timeSeries/0000755000176200001440000000000015117036003012357 5ustar liggesuserstimeSeries/tests/0000755000176200001440000000000015116770216013533 5ustar liggesuserstimeSeries/tests/doRUnit.R0000644000176200001440000000164215116770216015245 0ustar liggesusers#### doRUnit.R --- Run RUnit tests ####------------------------------------------------------------------------ ### Originally follows Gregor Gojanc's example in CRAN package 'gdata' ### and the corresponding section in the R Wiki: ### http://wiki.r-project.org/rwiki/doku.php?id=developers:runit ### MM: Vastly changed: This should also be "runnable" for *installed* ## package which has no ./tests/ ## ----> put the bulk of the code e.g. in ../inst/unitTests/runTests.R : if(require("RUnit", quietly = TRUE)) { ## --- Setup --- wd <- getwd() pkg <- sub("\\.Rcheck$", '', basename(dirname(wd))) library(package = pkg, character.only=TRUE) path <- system.file("unitTests", package = pkg) stopifnot(file.exists(path), file.info(path.expand(path))$isdir) source(file.path(path, "runTests.R"), echo = TRUE) } ################################################################################ timeSeries/tests/msft.dat.csv0000644000176200001440000003146415104730074015773 0ustar liggesusers"X.Y..m..d";"Open";"High";"Low";"Close";"Volume" "1";"2000-09-27";63.4375;63.5625;59.8125;60.625;53077800 "2";"2000-09-28";60.8125;61.875;60.625;61.3125;26180200 "3";"2000-09-29";61;61.3125;58.625;60.3125;37026800 "4";"2000-10-02";60.5;60.8125;58.25;59.125;29281200 "5";"2000-10-03";59.5625;59.8125;56.5;56.5625;42687000 "6";"2000-10-04";56.375;56.5625;54.5;55.4375;68226700 "7";"2000-10-05";55.5;57.25;55.25;55.375;40549700 "8";"2000-10-06";55.8125;56.75;54.75;55.5625;30897000 "9";"2000-10-09";55.625;55.75;53;54.1875;29161800 "10";"2000-10-10";53.9375;55.5625;53.8125;54.5625;31033100 "11";"2000-10-11";54;56.9375;54;55.75;50602900 "12";"2000-10-12";56.3125;56.875;53.8125;54.375;45109800 "13";"2000-10-13";53.875;54.875;52.125;53.75;52260600 "14";"2000-10-16";53.5;53.8125;49.5625;50.375;59879500 "15";"2000-10-17";51.875;52.4375;50.25;50.4375;40638300 "16";"2000-10-18";49.625;53.25;48.4375;51.75;55268200 "17";"2000-10-19";58.4375;62.1875;58;61.875;128496600 "18";"2000-10-20";61.3125;66.125;61.125;65.1875;80189300 "19";"2000-10-23";64.625;66.25;60.6875;62.125;92585200 "20";"2000-10-24";62.625;62.9375;60.1875;61.5;47213700 "21";"2000-10-25";61.9375;63.4375;60.4375;61.25;83801900 "22";"2000-10-26";61;65.0625;60.8125;64.4375;57413300 "23";"2000-10-27";64.6875;69.1875;64.625;67.6875;62146200 "24";"2000-10-30";67.5;70.125;67.375;69.0625;55028800 "25";"2000-10-31";69;69.5;68;68.875;52237000 "26";"2000-11-01";68.5;70.0625;68.4375;69.625;40654700 "27";"2000-11-02";70.375;70.8438;69.625;70.3125;38992600 "28";"2000-11-03";69.25;69.625;68.0625;68.25;34355500 "29";"2000-11-06";68.6875;70.125;68.25;69.5;37425700 "30";"2000-11-07";69.75;71.875;69.5;70.5;52165600 "31";"2000-11-08";71.125;72.375;68;69.4375;103074700 "32";"2000-11-09";68.5;71.3125;68.4375;70.875;45529300 "33";"2000-11-10";69.9375;70.3125;66.8125;67.375;46872200 "34";"2000-11-13";66.6875;68.125;64.4062;66.4375;41682400 "35";"2000-11-14";68;69.8125;67.3125;68.8125;42109300 "36";"2000-11-15";69.0625;70.875;68.6875;70.0625;30211100 "37";"2000-11-16";69.4375;71.5;68.9375;68.9375;46064300 "38";"2000-11-17";69.4375;70;67.7969;69.0625;53262800 "39";"2000-11-20";68.125;68.5;65.5625;67.1875;40078600 "40";"2000-11-21";67.375;69.25;67.375;67.75;29743800 "41";"2000-11-22";66.0625;69.5;66;68.25;38171600 "42";"2000-11-24";69;70.4375;68.5;69.9375;17219600 "43";"2000-11-27";71.4375;72.25;70.625;70.6875;42653800 "44";"2000-11-28";69.375;69.75;66.8125;67;63723100 "45";"2000-11-29";66.8125;67.125;63.25;65.0625;49140200 "46";"2000-11-30";62;62.0625;57;57.375;98600400 "47";"2000-12-01";58.0625;60.625;56.0625;56.625;54904900 "48";"2000-12-04";57.25;59;55.1875;56.4375;40203600 "49";"2000-12-05";59.1875;60.5;58.25;59.875;50867200 "50";"2000-12-06";60;60.0625;56.0625;56.6875;45280400 "51";"2000-12-07";53.4375;54;52.25;53.125;72654200 "52";"2000-12-08";54.625;55.875;53.4375;54.4375;60469900 "53";"2000-12-11";55.5;58.75;55;58.0625;47788100 "54";"2000-12-12";57.8125;60;56.75;58.375;31553000 "55";"2000-12-13";60.5;60.5;56.8125;57.25;49180200 "56";"2000-12-14";57.9375;58.7344;55.375;55.5;35600700 "57";"2000-12-15";51.0469;52;47.75;49.1875;58449900 "58";"2000-12-18";49;50;47;47.8125;53593700 "59";"2000-12-19";47.4375;48;44.5;44.8125;60135900 "60";"2000-12-20";42.8125;44;41.375;41.5;74518900 "61";"2000-12-21";40.75;45.125;40.3125;43.4375;81586500 "62";"2000-12-22";44.75;47.125;44.75;46.4375;54775900 "63";"2000-12-26";46.875;48.5625;45.875;46.875;33470800 "64";"2000-12-27";46.125;46.8125;45;46.4375;34501900 "65";"2000-12-28";45.125;46.25;43.875;44.5625;38809600 "66";"2000-12-29";43.9375;45.8125;43;43.375;49988800 "67";"2001-01-02";44.125;45;42.875;43.375;41206600 "68";"2001-01-03";43.1875;48.875;43.125;47.9375;67981100 "69";"2001-01-04";47.8125;50.5;46.875;48.4375;56198500 "70";"2001-01-05";48.5;49.875;47.5625;49.125;46707300 "71";"2001-01-08";48.9375;49.75;46.6875;48.9375;39908800 "72";"2001-01-09";50;52.625;49.75;51.8125;57482700 "73";"2001-01-10";51;53.8125;50.75;52.875;45115100 "74";"2001-01-11";53;55.75;52.3125;55;50927400 "75";"2001-01-12";54.875;55;52.5;53.5;36856000 "76";"2001-01-16";53.375;53.5;51.125;52.5625;34231200 "77";"2001-01-17";53.625;54.875;52.5625;52.9375;36422100 "78";"2001-01-18";53.6875;56.1875;52.625;55.5;54894400 "79";"2001-01-19";60;61.4375;58.875;61;104674400 "80";"2001-01-22";60.75;61;59;60.125;38336500 "81";"2001-01-23";59.75;60.9375;58.9375;60.5625;35147600 "82";"2001-01-24";61;63.4375;60.75;62.9375;55227500 "83";"2001-01-25";62.75;64;61.5625;61.8125;42828700 "84";"2001-01-26";61;64.3125;61;64;46540000 "85";"2001-01-29";63.5625;64.625;63.5;64.5;42491900 "86";"2001-01-30";64.5;64.75;62.875;63.375;28638400 "87";"2001-01-31";63;63.75;61;61.0625;40949400 "88";"2001-02-01";60.8125;62.625;60.375;62.375;35896400 "89";"2001-02-02";62.5;63.375;60.75;60.8125;35550000 "90";"2001-02-05";60.75;62.0625;60.25;61.9375;25699600 "91";"2001-02-06";62.0625;63.8125;61.6875;62.5625;48221000 "92";"2001-02-07";62;65.0625;61.8125;64.6875;63030900 "93";"2001-02-08";63.75;64.5;62;62.25;44020600 "94";"2001-02-09";61.3125;61.5625;58.5;59.125;50287600 "95";"2001-02-12";58.8125;59.4375;57.1875;58.75;35644700 "96";"2001-02-13";59.625;61.0625;58.125;58.1875;38035300 "97";"2001-02-14";57.625;59;56.375;58.375;30864200 "98";"2001-02-15";59;60.1875;57.875;58.8125;32813900 "99";"2001-02-16";57;58.25;56.125;57.3125;33479200 "100";"2001-02-20";57.375;58.25;55.375;55.875;30365400 "101";"2001-02-21";55.25;58.0625;55.1875;56.25;31973600 "102";"2001-02-22";56.3125;56.8125;53.875;55.1875;50408200 "103";"2001-02-23";54.4375;57.5;54.3125;56.75;46310300 "104";"2001-02-26";57.625;59.9375;57.375;59.5625;43968400 "105";"2001-02-27";59.375;61.1875;58.6719;59.375;49574300 "106";"2001-02-28";59.5625;60.0781;58.1875;59;42304200 "107";"2001-03-01";58.5625;59.5;56.25;59.3594;40890800 "108";"2001-03-02";57.5;58.125;56.4375;56.6875;39900400 "109";"2001-03-05";57.25;58.625;56.5625;57.4375;24691800 "110";"2001-03-06";58.625;60;58.375;59.4375;33390900 "111";"2001-03-07";59.875;61.125;59.3125;60.6875;29871800 "112";"2001-03-08";60.3125;60.5938;58.4375;59.25;27313000 "113";"2001-03-09";57.9375;58.1875;54.875;56.6875;51897200 "114";"2001-03-12";54.6875;55;51.625;51.9375;57188000 "115";"2001-03-13";52.1875;54.75;52;54.1875;45517800 "116";"2001-03-14";52.5;55.25;52.1875;54;45343100 "117";"2001-03-15";55.3125;56.0781;53.5;53.6875;35819200 "118";"2001-03-16";52.5;55.125;52.4844;54.5625;56424400 "119";"2001-03-19";54.5;55.5;53.125;54.3125;30518200 "120";"2001-03-20";54.5625;56.125;52.625;52.6875;45911400 "121";"2001-03-21";52.25;53.25;49.75;50.0625;62494300 "122";"2001-03-22";50.5625;54.0625;50.5;54;63181600 "123";"2001-03-23";54.9375;57;54.375;56.5625;49759800 "124";"2001-03-26";57.125;57.5;55.5625;56.0625;31559300 "125";"2001-03-27";56.0625;58.5625;55.875;58.25;47567800 "126";"2001-03-28";57.375;57.9375;55.375;55.5625;39340800 "127";"2001-03-29";55.375;57.1875;54.5625;55.375;43492500 "128";"2001-03-30";55.75;56.1875;53.875;54.6875;45600800 "129";"2001-04-02";54.8125;56.9375;54.625;55.8125;37962000 "130";"2001-04-03";55.3125;55.3125;52.75;53.375;47093800 "131";"2001-04-04";53.375;55;51.0625;51.9375;52023300 "132";"2001-04-05";53.75;57.375;53.5;56.75;56682000 "133";"2001-04-06";56.375;57.1875;55.0625;56.1875;46311000 "134";"2001-04-09";56.57;57.42;55.66;57.15;28147800 "135";"2001-04-10";57.95;60.09;57.78;59.68;54599700 "136";"2001-04-11";60.65;61.5;59.7;60.04;54939800 "137";"2001-04-12";59.56;62.31;59.35;62.18;43760000 "138";"2001-04-16";61.4;61.58;60.12;60.79;32928700 "139";"2001-04-17";60.52;62.11;60.04;61.48;42574600 "140";"2001-04-18";63.39;66.31;63;65.43;78348200 "141";"2001-04-19";65.81;69;65.75;68.04;79687800 "142";"2001-04-20";70.3;71.1;68.5;69;96459800 "143";"2001-04-23";68.11;68.47;66.9;68.25;46085600 "144";"2001-04-24";68.2;69.93;67.14;67.55;44588300 "145";"2001-04-25";67.57;69.79;67.25;69.69;38372000 "146";"2001-04-26";70.07;71;68.25;69.13;59368800 "147";"2001-04-27";69.53;69.68;66.21;67.12;60786200 "148";"2001-04-30";68.53;69.06;67.68;67.75;37184100 "149";"2001-05-01";67.66;70.3;67.6;70.17;41851400 "150";"2001-05-02";71;71.15;69.35;69.76;46432200 "151";"2001-05-03";69.25;70.18;68.14;68.53;33136700 "152";"2001-05-04";68;71.05;67.96;70.75;59769200 "153";"2001-05-07";70.83;72.15;70.7;71.38;54678100 "154";"2001-05-08";71.75;72.1;70.75;72.06;37542000 "155";"2001-05-09";71.24;71.3;69.86;70.4;38338300 "156";"2001-05-10";71.13;71.24;69.96;70;32167300 "157";"2001-05-11";69.96;70;68.65;69.4;25564400 "158";"2001-05-14";69.13;69.2;68.3;68.72;22484000 "159";"2001-05-15";68.74;69.3;68;68.27;30692800 "160";"2001-05-16";67.7;69.88;67.33;69.16;45946900 "161";"2001-05-17";69.1;70.14;67.55;68.17;53492400 "162";"2001-05-18";67.69;69.2;67.25;68.09;45302700 "163";"2001-05-21";68.05;69.99;67.75;68.79;51745800 "164";"2001-05-22";69.45;70.35;69.18;70.31;41727800 "165";"2001-05-23";70.39;71.6;69.51;69.7;46818700 "166";"2001-05-24";69.94;71.78;69.27;71.72;40390800 "167";"2001-05-25";71.66;71.9;70.36;70.91;26373800 "168";"2001-05-29";70.8;71.75;70.05;70.34;35605400 "169";"2001-05-30";69.56;70.58;68.65;69.19;43250900 "170";"2001-05-31";69.49;70.38;68.4;69.18;35341300 "171";"2001-06-01";69.6;70.7;68.7;70.34;28793800 "172";"2001-06-04";70.55;71.02;69.8;70.78;21868300 "173";"2001-06-05";70.76;73.08;70.5;72.6;44727100 "174";"2001-06-06";72.89;73.48;71.55;72.36;40011400 "175";"2001-06-07";72.12;73.73;72.08;73.68;33480000 "176";"2001-06-08";73.7;73.75;72.05;73.19;25933500 "177";"2001-06-11";72.85;72.85;71.51;72.12;23672800 "178";"2001-06-12";71.02;72.41;70.81;72.08;33357300 "179";"2001-06-13";72.05;72.3;70.64;70.69;27651200 "180";"2001-06-14";70.22;70.55;68.4;68.9;35986200 "181";"2001-06-15";67.51;68.3;66.4;68.02;54177200 "182";"2001-06-18";67.95;67.96;66.01;66.88;28423400 "183";"2001-06-19";68.21;68.85;66.85;67.32;31728700 "184";"2001-06-20";67.14;69.59;67.1;69.41;32054200 "185";"2001-06-21";69.15;70.55;68.92;69.84;34801900 "186";"2001-06-22";70;70.61;68.58;68.83;25546000 "187";"2001-06-25";69.1;69.81;67.77;68.85;24607800 "188";"2001-06-26";67.82;70.21;67.7;70.14;31538500 "189";"2001-06-27";69.86;71.53;69.36;71.14;34599900 "190";"2001-06-28";71.55;76.15;70.53;72.74;64487800 "191";"2001-06-29";72.6;73.41;71.4;73;47141900 "192";"2001-07-02";72.05;73.15;70.15;70.6;36405100 "193";"2001-07-03";70.3;70.8;69.93;70.47;14018700 "194";"2001-07-05";70.22;70.72;68.44;68.51;24621300 "195";"2001-07-06";68.3;68.4;65.67;66.06;33733900 "196";"2001-07-09";66.2;66.91;65.04;65.69;33238300 "197";"2001-07-10";65.9;66.25;64.35;64.48;33281300 "198";"2001-07-11";64.21;66.75;64.2;66.5;36911300 "199";"2001-07-12";70.7;72.05;70.33;71.6;64039000 "200";"2001-07-13";71.4;72;70.94;71.34;29467300 "201";"2001-07-16";71.45;72.16;70.15;71.18;27995400 "202";"2001-07-17";70.66;72.01;70.14;71.82;31620500 "203";"2001-07-18";70.6;71.5;69.87;70.57;28795400 "204";"2001-07-19";71.22;73;71.22;72.57;38274700 "205";"2001-07-20";68.03;69.4;67.94;69.18;62101800 "206";"2001-07-23";69.24;69.24;66.35;67.09;39999700 "207";"2001-07-24";67;67.99;65.7;66.32;33765100 "208";"2001-07-25";66.26;67.52;65.61;67.48;37032700 "209";"2001-07-26";67.12;67.32;65.5;66.59;38987000 "210";"2001-07-27";66.05;66.25;65.05;65.47;32698000 "211";"2001-07-30";65.65;66.88;65.54;65.8;21098200 "212";"2001-07-31";66.01;67.39;65.85;66.19;29515800 "213";"2001-08-01";66.8;66.81;65.76;66.47;27839500 "214";"2001-08-02";67.21;67.54;66.26;67.45;27099200 "215";"2001-08-03";67.3;67.36;66;66.89;21630200 "216";"2001-08-06";66.53;67.12;65.68;66.13;13915800 "217";"2001-08-07";66.04;67.05;65.99;66.35;15673900 "218";"2001-08-08";66.51;67.24;64.49;64.86;27498200 "219";"2001-08-09";64.98;65.55;64.3;65.01;22768100 "220";"2001-08-10";64.77;65.86;62.9;65.52;25878200 "221";"2001-08-13";65.24;65.99;64.75;65.83;16337700 "222";"2001-08-14";65.75;66.09;64.45;64.69;18240600 "223";"2001-08-15";64.71;65.05;63.2;63.2;19751500 "224";"2001-08-16";62.84;64.71;62.7;64.62;21952800 "225";"2001-08-17";63.78;64.13;61.5;61.88;26117100 "226";"2001-08-20";61.66;62.75;61.1;62.7;24185600 "227";"2001-08-21";62.7;63.2;60.71;60.78;23555900 "228";"2001-08-22";61.13;61.15;59.08;60.66;39053600 "229";"2001-08-23";60.67;61.53;59;59.12;25906600 "230";"2001-08-24";59.6;62.28;59.23;62.05;31699500 "231";"2001-08-27";61.9;63.36;61.57;62.31;22281400 "232";"2001-08-28";62.34;62.95;60.58;60.74;23711400 "233";"2001-08-29";61.05;61.3;59.54;60.25;24085000 "234";"2001-08-30";59.04;59.66;56.52;56.94;48816000 "235";"2001-08-31";56.85;58.06;56.3;57.05;28950400 "236";"2001-09-04";57.19;59.08;56.07;56.1;33594600 "237";"2001-09-05";56.18;58.39;55.39;57.74;44735300 "238";"2001-09-06";56.56;58.39;55.9;56.02;56178400 "239";"2001-09-07";56.11;57.36;55.31;55.4;44931900 "240";"2001-09-10";54.92;57.95;54.7;57.58;42235900 "241";"2001-09-17";54.02;55.1;52.8;52.91;63751000 "242";"2001-09-18";53.41;55;53.17;54.32;41591300 "243";"2001-09-19";54.46;54.7;50.6;53.87;63475100 "244";"2001-09-20";52.35;52.61;50.67;50.76;58991600 "245";"2001-09-21";47.92;50.6;47.5;49.71;92488300 "246";"2001-09-24";50.65;52.45;49.87;52.01;42790100 "247";"2001-09-25";52.27;53;50.16;51.3;42470300 "248";"2001-09-26";51.51;51.8;49.55;50.27;29262200 "249";"2001-09-27";50.1;50.68;48;49.96;40595600 timeSeries/MD50000644000176200001440000002754515117036003012704 0ustar liggesusers655dcc268ec612659050ace08950239c *ChangeLog 8fa4dbaa45cf5822b1df13a216709d64 *DESCRIPTION aa918c8939207e27d985257874a70c75 *NAMESPACE 979afb168c41c809d49dbea670184b78 *NEWS.md 7417a25041ce9a1531aa2dd338eba8fe *R/AllClass.R d3edf10575203354ea8dd35002dfc85d *R/AllGeneric.R 265850ce81bf120965f0fbff81a08847 *R/aaa-Deprecated.R 7f05b9160586788d7769cd477f37bbb7 *R/aaa-utils.R 92bdf85ad88e5dad33f2eb41f7f9e703 *R/base-apply.R 1ca0af4dd866f63058a2f835b2530eef *R/base-applySeries.R 968c421f42d0ceb9992dda28dbb92d2c *R/base-attach.R f2b2adb80b6b8ababb256f876dad2ead *R/base-cbind.R a8c557a596bce446e5c4449e9b6e6ed4 *R/base-diff.R a46fc4895fb6f5d27d1c6d928cf6ea31 *R/base-dim.R 7fe6efc4ba24fef61e45daedcabffdbc *R/base-merge.R 6ed798fb2f8a96ddca7221d2efc6d377 *R/base-rank.R d8a2ce279f26687ddf562c5db75ad91b *R/base-rev.R a4ab768410837ac64ea14dab3f463fe7 *R/base-sample.R 20ea1c4cc89a27463aef3bbfa66e4487 *R/base-scale.R b0f09d7acd1a7d27e0d2f5cd428836eb *R/base-sort.R 31105a6dc2b3455ea62cbf03d8dadc18 *R/base-start.R 8e547ccf563a5134f438b299514b718c *R/base-subsetting.R c36e64e19fc7cf9c0cf8beeb2ec7aff2 *R/base-summary.R 787d5636b21ee586692fa57b10271810 *R/base-t.R e38b8155ba182239b14be153ac03f1f7 *R/fin-align.R 35a05ba21a2743468d78e824f64786f0 *R/fin-cumulated.R ee0188b9b207406a9870a19331cfeece *R/fin-daily.R 7927233d883f79c8d7d40930fe6ef2f9 *R/fin-drawdowns.R 29283f111ed012385b7f23844bc9a1b3 *R/fin-durations.R 5d06a2c8bc2f9a895fad418b8705976c *R/fin-monthly.R adefaa5d26ecebe033cdda94cfbcd881 *R/fin-periodical.R 1f85e125241cb2c6a724cb979bd1d38f *R/fin-returns.R f2dc2572b00b06518008493859833ed3 *R/fin-runlengths.R 5d37cb40e1478469171d33d825395b2e *R/fin-splits.R 2aefc803b5781c629297e053a4e8a791 *R/fin-spreads.R eeaf1f19869cde428f05f23432d928b9 *R/fin-turnpoints.R 71580b37ec7a5f795a8fc6aeaa55d4f4 *R/fin-wealth.R 1931fd0fdd6e64db95814455c6a002ff *R/methods-as.R 58f5a50dfbf8669c239615e1ccdbf58f *R/methods-comment.R 62c43cc67894dbf23483cbfef6396564 *R/methods-is.R 8524c024939ea1297173fc1cbfbb5a1a *R/methods-mathOps.R 3cd57facd28eb9bcc970ddd0e1a16ba7 *R/methods-plot.R ff8a9deb554937cf44e6b7738e887cd7 *R/methods-plot2.R 0355507198adc0512ae493caa509c4b2 *R/methods-show.R c59ac79b5a1165c989f5fd0f2f374590 *R/statistics-colCumsums.R 0c4a729667fe29341e15dde9065f3922 *R/statistics-colSums.R 628cf16ba73fea29c0ab4d0f9ba050ff *R/statistics-orderColnames.R 97d48f32243b61df5214da1acb00006d *R/statistics-orderStatistics.R 75c8c4ed3e2c5a4e495c3986a16753d6 *R/statistics-rollMean.R 2127cd77fff199b213284e448c4af346 *R/statistics-rowCumsums.R d56e0782384982094a128f363e07e0df *R/statistics-smoothLowess.R f2f9ebb8ca20f32598639493c27722ab *R/stats-aggregate.R 8199839fd45182174f57e337f774685f *R/stats-filter.R e7b10a1ecb15fa46ac90740fb6a6cce8 *R/stats-lag.R 96e4b067a6f6de0b52987775e7dcde48 *R/stats-model.frame.R 1b2f4d6e8ea5cab7e17f9293630f9cd2 *R/stats-na.contiguous.R c8c8ff44d2f165ed70d99b90234f217c *R/stats-na.omit.R 8dbf0e86388a4c35659de207ee12979f *R/stats-window.R 0e6dc00950c93d137d65819d5ff54b89 *R/timeSeries-dummy.R b6019c6be24f38b69ca42404d3a48a56 *R/timeSeries-getDataPart.R 56489f9b98c52a583063b3bec3841172 *R/timeSeries-isOHLC.R e4f031dbbf5c58e0a70573a81f49fc27 *R/timeSeries-isRegular.R 41515d1a8ea45269896af20e17c45cca *R/timeSeries-isUnivariate.R aa6b610c7793a3e0fcaf2ec1aab16d96 *R/timeSeries-readSeries.R 184369cd20ebaf4cc05464ef17aa00f4 *R/timeSeries-signalCounts.R 43e0e604a42c831eea70c6d7ec497dd3 *R/timeSeries-slotDocumentation.R 3497ade73292c5dec2652335dd984588 *R/timeSeries-slotFinCenter.R 121962bec1e968195f3b968e4933a252 *R/timeSeries-slotSeries.R 790fa882672533bc7d1c250764255367 *R/timeSeries-slotTime.R 4918b16308209845926c89ec171d2bd9 *R/timeSeries-slotUnits.R 5dca5b8ed59e58f0890cb60d45110d01 *R/timeSeries.R 4f24ad3daf9dfd14c6f3b3ca847cb0a1 *R/utils-description.R 637ea07af0000fff9f57ad78fa299999 *R/utils-head.R c4d5d58ff1dfb91f1c1db4e742d87c0c *R/utils-old2new.R 0f096d915dbadb5091ff294062493040 *R/utils-structure.R e83fb0c03a5b5909b6be7103e364ec85 *R/zzz.R 219eb2bb1a7f10e4b401d7a8cb318bfa *README.md 0b80552b0182ca75c2f52fd07c81df19 *build/vignette.rds 08783f26722d600f1d895921a23adb91 *data/LPP2005REC.rda 99535a09b0b8189278aff6d651041d6f *data/MSFT.rda fef224cc858248dc279e326ed178f294 *data/USDCHF.rda 575a9649d181e899167b5b7fd7811c4d *inst/COPYING 29ddc76d19a7435c4101fdbd13ec0146 *inst/COPYRIGHTS df11bf613071e8b06eaeeccab8e9faac *inst/README 68b329da9893e34099c7d8ad5cb9c940 *inst/THANKS 6a055dde3c367d6039e57eca4b4c9658 *inst/_pkgdown.yml 26f1e7c7b1bfa86789532fed789ad211 *inst/doc/timeSeriesPlot.R 085e8932df19f10701373f78fa9b5d1d *inst/doc/timeSeriesPlot.Rnw 0698f272ee15947d90f35a5c48856db2 *inst/doc/timeSeriesPlot.pdf 0606ffb4675ae15060bd825afe8a7d16 *inst/doc/timeSeriesRefCard.pdf 63a08580cf247052d3e8c939e75e2928 *inst/extdata/msft.csv 00240e3af8ce216961a315a1c0dbeb79 *inst/extensionsTests/aggregateWrappers.R 7322559e6dae10dc40a67c7434c2e30d *inst/extensionsTests/alignWrappers.R 2c0fddccf06540c4ad0ac44173b002b9 *inst/extensionsTests/attributesExtension.R 30e3c046e97879e7293ce8cf5397b165 *inst/extensionsTests/chicPlots.R df134f2efe48f0a98c3728ffa6227404 *inst/extensionsTests/endpointsWrappers.R 6ff348d414cad9dba24940f24d066584 *inst/extensionsTests/xtsWrappers.R e5ac636feb7f0b932f8e6086c1ba28e7 *inst/pkgdown.yml c6df0a8aafc7cfa9f914919f7f043ff3 *inst/unitTests/Makefile 89dc29af114e6a2a3c80a502b4f4c95b *inst/unitTests/runTests.R 4ab6be780fa464291b2f4c0e8d1a1bce *inst/unitTests/runit.NA.R 94ab54bfea1b4edec5a3b5f04167d42b *inst/unitTests/runit.Omit.R 42504a80169f26a6d37ceda28a269139 *inst/unitTests/runit.TimeSeriesClass.R 8e685661d5ae69c3347782f6ce75d9a0 *inst/unitTests/runit.TimeSeriesCoercion.R 849fa2a5ae5a5b1f6d16e8b86ff3cac5 *inst/unitTests/runit.TimeSeriesData.R 90b7820b07832910daf1afd5dc224856 *inst/unitTests/runit.TimeSeriesPositions.R c132373dac907f3ce1ee549d855652d3 *inst/unitTests/runit.aggregate.R 54e1197ea897c2b1fed028f45cf3e1e8 *inst/unitTests/runit.align.R 3bda0d794fe86888602d8f23414f39f2 *inst/unitTests/runit.apply.R e191857ac1bc06d68bc96c0b351cf39a *inst/unitTests/runit.as.R f2dee6d70982efbc28ae37bbb8ee57da *inst/unitTests/runit.attach.R 85016a1405dd51befbd7325f60d42a4c *inst/unitTests/runit.bind.R 9af6d13110613bf27e278d9e18109ae7 *inst/unitTests/runit.colCum.R b29286da34454a7726303e760cf88c23 *inst/unitTests/runit.colStats.R 96e213d2502e0135529b0700a7ed7f11 *inst/unitTests/runit.cor.R a5852317cc1bfc175a3781cee79012a0 *inst/unitTests/runit.cumulated.R 21c034e99f1666e27dcee33d785bb2da *inst/unitTests/runit.daily.R ef380731926ca03e283be11bf34e3f32 *inst/unitTests/runit.dim.R ddf81974440ba5c80cb35bbe6ea5213a *inst/unitTests/runit.drawdowns.R 00d0cb67d039b13011e43567f187a4f6 *inst/unitTests/runit.durations.R 1db68ab137b059cdeaafb194ffad9bea *inst/unitTests/runit.lag.R 37d375e0d22e63b663a8fed4bf71d6a2 *inst/unitTests/runit.mathOps.R 458df9d4fcc5c19781b40f0036353311 *inst/unitTests/runit.merge.R e0352cd00443ed64c2a0004bd4fb3fdc *inst/unitTests/runit.methods-plot.R e6868109cc1c537fea76ac57c333740a *inst/unitTests/runit.methods-print.R 3ad927a3804d96a9736216041a9a87e9 *inst/unitTests/runit.methods-summary.R 18fbd863b2c420249df32513d41dad5f *inst/unitTests/runit.model.frame.R 334271359fb9c749a7dfde8f6608defc *inst/unitTests/runit.monthly.R eb124eec56065e3395c63cab251bcf13 *inst/unitTests/runit.na.contiguous.R c3bf679bb5a7bdd5e3b9df1cf1a0d124 *inst/unitTests/runit.order.R d073d9b64074dc094dff6f987c519288 *inst/unitTests/runit.periodical.R 0965a3517f6e21aca3d18841387b1c5b *inst/unitTests/runit.rank.R a49a96a4356e8deae4b44daedab98373 *inst/unitTests/runit.returns.R d8ba4ac6486f022a81ab101ed8e631a3 *inst/unitTests/runit.rowCum.R d33bd4274e713eb2d401b53feb7c9461 *inst/unitTests/runit.signalCounts.R 56b7c45751ad846a43a4f18c70337828 *inst/unitTests/runit.spreads.R e9dcc24a88ed5e4a547f57b8ebff16f2 *inst/unitTests/runit.subset.R 8c98a57d8736b0515851139c13a08036 *inst/unitTests/runit.time.R 31c51655d159665b6391203a329965ea *inst/unitTests/runit.timeSeries.R 3f789a72d969c9cfbc324162f3cbbf3b *man/00timeSeries-package.Rd bba1ee287c398eab001e1183eff84fa9 *man/base-apply.Rd 5640d89dbec45dc2c1b91b678c7a196c *man/base-attach.Rd 162eb074e5049944408de0336185d630 *man/base-cbind.Rd 04a07fdfa713adbe80953a40120a8f2b *man/base-diff.Rd 430dee87a135e18c266bfc5649640d6b *man/base-dim.Rd 673542baea273a24e8819fa56cb8937e *man/base-merge.Rd 9e42a67a8fa5295b45ad741f04c5132e *man/base-rank.Rd 6e79f4b84991c3a451f47e4ef30d54cf *man/base-rev.Rd b06c4f464878498805e943bd77766eee *man/base-sample.Rd b3863fac028d79bf07458f420b3e45a8 *man/base-scale.Rd 0154228d466e18ade1797cc8930ff569 *man/base-sort.Rd 437b4fc783011911844e90d8f0d2a2e3 *man/base-start.Rd 7351bb16573fe16b600cf39b4931a467 *man/base-subsetting.Rd a7e99ac5af71ebfaee6394c724cf10b8 *man/base-t.Rd 5957894e663eff31bbfb029dc83bae1b *man/data-examples.Rd 9f973540f2165c0527b5f993c8962ddd *man/fin-align.Rd cc0fc794bcd3648da79adc3bc8d99d48 *man/fin-cumulated.Rd e1c333ab4794ce0ae0522c93dda81220 *man/fin-drawdowns.Rd aadc3c82ce30a2755e67ff831dc64b2d *man/fin-dummy.Rd cfc133b2f19c1732233aeb117d94ca68 *man/fin-durations.Rd 0efede3d3877bc282497a30a105afc38 *man/fin-monthly.Rd 82fa2c9c70f536b43b56f0c0880ae4ee *man/fin-periodical.Rd 6558081d2a2235141e7deac75e92d2dd *man/fin-returns.Rd 071c7a96b4a461c113142fd47674c24a *man/fin-runlengths.Rd 47f21b16273c1940bb68915e5449d821 *man/fin-splits.Rd 6185b2f2fddbfd6dda755e0a0eac5812 *man/fin-spreads.Rd facfda2d7e2e2dda1856cc9c5314d7ff *man/fin-turnpoints.Rd c3fbfabac7dff8d72bc04acf58b060ef *man/fin-wealth.Rd b51c826bab54dda9ba212918ecbb6999 *man/internals.Rd fc45cdf9d6e780036d76040a48331b5a *man/methods-as.Rd c70c22e1b172a27f19477b13b4991427 *man/methods-comment.Rd dc181c5bb7fc9cd851bde301e988498a *man/methods-is.Rd d553c81643c254365f802f0219864cd9 *man/methods-mathOps.Rd fca9dde15f3880bf3a8db1e98326ad5e *man/methods-plot.Rd 24639d8d582c29f043e5816ce9349aaa *man/methods-show.Rd 3a9f64c02bacc430c88dd1c29bea386e *man/methods-stats.Rd d124ec6de752732c11c3415d0be57601 *man/statistics-colCumsums.Rd a9d50c6e1b6a7c2c591875f1260a31bc *man/statistics-colSums.Rd ed415ce0c92c56260574de872f92a578 *man/statistics-orderColnames.Rd 8f106cbebc15171d5de451ff900623f2 *man/statistics-orderStatistics.Rd 4cdcaee7ab831f692dbeba19a4e03767 *man/statistics-rollMean.Rd 02fa562a75b8611cdb41836182092d1f *man/statistics-rowCumsums.Rd f89b7b3a0db7a0e1eda3d21874a58c4a *man/statistics-smoothLowess.Rd 316df3844ef9e641c1d7408ae2175825 *man/stats-aggregate.Rd fcdff42245399a90ba25b672bb837b1c *man/stats-filter.Rd 7166eab5c067085895c5f5daf1f03655 *man/stats-lag.Rd 5b7af0b8a62d2abe3158367c3f26b096 *man/stats-na.contiguous.Rd 8cfc3eee8ffc505e34817523c2fbc7b7 *man/stats-na.omit.Rd a10f480bb53714fe9fac85339b47b739 *man/stats-window.Rd d6db205773307994cb2d8d47f82da9b5 *man/timeSeries-class.Rd 8c5bb3bfabc883c43a1c39a7d49e1ac0 *man/timeSeries-deprecated.Rd 76d23739621c7975470ac217e9465709 *man/timeSeries-getDataPart.Rd 3b55e80f339eae7ff317c87c29a5fbf8 *man/timeSeries-isRegular.Rd 27c24c5de3d2adb1b8bf279b239412ae *man/timeSeries-isUnivariate.Rd d02a1934d7f2286cc88eabeb0f0a7a85 *man/timeSeries-readSeries.Rd 157e7bb25da9cac4cd0e4dabcce43ed4 *man/timeSeries-slotDocumentation.Rd 91f0a86fdca3b423610f508c4700b2e9 *man/timeSeries-slotFinCenter.Rd 6f03d54ec413d65045436f42d72c75be *man/timeSeries-slotSeries.Rd bce2abf52cf969bd7ad0dfa3395b63f9 *man/timeSeries-slotTime.Rd 495af2ee0c3310f39f502cd398093434 *man/timeSeries-slotUnits.Rd cfcb36e3b7ba13ae9c5397d340e6e3b0 *man/timeSeries.Rd 7afbd0cd1eabdba46b48de0cfe44cf0a *man/utils-description.Rd 81f6e042d39af60ff2282cef34de9e09 *man/utils-structure.Rd defd44eea012d33b9219d999beccca12 *tests/doRUnit.R d9fbe377cad1b0d2f684d3b71f324966 *tests/msft.dat.csv e0082956f0c082294af55dff827fd17a *vignettes/characterTableFig.pdf c1f351531201d1c5a99319debe31a159 *vignettes/colorPalettes1Fig.pdf 96410e04a3f1eac571ec2de10f40c901 *vignettes/colorPalettes2Fig.pdf 61bf91e727aa32ae300c067888d136e1 *vignettes/colorTableFig.pdf efb5d1677a5e3f01559bf066ec86cc1a *vignettes/colorwheel.pdf 3c2c1851329140ae3c375a6da33bd27c *vignettes/symbolTableFig.pdf 085e8932df19f10701373f78fa9b5d1d *vignettes/timeSeriesPlot.Rnw timeSeries/R/0000755000176200001440000000000015117002161012556 5ustar liggesuserstimeSeries/R/base-merge.R0000644000176200001440000001376215104730074014730 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # merge,timeSeries,ANY Merges 'timeSeries' object and ANY # merge,timeSeries,missing Merges 'timeSeries' object and missing # merge,timeSeries,timeSeries Merges two 'timeSeries' objects # merge,ANY,timeSeries Merges ANY and 'timeSeries' object ################################################################################ setMethod("merge", c("timeSeries", "ANY"), function(x, y, ...) { callGeneric(x, as(y, "timeSeries"), ...) } ) # ------------------------------------------------------------------------------ setMethod("merge", c("timeSeries", "missing"), function(x, y, ...) { x } ) # ------------------------------------------------------------------------------ setMethod("merge", c("timeSeries", "numeric"), function(x, y, ...) { # Deal with names of numeric vectors units <- names(y) if (is.null(units)) units <- paste((substitute(x)), collapse = ".") if (length(y) == 1) { y <- rep(y, times = nrow(x)) return(merge(x, timeSeries(y, time(x), units = units), ...)) } else if (length(y) == nrow(x)) { return(merge(x, timeSeries(y, time(x), units = units), ...)) } else { stop("number of rows must match") } } ) # ------------------------------------------------------------------------------ setMethod("merge", c("timeSeries", "matrix"), function(x, y, ...) { # deal with names of matrix units <- colnames(y) if (is.null(units)) { units <- paste((substitute(y)), collapse = ".") if ((nc <- ncol(y)) > 1) units <- paste(units, seq(nc), sep = ".") } if (nrow(y) != nrow(x)) stop("number of rows must match") else merge(x, timeSeries(y, time(x), units = units), ...) }) # ------------------------------------------------------------------------------' merge.timeSeries <- function(x, y, ...) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Merges two objects of class 'timeSeries' # Arguments: # x, y - two objects of class 'timeSeries' # FUNCTION: # Compose Attributes - Documentation : xAttributes <- getAttributes(x) yAttributes <- getAttributes(y) Attributes <- .appendList(xAttributes, yAttributes) Documentation <- as.character(date()) attr(Documentation, "Attributes") <- Attributes # Merge: if (is.signalSeries(x) | is.signalSeries(y)) { data <- merge(getDataPart(x), getDataPart(x)) return(timeSeries(data = data, units = colnames(data))) } # Convert to Data Frame tx <- as.numeric(time(x), "sec") ty <- as.numeric(time(y), "sec") df.x <- if (prod(dim(rec.x <- x@recordIDs))) data.frame(positions = tx, getDataPart(x), rec.x) else data.frame(positions = tx, getDataPart(x)) df.y <- if (prod(dim(rec.y <- y@recordIDs))) data.frame(positions = ty, getDataPart(y), rec.y) else data.frame(positions = ty, getDataPart(y)) # Merge as Data Frame: df <- merge(df.x, df.y, all = TRUE) #-> To avoid problems when using invalid data.frame colnames nx <- make.names(colnames(x)) nxrec <- colnames(rec.x) ny <- make.names(colnames(y)) nyrec <- colnames(rec.y) dataIdx <- colnames(df) %in% c(nx, ny) recIdx <- colnames(df) %in% c(nxrec, nyrec) data <- as.matrix(df[,dataIdx, drop=FALSE]) recordIDs <- if (any(recIdx)) df[,recIdx, drop=FALSE] else data.frame() units <- names(df)[dataIdx] charvec <- as.numeric(df[,1]) # Return Value: ans <- timeSeries(data = data, charvec = charvec, units = units, zone = "GMT", FinCenter = finCenter(x), recordIDs = recordIDs) ans@documentation <- Documentation ans } setMethod("merge", c("timeSeries", "timeSeries"), ## function(x, y, ...) merge.timeSeries(x, y, ...)) merge.timeSeries) ## # until UseMethod dispatches S4 methods in 'base' functions ## merge.timeSeries <- function(x, y, ...) .merge.timeSeries(x, y, ...) # ------------------------------------------------------------------------------ setMethod("merge", c("numeric", "timeSeries"), function(x, y, ...) { # Deal with names of numeric vectors units <- names(x) if (is.null(units)) units <- paste((substitute(x)), collapse = ".") if (length(x) == 1) { x = rep(x, times = nrow(y)) return(merge(timeSeries(x, time(y), units = units), y, ...)) } else if (length(x) == nrow(y)) { return(merge(timeSeries(x, time(y), units = units), y, ...)) } else { stop("number of rows must match") } } ) # ------------------------------------------------------------------------------ setMethod("merge", c("matrix", "timeSeries"), function(x, y, ...) { # Deal with names of matrix units <- colnames(x) if (is.null(units)) { units <- paste((substitute(x)), collapse = ".") if ((nc <- ncol(x)) > 1) units <- paste(units, seq(nc), sep = ".") } if (nrow(x) != nrow(y)) stop("number of rows must match") else merge(timeSeries(x, time(y), units = units), y, ...) }) setMethod("merge", c("ANY", "timeSeries"), function(x, y, ...) { callGeneric(as(x, "timeSeries"), y, ...) } ) ################################################################################ timeSeries/R/stats-filter.R0000644000176200001440000000572615104730074015343 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # filter,timeSeries Applies linear filtering to a 'timeSeries' object ################################################################################ setMethod("filter", "timeSeries", function(x, filter, method = c("convolution", "recursive"), sides = 2, circular = FALSE, init = NULL) { # Description: # Applies linear filtering to a 'timeSeries' object # Arguments: # x - a univariate or multivariate time series. # filter - a vector of filter coefficients in reverse time order (as # for AR or MA coefficients). # method - Either "convolution" or "recursive" (and can be # abbreviated). If "convolution" a moving average is used: # if "recursive" an autoregression is used. # sides - for convolution filters only. If sides=1 the filter # coefficients are for past values only; if sides=2 they are # centred around lag 0. In this case the length of the filter # should be odd, but if it is even, more of the filter is # forward in time than backward. # circular - for convolution filters only. If TRUE, wrap the filter # around the ends of the series, otherwise assume external # values are missing (NA). # init - for recursive filters only. Specifies the initial values # of the time series just prior to the start value, in reverse # time order. The default is a set of zeros. # Value: # Returns a 'timeSeries' object. # FUNCTION: # Check Arguments: stopifnot(is.timeSeries(x)) # Extract Title and Documentation: Title <- x@title Documentation <- x@documentation # Filter: ans <- filter(getDataPart(x), filter = filter, method = method, sides = sides, circular = circular, init = init) # Note: do not use as.matrix because ts objects might # not be coerced properly ans <- as(ans, "matrix") # Add Column Names: colnames(ans) <- colnames(x) ans <- setDataPart(x, ans) # Preserve Title and Documentation: ans@title <- Title ans@documentation <- Documentation # Return Value: ans }) ################################################################################ timeSeries/R/statistics-smoothLowess.R0000644000176200001440000001513315117002161017602 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # smoothSupsmu Smoothes a timeSeries with the supsmu function # smoothLowess Smoothes a timeSeries with the lowess function # smoothSpline Smoothes a timeSeries with the smooth.spline function # DEPRECATED: DESCRIPTION: # .supsmuSmoother Smoothes a timeSeries with the supsmu function # .lowessSmoother Smoothes a timeSeries with the lowess function # .splineSmoother Smoothes a timeSeries with the smooth.spline function ################################################################################ # DW: # These are older functions which have to be rewritten ... # The functions are thought to be used to smooth financial # price or index series. # ------------------------------------------------------------------------------ smoothSupsmu <- function(x, bass = 5, ...) { # A function implemented by Diethelm Wuertz # Description: # Smoothes a time series with the supsmu function # Arguments: # x - an univariate timeSeries object, e.g. a price or index series # bass - controls the smoothness of the fitted curve. Values of up # to 10 indicate increasing smoothness. # ... - further arguments passed to the function supsmu() # Example: # x <- smoothSupsmu(MSFT[, 4], bass = 0.1); x; plot(x) # FUNCTION: # Settings: stopifnot(isUnivariate(x)) # Extract Title and Documentation: Title <- x@title Documentation <- x@documentation # Handle Missing Values: x <- na.omit(x) # Convert to Vector: X <- x x <- as.vector(x) # Smooth: ans <- stats::supsmu(x = 1:length(x), y = x, bass = bass, ... ) data <- cbind(x, ans$y) colnames(data) <- c(colnames(X), "supsmu") rownames(data) <- as.character(time(X)) series(X) <- data # Preserve Title and Documentation: X@title <- Title X@documentation <- Documentation # Return Value: X } # ------------------------------------------------------------------------------ smoothLowess <- function(x, f = 0.5, ...) { # A function implemented by Diethelm Wuertz # Description: # Smoothes a time series with the lowess function # Arguments: # x - an univariate timeSeries object, e.g. a price or index series # f - the smoother span. This gives the proportion of points in the # plot which influence the smooth at each value. Larger values # give more smoothness. # ... - further arguments passed to the function lowess() # Example: # x = smoothLowess(MSFT[, 4], f = 0.05); x; plot(x) # FUNCTION: # Settings: stopifnot(isUnivariate(x)) # Extract Title and Documentation: Title <- x@title Documentation <- x@documentation # Handle Missing Values: x <- na.omit(x) # Convert to Vector: X <- x x <- as.vector(x) # Smooth: ans <- stats::lowess(x, f = f, ...)$y data <- cbind(x, ans) colnames(data) <- c(colnames(X), "lowess") rownames(data) <- as.character(time(X)) series(X) <- data # Preserve Title and Documentation: X@title <- Title X@documentation <- Documentation # Return Value: X } # ------------------------------------------------------------------------------ smoothSpline <- function(x, spar = NULL, ...) { # A function implemented by Diethelm Wuertz # Description: # Smoothes a time series with the smooth.spline function # Arguments: # x - an univariate timeSeries object, e.g. a price or index series # f - the smoother span. This gives the proportion of points in the # plot which influence the smooth at each value. Larger values # give more smoothness. # ... - further arguments passed to the function smooth.spline() # Details: # smooth.spline(x, y = NULL, w = NULL, df, spar = NULL, cv = FALSE, # all.knots = FALSE, nknots = NULL, keep.data = TRUE, df.offset = 0, # penalty = 1, control.spar = list()) # Example: # x = smoothSpline(MSFT[, 4], spar = NULL); x; plot(x) # x = smoothSpline(MSFT[, 4], spar = 0.4); x; plot(x) # FUNCTION: # Settings: stopifnot(isUnivariate(x)) # Extract Title and Documentation: Title <- x@title Documentation <- x@documentation # Handle Missing Values: x <- na.omit(x) # Convert to Vector: X <- x x <- as.vector(x) # Smooth: ans <- stats::smooth.spline(x, spar = spar, ...)$y data <- cbind(x, ans) colnames(data) <- c(colnames(X), "spline") rownames(data) <- as.character(time(X)) series(X) <- data # Preserve Title and Documentation: X@title <- Title X@documentation <- Documentation # Return Value: X } ################################################################################ ## removed on 2025-12-12 by GNB ## ## .supsmuSmoother <- ## function(...) ## { ## # FUNCTION: ## ## # Deprecated: ## .Deprecated("smoothSupsmu") ## ## # Return Value: ## smoothSupsmu(...) ## } ## removed on 2025-12-12 by GNB ## ## .lowessSmoother <- ## function(...) ## { ## # FUNCTION: ## ## # Deprecated: ## .Deprecated("smoothLowess") ## ## # Return Value: ## smoothLowess(...) ## } ## removed on 2025-12-12 by GNB ## ## .splineSmoother <- ## function(...) ## { ## # FUNCTION: ## ## # Deprecated: ## .Deprecated("smoothSpline") ## ## # Return Value: ## smoothSpline(...) ## } ################################################################################ timeSeries/R/timeSeries.R0000644000176200001440000002551115104730074015025 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # .signalSeries Creates a signal series from scratch # .timeSeries Creates a time series from scratch # METHODS: # timeSeries,ANY,ANY # timeSeries,matrix,missing # timeSeries,matrix,timeDate # timeSeries,matrix,numeric # timeSeries,matrix,ANY ################################################################################ ## .signalSeries : generate units, title, documentation if NULL ## data must be a matrix .signalSeries <- function(data, charvec, units = NULL, format, zone = "", FinCenter = "", recordIDs = data.frame(), title = NULL, documentation = NULL, ...) { # Description: # Arguments: # Note: # it is possible that a ts object is considered as a # matrix when timeSeries method as dispatched. Hence this check # FUNCTION: if (!is(data, "matrix")) data <- as(data, "matrix") # Add units, title and Documentation: if (is.null(units)) units <- colnames(data) if (is.null(units)) units <- paste("SS.", seq.int(dim(data)[2]), sep = "") if (is.null(title)) title = "Signal Series Object" if (is.null(documentation)) documentation = as.character(date()) # remove rownames of data but keep colnames for # functions like var, cov ... # Note that if it fails, new("timeSeries" should fail to - normal try(dimnames(data) <- list(NULL, units), silent = TRUE) ### new("signalSeries", ### .Data = data, ### units = units, ### recordIDs = recordIDs, ### title = title, ### documentation = documentation) new("timeSeries", .Data = data, units = units, positions = numeric(0), FinCenter = "", format = "counts", recordIDs = recordIDs, title = title, documentation = documentation) } # ------------------------------------------------------------------------------ ## .timeSeries : generate units, title, documentation if NULL ## data must be a matrix and charvec a timeDate object .timeSeries <- SERIES <- function(data, charvec, units = NULL, format, zone = "", FinCenter = "", recordIDs = data.frame(), title = NULL, documentation = NULL, ...) { # Description: # Creates a time series from scratch # Arguments: # Note: # it is possible that a ts object is considered as a # matrix when timeSeries method as dispatched. Hence this check # FUNCTION: if (!is(data, "matrix")) data <- as(data, "matrix") stopifnot(is(charvec, "numeric")) # Add units, title and Documentation: if (is.null(units)) units <- colnames(data) if (is.null(units)) units <- paste("TS.", seq.int(dim(data)[2]), sep = "") if (is.null(title)) title <- "Time Series Object" if (is.null(documentation)) documentation <- as.character(date()) if (missing(format)) format <- "%Y-%m-%d" if (identical("", FinCenter)) FinCenter <- "GMT" # Remove rownames of data but keep colnames for # functions like var, cov ... # Note that if it fails, new("timeSeries" should fail to - normal try(dimnames(data) <- list(NULL, units), silent = TRUE) positions <- charvec # as.numeric(charvec, "sec") attributes(positions) <- NULL new("timeSeries", .Data = data, positions = positions, units = units, format = format, # charvec@format, FinCenter = FinCenter, # charvec@FinCenter, recordIDs = recordIDs, title = title, documentation = documentation) } # ------------------------------------------------------------------------------ ## missing ANY setMethod("timeSeries", signature(data = "missing", charvec = "ANY"), function (data, charvec, units = NULL, format = NULL, zone = "", FinCenter = "", recordIDs = data.frame(), title = NULL, documentation = NULL, ...) { .signalSeries(data = matrix(NA), units = units, recordIDs = recordIDs, title = title, documentation = documentation, ...) }) # ------------------------------------------------------------------------------ ## missing missing setMethod("timeSeries", signature(data = "missing", charvec = "missing"), function (data, charvec, units = NULL, format = NULL, zone = "", FinCenter = "", recordIDs = data.frame(), title = NULL, documentation = NULL, ...) { .signalSeries(data = matrix(NA), units = units, recordIDs = recordIDs, title = title, documentation = documentation, ...) }) # ------------------------------------------------------------------------------ ## ANY ANY setMethod("timeSeries", signature(data = "ANY", charvec = "ANY"), function (data, charvec, units = NULL, format = NULL, zone = "", FinCenter = "", recordIDs = data.frame(), title = NULL, documentation = NULL, ...) { data <- as(data, "matrix") if (!is(data, "matrix")) stop("Could not coerce 'data' to a matrix") callGeneric(data = data, charvec = charvec, units = units, format = format, zone = zone, FinCenter = FinCenter, recordIDs = recordIDs, title = title, documentation = documentation, ...) }) # ------------------------------------------------------------------------------ ## ANY missing setMethod("timeSeries", signature(data = "ANY", charvec = "missing"), function (data, charvec, units = NULL, format = NULL, zone = "", FinCenter = "", recordIDs = data.frame(), title = NULL, documentation = NULL, ...) { data <- as(data, "matrix") if (!is(data, "matrix")) stop("Could not coerce 'data' to a matrix") callGeneric(data = data, units = units, format = format, zone = zone, FinCenter = FinCenter, recordIDs = recordIDs, title = title, documentation = documentation, ...) }) # ------------------------------------------------------------------------------ ## matrix missing setMethod("timeSeries", signature(data = "matrix", charvec = "missing"), function (data, charvec, units = NULL, format = NULL, zone = "", FinCenter = "", recordIDs = data.frame(), title = NULL, documentation = NULL, ...) { charvec <- rownames(data) if (is.null(charvec)) { .signalSeries(data = data, units = units, recordIDs = recordIDs, title = title, documentation = documentation, ...) } else { callGeneric(data = data, charvec = charvec, units = units, format = format, zone = zone, FinCenter = FinCenter, recordIDs = recordIDs, title = title, documentation = documentation, ...) } } ) # ------------------------------------------------------------------------------ ## matrix timeDate setMethod("timeSeries", signature(data = "matrix", charvec = "timeDate"), function (data, charvec, units = NULL, format = NULL, zone = "", FinCenter = "", recordIDs = data.frame(), title = NULL, documentation = NULL, ...) { if (any(is.na(charvec))) return(.signalSeries(data = data, units = units, recordIDs = recordIDs, title = title, documentation = documentation, ...)) if (any(!c(zone, FinCenter) %in% "")) charvec <- timeDate(charvec, format = format, zone = zone, FinCenter = FinCenter) .timeSeries(data = data, charvec = as.numeric(charvec, "sec"), units = units, format = charvec@format, FinCenter = charvec@FinCenter, recordIDs = recordIDs, title = title, documentation = documentation, ...) } ) # ------------------------------------------------------------------------------ ## matrix numeric setMethod("timeSeries", signature(data = "matrix", charvec = "numeric"), function (data, charvec, units = NULL, format = NULL, zone = "", FinCenter = "", recordIDs = data.frame(), title = NULL, documentation = NULL, ...) { if (any(!c(zone, FinCenter) %in% "")) { td <- timeDate(charvec, zone = zone, FinCenter = FinCenter) charvec <- as.numeric(td, "sec") FinCenter <- finCenter(td) } .timeSeries(data = data, charvec = charvec, units = units, FinCenter = FinCenter, recordIDs = recordIDs, title = title, documentation = documentation, ...) } ) # ------------------------------------------------------------------------------ ## matrix ANY setMethod("timeSeries", signature(data = "matrix", charvec = "ANY"), function (data, charvec, units = NULL, format = NULL, zone = "", FinCenter = "", recordIDs = data.frame(), title = NULL, documentation = NULL, ...) { # if charvec NULL returns a signal series if (is.null(charvec)) return(.signalSeries(data = data, units = units, recordIDs = recordIDs, title = title, documentation = documentation, ...)) # coerce charvec to timeDate charvec <- timeDate(charvec = charvec, format = format, zone = zone, FinCenter = FinCenter) if (any(is.na(charvec))) # Note : there is already a warning in timeDate if there are NA's .signalSeries(data = data, units = units, recordIDs = recordIDs, title = title, documentation = documentation, ...) else .timeSeries(data = data, charvec = as.numeric(charvec, "sec"), units = units, format = charvec@format, FinCenter = charvec@FinCenter, recordIDs = recordIDs, title = title, documentation = documentation, ...) } ) ################################################################################ timeSeries/R/stats-aggregate.R0000644000176200001440000001350715104730074016000 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # aggregate,timeSeries Aggregates a 'timeSeries' object # FUNCTION: DESCRIPTION: # daily2monthly Aggregates a daily to monthly 'timeSeries' object # daily2weekly Aggregates a daily to weekly 'timeSeries' object ################################################################################ ## GNB: 2024-01-11 GNB: streamlined and removed the S4 method aggregate.timeSeries <- function(x, by, FUN, ...) { # A function implemented by Yohan Chalabi and Diethelm Wuertz # Description: # Aggregates a 'timeSeries' object # Details: # This function can be used to aggregate and coursen a # 'timeSeries' object. # Arguments: # x - a 'timeSeries' object to be aggregated # by - a calendarical block # FUN - function to be applied, by default 'colMeans' # ... - additional argument to be passed to the newly generated # 'timeSeries' object # Value: # Returns a S4 object of class 'timeSeries'. # Examples: # Quarterly Aggregation: # m = matrix(rep(1:12,2), ncol = 2) # ts = timeSeries(m, timeCalendar()) # Y = getRmetricsOptions("currentYear"); Y # from = paste(Y, "04-01", sep = "-"); to = paste(Y+1, "01-01", sep = "-") # by = timeSequence(from, to, by = "quarter") - 24*3600; by # ts; aggregate(ts, by, sum) # Weekly Aggregation: # dates = timeSequence(from = "2009-01-01", to = "2009-02-01", by = "day") # data = 10 * round(matrix(rnorm(2*length(dates)), ncol = 2), 1); data # ts = timeSeries(data = data, charvec = dates) # by = timeSequence(from = "2009-01-08", to = "2009-02-01", by = "week") # by = by - 24*3600; aggregate(ts, by, sum) # FUNCTION: # Check Arguments: if (!((inherits(by, "timeDate") && x@format != "counts") || (is.numeric(by) && x@format == "counts"))) stop("'by' should be of the same class as 'time(x)'", call.=FALSE) # Extract Title and Documentation: Title <- x@title Documentation <- x@documentation # Make sure that x is sorted (by timedate): if (is.unsorted(x)) x <- sort(x) # Sort and remove duplicated entries in by: by <- unique(sort(by)) INDEX <- findInterval(x@positions, as.numeric(by, "sec") + 1) INDEX <- INDEX + 1 is.na(INDEX) <- !(INDEX <= length(by)) # YC : ncol important to avoid problems of dimension dropped by apply data <- matrix(apply(getDataPart(x), 2, tapply, INDEX, FUN), ncol=ncol(x)) rownames(data) <- as.character(by[unique(na.omit(INDEX))]) colnames(data) <- colnames(x) ans <- timeSeries(data, ...) # Preserve Title and Documentation: ans@title <- Title ans@documentation <- Documentation # Return Value: ans } ## GNB: 2024-01-11 GNB: streamlined and removed the S4 method ## setMethod("aggregate", "timeSeries", function(x, by, FUN, ...) ## .aggregate.timeSeries(x, by, FUN, ...)) ## ## # until UseMethod dispatches S4 methods in 'base' functions ## aggregate.timeSeries <- function(x, ...) .aggregate.timeSeries(x, ...) ################################################################################ daily2monthly <- function (x, init = FALSE) { # A function implemented by Diethelm Wuertz # Description: # Converts daily to monthly series # Arguments: # x - daily time series # init - should the index series converted to a wealth series # FUNCTION: # Save Colnames: colNames <- colnames(x) # Fill to end of Month: Time <- unique(sort(timeLastDayInMonth(time(x)))) x.endOfMonth <- x[nrow(x), ] time(x.endOfMonth) <- rev(Time)[1] x <- rbind(x, x.endOfMonth) x <- alignDailySeries(x, include.weekends=TRUE) # Cut Properly on end of Month: today <- timeDate(Sys.Date()) first <- timeFirstDayInMonth(today) x <- x[time(x) < first, ] Time <- unique(sort(timeLastDayInMonth(time(x)))) # Align Properly: mSeries <- alignDailySeries(x, include.weekends=TRUE) mSeries <- mSeries[Time, ] # Optionally Initialize: if (init) for (i in 1:ncol(mSeries)) mSeries[, i] <- mSeries[, i]/as.vector(mSeries[1, i]) colnames(mSeries) <- colNames # Return Value: mSeries } # ----------------------------------------------------------------------------- daily2weekly <- function(x, startOn="Tue", init=FALSE) { # A function implemented by Diethelm Wuertz # Description: # Converts daily to weekly series # Arguments: # x - daily time series # init - should the index series converted to a wealth series # FUNCTION: # Convert Series: mSeries <- alignDailySeries(x, include.weekends = TRUE) start <- which(dayOfWeek(time(mSeries[1:7, ])) == startOn) mSeries <- mSeries[seq(start, nrow(mSeries), by = 7), ] # Wealth Initialization: if (init) for (i in 1:ncol(mSeries)) mSeries[, i] <- mSeries[, i]/as.vector(mSeries[1, i]) # Return Value: mSeries } ############################################################################### timeSeries/R/base-applySeries.R0000644000176200001440000002626115104730074016127 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # applySeries Applies a function to blocks of a 'timeSeries' # fapply Applies a function to 'timeSeries' windows # DEPRECATED: (now removed) DESCRIPTION: # .applySeries Applies a function to blocks of a 'timeSeries' # .fapply Applies a function to 'timeSeries' windows ################################################################################ applySeries <- function(x, from = NULL, to = NULL, by = c("monthly", "quarterly"), FUN = colMeans, units = NULL, format = x@format, zone = x@FinCenter, FinCenter = x@FinCenter, recordIDs = data.frame(), title = x@title, documentation = x@documentation, ...) { # A function implemented by Diethelm Wuertz # Description: # Apply a function to the margins of a 'timeSeries' object # Details: # This function can be used to aggregate and coursen a # 'timeSeries' object. # Arguments: # x - a 'timeSeries' object to be aggregated # from, to - two 'timeDate' position vectors which size the # blocks # by - calendarical block, only active when both 'from' # and 'to' are NULL # FUN - function to be applied, by default 'colMeans' # units - a character vector with column names, allows to # overwrite the column names of the input 'timeSeries' # object. # Value: # Returns a S4 object of class 'timeSeries'. # Notes: # The size of the 'moving' window and the selection of an # 'adj'-acent endpoint are not needed, all the information # is kept in the 'from' and 'to' position vectors. # FUNCTION: # .Deprecated("aggregate", "timeSeries") # Check object: if (!inherits(x, "timeSeries")) stop("s is not a timeSeries object") ### if (x@format == "counts") ### stop(as.character(match.call())[1], ### " is for time series and not for signal series.") # Monthly and Quarterly from and to: if (is.null(from) & is.null(to)) { if (by[1] == "monthly") { # Use monthly blocks: from = unique(timeFirstDayInMonth(time(x))) to = unique(timeLastDayInMonth(time(x))) } else if (by[1] == "quarterly") { from = unique(timeFirstDayInQuarter(time(x))) to = unique(timeLastDayInQuarter(time(x))) } else { stop("by must be eiter monthly or quarterly") } from@FinCenter = to@FinCenter = FinCenter } # Column Names: colNames = units # Function: fun = match.fun(FUN) ### # Blocks: ### j.pos = as.POSIXct(time(x)) ### j.from = as.POSIXct(from) ### j.to = as.POSIXct(to) # Blocks: j.pos = time(x) if (is(j.pos, "timeDate")) { j.from = as.timeDate(from) j.to = as.timeDate(to) } else { j.from = as.integer(from) j.to = as.integer(to) } # Iterate: pos = time(x) rowNames = rownames(x) rowBind = NULL for (i in seq_len(length(from))) { test <- (j.pos >= j.from[i] & j.pos <= j.to[i]) if (!sum(test)) stop("outsite of range") # make sure that cutted is a matrix ... cutted = as.matrix(x[test, ]) # YC : *AND* make sure the matrix is not subbsetted to a vector!!! # YC : here it is fine because as.matrix of a timeSeries checks it # YC : but prefer to check it one more time at the end of the loop... ### if (sum(test)>0) rownames(cutted) <- rowNames[test] ans = fun(cutted, ...) rowBind = rbind(rowBind, ans) } stopifnot(NCOL(rowBind) == NCOL(x)) # YC : see above # YC : length(to) might not be == NCOL(rowBind) if (length(as.character(to)) == NROW(rowBind)) rownames(rowBind) = as.character(to) if (is.null(colNames)) { units = x@units } else { units = colNames } # Return Value: timeSeries(data = rowBind, units = units, format = format, zone = zone, FinCenter = FinCenter, recordIDs = recordIDs, title = title, documentation = documentation, ...) } # ------------------------------------------------------------------------------ fapply <- function(x, from, to, FUN, ...) { # .Deprecated("aggregate", "timeSeries") # Check x: stopifnot(is(x, "timeSeries")) if (x@format == "counts") stop(as.character(match.call())[1], " is for time series and not for signal series.") # Check for missing form/to: if(missing(from)) from = start(x) if(missing(to)) to = end(x) # Return Value: applySeries(x = x, from = from, to = to, FUN = FUN, ...) } ################################################################################ # *** OLD *** # Check if it is still used somewhere ... # 2022-10-07 GNB : apparently not, commenting out # .applySeries <- # function (x, from = NULL, to = NULL, by = c("monthly", "quarterly"), # FUN = colMeans, units = NULL, ...) # { # # Old/Alternative Version # # # Chreck for 'timeSeries' Object: # stopifnot(is.timeSeries(x), # is(from, "timeDate") || is.null(from), # is(to, "timeDate") || is.null(to)) # # # Allow for colMeans: # if (substitute(FUN) == "colMeans") FUN = "colAvgs" # # # Monthly and Quarterly from and to: # if (is.null(from) & is.null(to)) { # by = match.arg(by) # if (by == "monthly") { # from = unique(timeFirstDayInMonth(time(x))) # to = unique(timeLastDayInMonth(time(x))) # } # else if (by == "quarterly") { # from = unique(timeFirstDayInQuarter(time(x))) # to = unique(timeLastDayInQuarter(time(x))) # } # from@FinCenter = to@FinCenter = x@FinCenter # } # # # Start Cutting Process: # fun = match.fun(FUN) # cutted = NULL # i = 1 # # # Find First Interval which is not empty: # while (is.null(cutted)) { # cutted = cut(x, from[i], to[i]) # if (!is.null(cutted)) { # # Non empty Interval: # ans = fun(cutted, ...) # } # i = i + 1 # } # # Continue up to the end: # for (j in seq_len(length(from))) { # cutted = cut(x, from[j], to[j]) # if (!is.null(cutted)) { # # Non empty Interval: # newAns = fun(cutted, ...) # ans = rbind(ans, newAns) # } # } # # # Return Value: # ans # } ################################################################################ # *** OLD *** # Check if it is still used somewhere ... ## removed by GNB ## ## .fapply <- ## function(x, from, to, FUN, ...) ## { ## # A function implemented by Diethelm Wuertz ## ## # Description: ## # Applies a function to 'timeSeries' windows ## ## # Details: ## # This function can be used to aggregate and coursen a ## # 'timeSeries' object. ## ## # Arguments: ## # x - a 'timeSeries' object to be aggregated ## # from, to - two 'timeDate' position vectors which size the blocks ## # FUN - function to be applied, by default 'colMeans' ## ## # Value: ## # Returns a S4 object of class 'timeSeries' if FUN returns ## # a time series object, otherwise a list, where the entries ## # for each window is the output of the function FUN. ## ## # Notes: ## # The size of the 'moving' window and the selection of an ## # 'adj'-acent endpoint are not needed, all the information ## # is kept in the 'from' and 'to' position vectors. ## ## # FUNCTION: ## ## # Check object: ## if (!inherits(x, "timeSeries")) stop("s is not a timeSeries object") ## ## # Monthly and Quarterly from and to: ## if (is.null(from) & is.null(to)) { ## if (by[1] == "monthly") { ## # Use monthly blocks: ## from = unique(timeFirstDayInMonth(time(x))) ## to = unique(timeLastDayInMonth(time(x))) ## } else if (by[1] == "quarterly") { ## from = unique(timeFirstDayInQuarter(time(x))) ## to = unique(timeLastDayInQuarter(time(x))) ## } else { ## stop("by must be eiter monthly or quarterly") ## } ## from@FinCenter = to@FinCenter = x@FinCenter ## } ## ## # Column Names: ## colNames = units ## ## # Function: ## fun = match.fun(FUN) ## ## # Blocks: ## j.pos = as.POSIXct(time(x)) ## j.from = as.POSIXct(from) ## j.to = as.POSIXct(to) ## ## # Iterate: ## y = series(x) ## pos = time(x) ## rowNames = rownames(x) ## ## # Compute for the first window ... ## i = 1 ## test = (j.pos >= j.from[i] & j.pos <= j.to[i]) ## # make sure that cutted is a matrix ... ## cutted = as.matrix(y[test, ]) ## ### if (sum(test)>0) rownames(cutted) <- rowNames[test] ## ans = fun(cutted, ...) ## ## if (is.timeSeries(ans)) { ## ## DW can this happen - check ? ## rowBind = ans ## for (i in 2L:length(from)) { ## test = (j.pos >= j.from[1] & j.pos <= j.to[1]) ## # make sure that cutted is a matrix ... ## cutted = as.matrix(y[test, ]) ## ### if (sum(test)>0) rownames(cutted) <- rowNames[test] ## ans = fun(cutted, ...) ## rowBind = rbind(rowBind, ans) ## } ## rownames(rowBind) = as.character(to) ## if (is.null(colNames)) { ## units = x@units ## } else { ## units = colNames ## } ## # Return Value: ## ans = timeSeries(data = rowBind, charvec = as.character(to), ## units = units, format = format, zone = x@zone, FinCenter = ## x@FinCenter, recordIDs = x@recordIDs, title = x@title, ## documentation = x@documentation, ...) ## return(ans) ## } else { ## listBind = list() ## ## DW [] -> [[]] ## listBind[[1]] = ans ## for (i in 2L:length(from)) { ## test = (j.pos >= j.from[i] & j.pos <= j.to[i]) ## # make sure that cutted is a matrix ... ## cutted = as.matrix(y[test, ]) ## ### if (sum(test)>0) rownames(cutted) <- rowNames[test] ## ans = fun(cutted, ...) ## ## DW [] -> [[]] ## listBind[[i]] = ans ## } ## # Return Value: ## ans = listBind ## attr(ans, "control") <- list(x = x, from = from, to = to) ## return(invisible(ans)) ## } ## ## # Return Value: ## return() ## } ################################################################################ timeSeries/R/stats-window.R0000644000176200001440000000756615104730074015371 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # window,timeSeries Extracts a piece from a 'timeSeries' object ################################################################################ window.timeSeries <- function(x, start, end, ...) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Windows a piece from a 'timeSeries' object. # Arguments: # x - a 'timeSeries' object # from, to - two 'timeDate' position vectors which size the # blocks # Details: # from and to, are both included in the window. # Value: # Returns a S4 object of class 'timeSeries'. # FUNCTION: # Check Arguments: stopifnot(is.timeSeries(x)) # Extract Title and Documentation: Title <- x@title Documentation <- x@documentation # Check for Signal Series if (x@format == "counts") stop(as.character(match.call())[1], " is for time series and not for signal series.") # check if all argument names are used if (length(dot <- list(...))) { if (any(names(dot) %in% c("from", "to"))) { if (!is.null(from <- dot$from)) start <- from if (!is.null(to <- dot$to)) end <- to warning("Arguments 'from/to' are deprecated.\nUse instead 'start/end'.", call. = FALSE) } } start <- timeDate(start) end <- timeDate(end) Positions <- time(x) test <- (Positions >= start & Positions <= end) ans <- x[test,] # Preserve Title and Documentation: ans@title <- Title ans@documentation <- Documentation # Return Value: ans } ## (2024-01-05) GNB: stop making window() S4 ## setMethod("window", "timeSeries", ## function(x, start, end, ...) .window.timeSeries(x, start, end, ...)) ############################################################################### ## 2023-05-26 removed this cut method ## (it is not compatible with the purpose of the generic function cut) ## ## .cut.timeSeries <- ## function (x, from, to, ...) ## { ## # A function implemented by Diethelm Wuertz and Yohan Chalabi ## ## # Description: ## # Cuts out a piece from a 'timeSeries' object. ## ## # Arguments: ## # x - a 'timeSeries' object ## # from, to - two 'timeDate' position vectors which size the ## # blocks ## ## # Value: ## # Returns a S4 object of class 'timeSeries'. ## ## # FUNCTION: ## ## .Deprecated("window", "timeSeries") ## ## stopifnot(is.timeSeries(x)) ## if (x@format == "counts") ## stop(as.character(match.call())[1], ## " is for time series and not for signal series.") ## ## from = timeDate(from) ## to = timeDate(to) ## Positions = time(x) ## ## test = (Positions >= from & Positions <= to) ## ans <- x[test,] ## ## # Return value: ## ans ## } ## ## ## setMethod("cut", "timeSeries", ## function (x, from, to, ...) .cut.timeSeries(x, from, to, ...)) ## ## ## # until UseMethod dispatches S4 methods in 'base' functions ## cut.timeSeries <- function(x, ...) .cut.timeSeries(x, ...) ################################################################################ timeSeries/R/statistics-orderStatistics.R0000644000176200001440000000301615104730074020266 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # orderStatistics Compute order statistic of a 'timeSeries' object ################################################################################ orderStatistics <- function(x) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Compute the order statistics for a 'timeSeries object # Value: # A named list with the order statistics for each column of # the inputted series. # FUNCTION: # Order Statistics: td <- time(x) # Return Value: mapply( function(cl, nm) { S <- sort(cl, index.return = TRUE) timeSeries(data = S$x, charvec = td[S$ix], units = nm)}, as.list(x), colnames(x), SIMPLIFY = FALSE) } ################################################################################ timeSeries/R/base-rank.R0000644000176200001440000000377015104730074014562 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: COLUMN STATISTICS IN FUTILITIES: # rank,timeSeries Returns sample ranks of a 'timeSeries' object ################################################################################ setMethod("rank", "timeSeries", function(x, na.last = TRUE, ties.method = eval(formals(rank)$ties.method)) { # Description: # Returns the sample ranks of the values in a 'timeSeries' # Arguments: # x - an object of class 'timeSeries' # ties.method - # "average", replaces them by their mean, # "first" method results in a permutation with increasing # values at each index set of ties. # "random" method puts these in random order whereas the # default, # "max" and "min" replaces them by their maximum and minimum # respectively, the latter being the typical sports ranking. # Note: # Ties (i.e., equal values) and missing values can be handled # in several ways. # FUNCION: # Return Value: apply(x, 2, rank, na.last = na.last, ties.method = ties.method) } ) ################################################################################ timeSeries/R/base-attach.R0000644000176200001440000000265615104730074015075 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # S4 METHOD: DATABASE ATTACHEMENT: # attach,timeSeries Attaches a 'timeSeries' object to the search path ################################################################################ setMethod("attach", "timeSeries", function(what, pos = 2, name = deparse(substitute(what)), warn.conflicts = TRUE) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Attaches a 'timeSeries' object # Details: # The function works in the same way as in the case of a # data.frame, i.e. the return values are vectors. # FUNCTION: # Return Value: callGeneric(as.data.frame(what), pos, name, warn.conflicts) }) ################################################################################ timeSeries/R/AllClass.R0000644000176200001440000001272515104730074014415 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # CLASS: REPRESENTATION: # setClass Classify 'timeSeries' # setValidity Validate 'timeSeries' # setMethod Initialize 'timeSeries' ################################################################################ # CLASS: REPRESENTATION: # 'signalSeries' Deprecated S4 Class representation # 'timeSeries' Deprecated S4 Class representation ################################################################################ # YC: Note if slots are added or removed, don't forget to edit # getDataPart,timeSeries-method and setDataPart,timeSeries-method !! setClass("timeSeries", representation(.Data = "matrix", units = "character", positions = "numeric", format = "character", FinCenter = "character", recordIDs = "data.frame", title = "character", documentation = "character"), contains = "structure", prototype(matrix(NA), units = character(0), positions = numeric(0), format = character(0), FinCenter = character(0), recordIDs = data.frame(), title = character(0), documentation = character(0))) # ------------------------------------------------------------------------------ .validity_timeSeries <- function(object) { if ((length(object@positions) > 0) && NROW(object) != length(object@positions)) return("length of '@positions' not equal to '@.Data' extent") if (NCOL(object) != length(object@units)) return("length of '@units' not equal to '@.Data' extent") if (NROW(object@recordIDs) > 0 & NROW(object@recordIDs) != nrow(object)) return("length of '@recordIDs' not equal to '@.Data' extent") # Return Value: TRUE } setValidity("timeSeries", .validity_timeSeries) # ------------------------------------------------------------------------------ # Note it is faster to assign manually all slots of the timeSeries objects. setMethod("initialize", "timeSeries", function(.Object, .Data = new("matrix"), units = character(0), positions = numeric(0), format = character(0), FinCenter = "", #<< FIXME: use identical in code rather than FinCenter == "" recordIDs = data.frame(), title = character(0), documentation = character(0)) { # as.double -> crucial for speed improvement in subsetting if (!is.double(positions)) positions <- as.double(positions) .Object <- timeSeries::setDataPart(.Object, value = .Data) `slot<-`(.Object, "units", value = units) `slot<-`(.Object, "positions", value = positions) `slot<-`(.Object, "format", value = format) `slot<-`(.Object, "FinCenter", value = FinCenter) `slot<-`(.Object, "recordIDs", value = recordIDs) `slot<-`(.Object, "title", value = title) `slot<-`(.Object, "documentation", value = documentation) # Check only one we needs rather than using validObject anyStrings <- function(x) if (identical(x, TRUE)) character() else x error <- anyStrings(.validity_timeSeries(.Object)) if (length(error) > 0) stop(paste("Initialize timeSeries :", error, collapse = "\n"), call. = FALSE, domain = NA) # Return Value: .Object }) ################################################################################ ## setClass("signalSeries", ## representation( ## .Data = "matrix", ## units = "character", ## recordIDs = "data.frame", ## title = "character", ## documentation = "character"), ## contains = "structure", ## validity = function(object) { ## if (NCOL(getDataPart(object)) != length(object@units)) ## return("length of '@units' not equal to '@.Data' extent") ## TRUE ## }) ## # ------------------------------------------------------------------------------ ## setClass("timeSeries", ## representation(positions = "numeric", ## format = "character", ## FinCenter = "character"), ## contains = "signalSeries", ## validity = function(object) { ## if (NROW(getDataPart(object)) != length(object@positions)) ## return("length of '@positions' not equal to '@.Data' extent") ## if (NCOL(getDataPart(object)) != length(object@units)) ## return("length of '@units' not equal to '@.Data' extent") ## TRUE ## }) ################################################################################ timeSeries/R/fin-cumulated.R0000644000176200001440000000573215104730074015454 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ############################################################################### # FUNCTION: DESCRIPTION: # cumulated Computes cumulated series from financial returns # cumulated.default Computes cumulated series, default method ############################################################################### cumulated <- function(x, ...) { # A function implemented by Diethelm Wuertz # Description: # Computes cumulated series from financial returns # Return Value: UseMethod("cumulated") } # ------------------------------------------------------------------------------ cumulated.default <- function(x, method = c("continuous", "discrete", "compound", "simple"), percentage = FALSE, ...) { # A function implemented by Diethelm Wuertz # Description: # Computes cumulated series from financial returns # supports 'matrix' and 'timeSeries'. # Arguments: # x - data object containing ordered price observations # method - "continuous == "compound" and "discrete" == "simple" # Example: # X = as.timeSeries(data(msft.dat))[1:10, "Close"]; X = X/series(X)[1, 1] # x = returns(X, "continuous"); x; X; cumulated(x, "continuous") # x = returns(X, "discrete"); x; X; cumulated(x, "discrete") # Note: # To make it conform with PortfolioAnalytics: # "compound" == "continuous", and "simple" == "discrete" # FUNCTION: # Check Arguments: stopifnot(is.timeSeries(x)) # Settings: method <- match.arg(method) # Extract Title and Documentation: Title <- x@title Documentation <- x@documentation # Handle Missing Values: # if (na.rm) x = na.omit(x, ...) # Transform data: if (percentage) x <- x/100 positions <- time(x) # Calculate Cumulates: # ... colCumsums and colCumprods are generic functions with # methods for 'matrix' and 'timeSeries'. if(method == "geometric") { ans <- colCumsums(x) } if(method == "compound" || method == "continuous") { ans <- exp(colCumsums(x)) } if(method == "simple" || method == "discrete") { ans <- colCumprods(1+x) } # Preserve Title and Documentation: ans@title <- Title ans@documentation <- Documentation # Return Value: ans } ################################################################################ timeSeries/R/timeSeries-slotUnits.R0000644000176200001440000000344615104730074017032 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # getUnits Get units slot from a 'timeSeries' # setUnits<- Set new units slot to a 'timeSeries' ################################################################################ getUnits <- function(x) { # A function implemented by Diethelm Wuertz # FUNCTION: # Return Value: UseMethod("getUnits") } getUnits.default <- function(x) { # Description: # Get units slot from a 'timeSeries' object. # Arguments: # x - a 'timeSeries' object # FUNCTION: # Return Value: colnames(x) } # ------------------------------------------------------------------------------ "setUnits<-" <- function(x, value) { # Description: # Set units slot to a 'timeSeries' object. # Arguments: # x - a 'timeSeries' object # FUNCTION: # Assign Time Slot: colnames(x) <- value # Return Value: x } ################################################################################ timeSeries/R/base-dim.R0000644000176200001440000002022415104730074014371 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # S4 METHOD: DIM OPERATIONS ON DATA: # dim,timeSeries Returns dimension of a 'timeSeries' object # dim<-,timeSeries Assigns dimension of a 'timeSeries' object # dimnames,timeDSeries Returns dimension names of a 'timeSeries' object # dimnames<-,timeSeries Assign dimension names of a 'timeSeries' object # colnames,timeSeries Return column names to a 'timeSeries' object # rownames,timeSeries Return row names to a 'timeSeries' object # colnames<-,timeSeries Assigns column names to a 'timeSeries' object # rownames<-,timeSeries Assigns row names to a 'timeSeries' object # names,timeSeries Return column names of a 'timeSeries' object # names<.,timeSeries Assigns column names of a 'timeSeries' object ################################################################################ # Base Functions: # Generate from Matrix: # edhec.tS = timeSeries(edhec.mat, rownames(edhec.mat)) # edhec.ts = ts(edhec.mat, start = c(1997, 1), frequency = 12) # Univariate time Series: # edhec1.tS = edhec.tS[, 1] # dim # dim(edhec.tS) # 20 4 # dim(edhec1.tS) # 20 1 # DIM # DIM = function(x) {c(NROW(x), NCOL(x))} # DIM(edhec.tS) # 20 4 # DIM(edhec1.tS) # 20 1 # length # length(edhec.tS) # 1 # # LENGTH # LENGTH = function(x) NROW(x) # LENGTH(edhec.tS) # 20 # LENGTH(edhec1.tS) # 20 # # ncol / nrow # ncol(edhec.tS) # 4 # # ncol(edhec1.tS) # 1 # # NCOL / NRWO # NCOL(edhec.tS) # 4 # # NCOL(edhec1.tS) # 1 # # isUnivariate # isUnivariate = function(x) NCOL(x) == 1 # isUnivariate(edhec.tS) # isUnivariate(edhec1.tS) # # isMultivariate # Just Negation of isUnivariate # # # # ------------------------------------------------------------------------------ # length # dim # ncol # nrow # LENGTH # DIM # NCOL # NROW # ------------------------------------------------------------------------------ # Note it is faster to access attribute rather than accessing @.Data setMethod("dim", "timeSeries", function(x) attr(x, "dim")) # This should make functions like # model.response(model.frame(dummyMonthlySeries() ~1)) work setReplaceMethod("dim", "timeSeries", function(x, value) { # dim(x) <- NULL returns a vector if (is.null(value)) return(as.vector(x)) else x #<< returns same object : # setting new dim # is forbidden for a timeSeries object } ) # ------------------------------------------------------------------------------ # colnames - faster to have dedicated method than relying on dimnames[[2]] setMethod("colnames", "timeSeries", # "signalSeries", function(x, do.NULL = TRUE, prefix = "col") x@units ) # ------------------------------------------------------------------------------ # rownames ## setMethod("rownames", "signalSeries", ## function (x, do.NULL = TRUE, prefix = "row") NULL) ## setMethod("rownames", "timeSeries", ## function (x, do.NULL = TRUE, prefix = "row") as.character(time(x))) setMethod("rownames", "timeSeries", function (x, do.NULL = TRUE, prefix = "row") { if (length(x@positions) > 0) as.character(time(x)) else NULL } ) # ------------------------------------------------------------------------------ setMethod("dimnames", "timeSeries", # "signalSeries", function(x) { list(rownames(x),colnames(x)) } ) # ------------------------------------------------------------------------------ setMethod("colnames<-", "timeSeries", function(x, value) { units <- as.character(value) if(!length(units)) if (x@format == "counts") units <- paste("SS", seq(NCOL(x)), sep = ".") else units <- paste("TS", seq(NCOL(x)), sep = ".") if (length(units) != NCOL(x)) stop("length of 'colnames' not equal to array extent",call.=FALSE) x@units <- units colnames(x@.Data) <- units x } ) # ------------------------------------------------------------------------------ setMethod("rownames<-", c("timeSeries", "timeDate"), #c("signalSeries", "timeDate"), function (x, value) { .timeSeries( data = getDataPart(x), charvec = as.numeric(value, "sec"), units = colnames(x), format = value@format, FinCenter = value@FinCenter, recordIDs = x@recordIDs, title = x@title, documentation = x@documentation) } ) # ------------------------------------------------------------------------------ setMethod("rownames<-", "timeSeries", # "signalSeries", function (x, value) { # if charvec NULL returns a signal series if (is.null(value)) return(.signalSeries(data = getDataPart(x), units = colnames(x), recordIDs = x@recordIDs, title = x@title, documentation = x@documentation)) # coerce charvec to timeDate charvec <- timeDate(charvec = value) if (any(is.na(charvec))) # Note : there is already a warning in timeDate if there are NA's .signalSeries(data = getDataPart(x), units = colnames(x), recordIDs = x@recordIDs, title = x@title, documentation = x@documentation) else .timeSeries(data = getDataPart(x), charvec = as.numeric(charvec, "sec"), units = colnames(x), format = charvec@format, FinCenter = charvec@FinCenter, recordIDs = x@recordIDs, title = x@title, documentation = x@documentation) } ) # ------------------------------------------------------------------------------ setMethod("dimnames<-", c("timeSeries", "list"), # c("signalSeries", "list"), function(x, value) { rownames(x) <- value[[1]] colnames(x) <- value[[2]] x } ) # ------------------------------------------------------------------------------ # important for completion with $ setMethod("names", "timeSeries", # "signalSeries", function(x) c(colnames(x), names(x@recordIDs))) setReplaceMethod("names", "timeSeries", # "signalSeries", function(x, value) { nc <- ncol(x) nv <- length(value) nr <- length(x@recordIDs) # Note that using [][] ensure that length of the # names are equal to array extent colnames(x) <- value[seq.int(nv)][seq.int(nc)] if (nv > nc) names(x@recordIDs) <- value[-seq.int(nc)][seq.int(nr)] x }) ################################################################################ timeSeries/R/fin-periodical.R0000644000176200001440000001442615104730074015604 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # endOfPeriodSeries Returns series back to a given period # endOfPeriodStats Returns statistics back to a given period # endOfPeriodBenchmarks Returns benchmarks back to a given period ################################################################################ endOfPeriodSeries <- function(x, nYearsBack = c("1y", "2y", "3y", "5y", "10y", "YTD")) { # A function implemented by Diethelm Wuertz # Description: # Returns series back to a given period # Arguments: # x - a monthly 'timeSeries' object of returns # nYearsBack - a period string. How long back should the series # be extracted? Options include values from 1 year to 10 years. # and year to date: "1y", "2y", "3y", "5y", "10y", "YTD". # FUNCTION: # Check: stopifnot(is.timeSeries(x)) # Match Arguments: nYearsBack <- match.arg(nYearsBack) # Settings: if (nYearsBack == "YTD") monthsBack = atoms(end(x))$m else if (nYearsBack == "1y") monthsBack = 12 else if (nYearsBack == "2y") monthsBack = 24 else if (nYearsBack == "3y") monthsBack = 36 else if (nYearsBack == "5y") monthsBack = 60 else if (nYearsBack == "10y") monthsBack = 120 stopifnot( nrow(x) >= monthsBack ) # ReturnValue: rev(rev(x)[1:monthsBack, ]) } # ------------------------------------------------------------------------------ endOfPeriodStats <- function(x, nYearsBack = c("1y", "2y", "3y", "5y", "10y", "YTD")) { # A function implemented by Diethelm Wuertz # Description: # Returns series statistics back to a given period # Arguments: # x - a monthly 'timeSeries' object of returns # nYearsBack - a period string. How long back should the series # be extracted? Options include values from 1 year to 10 years. # and year to date: "1y", "2y", "3y", "5y", "10y", "YTD". # FUNCTION: # Check: stopifnot(is.timeSeries(x)) # Match Arguments: nYearsBack <- match.arg(nYearsBack) # Series: Series <- endOfPeriodSeries(x, nYearsBack = nYearsBack) # Internal Function: .cl.vals <- function(x, ci) { x = x[!is.na(x)] n = length(x) if (n <= 1) return(c(NA, NA)) se.mean = sqrt(var(x)/n) t.val = qt((1 - ci)/2, n - 1) mn = mean(x) lcl = mn + se.mean * t.val ucl = mn - se.mean * t.val c(lcl, ucl) } # Statistics: for (i in 1:ncol(Series)) { # Basic Statistics: X = as.vector(Series[, i]) X.length = length(X) X = X[!is.na(X)] X.na = X.length - length(X) ci = 0.95 z = c(X.length, X.na, min(X), max(X), as.numeric(quantile(X, prob = 0.25, na.rm = TRUE)), as.numeric(quantile(X, prob = 0.75, na.rm = TRUE)), mean(X), median(X), sum(x), sqrt(var(X)/length(X)), .cl.vals(X, ci)[1], .cl.vals(X, ci)[2], var(X), sqrt(var(X)), skewness(X), kurtosis(X)) znames = c("nobs", "NAs", "Minimum", "Maximum", "1. Quartile", "3. Quartile", "Mean", "Median", "Sum", "SE Mean", "LCL Mean", "UCL Mean", "Variance", "Stdev", "Skewness", "Kurtosis") stats1 <- matrix(z, ncol = 1) row.names(stats1) <- znames # Monthly Return Statistics: xData <- as.vector(x) noNegativePeriods <- length(xData[xData < 0 ]) noPositivePeriods <- length(xData[xData > 0 ]) stats1 = rbind(stats1, worstPeriod = min(xData), negativeValues = noNegativePeriods, positiveValues = noPositivePeriods) MaximumDrawdown = NA TimeUnderWater = NA AnnualizedVolatility = NA SharpeRatio = NA InformationRatio = NA ValueAtRisk = NA ExpectedShortfall = NA # Bind: if (i > 1) { stats <- cbind.data.frame(stats, stats1) } else { stats <- stats1 } } colnames(stats) <- colnames(x) # Return Value: stats } # ------------------------------------------------------------------------------ ## 2022-10-09 :TODO: GNB: this function is unfinished and returns NULL. ## It should be removed or completed. ## I don't know what it is supposed to do. endOfPeriodBenchmarks <- function(x, benchmark = ncol(x), nYearsBack = c("1y", "2y", "3y", "5y", "10y", "YTD")) { # A function implemented by Diethelm Wuertz # Description: # Returns benchmarks back to a given period # Arguments: # x - a monthly 'timeSeries' object of financial returns # nYearsBack - a period string. How long back should the series # be extracted? Options include values from 1 year to 10 years. # and year to date: "1y", "2y", "3y", "5y", "10y", "YTD". # FUNCTION: # Checks: stopifnot(is.timeSeries(x)) # Match Arguments: nYearsBack <- match.arg(nYearsBack) # Series: Series <- endOfPeriodSeries(x[, -benchmark], nYearsBack = nYearsBack) y <- Benchmark <- endOfPeriodSeries(x[, benchmark], nYearsBack = nYearsBack) stats <- NULL for (i in 1:ncol(Series)) { # Gdet Series: x <- Series[, i] # Compute Statistics: stats1 <- c( TrackingError = NA, Alpha = NA, Beta = NA, CorrelationToBenchmark = NA) # Bind Results: stats <- rbind(stats, stats1) } # Return Value: ## GNB: stats? invisible() } ################################################################################ timeSeries/R/timeSeries-signalCounts.R0000644000176200001440000000267615104730074017503 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # .signalCounts Creates charvec for integer indexed time stamps ################################################################################ .signalCounts <- function(int) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Descriptions: # Creates the charvec for integer indexed time stamps # Arguments: # int - a vector of integers, the counts. # FUNCTION: # Check that int is an integer # ... # Check that all int's are positive ... # ... # Format: cint <- as.character(int) ans <- format(cint, width = max(nchar(cint)), justify = "right") # Return Value: ans } ################################################################################ timeSeries/R/utils-old2new.R0000644000176200001440000000524415104730074015425 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ .old2newTimeSeries <- function(x) { # Version 1: if ("Data" %in% slotNames(x)) { data <- x@Data charvec <- timeDate(x@positions, zone = x@FinCenter, FinCenter = x@FinCenter) units <- x@units recordIDs <- x@recordIDs title <- x@title documentation <- x@documentation x <- timeSeries(data = data, charvec = charvec, units = units, recordIDs = recordIDs, title = title, documentation = documentation) } # Version 2: if ((".Data" %in% slotNames(x)) && is.character(x@positions)) { data <- x@.Data charvec <- timeDate(x@positions, zone = x@FinCenter, FinCenter = x@FinCenter) units <- x@units recordIDs <- x@recordIDs title <- x@title documentation <- x@documentation x <- timeSeries(data = data, charvec = charvec, units = units, recordIDs = recordIDs, title = title, documentation = documentation) } x } # ------------------------------------------------------------------------------ ## # Example ## library(timeSeries) ## setwd("~/r/fPortfolio/data") ## rda <- dir() ## sapply(rda, .old2newRda, suffix = "") .old2newRda <- function(file, suffix = "_new") { stopifnot(length(file) == 1) local({ load(file) nm <- ls() lold <- mget(nm, envir = environment(NULL)) test <- sapply(lold, is.timeSeries) lold <- lold[test] lnew <- lapply(lold, .old2newTimeSeries) objects <- names(lold) for (nm in objects) assign(nm, lnew[[nm]]) newFile <- paste(file, suffix, sep = "") save(list = objects, file = newFile) }) invisible(TRUE) } ################################################################################ timeSeries/R/methods-plot2.R0000644000176200001440000004700415104730074015416 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ############################################################################### # FUNCTION: DESCRIPTION: # .xtplot.timeSeries Plots a 'timeSeries' object # ... support for at = c("pretty", "chic") ############################################################################### # FUNCTION: DESCRIPTION: # .xtsPlot Internal xts plot unitility # .axTicksByTime2 Takes care of "chic" axis creation # .endpoints2 ... determines appropriate axis end points # .periodicity2 ... determines appropriate axis periodicity # .colorwheelPalette ############################################################################### # .plot.timeSeries <- # function( # x, y, FinCenter = NULL, # plot.type = c("multiple", "single"), # format = "auto", at = pretty(x), # widths = 1, heights = 1, # xy.labels, xy.lines, panel = lines, nc, yax.flip = FALSE, # mar.multi = c(0, 5.1, 0, if (yax.flip) 5.1 else 2.1), # oma.multi = c(6, 0, 5, 0), axes = TRUE, ...) # x=dummyMonthlySeries(); y = NULL; FinCenter = NULL; plot.type = "s" # format = "auto"; at = "pretty"; panel = lines; yax.flip = FALSE # mar.multi = c(0, 5.1, 0, if (yax.flip) 5.1 else 2.1) # oma.multi = c(7.75, 1.1, 6.1, 1.1) # dots <- list() # y = NULL; FinCenter = NULL # plot.type = c("single", "multiple") # format = "auto"; at = c("pretty", "chic") # panel = lines; yax.flip = FALSE # mar.multi = c(0, 5.1, 0, if (yax.flip) 5.1 else 2.1) # oma.multi = c(7.75, 1.1, 6.1, 1.1) # axes = TRUE # ... <- NULL .xtplot.timeSeries <- function( x, y = NULL, FinCenter = NULL, plot.type = c("single", "multiple"), format = "auto", at = c("pretty", "chic"), panel = lines, yax.flip = FALSE, mar.multi = c(0, 5.1, 0, if (yax.flip) 5.1 else 2.1), oma.multi = c(7.75, 1.1, 6.1, 1.1), # oma.multi = c(6, 0, 5, 0), axes = TRUE, ...) { # A function implemented by Diethelm Wuertz # Description: # Plots timeSeries objects - Internal Function # Details: # A modified copy of R's internal 'plotts()' function, # see 'plot.ts()'. # FUNCTION: dots <- list(...) minor.ticks <- dots$minor.ticks %||% "auto" type <- dots$type %||% "l" col <- dots[["col"]] %||% { 1:NCOL(x) } pch <- dots$pch %||% 20 cex <- dots$cex %||% 1 lty <- dots$lty %||% 1 lwd <- dots[["lwd"]] %||% 1 grid <- dots$grid %||% TRUE col.grid <- dots$col.grid %||% "darkgrey" lwd.grid <- dots$lwd.grid %||% 1 frame.plot <- dots$frame.plot %||% TRUE ann <- dots$ann %||% TRUE cex.axis <- dots$cex.axis %||% 1 cex.lab <- dots$cex.lab %||% 1 cex.pch <- dots$cex.pch %||% 1 log <- dots$log %||% "" equilogs <- dots$equilogs %||% TRUE main <- dots$main %||% "" xlab <- dots$xlab %||% "" ylab <- dots$ylab %||% { cn <- colnames(x) if(length(cn) > 1 && (plot.type == "single" || plot.type == "s")) "Values" else cn } xax <- dots[["xax"]] %||% FALSE xaxs <- dots$xaxs %||% "r" yaxs <- dots$yaxs %||% "r" # Continue ... if (minor.ticks == "auto") minor.ticks <- .periodicity2(x)$units if (at[1] == "chic") minor.ticks <- TRUE if (format != "auto") minor.ticks <- TRUE # FinCenter - take care of it: if (!is.null(FinCenter)) { finCenter(x) <- FinCenter if (!missing(y)) finCenter(y) <- FinCenter if (is(at, "timeDate")) at@FinCenter <- FinCenter } # Plot Type: plot.type <- plot.type[1] if(isUnivariate(x)) plot.type <- "single" if(is.timeSeries(y)) plot.type <- "scatter" # Axis Positions and Format: AT <- at[1] FORMAT <- format[1] if (x@format == "counts") FORMAT <- "counts" # Decorations: # if (is.null(col)) col <- 1:ncol(x) # if (col[1] == 0) col = 1 else col <- .colorwheelPalette(ncol(x)) # if (is.null(pch)) pch <- 20 # if (is.null(cex)) cex <- 1 # if (is.null(lty)) lty <- 1 # if (is.null(lwd)) lwd <- 2 if(is.null(type[1])) type <- "l" if (length(type) == 1) type <- rep(type, times=NCOL(x)) if (length(col) == 1) col <- rep(col, times=NCOL(x)) if (length(pch) == 1) pch <- rep(pch, times=NCOL(x)) if (length(cex) == 1) cex <- rep(cex, times=NCOL(x)) if (length(lty) == 1) lty <- rep(lty, times=NCOL(x)) if (length(lwd) == 1) lwd <- rep(lwd, times=NCOL(x)) if (length(cex.pch) == 1) cex.pch <- rep(cex.pch, times=NCOL(x)) if (length(ylab) == 1) ylab <- rep(ylab, times=NCOL(x)) TIME <- time(x) if (is.integer(TIME)) { X <- TIME AT <- "counts" } else { X <- as.POSIXct(TIME) } Y <- series(x) if (AT == "pretty") { at <- pretty(x) } if (AT == "chic" ) { ep <- .axTicksByTime2(x, format=FORMAT) at <- TIME[ep] } # SINGLE PLOT: if (plot.type == "single" || plot.type == "s") { # All curves in one Frame: ylim <- dots$ylim %||% range(Y, na.rm=TRUE) xlim <- dots$xlim # even if it is NULL plot(X, Y[,1], type= "n", xlim = xlim, ylim = ylim, axes = FALSE, main = "", xlab = "", ylab = "", log=log, xaxs=xaxs, yaxs=yaxs) for (i in 1:ncol(x)) { lines(X, series(x)[, i], type = type[i], col = col[i], lty = lty[i], lwd = lwd[i], pch = pch[i], cex = cex.pch[i]) } if (ann) { title(main = main, xlab = xlab, ylab = ylab[1], cex.lab = cex.lab) } if (axes) { # Y - Axis: axis(2, cex.axis = cex.axis) } if (axes || xax) { # X - Axis: if (AT == "counts") { axis(1, cex.axis = cex.axis) } else if (AT == "pretty") { at <- pretty(time(x)) if (FORMAT == "auto") format <- "%Y-%m-%d" if (!is.null(minor.ticks)) { minor.at <- timeSequence(time(x)[1], time(x)[nrow(x)],by = minor.ticks) axis.POSIXct(1, at=minor.at, labels=FALSE, col='#BBBBBB',cex.axis = cex.axis) } axis.POSIXct(1, at = at, format = format, cex.axis = cex.axis) } else if (AT == "chic" ) { ep <- .axTicksByTime2(x, format=FORMAT) if (minor.ticks) axis.POSIXct(1, at=TIME, labels=FALSE, col='#BBBBBB',cex.axis = cex.axis) axis.POSIXct(1, at = TIME[ep], labels=names(ep),las=1, lwd=1, mgp=c(3, 1, 0), cex.axis = cex.axis) } else { if (minor.ticks) { axis.POSIXct(1, at=TIME, labels=FALSE, col='#BBBBBB', cex.axis = cex.axis) axis.POSIXct(1, at = at, format = format, cex.axis=cex.axis) } } } if (frame.plot) { box("plot") } if(grid) { if (!(AT %in% c("pretty","chic"))) at <- axTicks(1) abline(v = at, lty = 3, col = col.grid, lwd = lwd.grid) grid(NA, NULL, lty = 3, col = col.grid, lwd = lwd.grid, equilogs=equilogs) } return(invisible()) } # MULTIPLE PLOT: if (plot.type == "multiple" || plot.type == "m") { nser <- ncol(x) nc <- if (nser > 4) 2 else 1 nr <- ceiling(nser/nc) oldpar <- par(mar = mar.multi, oma = oma.multi, mfcol = c(nr, nc)) on.exit(par(oldpar)) for (i in 1:nser) { plot(X, Y[, i], axes = FALSE, ann = TRUE, type = "n", xlab = "", ylab = "", # log = log, col = col[i], pch = pch[i], lty = lty[i], lwd = lwd[i], cex = cex[i]) panel(X, Y[, i], type = type[i], xlab = "", ylab = "", col = col[i], pch = pch[i], lty = lty[i], lwd = lwd[i], cex = cex.pch[i]) y.side <- if (i%%2 || !yax.flip) 2 else 4 do.xax <- i%%nr == 0 || i == nser if (frame.plot) { box() } if (axes) { axis(y.side, xpd = NA, cex.axis=cex.axis) } if (axes || xax) { if (do.xax) { if (AT == "counts") { axis(1, cex.axis = 1.2 * cex.axis) at <- axTicks(1) } else if (AT == "pretty") { at <- pretty(time(x)) if (FORMAT == "auto") format <- "%Y-%m-%d" TIME <- time(x) if (!is.null(minor.ticks)) { minor.at <- timeSequence( time(x)[1], time(x)[nrow(x)], by=minor.ticks) axis.POSIXct(1, at=minor.at, labels=FALSE, cex.axis = 1.2 * cex.axis, col='#BBBBBB') } axis.POSIXct(1, at = at, format = format, cex.axis = 1.2 * cex.axis) } else if (AT == "chic" ) { ep <- .axTicksByTime2(x, format=FORMAT) at <- time(x)[ep] format <- attr(ep, "format") formatLabels <- names(ep) TIME <- time(x) if (minor.ticks) axis.POSIXct(1, at=TIME, labels=FALSE, col='#BBBBBB', cex.axis = 1.2 * cex.axis) axis.POSIXct(1, at = TIME[ep], labels=names(ep), las=1, lwd=1, mgp=c(3, 1, 0), cex.axis = cex.axis) } else { TIME <- time(x) if (minor.ticks) axis.POSIXct(1, at=TIME, labels=FALSE, col='#BBBBBB', cex.axis = 1.2 * cex.axis) axis.POSIXct(1, at = at, format = format, cex.axis = 1.2 *cex.axis) } } } if (ann) { mtext(text = ylab[i], side = y.side, line = 3, cex = cex.lab) if (do.xax) mtext(xlab, side = 1, line = 3, cex = cex.lab) if (i==1) { cex.main <- if (is.null(dots$cex.lab)) par("cex.main") else cex.lab mtext(main, side = 3, line = 3, cex = cex.main, font = par("font.main"), col = par("col.main")) } } if(grid) { abline(v = at, lty = 3, col = col.grid, lwd = lwd.grid) grid(NA, NULL, lty = 3, col = col.grid, lwd = lwd.grid, equilogs=equilogs) } } # end of nser loop return(invisible()) } # SCATTER PLOT: if (!is.null(y)) { stopifnot (isUnivariate(x)) stopifnot (isUnivariate(y)) plot(series(x), series(y), xlab="", ylab="", col=col, pch=pch, cex=cex) return(invisible()) } } ############################################################################### # Test function for xts-plot-like axis positions and labels. .xtsPlot <- function(x, y=NULL, type = "l", ann = TRUE, axes = TRUE, major.ticks = 'auto', minor.ticks = TRUE, major.format = TRUE, grid = TRUE, box = TRUE, ...) { # A function written by Diethelm Wuertz # Descroption: # A simple example to test the xts functions to generate # nice axis positions and Lebels # Example: # x <- 100 * cumulated(LPP2005REC[, 2]); xtsPlot(x) # Settings: # time.scale <- periodicity2(x)$scale ep <- .axTicksByTime2(x, major.ticks, format.labels=major.format) # PLOT COORDS: xycoords <- xy.coords(time(x), x[, 1]) # RAW PLOT: plot(xycoords$x, xycoords$y, type=type, axes=FALSE, ann=FALSE, ...) # ADD GRID: if (grid) { abline(v=xycoords$x[ep], col='grey', lty=3) grid(NA, NULL) } # ADD AXIS: if(axes) { if(minor.ticks) axis(1, at=xycoords$x, labels=FALSE, col='#BBBBBB') axis(1, at=xycoords$x[ep], labels=names(ep), las=1, lwd=1, mgp=c(3,2,0)) axis(2) } # ADD BOX: box() } # ----------------------------------------------------------------------------- # Borrowed from ... # Package: xts # Title: eXtensible Time Series # Version: 0.9-7 # Date: 2013-06-26 # Author: Jeffrey A. Ryan, Joshua M. Ulrich # Maintainer: Jeffrey A. Ryan # License: GPL (>= 2) # URL: http://r-forge.r-project.org/projects/xts/ # Packaged: 2014-01-02 18:00:13 UTC; ripley # NeedsCompilation: yes # Repository: CRAN # Date/Publication: 2014-01-02 19:18:28 .axTicksByTime2 <- function( x, ticks.on='auto', k=1, labels=TRUE, format.labels=TRUE, ends=TRUE, gt = 2, lt = 30, format = "auto") { # A modified function borrowed from the xts-package # Arguments: # x - a 'timeSerie' Object # Example: # x <- 100 * cumulated(LPP2005REC[, 2]); .axTicksByTime2(x) tick.opts <- c( "years", "months", "weeks", "days", "hours", "minutes", "seconds") tick.k.opts <- c( 10, 5, 2, 1, 6, 1, 1, 1, 4, 2, 1, 30, 15, 1, 1) if (ticks.on %in% tick.opts) { cl <- ticks.on[1] ck <- k } else { tick.opts <- paste(rep(tick.opts, c(4, 2, 1, 1, 3, 3, 1)), tick.k.opts) is <- structure(rep(0,length(tick.opts)), .Names = tick.opts) for(i in 1:length(tick.opts)) { y <- strsplit(tick.opts[i], ' ')[[1]] ep <- .endpoints2(x, y[1], as.numeric(y[2])) is[i] <- length(ep) -1 if(is[i] > lt) break } nms <- rev(names(is)[which(is > gt & is < lt)])[1] cl <- strsplit(nms, " ")[[1]][1] ck <- as.numeric(strsplit(nms, " ")[[1]][2]) } ep <- if (is.null(cl)) NULL else .endpoints2(x, cl, ck) if(ends) ep <- ep + c(rep(1,length(ep)-1),0) if (labels) { if(is.logical(format.labels) || is.character(format.labels)) { # format by level of time detail, and platform unix <- ifelse(.Platform$OS.type=="unix", TRUE, FALSE) time.scale <- .periodicity2(x)$scale fmt <- ifelse(unix, '%n%b%n%Y', '%b %Y') if (time.scale == "weekly" | time.scale == "daily") fmt <- ifelse(unix, '%b %d%n%Y', '%b %d %Y') if (time.scale == "minute" | time.scale == "hourly") fmt <- ifelse(unix, '%b %d%n%H:%M', '%b %d %H:%M') if (time.scale == "seconds") fmt <- ifelse(unix, '%b %d%n%H:%M:%S', '%b %d %H:%M:%S') if(is.character(format.labels)) fmt <- format.labels if (format != "auto") fmt <- format names(ep) <- format(time(x)[ep], fmt) } else { names(ep) <- as.character(time(x)[ep]) } } attr(ep, "format") <- fmt # Return Value: ep } ################################################################################ .endpoints2 <- function (x, on = c("months", "years", "quarters", "weeks", "days", "hours", "minutes", "seconds"), k = 1) { # A modified function borrowed from the xts-package # Arguments: # x - a 'timeDate' object # Example: # x <- 100 * cumulated(LPP2005REC[, 2]); .endpoints2(x) stopifnot(is(x, "timeSeries")) x <- time(x) on <- match.arg(on) posix <- as.POSIXct(x, origin="1970-01-01") .posix <- unclass(posix) if (on == "years") { ans <- as.integer(which(diff(as.POSIXlt(posix)$year%/%k + 1) != 0)) } else if (on == "quarters") { ans <- as.integer(which(diff((as.POSIXlt(posix)$mon%/%3) + 1) != 0)) } else if (on == "months") { ans <- as.integer(which(diff(as.POSIXlt(posix)$mon%/%k + 1) != 0)) } else if (on == "weeks") { ans <- as.integer( which(diff((.posix + (3L * 86400L))%/%604800L%/%k + 1) != 0)) } else if (on == "days") { ans <- as.integer(which(diff(.posix%/%86400L%/%k + 1) != 0)) } else if (on == "hours") { ans <- as.integer(which(diff(.posix%/%3600L%/%k + 1) != 0)) } else if (on == "minutes" || on == "mins") { ans <- as.integer(which(diff(.posix%/%60L%/%k + 1) != 0)) } else if (on == "seconds" || on == "secs") { ans <- as.integer(which(diff(.posix%/%k + 1) != 0)) } ans <- c(0, ans, NROW(x)) # Return Value: ans } ############################################################################### .periodicity2 <- function (x) { # A modified function borrowed from the xts-package # Arguments: # x - a 'timeDate' object # Example: # x <- 100 * cumulated(LPP2005REC[, 2]); .periodicity2(x) # FUNCTION: # Check Argument: stopifnot(is(x, "timeSeries")) x <- time(x) p <- median(diff(as.integer(as.POSIXct(x, origin="1970-01-01")))) if (is.na(p)) stop("cannot calculate periodicity of 1 observation") units <- "days" scale <- "yearly" label <- "year" if (p < 60) { units <- "secs" scale <- "seconds" label <- "second" } else if (p < 3600) { units <- "mins" scale <- "minute" label <- "minute" p <- p/60L } else if (p < 86400) { units <- "hours" scale <- "hourly" label <- "hour" } else if (p == 86400) { units <- "days" scale <- "daily" label <- "day" } else if (p <= 604800) { units <- "weeks" scale <- "weekly" label <- "week" } else if (p <= 2678400) { units <- "months" scale <- "monthly" label <- "month" } else if (p <= 7948800) { units <- "quarter" scale <- "quarterly" label <- "quarter" } # Return Value: list( difftime = structure(p, units = units, class = "difftime"), frequency = p, start = start(x), end = end(x), units = units, scale = scale, label = label) } ############################################################################### .colorwheelPalette <- function(n) { # A function implemented by Diethelm Wuertz # FUNCTION: # Color Wheel: orig <- c( "#FFF200", "#FBAA19", "#F26522", "#EF4823", "#ED1D24", "#A9285F", "#662D91", "#4D2F91", "#2E3092", "#00707E", "#00A650", "#8CC63F") orig <- orig[-1] # Splice Wheel if (n == 11) return(orig) rgb.tim <- t(col2rgb(orig)) temp <- matrix(NA, ncol = 3, nrow = n) x <- seq(0, 1, , 11) xg <- seq(0, 1, , n) for (k in 1:3) { hold <- spline(x, rgb.tim[, k], n = n)$y hold[hold < 0] <- 0 hold[hold > 255] <- 255 temp[, k] <- round(hold) } ans <- rgb(temp[, 1], temp[, 2], temp[, 3], maxColorValue = 255) # Return Value: ans } ############################################################################### timeSeries/R/base-diff.R0000644000176200001440000000641115104730074014532 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ############################################################################### # FUNCTION: DESCRIPTION: # diff.timeSeries Differences a 'timeSeries' object ############################################################################### diff.timeSeries <- function(x, lag = 1, diff = 1, trim = FALSE, pad = NA, ...) { # A function implemented by Diethelm Wuertz # Modified by Yohan Chalabi # Description: # Differences 'timeSeries' objects. # Arguments: # x - a 'timeSeries' object. # lag - an integer indicating which lag to use. # By default 1. # diff - an integer indicating the order of the difference. # By default 1. # trim - a logical. Should NAs at the beginning of the # series be removed? # pad - a umeric value with which NAs should be replaced # at the beginning of the series. # Value: # Returns a differenced object of class 'timeSeries'. # FUNCTION: # Ceck Arguments: stopifnot(is.timeSeries(x)) # Extract Title and Documentation: Title <- x@title Documentation <- x@documentation # Convert: y <- getDataPart(x) # as.matrix(x) # Check NAs: # if (any(is.na(y))) stop("NAs are not allowed in time series") # Difference: z <- diff(y, lag = lag, difference = diff) diffNums = dim(y)[1] - dim(z)[1] # Trim Positions: if (!trim) { zpad <- matrix(0*y[1:diffNums, ] + pad, nrow = diffNums) z <- rbind(zpad, z) } pos <- if (!trim) x@positions else x@positions[-(1:diffNums)] # Record IDs: df <- x@recordIDs if (trim && sum(dim(df)) > 0) { df <- df[-seq.int(diffNums), , drop = FALSE] rownames(df) <- seq.int(NROW(df)) } # Diff Result: ans <- timeSeries(data = z, charvec = pos, units = colnames(z), format = x@format, zone = x@FinCenter, FinCenter = x@FinCenter, recordIDs = df) # Preserve Title and Documentation: ans@title <- Title ans@documentation <- Documentation # Return Value: ans } # ----------------------------------------------------------------------------- ## 2024-01-06 GNB: removed the S4 method ## ## setMethod("diff", "timeSeries", ## function(x, lag = 1, diff = 1, trim = FALSE, pad = NA, ...) ## diff.timeSeries(x, lag, diff, trim, pad, ...) ## ##x <- getDataPart(x) ## ##callGeneric() ## ) ## # until UseMethod dispatches S4 methods in 'base' functions ## diff.timeSeries <- function(x, ...) .diff.timeSeries(x, ...) ############################################################################### timeSeries/R/stats-na.omit.R0000644000176200001440000002611315116772565015432 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # na.omit,timeSeries Handles missing values in objects # .naOmitMatrix Internal function called from na.omit.timeSeries # OLD FUNCTIONS: DESCRIPTION: # removeNA Remove NAs from a matrix object # substituteNA Substitute NAs by zero, the column mean or median # interpNA Interpolate NAs using R's "approx" function ################################################################################ # DW: # I think we should deprecate the following functions: # removeNA, substituteNA, and interpNA since the function # na.omit() can already handle all these cases. # DW: # note we do interpolation with approx(), zoo also offers # interpolation by splines, we should also add this. ################################################################################ ## 2023-05-28 GNB: added argument 'FUN' ## 2024-06-01 GNB: renamed .na.omit.timeSeries to na.omit.timeSeries; ## dropped the S4 method na.omit.timeSeries <- function(object, method = c("r", "s", "z", "ir", "iz", "ie"), interp = c("before", "linear", "after"), FUN, ...) { # Description # Handles NAs in timeSeries objects # Details: # Linear Interpolation is done by the function approx. # Spline interpolation like in zoo is not yet supported. # Arguments: # object - an object of class 'timeSeries' # method - how to handle NAs # interp - how to interpolate NAs # ... - arguments passed to function approx() # FUNCTION: # Check Arguments: stopifnot(is.timeSeries(object)) # Extract Title and Documentation: Title <- object@title Documentation <- object@documentation # Settings: method <- match.arg(method) interp <- match.arg(interp) # Skip ? if (method == "s") return(object) if(!missing(FUN)) { # GNB FUN <- match.fun(FUN) data <- object@.Data data <- apply(data, 2, function(z) { z[is.na(z)] = FUN(z, na.rm = TRUE) z }) object@.Data <- data return(object) } # Handle NAs: if (method == "r") { # Remove NAs: # DW: # object <- stats:::na.omit.default(object) object <- as.timeSeries(na.omit(series(object))) } else if (method == "z") { # Substitute NAs by Zero's: object[is.na(object)] <- 0 } else if (substr(method, 1, 1) == "i") { # Interpolate: interp = match.arg(interp) f = 0 if (interp == "before") { interp = "constant" f = 0 } if (interp == "after") { interp = "constant" f = 1 } n = nrow(object) for (i in 1:ncol(object)) { y = object[, i] idy = (1:n)[!is.na(y)] ## DW: ... added # Linear/Constant Interpolation: y = approx(x = idy, y = y[idy], xout = 1:n, method = interp, f = f, ...)$y object[, i] = y } modID = FALSE if (method == "ir") { # Remove Start and End NAs: # DW: # object <- stats:::na.omit.default(object) object <- as.timeSeries(na.omit(series(object))) } else if (method == "iz") { # Set Start and End NAs to Zero: object[is.na(object)] = 0 } else if (method == "ie") { n = nrow(object) for (k in 1:ncol(object)) { y = object[, k] if (NA %in% y) { start = sum(cumprod(is.na(y))) if (start > 0) for (i in start:1) y[i] = y[i+1] end = n+1 - sum(cumprod(rev(is.na(y)))) if (end <= n) for (i in end:n) y[i] = y[i-1] object[, k] = y } } } } # Handle recordIDs ... recordIDs <- object@recordIDs modID <- c(r = TRUE, z = FALSE, ir = TRUE, iz = FALSE, ie = FALSE) if(modID[method] > 0 && sum(dim(recordIDs)) > 0 ) { index <- attr(object, "n.action") recordIDs <- recordIDs[index, ] } # Preserve Title and Documentation: object@title <- Title object@documentation <- Documentation # Return Value: object } ## setMethod("na.omit", "timeSeries", function(object, ## method = c("r", "s", "z", "ir", "iz", "ie"), ## interp = c("before", "linear", "after"), FUN, ...) ## .na.omit.timeSeries(object, method, interp, FUN, ...)) ## # until UseMethod dispatches S4 methods in 'base' functions ## na.omit.timeSeries <- function(object, ...) .na.omit.timeSeries(object, ...) # ------------------------------------------------------------------------------ .naOmitMatrix <- function(object, method = c("r", "s", "z", "ir", "iz", "ie"), interp = c("before", "linear", "after")) { # Description: # Internal Function called from na.omit.timSeries() # Arguments: # FUNCTION: # Extract matrix: x = object stopifnot (is.matrix(x)) # Match Arguments: method <- match.arg(method) interp <- match.arg(interp) # Handle NAs: if (method == "r") { # Remove NAs: x <- na.omit(x) } else if (method == "z") { # Substitute NAs by Zero's: x[is.na(x)] <- 0 } else if (substr(method, 1, 1) == "i") { # Interpolate: interp = match.arg(interp) f = 0 if (interp == "before") { interp = "constant" f = 0 } if (interp == "after") { interp = "constant" f = 1 } n = nrow(x) for (i in 1:ncol(x)) { y = x[, i] idy = (1:n)[!is.na(y)] y = approx(idy, y[idy], 1:n, method = interp, f = f)$y x[, i] = y } modID = FALSE if (method == "ir") { # Remove Start and End NAs: x = na.omit(x) } else if (method == "iz") { # Set Start and End NAs to Zero: x[is.na(x)] = 0 } else if (method == "ie") { n = nrow(x) for (k in 1:ncol(x)) { y = x[, k] if (NA %in% y) { start = sum(cumprod(is.na(y))) if (start > 0) for (i in start:1) y[i] = y[i+1] end = n+1 - sum(cumprod(rev(is.na(y)))) if (end <= n) for (i in end:n) y[i] = y[i-1] x[, k] = y } } } } # Add Control: if (substr(method, 1, 1) == "i") { attr(x, "control") = c(method = method, interp = interp) } else { attr(x, "control") = c(method = method) } # Return Value: x } ################################################################################ removeNA <- function (x, ...) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Remove NA's from objects which can be transformed to a matrix # Arguments: # x - an object which can be transformed to a matrix # FUNCTION: .Deprecated("na.omit", package = "timeSeries", msg = c("'removeNA' is deprecated.\n", "Use 'na.omit' instead.", " See help('na.omit.timeSeries').\n")) # GNB na.omit(x, ...) } # ------------------------------------------------------------------------------ ## removed on 2025-12-12 ## ## substituteNA <- ## function(x, type = c("zeros", "mean", "median"), ...) ## { ## # A function implemented by Diethelm Wuertz ## ## # Description: ## # Imputes missing data by zeros, the median or the ## # mean values of all matrix elements ## ## # Arguments: ## # x - an object which can be transformed to a matrix ## # type - method specifies the substitution method to be ## # used. Choices are "zeros", "mean", or "constant" ## ## # FUNCTION: ## .Deprecated("na.omit", msg = c("'substituteNA' is deprecated.\n", ## "Use 'na.omit' instead.", " See help('na.omit.timeSeries').\n")) # GNB ## ## if (!inherits(x, "matrix") && !inherits(x, "timeSeries")) ## x <- as(x, "matrix") ## ## # Type: ## type <- match.arg(type) ## ans <- switch(type, ## "zeros" = apply(x, 2, function(z) { ## z[is.na(z)] <- 0 ## z}), ## "median" = apply(x, 2, function(z) { ## z[is.na(z)] = median(z, na.rm = TRUE) ## z}), ## "mean" = apply(x, 2, function(z) { ## z[is.na(z)] = mean(z, na.rm = TRUE) ## z})) ## ## # Return Value: ## ans ## } # ------------------------------------------------------------------------------ ## removed on 2025-12-12 ## ## interpNA <- ## function(x, method = c("linear", "before", "after"), ...) ## { ## # A function implemented by Diethelm Wuertz ## ## # Description: ## # Interpolates missing values in a matrix object ## ## # Arguments: ## # x - a numeric vector or time series object of class 'ts'. ## # method - the method how to interpolate the vector, one of ## # the applied vector strings: "linear", "before" or ## # after. ## ## # Details: ## # To interpolate the function 'approx' is used. ## ## # Value: ## # Returns a vector or time series object where the missing ## # values are interpolated. ## ## # FUNCTION: ## .Deprecated("na.omit", msg = c("'interpNA' is deprecated.\n", ## "Use 'na.omit' instead.", " See help('na.omit.timeSeries').\n")) # GNB ## ## if (!inherits(x, "matrix") && !inherits(x, "timeSeries")) ## x <- as(x, "matrix") ## ## # Internal Function: ## interpVectorNA <- function(x, method, f) { ## n <- length(x) ## idx <- (1:n)[!is.na(x)] ## x <- approx(idx, x[idx], 1:n, method = method, f = f)$y ## x} ## ## # Select Method: ## method = method[1]; ## f = 0 ## if (method == "before") { ## method = "constant" ## f = 0 ## } ## if (method == "after") { ## method = "constant" ## f = 1 ## } ## ## # For each Column: ## for (i in 1:ncol(x)) { ## x[, i] = interpVectorNA(x[, i], method, f) ## } ## ## # Return Value: ## x ## } ################################################################################ timeSeries/R/utils-head.R0000644000176200001440000000701115104730074014746 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # METHOD: SUBSETTING METHODS ON DATA: # head,timeSeries Returns the head of a 'timeSeries' object # tail,timeSeries Returns the tail of a 'timeSeries' object ################################################################################ setGeneric("head") setGeneric("tail") head.timeSeries <- function(x, n = 6, recordIDs = FALSE, ...) { # A function implemented by Diethelm Wuertz # Description: # Returns the head of a 'timeSeries' object # Arguments: # x - a 'timeSeries' object. # n - a single integer. If positive, number of the first n records (rows) # to be returned. If negative, all but the n first number of # elements of x are returned. # recordIDs - a logical flag, should the record identification # be shown? By default FALSE. # ... - # Value: # Returns the tail of an object of class 'timeSeries'. # FUNCTION: # Head: if (recordIDs & dim(x)[1] == dim(x@recordIDs)[1]) cbind(head.matrix(x, n = n, ...), head(x@recordIDs, n = n, ...)) else head.matrix(x, n = n, ...) } # ------------------------------------------------------------------------------ ##' @title Returns the tail of a 'timeSeries' object ##' ##' @param x a 'timeSeries' object. ##' @param n a single integer. If positive, number of the last n records (rows) ##' to be returned. If negative, all but the n last number of ##' elements of x are returned. ##' @param recordIDs a logical flag, should the record identification ##' be shown? By default FALSE. ##' @param ... ##' ##' @return ##' Returns the tail of an object of class 'timeSeries'. ##' ## Martin Maechler: if("keepnums" %in% names(formals(tail.matrix))) ## R-devel (2020-01) ## refactored somewhat by GNB; *TODO:* is a similar thing needed for head.timeSeries? tail.timeSeries <- if(getRversion() >= "4.0.0") { function(x, n = 6, recordIDs = FALSE, ...) { if (recordIDs && nrow(x) == nrow(x@recordIDs)) cbind(tail.matrix(x, n = n, keepnums = FALSE, ...), tail(x@recordIDs, n = n, keepnums = FALSE, ...)) else tail.matrix(x, n = n, keepnums = FALSE, ...) } } else { function(x, n = 6, recordIDs = FALSE, ...) { if (recordIDs & dim(x)[1] == dim(x@recordIDs)[1]) cbind(tail.matrix(x, n = n, addrownums = FALSE, ...), tail(x@recordIDs, n = n, addrownums = FALSE, ...)) else tail.matrix(x, n = n, addrownums = FALSE, ...) } } ## (2024-01-05) GNB: stop making head() and tail() S4 ## setMethod("head", "timeSeries", head.timeSeries) ## setMethod("tail", "timeSeries", tail.timeSeries) ################################################################################ timeSeries/R/timeSeries-readSeries.R0000644000176200001440000001026015104730074017104 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # readSeries Reads a CSV file and creates a 'timeSeries' ################################################################################ # DW: # I think we should add a similar function for writeSeries() using # write.table(). Proceed in the same way as in the case of the read # function. # ------------------------------------------------------------------------------ readSeries <- function(file, header = TRUE, sep = ";", zone = "", FinCenter = "", format, ...) { # A function implemented by Diethelm Wuertz # Description: # Reads from a spreadsheet and creates a 'timeSeries' object # Arguments: # file - the name of the file which the data are to be read # from. Each row of the table appears as one line of the # file. If it does not contain an absolute path, the file # name is relative to the current working directory, # getwd(). Tilde-expansion is performed where supported. # As from R 2.10.0 this can be a compressed file. # header - a logical value indicating whether the file contains # the names of the variables as its first line. If missing, # the value is determined from the file format: header is # set to TRUE if and only if the first row contains one fewer # field than the number of columns. # sep - he field separator character. Values on each line of # the file are separated by this character. If sep = "" (the # default for read.table) the separator is ?white space?, # that is one or more spaces, tabs, newlines or carriage # returns. # zone - the time zone or financial center where the data were # recorded. # FinCenter - a character with the the location of the # financial center named as "continent/city". By default # an empty string which means that internally "GMT" will # be used. # format - the format of the timestamps as recoreded in the # first column of the data in the.. # ... - optional arguments passed to the function read.table(). # Value: # Returns a S4 object of class 'timeSeries'. # Notes: # Note we expect that the header of the spreadsheet file in # the first cell holds the time/date format specification! # FUNCTION: # Read Data: df <- read.table(file = file, header = header, sep = sep, check.names = FALSE, ...) # Get 'timeDate' from first column with header specifying the format charvec <- as.character(df[[1]]) if (missing(format)) format <- names(df)[1] td <- try(timeDate(charvec = charvec, format = format, zone = zone, FinCenter = FinCenter), silent=TRUE) # DW: 2014-09-16 # If sep=";" fails try with sep=",": if (sep == ";" && inherits(td, "try-error")) { return(readSeries(file, header = header, sep = ",", zone = zone, FinCenter = FinCenter, ...)) } # If format provided in file or with format argument, try to guess it if (all(is.na(td))) warning("Conversion of timestamps to timeDate objects produced only NAs. Are you sure you provided the proper format with argument 'format' or in the header of your file ?") # Extract data data <- as.matrix(df[-1]) # Create Time Series from Data Frame: ans <- timeSeries(data = data, charvec = td) # Return Value: ans } ################################################################################ timeSeries/R/fin-spreads.R0000644000176200001440000000513415104730074015126 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # spreads Computes spreads from a 'timeSeries' object # midquotes Computes mid quotes from a 'timeSeries' object ################################################################################ # DW: # Setting bid and ask for column names is maybe the best choice. Examples # are the TED spread or the Libo OIS spread. The spread between High and Low # is the range. # ------------------------------------------------------------------------------ spreads <- function(x, which = c("Bid", "Ask"), tickSize = NULL) { # A function implemented by Diethelm Wuertz # Description: # Computes spreads from a 'timeSeries' object # FUNCTION: # Check arguments: stopifnot(is.timeSeries(x)) # Extract Title and Documentation: Title <- x@title Documentation <- x@documentation # Compute Spread: spread <- x[, which[2]] - x[, which[1]] if (!is.null(tickSize)) series(spread) <- round(series(spread)/tickSize) # Preserve Title and Documentation: spread@title <- Title spread@documentation <- Documentation # Return Value: spread } # ------------------------------------------------------------------------------ midquotes = function(x, which = c("Bid", "Ask")) { # A function implemented by Diethelm Wuertz # Description: # Computes mid quotes from a 'timeSeries' object # FUNCTION: # Check arguments: stopifnot(is.timeSeries(x)) # Extract Title and Documentation: Title <- x@title Documentation <- x@documentation # Compute Mid Quotes: midquotes = 0.5 * ( x[, which[1]] + x[, which[2]] ) # Preserve Title and Documentation: midquotes@title <- Title midquotes@documentation <- Documentation # Return Value: midquotes } ################################################################################ timeSeries/R/utils-description.R0000644000176200001440000000252715104730074016377 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received A copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # description Creates default description string ################################################################################ description <- function() { # A function implemented by Diethelm Wuertz # Description: # Sets default description string: # FUNCTION: # Get Description String: ans <- paste(as.character(date()), "by user:", Sys.getenv("USERNAME")) # Return Value: ans } ################################################################################ timeSeries/R/base-summary.R0000644000176200001440000000604515104730074015322 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ## Author: Georgi N. Boshnakov ## setMethod("summary", c(object = "timeSeries"), ## function(object, alwaysNA = TRUE, ...){ ## start = as.character(start(object)) ## end = as.character(end(object)) ## ## ## stats <- cbind( ## ## "Min." = colMins(object), ## ## "1st Qu." = colQuantiles(object, prob = 0.25, type = 1), ## ## "Median" = colQuantiles(object, prob = 0.50, type = 1), ## ## "3rd Qu." = colQuantiles(object, prob = 0.75, type = 1), ## ## "Max." = colMaxs(object) ## ## ## , check.names = FALSE ## ## ) ## ## stats <- summary(as.matrix(object)) ## ## attr(stats, "start") <- start ## attr(stats, "end") <- end ## attr(stats, "nobs") <- nrow(object) ## attr(stats, "Format") <- object@format ## attr(stats, "FinCenter") <- object@FinCenter ## ## class(stats) <- c("timeSeries_summary", class("stats")) ## stats ## }) summary.timeSeries <- function(object, ...) { ## stats <- cbind( ## "Min." = colMins(object), ## "1st Qu." = colQuantiles(object, prob = 0.25, type = 1), ## "Median" = colQuantiles(object, prob = 0.50, type = 1), ## "3rd Qu." = colQuantiles(object, prob = 0.75, type = 1), ## "Max." = colMaxs(object) ## ## , check.names = FALSE ## ) stats <- summary(as.matrix(object)) attr(stats, "start") <- as.character(start(object)) attr(stats, "end") <- as.character(end(object)) attr(stats, "nobs") <- nrow(object) attr(stats, "Format") <- object@format attr(stats, "FinCenter") <- object@FinCenter class(stats) <- c("timeSeries_summary", class("stats")) stats } print.timeSeries_summary <- function(x, quote = FALSE, ...) { cat("Start Record:", attr(x, "start") , "\n") cat("End Record: ", attr(x, "end") , "\n") cat("Observations:", attr(x, "nobs") , "\n") cat("Format: ", attr(x, "Format") , "\n") cat("FinCenter: ", attr(x, "FinCenter"), "\n") cat("\n") class(x) <- class(x)[-1] attr(x, "start") <- attr(x, "end") <- attr(x, "nobs") <- attr(x, "Format") <- attr(x, "FinCenter") <- NULL print(x, quote = quote, ...) invisible(x) } timeSeries/R/statistics-rowCumsums.R0000644000176200001440000000330015104730074017260 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # rowCumsums,ANY Computes cumulated sums by row # rowCumsums,timeSeries Computes cumulated sums by row for timeSeries ################################################################################ setMethod("rowCumsums", "ANY", function(x, na.rm = FALSE, ...) { # Transform: if (!inherits(x, 'matrix')) x <- as(x, "matrix") if (na.rm) x <- na.omit(x) ans <- apply(x, 1, cumsum, ...) # special treatment when x has one row because apply returns a vector if (NCOL(x) > 1) t(ans) else matrix(ans, ncol = 1, dimnames = dimnames(x)) }) # ------------------------------------------------------------------------------ setMethod("rowCumsums", "timeSeries", function(x, na.rm = FALSE, ...) setDataPart(x, callGeneric(getDataPart(x), na.rm = na.rm, ...))) ################################################################################ timeSeries/R/timeSeries-isOHLC.R0000644000176200001440000000432715104730074016106 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # .isOHLC Is the series an Open-High-Low-Close series? # .isOHLCV Is the series an Open-High-Low-Close-Volume series? ################################################################################ # DW: # I think we need a better method to detect if a series is a OHLC(V) series # or not. A possible approach would be: # any High >= Open, Close, Low # any Low <= Open, Close, High # Volume >= 0 # number of columns 4(5) # ----------------------------------------------------------------------------- .isOHLC <- function(object) { # A function implemented by Diethelm Wuertz # Description: # Is the series an Open-High-Low-Close series? # Arguments: # object - an object of class 'timeSeries' # FUNCTION: colNames <- c("Open", "High", "Low", "Close") if (colnames(object)[1:4] == colNames) { return(TRUE) } else { return(FALSE) } } # ------------------------------------------------------------------------------ .isOHLCV <- function(object) { # A function implemented by Diethelm Wuertz # Description: # Is the series an Open-High-Low-Close-Volume series? # Arguments: # object - an object of class 'timeSeries' # FUNCTION: colNames <- c("Open", "High", "Low", "Close", "Volume") if (colnames(object) == colNames) { return(TRUE) } else { return(FALSE) } } ################################################################################ timeSeries/R/fin-returns.R0000644000176200001440000001144215104730074015166 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # returns,ANY Computes returns from a 'matrix' object # returns,timeSeries Computes returns from a 'timeSeries' object # FUNCTION: DESCRIPTION: # returns0 Compute untrimmed returns # OLD FUNCTIONS: KEEP THESE FUNCTIONS FOR COMPATIBILITY: # returnSeries Deprecated, use returns() # getReturns Deprecated, use returns() ################################################################################ setMethod("returns", "ANY", function(x, method = c("continuous", "discrete", "compound", "simple"), percentage = FALSE, ...) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Computes returns from a 'matrix' object # Arguments: # x - data object containing ordered price observations # method - "continuous == "compound" and "discrete" == "simple" # percentage # Note: # To make it conform with PortfolioAnalytics: # "compound" == "continuous" # "simple" == "discrete" # FUNCTION: # Settings: method <- match.arg(method) # Calculate Returns: data <- as.matrix(x) positions <- time(x) if(method == "compound" || method == "continuous") { data <- rbind( data[1, , drop = FALSE]*NA, apply(log(data), 2, diff)) } if(method == "simple" || method == "discrete") { data <- apply(rbind(data, NA*data[1,]), 2, diff) / data data <- rbind(data[1, , drop = FALSE]*NA, data) data <- data[-(length(positions) + 1), , drop = FALSE] } if (percentage) data <- 100*data # Return Value: data } ) # ----------------------------------------------------------------------------- setMethod("returns", "timeSeries", function(x, method = c("continuous", "discrete", "compound", "simple"), percentage = FALSE, na.rm = TRUE, trim = TRUE, ...) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Returns the returns of an object of class 'timeSeries' # Arguments: # x - an object of class 'timeSeries' # method - # percentage - # na.rm - # trim - # FUNCTION: # Check Arguments: stopifnot(is.timeSeries(x)) # Extract Title and Documentation: Title <- x@title Documentation <- x@documentation # Make sure that series is ordered: x <- sort(x) # Get Returns: if (na.rm) x <- na.omit(x, ...) series(x) <- returns(as(x, "matrix"), method, percentage) if (trim) x <- na.omit(x, "r") # Preserve Title and Documentation: x@title <- Title x@documentation <- Documentation # Return Value: x } ) ############################################################################### returns0 <- function(x, ...) { # A function implemented by Diethelm Wuertz # Description: # Returns the untrimmed returns of an object of class 'timeSeries' # Arguments: # x - an object of class 'timeSeries' # FUNCTION: # Compute Untrimmed Returns: x <- returns(x = x, trim = FALSE) x[1, ] <-0 # Return Value: x } ############################################################################### # DEPRECATED: ## returnSeries <- ## function(...) ## { ## # A function implemented by Diethelm Wuertz ## ## # FUNCTION: ## # .Deprecated("returns", "timeSeries") ## ## returns(...) ## } # ----------------------------------------------------------------------------- ## TODO: remove when fTrading is updated, see note in file NAMESPACE getReturns <- function(...) { # A function implemented by Diethelm Wuertz # Description: # Computes returns # FUNCTION: # .Deprecated("returns", "timeSeries") # Return Value: returns(...) } ############################################################################### timeSeries/R/zzz.R0000644000176200001440000000201615104730074013544 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ .onLoad <- function(libname, pkgname) { if(!is.numeric(getRmetricsOptions("max.print"))) setRmetricsOptions(max.print = 100) #-> show() of large matrices # YC: This should really go in methods package. keep it here for # the time being. (R -2.9.0) setOldClass("difftime") } ################################################################################ timeSeries/R/methods-show.R0000644000176200001440000001175315104730074015340 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # show,timeSeries Prints a 'timeSeries' object # print,timeSeries Prints a 'timeSeries' object # .print.timeSeries Called by function print,timeSeries ################################################################################ setMethod("show", "timeSeries", function(object) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Print method for an S4 object of class "timeSeries" # FUNCTION: # Check records to get printed: maxRmetrics <- as.numeric(getRmetricsOptions("max.print")) maxR <- as.numeric(getOption("max.print")) maxR <- floor(maxR / (NCOL(object) + NCOL(object@recordIDs))) max <- min(na.omit(c(maxRmetrics, maxR, Inf))) #-> Inf to cast case when maxRmetrics and maxR are NULL if (ptest <- ((omitted <- NROW(object) - max) > 0)) object <- object[seq.int(max),] data <- as(object, "matrix") recordIDs <- object@recordIDs FinCenter <- finCenter(object) # Series: cat(FinCenter, "\n", sep = "") if (prod(dim(recordIDs)) & (nrow(data) == NROW(recordIDs))) { dataIDs <- as.matrix(recordIDs) colnames(dataIDs) <- paste(colnames(dataIDs), "*", sep = "") #-> use format(data) to have same number of digits when timeSeries # is printed without @recordIDs print(cbind(format(data), dataIDs), quote = FALSE, right = TRUE) } else { print(data, quote = FALSE) #-> to be consistent with @recordIDs print } # print message if (ptest) cat(gettextf("...\n [ reached getRmetricsOptions('max.print') | getOption('max.print') -- omitted %i rows ]]\n", omitted)) # Return Value: invisible(NULL) # as specified in ?show } ) # ------------------------------------------------------------------------------ ## GNB: streamlined somewhat the calls and removed the S4 method print.timeSeries <- function(x, FinCenter = NULL, format = NULL, style = c("tS", "h", "ts"), by = c("month", "quarter"), ...) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Allows for horizontal and ts like print output. # Arguments: # x - an object of class timeSeries # FinCenter - print with time stamps according to FinCenter # format - use specified format for printing # style - a character value specifying how to print: # "tS" Rmetrics' default vertical print style # "h" horizontal print style, # "ts" R's base style for regular time series # by - specifies the period for a regular time serie, # note only active for style="ts". # Example: # x = timeSeries(); print(x, format = "%d %b %Y"); x # FUNCTION: # Change Financial Center: if (!is.null(FinCenter)) finCenter(x) <- FinCenter # Match Arguments: style = match.arg(style) by = match.arg(by) # Change Format: if (is.null(format)) { charvec = rownames(x) } else { ans = timeDate(charvec = rownames(x), zone = "GMT", FinCenter = finCenter(x)) if (format == "%Q") { Quarters = rep(paste("Q", 1:4, sep = ""), each = 3) Y = atoms(ans)[, 1] Q = Quarters[atoms(ans)[, 2]] charvec = paste(Y, Q) } else { charvec = format(ans, format) } } # Styles: if (style == "tS") { cat(finCenter(x), "\n") X <- getDataPart(x) rownames(X) = charvec print(X, ...) } else if (style == "h") { stopifnot(isUnivariate(x)) # print(as.vector(x)) ans = as.matrix(x)[,1] names(ans) = charvec print(ans, ...) } else if (style == "ts") { freq = c(month = 12, quarter = 4) start(x) start = unlist(atoms(start(x))) end = unlist(atoms(end(x))) ts = ts(as.vector(x), start[1:2], end[1:2], freq[by]) print(ts, ...) } # Return Value: invisible(x) } # ------------------------------------------------------------------------------ ## setMethod("print", "timeSeries", ## .print.timeSeries) ################################################################################ timeSeries/R/fin-wealth.R0000644000176200001440000000237215104730074014752 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ############################################################################### # FUNCTION: DESCRIPTION: # index2wealth Converts an index to a wealth series ############################################################################### index2wealth <- function(x) { # A function implemented by Diethelm Wuertz # Description: # Converts an index to a wealth series # FUNCTION: # x - index time series to be converted # FUNCTION: # Wealth Initialization: for (i in 1:ncol(x)) x[, i] <- x[, i]/as.vector(x[1, i]) # Return Value: x } ############################################################################### timeSeries/R/fin-align.R0000644000176200001440000001077115104730074014562 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # align Aligns a 'timeSeries object' to time stamps # .align.timeSeries Aligns a 'timeSeries object' to time stamps ################################################################################ # DW: See also ... # in package timeDate # setMethod("align", "ANY", # setMethod("align", "timeDate", # ------------------------------------------------------------------------------ .align.timeSeries <- function(x, by = "1d", offset = "0s", method = c("before", "after", "interp", "fillNA", "fmm", "periodic", "natural", "monoH.FC"), include.weekends = FALSE, ...) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Aligns a 'timeSeries' object to equidistant time stamps # Arguments: # x - an object of class "timeSeries". # by - # offset - # method - # "before" - use the data from the row whose position is # just before the unmatched position; # "after" - use the data from the row whose position is # just after the unmatched position; # "linear" - interpolate linearly between "before" and # "after". # "fillNA" - fill missing days with NA values # include.weekends - a logical value. Should weekend dates be # included or removed? # Example: # data(usdthb) # data = matrix(usdthb[, "BID"]) # charvec = as.character(usdthb[, "XDATE"]) # USDTHB = timeSeries(data, charvec, format = "%Y%M%d%H%M") # align(USDTHB, by = "3h", offset = "92m") # MSFT = as.timeSeries(data(msft.dat)) # align(MSFT) # See also: # in package timeDate # setMethod("align", "ANY", # setMethod("align", "timeDate", # FUNCTION: # Settings: Title <- x@title Documentation <- x@documentation # Check for Signal Series: if (x@format == "counts") stop(as.character(match.call())[1], " is for time series and not for signal series.") # check if series sorted if (is.unsorted(x)) x <- sort(x) # Adjustment: Method <- match.arg(method) fun <- switch(Method, before = function(x, u, v) approxfun(x = u, y = v, method = "constant", f = 0, ...)(x), after = function(x, u, v) approxfun(x = u, y = v, method = "constant", f = 1, ...)(x), interp = , fillNA = function(x, u, v) approxfun(x = u, y = v, method = "linear", f = 0.5, ...)(x), fmm = , periodic = , natural = , monoH.FC = function(x, u, v) splinefun(x = u, y = v, method = Method, ...)(x)) # Align timeDate stamps td1 <- time(x) td2 <- align(td1, by = by, offset = offset) # extract numerical values u <- as.numeric(td1, units = "secs") xout <- as.numeric(td2, units = "secs") N = NCOL(x) data <- matrix(ncol = N, nrow = length(td2)) xx <- getDataPart(x) for (i in 1:N) { v <- as.vector(xx[, i]) # New Positions and approximated values: yout <- fun(xout, u, v) if (Method == "fillNA") yout[!(xout %in% u)] = NA # Compose data: data[, i] = yout } # build time series ans <- timeSeries(data, td2, units = colnames(x)) # Remove Weekends: if(!include.weekends) ans <- ans[isWeekday(td2), ] # Preserve Title and Documentation: ans@title <- Title ans@documentation <- Documentation # Return Value: ans } # ------------------------------------------------------------------------------ setMethod("align", "timeSeries", .align.timeSeries) ################################################################################ timeSeries/R/fin-runlengths.R0000644000176200001440000000422015104730074015651 0ustar liggesusers # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # runlengths Returns 'timeSeries' object of runlengths ################################################################################ runlengths <- function(x, ...) { # A function implemetned by Diethelm Wuertz # Description: # Returns 'timeSeries' object of runlengths # Arguments: # x - an univariate 'timeSeries' object of financial returns # ... - arguments passed to the function na.omit() # Value: # runlengths an object of class 'timeSeries'. # Note: # Zeroes are handled as NA. # Example: # set.seed(4711) # x.tS = timeSeries(data=rnorm(12), charvec=timeCalendar(), units="x") # runlengths(x.tS) # FUNCTION: # Check arguments: stopifnot(is.timeSeries(x)) stopifnot(isUnivariate(x)) # Extract Title and Documentation: Title <- x@title Documentation <- x@documentation # Handle Missing Values: x[x == 0] <- NA x.vec = sign(as.vector(na.omit(x, ...))) # Compute Run Lengths: n <- length(x.vec) y <- x.vec[-1L] != x.vec[-n] Index <- c(which(y | is.na(y)), n) X = x[Index, ] series(X) <- matrix(diff(c(0L, Index)), ncol = 1) # Reset recordIDs: X@recordIDs <- data.frame() # Preserve Title and Documentation: X@title <- Title X@documentation <- Documentation # Return Value: X } ################################################################################ timeSeries/R/stats-lag.R0000644000176200001440000000766115104730074014621 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # lag,timeSeries Lags a 'timeSeries' object ################################################################################ ## GNB: made it an S3 method and removed the S4 one ## setMethod("lag" , "timeSeries", lag.timeSeries <- function(x, k = 1, trim = FALSE, units = NULL, ...) { # A function implemented by Diethelm Wuertz # Description: # Lags a 'timeSeries' object. # Arguments: # x - a 'timeSeries' object. # k - an integer indicating which lag to use. By default 1. # Note, negative lags are to data in the future. # trim - a logical. Should NAs at the beginning of the # series be removed? By default FALSE. # units - # ... - # Details: # The arguments differ in the following way from the function # stats::lag - lag(x, k = 1, ...) # Value: # Returns a lagged object of class 'timeSeries'. # Example: # SPI = 100* as.timeSeries(data(LPP2005REC))[1:20, "SPI"] # Note negative lags are to data in the future ! # lag(SPI, k = -2:2) # lag(SPI, k = 0:2 , trim = TRUE) # FUNCTION: # Check Arguments: stopifnot(is.timeSeries(x)) # Extract Title and Documentation: Title <- x@title Documentation <- x@documentation # Internal Function: tslagMat <- function(x, k = 1) { # Internal Function: tslag1 = function(x, k) { y = x if (k > 0) y = c(rep(NA, times = k), x[1:(length(x)-k)]) if (k < 0) y = c(x[(-k+1):length(x)], rep(NA, times = -k)) y } # Bind: ans <- NULL for (i in k) { ans <- cbind(ans, tslag1(x, i)) } # As Vector: if (length(k) == 1) ans <- as.vector(ans) # Return Value: ans } # Convert: y <- getDataPart(x) Dim <- dim(y)[2] # Lag on each Column: z <- NULL for (i in 1:Dim) { ts <- tslagMat( y[, i], k = k) #, trim = FALSE) z <- cbind(z, ts) } # Positions pos <- x@positions # Record IDs: df <- x@recordIDs # Trim: if (trim){ idx <- !is.na(apply(z, 1, sum)) z <- z[idx, , drop = FALSE] pos <- pos[idx] if (sum(dim(df)) > 0) { df <- df[idx, , drop = FALSE] rownames(df) <- seq.int(sum(idx)) } } # Augment Colnames: cn <- colnames(x) a <- if (is.null(units)) # ensure that colnames is replicated according to the length # of lag indexes. as.vector(matrix(cn, nrow = length(k), ncol = length(cn), byrow = TRUE)) else units kcols <- rep(k, times = ncol(y)) b <- paste("[", kcols, "]", sep="") ab <- paste(a, b, sep = "") units <- ab # Result: ans <- timeSeries(data = z, charvec = pos, units = units, format = x@format, FinCenter = x@FinCenter, recordIDs = df) # Preserve Title and Documentation: ans@title <- Title ans@documentation <- Documentation # Return Value: ans } ## # until UseMethod dispatches S4 methods in 'base' functions ## lag.timeSeries <- function(x, ...) timeSeries::lag(x, ...) ################################################################################ timeSeries/R/timeSeries-isUnivariate.R0000644000176200001440000000314215104730074017462 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # isUnivariate Tests if a 'timeSeries' object is univariate # isMultivariate Tests if a 'timeSeries' object is multivariate ################################################################################ isUnivariate <- function(x) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Tests if a time series or rectangular object is univariate # FUNCTION: # Return Value: if (NCOL(x) == 1) TRUE else FALSE } # ------------------------------------------------------------------------------ isMultivariate <- function(x) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Tests if a time series or rectangular object is multivariate # FUNCTION: # Return Value: if (NCOL(x) > 1) TRUE else FALSE } ################################################################################ timeSeries/R/fin-monthly.R0000644000176200001440000001316515104730074015162 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: FOR MONTHLY OPERATIONS: # countMonthlyRecords Returns a series with monthly counts of records # rollMonthlyWindows Returns start/end dates for rolling time windows # rollMonthlySeries Rolls Monthly a 'timeSeries' on a given period ################################################################################ # DW: # I think we should call these functions: # countRecordsMonthly, rollWindowsMonthly, rollSeriesMonthly, ... # ------------------------------------------------------------------------------ countMonthlyRecords <- function(x) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Returns a series with monthly counts of records # Example: # x = as.timeSeries(data(msft.dat)); countMonthlyRecords(x) # x = as.timeSeries(data(edhec)); countMonthlyRecords(x) # FUNCTION: # Check Arguments: stopifnot(is.timeSeries(x)) # Extract Title and Documentation: Title <- x@title Documentation <- x@documentation # Check for Signal Series: if (x@format == "counts") stop(as.character(match.call())[1], " is for time series and not for signal series.") # Count: ans <- rollMonthlySeries(x[, 1], period = "1m", by = "1m", FUN = NROW) colnames(ans) <- "Counts" # Preserve Title and Documentation: ans@title <- Title ans@documentation <- Documentation # Return Value: ans } # ------------------------------------------------------------------------------ rollMonthlyWindows <- function(x, period = "12m", by = "1m") { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Returns start and end dates for rolling time windows # Arguments: # x - a 'timeSerie's object of asset returns # period - a character string denoting the length of the rolling # window, e.g. "24m" means 24 months # by - a character string denoting the shift of the rolling window, # e.g. "3m" means one quarter # FUNCTION: # Check Arguments: stopifnot(is.timeSeries(x)) # Extract Title and Documentation: Title <- x@title Documentation <- x@documentation # Check for Signal Series: if (x@format == "counts") stop(as.character(match.call())[1], " is for time series and not for signal series.") # Get Window Parameter: periodLength <- as.numeric(substr(period, 1, nchar(period)-1)) periodUnit <- substr(period, nchar(period), nchar(period)) byLength <- as.numeric(substr(by, 1, nchar(by)-1)) byUnit <- substr(by, nchar(by), nchar(by)) stopifnot(periodUnit == "m") stopifnot(byUnit == "m") # Get Window Parameter: periodLength <- as.numeric(substr(period, 1, nchar(period)-1)) periodUnit <- substr(period, nchar(period), nchar(period)) byLength <- as.numeric(substr(by, 1, nchar(by)-1)) byUnit <- substr(by, nchar(by), nchar(by)) stopifnot(periodUnit == "m") stopifnot(byUnit == "m") # Make Windows - We expect monthly data records ... positions <- time(x) Positions <- unique(timeFirstDayInMonth(positions)) numberOfPositions <- length(Positions) startDates <- Positions[1:(numberOfPositions-periodLength)] endDates <- Positions[(periodLength+1):numberOfPositions]-24*3600 # Windows: windows <- list(from = startDates, to = endDates) attr(windows, "control") = c(start = start(positions), end = end(positions)) # Return Value: windows } # ------------------------------------------------------------------------------ rollMonthlySeries <- function(x, period = "12m", by = "1m", FUN, ...) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Rolls monthly a 'timeSeries' on a given period # Arguments: # x - a 'timeSerie's object of asset returns # period - a character string denoting the length of the rolling # window, e.g. "24m" means 24 months # by - a character string denoting the shift of the rolling window, # e.g. "3m" means one quarter # FUN - function to be applied # FUNCTION: # Check Arguments: stopifnot(is.timeSeries(x)) # Extract Title and Documentation: Title <- x@title Documentation <- x@documentation # Check for Signal Series: stopifnot(is(x, "timeSeries")) if (x@format == "counts") stop(as.character(match.call())[1], " is for time series and not for signal series.") # Settings: windows <- rollMonthlyWindows(x = x[, 1], period = period, by = by) # Apply Function: ans <- applySeries(x = x, from = windows$from, to = windows$to, FUN = FUN, ...) # Preserve Title and Documentation: ans@title <- Title ans@documentation <- Documentation # Return Value: ans } ################################################################################ timeSeries/R/statistics-rollMean.R0000644000176200001440000003060515117002037016650 0ustar liggesusers # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # rollStats Returns rolling statistics of a 'timeSeries' object # rollMean Returns rolling mean of a 'timeSeries' object # rollMin Returns rolling minimum of a 'timeSeries' object # rollMax Returns rolling maximum of a 'timeSeries' object # rolMedian Returns rolling median of a 'timeSeries' object # DEPRECATED: DESCRIPTION: # .rollmean.timeSeries Returns rolling mean of a 'timeSeries' object # .rollmin.timeSeries Returns rolling minimum of a 'timeSeries' object # .rollmax.timeSeries Returns rolling maximum of a 'timeSeries' object # .rolmedian.timeSeries Returns rolling median of a 'timeSeries' object ################################################################################ rollStats <- function(x, k, FUN=mean, na.pad=FALSE, align=c("center", "left", "right"), ...) { # Description: # Returns rolling statistics of a 'timeSeries' object # Arguments: # x - an object of class timeSeries # k - integer width of the rolling window. # FUN - statistical function to be rolled. # na.pad - a logical. Should NA padding be added at beginning? # align - character specifying whether result should be left- or # right-aligned or centered (default). # ... - furter arguments passed to function FUN. # Note: # Internal function are borrowed from package zoo ... # Example: # x <- timeSeries(matrix(sample(1:24), ncol = 2), timeCalendar()) # cbind(x, roll(x, k=3, FUN = mean, align="right", na.pad = TRUE)) # Internal Function: .rollstats <- function(x, k, fun, na.pad = FALSE, align = c("center", "left", "right"), ...) { window <- matrix(1:(length(x)+1-k), ncol = 1) winFun <- function(i, fun, y, k, ...) { fun = match.fun(fun) from = 1:(length(y)+1-k) to = from + k -1 fun(y[i:to[i]], ...) } rval <- apply(window, 1, FUN=winFun, fun=FUN, y=x, k=k, ...) if (na.pad) { rval <- switch(match.arg(align), "left" = { c(rval, rep(NA, k-1)) }, "center" = { c(rep(NA, floor((k-1)/2)), rval, rep(NA, ceiling((k-1)/2))) }, "right" = { c(rep(NA, k-1), rval) }) } rval } # Roll: ans <- apply(x, 2, FUN=.rollstats, k=k, fun=FUN, na.pad=na.pad, align=align, ...) x <- setDataPart(x[seq.int(1,NROW(ans)),], ans) colnames(x) <- paste(colnames(x), "RSTATS", sep = "_") if(!na.pad) x = na.omit(x) # Return Value x } ############################################################################### rollMean <- function(x, k, na.pad=FALSE, align=c("center", "left", "right"), ...) { # Description: # Returns rolling mean of a 'timeSeries' object # Arguments: # x - an object of class timeSeries # k - integer width of the rolling window. # na.pad - a logical. Should NA padding be added at beginning? # align - character specifying whether result should be left- or # right-aligned or centered (default). # ... - furter arguments passed to methods. # Note: # Internal function are borrowed from package zoo ... # Example: # X = timeSeries(matrix(rnorm(24), ncol = 2), timeCalendar()) # R = rollMean(x = X, k = 3); R; plot(R) # FUNCTION: # Internal Function: .rollmean.default <- function(x, k, na.pad = FALSE, align = c("center", "left", "right"), ...) { n <- length(x) y <- x[k:n] - x[c(1, 1:(n-k))] y[1] <- sum(x[1:k]) rval <- cumsum(y)/k if (na.pad) { rval <- switch(match.arg(align), "left" = { c(rval, rep(NA, k-1)) }, "center" = { c(rep(NA, floor((k-1)/2)), rval, rep(NA, ceiling((k-1)/2))) }, "right" = { c(rep(NA, k-1), rval) }) } rval } # Roll: ans <- apply(x, 2, .rollmean.default, k = k, na.pad=na.pad, align=align) x <- setDataPart(x[seq.int(1,NROW(ans)),], ans) colnames(x) <- paste(colnames(x), "RMEAN", sep = "_") if(!na.pad) x = na.omit(x) # Return Value: x } # ------------------------------------------------------------------------------ rollMax <- function(x, k, na.pad=FALSE, align=c("center", "left", "right"), ...) { # Description: # Returns rolling maximum of a 'timeSeries' object # Arguments: # x - an object of class timeSeries # k - integer width of the rolling window. # na.pad - a logical. Should NA padding be added at beginning? # align - character specifying whether result should be left- or # right-aligned or centered (default). # ... - furter arguments passed to methods. # Note: # Internal function are borrowed from package zoo ... # Example: # X = timeSeries(matrix(rnorm(24), ncol = 2), timeCalendar()) # R = rollMax(x = X, k = 3); plot(R) # FUNCTION: # Internal Function: .rollmax.default <- function(x, k, na.pad = FALSE, align = c("center", "left", "right"), ...) { n <- length(x) rval <- rep(0, n) a <- 0 for (i in k:n) { rval[i] <- if (is.na(a) || is.na(rval[i=1]) || a==rval[i-1]) max(x[(i-k+1):i]) else max(rval[i-1], x[i]); # max of window = rval[i-1] a <- x[i-k+1] # point that will be removed from window } rval <- rval[-seq(k-1)] if (na.pad) { rval <- switch(match.arg(align), "left" = { c(rval, rep(NA, k-1)) }, "center" = { c(rep(NA, floor((k-1)/2)), rval, rep(NA, ceiling((k-1)/2))) }, "right" = { c(rep(NA, k-1), rval) }) } rval } # Roll: ans <- apply(getDataPart(x), 2, .rollmax.default, k = k, na.pad=na.pad, align=align) x <- setDataPart(x[seq.int(1,NROW(ans)),], ans) colnames(x) <- paste(colnames(x), "RMAX", sep = "_") if(!na.pad) x = na.omit(x) # Return Value: x } # ------------------------------------------------------------------------------ rollMin <- function(x, k, na.pad=FALSE, align=c("center", "left", "right"), ...) { # Description: # Returns rolling minimum of a 'timeSeries' object # Arguments: # x - an object of class timeSeries # k - integer width of the rolling window. # na.pad - a logical. Should NA padding be added at beginning? # align - character specifying whether result should be left- or # right-aligned or centered (default). # ... - furter arguments passed to methods. # Note: # Internal function are borrowed from package zoo ... # Example: # X = timeSeries(matrix(rnorm(24), ncol = 2), timeCalendar()) # R = rollMin(x = X, k = 3); R; plot(R) # FUNCTION: .rollmax.default <- function(x, k, na.pad = FALSE, align = c("center", "left", "right"), ...) { n <- length(x) rval <- rep(0, n) a <- 0 for (i in k:n) { rval[i] <- if (is.na(a) || is.na(rval[i=1]) || a==rval[i-1]) max(x[(i-k+1):i]) else max(rval[i-1], x[i]); # max of window = rval[i-1] a <- x[i-k+1] # point that will be removed from window } rval <- rval[-seq(k-1)] if (na.pad) { rval <- switch(match.arg(align), "left" = { c(rval, rep(NA, k-1)) }, "center" = { c(rep(NA, floor((k-1)/2)), rval, rep(NA, ceiling((k-1)/2))) }, "right" = { c(rep(NA, k-1), rval) }) } rval } .rollmin.default <- function(x, k, na.pad = FALSE, align = c("center", "left", "right"), ...) { ans = -.rollmax.default(-x, k, na.pad = na.pad, align = align, ...) ans } # Roll: ans <- apply(getDataPart(x), 2, .rollmin.default, k=k, na.pad=na.pad, align=align) x <- setDataPart(x[seq.int(1,NROW(ans)),], ans) colnames(x) <- paste(colnames(x), "RMIN", sep = "_") if(!na.pad) x = na.omit(x) # Return Value: x } # ------------------------------------------------------------------------------ rollMedian <- function(x, k, na.pad=FALSE, align=c("center", "left", "right"), ...) { # Description: # Returns rolling median of a 'timeSeries' object # Arguments: # x - an object of class timeSeries # k - integer width of the rolling window. Must be odd for rollmedian. # na.pad - a logical. Should NA padding be added at beginning? # align - character specifying whether result should be left- or # right-aligned or centered (default). # ... - furter arguments passed to methods. # Note: # Internal function are borrowed from package zoo ... # Example: # X = timeSeries(matrix(rnorm(24), ncol=2), timeCalendar()) # R = rollMedian(x = X, k = 3); R; plot(R) # FUNCTION: # Internal Function: .rollmedian.default <- function(x, k, na.pad = FALSE, align = c("center", "left", "right"), ...) { stopifnot(k <= length(x), k %% 2 == 1) n <- length(x) m <- k %/% 2 rval <- runmed(x, k, ...) attr(rval, "k") <- NULL rval <- rval[-c(1:m, (n-m+1):n)] if (na.pad) { rval <- switch(match.arg(align), "left" = { c(rval, rep(NA, k-1)) }, "center" = { c(rep(NA, floor((k-1)/2)), rval, rep(NA, ceiling((k-1)/2))) }, "right" = { c(rep(NA, k-1), rval) }) } rval } # Roll: ans <- apply(x, 2, .rollmedian.default, k=k, na.pad=na.pad, align=align) x <- setDataPart(x[seq.int(1,NROW(ans)),], ans) colnames(x) <- paste(colnames(x), "RMED", sep = "_") if(!na.pad) x = na.omit(x) # Return Value: x } ################################################################################ ## removed on 2025-12-12 by GNB ## ## .rollmean.timeSeries <- ## function(...) ## { ## # FUNCTION: ## ## # Deprecated: ## .Deprecated("rollMean") ## ## # Return Value: ## rollMean(...) ## } ## ## removed on 2025-12-12 by GNB ## ## .rollmin.timeSeries <- ## function(...) ## { ## # FUNCTION: ## ## # Deprecated: ## .Deprecated("rollMin") ## ## # Return Value: ## rollMin(...) ## } # ------------------------------------------------------------------------------ ## removed on 2025-12-12 by GNB ## ## .rollmax.timeSeries <- ## function(...) ## { ## # FUNCTION: ## ## # Deprecated: ## .Deprecated("rollMax") ## ## # Return Value: ## rollMax(...) ## } # ------------------------------------------------------------------------------ ## removed on 2025-12-12 by GNB ## ## .rollmedian.timeSeries <- ## function(...) ## { ## # FUNCTION: ## ## # Deprecated: ## .Deprecated("rollMedian") ## ## # Return Value: ## rollMedian(...) ## } ################################################################################ timeSeries/R/methods-is.R0000644000176200001440000001271015104730074014765 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # is.timeSeries Tests for a 'timeSeries' object ################################################################################ is.timeSeries <- function (x) { # A function implemented by Diethelm Wuertz # Description: # Tests for a 'timeSeries' object. # Arguments: # object - a 'timeSeries' object to be tested. # Value: # Returns 'TRUE' or 'FALSE' depending on whether its # argument is of 'timeSeries' type or not. # Changes: # # FUNCTION: # Check: ans <- is(x, "timeSeries") # Return Value: ans } # ------------------------------------------------------------------------------ is.signalSeries <- function(x) { !as.logical(length(x@positions)) } # ------------------------------------------------------------------------------ # YC: # Note if is.na returns a timeSeries object then we have problem # with the function quantile... setMethod("is.na", "timeSeries", function(x) setDataPart(x, is.na(getDataPart(x))) ) ## 2024-01-12 GNB: ## TODO: create method for anyNA? ## The default anyNA works fine (it calls is.na), ## but a timeSeries method might gain some efficiency/ # ------------------------------------------------------------------------------ # something like this would be needed if is.unsorted again became an internal generic #if(getRversion() >= "3.3.0") { # setGeneric("is.unsorted", signature = "x", useAsDefault = base::is.unsorted) #} ## ## 2024-01-11 GNB: the notes below need consolidation, I wrote them as I worked on this. ## ## 1. is.unsorted is internal generic, though I didn't find in NEWS R- x.x notes about it ## changing its status as internal generic. ## ## 2. changed the method to S3 ## setMethod("is.unsorted", "timeSeries", ## function(x, na.rm = FALSE, strictly = FALSE) ## callGeneric(x@positions, na.rm = na.rm, strictly = strictly)) ## ## 3. since is.unsorted is primitive, it may be better to define an S4 method but the ## implicit generic created by setMethod has the wrong signature and creates a new ## function which needs to be exported. So, an explicit setGeneric to limit the ## dispatch only to 'x', see the example above by a previous maintainer. ## I haven't tried that. ## ## 2024-01-12 GNB: ## ## This works: is.unsorted.timeSeries <- function(x, na.rm = FALSE, strictly = FALSE) { is.unsorted(x@positions, na.rm = na.rm, strictly = strictly) } ## (GNB: cont.) ## (in the sense that it is dispatched) but it gives wrong results if there are NA's in ## the data. Indeed we have ## ## > base::is.unsorted ## function (x, na.rm = FALSE, strictly = FALSE) ## { ## if (length(x) <= 1L) ## return(FALSE) ## if (!na.rm && anyNA(x)) ## return(NA) ## if (na.rm && any(ii <- is.na(x))) ## x <- x[!ii] ## .Internal(is.unsorted(x, na.rm, strictly)) ## } ## ## the internal function is internal generic but before calling that, 'base::is.unsorted' ## handles the 'NA' cases. The time series method for is.na() works on the data, and so we ## get wrong result if there are NA's in the time series data. ## ## The S4 method creates a new generic with default 'base::is.unsorted' so there is no ## problem there. ## ## The core problem in my (GNB) opinion is that the timeSeries methods for is.unsorted and ## sort() have different semantics from that of the method for is.na (the former work on ## the time stamps, while the latter works on the data). ## ## So, returning the S4 method. Notice that we do not try to keep is.unsorted internal but ## rather export the generic so that it is calledand the time series method kiks in for ## timeSeries objects and avoids the above NA problem. ## ## Note that we keep also the S3 method, so that if the S4 generic is not visible, the ## result will be correct at least when there are no NA's in the data. setMethod("is.unsorted", "timeSeries", function(x, na.rm = FALSE, strictly = FALSE) callGeneric(x@positions, na.rm = na.rm, strictly = strictly)) # if (getRversion() < "2.8.0") # { # setMethod("is.unsorted", "timeSeries", function(x, na.rm = FALSE) # callGeneric(x@positions, na.rm = na.rm)) # } else { # setMethod("is.unsorted", "timeSeries", function(x, na.rm = FALSE, strictly = FALSE) # callGeneric(x@positions, na.rm = na.rm, strictly = strictly)) # } ################################################################################ timeSeries/R/statistics-colSums.R0000644000176200001440000000772715104730074016542 0ustar liggesusers # This library is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This library is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: COLUMN STATISTICS: # colStats Computes sample statistics by column # colSums Computes sums of all values in each column # colMeans Computes means of all values in each column # colSds Computes standardard deviation of each column # colVars Computes sample variance by column # colSkewness Computes sample skewness by column # colKurtosis Computes sample kurtosis by column # colMaxs Computes maximum values in each colum # colMins Computes minimum values in each colum # colProds Computes product of all values in each colum # colQuantiles Computes quantiles of all values in each colum ################################################################################ colStats <- function(x, FUN, ...) { # A function implemented by Diethelm Wuertz # Description: # Computes sample statistics by column # FUNCTION: # Statistics: if (inherits(x, "timeSeries")) apply(na.omit(getDataPart(x), ...), 2, FUN, ...) #<< YC : as.matrix is slow ! else apply(na.omit(as.matrix(x), ...), 2, FUN, ...) } # ------------------------------------------------------------------------------ # YC important because default colSums is unefficient since it retrieves # full dimnames, i.e. rownames which is very time consuming if (getRversion() < "2.9.0") { setMethod("colSums", "timeSeries", function(x, na.rm = FALSE, dims = 1L) { x <- getDataPart(x) callGeneric() }) } else { setMethod("colSums", "timeSeries", function(x, na.rm = FALSE, dims = 1L, ...) { x <- getDataPart(x) callGeneric() }) } # ------------------------------------------------------------------------------ # YC important because default colSums is unefficient since it retrieves # full dimnames, i.e. rownames which is very time consuming if (getRversion() < "2.9.0") { setMethod("colMeans", "timeSeries", function(x, na.rm = FALSE, dims = 1L) { x <- getDataPart(x) callGeneric() }) } else { setMethod("colMeans", "timeSeries", function(x, na.rm = FALSE, dims = 1L, ...) { x <- getDataPart(x) callGeneric() }) } # ------------------------------------------------------------------------------ colSds <- function(x, ...) { colStats(x, "sd", ...) } colVars <- function(x, ...) { colStats(x, "var", ...) } colSkewness <- function(x, ...) { colStats(x, "skewness", ...) } colKurtosis <- function(x, ...) { colStats(x, "kurtosis", ...) } colMaxs <- function(x, ...) { colStats(x, "max", ...) } colMins <- function(x, ...) { colStats(x, "min", ...) } colProds <- function(x, ...) { colStats(x, "prod", ...) } # ------------------------------------------------------------------------------ colQuantiles <- function(x, prob = 0.05, ...) { # FUNCTION: stopifnot(length(prob) == 1) colStats(x, "quantile", probs = prob, ...) } timeSeries/R/fin-drawdowns.R0000644000176200001440000001525015104730074015475 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # drawdowns Generate 'timeSeries' object of drawdown levels # drawdownsStats Compute drawdown stats for univariate time series # FUNCTION: DESCRIPTION: # .drawdownsHistPlot Displays a histogram plot ################################################################################ drawdowns <- function(x, ...) { # A function implemented by Diethelm Wuertz and Tobias Setz # Description: # Generate 'timeSeries' object of drawdown levels # Arguments: # x - an uni- or multivariate 'timeSeries' object of financial # returns # ... - arguments passed to the function na.omit() # Value: # returns an object of class 'timeSeries'. # FUNCTION: # Check Arguments stopifnot(is.timeSeries(x)) # Extract Title and Documentation: Title <- x@title Documentation <- x@documentation # Handle Missing Values: r <- na.omit(x, ...) # The starting point of every time series is set to zero. # This ensures that the starting value for cumprodReturns below # (which is the wealth index) is 1. startup <- timeSeries(data=t(rep(0, ncol(r))), charvec=time(r)[1]) # Preset Drawdowns: nms <- colnames(r) drawdowns <- r <- rbind(startup,r) colnames(drawdowns) <- colnames(r) <- nms # Compute multivariate 'timeSeries' of Drawdowns: cumprodReturns <- colCumprods(1 + r) cummaxReturns <- colCummaxs(cumprodReturns) series(drawdowns) <- series(cumprodReturns)/series(cummaxReturns) - 1 drawdowns <- drawdowns[-1, ] # Preserve Title and Documentation: drawdowns@title <- Title drawdowns@documentation <- Documentation # Return Value: drawdowns } # ------------------------------------------------------------------------------ drawdownsStats = function(x, ...) { # A function implemented by Diethelm Wuertz and Tobias Setz # Description: # Finds the drawdowns in an univariate 'timeSeries' object # Arguments: # x - an uni- or multivariate 'timeSeries' object of financial # returns # ... - arguments passed to the function drawdowns() # Value: # returns an object of class 'data.frame' returning # drawdown - the depth of the drawdown # from - the start date # trough - the trough period # to - the end date # length - the length in number of records # peaktrough - the peak trough # recovery - the recovery length in number of records # Author: # Based on Peter Carl, # partly from contributed R package Performance Analytics # Note: # modified with permission from function by Sankalp Upadhyay # # Examples: # x = drawdownsStats(as.timeSeries(data(edhec))[,1]) # FUNCTION: # Check Arguments: stopifnot(isUnivariate(x)) # Compute Drawdowns: drawdowns <- as.vector(drawdowns(x, ...)) time <- time(x) # Find Drawdowns from a Numeric Vector of Returns: draw <- begin <- end <- length <- trough <- c() index <- 1 if (drawdowns[1] >= 0) { priorSign <- 1 } else { priorSign <- 0 } from <- 1 sofar <- drawdowns[1] to <- 1 dmin <- 1 for (i in 2:length(drawdowns)) { thisSign <- ifelse(drawdowns[i] < 0, 0, 1) if (thisSign == priorSign) { if(drawdowns[i]< sofar) { sofar <- drawdowns[i] dmin <- i } to <- i + 1 } else { # @todo: recovery time (in days) draw[index] <- sofar begin[index] <- from trough[index] <- dmin end[index] <- to #cat(sofar, from, to) from <- i sofar <- drawdowns[i] to <- i + 1 dmin <- i index <- index + 1 priorSign <- thisSign } } draw[index] <- sofar begin[index] <- from trough[index] <- dmin end[index] <- to ## length: as.timeDate(pos[x$to])-as.timeDate(pos[x$from]) # If the time series ends in the middle of a drawdown, return the last # date of the time series and set the recovery time to NA endt <- end; endr <- end; if(to > length(time)) { endt[index] <- to - 1 endr[index] <- NA } # Result - an index list with all drawdowns ... ans <- data.frame(from = as.vector(as.character(time[begin])), trough = as.vector(as.character(time[trough])), to = as.vector(as.character(time[endt])), drawdown = as.vector(draw), length = (end - begin + 1), peaktotrough = (trough - begin + 1), recovery = (endr - trough), stringsAsFactors = FALSE) attr(ans, "series") <- x attr(ans, "names") <- c("From", "Trough", "To", "Depth", "Length", "ToTrough", "Recovery") ans <- ans[ans[, "Depth"] < 0, ] # Order Drawdowns: ans <- ans[order(ans[, "Depth"]), ] rownames(ans) <- 1:dim(ans)[1] # Return Value: ans } # ------------------------------------------------------------------------------ if (FALSE) { .drawdownsHistPlot <- function(x, labels = TRUE, col = "steelblue", add.fit = TRUE, rug = TRUE, skipZeros = TRUE, ...) { # Note: # We require(fExtremes) move this function to fAssets # Check Arguments: stopifnot(isUnivariate(x)) # Plot Drawdowns Histogram: X = drawdowns(x, ...) histPlot(X, labels = labels, col = col, add.fit = FALSE, rug = rug, skipZeros = skipZeros, ...) # Add GPD Fit: if (add.fit) { z = -as.vector(X) par = gpdFit(z, u = 0)@fit$par.ests u = seq(0, max(abs(z)), length = 200) v = dgpd(u, xi = par[1], mu = 0, beta = par[2]) lines(-u, v, col = "brown", lwd = 2) } # return Value: invisible() } } ################################################################################ timeSeries/R/methods-as.R0000644000176200001440000002612715104730074014764 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # METHOD: CREATE A TIMESERIES FROM OTHER OBJECTS: # as.timeSeries Defines method for a 'timeSeries' object # as.timeSeries.default Returns the input # as.timeSeries.ts Transforms a 'data.frame' into a 'timeSeries' # as.timeSeries.data.frame Transforms a 'data.frame' into a 'timeSeries' # as.timeSeries.character Loads and transformas from a demo file # as.timeSeries.zoo Transforms a 'zoo' object into a 'timeSeries' # METHOD: TRANSFORM A TIMESERIES INTO OTHER OBJECTS: # as.vector.timeSeries Converts a univariate 'timeSeries' to a vector # as.matrix.timeSeries Converts a 'timeSeries' to a 'matrix' # as.numeric.timeSeries Converts a 'timeSeries' to a 'numeric' # as.data.frame.timeSeries Converts a 'timeSeries' to a 'data.frame' # as.ts.timeSeries Converts a 'timeSeries' to a 'ts' # as.ts.logical Converts a 'timeSeries' to 'logical' # as.list.timeSeries Converts a 'timeSeries' to 'list' ################################################################################ # YC: # here keep S3 methods because it should expect an oldClass object as argument # ------------------------------------------------------------------------------ as.timeSeries <- function(x, ...) { UseMethod("as.timeSeries") } # ------------------------------------------------------------------------------ as.timeSeries.default <- function(x, ...) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # FUNCTION: # Return Value: ans <- timeSeries(x, ...) ans } setAs("ANY", "timeSeries", function(from) as.timeSeries(from)) # ------------------------------------------------------------------------------ as.timeSeries.ts <- function(x, ...) { asTime <- unclass(time(x)) yearPart <- trunc(asTime) decimalPart <- asTime - yearPart leapYears <- yearPart%%4 == 0 & (yearPart%%100 != 0 | yearPart%%400 == 0) days <- trunc(decimalPart * (365 + leapYears)) + 1 freq <- frequency(x) charvec <- if (freq == 4) { # Quarterly Data: days <- days + 1 ans <- timeDate(format(strptime(paste(yearPart, days), format = "%Y %j")), zone = "GMT", FinCenter = "GMT") timeLastDayInQuarter(ans) } else if (freq == 12) { # Monthly Data: days <- days + 1 ans <- timeDate(format(strptime(paste(yearPart, days), format = "%Y %j")), zone = "GMT", FinCenter = "GMT") timeLastDayInMonth(ans) } else { NA } # Result: tS = timeSeries(x, charvec, ...) attr(tS, "ts") <- c(start = round(start(x)), frequency = round(frequency(x)), deltat = deltat(x)) # Return Value: tS } setAs("ts", "timeSeries", function(from) as.timeSeries(from)) # ------------------------------------------------------------------------------ as.timeSeries.data.frame <- function(x, ...) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Converts a data.frame into a timeSeries object # Notes: # The first column must contain the dates. # Examples: # data(bmwRet); head(as.timeSeries(data(bmwRet))) # FUNCTION: if (all(!(num <- unlist(lapply(x, is.numeric))))) stop("x contains no numeric columns") # Check if rownames(x) or the first column has a valid ISO-format: if (num[1]) # is.numeric() is better than format == "unkown" # which can give wrong result. i.e. whichFormat(0.1253328600) suppressWarnings(charvec <- timeDate(rownames(x))) else suppressWarnings(charvec <- timeDate(as.vector(x[,1]))) data <- as.matrix(x[, num]) units <- names(x)[num] if (any(!(cl <- num[-1]))) { recordIDs <- as.data.frame(x[, !c(TRUE, cl)]) # do not take first column names(recordIDs) <- names(x)[!c(TRUE, cl)] } else { recordIDs <- data.frame() } # Create Time Series Object: timeSeries(data = data, charvec = charvec, units = units, recordIDs = recordIDs, ...) } setAs("data.frame", "timeSeries", function(from) as.timeSeries(from)) # ------------------------------------------------------------------------------ as.timeSeries.character <- function(x, ...) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Example: # as.timeSeries(data(nyse)) # FUNCTION: # Load Demo File - Returns a data frame: x <- eval(parse(text = eval(x))) # timeSeries: ans <- as.timeSeries(x, ...) # Return Value: ans } setAs("character", "timeSeries", function(from) as.timeSeries(from)) # ------------------------------------------------------------------------------ as.timeSeries.zoo <- function(x, ...) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # FUNCTION: # as. timeSeries: ans <- timeSeries(data = as.matrix(x), charvec = as.character(attr(x, "index")), ...) # Return Value: ans } ################################################################################ # YC: # Since 2.9.0 must define proper S4 methods .as.matrix.timeSeries <- function(x, ...) { # A function implemented by Diethelm Wuertz # Description: # Converts a multivariate "timeSeries" to a matrix # Arguments: # x - a 'timeSeries' object # Value: # Returns the data slot of a 'timesSeries' object as a vector. # FUNCTION: # Check: if (!inherits(x, "timeSeries")) stop("x is not a timeSeries object!") # Convert: ans <- getDataPart(x) # is matrix dimnames(ans) <- dimnames(x) # Results ans } setMethod("as.matrix", "timeSeries", function(x, ...) .as.matrix.timeSeries(x, ...)) # until UseMethod dispatches S4 methods in 'base' functions as.matrix.timeSeries <- function(x, ...) .as.matrix.timeSeries(x, ...) setAs("timeSeries", "matrix", function(from) as.matrix(from)) # ------------------------------------------------------------------------------ .as.data.frame.timeSeries <- function(x, row.names = NULL, optional = FALSE, ...) { # A function implemented by Diethelm Wuertz # Description: # Converts a multivariate "timeSeries" to a data.frame # Arguments: # x - a 'timeSeries' object # row.names, optional - not used # Value: # Returns the data slot of a 'timesSeries' object as a data frame. # FUNCTION: # get rownames from timeSeries if (is.null(row.names)) row.names <- rownames(x) if (any(duplicated(row.names))) stop("cannot convert to data.frame with duplicate timestamps") ans <- if (!length(x@recordIDs)) data.frame(as.list(x), row.names = row.names, ...) else data.frame(as.list(x), x@recordIDs, row.names = row.names, ...) # Return Value: ans } setMethod("as.data.frame", "timeSeries", function(x, row.names = NULL, optional = FALSE, ...) .as.data.frame.timeSeries(x, row.names = row.names, optional = optional, ...)) # until UseMethod dispatches S4 methods in 'base' functions as.data.frame.timeSeries <- function(x, ...) .as.data.frame.timeSeries(x, ...) setAs("timeSeries", "data.frame", function(from) as.data.frame(from)) # ------------------------------------------------------------------------------ ## 2024-01-11 GNB: streamlined and removed the S4 method as.ts.timeSeries <- function(x, ...) { # A function implemented by Diethelm Wuertz # Description: # Converts a colum from a 'timeSeries' object into an object # of class 'ts'. # Example: # # x = dummyMonthlySeries(); as.ts(x) # # x = timeSeries(seq(12), timeSequence(by = "month", length.out = 12)) # as.ts(x) # # x = dummyMonthlySeries()[c(3,6,9,12),]; as.ts(x) # x = dummyMonthlySeries()[c(2,5,8,11),]; as.ts(x) # x = dummyMonthlySeries()[c(1,4,7,10),]; as.ts(x) # # x = dummyMonthlySeries()[c(4,7,10,1),]; as.ts(x) # Changes: # # FUNCTION: # check if monthly or quarterly data td <- time(x) ## m <- c(timeDate::months(td)) #-> c() to remove attributes m <- c(months(td)) #-> c() to remove attributes ## (m[1] -1) -> shift vector to match first entry in m monthly <- seq(from = m[1]-1, length.out=length(m)) %% 12 + 1 quarterly <- seq(from = m[1]-1, by = 3, length=length(m)) %% 12 + 1 # get year of first entry y1 <- as.numeric(format(td[1], "%Y")) # important to use vector/matrix to avoid troubles with ts() data <- if (isUnivariate(x)) as.vector(x) else matrix(x, ncol = ncol(x)) if (identical(monthly, m)) # Monthly data return(ts(data, start = c(y1, m[1]), frequency = 12)) if (identical(quarterly, m)) # Quarterly data return(ts(data, start = c(y1, m[1]%/%4+1), frequency = 4)) # other frequencies not implemented yet; return default value ans <- ts(data, names = colnames(x)) attr(ans, "positions") <- time(x) ans } setAs("timeSeries", "ts", function(from) as.ts(from)) ## GNB: 2024-01-11 GNB: streamlined and removed the S4 method ## setMethod("as.ts", "timeSeries", function(x, ...) .as.ts.timeSeries(x, ...)) ## ## # until UseMethod dispatches S4 methods in 'base' functions ## as.ts.timeSeries <- function(x, ...) .as.ts.timeSeries(x, ...) # ------------------------------------------------------------------------------ # YC: # Unneeded since timeSeries inherits from the structure class # as.logical.timeSeries <- function(x, ...) as.logical(series(x), ...) # ------------------------------------------------------------------------------ # YC: # Important for functions like lapply and sapply to work properly .as.list.timeSeries <- function(x, ...) { data <- getDataPart(x) ncols <- NCOL(data) value <- vector("list", ncols) for (i in seq.int(ncols)) value[[i]] <- as.vector(data[, i]) names(value) <- colnames(x) value } setMethod("as.list", "timeSeries", function(x, ...) .as.list.timeSeries(x, ...)) # until UseMethod dispatches S4 methods in 'base' functions as.list.timeSeries <- function(x, ...) .as.list.timeSeries(x, ...) setAs("timeSeries", "list", function(from) as.list(from)) ################################################################################ timeSeries/R/timeSeries-slotSeries.R0000644000176200001440000001467315104730074017166 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # series,timeSeries Get data slot from a'timeSeries' # series<-,timeSeries,ANY Set new data slot to a 'timeSeries' # series<-,timeSeries,matrix Set new data slot to a 'timeSeries' # SYNONYMES: DESCRIPTION: # coredata,timeSeries Get data slot from a'timeSeries' # coredata<-,timeSeries,ANY Set new data slot to a 'timeSeries' # coredata<-,timeSeries,matrix Set new data slot to a 'timeSeries' ################################################################################ # ------------------------------------------------------------------------------ ## 2022-10-08 GNB: ## ## TODO: In principle, we could just not define 'coredata<-' generic and do: ## ## "coredata<-" <- "series<-" ## ## but this doesn't seem desirable since 'coredata<-' may be exported by ## other packages, too (e.g., zoo). Maybe, do it the other way round: ## define the methods for 'coredata<-' and do ## ## "series<-" <- "coredata<-" ## ## This may have the analogous problem since other packages may rely on a ## generic 'series<-'. Admittedly, this is far less likely. ## 2023-05-27 GNB: renaming from .series_assign <- 'coredata<-.timeSeries' <- function(x, value) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Assign the series Data to a timeSeries object. # Arguments: # object - a 'timeSeries' object # Value: # Assign to be assign as series Data of a timeSeries. # FUNCTION: ## 2023-05-27 GNB: added this when converted the two S4 methods into a ## single S3 one. if(class(value)[1] != "matrix") value <- as.matrix(value) # if value same dimension as time series # we we can assign the value directly to @.Data # This can speed up math Ops significantly if (identical(dim(x), dim(value))) { x@.Data <- value if (!is.null(cn <- colnames(value))) colnames(x) <- cn return(x) } if (is.null(charvec <- rownames(value))) charvec <- rownames(x) if (is.null(units <- colnames(value))) units <- colnames(value) # now that we have charvec and units, better to remove # dimnames of value to avoid problems attr(value, "dimnames") <- NULL if (!identical(length(units), NCOL(value))) units <- NULL # if now same dim , drop charvec and returns .signalSeries if (!identical(length(charvec), NROW(value))) return(.signalSeries(value, units)) format <- x@format zone <- FinCenter <- finCenter(x) title <- x@title documentation <- x@documentation recordIDs <- if (identical(NROW(x), NROW(value))) x@recordIDs else data.frame() # Return Value: timeSeries(data = value, charvec = charvec, units = units, format = format, zone = zone, FinCenter = FinCenter, recordIDs = recordIDs, title = title) } # ------------------------------------------------------------------------------ setMethod("series", "timeSeries", function(x) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Returns the series Data of an ordered data object. # Arguments: # x - a 'timeSeries' object # Value: # Returns an object of class 'matrix'. # FUNCTION: # Get Data Slot: ans <- as.matrix(x) # Return Value: ans } ) # ------------------------------------------------------------------------------ setMethod("series<-", signature(x = "timeSeries", value = "ANY"), function(x, value) { # A function implemented by Yohan Chalabi # Return Value: callGeneric(x, as(value, "matrix")) } ) setMethod("series<-", signature(x = "timeSeries", value = "matrix"), `coredata<-.timeSeries`) ################################################################################ # COREDATA SYNONYM ## GNB: replacing the S4 generic coredata and its method with an S3 method, ## which is exported and registered directly as a method for zoo::coredata. ## ## The S4 coredata() in 'timeSeries' was not exported although is method ## was 'seen' by 'zoo::coredata' when zoo was attached. I suspect that that ## was by chance, not as a design in the S3/S4 methods handling in R. Of ## course, coredata() was only visible when zoo was attached (or xts which ## exports it). ## setMethod("coredata", "timeSeries", ## function(x) ## { ## # A function implemented by Diethelm Wuertz and Yohan Chalabi ## ## # Description: ## # Returns the series Data of an ordered data object. ## ## # Arguments: ## # x - a 'timeSeries' object ## ## # Value: ## # Returns an object of class 'matrix'. ## ## # FUNCTION: ## ## # Get Data Slot: ## ans <- as.matrix(x) ## ## # Return Value: ## ans ## }) coredata.timeSeries <- function(x) as.matrix(x) # ------------------------------------------------------------------------------ ## GNB: replacing the S4 generic 'coredata<-' and its methods with an S3 method, ## which is exported and registered directly as a method for zoo::coredata<-. ## ## Note that although the S4 methods were seen when zoo was loaded, they ## didn't work properly since they dispatch on two arguments, while the ## function is S3. ## setMethod("coredata<-", signature(x = "timeSeries", value = "ANY"), ## function(x, value) ## { ## # A function implemented by Diethelm Wuertz and Yohan Chalabi ## ## # Return Value: ## callGeneric(x, as(value, "matrix")) ## }) ## ## setMethod("coredata<-", signature(x = "timeSeries", value = "matrix"), ## .series_assign ) timeSeries/R/timeSeries-slotFinCenter.R0000644000176200001440000000610715104730074017602 0ustar liggesusers # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # finCenter,timeSeries Get financial center slot from a 'timeSeries' # finCenter<-,timeSeries Set financial center slot from a 'timeSeries' # FUNCTION: DESCRIPTION: # getFinCenter Get financial center slot from a 'timeSeries' # setFinCenter<- Set new financial center slot from a 'timeSeries' ################################################################################ setMethod("finCenter", "timeSeries", function(x) { # A function implemented by Yohan Chalabi and Diethelm Wuertz # Description: # Get financial center slot from a 'timeSeries' object # Arguments: # x - an object of class 'timeSeries' # FUNCTION: # Extract financial center: ans <- x@FinCenter # Return Value: ans }) # ------------------------------------------------------------------------------ setMethod("finCenter<-", "timeSeries", function(x, value) { # A function implemented by Yohan Chalabi and Diethelm Wuertz # Description: # Set financial center slot from a 'timeSeries' object # Arguments: # x - an object of class 'timeSeries' # value - a character string, setting the name of the financial # center. # FUNCTION: # Check: if (x@format == "counts") stop(as.character(match.call())[1], " is for time series and not for signal series.") # Convert to user financial centre: positions <- timeDate(charvec = time(x), zone = finCenter(x), FinCenter = value) # Assign Positions to Time Stamps: time(x) <- positions # Return Value: x }) ################################################################################ getFinCenter <- function(x) { # Description: # Get financial center slot from a 'timeSeries' object # Arguments: # x - a 'timeSeries' object # FUNCTION: # Return Value: finCenter(x) } # ------------------------------------------------------------------------------ "setFinCenter<-" <- function(x, value) { # Description: # Set new financial center slot from a 'timeSeries' object # FUNCTION: # Arguments: # x - a 'timeSeries' object # Assign Financial Center Slot: finCenter(x) <- value # Return Value: x } ################################################################################ timeSeries/R/methods-plot.R0000644000176200001440000004014515104730074015333 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # plot,timeSeries Plots a 'timeSeries' object # .plot.timeSeries Internal function called by plot.timeSeries # lines,timeSeries Adds lines to a 'timeSeries' plot # points,timeSeries Adds points to a 'timeSeries' plot # FUNCTION: DESCRIPTION: # pretty.timeSeries Returns a sequence of equally spaced round values ################################################################################ ## 2024-01-11 GNB: streamlined S4/S3 combination ## ## Keeping .plot.timeSeries for now since cached plot methods for timeSeries in fGarch, ## fBasics built with previous version of timeSeries cause test failure in fExtremes ## because .plot.timeSeries is not found! Took me some time to figure this out. ## ## To remove .plot.timeSeries will need to rebuild at least some of the above packages with ## this version of timeSeries. Alternatively, new releases of those packages could require ## this version of timeSeries. Or maybe ask CRAN to rebuild those packages with this ## version of R? Indeed, this is the case. Actually, it seems not necessary to rebuild ## fExtremes (which is good, I don't manage it), it looks like the imported offending ## method comes from fGarch (which defines plot methods and exports them. ## ## To summarize: keep .plot.timeSeries for now, remove it when the above packages are ## updated and/or rebuilt with timeSeries > 4032.108. Preferably, updated versions of those ## packages would require timeSeries > 4032.108. .plot.timeSeries <- plot.timeSeries <- function(x, y, FinCenter = NULL, plot.type = c("multiple", "single"), format = "auto", at = pretty(x), widths = 1, heights = 1, xy.labels, xy.lines, panel = lines, nc, yax.flip = FALSE, mar.multi = c(0, 5.1, 0, if (yax.flip) 5.1 else 2.1), oma.multi = c(6, 0, 5, 0), axes = TRUE, ...) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Plots 'timeSeries' objects # Arguments: # see plot.ts() # Additional Arguments: # format, at to beautify axis.POSIXct() function # widths, heights to handle layout() function # Details: # This function is build in exactly the same way as the function # plot.ts() for regular time series (R's ts) objects... # Examples: # x = as.timeSeries(data(msft.dat))[, 1:4] # .plot.timeSeries(x) # .plot.timeSeries(x[,1], x[,2], pch = 19) # .plot.timeSeries(x, plot.type = "single", col = 2:5) # FUNCTION: # Check Missing: if (missing(y)) y <- NULL # Check for "pretty' and "chic": if (is.character(at)) { if (at[1] == "pretty" || at[1] == "chic") { return(.xtplot.timeSeries( x=x, y=y, FinCenter = FinCenter, plot.type = plot.type, format = format, at = at, panel = panel, yax.flip = yax.flip, mar.multi = mar.multi, oma.multi = oma.multi, axes=axes, ...) ) } } # Labels: xlabel <- if (!missing(x)) deparse(substitute(x)) ylabel <- if (!missing(y)) deparse(substitute(y)) # Take care of FinCenter: if (!is.null(FinCenter)) { finCenter(x) <- FinCenter if (!is.null(y)) finCenter(y) <- FinCenter if (is(at, "timeDate")) at@FinCenter <- FinCenter } # Return Value: .plotTimeSeries(x = x, y = y, plot.type = plot.type, xy.labels = xy.labels, xy.lines = xy.lines, panel = panel, nc = nc, xlabel = xlabel, ylabel = ylabel, axes = axes, mar.multi = mar.multi, oma.multi = oma.multi, yax.flip = yax.flip, format = format, at = at, widths = widths, heights = heights, ...) } ## setMethod("plot", "timeSeries", ## function(x, y, FinCenter = NULL, ## plot.type = c("multiple", "single"), ## format = "auto", at = pretty(x), ## widths = 1, heights = 1, ## xy.labels, xy.lines, panel = lines, nc, yax.flip = FALSE, ## mar.multi = c(0, 5.1, 0, if (yax.flip) 5.1 else 2.1), ## oma.multi = c(6, 0, 5, 0), axes = TRUE, ...) ## .plot.timeSeries(x = x, y = y, FinCenter = FinCenter, ## plot.type = plot.type, ## format = format, at = at, ## widths = widths, heights = heights, ## xy.labels=xy.labels, xy.lines=xy.lines, ## panel = panel, nc = nc, yax.flip = yax.flip, ## mar.multi = mar.multi, ## oma.multi = oma.multi, axes = axes, ...)) setMethod("plot", "timeSeries", plot.timeSeries) ## # until UseMethod dispatches S4 methods in 'base' functions ## plot.timeSeries <- function(x, y, ...) .plot.timeSeries(x, y, ...) # ------------------------------------------------------------------------------ # Internal Function called by plot(): .plotTimeSeries <- function(x, y = NULL, plot.type = c("multiple", "single"), xy.labels, xy.lines, panel = lines, nc, xlabel, ylabel, type = "l", xlim = NULL, ylim = NULL, xlab = "Time", ylab, log = "", col = 1:ncol(x), bg = NA, pch = 1:ncol(x), cex = par("cex"), lty = par("lty"), lwd = par("lwd"), axes = TRUE, frame.plot = axes, ann = par("ann"), main = NULL, mar.multi, oma.multi, yax.flip, format, at, widths, heights, grid = FALSE, ...) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Plots timeSeries objects - Internal Function # Details: # A modified copy of R's internal 'plotts()' function, # see 'plot.ts()'. # FUNCTION: # Utility Function: plot.type <- match.arg(plot.type) nser <- NCOL(x) if (format == "auto") format = x@format X <- if (x@format == "counts") time(x) else as.POSIXct(time(x)) if (is.character(at) && identical(at, "auto")) { # Index = round(seq(1, length(time(x)), length = 6)) # at = X[Index] at = seq(X[1], X[length(X)], length = 6) } if(is(at, "timeDate")) at = as.POSIXct(at) # YC : force col and pch to be of same length as NCOL(x) otherwise # time series might not be plotted at all. col <- rep(col, length.out = nser) pch <- rep(pch, length.out = nser) # Multiple Plots, each one Curve, on one Page: if (plot.type == "multiple" && nser > 1) { ngraph = nser panel <- match.fun(panel) nser <- NCOL(x) if (nser > 10) stop("cannot plot more than 10 series as \"multiple\"") if (is.null(main)) main <- xlabel nm <- colnames(x) if (is.null(nm)) nm <- paste("Series", 1:nser) if (missing(nc)) nc <- if (nser > 4) 2 else 1 nr <- ceiling(nser/nc) oldpar <- par(mar = mar.multi, oma = oma.multi, mfcol = c(nr, nc)) on.exit(par(oldpar)) nr <- ceiling(ngraph/nc) layout(matrix(seq(nr * nc), nr), widths = widths, heights = heights) for (i in 1:nser) { plot(X, series(x)[, i], axes = FALSE, xlab = "", ylab = "", log = log, col = col[i], bg = bg, pch = pch[i], ann = ann, type = "n", ...) panel(X, series(x)[, i], col = col[i], bg = bg, pch = pch[i], type = type, ...) if (frame.plot) box(...) y.side <- if (i%%2 || !yax.flip) 2 else 4 do.xax <- i%%nr == 0 || i == nser if (axes) { axis(y.side, xpd = NA) if (do.xax) { if (x@format == "counts") { axis(1) } else { axis.POSIXct(1, at = at, format = format) } } } if (ann) { mtext(nm[i], y.side, line = 3, ...) if (do.xax) mtext(xlab, side = 1, line = 3, ...) } if(grid) abline(v = at, lty = 3, col = "grey") } if (ann && !is.null(main)) { par(mfcol = c(1, 1)) cex.main = par("cex.main") font.main = par("font.main") col.main = par("col.main") mtext(main, side = 3, line = 3, cex = cex.main, font = font.main, col = col.main, ...) } return(invisible()) } # Scatter Plot: if (!is.null(y)) { stopifnot(isUnivariate(x)) stopifnot(isUnivariate(y)) xy = cbind(x, y) xy <- xy.coords(series(xy)[, 1], series(xy)[, 2], xlabel, ylabel, log) xlab <- if (missing(xlab)) xy$xlab else xlab ylab <- if (missing(ylab)) xy$ylab else ylab xlim <- if (is.null(xlim)) range(xy$x[is.finite(xy$x)]) else xlim ylim <- if (is.null(ylim)) range(xy$y[is.finite(xy$y)]) else ylim n <- length(xy$x) if (missing(xy.labels)) xy.labels <- (n <= 150) if (!is.logical(xy.labels)) { if (!is.character(xy.labels)) stop("'xy.labels' must be logical or character") do.lab <- TRUE } else { do.lab <- xy.labels } ptype <- if (do.lab) "n" else if (missing(type)) "p" else type plot.default(xy, type = ptype, xlab = xlab, ylab = ylab, xlim = xlim, ylim = ylim, log = log, col = col, bg = bg, pch = pch, axes = axes, frame.plot = frame.plot, ann = ann, main = main, ...) if (missing(xy.lines)) { xy.lines <- do.lab } if (do.lab) text(xy, labels = if (is.character(xy.labels)) xy.labels else seq_along(xy$x), col = col, cex = cex) if (xy.lines) { type = if (do.lab) "c" else "l" lines(xy, col = col, lty = lty, lwd = lwd, type = type) } return(invisible()) } # Multiple Curves all in one Plot, on one Page: if (missing(ylab)) { ylab <- colnames(x) if (length(ylab) != 1) ylab <- xlabel } if (is.null(ylim)) ylim <- range(x, na.rm = TRUE) i = 1 X <- if (x@format == "counts") time(x) else as.POSIXct(time(x)) plot(X, series(x)[, i], ylim = ylim, col = col[(i - 1)%%length(col) + 1], lty = lty[(i - 1)%%length(lty) + 1], lwd = lwd[(i - 1)%%length(lwd) + 1], bg = bg[(i - 1)%%length(bg) + 1], pch = pch[(i - 1)%%length(pch) + 1], type = type, axes = FALSE, ylab = "", xlab = "") if (NCOL(x) > 1) for (i in 2:NCOL(x)) lines(X, series(x)[, i], col = col[(i - 1)%%length(col) + 1], lty = lty[(i - 1)%%length(lty) + 1], lwd = lwd[(i - 1)%%length(lwd) + 1], bg = bg[(i - 1)%%length(bg) + 1], pch = pch[(i - 1)%%length(pch) + 1], type = type) if (ann) title(main = main, xlab = xlab, ylab = ylab, ...) if (axes) { if (x@format == "counts") axis(1, ...) else axis.POSIXct(1, at = at, format = format) axis(2, ...) } if (frame.plot) box(...) if(grid) abline(v = at, lty = 3, col = "grey") return(invisible()) } # ------------------------------------------------------------------------------ ## GNB: 2024-01-11 GNB: streamlined and removed the S4 method lines.timeSeries <- function(x, FinCenter = NULL, ...) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # NEW lines method for an object of class "timeSeries" # Arguments: # x - a "timeSeries" object # Example: # plot(MSFT[, 1]); lines(MSFT[, 1], col = "red") # FUNCTION: # Change FinCenter: if (!is.null(FinCenter)) finCenter(x) <- FinCenter # Lines: positions <- time(x) if (x@format == "counts") { lines(x = positions, y = series(x), ...) } else { lines(x = as.POSIXct(positions), y = series(x), ...) } # Return Value: invisible(x) } ## GNB: 2024-01-11 GNB: streamlined and removed the S4 method ## setMethod("lines", "timeSeries", function(x, FinCenter = NULL, ...) ## .lines.timeSeries(x, FinCenter, ...)) ## ## # until UseMethod dispatches S4 methods in 'base' functions ## lines.timeSeries <- function(x, FinCenter = NULL, ...) ## .lines.timeSeries(x, FinCenter = FinCenter, ...) # ------------------------------------------------------------------------------ ## GNB: 2024-01-11 GNB: streamlined and removed the S4 method points.timeSeries <- function(x, FinCenter = NULL, ...) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Plot method for an object of class "timeSeries" # Arguments: # x - a "timeSeries" object # Value: # Plots a 'timeSeries' object. # FUNCTION: # Change FinCenter: if (!is.null(FinCenter)) finCenter(x) <- FinCenter # Points: positions <- time(x) if (x@format == "counts") { points(x = positions, y = series(x), ...) } else { points(x = as.POSIXct(positions), y = series(x), ...) } # Return Value: invisible(x) } ## GNB: 2024-01-11 GNB: streamlined and removed the S4 method ## setMethod("points", "timeSeries", ## function(x, FinCenter = NULL, ...) ## .points.timeSeries(x, FinCenter = FinCenter, ...)) ## ## # until UseMethod dispatches S4 methods in 'base' functions ## points.timeSeries <- function(x, FinCenter = NULL, ...) ## .points.timeSeries(x, FinCenter = FinCenter, ...) ################################################################################ pretty.timeSeries <- function(x, n = 5, min.n = n%/%3, shrink.sml = 0.75, high.u.bias = 1.5, u5.bias = 0.5 + 1.5 * high.u.bias, eps.correct = 0, ...) { # A function implemented by Diethelm Wuertz # Description: # Returns a sequence of equally spaced round values. # Details: # Computes a sequence of about n+1 equally spaced ?round? # values which cover the range of the values in x. # The values are chosen so that they are 1, 2 or 5 times # a power of 10. # Arguments: # x - a timeSeries object from which the time is # extracted # n - integer giving the desired number of intervals. # min.n - nonnegative integer giving the minimal # number of intervals. # shrink.sml - positive numeric by a which a default # scale is shrunk in the case when range(x) is # very small. # high.u.bias - non-negative numeric, typically > 1. # Larger high.u.bias values favor larger units. # u5.bias - non-negative numeric multiplier favoring # factor 5 over 2. # eps.correct - integer code, one of {0,1,2}. If # non-0, a correction is made at the boundaries. # ... - further arguments for methods. # FUNCTION: td <- time(x) if (inherits(x, "timeDate")) { x <- as.POSIXct(td) as.timeDate(pretty(x, n=n, min.n=min.n, shrink.sml=shrink.sml, high.u.bias=high.u.bias, u5.bias=u5.bias, eps.correct=eps.correct, ...)) } else { #-> signal series pretty(td) } } ############################################################################### timeSeries/R/fin-turnpoints.R0000644000176200001440000002370315117001527015712 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # turns Returns turnpoints from a 'timeSeries' # turnsStats Computes statistics of turn points # BUILTIN: DESCRIPTION: # .turnpointsPastecs Builtin function from package pastecs # .extract.turnpointsPastecs Extractor function from package pastecs # .plot.turnpointsPastecs Plot function from package pastecs # .summary.turnpointsPastecs Summary function from package pastecs # DEPRECATED: DESRIPTION: # .turnpoints2 Deprecated, use function turns # .turnpointsSeries Deprecated, use function turns # .turnpointsStats Deprecated, use function turnsStats ################################################################################ # DW: # This function is originally borrowed from the contributesd R package pastecs. # It is not necessary to load pastecs, the functions required are builtin. # ----------------------------------------------------------------------------- turns <- function(x, ...) { # A function implemented by Diethelm Wuertz # Description: # Returns turnpoints from a timeSeries # Arguments: # x - an univariate timeSeries object, e.g. a price or index series. # ... - arguments passed to the function na.omit() # FUNCTION: # Check arguments: stopifnot(is.timeSeries(x)) stopifnot(isUnivariate(x)) # Extract Title and Documentation: Title <- x@title Documentation <- x@documentation # Handle Missing Values: x <- na.omit(x, ...) # Convert to Vector: X <- x x <- as.vector(x) # Turnpoints: ans <- .turnpointsPastecs(x) tp <- .extract.turnpointsPastecs(ans) data <- cbind(x, tp) colnames(data) <- c(colnames(X), "TP") series(X) <- data # Preserve Title and Documentation: X@title <- Title X@documentation <- Documentation # Return Value: X } # ---------------------------------------------------------------------------- turnsStats <- function(x, doplot = TRUE) { # A function implemented by Diethelm Wuertz # Description: # Computes turnpoints statistics # Arguments: # x - an univariate timeSeries object, e.g. a price or index series # doplot - a logical flag, should an optional plot be displayed? # Value: # Returns an object of class turnpoints. # FUNCTION: # Settings stopifnot(isUnivariate(x)) X <- x x <- as.vector(x) # Turnpoints: ans <- .turnpointsPastecs(x) # Summary Statistics: .summary.turnpointsPastecs(ans) # Optional Plot: if(doplot) .plot.turnpointsPastecs(ans) # Return Value: invisible(ans) } ################################################################################ ## removed on 2025-12-12 by GNB ## ## .turnpoints2 <- ## function(...) ## { ## # Deprecated: ## .Deprecated(new = "turns", package = "timeSeries") ## ## # Return Value: ## turns(...) ## } ## removed on 2025-12-12 by GNB ## ## .turnpointsStats <- ## function(...) ## { ## # Deprecated: ## .Deprecated(new = "turnsStats", package = "timeSeries") ## ## # Return Value: ## turnsStats(...) ## } ################################################################################ # Package: pastecs # Title: Package for Analysis of Space-Time Ecological Series # Version: 1.3-4 # Date: 2006-11-28 # Author: Frederic Ibanez , # Philippe Grosjean & # Michele Etienne # Description: Regulation, decomposition and analysis of space-time series. # The pastecs library is a PNEC-Art4 and IFREMER # (Benoit Beliaeff ) initiative # to bring PASSTEC 2000 # (http://www.obs-vlfr.fr/~enseigne/anado/passtec/passtec.htm) # functionnalities to R. # URL: http://www.sciviews.org/pastecs # Maintainer: Philippe Grosjean # License: GNU Public Licence 2.0 or above at your convenience # Depends: boot, stats # Packaged: Tue Nov 28 15:33:42 2006; Philippe Grosjean .turnpointsPastecs <- function(x) { data <- deparse(substitute(x)) if (is.null(ncol(x)) == FALSE) stop("Only one series can be treated at a time") # if (exists("is.R") && is.function(is.R) && is.R()) # We are in R # Now done with Depends: field require(stats) x <- as.vector(x) n <- length(x) diffs <- c(x[1]-1, x[1:(n-1)]) != x uniques <- x[diffs] n2 <- length(uniques) poss <- (1:n)[diffs] exaequos <- c(poss[2:n2], n+1) - poss - 1 if (n2 < 3) { # We need at least 3 unique values!!! warning("Less than 3 unique values, no calculation!") nturns <- NA firstispeak <- FALSE peaks <- rep(FALSE, n2) pits <- rep(FALSE, n2) tppos <- NA proba <- NA info <- NA } else { # The following code is faster in R, but do not work all the time! #if (exists("is.R") && is.function(is.R) && is.R()) { # We are in R # ex <- embed(uniques, 3) # Works only in R! # peaks <- c(FALSE, max.col(ex) == 2, FALSE) # pits <- c(FALSE, max.col(-ex) == 2, FALSE) #} else { # We are in S+ m <- n2 - 2 ex <- matrix(uniques[1:m + rep(3:1, rep(m, 3)) - 1], m) peaks <- c(FALSE, apply(ex, 1, max, na.rm=TRUE) == ex[, 2], FALSE) pits <- c(FALSE, apply(ex, 1, min, na.rm=TRUE) == ex[, 2], FALSE) #} tpts <- peaks | pits if (sum(tpts) == 0) { # No turning point nturns <- 0 firstispeak <- FALSE peaks <- rep(FALSE, n2) pits <- rep(FALSE, n2) tppos <- NA proba <- NA info <- NA } else { # This way, we consider the last element of duplicates, as # in PASSTEC 2000 tppos <- (poss + exaequos)[tpts] tptspos <- (1:n2)[tpts] firstispeak <- tptspos[1] == (1:n2)[peaks][1] nturns <- length(tptspos) if (nturns < 2) { inter <- n2 + 1 posinter1 <- tptspos[1] } else { inter <- c(tptspos[2:nturns], n2) - c(1, tptspos[1:(nturns-1)]) + 1 posinter1 <- tptspos - c(1, tptspos[1:(nturns-1)]) } posinter2 <- inter - posinter1 posinter <- pmax(posinter1, posinter2) proba <- 2 / (inter * gamma(posinter) * gamma(inter - posinter + 1)) info <- -log(proba, base = 2) } } res <- list(data = data, n = n, points = uniques, pos = (poss + exaequos), exaequos = exaequos, nturns = nturns, firstispeak = firstispeak, peaks = peaks, pits = pits, tppos = tppos, proba = proba, info = info) class(res) <- "turnpoints" res } # ------------------------------------------------------------------------------ .extract.turnpointsPastecs <- function(e, n, no.tp = 0, peak = 1, pit = -1, ...) { if (missing(n)) n <- -1 res <- rep(no.tp, length.out= e$n) res[e$pos[e$peaks]] <- peak res[e$pos[e$pits]] <- pit # Keep only the first n points if (n < length(res) & n > 0) res <- res[1:n] res } # ------------------------------------------------------------------------------ .plot.turnpointsPastecs <- function(x, level = 0.05, lhorz = TRUE, lcol = 2, llty = 2, type = "l", xlab = "data number", ylab = paste("I (bits), level = ", level*100, "%", sep = ""), main = paste("Information (turning points) for:",x$data), ...) { # The next function actually draws the graph turnpoints.graph <- function(X, Level, Lhorz, Lcol, Llty, Type, Xlab, Ylab, Main, Sub, ...) { plot(X$tppos, X$info, type = Type, xlab = Xlab, ylab = Ylab, main = Main, ...) abline(h = -log(Level, base = 2), lty = Llty, col = Lcol) } # Return Value: invisible(turnpoints.graph(x, level[1], lhorz, lcol, llty, type, xlab, ylab, main, ...)) } # ------------------------------------------------------------------------------ .summary.turnpointsPastecs <- function(object, ...) { cat("Turning points for:", object$data, "\n\n") cat("nbr observations :", object$n, "\n") cat("nbr ex-aequos :", sum(object$exaequos), "\n") if (object$firstispeak) { cat("nbr turning points:", object$nturns, "(first point is a peak)\n") typep <- c("peak", "pit") } else { cat("nbr turning points:", object$nturns, "(first point is a pit)\n") typep <- c("pit", "peak") } cat("E(p) =", 2 / 3 * (object$n - 2), "Var(p) =", (16 * object$n - 29) / 90, "(theoretical)\n") cat("\n") # construct the table summarizing all turning points typepts <- rep(typep, length.out=object$nturns) tablepts <- as.data.frame(list(point = object$tppos, type = typepts, proba = object$proba, info = object$info)) print(tablepts) # Return Value: invisible(object) } ################################################################################ .turnpointsSeries = function(...) { # Deprecated: .Deprecated(new = "turns") # Return Value: turns(...) } ################################################################################ timeSeries/R/aaa-utils.R0000644000176200001440000000135115104730074014570 0ustar liggesusers## Copyright (C) 2020 Martin Maechler ## ## This program is free software; you can redistribute it and/or modify ## it under the terms of the GNU General Public License as published by ## the Free Software Foundation; either version 2 of the License, or ## (at your option) any later version. ## ## This program is distributed in the hope that it will be useful, ## but WITHOUT ANY WARRANTY; without even the implied warranty of ## MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the ## GNU General Public License for more details. ## ## A copy of the GNU General Public License is available at ## https://www.R-project.org/Licenses/ ## Not my idea .. but really nice : `%||%` <- function (L, R) if (is.null(L)) R else L timeSeries/R/statistics-colCumsums.R0000644000176200001440000001750115104730074017236 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: COLUMN CUMULATIVE SUMS: # colCumsums Computes sample cumulated sums by column # colCumsums,matrix S3 default method (for matrix objects) # colCumsums,timeSeries S3 method for timeSeries objects # FUNCTION: COLUMN CUMULATIVE MAXIMA: # colCummaxs Computes cumulated maximum values # colCummaxs,matrix S3 default method (for matrix objects) # colCummaxs,timeSeries S3 method for timeSeries objects # FUNCTION: COLUMN CUMULATIVE MAXIMA: # colCummins Computes cumulated maximum values # colCummins,matrix S3 default method (for matrix objects) # colCummins,timeSeries S3 method for timeSeries objects # FUNCTION: COLUMN CUMULATIVE MINIMA: # colCumprods Computes cumulated product values # colCumprods,matrix S3 default method (for matrix objects) # colCumprods,timeSeries S3 method for timeSeries objects # FUNCTION: COLUMN CUMULATIVE RETURNS: # colCumreturns Computes cumulated product values # colCumreturns,matrix S3 default method (for matrix objects) # colCumreturns,timeSeries S3 method for timeSeries objects ################################################################################ # ------------------------------------------------------------------------------ setMethod("colCumsums", "matrix", function(x, na.rm = FALSE, ...) { if (na.rm) x <- na.omit(x) ans <- apply(x, 2, cumsum, ...) # special treatment when x has one row because apply returns a vector if (NROW(x) == 1) ans <- matrix(ans, nrow = 1, dimnames = dimnames(x)) ans }) # ------------------------------------------------------------------------------ setMethod("colCumsums", "timeSeries", function(x, na.rm = FALSE, ...){ ## GB: split to debug #2121; was: ## setDataPart(x, callGeneric(getDataPart(x), na.rm = na.rm, ...)) ## 2022-07-27 bug #2121 ## can't delegate omitting NA to the matrix method since we need the time ## info after na.omit() here (to set the time properly if(na.rm) x <- na.omit(x) m = getDataPart(x) wrk = callGeneric(m, na.rm = FALSE, ...) # note: na.rm = FALSE res <- setDataPart(x, wrk) res }) # ------------------------------------------------------------------------------ setMethod("colCummaxs", "matrix", function(x, na.rm = FALSE, ...) { if (na.rm) x <- na.omit(x) ans <- apply(x, 2, cummax, ...) # special treatment when x has one row because apply returns a vector if (NROW(x) == 1) ans <- matrix(ans, nrow = 1, dimnames = dimnames(x)) ans }) # ------------------------------------------------------------------------------ setMethod("colCummaxs", "timeSeries", function(x, na.rm = FALSE, ...){ ## GB: split to debug #2121; was: ## setDataPart(x, callGeneric(getDataPart(x), na.rm = na.rm, ...)) ## 2022-07-27 bug #2121 ## can't delegate omitting NA to the matrix method since we need the time ## info after na.omit() here (to set the time properly if(na.rm) x <- na.omit(x) m = getDataPart(x) wrk = callGeneric(m, na.rm = FALSE, ...) # note: na.rm = FALSE res <- setDataPart(x, wrk) res }) # ------------------------------------------------------------------------------ setMethod("colCummins", "matrix", function(x, na.rm = FALSE, ...) { if (na.rm) x <- na.omit(x) ans <- apply(x, 2, cummin, ...) # special treatment when x has one row because apply returns a vector if (NROW(x) == 1) ans <- matrix(ans, nrow = 1, dimnames = dimnames(x)) ans }) # ------------------------------------------------------------------------------ setMethod("colCummins", "timeSeries", function(x, na.rm = FALSE, ...){ ## GB: split to debug #2121; was: ## setDataPart(x, callGeneric(getDataPart(x), na.rm = na.rm, ...)) ## 2022-07-27 bug #2121 ## can't delegate omitting NA to the matrix method since we need the time ## info after na.omit() here (to set the time properly if(na.rm) x <- na.omit(x) m = getDataPart(x) wrk = callGeneric(m, na.rm = FALSE, ...) # note: na.rm = FALSE res <- setDataPart(x, wrk) res }) # ------------------------------------------------------------------------------ setMethod("colCumprods", "matrix", function(x, na.rm = FALSE, ...) { if (na.rm) x <- na.omit(x) ans <- apply(x, 2, cumprod, ...) # special treatment when x has one row because apply returns a vector if (NROW(x) == 1) ans <- matrix(ans, nrow = 1, dimnames = dimnames(x)) ans }) # ------------------------------------------------------------------------------ setMethod("colCumprods", "timeSeries", function(x, na.rm = FALSE, ...){ ## GB: split to debug #2121; was: ## setDataPart(x, callGeneric(getDataPart(x), na.rm = na.rm, ...)) ## 2022-07-27 bug #2121 ## can't delegate omitting NA to the matrix method since we need the time ## info after na.omit() here (to set the time properly if(na.rm) x <- na.omit(x) m = getDataPart(x) wrk = callGeneric(m, na.rm = FALSE, ...) # note: na.rm = FALSE res <- setDataPart(x, wrk) res }) # ------------------------------------------------------------------------------ setMethod("colCumreturns", "matrix", function(x, method = c("geometric", "simple"), na.rm = FALSE, ...) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Cumulates Returns from a stream of returns # Arguments: # x : a matrix object # method : generate geometric or simple returns, # default "geometric". # FUNCTION: # Handle Missing Values: if (na.rm) x <- na.omit(x, ...) method <- match.arg(method) # Return Value switch(method, "geometric" = colCumsums(x), "simple" = colCumprods(1+x) - 1) }) # ------------------------------------------------------------------------------ setMethod("colCumreturns", "timeSeries", function(x, method = c("geometric", "simple"), na.rm = FALSE, ...) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Cumulates Returns from a stream of returns # Arguments: # x : a timeSeries object # method : generate geometric or simple returns, # default "geometric". # FUNCTION: # Handle Missing Values: if (na.rm) x <- na.omit(x, ...) method <- match.arg(method) # Return Value switch(method, "geometric" = colCumsums(x), "simple" = colCumprods(1+x) - 1) }) ################################################################################ timeSeries/R/timeSeries-isRegular.R0000644000176200001440000000353315104730074016760 0ustar liggesusers # This R package is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # This R package is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this R package; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # isDaily,timeSeries-method Tests if a time series is a daily series # isMonthly,timeSeries-method Tests if a time series is a monthly series # isQuarterly,timeSeries-method Tests if a time series is a quarterly series # isRegular,timeSeries-method Tests if a time series is a regular series # frequency,timeSeries-method Returns the frequency of a regular time series ################################################################################ setMethod("isDaily", "timeSeries", function(x) callGeneric(time(x))) setMethod("isQuarterly", "timeSeries", function(x) callGeneric(time(x))) setMethod("isMonthly", "timeSeries", function(x) callGeneric(time(x))) setMethod("isRegular", "timeSeries", function(x) callGeneric(time(x))) ## GNB: made the method S3 ## setMethod("frequency", "timeSeries", function(x, ...) callGeneric(time(x), ...)) frequency.timeSeries <- function(x, ...) { frequency(time(x), ...) } ################################################################################ timeSeries/R/base-apply.R0000644000176200001440000000606615104730074014755 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # apply Applies a function to blocks of a 'timeSeries' ################################################################################ setMethod("apply", "timeSeries", function(X, MARGIN, FUN, ...) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Apply Functions Over 'Array' timeSeries' Margins # Arguments: # X - an array, including a matrix. => GNB: actually the code throws error if X # is not 'timeSeries'. # MARGIN - a vector giving the subscripts which the function # will be applied over. E.g., for a matrix 1 indicates rows, # 2 indicates columns, c(1, 2) indicates rows and columns. # Where X has named dimnames, it can be a character vector # selecting dimension names. # FUN - the function to be applied: see ???Details???. In the case # of functions like +, %*%, etc., the function name must be # backquoted or quoted. # ... - optional arguments to FUN. # Value: # Returns a vector or array or list of values obtained by # applying a function to margins of a 'timeSeries'. If the # returned value is a matrix, and if the input argument X and # the returned value have the same number of rows, then the # returned value will be transformed into a 'timeSeries' object. # FUNCTION # Check arguments: stopifnot(is.timeSeries(X)) # Extract Title and Documentation: Title <- X@title Documentation <- X@documentation # Settings: pos <- X@positions rec <- X@recordIDs FinCenter <- finCenter(X) X <- getDataPart(X) ans <- callGeneric() # Manage when univariate timeSeries drops the apply to vector: if( is(ans, "vector") && identical(length(ans), NROW(X)) ) { ans <- matrix(ans, ncol=1) } # Result: if (is(ans, "matrix") && identical(NROW(ans), NROW(X))) { # Compose timeSeries ans <- timeSeries( data = ans, charvec = pos, one = FinCenter, FinCenter = FinCenter, recordIDs = rec) # Preserve Title and Documentation: ans@title <- Title ans@documentation <- Documentation } # Return Value: ans }) ############################################################################### timeSeries/R/stats-model.frame.R0000644000176200001440000000670515104730074016245 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # model.frame.default Allows to use model.frame for "timeSeries" ################################################################################ # YC : remove model.frame because more problems than benefits. Rely on # default model.frame as long as as.data.frame.timeSeries works in # 'base' function model.frame.default ## setMethod("model.frame.default", signature(data = "timeSeries"), ## function(formula, data = NULL, ## subset = NULL, na.action = na.fail, ## drop.unused.levels = FALSE, xlev = NULL, ...) ## { ## # A function implemented by Diethelm Wuertz ## # Description: ## # Extracting the Environment of a Model Formula ## # Arguments: ## # formula - a model formula ## # data - a 'timeSeries' object ## # Details: ## # Allows to use model.frame() for "timeSeries" objects. ## # Examples: ## # x = as.timeSeries(data(msft.dat))[1:12, ] ## # model.frame( ~ High + Low, data = x) ## # model.frame(Open ~ High + log(Low), data = x) ## # FUNCTION: ## data <- as(data, "data.frame") ## ### model.frame.default(formula, data, ## ### subset, na.action, ## ### drop.unused.levels, ## ### xlev, ...) ## model.frame(formula, data, ...) ## }) ## ## ### # Create Model Frame: ## ## ### format <- data@format ## ## ### FinCenter <- finCenter(data) ## ## ### recordIDs <- data@recordIDs ## ## ### title <- data@title ## ## data <- as(data, "data.frame") ## ## Model <- model.frame(formula, data, ...) ## ## #-> should be in parent.frame? ## ## ### recordIDs <- ## ## ### if (NROW(Model) == NROW(recordIDs)) ## ## ### recordIDs ## ## ### else ## ## ### data.frame() ## ## ### # Convert to timeSeries: ## ## ### ans <- timeSeries(data = as.matrix(Model), ## ## ### charvec = rownames(Model), ## ## ### units = colnames(Model), ## ## ### format = format, ## ## ### FinCenter = FinCenter, ## ## ### recordIDs = recordIDs, ## ## ### title = title, ## ## ### documentation = description() ## ## ### ) ## ## ### # Return value: ## ## ### ans ## ## Model ## ## }) ################################################################################ timeSeries/R/utils-structure.R0000644000176200001440000000453715104730074016117 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # str,timeSeries Displays the structure of a 'timeSeries' object ################################################################################ str.timeSeries <- function(object, ...) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Displays the structure of a 'timeSeries' object. # Arguments: # object - an object of class 'timeSeries'. # ... - # FUNCTION: # Series Name: cat("Time Series: ") cat("\n Name: ", as.character(c(substitute(object)))) # YC : as.character(c( important to handle str(timeSeries()) # Data Matrix: Dim = dim(object) cat("\nData Matrix: ") cat("\n Dimension: ", Dim) cat("\n Column Names: ", colnames(object) ) firstName = rownames(object)[1] lastName = rownames(object)[Dim[1]] cat("\n Row Names: ", firstName, " ... ", lastName) # Date/Time Positions: cat("\nPositions: ") cat("\n Start: ", as.character(start(object))) cat("\n End: ", as.character(end(object))) # Other Attributes: cat("\nWith: ") cat("\n Format: ", object@format) cat("\n FinCenter: ", object@FinCenter) cat("\n Units: ", object@units) cat("\n Title: ", object@title) cat("\n Documentation: ", object@documentation) cat("\n") # Return Value: invisible() } ## setMethod("str", "timeSeries", ## function(object, ...) .str.timeSeries(object, ...)) ################################################################################ timeSeries/R/aaa-Deprecated.R0000644000176200001440000000377215104730074015501 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # DEPRECATED: # .description # durationSeries # midquoteSeries # spreadSeries ################################################################################ ## removed on 2023-06-03 ## ## .description <- ## function() ## { ## # Deprecated: ## .Deprecated(new = "description", package = "timeSeries") ## ## # Return Value: ## description() ## } ## ## ## # ------------------------------------------------------------------------------ ## ## ## durationSeries <- ## function(...) ## { ## # Deprecated: ## .Deprecated(new = "returns", package = "timeSeries") ## ## # Return Value: ## durations(...) ## } ## ## ## # ------------------------------------------------------------------------------ ## ## midquoteSeries = ## function(...) ## { ## # Deprecated: ## .Deprecated(new = "midquotes", package = "timeSeries") ## ## # Return Value: ## midquotes(...) ## } ## ## ## # ------------------------------------------------------------------------------ ## ## ## spreadSeries = ## function(...) ## { ## # Deprecated: ## .Deprecated(new = "spreads", package = "timeSeries") ## ## # Return Value: ## spreads(...) ## } ################################################################################ timeSeries/R/statistics-orderColnames.R0000644000176200001440000001304115104730074017674 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # statsColnames Returns statistically rearranged column names # orderColnames Returns ordered column names of a time Series # sortColnames Returns sorted column names of a time Series # sampleColnames Returns sampled column names of a time Series # pcaColnames Returns PCA correlation ordered column names # hclustColnames Returns hierarchical clustered column names ################################################################################ statsColnames = function(x, FUN = colMeans, ...) { # A function implemented by Diethelm Wuertz # Description: # Returns statistically rearranged column names # Arguments: # x - an object of class timeSeries # FUN - a character string, the name of the function to be used # ... - optional arguments to be passed to FUN # Note: # Example of function Candidates: # colStats calculates column statistics, # colSums calculates column sums, # colMeans calculates column means, # colSds calculates column standard deviations, # colVars calculates column variances, # colSkewness calculates column skewness, # colKurtosis calculates column kurtosis, # colMaxs calculates maximum values in each column, # colMins calculates minimum values in each column, # colProds computes product of all values in each column, # colQuantiles computes quantiles of each column. # FUNCTION: # Apply colStats Function: fun = match.fun(FUN) Sort = sort(fun(x, ...)) Order = names(Sort) ans = colnames(as.matrix(x)[, Order]) attr(ans, "control") <- Sort # Return Value: ans } # ------------------------------------------------------------------------------ orderColnames = function(x, ...) { # A function implemented by Diethelm Wuertz # Description: # Returns ordered column names of a time Series # Arguments: # x - an object of class timeSeries # FUNCTION: # Order: ans = order(colnames(as.matrix(x)), ...) # Return Value: ans } # ------------------------------------------------------------------------------ sortColnames = function(x, ...) { # A function implemented by Diethelm Wuertz # Description: # Returns sorted column names of a time Series # Arguments: # x - an object of class timeSeries # FUNCTION: # Sort: ans = sort(colnames(as.matrix(x)), ...) # Return Value: ans } # ------------------------------------------------------------------------------ sampleColnames = function(x, ...) { # A function implemented by Diethelm Wuertz # Description: # Returns sampled column names of a time Series # Arguments: # x - an object of class timeSeries # FUNCTION: # Sample: ans = sample(colnames(as.matrix(x)), ...) # Return Value: ans } # ------------------------------------------------------------------------------ pcaColnames = function(x, robust = FALSE, ...) { # A function implemented by Diethelm Wuertz # Description: # Returns PCA correlation ordered column names # Arguments: # x - an object of class timeSeries # robust - a logical, should we use robust covariance estimation? # FUNCTION: # Order: if (robust) { x.cor = robustbase::covMcd(as.matrix(x), cor = TRUE, ...)$cor } else { x.cor = cor(as.matrix(x), ...) } x.eigen = eigen(x.cor)$vectors[,1:2] e1 = x.eigen[, 1] e2 = x.eigen[, 2] Order = order(ifelse(e1 > 0, atan(e2/e1), atan(e2/e1)+pi)) ans = colnames(as.matrix(x))[Order] # Return Value: ans } # ------------------------------------------------------------------------------ hclustColnames = function(x, method = c("euclidean", "complete"), ...) { # A function implemented by Diethelm Wuertz # Description: # Returns hierarchical clustered column names # Arguments: # x - an object of class timeSeries # method - the agglomeration method to be used. This should # be (an unambiguous abbreviation of) one of "ward", "single", # "complete", "average", "mcquitty", "median" or "centroid". # ... optional arguments passed to the function hclust # FUNCTION: # Order: Order = hclust(dist(t(as.matrix(x)), method = method[1]), method = method[2], ...)$order ans = colnames(as.matrix(x))[Order] # Return Value: ans } ################################################################################ timeSeries/R/methods-mathOps.R0000644000176200001440000001542515104730074015773 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # Ops,timeSeries Returns group 'Ops' for a 'timeSeries' object # cummax Returns cumulated maxima # cummin Returns cumulated minima # cumprod Returns cumulated products # cumsum Returns cumulated sums # ##diff,timeSeries Differences a timeSeries object # ##scale,timeSeries Scales a timeSeries object # quantile,timeSeries Samples quantiles of a timeSeries object # median,timeSeries Samples median of a timeSeries object ################################################################################ setMethod("Ops", c("vector", "timeSeries"), function(e1, e2) { lattrs <- attributes(e2) e2 <- getDataPart(e2) value <- callGeneric(e1, e2) if (identical(dim(value), dim(e2))) { attributes(value) <- lattrs value <- asS4(value, TRUE) } value } ) # ------------------------------------------------------------------------------ setMethod("Ops", c("array", "timeSeries"), function(e1, e2) { e1 <- as.vector(e1) lattrs <- attributes(e2) e2 <- getDataPart(e2) value <- callGeneric(e1, e2) if (identical(dim(value), dim(e2))) { attributes(value) <- lattrs value <- asS4(value, TRUE) } value } ) # ------------------------------------------------------------------------------ setMethod("Ops", c("ts", "timeSeries"), function(e1, e2) { e1 <- as(e1, "matrix") lattrs <- attributes(e2) e2 <- getDataPart(e2) value <- callGeneric(e1, e2) if (identical(dim(value), dim(e2))) { attributes(value) <- lattrs value <- asS4(value, TRUE) } value } ) # ------------------------------------------------------------------------------ setMethod("Ops", c("timeSeries", "vector"), function(e1, e2) { lattrs <- attributes(e1) e1 <- getDataPart(e1) value <- callGeneric(e1, e2) if (identical(dim(value), dim(e1))) { attributes(value) <- lattrs value <- asS4(value, TRUE) } value } ) # ------------------------------------------------------------------------------ setMethod("Ops", c("timeSeries", "array"), function(e1, e2) { lattrs <- attributes(e1) e1 <- getDataPart(e1) value <- callGeneric(e1, e2) if (identical(dim(value), dim(e1))) { attributes(value) <- lattrs value <- asS4(value, TRUE) } value } ) # ------------------------------------------------------------------------------ setMethod("Ops", c("timeSeries", "ts"), function(e1, e2) { lattrs <- attributes(e1) e1 <- getDataPart(e1) e2 <- as(e2, "matrix") value <- callGeneric(e1, e2) if (identical(dim(value), dim(e1))) { attributes(value) <- lattrs value <- asS4(value, TRUE) } value } ) # ------------------------------------------------------------------------------ setMethod("Ops", c("timeSeries", "timeSeries"), function(e1, e2) { # Note keep recordIDs of e1 only # check if conformable arrays if (!identical(dim(e1), dim(e2))) stop("non-conformable arrays") # check if positions are identical if (!identical(e1@positions, e2@positions)) stop("positions slot do not match") lattrs <- attributes(e1) e1 <- getDataPart(e1) e2 <- getDataPart(e2) value <- callGeneric(e1, e2) if (identical(dim(value), dim(e1))) { attributes(value) <- lattrs value <- asS4(value, TRUE) } value } ) ## 2023-05-31 GNB: making these work column-wise and return 'timeSeries' ## # ------------------------------------------------------------------------------ ## setMethod("cummax", "timeSeries", function(x) callGeneric(getDataPart(x))) ## setMethod("cummin", "timeSeries", function(x) callGeneric(getDataPart(x))) ## setMethod("cumprod", "timeSeries", function(x) callGeneric(getDataPart(x))) ## setMethod("cumsum", "timeSeries", function(x) callGeneric(getDataPart(x))) .cum_fun <- function(x, FUN){ wrk <- apply(getDataPart(x), 2, FUN) if (NROW(x) == 1) wrk <- matrix(wrk, nrow = 1, dimnames = dimnames(x)) x@.Data <- wrk x } setMethod("cummax", "timeSeries", function(x) .cum_fun(x, cummax)) setMethod("cummin", "timeSeries", function(x) .cum_fun(x, cummin)) setMethod("cumprod", "timeSeries", function(x) .cum_fun(x, cumprod)) setMethod("cumsum", "timeSeries", function(x) .cum_fun(x, cumsum)) # ------------------------------------------------------------------------------ ## setMethod("diff", "timeSeries", function(x, ...) { ## x <- getDataPart(x) ## callGeneric() ## }) # ------------------------------------------------------------------------------ ## setMethod("scale", "timeSeries", function(x, ...) { ## x <- getDataPart(x) ## callGeneric() ## }) # ------------------------------------------------------------------------------ ## GNB: make an S3 method, drop the S4 one ## ## setMethod("quantile", "timeSeries", function(x, ...) { ## x <- getDataPart(x) ## callGeneric() ## }) quantile.timeSeries <- function(x, ...) { x <- getDataPart(x) NextMethod("quantile") } # ------------------------------------------------------------------------------ ## GNB: make an S3 method, drop the S4 one ## ## setMethod("median", "timeSeries", function(x, na.rm, ...) { ## x <- getDataPart(x) ## callGeneric(x, na.rm=na.rm) ## }) median.timeSeries <- function(x, na.rm = FALSE, ...) { x <- getDataPart(x) NextMethod("median") } ################################################################################ timeSeries/R/base-sample.R0000644000176200001440000000211715104730074015102 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # sample,timeSeries Resamples a 'timeSeries' object in time ################################################################################ setMethod("sample", "timeSeries", function(x, size, replace = FALSE, prob = NULL) { x[sample(seq(NROW(x)), size, replace, prob), ] } ) ################################################################################ timeSeries/R/base-scale.R0000644000176200001440000000312615104730074014711 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # scale,timeSeries Centers and/or scales a 'timeSeries' object ################################################################################ scale.timeSeries <- function(x, center = TRUE, scale = TRUE) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Centers and/or scales a 'timeSeries' object. # Arguments: # FUNCTION: # Scale: setDataPart(x, scale(x = getDataPart(x), center = center, scale = scale)) } setMethod("scale", "timeSeries", function(x, center = TRUE, scale = TRUE) scale.timeSeries(x, center = center, scale = scale)) ## # until UseMethod dispatches S4 methods in 'base' functions ## scale.timeSeries <- function (x, center = TRUE, scale = TRUE) ## .scale.timeSeries(x, center = center, scale = scale) ################################################################################ timeSeries/R/timeSeries-getDataPart.R0000644000176200001440000000527715104730074017232 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # S4 METHODS: DESCRIPTION: # getDataPart,timeSeries Get data slot of an object of class 'timeSeries' # setDataPart,timeSeries Set data slot of an object of class 'timeSeries' ################################################################################ # YC: # This makes getDataPart a bit faster than default function setMethod("getDataPart", "timeSeries", #"signalSeries", function(object) { value <- object attributes(value) <- NULL attr(value, "dim") <- attr(object, "dim") attr(value, "dimnames") <- attr(object, "dimnames") value } ) # ------------------------------------------------------------------------------ # YC: # This makes setDataPart a bit faster than default function if (getRversion() < "2.8.0") { setMethod("setDataPart", "timeSeries", function(object, value) { #-> Note : do not use as.matrix because ts objects might #-> not be coerced properly value <- as(value, "matrix") supplied <- attributes(object) valueAttrs <- attributes(value) supplied[names(valueAttrs)] <- valueAttrs # YC: force @unit to be identical to colnames(value) supplied[["units"]] <- colnames(value) attributes(value) <- supplied asS4(value, TRUE) } ) } else { setMethod("setDataPart", "timeSeries", function(object, value, check = TRUE) { #-> Note : do not use as.matrix because ts objects might #-> not be coerced properly if (check) value <- as(value, "matrix") supplied <- attributes(object) valueAttrs <- attributes(value) supplied[names(valueAttrs)] <- valueAttrs # YC: force @unit to be identical to colnames(value) supplied[["units"]] <- colnames(value) attributes(value) <- supplied asS4(value, TRUE) } ) } ################################################################################ timeSeries/R/fin-durations.R0000644000176200001440000000457715104730074015507 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: FINANCIAL TIME SERIES: # durations Computes durations from a 'timeSeries' object ################################################################################ durations <- function(x, trim = FALSE, units = c("secs", "mins", "hours", "days")) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Computes durations from a financial price series # Arguments: # x - a univariate or multivariate 'timeSeries' object or a # numeric vector or matrix. # trim - a logical flag, by default TRUE, the first missing # observation in the return series will be removed. # units - a character value or vector which allows to set the # units in which the durations are measured # Value: # Returns a S4 object of class 'timeSeries'. # FUNCTION: # Check Arguments: stopifnot(is.timeSeries(x)) # Extract Title and Documentation: Title <- x@title Documentation <- x@documentation # Check for Signal Series: if (x@format == "counts") stop(as.character(match.call())[1], " is for time series and not for signal series.") # Match Arguments: units <- match.arg(units) # Get Positions and Durations: pos <- time(x) dur <- c(NA, diff(as.integer(difftime(pos, pos[1], units = units[1])))) # Data Matrix: ans <- timeSeries(data = dur, charvec = pos, units = "Duration") if (trim) ans <- ans[-1, ] # Preserve Title and Documentation: ans@title <- Title ans@documentation <- Documentation # Return Series: ans } ################################################################################ timeSeries/R/methods-comment.R0000644000176200001440000000266615104730074016025 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # comment, timeSeries Get documentation slot of a timeSeries object # comment<-,timeSeries Set documentation slot of a timeSeries object ################################################################################ setMethod("comment", "timeSeries", function(x) { # A function implemented by Yohan Chalabi and Diethelm Wuertz # Return Value: x@documentation } ) # ------------------------------------------------------------------------------ setMethod("comment<-", "timeSeries", function(x, value) { x@documentation <- paste(value, collapse = " ") # Return Value: x } ) ################################################################################ timeSeries/R/AllGeneric.R0000644000176200001440000000666615104730074014733 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # GENERIC: DESCRIPTION # returns Computes returns # rowCumsums Computes row cumulated sums # series Extracts series data # series<- Assigns series data # coredata Extracts series data # index deprecated # index <- deprecated # outlier Returns outliers # timeSeries Returns timeSeries # colCumsums Computes column cumulated sums # colCummaxs Computes column cumulated maxima # colCummins Computes column cumulated minima # colCumprods Computes column cumulated products # colCumreturns Computes column cumulated returns ################################################################################ setGeneric("returns", function(x, ...) standardGeneric("returns"), package = "timeSeries") setGeneric("rowCumsums", function(x, na.rm = FALSE, ...) standardGeneric("rowCumsums"), package = "timeSeries") setGeneric("series", function(x) standardGeneric("series"), package = "timeSeries") setGeneric("series<-", function(x, value) standardGeneric("series<-"), package = "timeSeries") ## 2023-05-27 GNB: making non-generic, see comments in timeSeries-slotSeries.R ## ## setGeneric("coredata", ## function(x) ## standardGeneric("coredata"), package = "timeSeries") setGeneric("coredata<-", function(x, value) standardGeneric("coredata<-"), package = "timeSeries") ## setGeneric("index", function(x, ...) ## standardGeneric("index"), package = "timeSeries") ## setGeneric("index<-", function(x, value) ## standardGeneric("index<-"), package = "timeSeries") setGeneric("outlier", function(x, sd = 5, complement = TRUE, ...) standardGeneric("outlier")) setGeneric("timeSeries", function (data, charvec, units = NULL, format = NULL, zone = "", FinCenter = "", recordIDs = data.frame(), title = NULL, documentation = NULL, ...) standardGeneric("timeSeries")) setGeneric("colCumsums", function(x, na.rm = FALSE, ...) standardGeneric("colCumsums")) setGeneric("colCummaxs", function(x, na.rm = FALSE, ...) standardGeneric("colCummaxs")) setGeneric("colCummins", function(x, na.rm = FALSE, ...) standardGeneric("colCummins")) setGeneric("colCumprods", function(x, na.rm = FALSE, ...) standardGeneric("colCumprods")) setGeneric("colCumreturns", function(x, method = c("geometric", "simple"), na.rm = FALSE, ...) standardGeneric("colCumreturns")) ################################################################################ timeSeries/R/stats-na.contiguous.R0000644000176200001440000000500115104730074016634 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # na.contiguous,timeSeries Finds the longest consecutive of non-missing values ################################################################################ ## 2024-01-06 GNB: converted the S4 method to an S3 method and removed the S4 one. na.contiguous.timeSeries <- function(object, ...) { # A function imlemented by Diethelm Wuertz and Yohan Chalabi # fixed by GNB for the case with tied stretches one of whom starts at # the beginning of the series, see comments below. # Description: # Finds the longest consecutive of non-missing values # Details: # adapted stats:::na.contingous.default to timeSeries objects # Yohan Chalabi # ## FUNCTION: good <- apply(!is.na(object), 1L, all) if (!sum(good)) stop("all times contain an NA") tt <- cumsum(!good) tt <- c(0, tt) # GNB, see my bug report to R-devel for stats::na.contiguous from # 2023-06-02 and the discussion there (see # https://stat.ethz.ch/pipermail/r-devel/2023-June/082642.html) # The fix is my proposed way to patch that. ln <- sapply(0:max(tt), function(i) sum(tt == i)) seg <- (seq_along(ln)[ln == max(ln)])[1L] - 1 keep <- (tt == seg) keep <- keep[-1] # GNB, see above comment st <- min(which(keep)) if (!good[st]) st <- st + 1 en <- max(which(keep)) omit <- integer(0L) n <- NROW(object) if (st > 1) omit <- c(omit, 1L:(st - 1)) if (en < n) omit <- c(omit, (en + 1):n) if (length(omit)) { object <- object[st:en, ] attr(omit, "class") <- "omit" attr(object, "na.action") <- omit } # Return Value: object } ################################################################################ timeSeries/R/timeSeries-dummy.R0000644000176200001440000000530115104730074016151 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # dummyMonthlySeries Creates a dummy monthly 'timeSeries' object # dummyDailySeries Creates a dummy daily 'timeSeries' object ################################################################################ # DW: # A more natural name for the function dummySeries() would be # dummyMonthlySeries() to have the same naming conventions like in the # case of the dummy daily series. dummyMonthlySeries <- function(...) { # A function implemented by Diethelm Wuertz # Description: # Creates a monthly dummy 'time Series' object # Arguments: # ... - optional arguments passed to the function timeSeries(). # FUnction: # Return Value: timeSeries(matrix(runif(24), ncol = 2), as.character(timeCalendar()), ...) } ## dummySeries <- function(...) { # GNB ## .Deprecated("dummyMonthlySeries") ## dummyMonthlySeries(...) ## } # ------------------------------------------------------------------------------ dummyDailySeries <- function(x = rnorm(365), units = NULL, zone = "", FinCenter = "") { # A function implemented by Diethelm Wuertz # Description: # Creates a dummy daily time Series # Arguments: # x - a numeric vector # origin - the first date in the series # FUNCTION: if (zone == "") zone <- getRmetricsOptions("myFinCenter") if (FinCenter == "") FinCenter <- getRmetricsOptions("myFinCenter") # Check: stopifnot(is.numeric(x)) if (is.null(units)) units <- paste("X", 1:NCOL(x), sep = "") stopifnot(length(units) == NCOL(x)) # Time Series: if (is.vector(x)) data = matrix(x, ncol = 1) if (is.matrix(x)) data = x positions <- timeSequence(from = "1970-01-01", length.out = NROW(data), zone = zone, FinCenter = FinCenter) ans <- timeSeries(data = data, charvec = positions, units = units, zone = zone, FinCenter = FinCenter) # Return Value: ans } ################################################################################ timeSeries/R/base-sort.R0000644000176200001440000000351415104730074014612 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # sort,timeSeries Sorts a 'timeSeries' object in time ################################################################################ sort.timeSeries <- function (x, decreasing = FALSE, ...) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Time sorts a 'timeSeries' object # Arguments: # x - a 'timeSeries' object. # Value: # Returns a time sorted object of class 'timeSeries'. # FUNCTION: # check if really necessary to sort x ## NB: is.unsorted can return NA if (!decreasing && !isTRUE(is.unsorted(x@positions))) return(x) if (length(x@positions)>0) x[order(x@positions, decreasing = decreasing), ] else x } ## setMethod("sort", "timeSeries", function (x, decreasing = FALSE, ...) ## sort.timeSeries(x, decreasing = decreasing, ...)) ## # until UseMethod dispatches S4 methods in 'base' functions ## sort.timeSeries <- function(x, decreasing = FALSE, ...) ## .sort.timeSeries(x, decreasing = decreasing, ...) ################################################################################ timeSeries/R/timeSeries-slotDocumentation.R0000644000176200001440000000550615104730074020540 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # getDocumentation # setDocumentation ################################################################################ # FUNCTION: MANAGING ATTRIBUTES # getAttributes # setAttributes<- # INTERNAL FUNCTION: # .appendList ################################################################################ getAttributes <- function (obj) { # A function implemented by Diethelm Wuertz # Description: # FUNCTION: # Check Argument: stopifnot(inherits(obj, "timeSeries")) # Extract Attributes: ans <- attr(obj@documentation, "Attributes") # Return Value: ans } # ----------------------------------------------------------------------------- `setAttributes<-` <- function(obj, value) { # A function implemented by Diethelm Wuertz # Description: # Example: # obj <- dummyMonthlySeries(); getAttributes(obj) # setAttributes(obj) <- list(mat=matrix(1:4, ncol=2)); getAttributes(obj) # getAttributes(obj)$mat[[1]] # FUNCTION: # Check Arguments: stopifnot(inherits(obj, "timeSeries") , is.list(value) , length(value) == 1) # Compose New Attribute: name <- names(value) names(value) <- NULL A <- list(value) names(A) <- name # print(A) # Get Already Existing Attribute B <- getAttributes(obj) if(is.null(B)) B <- list() # print(B) # Join Attributes: JOINED <- sapply(unique(c(names(A), names(B))), function(x) list(c(A[[x]], B[[x]]))) # print(JOINED) # Assign Attribute: attr(obj@documentation, "Attributes") <- JOINED # Return Value: obj } # ----------------------------------------------------------------------------- .appendList <- function (A, B) { # A function implemented by Diethelm Wuertz # Description: # Appends list B to list A # Arguments: # A - first named list element # B - second named list element # FUNCTION: # Append list B to list A JOINED <- sapply(unique(c(names(A), names(B))), function(x) list(c(A[[x]], B[[x]]))) # Return Value: JOINED } ############################################################################### timeSeries/R/base-rev.R0000644000176200001440000000172515104730074014421 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # rev.timeSeries Reverts a 'timeSeries' object in time ################################################################################ ## GNB: removed the S4 method rev.timeSeries <- function(x) x[NROW(x):1,] ## setMethod("rev", "timeSeries", rev.timeSeries) timeSeries/R/timeSeries-slotTime.R0000644000176200001440000001054215104730074016621 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # time,timeSeries Extracs time positions from a 'timeSeries' # time<- Defines S3 UseMethod # time<-.timeSeries ... to avoid problems with zoo # FUNCTION: DESCRIPTION: # getTime Get time slot from a 'timeSeries' # setTime<- Set new time slot to a 'timeSeries' ################################################################################ # DEPRECATED: DESCRIPTION: # seriesPositions Deprecated, use time # newPositions<- Deprecated, use time<- ################################################################################ ## GNB: swapped the definitions of .time.timeSeries and time.timeSeries ## in preparation to drop the former ## .time.timeSeries <- time.timeSeries <- function(x, ...) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Extracs time positions from a 'timeSeries' # Arguments: # x - a 'timeSeries' object. # Value: # Returns a time resampled object of class 'timeSeries'. # FUNCTION: if (length(x@positions)>0) timeDate(x@positions, zone = "GMT", FinCenter = x@FinCenter) else seq.int(NROW(x)) } ## setMethod("time", "timeSeries", time.timeSeries) # ------------------------------------------------------------------------------ `time<-` <- function(x, value) { UseMethod("time<-") } # ------------------------------------------------------------------------------ `time<-.default` <- function(x, value) { # A function implemented by Georgi Boshnakov zoo::time(x) <- value x } `time<-.timeSeries` <- function(x, value) { # A function implemented by Yohan Chalabi # Note: # To avoid conflict with zoo package. # FUNCTION: # Assign Rownames: rownames(x) <- value # Return Value: x } ############################################################################### getTime <- function(x) { # Description: # Get time slot from a 'timeSeries' object. # Arguments: # x - a 'timeSeries' object # FUNCTION: # Return Value: time(x) } # ------------------------------------------------------------------------------ "setTime<-" <- function(x, value) { # Description: # Set time slot to a 'timeSeries' object. # Arguments: # x - a 'timeSeries' object # FUNCTION: # Assign Time Slot: time(x) <- value # Return Value: x } ############################################################################### # DEPRECATED ## seriesPositions <- ## function(object) ## { ## # A function implemented by Diethelm Wuertz ## ## # Description: ## # Extracts the positions of a 'timeSeries' objects and ## # converts them to a 'timeDate' object. ## ## # Arguments: ## # object - a 'timeSeries' object ## ## # Value: ## # Returns 'timeSeries' positions as 'timeDate' objects. ## ## # FUNCTION: ## ## # Deprecated: ## .Deprecated(new = "time", package = "timeSeries") ## ## # Return Value: ## time(object) ## } # ------------------------------------------------------------------------------ # Deprecated: ## "newPositions<-" <- ## function(object, value) ## { # A function implemented by Diethelm Wuertz ## ## # FUNCTION: ## ## # Deprecated: ## .Deprecated(new = "time<-", package = "timeSeries") ## ## # Assign Rownames: ## rownames(object) <- value ## ## # Return Value: ## object ## } ################################################################################ timeSeries/R/base-start.R0000644000176200001440000000401415104730074014754 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # start,timeSeries Extracts start date of a 'timeSeries' object # end,timeSeries Extracts end date of a 'timeSeries' object ################################################################################ start.timeSeries <- function(x, ...) { # Description: # Extracts start date of a 'timeSeries' object # FUNCTION: # Extract Date: if (length(x@positions)>0) timeDate(min(x@positions), zone = "GMT", FinCenter = x@FinCenter) else NULL } ## setMethod("start" , "timeSeries", start.timeSeries) ## # until UseMethod dispatches S4 methods in 'base' functions ## start.timeSeries <- function(x, ...) .start.timeSeries(x, ...) # ------------------------------------------------------------------------------ end.timeSeries <- function(x, ...) { # Description: # Extracts start date of a 'timeSeries' object # FUNCTION: # Extract Date: if (length(x@positions)>0) timeDate(max(x@positions), zone = "GMT", FinCenter = x@FinCenter) else NULL } ## setMethod("end", "timeSeries", function(x, ...) end.timeSeries(x, ...)) ## # until UseMethod dispatches S4 methods in 'base' functions ## end.timeSeries <- function(x, ...) .end.timeSeries(x, ...) ################################################################################ timeSeries/R/fin-splits.R0000644000176200001440000000726015104730074015005 0ustar liggesusers # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # outlier,timeSeries Removes outliers from a 'timeSeries' object ################################################################################ # DW: # We should call this function no longer outlier, much better woud be # splits() since the function tries to detect splits by large outliers. # For outlier detection we should use better methods than just the sd(). # ------------------------------------------------------------------------------ splits <- function(x, sd = 3, complement = TRUE, ...) { # Return Value: outlier(x=x, sd=sd, complement=complement, ...) } # ------------------------------------------------------------------------------ setMethod("outlier", "ANY", function(x, sd = 3, complement = TRUE, ...) { # A function implemented by Diethelm Wuertz # Description: # Returns outlier splits # Arguments: # x - a numeric vector # sd - a numeric value of standard deviations, e.g. 5 # means that values larger or smaller tahn five # times the standard deviation of the series will # be detected. # complement - a logical flag, should the outlier series # or its complements be returned. # Note: # This function is thought to find splits in financial # price or index series If a price or index is splitted we # observe in the returns a big jump of several standard # deviations which is identified usual as an outlier. # FUNCTION: # Check arguments: stopifnot(is.timeSeries(x)) # Extract Title and Documentation: Title <- x@title Documentation <- x@documentation # Find Outliers: SD <- sd * sd(x) if (complement) { ans <- x[x <= SD] } else { ans <- x[x > SD] names(ans) <- as.character(which(x > SD)) } # Preserve Title and Documentation: ans@title <- Title ans@documentation <- Documentation # Return Value: ans }) # ------------------------------------------------------------------------------ setMethod("outlier", "timeSeries", function(x, sd = 3, complement = TRUE, ...) { # A function implemented by Diethelm Wuertz # Description: # Returns outliers in a timeSeries object or the complement # Arguments: # x - an object of class 'timeSeries'. # sd - a numeric value of standard deviations, e.g. 5 # means that values larger or smaller tahn ten # times the standard deviation of the series will # be removed. # complement - a logical flag, should the outler series # or its complement be returned. # FUNCTION: # Check if univariate Series: if (!isUnivariate(x)) stop("Supports only univariate timeSeries Objects") # Find Outliers: SD = sd * sd(x) if (complement) { x = x[abs(x) <= SD,] } else { x = x[abs(x) > SD,] } # Return Value: x }) ################################################################################ timeSeries/R/base-t.R0000644000176200001440000000177315104730074014073 0ustar liggesusers # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # t,timeSeries Returns the transpose of timeSeries object ################################################################################ setMethod("t", "timeSeries", function(x) callGeneric(getDataPart(x))) ################################################################################ timeSeries/R/fin-daily.R0000644000176200001440000002014315104730074014564 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # alignDailySeries Aligns a 'timeSeries' object to new positions # rollDailySeries Rolls daily a 'timeSeries' on a given period # OBSOLETE: DESCRIPTION: # .ohlcDailyPlot Plots open high low close bar chart # .plotOHLC Internal called by function ohlcDailyPlot() ################################################################################ alignDailySeries <- function (x, method = c("before", "after", "interp", "fillNA", "fmm", "periodic", "natural", "monoH.FC"), include.weekends = FALSE, units = NULL, zone = "", FinCenter = "", ...) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Aligns a 'timeSeries' object to new positions # Arguments: # x - an object of class "timeSeries". # method - # "before" - use the data from the row whose position is # just before the unmatched position; # "after" - use the data from the row whose position is # just after the unmatched position; # "linear" - interpolate linearly between "before" and # "after". # "fillNA" - fill missing days with NA values # include.weekends - a logical value. Should weekend dates be # included or removed? # Note: alignDailySeries is now based on align timeSeries method. # FUNCTION: # Preserve Title and Documentation: Title <- x@title Documentation <- x@documentation # Adjust zone and FinCenter if provided if (zone != "" || FinCenter != "") { if (zone == "") zone <- getRmetricsOptions("myFinCenter") if (FinCenter == "") FinCenter <- getRmetricsOptions("myFinCenter") x <- timeSeries(x, zone = zone, FinCenter = FinCenter) } # Run Generic Function align() ans <- .align.timeSeries(x = x, by = "1d", offset = "0s", method = method, include.weekends = include.weekends, ...) ans@title <- Title ans@documentation <- Documentation # Add New Units: if (!is.null(units)) colnames(ans) = units # Return Value: ans } # ------------------------------------------------------------------------------ rollDailySeries <- function(x, period = "7d", FUN, ...) { # A function implemented by Diethelm Wuertz # Description: # Rolls daily a 'timeSeries' on a given period # Arguments: # x - an univariate "timeSeries" object or a numeric vector. # n - an integer specifying the number of periods or # terms to use in each rolling/moving sample. # trim - a logical flag: if TRUE, the first n-1 missing values in # the returned object will be removed; if FALSE, they will # be saved in the returned object. The default is TRUE. # FUN - the rolling function, arguments to this function can be # passed through the \code{\dots} argument. # FUNCTION: # Check Arguments: stopifnot(is.timeSeries(x)) # Check for Signal Series: Message <- " is for time series and not for signal series." if (x@format == "counts") stop(as.character(match.call())[1], Message) # Preserve Title and Documentation: Title <- x@title Documentation <- x@documentation # Fix missing matrix method for quantile(), still to do ... .quantile.matrix = function(x, probs = 0.95, ...) { apply(as.matrix(x), 2, quantile, probs = probs) } # Settings: periodLength = as.numeric(substr(period, 1, nchar(period) - 1)) periodUnit = substr(period, nchar(period), nchar(period)) N = nrow(x) Start = start(x) + (periodLength-1)*24*3600 Positions = time(x) to = Positions[Positions > Start] from = to - periodLength*24*3600 # Apply Function: ans <- applySeries(x = x, from = from, to = to, FUN = FUN, ...) ans@title <- Title ans@documentation <- Documentation # Return Value: ans } ################################################################################ # OBSOLETE: .ohlcDailyPlot <- function(x, volume = TRUE, colOrder = c(1:5), units = 1e6, xlab = c("Date", "Date"), ylab = c("Price", "Volume"), main = c("O-H-L-C", "Volume"), grid.nx = 7, grid.lty = "solid", ...) { # A function implemented by Diethelm Wuertz # Description: # Plots open | high | low | close bar chart # Arguments: # x - an S4 object of class 'timeSeries' with named entries: # Open, High, Low, Close, and Volume # Reference: # Build on top of Adrian Trapletti's plotOHLC() # function from his R-package "tseries". # FUNCTION: stopifnot(is.timeSeries(x)) if (x@format == "counts") stop(as.character(match.call())[1], " is for time series and not for signal series.") # Next: x.filled = alignDailySeries(x, method = "fillNA", include.weekends = TRUE) jul = as.integer(julian(time(x.filled))) X = ts(as.matrix(x.filled)[, 1:4], start = min(jul), end = max(jul)) # Plot OHLC: .plotOHLC(X, origin = "1970-01-01", xlab = xlab[1], ylab = ylab[1]) # print(axTicks(1)) # print(axTicks(2)) title(main = main[1]) grid(nx = grid.nx, ny = NULL, lty = grid.lty, ...) # Include Volume? if (volume) { Volume = x[, 5]/units plot(Volume, type = "h", xlab = xlab[2], ylab = ylab[2]) title(main = main[2]) grid(nx = grid.nx, ny = NULL, lty = grid.lty, ...) } # Return value: invisible() } # ------------------------------------------------------------------------------ .plotOHLC = function (x, xlim = NULL, ylim = NULL, xlab = "Time", ylab, col = par("col"), bg = par("bg"), axes = TRUE, frame.plot = axes, ann = par("ann"), main = NULL, date = c("calendar", "julian"), format = "%Y-%m-%d", origin = "1899-12-30", ...) { # A Copy from Contributed R Package 'tseries' # Description: # Internal called by function .ohlcDailyPlot() # FUNCTION: # Check for mts: if ((!is.mts(x)) || (colnames(x)[1] != "Open") || (colnames(x)[2] != "High") || (colnames(x)[3] != "Low") || (colnames(x)[4] != "Close")) stop("x is not a open/high/low/close time series") xlabel <- if (!missing(x)) deparse(substitute(x)) else NULL if (missing(ylab)) ylab <- xlabel date <- match.arg(date) time.x <- time(x) dt <- min(lag(time.x) - time.x)/3 if (is.null(xlim)) xlim <- range(time.x) if (is.null(ylim)) ylim <- range(x[is.finite(x)]) plot.new() plot.window(xlim, ylim, ...) segments(time.x, x[, "High"], time.x, x[, "Low"], col = col[1], bg = bg) segments(time.x - dt, x[, "Open"], time.x, x[, "Open"], col = col[1], bg = bg) segments(time.x, x[, "Close"], time.x + dt, x[, "Close"], col = col[1], bg = bg) if (ann) title(main = main, xlab = xlab, ylab = ylab, ...) if (axes) { if (date == "julian") { axis(1, ...) axis(2, ...) } else { n <- NROW(x) lab.ind <- round(seq(1, n, length = 5)) D <- as.vector(time.x[lab.ind] * 86400) + as.POSIXct(origin, tz = "GMT") DD <- format.POSIXct(D, format = format, tz = "GMT") axis(1, at = time.x[lab.ind], labels = DD, ...) axis(2, ...) } } if (frame.plot) box(...) # Return Value: invisible() } ################################################################################ timeSeries/R/base-subsetting.R0000644000176200001440000005436215104730074016021 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # METHOD: SUBSETTING METHODS ON DATA: # .subset_timeSeries # .findIndex # $,timeSeries Subsets a time series by column names # $<-,timeSeries Replaces subset by column names # [,timeSeries Subsets a time series object # [<-,timeSeries Assigns value to subsets of a time series ################################################################################ ################################################################################ # index ################################################################################ # Note : no "character" -> because needs to be coerced to timeDate object. setClassUnion("index_timeSeries", members = c("numeric", "logical")) setClassUnion("time_timeSeries", members = c("POSIXt", "Date")) # ------------------------------------------------------------------------------ .subset_timeSeries <- function(x, i, j) { stopifnot(inherits(x, "timeSeries")) stopifnot(is(i, "index_timeSeries")) stopifnot(is(j, "index_timeSeries")) # subset data and positions t <- try(data <- .subset(x, i, j, drop = FALSE), silent = TRUE) if (inherits(t, "try-error")) { # cast error and remove calling function msg <- sub("Error in.*: \n *", "", t) stop(msg, call. = FALSE) } pos <- if (length(x@positions)>0) .subset(x@positions, i) else numeric(0) units <- .subset(x@units, j) # Record IDs: df <- x@recordIDs if (prod(dim(df))) df <- df[i, , drop = FALSE] # Result new("timeSeries", .Data = data, title = x@title, documentation = x@documentation, format = x@format, FinCenter = x@FinCenter, units = units, recordIDs = df, positions = pos) } # ------------------------------------------------------------------------------ .findIndex <- function(ipos, pos) { attributes(ipos) <- NULL if (unsorted <- is.unsorted(pos)) { or <- order(pos) pos <- pos[or] } i <- findInterval(ipos, pos) if (!identical(ipos, pos[i])) stop("subscript out of bounds", call. = FALSE) if (unsorted) i <- or[i] i } ################################################################################ # [,timeSeries Subsets of a 'timeSeries' object ################################################################################ ## i <- c("index_timeSeries", "character", "timeDate", ## "timeSeries", "missing", "ANY") ## j <- c("index_timeSeries", "character", "timeSeries", ## "missing", "ANY") ## expand.grid(i = i, j = j) ## > i j ## 1 index_timeSeries index_timeSeries ## 2 character index_timeSeries ## 3 timeDate index_timeSeries ## 4 timeSeries index_timeSeries ## 5 missing index_timeSeries ## 6 ANY index_timeSeries ## 7 index_timeSeries character ## 8 character character ## 9 timeDate character ## 10 timeSeries character ## 11 missing character ## 12 ANY character ## 13 index_timeSeries timeSeries ## 14 character timeSeries ## 15 timeDate timeSeries ## 16 timeSeries timeSeries ## 17 missing timeSeries ## 18 ANY timeSeries ## 19 index_timeSeries missing ## 20 character missing ## 21 timeDate missing ## 22 timeSeries missing ## 23 missing missing ## 24 ANY missing ## 25 index_timeSeries ANY ## 26 character ANY ## 27 timeDate ANY ## 28 timeSeries ANY ## 29 missing ANY ## 30 ANY ANY ## YC : Added i=time_timeSeries i <- "time_timeSeries" j <- c("index_timeSeries", "character", "timeSeries", "missing", "ANY") expand.grid(i = i, j = j) ## 1 time_timeSeries index_timeSeries ## 2 time_timeSeries character ## 3 time_timeSeries timeSeries ## 4 time_timeSeries missing ## 5 time_timeSeries ANY # ------------------------------------------------------------------------------ ## FIXME : deal with signal series # ------------------------------------------------------------------------------ ## 1 index_timeSeries index_timeSeries setMethod("[", signature(x = "timeSeries", i = "index_timeSeries", j = "index_timeSeries"), function(x, i, j, ..., drop = FALSE) .subset_timeSeries(x, i, j)) # ------------------------------------------------------------------------------ ## 2 character index_timeSeries setMethod("[", signature(x = "timeSeries", i = "character", j = "index_timeSeries"), function(x, i, j, ..., drop = FALSE) { td <- timeDate(i) if (any(is.na(td))) return(as.vector(NA)) # bad to use directly @Data but more efficient in this case i <- .findIndex(td@Data, x@positions) .subset_timeSeries(x, i, j) }) # ------------------------------------------------------------------------------ ## 3 timeDate index_timeSeries setMethod("[", signature(x = "timeSeries", i = "timeDate", j = "index_timeSeries"), function(x, i, j, ..., drop = FALSE) { # bad to use directly @Data but more efficient in this case i <- .findIndex(i@Data, x@positions) .subset_timeSeries(x, i, j) }) # ------------------------------------------------------------------------------ ## 4 timeSeries index_timeSeries setMethod("[", signature(x = "timeSeries", i = "timeSeries", j = "index_timeSeries"), function(x, i, j, ..., drop = FALSE) { if (x@format != "counts" && i@format != "counts" && finCenter(x) != finCenter(i)) stop("FinCenter of timeSeries and subset do not match") .subset_timeSeries(x, as.vector(i), j) }) # ------------------------------------------------------------------------------ ## 5 missing index_timeSeries setMethod("[", signature(x = "timeSeries", i = "missing", j = "index_timeSeries"), function(x, i, j, ..., drop = FALSE) .subset_timeSeries(x, TRUE, j)) # ------------------------------------------------------------------------------ ## 6 ANY index_timeSeries setMethod("[", signature(x = "timeSeries", i = "ANY", j = "index_timeSeries"), function(x,i,j, ..., drop = FALSE) stop("invalid or not-yet-implemented 'timeSeries' subsetting")) # ------------------------------------------------------------------------------ ## 7 index_timeSeries character setMethod("[", signature(x = "timeSeries", i = "index_timeSeries", j = "character"), function(x, i, j, ..., drop = FALSE) { j <- pmatch(j, slot(x, "units"), duplicates.ok = TRUE) if (any(is.na(j))) stop("subscript out of bounds", call. = FALSE) .subset_timeSeries(x, i, j) }) # ------------------------------------------------------------------------------ ## 8 character character setMethod("[", signature(x = "timeSeries", i = "character", j = "character"), function(x, i, j, ..., drop = FALSE) { j <- pmatch(j, slot(x, "units"), duplicates.ok = TRUE) if (any(is.na(j))) stop("subscript out of bounds", call. = FALSE) callGeneric(x=x, i=i, j=j, drop=drop) }) # ------------------------------------------------------------------------------ ## 9 timeDate character setMethod("[", signature(x = "timeSeries", i = "timeDate", j = "character"), function(x, i, j, ..., drop = FALSE) { # bad to use directly @Data but more efficient in this case i <- .findIndex(i@Data, x@positions) j <- pmatch(j, slot(x, "units"), duplicates.ok = TRUE) if (any(is.na(j))) stop("subscript out of bounds", call. = FALSE) .subset_timeSeries(x, i, j) }) # ------------------------------------------------------------------------------ ## 10 timeSeries character # inherited method works fine # ------------------------------------------------------------------------------ ## 11 missing character setMethod("[", signature(x = "timeSeries", i = "missing", j = "character"), function(x, i, j, ..., drop = FALSE) { j <- pmatch(j, slot(x, "units"), duplicates.ok = TRUE) if (any(is.na(j))) stop("subscript out of bounds", call. = FALSE) .subset_timeSeries(x, TRUE, j) }) # ------------------------------------------------------------------------------ ## 12 ANY character setMethod("[", signature(x = "timeSeries", i = "ANY", j = "index_timeSeries"), function(x,i,j, ..., drop = FALSE) stop("invalid or not-yet-implemented 'timeSeries' subsetting")) # ------------------------------------------------------------------------------ ## 13 index_timeSeries timeSeries ## 14 character timeSeries ## 15 timeDate timeSeries ## 16 timeSeries timeSeries ## 17 missing timeSeries ## 18 ANY timeSeries ## rely on inherited methods # ------------------------------------------------------------------------------ ## 19 index_timeSeries missing setMethod("[", signature(x = "timeSeries", i = "index_timeSeries", j = "missing"), function(x, i, j, ..., drop = FALSE) { if(nargs() == 2) { # same sub-setting as matrix if(any(as.logical(i)) || prod(dim(x)) == 0) as.vector(x)[i] } else { .subset_timeSeries(x, i, TRUE) } }) # ------------------------------------------------------------------------------ ## 20 character missing setMethod("[", signature(x = "timeSeries", i = "character", j = "missing"), function(x, i, j, ..., drop = FALSE) { if (nargs() == 2) as.numeric(NA) #-> return NA if comma missing else callGeneric(x=x, i=i, j=TRUE) }) # ------------------------------------------------------------------------------ ## 21 timeDate missing setMethod("[", signature(x = "timeSeries", i = "timeDate", j = "missing"), function(x, i, j, ..., drop = FALSE) { # do not return NA if comma missing because timeDate index # bad to use directly @Data but more efficient in this case i <- .findIndex(i@Data, x@positions) .subset_timeSeries(x, i, TRUE) }) # ------------------------------------------------------------------------------ ## 22 timeSeries missing setMethod("[", signature(x = "timeSeries", i = "timeSeries", j = "missing"), function(x, i, j, ..., drop = FALSE) { if (x@format != "counts" && i@format != "counts" && finCenter(x) != finCenter(i)) stop("FinCenter of timeSeries and subset do not match") if(nargs() == 2) { if(any(as.logical(i)) || prod(dim(x)) == 0) as.vector(x)[as.vector(i)] } else { .subset_timeSeries(x, as.vector(i), TRUE) } }) # ------------------------------------------------------------------------------ ## workaround i <- matrix. setMethod("[", signature(x = "timeSeries", i = "matrix", j = "missing"), function(x, i, j, ..., drop = FALSE) { if(nargs() == 2) { # same sub-setting as matrix if(any(as.logical(i)) || prod(dim(x)) == 0) as.vector(x)[i] } else { .subset_timeSeries(x, as.vector(i), TRUE) } }) # ------------------------------------------------------------------------------ ## 23 missing missing setMethod("[", signature(x = "timeSeries", i = "missing", j = "missing"), function(x, i, j, ..., drop = FALSE) x) # ------------------------------------------------------------------------------ ## 24 ANY missing setMethod("[", signature(x = "timeSeries", i = "ANY", j = "index_timeSeries"), function(x,i,j, ..., drop = FALSE) stop("invalid or not-yet-implemented 'timeSeries' subsetting")) # ------------------------------------------------------------------------------ ## 25 index_timeSeries ANY setMethod("[", signature(x = "timeSeries", i = "ANY", j = "index_timeSeries"), function(x,i,j, ..., drop = FALSE) stop("invalid or not-yet-implemented 'timeSeries' subsetting")) # ------------------------------------------------------------------------------ ## 26 character ANY setMethod("[", signature(x = "timeSeries", i = "ANY", j = "index_timeSeries"), function(x,i,j, ..., drop = FALSE) stop("invalid or not-yet-implemented 'timeSeries' subsetting")) # ------------------------------------------------------------------------------ ## 27 timeDate ANY setMethod("[", signature(x = "timeSeries", i = "ANY", j = "index_timeSeries"), function(x,i,j, ..., drop = FALSE) stop("invalid or not-yet-implemented 'timeSeries' subsetting")) # ------------------------------------------------------------------------------ ## 28 timeSeries ANY setMethod("[", signature(x = "timeSeries", i = "ANY", j = "index_timeSeries"), function(x,i,j, ..., drop = FALSE) stop("invalid or not-yet-implemented 'timeSeries' subsetting")) # ------------------------------------------------------------------------------ ## 29 missing ANY setMethod("[", signature(x = "timeSeries", i = "ANY", j = "index_timeSeries"), function(x,i,j, ..., drop = FALSE) stop("invalid or not-yet-implemented 'timeSeries' subsetting")) # ------------------------------------------------------------------------------ ## 30 ANY ANY setMethod("[", signature(x = "timeSeries", i = "ANY", j = "index_timeSeries"), function(x,i,j, ..., drop = FALSE) stop("invalid or not-yet-implemented 'timeSeries' subsetting")) # ------------------------------------------------------------------------------ ## 1 time_timeSeries index_timeSeries setMethod("[", signature(x = "timeSeries", i = "time_timeSeries", j = "index_timeSeries"), function(x,i,j, ..., drop = FALSE) { i <- timeDate(i) callGeneric(x=x, i=i, j=j, drop=drop) }) # ------------------------------------------------------------------------------ ## 2 time_timeSeries character setMethod("[", signature(x = "timeSeries", i = "time_timeSeries", j = "character"), function(x,i,j, ..., drop = FALSE) { i <- timeDate(i) callGeneric(x=x, i=i, j=j, drop=drop) }) # ------------------------------------------------------------------------------ ## 4 time_timeSeries missing setMethod("[", signature(x = "timeSeries", i = "time_timeSeries", j = "missing"), function(x,i,j, ..., drop = FALSE) { i <- timeDate(i) callGeneric(x=x, i=i, drop=drop) }) # ------------------------------------------------------------------------------ ## 5 time_timeSeries ANY setMethod("[", signature(x = "timeSeries", i = "time_timeSeries", j = "ANY"), function(x,i,j, ..., drop = FALSE) { i <- timeDate(i) callGeneric(x=x, i=i, j=j, drop=drop) }) ################################################################################ # $,timeSeries Subset by column names ################################################################################ # should behave the same way as $,data.frame setMethod("$", signature(x = "timeSeries"), function (x, name) { nc <- colnames(x) nr <- names(x@recordIDs) dataIdx <- pmatch(name, nc) recordIDsIdx <- pmatch(name, nr) ## if none or more than one match return NULL. ## ## GNB: 'more than one match' is in the sense that 'name' has a match in ## both colnames(x) and names(x@recordIDs). Note that 'name is of ## length 1, the matches are of length one and there is no problem in ## the 'if'. if ((is.na(dataIdx) && is.na(recordIDsIdx)) || (!is.na(dataIdx) && !is.na(recordIDsIdx))) return(NULL) if (!is.na(dataIdx)) return(.subset(x, TRUE, dataIdx)) if (!is.na(recordIDsIdx)) return(x@recordIDs[[recordIDsIdx]]) NULL }) # methods to generate completion after $ .DollarNames.timeSeries <- function(x, pattern) grep(pattern, names(x), value = TRUE) ################################################################################ # $<-,timeSeries Subset by column names ################################################################################ .dollar_assign <- function(x, name, value) { stopifnot(inherits(x, "timeSeries")) # check size of value if (NROW(value) < nrow(x)) { value <- rep(value, length.out = nrow(x)) } else if (NROW(value) > nrow(x)) { stop(gettextf("replacement has %i rows, time series has %i", NROW(value), nrow(x))) #, call. = FALSE) } # assign value to recordIDs if (length(x@recordIDs)) { x@recordIDs[[name]] <- value } else { x@recordIDs <- as.data.frame(value) colnames(x@recordIDs) <- name } # check if object is valid validObject(x) x } setReplaceMethod("$", signature(x = "timeSeries", value = "numeric"), function(x, name, value) { # check size of value if (NROW(value) < nrow(x)) { value <- rep(value, length.out = nrow(x)) } else if (NROW(value) > nrow(x)) { stop(gettextf("replacement has %i rows, time series has %i", NROW(value), nrow(x))) #, call. = FALSE) } # get data part data <- getDataPart(x) # coerce value to matrix ncol <- NCOL(value) value <- matrix(value, ncol = NCOL(value), dimnames = NULL) # set up colnames cn <- colnames(value) if (any(is.null(cn))) cn <- if (ncol > 1) paste(name, ".", seq.int(ncol), sep = "") else name colnames(value) <- cn # if name already present - subsitute ... if (any(cdata <- (colnames(data) %in% cn))) { cvalue <- cn %in% colnames(data) data[,cdata] <- value[,cvalue] value <- cbind(data, value[,!cvalue]) ans <- setDataPart(x, value) } else { ans <- .dollar_assign(x, name, as.vector(value)) } # return ans }) setReplaceMethod("$", signature(x = "timeSeries", value = "factor"), function(x, name, value) .dollar_assign(x, name, value)) setReplaceMethod("$", signature(x = "timeSeries", value = "ANY"), function(x, name, value) .dollar_assign(x, name, value)) ################################################################################ # [<-,timeSeries Assign value to subsets of a 'timeSeries' object ################################################################################ # Note that most of the generic function works by default with [<-,timeDate # only need to deal with special cases that are i <- ("timeDate", "character") # ------------------------------------------------------------------------------ # timeDate setReplaceMethod("[", signature(x = "timeSeries", i = "timeDate", j = "ANY"), function(x, i, j, value) { # bad to use directly @Data but more efficient in this case i <- .findIndex(i@Data, x@positions) callGeneric(x=x, i=i, j=j, value=value) }) setReplaceMethod("[", signature(x = "timeSeries", i = "timeDate", j = "missing"), function(x, i, j, value) callGeneric(x=x, i=i, j=TRUE, value=value)) # ------------------------------------------------------------------------------ # character setReplaceMethod("[", signature(x = "timeSeries", i = "character", j = "ANY"), function(x, i, j, value) { i <- timeDate(i) callGeneric(x=x, i=i, j=j, value=value) }) setReplaceMethod("[", signature(x = "timeSeries", i = "character", j = "missing"), function(x, i, j, value) { i <- timeDate(i) callGeneric(x=x, i=i, j=TRUE, value=value) }) ################################################################################ timeSeries/R/base-cbind.R0000644000176200001440000002575615104730074014716 0ustar liggesusers # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # Generic functions defined in {base} # cbind.timeSeries Combines two 'timeSeries' objects by column # rbind.timeSeries Combines two 'timeSeries' objects by row ################################################################################ # Generic functions defined in {methods} # cbind2.timeSeries Combines two objects by column # rbind2.timeSeries Combines two objects by row ################################################################################ cbind.timeSeries <- function(..., deparse.level = 1) { # A function implemented by Yohan Chalabi and Diethelm Wuertz # Description: # Binds columns of two 'timeSeries' objects # Arguments: # ... # deparse.level # FUNCTION: # Columnwise bind: dots <- list(...) # Preserve the title of the first ... element # counter <- NULL # for (i in 1:length(dots)) counter <- c(counter, is.timeSeries(dots[[i]])) # Index <- which(counter[1]) Title <- "" #dots[[Index]]@title # Compose Attributes - Documentation : Attributes <- list() for (i in 1:length(dots)) { if (inherits(dots[[i]], "timeSeries")) { nextAttributes <- getAttributes(dots[[i]]) Attributes <- .appendList(Attributes, nextAttributes) } } Documentation <- as.character(date()) attr(Documentation, "Attributes") <- Attributes # remove NULL from dots args if (any(t <- unlist(lapply(dots, is.null)))) dots[t] <- NULL # deal with numeric values vecIdx <- sapply(dots, function(obj) (!inherits(obj, "timeSeries") && prod(dim(obj)) == 1)) if (any(vecIdx)) dots[vecIdx] <- lapply( dots[vecIdx], function(vec) as.timeSeries(rep(as.vector(vec), len = NROW(dots[[1]])))) # coerce to timeSeries object if not a timeSeries if (any(t <- !unlist(lapply(dots, inherits, "timeSeries")))) dots[t] <- lapply(dots[t], as.timeSeries) # note that new timeSeries get FinCenter of first entry of args FinCenter = finCenter(dots[[1]]) # get names of arguments if any units <- unlist(lapply(dots, colnames)) if (length(t <- as.logical((nchar(nm <- names(units)))))) units[t] <- nm[t] # change colnames if they are the same if (length(unique(units)) != length(units)) { for (name in unique(units)) { pos <- grep(name, units) if (length(pos) != 1) units[pos] <- paste(units[pos], seq(pos), sep = ".") } } # ensure that data is sorted dots <- lapply(dots, sort) # get list of timestamps and recordIDs tds <- lapply(dots, slot, "positions") rec <- lapply(dots, slot, "recordIDs") # Fast version when timeSeries have identical timestamps # or with signal series if (any(co <- unlist(lapply(dots, function(ts) ts@format == "counts"))) || (any(!co) & all(sapply(tds[!co], identical, tds[!co][[1]])))) { # check if all have same number of rows if (diff(range((unlist(lapply(dots, NROW)))))) stop("number of rows must match") td <- if (any(!co)) tds[!co][[1]] else NULL data <- array(unlist(dots), dim=c(NROW(dots[[1]]), sum(sapply(dots, ncol)))) recordIDs <- if (sum(recIdx <- sapply(rec, length))) do.call(cbind, rec[recIdx]) else data.frame() ans <- timeSeries(data = data, charvec = td, units = units, zone = "GMT", FinCenter = FinCenter, recordIDs = recordIDs) } else { # Aligned timestamps: td <- sort(unique(unlist(tds))) fun <- function(ts, td, ref) { mm <- matrix(NA, ncol = ncol(ts), nrow = length(ref)) mm[findInterval(td, ref),] <- getDataPart(ts) mm} data <- mapply(fun, ts = dots, td = tds, MoreArgs = list(ref=td), SIMPLIFY = FALSE) data <- array(unlist(data), dim=c(length(td), sum(sapply(dots, ncol)))) # Note that recordIDs are not preserved when time stamps are # not equal because don't know what value we should use for # missing entries if (sum(sapply(rec, length))) { msg <- "@recordIDs cannot be binded when timestamps are not identical" warning(msg, call. = FALSE) } # note that new timeSeries get FinCenter of first entry of args ans <- timeSeries(data = data, charvec = td, units = units, zone = FinCenter, FinCenter = FinCenter) } # Preserve Title and Documentation: ans@title <- Title ans@documentation <- Documentation # Return Value: ans } # ------------------------------------------------------------------------------ ## # YC: ## # Note that since 2.9.0 S3 methods can not be defined for an S4 class ## # which extends an object like matrix. Therefore we turn all S3 ## # generics to S4 generics for backward compatibility ## # Note that since 2.8.0 it is possible to define methods for functions ## # with dots ... ## if (getRversion() < "2.9.0") { ## cbind.timeSeries <- ## function(..., deparse.level = 1) ## .cbind.timeSeries(..., deparse.level = deparse.level) ## } else { ## setGeneric("cbind", signature = "...") #-> creates warning but ## # cannot avoid it with ## # current dotsMethods scheme ## setMethod("cbind", "timeSeries", function(..., deparse.level = 1) ## .cbind.timeSeries(..., deparse.level = deparse.level)) ## } # ------------------------------------------------------------------------------ setMethod("cbind2", c("timeSeries", "timeSeries"), function(x, y) cbind(x, y)) setMethod("cbind2", c("timeSeries", "ANY"), function(x,y) callGeneric(x, as(y, "timeSeries"))) setMethod("cbind2", c("ANY", "timeSeries"), function(x,y) callGeneric(as(x, "timeSeries"), y)) setMethod("cbind2", c("timeSeries", "missing"), function(x,y) x) # ------------------------------------------------------------------------------ rbind.timeSeries <- function(..., deparse.level = 1) { # A function implemented by Yohan Chalabi and Diethelm Wuertz # Description: # Binds rows of two 'timeSeries' objects # Arguments: # ... # deparse.level # FUNCTION: # Row bind: dots <- list(...) # Preserve the title of the first ... element # counter <- NULL # for (i in 1:length(dots)) counter <- c(counter, is.timeSeries(dots[[i]])) # Index <- which(counter[1]) Title <- "" # dots[[Index]]@title # Compose Attributes - Documentation : Attributes <- list() for (i in 1:length(dots)) { if (inherits(dots[[i]], "timeSeries")) { nextAttributes <- getAttributes(dots[[i]]) Attributes <- .appendList(Attributes, nextAttributes) } } Documentation <- as.character(date()) attr(Documentation, "Attributes") <- Attributes # Remove NULL from dots args if (any(t <- unlist(lapply(dots, is.null)))) dots[t] <- NULL # Coerce to timeSeries object if not a timeSeries if (any(t <- !unlist(lapply(dots, inherits, "timeSeries")))) dots[t] <- lapply(dots[t], as.timeSeries) if (diff(range((unlist(lapply(dots, ncol)))))) stop("number of columns must match") # get names of arguments if any otherwise use colnames units <- unlist(lapply(dots, colnames)) if (length(t <- as.logical((nchar(nm <- names(units)))))) units[t] <- nm[t] units <- structure(units, dim = c(ncol(dots[[1]]), length(dots))) units <- apply(units, 1, paste, collapse = "_") # Bind: # data <- base::rbind(...) # no because S3 method dispatch done in C level data <- do.call(base::rbind, lapply(dots, getDataPart)) if (any(unlist(lapply(dots, function(ts) ts@format == "counts")))) { return(timeSeries(data=data, units = units)) } # recordIDs part if (length(dots) > 1) recordIDs <- tryCatch(do.call(rbind, lapply(dots, slot, "recordIDs")), error = function(e) { msg <- paste("@recordIDs cannot be binded :", conditionMessage(e)) warning(msg, call. = FALSE) data.frame()}) else recordIDs <- slot(dots[[1]], "recordIDs") tds <- unlist(lapply(dots, slot, "positions")) ans <- timeSeries(data = data, charvec = tds, zone = "GMT", FinCenter = finCenter(dots[[1]]), units = units, recordIDs = recordIDs) # Preserve Title and Documentation: ans@title <- Title ans@documentation <- Documentation # Return Value: ans } # ------------------------------------------------------------------------------ ## # YC: ## # Note that since 2.9.0 S3 methods can not be defined for S4 classes ## # which extends an object like matrix. Therefore we turn all S3 ## # generics to S4 generics for backward compatibility ## # Note that since 2.8.0 it is possible to define methods for functions ## # with dots ... ## if (getRversion() < "2.9.0") { ## rbind.timeSeries <- ## function(..., deparse.level = 1) ## .rbind.timeSeries(..., deparse.level = deparse.level) ## } else { ## setGeneric("rbind", signature = "...") #-> creates warning but ## # cannot avoid it with ## # current dotsMethods scheme ## setMethod("rbind", "timeSeries", function(..., deparse.level = 1) ## .rbind.timeSeries(..., deparse.level = deparse.level)) ## } # ------------------------------------------------------------------------------ setMethod("rbind2", c("timeSeries", "timeSeries"), function(x, y) rbind(x, y)) setMethod("rbind2", c("timeSeries", "ANY"), function(x,y) callGeneric(x, as(y, "timeSeries"))) setMethod("rbind2", c("ANY", "timeSeries"), function(x,y) callGeneric(as(x, "timeSeries"), y)) setMethod("rbind2", c("timeSeries", "missing"), function(x,y) x) ################################################################################ timeSeries/vignettes/0000755000176200001440000000000015117007154014374 5ustar liggesuserstimeSeries/vignettes/timeSeriesPlot.Rnw0000644000176200001440000015272615104730074020051 0ustar liggesusers%\VignetteIndexEntry{Plotting 'timeSeries' Objects} \documentclass[10pt,a4paper]{article} \usepackage{hyperref} \hypersetup{colorlinks,% citecolor=black,% linkcolor=blue,% urlcolor=darkgreen,% } \title{\bf Plotting 'timeSeries' Objects} \author{Diethelm W\"urtz and Tobias Setz\\ETH Zurich and Rmetrics Association Zurich} \date{May 12, 2014} \begin{document} \SweaveOpts{concordance=TRUE} \maketitle \tableofcontents \setlength{\parskip}{20pt} %\SweaveOpts{strip.white=FALSE} \setkeys{Gin}{width=0.9\textwidth} % plot.ts <- function ( % x, y = NULL, plot.type = c("multiple", "single"), % xy.labels, xy.lines, panel = lines, nc, % yax.flip = FALSE, % mar.multi = c(0, 5.1, 0, if (yax.flip) 5.1 else 2.1), % oma.multi = c(6, 0, 5, 0), axes = TRUE, ...) % plot.zoo <- function ( % x, y = NULL, screens, plot.type, panel = lines, % xlab = "Index", ylab = NULL, main = NULL, % xlim = NULL, ylim = NULL, % xy.labels = FALSE, xy.lines = NULL, % yax.flip = FALSE, % oma = c(6, 0, 5, 0), % mar = c(0, 5.1, 0, if (yax.flip) 5.1 else 2.1), % col = 1, lty = 1, lwd = 1, pch = 1, type = "l", log = "", % nc, widths = 1, heights = 1, ...) % plot.xts <- function ( % x, y = NULL, type = "l", auto.grid = TRUE, % major.ticks = "auto", minor.ticks = TRUE, major.format = TRUE, % bar.col = "grey", candle.col = "white", % ann = TRUE, axes = TRUE, ...) % .plot.timeSeries <-function( % x, y, FinCenter = NULL, type = NULL, plot.type = c("multiple", "single"), % format = "auto", at = c("chic", "pretty"), % col, pch, cex, lty, lwd, % grid = FALSE, frame.plot = TRUE, panel = lines, % axes = TRUE, ann = TRUE, cex.axis = 1, cex.lab = 1, % yax.flip = FALSE, % mar.multi = c(0, 5.1, 0, if (yax.flip) 5.1 else 2.1), % oma.multi = c(7.75, 1.1, 6.1, 1.1), % ...) % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 1 \newpage\thispagestyle{empty}\mbox{} \pagebreak \section{Introduction} The Rmetrics \texttt{timeDate} and \texttt{timeSeries} packages are workhorses to deal with chronological objects. Since their inception 2009 under their original names \texttt{fCalendar} and \texttt{fSeries} they have been only slightly modified. With version R 3.1. we have essentially improved the \texttt{plot} function, but we also took care that the functionality is almost upward compatible. In this vignette we show how to work with the recently updated S4 generic plot function \texttt{plot}. The function is written to display Rmetrics S4 \texttt{timeSeries} objects. The basic functionality of the \texttt{plot} function is to display single and multiple views on univariae and multivariate \texttt{timeSeries} objects. The function \texttt{plot.ts} from R's base environment, which displays basic \texttt{ts} time series objects, served as a model for our design of the generic S4 \texttt{plot} function for \texttt{timeSeries} objects. Similarily, \texttt{plot.ts} can be considered as the prototype for the S3 \texttt{plot.zoo} method. The \texttt{xts} plot function was build to display univariate \texttt{xts} time series objects which inherit from \texttt{zoo}'s objects for ordered time series objects. The generic S4 time series plotting function can dispay \emph{univariate} and \emph{multivariate} time series in \emph{single} and \emph{multiple} frames. The plots can be tailored with respect to several viewing components: colors (col), line types (lty), plot symbols (pch), line widths (lwd), symbol sizes (cex), axis layout (pretty, chic, tailored), minor tick mark appearence, font styles and font sizes, frame positioning (mar, oma), as well as tailored panel functions (panel). \noindent\emph{General Plot Settings and Design Apects}: \noindent\emph{Plot Type}: Univariate time series are displayed by default in \texttt{plot.type="single"} frames, multivariate time series are displayed by default in \texttt{plot.type="multiple"} frames. The default line style for a plot is \texttt{type ="l"} is drawn with "lines". \noindent\emph{Time Axis Layout}: For the time axis layout the function \texttt{pretty} determines in an automative way the \texttt{at="pretty"} positions of the ticks. The \texttt{format="auto"} is extracted from the time stamps of the time series object or can be overwritten by the user with a POSIX format string. Alternatively, one can select \texttt{"chic"} to generate time axis styles. We called this method "chic" to give reference to the underlying function \texttt{axTicksByTime} from the Chicago \texttt{xts} package which generates tick positions and axis labels. Furthermore, a "tailored" method can be applied which allows for fully arbitrary user defined positions and formatted labels. Minor ticks can be added in several fashions. \noindent\emph{Annotations}: The annotations of the plots are reduced to the y-label. These are taken by default from the column names of the time series object. This gives the user the freedom to have full control about his views how the plot should be look like. Note, multivariate time series in single plots show the string \texttt{"Values"} as label on the y-axis. Main title, sub title, and the x-label on the time axis are not shown by default. We prefer and recommend to add these decorations calling the function \texttt{title}. This allows also much more flexibility compared to passing the arguments through the plot functions. All default annotations (including the y-label) can be suppressed setting the plot function argument to \texttt{ann=FALSE}. The argument \texttt{axes=FALSE} suppresses to draw both axes on the plot frame. \noindent\emph{Decorations}: There are several options to decorate the plot: These include colors (col), plotting symbols (pch), scaling factor of plotting characters and symbols (cex), line types (lty), and lindwidths (lwd). Note, all these parameters may be vectors of the same length as the number of time series, so that each series can be addressed to its own individual style, color, and size. A grid and the plot frame (box) can be added or suppresse specifying the arguments \texttt{grid} and \texttt{frame.plot} in the argument list of the \texttt{plot} function. \noindent\emph{Panel Function}: In the case of multiple plots the plot frames, are also called \emph{panels}. By default in each panel the appropriate curve is drawn calling R's \texttt{lines} function \texttt{panel=lines}. This function can be replaced by a user defined function. This offers a wide range of new views on your time series. So for example yo can show zero or any other reference lines on the panels, or you can add rugs to (return) charts, or you can add for an example an EMA indicator (or any other kind of indicator) to curves shown in individual panels. \noindent\emph{Example "timeSeries" Objects}: To demonstrate the wide range of options to dispaly S4 \texttt{timeSeries} objects, we use the the daily index values from the Swiss Pension Fund Benchmark \emph{LPP2005}. The time series is part of the \texttt{timeSeries} package. For this we have introduced some abbreviations: <>= Sys.setlocale("LC_ALL", "C") @ <>= require(timeSeries) require(xts) require(PerformanceAnalytics) require(fTrading) tS1 <- 100 * cumulated(LPP2005REC[, 1]) # SBI (univariate) tS2 <- 100 * cumulated(LPP2005REC[, 1:2]) # SBI & SPI (bivariate) tS3 <- 100 * cumulated(LPP2005REC[, 1:3]) # SBI, SPI, SWIIT (Swiss Market) tS6 <- 100 * cumulated(LPP2005REC[, 1:6]) # Swiss and Foreign Market Indexes @ % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 2 \newpage\thispagestyle{empty}\mbox{} \newpage\thispagestyle{empty}\mbox{} \pagebreak \section{Standard Time Series Plots} The \texttt{plot} function from the \texttt{timeSeries} package allows for five different views on standard plot layouts. These include \begin{itemize} \item Univeriate single plots \item Multivariate single plots \item One column multiple plots \item Two column multiple plots \item Scatter plots \end{itemize} \noindent The only argument we have to set is the \texttt{plot.type} parameter to determine the layout of the plot. The default value is \texttt{"multiple"}, and the alternative value is \texttt{"single"}. The arguments can be abbreviated as \texttt{"m"} or \texttt{"s"}, respectively. \noindent \emph{Univariate Single Plots} were designed to plot univariate \texttt{timeSeries} objects in one single graph frame. Nothing then the \texttt{timeSeries} object has to be specified, the \texttt{plot.type} is forced to \texttt{"s"}. \noindent \emph{Multivariate Single Plots} will be used when a set of multivariate \texttt{timeSeries} objects should be drawn in one common data frame. For this argument the vlue \texttt{plot.type="s"} has to be specified. \noindent \emph{One Column Multiple Plots} display multivariate \texttt{timeSeries} objects, such that each series is plotted in its own frame. Up to four series, the frames are displayed in one column, for more series the frames are arranged in a two colum column display. \noindent \emph{Two Column Multiple Plots} handel the case of more than four \texttt{timeSeries} objects. Then the the series are displayed in two colums. In total, the number of rows is not restricted. % ---------------------------------------------------------------------------- \pagebreak \subsection{Univariate Single Plots} The most simple time series plot shows an univariate curve in a single plot. The axis is designed from "pretty" positions calculated from R's base function \texttt{pretty}. The labels are printed in the ISO 8601 standard date/time format. <>= par(mfrow=c(1, 1)) plot(tS1) @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{The chart shows an univariate time series (here the Swiss Bond Index) in a single frame. For all plot options default values have been chosen. You can decorate the plot, making it more conveniant to your needs, e.g.: change the color (col), add a main title and x-label calling the function \texttt{title}, or remove the grid lines setting the argument \texttt{grid=FALSE}. You can also design the minor tick marks, setting instead of the value \texttt{"auto"} oe of the following spreads: \texttt{"day"}, the default, \texttt{"week"}, or \texttt{"month"}. } \end{figure} \end{center} % ---------------------------------------------------------------------------- \pagebreak \subsection*{} Two other plot function implementations for \texttt{xts} time series objects can be found in the contributesd R packages \texttt{xts} and \texttt{PerformanceAnalytics}. Let us compare how they generate plot positions and time label formats. \vspace{-0.3cm} <>= require(PerformanceAnalytics) par(mfrow=c(3, 1)) xts::plot.xts(as.xts(tS1)) PerformanceAnalytics::chart.TimeSeries(as.xts(tS1)) plot(tS1) @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{The group of the three charts shows an univariate time series in a single frame for the plot functions as implemented in the packages \texttt{xts}, \texttt{PerformanceAnalytics}, and \texttt{timeSeries}. For example in the case of daily time series records \texttt{xts} uses U.S. style labels whereas \texttt{PerformanceAnalytics} and \texttt{timeSeries} use ISO standard date labels \texttt{YYYY-mm-dd}. The plot decorations are those from default settings.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Multivariate Single Plots} Multivariate time series plots in a single panel are constructed by default in the way that the first curve is plotted calling the function \texttt{plot} and the remaining curves by calling the function \texttt{lines}. <>= par(mfrow=c(1, 1)) plot(tS3, plot.type="s") @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{This chart shows a multivariate time series in a single frame. Note, we have to set the argument \texttt{plot.type="s"}. Again, for all plot options the default settings have been used. You can decorate the plot, making it more conveniant to your needs, e.g.: change the color vector (col), add a main title and x-label calling the function \texttt{title}, or remove grid lines setting the argument \texttt{grid=FALSE}. Note, to change the color settings you can set the argument \texttt{col=1:3} which would result in "black", "red", "green" for the three curves, or you can just set the colors by name, or selecting them from a color palette.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection*{} Now let us compare the plot function from the \texttt{timeSeries} package with the \texttt{chart.TimeSeries} plotting function from the \texttt{PerformanceAnalytics} function. (Note, the \texttt(xts) has no multivariate plot function implemented.) <>= par(mfrow=c(2, 1)) require(PerformanceAnalytics) PerformanceAnalytics::chart.TimeSeries(as.xts(tS3)) plot(tS3, plot.type="s") @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{The two charts show a multivariate time series plotted in a single frame. We use for the plot the functions as implemented in the packages \texttt{PerformanceAnalytics}, and \texttt{timeSeries}.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Multiple Plots} Multiple plots enormously simplify the display of different curves in multiple panels. These are the ideal plots when it comes to the task to create a quick overview over several time series. Multiple plotting is exclusive to \texttt{timeSeries} objects, \texttt(xts) and \texttt{PerformanceAnalytics} offer no multiple plotting tool. <>= par(mfrow=c(1, 1)) plot(tS3, plot.type="m") @ \begin{center} \begin{figure}[h] <>= <> @ \caption{As long as we plot less than 4 time series in a multivariate frame, we get a one column layout. Annotations show by default only the y-labels which are taken from the colmun names of the time series to be drawn. Feel free to add main title, sub title, and x-label calling the function \texttt{title}}. \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection*{} For more than four curves the frames of the plot design are arranged in two columns. <>= par(mfrow=c(1, 1)) plot(tS6, plot.type="m") @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{The graph shows the layout how it is created for six curves. There are two columns with three panels to the left and also three panels to the right. Note, it is easily possible to adapt the margin sizes and the gap between the two columns of plots calling the function \texttt{mar} and \texttt{oma}.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection*{} If you like a design with a small gap between the panel rows, you can modify the \texttt{mar} parameter to introduce a small gap, here with a width of 0.3. Feel free to modify it. <>= par(mfrow=c(1, 1)) plot(tS3, plot.type="m", mar=c(gap=0.3, 5.1, gap=0.3, 2.1)) @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{One can use in a multiple plot the \texttt{mar} parameter setting to create a small gap between the rows of the individual charts. This lets a plot look more elegant.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Combining Single Plots} You can also create your own multiple panel plots. Just combine single panels in an array of rows and columns using the parameter settings for \texttt{mfrow}, \texttt{mfcol}, and \texttt{mar}. <>= par(mfrow=c(2, 1)) par(mar = c(bottom=1.5, 5.1, top=4, 2.1)) plot(tS2[, 1]) par(mar = c(bottom=4, 5.1, top=1.5, 2.1)) plot(tS2[, 2]) @ \begin{center} \begin{figure}[h] <>= <> @ \caption{One can use in single plots the \texttt{mfrow} and \texttt{mar} parameter settings to place single plots either row by row or column by column. Here, \texttt{mfrow} and \texttt{mfcol} to the job. In this case a vector of the form \texttt{c(nr, nc)} draws subsequent figures in an nr-by-nc array on the device by columns (mfcol) or rows (mfrow), respectively.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Layout of Single Plots} There is another option in R to create panel layouts, not necessarilly in an rectangular array. Have a look to the help page of the function \texttt{layout}, her comes a simple example. <>= nf <- layout(mat=matrix(c(1, 1, 2, 3), byrow = TRUE, nrow=2)) par(mar = c(bottom=2, 5.1, top=3, 2.1)) plot(tS3[, 1]) par(mar = c(bottom=3, 5.1, top=2, 1.1)) plot(tS3[, 2]) par(mar = c(bottom=3, 4.1, top=2, 2.1)) plot(tS3[, 3]) @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{With the function \texttt{layout} you can devide the plot device in rows and columns expressed in matrix form defined by the argument \texttt{mat}. } \end{figure} \end{center} %To be more specific, the graph \texttt{i} is allocated a region composed %from a subset of these rows and columns, based on the rows and columns %in which \texttt{i} occurs in the matrix \texttt{mat}. %The argument \texttt{layout.show(n)} plots (part of) the current layout, %namely the outlines of the next \texttt{n} figures. % ----------------------------------------------------------------------------- \pagebreak \subsection*{} In addition widths and heights of the layout can be different from row to row, and/or from column to column. The sizes are expressed by the arguments \texttt{widths} and \texttt{heights} of the function \texttt{layout}. <>= nf <- layout(mat=matrix(c(1, 1, 2, 3), byrow=TRUE, nrow=2), heights=c(2.5,1)) par(mar = c(bottom=2, 5.1, top=3, 2.1)) plot(tS3[, 1]) par(mar = c(bottom=3, 5.1, top=1.5, 1.1)) plot(tS3[, 2]) par(mar = c(bottom=3, 4.1, top=1.5, 2.1)) plot(tS3[, 3]) @ \begin{center} \begin{figure}[h] <>= <> @ \caption{With the function \texttt{layout} one can also define the widths and heights of the columns and rows.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Bivariate Scatter Plots} For historical reasons, like in the function \texttt{plot.ts}, there is also the option to create an scatter plot from two univariaye time series. Since this is not a "true" time series plot, we will not go in further detail for this display. <>= par(mfrow=c(1,1)) plot(tS2[, 1], tS2[, 2]) @ \begin{center} \begin{figure}[h] <>= <> @ \caption{If \texttt(x) and \texttt(y) are univariate time series, then the plot function will display a scatter plot.} \end{figure} \end{center} % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 3 \newpage\thispagestyle{empty}\mbox{} \newpage\thispagestyle{empty}\mbox{} \pagebreak \section{Time Axis Layout} The function \texttt{plot} comes with three options to design the time axis layout: \texttt{"pretty"}, \texttt{"chic"}, and \emph{} (note this not a string argument. \emph{} should just abbreviate that we have to input character strings of fully arbitray \texttt{at} positions. For the first two options the style of the axis annotation is generated in a fully automated way, whereas in the tailored case each tick on the axis to be user defined. \noindent The \emph{"pretty"} time axis layout is the default setting for the argument \texttt{at}. Internally the function \texttt{pretty} is used to compute a sequence of about \texttt{n+1} equally spaced round values which cover the range of the values in the time stamps \texttt{time(x)} of the series \texttt{x}. The values are chosen so that they are 1, 2 or 5 times a power of 10. \noindent The \emph{"chic"} time axis layout is the alternative setting for the argument \texttt{at}. Internally the function \texttt{axTicksByTime} from the package \texttt{xts} is used to compute the sequence of axis positions and the format labels. \noindent The \emph{} time axis layout leaves it to the user to specify by himself the positions (at), the time label formatting (format), and the minor tick marks (minor.ticks). % ----------------------------------------------------------------------------- \pagebreak \subsection{Time Axis: "pretty" or "chic"?} Our plotting function comes with two axis-styles. The first is called \texttt{"pretty"}, which is the default style, and calculates positions from R's base function \texttt{pretty}. The other is called \texttt{"chic"} to remember its origin, arising from the "Chicago" \texttt{xts} package. \vspace{-0.7cm} <>= par(mfcol = c(2, 1)) plot(tS1, at = "pretty") plot(tS1, at = "chic") @ \vspace{-0.3cm} \begin{center} \begin{figure}[h] <>= <> @ \caption{The graph shows the two flavours from the \texttt{at="pretty"} and the \texttt{"chic"} axis designs. The first style uses the function \texttt{pretty} from R's \texttt{base} environment to compute the positions for the major ticks. The second style uses the function \texttt{axTicksByTime} from the \texttt{xts} package to compute x-axis tick mark locations by time. In the upper graph the minor ticks are calendar days, whereas in the lower graph weekdays are drawn (therefore the small gaps between the minor ticks become visible). Note, the time series is in both cases an object of class \texttt{timeSeries}.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection*{} Now let us plot a multivariate 3-column time series in a single panel. Again we compare the outcome of the \texttt{"pretty"} and the \texttt{"chic"} axis style. <>= par(mfcol=c(2, 1)) plot(tS3, plot.type="s", at="pretty") plot(tS3, plot.type="s", at="chic") @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{The only difference of this graph compared to the previous is the fact that we consider here a multivariate time series. Three curves are shown in a common plot. Note, when using the \texttt{"chic"} style, then the vertical gridlines are narrower compared to the \texttt{"pretty"} style.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Time Axis - Selecting Minor Tick Marks} The \texttt{"pretty"} style allows to draw the minor tick marks on different time scales. These are: \texttt{"day"}, \texttt{"week"}, and \texttt{"month"}. <>= par(mfrow=c(3, 1)) plot(tS1, minor.ticks="day", at="pretty") plot(tS1, minor.ticks="week", at="pretty") plot(tS1, minor.ticks="month", at="pretty") @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{It is worth to note that a good selection of minor tick marks makes a plot much better readable.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Time Axis - One Column Multiple Plot Layout} In the multiple plot layout the axis are drawn along the same principles as they are drawn in the case of the single plot layout. <>= par(mfrow=c(1, 1)) plot(tS3, plot.type="m", at="pretty") @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{This graph shows a one column multiple plot layout. The one column layout is generated for up to four time series. When the multivariate time series has more then four time series then a two column layout is displayed. It is up to you which axis style you prefer, \texttt{at="pretty"} or \texttt{at="chic".}} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Time Axis - Two Column Multiple Plot Layout} Concerning the style of the axis, there is now difference between the one and two column plot designs. <>= par(mfrow=c(1, 1)) plot(tS6, plot.type="m", at="chic") @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{When we have more then four time series, then the display will be generated in two columns. Note, it is possible to modify the width of the gap between the two columns.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Tick and Format Layout: The axis style} The third alternative to style the axis offers the users to define format positions according to his preferences. Here comes an example: <>= par(mfrow=c(2, 1)) at <- paste0("200", c("6-01", "6-04", "6-07", "6-10", "7-01", "7-04"), "-01") plot(tS3, plot.type="s", format="%B\n%Y", at=at) plot(tS3, plot.type="s", format="%b/%y", at=at) @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{This graph shows plots with user tailored positions and formatted axis labels.} \end{figure} \end{center} % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 4 \newpage\thispagestyle{empty}\mbox{} %\newpage\thispagestyle{empty}\mbox{} \pagebreak \section{Annotations} Plot annotations are elelents which can be added to plots or completely discarded. To discard all annotations you have to set \texttt{ann=FALSE} in the argument list of the timSeries \texttt{plot} function. To display annotation you can call the function \texttt{title}. This allows to add the main title, the sub title, and the x- and y-labels to a plot. Together with the appropriate character strings, you can also specify the placement of these annotations by the arguments \texttt{line} and \texttt{outer}. There are additional functions to add annotations to a plot. These are \texttt{text} and \texttt{mtext}. % ----------------------------------------------------------------------------- \pagebreak \subsection{Discarding all Annotations} In a default plot we display only the value-label(s) which are taken from the units or column names of the time time series object. Title, sub title, and time-label are not shown. To discard the appearance of all annotations on a plot you have to set the plot argument \texttt{ann=FALSE}. <>= par(mfrow=c(2, 2)) plot(tS1, ann=FALSE) plot(tS3, plot.type="s", ann=FALSE, at="pretty") plot(tS6, plot.type="s", ann=FALSE, at="pretty") @ \begin{center} \begin{figure}[h] <>= <> @ \caption{This graph shows a plot where all annotations have been discarded. Now feel free to add your own annotations.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Adding Title and Labels} To add a main title, a sub, title, and x- and y-labels you can call the function \texttt{title}. <>= par(mfrow=c(2, 2)) plot(tS1); title(main = "Index") plot(tS3, plot.type="s"); title(main = "Index") plot(tS3, plot.type="s"); title(main = "Index", xlab = "Date") plot(tS6, plot.type="s"); title(main = "Index", xlab = "Date") @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{This graph displays in a two by two array four single plots. We have added title and x-lable annotations.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Changing Axis Font Size} Sometimes the axis font size may be considered as too small or too large. Then you can use the plot argument \texttt{cex.axis} to upsize or downsize the font. <>= par(mfrow=c(3, 1)) plot(tS3, at="chic", plot.type="s", cex.axis=0.75) plot(tS3, at="chic", plot.type="s", cex.axis=1.00) plot(tS3, at="chic", plot.type="s", cex.axis=1.25) @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{This is an example how to change the size of the axis labels relatively to its default value. The upper graph shows a font size decreased by 20\%, the lower graph a font size increased by 25\%. You can proceed in the same way when using the \texttt{"pretty"} axis style.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Flipping Value Axes} Flipping every second axis label in a multiple plot from left to rigth might be meaningful in the case when axis labels overwrite themselves. <>= par(mfrow=c(1, 1)) plot(tS3, plot.type="m", yax.flip = TRUE) @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{The graph shows an one column multiple plot, where the axis of the middle panel is flipped from the left to the right. Note, the same procedure can also be applied two two column multiple plots.} \end{figure} \end{center} % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 5 \newpage\thispagestyle{empty}\mbox{} %\newpage\thispagestyle{empty}\mbox{} \pagebreak \section{Decorations} There exist several options to decorate plots in different ways. Plot types (lines, points, horizontal bars, etc.) can be modified, colors can be changed, lines can be modified by style and seize, points can be selected by symbol and size. \noindent In the following we will give some examples \begin{itemize} \item Modifying Types \item Changing Colors by Names \item Changing Colors by Color Palettes \item Changing Line Styles \item Modifying Line Widths \item Changing Plot Symbols \item Modifying Plot Symbol Sizes \item Discarding Grid Lines \item Drawing a Box \end{itemize} \noindent to show a few of the many types of cdecorations. Play around to achieve your perfect layout. % ----------------------------------------------------------------------------- \pagebreak \subsection{Modifying Types} <>= par(mfrow=c(1, 1)) plot(tS3, plot.type="m", type=c("l", "p", "h"), at="pretty") @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{If we like to consider in a multiple plot for each panel its own plot style then we can set the parameter \texttt{type}.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Changing Colors by Names} Colors can be changed in several ways. Just by their numbers, e.g. 1 (black), 2 (red), 3 (green) etc., or by name, e.g. "black", "red", "green", etc. or by using well designed color palettes. <>= par(mfrow=c(1, 1)) plot(tS3, plot.type="m", col=c("blue", "orange", "darkgreen")) @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{This graph shows how to assign colors by name in the case of a multiple plot. You can do it in the same way setting \texttt{plot.type="s"} if you like to display all three curves in a common single plot.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Changing Colors by Color Palettes} When the number of curves increases, then it can become quite difficult to find a set of nice colors. In such cases it is convenient to select the colors from color palettes. <>= par(mfrow=c(1, 1)) plot(tS6, plot.type="s", col=heat.colors(n=6, alpha = 1), at="chic", format = "%B\n%Y") @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{This graph shows an example of six curves diplayed in a sequence of reds. For this we called the function \texttt{seqPalette}.} \end{figure} \end{center} \pagebreak \begin{verbatim} FUNCTION: COLOUR PALETTE rainbowPalette Contiguous rainbow colour palette heatPalette Contiguous heat colour palette terrainPalette Contiguous terrain colour palette topoPalette Contiguous topo colour palette cmPalette Contiguous cm colour palette greyPalette R's gamma-corrected gray palette timPalette Tim's MATLAB-like colour palette rampPalette Colour ramp palettes seqPalette Sequential colour brewer palettes divPalette Diverging colour brewer palettes qualiPalette Qualified colour brewer palettes focusPalette Red, green and blue focus palettes monoPalette Red, green and blue mono palettes \end{verbatim} % ----------------------------------------------------------------------------- \pagebreak \subsection{Changing Line Styles} In multiple plot to each curve an own line style \texttt{lty} can be assigned: 0 "blank", 1 "solid", 2 "dashed", 3 "dotted", 4 "dotdash", 5 "longdash", or 6 "twodash". <>= par(mfrow=c(1, 1)) plot(tS3, plot.type="m", col=1, lty=1:3, at="chic") @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{When we like to print plots in black and white, then its makes much sense to use different line types so that we can distinguish the curves one from each other.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Modifying Line Widths} Not only the line type, but also the line width can be modified for each curve in an individual kind. <>= par(mfrow=c(1, 1)) plot(tS3, plot.type="m", col=1, lwd=3:1, at="chic") @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{The graph shows three line widths, the upper's curve width is thick, the middle's curve width is medium, and the lowest's curve width is the thinnest one.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Changing Plot Symbols} To use different plot symbols we can assign them by the parameter \texttt{pch}. Don't forget also to set \texttt{type="p"}. %<>= %par(mfrow=c(1, 1)) %tS3weekly <- align(tS3, by="1w") %plot(tS3weekly, plot.type="s", type="p", col=1:3, pch=21:23, at="chic") %@ \medskip %\begin{center} %\begin{figure}[h] %<>= %<> %@ %\caption{This plot shows how to assign different plot symbols to the curves %in a single plot.} %\end{figure} %\end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Modifying Plot Symbol Sizes} The argument \texttt{cex.pch} allows to increase or decrease plot symbol sizes with respect to the current plot symbol size. <>= par(mfrow=c(1, 1)) plot(tS3, plot.type="s", type="p", col=1:3, pch=21:23, cex.pch=c(0.2, 0.2, 0.2), at="pretty") @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{This plot show how to change the size of plot symbols in a single plot setting the argument \texttt{cex.pch}. Note, for each curve its own size can be set. The same approach can be used also for multiple plots.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Discarding Grid Lines} By default grid lines are displayed. To discard the grid lines from the plot set the arguments \texttt{grid=FALSE}. <>= par(mfrow=c(1, 1)) plot(tS3, plot.type="s", grid=FALSE) @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{By default a grid is drawn on top of the plot. You can remove it by setting the argument \texttt{grid=FALSE}} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Drawing a Box} <>= par(mfrow=c(1, 1)) plot(tS3, plot.type="s", frame.plot=FALSE, grid=FALSE) box() box(bty = "7", col = "white") # boxL grid(NA, NULL, col = "darkgrey") # hgrid @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{By default the plots are displayed as frame plots. This means that the graphs are surrounded by a box. This box can be discarded setting the plot argument \texttt{frame.plot=FALSE}.} \end{figure} \end{center} % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 6 \newpage\thispagestyle{empty}\mbox{} %\newpage\thispagestyle{empty}\mbox{} \pagebreak \section{The Panel Function} Multiple plots are very powerful plotting designs. Each panel in a graph can individually tailored by the user. By default each curve in a panel is generated by the function \texttt{lines}. You can define your own panel function(s) by setting the plot argument \texttt{panel} to a user dfined functions. In the following we will show three examples. % ----------------------------------------------------------------------------- \pagebreak \subsection{Adding a Horizental Zero Line} In this example we show how to write a panel function which allows to add a horizontal zero line to each plot panel. <>= par(mfrow=c(1, 1)) lines2 <- function(X, Y, type, xlab, ylab, col, pch, lty, lwd, cex) { lines(x=X, y=Y, col=col) abline(h=0, col = "brown", lwd=2)} plot(returns(tS3), plot.type="m", col = .colorwheelPalette(3), panel=lines2, at="pretty") @ \begin{center} \begin{figure}[h] <>= <> @ \caption{This multiple plot shows panels with curves having a horizontal zero reference line.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Adding an Rug to Multiple Return Plots} This example shows how to add in each panel rugs to the righ Y-axis. <>= par(mfrow=c(1, 1)) lines2 <- function(X, Y, type, xlab, ylab, col, pch, lty, lwd, cex) { lines(x=X, y=Y, type="h", col=col) rug(Y, side=4, col="steelblue") } plot(returns(tS6), plot.type="m", col = .colorwheelPalette(6), panel=lines2, at="pretty") @ \begin{center} \begin{figure}[h] <>= <> @ \caption{This multiple plot shows panels with rugs on the right Y-axis.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Adding an EMA to Multiple Index Plots} This example shows how to add an EMA indicator to each plot panel. The \texttt{emaTA()} function is provided by the \texttt{fTrading} package. <>= par(mfrow=c(1, 1)) lines2 <- function(X, Y, type, xlab, ylab, col, pch, lty, lwd, cex) { lines(x=X, y=Y, type="l", col=col) lines(x=X, y=emaTA(Y), col="black") } plot(tS3, plot.type="m", col = .colorwheelPalette(3), panel=lines2, grid=TRUE, at="pretty") @ \begin{center} \begin{figure}[h] <>= <> @ \caption{A multiple graph with EMA indicators in each panel.} \end{figure} \end{center} % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 7 \newpage\thispagestyle{empty}\mbox{} \newpage\thispagestyle{empty}\mbox{} \pagebreak \section{Conclusions} The \texttt{plot} function in the \texttt{timeSeries} package is a very powerful tool to create plots from time series objects. This includes to display univariate and multivariate time series in single and multiple panels, to select from two styles for the time-axis or even to tailor positions and formats according to his own needs, and to modifiy annotations and decorations of plots. % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 8 \newpage\thispagestyle{empty}\mbox{} \pagebreak \section{Appendix} In the appendix we have summarized graphs and tables which are useful tools to create plots. We have reprinted the default color table from R, we have summarized several color palettes as available in the \texttt{fBasics} package and other contributed R packages, and two tables with font characters and plot symbols. % ----------------------------------------------------------------------------- \pagebreak \subsection{Margins: mar and oma} <>= # Plot: # - oma stands for 'Outer Margin Area' # - mar represents the 'figure Margins' # - The default size is c(5,4,4,2) + 0.1 # - The axes tick marks will go in the first lines par(mfrow=c(1, 1)) par(oma=c(3,3,3,3)) # all sides have 3 lines of space par(mar=c(5,4,4,2) + 0.1) plot(x=1:10, y=1:10, type="n", xlab="X", ylab="Y") # Add Text tot the Plot Part - red text(5,5, "Plot", col="red", cex=2) text(5,4, "text(5,5, \"Plot\", col=\"red\", cex=2)", col="red", cex=1) box("plot", col="red", lwd=2) # Add text to thebThe Figure Part - grey mtext("Margins", side=3, line=2, cex=1.5, col="grey") mtext("par(mar=c(5,4,4,2) + 0.1)", side=3, line=1, cex=1, col="grey") mtext("Line 0", side=3, line=0, adj=1.0, cex=1, col="grey") mtext(" 1", side=3, line=1, adj=1.0, cex=1, col="grey") mtext("Line 2", side=3, line=2, adj=1.0, cex=1, col="grey") mtext("Line 3", side=3, line=3, adj=1.0, cex=1, col="grey") mtext("Line 0", side=2, line=0, adj=1.0, cex=1, col="grey") mtext("Line 1", side=2, line=1, adj=1.0, cex=1, col="grey") mtext("Line 2", side=2, line=2, adj=1.0, cex=1, col="grey") mtext("Line 3", side=2, line=3, adj=1.0, cex=1, col="grey") box("figure", col="grey") # The title will fit in the third line on the top of the graph. title("Ttitle - Third Line") # Note 'outer=TRUE' moves us from the figure to the outer margins. mtext("Outer Margin Area", side=1, line=1, cex=1.8, col="brown", outer=TRUE) mtext("par(oma=c(3,3,3,3))", side=1, line=2, cex=1, col="orange", outer=TRUE) mtext("Line 0", side=1, line=0, adj=0.0, cex=0.8, col="orange", outer=TRUE) mtext("Line 1", side=1, line=1, adj=0.0, cex=1, col="orange", outer=TRUE) mtext("Line 2", side=1, line=2, adj=0.0, cex=1, col="orange", outer=TRUE) box("outer", col="orange") @ \pagebreak \subsection*{} \begin{center} <>= <> @ \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Character Table} \begin{center} \begin{figure}[h] \includegraphics[width=\textwidth]{characterTableFig.pdf} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Color Table} \begin{center} \begin{figure}[h] \includegraphics[width=\textwidth]{colorTableFig.pdf} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Color Palettes I} \medskip \begin{center} \begin{figure}[h] \includegraphics[width=\textwidth]{colorPalettes1Fig.pdf} \end{figure} \end{center} \pagebreak \subsection{Color Palettes II} \medskip \begin{center} \begin{figure}[h] \includegraphics[width=\textwidth]{colorPalettes2Fig.pdf} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Symbol Table} \begin{center} \begin{figure}[h] \includegraphics[width=\textwidth]{symbolTableFig.pdf} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Axis Style "pretty"} <>= FORMAT <- tS1@format FORMAT POSITIONS <- pretty(tS1) POSITIONS LABELS <- pretty(tS1) LABELS @ % ----------------------------------------------------------------------------- \pagebreak \subsection{Axis Style "chic"} <>= axTicksByTime <- function (x, ticks.on = "auto", k = 1, labels = TRUE, format.labels = TRUE, ends = TRUE, gt = 2, lt = 30) { if (timeBased(x)) x <- xts(rep(1, length(x)), x) tick.opts <- c("years", "months", "weeks", "days", "hours", "minutes", "seconds") tick.k.opts <- c(10, 5, 2, 1, 6, 1, 1, 1, 4, 2, 1, 30, 15, 1, 1) if (ticks.on %in% tick.opts) { cl <- ticks.on[1] ck <- k } else { tick.opts <- paste(rep(tick.opts, c(4, 2, 1, 1, 3, 3, 1)), tick.k.opts) is <- structure(rep(0, length(tick.opts)), .Names = tick.opts) for (i in 1:length(tick.opts)) { y <- strsplit(tick.opts[i], " ")[[1]] ep <- endpoints(x, y[1], as.numeric(y[2])) is[i] <- length(ep) - 1 if (is[i] > lt) break } nms <- rev(names(is)[which(is > gt & is < lt)])[1] cl <- strsplit(nms, " ")[[1]][1] ck <- as.numeric(strsplit(nms, " ")[[1]][2]) } if (is.null(cl)) ep <- NULL else ep <- endpoints(x, cl, ck) if (ends) ep <- ep + c(rep(1, length(ep) - 1), 0) if (labels) { if (is.logical(format.labels) || is.character(format.labels)) { unix <- ifelse(.Platform$OS.type == "unix", TRUE, FALSE) time.scale <- periodicity(x)$scale fmt <- ifelse(unix, "%n%b%n%Y", "%b %Y") if (time.scale == "weekly" | time.scale == "daily") fmt <- ifelse(unix, "%b %d%n%Y", "%b %d %Y") if (time.scale == "minute" | time.scale == "hourly") fmt <- ifelse(unix, "%b %d%n%H:%M", "%b %d %H:%M") if (time.scale == "seconds") fmt <- ifelse(unix, "%b %d%n%H:%M:%S", "%b %d %H:%M:%S") if (is.character(format.labels)) fmt <- format.labels names(ep) <- format(index(x)[ep], fmt) } else { names(ep) <- as.character(index(x)[ep]) } ep } } @ <>= ticks <- axTicksByTime(as.xts(tS1)) ticks @ % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \pagebreak \section*{About the Authors} % ----------------------------------------------------------------------------- % Diethelm Wuertz \noindent\textbf{Diethelm W\"urtz} is professor at the Institute for Theoretical Physics, ITP, and for the Curriculum Computational Science and Engineering, CSE, at the Swiss Federal Institute of Technology in Zurich. He teaches Econophysics at ITP and supervises seminars in Financial Engineering. Diethelm is senior partner of Finance Online, an ETH spin-off company in Zurich, and co-founder of the Rmetrics Association in Zurich.\\ % ----------------------------------------------------------------------------- % Tobias Setz \noindent \textbf{Tobias Setz} has a Bachelor and Master in Computational Science from ETH in Zurich and has contributed with his Thesis projects on wavelet analytics and Bayesian change point analytics to this handbook. He is now a PhD student in the Econophysics group at ETH Zurich at the Institute for Theoretical Physics.\\ % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \pagebreak \section*{About Rmetrics} \noindent\emph{Rmetrics Open Source Project} With hundreds of functions built on modern methods, the Rmetrics open source software combines exploratory data analysis, statistical modelling and rapid model prototyping. The R/Rmetrics packages are embedded in R, building an environment which creates a first class system for applications in teaching statistics and finance. Rmetrics covers Time Series Econometrics, Hypothesis Testing, GARCH Modelling and Volatility Forecasting, Extreme Value Theory and Copulae, Pricing of Derivatives, Portfolio Analysis, Design and Optimization, and much more. \noindent\emph{The Rmetrics Association}\\ is a non-profit taking association working in the public interest. The Rmetrics Association provides support for innovations in financial computing. We believe that the Rmetrics Open Source software has become a valuable educational tool and that it is worth ensuring its continued development and the development of future innovations in software for statistical and computational research in finance. Rmetrics provides a reference point for individuals and institutions that want to support or interact with the Rmetrics development community. Rmetrics encourages students to participate in Rmetrics' activities in the context of Student Internships. \noindent\emph{Rmetrics Software Evalution}\\ If you like to get an impression of the size and quality of the Open Source Rmetrics Program have a look on the Ohloh Rmetrics Software Evaluation. The Evalutions gives an overview about the Software Development (Code Analysis, Estimated Cost), the people behind it, and its community. \noindent\emph{Contributions to Rmetrics}\\ are coming from several educuational institutions world wide. These include the Rmetrics web site and documentation project supported by ITP/CSE ETH Zurich, the organization of Summerschools and Workshops supported by ITP/CSE ETH Zurich, the R-sig-Finance Help and Mailing List, supported by SfS ETH Zurich, the R-forge development server, supported by University of Economics in Vienna, CRAN Test and Distribution Server for R software, supported by University of Economics Vienna, the Debian Linux integration supported by the Debian Association. Many thanks to all behind these projects who gave us continuous support over the last years.\\ \noindent Rmetrics Association\\ www.rmetrics.org % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \pagebreak % References \begin{thebibliography}{99} \bibitem{zoo} Achim Zeileis and Gabor Grothendieck (2005): \emph{ zoo: S3 Infrastructure for Regular and Irregular Time Series.} Journal of Statistical Software, 14(6), 1-27. URL http://www.jstatsoft.org/v14/i06/ \bibitem{tseries} Adrian Trapletti and Kurt Hornik (2007): \emph{tseries: Time Series Analysis and Computational Finance.} R package version 0.10-11. \bibitem{rmetrics} Diethelm W\"urtz et al. (2007): \emph{Rmetrics: Rmetrics - Financial Engineering and Computational Finance.} R package version 260.72. http://www.rmetrics.org \bibitem{ISO} International Organization for Standardization (2004): \emph{ISO 8601: Data elements and interchage formats --- Information interchange --- Representation of dates and time} URL http://www.iso.org \bibitem{R} R Development Core Team: \emph{R: A Language and Environment for Statistical Computing}, R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org \bibitem{quantmod} Jeffrey A. Ryan (2008): \emph{quantmod: Quantitative Financial Modelling Framework.} R package version 0.3-5. URL http://www.quantmod.com URL http://r-forge.r-project.org/projects/quantmod \end{thebibliography} \end{document} timeSeries/vignettes/symbolTableFig.pdf0000644000176200001440000001670515104730074020003 0ustar liggesusers%PDF-1.4 %âãÏÓ\r 1 0 obj << /CreationDate (D:20140417140611) /ModDate (D:20140417140611) /Title (R Graphics Output) /Producer (R 3.0.2) /Creator (R) >> endobj 2 0 obj << /Type /Catalog /Pages 3 0 R >> endobj 7 0 obj << /Type /Page /Parent 3 0 R /Contents 8 0 R /Resources 4 0 R >> endobj 8 0 obj << /Length 3433 /Filter /FlateDecode >> stream xœµ\i£Ý6ý~…ÙW3²6vÚB ¥ihZHÓ„^šÊÎ_G«g|Ó¤R4|ÈÍóµG>–ŽæèHW†åú˃åãÓøïÕ‹Ó+OßyãÕåÎÓ“Z•R ÿ|zçÑé•×u¼þâÞÛZO”r F­ܺ™åâáòÞ•‹«‹WË•Û^] þywy|oyûòñ'˵û·ŸÜ¾óÉÝ'ñŒY®<½úþrqýôÚE‚²˜°ªmqzݶEo¸:\0„Õàòäîòîò¨½vóϽyí­ß™åÓÓ{ï/jùèËõøïÁ 2Ö7OÁ®b¹aÕíê|ùˆEßìª,•àuA…by›_7›ªàŠºº\¼6åc(an­‰xÌ£3£Qµ+™x©2|˜ ʧÇË=+ö¸gÎæà™L­nÏ2zúܨOÇèôÞ¥÷#véÀmîõJ ;V¶ÅlËh=Z*ˆ–$žòðWåù1¿¾ëæZ§<Äì)šŸ:bõžÑŽÆ2èFcÉã¡U{/Ç0›]Uá‡V£±ä‹4°ØÏN0/.1Ñ DC“­r*qa¾Lj©£CªU£Q¢Å5jA"Ù ­ç!b20Ñô—¬øEˆäoô6 s‰ÑwTçú%ˆdd´‘ –vù² ÄX`kh; бQ­]¾"‘ ÔnbuU ¿*‘,ŠöóÝ%Š—Ô-•ø5ˆÉ®´†=ºø(}ëŠ F2*›’Á˜¦³ÊSߺ*ƒÑí³½›€¾$;`ãàÑå¿.‘üÊ6­/X K#®Tâ7d ’SÙô%J Äñ•+lü¦ Äæ˜ô%Õb´¾´Ë·d ’GÙ>ÇØ§q+ÄѦZ‚\Húˆâ¥Mgû€k›VҔѨTC÷ŠL=‘óÙ¦$ë0s0J"¸M+H†HnD†}¸µÍêGìª~ËŽ¨ù±I„&´+D3+"/-‘ 4³êQ!’¿Ûd &\˜hfÕ£BLkx\$›ƒH¾Ô̪G…HnÕÊ@¤ͬzˆÙÃq]m "_»œu'"yo/‘ 4³ÚR!’÷2S⬶l!»»Cü¶ D2ófV[*D³ êwd ’™7ê TZ…4¥»|W"™y+ .®,7Öñà÷d ’™·ê’ FÃ\þß—HfÞʨKòÞ¶<ôd ’™·ÓêÅ9hƒoÓs?È̼P—j½u)ñG2ÉË[u©Î»Ú‚We ÒŠ£P—¼Òg[C_“h÷Á˜V—ºÚh[CÿX"Yy;­.¾9ïÚЯÉ@¤G+ .6ž×[kè×e ’•wÓêâöeÄRâ2i]Ò ¨Kâ"ýŸÈ@$£ï¦Õ%”¥Ê}eå§2Éè»iu iþ0d»Ÿ ¼.‚&ܬ¶¸m7úå‘6‰Ðןûf(ݬ¶8“KÔ»¶ü\"}7«-±óOtwmySb2úÕPÎj‹+«â°kË[2Éè»Ym‰ë²tmè_È@$£ïfµ¥t »¶¼-‘-§ûYm)µ˜}y±7d ²™ƒYm)\L¾¼6ô;2iæÀÏjKmhZµ¿)‘ýxyV[b^L‹¹Ñ—×}!‘Œ¾P—ÔÐiÕ¾¬”þR"}?­.¥¡ÓïKCÿJ"}?­.:U_þp)ñ]ˆdô½€º4£_Jüµ D2ú^@]²/ßþoD 2£ï§ÕÅ”_ßÊ@$£¦ÕÅ£¿/Ú¿'‘Œ~˜V—‘Œþ­[2Éé‡iy)Ó¢}yê÷e ’Óòb“1ßÒ,c*ñˆäôô¼˜²hŸ7j¥'‘œ~˜–›'#Ò‚o±C¿—HKúaZ^J-¦] E¤oË@ZÇ™–—1óºú¡ DrúaZ^LZ̺¶;"iI?LŠ *H™-é4‡0mŠOÌ–ôAMªKÃ;t¥â]ŒdõAMÊ *,kðùë©È{2iQÔ¤¼´z—6×¥"ÿ ƒ‘Ì~ì:2Éíß—ÁHnÔ¤À¤íhÉb‘Ýÿ£Ff÷AM*LÃèMÚ‘Š| ƒ‘ü>¨I‰Im,7ü’ÁH†Ԥƴz$Ç)ƒ‘?¨I‘A¥²Ÿ6»>”ÁH–”ŒÎ¤ÅýºÅê‘ F¶ådt&ytWúÌcŒ|sñ´Î@[Þ÷¥ÿ,ƒ‘í3A€˨çcŒl?1Lë æx´é®`|"‚‘o.†iÉù1ÿ\¿`|*ƒ‘m+†iÁ}E¾ÐçŒlS1èLÚ™º/)÷/2Ùöb˜Ö™ÒÖf_Úù« F¶³¦u&ïJ›‹ë8üSŒl_1èLªÇèÕkÿ› F2ÿ€:“0¢Iÿ¥"ÿ.ƒ‘Öùt&a$ûÿŒ´Ð8­3%‡—·C¤"ÿ)ƒ‘ü? €Î„¼Z†ÿK"Û#€³*ƒQ±l~ÃWtü÷$D=0ŸÀY•AŸ1jß~úŒlgUF«²m;¤=ö©ÈÿÊ`¤}п¼3kÞ¿¼3ÏÜ¿¼s3Û¿¼3—yØ ÞÌìŸÆÑ`æË¨»‚™a:lmî fNFw¿h Óº"èî7 ´`Zñ=ʰÃK~FÆVÉ@2Œc·Q†±Ý °2Œü¶Q†ñÙ6Ê0¶·¶Q†ñ1Ì6Ê0¶´Û(øêo£ ãr¼2ŒfÊaë'XL:Ìô¬3i1ý«ÁlÖ×ô¬³œoú VƒYÎ7ý+Á<ç›~‚Õ`–óM?Áj0Ëù¦Ÿ`5˜å|ÓO°Ìr¾eÏùv”a<çÛQ†ñœoGÆs¾eÏùv”a<çÛQ†^T6Ê0¶Ìv”a<çÛQ†q{lGÆs¾e7”n”a<ç»Q†ñœïFÆÌ‘ë'˜?ߊ ®Ÿ`þ|“0¸~‚ù}ûîþ²º~‚ùóµàú æÏ·¼‚ë'˜§Í¨µ¥|?Áüù6Qðýó´³Âöýóç[+Á÷ÌŸoz?Ê0žóý(ÃxÎ÷£ ã9ß2Œç|?Ê0žóý(ÃxΣ ã9?Œ2Œçü0Ê0žóÃ(ïZeÏùa”a|œFÆ~Üa”a<ç‡Q†±q~è&XÊšxx=ªn‚µ`öŠ?ÕM°Lã|TÝkÁì%ª›`-˜½åOu¬³œª›`-˜r>ªn‚µ`Êù¨º Ö‚)ç£ê&X ¦œj”a,ç#Œ2Œ-#Œ2Œ­ë"Œ2Œå|„Q†±•P„Q†±œ0Ê0–óFÆævFÆr>Â(ÃXÎGeë-Ž2Œ¿¸Gvx;í(Ãø eq”aìµ°ØO°ô’|}Èùý¯umÁ,ç÷¿×µ³÷ºöÏç·`–óûçó[0Ëùýóù5˜çüþùüÌr~ÿ|~ f9¿>¿³œß?Ÿß‚YÎïŸÏoÁ,ç÷Ïç·`çcÿ|~ ¦q>öÏç·`–óûçók0çcÿ|~ f9¿>¿Ó|>öÏç·`–óûçó[0Ëùýóù-˜æó±>¿³œß?Ÿß‚iœýóù-˜åüþùüÌr~ÿ|~ ¦q>öÏçkò»Áù›¼û VƒYÎïŸÏoÁlœß?Ÿß‚YÎïŸÏoÁ,ç÷Ïç×`žói>ÿÆér Å>endstream endobj 3 0 obj << /Type /Pages /Kids [ 7 0 R ] /Count 1 /MediaBox [0 0 432 432] >> endobj 4 0 obj << /ProcSet [/PDF /Text] /Font << /F1 10 0 R /F2 11 0 R /F3 12 0 R >> /ExtGState << >> /ColorSpace << /sRGB 5 0 R >> >> endobj 5 0 obj [/ICCBased 6 0 R] endobj 6 0 obj << /Alternate /DeviceRGB /N 3 /Length 2596 /Filter /FlateDecode >> stream xœ–wTSهϽ7½P’Š”ÐkhRH ½H‘.*1 JÀ"6DTpDQ‘¦2(à€£C‘±"Š…Q±ëDÔqp–Id­ß¼yïÍ›ß÷~kŸ½ÏÝgï}ÖºüƒÂLX € ¡Xáçň‹g` ðlàp³³BøF™|ØŒl™ø½º ùû*Ó?ŒÁÿŸ”¹Y"1P˜ŒçòøÙ\É8=Wœ%·Oɘ¶4MÎ0JÎ"Y‚2V“sò,[|ö™e9ó2„<ËsÎâeðäÜ'ã9¾Œ‘`çø¹2¾&cƒtI†@Æoä±|N6(’Ü.æsSdl-c’(2‚-ãyàHÉ_ðÒ/XÌÏËÅÎÌZ.$§ˆ&\S†“‹áÏÏMç‹ÅÌ07#â1Ø™YárfÏüYym²";Ø8980m-m¾(Ô]ü›’÷v–^„îDøÃöW~™ °¦eµÙú‡mi]ëP»ý‡Í`/в¾u}qº|^RÄâ,g+«ÜÜ\KŸk)/èïúŸC_|ÏR¾Ýïåaxó“8’t1C^7nfz¦DÄÈÎâpù 柇øþuü$¾ˆ/”ED˦L L–µ[Ȉ™B†@øŸšøÃþ¤Ù¹–‰ÚøЖX¥!@~(* {d+Ðï} ÆGù͋љ˜ûÏ‚þ}W¸LþÈ$ŽcGD2¸QÎìšüZ4 E@ê@èÀ¶À¸àA(ˆq`1à‚D €µ ”‚­`'¨u 4ƒ6ptcà48.Ë`ÜR0ž€)ð Ì@„…ÈR‡t CȲ…XäCP”%CBH@ë R¨ª†ê¡fè[è(tº C· Qhúz#0 ¦ÁZ°l³`O8Ž„ÁÉð28.‚·À•p|î„O×àX ?§€:¢‹0ÂFB‘x$ !«¤i@Ú¤¹ŠH‘§È[EE1PL” Ê…⢖¡V¡6£ªQP¨>ÔUÔ(j õMFk¢ÍÑÎèt,:‹.FW ›Ðè³èô8úƒ¡cŒ1ŽL&³³³ÓŽ9…ÆŒa¦±X¬:ÖëŠ År°bl1¶ {{{;Ž}ƒ#âtp¶8_\¡8áú"ãEy‹.,ÖXœ¾øøÅ%œ%Gщ1‰-‰ï9¡œÎôÒ€¥µK§¸lî.îžoo’ïÊ/çO$¹&•'=JvMÞž<™âžR‘òTÀT ž§ú§Ö¥¾N MÛŸö)=&½=—‘˜qTH¦ û2µ3ó2‡³Ì³Š³¤Ëœ—í\6% 5eCÙ‹²»Å4ÙÏÔ€ÄD²^2šã–S“ó&7:÷Hžrž0o`¹ÙòMË'ò}ó¿^ZÁ]Ñ[ [°¶`t¥çÊúUЪ¥«zWë¯.Z=¾Æo͵„µik(´.,/|¹.f]O‘VÑš¢±õ~ë[‹ŠEÅ76¸l¨ÛˆÚ(Ø8¸iMKx%K­K+Jßoæn¾ø•ÍW•_}Ú’´e°Ì¡lÏVÌVáÖëÛÜ·(W.Ï/Û²½scGÉŽ—;—ì¼PaWQ·‹°K²KZ\Ù]ePµµê}uJõHWM{­fí¦Ú×»y»¯ìñØÓV§UWZ÷n¯`ïÍz¿úΣ†Š}˜}9û6F7öÍúº¹I£©´éÃ~á~éˆ}ÍŽÍÍ-š-e­p«¤uò`ÂÁËßxÓÝÆl«o§·—‡$‡›øíõÃA‡{°Ž´}gø]mµ£¤ê\Þ9Õ•Ò%íŽë>x´·Ç¥§ã{Ëï÷Ó=Vs\åx٠‰¢ŸN柜>•uêééäÓc½Kz=s­/¼oðlÐÙóç|Ïé÷ì?yÞõü± ÎŽ^d]ìºäp©sÀ~ ãû:;‡‡º/;]îž7|âŠû•ÓW½¯ž»píÒÈü‘áëQ×oÞH¸!½É»ùèVú­ç·snÏÜYs}·äžÒ½Šûš÷~4ý±]ê =>ê=:ð`Áƒ;cܱ'?eÿô~¼è!ùaÅ„ÎDó#ÛGÇ&}'/?^øxüIÖ“™§Å?+ÿ\ûÌäÙw¿xü20;5þ\ôüÓ¯›_¨¿ØÿÒîeïtØôýW¯f^—¼Qsà-ëmÿ»˜w3¹ï±ï+?˜~èùôñî§ŒOŸ~÷„óûendstream endobj 9 0 obj << /Type /Encoding /BaseEncoding /WinAnsiEncoding /Differences [ 45/minus 96/quoteleft 144/dotlessi /grave /acute /circumflex /tilde /macron /breve /dotaccent /dieresis /.notdef /ring /cedilla /.notdef /hungarumlaut /ogonek /caron /space] >> endobj 10 0 obj << /Type /Font /Subtype /Type1 /Name /F1 /BaseFont /ZapfDingbats >> endobj 11 0 obj << /Type /Font /Subtype /Type1 /Name /F2 /BaseFont /Helvetica /Encoding 9 0 R >> endobj 12 0 obj << /Type /Font /Subtype /Type1 /Name /F3 /BaseFont /Helvetica-Bold /Encoding 9 0 R >> endobj xref 0 13 0000000000 65535 f 0000000021 00000 n 0000000163 00000 n 0000003797 00000 n 0000003880 00000 n 0000004015 00000 n 0000004048 00000 n 0000000212 00000 n 0000000292 00000 n 0000006743 00000 n 0000007000 00000 n 0000007084 00000 n 0000007181 00000 n trailer << /Size 13 /Info 1 0 R /Root 2 0 R >> startxref 7283 %%EOF timeSeries/vignettes/colorPalettes2Fig.pdf0000644000176200001440000010337315104730074020426 0ustar liggesusers%PDF-1.4 %âãÏÓ\r 1 0 obj << /CreationDate (D:20140430203415) /ModDate (D:20140430203415) /Title (R Graphics Output) /Producer (R 3.0.2) /Creator (R) >> endobj 2 0 obj << /Type /Catalog /Pages 3 0 R >> endobj 7 0 obj << /Type /Page /Parent 3 0 R /Contents 8 0 R /Resources 4 0 R >> endobj 8 0 obj << /Length 30481 /Filter /FlateDecode >> stream xœ½½K“&;v$¶¿¿"—ä‚¥x?–âHC³1“fD¶Ù6 NwŒTGSì&%Ó¿ ò‹¬Œº‘±˜š vfU&àŽwÞ¿ý§·þíŸßþõ—ÿãí_ßæo{÷6.ë·qz[×oÓú6oßÖùíO¿û¯oüåúóßþÍ_¿ýöÏ¿ôߺ®{ßݷ-ýùçßêþ÷KÇÿ!þü»ÿð¿§ÿkú›þß_þ´uo¿û¥ûOéÿýó/}þ‚·ÿí—eù6¥{_¾­ÃÛ‘‡ãž†é‡ØÞþP†sn߆ Ãa+Ã}ǰÏ_¼ç¡ »õmêºos—‡é·Ò°ç÷Îù«òpß0œ—4ò¿[†ã˜†ã·‰_Üíi8}ë—<œ¾mCn+†s¦¿‚áKÃôÕ/¿msî†éW™ºõÛ’ªiú¶Lú}ÿðËÏÿÔ‚$ý¦eÖ¾ —1ýïWÖ§ÌÕ¸”ÿo+¶ù¿¹>ý›X¢÷kP–è?i¹ó?Þ¶oü¿ã2Lë¾.eÞúîí7ÇÛ_ôùö›þåýÍO`£K¿ôœþ\Ò/ñë±QOÝ‘‡eQÊr§)ÚÊpÛ1LëO@þÌáÙžÂFŸ1žçm 6†ŸÁFBždltCúsžÇ_fêŽ_æôÑ\ÊpÏ›4ì6úë;' íeX>ƒól,ùÇ/Ã.ñÊEIPYÊpÊP™»¼(y¸dlL{þ¦<ÜF ûüÅe²Òpû6 eØgˆNé›Ö2òß<¥°/ÃqÁß;•¿j΋W†«ÁP†Û+l¤ŸkX˲Nyò¼–/_÷‡°‘þÉ 3Ã}cü5ØØÒoÓ¥™¸aßh¦îøeóç%&vÌ[BL{ù”Ç¢ôß–¾Z²¸Ò‚vúÞ²ÜãþmÜ ãÖ@e\KÒpèfã"L޳0¹pØwàq®ïqÄu¶o¤Ÿ«ëéçZÇg±‘~à¿l‡}cú5ØXûüwý |£™º#û>6äqÈ?Œ·ë±/¥\›ùX¨„·ú1í s‚4,ÛËİcƒb gq‰6üC,@C‚Êå) §ªx¥aW•¶¡`Ä…o(,Èeq˜K‘8åéçæ¨)CùãIP÷ÎPÙç2,0·ÛHá¸mÆäš‡+1Yœ‡ý†a‚÷˜Ö¸*à·Ô 6òÏ•~a#ÿ [ÿ(6òÏ¿bßXéL¨Øò¾Ñ§_â¦}ƒSwpCÎÃ~÷v=nS&hÚÌÇmD)ÇVŸ‡ãàB0nC®Ø*yˆm¤|ÊÇ­ÇÞŽ“‡Ýê4nþ!”§<ìf¯qÝ ”¶<,%…o\·ÜSýe1 BÑÓ´n/¹(jJþAR­šR~åÑš’Ân)ØØ~}ÆFŸ~ôgúï›ø§î ‘ËÃRÊAóò°¬/Hà¸.ø ‚"æá<™@æ!¥ÐË<ì‚|ŽkÁ¢¨i.½‰k–Z›‡ÃnÒ›‡…ça·™0—aaÞ ×)#Õ`Èøhþîµ3Í_¾ÌrÑq-¿sÆÆþsØØú\Sº1ë}ÂûM} §î`[ÚØöÒ<Æ¢”Ö2–l®´ »¾wЦÕ`@Kk¨ á5Ðfh– B´Ò†(mm¸á&ý|ß@kl ‡}èa+lôÝÏcÙÿËýrmæî r互÷k¨NÞÍ¡Iy¯‡båJ=Ëuj—«´0×(e®@ÐÑ\Ÿ ²¹zAƒsmƒBçÊýÎuêÞ9á€øå¢ñëÑ¢ñ+c}íJÍÄ}p¤ÏMʨ$gS9Ò&z«M!f›$Bê6…„n‚ ™Üô"ºÉ)$vSWð&¶’çI{%Þ“KÚŸý½cE¨[àT57…jþ(…j^ØhpXÍó? ²{V„¦Ì«?ÿþ_ßþêíïÿðŸÿô·¿«–frübŸùšêwÓ;7V[þY†Ä”>‡­roz õ›e\ù7y¼TNÏU3›ÇSeáäñXy8yÜW&NŸJ×^¹8y¼V6NOUžÇCeäôiûß+''—ÊÊÉã¡òrÒg·H‰WúÄ¢»—ù'ì*;§Ÿ!0µ=4ÐøuÒ\~ß3Gçb悘±ñùõˆifð(ã©R¯ò¸«\~‚¦*í+ûJë§òù³r–ÇCåìô‰6-•îÖKt§,×'ÒµTæNw•»Ó§gª4¿>³ßÊßÉã¡2xú š‰Å>Që½²x^ &ýDSeò”ßàËóÕˆ™,@¾òy. fíóî2-¥oh®Û<Êx¨¬ž>õ2såõô©áØ*%=ûJhïG*žÔáÓ E˜2}w•Šß§.l¬Ÿ~„s, aË"Èã®2}R/ ë‘CŸZí¹²}2"¶Ê÷!Blü¼@Lú‰–ÊúÉãý÷óÕˆÉÝè¹ûs1Û”±²ì¹ñžº;ªR3ƒy~²ÚSͰ¤y­À°eE)V(»Êê‡ˆÓ §Â=W.Pê¿­• ”Ç ‚†²Y²”V9A=ÄÄ@h÷5‚‡>—Ã@øÐqú ö´ó=&k}•!”Çý;Gè«3t08_{B³Ù*ÜŠ¸{ËAƒvÓüô¥ÜÄ.ž&j«|¡<*c¨ÇRG•È¿ce õié¦Êê{œMqJã±®Rý„OU,ÿC•?Ô種‚}!õ¥.@ÿêXÎ<Œwœ¼m§ðäÛ8$±Øæ!™E€¤e’[”HrQfh„º AÒ‹2É/Ê$Á(s £ BRŒ2 É1Ê($É(³,?"2ð¢,AÂ{¸,AÂ;u«.Cf€Ñ~õ»Tß`2ÐÕƒ+Bw. ]>¸&tûà¢Ðõƒ«B÷. _ ¸.|ƒàÂð‚+Ãw.-_B\[¾…¸¸|9¾­¹|㋼„ |‚à¾ð æ¾ð ~ÎÃJk›E±0±~÷OÿÏÛ_½ýõŸþúojØõiXäaýð‹jKb^ËQö³áŽ“}#üpíc‰ñ¡É<®L¬<î*kq*+NdB,Š›N{ú<çVŽêÇqÏ ç=}t+â\]‹ØgIWœÒÔQÓ4++»ÊÄrA«L¬<*kȺäwGý6fÕ9ÐrXÛÊ÷+@þeGº¦W´^»XW0³ÚÔ­ÅšXo Míæš±F:„ÆSecåqs„;Ѥ¹>áÆ}ec ‰f-••Ç]}|\&‚N—eâãðyÚp¦ÊÆFÞÕ…™¼¿‡€9 ‹$pÄ  y]Yy<¾3²¾3¹É=w²®`fÊÝǹÈÌý };‡i†2¶zŽi$x f\0ôM0~¼†#0§5NϽ²²ò¸¯¬¬!‹|5†ú¨cøAƒ5£iœk § s¯¬¬<*+kHEj®¬¬˜É[`eeåñüÎÊúr̤Ÿs;÷².a¦X¸ý¶ßqâ¸Ã4C‰ì•™•ÇCef Ý«Gµ ë­C&"•™5tƒ­ Çú¦SÇ«·ªU™øÔwRA_ëËNoßÊÌJDj«Í¬½áafx…™•ÆK÷!ŸI?aWßyêzÂÖ¦~Çýô7ëf¦bNdV³/7œ'lçð(ã¡¶³öÇvdg¥ñ\ÛY;,rÛYéwÝk;+/Fmgí°Ðmg¥ñXÛY;ŽéØÎÊsWÛYûŒû7²³v\Á°•Ækmg¥ñRÛYy\ÙYï1“ÿÅ©¶³ÒwŒïì¬/ÇLêp·s?ëf`e­åö-Òa3‡G̱š`­šd­‘šh­¡šl¬q4áÀ@4éÀH4ñÀP4ùÀXˆÀ`ˆÀhˆÀpˆ Àxˆø „Hψu SÌDSÌ|×Ô?„™3GëžVï3ù`×]ûLA;o4IkÃ^ZjAÜ–êx:]×¥PKâ¾jM\˜êx@]7¦P«¬%²–Ykd­³ÉZh­’µÒZ&k©µNÖZk¡¬Å–›OkSÜBmúN;|¨6yZW0³”£@Î4Ý 7·sxˆ3ZÒ'§´äOÎiK€œÔ–9«-rZ[ä¼¶$ȉmY3ÛÒ §¶åAÎmK„œÜ– 9»-rz[.ˆL­Sl‹‚ø;‹â!|æj]ÁÌT:&Üòî8˜ÞÎá¡Þ´šãÒ»VkPzÛX#ô¾±†ècÑ;Ð[FÐ{†Ð›ÆÐ»ÑÛFÑû†¡ Æ¡ÄgÚÂGû zíÀ zí§1ƒ^ûÔ׺š¡œ´€|Ëu¼v‰`±™C$‹Í"Zƒ‘—ÉT, ÂE1Hņު‹ÑÈ R*V£˜A$Œb1Š!DÆ(–!ãæ1Dʸ˜ óBCQ/ŠD½‡‹E½Sgë húbååˆn¿Ñ´“xHmkõPããÖ=Ôú¸”5?îìCí+ýpâÆ?Ü‚êA€â&Tïlx0ÀÏ àÐM¼6@·ÂÐÍð[t;ü”ݽtи%¯AS܃x)îÁÃ,˜îÁOÞÏZó6üÎÛú/ÿô÷·5,¸¾ý¡·õÃ/ª·´¹÷o9ÜqLu५GYgÔ—qåmåñTy[y‡OuzZ¢Ãazê}yÜUÞÖØã ˜ôÂQ¾õÄ<ž*ok„8ï.õkû,SæÓ•·•Ç]åm=”¿ù”ÿ‡ÊÛz™ô•·U~ƒwÞÖ×cƺé+oë f梡 .wôhíeÜUÞÖ˜«ò¶Æ4uKýÜYZü½~ ­§B«ÇÒzxÁ~K­ÛõöžZKã¾ò¶Æžºjëà™Ë÷sïRy[yÜWÞÖ˜6œ±ò¶ÆÜ÷WÞ1boëfòqíÊÛÊãõ·õå˜É tîm]Á .öÍxm½ÁÛjçð(ãn«ç˜¾‚×€oQzøˆ§×°æäm¥¢5ÖÞV*jsíme½¯ö¶Ò¸¯½­}Ñ}%ô€Ð7ÃÛJ”i¯½­4kokÇkZö¶2«¼­÷˜É?áP{[é'Úßy[_™üsçÞÖÌlèë—ìŠn·œ»hæð(ã±ö¶é\jo+“ÐÚÛÚ»æ:ïµÒÚÛíokÃÙxÅ×ýŒ_ø”·µ­ð_åmm¼~¬si\¿R;l|rNçD6\ó9’4îºøLþ‰¦æ=¿ñ£µiØøpâkoë förVg†7ü÷¯ÇL3‡y†¦Â|*‡úa¿Ü?×/ûmðÌ}Þj±F:•Æ}ý¸ßOÝç¹Ð¯Çy¯ gw|,Ï]ýÀß6àbΓm¸âófY¨ßøKã®>¯¶áaäf¼g­ó]©¿_ßy[_Žˆ_gÞÖ%Ì”÷ºöu$rnÀL=‡G̱º`­ºd­‘ºh­¡ºl¬qtáÀ@téÀHtñÀPtùÀX¨À`¨Àh¨Àp¨ Àx¨ø „Jψ¥ SÌDWÌ|×Õ?„™3oëŸ)Ï ì…Õ¬7î3¡m¼N%¹mã3’ãP B®Ûx®]rjIõ žüŠÇy®]r!jUȉGȨu!G¢†\‰Zr&jiȨµ!‡¢[s>­MÕ»€+„jäÇjÓ™·u½oÚʵ­ùÌ4sxˆ3†®Nº?8gøôÜ퀳†¯N¾8oøàÄá[€3Û× §¶ïAÎm_„œÜ¾ 9»}rzû.ˆ¼­S>8ðw>ÅCøÌÛº‚™±S—þ{º3íêM«9žñžŸ×`Ácž^£×0½†P c7}ÿTõÖÆ{ïxi½yEyPc‹ÃuKÊG;]=ÚÄ:£>¸Œ+o«ˆ6•·•ÇkÕgçñRy[y¯¶à9 whï1“~¢¡ò¶òwôï¼­¯ÇÌ ^â•·u 3pBË“§¹NýzÌ4sxÄ« Ö¨KÖ©‹ÖªËÆG D—ŒD E—Œ… †JŒ†Š ‡ÊŒ‡ Ï@¨øŒ„t†™èê™ïºú‡0sæm]ÁÌŠS:e·É޵τ´ò^•ä¶•OJŽC-¹nåÑvÉy¨%!÷¡Ö„¸òh»äBÔªQËBnD­ 9µ0äJÔÊ3QKCîD­ 9µ84ç³Úò!jÓwòáCµéÌÛºÔk—s¤K/äÜÅgBrg ]œ2tpÎðè¹Û7g _œ¶ Àš QË—'ßœ9| pêð=À¹ÃÙø –|ú«öUÀéÃw!Fìmqà* «pàï|Ї8ð™·uIÓ+ÓTþúåŽ^»ÃC½i5Ç=žÔ ·5ÚG%J±·†í5Fï@oAï]¥¨Ai5ÆÐ»ÑÛFÑû†¡ ãÔü ¶ðÑ>ƒ^;0ƒ^ûiÌ ×>õ¶®€fDÄ^I™ï¸TÜNâ!Ì›9E2oöÑ\ (²¹XP„s1¡HçbCÏň"Ÿ‹E@3Š„.v] )2ºXR„t1¥HébKóBCQÏʼn¢ÞÃʼn¢Þ©·u4ýñj·ÜŒi'ñÚnÖH5Þ¬’m®Y'Õ|³Rªýf­tÌjé˜õÒM0+¦Û`ÖL7¬ÚnY·Ý ²r»s|ÿ^±úÖ-ùÀ=0 ¦{ð0 ¦{ðSÞVÞž²¢ñÎÛú÷ÿü§wÁmùè̹³õƒ/©k`y! ¿aÇÙCFŸ Ú3£ÁÊtlÚ° Af¨Ú° fäÚ°¢½f Û°1ÏsZëôæ0·4Ü™ü–qŒõ–¹ÉØ£R2&nT žùÍ÷C×7GÌ#3å@7NPO—†È…CxÝ87 åø}«l·±CÏl·gû] u>_ö% d¶[š·WÖç±±”»âEÜ ÙLÝ¡DÅq¢Æ¼Å4,‚ÓGr f5æû¤Ã›“Ç jsGÅâqgÌ'²¦R!ýíœ"›ÿ!æKN=„F¦O¦aÁ³)§ÕŽÉ•iXTLæZæŒÙòÅH½LÃyy‰®”îgÛG(dþA¶§°oùZí §êóØ@šìXÔ᎛fêE±N$ j&¬/c\§ ŸA¾_›†;‡¹6NdÑ ˆf˜ŒÍ…  —MÃb=0zvZX÷L;-0.[;‘?3Ô6ýÅöàKÏxNù ÄÍÃ5À0-ô÷¿ÇÓdÓw‡i²éË+ƒák±Á4Ùi~éH};Èqyî–7Tš©;áì‰EÀ³§ñÏ^„C{É âíô½];-00”ZPadµ€Ä@kÁŒq×!ðQFe À Ò¼³}ºo0…ZØ` õ“Ø` 5;ç<]ÀFQõ í-·xõÔŠ~׆Ì`xm׌×fÎPymõŒœW!` ½ÊÃTDf¯è{ â‹ÊSNUñ Ò6›Ò…o RYpîþ”o0½^5…éõOÖ¦×ÛK‡é6Ê^¾ xÇ ÀfêŽ<Ì¥\Dn(ª‹iOa–:Dó 8L¿rnWD/yrPä3 ³!j: 'q0 Z;€‰ôHÅ%†Ò‡‰03áDtzÀû_Ã{là<¾¸è€¨ƒ'¹h¶ªÁá_9I°Qd—}ÖM©žË©¦îðÄŽ¼!ÅiõŒeÐ+#X²^`A;½Q‚åîü½ ùñPé7C%É·|³|\y ö‹1™!ÚÏÄ$œö à ï~"®þ~lÊßa£ßy# Øè·wÔ_ôó¿rŒ>µì¸­»ß!É5SwhCNÃ~í:ÇYͱ™gÁbŒ­>ë#C‚|ê`Œ2ÑwºUŠH·ëv)1YÛY£u]"”§4ä™õR¼ºUGÚKiëV=[Qþ¡n¡C„²ØIøEÑÌÊßK.Êš‚L7×”üƒ,aƒ5%ý„¯œ¡ úF9ƒ¹–>e»å­ˆzê¹|"¡š—†0…@³;ELÃy ™õÚ5èe~³±"ŸùwEM»R?‰«Oë‚Ö¦á°éíÈ‚H‰q›É„9{ óNØ 0)†n òvÂEÓwÓ*\4P˜Â¸h¾jõÊú<6†!×”ïƒN7¼CÜLݡЋöÐÓŽæÑ‹‚ÖÒK†ÆÓ ºë{§ªiØÒ *lx$¶Ã‚›e­´ ÊF[f.x³I?Ý7ØÃ ìaŸÄzØ ­ÓóypôeË@ðT~ÿ.C•#íÈÔ•´_SuÒnNMJ{=+UêYªT»TE¨…©ÆP)S¢Ž¦úD•MÕ‹œj:U>êwª‹T÷N Å/Š_OŠ_ÝôÚÑù<8ºr‡¶/]ì¸Ýpâ¶™»C’³¨i=ÊÕ¢³E)u‹BRÁ¤L.úI]äT;©«x[Éó¤½ïIŠ%íþÞ±"Ô-p®š‹R5’R5Ïlôò­$8°Ø;øùÛßýõ¿¿Ï0ûعùÑ×|¥u£Ü/õDÊS·©Ü05£ÊS¯ªÜ1µ²Ê%S§«Ü25ÂÊ5SŸ¬Ü3µÑÊES—­Ü45áÊUS®Ü5µðÊeS‡¯Ü6 Êu“´¢œ³@¥rÎÔ@+ç¬í Å¯k“˜svâç\@Ìí†N;ƒ‡Ã¥])œPÒ–Â GçYâ c ?”n¦pDÉj O”ê¦pE‰r _”f§pFIz o”â§pG ‚ ”^¨pHɉ ”Pû1 S”§0ÅVûjÄL–_¹<s»ÍÓÎàá„ÓÉq–H@•P®„TéèJP•Ì®„U©ðJ`•H¯„ViøJp•į„W9J€•A „XùJ•½ „Y¹J •9!„Èöy&¶JÜWbk«î5b˜Øzâý\@LkþÜ‘<ßÌàáeϰ„y­S˜½BLiö 2ÅÙ+Ì”g#€)ÐFS¢ ¦HaL™6)%¡L©6‚™bm„3åÚŸ¦`°Ç0Úˆa*ô³ˆa*ô‰#t1¥ÇZÑiÝñÌ^;ƒ‡“Ú½‹3ÉÝ»<“Þ]˜ï*Á¤xW&É»Ê0ÁUˆIô®RLªwc’½«“î]{<¿ï*ÙC~víÁ³\e{_ÿ€Ç0yÞU‰ÉóÏV%&ÏŸøDŸFŒ.¯ñ©½ñŽPèfóüô…˜)æPÊ+êyÎL³‡þf&š•©™j_d0YÇ3ÓMS³WŽQ¯•eT¦®òŒòxª™v‡à3q4ÙÁÔ"b&ßáq-!äbºrôØÌ·CšÀ³Ì7ë]çîÑ•=¦ä,ìå)Ï[ì£v˜aö»^¶Ã^!vË^A6Ó^aöÚF@ß¿W6’¤F^SŸ/JB¥Á„pi úH‚Ð'DêÎ9bÔ 1mÿbN<¥+Ì·\ÝŠDœÿ¼m‘Æ£]\ò™vy©kªßT%¤Í©ŠHºS•‘²§*$áOUJº ª˜dCU9©Šª‚U%¥IªŠJ²T•í̳P…%ŸW%)‚ªJ­$øPU:qš®t×ÅjŠ?oã1’ŒÅ¥Æ‹IJ¬Ó”–/&*©_LUN€˜¬Œ1]ùb²Ä”ƒH&-“BL[†˜¸,1u9 bò2H„YOçÌWƒ˜oë0<Ã|Ïü§+ ÞIóŸ·õJr ÔÆ £[@7ëb·ëd7ìf·l°›6BØmAìÆ0vëF »y#”ݾL5À§ZàOÕ„öv×F »ëgƒîúÔ•º™¡l/ñçm‚Œ7J^ÞÆ)‰y›§dæ2@IÍe‚’›Ë%9—Ù *C”ô\¦(ù¹ŒQt™£dè2HIÑe’’£Ë(%I—YJ–Jx.K”ðž-K”ðμª éËžñçmª¯™ uusEêîæ’ÔåÍ5©Û›‹R×7W¥îo.K_À\—¾¹°|qeùâÒò%ĵå[ˆ‹Ë×ãûךË7¾ÈKÈÐ'0÷¥Oð,÷¥Oð3V!ïã+ ëoþ¿ae{X?ü¢w÷¶ibÝqC _>ØÇã#“Ló‰J&ˆùÀ%Æâ<&hn×Ä=ê8͉³8ì‰÷gã,(Ðâ¨(Òâ$)Þ¯õAS&¬ù*Ø|L• m>ÅÊ7‰?J4«ú!Ņ̃@™hÖ¶òL4ûº]L4;q±®`­Y¿ì3wär4sx8jÐG°EèÚŒ*ônFú|7£}ü›Qˆ>ΨD—‰ ³åŒZôÑsF1J€TT£JE9JÀTÔ£NEAJUT¤$æW˜At¢O•3:± ¿3£…ÒW>ÖÌàTùXl‰á–\ßfgšJÆWæ©d~e¢ÊPfªleªÊFPæªle²Ê†Pf«l eºÊÆPæ«leÂÊQf¬leÊÊFQæ¬laDFÖ Ì0£U¶„2Z[[âË1ÃÃ'NÖ%Ìà– ,Ð;Þ>oçðpvrÌ1¯²—cÍkˆÜ ¯1³f?#̆6†˜mŒ1[Ú¤¨iŒ2›Úfvµ1Îlk˜}ýÁ>Ã,hc†YÐc†YÐ'^Ö%Ì”;,[?”–þÙ°ÃÃíÞË™áï®Ì€w­`F¼k 3ä]k˜àZÄ z×*fÔ»–1ÃÞµŽ÷6³v¼·o3k‡n3kçE*™Y;Þëú€Ï0sÞµ‰™óÏÖ&eΟ¸YŸÇLkgÝrԴ㌇ÚÎÚWÛ‘•ck;k‡°g;k_°F²³ò³ µµC´•Æcmgíèùmgíx®ßvÖ¾e;kÇ ÛYi¼ÖvsClgíxOKy™ü/Nµµ#@àYÜç ›s?ëÒ>ó³¼”¶-w³hæðˆ9V¬5P“¬5R­5T“­5V. ¨I'FÜÄCnò‰1‹Ä EbÔ"1l‘·ÁÏ€E ~F¤cÆM=1Ó6õOaæÄѺÔ7uåýN¼ÈxGf;‡‡÷rkmÜë­Å±ø®k…/K±–ø¶k¯K±ù¾k•´DÕ2iªuÒ"U ¥UªVJËT-•Ö©Z+-TµXróymòÍ)Ö¦V;|ª6xZ—ô™>øÌ-o1¶sx˜3JÒ§”ä/Î)K@œT–8«,qZYâ¼²$ĉeYˆ3;ÿœZ–‡8·,qrY&âì²TÄée¹#2µÎ9°, qàÖ¢xˆŸ¹ZW03•Ži³>³u7ØZíîMcŽÑ»Æ ·õ±÷õ²7ö³w6Ø[#ì½!öæÆ{wc½½1ÊÞߦ6`ŒS;ðg€ÚÂû {mc†½öØA¯}êk]ÍP®ïN4Ë-S3‰‡E0oæɼÙSDs1 ÈæbAÎÅ„"‹¼U#Š|.V]Ì(ºØQDt1¤ÈèbIÒ÷Ž)RúZ2EÌsB#QÏʼn¢Þ³ÅI¢Þ™³u4}鶇RæíÐ4“xXm÷¥zªñ¾sOµÞWò©æûÆ>Õ~_è§àûþt â9¸ ñZÝ?&@7Âo ЭðSt3üRÝ?d@7Dï4nÉkÐÀ=ð;tžeÁr~ÊÛÚ‡²»¾÷¶þþí-Æ•}ìnýð‹¾ÒÝRÆ—º4e€©VF˜:eeˆ©“VƘ:me©WF™:ue˜©“WÆ™:}e I PFÚè`<¼`+%AkR”Á&%BmR*”á&5H™fÕutfš©³W¦YÛÙ3Óì뮤3ÓìÄݺ‚™ÛÝ­v‡ JqS¡9…J±S˜¡=…JñS¢A…%J1T˜¢E…-JqT£ß]bX£Ÿeb˜c¼Úe!uB_î7Ÿ)Õùfž(Qቭ‚øõ˜±rúÊݺ‚™ÆÝïØgš9<œjê˘zê÷̘ŠêçÎø~«_CcªªKcêªßRc*«ŸZcj«_bcª«jcꫜ¥ÂÊQj¬œ¥ÊÊYQ꬜aDîÖ Ì0¥Õo°1¥µu*¾3Li=q·.a¦q·îÀL3‡‡Ó“cŽé,x p?ÖOÄvÀœÜ-¦;ÛÝbú³Ý-¦CÛÝbz´Ý-¦KÛÝ¢Âiw‹éÔv·˜^mw‹éÖv·˜~}¾Ï( ÚîÓ ŸÅŒÒ OÜ­K˜©Ý­;®ƒ¶sx8¥ÝîSÜín1åÝîSàín1%ÞîSäín1!^ñãd?ã‡gXìn1ÅÞîSî}Òc[šWj‡OÎé¤ÈÆ«`:I’Æ]÷ŸQê|¼ç‡7ûŸ­MJ?q·>™p·æÒØß¡5s˜gh*üÀ'¢¶ §ytbjñ¬€NTmPø|âj±F:‘•Æ}ý¸ß…Ð'ºð~œøÚÐøûD؆û}blßò‰² A|â,¿´_¿ñÇäŸXÛð€0ò 3Þ³Ö ¯ Ïrà±gîÖ5>SÞrã+¡w¨ÎÍ1ÇꃵꓵFꣵ†ê³µÆêÃ….¾­_ý#†ÜçcÖˆAëĨubØ:1n‚ŸëüŒH :ÇŒûzb¦íëŸẨ»u3ka¿ùÝPä§Þ¶ÏX â^nÁ{½9Ö v¬ôXKâ-@Ôšx µÈ‚!k•EÖ2 ެu$Y -X²VZÐd-µàÉZkA”µXªóymŠwQ›Zñ©Útân]ÁÌŒŒ±rÉoêîx9²™ÃÜÑÊ>9¥•rN;ä¤vÈYí,ÓÚy çµ3ANl炜٠ˆäÔr>Ĺ匈“Ë9g—³"N/çE‘»uÎíT·NÅCøÌݺ‚™±ä_®ýmoµsx¸79Fïk€Þ6Ö½o¬!zãXãMß?Õ½µ0¢ÞÛ/M²7÷C”ìÝýN%{{?cÉÞ߯\Rð#˜ÔüF&µ…ó}F½¶ß˜d¯ý,fØkŸº[W@Óm°(òß?Þñ´u;‰‡E0¿ðJ‘ÌÀRDóû°ÙâùXˆpñº,D:?>+‹UoÓRäS±’¨b&‘PÅN"¢Š¡DFK‰*¦)Ul%bž‰z*Nõž-NõÎÜ­K )óTNí ý i&ñ°Ú.Ö(5^¬Rj½X§Ô|±R©ýb­rœë­«·p öÊݲÛ@Öl7¢“úO·‚¬ÛnY¹ÝŽ1¾®X}ë–|äˆË=x–Ë=ø©·×"¿t·þæ/Ë>v·~øEµ»5àÉìi¸ãܪR¾Ô¥)L°RÂÔ)+EL´RÆÔi+…L¸RÊÔ©+ÅL¼RÎÔé+MJ€RÒ¤(EMJ‚RÖ¤4(…MJ„RÚ¤T(ÅÍjSÍŸJ5Sg¯T³¶³gªÙ—áS©f'îÖÌl3Cñ¸r”âmIxRƒ7(ÅMq„RäW8:Ãq†Rôw(ÅOqˆR—(ÅPqŠR·(ÅQqŒR$×(ÅRqŽR4÷(ÅSqRDiÕù=fŸ(Qñ‰­‚øå˜é¬œ¾r·®`FîV©ƒËyBÍÎ5µ»ÅÜS»[ÌEµ»ÅÜT»[ÌUµ»ÅÜU»[Ìeµ»ÅÜV»[Ìu"ÔY»[Ì…µ3ÂÜX;'Ì•µ³ÂÜY;/Ĉݭw˜QN«Ý-æ´¶NÅWcF9­'îÖ%ÌÐÝ*xßqw«ÃÃùÉžc9 Zæ/{˜Ïì5d~³×˜ùÎÆóŸ#‹jj1ÄühcŒùÒÆ Nc”ùÔÆ0ó«qæ[û3Àüëó}FyБJ…<è‡1Ã<èwëfèn-w¹[íÎi÷Iæ¸û$sÞ}Ò9ð> ÁœxŸ”`޼OR0gÞ'-˜Cï“Ì©÷I æØû$sî}Ò#-Tý\o•»5®¼¦“$i™ïînÝáT´sx”qW¹[9%v©Ü­<+w+»ÊÝ—k¤YiXk >Yk¤>Zk¨>[k¬>\PŸNŒ¸'†ÜçcÖˆAëĨubØ:1n‚ŸëüŒX :ÅŒûzb¦íëŸẨ»u­o*XY{ž÷;r›9<¼—[pã^oA޵À‚k…=Ö ~¬5Y‹,²VYPd-³àÈZgA’µÐ‚%k¥MÖR ž¬µDY‹­:ŸÖ& ˆ¬M­€øTm:q·®`fÆíÐ)gæ-w<ÅÝÎáaÎheŸœÒÊ?9§rR;ä¬vÈi# œ×Î9± rf;äÔv>ȹ팓Û9‘Ã*g…œÞÎ 1bwë”G8pëT<ÄÏÜ­+˜KÂâZq¿å.z;‡‡{Ó˜cô®^ö¶^#ö¾^CöÆ^cöÎÆ{kc„½w䨡77ÆØ»ƒìíQöþÆ0µa\Ú>Ò>ØgØk3ìµÆ zíSwë hp gîóß¿Þqd½ÄÃ"˜6s‰dÚì%¢©HdS±§b"‘NÅF"žŠ‘D>+‰€*f Uì$"ªJdT±”©b*‘RÅV"æ9¡‘¨§â$QïÙâ$QïÌݺ´Ó”cÍ2Þšz«íbRãÅ*¥Ö‹uJÍ+•Ú/Ö*7@¬VnX¯Ü±b» dÍv#ȪíVuÛÍ +·Û1Æ÷ï«oݒܱ`¹ϲ`¹?ãnËTÖè{wëïþïßÿößþôxß–/žº[?ø’æiÞÈ)ÏÿýëÞðøc~Zr@ÀYÚ0†yDŒð´ažÐ$#Z-w&­õù‹g´Øˆeæ…épåú0¯üÞòUy¸3ÿ-mÃoiÿȆæÄ/NÈ¥–ç~óPÉrsöL–+1tC,_\BêòépåW–±Î)çï où)]<ÞD¶ oýò5@DÂ[ž·W.Öç±±Ì%1´ì\Û¼º™ºƒ¹ŠCžî²ÜÅ´ÏÃ"º!“1Ÿ#(|‰C†Èð¦<dz«´ÇaQ`yo#öY2†Ôq¹.wn@ò?„¥roÏa> ³ìÀ*‡Ü¹—„Ê’_™‡EÉDºåzÊBÚ}™‡óò].ßù_.ª ¢!Ëò}†ý—a£`<ÏÛ+·êóØÀ݈e-™oÀF3uYó° ì‹CîÕKlh sÍÃòÄ;¶y¸sØå/&“FLlN"›‡KDÌæa±@;¬k_‰§ÍÃb^ ¼6AÏ ;ÊÃb}àÅç<,ÎbqËp 0äáö È”Í?Hq)›¿¼2¾Ì”Íóö•ú<6p½Õ| M®§î`sLl‰yŽi/!б(%":–¬®´ ¾·‹ðiƒÑÔ† ‚« $ÄZf½6‰mˆ"0ÛFœ¶á°íó}YÔÆ²¨Å³¨×ñ¥ûôYlØG(gKçáŽ[¿õÔ €÷†Œxxo×÷fŽhyoõžw!@,½ËB\DiïƒÀ{ ¡0.Oi8UÅkXQbtXz)¼T…o RYf‰3¾ {×dØ?ZSaŸ»W.Ó…š2—Ø¢¢Ì,à 5¥™º#s)‘Ë) [Ð<œá3 LÃi Š˜_©dîMæ —8?hò™†óÔt(}‰ëP‚Lk° ‘Þ¡¤ã˜§&iƒ0#éÄtz(ï€ ï±QN囋%òàQ.šÃvÀá_¹IŸÇÆP˜Æ\â­î¹]WOÝá‰íyOŠÓÞë9,J§×FÊ’%®ÀÇHÊ‚&®À·JÊrO›¿7ƒ!Q¾tR ’ $ßõÌuÀ6¦u&3Dsý[À¹þmfxç\‹.öì©O`#ý\¸lLËû$ê/ÆFúù_¹FzØ‚¾Ô”[ôÿfêmÈiØï±]O¯'a3wÞ†ÂV?î¼ …B€Ší2‘,QDòc:]”˜q¥U„”ˆœ"”§4äÉõR¼rRÍ¥mœõxþ¡‰.Êâ(ñEsQ$ÎjJ~ôgŒš2.ï#쿸¦ä·¥^¸C¸h¹ŠÐá¬Ëgêš©;Däò©„>h^žî%H`>£0EÌhMA ÓÎ]¡—ù¹­Š|"ÙÈÔtì!÷“¸úÄ.hmÆÄ¤w$ "%Îq[æ<ì1Ì;áØÁ¨ òð#.š¾›NPá¢ç£\4}_¹@øF ¤J™¼{ÜñNh3u‡@O,ÚCO;šG/ ZK/O/è®ïª¦U``K+¨°áØ fl–B¶Ò‚(m˜m¸àÍ&ýtß`+l°‡}ìa­ÛóypÌE¶ÏlÓ_~ÇÃÃÍÜRŽ´#SWÒ~MÕI»95)íõT¬T ¨g©NPíR¡¦C¥Lˆ:šêU6U/jpªmTèTù¨ß©.RÝ;%¿TT(~=YT(~%¬¿tu.0ŽòׯŸw)£b”œEå(H‹èQ® ¤˜-’H©[’B¸&erÑOŠè"§”ØE])À‹ØRží•xOR,i¿÷÷Ž¡nlsÕ\l”ªù“l”ªyf£—_Þ[³"TÞV®þõßÿñÿôöWoÿóoûû?þÛ‹<³Ý›}MíH—gpAiºåytd€¹/bF˜;Nfˆ¹!eƘûUf¹eF™»]f˜¹fÆ™{ef ¹•fFš;mf¨¹gÆšûtf°¹gF›»|f¸Y`Æ›åfžUÈdæ™›hfž}×E3óìËZ%džy:³•§Çu©ÿé_…˜fZ¿bP¡å-ZýbСÅ1!Z;cP¢¥5)ZycТ…91Z·cP£e=9ZõcУEAAZ3dP¤%EIZ¬}ܰ$Ç`Åï4¹¯FÌd ò•Ós1òq=·ßn°ˆÛ<”vj5œi¨Ë™–j-iª–Ú™¶j%ži¬ê™ÖjŸi®–ù™öj€i°6 ˜ki²¶˜6k‚i´6(„Y?/ƒôV üLoýNáÿjÄ0½õÄÿ¹€˜©¹øs¹£éngðP¢rÌ0•¯™c…Ø+ˆDçXa$>Abt ‰Ò0$N)'¡H¬#Ñ:ŽÄëø û£= Ñ$D?Œ&DŸ¸BWö˜1W¥¡áÍOŽÜ•){ RÛcGª{ìòH}*€Tø¨H*‚Tù¨2ˆGˆ*„Tú¨RH­*†Tû¨rH½*بRª’=$hWÑ<ËU¶PcÎy Rè£*!…þ᪄ú3¯è )»ËPªRÞi~=bšÌóÓ^`¦˜6*¿¨Ç±¸`š=483Ñü¼LÍTû ’˜,ŽåÓívxbÂúx3宼ãLºÏ2Óîv´Xbâ8BL"Áä»ýÛФý6ˆéòä`¾]¹Gö0ó͞ϤKU©XHå Û-r_;ƒGÌ0{^¯[b¯;f­ j­°úm!@í¸¢n]R3/„©×%¡R „` B¸t}$Cè"…ç1jã…˜¶1'¾ÒÄ@Dmêî0¤Û<¼‹KBÓ./…MU@œª„ô9UÉwª2R÷T…$þ©JIT“t¨*'eQUP£ª¤tIUQÉ–ª²£yª°ãóª$UPU©•ªJ'nÓÄàâNvýôÍ fŠRäÅ$%Ø‹iJÏ•Ü/¦*7@LVf˜®¼1aY bÊŽD$“–Q!¦-CL\6‡˜º\1y™$Bˆì§sæ+“AÌ·ub¾'Ôæ»åg…æòÌÞ->C;ƒ‡»Ñ˜at«±èf½Bìv½‚솽Âì–vÓF»m#ˆÝ¸ÆnÝd7o„²Û7‚©áT ü  šðÁÃîÚˆawý,bØ]Ÿ9SWˆÌ®w<ó•¤;ˆL3…‡%/oã”ļÍS2s ¤æ2AÉÍe„’œËŒ¬P•!Jz.S”ü\Æ( ºÌQ2t¤¤è2IÉÑe”’¤Ë,%ˈ %<—%JxÏ–%Jxg~ÕÈt7VíÖÕÍ©»›KR—7פno.J]ß\•º¿¹,}s]úæÂôÌ•é;˜KÓ—0×–o!.._£ï_k.ßø"/!CŸÀÜ—>Á³Ü—>ÁO¹XÝR>µïm¬ÿåÿô5ê±²Ë>´±~øEﮚ ¸u»åx¿â|«ŒON"0,V"P,Î]"p¬:– ¦§67ùô¡ÎrŲ:ó¹áÐgxÖW>Óª¥å1Û8oŠÀµ8ŽŠ@¶8­ŠÀ¶8ÌŠ@7ë? 8kÎû•€³8 Š€³ïºyœ}Ù¹.œYW0ÓŠçÈ·ñïx<¿™ÃCɃqÉ„qPÉ…qŽɆqÌɇq ɈqH|ä(!'3ós$/Æ t$3Zƒdr£5J&;ZÃdò£5N&CZer¤UæW˜ ép9’¿Ó ¿3£µÒWVÖ¥}¦\wÜÊ©žùŽçÒÚ9<qj%Ÿ¨Vú‘j'€ªv ±j'¬vÑj'‚®v*ñj'ƒ°v:k'„²vJ1k'…´vZ„yY/0ƒÈV;ŒlýΙørÌ0²õÄ̺‚™¡üÅ âêÖܬvE)Ws •&Ö`Æ-C¯ÑïÇk8sZcD=ATt`QÒ1ü AêšÆ(¢ªȲŒ#ê:>ˆÂþhŸA4t`ÑÐOc†ÑÐ'vÖÌ쥫_:üyŸiæðPd{ìåˆt½‘ïQ  µ‘ñQK)µQ‹:Þ¿U­Bd}Ô2DÚG­ëxW~Ö¾£VÉÏÚ¡…ÛÏÚyŸJ~Ö¾¡ÒœóDÐGmBýõ‰ôg†ÖÌà8rßeVÓ÷wôhÍe<ÔŽVÕ¨­4žkGk‡¶gGk_°Fr´Òx­­Ú ­4kGkGÛoGk/o÷‡£µƒoÙÑÚqÃŽV¯µ£…‘p´ö=ÝЄüÖ=Z~ ¦v´ö’&ð0î·]½Ä+KëÒ>S^¤Y'dŠßá‚6sxī֨OÖ©æºÏæ»'ܧ#îã‰!÷ùĘubÐ:1j¶Î@Œ[‡àgÀ:?#’‚Î1㾞˜iûú§0sbj}3º‚‹‡?î9ØÌáá½Ür÷zËq¬¾2ÅZá;S¬%¾4ÅZã[S¬E¾6ÅZ%9QµLr£jäHÕBÉ•ª•’3UK%wªÖJU-–â|^›|е©•ŸªM'¶Ö•}f-û »§;‚wÛ9<Ì¥ê‹SJõç”+ N*×@œU®‚8­\q^¹âÄr-Ä™†HN-×Cœ[®ˆ8¹\qv¹*âôr]„ùZçX.…8pëR<ÅOŒ­+˜™Ê¥»Ó &|[ß$gK½iÌ1z×Xô¶^#ö¾^CöÆ^cöÎÆ{kc„½·1ÄÞÜcïn ²·7FÙûÃÔŒqjþ P[ø`Ÿa¯m̰×~3ìµÏ¬­«ÅÉoßÒ85“xXófN‘Ì›=E4Šl.á\L(Ò¹ØÈ^U1¢ÈçbEÐÅŒ"¡‹EDCŠŒ.–!}ý˜"¥o'SÄ<'4õ\œ(ê=[œ$ê™[×T½Üg…÷wB§Ãç¢ÖiŸ ¡Õás!Ô:|.„^‡Ï…Pìö†D‡Ï…è‡1£èŸëf–²ÏŸë–ÚÔÎá¡ðöð¹î>ÂßÃçB8|ø\Ÿ áòás!¡zÖ·‘ý®Þe±Ï…pûð¹6ÞFÖ™miž­6¾A§3#ï…éLIwÝG|†aôÕ+ª÷£µ‰aôg>×%<3eŸ¹ã Èvó MxÑWg£¶ k®³SÛþ¡³U´>Ÿ½Jã±>›•Æ}ýÚß­Ðg»Òx­ßûÛ ølØV^ñ³cø–Ï–m¸â³g¹E©ýCœHœ]Ûð¢0ò 3C9³í³^[Éx˜ë¢^â•ÏõyÌøÙPÜ®XïØgš9×%Ì”û¡®»§×næðposŒÞ5Ö½m¬zßXCôƱƛ¾ª{kaD½·Ÿždoî—)Ù»ûáJöö~×’½¿Ÿ½¤6àW1©øÑLj çûŒzm?:É^ûY̨×>󹮚m͆ú~Ç¡Òv‹`~ò•"™_„¥ˆæc)²Å{²áâ¹Yˆt~Vf««¥È§b%PÅL"¡ŠDDC‰Œ*–!UL%RªØJÄ<'4õTœ$ê=[œ$êù\—všâpÅŸ·)Áb4RÛťƋUJ­딚/V*µ_¬Un€Ã¾õ‚õnÁ^ù\rÄšåFˆUË­ë¶›AVn·£ïŸ+Vߺ%¹bÁržeÁr~ÆçÊ :ï®ø\þ·ßÿ¡oŒ.š}htýð‹jºcݶÛÈ7SÀܰ1%ÌM1SÄÜ43eÌM5SÈÜt3¥ÌM9SÌÜ´3åÌM=SÐÜô3%Í¢SÔ,0eÍ¢SØ,:0¥Í¢SÜ,Z0å-„!¤žUPeê™›|¦ž}×ä+õì« ÊÔ³3£ëó˜qM,——醚ØÎá¡8B‹oŒ+´8Ç8C‹wŒ;´¸Ç8D‹ŒK´8È8E‹‡Œ[´¸È8F‹Œk´8É8G‹—Œ{´¸É8H‹ŸŒ‹´8Ê8É ßcñНø˜øå˜é,¢¾2º®ì3$ßźÈµÜÉiš¹§at!5Œ.䦆х\Õ0º»FrYÃèBnk]Èu £ ¹¯UDJ®.䯆I‚\Ù0Q;& riÄ!Flt½Ã s\ÃèBŽëw¦ÅWcF9®'F×Ì@zÞÊÁÄí–\úfå+ÇS¸ñ Ÿ9Öùͱ†ÈwŽ5Fþs`ùÐUNÕÔbùÒ1äO)v£È¯ #ß:0Žüëø ûƒ}†yÑUbUÉ‹~3Ì‹>1º.a’P÷áêÝÌá¡÷8´€œ÷8Ô€ø8ô€œø8ù84œù8Tú8t±ò²²e Ç>m ç>u¬¼¬¬C,õ¡•ÏçéÐÈÊ c:T’ÆS÷Ÿa.}Â@.ýõ‰¹ôgF×¥Ú„#;%)q»Ã´hçð(ã®2ºÆµÃšëðTKetåqW]cËZÎJã¹2ºòx¬Œ®<î+£+ÏÕ^]eî*£+—úpÙ‚‹">|–ÆsetåñT^[ðTÛµ÷˜I?ÑP]ù;úwF××cfP/ñÊ躄™rübÇñ;kçðˆ9VK¬5Pˬ5RKÍ5tËÍ5vKN ¸e'FÜÒCnù‰1KÄ %bÔ’1lÉ·$ÁÏ€% ~F¬ bÆ->1Ó¶øOaæÄèºÎ‘t” Þ¶ÏXâ^ní{½µ9Ökw¬ÖöXK¬ý±ÖXd-²vÈZem‘µÌÚ#kµIÖBk—¬•Ö6YK­}²ÖZe-¶}Z›¬%²6µZâSµéÄèºÞkïS®PËvÃã)íæŒùÉ)msÚ$ 'µ‰@Îj“œ6â±ÀymRÛÄ g¶ÉANm„œÛ& 9¹M™­2YÈémÂ#6ºN9p$e·¦ÅSøÄ躴ϔÐX¤ù8òm}“.ö¦1Çè]½ìm½Fì}½†ì½ÆìöÖÆ{ïÈXConŒ±w7ÙÛ£ìýaj¸´}¤-|°Ï°×6fØk?ŒöÚgFץƩÜÍÙæÒlßqC½ÄÃ"˜6s‰dÚì%¢©HdS±§b"‘NÅF"žŠ‘D>+‰€*f Uì$"ªJdT±”©b*‘RÅV"æ9¡‘¨§â$QïÙâ$QïÌèú …ÆÞ¦‹ÑHmk”/V)µ^¬Sj¾X©Ô~±V¹bµr Äzå&ˆËmk–!V-·B¬ÛnY¹ÝŽ>¾¯X}ë–|äˆË=x–Ë=ø£+3èéÇF×Ð]%ã-?&ynsýàKÞím7¾Ø‚”´~ëЫ!C­ßz´ÃHXë·Ý2ò×òpg[Ÿ¿xD¯ì¶>qŽ™¹o{Z‡DŠð½¥#ÏÃ!qi§è·H…KI¿­(›H”ë7E–÷€óPñsóPÞ§˜9òe»’ìòr¥³ÏWñª0sþ¾—ÒÎC|ÊÃï«ó“³_‚HÄÀåy{eg}Cy+aDSvYj¦î`øbŸQ‘×͘‡E}Cpc¿“x Ö±Ot¨”@„>æa‘ö ™ß5Ab`y£<äRAKAy§e}SØd–BK~ee䰛ʰ`1–ù=Rúr™‡EÒDf~ ¤°7dæá¼¼ÄF—ëxy@¦w~dùA¶§°Qt´¸ˆ ÷Þ%&ç˯Q€†¢Ä¸<¥áT¯aE‰Ñê%cÄ…o RYf‰3¾ {×Ý?ZStŸ»WvÓ.ZÞAË ™áެšfêŽ<,¥œD.Ç„lAóp®Ï$0 §%(b~'§"éWÞç —8Shò™†óÔt( ‚‰ëP’Lk° ‘Þ¡Äç˜CÞ!L˜…b:=”W †÷Ø('õÍE‡’‰ð(íWqøW¶Òg±!±w×úþuêTSwhb×w§0íë®ÇFð»lz‹¤,Yú‰ùTIYÐuÑK&e¹³ÀßÖYï ¨¬“¡’äû¿€Ù:r×Á˜Ì]{bÎvû†a†÷ÚWÿ²×Yæï±±Î¼+ld“ kÿÒ>úü¾±—Œ+H»9Mï®}SwhC^vÞ‘Âv½l¼²„Í|YyC [ý²ò‚ AîÇ(Ë¢ëQ¥ˆ,³ng—“›Õ5 PîNç(OiÈÓì¥x-£»—Ò¶ŒzÐÿÐ@»eq‘ Œ¢¹ô(g5M´kJþA–‡°Áš’~ÂW6Ñç±±”C/SIåìïà¢ÍÔ"r¹­êMóòQ!ýÒQ÷\€ŽÚ{!yH ¯èèE>ûy—°Ÿ©i.½‰k…Öæá°›ôæ!UJœ‡Åsa.ÃüÎ ‚aÞ(ýæ¢ù»i edç/¯Ôý'¸hñôylàEHpÑiºãàe=uÀ˜Øò¡i/Íc,Ji-cÉJã ºë{§hZ ´´† ^ í°a†fÙ D+mˆ¢Ñ6€Ù† ÞlÒO÷ ô°ÆzØG±¶ÂFkû\%Kù3[?w Ú8¨iG¦®¤ýšª“vsjRÚë©X©PÏR Ú¥*B-L5†J™*u4Õ'ªlª^ÔàTۨЩòQ¿S]¤ºwJ8(~©¨Püz²¨PüZº×öÎ…ªRã–'›¹;$9‹ÊQÑ£\-H1[$‘R·($…pLÊ䢟ÑEN)±‹ºR€±¥¯JÒU•ZQð¡ªtâ5]î®·b6 w¼ÓÎàa¦(=žLÒr=™¦Õ|2Q‹ýdªöÈdméÚI ¶Ñ@¦l‚LÚ6™¶] 2q›dêö@Èäm‘!2ŸN™¯-2ßï<†g˜ï™uau½n¹6Mw¸ í êF«.Ýjµ¥›B·+ˆn8VÝr Ýt Ýv Ýx l¤—,¢›„¢ÛC „S-ð'€jÂ{ ºë@ ºë‡ƒîúÔ—ºB}ËKåët[f|;…‡%/oã”ļÍS2s ¤æ2AÉÍe„’œË %;—!Jz.S”ü\Æ( ºÌQ2t¤¤è2IÉÑe”’¤Ë,%ˈ %<—%JxÏ–%JxgnÕ&S®wÄŸ·©¾f2ÔÕÍ©»›KR—7פno.J]ß\•º¿¹,}s]úæÂôÌ•é;˜KÓ—0צoa.N_Ã\¾‡¹|㋼„ }s_úÏr_ú?ãae²¾ž™XxÌX³]¬~Q-â5ýòúìºÝq¶Y`q¸EÆÇ&‘%§*‘5‡.‘EVÉÑ#›Î{úDgÙjªŸN|VYh}ec1K­:Mºâœ¦›"‹-΢"«-Žª"Ë-N²"ëÍò³ÏšÃ~޳ê$(²Ï¾kæ™}öu‡º}væc}3mÈâ-[Z;‡‡B ã6B ã”6B ã7BãŒ7Bã8Bã„øÈÛO:@NbæóåeŒãçm´ÉPGK” }´„ÉPHKœ ´ÊPI‹Ì¯03@ÓÉr„,~'~9fFK¥¯œ¬+û dæuØï’™Û9<”~j!_é¨ú•ž*#@éª2 ”¾*#Aé¬2”Þ*#Bé®2*”þ*#Cé°2:”+#Dé²2J”>+#Eé´2Z”©(+ëf˜æ*cBi®­1ñå˜ašë‰—u3H;œ uºåŒ`;‡‡S–cŽ‘Âk€”æX#¤8Ç"UÃkÌhc€)ÑÆS¤!¦LcL¡6™RmŒ2ÅÚfʵ1Žìø %û£}†©ÑÆ S£Æ S£OܬK|¦CÒøDVsWÒxì3Hs½iï±×# >jÒâ£V M>j Òæ£Ö !!jQÇË·ªUH³Z†´û¨uïßÊÎÚwÔ*ÙY;¤pÛY;/SÉÎÚ7Tšs>ƒtú¨MH§¸61þÌϺ´Ïìûù|ãú鰙㌇ÚÐÊ`¨ ­4žkCk‡´gC+?,SZi¼Ö†ÖiІVOmhíèúmhíå1ÿ0´vð-Z;.aØÐJãµ6´*†Ö>ã(Z×äÿÖ-Z~¦6´ö/ð0μ;Z—ø (/·æ¦;Úkçðð» æ¸Mæ¹æºÍæ» 'ܦ#nã‰!·ùĘebÐ21j¶Ì@Œ[†àgÀ2?#R‚Î1ã¶ž˜iÛú§0sâi]ã3Yj¾1í°ÃÃ{¹Õ6îõVãX |_е¦XK|cеÆW¦X‹|gеJj¢j™ÔFÕ:©‘ª…R+U+¥fª–JíT­•ªZ,Áù¼6ùökS«>U›N\­+˜ÙÊ+ñçm|Fг8£D}rJ‹þäœ6ÈIm³ÚT §µé@ÎkS‚œØ¦9³M rj›äÜ6EÈÉmš³ÛT!§·é"ŒÈÖ:åÀ6)È¿3)âÀg¾Ö¥ÚTôÎ^Å]}“-ö¦Õ—ÿ¡ZƒÒÛÆ¡÷5DokŒÞ90€Þ:0‚Þ;0„Þ<06кÑÛFÑû†¡ Æ©ø3@máƒ}½v`½öÓ˜A¯}êl]ÚhÊß¼#,þŽD©v‹`ÞÌ)’y³§ˆæb@‘ÍÅ‚"œ‹ E:Šx.Fù\¬(º˜Q$t±£ˆèbH‘ÑÅ’"¤ïS¤ôÕdŠ˜ç„F¢ž‹E½g‹“D½3o몌íÓüŽ×ÛI<¬¶ûb=Õxß»§ZïkùTó}kŸj¿/õÓ ðºñ$Ü„x1nC<(7"ÞãŠçàfÄkp;â1¸!~ë qK^ƒîß  {ð, –{ðSîÖŽ÷w^º[¯âæ?v·~øEÍ-ãÜÓwxg¯¿#ª `îÒ˜æN˜ bî”™0æNš dî´™PæNœ fîÔ™pæN~ÔK)UÚ\¹[LP³RÀ„5+ £^KÙ•¸6B ¢Á7+Lx³ÄijúJ:ÏÜÙ3ñì»Îž‰g_w-‰ggîÖ̤}ýͯ2ÞqÆ£ÃCQ„VÜUhEŽQ†VìuhEQˆVü•hEQŠV µhE‘QŒVÕo/!Ê1žfBÔcõr”…xØ }¹ß}B”¤Uç˜A´¢DF+~§ ~=f¬œ¾r·®`¦/×'úÒßç?ïŠãŒ§µy–1Õoš13ÕOžñAW¿ˆÆÌU?˜ÆLV¿§ÆÌV?·ÆLW¿ÆÆÌW?ÖÆLX9ÊŒ•3¢LY9'Êœ•³¢LZ9/JR”»õ3Ìpõ;lÌpmŠ/Ç 3\OÜ­+˜Ë3Bu¾eŸiæðp¶rÌ1²—c Ík„ìæXîDiØÝbö³Ý-fCÛÝbv´Ý-fKÛÝbö´Ý-fSÛÝbvµÝ-f[ÛÝBöu¸[ÈÆþ`ŸQV´Ý-fE?‹eEŸ¸[W03íp,¬&ÿyW¾x¸[Èpw ïán!>Ü-dć»… ùp·1îbª—üxÙOùá)»[ȸwkãdôØ–æ¥Úaã³s:)²ñ2˜N’¤qW¹[/0ƒLúêM¿ÕûÑÚÄLú3wëfàQà-¦õŽ­™ÃqO ¹Ï'ƬƒÖ ˆQëİubÜ:?Ö)ø‘tŽ÷õÄLÛ×?…™wë f–òXAüyÛ>c-ˆ{¹7îõäX ,رVXÐc-‰÷QkâA@žm—`ÈZeA‘µÌ‚#kIÖB –¬•4YK-x²ÖZe-–ê|^›âm@Ô¦V@|ª6¸[—ô™rú8þ¼ÏXt&g´²NÊ?8g8tÝ퀳†³NÎ8o8àÄá\€3ÛÙ §¶óAÎmg„œÜÎ 9»rz;/ˆܭSN8ðwNÅCøÌݺ‚™mÀÙ.ýyWßdw‹½i5Ç¥w­Ö PÃjJï[­!4èXãMß?U½µ1ÂÞ;^›DoQöô°õV%zûxʽ¼t m ¤vàw2©-œï3ìµãIôÚc†½ö©»u 4[9®ã?ohü)E0¿òJ‘ÌÀRDó±Ùâ YˆpñÂ,D:?@KÏïÓRäS±’¨b&‘PÅN"¢Š¡DFK‰*¦)Ul%bž‰z*Nõž-NõÎÜ­+ A¼aüy›,F#µ]¬Qj¼X¥Ôz±N©ùb¥RûÅZå8è[VOáì•»%·A¬Yn„XµÜ ±n¹bår;ÄÚ冈շnÉGîX°ÜƒgY°ÜƒŸzpE´–Ý­ø‹ÿñ—oé§ý‹ùí¿ÿùí¯Òø»êÿФI1Óìc¿ë‡_T{sIÿÀ[þw<ÿÄ 0÷m soÌ 1÷Î soÍ 2÷Þ *soÎ 3÷î :so?é©¡uxª…ÚƒÔ¬0hÍÚ¤§^v¥õíЇ¨M0ÈÍÚƒÞBBðY…XŸ¹×gðÙw½>ƒÏ¾ ± >;ó»®`¦/o0%f¼¿ƒƒ·sx(‘Ð ­Ñ1ÑЭñ1Ñ ­2QÑ"­12‘Ñ$­Q2ÑÑ&­q2Ò(#­‘2Q2tè÷˜A¢5E&,~§)~9f:k©¯ü®K˜)¹d}чRµ¸-•Ó:4£OÃïb4ªý.F§Úïb´ªý.F¯Úïb4«ý.F·Úïb´«ý.F¿F8¢aíw1:Ö^ £eí¥0zÖ^ £iíÅ0PÑ~×;Ì(ÊÕ~£\[ïâ«1£(׿ë fätÉõº-þ×~#–=ÇŒ`ö0¢ÙkÄg¯!#ž½ÆŒ€6 US‹!FLcŒ 6QmŒ2ÂÚfĵ1Žìø "ûƒ}F‘Ñ‘U…Èè‡1ÃÈè¿ë fðÈ2\¯©»ávz;‡‡¢Üãì¢Þãl¢àãì¢âãl¢äãì¢æãl¢èãìÅÊ›Ê:›(û8»¨û8Û±ò¦²Î~¤…ªŸðÍã¡ò»Æ•·Åt¶$§ÚïzDÓÇY DÓ?\›Mæw] .¹^7`¦žÃ£Œ»ÊïSû¶T~W•ß•Ç]åw©ý[ë3ZiÒ>ØgÐkfÐk?ôÚ§~×%ÐÜíwµ“xXÓf.‘L›½D4‰l*áTL$Ò©ØHÄS1’ȧb%PÅL"¡ŠDDC‰Œ*–!UL%RªØJÄ<'4õTœ$ê=[œ$êù]W@s»ßÕNâaµ]¬Qj¼X¥Ôz±N©ùb¥RûÅZåˆÕÊ-ë•› V,·A¬Yn„XµÜ ±n¹bår;ÄÚ冈շnÉGîX°ÜƒgY°ÜƒŸñ»Æ¹\£<÷»þÏ?ýþ÷|˜fß‹à·4Ü™—{ô —ùJ?£z2R®Wf žîçÏe@÷+óçV—¯g—/F”]>:0½9$°ß›4sü¾U\~Ù|ޏ~z—X ,~Y \~!à…©õyd”²×—«ÌÃzÃ>ÖLÜ¡ìÅ¡ƒÆÆdÆü<øþæÜÆìƒ©ŽùqðáÍ™ù‚{ù_qükP` ÞãF‚"…`˜ ÒÏ–yþ‡˜D Þ9•à d0ÅrXPÿ˜q™†E×dæ°‚Â1sXQºÞ#£P³ü/ñ‘¸uÿ 2p,n^ZWŸFF_nNôå]¹~ºAl&îPdë@’Á@×aÃê2îuØðùã;·ùZ<‡¹VdÕ ’MÃ©Š™Íwæ«Ú4,V#jÇŽu¶c#ƒñ¶#ù4ÃoÇ6_„;¸$ Î;˜(„Bn¯ÁÔÙôƒÇ©³cW_Š †ÎûKƒêÓȈ{Xù#xà ¾fâ={ZíIGH´—Ò^0„‡x9;}oW…S Œ®Pl-1öZ c(¶ ÈÈl”Ú‚/ã¶n†qŸî̪2˜Uý 2U~€W6Ôç‘ÁØ8QwÜí¬'îP<¼¶b†Çk£f´¼¶qÏk“g,½JCëU ø òÁÀ{—,¿Fé Ĩ0¥áT•­É*jéÇÉüT%oûQAp.ÿ”g0á^Õ„ ÷Vܧ_î•ÙôidÄ*øM¿þôz=qGæ.ú–óA¶ w<Û'ê—†Yô1Ì“[ÑÆôçÆE¤’ç {¿zQÌÒÝèꀑÙìGTw@nŽˆpžÓ1h23PD¢¼&(¼GNë‹Cxæ÷W–Ò§‘qÿý©zâOëÊÛS˜ônÕ##åWÉzÄ ÖÍz¢¤,g7铲ØÝäïÍPÈ·-”|€`ù0@†7Ö Á®3"@÷€,èÍ>û†Q‚v¾,ÑyÃÀUŠsTt#oJù­¹þITd™ñ…gôiPÜAªš¶ƒ›p¾Ÿ²{‡Îo:ÎÞ¾÷™£ÊÞž_S¼ñgÍvtUØ']‰ÊŸê}Ô…ì\Oö‘öP)6Yž]‰öA§×óÇïu¶=×°½×óå_èh •ê·Kê-¥qÛQ N Gúxû)o*ûð.ÃþkËFúé^Ù@Ÿ†ÁýP5iYÚ¶Óÿ)nÛéþ~—Ï!L&i4Of†iDo.ÓÆ4ê‚Sn«ôúL8óÕ³ÞlÔçq UÍ&Ïn»‘Ù€ä¦QqÀ€ó¨Ç(ýôiÉöh:¶…ªÚkf™¾•OFî¶ÔRý¼2e/œOÃàv[§ž´ƒmœ'´ÐOvi½¥;ô"•ÖÑ ¸óû¦h:µðèH ´« zY ®€†.X D‹,€¢xÑ\Ÿîh=tžÂgÀ 5k>ƒÆ©¹áXd=k…í¯P´÷B"Ò¾ ýH{6Ä%íçPž´×C–R€f¥AKõj—j ¤0ÕèdªIÑT¯ °©–A~;¥§T M=X M%<¿ô_>ƒÛÍ—zÖJÁ"\ЉEÆ "‹¨Qa‹£þ,ŽGuZ Úµø!•m±GêÞâ–TÅÅ<©™‹—RQk¥Þ.N+5žŒ·ÑêÏ¥lQFJÙRFHÙûþ3aP…rç‡ _ú+ÿý/Ó—¼ýÅþý÷g& ÒÉ>tY~ð%5k­ó; Vž—zå}©GT˜ZHå…©ÃTž˜På©?U™ÚWå•©»UžYï<3ä©7VšZg奩³Vžšoå­©/W›ÚvåµIQ~Y€Uùej{•_Öö½5F¿.¾ìÄ{ù<^ñÓ•‹hùáÅÛò(RPj“"%F)’ppJ%žu”¥HC)]Š<”¦HDédŠL”Œ¦HE©lŠ\”§HFitŠl”„§HG)|Š|”¨HH «/ðˆD hŠHl´/ÆËd½ð•#óy¼ ±7Íÿ¼-RSúªRKGT"ÕT²¶RO¥z+U¢¸RS¥™+UU’ºRW¥¸+•U‚¼R[¥×+ÕUr¾R_¥ö+Vf€Rcå(UVV‚ð!‹æ^˜Â*)^)¬­ÿÅxaë‰Oóy¼à±á¡/FÞW\Ûù;œ‹ìù¥Œîùg®²×‡¹Ë^?æ2{}™Ûìõg®³ñÁÜgã‡¹ÐÆs£?ŠÆ's§_æRßÌ­6þ™kýÁþœgã…9Ïâ…1Ï'îͼ”$ù—\Ç;øK3‡“×½3™Ýû;“Û½ÿ3ÙÝõÉï®L†w}aÀë“å]Ÿ˜<ïúÅdz×7&×»þ1ÙÞõ±5áü÷À¯\_{;ÿ€¿0IÞõˆIòÖ#ÉŸx:ð‚<ð)?è:öw„´4ó—g§Ü ~Ø÷8]"þÈClæ—=ì_óÏüjœµ~ꫵ¾j»µþêÊ…5íÂzzáK-¿ð'E@ø”`@üJN ¼¥5ý"øá¶sŽuòÂJÛÊ?‚•ÿç*T–Bxçᨙ½C;·d3nìÒÔ¸ïKpcYǪ!©ŽEE:kŽD>–$)€¬X’YФ²ÞIXd9”êÈj)I’Åt7—*µVði鑸ÇÊÓªžWè*8Ö îà}›È¬.]d–Wœl„q79ÎE"+,ŽMâ×¾Êë+cIYdq&ïÄúÈ&³Ì|¢“Yg>ðÉ,4ŸeVš„e‡Õçæó”Ìkmf‡}Ù©)F‡8KW³if+gôÇ;Ð53x8ÒÏ™ùçsÎŒô1hFú”4#}ˆš‘ƒ>cÍHBÁ¦´ïÚŒ4ônFJT$¢¤CE&JZT¤¢¤GE.JšT$£¤ßWˆAD¡Ïf3¢°•ò¾1£ÌWÞÒÄdƒ ³Ä|Èj¸ÃŒlgðpr¨Äu%‹J|Wò¨Äy%“J¼Wr©Ä}%›JüWò©Ì%£ÊÛ<Êx¨-¦Ü«ÕÓ¾‚’ÉbÚaYÛbÊ/²ÔS¯µÅ´ÃÒ¶Å”Ÿ7¬-¦Gfl1íx ßÓže‹içÅ'YLi¼ÖS9l1íx™Jy˜ü/NµÅ´ãyþG™o~p;÷˜. f)/`n¥6ÍÛ o´3xÄ «ïå ¸/æ ¹oæ º¯æ »ï&Ü—!îÛ‰ ÷õD˜û~"кjݶ¬€[tþ-Iàã!µç-îቖ¶‡-'.Ó°¬]œ(_îx³™¿Cû·µ4lïVÚ°ûû®Šƒ/¡vø¶J‹¯¡òø¾ “ÔAÖ-i‡,kRYõ¤;²(J•dÍ”fÉ’*E“WbñiòÍ#À²•Ÿ)?'>Óx/jñzǪföB ñä‹’éI'%â“mJâ'•@®*{€TVæ™®¬a§‚'Ë– –iA–-Kƒ$\†9ºìâAFÓ)£•—@BÛz ÏðÙ§é<¶Bñ^ §û YMì0cnK3_úS¯ ÚW¯º[¯)š_¯8zc㭳тÎÚXw4ÒЗ‡hÛRtõÆ0š~#šÀ»ZdÃò³ð@ƒ|æ5]ÀÎ_âTÝÞß'©xû€få­’–7n(^ÞÖ!ˆyÓ‡^æ’9Íj›Ë Ä8hu.Eò\¨ ô¹ŒAt‘ƒNèÛ¶}*ã9ý èæêÍíÑêBÉíÌmº‚¥à£œfØo¹ú^Oß!ÜWÇ!’û^94t_:§Äî+é”à}c½/´SÂûîøã:<,€¸-‹ .Ó£µ»ö°â*>,ˆ¸©O‹Bù ã5V éû2<%ýG©*%ýŸºÝ´uåöÃ;é<÷½åÄ´®-§}Mý‹Ž¹¶ ¥ÂÝÒF)àJm”°Ô¨* K¬´Ôè*`K°¸Ô(+ K´¼Ôh+૯¾æÊrR@XïT8<ÕªF_c@&¡@e`&©F^ÕÝlz©ñV WÛx3ÐëËnh3ÏëÄrº‚˜"ÕŒ8¸wK˜@3ƒ‡sö$†)‡Ob™rú$¦)ÇOb›rþ$Æ)Pbr%æ)GPbŸr%*‡ÐO 1§Ð/1Ç0$Bãï¡uösFÌI”ü1Ì ”¸§ÜÀVÜûrÄXÓ|e9]@ B‘¦Ò}¯wòlgðpœ§ßábܧŸéb¨_ñâ#¥~ä‹q¢~Œq£~"Œq¤~AŒq¥~`Œq¦~Œq§2$‡*ÃBq©24§*ÃCq«2D„YN/ÃxR?-ÆxÒÖ@øjÄ0ôÄrº€˜¡Ü5ÀcAëzǃ Í  ކà+€×c…ð*e¬`Wgˉ¡Æ¶œzlˉ¡È¶œšlˉ¡Ê¶œ(>Úrb(³-'†6Ûrb¨³-'†>Ÿï1 A¶åÄäG£ äËébÆòJÀRØÌÖßðºw;ƒ‡£Ém91ºÜ–£Ím91úÜ–£Ñm91:Ý–Ÿù§éxG×oÓáÉJ[NŒn·åÄhw¼`ô»f0Þ7–?ç1ŠZGêð(ý£UIIë'–ÓÄLå>6•á[Tfóü‚‡“rPDýZÝ6‚’épÓÛ‡ŸÒx¬G¥q_¿X·Áæöáª4^ë7ë6¥ñᬠïÑûðÖžåÃ]oeéðW÷õÃu Æðᱠϳ !¯3àif¶ÚðBþ£ÌwÈO›Ÿ[NƒC3ù°yÖ> endobj 4 0 obj << /ProcSet [/PDF /Text] /Font <> /ExtGState << >> /ColorSpace << /sRGB 5 0 R >> >> endobj 5 0 obj [/ICCBased 6 0 R] endobj 6 0 obj << /Alternate /DeviceRGB /N 3 /Length 2596 /Filter /FlateDecode >> stream xœ–wTSهϽ7½P’Š”ÐkhRH ½H‘.*1 JÀ"6DTpDQ‘¦2(à€£C‘±"Š…Q±ëDÔqp–Id­ß¼yïÍ›ß÷~kŸ½ÏÝgï}ÖºüƒÂLX € ¡Xáçň‹g` ðlàp³³BøF™|ØŒl™ø½º ùû*Ó?ŒÁÿŸ”¹Y"1P˜ŒçòøÙ\É8=Wœ%·Oɘ¶4MÎ0JÎ"Y‚2V“sò,[|ö™e9ó2„<ËsÎâeðäÜ'ã9¾Œ‘`çø¹2¾&cƒtI†@Æoä±|N6(’Ü.æsSdl-c’(2‚-ãyàHÉ_ðÒ/XÌÏËÅÎÌZ.$§ˆ&\S†“‹áÏÏMç‹ÅÌ07#â1Ø™YárfÏüYym²";Ø8980m-m¾(Ô]ü›’÷v–^„îDøÃöW~™ °¦eµÙú‡mi]ëP»ý‡Í`/в¾u}qº|^RÄâ,g+«ÜÜ\KŸk)/èïúŸC_|ÏR¾Ýïåaxó“8’t1C^7nfz¦DÄÈÎâpù 柇øþuü$¾ˆ/”ED˦L L–µ[Ȉ™B†@øŸšøÃþ¤Ù¹–‰ÚøЖX¥!@~(* {d+Ðï} ÆGù͋љ˜ûÏ‚þ}W¸LþÈ$ŽcGD2¸QÎìšüZ4 E@ê@èÀ¶À¸àA(ˆq`1à‚D €µ ”‚­`'¨u 4ƒ6ptcà48.Ë`ÜR0ž€)ð Ì@„…ÈR‡t CȲ…XäCP”%CBH@ë R¨ª†ê¡fè[è(tº C· Qhúz#0 ¦ÁZ°l³`O8Ž„ÁÉð28.‚·À•p|î„O×àX ?§€:¢‹0ÂFB‘x$ !«¤i@Ú¤¹ŠH‘§È[EE1PL” Ê…⢖¡V¡6£ªQP¨>ÔUÔ(j õMFk¢ÍÑÎèt,:‹.FW ›Ðè³èô8úƒ¡cŒ1ŽL&³³³ÓŽ9…ÆŒa¦±X¬:ÖëŠ År°bl1¶ {{{;Ž}ƒ#âtp¶8_\¡8áú"ãEy‹.,ÖXœ¾øøÅ%œ%Gщ1‰-‰ï9¡œÎôÒ€¥µK§¸lî.îžoo’ïÊ/çO$¹&•'=JvMÞž<™âžR‘òTÀT ž§ú§Ö¥¾N MÛŸö)=&½=—‘˜qTH¦ û2µ3ó2‡³Ì³Š³¤Ëœ—í\6% 5eCÙ‹²»Å4ÙÏÔ€ÄD²^2šã–S“ó&7:÷Hžrž0o`¹ÙòMË'ò}ó¿^ZÁ]Ñ[ [°¶`t¥çÊúUЪ¥«zWë¯.Z=¾Æo͵„µik(´.,/|¹.f]O‘VÑš¢±õ~ë[‹ŠEÅ76¸l¨ÛˆÚ(Ø8¸iMKx%K­K+Jßoæn¾ø•ÍW•_}Ú’´e°Ì¡lÏVÌVáÖëÛÜ·(W.Ï/Û²½scGÉŽ—;—ì¼PaWQ·‹°K²KZ\Ù]ePµµê}uJõHWM{­fí¦Ú×»y»¯ìñØÓV§UWZ÷n¯`ïÍz¿úΣ†Š}˜}9û6F7öÍúº¹I£©´éÃ~á~éˆ}ÍŽÍÍ-š-e­p«¤uò`ÂÁËßxÓÝÆl«o§·—‡$‡›øíõÃA‡{°Ž´}gø]mµ£¤ê\Þ9Õ•Ò%íŽë>x´·Ç¥§ã{Ëï÷Ó=Vs\åx٠‰¢ŸN柜>•uêééäÓc½Kz=s­/¼oðlÐÙóç|Ïé÷ì?yÞõü± ÎŽ^d]ìºäp©sÀ~ ãû:;‡‡º/;]îž7|âŠû•ÓW½¯ž»píÒÈü‘áëQ×oÞH¸!½É»ùèVú­ç·snÏÜYs}·äžÒ½Šûš÷~4ý±]ê =>ê=:ð`Áƒ;cܱ'?eÿô~¼è!ùaÅ„ÎDó#ÛGÇ&}'/?^øxüIÖ“™§Å?+ÿ\ûÌäÙw¿xü20;5þ\ôüÓ¯›_¨¿ØÿÒîeïtØôýW¯f^—¼Qsà-ëmÿ»˜w3¹ï±ï+?˜~èùôñî§ŒOŸ~÷„óûendstream endobj 9 0 obj << /Type /Encoding /BaseEncoding /WinAnsiEncoding /Differences [ 45/minus 96/quoteleft 144/dotlessi /grave /acute /circumflex /tilde /macron /breve /dotaccent /dieresis /.notdef /ring /cedilla /.notdef /hungarumlaut /ogonek /caron /space] >> endobj 10 0 obj << /Type /Font /Subtype /Type1 /Name /F2 /BaseFont /Helvetica /Encoding 9 0 R >> endobj 11 0 obj << /Type /Font /Subtype /Type1 /Name /F3 /BaseFont /Helvetica-Bold /Encoding 9 0 R >> endobj xref 0 12 0000000000 65535 f 0000000021 00000 n 0000000163 00000 n 0000030846 00000 n 0000030929 00000 n 0000031052 00000 n 0000031085 00000 n 0000000212 00000 n 0000000292 00000 n 0000033780 00000 n 0000034037 00000 n 0000034134 00000 n trailer << /Size 12 /Info 1 0 R /Root 2 0 R >> startxref 34236 %%EOF timeSeries/vignettes/colorTableFig.pdf0000644000176200001440000004400115104730074017602 0ustar liggesusers%PDF-1.4 %âãÏÓ\r 1 0 obj << /CreationDate (D:20101228123754) /ModDate (D:20101228123754) /Title (R Graphics Output) /Producer (R 2.12.1) /Creator (R) >> endobj 2 0 obj << /Type /Catalog /Pages 3 0 R >> endobj 5 0 obj << /Type /Page /Parent 3 0 R /Contents 6 0 R /Resources 4 0 R >> endobj 6 0 obj << /Length 7 0 R >> stream 1 J 1 j q Q q BT 0.000 0.000 0.000 rg /F3 1 Tf 14.00 0.00 -0.00 14.00 160.63 397.45 Tm [(T) 80 (ab) 10 (le of Color Codes)] TJ ET Q q 59.04 73.44 342.72 299.52 re W n BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 82.49 331.40 Tm (0) Tj ET 66.33 301.00 m 77.13 301.00 l 77.13 311.80 l 66.33 311.80 l h f BT /F2 1 Tf 8.00 0.00 -0.00 8.00 82.49 303.59 Tm (1) Tj ET 1.000 0.000 0.000 rg 66.33 273.27 m 77.13 273.27 l 77.13 284.07 l 66.33 284.07 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 82.49 275.85 Tm (2) Tj ET 0.000 0.804 0.000 rg 66.33 245.53 m 77.13 245.53 l 77.13 256.33 l 66.33 256.33 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 82.49 248.20 Tm (3) Tj ET 0.000 0.000 1.000 rg 66.33 217.80 m 77.13 217.80 l 77.13 228.60 l 66.33 228.60 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 82.49 220.39 Tm (4) Tj ET 0.000 1.000 1.000 rg 66.33 190.07 m 77.13 190.07 l 77.13 200.87 l 66.33 200.87 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 82.49 192.79 Tm (5) Tj ET 1.000 0.000 1.000 rg 66.33 162.33 m 77.13 162.33 l 77.13 173.13 l 66.33 173.13 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 82.49 165.00 Tm (6) Tj ET 1.000 1.000 0.000 rg 66.33 134.60 m 77.13 134.60 l 77.13 145.40 l 66.33 145.40 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 82.49 137.25 Tm (7) Tj ET 0.745 0.745 0.745 rg 66.33 106.87 m 77.13 106.87 l 77.13 117.67 l 66.33 117.67 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 82.49 109.53 Tm (8) Tj ET 66.33 79.13 m 77.13 79.13 l 77.13 89.93 l 66.33 89.93 l h f BT /F2 1 Tf 8.00 0.00 -0.00 8.00 82.49 81.80 Tm (9) Tj ET 1.000 0.000 0.000 rg 95.18 328.73 m 105.98 328.73 l 105.98 339.53 l 95.18 339.53 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 109.12 331.40 Tm (10) Tj ET 0.000 0.804 0.000 rg 95.18 301.00 m 105.98 301.00 l 105.98 311.80 l 95.18 311.80 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 109.12 303.59 Tm (11) Tj ET 0.000 0.000 1.000 rg 95.18 273.27 m 105.98 273.27 l 105.98 284.07 l 95.18 284.07 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 109.12 275.85 Tm (12) Tj ET 0.000 1.000 1.000 rg 95.18 245.53 m 105.98 245.53 l 105.98 256.33 l 95.18 256.33 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 109.12 248.20 Tm (13) Tj ET 1.000 0.000 1.000 rg 95.18 217.80 m 105.98 217.80 l 105.98 228.60 l 95.18 228.60 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 109.12 220.39 Tm (14) Tj ET 1.000 1.000 0.000 rg 95.18 190.07 m 105.98 190.07 l 105.98 200.87 l 95.18 200.87 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 109.12 192.73 Tm (15) Tj ET 0.745 0.745 0.745 rg 95.18 162.33 m 105.98 162.33 l 105.98 173.13 l 95.18 173.13 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 109.12 165.00 Tm (16) Tj ET 95.18 134.60 m 105.98 134.60 l 105.98 145.40 l 95.18 145.40 l h f BT /F2 1 Tf 8.00 0.00 -0.00 8.00 109.12 137.19 Tm (17) Tj ET 1.000 0.000 0.000 rg 95.18 106.87 m 105.98 106.87 l 105.98 117.67 l 95.18 117.67 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 109.12 109.53 Tm (18) Tj ET 0.000 0.804 0.000 rg 95.18 79.13 m 105.98 79.13 l 105.98 89.93 l 95.18 89.93 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 109.12 81.80 Tm (19) Tj ET 0.000 0.000 1.000 rg 124.03 328.73 m 134.83 328.73 l 134.83 339.53 l 124.03 339.53 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 137.96 331.40 Tm (20) Tj ET 0.000 1.000 1.000 rg 124.03 301.00 m 134.83 301.00 l 134.83 311.80 l 124.03 311.80 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 137.96 303.59 Tm (21) Tj ET 1.000 0.000 1.000 rg 124.03 273.27 m 134.83 273.27 l 134.83 284.07 l 124.03 284.07 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 137.96 275.85 Tm (22) Tj ET 1.000 1.000 0.000 rg 124.03 245.53 m 134.83 245.53 l 134.83 256.33 l 124.03 256.33 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 137.96 248.20 Tm (23) Tj ET 0.745 0.745 0.745 rg 124.03 217.80 m 134.83 217.80 l 134.83 228.60 l 124.03 228.60 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 137.96 220.39 Tm (24) Tj ET 124.03 190.07 m 134.83 190.07 l 134.83 200.87 l 124.03 200.87 l h f BT /F2 1 Tf 8.00 0.00 -0.00 8.00 137.96 192.73 Tm (25) Tj ET 1.000 0.000 0.000 rg 124.03 162.33 m 134.83 162.33 l 134.83 173.13 l 124.03 173.13 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 137.96 165.00 Tm (26) Tj ET 0.000 0.804 0.000 rg 124.03 134.60 m 134.83 134.60 l 134.83 145.40 l 124.03 145.40 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 137.96 137.19 Tm (27) Tj ET 0.000 0.000 1.000 rg 124.03 106.87 m 134.83 106.87 l 134.83 117.67 l 124.03 117.67 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 137.96 109.53 Tm (28) Tj ET 0.000 1.000 1.000 rg 124.03 79.13 m 134.83 79.13 l 134.83 89.93 l 124.03 89.93 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 137.96 81.80 Tm (29) Tj ET 1.000 0.000 1.000 rg 152.88 328.73 m 163.68 328.73 l 163.68 339.53 l 152.88 339.53 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 166.81 331.40 Tm (30) Tj ET 1.000 1.000 0.000 rg 152.88 301.00 m 163.68 301.00 l 163.68 311.80 l 152.88 311.80 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 166.81 303.66 Tm (31) Tj ET 0.745 0.745 0.745 rg 152.88 273.27 m 163.68 273.27 l 163.68 284.07 l 152.88 284.07 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 166.81 275.93 Tm (32) Tj ET 152.88 245.53 m 163.68 245.53 l 163.68 256.33 l 152.88 256.33 l h f BT /F2 1 Tf 8.00 0.00 -0.00 8.00 166.81 248.20 Tm (33) Tj ET 1.000 0.000 0.000 rg 152.88 217.80 m 163.68 217.80 l 163.68 228.60 l 152.88 228.60 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 166.81 220.46 Tm (34) Tj ET 0.000 0.804 0.000 rg 152.88 190.07 m 163.68 190.07 l 163.68 200.87 l 152.88 200.87 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 166.81 192.73 Tm (35) Tj ET 0.000 0.000 1.000 rg 152.88 162.33 m 163.68 162.33 l 163.68 173.13 l 152.88 173.13 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 166.81 165.00 Tm (36) Tj ET 0.000 1.000 1.000 rg 152.88 134.60 m 163.68 134.60 l 163.68 145.40 l 152.88 145.40 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 166.81 137.26 Tm (37) Tj ET 1.000 0.000 1.000 rg 152.88 106.87 m 163.68 106.87 l 163.68 117.67 l 152.88 117.67 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 166.81 109.53 Tm (38) Tj ET 1.000 1.000 0.000 rg 152.88 79.13 m 163.68 79.13 l 163.68 89.93 l 152.88 89.93 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 166.81 81.80 Tm (39) Tj ET 0.745 0.745 0.745 rg 181.73 328.73 m 192.53 328.73 l 192.53 339.53 l 181.73 339.53 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 195.66 331.40 Tm (40) Tj ET 181.73 301.00 m 192.53 301.00 l 192.53 311.80 l 181.73 311.80 l h f BT /F2 1 Tf 8.00 0.00 -0.00 8.00 195.66 303.59 Tm (41) Tj ET 1.000 0.000 0.000 rg 181.73 273.27 m 192.53 273.27 l 192.53 284.07 l 181.73 284.07 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 195.66 275.85 Tm (42) Tj ET 0.000 0.804 0.000 rg 181.73 245.53 m 192.53 245.53 l 192.53 256.33 l 181.73 256.33 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 195.66 248.20 Tm (43) Tj ET 0.000 0.000 1.000 rg 181.73 217.80 m 192.53 217.80 l 192.53 228.60 l 181.73 228.60 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 195.66 220.39 Tm (44) Tj ET 0.000 1.000 1.000 rg 181.73 190.07 m 192.53 190.07 l 192.53 200.87 l 181.73 200.87 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 195.66 192.73 Tm (45) Tj ET 1.000 0.000 1.000 rg 181.73 162.33 m 192.53 162.33 l 192.53 173.13 l 181.73 173.13 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 195.66 165.00 Tm (46) Tj ET 1.000 1.000 0.000 rg 181.73 134.60 m 192.53 134.60 l 192.53 145.40 l 181.73 145.40 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 195.66 137.19 Tm (47) Tj ET 0.745 0.745 0.745 rg 181.73 106.87 m 192.53 106.87 l 192.53 117.67 l 181.73 117.67 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 195.66 109.53 Tm (48) Tj ET 181.73 79.13 m 192.53 79.13 l 192.53 89.93 l 181.73 89.93 l h f BT /F2 1 Tf 8.00 0.00 -0.00 8.00 195.66 81.80 Tm (49) Tj ET 1.000 0.000 0.000 rg 210.58 328.73 m 221.38 328.73 l 221.38 339.53 l 210.58 339.53 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 224.51 331.40 Tm (50) Tj ET 0.000 0.804 0.000 rg 210.58 301.00 m 221.38 301.00 l 221.38 311.80 l 210.58 311.80 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 224.51 303.66 Tm (51) Tj ET 0.000 0.000 1.000 rg 210.58 273.27 m 221.38 273.27 l 221.38 284.07 l 210.58 284.07 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 224.51 275.93 Tm (52) Tj ET 0.000 1.000 1.000 rg 210.58 245.53 m 221.38 245.53 l 221.38 256.33 l 210.58 256.33 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 224.51 248.20 Tm (53) Tj ET 1.000 0.000 1.000 rg 210.58 217.80 m 221.38 217.80 l 221.38 228.60 l 210.58 228.60 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 224.51 220.46 Tm (54) Tj ET 1.000 1.000 0.000 rg 210.58 190.07 m 221.38 190.07 l 221.38 200.87 l 210.58 200.87 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 224.51 192.79 Tm (55) Tj ET 0.745 0.745 0.745 rg 210.58 162.33 m 221.38 162.33 l 221.38 173.13 l 210.58 173.13 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 224.51 165.00 Tm (56) Tj ET 210.58 134.60 m 221.38 134.60 l 221.38 145.40 l 210.58 145.40 l h f BT /F2 1 Tf 8.00 0.00 -0.00 8.00 224.51 137.32 Tm (57) Tj ET 1.000 0.000 0.000 rg 210.58 106.87 m 221.38 106.87 l 221.38 117.67 l 210.58 117.67 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 224.51 109.53 Tm (58) Tj ET 0.000 0.804 0.000 rg 210.58 79.13 m 221.38 79.13 l 221.38 89.93 l 210.58 89.93 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 224.51 81.80 Tm (59) Tj ET 0.000 0.000 1.000 rg 239.42 328.73 m 250.22 328.73 l 250.22 339.53 l 239.42 339.53 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 253.36 331.40 Tm (60) Tj ET 0.000 1.000 1.000 rg 239.42 301.00 m 250.22 301.00 l 250.22 311.80 l 239.42 311.80 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 253.36 303.66 Tm (61) Tj ET 1.000 0.000 1.000 rg 239.42 273.27 m 250.22 273.27 l 250.22 284.07 l 239.42 284.07 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 253.36 275.93 Tm (62) Tj ET 1.000 1.000 0.000 rg 239.42 245.53 m 250.22 245.53 l 250.22 256.33 l 239.42 256.33 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 253.36 248.20 Tm (63) Tj ET 0.745 0.745 0.745 rg 239.42 217.80 m 250.22 217.80 l 250.22 228.60 l 239.42 228.60 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 253.36 220.46 Tm (64) Tj ET 239.42 190.07 m 250.22 190.07 l 250.22 200.87 l 239.42 200.87 l h f BT /F2 1 Tf 8.00 0.00 -0.00 8.00 253.36 192.73 Tm (65) Tj ET 1.000 0.000 0.000 rg 239.42 162.33 m 250.22 162.33 l 250.22 173.13 l 239.42 173.13 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 253.36 165.00 Tm (66) Tj ET 0.000 0.804 0.000 rg 239.42 134.60 m 250.22 134.60 l 250.22 145.40 l 239.42 145.40 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 253.36 137.26 Tm (67) Tj ET 0.000 0.000 1.000 rg 239.42 106.87 m 250.22 106.87 l 250.22 117.67 l 239.42 117.67 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 253.36 109.53 Tm (68) Tj ET 0.000 1.000 1.000 rg 239.42 79.13 m 250.22 79.13 l 250.22 89.93 l 239.42 89.93 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 253.36 81.80 Tm (69) Tj ET 1.000 0.000 1.000 rg 268.27 328.73 m 279.07 328.73 l 279.07 339.53 l 268.27 339.53 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 282.21 331.40 Tm (70) Tj ET 1.000 1.000 0.000 rg 268.27 301.00 m 279.07 301.00 l 279.07 311.80 l 268.27 311.80 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 282.21 303.59 Tm (71) Tj ET 0.745 0.745 0.745 rg 268.27 273.27 m 279.07 273.27 l 279.07 284.07 l 268.27 284.07 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 282.21 275.85 Tm (72) Tj ET 268.27 245.53 m 279.07 245.53 l 279.07 256.33 l 268.27 256.33 l h f BT /F2 1 Tf 8.00 0.00 -0.00 8.00 282.21 248.20 Tm (73) Tj ET 1.000 0.000 0.000 rg 268.27 217.80 m 279.07 217.80 l 279.07 228.60 l 268.27 228.60 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 282.21 220.39 Tm (74) Tj ET 0.000 0.804 0.000 rg 268.27 190.07 m 279.07 190.07 l 279.07 200.87 l 268.27 200.87 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 282.21 192.79 Tm (75) Tj ET 0.000 0.000 1.000 rg 268.27 162.33 m 279.07 162.33 l 279.07 173.13 l 268.27 173.13 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 282.21 165.00 Tm (76) Tj ET 0.000 1.000 1.000 rg 268.27 134.60 m 279.07 134.60 l 279.07 145.40 l 268.27 145.40 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 282.21 137.25 Tm (77) Tj ET 1.000 0.000 1.000 rg 268.27 106.87 m 279.07 106.87 l 279.07 117.67 l 268.27 117.67 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 282.21 109.53 Tm (78) Tj ET 1.000 1.000 0.000 rg 268.27 79.13 m 279.07 79.13 l 279.07 89.93 l 268.27 89.93 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 282.21 81.80 Tm (79) Tj ET 0.745 0.745 0.745 rg 297.12 328.73 m 307.92 328.73 l 307.92 339.53 l 297.12 339.53 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 311.06 331.40 Tm (80) Tj ET 297.12 301.00 m 307.92 301.00 l 307.92 311.80 l 297.12 311.80 l h f BT /F2 1 Tf 8.00 0.00 -0.00 8.00 311.06 303.66 Tm (81) Tj ET 1.000 0.000 0.000 rg 297.12 273.27 m 307.92 273.27 l 307.92 284.07 l 297.12 284.07 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 311.06 275.93 Tm (82) Tj ET 0.000 0.804 0.000 rg 297.12 245.53 m 307.92 245.53 l 307.92 256.33 l 297.12 256.33 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 311.06 248.20 Tm (83) Tj ET 0.000 0.000 1.000 rg 297.12 217.80 m 307.92 217.80 l 307.92 228.60 l 297.12 228.60 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 311.06 220.46 Tm (84) Tj ET 0.000 1.000 1.000 rg 297.12 190.07 m 307.92 190.07 l 307.92 200.87 l 297.12 200.87 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 311.06 192.73 Tm (85) Tj ET 1.000 0.000 1.000 rg 297.12 162.33 m 307.92 162.33 l 307.92 173.13 l 297.12 173.13 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 311.06 165.00 Tm (86) Tj ET 1.000 1.000 0.000 rg 297.12 134.60 m 307.92 134.60 l 307.92 145.40 l 297.12 145.40 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 311.06 137.26 Tm (87) Tj ET 0.745 0.745 0.745 rg 297.12 106.87 m 307.92 106.87 l 307.92 117.67 l 297.12 117.67 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 311.06 109.53 Tm (88) Tj ET 297.12 79.13 m 307.92 79.13 l 307.92 89.93 l 297.12 89.93 l h f BT /F2 1 Tf 8.00 0.00 -0.00 8.00 311.06 81.80 Tm (89) Tj ET 1.000 0.000 0.000 rg 325.97 328.73 m 336.77 328.73 l 336.77 339.53 l 325.97 339.53 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 339.90 331.40 Tm (90) Tj ET 0.000 0.804 0.000 rg 325.97 301.00 m 336.77 301.00 l 336.77 311.80 l 325.97 311.80 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 339.90 303.66 Tm (91) Tj ET 0.000 0.000 1.000 rg 325.97 273.27 m 336.77 273.27 l 336.77 284.07 l 325.97 284.07 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 339.90 275.93 Tm (92) Tj ET 0.000 1.000 1.000 rg 325.97 245.53 m 336.77 245.53 l 336.77 256.33 l 325.97 256.33 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 339.90 248.20 Tm (93) Tj ET 1.000 0.000 1.000 rg 325.97 217.80 m 336.77 217.80 l 336.77 228.60 l 325.97 228.60 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 339.90 220.46 Tm (94) Tj ET 1.000 1.000 0.000 rg 325.97 190.07 m 336.77 190.07 l 336.77 200.87 l 325.97 200.87 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 339.90 192.73 Tm (95) Tj ET 0.745 0.745 0.745 rg 325.97 162.33 m 336.77 162.33 l 336.77 173.13 l 325.97 173.13 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 339.90 165.00 Tm (96) Tj ET 325.97 134.60 m 336.77 134.60 l 336.77 145.40 l 325.97 145.40 l h f BT /F2 1 Tf 8.00 0.00 -0.00 8.00 339.90 137.26 Tm (97) Tj ET 1.000 0.000 0.000 rg 325.97 106.87 m 336.77 106.87 l 336.77 117.67 l 325.97 117.67 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 339.90 109.53 Tm (98) Tj ET 0.000 0.804 0.000 rg 325.97 79.13 m 336.77 79.13 l 336.77 89.93 l 325.97 89.93 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 339.90 81.80 Tm (99) Tj ET Q endstream endobj 7 0 obj 17300 endobj 3 0 obj << /Type /Pages /Kids [ 5 0 R ] /Count 1 /MediaBox [0 0 432 432] >> endobj 4 0 obj << /ProcSet [/PDF /Text] /Font <> /ExtGState << >> >> endobj 8 0 obj << /Type /Encoding /BaseEncoding /WinAnsiEncoding /Differences [ 45/minus ] >> endobj 9 0 obj << /Type /Font /Subtype /Type1 /Name /F2 /BaseFont /Helvetica /Encoding 8 0 R >> endobj 10 0 obj << /Type /Font /Subtype /Type1 /Name /F3 /BaseFont /Helvetica-Bold /Encoding 8 0 R >> endobj xref 0 11 0000000000 65535 f 0000000021 00000 n 0000000164 00000 n 0000017667 00000 n 0000017750 00000 n 0000000213 00000 n 0000000293 00000 n 0000017646 00000 n 0000017842 00000 n 0000017936 00000 n 0000018032 00000 n trailer << /Size 11 /Info 1 0 R /Root 2 0 R >> startxref 18134 %%EOF timeSeries/vignettes/colorPalettes1Fig.pdf0000644000176200001440000012056215104730074020424 0ustar liggesusers%PDF-1.4 %âãÏÓ\r 1 0 obj << /CreationDate (D:20140430203415) /ModDate (D:20140430203415) /Title (R Graphics Output) /Producer (R 3.0.2) /Creator (R) >> endobj 2 0 obj << /Type /Catalog /Pages 3 0 R >> endobj 7 0 obj << /Type /Page /Parent 3 0 R /Contents 8 0 R /Resources 4 0 R >> endobj 8 0 obj << /Length 37256 /Filter /FlateDecode >> stream xœ¼½K-9z$¸Ï_ËÌ…îøû±­Æ´€zZ#Ð A‹’”£n¡¼TÊRAп’öøèÇÏ ô㋾[¤~ö™‘´öí·oíÛ¿¾ýûÿïÛ¿¿ßÖæ­Ÿæoýð6Ï߆ùm\¾ÍãÛ/?¿ý¯·?þðýùoÿú7oÿôçÚoMÓ¼5Õ¿þ'ýŸÿÛßýÐ|ø?ÿÝûÒÿoú/ýçÿoÍÛ?ÿоý6ý¿ý¡Í_ðö?~˜¦oCúÙëômîÞ¶<ì×4L¿Äòö‡2ÓpùÖ vK®+†mþâ5ÿ„2læ·¡i¾M¦¿ªKÖß;æ¯ÊÃuÁpœÒ°Ë?· û> ûo¿¸YÓpøÖNy8|[º2\f Çýßÿ®¬O™«~*ÿc)6wùçúüæw±D× ,ÑïÒrÿîÿ{[¾ñÿÿ”aZ÷y*óÖ6o¿ÛÞ~lzûÝ¿þðÿîKØ›î2lÔS·åaY”†²ÜiŠ–2\V Ó:Íúmló°ý6§/Nÿ©¶Ã°oËp.ÿ×æÛ’‡mÆSvk.yÉÆµLH›–dÆ0¯o–”þü´4CÛg¤–a3”aÁƘ¦(ÿ_‡o}þ¹ã„4äÿb¶ ¢í˜ÿGŽHmÁâ#l¤‰èËOnÛ<é·æ/²Ü…6c<ÏÛÒlt_ÇFûþÿlì¦nûaLÍ© ×ü±IÃ6a£°¾cÒZ†å38vÀÆ”ý2lòÏ\”•© ‡ •±É‹’‡SÆÆ°æoÊåǰÍ_\&+ —oCW†m†è¾i.Ã.ÿ—‡ôÛ2ì' ù½CùOyñÊp6Êpy„ô{usùEæ!O@úäåËçõ&l¤¹`f¸oô_ÁvŒK±±›ºí‡¡ÏŸ—˜Ø>o 1íåS‹Ò~›ÚjÉàJ Úè{Ër÷ë·~ 0ôË*ý\ €”†]0ë'a²€°…ɉö ÷pEx÷=6¨£}#ý^MØH¿×Üß‹ô 7øcìÃ×°qù¾±›º-Û66ä¾Ë¿Œ·ë¾-¥\›y_¨„·ú>í c‚4,ÛËD·bƒbé gq‰éü  .AeŽò”†CU¼Ò°©J[W0âÂ×䲨¥Hòô{ucÔ”®|Hn­)]«ÉsÆÆøë±‘÷_ÝÔmy˜K¹ˆ\æõÍë°&’À4¦ ˆiØV2ýÉëô2 ç.ÈgŽ}PÓ4ÌHÄ5OW´¶+,Ȥ7› Ä]—w殀Ñtº+«l0|ÄFÿmê‚‹¦/ÏÅÿN.š7 pøT¿36¦_‹öš}c7u›'6ýFS5ícÞâcQ&ÆjÉÒì®^Ð~툫²Üy¨ïóªÍxTúµ1TÖ± KÌúeå6R@Ø/‹19çáLLça»`˜àݧ5® øûeÄu€ü{¥¿EØÈ¿ÈÒÞŠüûÏØ7æ¯c£f휺r¶«·ë~2AÓfÞ/=J9¶ú<ì;‚~érÅV™ÈCl#åSÞ/-öv”˜·rÑ~.sÆÆúõ>åRlì¦nc[ÚØöÒ<Æ¢”Ö2–l®´ «¾wˆ¦Õ`@Kk¨ á5Ðfh– B´Ò†(mm¸á&ýxß@kl ‡½èa+l´Í¯¿.Çnî6*GÞ‘¡+y¿†êäÝš”÷z(V®г\' v¹Š@ sRæ Íõ *›«48×6(t®|Ðï\¡îˆ_.*¿n-*¿2ÖWÑöW)£v*»¹Û(9›ÊA6у\m1Û$R·)$„pLÈ䦟ÑMN!±›ºB€7±•²zN#¦K‹ubêÜʸ«Üž6µ3ce÷´©çX*1=ÛJko{Šž”âÛ¨”úmóX6@ÛÖKÇMåû´© ì+ã§MÝöX9?Keý!ö~ &ýFSåþäñúÁþy5brCzlFL"Z—!f7ƒy~š ˜a©óZnÉ¢R¬P7• Ôv3§Nµ{¬Œ ´=~›+'(wêÊfËjZeµÐ¡iÜÖîÚ\á]ÄéÆmå=@L–û*O(ۦЫ“~ÍáØ:˜ Ïìg0ÍO[ÊMìâi¢–ÊÊã®ò†Z,uT‰ü7VîP›–n¨ì¡¶ÅñW¡4îë*ÕøyªbùUQ›P]ÛBx¢J¶EƒŽ*ÚžU¶ípjå˜Ç¤ßhªœ¢ò}°Š^˜ô÷LÇfÑiÄLiú 1»ÌóÓ^`¦˜ÆCeåq[9Fm oÙL4Çš©¦q_™F-–:˜nššµ²òx®|£2u•q”ÇCʹ󸲎ò¸¯¼£<îj&߬hàˆGˆISSùGmÛúÁ@z9bz5,¤Óˆ™Óœ]…˜Ý n1Ãlz½쉽Bl™½‚쨹Ân¸‰÷ãDˆÛu"ÈÝ<æfŸ´@„Z* ‚­$áø °ÁOˆ%žCĸ'bÞ5ò÷ æÈX:˜e¾®WÚÍà¦]ÜwyKl¬VàX%,бŠX¿c•±¼Ç*dõUÊâ «˜µCV9K‹¬‚VY%-L²ŠZ·d•µ¬É*lÅø°*YdUz§ ÞS•Žì¦“ˆ¹ô|Ë~71EKòd’VìÉ4-蓉Zï'Sµ@&k·€L×f™°½2e[dÒv*È´md‰Ûç S· B&o—D‘ÿtÈ|í2ù¾³îa¾G&ÔÓ¦?àª^É6»Ñj†K·Z­@éfc…ÐíÆ ¢ŽF·@7A·B7C·D7E·†‡ZŸ¨ Ïöt×t×7#Ýõ¡5õEÈ\b?ì§p“äÛ8$±Øæ!™E€¤e’[”HrQfè…º AÒ‹2É/Ê$Á(s £ BRŒ2 É1Ê($É(³,ŸHxQ– áÝ\– áV_„ÌÒ_±Ëì¦p“®\º{pIèòÁ5¡Û…®\ºpYøÁuᆯ\¾CpiùâÚò-ÄÅåkŒñýsÍåw¾ÈCÈÀ'î Ÿàfî ŸàK6V¡ ]åcýø¿þýÔ€kÓ0Ͻúî}øû¦ë¤å®‡îs},.>1™Ç•}•ÇMe_u=ŽdÅqLÈDqZsÁQOæ\Ê9ý8ë¹à°§‚.E–‹“¢s‘yâ éŒ#š:gšÆ}e_åqSÙW].e•}•Ç]e_uY‘|wÎoÁIVí!„í›øv¼_vžkhp?ë±u3©Xå6þ:©p?‡y†F¬‘N`§ñPXy¼;¿ÒXïNã¶2°ºD°¦ÊÀÊã¦>;.û@GËû2ñqòó¸¯ ¬G˜é`‰éPyßÛR[îÃLo‰ô‘ƒu3]úp^*ýìç0ŸËµ? ø]Wj¶þ<î* KMËX]G1vÖÁï•ÁÐuƒe@ti¯ž+ «ë`›ËÀÈ㮲°ºÜ¯TV—õ¹²°ºô¿¬•…•Ç]ea#¶°`&ŸŠ®,¬<î?XX/ÇLno=¬3˜挙åºV~?‡i†2–zŽi!x FÜ.ô °|¼†=0§5Nϵ2±ò¸­L¬.Ë{5†ÚÂ¥cøEƒ3£ik § s­L¬<î*«KEj¬L¬˜É[`ebåñøÁÄz9fÒï¹»Xg033ò™Ý¦JÄ`­l¬<î*«k˜<ªi\_gí2©l¬®éliÜ×לÞ»U­Êħ¾è” ú\ßtjxõV6V"RKmc­E Ä+l¬4žš§|&ý†M}á©iaßZ›Ú—Ó|¬3˜Y “Y/´>ws¸•qWYëŒ;2²Òx¬¬測ô·®µ‘•£6²V˜ç6²Ò¸¯¬tld幫¬uÄåY+î_ØÈJã¹6²Òxª¬<®Œ¬˜É?q¨¬ôý#ëå˜I½írìdÂL[¬¬¥ˆ†iǼ3õn1ÇjµjµFjŸµ†j¯±ÆÑ~Ñž#ѾCÑÞcÑþƒ!£!Ã!/ã!?à3ò>#V€1í<0ó®¿ 3G^Ö9ÌÜfüo¦Ëö™€V^g’ʆ½>T8Ô‚¸*ÕðhºîJ¡–Äe)Ôš¸-Õðtº®K¡VYEd-³ÊÈZg’µÐ*%k¥ULÖR«œ¬µVAY‹-4Ö¦¸8…ÚôN5¼©6¹Y§0SމNkÆÌ0]°Ïìçpg´˜ONi±ŸœÓf9©ÍrV› ä´6ÈymFÛ¬ g¶™ANm³ƒœÛf9¹Írv›)äô6[„ÙY‡Øæ9ð;sâ&|ägÂL‚~^„‘Çs®ê›lh±7­æ¸ô®Õ”Þ6Ö½o¬!zãXcôÎôÖôÞ!ôæ1ôîAôöQôþahqhñ€¶ðlŸA¯˜A¯}7fÐk:Z§@SD½¥í ­¹4»IÜ$‚Åf‘,6{ˆhQ zÞ$S±€Å"]ºª.F=oG©XAŒb‘0ŠDÄ(†£XB„ŒkÇ)ãV2DÌ'„†¢^'ˆz7'Šz‡žÖ9Ðd=/þ½J Ž›ßPÛãN=Ôø¸rµ>näCÍ ûPûã>?Ü€¸î· z  ¸ Õc ^ ð[8nO ЭðKt3üPÝ¿c@7DÏìÜ’Ç )îA<÷àfL÷àk—³ËËÝk¸Zÿñó/ù~VºnÂÅí§ÆÖw¿èƒà®ë¯¿wÞÁBWƒÖ³È¨ .ãÊØÊã¡2¶ò¸¯šì<î*c+ÛÊØêSQ[+c«×û(lòóx¬Œ­<î*‘ ›ÊØêõF E†<*c+›ÊØê;>G!¨O?hÙ_DïðF˜šúü¬Œ­Ž·c_v=óccë føSÓt_†™z·2î*±­OÄg©Œ­<®Ïœ÷‰8­•˜—ÇcelåqS[}‹›_ {™ óx¨Œ­Êt¼¸ÔÎû™2™®Œ­/Velõiê¦ú¡³´økýZKyVϤµ0‚ýŠZ³êÕ9<²–Æmelõ u?ÑÖÀ0—éÑçÆ¥2¶ò¸­Œ­>m8}elõ¹é¯Œ-bÄÆÖÌäSÚ•±•Çócëå˜É tllÇÌrá>³›Ã­Œ›¥žcš ^¾Bé5âó^Ø“±•ŠV_[©¨µ±•Å¾ÚØJã¶6¶ÖI×”ÐBÜ ck„ÙIã¾6¶V¼£ec+ó¯ÊØúˆ™üvµ±•~£õƒ±õjÌäß»;6¶ÎbÆ÷ú.y›m7‡[÷µ±•çT[™ÖÆVb õ-Þ. ¥µ±ÎÆÖ‚ƒ;ñ~oû?¾í)ck™a¾ÊØZxëX‡<Ò¸~Ÿ¶[øØœ‰,¸ÿåC$iÜ4ÏøLþ†ÝK~3ìá[kS·ðÉÄÇÆÖÌ´W¿ºŸÃ •Æ]ý¤_nžë7ýæ>lµôX#ÆJã¶~Öo¡îÃ\hÖã°×‚ƒ;> –ç®~ÚoépH‡ÉÜþða³,Ô¯û¥qSV[ðt0ò38²çÃ]©¹Ÿ?[/Ç ”¯#cë f.;v?‡[̱Z`­Zd­‘Zh­¡Zl¬q´àÀ@´èÀH´ðÀP´øÀXHÀ`HÀhHÀpH ÀxHø „Dψu CÌDK̼kéoÂÌ‘±u3ë‹ö™Þ¢’Ö¶ð@iq¨¡Õ-<Î.-µ¤z}Å3€<Î.­µ*´D€#´FÔºÐ"Q C«D­ -µ4´NÔÚÐBQ‹-8Ö¦êEÀOAí´Ã›jÓ‘±õy̼àžÖ~7qÆõÁ)Côç S€†»MpÖ0ÀiÃtç Sœ8L pf›äÔ6=ȹmŠ“Û4!g·©BNoÓE‘±uÈä~gRÜÄŒ­³˜Aß”­‚ö²¾ÉÆ{ÓjŽG¼äç5˜ðŒ§×hÂíK¯!äçXãEß?T½µ1ÂÞ;Þ˜DoOP‚ Ç •èíãKôþñ¾%´xþÚA¼Ž máÉ>Ã^;^—D¯}3fØk[_͸}¾L ‰H!‚ÅÛ®ÉâéWˆhñ2l;¿{8v†-Ôè]Ù§6ôì,ÝU¿JÛò.‹E@3Š„.v] )2ºXR„t1¥HébKó ¡¡¨çâDQïæâDQïÐØúhÊaÒùŠn{?‰›Ôv³Fªñf•TëÍ:©æ›•Rí7k¥`VK·À¬—n‚Y1ݳfºfÕv+Ⱥíf•ÛíãûÇŠÕïÝ’'îY0݃›Y0݃/¾:8–ãÝ•±õoú·rýˆ»áO]­ï~ÑÃîìªs[¸èêÎVuÀe\¹ZE®©\­<ž«;§ÊÕÊã±rµò¸¯\­Ao¸°Ã/²GåjåñT)yA’ÆCóŒÏäß`©\­ò}pµ^Ž™‰¯=>vµÎ`&_£¸3õneÜT®V–!¦ÊÕÊã¾rµò¸©\­~Z±F:‰•Æcåjåq_¹ZyÜV®Vž«µrµÊÜU®VOõI² ×@|Ò,ÇÊÕÊã¡>©6áý÷f1“~£®rµòw´\­×c¦S/ñÈÕúŒ†~ ‡¾Œ‡þ€Ï@èøŒ„t„™èç™wýüM˜9rµÎc&*ÔUûLh@3¯SIh›ù6¡„8Ô‚êfžh—‡ZBjM3O´K(D­ !µ,„FÔº"Q C¨D­ !µ4„NÔÚBQ‹Cm>ªM!¢6½oªMG®Öy̾ŠÏ„Ø ÎŠ>8e(þàœáÐm·cÎŽ8mz5@œ–#NŽ8s8àÔáx€s‡#²ð-9&tVí¨€Ó‡ãBŒØÕ:âÀUþUáÀ8ð‘«u3Ñq_Õ7…«…Þ´šãïj ÐÛÆ­½R¤Ø[C{ö£w  ·Œ ÷®’Ó ±cè݃èí£èýÃÐŒqjþ P[x¶Ï ×Ì ×¾3èµ]­ó  iï*Æ E0oæɼÙSDs1 ÈæbAÎÅ„"‹ E<#Š|.V]Ì(ºØQDt1¤ÈèbIÒÅ”"¥‹-EÌ'„†¢ž‹E½›‹E½CWë  ¹î¬ú~7©ífTãÍ*ÙæšuRÍ7+¥ÚoÖJ7À¬–nY/ݳbº fÍt#̪íVuÛÍ +·Û1Æ÷¯«ß»%Oܳ`º7³`º_rµò'f¨Ã´~ü§íCH[¶¾Ž­ï|Iõ—õåÑ•ø÷×ç¸á3Ú hË‚óÒi݄ƘjÝ„¾™ñjÝŒ¶šákÝ 8Ü3ÏɬÛƒÛÒpeÊ[>¸ÑG¬[æ$}‹ ÉH¸^™xÏ7_ß'×÷ÌCØ\?@m`]"Auý¸K#Çß[å¸õ :wæ¸åèÚw‰ÓùDÙKÀÇ·4oœ«Ïc#UŒ·úß«2þ0u›Òû J³Ó°mL^ìÉ1˜Ë˜¯voNmì¨xÌtìù‡g5ú•‘žH„t´sblþAÌ’ZŒLšL æPªS*Ó°¨—̰Ìy²å‹‘p™†ãôM)Ù9º¶Èü‹,wagÜò-ÚÕ l,yLj¯ ÅÔmŠ]H&Ê: X_F¶>ƒ|¨6 WsMÈž;Œ0 › @$›†År`Ìì0±Þ!„v˜`X0¢v of€múÅîà“Îx7ù ¿ÍÃ9À0LôõßcƒÉ±é»Š³ÀäØô啱ðZl09v:QŸÇÆX®£Ç¿W¥ cê6Å5{bæìiGÔ³AÐ^2äxA}oSEL  TO- 1¼Z0c´µ@ÈàkA”±Ø0C³oFjîLœ6˜8}'6˜8WÍ?8NŸÇÆÔä]ñïUiäÚ7ó® ™!ðÚ®¯ÍœòÚê/¯BÀðy• ¦6¨ˆ0¸^%†±ö*@ ½WyJá*^âTÚºbOºðu`A*‹NÚò &Õ«¦0©þΚ¤ú~yè,ÀF9"ÿþúéõÔmy˜K¹ˆ\WÔÓ<ž× ì 7EÌÞT2ýɹM½äYA‘Ï4ÌÖƒ¨i‡^@ĵC’€hm$ÒÛ!þF”¸ëJÿ%ÂÌ(ÑéÏ} ±øâ¢2 îä¢Ù¢‡ä }s““ãß ^Ç©¦nóÄö¼Åiïõj¥Ó£"X²VoŽ`A=I‚ånü½ ùÌðPiC%É÷z³|@y ¶“1™!ÚŽÄ$ÜŽ¼x·qð·ý.Œü6Ú•w €vù7ýjl¤ßÿ‘St…iÄ¿W혺Mr¶kl×9·jŒÍ< }lõYé¢äÓ}”‰¶Ñµ§RDšU·®K‰ÉšÎ¨Yè¡<¥!O©—âÕÌ:Ä^J[3롊òƒš‰ÎÊb#ÁE3+~¹(k ÂÛ\Sò/2Ý„ Ö”ô>r„N`c-ïiùß«ø¦n‘Ë'Ú yi3$0+¯CPÄ4‡ Y§ƒ^æ'+ò™mWÔ´é!ñ“¸ú”.hmvkÞ†,ˆ”÷—L˜ó°Å0ï„Ms‚`h:*n\4}7ÝŸÂEóÁ„ñ&l€‹æËUœŸÏccIÓ^ÿ{UŸ‚©ÛÔzbÑzÚÑÓÇÚâÓ’ÌóaJf…ù¬%³Äâ(&ømœÔÄÕé8ȉ¬²8ç‰Çfã(²Îâ”(²Ðâ)«õSf©ù*³Ö|B•Yl>Àʬ6©>Ê.«Ïø!»Ì@™]¶ïá™]öº³\Ì.;°¯Îc¦].ÄL=‡›C}úš¡ƒ>œÍPBŸÝfh¡v3ÔÐ'¿zèƒá Eô¹q¹:VÎPEŸ:g袔G…2J™Th£”K…:JÙT裔O…BJ[~„„$ú@9C÷JáË1Ó[!}d`ÇL?]æGìçpsz©ô{¥›JßWú©ô¥£ÊPzªü¥«Ê_Púªü¥³ÊŸPz«ü ¥»ÊßPú«ü¥ÃÊQz¬ü¥ËÊ_Fä`=À ÓXåG(uïG¼3Lc=°°Îcf®ÃÌn7§$ÇÓAð e9Ö)̱†Éð3ÅÙ`ʳ1Âhcˆ)ÑÆS¤Aª™Æ(S¨a¦TãL±ög€)×Oö¦>3L}¾3L}>0±Îc&§>_…™ÝnNc÷^δvïõLsw-`Ú»kÓà]K˜ïZÃÀ×"¦Í»V1ÞµŒiõ®uL³·‹Å´{»X+p»X+ïPÉÅZñD×>Ãty×&¦Ëß[›”.`cÇÌÚ^Ægös¸•qWûXëŒó:ò±Òx¬}¬Šž}¬uÂÉÇÊ/-Ô>Ö EÐ>V÷µµ¢Ù·µâm~ûX+ø–}¬w/ìc¥ñ\ûX ±µâ -aä#fòOjkEZÀ½¸Í5ÇFÖç1ÓV˜¹ª›ßÍás¬îWk îXk¤îYk¨îZk¬î[PwNŒ¸{'†ÜÝcîþ‰A«ĨÕbØê1nõŸ«üŒH:ÆŒ»ybfßÍß…™+ë2º¾Š™LÁ/ ÚôãZL3õ³eL;õ«fLCõ£g|½Õo¢1MÕO¦1mÕ/ª1Õ®1­Õï±1ÍÕϵ1íU&ˆÒ`e’(-V&ŠÒde²(mV&Œ0"£ëf˜Îê—Ø˜Îº7-^ަ³]_ÅLnõ/Kô•Ñ¥Ôä˜cš ^\Ê5±† 0'£‹©Î6º˜úl£‹©Ð6º˜m£‹©Ò6º(vÚèb*µ.¦VÛèbªµ.¦^ï3J¶ÑÅè{1£è£ë«˜É’âeÉá6º˜În£‹éí6º˜în£‹éï6º˜o£‹éñ6º˜oùñ²óÃc,6º˜^o£‹éö>ô±L»·j»…ÏéÐÈÂë`:T’ÆMóŒÏ(m>^õËý÷Ö&¥Í]_ÅL¶.~=fvs˜gh(üÀ‡£–{txjéñ´€W-û|øjé±F:œ•ÆmýÄ߱Ї»ð~þZ øpØ‚çú}xlßòá²—A|ø,¿³_¿ôÇÜ^[ðŒ0ò3^µÖa¯÷rà¡GF×ç1³oñ/ÂL=‡[̱Zb­Zf­‘Zj­¡Zn­±Zra ‰ïŸë·ÿˆ!·üĘ%bÐ’1jI¶ä@Œ[’àgÀ’?#Ò…Ž1㟘ٷøwaæÀèú*f®©M»9ܼ—[{ã^om޵ÀÚk…µ=Ö’xµ&žD-²vÈZem‘µÌÚ#kµIÖBk—¬•Ö6YK­}²ÖZe-–}\›âu@Ô¦½–xWm:0º¾Š™K8ð~7sF‹üä”6È9m“ÚD gµÉ@Nk‚œ×&9±M rfç’SËç–I"N.Eœ]&‹8½LaDF×1¶iA¼7-nâÀGF×W1sM¯½›ÃͽiÌ1z×Xô¶±Fè}c ÑÇ/úþ¡î­…õÞ~o’½¹Ÿ£dïî×*ÙÛû1Köþ~ë’Ú€ŸÂ¤và—2©-ï3êµýÒ${í{1Ã^ûÐèú*h®õv“¸Yó;¯Éü ,E4¿K‘-‘…oÌB¤ó´r[õB-E>+‰€*f Uì$"ªJdT±”©b*‘RÅV"æ1¡‘¨§â$QïÞâ$QïÈèú*h.rêIܬ¶‹5J«”Z/Ö)5_¬Tj¿X«Ü§zëÙê%Ü‚µ2ºì65Ûh¤þÓ­ ë¶›AVn·£ï+V¿wKž¹bÁrîeÁr¾btu3.ŠÇí-ú\ÿòËÏ?ÿ±X\#Ëž;\ßý¢W^åRΗ:5倩VN˜ºe利›VΘºmå©WN™ºu嘩›WΙº}å I PNšÔå¨IMPΚÔå°IPN›Ô å¸Yb®Y`T¹fêî•k¶ïî™kö2Œ*×ìÀá:ƒ™8ïqqž!JuS ¡T9öN±D ¡T=JõS ¢TA&J5T ¢TE.JuT £TI6JµT £TM>JõT ¤TQFZyþˆ(JET€â^E|9f«§®3˜‰ƒe‡nJyV²©.&ŸÚáb2ª.&§Úáb²ª.&¯Úáb2«.&·Úáb²k¡ÖÚáb2¬Ý&ÇÚ=a²¬Ý&ÏÚ}!Fìp}ÀŒ’Zíp1©uïV¼3Jj=p¸Î`&°^œîk‡‹ Êžc¹ Z&0{˜Ðì5d‚³×˜ ÏÆ #•jØcˆ ÒÆ¦AªœÆ(ªa&XãL¸ög€ ØÇûŒ¡#Ÿ ‰Ð7c†‰Ð×ÌÄAù‹SÄí¤3©Ý§˜äîÓ Lz÷i&Áû4“â}Z‚Iò>MÁ¤yŸ¶`½Oc0©Þ§5˜dïÓLº÷i´Põ³½yÜUW?óª˜N“¤ñÐ<ã3Jž÷é &Ïß[›”<àpÁL\ȹ 3õneÜTWޱž*‡+ûÊáÊã¦r¸úiÅéTV•ÕÇ}åpåq[9\y®ÖÊá*sW9\y<Õ§Ê&\ñ©³4+‡+‡úÔÚ„§Ü§}ÄLúºÊáÊßÑ~p¸^™N½Ä#‡ë,f®=¸›Ã-æX½°Ö@½²ÖH½´ÖP½¶ÖX½¸0 ^q/O ¹×'ƬƒÖ ˆQk ݵbÜZ?Ö*ø± tˆ÷öÄ̾·¿ 3×ÙÚt-fvs¸y/·èƽޢkE;Ö ‹z¬%ýXk, ²Y4d­²¨ÈZfÑ‘µÎ¢$k¡EKÖJ‹š¬¥=Yk-в[y>¬MY›ö"â]µéÀá:Ë/ÆL=‡›9£Õ}rJ«ÿäœvÈIí³Ú] §0,p^»äÄv/È™ínSÛý ç¶;BNn÷D.«Ürz»/Ĉ®C¹XàÀ{·â&|äpíµ¯ÅÌn7÷¦1Çè]½ìm½Fì}½†ì½ÆìöÖÆ{ïHTConŒ±w7ÙÛ£ìýaj¸´}¤-<ÙgØk3ìµoÆ zíC‡ë¬¨w)hö“¸YÓf.‘L›½D4‰l*áTL$Ò©ØHÄS1’ȧb%PÅL"¡ŠDDC‰Œ*–!UL%RªØJÄ<&4õTœ$êÝ[œ$ê9\g݃‹ASOâfµ]¬Qj¼X¥Ôz±N©ùb¥RûÅZåˆÕÊ-ë•› Vl·¬ÙnYµÝ ²n»dåv;úøþµbõ{·ä™{ ,÷à^,÷àK[#ÎC<½ÊUÜ®Ýe.dºå+²‡F×w¾äá;šµøHEëòk“RÏÒÞÑ=b$ªuã€~yky¸2~­Í_<¢ÛFV[7NŒŒ+WÓ»qæ÷–¯ÊÕ¡pi¯èÆHK[Iö7~qI7)"°¼œ‡Š›ó°eÜ\ɦërR`ùâ’\—‡ˆŒ+J7õux9ÿÞˆ}Ë¿HièûÖ!ßpûÖN¯Á$bßò¼=2´NccÍÿíK«î¦ncØb—§»,wñðó°èojÌÇ õ@Œc—!Ò½)ä±Ãì*²›”X^âÈC$€–Ø.5E9¤é{‘üƒ°4]nó9̇c¦Ø@le6£KñC¨eQ‘—]j/ C fŽÓCl4¹’çŸ\BäE–_ä}°ý˰Q0žçí‘qu0<—îlì¦ncJkŽ-²Ëm{É- ¯yX>ƒxÚ6W›üÅ$ÕÈŽÍÃ!’eópŠÜÙ<,NRi»y`,™µyX| $Úæ!˜Z!JyX\<‡Å$AVnΆ<\aA³ù)†‚fó—W~Ãk±Á ÙSR4q¾bR–{Xü½ ‰:ð ”•|Ž  ùê/`–¨¶€0­«0™!šëßÎõoÁ0Ã;G]4±od{}x‚ô{áš°1Lã©_Œôû?2Îb#W“‹..ì¦nÓ†œ†íÛõÐð¶6ó~åå(lõýÊ»Q(¨Ø.ù ÂE$?³ÓD‰égºF(@‰XÀ4ByJCd/Å+‡×ÌQÚúQoYà 4ŒP{éÀ(š}"qTSòs@}Ô”~ú˜kÿâš’_z`Å¿dFzßÀÔm"rù€B4/O÷$0W‚"æ§µ† iH¯ÐËüWE>vdjÚ·PþI\}x´6cb ÒÛ“‘ç„%s¶æ0Ák­ú”<|ÆEÓwÓ*\༕‹¦Ïá#Cè,6r÷zÑÝ•ÝÔmj=±h=íh½(h-½dh<½ «¾w¨šV-­ Â†W@b;,˜±YÙJ ¢l´`¶á‚7›ôÃ}ƒ=¬°ÁöNl°‡ l쟳àà ¨+Â5ws·I9ÒŽL]Iû5U'íæÔ¤´×S±R% ž¥:AµKU„Z˜j •2U êhªOTÙT½¨Á©¶Q¡Så£~§ºHuïpPüRQ¡øugQ¡ø•°þÐà9ŽKíÝÜm’œEå(H‹èQ® ¤˜-’H©[’B¸&erÑOŠè"§”ØE])À‹ØRží•xOR,i¿õ÷ö¡ÞÙǪ¹Ø(Uó;Ù(UóÌFÏ9CþËK »‹JúË/úƒœ1gOœï}Í+o*1̽£ÃÜu2ZÌM)£Çܳ2šÌ--£ËÜñ2ÚÌ 1£ÏÜ/3Íí4£ÓÜm3ZÍÍ8£×Ü«3šÍ­<£ÛÜé3ÚÍB£ß,±0 ­B'£ÐÜH3 í]'Í(´—µKˆB;òuÎ fUU»ÈØÙÏà¦|BkXÌ/´ÄÅ|C+`Ì?´@Æ|DëgÌO´¼Æ|E«oÌ_´8Ç|FkwÌo´´Ç|G+Ì´0È|Hë†Ì´¬È|I ¶ƒ«–嘷øN—{5bËÜžˆÑs¡Ý³ŸÁM!¨VÄ’jÁœ!ªÖÓ²j¹!¬VãÒj±ž!®Öòòj©Ÿ!°vk£€!²ö2k›!´v!Rk“B‘ýó1uµÈÏP×w*ÿ«ÃP×èb`ÿ\hígpSÐrÌ0Õ¯‚šc…ä+ˆ çXaAAšd AÓ0Q))¡²#è:Ž ìø (ûÙƒàè@ ‚£oF ƒ£œ¡ˆ)t¡5´ŸÁMa#ì=vy„ÁG@X|T „ÉGAØ|T„'DBX}T)„ÙGCØ}T¹|¸«®‚m*¥*ÙB†vmÁ³\eÛ5æ˜Ç œ>ªÂéo®J§?ò‹N VÑ…†Ñ~óü´…˜)æøÊ3jqJ.˜f ÎL4¿8S3Õ¶A“$&‹SzÁt›¾ƒ˜pƒ^ÞL¹)¯ü“nÀ³Ì´›m–˜8NTSGÜH0ùfE×íB€wˆiò‰ä`¾M¹Vv3óÍçŸ]¤ˆt¡´ŸÁ-f˜}¯W€m±Wˆ]³VPMµVX=· –\QÇ.©¡ÂÔï ’„P©B°Ä!\Zƒ>’"ô ‘ÊsŒµòB̾—¿ 1ÞÒ ÄÀVºÐ\ÚÏàæ]\2švy©lªáT%¤Ñ©ŠHÂS•‘§*$PUJú ª˜äCU9©‹ª‚U%¥MªŠJºT•íͳP…%W%)ƒªJ{ið¦ªtà8@ ̦ -§ý nfŠRåÅ$%Ú‹iJÓ•ä/¦*G@LV†˜®ü1aÙ bÊL$“–Y!¦-/CL\V‡˜ºœ1y%Bˆ,¨cæ+£AÌwï4ÜÄ||¨ˆu¡µŸÁÍÝhÌ0ºÕXt³^!v»^AvÃ^avËF»i#„ݶÄnÜc·n²›7BÙíÁTŒpªþPMx²Ç°»6bØ]ß‹v×GîÔ ÈÀ˜ºÐžÚOáfÉËÛ8%1oó”Ì\(©¹LPrs¡$ç2#;Teˆ’žË%?—1J‚.s” ]))ºLRrt¥$é2KÉò ‘¡„ç²D ïÞ²D ïȳú4dà \jZí§p³®n®HÝÝ\’º¼¹&u{sQêúæªÔýÍeé ˜ëÒ70¦¯`®LßÁ\𾄹¶| qqùm|ÿ\sù/ò2ô Ì}éÜË}é|ÅÉZñ­¶²ÝHú/ü¯?ÿáÿ–þ·ñíÇÿÜ]ORâÙS{ë»_ôð.ÝU!äˆ ‹³¬>>U‰˜±8t‰²8“‰˜²êÈ&pœè\pÔ>ËMÌê<è‚¡ULZ[ù[ŒY«›–woã,*bÚâ¨*bÜâ$+bÞâ +bଠ1mw°Ä¢ÅAQÄ¢½ëò‹ö²3_ˆE;2¸NaÆÞÖe˜©çpS^aœÒFžaâFÞaœñFbG^bœGžb ïy9JçËÉØ|üyq:yŽÖ&™÷hí’yÖ6™ií“y’ÖF™7iõùf:hc:xŽüÅwZâË1Ó[C}dqÁL¸[Waf7‡›‚Q­ð38ÕƒUí0xÕƒYí00¸Õƒ]íP0øÕƒaíp08Öƒeí0xÖ ƒií°0¸ÖŒ0"ëfôjÇ‚A¯ï‹—c†A¯&×Ì„¿ufvs¸)€¹šc¨7±#n zxB^ØÓ# :0€éÀ¦C Œá Rï4FpFv`Ùñ@€ö³}ÒJßJØ\g0×U˜ÙÍᦠ÷ØË{=‚⣠H>j‚棖 ˆ>j ²¢5¼›«Z… û¨eMy:× Ü>×Ê»Vò¹Ö•æ˜Ï ¸>j‚ëo®M ®?2ºÎ`&<®«Îxíæp+ã®vºrþFít¥ñX;]+4?;]ë„5’Ó•Æsít­Ð ít¥q_;]+ä;]kyæ?œ®|ËN׊;vºÒx®.ä„ÓµŽèõº]4pÝ»å7cj§k-Á7sàvYÕK<²ºÎ`&\®Ë0SÏás¬þXk þYk¤þškèþ›kìþœpÿNŒ¸¿'†ÜÿcÖˆAëĨõbØú1n}‚ŸëüŒH":ÆŒû}bfßïß…™³ë fÂçºxŸ±FĽÜ2÷zËt¬¾NÅZáûT¬%¾PÅZãU¬E¾RÅZ%™QµL2¤jdJÕBɘª•’9UK%ƒªÖJ&U-–}\›|¹Šµi/+ÞU›ì®3˜ §ëb>#)ZœQj¿8¥ÜqN¹â¤rÄYå6ˆÓÊç•[!N,7CœÙ¹‰äÔrCĹ喈“ËMg—Û"N/7F‘ßuÌå^ˆïÝ‹»8ðáu3áu]Ü7ÉñRosŒÞ5Ö½­×ˆ½¯×½±×˜½³1ÀÞÚaïm ±77ÆØ»ƒìíQöþÆ0µcœÚ?Ôžì3ìµöÚ7c†½ö‘åu4áv],Ðx£¡æÍœ"™7{Šh.Ù\,(¹˜P¤s±‘íªbD‘ÏÅŠ" ‹EB;Šˆ.†],)Búj2EJß\¦ˆyLh$ê¹8QÔ»·8IÔ;2½>šÚïºX öípªí¾wO5Þ×ò©ÖûÖ>Õ|_ê§Úï;ÿtü$Ý‚x1nB<(·!Þ€Ïqùµº~Ì€n‡ß: ¢§vnÉcÐÀ=ðStîeÁr¾b{µóXvÿ¾×?Áìú¯ŸÞúô?~ÿÇnû=þþå§·¿*6Øÿã÷;ÿKAhOý¯ï~Q½í¥eKÿ&°]õ ?ÒÃÜÇ1]̽2ÓÇÜKó9{÷ÚL/s/Ît3÷êL?s/Ït4÷úLO³Àt5kL_³–Àt6k Lo³Át7kL³–Át8ëELK«ï´#-ͽ?ÓÒÞõþJK{Ù½v¤¥ù_g0³¤?ü/äˇÿ…üùð¿Oþ’ª§y{Ùoâ-û_KyX<ü¯…·—ud™vOÝv ß­ÓY’…÷ÈtÖ$›æŸaž}õ(àŒê}kmbžý‘ÿu3C9Ï¥q¹äù·Ýæð °ÎL-Ö\gª–üCg®h€>“•Æ}}f+Ûú…À¢Ï|¥ñ\¿¸@ð™±¥„Ä™²|ËgÎÜ"ñ™´ÜºÔ"$δ-xHy„™®œñö°¥ÄÜÌ»yR/ñÈÿ:ƒ™~ÌUiú·‹î¬ïçp‹9V§¬5P'­5R§Í5t'Î5v§N ¸“'FÜéCVˆ1+Ä •bÔJ1l%‚·RÁÏ€• ~F$cÆ?1³ïüïÂÌÿu3]a2ã”™ð<]Àgös¸y/·$ǽޒk%=Ö K~¬%ñ  ß ó‹‚</I‘µÊ’#k™%IÖ:K–¬…–4Y+-y²–Ze­µdÊZ,]ú¸6ÅエM{‰ñ®ÚtàÁL[8ð0Í¥Bu×ñËÒäŒÖþÉ)í sÚ; 'µ·@ÎjïœÖÞ9¯½ rb{äÌNU$§–7"Î-ïDœ\ÞŠ8»¼qzy3ˆü¯cl/ƒxïeÜÅü¯3˜i Zú9#gJ{îe}“ü/õ¦1Çè]c ÐÛÆ¡÷5Dok¼èû‡º·FÔ{û¹Jöæ~Í’½»»doï·0Ùûû©Lj~I“ÚÚ¤¶p¼Ï¨×öC•ìµïÅŒzí#ÿëhŠišv®\¢æ+@³›ÄÍ"˜Ÿ‰¥HæWd)¢ù‘YŠlñ-D¸x¢"_°• «n)ò©XIT1“H¨b'QÅP"£Š¥DHS‰”*¶1 D='‰z÷'‰zGþ×)ÐU¯MÓ’ .qêIܬ¶‹5J«”Z/Ö)5_¬Tj¿X«Üg…ëÕë)Ü‚µò¿ä6ˆ5Ë«–[!Öm7ƒ¬ÜnGß?V¬~ï–`†Ñ¯an!úõQñjÌ(úõÀÜ:ƒ™;L;£â‚Ú´ŸÃM‘Ì1Çk¼ˆtŽ5Bäs¬!"¡c@¤t•g5ì1„HêÀ"«ƒ8QD^†‰Gdv|©ýdŸaÄt•lU"¦ïÆ #¦Ì­3˜™ð”Ë(‹ëªXò0Ñý -!=%M)]-f^\ÖAŒOÃø Fš´±2·ò¸©zd°ÔAf>±§ƒ"3/é IÍ3>Ã(û8x(û›k£ìÌ­3˜¡tX8ð´^Àgös¸•qS™[}ÇT™[yÜWæV7•¹Õ'°Ìõ¬4+s+ûÊÜÊã¶2·ò\­•¹Uæ®2·òxª”M¸4âgiN–« ËzÌnê¶<,¥œD.§‡,Aóp|Ï$0 ‡)(b~&§"éO^Ç —8:hò™†cÔ´+=‰kW Lk;° ‘Þ®¤ê˜w]Þ!L˜‘b:Ý•G †Ø(òÍE»•p+mgqøGNÒ l  ‘5WD¨í¦nÓÄÎ+¯HaÚçUoàoYôIY²ôó¥’² ó¤‡LÊrgM¿ 0ä—;¦€Ê<*H¾æ ˜Í=·€pîŒÉ ѹ%&àì°/fxÏ qðOksþóÈ+QÀFöÚ{±1·£3Ø(:n“étÉEÝzê6mÈÓÊ«PØ®§…7“°™O3/Ba«ŸfÞƒB!È]`ebšt ª‘iÔ%ìRbr³:GÊÝéå) yh½¯©×™öRÚ¦^ïVàutˆP' ¿(šS‹"qTSÐD»¦ä_dº ¬)é7|ä ÀFqž»„ p«ø¦n‘ËmUkš—‡0… l§†Rçl4”Û ÌCºvEß@(òÙŽ«´üLMópjM\[ŸÖ-´6»Õ¤7q޲Pâ<,6s¶æp\`R ãBåí1ÍßM(#;y%èßÁE[´ˆ Øèò¾Ñ¢²Œô°»©ÛØÆÄ–}L{icQJkKVÏXÐUß;DÓj0 ¥5TÐðHh‡ 34Ë!ZiC¶Ì6\ðf“~¸o ‡56ÐÃÞŠ ô°6öNÏçÁ1”óq•iè/hTvs·I9ÒŽL]Iû5U'íæÔ¤´×S±R% ž¥:AµKU„Z˜j •2U êhªOTÙT½¨Á©¶Q¡Så£~§ºHuïpPüRQ¡øugQ¡ø55σ#Ÿ0ÐOî®P8vs·Ir•£ -¢G¹Z4b¶H"¥nQH á"˜”ÉE?)¢‹œRbu¥/bKy^´—â½H1¥}Qf ÿ"Ô;[àX5¥j~'¥jžÙèékI]Öeòs½ïŒ›¿ùËÇ ´çÎÍ÷¾æÈº. 6Ì sOÄ\1w›Ìs3Ê\2÷ªÌ-s+Ë\3wºÌ=s#Ì\4÷ÉÌMsÍ\5wÙÌ]sÎ\6÷èÌms Ï\7wøÌ}³À\8K+ÌI«PÉœ47ÐÌI{×A‹¯k““väçœ@̲âBRûvÑEý n /´vÅpCK[ ?´òÅpD c O´nÆpEËj _´êÆpF‹r o´fÇpGKz ´âÇpH ‚ ´^ÈpIˉ Ÿ´Pû1c´Ç0ÆwzÜ«3X~|äòœA ^L+‡x/y›f?ƒ›R­„+AUB¹V¥£+U2»Z¥Â+ÁU"½^¥á+V¿bå(AVfå(Vö‚jå>(ÁV愲eû<@ _%î+ñu¯î¿1L|=ð~N f.éd¸Œ4_q>j?ƒ›S˜=ÃLiö 0ÅÙ+Ä”g¯ S ½ÂL‰6˜"m„0eÚb µÆ”j#)ÖF(S®`¦`áHÉŽOR´Ÿí1L•6b˜*}/b˜*}à@ÌT:­¥÷žÛ+ö˜Ý nJz]Ið±Ë#)>ª’ä£J i>ª’è£Ê 7!ª’ì£J!é>ªX[ž *×öE|vl{T)UÉò³«h žå*Ûv¨1Ç<ÉõQ•\sUBrý‘Ot1|—®\Gê¯Øcv3˜ç§/Sl[¬¸˜$ŽÃÓl¡¿™‰æWej¦Ú68÷$&‹ãxÁt›6˜pƒÞL¹)JO0é<ËL»YÑ^‰‰§q_ÙF-’F‚É7+^5»„àb²øQ1ߦ¨X73ßzsì@ÌÐfe/¹î¸ŸÁÍ3¬~W+ vX+¤nY+¨fZ+¬^[P+.„¨S‚ÔÈ aêó…@ÉB¨T!X"‚.AŸIú„HÝ9FŒZx!fßÃß„˜Oébºò¾ôTÞó̇.Ûc¤ñh—|¦]^ꚪ€Ä7U isª"’îTe¤ì© IøS•’.¨*&ÙPUNª¢ª DGUIi’ª¢’,Ue'ó,Ta‰ÅÇUIŠ ªÒ^¼©*8M'Ó®xð ;ÍzÌn73E©ñd’ëÉ4­å“‰Zê'Sµ@&k£€L×>™°m2e»dÒ6)È´ía‰Ûâ S·B&oƒD‘õtÈ|m0ù¾sîa¾GþÓ Ä4Å–À|¯¸°ŸÁMÝh5Ã¥[­V t³±BèvcÑ Ç £[ ›„ Û¡„õt’…@tóPtû`¨pªþPMx²Ç »Ä »¾1è®]©3)§*ûBd¦+îÆî§p³äåmœ’˜·yJf.”Ô\&(¹¹ŒP’s™¡dç2DIÏeŠ’ŸË%A—9J†.ƒ”]&)9ºŒR’t™¥dù„ÈPÂsY¢„woY¢„wäUL9rÙå‚ì%»Ìn 7ëêæŠÔÝÍ%©Ë›kR·7¥®o®JÝß\–¾€¹.}saú æÊôÌ¥éK˜kÓ·0§¯a®NßÃ\~ç‹<„ }s_ú÷r_ú_q°2YÏ·™ß[XýÇßì,,&š=÷°¾ûEG&Ö¤‰1`q°%ÆG&#'*3.CVÇÍãš Îzú4gÙhªÃž N{V1hmeb1F­:I:㌦š"†-Ρ"¦-Ž©"Æ-N±"æÍâcÏvýeÕ)PÄž½kå{öº]ˆ=;r±Na¦+·N€œ+6´ýnÊ#Œ#ØÈ+ŒÚÈ3ŒÜÈ;ŒóÝÈCŒãßÈKŒÓá=o>éð8i™Ï–#1Žž#¯Ñ$ó-P2ïÑ&ó -p2/Ò(ó$-1?ÂLL§Ê‘¯øN0|9fz ¥|¬3˜YJù“‘uA;¿ŸÃMÁ§–ñŒ*™_Á©²¬*›@Á«²Ì*›AÁ­²!ì*›BÁ¯²1 +›CÁ±²A,+›DÁ³²QL+›EqŠ2²`†A®²%亷%^޹8Yg03—`޵œõ™¯ÚÏáæ€å˜c0Ç  9Öα†Îð3Ú`@´1ÂicˆÓƨAT£ °6†pmŒ#;>È~¶Ï00Ú˜a`ô͘a`ô—u3¼½R^™.Ùgvs¸)È=ör½Ç^ ø¨ŠZ ù¨%šZƒ„¨E /ÞªV!È>j‚î£Ö5¼{+3k]Q«df­Âmf­¼H%3k]PiŽù ‚é£6!˜þæÚÄ`ú#7ë fp«Acã%èn·2îj;+ƒ¡¶³Òx¬í¬ží¬üŽLmg¥ñ\ÛY+„AÛY<µµ¢ç·µ–÷úÃÎZÁ·lg­¸€a;+çÚÎBpHØY눃hÍ.ú·nÐò 0µµ–›9pžàåØÏ:ƒ™2s¹Ý0\bOìæpó» æ¸I湉æºÉæ» 'ܤ#nâ‰!7ùĘEbÐ"1j¶È@Œ[„àgÀ"?#ÒŽ1㦞˜Ù7õwaæÀÑ:ƒ¾«WjSÉÑœÝnÞË­µq¯·ÇZà»R¬¾,ÅZâÛR¬5¾.ÅZäûR¬UÒUˤ5ªÖI‹T-”V©Z)-SµTZ§j­´PÕbÉÍǵÉ7§X›öÚá]µéÀÓ:ƒ™vDÐXÆL·^N·›ÃÍœQ’>9¥%rN[䤶 ÈYm)ÓÚr çµ%ANlË‚œÙ–9µ-rn["ää¶LÈÙm©ÓÛrFdjr`[äÀï,Š›8ð‘«u3|W¯¼ ’¦ã²¾É¶{ÓjŽËÿ¡ZƒÒÛÆ¡÷5DokŒÞ90€Þ:0‚Þ;0„Þ<0ÖѸÑÛFÑû†¡ Æ©ø3@máÉ>ƒ^;0ƒ^ûnÌ ×>ôµN¦ü—ù®ÞÁûIÜ,‚y3§HæÍž"š‹E6 Šp.&é\l(â¹Qäs±¢èbF‘ÐÅŽ"¢‹!EFKоwL‘Ò×’)b‰z.Nõî-NõŽœ­ó ¹ð]½ý$nVÛ}©žj¼ïÜS­÷•|ªù¾±Oµßúéø¾?Ý‚xnB¼·!€o €qÅSp3⥸ñÜ¿s°sKƒîß  {p/ –{ð%okÅëμ­_~þù»Ô(æ•=w·¾ûEþ;Ûò·Õÿ^j7]]CÀÜ 3$Ì2CÄÜI3dÌ6CÈ܉3¤Ì:CÌÜÉ÷z%¥ 9+w‹!iV ¢f%¡×K)«BÕz¨AT"Òf¥‚!nVƒjV_GG¨™;{†š½ëìjöº+é5;r·>]ÏŠ¯ ³Ä´A+nL#´"Ç´B+vL3´¢Ç´C+~LC´"È´D+†LS´¢È´E+ŽLcŒw—ÖÏ2!ͱzµ ÊB<ꄾÜo>!-ÒªóÌ =Ñ "Óß)ˆ¯ÇŒ•ÓGîÖ)Ì"þ½*q3žÕB¬i9æñʱˆ_Ž5BÑ•Æsý¼ß‚Æß'–ò`œ[À·|¢lÁEŸ8Ë~×oü!9$N¬-xÀGy„™ïYë„×RÌ‚›9p—›Ôcwë fÆ>ï0ñçp󻿏Oæ¹æºÏæ»'ܧ#îã‰!÷ùĘubÐ:1j¶Î@Œ[‡àgÀ:?#ƒŽ1㾞˜Ù÷õwaæÀÝ:ƒ™üHSýïeûŒµ îåܸ×[c-°`ÇZaAµ$ÞD­‰Çy²]‚!k•EÖ2 ެu$Y -X²VZÐd-µàÉZkA”µXªóqmŠwQ›öâ]µéÀÝ:ƒ™¾\$Ž/ã3É­ìƒS†òÎÎ]w;à¬á,€Ó†óÎÎ8q8àÌv6È©í|sÛ!'·sBÎng…œÞ΋0"wë‡SüΩ¸‰¹[g0Ó•kçñïU}“Ý-ö¦Õ—ÞµZƒB «5*½oµ†Ð c}ÿPõÖÆ{ïxi½y+‰€*f Uì$"ªJdT±”©b*‘RÅV"æ1¡‘¨§â$QïÞâ$QïÈÝ:šG?ü %XŒFj»X£Ôx±J©õbRóÅJ¥ö‹µÊ p®·¬Â-X+wKnƒX³Ü±j¹bÝr3ÄÊåvˆµË «ß»%Ïܱ`¹÷²`¹_z{0ñö¬tÚÝúû°·~J8{ûñ¿þüÓ?¼ýî·ïrËž›\ßý¢w&— ‹îñ™a_nÖ憘aan˜&憚acn¸F憜aenØfæ†~Ðë.;kð: †¥Y0`˜š…A¯»¬Jä[! Q`X› †¹…(„p³ ¦ 7sƒÏp³w >ÃÍ^S†›™\ŸÇL.ùøêÚæz¸\qy?‡›R-¼1•ÐÂS -Ü1ÕÐÂS-ü1Ñ S-2UÑÂ"S-<2•ÑÂ$S-\2ÕÑÂ&S-|2ÒÂ(S#C|þˆ¤(ZHdŠâ;!ñå˜i, >2¹Îcf™.{õi?‡›âMÃäbü©M.Æ£Úäb|ªM.Æ«ÚäbüªM.ƳÚäb|«M.Æ»Fâ_mr1Ö ãcm 0^Ö ãgmÀ04Ñ&×Ì(®Õ&ãZ÷†Å«1£¸Ö“ëfÊå-¼Ì²^p@h?‡›c”=ÇŒYö0†ÙkĘf¯!cœ½ÆŒy6‘TÃCŒ‘6Æ3m 2†ÚeLµ1Ìkc1×ñ@ ö“}F±ÐN…Xè›1ÃXè“ëM8qàÌap€S‡Î 8y(4Zm°€Ó‡CŒØä:âÀU(VáÀï ‹›8ð‘Éu3h튙Þ4\/ÞÏá¦Þ´šãһƠ·5BïkˆÞ8Ö½s`½u`½w§ÉÕ[ie ƒèí£èýÃÐŒqiú H[x²Ï ×Ì ×¾3èµM®3  nË¿Í÷þö“¸YÓf.‘L›½D4‰l*áTL$Ò©ØHÄS1’ȧb%PÅL"¡ŠDDC‰Œ*–!UL%RªØJÄ<&4õTœ$êÝ[œ$ê™\g@3O´-GJ/M=‰›Õv±F©ñb•RëÅ:¥æ‹•Jík• V+·@¬Wn‚X±Ü±f¹bÕr+ĺåfˆ•Ëík—"V¿wKž¹bÁrîeÁr¾breU®&×ÿüå÷ü—ŸÞþªmÞ~üyïs•4·ÌTM®ç_ñÀá*Îu„3 ­YШ1+­YÑ 3I­mÐ*3g­mÐI3…-£†±k™ïæÇÄ™ï–Ϧ¹Æ÷"ß- W†Áå“m¤¿e–ÒŽ¨™LŽk ˆ·ÛÉ1sÆí̘9dÒ囨勑X—O oÎl×]h9þÞ*î-?a>FÜ[;|¦_–ö–x`e}ö±Jè%—(v·)b±k ¬1€1¿¾¾9ž±#ç`xc~¼{s´c¾Ë^þ¯8éÕ)Oot=s?7Ð ixgw<ÿ NÂŽwe7 «ì&T=FY¦aQ3tÙÍ nŒÁìf¬È(„,ÿä¢ 2%ìïANÀuÝCÃê2r¿…Þk¾âJÖnâ6%³v¤Ìmí¬.S]»Ÿ?>h›oÀs˜+dG.ͼØ4ª4Ù|=¾ÊšMÃb@0‰¶oXýSÛ7°/˜bÛ“E3ã¶o`~ðéç¾7Â|ܾuB(äáò —M¿Hñ.Û7µÍðRd0[¶[ÚRg‘ÑJ1¾*v·)ÏÙÓŠ´gO:² ½$HŠö‚!%ÄËÙè{›*ƒZP`Bµ€ÂüjÁˆéÖ³¯A&c  ÌÍ|™ª-p3sûpÏ`$µÁHê‘ÁDêô <2ŸN"cF õNî&nS ¼¶bfÄk£f‚¼¶qæËk“gú¼J³éU ˜ì òÁ\{¦Þ«ôt_T˜Òp¨ÊV‡µôëdVª’×ý¨ v8‚È3d¯j û« sìÓ÷Èb:‰Œ iÔWè1»‰Ûò0—pÑ·²¹ã1>Q¿4ÌR‡ˆažÜŠ6¦¿8·+"•ŒòàPÜêþŠÓw»‰Û<­3/JaÒ›Y?%«c,X3ê5’²œÍ ÇJÊb7ƒ¿7C!¿Ñ6Pò±.`ä˾žS3›ÆˆLÓ°®dAov׌´ó½ˆÆnM£¢éy) ¨ÈÏʵw¢"‹‹œ¢Ïƒ¢4$}1»ùŠWõ«iÛ¸ ç«(«wèü|ãèí{yªìíùáÄÎVj{W…uÐí§ü©^{ݽÎõdíi •b“áÑ•hítP=ü×VÇØs [[½TQ~BC+¨T¿Uo)ËŠJpP8Ò/À‹NySY»Qõ¯-é·{dþ|Ë”U5\Ui¯¸®]OÚF–¶¬t} …[Vz>…ßåÓƒÉ_ƒ™aё˴1šà”Ë,•>Î|ˬ5õ)ÜBU³µ³šÇ.d6 ¹iT¼0àÜ Ðz è+MݵÒÓÔ|+]M½¹Òפy(,©42õ¶J#Û7·5_Fv`°|/ùÄ›\·‹žÃÛÏßæ€@IJ ”⤀ÁΙ“x¦Az• %g)ÀPj—%†)QZ™%¥)@QJ›%Ä)€Q:%ã)ÀQ*Ÿ¥ž>À ¥’)ðp/“½/ƒEÁG¶Ëçñ²”g§qKh½BCÝÏßæ ÒΓÈ(•v­ SIÛÊ8•ò­ T ãÊH•n® UÉêÊX•ê® V‰òÊh•f¯ WIúÊx•⯠Xʈ•_ |ȇy€fªJoW¦ê^p1^©z`Æ|/s‰\š¬Ÿ-ëõh7›SŽ=¿ÔÊ=ÿLIöú0EÙëÇ”e¯/S˜½þLi6>˜âlü0åÙøb ´ñG…ÏødŠ´ñË”iã›)ÔÆ?SªŸì/Lm6^˜Ú|+^Ú|`Ñ|/î–ª´\q{?›sÔ½3gÝû;sؽÿ3§Ýõ9î®Ìyw}a`ësâ]Ÿ˜#ïúÅœy×7æÐ»þ1§Þõ‘9ö®ŸÌ¹w}mq¢ü a.¼ësáo­GŒ…?0n>NË€ó÷Ï^ ðÒ”³Àæ» ^ú¿•ïæFôØÎù<^í]žeÍ÷V=^vó·Åü²¿Õü«ýÕú¨;Öú©yÖúª·Öú«õ>Ô™ ?jÜ…/õõŸÚ~áSªñ+Í€ð– @ôKmà‡CÎ1VÔ® +û~ý¬˜<Ÿ‡J[¨Ëˆ×X¯x½l7{›vnicÜØ%œqß—ªÆ² ÉUCz‹ŠÄ:Ö)y,I’ùX±¤² I d½“zÈr(i‘ÕRº#‹éj.Uj­tÞÃÒ#…•g/ñÝRx¬ŸÏƒ£ÁCˆ%Šg¾$õ½ž»M4P:Y¢Ôu’HIïä˜ÒåIA%Ú“¡JÑ'•ÜO~+/€ô×у`ÇrHže1[Ë õ–9Af.ç‚htÈceÆî}€[Xì!t¥Ýéy¨äŠ$¸zî6õ”1³åó^:R¯ V¯úY¯(Ú]¯7ºa£Ͳ±‚^ÚHB«mœ¡7 Ѩ£èã`´ùÆ7T€';šbƒ=ñ­à@K|d@G‡Ã$åÚGWw“·I¡ò¦ Ë[6ô-o迼ÝCs1€xæRmÍ…Ò›Ë ”9!w.QÐõ\À û¹¼AtñƒhèÒMñ í€Ä溅íÖºíÈ<:³w,åPAþ÷×!v“·Iï6£ƒn¾µÜlbºÉ"Åv“IŠñ&›ëMF)曬Rì7™¥`²K³Àd˜f‚É2Í“i™"Û;³â!R(Þ› R¼¿• B¼ÿŠ¥”éK;|ô”þæ/¿ù˃”¯§¦Ò÷¾æ•®’‚±| Ž*®2X˧¼åÉ 抳‹Pqâh#îÇÉGÅÁH<ÚÚVÁ`må*)X,N]âÑWÊd0™Ïl2¸ÌG:læŸ >“*£ °úh‚À|b’A`û.›A`/;Ű[éÓˆ±¯Ô”û;—DYìfps>Ÿ*3¿Ï'™™ïçƒÎÌÿó9hæú˜4ó}Ššù‚>dM]ßg°™Oè#ÚÌ/”.¨|Cé†Ê?”®¨|DéŽÊO”.©|E龃¼AŸ¾fÞà^Ç{5bzË—Œ¥ˆYæâ”C4—¼s¹ŸÁÍ1 RÖ*å]1¢Ræ3*å^1¤RöS*å_1¦rs*ç@1¨r“*çA1ªr&³*çB1¬r6Ó*çC‘³ô1Œ5•S XÓ½SðjÄ0ÕôÀZ:¼;·Ât‰·´ŸÁÍaÃ1ÃÐöcV+„0ãXAÄLx…†l0,Ùa˜²İe#ŒaÌF µF#”aÎF0Þp†AûÀ°è'{ Ó†'ß‹f'˜K'3ÍíµîÒ~7Gš{gä¹wyF¢» 02ÝU‚‘ê®"Œ\w•a`€«#Û]¥éî*ÆÈwW9FÂÛ]bd¼Ý%FÊÛ]bä¼Ý%FÒ?á1ŒhwUbDû­UI íöÒ Ä kþÒ%RÎn·2îj)7jµ¿´Î dò—VøÕö—òK+µ¿”Æsí/­ð³í/åg kiÅyûK+^¶·¿´‚gÙ_ZyµIþRϵ¿Äˆ ûK+^œB>"&ÿÄ¡ö—V¼µ+óÍ/û-ÇÓ Äôp$Ë­€¥¹àøç~·˜a5½\7Å\!7Í\A7Õ\a7ÝD€›r"ÄM;䦞sÓOZ B-ÁÖp+À¿õ|<$õ£Å <ѲoàïAËÅt,-žÎqèá ûºž¿Mû·…4lï–Ù°ûû6Šƒ¯¡vø>J‹/¡òøF “¤AÖ- ‡,k’Yõ$:²(J’dÍ”`É’*9“WJñaòÝ"Àr¯ÞS~L¦ðhú"—wÜÇKt—zö6B©ðä‹ÒèI'¥à“mJß'•úO®*o€TVΙ®|aG‚'Ë“ –cA–-?ƒ$\n9º¼âA.Ó!£•‘@B»7îá³6Óx”ÿìPŽL W“Ýìmê0cnK3_úS¯ ÚW¯º[¯)š_¯8zc㭳тÎÚXw4ÒЗ‡hÛRtõÆ0š~#šÀ“Ý-²áù^x A>2šÎà£<‚²¿Â¤ÞMß&ÍÊ[3$-oÜP¼¼­Có¦½Ì%rš Ô6—ˆq.6Ðê\Š å¹PAésƒè"Ð÷i!#ú²-TÆcúAÑÍÕšÛ­Õ…’Û‘ÕtjÿÈmMWn.¦-ù2‘Ö™¡‚ûr8Dr߇†îk唨}霼ï¤S¢÷•uJøq£\x‡÷áaÄuy´¶q›C\¶‡wñiQèªþÎÂxŒHú¾îNIÿVªJIÿKfSúœ¯.0e³é¯ÿø vë©Ýô½¯©ÍM…ç.HæÂ%µPJ²R“ª¤+5±JÂR“«¤,5ÁJÒR“¬¤-5ÑJâR“­¤®¶Jê+»II_­ãÝðüªš|%…IP’˜D%IDP™d%sU7¯™Ì¥¦[É\û¦›É\/»Í`®»é bp©®²›ö3¸90OB˜õ$”)pOBšù$´)°OBœý$Ô)ðOBž%ô)0PB ý`ýž ã¹!4ýñÚf?VÄÀCIÁÃ@ { Ü {/GŒõÌGvÓ Äàé8<¶^q g?ƒ›s9ýÊs;ýs=ýFõ^Ìõ _Ì õ`Ìõû`ÌõóaÌ%õëbÌ-•¡\S™Ê=•™¡\T™ÊM•"„Ènz€æŒúá0æŒî̓W#†1£vÓÄàI¹r»{¹$¸f7ƒ›Óc†!öÇ àíÄX!¼9+ØÄÙnb:±í&¦Ûnbº±í&¦Ûnb:²í& ¶›˜®l»‰é˶›˜Îl»‰éÍÇ{ŒÒŒm71ÍøVÄ(ÌøÀn:\~ñìÜrA4À~7gŒÛnb¹í&f”Ûnb†¹í&fœÛnbºí&>ÝÏñr®_žÃƒ”¶›˜Án»‰í>tÁ wÊ`Æ»m0Þ‡:˜Ìc”™OÐá¡ù[«’"Óì¦3{ Ž•ãYÓ+žBÞÏ`žŸBðâ`R¨ß¢[zP2lZ`aûàS÷õÁ¨4në÷èXÜ>X•Æsý"Ý‚c4>˜µàyÜZÀ³|°káu,üJã¶~–Ža>8¶àñU!äb:<¼¬ƒV ^½¿•ùvù¹òc»é bJ=êËA«i¸BÜÍà3¬¾—+྘+ä¾™+辚+ì¾›p_N„¸o'‚Ü×aîû‰@ëD¨u زnÑø·$‡ôžc´¸‡'Zö=ü=h9°›Î´IHÃâ ¨W>õümÚ¿­¦a{·Ö†ÝßJŠƒu:ÔŽx¾®”–x¿ÇÇ¥¢0YDݲzˆ²fmUÏÊ#Š¢uIÔL«–(©Ö4Qq% xʮԟ½ xOù9°›NUŸò‚Uyâ!_”¼Š¯X/!´¾h©tÒB>ئe~Q›àª¶€@`º¶@„Æž,k‚4ZÆY¶l ’p™äè²DˆÙM‡ŒÖ~íÞO¸‡ÏØM'àÖ§)wgÓ‡÷²Hv;̘ÛÒ€ÆÌ—þ4Ö¥0’XµÒÝÆš.üÞ¡ê…¶Î~µßHDãíÑ—ûuE´í~z]½ßeDÓïGAƒŽw¶È~ò­ð`ƒ|d7ÀÇØÏþÑÝ!î¦o“få'F!iùýQ(^~œ‚X¼\Zô²xÖ´Èi~ój›D…§bC­N¥ˆRž •>•1 *rÔ U)#ª@Re<¦ÝT]¨¹ÝZ](¹ÙM'ð1» ?º½‚}ì¦o“ .nG‘\̺x¡$vÑFI𢕒èM _´T¿h«,ÑZY¢½²D‹e1ˆ6Ë‚­¶EAÚ½·0žIú¢ª’ôo¥ª”ô¿ô\^¢ÖëûMû÷òõÔlúÞ×Ôœ¸ˆ4ùVÞÿõÏ·21J ”¥Ô¢*qJ-¬©Ôâ*±J-°­Ô"+ñJ-´±Ôb+1«ubµÔ¢+qK-¼¹Ôâ+±K€½$(ñK‚Á,Ò0!+©„,µÜJÈÚ·ÜLÈz2u`6ALybðfÞ4y?ƒ›ƒë$ƒ)ØN2™‚ï:§!"O2›‚ó$Ã)XO2‚÷$ã)˜O2Ÿ‚û$*ØO2¡‚ÿ$#*P2£‚%C*XP2¥‚-D ƒø$ë)ˆo/ë½1ÕÌGfÓÄyRÍzÅ¥Þý nÎÇ´ÙÄüL›MÌ×´ÙÄüM›MÌç´ÙÄüN›MÌ÷´ÙÄüO›MÌ(TW›M̵UÁüQ[Ì'µÕÁüR[!DˆÍ¦ˆQÞ§Í&æ}î­ƒ#FqŸfÓ Ä ‡œmíËbm61…×3L©ß+À_¯S~½‚Lö 3%Ø`Šp¤ {1…ØcJ±HÙÑeʱÌd#œ)Éþ0EùxQªpä!Uø^Ä0TøÀl:ÜjšKmZ®¥ØÏàæ¬o`¸0+ÜG˜%î# Ì÷‘f‘ûH³Ê}äYæ>Á¬s™`ºT0+ÝG.˜¥î#ÌZ÷‘ f±ûH³ÚyŒ²Ë}‚Ùå·V%E—˜M'3•üŠilËAˆ+ú±Ý "í¾©Ì¦~np¨FÇ–æ”LÇšæ/+ëØÓ´‚ ëXT•Ù”Ç}e6åq[™MyªÖÊl*SW™My<ÕǺ&ÞÍÒ±¯œ$\™My<ÔÇÆ&¼5ëžì#bÒoÔUfSþŽöƒÙôrÄtj ™M'3–4¤q*öätÙÏà3¬®—+஘+ä®™+讚+ì®›pWN„¸k'‚ÜÕaîú‰@«D¨U Ø¢nÉø· ‡ÕžC´¸ƒ'Zöü=h90›N€¡YC¹Ö?_"÷Ôó·iÿ¶–†íÝJvëp(VéP;¬á¡´XáCå±þ‡ÂduuËÚ!Êš•ET=ëŽ(ŠV%Q3­Y¢¤ZÑDŵ\|T€,¢þìåÀ{ÊÏÙtÅGè kÉÑ)Wñ«Å „–âÁ-ÔƒNZÆÛ´È2j \5B” •µ}¦ksDØÖx² ÐhÛ`Ù65@Âmy€£Ûl61ÚÈS*„vï&ÜÃg̦ðÀC­]y+m¼B–ÛÍÞ¦3æ¶4 žyô§^´¯^5t·^S4¿^qôÆÆZçÈÜ*¿€±„ÆÛHC_n¢m7JÑÕ Ãlú…pjOv´È†:ä{áùÈl:ƒ"©$j–ñqÅÍÙÝômÒ¬´5SÒÒÆMÅKÛ:1múÔËT(§©`PmS9¡§bC­N¥ˆRž •>•1 *rÔ U)#ª@Re<¦ÝT]¨¹ÝZ](¹™M'ðÑ aFæG®iÅ>¨‚‹ÛQ$ó£†.^(‰]´Q¼h¥$zÑNIø¢¥’øE[eˆÖÊ"í•… Z,‹A´Y„hµ- Òî½…ñLÒU•¤+U¥¤ÿ³©Ÿdo0›~ùÓ~þóÇà¯'fÓÓ/8zEïŠK*ÈÎê'ôNÖêgô¦HÝJ^Ç„®4ÜxS^W¿¢ëE˜W¾&ưTm‡–ßWbÀòû¢Œ K[ÁaiŸzT=¤‹ Š‹+̃ƒÈZFŽè† âÌrá𦀸dyÓøû" ,§OŽŽK£÷‘ÂÍkІ °œ’ùÀ>ú”4sxÙ¸(3¶1noX h!Š/ŠÞ…˜¾l~cƒ†x¿4*J¢ÿFÅ•W!Ò1å9ú±ƒF3yìð58öÐ÷C˜Fˆ(.ÄŽ”CDŽ#ØbDZN£¯0Ðä›ßm8vÇ_…Rás`äCè¨ð7` ž±Ñ›#9b9Ç k‰ÈÎqÂç ¯§¦ÑÊQúeFr\Ä€æU‹ˆÐq†vøÐq†²hÑqaé*±£iT\D’Žd·ˆ+8 xL8Šß€˜ÓqU:æï0€¼Ðôó‹n¼Ð<Ùë-@\hž§ϧ1PNP³¿„¾V3¶1…׳Yz=Óå³ëU(ɾ^¡áÕkø}M¤kÕ‘$,D eXhA±„tb¡ ÉÅB R…N$ ¹HC>Ú, :ø> 98ýôG¦Íç1ÿ“SñøæK<¾jÆ6rkWEX·v\yk7FÈ·vj€kG8¸vx¼ð¯Ý¡âª WÕ@¹* ‚ÊUmb®J„€sU)„Ÿ«‚!ýˆ E\µ)â÷Õ„ˆç³Ø0ç00,åúÆxÅ‘ëjƶ—‘W¶ïü}iÕó.“Ñ7FDB‹/$u+w…‚²ôIæÓnÌtfwyÁ(!7§‘4Þò¿Ã!ò[ï«1Ð7‚_Šô{?òK>‹¥¸°m9…xÍK_ÕŒmÜUóÕÕ;n>à:z7ΫÖ{§Î«Ýyï:ÝÉÉBö×'ïþ]«Ë¿¹2¤Q3»jt -’RQòÙåÑÕ&'Í®DiÄgÊEÊ…öÞæ—6Zª[;×!òïkAúù¼†“÷Ž®ùþÒZ‘ðÀù,æòÎ[S^ŽÛ $«zÆ6²«ìµ·f^i;¤°²v¢4X[ƒÙ\;É£*·ó&Y!åˆÅ(m»¼p0Bø{ô©SÄŽÌÁ:[r0Òv„ضšG-FY¹ Ó3| Lõ¦ï¤ýQîµ¼ýzNˆxµÎÇg10Î}È–Wœâ©glc—åÙÄßšiÕ*àf·V¨tu^½•ß7D7¨UG§(D ‹ZÐa IÌî ÊЙ èZ…Nt´B.ºÝ£}½¡0€Þð>  7 ìíÏ‚`(®^o® …õ”m”[´­BŠÑ– ™FÛ1$mÕw´CúÑYHÛ?$#•ÈI*šTR C©Ü@¢R)‚|¥2iK% ²×!€H¤b‘è¾b‘¨{ŸAWzîW°ÂzÊ6ê®âWÐdŽ ×Š—AËgƒÎ+> X\ú°x ´cqDèÊâМÅ-©G“wR«&'¥Ž=úûúವþ}¤‹B-¾B-îæ/܃鲦‘ç¤6&þöŸÿf—ðÃD©'¶Äó¯xàKdº^ò1b˜Ô€0¥IýCœÔì1ãI # Ô&2!J=$¤Ô`2_JÝ'ã§§O•t*õ­ ¯RSËl+u¼L¾R;Ì\,õÊLÍR#ÍL-錙 è1eJ])C¦öm) ÷ªžSfÅYd“}™®xý¾ž¸M9oR{˜')ˆqƒ3Ë‘˜/'…‰és’Ÿ˜M'mŠÉu®˜k'U‹©w’¼˜‰'=Œ‰y˘§'%i{’Ù˜Å'Õò#2T'ÍŠIu{ÑêµÈ$Ì=²0>‰ {…¢\ópy=q›ò!Gç–ôH ÅÌ–”ŠÌäIIÌÌ¥”þÌÔJ‰ÓÌ´”rÍÄKÉÚÌÔæÍ´L âÌÒ”ZΤMIéÿoç®$ÇqQ__³ïÙuéÈ”-ŸòÔ2øÿ†vêdžîæn‘3ˆÞvD€«uoæ­®gsöP:ÂG i|U„Ë&Ú!\î#íŸU€ËÉ`ãve0Ù×s^?Ž ãnn)VÖeM$ÝEIë–¬uÃÀÙºÀnÝlP¸JP®B£«Œ€ì*2R6% W‚ïU¾À}7èßù™WeÆ=Spq'ãŽ;•ñ:~|9‚¾³[¸¥4jbXÕÔ¬=Æá\{ÈCÁ¶Àȶ@ðæ¼å¾¶Åú¶¥6·… r·e ®·E ê·%&¸bø¼Ï€§m5§}f5§=‚Ü© ž=;zº]¸¥{Û7 ÷6wܶ²õþÛ^_Ù´Ÿ?}^‡!\ô²åü\urýée‡ŒÕZ"ã;’M«û0à6Ÿ?}^g"Ðl¢†«¾QÆ5I\;ЇñRû™èõ®Út4r»2Æ'Ü?äM‘ÝÂ-.ke=v³[/Ú ‹QÍvÖÅf³kq#…úߥæ82ªsŽÈj«#Ázî´†<ò­[¸kå#ýF#3eÔG{c|†2&“Û•ñ441:ÐÇ#¾EÚ-ÜÒ£¸Sê¦Q9ÆUåoŽ•Ð+¢ XÊGã±—fg)= ÖR˜šº¥l5’KQk^—’×0/ñÑîg”Ë&§³jÒØ,ÕdŸ›QM&c”›•ñ>âS®†òÎÌná–¶oÍ£ÓÜ5¬Në×$;acî´ÍÀÓT6 OËÙô< i£õ´«’ÝhfʧÕmbŸF¸q~Úäfýi¢;(';s•YÚT=è>V?£ WnVF&l˜óp1t·pK-ߺ¬?3p­*×z¦2p­³‘ËÍÒ¸ZÕë{ A.G”“íÊ-͉<‹I‘<©É˜<ÇI <åɧ¬¤WV²-ëÉ—Õ…\ÌÚCjfe"S³n‘¸YÕÈã¬y¤uVD²¼y£AÔe9!ê:³œuÍ17KcûnÙU G¥ v„Ë6pD϶wÓ6ÄÖ¶†„Ú6ŽDÞ¶•â6Ä嶤„é6¬Dí¶³ âÛì&¦o+œÿuý½—M½|' òq{Pòñ3{Pòñ_Ï\›”Ï¿æ—ùÌ~ß}5R Ó_ hþæ—ü)‡oÈWyEŒ á;ÐG^ǃŒä]=þc½È7zÎõ–ßøFh½8¨LëýÀ̽l˜MÏ››…ù´^;ozz'^”¡Iy›Ö”W!Q59 œi{el°™¼7šiïCfú! Ì4Ñܪ ߯åëòvÀé´[º¥„4oäÂOóº.t5ïòÂ^ó¢/d6oÃmóŠ0T7ï'>ïåbˆpÞ<†×€.4¹ÆwaÍ5Ü ‰®Ñ_8u C±kÞú6ƃ_Þ+†ñ¶OÕ~XÏ¿ÒÜ¡ °HãNÑÛûï}o—n)]±AvØ‹¹Cflnc#òP ‡ùØx=DȆïáE6šM²Á}X“õC¢lèNeG¡Xv`ÆeÇ C§4_µ²Y|øû,þ‡µr2¦¹]¸dR”·§#nœn—n)˜u]X"t—}üc\7e _×-oú»¡àbÝn²Q  f• Z…Ä_O™‘÷)B·J®«¼çÎÏ Ð²j´ì©Ú€,;ÔÜ®ðîGÿzD¶¶[º¥LgdˆÏ×ð =Ì¡E{ÔÃ’¶@š¶Lð\»EJµ%†µµå þµ“èØNj`g;©¬í¤îö¼ß€JmMáËï3kJ Ô“QÍíÚxYüõ½°kÇwÄ|w»tKðÎjÀÅ;«&ï¬Ö¼³PôÎj Õ;«dÿ²áØ?of5ÃF;«ù¯‹;«ù]³š‘¼8«ùüée3«]à¬æc<T1|ÑFöÎj ÚŸÙ‹†g?ÖÜ®çñýÂõ>Èõ}Ÿ.®î–nqak3³ì5¡Ù”ZÔlY ,ª½e»5¿ˆAkŒT4ÎI[Ì4݈PKŽD5ìX;¼5ûˆ¿!ÊL:e´±wʧhc2®¹]Oϼôtýñùˆ×vK·ô@6â¸6»â0÷+Žz?s¡ø eÂ]("~éB‰iÞ–Ô4.å©Y]ŠW“¼”¶æ|)|MS›¦h6{Õ¿y¡¦ì¶SjÊd`s»6ê÷u|÷t9"ߨ.ÝÒF®¡vÚ¼FÞiˆ§El\ž²azÚËFíi>ħ5mLŸÆU8mm#þ4½¤%îx s‡i§;Z(m:›Y/Ú\>½è>—?¥ŒlnׯÃ81^^‡6žÈ7¶K·Ô® Ë‹‘.û0n ÖÒ-Ãxº¡ØR·Óª°´Jë°ÃÊ,ø‡Š+­D1Ú ®¼1éós«6ð°§j;ÚÜ!ŽñÇ>K"Ÿgëa‡É‘'2¹’ç5©“§9™”g=‰••€<Ë:AÚe! ³Æ”YÈѬO¤lV/28k Ÿw’ßùí'éÞ´áHøeQ!ü:³¨$üšmîÇÓe|Y{`2êGµDÎ~£L íÌÄÕ~ÝL˜í§ÏDÝ~MîGÓÄäëÕ#D_?·ûú-öà×µ‰çýŠ›ðÞO¼‰öýþ›à¿‡oÇߊc¤æ~iMj~f7šÔü—7c»þ<¸ù÷ÿù¿ï€F1¸ù›_2Ü1: ¨†(” :Í@„êCêK ‚¨6„¢:ÜŒêÃ7ª;þèeC?z]7'½HÈïcÖ•‡¼TÏ.S}¨Mõûa:5H æhó/”£šå@Žöf9Œ£’`G“ÁÍ­Ú#3þ­óüËv閲ƚP…DÖü*œ²¦[¡˜5û ã¬ÉXhÍÍÂGkªzZ3·°ÕšÈ…¼æ{2pÙ|ljÛúÍ0éë35Ãîú† ½7€ýª hi ÙBKÛ‡l?­&Šß nnׯûx5…¼ñzåné–r }j Š¡ï0Á8ô‘&^‡ô'øˆ>ï=Ñ·Ÿ`+ú0äE_‚Ëè“RP›ó‡éØ)@ˆ„Ù Bh‘/D Ü|Õ¤EŒ‚´¸çX€'ƒ›Ûµq˜Å÷Ñ_y&b»tK§ëÂ’¨»ì<ç¦ðV [ö0tÕÁ àU7`YÜ𖪃®n¾:¸!úsp,ÖÁ (Y7€fÜ€¡ž´:¸Òz¦6Âh nnׯëøÎ_žŽ#ø:¸Žìàv²ƒÈÊnà.;¸Êìàf³ƒž>_ŸãcOßã!A™ßCn I{ëδw P{cFµ÷ XOûð×GÆÆ{ÝgÖ”à'ƒ›ÛµÁSÅBŠß,ÝR´º7p¯{?,»·w ¶{·¨»7`¾{/$¼·† Æ{§ ¼7ŽàÍ{ ½·• Õ{— ˜½7Àxê}œ?¥ nîÐmŽšòötœOéà&p]X ¶.û0ë¦ k¹nÙ0žë†¾ç÷¾lLkÅKë+w^ŸÀÃû>^P }<+íËzmŸÝÆû&&}znÄÃúÀöLmÄÃÎ7÷Wq<Ž›©‡à[vk·49òíHr%–$uòÕI2©õIÊaP×÷*Gžåc–¤]¾tIÖ“¤¬(9ZëSR¶V¯dp­mIèZù’ßµ.&Ý›6 ¿ZT~YT~Í7·‹ãa Á¯Uåçvk·4rn+—@º^âê¶ ³Û$&ên ™ \ê-1yÛÏ„èmN±·uM߯¶ñ|ÚÞ†÷iŠí¿®¿÷um¨wcyjÞn4©ù™ÝhRó_zíS-Ÿÿß n~Û½‰V<Ð_Œnþæ—üä77êÔ…¹S¯$Ohˆ=õ©úÔņ÷SPphAõÇ ½È¬¡zë ˆê¼C*ª/Ǩ®=”£zú0êøCH2J´Š0Ì Úå ƒöv9Ä a€A“ÑÍ}Úpts9àìné–’»šQ…ëÕ+Ô¯¹oƒ Öô+İfcá‰59 m¬¹ZXdMÝB*k&ŽY»PΚç…Ö´/„´fá§5) ]Íö‹6`5f {l³ý°6š)~7º¹[#¤;äßíÒ-¥þ9º èèb £x‚Žn  :ºEèèR¡£8†Žn ®4QÝ@Ht?Ñ)tEg°0 G7ÖF¸…ŽnàîãùŸÕF°…“ÑÍ­ÚèWzïÃ]£ú£ –Žn†º°ÉÔ»ìÀFÝP¤n R7Œ©Û ät¥¿¼ì¤ U!OUf„Šôª̪€Á¶*o ®Ós#ÈÓ•3§§jâédts¯6.£¦<1ºÙ-ÝRÖ°³tHÄNÚá;‡‡bì”Ʊ3|ÈNøá#;ÿ‡žìíØÊÞ€¼ì͸ÌÞ;€Úì­˜ÎÞY€øìxÐÓ~#´d¯@K>³¦–<ÝÜ[SÞÞ®nùéáˆ(e»tKAåÞÁcî  çÞßîíéÞý îÍ ë/¾úã:º žÝ;GÐÛ½‘ÜÝûJ°ß½ÍÞ»Nã½ X^CôE Ö½FiýÌ^4œõÉèæŽscœ¯ã››ÇCÎíÒ-.lf–½>4›R—š-«‡eCu¸l·þ1莑ŠÞ!鬑™¾êÊ‘¨žëè‘·~ñ›¤L´¡YF{³|Š6&£›;´1Þ; Nàˆ^t·tKd#*Žk,sã-ŽzÃ/ ÑeÂàŒ"b¬F‰1t£ÉQž ì(^Æy”6Ã> ŸQ eÑ ¢i;©)¦lÔ”}ÊvJM™ŒnnׯûàÌ?7ð°j{ª6ð°³ÑÍíâH«1>õ}8âáˆÝÚ-MŽz"'WêyÔ©§y2©žõI¬Z ’gµN$íjIÖ“¤¬(9ZëSR¶V¯dp­mIèZù’ßµ.&Ý›6 ¿ZT~YT~ÍF7·‹ãeü±ãK΃ıY»¥‘s[¹ÒmôW· L˜Ý&1Qw[Èám0“·ýLˆÞæ4{[×ðmlϧímxŸ¦¸Ñþëú{?Ö†z7˜§æíF“šŸÙ&5ÿ•ÑÍóÛÀ}ÝüëßþPaÿø?° îèendstream endobj 3 0 obj << /Type /Pages /Kids [ 7 0 R ] /Count 1 /MediaBox [0 0 432 432] >> endobj 4 0 obj << /ProcSet [/PDF /Text] /Font <> /ExtGState << >> /ColorSpace << /sRGB 5 0 R >> >> endobj 5 0 obj [/ICCBased 6 0 R] endobj 6 0 obj << /Alternate /DeviceRGB /N 3 /Length 2596 /Filter /FlateDecode >> stream xœ–wTSهϽ7½P’Š”ÐkhRH ½H‘.*1 JÀ"6DTpDQ‘¦2(à€£C‘±"Š…Q±ëDÔqp–Id­ß¼yïÍ›ß÷~kŸ½ÏÝgï}ÖºüƒÂLX € ¡Xáçň‹g` ðlàp³³BøF™|ØŒl™ø½º ùû*Ó?ŒÁÿŸ”¹Y"1P˜ŒçòøÙ\É8=Wœ%·Oɘ¶4MÎ0JÎ"Y‚2V“sò,[|ö™e9ó2„<ËsÎâeðäÜ'ã9¾Œ‘`çø¹2¾&cƒtI†@Æoä±|N6(’Ü.æsSdl-c’(2‚-ãyàHÉ_ðÒ/XÌÏËÅÎÌZ.$§ˆ&\S†“‹áÏÏMç‹ÅÌ07#â1Ø™YárfÏüYym²";Ø8980m-m¾(Ô]ü›’÷v–^„îDøÃöW~™ °¦eµÙú‡mi]ëP»ý‡Í`/в¾u}qº|^RÄâ,g+«ÜÜ\KŸk)/èïúŸC_|ÏR¾Ýïåaxó“8’t1C^7nfz¦DÄÈÎâpù 柇øþuü$¾ˆ/”ED˦L L–µ[Ȉ™B†@øŸšøÃþ¤Ù¹–‰ÚøЖX¥!@~(* {d+Ðï} ÆGù͋љ˜ûÏ‚þ}W¸LþÈ$ŽcGD2¸QÎìšüZ4 E@ê@èÀ¶À¸àA(ˆq`1à‚D €µ ”‚­`'¨u 4ƒ6ptcà48.Ë`ÜR0ž€)ð Ì@„…ÈR‡t CȲ…XäCP”%CBH@ë R¨ª†ê¡fè[è(tº C· Qhúz#0 ¦ÁZ°l³`O8Ž„ÁÉð28.‚·À•p|î„O×àX ?§€:¢‹0ÂFB‘x$ !«¤i@Ú¤¹ŠH‘§È[EE1PL” Ê…⢖¡V¡6£ªQP¨>ÔUÔ(j õMFk¢ÍÑÎèt,:‹.FW ›Ðè³èô8úƒ¡cŒ1ŽL&³³³ÓŽ9…ÆŒa¦±X¬:ÖëŠ År°bl1¶ {{{;Ž}ƒ#âtp¶8_\¡8áú"ãEy‹.,ÖXœ¾øøÅ%œ%Gщ1‰-‰ï9¡œÎôÒ€¥µK§¸lî.îžoo’ïÊ/çO$¹&•'=JvMÞž<™âžR‘òTÀT ž§ú§Ö¥¾N MÛŸö)=&½=—‘˜qTH¦ û2µ3ó2‡³Ì³Š³¤Ëœ—í\6% 5eCÙ‹²»Å4ÙÏÔ€ÄD²^2šã–S“ó&7:÷Hžrž0o`¹ÙòMË'ò}ó¿^ZÁ]Ñ[ [°¶`t¥çÊúUЪ¥«zWë¯.Z=¾Æo͵„µik(´.,/|¹.f]O‘VÑš¢±õ~ë[‹ŠEÅ76¸l¨ÛˆÚ(Ø8¸iMKx%K­K+Jßoæn¾ø•ÍW•_}Ú’´e°Ì¡lÏVÌVáÖëÛÜ·(W.Ï/Û²½scGÉŽ—;—ì¼PaWQ·‹°K²KZ\Ù]ePµµê}uJõHWM{­fí¦Ú×»y»¯ìñØÓV§UWZ÷n¯`ïÍz¿úΣ†Š}˜}9û6F7öÍúº¹I£©´éÃ~á~éˆ}ÍŽÍÍ-š-e­p«¤uò`ÂÁËßxÓÝÆl«o§·—‡$‡›øíõÃA‡{°Ž´}gø]mµ£¤ê\Þ9Õ•Ò%íŽë>x´·Ç¥§ã{Ëï÷Ó=Vs\åx٠‰¢ŸN柜>•uêééäÓc½Kz=s­/¼oðlÐÙóç|Ïé÷ì?yÞõü± ÎŽ^d]ìºäp©sÀ~ ãû:;‡‡º/;]îž7|âŠû•ÓW½¯ž»píÒÈü‘áëQ×oÞH¸!½É»ùèVú­ç·snÏÜYs}·äžÒ½Šûš÷~4ý±]ê =>ê=:ð`Áƒ;cܱ'?eÿô~¼è!ùaÅ„ÎDó#ÛGÇ&}'/?^øxüIÖ“™§Å?+ÿ\ûÌäÙw¿xü20;5þ\ôüÓ¯›_¨¿ØÿÒîeïtØôýW¯f^—¼Qsà-ëmÿ»˜w3¹ï±ï+?˜~èùôñî§ŒOŸ~÷„óûendstream endobj 9 0 obj << /Type /Encoding /BaseEncoding /WinAnsiEncoding /Differences [ 45/minus 96/quoteleft 144/dotlessi /grave /acute /circumflex /tilde /macron /breve /dotaccent /dieresis /.notdef /ring /cedilla /.notdef /hungarumlaut /ogonek /caron /space] >> endobj 10 0 obj << /Type /Font /Subtype /Type1 /Name /F2 /BaseFont /Helvetica /Encoding 9 0 R >> endobj 11 0 obj << /Type /Font /Subtype /Type1 /Name /F3 /BaseFont /Helvetica-Bold /Encoding 9 0 R >> endobj xref 0 12 0000000000 65535 f 0000000021 00000 n 0000000163 00000 n 0000037621 00000 n 0000037704 00000 n 0000037827 00000 n 0000037860 00000 n 0000000212 00000 n 0000000292 00000 n 0000040555 00000 n 0000040812 00000 n 0000040909 00000 n trailer << /Size 12 /Info 1 0 R /Root 2 0 R >> startxref 41011 %%EOF timeSeries/vignettes/characterTableFig.pdf0000644000176200001440000007333015104730074020427 0ustar liggesusers%PDF-1.4 %âãÏÓ\r 1 0 obj << /CreationDate (D:20090325151403) /ModDate (D:20090325151403) /Title (R Graphics Output) /Producer (R 2.8.1) /Creator (R) >> endobj 2 0 obj << /Type /Catalog /Pages 3 0 R >> endobj 5 0 obj << /Type /Page /Parent 3 0 R /Contents 6 0 R /Resources 4 0 R >> endobj 6 0 obj << /Length 7 0 R >> stream q Q q BT 0.000 0.000 0.000 rg /F3 1 Tf 14.00 0.00 -0.00 14.00 172.98 397.45 Tm (Table of Characters) Tj ET Q q 73.44 73.44 328.32 299.52 re W n BT 0.000 0.000 0.000 rg /F6 1 Tf 8.00 0.00 -0.00 8.00 122.60 342.74 Tm ( ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 122.60 342.74 Tm ( ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 160.27 340.12 Tm (!) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 160.27 340.12 Tm (!) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 196.75 339.92 Tm (") Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 196.75 339.92 Tm (") Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.60 340.11 Tm (#) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.60 340.11 Tm (#) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.40 339.91 Tm ($) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.40 339.91 Tm ($) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.27 340.26 Tm (%) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.27 340.26 Tm (%) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 348.49 340.17 Tm (&) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 348.49 340.17 Tm (&) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.84 340.81 Tm (') Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.84 340.81 Tm (') Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 122.27 331.25 Tm (\() Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 122.27 331.25 Tm (\() Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 160.27 331.25 Tm (\)) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 160.27 331.25 Tm (\)) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.60 330.44 Tm (*) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.60 330.44 Tm (*) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.40 331.05 Tm (+) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.40 331.05 Tm (+) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 274.60 333.37 Tm (,) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 274.60 333.37 Tm (,) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.40 331.09 Tm (-) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.40 331.09 Tm (-) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 350.60 332.87 Tm (.) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 350.60 332.87 Tm (.) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 388.49 330.67 Tm (/) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 388.49 330.67 Tm (/) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.60 320.93 Tm (0) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.60 320.93 Tm (0) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 159.60 320.92 Tm (1) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 159.60 320.92 Tm (1) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.60 320.87 Tm (2) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.60 320.87 Tm (2) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.60 320.93 Tm (3) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.60 320.93 Tm (3) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.60 320.87 Tm (4) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.60 320.87 Tm (4) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.60 320.91 Tm (5) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.60 320.91 Tm (5) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.60 320.93 Tm (6) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.60 320.93 Tm (6) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.60 320.99 Tm (7) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.60 320.99 Tm (7) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.60 311.37 Tm (8) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.60 311.37 Tm (8) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 159.60 311.38 Tm (9) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 159.60 311.38 Tm (9) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 198.49 312.28 Tm (:) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 198.49 312.28 Tm (:) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 236.49 312.82 Tm (;) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 236.49 312.82 Tm (;) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.40 311.96 Tm (<) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.40 311.96 Tm (<) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.40 311.93 Tm (=) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.40 311.93 Tm (=) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.40 311.96 Tm (>) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.40 311.96 Tm (>) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.82 311.37 Tm (?) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.82 311.37 Tm (?) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.40 302.59 Tm (@) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.40 302.59 Tm (@) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 158.71 301.80 Tm (A) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 158.71 301.80 Tm (A) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 196.93 301.80 Tm (B) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 196.93 301.80 Tm (B) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 234.71 301.80 Tm (C) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 234.71 301.80 Tm (C) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.15 301.74 Tm (D) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.15 301.74 Tm (D) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.16 301.80 Tm (E) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.16 301.80 Tm (E) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 348.55 301.80 Tm (F) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 348.55 301.80 Tm (F) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.19 301.80 Tm (G) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.19 301.80 Tm (G) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 120.71 292.23 Tm (H) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 120.71 292.23 Tm (H) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 160.27 292.23 Tm (I) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 160.27 292.23 Tm (I) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.08 292.24 Tm (J) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.08 292.24 Tm (J) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 234.71 292.23 Tm (K) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 234.71 292.23 Tm (K) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 272.86 292.17 Tm (L) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 272.86 292.17 Tm (L) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.04 292.23 Tm (M) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.04 292.23 Tm (M) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 348.71 292.26 Tm (N) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 348.71 292.26 Tm (N) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 386.71 292.25 Tm (O) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 386.71 292.25 Tm (O) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 120.53 282.67 Tm (P) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 120.53 282.67 Tm (P) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 158.64 282.69 Tm (Q) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 158.64 282.69 Tm (Q) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.38 282.67 Tm (R) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.38 282.67 Tm (R) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.23 282.67 Tm (S) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.23 282.67 Tm (S) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.16 282.67 Tm (T) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.16 282.67 Tm (T) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.84 282.67 Tm (U) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.84 282.67 Tm (U) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.84 284.29 Tm (V) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.84 284.29 Tm (V) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 386.53 282.61 Tm (W) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 386.53 282.61 Tm (W) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.02 273.11 Tm (X) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.02 273.11 Tm (X) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 158.42 273.06 Tm (Y) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 158.42 273.06 Tm (Y) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.16 273.11 Tm (Z) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.16 273.11 Tm (Z) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 236.27 273.72 Tm ([) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 236.27 273.72 Tm ([) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 272.15 273.85 Tm (\\) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 272.15 273.85 Tm (\\) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 312.27 273.72 Tm (]) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 312.27 273.72 Tm (]) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 348.97 273.10 Tm (^) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 348.97 273.10 Tm (^) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.60 276.60 Tm (_) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.60 276.60 Tm (_) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.60 259.04 Tm (`) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.60 259.04 Tm (`) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 159.08 264.31 Tm (a) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 159.08 264.31 Tm (a) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.40 264.16 Tm (b) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.40 264.16 Tm (b) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.40 265.16 Tm (c) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.40 265.16 Tm (c) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.62 263.35 Tm (d) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.62 263.35 Tm (d) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.84 264.30 Tm (e) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.84 264.30 Tm (e) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.52 264.44 Tm (f) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.52 264.44 Tm (f) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.96 265.14 Tm (g) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.96 265.14 Tm (g) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.19 255.42 Tm (h) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.19 255.42 Tm (h) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 160.28 254.73 Tm (i) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 160.28 254.73 Tm (i) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.19 255.57 Tm (j) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.19 255.57 Tm (j) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.40 254.67 Tm (k) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.40 254.67 Tm (k) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.40 253.78 Tm (l) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.40 253.78 Tm (l) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.30 255.56 Tm (m) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.30 255.56 Tm (m) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.52 254.71 Tm (n) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.52 254.71 Tm (n) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.40 254.75 Tm (o) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.40 254.75 Tm (o) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.40 245.24 Tm (p) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.40 245.24 Tm (p) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 159.52 244.42 Tm (q) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 159.52 244.42 Tm (q) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.40 246.03 Tm (r) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.40 246.03 Tm (r) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.19 245.19 Tm (s) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.19 245.19 Tm (s) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.84 245.18 Tm (t) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.84 245.18 Tm (t) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.30 245.15 Tm (u) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.30 245.15 Tm (u) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 348.75 244.85 Tm (v) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 348.75 244.85 Tm (v) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 386.86 245.18 Tm (w) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 386.86 245.18 Tm (w) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.63 235.38 Tm (x) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.63 235.38 Tm (x) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 158.86 236.46 Tm (y) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 158.86 236.46 Tm (y) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.62 235.42 Tm (z) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.62 235.42 Tm (z) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.68 235.58 Tm ({) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.68 235.58 Tm ({) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 274.80 235.89 Tm (|) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 274.80 235.89 Tm (|) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.68 235.58 Tm (}) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.68 235.58 Tm (}) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.40 235.50 Tm (~) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.40 235.50 Tm (~) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 389.60 237.54 Tm () Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 389.60 237.54 Tm () Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 123.60 227.98 Tm (€) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 123.60 227.98 Tm (€) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 161.60 227.98 Tm () Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 161.60 227.98 Tm () Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 199.60 227.98 Tm (‚) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 199.60 227.98 Tm (‚) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 237.60 227.98 Tm (ƒ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 237.60 227.98 Tm (ƒ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 275.60 227.98 Tm („) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 275.60 227.98 Tm („) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 313.60 227.98 Tm (…) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 313.60 227.98 Tm (…) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 351.60 227.98 Tm (†) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 351.60 227.98 Tm (†) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 389.60 227.98 Tm (‡) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 389.60 227.98 Tm (‡) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 123.60 218.42 Tm (ˆ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 123.60 218.42 Tm (ˆ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 161.60 218.42 Tm (‰) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 161.60 218.42 Tm (‰) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 199.60 218.42 Tm (Š) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 199.60 218.42 Tm (Š) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 237.60 218.42 Tm (‹) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 237.60 218.42 Tm (‹) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 275.60 218.42 Tm (Œ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 275.60 218.42 Tm (Œ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 313.60 218.42 Tm () Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 313.60 218.42 Tm () Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 351.60 218.42 Tm (Ž) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 351.60 218.42 Tm (Ž) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 389.60 218.42 Tm () Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 389.60 218.42 Tm () Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 123.60 208.86 Tm () Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 123.60 208.86 Tm () Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 161.60 208.86 Tm (‘) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 161.60 208.86 Tm (‘) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 199.60 208.86 Tm (’) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 199.60 208.86 Tm (’) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 237.60 208.86 Tm (“) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 237.60 208.86 Tm (“) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 275.60 208.86 Tm (”) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 275.60 208.86 Tm (”) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 313.60 208.86 Tm (•) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 313.60 208.86 Tm (•) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 351.60 208.86 Tm (–) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 351.60 208.86 Tm (–) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 389.60 208.86 Tm (—) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 389.60 208.86 Tm (—) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 123.60 199.29 Tm (˜) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 123.60 199.29 Tm (˜) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 161.60 199.29 Tm (™) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 161.60 199.29 Tm (™) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 199.60 199.29 Tm (š) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 199.60 199.29 Tm (š) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 237.60 199.29 Tm (›) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 237.60 199.29 Tm (›) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 275.60 199.29 Tm (œ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 275.60 199.29 Tm (œ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 313.60 199.29 Tm () Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 313.60 199.29 Tm () Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 351.60 199.29 Tm (ž) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 351.60 199.29 Tm (ž) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 389.60 199.29 Tm (Ÿ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 389.60 199.29 Tm (Ÿ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 120.60 187.04 Tm ( ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 120.60 187.04 Tm ( ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 159.12 186.99 Tm (¡) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 159.12 186.99 Tm (¡) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 198.61 184.95 Tm (¢) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 198.61 184.95 Tm (¢) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.40 187.17 Tm (£) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.40 187.17 Tm (£) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 274.93 187.07 Tm (¤) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 274.93 187.07 Tm (¤) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.75 187.62 Tm (¥) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.75 187.62 Tm (¥) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.60 187.76 Tm (¦) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.60 187.76 Tm (¦) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 386.59 187.70 Tm (§) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 386.59 187.70 Tm (§) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 120.59 178.11 Tm (¨) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 120.59 178.11 Tm (¨) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 158.59 178.17 Tm (©) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 158.59 178.17 Tm (©) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 196.59 178.12 Tm (ª) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 196.59 178.12 Tm (ª) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 233.43 178.18 Tm («) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 233.43 178.18 Tm («) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 271.65 178.18 Tm (¬) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 271.65 178.18 Tm (¬) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.19 176.53 Tm (­) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.19 176.53 Tm (­) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 347.65 178.18 Tm (®) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 347.65 178.18 Tm (®) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.19 176.70 Tm (¯) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.19 176.70 Tm (¯) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 122.00 166.32 Tm (°) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 122.00 166.32 Tm (°) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 159.40 168.02 Tm (±) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 159.40 168.02 Tm (±) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.96 165.82 Tm (²) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.96 165.82 Tm (²) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.40 168.05 Tm (³) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.40 168.05 Tm (³) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.40 168.47 Tm (´) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.40 168.47 Tm (´) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.75 168.49 Tm (µ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.75 168.49 Tm (µ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.62 167.70 Tm (¶) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.62 167.70 Tm (¶) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.76 168.26 Tm (·) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.76 168.26 Tm (·) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.40 158.93 Tm (¸) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.40 158.93 Tm (¸) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 159.40 158.94 Tm (¹) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 159.40 158.94 Tm (¹) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.40 158.94 Tm (º) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.40 158.94 Tm (º) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.40 158.92 Tm (») Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.40 158.92 Tm (») Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 271.60 160.73 Tm (¼) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 271.60 160.73 Tm (¼) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.19 157.48 Tm (½) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.19 157.48 Tm (½) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 347.60 159.06 Tm (¾) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 347.60 159.06 Tm (¾) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 386.97 158.59 Tm (¿) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 386.97 158.59 Tm (¿) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 120.31 148.92 Tm (À) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 120.31 148.92 Tm (À) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 158.86 148.73 Tm (Á) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 158.86 148.73 Tm (Á) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 196.42 148.60 Tm (Â) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 196.42 148.60 Tm (Â) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 233.65 150.03 Tm (Ã) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 233.65 150.03 Tm (Ã) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 272.53 148.85 Tm (Ä) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 272.53 148.85 Tm (Ä) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.53 148.84 Tm (Å) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.53 148.84 Tm (Å) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 348.31 148.70 Tm (Æ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 348.31 148.70 Tm (Æ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 386.53 149.44 Tm (Ç) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 386.53 149.44 Tm (Ç) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 120.53 140.01 Tm (È) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 120.53 140.01 Tm (È) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 158.75 140.03 Tm (É) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 158.75 140.03 Tm (É) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 196.75 140.53 Tm (Ê) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 196.75 140.53 Tm (Ê) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 234.75 140.03 Tm (Ë) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 234.75 140.03 Tm (Ë) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 272.75 140.03 Tm (Ì) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 272.75 140.03 Tm (Ì) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.75 140.53 Tm (Í) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.75 140.53 Tm (Í) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 348.75 140.04 Tm (Î) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 348.75 140.04 Tm (Î) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 386.75 139.92 Tm (Ï) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 386.75 139.92 Tm (Ï) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 120.53 129.66 Tm (Ð) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 120.53 129.66 Tm (Ð) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 158.75 129.55 Tm (Ñ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 158.75 129.55 Tm (Ñ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 196.44 129.73 Tm (Ò) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 196.44 129.73 Tm (Ò) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 234.44 129.71 Tm (Ó) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 234.44 129.71 Tm (Ó) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 272.04 128.49 Tm (Ô) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 272.04 128.49 Tm (Ô) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.31 129.75 Tm (Õ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.31 129.75 Tm (Õ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.40 128.83 Tm (Ö) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.40 128.83 Tm (Ö) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 388.60 130.27 Tm (×) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 388.60 130.27 Tm (×) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 120.75 121.63 Tm (Ø) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 120.75 121.63 Tm (Ø) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 159.19 120.97 Tm (Ù) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 159.19 120.97 Tm (Ù) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.19 120.88 Tm (Ú) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.19 120.88 Tm (Ú) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 233.43 120.83 Tm (Û) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 233.43 120.83 Tm (Û) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 271.65 120.79 Tm (Ü) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 271.65 120.79 Tm (Ü) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.19 119.13 Tm (Ý) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.19 119.13 Tm (Ý) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 347.65 120.83 Tm (Þ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 347.65 120.83 Tm (Þ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.19 119.30 Tm (ß) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.19 119.30 Tm (ß) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.62 110.24 Tm (à) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.62 110.24 Tm (à) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 160.28 111.03 Tm (á) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 160.28 111.03 Tm (á) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 196.44 110.62 Tm (â) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 196.44 110.62 Tm (â) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 234.44 110.58 Tm (ã) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 234.44 110.58 Tm (ã) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 272.46 109.36 Tm (ä) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 272.46 109.36 Tm (ä) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.75 110.65 Tm (å) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.75 110.65 Tm (å) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 350.06 110.69 Tm (æ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 350.06 110.69 Tm (æ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 388.06 109.86 Tm (ç) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 388.06 109.86 Tm (ç) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 122.06 101.13 Tm (è) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 122.06 101.13 Tm (è) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 160.06 100.28 Tm (é) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 160.06 100.28 Tm (é) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 198.06 100.28 Tm (ê) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 198.06 100.28 Tm (ê) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 236.06 100.28 Tm (ë) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 236.06 100.28 Tm (ë) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.62 100.30 Tm (ì) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.62 100.30 Tm (ì) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.62 100.26 Tm (í) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.62 100.26 Tm (í) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.62 100.22 Tm (î) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.62 100.22 Tm (î) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.62 100.26 Tm (ï) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.62 100.26 Tm (ï) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 123.60 94.10 Tm (ð) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 123.60 94.10 Tm (ð) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 160.28 91.90 Tm (ñ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 160.28 91.90 Tm (ñ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 198.50 90.86 Tm (ò) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 198.50 90.86 Tm (ò) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 234.86 90.77 Tm (ó) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 234.86 90.77 Tm (ó) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 272.86 90.55 Tm (ô) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 272.86 90.55 Tm (ô) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.86 90.76 Tm (õ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.86 90.76 Tm (õ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 350.06 91.56 Tm (ö) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 350.06 91.56 Tm (ö) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 388.06 90.74 Tm (÷) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 388.06 90.74 Tm (÷) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 122.06 82.00 Tm (ø) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 122.06 82.00 Tm (ø) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 160.06 81.15 Tm (ù) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 160.06 81.15 Tm (ù) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 198.06 81.15 Tm (ú) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 198.06 81.15 Tm (ú) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 236.06 81.15 Tm (û) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 236.06 81.15 Tm (û) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.62 81.17 Tm (ü) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.62 81.17 Tm (ü) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.62 81.13 Tm (ý) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.62 81.13 Tm (ý) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.62 81.09 Tm (þ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.62 81.09 Tm (þ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 389.60 84.53 Tm (ÿ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 389.60 84.53 Tm (ÿ) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 121.38 359.13 Tm (0) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 159.38 359.05 Tm (1) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 197.38 359.05 Tm (2) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 235.38 359.13 Tm (3) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 273.38 359.05 Tm (4) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 311.38 359.19 Tm (5) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 349.38 359.13 Tm (6) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 387.38 359.11 Tm (7) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 83.38 339.93 Tm (4) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 83.38 330.50 Tm (5) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 83.38 320.88 Tm (6) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 83.38 311.30 Tm (7) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 81.15 301.75 Tm (10) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 81.15 292.11 Tm (11) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 81.15 282.55 Tm (12) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 81.15 273.06 Tm (13) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 81.15 263.42 Tm (14) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 81.15 253.94 Tm (15) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 81.15 244.37 Tm (16) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 81.15 234.73 Tm (17) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 81.15 225.25 Tm (20) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 81.15 215.61 Tm (21) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 81.15 206.04 Tm (22) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 81.15 196.56 Tm (23) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 81.15 186.92 Tm (24) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 81.15 177.43 Tm (25) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 81.15 167.87 Tm (26) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 81.15 158.23 Tm (27) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 81.15 148.74 Tm (30) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 81.15 139.18 Tm (31) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 81.15 129.61 Tm (32) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 81.15 120.05 Tm (33) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 81.15 110.49 Tm (34) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 81.15 100.92 Tm (35) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 81.15 91.36 Tm (36) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 81.15 81.80 Tm (37) Tj ET Q endstream endobj 7 0 obj 29175 endobj 3 0 obj << /Type /Pages /Kids [ 5 0 R ] /Count 1 /MediaBox [0 0 432 432] >> endobj 4 0 obj << /ProcSet [/PDF /Text] /Font <> /ExtGState << >> >> endobj 8 0 obj << /Type /Encoding /BaseEncoding /WinAnsiEncoding /Differences [ 45/minus ] >> endobj 9 0 obj << /Type /Font /Subtype /Type1 /Name /F3 /BaseFont /Helvetica-Bold /Encoding 8 0 R >> endobj 10 0 obj << /Type /Font /Subtype /Type1 /Name /F4 /BaseFont /Helvetica-Oblique /Encoding 8 0 R >> endobj 11 0 obj << /Type /Font /Subtype /Type1 /Name /F6 /BaseFont /Symbol >> endobj xref 0 12 0000000000 65535 f 0000000021 00000 n 0000000163 00000 n 0000029541 00000 n 0000029624 00000 n 0000000212 00000 n 0000000292 00000 n 0000029520 00000 n 0000029727 00000 n 0000029821 00000 n 0000029922 00000 n 0000030027 00000 n trailer << /Size 12 /Info 1 0 R /Root 2 0 R >> startxref 30105 %%EOF timeSeries/vignettes/colorwheel.pdf0000644000176200001440000014243415104730074017242 0ustar liggesusers%PDF-1.6 %âãÏÓ 7 0 obj <> endobj 13 0 obj <>/Filter/FlateDecode/ID[<295E44FDAE254B2AA171513D3A17C178>]/Index[7 8]/Info 6 0 R/Length 40/Prev 50176/Root 8 0 R/Size 15/Type/XRef/W[1 2 0]>>stream hÞbbd`b`Òbb`°ab`< ¤#˜8ÿ01|y äÛ1°Æ endstream endobj startxref 0 %%EOF 14 0 obj <>stream hÞb```a``|Ï›Ö2`30D00ä20p,Yaâ230–ÏÒŒ@| Àðr¾ endstream endobj 8 0 obj <> endobj 9 0 obj <>>>/Rotate 0/Type/Page>> endobj 10 0 obj <>stream q 180 0 0 199.9199982 0 0 cm /Im0 Do Q endstream endobj 11 0 obj <>stream H‰ìÒA ¡þ/i1ç4ÈBÖŠ}"Î<ˆ°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ûé°Ï.ÔÂóÿÂÑ0FÈ„\«¡¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Óa;¶Ó1ïCĹP± Š#© endstream endobj 12 0 obj <>stream ÿØÿîAdobedÿÛÅ     '$''''$25552;;;;;;;;;;   %%#(((((#,0000,7;;;7;;;;;;;;;;   %%#(((((#,0000,7;;;7;;;;;;;;;;ÿÝÿÁAî"ÿÄr    a!16ƒ²Aa"2QTq“ÑBU‘¡¢±ÁÒ#$35CRSbert’Óá%&D‚£³Âñ '()*4789:EFGHIJVWXYZcdfghijsuvwxyz„…†‡ˆ‰Š”•–—˜™š¤¥¦§¨©ª´µ¶·¸¹ºÃÄÅÆÇÈÉÊÔÕÖרÙÚâãäåæçèéêðòóôõö÷øùú O9!AQ12Raq‘Ñ"#3B± $%&'()*456789:CDEFGHIJSTUVWXYZbcdefghijrstuvwxyz‚ƒ„…†‡ˆ‰Š’“”•–—˜™š¡á¢£¤¥¦§¨©ª²³´µ¶·¸¹ºÁÂÃÄÅÆÇÈÉÊÒÓÔÕÖרÙÚâãäåæçèéêðñòóôõö÷øùú !1Aa‘2q’¡ÁÑ 3BQ‚±á"#$%&'()*456789:CDEFGHIJRSTUVWXYZbcdefghijrstuvwxyzƒ„…†‡ˆ‰Š“”•–—˜™š¢£¤¥¦§¨©ª²³´µ¶·¸¹ºÂÃÄÅÆÇÈÉÊÒÓÔÕÖרÙÚâãäåæçèéêðñòóôõö÷øùúÿÚ "?édVEd寓”µõô¼Òy8|7ðœ˜àåDÌŠw<2C°¼Ûûâî5B‹i¾¥8ÇÆHv›|xd‡ay·÷Êp1àq’…æßß!Ø^mýòœLxd‡ay·÷ÇÆHv›|§!Ø^mýñàq’…æßß)ÀÇÆHv›|xd‡ay·÷Êp1àq’…æßß!Ø^mýòœLxd‡ay·÷ÇÆHv›|§!Ø^mýñàq’…æßß)ÀÇÆHv›|xd‡ay·÷Êp1àq’…æßß!Ø^mýòœLxd‡ay·÷ÇÆHv›|§!Ø^mýñàq’…æßß)ÀÇÆHv›|xd‡ay·÷Êp1àq’…æßß!Ø^mýòœLxd‡ay·÷ÇÆHv›|§!Ø^mýñàq’…æßß)ÀÇÆHv›|xd‡ay·÷Êp1àq’…æßß!Ø^mýòœLxd‡ay·÷ÇÆHv›|§!Ø^mýñàq’…æßß)ÀÇÆHv›|xd‡ay·÷Êp1àq’…æßß!Ø^mýòœLxd‡ay·÷ÇÆHv›|§!Ø^mýñàq’…æßß)ÀÇÆHv›|xd‡ay·÷Êp1àq’…æßß!Ø^mýòœLxd‡ay·÷ÇÆHv›|§!Ø^mýñàq’…æßß)ÀÇÆHv›|xd‡ay·÷Êp1àq’…æßß!Ø^mýòœLxd‡ay·÷ÇÆHv›|§!Ø^mýñàq’…æßß)ÀÇÆHv›|xd‡ay·÷Êp1àq’…æßß!Ø^mýòœLxd‡ay·÷ÇÆHv›|§!Ø^mýñàq’…æßß)ÀÇÆHv›|xd‡ay·÷Êp1àq’…æßß!Ø^mýòœLxd‡ay·÷ÇÆHv›|§!Ø^mýñàq’…æßß)ÀÇÆHv›|xd‡ay·÷Êp1àq’…æßß!Ø^mýòœLxd‡ay·÷ÇÆHv›|§!Ø^mýñàq’…æßß)ÀÇÆHv›|xd‡ay·÷Êp1àq’…æßß#/'&lkž‰öTÅÓI"=xNv(ˆŠšUzæ´g7ÅôËfýí—Îh~î5B‹i¾§“/m:Û''%­³åXgl±5ˆŠ¸*àºq>—qª[Mõ(*i)«bX*âdñ*¢«$DsqM”èÿ+¼PÀ³ÒŽò»Åü =)±t7“þ'Sw&w‰+Ͳ,ªlÔtÁ'=FÞlk‚£³bˆ ‘]åwŠøzQÑþWx ÿg¥'‚ï¬["³%©§ª£‚i\éq|‘µÎ\¸gT€èÿ+¼PÀ³Ò•VöT[´™cZTõNeMSœ“HˆÜ]‚/&ÇCy?âu7rgx½˜!¦±¬è)ØØ¢eKѬb#Z‰Á]€Á%_ek¥cVÐz¢½¨½+4*§¸›tJ®‰Ž\ê¬j¯~qƒ×ñýìgž‡èØ=s¼7Î@ež@0Zд(ìºGÖ×JØa1sç']OdÇo3(¤´í‡Yp¿ÖZà«SC¥ðÎ^Ö„÷í»Ù­–GEaÂØbEÁ&™8r//BseÖVN¸ºÑ•¼Œà±<¤8qÆùdlQ§ ïr5¨šUUpD6 š»{ϤŽ[V¬¬{QÏI3ÄÅ\üM ‡]A“:‹.r®%Ŷ”«ï\'–‡f̽L¡‚XÙ\ÔÄ®j=\ÞðUθµPÒß’ù;#x³i•>õ±<ä8V½×äõ{Ô(ê ¸–5WG+U|à øäl±¶VgkÚŽEäTűéXôµ4V]5[ÒI ‰±¹íÇps"çäCÝq²¾¶ªÎɺêÚ'¬SÅ+˜*¢ðšœ}³²Oåî¨Ú_z[¾Ð ¯£ü®ñAÿÏJkÙ)YShdí e[ÖI¦#×U\W>c7œˆÕK3ßtóÔgp [—™E•¿%=-k㥙–ðZ¨ˆ¹œ™Ó‰PàÓÞUGQæ®t‘¶F«Ø­fj.tÑÖ-/jÊç‹& R4Åô’ð¾á&o= ˜?HSNʪxªb\Y,m{W‘ÈŠžyÏÊu´aUËeʰÕEÉšˆ«ÒtÊ™ñâC•vÖ§©˜†7®2Q¹`w_ÎÏ)O>_Zþ¢2j¥ì\%¨Nw‹¯‹ó*ùƒ]ÑþWx ÿg¥*.÷--[FÚ[:Ø©YÛL¨€ÁIO•™j½Î¡š¢l4¤¢µ<†ày£¼ µ±§Hí,_׊¦.ªr*#TÖlË6’ɢІ‰q5˜&¯¯]Tôr¦Â¥·¬‰é¦b,­Ï‚L:f½©Š`¾xƒ$ò¾‡*iܱ'1ª‰›@«Š¦>«Æ‡|Ár>Ò–ÆÊJI‘x-tÉÉÄ­zðUÏ7 ÅU2SÓK:èŠ7¿àQWÐ?:UNêš™j¸ºYõ^W*©úßUm‡^­Ò”“að*~z@ewBÚüª£cÓ®™SÞòÍÈÇn¥¢uÇŠ’\<ɱ >Q̉¥Lÿ)ïJ:ßCaFÚ‰#UkêŠÄŠšQ¨˜cÛ:÷lÉddëÙNîÕæ rfTj¦/Tñ³´qºYs¹îF§mW¦èç-«ä^w©‘Ëí0F˜'Õ>c¼ ² —ƒ=Cœ©¦:ˆÓ¼ŠkVGbYQSFÖ«co4r"pžü:g*ñç8w“bÓWäôÕ©yæ‹ Y""p¸8¢9¸õ°PI^=-¹3lûI¥¬~h܋ꩬ˜çEä:¹{ª6—Þ–ï´Ã#‘ñHÙcUkØäs\™•3¢› µi-¯v²Úꦣ_ïHöµÞZ&8o9ª–g¾é穃vOW²ËÈkAú)è'mS…‚y 3é•Ù{C“Kηž«•1æH¸1ˆºËèýMãeu|¼yÒô±Ác壕IªÊ¹ëª¥¬©r¾Yžç½ËŸ:ç5«±°©i,6Z’F×TÖ9ÎæŠˆ®F"àÔN¶€ge®\P¹QS31ГFˆ‹ðMB'¯^GLÊl ‰¨Ç*'<Äœ+›Ÿ7h¾µìºK^‚j¸ÚöHÇ"b™Ú¸fru•ü÷Q ©ê%§UŽböÚªž€èÞjÉ)ù´.G5Ñð˜äÎŠŠ˜¢¡ŠO—ùZÉäck܈ÙˆœhETO [Ý…­%vNÍC3¸O¡r±¸ç^“§œÉê~šfûÚýå»±/:®ŠË©’Õ{««]+Rž5DcQ¼êåDщ͚ð2ÒÓ•Vëcš:h¸Hž>SÁ™*Ü¥´ÏX¥*#¦Ã2¹W©f<¸g6j+>ŠÎ…´ôP2Ú˜#XÔO›˜»«fص)*ã¶ÖGOÍବænà94hN4+ÏŒ3¢ ÀzVÕžËVɪ³Þ˜¤ð½©Èì1jù'ç©b|¾Æ÷1ÉÖV® ~’1Ų½Eå4îbaZ%C:Ø»3“É@Öº[Síj‹-ëƒj¢á±=Î=>RŸ±kóÕ« “±e|94sG÷šHØv›ì{Z–Òf+ÎòµÊ‰ÆÝOù«ž¦Þ¶d›tÕÕ=*i^Ø5<€dîÐd‹ªò&®ÝàªÔ2d’ºÅgù8ããgè{:ʧ¡²!²¨±GN¹:ø¦_UL"ß²ßc[VsÓ×2¹½v.v¯ •Ø[)Y“«I3º{=ÊÅÇÛkÓ5|ô2ë~½ö½¹WZ77¨w=Ç ÈD<Ù?”ØIZ‘b©YG$'—©wœòä]™ê[)h©œœ&6Nk'¼ÇÓ/–€6NYÉdØTt˜,P7‡ïNN¼µ0‹_صo¿So¸ýÄ~wµý‹Vûõ6û€GO&2¶¯&"ªm-–j®5ÏÅZÞz‘jÒ$LxO‡áËÒ|ž½zŽnÊl ®Ê爓‚æò¹º;Fš¨Š˜.tëýèX4öU­m8«˜ç9L’5zeNÞ Á¯Ç$sFÙbr=j9®L訹ÑI¬®Ëš™D§cyæ¹íŰ¢àÖ§½x»G:ï-îHÎú§p½EsDÅtð¼6§ ev|ötõõ.WI<ŽzªòèNÒ ÷‡–6œüÎ’^f®^–*xÑWµ¡Ê§’L¨¼K-¢­Õ ¯<(¬ññi_uö=ˆÛYìE©¬s•©Š¶6ªµ:Øá‰g,QÍ¢™¨ö=×&(¨¼J€y)yÌ´ªc³­¸Û²ª69Ùš79t#‘tbw/Tkýá›Í2̶±£°2ŠzZdàBî Ч´£³à¥BêÑ´ßkÝs«$\dZv2Eë¹Fªù@1½äf«Yžú°Á ï#5ZÌ÷Õ€Ë;@`s|_L¶oÞÙ|æš1œßÓ-›÷¶_9 û¸Õ -¦ú”äÅÜj…Ó}Jpzú²ß"óœZw¯«-÷ò/9Àñ¶Ý®¨Ò{Ü»ÊbFÛvº£Iïrï(2ʃ>¾a”ü¿qMϯƒØe¿/ÜP`Ëàõü{ç¡ú6\Çï óüå¯ãûØÏ=Ѱzæ?xoœ€Ë=¤öh{é6ê˜%ôÝM÷¾-ä7¼¤öh{é6ê˜%ôÝM÷¾-ä¼¾ Uj¬ØqÌÊü9UQ=‚²k:¶ êtE–žFÈÄrbÜ[1C@¾ wpìÚ¼:^±*òô®B̪J+Fš­ÈŽl3ÆõEΊˆ¨«åUø*åG´S÷7z`·©• Š‹> ˜zíÞ˜Õᦳ§…“ÇJÉ×µx ÎŽLSˆûócÅð ïçªu–JøåFà÷T5Ù‘pÅ\Š~Šf<Ǭ‡‰(h‘qJx‘SÜÞ<à«iÃÏuTܧ•¾KUήjµÊÕÒÕT_1úMSÁL +,·ØùAYFäÁ¼ÕÒGÊÇôÍ_,GVì&H²²¯³`™ž>úÒ~z°m%±íŠKI4A3\äëµs;ÊSô =DUPGS‘ñJÄ{™ÑQSäøsšÔW9QÜ›_ŽÂ͇‹ãô¨@ØGÛ4-v…«…àÐЯr&ÔÙöu£¤‘$¯g ¹ÛƒÑÎ,éR BšuÌ‘ÔDåí#‘A“ôiÌÊv$™=hµÚŽo)ª§EŽG±®LèäEOäå|í¦É›JW.²½©ÛwJžx0`F—g½ÏºJ”_ ’5;\ÕÑ3CRŠ•Ô·K#^˜,”ë.÷**y@É–š¥tŽŽébàøjh𽥓9•šÔ”®«º–FÄÅÍ¡lˆžðþùH’›îH5“k[£¾Ns7|…µ)g9«öÚª€3¼~|ÊF$yAh±4%dÛʧè%TDÅxÏÜÉSl×NÜé%TÊ®ƒ¶º'¯4µcâX#wÓ!SôÓ7Þ×ï)¢Ý+’šÔ¬Té]À‰´ŽrùæuSôÓ7Þ×ï(2i·=ì>Ð÷â=ÓC3Ûžöh{ñé¡;Ù²¹æÈ‚Ôbbú9x/_p“7žˆ^•³g²Õ²ª¬÷¦)÷=S{è›Êt®ž>meZðûqZß%Ž@dÍ^ªç¹Ë¥UWÉ(, ¶¶²v‰Ô6{bX"Ȫö+Š¢"çEN±Àž7C4‘=0tosU9QUÒn–¢’¢–²Íž8ß$r6fpÚ×/ÉÁ])×@?‚®T{E?sw¦8ùE•–¶S2Z,ÎV,mV¯M‚.9׬n\ãCØñ|{Üh{/€ox2,—’H23)Y S¶ìQ|¢8Þ2º‚92^Ò‚š&±VÏÁFãÀÁÜIÈ`àÉUeÞ&PYTÙÔ¬…a§gŠæ*» 9ׄ‡µà«•ÑOÜÝé‹ ¶šŠÑɘ£|1>ZG¾ªµªì1á5W7YJ®q¡ìx¾½à`ÁmûzÐÊ:Æ×Wµ©##HÓ™µZ˜"ªõ×®XYü/z´voVɇkš4ҹƇ±âø÷Ž_GY^ÈšÖ7€Þ•¨N©½` <Þò3U¬Ï}X`†÷‘š­f{êÀe 09¾/¦[7ïl¾sMÎo‹é–ÍûÛ/œÐýÜj…Ó}Jrbî5B‹i¾¥8‹½}Yo¿‘yÎ-}µaÐ[ôiChµÏ…’`×+Ç éÛüôm·kª4ž÷.òž/ü“ö̽Õå “dÑØ”,³¨­‚5rµªåé—ΠáŸ_°Ê~_¸¦‚ríÜœ³2Ž ´Øç²«ÙÀr±qTȃ×ñýìgž‡èØ=s¼7ÎB]·c’r9!—ª*zµüYʶµÔjhDDO”žÀ-}&ÝS³¾›©¾÷ż‡èzºX«i¥¤c™Žè‹‚àäÁs“Q]žJÃ#%d2££r9¾­zçEÅ=ì°Éîˆì7ÑÇ‚TG„´êº8mM:f0ÚºJš‡ÒÖFèfUǦ ˜£Î}©`XöÓx6•,sª&åL§& äåÅeÐGf[1¾F@œ¦9Ä×"ªhëÊ‹ÚÉèÚ«5;‰8-by*å<Ó]VKÈåXù¼\“óH¢ªÉxÕüñ.N“ò‘=L—ËKW+2‰ c–‚ž'ÊøÛÓ=ËÔµîÚèBôô,« ɱ±Ù”̃„˜9ÍL^ì:긪žø$rû#—(é[YBˆ•ôÍTj.d‘šxõúÅpüßSKQG3©ê£t2±psŠ×"øç{'òîÜÉèR–6zd^–±To¼ªgCd´¬;&×oÑ¥Ž~³œÔá'iS9==Öä¬ÎW1“C$\<Ò8%%½ÛmÍÂ*Jv/]xnôPàZÙe”vâ,5UNHÝ›˜ÂœÍ‹É‚g_Ñ¢ºœ˜bâõ¨“‘Ò"'”Ô;vfIdí¨ú*(ÚôÑ#Óš?Év ÁêTäó-Œ‚Æ“¤zQÃÌܾFµ«äé1kFά²ªßE]¡š7**90Ç•:è~Œ=RIJ­˜ù•¥MB&…rtÉÚT΀îM^”T6|T6Ì2JèŒdñ`ªæ¦dá"ªgÃŒô2Îð“(hýFYðº g9+äTῃÐ…l×U’ò¹]7‡&ÉŠy¤SÉKuù+Nä{â–£)dUoÔha“7Y”–ŒtÐ1R¹y°éÎ<ý~²ÎZÁ.EWSBœ⦌N²5ÌD;´t4v|)OC ‰º#PúZVu5­C-ŸXŠè'j5èÕV®¨ºS´ùÐݲ6&O‘ö|2§ ’R+\‹ÆŠ®E=ü“ö̽Õå%AOeÑCgÒ"¶Àb9Uˆ+ÛÃò³&+2nÑ’'±V–G+©æÃ¥V®„Uë¡ÒȬ¼~LÆú¸QF÷pÚTG±Ë§ s*/XØj¨ék¡u=dLž'icÑß,™«»•©z½‘IN«ÅÕä/‡mÞÅ,Ô2SÙò$Ò±X’˃Q˜¦ ¨ˆ«Š™Õ]¥TÊJ8Ý4ò»kSÅxÔÖẬ—Ü'óļޓòš…•`Ù+8e+ Å0W"bõí¹qPS&läí€Ë?t¼>g'ŽLþ2h0ªŸ¦™¾ö¿yOÑîDr+WB¦ J>ìrQïsÝ ¸¹Êåõkôªâ ›žöh{ñé¡» &켜ŠXlÆ9™È÷ðܯΉ‚i:€cùY“?.ÙCpŽÓ•“7 Ž_Vù*šäÇO tñ'‘1¬ju‘©‚ ¬ÚYí-9ŒôÌ‘‘»¬’aœ{`?”Ù,æåäT17-)Ù3pЈ«Œ‰ã`¦¼Æ6666& kQ¨œ‰™^{6–¦ºšÐ•¸ÏH’$NësDÁOhátíbÕ¾ýM¾ãôI+=ÚäµDòTK ªù^ç¹RW¢båÅ|ð ëê­=Ì,äX9-ddç6[-g7àðøoWõ8á§¶uÀ2KÜö=Mï¢o)׹ߦ;Gït[®*-ÌŽ±2†¥•v”o|‘³™µX÷10Ç Û<öMYy9±ÙŒs;šçðܯÎÔÁ4öÀ3;ÅÉ:›6Җפ_ETå{•©3zõH½dUΊNäõ»U“¶œv•.VâÙ#^¥ì]-Sô ãd¬Xäj=ŽL×"**r¢“•÷y’µïYKÌìê°9cO#G”Άöruñ#抢90ÎÄj;?o„‡6Ô½èR72È£r½S4“ª#S—‚˜ãä?lšácÍ*pëpÛé~‚îòVé"R¬ïjâ‹;–Dò3'”ÐÉÕ«¬°)äµ]Íf«…d›Á0“àáÖÁp1ì®ÉzÌ›´dc˜çQÈõu<ȘµZ¹ø*¼J†ìÖµF1­j""&dDN#ÇSKMY  ª‰³Dí,z#š¾H‘ù[>KV¾N6¥&‰Í¡Éʆ౓\ÍÀ©áaÔðÏáÝeÚd¥[•íôê¾Ùzµ<…ÅQ·O“H¸¬•.N· ©ó=§zõ5Žm…KÌ_+‘šeG9Ë‚`ÔÍŽ~2—-Ù$yTÉœ¯‘°DréWbÜWÉ=›/!òjÈ‘³SR#åbâÙ%U‘ȩƘæC©jY”¶½ ¶}j+ ™Ujæ\t§hó©½äf«Yžú°æx䟶eî¯)h(iìÚ8h)Q[ F1xK‚r€{g7ÅôËfýí—Îi£Íñ}2Ù¿{eóšß»P¢Úo©NL]ƨQm7Ô§s|_L¶oÞÙ|æš1œßÓ-›÷¶_9 û¸Õ -¦ú”äÅÜj…Ó}Jpg7ÅôËfýí—Îi£Íñ}2Ù¿{eóšß»P¢Úo©NL]ƨQm7Ô§Ç,ј¹sñ'èËW,™“¥oY òë“ÈqW-Kƒmoë!JSÌ[¹ï>x£êœ˜õ´©áu|iÔµWÊ=kÑò‘Ê¢.3FÂKÊ%ÁZ^¹PŠÇ„öÖÐwÉ h;‰äž :ÙKçžÎ^ÂÜ#ëLÃv=öׯ½SU<³ÌÉ¢“¨r/'Ê4¨HåP×µ±Rò…pR—„|Ê„^,dè«$3ºfòé=è¦d­Å‹ÛN3aجž_I‹C«Io«š'NPÇ‹w>à¾| æø¾™lß½²ùÍ4c9¾/¦[7ïl¾s@;÷qª[Mõ)É‹¸Õ -¦ú”àEJB˜&w®„>óʱ\ºx“”å¹Î{•Î\UH¶5b¸†Û&ãg¥­Å+ÞÖ7Q¥…âGËžç¹\åÅTú€iÎr¹U\³ª­5Zk9ÔÀû1îÜ&. ‡Ôšç1QÍUEE),ãÓ§¨lÍë94¡æ9 {£r=¹•¤2¶V#ÓÇNSr°kÄÖYQW1-nI^ö¹ÉV㺱3î,€Íñ}2Ù¿{eóšhÆs|_L¶oÞÙ|æ€wîãT(¶›êS“qª[Mõ)À⨓™Âçqá‚xç±â:ÚÆª­â$æRºÒÝÏJ®^k*¢u-̇€ÎåÝ)Šø¯¦ç¹Wľ;¢”RK Œ€Ø£›™ÉÁ^¥Ù¼s×3™¤²‡Ic2+-XéïRª_˜”T“O)ÙÒóHšþºg>ç¢C{b±¯m6¹¨©x´Î®;›°ŒÎo‹é–ÍûÛ/œÓF3›âúe³~öËç4¿w¡E´ßRœ˜»P¢Úo©NÔ´ƒZÞºâ{g¡h/«žâK±×«$fȨSPÙE]š=P£€UF¦*¸"iUÌ1Lè¼`{ôÆ77¬¾y핞¹Þœˆ{¦õ`¯X’*µS°ª¢u•ɰ)Àg7ÅôËfýí—Îi£Íñ}2Ù¿{eóšß»P¢Úo©NL]ƨQm7Ô§¢iùÇ ½t>@… ž¬jû‰ïž¥ Þ•®ë*¡.ÇX¯E›!V®¡²‹¹4z G;ëZuðÙ”2ÖÍ¢6æoœ¹šÔåU91ŽˆäkRw9Q.Ô7påÛŽ}«[NÀåF=jçµpVÄ‹™˜õܧͅ4–}L¹;Vås©ÓšRHí2@«™9UºóäýԴϬ­Ï[\þm:ûN=LiÈÔÌ2‚‚jˆc¯¡Í]Bîk¹'‡yý™ WçK.Ѿ·9ëOJðø¸õ,»¶fìuAëYµðÚtQVÁÔÈÜí]-rfsW•öNƒØæ9Zä™ÍUEE®‡ÚÂåž3בtõh„nw]|ãÚ7«bÃAEªŠ¸*ª‡gvl F°Ç,ðÀÞÏF'*œéíúvf…‹"õ×¥C iTñèÕ­W £R¦£órhPOIoÕ»¨kž:ž?SV‡µ§À¡ÓuŒI’Ò‰o MêÀª÷ežR‚q¶íst«]ÛNñìE”+¢x¼v¯|äË’¾’ª·&æ%`ÖŽDõLíƒÕ§´¨ês1èŽö—tª{Gu‘(˜ärWEœç”%rI§š3äg7ÅôËfýí—Îi£Íñ}2Ù¿{eóšß»P¢Úo©NL]ƨQm7Ô§éÚœ{1M"§JÄÒ¿â÷µˆ®r̉TÃiag´÷²6«är5©¥W2š¼£§‰U”ÍYíZq+- šçð¦wKÄÄÌÔ=blkrÒ†“%uµ5J«Èt'·m —3ùšu˜˜yg¨úš™3¾W»¶å<@éº+ßnr­êšÜ›ÆÏ·4“Ú—ÉSîʺ¨×Jööœ§ˆÊ–Ѝbéт޴!Tá9%N³Ó?’‡Z“()'TdéÌ\½|íòI€v!Ë#CȨ’³©Ÿj¤–[º²í®k‘ÕEEЩ’>†Ô©¡rp…k£Æëô5ðWÅÍ!\éÕ1z¤)IålJÚêËì ±š–¬ö@dûЦ>i ›Ç†)ãPc ±¡¾­ÕE¿IŒ§q§»`yª¢æR®K³¡á<ò<Iâ¾Òg1Ê‹†wE©$ÖP1Oëq!Nš‚Êz9þÓ%GyQ©åží½hÉAF‘Ò§ ²©é3=ÍÞ´‰O5“gGePGHÅá9©Â‘ë¥ïvw9{jvágÞÆ[{çl;¤ÈìÄ>_ ;™3ÜCèà'Ç~Ûàõ6}ªüSÚb©ër#¼ó¾z¶Ÿ ©C-Ù’Dé\šZäÎ×'*)êX„ÕTÍ]BîcPžÕ‡S"r93¸¹ø„‘r8s6%ÚZ5ÞÁo«Ÿ+„]ÌÕCè{qsI8KÔ·9šMҨ̄ËW¹úªß””So!îÀÎg[ÖLçÐáÃl&5¶±¨‰x‰1ÂÝ×ww ¨ÔUUÁ:©ÌÀUDLW2'ȯ·ÅX©0s´+×BvZÔµ]Rå‚Â$̪š]ña]bªª°à,È–¯ÂÃ$lk \R¨°äŽÌûË4³9_+•î^5>€ÕUË:¬êµTïI, ~ŠØ¨¦Tl‹Ícë.”í)è$(Ñ :Š•«uìO™ÓErJê+©ª¡«šBìS8Ó¶y‰ j™ieIa\4§§YJj*ØëaI™É™ÍãE6ˆ6T”Åb"ZT[´"¬›Ñá%†]«=@æg7ÅôËfýí—Îi£Íñ}2Ù¿{eóšß»P¢Úo©NL]ƨQm7Ô§ÒiYNšEÁ¬EU ³$ëP­§h²Ï‡…¦Wfc}Ñ'4ÒTHéer¹î\UTòVÕÉ[Péäã\dâCÀD•JV;©[Ñ=‰Ï9ß<À:ÇÀòÓTÍI2M àäò:ÊxõZ¨¨³*U‹: è«àIY™É™íãE=’:Ì®uJIášDë§\°kšö£Ú¸µÈŠ‹È¥©$¢Ïe;smpΈJùf£ävO³Å<)4jÞ4ΊsÕc•®LÁ੦I“„ÜÏO,‰cV²ÆÙÐS5)¥É0ÐÝF­î‰œÐªEs—DÅUt"!ò湊­r`¨pò‚yjŸ FåI«sÎôÓ Õ»¶ºÕ ÀtH–Zâ³OD«£mÊ·‡KwÓéd¢Û6œ¶ô©ë<\((º8(¸>_s'!Þeb¢[šëKÿbº®¼1†1Ïr5¹ÕNœ1$,F'޼§Öž°·ÎåÒ§˜Û,± ¶lTÆÏKKŠV½®rÕ©~î,H & qm»ASÖ8—ïb§œtë*–ó/…LÉ×UÐJ=î‘ê÷®.rªªöÉV/+XLH,Yœô§Æ|S¶Á£+ùbŽ-Yõù&=[èçIY£C›×CÀL{¡¹Õ™ÍYÑLJ*I§…2Æ)Y4m–5Å®LQO¹Å°*צ¤zû“=;FÙ$”$¦ b%ªÚ¥eKR½'Fn: 3šÀg7ÅôËfýí—Îi£Íñ}2Ù¿{eóšß»P¢Úo©NL]ƨQm7Ô§ÃÊJµkYFÅêºwö¸î¶œëQ],šS„­oi3!Ó—Ä …B–¯Y¯ªšê»‘Õž¨ŽhK“µk52Ó½qt+›ÞWA4{öüÂÐ1Í&,_G–v$‘,¸Í¬êK~}Sw$ŠÐpéNÔš’’ŠjÚÅàGj÷;7=`Y•k¶ís*m]1ž»“6u<ö¢®Q[‘Øq硳Օ‚¦‡¿LPú*R¢"& ™ˆèË,2 ­¯Ö·½íDQy–ºp¨ãs*Y8:§†N©¹úé™O ¬ö¯RåNÞs[”vW q TºZÀ\3¡W‹Ç€ôAí­žþ''–Ï“Ë:¹“KçšÈ\Ã>´ì7sÔ¼Û=‰Õ9Wµ˜ó²ž(ú–¦=uΧnQÉ\EÆŠØIv´K€˜gÄ«ÅbÂNÛVM¥F“Òú®²‘ÜÞ•ë™xmð½§&e:y=_OjY±ÖBÞ×LÅê™#s=‹ÚS¦LÕ|vmô¯oKfÚïlu)áb¨ÐÉ9Ú”ØäVDÖ¢bîló=ÓN“ÚÍY ¨ç™˜Sñð\¡Ÿ×téïnóâží±'4¯“¬Üž2‘ªKâY²˜‹YÓ%ãiV4/)AnêîŽ:¦àòÒÌ´õÌžÉkŒ®EG"*hTÄŒ*ìù9­/]<Eñ³¬"ÏI‘É’_`p[`„ód[#€g7ÅôËfýí—Îi£Íñ}2Ù¿{eóšß»P¢Úo©NL]ƨQm7Ô§ïæpHÿic—ÈBWU^5Ä´¯úbŸm;Î"É–$¸³éM5± '€}àzÇ4oO ö¯§Ð&”íŸQfTP]¢âˆ½t>O¤^»g¼§œ}Í-°sr‚ÖKÍ}KS‡;Õ"¦‰4¾Wæc|'H™¡øòeíGtÖ}”çCHžIôI/.¥“¶JÙ6rG2óJ¹Ü³ÕËÆé_Þ2hC¨Õ´¬ú{R†j ¤áG3«×Eârr¢çCÚ,—´*ɬKIq¯³U#s—Ù±{.Tí¦žSºOe5,ôrÔÖ{UÕ©QtËL½[yU4¡Û¤ª‚ºš*ºg#♈ö98Ñs€y€UËdʾÜwžxbÐo¶dûh§®iѱëç¹»&OSÔ#×+zôšÅ\lör+“Ë&J{¼>>^ùjS°]4;Ž×&‡°Ü¥×Kzgº6",s|_L¶oÞÙ|æš1œßÓ-›÷¶_9 û¸Õ -¦ú”äÅÜj…Ó}Jp†¢®š•¼:™YzïTož 6’ºÝÄyèË TI]*§¶ÚªžJàznËë5¥‚W| z'&×4J̱ãÔŽs»½˜f^ÙŠ½<37àWÑ=êl­°ªU›ó%^)[åçCíV©˜Ù–<ð*‘Ïw7³²SÃ;xp=²5xÚ¨äò!ôÞÜ@xªY敞Õ“Ê"4f/4‘UЭ=\±/…záÚ\èN±&Ûz†ªË<a¤Ú&«äcÃ9ÉSê{–<ÞЉ¼M^ö›œç ´okRª¢JëH¯j`ÔN²" ñÏÈ­rbŠ˜*.…BjÉUÉÛfLŸ•p¢¬WÏg9t5tÉ¥ c—”6G©{=c‰ÜΪ$Ô²¦–JÌíñ—B€uÌÉû]-‹=³HÞgS–¨—K%fg'¢‡LnÛ‹™×+¸¤j;Ð_8çû~Ÿš@ÙÚ™ã\´§Õ¬J”¾³–‰/üRfÄýîëΦoœÈWRGÌi¢ÚXˆ¤Ý›Nµ5‘³Â¢ðÚB¨¹`•$U«3Rúš‘ÖÂxc Û ØdॣiÒÙóZ‡g^¥‰Õ9yŽr1\³"USê”ä£u¼IÒª5\¸"iU̇"»)ìê5VF«Q"q3©òt6»[i9Zçs8x£jàž?\æ“£XŠÚBN|¾Á kÔ«ŠUžqŽÅÚœ¯´eUHÈ[Úá;Ë= -»VUéªdñ—ƒçˆ:Nö¯\‰X´)ê4â³®½{>¤íqç™~ {çš+vÖ‰qmKבØ9<³Ðˆô´r¥úŸN9c„^ñE.XVƨ•Q²VñªtŽôPîÐe ›]ƒüÊEð’fòB@ìC—FeªÑ¥eÃ9kZÚq+ǻǨÊÊJºH¦Uži^©½¥,hëiëáIéžjéë¢õ• P%,Ž”©:«VÔ‡²l:–+á,1x¤±o‡œg9€Íñ}2Ù¿{eóšhÆs|_L¶oÞÙ|æ€wîãT(¶›êS“qª[Mõ)À½u¡IgB³ÖH‘±4c¥y8ÏNÜ·i¬Z~úyž‹ÌâM+ʽd3«FÓ«µ*¢­êå^¥¾©ÖD89ô7§ ™gÆÆáð•MÛ"Õë[.*êÑYæèæŽÏ"úMÏQ=KÖJ‰+—J½UËåž0bW*Ú•¬ŠµÝÙɼ̚|4€š–¶®‰é%,¯‰Éí*¨žF‚¦È˧¢¶Yœ$Ñ͘™Ó¶â@QÊ–†ú6U[Ý„š[³Â´ zž¦ ¸›=3Û$nLQÍ\Pò™emÖXÓ¤;…¯«"^¥Éè/)£YV­-¯J•4Îä{ªjõ”Ì×£¯IËÎ…”®j3Xã±Gº9€žÊJElŒ«jfrpÛM ᫦e];à~‡&eë/˜e¬ènm[T½Cæq¾W}ç†Jy] ©ƒ˜¸)ô!**,Ëj‡0(rn“ƒêÜ™ßÒ³´šTâQÒÉYPÈ#ðËzÉÆ¥œ0²› iƒXÔD;ö‰öbÚ6ÒôÙJ7]ÝØû“y@÷ÛV”9/N«Ì•Qi=¾^–>Û—Ê:öŧ gM_>t½+SKÞ¹šÔåU=<˜³&¢¤}mMhZ犧q¢»©9™Š¦ó®Æ2&68ÑÖ"5­LȈ™‘°3j"äå¶Ëv<Ô5êØ-¦†?Dsú R¢¢¢*.(©Š)⬤‚¾–Z:–£â™ŠÇµzÊqrb®zgÍ“V‹•Õ6~ÆGi–}vþUM Ý–6Ë¢zb×"¢øäU;égt/ð«™zéÄ¥yèZÖ>EÍ#OV±3r§XŸbr5”âbNöZ]¥T:¬:újw%¨Ë\÷rh*ŠÕTTÁS2¢Ÿ´K…“g-T¼ÖDõSà—¬dƒñÞØ“ª¯è§ÍJ‘§)bGJÄ£X æïL.täoÒ>0Lù6È['†Øm´jxuT„©QÕ›“ÆØúK+ ‰ÓJ¼1ªç/"V‘ð•Üõm[R.™f“;×4lãr÷ˆ:ÊÊŠéÝQPîä"uóZÖ”–[§~f&hÛÖoéer•Žé’Ø–‰^ì±X6±¡vJíI,/vÌ@uŽÐîY–E™P“B¸µz¶/Rä=0}k•ŠŽjÌ©Tùœ#8¸É]Of‘C[¡NÚ˜¹3§/)ì™?k:Í«F½}Q*£dN$ë8ºEEDT΋µ%”$vO‘%ªË6Åv-K˜á,1{ ãävPÍñ}2Ù¿{eóšhÆs|_L¶oÞÙ|æ€wîãT(¶›êS“qª[Mõ)Àzvµ§“Dú¹¼*`Æñ¹Ë¡pÎrº×[FÑZxÝŒʬnÞǺ…[6ÉÜ5'%©<ÕîùÇ*¾º¢Òª}]K¸OzøÈœHœ‡®€­ÊNM¶î¶ð°¿cZõ=cj"\X¸$±ñ9½þ±è‹1õ Êœ”¢î4ð3]¤ª†¶ž:ªwp£‘¨æ¯ yˆ|‡µÖ‡YS;¤—EøÓÇ.Ã]D“–kÈVM%<¸šÍ@}7œ»jËçÈù¼)êæ&jN·l˜TVªµÉ‚¢à¨ºK³—jج¬Æx0dÜ}gvùN„²GfcHvê©_Å5T§w ÆKƒï42ÀõŽf«šQO¡-QQfZJià}˜ÇÈôˆ®s—DÒ}é駪‘"ªç/²šÌ²" o4~™S;¸“‘ÄžLøëJ“j»úŒµGÍ“f¶‚_žg§N½nD:ãå5«-BØ(“…_\ô§¤g Ú_ÚjgRÓØmFµ&D:¸®#ÑÇ—(’>ªÍ±_‹½¦Z®$åF'–S…‹eEcY±Pļ%bp¤zé|ŽÎ÷¯*©ïœŒ€TPT#a·¬Öã]fª½še…}yøÙÓ”ï 9”Ö³«éí:(ké]Šv#›×Lt¢ò¢æ=’fãµo:ÍwKfÚ¯t´‹ábŸKâäGiB˜™jY)SŒôè‰/q;✱ѹXôV¹3*.e,V²Î§­OV&âzi%ˬ-#ªÄ…3^¶©Qp”챬ÇNäg†9TJƒÞª±êé±V§5gµ7Oއ¤¨¨¸.eå!Eƒ ÐÄjµnÉ(TEv-5˜| gÐæ‚’¦¥p…Šî] äŠ+ 8°’©y£½¡:”ï™<Š4¥qFÌÛ’ÒCU["ÂO챞…eÉXä’LY i^5äBŽ8Ù8Ó‚Ö¦ˆ|¢#SL4"&Å#‘C’6fÓrÚ¹mQ^È•w‡X£H'2¾ÐXáek‚ËÓÉï)¡<’ŒÏmº¥¬´ç—ZV7´ÜÇR_‚Éjõšú©ßk(éÚ÷Ï5w?!è€å€¾KÚ Ygó)×€½uo…R ìä­RÓÚ‰W¥ªÅíéC±#‹eÆmgR[óŽØQÓÔ%»Ã„–v=qn-•ÀÍñ}2Ù¿{eóšhÆs|_L¶oÞÙ|æ€wîãT(¶›êS“qª[Mõ)À”þ£l™êQp½;2jªªª®u\êZÞJ¶ZD^­Î‘Éï9“Ï"Œ1u¼ -­[ú÷™ –‹ÂÀàG€Þ ŸM5šÕÑÀEÁóvܺ9¾SÔM]41BålÕÝ5SÛ¦*fõkȮЇvšžJxéiÚŒŠ#ÔЈ‰‚AåsíÛ&;fÎ’ËÀ“3à•:¨ånv9QÉ‹Wë¦sä @ ¨‰Š® ×SëÍ¢ö¶ù( ¨ÉâMê‘÷,îúEàVEÅÂÞz<Ú/ko’…Þ"9k$jâž«LS:qœ™nC®×FQ²au5&c,À>²I,Y%r1L\ç*5©ÛU3–#ìSÔ½•ÙwVwÇ©{+³ î¬ï€{`ñÃ<,æ”ò6Vc‡ ŠŽn=lSÈÖI#‰‹$®F1©‹œåDj']UO[Ô½•ÙwVwÀ=°zž¥ì®Ìƒº³¾yਧ©g4§‘’·8Lr=1ëbŠ @«ÞÈÚ¯z£ZÔÅ\«‚"'€}^¾º 6Šjê§pbŠ÷/k‹¶§ÓÔ½•ÙwVwÉËVÑ ·­Èl…©‰,ú Mc•íFË&˜¢EÇ:q¨C%¨j“[Ö‹p­´Ü’+WLP§®¢NÒg^S¼x ©¦©jº–VJÖ® ±¹‰É™Tò€ðç5W½Q­jbª¹‘8ÏWÔ½•ÙwVwÀ=l¥¥µ+,yéìiy[¸<cÀUDTW5Īœd½.RÅ“éQKSKS ¢ÆÀÕ‚yÝS ¹£¸)";¦TåÀ««¬±k`u<Õ±#]†vNØÞŠ™ÑQQȨzTù7“uQÔpŸ]?%™Ó,Òô‹Âb#¸J©‚èÀÖfUVÍUOfÅLÞy¨™ìlæŒXÆ#ÕéÂj9tာʪÚWV¹)£|VRÀÚµá¹çI‚¯3Í¡xΣò~‚VD’ºgÉ‹$S:W¬ÌUN àìqÃ#Å>KØò½ÓN’`ôb΋#Ñ’ó,íY3àì0ãå3-*ÒÖ„´±ó›k¥¥é\îl¼«‘Øa‡á–Û´Ù³kªÝ<Yu5 Hä‹:5[ÃUÍŠc¥ê¶%;¤ð,|ôê® ÌÕNhìQWªÑŸAëºÆÈ÷ðy¥Dr5ºÚúžXÇ`ªÖ¢¿6ŒÀ£òê¥$}44ñÏ+j)bcÑdŽ'%BªcÓ52¡ïÖå-U•Z´„óIiRJGF®àË/ ±gã·’Ÿ'rvªU©ëU#dî“›,«Â…UcÅxK ÷kì–Ú…\üŽ…î•‰†/Y©ŸF`†ZZ5ÖnMMSJîePå†5{|'4r5ÊžIã“™dÅ=E+¦ª¨­|Ìló=ñ«äç\qÃÆ;µ´”µô²RV±$‚VªH×hT8Iedª@´ï¬Iœ4ªW¬jÅŪÌ_Òª*r-ª©ŽØ¦J¦¬N³g¬mTp¹V)Q°¤TÇ¿íÖåµ}ŸV¢Ž%–¢–*ªv±îVð^ö±ZåTÌ©ÂLè{­²²A×¾xez>W¹òNsÝ+xW*»?K˜ûÁ“y5Wã‰üôˆÈãÇ›,«ä{‹Â^ b€ªGTºº­ÙW:¶5Uju“<ÇÂ& ‚q\3¨È=Eµ¬´\²èÎøõ/evdÕðlñÏU0¾žv£ã•ŠÇµs¢¢¦ ‡Ö ÊJ¥T¦š9•½W3s_‡oSÌ9“sËeÕÍ’Õ®W-2sZ¦Ju\ÉÛn…(Î&SÙ“ÕSÇiYÙ­ 9ÜÚ}­«‰y‡žÎÊ;&Т†±*b‰eb+£{Ú×µÚÕET\Q@:€õ=KÙ]™ug|òÁ[GTªÚiã™Z˜ªFö½S·‚¨˜ªëVÌk•®«E‘ˆ¨¾Iñê^ÊìÈ;«;àØ<WQU9YM‡BÁ§Z›VaŠ5Ü7v›œäÆÑ¹­®¨‡ÅY¨BR{¦-è"ý©ƒQ:Ȉ|€l%Ls|_L¶oÞÙ|æš1œßÓ-›÷¶_9 û¸Õ -¦ú”äÅÜj…Ó}Jpм&¤©M ׯ«Ú\P‘4|±¡ZËG±1};’Tí&gyJg‰3¯Jõ³§yd7’i=†À ˆ¨¨º0= jjxä}$±µU½4nTEW5}:ÕXÕr=Q8MÇãLO¨³iVtã(ÝiIdwÕ‰†1±µÄFµ4"fCìðÖÛnëÂÙðæµíV½Í](¹Ðñs/¶Yä!æs1©8àŒšY£ÃÎt¾Ùg†wô‚ËšV5ÙfÁÁ©ßRUpLêº R¢õeSS*`äÿzwL¾y’ê·„•«¿©YÊRmB9[x^ ™ïž:Šx*á}=Lm–)±é‹U:ʇI³•ЮNx›MÜÚ:ÉÏi»›N¨ðQÐÑÙðó½ ,‚.»#[Šé\ÇœðCS éê’E"p^Ç&-T^%9Ý ä߉´ÝͧTrºÉÏi»›OvŽ‚ŠÎ‰`¡…”ñ«•ÊÈÑÜW1ìô–(ç‰ÐÌÔ|r5Zö»:*.eE>ãFu—Ê 3&ì{9õ ³)¤¨‘R*h’6âù_™©è©å±r*Å¢³a†¶ŽŠ•NÒ=ˆ¸½ÙÕ‘4!ô øñÛﵞϲÜè(“ÂÉ6‰%ñ´!LëQYÔ6lnŠ‚S±Îá9±¢5tc˜ö@úÉ%ÑJÔ{Õkš¹ÑQs*)ÌèW&üM¦îm: •ЮNx›MÜÚ{”6mšÇGAO;^¼'66£QWF+ì€õ{#Ñ×"£‘t*.eCì9] ä牴ÝÍ£¡\œñ6›¹´ê€V†Ì³ìÖ¹¶}fÔn8hÇÛáQ:.e>@)r['UVΧUUÅW™´t+“ž&Ów6PêPÙ6eš®uŸM:ȈXÚÇ à{`|i̤]MƒcXÙFŽ«£†K:×r5®{R”âäk¼òÔô­{2 bÏš‚|É#zW'TǦv½9Q@=~…rsÄÚnæÓÙ¡²,»5Î},Tîz"9cj5U> èäŧ=]4–}¡šÐ³ŸÌ*SÚ°ê$NG&s´rß“=#Ý$–};œåW9UUU\ê§ÇB¹9âm7siÕ•eY²:Z H©Þæð\èÚUM8f=Ð2L™Éù¤t²Ùôï{Ü®s•UU\êªtÀ+¡\œñ6›¹´ö(¬[&Εf¡¤ŠžEo]Qª©ÖÌ{ GeuÃXÊÆ§K;prû“~!bzvµm*))—ªTá1zÎM T+:µ-Rš^¡ÓaWÜ=hÉê/Õ34uܯ|¹Î\UU©Š©õõAØñü ܱ¾‰Á{Zä^%CêE³”¨–•Ú–-5MὋ»–â<1RRÀî11ŽÃ Zˆ‹æøª®¦«=é÷¨«‰$³ÀzuYñG%UD,v ŠªµʼIÛ=ÃêøÙ"pdj91EÁs¦) úÇPª,ê‰Ve™f>jBþ-\Mä¾WUÓÓ²¨ùÞJö£$Ü71¹‘©á[ã!ïõ{•Ëj¢5J*+‰ö«#¨U-{Óªõ\}¤Îå&èé%­©e4)‹ž¸v“M’š::hé¢LQ—®§vÃàѾÌ[FÚ^‘¶ÖÈP§¦“á)ã땳<À±9¾/¦[7ïl¾sMÎo‹é–ÍûÛ/œÐýÜj…Ó}Jrbî5B‹i¾¥8}dcdc£zb×µZ©×E̦WlÙϲíi*;×®ÅΊjÇ *ì?R´|Úõ¦Y×sxÛÞ8=³¥Ú6ÆÆué]Šá8aY«*3ª­r`¨¸*.a+À|.8.— Êxigt¬VKšXׂôó—´¢céA¸¹,Q¹wwÔó€}á†Z‰Y-WÉ#‘­jiUPa+¸ÖÉK)m+QŽzc :¤’/n¥@z•H°JÚÖhN–TN6õücÛ=û6´-‡p)â^f¹#óF‰Ûã>¥©¶…õúQ‹•Ü+*xÏF>w5¢½ÏTF£sªã Ð2_&[e±++VôÌšR4^.ß\ûd¶JE“ñ=%sj&á¯2—Ek8›Ÿ€ÊÖMMI›×F„¯æï¤Ÿ­Û7V2Dz6Í© gM_6~fÜÄÒ÷®f19U@9ûÝnZpä½:¯1N E¤ôâ‰EÛrùEØØØØˆÖµ‰™2!ÈÉ›.j 7ÕWtÕõï犧q£Ô³´ÔÌv@ÈÊ[&KJ‰³Q¯¾‰é=$œ|6øUärfS®9ö­³gGZÄà=qdѯUÌö/iN3UñÙ·Ûh7¥³mg¶:”ð±Thdœˆí S¡iX´6›q™¼0Í#s;Ç땇WWjKeQcUõobq®vÅÛãT4¹›+¡{ar2Ec‘ŽTÅÊ™—ÙèØV;,j§áóYäs¥¨™z©$vw9} I47­Ó:ä™3¢•—Vœo.ßC¼W7b ñI ð$b±S6Eiõ4ÉiéçN ѶD÷$E=2zÇ•qZv§¼ª·ÎS¢ë ~Bô[ÚXd”-ħµM=‘ ®…ìom/Á8óE`Ygm3}ËyêqK‹UZ˜8GÛ¶ôV(Éç%± â‚yÝÀ†7Håâj*ª ’­¨T}b¥;:ÝS×ÐB¾8b…811¬N³Q<£îv!ØsMꮺ´C–µ¶©,⡚ð³Ô ²èìÖp)˜ˆ«Õ=s½{j{`Öµ“5&D¨„t§)·)6ÛÊÀú|€3›âúe³~öËç4ÑŒæø¾™lß½²ùÍïÝÆ¨Qm7Ô§&.ãT(¶›êS€x悈Ö)ØÙºZäEC]ö]J«éUÔ®^&ôÌò¾Qƒâ¢-©®­ u•ÉÅKWD‚¨È;N5^w–)S‹X¾‰é»#íæ¯®ݧ·¾i ãe´ã•ª±Þ+èêžÌÍÙ‘Öó×<-o½=¿÷©²½ê‹UV­KÓŽN§àS1ÜcQ‘µÔÐL°9""ZtèÓ¢®B*:¤=JëN’ÍX­ÊÄ©™!Ø*·†í«Ä}6ØLë.QáÕY¶+þK-W¢ŒO,÷²šÔš‚‰´Ô=5}{ùÞ•¼|'ii©œö¬k.Ά‚,üã#×KÞìïzòª€{Àõ­* {R†j ¤áG;«×N²§*.s•’õõ ɬ;IØ×Ùª‘¹ËìØ½—*vÓO)Þ'²ž’zIaÊk=ªêŠ R¢6é–™z¶òªi@ xi* ®¥Š²™Èø¦b=ŽN4\ç˜ê6Ô£u¦ë%ŽWT²™èˆªÖµWÅt"¯XÛ3›âúe³~öËç4ÑŒæø¾™lß½²ùÍïÝÆ¨Qm7Ô§&.ãT(¶›êS€ðªLUpDÒ« ãZ9_`Ùʬ’¡%‘<$>¬_+1õVÑ'>e8Á]“If³´j›Ìb*¥¹8+°ò‘Ï=ÞM®«ÒSÀÔù:üÈæ^µ Π·UÝRfŽ æ;ɵZ¾¬¦éÉÃo¢§B–ò霨•´®èÜO!p éPFÍ¡-ÕsV™lU”ö%©ƒiªZ’/²äéä)Õ8**Z¤ÇDe«±i¬Ì ôí{2 ^ϚϨêeng&–¹3µÉÊŠ{€ødâdŧ=U4–u¡šÐ³ŸÌ*ÚÑ:‰S‘ÈvÉÜ¢§šÌ­ƒ*(X¯t8C]’ËÕaÆ­\èP1í‘‘ºˆäÇ6eÎöú½íŠ÷ª5­EW*æDDΪ}‰Ü¦¨–О —¢r¶ZÞž­íÓ3W¦í+´ ÒÁc­ËRl§˜7…Ofµx£EÁòöܺ9 SÇOO,Ó@ÔdQ1Ʀ„DL áQŠ×&(©‚¢è>@Í’«“¶Óì W Õ|ös—C]¦H=)Ž^PÙ¥ìõŠ's:¨\“RÊšY+3µ{K¡OœžµýLYíšFó:˜\°ÕDºY+39;\hÓè[v¬65%t©ÂsplQ§U$ŽÌÆ'*©ëdÕ•5+êëׇhW¿›Õ?¬«ÔÆœLǧ RåPº²¡ŽmŸd=c¦cÑSšÔxip^&èB=¯”Ô^1"óyÓÙl\ÉÛ^"FÐÊkVÐUE‘a}—Jž:éSYT8T­ËYëÖײR•ËÈ=Õ-‚.êmK:馢8׬®Ew™Ît¹cbF¸6GÉï,\<¼UUÅW^5ΧÁÕt¹ëhˆž% KEjs”žeĶ%ßFÖG´Íð)é<9_bJ¸,®ÞÚ¨žV&z)-‹t·ÇÛ´Ö3Å|³÷ÃT¦´(ªÓiÙ/#\оF“Ø2F½ìr9ŽV¹4*. v¬ì¬´èU+¹æ$ð²uXr.“3%È´ž“]¥3’µ¥œUÚS¾éY`z&‚m•oPZÍÂp%ñÉÓ;JD‹Æ¼½d3Y¦–¢WM;ÖI¸¹Î\UTÍ 7ZVUš©]„0Ë+ÈŽ­±•Vµ²ålÒ¬P®ˆcUk0åãSŽÙDFÒD˜‹œå7vM·šÓ䪋ŠfT(,\´µl•lr¹j©Ó2Ç"âäOqv”'ÁñZŽI•'>¡RTÝØ¶ža²XÖõŸnAÍhßÓ¢tñ;4í§¢tLFжªÏ¨eU‹¬\QSÎ^ºŽLe<ý?øGWz¶>%÷&òX¨)¥4%¬[1Vá`ž¹Ý:@#¬Ö´k©ìÊ)«êÁŠ+݈„NU\Ç+%¨gHæ·-á]i¹$r.˜âöTIÚM<§¯h|xíøì†tÔc›=jøWͦ8|m*RèÌ€ 3j¢äå´Ëz$†¹Y¢ÔÐÇhŽAJcÃYIu,´u-GÅ3jõ—0•ˆæ®(©Š*h>I옫ž–I²jÐrº¦ƒ‚Gi–}vþUM P€ð¸5W2&uRC(r­ÎWQYnÁ©ŠI:i^º7¾}òºßV«¬ª7`¾Ïzn'¢H T¥gV1oWØv¶×&•jÊíÜ1‹ÞÙòªªªª¸ªéUÒ|tI ´r>'¤‘¹Xö®(ä\´ÉÜ©Jµm ¢¨Ù´2]þEë)|¢ª.)™SB™aFtN–•R¢œöU‰NË…ìÖ’Ê™­‚w%m寋œjëDMé\¾©è¡DU‡"5ÛE*õèNǨéÍaZõ» 9š€g7ÅôËfýí—Îi£Íñ}2Ù¿{eóšß»P¢Úo©NLÝÆ¨Qm7Ô¦ÅÊ›}–ž²1QjfŰ7—ËȇeÎF¢¹Ë‚"b«È†E•í›ZYÑq†5XáN$kxü}&HL£u;D9¬ºúfž JXÌåÍ4“Êé¦r¾G¹\ç.uU]*};„.0{Õm\u”®àI±Eâ^º/"ž¸¡6Õ£f±-h-«>:Øs+“ Æ×¦”=ó0È[ilëQ(åvõŠŒ\t#ü*úžt¢2ÓT¨NXÕ´ý4÷RÀõdõuUM‡”UO#ŸfÚ|Ž\[éÔ*uší ÊPž¥©gSÚÔYõ)Œs1[W‰ÉÊ‹œçd½£Q,2ÙŠühY®H¥Uöc=—*r*ygyÜfPÚÞ¢,çMsJ™\ÒÅÆù_™©è©ÒÑI«9(íé-—ç ³\ø(Qz—Ë¢Y¼mÓÉë'Ô=œØ$^iS*¬ÕR®—Êüî_A˜ÁÊŠ Ž 6íšÜk¬ÕW£SL°¯¯"^ÚgNS«gWÓÚtP×Ò»…ìG7®t^TSÙ&i>;6û¬çt¶m¬÷KJ¾*/‹‘¥)€6RÚ³PÒ²Ž8v…{ù…+zʽT‹ÈÔÎë-UM¹”IOG#£ ²]¨{–uLÑcÆEÅN½i¥•g¾tõãºH“Ü—Æ>ö-• gECð•©Â’EêŸ#³½ëʪIe¥zÔZ)HÕé)›‚§¹;:ùFLK*ªZ­$¿:í}¸šñ‹ÔVj–Ìà=î‘î‘ëÂs•UʺUWIõZ€ËKS-Du0/ñ¹‹èuŸ[¡G\Z$j*§YxÓÉ2·!«×…5œõÌ©ÍcO)Èväqh_B¶ŽÉ‘–ÞÇU(éÔ¸JŸ ¼ex¤W@g7ÅôËfýí—Îi£Íñ}2Ù¿{eóšß»P¢Úo©NL]ƨQm7Ô§YZ+gX3¹‹„“á :ý6•ò12bêó*—*4\Ø>UO!¨B¸ 3'®CÙó¾­s$R[2œ€òǺ7µì\ÕEEë*gCf±k’Ò²©«SL±7…ïI™ÞZÁ¤ÝÍRËcKN«ë‰×ÓÑÏÄÃ(IÚ‹YNÛ];•rIkÑXz/²iŸkGl"¹•Âè]Á\ö.tG'  ÷Õ%€Ц¢Jy*jŒŠ&+ÞåЈ‰Š€q²¦º¡c†Â³…m¦åšb…={*ö“2rk>†žÍ¢††•¼ b1©Úҫʧ&)毚|¨®j¶Zî–•ŽÓ3z„äWiRˆÏ·,˜í›:J7/LÏ‚T꣕¹Øäñ΀ädÝ­%§D±V'ºë\|höønÓ“:rjÝcì+N,§§EæàÁi±¼qªàÉ»m]<…ÉÙ#Ts^ˆæ¹3¢¢çEû¨šUµ–Ùz¹õ BÄrâÆ7W‚œJ¼g¼>¯r1ªõÐÔU_Êë'uM\ÕβHçy*i–¤‹›S"iHç)– {©±/T›´pÁVy±[:$ÐŽOÔ­-±M&8"ÈŒwiÙŽq÷ÊÉ£zikÚ¾B¢œš´.E¬¨|U‚© Eâ”ZÑF²«„Æ»®ˆ§Ø¶S@g7ÅôËfýí—Îi£Íñ}2Ù¿{eóšß»P¢Úo©NL]ƨQm7Ô§ ÖñÞ®¶âgi›‡Ž®% ËÇb¶Ú‰üO¦o”çgv±·„•¹Óë 9š]]“×Öèø½Tï=Rîì£\+¥âÆ6ùêcl[ã¦ÂÜøçïEÐé“GÒW=‘=ñ·š9­Uk1Ĩ™–ÖL´––Ʀc¢…›ZÍ^©‰°JuåW&~B’Úµb±¬é«åN±0‰¥ò;3œª¤µ›O]’•°Z–“øq[ND´àÃPõU}ç?@-šÖ±¨Æ"5­DDDЈšùÇ<TÂúyÚŽV«ÕÎŠŠ˜*4ÖÛòM³Ø­}D°¹¾¢ÚTñHìÌzí\ËÈX¹ÍkUÎ\UUt"!WOhå5D¹Ogªµ¶[Õ,¨Õ3MÀ\erò; ÝŠç1®zp\­EVéÁxÐû‘iÁlYð×Óõ2·¦jék“3𼍧¸é[MWY5hžØœf¯U6¦–kÍòQPÊ^ÕcÜÅÒÕT_1>^˜³éIÛG.«ÚIè­ðø"`Ù‰‹Úw'ž}OfÍ…j- xS?f'—ŠŸQ'TJêbNö-¼I7 j&1:ÍO8ûŸ ™0>K…-áœßÓ-›÷¶_9¦Œg7ÅôËfýí—Îh~î5B‹i¾¥91w¡E´ßRœ!o2•U´u¨™‘_—·ƒ„5Ì®³VÓ°ª"bc$IÍcëâÌøy™Û€³¶jÄ=Ÿ Ú·Ù$“ÐÀS—wt« ˆú‡& Q;•;MéSËÄÍ¢óHØ£L^÷#ZuUÁ ¢É¢mfÓÑ7Ù15«ÛÒ«ä˜e™¨•ÔîµÐ»QÏ$V½žØ)íI¨hÙGCÓZƒùÞ•¼h®ê¤^F¦sªJž“~<¹EÃê¬Ûø'´ËUƼ¨ÄòÎí£AOjPÍATœ(§bµzéÖTåEÎxì{2Ά‚ézw®—½s¹ëʪ{ ¼m© ž”–³}]J÷BÙqEæ±·¨“ÇM'P6RÓÚV…<6]+"«“WPŠˆ±Â™Ü‰Ç‹´JZhhé㥧j2(XŒcSB"fCÊ&5c(=¦Ì¶dù,5^‚?Ï)ÏN׳ ¶,ù¨*36Vô®M,rgk“•ôrbÓž®šK:ÐÍhYÏæ5 íHD©ÈäÎÚ3l££Z+^xðÁ¯w4giÙüóI&²ÒÌZŠFWƾŸ3ðÓÀ^?N´²9Òմ朗ªº£d(¼¨¯sòYeßÈÚ5¨µyáS¤¦b»r\Èp %lÅ³ìÆºDÂZV?®ˆ½JybKŽ"Vm5ö ³®¨Øò[ª§¬üzã²*•€Íñ}2Ù¿{eóšhÆs|_L¶oÞÙ|æ€wîãT(¶›êS“qª[Mõ)ÀáQ0\è¦M•¶+¬{ZF±¸SΫ$+Å‚éoŒ¦´r²ŽÃŠÝ³Lì3:h_Öw[´¼fHO uÒÚœö] ?NâÔ£…lŒ|ZªièêMRÅd±¹Zæ¯]Ü!¹€n˳*mzØè©[‹ž¹×‰­ãròY©™Iɤ•Öòü±VºÑõ%3}QH¸·‘tyM,ôì«6žÉ¡Š†™:XÓ;¸Üî7)î(£tõ*–51MG+ÂõgÕïdlt’*5¬Es•s""gU'l:Û´¦ÊŠ„^cÓSÙ¬wH¸:^Û—Ê>ÙM<¶L-DåGÕú²±íÓ3W¦í+´!Þ‚©¡Žž£#‰ˆÆ54""`ˆp7@ú¾HØ­kÜW¯¨ªˆª½dëŸc—”VK­k=YNîgWNôž’DÒÙY¯c$c£‘Íz+\ÕΊ‹™PëñTÂʈŽV£ØäÎŠŠ˜¢Ÿi#d¬tr' ¯Ek‘t*.’rÂ{ì+N\˜©UæáOf=Üqªâø{m]…(ŒÖÞ²$²+] °½UÑ;®nÚÓPµ,Ê{V•ÔÕ §;šZî%C:´ìÊ«*¡iê[ïN¥É×B\¦N°–t¶¯…tYmuœ¬˜(MÜ«‡5nëbz€¬H:V-‰SlT#XŠÈZ¾¬—‰¬œ§&µ^¨ˆ“ªŸ5*F”\æîEclö²ZÅ[J­*fo¬Ð9qÐçq7¾h3!ᤤ‚†”ÔíàÆÄÁÑ^SÌU ¶j«j¥ZͲݗVûV¬Íô Ç( æø¾™lß½²ùÍ4c9¾/¦[7ïl¾s@;÷qª[Mõ)É‹¸Õ -¦ú”àOeVJCnÅÏøG[zWhG§´»ÐS1ª¤¨¢ÔÕQ¬R±ps\˜)·œËk'ìër[0‘¤™¹¤oÆœ†Xq¨)-48ì« Vá(`ž¹˜è(mœŠµ¬µt·žéÓCãL\‰ÊÝ$ú¢µpr`©¥2¤r:š,äTéΛ¹$Ó>Óäû27ÊädmW¹s#ZŠª¾AObd¥^­šÐÆ’ 8/¯\œ‰Åãœ\äm5Yºt§UÜ‚oz86m—[kT¶–Š5{×JøV§]W‰ O'rv–À¥æqôó½f•tªõ“¬‡·fY46E:SÐÆ‘·Ã;Kܽu^3Ü:Ñ"«é%$%¬kPá'†{¾Eª­iWÓÙt3WÔ¯8®^ºõ‘9UsÉ3]ñã·Ùe7¦³ì§6jÅð²O¦8¼m*b:g%¨* ÖÕ¢˜WZnI^‹¦8ý—v“Ë; Í£ŽMÛ¬¶šÏ´œØk‘:˜åÑÞ>…)QQSΊxê©iëißKUe†DÁìvtT>ìkXÔcµ‰2 Ø<µYô’ÖÕ9,W½ËÖCÎxjèé« Zj¸Û4NTUc³¢«WòЋ“4uRM”¶“UµUè‰nÓ :u äUÒ¥ðˆˆˆ‰™2!òŒ¤²d´¨’Z5àWQ½'¤“ß ÚrfSÏaÚÑÛ6tuN™Ù4KÕG+s=‹ÚS LÕüvmöÚ-élÛY펩<,U/";B€Sµ}ŸKi@´õlGµt/†jõÑOdÉ2ÓE385(»bkŸÛ+]g+¥¢ŸO ©Óµ9PáèÒk‡.ÐÉË*ÑÅòEÌä_fGÒ»ÇâS¥C=8k|¸dÍnZJ5ãw¥ã=¸*j²¡ª«GP×§dEjù)ŠÙrR܉}qÃN»ÕôPêºO¶¬[êy"JuSXç»×¯9ét9mã‡:?Êïžx²JÜ•s‘§]îDó±>$(‹.ŸnÊ¡.¬5¤q–µÎTkQUW2"gR®“!ªŽ­¨DN6Ɲ’¥ŸaÙ–j"ÓBœ?n?¦’¦fHâ:ÝŠ¥Ý©ÉZÛØô×]¨ó0-KXÙ!SV­žÐÆtð=˜îñiMM-§¦bGSjPw¡@dÅmjªÚ–U™VËwfîEbŠÄ€Ês3›âúe³~öËç4ÑŒæø¾™lß½²ùÍïÝÆ¨Qm7Ô§&.ãT(¶›êS€m¡“Ö5§ŠÕÒ±Î_Ôà?ÉL>¢ªZR1(©+’I­Ùá#ênÚÌ‘Uij%‡‘p‘==Ý”øú®¹ŠžäÅO9Ê_šFzU4;„·Fƒh‚Žì¤ÇÕµÍD÷Õ|÷[¹²!TZ™e¨TâÅ#o•ŸË+X¯Z¢6î…Ÿuž•eÙ‰…4q/µ"bÿ%qSÝÆ«=©½%q+‹0|*¢&+¡“™”V²Ùs¤…9¥TîH)bã|¯Ìß4©öÉû%,{5”î^DвÔʺ_+ó½|œÈrìÄ\¢·d¶ßž†ÏWÓÐ"õ/“D³zSÆu¯SI”Ie×#Rš²$u ¨˜böuq¹zühv@}%–8ctÒ¹Æ5\ç.dDLê pr2zÒ­µâžÐ™‰$³*Q7I&æá»¶¹Ðë€=kF‚žÔ¢š‚©¼(§bµzéÖTåE·²89/_QÀšÂ´uš¨Åré–%õÜ©ÛLËÊw‰ì§¤ž–Hr–Ïjº¦'ºe§^­¼ªšPíÑÕÁ]Ke3‘ñLÄ{œh¹À<Às|_L¶oÞÙ|æš1œßÓ-›÷¶_9 û¸Õ -¦ú”äÅÜj…Ó}Jp=ZûJ‚̉g¯18Þ¸*ö“J’6èЫ—Nê…MIê¶yÕLà¾%µª·u 6‘p ’®ñ²’¡W™I;zѱ|—bsß•ùK"âëBoQ<ä3¤‚"Úª!‹ôm`Åc˦‰qm¡*û×Éå¡Ò£¼¬¡§TçŽeRÔÓÂo|–àA-_£Xm—y–MR¤vŒO£zæáúò/%3§VSUÓVÄ“ÒJÙ£v‡1QÉåwÂ|;sU2FSLó ©­í†À³…e¦ªÎiŠõì‹ãfCµUS <•UFE ÷¹t""b§&)¦­–|§®j¶zþ–™ŽÓ3z†ò*éS“³AEG +x1@ÄcSµÇÛ=€9™Cdz˜³ næu19&¥•4²Vgjú |dõ¯ê^ÏIeo3ª…Ë TK¥’³3“´ºPê–XòÓe­j9È*¡àÖB¸ôÏoQ#x±Ã2€u€&íùd¶íòZ‘Ê‘`Ù­)áaEébÇ®åòŠ)8|s<üàð´cʼnÌÉûÖM4Ž©zO[U+¦ª™<3×B&>2t¢Š8cl15ÈÚkS2"&dCî¢9ª˜¢¦ ŠMYJ¹9m>Á•p¡®WÏg9t5ÚdƒÑB˜æe ‘êbÏtQ»™ÔÂäš–TÒÉY«Ú] Ó/'­RörM*s:˜°ÕĹ•’³3“´ºPðÚy[cYб¾^m*{..™|uЇÇ9“ªÌl¥F¥y^Ó‹›Ý’ºvYx•Ò*¥;!o½UîôåM–C2®5JÎF5­ô +)bZN¤;EeM]•ì3ºõ×MP/DÖö8óô¾Jwb³ÊUžy¢užÖ»ÐCâJ›YM’Ò~ÈKàôVÀÔ Exµ Tm¡L×§¢^ ¼…Å {3)l‹WÓL‘}•'Hÿ/OŒdlV>ÑiÖSŠÈµÖMŒ®Î›½[ª<$µÇPd8Às|_L¶oÞÙ|æš1œßÓ-›÷¶_9 û¸Õ -¦ú”äÅÜj…Ó}Jp>4gR3*/ =Ï¡±ø3Ô¦(ù—%ópωKv=ŠëB¶Òjk¦tÒ;뎤Ї®ßDD¤”ù`½eÛ6•:OgÌè—íÒÇr*hSÑ>äjb|u ÑZMNs&ƒO• –³QXU(ÚF+ù­oMƒeF`­‰¾ôºPКֱ¨Æ¦ j""&„DЇçÈ•ñ*=ª¨ô^ 3*/!§ä>Zz’FÙ6£ýjja «›š¢q/¹yäØÒUklÆ¥+Umd>Ô² ¨}Idd1>iFÕs—NdLTñÑVÓZ±ÖR=$†fð˜äã@8ë:Ñ£m{lÅ‘9éñ,©b«ÀEÃë{ zv«EdS­McÑ©á[¥Î^²!ô¶mŠkÕu Šè‰Õ=ÜH†[jÚµvÅSªªÝНPÄêZd0ÅŒé%7V¶ÖJÍwò»Iáy[Ý‘ö¶²‚¢{Jzê>%=g•±Êœ.f½Øqñ)èŸk^Õc“TÁPðÓ¹ÍU§‘qt}JõÛħUʱu¦©’,TiBš§{N¦£™4°«¹« Ï<àØŠ­TsWM ™” {-«(´öŠ­M>Ž畉ÛãBþ޶š¾µ4’$‘½3*yËÖS:v ½WaÔ¤‘*¾*sX—C“®e3ÂŽ­¤êhCÛ O éÔ¢”*nËe²f´^‚ºšÒ¥ee+¸QȘ§]”öâ,ôЬJ. ÆJãNãOw`s|_L¶oÞÙ|æš1œßÓ-›÷¶_9 û¸Õ -¦ú”äÅÜj…Ó}JpY”«dÑ%Hì*ꚸªidz{k¡ ™æŽš *%^ "cžå^$jb¦mÚ’Û6¤ö„«ëׯ=¥‰™©ä©1ó­µ¹:‡ÌÄz:s¨¨kN­øñ7Ï>^ª‰‚i\È|µ¨ÔDN#±wPÔm5½¨žÄÎ#äûG$ÈÙbr±ìr9®LÊŠ™ÑO¨26\Ê&å–Ù$TJ¨0díåâwiNñŒäm´ëÛ†G;'T†dâÁË™|e6TTTÅ4)&UÊ}+k©¦¶.ê>@\Èñ§2“T»yÖKóYö›5¯SÚd‡‘JÇ6ß²[lÙÏ¥Eæs5RZySLr³;ž>9y;jIjYÈú–,up=ÐU1S %fg*r.”:€­§hÓÙT3WÕ.ÂÅvj¼MNU\Ç3&,ê†2[jÓO IRG¢û*?eĤÓÊzÓ5ÙK”îä_Q–<ˆéSÍS¥­åFéí”ÀúK,pDé¥TkÕs•t"&u>ä–_ÚËMFË2`úžšL4ð‹ÇS‹Ý@ÕZÆûÇ••Z–êx^ì–6Iå·-·h:eUHªØYÄëöÔå€OUW,ëj¥Ú•8Ñ„ar1W†¡ŽÁ&בçDë§`fYÅZj¬^ ¸šÆšÄÖ©ŸXÞÙ’3:91Cìzíõž~gì¹UU¼ŽãOö’e¥h¶‡Í ޤZ– ÁÜ’ÍYuOá´ ·Ýd×%4îõ–¡È×c¡®\Èîù¦"¢¦)ÄÍ;#-e´ì–Ç+±š•R'ã¥S¯vdÑ2¾+ööÃJå“–äö`wÀh¯€Îo‹é–ÍûÛ/œÓF3›âúe³~öËç4¿w¡E´ßRœ˜»P¢Úo©N׋h­Oº.¬‘"åàõNòÉK»Õ©WUÐÒ"ædO‘S•ËÁO8„+HÛC 䪦¹c°|€ú¿:¤iǧ´q{¨R{U¨—fVá½2«×´Þ×\ûŽÐ m Mj¶ª·aá‘ʆْ–ŠÚ–%S—ó>g'½3¥_8ÄÍ6ëjVK*ª•W×5äí=>!Õ—6xtWÉŸPÆ[ fÀøDDÇDÅq\9—6…]dE5ÏÏ­‚7><ïà9U‰™x€(ÁÌ¡´¬ŠÉ§D«¯³°EÕ5!“›Èþ W5˜¦sÕnTUY–ıÔE#Õa¤kiæs6ºg¹¨ävã›8h'áÊ™PÔQ:&ó㨞ô‘ŽD•ÃL4b˜qž­^^SQºhä$’(9³[ ¬•ÈÅjªfGtÀ@âK”œå=4v2Ò²®dŽN=¨±·‡À\3ª[Jך›&ç¶c‹+ifFìꊩ‹qò@:À–³ê*,» ,¥µ+ê*ÕôÌ–HÌ•ÒáÁkQ˜gr&“ÁkeUSj)¨•¡¨Ž¶‘Ó5Ùø%Åt¢rg@ KÉ—ENÊÙhÞÚz˜§’‘ü&ªÉÌQUQSÂ≘îYu³Úl«šwIXׯÅr=Ü&)Ž=ÀÔLpDLWÀù>T×-¡mÔÊ‹‹#2gi™¼óR¬—˜RM?¶â{¼„U1—¹^÷=t¹Ê«ãç:Ò§Rj_“ºORNuj½Ò”V~½ªXÀé4I4jÅ̺QzÊšúÓʲ3æ{‚ôåN3Êzóz¦D©oSÔÊœœNñŽMÅ’‡ôæ­Â™*ËW&ºW#Þ;ÕÓØ3ç@q:@y \´¶ÒSªàʦ+=É:f“‡µeN´Ö•,é›J%s|_L¶oÞÙ|æš1œßÓ-›÷¶_9 û¸Õ -¦ú”äÅÜj…Ó}JpÊï9ʹA.„¤fK‰ÎôbVÛTòñIJ‰ð.^ùY“c–^¥€”ÀèÅTÅ\Õá*çÍä!÷±\¨¨³M=IíL¦|p½ÅÞAñÂ÷yØ Üü$7cëÂ÷yÏ Ü]ä P¾çá Á»/qwXÝí²¶c«*j’Dp§g4à᎜éh—S ¤5óñ+âbxȪ`•#’§téJ”×fcŒïô]î¦Ñî)é‡EÞêm➘ ³a?Ñwº›G¸§¦{©´{Šzb€Oô]î¦Ñî)é‡EÞêm➘ ý{©´{ŠzaÑwº›G¸§¦(ÿEÞêm➘t]î¦Ñî)éŠ?Ñwº›G¸§¦{©´{Šzb€Oô]î¦Ñî)éŽm¹”¶ŒÔñMeÐZ0ÍM3eVº"‘‰Õ±Ù×6š¥Ëˆ+ eE=›_,oLÎdHæâ™•1áuÏ/EÞêm➘ðÒüv­÷YîélÛYî’™|,U_";JÀý{©´{ŠzaÑwº›G¸§¦(ÿEÞêm➘ô-‹ZšÚ§ŽšªËµاŽv«!D^kŠ&w.b¼IZ¶Í=¯NÊj‹.Ôkcž)‘Y "ð£r9;—6(zv„¶}¥^¶Œöe¬ÙWó6&£}På{xú뜹ÔPT¶FËeÚ¼kVäH‘:w7€©§F¢ú.Vµ’P[k)ù݉ÌcN 8Mñn“Eå¤ËbJ7Øõë 5Ê÷º,‰".|t.s¥Q”°USII=hºX±½¼Å0Vª`©Õ ‰J”N³%¤¶æ¢æ|Î:wÆÞ Vª*`ª¨©›>Ê fJšª b¢t–¬¯‰˜ªCŠ1˜"¢aŸ9~3Õ§³_SIgÛ‚(掞%‰¼RlÎàçÅW Úl¦e-âŽyûe'Àž``· ™ÓU¹ÝãˆðóÏÛ)>sÏÛ)>âž`'mÇÃ1¦«s»Çá矶R|Å>tr*,R**`©Á<àNÛ†*Ï­ãˆõé9¢1Ìs\c°b»2«~!ì|rÑ*­¤æÊ4ôÍ8ÂùÊõ\º÷lš|6ûF¸HÅë9<óêy)˜²TEi|Œo’¨€ÃÀž¨Ù¡\bb¯çsêÄà±­ë5ûÊ ÆÀs|_L¶oÞÙ|æš1œßÓ-›÷¶_9 û¸Õ -¦ú”äÅÜj…Ó}Jp„½:%}{S×R:7/#ÓòÐÍÍÃ),´¶,Zª1{£WGïmé›å¡ˆ9®c•LÕTT](©™P©!}:­_ MsXO€Ú>@kWsD´¹:Éœ˜:ªWËò^¥¾q–QRK_Y )‹ç‘¬n«¤Ýhi#¡£†Ž$ÁDÖ'ÉS§/|Ík*ªÏ|‡ÜçO°ëWÚve3ªëdH¢bi]*½dN5Š«2SP{'*ÕÊ{Çŵ• æ‰ì¦tòy £Ç ò‡/ëíu=˜«IM8Iëç§*ñxÄ›œç¹\åW9sª®uS¹ D«Më5ÒZŸJ;¹}]z9Õ¶un'ÌïA½ó=áå$«ÒIIÖcÑÄ™i²xMÈQoi™¸ŽÿGYOŽ<ùæÞ=˜/(áT掊dë=ˆž[p%Áõ`Ã\0 ÜF…Az9Q¶•"³®øW„žBà¾YWeå‘l7†½Üq¯K"x˜ĴrIÒHœ¬{WsUQQ{hb|Ž­¸ªà¡‡o€Írvðê©\Ú[kˆs"Nž½ooÚÑ)j©ë`eM,–)½«Š)Ñ‹ð—JUÒÐùjáëÛM=³Dê:…VtÍ|r33ã{W¹¼¨{Œj±b¹\­j"¹t®j}ŒÀ|¶<Û[=Ï}<+1.ÍŠå鞞䩘è>¯{#j½êkSÜ„VPeò1]IbàåLQÕ.Ο%N>Øšsm®îAjÞDV×Ztl|ÒºvBœHåé—´šT˜¯¼j(•YgÓ¾uMzó6ú*AÔÔÔUʳÕHédrâ®zª©â9P’T­u8êmÉè"ž¢ðmÙUyŠE {‹xKä¹TõW-rWzôÆwŽ>̇B±¨¬Tã¡t¡‹.òŠ5é¦d‰Ösè`u(ï&¡ª‰]H×§¢Ujù ‰ÈbV% c‚ÎÁ½Å™•¶%¨¨ÈææR¯²åéÆ] vtçC;ÖXZvC›ŽZšdÓ×D÷â>+N:Ö¶æNïJ½™ªвmªj'£~8uq®gµzʇ¾q#¥ԕƲ0«Úc˜íEEñóå¡Lê:éé\˜,R½¾Bæ6S:Ëë5i­6×±=WTÜëÅÃneò޼¥³µ²äÉ›A]B¼é>RG®;åÒ\u 8¦LR-e»I£dæŽí3¦9eµÝÙ«êûVD̾©‰|·/œ‡8M¢z%ÙÉlk«Å«<®--\°"ÜP)@3›âúe³~öËç4ÑŒæø¾™lß½²ùÍïÝÆ¨Qm7Ô§&.ãT(¶›êS€Ê/ 'ÖÌ´ÖѾ³V*¹pÐÙ<2xúPÕÏNֲ魊 h*ÓHÜËÆ×q99PÍ'd½¢Ò[à ]F~Ú±ªì:çÐÕ·;W?½¼NCÐ+¢£‘)¢šÀú`ÚÉ|›¨Ê*ô‰¨­¦Qg—‰ÚS•OŽr1ʳ"ÆR]¦O«¤u½RÞ•¸ÇL‹ÆºÿA ñRÓAGO-3Q‘DÔkšò‘£EXÏW-ò]¸€ÕïlltTkZŠ®UЈ™ÕLfOVÖµi,j'×V;‚Æ&dðÎwS”Èmü ­·êÖz—+bj¯2…¥bwúê{Y]”R[Ö‹¹›•) Ul â^»×•N JM'HiDërøGÚW왼™ÊzÌž©N ¬”Tæ°®zoYN >9¨ôTTµ m5£Ke#ÒH¥n-Tó—”ö «!²•ÖErPÕ?Ö:—"gÐÇ®dwixÍSNt%G„°]5E´S᫇¡± 퉬Y覎&͇V.eVö—2‡”ömDðEjÙÉñ¡f¹f‡ßfD¼ŽC£eZTöµ6…2ô“16»C𼍹ŒFlÒ¶-Z{‚JêŒ\Á¬½[ÞåÁ¬o*©î“ß{ykÓY–LŠÊtð³T&gIÊМ ½ÒÂÉÅÏb9Xí-ULpSæI$ŽF±ˆªç.dDM*}ˆL¼Ê%sÖÅ£v L¥ÉƼLï„IÍ´(ʼÔV{Ý‘Îʼ¬–Ö‘ÔTNVQ1pULË"§òu™çhNÓ§QQЏO°jδªìª¦ÕѽXö®tð®N²§¦OÛÔÖõMI30I¢ãjõû]c!=ûÖ¨±«ÙY∸HÎ'7Š“œ¶]Š«ÆêÁ5‰îùŒØÁࢫ†¾–:ºwp£•ˆæ¯oˆóœVšw4fPÙ-¶lÉ)}š‰Ã…zÏMNƒ¦ŠˆäT[E>©Ô•)Æqw%šz£’7Ã#¢‘¯c•®jéELÊ}K¬¶É¥—…lP3¢zÑtª'‡OD…'ÄbÃtÊ],;.e%R8÷Twg»ÄéÊjbª¸"&uP=ÕõQQÓ§ I\NNºšå›A™C =LLDÇ®¼käœ<ŽÉ¿Qpsõc}k™¹š¾Ëjñv׌¦;’xt D¶«’*¶âÏVMEJ›»N›Ç»ËwÎ99¾/¦[7ïl¾sMÎo‹é–ÍûÛ/œÐýÜj…Ó}Jrfî5B‹i¾¥0»'èr‚iª“‚öâ±L[½×C#·rzѰ*V Ætо«™¹ãzr/_Üe-|¦¬‰³DôÎצ(v J]’Óml#7L öíÙ=ÓØRbšyÞUÁS‘®ï‘uÖM¥f=c¯§’N75x+Ú](q™Ú´ëU5´Ñê †…›`Úö³Ñ´4¯‘êpcO“.\Ø7iO¶¢Û‘'z`©3FžôºTÅ<8Vå§Y)©”›$òk$í ¡™ÔXi¾¬É›´ÞºšÕ—eQØôl¢¡b26&uðÎ^7*ñ©ìà Tñ¶Øãb`Öµˆ}ɱ匵š–ˆlJà ‚Fñ-¥ ³g@ì&­Å†”:¯'A\cùkh­£”5 ‹ŒtëÌÖé4ùx‰,:8‰=£i™ŠºÎ¦}€É­ä=´¶½ŒÆÌìj)U"“*‰Ô»È22¦îí£·R•Ë„u‘«8¸Méš`•C£†µÛM I]Fªp((*ìk~xi£WÙv‚:£7SéÕ'iÚ{g|Ï€FR-§-(,¦;šÖȾtÑ kÕ½yṗ½gPSÙ”PÐR·ƒ F·®½u^U]'²= rÓe‘eÏZî©Â4ë½s5<“–Y'•óJ¼'Èås•tª®u-o"½UÔÖk2"Í"y–ú$9ɤů¥yJÿ,÷¥ˆGh[Ýݰ¨ù,y™Qeƒ¿†o¢]˜Å•Zû>ѧ¬báÌ¥j¯k<ƒecÛ##s£šŽNÒç8)l)^TSX¦µëö#áQ0\è¤NTd[•Ï´,†ãŽ.’<µox·±"$Ë‚tX¶][wôÞ­\×1U¯Ej¦”TÁOƒ"}£ŠIœŒ‰Ž{—CZŠåòŠ +"-jõGÕ':BºUùÞ©ÈÞùõ­s©"Nj­dR±ÕõY¨¬×‡9a–¢FÃI¸5­LUT¿Élmž­¯´‘S¥‘él|«×S±cäõ›b³ fp¥Té¦~w¯{Æ:gjž†›©­b»l-̬„éÑ» ož¤öHÁ Îo‹é–ÍûÛ/œÓF3›âúe³~öËç4¿w¡E´ßRœ˜»P¢Úo©N>’EÍVLÆÈÕÒ×"9<³î8µy“uŠ®–†6¹xãÆ5ó*ˆsŸvÙ6åÅ­™©;èUƒ"FˆÛG® ‹ˆ–Žíòi‹‹™,žõ"áå"*<“ÉÚGAC 49éÍÍbuÀXÑjõ[ñqV±¬j5ˆDЈ˜!öÆdT©4³¯²ã{þL&i4Ï•ÙÕïs•yUq6»}ê˹ɥ)eÝS;Ò¤õ»Cê wO Ý“P´–%KWgQ¼lS(õ³µS‰P*NŠ•Á½¢âˆ©Æ˜Ÿ'Š™UÔÑ9t¬l_))Ö|*¢&+™Œ5ÈäG5QQs¢¦tSâXÙ4nŠDÅjµÉ×ELžÉÙ¥²kfÉjÇ+¹Š,Ö|ŽÒúu^§5j棆¹®LZ¨©Ž¦}#)mY¬úVRЧЮ0¥gYËÕH¼Lê{V5™gEBÇ+Õˆ«$Ž\\÷»;ܽµPx”åJÔeV|R%lIòTL|³†{öó•öÕk—JÔÉç©èбQW´à·h­è§Ø“à|Ÿ×òj¥j¬*)•q^bÖ¯m½/ d¥ÎWdä¾ò¢|œ\pÛ%v”^í-퉫UeÙÕ©…]4rò¹©“¤åÍ‘9=*ª¤h{“ÏÄïб®µD[ãu;&½,©(­ÙI¤Mô`ã«»\?ˆ{ä^OB¸ó¿4TöãœïEà>YLL…0¹YöT•ÇZzÓG¯MAEFÞ ,žàÔE=€h“Zò““ºÛowx@Íñ}2Ù¿{eóšhÆs|_L¶oÞÙ|æ€wîãT(¶›êS“qª[Mõ)ÀÒ¶bY¬ŠÈ“:ºšTó*aÆøö$Œs¡ÍT^Òæ0ËJ•ÔV…E#Ó†g³ÈUÀïHVÞ—Š}Dõ€tú÷…ªùXÄÒçµÇTCètòjk­Ú*tLQgkÚgL¾qñËBеD àC=¥O!¸SX8¹OfOWM¡gæ´,çózeö¬:¸×‘ɘíJÈ´à¶,økàÌ’7¦jécÓ3𼍧ºzôt”Õ)#H’y]4ˆ˜à¯v•ä<Ï{#j½îFµ3ªªàˆöõ¥—6 žªÆÊµR'……8IðZ ê«Î©rªQѱ‰Ä²¹\¾B`elž#íƒHûTäò2çÞ.P¹qo1bu‘˜ùê#¼l bôÉ Ó¬¬Tó”爑.°Lé™fˆ3êK϶‰®N5‰Ê‹ä.%›–Ö ¤¨ÎmÎò/„›¤ÏȺ–­À¦|ºrYùãžh©¡}DîFGUïræDDLUO»\×"9ªŠ‹¡S:Ú*kF–J:¶sHeLÜU1Lqâìb>NOC-¯].UVµQ%EŠÏÞ˫îåÏÚ)«ȘØãDkˆÖµ3""fD>À?”q¬Vís²¾JãèœÒ/ižÞ|¸tµ²Dí§J¾q8sKBÃBWô ÷x >›j¹Ç“”¸ø~ü—)•¢+•™ÕW6["—œ¬ºZ]  EíáŸË8¸à¶r¹N1ÝåwAo§¸8‘ 3›âúe³~öËç4ÑŒæø¾™lß½²ùÍïÝÆ¨Qm7Ô§&.ãT(¶›êS€Ë¯ËZKaµìL#¬b*¯ ¹—ÊÀÔN>UX‰nYÓ5=_« _sN/3¤ñ,¸ˆ«h´”ÊwŒƒìö>7º9Zæ*µÍ](©™Pú•O°7ee¬•U´‰ÒÄÞcû“³¹|d"驦¬¨Ž–ªùez1N5Si°¬¨ì[. ó¬mÅîö§®w/’u¥q(B–®É“ÀtÓàIåŽX%’Õ³¬ç#«;ô¤H¿291‹Ô-µ3¹;ˆ÷ò‡+¬û«¯7«Téajèårñ!›ÛKk[oUª™[=,,ű§ÇãœÙe’i,®W½êªç9qUUãZ×=ÈÆ"¹Î\:ªõ#òbs-Ki:`èiŸ³ËÈp‹°›;¯’¸náçÈ•uilÚ Âyê†;K¾yTµ•"År¹MmÝ ¨ÔW.dDÅTà.Uå,9db¢ÔÍ‹ o/—‘ ŽYdžWM3•ï{•ÎrçUUÒ§[*í—[Vij¢ã J±Bœ\ñøëœã”äð¬¶S·-©ÓN«5ƒãNe>A˜Øx¢^få…tibòu¼cÈ}db½[™Í\Z§ØàÄVÎÚ‰h·U¯•ƒ€Ï xåUr¤-Òî©zÍ<‡Ò6+qs³¹ËНœ‡¢¬ÍKEµ[£åáÀ}ѨLÈ™ù>“¡ôy)ê&¤•4îVK‘ÌriEC_É«v+zÍeJ`Ù™ÒNγ“´¼F8w²2Ú["Øì)êU"•8“¥Þ2˜%0¬ÆN–æÚaêBùf£[N`OåvN¥·GÍ`DJ¸V5ö¤ãbúÝ:’¥%(àhÃäñ=ÑÈÕkÚªŽjæTTÒ‡ÔÒ²«$#µ‘k¨Q#¬D雡²a×åå3™é楕ÐT1ÑÈÅÁÍr`¨sEœ›±ìˆYº°5Ž'Œ}7€ lɧZµ)]VÜ(áv(‹ìÇ'k®}rg$*mw¶ª±4\q\Γ‘¼œ¦•OO ,,§§bGmFµ­ÌˆˆqU8,Ë1A:pwdñµ“}>舉‚fDÐ|€q"@Íñ}2Ù¿{eóšhÆs|_L¶oÞÙ|æ€wîãT(¶›êS“qª[Mõ)À&rƒ!¬Ûe]QOë%RçW±:G/¹'¢†kd·c¹V¢’$Ñ4X½ž>ÓÇ6cáQ0\èv!JŸ•¹+.•&Œ‘Oƒj®É› ÑUZª8ÕËášœy-ÀãÏvÖŠ«§‡‘¯G'–Šv[-†¶¨©áŸWÈËA¥øY8ý5TaÖé=)ìÁw=¢ËͦÉÏÁ<Ê!Ée®Öø_#,DU\1UâLêw,ŒŒ·-uk› Óºe™©‡"iSP¡ÉûÎÁhé"c“ÃpxNòW:&Ë— l×jaÈàdþFÙv$ÈœñU†y¤MòœG|¨ç¹ë;–u>@âÄËElÛ¢F.J‰ :ø¿2ùX²óê•"¢£EÌç>W'k§žd€Ú8K¼‘õvIÁò §Yðð|ŸÈÀ$UV¢çM(1LQ¸ç]|+˜º¦O€|€ET\S2¡ð|€l™1hú“°éjœ¸¿™ð$÷¦t«çR.쪕ö}U"¯®¦Gµ9™ü¶–„¨Í ˆä»9&®I¬Ð1Ÿ æ[9=fÛqðj™„¨$ÌÍ"wÓ‘N˜Ôe(5(»eFakd=¯g+¤¦o=™øQõhœ­ÓäÏcãr²F«šQȨ¾Y¸¥e•fצ”ÑËÊæ§ ÉÒ}¢;©[),#}š°3ŸQY?2ª±’B«í\<…Äõíì¥\Õ3¢|‘~b}¢C¥[ Õ«Fv &+»±¸É$Òr+‘©å!Ó£Ék‰QÐÒ1ÎO '«ËÄQ•±¤±)K:á™ÙÖ«j¹GNç7ŽG'4ñÔ¶°ò މ[Qi¹*fLézé«è•mkXˆÖ"5B&d>OЧk>¥\á™DøkZÔFµD̈|€|8Às|_L¶oÞÙ|æš1œßÓ-›÷¶_9 û¸Õ -¦ú”äÍÜj…Ó}J`µæ½VÖ¦gi±OËÞ4“8¼ØÕ-:IxN©ð.^ùžKRñM”µ$FƒàŽ“àäø>@ŠTVªLÔÅS3‘4ªšª«+Ó;Bu“‰(8P%õ-f»®|Þá»®Íø3èù÷`õJºèø–(×ÈUODÐŒúëã^o]/G|•Uô ›*Ç®¾Éµu6às|_L¶oÞÙ¼æš1œßÓ-›÷¶_9 û¸Õ -¦ú”äÅÜj…Ó}JpW›H¯¢¤­DõÔ®c—‘éŠyhZœÌ¤³}Jص4h˜½cWGïMé“Î2AuF­Ùõ{$Ì`ù ŠÕV¹0T\Tëø|Ÿ€€ù>´l|¯lq¦.{‘­DãUÌ€MÚÒ,6DÕNLõ®¦&z©^z6-Y–U5 iŠ&£½és»ËSÞ$ÅuÜêêrIÝ“`p>@g7ÅôËfýí—Îi£Íñ}2Ù¿{eóšß»P¢Úo©NL]ƨQm7Ô§*Ë› l«UÕQ7 jÅWµSB?Ã7Ñ&ͦ۲)í»>Jóp“߯צ…1ûFÏ©²ë$¢«oH×EN''"”dÑlÆÐ­¹¹#¦”ï•̨õðÁ°ù> | ^@Xk_iz‘™¾³Ñ®-ÇC¤âOIÁ²lº«b¶:Fâç¯Lï Öñ¹M†É³)슨)“Æ™×Î].^ÙÖ•E m [’5U긱³ÜÎps|_L¶oÞÙ|æš1œßÓ-›÷¶_9 qª[Mõ)‰‹¸Õ -¦ú”à.RäÕ.PS`ì#ª™M†t÷ë¡ÚÖ¹X¨¨³*M§u…¥fVÙ5.¤®c{W2øW']TÛ-K"‚اZzø’FøWh{W®‹ÄgÖÝßZT*é¬ÕçÈ4ðS4ÍN׌P…*ké;w„o…Tñàd˜>òÅ,/Xæc£zik‘Z¾Yô;Ðð|ƒï TH‘@ÇJõÐÖ"¹| îÙVEu³T”´1«•z§øF']T£±.ò¾±[5ª¼ëžf™æw …ýfPÙTéMCDÄÓ‡TåëªéU:ÑeMe&âËá§U,XYêdöNÒdý'2‡§™è‹4Ê3—¬d:À9\ªª³ªšmÝ`|0Îo‹é–ÍûÛ/œÓF3›âúe³~öËç4‚î5B‹i¾¥16íG’Ô”õu°Ã+y§ {ZäÅʺJ‰ò{ÅnèÞøP¾‰ò{ÅnèÞøèŸ'¼Q¦îï€uËèŸ'¼Q¦îò{ÅnèÞøP¾‰ò{ÅnèÞøèŸ'¼Q¦îï€uËèŸ'¼Q¦îò{ÅnèÞøP¾‰ò{ÅnèÞøèŸ'¼Q¦îï€uËèŸ'¼Q¦îò{ÅnèÞøP¾‰ò{ÅnèÞøèŸ'¼Q¦îï€uËèŸ'¼Q¦îò{ÅnèÞøP¾‰ò{ÅnèÞøèŸ'¼Q¦îï€uËèŸ'¼Q¦îò{ÅnèÞøP¾‰ò{ÅnèÞøèŸ'¼Q¦îï€uËèŸ'¼Q¦îò{ÅnèÞøP¾‰ò{ÅnèÞøèŸ'¼Q¦îï€uËèŸ'¼Q¦îò{ÅnèÞøP¾‰ò{ÅnèÞøèŸ'¼Q¦îï€uËèŸ'¼Q¦îò{ÅnèÞøP¾‰ò{ÅnèÞøèŸ'¼Q¦îï€uËèŸ'¼Q¦îò{ÅnèÞøP¾‰ò{ÅnèÞøèŸ'¼Q¦îï€uËèŸ'¼Q¦îò{ÅnèÞøP¾‰ò{ÅnèÞøèŸ'¼Q¦îï€{5¶Uh·ƒ[OÜ®jp¼'ªî¬Õ]5§Uâc¸MòŠuz'Éïi»£{ã¢|žñF›º7¾slG²ÚåC*MbdÔ—_Oª«ž‰îLEôO˜î¾™ÕµÏ_ycSÑ):'Éïi»£{ã¢|žñF›º7¾sĘ·/ ­9=ÜåÒ]åN¨é’Z…Okv òߣ³h,öp(©ã…=Á¨‹äé=N‰ò{ÅnèÞøèŸ'¼Q¦îï˜Ýï·9Tùro:€åôO“Þ(ÓwF÷ÇDù=â7to|â`ê—Ñ>Ox£MÝßä÷Š4Ýѽð 9}ä÷Š4ÝѽñÑ>Ox£MÝßê—Ñ>Ox£MÝßä÷Š4Ýѽð 9}ä÷Š4ÝѽñÑ>Ox£MÝßê—Ñ>Ox£MÝßä÷Š4Ýѽð 9}ä÷Š4ÝѽñÑ>Ox£MÝßê—Ñ>Ox£MÝßä÷Š4Ýѽð 9}ä÷Š4ÝѽñÑ>Ox£MÝßê—Ñ>Ox£MÝßä÷Š4Ýѽð 9}ä÷Š4ÝѽñÑ>Ox£MÝßê—Ñ>Ox£MÝßä÷Š4Ýѽð 9}ä÷Š4ÝѽñÑ>Ox£MÝßê—Ñ>Ox£MÝßä÷Š4Ýѽð 9}ä÷Š4ÝѽñÑ>Ox£MÝßê—Ñ>Ox£MÝßä÷Š4Ýѽð 9}ä÷Š4ÝѽñÑ>Ox£MÝßê—Ñ>Ox£MÝßä÷Š4Ýѽð 9}ä÷Š4ÝѽñÑ>Ox£MÝßê—Ñ>Ox£MÝßä÷Š4Ýѽð 9}ä÷Š4ÝѽñÑ>Ox£MÝßêÍñ}2Ù¿{eóšYtO“Þ(ÓwF÷È;ÕµlÛFš´1T+%•\‘¹†(˜c€pí'Áòí'À2|'Áòð'ÁòÁò| ø>O€ò| Áò| Áò| Àäðù>“àäàù>>Oƒäøùø>O€ð|ŸÈ>“à|ŸÈð|ŸÈÁöiõ>ÍÿÙ endstream endobj 1 0 obj <>stream 2014-05-14T21:14:07+02:00 2014-05-14T21:13:01+02:00 2014-05-14T21:14:07+02:00 Adobe Acrobat 10.0 application/pdf uuid:9cb91169-7cbd-f84f-b1db-2ddbe477f4c4 uuid:ccc87a8c-c8d3-f143-b142-fbf798839731 Adobe Acrobat 10.0 Image Conversion Plug-in endstream endobj 2 0 obj <>stream hÞ2U0P°±ÑwÎ/Í+Q0Ô÷ÎL)޶ŠÅê‡T¤ê$¦§ÛÙÖw € endstream endobj 3 0 obj <>stream hÞ2S0P°±Ñw.JM,ÉÌÏsI,IÕp±22041054124460Ô60R70Pׄ¨Ê/ÒpLÉOJUpL.ÊOJ,Q04Ð3ÐÔ÷ÍOÁÔlb`×P”ŸRšœŠM·‚gnbzª‚s~^YjQ1Ð 9¥éº™yšvvð7. endstream endobj 4 0 obj <>/Filter/FlateDecode/ID[<295E44FDAE254B2AA171513D3A17C178>]/Info 6 0 R/Length 37/Root 8 0 R/Size 7/Type/XRef/W[1 2 0]>>stream hÞbb```bܪÌÄÀ[ÁÄÀØ Ä·˜m˜˜ ,­ endstream endobj startxref 116 %%EOF timeSeries/data/0000755000176200001440000000000015104730074013275 5ustar liggesuserstimeSeries/data/LPP2005REC.rda0000644000176200001440000006540115104730074015267 0ustar liggesusers‹½ýe\UíÖ?ðŠtH‹Ò(Ý9èîîîîî¤Á@P Á*b'!((* ˆ”Ôa﹟ûùŸýœ×Ç\kÉwò›s®kÍk >‹µ,´ì$©í¨IHHÈHÈ·¾nÛº¹lë )Évª­‘ÚÈÌLR\\ÆB[“„t= :j±0Šê³ O­ƒAXÛw´˜Üoz҇鮆 ƒuB•yãñ ~‰¹-õs*z½7¼ýdBÌÂ<#þT¿-9'±&oAçzÌèû²Và•¸ÿŒô˜ø½Ij‡Ö¾+ÊÉUèa@“Ö”0 ¿vG†9¥£¨ŽTapÞMÐi3úá‘“ î$áçw÷¨ Ö–þ§${Qš1»]:¬4wüR? Z{çL·wGуAý£íÒ¨^Åx²# íElaîŒø z›L¨^AšXr›à4~Ù¾l·Ž/n ]yôwqé'±£N… ”‹³c€¯ñ¾G&\`;µÀzé0X;³ª±X:/{­Úçá] SÉã Ä:ef´šl ÍàðºÁÖTYØ’$wEüç˜é~—€Àä¥(ÚîÀü{&¥[ U~³Tp™”üCõŽ '­ d^¹JÉçŠ<«~û¬)MP~uîÑëH °¸q@KÙåj®„WÅÝE‘o INð`pŽÊøŠ–ó›Ÿ?+ùùK®'~¥€Þ칩ªàt6(¨vE=š Èw<Ý@Í…ÂC½B;Ñ0¦‹bÇC9Ô[»òÀ.h-î±åÊ%£yÎw{Õø$Ô)\ŒSbUæ4!ýïû—VîÒêUS4/ ÛîÙt¥™&™ªƒ}ÁâiË¥¡ò[ Oß;Q7Ø¢t“g}[#ÿ9~UMkû0Ódù÷\§Ü¥‹Üv¹2ÂFô ´Ýuzú Ú¼î¢IyáÂE}D£É2F‹ŠÐo;5‚úÓowlx3Ðp£åø¶Ã'QµØùkÝ2‘Qè¿üʼŽsN*;òÁqé–E¨ª&÷÷Eºya©”BØÏ\’ÿú‡Ò¾íÜŸÛ¸Q+àÀWf‹3¨»úÉ‹®uÖOˆÖ~ŽÞpÖퟯX£ë2Yv…mšÿ¢4¨`åD«Ì¯Ë3G>ƒõã™Cz’Qÿ;OçÂU-w™¢“bLã3Eä=Rrh&/ì›è3#F%çôÝç}”h»k‡»&ãоÈi¸rk C>YÓ3ú&6š,¼>ƒU]ê;ÏrZðœ_‰¸bŸ†nlNŠËMÀù¤ûµeÃApk“n¼É ~khÿmåŸÇÁú¦zNc Ù?÷Ž”ø3^åÞP*ʸìvÔ S d´ŽÑ‹Íω¶dãäß¿€§pÏß*_ÞŽÃ|ìqñ·h`‰H~<Ò–hb¤‚ÀN1ÐN†½LJ%é}CtÀæ ƒí³50IYÍ“åDw¡u†Þw íd–‰î¯éNËr£%ÙlÉæcÔY»´|Ð í ¼t~|s L$ÕÞp*Ôo]ÇÓNÞ6õ2æ»üQõ„‘…G¡ƒ×Íã;P½j\/tlj®|¸@yͬêv`éÝ¢¦yÁÕ=÷Ó„;¨ÏÎý0Xæh×At›ç¬žžz 6U1ïTœ÷€ÓÛ“,Ý E­.¼?ð#¸iLŸþ´Ë<º¹uWAsN{Át—7Z´ÅåÝýýϺ`¹¬»ÛL ¤þžR×çÊF½¿×ýTêÃÁM‘zå»q,ŠüÒŸN-Ý@õXW†®B;øŸõÂU¡›¤=YT6óGÖ®¢éÙ}§¿n]Ï«Gv /uýïÏŸnƒµžøœÂ±ZTϦ«”G{ºNÖzà¥/Y®=…ölg;yi€—õ\DVÕiÔ91•tX‡cë|¥îŸÚ/Oyü»oƒ«žk¹Ò—qðåò¼óúxþxÏt3Ýy¾PZeµ­¡ÔWé¿ÖLõ(2üVÐ÷Ñ~:àXøÀ -ª¾YstйKb‚üI0Fûâhq|îhe—è=~¬c`‹4 oœ¬©¡áPw1Ëå|ÝNi®5Û{tþ™"û\K ÈP¯gŸþDø_+¾Uš¯Ç´ì¯/ò‚ރŒ”PäÚmß¾? Ò¹ÞyÈjy ÜTL>¡ÔÓ¶ëùmihnw©N¿Uî¥1ð;’ ECGG:pœL;mt ½úÇÄú<žßÏ9Üþ|‘ïGY8A~Ú‘†auB忤(+Ð'~çéÅ›»Q‹,0@ÂHœ7~¿,¿ð?ë:ƈV\øçéû¿×iؼ]2îh{+ÿ•^Ëg°¸ý¨ô!Õ;°[õ žùÓj3oÚÑÏþòDŒ yaêºäm´6zûEî/Z[~4“ܓҬïðÞA¾'Ëñé©è„ßÄ×çÀ§ž\üáæ8 oð3þ¼ ®ÜÍÃÀ…±8vLu¬¶IK;E‡€È¦Ò[UZc4LhèôŠCÃ\—~óƒC¨OóVg×'n´)y½zúëÿ®7ÌFüŸGÍÀ¼XPͪÿØÓ™²ºØE€íý££Ï³QdóMôE™FÔÉH'}v_ Ô3 YyQ¡aÖå°6¿ÀB±4¢·­( ¯ó|žQFÝ\z×`­ë¨•¤ŒêYörKTͨsx¯‚w7Jÿg^ÿÇ£E_¥CªE8èÓØÍ`"§û=©Š²gÀA^Øï£r)Èÿ–íd[ت§®n›oÌDã©;åÕÀù:z-Ýéê¾ÙçÒOm•ʘÅ9àÛx³\¦Ì_è'+aØaÁ6|)(q |:• I_H×ÉM¢DÕÌœ™!øÆ^ØÙí‰Þ{©³#ÎbPžæÐÀÌè%™¯ŒW_BI¥}Çë-FÁFãú}•üLL~íA“t¶SX>üý wÂË©;)G‚ ʧz¡N¶î3¸Ü»Âá­–ÉRŠ{1øæaÉžYèaLsÿé¬÷?OÂ÷û¿cØÁ±À¶¦uÊR ¢¤Í».`˜~í³Ê·ÿ¸Äíä‰ýi? ï›ÒâûôèþYò&p뺯ư©fcˆüä8ªHã1¡8¢ÃÛSr²˜"­ÿ€¸½ýS7GƒVÍù¹fl¾ói+åŒ{™ÖKÏփɻ„lWŒB ŠÙræ®f¦¿£|´Ø¬œšq¼¯дWAúÕüg´ò9ùt¬û¤ï¢•û¶g"?Ô=ØÅ ¶n |7ƒÀ'€ô¹W:„{×|ËÇèàFraÕEˆÏ:X~ÁŸBuRÔ;„ã“)ÊmrýÚëÝ€ú…Þd4d=èÆ®`²‚fCK¼vä‚?É“®ÏeÁ¨öøÀOûú¿˜e—ÉfÝùQÖœl®:`M£êãøËÂýU…’ Bãâ¼Ò’DQ>4“Ǹ÷MÛ}Ñ'Eiv·xÄœz=tó’¾*|£KìÌԇ⶙¾§9°7MœîeeWG ‚ªõy²ç˜ôp4{xÐí˜D;ÿ€ ?Ücϼ!ß…b|2©Ò¾^ÑùÍ«ê‹J/=dÐiÉך§´ß_ºc²? Âã6ÚTNû5º+«>OQ’†m×{=°F^ƨ< oq¹LU8>ùÓ¤½V˜‘bmÂñuß÷ŠÅß‘ó 'Í7F¸ÒqšM"÷[>½¬Iá׿å4Тw¤[w%I:DÆV]ä-T{{þÄ+%_@­·ÛþQ‰êlïRmÇÏB̬šV ¼yý|Nò²hƒ069ÛÂî]ÿçùUuAqzò7ä˜úÆwp€p™—W›ô/AäsŽ„î?ºèØ£.¿íB*†sfNÞ„Hh¾~½æ-i­Ð,£ßÞ¸ïÖüéÿÌÓ®´IÁKÌ‹^_zó:ü®ò½MÛ¾rÉJ&ùu¸!ÞðýÞzð:uôEå!´¹îfá?¿æK¿/LçâÑÀ/ †½X\7µË¾EÇoÖºb [Ûð“€™ŸË,#.§ŸHžž<-(C_2Ç‚iË%¸30çþƒ]ïÎ<þgNÆ8žŸ¹Δ6jg±Ì¼ôq]ÞGÈäØ]Çâò ì%0pXùAÒÙ;«šÇ&ÿ9/ØBÔ/@,‡ìønu2¬¦²ðâ›±„ÃQ1 VotðHthðV–†^ÌẠ5Ç B›ÞúПè»Üïø6ê)¤õ±È&¹AhM¥ïZü#ì$µ4̓c΋·5¯ú熟Ù7Ð+XæOÙ &ítb5¢ŸŸs¼ÏåFÑ»?Ÿ^£‚³!Šf }ý‰`è73pcº9G[³3¨ž/±8(AÌãòöÓò‹Õ]¹sæ—^ÒÒ.Ý¡þCš%—˜!ÂŒ;-So[ÔŽ_ (ÁlUCšnô(é gÝêûc.Ú]3‡£Ãål/I4!-$°2ÕO £Gw_U뢆cm'J:V@óÓ.!êFv¿óñý‰‰ûÓkÜ#c ÙìP®?l™ñãNÐùÏy ]Ýfqð"æ”úÞ6ªGÈ̳y¾—ùæõ‘)që¼ ž´Ö+W!‡éXS™/F­v2Þ¡çÇÞÌ%éž= éÁý:Š‚¸5W©B„ÁfÆ¥J^ð}¸ÑóH º«®®C¿–§æ§+À¿õ¡5í—\<$¸4eö^ÜŠöß‹í'ì9åÇ™À9+‚6 ä îS‘0ïÝ&ð9ÁF Œv;2ížøþ5,Z/&å ¢Ùक़Ë*½Ú½˜vÎãDgUØk0ž´EëÄOˆªë)9&sÓ/Ýnôá†Ü³e §'Àoi’Cö·>,Š)(BŒbNÝÏ%Ýö£ÌáÈ8zSÕƒÃÌŽzóŽ&¦•åÖÓ¦Ï7¿¦Ûï@õI¶Ã‘\'ìU97!8­•ö‘Œ"èò*'O©=[öòmš;Û1*ó(Sûv®­>ëµ×ãáBHÔ6´H.ß É|N/åØÁìtڣą7à· l{šG2|+"_ƒyÂëåª"î‰2ÐŽrÆæ"˜]ºn”³5¿¼³£^°WBLÙ«®W.Ô럌ÃàkqcÃCú'j)äçžÓ}Þm/”Š‘§þŽÑóÝÀ´¾Y{[ƒ r¹SEgr‚XzÝêHu!®tãp,ä0ëzî+ù ¡m½ŸÄ0N»`ÕÐÛ5™·»“ïÿÞSLS‚ŸJÁn–lS\óÚjë³¼c„ijtýÌv…w~ŸÔ/bÄL€x­ã°¦Ÿ1ø>Ø ‚ŸßæÍQ‚ÑÀžƒîëèkÀÅ“Ð/‡yW6¦ïô(c!Ãd9UÚ&œ>ž}]ò“;ÞÁôŽ€é‡öb‚yvÄá¿Û á­æÇÒô¥¸74@1 Éuvëª %þ½­:ÿ(úŽûôÏSµC|ŒÇ ÿ¦S:~ßû 8DýìØÃ Yʶj‘k¹Œ¦‡«Ñ^—öCË ãËDÖÃ'À>BíåŸ8~0yþõö“ÅUDçpüšÅÿ©Ï³…&ßæß™@·¦˜­Ê¸-èɻܧüWä~ÿÜñÉàzðèïdÑôæ­åßf‡' Í `Ì‹f±ó#Ú±è}Ò¥H„‚j¡¨C[ó´««²KÒ.ŠÇœ/¸„ž‚òWtÍê 8õl™Ð’l5# =÷ jçïüñ_1BÏ>\çœ/ªÕ–¾=ûâZÊ4…ZKÐÉ]Lë+Cfžg³j¤…ÿ›;lÈà \Yæ×ÛŸœ¸s›sÆQ6ÈÛPB",¿ž˜¢P]¶lVÜøÆ“1Ü_ЀC¤w²³ Æý|uþ²ÁCH8#¾ë¹†=y´³ÖZ=ó™&ľ€¤mŠ{Ä •Áέ2ÿ{å&øÓ… «e€±YêÝú*= x´äZ†Yæ M£öéFZñû: êtfž¢ëÇ «®»oa±Õ·Ñº!Ì‹ 4¥¥¶C7Ú¨ø¹×Sàéª<ßßš…§Ì‹Uz ÷E€‰8»%äñ]?–ÂyAñ{‚§@¦zý:Y¡ÿÆÏã™Ï¦1uÈ˽ö¹*V‰>ŸEòá£`‘n+Rdw /9{l,È@8_2É¢¤„¶>}›êGªÙƒ_Ž€ã˱)¹kæ˜Êäë5w#㬱ӳdyÊä¼ýLE:1 jú?•¤¡mÐÞcl‰-\K’ÚÖ§õs!*ÑÛþb ßÿ™'ñӟبE¶úÓÝ¥$ìëƒÀ3ä@ó³ÃF(±¨£mbXÊ£2Ð"ö’ÔêvMt2 ÏÕÞªk”8ÌÍWÊЕ£éû€K>˜=›]s-çUàq¥´éA££Q™—¾_À¸±Ð–{wá cÝرÕ÷f³¾aŽþÛrhdE^‚–ëZD¾ÿtÏíù0ù,Z”·2ãnªA×pzÿqïÅIt1XÇ^K‡ºee¥îàDEòãa\ X9ÎÈ&Î^;}úvNt+hvrí: ^ýîãg7ZÐZ¢âˆ˜ôÌþkô³¢ ùÇ= hI,5™Í,5_f*ì{F‘,Ó¿b¯ƒ®?;Uí¥Ø­u…E`©P2hzc ”bQ1,À)ÑW ’j¾\ˆ¼„¾¯µÖN¦ï@mߡ֠}²ÿœOŸšÉš µj°;õDF • ¼vr%½Û€Öz¿Øý³J] ¨!}7J3ÂMãè{{×®*C/pûX/S½G«HbæÍÁjë,ž?5‚ñ†ñG´ÝDƒ½#«.˜šæÙc¿MëÝŠE-÷üoþU$Ït¤ ÿ¤5ƒ‰Ÿ?dGpšBŒe±‘†ó}z®ý  ø¶ MC$cq;©ß9ð|Ñ÷aúg š:KvRߎß4^‹“—Ê@¥îVs}¸4Ÿ|ðr'x{~ܵÜ;R]Ͻ®Äö O÷œÀ”)þPlDyrþN°Ì¡¹ìžNç sðb*[·ügÿÜ…¨†~hOÅßðëáZàÀñÒŸÿV„}¼Åw†º¼™ªr¬oBpòÐÍûYÌÏ•«ÜàîÒ-Ên A˜²rCúOpüׯËxN€é,¥æà÷­záĦËÇVh~¡[P)äX1\ sû²;Çé]âî­ùŽõWÓÀR{½DaïôËüËž¶û&˜Mú>¸4‚{9°,uóŸÇÇ£è~‹›¸4ÖMꡞ¤cõhÚSrq[ ¡ùÃZY‡Sàù¸“Q¶æšý¹s|ΕUØž8ó_}aÖ¾¹³¿™Ðà“JÏE*ðõ(–ï^—Æ 3ŸœšÑþ“Ïáƒ+ &v—ÍïuhS<Ø=ËVR“Ú«§ÈPýÁÕ½w-ý!@^öîÒã`ˆK1êWáGÊ)ƒ^Ÿ*t4Þ^`MùêxLj0p¸Òá*û¼¿Qû!UùK¥Ä·;ÿ缡z™¼úi ÚJL6©QK€ï±¡é+>àv›ÿ´}ã鯰ýW6Ïl$U>‰‹Ü«ò„yç 5 `&µO1¹5Öª#U®¢ûËݾﮯwФ 7][õ³í_Ð÷‹Ù¿q&@•ç«!×:v¡n~Yкôœ™“(ðˆn(7<’Gþ5ÉmK‹h_Ø?zXÍ ,JÚ½B¿þØ{z‚ÿ¬ƒòIÙñƒ"øÄÍù×ú½@ç+%%Ѻ¡µCëè%–* Ù~õ§@§îº¸ :QöÏqXŸÝîª2Ô‰öç±ïMåK´I°¿ ÌrBFïÔô“sbÈÎ=\ã7:1V°EkP²mÃã#Jô¦1‚š&õ´Ð°Þø«ˆ7ÄÍ/ƈèa$ÕÇGÜ3hmTéºc± íªuž]H  o²‚úQåpCÇ]4 rêí‘ÿeŒš4ö^Ëû×%÷1ýúg¿¨Í¯tnc4HGšŠ0 £ÅÑl…˜TÎZºªð±T^:Ý!µºâ<ü»Ð -•Í­¬µDn׳˜AOvÅù±ó]à{^ñ_•&|jöé+Bw§–Èï±:t2-iö4Øítëûlææ~4Uï-Û `Ü0L€µ|Æ.íçm‚@ÁÙâ}wÝÁg%¬TŒÊÃUEs_†-ÛÄÐòÊÿ=ÿÎ19F·-0<ÃVXø¾6D“d¦þá‰F¯iÑ…„×zk¶«dbö'‹í‹ Š}üÚ(,#øû¸º þ¬øëvœ³M„“ž4l]'z™ãž¡[ íÍ×[}¶WÖðÃ] ´èFÂùö÷É[èF¹ï~íuGðO®¼˜D“KæßN ¶÷nóÌ™ ðßi=&x·mŸ½ZêÚËN½AkÔÒm:Ì·Oì2{æh´3Ñ[}ÍÖ©äØv{¾ø“¶µní(>ÑÂΙTÚùÒà{¯A}BN ÃÉ–ßõ˜¿BÿÞ"œ¢šhà±çËó»“à´}3Õ£oHfÝ£žF#Ê Û²ž ½L¨ÖF3šzD‹ÎÌÙ£px¯Út–1¸M¿¿¸d+þU…×m9 Pýe•¨µZÕ>ˆiYåE©ç©ú2Å|ùÔòÏ7ï¾f¾!ÂÆ %.Ä §2I" ÄŸ¿UǺÝûÌ·®€å`t@¿Ò¸­³ØuîßÒ»i®ïù!àq”`Í{pê¯P˜ÓÙÖ_{›^Ñ‚ýψ¸\@wä4QùzÐàžy׬;‹N#ŽÞ6a«hužƒú®í%ãÙs½ö™681Nóé$X å`h´ÿB=ˆgz¦~¼Ù‡j™ß%§­€ƒßò‹EÑîõC+æÇà–fÄ?¤kR}†ór.i™=BüÒB µôìà{÷nܵV§n=xˆ‘×F76âÔQªäÔùZེºPžÊ‚V¶¶±‡.žE©ü/;eÐ)×f‡çvà9¶>ÛP‘…®;ŽvQåBÛkG'É»'P[_ý qH’|HB_y<ÏÝ3›¾¡Ç>+11W¢ûòô@F§ú*ÝÖÜç Ç[ÿ°bd£öôów™`ž©­eÛyø¯û…‘­>°`…%ú™&èAºzÓÿ­»Ö’e·17 Ù)—ñHJ]t;ûáSkîÌÿq–/òÒ¹É}Áf±O—ˆ56%Æ(y µ—º½Ñh)hUɽ²m©@×î¼ ;Ž€yTúÕ=º.hI½<'ñè„]¿8{ý/„ÿ¡|ö‘ä †¸êF…,ÿD—&‘Rt`í|Ž»‹¦´ Ww51^·²=O­!PØ»YÂ1ÌõIØøç¢-Ûìõ9‰7ïû!E iÛâ%´t€™ÏŸ›;êÛž`w·½ãŠÀŸrÎ<¶Ý;ò"oÕïÇèú†×_ú èÔ‹p±ýÃh5Üt”¥œ}Kt(Ÿ|[•ó†| ÷Áeæ^}ëEzt÷õâœT–GçÈùîỘáwÙä¶·2D^s›^M†@¹ùD~áç¨}{a;Uç^âã>±Jƒ>…á–ÞǾ£ütkãýS×P²mäÀ=nÔÒrgTÖFƒìsÿú „_cgúöÿgÔ2|e8GV>”ý‰YàýPÛîéÙ_h%'uþÁ:Ú:Ú(/gޡ߲ ¯Ô2#¤Á[/ýÌÀÀþ<»4é}ð´Ïá´¿ÖþÅâsÔoï+Âì» b›n>.m82fõÑVö] ©ÙQsÜÇÍ3ÒpBÕø©³šwÏ@ gÍ!út>Ï^ÅF­ j‘æÑ‡‚ 6¥ê$YòM´ïü}Ô¬:,†– ¬þϺÏ!É7½rt­ç¬.´ém …ðÚªŒœ/Zj©~Þvø&ši\ÌÚV…–uëgéz‚@®fVLÝt<Ùô+ЭÃF ¸÷7Øç ÎëÉm—¡ðÐ=#¨3пr„8 Þç²×ŠŸ.ƒKý ÃXúÉþÇ)tWÑáò:¦ž°{;Ïg0Z(ú4l׊ÒMÂ’|tõà¸ÿ³7ÇtìÚ9*õ[n_ª8ýÌì 3ºÉÏkɇ#Êm6º$ÙÁ£ž‡ò#Ýic¹»rÁèP§^w>ÖÜ«n%Й3¢)ó³Ñêbpê2ͯBô¡¹ì¬ýÆÄ?ˆ8çh¬‚VG«~ËlØV=Ýt2Žq™õQú^ùÚك̠²<§èÞÏŒÒ#_DLj­ÑòHòvVu:°¼Á*{Çâ3XÝTñáK¾ÏŸ¯WÍm[õ[<_ÐŽ¨Ó¨ÏÎòeB7y%WhßNnÕ-»/nVšs¢Þɫۮü¶«‰Ly÷§¨)óðê¨cáV]"äEwéÉíUººÔß‚ÐúÅÑ)2ÅE4÷S“yÔNg<¨ÏM‚Ç k¶Ä·ûèÒ;\® Æ«lóº½AíßO— êlȲÒp³Ÿ·G·Ùˆ™wV¨÷VQ½î‡8êÅU?|zÏ·¯ÁCiÏ%¿>@û3vO88jЂ»`š©BxzšÔdxQíEŸÞdž:ZW’ç“ðÙy‰Zp8eš©áš_#ÕÕ”ÅA_ð‘J€ä1ð”‚#†÷ÐCm{7eöoôë¡ÞA‰š|êd‚O¡erXˆ%ÚsuÊ–|3Eåêá÷GTÐ…ª4ß·­mÞàK=‰Zšº÷ø4‚.ÝUi*ê¨ß²Û(b(œ¨¦¥ýÁîWt§ÓÔ‰;)Ì: Ê‹BÓøo¡weòúðu0ÏúNª+àÑõÔŒ# EN*ó¨Â Ø-oò½Të´ OYÛ;í! O¨À=±‘JCî ªG|tÒ“Ú:Þϳå\à"qëÍfh"XLsJ œ]M­>µ Ú0Ÿ^Ê ƒZgyïäõ€|÷DzÏ(Ú }ŠØ&vSäY½U §ÞÂsêîí¿D¿Ág?}maG=¼eì˜Ãžu}ÏbA™ô¯¯Á |¼]J«µž¦sXU=B]­Øý·3›Ð’â^Ôµ;©h#ßéÝðx•r.Ú<—©¾ºFK€Ú)jzU 0Ú¥¤è×zlBùZtÜ‚½b.«å!êÎôý·×@1aÏ©9°—óÎÝšßv²F¿8MQ[ ãSg£$X®œ°þcÐÎê C'Ûo€& S§æù5”ïLªm=ˆ¶ñÀn©ºŽ¤óÉ=¢g@3êqÄC¯u0 ¡Ì~;u¥/ œ°º<†‹ïiq缋Äþ8+Ó"TËßÔò¾ôdòÓfvFëûö Ò#àrBßøÓ<#EþÑ<¬Þöæïw©ÎO€Ï¯Ùõ~'pð”øi~×rx8kP;æîi @½[_I^Au’Ž%¹½ ݺnǘQÆ·~èA`8¼zÑxU» ZgÉU;_¢ËLEM¿0JˆŽ_©%A7ã$íáï¨y¤F1° ÔE2¢á;Ó¦oAi<æî½Ýy vóù!‘íU ÄIæÚtMMR²ï.:0€„})YÐZ=R=ê÷DHÏìM`[‹‘F1kðH{¿Íçë]pm€Í‰¶)·B0ç´¯I¥A¦9}»‰H “¼·0êÚvûîTƒu˜N«û•ÛxpVˆo®ý.XÖœZ8‚¦S<‰®Åàγ¬òw¾\×Üô“)ž£”åwQ6é{xVê|Ža`é@Óoýž×ÙuÐ‘Ô A‹@óPùÔ­3ÛÂ|ÚEFÑZ—KV¹H-¿ëüYxZ†·ö{š)R¢ü3‹±“$e¨ÃñbŸ«0hñßÞ>2¦ Úc NÚ"“hQEË þJ°£À ÿÄʂʋ¡ókçVÑeÍà︦'Z< *K~öTNû¬½Üªëé>ù³i1ð‚îÔSð3£uº3ìõÌ ù,%ÑšZõ}ÀsV Ž gœ°BÍ/ïñv¢Î¥©ÃQ>´¨û®ñZ‡~Úmª¾Ä} ¶g^QQƒž“mO‰òˆýf¢æ düÍÇ%Æ@å>[A‰XhÄ>¼±? lã'"²ß#×±I4{± ݶ=M)cݪç†þ}a@ý1a¥ôs\ ó@ð[¦ÒG°ÿår.áöâŽOÿç¡¿½¬ù`-i"hÍzú›òw–¾vðèW†¼ªë±T†ï÷ø -ÍÐ×Lö6ÔHÛEë‰êäNÕQfUàa œã}Ñàà!©îQ ò]‰×Ë8š‘Ï·ƒê¹åGP¡nñ¾›âÇ:ÓZ˜›Ñ‚Ó!;xpl'þô­\O7=‡rëÐàÍ»}Càᔓû"‘· y{ÍôîG)’QÐ)Þ÷¢:ÀòYcKôp3ª41ßN û?·É²ÊQ ƒª¬uÝØ«ÏˆØsƒ‘¯ýÌ>°w Þ;`µu¶_EF´øÐb£öGæeä|^Õ9|jâ÷¶Ãë ñNÿ])¿(¨ ©·–­ÝE ¤|×Uô?Ý mg--ÙþH4d±Kª|û•ïÚ}à*5åßðX8ƒäÌ•w;|©›êŒõ¾=rã*+h>N}3NE‚Y–Wj¥\nO>ÞÛmˆfß]Y÷ç±#IæÐ=)Ö·ÈþJr÷ÐÆ °w¦þî.Ô²qéҟΖAvÆ1C«ÐÈýyë€-øH&²)Vöì ÛLujp¼1Ø65î‹üiòJ¿( eW•Æ5Ã^wÁJÍ]rB;3–%4] Z”ì;m#ĪÝHñ“TÇäI).¦Yʈèݸ .gÝüõú ¨ÿÖ³<^gÎjycÚµãs—ÿ‚ûÒ­Ø´D# üÉvfìNÔ¶žˆ”ƒðX¿¨¯Œ&­[çÛ¥ˆv¶WÞ:ËÛƒ3¯8†ìÝ‹¯Þ÷Ý—zþd®Æ _VÐ{¶†¦Œâ#¤æsÒô|À„´€?µyà{±|Ijìot7Ýs$®iP>R„ô§ædôCfé÷ög¾ó`Ü3J¢Ü`=~÷¥€>ØL{µFi—bô©cÑcñúhf9gµ~""s„ÚùÞc¸¿SÊ£g´ á¹n¯)Êl¿Ç>¨VÜöbc#,h+Û_¶ôòô{Ýx¸ÉU‘ÙoºÑ­œL‘b/D*º‹DÓ ‚Où˜;×Yôl§‰?}ü/ê‡ÇQ=4ÃØR¾§Ìs:xøèpêøš ä,=vçk)fÒ-sG§÷cñ—Âx·U!¿?ô\ÉM¼#©ähìW!ç5Õ‘ÛPÊœÁô]%$n~Ièˆ^³»_ÞŠðí)¬h/O¨¿ #'ß}U]š8ÞsY ¢Wƒÿ¬²HaYׯ~Æ8ôèùv•9°â⋾÷­m2º*Rsœ¿¬¾ëÙ5ÈHtdåMõ*†ª”sœ-ßòóG ü#¢ž¯yñæWøÁqZ·îèH•m›ª¾‚ö†ó›jZ!…ç5 O‘?$Úø<ùn’ó×h¤¬ fgl£Â¡s•ò¶·Æ0fnf÷A«J k‘;?—±¤º²îjc´+]ýÊW1ˆÎL‹Z,‡Ì]/†§ß@ØÕ9ZÞ”hU$ÖÙœ[ ¡Ç?ê³ÒœDï¯÷¾Ý ¯ J;ÿW}Êjç3Ú=àþq™rñà ÃÛ–'c[ÿAˆó¤)È'é{ 5œÀÀ ý‚ão^aÚ¸g#ëdçýe¿©‚vVƒêû:ßM‰Ù›û6ß V'ºEÜå'&0˜-Ë¥s€‰C’ðšÛqˆrüÜTpøXxðTú•Aósêi¯b̼fú#Ód½"9rȃÛà/®Dá‡ÏOÛjc!ƒö¦ÉKÈ|Dæ•Ö˜Qǽϗގ„$·¢ÛO€SõüvÉ÷n˜pîSË=ç›è­ÃâÇ—!Zqžç’Œ8Fš~}`‚G^}dYK ¦õRM_Hð*×g1€Žw•Üã_Aw†ò|~+”|ô´áÛRˆãë‹Ù±‘‡AÔ¼òÖ1ðÉJŽ"ÌõîzÑ´Ë+y^WbÄÎçE¢oGíåKÁ)x¤UžŒFT PýÃÚR$¤¿o¸Âc‡§žíÏ' |If$=;À2çëHÅÈaVîo“;QåÔoäÐLñ&ïö<¬. 5#í€4fÇu›wVÙ¶rºOþ¤¼¼¹ìý ßöÖXKÚcdh†Ë#tÿö7›íu3f|Ÿ‹Xþ~×¢XÜÁ Ž_žÑaÚx\-[›©&m«ÚS²­ò&T^¬“ò„p‰|Æë…`uqÕç˜Ä"z¼](:C ¢˜J@½^â7Uh>' %wá1àTÜ0rd›ÿÑeÁd&á/”EÞþÛeÙˆž1®‡Æ¥‹ ~–_S¦ý>sŒì>ƒqõ7)l·ê¸œ«÷\˜ú±øŠVn¤.Ÿ~)åÌŒ~ìÌ‹çК] ÇDKÏ/ε¼Rô• ‡Ÿ¢à{­°Ê®ËÓ§Ð|†¤OE¥ T{À<:i>? ø8LJOBªz„‘ËDêÆÚßËkG–ßL¸Ð…««Í`,¯¢ôR/ ¯ØùÓxºØv$CPÇ—ƒÁÇ)P[;¬ÔÜÛR;XŠv›‘¢3Ó³uJ!ˆ;¹ýã©ß-Ƹ˜°R¿FÏÂ_ xë)ß¿¯AûÞ¼8ØÐ™dg\έ€,’=Qš]i¦wûFô1PÕŽ9ú½x'¸gú}ÈGÙ^«¤ˆaˆ¸.6zÄU= èý4À?€œéôy*ˆÞ¨¢Õ©JσàÑ¢¢aº‡Oý¸Œé¯ƒúµXû1VùÓNEˆS~y‰K3‹{»ú!{vϦ]ë¤ÑØ­ԋΦ37?“AºBZ³î#t£¨˜9Z1…AoFúz_ƒë«ýr‚¦[õKÖn,ÛÃsc¬"ÏÝí¥, †‹§½µtÐ?3‘"Çwn?‹Ûá_s1©ù¨ÇÓ÷¨wÃŽÜ´–r ˜>×/Òßx<‘´‡«ñ{Óž€åÁûQM>C ÍÚv{.4°‹5¬ãgƒdµ¦¨;«÷#x†Y ¼H…¯>|‰)i›OœNAL²VGìÛnŒ;ÓÛ¯ òäÆ ^/k %‹1Ì»²\oQÖÛDWƒ“IÍ3¿ ÔkX¡dá:…)-]o$¨cø‡OûÓòÐîÌ÷ͰÀ&tÛ).q±~å^<©õú¹uüïªê 9ä™Jq,è)Øâ+lŽ—´’5Û`TN8íßQŒ8×ûjó x¿ù&p¿£âƒÈû_l˜ATð-<ªNÝþÁ—ÞbÔxõJke'idGõ¨²¤xcô äî—[~ŽWÄ5íïÅÜWÏì×g0OÑÖ®J) YšÃ#¤x Àq°g;m F®ýŽTµÆDnÖîâ~ÈÈ*ðJhÏ;_Ëüvƒz7·XÁRp§O{õAmZÎ^¢â;©œQ9µ¯ÚF0!âȤ „žÔã;fù÷GG¿§CìÝiÖÃþ gyõ±ËL*jNdS‡àXïzû=rèMCmÑ÷æ ú&ZÝ:Ç)òI—Öîƒà]Oj Ý!w²ë–D~zý¬ä%€¾ÃC7»®€l°â:“!;¸°¦jâ=á:ÚGÈ»Á5xûHz¿†+n[<Ž¢7Ï“gïIߎ«¿N° »õ©1döû$ÄŠšBü“'~@¤SmRÿ$:|‘r÷ÍG‹½†Jspè|!AQ¾Û”îr´¢7ÓΔ‰è÷ÞíH‰©ä¥í}‡b¥˜~ClHpkþØD-.ý**£Šm´<[!ÀܼþÊÎ_haýy±þÄÉ×:$¯B¤0Ý®û‹^(Krná9ùiPJøñ½%ò{í·=œºÇ[HÄúÁ’e†EA¶ƒöšçJlÕo=¬šRâYöwž–vâ9jËøu/lõ­º}¯®=<»¹Èû[ê¶ÎÃíÓ9Ψå<-\­»"¼oµÈÄÀ¸ç/ân ³ƒ™jfùýÊcxFO—Ügl3ë)¿k—õbŒŠÉ|A+)f®<륋GµGh•=ƒ’•Ð`.ÕIpÐéáéb†’ô|QõÖçs‚ê; )º%TÎôÍ~Å’Óý7ŒŸ¯`éd¸VéÈy-Ú¤ „qËnT7NCñ»ó7i8!Õ?Òë’Öe1v…(zŸ¢êˆ÷Y“VÆ Ú5íµû1Xt¡,ê‘ fºøì|{òX„ª£&·êƒˆýýÚÑ?|æÞ%½fȧǷ V“;ÂOvõ/z S7€ªRI½ÑÖuôŽ’ä£>K´õiî¯ÐÈC3å#K­êèÍüM¿°W -Ú•Y$/ATÆ•¿;N»Bfà',ô„Hû ™’‰=`¸|é‡')g~‰ñL[ÏwN±«ôVýkJøŒªšŽ‹HŸ>€¶é?*ÞHŸ¡½O¨Œ"o@Ìç¶^~sˆáy’³a÷Üu_îï)‚èKéµ\L_!è´ì­öTt$7¡ ’^,ùYâYnsúì+¦™§½Yz+¾œDz4§6 ;hÁiê6¸[ç³8Ž^úâ÷žG™îZ¹P6jž8ª¹U/•ëÌ.U=ƒÈÈvÜ©´kçÎþ´U·“½%„¡ émÛe Ò¿òeJ­1¨LJÒIJ„U=H?”ñ‡t[3ó¬ŽbH?Ü©Åv¡å:vç=Û:“:®¤Þà¿ÿJ5µœ2(kúõžåýƒ¦ÛºÏçþ‡ð¯XtŸöíž.½aå·F`9MõóŠs †„)Ó»šf€³ü»ŸûÅÍÐÆ*:N@”Ùh>l–Ç,@Fw㆟n;j†¼¹)x1B?Óå¢ ‰'÷ËvIs¡Óé™l­v.49fût"’ 3ªw^1¨ÇìÃ/D¿“ü‚Œ%šë§Œv ÇèÆ=[¶·X0ïXUû§z«nØQazç¤L ~+«€¸§µ_+.ÔAÁç0ù¼àQ+»@nó`l™¼dǃ¿jqªøjt?ùØž5]<ɘbtÎÕC0Ÿ“…ó- °îøÓCÛÞƒ±k»)F(~CøòJ3ÍY[´|D7Nu “:K؆ ­Ê:y«‹B÷ýÍóõ|®à¢¡w9&n½÷¸¢Õz…¾±£Î¿úk!ý…¨n)=d½i`?Å 3÷”kÜk`ëì¬Á`(D¶{¬RMÃ\‡{7NéÕÏwozî¦ôÿ Ko®÷#^ï«3™Ö‚Xé ‰‰/è?Ì~†6£F>ñg¾ž†Tf‹Zt´„4‹xu’ù£`© ¸Ö~Y0‹]„Øo/¹Òó\02]+iÑ«â0_³äåGK÷Gü®'Íý4o´ ÂåéÈÛ D©ßd­§¤A÷Äè…+\ìè(ëE[jm€~Ï¡^”ýdEnÄŒYæcë¦kþ¯Çðôß)q?0¾ò¤'õj<ÆÍvÒ°8¯‘{Ò“W5!ȸ‘yC=€>/óÛ^Œñ ü*7Ù€éÛ/ð³IGK¥ÕšÃôèvæŒH¹˜?0SrÍÆ‚KÒöo íJ¤MíRˆ¿Â³²C|œJTЊ ×öø+ש1‘ÿñXF"xÖÿl9‚^¿ÞqÐqÖ£¿ yÈæ-Œö¹ý‰¾û(ÆOúý¶RQÚz^Žíu/¾*_7Më€ÌTu·z$“›5†Q‰CúHw¹^—2FäåÕE¯ÎyËõk˜6ÜU“y t{©Z“v`†²MfSúFixqQ¤ôaZEé²&?ÆÅ-L]Î)ƒHG ¶·šXÚÜU6–i/÷|÷÷@Ž6Ú›2òsv—ªY3¾?ÃLFÑîA‹G%Ævböòê’Â!0Ó_•XI—ÚÖ Ò UVë˘iú.dH ÝÃøk|#u0ÓjÿBü*$³ÚL& 7¢ù É|‡ó¢X´Ø¥:ú ½ú[4o˜C©¤Ùʰ¿28¿æälì/+OÞ•“}¥ó1é¼ ê$ <íZ< ‰£åÎŽ4|¨Mçü[­B¾F ÆàÁ¸¬«*ŒàJk»÷NR#ä88—,dOÕÒZVˆ —•ƒhÍ+¨d/ƒZjå§®÷hÚk½¿ÁøFÔˆ= 7¤*nÝ)†Ïpâ¹–±¨%ØGÛPÈ…Â<‹Gª‡~axû"ÝÐ!ôýÞ>^¾±¶ºJ…š~BÜTè‘eö%ˆÑ=3r;St./d¸<‡,{n5^=·Ì}]aE=sòã­Rýº³,é/„,¿æ­2g?æÞEÚÕ7è$P~´J›¬·ŸµÊ« Ãô³¬ÍçÁ²¢çîk¥Çè+õñœêm þý¨Ð6pÔLEŠ[db¢ïC)x'¿Ùu¡ê Îx"OÕ;§§‚\ š9¾B²?O ”ák}¾»žóæ”ÓÀ¤1…-êÿ@{¦¾ð«“èI{ùOÚ¥}àöñ”ìi70%Ùi·IþÂLp®GÛò„ˆ,3ˆéÒ jbz~}çLdbÀ+ïgŸbÛ:ܰaæÐù€2+x¡áFR½¹vù(D—ŸJÚ­g€aÚ'ŽÊ=ƒÄ*F-9¬ ;ô¹ë z q²˜ýie×¼÷3cðù¯®‹T#èqÁ×£øì·ì˜’ ņÄ1/1»í,Cø<´á unx´¢¯îUK`„øŸéû´) òšËü“îMð?*ÅÜpƒ’VÙ ¸„üX¾ˆÄZ~ÛÖšÔz5låyàôm=ýø×|tp³®«Pƒ8ëì•¥%tþrø<—UÄuž™m»î©×‹èSÎg€Ó¦<ö/k˜>Ú¾Â4¯ÃòÕ‹SuýnˆÓëE>x7ɼù°?ÒÎëqê¨N÷ŒêK4÷eÌÿ‚C¡g f¦‰U¾Húö¶*áÐÙª‰^×¼ÌÖ]µÚÐ?3X³ ]üu­Z&~º@øÏ}wÎ*Í„K?=7Øe8l7½eŠÑç×Ç{ÞDÏM«Òï¾mÎõ‘ú£j+ܼÕ_íízRüÄyÏäHé”`ø ’¨áu°2a{”IÚ ‰Â“ѿӣêÎ\ª–hsö)¯œ— FjËEó+˜üãoqrç$ê“Ë÷ú&@†®º$o@jw¬Ê˜|‰A®îä‹­¡QâÉÞ¼~5D’~…!8ÍS¶~Ií¹½g"Ñùsüsu–#äÅýÖÒyÓ Æ%í†ÐÎ*®ß¤¯ žÞQKZbÌ…zß„k]ÖºOæË^Ì Vä·èÔ€˜7‡[}Žè¡+ɰÞK² h 2€Ú¤µF³#fR¾ýš+~z̬Hœõ¶_@‡â¢‰Ùé!\;ì+ÉnöÍÍFµç†} {ѧöå鳓 ——nÏ€2 Ë£mýÝZ'î›|΀Ȃ֧aÀk;ÿhÏÙchöîÒ’Ò{H^®w¿ñ ’†V¬ìin>³çä7¶P™ô{º Õç?çZžêˆ“YïëÐÙÖ9îÁKc w¹y°&Lb%‚;3z‚/gàl´˜µYÈWJñ »ãëßš;*Áß-áÝËíò~§Åç—è½2êJ,…èƒ,».©˜¢Ï—ë'ßjd¢÷¿_®çާv¼Ì|Ì/†á«!N Žg1擾jÀº¦|tÿ-öZ˯qóñ~~ ½Îá‹;ËÁ^©ogÛ&äé´'–UBÛÛaŸ¢Ðëã%—‹þo1ëCAC‰8eÖ‹Þ4 ƒÃ—Î6lwÐÅà ÖË-¶QnSÁ qýö“*1_XÑ´Ãéo4sßý¸ÞgÚoe>[Ç(ßÃ]²&[£œB·vÐeLéùÓü!U-Ñ÷LÄê¤÷”Ü‚k9+WÞqBÂïôÍÊ7ÀÿÖñïŽ÷ºÐ,%;ˆq?:ÑYŒQ¬ˆAÊóÃê©{¯ƒË­'Fs÷jеMfF·Ò¹©íäü¼?¢}93¥Í™Q°W?´Rò® ”_GÅ:ù”¹Qr`þ,?“=Úx=]ßs½ÌSöujÏp‚ÿ¬UÈB{*ôí¹D¢úgd¤Bï’:¸œ<}ä45X6ÞåaP‰ź„‘)¿­>»$ÕôÅ0“´ˆísAi1EÉ[ó“$Ú‚AJ@ÆLì8zq³·XÆ‘€Þìíój h4(l\LQ¡ßšfb\zÁ,ìo¾– 7+Á3ÿlï¢m­ß–¬š·M7nÙŠƒÿó+¥iÁE. zÐä)Z­•7› €iÒ3‰Þàõ±ù¸á9W´¤\g˜{Ü B^±a:މhýmØ‚ïëÖñç¿8˜XŽ4eÎ2IÑçdˆðëéôe¼uËÛ숟–߸« 5__ h‰Ú÷†¶×!ðÉü!ÃÂ`}*ê8 /ˆ(R>.}”\YŠuô2ÚfSGn~¼v¿¿ŽÍáVý®Yœ·Tu æãg¡f³ÈŒšÝ_p-íX}Ø^‹VíÝà@ãCÛíOâ 8¹7.ÌŸBõ[܇w^£Ïæ…½œ^`j˜¯Aw7´…jœõ²ÓÀ²›iBiø=xß­¦\ñC”{qb›(ˆ¼Ý ¶æQEeçd’Ð+ZB_yå4è¿:ê·|óX·µ±ßÜêî:DË–à.#R7 31´ºq°Rùõ tÝÉÀìÝüÔžíË3îV[Cþ&®ý`þIèä-0ê  {´Ôöžøõв 4'×NÁíbî?ÆÁÈ×;‘UÜ•z”§Ó.Ç€•íøß ‹ÎÀÖVÛ4|Td‚¯ÚÆmóG3/gŠMz^ô+š&÷¡V‡ËÅ.Õ;JÛгéÌ6€ü½ï¶o ÊÎѳ7 ®¢•÷àf«w Îj©Ýsá*¨tï?Äï:Üm÷¿”Eå'_Ô¶úCëŒË’÷Ð2þß/”CgϘ+~ªºÑùaWÑ›>d•tì Øë¾Scë§b§)j•Àþ’ˆÔû’]À«åP÷ùš—Ûsì¯A7Ñf½‚pûÞGÄ •28KÚ-}iºIÿN¡EœÂÌd‚#d—?ðC—ëSâºLÈwYòÑßáe°9š¦!@Žý\z¹wÐfÈ£)ÿÌ)4\ºiÞ2Žá4]¾NI«/ŵ`¥Œ6k ÇRfÁ¬˜Oò\ãÆþ*Tš’/B댻'Âr³Á»!¶º-D£ŽG2Ð<‡ ‘7Ù%oZÐÔZCÚŸl«wý\ª'Î/ÇžÁ½³ƒZc1>Ž‚sïvpþLvf0~d_VOv¥ïB¶á_ãèZúšc2ø7LÜC–Žn¥Šè7öT^vcø¦;8¿r ]ï2„p^èÓ1ê(>¿ºÃ/Ü Â§7;ç;AÎ] «¶%Շϫë>D;¹Wû#,A&¶vÑAatîv~ÌfAóOê A!ñ·7ÁÍoÿYjtûP7{{@GÈKÿÉâ ˆjÌ:\¯ 1ÃËž¿À±Áòå¡!Ðd4ª<0ù}º•Äo?šKõ7Ù>h(.ÉýEô!¸ŽìÎo;{¦®Z}†‡"hû—|¼1t›Ú‡…¨Õëõ3K]rk†ìô-ŸíùsìL„ß5ç–÷ͫÔ¬7gâÑeâö†i¤DÇOd¶M:‚õçˆÒWôÕ ¬É.ŽS–çý@ÝJ`úYèMÐ^ zû€Ç 9þóúçÝÒÝ}ÁxÅ0ˆËà#Ø)úÒ^ÝÛ€âÔô†ýÂI _ÉÏún¯x{ó¸îÊzÆ­‰´ó5P¦GÑfz•«*¨ü!Êùe\ Rö¼'N¹ÑBO®<e ¶¦¹c‡s!¥®Rm­ïcožä«î‰3©¯€îç3 E¥)(Uù±7ÿiZåí#sh÷C·óŸŽèÌÁ`ó3—òÞGã\Kë3øû.8u}C{z›;%BÑüýWߌޭÇÁù8oJäÞM7+)AŠê»ã¢† ðŽ¹¿¦ùæ4ŸÔÀjoË'ÛÙ§`¡–´êø‘ ÔTÛ¾j³¦—ƒ|ÅP¾8 ÞRfEsè­aLBÏ”EÉ׺+`×/#öaû Ð9û®Ýž-K>2§üˆA6r>²_}AŸ9/K´}ëzáP¦‹æï ^òÓÜž'^×úÏëâ5jöŸ}Ú >tëÞäã…½QŒè©î|ߪá/Z:Ÿ¸°È^ê›×ýâ{ªÐ"F•¾F-|êÏrvñØ7íc=ÎM?°ç˜&úàŸ¿“THY8Pî |ïÖ¬˜Á˸uhî5ì/vˆøx 5™&^yB ÞÓ‰¿u= Ð#«rVMJ õrêÝó ¶öë AÀ8^ÖÞËZ|¹¦çC©¤‚ÀäéÝýžOjòûã©Àüó%~Å+ CIs kò(àp·tñµg n}CÉÚ}†õû%ѦM¤oz®$i;;ão-‚ÛeÅêP½}`”ÎÊì‘熛¦”Íö ¨#ÃÆ«µ´§Ô™§>ÅÃ6q— êqcPYŸãz÷Ö¥7$ÖÅÛvŲ?l0a(@?êzf¸¶5…¡ai‹ØÒ{1tI&wlšiÄÐ’8îcG!€ÜÖöõÅ 6vô…õúxoÏlâÅ­:ì¶CÏîAÈ'9í!‹¾§D7«ßÝÆèPª*ÇK Ôÿ¤qäy545¹ ¾'®|¼|{=žÙ?¥&®FŸohi;m×ù-ÜŒT|; EѶÞBpÿ£åWiÒ)ÆQtVò(su~)öã_ðv=šµw㘽ìiaÜšÿ N9ï¼Ï ³‹¿fIVmÕÇv©?€ œ”…ìOIÛ‘£C+O@CåfUdËC°ŠI˜¾F…Áï\“ ›ÕgTNôèDóçõkWˆ;“©;ŸN”]—ªõГڒ¶è9 ˜»½ßédŒR÷Øßí?€îf»ï×™°É…jîY³ß(ÓAû&p± ܨ»ÊÛÄÑŸëOrÓ0˜ýü<ßhàkDó­ø^¤¬óž]¿þ‚ÓÙÓˆöʲ·H×À]0w¦}«¯ð/¬“ýC‹–—‚‹í„ïŸêòPùÇ~ô15ÆpŠ«×ëîAÐî=Oø£wcð‰Æsúþ³ÕçÙXF V;øÆ&¥@ÕÌt1ü I?<ô¼ôÄvCÅŽ7ƒànÿèrä:p'DŠsö™€Á+Q7ÿèÖ§.òîk$8´Ž[|ÈU—!Jæ 4™ˆÚu¬­wå€OXÂI²†æÛëÄëüÐAX}üFøe*Sïñ,BsÎ0¡™Á»àÛãPAª¬ê.Ú,½ª`Ûó=*Æ~àÑðüÁ‡û Œûj8g·(jJ¬šš Ûî¢^í®qpoÖY²½ª„‘sۦͭÁå0{…þå?´MõÀªí9tIR½¦¡é¯ºÞWÊsÑ" íÎéËzo´ñn ßq®u4¬äè?N_ă|âuÚÑ_Á”÷‰HûÖõOk¾èïób#Ø[*¾ÖW ÎJúï^ÉõPÖ5±4íx£9Pºí6M¼ã¿aÐõÆ„ƒöôàs,¸è`ÖoTbg)Ù % Aj^jeЖÛù³êíÖóø=­Dì;o0y´!·(9V|÷ߘ%–€ÄË»F©O%QíD†ÿŸÜ‡è_¯Úd÷FmÞÊ(ð…BKÒk¼¬M=âѬFÊÉÿØ3ò|’øiºY€ï šg¿N&ƒ'ÝáÔC& ¡^{@¾áº/ÞIôDçÌu²—ÜW *Ã#Þlõãû ]¹î‡~)sŒG»9х߃¯VjêöÕ{ÖÒL™~èaôÞog#/a ªÊ¯£_߯bV)ð¿ÛdzrÓì©ÍY0ð„´µH€1 ®Õ§rcæ ¬ž°5DIh™¨K‹£Å¦ê½R&oúkí‰atCi™K ùn~aJŸƒ$noõ} ֧߀=çhòYí“àóbÛÌÎô³Ù¬NPÇèmŸ²™´£Pô¼1%÷F—Ž«F¾gƒª†ZݶSèÏû©‚Õ0ýTî÷ÙxS”fL™ˆ¸» i1gnå±€^gg5'Ýþ]$‚aè°”;îfÛ †³Ó¥Ý|‰à^Ô$DϦ‰:wjzn€²ôƒ„´Cè›.Ö,µ–Z?§ö¼é‡TëSÑÛ^‚ÅËщàópTlñhØüÍ•&áÏFžÒ¥á9`EÒ®Îõ³Ý•ÂÃÄnB€øßí<×Ìа£OÁò r÷h>”ü ŒS[¢»¨¿EnÙlOýöà û0šY^ª~²ü âç¨Cî¿£æwãÌ>‹´Ô#Ë"0iÔiýõ™Eàî¡4›¡õ4KŸVòÕT€v¤ø\Χ×tNÛ|ÉH´iú iZP·øìG{ZP¯¤r–‘@û_ã,‹_ÀeA&„Ãî#úšlN<,‹£×Zºã}À4ÂÿjÊã@pù媴Û&é9þ]¡‚“lpAÖ÷Ý™lÏ\´ ^G„ÛWdLÁÖø9µï^0éNÐ~‘iƒî$æ+2Ïd ’òjûy ´á‘ŠØVtT “y—EàÏî·æ ;1ê“,WÎNT§î½~Åó}Æ,àTô'á x¾;*0¦ù¬Ž=6‰Hã{êýJ‘ܵ8¥ÛŠg¹}/Ò„‘¢ïçdÙ0ŒcóM/„«ñ–Œ¿ ~‡H&Þ:œDëÓ ¬5Ý xTü+{ÀI´³ªèÌmCP5Îu箟¤¹ûü£`·¯~³” #-ã¿ù¿ë¯"\úgò!"á2Iñ{:p§õ±/§ÉûU#­æëK û7{ñ*zÛÞz˸UO ÷$…C›>DIeÜyIB ÿt/¸ ”û¶-‘g'‚Ô×ëû%ì·êd‘µ*^eNåR˜œ-žLࢯøXçU9¨8m! Á€Ö§;Ž•€ÿoþ:jô`T}rÉæ7è»5ØmÕ^áU½;™µ‡O±dÂnhç6ÎÇ.r&—|VÓãÇèPí0êW‚Ê7nêQíYQÿOýdeÇm%_„ ׿%ÄÉ‚ eêÍÃ3aºn]y ·•R ¸Ï#Wí•Lf´rð$É›ÉA“zn7ë¼ç "(—/Ÿ#ž_ïuÊO€Ý=qúýµp(ìVƒ\(:Kê;pAŽ7ʲhÞl®ª†N£z¢ENG©.Úg¼\}ºCü 쯻bt†ÀC‘ŸT(y´xQe¶¢âÎvPÚ,»(Œ}áAwí¹&*‰þï?ÿ—CÑU£Þø xÓ§þ€(èfT]y¦&€Vo‚zÆÒBÁû¬DÜÞ½{À¢ýôón?:°½´ÖàáIÇÙÀs2ÛÚÿÛ+T°øRuâV/†˜{O‘ì÷Ò™“sOøoÿþø¦l  ¶æm6íͽQȧ57Ò]®–?g­]!bçoéFÎ|Ô/Éž”FY{–UšùÿÔñ†4of¥ÎWB°Ø(Mö׌­ú.0«N8 ½˜ýY]C›¯y XÀ¾ dxÙš­Ñº0ŒBøõ_µ–eQ`©ÿÞ9ó‚GäªÛIqз֠]]æ¡êºuiXÐøÆ®c) C+¿–bÏ¢’zŠªçÁÏhîOVÚ“4w}Ú8– ¿ÏžñQhàµ?:>¬Âþšc®7™ué@z:Të£ãÔzz<ŸÕwÜÿoq#e+ ôÝØÁxÐñNŽÊ¯I—â¾];`wÁÈë_;TÛŸzÛ»úݺýv›€yÓí1¾]^#¥Ÿ½dl@?ñÃéÄ G”¹{å÷å`]ÜÛç|"û$ ˜|3f¾V‰JqÒ܃ GÁŽ|~*îP9Zn[z̾mÄÉìB>ãÍ×9§²0äýLJÔot¾ö¦*ãÂô»“•(SZ‰)Ÿyø; ÎÞŽfÛwN°f)‹{q±ÛiýÁi«Z)ü5ò^\u{%V1zÈ™©#n˜Þ–-Þžã ±ÏK¬WÈ¿ SiÔâ'Þ}T©ý{AøO³¼î|;ºý^ÒAÈ{9¹ö °šüÝBÖNîÇ<ì•rФv#½, Cw¾ó%•w‚°¹Î™S>¥[Ç÷ŒéE@j 8Ï\X¼ á7RÍöUýõAÿuõƒ¨0ÔÅ8ýœùÞX±´×?ÿJõXPœ³³ ê@Mé3v%‹3`Gîn[W‰ªý÷Ö¾N@liûo®×ù¼ŸJ³¶ ü.{0Iž±Ã\O|dVA¤­Édt.Ìú(nŒ³KBâtÃØ&%4eÜ¿+ñ?ë ˜Ùv=XãÜêcüœÄíýa—UJì­¶ƒkpÓïÉFðv×2yt¶Üö~í‹_«úç÷ .•ÇÞf‹.btIˆÖ› àv˜{]øs&ú<ÿNïa W?’=z·.½Mô^È+pšO…C“~hŠáv–Ãïðíǰï±}fçž‚“c·ú(ošŒ|Ÿcþy ´¹Ç ŸÁ°ðÙ} Ö`þ”¶8ém8„}]—Œ{ ÃêU;yù!ü’Pg*ïÄ´«.iâDᬂ­¼|`"Ü”ñîN*XX¥ÏW½º{ŸŠqIƒìÊïaVRtbix®Ú¤úƧ@…k›1å·&ÕÏÿ©[ý¸œØêhÑ|:T’óÈ:X —P´´Ÿ…ðkMêfÕ,覼gT¨p‚ïäÅûÛÐC(ÇûÂÝ›hEÝ:e\ìB×dåN§`„ˆ³zþã˜$ KOÕ±1B¬_¯Å¨íÏ-ëúþ`jXƒ »£xl°Ž¾4q‡Ò„&ò» óñØÎ÷c‹>÷­ž,_ üõ[e¾HÉ×9RWp’¼|ªéÔô}ÔÖzíˆ'¸0”ÉFì¾Þd‹¯]¦O£ £k Û\º3¹è½#Ë€Pùà;£ßXQønÈ=îYôÐ{¼iF ªîl+AçûÛèÕù!ÂcãÂù,`íGn¦Ó€š+‹w¬©h ¸§©+6¶bN\rt=Q‚Ú¤ º¯Uƒ÷“µyG¾éAÚµÒú«/œü(jΫ‚å«ÅÒP .¦:âö:ë¢v:ïÀ}AѰ®k(^^®<ûÇMTk]©ÆXnm¶ô_Ñi§òå}yx¨UŠúÆ-àµÜFàŒÉŽߥ!¬£åSuÃ&Dxkî.ÙêÃ;îÀb hܸòSe(ƒ>¡@DG#:1¾½]iSÖâ.oŸ¢«s×Ͼ^Œ¾ÈóûäGônšvã¨cƒàÄ› «Öyè5ùsˆÁá& §ZU˜_ý ±÷.÷2_‡ˆö%†ÂÑytK»?ƒ§'\îÙ€Íwνó^Ò³ÞzYw"ëàèÉFt>²o“½ôŸ÷)ÈÒJ-xnÿÃÍkõiQš“úzIz1é}HfÓ„Œ. ¹ålp[”÷•y톱LN·Úg/@®îÎþ’"UðW -æK“”ÊñÌjøðG¼ Jóù?ŸÕAû–´Ó94¯Zô(»·tËŸÖ–—£ÃEZòUup¡x’‘j„‘IÚݘ€õ_‹»¿žX‚G\BðµßàÓX$üû»"ì*>2@*»$­‡ !¾þÖ¿ÞÁ|_høÝ —‡Úkäl´`ÈÕ°ÁFU5§÷Mbgm×SÞ@§ãm÷g>mƒm†²®r5k`{†%`°-dè¬h/?,¿±•+¿[QÂóÇGýuðâe¾ðgkðW˜[®z»g~nï‡o«­7ÏÓc${úUë ³¾;²¾£†˜ù¼³È0z¬¢àYçHй8à˜tBR²šY«Ž Ó+Ï›®ž}š6ÇÉ] måmm»¡'Õã»sttãÐÌëû àœ_€È1mˆò7}ÄA°R^ʤ †ß”ÌC‡K øêã—ý[õŠý“;é)í`sC¢­j§†TÏËÝ\çõs­±–jŒw“×èµtÄŒs”>}¥ Z6VÕ ]2™gáÉÙï¾ @ÕóoJ|=¥ ì}¸ÄÁwàßDÊ_r Z+M¦Ë0Æš5j‘‘¢B\¨…·íDŸÀc3"lŽ`¡åÝQþ"ÔJŽG…4ÿ ˜TÏ›V†‚r… Åai | F—lÖnï^h¼ˆV-ûôæÚ€©Åbßïy T—z|Ó¼Õ*õªBì'·FA0¬nm®<ʈ,ÿé+½¶½~’½÷¯ÎÂ2˜¯ß¹´ªQr(ìS ~4¥ïsz@¶%ÈšwûúÉ4×&ëßFÞ„cËq©à±—5ç·Vj°]£}§Œn2Œ«ºÖK×ZÿC{9S úÍ¢‘>ͧ#æW!(xvïCoc]/.ÍZ ’rÝÚãyˆ;šèD~q«Î&êH°•ˆezéy ìoØô%ÞCûír²ŒKQ¤s‚Üsl¤* 7ôÀíõš}ÔHÜZ좇# ã`Œ#¨·½¡êq£-ä<´ ¾tƒû†½8QG]¼68þõ×Õ>v(îFêÿþÃ#pê=iתK ‰OÃìÿR¢Ú£¿öuÑàKS˜8–¨ŽÏׄ³”˃Eòså½Ê ²ðÛólçA¨nìZn×1ôÚpš5)îFG2•ðÕ0ðyÛ#RsMµ$þü„„ˆ“pD¼mæÿnï@£‚ÑôÞŸøöß £ÏÌo€™ç\ƒt½„ÛÎvøŽ~GËaS#t˜¦†SkT"ôéãW §õ~ÂSGžºð4{O»‹È§ &<í=ÂÓ±ž.€ðô~D>ýMÂ30žÁ›ð Ý„gì$òwÒ~§+áw¶ž‰’ðÌäD>³=ᙯž…Œð,6„gµ$òYϺFx6s³#<{‘ϾHxCÂsœ&<ÇÂïš#ò9µ Ïy‚𜿿[ƒð{€ÈßSFø=S„ß«Lø½¥„ßWDäïûDøýr„ß_@øý Ï5BäsKž;›ðÜ@”ð<‰|ž4Âó¼"<¯ áy“ Ï—@äó žŸ‡ðü±„çLx~"_p/áà /ØKx!N ³ù„¾Cøƒ,„?èOøC>Dþ¡ë„¡'¼ˆ'áE®^´È£"¼˜3áÅZ /¾ƒðÛˆ| [ÂK\"¼$ á%­/eAäK'¼Ô_ÂK›^ºžð2µD¾Ìáeõ /[CxÙß„—›!òå5 /_IxùiÂ+¨^Q…ÈW‘ïð‘ðŽR„wÌ%¼ã0áÞùÎ"„wÎ ¼ó»ÞU€ÈwM"¼ë3»ñÞ-žðî1D¾û#Â{ì'¼G$á=ú ïù€È÷ÚEx¯`Â{Ý#¼7á}˜‰|?ÂûÜ$¼/#á}½ ïçAäûuÞŸ†ðþ®„÷o#|@+‘HNø@{Â^!|áƒ6‰ü`+Â7>xð!æ„5!òCëºDø0C‡&|x5‘>GømÂGœ$|Ä/ÂGþ ò£€ðQe„š"|´ ác‰ü˜"ÂÇ|"|¬ác —GäÇ>^‚ðñÙ„ÿ@ø„·D~âAÂ'¦>ñá“ ŸÌOä''>yð)<„O‰%|j‘Ÿúði{ ŸNø´^§ß'ò3Ø ŸDøŒ;„Ïd!|ÖN"?ˇðY× ŸÍ@ølOÂç¸ù9í„Ï¥ú—ÿ¯B‡OhD°û¿>§’ñÿù_J>{¾`>¯ÿn tüC4½C¢¼#þkƒmºÆVÿm#¼=C#¼ôµ¶ZˆmÌ$ÿ7š<Ä=Øû:ÿþ&UDh¬èÿºÿ_[{¹GFþ×nP{¹G¹‹úDlm·uoã¿7‰ò òþ¯M˜¬üƒ½¹,½#ü½#¹L=¼=£þk3:¯PÏèà­£vÿWCô_›sXùEs©GûrIÊqIÈ)JÊ)JÈpIŠ‹+?„z“äßü¿{µG¤ýW E˜»g »ïïßÿ×ÿç(·Êÿ9Kÿ:Çd„ùÿ{óøïÐÿ­É^¨+ytimeSeries/data/USDCHF.rda0000644000176200001440000053127015104730074014751 0ustar liggesusersý7zXZi"Þ6!ÏXÌåÔØïÿ])TW"änRÊŸ’Ù#Åߨa…ø¤T”²Yd{ªºÙPæ2Iž++eÐÆØ“@䱪^›÷¦9ÐiØðD9¦À‚Í X+b×·XÖÊžä9 _’@¶¤·©ÒÀZn8…äÆŽ©æ®BŸï¡›N> ¤2wGDüƒwœ2ÎHÎ/_BF7€‰–5LàiBõ‰Ï[|ˆøõÉ„›Î¹Úp׈ *­Ô -s—o|ãWò‚æm!‹û/XÕPÇýØzk#1CÌ)/þð‘’¡¬|bìÁ›jzNÞÉëõÿ30³D0µÅ$»D‘ÿvZ‰ ˜NHÔéyÍ_á@W¸€ï3å’0pkˆZÙÏ+ÆÁÛL¾jîÞÏ„ŽœŸ(?Þ½®‚Ú÷8#'ý“ãþ¾-£µ@-àh =eJ"’¸!Þ][Ö<@$ãþpâ4}൓ö…šËöíŸï‚¯°(û³ÌOó™÷±;„Féøy±â‹LþÔ¼ø®0¯ml˨Ú:#l`Ó騰èä4Ñ;&!‚\§B÷2ϳP÷Ï%}D{u2ì†™Æ 7íÛ¯õäû–S êxˆÞÑ+¯Ó® =BÓš5pSLýÖi7Œí±‚z$É8a9)Ìñ1ØòœnêÈroþïŠt/ñZ_.ý´ºÕÅ*_nÍ$?5–ÿU±H„:5Ãüy†–ÿ?¼GUË^1U³}ቨh•6ýˆèt²AÒ~œàÂ<‚¾Û+)XÁŠÞ¼[/h‰ùköÙ¨xóàåî5M„#Rgm¦©ÿd%Ûú"ÅÆÜüÔ[Ó‘¸¥‚w0=N]yû!. –IçX›ÀzÌIWèß ×_3y'çYšߪ!ýɸ ´ŸºC,‹ÒÚhæS‰®Ž-G¸°òÜêvÖ³ðʆ–ØvÑŸÇ,j °hožA°ai¥dÃt”ºYhÔfCÀ^`¦Pô0)åé«rªɉŒ¾âq–Íuh5ñæî¶|¢FèÇn À¨§²‡±ûwÈþÐÓ^Uf̉0Ê[á@©kâ4äPÒ&@Õ%½…¥¤-7éÅ„M‰³ˆX^pBVÃù··yâä ì_!y耜í©ÄãŒIsl÷èe^¹(×ÔRˈê.×yFÂßÒ0˜ðÁ©r)GƒJs®Ó½ôbì_f4ÑýûIm%å‹FZTóá>w ‚ùY¬\ªh éA š9óx#É~ÏpElÔ±šÞU§îÑÓÞ+á‰Æ¨øÀaÞ›ÃÔŠëÏÔºáv†Ê! ”»^ªªíû´È?#¤^Ö°C?pöëxÞ"•g6©›íÝSw PÛ7|WmmYà¡ÔN"…@é<®Bÿ ý¸‘/q3¬0uIG˜W«çµ˜|JV——ÚaŒØ½ã„9%ÍéäÁJ´°Åý[ÕßCè=Øcøìh£}òX´ñuå,{£HD)~%®$#ŠŸ°Sw´•RÀ¡çeÞÊtCoÅý£çïN›YbÖr>pð¢•ü©¸CáÜ@ˆtuæ·fe”uÿz•éÄ|²S€Wrœ.‡öâ§lÑ6"\8pT|³öXz„åXMÍ›G•È ¨CЦ‡YÄì:²±Ò·Å#¥žƒo&…¥ÑTªê¶"íš¿³cÕÕ’R0°fyY"Æõ|$|ŠºÅ¸ƒ·Òæ× %˜üAnà<%rï,Xád_o æpQÒߢ<bâ~ðð€K¤„6›ºm¿Tžõ™Õq&G/`I¢y«¹[õ96ÅsOÌÊi0¤È_ {¦©«É:¥¿Ãø[Jì"œO4iF ©ÊåIQ\Qá‡\XÔˆ6g“ÝÑÔ3Å÷  ³6娈Ož*!±žiÐ’«ÌœÿÛn¡L,C‚ãW̽h½øÄê§e‚ç»tŒ£ïÌÞŸ}ˆ2ôRY 2RÖø+G±ª ªµ<´ü$£ºƒ«íÀÎvß,o?äó5wKT‰D‹ó‘ÙÕû†ìðòⲨKÜ^Ÿ[Ð Ç)1¥+Ôêýíiw6¥ÀºpÆ–”ó/ZaȲßktIl½ñªª†€†I¡ûÅ'Q+4«Y ó̤@³5ÝA€ÂÏÇô…WJiîeãïýW¹ØôZà6Ý%zªKѳ*ºR¥ÕF–y —ÏzGIj`*¬.X,&Rq©tƒäº,:Âd¥Ò¸Úß©žú‘æ‰'î©âã³…÷f›.2·Kh(¸Ð‘3˜¯š£fE0”޶ôáÔ‘X:B'ôÇM¶>ÚàôýO¼‰‡)iš8ɸW:lOƉy=(q˜Ÿ£É›(®âŒ ë(½ ¤sŒ„¬g»¤á€4ÒP¾ç‡dì¢òÕÞÜu¼Åß©¤•ºê):­¸ô=(L,­ [¸ú´F4IÉLòš¶o ëB")C³\©jÞ'šƒ2þñŸˆÅ|¹9êþ¦å¿!Éà=8vþˆ]ÊYϧ4Ñg¯ø~µµy,¿ß¨¨0˜S´l¬`{áRF‡ŒÑíêpÿ¿cÃë¢ÉË#GKݦžèŠ/Zèsr2ªÒ„ Å`u¶óm¶Â0ë:3;…‚Iê\¯ÿI‰pñ,A`ÇG’n7L'N¿ë|» t—ñÊ6,oGAÑûÉŽ·0Sh4O»…ªÐ©Z9ÏEãDZ¡â´)úš¼Nmˆ?ø„fk§q?â¨ùUòÒ¨W ¡”9ÕëêãÊÑ—KÎ×/ÏÇ>“D’ió-î*à±ÝYC% Ï\‰ÆÚ‰c×Ú¡gR0Zá1râ(ó6Ê„aKj÷£G­÷QÍ>6K“}m°Gú1+áF"œFKjÓ¯ð– gäòvÕ¾<\Uö{U>HØOV›¿Ÿíý¹¸¨“ÖîöܼÚ{m¼¬¥îù)wÌx=ý˜YïÚääG nDéÜñªB-ÁÖº'XøÉba÷ˆaC¨}{¤{¬=C}ºØêµsãn¬ØÍÀEe^‹ÀÀv;"ËÃVdž¯ñ#U±“ þùµ¶–Æ6 ¿â%€Œh‘ÔˆÉþÇŽÜ zfýnMÍþJì{3¹ƒí•!@”› Ù‹"—Ì8ÊÉ`)¢ÄVUôíðPWž^ª.¶í©£Y¹ Ú𖼈êË5dÙ#e3äÝ &*ÁT™@W|å)y»¸4à>1záe9’Õl7—_XwJF*ŠÕlæò¦E+@µ¢Ð6ãR-ë·jc“üÏCº§n:®ÓsF„yïg Œ“>Ö¸x¬2©¦–%ÃOЈ´,šøÛ5­Þõ䫘4Š×®û`4²áæ(Ãü-ô‚ã4•(*)Öuø=+jø9À@1jø.šÊC—u;Üäö®é(ÅÔw‹FÖ¦±Ë0j7Ñ/ýÜ4;)PÀíE΄˜u¦ KUÂÓþNÙjB~ûâUÍ4üjzÇE¨D‘o)0õ«’}ŒŠæ½ðJ\òÞº[Lƒ­áR‹ŠÖQϼ1Ô™#Œänÿ½QÈ’—ÿâ%¬Yn,žYtiƒñOxÙ¦ ªÜ× ø{q€Õø£¢]sF‰¾X¨üe*¨ÊÌkûŠLö‘‡h±ê“©~ÝÓŽÃ1éך1ýŽRÖYÅúV´·Mäke?m܆èßôÙ­V"×o°azÞpø}óGAÄ4aÎw7œ×»SGL|‘›…Qß…ôÑg+y^ÈÛ4‚9|Š¥18)>Ô2rlÞÿÊ;_ýŠß˜Zµ‰=|è&ƒa´;Ø"ÿÝÍ_´@`é°CT¿€î_q×a$w ¢âS™‰e5Îé°ßûä/øÜ¨ƒTYÉzeÐÉy å¨zr‘ŽM¨ã¤\<:‹¶ÙÄZBZÆc©PF&hT¾ðbv~ä$•âÛâK·øA^ LJ†©Ë•cxhUTd¡ŒÍµOÚÌ¢–àó>†jNdŽàP"'óR{¼õ 5În¥\¦°N%ï¼,(ùà:Ù4*–)( ®*å5|t-xùV~£ëÛNeümï0A‘QÕ‚Çïp9;H=À‡Ûbs=5t‰Ž¸¾a¥‹®„»ûïYzö‡?ë'lÕ |Ãý9¥cY–ýTKúÈ71@°îñ¢s™±ß}~è_–]9Œ¤§iñbü¸#bdfJ§«Ÿªæ{ÑIsµU)‘î?âÈ|;H‰0‡Þ¥Xâ÷Ùç jZ%À©);kC7C*ÅAP‚. t-ÚxŒŒß<1ÔÙ¥.ù©eÃ've<›ÿ4˜‚Ó¼Æsw½ƒ8§ç0ÕÆ«CòuWä"ñ!ñžB˜Ìñu\Xê}û!iRÑ@ru ›ÎÂö&¦M‰UÍÐï ®•2ˆV~œͶuÝ%OöéSª†FKÇÃLUêí*‘Š…vûmµ2ÿ #Y‘°Ì"¼¾.îl&¥hâ^E9/°X:FŠŸ K5ZÒ”³Õ岦ü† £k •À\ov{|Rp¨X6£@0€æMÔ•C_?–Dýú¤EúˆœQƒ¼Õ¤Š½ á:kÊñjnkÈ!~˜òÒyë‚ š3 ý³Ì7X×?t Mk¤úl®˜4zE¼†WÁ¸~à󹄽cük{^qüO¹¨©äÑ+hMš‚äÑgº¸j·ÃQ1´LjýÈâìMÀPü±ë6ÓÍs6w\~wºS÷Sƒ×¼õÝ~x ^&ÐÂ/Ú!Šê$p’ãôAÃsw ê ö¶ûßý)„V5™(² dEcãêkÔ—¢‰(Á÷1寗þ± ‘‘Kã×îrÏ“»ýº¯Œ‚‘•T?OíTŸ)hüi@Åø &ÔOÄYÅNÍÎGÅVæüŠîZ*ÔsRËÀ}PõÈ÷NéŸï6æp‰v¥ÂPôQÂ\ªùgµôw-3»ÎªÕÜ´&E<È!Ž*åævÒd1ÛøñQZäÁÁö* Z¼´if’E6rs“VÇGÂ)`œwDö²À¿CMVbÈÌfä§¡Œ»Õ9ûóÁàNX~»Åy†ƒ)1v'ÀÞ` \ˆÉÁŤ]„jÇ´Qiô)¿ÐÝŒ÷†7äU{„y¤™ñ{úõy&Ú 5ÌŒWE»ìÇ[î˜÷{Kº0þÏâ$öŽ=9m=)Çóňm¾¥;ó¤nŠfÌÉ÷C@jYÜœ~VK|ÞÑïIÇÖ£Jb˜žg—gOlD"×g5ȼX½öÁ£C"=;üúF‰Ö·ì~³>eç•"à4†ø®Q‚²V÷>ädA‚{S/’–¡)ÐÆåÖó3[ì{¶ª¼›Çs„NûU¨N/[žôEPÓchÎï‰yâ `…t½Ÿ’B¶M³·Å^MÁ‡0cŠöwʸ ­Ðy/ãÍi)Íp#+‘ÛŽNЭæÐ7Ò×ÐP4’èÕõëã:?„Î[Sk×özº‘kyȹL£,õtöïZõ1ñ»\œ2ÅîŽ_¥JëcþðzVÉó1ðé{øyìÿNÿÙ#©ÿ)íø»c2ŠVT¶-°8ß¦Ï uÉ_ jâ\‘€¼Zi©æançu¯·?;obaõÌíÒÛ ½ø’ç'KA1/~ÌÒu?h ¸­6zsi$m$²¸©Ù¦ãázjˆðpP5>JGêš?BÞJ ƒKÔÕpˆK1¢Rr‹ºz1ÒS‡ÛÄö¸SeFäÙŒï¼Ww‹;p…“åTõÜîmý3ô=¥£ÈAÓï sò}Ô½ÀG¼“š!YT®åé‡þo{[ØA ã{cè1öUä¿õw,ï6‹³1 ˪ïfY¤ßÌpÐB?WØ­ø¡³¥Œ5Ëh¹/Ž[œc±Vº÷-Xnf€å6Þ³˜gþ1‚zb/'*zn‚€£Ã3ÇΆ» 1ç)5¯ãÔ[ÃÐ%§´jÆ–¦zSOÚÌ úÁ°*7áŠWK+ò¬:ÍÙžtS@Pêà6ñ}úÆ4´#IŽI"IÝä@xDÖ}HÀ'ûÇ¥IiuâPæ†mr"ódödƒÿFƒ_Úà<È*,ó+qª?J–(âM,·gz‚îë”Ü—Á€(ꔊ'—²Ó –ü&­—¾s©¤ôsKÁš†”U4*Â)F¡å¬]»OgÓ‘±ïïì¿ìÇoVY€ûGs®^_O- –\¢Ç-šT¬ùÄ«C—ºit`‘œú'‡5ü¬Ð5Egg6Í~÷M¦?ôI¨Õ©Ø¡ðå©Y¦è=ÿýî£íR§±×ç¤l².£4.*ãã«*Å.lšNÛ0ei cvè}ÑÁÊÁ’{+4`EÎý0ëm` ¥_W&•1Beh°å/ÿÿàÁo¬+7uŽ07—gÓ˹ÇÝÔjK`Á@Œþ2/ Š_*&M•ž½×ÒˆÑKÕˆ¨q¡„ÓÌrä݉J›¼ÊT“¹‹g™ò€\ùOxîpù2$*ÇžczûЗãÍ" ™‰{ûüTŒoÖp“N5WvÀø·L{ây­ºÇòÏÚiJŠŠ-BþèÚÊúÉOÐà!ç]fÂ_—z$ät2Ív…ÿŒ1üªÊÌ•ûH•¥^W*g©ìõm„ºk ˜šÎuºj ÌõF§1b0Ô­c« Ûë\ΉµKèä%t¥pÐi3ä†Mɓà ¸"¯æŠŽDY¬Áø*x³ãtÍ7ª0÷ Ö7…êƒãqsò Æž1lÖ[.›øæÉÓ'¼Pf·>kÿ/UËF‹º6‡¦¬ôKW|.{³`ý´­ëŠÚ®Ç‰¨¦º¥/ôú¡_4åE UÁ¦.ÏØÓè‘OÌRœÁÇì‘Ô…&ðOKìT´1±™*ßťœ”ôä¬8Gh¼„gwžEˆœŠâCŹÓ8!>ѱÿBüJü¦óenEAtª$á1ôڇ̈́tƒÙÏ#7ñ¶‚ŸÆËPFnôúYSÞ¦‰ºq`"ª;r‹¢‡Îª ê{qØÊ )¢;£Aë1ëôæÞf"é¼×]÷¡ë0èç2Š;Ô S ø.w[]YÄzUÆW5¶{vEªÇÀ &´ßPɱUR‘;çqÄÆÜæ?JâÍ"æg)}dR¹ÅùCiJ³•h¸z«/°@»H2Ë_‡Ø lüND/Æo]bŒN=t_R»(üË'ü"ä0›£xAɦH¡÷ŒæŒƒj¼…h¹8l©Wå×ì“'/ܨÛgÝ';%n†9V +}ø`?Ù³^¯Á-!ÏED.$k‚r¾w«&5ðÅtZlóc,Ч"Á|þ3’÷ú÷“—éÙšÔ݇ ²Û3'wI½NF+f$üŠ©è*™Dþä%+fe ¢ÆLΖÛá÷hµ%óΘöÛ7Ò;ŸÔ{’-blTžÒ˾¬u2I¹C™w9‡¼Þ@ÐÕV\ÙBFÔ5qÖ0-Yg`.h™_`Ýì¹Ð@uwâÎ`´åí§hê_@O)® ˆÉz<ÝV‰Ã åT8HÍþÒ%BTÒ1êQ5}'±}»¿0ý¤¹hÍ ™\®6HG"œùh ùu°ƒ_{ôí›Ú ÁiU €;rD>Ø̇Ôe›±àôc¤™çý«RÀi——¹¢Áoz»¹e ðGʼn˃ÓdÌP´Ârâšq±Rú€?Êô ðšïæ b:»¿˜ÈÆmØ|®ñÍUá2#_ßþª“ÖûrS\J†%錜µï4(xr_‡zñõ£'ø’ºéiËœ‰¹¨9äI¤iƘˆ󎃫^Ù• dÑç5ñŠ>¼Î:ó„Ñ&¡é ¶ià•½Ž"“‚ y„˜84lñÑ×íFAêFåå^C]𱀉y R÷}ž}Ÿ…$m÷ë:u&Öù]ÒçpŽR£¡!ß$IšÄ´õÎM*DîÙðÝ#Ÿw5¯„Vp 0MÎ>³cÒÛBki=¢{YiÓc¾ý¾W“Âtç§–Îø<¸ò Lõîõà²%r2øGËN2)±Ë‹T%8 }Sµ!qŠ)hІ BÿéÊkÊ……ªX%s}‰€[óê;½½bÆŸ¾ J¥õN:‘²ª¸bÿ®•_SW5šÈŸUyÉúX¸Q~U×=Uî­nzu|oӂЍ,×ÕS¢P4­ÜŠ¿„,PüÂXxH4}RA”ÎCö|5fØ|­EÕ‰òD¦€NÄd‹¬’(»wM ï"#¨(|Ó0’»áÍàØÞŽ Nª.gYtózZk±°>¸Ípšr/÷õÒÕ-ÎRÆ”xÄà®àû=‡Ø]knàwÅ o¹í"3Äw øÄËfƒ£åÀO€”&ý] ‘ZB ®†¡·ªéýÂ-ú÷=ä…½÷hÏCE’3°õgܧã„î7]± ¨‚Réß2ÉG·“!ÿTEĹ|E|Ǭ(³ï¹­™Ûæg¥Q‚Hí¡t¥kHJ’¶µc»£!ýz)dò‰ô—Ž…**ÎÐÔìV,Y¹”EoÈ>wõE¬g"€­—œ‡|Ð<áéaUaz’âƒE]œÃÄÌVoòî‘&îW­Î³rãe«˜”uÎDп‘ÔeMÒürt®bá.B¬ªðv½ ¯ø]æ–¢#éÅJùSùÚœrd×aó¨g¼¥ƒVðzë#?ÑFËÊę֢Ì.}p_éþùRIËYÃJ)j\‚“¨‰`fŽÛÙ²ó]‰ÔƒÍ~YöÁвw3NÆNs•åz{_>=È .šÆ åª|‘½ÿ^×Ñ 4 Ül¿Îæ%¥ÿ7rÍ<ý|Ç• õCW¸ëÚ®ÁŠhi':cúÿ…úQ™SV|H°l=Ò¢è:Ùˆ7ŸrðGëE/¿.»>[b ÒŒ€D’Êìd8½£?ÅÆüÞŒHt>‡Ik…(>ø¿ZÝÞÁJŸ~Æ ¾ZžÍÓ@y-žÕAxl ç¸Oñ%yB­Â®ï DM×}æ/ùZù“CeÄ< $u·hÌ-ò羉DT]è¸ü„ÎïL8íàÅ2ˆÌâ¾³OùŒ«ô1%nt$ôåøé‚‰J0"-Ee.Œ§d5ÙN Õý)GçÎZ6Ž¥·þ-XmBÆ÷ÒÚOL•Ëîu¯_D3@ÑwÄêÑ)§}éh7^\KúêCÅ„l ¶ÆAÈoÖO P>Û†Cf„”î$ ™,7w/HÔhQ×I_¡€“ä]É&äÇm.ŸÊúäËR\í FЧsX;UÆäx¹ûw û—qh“¬dšÚpÎÝY£ÑÇ‚¶ŒÕl\³óþó¢1ê Oøé•›œÍT^BC%æò3àŽ™z{(HÙÝàQk·®ù«eÊNá¦CNVùJ7†yc//û)ë{˜ôl 9—æÈÊzŽ˜cama°z`R[©ø èr“­¹™gîкˉMƒ t³_Àþ¥˜'ý§3²ú1ĬAÝ2úȸ5&áµíãÈ r«e$¹CÇt{òf9÷Å·Ô‡êÛ¾”¡]l4\Dë]°$ õÖµ–Ï96‰·TÞz‰éÎÛðÁEÔŒ©isç9j%IâÄl¾tÅ#Š{Tü“â(Ó»kÚüW¦,šRÓIwŸ`·z½;¯õ¶¸D Ä¹¤Üsîèm»]}àš»òÆjB-nÐ(ÉfߣwH[ÁG(Ð ¼™“P•) ”h›Ñ4|ÖêEÆ@¨o ̈­Ñ§õ±táö¼=¯˜ªê¿Šª’¤ðïûVwÆ]1>ɪ/,¾¸õu“±Š^ñ(8 u/\ 4«›ãÒÈGjÛ±æUž>ò&. ÞÑEÙZ sø%ÅŒ&ŠI¼UTcؘuŒëÖ©‰'V}{(ÇŒýe( £3YïáñÖÿb!Ô#i‚r•d9ï˜iõ}ÄàªccQ… +Šåüß)Œo‚ôõ0´6ãüi‚<¨“Èo¼®7 ¢E›–sÏÕ ·œo–ÀSõáP+ÝÛê¸òÞxx´ð[ÁòË–Óso[|²÷á’P/·³øŠ´Éoeoݸ蚠- ·m}‹)Ïø‚#:H0« N¨àìãúGˆá—°7Nxì¾uxfé'D@ü‹:½¹ Ã+õ -›Bª6M'ÒþÂ4˜Ïî R¨…_˜$šœµ~a &˜jr™aʈÓ ‰M{{,Ç¢‹’ßÂï\й:~$"q†bVDl ñå]]©4zݯêFæï®¼Cö_[¬P¼QÉÛßÙ—*¶y¨/h—ŒÁb‘ï¶4P†s<Î%ŒÈ?„{ùFh®Ì×cI7;H·†.÷+·8  ’I¡ìá÷µÇïÁ¯´-‰ ÜŠ„.µŽPJLÂQ%ë 2ÚM'…^ùÂuÝÒÊõ|[PUÅÚÛ&ÄÆó/Q©ãÞ‹i'¾‚¤CNT§ˆA˜át´ƒÿ™}.ÚD¡<)ú{`ÑÙx›Âb{Ø€T]H*\ãxÌ©DmÁ&’i¢ïg1@Ê ¯Æ%$Ä 4`†ù{«æÈE|G$pM™ËJÃ\® y@Ázk¬ž¯r•Nó¥±JŸÅ@¸ÿ©V‘Å"r7 $'…À‚ ƒsEDR~Îw ¿,qé¶}™¶{rˆÚÐÕí6:‘ü+«p[”i¤T®þ¹™ùU½”E³¨4ßч[ð“çøFv&˜‡Û/5>CóŒÁ€šÝ‰Œf¥ØAä”.D—=$P~a× Ðˆ5{™Ô‚0E’“êÆ:§Æ½îÙX–•”¢þHÒc’LyU«¤ˆH[á¦^û†¡Ž„ˆWóɃÊÇ€-¤Œ 0£Î3é&4çÂŒùQKQ¼ãëŠjeÕÍ2fÙ«|Ëb—áÆI!ÂO™[âéσjè К••’‚À2Rí°ú§ä JŽàß(Œ!7v·àݯÍj—pW²K¬|öºî­£$!èæø, 0'ÈY‡0ñÿU8 51èÖ]áÆ–E£'‚Ȥ ûO· ®ÃE.ɉØÔWÝ©w`| ;­»ô|<• þt3Ú=rôÓД-à¥íAläÓÆ±¤DOvH,ž`[@c#?Z Zo‰ØÆ,>×m¨*?²EÍ¿‡˜®ß¾xbBù´íð™ ƒ+gŽ^0|v‹Í'Q´ÉN%•'Kjÿhš×3t-–¾7HŒ¬,£È/íY]’Ÿ1¦Þ0`uð!NÀZ%Hž)ÇX¦Ñ#³/r’L׬OÀ„ŽiæÍ¿‹!p²aK•°¼(Æ®ÚZ~VÐc?L>§#;œÀŸ\|î»C6#ˆ êÕÕ.]ShˆÜæ"€J¥°¢A¶Lü"0"ª>R>g>Ó KTòNtuеf*ö½Ý[]5N »ì¯šñ.#·ÅTÀÿ8&yÊHi.“M tc% 6óÜ+Ì-‘9¥°,¡q¤J‰pÚ^ˆRVé¢IÚfïtؼKeŠÎšßšØ›Ñçt€´€yÙ£ªßœúçVçê@œ’½úqŒCüPk'‚o#ªÎŽ;†ñŒ YVz¶öeœ%«ôòhK؇H’pTUœgDú×Eé€uû%ñ—7µZ_K°O#™4`ÕY7>ì×8à;æ3lÜ+“^Šlb.p3Òüf”Ç“BÔÒ°5™»”ÕR¡5˜ä#pTD#WÜ•ÿÈ€²Ùtf#ð¬è]ÃÝ’ÿ·ÉB’¿‡Iƒ¶ôú´Qzâb# ¢—a™¶:¡.$ù“Ô‹„¾,Äs黤󟸹o˜=‚€vghXÀc÷ŒžàËËÔ¿CQ*" C^ò$·M™úƒǺ`siP]|ìþ ÒŒ?öÊ_kB÷G&½Œ ÀËW=‰»2”ßlU沉=Ñ×ݨ±ÄŠ­×ßèXŒXÙW†-Ûf!?c$ kÞìšaÙŸ¸ vÎñ£ÖE0C+ˆyª¹›dS$o»gL´²IÈÇ‚Ág•`º¯ãéSùH %ê£T¹Ý Ð^áå%;ÒoøY¦ƒªµ;;‹ºÞ\ñG¾+ìÒפ y8>Fbå€&½T±å¥øe.i®†[_©\_©ñ-¾%’aÊ ëí)&¤'gÃa¶cØÐ4 ™íß“Rò­(wR~ƒïŒC¼úTÙp”ý'À7͆äsíØæSÚ†I–ûCËï¾£PpüðÖ0H]_:S+¼{t¼PšÖhI–ê–y;¢rå¸Úá÷›a×™ñ°·¹Ÿ¯u™˜oŠþ8³d±91}/¨Ïp¾PfMM#ÛzÙä–ÿŸ4£QVWA *‰šÌYˆ MÝF¡ðÁ®Kê@^™•Q^iÖ…ÿ‡Hã@\@ 丞!™>RZafzs˜6µ)N–Ÿ‘ þ¯* 9Ÿ{‚_e«Ù0k‡.“&³kù^›=w&œxbùélñ2¤ÒVM­ly¬c~0ÐýTs6—9S9›¿ë¾ËÜ)%«ðŸ9Z‰2´v‘2ü$½Íèî‰(Þ¶ =t¸#¨ùo¾ ¼ÈëèW”ÃÀXX}Ö?å´¸ë !nWBMåKÓä¬aêÝÙh>_$¶n\ÅŽ‰Ä¢ðuàì4¡LûV—RøD©ëí¨ŽMg颣°Œ³¸¢i¨¾2}Æ=.Z¥I{B#Àƒ‡dêž~šùw£»™ÎY·Äû¬²'!®¨tG9Šw2JÔ”óLH}òeãUÁ©A¢£-Öîà0[òÆúme5,l¯×džHµÓëVUÁr_ATB ~¢mæöîZoVMˆq 7ÕH¢ôuÿæƒ8T"F=Êg€ŒÂËr’§gQœ8Ñ`L ë€ÈªãI¡÷á7]ôfÍŸ÷jnA‚¶‰¬×5èWà‰iÃC”ˆë€ˆ¯)S’|¾J¿¨eW#žYÞ ž 15Í’×Ü ÿñƒ³Œ,"åØ¥Òµ´N°‡_»dåõ6'¶Ã{¡n¾l4¯#ðÒ›vÎk¯Ûí EÜ`+›‘¥%Ç£©Ù2®áI˜ˆ•æ° s¤ÅÖî07uÕ»°Þ Ûý1¤üzN%-ÂKvÝì«ðÃ÷ëöe\Â7O Ïá=ìm¦cƒ²Úê“C<ªAŠpJ~¨wÿµ˜¤ f¢g•b^ s¸Þ¼ó9G?ï#÷Ÿ¾ÏS›²¥•¡Sx§Îãk?W²ß±aÊŠõp­žW›Lʱ¨KÂv$Ý„Çô¿tµ·}FÉltØ¢¨½Þ/ŽâŽˆƒ!$bÔ~ñ áKSôbÔµMM¸­ê-´ mýofBüLíØe;ý©]èÝg÷L\U{8þÓm¿øË»mŠóC‡;°S0ú#?¥f"Z›=æÎtæI=!lT.1Bí 'Õ Ήƒ §±g£1»‚ö®'›Â6š„É›HQ]ɪÔqÝÖON~*ù´ž°¦MI ZV"’0Bm6 ‹œYü™± WO¤%˜€ôþñÕ¶•ê ÄÅ+4´Pž¡²„‚¼ËžÈÏÌᓾªeæÙ*\1,'žï¡C;€j¡:¶ ~Où@¨Æ@ü%¦A©YªôÍ0ë½É»â°"±7~Ô¶P ºiþµÌìIÍfÕÑD4¹³ÛУ)‡0¥ÖH¦ä‰‡ß5þ%Žá…¨g¾¤—z<*Äs°jXržuv¡+9¾ÚkMÃ*îÚ(D©±.Î50Ü)G‡%à øÍÂ8Œ™¸H[o*ác ]PcœÄHY =— rÉ ügã:oÿy³¦]'p ~áÍåݶ :W½I‡-6&%éíË»ÒÁÇ «¹PÓÂnOž!i—¤ÂtGrI|²ðÿ§’à …ÇNxZИ4íšrÌ…¢'Còšh‰yžå°§$Ú'#IvY#d7™n`vî6u¶ ݱ"s@é¹8¾ª½Ûøé bä\™8ÿm½Zò KÜ8!®œÖ[Eƒ<Jé¯[HS2êJ6²扗Ô™*c;ö[û/pß2÷·•ç{3.•™£ÓGD²¹Ý9iÂHöå ‡ÿŸ'ºkÛù•£Çqm Í­y¾¬‚~iz¢¾­Ÿ  ÑI*ï›Ì½s[ÒÌÓ÷ªè°ÞÉ?’W³•è“!D2ó†ŒR{IŠ0{€¤ö÷èküˆmH@À~”Še+ „´ ˆŠw›v?žØE)]ÃÏ»²^óÃ[íñ@”¾¤* Ñ”06fy¾€xyá:¦Öç\(d¿>–ƒÏ3çDPÖâ—ÎÔ°ÛKãv¡§^u­¶J[MþuóDw‰Ý°/¦rG˽Aä°@àxnÒPå°½i 16ئm á¢ìò“eoP´£räÉ@þ€î«vF¶¥ÿÕ9BÁúvÚ«\ŠÚ¬èYJEqsGs$á°â[!ËÐæ¢žI.Å‚îÊ©GŒV¶>`ÂWÞüç£þGYXNÆc¶aoŸNÌçSú $1¬Ey™×yàm»Û• ôu™¤°•ê1!þW®`¤ûP ò!U0SïQÎg±ªÌp¿[g&™X…ç·&ÇJs9ß"U}TØÍjš[Ê´ôãIbh4Éj'×â¿!&!N@2Œð<Ø~Šn·¢Vûï¯ »½²L‘…œ›˜$ÁQhl. ˜|‹;µ:ô\œJ)[h”ïxP ½‡.ñóVËÕMŒÂ€Å½q°a2å¢%7ýHd ô^k–Úô—e‰óÆ5MóNµoÊëwZk¾LÒŸ dÆ"¢1Ú&èxåz.;½ÿ2šé>8ºôâƒÐ ¢?òÚ‚¬×ÌÁ8ÙÂaÒ:ü®›]}àáˆãTe¸9ðV¡¨mûùµéúÜŽñð Q@’F¤+ín çe) é»"¿Ô³Œ\8ZL*¢e(¸gH<ÇÓœ’p Hº¹oÀE×R‚8}¦—ؽ‹jü¬ZéÀÜÅt£¹ÑG~È¥ˆN” MU[åôãOÍfÝÉmÒºLlyrnD-lHg˜g,º…f/8ûæ-<Ú6#QµUCâÃÖþ˜@öY+¶-tHa¨öŒÈqå‹ÕösZnôâIÚNÑ,i§°—&ú:ú Íœ²àhʯßI9.Åve\cò%¯pWM\'¯Å¿fU‡Ôÿgþ®*ÃÙ¦m¡“ÍI†`õó•æqzi*Rjp¯ŽZ¼6t™ZŠ nð/~Îp~ä7½™ˆ9ÖaíɆÝð¨?|úÿS·Ü­U4#¡:f¼FM{áSˆs™8±I È⬆’ò å÷òôÜmy&vP,´ª’øçUŒóbĪ= ŸcÌÞ ó¯V’S˜ad>;¥:í-½¤ª=[¨EÁëtºçë·‘©º=[•ð}kn Úídí/*Ž"àLLù*H  4*?‡ŽµLþB>·ÝáøØßA¶BRì6K XÊp5ø…ÁΫM¨3tUŸoÅ"Õž"³fá±D>.[t:N§‡Õ>[‹0æÓ†kë!,­-FMŸì«#ÚKÜꯙ¢³y²ÃýÉ„ŠhV²ßž Á¼ŽD56ÎK|iY;;$šh÷ ޤ—ºù‘™h_ušz§ŽÎ]î_Ý‹Ät:Ôz®©yÊî¾M²ÿ·œuÙ)†U³F‰L«®ï\xÞAIİ«èwÃÒCÙl–Ðv§¦7²$lŠÏùõ¨ÓQTÞ¥FrœŒ6—p„¯ž¿kDð¹e<<)6få¯Kú댞P:_FVÊ¢óôÈìåžs]¾ &C½‰¯¦Â1ýˆ¶í íŠI ¦Q¨ß«±§ŒFvæ‡<ØŠP|`Š”Sc ëoAö^â{xS‰tŸ‰òë‚ÓµÎn(W»øÕ¡E_Ú}ü»sâÿ¨éº] iO[=ܚф©oü„xv  FfÐÚoBwŽôêN1‚s o`@lCKú˜Ô:ð³ï{ò”=F[Di©.¨ƒÅLuã»6xëývR~Û¡ºgZ¬3‰òˆ†x‚j4Õÿ;ë%ë•£Ö–ÜgJíǾ¥Ç<.+~G/cü¥ÒýN§ $gbÔPK:[`˜ŽR¸z‡÷ÑϧBÔt|’Sˆ>!{ÓîÖ3zã0®ôXïÌòðâlsc{¤ËBH7[vfU¯œºY‰o$º¡ÍádÿÝjË»ï¨!^³ŸÜ»mr|õ¸’‡ …[y­—z„{°géUã¿ å 7ýùäJŽqþëÈŸ?#-a+œx¤¾Š %ž<΋SÆïålf”°Xf°§ºãçlÔ³ØYJ7Å$"å´õØÿ­|Œ9{ö?Ç]‡X`ì ð5 2>évsÏZ(£üz¥õ:·ìäâ ö‘0C1ô‰ÜW2….0^„ÇÖLž ÿ7HUl€D\…ˇ6„ÉQ‹~ÅReÊŽöÀšÝJôîÿt}zÙO \aåŸê’¦ú ã%yB÷:ò‚ËE›«Ÿ‚Aq€=梓˜Á9– <°ºî«kJŸ‡ží(¹ºî‰á„ñbíFéö_e¬28´#÷r€¦(i¢V œI·ˆÔ:éL¢’JMļO²yG ü~3<¯ Ï·Ã÷ç”%5lqÝ5ANÑ Ý“óŸUG¤Î J#¹Aî¿QpÝë*žNÖKs;ÿM>yî“[‡ Ný‰dE?=²jÑ»‰¢RPí"Œ¾è? ÇÅt˜Ãè™YÇfÎNùàO£B“ 2X8m`û¹Ø¯Š:Ä<)vßWE¬Û(ô­ÒÕ>M!éÙQ£o…lR°°ƒüüdÍX±›èãÿ È\–z–tðÈQÙJáTg]ÊÇ,ËŒï÷…ÿ¡Ÿ6¼ˆwsÄ?~üP´ÅZñírG®î£MÇp—ÿ—ÊÍδ—½ÑšØûÛXîýãp€@šh-ﬖ™.–¸þ; q¸Å5ïlIwñ4lŽú»tRŠŸ>$¨ŠÔËsúï?5‚ÆFxyMß?ªˆârâS¶o´É­ç¼RzQãhMRðG„‰– RS’§4¡TóþÁÃ÷H+š8ÄTÖêò‹Ö‚%u†ÍaUŒjãunM!¶e¿X_#heëåfãõjþ‡}sÆU‹h%!D½"õåQè=7TI“®MÍHöAݱÕÞ˜cTj$8¾) ž‹&¾`c Àî±¢ºú¾Ž5ü!0 5ånì~ ù\‹åEÃï>ÈÛ‰WÙd®Ú“(µ%•°Ýß!ÔÈžû2SÅ/®v¥t·1Cü¿B[b„G]¦î0„j×}¬é ¡ ÇdRWqál“d”še‡Ý£ò©vva'ç`ß›BrÀCÿ•:;ȯNs÷VL8{TאּrŽ ¨Tfü{õ¨\8Æâáç›:#ЧÓU.äíjøYÒu¾ZVòo¢e”Hîï‘$ r5œž$BŠköÑÝ‹øÀ¬b‹jfA :gÊï§ „(A™ÊÈÛ!ÔbÕM›˜ªv—nfãÑKޙωæ)8N¢uÜ y$¹˜u"‚û¿íöHxÇ éî«öTò¾”‰t-ƒèOá;Ô8NZG9€ï¡_ì§‚{ ÓÐöÐò¶øL%Ÿ¸Ûï0šÃÏrçÅ[HQgF(0×?œ¶¢ROÇFy²jΣ#† ‘tI»qZ}z½„@&š76®ÏÅ,cljaÜ /… "Íò%4bÊÄÕ­xø’€g]ÓëÁ·D## `æ%‚±Òqàùðp1Ž( ¶œiÂÚ ˜6oÈ:_’„€o#1§sB•e3ñA‰ƒ%OàLºN;¾Õ´œ¡o$=WW¢X;Ž&‹É<ªùæ\ZNž¯»Ô0]õ?"n{È®&§€ú›Xžr´XQñø©‚ž­&?ÇÂ¥¯(ɼR3µÇSx(ý»þÍ#ék ù¤ümˆNIjI‚v@rÊ©å;FМ¹Ãx]É3q’óÇ”µör‹OU8ª£Nû%$ðFDþwZ­(•ü’;ÔMÅù{ ªc±TˆUY½-%BÆÞ„Êa¹!¼ý»nÔÏCÞ¾gÉ·mHµ•z¸kT«r¶¾1} @4ú‘ÿö Àß‚éòq Ni$³8ÏæhMD×+̾:n±ÍÖÕÍí œÌTXEZĺ€ÛYzmÇ”Cn=ìÊEQ8tÚ¬CfyVT_›'Bc2|Õ©h¹Ç‘ÌX¡§lxf~þmòá%lIÎl”`°g&;¶®€ùÓ´(_ñI¤‰m"“qÉI¶®žIãM£“Òr(1Ð×Ý mÕ³,'ï°üp‹ƽ9Žùa1Z0#Ü—8ZÖhœÝ û-aRò:úÑo-~S Ý‚ûAv¶ÞùûàKF†rª¼)(À:øÈkýa® ¾ésðØ«œ"^f”’ ” sº©÷Y c@©õ¤c5‘)ÚÏPq·û{N¼îñ“ä9òÛ¦¹XL§V÷ßÌJÄÀýŸcë›yŠŽ[ÿíFYÒgZI^wÉÇÇç9¸°Ê¿Cãƒh-;f¹«Å!âp'$îÀp>‚&" z’—’×ó ªt6]îá5Í|ÊÙ…«ìtõFMÓ ´;e6nNNq؆4¼ Da»\Ëk- Šèìâ|^æp¥1ÁDÿ°?¤z8§5㇭$MŸ\§dOH{ƈuýí%KO–kq—*œ3bØô=Þ9§Ø:7ÙÄñز’ ¬Äïɾœ!9뉧ys‹mV uå½MÛ®tDÌs*Õ¿áu°ë:jb)BîŒ^{¡™á›lËRÔjÞxʃ;™ ‚R¾ÖìÑH•V ´•ú+í§Ý¹CøòŒœfa´ŒyƒÊíÎRècr©7mâ¹áŸ®³•ÀÔÛЈtɹG‚š›{­Î:v’©ûYdÏIþ=AŒæÒï³Õü¢¤DÆ-×|új ‘k‚e|Þ¤;TRy?ÇI\ VH¶• ƒh:KfÙ¸‚’³PE9 š.¥-rüŠF*zŽ9TQzH¯Øtß7KnÖÊ:Î.fJhæËJòuË/‘3’ÿ%ôS b˜4ªISÁëõÆSue 2øf ä•#…vœœ’§­´x`SºÙšëØÿþê•ÞË%ÙL xKª5Û¼ÄmqÔôE;ú¥’ôÖÞÅãÑ çì’ O„! [t߆5l²àÅznvŠkbZÄwð%á0MO].sVuoÌZ/÷$Iæsì ƒAñÈö÷á~_Þ¯¤`Ñ*î“<Šæ1þÎŽû»Øæ[›_ºä,» oôUi"òO;©$¬ñéµú°Ïµ¼ŽÚ6U G†4Ö \ ~º‹pcr×¹¾¿U3šèá–+º0zq(®Ü ´üË£õÉ$C6OãLô­M+Yñ•Áhùp CxÏþÖ´piu‘©ñˆûXÏÏ:oÛêãçƒ4‚@k÷ß$ÖçáäÝ›È'ÂõÛ{Å!W(/™tb—Àö×®¿«ŽŒR7ò E£=mnÄG^*Ãø¸ 38\°æ|%%ø6œçi60»ñ!HõÙ$T Kô€±+#l†­é¢'!2t:è+âL_1:¬ô'’ì­¼@EM"—–É•4÷’=šj¹±$Í&kg¦Ë°ÿ!\ñ5Z1TsñŠ1»8Їª7Dq&js œ–ê‚êä¼C —K@'Ù?£ÞÝWT$v×*û¾0¸g¨ú §oûå—þ&ÛBªsìL:!mu‰7q¢®ëOêNp³€Ó¬‹!NºÀ›î²”é¡¿g±Ï{¥Z'—zaÓÕ`§ðíkƒè˜è4*ŠY‡vá­ú{Òõh’,i¤Û`©®¿Ñ ²60ûZüsªh¹J†¡—O¦%?U¶ lg;8þôš§kDM<ç:øÅ#kä–èýW>B^k‹"ظ_&Vª-û¯–ü þ"Á«lˆ9 .SwÇxgÇåÔKø Ùwz>«êN†Õ$R€¤¯çràS}4eø%¯sÚdäHuØÓMÍ2&mеE=eC„·fÒlsn vnø¦Œå¹{¨*hšíÒçñ×í^Ö;RôGËŸ9¾–iX®ê>Ï#*ùÊíCÿWWd“L–( #,äÏ¿T¥jvî®âk»ï^_uüJ6vµ/@‰CÝbòr=bÞ2GK"—=·l¾ÛÆ)ˆ+̬³¿_ù¡^± ’UT …b6Q1Rúoס –õ#d7ÏÎÓ>‰·›ÞÇxûÚe%^;Þf ²+61×xð„G™h¸Ò¬T˜Ú¢eÙ“ÁIý#_¦e¶á|TMV8^ÿ¥7ðË¿-ÞÿPE€¯ éiM’.íñ^NFzzbÁÊÞ^ͪ8WeåÉ*Wôv…:xõ9ï5Ê0êsûž{Ö4æÄçVF17ë9 –†Ë´®|µD½Wò"Òºëß oÇãxÍÝ)zaÜÖN>hÎrÕà âF†XL¬ñïh­¾ÈÒØã½”³Á!N¶9 áöOš[2¸¥ï>·]Ztýòúž Õô¢ B¡óUÌMt¡vÉ‡Ý B´ò—Áö Îj  •œ¶!Úsáʳ†ézwAjðÎ=AîÐÄJ«oq©ù ÷ ¶´¡lIÁ q„T2/~[Ÿøé ]3U4îyÁmƒ¶Ä¬ ÃùPˆNIG¨$SòÕ¯qo¯P¯ÏXö [Þ¦Ü/Ÿ{¼gÑ@¤A+éIµ –¦šâ€Ô0Y†~ª¼PpŸ…ë”’ÎFY€C?N(&±Áð|ãÞ¿°Ù9¢Ö MS4¡ÚŠÌÚÙ¢päøYƒfõ¨aU-öªBg5ÚZL£8fy$øÙþï:8†HÈùâ#E¤`—*§4Ç;}zWG;ÿð_(‚>ŠC©qæFù{Gz ËRÊü»_ÒÔ³›riþGs8y¢yŒ^²Ñí5æÅ_¿bb¹ƒfÓ™«ÉuS{æõÆknÒûè §œæP=‹#¿ªnèâw‹ù÷{/ËpµËÄñ·-LU××h0úq/e @¿ÍÄ×]ÈÛæ¶w^ ï xp ëW.S““†}×éSšÂЈˤ¾HµŠ6?\ïW}³·“¢Èݠ䆀ü14˜ôa¯½4…ð)#IEùäÖú[uà$ #"a K@¢—æÐ¼@ÄzM«æF:m#–a›™|„!`âêkN¹-eÝ›wæ›^D‚-¸Åi›þÂþÌb„;ïÃàLSÇ£BH”›xa«Æ>¿bñÍÑA|'à$/ä Æ”ßù(•þâ‘FÁÏêh€¼É×Ó̤jWíh³Ҏ!Þ gË0td§@ƒ¶²7ùØÙb¨sp@Á@•Ô¬9ì3WÕ¼qRÿ[Z@Œ$:ƒO¶V˜÷懵F$èºÊNÜ®4™Œ}ø> `÷Ñ]zî¶mÅ…À¥ßÌûîfXÐ*’¥"Óη†,î ¨¿œ†ëìÙg–ÒÄ*Ê_ÞÀ'‚ÇŠŽ`~s”Ö#>*~‘º´Ý}ÿj eC8vODm)?°tEó÷–3wžÕ¾vöÂèùEÛMý@gS’ŒLD™Ò­ W(á.ê_0«O]eãÙïëá9Y°1zCЇPA 2v]¤’UR{nƇ?ÕÌçµÉŸ%Ú˜0íÍßn!þ¬]Ðû[ôžìa*¤Ë“ÁÊtÀr+zHGìm¦q ã1þËÄ,oD3R£ÛÈ•óG$¢5ÔA ÄD›¬=N£âQ-{V£@ޝ¤]’´Ìòñ|6&/Ù'¿2e"lÛ+Uìµ×`5ø)õ¼×“Mò8ú[ƒ60;8'«ÒCí<¾}²·8,œÖRÆ–üÖF§< µ˜†óŸw"jéX¢´Óï^”Ää£Ù>ÿmOE¡$¿Øògý*[ñ[馋c3=û½K¤²3ùï“4 mj …‹Äõ¶…ÒÜp”N‰ô¥¬½ Ín Fûbõ s-øÉf4×­9ÿZ]c¯9Ý#Hð.Öˆk5*1­®îŒÏö–ÍdÛžœ.?ÿ7 §Î.ÃÊ6Ž÷¹Ç]+à¾2PCƒ¾Z]†É=ËÖÌN´Ü¤.ï“åC~k 2ÿîÝÆÐqâÎ`Ò?ò”wYÙ‰o]à«hŽz[|‹ñÖΤå+´ÔÂӉDøæŽ×ßß“/ðpÄžËUªÙÆÍ=KÝ5âJ̧hTH>ˆŸjEåwßF†â±KÇGI>šllEÿFxˆè[ˆ` iF—¶ m&ã• ÓzSÔQ/Çõü}ÌZ¢¯r10^B4ÐħÃü9„bEn_Ÿ¥©|ÍÏ;@»8Íe$7KSPyLn|MÓwípMD!æfïî]ߥú Ý=¤Æ?M”sèŤ€i ƒ˜Á|ž?}æ„Tsh¤üÍs„‰€tzÊÇH ù“øóLsš¡&9&EEKRÒà=¹­/èÕdc¦‹4•wÖ Ê¸Ã³¥ê,l‹–ÑÁöŠÖRîÍ(ÖrgËFÛ³“á‡ÐÔ©À¨Áø ™¯H¾«äøŸ×øô©(n•¥(qŠõzùåâÌW|Ù!òƒì}ƒnsß;“þ¦ áÝM Älì§ßr– ¢ì0ÿæ’”zé9€Gè\uë£ã´ñçÅbJz^Mä]–tØ£ýI7©‚Änye€«ONçÂήú%@W­aSA8èwA¯{9¶§ ‘ÚXòpß“áâ1QL¬Ï)KvÁý¼&ý‘…·]fFÌDÍ÷Ÿ™ ÖÿG¼ÎTh[2û² "áÁu[磬š(Œ9pp=5m`„>+ðê¸HGñâ®8F/£ Ó¥u›{īႛë§:©N¦æ}K+ )Þ÷¯zš,4ZÚS›ðþ C‡´VÉì´>¦ HpГC=#Çÿ—„NÆYêÆH`c: !k«Ô­‡Ç¾ÑÓÛ%94„0…@Ñ).Ø¢•‘‘A—þatÂkô&¾h†Pwf0×+þg üN·e°{ðB™þb J·Î’?ºh4ôn>mçG§²ÍÝ ‰D}õ„´Éú –¢Ü ·i´ŒRÏÓ÷\uÃíKâžà?@;öåbeéL<27-±ýSÑ:üJÊeŠ Ëãh<¬ù¤Þ- 4eTa$¦‘ÈذÎos€dÕuBÛzþø,j·o÷ £‡¯¥*ŽT¥†!L&¬-ÙÄâÑ=ª÷p>Ñ´ð8.UÀÈFëMXr/3 ›hOd ,Û´( & j›_ÙGqÃL¯‘Ž$ÐÏàwdd]H ñ×Ò=âúo öi¿Â‚HÐåàøœ}]]'uÇFÞ,2jeˆ´û Ò6kòØöbuÄÓ5[+ŸÙÊ@™¾„d"øßÍ™ôKýüáë3OD!‚Šeâíi§’´¾¾sÈázêÕà «n«Pð|öÙªí¥à »yC ~[àÓµ8U'ƒ…ø^³ ûÝçBÌŠ¯ ˜Kî‹sSéõÑbw%~^™7ýÍ;–Ur¿C7¸…Ý,UŽˆ4›œªF!·óßœ6Qá©Á·µ´%g÷1sJ‹½€¯ä>ÕÞÝF·Lè#6®2{2ä€â)Ôåºò‰ŸK¼¸íGuÔ! `2ƒˆiÖºÝÖLûëÞ–‘«áœ3x4ÒZ±± db t¦$ÿŽ]„áÂ=Ülhi>‡QÜïÜúLT¯$Ñjø ·ó×;#±(ý¿Y.˜_1yrêCö6Óú3HsŽÀ+‰2•´êíYI(&µ¯|é^p¸ìÍdu…£ß—­ëÏ­Ô«ÕÛ¢Oã°0]ŠðªlÔ ;/Qjý8zfký¯³ê‡ÎgUñÝ{¯ˆH.$ИwugA¸çcéWï5âã÷­Q…UðúÈ¡”ÎT£pÛ6œÙʨð³ù›+Ýëœeo êè‘pɲÌÕ‘)CbW‚{;:c•Eìq7nñ ˆ~ò"ö®À”e™µužiܰŽ1݇ü4µj¶MåÔ®¯wcÝöA²°pŒ«ÑíÔà‹|}cAcI6Tù†[ÔìŒÑtä»ò¿ƒv2.òۦ£]e†ºÞQ=ˆ€6­´sB§Â”3m¾³ª ^ý!“CæÂdð²â§™¸H_–ÊúKâ©v• ´ BÑ]¼œ1/Ç›.ͤaýÐ×Þ‚“´™#n–)ÓªÍßáÉŠ¥´#.9ÒÜ‘š0À ?Éñ¹,o‚*O)™”ÂãÜ^–^ò¢€Ô æ[uº;SátÁ賸ÍXiíîxüÇ!4œLS-I¬TŠ@´¸Cá;û ^ϵþh~á/ri§ì5•XÙ;miSä ÿ7쉧6DõV\Ìk>è}t(ÿOЛÕú[S¯~AE‹6tOKF6®³:¼0€w§ÓpÑî;âãFìZ·0o’ã¨HR“_8 OÈ]X\Ço¸˜ÛÈWMÚ %É–ÌtZ2ʺ;h¿uJÜ“ÒVÁ®#{ܶ–`dÓwÄš‚`Ÿ"hÌ,õ3ÁÊ«YSŸ_¨:­ÊT«s=¡”u%Ä­Fj“ÎèÍ8_Ø+~kb'ÈÒn.F@pøÀ}·ÔŽé¾¬ÜÁËÚB‰×ôšAT窤pJヰK¡^‘cÍ‹åBË R ±ôCn×,$Óãõ1 ¡³ü‚A8påG.¯ª6¨xp° {ŠÛl«Ï,bÙ°XÀ‹ú´@³BH{²ZJØ}&©¿úRþ&¶0xûwE«)Õ‚q«Úl ½öŠ¥oÐÌ+ÇW±£*°`j¤Ç~l3ÜekÁš?²/ÉE zÏS_6 i.’ç 2ŸšDßPV ëœ¨EÑóŠš|Þ}®fÞšŒPž§‰þè64íösSä So“th€¢‚áÑÆå2¤†û×3¬¢ò»§ÿžp³ 7Ð9ÃìѺa«íï‘ x9&ÐòÓ,E˜ÄuÛsü@ú€‚.ù™‚¸‘ÃrJO'Ê8 Œ<Ÿû»;ÐHÏŸ÷e8>@ïæÃEm©É‡J®t…Ì}ùaÐñŽEK¬íÚ^? Ÿª¶ 7 ”YÕOêu)U½&!¶ý½Š‚ó¨òïPu5&å´)ÐÕЇ¸¡u©ïƒibeɯڌôhc_¡ p VJX±¬™Nˆ/ÚºÕ¢·½ŽÊNNCÒ¯V"—SÕ"Æ.d¸‡ÞOW~6wºæŸ~ù»â iKÄæÞU]ðHt+ç+þ'÷P;±êÁ¢«²G—äÂ6õáu1ðhÁ󘮊¤»€-¢…­n}àÿ±N é(E©ƒŠ(RÍ™LoØ,TÎ¥$8oå4ÊRŠ [™¾:[z½yâFt—Ý2D—²q·³.úÏÖ'ñX æ²N8Þ ›d»Jýi.f¯Ÿöa¥2ihM–áµÕ› _*žöØ·ïn¾zÝ7":âdO•%è^3Ò©ÝUBe±§-A¶ê••/ïÉï”tl˜t‚Ƀ'ÿ=ñK¼mëŸ>M÷ިɞ tÑàƒF¨TíÇK<¯U)©5pƒB«/ŸÄågø(F\¸½f²çÄJdÚšmÄ´n‡·ÂR(¸Dyi®mÕæO c›ŽŒbx`í‰ðâc —£=N`qpwüLšû]òE¾Œ n«mdˆC_áŽ?€ ¸vAŒé7;Íc®i Þ[.ذõO—ËTò&¤ë…X”·™G°¹ÝùºvˆÍ‰O½ìÔ6¹@ÓX(ÙÓfϤ\:~µæßìt+ï [ à“¨SÛÆ(¥â<Çq~-”7¢¯îd3r’KDj÷Óªü(·7» Øn›*£ |†â’Ï+y-åÔ°ë~€@ì[Çï̬ÉrÜ==¤‘ÊÈ+ áÚP$!ä_(|ý,ÿùõ­4eõÅ15ÞYhåŠ!Êß= +µù)€ŒŒ7çEÒt Lˆí##ñÛA˜~daqMޗŏÓc51óȼ׆IÏöê»A2a(icràˆD¾ËÄóÒ=ËMGÄù!Á´ ÇìýƒY¾·\‹õÑ´Àí÷Û–ÏR[ZTiŽ2¸µðŽ!Ѭû1¢>l Ëe3ÝèH U¾µAÉP¶š“£©ØºËRÿ³Òp€ÔĪi6Jñ$wÇøRìY9 ¢ÛÏsaLYï@ úDîÀ[Î/­8Íü|û‚Š—&˜î ÈÊi=-¨µ¤m= Mó+>†½Rêá«r#”œVƒHí¿N¿˜–²?JW4ü¯š1ù{€håYgiø…åéŠË-ZFjÆ>»€Ø‹‘–{ý!Wãó¸ÁkŠ@Õ-‡Öˆbü£=帯3ÛÁRäˆùR™^Šx¹F°˜E®!jÜ8‘³”ç‚ú«÷Þ½¸F¿ö¶aºD¤”ç‚¶ª"Û0€0g—²b¡ö5žþ×Ã纗­õ Lâ‚1äˆÀWJÒ½N~½Ëš xow €}ÑÝŽŸ®ß½¢—ü-Dï4¨¾œ0ˆ­ñÖºîøº;ëufq]œޏH­rU^'Ôj!l‹Ï*1ÄíŸÿnœ8·«wÃ$Š„ge­ ÿTºÙsâÄQ›'nè’¼–hv èG"Én{JÑåÕGÀ "×wóü0X}{srŒV’…MzÁ¦W)[‰õ²S›9÷´çÇ2""|õ¥Ú¶]HÉ&o©§ÆLKr„{Pžé?yS3!©£Tø"GŸÃ´òM·p¿Î€~Ló!‹m+øŽÝ"ç^ZQ½ ½•ÏÁB=~Ø^ª#S͆Tò§» :€®Ø´l!{W>ܰ ‚˜[DAo³sÚ s`wØÿíi+ ¾ ˳ŸfžÛ}$ýõqÀºtPŠžÃmüù+ý Ò Œxº ëí4ì´·í%O‚àfT»ßDçlšƒcí)Ó+â 4g" ³r’§™1Sp~‹}0¹æÓfÁª7†ge’Œ–¡¤/Ðüïòïí`®ÇÌp‹…]¼Ñ4‰›nm8áVè¿¥í3*ØòÀJ¬'½â¢‡¢&Yµ8\O?™5¢–bÅÆ>îѦtš¨ó`ÂZc ­¥ëm|†Ð$*•˜‘Ác‚µßôXÝ`䑸_[1˜m¤Ñ­¾²À›ÿ ì˜/l[ÊÈzñò&#.‰k¬ø‹×?GL¹íÑãy"Páï—c•ŠRjLÕˆm9îæEÊø CyÅkšeПâŽ3—™ò:QœªùÔqÛ+c‰[ÿ{‘‹Jx§ÅVVCj+ºnî½²s «2 líªÉÏ5Ï•Y|üAdHÅδí™4ç**ŠO9$÷…·:‹Ë½Ž§ô;…U{îÆ’™¨A‘0úÔÊØ)ç| ›{SSt$»—¢ôÒ¿—TéuZï ¢·èÆf¡ýÇW?ι#UThı>ú´_î’gà ÷fÑß÷SgÅï6…ˆæ­$™<ËÞ\ªY%#§ÒÔd¼kw7:Jµñb\Àá®’ Ó¿ä ÇOŸå׋ íÅ#í äñÿ¦”á?8&£-ìkwêSzÊ‰ÚæüºLÙ!WDGŽvwV[‘¤‰}p2kQ0a†! 2)Û¡mºæïÎY£ê|A²A¨b+ëÆÜz诟¡¤4T0²s |IV×ÝŸ¯ˆÝÖ¦= bÈ“ôšn騠…Ö¶~{»­º~òùl‰7Vi»p xoJG ã6Š«yÔã4«ìsÝÎI·'%$½Sß³û¸ˆ6 n‡9Aެ/ì)sFÖ”?Œõ&ØvJ^ºùãêB %õ&Èi£WÅïH€ÆV¦ÀÒ-èÏ Ff@­½|_³Ã~ùÆ[¹Õ8•꿟]ˆ '‹%C@ŽÁ5¯ãLÈO8öâ–ÂûßÙ÷,g&;6·Ñ1Ÿqûú­êà]˜”uÝæåªB0ð¾êÔߦ`6ƒÀ R_<§‰ÁerËß¡uúÁA.±NËœ«àÝ^ƒ|:†Nïê› ïAl–_zVÙ§nÕ^ÙPJ vYê¸LjAýß_~h ¦Ú{ÙÈRF¯„Ù•È#¹\‰ ™ QŠ:³Î£%i_Ï:«kÿ ¯¯àò›|]BßG˜ÛÐÙ äSäÍÞXp£2Ô#œXoP%Éào¾{æÌÜ*(ÿÊ |Ç/Ä~'áõGyâ"ã ƒ²æ:»ÞF¤Å_HNº ú^þÆ#>&m· B5wˆ…Ë»¦ Ó®èþªôݵcbqAF,/+u!‚~OƬCÀ²HÌÀÝÇ›”]I…Ö= ˜´{Œêv6.ÚB…dÊ-xž2€„Í£[© _g™r¶™–AÉ_7½ï”ß„WûÇK!d›¹Q ¤a"¹™#¾A²Î4›uø/´ Û aÇâÇÜþùeaP—ÏÇÃ@ ¢o µI¾L~š™«îyÑ‚¢ìóšÎÊBÓæã”.†ù¼ê ‡©ÉÍ [˜È~$+b~ÄIùQ‰µ9a¤ÅM€¯á†MfcÝZÎsp¡ÿÌJ-bóŽÜPÊŠå¯Ì¡4&ÅU¦Ñ¶™@0åÚÛG[‘á$ÈÐý|IÆÈJÓ‹$€“ £ÿaÂÆ¤HÉ­s‡¾éô1OìrÕ¸ 1èʸÁ2„D»‰©àœ>ë“òOM˜àþçÜÓ1A†»p3×þ‰ŠÐ"ã䊱ü\(êÑ?~~CW ‡»F› N‹®˜ÙC™ÍmyÃC÷>õŸ%¶9<ÚwãÌ'ª†Oê^–ÿ(ÿMÿÔNæL†Ž V9#¬@ƒG=zG{µtÿÁ ¬65sÖÁÒ(Rd~LZ+Õ—4‘‰caQÎÒ‰»Hî Ù[¢»Î»Ã³iâæ?Nn”i2êò*èuèEÔgævº’1¢¦+‡¼Ô|NWˆ!’‘ü­hb«Ž™ÍHƒ¨7Y­];-câD¬$ë>Ñ´R0…¾e 0ã†m” P+¦¢Tmú>ú_Åc.ÿË÷óMOü@S‘ãÇ©O¯ŠS®¨Ù·i³‘“Å"9Êc7i”åt ;(†0)ä…y—*ˆ+b>Ö ´ 3¶Œÿ³~PÐ0å&¤t`Ïaf>Fê8ìï½ìî õhý–ݧ¾O `“¸ŠA»JëGÕž0ùÚ!ÔB|Ú©~”ŸT@5ÿ~> ¿ h+Vo\>„5öØÈû¸‹IÓWzÏ5NÛÚé÷vpZ_Â4A%Žüý܈Ív ¾ªŸýÍ q"å˜Í©Œ§–ãÞÙ¨”¤_ÂwÖ:ÄSø¹Ò¸$vÎYR mUK¼Ë •˜Öu}ç»z$ÁÒ|7â£ðËE?Óý¼@BØJzh’²£0ªfî@§ñ˜Æ%’¥áa@rœwü—¦£ŠŒzLâ>ãP7ŒCÕEÄè\ m‚Þsyœá<;G‚Š“6ŸmÓ,$Q ‡jËnÍÄ~7eæßƒ¢î0¡?¾…óBˆð*¿(®ëo‹¾ì´N‘ÈÀ˜/ÍĦ|¡à†¨Å  ¹Ž&^[Û䦋úç9ÅF„]Pì€G ah™•—ÔÂi0ùTØ Ö†ÏØ i%¤‘àê뮚DÚŸŠ üÍ áGž{«×ÐçÇFØÇ,ÚÃ.”Ó†•´ì¬6$†i¥¨×à(£—vä…›z#¦pûµHKgeé¨^„Í—[͇X¡è#ûº‘4i~¼…” £T _'+ Ç¡y,è(;ååºýÓjÄ·„Š>ãÔ§XážÒÔzݪ2ïn!_³K½˜¨ô1ëž]¸ãnˆñ' R>ÙÓ§ñ@é–dÂCÆ,Ç|BÙ£K³’„¡*ˆSãOÄ',²&„ )áä¶±ŽjÁèm€VI¥³Ê {ƒ¾x¢ÎÚˆçÿ>•zÆšZÊLŠVEõIïʽ¤=jÆÑ ¢âÃû’/C@h›ž8zÖ×0°º¡Ô¿W6Kòµ.FdÌ6Åp"sïì—%[ZsÁJÂ<¶Pœà•IXü=hF<b¼!´#$ì,Zc_AÆÿç+Ú¤êBlÀ«¥F.Ò= t2xgL}¯6#ñóí_ŸÒH‘§t­•Lç°šM©zþv hjt—à <óU#›§^¬€ÿW©Úd b…®BFžŠo™‚4/²âÌžÀ™˜aq´Ñ«8ýÃ\ˆ:ësŠîŠ#*´L‘.Üÿnj£ š€ªƒMIgè²×àÕõ*R#¼hko˜ø;j,ؤ‹Mꘒ'fd¹h1¢oò–Ù´s¾½¨Z}‹tã1´?K.ùÊnÖˆ|1Ø&·ïz…­Ph±Kô3È¢jg1÷m]ú`[ ¯ŸjÈgáëxg—¢Â:ÓEq.‚*¤l\žï ×Úõ·ÊS°—MCvº˜xOÑ÷-M5?£íV×mòkÖ£e"·§G#$½ò—­=R/ÛÏÕRýjêYµÚ½EêØýŠó/K3ŽUˆµ)›’Ÿ{!“ìû/T×§}¥¯À£3‡Zq5kµ´ˆN ?+q íö ¾”h¡Í æ˜ûLÔ¤vrÕ™ÿý}²upçŒì“IFF°{|mDn’Ë»—x1âvŸ¨Q,ÌYzµm†ôð;ۢו„üböoÁÕöRü¼²iìa-®å碥ҎdŒYà`:j­tàËý›!áç´_,¿[¤ ªzëbÛCÄ•³9všoàóù”ìicuÈö *¶û¨kÄXY §o‹÷ò‰;X`…ò¬!ðrèÔÿ=ƒÜ©LÍ÷R8ú=Ý ìPà§håE’Dë«ÃÀPÒ”=úò£“üJìDÓ´S‹«Ve®¹©•á>þÔ†>¿ŒÃjVÍŠ«Ûþ1çâ3áwÐ!¢"±Ä†Åï„åüPÉÒÿÎqw¼÷p_>l˜k8ÝŽËÌ,’Û •j5ö¾ñóq˜ZŸŠöÑwOÕ-u&³'J]Ú¿ÍôøÊ/ƒåâ†Î%}E0”âtSi{ ¬¿O•ØÙ—–6š¿-þT–˜m—ÔôSå÷ÛÇ»ç±ÊDÓíº cUjp8”pŒYÃO¬èkŽù5Èð3žºW<&9í5wàh®µ¡Èâµï~‚¼Ó»ò6 ×ÄKVg lQÂcUÌí%Áð$†ž_×B,ô¿5U×#F—u¾à7‹¦SSo¨òD S)P°Î“Âð°¾®XFضyjNÜÕŽ•î'ÃäÆ+äœùÀqõoWMÁDõõßF6‡)KsH‹âZ#t‹³)Q³álÜCY- ³vL Œ <Ц?ô„ï€Ñ&Ý<-’R€Ù§0krè+ºqŸÐ4ÿ&dOèa:À /a®/Óá^¨jgvßAá…2R9&ãOÖ§ñ,(÷G #ÃrÈÛËMÓh”)ÿùa€ÊÐ̳³ê¬¡@4‘>r¸… _Vr’v™mŒ Ö —•7Ñ™„®qO²ÿWm㥥(ýüT¼y ö6KÄY¼KÈ@ÃU«Ò§peOFÚcѳ*©äEëø¼žíí8í£E÷Û,íì{ð´¡G‚{Å}éÈwîT>5Žš÷Ÿs=ËgZ+S²<‡£0ÝÑ>_Ý1wMè Éùm]ìè£Ø^Û­ Gó˜Üû®'&*u©Ui)€éx+ëb@§ä5™ÃÇ|‹M}¦÷ì€~Ñ ƒj(›G).P>‡§Å¡ iñ‹ž4êdû;rÜáÇÔ¨¦1(Íÿ=¿›ˆM³Úo|´ù• EŒJùjü¼PåÆéÇÇr8;ù§êŠ ]‹Û9 Îií¹VòÄÕÜ,·,ùnY¹Ð4þëWÜC ÄŸÅR0õÝœˆ©†nÜì:u0¾ß&[› Å+…õ¶ÀРAW6ÉÁ}|(WϱZÌÒ>ùp—lú5µ ß·íË\žr쵺eú4ã€*ÿÞÙ(ÞÚ¾Ó½ËrJÒSÍ+çqDÑ)}}ä.‹ŠõŒnõmgÓß9`ºÿÕžTœ@¬Î䡨¼Óz| ‹9]ßEdq¼¨ªôäôõRk§tÈÚ7UÁ,X…Òç|³]E±wnø– Õ[\‡ê8_°|øØ–Æp&£@•àE‚F£?›Cî\§Z½Š¹Ì=às‚|ÚNs’ °ÂÖ@ùÀÎBzY4øˆ£«ŽsözS]§„ºîF 9XŇHþ,ë&BiÆôîÝ—±s©8¶gU²L׌ÒÎ…Fw˜ÄJ§íŽ~ƒï’ÓUAŒSB…“%džZðåmš”ÄþyQ¬2Ar+—}Bø×Û%§æ§ÌÝaáÍAô­4CƒÂ\PÈ+îrd>¸Ñ0ó÷¨Ëà ¥e$jì ¼Wþø/¯_ˆ}õìÌM¥Â£›šüÁ1£#½½eÝÐ<ýCøàe3Ï¡¥ƒ³Â5`:fÐ µÚMDlx v8Á¿Tk³‘ýèlëTÊùEì/ß&¶C¬#<¢ï*Ç­®¼­cc-èø#•ب —B4Ê¡Y¤€–Høz9Î`9($÷n{ÿm$µÐ+=Ùö¼ùc´x ½Sjùèã,Ú%/mòiO%¾¬ð/(°msÈz^Øú—Æ~|%Ó°ÖuD ¬3Efâ—ªÝïdïuЍ·˜´ÁœdQ§ÛZ¢ŽÉbÉ*|;τ†*>E~WÞ‡ˆEÝà2ú-I#"dö‡;`Û ´s\ÒìŒä_]nЪ6¨=Kû}Ñc­h¡Šë·j©kE„—s-œk]Ê7H9ÑÏXYd†Ü@A#ñ©ÇñÛ° #þ‹ã!ަEaîeÿËü>'ŠÝ¨f¿60ˆ"å]ãf¾×K¤¦½/ý=v_ùƒ§Ä"Áù¥MCÁc•Ö:çRmì;Ò6@úù8åóOߘjšQ³7¬Úœg_˜p=Y[3öbj¢ÆšÙªx—4$©§^x’qg/Ïô¿#Ù .ò̳È) =x`Ñ0f ÈÕ«àWçà|1© —¢‹çF—Üt;Áá˜t‹çÞÍÉŠq?èliÕ5üæ])MÖ9…€/þÑh¿ÔÑS3ˆÿŸ‰Œ3ß80ÑL·¶,gxZ)píi±ÆšÒ1bZŒº œ2öÙ¼EÔöŸlÈ ÖË`½ÂM » ü¸ô¨øã^d.µÑff‰ ó3.áÍÐX¯¯r¼Z¡ wt éÜLü±Sš3 ×=p,æÖ|œÌ•Êè¢Èl¦ƒW^³Î,_±9C³0ç|¥AƯ_¤»¶n²§§ÿ(víæÌbä_v‡« sÒmÄD69oìäõß±Ó5°` ÆIÞò[PÊ x×Xrœ²‰,Ý·3¼YQª©l8h’Ü8¯¦Å&öCž­pCQŽÃ”©ÿëžšƒò Òø²|á¿Ç¹¬f.ò<aŸx´‚0_rþœ¾nl‹íÑ㜠µÈÏS½­ðëL«ÖËö߇\?2oŽ ŒÛí N“ÚØÆœ]Ü ÜÏóOô–°Éº®tv×îMíöV(¿6Êï…³A”~ˆÅÛ2ñ°ªä”ïT žs™Ò“Xîr1ðß›ž—wÀ·TÉtËÔ=c´ôž“ñƒ«4˜œØ\­·Ð`;ÝߨÍdd(€ÃMÒ“W‘Œ_pZ[êÄ¥Îq5nž•¥ÉYÎíLƒoÖàû{ѲsäBp>À¶ ™(̹>|€’u$¼óot"9.ÏÒ„@j´¬è6®ŒnÁú‘BÞV§ðTzûò°1TþÔç ës¤/ˆm²è¿Ææ-Š¢ª•˜8íÈýí ®`l;ª9S¤à>ˆ:"ðuÁsÿøbÍDw¥v¬9<¢ì}{ê3‰gÑ6A!Ða Ò ùjÅ™èÂøu^Ás9}Ńe#Ý áDFœ*¯¿&s…‡sÔ'º‘LÕžŠ± "⯥u¬áÿèYäÐ>ÆÖXKž¢¸¦Åà*^Í0ô ½5Ÿ~Ö¡š•(—F—³·åÚ˜S¸Yl”ìîQ÷,„f“£;Hƒoº}^CY§Ð>XÙ6»Ž!~©2‚ØîBé ¤½ZÄ‹ °‹$VG•>ÌmÈ%æÅž ßJ±sÌV°ÌQ¥lË]o烈ìg0FQQÿzýŠÅêY®ê´+÷ä|ô” Œ‘¯;€6˜änÁ1·»]Œlfú«gÄQI¿_ªÖ.yžx»¢Tm)0´ŽÕnðèAÒ€ )D}Þ(àûXÓm“š%i˜¾z&–Ðö˜ˆ7kª‰»<“˜håÍÓÎ oåð>ÈuÓúö>A„Ív|{ó­±\…‰KâñÓ #mŽ{Imždªª¾[ËFõÔ¸–’¥4*MÿñŽ'”ŽsŽ-NZ¦@6\¢(9€3ÂbNçÄÓ5·CÕÎÇQ  ,·Ž¶ë:6a9~9xâ‚ÔR³@Ã4Ræ“9±Ù™ Äü½ê½“~É­oòxà~É Þáž3ùÅ,d;~jN:4VÞÆ§p_ly¼‹Ì»ÿÇZY ìü@p‹„*-—…ïv²®¨¹÷t¯ç5÷µX¾ùžEJ­QoDFps”t+Â8fJ\‰·¼_AQgm5éæâ*}t†ÿˆ`\·ŸV¶šd$Y¦¤”¤ ~<ñ‘ íñù‡@QGÜ;†`wå‡ý¤š¶F¯ŽÓ ªn,ѳùã¡"í„Ëí9pg¢¥¿[¤®svò \:ßN䆃“†é2Pº>~M)¶ˆ°m2õ0·x +4s˜ç…–*j6r%a§V¼s@Æk«g6ÿ–¬“0»Ö§5唎SZ&â Œ™NÏŠ¬a̹ÊmžÄÆ Æj‡ ŸùrÜàœÎf—ÆÜ_‡ÌÖÇ.jhø•‡mC6ðl¶¦Ën‹ææíÒ¦óbÂq{Ñ9|—á%à:uKæ°{Ë>Š& æÑï¼_ç¨tªuN·“uFÁÒgfM", » 98D¥kŽ>ãÓ±Hl5ínàüï†(Î5%V‹ýGGÃV0zˆ1ýRöT •ú ¿fŒ>Ôñd5ÆøÎ‰Ã:®Œ—c]ê9õÖ+æ‚Þ›õå©BVŠý-¥jÔ5 ¿gOÿ á•Æ1ìÚ"é.T¹kõ?P}OÁªËa•§ª¾Ø¹Õ×C KHžA@—tSz ,ö5Š}æÃHÏ"—dFÔ l8#s㓪ٽ{ûòP­‚hóÆt}p±,ñ룢ÂÓ½¾¡4U…ÚrÊNÈAt äÌ_]º\e,ÒÕ•CKç…w€ã·¾Î«ú>¬Пoó÷£2!> aNP#<‘±ßœTÔu Ï(&÷n¡:6såe>Ë!•ŸE¨° "âäŰEÔ#¾Y°Ù#ñƒ8u *Áèbdþ™`6znœ)ÈT¬b0¹ ^{®˜4æjý5ÆC®8®~ij‡ð´âMÌP=v˜‚7ìŒá«‰jóš‰o¨ "L¢=¬òYa.x ú`·Th»§¤“kW’ ýÀm!(cÊnÜ Lx±oŸ>Ò·8 “´Ô8¢ ×Ò§O•W!…Ã6Ó‹›¶Ÿ4ò+íÑ6ÃËÒ¯²­åûŠBÛþW##PH¢4 sb¾Š“EfO<ßëfRàæ¸‡æuk-¢?Ë1»d­¯9f80meI ­N£phgWmʵvŸÔÕ¤LƒÜQ}T¤ÿÂH†©ía¼#ÅéDæ¢ SÆò ÛÙäæ”E“¤yÇ´-¹â}2Oܲ1•ŠI»]“#³Î%Ø,î׈GêL)ÿ:ZUe>>¹×'/YÞ´r˵¢*ð 8¾ßî{á¦s¬rI­6eº¢–ÉŽ°å¢ó“è©ìL<ÉÛëOÀ1ÐO€ 'OÊ¡Îü)a>X÷:Šï`ÝèmÄdÇöØkæü6牬ÎJ¨¿R :IÇË‹–½O Ê&NÜzV¸~HåÝû{ðÔdêÖÓW­Ê32yÆU”¨Žž¾þþ¼êýQâvß3îfƒÖ_Š0–Ø•X½öàÇ9’ ò*ÑÓS >œåê_p"àX»~Œg [±>.&'lGñSÀ}j—%qO+×-阦¤·ÃæðuYlqU2¾M &½ß„´i8xSh«ûf<×Ê€?Üóœ"³DŠß ÏýØ ŽW+·€–·ð«àÜ” Ü5H&ðÿJbH'8¥5ƒõuöºçñŒ•SßӬ칿Ç7¾Ãä) ¢Wg°§YaCzoã­ãø~ùflA^büÕ 3CK˨±Ç{œçõ¦#0l5®özþý¬_ lÙõß>g„·Âd±°$07¬o=<*ºf$N4ï…´ÿaÝ‚ºpÇ\üË"!…c?Šûl¥Âü6鵪¨š ¨ìªTrH¬{VÀDM%¢Tg£ôÕà3ŸÁ­û94ëíaM—èxÇ-Ñô³5&|Óx:`Ù’Ž?*)ú”bûè8Ô1©;:)UÏÝÆ¬§ð•tÿ,‹ve©Hâ1Jðõ_& ¼©Œ7¨ ì,d[ V€ì#ƒóª\âñLª.r¬Aª·;ïõ‹ôɨˆïý±ž…Vñ«û™%¤í*v¿ê „{àšˆ›„3eÄsYÙËâ*˜ÎU¤Fò P9^ ìûxïYÓ³»-™?u å1Æ’ù–²V`û•»µ °ã²[…~/–´Ú²%¾cpaœ•ºJGdžrEà]ò8²+ŠH¹À[~jšØÐÇßÇq,Ú¸¾#,Hܳƒ“c'cŸ‡ó\7E³ Bÿ½Ë…†FþU":(Aެû\E¹Á rØÎ k$c™}(§€–Äpn0—c·]gv,½øŽj‰¸ mOáÀ‡—µ5²ÇI °A÷p¬>§YÐ*g1˜\WFç`´#”¦^à´w?c×ivÖtǤœùCxbŽ xÌgÿ[m‰J^7¨îÖæUAL¸kLñöÁíÉP3;¹$Á”ðd\6±1úÜR!³ ›íÙ¤|ç*'_½ùiKä?Vãl÷ødÍ€K¹&u¨¥–;À ïF!ö¼ýN QîÇ]‰Q‰™-#›]²îzÀîî„?gÂm(ظÇF¯ÊÒ+ü‹jMâíK,9×䤈ͅÕë§¾ ?o¼‚ØÚÃg•˜>—òQp€·†±5ÓW%àl2]MÓÏ‘ù‹¸±|[ÅIŒcdQÂïpSú_Íà²dQw¸Ý’[0¹eà3ì(¶æryWÍ›j²ü-²­(4†À¤WªÑ‚ó4áö8J¸e¢)?”ìðÝ6£b 2ý™ sn·DIIdÊÄ#—Œ@)?º:««Ã™?*”A¬eS†hÆöÊ ßÂä¸{|rìSë†-¿JO!’ÖÀÁjB_⣭yhÀÎè¦èL*S`oý¦ †Až£…œtè¯×#XX§ žýâ塉‰“2F^^õ²A'h/¡®Q…žJžš™”Í{•hÚŒ(¥gg6· *à0Ð…Ëøi†àÃÕGKa‹¦)èxu­ô.·\Žy­õ2w¸&–ô J¢æìX *ò×SȇG©r»N\ßíÚ3ÌÁk*¨5—‚fqÛ›Ø@‡:yÀ¾snEŸvÞÂü·í#ÊÝäáìü>(dÈm.ÎÁKTý€²dfpDq÷>v?¤’KĈNöÿ­4c´Ÿ¿@¾êD|2ç~þ½»`|0Î¥#únY;Ê•%”ÛâT.|‡‡à…~d¬–WâÛͨ_ ]³‘š+PU|èÕ%5¨5Á­ýÑ—Hä:¸£úQ:@Èfx–A¡åáÙ"?Ô¦dÓTñäϰ ê–ÆøÛè´6“a8üs,d´ç¹ý>Åûæö=ó®Éòò­fŸ‡+mZ…l;Ѫ?v…¾¤òeÄ÷Øsºšu*Ĥ:¹sø\ÇåæŸb;òû!þ²~§«Å}*Tc‹7žy:¢òý—C÷rùëÝ9´ .ÙÄXëC’5þPÇÎ}6O²»T|—ßÿjgŽºS[Bl¸Á!ÝÜ‹·ðùÄ¢ðÐÊGÏwÝ«Ã"$í$6hAqÖ9ÊÆ= 0¥î 8"WÞ Jt?²£]%ö=\òÖÈOHÜõÒŒUêî{%öÏ0ŒX`¾aí¤xØt}Ÿëè=ãhÈ4'áwD%Âô܂š|uRPö‰Ý­i¸ó¬Ö€l‰ $r>Ãj(/\ÓÓ&+úAÔä,ŒÝ¢Eýá+Ôä¤}|©¬ ©ßìÕÿípßüvA˜2Ó» beö#,lM“4W÷ž\Ü^IÏ S|è¯âf:åð–û0¬›ç4CèlG/Xãe“Ëß}‡#H™>%«´ãx‡¿·öy|d¦^c&Yq¤2®JÎNÔ]Ë%Oø@¢HcðÊó*rs”0­-f…N9=³Ï Xu’l›ÖÿÙù0œ”œ š9µ£ÛËÅ,è?,0Pì¶^ÚLÈ»¬I¡rúÈ’,L…S8áK^RöŸá¥7[àî·ÂÆ]Ð3ž"Ô4Ú3ÁØX¸]ŠtŸÜªÿ%*Kˆ;VÔçäýê3ð}×ýŽXä¨Ķy¨‹2"}E3aê…°£'|#”ëEC½@>ÙH4à2ä£Ë¶Gv}Tõ(ÐðLH¯1Sý˜Á%ÐúvÖÙÊ|7ë7y \ºÎ:ƒS0'§î†;DqÈÍó¶ÏFéy7Ñ <ˆx“}X¬Y§Qªæó½y„Ÿ†®¦wNôìˆ u"ÖuXwXÞ†•¾‘ySl£ÓãyG£šf2¨ýʳsC=h机mþÞ:½EƒbW%6{7š 3þÛ×ò$Í<!ŸkÌ W©Z}b@ŒºŽ›>îùmñý*BBtÛõcM µ´£~:üšù!O+Ìú=‚Eå@èøå{â×r¯à$Cô÷&1:ši®·Ï"ùœwså*‰$¦­O}ŒeÚßtŒ¼%úßJVþ2¦›Ê̘/ŠV&élѹ²X|=‚CƒùÉ<«€ë˜¶uu/nN‘Íüž"Ï}ñs<[9ѤÒVi’SjžÓã>nm2WQaiÊ’ˆ +ÐDv`T¶±8➦&[zvÿ®ôûÓÃTþ îžó cÑ|Ä¿Ú<»‡ùç‚n„Gf‘ð¯ã±DT3tóuèd ¬ØÃd²¿âH Ù‰Âj¤3ælvÞ’ó“n<`MG¥´:ü<õ¸Ç²ã7 ù¼N {Ê^Ü8Šž‹M¨4…pÃ9Ä“ŸóË1pq ‘~Ž„wY´éC¢z1'žÚOnW ßNÕ¡­‡õ†úC£©k8kѧ'ÇÀ¬1OÈ·u¾Å‰.öÜ›çîé™ßŒÊ–Y½~dL*µ¤ÏgÛÀ2ñ]{ø­Í .¿½…>M”û‰„a2 P¨5:š>Õä¶Ž…/ÏÐ_Cî³÷B,Xij6Žy"‚N¹ãš˜<ÚzRæüè;tm=”ÔË,}ïó-ø…«Oæ«T: m„zçY–æð“®2&ÿ. ¸ø³¬Ïøãy*Å9ñö\ ÍÆ(ˆYïWß²š¼uó¤‰á¯‰GCZÖ“Úã-úZÊyhñV ö¸É·W.aþ]òí÷º€ [hU:öpóÂúĦ¬í¾-Îsê•r`+Ã>ÿ ° ‰òR¨( í7ª1Ó{˜’M´§3ìµ%¦~¸’TÖ‚éOX½› Gÿ×–’È™C5Èsÿ•ÚîcvsXK}R¢ÃÛgš_ ©±Ü2b>»ÊD·ð Ãõ5âñïõ]pæÞÜÞ3†»+v#zoÕ£¹NJð~ÿᨴ*¼ÂQ»YMNÑùÖŸ¤p"Þ>UkÒÒ2€Ì áÇ€W‘z…> _i¥#¨­¥Ê]†W<¿ÏxêV5hÌ4þ€ž³YÍ}›ƒQÏ=xµ¾ýÎUT¥a_×}ißÒ¥B'¯êD[§FU][Í# 5ŒfÏC-èîØì×yúQ ‹ä_廈´{;TxáFi\@ É B›êÆq§‡¾"¬í^ù/Bb7qµmíÖ+Ø©;(1†TARâ,*Éú}‰EÞ’öBáÕe(¤LþW­Y=åtQ‹îšgÈrH.w]ò?{}àUî8äÖ(¥ Ò¥£fuRéÞYrÏm®”:ÌS/ZSl«Z¾*ÈmgX:8ŽQ HMÇrRÐ Ò÷˜4‘=.ën‹¾YHz}â+ôüxzÔÕõ:´ŸÐÛ1¡û£ŸÞµºÎB*&k©DðPâìI·I뤣% ¥ËD",„~1‰•¢#ZNôªN¡8¿„ãÜJQaìeípØÆï‘:£ ôÑ®Þ ëÏâÔh´gU߇aÄ…ˆJçg®QÙ|ªÏ®Õ ìë š§­ô[ŒÁ>›v|¥\¡°øñT—ýã…çÏ + ùæ§¼¾r™­–²ŒT_…Ňä=°“c6Ї÷ºì%äæœæF*Ì”úÃt4óÃ5g*„:!·«Uº­^ ¸2í¶„—P› 'Fsò”UŸ‹Kž@Sü¥£  uöN®g{3qlÕ?9t×ñ…"ê~#oI’Ø1ÆfÏ>Ù»ÌØ/Ì'Å>t£“¤šv@íJø¦¿PtšQïÑÔàÈàôaC»Öåz…âîyŸ'¬±é…XûÃ_gÑdùø¡]ùÑa‡5𻬯„ÏÈìÁy ¶§Â¶QµNêŸ ð!àðáãÄ›À:eû®`Ö[§i' zÃó¼RŒ|!àf3c–´*!‰õ…Y;všÉåVpV ‚“O¾aèVUÚrÆ!éŽù ý\иR”½èRRÈ?-¥sÄ$Ùh@„ÐäÄ ºг¾NDâ"zml)“!â " ±Éþ€ c½SD¡lÿŠ TFO% C÷¦Ê5Ζ+@Ô¤GA¸pŒl`RºVã€áŽ¬Í‡LÈ_Á¾„å*ÊÎL½Þ5 6S;ÙtŒ3lžhiè£ÑÝ-— ¯pÂKZ{Ó"Á^&‚u)nDŠÆÝ¾ÂýŸ€†¸„¾u*K'…~ð²_¥}/4i˜$À_›©Õ'f꜂©-¤º›WÊ{§èr]A|.dɰûÕ¸T oTº3ô’– VøàÐØëåLlÈ ·äô¡Ù"yt¦¨L~ÚÌ—ÜA¡çã”Nׄ!Å ßÜtpþÔm×ÃÐÌ¥ZÂ-ßÍÄ'Ó„·û~ÞC"¬Ö}ľùçjÜÎx¢mdzØZWÅÜ)Ô6ß­ ïm3áw‹Á‰Ì¤p覈4BŽšˆžÒ9Áî*L5(S†­GÍ>kP—QÆSni£Óé& "ÓÊ”/®ï*à«—½ÎéGKP".=ÃîÑÓkìi’Iæ±i‚®S X1²9ò‹8ÖRNY2ѹñÖŒ•gA/)Â!§È 1!+ûŠ)ˆfwáNBiÁOFL%鬆³tþ4÷'Bíë új]J5\ õ>øðmã;·  ºÁWÇK©l™ówÿ/³Áë`3Giµ@œ’ÌY$Io÷ÈuÇÏ8ìÇØIõMž<ýTÁ¸"÷‰Çt¨þx†5ëúWr¿BA‹^ÃÂtÂU`mà¡C)¥çÊŽv„ùè¬ê+Ç~0ÏÜ ”„q¾˜u~«B~]xCÃÂÍÆÄtœ†¼Šç£¶bÔƒ²xã‘^Ê @,.™º©íX}.ßtNa!ÃêÉ6gQékîR©VK~¡`‚ÆHJöC»yšx RÊ—æÒœE=d¨à»Sg­C=]p E'IDÑÞ¥#µxg«Ã6M‹ð8Iõ)ùЉð× >—ÙS Q)ýëƒëû`‹ÙòÐaP™H++ yu%ukݦUµ=¬gkïžù ꎙý.Õê[—¿ÖrôlÀ/p0YÒ‰$q›Ó×§\úƒº 2Î_A_4–©aÒ­¿ $‰Â¼á½«{þçõÚ>Ϫ¤;CÅ65Uÿ@LŠž Ž3Ì>„9g§ÉšÃV„åkùÂÍÏ;† |›:k¶ïÅœ¡cþ·—ƒ˜g Á¶Hß’€Û@½óJd'ü?7D±øÔ»á#7.›+LÒRûÃî/×XO&i|’³ç»íŸ_‰ÈY½‚8OùþÞÃv ËRk(â H° #~3¼ß#àl ¹Xt­s’ï£~D&¼ƒ•H¤"ÿíÜ<€‰s5—±<äì–ò&{”/•¾ê®äÅéàÛ‘š2ñ”ª‰YMÝL¡Ù¢nC¸?Œ{mªó‰ÈñLÿÍÍVGÓ`z=ß{iÚCÿ7²u¦%0ø/},´ô÷ò•J€ô KO‚.7\AÜI'7ÊÄpz$½o­BïTGèÐúAxãs¨Fgúž£æé¶ôAÕ}‚…4gœÛ_ŠŒ/³3ä`Þµðçïº< Oߌ\é³|R;*À\în)FÑ "Ë£TF)b.rÿöV¼µèÇ$,x ’OÐÏþ …>ÖNÑÑñ.‹Ç-ƒÔ¶þ²8±m‘ô0ºÙë!ØßbÃÁÎÄÜœG%ÆO@û!þ0˜€ŽÓ²¹²ë7ÿ{ØQ0‰ë»sÖ.Y’n¯HLŽT+²fï*),`“s‚âSh’6tBZ<  ·¥mÞo4'sú/»‹ýN<ÑeC¤…_k«”‡åú§Z%¶m÷m†ô2‹Ó4ÏÿÙÊàVG  B6”;±hÆì?¹2À朑lǵžd¡oh)2< ã,:Yi$ºì“óû;wæ›ú7ÜK› l?Iè3/dævB98Q³ÑÃe‰°—8 _ë$ð-ó7÷JØ1 ŽaeR=ò´ø–WÂë pŸÕÁ©"T»y^lÎýMÙ+˜ôV/##Ó”“RxéÉylc'x³h]¤!ÃGð¥ÅîP+çhôÎX¬m7©Ï“¤tÿ!’áëúL?ÜùÎæydùN6[t$x¹4…¤0ÝCê¸1_=aÚoýz%}ØûÃtlzH• ÿ_ÖKÃWØQɬ:Ì„œ-­Öƒb`‡ÆTJà{÷ä'ôÿýB’ý¬± R>ØD\*Ñ.ÏæõüÖ°êg¿s»qÑöðh¡±=ŒoùøÿÐdŽZ9ËH$l5ËÐÂ#á+?1—~d=jÕï("bÖ_ou]­t¾ÙŒg‡³–𫬏cä>гÚtRº³Ä—”Û¹<¤ËiŽbõø]óç®m—±¹»Lñf¦•éÏF7šJá='¸¾GJßÏxbêÜKÛE’AdVýÅ`ÌÉÔ Ç~q¾úâ°¨óùyöTíÐ_Ö¥¸O.?z†9—à³ÇÃ;uo{hd£×éèä呂c›ñbù´§Q]D?uÀÙS#t¯·S |þ-eAšðþ(â¶ väÅA ¹€÷jé]ÑiªÉGS?‡è+ŒSaü™ƒdìN´ƒÈxì²zA±¸,(›*Ÿ7Ûܘ¹pŒia$aö“ƆhW¢¢tkwÊápr5pf_É‹€¼VˆÂ(ÔáÛõRZË'x÷¨QÕ9&ªÖ0¨Àë.ÑRcëF µ½ª~+>‡ùäaä*‰Óc ÷î(H»Gª›9éèÛˆpv©˜D4÷×3ž£[:(P^?µ"•ë.Âiň¿»³l@_R¶Ç]iHéQ IÌ]¤ø~ýq¡´ü òÊ‹þU -‘mæ±Ì¸%‡ìdýSb®ÍÈè±Ì µJÑû4PyßÊŠ¶)äÿá#øÂt»Ç)Èø4™Ñm•Šï7.¸fåÛû,‚ïÁc/ÿÐgüQ–‰‘È/f0üüÜë_zÙ¡™wçáúk‹!ͦ0=6„B ln]4)óeQ£ÝË^R8Ýë³[F`Š£ 4¹ª>WBÝ}xŽ’u-] ¸CÁ_I%O ý_„©ÖŸkÝ®"_HEötucú«Ó¤­JLñxXõ•u’)¾RÀCQ•1¤rQÐcºç+QUöç‰)#H—4 Ei•0Áp¨KÓÞ—‰hVÙµ9!kj6tªòr¶?Hšùáwª„Y†3möšP Àï.G2ENË3vÞMòMÜeàØkž)vlCÝyÏÔÿ>y>¸— i#6¤j2“R”Šd§Ø’„R_ ƒÂß•~ç®ä`_mÓ›\Úw†fDÇóË$©)…z§¼Ë.)[Þš(1ñ ²™ˆ[•V!Vø‡(Ò |GïhtXˆ¾ø›¶,Öc­z/ü.œaÃt†Ch8\ïÄ0ÌØçbLµ@}Ÿ„¨“é,Ž«žH'L_2pÈ*w*4?ú?{"R5º^Ì)“Bëß¿Ì9fÔOŽ~¿ÛƒÑ_<‰ÂßZô¡Õ\lâ}¹•†œC8B‰Œ1Ûr–S?¡%Í ,ƒ–1mc~*"´hƒsz¶Ey³×N.s‡´.õó¬–MíÌè v —/ÈÂêÙq9…dÔ/@œr)ã¨Î0l9) Ñ@ Àª(IJßIµ/ì(Á\ˆù“Ø÷Þ¢¤Ä0Ím\ý`€KÇguXÇ#Š&áHøîú5UC¤C¦+¤Í’]m†&©ÎöPXÌÈ—ð¶4š$67$,9u+{„•]ö1Í©Ã!íËñŽm‘Üš£SdW1åÈ™;@ø&uñÄÌ.!Õkä¾Zþ¸V&VƇnF ¸HÕeFdE¬ôNúß6q?2w£oõQ ÙίT}ÇÆj»ÃÒö°)¢¿£øÂÅ`Oui~sÅ<ŸÌ׉µ­Ü#qÿžÿœx´ë”øf¸!¨ÆÝ«¹©â¹» Ë«ñèpc @® 9o¬L nÝÆ’ö}¯}Ðü{È»UйВQJG›f*å oy•SÁŽ¡„ǤÖÊÐÿþÀâ½ßueÐ ‹ñ9*"ª*ÊkưƒcPg¾È[˜Þä†GB²¦¸é›©VTNDñMyLU1݆Ay¯…°˜Óu0úÄ3©ZŠ¡6+R¥üF—: ÈþîáNHÕÄNF!¯BÃQæ`UçØt# kUŸÅ=Ì)ˆ¢±ó dÂQ æ‡Çî•‚&’¸Ñ °‡6…û<^Y[A†"7HýhÎæÓ•Œ“aÕºüdâ³çðÜ´Ë‚Ä8=ýN®»ÔÀ@}°Ð{øP:ó$Wu˜ù…ÂzÛ˜€Ïû¢Œ³„hXl;Ûÿ…_Ô)|‚vLLË?ÒwIl߇̄üF«â]Û`u¢Ü·L¤oã-ær:3.­<º)»/å¦;â?KöçV„”Ã×Ë8bç ’FSˆë1„÷º§-DoY–ŽI6gß*]¸Ë©Ú—F)>–,ÙìVTAîë‚nÝ.7ixu‚Á(‘ï¬Nðìeß ã!Áf÷8¥¤ÍÉù^Ô;/6ËþÃ|1n9ÌvÅu¦»Qø»Iþƒ¼!)KœjÕòïuW³ªVRˆO‰÷ Wö ŠbBfˆyH<ôoë’…1«Û×ËÞ L.RïŸÃÝ+û[K?élñ'aýýaßD|¦£ .ƒ ®ï„O « žÝ‚¯™ÈnhkåÄ Qu“Ž󞯇@ùÚ½Å*´°#ê•@dÃ-9ÜDCsß·«X+sPÄúèë9fñÙ’¬wâ_E5ËŸkïÜ·Ž˜‰J?<ŽïÍ ØóÜòª,~²*ÐÜyÊèþú¼‚s ’¯tzçÙÀÍR‹±ñ¢#d¸9?E(J!4ÞZ‡úóÆ;¤»±)zྊþFöÌ?$Ú„UâÔé¸äžh,u¢Ñ4¥õ‰Å.Åú :~ÏæÞ©ÄýüíÅÔfq $±6ÛþSu¯ 0æüÓß-G‚æ¼ìˆËP#”3vîÃLü?üÛ,åÙÂOíÑæÁaÎÉø#Êö™÷6]Òf~tŒŸ³ sÜç-ÎUÖ)5hÑ€aVý¯B¡«Mrx ŸEGŸÑ<6›ˆoœÕ=^Ÿå=vÖ#>F…Åê:¥ð—N‹òýë½òúÆ‚h:R½I]mr³p(OµÉg.Ði%J;M+á›VÓ¾£…L07f«§æ&"Qš×÷¡4„àò¸8ðq8û Lú¸·’Œšüã)Irè~©Q~+ŧvžWË#óSÎ ;ešÌ 2¸Ï=²ø²»¾ùQ+îZ}ÊqL·Økço¾cHØ –¥õr¹)ü·“žE­qËy)8ÏîwðÉã—^þ—Ê3þ]4ÄtÔí§lÚ˜¨ÉÂ×—ÎìèÝbè±U¼$F>—õ%‡IÅÝž­ý9 g‚‚ùv^xLùóù¹îF†kÔgË~IG;.!Ÿé¥×•ª†§Zë*3„¿¸û # ’}gÈÍy‘଼Õ(ÐLÕQ»¢_¯Ô>¾®“9†oíÈ¡31Nõ»K=6u îPWøÕ.!¨ÓMüfÔý+Ÿ˜‚¡dCí¦)n²ñJØàÊP—ÿt"7ÕÖº ¸%ß“Ñú¼$Tïc/TøÖÇóHºÉ¿Œ’¿úW·¿4åA…6Ieõt,¾Fþ&ö92;Ϲsà·nyôx̃ˆ†%žµÑlÑçæ8¼õiç €®mÄ®øõdAÂTµžƒ2!᪳瘟 ¨p%¸í3+¤e6a#Ùr®˜ènνU ­…±tý)åøí.k¨³]ÕÄÅU_¡P‘Lèà.áÜ>÷§)D]³xÌúèNɪ ~LMæ% ±ï>álýüK¬¬  ؉ҢýÍÓíÑáJ·¥kÊï ½ò•Ût¶›äê•®£E:›¹‚’ÿoøŽW~ó¢>ºóÜŒø$vMô(JÒ¬M‚EªÿL„?Ü^¡Œ—Þ4’ ,’ÄŒ@…„‡¤ÊÁeVNA ³µÐ_ãoRª7Ň [e37ய”5~×)ÛñŒ1ë6 ÌB OŽì°WTÑÄÊã‰EÂÂÇwv›I¼ù-½¥Ð:pY¢y —¿=‚Í|aÌ•öB uÞ÷ù®ÐÛ G›¥y¹X}(÷K+=\÷*«ˆ+;ÏÈ+z:xÁCï©ñ¯#a²l WÝ—¹AT¸˜æNòƒe¨¸Þ]9s1¸ø¸í2ØA¼6µ!¦”õ¸dªa© ©¨W4”¦qëW76FîÖUåû,è%òÃÁ¯Óô°øÕ}ÿë hv^ñ'îyÂÓrl KpÜg$<Ò¡oÅð†MY¶nOg¤ä/F€7«Î-ÍΨ;’(ÖÁ(À{{ñVOj@9o Uå—”RÓ'çTd"o3w¹Ý$Y‘€±Uµ|·HùÖîo ¤¯ÙëAz‡vXÁŒÚn0)âæ¢è¢µí€ÜÒB“­¿7ÐU,‚&BïNy$_+ zQéâÖvrD›’ÌBÍ¿‘ Iùhv«âVà XUÄú„ŠÖš  Vî…ÞpaÃÃÚ¦ÃGüøt([å™PûLrP•EŽ<‹ŸÎÞÇwœ±@µ'‘oÔ5Œ?Å´Úgô÷ÄOêÕª1»gRüÑÑ’gXíÞâͲĮm½IvÈå¢95E.Y ”åžfÔ×((3€‘ýµú+OÚ×aOY.U ³"]àÕêÂØ¬¶zÅyŽác–ÐX­|kÑ2Ý%gç~X.®ê™Í¨$úžéòÍÝ”·\TQ seœHU>öÂ|¢ÆMÒ¿b+ž+aZÙÒê„ø"w(b“b~>éifQP›Î÷QlȃO_¢(‚à÷ØGÌi:‚ð½4ùøgèÞ‰‡õffiOo¿N‘-aHªìÎê+Æ<ÔN+>رÛ žXÐd¶3°åÊ~²7§ ÉSö1ÃRÉYUcáÜWYž9z¢Ü`éOSÖ5÷´N÷îcçÂ$”v‚tMøH­ÀC1T^¯¿ ½J;É‚µÁÌÐS§ø%4zU¯ (ñ¦§Êm ,+c¾Ù¢ä¸£d J¾!>ixÓâ£YÀ°î+ƽγ´ÏÜÜh ítõ½"d1ÁõÈŸÀaðn¹ùa;srÀªøNbBùzÑú)aöôþ&úô vÍiù(ðÝÞz¨3áb;•&@iÝ‚Šœ*IO‹¬ëÏ7S+ 2›æ˜ª$n(¾P‡”¼Ñ×^+ç8§?!>¶¸…«PÁcT)\µs:kóÇ"U¼¢Ü¨¼VÈý¥z†°{:£ß¸´P®”'QXæÝYbP#‚uKÇ«®99o¬L+ÈWg•\aË*=VÌ.hé¢K¦~uÞgÖ6B´Ú¦½vYú½Á(ÌgA;p8,ß®áu¼ƒ¶}x j‹Ó5î%¡}×I{»§Þ™ò¦}u‹Yü…–G9”¤ÁÐõûyÜ… ¼Ïs^fe¢™ .zîÁlÙø"Lf‡è·cijªChK¿‰í>ÍGòìTŸpÓáØàODÁˆâ78x’Ú/úfÓêEP™B|`›÷n,‚y]N-û„T'j ׬ÌK°…>Í.ލqÛÅÙà ,~µƒ%ŽŠ&”—‘¤€¬Aš;f³ê­H¨Ç9h³c)¿ùŠÚ5fÍdÖt‡€ç—/ ~yCŠ»Mas1"ëþVÑ„S+Ôy\]Ñ:Î\¥1°v‘/ù`»0Aq¥« mÕ 8äÐú”É÷§ŒÛ V^XfzŠÃ£D XìÚÝG§HZ}ÀÝódã‡7³ñæ‚TA©ŽÞ³·qQTU6=”ò‚Å«üÚµ…t’ÀmµN«Mýìøö{¸›JÀFѻƭ(Œnp$×èPËi:|õëùsÜCë^øÄÌj_·Æ–‹Ý–Å0Ø6¦èÛ½ÚtˆJÙTÇÿ_ýÊD ÝÖÛ±¦ &ÌXIòHÕ‹‘ÊþÕÃç×¹Åñ¨æçiV2©™ÏÝié’å¬eöøÛNh´«Jßhº&pð÷ê׸å¾ÎxÑ»jë-§ý'¢‡‹˜_7poZ»Þ¤LüÅ®¹iˆã/¨÷rûSâܧD ÿ'2 î·ec÷¾r£1ª·b¹-`5ÓÓ~ä|¾…ÚtõT4–9ïôºkL@¤òûåØb T‰¹YU‰~ú¨—¾QSJæ„ÔËBš¾mJi–L?“nT'fo'ïmK¯á*ãÔ ×æŒa§·€ØCn O+ šÐ8|£¹¨¤*4–¶¸…FU3ṵ̀Þ3um²wwFNT¥¾‹Ð—ÂIIï9•·©•J7 £𿆔vÅáé/õø&ü]Cf#)§¸VQɦäñOÇÎW‰¦Ts·`Cby.cò¾æÒˆ(Ùj"«¢8Ø÷ O÷Ï9=׊L~ƒhgý ½y‰ÎFDж0†I‰¤^MüCÅ)Í€²µÜ±/žEª '9ztN¯ƒö9 AªÅQÌ׬°p}f'_‹k„ß%bq«Æ@³ˆ1FdžÓ³]:±ã›vT´Mñh’>%ØzÎéÍQ|[c» ‹’ !ûÙMPÊ­uWî?}ÈçÈ—šX¿­"¤'ë)I9IoóiS¬ÕrT9Ÿ¥3>áÓã“.>''Ùs›¬l/'Á´k-ºYâ©‚­Ð×A4´'èG SkƒËõ¦.޾(`÷ç'«'PJ8'U·g°¿Áñ¤ò7ÚÄ:`'} Óð»ÁÐJ€¡ÊɈJRØ‚Î(u ódQÏ´üÓË<~ ),Š£ÿv¡ˆ2åEG¾@Ã9ê™&[žŒ¼rnë¶¾<ò|YJ»bmßúa=üæU°vØy­ÄPwÚæ¿ûÜpÒ3˜_ðcM’c ã?ó&¦=Ë-[¾Mʉ”JrÅ¥«ˆÍ íþ¢Ï­úþ‰‚C­g9˜‰o»åh?DÛß‘ÚÂÅã¼gíã@Çg4Úª°aØ;w kKIƒE“Á-ÛÔ]+g1,ƒØ Ž«áÞ•cóéâóÌó@íƒ3*C—R»«IÏÃÐþ,z@Á‚á|ð@¦ÜDE5*÷Ëé<õLÕìÛ²õŒQSéTa¦á¢€·°“Gä/i6O°Ù'«A‡gO¨ïOK…ÊÌX»ßŠ8´DäSY£q”£3œ+öáåÅ‚ê`ÑC;kƒ]¢'v}7ž'0+»˜./r{Ÿ +‡ID†åµu7…T°1ÊèÏàŽ³(kÄÆU¿(ȸ@9¿‚$©”FRSÔ·­¢XŒû^Æg¦+¯­Yœo¼)œ´ï¦Š U¤/Ìk«aÒwj©„Lš!I1§õ©—Kª’ò‡,Fg§P¡á•˜fÏ+x<Ójhw\vQdðG|;ОÖR5×Váu$- ÷á­ºÎq2€”%zfp¢™ëÇUžÜ@¯SÔ”ñ\ìÕdîDº3–#“TT×%5ÜXƒòÝ$Þ÷ µÒà‡T•§ËýCEÚàh .‰¹Žçqj±Öòf­†_¸bƒüÔpª½h$Ê,%V3©šàÄ:P¦;‚g²Únk@W´Á&ýß÷Å[ú2v—+ÇéVÁ¨Ìßy/Tì75ldÍ%äMx¹št| ‘ûX ôâÁ‰‹ÑŠt?ø9ì!yGeE&b›ú=¯(oD-:aÑ=U6Ä!Œ¹ÂRv—G)_¤F7æ«Ï¿…Wa42%Çs.¹4¡)e ÝVSæßQ®Û°÷Ÿ^˜j]ò½Kƒ>xÑûw$!rºÉB˜&/’ÅìLy©-“}ø`&ø±Z)ô‘)‹)׿àŸ3Þ ýÔØQo<øáÙª$ÀhP”½A޳ñ ˆûÛårË·5¼>¿ ®LÌF²x«X…ó&IQ %mkQ%zËÐãCýº%@¡QèÿùÿæÏ»Q¤ˆ©¯:bÁ-+Ο Xê,6ÓW‚ÏÝP ÇfZºåó§»ÊlÄ“®9ÎŒúè á'Kí*”j÷åsQüa{3°PÂeiN£½bÇHH×b1”J‰q`)9zÃ¥êžkçÂû›ÑvbÕçš³ÅÊG§óxÌøÀ—ø±@ ks–Ä÷uè©£dW­LÉÆ»²(Ìý8cÎífn…¤ Ä׿hÂELˆ¾ùRsí·÷×ëï¬J'ÎܮƧRk³˜–­q×ô˜WêZð Ï:ŒÃ¹Üq êK§-ßš)YÎâ—ü©{†Ž ºÞÈÃbêÒ»|ƒÍÀAÕœ.?å†"5ω˜¤ü„¤–zf‡óœÑgIÿ.v¤e'YSHÇP쿹ÆãxxoŒ~Çõ¹Ct=Û¬|•T“X´èò\Áèmª¬ã-¥PÊ•¹5Hãõ† ±±¢Ób¤©—&\-Üq%y«É2D'u¥³!™cî“ i–›Ò­8’ˆ½– æXßF£V¤!Ðçݱ|øIKÀ¿´pˆ¬\ÂE²' ¿_ž°Ó…·Â}»bt_š·ÍJ*£{tõÙ øNÍ VyõÖu^Ùø²iW%ÄM‚õß‹ö¦‰îNý »fY\q|ÞÕn#¨áìnäˆöå¶1]OÄÏÙ­¾º£¯˜ÑùD£W(;䊆/räô‚jùtwÀD ¦%xžM%zTühÆ1u¶cÅ7’Ë€~æpGÑ^Á_êï Ï?ÅÞä*4¼zrl/ú…†cPþuûoûèÞer¾SÓS»g¯Á~óºQ-ô‹Îd%cÓÁ`[€Òø“|ì)ἩC3ØNªY$K$*ô4UÏñÄèKÿR^>¶õþ7Z­¶Ñ ò‘ t|íä±)T'èòó½ïºœÌÂk,ý«Ã§,h.{ .s¸,2£ýU «dL¥l$‡Íe³„¹Þl2ûÍs¶µXÔQTî² x‘öAGW㦟ÍÑ-í>Š[§²1Gqƒ%Õ8Ökh½h5*ö:£PdÏX¿9¶v–m¢ä¼÷¨jr\Ì„»l¸°°Bøzs”f?høÕý+Œìe½×Æ»‹&=klçô|ùìNU‘ýZÁMigâ±@v€Ï#ø›ñÉ0µ}WrðSˆ«¬GDŬ0µû¥ä4§&Ö}uDÇa Á±¯phÓ.©¨Ö¨w±°ëœQ‘!ÿí&©€È½Ã~ 1fæ) 5Fºq†çÉå@c“ì À›¸t¸bÜü7íL³Œöœ§Nì»'ë¥zk±K ëLA²)&=‹¡¶p_ì$5™¢” Ü¿mìö»Öñ Ó]ŒØÒà$ÎEä§R$‡õCl±KžÖ4ùÐ"O@¥¼K88ϘÀÿƒvz:ÿž@úî—­Ž”³Î ßÔÚH>ù´ÙÇSEŸ5nv¸{œÒø½ ¿¯Œù—qîÔ!1„ÒG‰º‘úÒü-¿ºmzî¡]ü\9yNÞÍ^*±ÎÖpËP­Â>Ûí›iêVûð‰”¼ ©"‰ л‰­ìHs¾^j)©+%üIlݬˆßÜÙ¶ žPûÃÿ§)3«—HSJàf]“^\<0e ¨¡Ø6\•þF9’–Ö.d 3{Ô¯ÄXh™ú”r¼§97®±[™¿h;öKþõéó Åm£piЀ9ô×Ö§_ ÃäÄĘÐeFCÐÀNGH**Å8”³0˜ý‘ÙOMW¦Û¾”IV8_}<Üçã2x1‘;0ôËýºªÌÃϪïU`c>°™*­[‘øý«'ƒ_AÔwj2TŒ…9è02CSÂÀâ éãQ÷îÔè‘t_”žUÍÈ£4%•%=í µ6i¤ÝâX´Ž#sÁü@ z ŸÖžlóNÝИã¨ß®ùÆÑ~¤ÃÎkîÊÏ6°}6ÍœD(S¹>h©û¨à_—•Ò"ÏQC֭Ƕ®Ç.üµ®æÒ, Ûñžˆø¶:Rå^ÝØ"ÐÖ•)Kz©z4ÈÔ\)ÿŠyBúô;0uÄn¥v,]dàÖcÛ0ƒ¿CµÔÑRÆ÷E) ¿ÓfÕ3ìwmx†£ðq éÔv¤²YFð9«%€>u0ôåua¶O e×;{ýaZi&É¢ž–GÀ+„|Çmïå×Z|A²Lø¥*ÈT3Buh>££QÂÖ fLMž…5X¡äžH-‰L܃ :ÃI)˜«Ö&x( ¤¬o®kÁ7´”þNË\œ(•”xJ–Ç“q=™£fÞgëm·nÞ·}è¡ kq™Ó6s78•¦¡üÝÈw«Æ²`‘îÊt*ƒ8R€í2¬úr Ìð[ó m–Á«:MÙÀÏF­EÒ|ìÉÔCYAT]5OM‹ÑUÆjžk”Ÿ‰¨¼q÷æñB*–¢÷^{ÆM5"(ºBñ+¦ÿõ•~dnRlØv-3¬-¶ðž¯^¸ÌŸ¨¡Ç O«Pp1|ýʨú¥ýeKó Œgň´²8fZñúû‡ö*-ÔËŠd¨ ‹õo%œßZèÍÁÀh»XªiÊ o“Ì—¯°jœ›Z¥‘޼Ó«2è(qðPç?%˜~Ž[w`ä…„Å.ÕÓœÈd».R¬=ªxµ¿N špP°m©ŒÊs¿Ù¡.¿Å˜øF²Æ¿ ÓT¤[­i¬ÚOl‘w7$;Â~œ7§:‹V:UûÑ<Ø@½3>­Þ=ÔÌ:6¶}©_Œ82›E-ÎG67¼÷­í˜»™¯ÏûYpr,àÐ#-š }ö>Bµ~JOñ‹[&ŠOZÐü È9w µi¯zÂ0Ðn¼ëHûvâ©B˜LÎ÷8ñsµÔqtdMnwãX&ËJsj1Ð;ÅŠãíitÖ)íyÛl5Y3gT‹ œË§‹ë—ª&‚$i悺…0Æøôó(}‰ HòʲœuVGUd•$wØÉÐÌ—?¹ožGçi½¤}_êoh§Q#Ïû9ç–ÙÖ½­‘sÅ~K"Jr3rþ¾Nq™’[0B>X …®ÊÛ+vä¸è“éÉš²):¶žwhÒ“oEÆê‘ðžsíÚôkç)á0ÈÂ… h!Mô?-7˜Ò(Ã\²B‰9øŠ÷¢pÅcæMç"¶E§ç؇ϸGŒÅ¢Áר‚>ƒ[§®÷ë§<Æ–ŒIò<É*¡ž„¿g:Ä> f™€:ÎÄŒF\bÌÝ€3ç)¹¨*wšóc}„À›áê¹ë‡š`Ý•t2e ä s«üI¾¼°{»ì<ÈÀùÚëÍÄÕ,0q&æ•CêpjÂÔ¹ùT,©ëËc•@Îbç*¡‹[ˆ9(e.G9Är<¯]oÒÇ?>3ϼ=h_>ªv¦Àú³t"ÙøÃÒ˜ö9ºB>Zvô€îuÈY‰ǶÜŠB@d+kó\%ÍDu_ù†:±¥öÝ¥‹xÑ7Ñ’éçºȱy \“¶§Qò7µz[IE’çVnsfNÙɧÆ>ã”pQ,å_ °† ë ©W‹ÌÔ)z^üß<¾3ÄÕÓ$üÁV²£v% ÜfÂ*Ÿé]öÿO·zœ=ËÆI˜Åª»ØékdÓšZ£³MúDgØË kóŠ¡ퟴƒ·~yW>¶T£ÊÉ@Å )bè]³r&b”œn-€÷þRѦÇ󨋬ï¾UïÌö†c+Ðnr–}:h<²›ôœÄTmGkˆr$k²w¦41ÆJÂÂZ¼Íé"±<;4u°xׄ= o[ÑkˆÐ¶FZDZ2ª'‡‡PâÛð> v¹ƒ*æLðVƒ0¼Â( BE5´T9ꬒf´~5rûD=ŸÃ%«ã À NÌ‘Àîì0LŠpƒMƒ=à{òMe#âHƒÄ1+õX ¦m¦&qÒCæ~¨AE¢gŽÇ29$}g›Zi|,¯Ö½´Œ\;L`¾Ô…:D§£Âª,ɜ׻¸ìý"Ðnˆ©È‘ˆÍÓ”­wœBoAeÏ£E éCÓçù:`‹¹Àö´XÎö~ÿMŠê ‹¢ª(j¼]³‘™f©†“¼Õèú,Ã`GëD(*˜8j=nW¿IÊ…ª&û³nƒýäÙ4é¬ ÛÆÐuuEÓ.Ûÿ¢i°¾ñ^h;=ô+ŽlWö-¬$˜Ñ1K0…‰H Vͬ•O$ýô –±¿tçÛ@¾ÍÑ¥ õ®è6ü.ëòÔ$x ¤Lñ–Ö›ËTã}Íèñš¬ R.kZ"–Éò~|ŸöïLU)Uëêž¡%;–}9)û,S2’‚Õ’â‘#𜟽«g0E»1réFË5/ì„Ñÿ…Ò¦àVó£s €x€i¢úSá{=÷ù…s›(YÂüjÖSÖâØjÐtÞ0¡¨Š\Q–Ó {VÕŽ®®.±ÐÙ·Õë 'þ;—©ˆ·¡.ä8² ;ŒvÝç¦%UUÌp±rª÷ƒ¬¬ç"‚Ùí²ÿ“|ÕwóCw}_.V¿ë¬ì Ÿ’[ˆPõz{”Jãºm€ „=ÍwB­ãÜ…jðõó‹xi"Ðô¿k¤T.keµ—^€=ÚEï›#ðh¡0â–Ãrknh)1iŽ…Á™“/´Eôån²bŠd×Ë ¢-ƒã…â-íB$yü€V®r”j€‰íÉõ¶[«K<Š5:CØŽÎÍ9&Hêˆ^Z™“d¬ËKßøÕ{ eM£Y‹ó®Ñ·óô…Ñ.cÙÔÄtëÝ[¹|üT d–ä^Zù* xšÿdj¨‘w—*µ˜h:O‹CWéâ!ˆ!{¾5fÃD_¸^áö¢„ŠUUà™´É“Y Õt1®uÉjM“O†wóÀð›OZ 24ã«úÑÇ0KqzìbÑÈGªÛô w§NqÈÕP˜“±bLöd•ͽ™¹Ñ:m6R < å¿ õ@ñ ÊÉÚ½a(pk¨\d݇R‹@~„‚ÈðñcYŽB “Ùr¾D²Æq¿Õxsxp­”¬Uè¹0s õðû„Ì´²¿öØ=FÔ4Šì£/)ù”;RBߪ\ÿ9pSùA¯g›Y´ƒ¼æ‡ÌøD½>`RÉÿ·*R«NÂK»Nº‰r_ïz2*…±|0ïÃÌ~àé‚â-ò)gÏ_—¯«Ò%ØÞ“;Oq»cŸ 'Tó^à÷¿·k¥«ó\´ü·¦HNêÿŽ?pK<áí\¦œ§#çý1|ªš¡$nä0Ép+#åàöÌ­#BÖýÝ„ ç¶ Ç‰¹’Æ Dï 4»96ç{GÞèw°~Þ ] jñ¨Z¯ÖÀ&•“ÍÄ&çb¢mZŒ!ÑžÉuáK… 2®ZÌœæÌîMÃüÛ©`_Ö&\¨~Ù¥¹JfÞ.רæ¯Ù˜D*C "¢É1ÚúðÜ\¹Ü+ÒË8"¼Nƒm¼H!¨ËUÓ‹áD Lkà¶8•7Ó¶×~[XFjýî5ýº[·PsÛ'ônú/·†-fýšãGÎþéÝð~ Nô×} ¬úòTœß UŠæŒŠIa¶¾û<ƒ Ÿ›ËòÇ 7ã°#h>ná+ì |U‹L´Xju4šF“ˤg™¤Äô¡¢î¨û»©B‹(ÞU¾¢@ 5¯Ð76˜öL\â45x:NWr:?eò÷„1Å™JqýÀ«ñ0Òä1.?È$ƒïNÎÂ!&"Úô•ϲø< ²z¢S ¥}´TKxýÆuÊÚ×ú0q+RB4¨:4š„ºð½¦SÖ1°üåÍ9XÒøàÆì>7!Á›כƫõ#ø'Ö×UK›¾+®y•2±Y$êóS˜Ëiߌ³:Ë–ÔZ2A‘îÃn3#æ ¤¡`(gÏVŸ6àÜ`ØÜxýÄDŸ/ h>Y_$‚ M/ I謧ÏìBððÆT&Dý‡SÚƒ×{·ZÆ›¾–¸}ÉMܬÈú³}Çïèò…Ï ÔýÔ¶.s)²­X_Ï^`Ø0iexYã"Eቯÿš~y7 øW ÂyM±Ò—3ÆíYYêGnµPƒ<,Œ9å ÛÉps= Y2¨w\œž¯ž`Í'”({f¦‹²„¶ô×$Sý-i^s“<ËëîUÖ$®Z¤×Àè­°1jry¬ Žî¥ï£*Ñü‡Ò©†°b2˽m"ójh˜ÕqÅjÐk¢߇ÅSˆéîAõÀ8¿ÌÜYùÇ ZÀ…ñ®Oã9±¦at>CŸ#½ÛÅ_ÃsšE&íÆÏñ÷½A–sÛv®Ø®Ôé⃮R`.ÚZàíçÊÕË™~òÿÃîØ;Lk`,NÝ~Ðÿ¼µÇn\J¯ÏÈ:³Q™ž]›'3vÌðÂç%Rÿÿžï£ç8? ]àÎh˜í\©fÀCÌ­ ©À8(Åb/ ¸¬ÐËc¢æU†`²z§ó>;UÌU.ž)!ÎñXòÀïJÁcÄT{™Ålp! ¢”2ËuY!ˆfë´ÀÉõvuù×5Å@VîWBªÄšjîÕ eå0ü_§O(yŒx9Wµ¸'²iµsÚç«v¬ÈŸ2x7ÇP>ÁV¡HwL°+ÑëÇ hÎ"1ÈÔ‰JòkÃŽä=ôT»…eͬ%Vw&ð-R‡Ò%>*5NôèÆ±F&²”£elУD{·…lDŠi@‰™©@(aúLõ¬¬ªèg;F…¯?r$!›ßõÔ‘’—Äò ˜ðæ“ûdz`?2k­ûM©àeKŸê!SœIyüžHÏêˆß@á0×åÚ{×Ñu|[ ²S¸âTÑ2Z•Á„÷v½ýÄ´¾.SMˆÎ¥BV®Õ’ÉGàò$=ÿߎœNɹ´£—‘qËÜü°ì¬Ñ]ÛÝ^IÁÏü¾Ì=Úž2†>›óôÍ™¦Â­çË«ž¹GDy07Ç>*~ žþ›=¼ÝzQmì­9ŸDjù¼mó_¹˜ ÕK‡æOW^8¿3+Öþ7è9Oá–²4…Ëæß@Ñ(ÂqÊhµ# ¶´]ÔèNinM#NC@ãe ¢*wô="pä;mЫ–L”Cÿ8ûÜô×â9È‚¡OÆ/tñ‘u¸X.Éx=ãÆë²‹C¿l¾„š’ç¹ðT.²ôH¥_ ªÜØ']+¢aß²Þê·­êÞ8[} é3·` T˜jíf¾zxWù¡aü󽼯< \³kªÔòXª¨I›¨ÍshàN°R2}÷I$sv)M¡Dö4áÕv“?õ2&I¼U¶ZGó»í‹:߈ î.6Ε¼Wõ†)]HwW´œõ”bD_\ÚÆK3Ê.k€\¬hèG.ì@WlÒ¢[9 à°²¢ç_£tç‹bHÇú7²lq8[à3‚ì_µRçÏ7C­ä«HÅ|Û.9b1HHòãÑ-”Ε«ënÙÚâ’®Pm-P¢?Z%sk’’ŒÅv΄Ì6 úòYöSè|]¥¹`¢h|3Ì ó—xX ˆ©°àœ IȈë¾1û¶â¯‘Øùõiþ)Ú‚ìy–«²þÌ Ëôþu»‘–f’¡[ èBD$tÙ¾fÙ&ç²ç%¿ÞznLtV¾EqYY…ä>t“ ® Óùíeççg>*T>®“4Е®f²ÙʨÐ*½#¹¨7¡ ‡{7á'äÛ²åmaªÁô´5`¥Û«;¨.téfˆMuB^liç0··X_¼kavr8ëtîIZ¡ç8Ý~à n7Br× m´G(¯,yÏæÎ~£ …ŒçÛ!ÕÕÇ,ã.i¿ „‡?^ 寕Ò6z,5;»”¢%K…ç©.æáDùã·é ©^G¹~J.’‡ì–ò”Ú‡2æ…÷ wlìå_Z÷³³ÆƒÁùm uïb;Õ Ä¿ž¾ó—ŠèÎÊbyG^„f ž²ídÂZÏùiÙ´üørˆ€÷ÃmXûÀÎ%›@ŒjvIà€à+Óê»B“þKЭÌ‹±l, n”Aœp쟲/Øg~}FÚúŠÀÂ.êÁžlüo×·^úå JƒF¡2›ž’t™sl?ŒFcå+õÖÍã#˜ÅéWE´p=Š›‚DŸ•rêðN5œÚ“«Mw ¹¬”ÌçÆû³•µ“ÊX‰šCo]t{ý±5Mα,UÒ8 Ð?²®`=Ó뮕¦vßg?È+_M¯‹ tšuPFíí–Jãé{žA8ì”;&º.ÝnZå¹~®·e°M4-v „H“ýw]hp‘ÃMΖŸ¸²Y«í|'¤-¶"Ý#L¿FâB†›áö%—X™ÍQ}™j+‹¥µÍwz˜è}‰Ù´WS¬ü°¡2®‹¨pÙý £P– [u˜2ºý±©/€ðtO’$Õ p #`­e|£×·ì=îpì*žv'ùÏ?ó›à˜¿É¹$ÿ?‡SO×Îý]{`¦xø0_±­z²æjŠÝq7£À†ûRu6™‰ýsññ¿ºËº¤Â)0ª0ÚŠ_ê9s¬"AM î¬åwFJ +%² •)lî¿ËDò¡åÖ%D#ÏA­£˜lçO¤ë?„@"°Qɧ'0ši³˜7BšúY:É•½¯†oÏå™ó3JÈ9«Tž¼F¢àò·ßÿ@ä¬ù¬—¢Û˜¤ûòç„wØï«lG0 i:öç­2P/6(pgœµ5Ȳ½“èiöwetû÷TÿÓpú‘/ú(•)Ýñž`gã’< Ñ*–˜Ñ¢ÙÂá—”%ömµž²¤‰"-÷§]ø×ßâ*Ñ.3†û‡zü€WgŽÁ¬HpÛù8zÖŠ¯ ¹À-£±;‹…  ÑÈŠÏó¤%ÜÇ)a ´ Ë0š3Ñç/‘Q ¢î_l}ëy'ˆ‚4÷Ñç}9µž­±±Eøÿ#lî_r+Òæ>ø\a¨Ôè‚—]‰­§?FÞUJ^æ³}<=é@ÇÔÂB ’Þ~{È­ß%¾¢›¯oRc@MØ]v\è·kývYÙ©®ý¥Êg–[¾7„_A `I¦ÙIhÉýÒÐì|à¿‹Éñù¥üÈç€É:d!‘àv¶ãoä/šÄ"Îk$Ǽâ·bxþÇKbvµ•$ÆOMNâ–‰\ÅüÃ%æÂ¨ï Ãˆûå±»ÎêZC(À‰ 0ª®…ÅÖC5^ÛRPµŒ’€70ƪclàJ` i)¨ƒ ›Rõ/ÔM31L ,sß×£*Ú Ó§í±|ÿ×ï©D´—‚W¡¶‚|S3õÊE·ë5ò=×2äÝ^`òe¦¶p%u8"SоŠož‘™EjµžüúqiÎ!j7ë+•«³A[cÀ²ž'ìÞË ×?œ¾Ã}¥Œñ%3<×b¦Ïqb­áe«kTâ|ç+œ˜:AÍ`ð ŒÝáÊÚ]B …<×T@Ô¥ÄNI¯FèÎE‰Î.UTû®ÚìkfÃjý4˜¥Âƒj¼¦Ëé™Rè_×i')0ÆzÖl¥îäfî“mH¹© € º¿«ªuèáN`Å´ áJ¢‘=´FíÏ}AJ@¶--8Ž~ó»x?ň+7rÈUg<ö“nLƒ `æ•i`Ó–óäˆ(VeÑ ó“¼“°¥\ŸÚq.|öVƇSí‡[›âÑ’bùäkä9Ðb'‰Æ}´Úå2¯ù®­ßñG†Lî‰ÝÅëÕ×,[ÍL3ªÙæ ÊÕpŒÄ7U"Ò¬;–„­¶C:êq'¿­”q´.öÞ +MI<¢½›¬|aeܟ☱‰ÍÆÅÌv̆`¾Y <Ž[ÛÂL—f+×Hqù.”òAÐ"ÚÚÈ~/ôƒÖì°+XÏÛŸÝ;;PÎ[+×Q6;Îÿá¢Þ!–%–Ñ|Hâ‚~鑈–C÷˜FOR4“jÑvˆAÚþÀ¾e½€…¯áâ¼WKÞ½Gr žTÕ“nb†cKsãåKλC ÿø¦qÐ*Ú9Æôºª½+ù [Gì–ÁšrÂn7¶“@@Üñ €¹Š“ãC»2+ýÕ …­|{©Æ¡Í©Þ1Xc¨T…2Ï™µ¤ æwï©N§˜@uq:€|=^ñ†ñx.¿rÙç°D;n©z}ßÕÊ`¤lýÌü%ÕzKõqíE™õähû3ƒàÖ>Ò_‚VÌQ£²ó×âó ,‚…‘U諨Üð·SœtÓÞôkÁWOÀgÝNø.¨®M/1†Q; ™ª›"yÚàùê:ì$§5v é „ÔÈ9i4÷¨(:ÃJ†ë‰Q¹¯ç Hˆ/l§Å`u0žd@Õr¹tŸê<³£³… sÉ¿Ï|ÖÈGÁâí»@(P&M} ii§¾ó_¯@„}?{Ç2ñAË6Ç]ƒ³Ý\è“Û±ýK¨2“¢})£·ÇâÙÌÃj9V|÷ >±^ã¢éfæÅß*Þƒ33pn°Y[žåQ([P–±ñe€dbØÐÐP ›`V9öW(/Y™ÓÑ»'¬@Îr Zç5jw@Ÿ²&ŠÓÿÇl`Ô41jĨw•T&ÀζàèzºÝÿϳ@`©•ÈÔÜ~ÇE"~ˆíN,8k¹QúáÌíÁ-‘¥‹ÊÄ„âKŸ:Õ‹4üÛ2ª >æþÄA9žŠj’øã*>bRùòÚ˜{cYÕsúðÀÀ«ÿ¤¾°pÈBÛt,½Ä¤Šè[ØÝ”6öÊð8¤½ÅTj÷Y²][3Ñ<šydvH’1¥Ôàó?º–ÃmóÆ)å3ŸÉ£ëß"/.š‘´ä}ÄGÁ[äùº!™X3 ÅHÍrð­p `|[Q°ÔœªZhyc‹Ï6ÉõwÙtѰšÉ»þ6æçÁ\ë»ÅÞ`ô_‡Íbyºw ‡ùjz^½,ª"g¦ø7×é˜ÇX0Ûù¿@¨ÏÔ¿^³HôÛæ‚³uÐâÍ_sÁ^¨ÌDKß¶>SrˆÉ*ƒÝ\ô— ">óYz¶²†¢ð4pe Ïk^Ð'™v"lÄF¬þŠbóe©IÍþß["1‰‚šv(]©•°î@ì›'”2žn¬ï)¶¤†îø-ÌíRÇ}VÉQˆRQ{Ó1ä^‹ í)TkŸ”Ôнç¦%,C?Ëß95WO®»þ²®ÙCîmæ;Å´ÕÌpLÖÿ¦x;ŸÂЕæW|剒U4‰uúæäeÝ€ñ·Øþ.ð#é2áÜí¾0oÕñÉvµ÷Æ øt/›6W¢§¦?õ“]pÚ´ ?ר²…GK¬½W%°®Ö½ØP"A•“q¬N¤L} 3Ä¥ÅIÍ›>hÙ°9\œÃÛÈ&á›ö@Ô— þ£’˜çíŽÈ¶>SbsB¢=oûU…÷øT)âÃCÇž‚Ò?­;(žEœu½(«³Åù>p—ýœd€çø0E=fó²Yh0Ä[‰žÇîw1‰‚)³¼úO´þM¬œ©VC¢ƒ~¥Â´‹^´[4xC:¸§…Ž}ZþQ+vL ]"ÎîRI!yÂâ†PËœ¼àO¨›Òú9û7Û—)$l™„Vr ä)±á.¨ÈDŠñŽ3”B²Œž‡2ô·¾s¢ízîÓuFöê°ö6F¥§©Ü q°OTLL5†Ð¹hÄ×Ðü—=ÑWwFüSåõâ­IÂí UÅ9+ø-Ì šA&ؽϷ Öá0ñÇ>À(÷Ž1”çëÈü!®²&”Rï€OS„ü¡yâ£}/tÔÓüc¤¼LAs,tàËëÊ•¯?t ô™99ÅY~ó`>a¨ÇÎRÕÓ·õ<ª7?…#ÐHû½ÿFKÎù¿´Iù§ sòÁ9¦m0ÔÒ£¯©Û“phö¿7nv\» ¡»Àp¼%Ä^*ÚQ†áUì»x£ô›Ÿï!reó‚÷§ÅM#•ŠÇüWþ„Ön¾%'hsq_mI ªüÿ¥Ž¿ “þ8•_ŸEÙ“Ù`{ˆ¼a™n(GÕðø¸*>|)Ä‚Æv]ð†¾Ès X¾u\‡D©˜|YÿVvYWÏíµ!yË_Ž¿½F¸aw^C™§®WÏ(îA™ 5žCÚny[ëʽ“ »ÉœËÙ|t×E«œižL(7á„Vâ³u¥ŸË)ŧ—ýMòB”¾‘iÜ&û<:ªZ†ÐC¯VzæçÊ+ôjö‹qÛå})Ðõö•€ÛÖô@P¦ö‰Ÿ¿t!ËñÔ(a`qÔ!Q;DéÅøÔã]<ûKMð‰ÆžëYpÊåЩSßµ„!’(ÖšÀ´c^‰Ð ý5ÿ¥ÁJž+?‹»Wvú§vÇÒ‰ÍpÕ™›æ#c’¾qs„‹t“HzlXq!»í€6qF7€2¡4ZßLòß¾{ðu&6«>·ÏÙý,·O9Eì{´ŒS36ô¹êåv}P¼Ü^;®èàQ§ ­É;([âíà£&(ŠÏðf6[(NuˆÅòG>‚*çøƒ#]ä¦þrIäñfÚñO;D´dü}PI2Û±JÛžƒ^§ ßzcÓÒ¶cÄL·Û%ç oø…xRú|‚Bj޵ô'_ëQËø½A ­U¡e-¢×ïuÈrާƒîÄaup³ËŒñ”eÐ:1s°Ÿ³¿ìMBþKg]îæ÷$ÆX\®¿5UÖ›K+E×0cæÁk ’>s¶Ð6Ö³UØq{NF-ÀíU°ˆý‰ÆØw¥\Ö®,r;¹Û ü‡\ú{‹ohx÷bjÛÙµ{7©b»„‚©g}®ƒ%y`azŠ‹2ªð“ëpd{y.o`pP¼< è›rw®„º·L*F8˜ËlÜž„‚ÄlÀ}ãO7ùüc9®gd`¨-ík¯Å¾ pYå/ù¤Cv ¼hæ·¥î]I%Êßc„¤À~½ùHd ”÷mŸÀ“ºñíæžÔfÇÇR}CT2ùt©oá*6ÁÁð ¢áÎ ¨!#Œˆx£:¨Yí4Îþ  ú®ö<‹'!]ÞO½$;AÊSHbQ—œÍªÀ̤nBm ›¯,i*O©÷mRô§OØ[¥¥ø^、Lÿ2ù+Ý8…áýíáúíÂò§t‘ÔN-µQ²o]a! Îi¹øã\]¦ñ8Q÷£éÄý§@rLÍL´±%3H›\Dð{>†’ƒãi8öš+JWÈnDÇxS(’Æø?Åq Þ-TÉ„zÓ¯s€²˜D¸÷ôÊÓ¹ñäµb1 {g7€& 訄d7*óN9ãïÀå•çðUÀ¿]‡“’ð"¦<¨ôn«Óìh(* G|2V'쯾“Š?+öl?È´wGjßÒ_õw_ìÿ+Ž4˜ZÖ JúÛþæ%Û,"4‰žš Þ›ª{2Nû›þ¤@Ê_ЇWC»–à§ÐÞ ’F³Ðî‚‚±Ò¨Oß´'ò£¼âÓ‚öG3±áQP{üÉÈ·œí¬ÒšcâJOó :*s»}ú)UŒšóç¸x)<>GQÙ¸ºA.ôvŠOô…ŠþGæ ¨ôÿ“WêoÄÝx;’©‰ ¢XÕîÜvÏÁ €^Çp4Âó§bƒ-Æl;tFæý“[wŽ,)»ÙþÚå„c[Šº£5)q±hŽ­¿y”Ûs-Íî9M?@ÅÍŒ¨CÔØ)ãcÁ-/É—qNiìæé»­¤5)@Bé»A†*äG_¼·¹ñwŠ|#=ò¸m,éõyy[ÜU`Þ_âZ¸¨•}¤$¾'1;ò•>ó£+•e U]¹Å,ü5"Ï.ÄXöM'EÔÊâ^î–“1Ë·ÛÑò9ýqËj™ ·aQ`–a‚t„´âÉßKehög6œ°’”s¹ŸÑñ2-^RT.°ì² ,Hê3˜×ÆÌùÍR8%?pÛVöEE3êW~ACŠÁÜÊxU0ø…UžÁ}>j¾Aj:”õ£(ÒÇnl¯¡+HÈžRíjˆô¿Ê6q\ø §Òu}oRæÔö"È’vÎvE Ý8¥uðzúì0^ƒÎ™õ#ý-{'nuOÅ=·?5–Ð[=dÈ~ûpØd‘ôJŸ´Çqó\ Köâ¼U.ÛØo|!J+^“ô» [ѹc:=SÿB³å‰˜`ÿ¬}ëÜщ·ïTsàNéÂ’}8!ÚY:Ôý‰«VÏ÷UK~“ŸWÞž×ú”jÁÐ ‹$Q© æg#àWé|‹§dxÿájTÒ{lN¼"KË´È'Ò®Þ¡LÝ_97ìÕ¹ IÜWaBséìhöiò?ê½- çbðÇ6Z†¨¿fïF§ÕO Z ž@ho@ÁÚ¸ÅbÖ,ªàBÑgrx<0äâPmiÙHš'·ufYóÜžýž§üY´Ìæ0r¼ð-ºµÊëHbÀ’^££ {óE×%×w ª–ðM+;å¤ Ç#“·“xÏ?>PäÕÞoU¸ˆ¡£¯Ím ÊŸ0¹¹ë(VMòÞˆ»hV›ÀMÐñÕG6îš„_uæHšÙÜÀr÷ážMMʇÂ+)T¶ñŒ×Ö'›Rc°í{–Ÿ°LÔ¸Ô÷0ãŠË´óRù9²(Š (Þ@:ß}cÊFÚa-ÃÒa% ö‹‡¶ë×vdPºv·È'9ÿØLÏ¡»·‡“އ°ôÄÈlU]™›¯}%t«"Mç»6_MGé¹ì“6ÙŒYooáHž¸Tµãî^X9H»L^•<ÖE®¬ÕJ:E ±£¥Ó˜MíBÿ«äDK.a÷°Öô ¤Ò1mšïnÌ‘°‹‹ÿÿ°¯ß`,­¦Oé–‚³GeIráíWO¶ q gÔÏÖ§¶¾ÝA†Y¢ù}¾_ÖÊèn)£¼ ÿ# ­ÛS˜´WÕO…0'+l(ˆ%#?].ïVˆj-Y3 –COæsœÍp¸¡`ôÐÿ¤Aöpâ üj§ MâÍ…p¼)»å w™ç=—õQd\æ jÔÇQ^¯€F…*¶2ÁݹŠYa=+`Adt«Ä¥Ì2Ãdñ Ùt\ña²Ý§@<Ý èà w¨þmçÑ&›.´‹õèu¿]»š|åÅúØØÛ¦5Diø[À¦ñÊi{L¦ òŸ>ú(gc˜Å¤?`ÈŠÚoáMó2,=¨¦ƒéÖ‡r÷Ó;ð¼RÂ]¹Wk õøµ£-ÙXD —ë‘80C¾¾{Gâ.U¿\hŽ}à¶Y)ö.Õ‚}ÊÑrjž«¾™ƒ ZõÇ÷Ǹ±³¬t“üÖŽ•©ŸwE;9pûÝ‚!@)€ÈI˜kÁ½wpÔŽ‰SŒ ´1“& ìB‘aKæ"»SÒH6óæÜE²÷üÁ™ü–3pœ%íí’p)dÄš†A‰ƒ’?3æ>M;Ík,æâX¨¦áƒÄè\HW(]”“Mݧ› µSAæ`3s" ‚ï.á} ²¹Uö%Óp#ÜZ‹ƒþKʉ¬ãŒÝ:ÝÅŠ7X„8l‡ÅG«*ˆ£O*ž²¢Œ ÷VÛ]Û h¿3{ÖR×Â,δc§ùnÀÒ#±ûŒêm™>ßQå¿Qd~Ky›‡¹…Ãïþ .¿µæ”Dó¾±ÂÉš¿óÛdÇ Íy3ÿ—« ~ÅQ»ŒàÆKj­ØQšžæTG¤LìæjÁ–¹ãÕå9Bž»;žZ«_!±%Q;Hb²ØÀ옙T¨S'¥“hÄŒ§8ô€‘rê®^Å9mÑéÉ¥vvmâ •¨¨üÞ›™¿óJW°Cqw“Ý›oe;k9°h÷7…ìç~©gL†h®¤d)Ÿ('§drùwóYQ¦ªç¹ä&qý Â:9zAÊSÏΨÜüM°%`ײçT¿ž÷[X2JŒ“‡­¤–vùw¾F/¼ðïðÆ` õÎî|¦þT8R3´vŽúË•A[^Ë̶5LÒ£Žåû>•ÕZb÷yŸpÓ?ì ¥£öÙýrÿÕZpìK`ÿŽD+'Œ½ @— –ˆm:Kò¤xÑ5 }6˜F_à¬(9’_ÍÛfX&×%³ž·—Ô~ÎÀ‚Œ>ˆÚ¥ã\Kð ­º¤µLÏñt£”À_Ó¿ÐzéSt›fºe šÿÓ?Væû˜Š‹tŽŽTádq,¥?ƒGTW‚6=$‹a„…G0ò]¸:nFû‰rDÿøÒ‚"b&Égv2ÆN7ßu4ËtËž2Ñ"PUÀT’Áó´EàŒÁfÔQ—sÉ1‹ô€fPàíPЗBñ±íî_ŽTjm¾œQ´X'2«>½r€ŽdðߣT#ÜÖÁ9­Ž¤!ÒgHêBªŠ»ÖM¡†¤\*+y¤ý m6²õ»ö ²—h© œ§£è}G±ÚHcŒnvÛé¡+A‘sf5·* íˆ1 ã.®6‰)¬9ÛH%¡ äº7PݺÆxS㸭Xþ¢Ò^ë pˆI ߎ`¬ÖŽüˆµ#åÂ)ª±êù$ü>¬‰¢öõ)úÂW~ËWÕ¼°YÔÝÕ³åVY½ãÕûóý»ÖüTL`Äõ´³þ•:¤­€c¿må‚jUDl£I›³rPû„#GW¯bî|®ñÕHë½ $þú»&HhÛuÆÜq.Íxýœ¡Ÿƒ‡Þ¢w“n˜í­kvSš>´µ‡> íLJ>FãŽ=¦ÀB:³æN^Τ¦Ï‘äŸ@ðŸ·\çeÁ’>¬,o¢ØEx¸Ä°¿!uwÏt,%ÆÙ›Þ²˜ð{á° °lnrZ^_ÝÕêxÎÒŽ\ïUÛbø4 ¼,/u¬¹ ”ÐKwþ£¢}{Kب>ÙÛЂ? ÿ߆Ú?®§¹ÊlöZ§: ÐB˜ùºîqžÃu)’‹Ý§{Ì®%ÖÜ ©‹µjÑ|ST¦$]FP5Þ$È€zñÒ¡¸‹µ#ÓûòÐ÷áÐwç bMYqk_Ù"Ò“0³Cåò˜?§ ·DtFwZøàŸ2Ê< ¬Ç¦Àjû<¢5ãöí[œ¤HB«¹àÃX²c£T¥ª•'Ì›_Õ˜ã¾tKΠÓCÛ79;[€0N°Kó-‹ÃŒWû†}lP›ŽdšjÕÙ]Gš?lò(>ç•Q‡£ÉÙ ©vÛÎTº9àzŠïÑIkS'uI ÁM\‹šÊèüðîw̽…ϸÉJßB{WÓ’àŠÕÿ-óo‹~´d™«Ž­¦tÉ .N¿¤.æ®sýëã‹ú:AVÜ|ÚS´AWÃí^æŠ6¦ê˜ 4Åê ¥HñêѶÈ(ç )õÁZ’î€E,|•èH.5 ‘/?“oÙŸ”Š»O™«xCfêÊqêþ”ëÀtU?D ôž¯=˜¾ñX#…®R<öÐR(1ÉOQÎÜv6Ëž>|)3”ê¶bWÔr{¶¢ð‹1ëý…÷ûìêŽÂ8¬MºÈ²ÂfødQ»;ޝ¼¯–»?NνËÆâNyl¹Î®Õ”ðþ`m¬ û¤¹¯þÑšeµ;ÍZU­½*Ipu8Ÿ-Ò#óüëÕn(Î;,¤àúÒ’Û6ä<6å·£+úsËüüoþ¢l|£¥=; há'3ùå‡U¯EUW˜P¾Ç¡ôš•0H{Ä sV[ù!ÆÁë‘zMÙq™ßÇÂ?9@98½®§‰|·‰–k/XË¢‰PÛºÞ{zäáØ¹ñG…¹fäøø¶]©›3þÿ¨:ÍÉ…CÙ ÙÙ*Û-ú>qä¸jÅà[ÚýéQP†2M;ŒO!Ϙý#Œ7d ÂÕîÁÞ9Õ)ž,=ᆭÔ'LDÂGr§3áÔ5!Žý7á uo•÷Ã×ãYãâ›8‚©ÓýŽÊA!+榌4âíF"~nl0ÍÌ»Àr!û4Yï÷ÔJó(µZäN/®×æîw#à¡{m;õœ¦bþe>‰?wsÜX̉䚿®søJ™' ˜d' c4h¢¨’¹}ô©ñ¥Æ¢+Vùßk›q#ªœ„šfZPCI˜Chê­JÜle‚0v«Fmf„–È¥~TTRãvê\žÑÑSÙŒs®ÚÜ”üÀ‹$%<äÂ}£|0ìÿ£B¡ÛjaÀFž›oá˜dS:ª7|‡F†*ø¾Çï¸ÆUwkW…À‚ùÃ|1ß»)¥å ¢È>1)²¡g§Èø,[0 xHJñݯ¢5ûF"ŒæðÇ~4ex0Δ¸fàu5 ºòs‰ø5ï¼`¼¹ïýÒ}’Ð]ÌÄ&srrr¥s¸b?@ÊVY³ñnÚ^¬ŽÇæ”MÃ`·´ÀK™|YâÄìšì8?“L·(ì§•*DžÐd8(ýîv£¼« ©­v^C ¶Ós‘EoüÁ_:ÞjƒÆñŸ,оU§¥z~},ý"ÞMÑ^‡혎‘Ã=% véG€ÍÐìQî$Š)¬§K"RWI òhýèW2çcWfj[5¨­M´K5‰¥$ PIÉZê“ô4aÄíÈ,C‘sS÷Tþoj¼ìºyŒÏÞT¢„=ßô‰Òò›$„Ø?f^©3Z^žºu]Ä“”ªã'87Ü‘ŸM³zj ü}Ê®a¦wQs;“@«¬@a|­@ÍSv‡Ì˜±ñ½qÇùûå]ã¶®E˜ZçÁD8¥O+åórþmˆn;¡Ú×O;Ÿ;99’˜”ãð ¿sî²Ö?ëÄ8bv©£ÒÈ\O7P/9Ú‘?«!–4è´&«G)¸I;UBלY`ÅÄ®üû»ìc¾rR¶ÀY]œ|±¬X®ntö’=V+lÓ-ˆX !‹X›á8? 0[®¾hpDáRÐ@ÿ¡¯z2 ÿ¯@’KÍa¦$W:]cˆ¿ÄÏ3b£}$k”¬†ÚŽå©®x«Õ:RœF`œ’îoötø’¸Ý}†¤ˆ2›ç{ØGHì( ›òp êËk+¡Å]<*†zCÔùJCÍ[é¼$ 1<ޱï)@ Ú%Âþ1¯+éô-‚ú©ÿßµªÆÊð½F´¸ ½B„»)c=æþ6Ú@?ÕÓ5¼ä.ZIƒúsþ]]˜4h&1@ÄÚÎcuL¿úp |ç¶½‰Ðò *¾-O ÏFk×lcL+Á»@;“¹Áƽ¤w™Sº•ÿ ú*¡’IÈšVö$& Åô]{Ϭ]êÕˆÊõ¥ÁÓè9†¶×°“tšÉíñÚF¦À  °uK¨>çÍF§ikÇHÔ#O¸YP¦f”ªÿB-NƒHz<Õ²Fí›Üï!€ è:ö(Ôiâ <¥8‹À¥!û(ªÙ½¨x5B±ÏÛ  Ô~9®BOÎÆ§›ÊŽäôbµ PWN_G†<”†?àL9 ë‚ꢃ ‹{íß48ãíö"O >3yñµ6DîüÀ_"ª5äzØTûUèPÙ£Ç*oà¸ê³Ø›™+šÎD¼²qû Q±Ð¿ô¸P³q •Wx€Òíá@x^˜‹ÐÞåx²Ì)ø˜ÊÅGëèDyÝ¿*ò¦£ßÜRò¨o ¯ØbP»ec¦è];N³} ,®¤ú*T[9\ÕSý4›‡õµâÃÖÈ #¥ …–[B€RPÇÞàðÉžXà{¿óeNê>^˜ÿ^ÁÌhyš;Ìà*EÐÐ_o„Ø·q¦ð)à°¢aú(~GBŠbb¾.‘1‹SJ@ Ð[ÖR-V|Kû[êM`ÿRÁ²Üï¦ö»®æûÞ'Úð® 8âx{Õà¡£‡[í2ÎÉcå‰x÷Û^X wäÂíXníé©¢ø¯ýùwÎó·¦AWšµÉÔü< öˆãK¶¤)lL G¤Ä¢Ioº›Ê?KžtÁjúÄÒ*C{YujKAÿE댌Gv[žZȃc,L‹HÁõЊ bU+±Êš:)µȱõ„älÇv ´%øÂãÑMdƒË[LÇ^SÉ“§ÄЊ©ÏçŸ3˜‹‹8™¼¯Öý´õ æ|¸žØ{¥%Ïp{o;6­|ïFÑ?©ú"Õö"͆s«´B”~€B•›s”ŸB~y€’ xÐÚxîNçH&~®A2„Ù¿Äœ4V¤MF;Riì.OÝGÖtf¾ñ©è/Ò·bLv»—êˆoœ/ÂI¦˜ÿ.Â@õú …MtçØf)xeñ1-:=q@©Ÿ| Å¾‰yÎ÷Z7çÁ°cÔêè‹ÂôR9lëáÃßÞ3Zíï¥v_sî/•]°•^¾‹ëyÿ öó‚Þ‘t=˸qPaóHñ)ZQQRð%ïjfiATlõ¤CXÓ·Á6rªà9 žªðr\Ǩ/úÜX¼[Lúc xuR§€;öZ þ½¢ª®†c¤ÌSÆ%mùëóèéŽ*0H|n­œcó‡d0VóDD[+×{#ú¹ïï­ÂGÛš¤í§Ì ÆV1¡«ÿ¯ñ÷žl9|1ËËÉëÌEGnCúVíúÆ«†É)³ÒsX¡‘ī۽k k–Þ ËºÉ=iE·ûý ¯s㼩$y¸Q ÿœ×y[©)hSÑ“ UAG#‰zZV}®¸P%¥Ã~BK²º;é‹¢tðR÷«f9’ú«˜$j÷,:ëUÚ='Œˆ¾¼ª¤$Ù<‡¢yS¨ïv¼|i(‚bª9¨üM~tÆ÷>ÃxDåCƪªº‘ÕTBÄï0œTE[áM^T‡‘8$¬ÚSlß÷0kËZ„›úà98á©õHÖ(~‡„âûà|®ë¨PN*»ÉPVI³Ë@ú[«Íêù†—mß? ìòùØaÞLËn.Ž |1d>“A[ÀiÊþ´«ÏW~ÀýÈ÷ƒ„aüÖY¬Áµz{M[¨ ¥Pó¹¨+~ öŠ›Ù»ãwѰ]ÄžÀx!F¬ª@½…ÓkQ¸ƒ¡*šµý‰©Ša›»Å õZ?ÈÂÐ3“dŸtQyÙïÜ FËùõº,¿wàÍsXou¦úày.:aè±²g’kÊ~P•’"GòÛ)vÝ´U˜R¬ Œ ˜üµ¿‘øûæ©÷­Dpño­"w#x:z‰1R[x(Íx$ø&æ-âR«‰Xö•b˜{"ø®Î–l‡žÐéw¥bſߜ³Š•©½Ø ´&s>VW%€ÞL« 7ÀIçvlLœ'€.× ŠÞ_˜lìÒkŽpN4J$IOÏc–Ð.)˜£X±8ÞÈIâ_ƒ‘¸úKó+¢e ­ú9: ¢ËήE„ò1•ÆÂ¢ÙÜBìÆ ]±k EíHÙwm—MXñŒ™7ë>#Ü{µVú¿£?bˆ£pRû'͘ÓZ–aåD¢ò”oÊ3w×¶ÝЋ΅í¨\WÁ42µHÅ›0~§öLœ8‰·Ç¢þó£“&tÞé àǯ[7.y<‚£-„9ŠBœÀ¤Åª°¦>†Ï´DŽä}Ñ|ݪl†Ë1Pà¸6Ô¡ÓÀ’·]| Y·6«„ì7:ÚÓw¶€µsª%“†ÌX°Üe?ï±m®Aãb´†ü8ƒóGÙ’‚¨âH ª-Š/«oû¯EmS]O„J`—s£:• ¹q$)4Ð'¤;>X¯•ßâ àb'l·‡wgn¦ß›–Óe󪫾”¢oðÖ¢¼µüÙ¥U° .ßÏ\«Ð¬TQudiØly û³qáýüˆ¹.·OÂÙï!ñmÇs.Ä•FeÉ dÚ9åFa§ õ/{ •© WËžïCÉž)mõïhgÉm¿¶ð+¹Œ;S— ²aÙ7ÅP]ÔŒƒÊU‹Û‚vˆ Ü5HpæçÏ>ã'*\ôüc‡^:Ë–Èå|Šò •t=w1~ ÒÞy¡\¦šíOÄ¡^íçÛ¨¡¼{Q KÜWžÇ,}A»&EÊùëTÑGX*RYM*ÀÜÔAŒëŠÝPbéÝnÎÓxH³˜À2¾={©À!&QÈ7 +¡Ä‡²bÔ¨NÚó„ì.ó„“<Å\Ùú¿Yg‰ñ’˜µ²JáÔÇ,kCNö’޶åC…×>J?—p*Tt_æÙP¹9Єò‰ÍÎú¼š^â{žŸál|ýPsé‰<–8Ô¤ë%ú¿ŒgŸ&VÊ’¦«¼× Æ[õ©åð?¡9~)Cç1Åz ±f>ÌŒšJSkõíTáìѦR›Væb >R¼;ùœqÖùWƒÁÎqºFÜ*Ô× …©mÝc-»ôê ,{YΨ¦8ŒòQÄäîÚ3™‘ž“°hU…áªí‘‹Å5¸wÜw_¸ƒØüö•8wu[u§EÇ2§õzF_=äć9Óã ½mnÝå5¿cMkéÛö;tØô=,ï°XˆL¸©+="‰±bŠc:”ÉѸÜi4¤„€-0³S*ˆø³,fûPd•sm]üÙp÷Ý.'vø4)Èêô—Ï£BÏ™Ÿ¼Öƒ;”×\áW¹<çU™'+/þ•ž"ØÎÑn¿8êü>ómH»à®ƒÚ!ã¥*@F‡‚—Üɬ¹€ô”é×S¥«š¿]ËÃÛ5׈Sòu&@&룴‡áUt%´V¸aާ\x6‚ÇkÑ ‡vî—êö2F@ÏÜ®’óŒÞ´ð¢?ìÜ?T€åà¼%×fjUÞú»x¯¼M~Ó^]Wò$¥âÁv¼iaùç¼´ºƒcÆ; âuù"8€µ-•)t7W’KšÎ{Q$XÉÎ!ÓjD©Á+è1é—=‰IÃKn›Eš»NÒ*ŽuS“kàúÒ.¼z¹&½r˜9h{° ŒôÚ P'M#ß^cý_-Öy9~`Ï’Æô:Ìd;8H|Æ·qŽêð£+//þÌÓ Õó¾Éfæ3«Ó\ئý  ÚǯQÔ*©ž°ØEw¦¡>€7J„u“¸†VáeÅt„ŸÉ0S¾/‘iˆ9-±•Â$‚¿ÙžÆPÒØ‡úÆÔÄs£éwÛ<¡*,l€Llou8o¯â ÔHþPOÒD³Ú2Æò¢)ôÑ$К€›’ÔÑãž®­ÙI³ˆz`Ž}ÏEô¶‡“É“à‚†Ÿâ<ÒÅ>xW”ÿÉÈsýFŽ·ûë¹³Àˆ6‚4¸"²‰ë.©76éÁ­ÀÛîÏŠ˜ÉVÆb(ï2 ÚËéUG 쮺lYOêT3M]=W‡ú‰cÎÝ3]»ûêΚaÅÃæZ½ò;æÅ„<{2QFïÑ7r¸Â I¿Z ” >P*U²*(~ØÚŒ+[X>–Ú(Ñl¨5”¼j÷ÒÁÍ^™àÿäª|Rdy59Q,ïßNQp/5Öè÷>G‡ÐvšB2gÿwyMŠK‹^H¡ mXÑ Fá#NcâN2&=sé—»ÂÓ0HÇ}|›R¦±»Vyþ&¿ôGqáÇ¹Ãøç¼LuJ¦Ë{uÿ qµ}t•vúhWп︾›\—=Ðø1Œ ñé–g E‘¼«=ƒâ‹ï9 ¯V²b¦òñÌO(›ì¥Œ4 “&Æ—TË.ilÊÁl.Ú¿¸!»FªöÞÙ;ÂèŸÏ÷YÒ ø}Oï+TÓtÌc®çöÛ…óMˆ$À ªå3}3¨C¤²IЊÅ|«Å>–‰\mÖ£bVÓ`7uc©Îò¿Ú²ÆØ3Ó@kÂP«òk>&·hðÚQŸ£†ÖÅS±óg6PAþ$å+4W [ÉW¶ÁCérc•ƒóR×oBýîúÍÿdF‹îÛv„ò(ÊÁ+¶o„ƒ.ËJi©"ó’£_>þkÿ)/ö/qdcX$ û«Ô Þ Üï¥ìC%<³«!rË.¶x'Ç&t›¥—ô1ã/ßÃj Ng££¡]ë…N}»H¶ÔØ ì²Bø ðD¢2ÎrBÀ“_ ™øcš–4f× +ñŸVÌ¿¦ÿø kšqÈ„­)°ö%Ü0³´,ªéC¥—j“FÜùßX(¯-Z^L¸²)”w <üì0Á‚©0XêåüìiOáÈYPT˜]Ä‹me4e4°‹ÜÙµe…´#ÿœ/敉·DººUfÒª"îu¼4é]ËO,…0g9æ5Ç8hjÛßÈù'é‚‹ÉÈZS¹:•bÊ”ºI”gˆ7¹~P^.Äï\í™bŸê>~e#„°L9ÁV$?ÞIKdYž‚˜!5I;QŸ´ÜŠœôˆÁ.}0@ohj°‡oT¨3gEˆÉŸ5ý¾ØŒùáë›ikAXŠC—DôÏÈ00z;P‘€jö`˽´ f«R?9ýøÕsÜÚŽÕ×ÁX‚(OI2Œ¨ÆÅ¼¦ˆÆôvÛØg~í¿"²PÜ­O< y¥2zª˜›Ïñ¨-çmÜYÒïüÝ»öÛ2ÑÌ…±ÂMñ\+ö±ÁF-¶]Ä­T¿;¸Y«‚RÖê†M>«y\×sìEczà¡n;ÚJ㤡'I§>G¦ûA½¯9؇òüÆ1O÷ÄÁ¼…áø’WŸ×¶ DŠÊpÖϤôÖ>ŸE¾ÐÑœ©{¡; "ý”Ôü™«€xÑžõØp ¨ÏÌÍñŽ‹vƒ‚Jàƒî¼å¨A1Ë,ý¯'‘¿ˆHò£,Ëœ„ˆŒ ‘ó»iðý6²,£Pb…)ÌÈ›0µƒ¦âUx^Ou·Ö‹É—ÏäâöEòTùË<.·æ³ßÜõP ¡üYì5 ëdÕ9p^Ô`G~¬Òʘ{+ ;¤R¼v¾¥ì¯ÒC.7¾ã•A¼á˜aØ ñ L¬©­ég«Wä•c”4üqË6Kï$nó0ûÇWÿç´»†³¤Êb2¿cþs+šÒ:{en\eôbœIß>ÀTL´=òŠdøÌÝUÖÇt”ªlüDÝV  @(Ì¢ÁîyÌhÍPzÈéçq—f¾Ä>‰ fi&ëNAl#dy"´ r£gÀw.)6Cžþ æ0Æz…@BÔç:-©ÇNá•»´UÞÔ(ɰ=*š#^îºrÁPÚ%…sC¼þÌɮƣr;’—*˜$²üõh$çì˜@¸«»m–e’e|uÎ#%XF‡ÍŽ×Ê¡ÑVµônÜÁÔØ>ЃŠTæ¤fãÐg‹Ý·ÜÒ(p÷°¯àŽ÷OØš§Y«º«XÓ…(±&YiÍ“A&… À—úÕ–_¡ËR›t+}R¤Ñèä&•Š8â÷Ÿž°öly`Ýuù’Á>£e4@îýzg™ØÀCx6”U¸†Ç^÷š´¤T ÷-cqôÄbË+·}.ì*ä>=ÝYC/H÷ÍߣÜömÖ-Úë\[(€›óÉ6éË\@NÖÐP<ƒ·b‡™1i™GŒ©Œ,/ëkÝ‘$øÚèaª€jUN“‡!^ˆ“iÐUV/Þ-˜yvóZΉl^¶× s(ĸ¬odò‰¥Êˆ¹5/‰<]¼vMé2©Œèœ¥îtr"p7±ú'$͵WëP¡Zrb÷b¿Ê{¶e#ÝG»ìk„_¤S˜5Rû]ÍznyÈY¬B‡j{ ¯<³Ú?I+$KxE¸ÌAÿyë±Û´M]ªýæe¸Ãd²3ŸB´_³«Ž~­ý»„wÍÆ,_Ìm£ì$ÌI³¹ý²¥%%œ*»{æóƦ)&hØM0Go¹Ù—¸/P®«3¶æ¹,C/(²ÈNÇWæxnU]'â;ª dåÁL˜6Ô`Fg5}( ¬Ÿ•hEÜ\ÍD"¯ãc}¾€òࡃX©x_µ{Z9 ô¡ ²1(óq3¤<7¥åþŒ¿a³eâÛ?„e¯^» vB}Ÿ›,~–S­ŽÑ£œþ‰.íJÆLºK›fà×=Tvœ[ÁRMˆ%c»s{4RrVïTµUßázH°é™<߸\ºÐBÖéÜ©W"qt¡w-³¹ m«ôê®Òã0@•ìvµ4¼Ê©”ý¹|†ÃSвÃÿç"¥>ºx·°î {‹V°öòƆW&þÉI¯&zn‚'yÜÍ|”k¬c6»Œßó6ŒÌØÄ¨H½ÜòÚ„uÛV®8tvoñᮬÏð«Ú퓜 ø*Þ²0 ¼åmí⩉~;’†£ô;óxt“ Pàm¯Ž<gn†$z÷ŠZT €ª$i󪌆IOCŸ#˜¸öYvuE¾äíTšÇ8:vô©¿cY #ÚŸ~¶þ´ ã%‡»Æ1Øä< Àª«â!ür\³ªÏž:^úèKRäj1”ŠqÉk5#½‚tÈ_/ŒÃPÀÍXTªÞ,Kù5ƒ¹7íxEÒž\J•~+RA{ûÛYÒe“W={j`KœŽ9©¸‰¯Vôr}¬ ‹• ëuÄíJÆq f6Ø!+ÕÛÈœáæœ®/?ÔÇ´DI~$j¡ÿâ5×ê ÷­3ws_B«y~¡m"ºDn$ðcµU ä½…`lªið©œcØùŠŒ’œ‰’12üi1KV}”LøXƒ$Q_Ò$À/;ÕÌÍÅÌwÛ$$èssY@ÒcB–+ôþÎ>6rRdÿ4‡3<^Tˆ‘­Q ”NÈ€h©¯¸ÌÓ[øl‘˜óÜ^¾"ÎY{ Àì‘ÎËã›_D½T'9øQÇ_OÿY8ãKœ‚Žþà&\+šç_$ÞÙóÔaŽV2z êÛÐ9Új¬DO@åhl‹æ«¿g„Äú½Å­¾o$ííx¸¤Öò?ÛÞg(Å!{ñ8'kBe„h²(úÏ~r_Ío–!†ƒ}ñ!PÙBõ6„¡S¾r˜2xnÁn?ž¢}NúT¶éÃtœãm•S3ç¨9>¨„¹v°ÆßÉ?6›†äH›kÃÛó;øÃs€Sð’Üdª ‰º%£K¤í)°Æ¸ xV 5”äVÀaÎyýÉ ân¦øCP '=;! (yu/Ë­Öƒ”ß á.K@îçIùZÝñU Kýªæ ò[ÄÑÒòª‰þS¢„Ý€_v(NR J©G¹ÿûç/ñÝhר?¼ E &ª]|? ±ï»ó9š´Ê$ a+o˜X€Èám|Ãë„›NÜß?y~šÛàÿ¹V2ò<¥&ðR"[JAä…Ã|Ïe÷–ôáêƒÜª…üóPà›Õ,ͨ¿+0v®•Ù:Ãa¹w°Vr'áÙ)·;ä™0.¶Ï‘“¾NQ²Ÿ'A—ÕÒ™*9"åýÞÃÄ–¢Jö×Pd…B“ãˆSôé^rçò“ÝÚ9øå#zƒK¥R! ì‚ôH޹]OPDJPàÒzc‚g‘î\®±mßú|˜Ù…„tM¬aáxÿ¹å0ÀˆÍ ûΜ¾ÜY³“ó⦂†Ã$¸Ì‹BÇY¡JbT;Ø2iL·. Û’ý9ÏR¿0ÜCŸéú<ƒAÐ…ðæol¿‡FHmKçmØád·d©'Ý4ÜÔ¨¾r: lØ X\ NÅ?-µ“¬å²Wkˆÿ‡Ç¤Y©X®…õ6©ÌNÜ¤Ì Bª+†xÜœrbɯJîU¯ `É=þ~¿3/˜iyÞ‡(Û²‡àØrÛ ›´SŠe­Â…õqQrÍcc0ÔœlÜi•<ì¢â¨ð ÐÔÖ'ñ¡ÍÚdŒÔÃi¿ç™xƒÈŽïç^ 7½Šã0ؽô~õ”Ÿ!8뉒P÷äÜ£c©1p[e\1j ÈÏeëžõrŒäéœÁ9÷€¤ù.þM®’ò’~²ISqî»4°è !v~íÐfÅBjÑïô-g·¸$´ ô TÀ4hÞOÁºÊºáv²¹^H|)ôvœ°ª)Ñ—…''fÀxˆenwÉÑ¥V[ÿ BÛÇR‡,ÌtO>…‡K>—ÒBý5bvº¢¢Ç;Ó^³xvÝJÜõQæY ŸUöù;P3ÉS,ªÕ²²]5yëþp’D—ŠG«ä´~ª/øF}xÐ%ûÉæ«¦‹Mx"Ø’)ô¦ÿ¿gY‘ruÁÓÛ-$ Úšùà+í¹8›Š¿ÄÙ*³Þ>4gÅKì •3‘M WÎþúÄ@ÿ³Fñà02šF]‘VìL»Q¹.ÙîD[`î^ˆn`ßÀú¡µª»fr}ͦž¾wè†p[¶ðtíµCâÙ6øºÏ£î>Ó‹|é¨dSϬ,!pY¯P…¬u‘æOÖõ27cU8(–‘’¾Ð\y™ŽHhÎ —Czì»ßrÃϽ¼dhÎÑÁ˜P þ ƒ¤±ºÖLÄñȄՙ! óZkÿWÖAqDIiNurÍhFö™>l[J)CJ;a"®Înþ]™Ö^áõ÷.Âa"1éé[ÿé' ;Úç+ZƒO«)ã>\ ¦Oqð}n{¿öí9%Rç ªÐîÎ8ÐXßœ§t„‚Ú Z$¿í I}Ä dó¶Ë®§Yü\ÇÙ©Ä创xœ7•Ñä+ÍaÈë¬é-Ê=É¥ªY‡E:°¤i´ÀïÞ.†µ9æ~+ÍÑäBjôê_@äÏ¥SQ[/eAS¡Âˆ3Á÷àv2ÕÔ&T·’=87ÿl÷<©ˆÜµÚ!¾úZ¯4iŽøYµZ~Eù¢Ïo(‰î0 °ÑìØ>ÐCmiÈ |¯n¨?k¥ÎÃ륌7‘,^A \-¨e n»üZÕï®øŠ³~,fñŒx}öæ\;ÓzGnISþŽÜ3ó:7\•·«)ìï‚4ô'вÐ÷‰!ÍŒÉ4oFØåIüªo²úV˜£S"sÀqÓÛ@k¼z[H0³Ð¥lƒR·,kÆ0žôQùS³ùämy5k !…í¶¸ã±ù¶Â/ˆÄ&7  4ƒï§Wœ ¸Tí…:ÈF²N¡` P™¾Ÿás°Ñð¶"“ÇPæ«7ÒÙ€U”Ôxç!BBë&Óy0§ïô²¹gQcv­8þ¥ó¯ôËhºµÝßléöVGÏ¿)…×WåcäÁ16Ë»Ö,ᔊÂ<Â…œÿC7%˜Š¤Þäp½…­–:*÷œZéˆW¯³okV‹|¤m(²°#VÅúÓù¸êúõIS¼“g´<·i渾eü 3âv¯ÁôÔõÄÔÁ¿ZHƒeþvlÒͶFÖå3zæ\‹Ègl¯è“y·€é’{l¦¡kÅ2 ÜV`j1ìpaÐØSÂ15ßuñÜÖO ÐÀs}Â:+`†bN>›A”Vë°3}xvL²³ÎR¡º)ïé•u¯øfÖ0%nÉê`ñ!x³û–Ëâ‡úɹkSқȋ‡—Âh‡ÿ âÏ%ñ ‡€ü‘òæ¡I¦7& B½gyç* 9/O òǨɃrr2Uà=|ÆMi>œÙŸ«¾Ï§=}D{X,ò› ^¡SHŽ<r7>øê!ôsFZɲ:¼ ®Ym¥T‘ËDI;÷÷RWÞµÙÀþ¤ë”c о²¿Ü>”þ¿­IQTh±ÞަW€µn4BÄ2 ¾HéUįeóN¾Ó!9žžŒ7ùÆŸôúªSÖ;@GCT¿¡‘Úk\iVçUO‡¦uh§èç\-ùPaKcÇý{I`nÇÏ §4”îÓ_éñ©ƒ¤¬»ÏF,ê™ɺÃ_ªÄÑÉ}‹½êÈëoé‡ ÛÛ+)Y¢íò!ôþ ãåm€~†÷ñîÅÊã]†õ/ë$eò=%ȲaˆHÌÿ…BCh°&l R±βŠD™ÌN¡@Ü÷¡ùYñ¦üWèž/&´sñÞígÇ7<¯ôš¥öÛ £)C(5ïããÉ:RtìEK€þªST¹ÜGô²Ÿ ½uî7×GÍA,.ë„•þíôº‚V/•ÉRo£äûLYsx¬¨tttáö…îÑX s*Lpyǃ ;¬›|‰Þ÷#ôÓòÃ~"OõÂ,§.¿ü‡f[w9Â(á+ñš¦8-$,ñ["Bu¡ËJ:Þ8@¼~ÁDÝA o ŸaÖ´û”ãÛƒ½Й±\þ‡Ç ¯aqójnÈÒhqã)s/àñáÑš{Ù9(M”S¼¼º.;§Ñ„¾Sèå´%Hò@Ññ1Ù¿g‘áýI².D"}YCt©?§Iä)8``Áøâ¢wz/ð³,Tºí.YZî[C|Í)ãç\“<¿Ùx‡¾aÕÃÄ%SZ4ë†;Ü›àÙ#õßuêöõ÷7ó±â¿uÂ"Òñ_[”׃wÕT-åÝæJç*³ü¿ÉÙÐzÁ ˜ áÁÜPšx4È{5°v‘¸”h=µå¡‰z¥òž7¯·›d>5–&l“£õ…Yyj}Û‹—HJ8’wðü¨ìÎ9 >è*\6°‚¼ ýP7kKG@{¨”ÏýYDç Œ²±ØM”ñÔ›‰ymÎC$åpYGõBö¬`k¤´3¶_[ÝpëFægò?zò`²K¤/¬Ž£BU NÚ[3þß6ôFŽ"^JF£†éFŠ*~¶`ŠL ô¡ð%÷¹€ M¡â²bÊKhãE^Q#Eå¹FDL©˜“¦i£ý/´¡{Èþï8ÁÅNQ¶/BïQÇL001Ê~ç?ªùˆI7ˆy퀘ÿ{%s½[ÁNÓ(¨@júò†<z£ãýwͨ¦ÆÁLg‘åü'æ²úV‘4OÂQ‹'çAàök£I’5­Òt:Ùþ½W 3H¨é$1rÌÐb€ê„ɳ®)ï3]Tÿ_4Üí—/S;k7€óÁ 0+¥yÇˉùeÑDx*›Ä a_o1x„Ò‰AözAÛqàÐGRãŒÑãSø¶zˆ1–ÇKEBš´d,Ê3µ˜2çZãòkò'½Z¹â—w.#Ã8–‹TŽ5ÇïÈqªÚÖ!Ö‚¤Ðÿ µªa•ys4QsF9sY?`wüöØyC÷@’¾þKÿù3éß_ÿóOðvChÔŒ%ïùPfýBkÖöÖ9o†õÅ?%MLà ž¿iP’ã¼à‹’ÿĨI8ä¢Ç†5¦Ü‘\_l(Ö)U1}1”V¢íKÃÀSºÄá0›ÆÓ i;ƒè"Õ¦Õô¸û!Ucéá[±‹bÜ"XÕ¢ «íT¾!@¾vHñ³·™Y]¥‡ÝPý£†ÇÜØSŸB†|²AVu-é ìáË™DÒðiüÃÉ 'µ½PöLí@\R^¬iȾiþø¦JŠæ(OÓFÚhãæãÚÕ ÜCý¿eü±öÚNv·§Ó7ë‹*<´þ5Œ’Ïçcño/üÚÌ_Ëœ $ÄsÄ7 =_ßz¹¸+l©ý™¨aßØÚ¬øðA!fYGÀ²ÿ»Õ½Aþ+ZKòìZ ñÛSûöÜîôÜ ÷þ¸ë«¾!çfû¾ýÃ;5óù&}:¾¨{Yä"S"4ö€7Ú6 ò}ÚT3e›óÞ‰-O¸]B®ÚùC¾qÈ¿"? M¥•ðÇè8É9-Ñ2¿,=ÕÌv¡9†ëÜeKÄä¨@ºý,d·hЩumQ©þ|czwÈèJ«02¡ÅúO(ÖÒb1Õ‹~M^Œ =ü˜™…&)MÎg‚Li¶Ð“ù~òd°„£i„¥'õW|uO+R “|ØDvoÕY¦Î…ÌŽ‰‡ßÛü6Ö'Ûr-¶L ^Q@Õ¼ÙËñÙ¬oŽžv%DQœ1µÆ¦ŒBÁ(ÝJNŠ|'Á$ÿéMp(q\’Ҟe™•vü9?L÷´UùMP‘iíßMiãÿŒHgÿL_›E-:é-²Z!Ó㈯¦ø2«’ªLD³ï@x-òà  á4{MС£½f²ô|0ZêÏUìš™1Á8Œ&ÁìáÏQ€É­«‹ÄaêH–cê+’PpšPŸõd³…Ÿ¿r¶ÊEv“–@áx‡™%€ãÿ­pOr¶e~ý0Ñ))au£èM€-tù€™@Øk¡x­Ð^p)¢¹ÙǤ­‡I ÊHÐÍ ì­>¾àXéýìÒ`"ÌE£¨ïŽƒžë{÷ y ¥Lf·>yÜ'Û5§”ôïKXœÈÏ´WqÞô™ýäxF^µG"¿—º/`À6«¾“eO~`’»è` ¯b£›Û‹³±æåÚmAZë~þa9Þ|.hyÞpéø–ª+_s!½µÇ@ŠgbZüW,¦ÇÎU§¾4ÌsR@[7¾<5ùŽ©VWÈ öÂÚ¸Ó„ì]MR.¾ ²N¿’[R)áJ¤™¹õW âºòÙÇÆ'ºJ›<‘Ë1)t“‹£èU¬ŽÆr™r®!S¿º*{á‹uKŸ·ž®÷6ªO‚l$H€óÔÃmµÏµC¡Y’)cÖÙŸ5£~lâôú)º$D>/¤ l± Æwå²Ïøo~‡­c¢d“èÒ´ÅÃ×f&FÙ?vs¡tcF"„"ïS¦ ô(^<õk¢i³ÿÿ$6úGxZ5SÃÙ$Dcvp[v ê’kÖÜ»  ¬Fp)¥®Xß„{¨®ÕÈaóó±a€"ÿcÂåõ–ô¨0U¬†f“<\mž ­ó}ô;®.er_ôäÉ —Ëeú €bÊ©.T]<¾ žÆ°¯ïÅ×2¦­ûlJ0…{³kÈa»¤û˜’b¶ê±_ã$‘F.åi· ;ë§Té^Æœeïd4öÅ rÀÄ(‡¸E"ù_R{%@!NïöIÀÚ¨^c{¢·YÄ­lT…¹ÀN×–Ø$£ù—%F‡^}:—ÛÉî«bÄSRoRˆfáÛN&*jH‰(d:*P©å%h£¦2A·\´1L^RÇ‹­_ÄŠ”N¼v£|J½J&z“5ÌéTƒêà'·s‰ÎL÷Ê ¾jŸì·"S:—Ì8þÆy çƢƃ™æ!ô"¡Þ^«á/Ë ©+ˆ4:S”F¬º6ÊlÌ[Ëú:B¢ŠÃ<å‰I²+„-®¿+ž§ºŒ´+˜X9T †»%‰üK^OhìÅFA!œq¿‡]þ†µ‰.Ä"! ÀL[#¹kØ ôã›B|¥nžýBÎ…x/š&0µ±š³óD†Ú©¡±ÜÎø-¾9{ßÚý:W˜pÑí×b=ñµ»>eä]<ÅQ÷A,Uœ~?«°ï½¸u»î­o·CIƒñ-°C.¼(ðžÃ´„ÜbˆîPÀ/{æƒzH±ê¹ÒÕ´ÑŸHêï{ ïãn2¡û‡À^I¥WIæLA›ë3EÚŠjÙHRÿÈŒú €#³\'X{rohñÑGî@–Ê_"Bç”( 7ý„üÇIã¼’y%ª¥Žeל´$“º¤[˜ƒ©]>FE=èÉŸ&ª"Mºˆ–¨Õ„,à ab52,1°˜W^‰ø<1åóMÜn E©ó]yо ¬âP¶€ îb&PÆk»õG%!>‡öÉiÙF'I³UÞég‚ߨß_ɯôƒ‰;aÁ ÿÂ¥™0—@§šcG|ÀQsËK%®.?Ø ›…Σ  :+âV@v°\I­`P9¾Q_{âé·¹ûÂô òíµÞM¹¹ì¢bQþýaÞü,»·øØgF,¿1 Ôçy &ë¤Kœ YÙ:‘2΢›¹€•šZm“;];’;NgUEÙ[6Ü 1ݱA°62Å+Xƒƒ¬$²¢YÜ©d•)!H5ê4„Y~U|‰²i*ü‰6‘ '\]xµ[ýÕ \CÍÄEÏGé{||}’b‘ŒÅ{¯ÌÂÙ` Ñ|GA“™æóìPãÛü»IÄÁŒÊxžO‘£“—&QHˆ¨[ã@}¥àƒgf/©ÕÉòÊA׌Dêrh^‡\­6«ŸiÁxs×ñòàb´ C†ïÿOBÀ=™æ|‘i´·yd°ÕS²2K!1·9ÐÍÇU¹{Yêñ¶ýu.ZÄ·üâŸ5çþ‚¡Èbýàžü¸0››•ˆÔQ«Fn©#°,^@Ðû䳉 ¹ÈôÇÚkѵ/'Ôg&ûÀ‘­MÕ/‰”yiü?¨Nò†}³}£ yWÛöÙvƒW PÌ5×\í—[Ü6àŒ!`©‚dõÿQµ.Ö °ygÀ½¿ŠÙào”;VhÂ8ÐC;tÝ‹’T{É™û?Ä$̦ѩñÁ¼QÉ88‰S÷uz„å˯sï1ºäOYÂÆ¶˜_&‹È´ü_ì-£÷&³ 'þ˜ÌŸÔLu­ãöyb~„¶ †YˆÙZgô¤m^Ú$ÛŽ-SgÍÝì¾Z®I’¶Ù‘ίûw£ŽÈèôksÎZÍ…¸÷ɤn‡ð'ˆ C¾ÝÛ5ÈsAŽÏ°@³„ï ¡åÅFæCô í¢ÑiQ 47}j¢‹€ฅ{qeqIøý}y8ãÄ×h·#>ÓL¹VWù<—ø•Ú-ܘEÓþ)P‡ó=˜h÷©å[¨pîd¶)Ö¼õ`úÞC^Ø.óî÷GÈr•65Œ»%EŠôAø—=øîK!’ O“ˆzðųˣ"¢ãÙ¬Y©:¸de'^w(^;aó{¯Áþ鑸ÖpÄ]ÜNV(ÃÞò›SãçáKö?ÊHiG=½@„;̱ð¦í$ÙÜö”ø´€Ž¨ÄŸô/7Øò퀧¥,'T‚•DmÓ<3)L»‹ë "s©©žxìÎv™Rœˆ¹QO‰|>{÷ØÂå{ìž°%ÎgÙ ´[°ŽóՈΥê9 ¸QÈÌØ®Y\ù8t—ŠC!Þ‡ xŽÆc2ìëxë›Þ×}hIñÍSeÈW¸RêZ¦Ìz ®³Å/~Ïl!°Ã§œ‚T‡ÝAy„ƒ4öâ­N)–ÚvÄ’oŒóìåD âFìFû\RS ã¨~Â’ˆó„¦è§ÌSú‰ty\ŒSg’Aìå2¾¡åZQ÷ÔZMU§‚?¥ÔùØéUjÂËbõ’ÍI¨¢.}OCÀp ÒËUfЖ­hêO¦ ´„Ÿ¾ ¡Æ'Öø`l¸¡í2- †;@~éúÅ)¶„èg#´“mþXFîü 9¯o_pÑ9Üfñ~}°´zŒ ¦é0‹®&.—lÀ©ã7f€+'¹R­~ÙðúÂ_çü÷1›ž$²»-'º|^­™ðKÁÀ¨$ÒWVýÆÖ¹r>ÒKB\71úx·Ý¤ÕýQ›Šì2| ²Mtšyíê‰t6‰4²ë$ x}·ŒÕžÀåýZµ É(Rê£<`K[ÚÓÞ8¼åY®>šÕuõ(EÜÄ#ÿRÅßÞL á?sØ•Ò7ª†œ.'Ÿõ܇6€ æa ûâ?ŒïgzŠçÄu&lxa =>»¨Çͬ œàe¤L³P¿óPò2°öŒ {ÛÔ1Cçq´0°eØþ^è´.y/ö&áµÄ K× ŽmZáMÌãB8”G€‘.ÞsؽF¬J[ÀYøá¨:DZñNˆ? ± BT®D”ËòXq£kçÚÏÖy]XÙ«Çœ Ù;ºMîêqã1ÇQ0/yª¢kvð ;Úƒîƒ?Öõ{Ó÷€Ñ-=`-ïo÷0Öu ȉDZ04?òO}{iÁ ßÑÏšV˜&6K’–-ÒP3­R]n¾-´¢·'•ž¯®Í×57˜N©Œ+µI=Èûhfàõª½ã刡;ÙŠ¤Ò?<4h¡þÔqu Ñ–;òAzlâvñÒWõßêp&N’•´ ì!Rémïrâ‡ÑpVk«äÇ*ññ÷óÎWOH¦–Ì’°÷­é÷;Ì^ݠȰ‹~¨çèßþvÈÖ~ 7¿Älèž‹ýø@ÿÃlí}¼ÈžHícšäÝëêhtU7äa‡ýÙuµ‘tÅCrú覟øOü3à +;\r¥ÒGu”™ ¬ƒ¶Åh<ÓÒ¬|zîJ{„Ž÷x8²§>î¬7OFæèáFúncŒç‚ãÏõÂD÷þwÝ{ýiÝúÆ,Ìò@—õT¼¦×weB„D@³G°Í•ñºÇäÖ9¡Ûz¹51»xuîS|Ñ.˜4!^¥ùn–øjªmxŒ{‡†¬SÐy¾®ðeß_Öw)‚u;h±ÔeÏ{CÛŽš¾Å""ì…²ídת´~Õ‚­}på[ËЀ¿m˜jœÃi%%s³Ád‚úÙ:…®Ï—æµdn×!‚‡¶w J§71ÐëiÇê/³A óÌ~¥C–aŽaÛ|ÏÝÅ_&½œÐ@ É+";ryÀ C9,8ºÝ•C¯j&<äÁüBÜkBÆ>ÂNÐôà™¯³ƒ: õ ­|4Ð â•Ò¯rHL˜ï„öÔOw)³2‡¼šß$rö°l'0ƒ ®¼lõž¿BqŠ¢} %ˆù†|EëüxÜ´ß’õŽ«ñèèÇ¢ÚYßöºÁ—‡Iú™2â£ð/‚ÆE+mU+‘@fÕÃf˜l^0Ȉן ƒ@ç#7ž ¹fvð©Ç\}è ‚êÔó!ý":£¤“i`o=ᑘ±AGXGêjé ³o”«ºD^Ôt|Ä@JW4IfÜ”&'8jÖ— V4š(o%*H–$ðt'1sÿ–"o§Rç›(NÍ£§c¥j”ñ*=B㣗ø²{úx¨ÔÚ¤ã@øc¸e—®mݱH,“žãìåÖ*X·Ô¡î3tfÿ¹Ñ´üÃËéi©-LG·¥¼Ðb@Öñ·f ±º\*¼i=kxêö1“ûÚ<é‚¶mÇ@]cZ+<ú.óîïí=F˜D´b¤pƒGu36‚¹Ê.QR%Xsv Á¦/eÙhÌ®-“W„Ø_xb—©]+@®Ö›Üx‰”Tí{z4òl®BªŠ¬oCʼn/Z@åãS•àñ~ëÀ…懴‹)0=ù­Œ¸Jš‰£¼„[D±ë‡W‰c¸ "µÈ\XJµ›¤WŽ7ßãámʪØAòNLLŽNÏQÔœ6"øsÁÿ Ë8¿ˆ^5Y_*‹ßsu_úÍ”Ô}Ò¡ë3UåªM2}sý®Ö6cwϦ[_Ì÷UËU>°•4l(üR`zŠß‹xö1Úd!)åÉ?^ž6ÜΠֈ̹Zרº¦©)Qø¡°à¢ŽBPô§Jbî¦k÷øÀk© Ç–Š´‡™‘ F²”V§Œ˜S=-µe(új’2&az&{Ïñ‰ú/*1 {¨û5£6)•Ÿ«|á¨Èw´môŽU§W‘òuà*‰_ýpÍëÄ+4ñòD!”nr\ÇÔr{ÝÖãBÜpÉ?Úô~áÿ„º‡¸·µx#•î´ú…žÚ¬4#[.Ž¥÷}o":@úXË‹7Ö}2'5Q:c´­.߬/EI†]œ«ûLnÈ›€ð¾¸eE(‰ÅJ™fSÃýêûsnŒÓ#ö?F‡ª£":³—7D‡»Ò‰ êØ‹lÑÅK^{×*Wkp!;_½ú9æ9i·t8 {Çi ³¦Ã°âÒ³‘>1Â:¤°Ë~ŸÂ£Z›{s 4yF˜Çz‡åŽÀÃ'+Ü 3î3 B£Ù"æ3kYa¾§'“Ü‚!%3S‚`þ²H¿Ïå´0g^h”xšmJOÙ& ^Å›€¡Vj¬&ÅØ-Zk‹®â®+PûÒf‘´P«6Œ®ÕE±x.ó”íÃxÙÔbéó"0/oÊ0Þ£òíclÄš^Ô­Yqjˆèb²$Ø”²òjfÒÏ¡µ¨61‹ŸºÕÇìØúð8¾L+h÷”x½›ÿªõ9S‘6ÿù šUƒAœ/Cõë5I2 iæBB‚O=¬ò<°ÌC 9+¦_£Ëºåæ×ÃÙ.·Ï v6òœ9›®öŒeüÿפYÊ×§ÿÓòÕBàç\þñi«f«a[ŸùeW7šnÇ.a â ‘üZJ%pWäš¹àøÿ¤ÜüP­t<àk¸ÏªNsšÑñj;£ŒåMƒ–ªÅÌ>.ð×Õ ÄzŠy™ñùT‘RïEÖÛÿLÊtÜÔþ?Á%ÌøÜ÷ÑöÛªû3E]¿ úê ›wžò(ŸkÜêX‚Ö@ºŸ VrFLÛ4IQJñ'ë0“A®Ÿ=SüÐÐ%¼ßœ“É^“˜S0 _eݯñ$ŽyÈÆuGW¢‘lnM¼rãf_J1û·T0ÔW LNb™Ì‡¶¨•šÛ$Á9û˜F2É‹‰h˜„-Ã|JÅu§ÓNžN85ÌÚØ/_0ŒmÌxÎþ1ÏÈň§cNó^¹J \½5¤¬Ì5¢5l/Òogl"}Ï»). cå—“”lgãp_íˆ$&þŸê‘8ű-ÈÉm ?õºÒÙ² ^¥"˜V Ïx·|=5xÄÎMô:ÝØK¹mâ4“=0Ö€áˆO.âj‘3·½6% ?š™— é(øfÓP-©Õ]« # ²Ó>¢Œè#R6½,Ê·†l1þiýØÃ¢­C/,M¦¯dTÞ¤wÒ˜7ž=1ãÂ¥J}QU7<ç*ƒûœTÁ7eI3Šû|-i·QèùúʹÜ2IðÔl.x1‘6BÔ&˜w¿·o׌Zbò³…쨟DÒÙ—fŸ›;@ÔP¥,dí¦,®4¼[‡ÉÐHP@ìŸ ¤¤H¶ùSWCÐ×qt|aß&Ñ%lò!úËÏ4DÒî²ÒÒ£“Èô¬t¶Êç¹Ïù°º`c‘‡;yÔ¡Ô'|ÔÅÍ¡<®#¤hÛo@òf ç7#óažLÕiµ:è@9Ý»®énª£û5½¨n_ßïîÇ ÍàGW„t{_˾tJ·7nÉMaШÅ!–ÏNmøÂo_ê¾ë ì3hÕTWŒ’ë4K7)ˆø)ª‹Îßßð“äåšÖqS°6¬Áƒ5„ÍE¢I#*2wwVt©z]ó–)oÝTfù}¦¾…Ô¯{•B…òkÀtÉ7ïö¶Öÿ±zm¡]G±$" Ô_“¨çù9yž¾€9öá˜Áç=¼rÔ´EyÏÐV„d+Õ[V³yž´ýý’û†Þi^³éºbi|ËÑLª%o«Å “¡±ÝÉÁjTÁ´çUìýÝíõá¿óÏKƒ •-ˆŸw$Ô'ñkSÔÀ;8É0ö/øøò(l§ Ia"{L¥ð¿¨´ß¾ŽŠmµ1Åh@^Á ½¡-5²Ýë6Å O¨Áó€Ëä¹á:‹ý–©G$tÓ +Ç"Îë…™aí7ù<£\ª¢sŸÔÅÇó‹Ís s·¸„Òß-ߟ©÷«dua~¹JIc)šsºÚ´M²,¬Äš¸©a¯^ã*gÀ}{ u•ÐÏ çÙ„·* x¶!””µ¯úºrëÎx—FeÁ…`8Û  ³p²ý[Gµ>@”éw×{A”aGû»O¥ÉØ-T% ä·6L)·m"uŸz-E´·/ÓK«.CÜvÔ]–¿r¢óнT›—/#¾%dº¯k© Ö>Sg’wlµ•ò_Ûù„ ¢‹ çVÿO—dòƒ0}Çõ|æý‚qp½Gç"¹*ʾLý[™ÑÆL"ðK_ rO×S›=›W>ÐJ\ê©¶µ[“zm•ñl~*üDÑ“‡›re48èVÀÃré!Gâ5XëÚMp%a`êO4Êò÷óÇ’ØDÆ‹ 'Ž‹±3\BòWU+Åýß2üŒt ÖÔÞïÜØ–hdxR…¬crˆWØmâÉ3Òá¶N–À$Ùpd¡3ƒÉF±Æ¸{H)Ó±ý@‚½Gh»U‰Ó›\t»OeB¿ 1Úâ(œ¥uš…Ígx“0 ë _¨ÓLÒ¼æ¦xyÏl–( é¢^¹Ù¶Z¶ÀÞŽ>~T+4pv è£áù}c IÁü¨Â"úÝ+½ÍJ ×Ë]S}}ÄÀ›ÿ«WiÎ/¾¦pÌw9Ž/æÆ“.ñ|+" #;,ŠÖHÃe$â ê=65e¸åxÓ÷ÄK†ü—¦ßcü±Éàb—WQ®oUDløgмªÚº?Úß0²¥þÆCñ£õí}ÉGRK˜à÷[x`šÑº‰¬=»NñºªÖÁÁ7Ö¨k¯®Gd¶rJ·"ÔÇêâ¶htÀ=±í”Ú¼4tÄ ¨žFÑ—±cÃ׋շúŸNýE¦>ÔŒa|ÏzØÚz>!YúÂàÚªðn‰èãPç¨EÛ'ð®oê CŒš{±Õæïí®³°þýßuâçb¨ÙÊñÈe ú†l¤EtûÑ™,d²Mfo7@Ìïû®TÀcÙ øìª”*FJM‹V]T^ç@¿ ŽêТ¾¶È‰,°URS¨*ôÁkÁbB*W4é‘ðÚ+:ôv§ÖÛzEÌžß¹Wã®|Uò¼À™íç—:e\bQ~r#U›ìßàqáu˜Kòðg65ØêØ/p‹ ÷¤ŠÍØ7Œ*Jæà›Ôá[ëÖ”é÷ÐMš‚ ÉÐ.¥$ضIÝ|ûDãN;|šâïÅäøÆh‹n|ú àZàŵ•9)§ˆN+üû<€RnÞ!R[¡"~Ôx¥Â¡pþ4¦*F4±î¦–êP.™ ^ŽêÌ:±ûÜ­àÝ JC+_IM;yZŒkéßVˆØ¹Õ¢C{Б5{µÀ9Õ-ÑçÝ œ¡p,¹Î¶AV‹MÊjóó V%ù‘hœ¾ì™ÑÍïPe‚îl¥Âe;tíòÿÚSòõF3“<ôBËà1ÏLBÄôŒõXƒ-AÏÿ?©fçµòùÆ€ýE ìÉÝobº'.x¡TöÿÔM^ZI-<}Õ1"š£Ô¸tÖÅNtvçØ\CÖ8p¥n/‚eÌ;‡Ä, p¡zH‹=‰ôK0åŒÅ ˆŒ[Ë>]Ö¶0F⹊ð“Øpó*œun›3qF¶_ñ¹˜ÁÀAs#“i±^. âV¡ö²¾l, ôj‡K…rUøMw—¯éÈ¥E¹Õ%I)jTx´)ìPçû{—¤õ)žã¿™+ÃÏ.nƒ7À×l³˜9³Çž¾Í§­ :ŧUT~˜xáÒ€íµè¿Ú¤æ U#~¡·Ýé­Ê3`¬!تi°ÎŽzHRéà £vvZ0¨U>`G=ø~ö‰V…/¸Ìï+ß…x=¦PˆsŃ참îkßBŽ+MdJ”œþÁçÅÞ½àæ“oIBŸ1¦qöGnµÂŽŒ'ÝDÜËnònf¦kTÜÈÙ†F¯ 7²É—û†ZyÎ!=Z—OɱCÛýa0À„ÏÑâù×C¸‚Ф|×îb‰ŠCmҹĕ[®¯ç¶6«„SE”¶XÄ­+áJfpuŽþÚÆTCŒAý7œYÌX2¥ëÀ.h¨ )í MÔ ®ëúUÃ`máÉœ Òg+ŠKáÆ[M¥0Rîg§YU,¼ÈŽÑ¬YýÓ’²9*be]Bôr [º¾MùÜÁaAœ•|ðŽþnÕ™cFE=0ß‘¥cÿ>‚ª`¿—¹â%j~|Áæ͵õ/õ=—¨ûLpð¡H)Œ…ØM`¡ÔòBö‡‚d$„%ôGìßhQѦ7 9óÙÆŽM×Á¾ê9¸ÜV†¯‡#½iµU 4€‡ûàš<-n©Üó?f”>KW Fý‚]I÷2ÜmS1jË äq܆¡tÈŽŒyÍž “³ÐH;`™U 丧ÀvLd¼W/ï)îOŽ–õ?=s/ßEp‘P)ίêUûU=nÞ§Fq>9Rsävë5†$åÚA—"âBå '.5ÑÈ× D‹ ¼¡ :é9 '׿ŠónˆÎ¦áŽi¿{Uq,ªœ€R¸ÿu=G—c"ݱª0×MìâŒÅQʯŒw”•M$Ë=®Y}K $ÖÔûý‡>³{1—<íÃ$ÔË_!μ?ºl'Ÿ“Y8©§¿o1wÙ:&•lØ[ºÚŸÖi *М%ï×íq£S1“Vâ|íHcQ¾.ºJìÁûiVr¦lPÖ¨ GŽ¢]þµ­´bÚYº? àM9Ä]Éb»×@L×”0±”1î@nׯ©oåЭoG«xÁþòK¨Q]EÁÆúöú ”v+Ðwž.‰œÅzN½Öþ ûd%ëýVCéZ¤‹vàå>ÙÕ/“x _¬CS‹†¹3ÞF—lm.ð}Ö=>i^AÀOòj©Nœ0ø¤þ+ÞºwXHôŒ5Ã,Žc‚¾8µ¡„ßf=vAÚ(ßÔCäÛ(èQ3rN?s‹¿©€ûöÿ/ÎÅã =Þ=Ìccá{ê¢&¾GÒ»¡? HÛ;¨e»lKt„Ú73ežhø¾é\²Ñ‰'÷­‡+a—é™1ç±·ô*vÛiíT-JH²žÎ|#0!ìQ ™~zkÀ©ƒrN¶{—èõ0Q^}J†XƯ©œìÕ>8&ÊÊ뙯³RÙe éÖ þͼ#2 ¹3\êXÿÔ^–I.4 Ï5^Ñóyœ*:3‰x™ýæ›Ró—K¿RB¤o.A?Ì]¢ïÕÃNÿ`q¡}Øà¶£:Á0æðJË"â.Aò7sš*ÃÔ)|R:eZGÉ ,}±ˆLÀ ƒpÊÅp¡ï :çQ†¬ª.|]û‡#4 T™ÒMÀåæçI÷ñ¿¯zZö5‡‹é’¢HbVäèÚ÷ÓB#l(áãƒÆÜåjWDü§€JÖëž:ÛÒÑ¡j¶ÓÔKŒ½âŸÊ¬ýÙ{}æWóSç}¶¯uÙ8.(Bí ?I Äú k!à'ÙKTOmBK¬‡'‰•U–– ,ãg=gvÁ&ã„o‘(Þ ÓÜ ß:Êç>‚î¦Aƒ4EvÏe4ÍÇÌÒÈíèøÀ8ÙZµ§ŽHÖËÒjƒÓÅÛ€ÿ¯¬ÂGk¨?’¸ß­jßêMn°ùϤ<a¿úÃi‚ÿœÔŸfMÚ|G¹:cÇ>0æ!ž¼’ùæø½Ó5A¤å òì.¶E˜gB*úϾ†î¥% â^­%7žrK…òý]úrßÈ;¥Ç¸çr>J²ŒŒ¹ÿ‡CK ‡ÉæK’BOå?šf ¬x*å)¬ÓîZ† ¥ÇBÊ­h ia¡‘¼öÂb{²,mDÁ=x:›.#ý÷w Ê$réÀ•£2ïÎLsiR•ðçœDþÜÕËÓì•I§ýîÈû@! 7C÷µZ¾R³A‰5ä& »¯tó;ôv˜kaýÈâLgG9n¿/³ËmÍK®ÂBfz˜`×4{ø£u”zÒq±–µÎZZÍÈ2|9éàqÛUß7?2 Ô¯:+·&™i'ÒÜM[aHq¡î÷÷\29ˆà¬Å„Zó¹7è”}!S©Fu~ß"mÉ̾`èù2©îßÜ.€y"€ß:²«Ó¾9år“‘f8Ýk¹œ e\‡9­3\{—ÉÎ 90£¦ù;˜Å±x™'ÚQf;ÌÀÓ”•ÒþboM+êâÚd´Ð&ÿôÆ¿÷ý¾T¾ðçÚNá¼xÔ€²­ñ/PíµÑøk{t _¥QˆÑ[b31†ÈÐ;jaQ)¦~ÏkÙu&ôãø4úê?– 7­%Ž5‰±+OÖ¤a_‡å ߉GƒËo­†çoGÑÿh%ÜÂPO«‹t7â]­‡Íæá”0Ã6Kü 8ì[^MË­×ÕõayëÌÌØÍíø´€ð¯S!èÕn/‚Ãtbp¨2¹œÃe–­2!BheXë _[ÿv³rp@ˆ«HÿÅÞ‚ÃËQÀŠƒ0ÎÔ…™M·v{dh¶¤é7ùªlŧúqØó°ñY,$ÿm–‡‚tkI¨†1ØQTÝÚ—½B‡â8yòóq¥ƒfyw÷o·…#çk7¼‡ šP3R0KÊÏG<*£@ZtVÇ„¥YæŸ>%"Ì{/†qëyŠ.¹õÁíeÛ=9&÷*bíÁ>æ?‚ zQÖhÔsB‹ßuçn’µÙ^ ïG/¥¦¨ ý©¾ åœðç]Sº¹aHt.\üÛ½·üð ¸×ªû†êÓ²"YOy¤ýeXÒ¢ ’+(¼m²f\3õ pV}c§Ž´æyC¡ PÊJC1œŠñr™Â€ÒÑéb*EpSR{y¤m­ƒ„¢àîÄ+û{¯ž¡€Rÿj ‡ðÒ Î´tó¾¬Ä¼†ì™±Ÿ™³;}½¢ÇéÿÕ8ÞŒ!÷Qb^¾P&@ÑäßËÅ $Rýãå±_OîÂÇMËýFfI>A®LLæ¬ÕYîÞ$˜»yc_~ìjý{BÀ”­Õ´|é‹ðpÒsvY‡ì Ù £mñC3uß ˜ É ¨KøšvÂÞÝú ‰nÝê¦Õѧš•jÁôî’ÑØElXÊòÀ±;$ÓŲyiïü͘uOçzBœ)ÉVvyÉ2çùAÂhqÞ)Z1©Å²3±|—Én½‡Tâ2‚ÀœòùÓ¼DÖÃö´°eþy™ãÕ.Z¢ ¼‹Ù1ØÓ¨kŽ…Ûokö²ª‡WÑ”x¾9ùF=Ç« Ä 8uÿ onÑÖ”þlM¯øš•ÌðæQAõœøiu8¨È¾ÙAIÀœrïpïzq[اlM؆L¸ï™,›Ôn—èëèaF„`°Þ<€M æ¯99ËGú. ‰‡´*J€Ú, BóKäàm™y‰Ö¿Üív"²:ð]Ü­ªbTMÙ\²‡ús aÒ™€Ý“|IjJ„fBc»ÂcïSþ˜¼§Y'ÆÛ…Xt+ÁæÚ3߬ÃKߟë¸ÁùÝüJ'Ìëþ¯R㇠›ët®æHެ|›RðW*ü·n…þd–`[›²&H—ÔÇË ÛöžîŠŽs› Ue£ï h¬%Å${¸ÿŸ)éß©0S¤¬ÿå¦ð£Š…E$5hŢ^n5z^ç~$9sœ¿65¦e“ùÜH¤)HŽ0ªÕDÇÝ#Yßlfw¯êMÛéD ŒWg  ›îÈÃ,§ü”4~2Ô­¬ï#xLß‹íhÅú{ð$rÇå™o ”ôåe'0¨Ð±£­v#¬ÙþÝ2ë9QE°Êƒ´pAÂLÓŒò(18w¡.ÊdÈÍwŒŒ€º¦zU²Ïk ù†ÇÆ™ØÁ´ˆGßEÉBà3”·ÊJø¯ŠO÷ òôBÜ¢ˆ@c¡öÂ@ûç2…³2‚B÷b ãM¡nváQeÒ¥¼Ò`&Ò_3¢Œ©üûô˜Lßûï¢ó eÄÀ¬fdì W¿y'»Å.|LÇ RQ˜×·a`}x žNœÇ‹ÿ]Ø ¥#kO¤Ó!KsFr û?í‘n[²o0íäPÙùŸ'æÑøn*öÏYÖBýÇh4¯5ÿÊì”#SÍ<œ Ñ_”P.hðÚ ¬"—óÆ¥U¼F3“PJ°g™,%Y•‰c…8â¹Ée·è*mXº‘Š|h•{ø8Ü ˜|¿ÈœÃ¾±ì˜¬òVТÁ™øž…Rµ»„ñð ´_A:³¯}ëÕÁসãÿ¾ßT ?xè?Ť%ÛÅs~õ6+x¸õŸB‘·%8tpž™‡¥LèÝâ!½Ž™(°ýxövûVóbbg…÷ª–ï²,¦Èî°%båhõûM@Öv8ŠÉ@Qwœˆžù¿¤!Ï𮄇~„ÎÜ ©`êU0(ùƒÆÊÑ“iÐá5˜õdRoj^êÛÞChH‘y/ø;`:Èߦ2Hkð †ç»z_!^a1ˆb#Aèï¼;àõj P—ºF0môå_1´@T»¢ªÖ>E;L8‘ΛpÒºÚ; ßrˆQä`nä·‰­qI¼jâÔ,¢x샑‹|²\er«ÍÕú}D oÙ®c4ÆzšE­_š³Ç$i°cU’kKa³wYûS"4Ì1U¾ó€Ù [h¿]ø½˜†—ÑÚÌ~ñEèIæi2;6}id”¨P–¿MO¼!lFƒfgrå ˜—ÉâÄÀm,>%'åAå¦vV¥ðÜ©Rñ¥üº•{‰7Ìo¤)D†ªjŒ·ÚŽ’å¯ÑòØÕ"ñ’m¨€™±6éÍ,0g n| ÓgÈ(&ôóÃ,–Xr<cô2Ö[Û+ÌïaãOa¼š"-¸yÙk½°É? epÎ!ȹ“|F3ò‹ùŠ%¡¸ó®Lµb˜Ê´öŽI› kÕ£ñ[ÿ1Ò±L{M%ï6jJØÿ^ Çⵜ¥"¾Áõï¤ðf’e£¾È»j–îÀƒó± 8~Ôv]Ô«Ô®jþsÍ’ AÅfë_Œ!YFeÓ><0axÕ¹î>òsŠÄâ`ºÇ8 0iió<·S÷ì¢H] f@íëÜ‹f Ž;k9ò?j­÷u’±`KˆC›môwÁSòxˆ-sr¾Š¹9@%NšöXϦåÂA×bEÕbé»ài%ªB “‹Æ³£'ÿ+¥n(©ËÊ$Ë‚“"°fìi¢ÿŒ~Å÷;Í^¬Lu8sŸN@fÙmÌ;k¸ˆØEnÔyî_à çv¥Yuøê¢¬^Ðý¯2)ÐÛ%NKðÝ@l²LY)µ‘ÅHÙKU=ÛC@öœ÷_þDb[[†Êo´Ã¢þðœ¦ß4ìOu-P@!¬‹ÃzÑÒE\~\QLhv½ãæ²'@“?„bå¥áó*Ví7ˆåx¾mrPe`QÐÆ-ý—‹Õ»åÈD+g+nŠÞðù8“=e¥³÷±– sêR2?›”]ô8ûÝx¤ZöÀÇî7ÇËIZ“&¯ÉZÜ<Á/‰ÂÃ: .Žúb¬Íè;­³ƒ¤¨§™] c@ŒÓ8Æû^®Ç#ŽG€×¾¨åÍIÕ¡3'ˆæ&àO¡«GNC™øõñ¯ ÙÄoòTƒ±ÔlÐ!W½·«³VnöˆÈö#R"à`ÓºÚp¿–ð™‰ñ{u—aÈú+}öi)sîoáÑ骜uzæo¸M<ã·P冗¦†ï#^^$bRÛ¶zi2A&<ŽåˆÝýwþ!¾µõÜ`‚2ûdUß)¦Ö=rÎZ9ºë',L %ché™ô¦‰I¯¯.õÅùOêðUÈÔ­šçoºç/ èF[uQ©4šñ˜pìˆëoé\“­PX’ëz+·oÁÖ€pk_3 rug*D÷ p?Cóõ‡ÜùwNH†/ÙWv>9XV(1VÑkFLù5qõî!?G瓹ÛÚãÆHÞþK¦-…)pøxÜÆªþì!À1,Hfˆ£<©ÑÌÞ<ô[ájë!Ï^gf °L_€G}DiÓ=L¬ç ±€’$H-ÆŠ|º”*>ŒäÑú¯QõË‚ü?c¹‹à¤ÌP=†0f37EºÖ‚SGÔ'+ç¬ îÏ ðu{Éœ0¡Á¨ÀzêÚðíVF§Z|c¬ ]Ëï e¾ƒ, ¡É?ÜðW÷32÷aYN˜c'Š•ò™æ!h™ νþ~×GHÕ€¿/½ìvŸn®ï~²ÙÓ~HÃx¡TÖ„€+@ˆ¡!ˆy÷¶¥L!þsÙ;²mN–·}Úƒê—bcØ@yŠ~B7.VqRù‚Œ¨•¬.'?ŸË,ÄxœiéEV®EÓÁo)¦kÀIx(Ì)ŠÐ"´&£åX¸k1ggdõP0rEà†›r6‘DFE8jù&\˜ŸYˆý)¡]o‹áuó™-\ÄÐÑ$(ðÜcäžÁÙŒØÀ»w {ÐJ @TÐ]¢îÏ~-8Öa­}y9r3O+â¢×äh?½¢î>`®æ hãõ‡)€)Ü}~û Y^èלÜ+ÈÆ-a°5~ß[ φ±|' •yžÛ¤Ï{X5ˆZbÊÙ’xyŒ( ,…¾¾-:/Ⱦë¥J.]hNÙ Cª¦óîÐ’ju‹½m’"‹€š¼N­Úr] §®ŠÙbò6–9ýDä rœ‹Z2vr$ÑeÖÂVÓ—/K=Qž2·M&¤Vñ@\N…öŽ Âå@V‚B"Z©¼Ô}¬ËäAÀ%–Ú‡ ½KL+;ŸÍ±¤ó ˜NQyÄh¾ÃÑ”bÝú…ÙA?²4û'ƒ@2ÉØz©AÛL`RmT!ã4׳ú&°ü7ÏÐÔB¸”bÍCq÷¦iD°PŸX´ÀBD ™'b¼™Õ*£^‰ì[×E\®|Á—ò”pøGåý¸ïvÎ8Ôt)ñ<9åR|¢™Ñžó+B·¾PþIý5Ü$·pÝt¿œJ4»î§ 7«ÿxP#–ó•FP·€N,wbÙË‹1 ŽÌ|ï}Ï72„ðžútÆæ"ÜwÌÀÉ%$ˆ[4‚¹Âµ…¹)<ܪãE+f-eµm[¨I¡ëp=Kx_Šc®*I†̊I0Sè§ÏÌhöË!U)k>üs®&'pTr ç° `òæø_-ý†cgxb½sN¶rÁ~§ 8¶ š: g,/äÐC!4zƒ»œÝÏZ3Õdl^5;ÖòO˜ÝA#²”P1ñÑèÜ„Âëè6hg}šq˜Zbâ,'òÿLþ£uúÌâ^3ÁÜ@L")çÙV”>ñ…óŸý”>\6ôK3¬|½¼fº‰à…«œü.þ ùë»bñJ±=pÉsÿ­çIŠLŸEœ-íæ›EÐð#ºÀñoq8{hÕT®6… –¥/<0 Bµ<Íͽ¬ž|•f/“ nò4c‘ðyX^mcA§Mö•:¸j^ôqm¥YQ™ºæÂéî+õ YÕ!Æt•Îô¿z }"âUn1~L<ÊtÁL0ß),ŽƒFi熽aéË>ÔsÕ^Þ˧“&¨Î-œ®„/Ôã¶Ð ΂8&Z¨vmE#+ÔJû3±4HO·î"¥PÈgÁypÈïü_P~”X¹®ˆ(E[6«•­UÏ,¶n¾ hßAEð–Š'Ÿ n˪ï¤s;%.6 c$¡T ôîæògdrÀèéxL"AL£7ÆÅ{3.…2‡8Ó¦´Y23/së]mŠw8FŽéÒ"Pöò¨`ÍÑPRjCÁ1F<×·oð]k~¿çšZb ¨O ô÷+ç“íi× 1‘ còq‚åK2c·¡âœGäuÊ%Få^ ªï>ñ´—™ƒ‚ý1×€fxLÌd‘•š=¾jÔ9H*Ä0óþTÜ3@9tòVY^4uy’ÄM~?^¢~8ÍHÒ= •¯ôüïþÊN¦%öÈæÛU[8À2¼á<øÅZ›Ö^7éÍÛ±-ëéà ¹‚Ù Œ…’éÀï¾€Óþeƒ5öñ7ª„È„_î2‡•Ý>ñ PœF]®TÜäÕìU@=47L;VOO‰Øð V­Í).ÚµÖâõ2ûÁ¡IAÔëÐ+ÂGhK¬ í;ÍXPÞÄ©\¸¬Q0£ôå¹¼Y¡I9;t>†×rÚ¢)ÜM׫ÕÈÁßHÌÀRkÆøk”ý© !0Ç+P±ßMö~Fãz>iÓU ƒÒ¦8“Ô]Š¾Æ™¬aZRÏ`”ÝŸ·ÑOVøméa‹8É›u‰ËKF±+•½Ä: ËΕ£åˆ©5 z©?©•ÚhXݼšNwƒÁ$š ñ3ÛÚ6šD4­ïTFLñ&£ëNüŒw]×Uf8+ÝfùÎJ•0#¹‘çs\ônmGšk7Îÿm¨†ã jU.¬€Ì²3¥ÝI™ Yí-.¨ˆt¹R0ºï÷‡ž'T”>dï²ô}e}/?‰4ê Ò‚Åâ3©ú­¿R ¸…ŽM Â¦Žæ¾Yòeº¥Ç®WIs=ÃåžÅã/%Ín¼ðoúcl3kF‰ 4 ãŸH”aÇ쫯Çî3Ó ¯ðb'.»J¦÷ª]ØG²‡ Ã$î• uê’n7:~ØŒÀJq‡ýàõšŠÑâÃMÊ´Uþh}-Ö^#¾7ß«Æfà¼=³sAp·Æ’ÜU®wKš`ÀâÞÙâàRçÿÖ!Ìù…>U }‚M¼0`¼?é_ÿáƒ[¡ý ‰SW¶ÊzØ‘qMS¶ð‚ã§GPt¿ûdþËwu"-Pw oõ±‘vž5€´ ½ÄQJϪ®ÿUú¢Êx¹Œà° èÙÁB)vô#F»øÈÛ#«ÑûœÛ÷÷Z€“çº*Á¨Ü5ˆâø7KÚ¿++OTôo‚ʰA˜ÚóÕ: BJfáÖ«,¢»(yÖ^m‚¤¦æ<‰a¾UÒ¯âKŽž2sYi±‡)¥ÌŠZ‡Á©;î1h§7¬†-¨ÃÂò)]Qabº¯Úè».蛌Z#Ø#ùgóA»ùÈÎÑ|Lü~t“éò%†ªÖN?sýqlt>oƒœ‹õÕ„Qq!í·KhC3à¯,¢·TTî=˜?cJRI'ÅvÀ‚/œU:FŠõŽwúê:6V…r5O;Å… ~H¤ÖÿŒôm®Œ|iû”œ@pðûÏ©ôy‹ÀNÃX ë_^§ˆ»3(ʼnY½Å¨ó|o,gïív@ÙX½p¶—§¡hKå‰ål”õ BQ¸‹ÞŠØ˜üæËÝ| j­øŠQ®ôwŸ²I˜AFÑ}-ç Œ¤wÖ€…›9íüÜlC}½iŰ3è!ƒ„«÷Ð&Žq´›ç,Û\%|V€%¤S¢xZÀ´öWL•\;2­\t,‰éøÎÒ¾T~ ¬• n„ÆøgÐ×Ig4€ÇrÚ¤ýpÑ2²T¹úHª¼Ýè~iË’ø˜<~‚dW0hNôªÜ|µÄ÷ÎD°WTK°=ÛT ¥ÛµKÌñc‘hàÓ¢A䌬gÀψP1ø²ëqÊkîMS¯=,E×Ñ`†óeâ=ãLËFòn‹##Ïå[í¤Š>´rúlÓd!üÜ:KšM9¨v0ƒÎâÅî¦Ë!äë•QP]’ñ0‚luS©I…@Þœ7”Ä &íxÈœÙï÷ÂÎOª«úu¿J계3D'¹?ócò?Ÿ,Õ¦^Ý¿× :Ø™’ŠÀIxƒgâªH‘Z‚ÔÝXS… O ðý%dõDÓ7:RÑŽ \¦@$ý¨öãê3£g g¹û?õ|SkÔt¹H’ýugr4*ØWžÑ ÇñªF«‘Uæäßá.˜úÒüH¾1÷ëVtz<œ‚eS^ÄÓ.ËZ€Ü8Šf"NèñmêÎA ÕìÌÈ(!ž¦ €©Iz(hÙ2’ÔºÍIèÈ ŸÕOAýÞ¯aÅpqñ°E¥Ò¯Æ‰@¢­-®ñ£ä-ºaO7’ü=/·>š^»ÒTFI58mÆ8ª­öF[&+ó;jÓ1Øåkc…òó²’ÞÒ¢ðnñ¿! WƯo’Ò y!ĽÀªLÓ þ`ÿ\iÃE"Û*yÉ@Ö R@„ -ík]#z­ì3GP¢—ÃôöÀÒÈpŽãž/*Ù‰ž5z–ðc¸‡RÔaÞùH—´‹ÌÎ2kþ°ãF €ØÞðŽ‚¤¶#èjã¨}óìíò¦wñŸ>þ'¶ûm˜X°Éâ¬?&/²Uwk‡`h¿²»Âq-N"&àkÐYd$Âû;qc§ó=mê^%³Ã: ‡7” ôœßšðSwŽý£%$6Æ‚ÑZz;÷îu »Ü_ÔÇX ¸>©ÙÖ±Õ¥¾^:ãG^+†G’§¢é‘hãVNœË3…>BCÍl(íDÂ1îŽ֟VÒFHózP¤Ç5­ÌÔÏ™ˆwú=e¸4W_Ò†N`>É3çj¨xúû¾oĤ³j9RI¦©cYÎák±Ü<Š[‰ù$Äíê…Ñ¢ÔõnPu†£áÊÿ’±‹šå:(«‡OrV3râò2r§Œæ‡Z@›V§(Þ·€b±·{óÇ•œ3ÉÇäW<”¼_Wf½”L@zsÂtDsîö)ã~@K8¤ò«1ŸkúÌŽÞ½´R„Hƒò>0S€î—’V%Ÿëßѧ+vu7‰ G^M·.o¿Æ³¨uGýbÎ'ÏÂð!µÐ’õ×gÎüŒ÷9ãíP¬$Ë/i7r?ÛëáIô{ 7 Iðƒ™"­ïxÏP¡YJˆ/ì²Ïïl(DÐ$Ü>GmDA ¯‡ÖiŠ¥´Ö4¼„z.ïÛ C©LÞD®ê ¶d?§Ân/GpÂ]·úPr¡C}Ó€º¥Ô%ê\¬TsáuŸæ™9(6—¦0h(íÛ_;Yšdò6U©nyHN™ˆ‘á¹IÁ‹¥úÒuAtüG¸ »RèíÕ]M÷776©r[¢è_¬âÐ7¦}uâÈÝŠi6êó>/sÑNópæúªcÿ~ô} ñ2uÛHÄ ePUDä‡@ áV¼f#æ°Y“Zik϶â;d»áOɪïjë“2L×4¨ä„Zù8sÎí«…NUi¬ºlkžî&Á©dRN¯_6­Ø=…üz·ýÂ%¡@¯ØeÝ[÷h?n“ÕÑŒ=h­”) â'Çég©+™û^–|Ç(bÄÚˆ>3 bzט‚YŽ5$iB•o£W‘ÙbÿCاϾä‡Z[)õës–lìs~`AI—¼¦ƒÓ)ªc~éÞìJ ö³›elµ}°"ºè¿Ú˜uͯEÝ4XÆéÝ\zƒì‡¦¦u›^«šÚ«Â÷‡æÿ@QjÜëÎÒñ²B3;Mf¤ÄôÝTYŸœÈ Ï]Óz†<¶øžM`µÄ”¼&À®Ax{7€p± UËÝà" žÛx=C³²×gÇìá}QU4\“òÕ?ü‘'„°á•¿—A¾»G»åÈø’¤• a’@¿ý±u[¹Þ=±óªCÿzy¾‘»,zxQ¬ÏØ{ A°”—GzeÔyÚb(Y»Ó„ñиúRS ñª9=´­‹Óèz}þXN€Z `5/:(FYýÓˑԕJ+nCŸÆö¡-µp] .À*’âp;Ñfv"&•â&ÑܤS ãæ#d0 ¸ooãåç´aS8e\šRäÔXí6üá¶ÙºY.vEüÀ0ì&ñ)õ0ëÑÓ†ºu¼õ¸(Ф¶n¡H‘ÑÂŒ›£Â"‚VÐ<ŒQC®bïo©æ'C†ñ{Üc{_€upô²S{Õ|¸ ‚ŸK¢kNŽÄ¤¡Ëø4YrZJŒ¹þéÈSìA¼jûÒ¢ÀS¶ÈdëÖk‰{yr¡­"¶9jZ•[95UÔŒ+kF kâ9OßÀ¯„è•ᢠ8Ö”?Çé ý˜¯,“ýŒ"¼0ÞYCÇæåpþfÚ¿¾Œ)·ˆ!¯9¤„ õÆ3tFPX›ˆ5éNIÀŠ—»IÐ=„«üVØ÷R;d¿ <¶gAJþ÷¶;5ä¹T ¤@¹-øÎöG9ñ2L[ÉÈ·i -Á;„G²c·qÞ³d9až™Óò<Êh™–éáÐÞ šI×RÛ(ÙŒˆ·êA9¹È`³ÈÖ+²˜ö€}s´d]šwW Æ¬*ºQOv-±E g"Ævº <w!å™075툶¸þ5Íò9D™>tu¸¨à ¿V8àµÃHx23YÓcù#;OE~:°{`ðj„±ô¯‘¹‚ò-“Q`Zš(|Ô´*É—d¥¡@pqáH˜w4K!$‚ ss<#F ÿƒEiª˜Vöænl(OO¶ŸÔ¹’£<»©õ;¨•ëT¬Ø_*ûšªnèB¨y½ n;Cèì““åHUÚ´³iŒ2äÿþ j<‘þ›)UF…‚ÊD”uå±N¤•‰65|½ÏŠÃ›¦hú³~0ÏJÊÛTRô¹2~c*\)¨ù!L´”_4çÆÁ÷U¯p_(ñU/Ñj-ïžA·éM):z-ÎaIç„òŠ[w´6'Ñn‰t‹÷ÐÊ[jàðÀgcÚ0ùÒZ¶aºû y§wïà´rÆÈɦK³Ê•‚Q\ù›DzŠE|ZӚǘ’“—¯Š[?‘ç3ãì‚¡ãÒÊÔl­ï-ÀƒèO½+—¯y4”!11‹?(âŸ=‡¨0Ÿ×.…`dXc„Ž€ÿ~T#§îß#Í®i‡sÑ2#Î;¬÷/F==MH‡êžéø€šªf^µ¿‹µÔŽ–ýñœö«>lÆø,k_ Fôó Ösk$L¯ÅȃH±©«¸f=HQ¤êøGï_ÔÏ2ÒwBÝòövC÷œˆË<@NŸ‚¿,OZóœ ì /ÈÀ$ì¡Æšr²ïd lœPkÿ/ZMap}çŒÎ~̤£—¢m®`gd??d̶ãf~ü_¦Ô鸅üÆx‹ÿò¢©‹×"&^ôH4"2¬d™XãždêšÙ£ŠndųpWÞYm-ûVÇi ‚‘b(±±§-ãn8›€R ”Ššá©¥+*Âu}j¾m ¬>;®¿"ëþ 84NàŽHìX20 ±*`ê!c@Ñ– °…œìÒ`¥«T™ÔÁÔÙLˆ%Ôsü½Ø z™ôÐhò¿Õ{û$ºù˜š7g+Q€ž6¤Uk© CŸ’!Ùïž»ïMú*š@R$ïЦN?˜_°V×£ øóá»ñ.V _z\áR˜nY:WQÞñ¹Atž­sizߢvLGúWlÙÑõé’ 8àãèK%¬' ¥ªè’†6òñÙŒKjé´¦ ¢—áÉ>÷\”% "‘Ëm .ÖXJ±Eå¥Ú¿ÇlÏþÈ¢¦mO·ËjV¹§¼ ˜S£5Æ{ï^bküWv/4Ò@—¥j›&¿„NãJ³÷Ò,Þ&5ÒK x^U:iwéuh{æ !5¢À/T  UP0+Ô‚ mƒÇ©hFZs–ä|‰]È“cÑÝì'ö¸ ÿû„šõV›)‚ Ý\0sYÅ,€†V a¤…¡_¬Uid×toˆq¦dâ‚Gwý}x´Ï¡š0.à°›â¿`f>WÇ£Ÿ€äóWáùÙ³ð¤ÇJT5oLntÒX-R ÕÝïµàÎoïå&GÖV»”Á2/€CÕð-Ž™no4ƒEâξKį÷λ±×¼=´nê®bSóD4Yþ$ùó»äÎ¥HnÊý¨îJœþóiuXÙn—y5£º†iøÏóGÅ0Ä_ÀÊBêeXŒ¤”§ÈþØû­¥F ¾´*èÏ9*Ü÷…ÎX¾¨[—E±ë}KÊk—ÇöáÖlÁ\{œ…£‚G&iËÈ]Ž™¶š;R¿º|¡»Ü2TX¿ö™ìd§ík,žoõårœx|)ƒ#®–kÄ´»cxüèŠà×ôl:5#úèê3¨æÌlÓj—˜f«1Ð`·–†……Mz“ ~mKÏð°¤a`/ЭU7ÙÈ…CŠè±º—:lª ÇA ´O[Y¸fKgØlcBL+#î^Ä ·v¹œInz25ì³ëáú«Xhp!xïÖwA¼°¢gÚwÞ1hw…ò )‚ûàXû³V“OJpkÜ2üX’Z}æå…PœÂ¨€œUì?MÌsDK Õ1‡ iÀ®¥¢\Ïl‹˜U·Òþ4tbN ×YsÄ ¬×µéb*nÈgœ0 ksí›þüi[6=eýááÛ#©ç‡ÖÞ·úVñ¿?¶Bñì :.ß`Mèøë|Á°Nå-L%<M§w5Í3qIvâÀ¡µÁ—æ)*˜„•a-Þð ò+óHj¿€­êQ5ëP=%å° ±_©¿v]k¡Ì‰Ù™"UcÐûuݲNu Á%ùÙ—ÿ5WÓ%ÅDª•ž‹a Ê`ו©+…üæØ‡ íhKpé` OÙ?‚óTWKÜZŸÒÆæR~¯ËÓOá§¶“ׄ¦NE­,/†æD©1£¾ˆã- ZÀàO‹ÈÆ×¡ý‡ä~ßxqKì+Tò¹dâߤ‰þèt„œ®?Å^´6!—vi#ÕÈv Þ Ô>B_~ð¹Í;,ÏLš”îÙÂj¶ ÉélöS¾å„XÆ|¹•s+~A9“QŸõ&šT”_÷6¤qHð1\w[Zå1xÓÌšŒ†Ð jéå_(WQ¾1Žˆ(ûñ_aT{â™ÿv$ ÍÌNýjúÖsš‹Ngt‘¼õ‘Pk•Ö®iºGxïËö‡\1Â`î:)¡™{ÿ"’1Úº‚”2S»–¼¡o"ù¿lEÒÀQ F¦5æ­"Öž½Igˆ ɸ8é¯ÜY•ù!²}´­c“ûTË8"u¤qS—% aß^ya§‰´üÓlD*–)¸•Ù7@Ržìs7DØ"šË_kÁq Ã!÷f£!ÿõ/TCâ4DçˆÈ=ÑwÛ¬]ÇF\Ÿ4Çê#t/ˆ@à!ÀøV‹ø«¸K·/‘_ðZ d>°ï?îþ–A?b-BÊitjB'»ê‡;ýuÆ‹¸"±–u«rý)¼‰ãbAÌ…ÙÉ" D#e·s ´Èóç÷ öº½ð6ËÒ\v2t3áÎÜöv*ÚIãþòë\(DiÌÓåõ{èRæëzű?­*Yfe¾ÛXÔ 01@#…æ™fH„Ž!¼se".ÔêÏ>³„€o²ö’à´NÔîªÀ~?Ï“¤z¸™ôÃIóËzT´~Lù (t³ŽMß4»Z’›|Mv®\ÞYcAU`}™¯‚ š<±)cZ—êôêD›îô'‚^ñé–§‡ä>®L²½®Îáp'–‰îKÛ[ËxQLÃŒcÀ®ÃÆó<ì.šwI¾lôo½í$þÀð·V”$'Š£!¸W5~5âø\ÆŽÎÀiÖt&ð¬…6ϯ¡J 8‘’ZàAšê̘ÄM¢  ŒjŸìb)`~º]—`RP²”›Ãµ´_dŠKž·Í—`≗Ǒᛚ‚½æï\0§Ö%©ðYr†`·‡•í)ÙDZÉ’Tåþ)÷Ì}ºk_QîÜ €}z¤@ÔzI4*ºSNôÀMÀì¦:¤Oíz@gb¾t.t‰lvÈ›F(ÎÖå+ÑžR¶·¶{5 Ì;OÞÈ‘ßã*…³ëÀ««xœ; Ñl|Õn_×]%ô×¢ñˆÌïZAvŽ(ƒ'•¹;àFKÇ-ÞO£¶8ÿð?<„‚“ÞÁa)ÃÊ—’çÊ~ÕäxÕW<ˆ×ÆžÛö'é:ôMb8Ô8gø.øQ 2–²ÞRu Ï6·ž#>&!é0þ¾#óh„Žqå ¾ñ}¿Âú±ËÜ‹„ª|Š"_üÍd6³Jzçâ(©Ëã•>â(¹SÃGiUΕDã:É‘q/Åê7½ NήÝ1zß2Ë[c“¢oÜ‚/—gÒà½ÈìΛäv„jÐÃ*±M&éä“ÎîG’¾ùaBR°Âþ”MüëU¦-uœ:ÃRV‚ÈÐäë(Æ·Ÿñk1ý;e¥âódêF9ZÐȨ°n“3ƒ)ˆÑ« H€ÏM'¾C×R!ÙÔ§šß‘çÒ “tébR¦Ôp„˜bC¢Ÿj ã­·Ðø©bCIÁ’´-* F¸-ˆÊòzU2«9J‘}UB¬F0žR…ž ä37¿*ƒz“ˆ"Á„–QÊ)ŸÉõ5Š)°LËqqºßø{ÝÞ‡šᬄQ¡³¹óv›ä¨5Bü´çDJOÎÏZÇÁ2ý'Qlë̇Σ橇Ò`«¹J›9:‘A±Ð8^qcñU€\JC7>8Ö{ޤû®ªº¥"l²ªötôn‚”×%¢IÅ‹X:{㨠4Ê<‘…æƒ _Ñÿ"ÐöÓ%$Z®„ó li¹Iþm†åkîZ׬œ9ߦ)𣠽û¿ÙþT†–¿èº4#gt¦À&ËÍFÕ)ØèMÈ,‰)à¸ÎçhìÙqÚñ¦™ôIÔø±YÔô·¬Ü, ¿‘y ³Dpm0ÓR·Ò¥›¯UÚ˜6‘ä¹Tóô×Cæ-ëw2ÍÊNØø‰ÖÉ8D‹2  ˜î½6ÐboŠoR¶"Á쎹0ÚT ¢”ÓüGi|K®Û´ˆáð*Áég²rŠ6ß&B™\"†.ÿæ}r¬ÐVOž£ŒUµqr¬€íV«®r¢’é9í frƒ´%›³g]ôR9}Vá”ò9‹òAÓâeÔDÖcãs´°÷<]¹º± iÏ_]²rš` óàȸœ·~µ÷ `ë…~øø¨à¨²Ò÷Q^üDÇ–ì<‚‚Xw#[†ðŸEÍ{nv¿€wZ ÑM‚Œu×N_Â¥—1—PÙ«<»ý¯/Á¿ÕYíUÏzþ6Î<y"™À"q„,‡ýY]ù)ë!ËåG+Æ£SÌ\cÙ©œT± D‹{î¤d«õ…ˆ÷U2O^ ½¦èØqä"½[S=´â/¨/‰¬Kž²~y‡oºÆ#˜ö-Åvk¹¬Z-Qœ)k-/|«'Kç1O¦¸"a„g5'†œžX¸uSÍ-‹¾¬t¹’!éƒÏlé`ë¡!’ÒY"« ÈÞ{«SAC]”$ŽJ>‘ïô\ÿAioa‡QӴʪòÿ–è6 óÊ¿z÷‡k‘•S¢5BS#eY+䣳šGßòÏuÇÏpdhÿm+Üi·ÊÚE½”„x˜ÒÉ)tÇ’  ÒðK*-Fá¼î0Ò¼ñCÅ­´S’P¸ý¥zD²ˆ7Ì5ÓWÈúÁ ¡QGçšd Ò¯‹£ä~Ò‚z‡@n³CÊBE±Ÿ³'ñ¡õó1 Ø µCqBúáØ…#†ËöÚÕöK=ÆOå²¶HEò³Ðj÷ò‡Ri_“c¤Q±Õðuã Dž´Ì Š@ðLäZÖ½%{þTyzþ0o¼§©~àmÁXR"äD<âôXzWˆ§Yü'S²8Ãi²ÿMýä us‡‰Ý׫m ôh‚tÇ9Ѿ¦Ôyx)füE· Ài°²“# ŽÅ¦ëP‚ƒÖÒæÓ(«Æ¨çŠQaÂfë {|²ùÕ»•@Ñ÷û7sÿ&0M`³Þ'ÉÔx%•æS9¨É:âʤ±-Û­²·Moa§ˆ&÷¢Üz OôÙÓï3:@.Îj¯nƒêÛtZwÙ ½‚é «hõlÖ|™c@$Ó¯½üÚU—_ ?Ä#~_À¦ä íÓ“ÝÖ±™;1@ÞÁ´IfÈ´Pñ\pF˜A–8<Úþa’ží¼Hõß^Ön^aWª²f5ÃQf ¸jÊè÷!º‰,;¿½ØÐ"2Žš4¹ À˜ÈHÂÙL>gx)WÄdz£Â¼~íýW4±¡ÿLXD¦^`GûßJ’î×dz³°ßsJd“¶*“¨9‹¨«ŒÂx34ìr/; ÂÛZõ¹ôxôs—X;£–¦¤ƒ+Åîõ=¿ – Žñ}—QÔ½äÖŠ·R1¡À¢'þÃôNá‡w ´xÖ”œÿFÏ[s#H>É +œYÔo·L¬°~w`ÉÛëB4‰G™îûgªøözn0–õÕgõx3äS‚Ëê–£B·ŒH®×ýZàVµ„èù·¿;¢NÇì ŠÃÕš‘|‚”nÉÀà-î°ðãé³PU`áIÕ_n óï?ô€>Í¥_b?á1™B‘±¦ OÂ=¸‡”1"‡öý|ª}í#÷;Í ÷ÏÉÜ“üQÕÉô¬žß_6Â1 :¯0ÔöyÉÜi|{›š‘Ùgú]GìT¶'ï”ÅŸæN18ûOznºÜZ9#.TÓ«¥I ÕóÄ™C¤Å‹:Â3 5 Drx¥jÙ}ê,;j5nzÆ4͵ãĉ Ÿ³ƒÒoß{¬\].õ_v'>öF%Ìfnª²)$ÿ–ÎÐùfþ!7U’Ï'MeÛŸ8r߉Ù:ñê]žÆƒ™' q@>Òõ9Àß/©¥è,‡—Us?Ûâ) X² Ès2'zÒÔª.ˆà>ú½M鼞 $i¥÷ú2ÆjÏð²+£Ô§uÝ“¾ÒÝ êS^÷][A{„9pøÛÐk±Ã°OÂN¤…uÇ}Löa³ï©Qk²ª?ÎO› oÒ`óøòÍ»)´Ç¾7’å|?uÚ8%%º€÷¹ßjEÈõõD#O,‹KpRŒE=!AGƒ¢žœ¨»É úØøVðŒO ¿E(9|n»9½ý7_ZÌÖY ’ƒ é ¡gÐ0•ãƒW²iYúXÿF²² Ò•)3„.-T¡C'{þž£; G¼Wp«þj3Ø¿à$Ö]£p_J•÷wßxQÎ,ïd °¬J;F‡ð¿âµC×Ð;×êF]ÂuzÒù2É) [[¬qPܨÓƒ %Tò@¦.29û¬9£ðt›Úv™u›OŸ˜ôBð„méÕ>„qÚxFÈÛ˜•ƒŸü­åsþ9ôqÓsy3q}Xæ*5`­Mø–Ñ Òdïœß¥³»ÓÃjá³Û·%¢( •_(Òv@Û{‡ð0_Í…æÃŽ_øCF¹’6Ó&øþé:¦’2²—µ¦è™Òôépñ—»?ì±3$ý;½‡ÜfÀçò¹© ž¯òWeäNÝ—ú€ #3Uîù„‡ÖçGäÕfe2¡ççåöOGÌ åM«( ˜Ášr#BÜÅ$-0‡$è¾Ñ²¯ÎRZEù öÞMh³ôSàúyð×±—SaÁýè$uÏ¥[…%ǵüvÉèy§(-êÆ@PôñÅ“]-­ép&‡ŠñmyÂÆÅ†ÓµÀ;Dìr¿qíÿ±âõ"×%aW)úO—>,&®x r€ÁQp̵# tx2Sãɲ¿·ŽF!K×òŤ"yþ®A¾†Ã¦ÒhÉÿæ¥ÚhÏ|éx‡Û·Š)­(„÷¢¹Ö½1^  Ë<Þè!1)ð®ÅyW»ê¡ê`³½sŽÏ9†<,k{Û· O„&i ô¬†½ºÉ¶&ëÎU‘OxH»kµ&„Â|í{l‘ÝÆ@*è~\ž)º×=஑‚S0µ*ª p×­x¸ÚÓ{£`£‚ÛÝÚáp»×›džßÜ~íf¢U/¡UIŸú<ìQ"—‹V/ŒÁ±É÷'²’ÿV†Ë¡XÒé“ä{á’˜2ú=„iì?Ò¬Yã¼./^˜î.•û:mb¸~r,(XCâ-kAM5*Þ²Ál`†„ÄG4"Êÿ¦Xt3&¸“Ö@¯f­<ƒ÷±L 1îK èœã& _U‚ÿôq 'û8åGz@IÑâD¤s·9såD©ÙŠ{õޤ•vC_ ³úW§ÿ H»µ8)£sÕ—Ñ@–hþñ‚Ï;)MúŠÈOPÐe^!ã Ïªn0ª!îÝã˜óŒ„XȾÀYÿhu•DQäz¨ã¥Ö˜#=±ÊÜ €Jìà±ÍáØ0ÃR|ŒµKgK> ˆÁjðÓŽ)•ñ$Ÿ‰*VQŵÄrßg´™N±Ôœ7 н¢G5úou@…4ÀDžÇÎö÷î•ü ‹¿3ŽoÅ ¡ËÁ¢–8C¶6RãÅBœ–‹2•ú¸Pïèà™•ˆâ,ÇÍõ~2ÄrNž) *›g™}X‘È|0Wë¸(¦ýDºoˆÖÒSr çÆ€FÀ_A‚é»”Þô~R}±Ü®Á îy.á•ø8H™Î›Àß­9ä%–#f@‚­ÑÔr;¶¼ô¤t±uw¸êº5jseÍì2éÔ6¥µt<÷ÌI‰V®ÎÌ]=7ÎèçÃXä£|Û'¿ÂR©G²b÷z4M Ç>É;݆ÑçU¿O2ªžJmÀØùj*¾­Šo\'50¦Úª2-q5OŒ@b¶†5Ê‘e’¼¯—¥Øæg#Ã_¨fî€Ha²$ˆwFF½UÖÜbB]õÅ©²?×±+kÞìbkB #kF/¸HoNE™Ôè¿ßË@Dq’ŒŠaåüQjA`—ÜÄn—'±æUÐÈÒÿp„wn.z)Á×¥êT7±jϬ[œ‘•0Á¤Uë3EÓPa8«_)æP)¼=5®Z{ã=Ë_‘0s ¢nm¤û·ÇÇrµ ×#ý¾¶¶bA¡¸U[OécÑsÃFU³EwFÚ „g<Ìæ™Œv í÷"È9Áù/ëÔ'#/*D¡«Ÿø¾§"Wb±.gðÙÂR™ë™àò{o>ØAýÚWß[:ka¢ŠWgnÞc7€¨I˜»õ’§é-Ò/‡£îÉHÁc»u¯%¶û¿ÆêÍŠ{´<ÚÁ ¿@Ç:°8NEmÃ&ÄéºæLJ©‰î|Ž]åÝ/aÏapމï=± ^+~±r¬ŸWצ §[akG6lȷﱩYË,½£Füü”˜¼§G¶¼Ç6µ°ÏÖþ€hpÒ Zéj©\/ 9wQeL èü¡ÎÙ5Í ]cõfþ½ÐùêÌ_ÀW“ÕÛ‚¿b!=)ÛèHv¶Sq9|ö†=ŒŸUŒÜfÉßQõ›¥˜Óñm£­: NIÚ5¥^÷¼Ò&<©,v/Î$`¦úäO¨Œ\m“ S˜à9yïë¢Ñq‚.¦e{] ‘;.ËŒæ%€r]7þv$ØYñaï Xœ¸Ã¿î‡”ýÃëèƒý7™ó% Í÷bûü9+–³»ìŽ©\KbαyP³œµÂÀštƒ†2­zÝÜžÆ~ó™6|°2£kÿ ÏàZªP!%_Úá¶@PJ”A¤À¨Bͯ_=ô%ÒràL£¼¢n üûÀÜ3õìÄǾŽÕg›™Uo#ðâ<­0$o…ÒƒHgds‹…£×ûè Ž.*ŒlZøœ˜A>¥ÿ?¸BÕ#ºÉò6aþªÚÚ·i¢Å³{pe< Uã7ÉĦ~^s’ßsò¯¦aY Uç$ˆ¡¢Ò=ok¦ù&ú^ÉnîKö3Ö&±½ªùûÈÖmÔ²Lec®ãAgGVÇ™2xhö T»~X¶-”ùÈÞþ-:ëÕ)0òÈvÕ¥ìœáEÖÓù\<½4y‡±K-pK‡¼5ããlåcRƒ’#N½ú$6ù¡E©‚¼-°<Ä/ ž|'3l»Né¹E…7–ÑHPíàÆ9Ñ5]·â®ôè_ X‚ßCÚUF‚°ãIô°mw[ d^Sñi“'ƒ¹Ž„õG<ÅvÝ’0P¬‹sçþs~X¿mY{t÷½8HYÕG-DŸMžã”™ZŸ§öL²_¨:Qœº5%XÈ\{R@2]™N™ÿk ý»xph[eyÓÏk*ïÓF@½˜bJ3ˆR»VC’ádó ö @Ì(N•JO„ÇÔÂý¹¤@DVó¶çäi¢5®Iã‹ÙÛ¦béÕ›[ØÔþ:ŒOèbõ|}“qè ޥ᜙ž—Ëü¿¢Û ùô5¶j jôƒÆK,íÖI,j< Ë~¢éEÀ>r¶­__¶‡¼Eâtïˆ ¡Š'c¸åå'Í`0.Þð{+ú´÷lÄ%v›l}Hôèü½à‘¢H\ñáE|Y㟇ñDBÉ9¦E毃̎«“÷€’¶ª?g(ÀtTÏegÐ÷Œt4¤2½Þ¬'’/<>Dw#í;‚ÿL¥5ÈSéi©ÂÝâç\d>™){yfÚ°é…4Jæ†f¤[ÁÎÜ1=³»ð?P„­ º¾:=û¬‰>n”gs Ssç(õ6ÚŒwm‡µ;8“NÔé»s¦U,œ™³JBLZëƒö¹Å0zVõx:YEèÑ9! wœ|z* õ”E¢¹ˆaUM‚¶Ðjú`Û'8‘O¯_gŽ_†P®˜3u}oLc]æÐài“MgÍUZ9'þ¹…®•ëR-i„͘• ,ݼ68 ‡'„¿ÍÏëv°~}7-3aÀ|1Ê Ñ“â ʤì7;èà©üsh*ÿ¡qųÞýèÚ‡‚W\Øp ²½ø­ŒSŒÔÀÙ¾Ë ás­×yH'R2| Ýˆî·ÑüŽ×d8º=÷^ó/=ïŸ/ð|ÿe™ˆ§z|j.ÔüºèQ´‡…TÈJÿ”åãÖGÙ²ÕfYoùWœLˆ^‰éiÑ$0Û½kR–vv_êBªg¯ó<½7’B±™Àú)¡Šå2hW?¨ùÂ¥Öé5¿‡Ñ°N–ëhÐx~´=nÃ;½Qy¶óé|½K!8M^D¯ÔÂÀ <ó"ÿ=áÄø‚º‚Ká¡/]”ÐÕpg:«çº(g^wçøÛŠ ÉM¶¾“„Zß'46ð>§x¬í©UwâȄ’õ †LØè/ŽGWÓÎâÙ¹‘ß›)“¯ƒï‚8%1ÁÕ»û’nXC"ÛÒÓÄoäÄ`ñ…, ÚΖÌR$–n°î0‹?-ߨ¬ÎÆ?m`âu?‰ü€ –NŸ/ðÞÑ•Ž&hÈöJËÝ(R•MÍ匂[‘~šDÖZ8Å)Âg¬ï??Ø7¢U:ÄÄ¿Îúµ°rÄÓZ<(ÚÊÓ¦µ¯Å´x¼¯æV A·Ÿp†[öy§Dyþ•æÇ3`Á =¥Zƒ/SÏôÙôšÒ”}C; /gqrÃðXCt†îûÆ=PÖ!1Ñ0gXþ$ûrÍ~!=¤5_âx^û—dßäQwœËgðN·£Êié|~ºo?˜ër¬Ä.á÷èº s+D¥GrãËÙya)ñÄ«dVY‘¤ÅëpŸ€«ÅºÔ£Ý*îxÍ6œSž‹›/ºç8šŸn~­&üç}QxðÞ v†§ÝOJ·k:9Ê÷r:~ÐmBR³Á[u÷¨Û-àoÀã†;¯?þ ÇVÒã…ˆ 1dÌöš§Ý}YÙ`ççëÜ^)èbš¡ˆˆ‘'˜ É ÓÔ!×§Ú b^…°Otó ÞÅ”Tܵnõ5R׸aóê%¦O‹\‡îýkµNÓ§úú$íàò"âzÒI½eó K+I˜þ¦©[ƒñ÷ÄIno»¢·`Ó  «ójˆ!%¹j>s _†r}؇(Ó¿tv¬dÇ!ˆ2O®ÔºœaÁß €M¿ò°D¤A°œÑ#Î¥¼ õ‰ 2]5l8QÏ$æ)E[8ë烱o†½É ÒÏo€ 4 ˜¸+â'|Vêð?ÌX»*šÝÞ=  üÎ*’&TÎF,“˜wuí6$6£Å«r&í‚þ>¹ï¢”@ÖÓ ´fV¬e¿”±E51P¼ENJõAWƒÝ8š;Ë,c»/L‹óí:º<?iëp¡ŽŽ2§=SÕ?áVS‘°yˤã²}Äú‡9×Ì«? æ"é„峯 ÄÓ5¬¾×S …ô!Äb3^:§"kø¥\t¿ßÃwm…Îáü{,FÔɊÝöÐDk]sê©6Õ T‰Ý mk‘ŒË 6³õŠœð˜Ÿ¥{bàšë]kZýliþ/Þf#—eô®HxÊÍ€¼r jÏmànŒímÁS¡Û曂4ÌÁŸ‚Y¾'=H/:órè:oô¹}Šÿè‚‚ÝXÜåª-˜:eh¦å;>»cñ1-Ø+Fo>:òuTºâî׿*Ïâ=ðá첂T½´u'@Éôòž´M»ûxƒŽ(È 6¾Ösj¬Ã—DmïÁâ…‡¼Sª¯rÁ—ezñWôÎâ/¶ÿ·XqŸ"BÈР”@‹”hÒî¹ìO`HAëË©ÊzßÊ%°M.›XÑ"ßdƒÉ5ôJ©v ”üÇÙúC¨4?λA¢”H³Äúã:‹ºM|оdîàóL6ìP“̘Au×!g1þt œR,ªª܄ԺrWb[ÂXUR¹ãGÖ–‘óŸòÑh5Ç»kH$j]$ãï€Bwk’"v£”Ž,!k½±œš«ƒ¿V9¿ä-\|g!€ sÁ€¦ 84mQŒ³e6Öo©Æoä|ÿNÌÓ!Üg{æHôf4C¦È=&^d¼]6ÆÜoÉüIß\.·Ü†„ˆýÁä‰x} ·I/øš9s`ŽFBm¨3´°·P²f×­£ç(%UÆÒ^'c?¹¢AM_µêzÄœADè§%!ߢçüW¢Š¦vÿG¬C!Ašwz-‰-"ó¤ÂO÷9léÊ6½Ðw@<뫵´Œ¥hý;å¤Í6²Ñêý…Ï„X„­%œA]3JæŒØi؇îw…6 j¤ãO:¼¯D]€Í8‚ÛJ ±›lh„³t‘üÈ|tý_¸hõ a¥oå°Š” Ž ‰Ü]ÎoI˜•[gÁì¹¾| .yñ^‡§íåÔäm.ÛžnIŽBÿûjÛp”ûÓŒøÄÈ˲pîRÛ[J|Âz’“‚ßO´ìŸlwÏ~âLðz¼ÛÊ|œÊ ا0xµA“!ç@Ì7@Xâ…S©ìáNØÀôcò:QVPqh‘2 vŸ€1¶ªBZŽv—•6g„*ùDÚ~\,¾Pª«èAQ>^¤›øD–D·^Š^òƒølæÜt2 æÎÇcßJøËšÍD §NœW"ÍåsÀ¤&©+[ILl1zrñì›[2Ôät^åÜá:=|Ìåì›}ø8õ+ut£‹“èAÄW û97hdð'2 Ø ¼()-Ι rì$9¥äÌ›¼™òk ŠsæHËzôCø5É¥åØôœkŠ¦Ø­ó Á@†“kuδ¥¼cd²äÜO; ( Ǧ$/“üéÕS6GÎõÀ|§Ót$p TÎË€äyX´å›ÉíZ(¼zwB÷ŽBƘ 40FrAH«g¾9'Šˆ¾=UèÓçœ-Q›.z•ù… —ô\ú.??¤@«tŒ.l€}óuÈö¢hÔÂ}7ë³M»l«Ïä¨3F×£…d5$;íáüêÀúÇþ7¡x Ê$¢™Ñ‡28”ôX]õ¢(X —µ:ÃPßv5H<¥:ŽJ…ª½9e¥Úsµ“¾‚5•Ÿ4ÅÛ= îõÉÛz¿áÝøêQ*ݰ‡ð[¤­ð^óøI3óPpæê kfaèÊóAytJ°P3¤¤ôB°A¤8{˵;AÿUù_tÈ=ó°³6—GH|'n–!L8'7ÄcÉgEY¡2FÍ{ôòùeJ뜄 e?-;!}^ò]Èd¤—ï9÷“kœ'õÕ ím:мøÅª*á´>´>ÂW®:{¾6Šîú”ÇÒs ìk€'¬°4;ÌWÊ<‘Ô>ÁEýÕâþKho*‹6œ…+†¶a€QNv 6¸Ä3ã{Í̶º­“KƳD‡êXvj¸2Ñ êìq žd)}ºœYöXÍŠ_/ãQJÍûkþÔº8ïn…¸Úk‹ "î”Ãz:¸èЕ¦;WößkÌmxîy|¢ýPÂ>™ý^{®Ç¾¢™•šÄFR ö;kf5`eœ‰Ç €ß…GÆ©$Xh‹¹ý¤˜Û­úKVçUãGLRÛš—fYhÆA¡šÈÍk¶cÿ0;nµûÛh„fž˜ë»}¢ÑÓR» 3|aßÞ‡pyˆ¹} nµ{Ùêh…%Íê¹iÊ.ž¾K Ùb¨Í¾;›]Ïá;bìUF±pÌ™ ا(¯³a슬Bâ@|7÷Å1nUM‰ˆ) TD›"¶ NSÀo,À\LåóŠlNZÔÏ™z„Þ–`,V,ß9>°ôÔ\p:÷¢,³¦j¸ Ï*VÜ ÿűLôÎüûðZªnq׸äF !jU›pKË݃]ÃuË8ü»nÄ¿å@ØY–ŽŽ–âÛ°yý¸¹Àf` …vK8p2— lGd ks)]#¢Šäg…†:¬ü–Ÿ´lV+¾„';P‚ȳã8y¨‘ë[s¤a‚áÕW0!…a¾w•íxöm¢ëO*];,L«ë¦•ô¥‡õPhÑõfyX@Ëô°$|Ü­¸1ùA¶źjô‰ §×5å‹2öUœéÆr ¶PE¤t¸ã†ÊÜZMÇ/ÓDCO\LdÕ“ûîŸQ®·äð £.[x¨íD!ÞWóÚî8’Ôù±ý¦€ÃëÉ^($ëÔµªÉ8.R°óJ“wbiŸÃ?¸ƒWe·<»>,¿M”Íñ%PG¶dê%5ûiwÒ8…U0…vF—×”óe^ª]C½7 3À©Ÿzûqƒ)K§˜ÈÞn*Œ$Æuov¦qøh (ǘûµ(ðÑ…fу>Ôù þoÁ‡HÖÙ{ÂWål1ÝNÀ>=ýA †Ê«~ 5Ä)…Ö-I!×e‘*CŒ{·vp ï…Iy9˜®†GgŒ­ÿÆ6\òéÎÜÐ>ô±Ù"Ý><ae0C 46;Èd[‰¿||Í=”Zæ®ê³¼—`à¦f ¯ù~~¸Îy›V—<{ö\ÇôÓÀzmЏyfyʼnkï7ó7¬(sö^ÅÏ×Â2„',«#é• QÞýÝ­?çmä€1ªºá ‘-„a*ÜcD´Õì?eUý--ÖòL¡×eJ‡‚æ“|ϧä„/|Á.nÖŸV-Ìɳ‚A.Š¡Èÿ =G ÕÂÍ+?õô[¼þÙ-Ö]­‚WèÎÀ°ãÕ÷Š[Õ­éÀÏ€©;^èåVÚj”àr7ýIQŠ‹%¥ì«úšg³EàX-Ú è0¿`b‹ù¨"Möa –|Ô¾"²e[\ÿ.?­͘í¹ÐOïr“Üáú×(•° uþÄLÔ.!J"ý†÷λâL‰å¼·­¦ vU1ÛAíÛªï­E¶b%ÔwdÃ[2°§ö>if+é#1ip;ûu™Ø…Õ:?ïVf8/ʻɛêdÓÐL{¾b –úðÎXi®øV‰ƒ¼¦µ3(Z°}fíŠëÅÊa¸m¨ëýþ¢Šù· `ÿ’‚× ¯¹­…îfÄåƒ –”îŽÒÅ ÇîŽÜïÏ®³¦_ 9ó¢eV”wa)%¢9ûL¾9§6ëRJ«æžÞó7£Î†LwC(gdè¢7„õܯü¸s‰éÿÉñˆÕò{??ëe€ÝÝbÅÕ¯‰ûâTS=®Ñ«tĸ“ì ò b¼[ª;Ú¤7ÕbèK,Ûvs$ÿ47ˆj9›œ˜Ì2i‘Œ®$ÏY)uò÷OË¥æJŠú×$Àƒ§ÅTý«þÓB¤àµE}ï}µÞe“[mÃRm÷?N…nM…笠nPÒ°çéä4äÌþ=¸ØRÉÐ ×]éæ± cq´.®ßIöMbâLzdlj–Q¥EŽxBR‡ Ø2ÑñÖ¨QS®Ïƒ%}JÞ›,dfN§¼v!³ÿª²î¯I³-Ác7wèU„Ïoyvp&áÑÇä…béªÒ°úºÊúŠœŠŠÒ½4r`u,\cB/¬$3PGÏ)µùÇ6He¿‘×’¿4ð¹Ãä3/­§Ò¬W”·qžnmåûçdv{öOpðû4:òh9&‰™Ïލ¤’G?$ÊÌõ î€Eðö ñÙ@šëºµw¿yEÅ×.Tc‘3ÿȘ1 P¾¬Ýú„òî>ZN÷Œ<ÑÇ™9ïÇUïѶp9·h”¸™ðÉümÖ8³„¸zHÓ­Au4¢®‰« 1|,ª¯¥Ýç„]Kq¼ ‘0~Ô5\H2zÖÕØåœt2ŠQ>ü‹©¾ª™ –îãCUãUÆh ‰ñ¹íÃ*ÞD¶ì»¬úþ®z?ã²Î² õf £C B+4ô¹ô2‡×RmkÃþà=€ÂÜSþc÷JU¯îf!¸ÆÇ%ø_ЛoÞ[µ}‚¶1S—qMge)æ-þùÅÚh‡A<,@s ôªÜ±ÌÑÖFÄT߃‡‹¶ñ|Ô³ý—±‘_=‚p7JQ¸ešŠðö®Š)D°%GP!\ücë)uª©°â™ùÀ,X> ¸YØ$wåoï¤:å˜çÁ3Ó=ÀÐNðÅÙ¡Ä~³HésGŪ‰Ê+ɸ{·À*ŠÉM‰Üh$Å‚SQGß‚S^—Ò¡U÷Q>Jÿ›·@-+¢ž2:ž„˜ÊY)o< йFèèóßûš3 “æý$\”ôfÞèì4ÊêlœUÜ/câD)àö¢è¢“H3ÛïÈWŸk"ÃÍÝþRÑË ’]«íyÓµ^ÿîÁCÏ<Ui?Vy!1Îï»CÉJÀþïõq°M­=ЕÎÎ$jwbâ¹îµ<øæ`ràˆ‹yÿE¶¥Ìb`Šnx$g7Ö‡=[4Òñœ$c4Ý^®UäÓð Í0'ŒÓc»Ãdcîy—ÁÐaØüÀ®g¶¬¶%ãþØ‹ßWe2)Žn]3«q/óš:dZFˆLÃÔÍ¡§š2‘¦4G?Bœ*ˆhÛþ¬ÑDñÇÈ5{¡éßéB"cù9¡,50N²c{{鱿ϳHùctkÙñì#á‚ÏÊ„4:žINyUøÎùÝÅ€…ðQ5ÍEZF–ž«XË5öÜëî놷 ­¿ˆ‰ôêÙwFý (È ³ºMÌ"âÖ6Œ9"¨ ü¦¸ Ýü<ÑCdÛWéü½[,e< §õš¿)yÌ’¸ˆ.íïÊËøI¤UÉ0`{üSHWào"RE^•%­¢a“LƼ–Èï>â‘êñ»>œŽæ–¸¶äýO‚;óÊî=èe±vKÂ$¸ýO—`Eu׈_žåçà'–ÜXªp§¸Z8!Úty™Rù‘€*íSE…”E™ ¬hJt“ÖÒµ ÜÁþI}8Èëh²|Ž5¥ýÝÀàfaVø´_ÔÏ0OOh+0²ºÒÍÌÅZ‚€EÔ·¦SieYV.ð‚ÿìk$ÔHsˆe¨¾Jn±ü‰[ÒZpIô§ö2àÆ^¶GQÐѼÂü•:."‹ˆLx )%‹^Qîפ<ÜE‚Qúbµšjuùî[oZÃ)uÈ]ûŒµï@ÏÆÀ½b§…æ§(³‚ü'¹Šäq€GÔËß8ïþι߽ØÈ™Ë´påmrEz­$ ªÆ‰™c£é¦&e“þÀ’¶ŽUmñ"¡•9W¶´Œ„Ü-k#ʨ3Ñ2®4™©­V3Z•=ŒÊUkîÕ \8dqØ;R+!‹+€| tN*Û^°GÓÞñœ]GÙÔT+HŒDÕƒ©øp$¯eñ}½vøk~=ûìŒÑ=žf *xÄ®RÜÜ>æJ£W±€¶ÔòÑü$µf«"í;?ˆâáj{]¾«•b,‰p *”ÐàÏ™Hð- ª û–[Z`õª!ìú3YzÚ´uêU­Ñ£TÀé2(çJÙýÂ>¨˜F½‘òƒ<øAeó©|YµâÉeÓ¼cD›/xfÅM¦Ñ³Øtfz-ð´—þ‘ÅÑ-k$t§eÊÄ«êQ´j„eÜGR§ÈeÃÖ£—)©±<$òp¼;zíy^¯uÐõÕ™8„»yR÷»á¤1y˜ŸÞ÷ú™YŸ‚¨òþˆ¡ -ÓÍ:(=ªì+~h‹õ2”óÝ´eýr]–Zý0Œ¬»wîŽÛ iâû5óñP{‰Á[0'wµ*IÑšî‡ÞMdÚ/GéøQ¥ß-ï*ª 2­û†Þóñ¬jÂä#¼ûƒ2ÆÃæ4FÏôOú%ΚáVŠúŠrLÃŒ£™äúw_ÞþˆpžâÛt<ð^zй¿ +›DO>þLÏÛ‰Uš ¥éE ºÌØãFy(Øœ.Œ49óI`/yáÃQaOõÏJ¨Ò·çvðKF%%`¹w?/¶ ÚÁâ;¦{1I>@‹è„Nâ•ì½þödܣꖠü8GñRfÞQÕ»†< ›_ˆÆí®(ß’3:É:ùPaõ¶ªúŽˆ!z–¯&dG6¬ú«<²ó¡&Rj8Óu"›28w>^¯”Ï?\Š×Ú\(RLÒ>h©S3 "iRßÙLÌ›:çÖƒ‚õü¿@t½¥žD/ZñˆáfÏš&2Ãɨ½>ªäø<ùC±gÝ·"æ§sè3)üÔŽ¥F©÷Îбp t/£­—|@±æ`!qUéÁ.¯)ÂfÐcgZE!Žsb’œtÔiÿi.± Ѹ=àåg K³h$6’èqf]† ‹ðÖ+é:UTÁJ¯øáé ³¹{ûŸ Š×Hh(Ô ùêÿ#Õº—RË"kW.þùA!ÞW•TeN9"[ÎqOûÛŒ'?&8áUÓð@w?õÿ†_ˆšëuÀí¬÷~Ū–&‰S6Ëq¹7ƒ%Û¬Ÿo”QªÂ–ª¬4Ï™¦3{QG~²4ì~Ö«éÏ].»pOà'X Oü –fTZC×6íaY…ƺÇA¦Rš‡œÃ®X_¨†oÑ2`í*ž‡Á´0á:…»–jÍ‘JGfæ FË…sKnî~3sµ†/Mz7Ͻª§(×Í[|ú*G‰:2`3­lÌ¢W*Xœ)z5ãN¼ /C2Lr„¦ÌÚ›¿A&ˆŽ¾T•KÓìˆB§b{ ž©å*©<ò®ICO3Å®ÓGÓ“|>£ž)˜„vc#àþë|êi±°sÊIò­Í¶_0çÍ®¼|\H+ɪ€eÖ·² !ÌêëÛ¨|ëºÝ‘r¤†‡a*¡Ì1Y }/Dk’QNGÃyÍ$&Œ’dÆà Û'Ú(°‚Ö!žÃYð8Êo'‰ï7Y‡ŸŽpø 5w`6¡e½1ô™Ö¢M ƒÔˆráœ)²!(ªÚ¯°û %öë?WßóëŸNS±Óìuò²M!…žÝ:çnòêL/«ûçö‡ó®ø:yí s¶§”^Û•_£”K*l…1õƒ„t^ag~‘:»ê>×UŸZQé6*¬\¥ìž‹Ý;ÕŸy }ɸHûœðJ¦Ç9¾’¤_L‚‚ÿc9 Á!lþµÛOâ‚ñz3ÕJáÆ»åR;6ð¦ó.|\%4±J£qR˜_Ãð .‡ÜðڌʣF¼]Ò7çVE]ʨ5kÖºp©p–•¥«µj|üG »OÄk®Öà 4HÍÙb¶îˆï/l(TýâæÁõ?{ΰ”„&aÝR'Ð)«ƒ² Ý•? j(¾•.–ߦšìS|D¢ñŸ"ƒ­pë{‹«âÞú•d}vºD~“;ÓUãñH­¼…:â…ë_h¾P,y!@A Á ^'âP‚2•¥¶˜Îþ—~Sß{°m’bŒY¹7pÈ#T«?ƒlWúCbá1óáÞÜb™Tud­,+IOáYÐÁhùƒö¯¨*‹>FÀ>4¿²z@ja¿uaƪH†Ï¨å-¬¼c½¿ºD ±)y&7_¢Ê•’ß°’p€€vÍÕŠ¹“| í*''øœÌjÝóýOBÒ5ApÖóæb]ïëk¡”ŸÃ ú‘Ðw H"lD7]lbϺýiŽÕ>Ô´½Üj^á2~ݽ¶=è–7Ø3©PÓA'ÌÁP_ÈöInT¦÷ŒE—äz‚n ðNJ”ƒîËIf ô\É•59^,aïwb tG̬•qîÓ9ã…¬Å3K3‘z1”®¬à**œ–ÄKÐæHZâr±˜øÿ„||˜[³š!æî³¡>œJ*H¼¾?µëº•qÜöÍDDäŽGÖî9ÏQ %Û· hГK¶4ú/&О; —»ö&„ùßÅη>Q’ú›ôè›ƆÕü«âÃHÞì6Æf΢n¬87²]þ*Âzc|{ \±£ÅpÔ±¶¤‰–Ù‰ªødT»œ~à’-sX,ú}ðx¯–Ñ}´/<%Æò`©‡¯°Q¡Îtlý.îÝUfW5vaF¸¸°zEÚUÄIÏåÒ¦ÕÍ}ã92ðAR㤌¼ÁS`VZ| å·õ£i ëÖ Aájd Éˆ“JˆcY§Ò>Z´®JÛÌ(0©䨓۟!ë:’ór—AÓà ª‘qèÛCr% 9}-Hx»ù„c‹®-;‰#¿ˆŒHôÖ ò¬$f‘'*GoÑž²%]4*7Lðˆí% ƒC†¯ªÇNþ·‰;‚´+fóD‘Ÿ/\sVÔy 4ÐlÑ ƒ›ú€gL"ÖÆæ|ó®†Ì:DÆ3×äM2˜u T€8šÍ&©x¦´â ŒQñ—7ÅŸXAáу¹…AùDÊ=ɦ޻÷ç롆ïwYÅÒÝæÇ$ùÈ_¾0ò"øY‹*h-ð*—E h,RMqÃTQð@§³šôyëLg‘z·Ìü>ŠŽjvBóQ:HjD"ºW;@%skÄmf÷‰XÞQ‘W©¢ªÒ|Öâ˜5èãþyœ¿¿Õ>Ƶp´ÇMWwCý%¾Á[~ã2â^áñˆ%Œ…pëVEŸÆp5ìFŠV´"Úf/À¸ÂÜàßx(+z6Ç=™í9W“DgR¸Ê­Æˆñ‹™¹_Q̬=ïÄäÔxÑ:V´¼„¼¶Q}ŽÕ8DIxŒ%LŸ ñ‹¸&õà–øzü–´OF=eõ^ä-üÉÙ`éƒç*ö×Év@B”¯GÔÉJÍÝÝ~àyѸ~!ê~ñ¹pH¹•‚·¸ÉÇé¤ÈˆCϱÂjaýÕ¦¬®ìg]B ZZy¿Óã?—Ѭ qD­çP~D¾ß¥¸?™Ó'{Û¥Ÿ}À×µ ó:Ûbj’ƒ.i|[|¡23g`ÏH‹¨ÿòx˜,HÝ•1E/ÕÑ="Ëx 1A7—Sñ¥ô³-‹@ae¥¾Ër²Í©¡Âï+Äœ¯•ÏàWÁ}ÿ*08™Im„5jÝ3%2ý;e£·–òeð½¶¾S‘eS¦¹phÓ­7£}åJß;µö"½ ÚN+r­è0¤÷ŧÈn'ÔcƘ(ð ݾY¸þVS¦*R'vVÆN뀔Ðn›…»nº):NHÚÀ£¡÷£Š †#íJ —àI(äù·KâU‘µ¢G5?FÐ.ué<‹¯+z±é†Á(9‹²˜lèXà)ÁhGwm$âï†0תxº¯µ—øƒÉ‡Çv ò·›¢òTؼÆ5KƒTá,{8ÅÈŒØàÐh&–+R÷Xé§É=Ðx«?ÌÅÍ«¾áßß ·^Ù³Ií„s;ý¥’<²gÜr$èý¸ZîjDX3Öøàv½1Pæñà½ð€H,/Ñ8ݶeÃÜÛ碫•]\tç¶2ô,¸–à¬#ÔÃIÍeÚ=ÐV8bL«N⯑Z»Hð˜Aôðp˜‰Ï=1P»adæ~Uå_8Õnã¨c§i…´ »E-¼ˆÄØ«Ä*áÌwx Lnuy*ó£Sp„jžÒr½B'ñÙ­Âh±¿0 iÝÑæìëºnZX©_xæ?eá­3ðÛÒíH9íÏëÛŽ_Ä6;ÌX òüxa ñrÃú*CÇéÓò¬–î^QŠ+dðìóÞýúÕ9#Ö‰Âcúg«ºÃYõÎc˜ ÿúÎõžödD1XDZ[!vO4IômõI0ºðaú§LáØ:Hà¨M1:\•z™’©çýÓûÈKÔY‚™ÊÚ®¥îÀ:¯ÒgÐÁ*“Ž„7‚*9ØµÄ íØSÃ6¹iá­Ê§#TxH•n3I`ÌIU0°Ã™ˆ ãµÆ­¹¼c™"øâ±]LjóÌm6Çý¿Z½¨ƒôðC±(Û}rÚà~Qà*ó%òðS(NÑå¶°¡N,êæ‰«³’×¼ËW/0Kƒ‹´¦6{2o@¾'…¬¯C„ý§Ù|¢É}S'DaN$¹ZR Kù1œ¦4jÒËÞïñ,ôyµ6P€Ü|·ñ1ï9Xù043a<Žë˜«M¢’ð} g)…+÷ë!¾½)*)ÂZ½åŠ55xAJ*~Óú6>ËÉ*”£kßð:Xæ¦÷óc–è¼é¾áC.7{²ýWfF*|^0Çf÷ò¨­ãX‹g©F["ôœý\4§ÅñfÁ÷ˆ×Â,ÒÐGþµìAäª ‚)¡[YñõW Ä7œ#aoN‹'¥™H…PÐøó¡é(YËU†Í|wÍY9L.|•$°3!ÑŽÍ0¥vgßs]Í~5¨Ï“-õ âЊdVhS\óôŒŽŠõƒc<¬ÃÅxèUÐXjΠë‚GW$õÜW¿õP5ÀØdwŒE±ÂL3†æ¶áï! CŠxÿÎ3]qß9¤‰Ð5d Wà<®út_“üd%%‡±áýêߊB’=² ¬[O²ØJZóû¯’',ž™Uú÷ëÞ}*'™~«~ƒ°×Œ³+Yî-Êý‚‚P€oÂ¥šùmâÂÒ$rs ¾ЧëAÏØÀDE8¦]Ïu&fÑ«ëè•«äð‰5)Ã?>æ0Ayn:Érq~ßµi¨R×8¬M(1"~&Sô³Ïªó'pw¶=ö¬‡n¸|ÌÌ€ÉÓ´-€Ö`P¦‰ª¼:à¸ô“P…iÆÜ‡ÜúŒØÁÿ<°‡žú“K¸É¨{*oÔ¢Kï ‚bF"Û-ñÀ¯g#Ž¥éþ=Ò<¸}ñ‡ßÁ$2Ôå¨âÂÛ­©H+bþn»D¾˜€ofW;ßE*œ«¹y+_…” v£¸+S,Äü´ºžíó%¶P½Ô’ª€öqf¤'ÁwAxdÅãËô'¸÷†¿š5­í¥jù£bûUdƒˆ°%ÝŒZËŠ\mùŠüU\>»N^V5Ž~½~-H¥ÜÞ‹òå¿䠥`/¾R†¶¶òTbotžà ÛFÓ¬IY¹˜TM”|TRgÿÒýÈAiNf†1+t4@îçœ2ÔÌ“<œ¸r Øo_´\‹¶Åö¾¸¿¯VÑÓP„@ò\îÄîœ ;Ý-I(âd(œc¶ ß<EøvÛn‡@wú¿Úô™3½?rŒLÊ:ÀJPå(G¥º†Ö{Çqg /šÒˆ“†SúÕ¾ "í°Éë²:Ûa(øÂBÉVâ‚;Œ’Lò6‰h}zÖ¥‡Óå¬ý!“©•sYÈÑÑùL$‹PÎSi^ç²™ÎL_¶€erZÑbÚ>Ûâ•Ý5ODvÃÓS[î‚ÍhÀDßðÌô=µs½GaÐ’Š¹Û¬Ó/3AŸ„(汃€K?Øä¿¯H°>h–#IÇËA•Û\êËvtCó²Ÿ/e'Wó¥)†x|†¼+pÇN{Ãcs Ó5éá1êõ'þnn©±A—IBMÞTtê0è»Awû3~Œ‘„[íÛÈzÆŽ›À«ÌpœûÎOòcä£5»ÍâQ<ÎʛГRÿ†ŒÆ`X?G™?€Ÿäp ¿í~Œ_Ĺ´ìøÀ32Þ9‘0ù,,B'ÌAö£IycÖ¿ .R‰ u·¸¾š+ÓEµÀ—¾x+:M$xþMÝ€Ýjû×rc£‘sÙ†WßÀ2±rã(‚_t«Ÿƒµ9gO`ìf1å¬é†.†Ò£0°Y¹è2øh”öâÌݱ'‘XÎVe€Œ—[²ošÍåæÌY‹ V5³ÀC?Ŧ$5ãÒÿ%|gâ$ÚÖI†;¢ÆR ¬í³¬WCb)8XæZËØ?n•·­Öu=ÅØañ4ýßÁîÖ xým}b6[{@tâqúR”6ÇçQHHÉãÝL%¾ÿä8(åu·,؆l¯1yáVœÖ¤k7H3c§çE¿;˦N©ýУ¾V@ˆÈ.«‡£õ·  ÖìCO¥Vò5Kø–/«Ú,rù',6'ºí•ñ†§§c$£~!ŸjgOr¨V/šF_F¤¢dù§´tR'`ÅÙ,·›T‘»éf“/.ˆMO‘é‚._áä˜I‡í[³bIv° °¿ˆÐ‘H­€}­|—©ì(Ù_¼À>£Ô½ªhƒÕ%Ôþ¿€„d“Œþ ys¨H>&gšœîî AÌpUjh G–Ô‹âpÍ-G‘"u2š’º áyŽë¡Ý +&rduVTÛ#Rlµ°gâþfóÈã2>R^¦Íµo”éyöçT‘@OS”qNmR;÷I0&Ü-Ì(|¼„EÈ„`9”¸¨Ré«\zY§Ô$ Ÿsïe”JÒEù•c,Neyq)0zK„ˆ…ðÈãæ°Zž–µ2±ÓA©ØÇ»hØ@¢õ,pöZµw |Õ<Ô$œy¨ SôéÁŠ˜Õܼ 1á±_{À‚†[ÕÙöŒ—ñÁEd3r–_)epú–¨l<÷¶$žIYêˆÁî}÷$l».ʆ‘ksšfë(øú1§1§YåÑÇÍ£Ü;qL>¾¨N˼ôûñÇFĂ{—]³¦pûl³nA¡ßß”…ü°ü jÊ,ýµ¹Øí iCI%`ä·`Kno_º÷6«÷Íí²…Ä…rAŒ®±) ªv¢çÚ†°´A> ² æ!åJ>po%šå³ãbßjh®aÉÓ£ —rÿ…¥Ó¿É}V1„ÚMЙËO5[ÄÐ9¸Þ}Ç4=3µÎ—ðL¡,Å)ìlá´; ¸ü"]m “­Ì‰&Ç_´}C5üŠªž»çºùà²ZÜmÇI[dæ,#7íøk‰,¡×D¨4LcÙLtO„½NévÔŒ¼@ØÜ:“Da2/ƒW»æk±¶ã¨¾k¨Zlì€f'´Ä™>ìU¥ë߇ÁŽÏ?«À$èÎÈp‹ fQÔQõÑÀÌ 5»ÚOr‰ËSzŸK_aÿªÎu.õ?Á6b/þÃ@¯VV>>G}½Ð?Ï‹ƒßÂŽÛñÍ_ôŠ'ûx:„ÎÇó¢Þ®c/ORŽÊ^ tnç‹E¼{Ž@¯Pæi¬€Pï”=ƒé~) 8_ѧÔ"[þOe?%€ÃMãÛ7ÄÇ$x‘L»8Õ—¶‰0‡åŠ! ´ê!€Þ¥ƒ #.¥ßœa–™á“ZV*èlÝÙ@Ôqe¸$1;˜D'Zü‹­cwDª&qü›ï\ù´Ê{Y5D°«ÍÖ:x54Iáë¢ä)À·1lƒùÐjýUˆø#à[`û% T0ó‡ÇÜ7¹Žàf‹?¦ž$ì)]ý»ÒºP&$¥ê5ˆ+Ε#À\ ‰ãп¶+P‹èÝDIÞéÞa1¹h¦Eão®?\w[}Œ´HÜUrz_Ü„„ºž×ñ OÔIæC›?•6BÌ>d+w"ùÉ!zÒ”Ê:UXÃ|ˆ7 ´û$Œ1På{Ëö´¿ëA®ùO¿‚¯6”oåĚ͛Po”v6üð,=*„YƒO~Kò‘äÚ`:z|‘{Úe2 =0uA£Ïµƒë÷œKÙ*uލ ±ðj̸BuC’ÆÛ¢%m]~Ù’¦ΉFF%¨OŒ´±Ìó÷Cø^ ßÖeòÕàrS3&a¾2À­¬´f&·6övšuh`³Ò|0è™MßgG€Î÷‡ wÞŽSš>šúglXuq¨c’7/ã§ù=M ÝὦR5\ÄmîqgN ¿ †UQÏ(C18pœ×x„CÈÊâM9NÜ‚òßÓ[#«}™´&Ôˆ ~A¨b×Oõƒõ•â†à&>9O÷oÁúÚݹ'öôBóþ)¹9ÉB½sòeàV2¼$Þ–ÎÚ8N¡[Ð̯žÞXG̹p§rìձײַÝê(þJEyà݈n‚.As“§ãÙÞÙ¬Åd¦W|`ÕNçCn˜Žâ]UÃ…7շŧ•_ÈÔûó5àœ…ºéwYqJ(´ËSð‹Å(‰Æo§#*¤žúaàBÍD¦ðHîøtÉä €]|èo×…¤ßGÎÂ<¿uZrïgdºÀ©Oq(Òqƒ\HHÚÊ‹Ê}Öí9Í(ÿŸEȤήøÓöJW.–Å—çI¥Õe¢#íeÀȬ>nØû4^-&x›€õ–‚!×ð¶¦è°µƒÛÐÐÂåä4EÕÜß4/ñ÷Ô¬¾#=ºQ!òÓHWøÂÓïÓ‰ÊѪ]CŠ—«¨ñF‰°E|yÀès ¿Á°¡È¢E˃7Ý/瀈Cþ¸«§øÀÍD4ÌÃþ71ßñgçåbkð$¢Ež¤FÊÚÌÒTÞ%f¯Í{ é°ÎÚ«ÜØ,„¶âØÀ b¶ÛГzöñ2w÷¿ñL×”'Ð1ÀAê¨YÞ{ùÈ^exš$$ÁÀ/4MfÝÝä £ Â}"¾´k\²XÍýXãá¦1pM& @_–EŒ¶k­\{“«Œ†ABÿŽºJeo‡|ÄCÅŒÂÜ;%ªÛUF†qÀ°xëØ”h!±§K:ìÈ„¾Ï›[>ôņ ›qHS«ߟ$q{N.=üa<9>üè°6 ̳µ …35êzƒ 2(ŠuÊã¡!°lÅm€ô(0Ë^h†·(¶ŒŠU± Lêl~Gw3Šs­þÛ’3"ää»ý=)ªó1 eçóú§Ê¥€)âg¬¡Ãå+XWÌ8X¸–0m¾ЗR·*jG¡T ¶ÏS–c&«2Œ)‘tIŠE±ó+>bo5uVØü>+Ÿ~ºÿù¦%†Ù€=‚ÊÖŠ7UÙ©N5…ƒ-2&*6–™>oœ;mç]ý2>Þ3C© ]'…g“çQ[?ÙX;ŸÒ29qUK-—k¹¬gŒ·CçNó Î}k€R/Þ·}5¯ØŸ MÒ›'Ï·ýbB ›ý!ýaƒYŽ\ßk‚$Þ&‚ØT?ûV`Ù¨|û¹Rè·à/Ü n'i…ÕøÕº ·A‰=<Ì`?Uæ a©ƒÆyƒ‡¬«­Æ×˜ž¹s' ìÍT~Sñá kƒØzGý.âÕò¹x×;O  Ê̯û2öW¾^7¬,xU ÕǸå-»¯l†A`³˜C/!´ªÉÝ(‰ #«ì¢ÊŒÙŽ”ŸTð< Ì­›T«¸)õ†ùÈqÈÓ’å"B:ö\³‚KÈ’‰ÞK×èlÖjØK†Nv„­- µNá¨_# R‘?Œ9yŠù/ Vkoösе›ÔÒ÷–qgóHäÐÈ5?G‡¾ZP_e¶uÙúz‹Ê€2ò6H"õwÖCô¬ü/3ÿ9›ú¼33çŒQ‚B‹æX—S hЉ…¹óžÌ'‹d]J;ÚØñ½Ã¶Ð¾ÉõšñIÀ˜Ó‡R6õ’…5£¦J4‹3áá;2,©k62xV8°uþU"tk1³=#óùsºžçñ¶ØõH®½"rãW`X~oX|7Q™ËN%Š%°ÎÌåìOaæPí¨¤oiZáiS¯}¿B*éò*à‚™UÈó@Š#ü—rxNW÷Åáøfì—­ëטÒßA[¥Wi|B5¥0ÐUõU·Q'i1~‘#K&.ÂèT»©„6½~«³/)îj¤Ïþ7a3fTO $ûž0Ï€W2èe­Y*èýk¡ª«p¯K#Š|QM Á¾á¹Ð`%µA•d9 žl†ôJ«aÌDµÝ°Øl¼È&îÅp™†`ÚQ òbÉ:?fJå¼rq–ÀÜ!•47¬á¸|¤îaO4vÐùeìÛ»¹Péási {¾Nš×ï,F¨¬ß0ɹCä;©=@;XÎâ¶ã´²ˆz«màbÊŠ¼°-DG`röPe#¡J„ñŽZàãÉéåÕTt1”g‚z‘…[Lo×µ”ØõuJ,;ñ¨yü˜U7Z©Ö4 6*NwÂà'j.½‰s¹ª4Ž‰Žº >à®öØmÇúr&ÀO„œŠµk.¯j¹ÌPϚܬ»ŠzbUÒ*%dßub`kÊ®C"ê‰ëâ2â;)õ²¬JËð)BŠË>ª¦zü½aI›¨Vü]|Z¼ïÂYäÊåS‚;´èF¬eUm¯«ƒÒ€tÀ°ýJå†CoÀf= Ï-f¯iðBwŠ?¤n!ÚhñXYVœˆOÈ×Â[سI³Vû_%تʼ嬑 ªøç2³Ž;ˆ,È)Ù=¥¦ÚØYϦLˆ(„Œ8plõHØtàPΜlKÅ'èqH¼Ðà 9|¿Òp½¯<=†Ø»’æ`÷šÑ¡QŠ í",TjÉ¿a²Z¡6ІÎvWf±†SKE6ɀϭõÀf«s½Ð|çu’"3¿ñK;„‚<òOúëPm²¶ÄÏØ’ågòH¼ 5i9%öÐw!4MðPd°õe)Ý·¡xèÒ¼[Ó9¹²Ì¿Å.;]8sqÀäÑbí¦íˆ±38“ Vë6•ªÂÖ§fòeUa1“GÞ<[^œ ÐIQ ÛÁ®6jÑAÙúGዬÚR½+ÔSÊŠ][6`"ÀZ5µsZQk•‰!ª]D÷f®nýò`k/Õ b7«n›dàÑäëð¼±A`SXpÿÄfÓæ<]T¦J`Aé¼ß;cRÄê®ÚåeztåûÌß"‘åLü®&ä=Zl›ý¶ »ðcÕ±Š(xx6ð[›z,7Þ»¼,e<ŽDù&¼Ze•…ÂäèI¿ ¼òð¸´Û»E“}ÓË¿Gêbû³õ.¿« Ý.\ŒöÃÔRYu÷KFL~®ÈËKwRýØÓÁ.]›íÄo¿ßÿL$OͺéñÍx”ž•ÍäA¢½ƒâ 5{ cDM;îý˜¬á·ªâÌÒ}iº_‹«ÏS«¡{Èw‘ìšú¬ý¬©Põõ‹WAŸXŠ{µ¶j™|*ËUâ^þH‘žá$“mSƲúËaC’¾õ"Y˜m>^¶2;6¬`É(ý¶ãÛ pçþ”ìøé¨iõ_êœVíä„Ü“R>ÓÈõ“±#¾ü#ßçLÇJ|t£{¼69Õ‰6!­h …‚),¿=1N{B{hßB’cÙ<×Fßög-ºlÆ`ûmß^½:>Ü Ž#óv ÕwzÇñ´h}3¥a¹¾25䳎`™SUö^“UÃs\T¨ÜØÔÎO†‡;Æô‰$–‹À°ÅˆÂkvñ´´™+ÚàÄ—žiÚ™^×;AÞ4¤s¤M¯•™Í¥s—– ÅÆ[ÚTŠJ(Ñ7ÙºKÚÙ_À^(¸ãÉ©9ø„¨)˜-ÜØ I½º…{-øâ uHÏ}cªS ÒËç!Éø½IUØj1o:›ÉG_áçšÍv‡JÀAþV€¸h9“F·®æÕ­¸]7)vaj(V’ ;¿Íû'&$‡Ç¢néßÈ ØÄ ^SHù}m©„å‘ ×ˆÆÇ±0§ÕŸŽÄåá èøl¬lYp—tá¢mE(âx®’¤«5œ'×Vrx¡“3_SÓöì½µ—GõÈ i1ÛÔ@=“Ct³Ã¡‡ù3jñ˜ñ)ê­×™ëœéæÐ÷a^6d¦“Ü$í®J>’ö}`=ñ4Þbû£‡j›vÌøMáßÀŽúÕ*—ïWþâé}èzòå§ŠÜT›b>³º@›ë/ Òu_g"ÞÝñ3 U$"Ãg`¥?ÑùQðtÇHf^†ï AS#¨6®[ž17¾™kÏn,æ~¹bw<=›Ï–æMõ›Ô|’ñ@w¹Ÿe+¦ÇH;ÌÄúôÚ$©ËQ¨Îöäg9³ãP€¦Om`R6zóI‡TÕ¡TÖÝîØŸ)³ËëžMâáw‹;ìÀ†ÉVÕí¤¤%2Dü3½ÚO]O² À·\Á^áÝ›„w®wŠDL›¹SÉ•˜¥6°Ó Æâ¶ãƒµZE!AX³6)i—´ž‹oVplTUfv\ÚŒRôˆCwEÝH 1uvýPû¥®L þUeÃZ LŸ˜ûžr8•iW>ö ÆÏýtº"ÑääôÈFËÛŽ (]±܇t¢¼RkêÁ¬BÉt¤£P]È:S¾^µ¸‹wÐýÞ(å]·§›“lx0§…¬ˆÌW¢²Ê©ˆWÌç ÷·gAõé¤X¬Äv þýÍ3¯–~Éülqxª*@ {Yè¦õ!óÃÓU+kiôë…yþ-—ù¾Zžî 䟮̘” „!ÂihBÇäß‚g†ëí žx^¿Ô]Õe0ò†öv}×RÀþK¹ž| I˜Ÿ…³ÚÑtÈcrÎzwãgQ}®†F>ëß]þÊ@®Pž9.rj¦E"‘-r!®š‚û ‹SDÏ€?#µª \¢–ßïö:‰£æ‡‹Ș|i»lµrŒ"Õlå f1µ¼tnágöÖª§ÞZØÛË « K•Ì~‹Eõ»hË{`¾¤ðÇ9›ê_êqÜdì–š¦*w_j¨­pŒÿMɃ­-ã¢Ó—ž—#,æƒð„%¨Ù7Ùò1ñ³ë°†ŽšEGþÊŒ$˜Ek÷j„©`øÜí‘ÐñZ3Šý.÷U6o Ë=+pùÏ 0È—ÄŸ‰YH²Šn7kÖ…æã/(*â;ÞÔƒ¹o%g´¢´ö¸š#tðP‘Š¥ ¶H2ìöàT¿i¯%"3àQÄâZ¼ù¦h“€錀iŒl¶û ë µØîÔïù†ÚÜPóÅ.?Â"íÆ?xðÖ"õj$–Ävck žä )jÂ]d+³A…ãx/0Ì ³Ž)ÁÇç:ƒÎI6嶸¡=¶U_GkØg²u ÀV|BÞCûnËL£)G "¾ìË î.6½ubq¿ü…5¸=Ññº KÁò¡È{ßÁ¬V¥4NÙri¨à¬nˆµBˆBÓ8ÿ>ì–䂎æ:e–o,–hÀn q⃤pGp„àß‚º½­~òôh¼Ò þ³úX®(75 Ô‰<;V…Y‚>ç8’ã.¦Œh΀×'«dHJonÒ`Ö4ÉïñÜn«iiîZ²²—*hQ¯-ŸË‰)Í•_œ‹“޲ ñkMŽœü¯PË÷(k³Æ"á­?΀þ厅ŒÓaHѨ2œ×Ȥ¶(f'0gaþ“FØË#€hcYÅ´›KôJ°Š¿R?˵=°¸ÝÀ™<ÞB¾¤&Žb8¢`„š©Ð½Æ4­œÙ™§ èpp«œ³J0Õž“A6ÕÄy¸éi ~%dLñšœüN2s:™•âÇ6ð´ÉºŽÒÞ¾›pXüïád^Ö OoüL™ ÷r¸1(îŒÂûÊvhÃ2Ô¿+LÜmŒg˜¼êzÔƒêÎì²ÓGÃnàA¿}²„k;ÎÑ_´2nLȶٲE:lÕÀ‹8‹2CB<7©¥d¨’ÊŸ”»l$™=º|×ZßÅnЦ+Z48àÐ+ÏjôÚÍõÅrÎv¿S=–Ñm EÒ£2%ð«^j,%œøÚ¾$ý®h§‰ŠŠ> t¾,sRé.]¥H®bâ? rþÖ¬æÒ–/“Õ>` QR₊ì> `$r7‹?³c¼0Ñì_·/ë> Z;ÈK¬È`~^¯*o%ôÎÆÑ4›=È2 Oê^ð·M%ihwùkoWK"PÈsκÏ]$F'›$Àš‚,8˜¸Ä×Bpâ]fÍkÒ“¥jnBcß>éñéOqb=GPùe2iê¾Â9Õ€x¹«•Šƒ¡•}»Z©×>Îs›êlâøÿàÆ›<Ÿb剂DgÞ}¿{l"ÊÓT ¢½¿«³kãéD/ò¹*¤Nª1—<3ØŠWÜ2°R¯¤å4ÀֽÌÒãËÊø.þ"i)ßZ0>1)j{ŒòçQX>˜~N¨TÔû0ݘÀDUx&ÎP 7lýʇGcåddö<~ò7koû ¹ Q#1Íw©o« ŸÀq£?Þá+Y-Êh2xG «…EtÅihCjÿ»#rú& à2ô_Ž«ýüŸöa³a‹¬ÒXGÁ|ôý½b80«.©@ÆxÇ'lÎo‹Œ}`ÐkF}ƒ««Ò~‰œ7k–¨j5AÄ_¤÷éÓtuû”o#ü"ù– 8Iž¿}Ñ?´Þ}(<¢DÒ îŽñ#c_óÊÿóH1®í“ؤÏhpÚ{V’ØÄuF{»­ µœÙ—OÔ}ýv ÙJŒPÅŽª x¯¶ÈŒ þT3dñ‘'û}ÖBVNuH«ìŒj.ÔŠôcÜV"}8}½ÝøCéRÊúÕ× ðL(0”BlнC½ß§hœ8ø£Z¶2•?Ö¯N‘Y)EýBØ„{[ãëqïéJd]âÛ¹ð?Ù 8Ì10 Àì`Æù5$0ïªèÈ€S AÅ«¼ÄÊ=¾¼ëN÷OdˆY?B¸²M ¨êÅÐlù0Â!íh¨<ÓDèa¯‰zú=ÈuÁΑuTÁ®‚èÇã@U<Šf¢Ñg>uÙ9Švr?º¶¨D‡/þÔ7;yÛ‚pU€ŒzH”‘ëi±c é(`Õ)J¿ḩË+<éXâÕ*³?Ïì”@†®ìpÅX/ƒG¸ZíËl¦;8M ²K èÈ”Iï“ «>ñO¦VÖ|»å ·`gi½T5Ïõ+äðNNò ]ï¶<%vÞO« ·¢; %ç4´jüQùnÆÓ€Çµu²¸ú=‰«âBe=²kõžˆü»¬èwÏAó›;5úèN¥Bu.({¸ÎÍ7„¡‡-áwÙ'6*A]RÀ1QìK+.­‡¯ÊÅðý S†¨Ë@Ä\}ŽO*kŠìM‰ó:ó‘üØŸm¯JðuÎM´öx(êJVLQ^?ƹ4ïÊ@T\\EWHX9o(!5DõP´—>)Ö±ÛPð£Q.€S.ðƒR­ëЂœurï:ìïiDÕ éù”‘Uh ™Áø›0í”L@ìp@É=OÔý¡É=»Á¤UÄÿ„ KC°VÒDZæQ μ7pŒlSÀóY7;}*=FÓ,ÞºÍeå¾/n²õöw„ü½á­!TVÍ×.OÚù²%wˆPwÚδé-/ î`ч-1$÷¤›:L˜Wå60|„ÏÍ?¥fÚ„&vmzf ‡Y¾Æ9›‡ÞÂVzzß2³ÏHø”Ò.ck…˜­ƒvÄJøˆâ\•Ù~ó7hÂX,r²Ï°­&ÀÈO¥ç…Ój°ÜóÑA¸AÓ"€æbÄâQyµzÊ y¡3,鸷xü\)¢óópC"fھ泷¸ÙãŠýùzçÉ&³ÜU…Y®sUˆ?§0¶S+ùÌq¶ÄXWõ‰õï‘.%Šâ¿Nét Ç„aV‰`éöÕ½—yct~°qÌX–+Q§mÇ’MÜ2õÞzíZ M?ôãË‘¸5é«?ÿp÷ˆ¦Y€!Oõ;ZHˆòÞQâÞ5¦ß:}3ê¡wÑM¡FdzÀlõHã ½_?jxÿ~#éÉ· ­ç²Ã,^é>$ÍGL¼*êšíÛõ;Þ´Þ²?£þ,IšwD(lmd—0Êdž„]H.ɧÇ*Ã$¿T}eŒˆŒFü”ÇM¢· ­ µ;½ÐLi_±Ø5%Sðì Ó­Â|êÄ÷š4|ÂÐDìÁ7$™ôë®Òž32ë&KÜŠ&QÕP–¬Âï µ€'ö²¼?^"Õqñý úKÓ¤å>åqJ×ßêâs<›qøà•»vb$Åt§¥JRbîZ,û[ç¥Æ!ëÂx浫?‚· ']‘„ˆ?¢J£î¥.ØD­$AÄ›&ΠÆO]†$¶% —(:7Û iQCÀ2Gí¹hÂEÛíSÜÖ ÷äÓÒ¶LÈ‚¨gß{èÆI>ügïÆ*ør uÆÈQ4Ý7<ž¬×Ó6 á'{­-öÖÇ‚_¶Îô¸˜¡)AÕ8þR¨Zí(ð™ßIà!E F×–è›õD}ÝØ2žþK»©Þë ð¡|à(ØýîåZj£¬#fyéÂy¾¦YëFðÆÍ:½føH¶/­é¥F`ž@4ÕøösîàFÆ(Èc =–æÂ¿+×ý'èäØëFÂÝ|»¿¸Þ`Ø«“)†ß©½˜÷Y¦ÅÎb1lk†9åÄÈÇ!bòë€5A:?Â!‚Šˆ¶²\ꥬÒAeíp–NÉÊ–Å~ÆÆ!xð¬(‚ÂL¬lm5ij ¯ó‡@´| áßzî çº2š ëANc>±ßä ¥e”u"±’Ç:âå‘>«±úFÔ„—%¶x¹Û·i\’ ÔëÖj )%TWЛŸö|;¨ ˯<`·òÇ®wí tp„6ÿ£Ù:miÏ ò¶]ÃaFå ÖÖ×'@2þ°fA8~b»Áú!©˜(Ã<¼ä :1—d;Ù®2ó©{¤)—ÛY¢ÇŠð¿'^ÜæaÀY:Íÿ÷ºBwünßKÞ(lm$ÄV"•ï¸ç»cÚ¦H‘cÇzù?ª“îØÿôtâPӗ馺½¯ÂCŸì™›™gÛ¬ô;ä î^Ÿp¼n*cŸÛȾV-6älÝ|õÙÃ…e«õ2£”Uò<æ“Ô‰YXÌ¢TYþe†¯U{Ò!róíjI8'P!cK~ÑÎÝNws”(Eׇ.üÅäõfY*`¨‹xˆd¦h)lé7>±Ÿðu+$¿ú‰ÕŒK¶»Gï/ƒ]Ô+Éc ‡JÕÏÂé>[­¦f=CJ¦Tùz‚÷bížL½ë_ ˆðàÛ6ÀÔÌëG¹UÇòp}´ªaœ {k‰ yø:ƒgL°7‡nƒI’Swþ·mqyŠÄ.Nø{–KÖP®€#ÉÌ!ìòþ/ð6,f×?}T2Sü€òàx4¹ÛK[2•¡í2B0_ùë:oqVÔw`ÉM~¶N]mÄ^¨ –¾ /Zð)Ktײ¿Þ¢Vê`Ùf¦Q®¹1Ò¶š¡1˦tŸFýÄ}c è¾ÏSŸ(y¯îʨ  ÛÍÙi¦(<¥WJœé1& àßOw÷Šfv»@ÉÊÿÀtAüÞ×£YW{1®.hcN´xzlÍx&?ЉŒÃr¤&)çxhÝ6lPìÇÛZãøhA†µ-£¿qϾúÚ˜žé€Ì"ó*LÆ›&Že>I¨„êm®;Üš+MÉÿhirý(:—SÍ:¤}á½ÿ©»(‹Õo;­§Olç»~|›ƒ9§ôEo´JK= §ƒõ&Óp„“Uÿ’€Ål¨ÃP±k7a”ª,œP³¸»;vmîÉ}“;ªöÈiV ý¡BɸMN‡ Ô¥ó€Ûý”_0µÓ»§dx¤/Åß IwÈùâàê3¸-H##>’Ô=uz¢¿l¥¶:C'‰7Á4ocT­ÑÛÍÜÁì"ÑËÄ1ËxÖ°âµë©sÕ¼‹‡‚Ø>†×a#u±Þ’|(Œ‘£9ã¸5à¾JYzÉ¿-n­jƒ+OâK=ä¿ÌJó«¨Dmé'UP±,Dä@ä×QŠf*]“~•-‡°ŸÇÜšàî2ÌÍeß™Ô~hWʆ ¤wK‘ß²Ùh·K‹Dvëd'Dus&ôfA-¹‚ØŒ6KZHÁ¬YÙ'¢ñ>@•†Q1> °AÑJ½! 0òÀ{ò<‰È‰àQ2è$ŽßxCáæe "æÍ¯ÃÕ¾ pg@dá›ÄI[ÿFó)iË~ë Ñ~½Ÿ@MûÎöV¬ÿ…¾­a|é>¤ÝÍáƒ3`_np-¿„7‰ÐXbÍ’€½1EÓ"Y˜%hªµ«ûÕüÊQ)ëfr&riESµ¯O eSÙªÅo¼ëæ-ÇÛùN%¦A^/«ã7ö]çE¨Ÿs¿/>= XÄjM4ÑÀOÔ×ï)ok(Mý…Ç’ô?n•èó"§R+|Ìå g¾È¤Î0VîD;PbfvÑc XÎpgÎyc‰èoâË¡³9ÙPœêDz0ïpeXv¾5]N¶¤V€çá.áµ:¬öçj¥LŽü¤ðd<÷+«çwl¯c3ؽه@Ú¦jû;Ø,Þ¨}Òl,æVÅ— Y±¼¨BRc®×iš»p!à†ÌWÈK( óê!ÊŠ‡¯˜ÀN?áÖñ2¿ ?EÕêËp×ý÷Áä Ì×ÔÕÞÐ# ÖÍj (*郵Ê.R²¶ÅŽˆ47×öxb¡ŒƒÕµ¡¬äI^È ¡1¥ÎcÐ\©· …¯]]éâµ®p_ ¼áƒôŽT!k̲wìá¥Dá{"Ž×tßéê…Õ{yÈÕžøÓ{§–z’Õ´zEïQõ[8šHÁ};àŽ©W8]~EÌ>ì{àê>Pê÷8b­ú¬9¬ ((aE"QاΙó]Ì3w¿uf?‚hç]ÓÕ?çä>«8A,#Á I•Ïnüˆ“,¨*ØÍ”ž£:q¹GkÁ_£úVûõ7Glcé2'>ÇÂáÂwÜY¤Œ¢¥b˜5ö¤Å2Z ëS)4ƒö'vpE;QÍ%Z*î¼q‚¹ÐΞóŒ·!ZÇŸ¯´­‹®.UzþÑYk¼h* Á:½&L(g «p†þKè$ hG1cÞA¥Ncÿ°qÔ=Ïjå$yBm²èù÷±¥‹z"dˆe©|5½`›ŽqŤz‹Âû˜ôç·7P†Y¶g9þ;» ¬L6-H‘?†`ñ~er4}¼ÿ]í\ö£bñ7 Z`è3€5ÃÒ$6½·š%¹ïÅæ Î( aZÓþ¾ÄXk؉ ç2Ãç%ƒÇ½UÉRHCCe¼µÎ®gê}~/ÑU†Züênýs¡€eQ?™¹†Ž4ºd;Ù9ãQ†H`/ÐCû!.§xGiOõû{#$qÒÇez`Eöoîon[³|U6¦\R(M6t¸2­—:)–æÏuºÕF3Áÿ!ãÀjÏ»Ôu˜m¬I°àÇ:ð}†ô7ÔÏä£Xhè;ÌÃRL7e>âíeŸÖÐPÚúó²I¦ÿV~Pô%  íšSô[a:üÎþK‰y!nú¥5J~çN¸i­ ºœaœc}k½§—@¶|S BÃïÁÊ.(ˆ¦5/\÷VÜ4ÖæÎ«–YÔm’Ÿ‡" 5ÞñºÚŸøoà˜…Ä¹”~‚ÓšŒãîN¼PÐçÛ@Û•·VÙ)†>A=WH( ü®a‰cV5>B|£lŸ5ÜlÓ‡L"ü\Å*ˆY!:ø_bÍÝQ%ѤA;£.Û– â‘$ïý,p€{ìéþfŸ'{ÎgÊv~°MWBc£sh˜+¦Hƒ/ÎO[èáêaݧßäÙÐ#ÝJÇþ¢<œ¥;…¸a‰qï’t†^"iiñß 9iÚ&ÛrPЗ5çGØ„8¸zç½2\;S$-Þ²Ž+‹ùÛ  D†f™IVM— #Ö—w9hï´Ø@?ŠôYmgþB]°å*ØŒÐã‚N+2ˆ]=º–}ÚÂPÜX¤¬™àµ…£“yàà¢RÔ·oZp,^*,Ø®«GùqMø´œ½Ê·¦¥åÈzªtü3ü Ôù™@•ÿë‚J“È~г[ÏúÊL`Ÿ8Ô€ÊÅëê#þtÏx5’ðsÞØ¯:UwßD[œí |•&¥ßnƒkó{WßÄõ”›_€zâÎóüHSÎZŒ{±Xq!½Ô¹[¤´o”âÐ*ÀŒ2àvÞÕUª¯L(…Ox:dVÃh"x0Ó$8&ƒŠêÀRj–çIXd±þÐ ÇDúñ`Xyù³W~¥è šÿ¾÷J$'¢¾¹:nΠcÖD›úA¤Yâ òE!j^$EÕB£¿y óí~Gv.Ž*²n?ô½È’û¬„f¼¼Jht,M3sh؆CJŸÞ0L§‚=—‚dŠòU­äÞ:ïì>5ÎöDù¯44ãó”ÂÌéßhÏ›FüÆYÅWÈ ¨R1žVÐk)í”â^Ó‹k!)œøKMnjí!$y\¤Ä.­ëum¯xÞN¾YÒý’™Œ€¢B îY‡dv¡ÄpyVÓŽãêáCª=l”N+h+„2“KuÉdjѦP>­…#‚ÿ®ú1Ëél¼Iv\Ù~có…³Çxj?/5¥j4zQZ)2ìb®žSM÷=&óV‡¾Ö–h~?œ$¼1ëpš¥•w¥«:ÎýØœzªNÌÆ<¦œBܤãÅ: ؈ðB¦[Fïê@¸$“¢ ꊷ¢Þ  t1f¢¦ƒÓ¦£âÑòR¥Ãw»ÆQ¸)UÜ ‡?æ9U‚:±Ì[òBñžŽ»r̬Ìá_¿¥w&Rk t C®ª]ųÔl‘Ýøƒ ­Yî‹îUÍÌÍl‹<"¾NI‹Ô‡åÑ”I·A^†!8;†tNé¨|’ĺñCã|&"¦&òç!5+(f~ƮչϷ\ Zßm¬øÞj8ýTÎÎg|Ë}¶r‹Ê>¬Ë $-ŒLLâµÐ§ `FWÕ³¶'jvP•`riÏåh5›×ËßI²^[5¡œ(‡éÉÌ`§Ý}D[%)ÈÂs9Ô¦ˆ/˜ßœÚlL R‘ T§„`mA—U~þu¸†÷Úü‘P,C4Ó=®ýŽ·¹ê·}o.Eë (‹˜8= ¾nêë OÀ÷‡M³1©¯~ìW3„w’Û¯VôY¦‘úm│ éë®I ]-ñÓèû;µ‚6Ôžoo©}âúDëöåì˜)¦‰ )¤çÆ"1C°Ÿ$ßÕŽSòO|ÝHc™ä`;Ð0·n°X:>䈓Ñí™ϳ3Ÿ7¡eÏ¿“ ç 1bê¿‚_7¿ë…Pžgðån¿Ú^’wvï‘<¯·Å(ê8 ¬Î£¤4§¼3ÃÝF/ò–¯ŒŸ>ʧ‡¥ÄÑ75>$äÓ·Q:šTÈr”#[¶o(ö&„¸»ÍŽÞº”AïÐ+‘xçX†„â~¥B'ÕªÜ]¤ U#C‚ž 9ž+-÷}Nl]y[µ¢Q|ÎýqüBBëÑo†å?Ä|®hUg¸Cgt|Jª:.<ÍÒ²•ˆQ7PuËÍ#ÔkíúÔÓû,[d^^†Ömžž÷LMõá6™BZ‰»B<‘¨„ÿrc?øÍ´‘‰Þ¨ËÍî£$¥»ÅIj=kž.d\õýO#¿L#KÕÖgˆðy‘L™á† ¹Ù´ö¤ó'·ˆ¸/@DðÈø÷Ýl_é|E"ài¸EÅ (!.#§žwrÞ¿*že_r¶ar »öpÄ4Y§š£÷ðÙ0˜[Õ ˜÷»õ{|°u³Ž&9KWÄßhl£tØF5†<Ñ[ÓS”$u$RL%Ìlµä«gôìÆ[C1†}!- )Hl€n`ŒJö\®ZGCðþõvKå²ÿT{#†bŽgn_óâì§+ØŠ\D¤š“ÀÆ“!™™’BH¾øB )…fSÙkQå¡È¸ä9ØÃÛ5Gƒ‡m§áœÔ ÂD"ÏSç5¶Jƒ_¨±ßŸ#Aáñ»Ï¨ Ù9j?¡ÍaËë65œ \! Ã^ú8 w´°6_˜ßÑÄ¿›&w±}ÆçXûÓGÍ0ÁÅ̰ôf¡Ö„-Gа`>¿ýÂÝ">!gWµ‹–Cð.¥ÀNÚØäâÐ ²*p¾¢3ëz/É£¥4”|>D˜¤\¨ p4Î5¢6vJ_¶¼\I¯ö[mŸZÝ\ʸùMò@lÍ(ô %ˆwâ®l/·ˆ( ¥R`28ϘØÅàÈÂäCÜ…Ïß$gKÚ÷e H®câbmåíH? JÚFFŽáø„rÔŸ¶„±o»M{ö[é‹¶°Q§v¡tó:$”µ†í+ÞævÜ DwÞw0<Ÿ¾uÜzlS%f$íTh&7ø× •£Pi[Åù×Üš¼âþø¡Jüœ°§…¶ÛÔb-»ëD£÷wÇ…&{¥\ c>°¼œ{Á ” z‰ÿlO°K[Ï2£¡«˜¹ZŽÁ¡s¦»-UëÛ€5Æo°é¿ßƒÔ96 Ÿ†÷B.ÝìÄø™¯ÆW†þL¾XøÀ4߈ä( šߨ1{+÷=Â…b‚®\•v=NEyeéò·yÞ:à~ÒÄs1HäšÛ#Ò7˜âËÍYjsëø@¯$ÛèÍ'ó)Æä%m¾•9eWùJÜbLøº‹]–:€d@¸@fÿ Åïú-@ÌA6ÕOՂꨶæ3®>a þM ½Ë…N:Ð V¦}u‡Ð}äÌM×omZ!W®´&åG»ÜÖV&XWÎo° ‹‘üSÌ:€\­ÂTwWqþ]«.‚‚båQ˜£%ˆÙH&aÌ…Ú?bGÑI›«f§u‚sxØÕÙS£óÉ‹ÕÒA/‡†Q#[‘{5/—P¢¬XãƒFï%Æu4ßLÑjU0i¡G—ì’±¦ÕÜóÆã9²Ìâ€5‹‚†rá.ϰ8yNŽ(’Ö­×KšÙS¤¿I~›–Yö%ÏÇïá—у˜ˆ”PÂÈÝù~¨Ýu³!²Ü$j}Å΀zå‰êší%faT=tɶßDå‚%Î—Õ )˜Ë?\lgÓu+æbJ؞˜ƒÿ¡pqG9Ï͈š³³Ì·mI!\p|pܤÞ:LJfÈ~d ¡˜"š’‡ û+osFİ4j1–¾O¢”ÏáOm`rÓ›Û³Êã-2‚žþH3KþañíC}ÂÆ©Ì’Ÿ"[Ì'«¤ ±½*?6Š‚ÒÊÿ 6è"EÕôXÑ›I (¯ã)§Õ±ÊÈ&;ýi¸®Ñ3ÐSŠUWçžœ`\öôòs9sxÏýíž-%! ø.ï6—ùÀx¿²t3á8KòýFΣμß9Î!8#ö!¦£O¡/AO',âQ‹ƒþÙëíï©°gÛž€ÓX,ÖŒžøKŸ‹qW"u×·ëÂ*-ƒÞ¢óRóóÕknÆßP/cµ³w‘¶OÐTx Cî‹>ë9ôã>õYǃšþi¿ÆÏIyž¼D8ù¤ÄHªªM«å†r@~<Ǫ®‡k{ÀwDŽä*`sTé0m+2›“n÷~òI²;"Á9k3!¦q§æ WŒÃDN½–nx 2*¶õ&ÿEÙ)¢Ï6X–ó @Kº«iŒ à„Úö ¦ž~•Lþ,ž¤M ÷z±–\ãÝÙ{.‹Ê˜š ‡£ÆFü”äR ä;Œk\šã.˃¿°Dܹ¼IÊ£ŒQø#ýøpZÓjúÒ"g üPª-h¡£±Ù±ˆåÁ ü‰ZÞ1ÍQ²“OäRœ°P§ a­³—bm8‘YYÊãÒ/‡V§_ßkìPÃö'x„€FE­³ðj!÷å´ºÄæ×/'–!#[k¢a?µ™ewbȉrx÷ˆH°8póÁ¾š¢QΑcMþ5’*ùjõ½‘$lŸ Ç;ŠÃº:BhØìå¥.}kóuçÐ[i:™…rUø PDH5B;W49gs¾MF¯µiæŒØti â`|ï2ÒgåvL./D”„#&¨¿÷°’mœ{\Ü™¾¦Š—HŒiº>Ò\(å%\÷QÂ3²3ŠYέ *`BÀàµIiÙŽ§Ù¹RhÃ<igܦŒ¶°Ñòý¼Õ»x¸‰äç“ YÝ¢Úc!3CfÙ#í;ÿrXX&1ow}dKUµ3¼XÌ@Fj9ÿt+¾:†¥ÑA©8$“™GÛ+VÓÔ—np’ÖóÇè¥Ú­^2'²܈y“׈@ •.îÉ:”çÐicN;˜èqrc%žî²asµ/oô×@I>×ÿ j_Ot[tZùÜ ƒq–’ìl4MùçÁßÔmxvÐzu….1¶`i¢¸æˆÍ^„—®ÄÉê«‘¤¬ŽÇ_sþñ¸O3¹ Š«¾€”–¥Y–+·Ä!uWߺ†t–ÁsŸuÕjK·vù- Ì0]`LõP6œÔ“*~¢ ì¢Ì¢ û \Ùìynˆñð]0R_/ÂC‡„©g|Ê[l˜b«Ã5Ó‡_ââq>Õ][BÕµásç„uÔ½åfß`B%µµnµ_½ª£½õBŬòxÿ²O J§ÈƒNòYž€zxIWUÜp·ÞJš¼Í½ÈÂKËØÂK-æˆb«7rÑÑù çi'ü(úsZ3ÕkrO¥'cy„:£a¤åÅ0»Y´ãhx»òx@¥818ŒiÕ?ѹÛO1Å/u’J†·9sã7(yÛM˜øÑGÑšªºRçº^Ðþ8Õ}5fÍ Š¦xzo\!\4›Ñ‹+ÿ^!»=R=~Yù Ì©Å2ð:'Ûa“3ÐòxT•àkh¿Õý?@˜×?/ö2­|.õh»nÍÔÀSpR(A±Ö«C½C+T¨/b5¦Síý}oŒ°K%/›lZwn0O§ 4U¸&¨ºðüXß–ïÆøZ>5!‡º·.…ÔÀzíः ¯¤Ñ§ø§3)/ þ¯h  Ë/ %㊥eísSò‹âµµ÷"¨ù¬1H—¨‚²ÂÝë¼XB°Ñï#·~°×ü}Ny}ÿË_¯”V¬m"ŸÎÆZ]vPðe¡’U.‰ÞßLÊÔýOœº­¾£:ðTøÂß´[.x!ærôŠ„cºÔÿb<6~ì?ýÁ4kŠ÷¦Š·Íˆ›ÈÒP›¡¶8ó€”üAXU>äÜ®œ^¼%7¿¸cM)Ûh±ÉƒPØ£Psð'‰‡ó%>4 nò’Ș(ÆÃ¸qaœáºeM-º¼‹dÓ ¢Çl{4ŸÆÜ~VúI®rŒŽ#›>Ñù¼q3ÞA/™¶Â2»3ÓÇVÇd×*æ'í×™#ªM³³«Rüñ=Ù¡.9Ü *ÚXi¯fýô>ù…†$s ç;ü$ÝsKÅšƒ.8p6éÈJéì* :ºØÈn‹³°#×zOg×¥T,6ÈÜú«€ãÃl‰÷¬È÷˜A.òèVì €·×D>Ztˆ˜i>PupÆ-ÑÝ4c‘›æ¶¶W©CGZèß% ÞhqÜ’eøaË<ô‹­Cs¹±÷œ%òåô·*yüjY+qÃ>PÌß'KÛd®ÍÌtžÝó¹Ö¥:Ü™Ä=@ »˜CbÀ5£ùÒ“†çj‡Mèz2Çññ‚¯‚ü–ÆêQÉÍÂWy:ódSs„C•,‘üzþ>-‰Ü^Lêóß,  xNÚÙùl`@ µ‡([c±¤7zãîõfl2t³¬Ö b ?|`µ6Óüûl+W|­{n&~‚ Uß]¼õ¯¡Kl¶ÏÁ%öN™äld`^Äâaÿ{¯D¸³¬¸-îæšòQÇ÷}ì™’")#«§ªTi½˜&éÚ•W^ÞæcÒ.ÿу…³5ê`9Õ²¿¢Vmïµ*Õ#E8&“\+ïI)“0ŒÛ îä=³Üï€.Ú^î;±iÅ9¢¯¡£SD·Æ-Дá|ˆæÉãE8a¹÷¶9l’€Ëç›J‹äÀyœu™“/—…ÌÏàáªdý7«S*€šBÿ½œå/”Î+>OÆ O¹®ÅmmÇ*, ¤OÁ ’µŽôÓ€ê®9H5E läˆ0, p½×¿Ê © }‹i_È ‹>ó¥ï,-• è€ ÷¥yÍÛ„‚² 5ôˆÍßgÚn{6[n›¸úÒÊi&Gü;>Ï 3ø9±1å.°š¢à×ì¸Ç®{ ^Å ÷£ m?ÉÅPn6ˆ„(HTGßub{Ð:B¾†sâ´ÿJÞ]öÃÓKs×áý¦*ïò…ŠSàþÑã–€é½~ÊæÑê0'Ào6 ý¯g†Üàü]kÛ9[CSE1®9}€;(KÃ5Ã*C¿}’vs3()ðâÌ%VÅ ?sÎmH,¿bFP#¦Šþ C#L#tÆb›h síóA&+(-X¢}ÿ‹~Í?ÈßípÄžv—‰™ðØu6üã"uÃØkQ:=%æi»‡ ôûBWÌÙŸ¦k×\ÞX<â»âШcëú!+àn¾3lîªs•œåsU¬ YHβ]·eY©Åoál-`i«ê”ÌÿŰAW61èQð#ÇjX&X':´+{µp5,¹0aœƒS|ø^´¡ÚÃfØ äÅ7õБÞT<7{n(Md·¢œõ]%þ¢³j½Aíä-^aDýÅ*$›c:xêç’ç”­\—ó¹,Ì-‹”qÚáüFù/ Âh»\ þÇÿjD\4},ö Ç¨,u@KÈ)]ûÀÇ%7œn¨É}T›jÝ‘×|q³ç¤ÕG‡W¹=?¬çiž²Ò8¨bÆo´ÚA àÏ>‘ÿN«vLަ_8ÏSÅ'¦ vòîÇè6ÓIË´ö½Ñ¹ˆA;ÂâQ2êz¼,îsÓza§[’j«Óß’ÂF{¥ñ•åýÀú}Í–&ÑÞ>ßA]‘C¢#*-¨N³,tͶ|þcîX[W‰nÜô« Ig¶ñ›Üöxw˜¨ µK™#Ü0RÞ[¸ Îs|d‘HHè }ý¿ø›H6B3k\dÕZÑ#Ýÿ þs y[Û%LZÀ jnÝ YÇæmNò€´¼ð3`–ãá8æl²h5fÞx([¿^YÅõÉg}ÞsîÔqp´ÓI‚r/д{˜))ܶ".Îb²Ùdúx9Ç„þº× ;Ú#ö}Éd²ÁÎ¥aSåEPèØwudåÖO'FÈ‘œ¾W&*Ï' Â;ËÌVÅ%°¸7£³ÞÇ ühÐótàØBÅ‚)œÞ¤ ¸¬f28h6€ I8¨t j¹Æ¿N.99ò/š)(òÜfÝÊ‚+k¼&Ôµq?•Tþ l’ãï«/Eøè¯µ®’½˜ Ÿh[$"½n¿áo½5]&b»|º?ÿ#ƒÑå^•øRQì‹Äò2¥ÆrÈkðáý0ž27/’$†žì•F`ªg³´zB¦ÖÏäwćN¿·…æY]wÂ|ÀT_¯Ý†*‹~Ò×øx÷ùP­>cÛdïÅõŒ'UÓ ðýnb‡&£7Ûh¢N󥜷0P¨èÅÞ•›…~™þI“B±¸ñ0¦?W­³à¦;c3ˆ~ÂÒZMDJÃ`ü«ÒõI¡roTm-ëA(›µK5 ËÎW ¯sššy5È‹tÔwóF/´sLJV†ˆŠŽ pfk}F½^uñ¥‚©€ú]5ŸŒüþ¢æ"¨CÒ…ûõ @fì# âŠçÊ,ïæná-Ü4)}‡¢Ê)ØŒ´{Oaô“Ä…“ ÈsÜôˆe ¤ ݤn¤¶/.kwF¬‚TqÃŒ ækÖÁl.Ñ!ŽF(•¡Ì§”ïI¿ÒÄy’?üñ€3‘Px0û&Ö€ñÞJKDR£¨oÄô†k*Lš[’¬ºÞßärºÕ¶ÜD…ÅïËÍq DK‘¹rRÜ…óãe'ÈeÃkJ]o„ÙÛ.ù¡„£x%rËtôøô*ój0ø±Fyd‡bß, \ŸÞÓÙhg³ÆnZ£å´¢S²›«òz!ƒá«3®çsD“tÉã;÷Q^`Cз{ùD4À™µ3ÿíáR´Ì‘ZÕyåt† 9¬%z ƒ[Òˆ'¿›ÁÌžeL'<‘FìüyœÐ):Û~rñøöäz ¾ÔfSHG$5xWñ˜eÊì¢úyðÏáÝÞ9V,%÷+âèÞÁLþWèg¸ 츯>œ¯'~…¬]2žO€g3Í]« °J}hqŒú6޶"ÞéTe‡X;ÙÛîÚ  ßAÖR®nÆGc¶ÌmÚtŸ«—‹;EPˆš²„N·r¤agPË<Þùäò«* fÂ#œÒ‘°ñ ‡ÐWüó=d|‡òM{_g`©¨%8âVöU!ˆ?UŽ’ÅÅe_¬Î—ièÄh"¤CÆ!]ª­þ§²â–‡  í®—Û˜ŸÆ~.o&™þbÀGGiOXe4GïÚ|Àp’ËÝê aù°5Ø)PxñF]öxý&š5ipÞ•í£˜©yyžô@ÿã~ÞÒZs¹Æ`N¨ì¾ÑE¾ Œ4£×&ž=õãö¸ÀܘC#ÃiDaÄ8Ã0;&E•±ÉtV×nd~Ã6Ë YlFÁú¸†-]xÜ’;Û8o+u³`tÌä ¢ƒÓœ×… xÁã7h5 GpHÃ6³ÖÞ\cPË£Ó¡íR±ÊÆX€©Hdú-Ùí&ÙFn°k¾µ§>kŒüåb¯GNû¬qž¡0ÚŒ†`ðÝumº!g¦5¶PjC¾‘ndÝ+˜Ú_e«³qf8§1ªqð [5¼YÉý aä_v:KÈ.+\0ØRçw©»=\õ®Ò¸ôÀÎcSÜ!DA' ‘«û`áÌãÊ×,uj"ïx¼¯¯#!œÌ¢sÊ{L<£˜>ø0:»IÙÍßÿ:ñ.CŠ Í,¡æîùüÁª9Žâ8«š‡ŽÍ+ìOQ®„%¼F·êžœµó[©¼PO$„Y*~ÐéºXÝdDÖ­‰oé$;­…Ïí±x> Sú8Ȧí*#Ú ¥é”,`?öг£V£¯ãGUˆ·Äh<ÓҼ˗gl2Õ±z¹$röˆãOœ¦RÈÚ«óàj »Ã RuóîÖÐ@¹_󄜚K¥îBâ¬Ö{¯Á‘LL©z@­ýxCóhƒAÅ<ÛO­¡Ñ7”Â*>V_=u&‰Ä.QtæŸ^¦ÏLû*êÝ%œ“‘CMüJÞ"ûz© ™Ñœ™Ø&ˆ–g¹cë5ŽÓXÿ4øtU°Ý§^I€!¸ãšr¸v(r&½s!]î>j8À>©CaödHóˆÿïD³î¨ voðå,«"­i"{¢œ"ˆËW{¥<*þÇäM&E½7B…ˆÁЫ’ü¸!­¦ä†íPôvº Œ#?Ö ‰B†­.Ïb"ÇŽÉÁše›²qÆŽe–Û|äÐkÿB•cTŠ;r&å:yÓ¦Ï#ý튃½xdu¦!ËC²~8Ê8»ÃÿLÖKعÁD°§V“íáó㾟¢Yyf!ªÄ¼œ‘ë$ðU¿|2Zyÿ±&ÊXa0ÉnÔ"fäiü9ÚÁåÜÂýÅѯR¸%x7œ¸,”uÑ€ãù0ís|éa*sÌ×PÀI9’`Ȇû…‹}#\÷7ÔŒ‰Ñv}¤V™#TœŠ×N9°w„&ömq´µµP§ãN!J¤ùŸec>“2M}æùѧТsçöúhà ÃÁ¾à‰šC_¿ñ ïÙ&`,F•h¥pÄHTOìÉÔ)1ØkqÓ$x6ز.”Þ•ú÷²ïhŠPN?OVcØ ò{®ÛSsAp«_’Z_¥Û2¹š:Õ._ÍÚ£šÎðË[Ñ9Â÷Ý%ÐÚ×ÏÍ5?öe-F%Þ¹“óÉSï]ªŽPÀ‚Ú¨Ÿ5.a xÍ·»þ´´'ÛVlº9à–Õí$Úï´²7zÝX=‘cRzq—dbuÈÒ´ÂT«nX X’D4†Ö·'£+ˆ ]©Ëx˜s¸C òH<.æèjÖoiù93êØ éÚ7Úb\€1¶ú¸~}PëÒDÝLöH5äçy„0_ Åt0xœ@Ecg¹$¬„áµ"$ïoàͯK›\ªÒˆäÎŽ}ÄoNȈÆÖY4:wF³þwÃý‡ N© ºÓ ‘XºÅ]—%Ï%XçuV>góCÜ›,1fégŽMJ¤XÞòâ†BagêÓ«ÓS)¼¯Üƒ¼ºiQâf£)Ó±¨ÈSWŸ÷L£Aã¾­7çðOÇf0ˆ<tÜr–PdÐNPÛÁËh‹s»Ié4=;ßz ö#¹uÇÊÊ¥ú~{¶ºñ¸/îþò#]®`P@ÎÊÞúrØÒŽÑ&G„s"(€(àþE ú~Éx˜ÙïË=¬à˨ýTˆúJðíãÇÙþe @Âñ«°æõ½»”È5بMŠƒ€¢‚¦ö‚WÝAäËÆS-ƒ"%b—Ä$‚ÙóQÁ%;‡Ìr>Ä$¡:¿P6öƒ4HÅô¨Z–.™Dd¹T@_©¸’û¾e“ 8<¬œ²ok“jœÂªÙg"‡–svå³÷#ÔPzÇ.¶V,ÂNPØ?¨‹ïÂ8{ˆ¯?V_ÔêHjèp›2Ák#[óyeÇa§¬ _•«vZ·1s"‚ÍûÃÿDz"ÐtÂ%?{6’2ÌTd9óF_–)ª-*Á«—@àò ·WÒüNHµ^šD)«ÙbVW×6¸×àsÃ?L“¾UJº;ÆFiF·%‹Yf“ñ'tšdZ$=1êyQê@Ö53äY4ƒ‘Àä—Í-F> Œ 5à5 ÷¢T!0ú(M0…ÂÃë3ûáãÚ&]PÈ»¬Æ:¥ß2…µ²#L“›Ü«å‹ïpùg§7¶,`øùEÐU2(þ§By•ÚgðŠÜ1ZB¬þiu8Z”’¦ýÈ’$gË }"aà¦ÀŒ°¯Ž§#RdH Î 3©2aWîÖ•ñÁ'±JîšäNjW- F4ÿ]±*ø êX©èü¨¯øF%ÎÅÏ[ï„`Þ¥ãc´A­Š5Å=òë *´ñ·W9‡|¡Â3µÙó¬¿+½ - ‹¹ºÛ†ö_.b$kbs¦X©*Ìo$Ì …Ýk±5Úßh ]z=è¨x® ÄIñ¤FÞìǫ̈e˜›#¿ ÚåÃàÎJßÖÔ M=¸a^4ÐýÙùáx57¿oÖUT¦×¤—[¬-œÙ\͘´¯$pD.̺ÒÄkL,ñ|°…û…øDí*ø/ñIoíhÕÀö×Lu K|#2Pujå©&U;›¿O8ú¿øt‚³Ö w £³“Iÿ$aZ×þ²÷Ó³VÎ.[šYj³–Ô°ùåÇ9-Y^2ï0žûãÌÓìSÒU&K‘ÔþßïõQ™ìH(š^wãi©¾aÀ«ÉÒ´‡*‰vPg±ÿ4þø×šs3¬œÐT¾…¬ú¡cy{øØC •ó¼åµFG* ¶G&>3+a·—h;BDmæ˜l®”æ@1õ|xADÚ/q(p—Ì}ØÛª]ºŸJ>…8ßêI.'‹½™WŸ0ÒÂDÚyGÉÚÿ8¼ èø|~0‡ø¡¡þº5tYó8?ú§ÓÖ`1'®ŒmËÜ4rHd¿§äÒ?~áÍÓx™O‡#ŽÛ,ú7©Ëù˰ŻAY‚ÃÏgOµÙ™T#òTI"±½Ô ¶—òØóªw튙­òÌØ,]5?|üú¥fQŒ(’¶Ñèúc;âÈ%õбýþ¯¥ú¦ § «‡ßf‡ÒËÒi'— B'Ï#Ê{ý{ƒø‰³ÚI7ÀôŒf©#(ˆ 8ϸ­°ZÁ^ÌAÚóߥ«Kòµñ»p+1c÷—z:®ÝpàÜrhQSæ -Ü ~9À>¦Ìg¢t,/(s<¸™Æ‡Ý‚lòÝýК׆ƒ7¿ ¨e27^Ú™¡«ž=¶Ãzà6¹‰F7Á¶§S_|v"8ó«íü h€œyGè)ÿÏÕ×X.9‚Ç]߈æð²äy‘Ñ+g®hsç¤lË"k- pŸ¡4Æ6?‘&Éœ;q­È¡^ßé¤À™Ò|(|Vüž5ä´åųÈþÔ…½¹ùÔ[_;óÙ3j7 ëÍ9…üñËÐÍí.½h“~8u³í~·÷Í.1p[„ùƒÖª˜ "ŒE=Ѷ ÃjÃ3“Ñ=TµŠì"rܬ¼If¯Ä£?õ¿€×ˆ°˜áÇpô0×‚Šªª¿—>­˜Ô‰„Éôõmme˜áZæŸ+Ý›ÊK7êY<W§a…xÉËá–È~1¦¤8%ƒÛŒ¦7GŒÐÉÖ:ÊŠãÍ-“ ÈuöG$²mRg8/f'(>Ú•AÛSk'màÀoÝЖÂV;Ú™jѨèhê#3Út‡\6ªÓ8ì?c½b¾oþò•|¶z¶úŸÍ£¨º@¶dSùo7ô1†s³ ‡’{Ɉ&¯õ"A üzé/{6`ûu-|vK¹ÖwÇ/6€»oùQÁñ-}+¯PR}nìAwæÇâø°ÑI›*hP›¿|ÉÂÿ’]Ñêqˆôi[qÚ¼óÏ£omë ê«Ö˜ÑiiÓ*Ç£¹êáQ,»œÙK€L¤;P/–Ìï—L™ÿo%‡¡©ê¨Á6жf{Á•Ÿ§ ÛòôiÓ†é_ò:r{Ø4Õ®lL’YSY¼÷ý³èᢿâqÙý'PÒþ=‰(}Ü #ØÀ.‡¨`9<>ÁÙ3C6I¿„V/î]Ôɵ£¢\È•º*ì.ªsÆä®Ðk ‰Ç: …YÜ‹0 £4ÈøùdÉâ!÷Õ ¾jé†Xép²‰ÊÍ06÷9±d¸ÉÖ1ΨL,QÊl<ÎÀ­ç²>×ðóŠk;ñÔÖW¤]{`æóc™ô;æÃ âÓ’¦©MQ㺙…“Ðß¼®çŒŸö,‰¯SHGÏz~¨tMÓSC (XoDöÔ*a—91 -ÓJö[jQ.nÁ`D¤÷Ð5’NÓåÒ RP‚è®bÐÚÌ9R«¹¸î´6/hÚÁ7Ãîø– +E–Q×ïÀÂÛWjŸ‹ Êãy 0x«K”/ĵU96›zá¤GË)©ºžŸWê:?¼Q&ò† §´¿ÂÎ8ìØËÆ&{B–‡EgEŒC.èµ,.GLšŽa¦éc,]Œ­Ù óš=”’Pù´|°å¼×+!­!B&£xŸ²ˆ¢ñÔÜ޶¼ ŽÅÓyž1ð÷'ñ& a›iÕä< ÿñ—B'~(…XNr}&4” ÈÓ´oÿTÙ {t„¢=ƒR] t=d=˜ÝNl¶ýÒd&¢dIQÛò³ö³+F™ÔïªTܶ€ vÅIÉÝË 3t£<Œ[u²ÉzA nªJœçÞøbkD^ŽØÔØ`œËÇŠ¯GŒV$¦‰·2JJ¡O5À"·©¸ÔÓNb{ÇЮQlíäR˜gx(d •Y}”¶KÍ[•}›+ŸŠÔ£¸ õóí^«úw±Nyœœ \ ¨ÙH¤z¨eRªcÇ ¤@éqQ ?œ(KÂ&^œ TI‰h¬.Pø%Iâž½´L*Þ…_ÁÛÃq¤\hI9Ô«þÐz¬åQÛ@G·K7Jƒ¸ÁÕ%¹à·M+¤Áo5ÞâÞdS•¾¼Õ$¸ü)ʯ³,77Ѩò{œeŒrÿíÉ}!¿Ædd”¹ï ÑO,ô^ù³\Øph‚dÇòð«{Ýuæh9ž\Ñu?lª!j«(Œ›FˈÈnwƒ°ê|ÒÆÁoóAâTî†OœÓ-™‰(åQÒò„D…ÚÆ]qÝOÅ™Zaz [úæ#9í¼¬>çLÐ2Fc©‘òCåJ¥C"%ê·(w‹C™µH¢mêf¹ø1 vTÚ¬[MÔÖioIv?ŠéOtAÃâ¿Ø¸5ë¯*ûf·’îŠo¡ó‚e𷇿ÐÄà‡ƒÿ±¯RŒR]›üÞÁ÷0ÞN#T@rsÑÇâ—U`K æ3úÏgþÎ*·9~Áì¢J6ÔÁZ$ŠQXx­“‰˜†„¢­BÀXPÇ䆸ýÒ ™CŽNƒϜαÿwà*Ózæ]ò0ú#´»”emiû¶Þ5ÃȹM%Ãm );ñ€lø"ÆÖ˜­Uˆ⊪R]ë'CÚ<5n?Š–ýa@o†uuPE±® =ïÝÈ<ç»FcI¤ç™7sFt1¯ RíQ’‡lm×tÖ1\óѨô5Û ö¢â-ôTð*:ìÎFU:VÆ  ¥Ž'\7óŽ(òRÑÖÞû‚m'r+ S|7à#õWøÍ àËh›ÔŠê†^ƒ­f@˜\"Ù?û0«øS½úVos@v½Å ĉ&™ú¡= ÖŠo°^K±H7‘¢Ç0”¸vòà~[êøUgÇ'\‘&×upf‰•æ$_#;Ã2“óÑa”£åÿ4z9Ú}ÜŸÄÉ÷3$#åHÃÌïªSO„"†ÅcчFvŠÎJdÁîÛ•½q^ø@œ®À #XÓç"3D*¾ u|¾U” +¼ÈµK¢¬Š—¸6;¾ú@üu}aÌ6ÀÂŒˆt,}nLž 4Nâm_÷(úÁH²5ÄÎÇ“õ¡]J,‚!K’.Òüi^Ü{9ÃåI¾ø—½)R€·¢ûÁÜ ˆe[atM¸—;}¦º€¾ò@z=Ì®YUõ¿Ã#Qçw)—Î3Låvôk?èÇøQú œÓÅÆx8ž£ìðýGdù 눛¿™öJ£zXjY®†ëä-¬¦’éÙ. FY—ü¨arÛÜØ«eÛ_ñ56ë2ˆˆ`cÃêê:\Ò-KêlÕ<ý¯GZHu=L ºbÄ5üSï”ÖÚ,ÍĈjšbz}ý¿†B¶£‘'U΋D‡¤CÀ2v•,ùÉâáö”Á«ì´˜¡éëìoJR¥ƒŸÏ]WmÏc޽ñGÅÐÉl4_>ïl-5Æ•›yNi›ÛÎ%©uF8ˆÑ(ÝOŽ&ÛŒ¯††îÚˆJS üSgÚ~žQåfQØ=U÷DZ)؈«?os0˹~"9—ÈôSéMô ðïøaäR¹õåÔ9Ô3aJÅ3î¨>ô©WÈ y!ns´Ëà¡ k>‚ÛàªÅ­¨qc#ûÛ¯‹W¥#}1dF+åçN2 •WÀí"f@1™<~̼ÜçYãÚœòZRÚ[ìÑïLÅüûH;E0X¯þ˜Üœ fò^ÝñwY`É:†'1Œã¦ÄLaôþÙ2Cu4zûîµ6>lÿ-óîÒŸL¼YkÀß Â.10ÁÕ_m*Tìˆf p&ék y´áùµŠÆÚÄ;a¥ó 8B3ôc¢ó¿rNT­@Xƒ¨oØ›E:´¶ŽNþíù'Ø~¬BGçÞªö3 ‡ðB»cÑžÕïàya³*»¾Y ´×%?=áþ̳Pä\8Sb­"r»q·$ÝWjJk¨“µû;¸”¤9äaØW+z¶n±ƒ®·v[±~ú_DÅxÕoo>¾•ǦÀvL`†íž_꤂¶¦öOhÝ^À€˜z Û/v5ÇÕs| #í•Pù°ê~íé˜ë$Âä34e3 xsJØ}§þAÿ‹SÐÚM ;7›2*m´½¬9”ßGb±ë;X-öW3?Ñ›³o;áŒ$p÷ÇØ÷cm§Tœñòj—AC gß'Ic(6¥Àu\/÷¶–h›·ølhœãšË‰‚}EÊ™wÕÐd÷ò¾;¦ë­ØøCbu?ïÜ©(…`u9Å*Á€›rËPÈ57l“o¿*spSÑ𬽖Xp‡ži7€?P&MŽëoM9°Àåt*‘—Qˆ¹)YsA~ç¶Vª$núvR [X_콈¢u,°Ö=ÎÓ©¹þÓóè½ÚÂ5‘ŒÑ] :/ûÐo·:àß´ÄZÝ A3<Þ¹sÓcj[lQºº’¼™ê+M÷¨$ˆ¹‘[=É_NÒvq–Ã*‹ùã+aã)¥Oîæ}þçú~é%ƒñq/vÐr=á0t¼ ½kG®äÕ†€òPÊ©4 B½é\>Ê4âb–9[¿Žtdx¢B½žÉÇ/%@å5æZBYÀ72î4LÞôÒ‹‹ô„ËëÀèP•«Jº ÿ`eͱ6YHî°û5À|¶/%\SH|šR¡eÉã‘-õ/ ¸…³˜,6ʻϋÓeÅjòp°g¹Üå©Öî¨Ç–ȈtI1…)¨ãåQ]rNæÓ8“ÎlÝç©):ë”ÕÃÔÎÓ8±AŠ®é%#Þ™ƒv™s”>î.ªý‡ë‘öTãsÑV•::ðÂD:=>ê˜ÝE¦­KWµÕ*Thr¬(úÌ{ŽE7±f£Z0¬c£šZ,4Ü0üaí& -ëW Ž¥+þòK!¿?†ÿÔVðÏÜT‡»íùMí¾wƒœîïÇ(¥±nýy•‹Ší ³½¬ž²ƒÈ*ˆ½ldžÉVè¢aëaÁûR]’„*ÝÉ~ö÷Áº ÏïiaçÂѦ¶{#•qø\ƒ4”GCCÑ4sÕ‰~;Ý¥Rb\vDÆ]ËÄÎ`5Äÿû“ôåQ°›V*Ö¹ïðÛ 5Y.$föŒ^HáHçJöƒc•“‹ž•eÁïîzLo¿Œüh¢7;sÏG¦ÊËUa1•$è5á;r?º¢Òq›îTÛPñÜôEz‡öOqÈ¿2:îRDäï BÛd2_oÿ½`€>˜Šñ«9±Óª¿Ù1æG€Šp˜æ˜ÒPëY¢ƒh2˜¬Þa*‘ÂúK@I™Î>š{;´8ù™r¨:2”* Ý,ëÕ®“ð‡tvá„øgýXqZ§ÆÙ8ýíƒ|DrÐaü—ÕÌ«+ˆ¨ývâd”=<v½ÞSQŒ,-“£‹à`mÁpiçÇjøÚO F1¢UuŠÉFÀYàN/ ·E“‹wYÿcÚ`¥"«I­-ZàkF·1ã´.zuqÎ~@¾#”" (9gÍ7òÖ Ð+–Ç”Xr9Jk³ÈZ"ý…‰îFµpþ͢IJ”Çh\Í6÷c'ˆlËì´·prp†2¬Ø·»Ë™‰Í'7~o|“tš÷ÔQ ˆ~FÕ± š£C0¤5kçš;M£pÞ¯ãß p%ò-ö5 o©¨µ!ÀãZ¦Luns®W9¾éØJXâíÁ&fÇÓÿJp˜ÃM·ð6ªÍ”øß¢¢‡ë† ¹Š¦DÁX; $‡î:ÞOßÖ7N¿ܹl[¥çi ÷F“‰ AÄ=‡šÐÐ`§ƒã~Pƒõ¸nÈ-Ѹd.ž¨J“²¹#d†…ÓÒ”™µì`¾ ò”í*2j½/'[‚ÃÓÇ”Ú)ÜX`–¹ƒêÚ²ž ÇðÆ‹œâ¡OέB/jdP ´>?zÄ__²Þú£öÞŠSí£­Á<ÿÂ(õ%|&®Ã†yÔýÄF"2¿HlêiÑ6B3›?‰Ëë{è??š²x3½¢ÊÚaz6ÇiáX‰±¡ë®áíN‰":Ÿ¶ªpÙë,+ñaWqŠ/‚;ÓÄú.EU}•‡¡ù6;¼ëgUo|õ|;µ}ÿy)âí ¿è ²†ruëÓhd$µRÄXax¸£;_{^tr—¾2œ¶b e*Áãý" YÒ¨N óã²ÐýG£šúš\Bå$PbÍø"¡aÔ/™AŠôbÁm¹\g”0Èî`FxŸ¬®ÂdsP:%Ÿ#ˆA.òÌØ8q#Ÿ`®—;*ÊÖüã³û5Ùc| 7j<ï ɾÂ!©9òÜYQ5Þº½fðßÞò/è<³©Þþ(ºB˜Ã²iowL‹ÃÜ/îuØü¹&çЈ«óŸX;ÿzÎnø Ìß`Âñ!†žŒ+ÙÄÏ»ù¬òV¾ü~*:×øââz‘‡ò4â[l/á|)Y-½1@¸h§—PÒ¨…Go/ˆáS~P'WsMõjÃSôÑh¯Iþø·Öå’£ÓÞ¶"'Ð%ûëh8,•ÛÆJ]€"㾑6pB+IÐ\ ëð1V¾eЦnüžÔûûëÙ^Ö¥òà¾pBn˜M1Zħq)í9k¡>"X¤CHM¬(Ö%§ÜËÅÍ….î_ ïhÖŸpÙPLëÔU …‚¶ó´á»¾¤’ Ûs¦8:óšÈ½á=4OŒ!×Öºÿâû¬îw]dõ.ÅU,üSÃ]ͩƢ ?©œÛ‘gø`hHú¶D ó¯ìézQÑzqµ¸ ÿ™\Ã+:øv3úD<@bb CƒCØWEßÏ*€§a÷`D;T—Õb9 IJJ„/£œA†²¶TóžŒÙžë’2uR‰º*“7Ó ôlYmìôº•œ–vgÄš¡!âû&‚ÀôD#ޏ<3“ /øx¹õéohƒ{ëu‰|ÛRN4Íp’\±ÒãÉ N—œkcfÇÏ &Fä`S„FÙŽ Pui… ”€Q/€KŽ;‡j;Ô»ˆþƒYÇR aÜ\ç4mj·#àйÐͦ¾+-%fÎ/¨ öŒ+¢óUVwåÚûwG½Žâ1ÅV’Çö¬Œl¬@´LÕª$ã¤÷j<QeÕ _£½r ‘6?S‡ãsùKYE9ÎuY+FhóÒ̧҂x&‚±t˜ÖhrÆh›B%vOC]’ ¢«ö ×#?ìY}ÆÜžbª³Ïë#~¥ö9ݹ´‘dÑ*B§;cW.<Î6Ü÷ß±)W`Ù6sÐ:&eÓb–ÒµáX‰.­ŠDŠ@¨M"[¼¢pñ}”;¬“e²–ÊùÑ*¸ttk>]¼)ø¡O(?oyyŠÌ̪¢dRæÊ–÷‘WñQ°¤ô‘z¼ø­aü€½¾¥>®(>îu½ìÈ ±ÛÐ ÒÙ:?l;õ¶ù³Î^I$–„„ü¨?%Œ\Ó‹¾‹ £u\>Éáû%²æ7Ý OГÛÇ‹ÒüÛKó„·„Ù–7ø¹ šˆ)Åõ.ú!ÝGñãù3> g;6#á¼{hî …+ÔÖÂ<“Zb\Ü.ÓÖ¡=nÁ#x áùcºçsM%2'‘ô&Ì™‰4î+ËÉüâ~ŠQ‘ªc¸Ù£ŸÎ†x°F ¦iÌ`í«¯Pô›d˜ýÄþÛ½ž/€š¼Á%cP„æCJkôZí¿ý•EO~„–\gHbgH½x¯B–DJ^qðX<ãû*f ×WŒd—¿²m nw…#@~“™F¢7 \óÇUÕD)4‚äÏW.á)• „Ž<ò}“ü4,×+¤WFŠÈÞ¦âÒ†‹?¿NIÁo0E)¯¹%ì¤hl©Ó'Ú ðˋ֯,m7 øs€·½R])ðì¯6%¹Ïˆy®“¶àFÍZP0çBþâµqd ®Ú•[†¶a!¾­‹Œë¡p±M2qÌÀÚÁ¬2ò鄬[aåÒÚ‰ÉÍô|kõ*ßeó®öþÖB³zf½ëRŒªFœ6OäÊ|jQµï–¸ ôŠ3è:½&xÞZia¥ŒúU™ÁmìIÄÚ–ö Dçs£J±ö„ñ£SÀËð+EàWª)Lfaä zÃBmèëdé@Ê#ÞBÒýûaºÙ8Aè mBý0t°·Ctž€º2mûMI?*nZÓP‰í(Äs—_kwÝeØLC%}6ÖðkÛê plŸD?kgåô"’T E>!AÍŸ8PgJUø6:§ünÎçÀÂ8Ý€kêv¾Vóã²{ÆtЫÆÔ„­)2Ôëš4‘ÙGâªú‘³ðóߘ~™RgEÚ·â¡þðü—;-ºRäÈåE—ëúãèN=/´Ù|P\üÕR Õί†Æ^°7ÁáÑ Å[¡Pd‘·WìøU$«-‹ײŽ)'C²ãޝi"¼II“IŠ2]õ>Hý²GA_P„ny-tùB7µÕ«ß8áåNOÏ7,U³…ÒôþÏÞ(µ&].Q©1˜Na?˜<â{¾[%Ë6ô¨tˆÞZi‘ÙÄ&é=cœèâ! Ýõ¶x†Ù—1„(z‹O `C!ˆÐÕ¬á%l°Ü&ßC©²+MFB“Ü©§¨ØO J¡§Cä&;èeþ÷õ$r¦à.êA AaœªË=r—ñÄ y!8ò'™`á9¥r\ßѳRÙŸS*óUÞ4>¹yùË'½nIcTñ´ËpgdgKC-жÉJëí|Ût:†¸Î˜Ø›½þ£v|92©†[…º2’d¬ª?»²fr%Ê øbÚl€¶,…ºîJ«t.JvöØR’:ª.À{àËš¸TO”<"’Ø]ЊŒÂ//Ú2£‚h÷µ‹œ\˜~Óº/0:&ŒôHmH59úâ½FÃ(—4ñý)ôÍ07ð/—&åƒM0lŸ~ˆE”sÙj¬Vþ2E/¢ŽTÉ8+˜hP?ò µgÂì­#sŠ5J¾üÚñzé|‹d„ˆÌ"’Ù,}ý # ¥æª`¡4½wIåi  µtº¶VLN² oØ D„õK€k4½¹£Léì/™á¡+A‡ =6e "Ê…_ÐŽæß×PìcÌ£KF… &ÏòU(•;RoeKPëJU„s§Ø‘…÷œWMøÒë8ÝžyF ׇNõ€ƒ¼`VZ}7!ŒèÐÚº—y÷²¡ŽÿŽä¤5[û&ƤL{Žu©„¿>rAÏ(ÿb“Dñµß:Ï<_äŽìò‚[Fdº•Q+!¿~ÝKð6VÂ&Ú•ržñѯSNÏHL­Œ¦•ò­7=ÅX4vèòL4€¤þ–Ÿÿ%É_iæs ,­&»E3¨¢]ìu= á5)‰Ànɹ™buBL’®ªÁöê‚¬Š¹êt¦ç«•¸¹yÞù<—ýå·ÕÚù™ß牠H*Á$œæ©Æ"À÷·—(Ò™"E…•}B"žk2Z@yšàfô/_ZÇ9ߨ:àló©ñ »ŸŸ±ZÞ£(¥•1ôýW¼il¹:ÛÞ‡ˆ•”d„V_djü± báÛ¼ZúÈ—Ýô§mñÁcˆv¬¿Y@r„Úh¸¦3ýÀ.}³pZáY\>Mmjòbõÿhä´[¢ ì`¿9´£y¶EaHØG®R+_†t” »£ 2ðˆþ•–ºÕ §îFÎ9­£\¬ñŸ–½¬ [Ý$ÕœSR¡×„$ßϾêéÃû+jG«ø+˜á_pªØœ@˜û|1©ñ€Lö=ä„Áu`üé™tMLÞrô¬Jˆ#Ö&‚1±d ;³ÃDÇK—×i_‹©abVdÜ{…}¡6ÈìèLF#iaµ6 ^ì™YŽ0`¦:ÂQïà ;S*µ5ã§Ÿ›òL™o¦aï…·AGÌ¥ë†ô¨7ON\ãò%L¶«¯ï ¹W-Úú´Ü„“Ϻ+%TÖ9 K>ÐF.%[ý\8PçºNήóÌùŸUJ€bNãO ï*‘¦9ñ™g!-3' f—°f'X²Â3{ª¨º@ÙèúR¹Þ‰¼áúüI $.^}Cg¸.ÕÉ„9׋Ø#NÓãz÷Iæ¤#è ­˜æÚÒU‚=ükùzª8º>·»¬eAzʶ¤;¡ücÉk&|M3Ú–Î}mÞ•sžƒa2¯š˜á€Rµ<÷šøè7W{LDÝ·XÔWX€‡œQ o…z — G¬˜Z=÷¥|u&™; ´ø³.Öly‹§ç!Z½_-w)Î.{Y“ã—¤eß}4“ j‚“Uºœ†J>Ÿ Eà±¥ªž»"®‹æ±·Õ½"ð…c‘ ¨æÒêZTé°è Äo*q‰õ¶qpwÖ57.ÂýѾ*BWÉ¿³Ä©ÕÌE¼ü&*Æð7ùÈÄnΕÉÚS\‚*ºyZ»P&6XKŽÇ>—Q­x†*)¤÷]ìLóVsZ÷þcÙ˜3ê5c2£â¹z°öŸ ÔŸ+gêC¬‚Ý>·UaÊz qQà,:Ìs¾/¨¥ TÞ鸩ùÔÉè»Ü‹¾…RëYQtt’['ªrdi•z±‹]„À‹ã»Ú®ãÒ¹|ü‚áßlˆÀ9È?¹xIg¨ÿ´$Lª©ÏKhMÊÄÔüüþZ5UÀÚʧdqLCf¸`T“ÿýÃ=teûÒà±~RˆØs§hÖКi5Î’¹^]6¨z€9HA´LÙÒ„®M'8¹ÑASzÒŽü¤mìPÄ®ž¡»åå \àüÝÇ*±˜ø‰±[BB ¤äñrÓæ¾ß*æ…ŒeÜ›ÕÃ_怦áÂx¾ˆ]Ó[o“Q╟ܜýcµ¶’¡ú?{ø–Wî6„ÚHÔ´lwR3-:Šû3Z¯›,[Ân–m¿ô—O)EEÃîÈÁF2kóøTÁr„Æ!Û¿l’îOp§=[¶,|r`9åwX°"-öì¢|+ù ÂÄW0Â>5ˆéCèªoÕÍt_Ž:aøß‹tí@!"ñm¸«j•wôæ»±@… Î8. =©tjäÑž 1o@x·L‡™y\ò"W÷xFm³Çž¼¥ì"W׿KA[:_>»+»wð»emЀ¾K|.¬Ô\px±2õµßk®3©#ôµodJ<¸èýªVM6–ñýyLPþÜj¢iT`Ìu>Dn Ó¸&Û4LøU§ÊpøÍBÁd¯ ™8@Õ¡ED»£áá8|þr’„Ú[åèã"uö—nóö?q}‰a[OËØþX#¢‰éôìVÇ•’£òŠ'®Ù­®b…5.²ÒAp?fÁÛ00Ç€_¥ zBºGŽ‚°Í"mؽÏÐùâÔ;–¯5<uõ÷À§ÖãÕf¹­=óP‚bcðìØù¯zÁæê)_î®';긣Ïv¡àöŒ†°9sÃñ~c¼FC“Ð2 ïô›‚®fg)¯&O W¿\JÀÞ8 ŠhëÀ²jÅ6Mç)¯´éµø`½®QK »`'ª†­‚Úß7NO»[9P¦žŽ<ŒèŠ1¾ö76Ë}žJ7a$ƒ3‹ s¿²AñBÂgµ¦Ë!] ™$ò€Èí_¯ŠìaÀ@“ZìêœL+£9º†ÌL ˆ×2eŠÙ%=‰›JfN ëÙ/öOw.íÜŠÆ$¤$oÈGcW†àrKÏ Q'…ÚaHÄëíÀ*5Ï?Vjàìá“•Fün“®ë×áwótIPÿùjvõ³Â¾TRÚ¼9uæóã-ÍÛËìrþÚJKd®l£oy28¸0b;L§\5&ŒÏè DY+D÷Ü-f¥ÑÎ9eOd±‚¿éú=,7mÃ+B^­«àeAŸ ?ó ¤Až®ñ“Î2ï?)=6ùbÖ;OÛPv’©|òÂlÀbf™ðF92¥Ñý–)BlŒÌÖúP[V²±»ÞîÿH[¬T9òž~Õâñͽ¢"™œÇþ˜6¬WLÂOÀþʆgN×ä8̰E­ç eZ[ýW¼ªd’(l=KFt x€Ä vA‰üúÔýåߋ"؈>„Ë]Ýc&/ôK¼‘_¤ê“ÈâGC[¾õõÅâ¼LG·WÈJ/Ô: ’¥¬gOm?i´0SQ ’5'–;l¦bVH©FC°4ø®Ý FfpAñARO8£³?ùeªˆRÏmN|òæ–D—´²J[4êƒt»jÀ¦–| *‡Ðúšw¡ö䦦ìú§K0“ÀzK ñ 7yW‰Ü¾ ~³¡ób…Áçõ~”—MQSg‚‚K#ÀTW_Uƾ0SM5ÐGª§ Ò1±ß]´Ãéj±}d}šAzÝ ;Ù5mð“‘Âˈ߳8\W” KËîµ2N{§¥ÝWþOÙìZ§½#ïTN»bº¸ÊtAK¶zTÛ5J„‡j"$Éçi0LÜî’&³§6nëˆÛ=­È‡?=-ln>~C*Ùý‡üE„U hÄ™´?¥Ø†VwE=¿ú«Î^êg+#áÁÓäF”`ôBXåu#{O¾§ç/æ=…'&6Lꕞ´P懫Ҫ"£üÖ\IŽî̉©dKކ ¤Ï=µ‡hÞ|©Q*ß‚p–nül|±ž:Åí4jÙ×ÐëíSçdUWÏ…2Él9Ê|6!Tà[Dlò{:ûòH`ÿ¬GqÔC§£×œ?ääV«/ËýTx à ³!ǧÀ:UãÕ½=?,’bø2xK‡”-‚´NÀyA²ÛN4ù"]«×‘û¸ ¨°|)tzÞ[Ï”¢–bRUðÈ#ã²¼ÇZüß]e  åä±/,)†±·Uz`ËO´˜ÁÁ¿|”œÃxÏ0ˆfêº L­SèÕíÜϱ¦»â¨äA¨#°Ãð’î°\À"hËA:¡;'ü*>tÑŠÜçΟØõj â<¤ë›o™£(¹Ž}Žþl©Ñcl©¿d¨šÂø“€êó ˜,¬Ðѹcó`Ž#T@?ÍÕ®³}•&.ôRç1íž©„î)§?"î8˜ï;xLˆÅÑDˆ—Ñë$Qõr‚Š«Oƒì’JРêSd·–ŸÛRU.Î]¦iæ-‘,ÅðZMHßpÛDm€ÛZ±­¢È ÞGöasK7hÛ®«uC¥Cy)3‡ž´ žc°¢ÕMÕPA¨šA·@ôW»'„x9ëA@aÏLÌ×ÅV 5wȰ j_Ø“Klfd¬ ÚÙW•šw¥7íAÄE7ÆÛFä0âx1Š„š|òòÔUçP1sˆCânYhæE€ ÁÆBsŒ•zT¯.TúÔµEQ`ºnôÙ†'J¤‹‰×K¶gIf¦­«!H Ó‹ák\LñµcôºŸ3ÄàCÏZ gEDPÒ`ÿè÷!lá È×åˆgÇ«`zgz¼`<êÕ7ÅëéÁüæaºN'9ó²?1²×Û<.Íôø#Y]š\2ïÉ0F„îbQ…¬uïóïÑårmSFÿ õN¦z|”Q¯ÊÔ9$ähœ‡ôL{à—›17ÐÞÕow/ö¨â^·*•óÔ-k±±B˜’ósÒ=žÃƒzC­P–FIˤ)j µÊ}i-~ ¿¬î ˆI}?[“>„¦mk´ãªl¤Vºoõé„OUê/8þ•)‹æÕ’GI¬YÌ“(Yñå¬ |3¢—Ÿ\‹ï3³ÕÖY¤„ªåárÓd:ŽÞe»0˜·™Ãœ·ÇV¬ºóB¡f84M‚aŽŸË}$™ËŠO ,j 3ź~Æ_ ¶òÉ{rm¶ß+D›õ•t¼uá“J×·[f'ôÿ‡ÅÛ"×­C4Ó¥šõ¶r“thìeÂ8¡8·oäQ’]ê»ø ÞO°ëS£iãYFcÿÞXp|¹VO#~žÜ=¾Év¤Û׃T× ×X"5‡*`‚bk¾H¥¼,p2n"xÑ{–•8é§bn ‹”uHdñþë̽äØF"²G#QXîZ 9ý‰ÊJ' æ¶×1 …otºY¨œ·,Õ»! Dù®*ÑHÛhÏñÜ<Ε!c÷çí½×Ù҈80Ï ÓÕN­æ 7EœäƒÖ±Nîéº@TÞyAŠŠîè…€…ž väþò)ZÐ-½¡)³Ë¿Òò,X»Ð$Y‹®KÙXIý> gd¬ÏɦúÝ­,6”Ñ ]\5VQiz"*yã6˨›Û2äo r;NUuŒ©`Žç›Ž²îʳd:¹<_0÷'Oà†eny‘¥ÄÅf$M¾! Òd¹\ë?¯ŒH<Þ—ÊŸÂi½<°ò…’E–ÑäIõf´ÒÆ+z Ô!0%±ƒÈmÛ+à‚©^E•ÕLÈ_Ù9H¾‚°EµòiªP6eZÔŽ™jvüØ =ކ?7u/ºçá}€FqúA7DGõž)(Áó§ß8TŽÉ}Ÿ“ŸSŽ ¹È1I¯µq$?´ùSÚ=®ù¨Ýð"mÊÇ2ÁHáúÀi‚HTmVåÿ…85€ŽD!{bs¬ÿj„› Ãø#ñË!$ ‹—O.ÕsîæAâoú‰8¨ì'bÈ)qÌ4¢Wÿ:·º9Bƒ}Óà~5[o’LxW©;2ÙH4+·͔P×z…rM$ üÏíX‘µWšùvA2üÅKÞ>80×\&öËÏã‘ Î{>ߣ8­±µdShïJò†kªi± GKCØ)9Ö¦¿@RÛºÛwö¢:r,ia|C—Jlñ¡ûéó”yºj7 ø²®Xßœ½@ô¦Œt;_k=±EÄØÁ :Æ;‘ M]É’S²ÃÉ´Ayÿsd›tÙ<ýÀÒk™¼Í1Þìüàœ ©õOÕö‚g(ž5_.eÈá Í6ì,Å õSžá|¢Xd® ÇòAé¹3}´[!#£=Ågh'(ÚñC ÉIPZ4yËp±• È.À¾ ±³™ÍÝKqúqÙʱJ8Š#Œ@yµ!oñOkË9˜ ¹ÆIk¹ü3½ºAä¤ûüÑrK(UüúÙ²Ë0FpüDòxq…Ñ+™©ÿ‰Û ^éÕØ×Ûz@3@j±ýµ Wê ºØdÀ»+º7`9‹$tj>þ1ˆ]÷¾O^O¦ÌÈSY' $vÜ…RƬ–˃}äuÿãc¼~œ­ã¤í¦•ÜŸosþÇvÓ@ï¦ÁD‘@¯”³½w0qIÌ[ZŒ›TLo+½A#»3ˆ ²Þs oˆq phÂS°£=ãÖ5רžl¯¢ã>Þu%ÈÿÕ$_ž¼¾£™×€‰ BC·?ª$a/zsqóKJ;ìn%µ”d+oJ› Äš_ãæ¤¢äj!‘‚÷Ö ¶ßÔ'×ÔÿðÁèÆË\XFÌ´Ü>ïÓÁ9¬íÙÕx,½‚“öéyÓÁQNƒ5?$¥ÖËSCÉç ~AΖŽnØ“]Á47ÕÊ„+ù×¶›ØÅ›?¹÷RÝ'†‡!üØ úÍ­£‡½q,™x6M¥q 'y=õþY®q}¥wZ—lb´¸ t¡ð%RÖäÊ’-ñÚ'Ï,¢tÑ« :ÈžkzI–X¡TYTü´êLØOݺž›ø–‹Jf×­a°<új²-ýf_-#ÑÍó~")Äk–¯LúìR²ß»‘FWªGU"ˆ}7-K]¦.TIóÕ!y2ç@ÀXT¯óôË«Úey©¾ÇdÚŠÖô‡xÃÿOµðé†Þ´‘Ls „ëÝâšé‹ Z…—·¸§èùßéÏÒÓ²Ùý0¤ýÚéÍ;54»g0'qC_N4I!ɶ{.´<áÇËX‘Nê=Ãódƒ&õßa†ˆÙ°9F»JjqøŒ[ȦŸ¸¤wJ‡BˆÑÃ0ëóOf¾ë¶~‚çN5å#Uá‰:CĨΊؤ•Ü}Óz§|Ýz„zEéU…ñÿÕW l·® ÆÙ)¾‰!”Ì{´·k‡9ú¸½àà/5¦ÜÚ&jã·ÄVOKxpì%£ö‰òh…y]íÓ¥¨Ÿ$Ž"KKöeü´Ò[)ñÍä—6Á|Žx1”V]ŽuUøz<4=OLof>ž‘‘Y›Iƒ ÔT@Ú-áqbMOÒ ©_o¬|gQÀÁ—¬íÔ“ôŠ?È6Z—Tu0 lª”Ê«éîöû€Òz‘@â fãwMOè|ØGßµ)ø›´ëJç5LW†"Œ3?’·* ÚÒœa3j•Y>~Dƒy:Ø<+q0#Œiï¢xȸÜ è[tjÁ»Ý郓3‘6èµRÖÄÃat†³.äüãÔòà¸ð‰U¾c‘ „óƒƒ`Ñk7ÆíËd}Ƀ¯ûÛ+x‰„’vÉ0Tt4YÐЈ +Hè¶9ñ¤ýeýûùçû3q<6¿¤*À@f Ò§rnM=yb»?ì”'s´Ÿ{X±¹£3ÑJhòpuçBº´Ö|&ÝÿºŽ Óâ}J'éÝÿT1ø½Ã6«¥£22ztTC%ïRT>½hÆ;“àœ5*}‹èd9`qŸæâÄÞ™O`¹^)“E‘ÎóΣ,¶W`lφÂf­Ò÷Ã#¿˜-7½5Û"rôü¦·ÊÑJ„nDQð D«Ä7zA4Ðébk˜ÑÓÁ|»Kó±Ë™Œw©´G.×+³§ †Jì^7êíÅNn2#ÑÑ…!C»}Q·z8‰ÏJøÒÓH=nð‚3ÖüýãOg`<‹\Ê#xåY!üË]ψö¤C,åüauŠfˆ¸®E*ÙÎ2ê+r.ŸÄ¨€Š¢šÎKVÑÂÕ­¹o!ÿZjå6:£€1ã¸;ÞòÝF²nsO7˜Îã@ÜÈÉ-b$‹WtŒÖ¥4YÝ¢ãŒÞÝÀ䣢=RåC¼êÔ ù÷Ïvw˜:{„bœôQôDÞßÀôš5ÍS‰ÙÃò·¹¼Äã–©6- 9Œjd3„RßÃeE–·À±´*‚ø ›z3Ï_"`ë [ÜQæwFD®”•›9ŽÈ·L´J$zï{›ƒ5r±P¤;0½o±,˜ƒÉã7HÌË¥M/Ü”Ùv]¾×3‡–‡µÍâéLæ‘pú¯EîÄ»z>0îÑ4ŸNš¤˜÷ï¿EÚ-k²Ü×Ð]¨sOüýԗ牼²ôá± \݂ͱùQ=úb+Ưíaù*¦`|Áp©Dx Ðv]oêˆí¼ ,ø¤]µyLp“ýŸOf'Ñ¡"JÓ-×ÉAvõ»*!—:§O»^üÝŠ­”n¨¡ÂwÖÈßÌ%´N;yÍ碼dhqSðÓÒ*cze¤ô\{SR ê‘é÷R{.ÛîºZïÊ¢h6“ÜM~ü‰Ï ¿D^0G }¶ õ rÛá6àôóÉNª“Ä„¢“1æoñ82¥Š^ø²Óḭ̈ÌúÃ/_ÝtÏý-ºÒËX"ÌLG›Ûµ G¡*„œx•ÏV»xÎû+Œ¦Ë;¨£ß9IJyM…9ˆŸv¯+ó—yÁúÆû–”Ï¢o‹Ö¦ý ©Œ/ ²é#hÞ&tô-ÊßûȉÙ<,î(h¢˜*=aµLxí¯ƒ ðû†ÏûŒ2ÇÇ¢Ä cn•ôƒ¥nÉ`ãTò5½p¹ÏJ®›ÊÙ•~öþaxnÂ4j1¦W]Ù üd'“_p¹gÛ‚CȰ#!ü~ñy>Ô½RwõXUS*tm8Cìr3Äoâƒ3`£Õ½ù!ôGµ©î6U3h 4¥Tü$˜¢±åœÖ´š°îßÐævÛ¯±àcÒ,M·-‡ÚÅJSêû.,l#NÖÖÀ¬Bˆï›Þt½QÙ‰f ½nŠyé2°MÌpOa/êÉ ŽDÃ{b_§T$‰°æÚé9¢‰Ï¼Ô(úh·«xÚ«œqcŒ7â-Ù–é¾;à5»(1ïhzc³cn†¢`&ñ)wÂ)þ9WÚs€)ù­±”Ù훯m`ŠNˆB¤²É j«gLö[_F'œs«º{ ~™Éz6S”Ì‚]æŒ2šîojKÀ©Þ ÷ÅM˜Òïñ$óÊä2U "šªzxÚô}Ðý¿”íŠ}%f"ÊNïdßW©hÝØ6²šÐn)aÍtåýÊ8§¹PjÎoëóU ­ÊžO“™á{£·^_²å¥åwôê)Í!ß*y~"x |™ê*¡‚w×&?VPœNãyT¦Ë^§&ú¸C4á#© ¢Ø/V??[â!é¹T™&]b¶>sk‚ÿ“Uud$:ÀëA‘_”º¨ÆÂM@Ð9R ¹‹¿ ›/=Ô:{ôåJ–Áj2s˜¤Â»­ˆü7ôÊ ;T»ü­“wÒ^Ì-©GtÜ·l£¨ŽŸ¬ûÚHººÕMCSŽå3åãöXDPýái£‡f¦".¢5eÁÕòÝ(¼ Ô©ä*‰ÄY¹©hì·™ö»g¶N™ ¹Â’¼dPã8íŒA\ÙI šë‚|Ípº©3J¢5åê’‹/T³)¸}[è=c÷§e.ž¹‘wjì¦$èÁ ¦æ1\h¸Û†|ÊÝp9s­Qäà0ý%¡g-—dY$ñ'Ü]ƒ}˜®÷–L}?‡˜P:y;mFý¢ŠÜG£èLmÉ_ ?ϰôf£? ¶’ó‰©V¶Ï î9{Sr(0öƒ»‚øÁK©äw!÷7ÒnxËïÏE¹Àøòй‡,÷âR•vß7•¬1ƒZØÉ[Gúžàå}FNS•4…t”,"Spÿ:ŒÀ¬Á½-Xçky§&Îì™Öx€C'`ŸÊ¨—£®m9›ëUiÕ£Kýæ½ç³¤È(‹Ö–ž_1{*Oé¯ÌÎÔuE$ÖëkøÇ„ëv!^Ïk׫̳qà‚½÷[Ós¦a92¼‰Ûnâ”r±’›j}Ÿ'³0}4 ==õ@ß=ñP ¦Ö¥ºh‘kFMBáñÐx¦šjXºï,Ä¡Ú ÎÍWÓMƒº¨pê, =TÙ…®â¹çUvôµ&&€ò?};ë•O+ï†Ö×-Q+ XI}ža²í £þ¸q2ˆ?¿6–$Ñ‘SÃË`S>f™[@lØ8 “*ûdf‡b‘á¦f€B–&´¡¼Ghª7…^è4þ:gÌxòÙŠA‡õÓ›\šÿîÒ&øA¨þÒZÉFu½$*«õÖ¾;»úfË` }( –5]§p¿YVÑ™òBݲ>ÉXØNº¢=|R­ŽB \I€ŠJiÊXýiXYgÄýUÙiÏ¡Øu'×;ý×ZcfºLpã…{yPp–ºIë‘Ú„@ÔÈBr%lûO„²»sö*Ð¡Šøt>K c‰U/²]sŠà(¿£½/:kàå3,q|Òß%5œqÝÜÙ.ëÝñ,Sã—š;$]&ë“{ ’~o©Ì ˆ#g(@o«k•9•ânb#*&³aLo„ÌMªŽÊ;luoźҗ¦ñ¨PáùÉŸ'Úu´³&nJÜ< .F&¢ ÛˆÉÑàDçÏ5d(ùGë£Öî!È|‚a[%5 c¡HÙ¤ºÎ§eÔø©ÖG3 Ð}UÂg6ÝbÀºb¹>jù ¾­òWÂí$ÖÙ¨Ïõ’ª¶æÍÜ¿-¡Í€¼u‘—»¨$Ò‰ ïePráMífÔûÎÁ¦²;ÛVmõ~#!ôÂm·&.¢évï<ëDE£¯,NÉ*áÚ;÷û¨|R‘Ÿ8[!ºs{`±ðÌmödrÍkž*ÔË‚k…ùxo¾Ð/س)ÚÈ(ìôœ ©ßß_Ááç&Ø,Ømƒ‰§߬9ñšÍèy³'’»&^ÑéqcgS˜™Ñ¦Ü_}ÉŸ03ÿXN4XÖ'*žÿÔÆfHèÌ*%$½dÍÜå¥'ó‰{&>)¬(š³ézu46/öëWebŸ¤Q¢ë,6³yó ÛE“ç0xí󬫙·@œÔÝýîÀä÷ã“Yu·HŶ_ · íÎÑ/èdñÂWý­C_ȇ=XV}ÂNœñòÅ04TÀ¼ç/äád÷™u.³‰JÚ£KeBÀ§›Íý®zÇÊ=Êg6ö¥ËÓƒ„_y¢nþÓu¢’g8ÂuÉ6ëˆHÝû®Û—Ê»·5 us\Jœá?º‚AT”;=è† ˜ì`ÊЩD(½uÌÿïi”kB6.bW€,„²'%4²Quz¤±³oâáÜR ‰&?p§'à—GäCåô Ÿú¢$ó£’ WÜoàO”%ÆDѦf{-\-|,¡\ůŽOw‹n!jö ª-ÛP­$ŽCMTà…niÙY„ %¸xrÃé=ðoï쇖£e©Èa>ºª6"òÒ9KdÚæËpeï¿ ëÆ&$¶bujñ8Hp÷ð'ÅPó‡s© ûŠªYã5gâ°Œ½¦lŽÊ[ÊOÔªuq¡gôRSye8ÍåFIl4Ëm?].éOßD»òÞb yé$Úžð #œ²,1ž©}?‘‰Q¾Ä)̪pÌÃ@ÿu2K—Ý' ±uhD—\’øEÉËo™"šdA5¸jã SdɱùÕó¶Òt&±Åó£_FðÞÞu:ŸÇ\=vÌAȃ{ž>Œ ‘Pû¹*p>«³™2ðÝ¿r I3sŒzßÎXiA\þ–’!<­æ ÄNFÙöð_Ý)D3VYԪ˂½jwh~Pú²¥&èÙ^xvŸ[÷Ìèè´¾PUÞKSõ2h÷ÜaöõiÞy\M¹O‰QVp‡·®ŠBLÃaÿh‘,óà ÄÉd Eè¬hžy–r©V |†ä~ëÎ|$KýÁƒ¯*©èÚýGYËÄk(î=s›îmo:hBÕ³õîÂYÞŽA*nŽa"Rí™»ßM®‰  +m„¹0† …åX×–Õ!®Å'˱€Ð—à†ÞÀDŽkÑB‘,hûèƒæNOk4&<ë.ZNÏÐT˜$èf¼Øw Ж?šëõ¤ˆ‹u-;¤®ùõ‹ôcâ?u ¼7ïkmӪЈ.ú7B¤ìuú\íÿªÁUüÏJ¡ ÉÇÃIÏpÆáyïÄŒVHK2ÿ°wjâMFômÅ™~$nz’g§¥ÓÅѹíði£4u6ÜÍGÓ+D­ÌÚ®ÓÙk í³z”æ\Vï4N´¢¡éB¿lÂáþ ÄÛ%'&Spî²9©ù a°. ѯÿ› Èït‚nœ„~:ìÔŠ_!w ‡U:.Cjïoagl9›ÐßÂV !W®»EB†qÈ\ˆ…QÎ,W0ôŽéAA2<µy¼†':º¼úÖz!¼SëÌt>YÐ}XEž²eKܾU-‘¨Ìár<•ÿ™ÔÐ!¶ôUè1ü†ŠýµcÁfûøÃú3ý´ ~Á¾ô=¥yÕ@p¡…§V…«·‰Zñõ• 9„X½“Ƴ€« AVc¹˜@OaQV_vƒš—;xE®µØb ÜÀ>ÒBÉC—Fœ¯é•€û›M’8“ªõù‚ùI§J1£€ÂÏ«y.O³eq€fn¸U-í%]C±Â͉ÚN0\ЗþWÕÍê覈?ð÷öäéÈ–ÌÛ4û1n[㟘þyÌ®þ·vú-yâ$U%½ò7±\ûåbNH®¸CTl¢ãQ¸GìõŒN³¼#rØGì?ŸŒÆFÄ‘&HÒߪl4&WD†ØÈ^ޤá)”“¹*ú–knIÖëÂDÖ%¿~º{ïIVÊßA™Ë³ ôçp¤bF`$wrb˜9†#ê f–@"›]*žä=ÁêVPì"K¬”²v’ß,+!‹_¯ƒvÅÏš§ÌtxË‘ÉqžÞ·r]ÌË–d½c%: ·:ÖEº–éhúß\@˜x–à©Ma×Úqn/„i:l/s^R8+üô !‚‘Ù_¬‡l‘íÊ ׯõ<1Qgãð󘱈¢ÁÉNøaNÏO"GK;ñOê¸q²iS_UIøqƒU¸ò€%Ó¼9¥×8#Ç!­ 'å…¢þØ,\I.z s¼‰õçÀQìÍüÆøÜïwÁŸÍpþUtM dX5üÙZóα®ppÉ€àØúZ›Õà:p±\ŒUA8wÚ<“ÄAØaŠŠ¦¦á:˜«ßô-/Ú¨†`-®ÔJ›‘‹só†sä¿uŠÿc“ݸÀNÄÉbÿw•ŒPÄŽN¾WZôÎ%ñg_9×8%Ó3‹Ÿ ï*ßܾ»iÐ/‚&›Áþ<ùÃ`/P%SLãá$²G¨_¿DÊÔ ÄKØÇÄ)G¢Œ¢(qPY_RDç:2dI’êþ1ðît¯?Ô}ø¤¬Ö©aÕ¥2ûžjÿÖý½ÓÐ šH&¼xç‚3.HwÑáe>cöžë7”6^[€.6#’Ô Ì “ê0ÏŒJyç&½¦H°´=‘{٠†^bÚì m<·4§±†Öèø@CÕE¶´ÐŒî†õ¿«ræ ‰áj_¬Åþ4Ý[ ›t}ZA[ÕÛ.Í9ct ÅfãÒf²@vìÚKp%y¹Uâ÷4¦vêý íülI,(Æzùi°øþ÷'y½VD0,øˆSbF¡¶ÎþÙRkžóˆ¬Ì×êÆR“þÔô¤ Ÿ-Xó ƒ9«lMç“䵯C¼´-¡? ™Md6ê‹ Þ+…‰}µ:ËsžE)¿=ÕóKB’Ü‚¼Umò\Ú:Ú·Ç]¶Âú™žÏ<ØÑ©ü{Á2m ÁPVÿ$—ƒó i¨ô3“QÕ³œÏÜØ 1$sÖÐ×»hé+:Ç[HÇ"ªnìµ÷ân<ñkÍ®Ý>±LÅtu9x^ç}•U¶ôñßÄ‘1Ëî|ÈöÆc[owJÛoÁÍÝ®ÜØ•J¹¨ÔMÁ6:ßxPxù Ô…µ´µ§R}o¾51)þ°"¦ã£”¼(°>Ì á²i¹Ã¬,#ÌhC<Ä|©Ÿ,|g²h°øÄ´ÊF$GÊt \ʀ⧒LýnÙ×eмמžÔd7¤V‡Jç”$ÖÄC»œKÙp–Ùroþí&e<³&¡¢úŠÌ¦E¿µí{rÙÆ¿e§ŒNoõ ×,*èLc+°gªåê‘°œ\,t-3ëŸVÀyA¼»bv6ÓËÃQ¢(_›G1#±ºÇ©w7eo#e?ÔƒˆúK]ÚN°—ÁÆ?Ьbü ›¢c`0ÞÍ©ä( ²5Zw+e8`/ƒãRþºù@Ì#õHOéûÉJ³^º³s3ìè‰w_N¿Õ~¹05ñÞKR€VdýH8b¸<÷q|é¡L°Ð•(ñJÎ;£S8!Z¤ïÁåÏ*ìˆ>›CÆ”ˆ%Õn"škÙ6ß;ͳ(P…Pÿ¸ì0e Òž©tS÷ó–ØP8¨¾CdÉ07Ù® ƒÝ”î"J¼L¿ ëQ{sØV©@±vÙgWhm¨¯_á= XÜ"Z¯!,f¿òÖMŽ ¢$dUø`ôy k%×:üš;€]Û_ø1ë¯%‡ƒpáªòÛ?»Ø±Wb}ZbÉzKjs_¹){e9W¤xž|–W®ÔœÖø°T ßCWæžlÌ™’Ê>­–í(6@Ïr(¦r W½õ„">ØÈÊo19ÁÞh‚SM¥¢&H¥f7ê-{úAq3‘û5.–e! ÚTùSOÖ¸•«¦"„Õ¼]6€¤)øb•Êæz«†°)°t(aš&î‘ S´¼6û1sëFkßàléJk±äD—`KÚìEÛÈaÔ&OѦe9N’i–ü2\äó0øÀa1|{ÃÀ÷“a¤¶h}a¤Uœ hŽÃ+(ÝDL>µ J(`ëá<3 A¾§NØñø³©hí,=h]{eé®bo¼HÀêhøQ¿[åôUoXÏIj5Ä>ãµgËêíKNBþS!ìjD%úsnNDf.ÒňÂçݶ0fEÿ «éͶœ¬èóä3°ˆž;I…5A]u⽩mÖ§‚*íÎoÊ.?¿<Êhï¡Üg¢êHŒ)2dz½žýUGUMÝ¢b`~_*+—òs &•sNƒ ùã×GÅ :¶àÁÏ $ö¿»¤¢É‚û¾[.ç^›’úO­8ªCVUëAMŸ´<|Xùa[5+föœÛ ŽÙ¦î!´*ÆíqìÚÅ­¶àˆØK¯fzïjðt¿áAÜUÜc¬QݲŸåYêxu’›…”¸£A 00 CG¸#7ök6*#–»æ©©MJ˯±,'ÉŸJ² :Û£cÊè4W†óq~ ª}.aäUu_V¨±Õo“L¨óÿFÙXMðÜxË9¾Ä·ÊF<ìí“+%|ºVá÷?.E›Cíã‰WéÁR¥²Brb:F‹+0¦¸¹sîü¬sI%Ÿç¥°S³¢íp¥¦ˆ¹Õ_…÷\^Ø w‘©Ë/§nîb-üS¾0£Ý$ÈR¢ú Ô¥KbLC¾ç…‘‘àô_Ψù¶.í{SпŠðÀ'§—cxPJô…5ž|D¬áu³FG^ä'ãàøC“úK¹=Q#Ž0Â1xÊ•ËgF°×ŸÿÂþmË jáÄåþü’_ŸVu*b{«ß¡bqƒµg¨ê|zÍ„*ÊMöw9W/v}œ' •7_µá(mg¨kUt™æžlŠ˜*›ÓâßS†ÄFa&ëǤŠâkù¶9h–ôDH¸ã2 xÚsG¨ì¨øtb›ãhEBê)-=™\æíÙ_µ:ð@0L80{:úkg=žú®OŠŸ?Ðá©´9O%¨Ó¬–ró§sI9e×è±lR‚Ød ñ}7<j|䕇/}—‚©Äâ&Ù61L‚úk’Ü¥o^9a ŽBçÝ0Ø S­ù1è–@ÍkA‘^ÓAª}ÃS÷ïâÁ?ËH:1QG ¨š€˜8~Èü²ô–j^Úçå"a[‹4÷<ç<Ør`ãºñ3ìžÓý7éO4æ—<œ›tH¬=á)ñÜË9`Êg—L,2ú"d›FÁb쥌˜ç£ºCp ØÕr¬…o1Çwgýs*h¼à'üxc£s!ðörlûÝØu²Ì›ì¾?}Âv+Ö˦‹…pcÏjîmõ<(²i礛ÏW0Ö¥[ÀÁ7:ðšÿd-x·éÀH¡ÔØ?:F̦âe]õT²XDã×»£Ë\ÞqIÇárªn;=Z©˜©Pã„åoœL))M‡NíÆvWmx]"Rr½Tw¾©Éð¯ ÛöÁ™õjk‘y“¦‹KíÆDBq“ü3'¿àß„Eúž¤ûx‚˜ß)B˜í&–9¡¼ÅGNk=<[´=Pþ8¹ãS|mJ Gf^œðçãT×"µ9¬—Râ·Ä˜¼…j#|û‚óù¨àa1(ÑÚ‹âDŽ|]Û× àAÈDäDôÑßúŸÚ]Ëýr£[/A…“ëH<ØÅ>—Ü%ÕFVaiõ¯ÝŒ]ê Ýíw>K𮎎¹Æ"¡0´º³é§vNÎÕ|T°]sˆÄÁˆ€:†˜OCS òd« ‰vóŠÝ bé¨fˆ†žTžgïšÈ_dÒÛƒ$ÁÁß[ÿ¿\$iæ%Šˆƒ ƒ †K{¹a÷_€wWÞ›/×Åeþ-ª}·ª¦yà@üܬæõ­9 eZu¸5¼Ç¶=UüîèûË&/—íЬTuõé=Œç:QÌ#êbñ1f+ŠîžÁ>¸µsIÖ0+ à-´”óå\=äñ4+OU|§_[‡¯ùCERœT>vš‚m*h=–_ùé£4“"ˆIîob[u®Ü§ÝZ%ýŸHügWÅ‹q^x‡§>bIà¶‚”ôÊHÞ¯¤@gÔ9ˆ@ æ¥ð[ë¤ãžœòÅ.®§&<Ûpp8!ê`zš£“àßÚ•f»“)»g{½ûÌSfÂ0àÍìðRö©kÑnlœY‡–9âÖBJ7æ[¤NÆt“ßY fñ± ¹¹ £1½(õTk8†ê²¼«ŸY4ñ]šì¼1¼e:ñ—pç[áÜŽºßvgÈç­ÎÇO¦…à:®G%˜îœ5× ÔïhhO³þ>ù—\‰î'㙞:ñ (o¯#;6ƒ`xöz›~ü‰uMØÙš5{³<©¢;fNz$Ý‘O­¸÷1Ü¢l{ÉLü>L›+ú¼š?ƒf_mOØKŸíò gÛ…‘iš”щFˆb‡‹±ÖvkgÉAÏWžó|’oÀ yr%ô©sy—ù '“-—Ô+4-¾÷k|Ïâz{¡¿nÔ÷³¤ØL®QDxA¿ñ™ø’æ ìèO‹0<` S¬áP§ï‰µW¹á{ü%úi†v—˜Ñ·#5:·Pf§N‘­ ¼aàaþGÃÁ _^šH÷BTg–/>L¢¬öqȈóu8:$~‡Žü‡dü$páy’ Î×QÜ”“â=ëÍb†Âq»f—zÖ_tù0;æ05®í®—³ Qsï*¡_Äc¬t¦ X–Tám£™4¤Ø7z¶´iEœ[à“ŸáHÄJûK˜,Œ­BëYt#̾8÷ô3Q£ˆ4àÊ‹›WœA%¨øtMÚ¡yßL£7õĉa*µŒöD)Ý€8fuªvq7U-=ç1·Ÿ6¨/áwDßá^§ÓúŠh\Ú%èUÇJ’üÑÇ¥ë  ¯Ÿô³Mü?@§¨(®=D¶ ®5°Ö`øì=îÉÀ”@ÖûS7æ$ZË·w_µÁUÃYæ¼½̹ž Eը䮹XˆbO.}_4n,­)€03pUìž^\Ã]sÄ”&`†óÑkÖ°Sÿ<Œ:±èþ»K™Ì‰:âцóázr»ŠÒT0ú£&W^äáuè…éáûF»yà½C}œÆz #^à]"õ“ebü׊W9/ðÐDγKáó‘51~jc“'@:¯J{FJÝ]ÝTžæ:­è·><§”3ͤj;מҧ5g+ÍÇXö}çMv’SipéΙ/yð3îzg§7ýÂrHa7ô+ù<™  ³‘ÓÐ Ó׃üœ#>m¶dRäññ¡Ö–Ûzÿ×NÌ4ÔÃܽÉfm‹=ŒýТÜʨÚZ6‡Kõ?tP÷Nˆ2ø¿$¿ýXùØcФ©Ü³³h©à‡ôbCR À¨Ú1†»7º®“…6á¦ZCq![‹+pæ;¶/­Î@Ò0]p„®n'ðŒ‘ýÚACO˜|:´4p¯”‚HÒEÛ^µ]h¹yÖ"§ðPúÍžýYLbRIœ…@tÙÙæíD KÉ0~‚ÝpïFüã6Ò­n²åÀ©<.ÝÙq“s4CΞϠ•-…‹ùhéb¼k³÷ÒæËCãš g!®r/X/H Æ núó–Æ„¢mÐ ïË ¢<¯€`†ªP^”Õæ¨±>X„"DâcÉŸKb ³·PæS§sª¡{z ˜mÛvöذ?ÜW[}1NõÙÂK(ãXÑþs4ÙçN éÖ:Ò>'À2äÀ­Â&4üŠc•0Ÿ·Ò“å ¼Š=_#n‚>0 ‹YZtimeSeries/data/MSFT.rda0000644000176200001440000001172615104730074014545 0ustar liggesusers‹­[y|”EÒîC`#‡("D…AñB@޹ç}DÄ I Á$;„ ä¬(°ŠÈ"*Š(‡¢¬ÀA>tÕŠ‹ˆ ’©ç©÷ÛÌ·~ùý¤¨®§«««ûí§«ƒØ1ƒÛŽik³ÙšÚšF…þlúkó¦¡?šØšÛÚ„ïÈøQ¶&Í:„þú¹Ý¿Ãþ±û¦QRÚ!½ïAzAºwSÖS†týFÝF|_¶Ÿ¡d?'uûyïäx› öólÿ­q\FÊ&ã4£)WR²¿I¼™Aù^>Ÿþ6Sgüf,åøÙšøc”¥q|F9†öíœýzÙßSKñúhwq=ÜŒ_óía¾u]<Ì‹³ÜöÁ«]²;˜ŸöãzÆ&BÆS&0 Ql¯&žþâÙǸ8®ƒã:fs\Æëä¸.J7ý»¶R¦<Öxž>Æã=Aýý‰:×ˈ¦Îvݯ~{c~æWóíWÿÜ_^öó2.¥—~=*¿»–:÷¥ûΈ~Äéwâuо›’ý½åÆCºŽnî×Õ”œ—{S„®’㸘'u7×ÇSM©ûM磒yqO‹‡víúÝ{çö.ßÏî¹°*ôó¤Ý7rHøÇî}wKçܯvßKÐ}Ãï{nŸÝÿ·“e=vìFæÑ¶÷f­ïÚFùræÉ2»É¼š‡BÃï]úÎͰ»9'Üý^»é ~ ìÆ)ŒoÆO ÿ„ôpóx»ÙŒqާ}%æmæÓ®ñp¾æ$ÎïŽ;þÌõ´o ?ø~­¯@*ü†¿'!l°tüEà sèo\b<í1ã=ÆåÄz˜…œ¿Îçæ3‡ùþün„ݧ¦ñxf'èF×çqø78®¹™q|Êq?£þó²ó9HM¸ž~æëhTs^Ü?F ÇMáx…´3ÏFóþˆú_¸?Š±ßŒZâï o‡žvãVäÓÐq38îøñ?ênž²ûÿõó}Œ|ùâþf|¾Biîû«Ý[‡yúÎb|ÿÍèï+Ç|½e؇®«g%ö›§+òïIÀ<=w6üº›"~×vŒï¾;,òì®þð“p8'¿7× À;¹_œüŽôœôéùÂïYÏIo½~Ç”µ”‰çò½ž#Ì›u®Ó‹ç ‹~ü<·Œ ]_¶ÓÞ3 åU%ñ&ã0•O_¯™Ó"pòR´ZÒ?Ïy‹Ï9ÓC¾áyhÎ&¯WÕ^À~Ëéï;ÊŸÉG¼ÇX÷†gyà¹mñœòrì à—@eú+º!ò¨uP^ýƒ<ª¼ŸŒõ°pÉ×zoZHüWˆ{¤æi,ÇQþ×ûÈ®Ë5äÑ×ÓóØà~Pþp?ÚÒŸ ¼§çe@×¼hñxåWäYò§5ÎäÑ8†u1^Â|Œ¯É×G1O‹ÿ¹ ®·1‰:ï/×Ó(C¾Œ´“ÏÖOëÿ*ò8÷oÆ÷÷¢džü¼ßøxoñr^ÞÓ¼•È‹÷Vä_¥‡ýõ>ìæ}Éúþ™/—8'÷¥ë.¶çä¾t>ˆy*O[ü¢2šß/×êôÜà¼Õ¯užÛë®(HÏQ'ý:Èç^â­óu‰ž«>­×yŽZç:ë;ë^ÞRù»9%Ç·ø6¯õ²q ÎKCù]ëøa´ŸµMûŸ›íÆäÆãœ¿Åï›ûõ3_Öùª|ÍóÑÊ3Ûµ¾²êríÇø•O´®J8AI\¼2–öXæKëñø3”ÄÇQS=ƒ²š~©'pý¸>ÊçNúuéû Çw²¿KëFƒó¥?åO/yDëiå[«ßM™Ñ¸Ý7(¢_4¥ÖýÜÖ;ãR^RžµêMæAùѪ[‰×w%·ÖÑ´{Gã>¥|ìa­Û9/Çñê»ízÏqæ8Œ×ªË9×}¼·US7 ­uàüÜGïÕz´æ£ë¡íôãJ¤ŸŠÆãº©»yNyX¿xYyÉ[¾?á¼ôµo黉ÖAú}¯‘'XOœ·~§ùÇ8 ¾6”GGS¶f¿?"êCÎϪ?µïGQ~d~#ˆÓ`ªqÌ‹éæ}!‘üd‚wÌôÏúÑòOþ3YG™™´×ÒŸÖÃÌU³Þ6ÉWæ&Þ+”OWEÔ§¯°ÞÖ{ë7³çó3ùQßxΛ¬_­:z>¥Þ§ºã½Áxþ ¾«Wq<Ö}æ¼·¼Á÷ õ£÷¡ð˜ÖãÆ%úå½È`½jL%ÏrßYü:‘ãÎ">ƒóÖú]÷ï{O÷¤ä:û™½ÿÝY·2ŸVÝÍ}î#ŸúÆbþs<ïZì'÷­ÏEû;¬Ã™/ß;<ܧn¾K¹YÇ»7"?_óýÆõënò¶“ï ŒÏñæçìŒïÐÁõ׺MùÛªÃyNy«!õr+¯èy ç„ÖŠ;AÉüéûœ“º“ç—“íú`°Îöó{¶ò[«ë@yŠß…Öçôgñ·Ö×ôcñ7ýXïãZÿ)³ž6o£Ôú~XãñŒ:Ê3ãð}Þäû¼õ~Ä~V}cPê=†~<ú®Z ©ï·Z*è{ˆÖeòŒ¾Ç3±´Çqž ôgñ2õx®[ÜÖÆ2õ›Þ·ŒËzW×{ýEÖák½Çv‹‡£(‰Ó÷å_¤é?ÞÓ—ÖÑ~æÃú}ãõÓŸþþÅÏþºŸõ}Cy×âW­·µ.=Cý•Ïõ>e½£3.ë{ß¾Õõô0ÿÖ;Û½µ´Sw2~ë{âüõ÷Ö÷¥÷%í׺1Þz7Ó}£ñë÷Êxõ_?¦ïé‡uoÔû ë/ë!‹¯ùnèK†îÛEG~໡i‡‹¯Ékf*ù€ïÞVÅü›•ä)žçúÞnñö1ô 4#®ŠxgÖzp ëÍ+ÉÇ<‡Í¶ôß‚õí`Æ«ï äóGögjòÜ5=Œ_y_Ûy~[ïØÌS€ü`?ëù47Óë/³ yYëTÞ§LÖÕ&ßgÍÛÙ_ëuåUòN€¿O°êd}ÿ~ŠóŠ&O–°þåº=øNÍýj½Oá¸ïsÞúŽÿ d 7uæÅx‡õð2Öß_Ðï³lŸ@ÞWþ­âøŒÏàû½±ƒ÷¸ æCïwð^`ç<’˜·lÞoúßò¸¡ïHg€·x¿'ñb}½yóó½ÇzâûŠÖ¾ïxøÎîá½ÇÃyzø¸ùþááû•+‹ïé}1/—ò5ãwç;;÷“ëæäûºã¢Øcæûï ¯lÌìóÂ;?¦hã—"óNí ÇS:öðî°¬Ž½{PX–<ðC8ò˜99C;´L䜕‚«¸¾{xÅcÊŠ—ˆßŠ“9Ò^µ°8Qú'¾~",+Ç®ûSO’öšËåÏ ¾¼d²ŒSsñ³ð³ 1[ü.|ú6±WÞ;Q⫨_.~J&î?Å+>{aÒÑ‹Ž-?_~)ø•5ee/ïåmGÿ·`¾åÅ!&ÿÐñSv¾‹ø™Ÿ|PæYÒ}šàó~^ôâ!O‰ÌHm‚þ£÷‰}ÑÄm2Ïyi‹E_QÜUdåÀáOÉÍÏI¿Š˜DéW¶b²èKÙÒ¯êÈ2·ü@±àçv öy™3ÄOá…‚[ðEâ[4DpU›Jû’·ã$Ÿ5™ÏFɸ=Š¥½àä2^aæ&䫨PôyŸÜ(²dC7ñ_}û0É{eÝÍâ·<®q·Ú"¸Ó~”þe%CÅo…ã5éWTÓþkEMè ù¬ìùÿåä-á-i/Ü›+zåëÅ_ijwñW~ëMÈçðÑóüuˆ¯|»¬KÑu_K¿Â³«DÎ.–J+¦üòXä¿Ãñ_úÇjÁÏ»¸ø†¾â¯ø†ße>sÒo“ö7öUÁé§ÅOþ¹éÒ>wnÑ+ÚŒ¿å]_éùaXõUÐû_%ö’‚…Ò^Òr£Èœª8ø?ÖIìùù'EÏm‡ï§â­kE.ˆ='ã”mDpe뮿EgI{å?ßÜœn;E–}ó˜àÖaŸ-Š^…u|ýV™ÇÜ«ëï–µ¢ïƒ|îê-y){³Hôân­Å_yÖÁW,”¼,h5í]KÿÜc-°NmlXÏÁ¥Ò^ú9Ö¥`ê=нW‹Ÿ¥/\ÜÒ†=ßò† Yß² /bžÃëE/öt½Ê3ä¥AXß$Ž’_c½.‹,x²™Ø« ¯@<—Ê8Eßí½8Ἄ7÷¬×ì$y‘‰Éž1\䜸ñ¿~·ô›_tù|ò:‰§bs-ÖñÛ×dœò‘ý¥½dx©à¿ñ‘…Çc¾ë6ˆŸÂã=Eæe¿!ö¬ãgÄ^6òS‰§¤ómˆÿ´<øÅÌÞù˜ØgÕÌEþ})öÜ ráŠ)ê{Lìó`æ]+7à˜¹ÃG`}—`®Løfÿ@úåä s;},þ ž’ñí¾óÉ­|ѸßE>ºëJÁçäý ø.X—‚m¾`—)íE«öËz,š?\Úó»ýñn;ŒñnúTô¼YÑXûmâgášÈgGœ·…×¼Šñ7­¸ŠWÜ„q~/ñgõ8-ýóol"2·j·Ø?ÖöÚCÒþhåG¢OÿßWî]ÈSö+OaÝ_Àw–ÞçêŒdàf~Ò[üeý´ëPŸè9Q[EŸ5ë¬èÅ뜰o?ŽùÝsƒ´gßÿ¾è³Ö`Ìú óIðy«å"SØüŸG٨㒷ʯJ{Y*öCi¯·°.Sº‰ß’#QÈolª´W]ßSü.oßf«à§N‘ü•ÚÇ‹=¯¾«ØKÆÉ¾Œø7pÍ&§L‰ö¶ð?“³Ù.„þki‘–’•úK”ùoçüÉiúwGÊÔiêÏ“ž£ýF¤¦g&Si9:=5{zr„ë6é™)Y)éia÷áãóBL]ýIGÝ÷q6ÈÓ2íº“õÐOUC?}W!ä×Àÿ¸ö3eÐýgÐ|Ý¿ €?Ûös7@?÷>ô_³€?ßøóïÀ~á-è§AÿýJàøKa¿ü0ô†Ð¶>h{PðAaýlzz³õЛKá’¿ßb-ì­V@om‡ÞÛ>Øf ðmè(ØîsèíËïpíQ¢ß•¹Ð;ö®cp²aï’ýª«¡_…Ï)Ø5ø«;ÂÞ­ôn/C¿vðÝ[ßýEØ{üzÏôž¿ý ð×{`ïõdï•Àõ‰®ÏÀõ]Ùïvàú}ÜŸç׿ú3ÁøÜƒ󀿥7ì·¾ 98¸!×7×·àío@:úí¿ãàï|ö»‚~OSè÷<ü°‘À»û}¿B¿t<ˆ…$Žû` ®#Á5ÐcqMÆ~ |Ü"àã‡ÂîøÒY œëfà\ ‰ ç tot_Oà}ïùô@gè]ÀL~TØG·†>úoÐÄu)8¦ðcž‡}ìèã|ÐÇ~<è1ø0ŽÇà## ?r zâ2à“î>é;Ø'€>y>ôäÁÀ'~J ìÓæ@O¹zÊ!à üôk)÷—– \zàÒß„}Æè3“ g¶>sðYã`ŸõôY¸^s6?Ûüìß`Ïýô¼ÕÐócÏ? üœ'`/¨†^x'ô¯€/ª¾x쥠—~½l.ðå7_ŽkOp~&dewà*ßnè.¸p*ôEQÐá\Œò(XÝö¥- /}zÍhàk€_¶ öåOC_ᆾâ,ð+׿*öÕ÷C_kfpÍà×ÞüÚ¯a_÷ôÚ2è9 ÜÓh&ú³ ×à³ ×à†,øÝxìEù”ã'$Ÿ›ü󸾟ö¶Cßò0ôQŽ_ÜüÖaßfBßvúKëßî~û/°¿òôWW@ßa~Ç÷À¿†kDpgô7n‡þÆçÀï*þÍ[`ßÝúî¡ïÉ~o/à÷ÖÁþö~èï¤CßkupÿàßMýïí(_îý ÀÕµî`è¢L ~”ý£3°¼ýÄÀ~×øàÑo [ü§CmòÓˆè[NIŸ9=)̰QÿÑÚºÏCúLÐgräµ >%mDrZVò̈ͼ£"±3“'¥ÏœìŒ ]!šuú/C·HKšž¬×—Hc›™é9Ðþ¿õž”š”™FÛÉIYI§ÌL _hl—#»d¥d¥&Gté8*ezrôÈä™)É™Ñþ‰&OÊŠèÖ~rú¤Ð )-+)|!ŠèΰÚ6ØäÿeøßH²Bná5Â]«Œ¤I%MŒ£Q‡ÿzëÐmP³ÎeS`þn~âéò¿´ÃóŒ—1timeSeries/ChangeLog0000644000176200001440000006433015104730074014144 0ustar liggesusers2020-01-24 Martin Maechler * DESCRIPTION (Version): 3062.100 ; using Authors@R * R/utils-head.R: tail.matrix() in R-devel uses 'keepnums'. * R/timeSeries-readSeries.R (et all): do *NOT* use `if(class(.) == "timeSeries")` but use `inherits(*, "...")` !! * R/methods-plot.R (.plotTimeSeries): fix wrong logic in `if(.. && at == "auto")` when `at` can be a vector (!) * R/aaa-utils.R (`%||%`): utility, to be used extensively in * R/methods-plot2.R (.xtplot.timeSeries). 2015-11-09 tsetz * updated rank function * exported getUnits and .DollarNames as S3 methods 2014-06-17 wuertz * vignette 'plotting timeSeries objects' added * DESCRIPTION, required packages added 2014-06-16 wuertz * Refcard PDF added to doc directory 2013-03-25 chalabi * DESCRIPTION: Updated version number * R/methods-mathOps.R: callGeneric() seems to have troubles in finding variables defined in the function frame and passed to the generic with the dots arguments. 2013-03-15 chalabi * ChangeLog, DESCRIPTION: Updated ChangeLog and DSC files * DESCRIPTION: Updated maintainer field and version number * R/zzz.R: Removed deprecated .First.lib() * R/base-apply.R: Added out of range test in apply,method-timeSeries 2013-02-22 tsetz * R/statistics-rollMean.R: functions rollMin and rollMax exchanged and function rollMin corrected, * rollStats timeSeries name corrected 2012-08-12 chalabi * ChangeLog, DESCRIPTION: updated DESC and ChangeLog 2012-08-07 chalabi * DESCRIPTION: updated version number * R/base-subsetting.R: Removed C level call to 'find_interv_vec' due to changes in R-devel 2012-03-21 chalabi * ChangeLog, DESCRIPTION: updated ChangeLog and DESC * NAMESPACE, R/utils-getArgs.R, man/utils-getArgs.Rd: moved getArgs to fBasics where it was already defined * ChangeLog, DESCRIPTION: updated DESC and ChangeLog * man/utils-getArgs.Rd: added manual page for getArgs * R/utils-getArgs.R: getArgs() can now handle signature of length > 1 * NAMESPACE: added getArgs to NAMESPACE * R/methods-plot.R: pretty.timeSeries can not handle signal series * NAMESPACE: updated NAMESPACE * ChangeLog, DESCRIPTION: updated DESCRIPTION and ChangeLog * DESCRIPTION: updated version number 2012-03-20 chalabi * DESCRIPTION: updated DESC file 2012-03-20 wuertz * NAMESPACE: updated * R/timeSeries-slotSeries.R: had conflicts with mu fPortfolio * man/timeSeries-slotSeries.Rd: 2012-03-19 chalabi * DESCRIPTION: updated DSC * man/methods-plot.Rd: updated manual page * R/fin-daily.R: fixed patial argument names * data/LPP2005REC.rda, data/MSFT.rda, data/USDCHF.rda: resaved data to reduce file disk storage 2012-03-17 wuertz * man/methods-plot.Rd: plot examples updated for pretty label positions * ChangeLog, NAMESPACE, R/methods-plot.R, man/methods-plot.Rd: pretty added 2012-03-16 wuertz * R/fin-drawdowns.R: drawdowns made visible 2011-11-01 chalabi * DESCRIPTION, R/fin-align.R, R/fin-daily.R, man/fin-align.Rd, man/fin-daily.Rd: alignDailySeries is now based on the align timeSeries method which is now based on the align timeDate method in timeDate (>= 2150.95). * inst/unitTests/runit.TimeSeriesData.R: updated unit test with new lag,timeSeries-method * R/stats-lag.R: fixed lag,timeSeries method when colnames are provided by 'units' argument. * R/base-cbind.R: Better handling of FinCenter in cbind.timeSeries method. * R/timeSeries.R: timeSeries method now works also with non default FinCenter when timestamps are given as a numerical vector. 2011-10-24 chalabi * R/stats-lag.R, inst/unitTests/runit.lag.R: Thanks to Daniele Amberti, lag timeSeries methods now returns proper colnames when used with multiple lag indexes. * inst/unitTests/runit.merge.R: * R/base-merge.R: Thanks to Daniele Amberti, merge timeSeries method now poperly converts colnames to valid data.frame names. 2011-10-11 tsetz * R/fin-drawdowns.R: Drawdowns are now compatible to the results of the drawdown function from performanceAnalytics ... 2011-09-23 mmaechler * DESCRIPTION: remove deprecated "LazyLoad" entry 2011-08-02 chalabi * ChangeLog, DESCRIPTION: updated DESC and ChangeLog 2011-05-04 chalabi * R/fin-align.R: Fixed integer overflow when dealing with very long historical data (1800 - today) in align method. 2011-04-21 wuertz * NAMESPACE, R/statistics-rollMean.R, man/statistics-rollMean.Rd: function rollStats added 2011-03-31 wuertz * NAMESPACE, R/fin-smooth.R, R/statistics-smoothLowess.R, man/base-rev.Rd, man/base-sort.Rd, man/base-start.Rd, man/fin-smooth.Rd, man/statistics-smoothLowess.Rd, man/timeSeries-readSeries.Rd, man/timeSeries-slotFinCenter.Rd, man/timeSeries-slotSeries.Rd, man/timeSeries-slotTime.Rd, man/timeSeries-slotUnits.Rd: help pages added and script files renamed * man/timeSeries-finCenter.Rd, man/timeSeries-slotFinCenter.Rd, man/timeSeries-slotUnits.Rd, man/utils-str.Rd, man/utils-structure.Rd: function renames * R/timeSeries-description.R, R/timeSeries-finCenter.R, R/timeSeries-series.R, R/timeSeries-slotFinCenter.R, R/timeSeries-slotSeries.R, R/timeSeries-slotTime.R, R/timeSeries-slotUnits.R, R/timeSeries-time.R, R/utils-description.R, R/utils-str.R, R/utils-structure.R: files renamed * man/timeSeries-description.Rd, man/timeSeries-series.Rd, man/timeSeries-slotSeries.Rd, man/timeSeries-slotTime.Rd, man/timeSeries-time.Rd, man/utils-description.Rd: files renamed * R/base-colCumsums.R, R/base-colSums.R, R/base-rowCumsums.R, R/fin-orderColnames.R, R/fin-orderStatistics.R, R/fin-rollmean.R, R/statistics-colCumsums.R, R/statistics-colSums.R, R/statistics-orderColnames.R, R/statistics-orderStatistics.R, R/statistics-rollMean.R, R/statistics-rowCumsums.R, man/base-colCumsums.Rd, man/base-colSums.Rd, man/base-rowCumsums.Rd, man/fin-orderColnames.Rd, man/fin-orderStatistics.Rd, man/fin-rollMean.Rd, man/statistics-colCumsums.Rd, man/statistics-colSums.Rd, man/statistics-orderColnames.Rd, man/statistics-orderStatistics.Rd, man/statistics-rollMean.Rd, man/statistics-rowCumsums.Rd: new file group for statistics and inference introduced. * NAMESPACE, R/aaa-Deprecated.R, R/base-Extract.R, R/base-attach.R, R/base-colSums.R, R/base-comment.R, R/base-rowCumsums.R, R/base-sort.R, R/base-start.R, R/base-subsetting.R, R/base-t.R, R/data-examples.R, R/fin-align.R, R/fin-daily.R, R/fin-drawdowns.R, R/fin-durations.R, R/fin-monthly.R, R/fin-periodical.R, R/fin-rollmean.R, R/fin-runlengths.R, R/fin-smooth.R, R/fin-splits.R, R/fin-spreads.R, R/fin-turnpoints.R, R/graphics-plot.R, R/methods-as.R, R/methods-comment.R, R/methods-is.R, R/methods-mathOps.R, R/methods-plot.R, R/old2new.R, R/stats-aggregate.R, R/stats-filter.R, R/stats-lag.R, R/stats-model.frame.R, R/stats-na.contiguous.R, R/stats-na.omit.R, R/stats-window.R, R/timeSeries-description.R, R/timeSeries-dummy.R, R/timeSeries-finCenter.R, R/timeSeries-getDataPart.R, R/timeSeries-isOHLC.R, R/timeSeries-isRegular.R, R/timeSeries-isUnivariate.R, R/timeSeries-readSeries.R, R/timeSeries-series.R, R/timeSeries-signalCounts.R, R/timeSeries-time.R, R/timeSeries.R, R/utils-getArgs.R, R/utils-head.R, R/utils-old2new.R, R/utils-str.R, man/00timeSeries-package.Rd, man/base-diff.Rd, man/base-merge.Rd, man/base-rev.Rd, man/base-sample.Rd, man/base-scale.Rd, man/base-sort.Rd, man/base-start.Rd, man/base-subsetting.Rd, man/data-examples.Rd, man/data.Rd, man/fin-align.Rd, man/fin-cumulated.Rd, man/fin-daily.Rd, man/fin-drawdowns.Rd, man/fin-durations.Rd, man/fin-periodical.Rd, man/fin-rollMean.Rd, man/fin-runlengths.Rd, man/fin-smooth.Rd, man/fin-splits.Rd, man/fin-turnpoints.Rd, man/graphics-plot.Rd, man/methods-comment.Rd, man/methods-plot.Rd, man/stats-window.Rd: several smaller updates: man pages improved, rolling statistics and smoother function added, some obsolete functions declared as deprecated. 2011-03-09 chalabi * ChangeLog, DESCRIPTION: updated DESC and ChangeLog * inst/doc: removed empty directory * DESCRIPTION: updated version number * R/timeSeries-readSeries.R: Fixed readSeries when reading tables with multiple columns. (Reported by Chris Breton). 2011-02-10 chalabi * ChangeLog, DESCRIPTION: updated Date and Revision field in DESC file and updated Changelog file * DESCRIPTION: updated DESC file * man/timeSeries-readSeries.Rd, man/timeSeries.Rd: added manual page for function readSeries() * inst/doc/TimeSeriesFAQ.pdf: removed pdf file because new version can be found on the website 2011-01-31 chalabi * R/timeSeries-readSeries.R, man/timeSeries.Rd: Added the optional 'format' argument and and a warning when the provided format produces NAs in readSeries function 2010-10-27 chalabi * ChangeLog, DESCRIPTION: updated DESC and ChangeLog file 2010-10-26 chalabi * R/base-cbind.R: cbind now extends vectors to match number of rows of the timeSeries object. 2010-09-30 chalabi * NAMESPACE: updated NAMESPACE 2010-09-24 chalabi * DESCRIPTION, NAMESPACE: updated NAMESPACE and version number in DESC file 2010-08-20 chalabi * R/stats-na.omit.R: removeNA is now the same as na.omit 2010-08-12 chalabi * R/graphics-plot.R: added grid function in .plotTimeSeries when using single plot type. * R/base-cbind.R, inst/unitTests/runit.bind.R: fixed issued reported by Thomas Etheber when using cbind method with timeSeries object with one record. 2010-07-26 chalabi * ChangeLog, DESCRIPTION: updated DESC and ChangeLog * DESCRIPTION: updated version number 2010-07-08 chalabi * R/methods-mathOps.R, R/stats-lag.R, R/stats-na.omit.R: improved support recordIDs * R/timeSeries-finCenter.R: cleanup code * R/base-apply.R, R/base-cbind.R, R/base-diff.R, R/base-rowCumsums.R, R/fin-runlengths.R: improved support of recordIDs 2010-07-06 chalabi * ChangeLog, DESCRIPTION: updated DESC and Changelog 2010-07-05 chalabi * R/methods-show.R: code cleanup 2010-07-02 chalabi * R/methods-show.R: Improved recordIDs handling in show method. 2010-05-17 chalabi * R/base-start.R: updated start/end to handle TZ 2010-04-22 chalabi * R/timeSeries-readSeries.R: improved dates management in readSeries() 2010-04-14 chalabi * NAMESPACE: updated NAMESPACE 2010-01-23 wuertz * NAMESPACE, R/aaa-Deprecated.R, R/fin-align.R, R/timeSeries-description.R, R/utils-getArgs.R: internal function .getArgs added 2010-01-22 wuertz * R/aaa-Deprecated.R, R/fin-durations.R, R/fin-spreads.R: deprecated functions moved to aaa-deprecated.R, started to clean up .... * R/fin-rollmean.R: code cleaned and description added * NAMESPACE, R/fin-runlength.R, R/fin-runlengths.R, man/fin-runlengths.Rd: .runlengths added 2010-01-06 chalabi * DESCRIPTION: updated version number * ChangeLog, DESCRIPTION: updated Changelog and DESC file * inst/unitTests/runit.aggregate.R, inst/unitTests/runit.subset.R: fixed unit tests for new years 2009-12-24 chalabi * NAMESPACE: updated NAMESPACE 2009-12-20 wuertz * NAMESPACE, R/fin-runlength.R: .runlengths() function added 2009-12-13 chalabi * DESCRIPTION: updated version number * ChangeLog, DESCRIPTION: updated Changelog and DESC file * man/base-subset.Rd: added aliased in manual pages 2009-12-10 chalabi * DESCRIPTION: updated version number * ChangeLog, DESCRIPTION: updated Changelog and DESC file * R/base-Extract.R: added completion method after the $ sign. * R/base-Extract.R, man/base-subset.Rd: updated signature list of timeSeries,$ method according to changes in r50609 in R-devel. 2009-10-26 wuertz * R/fin-drawdowns.R: example lin in script corrected 2009-10-05 chalabi * R/graphics-plot.R: improved handling of NA's in plot,timeSeries-method 2009-09-30 chalabi * inst/doc, inst/doc/TimeSeriesFAQ.pdf: added pdf files in inst/doc 2009-09-28 chalabi * DESCRIPTION: updated version number * ChangeLog, DESCRIPTION: updated DESCR and ChangeLog * R/AllClass.R: Added prototype in timeSeries class definition. 2009-09-02 chalabi * NAMESPACE, R/base-Extract.R, man/base-subset.Rd, man/timeSeries.Rd: added methods to subset timeSeries object with POSIXt and Date time stamps * NAMESPACE, R/stats-filter.R, man/stats-filter.Rd: added filter,timeSeries-method * NAMESPACE, R/base-t.R, man/base-t.Rd: added t,timeSeries-method * R/stats-na.contiguous.R, inst/unitTests/runit.na.contiguous.R, man/stats-na.contiguous.Rd: added na.contiguous,timeSeries-method. 2009-08-30 wuertz * R/base-apply.R: back to the previous version * R/base-attach.R: description added to attach * R/base-apply.R: apply should work now in all cases 2009-08-30 chalabi * NAMESPACE, R/timeSeries-isRegular.R, man/timeSeries-isRegular.Rd: isRegular methods are now proper S4 methods. 2009-08-30 wuertz * man/base-colCumsumsRd: file with missing dot deleted * man/base-colCumsums.Rd: missing dot in file name added 2009-08-28 wuertz * R/base-diff.R, R/methods-Ops.R, R/methods-mathOps.R, man/base-rowCumsums.Rd, man/fin-align.Rd, man/methods-DataPart.Rd, man/methods-align.Rd, man/methods-rowCum.Rd, man/time.Rd, man/timeSeries-getDataPart.Rd, man/timeSeries-time.Rd: renaming of man files finished * NAMESPACE, R/fin-monthly.R, R/fin-rollmean.R, R/timeSeries-isRegular.R, man/apply.Rd, man/base-apply.Rd, man/base-attach.Rd, man/base-cbind.Rd, man/base-colCumsumsRd, man/base-colSums.Rd, man/base-dim.Rd, man/base-rank.Rd, man/base-subset.Rd, man/colCum.Rd, man/colStats.Rd, man/cumulated.Rd, man/daily.Rd, man/description.Rd, man/drawdowns.Rd, man/durations.Rd, man/fin-cumulated.Rd, man/fin-daily.Rd, man/fin-drawdowns.Rd, man/fin-durations.Rd, man/fin-monthly.Rd, man/fin-orderColnames.Rd, man/fin-orderStatistics.Rd, man/fin-returns.Rd, man/fin-spreads.Rd, man/finCenter.Rd, man/graphics-plot.Rd, man/is.Rd, man/isUnivariate.Rd, man/lag.Rd, man/methods-aggregate.Rd, man/methods-attach.Rd, man/methods-bind.Rd, man/methods-dim.Rd, man/methods-is.Rd, man/methods-na.Rd, man/methods-plot.Rd, man/methods-returns.Rd, man/methods-str.Rd, man/methods-subset.Rd, man/model.frame.Rd, man/monthly.Rd, man/order.Rd, man/orderStatistics.Rd, man/rank.Rd, man/series.Rd, man/spreads.Rd, man/stats-aggregate.Rd, man/stats-lag.Rd, man/stats-model.frame.Rd, man/stats-na.omit.Rd, man/timeSeries-description.Rd, man/timeSeries-finCenter.Rd, man/timeSeries-isRegular.Rd, man/timeSeries-isUnivariate.Rd, man/timeSeries-series.Rd, man/utils-str.Rd: man script files Rd renamed to be more compatible with the script R files and the eBook * man/colCum.Rd: deleted by mistake * man/colCum.Rd: * R/timeSeries-isPeriod.R: isPeriod file no longer needed 2009-08-27 wuertz * inst/unitTests/runit.NA.R, inst/unitTests/runit.Omit.R, inst/unitTests/runit.TimeSeriesClass.R, inst/unitTests/runit.TimeSeriesCoercion.R, inst/unitTests/runit.TimeSeriesData.R, inst/unitTests/runit.TimeSeriesPositions.R, inst/unitTests/runit.aggregate.R, inst/unitTests/runit.align.R, inst/unitTests/runit.apply.R, inst/unitTests/runit.as.R, inst/unitTests/runit.attach.R, inst/unitTests/runit.bind.R, inst/unitTests/runit.colCum.R, inst/unitTests/runit.colStats.R, inst/unitTests/runit.cor.R, inst/unitTests/runit.cumulated.R, inst/unitTests/runit.daily.R, inst/unitTests/runit.dim.R, inst/unitTests/runit.drawdowns.R, inst/unitTests/runit.durations.R, inst/unitTests/runit.lag.R, inst/unitTests/runit.mathOps.R, inst/unitTests/runit.merge.R, inst/unitTests/runit.methods-plot.R, inst/unitTests/runit.methods-print.R, inst/unitTests/runit.methods-summary.R, inst/unitTests/runit.model.frame.R, inst/unitTests/runit.monthly.R, inst/unitTests/runit.order.R, inst/unitTests/runit.periodical.R, inst/unitTests/runit.rank.R, inst/unitTests/runit.returns.R, inst/unitTests/runit.rowCum.R, inst/unitTests/runit.signalCounts.R, inst/unitTests/runit.spreads.R, inst/unitTests/runit.subset.R, inst/unitTests/runit.time.R, inst/unitTests/runit.timeSeries.R, man/apply.Rd, man/colCum.Rd, man/colStats.Rd, man/cumulated.Rd, man/daily.Rd, man/data.Rd, man/description.Rd, man/drawdowns.Rd, man/durations.Rd, man/finCenter.Rd, man/is.Rd, man/isUnivariate.Rd, man/lag.Rd, man/methods-DataPart.Rd, man/methods-aggregate.Rd, man/methods-align.Rd, man/methods-as.Rd, man/methods-attach.Rd, man/methods-base.Rd, man/methods-bind.Rd, man/methods-comment.Rd, man/methods-dim.Rd, man/methods-mathOps.Rd, man/methods-na.Rd, man/methods-plot.Rd, man/methods-returns.Rd, man/methods-rowCum.Rd, man/methods-show.Rd, man/methods-stats.Rd, man/methods-str.Rd, man/methods-subset.Rd, man/model.frame.Rd, man/monthly.Rd, man/order.Rd, man/orderStatistics.Rd, man/rank.Rd, man/series.Rd, man/spreads.Rd, man/time.Rd, man/timeSeries.Rd: As a consequnece of introducing rda data files I have adapted all manual pages and all unit test files where it was necessary * data/LPP2005REC.rda, data/MSFT.rda, data/USDCHF.rda: now the rda files are there * data/LPP2005REC.Rda, data/MSFT.Rda, data/USDCHF.Rda: data files removed * data/LPP2005REC.Rda, data/LPP2005REC.csv, data/MSFT.Rda, data/USDCHF.Rda: Rda data files added, csv deleted, now all data files are in the same format makes life easier * data/MSFT.rda, data/msft.dat.csv, data/usdchf.csv: csv and rda files deleted 2009-08-17 chalabi * R/base-colCumsums.R, inst/unitTests/runit.colCum.R: Rewrite all Colcum methods to take advantages of new apply,timeSeries-methods. * R/base-apply.R: Improved apply,timeSeries-method to handle timeSeries with one row. * NAMESPACE: new NAMESPACE structure which should ease maintenance of packages. * R/methods-show.R: show,timeSeries-method is now more friendly with default "max.print" R option. * DESCRIPTION, NAMESPACE, R/base-Extract.R, R/base-cbind.R, R/base-merge.R, R/methods-Ops.R, R/methods-as.R, R/methods-show.R, R/timeSeries.R, inst/unitTests/runit.bind.R, man/methods-bind.Rd: Merge branch 'devel-timeSeries' Conflicts: pkg/timeSeries/R/base-Extract.R pkg/timeSeries/R/timeSeries.R * NAMESPACE, R/AllClass.R, R/base-Extract.R, R/timeSeries-getDataPart.R, man/methods-subset.Rd: better handling of @recordIDs with $<-,timeSeries-method. * R/methods-as.R: improved as.ts,timeSeries-method with monthly and quarterly data. 2009-06-13 chalabi * R/base-Extract.R: Improved $,timeSeries-method when matching names in @recordIDs * R/AllClass.R, R/base-Extract.R, R/base-dim.R: names,timeSeries-method returns now also the names of data in @recordIDs. names<-,timeSeries-methods works both for data part and @recordIDs. 2009-05-17 wuertz * inst/unitTests/runit.TimeSeriesCoercion.R: unit tests - still to be updated as.ts * man/methods-as.Rd, man/methods-bind.Rd, man/timeSeries.Rd: man pages updated * NAMESPACE: namespace adapted * NAMESPACE: * R/AllClass.R, R/AllGeneric.R, R/base-Extract.R, R/base-apply.R, R/base-attach.R, R/base-cbind.R, R/base-colCumsums.R, R/base-colSums.R, R/base-comment.R, R/base-diff.R, R/base-dim.R, R/base-merge.R, R/base-rank.R, R/base-rev.R, R/base-rowCumsums.R, R/base-sample.R, R/base-scale.R, R/base-sort.R, R/base-start.R, R/base-subset.R, R/fin-align.R, R/fin-cumulated.R, R/fin-daily.R, R/fin-drawdowns.R, R/fin-durations.R, R/fin-monthly.R, R/fin-orderColnames.R, R/fin-orderStatistics.R, R/fin-periodical.R, R/fin-returns.R, R/fin-smooth.R, R/fin-splits.R, R/fin-spreads.R, R/fin-turnpoints.R, R/graphics-plot.R, R/methods-Ops.R, R/methods-as.R, R/methods-is.R, R/methods-show.R, R/old2new.R, R/stats-aggregate.R, R/stats-lag.R, R/stats-model.frame.R, R/stats-na.omit.R, R/stats-window.R, R/timeSeries-description.R, R/timeSeries-dummy.R, R/timeSeries-finCenter.R, R/timeSeries-getDataPart.R, R/timeSeries-isOHLC.R, R/timeSeries-isPeriod.R, R/timeSeries-isUnivariate.R, R/timeSeries-readSeries.R, R/timeSeries-series.R, R/timeSeries-signalCounts.R, R/timeSeries-time.R, R/timeSeries.R, R/utils-head.R, R/utils-str.R, R/zzz.R: NEW FILE ORDERING CHECKED IN ... * R/AllClass.R, R/AllGeneric.R, R/colCum.R, R/colStats.R, R/cumulated.R, R/daily.R, R/description.R, R/drawdowns.R, R/dummy.R, R/durations.R, R/is.R, R/isUnivariate.R, R/methods-DataPart.R, R/methods-aggregate.R, R/methods-align.R, R/methods-apply.R, R/methods-as.R, R/methods-attach.R, R/methods-bind.R, R/methods-comment.R, R/methods-dim.R, R/methods-finCenter.R, R/methods-head.R, R/methods-lag.R, R/methods-mathOps.R, R/methods-merge.R, R/methods-na.R, R/methods-outlier.R, R/methods-plot.R, R/methods-returns.R, R/methods-rowCum.R, R/methods-series.R, R/methods-show.R, R/methods-str.R, R/methods-subset.R, R/methods-tail.R, R/methods-window.R, R/model.frame.R, R/monthly.R, R/old2new.R, R/order.R, R/orderStatistics.R, R/periodical.R, R/rank.R, R/readSeries.R, R/signalCounts.R, R/spreads.R, R/time.R, R/timeSeries.R, R/turnpoints.R, R/zzz.R: 2009-05-07 wuertz * R/methods-show.R: .print.timeSeries missing column names fixed on "h" style can now also handle "%Q" format for quarterly data * R/methods-plot.R: .plotTimeSeries can now handle different colors and plot symbols for multivariate series 2009-04-19 chalabi * DESCRIPTION: added explicit version number in Depends field for key packages * R/methods-aggregate.R: fixed colnames in aggregate,timeSeries-method * R/AllClass.R: initialize,timeSeries-method checks object with validObject * R/colStats.R: added colMeans and colSums,timeSeries-method because default function is unefficient with large timeSeries objects. 2009-04-02 chalabi * NAMESPACE: updated NAMESPACE * DESCRIPTION: more explicit depends and suggests field in DESC file. 2009-04-01 chalabi * DESCRIPTION: updated DESC file 2009-03-31 chalabi * R/AllClass.R, R/is.R, R/methods-DataPart.R: small changes to make timeSeries work with R-2.7.0. 2009-03-30 chalabi * man/data.Rd: added MSFT (timeSeries version of mstf.dat) in data folder. * NAMESPACE, R/AllClass.R, R/zzz.R: define S4 class 'difftime' with 'setOldClass()'. We will keep it until 'methods' pkg will define it alongside the other old 'base' classes. * R/methods-mathOps.R, R/timeSeries.R, inst/unitTests/runit.mathOps.R, inst/unitTests/runit.timeSeries.R, man/methods-mathOps.Rd: added explicit methods for Ops with 'ts' and 'timeSeries' arguments. 2009-03-25 chalabi * data/MSFT.rda: added timeSeries version of msft.dat dataset 2009-03-19 chalabi * R/is.R, R/methods-aggregate.R, R/methods-as.R, R/methods-bind.R, R/methods-dim.R, R/methods-head.R, R/methods-lag.R, R/methods-mathOps.R, R/methods-merge.R, R/methods-na.R, R/methods-plot.R, R/methods-str.R, R/methods-tail.R, R/methods-window.R, R/model.frame.R, R/time.R, man/lag.Rd, man/methods-aggregate.Rd, man/methods-as.Rd, man/methods-bind.Rd, man/methods-mathOps.Rd, man/methods-na.Rd, man/methods-subset.Rd, man/time.Rd: 'base' generics have now S3 and S4 methods. S3 methods are used because 'UseMethod' does not dispatch S4 methods in 'base' functions. For example 'base' functions starting with something like 'as.list' would failed without the S3 method. * R/old2new.R: added functions to convert old timeSeries format to new class 'timeSeries' * R/methods-subset.R, R/time.R, R/timeSeries.R: @positions is numeric and makes timeSeries object much faster. * R/methods-show.R, R/zzz.R: added getRmetricsOptions("max.print") * R/methods-bind.R, R/methods-merge.R: new implementation of [cb]bind and merge functions * R/is.R: added function is.signalSeries * R/methods-tail.R: optimized tail for large timeSeries * R/methods-as.R, man/methods-as.Rd: new as.list.timeSeries S3 methods. This means that functions like sapply and lapply can now work with timeSeries objects. * R/methods-bind.R, man/methods-bind.Rd: new cbind and rbind implementation in S3 method since methods:::bind_activation might create problems. Names of arguments are now supported. * R/methods-series.R: series<-,matrix-method uses now the new timeSeires() methods. * R/methods-as.R: new implementation of as.timeSeries.data.frame. Should has the same features as the previous implementation. * R/daily.R, R/methods-dim.R: improved handling of colnames and unit. functions like var() should now returns with the appropriate colnames. * R/AllClass.R, R/AllGeneric.R, R/timeSeries.R, man/timeSeries.Rd: timeSeries() is now a generic function with methods. timeSeries() should now take advantage of new implementation of timeDate() and should be faster in creating new timeSeries objects. * R/methods-subset.R: added $,timeSeries method with auto-completion of column names * NAMESPACE, R/methods-dim.R: added name,timeSeries method which return the column names 2009-02-04 chalabi * ChangeLog: * inst/NEWS: * DESCRIPTION: updated version number 2009-01-29 chalabi * R/methods-aggregate.R, man/methods-aggregate.Rd: improved aggregate,timeSeries-method 2009-01-28 chalabi * R/methods-plot.R: small changed in plot,timeSeries to avoid warning when dealing with signal series * R/methods-subset.R: timeSeries()[''] now returns a nuermic(NA) instead of logical(NA) * inst/unitTests/runit.subset.R: added RUnit test with subsetting 2009-01-12 chalabi * man/apply.Rd: fixed warning with new Rd parser * R/AllClass.R: use getDataPart method * R/is.R: use getDataPart method * R/methods-as.R: change as.matrix to use the getDatPart method * R/methods-subset.R: improved speed of sub-setting and sub-assignment and fixed problem when sub-setting with character argument without comma. * R/methods-dim.R, R/methods-head.R, R/methods-show.R, R/methods-tail.R: faster method * R/methods-bind.R: fixed colnames problem with c,rbind * NAMESPACE, R/methods-DataPart.R: added getDataPart,timeSeries method 2009-01-11 wuertz * R/align.R: old align.R script removed * R/methods-align.R: align established as method filename renamed 2009-01-07 wuertz * NAMESPACE, R/align.R, inst/unitTests/runit.aggregate.R, inst/unitTests/runit.subset.R, man/methods-align.Rd: unit tests which failed from the change 2008 to 2009 repaired, function align added the same as .align.timeSeries and documented, added to namespace 2009-01-06 wuertz * R/methods-aggregate.R: example modified was not working for 2009 * R/methods-lag.R: example modified timeSeries/NAMESPACE0000644000176200001440000002045315116777003013615 0ustar liggesusers################################################ ## import name space ################################################ ## MM: Importing all is almost surely a waste [FIXME!] import("methods") # , show importFrom("grDevices", col2rgb, rgb , xy.coords) importFrom("stats", approx, approxfun, spline, splinefun, var, cor, dist, hclust, qt, rnorm, runif, runmed, ts, is.mts, deltat, ## for these, we provide methods: start, end, time, window, lag, filter, frequency, aggregate, as.ts, median, quantile, na.contiguous, na.omit ) importFrom("graphics", # Note: lines, plot, points imported from timeDate axTicks, axis, axis.POSIXct, box, grid, layout, mtext, par, plot.default, plot.new, plot.window, segments, text, title ) importFrom("utils", head.matrix, tail.matrix, read.table, .DollarNames, # as we provide an S3 method ## provide methods for these : head, tail, str ) importFrom("timeDate", abline, # the S4 generic from here, not "graphics" (graphics::abline is not generic) ##--- For these we provide and export methods : plot, # base lines, points, # graphics isDaily, isMonthly, isQuarterly, isRegular, align, ##---- end of generics for which we define/export methods getRmetricsOptions, setRmetricsOptions, finCenter, "finCenter<-", as.timeDate, atoms, dayOfWeek, isWeekday, timeCalendar, timeDate, timeSequence, timeFirstDayInMonth, timeFirstDayInQuarter, timeLastDayInMonth, timeLastDayInQuarter, kurtosis, skewness ) importMethodsFrom("timeDate", "+", "-", "Ops", "[" ) ################################################ ## S3 methods ################################################ S3method(".DollarNames", "timeSeries") S3method("aggregate", "timeSeries") S3method("as.data.frame", "timeSeries") S3method("as.list", "timeSeries") S3method("as.matrix", "timeSeries") S3method("as.timeSeries", character) S3method("as.timeSeries", data.frame) S3method("as.timeSeries", default) S3method("as.timeSeries", ts) S3method("as.timeSeries", zoo) S3method("as.ts", "timeSeries") S3method("cbind", "timeSeries") S3method("cumulated", default) # S3method("cut", "timeSeries") # timeSeries method was removed on 2023-05-26 S3method("diff", "timeSeries") S3method("end", "timeSeries") S3method("getUnits", default) S3method("head", "timeSeries") S3method("lag", "timeSeries") S3method("lines", "timeSeries") S3method("median", "timeSeries") S3method("merge", "timeSeries") S3method("na.contiguous", "timeSeries") S3method("na.omit", "timeSeries") S3method("plot", "timeSeries") S3method("points", "timeSeries") S3method("pretty", "timeSeries") S3method("print", timeSeries) S3method("print", timeSeries_summary) S3method("rbind", "timeSeries") S3method("rev", "timeSeries") S3method("scale", "timeSeries") S3method("sort", "timeSeries") S3method("start", "timeSeries") S3method("summary", "timeSeries") S3method("str", "timeSeries") S3method("tail", "timeSeries") S3method("window", "timeSeries") S3method("time", "timeSeries") S3method("frequency", "timeSeries") S3method("is.unsorted", "timeSeries") S3method("time<-", "timeSeries") S3method("time<-", default) if(getRversion() >= "3.6.0") { # GNB ## we ensure that when zoo is loaded zoo::`time<-` gets the 'timeSeries' ## method. This works nicely, if 'zoo' is attached after 'timeSeries' (or ## if 'timeSeries' is loaded but not attached). It may seem that if ## 'timeSeries' is attached after 'zoo', zoo::`time<-` will be masked, so ## 'time(x) <- value' will see only the methods for the timeSeries version ## of 'time<-'. But this is not a problem since the default method for ## timeSeries::`time<-` calls zoo::`time<-` S3method(zoo::`time<-`, "timeSeries") S3method(zoo::coredata, "timeSeries") S3method(zoo::'coredata<-', "timeSeries") } ################################################ ## S4 classes ################################################ exportClasses("index_timeSeries", "timeSeries", "time_timeSeries" ) exportMethods( "$", "$<-", "+", "-", "Ops", "[", ## from timeDate "align", "finCenter", "finCenter<-", "isDaily", "isMonthly", "isQuarterly", "isRegular", ## from methods "cbind2", "rbind2", "coerce", "getDataPart", "setDataPart", # not generic in methods "initialize", "show", ## primitive R functions (internally S3 and S4 generic) "cummax", "cummin", "cumprod", "cumsum", "dim", "dim<-", "dimnames", "dimnames<-", "names", "names<-", ## stats ## "aggregate", ## "as.ts", "filter", # not generic in stats; let it stay S4 here ## graphics ## "lines", "points", ## from base "apply", # not generic in base "as.data.frame", "as.list", "as.matrix", "attach", # not generic in base "colMeans", "colSums", # not generic in base "colnames", "colnames<-", "rownames", "rownames<-", # not generic in base "comment", "comment<-", # not generic in base # "cut", # timeSeries method was removed on 2023-05-26 "is.unsorted", # internal generic in base (but seemingly only S3, not S4) "merge", # the S4 methods are for x and y, so S4 is essential "plot", "rank", # not generic in base "sample", # not generic in base "t" ) ################################################ ## functions ################################################ export( ".colorwheelPalette", # used in the vignette "alignDailySeries", "applySeries", "as.timeSeries", "colCummaxs", "colCummins", "colCumprods", "colCumreturns", "colCumsums", "colKurtosis", "colMaxs", "colMins", "colProds", "colQuantiles", "colSds", "colSkewness", "colStats", "colVars", "coredata.timeSeries", "coredata<-.timeSeries", "countMonthlyRecords", "cumulated", "daily2monthly", "daily2weekly", "description", "drawdowns", "drawdownsStats", "dummyDailySeries", "dummyMonthlySeries", "durations", "endOfPeriodBenchmarks", "endOfPeriodSeries", "endOfPeriodStats", "fapply", "getAttributes", "getFinCenter", "getReturns", # several uses in the tests in 'fTrading' # I corrected the devel version of 'fTrading'. # TODO: remove when fTrading is updated on CRAN. "getTime", "getUnits", "getUnits.default", "hclustColnames", "index2wealth", ## "interpNA", # removed on 2025-12-12 "is.signalSeries", "is.timeSeries", "isMultivariate", "isUnivariate", "midquotes", "orderColnames", "orderStatistics", "outlier", "pcaColnames", "readSeries", "removeNA", # used in fTrading. Remove when that is updated "returns", "returns0", "rollDailySeries", "rollMax", "rollMean", "rollMedian", "rollMin", "rollMonthlySeries", "rollMonthlyWindows", "rollStats", "rowCumsums", "runlengths", "sampleColnames", "series", "series<-", "setAttributes<-", "setFinCenter<-", "setTime<-", "setUnits<-", "smoothLowess", "smoothSpline", "smoothSupsmu", "sortColnames", "splits", "spreads", "statsColnames", ## "substituteNA", # removed on 2025-12-12 "time", "time<-", "timeSeries", "turns", "turnsStats", "head", # 2024-01-05 GNB: were in exportMethods but no longer S4 generic "tail", "str", "start", "end", "median", "quantile", "na.contiguous", ## "sort", # from 'base', no need to export ## "rev", ## "print", ## "diff", ## "is.na", # primitive ## "frequency", "aggregate", "as.ts", # stats, reexport ## graphics "lines", "points", # graphics, reexport "na.omit", "lag", "window" ) timeSeries/NEWS.md0000644000176200001440000002112515117004065013461 0ustar liggesusers## timeSeries 4052.112 - removed deprecated functions `interpNA` and `substituteNA`. Use `na.omit` instead, see `?na.omit.timeSeries` for details. (The deprecated `removeNA` is kept for now, since it is used in a package.) - removed a number of unexported deprecated functions starting with a '.', which had exported variants without the starting '.'. - removed the unnecessary tabular environment in 'timeSeries-deprecated.Rd'. - corrected minor typo's (e.g., changed a `getRmetricsOption` to `getRmetricsOptions` in an error message). ## timeSeries 4041.111 - now 'timeSeries' depends on 'timeDate' version 4041.110 or later. If you have an older version of 'timeSeries' and it stops working after updating 'timeDate', install the latest version of 'timeSeries'. The incompatibility was necessitated by the need to fix a bug. ## timeSeries 4041.110 - this is a technical update, there are no user visible changes. - There was an explicit call to `timeDate::months` in the code, but the upcoming v4041.110 of timeDate doesn't export `months` anymore (in package code call `stats::months` or import `months` from `stats` and use `months` without the package prefix). ## timeSeries 4032.109 - deprecated function `returnSeries` is now defunct, use `returns` instead. - a number of generic functions from base R now get only S3 methods for 'timeSeries' objects. Previously they were turned into S4 generics with S4 methods. - streamlined timeSeries methods for a number of functions. Left only S3 methods or only S4 methods were suitable. - consolidated the NAMESPACE. ## timeSeries 4032.108 - fixed 'Lost braces; missing escapes or markup?' NOTE from CRAN. ## timeSeries 4031.107 - refactored the 'timeSeries' methods for `head` and `tail`. - fixed a bug in the 'timeSeries' method for `stats::na.contiguous`, which caused the wrong stretch to be returned in the case of tied longest stretches one of whom starts at the beginning of the series. Similar bug was present in `stats::na.contiguous.default`, see my bug report to R-devel from 2023-06-02 and the discussion there (https://stat.ethz.ch/pipermail/r-devel/2023-June/082642.html) - removed deprecated functions `spreadSeries`, `midquoteSeries`, and `durationSeries`. Use `spreads`, `midquotes`, and `durations`, respectively. - removed deprecated function `colStdevs`, use `colSds()` instead. - removed deprecated function `.description`, use `description()` instead. - removed deprecated 'timeSeries' method for function `cut()`, use `window()` instead. The method was not compatible with the generic function `cut()`. Now applying `cut(x)` on a 'timeSeries' object `x` will work on the underlying time series data. - replaced the S4 methods for `zoo::coredata` and `zoo::'coredata<-'`. The ones for `zoo::'coredata<-'` were not working at all, since `zoo::'coredata<-'` is an S3 generic and the methods dispatch on two arguments. It is also a mistery why the methods for the unexported S4 generics in 'timeSeries' were associated with the corresponding 'zoo' generics. If `zoo` is not attached, the calls need to be prefixed with `zoo::` or, alternatively, since the new methods are exported, they can be called directly as `coredata.timeSeries()` and ``coredata.'timeSeries<-'() <- value`. - added a default method for `time<-` to improve its interaction with 'zoo'. - added 'zoo' to 'Suggests:'. - removed the deprecated `dummySeries`, use `dummyMonthlySeries` instead. - added argument `FUN` to the `timeSeries` method for `na.omit` to allow it to compute replacement values using functions, such as `mean`, `median`, or user defined. - formally deprecated `removeNA`, `interpNA`, and `substituteNA`. These had been informally deprecated in the documentation for a long time. - the help page for `orderStatistics` erroneously claimed that the input should be an univariate `timeSeries` object, while it is explicitly written to cover the multivariate case. - moved package 'methods' back to 'Depends' to avoid subtle problems when 'methods' is loaded but not attached. For example, it seems that 'Math' methods for 'structure' are not seen for `cummin` and other `cumXXX` functions, when called on time series objects (the other math functions work ok). - `cumsum`, `cumprod`, `cummin`, and `cummax` now work on the columns of the 'timeSeries' object and keep its class and other attributes. This is a breaking change since previously the return value was numeric vector, the result of applying the base R functions to the data part of the object. This was not particularly useful, especilly for multivariate time series. With this change all functions from the S4 `Math` group return 'timeSeries' when their argument is 'timeSeries' object. - stopped exporting some internal functions that were accidentally used by other packages (after those packages were updated on CRAN). - Numerous improvements to the documentation and further changes in the code. ## timeSeries 4030.106 - removed UTF8 characters from NAMESPACE (fixes CRAN warning to that effect). ## timeSeries 4021.105 - updated and significantly improved the documentation. - class `timeSeries` now has a dedicated summary method. Previously it was falling back to the method for matrices. - `colCumsums`, `colCummaxs`, `colCummins`, and `colCumprods` no longer throw error for `timeSeries` objects when called with `na.rm = TRUE`. Fixes bug #2121 reported by Shane Haas. - corrected USDCHF dataset. The year information was wrong (the data started from year 8295). The bug had been introduced in version 2100.84 when the dataset file was converted from a `usdchf.csv` to `USDCHF.rda`. `USDCHF@documentation` contains a short note about this change. Also changed the FinCenter to Zurich (neither the documentation nor the csv file contain FinCenter information). - the original source file `msft.dat.csv` of the `MSFT` data is included now as `inst/extdata/msft.csv` (note the different name). The file had been removed in v2100.84. Note that there is a file ``msft.dat.csv` in `test/` but it is a modified and abbreviated version of the original file. - `dummySeries` has been renamed to the more expressive `dummyMonthlySeries`. The old name is still available but is deprecated. - The functions `returnSeries` and `getReturns` are no longer exported and will be removed in the near future. They are synonyms for the function `returns` and their use was discouraged for many years. Just use `returns`. - 'timeSeries' method for function `cut()` is now formally deprecated. Use `window` instead. - deprecated function `seriesData` is now defunct. Use `as.matrix()` instead. - deprecated function `seriesPositions` is now defunct. Use `time()` instead. - deprecated function `newPositions<-` is now defunct. Use `time<-` instead. - deprecated function `colAvgs` is now defunct. Use `colMeans()` instead. - deprecated function `colStdevs` is now defunct. Use `colSds()` instead. ### Technical changes - stopped exporting (almost) all functions whose names start with a '.'. Historically, the package was exporting all functions, including those start with a '.'. This should be of no concern for users since these functions were not documented but the developers of some Rmetrics packages where using such functions. - the additional arguments of the S3 `timeSeries` method for `diff()` are now in its signature, which previously was `diff(x, ...)`. An intermediate function, `.diff.timeSeries`, was eliminated in the process. - the bodies of the methods of `series<-()` and `coredata<-` for signature `"matrix"` of value were identical. Now the body is a separate, unexported function, which is used as the definition of both of these methods. - eliminated `.merge.timeSeries` and other redundancy in the implementation of the `c("timeSeries", "timeSeries")` method. - eliminated `.rev.timeSeries` in the definition of the `rev` method. - eliminated `.scale.timeSeries` in the definition of the `scale` timeSeries method. - same as above for `.sort.timeSeries`. - eliminated `.start.timeSeries`and redundancy in the implementation of the `timeSeries` method. - eliminated `.end.timeSeries`and redundancy in the implementation of the `timeSeries` method. - the function `.applySeries` is now defunct. It was obsoleted long time ago and was exported for historical reasons only. Use `applySeries()` instead. ## timeSeries 4021.104 - new maintainer: Georgi Boshnakov. - moved package `methods` to `Imports`. - fixed CRAN NOTE `Escaped LaTeX specials: \_ \_` in `methods-plot.Rd`. ## timeSeries 3062.100 and older See file `ChangeLog`. timeSeries/inst/0000755000176200001440000000000015104730074013341 5ustar liggesuserstimeSeries/inst/README0000644000176200001440000000012015104730074014212 0ustar liggesusersintroduction of timeSeries package in the Rmetrics suite after svn revision 3319timeSeries/inst/THANKS0000644000176200001440000000000115104730074014243 0ustar liggesusers timeSeries/inst/pkgdown.yml0000644000176200001440000000033015116764443015543 0ustar liggesuserspandoc: 3.1.3 pkgdown: 2.2.0 pkgdown_sha: ~ articles: {} last_built: 2025-12-12T10:06Z urls: reference: https://geobosh.github.io/timeSeriesDoc/reference article: https://geobosh.github.io/timeSeriesDoc/articles timeSeries/inst/_pkgdown.yml0000644000176200001440000000721015104730074015674 0ustar liggesusersurl: https://geobosh.github.io/timeSeriesDoc/ deploy: install_metadata: true template: bootstrap: 5 search: exclude: ['news/index.html'] reference: - title: "Overview of package timeSeries" contents: - "timeSeries-package" - TimeSeriesData - title: "Create 'timeSeries' objects" contents: - timeSeries - readSeries - dummyMonthlySeries - dummyDailySeries - as.timeSeries - as.matrix.timeSeries - as.ts.timeSeries - as.data.frame.timeSeries - title: "Explore 'timeSeries' objects" contents: - plot - "lines,timeSeries-method" - "points,timeSeries-method" - pretty.timeSeries - "print.timeSeries" - str - is.timeSeries - is.signalSeries - isUnivariate - isMultivariate - isDaily.timeSeries - isMonthly.timeSeries - isQuarterly.timeSeries - isRegular - "is.na" - "is.unsorted,timeSeries-method" - title: "Subset 'timeSeries' objects" desc: > There are 'timeSeries' methods for subsetting operators, like '[' and '[<-', as well as functions and methods which broadly perform some kind of subsetting. contents: - window - TimeSeriesSubsettings - na.contiguous - na.omit - removeNA - substituteNA - interpNA - endOfPeriodSeries - endOfPeriodStats - endOfPeriodBenchmarks - title: "Aggregate and smooth 'timeSeries' objects" contents: - filter - smoothLowess - smoothSpline - smoothSupsmu - aggregate - align - alignDailySeries - daily2monthly - daily2weekly - fapply - applySeries - rollDailySeries - rollMonthlySeries - countMonthlyRecords - rollMonthlyWindows - title: "Manipulate 'timeSeries' objects" contents: - series - "series<-" - getFinCenter - "setFinCenter<-" - finCenter - "`finCenter<-`" - time - "time<-" - getUnits - "setUnits<-" - start - end - getAttributes - "setAttributes<-" - comment - "`comment<-`" - orderColnames - sortColnames - sampleColnames - statsColnames - pcaColnames - hclustColnames - title: "Transform 'timeSeries' objects" contents: - scale - diff - colCum - colCummaxs - colCummins - colCumprods - colCumreturns - colCumsums - rowCumsums - lag - sort - rev - runlengths - durations - rank - sample - math - title: "Financial computations on 'timeSeries' objects" contents: - returns - returns0 - cumulated - drawdowns - drawdownsStats - splits - spreads - midquotes - midquoteSeries - spreadSeries - index2wealth - title: "Compute statistics on timeSeries objects" contents: - "cov-methods" - "cor-methods" - colStats - colSds - colVars - colSkewness - colKurtosis - colMaxs - colMins - colProds - colQuantiles - turns - turnsStats - orderStatistics - rollStats - rollMean - rollMin - rollMax - rollMedian - title: "Combine time series" contents: - cbind - rbind - cbind2 - rbind2 - merge - title: "Mathematical operations on 'timeSeries'" contents: - math - t - title: "Other" contents: - "timeSeries-class" - dimnames - "DataPart,timeSeries-method" - description - attach - internals timeSeries/inst/extensionsTests/0000755000176200001440000000000015104730074016563 5ustar liggesuserstimeSeries/inst/extensionsTests/attributesExtension.R0000644000176200001440000002312715104730074022776 0ustar liggesusers # Enhancing the Documentation Slot # Series: # @.Data # @ positions # @ format # @ FinCenter # @ units # @ recordIDs # @ title # @ documentation # attributes(@documentation, "Attributes") # inspect the Information use # slotNames(object) # slot(object, name) # Load Library: require(timeSeries) ############################################################################### # Data obj1 <- timeSeries(rnorm(12), timeCalendar()) getAttributes(obj1) setAttributes(obj1) <- list(series=series(obj1)[1:6, , drop=FALSE]) getAttributes(obj1) obj2 <- timeSeries(rnorm(12), timeCalendar()) getAttributes(obj2) setAttributes(obj2) <- list(series=as.matrix(obj2)[7:12, , drop=FALSE]) getAttributes(obj2) ############################################################################### # Base Functions: # base-apply.R getAttributes( apply(obj1, 1, mean) ) # ok # base-applySeries.R # should be deprecated, use generic apply() and aggregate() functions # base-cbind.R cbind(obj1, obj2) getAttributes( cbind(obj1, obj2) ) # ok getAttributes( rbind(obj1, obj2) ) # ok # ... more base-cbind.R # cbind ... getAttributes(cbind(obj1, obj2)) # ok getAttributes(cbind(obj1, as.matrix(obj2))) # ok getAttributes(cbind(as.matrix(obj1), obj2)) # ok getAttributes(cbind(obj1)) # ok # rbind ... getAttributes(rbind(obj1, obj2)) # ok getAttributes(rbind(obj1, as.matrix(obj2))) # ok getAttributes(rbind(as.matrix(obj1), obj2)) # ok getAttributes(rbind(obj1)) # ok # base-diff.R getAttributes( diff(obj1) ) # ok # base-merge.R getAttributes( merge(obj1, obj2) ) # ok # base-rank.R getAttributes( rank(obj1) ) # ok # base-rev.R getAttributes( rev(obj1) ) # ok # base-sample.R getAttributes( sample(obj1) ) # ok # base-scale.R getAttributes( scale(obj1) ) # ok # base-sort.R getAttributes( sort(obj1) ) # ok ################################################################################ # Subsetting: # base-subsetting.R # .subset_timeSeries # .findIndex # $,timeSeries Subsets a time series by column names # $<-,timeSeries Replaces subset by column names # [,timeSeries Subsets a time series object # [<-,timeSeries Assigns value to subsets of a time series # Should work by dafault ... getAttributes( obj1[3:4, 1] ) # ok getAttributes( head(obj1) ) # ok getAttributes( tail(obj1) ) # ok ################################################################################ # Methods: # methods-mathOps.R # here the multiplications "*", works also with "+", "=", "/". ... getAttributes( obj1 * 2) # ok getAttributes( obj1 * (1:12) ) # ok getAttributes( obj1 * matrix(1:12, ncol=1) ) # ok getAttributes( obj1 * as.ts(1:12) ) # ok getAttributes( obj1 * obj2 ) # ok ??? getAttributes( 2 * obj2 ) # ok getAttributes( (1:12) * obj2 ) # ok getAttributes( matrix(1:12, ncol=1) * obj2) # ok getAttributes( as.ts(1:12) * obj2) # ok getAttributes( obj2 * obj1) # ok ??? # More Math Functions getAttributes( abs(obj1) ) # ok getAttributes( exp(obj1) ) # ok getAttributes( obj1^2 ) # ok # ... # Round and Truncate: getAttributes( round(obj1, digits=2) ) # ok getAttributes( trunc(obj1, digits=2) ) # ok getAttributes( signif(obj1, digits=3) ) # ok getAttributes( ceiling(100*obj1) ) # ok getAttributes( floor(100*obj1) ) # ok ################################################################################ # Financial 'timeSeries' Functions # fin-align.R getAttributes( align(obj1) ) # ok # fin-cumulated.R getAttributes( cumulated(obj1) ) # ok # fin-daily.R # align(obj1) and alignDailySeries(obj1) are the same # deprecate align Daily Series getAttributes( alignDailySeries(obj1) ) # ok # Can we use the generic function aggregate ? getAttributes( rollDailySeries(obj1, FUN=mean) ) # ok # fin-drawdowns.R getAttributes( drawdowns(obj1) ) # ok # fin-durations.R getAttributes( durations(obj1) ) # ok # fin-monthly.R getAttributes( rollMonthlySeries(obj1, "3m", FUN=mean) ) # ok getAttributes( countMonthlyRecords(obj1) ) # ok # fin-periodical.R # todo .endOfPeriodSeries .endOfPeriodStats .endOfPeriodBenchmarks # fin-returns.R OBJ1 <- cumulated(obj1) getAttributes( returns(OBJ1) ) # ok # fin-runlengths.R getAttributes( runlengths(obj1) ) # ok # fin-splits.R getAttributes( outlier(obj1) ) # ok # fin-spreads.R SPREADS <- spreads(obj3, which=c(1, 2)) getAttributes( SPREADS) # ok fails MIDQUOTES <- midquotes(obj3, which=c(1,2)) getAttributes( MIDQUOTES ) # ok fails # fin-turns.R INDEX <- cumulated(obj1) getAttributes( turns(INDEX) ) # ok ################################################################################ # Statistics timeSeries Functions # statistics-colCumsums.R getAttributes( colCumsums(obj1) ) # ok # statistics-colSums.R # returns no timeSeries objects # statistics-orderColnames.R # returns no timeSeries objects # statistics-orderStatistics.R # returns no timeSeries objects # statistics-rollMean.R getAttributes( rollStats(obj1, k=1, FUN=mean) ) # ok getAttributes( rollMean(obj1, k=1) ) # ok getAttributes( rollMin(obj1, k=1) ) # FAILS getAttributes( rollMax(obj1, k=1) ) # FAILS getAttributes( rollMedian(obj1, k=1) ) # ok # statistics-rowCumsums.R # statistics-smoothLowess.R getAttributes( smoothLowess(obj1) ) # ok getAttributes( smoothSupsmu(obj1) ) # ok getAttributes( smoothSpline(obj1) ) # ok ################################################################################ # stats # stats-aggregate.R by1 <- time(obj1[3*(1:4),]) getAttributes( aggregate(obj1, by=by1, FUN=mean) ) # ok # stats-filter.R getAttributes( filter(obj1, filter=c(1,1)) ) # ok # stats-lag.R getAttributes( lag(obj1) ) # ok # stats-na.contiguous.R # returns no timeSeries objects # stats-na.omit.R obj3 <- obj1; obj3[4, 1] <- NA; obj3 getAttributes( na.omit(obj3) ) # ok # What about? - They should be deprecated. # removeNA # substituteNA # interp NA # stats-window.R Time <- time(obj1) getAttributes( window(obj1, Time[3], Time[6]) ) # ok ################################################################################ # Attributes Functions getAttributes <- function (obj) { # FUNCTION: # Check Argument: stopifnot(class(obj) == "timeSeries") # Extract Attributes: ans <- attr(obj@documentation, "Attributes") # return Value: ans } # ----------------------------------------------------------------------------- `setAttributes<-` <- function(obj, value) { # Example: # obj <- dummyMonthlySeries(); getAttributes(obj) # setAttributes(obj) <- list(mat=matrix(1:4, ncol=2)); getAttributes(obj) # getAttributes(obj)$mat[[1]] # FUNCTION: # Check Arguments: stopifnot(class(obj) == "timeSeries") stopifnot(is.list(value)) stopifnot(length(value) == 1) stopifnot(!is.null(value)) # Compose New Attribute: name <- names(value) names(value) <- NULL A <- list(value) names(A) <- name # print(A) # Get Already Existing Attribute B <- getAttributes(obj) if(is.null(B)) B <- list() # print(B) # Join Attributes: JOINED <- sapply(unique(c(names(A), names(B))), function(x) list(c(A[[x]], B[[x]]))) # print(JOINED) # Assign Attribute: attr(obj@documentation, "Attributes") <- JOINED # Return Value: obj } ############################################################################### timeSeries/inst/extensionsTests/aggregateWrappers.R0000644000176200001440000002151215104730074022361 0ustar liggesusers require(timeSeries) X <- cumulated(LPP2005REC)[, 1:3] for (i in 1:3) X[, i] <- 100*X[, i]/as.vector(X[1,i]) Data <- alignDailySeries(X) # add: startDate Index <- time(Data) # Generate time Series: tS <- timeSeries(data=Data, charvec=format(Index)) tR <- returns(tS) ############################################################################### # aggregate: # from stats Package: The function aggregate splits the data into subsets, # computes summary statistics for each, and returns the result in a # convenient form. # AGGREGATION OVER NON-OVEWRLAPPING PERIODS # starting point: aligned daily Data # Aggregation Function: # function (x, by, FUN, ...) # Aggregation Levels: # weekly/biweekly: endOfWeek, onTuesdays, lastBusinessDay # monthly: endOMonth, lastFriday, lastBusinessDay # quarterly: 3-monthly # half-annually: 6-monthly # yearly: 12-monthly # Aggregation Statistics: # mean, sd, var, median, ... open <- function(x) as.vector(x)[1] close <- function(x) rev(as.vector(x))[1] high <- function(x) max(x) low <- function(x) min(x) spread <- function(x) max(x) - min(x) # ----------------------------------------------------------------------------- # Weekly # End-of-week: by <- timeLastDayInMonth(time(tS)) mean.tR <- aggregate(tR[, "SPI"], by, mean) sd.tR <- aggregate(tR[, "SPI"], by, sd) plot(cbind(mean.tR, sd.tR), type="h") # Weekly - Last Zurich Business Day In Week by <- timeLastBizdayInMonth(time(tS), holidays = holidayZURICH()) mean.tR <- aggregate(tR[, "SPI"], by, mean) sd.tR <- aggregate(tR[, "SPI"], by, sd) plot(cbind(mean.tR, sd.tR), type="h") # Weekly on Tuesdays by <- timeSequence(from=start(tD), to=end(tD), by = "week") mean.tR <- aggregate(tR[, "SPI"], by, mean) sd.tR <- aggregate(tR[, "SPI"], by, sd) plot(cbind(mean.tR, sd.tR), type="h") # ----------------------------------------------------------------------------- # Monthly # ----------------------------------------------------------------------------- # Quarterly ############################################################################### # ----------------------------------------------------------------------------- # End-of-Month Statistics # ----------------------------------------------------------------------------- # Monthly Open-High-Low-Close open <- function(x) as.vector(x)[1] close <- function(x) rev(as.vector(x))[1] high <- function(x) max(x) low <- function(x) min(x) SPI <- tS[, "SPI"] by <- timeLastDayInMonth(time(tS)) OHLC <- cbind( aggregate(SPI, by, open), aggregate(SPI, by, high), aggregate(SPI, by, low), aggregate(SPI, by, close)) OHLC # ----------------------------------------------------------------------------- # Monthly Spread / Percentual Spread spread <- function(x) max(x) - min(x) pspread <- function(x) (max(x) - min(x)) / (0.5 * (max(x) + min(x))) SPI <- tS[, "SPI"] by <- timeLastDayInMonth(time(tS)) SPREAD <- cbind( Points=aggregate(SPI, by, spread), Percent=100*aggregate(SPI, by, pspread)) SPREAD <- round(SPREAD, 2) SPREAD ################################################################################ # Rolling: Aggregation with Overlappinng Periods # ----------------------------------------------------------------------------- # rolling 52-weekly-highs and lows # xts: Mean on weekly Periods ep <- xts::endpoints(x.xts, on='weeks', k=1) by1 <- index(x.xts)[ep[-1]] period1 <- xts::period.apply(x.xts, INDEX=ep, FUN=mean) ############################################################################### # xts::apply.monthly FUN <- mean x <- x.xts apply.daily(x, FUN) apply.weekly(x, FUN) apply.monthly(x, FUN) apply.quarterly(x, FUN) apply.yearly(x, FUN) # timeDate::align FUN <- mean x <- unique(time(x.tS)) alignDaily(x, include.weekends=FALSE) by1 <- unique(alignMonthly(x, include.weekends=FALSE)) x1 <- timeSeries::aggregate(x.tS, by1, FUN) by2 <- unique(alignMonthly(x, include.weekends=TRUE)) x2 <- timeSeries::aggregate(x.tS, by2, FUN) by1 <- unique(alignQuarterly(x, include.weekends=FALSE)) x1 <- timeSeries::aggregate(x.tS, by1, FUN) by2 <- unique(alignQuarterly(x, include.weekends=TRUE)) x2 <- timeSeries::aggregate(x.tS, by2, FUN) cbind(x1,x2) ############################################################################### xts::first(x.xts) xts::last(x.xts) first2 <- function(x) x[start(x), ] last2 <- function(x) x[end(x), ] first2(x.tS) last2(x.tS) # ----------------------------------------------------------------------------- INDEX <- seq(1, nrow(xts), by=21) INDEX .period.apply(tS, INDEX, FUN=max) .period.max <- function(x, INDEX, FUN=max) .period.apply(x, INDEX, max) .period.max(tS[, 1], INDEX) .period.min <- function(x, INDEX) .period.apply(x, INDEX, min) .period.min(tS[, 1], INDEX) xts::period.apply(xts[, 1], INDEX, FUN=max) xts::period.max(xts[, 1], INDEX) xts::period.min(xts[, 1], INDEX) xts::period.prod(xts[, 1], INDEX) xts::period.sum(xts[, 1], INDEX) # ----------------------------------------------------------------------------- # timeBased is.timeBased <- function (x) { if (!any(sapply(c( "Date", "POSIXt", "chron", "dates", "times", "timeDate", "yearmon", "yearqtr", "xtime"), function(xx) inherits(x, xx)))) { ans <- FALSE } else { ans <- TRUE } ans } timeBased <- function(x) { is.timeBased(x) } # ----------------------------------------------------------------------------- alignDaily(x=time(tS), include.weekends=FALSE) alignMonthly(x=time(tS), include.weekends=FALSE) # error alignQuarterly(x=time(tS), include.weekends=FALSE) # error tD <- Sys.timeDate() + 1:1000 timeDate::align(tD, by="10s") timeDate::align(tD, by="60s") timeDate::align(tD, by="10m") # error td <- as.xts(Sys.time()) + 1:1000 xts::align.time(td, n=10) # every 10 seconds xts::align.time(td, n=60) # align to next whole minute xts::align.time(td, n=10*60) # align to next whole 10 min interval xts::shift.time(td, n=10) xts::shift.time(td, n=60) xts::shift.time(td) # ----------------------------------------------------------------------------- xts::to.minutes(x,k,name,...) xts::to.minutes3(x,name,...) xts::to.minutes5(x,name,...) xts::to.minutes10(x,name,...) xts::to.minutes15(x,name,...) xts::to.minutes30(x,name,...) xts::to.hourly(x,name,...) xts::to.daily(x,drop.time=TRUE,name,...) xts::to.weekly(x, drop.time=TRUE, name,...) xts::to.monthly(x, indexAt='yearmon', drop.time=TRUE,name,...) xts::to.quarterly(x, indexAt='yearqtr', drop.time=TRUE,name,...) xts::to.yearly(x,drop.time=TRUE,name,...) xts::to.period( x, period = 'months', k = 1, indexAt, name=NULL, OHLC = TRUE, ...) # ----------------------------------------------------------------------------- Convert an object to a specified periodicity lower than the given data object. For example, convert a daily series to a monthly series, or a monthly series to a yearly one, or a one minute series to an hourly series. data(sample_matrix) xts <- as.xts(sample_matrix) # is daily to.weekly(xts) to.monthly(xts) to.quarterly(xts) to.yearly(xts) tS <- as.timeSeries(sample_matrix) % ----------------------------------------------------------------------------- as.numeric(as.POSIXct(time(tS))) getFinCenter(tS) indexTZ(xts, ) tzone(xts, ) tzone(xts) <- "GMT" .index(xts, ) indexClass(xts) class(time(tS)) % ----------------------------------------------------------------------------- .index <- function(x) as.numeric(as.POSIXct(time(x))) .indexDate <- function(x) .index(x)%/%86400L .indexday <- function(x) .index(x)%/%86400L .indexmday <- function(x) as.POSIXlt(.POSIXct(.index(x)))$mday .indexwday <- function(x) as.POSIXlt(.POSIXct(.index(x)))$wday .indexweek <- function(x) .indexmon <- function(x) .indexyday <- function(x) .indexyear <- function(x) .indexhour <- function(x) .indexmin <- function(x) .indexsec <- function(x) atoms # Roll over fixed periods of length k point by point ... # Functions borrowed from zoo timeSeries::rollMin( x, k, na.pad = FALSE, align = c("center", "left", "right"), ...) timeSeries::rollMax( x, k, na.pad = FALSE, align = c("center", "left", "right"), ...) timeSeries::rollMean( x, k, na.pad = FALSE, align = c("center", "left", "right"), ...) timeSeries::rollMedian( x, k, na.pad = FALSE, align = c("center", "left", "right"), ...) timeSeries::rollStats( x, k, FUN = mean, na.pad = FALSE, align = c("center", "left", "right"), ...) # Roll over Calendarical periods: rollDailySeries(x, period="7d", FUN, ...) rollMonthlySeries(x, period="12m", by="1m", FUN, ...) # e.g. rollQuarterlySeries(x, period="12m", by="3m", FUN) # e.g. rollYearlySeries rollMonthlyWindows(x, period="12m", by="1m") apply applySeries # period.apply # Apply a specified function to data over a given interval, where the # interval is taken to be the data from INDEX[k] to INDEX[k+1], for # k=1:(length(INDEX)-1). timeSeries/inst/extensionsTests/alignWrappers.R0000644000176200001440000001722515104730074021533 0ustar liggesusers # artificial 1 sec data with missing Data tX <- timeSequence("2014-03-07 00:00:00", "2014-03-07 23:59:59", by="sec") s <- sample(1:length(tX))[1:length(tX)/10] tX <- tX[-s] ############################################################################### # align # extract index values of a given xts object corresponding to the last # observations given a period specified by on require(timeSeries) # Random Seed: set.seed(1953) # Create a day of 1s time stamps: tX <- timeSequence("2014-03-07 09:03:17", "2014-03-07 15:53:16", by="sec") # Remove randomly 10% of the data: s <- sample(1:length(tX))[1:length(tX)/10] tX <- sort(tX[-s]) tS <- 201.7*cumulated(timeSeries(data=rnorm(length(tX))/(24*3600), charvec=tX)) plot(tS) head(tS) tZ <- align(tS, by="1min", method="fillNA", offset="42s") head(tZ) tZ <- align(tS, by="3min", method="fillNA", offset="162s") head(tZ) tZ <- align(tS, by="5min", method="fillNA", offset="102") head(tZ) tZ <- align(tS, by="15min", method="fillNA", offset="702s") head(tZ) tZ <- align(tS, by="30min", method="fillNA", offset="1602s") head(tZ) tZ <- align(tS, by="60min", method="fillNA", offset="3402") head(tZ) toPeriod <- function(x, by, method, offset="0s"") { open <- function(x) as.vector(x)[1] high <- function(x) max(x) low <- function(x) min(x) close <- function(x) rev(as.vector(x))[1] cbind( aggregate(SPI, by, open), aggregate(SPI, by, high), aggregate(SPI, by, low), aggregate(SPI, by, close)) } A1 <- timeSeries::align(tS, by="60min") A2 <- xts::to.period(as.xts(tS), period = "minutes", k = 2) open <- function(x) as.vector(x)[1] close <- function(x) rev(as.vector(x))[1] high <- function(x) max(x) low <- function(x) min(x) SPI <- tS[, "SPI"] by <- timeLastDayInMonth(time(tS)) OHLC <- cbind( aggregate(SPI, by, open), aggregate(SPI, by, high), aggregate(SPI, by, low), aggregate(SPI, by, close)) OHLC xts::to.minutes(x,k,name,...) xts::to.minutes3(x,name,...) xts::to.minutes5(x,name,...) xts::to.minutes10(x,name,...) xts::to.minutes15(x,name,...) xts::to.minutes30(x,name,...) xts::to.hourly(x,name,...) # ----------------------------------------------------------------------------- # Time alignment: alignDaily(x=time(tS), include.weekends=FALSE) alignMonthly(x=time(tS), include.weekends=FALSE) # error alignQuarterly(x=time(tS), include.weekends=FALSE) # error tD <- Sys.timeDate() + 1:1000 timeDate::align(tD, by="10s") timeDate::align(tD, by="60s") timeDate::align(tD, by="10m") # error td <- as.xts(Sys.time()) + 1:1000 xts::align.time(td, n=10) # every 10 seconds xts::align.time(td, n=60) # align to next whole minute xts::align.time(td, n=10*60) # align to next whole 10 min interval xts::shift.time(td, n=10) xts::shift.time(td, n=60) xts::shift.time(td) # ----------------------------------------------------------------------------- xts::to.daily(x,drop.time=TRUE,name,...) xts::to.weekly(x,drop.time=TRUE,name,...) xts::to.monthly(x,indexAt='yearmon',drop.time=TRUE,name,...) xts::to.quarterly(x,indexAt='yearqtr',drop.time=TRUE,name,...) xts::to.yearly(x,drop.time=TRUE,name,...) xts::to.period( x, period = 'months', k = 1, indexAt, name=NULL, OHLC = TRUE, ...) # ----------------------------------------------------------------------------- Convert an object to a specified periodicity lower than the given data object. For example, convert a daily series to a monthly series, or a monthly series to a yearly one, or a one minute series to an hourly series. data(sample_matrix) xts <- as.xts(sample_matrix) # is daily to.weekly(xts) to.monthly(xts) to.quarterly(xts) to.yearly(xts) tS <- as.timeSeries(sample_matrix) % ----------------------------------------------------------------------------- as.numeric(as.POSIXct(time(tS))) getFinCenter(tS) indexTZ(xts, ) tzone(xts, ) tzone(xts) <- "GMT" .index(xts, ) indexClass(xts) class(time(tS)) % ----------------------------------------------------------------------------- .index <- function(x) as.numeric(as.POSIXct(time(x))) .indexDate <- function(x) .index(x)%/%86400L .indexday <- function(x) .index(x)%/%86400L .indexmday <- function(x) as.POSIXlt(.POSIXct(.index(x)))$mday .indexwday <- function(x) as.POSIXlt(.POSIXct(.index(x)))$wday .indexweek <- function(x) .indexmon <- function(x) .indexyday <- function(x) .indexyear <- function(x) .indexhour <- function(x) .indexmin <- function(x) .indexsec <- function(x) atoms # Roll over fixed periods of length k point by point ... # Functions borrowed from zoo timeSeries::rollMin( x, k, na.pad = FALSE, align = c("center", "left", "right"), ...) timeSeries::rollMax( x, k, na.pad = FALSE, align = c("center", "left", "right"), ...) timeSeries::rollMean( x, k, na.pad = FALSE, align = c("center", "left", "right"), ...) timeSeries::rollMedian( x, k, na.pad = FALSE, align = c("center", "left", "right"), ...) timeSeries::rollStats( x, k, FUN = mean, na.pad = FALSE, align = c("center", "left", "right"), ...) # Roll over Calendarical periods: rollDailySeries(x, period="7d", FUN, ...) rollMonthlySeries(x, period="12m", by="1m", FUN, ...) # e.g. rollQuarterlySeries(x, period="12m", by="3m", FUN) # e.g. rollYearlySeries rollMonthlyWindows(x, period="12m", by="1m") apply applySeries # period.apply # Apply a specified function to data over a given interval, where the # interval is taken to be the data from INDEX[k] to INDEX[k+1], for # k=1:(length(INDEX)-1). x1 <- xts(matrix(1:(9*6),nc=6), order.by=as.Date(13000,origin="1970-01-01")+1:9) x2 <- x1 xtsAttributes(x1) <- list(series1="1") xtsAttributes(x2) <- list(series2="2") xtsAttributes(x1) xtsAttributes(x2) x3 <- x1+x2 xtsAttributes(x3) x33 <- cbind(x1, x2) xtsAttributes(x33) x33 <- rbind(x2, x1) xtsAttributes(x33) ############################################################################### appendList <- function (x, value) { stopifnot(is.list(x), is.list(value)) xnames <- names(x) for (v in names(value)) { x[[v]] <- if (v %in% xnames && is.list(x[[v]]) && is.list(value[[v]])) appendList(x[[v]], value[[v]]) else c(x[[v]], value[[v]]) } x } "setAttributes<-" <- function(obj, value) { stopifnot(is.list(value)) ATTRIBUTES <- getAttributes(obj) VALUE <- appendList(ATTRIBUTES, value) attr(obj@documentation, "Attributes") <- VALUE obj } getAttributes <- function(obj) { attr(obj@documentation, "Attributes") } obj1 <- dummyMonthlySeries() getAttributes(obj1) setAttributes(obj1) <- list(series="obj1") getAttributes(obj1) obj2 <- dummyMonthlySeries() getAttributes(obj2) setAttributes(obj2) <- list(series="obj2") getAttributes(obj2) getAttributes(obj1+obj2) # returns the attributes only for the first getAttributes(obj1-obj2) # returns the attributes only for the first getAttributes(cbind(obj1, obj2)) getAttributes(cbind(obj1, as.matrix(obj2))) # matrix fails getAttributes(rbind(obj1, obj2)) getAttributes(rbind(obj1, as.matrix(obj2))) # matrix fails getAttributes( rev(obj) ) getAttributes( obj[, 1] ) getAttributes( sample(obj) ) getAttributes( sort(sample(obj)) ) getAttributes( scale(obj) ) getAttributes( returns(obj) ) getAttributes( cumulated(returns(obj)) ) BIND(# Add another Attribute: ATTRIBUTES <- attr(obj@documentation, "Attributes") ATTRIBUTES ATTRIBUTES <- appendList(ATTRIBUTES, list(say="hello")) ATTRIBUTES attr(obj@documentation, "Attributes") <- ATTRIBUTES cbind(obj, obj, documentation = obj@documentation) # Documentation # Series: # dim(@.Data) # @units # @positions # @format # @FinCenter # @recordIDs # @title # @documentation # attributes(@documentation, "attributes) timeSeries/inst/extensionsTests/chicPlots.R0000644000176200001440000002521315104730074020641 0ustar liggesusers x = tS1 FinCenter = NULL type = NULL plot.type = c("multiple", "single") format = "auto" at = c("pretty", "chic") main <- xlab <- ylab <- ""; nm = colnames(x); log = "" col = 1; pch = 19; cex = 1; lty = 1; lwd = 1 grid = TRUE; frame.plot = TRUE xlim = NULL; ylim = NULL axes = TRUE; ann = TRUE; cex.axis = 1, cex.lab =1, yax.flip = FALSE mar.multi = c(0, 5.1, 0, if (yax.flip) 5.1 else 2.1) oma.multi = c(7.75, 1.1, 6.1, 1.1) # Plot Function Extensions written by Diethel Wuertz # ... first Version 2014-05-12 ############################################################################### # 1 Standard Plots # 1.1 Single Plots # 1.2 Multiple Plots # 1.2 Scatter Plots # 2 Time Axis Layout # 2.1 Pretty Axis Layout # 2.2 Chic Axis Layout # 2.3 Tailored Axis Layout # 3 Annotations # 3.1 Adding Title and Labels # 3.2 Removing Annotations # 3.3 Changing Font Size # 3.4 Flipping Value Axes # 4 Decorations # 4.1 Modifying Types # 4.2 Changing Colors # 4.3 Changing Line Styles # 4.4 Changing Plot Symbols # 4.5 Modifying Line Widths # 4.6 Modifying Plot Symbol Sizes ############################################################################### # First let us see what plot.ts can do in the multiple plot mode: require(timeSeries) tS1 <- 100 * cumulated(LPP2005REC[, 2]) tS2 <- 100 * cumulated(LPP2005REC[, 2:3]) tS3 <- 100 * cumulated(LPP2005REC[, 1:3]) tS6 <- 100 * cumulated(LPP2005REC[, 1:6]) tS7 <- 100 * cumulated(LPP2005REC[, 1:7]) # ----------------------------------------------------------------------------- # 1.3 Scatter Plots: mat <- getDataPart(tS2) par(mfrow=c(2,2)) plot(mat[, 1], mat[, 2]) plot(mat[, 1], mat[, 2], pch=19, cex=0.2) ################################################################################ # 2. Time Axis Layout: # Changing Time-Axis Size: # One Column Multiple Plots - Each curve in its own Graph: par(mfrow=c(1, 1)) plot(tS3, at="chic", plot.type="m", cex.axis=0.8) par(mfrow=c(1, 1)) plot(tS3, at="chic", plot.type="m", cex.axis=1.1) # Two Columns Multiple Plots - Each curve in its own Graph: par(mfrow=c(1, 1)) plot(tS6, at="chic", plot.type="m", cex.axis=0.8) par(mfrow=c(1, 1)) plot(tS6, at="chic", plot.type="m", cex.axis=1.1) ################################################################################ # 3 Annotations # ------------------------------------------------------------------------------ # 3.1 Adding Title and Labels # Single Plot - All Curves in one Graph: par(mfrow=c(2, 2)) plot(tS1); title(main = "Index") plot(tS3, plot.type="s"); title(main = "Index") plot(tS3, plot.type="s"); title(main = "Index", xlab = "Date") plot(tS6, plot.type="s"); title(main = "Index", xlab = "Date") # One Column Multiple Plots - Each curve in its own Graph: par(mfrow=c(1, 1)) plot(tS3, plot.type="m"); title(main = "Index", xlab = "Date") # Two Column Multiple Plots: par(mfrow=c(1, 1)) plot(tS6, plot.type="m"); title(main = "Index", xlab = "Date") # One Column Multiple Plots - User designed Title par(mfrow=c(1, 1)) plot(tS3, plot.type="m", at = "chic") mtext("Swiss Market", side=3, line=1, adj=-0.025) # Two Column Multiple Plots - User designed Title par(mfrow=c(1, 1)) plot(tS6, plot.type="m", at = "chic") mtext("Swiss Market", side=3, line=1, adj=-0.3) mtext("Foreign Market", side=3, line=1, adj=0.7) # ------------------------------------------------------------------------------ # 3.2 Remove all Annotations: # Two Column Multiple Plots - Each curve in its own Graph: par(mfrow=c(1, 1)) plot(tS6, plot.type="m", ann=FALSE) # Single Plot - All Curves in one Graph: par(mfrow=c(2, 1), mar = c(4, 4, 1, 2) + 0.1) plot(tS1, at="chic", ann=FALSE) title(ylab = colnames(tS1), cex.axis=0.8, cex.lab=0.8) plot(tS1, at="chic", ann=FALSE) title(ylab = colnames(tS1), cex.axis=1.2, cex.lab=1.2) ################################################################################ # 4 Decorations # ------------------------------------------------------------------------------ # 4.1 Modifying Types # "type" par(mfrow=c(1,1)) plot(tS3, type = c("l", "p", "h"), plot.type="m") # ------------------------------------------------------------------------------ # 4.2 Changing Colors # Selecting Colors: par(mfrow=c(2, 2)) plot(tS3, col = 1, plot.type="s") plot(tS3, col = 1:3, plot.type="s") plot(tS3, col = c("blue", "orange", "brown"), plot.type="s") # ------------------------------------------------------------------------------ # 4.3 Changing Line Styles # Single Plot: par(mfrow=c(1,1)) plot(tS3, lty = 3:1, plot.type="s") # One Column Multiple Plot: par(mfrow=c(1,1)) plot(tS3, lty = 3:1, plot.type="m") # ------------------------------------------------------------------------------ # 4.4 Changing Plot Symbols par(mfrow=c(1,1)) plot(tS3, pch = c(17, 18, 19), plot.type="m") # 4.5 Modifying Line Widths par(mfrow=c(1,1)) plot(tS3, lwd = c(17, 18, 19), plot.type="m") par(mfrow=c(2, 2)) plot(tS3, type=rep("p", 3), cex = rep(1.2, 3), plot.type="s") plot(tS3, type = rep("p", 3), pch = 1:3, col = c("blue", "orange", "brown"), plot.type="s") plot(tS3, type = "p", pch = 19, col = c("blue", "orange", "brown"), plot.type="s") # ----------------------------------------------------------------------------- chart.TimeSeries function (R, auto.grid = TRUE, xaxis = TRUE, yaxis = TRUE, yaxis.right = FALSE, type = "l", lty = 1, lwd = 2, main = NULL, ylab = NULL, xlab = "Date", date.format.in = "%Y-%m-%d", date.format = NULL, xlim = NULL, ylim = NULL, element.color = "darkgray", event.lines = NULL, event.labels = NULL, period.areas = NULL, event.color = "darkgray", period.color = "aliceblue", colorset = (1:12), pch = (1:12), legend.loc = NULL, ylog = FALSE, cex.axis = 0.8, cex.legend = 0.8, cex.lab = 1, cex.labels = 0.8, cex.main = 1, major.ticks = "auto", minor.ticks = TRUE, grid.color = "lightgray", grid.lty = "dotted", xaxis.labels = NULL, ...) { y = checkData(R) columns = ncol(y) rows = nrow(y) columnnames = colnames(y) if (is.null(date.format)) { freq = periodicity(y) yr_eq <- ifelse(format(index(first(y)), format = "%Y") == format(index(last(y)), format = "%Y"), TRUE, FALSE) switch(freq$scale, seconds = { date.format = "%H:%M" }, minute = { date.format = "%H:%M" }, hourly = { date.format = "%d %H" }, daily = { if (yr_eq) date.format = "%b %d" else date.format = "%Y-%m-%d" }, weekly = { if (yr_eq) date.format = "%b %d" else date.format = "%Y-%m-%d" }, monthly = { if (yr_eq) date.format = "%b" else date.format = "%b %y" }, quarterly = { if (yr_eq) date.format = "%b" else date.format = "%b %y" }, yearly = { date.format = "%Y" }) } rownames = as.Date(xts:::time.xts(y)) rownames = format(strptime(rownames, format = date.format.in), date.format) time.scale = periodicity(y)$scale ep = axTicksByTime(y, major.ticks, format.labels = date.format) logaxis = "" if (ylog) { logaxis = "y" } plot.new() if (is.null(xlim[1])) xlim = c(1, rows) if (is.null(ylim[1])) { ylim = as.numeric(range(y, na.rm = TRUE)) } plot.window(xlim, ylim, xaxs = "r", log = logaxis) if (is.null(ylab)) { if (ylog) ylab = "ln(Value)" else ylab = "Value" } if (ylog) dimensions = 10^par("usr") else dimensions = par("usr") if (!is.null(period.areas)) { period.dat = lapply(period.areas, function(x, y) c(first(index(y[x])), last(index(y[x]))), y = y) period.ind = NULL for (period in 1:length(period.dat)) { if (!is.na(period.dat[[period]][1])) { period.ind = list(grep(period.dat[[period]][1], index(y)), grep(period.dat[[period]][2], index(y))) rect(period.ind[1], dimensions[3], period.ind[2], dimensions[4], col = period.color, border = NA) } } } if (auto.grid) { abline(v = ep, col = grid.color, lty = grid.lty) grid(NA, NULL, col = grid.color) } abline(h = 0, col = element.color) if (!is.null(event.lines)) { event.ind = NULL for (event in 1:length(event.lines)) { event.ind = c(event.ind, grep(event.lines[event], rownames)) } number.event.labels = ((length(event.labels) - length(event.ind) + 1):length(event.labels)) abline(v = event.ind, col = event.color, lty = 2) if (!is.null(event.labels)) { text(x = event.ind, y = ylim[2], label = event.labels[number.event.labels], offset = 0.2, pos = 2, cex = cex.labels, srt = 90, col = event.color) } } if (length(lwd) < columns) lwd = rep(lwd, columns) if (length(lty) < columns) lty = rep(lty, columns) if (length(pch) < columns) pch = rep(pch, columns) for (column in columns:1) { lines(1:rows, y[, column], col = colorset[column], lwd = lwd[column], pch = pch[column], lty = lty[column], type = type, ...) } if (xaxis) { if (minor.ticks) axis(1, at = 1:NROW(y), labels = FALSE, col = "#BBBBBB") label.height = cex.axis * (0.5 + apply(t(names(ep)), 1, function(X) max(strheight(X, units = "in")/par("cin")[2]))) if (is.null(xaxis.labels)) xaxis.labels = names(ep) else ep = 1:length(xaxis.labels) axis(1, at = ep, labels = xaxis.labels, las = 1, lwd = 1, mgp = c(3, label.height, 0), cex.axis = cex.axis) title(xlab = xlab, cex = cex.lab) } if (yaxis) if (yaxis.right) axis(4, cex.axis = cex.axis, col = element.color, ylog = ylog) else axis(2, cex.axis = cex.axis, col = element.color, ylog = ylog) box(col = element.color) if (!is.null(legend.loc)) { legend(legend.loc, inset = 0.02, text.col = colorset, col = colorset, cex = cex.legend, border.col = element.color, lty = lty, lwd = 2, bg = "white", legend = columnnames, pch = pch) } if (is.null(main)) main = columnnames[1] title(ylab = ylab, cex = cex.lab) title(main = main, cex = cex.main) } timeSeries/inst/extensionsTests/endpointsWrappers.R0000644000176200001440000001457115104730074022445 0ustar liggesusers require(timeSeries) ############################################################################### # FUNCTION: # timeNdayInWeek # timeLastBizdayInWeek # FUNCTION: # timeLastDayInMonth # timeLastNdayInMonth # timeLastBizdayInMonth # timeNthNdayInMonth # FUNCTION: # timeLastDayInQuarter # timeLastNdayInQuarter # timeLastBizdayInQuarter # timeNthNdayInQuarter ############################################################################### ############################################################################### # endpoints # extract index values of a given time Series object corresponding to # the last calendarical observation in the specified period require(timeSeries) # Daily and Monthly Series in 2011: tD <- timeCurrentYear(2011) tM <- timeCalendar(2011) ############################################################################### # Weekly Endpoints # ----------------------------------------------------------------------------- # On Given nDay of Week: timeNdayInWeek <- function(x, nday=5) { X <- align(x) DOW <- c("Sun", "Mon", "Tue", "Wed", "Thu", "Fri", "Sat") X[dayOfWeek(X) == DOW[nday+1], ] } LastDayInWeek <- timeNdayInWeek(tD) LastDayInWeek dayOfWeek(LastDayInWeek) # ----------------------------------------------------------------------------- # Last Bizday In Week: timeLastBizdayInWeek <- function(x, holidays=holidayNYSE) { # Extend time Sequence: x <- timeSequence( from = timeDate(x[1]), to = timeDate(x[length(x)]), by = "day") # Bizdays Function: FUN <- function(x, holidays = holidays) { holidays <- holidayNYSE() posix <- as.POSIXct(x, zone = "", origin = "1970-01-01") check <- isBizday(as.timeDate(posix), holidays = holidays, wday = 1:5) ans <- rev(x[check])[1] ans } # Create Periods from: by <- timeDayInWeek(x, nday=5) bySec <- as.numeric(by, "sec") xSec <- as.numeric(x, "sec") # Compute Index: INDEX <- findInterval(xSec, bySec + 1) INDEX <- INDEX + 1 is.na(INDEX) <- !(INDEX <= length(by)) dates <- matrix(apply(matrix(xSec, ncol=1), 2, tapply, INDEX, FUN), ncol=1) dates <- as.timeDate(as.POSIXct(dates, zone="GMT", origin="1970-01-01")) # Return Value: dates } LastNYBizdayInWeek <- timeLastBizdayInWeek(tD, holidays=holidayNYSE) LastNYBizdayInWeek dayOfWeek(LastNYBizdayInWeek) # Can we attribute the day of week? ans <- timeSeries( data = rnorm(length(LastBizdayInWeek)), charvec = timeLastBizdayInWeek(tD), recordIDs = data.frame(DOW=dayOfWeek(LastBizdayInWeek))) ############################################################################### # Monthly Endpoints # ----------------------------------------------------------------------------- # Last Calendar Day in Month: LastDayInMonth <- timeLastDayInMonth(tX, unique=TRUE) LastDayInMonth dayOfWeek(LastDayInMonth) # ----------------------------------------------------------------------------- # Last Friday in Month: LastFridayInMonth <- timeLastNdayInMonth(tX, nday=5, unique=TRUE) LastFridayInMonth dayOfWeek(LastFridayInMonth) # ----------------------------------------------------------------------------- # Last New-York Bizday in Month holidayNYSE(2011) LastNYBizdayInMonth <- timeLastBizdayInMonth(tD, holidays=holidayNYSE(), unique=TRUE) LastNYBizdayInMonth dayOfWeek(LastNYBizdayInMonth) # ----------------------------------------------------------------------------- # 2nd Tuesday in Month: SecondTuesdayInMonth <- timeNthNdayInMonth(tX, nth=2, nday=2, unique=TRUE) SecondTuesdayInMonth dayOfWeek(SecondTuesdayInMonth) ############################################################################### # Quarterly Endpoints # ----------------------------------------------------------------------------- # Last Day in Quarter: timeLastDayInQuarter <- function(charvec, format="%Y-%m-%d", zone="", FinCenter="", unique = FALSE) { # Description: # Returns Last Nday in Quarter ans <- timeLastDayInMonth(charvec, format, zone = "", FinCenter, unique) INDEX <- which ( atoms(ans)[, 2] %in% c(3, 6, 9, 12) ) ans[INDEX] } LastDayInQuarter <- timeLastDayInQuarter(tD, unique=TRUE) LastDayInQuarter dayOfWeek(LastDayInQuarter) # ----------------------------------------------------------------------------- # Last Friday in Quarter: timeLastNdayInQuarter <- function(charvec, nday = 1, format = "%Y-%m-%d", zone = "", FinCenter = "", unique = FALSE) { # Description: # Returns Last Nday first/mid/last inMonths of Quarters ans <- timeLastNdayInMonth(charvec, nday, format, zone, FinCenter,unique) INDEX <- which ( atoms(ans)[, 2] %in% c(3, 6, 9, 12) ) ans[INDEX] } LastFridayInQuarter <- timeLastNdayInQuarter(tD, nday=5, unique=TRUE) LastFridayInQuarter dayOfWeek(LastFridayInQuarter) # ----------------------------------------------------------------------------- # Last Bizday in Quarter: timeLastBizdayInQuarter <- function(charvec, holidays = holidayNYSE(), format = "%Y-%m-%d", zone = "", FinCenter = "", unique = FALSE) { ans <- timeLastBizdayInMonth(charvec, holidays, format, zone, FinCenter, unique) INDEX <- which ( atoms(ans)[, 2] %in% c(3, 6, 9, 12) ) ans[INDEX] } LastNYBizdayInQuarter <- timeLastBizdayInQuarter(tD, holidayNYSE(), unique=TRUE) LastNYBizdayInQuarter dayOfWeek(LastNYBizdayInQuarter) # ----------------------------------------------------------------------------- # nth-of Mar/Jun/Sep/Dec Nday in Quarter: timeNthNdayInQuarter <- function(charvec, nday = 1, nth = 1, inMonths = c(3, 6, 9, 12), format = "%Y-%m-%d", zone = "", FinCenter = "", unique = FALSE) { ans <- timeNthNdayInMonth(charvec, nday, nth, format, zone, FinCenter, unique) INDEX <- which ( atoms(ans)[, 2] %in% inMonths) ans[INDEX] } # 2nd Tuesday in (last) Months 3/6/9/12: SecondTuesdayInLastQuarterMonth <- timeNthNdayInQuarter(tD, nth=2, nday=2, unique=TRUE) SecondTuesdayInLastQuarterMonth dayOfWeek(SecondTuesdayInLastQuarterMonth) # 2nd Tuesday in (first) Months 1/4/7/10: SecondTuesdayInFirstQuarterMonth <- timeNthNdayInQuarter(tD, nth=2, nday=2, inMonths=c(1, 3, 7, 10), unique=TRUE) SecondTuesdayInFirstQuarterMonth dayOfWeek(SecondTuesdayInFirstQuarterMonth) # IMM Dates: # The dates are the third Wednesday of March, June, September and December datesIMM <- timeNthNdayInQuarter(tD, nth=3, nday=3, inMonths=c(3, 6, 9, 12), unique=TRUE) datesIMM dayOfWeek(datesIMM) ############################################################################### timeSeries/inst/extensionsTests/xtsWrappers.R0000644000176200001440000002451015104730074021252 0ustar liggesusers require(xts) require(timeSeries) X <- cumulated(LPP2005REC)[, 1:3] for (i in 1:3) X[, i] <- 100*X[, i]/as.vector(X[1,i]) Data <- alignDailySeries(X) # add: startDate Index <- time(Data) # Generate time Series: x.tS <- timeSeries(data=Data, charvec=format(Index)) x.xts <- xts(x=Data, order.by=strptime(Index, format="%Y-%m-%d"), tzone="GMT") ############################################################################### # Class: class(x.xts) class(x.tS) # ----------------------------------------------------------------------------- # coredata # "coredata" methods for time series objects strip off the index/time # attributes and return only the observations. # xts/zoo: COREDATA <- zoo::coredata(x=x.xts) class(COREDATA) head(COREDATA) dimnames(COREDATA) # timeSeries: coredata2 <- function(x) methods::getDataPart(x) COREDATA <- coredata2(tS) class(COREDATA) head(COREDATA) dimnames(COREDATA) # timeSeries: SERIES <- series(tS) class(SERIES) head(SERIES) dimnames(SERIES) # Extractor Function # getDataPart # setDataPart # ----------------------------------------------------------------------------- # index # Generic functions for extracting the index of an object and replacing it. # xts/zoo: INDEX <- zoo::index(x=x.xts) class(INDEX) head(INDEX) # xts/zoo: TIME <- stats::time(x=x.xts) class(TIME) head(TIME) # xts/zoo: INDEX <- index(x=x.tS) class(INDEX) head(INDEX) TIME <- stats::time(x=x.tS) class(TIME) head(TIME) # Extractor Function: # getTime # setTime # ----------------------------------------------------------------------------- # indexClass # The specified value for indexClass<- must be a character string # containing one of the following: Date, POSIXct, chron, yearmon, # yearqtr or timeDate. indexClass(x.xts) tclass(x.xts) indexClass <- function(x) class(time(x)) tclass <- function(x) class(time(x)) class(x.tS) indexClass(x.tS) tclass(x.tS) # ----------------------------------------------------------------------------- # indexFormat # Functions to extract, replace, and format the class of the index of # an xts object. FORMAT <- indexFormat(x.xts) FORMAT indexFormat <- function(x) getSlot(x, "format") indexFormat(x.tS) indexFormat <- function(x) slot(x, "format") indexFormat(x.tS) indexFormat <- function(x) x.tS@format indexFormat(x.tS) # Extractor Function # getFormat # setFormat # ----------------------------------------------------------------------------- # indexTZ xts::indexTZ(x.xts) xts::tzone(x.xts) indexTZ <- function(x) getSlot(x.tS, "FinCenter") tzone(x.xts) getFinCenter(x.tS) x.tS@FinCenter getSlot(x.tS, "FinCenter") ############################################################################### # endpoints # extract index values of a given xts object corresponding to the last # observations given a period specified by on require(timeSeries) tD <- timeCurrentYear(2011) tM <- timeCalendar(2011) ############################################################################### # aggregate: # from stats Package: The function aggregate splits the data into subsets, # computes summary statistics for each, and returns the result in a # convenient form. # AGGREGATION OVER NON-OVEWRLAPPING PERIODS # starting point: aligned daily Data # Aggregation Statistics: open <- function(x) as.vector(x)[1] close <- function(x) rev(as.vector(x))[1] high <- function(x) max(x) low <- function(x) min(x) spread <- function(x) max(x) - min(x) # Aggregation Levels: # weekly/biweekly: endOfWeek, onTuesdays, lastBusinessDay # monthly: endOMonth, lastFriday, lastBusinessDay # quarterly: 3-monthly # half-annually: 6-monthly # yearly: 12-monthly # timeSeries: Weekly - end of week tD <- time(x.tS) tD <- tD[dayOfWeek(tD) == "Fri"] by <- timeSequence(from=start(tD), to=end(tD), by = "week") endOfWeek <- aggregate(x.tS, by, mean) endOfWeek # timeSeries: Weekly on Tuesdays tD <- time(x.tS[-(1:5), ]) by <- timeSequence(from=start(tD), to=end(tD), by = "week") tuesdays.period <- aggregate(x.tS, by, last) dayOfWeek(time(tuesdays.period)) cbind( open=aggregate(x.tS[, 1], by, open), high=aggregate(x.tS[, 1], by, high), low=aggregate(x.tS[, 1], by, low), close=aggregate(x.tS[, 1], by, close)) period1 <- as.timeSeries(period1) cbind(period1, period2, period3) # Aggregate to Last Friday of Month - tD <- timeSequence(from=start(tD), to=end(tD), by = "week") by <- unique(timeLastNdayInMonth(tD, nday=5)) aggregate(x.tS, by, mean) # Aggregate to Last Day of Quarter - by <- unique(timeLastDayInQuarter(tD)) aggregate(x.tS, by, mean) # ----------------------------------------------------------------------------- # rolling 52-weekly-highs and lows # xts: Mean on weekly Periods ep <- xts::endpoints(x.xts, on='weeks', k=1) by1 <- index(x.xts)[ep[-1]] period1 <- xts::period.apply(x.xts, INDEX=ep, FUN=mean) ############################################################################### # xts::apply.monthly FUN <- mean x <- x.xts apply.daily(x, FUN) apply.weekly(x, FUN) apply.monthly(x, FUN) apply.quarterly(x, FUN) apply.yearly(x, FUN) # timeDate::align FUN <- mean x <- unique(time(x.tS)) alignDaily(x, include.weekends=FALSE) by1 <- unique(alignMonthly(x, include.weekends=FALSE)) x1 <- timeSeries::aggregate(x.tS, by1, FUN) by2 <- unique(alignMonthly(x, include.weekends=TRUE)) x2 <- timeSeries::aggregate(x.tS, by2, FUN) by1 <- unique(alignQuarterly(x, include.weekends=FALSE)) x1 <- timeSeries::aggregate(x.tS, by1, FUN) by2 <- unique(alignQuarterly(x, include.weekends=TRUE)) x2 <- timeSeries::aggregate(x.tS, by2, FUN) cbind(x1,x2) ############################################################################### xts::first(x.xts) xts::last(x.xts) first2 <- function(x) x[start(x), ] last2 <- function(x) x[end(x), ] first2(x.tS) last2(x.tS) # ----------------------------------------------------------------------------- INDEX <- seq(1, nrow(xts), by=21) INDEX .period.apply(tS, INDEX, FUN=max) .period.max <- function(x, INDEX, FUN=max) .period.apply(x, INDEX, max) .period.max(tS[, 1], INDEX) .period.min <- function(x, INDEX) .period.apply(x, INDEX, min) .period.min(tS[, 1], INDEX) xts::period.apply(xts[, 1], INDEX, FUN=max) xts::period.max(xts[, 1], INDEX) xts::period.min(xts[, 1], INDEX) xts::period.prod(xts[, 1], INDEX) xts::period.sum(xts[, 1], INDEX) # ----------------------------------------------------------------------------- # timeBased is.timeBased <- function (x) { if (!any(sapply(c( "Date", "POSIXt", "chron", "dates", "times", "timeDate", "yearmon", "yearqtr", "xtime"), function(xx) inherits(x, xx)))) { ans <- FALSE } else { ans <- TRUE } ans } timeBased <- function(x) { is.timeBased(x) } # ----------------------------------------------------------------------------- alignDaily(x=time(tS), include.weekends=FALSE) alignMonthly(x=time(tS), include.weekends=FALSE) # error alignQuarterly(x=time(tS), include.weekends=FALSE) # error tD <- Sys.timeDate() + 1:1000 timeDate::align(tD, by="10s") timeDate::align(tD, by="60s") timeDate::align(tD, by="10m") # error td <- as.xts(Sys.time()) + 1:1000 xts::align.time(td, n=10) # every 10 seconds xts::align.time(td, n=60) # align to next whole minute xts::align.time(td, n=10*60) # align to next whole 10 min interval xts::shift.time(td, n=10) xts::shift.time(td, n=60) xts::shift.time(td) # ----------------------------------------------------------------------------- xts::to.minutes(x,k,name,...) xts::to.minutes3(x,name,...) xts::to.minutes5(x,name,...) xts::to.minutes10(x,name,...) xts::to.minutes15(x,name,...) xts::to.minutes30(x,name,...) xts::to.hourly(x,name,...) xts::to.daily(x,drop.time=TRUE,name,...) xts::to.weekly(x,drop.time=TRUE,name,...) xts::to.monthly(x,indexAt='yearmon',drop.time=TRUE,name,...) xts::to.quarterly(x,indexAt='yearqtr',drop.time=TRUE,name,...) xts::to.yearly(x,drop.time=TRUE,name,...) xts::to.period( x, period = 'months', k = 1, indexAt, name=NULL, OHLC = TRUE, ...) # ----------------------------------------------------------------------------- # Convert an object to a specified periodicity lower than the given data # object. For example, convert a daily series to a monthly series, or a # monthly series to a yearly one, or a one minute series to an hourly # series. data(sample_matrix) xts <- as.xts(sample_matrix) # is daily to.weekly(xts) to.monthly(xts) to.quarterly(xts) to.yearly(xts) tS <- as.timeSeries(sample_matrix) % ----------------------------------------------------------------------------- as.numeric(as.POSIXct(time(tS))) getFinCenter(tS) indexTZ(xts, ) tzone(xts, ) tzone(xts) <- "GMT" .index(xts, ) indexClass(xts) class(time(tS)) % ----------------------------------------------------------------------------- .index <- function(x) as.numeric(as.POSIXct(time(x))) .indexDate <- function(x) .index(x)%/%86400L .indexday <- function(x) .index(x)%/%86400L .indexmday <- function(x) as.POSIXlt(.POSIXct(.index(x)))$mday .indexwday <- function(x) as.POSIXlt(.POSIXct(.index(x)))$wday .indexweek <- function(x) .indexmon <- function(x) .indexyday <- function(x) .indexyear <- function(x) .indexhour <- function(x) .indexmin <- function(x) .indexsec <- function(x) # Atoms # atoms # Roll over fixed periods of length k point by point ... # Functions borrowed from zoo timeSeries::rollMin( x, k, na.pad = FALSE, align = c("center", "left", "right"), ...) timeSeries::rollMax( x, k, na.pad = FALSE, align = c("center", "left", "right"), ...) timeSeries::rollMean( x, k, na.pad = FALSE, align = c("center", "left", "right"), ...) timeSeries::rollMedian( x, k, na.pad = FALSE, align = c("center", "left", "right"), ...) timeSeries::rollStats( x, k, FUN = mean, na.pad = FALSE, align = c("center", "left", "right"), ...) # Roll over Calendarical periods: rollDailySeries(x, period="7d", FUN, ...) rollMonthlySeries(x, period="12m", by="1m", FUN, ...) # e.g. rollQuarterlySeries(x, period="12m", by="3m", FUN) # e.g. rollYearlySeries rollMonthlyWindows(x, period="12m", by="1m") # apply # applySeries # period.apply # Apply a specified function to data over a given interval, where the # interval is taken to be the data from INDEX[k] to INDEX[k+1], for # k=1:(length(INDEX)-1). # ----------------------------------------------------------------------------- timeSeries/inst/doc/0000755000176200001440000000000015117007154014106 5ustar liggesuserstimeSeries/inst/doc/timeSeriesPlot.Rnw0000644000176200001440000015272615104730074017563 0ustar liggesusers%\VignetteIndexEntry{Plotting 'timeSeries' Objects} \documentclass[10pt,a4paper]{article} \usepackage{hyperref} \hypersetup{colorlinks,% citecolor=black,% linkcolor=blue,% urlcolor=darkgreen,% } \title{\bf Plotting 'timeSeries' Objects} \author{Diethelm W\"urtz and Tobias Setz\\ETH Zurich and Rmetrics Association Zurich} \date{May 12, 2014} \begin{document} \SweaveOpts{concordance=TRUE} \maketitle \tableofcontents \setlength{\parskip}{20pt} %\SweaveOpts{strip.white=FALSE} \setkeys{Gin}{width=0.9\textwidth} % plot.ts <- function ( % x, y = NULL, plot.type = c("multiple", "single"), % xy.labels, xy.lines, panel = lines, nc, % yax.flip = FALSE, % mar.multi = c(0, 5.1, 0, if (yax.flip) 5.1 else 2.1), % oma.multi = c(6, 0, 5, 0), axes = TRUE, ...) % plot.zoo <- function ( % x, y = NULL, screens, plot.type, panel = lines, % xlab = "Index", ylab = NULL, main = NULL, % xlim = NULL, ylim = NULL, % xy.labels = FALSE, xy.lines = NULL, % yax.flip = FALSE, % oma = c(6, 0, 5, 0), % mar = c(0, 5.1, 0, if (yax.flip) 5.1 else 2.1), % col = 1, lty = 1, lwd = 1, pch = 1, type = "l", log = "", % nc, widths = 1, heights = 1, ...) % plot.xts <- function ( % x, y = NULL, type = "l", auto.grid = TRUE, % major.ticks = "auto", minor.ticks = TRUE, major.format = TRUE, % bar.col = "grey", candle.col = "white", % ann = TRUE, axes = TRUE, ...) % .plot.timeSeries <-function( % x, y, FinCenter = NULL, type = NULL, plot.type = c("multiple", "single"), % format = "auto", at = c("chic", "pretty"), % col, pch, cex, lty, lwd, % grid = FALSE, frame.plot = TRUE, panel = lines, % axes = TRUE, ann = TRUE, cex.axis = 1, cex.lab = 1, % yax.flip = FALSE, % mar.multi = c(0, 5.1, 0, if (yax.flip) 5.1 else 2.1), % oma.multi = c(7.75, 1.1, 6.1, 1.1), % ...) % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 1 \newpage\thispagestyle{empty}\mbox{} \pagebreak \section{Introduction} The Rmetrics \texttt{timeDate} and \texttt{timeSeries} packages are workhorses to deal with chronological objects. Since their inception 2009 under their original names \texttt{fCalendar} and \texttt{fSeries} they have been only slightly modified. With version R 3.1. we have essentially improved the \texttt{plot} function, but we also took care that the functionality is almost upward compatible. In this vignette we show how to work with the recently updated S4 generic plot function \texttt{plot}. The function is written to display Rmetrics S4 \texttt{timeSeries} objects. The basic functionality of the \texttt{plot} function is to display single and multiple views on univariae and multivariate \texttt{timeSeries} objects. The function \texttt{plot.ts} from R's base environment, which displays basic \texttt{ts} time series objects, served as a model for our design of the generic S4 \texttt{plot} function for \texttt{timeSeries} objects. Similarily, \texttt{plot.ts} can be considered as the prototype for the S3 \texttt{plot.zoo} method. The \texttt{xts} plot function was build to display univariate \texttt{xts} time series objects which inherit from \texttt{zoo}'s objects for ordered time series objects. The generic S4 time series plotting function can dispay \emph{univariate} and \emph{multivariate} time series in \emph{single} and \emph{multiple} frames. The plots can be tailored with respect to several viewing components: colors (col), line types (lty), plot symbols (pch), line widths (lwd), symbol sizes (cex), axis layout (pretty, chic, tailored), minor tick mark appearence, font styles and font sizes, frame positioning (mar, oma), as well as tailored panel functions (panel). \noindent\emph{General Plot Settings and Design Apects}: \noindent\emph{Plot Type}: Univariate time series are displayed by default in \texttt{plot.type="single"} frames, multivariate time series are displayed by default in \texttt{plot.type="multiple"} frames. The default line style for a plot is \texttt{type ="l"} is drawn with "lines". \noindent\emph{Time Axis Layout}: For the time axis layout the function \texttt{pretty} determines in an automative way the \texttt{at="pretty"} positions of the ticks. The \texttt{format="auto"} is extracted from the time stamps of the time series object or can be overwritten by the user with a POSIX format string. Alternatively, one can select \texttt{"chic"} to generate time axis styles. We called this method "chic" to give reference to the underlying function \texttt{axTicksByTime} from the Chicago \texttt{xts} package which generates tick positions and axis labels. Furthermore, a "tailored" method can be applied which allows for fully arbitrary user defined positions and formatted labels. Minor ticks can be added in several fashions. \noindent\emph{Annotations}: The annotations of the plots are reduced to the y-label. These are taken by default from the column names of the time series object. This gives the user the freedom to have full control about his views how the plot should be look like. Note, multivariate time series in single plots show the string \texttt{"Values"} as label on the y-axis. Main title, sub title, and the x-label on the time axis are not shown by default. We prefer and recommend to add these decorations calling the function \texttt{title}. This allows also much more flexibility compared to passing the arguments through the plot functions. All default annotations (including the y-label) can be suppressed setting the plot function argument to \texttt{ann=FALSE}. The argument \texttt{axes=FALSE} suppresses to draw both axes on the plot frame. \noindent\emph{Decorations}: There are several options to decorate the plot: These include colors (col), plotting symbols (pch), scaling factor of plotting characters and symbols (cex), line types (lty), and lindwidths (lwd). Note, all these parameters may be vectors of the same length as the number of time series, so that each series can be addressed to its own individual style, color, and size. A grid and the plot frame (box) can be added or suppresse specifying the arguments \texttt{grid} and \texttt{frame.plot} in the argument list of the \texttt{plot} function. \noindent\emph{Panel Function}: In the case of multiple plots the plot frames, are also called \emph{panels}. By default in each panel the appropriate curve is drawn calling R's \texttt{lines} function \texttt{panel=lines}. This function can be replaced by a user defined function. This offers a wide range of new views on your time series. So for example yo can show zero or any other reference lines on the panels, or you can add rugs to (return) charts, or you can add for an example an EMA indicator (or any other kind of indicator) to curves shown in individual panels. \noindent\emph{Example "timeSeries" Objects}: To demonstrate the wide range of options to dispaly S4 \texttt{timeSeries} objects, we use the the daily index values from the Swiss Pension Fund Benchmark \emph{LPP2005}. The time series is part of the \texttt{timeSeries} package. For this we have introduced some abbreviations: <>= Sys.setlocale("LC_ALL", "C") @ <>= require(timeSeries) require(xts) require(PerformanceAnalytics) require(fTrading) tS1 <- 100 * cumulated(LPP2005REC[, 1]) # SBI (univariate) tS2 <- 100 * cumulated(LPP2005REC[, 1:2]) # SBI & SPI (bivariate) tS3 <- 100 * cumulated(LPP2005REC[, 1:3]) # SBI, SPI, SWIIT (Swiss Market) tS6 <- 100 * cumulated(LPP2005REC[, 1:6]) # Swiss and Foreign Market Indexes @ % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 2 \newpage\thispagestyle{empty}\mbox{} \newpage\thispagestyle{empty}\mbox{} \pagebreak \section{Standard Time Series Plots} The \texttt{plot} function from the \texttt{timeSeries} package allows for five different views on standard plot layouts. These include \begin{itemize} \item Univeriate single plots \item Multivariate single plots \item One column multiple plots \item Two column multiple plots \item Scatter plots \end{itemize} \noindent The only argument we have to set is the \texttt{plot.type} parameter to determine the layout of the plot. The default value is \texttt{"multiple"}, and the alternative value is \texttt{"single"}. The arguments can be abbreviated as \texttt{"m"} or \texttt{"s"}, respectively. \noindent \emph{Univariate Single Plots} were designed to plot univariate \texttt{timeSeries} objects in one single graph frame. Nothing then the \texttt{timeSeries} object has to be specified, the \texttt{plot.type} is forced to \texttt{"s"}. \noindent \emph{Multivariate Single Plots} will be used when a set of multivariate \texttt{timeSeries} objects should be drawn in one common data frame. For this argument the vlue \texttt{plot.type="s"} has to be specified. \noindent \emph{One Column Multiple Plots} display multivariate \texttt{timeSeries} objects, such that each series is plotted in its own frame. Up to four series, the frames are displayed in one column, for more series the frames are arranged in a two colum column display. \noindent \emph{Two Column Multiple Plots} handel the case of more than four \texttt{timeSeries} objects. Then the the series are displayed in two colums. In total, the number of rows is not restricted. % ---------------------------------------------------------------------------- \pagebreak \subsection{Univariate Single Plots} The most simple time series plot shows an univariate curve in a single plot. The axis is designed from "pretty" positions calculated from R's base function \texttt{pretty}. The labels are printed in the ISO 8601 standard date/time format. <>= par(mfrow=c(1, 1)) plot(tS1) @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{The chart shows an univariate time series (here the Swiss Bond Index) in a single frame. For all plot options default values have been chosen. You can decorate the plot, making it more conveniant to your needs, e.g.: change the color (col), add a main title and x-label calling the function \texttt{title}, or remove the grid lines setting the argument \texttt{grid=FALSE}. You can also design the minor tick marks, setting instead of the value \texttt{"auto"} oe of the following spreads: \texttt{"day"}, the default, \texttt{"week"}, or \texttt{"month"}. } \end{figure} \end{center} % ---------------------------------------------------------------------------- \pagebreak \subsection*{} Two other plot function implementations for \texttt{xts} time series objects can be found in the contributesd R packages \texttt{xts} and \texttt{PerformanceAnalytics}. Let us compare how they generate plot positions and time label formats. \vspace{-0.3cm} <>= require(PerformanceAnalytics) par(mfrow=c(3, 1)) xts::plot.xts(as.xts(tS1)) PerformanceAnalytics::chart.TimeSeries(as.xts(tS1)) plot(tS1) @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{The group of the three charts shows an univariate time series in a single frame for the plot functions as implemented in the packages \texttt{xts}, \texttt{PerformanceAnalytics}, and \texttt{timeSeries}. For example in the case of daily time series records \texttt{xts} uses U.S. style labels whereas \texttt{PerformanceAnalytics} and \texttt{timeSeries} use ISO standard date labels \texttt{YYYY-mm-dd}. The plot decorations are those from default settings.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Multivariate Single Plots} Multivariate time series plots in a single panel are constructed by default in the way that the first curve is plotted calling the function \texttt{plot} and the remaining curves by calling the function \texttt{lines}. <>= par(mfrow=c(1, 1)) plot(tS3, plot.type="s") @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{This chart shows a multivariate time series in a single frame. Note, we have to set the argument \texttt{plot.type="s"}. Again, for all plot options the default settings have been used. You can decorate the plot, making it more conveniant to your needs, e.g.: change the color vector (col), add a main title and x-label calling the function \texttt{title}, or remove grid lines setting the argument \texttt{grid=FALSE}. Note, to change the color settings you can set the argument \texttt{col=1:3} which would result in "black", "red", "green" for the three curves, or you can just set the colors by name, or selecting them from a color palette.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection*{} Now let us compare the plot function from the \texttt{timeSeries} package with the \texttt{chart.TimeSeries} plotting function from the \texttt{PerformanceAnalytics} function. (Note, the \texttt(xts) has no multivariate plot function implemented.) <>= par(mfrow=c(2, 1)) require(PerformanceAnalytics) PerformanceAnalytics::chart.TimeSeries(as.xts(tS3)) plot(tS3, plot.type="s") @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{The two charts show a multivariate time series plotted in a single frame. We use for the plot the functions as implemented in the packages \texttt{PerformanceAnalytics}, and \texttt{timeSeries}.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Multiple Plots} Multiple plots enormously simplify the display of different curves in multiple panels. These are the ideal plots when it comes to the task to create a quick overview over several time series. Multiple plotting is exclusive to \texttt{timeSeries} objects, \texttt(xts) and \texttt{PerformanceAnalytics} offer no multiple plotting tool. <>= par(mfrow=c(1, 1)) plot(tS3, plot.type="m") @ \begin{center} \begin{figure}[h] <>= <> @ \caption{As long as we plot less than 4 time series in a multivariate frame, we get a one column layout. Annotations show by default only the y-labels which are taken from the colmun names of the time series to be drawn. Feel free to add main title, sub title, and x-label calling the function \texttt{title}}. \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection*{} For more than four curves the frames of the plot design are arranged in two columns. <>= par(mfrow=c(1, 1)) plot(tS6, plot.type="m") @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{The graph shows the layout how it is created for six curves. There are two columns with three panels to the left and also three panels to the right. Note, it is easily possible to adapt the margin sizes and the gap between the two columns of plots calling the function \texttt{mar} and \texttt{oma}.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection*{} If you like a design with a small gap between the panel rows, you can modify the \texttt{mar} parameter to introduce a small gap, here with a width of 0.3. Feel free to modify it. <>= par(mfrow=c(1, 1)) plot(tS3, plot.type="m", mar=c(gap=0.3, 5.1, gap=0.3, 2.1)) @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{One can use in a multiple plot the \texttt{mar} parameter setting to create a small gap between the rows of the individual charts. This lets a plot look more elegant.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Combining Single Plots} You can also create your own multiple panel plots. Just combine single panels in an array of rows and columns using the parameter settings for \texttt{mfrow}, \texttt{mfcol}, and \texttt{mar}. <>= par(mfrow=c(2, 1)) par(mar = c(bottom=1.5, 5.1, top=4, 2.1)) plot(tS2[, 1]) par(mar = c(bottom=4, 5.1, top=1.5, 2.1)) plot(tS2[, 2]) @ \begin{center} \begin{figure}[h] <>= <> @ \caption{One can use in single plots the \texttt{mfrow} and \texttt{mar} parameter settings to place single plots either row by row or column by column. Here, \texttt{mfrow} and \texttt{mfcol} to the job. In this case a vector of the form \texttt{c(nr, nc)} draws subsequent figures in an nr-by-nc array on the device by columns (mfcol) or rows (mfrow), respectively.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Layout of Single Plots} There is another option in R to create panel layouts, not necessarilly in an rectangular array. Have a look to the help page of the function \texttt{layout}, her comes a simple example. <>= nf <- layout(mat=matrix(c(1, 1, 2, 3), byrow = TRUE, nrow=2)) par(mar = c(bottom=2, 5.1, top=3, 2.1)) plot(tS3[, 1]) par(mar = c(bottom=3, 5.1, top=2, 1.1)) plot(tS3[, 2]) par(mar = c(bottom=3, 4.1, top=2, 2.1)) plot(tS3[, 3]) @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{With the function \texttt{layout} you can devide the plot device in rows and columns expressed in matrix form defined by the argument \texttt{mat}. } \end{figure} \end{center} %To be more specific, the graph \texttt{i} is allocated a region composed %from a subset of these rows and columns, based on the rows and columns %in which \texttt{i} occurs in the matrix \texttt{mat}. %The argument \texttt{layout.show(n)} plots (part of) the current layout, %namely the outlines of the next \texttt{n} figures. % ----------------------------------------------------------------------------- \pagebreak \subsection*{} In addition widths and heights of the layout can be different from row to row, and/or from column to column. The sizes are expressed by the arguments \texttt{widths} and \texttt{heights} of the function \texttt{layout}. <>= nf <- layout(mat=matrix(c(1, 1, 2, 3), byrow=TRUE, nrow=2), heights=c(2.5,1)) par(mar = c(bottom=2, 5.1, top=3, 2.1)) plot(tS3[, 1]) par(mar = c(bottom=3, 5.1, top=1.5, 1.1)) plot(tS3[, 2]) par(mar = c(bottom=3, 4.1, top=1.5, 2.1)) plot(tS3[, 3]) @ \begin{center} \begin{figure}[h] <>= <> @ \caption{With the function \texttt{layout} one can also define the widths and heights of the columns and rows.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Bivariate Scatter Plots} For historical reasons, like in the function \texttt{plot.ts}, there is also the option to create an scatter plot from two univariaye time series. Since this is not a "true" time series plot, we will not go in further detail for this display. <>= par(mfrow=c(1,1)) plot(tS2[, 1], tS2[, 2]) @ \begin{center} \begin{figure}[h] <>= <> @ \caption{If \texttt(x) and \texttt(y) are univariate time series, then the plot function will display a scatter plot.} \end{figure} \end{center} % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 3 \newpage\thispagestyle{empty}\mbox{} \newpage\thispagestyle{empty}\mbox{} \pagebreak \section{Time Axis Layout} The function \texttt{plot} comes with three options to design the time axis layout: \texttt{"pretty"}, \texttt{"chic"}, and \emph{} (note this not a string argument. \emph{} should just abbreviate that we have to input character strings of fully arbitray \texttt{at} positions. For the first two options the style of the axis annotation is generated in a fully automated way, whereas in the tailored case each tick on the axis to be user defined. \noindent The \emph{"pretty"} time axis layout is the default setting for the argument \texttt{at}. Internally the function \texttt{pretty} is used to compute a sequence of about \texttt{n+1} equally spaced round values which cover the range of the values in the time stamps \texttt{time(x)} of the series \texttt{x}. The values are chosen so that they are 1, 2 or 5 times a power of 10. \noindent The \emph{"chic"} time axis layout is the alternative setting for the argument \texttt{at}. Internally the function \texttt{axTicksByTime} from the package \texttt{xts} is used to compute the sequence of axis positions and the format labels. \noindent The \emph{} time axis layout leaves it to the user to specify by himself the positions (at), the time label formatting (format), and the minor tick marks (minor.ticks). % ----------------------------------------------------------------------------- \pagebreak \subsection{Time Axis: "pretty" or "chic"?} Our plotting function comes with two axis-styles. The first is called \texttt{"pretty"}, which is the default style, and calculates positions from R's base function \texttt{pretty}. The other is called \texttt{"chic"} to remember its origin, arising from the "Chicago" \texttt{xts} package. \vspace{-0.7cm} <>= par(mfcol = c(2, 1)) plot(tS1, at = "pretty") plot(tS1, at = "chic") @ \vspace{-0.3cm} \begin{center} \begin{figure}[h] <>= <> @ \caption{The graph shows the two flavours from the \texttt{at="pretty"} and the \texttt{"chic"} axis designs. The first style uses the function \texttt{pretty} from R's \texttt{base} environment to compute the positions for the major ticks. The second style uses the function \texttt{axTicksByTime} from the \texttt{xts} package to compute x-axis tick mark locations by time. In the upper graph the minor ticks are calendar days, whereas in the lower graph weekdays are drawn (therefore the small gaps between the minor ticks become visible). Note, the time series is in both cases an object of class \texttt{timeSeries}.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection*{} Now let us plot a multivariate 3-column time series in a single panel. Again we compare the outcome of the \texttt{"pretty"} and the \texttt{"chic"} axis style. <>= par(mfcol=c(2, 1)) plot(tS3, plot.type="s", at="pretty") plot(tS3, plot.type="s", at="chic") @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{The only difference of this graph compared to the previous is the fact that we consider here a multivariate time series. Three curves are shown in a common plot. Note, when using the \texttt{"chic"} style, then the vertical gridlines are narrower compared to the \texttt{"pretty"} style.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Time Axis - Selecting Minor Tick Marks} The \texttt{"pretty"} style allows to draw the minor tick marks on different time scales. These are: \texttt{"day"}, \texttt{"week"}, and \texttt{"month"}. <>= par(mfrow=c(3, 1)) plot(tS1, minor.ticks="day", at="pretty") plot(tS1, minor.ticks="week", at="pretty") plot(tS1, minor.ticks="month", at="pretty") @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{It is worth to note that a good selection of minor tick marks makes a plot much better readable.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Time Axis - One Column Multiple Plot Layout} In the multiple plot layout the axis are drawn along the same principles as they are drawn in the case of the single plot layout. <>= par(mfrow=c(1, 1)) plot(tS3, plot.type="m", at="pretty") @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{This graph shows a one column multiple plot layout. The one column layout is generated for up to four time series. When the multivariate time series has more then four time series then a two column layout is displayed. It is up to you which axis style you prefer, \texttt{at="pretty"} or \texttt{at="chic".}} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Time Axis - Two Column Multiple Plot Layout} Concerning the style of the axis, there is now difference between the one and two column plot designs. <>= par(mfrow=c(1, 1)) plot(tS6, plot.type="m", at="chic") @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{When we have more then four time series, then the display will be generated in two columns. Note, it is possible to modify the width of the gap between the two columns.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Tick and Format Layout: The axis style} The third alternative to style the axis offers the users to define format positions according to his preferences. Here comes an example: <>= par(mfrow=c(2, 1)) at <- paste0("200", c("6-01", "6-04", "6-07", "6-10", "7-01", "7-04"), "-01") plot(tS3, plot.type="s", format="%B\n%Y", at=at) plot(tS3, plot.type="s", format="%b/%y", at=at) @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{This graph shows plots with user tailored positions and formatted axis labels.} \end{figure} \end{center} % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 4 \newpage\thispagestyle{empty}\mbox{} %\newpage\thispagestyle{empty}\mbox{} \pagebreak \section{Annotations} Plot annotations are elelents which can be added to plots or completely discarded. To discard all annotations you have to set \texttt{ann=FALSE} in the argument list of the timSeries \texttt{plot} function. To display annotation you can call the function \texttt{title}. This allows to add the main title, the sub title, and the x- and y-labels to a plot. Together with the appropriate character strings, you can also specify the placement of these annotations by the arguments \texttt{line} and \texttt{outer}. There are additional functions to add annotations to a plot. These are \texttt{text} and \texttt{mtext}. % ----------------------------------------------------------------------------- \pagebreak \subsection{Discarding all Annotations} In a default plot we display only the value-label(s) which are taken from the units or column names of the time time series object. Title, sub title, and time-label are not shown. To discard the appearance of all annotations on a plot you have to set the plot argument \texttt{ann=FALSE}. <>= par(mfrow=c(2, 2)) plot(tS1, ann=FALSE) plot(tS3, plot.type="s", ann=FALSE, at="pretty") plot(tS6, plot.type="s", ann=FALSE, at="pretty") @ \begin{center} \begin{figure}[h] <>= <> @ \caption{This graph shows a plot where all annotations have been discarded. Now feel free to add your own annotations.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Adding Title and Labels} To add a main title, a sub, title, and x- and y-labels you can call the function \texttt{title}. <>= par(mfrow=c(2, 2)) plot(tS1); title(main = "Index") plot(tS3, plot.type="s"); title(main = "Index") plot(tS3, plot.type="s"); title(main = "Index", xlab = "Date") plot(tS6, plot.type="s"); title(main = "Index", xlab = "Date") @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{This graph displays in a two by two array four single plots. We have added title and x-lable annotations.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Changing Axis Font Size} Sometimes the axis font size may be considered as too small or too large. Then you can use the plot argument \texttt{cex.axis} to upsize or downsize the font. <>= par(mfrow=c(3, 1)) plot(tS3, at="chic", plot.type="s", cex.axis=0.75) plot(tS3, at="chic", plot.type="s", cex.axis=1.00) plot(tS3, at="chic", plot.type="s", cex.axis=1.25) @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{This is an example how to change the size of the axis labels relatively to its default value. The upper graph shows a font size decreased by 20\%, the lower graph a font size increased by 25\%. You can proceed in the same way when using the \texttt{"pretty"} axis style.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Flipping Value Axes} Flipping every second axis label in a multiple plot from left to rigth might be meaningful in the case when axis labels overwrite themselves. <>= par(mfrow=c(1, 1)) plot(tS3, plot.type="m", yax.flip = TRUE) @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{The graph shows an one column multiple plot, where the axis of the middle panel is flipped from the left to the right. Note, the same procedure can also be applied two two column multiple plots.} \end{figure} \end{center} % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 5 \newpage\thispagestyle{empty}\mbox{} %\newpage\thispagestyle{empty}\mbox{} \pagebreak \section{Decorations} There exist several options to decorate plots in different ways. Plot types (lines, points, horizontal bars, etc.) can be modified, colors can be changed, lines can be modified by style and seize, points can be selected by symbol and size. \noindent In the following we will give some examples \begin{itemize} \item Modifying Types \item Changing Colors by Names \item Changing Colors by Color Palettes \item Changing Line Styles \item Modifying Line Widths \item Changing Plot Symbols \item Modifying Plot Symbol Sizes \item Discarding Grid Lines \item Drawing a Box \end{itemize} \noindent to show a few of the many types of cdecorations. Play around to achieve your perfect layout. % ----------------------------------------------------------------------------- \pagebreak \subsection{Modifying Types} <>= par(mfrow=c(1, 1)) plot(tS3, plot.type="m", type=c("l", "p", "h"), at="pretty") @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{If we like to consider in a multiple plot for each panel its own plot style then we can set the parameter \texttt{type}.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Changing Colors by Names} Colors can be changed in several ways. Just by their numbers, e.g. 1 (black), 2 (red), 3 (green) etc., or by name, e.g. "black", "red", "green", etc. or by using well designed color palettes. <>= par(mfrow=c(1, 1)) plot(tS3, plot.type="m", col=c("blue", "orange", "darkgreen")) @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{This graph shows how to assign colors by name in the case of a multiple plot. You can do it in the same way setting \texttt{plot.type="s"} if you like to display all three curves in a common single plot.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Changing Colors by Color Palettes} When the number of curves increases, then it can become quite difficult to find a set of nice colors. In such cases it is convenient to select the colors from color palettes. <>= par(mfrow=c(1, 1)) plot(tS6, plot.type="s", col=heat.colors(n=6, alpha = 1), at="chic", format = "%B\n%Y") @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{This graph shows an example of six curves diplayed in a sequence of reds. For this we called the function \texttt{seqPalette}.} \end{figure} \end{center} \pagebreak \begin{verbatim} FUNCTION: COLOUR PALETTE rainbowPalette Contiguous rainbow colour palette heatPalette Contiguous heat colour palette terrainPalette Contiguous terrain colour palette topoPalette Contiguous topo colour palette cmPalette Contiguous cm colour palette greyPalette R's gamma-corrected gray palette timPalette Tim's MATLAB-like colour palette rampPalette Colour ramp palettes seqPalette Sequential colour brewer palettes divPalette Diverging colour brewer palettes qualiPalette Qualified colour brewer palettes focusPalette Red, green and blue focus palettes monoPalette Red, green and blue mono palettes \end{verbatim} % ----------------------------------------------------------------------------- \pagebreak \subsection{Changing Line Styles} In multiple plot to each curve an own line style \texttt{lty} can be assigned: 0 "blank", 1 "solid", 2 "dashed", 3 "dotted", 4 "dotdash", 5 "longdash", or 6 "twodash". <>= par(mfrow=c(1, 1)) plot(tS3, plot.type="m", col=1, lty=1:3, at="chic") @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{When we like to print plots in black and white, then its makes much sense to use different line types so that we can distinguish the curves one from each other.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Modifying Line Widths} Not only the line type, but also the line width can be modified for each curve in an individual kind. <>= par(mfrow=c(1, 1)) plot(tS3, plot.type="m", col=1, lwd=3:1, at="chic") @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{The graph shows three line widths, the upper's curve width is thick, the middle's curve width is medium, and the lowest's curve width is the thinnest one.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Changing Plot Symbols} To use different plot symbols we can assign them by the parameter \texttt{pch}. Don't forget also to set \texttt{type="p"}. %<>= %par(mfrow=c(1, 1)) %tS3weekly <- align(tS3, by="1w") %plot(tS3weekly, plot.type="s", type="p", col=1:3, pch=21:23, at="chic") %@ \medskip %\begin{center} %\begin{figure}[h] %<>= %<> %@ %\caption{This plot shows how to assign different plot symbols to the curves %in a single plot.} %\end{figure} %\end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Modifying Plot Symbol Sizes} The argument \texttt{cex.pch} allows to increase or decrease plot symbol sizes with respect to the current plot symbol size. <>= par(mfrow=c(1, 1)) plot(tS3, plot.type="s", type="p", col=1:3, pch=21:23, cex.pch=c(0.2, 0.2, 0.2), at="pretty") @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{This plot show how to change the size of plot symbols in a single plot setting the argument \texttt{cex.pch}. Note, for each curve its own size can be set. The same approach can be used also for multiple plots.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Discarding Grid Lines} By default grid lines are displayed. To discard the grid lines from the plot set the arguments \texttt{grid=FALSE}. <>= par(mfrow=c(1, 1)) plot(tS3, plot.type="s", grid=FALSE) @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{By default a grid is drawn on top of the plot. You can remove it by setting the argument \texttt{grid=FALSE}} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Drawing a Box} <>= par(mfrow=c(1, 1)) plot(tS3, plot.type="s", frame.plot=FALSE, grid=FALSE) box() box(bty = "7", col = "white") # boxL grid(NA, NULL, col = "darkgrey") # hgrid @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{By default the plots are displayed as frame plots. This means that the graphs are surrounded by a box. This box can be discarded setting the plot argument \texttt{frame.plot=FALSE}.} \end{figure} \end{center} % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 6 \newpage\thispagestyle{empty}\mbox{} %\newpage\thispagestyle{empty}\mbox{} \pagebreak \section{The Panel Function} Multiple plots are very powerful plotting designs. Each panel in a graph can individually tailored by the user. By default each curve in a panel is generated by the function \texttt{lines}. You can define your own panel function(s) by setting the plot argument \texttt{panel} to a user dfined functions. In the following we will show three examples. % ----------------------------------------------------------------------------- \pagebreak \subsection{Adding a Horizental Zero Line} In this example we show how to write a panel function which allows to add a horizontal zero line to each plot panel. <>= par(mfrow=c(1, 1)) lines2 <- function(X, Y, type, xlab, ylab, col, pch, lty, lwd, cex) { lines(x=X, y=Y, col=col) abline(h=0, col = "brown", lwd=2)} plot(returns(tS3), plot.type="m", col = .colorwheelPalette(3), panel=lines2, at="pretty") @ \begin{center} \begin{figure}[h] <>= <> @ \caption{This multiple plot shows panels with curves having a horizontal zero reference line.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Adding an Rug to Multiple Return Plots} This example shows how to add in each panel rugs to the righ Y-axis. <>= par(mfrow=c(1, 1)) lines2 <- function(X, Y, type, xlab, ylab, col, pch, lty, lwd, cex) { lines(x=X, y=Y, type="h", col=col) rug(Y, side=4, col="steelblue") } plot(returns(tS6), plot.type="m", col = .colorwheelPalette(6), panel=lines2, at="pretty") @ \begin{center} \begin{figure}[h] <>= <> @ \caption{This multiple plot shows panels with rugs on the right Y-axis.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Adding an EMA to Multiple Index Plots} This example shows how to add an EMA indicator to each plot panel. The \texttt{emaTA()} function is provided by the \texttt{fTrading} package. <>= par(mfrow=c(1, 1)) lines2 <- function(X, Y, type, xlab, ylab, col, pch, lty, lwd, cex) { lines(x=X, y=Y, type="l", col=col) lines(x=X, y=emaTA(Y), col="black") } plot(tS3, plot.type="m", col = .colorwheelPalette(3), panel=lines2, grid=TRUE, at="pretty") @ \begin{center} \begin{figure}[h] <>= <> @ \caption{A multiple graph with EMA indicators in each panel.} \end{figure} \end{center} % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 7 \newpage\thispagestyle{empty}\mbox{} \newpage\thispagestyle{empty}\mbox{} \pagebreak \section{Conclusions} The \texttt{plot} function in the \texttt{timeSeries} package is a very powerful tool to create plots from time series objects. This includes to display univariate and multivariate time series in single and multiple panels, to select from two styles for the time-axis or even to tailor positions and formats according to his own needs, and to modifiy annotations and decorations of plots. % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 8 \newpage\thispagestyle{empty}\mbox{} \pagebreak \section{Appendix} In the appendix we have summarized graphs and tables which are useful tools to create plots. We have reprinted the default color table from R, we have summarized several color palettes as available in the \texttt{fBasics} package and other contributed R packages, and two tables with font characters and plot symbols. % ----------------------------------------------------------------------------- \pagebreak \subsection{Margins: mar and oma} <>= # Plot: # - oma stands for 'Outer Margin Area' # - mar represents the 'figure Margins' # - The default size is c(5,4,4,2) + 0.1 # - The axes tick marks will go in the first lines par(mfrow=c(1, 1)) par(oma=c(3,3,3,3)) # all sides have 3 lines of space par(mar=c(5,4,4,2) + 0.1) plot(x=1:10, y=1:10, type="n", xlab="X", ylab="Y") # Add Text tot the Plot Part - red text(5,5, "Plot", col="red", cex=2) text(5,4, "text(5,5, \"Plot\", col=\"red\", cex=2)", col="red", cex=1) box("plot", col="red", lwd=2) # Add text to thebThe Figure Part - grey mtext("Margins", side=3, line=2, cex=1.5, col="grey") mtext("par(mar=c(5,4,4,2) + 0.1)", side=3, line=1, cex=1, col="grey") mtext("Line 0", side=3, line=0, adj=1.0, cex=1, col="grey") mtext(" 1", side=3, line=1, adj=1.0, cex=1, col="grey") mtext("Line 2", side=3, line=2, adj=1.0, cex=1, col="grey") mtext("Line 3", side=3, line=3, adj=1.0, cex=1, col="grey") mtext("Line 0", side=2, line=0, adj=1.0, cex=1, col="grey") mtext("Line 1", side=2, line=1, adj=1.0, cex=1, col="grey") mtext("Line 2", side=2, line=2, adj=1.0, cex=1, col="grey") mtext("Line 3", side=2, line=3, adj=1.0, cex=1, col="grey") box("figure", col="grey") # The title will fit in the third line on the top of the graph. title("Ttitle - Third Line") # Note 'outer=TRUE' moves us from the figure to the outer margins. mtext("Outer Margin Area", side=1, line=1, cex=1.8, col="brown", outer=TRUE) mtext("par(oma=c(3,3,3,3))", side=1, line=2, cex=1, col="orange", outer=TRUE) mtext("Line 0", side=1, line=0, adj=0.0, cex=0.8, col="orange", outer=TRUE) mtext("Line 1", side=1, line=1, adj=0.0, cex=1, col="orange", outer=TRUE) mtext("Line 2", side=1, line=2, adj=0.0, cex=1, col="orange", outer=TRUE) box("outer", col="orange") @ \pagebreak \subsection*{} \begin{center} <>= <> @ \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Character Table} \begin{center} \begin{figure}[h] \includegraphics[width=\textwidth]{characterTableFig.pdf} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Color Table} \begin{center} \begin{figure}[h] \includegraphics[width=\textwidth]{colorTableFig.pdf} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Color Palettes I} \medskip \begin{center} \begin{figure}[h] \includegraphics[width=\textwidth]{colorPalettes1Fig.pdf} \end{figure} \end{center} \pagebreak \subsection{Color Palettes II} \medskip \begin{center} \begin{figure}[h] \includegraphics[width=\textwidth]{colorPalettes2Fig.pdf} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Symbol Table} \begin{center} \begin{figure}[h] \includegraphics[width=\textwidth]{symbolTableFig.pdf} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Axis Style "pretty"} <>= FORMAT <- tS1@format FORMAT POSITIONS <- pretty(tS1) POSITIONS LABELS <- pretty(tS1) LABELS @ % ----------------------------------------------------------------------------- \pagebreak \subsection{Axis Style "chic"} <>= axTicksByTime <- function (x, ticks.on = "auto", k = 1, labels = TRUE, format.labels = TRUE, ends = TRUE, gt = 2, lt = 30) { if (timeBased(x)) x <- xts(rep(1, length(x)), x) tick.opts <- c("years", "months", "weeks", "days", "hours", "minutes", "seconds") tick.k.opts <- c(10, 5, 2, 1, 6, 1, 1, 1, 4, 2, 1, 30, 15, 1, 1) if (ticks.on %in% tick.opts) { cl <- ticks.on[1] ck <- k } else { tick.opts <- paste(rep(tick.opts, c(4, 2, 1, 1, 3, 3, 1)), tick.k.opts) is <- structure(rep(0, length(tick.opts)), .Names = tick.opts) for (i in 1:length(tick.opts)) { y <- strsplit(tick.opts[i], " ")[[1]] ep <- endpoints(x, y[1], as.numeric(y[2])) is[i] <- length(ep) - 1 if (is[i] > lt) break } nms <- rev(names(is)[which(is > gt & is < lt)])[1] cl <- strsplit(nms, " ")[[1]][1] ck <- as.numeric(strsplit(nms, " ")[[1]][2]) } if (is.null(cl)) ep <- NULL else ep <- endpoints(x, cl, ck) if (ends) ep <- ep + c(rep(1, length(ep) - 1), 0) if (labels) { if (is.logical(format.labels) || is.character(format.labels)) { unix <- ifelse(.Platform$OS.type == "unix", TRUE, FALSE) time.scale <- periodicity(x)$scale fmt <- ifelse(unix, "%n%b%n%Y", "%b %Y") if (time.scale == "weekly" | time.scale == "daily") fmt <- ifelse(unix, "%b %d%n%Y", "%b %d %Y") if (time.scale == "minute" | time.scale == "hourly") fmt <- ifelse(unix, "%b %d%n%H:%M", "%b %d %H:%M") if (time.scale == "seconds") fmt <- ifelse(unix, "%b %d%n%H:%M:%S", "%b %d %H:%M:%S") if (is.character(format.labels)) fmt <- format.labels names(ep) <- format(index(x)[ep], fmt) } else { names(ep) <- as.character(index(x)[ep]) } ep } } @ <>= ticks <- axTicksByTime(as.xts(tS1)) ticks @ % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \pagebreak \section*{About the Authors} % ----------------------------------------------------------------------------- % Diethelm Wuertz \noindent\textbf{Diethelm W\"urtz} is professor at the Institute for Theoretical Physics, ITP, and for the Curriculum Computational Science and Engineering, CSE, at the Swiss Federal Institute of Technology in Zurich. He teaches Econophysics at ITP and supervises seminars in Financial Engineering. Diethelm is senior partner of Finance Online, an ETH spin-off company in Zurich, and co-founder of the Rmetrics Association in Zurich.\\ % ----------------------------------------------------------------------------- % Tobias Setz \noindent \textbf{Tobias Setz} has a Bachelor and Master in Computational Science from ETH in Zurich and has contributed with his Thesis projects on wavelet analytics and Bayesian change point analytics to this handbook. He is now a PhD student in the Econophysics group at ETH Zurich at the Institute for Theoretical Physics.\\ % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \pagebreak \section*{About Rmetrics} \noindent\emph{Rmetrics Open Source Project} With hundreds of functions built on modern methods, the Rmetrics open source software combines exploratory data analysis, statistical modelling and rapid model prototyping. The R/Rmetrics packages are embedded in R, building an environment which creates a first class system for applications in teaching statistics and finance. Rmetrics covers Time Series Econometrics, Hypothesis Testing, GARCH Modelling and Volatility Forecasting, Extreme Value Theory and Copulae, Pricing of Derivatives, Portfolio Analysis, Design and Optimization, and much more. \noindent\emph{The Rmetrics Association}\\ is a non-profit taking association working in the public interest. The Rmetrics Association provides support for innovations in financial computing. We believe that the Rmetrics Open Source software has become a valuable educational tool and that it is worth ensuring its continued development and the development of future innovations in software for statistical and computational research in finance. Rmetrics provides a reference point for individuals and institutions that want to support or interact with the Rmetrics development community. Rmetrics encourages students to participate in Rmetrics' activities in the context of Student Internships. \noindent\emph{Rmetrics Software Evalution}\\ If you like to get an impression of the size and quality of the Open Source Rmetrics Program have a look on the Ohloh Rmetrics Software Evaluation. The Evalutions gives an overview about the Software Development (Code Analysis, Estimated Cost), the people behind it, and its community. \noindent\emph{Contributions to Rmetrics}\\ are coming from several educuational institutions world wide. These include the Rmetrics web site and documentation project supported by ITP/CSE ETH Zurich, the organization of Summerschools and Workshops supported by ITP/CSE ETH Zurich, the R-sig-Finance Help and Mailing List, supported by SfS ETH Zurich, the R-forge development server, supported by University of Economics in Vienna, CRAN Test and Distribution Server for R software, supported by University of Economics Vienna, the Debian Linux integration supported by the Debian Association. Many thanks to all behind these projects who gave us continuous support over the last years.\\ \noindent Rmetrics Association\\ www.rmetrics.org % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \pagebreak % References \begin{thebibliography}{99} \bibitem{zoo} Achim Zeileis and Gabor Grothendieck (2005): \emph{ zoo: S3 Infrastructure for Regular and Irregular Time Series.} Journal of Statistical Software, 14(6), 1-27. URL http://www.jstatsoft.org/v14/i06/ \bibitem{tseries} Adrian Trapletti and Kurt Hornik (2007): \emph{tseries: Time Series Analysis and Computational Finance.} R package version 0.10-11. \bibitem{rmetrics} Diethelm W\"urtz et al. (2007): \emph{Rmetrics: Rmetrics - Financial Engineering and Computational Finance.} R package version 260.72. http://www.rmetrics.org \bibitem{ISO} International Organization for Standardization (2004): \emph{ISO 8601: Data elements and interchage formats --- Information interchange --- Representation of dates and time} URL http://www.iso.org \bibitem{R} R Development Core Team: \emph{R: A Language and Environment for Statistical Computing}, R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org \bibitem{quantmod} Jeffrey A. Ryan (2008): \emph{quantmod: Quantitative Financial Modelling Framework.} R package version 0.3-5. URL http://www.quantmod.com URL http://r-forge.r-project.org/projects/quantmod \end{thebibliography} \end{document} timeSeries/inst/doc/timeSeriesPlot.R0000644000176200001440000006142215117007153017205 0ustar liggesusers### R code from vignette source 'timeSeriesPlot.Rnw' ################################################### ### code chunk number 1: environment ################################################### Sys.setlocale("LC_ALL", "C") ################################################### ### code chunk number 2: library ################################################### require(timeSeries) require(xts) require(PerformanceAnalytics) require(fTrading) tS1 <- 100 * cumulated(LPP2005REC[, 1]) # SBI (univariate) tS2 <- 100 * cumulated(LPP2005REC[, 1:2]) # SBI & SPI (bivariate) tS3 <- 100 * cumulated(LPP2005REC[, 1:3]) # SBI, SPI, SWIIT (Swiss Market) tS6 <- 100 * cumulated(LPP2005REC[, 1:6]) # Swiss and Foreign Market Indexes ################################################### ### code chunk number 3: univariateSingle ################################################### par(mfrow=c(1, 1)) plot(tS1) ################################################### ### code chunk number 4: univariateSinglePlot ################################################### par(mfrow=c(1, 1)) plot(tS1) ################################################### ### code chunk number 5: univariateSingle2 ################################################### require(PerformanceAnalytics) par(mfrow=c(3, 1)) xts::plot.xts(as.xts(tS1)) PerformanceAnalytics::chart.TimeSeries(as.xts(tS1)) plot(tS1) ################################################### ### code chunk number 6: univariateSingle2Plot ################################################### require(PerformanceAnalytics) par(mfrow=c(3, 1)) xts::plot.xts(as.xts(tS1)) PerformanceAnalytics::chart.TimeSeries(as.xts(tS1)) plot(tS1) ################################################### ### code chunk number 7: multivariateSingle ################################################### par(mfrow=c(1, 1)) plot(tS3, plot.type="s") ################################################### ### code chunk number 8: multivariateSinglePlot ################################################### par(mfrow=c(1, 1)) plot(tS3, plot.type="s") ################################################### ### code chunk number 9: multivariateSingle2 ################################################### par(mfrow=c(2, 1)) require(PerformanceAnalytics) PerformanceAnalytics::chart.TimeSeries(as.xts(tS3)) plot(tS3, plot.type="s") ################################################### ### code chunk number 10: multivariateSingle2Plot ################################################### par(mfrow=c(2, 1)) require(PerformanceAnalytics) PerformanceAnalytics::chart.TimeSeries(as.xts(tS3)) plot(tS3, plot.type="s") ################################################### ### code chunk number 11: oneColMultiple ################################################### par(mfrow=c(1, 1)) plot(tS3, plot.type="m") ################################################### ### code chunk number 12: oneColMultiplePlot ################################################### par(mfrow=c(1, 1)) plot(tS3, plot.type="m") ################################################### ### code chunk number 13: twoColMultiple ################################################### par(mfrow=c(1, 1)) plot(tS6, plot.type="m") ################################################### ### code chunk number 14: twoColMultiplePlot ################################################### par(mfrow=c(1, 1)) plot(tS6, plot.type="m") ################################################### ### code chunk number 15: gapMultiple ################################################### par(mfrow=c(1, 1)) plot(tS3, plot.type="m", mar=c(gap=0.3, 5.1, gap=0.3, 2.1)) ################################################### ### code chunk number 16: gapMultiplePlot ################################################### par(mfrow=c(1, 1)) plot(tS3, plot.type="m", mar=c(gap=0.3, 5.1, gap=0.3, 2.1)) ################################################### ### code chunk number 17: combineSingle ################################################### par(mfrow=c(2, 1)) par(mar = c(bottom=1.5, 5.1, top=4, 2.1)) plot(tS2[, 1]) par(mar = c(bottom=4, 5.1, top=1.5, 2.1)) plot(tS2[, 2]) ################################################### ### code chunk number 18: combineSinglePlot ################################################### par(mfrow=c(2, 1)) par(mar = c(bottom=1.5, 5.1, top=4, 2.1)) plot(tS2[, 1]) par(mar = c(bottom=4, 5.1, top=1.5, 2.1)) plot(tS2[, 2]) ################################################### ### code chunk number 19: layoutSingle ################################################### nf <- layout(mat=matrix(c(1, 1, 2, 3), byrow = TRUE, nrow=2)) par(mar = c(bottom=2, 5.1, top=3, 2.1)) plot(tS3[, 1]) par(mar = c(bottom=3, 5.1, top=2, 1.1)) plot(tS3[, 2]) par(mar = c(bottom=3, 4.1, top=2, 2.1)) plot(tS3[, 3]) ################################################### ### code chunk number 20: layoutSinglePlot ################################################### nf <- layout(mat=matrix(c(1, 1, 2, 3), byrow = TRUE, nrow=2)) par(mar = c(bottom=2, 5.1, top=3, 2.1)) plot(tS3[, 1]) par(mar = c(bottom=3, 5.1, top=2, 1.1)) plot(tS3[, 2]) par(mar = c(bottom=3, 4.1, top=2, 2.1)) plot(tS3[, 3]) ################################################### ### code chunk number 21: layout2Single ################################################### nf <- layout(mat=matrix(c(1, 1, 2, 3), byrow=TRUE, nrow=2), heights=c(2.5,1)) par(mar = c(bottom=2, 5.1, top=3, 2.1)) plot(tS3[, 1]) par(mar = c(bottom=3, 5.1, top=1.5, 1.1)) plot(tS3[, 2]) par(mar = c(bottom=3, 4.1, top=1.5, 2.1)) plot(tS3[, 3]) ################################################### ### code chunk number 22: layout2SinglePlot ################################################### nf <- layout(mat=matrix(c(1, 1, 2, 3), byrow=TRUE, nrow=2), heights=c(2.5,1)) par(mar = c(bottom=2, 5.1, top=3, 2.1)) plot(tS3[, 1]) par(mar = c(bottom=3, 5.1, top=1.5, 1.1)) plot(tS3[, 2]) par(mar = c(bottom=3, 4.1, top=1.5, 2.1)) plot(tS3[, 3]) ################################################### ### code chunk number 23: scatter ################################################### par(mfrow=c(1,1)) plot(tS2[, 1], tS2[, 2]) ################################################### ### code chunk number 24: scatterPlot ################################################### par(mfrow=c(1,1)) plot(tS2[, 1], tS2[, 2]) ################################################### ### code chunk number 25: pretty ################################################### par(mfcol = c(2, 1)) plot(tS1, at = "pretty") plot(tS1, at = "chic") ################################################### ### code chunk number 26: prettyPlot ################################################### par(mfcol = c(2, 1)) plot(tS1, at = "pretty") plot(tS1, at = "chic") ################################################### ### code chunk number 27: chicUnivariateSingle ################################################### par(mfcol=c(2, 1)) plot(tS3, plot.type="s", at="pretty") plot(tS3, plot.type="s", at="chic") ################################################### ### code chunk number 28: chicUnivariateSinglePlot ################################################### par(mfcol=c(2, 1)) plot(tS3, plot.type="s", at="pretty") plot(tS3, plot.type="s", at="chic") ################################################### ### code chunk number 29: minorTicks ################################################### par(mfrow=c(3, 1)) plot(tS1, minor.ticks="day", at="pretty") plot(tS1, minor.ticks="week", at="pretty") plot(tS1, minor.ticks="month", at="pretty") ################################################### ### code chunk number 30: minorTicksPlot ################################################### par(mfrow=c(3, 1)) plot(tS1, minor.ticks="day", at="pretty") plot(tS1, minor.ticks="week", at="pretty") plot(tS1, minor.ticks="month", at="pretty") ################################################### ### code chunk number 31: chicOneColMultiple ################################################### par(mfrow=c(1, 1)) plot(tS3, plot.type="m", at="pretty") ################################################### ### code chunk number 32: chicOneColMultiplePlot ################################################### par(mfrow=c(1, 1)) plot(tS3, plot.type="m", at="pretty") ################################################### ### code chunk number 33: chicTwoColMultiple ################################################### par(mfrow=c(1, 1)) plot(tS6, plot.type="m", at="chic") ################################################### ### code chunk number 34: chicTwoColMultiplePlot ################################################### par(mfrow=c(1, 1)) plot(tS6, plot.type="m", at="chic") ################################################### ### code chunk number 35: tailoredAxis ################################################### par(mfrow=c(2, 1)) at <- paste0("200", c("6-01", "6-04", "6-07", "6-10", "7-01", "7-04"), "-01") plot(tS3, plot.type="s", format="%B\n%Y", at=at) plot(tS3, plot.type="s", format="%b/%y", at=at) ################################################### ### code chunk number 36: tailoredAxisPlot ################################################### par(mfrow=c(2, 1)) at <- paste0("200", c("6-01", "6-04", "6-07", "6-10", "7-01", "7-04"), "-01") plot(tS3, plot.type="s", format="%B\n%Y", at=at) plot(tS3, plot.type="s", format="%b/%y", at=at) ################################################### ### code chunk number 37: annSingle ################################################### par(mfrow=c(2, 2)) plot(tS1, ann=FALSE) plot(tS3, plot.type="s", ann=FALSE, at="pretty") plot(tS6, plot.type="s", ann=FALSE, at="pretty") ################################################### ### code chunk number 38: annSinglePlot ################################################### par(mfrow=c(2, 2)) plot(tS1, ann=FALSE) plot(tS3, plot.type="s", ann=FALSE, at="pretty") plot(tS6, plot.type="s", ann=FALSE, at="pretty") ################################################### ### code chunk number 39: titleSingle ################################################### par(mfrow=c(2, 2)) plot(tS1); title(main = "Index") plot(tS3, plot.type="s"); title(main = "Index") plot(tS3, plot.type="s"); title(main = "Index", xlab = "Date") plot(tS6, plot.type="s"); title(main = "Index", xlab = "Date") ################################################### ### code chunk number 40: titleSinglePlot ################################################### par(mfrow=c(2, 2)) plot(tS1); title(main = "Index") plot(tS3, plot.type="s"); title(main = "Index") plot(tS3, plot.type="s"); title(main = "Index", xlab = "Date") plot(tS6, plot.type="s"); title(main = "Index", xlab = "Date") ################################################### ### code chunk number 41: axisFontSize ################################################### par(mfrow=c(3, 1)) plot(tS3, at="chic", plot.type="s", cex.axis=0.75) plot(tS3, at="chic", plot.type="s", cex.axis=1.00) plot(tS3, at="chic", plot.type="s", cex.axis=1.25) ################################################### ### code chunk number 42: axisFontSizePlot ################################################### par(mfrow=c(3, 1)) plot(tS3, at="chic", plot.type="s", cex.axis=0.75) plot(tS3, at="chic", plot.type="s", cex.axis=1.00) plot(tS3, at="chic", plot.type="s", cex.axis=1.25) ################################################### ### code chunk number 43: flipAxisOne ################################################### par(mfrow=c(1, 1)) plot(tS3, plot.type="m", yax.flip = TRUE) ################################################### ### code chunk number 44: flipAxisOnePlot ################################################### par(mfrow=c(1, 1)) plot(tS3, plot.type="m", yax.flip = TRUE) ################################################### ### code chunk number 45: typeMultiple ################################################### par(mfrow=c(1, 1)) plot(tS3, plot.type="m", type=c("l", "p", "h"), at="pretty") ################################################### ### code chunk number 46: typeMultiplePlot ################################################### par(mfrow=c(1, 1)) plot(tS3, plot.type="m", type=c("l", "p", "h"), at="pretty") ################################################### ### code chunk number 47: colorNamesMultiple ################################################### par(mfrow=c(1, 1)) plot(tS3, plot.type="m", col=c("blue", "orange", "darkgreen")) ################################################### ### code chunk number 48: colorNamesMultiplePlot ################################################### par(mfrow=c(1, 1)) plot(tS3, plot.type="m", col=c("blue", "orange", "darkgreen")) ################################################### ### code chunk number 49: palettesMultiple ################################################### par(mfrow=c(1, 1)) plot(tS6, plot.type="s", col=heat.colors(n=6, alpha = 1), at="chic", format = "%B\n%Y") ################################################### ### code chunk number 50: palettesMultiplePlot ################################################### par(mfrow=c(1, 1)) plot(tS6, plot.type="s", col=heat.colors(n=6, alpha = 1), at="chic", format = "%B\n%Y") ################################################### ### code chunk number 51: ltyMultiple ################################################### par(mfrow=c(1, 1)) plot(tS3, plot.type="m", col=1, lty=1:3, at="chic") ################################################### ### code chunk number 52: ltyMultiplePlot ################################################### par(mfrow=c(1, 1)) plot(tS3, plot.type="m", col=1, lty=1:3, at="chic") ################################################### ### code chunk number 53: lwdMultiple ################################################### par(mfrow=c(1, 1)) plot(tS3, plot.type="m", col=1, lwd=3:1, at="chic") ################################################### ### code chunk number 54: lwdMultiplePlot ################################################### par(mfrow=c(1, 1)) plot(tS3, plot.type="m", col=1, lwd=3:1, at="chic") ################################################### ### code chunk number 55: symbolsSizeMultiple ################################################### par(mfrow=c(1, 1)) plot(tS3, plot.type="s", type="p", col=1:3, pch=21:23, cex.pch=c(0.2, 0.2, 0.2), at="pretty") ################################################### ### code chunk number 56: symbolsSizeMultiplePlot ################################################### par(mfrow=c(1, 1)) plot(tS3, plot.type="s", type="p", col=1:3, pch=21:23, cex.pch=c(0.2, 0.2, 0.2), at="pretty") ################################################### ### code chunk number 57: gridSingle ################################################### par(mfrow=c(1, 1)) plot(tS3, plot.type="s", grid=FALSE) ################################################### ### code chunk number 58: gridSinglePlot ################################################### par(mfrow=c(1, 1)) plot(tS3, plot.type="s", grid=FALSE) ################################################### ### code chunk number 59: noBoxSingle ################################################### par(mfrow=c(1, 1)) plot(tS3, plot.type="s", frame.plot=FALSE, grid=FALSE) box() box(bty = "7", col = "white") # boxL grid(NA, NULL, col = "darkgrey") # hgrid ################################################### ### code chunk number 60: gridSinglePlot ################################################### par(mfrow=c(1, 1)) plot(tS3, plot.type="s", grid=FALSE) ################################################### ### code chunk number 61: horizMultiple ################################################### par(mfrow=c(1, 1)) lines2 <- function(X, Y, type, xlab, ylab, col, pch, lty, lwd, cex) { lines(x=X, y=Y, col=col) abline(h=0, col = "brown", lwd=2)} plot(returns(tS3), plot.type="m", col = .colorwheelPalette(3), panel=lines2, at="pretty") ################################################### ### code chunk number 62: horizMultiplePlot ################################################### par(mfrow=c(1, 1)) lines2 <- function(X, Y, type, xlab, ylab, col, pch, lty, lwd, cex) { lines(x=X, y=Y, col=col) abline(h=0, col = "brown", lwd=2)} plot(returns(tS3), plot.type="m", col = .colorwheelPalette(3), panel=lines2, at="pretty") ################################################### ### code chunk number 63: rugMultiple ################################################### par(mfrow=c(1, 1)) lines2 <- function(X, Y, type, xlab, ylab, col, pch, lty, lwd, cex) { lines(x=X, y=Y, type="h", col=col) rug(Y, side=4, col="steelblue") } plot(returns(tS6), plot.type="m", col = .colorwheelPalette(6), panel=lines2, at="pretty") ################################################### ### code chunk number 64: rugMultiplePlot ################################################### par(mfrow=c(1, 1)) lines2 <- function(X, Y, type, xlab, ylab, col, pch, lty, lwd, cex) { lines(x=X, y=Y, type="h", col=col) rug(Y, side=4, col="steelblue") } plot(returns(tS6), plot.type="m", col = .colorwheelPalette(6), panel=lines2, at="pretty") ################################################### ### code chunk number 65: emaMultiple ################################################### par(mfrow=c(1, 1)) lines2 <- function(X, Y, type, xlab, ylab, col, pch, lty, lwd, cex) { lines(x=X, y=Y, type="l", col=col) lines(x=X, y=emaTA(Y), col="black") } plot(tS3, plot.type="m", col = .colorwheelPalette(3), panel=lines2, grid=TRUE, at="pretty") ################################################### ### code chunk number 66: emaMultiplePlot ################################################### par(mfrow=c(1, 1)) lines2 <- function(X, Y, type, xlab, ylab, col, pch, lty, lwd, cex) { lines(x=X, y=Y, type="l", col=col) lines(x=X, y=emaTA(Y), col="black") } plot(tS3, plot.type="m", col = .colorwheelPalette(3), panel=lines2, grid=TRUE, at="pretty") ################################################### ### code chunk number 67: margins ################################################### # Plot: # - oma stands for 'Outer Margin Area' # - mar represents the 'figure Margins' # - The default size is c(5,4,4,2) + 0.1 # - The axes tick marks will go in the first lines par(mfrow=c(1, 1)) par(oma=c(3,3,3,3)) # all sides have 3 lines of space par(mar=c(5,4,4,2) + 0.1) plot(x=1:10, y=1:10, type="n", xlab="X", ylab="Y") # Add Text tot the Plot Part - red text(5,5, "Plot", col="red", cex=2) text(5,4, "text(5,5, \"Plot\", col=\"red\", cex=2)", col="red", cex=1) box("plot", col="red", lwd=2) # Add text to thebThe Figure Part - grey mtext("Margins", side=3, line=2, cex=1.5, col="grey") mtext("par(mar=c(5,4,4,2) + 0.1)", side=3, line=1, cex=1, col="grey") mtext("Line 0", side=3, line=0, adj=1.0, cex=1, col="grey") mtext(" 1", side=3, line=1, adj=1.0, cex=1, col="grey") mtext("Line 2", side=3, line=2, adj=1.0, cex=1, col="grey") mtext("Line 3", side=3, line=3, adj=1.0, cex=1, col="grey") mtext("Line 0", side=2, line=0, adj=1.0, cex=1, col="grey") mtext("Line 1", side=2, line=1, adj=1.0, cex=1, col="grey") mtext("Line 2", side=2, line=2, adj=1.0, cex=1, col="grey") mtext("Line 3", side=2, line=3, adj=1.0, cex=1, col="grey") box("figure", col="grey") # The title will fit in the third line on the top of the graph. title("Ttitle - Third Line") # Note 'outer=TRUE' moves us from the figure to the outer margins. mtext("Outer Margin Area", side=1, line=1, cex=1.8, col="brown", outer=TRUE) mtext("par(oma=c(3,3,3,3))", side=1, line=2, cex=1, col="orange", outer=TRUE) mtext("Line 0", side=1, line=0, adj=0.0, cex=0.8, col="orange", outer=TRUE) mtext("Line 1", side=1, line=1, adj=0.0, cex=1, col="orange", outer=TRUE) mtext("Line 2", side=1, line=2, adj=0.0, cex=1, col="orange", outer=TRUE) box("outer", col="orange") ################################################### ### code chunk number 68: marginsPlot ################################################### # Plot: # - oma stands for 'Outer Margin Area' # - mar represents the 'figure Margins' # - The default size is c(5,4,4,2) + 0.1 # - The axes tick marks will go in the first lines par(mfrow=c(1, 1)) par(oma=c(3,3,3,3)) # all sides have 3 lines of space par(mar=c(5,4,4,2) + 0.1) plot(x=1:10, y=1:10, type="n", xlab="X", ylab="Y") # Add Text tot the Plot Part - red text(5,5, "Plot", col="red", cex=2) text(5,4, "text(5,5, \"Plot\", col=\"red\", cex=2)", col="red", cex=1) box("plot", col="red", lwd=2) # Add text to thebThe Figure Part - grey mtext("Margins", side=3, line=2, cex=1.5, col="grey") mtext("par(mar=c(5,4,4,2) + 0.1)", side=3, line=1, cex=1, col="grey") mtext("Line 0", side=3, line=0, adj=1.0, cex=1, col="grey") mtext(" 1", side=3, line=1, adj=1.0, cex=1, col="grey") mtext("Line 2", side=3, line=2, adj=1.0, cex=1, col="grey") mtext("Line 3", side=3, line=3, adj=1.0, cex=1, col="grey") mtext("Line 0", side=2, line=0, adj=1.0, cex=1, col="grey") mtext("Line 1", side=2, line=1, adj=1.0, cex=1, col="grey") mtext("Line 2", side=2, line=2, adj=1.0, cex=1, col="grey") mtext("Line 3", side=2, line=3, adj=1.0, cex=1, col="grey") box("figure", col="grey") # The title will fit in the third line on the top of the graph. title("Ttitle - Third Line") # Note 'outer=TRUE' moves us from the figure to the outer margins. mtext("Outer Margin Area", side=1, line=1, cex=1.8, col="brown", outer=TRUE) mtext("par(oma=c(3,3,3,3))", side=1, line=2, cex=1, col="orange", outer=TRUE) mtext("Line 0", side=1, line=0, adj=0.0, cex=0.8, col="orange", outer=TRUE) mtext("Line 1", side=1, line=1, adj=0.0, cex=1, col="orange", outer=TRUE) mtext("Line 2", side=1, line=2, adj=0.0, cex=1, col="orange", outer=TRUE) box("outer", col="orange") ################################################### ### code chunk number 69: prettyAppendix ################################################### FORMAT <- tS1@format FORMAT POSITIONS <- pretty(tS1) POSITIONS LABELS <- pretty(tS1) LABELS ################################################### ### code chunk number 70: axTicks ################################################### axTicksByTime <- function (x, ticks.on = "auto", k = 1, labels = TRUE, format.labels = TRUE, ends = TRUE, gt = 2, lt = 30) { if (timeBased(x)) x <- xts(rep(1, length(x)), x) tick.opts <- c("years", "months", "weeks", "days", "hours", "minutes", "seconds") tick.k.opts <- c(10, 5, 2, 1, 6, 1, 1, 1, 4, 2, 1, 30, 15, 1, 1) if (ticks.on %in% tick.opts) { cl <- ticks.on[1] ck <- k } else { tick.opts <- paste(rep(tick.opts, c(4, 2, 1, 1, 3, 3, 1)), tick.k.opts) is <- structure(rep(0, length(tick.opts)), .Names = tick.opts) for (i in 1:length(tick.opts)) { y <- strsplit(tick.opts[i], " ")[[1]] ep <- endpoints(x, y[1], as.numeric(y[2])) is[i] <- length(ep) - 1 if (is[i] > lt) break } nms <- rev(names(is)[which(is > gt & is < lt)])[1] cl <- strsplit(nms, " ")[[1]][1] ck <- as.numeric(strsplit(nms, " ")[[1]][2]) } if (is.null(cl)) ep <- NULL else ep <- endpoints(x, cl, ck) if (ends) ep <- ep + c(rep(1, length(ep) - 1), 0) if (labels) { if (is.logical(format.labels) || is.character(format.labels)) { unix <- ifelse(.Platform$OS.type == "unix", TRUE, FALSE) time.scale <- periodicity(x)$scale fmt <- ifelse(unix, "%n%b%n%Y", "%b %Y") if (time.scale == "weekly" | time.scale == "daily") fmt <- ifelse(unix, "%b %d%n%Y", "%b %d %Y") if (time.scale == "minute" | time.scale == "hourly") fmt <- ifelse(unix, "%b %d%n%H:%M", "%b %d %H:%M") if (time.scale == "seconds") fmt <- ifelse(unix, "%b %d%n%H:%M:%S", "%b %d %H:%M:%S") if (is.character(format.labels)) fmt <- format.labels names(ep) <- format(index(x)[ep], fmt) } else { names(ep) <- as.character(index(x)[ep]) } ep } } ################################################### ### code chunk number 71: axTicks2 ################################################### ticks <- axTicksByTime(as.xts(tS1)) ticks timeSeries/inst/doc/timeSeriesRefCard.pdf0000644000176200001440000032713215104730074020151 0ustar liggesusers%PDF-1.3 %Äåòåë§ó ÐÄÆ 4 0 obj << /Length 5 0 R /Filter /FlateDecode >> stream xí]kwä¸qýÞ¿‚¶F³’WÓÃg“mgÄëÝØNr'“äƒ×'GÖhÖræaK3Nüïs UxAªeÇ9Þ=gZÝ$‹`áÖ­B¡þ®úyõ»ªëöÝ¡êÛf˜ªvöS_u}[ÝßVÿQ½¯^~ùÐT7U½¦º;¶-þj›f<ô#ýE?5‡Ýà $ák_Õô?ýÑ û¡©ë¢Ç}[ݼ«~ô Gê"^ÝTS§N•W流_7û¦jv¯ÞT¿¨.¾óÝËêE[]œ=»¬šêâüùe…oŸ}s¡¾~s©>¾sö=õyv~öùggòãÕ ¾b¹ûeõêgÕW¯Ôcúk»ã~j½×¸T“ªWov$ýÕoX©­™ª¦­†ñPÐÛ\Yus<Ô“zF(§kŽÐVòÐîÇ–”¼Ãuo¸â' •Š[‘{Ø÷ãñ8±p¨ÿGú§ï»}?ÔÇjì{´ÔvPÓS5ÒzùÜ©.j©‹ ÝEèš3t>ž?Cát}ýì²J«8 ¯G}ïß;¸cåßñ‚ú~©v®Zó3Xîu]Uw<îуö±Ç‰»¬ñ?Ìú›K†Ã’š4TŸ_V}uqõb¯@ú˪SjÀo/•2^|v¹zåìZ 5á8´„|‹lÔr*ŸôB¾ñi߻ܱ–ÍårêÇ}Y ·ˆÿv—&šÖïÐznu«îËçzmp˜ëè©ù@§®kFñóñÑž¿x×qÛøN¬‚žÆ<æÏQó nu`¼ü1ñ‡\Êêt»S s9ª.“ XÁ¬X¾X° ø:~ÄgJ?7ûûÒn>ëK‘ëõ5d ü¨,žoøRÉRýš6’¾öM?s¤v%Hý·%ÃD[_@ŸÔÖ‰uŒÑV:%Ó1<}PÓUs3ÄÙûc?ùmòlÆ7ÛÝŬՆ¶ØŽûcwè|iä#ÊžPMô¯bsîý(gÎcË_d ô\÷ǃßRï¹®˜Ñü,.o÷ÓXµp]$Ÿ;÷ìhwpY¡£y7íÝq:‚³&ò+B¦Åcúà¶)ÿJäí9³°S¬¸Ã´ï~œÈoïúFÉ•ë·Å)¼„6Á €ù‹@G^– «°ÎÐ`;>ã hS¤Èg°½µŸÃJp& <ò‰Ýç„K\×»¤ÂŒ˜6š®[£øƒš/ž%ÒèN{2²íVÖ9F÷ƒ«ªæH®¤=*UɇRU/wÝ©‡l’Ì@JOýü»y·‰'ª‡fh[é»y÷ànÖ÷`ƒÖvóXÃ÷,ÙÈRÁÙ¼åT´\;A0yš¾FD<`/÷ÙÆ^9Ý ô‰Y,6ûÖd}w7ìâ ö5E˜¯*{ƒPýënZ2IÈÐ(iå~¼{wû¯·÷w·iù±Ç„~sÄ{Ê`Ò´B3mmZÉ„ØÉ–mëAC]âj2E(Û ðÃßÏ•9òïl…bw|X¾È-.ÔÑ·º`ÀOrÚ¼¥ýC¦/|Âs;ßhï¶CN0M0ØÚ…ýÕ6ÐKFîà ¢Póä–w¼ƒæ°Ç^4ê *iÆJ®ŒT^>ri¨Bf½(EYkã~^ƒâÌÍÛ€ØýpD4îöj䉊 bͤ÷½&Á,F¹ìY;Ù øõ§wïþ¶"·¾‡?ì\ÙWA šÖ™t…ÿÒ è´=`·A´RÂ]45ã†ÎÞ¶ªÂƬ½]‚݌݀1Z8c ›­RSÆwU›¦Š®S—ß… ä·«3pd5Ÿ+¾ðNá#«YÒpbŸ« ¦éƒ@hÆMÍTšb&ØmÕ»µÂ¶Þ#Õpón'nÙ«CD¯C·‚à¯ïÞþa‡-Hi@v¨SO ·Ùª‡a£ÿt8ìÄ»±oú#¢ašcƒhOÑŒ£®­Ê·`lZÄeë“cCë6þööã¯?^ÿóõýÇ4ß;HbìÞa«", ]iÊ7"NŒí.NH ë˜›xà®™…ò2v”¶Äç^ð„@ ¿] ½ñ؆ , Ì…r‘¾XÎPqLè3 ¢è*>AQÔ&¤è_ùntÝþx@6ð$Š·¬À®`‚Ö!¯—üP‚@ë<€Ž0Â.ãïû 0÷µ„ጀ„æ`Ègx`¼bIŒÉ%ÿjCy…3› `'Ø,Þ*DvÜöÈitŽî¶v„d{¤ˆn-"Ó„e(q‹à»‡úÉ?|™ ?G¤÷ûCÝ#½à4ûñJ˜0Kó4qr»AôžõVòÔc¢jDbÇ“¾U Æ+xÒŠ½Â4ÝZß`ìP'¹€ó¨Eñ‰,€@@á$SYáî¢97b)°¥Œ'~Á#›”m€¥Á¨eQ]ÆÙ]ü0­üæH`iJ]‡ûk ꙸÜqqd¤±z{ÐÝ,ƒóH7ÏÛé·˜ì<Òùª”zJ® õÿFÈëosšnÛd>v¢éX‹9£6oql¢²ÅiY»2"‘¦4MAslpbäÒà³2W.È„Ñ9¸â0TS1ÇNLƒ¨¥ä«¦‰©ÿcfY ~•uKƒÈò/òÖj|¤gˆ#ã#V™›¥(ŠÚ¹èÅD¡PÏ¿§[®Óy,ýTÃ"OÚÿKêQ‘V—nõ¸Òž–z\¥(B¡]Sf–z\iOE=î=¼o¢WÚvêÉðJ9› ϼv3¯!¤EaÃÐ÷´b„üÈR¢“¨&Òüå.YNriªö(%F¸sª'ÉDx1ªDµ;Í1ó®sbLó– L1ûƒìy 8æL,ãÞ=üËí·ŸÞ^ß§åÛ˜LÉO[º•šQƒÉ\iªKFê‰ÉÒcmqžÖŸî¤â"´™¹¹éA=|¿qaGÏés 9=`o§æF) ô‡ù9§9çfÀD_ëª1b\Eb±‰ž§Å#Ú[+ùîAå3Kéߪ LGØŸ—áè›ÆÝî Dp+°‰b±;—£Œ@“‹§Ì£ˆ€¸¡ ÑÑÖVÕ[ø È ®²—…Œë%ß=üã‡÷ý63»e’˜­#~«,aþœÏ ÑÐwPÓL3ÞÝB„=*RQÙzŠ.°@ì!õ‰ˆpƒè»‡ŸB6ýö>F“ÏlÝ;<®´?8~Ù‹æh@ghˆY†¤\Ì2C’צ(Ò%S…—S1ÝT2›wËr‰¢qy‘‡E(*±º,LuKvú‘r(–)ãÊÑa€úæþöwŸnßß‘¥+ßÃg(U+"’B±léJ3øŒE½fœʹã=‡e”&‘îD×ó¡†çÃ…¨Ü ðeÏŸgœx1î•Às5Ë ’ÓõƒCºa48\VwÐt0ÖÆ pÞ-ªZ¥¼G†ô“(XVûït|gG6ß=üÛû»ß_ßß]¼Mß)Ͼ?öÈyº·ð@i’_jŒg­3 JWše¬vÐåÓ n ƒÈÄD¢5ˆy”¹»èõúŒ¯xJTÆ7<Œ‘ *6€$C@RuÐ@´®â¶vƒd3¡2÷iÊ ¢Q~zûq”MÓï±ìÚpo²U–)]idP¦¹¨!.:¢ Þ{Xj’=ËS§//°*–;©ü7@H{——´â€³ã$ ~B¾çb®ÿ¢–Èœ ÇI ^ºšO%Ìã›]é//P²Œ+-à‹œÍ§Á µ'ø×4‚ÎâŸø\8”´2ZäÌFÔ)‰2–ƒ…”SSïQÞd†|:šùÞŒóµÄs¾6q{ýz©FÉäâ•üGú'ÇH .”¤œ$T8ï¼ð€r=äÎÛýx¾Žƒïâó™9¢9~~ÖÉ_1èp€P0gwp’(–ž*áem8olåq‡%λê1Î i§jù·ÆÌÜ,HÍÃ#¢ZÜiÁ+Šïî¾}ýöËŸÞÌuÛ€@Ýâ‘Ð3š@U>Ò%.ô²áºÔ¶çtaêc¶ˆÞ—)£hmBåÝÃŖèÈ“¦Ñ©KÙ¶ð.Ì&Ã9“àÞ޶ÎÂozjߤޕíˆçµùos²ºæH'w|ŒkÉèïÝ…¶uÍãDÏn¥l¤|·ZÎúGÈnwñ¯È¾õˆãY£Ëî‚¢×]jñˆëä¯ÂH¸aPºž.ñÍ’ÔaÞ~øøõÝû/oß#ó‘f“÷°7ðŠÍ´Õj`iÅtà0—ˆ«Ì\•èuLðFk!ÓvC\{O›lÕ•V4®*¨ô\&V}‹u²“;gêùL¶3§Í‡³õŒ‰/ÌVdª©@uÑΔsZS]x¦¤,Ÿˆªuäób0õx±ÄÚUQÇä@ –YÎ\«Ò9ý½&ÿS 9´)4m f HTpÞ¨ØøQ•"- {ÒG‰»FŸ¦„Y*)–1HvªÇv ]¿<®YWꛤç´Κ“µ 9›áo¼®KSÑ/Éb;”Š*²ö©äõìèÌÀpŒïg Œ$IËG\‹¬²BEóxD”☄g_!\3½f]À¡Þ¯2•y€˜ä¼ ¤¾ÈbŸ;<4t‡[È(¯ö`0žÄgÆ8ïáÓ!j¯¾.ËÿX[ˆšé D9(šJî B3tmGÎ=òØÏñµN”5Z‹T›ËÞ²´§el¡[¦7”ž:kCòdîpc&6r :Cæ$LÈ: s8Ùã dN’䔀ÌIRh,ôÜF&!*ïjÔôz}±µg-©c2óIÖI5Óø¦4æÉ¼0í3­^QæÜ6ˆ~XZhOK娣q…oÕ‚ul®´§ñlÿ‡ÖŠelªÑi6Õ뺔yžyßµÚ° WV ¤;­mcêhý‚Ÿ2Po͠θBSWÙ¸Ò³¨Î¸B1/Vû¹M#AóN=vÉxBì·D~1±Ò-弨“‰[“ÑK~ðA’Ìà#ã¯0R±™•nÙél‡p$"Sz]ÎוMï44±çšm4·¼{ùµ„),)‚ª,95S6çŠ÷€ -Âj¨…ùäΜ¼r‰*çeD.›ÛúÉUy¦È0XGN¸[yäDH¤ÌE4ÊßÑtd†çQB¯Vþ&ÕnÆ!hSé:Šèg™R?ŸõfYRTr~¬€Qƒ/û6cD-/%gh×fŒJ…•AÙ¶ì/¬{:Öu êÑÁ=ì©tGƒùÁùj‹%à§sfn3.x÷J¼Â–U¹hÆDªá%¸Ï@ÕD]˜×¬iï;g´Yèç+¸¸q›pÚ»+­ç‘º¶? „gQVóÈ{šÅ,yj€—¦åÓÎi@ŽhÎAÎàô‚$j匥(ÉÜEEIË«cæ¾I•Êd|ª€º¶éNÕ+ŸØú¸ïŸ ŸÛ„>3þÉlHÏ4ã¢&=z^#/cúfüÈ{"G5C¼™.𯰎CØð“ Š-ü+g°ˆ5Š«Ì€@IâÙHм py@`C²yj¸§¥ëC[i]. „Œâõö˜j¬ ö‹£ìÐíÎã爩8)F}‹J3³ò=à‡RK€È3Ìó©nfcÅà@…Þ„¸Hi–‡Ó3œjf&ÀцçNN89~Å©¹Q‰òá:5”2³Öâ©"ÚÉ{v?Mä°M8†Ñ 9ë *ïA4 v2ÜlFÂkä•@>÷n†„ÃeøinÖº<7c°ÝÂÍiæ4c‡2Ñ!‡ÒDKäÊ" 7ûwˆ1¨“dJ·Ø8*LÜ=Ýøa›p8ª%mÈοü, ”Xm Ï8*TÉüRê¨fÀ¬Ù`Wäyþ’Êhé¸ÿ¾ig‹“3ƒ‚ÓT‡®«´^b‚9£g:$ÂjÃö`ö|Îei+-|݈®G.ã{Œ³î…]ÎùèµOkäeÔk¼'r>™y¬Ð ØIΘLÃ]…ó\³äÁ¯á³ ¦Òž‚53.1E„3ß9´ã½ ˜… ´é]cúj€“*f3hÇ4§ ¤·¸@õ!¤ÌPPdßpgˆ¯êô*šâ— ÅvõïhÃï–vDÆ^4£ùå¨aRûÅûï"PêL>°GÛ1?,.¤UäuæÝ X: ©¿j]^y@!¹ŠíåõøÐ‡¸®Ž^Àë€Ô!¾š:_ç¯?ТV¾þ ÇÎÅã0AñôdÌ‚±w6`¿•ÁT¯Ñjʾþ@uK°bÙv‹y‰@„XÏ ÷r)f& ‹=݃÷tó©vjF(:dÏcx$ªÒ6Óám*Ý„ñ³}ßA¼©0zšÐ ÞÕz 7Œï:hªÅ뢬¬s±ËqÛZô§¦/)Šë5KSº­ÉcؾröÌWqÃãÞÐá}6ä/ß¹þÚ¶v¦YÌvŽ-Ö3ºÛàs#³É6†üíos{t´9A«7ùOœ•—ñ_جj?DGÎ ˜Ç`ß—Ì“|HÙ:¥œXÇŒÐó˜ßwXÌ,â‰8¡ Y¦3oa>ÃÏkÉÄ´„pìÓXÞ׊ߜpo§'³¹ Â|ºaÞíü›ó„i[©ãˆ:øÜëüIê ¤ ×ã=gxƒOÈùó,DÌ€Àßì‘J(®ù®4ý|¼ùñãõͯ3*èÉib»ÎNÉ>ø]iåèg¸ð¿²w6ÜEa±7 xñ•“3[Œ¡ô¾žß¸ ææŽSgìsº »€P Ȧ!'‹P±`©:Á± ¦ñ’‘c…M'éKæäÖt+¦ÓÆ@fd‡Å J'±A04¼ž9ñö³¥dBªË— uxKÞ%Š0u.zqƒÖû˜!€]j)Ù'c;WÚcaFsª€™ÀÆñ´Hç{>6/B”‚—v«›á¥2œfgÃ2(«ñbB 2ü΋۱DÓ97jüsÓØM…T6GYš ,•Eç=ê͇wï°š4#Ý¢Œš}:”9ÒŠQ–ߣˆ к+ö_šÒصɪeñ_ˆWôj>Z†Å° S|$ÍÇh2žø>ž@þ‰-@nÉ?yf —8@"é0ÖHˆtª§ÙÖ·¢üeíËuÊ|ëÁ¯ï޼ɠ±¯`o»Î7I‹gLÒºVWšBcv›t8õþ„z(‘™u§zøoZ¼k¾x]ϫڥçA€8K$ú®Ô_ÔkÏŠ iVy޳E²äad5¿ù–ñ²xè~)• Ôó²3žÊtæ¿eAó2±a=:¦“A³æ¼4³y\Ãl.Å…À_†ÒP¢|8±×‘‹Š¸ŠWøXz¿ûê!pd:‹J΃ËÌatzЪ$—p:úWgÁfy%í­‹…•У+ŒðŠî9õ’Y×à Øp¦ÌYuf’?2èõâʤÏ6â ² «rÿ³òè&³ÊÁ¢|6q>4#’²´ã¥ƒË8ȯ[ïjˆõ'¥åL×÷»4£•vYrÈüùûëw¹—⪋³É®tO ¡Ì"l†MŠY¬;(ÎÇ‘äˆ;‰†Á¥HLüx¦«Jc°I¢1γ‰rŽL‡Rƒ†/1œ°‡6B·øðôœÎ>Ìk‚D¼ûÿ泊¾\¬ÔÉN[´ˆ¡±n®xO¡Ð"¼¹Ò Æ€ ¸ÜÞ*kbC5ˆa®q hØ*K†J’ Hòâº4M¾K sôÎÓC—4&_O—;É/æƒì}9Õô2$WÙ^×mIï´Ã“¥w"¢Ó;7Þ3¦jyIlPaWÚ ãOy‡9EÇ I “N*’Ë&+à_#0<\ŠAUØ©SFܺÜÕûq¢×f¹Hñp·‚2,u _H1Êp&å ©S‰`¨®Œ.tä’f -î?üw9î¨å§Ã#털£ à#IÆMx#—Ï›g=o<î/ã9xjÔ`ðã"ÄÃÛ&žC =*#VN–~梋y.7ÂÞ#ž™»;q¨Ĺ1ЈYÕ–´Z,­ˆ5]iÆñÇf J¿zqüAaPvÌ27Ã_hLtý§oä˜ÕqО@aV–!–¡†DNøl<ÝcuLYO{ô¼Mɨïù7—ÔTÔÿ â é lC,Sžôñì›Kùͤ¨Md¼ø†Ç£Ñ>F9§Î³5J€úéúÙ²5Þ½êo‰»L©¥îÑš­K¬Çÿxë)–Vd=®´'²ñ ¬Àð‰hØFþ¼ùŠä®ƒ†‡éP»(y0ÛÌmÀ„Šãa‘®³ý›é‹s*%vk½HÕè6ˆ^ ILbË•½U &ñߺÒ–É:<—ÔóéBÓæPxž—µà|th²20Ãë&'Ëý#6‹„}:‹° #á˜ä|Œ­ —ãÕÕ£í¶žG«K¢à\T‚}‰»v„BK…eŒØâ×öD¬zò˜d}š–XµtÌÇDŸaZ•Á³Ó_0V‰‹ ?œÈ)L¼¡N}¨;ÁKÌdü™»ˆ>“xCÂB0ï‹¥¼[f†@—±¶ËrCS|w{ÿmfµª)=uE{ Ì´ÓB Sù:3Å’hV¬ö®¥`…¨°b»ÅlÊ•eSÍzéŽ2ÑD3RÎ,Ϧ3’ʰ”)i ޱ”:y¸¹ÎcUªóYö#íÕbU©Àµ×¢7ø0v¼·ê°³µÊ€{ë³îgŠçì‹y,@x”Æçê¸&@%žÑ$Žx4i‡Z8BÐŒKK8}KÄKPlâ #^â$\·r†…âá€å¹n?œŠ77~øpŸ+­ÖmãŠÞª‹DWšñì~’e2î Uè»G-OÈ ¤|†TteHÂâ*†4§{8Å]sî<ã²kTN#½ƒÀ¼•'•Y™˜ù"¼¡ñæÝŠúÓ¢¡z£¯ÃôÃG¼;CÝz™¨'ûñØ›µ´Äcçv•"?ëùk|I÷lõ@ÊØ$_ú¢ÂëÈšcb¢Ò oŸƒ!Õ4ÕsB4îLe‰«üüœ*“VÔ,™´z)çÊIò-=Æwp&ÂÔ< \¶i" IÀzÄ’Ï“ô¥eRliP?ÍA³AômÑ’;Oòãá¢Q¿òdñ’L\“ëPÉÞc¿”J?ÚJg33{²c?Ô;Í7…Y¾ɲ)i-|*÷xsä럾}û?èè·à†N  Ó”L8ü=9RDâe‰“ñÞß”ݢΕ,,}è 9 úšÝN¢G)øØa ¢ e[ŸÔx»‘ÊN¼×ª)€ „gû‹L7ëÔÿy¾4 È;!C,gS‹˜{¬Ý•·û–Vúá˜#,9£|Í`WÞ§q.Û„ÿ"Wµ¢³Jè,UH§^Ö;u¦`¼òLÔµ”©Ê­¥™—Ô~¥Æñò^@„ßÂCä¥xpÅ>Í/ü#7¸£`ΖCÆ5*ÏÆ''VÓPºÀq›`Jo€çÊæ4\Ú…¢Ù™÷) XOÛú±ÒÚyJg.†WXÁ€Ä YÈS”1ϬÎ|ež2îA5 ý¤£y„3´^{¼¨ò _ÆÈ¥<¢"ØX¾ Àl†ÈaàuŠ p)àâB½/†ž™š¯]Ld†¦S‰4£ÞôXƉ•2Ý¥v JŒ§òÅÛ„?+aØ@´[ÇÆÔ|g î‘WÛˆ^KXRä"µ+²w£Ñ›–bÚŠ„[D5 ¢"ë4-E“´ ,±;"ø)ψµ[@™Á»Y¢³¸É ¦õ&’kä•à&gènÉ3/G±³Õk¤;·Ç¡u?öØ«–»C¢:jöà +çóZÏd´!@–|:mhöUÙvµž„ôñ“Ø79ýÂt¾‘e„Pô€;™%-|è!ýÌØrußLMkžy%ÏÒ.Å ¦@`(qSålK¼»yP»×´ÏmG5&U7µ´´/X±Û2Þ¿(›ÒÐuo–N¢=ÕIØòQ•s Ù@s˜ï3Õ³ûÈÆÊ"K'#†o2`SdÚò×mï\ÎÒY¦Áôèj¥œ}rÞÆÙ{nÌRÓ;9|õ°¢IÉóŒ<è@‹%‰ÆïowÐ!kÉi=œ×$}†{+Ñ€'ÉìQÍ'Ò:j¥LsºLë)}¿=%wCOVòDVÖú´?’ýÐ?ôÀ¢Õ4¿ŠÇÁÿü¯ÚÛÄ]*°×!âÏÿ7Š× endstream endobj 5 0 obj 8638 endobj 2 0 obj << /Type /Page /Parent 3 0 R /Resources 6 0 R /Contents 4 0 R /MediaBox [0 0 612 792] >> endobj 6 0 obj << /ProcSet [ /PDF /Text ] /ColorSpace << /Cs1 7 0 R >> /Font << /F2.1 11 0 R /F3.1 13 0 R /F6.1 18 0 R /F7.1 20 0 R /F1.1 9 0 R /F5.0 16 0 R /F4.1 15 0 R >> >> endobj 21 0 obj << /Length 22 0 R /N 3 /Alternate /DeviceRGB /Filter /FlateDecode >> stream x–wTSهϽ7½Ð" %ôz Ò;HQ‰I€P†„&vDF)VdTÀG‡"cE ƒ‚b× òPÆÁQDEåÝŒk ï­5óÞšýÇYßÙç·×Ùgï}׺Pü‚ÂtX€4¡XîëÁ\ËÄ÷XÀáffGøDÔü½=™™¨HƳöî.€d»Û,¿P&sÖÿ‘"7C$ EÕ6<~&å”S³Å2ÿÊô•)2†12¡ ¢¬"ãįlö§æ+»É˜—&ä¡Yμ4žŒ»PÞš%ᣌ¡\˜%àg£|e½TIšå÷(ÓÓøœL0™_Ìç&¡l‰2Eî‰ò”Ä9¼r‹ù9hžx¦g䊉Ib¦טiåèÈfúñ³Sùb1+”ÃMáˆxLÏô´ Ž0€¯o–E%Ym™h‘í­ííYÖæhù¿Ùß~Sý=ÈzûUñ&ìÏžAŒžYßlì¬/½ö$Z›³¾•U´m@åá¬Oï ò´Þœó†l^’Äâ ' ‹ììlsŸk.+è7ûŸ‚oÊ¿†9÷™ËîûV;¦?#I3eE妧¦KDÌÌ —Ïdý÷ÿãÀ9iÍÉÃ,œŸÀñ…èUQè” „‰h»…Ø A1ØvƒjpÔzÐN‚6p\WÀ p €G@ †ÁK0Þi‚ð¢Aª¤™BÖZyCAP8ÅC‰’@ùÐ&¨*ƒª¡CP=ô#tº]ƒú Ð 4ý}„˜Óa ض€Ù°;GÂËàDxœÀÛáJ¸>·Âáð,…_“@ÈÑFXñDBX$!k‘"¤©Eš¤¹H‘q䇡a˜Æã‡YŒábVaÖbJ0Õ˜c˜VLæ6f3ù‚¥bÕ±¦X'¬?v 6›-ÄV``[°—±Øaì;ÇÀâp~¸\2n5®·׌»€ëà á&ñx¼*Þï‚Ásðb|!¾ ߯¿' Zk‚!– $l$Tçý„Â4Q¨Ot"†yÄ\b)±ŽØA¼I&N“I†$R$)™´TIj"]&=&½!“É:dGrY@^O®$Ÿ _%’?P”(&OJEBÙN9J¹@y@yC¥R ¨nÔXª˜ºZO½D}J}/G“3—ó—ãÉ­“«‘k•ë—{%O”×—w—_.Ÿ'_!Jþ¦ü¸QÁ@ÁS£°V¡Fá´Â=…IEš¢•bˆbšb‰bƒâ5ÅQ%¼’’·O©@é°Ò%¥!BÓ¥yÒ¸´M´:ÚeÚ0G7¤ûÓ“éÅôè½ô e%e[å(ååå³ÊRÂ0`ø3R¥Œ“Œ»Œó4æ¹ÏãÏÛ6¯i^ÿ¼)•ù*n*|•"•f••ªLUoÕÕªmªOÔ0j&jajÙjûÕ.«Ï§ÏwžÏ_4ÿäü‡ê°º‰z¸újõÃê=ꓚ¾U—4Æ5šnšÉšåšç4Ç´hZ µZåZçµ^0•™îÌTf%³‹9¡­®í§-Ñ>¤Ý«=­c¨³Xg£N³Î]’.[7A·\·SwBOK/X/_¯Qï¡>QŸ­Ÿ¤¿G¿[ÊÀÐ Ú`‹A›Á¨¡Š¡¿aža£ác#ª‘«Ñ*£Z£;Æ8c¶qŠñ>ã[&°‰I’IÉMSØÔÞT`ºÏ´Ï kæh&4«5»Ç¢°ÜYY¬FÖ 9Ã<È|£y›ù+ =‹X‹Ý_,í,S-ë,Y)YXm´ê°úÃÚÄšk]c}džjãc³Î¦Ýæµ­©-ßv¿í};š]°Ý»N»Ïöö"û&û1=‡x‡½÷Øtv(»„}Õëèá¸ÎñŒã'{'±ÓI§ßYÎ)ΠΣ ðÔ-rÑqá¸r‘.d.Œ_xp¡ÔUÛ•ãZëúÌM×çvÄmÄÝØ=Ùý¸û+K‘G‹Ç”§“çÏ ^ˆ—¯W‘W¯·’÷bïjï§>:>‰>>¾v¾«}/øaýývúÝó×ðçú×ûO8¬ è ¤FV> 2 uÃÁÁ»‚/Ò_$\ÔBüCv…< 5 ]ús.,4¬&ìy¸Ux~xw-bEDCÄ»HÈÒÈG‹KwFÉGÅEÕGME{E—EK—X,Y³äFŒZŒ ¦={$vr©÷ÒÝK‡ãìâ ãî.3\–³ìÚrµå©ËÏ®_ÁYq*ßÿ‰©åL®ô_¹wåד»‡û’çÆ+çñ]øeü‘—„²„ÑD—Ä]‰cI®IIãOAµàu²_òä©””£)3©Ñ©Íi„´ø´ÓB%aа+]3='½/Ã4£0CºÊiÕîU¢@Ñ‘L(sYf»˜ŽþLõHŒ$›%ƒY ³j²ÞgGeŸÊQÌæôäšänËÉóÉû~5f5wug¾vþ†üÁ5îk­…Ö®\Û¹Nw]Áºáõ¾ëm mHÙðËFËeßnŠÞÔQ Q°¾`h³ïæÆB¹BQá½-Î[lÅllíÝf³­jÛ—"^ÑõbËâŠâO%Ü’ëßY}WùÝÌö„í½¥ö¥ûwàvwÜÝéºóX™bY^ÙЮà]­åÌò¢ò·»Wì¾Va[q`id´2¨²½J¯jGÕ§ê¤êšæ½ê{·íÚÇÛ׿ßmÓÅ>¼È÷Pk­AmÅaÜá¬ÃÏë¢êº¿g_DíHñ‘ÏG…G¥ÇÂuÕ;Ô×7¨7”6’ƱãqÇoýàõC{«éP3£¹ø8!9ñâÇøïž <ÙyŠ}ªé'ýŸö¶ÐZŠZ¡ÖÜÖ‰¶¤6i{L{ßé€ÓÎ-?›ÿ|ôŒö™š³ÊgKϑΜ›9Ÿw~òBÆ…ñ‹‰‡:Wt>º´äÒ®°®ÞË—¯^ñ¹r©Û½ûüU—«g®9];}}½í†ýÖ»ž–_ì~iéµïm½ép³ý–ã­Ž¾}çú]û/Þöº}åŽÿ‹úî.¾{ÿ^Ü=é}ÞýÑ©^?Ìz8ýhýcìã¢' O*žª?­ýÕø×f©½ôì ×`ϳˆg†¸C/ÿ•ù¯OÃÏ©Ï+F´FêG­GÏŒùŒÝz±ôÅðËŒ—Óã…¿)þ¶÷•Ñ«Ÿ~wû½gbÉÄðkÑë™?JÞ¨¾9úÖömçdèäÓwi獵ŠÞ«¾?öý¡ûcôÇ‘éìOøO•Ÿ?w| üòx&mfæß÷„óû endstream endobj 22 0 obj 2612 endobj 7 0 obj [ /ICCBased 21 0 R ] endobj 24 0 obj << /Length 25 0 R /Filter /FlateDecode >> stream xí]iwäÆuýÞ¿Šiqz;ZÎÄ‘eÍI+^4‰?DþÀáfèps‰’üúÜWïÕ  4è%ÎÌ9$@_ªn½­^=ü1ûMöǬ,·MVÕõ¶.²¢é¶]VîÚmÓe÷—Ùï²ÛìÕWyvñí¶u·+÷E¿ŠÊS‘R˜”VAÃÉTšFf1e|þ°…” IÀ¶À|¯¡áÚ1ä11e‘·ï.¿?º~ £Û‰Eè"¦ŸZ˜"å¨Ö1tƒÖù¹™.`Dzã×t ~abâMð!çëd²Ð4¡ _WB¢qõšwûmµk@"§iKŸÓÒh×m÷º×å]*@§iôö1"» ´…qÔºÈK{× h-™F?aæ u mA’¶ùFËeC*ÇÇB¤_*©T3ÉŽ¿Qßão ¢#Š7'|y„üÝ39ª‘ù¨ÕÉN¶/”Ö`|¹ E”7Ŷ,öº¿Ç„b_ÆDD—Ãæ`ûR1.a’ÙLÐó¬ b3^P.6¸ÍvÐn>ö»óÇóí÷÷ç7—áäy·­j°Ø»Á ¬î77E8þm±Úá¡×ßKXÝìóíþyXÍÐ3Y½}Hbõì‹ç÷ç—÷1RWÛUæá{<ðF"hÚ–öÐHT)æô?À";ùÎ2ýþŒ'}>:ÝÐg9,GYŒjé.ßñΟøIX’ëí•#cå†r‰¾ü–Û½³r­œäAF“™w?!©Ãþ|Q—[µtôZ2ºéÊ-úÖÄ'Ør]EF3ôL6§™®K ·ÿswf̹]Õ噇|8Uïº]ðü²ù_—ÎÎ<ƒB9Irí "×Þ|5ïR\°;1Ixºiš&ÑFŒlŸüÊ’ã– æÙ èhåìúV üáHûHø}®‡olrøRhs’bÀ4ˆätƒÉáG»*$\µ Y¯sxjÖ…Ùk¼E¼Að2Ñ€]ôŸ—w)¿‡¨º¶•†»„žŽ>ÅœÑ|—o›z‡S]-À‡Ï;MuC™à0gG_kA r‚ÆGô¢1^p¥:"ºãBþ*Ÿã¯Ê >ÄT'c|££:–ýøzߎ'…àÁ;'H‡¨¶‘ò0îj»œâÖðwLL„.fl•Û®­Ö ã4MúÌ#M¢°|¿9¼¿ú¯ˆ3°oá ìÜönåe$äbYîöD-"EŒ?ëµíyY>Ÿª ë»T¥£TeE%üw'‹¡?Åj° \aoÙ zR¾ª›Ç´À@Z‡eª¥o=ÛÀ¦Åöš1ä¸yoŸnݾˆ°×òn"T£ä•P¹jm Xw–ýµS÷¨ò„·¡mVlhµk’¶U±Ý•”¸tè ‡Í«76 é`ËÚj¿x©S¦KRDºCžd­Äˆ›ko’qu¼E58,Ò¯Vè:-ûk'néJ[þûÅWãâBðòqáq)Œå•Ý‹€š'dŠöéŒuô¹tÆŠ¾eªß§ ÏT¢š 1üø!r·ÃòeÓR'8àÑ^ˆ0ÙÄÃÓÑÒ¨ì´í¯Ël¡°ÍáósãgfRHÚ¾Œš1+Á÷3ã+n(ÅG³ò¶„ º'€HR šÝ·@˜ ×wï¯.ίÃFNžcÜkdW4Î=â²<‰Y͸¡Ïæ­l™‚:yˆÇáç¦$Y°ìß±&giÈç I`y¼m÷¥×³19˜’{‘úÔ‹×ù¸}.¤Òl½_eµ¢4»¾zxŒ¹ø6v ðWÓÃ.Ú_•ðr)&Üb±ÅTæÓ䯓§3žw@2¬.;¸§î˜¨³®–¼CK_VˆÁþš xá8/¡q¡û-Mµn­梩ÑWiu.OÓÈøÕw1‡E~­&¹îèÀ2?Ü”]‡I¬î殹‹ Í„ú9¯‘dØ 'σSÍO‰ KBÃk^-8fÄÏ‚§ˆ„Gó®Ø6X~”û†Už~ŒÍÉo":æHYwp³ÝAðºž9¢Ð”¢ûm ­ålœ4aÜ·14$cís=`Ñ'4"@pÀ°Ö—·e¯ãÔ€ ãâC¾55+™ËŠí‘ê¶+4)¸Éc*XNœÕ²ÂR¾éN­‚óa~£3L”Њò£ïNÉÒGš&xEQxظ*O¿6'Ÿ~w*Ç!±bjD”o½Î:‰Œ©Jë¢|h¼ðZY·Ã‚ÿ ¸?ÖñVÝÅücÜ×Àºç’/înn.o#yU­ÎNhzX9 w‚•£.šâuÈ­2ìK>a¡a +É ó•g;µ éGDŽøJãmâ[¢øÎŽhú±»ør’id…a¥”¾À×1†|ëØ>óá.žn®nijÊÃÅ>œq.š‘™cNþBÊ©¼bK9’m,ÎÊE¬l5èºÝ>ãuìEOÆcïn;¿;Å>aÊM6Q¸Ç$¯¿;yõéržqŠŒpÎw´ŽË§¢1øÌ#1žžÑæë¦Á¶Q Þ€²v< mY‹ ð6uM"x‰ëuôk?Þß½ óÖ,Ízè^7ô1“$å ­$*ÿ”¼}-°¬\_ÒXà$wµ¤$9{f%%ÒÂT<Ðd·– pÈŒÁöEdæAVº=äõ·¡¦GxôÚ!Ðû<Ëj5{ГËj–O7áv[õ¬À׊rS)„ý×ÓR(® Io†ôPZ2¿´*ï±È1ÐËËØFPÆìŒ° Vå¾kK°ÍDm3f·e[Ï=§E´;„¡€® ¼üñéüöñêú2Â7Ù>højtsÑŒröײ‡KÓtcÚ‹D’å2mqn¾ªŽü¸ÛŒÞ5ϧ£D‡0O×’Vu)r8á°R‡b½¬1ubY£›#hàk¯ï" ¨÷ÑU%¶¢Ô z5®¹hÄ5ì˜mo™gl:±2ãm`Vq#ÚÃBƒ¯^ð-;*´‡&BŒ6sèî–1w‘ÞC $L÷ˆáOÁ„Kôp‡Ð“zïúê6VµÃ: {5n¸hŠ)K¥_”<|òËÑUÙÏ'þ%Ù论Áß"†˜:Â96°œHU‘(#Z)öص-B2!”8wºyõàA‡«ØC«÷=¾æçp¦„ÚܹorÕØ²È÷]‹³°þ°êY8;=ÉÙwW·Ñb:”Á×"­‡¶ ie|5™XB^}n¬=L^½çω¯L‘Lv&ŽškÄ~+~#\+ÑÈðFð`½Yuز?—ia3ʨÍQܸ:~øp÷CÙhM…¼Çèñ,]g²‰î ?ád ¨’qøÍG{4Ñ'Ì -„\Õ¹9AåÀ¨í§jä0â‘ÃãvºÇ ˆ¼Ô4cÂWítœb¡âžB÷BazÅÉØ«ñCuÂÿ‚¸c:ÂzSú[§3 ‡>\i0ZÇ­BÍxS#Ç離 b¿6ô¹©ƒˆÍ2ºâfN!ÄÑæ¡JN^ÃçBˆ2ºS…ƒÙ¥C(V»Ž$LàUÃylxÚ˜Õœ¶ƒ€îxÉ¿ /*6ž0ë?Õ%œpHK%øˆÀY\|9Œ¦—¨¥(ŠL®ØÉGN'ÂR¾¥¯`].Ì ­‰ägnCÄòƒAÄU%Œ*73X˯ÂÞƒ²EWUg S–\‘©~ØSI!2—SÎ B¿~#*_Ž»AnýÆÏP¹ý)’™?°-Š3$ÐùC Ã%ÕxÛïPw,XÈ‘¦¹³÷̃%Ö…K*cæÂChL™`¤°%r,+,ãv-FÉí¶¾dÀ^&ƒL¶Š%5šc…Íh˜2‰Ôî„4Á˜O^æ vH!õÕ_LD½¸»þJ#‹ûHø: dXô‘~p¢¶#G _ ¼7qƒ´ ":¦CÅÙ ïŒå8k„üÐÓÜ~G/ÏðwGB¢ÄCÜCûeîk¸pâùßwÎp†dö€ÊTqœÌ`\ÇñÑ3|Ðm8 O¦ºBÓ¦†6Z<†N%»DæÄW^Ê¢Â~lZÜy‰ÈÌI¬QÇ8‰mÞûþ¥‹¿¸„•ýN8—&?oÇX‡y)¤â Ìr5ÑRÎhš=¿VìâË…GQÚÙ©2‹vƒ¨ŠCÝÄjkLÒ¦ÉC<üÌ)GLǬS™§  5AÈ õ®n“¨çÞàPî¹Xjžü͑ؑz(ðPC!K]HaÌâ^Gw³¡» à_2ÿúÅù¦©Íü£eç[˜ÉísÝÆ/î ~.ÖŸ“€´ø QwfŸÉÊ\(êÉѰ6;®a ¥nw0˺‰ÀxÄæÑ\ãÂv‚ftá„ Ü–ÔÌK–u÷—O÷Qqϸni›°*~x`ü d¶yXÿÏ6f›Ço:¢I)l[TÝ/¬} S6ÐØïIrí[¸tµû¦ÑUÜVbÙ ßZJ5à:(Á5â:°U†ŸF¿múžQUc™¶,PšÀ7½åq‘Ç“ª(<ÔäØ-îÁÑL+"}Àñ¸È_Ú¤ÀšŒŸ•x9n¨‡ý É ùʯw ý‚$±qÑN¢¶Ù±Ìq!Œ.%jæúu G:džÁé›3BØîf5 R.…ð ò\ÒÄøæò<*µ÷íµ|q?ˆü45 î°¢Ð™^ª@:׺Æ×ñ)tÄV¥ÿAXS2m˜q`Ñ¿ìÅzÌþ>?84Î96<Érçcc¹j‡œàuªØÎÕšóEÞÀÂHycÈñIçßÎïc¦4a aÇ5ÑV²–•œawL8f‹¾šµ=óB(ÆÜ⢉"¹ŽU ýˆÅ“÷•ƒÃ„¨°ËÉ pž}ñÄ5Tê—@›1©Òk>2¨ôí\þ€¼¡´·¼F)8í¾ôÛº–öL¤_öF©H&Í—l¢áB$sø<ÿN'اÅn óì+Щ_†lE>͇¡þùéþñî!¶mkw~Y¥"›aT¿µ3)åøƒBeUŽ’A•Èv+Ò‘âM+ Š‘3>9‹êR!ù É>l5¡ÜhÏÏåÄ=|ñ 98üéÈÀéTÿÁ¹!”‰³µ|M¹„&_—{D½† UJ”½'=œ¦ª¦Ù¼PrªØ[ š~_1*ÔE_Ü â3¸X«Zm¢8Y=š2¡äLÊ‚‡•{"ñ’|¶Óˆ¤ÖN‹TSªs$_ìè]ª3Æ"ƒ¾—¾!'W¯ ÷}þ!•ª«QbhM¹¸\60Œf²-¼Â`w©½çÖcèÉü$bk|AŰխëv([]¬ç`«¡-ôþ%25w-ôµ;d#ª3&˜ZoËŽ‚1h3™ŠÌü©šsK™:„Naê¯'–^L0FÁ¯äœ¸X«Ru¸–B’Nä£ïƲðe×…ªË6ψ1!ÑÙBû`è½¶ž½–×£0“CSXEƒÂ¶h¶»ïê4ÕÑh3㉺â¡awSMƒä]¶Éä=žÇrÒM6Ï:u¬Ä64u¦ùÊ‘öe`ÚeÐŽk€vuAâòÅBf¾Ì ü˜Ø …sÜÓ"¸Ùåæ2é#)!,\ðK °&´Ì¡vS^xì5ÒÚçæòLýuñ‰`y”-6çîÝý»Ëû¯î®oñ¾»¯{ÚRÌ2K͵uö¨ª7:âaÌ(ÄgÀ8þ[§ŠÙð1NikTGx»áh²WOÈ Ñ 7éNÂj¾¡Úàƒb‹ª ÄTÓ05Â<·}‡„NoH=æ ôh‚%I…±VÝm¬G‘ãÌ{¸»L!,¼;Ř_#Ù“/ι µ"íXöù\8‡uÄÔ볈u*­t›Ã:,‘4%ök:ágèW­[ÕÝ쪪5ðŒœå'S(¯H¹ºˆzÊš~ê.‡ÎEÑà.–¢ßZñrW0ÜVý⃈5—,­Ÿ‹«ôiÒÞáLÏuQ‚mà5M ;mƒÚ²ÔÈ3wãb…Uð†øŠnw-žù†‡Ø)¾s·»&$cGµ»Î¼Åk_fßß]_S†adþè/~q/M]‹€Ì?Y­c*#.BKØâiC¦#lPav$E7È1-ÑÆ9&®·±?†Aìv<69­£Á“uÆ@Ø”n8VìW.ÞcUízÒ†T,‹Õø,õ_­ÐW’r.ÖªRn&É4å·È&m¦ µB¬ s§B…Éš6Ë{Fš–·aJƒBRæ†.¯ê ø4VìsÄ ¡¢S°ž]Rx²a‘¦.›6(®²Ž§­ …_ŠÓEн±’È{©ák±×Áús²—…c/ï‚‹ÛŠ`0”®§é=Ñ ¹IB’gÔ ÝœcÃùfµË•Ù‡Žñ¦–R¢ÊÙ¸ÞcÈqJ³n~wÕÎz£3¥ž¬Å<êÏI<í^°ÁÆŽ²^T IË)®Q䈸† 3ì@8@µ2سì8œjÐüÃ0Oœ©áD=_ÈM- 7Z¬ŠÅ­KpÐÖ?µ%¨TT…‘V}—Z8ÃŽ3o†ì#´¾ìÓ|œå—PÕª‚ »}çÒØŒÀŸ°)Ë,/¡nWA3YfÕ m:t”¨h£‚T{9K€ïï~H/câÝ`i¯ÊÒ ‡µª,ej’Ễ]®[­¥'rlq†Éª/§OfKÖ"Q*´gؘ ‡¼•kýô 1¡%1.Cw¤ <ùFy‡ŠÓÜQ'É%üøÞGKÑ®Ùâ=ÜÑ §f"iç+f7MîÛÅ7ww~y÷C|·‚V÷•s‹ qíYOa逷òBùSȪÞVÖÜ šŽ†¡ˆìx)ƒ‹@úK¤~ƒ*üòÉâØ(Š# 1”5xKp,·ø ]¥;½ÏŠ¿ø÷Å/qjƒS.¾`bþàßþK÷«¸ ùæhØ‘²[1 qc”3òz…úa®FPƒ­¸Òg‡ú Úù¯Zl}ž8ûhfö·Onž"\l%1ûšâwÐ)+•[QÒgòv¾|ÀX‚ VhêZ¼tP‡€ ÒhÈA?sˆO‹Ô²Ž/à—ÐÐTb_XÉÛ”¤•œäxT%ÛÀŒuB·áuÏßoÞþbü­”H7ãrîÖiÑ™¶FYá­4ŽV´{_Â%¯#Гñ%¡ØG*B1ÕúC§›OKõÄ_6ÅúÜþF±•é'õð…ÁØ,¦øÌåWS8h”÷X€ˆkU‘U‡3É*kº„æf·Eb›*§HXíŽ\l“®1RKÒh¥V”ZƒéA%4Q{‘ª©Ã9ƒ·“õ‹fêÒ}¨Yˆ«Z¬HìEõTü«å¯nWS±Íﳉ‹\]D·" zuNgAÔùM‰’ž•½S‡ø^Þ»­Ê~jUõ“[…¶ðm$KÁ´8궘«¸ÅSW™&ãᩊª‹Ú§/w-<\²ÑNu+Ü+ÌÝ|œðEŒÔà^tÍXÿ¨gGygÓî ÓêaÕƒ¦-½”¡Ò…Sù>‘ôîD]hn…ÆJÿ¹÷òzÙ‹ØUÀš&?.ñÈ_nËEÅP™€F†MÛškò/–ʦªRWÚ¦SõcñVÉ,G-x„ÜÛª³6µûwÙmö꫇<»x°m£ù—çmƒìá¯eä7£ÅcRî»}V¡<'ÊqsÛp-µŽÅÚÖß—@àPJ;O¬­áxÒÈŒ7 {3‡EaW˜}ë~)¿?ã£/¿àj²PÔªd¬ vò)ÈÄé’±ô-ØÎnH©8ˆ’± Kߎ F.ù rŠ4?S3l>¢Èò+iª.}Vbð_~©R±­.›{‚­z°ÃŠð‚IÓ1ÀlES´.„†¤m«Ú梑~ ¾¾Ìâ¾@B…€º¼\àh3©/ƒpPEQïÁQ;9î¶ØvªJ±!äs±í°²7@¾:É"“/Œ7î€ðlôùáh,–c…gÍt‚lg;ö}ZÙÂö.ðH{íæ„ó0ZCÞåx Nå¢ Pã»c&Qìúê}dšwW¡î¶mh$€ÚJ¨×„íDƒ†ye ˆ§/ 0ýÅãl‘(HI¤3l•i7"Ä^N&\ Ï| ø):CÂóS ¬ÑŒ=Ü©P³ø«*y_’W5<,Ä ¤à=‡˜€jDYŽ'›‡}¶­PîåµF: q`’…¡1Ï‹­#öøt}þxù.Lló²@ÿ`öyhjš¬]_dJ²Ö·) ·<þ±»ËWé’Rž'LÓ_=âp§r‹ÍzÃôÁò%Fâ1A+>D¹sf‚B%Þ¸•y¸tH,3¹ËW Œ[f.~w~uýßaV™XºØ‡wAÛÚ´ÒDáÀö¯},`ö̇>'Åðsê‰o/ï¯bÛÖrl9mjT–÷n³´?ŒŽðТ¶Ç_´ŽÅM»XO™)óWf»Žƒ9ªK”ŽQu±é‰1/áv¯3vz"J1Ý*¶re¹¹šrÒ¨‰zˆ(‡ˆîpïr85]45USÂ[?§ü;˜üÓîå1 ~sÎ$“¼Z3¸œYe³ìpHYG£ŒÙ ¢Ù‚R_еPç¼6kX±U¸dõдpca¸p‡‹êuø –´ÿà9¹î¬2\& ¢ˆ!jÕC#À3,¢w÷ç?¼»û!VÒ. ÿÀ ª‰.ZTE¸"ñTþÅJž DÛÇ >-–¬2ؼH)cÉLeÃ{1_å1ÖUé–€¥ÛŽåÙ—Ð,d³…±¾V3ˆÿæã@Ð/r*Pt¤q>p,É™¬œ&@3!ÃÓȼý¸\žOdÀäHväNvá½9ßw:#SÓ’ÝESd_ËJ±N¹všuÌZqÝëøo5ÔÚ+d¢sH×L÷Ø1ÚÍ£)èáxSÌÊö‘i!>b•ȶÀK®Ü^\:&–øˆ 6³‰f¦•à €ß=Ýc§È]švñ—vƒ¥¦‹¶.5Å ÓòY[ü3»©‰$h€ZÄ&C-ºn!µHá£À\„aXš)wçW1¦éýY_CÕ‘¶qp/p=¢Xqb6 7!nîn?Ä\Aóž@nöjú¥hŸÍÜ^}q÷tûø ÷Åo//°KËnÍìÍé=cu PÝišvgàH’“r.Ú´ÉMuß1Œ¿ûœ¦‡¶…Yl³a£ÄÚ»lÖxR]{q®«¦SDxŸñ¼ËÙØ7=MOUr‰í§qC¸Qò->„7ÇŦf#¼FAÇÔ!Hœšp¦†Fø!yàÖ BO¦7¨Dp&åï®na[R6å%–îê ~ ºÑóÔ’ÒE›&%ÅÛ'II aJ0ƒd)ø…cþémÎ¥ìiež8tÚˆ’j)bs3ƒ<_T\@/˜U˜ /Ù$ç[? Žâ~!^ˆR”Hð@uYJ"\eœáá"i7Gãýnþ=–*{kóäó_I”—[íÐ~˜ëe}äH2éê ÞºÏj¢ØE›f½Åã–f'ÿÄK3!™iÅôÈ‹²ÁM&¥5”]ÙM‚Ü»—APƒWXœ!O)8A¥U Á®n±Ì5³ß™¹Æ4ÂZÉÝQýÙ¥•™kM£à¾ÍõAá»wWç×aëŽvê¾R¢‰i¸7û¨CÎÌÇ 6¯!É¢V¦åVGû3âôrp¹º0„}x§H€îOañ •FÖó}Vã˜jµ ­N3æJbIOŒ3õ6'êY•‘¤Q¯W>Ðð#Ù›Eƒt¸©dÞ zìëe„"–}p'Õ{=W+‹jÙ§ ÝqŒè¨;Ê qÙ÷³ËÛ‹7ç÷ÿ¡ìµ—C¡tuÛf…û^¯8sGÝBõʸÚ3F¶‡6EAV{“F6ñ£a‰&ùã¹8nbn¿Q«1>墶'vÞvvCZ[tOÆ Ñ) ¤6áÇÈh¡e¢û‹6~Z,+¾ðÄ·¬!ÂH×ù+€!´ˆ= 3i“°‰ã™æFRþd9XË™r#ÃÖ˜1ãÀq>ù&G<:òŸ$íÓì™&õøÆKjÕ î|\+¸Q¹™ÛL!'¯ƒÇœ.„•àts©dÙçHj×ð†wvC¥P¶\Á¦ß—ÏÓZz×îÊk5ŸË›gâV Âé7±Ï E®ÊÆ=xX¬®÷HÕv‡eZLÆ´¿åæàû§ÛëËÛ÷´ÌÑDÚÄ†è± ?œ.Ú”Èem1)r]ky)2,°ÜAT囡ƒfo¿ ºêˆcm|¹Ä2øNyœKH¶+ 8«t¢Ã%¶~Ž”¦ùžs° (VോŠrx<ð¥D²}@ks§SlBYƒf>òÝÓãõÕå}X“˜UûÂ_ÚVÔ;`j.uj× øGf1èk„9,þæ%{Ï0á‹Dvú³É ŽJX²%óÛ^|†ãdÒ˹ñ˼œûXBÔ²`'«q²æHòïä"Vê](8'«x»7Q9íú Øèî÷;}`DFˆ¡å$ïøuDÛ›°Ìì0À´ +ʸï#Í”ÙáíKNœW×±¸p ’´M2Jjÿ|XìXÓyzjxXÔδ©!‹»ò‹é¤ˆ3ºåÏ9½ÍdgKŽÏ²%D0Tã94â ÏN"©±b…rŒ]¯‘”=\ϰ6ò†ûÉÍùL”Þôºz¼¥y¥Ñ0ì†Ý¼æÖËîÀ×çïcLß#äX–^.m›7 tt±Ô´YkAÚ¸;D?ù+åyØuîk%5÷ÈM!„#ø`6²ú9ïFVà Æ¯"b[Öá ±¿XWçXÊB™°•zÝÐ[óMqüDq[ ³´[|{¾½@F=6þß=EÂK¥~mÑ7?”€.–ꈩh|M8-ÿ*bãÈ_O ¹‡UbïLL!á‹-vnôÆ*&£u-+”¾V‘Ï‹tó©w¤—®ÆÓ>*L®}…ÚÔSfíäïÃó¿ê—³/zhÔeiEe©Ë࣡?À|þ)‡¸z+æ%Í t®ÂTÄL-üD¯šB¯è þvضRo$Ûjê©ÓÔ¦™1y¼ê„™‹ùrwsõª²ÂÀ7 R ðC'Š¥ž¿!ç~Ô óä§,JÀFÞÇ9å/‡ðdáŸ"#ýÝ!à¾Ë¨Œ©’{ôÆX™%ú«ò‘/—˜aD3ÀFB*œaüŒí'‘CÛ£Š IØEžË¹]ßîõ{|`OÇ4®®ˆMéóPPûœbtÓ†ªAöx1heŠ ¹öš8É éR 1(žlaJj#B±ôÕ±yÙçgùëËZÏVÕ_“eb"ØrDeLn#ŠcëE:yÈ¢| O!z5´#•SÏtV<òjäÒ}LeÈÔáŒFÃF QšÛÁyl´|ŠEšØÊ VaàvÎêÖápˆz•rdfC›/Þq÷w â‘à B²(c‰Ÿü7òpÎô 'ÎćB+ï…ñˆÁQoS[CÐA°Á@!ö×RÞ² Fje$nÉsÝÆÖ>yÑb:‘áï*zjÚkgú2 ‡MD*xpÔR@ND+¡UUJÌ_Š.†'pI¥‰ñOnöôm'Ì 0•}‘ÒÝ+Åàþüòáâþê#Pˆtº~¹{oç$·°Jq¡T • ƒ9‰á§mD‹ 1™¼:÷ÇôÊ|âëÄÐÔeÖ=¬^D¡°êx-WÓ'räk|=Ÿó®'C(L!*)¿C2ÒÔìX/î23×K-’W¥Í\Ô÷—_Þ¿¹†ÚàE„gOšñ%tAW^ܘòL)dé8KÝ:Ç é™¨|å`„~ ›c«Â=³BUnÂhÃÄ¡“z ZÚÐ>ˆá¢Mq"±ñ=U•Çöµ59Vè}Ã<¬ÊÍ/ý)Úú-@zdÒ»TéON“O=¡ß •) Œ×°ëþò wùt¬Ã`~Œ!@/Å@&QŒ^D?¹ÍYÓŠƒEY ‚³±×e & ½âŒ‡,lqèyßd˜& ·»MùUÑà—~¯ ª­÷¡»°t“³“GÔºŒpK¯0ôJ²M5Óe<¹X*QAuV“š,pä“å™I|½ïŸ3Ý<¥¨ÅX€g‡gÈð“Õä+tØ0î _o^½±ƒs³ ÏðÇšÎLˆµŽçT=ìèÄ7ÕèáÔkQT/Ú[I/º.}hof{ÚÜÍ…rºHÄ\†g›Š Êy‘I¥^ž’cû‰6x/Oϱ ¤I8l¡×±‹)´HÆ›n\…%a7ã`^0Ä`‡|BP’#+ãpдGžDÍ4"!تºÜÎyUÌá­C¥H$!îðþšª;ЄºOŸ4²kRÒÝO¸´îHò\÷9Ùa±8 œ}*Xû’ •d‡î@| ªA8¨ŽÆ¬YaÝBÂv¸ëezc;äÅ+Äé ¼ ˆXëà«Ð8 Æ× ç!¬òb•¥²Þ…CÅÜ’˜W’€wzÅÔM“™Q åœŸþ™¥È2E·à©¨¿ðˆè/ôaL>GÜ«@QŸ4ÊHÅ÷ðÔÜ©ø6¼È³ãUy» ê5Õ{ö^ÓfòÍŠÊöªH`G‡1ä4Y¶ð÷ñÔãù.;ùõ E„qìgêo ?a‡êõyƒ ö^ƒ’Ÿð7ÿ ÌMN endstream endobj 25 0 obj 12480 endobj 23 0 obj << /Type /Page /Parent 3 0 R /Resources 26 0 R /Contents 24 0 R /MediaBox [0 0 612 792] >> endobj 26 0 obj << /ProcSet [ /PDF /Text ] /ColorSpace << /Cs1 7 0 R >> /Font << /F2.1 11 0 R /F6.1 18 0 R /F7.1 20 0 R /F1.1 9 0 R /F5.0 16 0 R /F4.1 15 0 R /F8.1 28 0 R >> >> endobj 3 0 obj << /Type /Pages /MediaBox [0 0 612 792] /Count 2 /Kids [ 2 0 R 23 0 R ] >> endobj 29 0 obj << /Type /Catalog /Pages 3 0 R >> endobj 30 0 obj << /Length 31 0 R /Length1 17180 /Filter /FlateDecode >> stream xí{y`S×™ï9÷^í’%KÖfy‘,K²-Û²,ÛX Û×¶lŒmÀ6f±±†°†%KCpÄ$$$¡4KÚ@è¤}’)tÒÔɤ’6i:™6]YZš„@2ÝHƒ<¿s%±µ}ïýñÞü5²¾å¬÷œo;‹®7Ý´y9ÑÂ“Š¡5ƒë‰ôñ}Ò=´e“3™6ÞBˆò‰Ö¯X“L;l„h{V¬¾õ†dºåÙ;†—Æ’iò%hÍ02’iZZ8¼fê±o5Ð-«× ¥Ê‹j‘®_3xKêùä—H;×®YŠOëϹþ¦å©rÚKHf¾™0Âí,‘x–g”øŒdF™ ed?‘ŽH€`ĪþÌ|Ì—J dî.•ˆ/ÕGþ@JÖ 9þ›¯Õ1z¢.gçï«>é°|f;¤ =$?x¨âÑD˜ëÂßW]:hùLê)U(‘¼ãÄIÏ©l´ÝyŒ~‘f.¦™?§™?¥™?¦™ iæ|šù4ÍœK3Ÿ¤™ÓÌoÒ̇iæƒ4ó~šy/ÍœI3§ÓÌ[iæ'iæÍ4óã4ó£4s*ÍœL3ÒÌýifOšK3»ÒÌÎ4swšéO3 ÓL_šéM3=i¦+Ít¦™Ž4ÓžfjÒLE𠤙²4SšfTiF‘fd⤤¹ßKøs &á >/ásþDÂIøC  á÷%|F¿–ð;þ™„ß’ð) Ÿ”ðë~MÂ'$üª„_‘ðËžð$ü} •𸄟•ð! ”ð ï‘ð}¾W»%<&á{$<*áÞ,Ö·;G¤Ô6 ß!á­^&á9î’p›„›$œÁ°¾qHh$ù€ 0°°° p?àIÀ³€~ Б¥üGp¥þ÷äÀ@0xpp D¯!ôB¯!ôB¯!ôB¯!ôB¯!ô"jŒ¡ µ«P» µ«P» µ«P»Š(ðT7ypÀ=p> °ð¤àݲ ïÑø¥‰KÜÄ¥7.¾tá’$üÄä“§'/L ëÕ‚Þ~ppAðˆZáô‹^ä$¤oÌ\èØÅ¢׋ÚzàÓU³´ |Žê½TßèRZ¼³Ju'ùÜã$hÌ,ÈÉ»Àç“Üãâ\þÝÓkÎOÿè+·[_¹ÝþæOÀo¹hÍz Õë€n\kqܸvÛMÙ›6g™sV¬ºa%Ðòá,ÇòáÑ Ùö–Ûší®[öÆ ÷ yÀ‘àRÆqpr-w·‡»tŒÛÍÝK´ÄÁ=Bv0%à'ÿ ø@à¡Îa¢ãžDÛo€>޶OÝäYnÏ‘,wø8˜GÓ˜ÍÝÅm…ŠýÜÜíDzw@·¦èmÜ)ÿfn…DWp ŽÈüÎcÜú#gøûÜM(gõ×"_`ù ŽCaUc#·ØÏ ™¨³©wÀðÜîVHÔÏ€²öÛ@Ù8¾’¢·ró¥ò[8¶êù¹- ¬|sŠnLÑRõ6²zSt7ÿˆÂ_ÜØ…4%w3Ì-æ–pK!Â9\77t7›ë‚(5Ü,À¢æ“iàûÀolFú1¤Ÿý9¨š[‰7B Cèi9èzZº’D¸!À`1`` ÊE$©5s™P”ŸSéz¤Ù¬ë¸LH­µÑŒ|JZ_pÜ4”+PeRš’ªïB}“rèˆÉn´pTAyŠ–25–¦Òþ-AC™zcҔȀ8L7D:1¤6®‰3Hne=5€²¡OMåצhMŠV§¨3E«@Y»`ŠV¤ò‹S´ˆ3` ck‘¦$ø8W‰)[9g‡R4œ–Ó*9§–”£„r4¾£UB9(GåX¡%”c…r”(w£…ÊÈEOù Ùè)Ô Eä²V€ $:—Îd3£³Rt>]Ä”Bç¥èPVþý)b›Ÿþ,E?¤§Ù é™=M?–ÒçAYýOèǵx D¥†³MPáH0˜bà4Ç&'žû×|g5ø#¥¥áïQžíŽŽä¸3öèD^ž;™››ÎÌɹœép¤3³²S܈ƔâD•GéQ±k78ŠרF&!³I>Ëb##GºæI##GÝn6"òÝܼ°xÖá†ùÛBOxþ1ªMôW?“ù§½Ýñ6'Æ5ºðK2?æ!NyÒd ‹*Â?Jý=*ó?ºWðÿÃ#‚ÿ‘y¿øÃÒ`øÁ½¼×Þ¯íåTC¶¡âC:=:¿ðÜô|OøßŽQµ˜C¿¶ú§x‰÷KJ§N‚N6E¥ú'}>F¿{Òš~ù#ŠY«Ž¼#=X }äñ„ßýˆŠ¯8rÃGÇeþq(Fœ¨« O<+øßzVæv+Âõ;FKø‡ß§Î=Ô°‡²!쮩•ºÞíóKC©Ü¾ï½Oæ¿oLðß3&óAŽ¿?Ïû??/ó6Âù/üç!ñ“ʪ°ø žÆº90§;I[¦'imDêNsŠ÷=€–¬ÞðPñ§#ÏÛ¨ÿŒj+qð³mtÛ¨'×(õïìÀS¶ŠGã3FùFië(­¥ÞQê˜b¶Õ˜ÍÕfc•Y2k+ͪ Y^aæfRn¾ø…Þy±â"çõeùô%þŒR¿¾ÀQèÖçåg8q€‘d\¤.CÙy$Âë ™Z•Z£•+”Z^i±@hå|,} Õ—P¾CH1DùMü·É/ôr Ñðý42MÕÇ÷«¶ð‘ÇTèN´Ç©†jŽƒæêlŠlÙ`Õ…,]à⺋O^@/ ÐètÚ5€ @P(¨ €L\û}ìóØg± ±ó±s±ObÅ>Œ}{?v&öëØ;±ŸÅÞŠŠŒ½{-v"öjì•ØË±‰ØbßÇžŠŒˆí‰Ý»7¶;6»'6ÛÛ‰m‹ÝÛ[›늵Śb±ëóÿ' ×ûïøÙ}ØÀÙ<ÜÉø¥"XÁ§>ü IÇ«¯2y!Ÿè¾Â£õ8ÑñuDÇzáÌ“¸ÓÄ0yàêévW(:É»ëa °dÙÊ>CIš¼kb{ø¿ÿyùïýÝ’‘×È?“íRù ä(ùNªæwÈód”¼L^ É[²>lˆvÀ=ÈYHfyd Y‰ÚÈAr(Õj Aüáþ Kå¾NÎ’¢_¢Þc©œ+ä!<å&r OzŒ´£¿z²³}˜|›A ùvî¸þsýXÐb·l¹'Põ¯Çk¿ïê±À/“Çá_[aCÏÁ×_"’½©ð ýäÁž†-`¬—?²…$¬`X’Aù—T|BšŸ`ù“oNžb¥I̸ĞËü¿Ã›îB¬øŸÏÿHà¿QœâË÷eïr3dzüXxF!$úé0€Cðø‡€¿‚¿{@ü%þ¬ìÙÉOeßK4É2e…‰ ‰Û±–ýù9ù1ù!yŸ¼Eþ¼N~ËWð?äÏ🠂\vJöMò¼PNn&_½¾Wa­0,Ì …r™”Cf"®.ÀZ5@–‘׈ìEPxP6_ã?ç¿} }¬FÜ»±é!D2".\[ºdñ¢þ…}½ózºgvv´Ïh›Þmnjêë"Ó¦†k§ÔTW…*ƒò²RIq‘Ïë)t¸œùy¹9Žl»Íj1g™Œ™}†N«Q«” ¹LàqáQJmq[so˪¸½y{ç¨ÛàŒkg]˜ˆ£ÃåÎt†}e©Zq™?NLñ¬®Þq"ÖöÅåþë«ÌŠóÃç.4žép¶Ä¾îöÁX¼¨»×å6¼í¸\Þ‡nãÙͽ.—#Îyð"|Û±¸¡ ù(rfÄIW/ƒc“ïÕ"“Ôºú€»{ãyédë-9•«y±iâºa΢c†q­½9'YãDû^œ˜Yµ µØODâE8z à¤ÞH N³>SSœšgbJ×>‚5;]û7dÐ[ån‰­„DcWdz!)Q—sÌ9ÖÝ›r¸\Ò qr™Ó;®Q7»›—«1 -‘AÆÕähXÔ²~œjë©ÄpÚ–©ãQê >#n ƒUqq÷wrC‰éJ nˆ Y²A5‰£Ò3ãòæ¸"9çʸ8'»ã¥c÷3e~mÌ\Ôç1¨qÂ{Z†{â9] ‘…A†LÝQ 1å9[†cH³ºÀî(š^›^>ÀÌ„¸£(S5÷îtM8pïÝÙÏôÇuh®»í?Öb[édɱ±Îø9½W—ºX­¬Ô9ÖâÆÓÐY˪&¦±ÀeµIÖ8#&)GÜ=èŒ,[™á;xoÚþ]c†¸ö.húAKæLÀ b«ØTV¡¥âÛ½\šê½ÒÔ`¯Î–UQ¬!¬ŸÌCë…½-ÃîÈ3õ@íyÏõm]®¸ÝÏ޵°!Æ0z&|í8cÉ|Âá§Os\ì‘é‘t€'ŠƒÑ¾TVªJè!.Dûúؤ’ ˆ+<;eånçëTá‰gù ®WP6QVÚÑÝÛeÖ‰š\soÝ9›ãøŽ®ËÙÔ†:csLH¬d®»cNÒ †™|èI:0¤–Ò<ª¦êK½ž²9N%Ÿ°¨·ÕÝ:06Öêv¶Ž Œ ›YævÜcãZíØú–§äþùßÛ툷ÞÛ7 Ó©’†Øã19ÞÓŠÓ¿iN?SU«sx9ø6¸]µWæå:ˆ"»8ås°~øó¹1Ã'˜½ÑÉále¡7²µŽ¸¡–¹,4¯>1„G´Ä$_ÁÅç`^Ã÷yZVÎM ËáÂ#%ãa1pN*¸\ÌŸvÉ2$â#sz“i'Yæ8BÄ€z`%éó&lbI ,Û]O✻~œrrm\í^Þ׸›X~ËoHæËY¾ÂÝó‡s8ÁÕÇÜpà^â }Ì„™•sç±ÉIDÐSˆ¼®¸Ü³€«ò÷9ã2O;êMg0€ìéñ‘¡A6f¦h«ðÌê‹+/wˆ*3â*ô Jõ€­R,ϬÑŒuÐ-±È†sŒôÅûü졽+ÙˆœNì‡ÚÜSãror2/{P oÌè®”–¹'®öìD <£] „RŽI<Œ­Gø*´ùµ†œÐ€@†æÂ/ûª™Þ³«ºàÅ¢ PÑ¥B¼wÁ_£SÇUåè_ÆkÊÑ!¾Š>…M^JíLUÀ³ q Fä½J”©Šf°±à»ƒgU_bÝÌ9FºÝ·Ä©$QéQ ÇužƒØ,$ÛkãÆ¾/Ù})=,‹õñJ2WÁf®•6´=Ç&»oeN’þ”•ºã¤§—&Áï D$}c×gÄû8•×çê¤ì±1¥îo7HÊK©»LY/Ζ•°UâÄš1ʽ3w׫ÊdQò"e'ú/‰\!7Ën"›…?“[ù?“õ‹¤‰?Kº„ARÁï$+…õd%ÿ[2Ì­&³ùßãÍ‘UDäÂd.WJäß"s…› ¨·‘Ìàg“¹üz²„{žx„É AILô ±Öpß¹†À÷’÷ZühEÚEr‰ðª˱ö–‰‚èñ Ž˜pöÊ"™x+PI²ñÖŸ’9XŠƒ˜Ñ*ƒä =ûlÁßyºˆžäp§ùiüEaµðެ_vRÞ¨(W<¯Ô(ÛTaÕ u¶úUóm-¯½Y{Z§ÕêšÐZ /Ý-ë!E¤”T*Ò!z̪¢âRRš« –W•jÊË5¥UBu )öW„Œ&S†ÍVäIéʾ ¿zûTe¦‘ZÃ| § §2C†S•†_½¬ ÕUõÜ”z¾ºÊë.ÈàîêššPegÎB"ƒ7›­fw5Íte2à¦È-%…V¯CßXï¬(´«"÷4·Õçè #¥N¯Ya|€~yIÎ~YKk±xJª}ö@(ìîèÎ*¬Ì»+¯<7ÔZì­¯k-s•úŠräk¿ñÄ£¹AøÓßÁ!=9æú;Ùr’O ÈV1ªT¹ dµ³ £Àn+pº xY†,;?¿0ÃjRj ²œj^½WÔ;g;¹LÞé´iVŸiÌ|:J,r‡ÌX`HC(` ù3‰5”i Û8­fŸógI8\ x(d e­áÌPȰsbb‚AÐU]ÏA*>ŸK!—›³,V³ ²™BC4³š©‹çs*ƒ6ƒÞ“(/È6Ô5$æÕÎõÑoc¯h5åÉyùÞ¼º{xºshÉ·$_î¤òêTV›øxÁŠÒ{çþå•ÄÇ÷nJ¼g·Ó^:ïmŠ81³Í“_ÈxÜTV‘f±°Èãõ<¬^ƒ®²ªòéhNFoá+¯TÖ8rªxçHiàŒµC·†Î@gLc¡-ðj%c0Z‹ÅòrÕÕU圯Úe†qfº©×ës[ŒfsSФ²Ìd9ÿxa0['S*Ížr:\æ±j·^z«ªÔ”©«ÌÅ則êKtj«·œ†¹ÜÈÂÚ:zãƒ?i ̺`P›ågüåIþé]¾™‰5'N6×Ú´Á Á×Öζ4¸Q‰Ùê­°Õ?c~!ËÅê  Ü+2MQ¨’+äOG]^E–ééhÖ4~ÚÞ,¾$?ßáõà>õŽ)Sê=AºM8SÈ ÎdÂø .¼­w?o2«”¦/lÚZ™Æþjž•¸;‘+ÌŽUâ+çÝ|f¨R²_¸5}Fcñ–'¦g|þ£³û÷×-¨^è«Z‘øØuõ…üÁàÚ V/šÒ¸cýBnNâ;m.&Œ«'ýÌÑ{&j”öÅ˶u)ƒÕ·5îlËÕpߺôöÖ­ýKïh‚Ö×O^à÷Áž+Ȩ¨0{”O4‚…™s9lÑæ´Ø„ ÞfËÑ 9e/ìËL¦>g¯I°œm¥Vk‘óœ^_QtN¡¨$+$Ófæ ÃLÒ¶ÍÜ™IProcîrÐ`…)ƒmÓ½¾r84 okÊò™_[ 7íܾ̟fEWÏÛ¼µjË­›w5¯ü·í{× Y[—t4®ˆ„V¯¹ovÓæ§¿~ŠNé½!xÛæŽF¦®»kæúC 9‰Ï{—ù*›§/ë©×Þ·dåÞþâjŠ7¬)îö‰p;ìÄIæ‰ezc¦ñéh&¯Ï§:>??Se'vޛṃ|†:ßÖ”Ðét©·Ùl.6 i¶~jøüLÒ‹á ©i†IÏ0¹0Þí¾lBuf2˜7 S—YüS¦lÓ9ʃ‰)ˆÀ&ͺ*¿iH6/qp~¬Êò%™ÖZlÕƒ ƒµ°>"cxe¬» zŒó§ˆ—T’ßà•ƒÉ³Ïé øÁ÷ØäYQÍ8®œæ E8fˆQ•¾­H£ññ¾¯Šë5#š¸fB#AÓ¥y@s@#Óò½‚– å|ù¢`ñŽzÌNS…é€)n*€8{ÇÄç+mLYHÙuæ\s™™WñfƒÝÀ¾f7˜²²”y£Tãå+ÙCUz:³²²ªdT‰ÔQ$”?“•áä³ È aoƒñ††Tda%%Á ̵HÈJÁ ¿ßãcáÏë­®*ô0ƒAlÄ‚Xû±XÌ.ÃK‹.«¹§ô ™ß¹©?縊æ·ÍZjÛ>02Ò4tK„SgùJŸjÿíõòÖŠèºúû…¾öi7¶>ø¤®qø–Úîî»*ކ»¶'¶×Uå[´Az‚^n²7Ý€êHdQ¶;ˆr²Dô”ÒbY1_¼_”ŒæL‹AçÖ¹y÷>QgɶŽf“ªÚîeòÓ1½x½†òòœQb‹1Ì$?¹PÞWµNz3&3],´.švã#s‡^ÚѾ½­µ…×Y}¡Ï§e¸fUÜrû†›Â]³=|>Ñä^óÎOÙè(¨3 ÕyFU•ú/oÎìTš^yåÕu··•Ã/Vb–ݰ0¹YŒ:ñãÇëmù#¦éNb4F^•o¤z#µóFÑ[ÜfÔÛô¼~¿h³äcáÛ'æ µÑi2¹ñÆœ[½Ý!E…S˜q¶Ø ÉU/µö½ÍÔ|û¹l›áÔâ ˜¼9¹"HQb VV@*JðíÛ2+›‚­«lS¦·Ü,†C³ú„¾býë;ºwó‡ÞlìÈ[xltÞ}±ÚšhSm]±é/çþðv[07Á ²B,Ï|Æ`ÐŽÌ¡¬€-ÀØ$ Šóó¡U6…üpTA”T=jÌ*àÝL•̼ÝîÚR6«”U_¯Å”mL·†1›+ŠÃb¨pOñ±¨–ž•釥A¨æ…”þäYþšO"Z}Ãâ•‘ƒG6ýð+M«Ã®ÚæÀÈÝk×—N­êȸJ‹·Íj.²kªÔ…ææ¢‹üp½Ù–ŸµD,Í:51qBïšZQ/­ŽÃÐîÃЮú½Elrš,y®L>ó«¢Ë¥±éÎwíçí±ìNû;¶5v‹`á-û4y|Þ>Qƒ«‘¸Œ†íÙÙnã…$‡ÏŒá·3ÃÐozK“V.t‹7²Ï]·`z®’–G‹÷°é)¼´ñQð#â#3ô“_½sùžyþÒXõ­ƒÝÛo”—ôDW|EóLs§ÿOŸíûàvqÝ?ì2nùF] ·úž‡aq}6ý[ÙãØ•¯j…Rq8ª4ŒP³…~ê×§ÁétN8ßpÊt<z¢­ÀÓæÌs ù¯æbEä\‡œ~nÔ¦GáQ©øCŸUþ”é[ZÙ˜™2£m8ªdÁ+îé•@Ï#ÐW× Ø¿‡*±Qg‹íÙCÜÐy½¸,±¢ÔkVßêµ[3u‚ªnóèÂÙS†L¡rW™×¡;Ï/¸ôTsÔmVcÇc)[¸Å!¹"#;T¾f×€Oóü´ Ñ+.íÄk8H$ù÷ ËJÒHæ’ â‚&W·Ï§V”5¼rßD Õ×ä×jxUMM@ßM»Eb fÄ"½!ß0œ7LdnÞ`´QtÁ“OÅJ½½oÛ'Ús |þ€Á§Öt2!C……y®¦¦Î§ÁD;[òòz8UQP(™¶Cf€[ÈdfüÀ;¯“­=jtšÍóB;J$9å‡ôغ‰Å;z,~¶·ƒ‚S••W¶ ——SÉf˜ý 6œÂ¦!á‘P†cùX²°6ø¤CHZ¤€)m)¦°%£FÂ,˜Z]Tò1v|`kˆIZ2Ø›í]Ýîþ½ãªüÂ׎Ïtæ6G/¬º;¼úä®å‡on왨ç´u„—uµM§Ë.µß0j+3Wί]³‡B÷?´ðÎh†¯mê¾9ü…&ÿÆú§ÆÍSk=^CÓºö%uÛ‹[VøLí•á¥u%,š¿mnqfâ­»|Ѿ`ï–)w|yÎ;¯fἊ…‘œê;l{U¡qj*Y%Fx&]®ˆ2D2˜ü|QqÑᨱØì¤¹²\>—­CµSGkI˜jGm¶qV¤L¸rg{ø«—¶oHÀØ*‹—þ¤5'x¶ýbÛv²$+¬3—w1,/¹Ñ5¬þ¾Ð”ùÅYzß´Ä_Uç˜=½Hìñ+²Šý‰MÌÎ¥pöY+ÐìgvõÌ»;4¸ 0o£³9Jå£÷µæ´uúwF§úìØðHVÏ·¤(Dí¹pëßáöGÔš/–X»p8j7§£·žZÏS=ÿ.Ïéù¥ 0NÓáh1ãŒêRƒÑ%7Ò AßœÛV$vvNuZqDMYú¼uMÞƒ³sfUâ¡kKúÖGK,Okgiâžp¤ÀlbÑÿc¶ h8B¶ˆuåLe,”2ägÈžÁô6ŸÔ\üLQ‘ïp´H_jÔW° Z…¡fT©¬/-É-86ùÓèÄs†LÚY$u‘܃^¿¤Œ¤è]iÑ¥ÅyUœHŠSq%†`aÉ  49<>_kWr¡´G}T§Éhï_\U3¯±Ä¬ÌòÒ‚j½iFqnf~g{EbwÒÞ¯ÆSB‡è¶WÍ\=7±³¥ŠJííø·±ê°?Ÿ!æìjö‰Ä¢Èæ³÷) ƒ†Î´lW;µð u^¬ðyädlç™Ý"“‡?²E øÊb¬]Þ©a¥‘_»‚pYoüb㉑·¹öõÄ®í·5/™–ݸ¾íÖ; ¸pèƒu÷ôè?úUãÚ½³z¹ÿ'ˆd3Ý‚,"µdD,a—Øb:ˆ·Ü‹CØ>Ñk©É凸Ð~l?­Ï`y;µè ¶S‘ Ç xO08•زùÚQ¶IUÈÑ3{ÀVÅŸ¼­%)º¼!eŒtÐHÅ›Ë×3Wb:ôÇ‹Í{T>¹ÿ®6$ëUVEb¸°Ì¦´V_ÙÇu:}}ÛÌéE‡Æc?ØÑ~K¥­nnôÖÛ>¬ÓåÊ{«QôØ`ÆjsqëT~a[]Q¶QU­zJh{ úôЙ vºt`¸ÑùÚ+t—{ÖŒ’”…óeСÄÄ<‹Ý†s”ÍÌÎ¥ÞgLY²¦ej`ƒ­&P8½Én …k®òy ß<³Dþ›š`IWkEâ›ý-N\(^m®UxQ˜’%Øw«1³ yD´²²ggó?ÈÆ¿k²°Ðlo.ešl3ZÚ8nQ™W»E{·–óh«µO´­¨íÒhe*™6·ÜŒõ~Ÿh¶”ã&o_¹!ûÝ^RIñN­Ò0ZPRŽ&w]ì:æ ÛuI^ÚYÁ ‰¥‹! v[œ¼”Y|6TÉc×5·/^_uˆmΓ·VWÝÕp“–Hç”®¥Þ®å7¬k¨Y¶³«ç‰ŽeŽU‹ [jœE=«ú×6ô}kCÓWsg"-9å‘*iû²èìÕÑ<{Ö›‹æêÝ‘²X]æm[ÖÜ}«¨319yðôËÂQøð|1h5ðYN“è ´™D­¾ÍdØoµpj½œjy¹BT“ÃQ‡úS…Þ”¥Rå)dšQܱ†™Òßf× Ø›§µn¸ô¶ßã »xªç§„Ì.³‹-ìÐa•L[vOe½;K³‡6'¾o(­--j-(›¾-zçócÊ¢†¥ýZžhÜØ˜ãʯi©ä)/~S2'JÞ÷ÆkõbAî399ŽÃÑ3În¯Ø/º-¦RZŠÕŸê-£ÎžÎQJ;5[âÚŒ÷ÌuÄ-åG&t¢Jצ“+tܸF£ÏÜæÕW+IÀŸýI°Â#ÅD…ùVgT¬\T*ÏRœm¶nÚØRW1¿&´3‹X4ýòÄ*ªùøGŸå8™tÃ@ö9ôÅ~¼™òúÙ³gYýÕô%.Â-B}§hä(Ï='JÍ<d”ME´Jý6uU»¸H¢‚¾A_z ?B¡µÀ>rü²EzôõôÍð7®YvÓÊÁÿͯ endstream endobj 31 0 obj 11010 endobj 32 0 obj << /Type /FontDescriptor /Ascent 950 /CapHeight 674 /Descent -222 /Flags 4 /FontBBox [-1475 -2463 2867 3117] /FontName /TWYTYI+Cambria /ItalicAngle 0 /StemV 0 /AvgWidth 615 /MaxWidth 2919 /XHeight 475 /FontFile2 30 0 R >> endobj 33 0 obj [ 338 278 832 488 496 414 430 220 593 552 303 552 558 441 531 271 555 488 504 547 563 621 623 681 205 556 332 494 205 ] endobj 34 0 obj << /Length 35 0 R /Filter /FlateDecode >> stream x]“ÍnÛ0„ïz ÓC i[1 (Rð¡?¨Û È•+ ¦Z>øí;ø)šÃø¸»Ük]?>Ò´šú[žÃQW3N)f½Ì×Ô zšRÕX§°Þ©Ü…³_ªÅÇÛeÕó!³éºÊ˜ú;J.k¾™‡qôï¾æ¨yJ'óðóùXnŽ×eù­gM«‘ªïMÔÏ}öËVS—ÒÇCD|Zo¨ú—ñ㶨ÁD¨h^G sÔËâƒfŸNZu"}÷òÒWšâ»Ð½`Ã/Ÿ«Î>õ‘=ˆÃ /¦ÔÞ³ÞÕ¸¡ÔX¦ûh¬4ÿ§ÛÝëPÃxŸÆ6}G‰´›=-Ùí‰#q„€[â‰lql‰­%î¢åe„PûÄ(¥€;bB¨-/G „¨2ª@X@8"êðƒRÀ@„ Ⱦn(`C„ Ø×Á …(gvpC‰lØÈÁ % s®Ü”§ð}„déà•Âw*cÀ«+~[aæ(4j‰0G!µX…¿„[Áí}Û¶pÍ‹VV¼ì wkJúö/Xæ…ý¾¡Ü¹ endstream endobj 35 0 obj 430 endobj 11 0 obj << /Type /Font /Subtype /TrueType /BaseFont /TWYTYI+Cambria /FontDescriptor 32 0 R /Widths 33 0 R /FirstChar 33 /LastChar 61 /ToUnicode 34 0 R >> endobj 36 0 obj << /Length 37 0 R /Length1 5168 /Filter /FlateDecode >> stream xX{p[W™ÿΕ,YÖÓ²e˾I|•k9®¯¹vœØ©b«–äÔ)¶ãÌÜ›*Er*›$ ‰[ÚÒ‡ ÎÞ„X(%–2Ã;»wwvþfèLy…˜] C`ØÙ¡3™NJÍïÜ+©I&áúž×ïûÎ9ßù^çÊ˧^ /­ƒ‹' §Èz¿FÓY|dY±ÇΑ¤;õà {ìz†È·ÿÁã³Ç-h˜T^(€ÏzÞF½³ À²h»Ë'–µÇ១m<þP±JßÀØu¢ðhuû+' 'lþV'ÚþS§ªt¦9|A€ eEZ½Kð‰¾ÀÂV?`§Fú*¹H¢%K“¾ý8/³&4¼ñ/}ú##“o·3‰.ÿáó{Dûòê7>ùV×ß¾èu5æÁïÁ öƒÜÏ¿3Fäíx«ëíÏx]´LÏU©¢‘Rgs­­}ÓÏ~Žê£O´É}¢ã'?Eÿ‘ :q Õñ‡P}èd›ü¡“OŸî\~¸5²éÁ%TÇQ-”[å…ò¹wvœi{<Ý{ ÅýrûËÒ©++W¤UÎþøLK=Ï/¥.ñKë—œŸþIK}–yŠÑâËE‡RôÇ*×ÿsoW|¬Â|©>Ï´]ÿÊ>÷¬¤EŸ»«oÌý\ûsRèÙ‰ÔØÿ>˾ú,;û4Óž|ªA{ê™­]çÏ1må™s ÚÇQÏyvQZÞ‰îŒDF"á‘àpÄ7ñÜq F‰mTXSj8=ëÙèÝ ö±ÞÚ?ß|+ðú‰×oH¯³>-Я·ªn5¸¥+ t+ë©ÙOÓX0Ôìó4y}.w£ÏálðÁ|.G©ËÌ%/ÝCÇ1ϲcÕóïô5ϯ‚/yÞà=tÇpö<âX~¾à¹¼ìù%.3? ¤ÂA™möGÝþH¨Ýv¶ú»î 0¿°:êJeåË(ßbþT«?Ù—ìMö$»“[“JrKRNF“‘d8Lz’®¤#IÉ™áyÆÃ9ÊÍOò†öÀ$Ör‡2LJ´÷ÌÖ×ûg(—ÎWÍsçùŠ„&œ>tX¯°A>'_&ƈçòç>ihÚf^ÊÐùÊfƒ‰Î§6”ãC³\V'µÛŸ3Ërfùá[(gÎð׳üFv±Ào¨þfvƒ<SͶ3¼/Ëû³Þ ¬ÇÆêÓY­GX«‹ –ÏhgÄ‹>ò œ³ÆTk×<âÀ3s“\JÉñÒ\ŽË3‡ó¼SÌñïb´sæ0÷©“ &!"þ4«&çúÕÇqÍîo\ßøÑÆu‹ú9»_ã³[דrìÙ¸.aÖÆ àh¾•~瑈kQD¡o‰êOž>|Ô†NÒ7è÷¤¾'} Ðo默ýqÖÁÌI¯ feC¬—®Ô'>EÓè/ôoôz’ÊpÑ×è} yð¿èdë$=M“ø#:DMu”Øöjÿ¿ßÅê½—¬žÈ§„,µŸ>‡õ 2‰çÿ±Ç“¾X‡kk¾@Ÿ €r*awûÙWm«ë 5J˰ÏYØå}Ÿ^„ìi¼ÆÅv³>¶ºø,¬û?t‘N¿óаqÊXxàƒ8rø¡œŸÛÿ¾Ü¾éûöNeÒ“÷¦&Æ÷$ïÙ=6ºkçÈŽá¡»Ûúµ¾»z·õĻխ1¥kËæMrgG´½-ÒÚn~Ÿ·ÉÓèv58£~åÑ´ž]âé<|1£†î»ÿúþ§°S›•á„1Påâ §–oÑ×(5jp—v;ËýܽÃäý²’åÎ8^u_¡Ä{çô˜ú…\§X–w¦õXLæRï4Hx÷”ÍÁB¦9Íè¢T6~7 Fcê9o© ±š}”›„¼Œ@Y¿MÌû™Zóu¤3œZ×È÷;NÁv}†Kò^¤¤x=k5JpÖúg-œEöãH·n!¦]½ƒ²¥%5[Z„FKùwuzÝÖhL1sNo–c1Khd‚Y}ÍÛ”VÓ M8ÒZkòñ f9µÆ|ãÌêH¾ìî5‰ýP_Xˆ›e‰§.äÑQ3Ð(-ïRp…\¼™D˜f3ج³öä®4wÛB(‹7㚘hšY!b¡é…fðvà†ö1!k ò¤yjÞjhÞ²vL2Fª2€â„x*Ÿ1 q(ÛÜ_mØ®*¦XÔç­Z(öhëý¹9=›Þ N)­ïy5*¿Š~n¦³(xÌÄ«BI‚r@ÍÍÚ^PúU~Þ`h­jy°Vù­U¯Få«öGô)u*ošSª2eæÍBec娪„TsÍç3OeóŠþ ø7/È|ê¢ÁCù2ÛmYHlÃ9âS¸M[f SM)å¼jlTŽ5×yEîL®Æ¼1 bÎ ý §÷!;ÉÊ”H5øV•yhT„,:¨#&ŠØ"[²*Ä >J$YDÈgT•%ǰ¥å<"ÎVQ,‹‰xºPIÑQ øÊ¬n:*¿H©„;æe½F‰”•¥>=¯ÂnQñQdùÇ{ù7r{Ý·Íf5¬Œ‰ÄéðN—øú<Îxc”7Bc–é[ÒºC– z’ì½& ×C’·kÖD¡dL3¤*?VyHã i}]NJ¨É’ç>0 O ýXýy”ZCœ%9k8!¯B{Èûí£ ÖIÉšùªÞ|,° îR¹J¶ðˆ]q6œ>¤"te[ ÍaUœð‡ÂákC|JÄLb)jŸÁâ¾ã?Yä•Óº‚L„ȵ:JV) cs%Ÿ±R‚! z ®l\ËgD Ôáƒ`‘«.G·U{«+ôÿ£Ž¾G?{Ñ(ï†L©>œ@Á¶Bééy½n–¬ À^Óâ(·ÒëZ¬ñ ±!œc|°ó{Q8jgÔŠj;vëÌ0ÂÞ¤.Lr¯:)ð OظKànuîàP*u3¯"ü‘€u’™!\Xx¹W*È W‘ycÜ?‚‚ëÑ …7Ä÷o¯(yÀ{ùJ± änйîøtÑàõÁ2Í=XÁS]SÖ\ÏbRÎZP­.`ÇŠÁ Mlª/ ‰ßC÷©»¹«Ç²¡Gl”0̰:d]'®8oНböØg%B ‘1Äfâ>ÂëöAò¢ ®b^œT<gtöˆ·IØ Ènug.U”&²E$q,GÜëoâžíX¯è{·cA¼nJ‡·F«Uìâ^HÔs“*« ¦…,xW!¼`ý¶Xf¶Bs꣜Yµ¶rƒÌýñé>ìù^ *¾ûìÉX«1. ±ÆK6ê'÷Y´ó•¯«‰ ©=ý*§y]8&á7.¥È0oøa$ÎÆÛQ¿›f£ÿÎl}5úë­X…Ü&P «gºpa4¼cÛ6dè û:„@ÀO)<>üÿLJ6VGˆÎâOü$į0ç'æ©—úivP!ÕÒÚØêr¨_ÓÑèàŽTñ”’i84, oööõi-­C­š×1<0°sp8q5<–·]MàoLT”øÀÕÎW:~µy,ñʚǚ‡1¸{ì—v;Fvô¨[’[Ù¹sxh‹iÅ àˆDÚ#êkŽ5‹"írµõu·÷ÈÁ{Ç•ÁîO>ùOé©âø¦`w²_鉸ßboÿÍå(¼=ÊþØÖïÙÖ‘Sss­ÝC[>¶eûæá©»zÆ÷L Äú·õnrüÊWÞù½óù¿s¾õ—ÿÀ±­‡áŸf¶¦\ÔAôþyã}{§´táÄÑÓ‹…ɇޗ¦— Ç‹ôw?0  endstream endobj 37 0 obj 3262 endobj 38 0 obj << /Type /FontDescriptor /Ascent 950 /CapHeight 674 /Descent -222 /Flags 68 /FontBBox [-1110 -299 1378 1047] /FontName /OTYLGF+Cambria-BoldItalic /ItalicAngle -6 /StemV 0 /AvgWidth 585 /MaxWidth 1387 /XHeight 492 /FontFile2 36 0 R >> endobj 39 0 obj [ 220 ] endobj 40 0 obj << /Length 41 0 R /Filter /FlateDecode >> stream x]ÏjÄ Æï>Å·‡%&·‚eËBýCÓ>€ÑIVhT&æ·ïhÃzð¿™ßø9Í¥îƒÏмS´f˜|p„kÜÈ"Œ8û Úœ·ù¸UÏ.&‰†áa_3.}˜"(%šFÖL;œž\ñ¡xoä|˜áôuª3l)}ã‚!ƒZƒÃ‰Ç½˜ôj„¦¢çÞqÝçýÌÔ_Ççž8ío$®ÉX$fJJ­®W-0¸¥'{3$T×jRÊÇ"Ž¥“,FBe®2¥üöžÎnD¬®¤f.Y|ÀûÖRLåíz~–q¥ endstream endobj 41 0 obj 234 endobj 13 0 obj << /Type /Font /Subtype /TrueType /BaseFont /OTYLGF+Cambria-BoldItalic /FontDescriptor 38 0 R /Widths 39 0 R /FirstChar 33 /LastChar 33 /ToUnicode 40 0 R >> endobj 42 0 obj << /Length 43 0 R /Length1 27520 /Filter /FlateDecode >> stream xÕ½y|TÕý>~î½³ïûšdf2ÉL’ÉB²°$Ù °*û*»ˆJÕJµ¸ÛÖ­X AZ¥-­ÝPk]ºZýtѪ´ÚME“|ŸsÏœÔ~¾¿×ë÷O¿‘gžç,÷Î=ﳿÏv˦­Ë‰žì'™4°¶‘ÿÊv€ lÛdáìU„(ÓVlX¹–…ó¢õ­¼òª,<ùJB&ÿ}ÕòþA&Ÿ‚ËW!‚……2pÖªµ[è}ñWú>~yåúTzÍ¿nmÿŽÔ÷“ß"\׿v9Wôá#¸aÓòTºÐÛŸ€ýâõ¹ YÓ8›¬M,¢\ ù>Q\DpGë­Â¢@vz±ò¾áÃþÔºÔ<í_Ä«A!gÞÝý3ÊÏ]ÿèMŸ\¹Iûžú)µ¸ûÃuêûF~MˆîO.^|@ûž|§T¢LåI­žÐz„ˆk¸8ÀÅÕ\ìçb{¹ØÃÅn.vq±“‹«¸ØÁÅv.¶q±•‹-\læb#¸XÏÅ:.Örq%Wp±†‹Õ\¬âb%+¸XÎÅ \,㢟‹>.–r±„‹^.s±ˆ‹…\ôpÑÍÅ.æsÑÅE'ó¸˜ËEs¸hçb6³¸h㢕‹.š¹h⢑‹.깨㢖‹™\Ìà"ÎE Õ\LçbS¹˜ÂE•\TpQÎÅd.ʸ(墄‹b.&qQÄE!\äsã"‹\.r¸ˆrá"›‹,.Â\drâ"ÈE€‹ .Ò¹HãÂÏ… /.Ü\¸¸práàÂÎ… +.Ì\˜¸0raàBÏ…Ž -.Ô\¨¸Pr¡àBâBäBà‚¤„0ÆÅ(#\|ÊÅ'\\äâc.>ââC.þÍÅ¿¸ø'ÿàâï\|ÀÅû\ü‹¿rq‹÷¸x—‹w¸ø osñæâO\ü‘‹?pñ?\¼ÉÅ\üž‹×¹ø¿åâ7\üš‹_qñK.^ãâU.^áâe.~ÁÅK\üœ‹¹x‹ç¹8ÏÅϸø)?áâÇ\üˆ‹ç¸ø!?àâßçâ{\|—‹³\<ËÅ3\|‡‹osq†‹§¹8ÍÅ0§¸xŠ‹“\œàâ8I.†¸Hpñ$Opñ-.çâßäâ1.åâ.æâ!.¾ÁÅ×¹ørñ÷sq÷rqwsqG¹ø*wrq_áâ·sñe.nãâV.náâK\æâf.nââ7rq×sq×rq‹k¸8ÀÅÕ\ìçb{¹ØÃÅn.vq±“‹«¸ØÁÅv.¶q±•‹-\læb¹ØÀÅz.Öq±–‹+¹¸‚‹5\¬æb+¹XÁÅr.¹àbý\ôq±”‹%\ôr±˜‹E\,䢇‹n.p1Ÿ‹..:¹˜ÇÅ\.æpÑÎÅl.Ú¸h墅‹f.š¸h䢋z.긨=NWËX5'3ªX3'3œ ,tu2c BûYh£½É "÷°ÐnF»ídtU2}²ìH¦×‚¶3ÚÆh+KÛÂB›mb‘“é3qÁFë­cYÖ2º’ÑÉ´zä\Ãh5£UŒV2Z‘L«C–å,4Èh€Ñ2FýŒú-e´„]×ËB‹-b´Q£nF ÍgÔŨ“ÑFÜGˆûø7ð/àŸˆÿðw¤}€ðûÀ߀¿ÿð.ÒÞAø/ÀÛÀ[ÀŸM+2­ üøð?À›ˆ{ü{àuàwÿüà×À¯€_¯¼f,¼ ~Åxeàec$ð à%èŸc€ç‘~q?3® üú'Ð?†þ‘qMà9ãêÀ«?0® œÃµßÇý¾|ˆÅç³À3Àw ß6l œ1l¶©‹lš³iÿ¦Ä&ÅÔĦ76‰d“ ;{|“?£ß½ÉhiØØµ¾kñõ]ëV¬íZƒ\]¹²kÕ±•]+*»–ì¨\ÖÕ_Ù×µ´²·kɱޮŕ »[ØÕSÙݵùçWvvuëìšWÙÑ5÷XGW{åì®ÙˆŸUÙÚÕv¬µ«¥²©«ùXSWceCW= OÒ,iÁ4ÉB`vž„ø…™“üqÿþ÷ý âOøÏú%›Ùð‰¹f¯PÛîÖ{÷yoñJfÏ 1îÉÍo0»_pÿÞý7·Âwç6—ÅtINZ6׬NZ¶ã®š:ÆÅ“å²Îr…# f§`vœb}À)ëÖ÷­’óYË ÑlÌæ1³7#»Ù0‰ôcÌ$ÅMÅ fcÀ(Ò1£äŠC>j˜ÓÙ`ÖôbW¾]/Æõ5µ q}Á¤" A'?¤¡O#8 Ã9î”°pëPç¼X¬uXCæ¶&4s%„Ùóèg¼caBuC‚t-\Ô=$_êÄÚ΄£µc! _{ø0™™ÞšHŸ×x ½§5±"NÅIr‘™=±%›·nŽÅ¶,ÁÇ’Í[bò?„„­4„?$àßæ-Óÿ@šòŸÿX6ä[ºòmØÝÿó%ÿ¤ÿ<ãù#4ÑîcâA2(^®öû€½À`7° Ø \ì¶Û€­À`3°جÖk++€5Àj`°X,`ÐôK%@/°X,z€n`0è:yÀ\ ˜´³Y@Ð ´Í@Ð4õ@P Ìfq ¨¦Ó€©À  ¨*€r`2P”%@10 (  ˆy@.D da A dé@à|€ðnÀ8`l€°fÀ t€Ðj@(ÅŒ1|J€!ƒâ„Q`øø¸| ||üøðOàÀ߀÷¿.ïïïÞÞþ ü ø#ðà€77€ß¯¿~ üø5ð+à—ÀkÀ«À+ÀËÀ/€—€Ÿ//ÏçŸ?~üøððCàÀ9àûÀ÷€ïggg€ïßÎO§aàðp8’Àžž¾<¾ << << <|ø:ð5àAàà~à>à^àànà.à(ðUàNàà+ÀàvàËÀmÀ­À-À—€ÃÀÍÀMÀ!àFààzà:àZ28c¿pêàp5°Øìö»]ÀNà*`°Øl¶›MÀF`°X¬®®Ö«UÀJ`°€e@?Ð,–½Àb`°èºÀ|  èæs9@;0hZ h ¨jÉàù0ýßþx=ÿíø_þ|ž¥KèC„ŒÞ>ñ%!2‡¬!›É~üw9Ln'Ï’ße䨣äò0yŒ$ÈwÉÉk—]õÿ30z•r-1H§ˆŠØ »8vaôa`Xišs;BvEðR̘e쯟‰ûëèíc–Ña•èäkâK¸Û?„‘±‹˜_UÄ8VNÃâõÐx- úõ}£OŽ>rYæ²,"‹I/é#ý(ÿ YEVÃ2W+ÉZ²N­CÚJè-E.Œ%²¾”k=Ù@Ö“Md ÙJ¶á¿ ЛS!š¶Qo%ÛñßrÙIv‘ÝdOês»³);åØHÙKö¡f®&dÅ™Å\C’kQkדȨ±ÿºq<×!r¹õü%r ùOúðe)·’[ÉmäËhGÈWÈä«hw“{>{§¹Ü6C¯ø bî—ÕäNòmòCr’”*÷ØïÀ„+¶¥ìH­w rRëJս˞T ·Ä­(Ó—ÊImDËpËeåäWüßbi‰©î½¸e¨Íî@Ü]Ÿ‹˜c¢¾ƒÜ‹ø >©U©ú4S÷Ëzbü}ãyÓ¾N¾AB]ÜÐLDúŠH¸©©€†ÃýˆèŸÑ—"ªáò<‰ ½®I—åŒ#çŠÏ䌳œññœ‚%8L+ÈÖ‡ƒ‰óuáà°°°£úp]¸'˜¸ ëY²VDä€PWë=«ê‚ ¡/XŸhضêP}_]A¾0¤×Õ†k—ë òÉN©‡Jä„7 9Õ‚,Äœú)C"Ñé×&¤ìúþÁÄœŽîú:(Ô#Ç‘Zù^ UmB-ß+¸:g&7‡òϺyØB–õÅ ƒáÁþÅÝ ©’êº>a%rÃu‰ÜôÀ€ËùáºúD,Œk;þBB™m ý‹àáÃÞÃSOˆéOŨ²-ÿ"4‘qÜL ¡Ÿk‚gâ|¡}–›†ãd‰ýÝ,$ËüI/Šõ$Ä>šr–§8»hÊ~ž2~y_–­×÷¥þm[åIì_,ÈGÍÊÿ²Šl¤R¤oÙÀ*ÊýË…ëPBØ’tÂiSïO³~hRò÷÷¡«©:ºEá Gx&³6"p“ìúÕóºåKXl}ÂQ› }©«Eõ¸M¤þ­ú€ô^áŽîÓ¤t졲 ÿx))#=ô9®ZTJ¤þP÷àŠD Ï?ˆö¹"Øí%â=0_O¸{y­¥°%‘û¾¨@ù*”í3¹yf;¡ÎÖ»E¿ÔCk Á|„gNC‚%¡bAZ£3§»?áÙð-©T]v¤ìÚ&\ Æ¥µMþ·ü÷¿<’Ÿ‘ÐŒ?“¡¼ôLì{þ㣱ÜôrƒõËë&<àe7E@~ÀÔݾø9Ej‹”1ðZM´ ù"tÉš„ˆrÊQ´=Á™ì/÷„цâsºiåP[ËõÛ:/Lƒrm§ZIçe!–^ÉÒ$ÔÚÙÍÔg“hˆÉõJ«U7Êáñ`Óg’›yrð&Ü:ïýòpê†$ˆ„ÊQEšûoª´•¡³6`  7ô‡ƒ–`áþá±ýË Åã‡6Ô÷­š‚np(Ük”£Ò¿øf/qœé]‚õ˜kQП±¼~„ ÉãŠ:Ò¯x<.½ |‹<®4EŠOÉã¢á^ò¸ê5Äåmd@‘‰üÝr¾FéÏĬ8D2¥WÈbE9*-# Á}Ò'¤WÜH²¥sd2‡ÏíZá±W¤¯Ëú¨j¥ñŠJ9?Õ}âOp}ˆtˆOÂG¤{I¦r˜L–¶“\é~|G.é³Èxñf OräzéSrüq7Áˆì÷7„°?Ü‚pˆL&âg‰’r¤ L$Vâ%.âGks“’Oœ$×ňˆŸû˜H˜”â÷9z’‡=m~ç“K2ð‹&%î]Bt¸o) FRH&a†-ÆnT…"áOâ2ñ©U:¡X¤øDÙ <¢š¡ú­ºS}^³CÛ¢Ò…u/é¯Ðhø’±È˜4Ýo›ï±„-߱뛶Mvb¿Ó1Éñ˜s½ó]×.w‰Gç¹Ë3æ]â}Øû#ïG¾\_‡o‹ïEßÇø6%<›¥—°[–ðUd™M}›áÖr‘)Âɓκ:Mú¸¬D:aA¨›¢ñ”ÏW>5YuX²6 'jÔ‡áέy}äù¢‘×/تŠ.E¿{óõ7-ó×jkºÓ•nÓhlé.gºU=ò¥éâß•¦OjW~rDRM]\“%}U§*Õp†Ç›75Ô<ßl·(ôv‹Õ¥QÛ¬†œºÅ#×9Óè=ÒœNv¯‘Y0Kxì¢b¯Ò6!÷ž&YcoŸ0X„¶ðpJD†ÇÞ?¡GŒž œ«¾÷Ѩl ý4ÊŸù3ž#dÓä|½0++Éþ§Aoðd¦‡uFÁ¥0ƒÅ >~6üBX Â[ú\[—²‹ÔÔÔØªªŠŠz{­î*+¤µÔr¡ÄZ ‹Çz™/ 'vÙ.—J6yT I&)œ‰”WÌÎnuX )¶jKv m×*Öüy¤³‡Óҳ͂FH*ŒÞhF0ÏgRì~/|oºËoRHjƒV˜:úc­Q«Pšü.ERoÒH’Ƭ?<²‹ M=ŽqB@ëÊ@¿¨$?Šû‹0+`1Ó#><|QV¼åWÏñ9ãHwÆ‘îtêóiæ|š9ŸfΧ™óiæü§ÅøoΞ„&‘RXú8r‚ß?ŽÌ2#?øßÇq‰œŽœ¥Ã¢%n|@V/ê}Ñ«³†¼}ÑQ6,è‡Ô¤æBÜn«„¢Þ7e«•¼cÍ9«bFu˜áPfd²µ¬¼4„Vé¤í9CÊ ÅpØJ³ý’TÊöÍ£O¸ssÝBdË‘WlFÞäÅõ9£#¾Ê…-Ésµs˽³³¯èxþâÔîÚˆ°yúʹÕyÎ@Tq ÈïÜ9«°³±Ò¦›¸÷ÁOŸ\ì æùMvŸÍ‘f×F‹¢õ×}w÷®ïì›)Ѝ¬è´•Ý‚VfÃȼ=ž^ì´åØi˱;Pf» ¶{PZûÚrˆµ_Ê62#Xn1à·#·ï η´°!iêð ‘!%k%Ü/óÑ‹&!^Ö$ÔÀ-ózÿáÑ¿ÊÕŸýèÛ÷vœ,[ÿÍëžÚýÍMUâ]~òÐ\VÑ ¾þöÑÕ'¶|j­Þÿ]:¡dÒn”,ŸlòES5 –kTf<5X~j9O­q­Ö´ñð¾aA7îg#‹!Qy‡QcG4¤oõ½7¡Š‹äaÄŠU"×óåÅ’+:då>̤´[¡3jFn§%WhŒ¥£*!©ÁРÐBÏQ§h´ùmVZÍï°ù­šÑ5ZKšÝ注G‹5V¿\R'Ê%‹‡ÔöT¹Ár¹e¦µš*·œNëå>iL'éjí¸ÝîU 9Ç3;¼t€LÍHE笩nŒÒ±J›X>ÛðŽ-u¢`êQXO‡—u\ãú<™ ŠÚ Çž³§¡Mj‹ßi÷[µ#RÕJ%>OD˜rh‰ýU±C$5äkñô´4³‡¶Pm¡:¶yth¡ Jᡵg$ÏF…`4í‹JQsªü`¹ü`¹'ƒåž,§ãJó°Xr¢¨L(ó º™™UEÕgæx›¬šçò‡ŠÐŸåÞŒI‚NЩqîåÞÞs¬Î²Ëe½¹¼ÂJÇ;ÚÛ媷ÒNÞ¬ÿ+;ƒÚP¹äš…W|s[MýÎÇ–OÛ5yôe«U¡Åq·ÞeÓÙ¦,^6X|Ç{_ŸßûØ…[[,¯÷éKìévM¤02ûÐ3ëwŸ=X—ž.\•™3j4–4Û¨ÝIÏôzÿÈ]ý¾p®/“ö ̹s0ç‘á5ÅBØ2X6‘Ì0+Xîr:Ld ÆMsgé©õõÔúzj}=µ¾žŽz:G¸I܉‰%n§«ÐFâH'núr(?…4wÞ\L ùqóYƒð¢A0\>õn¼P#`Öx™Î$©u©cõbe ,‰Æ%³¦èD—Š9GÈã :4#Ç¡¼´åi™oÈ¡gÉmÊë£É4bõÈ÷¸Vüš«‘‹¢ŠëTÿºa?'™sªÆÝî~Ò-‘” Á² e†mÀ² åtXˆ<1Q7vö,¡³Ì•(æø@x©\¼B7n­3ä¦Ï=þ´—žv­x*i:MœlˆÆ{ZòãÈŒÇË–‡h'jôÑšç:‡…Xj8ŠÎóÇ‘Ÿ†ÍÔlB‚i骲c’väœ;—›Rx‘.bZ~»£ÓÜXŸ<¨µ¦¥,¦ŠaDšF[úª7T‹ÆI“ÜEEºBGž.РäimJf<+X~V9¦óÑÖ—‘Ul0èhûÓÑö§£íOGÛŸŽ¶?µ.V6q/5uVy‡Þã6yŠ UœŽ@_ìÕØ°Ì+Eóâë¬õxÛ²–Z«¦•–ÒÕß„V芯PŒ á í®¼3D·PJ—T:U1#àu‡ìq´TÒ;ÓÎ ‡^mÐÖ¼ž ]ï_œ”åÑ Û•Âuz_ â]köÛ —*uå'GÔ:µ¤ÀdŽåõQnKÅÃyY_ŽÿÓÒÃy^½ÖžîLõå½J+™N®=5›©v(3 $3l~Ÿ®[ä0Œã™¡+,,¡Æ,ñ o‰K,ÈUBYB³XHFå\]¡9ªðÒ™€NxX,»«¨ñR½ò’íŠ`3¹É0KE"ѰËåü{eHîÒ]ÿ¥Z•b¯Ñé3Vø¢á°stUpFš(Š{Àã Ø4ù¾¹éÑ@ºU˜’^^Rì0Ú^WЦit`?¡O/‰ŠoTí™ÚtG˧ÿŸ:¾™“©sçF~T6Ð×[Ô~¬]|«mÌ¥láa» x[Âî2JvÇ}jmPºàqЃÚf*kƒØgîÇz<#e\°Ü«ÀòT–§9WeœÁ¢PG¼˜8Ìó´g)å‰c|ørjâ¸4W¤fQ^÷LX*Þn¹ýõ#_~妺–#¯¹ååÃõ'£‹¾ºaÃW—æFÞ¹iã]KrÄ;îýth邇ÿýÀÑ‹O.ÿÐ?[÷›fwÞ|f妳7Íê¼åÛt‡ÁsèiØIïÊR¥ – "3 –»œœŽ‚¨hp[Ó©yÒ©yÒ-£Ð–Nwé˜/“ÄšÙò¸Je@1õdž ‹Ö@xÇJ•õòîƒÑD1a©'=ßþ­·kí!/èò|‚3oÖêµm¹'§.èÍ¿ÿîÙ+²¤ÛûïY7m´p¼_ ªÕîšÅW-h_Sfù8§q€Ö0ãô(q9©#·Å3,…Ö žº‚–¢B.E-U­å Ôò©\ºwÊ­±R“@ÉŒ¼2Ã4`Ù4`yä´Â4É´B Ö‡Omˆ ñ¸{:,p2ÔáNí(銩÷6C©nÂÇ,S½„'R¡„-Ï¥Œnˆ\î )µ%rÛ].¡,DR#…^åÈÊð…zÅvgAuçÔÍÜXØÙ‹gøZ7ÏŽ†g.® –ä8¶˜4£#us¼5¥·=Z703€AFƒ>€.^\¶ &<ò«q#b©¥”Œ•ó××ÎXÙ>ÅaŠM›]<ú‡¬téÚ¶Õnµj´-4uF›Æ± ÒúM3yë4™-ºðÔd0‘Ì0ÌèA`ÙT3†Åüx¬$nwm%q+væ%Y%¿‡^맸߂«ütàñÓêð?÷ò0Š÷ËóçÙãÞ;?e¦‹ Cá!J*°<‹ÄõÖ`…Pׄ6ÔÏÙ¸Žª k…Õ5 kÙ“3üÊÜy®a!7ÕQ¬tËõZ.XÐ-'¬2ä%<_rŒwPï ÌÕR¨J…?»uSIµÛì±~ÁT·‹:©tÎÆ–ÊÞÚ¬’¹«×­š[:uõm±³¦ÙU QRéÕú¢ºÞ)åsÊ|%óÖ¬[3¯T¸bÑ—°Á fz²ð¹¨3sÂsJ+fO-.­îÜØÞ±o~Ù°ë­» ;º´pzú¤™Ùå³§•”NŸ·udF_ -?“,?å‰Ã¼+VgO@¹cÃØr‡Gë– àË;>H­p ͪ²Ñ@zªo—`¹ò¼½ÿAÌr.–²Ð„UXˆ/lå¥Âkòöå_ÃŒáÛé ¼¹‘WÿŸÜ7Þ—i¬iv;sÑ•C&úñ*¬j²ÈºxzíÂ9Y‚rÄ'äÀ`ò½B¾Gð¢µÉÍQt˜òð*â6åõx=‘ìÀ\ÒÆÖ^¶ª«M`m!†ÂÞ^¡··nŸlyÂWD±ç+/Ÿ0Í—À ¤O)LÞhº+ä±ÔÒhF°åd¦…lZ…°YVKtÒ@–QÒdP—Ž Pb»®HÊNlã>yVQCã©Ó‡ŽU‹Ñ«j¤ŸÀç'‰xÐ<30³h¦¤×ºË èe´‹”ÑŽQf¡]þ–ãØÜEÍD0:ž‘)´"+ømÚeÆ”e›L5q‡ÕýRf)§ž-v:e…3ò†Üüb¦™©H§°eúo ³¤(åËé½@7:½—ôò©ë\lIoUÓK0-Á‰zÅ0›OfÞ1¹c”N¦û÷qwdµB^©™ÃUZR^!ÕXÒü¾€iêm›; ª·<ºz·«xvÕôþæbƒSµÚ?sþвþ:#ß8\783Ð3gÆúéƒsaaMCvÊmZ²ÊæLö§‡Ó5¯Ù›î §Ûó»övžsÔä6Ì›Yë…u_Qn„/k¤“55‚.Tžjî`yäË# Ëö*>Šû1ºˆaѵŒÎ1jñذ¨‹k‰SW>9¤PN”OEZü –¶*È!å,ºM¤›f7ÆÿÔ:é’͘·ƒŽþÜïsi%deþD¾ P[]°Vµ(½R:pko¬¹¡!Š}¿ •Úôx± ÊimjÊYvÓ‚œ'œeóãÁêx}´nwmuw…Wxk뙃 ÖÈ”Üuè^ ö7ÊJyÀÇÈŸr+ÖÙ×$¶ÖœnË›Y2ztÞ‚i»ÐçÂbAéÇpbÝ8”FÇ:!‚ß m‹îdNÀDv˜ Av¤À&`ytÉ‘2ö½}ÜXdLÞ·q±)€}ŸxÂÞ"½[LÇ%­±©8XP ia¶‘—ctƒ»´¹>‡±†¹R>ã2“ƒáLŒÀã3)(*ÕÞi­ÝEýw,Ÿó󩵘‹ZßÐ>>YûFL¤Îד´·•)R,·19ŒËÀl€SPû…BùÏíWܪÏ*„‚B‘VôÛH‹ç>Ó“hÒ¾“&7°^æ§f;Ù˜%¿‹±9;:  ƒªÂ¡ Í ýtbãÑrÙ jéhÔ;’ÌhØÐl.2¨õ*I”ÔúòùãëÙ4eÚÆÖ|¥¯àaéªíÓWgb[ µî˜_èô9Õ&¯Íh7ô^½zçðÎ-§¯®¯Û|w·ýÀ‘¶åtîËÆ›—×)w`×<˜tYh”;ž?5jQ–G+yöËÃ|­''åá$äŸz`²uÊ}‘ “š‚m–&º…»PRƒ.;W*Oåçb¥ðJËþ< KrSqÊ#6ì0auŠažîòÆM!^‡™M¥vfäú³Ë‚¦kôZ¥Íüc †&lu5û,ºÚnZÛž™eÀŒg¶»MJ­^ë)혲LmõÙ³‚Ÿ¾K'GêЖœÁ,»Ïªî]rýü\£Ù`‡7S"“Go—n”~Dªqº¶”¼wÚ i/kÔ ñ4-v¡­±´fxì#:‚åþ~ã)šT£n‡ŒÍ6¡­Ý¯0O’JÕjÚšÐa¯³q#DA©ÚïW—(¨ãehX¤›~EwЂ˺ó²ãzp¶y’ZªlùµaÞÛNg_¥ô—iMyÁ™¿ªlYô«`{êø£Fž1/¼Ê†þXéùXì\Ì¥&]lZ1XÎÇð/Æ?¨Õacl“å=@$ªÂxær§vÜGXégzô“ÞåÆ&Û‚ñ锘D¢Q¶ lº¸Ñn¾:œVÒ»vŀ߿žQþn톹…eW<¼qíÑeù–Pq°¸¨$;U¶øê¶ÜÆ€`±ZGG—÷Nj,r/_TÜTäž·´ã/Á\öà¶ÖåÕ~iK8µ höŽyùé.[aF¸PÔ‰¡é=S«7tgÇ{ÊBÕ•¥^o[þô¾HvïÌY;; ´šÐè‹W+›szV*šF–L©5Þ‚ÜçŒÚôIÕ´}ÅJõÌÌ%äª5eBÞ%WtªaOðQËó´NËî æp¤/ó:ÊÆž¦é˜¯þlÌT§ Z²¼mòðIgcLÆ)W›Œ/;­òŠE¥ž°Ky'¬lýç”ÐØØœë)lžT½»A٥çâÆ[›îj yy{ͳ–ÔeuwÜÄc&ο­ÍÓWÜØOGÊkÇ. Ê"øCäæS5áöðú°ä¢EË[Ão –/Xnér„NÍ ç{¦Sk4>-¬3êe—„¤6j…¿?ßM>}Iئ3j%LªZƒÇ2úôh¶Õ™;„jØÌIâ²~½ìƒ—Ûþ„¶‘jãmDøè„ÎÒ 7øTøÂVþù–=Þ /õàÔS(_Ägy'î·Ñ(ùœ4b¡ˆ¨‡~n˜+4LÇä'BXv2ÉŒ*Ë­Zß22\èö%̯M‡9æÜ–‡9Ú÷©9Ô‹2§+m¹LXqË·EXdÆåÑ3ø ` ±ªdk ߪ¸qFKuCAesAÛøðˆú§kL¾¾¬Jù«ðòIêt‚Ž–ò—'Œ t±pÙù¹ˆÔêLí¡ÙöÆ©|‘ ¥v#¿®°js=í=ðš«]ùµ…U[ÆGV•-ÍíJ·¨Ûni®ì©›d)èhmÌZ°­90^b¸ê3cìçcàjУ iõší]í¾¢9Åuyv ¾m|B –#q3«AZ©éè³µ”š…˜ÙQk©Ú¤›Å =]ÿ³1ºv˜x*&|t*51Ñi)®+hÉóf5sÓós3§ü¥)kÿo¶¾Ü´ÿyz7â³þ/ÓÓe†‚úèìDwƒ¯ÃBÔWþh<­&Wȱ ¹Vêy‰„ˆFˆ¨…çW—^Ÿ²ù[›Ö?´®¼jóã›ÁOø«×´7¯® ùkÖ´7­© ZwúºÖ™{Ol·€w7XVU¶ôÀ¬–ýUeKÀ6GGH¯À6Ô·°ŸúBåôõ$:oƒåÎIÃò !7ô`,bœÌ­ ;dO(ó0|¡_¡ÙÒþý _äVøü(íüÏn…//É©›ÏâC5‹Ãé·©sÛfu,;DÝ ¥²[¡!Z·³¶º§Â'üeÛ·¯i´d–…G«¹7Añt.¼º¤×^•Wël;øäÖú«§Ùsk‹GïÂ{ǃ»iKꃵîIY뺸æ èct|‹Ñý33€<ÈÅèÞ9ïÑËͦ4ÕœÀò( æo,ÉÅ›IØ;;³›õÓc…S¾2ék©¤{gË,:çñÞ™º¯Æ]1“­ì|·7ÎÏïµtp¨s[šš£ÔD%·-Íi¨oÌ£/·9Ò¬êÏíŸGOpK çs«Âf¾‡¶fOÍ]ËM7ú/¶‰fl¢å•ø,VJNl˜,DÌ©F–‹f‹ ÚêÌ´qÙR‡ï˜$íVć6—ׯZ"fg°ÙIÝ òp/Oø¬M4ÝW|Á:˜5"•øˆ¨Òj4îô,§wÒä)á -G«³gL©J7†²Ò I–¹2¬Z­Vã(l«Ið þÒ@sMy]Ô,it:­I~w¥cì‚øÙªœpÈ w+9Œa|ö8–ÅrcÌÀg ¿ØImb~ÚÄR HöÓ!Úÿ4~O‹uCñXBGp¿ÓÑPø» Ý»Ö9Ö>ë«Ä~COZ\o3'ÌÈŽR x]pÂAOQÊYÃ| &úÆÂÿçƒñùÒ%fOZP?É¥SЃ‚XÍüʼº4>§«#Í»knVÓ”\§ZÂêH§Òf–7åÅs9ñ¹]óâQÁT%êÛíudìXúƒ~[¸<;R–ÈŒUÏŸ6¹¿9ß`sZ f—Å굨]^—=<)-:9'˜™7­“®nBc×*¾E¦Å'r‰5\êˆ2æ`¹.Àr‡”F, Ðà6\7¥/¸›Šéê[-{E/œ§“]iêPìü9æÚcoÃX?ë`Ýòšœº%ĵK0·ÐÝ0Oßk¶Ñ—¥öð=Ø[Ôwl3¿UÑèÎJsh”Z¥bQz¦Å¤UeãhL41ëü0øUæƒÕõ.Õê´J“‡–ûõóI߯šàËðò• ú(mAQÚ‚¢ôä!*RQºNÀk`?EèrŒR],[ü‘<ðSA·04÷4Ø€}×Ú š£z¥· 3å%gíŸ|-6Þ¤h'_pg߸—Ï*{¬ð®?V‡›Ï–ît§[U³î§~µƒ9fÜEM“ªwÕÃ݇ý«M;¾˜ÚÞ5{ÚÊ—‰™|½4òÏö¥µÙÝ]âVCíƒ3iì“Oþp/ðb6£ Ý€†~f„ &2y‹Š‚ËoŸ€ùË|ádKbV&^ XUX…¨EÈQ ™9ˆ˜ž)de !*ñ^bVHʱA!+(DͶ¢N.­ÕÙ ¢×"ôv\‹a D—¦!º]¿7࡜æÞ׬g ì+oqH¬W^9Äè¹OoŒžÿ¤Þú¥ç@1º”P¿þ1þÒ–è¶»+ìl^•v ¢$ŽžW}99^“bôy…’¾¨àNãmàQ…ô‰ï®ßaUK÷+´:ƒúÓÇèBcÒI 6­„-²ˆíˆÏ`ÿ¬…³LÔ詵']T„µëÉë§q{6>Eƒ§Õ•BåìB!"A!"B$]ˆ¦ 9 !W¦L¦N¦ÓòK/á JvP†3AÜÁ‚ùCަÇIÙ,36Ïh–óQcÖXÚ-ë-û, KÜæj²”6g7O¹5_ȧiùtÔ´Ø]M+ó·ç‹õˆu·i©‘_¡–ì=WSs–dö–m.´±£6¶`c†Æ2õšUËïXÓ7n¸Û oÚ\2ù©<¨PŽ~(Ý9<¯AúŽ(>)}¹(B£+Ø]¸Ó2méW¢øœ¨µ¡ÙãÍñ5QxUÄù¹Ïƒ—Ú¥ûÕó¥Jkµ#›/U‘Ù¡ÖêQCØ©Žø´ZÔ/vó#5X² $½£õUD®;MŠa+Z^7 éˆ1µPÀyäûOA–y¼ ' ´«ÈQ.AK[k’ ½f*ÃB¹^Ðéö‚ÖŠ^_<)·9¬·¦7óÝ;-è9¦ì¾–Ï0avú6äl—|DGß6¸ôÞú¥cM»Üˆé©¦ ÕjìÑ@FØ©Wüò5…Þ™‰××­‚VðŒ~¨ìÑ`zØ¡SœQ¡³üéÙ6Q;úq¾ÉnPbw®–Þ ’”»I8%:%Oñ ÞJðT¯höêìͺVE;i¥^l¬bjÐuQJvhÛ Iì²ÂŽ·„HÙøÑ­]vœºj±t‡ª¸Ä´ŠªÝZ‹4ú¬Æ’•‘‘éÐ*AúHeÍ ¦eYU£'-V¥Áaª6´Øé1)ñb¾q¤P|Õ®Wbž°¡$=XÔ¾†ÿå…™zšXP=cŽÈo—!½L[§µÙVlZŽ{›Ì˜+°yÁƒSç;ŽÑ{ÏcÜAõPÇ}ÉŽ.9+„Ë^•_èAºøšJcÒŒ¼êôÓ!C8<ºÏb§oበ=Ž iÜèVáa©«ðú§:-”ir¹¼qM(o «U&—5hò¸}–‘;ð¦-]ŸûP8,}EÞ×ù‡^uÚuJ—ƮԌ#‚ó5çé4Í_óågúx’ñÝEjšk½9`ú''Èñj?–‚Á|¿^ïÏfP.É ±ün Ã?ŒÈxžuä üêÇ=D_T8ûÚºJ+aÁ£Ä¾Kßš™à|\WT=­bmcQa=@ïq³´Ux'&~¢Mª\¸öñSVj_ù‡1.´¡ÍàÉòz³Üz•Ém½ÍÖk³á EÃ>ЪRYƒþŒ²ÂBÏóšþTÿ_B~÷´UØœº¿Þº?Ôlܪ°ó}ƒ°™ÝÖh÷Ú­n ¸VïÉòѯSF¥EžçÕxs@þ ˆ}Ÿ/hS©lAú Âè[’Nù üZš!‹’á}bwêÞ)O›úQ…Ñ‘îô†l •Ø«0Ú3œ8³S(?0š5 µÑnTí2šµ¸³ÃˆûÕ 'ÄBq:~#e:AÔú 8ÜÇú‹šÆ”Ûû †Xh³Ž.±áOøZ‘Rø8šˆD2TVîrŸx´@yÆ4OÜ”‘ˆ¹Õf‹J§ëñˆ8ÖÀý0€¨TѨÝE+©Â®VÑ×–**ðû¤Šòr·[Âï”J\j©¢Üåv¹Ôj©Ù$ºÝé†WÒ¤`aaPJ{Ùáv ¦>0 8ÿ0¼Ìã_1¤»Ý¢ééU8šcÓÞ=zÑlÁò]u·Ö– «®X£G£6í]‚Ò‚¿ÑOîB|$¬^ƒç¾IZ!T)·ÂšÚ¤Òr©¾äƒ)Õ€ÕB¦Úì±Ù¼&µ[‡wl=!§V®ß±½@•„ǹøðK AV*üòƒ4Ím«íìˆÕö_¹zÙ¦ÕÿØ endstream endobj 43 0 obj 15298 endobj 44 0 obj << /Type /FontDescriptor /Ascent 952 /CapHeight 644 /Descent -269 /Flags 4 /FontBBox [-503 -307 1240 964] /FontName /HRLCTQ+Calibri /ItalicAngle 0 /StemV 0 /AvgWidth 521 /MaxWidth 1328 /XHeight 476 /FontFile2 42 0 R >> endobj 45 0 obj [ 631 498 525 349 479 335 391 226 229 471 229 527 799 459 305 423 525 533 525 525 453 525 239 488 433 579 487 662 525 306 623 420 715 567 525 543 452 455 544 615 855 386 252 221 252 395 646 268 250 498 250 517 459 ] endobj 46 0 obj << /Length 47 0 R /Filter /FlateDecode >> stream x]”ËnÛ0E÷ú .ÓE`Z¤í)xÑêöô µ,ÈòÂßsÇnÚfq\ g8wDjñ¼û´úÙ-¾M§fŸf×õC;¥óé25ÉÕéÐÙ2wmßÌweïšc5f ’÷×󜎻¡;¹¢Èœ[|'å> endobj 48 0 obj << /Length 49 0 R /Length1 5472 /Filter /FlateDecode >> stream xX l[×yþÏåKŸ’H‰ÒµåK_Q–u%S–dY´i‰IÙ2ãD’eà^ÛYI“²i×N][Î’4©•¥®\ÚÝ’µy´ÞÚt@÷@7à0¹X3-m‘v˜‹nØŠ[Ь(¶!k'[—´qÍ}ç^J~,-†]ÝsÎþÇ9ÿù_çR gÏÏ“‡ÉFÅÓ…3d>¡ †Žâà Š5wDˆ¤9~Úš»–‰¼ûŸzô˜5 1©<_(Ysº‰q¤ „5g SWùôÂ#Ö<Ô€±áÔÇŠuz¨sçéÂ#õýéŸ1W*œž·øÃ£{Μ¯Ó™NdûFH†¶(-m|¸fö[‰¾@ ô'ä"‰‚'h,}Õ»Ÿì`ŽŸ}sD~Ð÷‘@ògäj]û×v‰ñµ¥?¾òÞæ_~ÛýZCÒn4ëœë‹·Dî¿yoóÍçܯѵמ¯SÅ ¥f~ôfkÛºøGtŸx¼UþÄãí÷÷€þMt§Ï ;õ1t}¨UþèCÎv,œ…×?‰îØ tóå<_¾øñŽös­¥Û£¢-×n¼|µ3šÀ˜jý=EM(?ðú¯¿Âîñ%RßîÛ ÒÊË/·´˜,oÈë<#i™÷/=séžyáÇò;Ì]Œ_+Ú”¢/ Ø^Þ³!f²o~ac,±ýKì¹g%-òüæÞDÛó,øìx*ñÃgÙÛoIZê­P["õPHl’R«Øå3ÚâI{òÓ>yÁ¡Ý|JÒ>}Ѧ½~]¸Û°»ƒíg÷Ñ m`Y–1ÇílÄÓlÂw±¤9޳1sfC©L»t‘iKhŸÂO¡5\to§tj«;˜·‡##áð¶póp80ö†Ý[Ãΰ-¦-áÝs± 0s’ŸÙ˜0cùiœ14šƒRÅqQM¬`eðàÚ ÕÈ|©Sé±h÷&Ϧ@ w GêÕü}Z`£êïRüʆ9‚)lòº=^§«Ák³;¼È ¯ÓVÚÐI=<\@òÐNÊØl_£ 8=ä±y;i§Û°v?l»JWÝ_ü¼×˜ŸRÍ™­÷E\¾p°Í×lùh·›ù)Ž6Žö;hvØ«;Ù—ìMö$»“]ÉI%Ù™”“‘d8Ùœ $ÝIgÒ–¤äôÐãÍ9ÊÍMð†ñÀÒrË6e–j9îž>¬WûmX.]Zf4Çí—–% ÍéC‡õeÖ.Èåkƒð\þâg M[ÏK¹:_\oðA<½Þ œá²:¡ý¯çœ‰9·p¾N±æÚ9Þ›å}ÙïÉæy·šõÜÂ=Âì®ùTZÐ0ˆ…ºµ*ðqœñ.1LªnqØéÙ .¥äxi6Ç;§çy‡:‘ãßÁldú0÷ªH`Çg©‘4ÇAÔ Íq¹Oö6‹äFíùK ®Ý¨ý>Qí†À£§[³vGW¡ÿ_ÑГô»tÕù¦×þÓQ%Ÿ´Þl€¦‰[¯ßzUøúÿù P”º)tvû:s¼OnW7ª¢Ê¦ˆ:ÄP£óqj²ÏÔ~îx¾ös_"ºlµÿ°O×~‘2æ?ò9|ÈÐÎÍî¿/·ojïžÉLzbwj|lWrçŽÄèö‘mÃCƒ[â[úû´ÞÍ=›ºc]êÆ¨²¡sý:¹£=ÒÖµ47~Ÿ×Óènp9v›Ä¨Ex$­gOòötéQƒ ÷ÞcœS³U›”¡¸Ñ_çâSKއ¦õ*¥F îÔîe¹ŸÛbÁw£Þ/+YnáU÷J¼gVªÁÈktËòŽ´Ê\Šá ツRâÁiàA01Sœ¦uÑ–k?’F£úYw®N ±šu”;”¼C®Ü£æý¬¬zÛÓN¡*yÌ),ØnŒ"X’¼1d®FqÎBïrÖÂYx?Žt÷BìÍѱA¶tRÍ–NÀ¢¥üm›Þ°,U*JeVo’£QSi£½êiL«éùFœUª6z€ñÜr¦Ê¼cÌ$ovGU¢Ì×,ÔÍŠv’§.ç¨Ø ”–Û\áWî$Ä,&› 1sOîLs—¥„r‚§ œ.+Õ¾•Ê•å ÍkÞ’Z*ѹ­¥ªd‹eËs|]núPP-_V„»3f'œ§dËJsÁ›G¯f z7¾TžÏ‹0ay5š;­/EWdÜDúR–7iÜqßc?‘m•lä„"¦•Ê’Â_œÑï¤F‚ Òß§T²*vÃbÙ“Âcñ5·™Ñ8U2“º\PøâÑ“°ÞÂ•ÕøV‚ÜûßQxþ¤Èa`ÑJù“â('!iÇ T.Ï›G½b ñªdOfD‚ˆ~:éCz¶¬faÏú†0äm±{e£QÞ® ÁJ%+T,” ½° ÞvÍTÚ 'dAŸ4O͙͙>ÀŽ©Bƨ£ê  ØážÊg CÊrwÅ–[T¥"–wÅxH F¿ÚJ_nVÏfDt‚SJë»ÞŽÈoÎM¯¡Y<•øÛÂH‚r@ÍÍXQPö]~ÎJ`X­îy°ÖùÍU¯GäëÖGôIu2_©LªÊd%_),תJP­T½ÞÊ™l^1ÓŸÿõË2Ÿ¼bð`¾Ìv˜Ûãp¶Ø$.ô–™ÃÂU“J¹ Þq5:*G›ÖxPE>œ\Ï9D?r@ä\%øSœÞ‹ê$+“¢ÔàëyTæÁQ‘²Pè Žœ(b‹lÉì+ø&’d‘56#–=q n,9Š-Íà5p¦ŽÅ"ѨȧËË):Š _œÑ­¹BGå—(×àǼ ¬¬RÂeq•²&žWá·ˆø&3ããWÅ7jûZlWšÔf%! ;´Ã;Uâ+s8ãû£¼3]ß’Öm²$XI²M@®‡$oÓLAaTÌJPU¾¯ò Æi}ENJ° Å’g/E¤¿¯~—‰:J¡ gIÎZžPWa=Ôý¶Q×IÉVòõ¼óX`Ü¥òZ*YÊ#wÅÙpú ŠÔ•-345«â„+~õbˆMм‚KLCí3¸_ÜwÜÿS³ƒ¾rZWP‰¹3& d•²p6Wò³$² ¯¢—koæ3¢êˆA°ÈõG [¦½;ûûþ¯¾ˆ@òŠQÞR½8² Û £§çôzº™~2“{M‰£ÜM_³â* Ò9Ê:¾A vD̬¶rwN˜ÃiÖpçf&m5<„&|÷¿ULÍøsnž]÷ÞCžZ%£|\dU•]𩦨¥‡ôkA"åÒœþ’Ĥt~¨v¦_SˆR&VX,Š˜PŽaµ—¤“_¾–¿LªÝD˜ó"~ã˜8‹ 8FEüà1qÁU> 8»…K™8Ü'P1)£¼é*œ^â©iýq£\ÉÂØÔj "[#.©cU&9½¼QŸàuBàÇ~ÜÂ;Þ¥N ü‘Ê2R½’W‘þ(À:ÉÌ!,¢\Š)˵*èuTÞ(wÆŽ ¡Àº5CáŽØ>ðí-ô¾X,=D˜BÖ›*¼amA°Lq7Vp×WǤ)ƒëY¬ÕFr,ÜÐĦú ¡‘¢à{h¯ºƒ;»-%Ýb£¸QiVÍëÄã±%H`}f!412¦ØLÜGx]^h^TÁUÌ+ð€ŠŒönñ6 ¿3[ÝÞK­‰lIËóø¹{ Ä+`Ï,ˆ×eÀ(âðæl©Î€½ƒÜºï0e]ÖiJè‚w Ê Ö¿ËÌ,Ó¬úg¦EÍ­\ s_lª€KÞŒŠï>Kk5ÄJ¬ñ- ë'÷š´s˵?RI²úô÷©œæt˜„ŸØøu`TîEðÃ(œ ÷b}&ºRið}¸€e¯ßÚ(V!· èìž*\mîÇ¶Ž ½Â¾ €âÿbø9Ñ¿WV1„_FOc§WÐ}Ú1G=ø :@ÃTHµ„BþN§Ÿú4mÀ‰ §âȧ”<@CÁ!ih½§‡¨Wk †4m¨¿d`(~½9onK\ã/!:Š?x½ã¿Þ”ˆ¿ñ½¦DÓ&[ضá1iû˜mÛp·ºÑ/¹Ôm##CƒR8„‰ß·…Õm¬)Ú$š´ÝÙÚÛÕÖ-v)]íî|ò3éÉâØº@W²O黚Ÿf7é´n޲kmõnÛÔJ¨¹ÙP×`çounY?4¹¹{l×d´oSÏ:çC_ùÊ­ŸØ¿øÁ1û{¿øSÛ|þÑhYÊIa¢æŒûöLjéÂé£gOú§ §Né¿dC, endstream endobj 49 0 obj 3462 endobj 50 0 obj << /Type /FontDescriptor /Ascent 950 /CapHeight 674 /Descent -222 /Flags 68 /FontBBox [-1105 -279 1222 1047] /FontName /OTYLGF+Cambria-Italic /ItalicAngle -6 /StemV 0 /AvgWidth 542 /MaxWidth 1224 /XHeight 475 /FontFile2 48 0 R >> endobj 51 0 obj [ 220 ] endobj 52 0 obj << /Length 53 0 R /Filter /FlateDecode >> stream x]ÏjÄ Æï>Å·‡%&·‚eËBýCÓ>€ÑIVhT&æ·ïhÃzð¿™ßø9Í¥îƒÏмS´f˜|p„kÜÈ"Œ8û Úœ·ù¸UÏ.&‰†áa_3.}˜"(%šFÖL;œž\ñ¡xoä|˜áôuª3l)}ã‚!ƒZƒÃ‰Ç½˜ôj„¦¢çÞqÝçýÌÔ_Ççž8ío$®ÉX$fJJ­®W-0¸¥'{3$T×jRÊÇ"Ž¥“,FBe®2¥üöžÎnD¬®¤f.Y|ÀûÖRLåíz~–q¥ endstream endobj 53 0 obj 234 endobj 20 0 obj << /Type /Font /Subtype /TrueType /BaseFont /OTYLGF+Cambria-Italic /FontDescriptor 50 0 R /Widths 51 0 R /FirstChar 33 /LastChar 33 /ToUnicode 52 0 R >> endobj 54 0 obj << /Length 55 0 R /Length1 20348 /Filter /FlateDecode >> stream xåœy`ÅõøgvuX·dY—eëðZò![²åûˆ½qlÇGÛ±Eì$Nì\„«9MH  0(g9CÒÒ‘•R B)„RH)g ”¶\†”³$Øþ½Ù'9NJoÛßïûÇOñÛÏα³³ofÞ¼™]ذnxÑ­„'EËÎZC¤Ÿÿ6À¢egoð`Øú9!Š”•kN= Ãߢ2Ÿz榕Î ÉM]µbh9† ¤“òUaZ Ì^uÖ†s0ì7¯b â<ĹˆÍˆMˆsg#†ëëkk«céeP‰ï ÎBœ‰8q:â4Ä*Ä©ˆ•ˆˆåˆeˆ¥ˆ!Ä b b1b±±±ÑsT@Íúó§ "ˆ^Db¢Ñ…èDÌEt æ ÚmˆVD b6¢Ñ„hDÌB4 f"DD=¢1Q‹¨AT#ªbö*x¾JD¢Q†(E” ˆbD‘žÆìA(%„‘AD!¢@ä#ò¹ˆ„á‹Ùj °l„³±ž³U¼éA¸.D&"áD¤#;†°",x‡4¼ƒ#S&„a@è:„¡A¨*,3¡ÄHBŽ!x‡ "N"&ãˆoÇG_#þŠøJº-ýRz"úF~Žø ñ)â/ˆ#ˆO#Æ!>D|€xñâÏx¿?Ŭ‚;Nÿˆx7f…‘Cÿ€x'f­„ÐÛˆ·bÖYú}ÌÚøâMÄ1kD¾³6^C¼Šx‹þ-âe,ì7XØaÄKˆ±°_ãu/ ~…xqñâY¼î—Xô3ˆ_`åŸF<…÷ûyÌÚ5;ˆ<‰7zký3,ìqÄcˆG?E@<‚x‹ÞEDZ臰è}ˆ½ˆ=x£ÝˆboEìB<ˆE?€¸±ñÄ}1 X}úã˜e&àGˆ{c–ý0f™ ¸'féܳÌܳˆ€`–;1ËÌrf¹ÓnÜ·bèÌy3âûxÁMˆc–.(ó¼üzÄuˆk±JßÜ×`ΫWÅ,ÝpÝvÌy%â ÄH,­Ò.¥õ.‹¥-l‹¥ .¥µ.‰¥-|Ó.Æœa– Å]õˆ¡Éý‰¾Åý–v®û Ÿ<ò˜æw d$ ² äA@îÙ òû@~ ò#{A~rÈÝ wüäN w¨W¹o¹äû 7ÜrÈõ ×\ ò=kT«ÜWƒ\²äJ™*îî(9…¸¹cÀUÄM/ˆ™ÁdÒóc©ln@¬™X¯]‡X‹XƒXøâ,Ä™ˆ3§#j51#+¬Q…¨DT ÊeˆRD "Çi1¢‘Š0!ŒBÐÅ QâT‹Ð Ô"¡ŒéXS+Ä…ÀAÆ@>ùä÷¡9ò;7AÞyä5W¡Y^ù-È£ ?9òÈà ·CSܧ[QÓ›c&686¡rÎAlDœFÌB4 f"DD=¢1Ù‚HC˜öó<ÏÅD÷=òÙr„ç Öå\D¶ú<¬Y7¢ щ˜‹è@ÌA´#Ú­ˆÄlD3¢ шÈBx±ò„áBd"2ND:°ãcÚVñVxÜqo@ŽùúÀ_A¾ùä ÏA>ƒVýä/ ùÈAÞùÈ; oCëyäY_‚<ò §Ažù9ÈA'Aâ A‹ïÙ ²d7È­¬õ¹qÔñÄyˆÓb&p…è*Ä©¨–•ˆˆåˆeˆ¥ˆ!Ä b b1b±±±ÑèCÌGœ‚ˆ z!DU]ˆ(@ùˆÌ§ ÷/Þ0|ýðaˆPÞ3¼gøà0Ÿ|\L®¬iÞ:|Í0—é¦íÖè›7´¬‹¬ß¹."[WºŽ«ù|}kåŠÖÑ®uƒë8ȵ{]vn3Ë]¶ÎšÞl\W´N\ǯmYY³su¤sõêÕ¬Þ±ú±Õò V_½šÛgœ¸Z¥kþNËY‘ߟEÉn’Aç&c¼zõ#Üìz|ÂMˆ“ô PÀé ˆÓ‚§FVí<5²2¸<²bçòȲàÒÈPp0²$8Y¼s ²(¸ ²pç‚H°/2òŸìDvöFz‚Ý‘y;»#Á¹‘¹ßlÌÙÙi ¶DZw¶DºZèì`s¤‰/wà B\ð·ÆµÕuÄ%Ó f®ÉäÖd¾•y$“_“q$ƒ»ÀI é¤_ÎàÀáÁáv\íØáØå¤^»&uk*·Æ´ÕÄ™DÓ ¦·L2bºÓÄ®6ì0ì2ð†%†O “Ù.Ý¥Lÿ+=ß©_¢_­ç zæ¢>XÜlйuâ쎯 éêu:þjuÁp³¨ËÎi®×vj—hùZ*jýyÍŸ¨'Õœ¨†„OT“*nRE O=ö¡Œ>…µµ¸›ã”ì¶R9ÓkF{{ö¸rr^{4¥ka”^õõ°£Ø½ ª¸,J" öRzUÿ(åfõFÓÚ»`ø’íÛICf{4³§/zgf{t+œˆìdNH樕4ô¯^¿~C`} ‹×C̆aø“@áçÃp`g²þÎå€DÈ-eZ?¼dÊ€ÌÍJ†`YþNÿo£YÝþ×~ôíÎÿßߨ¾d1Û½%dâºi¶’ Émd'ÙK&?#¿$/‘ϨöŠ/!‘?ȧä S%µÐ *í5O»ö?8¸X~Ññ±2ytòý‰û&߇Miý´˜ë d“ùÇL¦NŽ7qÝD|ây…†¥kܳPÚ:6y”«‡+“å,ÌmcçÒŽ(ï˜Ø5±ã„ʯ!ëÈ09‡l"›É¹d 9Ÿ\@.&—’mä2r9èâ8¿‚\I¶“«ÈÕäò=r-¹Ž\On 7’›È÷ÉÍär+èñvrÙ‘Hcá;àßR*K¹‹ÜKî#÷ï&÷’‘Cø' ýûɃ‡1~bî$?€Ø{!Ëu?y€ì‚Q2Jbd7Ùm†ád(N'ûÈC$NöCk>BÀîÿ£ÐŽCË>!ű˜døïçÄüO’ƒäçä)ò4ùyzƳä9rˆ1ÕÇ[ééï·$©7¦éðäO’f˜v_‘t÷Æ4í1-¿ d­ÀÊ8Q·ïÀµØ:ìZ¦s¦Óä5,í5¿Öá#Ð4ã‡RK|Hþ‡ð—sBŸÀñÄØ“c¾"_‘¿’¯ÉQhÁoÈø´Ðôs–2N&  ¥”£<™8~v<–¥P¸ °i)TEÕTKuTO àŠ(OJÑL¥˜þ&åøUÇÓTR9©ÔLÓÀ^Ú¨¦S'ØÍLê¢nê¥Yôxšc*Å)ͦ¾ÄuVéJÇÔµnp‘l‰RXÞ£àù‚@å…± ÖÓb†1 Ÿñ ÂÙ#N©)J‡’çê5„çózY]®ˆ‹d)¢[»û0ì!K1"†ýQn¥<žL±DXÊÖdÊÔåƒh¶IhLü½ÊݺÔSX-+ýù¢2¤{¢¼pé²UŒC+F„FxBÐ%éï¼NÄ¡„2›F‹Bhâ4¦†î¾hHXMPÛ…øšNëé“.ÁئhÚ¬(\–¸*j‚k¡‹4°†ade Ý}ûIÉä[£¥çîRJúY=¢ÖYÐ(þ¦‘¾å+£îAçrèŸ+=}NoTìõõ }+úY+ ÆhÞ[p;øAJWÁ³”;™;ªô¥xú8'ßÏZ "<Ípj!ÁU`µhC­§:I2Ü%‘ƒPx߬¸—Îjqz¡sK¿P%'>T#š2U'TB~¼NxŸ¿[5ÌÍ*”çiZÑ8­‚' ©‚‰Ò¾½žÓEBP…Öœ-ì 88÷@rJ”ƒç”¢X+Ú=QÒåéVýô!±«5ÓµÔ¾í=[J­è%½'„0½Ó¢ÄÛÞÛ— Àú±/ÚÚ•5«ž-…§‚-'%·&“=#)B{Ï»¹(x`Aã(ü­CWT¦–Â`mC)4 £§yd(>¹uéȨ(ެi\U Ã`Dh]>"ôôÕB[Jã~‹s3»u*i§í½ …`{FzY÷¨H/ëYз|YÏe½}1V¿ƒ ý£ÙÖ·ßCˆ(År,–E²,`%̓@Д߹_„ÏG¤T™!…—Á\ŠÃLGɲ8‡qÆd>âd'Jqýðƒf_Mv¸É³œ5Ïyý«FûÙà"VhJø£Q*Ô‘('ÔÁš]¡ª… QÐÀâëY|=Æ+X¼RhˆR+åÄÁ& `§ Ëõ'í‡Þad½Ÿóyâ““½}ÞCα~/ ‰E ú¢ªÌr_ä›Íd¢gG·.bõ êld¶.뇱,²´FUP‚*Qäh–®aÝ.Zm (]¿Ñ­ýÑþ»ißi¬F1JZ„jhv,Sîg7 õ¤ aÖ±!kTíÛÆ ‚ºØbœ„›ÁeO¤ÔBÍ— ´lÐ- #Ëz «£-U³vƒ˜`eþ’¨‰D‹÷itê¨*Â;ס@øSöƒRØÃK¡m‰ pocT5òOSeâÐ$µ²ºÀß6¨<Ëú3VLwœÌÎÓÈ*-ÝJ ÉQ¯uŒ?^¯¡2y1”•âcQ¬Œƒ«dO®½ó¾Þøä„MÌ$…›XÇ$ÎýбIÿÈÉх‚”“cuRôÈHŠîÛ/@}¥è¦¥ûlêW„È^…5çyd»¬–tò÷“íŠ 8¿lçAêù/‰V^E®ä?#³eíä|¾Ÿ´ÛeJÒÆ]Fü³ÄÉâéWä ó‘óYXÖAZ¸W 1iåž‚òÛÉ&ù—DàI¬\¹“ȸÈõ ð <ö|²ëÖ¹@/É‚/¹,ð™W:±3Ñä0+t ÑÃ7Z)DNœ°švÁmZb‚ëRIq5É$É€ë=P ûÁª”vÐO¸‹xÿ¦ì*y›ü>…Y1©üAJEÊKªê»§±1¥Pù(9â¡ÏBý(½]4Ë8ÓY/”)®ä»M­õÊ+¹^R?þ»7Ÿ‚áԪÐ!zsìå1ãøS¦ªÐØá±â"jòš$IÓsJ¥B!d¹²yII¸Ž++õ YzNŠ+-¯¨ãKÂ.އœSDZ0å_ÿ¦“oÏæ6ykzŠå4à³¹Í))¼Û¥ó•x íBynº\–¢àå)Êœò!²±-ëyµ='#3Ç®ffÇŸë~*×›/k§.Ï:u†êÇ¿.œ]”>q­p³?“Ê´rFö¨¿X <Ê= ÆÆN‡À”øÚòÇé`ÌÜ#ƒeØCeE’ÊŠâtiLT"©l¶éÏL^8\OC‡Ç¦#;˜ªf„JJL% Ë½ÿÝ»`ÅÉÇ$Pfê‚\NÐ(›’l´„Ù?vjQ¬Õdù²‹2´ÜÄå²TwQVV‘;•Ÿ¸‘Ó¸BŸ©)/¼?ØPäÑR»ŒféÜy•¾QgŽC—­6ª 8È2½«3©y9ÓvƱ?LÅ_XRnªò¿çi~u¶AW˜} ×Ê: ÊI#¹[b1hÊS?î !Ü­±¼zSœƒ¯_ƒÆ¤U4‚UÜ-жɈ`÷‰Þn[r®a–ÙÆ€dÃì óN3Žÿ^)Ó´™Ãy°…'vL«ÍÅ'l¥ÍfµÒRŽßŸ°˜²ŽWu8?œ©•m°ä‹ùót.©ÃjÁhv–48çn™ôŠ‹k3K sÍgÔT7¤•ž}ieoeF–Æ –É4&-õÏ)IŸ0Oiô¦‚¯)Ÿ¿±cæ½uf}nUkpÒ/ðËžT¹bâ{ÎâFfê'ß篔ûH+yõ»ŸÌänÚ›Îklö!Ú ëÚDM ÷™*àŸµ6©ØÚ8-µ3ò¼«dO­qÚåhOa ­ŽLUU¡qÌvõðÄ„Bl:@‚ÿ¥b“–!+(++E£ŠWP‘Ÿl|ü•s.zpÙ¬õ}5é™Ê Ö—t­n-šS–QÔ±tÕÒŽ¢¦áýÁE]uiJ9Ç+uMQ󢊀°„:—¯Z>·ˆ~wå-§–ZÝYéÅAw~ºÆ›ëµå×ù ê‹E3"º¶õvWšÞ&¤gæ¦k3¼N‹¯43€éëAïZ°Ç@ÏÎ"‘„=& °Ç»í&EjR½©’•ÍìÖNYÙ0 ?Ä:ê?Ì•ÔÈ4éeÎ(Ó³žpgµA5q@ç*ñ3Ó8q@­WËåpà¯m€‰ÌÌshMu&³Ö‘—éÊwh˜y„Ú_9ù¾ìð d>Öþñp×Àˆ´r׉ZµžqžSêÎ8]4½C€y“|e§¨ù™’õgu6%šÔbJLšÇcd4_ö‹‹6?qélÉÆ‡]:ÿìe3ê–6ú´ìÁŠ]ZúÎÆ5Î8oÿyüÔÈ—u¬móù[Ïhä5Éd¶f6Œ„³ùß’"Ò<|ª˜ÊVçî!99¤:Î5‰Fo£ŸÙ¨-®-¥ß”ÒRöµ†ŠM›¥¥Á™ùqjoeQ~KÖö,NÌêÊÌâ Yî,N+ËÊ’eÂ4*êµàªeÚ´#óh°Ù(ðàhÇŒwEm‡ŒØCIß(ð„– 0ë ¬X ê;Xƒ)̬•hø_®d÷˜Ãì÷—•%<¬¹Jʘ8µÜ©“IÓ‡]kI¸¼‚?;-_˜gªØ~Êìó‹flÚ³q¾)gfQý²9%FI£Pg4/^]sÚ ƒ_ Î8¥Ü1»¾¬?èÖ•J£~vMƒ¯õÌ–¹ëÛ³ËóëóÓ2²2ôé~›;;Sp™ó"—.z-5»Ä[)–ÃÔÄ‘ó¡¯ù’Ofíªö–? Â25À}WT‹º¼Ì+“%¸…í¢Îßæl6Ω’úqUœ¶A?îHúÖõÌ»¶ÁôqìšÔûþÝ2¦uöœ¤“x¼›pñ!O(Ti²‚þê8)]zõ¹³›²aHºÜyµæf_Q¦6«±±%wÙÈü܉c¦üY%Ž¢’rWÙPYqcaýh㣗¶˜üÕyCÒ¼¡6h䂽õ„fs}祻‡«NŸW¬Ï*Ïx¥qv¸k%Œ÷–Éx/ÿ2)KÎÂàcç<Êm|løägj)+wÌÜ&{˜¶bp ‹ $Äi3øÚ¨Bp³SÎöAæ,2gû?+IêI¯[šÀîKN¸g®)—E®´W·ÍžºãÌŠYçܳ4·cV™U%çÓŒ&iKxéªô’Ž’ÒöJ¿N¥UÊ¢é‚Ý`ó¦Å-{6\úäÖ:0íVƒ]pT‡ ëÝtmËwÚ|n¿[íÛÈ‘v°#ÏÁW~X‘ÜèogÕ#|Ö@BÜ:Qmö6kªrœ2}~²ÃåhUö¶©…\ëQß!ŸÃVÆÉµœ­ '}Õ¿[j =¿éc6lµM™Z¼“iË” þ9µ=ÏåÉuhšnZ´r{nÉÒk—´o®eî ÜÁ£åËÊ‹g,©y¥éÅ%åtK {-k›=jëv3j(ø{’§­/ml)ž·¢¬òôž°!«"—é­ ô¶ìo€”R9êm·Ùì-ˆs³bRYœi΢ 8gÁ“2fxm:ÚAdF7§K6(ãî”EeœL–­²Õ £è<¡wýmö/‰Þ¨çL¼^e×Ò•2¨¾3’Ý1pŒíŒjfwÖ.Œ-`Kç7Áe 1c«ú{oÉ,(ï´~ Ö!±¦”z7gÉ)—ÚIÉïËËÛY30³ayk‘A¥Má9YŠ®zÁ††»Ï©©;û¾Ó×ìXYô9¿pIÑ샣GƒU3³Ì6³2Õë°º­½ÝfªÝüð–]ÒÜ0|çbÏ雲gô„`ì;à •ïËϵÐúD«X\Ã%»‹ò}ê8ÍÜ]>;ÝŸìɰÈvï‹ZsMKýît·Y±bQÕÜ¢4~°(hŸá»©'`Ñë²­™>.Þ.®m>%œ-ö—yk+Â6[g¨f(GXÚ:÷ÜH¡ZU0ñuK—#Pånì´çWŒŸRXÄÉÍ‚Çe —Úü!æ Ÿžü‹à_„ÉYØö ·$ÎO‹sƒ»Áiž¾0íUba[v³cw©‰u(4eì_Ë?ÝŠ›$ßzüñEgÂ>˜¤­'ο¨Í(ÎöghÍÙUþ¢¥eI_!É™ÛZnéÈÊJvz:>³­,³yÖø®dÌt?A¬¯]uÅ2f³Ï˜pÀ†*’+穉 ¦ªÚU—¯ä¦"&Rš¥™ŽëNŽpxnaò¨ü%xî&rS¢—dd¦ Œù°J5™ÆJ½QÆWWkã\@Ô‰¼qfkI«±Hch©ŽO¾°Xõì¤ÚÈÛ|­¶9*ÉÈ%^CÕ×Ãkwûa˜ÜR«ðÊ4e7²m{G¨ l±a`e~ËÕõõÒÈS(ùÄë$>çøiÒë‡7Jf[…ƒi§ò—)ËÞÅÅu‚Qv#ÇÈ ÙuÅáúH%ýùrÃ~”ãîåué!Ÿ/èÔð1žû Çœ _È©æïÔx\ß¼j²her­ÅȹTªñw’!>'Ó«]o™LmÒŽ¯Õj¹«µ&µŒ­ÇÏÔ$Bð.´l¸ßZÎ&«PËû¨J¥'é°èmØ'f§{Ôéö8·^4ˆútw«CmnU·Ë:I»´Þ…wÓT˜n|3”ãíÚ‹ÚoÍjóò¸}Zaöûs¨¿4á?™KḬ̀ZÓ”ÜÅgªº:r‹ìœr£Î"Ÿ8¤³Ã¦p8C¯|‘\a.¨T9S&:¬J£ÝD ‡ž/|–^ë°ïä†ÒM))VŸôVíÎÉÏèÃü.iÖwŽX§ÄR»pn -¤þPý!0ìîÉóóÔ#1_Ó‡õÞò¼¼r¯V‹ÔŸæ­ù•ÙCve~ :ÛhÌ®oɯbUùù5Œ5Ì'Ûõ¹—æÂ÷Aª˜ŠŸU(.òM÷úïÙÛ+ÎŒôˆ× ˆõ}‹Åz¶ö¨ð2¼¹2ÃŽÑÓØNûIáäãû \)¤is7ÀGsXT³0¡žx(5qâÌíLPxGÑ+ê =>;L|p€i¾Z‰L{mÁ¾¥\6ÃÁª` àSá*U©)—F9V>+¹ÿã‚q‘Å~›GvwêƒÿrxíÞ‹/ype^ÉÚ½]²keîÄWj‹» 2«¦£0Õj+Í©-t™•Ü•·.^¸ó«Ûn9&ñ¾EÛWµÀÖÖºŸ¬Ù{FÀž³ü|Ðüõ„ðQ¹É^Ô ¨SåQU.MÉ¡ðÀ†8¼Þý‰Eð*äŹkw»ìS|òw{!Òd†T[D•0/Ï`¤9xʘ¨H|®‡®8;?Тà  ›NÑž—Kóà>ÓnÅî𯔽rÉ–30À¾²€/’¯¶J`2Q ¡©ð%–ðU ¼ïŠ*à³€ñò=Ì5pö—l™&—¢×R«Ü`ÏqûCvø¢ v×gä°o ¤ï©4|Ûzܔﷻ­ú”=29OaîW{IcÏÝÁ×oüèu45¡;½¬€ÊTUMUUT#‚ò¤¾(Rkœûx_‰þ‘ª‡¹‰fòì–è6Øü=mŸ©²Êã©ú¶.tš¨+±*‚=Æ©7ýǵã;VxÉ Æº V×R=„¯\YO%°sZwÂmÓk/¹ üóάa$îvbÃTÀçn'½RàNûPNz!y@¯âÆíVOšJat¤½5k^Ðdɫ˯YØÔ©t)røHÆ1kéÙ⊛–Û猬»‰N€Wœ‘™—®I±ÞO°i^¿¤+Û[SàpùÜÚŒP–Ím3Ù}‚½dá––úÍÛw®½^d‚—À¬N*û)À‚¦¹ó›ºff yÚÒu§6¬>“ýòþå¿˜Ú endstream endobj 55 0 obj 11244 endobj 56 0 obj << /Type /FontDescriptor /Ascent 952 /CapHeight 646 /Descent -269 /Flags 4 /FontBBox [-519 -306 1240 971] /FontName /ENWEPA+Calibri-Bold /ItalicAngle 0 /StemV 0 /AvgWidth 536 /MaxWidth 1328 /XHeight 483 /FontFile2 54 0 R >> endobj 57 0 obj [ 563 813 503 347 355 246 418 399 226 316 537 529 494 537 495 473 459 537 538 276 561 537 474 874 537 246 267 653 474 606 745 507 507 507 ] endobj 58 0 obj << /Length 59 0 R /Filter /FlateDecode >> stream x]“ÍŽ›0F÷<…—ÓÅçg"!¤jª‘²èšöÀ6¤CyûžÏ‰¦Réà{ÍýÀ”¯‡/‡qXLù#MþÓcHñ2]“¦‹§a,V• ƒ_–ïùs;%ÍÇÛe‰çÃØO¦® cÊŸ´\–t3OŸÃÔÅOº÷=…˜†ñdž~¿óãužÿÄsc‹¦1!öl÷µ¿µçhÊÜú|¬Ëí™®¿ns4LDÇê>’ŸB¼Ì­©O±¨­mê··¦ˆcøoéÑÐõþ½ME]í›ÚXk÷º.•åÒZ“{U®º?¥ëÛW«¦Önª†M*¬Ý©CÝH×(X»[K7( ¹w‹Å{­îP@ôг¶(°ºÕj‡¥k×¹8 ÀêJ«4Ñ£@©ã… T½Žp‚­ô G8ÁJä'ت—NXëZ)á½ è'(ÎJ8w¸Ó*áÅzuŽÏ!(~‘’U ^JVÁWR^GVÁÌy*²ºœw§7éÈ*ØYñYôÜ5Y3k*Jj¦•ÚÇ—¿j ôƒé¯)q&óß«Žá0Æfžf»Ì_ xâË endstream endobj 59 0 obj 447 endobj 9 0 obj << /Type /Font /Subtype /TrueType /BaseFont /ENWEPA+Calibri-Bold /FontDescriptor 56 0 R /Widths 57 0 R /FirstChar 33 /LastChar 66 /ToUnicode 58 0 R >> endobj 60 0 obj << /Length 61 0 R /Length1 20776 /Filter /FlateDecode >> stream xÕ|y`TÕ½ÿ9÷ÞY’LfŸL&³¯wÖÌ’} €ˆÈ"DWTdQ¤Öò”ZÄH-ÑZD*hY´ n5hm¡êõÑbE›Zû:ÍZ*> “ß眙`Ìë{ÿ¿ ß³ßsÏùžïùnç\–ߺb)%ß#"™zÅü[þו%Dj¾zÉü[òyÝÄ¿½zårO>_ò!ÂßrÍ’|^ûÄ=×ܸªð¼!EH“æÚEóæëÉâÚkQÏÓjÄk—,¿=Ÿ×•#þo¾ºP¯ ù›–Ì¿½ð~r yÏMó—,BŒ¿®£<·Ü¼l9Ï’®fÄ?¼åÖE…öt!ÅŠÒZú )"wˆž´!ªÏ‹ŠùR^6ëÞ½©ï*]ó?©AÍ»{bM>~ÝÑpËù7^RºÔ/¢BÉÛ³xF©Î¡‘rÓù7ÎÿRéºXßGPÛ:(|¶i Ñ MrGñ°‘ŒFº¼„°ž—Ôñt-éFI Ù„°š—Wñò Yˆ’/Ið0Nˆ4Êsrêä¡ÌÓ!þÎ ¯e-Eêç½z¨›øðœ‡—±´H]¼­“:Èe¨qòv,-R;éBXÁÓ6þD9µ"VðP¤eäuž3ó:¿‘tàÕ“óhgà5,-ROkxXÂÃbZDœhÅB‘ªÉ?H1rj¬“HUäßÑ“qrJÞ^ÁC©ÐNâ9‘‡Ç(%•hKØ È ÊµXu³6@ìy-r,-‚&Ñš|ÍëÏ“ÿ"w¢þ<ϱ´H¾"F„çÈ—ä!Ôœã5çÈkDBÉ?É|”±á÷PöOrý)xHþÙ:Z“PÆçÄëDžÉbÁSÿ÷×OþFJðT?ϱ´H²ä3bEY–—ý•ü'oñWžci‘ü…¸~Nv"ü3i@øùQãö¤ÈÓ"é#Ï1|"fø#?eF>áéÓ¨ÉÇ<ýþž‡¿#f”Ÿ$ÿÁ1r’—±´H>ä5ð’÷É~ÒŠÞßç¹<<ž_3rœ¯[?‘¼ÇkÞåá;¤%¿å½ã飼ü7ä×l­ÉoxŽ¥Eò6ùÚ)³Ñ³´HÞ"oò2Šä£tÒËvyƒü’×¼A‚,7ÈVé—…ù³‘SªH^!/“ûÑë+¼×Wøj¾L“Ù(c5"B¶š‡Ñke¬FDÈÖ’•ˆäPaÞ‡H¹ƒ/¿à½½ÈÃø¼`ýóø9ÀK ¾‹X‰Hö‘½| ûxÍ>>†½äy>V#¢žáy²‡ÕˆÈ±1ì)̉Ո<-Òñ$ ªoc!y–¯éÏyÏÏððiîuˆä)žþwòpy‚íSŠd;ۧ䧤áãä'Œ føei‘<ÆŸy”lã”ÁB‘l%[Pªà¡H6ó?æ5c6¢æ!ÞßFÆeÈxýƒä‡œ¦Y(’ lï’H‰ õ|W²´\°µ_ÏÃûx¸Žü­dK‹ä^^ó}NÙk9MÜCîF™‚‡"ù7^¿cWpzyÈ%1_ øx[§ '°(G¡ÌÁ[Ûï0z³óK‹ÄVxƒ—±·‰l¼e<äÒšˆ‘KŠÐ‚ôÀ´‚‡"(W I¯@Ìö?K‹ ­üî-å}h°þlG±Pî‹Ð·‚‡"úceªB{Ç{VD‹ü|œ°´ˆkM9Ý.a)-_ÛCcÿ‡ÿÈÿ­±;óZêfȲ+ Ykï$Å‚]˜@VƒŽ÷#¿–S^²Z±ºÖ2á=²Œ‡­U»c4ø»kcÚ jQ)’Ä=ú=BpâÂ=-—Îòüj¶7‘õèUž=dêžÒUž_ N%Ù³÷({Ä zôò?U~’ˆwNåù7¾­Ðíøym(¼lÞ€¬¯ß–ÀȤcd1€ÅoÞ“0GÄ[oGÜ&Ì!ž˜X˜[ˆ_A<ÐÐ&fâ&Ĭí(€  Ïmè¯é{è›Ì°gØÖv®*¼Ÿ¥¯Œ°q°1î°gÙ"h—E< ÀÆó¦–·{ (F Ý“´MüO(Ä,x¸rXÙȤêbÒ òõK =´uà°·ÁåMàýÈ +d• ¼?ÿg/ÄÄNH?7Æâ…†æ‡´ ¢,DdH¸‰BòÆ¡óT’$I‘44ï*HðšÂÓ>ð§Ü&•ö«/nUFØ )ykP)~¥˜ýj_˼Q-͵U éXetò@j1•5NŠN—Št¤¥ã`eCÔyPGÊh™D¦k*&ɵÍSÒžÀ$Õä"ÉOÓÏÄ*é3ªØÏ«ž‘<ªæKU?'ÏDñä¥ôÒI*£¬˜®?Û›íí>Ñ›Éöé{ûõýŸéû3ú~cƒaèØØ{{»{³³½ùJÖ¦ÁÐ`´òÚ54RA•–ú}¡Q´®’Ê…dumUÆZfuѪLmMu(I+iMuí:eNªrQk!iVú}rH®DJ‹¹ÌM]ÔbV =!öº4‘.Y볆+ÁÆ •ÂòÈ´+—MëynÑíû~"‰!¯/  vxu.£ÏðeJÉ|ãÄEwtMzíGëÞhû< EŸª¼vkYMåú@å”ò¢à¨Yí£/I7ùrõšÇ4]‘HЯ™ZlÓ ÁÄê óª'k[:k’µ™p(æ(7ÄúCÎôã¬åL-­Í =-ïÓ’^•ŠÈA›ßQnÔª%} •Hœ®ô§©¤g*ie]Pç°”èô§z3½ͽÝKAäPO $ΣÆ:•º—öåúØr¸BÂÈ~ø~†Ó:æÎ•¨aŒü \1y¯A¸Rކ¾œÉ“Þ1cºbŸ¸Âç÷û¢® ŸPµ#àqÚ}Gî«oÀ úƒž<ÙGä\ûdò†Üs3¡h.ÊLtæÆ7 €›ë›v²ªe‚ïàøÏªÑp0VÞ7®FGu³ª*Ñ\+U§ÆŠ¥°_WTn”ôO”‰´„ëÛüº†TuµSª÷]µES­Ïtèú2Gšû2ú¾L#‚áÀ¸j¿U,èÂGL(ð¿!R8šbjiž4],ÉcðâñÒÛÆ5¥¢¿N%ïMÕv&èÇm éĬ·`´¶\ßv½si¼zb:çíñ#îoˆÆ {r;è\8%J½«‡x&}œñŠW€+-på#u-nþ •Q‹R«¶$]‰§BI$§ØàÕ™Kt~ý©¾ °Ÿ7æ›Í`O¤‚……/Lç"ÑSïЄ â¦É±Øä ?MBñÉŸ~ÚY %„«’`¢óÓg˜,ÏÍ‚þ@ â`†-Æ6cÛˆ±M /¶´—tÄ:"UÁšX¦ÍÛæ›îî+ Wy;&HÄñ¹±¼Ñò¾ZŒ}”Ê—©ªÝÚ¡ª$©~Â™Ê•Þ :Pwr¢~ÊwæÄ‰Þný—Ílùتeò«Æ²yqØ 1WK:vàåU,0ƒÀR€ j&ðR”©ôÒ€*«ÈêUÚì€R›Õ+²³SiÒý¿í ³T@‡ÉLpÄ…a ®¾Ë*º•QBSn·Ï/'=--]Q¿ÎhŠ2Rø©¦,‡v#ûÉfmîΙ۟6ŽFýÜN^u¾Ü 9p9[©Ê;Ê[[rp”ÔW²‹¥½ sÀEZ©Â,oq8µêfM“CgSm µÍµM¥£F‡té15î±ú³}ÙLßÙ¾>ýgúÏúY Ž™×¿òœ"ó¦‘éØÝÔtQËŽ -ÿEÍ©$¯wË%#U*ÓÞ2eVáÚµéÊo¤Nï°¶BsÈý¹1šéˆYÞÔÅãѸömcž‚>Ïl-sêë¢>ÏŽÊt)Í ·<~oÈ9p«3 ™{áõ(ºÆ ì`”Ãû€3äõ{L¦NüDº[z:·•Œj‰kzEcoÑ>ñ@yÀ°Æ ±Ò¦ÒjSµµÉ°^½^º§øA£+%m–ReªZÃÙl^c`Ú'Õ“! €ÐŒ±¦ZনQº;÷îßþš{‡Vöÿ¦.üñ©×_j׫¯ Órý¹ŸÒyØ7fÚ{ü‚†ÒO?¥t°ïÓÜ ãüay¹¨Õ^Ù2Š$!*‡–ÜWVÔ« ùB9ª9â ªÄÕªÕ¢Úm+ÓKV“®H-„£u¹ÿ§Í^zCócãÝ3u¶Àcëß«éÜU\–˜WypÇ‘Þ'wôö :þ÷TM¯ÉmÎËesÏ ×•[“Á˜Ó=nì„T…}N|=ÿð1•rœƒ¹$&ÐÂ^ì}5ί~Ö2“-üÜf¶ô‹+WVªÍ4Ü\9¦(£#Œ© ½}IkQo­¾6U¶[ ^Éf®lŽ©…1 Iu˜ê‹}c¤RR¥( —ÖÃ$»Ì¶d+)ИúôýÀüÀTž\†„Kä’W“ _^% IÒŒ°’yÒTZ}æÔlÈ ¨®CD%ðW0†+Ty7sä|#*àÇ+!Mè½ïÜßsߺ÷ÛSÑÆÜ_ \émlìŒûýtç–‡«ÇMîùnEÄô¶·bi:©_kšU_ò@U4ýþ˜Ïá®Ðé5Pû‰»¾B–«cšz½.&U«I+¬ýæ¾^Xµy;‡o_†Ž¼«–y !àFᮀT÷0¾¨Í›‡\°ÿMÏçÖPÍ °ú cj{ÌRkÃÝ5Sj¢c?J‚ñFíÉßù«ddvSá/œºyQtTÛ3¯ uî×—=öýãÿø-Û‡ÚÁ³Ò+Àξ[’Óõ4,ëÃÞ€Û Å„ËU}OIÒ"@®<^}Xí,õê(™(E°æ'º¹ZÒ}qJØ*ØLÖ=¸AÏÄ~*ÀJt¼åÜ'wppQð1ßô70'-Ów“ô¶ÒLâ/Ù‰§n¢ÓiçÚd"ÚH}þhµ&·Ùp4Œ>L! ú6‹×ú¹ fZgðzvBÁá‰tón$ë[2¯úhGÅÌ Áç£á†X¹X|0cè“BNOmR+Ø*¢1ªn$¾¢b`B;ÉR;)ÙĦ>4ódó›Ürã³6“"̯r£‚#eËŸ”ð9Sœ4"Ã/Êf¯Wà Í?ïë/(sVûÈé‹|ú!hr…„8? Ÿ)²ºN[Ñ ­ŒEêé¸2_(¤y@4˜:¬åE'_S•Ù:Í&i³F–=VÚV‰%z€ˆƒÑ~a§h÷øC.Ùéó”»ÍF›°ç›Þä?óy]!WÈïax£Ûr=bx³’ƦLWBþ®ú¢ìﺾrº¤L϶ƒ .`ø’’rø9»aír‡8sƒC°½R “O†¼Ók;„§œŸ3÷w4ꦥN_ÀùÔW»Ór(t„¼°æ1†ÁþœSú8·^;C‹’âxKàÆ Nô¦¨Å[#}üµ.·qåJ&Çïü³t¯¸º| ¹»eæŠõŠG[-?V=­Ø¡Ú|:¼Ûr ø%÷!Ci…ÓVSš."š¨-"ž>sš¢ózÏWÎÓ¡súãѯÓ1C£ñQÄ OM¦7KÜ6"G¦*Ã~S-g¡¯3O4½lßö›‰3„º^‰•e'&Lÿ³æÙ ·g¬8oÒv¸þaž×aj‰mu×TmÝ{óŒ»Nª§½¾øÇ/þãTãÊÑ7-ŸòšÛúè™=ûÓ©pøQG@I ×Îj›µvÂ;“¦ì\ûس:½jÙMÓ“Á¦iûžË5¹`éø<ÀKN*ïÆ r <}'[/‡.Å9V)n ”áY)ΦËpÇÝIÁûþtH³[*ìœï4΃8nŒ£ì4jÎ!¹Aœú}8qì ÌAÌÎ%Ÿ„WõIœ@|±§‚´uâœÁsÁv ëŸÝ‘ ã´+“C#¹-/C›ihq)I×Ò×7ÀôêV™xfïïïËÁë4TÁè¬;ȱò™rI,fX !™“Û÷ ¾…m={öÝ·þùç^ÿÔõoSMîoo^·-c*{QU¶YLm™°¼Ùe_¿÷õû÷Ýÿ~aMû¤Üßu$×?©sª½œ;ñ„³³žÚK€öâduËÜµŽ º-þŸèÑn1¤;äß/Ʊ%¢Aº¤äª’›K:–;î*ùIÉs%;{\Å.ëù@‰á´=8žh3¶•M7N/ÛÚ>:VkÍ$íUM7‡åÌ¿ÈÔì‚;ç~ú\o7W$™ø-¿ÙÅ;Д꼦-Áª©æ®Ô!¸TB!»ìH­›½í—6Ž[Ukò´Ýrîý§Næ>¦žÿèÚ"Γ¼îTç¡`оô²_üèÇ/ƒ[ì¾äIZöî»ÔÊHaOaþ[AcÐЇ­3@c:¬¤4¦À=5èL:Sà‹´¦CèÀúpÞ\ ʱ±™ô8ãh­¥§Q~ùãÈ ‚z¾F ¦·R¦#žŽx7hz7¨çâCœ3Ïv £pÁÃÞ`DϬw ÚÅð6å3Qs9è<ŒóïŒÊ²gAe8QÊËíüžÆ¦fLô8…AÅÅl!@ëÈfÔTØÂ~šßî|ƒ‹-†æý×½zgÈáLYÓÕTuò#j$/Ô‡î¾íÁû1@Œö0d“ ½wCkVŸÝßy‘:;i<ˆQ㰭˨€²AP½)Q¦GGÀA1òü)‰;Û²¢7± {^F‰ ã$,‘èÅ™<ˆgÈÀüáð!b»#Ì æ,Hïò4¶0·˜pR¨Ì,ç5ˆ[#±ˆ|áVîÞIDÃýö³[n¨ ×¥—.  "±x(·sCÎÑáêRmžÈÔ¸ÃåWÝÔq _x”áE ksK¥µâcØ7uäo­ó±_£¸EèÁžâÌÀƒÛ±ÜŠ’ï ~ðÒ‡ííCš“ÄàüLÂQÆ¿G >à§–”0 Ÿm0:c÷-O£ÎH§?G’TÂYDpÝ*Žx:âû@9»!'vcU!>„¸½§po$‰wá].`¸ oÛ€üs0ø—AÃAR… {¶›¹apûËe9öáÜ¥Z f¿Ñ‹"¹:„Sãüá|žo‚Oæ78×Fa© ¸`ŸÄ¨0éî;ïþ·ŸýŒ¦¾ú_¯»õº*¿ýçCßõм—þ9phòÆN»óáp83Þ(ª·¯¹ë‰'îºkÇ…Äý+â“&ÇSî¤îO®êû_¯¾v¡¡q‚Åì÷‡=˜ýBÐçjðÍòvëК ŠÓ€;2ý1ŠbócºA™ŒÛžzàP §ìv&£Ó ðí@ÞÏœ–H—ã¹8Jæ€3v´8d8Sà 7ä««\NhB«™|=eÔ]†ü´†V³I#ä/Ø \…3œ¡k% `Î+ gýL|¨JÕ}‹/r?ÏEöxñŒ“3ÈÅUý¦6°ÈÍv×Ä “þy2‡s_&½‘ÿõ‹®yÄßõ&s_Êr²­'/zËL¹¦¶ÖÃ;sM^< '}|Åêûçæ±³_&¢­ïާ*æÖerGk¡øbߨQq·~Peðó5æoFʃÝ&5È—çJ<áD)ÁÝ9%iA åë$£íÂØó½¸²Á<¤ÿ1$?ÎfZò¾ìüÆ6 mê‚áSÀ7‰„b¹et8ØÚ"¤­rПø*ŽFiúH,¦A»[:|c}rÖŒHh Ô‹slvŸ°A™‰ñCþîMÞòT±½¯£ßv—×QºÇ[qP''§"32ùU»‹z½îx”êBáù¦’Ȃ̃tõŒX‹‡e9÷!½+w2œÊ+„Lz—™.T}+Q.—Ï×\,Ššø]¹…ì`ÅåµËX…ŽHìV§Ìnu“(‚œ¼H…yºS£F…*F“~TǨx*èŽX‰œ¯·{€£h¼Ht ¸af£aÁ þÝóÓ™NÂô!g’°c@d©'?¦€äI ï ü^ÉŸ¡{\B+Z¯Ç"õäÇÓF~Gž@z˜1%§µKÀùµõ(H–*ð#¦Í‰à\X=‚C2œœ"}ü?Êv!Z~ ûÁgƃ>±¾ÐÍÆšÉ"À €’ŒÁœ)ê:q˜6t–ÝÅ@e~Âvß(HovK^ Ü(!ÅZ]p.Úzðí…åLÂ]…Ú:äæâù™è‘íVÆ,©°_qò’ű-ؾ¿+.L’°}ÌðÊbv'ÙÌŽnÙñ6;ö2ø@Ï-ßbjŒGQCÞÖS é‡`~q˜xÉû óáGää+ž¬=i¦qTÄç¿+ænkJtÚƒÍQ_ <0˜l3Û«Âá-^‹¹²©}NYô¦Ž5·éGGþUáè¹ú{·äæ9a „Ç:éÎ)3kª/œd|.§°Æöû­ÁxtÔè1ÍOΛ‹)xlóüã.Ø2MäÖIàZð5V89˾`šúyìh.•°ž¬fkÁ¾’cò¨ ëÊøeØw@‡²áÙ*¾j¥è‡}Õ× }ÑW©´ •òVß\´ž‰öl…b¨ËK%3¤R=Ö‹9”g“færûW‚‰-Ñü Ù4L2Áýæ,Ü=f{sqoøö"æ櫾€dŠ&“ó¾MÉæZÑFH¦ÅÛ ™í/ÔF"L ³Ao™©mH0É^WTv}#˜¢.¶˜õÐç—‰{AV2¾¥šœÖ)O[Î鎗·©ÚJ:tºjzÉź۰Ûô¤õÉÒC†C¦Ö¥z1¬YX6Î(ç~®^36 ’]åÌÛÀ”Y¿yS‹·ysË–#½ÂÏr§þòyî |þ9 .{ãá‡yxó/éäÎPýP]Ž}9,±Ðï†~ÎÿÐ::` CtB dèNìvß7ˆ]åÀ,ô F*”Fitâ†oàž¿„‹r"< *”œÃúwÂÆë˜ƒxâ'¡“ïÝ@üb5øF}ЧœN<1^Èx^oõ!Å4&]|àfÔ3‹óRRÃî`0Më}Œ$ ~}în‚äo¬0bÁ®æ¡búÞ0í%O#0K`5‡äoawá™P(˜Ìu„#UãM¦ñU‘0œmO^õ6Õ’·®ßÛLk×ïÝwß}{ž$¸Pá @ß“´lÓ™-óÛÛsgŽÉu¶ Ï­ÿÁó{ÖÝ·‡á| p¾ëäÕäü4»1÷§1û'€ f}½„ô>¤•à¼6¬û§pЗ™æÂ¾0;|ØvbwÆÎ;ù)ÿ*âþkÀs‹ÌÎ+xƒ=9ŒBÃfž¬V «’F¿Nä˜Lgþ æ£aÖsx㌘`Aߌw2„¡ù ø?`œU >ÌF¦æ ,êá ÷·9fÕÀžõðÕ¬¿ÿ9aÙè½7É Rݯ¯ÜÑnw=¥Ç™˜¿&œkOÃ!úÖž}~Ýžß;ð,ÝÐÞ™;ò[ªkoŸo1ã”ËóõXƒ€¾1ŒŸâ[@Ü]¯‹‘»[“ ¦0(ÎÁ9d-£[Ì”ù%Í2¢ï‡€‰€ýö£SF¥f¬®@§yÚ4ƒ:KPg:b/ÄÅIáfÔîßÊ3®Tð[Rú_"·ùQ©JhÓ/Ü*‡#±gÛæ§äpŸÝqÕoo›}S׺$6åç×á.Ôzˆ+€~‹y÷mË'4FÝ|;æ¾wð/’so¡sZŒ+,般okjÀ÷MøÎ£`8ðU§‰¬­Ü^þ}´yõ¹`Gì  ~ êBý&Ôoun­üíž@»'ÐÏh·‹ïõR`/ú«U7ª+v€ô…EžfÓÀ½àEMÔ‰sZHômÄø˜ô(çü ëSoT‚øÒ% ªÒ´?'ʱj£QwœÜ‚µï@M+tÒj¼¡\†Ùñ%èW>ñ=ïׂÕ\„ïg½°2½äU¾¾^ì$J d\+Óbq1ˆ«ú…Ónx¾z³Ùnh²yn3$ˆ@ìuܶvPXÐLÁu`o"äw—‡_°ÏŸÛ2ŽÃ}Æ{½ÁH¹¾4ºãêë¿wÍuG?xïåÉK%£]>¯ÇÍ5·_z岕o¼ûÚ‰ý ÷_ïÏpþº7ª÷j[g´w4?pï=?ŠÉ™ÌŠšd•ߘŽ]Ö2¦VRÜÛsïv‹Íjeú:…ÿ®_Z Fom„UZJ–î<ØP€X)@ ~ãY¦uGeQâ&lªFéq¬bˆxÀÏ™¬`:®ˆ´Oã¦!~:”SîwÃÀ)XPü”©»CÞcRà+Œ_ùC8æDbŸ(@áR¬øÝp†^ñ¶t"ÍÝpù5sr.‡œnZðèø?‘͆ÝÑPõåKƒrÜ'.ôÁ~ÊíÝqíõa§7m•“üóºé ßu¬&ÉÌþ ÃÓ|³ ß^¥ÉS­ŒwAùiÐÓzÙ71(a>$%kÁ7|À„ \ºsN€ÂƒH™hûݵåød>¦—Z±ðó1ŽËä\Ò-nËîÑ6÷˜-£µ¼ÞÃÌMTeCs¦–<’ñò³2¯¡r"«^M—\¿lÉ#ëd(ú~È]™†¿ 6ù¼u]O>iiËÈ‘‡ývºô»Ëï»–>êõ‡Þ– Ó¨¦·;ù¤T–Q-Èqr™NzqÁÑ-—û%…tá)ºçœéÜ^Çž `CËðgÙS€?7TWÅöè®™BÀ‰„çò—˜F\{½¸ã.ú\­uyf>lOæù}tð½]ïÌÔ§krYW¸¶ÉLÛr¿Ô6&ª&«M×U«?–GâųSÞW6 ËC2”‹Z•`M¬†¿«*”å“Ûsrh­çPGÍòøêPùˆ¯$Ð3¹vMÇï09ÀyàIAÆ…Qð@ì? ¸| {O\ÊTh½žÝS†´“±{Ìdò«¯Z ¸éuH3ýs Ò[fºæ.¤ógUF`Ü 0ŒHá2l œÕN)cߘ°Ïd`Ö„•av‹ŒVÌ«yØS`%lX=pgà Õb«¨À>/Ek3Ö’I$%  ½9Š­À®´ Öc'†±ZÙ¾\$ÉÞ,ÂÌñË<š¯ò=„n°$Lí!ŒU2©“b—ù‚ÿwÈ;ˆê¨7ï)ú 9í¡_¿¾ê±ž¨o´U Í9Üú ÆM¬í‰DOõ x„v&äʸ=U)4UÍ„M´;"Weœ£‚Üå¸O±2Ÿ2ȸnþnŠÕ¢<¥dû¦krû¬KÚbãn^qëu‹n-Ô°êÓ€,`ˆÔ Ý(´OÚ èÌÜøà~À6À3€Ã€£€S€,`HЀ8 Ð˜ ¸ðÀý€m€g‡G§YÀ¤8q@3  0p#à;€ûÛÏŽN²€F÷ >XøÃ¼ÈÅ4~;Ï,ÕáõLSžÈÃ7þ­úÊùæùÖy&m†÷?nD¾mD¾}Dßc|ëù‰#ò#ò]#ò“Gä/‘Ÿ:"éˆ<;½>~¦;ÏO‘gžŒáõ3FägŽÈÏ‘¿jD~þˆü‚ù«GäŽÈ3î7|<‹G䯑g_a oÏ4ÈáùFäo‘‡ä[ío‘¿yDþ–ù¥#ò·ŽÈ/‘_>"Ï4®áãå¼iØ~¸mDýí#ò«Fäï`ùÿ’'°1 endstream endobj 61 0 obj 13670 endobj 62 0 obj << /Type /FontDescriptor /Ascent 754 /CapHeight 595 /Descent -246 /Flags 32 /FontBBox [-655 -409 764 1089] /FontName /LMFXOD+Courier /ItalicAngle 0 /StemV 0 /MaxWidth 823 /XHeight 462 /FontFile2 60 0 R >> endobj 63 0 obj [ 600 0 0 0 600 0 0 0 0 0 600 0 600 600 600 0 0 0 0 0 0 0 0 0 0 0 0 0 600 0 0 0 0 600 600 600 600 0 600 0 600 600 0 600 600 600 0 600 600 600 600 600 600 600 600 600 0 0 0 600 0 0 0 600 0 600 600 600 600 600 600 600 600 600 0 600 600 600 600 600 600 600 600 600 600 600 600 600 600 600 600 ] endobj 16 0 obj << /Type /Font /Subtype /TrueType /BaseFont /LMFXOD+Courier /FontDescriptor 62 0 R /Widths 63 0 R /FirstChar 32 /LastChar 122 /Encoding /MacRomanEncoding >> endobj 64 0 obj << /Length 65 0 R /Length1 15076 /Filter /FlateDecode >> stream xÕ{x\ŵÿÜ{w÷nïU«mºÒªìª—ݵU®ªÕlI–Ö^¹J–ä^e›b08b"Ó %…‰(ë5E”„^ŠC Ïä‘@H ¤¥CÀ`é¹gW–œä…|ïÿý¿ï­÷Üßœ3gæÎœ9s¦h½o|ÿÑC„#e#;†wéã mä¼}~äÍk‘[7îÞ´yè©,›¶_¸ùÀ!®U›Ç†G‘'ŸÖlòL`îæû.@Þ9àÐö]#éü@øŠÃ¤ßO~ ¼çðŽ1Ô_ô`Áîñ±t>“ ÄÄîÞµwæw×^·0_OR'ã'Fòs°” A˧™eD¹4_~çÔ1Ïy¿]o¨}Ÿ¸” äÉ÷.þÅßwätՙ﫭Êl(­Â”ãï<ó:!ê/®:­|ÂÜ–ÎCpSqSì”·Þ7Å^ˆpAÊ«î|„óRÞEÀíG؇*{SÞÅ Oykö ìFØ•òÖp'Â,°a[ÊÓy[¶¤A G¡ ‡‹À  ˜Y„„3Ræä>F8ð‡Cø åì÷M1ï#ü5åî/FøÂQå¿Gá4ÂïÞCø-ªüá׿¼w~‰ðÂÛ¨ò „Ÿ£ð-„Ÿ!¼‰ðFʱøS„Ÿ¤+{á¿Pøc„×Px á?^Eøª¼‚ÜËȽ„ð" ˆpáßGøj~á?PøÂwžG8‘²Cäc¾²7<‡ðlʾ¸gžFøÂ7žBxá ,÷8 CxᄇŽ#¤Ža¹$¶å!äDxUîG˜DøÂ×îÃr÷b{Pø5„»¾Šð„/#| á.„;S¶ Ðé;nOÙF€ûbÊ6 p[Ê6pkʶà„/ ÜŒpÂGnHÙ†¡ÀõXçuXçµXç5WcÕWa#¨ùyT¹2e‹C-‡±²ÏaeW \ŽšŸÅZ.ÃâŸA8„p)Â%.F¸á@Êks!¾á¬ú|„óð û±-ûöâûƱø„Ý»v"ì@ØŽ° »²ß·asÊVoß„°1e½ ¸±”•ΣєõR€‘”•š` ‡SV¸!®GẔõ®MY? °&e½`uÊk1³*eñ "$R5p+V¤,°Ú3ñ”–yf¡ayÊË<Ó—²À2Ïô"ô¤ÌÔXËRæ6€¥Ý(ìBèDaB;Â’”ÖM¦ UZQ؂М2-¼¦”‰†‹Æ”) ¦Lƒ )Ó*€z„º”‰ÎÍZ„Å‹b)Sò¢)S ’2ÅjªS&ÚÜ*|Q%BEÊDÍZŽP–2Q“—"”`[ŠÂؤ6©¡›T€"ä!ä"X 5Ø$?6‡ïó"xP3Áų\NÔt ر6+¶Ó‚/2#˜°œÁ€ GСŠ9MʸL NרRÆõJA GMjr(d"ÎBùY¨qð Ð'@ÙGðŠ!ý7 €Þú«aƒï/@6Œøþdõýè@¿šùï€Þƒ¼ßÿ _ý è]ÿèH¿ ø  ŸƒÞ[Àÿ èM 7€~ ô ×õ›|ÿ¥ßìû1Ðk@§€þd¯þè — ðE úÐ÷¾ô] ÿÐmó½ ÛîûŽ®È÷<à ]Ø÷m=égu;|âì3º­¾§u[|ßÒmö}ržÒ•ûžzèqíß”vÜ÷˜v¯ïQí>ß#@>x t’@=ôÐý@“@ßúºæß}š¾{5úîüšæbßÝšƒ¾¯‚ü+@_úÐ]@wÝt;ÐnÓûnºE}¯ï ê¯ùn¼ èF £@7¨7û®W_æ»NýEßµê;|רïò] ò«€®àò|—sQßg™¨ï²ø¡øg&Å/Œ_2y0®9Èhºv¼èàäÁŸÍ õÅññ‹&Ä/ŒŸ¿`òüøìçÉFöJ±6~Þäþ¸l¿uÿ¾ýÜ_÷3“û™–ýLÙ~†%ûûýû9í¾øx|ïäxœŒ÷ŽOŽË'ÇßgÉ8£žš}æø¸ÛÛ(^<®3¶í‰ïŠïžÜß¹qG|+4pKtS|ó䦸Æèh|lr4>ÝŽÅ×Gׯ×M®¯‰®Š¯ž\Œ&â+AEt Ÿˆ÷GûâË'ûâ=Ñeñe _íŠwOvÅ;£íñŽÉöø’h[¼:O²ÙþlÎH°,ZBÜLS™[t¿åþ£[FÜI÷3nÎlÈòe±…ÓÜãbv¹.u]ëâ άè, ·/:~æøƒCf…%mÄn´ûíœö;t€öí¸½¡±¼ZêëR»l3؃Ígc[}6†˜Þ2ýÑÄÙž6¾hd Æ`˜5°¢Ô zŸž¥Y='êË#mOÇÒÇ¬Ž³‹:ÐÆçk{Ú Ÿ†7hz4¬¨ihn5Åem„cü >œ’¶†±ùÚ¦rÜÎÈ™)æºcý¡P×?»¼+©ì]d®LæõÓ§Ø·*©¸2Iâ«V'Ž1Ì5ƒÇ¶y iíê[…üW_Mš<]IO"ù%Ï`Wò$Dš˜…ñ³“¦Áк½û÷†BûÖÁcÝÞ}!é ³Ÿrð øîÝ<ý<¡9ÿüƒj ·~/|¤j°ö^äÿró¹ñÿÚî\¿Ž^„ðw2stÁÍH/ÙJöÂ}Ó!ò9r59Jž&?!Èg!u+ù¹‡|$ɳä»äµ¥þ—ÌÌ…òDË=FÄBÈìéÙé™{€¦äúy’£ÀYdþ³’YãìïÏ‘ý~æè¬qfJa&j©¬Ž}jû sfö4Û%u³5”gCÚ ½éOü3ÍÜ» ¤› 8YAV’AÒC–õ’>²”¬%ëÉ0!£dŒl$›Èf²ìµl';ÈN dÙMöq°á>²Ÿœé}i ò Ér ^D.†ô…ð< ¥.!—‚å?C2xYš¿lžä³ä Ëáù9r˜\I>HŸ e ¹ r„\ãy ¹–dÒgSó¥4}¹ èzrŒú¾Æþ6òEr»$=Jn&_¸»ÈW ÿæº4ï¬þäNrøÍ—Aó«à=÷ž£K5ï"O‘o‚O}‡| ¼íiH=G‡ôsägä-òùù5ù bj˜%äÏä¯äE°þF°:µùn鹞›æ,~>ØöAË^¶œo© ÌC _&Y ­8AΛ†Ñ¸l^™ i¼è(Ѻ¨U3V?kQ´µÔYZîèœä¬=3ãpVrÓ›-´àm`õ›çY4cûù–Ÿþ2™ÏÍO•| ÆànxÒq8—ËÈïƒNéd’Ü)|žå3©ȃä!ˆÇÈqòy”ù!øÇËäˆZ?%o¦=è”äA"/‘—eAòc¹ž‘sÏçØeäà_co_'òwˆ^l[¿níšÕ«ñþå}½=Ë–vwuv´/ikminjêëj/ŠE#5Õ¥%Åá‚`^®ãsZMFƒN£V)y…\Ʊ · mCþdp() ííÅ”†A0¡™´‹sfJ2Ù4¶A ¡mË‘)‘l&y¨/¼Ÿlp§ˆXL²C4ç™LŽ-NseræŠ `ÙV¡u(ý=o³3yhƒ¿8 #+}ó’²<È÷'¹àІ‘͇Ç&„è!Ø’ Àæ°âpÚ˜­ÇÊJAx:±…š¡/‘,v'­BZPI^ë–þ„T¥­Iks’ ¤K%K[¡,¸HëÚ@Z—ЗxœTξu¬Êï>^IªÈ mGÒÞ ƒlHŒnLú†Ü£àŸý w )‚ù…ÄØ %Á˜,| ^@©ôííŒ2t;Éç)ý ÖÍ ÒÑ¿ BS-d“ déˆ6ÕúŒ›dÔà-i šZP0\^s;„¢Ííî8·ôùšäÆ@3’ʹ6É ò³mÂ÷üÓ¦¡6mP¡¿u¬e^T ŒÔÀtmÿ¸,µEÚÐ%ÎvÚ‡â0 i?d+“,ôSÑQtú“¤×ŸÆ„A|HìMÐÁ¡¶–Æ·«_ i´Ó^2°€Ãü(æ%I k ‘aàø’H¶…¤q¥Ã*ñK$~Žm?'»#“íŸP ]ýôåBºB⇃£v ‰š«`²¶A Ú†¿Ñß61<5{hÃÄ1QœØÝ:´yLƒ ¡ctBèOÔÂXJóþ û}µ™t1]MÅaˆ=MÇæÊ¾c"seÿªÄãFøÓ䕉 ‡¯¡¦Ác¹—xÜOˆ(IY*¥Bªâ§ ­i90JIßý¸HÈ!)W& $~Î’ •@Æ‘)eÆŒ 2ÊDI6˜aÎÍ0‡[ý£tx.Ü<14H'±ÃP—I2B=I²B=Ú¤ZkJj„&*o ò”+¨œš’ŒãLALš NË%à(>Þa¤ÞÏæù§fg“îéÁL‰5@«IUÖy^'è-¡4â%ÉC#ô$SÎÌŽ‘A˜ ™ A¥#©‚Té@£M*CÝ ÀØÀJå“<4˜ Ñ—&¶ÐùýÆ$iÁ°cò }Qéà„Y¨ Ž ªIuÞa *hð$q /ƒ€K{Äk¡å#d ùadd¤\c©šŽHÆ $Ê‚c©ÝéLB»Ååitꤪ*„/MkJ Bøòƒ`Úy‰;œV€w“hQpž)ÓÀ:ÕAÛßÃÐxªú,­¦oŠ,.€ÐH-½Š‡ì¤.¯c‚?–×€Dˆf C]Ê<*¢uœ@)O{®»syS³÷ Òù‡º8PÇ$îÇÁ±ÉàĹ‚äêPqXy®T'‰'&”º\í¥ÔÍ!ÔBdôOõ/"KåÜD&ó> :n€tÓ4÷ YÊî™­ƒts I1@(/s“eì·Hç!*öA²d»Ù\²ƒ•“Nv/iç>!@/ðoý„há\>@Ä ¿{Ð%üòÁHLÄaASÃl%j8¿ñ ­"p*„rø¹ÜÇ$Ø,ön7-›ä¯+ÎWüޝRòÊÛTJÕçÕõê_höj^Ð|¬-Ô>…äpÞ˽§Eê[,úz)¶Û•µjfštž…û™« 1 3*šel^DÁõ¹u¦Ý}L_ ÏBgßxóµo¾qð$SúÆô©iã™SÓæX¬´´¼Œ1LYõ,Ï+BN ‰ÔÔTVVÔ³ÕU%¬£ VWÕ³‘z®²ÂË2T5%)(S)÷ú'«¹ž3 ö"_ëÎe¹¬Ï­·jåŒ_îs(ëzJ,†@uAXêãÕ V®T( µä´¬[”5óÇkxµßnÏÒËe¼V©ò»,.½l¦M®?ýg¹þãfÙöoäÊ«6-¯‘ߢV²2…â)·#oq[Àò[ £V/·ØÍ ÞbÖë:ÏQ:²¼ZÍkj•ÓiWªÔ ­ñL µ|ö´,ö,'õäž'؃ì%$¢7añÄ£»R+/ S쀨«(ˆy¼QøWX M1Œ¨/ k ¼v•CÉ{=Gt/ãneå¼Z§Ôä× DêÖÔûòú–7ä·}æÑÝu[eq ^£SË–lhjÛÙU¬ïëï­Ï¯ÿÊhÙʶ ³Rv¿ÒæpY´Yž¬¼†ÞP¸©¦¬"Ö5,ÆoÚÕhó²\ {–˦óçùó›WU7×”ÐìÆž‰ÍµF‡Û T,éƒ1ø9÷=„ˆqõì¥ì¡9?®ò(½SÌC󃋕S̃C±pÁò)Ö+:,Dµ8ßTpŽ¢²:k>õK¹nÉÁ%Ó7˜`ìéWÁøoLWš*Áþ±˜\´Š‚`μLxAÏE+Ú4ÚPÿåƒA0´ -6Â…e¹EÖ,#´Gײv|qï–z‡­´këUƒƒ—VXdÁ«Û(c~Tº£¥fes¹.ÌkB‘]Cf—I/ã5ªoø»Å¢èš}uÑkn¼jWs{Ãj£žSjùßµ¶VlßZcBÝöÔju`µ—ä{H1i"·/´šhÖ˜<^Ÿ_ˆDcÙ±lsÌd†(á³KLêX4GÆW~”ß™m6idzG›¾»öC‘_šqWj²†iÉn¯N—š¨ÑÖŸÀ™‘¬—õék™óÉ`¾ƒc2Ñ€—’2)€Ãò¼Ý*ã^²—wn;²rÕ¡ 3›_P”-cÔ¬Êp9½fÓ+× cëÚÑÚµyVåjO¤¤f÷P—)Pº½¥j ¥"`b/¯½þè‘m-b¤7äQ¥‚Êî\tîÔ¤n+:Ú¥²Bò1j×§.úwóS–‰rKÚǘÌ•LኹÜ"[–QÎú¥ù¹xåâ<o/ëÚz$ꮯ²mdÔV¿Ëé3ËÙ™S0M«ã-å~cSÇüIz_ «¡ÐWÕÚÑé[tÝõG¶5Y%.f†×ñr9<Îlhm/_¾uÏÎ’áMµ[oX 3u)øÓßJHí¹–{´¨"¢Õ«U‚Iëå¬V¡tŠÕ‰6"(¾‰yM&mÅËEÚŸ‰Þ¹I a¬ÔË7¸Õ«¸ŒCdsHsÓò)JeQä^´RJ =h+ï‰ÖnXË3ÛË—Eë†iŠÝÛyÛµ—¬‹”$õuÞvÍ¥ë"¥‰C«*z#ÞPdž]û£½Qo¨sÃî}7Ø?Cß*Éö…=Õ&“ÇœM<ÙppvŠF±¸Óœm*ðä+9zºI„e…®)4:J“ïq¢ýêóz+m_æÆþœ9e‡–A|Ó)•‡×š3¸b‰©çl¢Q2=cŽ†Î¾|“àu(Üí2‡×ï6ój~ñækúgveÆûìl¸»°;–#çU ªÙiö=°@y`¡ž×?›™*Øû…ì‹á U¢»õÅ¿¼L.Êá*äE±ÓÿQ)2Á-CQ離û/è†fá­‚˜û¿© M(ÝÝP %}—³p§+5ÝwÓÛÖagßSh *m ¬¹$ÜRâ¬î]ÛSÙtêÒþæ2’gÒ-WNdy]¤§ÊUÕ³¦§ºjý}Á%µa†Û®øí§ÕŠx ª‹ ÷7´]¸²\owk•&­ÒN·.nŸÛ]\(ªÅúŦ=ý%Z³]£¦–Þ3ûöyÙ¤•L,´´XXEBMJU£ª1¢ …Ê"Žˆƒ”5µGk•á·U¡@M»áC10·ÁsNWœŒÅ`€gi)¡ñ$|â„®Éè4³|ŠÒO¸¾Óf#é½"[™Ù+J»KöyV¡ÖèUïŽÉ¡2wÇ®Tª`È+ýE¥Žèò¨›•˹±ƒ­BkÑ]b4ë¥E=ô®AÍUÙìv“zFm«2U–ªÔ* üjÏëäy½Fá¬\Z£õøýzæ´Î¢ÏóÛOñZ•L¦Òò§ì`ÇÝ~Á=§•ýçØ1Gã$åµåB®ËI4ÎÜr—P[¡’G:¼áEãÒôêOï^ðêv'èvÐ$MŸ¢ ˜,³L磌j"™dÇômËœ,̨Í›Õm³¸ØkYs©x¸7ÄuRÎìT(4ÒÞp¨ËÜÃh2bXSF/p¿žÓ%ä5VZ¶*³ªy%+êñ–çZë¶ß”`®Éˆ!bï˜ýówìûòIÙÓ£·Ãº•ÝA¼D`úS®n9ý¼¡½ c¿P\Ò záîM NÁ`Uæªú%…Åòyƒ†W˜³!•oen[¼!Ÿ/hQ(Ìž"¯?ßÌr~Ø­ÕºÃþ@1Åâ3…T Î*Îñ£€ÎˆNhgüîÇA|ðŸ Ùq¸Ú·À¯ôùîù ƒf•—åѳ̼å)’« ÎpNv®U)×…A`åoÕ rœZ¥Åm2æÚ’EßÓÎ<ÌÆØ:Ø:!óÛʳõÓ3êIè4­?¾¬TØÙ£PjÀ?×ê´ôlcÐ0_ÕkØl…Õ³Ynq8-z:ù€½{W~%¬ «ÅN^®)••iu…ÖlK¾Íc"NU~yA™,[kÕØÊÊ­Ù¶r[¶-»œ3†ÕO°±Ã]b¨ô¤#VWê”~¦˜ñ„ñÙ:ãóÆgÆÅЃ3c‡• E~>g¥÷* ¯€s]M$w55‘zÖîpлë ;ÏEjìÜ)£Â0óñk:E¶Ï§yÜ™.õ)úÞ3{ gø©”ð[ZƒÃlä“.&'\æ•÷ÿEŸ-;YP Ø²CÈ+4¨o9m0 ŒâV-7÷ÛÆdZ‹Ûbuª8þ&Fn„ÏÌÇ·«¬ø÷þŠCÿ'¿ÔØiléY±¤?Ô<¼}ˆñ-Åû 1Bþù&»N endstream endobj 65 0 obj 8690 endobj 66 0 obj << /Type /FontDescriptor /Ascent 952 /CapHeight 644 /Descent -269 /Flags 68 /FontBBox [-725 -276 1259 1014] /FontName /ADOVGS+Calibri-Italic /ItalicAngle -5 /StemV 0 /AvgWidth 521 /MaxWidth 1328 /XHeight 476 /FontFile2 64 0 R >> endobj 67 0 obj [ 335 229 791 478 452 343 389 306 498 252 543 226 514 514 268 514 513 514 416 305 514 514 229 ] endobj 68 0 obj << /Length 69 0 R /Filter /FlateDecode >> stream x]’ÍNÃ0„ïy ဲIhK¥(!õÀ(<€coJ$êDnzèÛ3ãFEp˜HŸ½ãÌÚ›?l7¡ŸLþ·ÕÉt}ðQÃ1:5­îú¥ñ½›fJknoÇ,‡y{:Lºß„n0u“¿Ãr˜âÉ\Ýû¡Õk®½F¯±;sõù°M+Ûã8~ë^Ãd$kãµÃqÏv|±{5y²Þl<öûét×oÅÇiTƒDpçHnðz­ÓhÃN³Z¤©ŸžšLƒÿ·5ÚÎ}Ù˜Õå]S‘Òãc½)¥“lsÁÿr—Êer¡§ÿz°–n¨íæ4eÑÔ”Èê¶Á?K $²\+ ôÄ[ \@HdQ—ÀqU×@A,Ñ! [ o*ö@''T ,XÜ!‘ŠGU¸A »wDħ€ñ) ;ªŸ2d…øpID| ÈTZ Ð¬Ð …]7ßüùÚøvœ±ËL¸cŒ‡4ˆiR8}ÐˬŽÃÈOúQ¤ÈÌ endstream endobj 69 0 obj 386 endobj 15 0 obj << /Type /Font /Subtype /TrueType /BaseFont /ADOVGS+Calibri-Italic /FontDescriptor 66 0 R /Widths 67 0 R /FirstChar 33 /LastChar 55 /ToUnicode 68 0 R >> endobj 70 0 obj << /Length 71 0 R /Length1 5940 /Filter /FlateDecode >> stream xX p[ÇqÞÃ/A€H$¨GŠzÅð‘"Mý˜”!"J,™¤ @T#@|”AÅú‰H¹²cI”-‰È4NSÛŠ’ÆÎt¦É¤?>¸ Ù™Èl¢‰åkê&®Çm]Wq4IëÆ3-«–#ö»@K{&ÓÇ·{{»{w»{»w˜8rt”œ4Ifj9:Lò©TÑ,ytB´xìD¦íû?| ×w\&rÅ~ä±}¹~e/Ú+éÑ”‘ëÓ§h×§ÁÈõÙZ´õéÇrýJÚ¢Gäå•AômRÇòëÓÛè«SFsúU.´‡Œæå,õšÃ‡Æa—x㟾[^JÄÀ5Ñ9*¢ódå¡V‚Åæ_¸bð—I¹õ£Ÿ™.nú›=îàGä,“ÑÜo.lí+S?œù¸î÷ß-¾U”„®3äÌk¿x»“ÈY ù·ŠoÉ™òBÙ˜æHe+þÞác[ÕYV[ j DU¨,墬@¸ DI(.ŽQT lÂZ ,¡ÿ–Ëß’ø‰ß“øºÄ¿–øW¿#ñ[ÿRâ«¿&ñ«_‘øç_–ø§ÏKü‰³¿(ñŒÄÓg$>/ñY‰ÏH|Zâ§$~RâSOJ|Râ—xPâ‰û%Þ"pë¦VÖ@Ý€‡{‡'ß<xð2àŸNªcõÔ è<Ø88 øàyÀ‹€—Nl¤:ÆþóZeUÍÿôµ'*•¯=QýÏ¿ýè8 ôÈ! ¯¬T¾rðä‘åG+¼5ïÚ74š®PFÓg¾º¼z¼òñpµÿ1€ý•ªWL¿ý/¦Oü«ºÄV½™¼tøÒä%Ë·/šôÐE¶ç[ì›jÒgBžÿQj;#¾‘WFÌêH‰»S0›7×:=?=Ñù½ Z﹆¦Îç.0}Ëöì3&ÝóLw¨ó_ŸaN®ðÓܼ©„Ù™™©3[¾µä[k¨?Cú4à< sƦŸ:Éôã'¬ú‰Ó+ëÎaúàô«þ@¹ßë[ïõ®ó–¯õº×x]í^Ç}^[›×Üê¥ÕÞY¦†&Ã]þ†U¥«Üî&ÖxsQ¿ù¿î—~øQiÛ¶›¦…›¬I/mÖÝ+µÒzͽ¢®T­s»=e.G±Óe³¹Ì«‹˜Ée3uNwÔmrÒÔkÞç˜0O9þŠþÒñïn‡“œf§ûzÀ‘0;5O¸¿Cßq|Û=çø7*c~¶2TîVXm‰Ï¾¼Äë©*)·T”Ôm*e~Q¨À@+ ð<àeæ5ØšƒMÁÆ`C°>¸2¨W• /è –ÝAGÐ4)8°&Æxy”¢±¾Œ¡ÝÑÃ×èÑY³:ÄÛõ(w dzŒýI\n:7Ë(Æ-çfMhÊû†ã³¬ZˆÏ(sÄñhòÌ׺^ËèŽ8Ÿ¬MðvA<]› (oäŠÖ£ßûŒOHÎøÄÑ;%ãüÿKñ›Z/¿C'Éoh½ãBkœ7Exs$ÅÁlÐz侀»zú8aÜ¢×Ç'ÆÅ}¼þÞ­­ëY‡p|`¨‡›Â»£ÜŠre`8É—k=Q~½õÃÜ¥õŒgÉŽeMÙ€†‡ã›jÙ 2X- P¨”Ên@  àl+ÀÚfÜ2>1Þ3®¿6~e¼c¼eüÒ¸j¼f¼j\1~n\6~jÌ?1²Æ‹ÆŒ1mdŒóÆYãŒqÚxÊxÒ8eL'ÆqcÐ0ú-ƽ~ýA}lÞÿã±f—“u'î#]b²´ƒÎ?æk9zqañω[àÛC9º —kmÇÉcÞ¸¸`¨Å Qv·üó{âž`âãô]ýMRí𦔤¯JHðÅ|ÖÅÖ³œªAçY ÕG3BüvÖH»4ò¥×é{ô]ú&SUû]£'!ßK—´„}=ø#Ú%Ý‘VÊVÓ‡¸ø‡–ô>#Þ ×°Z9ä¯Ó—ém§gé¾6Þ…J’ÞÃKk¤ÄØñ:_ü ¬xÎJ'«ýˆŽÐV)[B¶KTdšÀþœÂ¾\£7!8J;©« À6°&äÿ÷ëô=k²ÐÛìšÇ ¬œÃãkìÚe¶ÁÊgi…Ýoß~ëö,.Xúq”ÿÀ% í5ö|ùvïJÄwƆ¶=ÝÚ¿es_o¸gS¨»kcð ÷¯_·vMû}m­«[šõ¦/5®jÔk+ýjÝŠÚeyµ¯ªÒ[±¬¼Ìã.-q9‹Ev›Õb61jf>î Ç#ûyu8‰:îÕ<*wm_ØÖÊ©\ñkeêšÖDK^‹[uNË¢¼b ž¥PG‚Ûô{U¶ssÀóƒ·)j„[xµ­)ƒ7ÅýšçMeIžÀ´|y8î÷+ÜÀÛÞ­)Õàžð!œ~Nq³‹ïv€IþðPœ¯(tb¶œ+w9‡Êš¿ÇÌí,ãɺªÃ½œ*²äz—“W¨-t§ oıð€’³Q+gp¶Œ3ï6¸t÷bصŽÏ‰AÄØ¯EŒ1DÔH~Ó…\DýjFÍ ÅËÖ(~¿4§è`<ë,káÑbx« Ê;Áq ¶åp–¹º˜$L®È†¬‰ŠJ¾ranDÀ~šN‚Ðz7H–}&™]œŸ¹SD–S"¨IŠÉ5¹-Ìí9#Ô1JqšV³Íó™™YíMê.C3R»ãÜœ‚QY2"鯉ì F’iUlw¯DbóÔHZÍ /t“ÀZ/†ÞÍ7Ò£I‘&,©õBæǧüó ®ÓøT„—é¼ÃK¿®˜3ߘ*º™Ì”Ê_Œß)õ $¯¥YÍD4¬†É"û{ÄŽµ.m›ÌÆ~CnNh:¥òɽû3¼©™Bþû3îºáÇî`0RT‡°#¹_¸²#-hÔÌô¨tuFº†|U#û{ˆÈ~ډѻ⑴† »° ‚ñæÀ½cý~^­‹™LD˜˜2`½ˆ ÞjÜÎ0#×AM(:ƒ=aŠÉ†br°b(՛ȳò X°<”ìM$„S¹ àöÀ”uµ¦fĤö¯Ð=þËÍ·4G‡â‘^‘Ð4…ãß÷),±™:™Ö÷E„d‡ÌeAZÄG d,WÀˆZ~硚ח³^õ)Ws+ìŽ÷i}ÉL¦OSû2ÉLjvqr¯¦z´LÖåÊŽ$UYþ ü˜VxßL‚{’i¶AîXΙ}øY68,¶ªOM§ÀÁÛ­ù;Ù’N‘ÏçkÙ5—ñüÞ»p:)jŸ8jðIÞ¡pO‡(Y´3ŽšÁC"Ô >ìLЍs"Û‘–âÇ’2yÄ8˜çb¿_ÔÓôlˆö¢Ã'ã¹¾J{•—(Ôªc“B2_xw ÉdA²4<©aß|âÃRæÇå7Îö¥ÜΔiåj§8ØaÞ~ƒÏÇàãÍ^„ˆÉ­_Ž›“PeRÌ‚*Öq=y•.Š˜àÄÌx4õu{tn Çç•`Bõ”á°dÐÙE‘©ž×µW™8G©ÂÃY³JÁ'œ«ˆÎýª—Id’ù¼Ó-¨ m#½TJ9ãQ»Â7xïÑPºJ. eåšðð5‘ð…‹!Ð'ê ["µ5ÁKÅ}ÇK'ìUÂq'*wPjDM‹Íæj²W EÈ ìÙÅkÉ^qÆ‘ƒPQò)ŽDÏ…öîTliþC}‰~j&‘Þ›BMð@]‡eEÐñx¾Üä>É"ÀZý•»åKQ,èà`C9ûyÛòW}HÔå>YÕ¹Ú]RÆ&ÄàÍÒܹ˜”ÒCXÂûpÿçÎiß,ûÒw!Þr¸¿ Æñq\yz¸Èz²;7˜ ±s;vÅç𙬞‹Å_21S8Ù“ÈÖCŸSñ$¹øç"Ù#˜BEŠ2Ìö’©Hê+s!¢I)µH†ìà5ÉË)ÇhÿµIž§ gÏ’ã…$÷ LŒøÒ8Þâ6Ýà¡ø‰t&™Á¦Ê\"³µ.â&­+ËL6/ÖF{¸SëünÁïÎñm‚o×zþ(u¥žIj(ÀqRXB¤°ÈrS@]\Ä z'¯ŸÛ»8`zBåÖÀVèm{3ŸI ;Dšb¬=Ð?’àEKB¥Ÿ;0ƒ#?4úä\ÏbÐ’5¥IlÇd‚'t±h|LX¤ªøÚ¢mආœ‘Ö±Pk"S®µËëÄàÅ)ŒÀ[åA(9 ºXLÜGxí.X>¢Ak$©b,4²Éhio±Ø7pFq«[p©ŠQÈRHÂ-sÀYRÌ«1!^A;WcB¼ö‚"œ—½©¼Ööp',j¸#”ùˆDý¼S0^¨þ£˜fp–†´cœÉˆÊ¥ìó’@  ¹ñNp4|÷åc®¢€`‰9.ç¸vá¹K~ÐÆf =&Фð´4kœbq‘˜„ß (D‰Ì½ >Œƒ³è^n‰dg2E%Ÿ? ¯¢’¥VÌ¢FÆ«¤âNAm ý©éŽòµ-Ö^ºÄ~µ ~Gžd‰QrÂÿ>â_ ]:kQ#5S­¥Æ—N»«Ïú”g¼žçVº.X½•Öš•nêîîöüÆsÝsµ¾ßzý¾6¶nm—éþ.óºµ ÚÊR“][·~ýšö&o:¥f¯·Ê«­ceþ2¦ûm•MõU Š{S—ÚV_íHχûFºjÜõÁfµÁk/š}ú{›9õiûmee iݪêÖ5Zt¨¢¾}Å“+V×®éûRCׯ¾óªÆÛÁïÿöuËÅ[û,ò×°^øZž÷ÍFˈŠ%Üܧ‡SöKµôzD¸ûmÅ3 endstream endobj 71 0 obj 3653 endobj 72 0 obj << /Type /FontDescriptor /Ascent 950 /CapHeight 674 /Descent -222 /Flags 4 /FontBBox [-1110 -299 1372 1047] /FontName /OTYLGF+Cambria-Bold /ItalicAngle 0 /StemV 0 /AvgWidth 600 /MaxWidth 1380 /XHeight 492 /FontFile2 70 0 R >> endobj 73 0 obj [ 220 ] endobj 74 0 obj << /Length 75 0 R /Filter /FlateDecode >> stream x]ÏjÄ Æï>Å·‡%&·‚eËBýCÓ>€ÑIVhT&æ·ïhÃzð¿™ßø9Í¥îƒÏмS´f˜|p„kÜÈ"Œ8û Úœ·ù¸UÏ.&‰†áa_3.}˜"(%šFÖL;œž\ñ¡xoä|˜áôuª3l)}ã‚!ƒZƒÃ‰Ç½˜ôj„¦¢çÞqÝçýÌÔ_Ççž8ío$®ÉX$fJJ­®W-0¸¥'{3$T×jRÊÇ"Ž¥“,FBe®2¥üöžÎnD¬®¤f.Y|ÀûÖRLåíz~–q¥ endstream endobj 75 0 obj 234 endobj 28 0 obj << /Type /Font /Subtype /TrueType /BaseFont /OTYLGF+Cambria-Bold /FontDescriptor 72 0 R /Widths 73 0 R /FirstChar 33 /LastChar 33 /ToUnicode 74 0 R >> endobj 76 0 obj (Microsoft Word - timeSeriesRefCard2.docx) endobj 77 0 obj (Mac OS X 10.6.8 Quartz PDFContext) endobj 78 0 obj (Diethelm Wuertz) endobj 79 0 obj (Microsoft Word) endobj 80 0 obj (D:20120319140701Z00'00') endobj 81 0 obj () endobj 82 0 obj [ ] endobj 1 0 obj << /Title 76 0 R /Author 78 0 R /Producer 77 0 R /Creator 79 0 R /CreationDate 80 0 R /ModDate 80 0 R /Keywords 81 0 R /AAPL:Keywords 82 0 R >> endobj xref 0 83 0000000000 65535 f 0000103745 00000 n 0000008754 00000 n 0000024668 00000 n 0000000022 00000 n 0000008734 00000 n 0000008858 00000 n 0000011770 00000 n 0000000000 00000 n 0000074498 00000 n 0000000000 00000 n 0000036831 00000 n 0000000000 00000 n 0000040970 00000 n 0000000000 00000 n 0000098790 00000 n 0000088976 00000 n 0000000000 00000 n 0000057698 00000 n 0000000000 00000 n 0000062033 00000 n 0000009034 00000 n 0000011749 00000 n 0000024384 00000 n 0000011806 00000 n 0000024362 00000 n 0000024491 00000 n 0000000000 00000 n 0000103319 00000 n 0000024758 00000 n 0000024808 00000 n 0000035909 00000 n 0000035931 00000 n 0000036169 00000 n 0000036305 00000 n 0000036811 00000 n 0000036993 00000 n 0000040345 00000 n 0000040366 00000 n 0000040616 00000 n 0000040640 00000 n 0000040950 00000 n 0000041143 00000 n 0000056532 00000 n 0000056554 00000 n 0000056789 00000 n 0000057021 00000 n 0000057678 00000 n 0000057860 00000 n 0000061412 00000 n 0000061433 00000 n 0000061679 00000 n 0000061703 00000 n 0000062013 00000 n 0000062202 00000 n 0000073537 00000 n 0000073559 00000 n 0000073799 00000 n 0000073955 00000 n 0000074478 00000 n 0000074664 00000 n 0000088425 00000 n 0000088447 00000 n 0000088668 00000 n 0000089149 00000 n 0000097930 00000 n 0000097951 00000 n 0000098196 00000 n 0000098308 00000 n 0000098770 00000 n 0000098959 00000 n 0000102702 00000 n 0000102723 00000 n 0000102965 00000 n 0000102989 00000 n 0000103299 00000 n 0000103486 00000 n 0000103545 00000 n 0000103597 00000 n 0000103631 00000 n 0000103664 00000 n 0000103706 00000 n 0000103725 00000 n trailer << /Size 83 /Root 29 0 R /Info 1 0 R /ID [ ] >> startxref 103904 %%EOF 1 0 obj <> endobj 29 0 obj <> endobj 83 0 obj <>stream application/pdf Diethelm Wuertz Microsoft Word - timeSeriesRefCard2.docx 2012-03-19T14:07:01Z Microsoft Word 2012-03-19T15:07:25+01:00 2012-03-19T15:07:25+01:00 Mac OS X 10.6.8 Quartz PDFContext uuid:9be220ff-bcb7-1a48-8336-e3d0105b57a8 uuid:93a93106-32da-534b-b718-6c8847c64fa3 endstream endobj 84 0 obj <> endobj xref 0 2 0000000000 65535 f 0000105723 00000 n 29 1 0000105980 00000 n 83 2 0000106083 00000 n 0000109858 00000 n trailer <<6F42369626D0482CB77739AF2A8958FB>]/Prev 103904>> startxref 109899 %%EOF timeSeries/inst/doc/timeSeriesPlot.pdf0000644000176200001440000356452715117007153017576 0ustar liggesusers%PDF-1.5 %ÐÔÅØ 176 0 obj << /Length 1525 >> stream concordance:timeSeriesPlot.tex:timeSeriesPlot.Rnw:1 13 1 1 0 156 1 1 2 7 0 2 2 1 0 7 1 3 0 1 2 69 1 1 2 1 0 1 1 3 0 1 2 4 1 1 -6 1 10 23 1 1 2 1 0 4 1 3 0 1 2 4 1 1 -6 1 10 20 1 1 2 1 0 1 1 3 0 1 2 4 1 1 -6 1 10 24 1 1 2 1 0 3 1 3 0 1 2 4 1 1 -6 1 10 18 1 1 2 1 0 1 1 3 0 1 2 3 1 1 -5 1 9 17 1 1 2 1 0 1 1 3 0 1 2 4 1 1 -6 1 10 18 1 1 2 1 0 1 1 3 0 1 2 4 1 1 -6 1 10 16 1 1 2 1 0 4 1 3 0 1 2 3 1 1 -5 1 9 19 1 1 2 1 0 6 1 3 0 1 2 4 1 1 -6 1 10 25 1 1 2 1 0 6 1 3 0 1 2 3 1 1 -5 1 9 17 1 1 2 1 0 1 1 3 0 1 2 2 1 1 -4 1 8 58 1 1 2 1 0 2 1 3 0 1 2 4 1 1 -6 1 10 22 1 1 2 1 0 2 1 3 0 1 2 4 1 1 -6 1 10 17 1 1 2 1 0 3 1 3 0 1 2 4 1 1 -6 1 10 15 1 1 2 1 0 1 1 3 0 1 2 4 1 1 -6 1 10 17 1 1 2 1 0 1 1 3 0 1 2 4 1 1 -6 1 10 15 1 1 2 1 0 3 1 3 0 1 2 4 1 1 -6 1 10 39 1 1 2 1 0 3 1 3 0 1 2 2 1 1 -4 1 8 15 1 1 2 1 0 4 1 3 0 1 2 4 1 1 -6 1 10 15 1 1 2 1 0 3 1 3 0 1 2 4 1 1 -6 1 10 18 1 1 2 1 0 1 1 3 0 1 2 4 1 1 -6 1 10 49 1 1 2 1 0 1 1 3 0 1 2 4 1 1 -6 1 10 16 1 1 2 1 0 1 1 3 0 1 2 4 1 1 -6 1 10 16 1 1 2 1 0 1 2 4 0 1 2 4 1 1 -6 1 10 34 1 1 2 1 0 1 1 3 0 1 2 4 1 1 -6 1 10 14 1 1 2 1 0 1 1 3 0 1 2 4 1 1 -6 1 10 43 1 1 2 1 0 1 2 4 0 1 2 4 1 1 -6 1 10 15 1 1 2 1 0 1 1 3 0 1 2 4 1 1 -6 1 10 10 1 1 2 1 0 4 1 3 0 1 2 4 1 1 -32 1 36 34 1 1 2 1 0 1 3 2 0 1 2 4 0 1 2 3 1 1 -5 1 9 12 1 1 2 1 0 1 3 2 0 1 2 4 0 1 2 3 1 1 -5 1 9 12 1 1 2 1 0 1 3 2 0 1 2 4 0 1 2 3 1 1 -5 1 9 39 1 1 7 6 0 3 1 1 3 1 0 2 1 1 3 1 0 10 1 1 3 1 0 1 3 1 0 5 1 3 0 1 2 6 1 1 -8 1 12 66 1 1 2 1 0 1 1 5 0 2 1 6 0 2 1 7 0 1 2 6 1 1 49 51 0 2 2 1 0 1 1 13 0 1 2 122 1 endstream endobj 210 0 obj << /Length 1238 /Filter /FlateDecode >> stream xÚÝ™[oÛ6Çßó)„¼”&U¼Š† ¹4é†foÖíA±•„«,2½Öùô;i%±ì¤rP,ê‹E‰/?þÏ9t\Ipº—l¹Žö^žPà4&˜³`t ES’B°˜(Œ&Áô[Q£Ë«0¢"A/Lˆ‘žæçy­óù ÷ðíEÁåŸ|læáߣ_^ž°$À8VœÛ.%±ä*ˆˆŒ¥”®Ùc›ë¼˜B D ÷Ñ”ÕæÆÞ¥(+'îñ(”U:›»Šó^¸kFa+æ›|5zí>ûsQëqï_¯µ÷nš¨š»»ƒù¼ aPh¬3£+˜XéÞ¿ó½íL’˜' †Ocšr××Yfë—®!L¾s’`æp`S&0Ì4æ#±J|GURŒL—9t°<€.¨íÙ»SX% ¨b%ˆ°m%0@‹}#8Œáèghƒ SWv h²ÄJß [ïàCDD"SĶô³Þ2q-ŸÀ—Õ– ¤hkïJÎ\ÙÚÈ|½7,9WHnMïÌ `)@ìŒÆ÷ÃÜpBú½Ôÿ†\ ¬†…²ÝS†ÎÁ$‹¦¼¥{0(‡O’g}Y‡Æa…Ô½§›Wª!%V¤ˆ'u¶(Ì.¬°$kÆ)¥Öžè]J³‰E‡`=»²£½Ù1Ï† ™ºt>áKÌK‰"½qèMf -íOµ0ŽQuéø<Ê*åd°V•öF&<²Ã ¢5ÎŒÉëP1!‹ê1‰_sú‘÷öªO¾õÈŸµ÷Ç`,íµÀum#iš"BvöÌ´õ̾sê:ÿ>ŒãhVçÆ4{`ßUV~÷]T¥Çû?­Jú,WŠô•JÚ:ãûpœUG^ò‚e¯ éÒ!bðÑØ’ûè+²úã»gÿÞ›}œÃÛÒ×UÅbZºº®{v¥VWÓMV-ùJ3½gΟ9¤6}j|Ä:XvøíDKñ•(¤=ƒRÚúû&ÍPæò,Œ&n>'¡÷XO3ï)ï{ÏFR`WØ®=‹L¦‹ªÎ'?ºû̳¥hn𬠠­MArútò šíMƒ9Í>(ËÊØœ³ìž\H…h²³8³Vœõ| ‰[«2YQ8ÄvŸNjҾ)kµùÀÚ×dÒÆ°#mV[Í4;í¢9È‹&ŽÅàbJzÓZ)øÑuV^µvt«enï–vÓ™U|{ÓÙ|,áÃE{ÊkEÿ¤Ð³Y ë«óY±hý@Þ ùx>IÙÓ¥‘;i<ÎÇU½M›¤J;K#o¥ñÌüMôå²]©QãwfnËw:J|SG´o²Æ[í¼¯·Tµ×ƒ 纛ç¿fÓ.EÉùØÈž›ŸoQÊmlšç>°³âic"ë~0‚”·kz)y.`To£aÁ¼Ñå*Íw‰b‘o8ÀM¾Çî‹? P+h½×sÝ=pO•0Ò›‘ØhR« Lj Ûij÷•‹ßªnü¦6> stream xÚÕZ[SÛV~ׯ8Ó§ä¡ò¹_:L:”$3¡ÍmgJxãOÀfŒÝ@}¿âˆcÝ Øiû`­Žt´ûí·{V+É‚qf˜âÌ2Á L(=&=„bJà¸fZ{&Ó^1ᙕ†IÁ¬å™”ÌiÍpÀ¹À¤e[& ZRÁ$.8¬4Ry¦¤…„Ne ÃT¡9Ï´dÂ8Å´aÂrÏ4L{ «å­d¨Ì(H5°«‚`Æ1©aÚx&WÌ H¥2\"] &¤‡qKÈà– €¿‡{F0GnzŒ5 Â/ç Æ€¨—(|æ%ÃaÍ<ø2ЋK”õ8}N*¸—,$ôùàÁÓ“\ЋCšü'ž5f‚K¦U€»,è‚/ ÷ ˆ€ŸÚxGÌ1íˆDC;GG ×þ ˆb11äY1\ÛL .F€(È‚"¯„$E‘·P/ øÐe¼¡pØñ´ÍÁà‚d‚7™Ëe…ÁŽ£ØYPN (+aG RÔ2B`F±²Z 0v0ˆ–5`_ \Öx`F¼,ñ)0K„ DÌ:Ð'2ëH@Ìl@ì‚fRN jŽS²:ä q#À‘ÓF@ô™À<çˆ^ÏUG;‚yN¾#¨žð ¤¶—vá"ˆ³7ÅdËp4#ç˜(¢îU¶µÅlðvz8eƒ7ìÅõh8O'¹xÉ^½Ê^|RÎâç>qÎßÓfB›9mf´™ÒæŒ6 Ú ãÙq<;yÙjDÖD 'ÑÜYÎâðלäa4vI›ÑÃsñÈ,Î*†×g}¤ÍE„[ؾnÀ¼8°›Øù5Â-ìü•bÇá¼æ8^þ9â=f¿{1Vi¬þ€Õ#_­Ï¬®›}5yušº¼äüæ‰1u„b„n£®ETxo¬8üç·ië wžœ_ÃtÂ<]Ë…Yblȯ®²M››8!)íôw²¤šjÇc1üðpÂwq1ÌâÅe·qJ’³š†‚ïóhgOüØã\§ß7Wï*;—î/õ¸—ž«ûz˜zø¥vq•¢_ús^­3£ï‰>$×Weé"&Üeœš8òÌšùì<×b¦Pò5bù2cº˜©§åR#tx7MðË´^öƒ¬’ÃX ¶ÒŽlœ2R՜ܫð–^§êŽùöÂÝLÛ‘–IŸ“¦&´-ë¦Êü&E?lèD[*ÑIt뢖ækE-[¨9ëCXåܼ9ù[¯J©Ó´D÷!U-mÖyjìsæ®*±›ò6onzþnË´‡HÒm7ºYÕ‹«¤¿¥I°è(x£ž®Å´äeuK¬–âÓÈ4%ÿ^ú„7Ž­äm_VÝFvF«Ø–ÏOŒ¥ò_’¤Èiwäçhâre¸ê߃Ûz]rÃ:I×óRÝçž~¾{Òsõî~¹ò¯‚ˬ)C»ÀýÏE˜ç«€³Ï'­«×8ˆNU£Ó ¯ÉÝš˜{:¶:¼ z¿ÎÛòÛô ô¬/1úÀ…pU1þÚ×$Üv"e7í÷ÛÒ;ž7·®'©7µûå"NXõÝ]å½mºi¬Ö{$Ž¿KKÙRzLÒ)qáô­À´+š=®Èg¹’ì§%}Ÿ<ïÕmº¥=ÕûŸ¢gÛëõþ}ÚŽŽbÿôÄ—A®ë­_µ$ªöízŶªÔï[8½J›£IJñMg¤|kŸV•¿¥{LýÑe:¿§Ý¯^‚žô@ëo㪪8ìx©w˜>`\ô/_ßÚ“uõJO±£Ÿ`g•ž,Iô æ[`Xˆmù~ÓÛ<…yײœZž[ÛÌǼ7íä7©ñEl È}-älŸ vÇóc‚±µ• o¯Flðñäó(¼žNæ£Éüš>9ÓÌl°?ºž.fÃñpwhot6>Ù™Þ°#šcžÅœe^‹Ü‡ã šfP åôíÉd GÅ'J²N_(ïd¸“ž—R”R–R•R—Ò”Ò–²ÔçK}¾ÔJ}¡ÔJ}¡ÔJ}¡ÔJ}¡ÔJ}áNŸä¼”¢”²”ª”º”wú޳„ÙÂÿlp°8ããÉ—l°3fƒüxðnð~ðúHâ|8G°¤Ê=§ï×2—V“¹œ¾N-”ļmÖý%v½0Ï•¦? ‡éÿ"äRØNr0V³¶L›kúïƒÕ9—šîr©]3Œúç×õbñ:7œþw!s,š–BýX6Á 0H)mΑðÚ«\©xQÀbyn"ÒâYGÑ8D®ü ¼è `Ѹå(ðLÒ ¦Mȵ\³,VP£¸iírpÓÅ®9k¡sGÿRTM0–&7®s5o"S¸ÌQõe(mŽP¹å½„¨M,fxîaJs`p€&r«m?– ,f…˜²`E«ÊmX• ¬b¸ endstream endobj 234 0 obj << /Length 578 /Filter /FlateDecode >> stream xÚÝ–MÚ0†ïü ‹“}HÖß±{£hÙ¶*ÒŠÍ©«²Ä…HÁA!HÐ__§h7é.¤§ñ—<“gæƒÀàa€ÊzRfƒÏñàn"(С–T‚ø ”‡ SaªAœ‚g(Q ©€ñÒ €)£0±&÷Ó ÒnQ@ WYœE?ãoG¿à9 ¤¾€«f½ÇÝ„E¯ƒà¡æ $ÔB4!„Ä9À”¢ ¦ifÎ;ã0© ƒ_Š2ûml½Y%¹_ûaÊÂúžYÓŽI+CŒ/gZ!¬"Èõ›å.-g[lè{l¬1Û.ü *¼nó*[çÆ›™j[6Gó¢Ú´ãbô>—ƒ ¥/þŽ@kÔ¯/TþÂÑzípQhlší:WJÍ ²¿æ›|ª£æ§I¹Èìæ 8çp•”‡´¦>[Å*éäJDW÷ÿf<êÙÔ±Œ—I™Ì+Sz.1rí9yÉM7Ýôèô&§{“;´Œq‘§¨IήÚi†÷fÈ» ëœä¦ªÌ¦i®moœê›äGzóçñ ÜR‡¢"ê¶ðÑÞødƒïi¿ª©½üUä'Ä,$½e³ž'F‡ÇÓ.ÛxvO¢Ü»ýá…4\—®$kÊûaGÏרÿÉŽ÷.Au»yÍm™Í‡ÝÿÊŸhl¶‰à!§¸RÁ!ÌÓ{+øûxðÆ}÷ endstream endobj 238 0 obj << /Length 19 /Filter /FlateDecode >> stream xÚ3PHW0Ppç2ÀAc(á endstream endobj 242 0 obj << /Length 2823 /Filter /FlateDecode >> stream xÚÅYYã¸~Ÿ_aøee`Ì%)ê`fw3‹ d0ÝÁÈæAmɶҲdHrûëS©ÃÍžÞ·¼Ø<Šd±Ž¯ª(¹:¬äê×wÒþÿtûîÇÏ‘Z)#BëÕí~¥´©Ô«DÆBélu[¬þ¨ÍV))ƒ/Í&TÁе›-ü—ÝPµÍæ?·ýñs˜¬2‘Å:ÆMäjk”HŒáå·Çè,øv*‡®Úõ¼$ gKb-âXÁJZ1T§ò—|(={›H(™9¼)<›©L„:œovSvUÙ{¶‹"ʼn#=ç»NƒûMùÛùF½ÂãF'AÛÝÃФ´8×c‹©Ó`h™²(óÚ®©†#½Úª¯ ÿJdQÄg҉ǮmÚº=T;\dâ0hï6Û(þ[î†^l¶ih‚›ªÙ•<;˪ã&žIÔÕRfܺ4EÙ½ o»êP5î”&?•>uÈ%ûŸóºlмóIP ™Fo*DŽBÞ¿ªŒ0±é€ég  ƒcŽR@A•<ÂÁNÙ`×mS[Ò¾®G$xÄ'Ь.ªßehHQÈÓ€<C½ÐªÜ¿ìz’"|³ú’©HR½F(m£1XŽRâ1ñ}üÁÆC•×ÌMT§3¸ÏHYð(\Õ'µØˆHÅ£qÖíàšE–ŽÆ¾¿4ä•ïÑEAN—O˜³š×}kÏmIŽí=v0E(Y9Êá˜ÎrSa²ÌJ"5ta8žš×Õ€7~æáªçÿ¼>µýÀíË ˆ¼+xlמÎùPÝÕ¥ Ó2¸HS>ê *ÆøiSh=T‡¦†’{ãý ÝIÂÜÃNâ:äœFNnÌ‹Ùh{»GWîPyÎŽ`GYKÜÞìP¢Ü°ê–ån5¤=’2 Là¹Ðz”Š,Qo)]Šl² kŽŒ³ö„ÁÚrÊZ€ÑÇ®q5“æq°¨ús;ÁÀˆÑDW|É¡†{EáŸÃ׭Δ0Wt…mqœ2ïFfÁ]Þƒ¡™^YUŠâp»çÿWœ&‰…ÖÑ[ò __9 ï‹ò2VDø¿ôUs¨Kn[¼ƒ{J%4€ÜÂANÈö¥ª3ÑkV[>âþ¸á¡KS=l@yWå%Oá®47nð@! (ߥµ !âý¹€‹hŠŸNìò*`…$ÊX… (œ­½c”‰TgKe¾bÆÄ‘.Ô ?FŠ$-yßµ'8VEÁ·PLÀÈ0ÈQ–X*›'ë§ïÑ ¢àñXqDåmœòÀzzaó0 >¥gYƒYpì(Íæræ=ûQÚhð±HÁ;^µø÷}`ÍðJ¹½oÎ'†æ¢¬¹¿o;n´j„AÁYÀ Øó8aʆ”Š€^‘v™àÛ^ÇŽc‘…obO$¤Qß±\¾Ü—@ Kôµá|?/SS*à7Sò¹©NU ~(šà½çðÔ“è· ’ŽºÝå Ê?¢L#ÁS±³k›¾‚¬ u†}Ôþ“È50Á}h¬8\Ø…V. €R¡0½’„[~z.B‚%Y€ø£m}®d ¹o 8~´6deuëLðé ²«žüvŸ¾HDˆßIùØã ÞsçîRÕ¨ª‚»œi˜¹?òÀý¼Ø¶Ud\úÊx^á„+Ã¥b‚aý“ÚdK»"0d$a¨!¡>Nnc³Â%_†–ˆXŽÂó«EE"NGš^eƒ<œ¦:kbØyq m´Ð×éØ2¬¾’Hq’ 2BÌX¨C©ŒJÝi02Š Ú¨ð‚÷fÙôÈQp)jÖFI”YÄ[-”2N¨ô¹Â—fœ~ª—…Y*z†Õ§Km#T]©@óÔ±¾Bt×ÇvÕx8ˆÁ(“ñB6%ð€%2ÿ2+¨lvÎü5YÛ¾Ãâò'íò§˜teù&Õ¹j‰G‡¼ª[4/Æ£Lƒ ®BºÍ‡“ÒàÞâØnàJ _½:ªra3¶˜ÅŒž€³ml ÔãÔ8§÷¼äwIèßÂzР®0dÑ9sÑ×<1[ãÂÔX´ZwèŸ){âË·5í‘Ѽ+§ n£L‡cÅ^ ÇÙ‚ú±˜Ñ]mÌd}õG9[±+Ÿf+ò§ÊÎ9ìÕT›9–:¨eØ{(ŽÑ2æ°Ú½_\ÏYµÕ£=Æœª†²-Á’ Òî¹wÊ;ÛÊÏV¤yW6»Ò.Ü·œC1Mo‰öv)'¥rA­øân4I&âcÚ¾Úz™ü`c Y­À!^ò"|Q9QÎ爤ƒj²¹ºfR7:š4õÎySZ‚=ìu¡âŒ«ŸÎ#"<@xR.óWÊM á®Q|­a[ªæ sS4öÜ#a㛘…Q|:ãêvþ°•$ÂD#œ}ð°´u4Ì™bʯ‰!ŽÜn’Ý…òÂp5Ž“ÿ\†Þ­ÖÚ" `눀8šwvz¾1-*xøÎt$)÷9@w*i€a)Y¦dÏçòãšatí­#¡p etõ.E6¢ ÕJ‰«•þ—2€ô©ö\jtøofWzn†‰›±µÚóÈš±…+Ì Øaëò*‘È­CðÓ¬¯|o}*DOU 0P×õÚÿàNuoÊJty¶4#,ü3ÂBÃ¥ØtŸ—° ھ쿑¸½Zô¼LHð[4Ù—nŸ’ Øò¶ôéüž„Ñp…8‚.Øß”Báûù3§TŸ^N ò×QÆ+-è,²¥³Ç%$›‚‚[‰9ƒX:a xü¿Rq&Bðk€(·ýdn4¹}÷?,8 endstream endobj 248 0 obj << /Length 2492 /Filter /FlateDecode >> stream xÚ¥YmoÛ8þÞ_ä€;åPk%‘”äÞå7uz>$i»·Xt‹cѶ¶²ä¥¼ì¯¿õ樷]ôKD‡Ã™áÌ3CÇ;Ùžx'ï_yöûvõê‡KLÝi„'«Í‰p7ö‚“È ]?˜ž¬’“ONµSg“ ŽœZÃÀwJšµäM©TRìq;UAÄ< bçÿ(ZÙÔYFkë"GzU-É{øFNQW4ߥš8Rõ¨-­À=ƒ³cç‘ô®¨³„Æ(Ïý¼ú:ñ}w*“ýœâ |BßÉÒ/FE÷lÂϹ)*õu =Òë¬J΄pd™ÊJÑž*ÝÛ‘Veª4Ó¿£Ó|›ÙuÔÎ.ëN`"ý‘\•ÀŠþp)Xï&˜ç»qƒúFíÓÿʬVú”8w6iXvJ8—{ÜɤuFFsp=|h€W©hò<‘O©„"r®eÚð¤Ufnü5z”­ïIˆY0ÄÀ‘yb‰;+íi2~hË­ q„î)´ú—ÀÀ@/˜‰¸õ`NÓ{œ<O¢6® ~äüx3GסTU—щ¥Zû½¢idBÉ2i;¥íþXKY¥E®Ç¢i-³ ïo¹g­ƒÁ¦Î׸eäZ§žÇQs«äà—W@öñ°á³¢;+“(%W´s &p6Å …4]ãâŽÖö:×~ö˜POé}š¥²=\qg'Æ<0.r=ƆV¢ƒ¸á,µ&k½ÆZ Êr[ƒ3MFk"U»²¨·»#FÊU52!ÇCg–e´`/Ò Îáî¿îûŸ=á¥ù:«“££ž{!|>-¯eNË >ª®%Z«ÄÎUU½”h•ïß.ù¾<óp€É&r˜²à—Áà²~sÍ`îùåìj9 ·½€`pÐ uÃAÿFމ¤I-3ZÔtTþY@amƬ¹7ì~ÇŽ*ŒgF”m™Ú5ËÄZX8º‹ÐŒ< !^¼ <<þ‰0WŒ‰8èî&`"š‡CñA óè.m¹´ Æuºy¦,⃺6Ö¡±>X GJ9w}OtŸŒH‚ã"7 ùЇƣ@úméÀ† ”tg|XÌR–j;2ÇxãÿãšäÇ.Ÿ¶V}åx° 0­aj*âvOüiìš üVæ¦5 ˜syY<è—…´¶¼áÚûŰÚ¶“hRÚîýYÛ‹ö5 'gëפ“© ¦9¥ÆÊœe*³Ïj8¬i¦,z¼¹ãQ¿–Ço±a¯¡¹Vüv)³ƒuž°õb»8Ø7 ÖuÙ`˜‘¤­x[¹­à®}…ÉÝ߯¢|bÕ>¢ÔGM›ºQ‡ÅXîLÝ bm¡Eç_*"WôÝ%à†¨fa¿cšYLoi'=DŸBÏÈäÚ eÒ,ÛH‘D‚nI„DA»Ìó¶-/ˆ#'tQ?‰DÜ´ç ¶2*90“ô2¦hTÊ|k‡&Z᛫GØ÷®Y3½*¶éæe\ÒÌ>œXܾ=}Ç*°,haˆ7‚«êIîM20|ð¡4&…hHþ]•Ñ ‚zïDZg鿉¥òµ¢ºG³RXÉ„K@07mj›ïµrŸQlQ+j3¢=½ÍàbÊzkoÞt£!Õ†RUu™Ûª´¦‘©¨ŽFt,tg…Mí½Nú¦ákV:¯õˆóë °8®e5îpÔˈc}×±Îu@ÿ’šîªEeÖ‰´Æ P´Ôæ´¦¹¹¯ˆÄfg'¡©Ø¸|þÿújŸž7¶r>uN1Ò–cð¾Ñ§Düpÿ+5ßÕX¢úŒ¹AWßàÛ.pVø/¨YHÔ:êŠh$ØçkÓ#èÒ#°ét¸ÙRØ”´§ |ž¤ú ³gì²w£®À^ÝÞž'Æ^ ¼UY†9÷›¨hº×€·°„TƒÑF`YÑÈD9ò÷ Q–}ÓåL‚)<¢Ž ù@…ò‹qëÖü¨—KeÓ"¥6}ϨÇñð×JÌš´ù‰Ò‘¤^+›¨ºØÛMòþ¾T)=?ßX{Ä0¿Àm1H»aíÎòY»ZUYÕW!H@‚^]ü2»º:}M,§§&ý_ºèHä'ÿ³Ý.V?ÝÎÏ/þò`vóñz~·¸€9LV‹ë9.>\]ÍV4Æ_®?ÜÌW³»Ÿhõz¾\ÎÞÏ—ç*ÿåý[÷ãêr#8og·ó»cúͬ‘;{÷î6Ÿ'8¿šßþ„7‚gËwóëùÍÊÊÆVÌâЗ‹‹Ùjñáæüâô›Yªßê´4n¤p9£×Pã¾cP~¹í©úÌ·ªÄI¨w³°¦J×BÀfUÊ$ùöŦp°©Zú4øç¿Üñ=§ÏºÞ× )>‡=›¸wó‹O¯-ûgª A¼±¡÷N^ÐwÕyú@¿lÿÕ2ø>eÞV_D=u¬.%®åmO·ûoV}§jl\5›‹KŒývòãb±êt$p6ÓkÀWUý¡®áwêŽéÚhÁéq´Ë¢Té6ïëFãVBÔöÿ@Çu<œº,ÐÄn ÕÂX ;æ«Wÿ¼öÓº endstream endobj 253 0 obj << /Length 19 /Filter /FlateDecode >> stream xÚ3PHW0Ppç2ÀAc(á endstream endobj 257 0 obj << /Length 19 /Filter /FlateDecode >> stream xÚ3PHW0Ppç2ÀAc(á endstream endobj 262 0 obj << /Length 1449 /Filter /FlateDecode >> stream xÚÍWK£F¾ï¯@{ÂÒší' ‡\e£DÚ$ÒxOÙz c“`°µÿ>U]M^¼¢(ÓT]þê«2‹‹~zÃüóûÝ›÷4¸J¤JE´{ЏPIÆDdXšp‘G»"ú#›-çŒÅƒm Û›­LÓxWJ\™ø¡ìª²'éïu;ô›?w¿¼ÿ M”'y*R<˜E[Å£¹;–¤¤åLI°D ºNç G­œŠÌŒJO—f?TmÖe?uí Wy<¬0y’¥|üv€¼ï_›Ñ:I'Õ³ÝoDÿ½Ñ:¶‡’¬ÙºnQzíÉæSÛÑÆg&Õ3îxÅ¢‰,»²Ù$|®Ê«3myÆ)%,x’kM&)&÷SÖ¥¢¤8ymÑÂüi/CŸl¶JiLl_’BÕìëKáÒ®ò‘΂„›Ï•vW“)Iv? În¾Y$ƭƼ|j* ´«ìàíB68?Ôþýᓹ/À¹´Ùj‘RzPV”O®‘^ùõÅïUý0ÏFçßž<Þ®QŸHDâ|G$$DÇ¿l ‘6v¨Õ×ÏŽ!ѵI ˗̶î KOƒT¼kÞ!\d—£=埣¥÷ø·üp%&»òÙ¢ 5»ž6MËLi[ó*ƒ± M`åHé·ýê1Ðq²lžvðª+û3").÷c¢¡0 ÂoEÍKU™(¸º­„$eEO´˜ÅÍÝnÍŒ:Å•(?â°×W‡Æ¥ Þ÷’ó±1äÒ,¸x%8 äâ¥mXBÕŒ¹…+„“ÿ‚dôd¶jh’ñD+~ÛB± UZ®=q‰ãT0"ˆ§ñ¯íp„àuhpÞÐ÷ˆ®]½p˜¥ùJdêh{o©¥ç#]9íà¡Êâ/-)“Œ/ùäž§ ×/b8 sH.¦òs;ôóxøµ h/A·™Êv½\Ä8G/µö¿À`¿¸¾‹_.!)~«º÷óYbñåÒ»¨`uuw+K×Èà Æù"þ8Í>âô'4Ëè¢êÏc§¢a 0|ƒç2 ýbšf•æ°0@ÁÅý9’ÃÑžÒ412[æ¿´£²P8Ñß3¡Rš…@†7ä:¾8äàsð»mÀ¼L$©ãOg’OÁ^{éæFÀY‘J—ñ[¨í¼pL§¡Á¼ÀHn „>IÍ Í¸ )³¡µÿ§ÒSÛùý1J©ÇÑ „£}\ÛQÓvm®ô5†¬ÿý Óóhwîš·s„ÜéÍl‰ÎÝÎWyÐ‰ë øvXZˆ©|š¢zSPË.p•áÌÓ—$BC eŠ”p B™»¾K9ŒÄÙ¿Cbˆ“ဆ‰VÞG©ô´W§”¿¥J²€'®ÆS÷‚=¶nÌåhñçQ³l¸¡žNBGnWÇžS»µ{uìSØì¾ }±ÿŠÃ6tÕ~ðìþA&óD¦8ôË<ÑÆwél¡ôãîÍ?ZŸy endstream endobj 268 0 obj << /Length 1063 /Filter /FlateDecode >> stream xÚ}VKoã6¾ûW¾TbF$E=Ø]tS¤X ¸‡EÛ#ѶIt%j“üûÎh([N•"43Î|ó¤£àDÁO«È?íV·w* 8g¹R"Øí.b–E"H£„q‘»2ø3Œo¶œ þÑVß6‰ uWig6[™ªð¡jµ§­­ë7ï~¾½“i³< š‚­Ì˜ä’ ÇIØØÞ!¥Â¾jÀGxª ñ®j¼NoºÊôDŸÀ¾×?ÚHÃçžXݒ€Urˆ²bè¾mDz¶òªzrLð'ó á#`ÊÊ °ÄK/UO}Ó°4}uhMIÒ}g¢Ö§Î8‡~_×$9m¶ÀÙ¾r•m½•B×ÅPÔ’¬]îÿþWyÔ½w¾ÚïR‚•¼Nðˆ7!¼£ï×…BHΠN >ª1 ;öÑÉ8¬õ#`LCSc-пÄZ"N¤" ° Ž™Äwô÷¿‘%'Þé¶Ô]Iò"½¥Ò"»·]£)ãšáÜz Û8bŠg÷ûÍVAžt÷W¤¢rõü±@’ßàIr`Æÿ¥N·¡ÆxÇ=œ5Ïñy·â@D?‚ˆKyÍêŸP )\¨ñ`ºå·÷ ~´«ßào:ÚN·3“ã^èìWH–æ~ïªÃ€U ÄøŠåT4X“£î²r>Èâ` ÖÛÁ@b¼äg ¥˜œ£éü‰›Ü<,u¨Î9]—úuý^û¨KûŒ¹’ðtäq|«óSåÉÍ‚OèX™ggŸÏÆ<­ß{JãYÏ¢Y(ÛBÙÅ`c[w\´˜°$Š/ 4šÞû"giÏKÂYâ^åoµäʃY endstream endobj 265 0 obj << /Type /XObject /Subtype /Form /FormType 1 /PTEX.FileName (/tmp/RtmpBlhU1W/Rbuild4444671013800/timeSeries/vignettes/timeSeriesPlot-univariateSinglePlot.pdf) /PTEX.PageNumber 1 /PTEX.InfoDict 272 0 R /BBox [0 0 432 432] /Resources << /ProcSet [ /PDF /Text ] /Font << /F2 273 0 R>> /ExtGState << >>/ColorSpace << /sRGB 274 0 R >>>> /Length 2727 /Filter /FlateDecode >> stream xœ¥YK&G¼¿¢pp»*³ž×µa $³#q°8#Xy-™=ð÷‰ˆ¬þªgØ+KöîÆt=3##3kòñí‘Ç/ïŽ_Ž:ÏTŽîg)‡;»6çYíø÷Ç_Ÿ_úËÞß¼¤3¥tÜÿ|ÿÍŸñÓ^ÿ<¾ÿÛ‘Ž¿?òñ-þÿðÈpüéÑóÙý°ÖÎ6n§—ÃÊ8­?NÂt¶DègÌóÌ™°ž^³~Žp¶Ã<Ÿ> ÛYs9«¾ö3Z>s Ø;`=M€ó„#ÐÓ9FÀXP;l‘œSÇ0î;œÇ1/çäF9 !ì§ÁõLpШ‚ °æµo; °àp£`íRÏž6تú9y£1O#ìÜ^p¶³pîLt˜U‹+LãEyæVbCk–œ8`g,g‚­z /¶&e-…`:xÎyŒÙÏÔ/ ¶¡3›¾Â“4¬aqîÛsJœe ;TáL#Z›ábÜÕàøÑ„Þ4º: ãÄ#ŒöÉw\IT£™]w°ÃÓ8sY¸^û÷Ó¸’Ä“_áNld/0hìÄù,sáYS8!ç|VF™YØOo·õ€g»Ï[»âkê<¹ž¹Ž÷¹0hë8v‹ý;r3öÃŦÆÛXÝÚ™b½Iê:ŽÙ5߆{=„ÇÈ:ÿÔ|³³Àž³.û›‹¾`b 7Ú7“¨ÄرWt_n R"¼ÊX˜†Â> š²¹D<ÊÍÿ6Äb¬7å?D$i<,‡3Ä\ØÁ_^a“Wá_àFûúÚQŠƒ‚Åb}„'q¦™“͈€û5ñ±ÖmáŒð?hXú7žÜÈ@ûb “ŸðoÑzP¶šoû—Ì€äùc>âuҞʓ .û´øW 5hÛ ˜r =ñX¿JO¡q>(K®’i$q¯R6×ù ¼”T£]˜š 7Kr™!ª™º ̺°ÔxRVEbŸ7öÖɦ²b}©E®Œ)µ\ë7òù©­ÄK\ƒ??ª+hœcÿAÃhŸä$/ðyo‰Ž0ÊŒök"nž94”¸;eƒØè˜ þ'í¡ñr—lnÄ“Ç$®äsf¢ê Cg3”´Æ~B›»dˆòØ€{d%bð9#¾JŒŸÔÚ ýr <Ö°…Û+läó ƒHóൃÏÀÐ[š±É>½Ð°ò熰ÊÍŸÁ*à ì¢MàIœV¼!°1Q²¡óÓ0ÀË, â"o8“Ï {%žŒÀ†ÞæÜh–À¸®ø@`€Ïi»è>0 GzŒÌÉùq_¾·ûüF>OY°ÓOHÑ5¾"ZÇ1m7 VÍHq¹©[‚ ¡Å œÎ”QÛÂø@I ­‰m™–/ "öƒ›ÛÖX¸¼ÂZË .#‰`¢BH¶£¨¸Æ'Ù¢­Ý¾nüžƒ;Q'7/ß“X}!’Ïý ¹ÕªÖK¾0¹‰Sî°¤ Ì­Gì’Ê…'÷‰¹k qâ\Q XR2–rZ¸ †$Uææ2E†\®õúžyç}-µöi_brÚSâ{—-‡HÑSJ­ Oâþ5H\ΔxâNaJUî ÈÌ"¼ö›<ç+÷JtŠø¡ÜKŒ˜ò¥,E½ÞÖƒæö“Ë·)¢88S®r!ñì;Úax¦ ÅElfJ(iaˆc×däVh-SÌ=L"v?©µLªMXX·¼µÂK-ßøL>#Ä}YòÚÖŠ6µ¨ZÈ[‘Ô6KË+ÜCkóº0µ|ð˜?BksðŸ]ù S¼¨khÒZ•¼LjKk[[XZ›"7±6&?!Y=ÖSõÔnbÔ®·ñUZ‹0­²Oiä'èû¬mQ›Í˜?T»^-‡!ÐX»"?qÏ;7*)³(QX³v¸º&uöbØFÚ¦¤î»-QŸà»—b ÎÚ•˜_ÉÏgÀÎÍÖUȳ±¨u×êìT;¬Ž‚XµCZÕkmBZÔKQ»>› b¶\àCÕø¦¢„µ½_x²7ë>MEkÕ&\˜m×^¯ªï_z|W«ZSçÇèí³$fíÅù‘[Ág϶öër¬#ìýÂãÖ°H-¶km,ù®•Õ¤°𨅭Kxžµ?›ðÙ![váVµž’‹\«ªÅ“Î\šÚ®Í‰Ù†!­Œ˜?éØ}`2kóÐg•}Z¤XbS+6Ò²ðˆZ6ÎC•¹{%CZ$Ÿ{Z‘¸§¨íu?äRÖ®W[hH¦¬]iö¼0ù ·Æùq08ò6^‰`Ÿ镵+Òbø{*q9ô2ôlªèt›ë|ÈÓ•xD-FðW³ù˜ ±C¼EþB~Åg~Ó}`¸Lœ—€{|WíMÇŸ~Ç}OqpG¼F¾Fbsăô„Dð±×1ÐkµxÄ™?ç“(ZgØÄø¢^ìŽÉOÄˈñ:Ï#}!f¯†°’~; Õ©ÏSXt[oPo½öÐcB»×ÏJô¯|F¢’ÏÏñÔ®’½éèí¾0ù ýRý£&´É_9Ök4,—ín]ñ§üëLœ}÷®Ä 6‡­ó êí3^ù”½ØÄãÖ›9òkM»Ww¥9í^›X½Z_çC>­ùŽk¼Ü°Þ ,â‡D#ŸY.Î…§ïÞŸÄÔ[ÁÏx+¸aòùêÅÝ%ŒÛ>®§ªg/êȯóÖÛ:ò+{±^"þù5÷_ÄÒÛ¥§Ž|K½½ôÚ‘Oóí¥†¸Ï­÷Žüê·ø'¦ÞöõM½½zk_o«Wæ[!ß ®Þ˜¸E¯Ûã»ë­€%|^xØîm½Ôx+H˾‘è^a½Œk½oëmÈ‹ÞØx¾ßõ¸÷|Ë ‘ùVpé³pi7þ/½U=Æ@§ÞBÖ¤× 4¾\o!ÄmÜÎOá[߈õV°z}çKݼٯªð{¾móík¯7¥·Ïý¸z+XzNL½½ôÛ!¼|û¢ŒÉ^Lô··/âî7þ¡—•ÞŽå/`òùo ùŒ2·ÄøÎÄͰ~ëíëŠ78¦†½•ß½)qnûÁ‘³ßâ‘…^ßo'ŽüšïúÐÕHl}`á0nñÂFbî·bòùzkq&®tó? Á”nüda˜oûÍàó…éˆ{¼7ÛouG¿â30d¯·Ç?ùX"¿;þaeçSbòùz»t,ÄGÙg¼ =âÝp×ÛׯzTcY£ÇcJd,[¤ïùšÿx÷²Þëøô™÷úO?üüøú÷(Ò— ³8ׇø#0Û >A Zýåãñ›—}üñ·ÇˇÇï^´ú5ý6ç«ÛR9T,ë!‹óß¿ûã5üU¿MP¯îë—%Á:Ž÷o,fÛÀëõ=`ÁÛ€Wk>·¸ øÕîâAQ[G¡qo_¥Œÿ>gæÏ¯‘É2ÿŸEú-ÂPIoNÒ_Ÿ$~9Äž=ÇÇõ»"f–´ÆÛª)¼ àãhq Xð> dÿ9`e=`mz ¸Ÿáÿ`%r{mWJàUçøN¯ÙS¯”š=¿x6û"Èg甾xº›Ô+¦¿qÏâê£$ ]ápAêª ÔeÇ þ–ûîñ_Ùýw4 endstream endobj 276 0 obj << /Alternate /DeviceRGB /N 3 /Length 2596 /Filter /FlateDecode >> stream xœ–wTSهϽ7½P’Š”ÐkhRH ½H‘.*1 JÀ"6DTpDQ‘¦2(à€£C‘±"Š…Q±ëDÔqp–Id­ß¼yïÍ›ß÷~kŸ½ÏÝgï}ÖºüƒÂLX € ¡Xáçň‹g` ðlàp³³BøF™|ØŒl™ø½º ùû*Ó?ŒÁÿŸ”¹Y"1P˜ŒçòøÙ\É8=Wœ%·Oɘ¶4MÎ0JÎ"Y‚2V“sò,[|ö™e9ó2„<ËsÎâeðäÜ'ã9¾Œ‘`çø¹2¾&cƒtI†@Æoä±|N6(’Ü.æsSdl-c’(2‚-ãyàHÉ_ðÒ/XÌÏËÅÎÌZ.$§ˆ&\S†“‹áÏÏMç‹ÅÌ07#â1Ø™YárfÏüYym²";Ø8980m-m¾(Ô]ü›’÷v–^„îDøÃöW~™ °¦eµÙú‡mi]ëP»ý‡Í`/в¾u}qº|^RÄâ,g+«ÜÜ\KŸk)/èïúŸC_|ÏR¾Ýïåaxó“8’t1C^7nfz¦DÄÈÎâpù 柇øþuü$¾ˆ/”ED˦L L–µ[Ȉ™B†@øŸšøÃþ¤Ù¹–‰ÚøЖX¥!@~(* {d+Ðï} ÆGù͋љ˜ûÏ‚þ}W¸LþÈ$ŽcGD2¸QÎìšüZ4 E@ê@èÀ¶À¸àA(ˆq`1à‚D €µ ”‚­`'¨u 4ƒ6ptcà48.Ë`ÜR0ž€)ð Ì@„…ÈR‡t CȲ…XäCP”%CBH@ë R¨ª†ê¡fè[è(tº C· Qhúz#0 ¦ÁZ°l³`O8Ž„ÁÉð28.‚·À•p|î„O×àX ?§€:¢‹0ÂFB‘x$ !«¤i@Ú¤¹ŠH‘§È[EE1PL” Ê…⢖¡V¡6£ªQP¨>ÔUÔ(j õMFk¢ÍÑÎèt,:‹.FW ›Ðè³èô8úƒ¡cŒ1ŽL&³³³ÓŽ9…ÆŒa¦±X¬:ÖëŠ År°bl1¶ {{{;Ž}ƒ#âtp¶8_\¡8áú"ãEy‹.,ÖXœ¾øøÅ%œ%Gщ1‰-‰ï9¡œÎôÒ€¥µK§¸lî.îžoo’ïÊ/çO$¹&•'=JvMÞž<™âžR‘òTÀT ž§ú§Ö¥¾N MÛŸö)=&½=—‘˜qTH¦ û2µ3ó2‡³Ì³Š³¤Ëœ—í\6% 5eCÙ‹²»Å4ÙÏÔ€ÄD²^2šã–S“ó&7:÷Hžrž0o`¹ÙòMË'ò}ó¿^ZÁ]Ñ[ [°¶`t¥çÊúUЪ¥«zWë¯.Z=¾Æo͵„µik(´.,/|¹.f]O‘VÑš¢±õ~ë[‹ŠEÅ76¸l¨ÛˆÚ(Ø8¸iMKx%K­K+Jßoæn¾ø•ÍW•_}Ú’´e°Ì¡lÏVÌVáÖëÛÜ·(W.Ï/Û²½scGÉŽ—;—ì¼PaWQ·‹°K²KZ\Ù]ePµµê}uJõHWM{­fí¦Ú×»y»¯ìñØÓV§UWZ÷n¯`ïÍz¿úΣ†Š}˜}9û6F7öÍúº¹I£©´éÃ~á~éˆ}ÍŽÍÍ-š-e­p«¤uò`ÂÁËßxÓÝÆl«o§·—‡$‡›øíõÃA‡{°Ž´}gø]mµ£¤ê\Þ9Õ•Ò%íŽë>x´·Ç¥§ã{Ëï÷Ó=Vs\åx٠‰¢ŸN柜>•uêééäÓc½Kz=s­/¼oðlÐÙóç|Ïé÷ì?yÞõü± ÎŽ^d]ìºäp©sÀ~ ãû:;‡‡º/;]îž7|âŠû•ÓW½¯ž»píÒÈü‘áëQ×oÞH¸!½É»ùèVú­ç·snÏÜYs}·äžÒ½Šûš÷~4ý±]ê =>ê=:ð`Áƒ;cܱ'?eÿô~¼è!ùaÅ„ÎDó#ÛGÇ&}'/?^øxüIÖ“™§Å?+ÿ\ûÌäÙw¿xü20;5þ\ôüÓ¯›_¨¿ØÿÒîeïtØôýW¯f^—¼Qsà-ëmÿ»˜w3¹ï±ï+?˜~èùôñî§ŒOŸ~÷„óû endstream endobj 280 0 obj << /Length 1000 /Filter /FlateDecode >> stream xÚVÝ“ã4 ï_‘Ggæâõg>:30°Ì1Ìp°å|©Û𤗤ìíd¹Ý¶—åv,K²lý¤üT‘l‘|·qýzµ¸»×ERñ*Wy²Ú$R^ •äyÁK ªuò[¥¥f©*YŸf¦Ð¬Ÿv~@ѰþŸH¹9võÔôé›ö°÷­ïðÔäP?F·~HÿX}woõŽJX^X¯ 7~œFrºzœ܈³ÏÔ´žBްJ6¤’5AŠ7õïÓÌZögÐÕSÔÖ®#¬ð6÷$™R9—¶L2)ye-ݰéÝ:Í´.YÓáZ0HœuO© ÍûãäÇèö3-W£ñ¯ÔæÌmý8“°Ì-¯ªÏWV\Xyòqð–™87òìó΀oëºÚÕ¹ýÓÔÔ³…á¶T§S±%ûÁO”äq$P´€hA‰ÍP÷íÁ Ív}ª h°lžHÚúÎnŠNÔ"ZC³äëÇfj°&=T¬‹CGGhëXZ”ö.–iOûÝ4òˆ„½HJ@ °ðÎ/¡øÊ²Á86ƒÿ]XAàÀ×ø€I†|Åu¢1$‹‡ÛÍÐ?~Q£¨ß Å0‰'?9m¯NCa—K€ƒ„‡áz7ž6ÓÃkb\л—Ëz熉¯¬?4>„ü¯ÁñmÞèy¦‡oW ì,‘È3/(€8—:©ÛŇ/dNÏR0œNEÅÝÛV%ßô‹ŸàÿdÊN³‹/ó‘Ï«X™ûf{Ä4¥bj™f…Ñlµ 轡?ÈÖohvØdþ´|kl^Äp¤ýˆí\b;‡€*¬Ç®ù;2qCú:D"*‘€~ÒSÌ ím †±é¶{rÅ6ƒk=©±-ƒèôÍ[¤Õ‘¶n$‡+~õk2"K¡1†¹$"ûie¹²Åg˜Èò¼<û¼™ “i#xenÈâÿñ|_¦*_ - C¼Mž3ÿ¿§ ~Àº˜Üq ¢SíÆh ëÚ5û'âHY|åÍàˆüU†6‚ΣîÀýàë~XÏÕãýÙ`žËp)ªb¿ðï›°ÞO{O¢Ï‚…¸ox„áíÝìPÒ†ëç©4S½Súôa™Ò—ªºiúù .„|]sÀ$¿È¨ ööáGÆ ®pÚvkš8 =Où,±/ÎPÿ YÛfëõkZ(×–¸ ïÆºº,þ˜È/µ¼í‰ð ‡9GãÓ@‹õct0^Z’Ö~ãŽû‰6£Ÿ¦&ÌÈm»‰_qôK+üÀÈyYØ$³4¢GHqKðÿ_Ù’ endstream endobj 277 0 obj << /Type /XObject /Subtype /Form /FormType 1 /PTEX.FileName (/tmp/RtmpBlhU1W/Rbuild4444671013800/timeSeries/vignettes/timeSeriesPlot-univariateSingle2Plot.pdf) /PTEX.PageNumber 1 /PTEX.InfoDict 284 0 R /BBox [0 0 432 432] /Resources << /ProcSet [ /PDF /Text ] /Font << /F2 285 0 R/F3 286 0 R>> /ExtGState << >>/ColorSpace << /sRGB 287 0 R >>>> /Length 12000 /Filter /FlateDecode >> stream xœ½K³4Çqž÷߯8K`aß«jK…å0#¨ˆðBÒ‚!Sa3´%Ê·ïÊk½oõ ï€Ò‚_Ìsºzjº«3³2³×ß|¬øøç/¿úÓþ¿þøÇ?}Y˲|à?ÿôü²|,Ǿuÿú—ßüÓ—¿ýøçí|´ícß—G){]ëõÑÊcßù/üòëÆÑ—ÇÖÿÿ)GÿÕ_ïý4~ø§U½ä?Lúÿµ=ŽöñÃßüîOÿû¯úûoþõûõï¿ýöã‡?|ù?è0½ùqÖé8ª·³=¶òql}äUÔOàün]¿[Ö_}tQ¾[Ž®ã›óWß¿8õïÿêoú-çÇÿùòwÿÐ'è¿~Y?~Óÿ÷‡/«ŽúÛ/væûv=jýøñcoèçÇÇ÷3áÒ&3€« õ±˜D º`ã±l˜dઈDà|ì'&¸xˆë6Äõ¸"¨‚eöØq¢Lpö¹…‰rÉ@Y (V˜(— ”J@™&ªÏ-΃IJ_pa\"°1°½J# ðD•>·0Q.8+L”KêB@å›·ôO`¢\"Ðç&Ê%}na¢\2P7ê4ú|€IêJ€K6yø À$u' òDÕýqÂD¹D Ï-ܼ.¸+L”KêI@剪åqÂD¹D Ï-΃IêE@åyh .X'L”K¶þÀ$µPy¢ZŸ[¸y]"p>N˜(—\ &Ê%µPy¢ºtâ<˜D >6œ“¬ËBDè i #m™>ÁÓ„ô)>1=!me¤­r¨µ8¬GGúD7DLÒ§úBÄô„´‘6ÏK™*÷Ú㢩3H·õ6œ:×ÒFÚ4uÝB½pê\Ò'¼"bšã±á7r=!íb¤MS·ž §Î5!×cÇyq=!<Ðu¨>MiBú„ÓÔ™F¤›ó;Në i•‘6MÝÖ'üDÄ4!ûãÂoäšC@Lr’ÅzBx ó>Ð5 tݪä„&¤‘kzB®ÊÈdü¯ýÿ£õš•ìÿЄì¿‘ë A 4!}Âqê\rN÷Îi ó>PáyqMHæ¥Þç¥MóÒîóÒÈ3 Hw éK»&d#ï(4!;ùG¡'=¤Ð„ä#…&ä$'(4!eBÊ+=¥Ð„Tò•BÒÈ[ =!eg¤L×K÷Ñc MÈF>ShBvòšBOºE¡ 9¦Žû@ùN¡ )ä=…žôŸBRɃ MH{¬x«¹FäZÉ‹ =!èG…&d#O*4!û4Ð~hŸÚïäO…&ä"*4!…|ªÐ‚^UhBú„ãmï‘î1¢gš•|«Ð‚ÞUhB¶i í>Ð> ´ß:ÉÇ =!èe…&ä"?+4!…<­ÐRFêtÛw/²Ò7j³»µÖ…ü­Ð„¬äÔ…žÈ5!Û4Ðvè ¿+4!'y^¡'}¯Ð„\ä}…&¤H˜Ó„4òÀBOú`¡éî':z¡ Yy ×B¹&dgGÌ5!;b® 9Ùs=!䈹&¤O8^R® ©ì¹&¤O8Mé !Ï5 [wHq Ð„¬äñ…&d'G,ô„ #šƒ±Ð„œäˆ…žv2ÒÎ )ä…&¤>® Ó„4òøBOÔnu‡=¾Ð„l䈅&d'G,ô„ #šƒ±Ð„œçÅ5!eBÊ+¤5FÚ4uÝ!EG,4!±Ðˆl¼ÇšÞ£ =!èp„&d'‡#4!¼×zBÐáMÈ5!·íšm+ä“„&¤’#zBÊÂH™§Ž÷}B#²ó¾MhB6r8BO:¡ áýŸÐ„äp„&äšÛŽü'ôIBRNkBx'(ô„ g‘îm¢Oš•ŽÐ„läp„žt8B²“ÚsBnÛ:Ûq‘OzBJa¤LSwðîPhBx(4"'ït8B²’ڎЂGhBŽ ¹mõlçI>IhBú„ãÔ¹žÜ1 Mï…&„÷|B#r-äp„žt8B²’Úmè¶}´]¼ûzBÐ' MÈù8qê\»H¡'ŽÐ„ð>PhB9¡) 9¡'„*· ¥­ðŽRhBxG(4!ù$¡'¤VFê4u…w–BÂ{K¡ ὡÐÒFÚÙ/øŒÊø~Λž†¾WÍô|FÕ»”Ë9{Q{Õò’gT´WŸ/Xo«ý‚^ž.WéaR2”šnÆ>£=¥å µS æ§W™¶¹ûôšr‘§Âš=tÈb(Göu3¥æóËbSgÅßÇâ“ã¥ÞMƒ·Ï¨ó–íj9g+ㇺôVοµíÖìò¶‚죯G5Y/ýÛ*ólÅÖrΗ|j¥ÕëÄZ!õÑmHý¬lúèëå*?¨ICiÞŠœÆ¢ÒrTäé·™”˜¸äžëßZq³¤¨´lï™uÌý㵺–(¶œÀ©Úž8qµDùqüÆQk,yö¹75ÇžYUÜõÑT[É0j‰·Ó.Ý(–¬û{«üí_Joå(ómºkøÌßæWdð6­ìyfõ®åÐ?£TW¦RïɨÕúCµÝ6MÇyfÅíßÊi›˜>³–¶éÞù3 gÛnWj<Žs¾¼Ö®³gÖ¿JÁ‡ž¿·v]õø^ÉZ}ÍŒ²Õ~ëo5ª}!´óÙ¬ UÒ¬ìs«>íú°ã[©içmþ½®4õnE¤}É>ô|½bÔÊ}žY.Zªÿ>^ «g~öÏ‹i«ò”›¿ºV~—Ëÿ™õœãs+Ö”UIçÛ+3ûfóåe˜×iëbÔ\J¢œÎÛ6²£Ç÷jÊn¿è ¥“Ö’è™u“gµ'GEÊjlº„Ö›+ʯÃÖ©¨m¼v[Õ¢ñ<ý÷óªÅÔ^¢8´Õ#ž§/>”´âZ~ùÏúý¼¬°æ4Þj»Öõ+ ûiêc$ªû×Ò¥2Jû4Uý~^÷×?×e) ?Ôò<8ÿ½¼|¯O»Í¯×êɃq­¼î>³*Ožõ:?^rw¬>¿^_7þÞ‹é6™¦ç—©r.Ëæ,Ãû™5r{õë/ à{èdµ[•Ýôg–ºõïo÷ƒ×±‰Ñ\ËöòÐV¡–óíåh¨Å[;óxVhvj’ú3«Ì$ñW/!ËßË­èCÔžY,–¿¿W‚ me_9¾×xÙeõ̯~™ØúîÕ[ý2Øm|+ÕÊëßë²úϪÆPa‰­ßß+®:oóïåUy?x-•\Ï:¾N‰¥ççURù}½$*ç×ëŸúõrêõíÅNý÷±õ×+›Žøý½ŒÉ2ÙŸYÃ$9é»j+P:|~Â麜®åyq(ö̺£]·hžYdÔ—™vº–Ãq<+ÚjƒâúŽB Ã¯Ï¨ú‰û'J|ŽbOç¨ç9šYrQ¼#q•ÓµFaK?žšQÅ"ËŽÎ÷f†»¿zþ^ŸÒ¯'û=¼åZìy•'—?¿¢Ì¤k}^GMI¬ßQ@’¼W‹\—ϧ—†È޳ž¿×”Å®Ï(úÇ"*<ÄjÔóórŽx>FíFu«6 5Âæª ¹­õ÷÷Œp>¢Þ"<¦(®¨þ¼ ç7,ý(›s=j$Â"‚ˆð¢úA2mõû{©CõçwÔ5„ëE }<}þFÅB‹óõò„>¾]ÿ^‹ Ö­žŒãY•Am>_^RöfÔ„=Å}°ûõtÛÞÍW/»ÙîWËùOWÀüÓÌölþ´•#2ÐuÙ\Ëú¿'åËxêÑy¾Oí/O·û±èü]låG"}·Ou½Š¬ùü>ž"ß—½U¯χo»­Ÿ‘ü.¿Î—gº‹ß\WpŒ"‡]–eÏ·ï#;=œ¿HE¯Í× Ï;¯îuG’yòžQžççéã­ØóßsÅe¾Ô©°ÊÒÌEò,pqlÔþõ”oÑ6¾åw‹š)ÏHî>ÖÓ×ËÜ>V÷„·õÄr²¾ÌÙóÀ°1ë‹k‰›¬—ßï–Z=ŽgyÔ¢í~·¤iøûËtµç«§CƒÖÜg‰ÌÛ÷µDg9[¿,«Ù#÷ÏHi–Ïõ~õ|å<ž''‹ÞB_m?bRkµõÊsŒ×„b™Ÿvº.Ëø>ž*|ôÇŽ^_ž,¿—>< X~OõG<ãW´Ú«žÞ›~«çòËiö•'îæýYºq?DJnøgž›Þ¹'Û¦‹í™µrÿiHÅÓhA«!IºØýnóíÙ°­Øó&R_Ã߀êRPkkzâž®:æÇsSÃDÔXÏ"ë´ßŸE?÷Ó¸¿"Ÿ4ÖÓHõ:2E#i¡±ÞGhÜÿ‘ðÙÑ?§¿³šÿžýÅWÆ×°‘œDÈ ³Þú ƒ€Jtïb.PÏh.PWh. è¶*Ð=¸àâ!®Ûên! õ¯àu¸àr¶»’@HÊJ@ቲ˜¸@¶¸@wià’€²Px¢l'gçmu àõà’€rP¦‰R'i.`{D „$ œžÛGÀvtgi.н¦¸$ \ž(Û€Ktñ€KÔ‰€KJ% L¥ñ”¸€ít%€íP…'ÊöÇàÝ1€KêB@å›×vÕàu·àÝy€KêF@æA=ø¸€í×%õáà’€ºPy¢l—o.аÀ\ q€¸$ žTž(Û-€Ktÿp. ¨•çÁö €Fàµsà’€Z¨ùqˆnSâÛ¹HhFZe¤MSgûÛ€¸FD·¼qˆºg€¸Fä$‹?5#<Ðy蚺îUr R#ÒÈ5HÍÈU™ŒߊHhDV²ÿS#¢ö€¸f}€ÔˆhØ׈œÓ@ç} sè¼Tx^B#R§y©÷yiÓ¼´û¼4òŒRb¹ ÈFÞQjDvòR3‚RjDò‘R#r’”‘2!傞RjD*ùJ©iä-¥f¤ìŒ”éz±]í„Fd#Ÿ)5";yM©A·(5"Ç4Ðqè"ß)5"…¼§ÔŒ ÿ”‘JTjD4¯×€XªÇ@B3‚~TjD6ò¤R#²Oí÷öi ý>ÐIþTjD.ò¨R#RȧJÍzU©Ñt@\b‰* ÈJ¾UjFлJÈ6 ´ÝÚ§öû@'ùX©A/+5"ùY©)äi¥f¤ŒÔé¶·4@ÚìnyæÍ@B#²’S—š(4"Û4Ðvè ¿+5"'y^©Aß+5"y_©ÑŒ @\#ÒÈKÍú`©±T¢„FdåB3B…FdgG,4";b¡9Ù Í9b¡Ñ|&@\#RÙ? ˆnÏâšòøBÄs£IÈJ_jDvrÄR3‚ŽXjDrÄR#r’#–š‘v2ÒÎ )ä¥FD“•qH#/5#‰'âÞ JÍz6©9x/'5"+9©ÙÈáHÍ:©ÙÉáHÈ9!·mÏUäš}O_$¤LSwðîPjDx(5 'ïï¤fŽÔˆ¬äp¤Fd#‡#5#èp¤Fä˜ÛVgZrÎ>‰'_âšÜ1Jï¥F„÷|Rb‰œ Í:©YÉáHÈ6 tÛ>ò¤P@nÛ?ž'JÈä“xê( ®á]¤ÔŒ Ã‘ÞJH#‡#5 –™:ÐŒÐ@å¶¡äù«€Ü¶”<¥Û–g¹rÌ>‰'¾R§©+¼³”Þ[Jï ¥f¤-Œ´yê9©©¼Å”š¨Þ¶˜ ´ß:ÉöNÍÚÞ©¹ÈöNH!Û;5#ìOH#Û;5 –Y?Ј¬d{§f„ È6 ´Ý:ÈöNÈI¶wjFÐöNÈE¶wjD ûS#ÒÈ$NÍHÝ™lo/HhDV(4#4PhDv²½S#rí‘“lïÔŒ í‘‹‚ý©©d§F¤‘íš‘z12ÙÞ^û0Ј¬IjF0½+5"u¨ÞjäÙ¤Äjkšš—ЈlÙßùóg`ýÿ7Šõwþô™xç7·Ê'ú;ó™|}ç_p¯xç7g0Št£™ÇŸëïìOöŸhäYûã ÿùsKjåɆý]ƒ±Y^ý L#q\ÔßÙ5ÝeÀþή•ÈògïèüoÙßY³>ÝßYñŸîï¼ÙúdgÙ[üs=Žÿm¯’É6ôNÜïmC^Ú†¼± ymòÚ6$äµmHÈkÛ‘7¶!!¯mCB^Û†„¼¶ ym"òÆ6$äµmHÈkÛ×¶!!¯mCB^Û†ˆ|íEÿÙÇ’Ð=Ž·½acÏŠÇ’-=Žå± =Ž% zKöô8Þö{KVô8–¿…Ç"¡Ç±$ AcIN‚ÇrÐãXr¤ Ç±Hèq¼í'ö8–#CcùúÐãXò— Ç±ÀÐãX2Š Ç±¤)Ac¡Ç±œ3ô8–;zË¡ Ç±<[ Ç±,ªÐãX>…Çþ Fc9gèq,g=ŽåPÐãX$ô8–CAcÉA‚Ç’o=Ž·£aã!5oFr† ÇñvVìq,zK® ô8RsZ$Yz‹„Ç’=ŽEBãq(Í7«eÊCcÉÄÇò·ÐãXÎz =Ž7û!£Ç±¤Aãq(5…6»Ú¢Ç±¤=Ac™:èq, =ŽåWÀÇò1ö8–ϱÇq^-^‚›¿±×Û =Ž%lÇ¢±Ç1é¶[rXùÈÇ[¿ ±Ç±¤}acù{Ë—ÇÇÂccù{ËTbcù ±Ç±hìq ã_Æ/Ôãx³b±ìq,ó‰=ŽÇ|Y¨Üרãx³ý¡ìq,{Ë’‚=Že!ÀÇš^ =ŽÕ[‚Ç¢±Ç±ðØãxh+¤”%{ËçØãXÖCìqœ«§?ÊçÅôiüB=Žå€=ŽáójËþA=Ž·>MØãX4ö8³{Kz6ö8–Dkìq,ŸcãÍ.‹ìq¼õŸ{‹ÆÇšO =Ž%{oëJ=އ¶2=ÐZ“'{KÂ<ö8=Ž%s{k&?ô8K {‹5=Ž¥®{o–]’=ŽIËó`Y¨Ç±”n`cÑØãX*.*ô8–¿ÇÇk+ÔãþÞ Ê*õ8Ž˜í(kÔãXjV°Ç±Ä-°Ç±|Ž=Žåûcc)¹ÀÇ µJkÌ·•d‘Ÿc©ÔãX4ö8{ßË­æå Çã÷·j(ÐZú4Æ·:'ÑØãXª)°Ç±×Edãqý[m’T`c¹¾±Ç±ð6ÿVb4î«'ÒëzËùbãñ}­,h̯׵F=Žõ_6žU÷´øý½”§êq,<ö8–ϱDZhìq,{ë×€ÇzšÐãŽg%3CWó–êq,§‰=Žóþñ2=ò‘=Žuèq¬±èq,ŸccàµEN {çzá&yýx9‰œ6ö8ÎõÇ EdXìqœ×OøgCk½‡žô8–ãacù{Ëicc¹ž°Ç±hìq,_{‹Æǹ~{Åà­bBOzëiAc9-ìqœŽ…û.bcã|>zý‚þ z§Íï• ¢±Çq:^s“lÇJ=ŽÓÒ÷Ò4×½N -r/ H¿Á+äxØãXxìqœ®ƒ'òËxØãXƒ=Že|ìq,Ãbc8žfÚëiCã´7=‡>íAO˜—Ã`cùö8–aãt<É=ÍlÏhO[9"]cã4ø=1]ÆÃÇr<ìq,§‰=ŽÓ4÷dr±Ou½òÌññ}Š»õ8–ùÀÇ¢±Ç±ðØã8#‹Êïƒ=ŽÓ¾÷ ítþ<[~ìq,¿7ö8|u Qcù~ØãXæ {Ë4bc™fìq,{ob6@c™ìq,ÓŠ=Žåsìq,Ó†=Žeš±ÇñV6êq<Žg¹Ä¢±Ç1üýeú Ç 5ÿW~ìq¬?#ô8öŸ){ëô8Îãy‚®þìÐã81©þ9ö8^“j7KöÈÇù}<]Ö÷2²Ç±ü^ØãX~Oìq,{§ßêù¬rÛaã¼_=S5ïOKMÿÌsPÓ;÷„Ót±=»Tî?ìq ZóFI»ß±Ç±Œ=ŽÓßêRPkgzâž²9æÇò3ÓõdÌ\Ï<óRîOìqœ÷—çTæzê ”¹^{¶dÆc<52×{σÌûß“Ecc]Æ¡ÇqúËž»˜þ±'*Êïƒ=Žåû`ãôo=ß0ö1"Ù´fÊ÷ÃÇòA9?ìqœÁ ÏþËõÆSýÆõcy}/ð$>9ö8Îhˆ§çó·\¼\ß<ñ.ý}ϲóg)ußðü98ž&ËÁx-ÖOìqœë·ç¼É÷ÅÇé‚ÇãúóPq¿ß°Çñ¸ß,)M>ÇÇþØÌÇã~³Ü2{ù³…dÜ–"–ñÏëƒ%õÁ2½Æýbi]oñ®Œ·xÂÖøý-;k\Ÿ–Š%¿ö8&­ñ™zgŒÎÓ§Æõl¹Rω8û¹Pã|žzÊSÆ)=¿iÜo–Ì4´e.Ö@ÜØãXͶÑãø'cöo!ײ¼l„|Ôw}"üÿç÷¼d™¨Ò²ñã›ôê¡$[bª;Ff•Ó¦Ý̯|¡Fˆû/­ðS—ñËWSýùׯ ¡ëm&>¸†½5*ÝÿNÃúrÉëQô‡ºôw¯~^z•Vÿ‘åžÒ£¨Éxéd—ê¤ú£ÅÍG¹›viË©‰M4o±©‘òþ_ã­ƒEýüºÚ²P4\%¾üiªßråi´z“ù‰÷ Š!p©·»}ëê/*ú,ªîÚÈkÄô(‡Èû/)ÖÔaª/¯òb4ùý|ûæúk‡ª¦ÃTÉAÕxžXu«©þ=«¿PJÎBbòCî4‰?TSý¾ªî†šׯOýFýßÝæ“«u1ÕW‰;x¼¯©š\yý¿éÍ /aܤM¦>j$@^´i¦*µ‹Íš³ø2”6lo¾6Mˆ&âö²¼¶šùn¯±Ð»Æ¾W8JK!ÿNwcš¿*HÂàýþl‡GÝmAÞÿÒ‚´Ýh+z f1lõÛnK„Àû)C—}iîŸn`W‡}J_ËÙÇÕåJd?ˆ¼!ÀþV_ÅS}±‘`x_¥Åd..›þ 5Bßryúv‹D¾åú,Èé†Y¿1â¢YÇï)aðSeQ±ÒôPÕ‚èzÃÕê1ÖUctCêû‹ªG`$¾ê9Ûälúv¹xÇH¹R›]+ë¦ï“ AÏyÓW3ÕæÁªMƒµmñXЦ/^kþº uò©EÎ6µÔ›?X×Mß5Æ­z5W›ï‹_²e^üš=,½ÊÓ&'§Ûdýª¨þZ(‘ýæér³C©GV·€O¹p«¿sbíöت+϶»ìæO¹<šß­³~Ø3Ò‚Ûýw/ñ>9Ù$ضëêP×|H½fŠ.øîwÿ´Ø§‡œm©æÖ‰ì¿N,yå¶ׂ´ò4:äoíŠ=ôN.§‡èú³£_oãÓ¦«îæ[É0«Åo±UœýPñ+œúx.qy‹5vIÓêÁí~_5>ÕµæŠ øTŸçŠËÛ,±+®I󏝸A-PbÁ쥫ùIšyr¾)s©u>¤“Cª-EDöR×%_úvéfçµ›=/ñí~y÷#دpé¾Ì勻Ȏõá.ƒÕù:›_9—îŸÅCÅ—nÅÈ#N?-ê ©ËVÄ5DöõüŒm¹¢[Ýç°zá§?Q%ˆÝ/à³FÌü’ øô×~I»dü­†‚ÏÕÏÊ.’3îÁ~%÷KWÂÎîðy†Ôg£ÏÍe÷ªO÷ýת©^áÑÉ©ÏÑ`d¡ìËsNlÕúv»ü‘ªiЇH5²Eö øŠ÷$ÊÓà?JÓæµúŽO—ü¾òv‹ä)XŽkqá.m÷£é«“®x¡¢E•¯+béê äõÜ4Ô.‘‡Œ\ŸrÅnHVÇåòµ}³—fÅå½Ù;D╨µ\À»-}cJ|A‘uÌä¶è–ËoA[tèô¥@#ÅM~}‹§,ú2šÓ( W÷õùtCMº‹^*¬¶ x y©uY"TÝ×g ÚßêÎåé H¹¼óPÅ/`—5.àæ²ÿ÷Ó-= Z÷õ9c|Û"—÷µØ-)±Þ>ÿW¼õkÓü˜kñï»éÛU¯xGá¦?ô€ùâ—§¤HX¸Ÿ|Ü캋:.‰M¯ºX(oÓ÷6ƺ!‘j¹¼ý£;¬k^§.$‹®uEÛõ‘Wév}ƒâ…1êU[ÿW—rwט\þtÞìíWõo´ëÿ¹ÜÓ8ñšk¬„º7˜p—b¯¾·r¨¯ZÜB“˜a÷á"ò¤!Å5ms‘b`ø]¶™ŸO+ñ‡úB-‹Éê²îi=«7¾ˆ\#F-†[ïê;oéfˆ,jk[€ûÔ€OÍêH.iýjœ÷JWÝÂ5­oõŠõPvžK«‡_f§†öÂW—ûi_áÒþ?襷zú¶ÆµºÁlwè¥áÁ<”ECó½†, CNCÒWš[›]3m‰€¸fˆ²_ÀíˆðusÃÙ~²b¯¸qÃUƒÑ[Ÿ›åï4ßšÖ˜ê™Æ³„ÅzÞ}c£h’QÛ=n./IV³ÖÂüE•ÂÊ•¨ó¢¦šmZÝÔɯPôWca¬š§Uã-Uý:ɵ1©÷¦mcZüy_Ò£Ù×ço´«ºÍòVÕ¹ 7IbÏýÒ­§¯±ÕÜ»¸»«ùw 7¹¼ó¬šn˜5·76›´Qì¦>^‹U´énš<,.—ý|$mÇ¥n^‹…±©Ÿ'.‡­¹¶UÞ2`mùK¬g¶“½Æ†ý¾XõC¾”ª ÚöxÉtNãχHµ#4ü¼à_ë#…ÛìÝZ ÕõS‡èt-Û«Ëjv§„Ÿ¥ºqñ[JÂÍ’ÀºøÍ+™ìRçšÇëºÚñÖGK?œ<~wj$ó}qÏz_Õ^Ÿ6rþzWJøY¶ÿÇ+óÌ!t³XdÑßÉ^ýš>ì`M¯éÝCA›ù„îæ‰ŸðˆÀ²â>”8s¿¦ãŠß7õ ñy•ô`%èÜ—¶|Ö¦—Bs [BÌâ¦TŸpH à5_£EŠOØì†—ðs¿âÖèrŸ·!58в^é©JèùÜÆlìÉ ÏÍ˱*‰”%ûò0çn>áQi½·c5Yj®³þâˆIøû§c–³,Ù~K‹,º’z|øÐ%ÛO '›Oh_A²w–t õÍÁºÐœk6ŸÐ~ÁCC@áJd¹éê`ëþ˜¹›/Ùö5ŸÐC e>õ¬®*ŸGzõû©>a¬»[ymˆe6üg 0[ôÏâß—ú„YÚ8gyµï™‹”—%ç>°È2u %—=|‰$ïx(õ Ç@úÕb–°ò>95Þ5ÎR2ˆ\3°#/îó×U·'ú’Ý? ‹0çSÔ'l/ê¶ÝO²hÜ8oœ¢Û4‘ì#ÁåósUÔ'ÌÛªèžs„v³Eòî®êæÝ]Ճ͋¿ê~xÄD–š±y£ð±ß·êæ|^urÍèï{E¹l Õ'̶ªOq(‘Ý•ÊK´i¢qD1v eU_¬H¹€ýi'ïá»F N^Ûg!»qd†—_À.õ‡®±kÙÔ'¬žT7¿ˆïgÔ¡Ì¡eªC©\ÌQ-Eß2¹ªi&µ?ü÷ÿªdüÉwa®Õnu ßÿú?½y¿Þׇ¼›º‚O·†'}(“šsÏŸ»Ò5ËêV}Ûi|rtÄ1À~áĽY$mæÊÒ³ïyá«Ù}u„Õ^<¢|Å!ä9/¯šÅC> Û·¸t;òGßÅh¶+fô±fI˜‚‹Þµñ±)øØóøØ—öñ±ÃØ?yùmQYNýv¯ ¨þÌŸ–ÕpP…;9?~‘×/q¡=CJxÙ6Âí½6!ÿ›Tƒ|ùÿo endstream endobj 289 0 obj << /Alternate /DeviceRGB /N 3 /Length 2596 /Filter /FlateDecode >> stream xœ–wTSهϽ7½P’Š”ÐkhRH ½H‘.*1 JÀ"6DTpDQ‘¦2(à€£C‘±"Š…Q±ëDÔqp–Id­ß¼yïÍ›ß÷~kŸ½ÏÝgï}ÖºüƒÂLX € ¡Xáçň‹g` ðlàp³³BøF™|ØŒl™ø½º ùû*Ó?ŒÁÿŸ”¹Y"1P˜ŒçòøÙ\É8=Wœ%·Oɘ¶4MÎ0JÎ"Y‚2V“sò,[|ö™e9ó2„<ËsÎâeðäÜ'ã9¾Œ‘`çø¹2¾&cƒtI†@Æoä±|N6(’Ü.æsSdl-c’(2‚-ãyàHÉ_ðÒ/XÌÏËÅÎÌZ.$§ˆ&\S†“‹áÏÏMç‹ÅÌ07#â1Ø™YárfÏüYym²";Ø8980m-m¾(Ô]ü›’÷v–^„îDøÃöW~™ °¦eµÙú‡mi]ëP»ý‡Í`/в¾u}qº|^RÄâ,g+«ÜÜ\KŸk)/èïúŸC_|ÏR¾Ýïåaxó“8’t1C^7nfz¦DÄÈÎâpù 柇øþuü$¾ˆ/”ED˦L L–µ[Ȉ™B†@øŸšøÃþ¤Ù¹–‰ÚøЖX¥!@~(* {d+Ðï} ÆGù͋љ˜ûÏ‚þ}W¸LþÈ$ŽcGD2¸QÎìšüZ4 E@ê@èÀ¶À¸àA(ˆq`1à‚D €µ ”‚­`'¨u 4ƒ6ptcà48.Ë`ÜR0ž€)ð Ì@„…ÈR‡t CȲ…XäCP”%CBH@ë R¨ª†ê¡fè[è(tº C· Qhúz#0 ¦ÁZ°l³`O8Ž„ÁÉð28.‚·À•p|î„O×àX ?§€:¢‹0ÂFB‘x$ !«¤i@Ú¤¹ŠH‘§È[EE1PL” Ê…⢖¡V¡6£ªQP¨>ÔUÔ(j õMFk¢ÍÑÎèt,:‹.FW ›Ðè³èô8úƒ¡cŒ1ŽL&³³³ÓŽ9…ÆŒa¦±X¬:ÖëŠ År°bl1¶ {{{;Ž}ƒ#âtp¶8_\¡8áú"ãEy‹.,ÖXœ¾øøÅ%œ%Gщ1‰-‰ï9¡œÎôÒ€¥µK§¸lî.îžoo’ïÊ/çO$¹&•'=JvMÞž<™âžR‘òTÀT ž§ú§Ö¥¾N MÛŸö)=&½=—‘˜qTH¦ û2µ3ó2‡³Ì³Š³¤Ëœ—í\6% 5eCÙ‹²»Å4ÙÏÔ€ÄD²^2šã–S“ó&7:÷Hžrž0o`¹ÙòMË'ò}ó¿^ZÁ]Ñ[ [°¶`t¥çÊúUЪ¥«zWë¯.Z=¾Æo͵„µik(´.,/|¹.f]O‘VÑš¢±õ~ë[‹ŠEÅ76¸l¨ÛˆÚ(Ø8¸iMKx%K­K+Jßoæn¾ø•ÍW•_}Ú’´e°Ì¡lÏVÌVáÖëÛÜ·(W.Ï/Û²½scGÉŽ—;—ì¼PaWQ·‹°K²KZ\Ù]ePµµê}uJõHWM{­fí¦Ú×»y»¯ìñØÓV§UWZ÷n¯`ïÍz¿úΣ†Š}˜}9û6F7öÍúº¹I£©´éÃ~á~éˆ}ÍŽÍÍ-š-e­p«¤uò`ÂÁËßxÓÝÆl«o§·—‡$‡›øíõÃA‡{°Ž´}gø]mµ£¤ê\Þ9Õ•Ò%íŽë>x´·Ç¥§ã{Ëï÷Ó=Vs\åx٠‰¢ŸN柜>•uêééäÓc½Kz=s­/¼oðlÐÙóç|Ïé÷ì?yÞõü± ÎŽ^d]ìºäp©sÀ~ ãû:;‡‡º/;]îž7|âŠû•ÓW½¯ž»píÒÈü‘áëQ×oÞH¸!½É»ùèVú­ç·snÏÜYs}·äžÒ½Šûš÷~4ý±]ê =>ê=:ð`Áƒ;cܱ'?eÿô~¼è!ùaÅ„ÎDó#ÛGÇ&}'/?^øxüIÖ“™§Å?+ÿ\ûÌäÙw¿xü20;5þ\ôüÓ¯›_¨¿ØÿÒîeïtØôýW¯f^—¼Qsà-ëmÿ»˜w3¹ï±ï+?˜~èùôñî§ŒOŸ~÷„óû endstream endobj 293 0 obj << /Length 1113 /Filter /FlateDecode >> stream xÚ…VÝoã6 Ï_aäÉjÕ’,Ù> nØuذ š—a·ÕqïüÑÙr»þ÷#%Úqº\‡¢0E‘?~$Ußobú~»ßÜÞ«4àœåJ‰` ¸HX‹ 5ã"ö‡àP0±‹8*üyjlý¼Ó24Cml¹‹dªÂ‡º«¢mz;îþÜÿx{/Ó g¹ ÇA$3&¹ô&ÉÒ‹!‘ç¡­[¢Ær¨ËÑÓOΤ#ëÎ IÑÃNÊteC·ñоí0¶Ç2FâÓðŒâtWþ‹¡8gœŒi|—‡ÕSÇ©+lÝw>J®ò(dÎd&ÀAç¼’m¨¢ÔÉ,dºÃ›†²5uÙsÁFBK–&šbÖ^k }—É’;)%¹^ùƒ· ï¸Í¥`1X&@¹¼†¨KÔ,ÅÈZID\eLÄÜÁIpBè7»H<ŸÌð9Vq{ú—»I~ƒ7IÈáàþ¯UwÖ†\¢Ž}¤…f_ŸÊ»í¸µ—öù´ßp â€/m#T¼h7oXʵ8SîbÖ"Æí­ ¾ë7¿Áß|Í£•IׯùZÞ…ô*IáÜ×Õ4¸v”¡üàcÛŸSìDžÌ`ýq<õX×w›`c!·Eµ¨Zõº¤EѹE‘‹0Ư¡«e Èð8˜¶dà…Vá/½-o¾Ú_ p ¾¡‡Ï+Žíýw,-1Nîܪ©-;”µWQÇY¦.še)çôé”åi~F_¤³$üXA§@ú±¼O¸þs®ôOýñì ë,žç,“ü2Úeò$2à,¤ eê‚Ï0øÔœGL Ò‰OcyÏR•†¿ï2ö“+ Ê¢\¹í3lt£€ÑÝš/®w‘][/×âêw&õúÀ %¿À7²jÂú途º"á÷eúið§®,#¼+3PdC8J>ƒ°«JR>QôMOªþ±ÂÎglM¸Æ6$ƒæ€s-Õv„ñ8{µm.Ñ«Qˆú'j ¦òL¥\fÚ aâÝ™–H¦Ó]Ë›×fÚ2ù’D8 qάçUCMNú1éüð0¡e òPºˆmÐÆ³ËÚ½ÓB±8_Æ-¾{wÿñ§‡OW¢Pð# ç«Þbîg ©üÀ*ð­¹²pö•Å ª,gÈã鱀Лð¬=’Iìÿi÷(Ma7ê·Ðmîøy%¤$†•´”ïåT{ßý†|q^MÍa^˜£ëX·Ì;ÿÝ>6Æ©|ÙbTn‡òàè$·ÕM»¥…îB_o`{‚kÿ[â¼hI„]9ÉÁ‹p¨½æ5ÜùÍû×ä~xHš‘[Ù%~ôÌõï`.ßxçSn áÖ‹½¥Í>ô­çz™ª ä“i ž%»Ø_û\k–¥*ˆ`'œû øë¿g1¶* endstream endobj 290 0 obj << /Type /XObject /Subtype /Form /FormType 1 /PTEX.FileName (/tmp/RtmpBlhU1W/Rbuild4444671013800/timeSeries/vignettes/timeSeriesPlot-multivariateSinglePlot.pdf) /PTEX.PageNumber 1 /PTEX.InfoDict 296 0 R /BBox [0 0 432 432] /Resources << /ProcSet [ /PDF /Text ] /Font << /F2 297 0 R>> /ExtGState << >>/ColorSpace << /sRGB 298 0 R >>>> /Length 6675 /Filter /FlateDecode >> stream xœ¥œMï.¹QÅ÷÷Sôyâ÷—m"@ŠR˜‘X ¬BD™H! øøÔ©sÊíÿ( †HÉÜ9·»Ýn»\¶>ÏäçWO~~ÿüéÛ¯Ÿ?=}R{fý´öÔV>³c>?|›åS›ÉôYíùä¦L²~z{öüì Õ?µ?{}V¢Ú㱊å5>}àζ &þÖîÌjN¨Þ¡ìSžK(e¥Ov•3Õ\öúüI2êöÚ”J¹7Ê™(gUÔ ãO¶OsUQ›Õ?)C­B5²—Zü•ãS²—:ý%듊˾(Gõ››ß¼ñnÈ•)å@vB'AVܼ˧tÖwQ®îM]Qm=ÚYê¸Û' “õ“*åì¿y|Úô«Ù¯NtˆzÍåX¬³_]x!Jî‰r-¾7ã¶±]7jkjïVèü]u».(ºS[%¨wrÝ>;ߺ#8ñ×}¸Ÿ =(ø—)Ý]ÏO­®;õFœ|Ñ“:ûû-t«t“ÞÝu¢ÎxÕ˜ËuE¼å™Þã~Þ*:ýþÍòû'C[ðé9ýþÁç'Bšß›*æ÷gé ½A9o†íD›.>ôÞòŠ“×ßß_ òfôçKEô¢==æ Û7ûûKû”âí?ü{Ê@ytéAÍö.óSÚKЫ]ý_£¸"LL×Ä0n/Ðk¸®^^Íh¨KF²G0õ`ûzÜçÚ µý užÒƒÚ“G®ƒñia6¥×öëÃë[­½ÙžõÝ~¸¾ªôf{òûZBŽxßß2²×ß¿Ïrìf{ú°Í­bXázâõ††{ÛËôd`<µÎøÌh6hË~è5ô¤f¼¶… –yIoÏ–~m+•¯mÏÆ&ÙX&·'Óæeõ‚X~eU:eË™´!’yµcDÛUÆm÷ù’ •£ OϨK•î ­aÕ`Û$eÁy®K„層•½¨‘ÑÔ»3oC—üÀQð÷&;Ÿm(IJãH’ÃåöjŒŽàEîL’Ô&9tÆ@Nų|¯%Â9‡¤®eÖåí<6àöÑorú$gIºVÉqË‚h|¥¿Ñdiceg$6ȆˆÝYMgÒ2…IÆÍ3Cú—m:¬ùáLB¹]fµLmblNÿ{\õ:Ûi1øÊŒHµ›™(VAD®©qlrºd· ÓÂóoîè,È!if7Oï›$븞è<«mhs®O?ÙêS®2ÞMn“CÃeûÌŠIد¢6®òóцÕ`ä0 ì¾h{ºåÊoÃZµ[Ÿõ3brbú7Y)mŒTÅ$‰H};Åäð«>åLáíôo±¡Ÿú9úyoAâ ’ˆºÌÅG±4bQa‘3Y²%·yâryLVÞ<¢ÞtŦË”v³Odcð.6¶}B´d_5Ù{=¼!g:XrADEKBZˆZ³w^mQ“žñKöÔ@j’Û¥Ï'%…h¡œvuóÙ…%$$¯n sÄ•~ñ XïûT iÉÇ®V^õ”yв‰//!ªˆ-6 Zb4ÙBîyÆB)ÝCtsì›ó,DŠIÒæ~»ªj,OŒ‰y²”í‰ñܼ=1&Uæ¿‘Ï`/6Û•üƆI†hæÕê‰Qy£ØT˜\ú(+Õ§¬6ѵ[N%FvJõîÀÓël³ #;Ô&¹ä™°uÉÁLè­Ñ²ã ÉÄȈmEQ—xµá‘c!­ ß›}š¶¡7xÕW16ô<_A"1NNµÅ¦¶žÎÒ¿ØÔæ+ɤï7=ó™®JOÈŒ¸ì èߥyé… É¤¸´éÍ’Ú@ïúnfJoœÈ³¥w.4µ /6ãíö®ª‹My½¿ çÒיȓ´&òA½¹Ð,¦mžó…¤VøÐ“«v6Ýð‚}¡Ý¤7êž Ë¨\hV}Ïè\Hžò|ÅŠû¶6ûô.üʘ•³0ƒ^\ Þ¿¹šˆXÓ6åùBrrϽ®…z±½b+ï·؜˜ê»p-6 ..Ì uãBR ñb[ÄÔ¼|æ és€—ÇòšCíis_ïBÚ÷D]ýa“a™×÷˜^“ eo›µO&eõÒÂÒ¿×fĶßK± -mn|šôLïB»XÚò…¤öheM.$‹Æ>²uñúb/÷ý‹ ͨŸ¥_Hv43tf<ûÚãÓ3*tAÅ ßüо0.˜7ºkŽ,±¬ýrÒt‚õ5““éIÍ$iÃñi3‡/¬Al5‰ëü¾íù×=>« ´ºÞò ŸØ¯M×iõ<_“ÏzxÞã©r…ýE#>saUkìÔ±¿kÒ‹×3¯{àâºOÕ>›õ·¼å»uÌ„|~#¿¾åƒ‰è~êìñ|î·±cÇÄêßkÉÔ¯ïÉ•ñ¼¹V­6‹–ÁþñúØD™¸±dû˜Ô…z2žµ‘„öûd>ªyqã¤ñZm¶,ûÐëÚ(Õ’¹qׯ¹ÚÆ2§wã ­Óôö±)³ç[÷ظ­{áóƒñÜßл¾ñjÓ¨6îÒ;6îGïönŒ«m,=ßFûØÌšû»1¬6—îk£Ym2íD&ŸÕfÓ<ßñ­|ëù´ÖÎ|«|]mÍ6žûÍ÷µ.æ[èMݨ7ó­6ºÕ¦QmÜ}¼Ö¥ünT¡‡6ž^¾m,‹@I–^åÝhÖÖcãžXÞ |º´6î‹zÆÆÝWéµ-æÛÆ%~µéuõ,T›OÛxóMí96îŒÓ‘oy½0>œÕæÓÄ|êËè±®úÛüª|ËñiZ÷ÄòFä[¶__ܸ 4@ït—·Ÿñ>ÛejãÎñkzæ7×Q¢:Ò*tý¢ g½â϶šÊ·¾T‡Þõo6¿NÆëæõÉûä–Z Šãm¬QŒ¯ápôm?›_÷¼ÆãÌQÕæ×|ç‡YˆC#?Ø|[×5^l>Íl_Æ»éI½©‡çÛÓÿ¶ÕLéŠOÓƒÚ÷žuîˆgj›OÓ=ÞMjgÕ¶Ÿ_âÙô"8ñõR]>qB³þ6Ÿ Dù| ½¨™¯mè„4ƛͷ鋞QG/Ƴ/£ 7óm%Xù8ý'#õeK<›À þÙÊOAꎥRÇîÌ99‘zð&R3ô5#™: ‡¡:¤·0©:€¡±:$¯:WŽóÏ%XÝóÞ'YÜ[툥"((´ŽÎ‹ÖsÕl m¾pܬˆ§#͵Êa&¼nWù^âuÛ5ø"Fx½Nñtâu ´…×Mú–Gxûõ¼néx‰§#­µ$4O¼Þ: ¯· tÂëm°ìÀëVÏâ×íz''^oZ•^5ñzÛÂÃÂë= w ¯wmÁ¯w’Àë·FVÃæ†¸šx}Laául>œÐ¯!"'¼>‚à¯[ò%Á^·d'M¼ŽÍA^¼>ý3_¼>gàxâu ”ò¼xÝ’kåýÄëS«¼ÀëCY7ðúèbWÂë#0 ðºM;4²ÚÔ™Wàu >–G¼>#)ðúÔ*)ðú ('¼Ž‘ÍëÄësë{…סËóâuÓÄùÂës«>Âë6&…ωױ¹!n'^?4Nx}i¼ŽúNG|‚ðnÇ, Ã÷¯P¯ï׉×Áý{„×m„¥ ¯ïÂqx›âtâu{ǃðº•p;ÒéÎÜå^ß•;ð:¨X~^¼E¼O¼Ž,ÕŸƒ×óÁ¢äëÀƒÂàØífèMÍx#b/I»1v@@r‡ì%uµ/)»£<¢r_@ èûr0¯¬{j,±+iÇý¬?Q;Þ?IÓµC3ÞÛ¡…¹¶ƒ,2žˆÛQ?–OÞ>Xæs€{±´Àã w'„õ9Ȉì›ÌšñFèîº=‡º£þºß±;¾ÇQh¸¾‚Ã/ÚÉû¥D•sCöî@Óû“ðr\ôà‘íOü.yówÔWõqëŒ'x<_¨Áƒzrü’Á_Ú!¼¿ÏëG :ËüG _¸™<¬“Çkñiæ"ñ`’éBñ¸Îø#‹÷û½¿ ãQ~ºh¼ÇKyŽGû•‹Ç{µÆ3óm×ñ-‰<šñB$09÷å:òï ½˜O™/Iå/í!ÞÇx$—G3ì Ì#L˜/Iæñþ¤Þóíe"›Gy¤„ó_ôà|•Ñ+ómu!ŸÇüIªD@aF°JB4!&ïˆi—‘Œi7]÷“ª‘Ò{š"—wL4²̃2Yš %¨÷ù˜×Ô£ýH‰ê}¾¢vVïó-Ù½o×Ð?¤:¤õøž|ázo_^÷Še.î°GXéàÀ‰=ò)©‘=Â\ =1ßvî2íqýCjïaÜŸƒí3áÛáöx)'Á=†­w‘{¬G ßçèiHïó…Ò)á½{–ïô^Ãôà{g‚sç÷sRX|ŸoÒs¾ÏG^"|„!)0¾ç7–çÛ/èzQüK;ÆG˜]‘•¿/¯0=$Ã*ÙÇÒu7µ?Y¾‡yhÄ3æã‹æc˜°ÿˆóƬ/y>€•@ݨëŽwÑm:Jp¤n'&Ó÷|ß&v¿¾Ÿõ¡yFª_¸ ;TšýCªïaAJïTß»1(?òmÍ:ç"Õ/5(&©>ºÔ˜TÝÀñLªn"E%ÕGy%ôdþUý|  |Ñ‘ê£=x Aª÷ñl…Tåç‹êã:ÏÖHõQR[RýR·âTaÁÃRýÒDEõ½ÙÒs¨~±eãTß”ܩ~ÌiÏ¡úÞl¼îTß›”Ý'ŽûQ}t‹N œê«›Õ/'_‘ê,+¨êCo>ïTÕöùXTÅ2Ÿ‘꣘zAo®OEõËŒÓHR}¼Æ×“¢ú(Ÿã™TßË'…wªïå×çP}¯–×TÅp<‘êãý3=‡êãºÞçT¿`Ú_Ï¡úx-ëOªïÏûûIõÑÌ<¥ ÕGýoªïŸMJïTÕd¾$ÕÇé¢ú€õŒOR}ÃùŠT¿œhRý²¶Æ+©>ª¹Bcýº¶¨©>^ÓÒs¨¾ë‹ê£|RR}¼ŸÔ‘ 5|© q$êO*GªïõMÏ¡úh_R9R}„­Nœê#žæ|ÕGêý¾Ð+X†’º;Õ÷aHŠî \ç÷p!†aF*Kª¯ap¨>â£\Tÿħ¨>úŸT›TaÂö#ÕG{¦‹ê£H½HõKønDõ+òù~Õ÷nãu§ú¸\TßùUzÕGÿˆú;Ž»´ã¼/Ú²wµS}¼ßó«¨>ºÙÇŸ¨þ¥êÑÈÞL¤òNõ¡ÙÞ¤úhvROR}t£Ï7¢úè&¶'©>º•Ô”TÍ ŠïT¿F¾ÕG1<5!ÕÇkûEõQ )0©¾Š=T°>¤úx ãTßïÏÏ¡úÕ¦åLÊïTÅö ü(Þ,ÔŽ[«åžRêãyÖŸTšýMª˜¯S߯óûIõ]“ª;Õ¯]óµ¨>`{= “êGªë¤Ô¤úøk¶©¾ßïýIªû×EõëËEõëˆþ#Õ‡^ó9Tèy§úø ö/©~å´¨¾©ºS}ÜOJNªaÅ|Dª´‘‚âWè8Å ÕÇuÆ+©>òOéHõkì¿Eõ«-£y*AªïÃæ¢úµh!ª_m}ÀSR}¤ö'©¾§ýüª_Ï) ©>êËö&ÕGš(AùÏ%¡ú–ÔÞÁ–ù“TaªòvÄ3ó þlOR}O#Añ=žKPz§ú_̤úx~R;Õ÷÷¯çP}¼Ÿõ'ÕG˜ë~§ú—vªéÅx.ŠWR}cÔŒÞêüX–´nÀÚ’Œ>(|Bv ¿7„ð[)@¶v9Zdkסšlí•—­]èM¶vùdek¯‚ñ´µW­¾[Í}H’½c~]ík¿i‡s㺃÷¥­¹û ÄîçNß-5¡;œRè22÷5 Dîlõ îK›Ew<ç€Ýy;ž~qû¦¤íèæžlH‚µÃMÕŸ@íXŸrÒ¾uDJо·:ä즖¸:lGÚæ“²ï€0áaצìxØ[xÌåaßáq—‡} "‡‡}Ê8ö£åa—ÕâxØ· ‡ {Îá‰'d+@@ö[OjAlBöiÙ±V!„'d+Ãñ°oAÓð°G}^;­Ÿáa×QÝñ° z²Ç¯%ÄØo9i4¢Wòr°¡ËÁ^„ø^;ÂÁ^…”ÃÁ.£Èå`ç‘B8ص%;öƒÌå`döèÍp°¢ ûâà8Ø—¢!ìKß'Äžõ@ì9…^ö­#!vëq#ö[ù`%IDNÄžeÎ Äž›¾GˆÝ:…GDBìY[ø@ì%Ò&b·)ˆHSˆÝš•í'Ä^†¾_ˆ½hKˆÍ´Ÿ±—ãˆ'b/-ðDìEÁØ­›9Ú„ØKÕ”;VžÔDìv?1;îòvÄŽïãha®¿tåÙe8䉨q•ˆ×wX×|r±±CË4ï{¥HBˆÝËçý›Æ¢­ö"bëOÄŽûë…ØqñLÄŽçg õÄCÞyyÚý 8z¡f|±{ù|ÿ ñh«‰Øñ¾Èñ\Fì½ ´¿üèŽØÑÞÇê>¾hg¥—ö¹:_ˆÝ‹‰À}m÷ñ{ˆØßx#b㈚GŽD츟ßKÄîñHÑë²¹_Ú»K"rßKä¿ b×0=ˆšˆšˆšGXDì(G˜DìŽx/»;Ò„8Ñ¡¾rÖ;›€¾;4P„ØK© ±Ÿöb'¹|»]g>b/K¿ b?ý-Äþj"öR¿"öG¢Bìg| ±ÛûÆØc¼bG~ ¥ˆýÖƒz‡åù¶²büˆÝêK!Ä^Âq,Ä^Â.Äã7»-©ö|Ûeb·|K„'Äžõ;Â@ìY§ÿØÓ~µVBòDìXM„¾Ò(Ëöbç¢àEì1?bõ ã¦Bì±ÄF–@ì©Èˆ+Äž´¦ ÄžªŒ¥Bìa Äž´„ÄŽ#C:Ú‰ØSUû ±'my±ãH”õ#b‡‘šˆžˆG a˜_骟{ê_{ Ä*Ä~Œ«Bì±Zbw#$‘y§‘3…4r=i|[_¬ò×û×KHNˆ=Ö7Ø“æ!v7¶öçEì9)¾‰Ø}ýp!v<Ï#%"vÿÞßiäŒ_#±û/ësûŸDì¾%rß±zíb÷ø ¤^¹š%b ãü”±8ŒóšoŽq~éaœß2Ë8Ÿ³¤Œó6Ld¬§qþŒ/籺½ó±¾:ÆùF~çsÖ÷‡q~q‹}ŒóCG>aœ×‘ç1Îk}xŒóaãü¹_Æù®ï ã|Ç_ã¼Þ/ãüÐ[盎ÌÂ8/cì1Î ™ã|Œ§0Σ} ã,Ã8ù Œó%Œò2Îwß0Î××(/#'Û#ŒóUG*¯q¾„‘~]¿x=Æùaœª_çãWÚ;ŒóÚã¼ôeœçkç‡ÆOç£ýÃ8¯_ã|Ô7ŒóÚ]ã¼Ä1Îwù†q~r—q>~hÆy™íŽq¾ Q†q¾É¸Æù&¤Æù@zaœ×ÁŽq^?48ÆyÁã|r ã¼òÕ1Î'!¯×8OÆyßcœÏÜÜÆy"uç±M¼óE?ä9Æy©„q>o!Mç‹~ÆùX_„që/^§q¾è‡%aœ/leœ¯úéTçc=Æy[ßÁÉ8_ôË0ÎÛ´S¹#ž±­äó4οšÆù[#ßVYÆÂ8oõÙ·qG„^_ç_Mãü­7×»Bê4Îc=K„Nãü)_Æy ;"tçñŸ§q¾–0ÞÓ8_‹úSÆyl‹‰ôiœõtçãÈ2Œóh¯òÄîõáuçk¹eœÇýDÞ4ÎWí'Â8Ïbbw?j wäÛHUÆù& Rç1-Žç vh¶ŸŒó°Pò}4Îwý¶.Œó6 1Ë8ߋƋŒómAì9Ž\Ã8ß“ê+ã|[_óMÿŠ0ÎwYºÂ8ó*˧qÛnùiœG³•ç vlãy¤ ã|›ú>çMóÈMÆù&~Æy4#8óM¿è ã|Ó‘qç[‰È8Ìàß/ã|›Ðiœ¯Ó8ñ…ØQ>Û_ÆùvŒî4Î7ýÐ,Œóç~ç›þ aœoqÄ&ã|XzÂ8ßõ Þ0Îw­_Ã8ß§ò“Œó\–ÄŽm?ødœ:2ãüˆ|)ãüÐú;Œóañ ãüû<óCëó0Î[³å@îˆç?¬‘q~ÄŒó¯¦qþÕ4Îý 7ŒóC4‘Æù_|/[üoÿüþK3þí¿ýüïí#žïÿýÁÂøjlƒP¬ï’¿ÿáù›ïÿó‡ßýíóýï¿ýÝ÷^z<~=󳫨\htKˆ"<ÿ?ñ0ª÷WýWp´ôæ/~x”€)ÿð<ßýøÉÁnÀÇÍ÷Éë†/ežW\7üÕí;= šo2Ñ<¶m?KÙþ÷—ù/—›°Må?.dþ¤B°^J?ªÉüZþC;Ôôƒ ŒsL˜hßc…ŒüuÃpTqn ¼nÀÞÐrAÜ yÝPùs͸Aò¾µ:7\•ü?­ÍI“'FÓ›"§ôBžOƒG¶¤­Á~òã°2e½½ü?ß~èןþ8&[Ìùã-}í †o-ù­o!‘¶}½§~ùÖò¿þö¿zБH endstream endobj 300 0 obj << /Alternate /DeviceRGB /N 3 /Length 2596 /Filter /FlateDecode >> stream xœ–wTSهϽ7½P’Š”ÐkhRH ½H‘.*1 JÀ"6DTpDQ‘¦2(à€£C‘±"Š…Q±ëDÔqp–Id­ß¼yïÍ›ß÷~kŸ½ÏÝgï}ÖºüƒÂLX € ¡Xáçň‹g` ðlàp³³BøF™|ØŒl™ø½º ùû*Ó?ŒÁÿŸ”¹Y"1P˜ŒçòøÙ\É8=Wœ%·Oɘ¶4MÎ0JÎ"Y‚2V“sò,[|ö™e9ó2„<ËsÎâeðäÜ'ã9¾Œ‘`çø¹2¾&cƒtI†@Æoä±|N6(’Ü.æsSdl-c’(2‚-ãyàHÉ_ðÒ/XÌÏËÅÎÌZ.$§ˆ&\S†“‹áÏÏMç‹ÅÌ07#â1Ø™YárfÏüYym²";Ø8980m-m¾(Ô]ü›’÷v–^„îDøÃöW~™ °¦eµÙú‡mi]ëP»ý‡Í`/в¾u}qº|^RÄâ,g+«ÜÜ\KŸk)/èïúŸC_|ÏR¾Ýïåaxó“8’t1C^7nfz¦DÄÈÎâpù 柇øþuü$¾ˆ/”ED˦L L–µ[Ȉ™B†@øŸšøÃþ¤Ù¹–‰ÚøЖX¥!@~(* {d+Ðï} ÆGù͋љ˜ûÏ‚þ}W¸LþÈ$ŽcGD2¸QÎìšüZ4 E@ê@èÀ¶À¸àA(ˆq`1à‚D €µ ”‚­`'¨u 4ƒ6ptcà48.Ë`ÜR0ž€)ð Ì@„…ÈR‡t CȲ…XäCP”%CBH@ë R¨ª†ê¡fè[è(tº C· Qhúz#0 ¦ÁZ°l³`O8Ž„ÁÉð28.‚·À•p|î„O×àX ?§€:¢‹0ÂFB‘x$ !«¤i@Ú¤¹ŠH‘§È[EE1PL” Ê…⢖¡V¡6£ªQP¨>ÔUÔ(j õMFk¢ÍÑÎèt,:‹.FW ›Ðè³èô8úƒ¡cŒ1ŽL&³³³ÓŽ9…ÆŒa¦±X¬:ÖëŠ År°bl1¶ {{{;Ž}ƒ#âtp¶8_\¡8áú"ãEy‹.,ÖXœ¾øøÅ%œ%Gщ1‰-‰ï9¡œÎôÒ€¥µK§¸lî.îžoo’ïÊ/çO$¹&•'=JvMÞž<™âžR‘òTÀT ž§ú§Ö¥¾N MÛŸö)=&½=—‘˜qTH¦ û2µ3ó2‡³Ì³Š³¤Ëœ—í\6% 5eCÙ‹²»Å4ÙÏÔ€ÄD²^2šã–S“ó&7:÷Hžrž0o`¹ÙòMË'ò}ó¿^ZÁ]Ñ[ [°¶`t¥çÊúUЪ¥«zWë¯.Z=¾Æo͵„µik(´.,/|¹.f]O‘VÑš¢±õ~ë[‹ŠEÅ76¸l¨ÛˆÚ(Ø8¸iMKx%K­K+Jßoæn¾ø•ÍW•_}Ú’´e°Ì¡lÏVÌVáÖëÛÜ·(W.Ï/Û²½scGÉŽ—;—ì¼PaWQ·‹°K²KZ\Ù]ePµµê}uJõHWM{­fí¦Ú×»y»¯ìñØÓV§UWZ÷n¯`ïÍz¿úΣ†Š}˜}9û6F7öÍúº¹I£©´éÃ~á~éˆ}ÍŽÍÍ-š-e­p«¤uò`ÂÁËßxÓÝÆl«o§·—‡$‡›øíõÃA‡{°Ž´}gø]mµ£¤ê\Þ9Õ•Ò%íŽë>x´·Ç¥§ã{Ëï÷Ó=Vs\åx٠‰¢ŸN柜>•uêééäÓc½Kz=s­/¼oðlÐÙóç|Ïé÷ì?yÞõü± ÎŽ^d]ìºäp©sÀ~ ãû:;‡‡º/;]îž7|âŠû•ÓW½¯ž»píÒÈü‘áëQ×oÞH¸!½É»ùèVú­ç·snÏÜYs}·äžÒ½Šûš÷~4ý±]ê =>ê=:ð`Áƒ;cܱ'?eÿô~¼è!ùaÅ„ÎDó#ÛGÇ&}'/?^øxüIÖ“™§Å?+ÿ\ûÌäÙw¿xü20;5þ\ôüÓ¯›_¨¿ØÿÒîeïtØôýW¯f^—¼Qsà-ëmÿ»˜w3¹ï±ï+?˜~èùôñî§ŒOŸ~÷„óû endstream endobj 304 0 obj << /Length 709 /Filter /FlateDecode >> stream xÚTÁnÛ0 ½ç+„žd V-Y’í°aë°Š °Ã¶ƒà(‰±ØNme]ÿ~¤e¥qávE€X¢È§G‘| Ù„|^$ã÷Ãrqqf¤`…š,ׄ ÉòD­3–s0­ÈzÓF"§Qœšî¬Ã…¢‡ÞʶޛÎú‹8ÝF1§Ö;íw­ó'ëCSºªm¼}ݵõ±µÑ¯å׋k•žð\0-%°¸ª¶w¶«lï}'œ•f"ÁuoJdû;RŠšÍHã¡rÛ×®‹…ÎYžisÎ ¥|YexßÎuµ9 ŒÈ¢ ò2‚r*ºÜŽçþZo†_¯÷û~D wÆæ{mÀƒ'ýhëa~í 79­š V_5ñ\6;â ¸©-L–Î$ýå’ŽöC qb™à ßñ¸:šBë÷~‹Óƒ!ã  ²6˜”g Ë æTÿú™‘~–ÁŒÔÌëŒd*?*Á¹NÓ¬æä[1©Þ¨Þ /O Ê&­úÒøãÐfy2hßËÅó>ÿnóŪ endstream endobj 301 0 obj << /Type /XObject /Subtype /Form /FormType 1 /PTEX.FileName (/tmp/RtmpBlhU1W/Rbuild4444671013800/timeSeries/vignettes/timeSeriesPlot-multivariateSingle2Plot.pdf) /PTEX.PageNumber 1 /PTEX.InfoDict 308 0 R /BBox [0 0 432 432] /Resources << /ProcSet [ /PDF /Text ] /Font << /F2 309 0 R>> /ExtGState << >>/ColorSpace << /sRGB 310 0 R >>>> /Length 16690 /Filter /FlateDecode >> stream xœµK¯$É‘÷õ+rÙ½èËxúcËÁŒ`Ä´Ì‚ HHD6¥™†^ÿ^avα0Ë{ï°«)dUŸ ˈHOó/Žy¬ß<ÖÇŸÿúåW?ýÇÿðëÇ~ú²¾-ËòÈþô‡¿|YËãØ·Kë¯ûããO_þéñ¯í|›ÛckëÛq<ö±¼­í±¶ƒ1ÿéñ—/¿þzï~yÛ® ùOÛý¯þa»Îã럫òÚ ? ·s¾mýq¬ýí<_||w}ðüa]XÖǯ—è?,Ç¥¿|ýó—¿ÿjçÅCþÝï>8äïþî¯íçã}ùç¹¾Ùþ²>~sýÿÏ_V?êo¿ Ž{¼ñøñ±Ï-éçãñ»×ÉñÖ{´^\¦€õm?Re ¸º¦Êpµò–(K@%Àe 8ßö3P¦€VÑÞ¢½µY\¦€ë?sCQ¦€ù¶ç†¢¼ΫmSCI–€¾–€^ê¼Ú6µƒd Øßöô-$SÀÕ¶©¡$K@ßJ@¯ užõç»C\mÛRe oKj(ÉÐÐ_j¾¹¡(ï€vµmj(ÉÐÏÐk;´­lïö·#5”d ¸Ú65”d è­ôÚPí|;ö@™º Vwe okj(ÉÐG è/ uµmnÊ; _#ejɰՀ타>K@¯ Õ¯¶M %™Ž·55”d K õâíím¤†’LWÛ¦†’LWÛ¦†’,c+㥮¶Íí@yŒµH¦€ÍîZwe { µ¡Æþv¦†’LWÛ¦‹W2´·55”d g µ¡Æu{L %™®¶Íí@YF+£¶Ã\J€d XßÎÔP’)`»nÁ)€²Œ^Fm¨¹[pP¦€óíL %™ÚÛ–J²ŒYFm¨yµmnÊ0Þ¶Ü”wÀº,%"t ™K ™ËKÈÕÀ=‡P竉B]CæZCæúrxšw‡Pç«¡g¡Î!WS·B]Cæ^Cæk»ô—õ÷šo­4u ¹r½-7t ™G ™/Mwe¨-7t¹|äêr¼mùI×ÙjÈ|iºõ|k¹é¤sH{Ûs»H×z öþ@ãm–¦£Î!Wƒ—¦£N!Ûò¶ç¦“®!sÔùÒtÛÕàg¡Î!û[ËßH:‡o{n:ér–Œ?t ©:ߨ½¨½?Ð(ƒÐ9d–©AèÒF yIþ×})Ùè²–ü?tÙß–ü¤kHž„Î!Wƒç¦“Î!çËÎ÷:_t¾?P¯í"CÆK»Œ÷í2_Úe¾o—YfF¡SÈ5),_Z:‡lev:‡ìe~º†äRèr”9Rèr–IPèÒ_BúG!y¦:‡Œ2W Cf™-…®!}¯!ý¥¿\3Á£ ]Cò„#tÙË„#t©ÏzB×<áCÚKÈ»Ç5ÛÖËœ$te"º†ô¥†ôצ«Ï}B§½>· C¶2á]Cò„#t©ÏBç£L8BçöòîŽýSž“„Î!ýíÈM'Cê“ Ð5$ÏlB§£>Ë CÖ2áC¶2á]Cò„#tÙË„#t9_BÞ=ÖÙŽVæ$¡kHï5¤¿4ÝQŸ…Î!õùPèrÖç;¡kHžp„Î!k™p„Î![™p„®!yÂ:‡/!Çû³ÌIBç«ÁsÓI×üÄ(t©ÏŒBçúÌ't iK™p„®!yÂ:‡¬eÂ:‡l/z÷øhkõéOè’ç$¡sÈùv榓Î!õ)Rè’'¡sH}:‡Ì2áBúR&¡kH9P÷@iëõ‰RèRŸ…Î!G™“„®!cÔñÒt½>Y Cê³¥Ð9¤> ]CæRCækÓÍ2áBF}冔w˜¶k¶Yroé²×Ü[:‡5÷–®!%÷–Î!gM¬¥sH éïCFͽ¥kHɽ¥sȬ¹·t ¹&¤ùiKè²ÕÜ[º†”Ü[:‡ì5÷–Î!GM¬¥kHI¬¥sH{ iïCzͽ¥sȨ¹·t™5÷–®!ÙaúÙ¯ÙfνCç­äÞ¡sÈ^rïÐ5$'Ö¡sH}Â:‡´—ö>¤—Ü;t ɹwè2Jî:‡ÌòT!t 1w^n:é’sïÐ9d+¹wè²—Ä:t ɉuèr¾„œïCZɽCç^rïÐ5$çÞ¡sÈ(°?t ¹f›9÷CÖ’{‡®!9÷C¶’X‡Î!ûËö÷:KäÜ;ti%÷CzɽC× ûCçYrïÐ)d_Jî:‡¬%÷]CʤsÈör íýŽ’{‡Î!gɽCל{‡Î!­äÞ¡sH/°?t™%%]CÆ^C^rïýšæÜ;tYë¤kH9tÙKî:‡%÷CÎ’{‡®!9÷CZý¡sÈ()qè2KŒVC^rïýš–IçµH:‡ì%÷]Crî:‡%÷CÎ’{‡®!ö‡Î!½¤Ä¡sÈ(¹wè2KÔÍwº&¤å@Ò9d+ÏBç½ÀþÐ5$ÃþÐ9ä(°?t9 ìCzý¡kHv€…Î!£Ø»Bçùr ùþ@³ÌlB§k¶™ç$¡sÈV&¡sÈ^&¡kHžp„Î!G™p„Î!­ÀþÐ9¤—9Iè’í]¡sÈx9Ðx Yf6¡SÈXk»H×Ò.Ò9d«í"Cöú¥¥kHùÒÒ9ä,±Ð9¤•‰XèRŸp„®!õ@ýýÆËÆ»]³Í< CÖ2 C¶Kaî¿^,³úŒb›Kg̳ üã*„ð±¡;|æ‹Í!ŸØsÈ'æ«ò‰Ç%‡|ò„?…|ö4‡|ò)‡|ÑsÈ' 2‡|BjRÈgSÙòÉt ‡|’ åOî99ä“‹2‡|Ò…SÈ·”¨-/%jì¾~{·Ç9^¡öÏßýãûþq¥ÅßýÏDz~ÿ/¯¿±Â4;Î_ÙÑðÌÇÌ¥=JÝT×ö×?ÞàHçñ›ï¯îôøî÷y,Û7œG_¼«—óh?ÿ<Ìn“âD¾ûíïÿÍZâö0½<áŸÁ•¡[>›šâ·¿ÿþê«ïþÏ·ý$fœË™\ï²ô£üçcÙ¿åL®žkS­_z&ö0·tï~÷ÇÿþM¿Š=<µQòŸÁ•©Ù¼éÿÁ…b³öËÏd¿þ²þõñ¥ò-g²Ó QΤÙ˜wuÿ[®›/¯WkOÕ°TéFèvUé‘Y£ûi%ïun׈˨TS;®ûSþóg×Ôvßý= C§DÂxåž"¨sÄuc»FÛ;:G\Éæš# =¾*Nem© ™êÔ,|©6G×”b9ýY—å† ÿ´ÝW'?½~û§¯¦èOWnóÍŸîîLðOïËû®ôÿ»›Ôì¿ÑçÙaø0;ÌŸd‡%äãì°„|œ–³Ãòqv˜C>ÉKÈÇÙa ù8;,!g‡%äãì0‡|’–³ÃòqvXB>ÎKÈÇÙa ù8;Ì!ßÜë¯íÖ߯ë5XpÀ}Ñë—OG@ø£°çîר¥¥®äî:ù§@®÷Ô2í4zñTÑsk×S%þÍ+÷Ÿ*è¿ä> í9UsïSÅú—Üü¸°d¡û©B|ôЧÊîÍIïæ,«ø^LÂBÕ¼”ð©úKî ¤=ŠàSÁ× õ©RxãTv’'ìMF¶VH{œrÉ̓QÅ~Éa’ÃCsÄòTÁzsGÝSåéÍMO£_ö}YkÞ¼~ã©Bóë/?ëÈ­%}ϰ5ÜT2Þ¼˜â©ñæ§þT9ø%wûXí}ýënÁ¬íîîë~ª°»ûxòT·ù츬ÒîΨŸ*Ѿd³–dvHXg ¹DuµyïOH3û_ŸY|W0ј«ÁΊãhHØZ.yPF7ƒ$+ž»#×§Ê/9­aYÍÜôIÖ.ë4X©ÜÙUX— ŽöTQòÕVÃÄšcó›ØVV‡D=q_Bš]­ÁZa”A£Î)Æ'1YFÅ’mß©ína×£Ÿ/k‘âúeáQïYedÞ?ßKŠ®f@û³~èjFO'T,d÷×IÍûEÇñþ®í¿XócÙ÷oøØýh£öûËŽþ¨ÒÝŸT§skÌAL7ê‰[©ßÿTnséµ5¦7j{pf³ýáU3—ööS‰ i@ÔÇ\Ú¿K>£òÅâ};ËZ,…ò㳆¥s|SÁJ÷¨Ï¨V±nïߟ¥(q~¬;éÌHUdbOîüüXQb÷kh”(;R­HgÿVaˆîÏš°Ý%v÷óa}GŸÅý@ÿQå†2•itŽGªÉè¼ÞU€ÑOí©½ß¿/K+¬¿ø÷a…}ŸImãµíßÏ ‰~`¼R9„ú§jì|ugÖ ª†~°?±„¡èÿªWè¼>Uœ $X•v|ÿ=Yv€lø5×°³úþ9û5ªáçÏê]_*°tvR÷~çW*èNdžQÐ=ÃîoýaRwü>>þʸßwŒ‡ré+?”%ßâýûœ‘Ö3fû¾áz—³¾oìÿ´ÑÛœÁzæ;§22Èãùõ3ÜñÆŸa…ïïäs뉦vûý ™à¯ìO´«Çx@oº]ÿþ}hDïßå:·ëÇG–mÇ÷¥ŸÜÜÞ¾?šÇ5)SÜÎÇÏ—¶ð¸éïÌeø¶ëqR´Ï†ýÅ\à€æd`cÿë‘ï£}éÈî¯wÚ¯£½GÈXÝy¿‹ÚÚÿ?-Óö{`;8K´?ÍÐ}Çý]Îç8_Úœ5¥’§Ùn»ÚÇÏÎßneã¢þyZ“{ÇŒT>äKo'µŸù¼ÆÝuϰ_Úó]y‡»ÛSŸaî“g¹‚{ãý‡à~â~%¿¯]uK㕜¼¶½»†m·3ß’GWׯ ¹ýDÿ¤²ŸÛ›òߟ‡¯v,¸ÈDkùñêŽÙ± ½dµŸÕ÷G/¬ò _çÇr¹^?ëxXZÇ ùWígÅçaVU>)gêàx%êàýAžS›4&Næ3r!Î ù’,‡×eÅxø í²ñïK3ál˜o ^—Ñ ›àì@„òΩí0â2{†ûoªÿÐÚg—¥ÿ^ôñÍŽñD¦½©þA‡Þ¥}ü—ïŠ÷|@Þ;KK;µå»WÚ:°®º¹a¾) Ý<0^Ë/7™É7™_È wiïe{³aÛáq³ýÔ–ï΃ý…ìi÷3¬kFˆðyøÒv?Ôvo‡ãlêz¥½lü}é%›ºÞi›Ì×å‹xZÂ&ùƒü_WûùýDf¯In+g×ÕÞhÚ¸®ßí ÏÖ~¥õ¸^`вaÐó]º±LûxNë•i\¯ðYÙ°:ýü1‘6ݱÝ'R6Œ.Ò~¼ø¼ƒvÓÎh„²avÁþ<¿Þ¯iÆSXœL|~ ^楤ݩäÚ¿žr>ëÏž®–¾†çÍþ<6PxqöîîàìýD¯!g nZäìWSùw gLQÈÙ ‹:I?\|‚IÎ~I¿žÈÙ­|+²ñk´ö_‡œ}œ ÉÙ­ÉÏGpöÁTœœ}.¸s‘³Ï•œ}nàDäì“i%9»]uŒ$}2 %gc 9û$Â"g¿¶úˆ@În|}g·ñǾ9û%'’³_½[ÁÙ#œ}òy9û$z$g¿¤ÀºÝ8¬ãGpöɼ›œýj ¿m’³Û×'Xwy¢Ó“³OÁqpöÙ0e"gŸœA“³_Ò–œ=$Òmû ¾gpöÙA—ÀÙíbvG.~µ†³Gröži[‚³ÞœÝ:¶·ªÉåæì¾Õ¾8»_ÄîãÄEîØÝ9»¥2íæì–¹8"g§-BœÝòï¢àììã-8»]ÎŽ×ÀÙ-)ò»8»†¶Ä]úVO­mèð[-8»I?Ipv;Iï àìv\p>pv;Kp%pv;MpapvK¯pw‚îÎnÛÁ‘ÀÙ]ƒ«;g7 Ž ÎîûÛÁÙ÷õÀ7%g7Íã;g·t œÝ4¹¶sv?_?8»¥sŒwÎnçÎné¸ 8»¥gàTà잎%Îné¸+8û¾NÌÈÙíóÜŸsv;^W·û¶’C³Ûçɽ³Ûþq&În:söý: 9¶svÛ. Îné&¸68»§—Ò}qÝg߯Ãâ÷g·Ïã÷g·xrù®ãƒó³›&ן8þ¡ã9g·Ïƒ;‚³ÛþÁ}ÁÙ-ÝØî €O_ÁÙ-ýçg·twOœÝ4ú#8{Ñ}Ãùƒã;g÷ï‡Ï;g·íxŽÎ~kpö¤8X{ãúg÷ô|}g·ô›\Ý9»¥ßàœàìûõ3‰³{ú¾?‚³Ûv|pvû<ú 8»íÏÑÀÙ‹îÃãWptO0­}Wqõíyàì>ðïÎné~G¼söýú™8ºO8Lƒ£ƒ³{<8¼svÛ?®Opö»¿€³[û/Ë#8»¸>ÁÙm¼Âõ @cã…?f%g·»8/8»¼ïÀÙm4§g/ÚÜ=ëy+9»]Ÿ>Ï!g·áíÎnç3¤ÎÏ™ÀÙm8?pö¤OÜ(NŽ¿àì~òïKήy9»}?porvË{ç#qvÞþƒ³›ÎÝœ}6Ž'äìJSÄÙçàõJÎ>'_$üq÷%g<—œÝïVØîœÝo±8çìv«ÇsEpvÇàâ â;Ærv»¿€Ë³ûô~<‚³ÛxéíIÎîx@\Ýï'®OrvÛÞÀÕ;Æ>â'g÷þ†ãOÜ_Ä=ÁÙ= ÷^÷7r9pv¿—ƒ£ï˜Glv[ ÎnŸ'g·ó!'÷×úÏîŽyÀ:1~“³ûýÛî/«Ž?q¿à<ƒœÝÇ7?pvï×Gpö¤wŒ×4q³ÛþÏÄÙm|Cûƒ³ûýŸï¿r?ᦉœÝqWâì{ãøHÎný ÜœÝ~Op'pvÇsØŽüúÇg·í Ý9»á=rtçìŽóÀõ³ï27’³Gÿ!g·óõ드}¿ºíwÀºë939»ý^à„àì¦gâìî7wÎî¿/>ïœÝãÏGpvÇoø¼svû}ü÷#gOÚ9{Ñ6~ÊgCÎn¸Ü œÝ4ž€³mãç­ ª'îäì¦Ñ>àìûwg7¼GÎíœ}ÇeœÝ¶ã|ÀÙ÷1q}’³ŽGgßó&ξOú4xãÙ¯nŠógwý9pÞ¯nŠç&àì¶½íàì{pPpö}ê|ÀÙíóG{gß«ûù€³ï×eq$ÎnŸ?gw½=‚³W·Æs pöÃò!hç쮽‚³›ÆspöÃÒ0põñ‡ôŠøQ8»}Þï?äìÇÂû79»Åã¹ 8û±Lö'pöÃæ‰³³ð#8»Å£¿€³ÛþOéÉó÷ýƒ³û÷óý³[<¹·l†o¹Ý9»ác´'8»¦ñ\œ}%g·ñˆÞ9»Åã÷g·þëórvëoçñÎnºƒk;gßã¹8»áçzÇxM9»ºÁɳ›n‰³Û÷Ãs5pvëÿ‡¸»ñ”›Ûƒ³OÎçÉÙ…>=qv¦3gŸœгƒg·iIÜœ}ž<rv£”ÐàìvüÄÙíøø=ÀÙEÏ)ÁÙo Ξ´'rþûûþéŽ_ÈcŒ³/¬ÑŸ™³ûlìæìœÈÏΓ”Ÿ}àž%?ûD ,?ûDF)?û@•ŸÏíägŸHägŸ¸ûÊÏÎÉ9»K?ûŽkF~v>Ú¥Ÿ]NŸF¿ égï;á8ýìLßn?»7û “ vÿfµ°ÈϾáÎ)?; ;ò³Ó~ö•œR~ö•H~vq ùÙù‡Ÿ}%§•Ÿ}#§•Ÿ}£V~ö¤'ý6àö]ñ-qv÷߀“ÓÏμ1üìâøò³Ó¯~öM~wúÙ9O ?ûF*ýì}C^,?û}ü¦ø5ûÙmº¿<üì=üä‹ü‰h/úÙ»žSÐÏ®êùÙ;9„üì6vað³Û*»Å7æ‘ò³·!¿<üìM“~v‹?ÁÙoM?{›âî«Æ]<§ ŸÝÆ…þΣ§üì÷MùÙmX‘¿½£<ÜŒ~ö{;üìG~öÖÉõègo|.?{£A~ö&.H?{käúô³·Æçô³7r/ùÙ[ãïE?{cÞ*?{“—~öFŽ"?»Í›¥ÍÏš~ö[ÃÏnÓûìgo|D,?{ã¼P~ökˆG¥Ÿ½‘ÉÏ~i\Oô³[º–8»iü~ô³·¿?ýìYw”Ya¼ ŸÝðº¸:ã—ùÎnõ1ô³7ÎSåg·AÃÏ~~ö¦ñ„~öF¿°üì÷ ùÙ›üÙô³_šÜ~ö¶±>‡~ö¶“3ÓÏÞTC?û­ágö¦Ÿ=ëß\~vû½À½ágoÌ{ågß‹~öFN+?{üþô³ß~ö8>ýìíàsröÆy¡üìMõ äìÑÿÉÙƒÀ/ÎÞ:ÇoröÖØŸÈÙãz gþLÎÞÈÙägïK?{´/ýìV™®?{£ßQ~ö¦zúÙý,ò³7fò³7qúÙUb(?»iøáágo¼ßÈÏÞ8þËÏ~ï~ö[ÃÏ®þ-?»}ŸöÎ×üìí 7¥ŸÝ8¸7üìÏUägoGõ³ßñð³·ƒÜ~vò³«ÿÈÏný~zøÙ5þÈÏÞ8“Ÿ]ýG~ö[ï*;çÝ¢HÜ~öÆy¥üìÖÿÀÍágoä4ò³7qß-*Uá/§ŸÝtâì1~ËÏñô³[Ò‹íLïùO~öÆçÂò³kb~vÎ3åg×ý1üìâ {$ùà´Gäñà®ô³kò~v=G‘Ÿ\-üìÌôÃÏÎt=üìzn"?;ç ágg½JøÙW>wŸS‡ð³/ü}åg_Ùžò³¯ä¶ò³¯ô{ËÏû£Ÿ}åõ'?;óÍäg_‹Ÿ}ãs‰3rû&®^ýé‹Òy|_ùÙÅ9o?û‚Ï3Áßù{ÊÏ΄?ü쬟 ?ûþâg×s©V³üð³ó~“üìø>ò³ËÏ/?»8qÒÕ™8»ÇïçígçÄ(üìzîØ£¸ãM|wg~ëO~öµpö;þö³“£‡Ÿœþö³÷âg§×*üì‡üïô³Ë¿/?;ŸS‡ŸýdûÉÏ®úùÙy¿ ?»žÃÈÏ~r¼”Ÿ~Âð³kò³ó~~öø<ýìô'?ûVüìý[~vr”ð³Ó‡‘üìà¬ò³‡?~vqAùÙ¹ÿìg‡_½I3¾«¨g?»¾ü쪟]õ;ò³wæoò³÷Ü~vÍ[ågï;¿ýìº^åg×õ ?»ægò³kv.?»¦Øò³«~M~ö[ÃÏžuOõ.ò³wÖ‡ÊÏ®ù¾üìY÷ž5ü욉ËÏíC?»æ£ò³k<“Ÿ]õ3ò³ëú’Ÿ]ã©üì¯åg‘Ÿ]ã½üìºþågïâ®ô³k~-?»æËò³k~,?»–(ŸÝÆ›ìg×üV~vÕóÉÏ~køÙû*?úõ@ØÓšàžô³ fÈÏ®ñF~öè?ô³‹ÈÏÞ7rkúÙECägó§Ÿ]ã›üìšïËÏíG?»ø†üì÷þfÔÉßÎñÖÇsùÙ5~ËÏÞ7>¢Ÿ]¤K~öèô³[ý\ö³ÇõF?{gý²üìù§üìq½ÑÏÞ7PHùÙ£ýègë‘~vñùÙc| Ÿ=ÆúÙãz¡Ÿ]¼E~vñ­A¿?þÉÕE­Š«o³jòrøMÇ_g¿û3—ÿÏÑB2Ïݵ°§î§ZÅSœRKvÆõÆõ9Cs1Î[cåMÕϦձÈÙ¿yšv}¿üçû¥—>^„æ×÷"4”‚ö·ô À-=¿Ïò×oéÐþ–­{îKOíoé™}H@û,{: @û[zVK‡öYöt€ö·ôŒÿ–žÐgÙói8´ hŸeOÇ´¿¥ÏioéÐ>ËžNÐþ–žÆßÒ³ø,{> ‡ö!ío¹½ÊžNÐþ–í³é'´¿¥çî·thŸåÈÇõÄ>$ ý-ýúËr¤Ó´¿¥çô·ô”=Ë‘NÐþ–í³鸀ö·ôdý–í³é4íoé‰ú-=OÏr¤Ó´¿¥'ñ! 틞KÖí“ö»MÑsÍÚ¡}Òí“vh_ô,Çï/ñío h_ôÌçhŸ´Cû¤Ú=óùÚ'í´ªèïÐ>i‡ö·´/zæó´OÚ¡}ÒNw’>Ó€Ð>éö?ÊLh_tu í“^˸Kh_ty í“>_âÏ—øþrüñrüùrüY~Bû¤·2ôÚBû¤Ï2ÞÚo„öIÏr ´¿5 }Ò[¹ Ú~Bû¤[¹Úï„öI;´¿5 }Ñù~@hŸôþ¿¿ÄŸåž@hŸt/wBû¤Úß4*éµÜí“Þ^â÷—ø³Üí“nåþ@h_ôÈ¿' ý­í“^Ë…Ð>éí%þ(÷ Bû¢ó‚Ð>i‡öIÏr³ ´¿5 }Òk´Oz¯÷ @û¤Ïz¿´OÚéjÒ£Žÿ€öEçû ¡}Òk¹¿Úï„öIŸå~AhŸt/ã?¡}Ò³Ü_ío hŸôVî„öEçû¡}ÒNã“îïôÌçhŸô,÷ Bû¤kÎNhŸô^ÆkBû¢óxMhŸt/ã7¡}ѽœOMí í“ÞÊxMhŸtMï í“nït¿ í“®9>¡ý­šÇÚ'½•ñšÐ>齌ׄöI·2~Ú']³}Bû[Ÿ5£'´Oz-ã5¡}Ñy¼&´Oú,ã7¡}Ñ£œOMü ío h_t¯ í“Þ^âk‚OhŸ´Cû¤ë €Ð>éšåÚßоèß×—øšêÚ=òùô: ´Oºæû„öIÏ2^Ú]âí“Þëø h_t?í“î/zÔñÐ>éYÇO@û¤·:~Ú'½×ñоè2^Ú'Ýëø hŸô¬ã' }hBû¤·2~ÚÇKBû¤Û‹îeü$´Oz”ñ“ÐþÖ€öEçñ“Ð>齌—„öIŸ/º•ñ“оè^Îg”|—Ð>鵌Ÿ„öIoe¼$´Oú,ã'¡}Ò­ŒŸ„öEç|—ÐþÖ€öI¯eü$´Oz{‰?ÊøIh_t? í“î%ß%´/:Ÿ„öI¯5Ð>齌Ÿ„öIŸeü$´Oº•|—Ð>éYÆOBû[Ú'½Öx@û¢óøIhŸôYÆOBû¤{ 퓞eü$´¿5 }Ò[É í‹Îù.¡}ÒgÉw í‹Î|…Ð>éù?ËøNhŸôVÆkBû¢óxMhŸt+ù.¡}Ñ™§Ú'=ËøNh_t9> }Ò{= }Òg¹_Ú']ókBû¤G´Oz-÷ BûÐé]JÄW^» léݧÜÝ«d­xñ¦‡—½çúÙíÿýW* øiøRz?ØÝ‡îžØ2g\jütåݺSûAËi÷ãÁÂ-,,g ܇íåV´±C]Ãî`‰½%%Ã÷âë„»aÆö²@]îàâÃ~X’B»ötåm9|¡¿±àq–MÓâÊÃMKö¨ë 15wÊ»¹òǤ¶œ×öÐ"Êöóz»ø#·á»h>”>~þ]Ý0¾ëí2µÆøÉ·L8Ý=\¹maú„dð!ìtsÖ eÆJžúC "O¿u.w8¤åöf{+¹5aú¢,RÆìkçƒ>C¼›Ë&›ö5b•«,¾\òàš.+Vƒ²†Ægkù[žÖ<_Q’4ÒÂ(Rë \¿ý`Ù­¿¼é(²» söÙ(¯a)–þ]Ý3=©½[[Æ®V“îÏ:¢|,½b•‘\ÛÝ%ïÞ7zHWgüƒËΛ¼Zip…Wƒ¿»o0\ûÅ»ºR“ëß/®›“¿±É½í­é&eón¯£½,Ì%¬ô›×’ ®Ìi½’Æínçëfoލâ÷½fÔè˜p½Ú²Þt0!ï>?Ñ‚U†,ý?ÆÆ•kvoá¡…Xv_"klð9ú…”}»¹„›t÷ålëN9\ònwo:œóîå7¶U+‘O´¤7ÝáÃM÷p„9T6pxâÐê‡Û¯p›ÿ]÷»qŽƒã,ÅöÒÛ ?ÊáÉÔXeøî6†úßã_(1øá÷=&G?˜{O_åthM¢ÓêXÅ»ýH+¤`2vw—ð©ŸþˆEågF„×{4ÉQ²s¤§Û‹¢môÆž§wQmmžlk13cÁ‹K®/î u._hdøú)ûäÛš/ÑYdh²¹Œ5S6l])¯áE¬‰OߊU¼â@õµ+¦Ü]Ë¡~ ³Ök…½¥k1AÝÒqð-}6ЇVZñÉ–-¸ ÷?+ꃽ‡}°|ë÷ÁþÜ}õKâu_¥Ýè0¤/"Ù;­ÉXCÕ¶Ây½ØàsK™»Ö²Ëí0‰Î€áTÕÃëpû Q]ñ¶ƒÞÙ†/VÜYêltxoé³þ0˾ˆÿd–„t“§Ö'¿:Xo÷²*W¦c«»zðôrÑ®b…é×Hçêe&»K´äô5§û`¥Ëô¥}²ëݰÓø»¥ûßúKÜׇëƒ&þéÏ3:k[ /ûý£L§+„,¶^Çßw[|ˆÈ²q\'Çr[¯4ó-î:·Ñöwš¨?›.á ¼ît袇.¿:v×ú%†Gto#ÄÇŒ.º­+»(_Y½jU_Ððñ~·¤Ië¢\‘Ó`q÷Nx&ƒwçûLNHlmê¢Ø³?˜é‹L¬^ÕÓ»NcZ£õ.kõÂ.ºj%óîÆôͤØÕæ Yd‰. —ææËs›Ü)gkÁ^Wwúå óæµ:Zßäô¶‚uóg uÒ†Ž×™‚½Þ¶kÕˆÝëb7n¼­wߨ7uQX·Ý¡qÃ!²K,°û3¤è{cZ^eñNÈÕUüDz]ÁD>ØE±Åá/Ïè“–ÙÃg&µ¢9Æ#Ö :6ö:Ø“'scMŽ‘‚}õš>Ùs_Ü¥³ÚvƒÝÆîVpšÏ -ð2™¢?Û#Ï5îVF”w—S®pdŒ‹Lßé5ÖNñ—1(Ÿ79÷˜x\ƽ†ãÓYˆÒhCÉóˆ|ØHòyFÒkoæ½[÷n¬1ƒÅ$KïŒ"¯=Rr– Áú©†ÊéÙ5z,êOF¬Rî¡{W§OQÖÛÞ–Hä¼m‰tkÃÃÐ!ö• {bƒzŠî è -£ddùήY²«ÄÕ]†{$Ÿ|IƒåW~-ÀC­ü™¯„€S²yŠØÃî²i™sä„X¾û¦±qíÎâ+`ÑŠ±Ý–ðÃ%ãg-è*ccNÈuV|8M.ü½*Þ’Cnñ¾D†l˦žkLœ|Ñg—MK²t—‡œáû–‚ý‰³š¾p“}#ؼWN[P%3Ý®9dâŸÞÂCk®O/n±”‹Óp½1Ä—*n&Q€0ýÍCëûcùÒ¡×L_q¼Ê¦_×C/Û¸dwÉ5X<9Ô®LNŸ§t9ÇÏŸ5Æl9!×Ûñ…˪´^§Õ?_M/µñuC±Uq›¶ð• F›m‚»ò—ÓCs¿!ÆžW_ rp>_Fô¸ƒW7Œd_Îû+¬ŽõÇ®%^|­µÁ5\})3ïf nœ¶À'‹…Ò4§39|ž‚–\'5C‹³`ÚÂ5ÊuQXÂýéÓ·[A5ñ4‰I üÇÛÁi å©É²$&Ë4‹7Ö>w۔濾öã‘äÔdYr1-5$É2Zcw”¢išèyÏøl•°ÓF®Írp`„Å?80r¥•“#wÕ9m™’}Æ€lë!î÷õkc,ŽÞ–˜ivÆdKÔÀIªÙ¢/Ý… pÙ90®¨·˜ñÆd™.ðF†’“e·k² 980®±"úSxcј,c]¢såÀˆ¾q®!7öº‹œ,¯“²ûœOw%hD"”Ö„w?›FùÈ1Y¦ }çpW“½nÕ¢èÑÛ›Ó2±N§½%­ˆžxŽI ŒèW(D\VvG]i\8Í_U<öÛL¾{ÓÝk¸œ÷…c¯|vÉ•Ù}Úme+5õû²ê+yÊ"úFžƒ«ÛL©ã¾º­ºjÜßì?3À‚É>,ìxÕDü¾ö&Šåîu–£ºD¹AŸì¢\ueqn¨ vøêw‚N;BtQ\B¾”’w3jŒƒ<-9vQØÞm¦ó¾p†¿žï–ÝyNH_äh¬¼/\s: DÏ®_áó‡¯9þFôüÑŠê? =»o'Ð3kˆž2Ð3룈ž¹J&ÑsçÂÜ@Ï ­@ôÌW£=OdFDÏ#ÑóÀ-˜è™¨žèy ý©»’ãFÏ“@èyòžô¬Õ€ž'ó2 çÉŠb çÉzGœÓ¤ûèyrU çÉ7Õ=O®PôÁôÊ7Ù‹LÛçù¶M¿ MÁèõt=œ¶ý1ÞA–}?®òíxÚ¶ï‰OÛù㊠ökAÀzBÇj#'ô¦U½×áúˆÕF.½jµPjÓC\z…FW§^µêAµm_©öxhGÕ~|PagÕëÀÙ‘‚o?A«W­rG\íçí¼ÚŽ·X›Þ…¨­?¯ʼn¬Mß«‘@÷­‹¶þl矰µi@npk]¶G€ë¤\'íèÚ¾ßHìÚŽ‡g€×6qo§×k¼uøÚ¶ãüÀ¯íóC<{b;®7lû<ú3¨“mGÿÃNÚ!¶ÏÛ ›Ãe`l¯Ï|ö´ÅãzÈ^W­z’mšŸw”mz“ž-Ç;È´ý£}A³ïþœmòÀ³mü@ûhû-IºO/¸z‰#m»Ià㎴W½<‡Lnæ`Ú·ôœÅ†ÆÔ^óáÇî{[ű ®‹– ç`¯âÜ0˜ŽD¶‹n~“"Þ1ÖòùÙöÔ‹Á¶õ^&²m½ò„l{L;Ç‹¨¹³í©5FÀ¶•˜mO­è¶­…Úɶuß%ÛžCôÚç “+þ‘mÛ5±mC&ól{rð$ÛööÄæŽÁUK*ƒnûàŸðöª%èÉ·-þL€Ûâ±/·o×’á+nN€q`ÜÞ¹´d¸®jqPn¿ïc޹çLoêqã&æž©‰¹W­OÎmï Ðí=7‘n»S` n¿³$Öíw­sâ#ëຠÝ>’A;îv­õÅ¢xû•–ˆ÷ªwÊyû8yAçU?ô^cý{Po¿ÓôG`o¿ótûlÎtÇþ|Ûù`]o©°î ÐwŒÁ¿]'î•f‰€{îæí(Ógbà6’e‚[.ˆï nšë®œYùøÜrC®{â ÜsEè‘Uo€ÂíxxˆnÛA)ÃíüviKRW¾W8ÜöÏuMùv߀¸%§à© ⦇ù„Žõǯnoé)ž Š›žÒÖŸ·EŸw,nšë˜8·6Ö'Ðè ã–Ðâиi2j5-ÏÇMÂáÖŸ7­<î 0¸ß’V½O†€ÜàSÚú³UÞaGä¦ñK0r&Àɽ2OXÜúó¦ë ˜Ü÷¯uTމýc]¿eßû)·ý‘~{Jj vO¬Ü¶sÿËm;δÜò„Ë-_/÷ÏcÝæ^yx>‚˜Ûv\Ϙüz<´3sÛßóé m =OPsKà36_á” nÞÞç¬T r¾î‡ÖKwt¾ê}dç¶:Às×XçÄé¹}~Óúå–©îǽ~ùÍõÇ Ûç¹NŠô8_"ôu_ïõÍ-SÝ1* ºõ§.=ОԎÑí÷Ç{óÀÑ-žïù\ÑŸõMtÓ8 t;ÞZ·þ L0=úiº/Ú8ÝÚoM<Ýô©õÎÚDÝ'\ äŽÔ­t—ho¬‹¨îñZ‡e…ÆúÌÀêÏýèÏ¡} -ÚÆÛ½s  uÓÜîlÝ4h+àzÒN׋¶þ|РH¾îdÛûŠ¥‚°Û„’ëŸïèÏSë®8c÷ æñÈnF®Ãâ”Ý&Œ`¶ÀìëÑ´çì6A§h·í»ô¤ˆ÷4Ã&ŒX÷¬Ý¶]_ ÑŸ@Û-žïuÜnšpÞy»MX±N €»}>ÖC·þ|tþ¾@î®ï7¦õ¤Q”ÐÝ& X·ÔÝ5X¹cwÓ`«àîë) ðnûÃõ ¶ëª8z÷ ïþöžâ¾{¥n}7=~· /߃êüÝ4¾¼WöbÿNàmÿGBðvüКë™;4öx@y§ð¿c¼‚ç>ð™^eŸÐ\×¥¡?Ÿzà(Þúó”žè¯$÷ýYï%7=Ž÷Jk¬³â<Þ‡¼÷g®ËE"ïÇ“eÜÇÛ¡x?1û<ÉÊ[ÿÇ:T òÖ¿ð}å hàѸ¼i®·>ÕŸñ( dÞ¯åhÞ èûŠþÌ÷ÉΛæ{Fý‡^¾ï‡xÞ/\? YÊm¼½ºmÓ:0KÑŽè“êÏ|’àÞ´çûFÕ¯s~Øk‚›±õT=,ÛWg n¾àžnÞ¹‚?¸¹¬àæ}à×7ï¤&àæ#¸¹Ü'àæ¶7~;7— ܼó7ï|Ÿ¸yïÈÎÁÍ{§õÚ¹yçŠ¼àæœÜ<"›ËqnŽECÄÍõÞppóη ƒ›wŽõàær5››Š&íÍ]K~—7€›wÎÁÍ;¯póN¬ nŽÖ7ï\éܼóÝ©´ló-;´ló"Ë6ŸpʲÍ÷Èʲ­äѲ½ìÒ²­e«iÙ Ë6ý ²l“å˲­WéѲM¬ Ëv’xÄ¢D˶^ãGË6g]²lÓ) Ëö®wZ²­ÓË6œ˜aÙÞåàFñ)xgõ@—G~Øx L}[¶A©aÙÖ«oË67,Û²ÆÓ²½V˶|Ç´lˤL˶Ι–íM†nX¶õ“Ѳ-×9-Û|á—,Û»Ö±†e›ËPʲ½DѲ½ëå–°ló]o²l êѲds‰‡´ló}ñ²ló5²lŸD\´ls±VY¶e&¥e›C‚,Û]+kò­…ÇiÙtÓ²Í׽ȲÝÅÁaÙÖËiÙîZS–m™XiÙæ¸!Ëv'b¦e›ï‹—e›®FY¶ŸµÐ²–í¦µ±aÙÖ+FiÙn|ÖBËvãS!Z¶û-ÛMà–m= e›/{#s²ô‚=ÙZ |´bÙ¶Ó@w>¸z$ù÷Ðã àœA¿‡–6üCîoG7ƒ·¢oûù¾¥ƒï[zš1´h;°÷àNê=†¶:ôŽŽæ È{ ^¡ ÞcpdðC|Üy÷à편û–N»ÇÔ+)vO½á¬{.,OÁ`;õ¾NîÉôŽ {.âØþëèå.ÄÜcÊ î”{èU£€Ücè͛θÇH r»<äÑ^çýõ¸gäÛcð’ßdjÄÛñƒoßÒñöèòhûµ9ô>XÀí¸BÁ¶1/Ѷ.X’í!¶°¥Ù¾ý°àÚC 2kkT!Ö¶ß{öùçèrk7Z µu kÛ+²Ú#°öà³AbíqÐy ¬=¸î=±öØé—Ö¢³ÀÚCdX{h b`ípbk½ù X[w Rí[Ú¾€qÀ´UQD¤-'‰¶Þ±D =‚G;Ïs%p¶ «ÇýjL˜gÑciÙ>nözF&#˶^“H˶(-ÛZœŸ–m™”iÙ–ý™–m9©iÙ–…™–í†e›¸@–mY˜Ã²Eßò}&6=ÖÛ£½Ý¦ÎÛ²½'0—FX¶ùÙ¶/ô+Z¶u9Ó²M7ÆmÙÆ# Z¶uý†e› …™âÒËö.<=îúHY¶uYѲ­wв¯Ê”e»eËöv/ðÝo{,ÛjIZ¶é'‘e[ K˶N2,Û\z[–íMmX¶ÏlÙV;Ó²­×¿Ò²­“ Ë6¾-Û|—,Ûzu.-Ûçý&MX¶»@óêét×zÞ7`_X¶ÛÁ=ýžÂŽaÙæCØÛ² ¿sX¶A hÙæSWY¶ÅkhÙ÷7,Û|}[¶iÙæ5x[¶²nË6È.,ÛzQ#-Û\œ–m-_N˶($-Û¬û‘e{hínX¶"-Û¬ “e»X0O:GdÙfª&Ëö$ý£e[/\ž Y,-Û“¨‘–í'sÂf2[È!Á•íÉx¶lóµk²l‡„e;Éé)"Hy.zæÊŒ‘@ׇñëz{.1ùxE–í)÷·Óä¹ÐL˶ž%€%+5%JžZa$y²†ž y.÷zàÍ›îL–í©·"‚"OVÕ"ÏX»Ûo2L!OØAç®…½ Ë®@~<õ:Hàãy²ƒOÁpÀã©wq‚O¾š˜èxò $Ƕ„äF#Ä”GV.îØØ’ ÀN¯Ï⥰€ÆñNS0c[|(!ã)G3ˆñ¤›€ÀxìÀàÅ“¯—'.ž|šIZlŸÕ"à«ËÌŠ'eÏ]¤ÙIñÔ‚÷Åv luN|ouL<Ãîí”x2… $–Ñ“Œxjép â!žÕ²=ù^òaùOˆ‡'ß’D:<9äÛªM`ÇΆ§Ð,а™F°çIWΚÀðÔ{ŒÁ…§S ËŽB*|Ö¡ðä+&É„'±‘°ü7$ÂS/¾¥c·ô¡iÒí@<'ÒŸeÙþõW:²ÿðÓ‹üô‡¿|ùÕ?lõñõOu‹]ù+wçÄ`õ‰ä×ß}ý¯?þñûÇ×?ùû¯¾w}<}懴«O¼àõ²Ïÿo}ø:û¿m¡ÚRàEÿñÁaòùxüî5€Ò^Äûrý LeŸqˆð7·o÷îqøDÕšçšõ¶–õúßGüñ>VÜ”_wÒ¿i'–Þ,/gÒë™Àû¹ß¬0že÷-kŒ—í+“VKyî;ìt‹í”9`u9‹#€OSÎ)Ò)þŒ{¸™ªûÌÝÚá:Ãoèïø´uµëžëß~ÁÇŸìûÇ¥6?ûâ—¦/vwIˆ>¯SKPþ—ë»ÿÓ—ÿ Ë™Ke endstream endobj 312 0 obj << /Alternate /DeviceRGB /N 3 /Length 2596 /Filter /FlateDecode >> stream xœ–wTSهϽ7½P’Š”ÐkhRH ½H‘.*1 JÀ"6DTpDQ‘¦2(à€£C‘±"Š…Q±ëDÔqp–Id­ß¼yïÍ›ß÷~kŸ½ÏÝgï}ÖºüƒÂLX € ¡Xáçň‹g` ðlàp³³BøF™|ØŒl™ø½º ùû*Ó?ŒÁÿŸ”¹Y"1P˜ŒçòøÙ\É8=Wœ%·Oɘ¶4MÎ0JÎ"Y‚2V“sò,[|ö™e9ó2„<ËsÎâeðäÜ'ã9¾Œ‘`çø¹2¾&cƒtI†@Æoä±|N6(’Ü.æsSdl-c’(2‚-ãyàHÉ_ðÒ/XÌÏËÅÎÌZ.$§ˆ&\S†“‹áÏÏMç‹ÅÌ07#â1Ø™YárfÏüYym²";Ø8980m-m¾(Ô]ü›’÷v–^„îDøÃöW~™ °¦eµÙú‡mi]ëP»ý‡Í`/в¾u}qº|^RÄâ,g+«ÜÜ\KŸk)/èïúŸC_|ÏR¾Ýïåaxó“8’t1C^7nfz¦DÄÈÎâpù 柇øþuü$¾ˆ/”ED˦L L–µ[Ȉ™B†@øŸšøÃþ¤Ù¹–‰ÚøЖX¥!@~(* {d+Ðï} ÆGù͋љ˜ûÏ‚þ}W¸LþÈ$ŽcGD2¸QÎìšüZ4 E@ê@èÀ¶À¸àA(ˆq`1à‚D €µ ”‚­`'¨u 4ƒ6ptcà48.Ë`ÜR0ž€)ð Ì@„…ÈR‡t CȲ…XäCP”%CBH@ë R¨ª†ê¡fè[è(tº C· Qhúz#0 ¦ÁZ°l³`O8Ž„ÁÉð28.‚·À•p|î„O×àX ?§€:¢‹0ÂFB‘x$ !«¤i@Ú¤¹ŠH‘§È[EE1PL” Ê…⢖¡V¡6£ªQP¨>ÔUÔ(j õMFk¢ÍÑÎèt,:‹.FW ›Ðè³èô8úƒ¡cŒ1ŽL&³³³ÓŽ9…ÆŒa¦±X¬:ÖëŠ År°bl1¶ {{{;Ž}ƒ#âtp¶8_\¡8áú"ãEy‹.,ÖXœ¾øøÅ%œ%Gщ1‰-‰ï9¡œÎôÒ€¥µK§¸lî.îžoo’ïÊ/çO$¹&•'=JvMÞž<™âžR‘òTÀT ž§ú§Ö¥¾N MÛŸö)=&½=—‘˜qTH¦ û2µ3ó2‡³Ì³Š³¤Ëœ—í\6% 5eCÙ‹²»Å4ÙÏÔ€ÄD²^2šã–S“ó&7:÷Hžrž0o`¹ÙòMË'ò}ó¿^ZÁ]Ñ[ [°¶`t¥çÊúUЪ¥«zWë¯.Z=¾Æo͵„µik(´.,/|¹.f]O‘VÑš¢±õ~ë[‹ŠEÅ76¸l¨ÛˆÚ(Ø8¸iMKx%K­K+Jßoæn¾ø•ÍW•_}Ú’´e°Ì¡lÏVÌVáÖëÛÜ·(W.Ï/Û²½scGÉŽ—;—ì¼PaWQ·‹°K²KZ\Ù]ePµµê}uJõHWM{­fí¦Ú×»y»¯ìñØÓV§UWZ÷n¯`ïÍz¿úΣ†Š}˜}9û6F7öÍúº¹I£©´éÃ~á~éˆ}ÍŽÍÍ-š-e­p«¤uò`ÂÁËßxÓÝÆl«o§·—‡$‡›øíõÃA‡{°Ž´}gø]mµ£¤ê\Þ9Õ•Ò%íŽë>x´·Ç¥§ã{Ëï÷Ó=Vs\åx٠‰¢ŸN柜>•uêééäÓc½Kz=s­/¼oðlÐÙóç|Ïé÷ì?yÞõü± ÎŽ^d]ìºäp©sÀ~ ãû:;‡‡º/;]îž7|âŠû•ÓW½¯ž»píÒÈü‘áëQ×oÞH¸!½É»ùèVú­ç·snÏÜYs}·äžÒ½Šûš÷~4ý±]ê =>ê=:ð`Áƒ;cܱ'?eÿô~¼è!ùaÅ„ÎDó#ÛGÇ&}'/?^øxüIÖ“™§Å?+ÿ\ûÌäÙw¿xü20;5þ\ôüÓ¯›_¨¿ØÿÒîeïtØôýW¯f^—¼Qsà-ëmÿ»˜w3¹ï±ï+?˜~èùôñî§ŒOŸ~÷„óû endstream endobj 316 0 obj << /Length 903 /Filter /FlateDecode >> stream xÚuUKoã6¾ûW{’€ˆ%R[ E›¢ l±¾u{`d*VC‘^‰Šã¿CåX»*|àð›‡f†3Ÿ³è9Ê¢ßwY8ÙïîyQJÎóhßE4g¤Îò¨ÊJBó&Ú¢âœIJiÎã¿feû“’IZT<þ¤Œ’÷Þ?UÔ¦ÌK$‹Ò¢&-Ðý݉Aˆ“wò¢ÔfÌ<© Þ§~8©¾ 7{ ‡~:)‘äu4¦[_²¢£ÔNilçñÕ]eøH¯ñ’¼Š—L g—%ŽYž„–j"IZÒ2ÞC½ñQN®Ð&‹ÅèŠ99¡?H¡P rèù© Þ¢²5ƒ JkºÆ°bzY«ÚQ ´¯ ç¾uÉ¿¬Ò.1mãjÅ‚Ç×^žÁ‹ñŠÈ$ÃÕ§ €í¹¨Æ^ºÊYUß¾0ÃDz½~v7÷®+Êò­UóÔcÐÑà,ðâfò†–CÖ>[÷ÑÏþsc“ÛÕ³˜§$åeüŸlít·¿(I @ˆÿ%ãÙFdNr˜å`óf'0£sQÄB¶‚6¤¤W‡Orì`P…nåƒêbûv+}šU¤É®©œMüŒ6x®†Ð#7†›h¨Q$dÅoûCkÈ+cQÊ(©YØ®Ÿ¡?n­Ä芺ќ?¶N¤wNÃbêªõoLýâ Y8û¹^!ör’? ï+sü¶ßQ²ˆ^#gŒ”eµÃîëŽT´DƒwÉ+¯Üÿ1ðèW³û~‹*]"¦7!=U­Û½|7ÏIÅ)–óØ?Ï~W¡‰ü'˜h–ÇÞ•ñ]Ix¤‰ÏËðÂáê vr z{1†GØpgЬFÁEuoüšpÔÑãJØbw«oo<ɳt‰°ÆÇc0 Zâ½5j4‚ Â8™ÙºÝúxÐÚXa{£'´špbDè|tTTÇÇ-*B–¬áÄ‹¯NãFrpÒBŸµ£E…m &Z M‚#wd± MéÚt‡<ŸüƒÃ(\}g á5“˜J*ŒÜR^7/‡°¥Â?iÁàÛVùtšŸÂ’^AæùÃo¡Y‹B¤J½/ö1ì{7ëÖ½ÐU²†Ô }çJ‹ÿT?Ðd°[%OVûùgDKàËŠGiÍkrô¥Å÷Ëý çÇ8 endstream endobj 313 0 obj << /Type /XObject /Subtype /Form /FormType 1 /PTEX.FileName (/tmp/RtmpBlhU1W/Rbuild4444671013800/timeSeries/vignettes/timeSeriesPlot-oneColMultiplePlot.pdf) /PTEX.PageNumber 1 /PTEX.InfoDict 320 0 R /BBox [0 0 432 432] /Resources << /ProcSet [ /PDF /Text ] /Font << /F2 321 0 R/F3 322 0 R>> /ExtGState << >>/ColorSpace << /sRGB 323 0 R >>>> /Length 6917 /Filter /FlateDecode >> stream xœ­œMÏ.7R†÷ï¯è%,òLÛýa{› Œr$ˆÕ0¢œ‘†,àçSw]Un¿''™œ!‹œ¤N»Ýîrùv¹®§S¶ßmeûnûÓÛ?mÚö×¾oµ]¯ãØÎ£Ê*ûx•kûï?lÿ²ýÑýu¶luÜÇëjŸ´úÍÿüw_o¿ýöMýíÛúç·¿ýûÛvmÿóö¯ÿ¶íÛ¿¿•íwöÏwoÅþ÷owys;ÎK­>¾Ýõuõí8Æk?·ïßìqµšy¿j˜ýÞŽjC*2í&5.ºÇÌûuªq}õ!³½v5¾_íÀl»™6ò[föÜcÝ—™mÕÛï=̱»i÷˜iO8}T£É¬¯Ò}T}ÇìÕ‡qºy¾šŸ¯¢aÈqj|¼nïù~íjl[0o{F{znu7¯ß_¥`êõÏòÚÕsß_—5>+¯ßË«œnÚÜ”s¬çææá¯oc~ï鯞¯óÂÝïmzÁ~½n{îé4óvçØ]~µûëÛÕóÀÔë3ƒfŽ×)O¶×ÐsÇþ*»ù¼1ÏuoŒÊë_¯]/8ýÓœ20=6lªüÞÓcãxc\îœËfλjþú‹©×¿*ó;ºÇƽ˃f >33åœËæW÷–½øû?vu\]­ð0ûäúáña¶û«ì§{è²!qýv'<ýÝî…çþæ1bí{s»{(ú{Ør“þP„[<ò„ ßßµ”â®°öe[r{·²«GеoÃíÃ}õôw¹;l<õ Ûýqçý·GËe¯9ÂV¸˜¯}=”Òˆ—‘ý ü±Ëéf×ì¸[v¯O•êÑ1ç½ÔJÐX(ùóëéþ¸ «D¶üaö(n_7«=ïNÿ·û뺈öb"\'«½HN·ûÛƒç&,Ëá/¢ëÅÇoKÝýu³ Šé„ûchZeÄOc‘ÓŸïgöXŸojàþ*¬Õb‹ÇýÑX¬E°»ÿî=l_Eé/¦ƒµîÚVì?ä/û×åýŸ!²{øçœ*K<œSf‰ïµ×Þ¹~º¿Ì­.xåDi;驵öÄë‰Ö>vˆíñm¶üenÞßr{(,d‡Þ†¢–+÷Œù¸P\›ÆæýÛDŸì Ä×…æ>×] 3æ÷BuKÿ\È®…-ïk¶üe¶‹G¹^-#÷ïòÚ2â}n¤WË®†­ø±eêÛG±]Mþ²û]"ËújÖ°å[FýµeæJWnØd™-=6üØh°ÙÅŸ×áýÒkc+~L¦|k,¶¿É_&c;íÑa³YŸÍ…¸šˆ6îw%®ãÎþ]Š«éuia›¿týðùh.Æ‹íj\MÁYŸÍå¸Úæ0Ò6ÇÉÆß¶Óz¾-#ŸŸîz,ûlaµ/áÏîz¼Üïz\mK$^Í6U{ÌÁý®ÇÕºA_º gÕFçãé®ÇÕºç¹Wk†?º _Õnæý‚?,lzØîi£ÇÓß=^íÆ| Ú£Ç6_¾yØ6Dü ôxÎ×@w[¿\ïÄOÌÝÑãÕöø‰ç×=6ÛÓ®º£Ç¶míi{ü 2º£ÇÿuGmñxíë©‘·Õ=¶õpp¿þ\uG3že7Æëû[-èq¾o-…ø ÿÊöø9ÈAjq=Öütî?‰Ÿõ(;âçn_ÄOÅÿU ±ûõÛõ¸2­ØŠ³úÄO ÿÔø±ñ•°?³¿ZYOÓ>ˆŸˆoÙ?ëµV×ã¹~ª qa}ŸŠÛ4z *ÛýÑs<è±Mû}„íñ3Ûâ§Æxô8õ¢õ}ü5ãÇã¿Ç;ý©Ê…w—Ãû?Ðã?züءDžø•=ˆ‡FÿøÙc¾ÏÐc?ÂÈ=®º Ûõø`?®gèññmûüe6ñ†‡~×3ôx¶Gmk [zlÓxrsÕ‹|°ž‘Ça£^è±½ÆÝÂnËþX/ôØ^Ûó‰zU q¨‰±ÙÄÛEfœ’z‘ç)Jv|?®J\ö'û¯Ùq¦ðõ"=V–~†-ýɳD½wNÿgØ~|¸"~nòã-òã8ÈnK¾]mÿó|0òåÚ"?îäk²Ç’ÿ׆ë¬ëïÛÐc³¯Ùò—¹‘ùíèqæçÚ¸ä›F棣Çó}:zlal£òóD!¬=òã|Oväǃë‘ÇaIÂ¥øQšVÃöø9ô8„ÒˆÖÛ(?õÛãg¶¯ø+Ç7"?¾bÿäÇ6?ÄÛ ?Ö4ûø‡ë±ì£†íþjÙ?ù±*îÏA~li‰¿¯oŒÖ¾E¾âBxûuÏ%„ò—M‹ßïaõ랟ɖ?lZwÚ“?ý¹Ë%ìÖ×ûÉ-ÿ¸v·ÑãÕVüXìŒ=VØøõBµÂ®Ÿ§Ûè±Â,mùcöWÐãûƒ„Lñ3û/”,¬½ûW‰çý®=zÜ"“­ø™ïSÐãV‰g%Î~íñüŠÛ|ú|ËöõT5­²Ñã<¿=6YðýMvÛŸõêú¬Õx<~NöÙq>÷óÌQ[žÏOú£‚aëË÷{ sÙßÛí\môØîw=R]hçùß²ïû9ÿë rõÕŽRÅb÷û9K¸¯}ñÏÑÞG]È—ó®Üv_Ÿ¾?xùè|Ö—ôú£§j?Ÿ‡^»ŸOF Õõ8ô^ÿÑ—õ¯Ž]m>Ý¿gÔ3â|­ î8/˾—óq‡Þçªa»þTò ÓÕŸó­.øþÔÐW=ÈK[‹Ýx?âëB³2¤‰qÕxß+ô8ê~¯Þ¸¯ñs¡ÇY/ÐÁþB/ý<êÂ}?õŸ¸å|îµè›Ûã'ÎûØuñßzõã¾ßûãFŸç¡Ç©ç±Ð¦~-ê7z|4ô8«_²ÛRÒÂUüذ®´G_Ö[ =>C?[èqxlèñ\o =¶ç£W­¥¿ð_¹žX=Îú‰ìû^ô¡£ÇS:õŒ¹^:z¬c¼?¿£ÇYo9:z<ç¿_Yß!>ûõFÿèñj¾¬÷>²¾ãùÚ1ÐãσzFÖsŽ+óñ ô8÷Ócœ?ž_Ènç²ÞõŒÇFW{PŠñ Çgc>¾U!ÿÿW¦G>¾JµÏ´¿O[Ž÷ŠͧùŸÛ·Ùƒ†vn+(âÛgr^˜Km¦ŠÙ ̵21„j, xèl°Œáëá¦ßÿð7ýðû?¾ýæoëV¶ÿ<éúã+ÿÓmSÔ½ùÁÍ&úÃÇí¯FÿëíÃwoóÁ»þ÷j/µMÝï_z¯ÎAÍï-ûþ¥7kuqsùÉ›9c}Å¿ø›gñàÕíß~ýMÞþ$Õ¬|Ïý @š,*ýõs]-AÞ-žm_¬ú󬿜E©:fj›,J&<É“@Õ¾<äaQ2½& ‹òÊ—_uE‘é;(,ÊëdmK%Ó÷CX”ª^#WÍz˜C;‡OXT1‰­‹ÒÕ#LSšb @r!Q}Ì7X”Êc®Ë°(UÃÒ4ÕÕU_´°(]u‘†E©g¯ À¢¼§{aQºº?,ªô$Fžï©¬†ééœÌóaQ*ªy¶‹z{®W8¢$‹òŠß뙜›eK¥{}£€Ei¾Ï¢¼Zø°(/êA°(³‹’YM)6z¤À°(]=¥®Ü9°¨Õl Ò·3X”é ¥b ÓÓñd°¨Å®8 Xd°(• Ý™Á¢Tbì°%Ïíü:í=ws»n“E©Déƒ UØ‚&‹R‰ñL6%7™Mm¥’#µMX”®SK‡E©?j÷°(•4a1°(w,,Êíc›,Jv<Ïg@vM65b¼þ>°(=V‹’M-¥ñi+dlR‚-yn§ö°X”®ß°«ô,ªÄ>•,Êû[XTQm¶t㯨u‹R öLö¤ZÞ¾G­åã…õxn§-ì%›Ú3,J%Zž‹R‰¶/,J%Xj÷°(]VEís¥.,¥ç]i«ö©0,ŒÚ§î‡ QûÜrÑ`QjO<âÔ߀]QûÜ“À¢tv‹ÒxnØÖ?z¼,J%eØ,J6ñ‹’}§íþ걞`Q*9Ó?,Jö rÁ•ÍxaQº?Úø+Ù%,JÏ#¾`Q²a °(lËeW÷iž+E©Ä kE©$N<âtõ‹ªªµÃ’ü¬-û¤½«¯Ûm›,Êß—öñÓƒ=¢t½'› …ÝñÇ´þèäÎÁ¢4>â¥çûN,J%yô ¥>ñ,ªÄ––,Jµ[ú‡E•d«Á¢Jê]°¨Ç†EéyŒ5Ù]°(‹ÛÀ†E•¬%XTiœµ’E‰}À¦`Q%jGÉ¢¬ÿseQ3^‚Eí©O=ôxÛìSa»°(éñ‹Ò_¯,Jú‹žŒÐãëgL=žvC¯`…°(­Ïº°(é/þ¡Ç-ôw„·˜/X”ú£ö½‡/¶ëqœõ‚E¹¾Ã¦ÐcsSOÛ÷¯=Ùz¬#×^aQ1“Eù4r=ÖÆîµEX”§°±Á^ÙX°(¹Z/,jîÇÁ¢Üû6Y”ïW°¢Ä~,J÷ó’-…ÇÙ=X”¦Ö‹r}ƒe…dW¡ÇÓ=îQ«?B“íÀ¢|¿õ Ç{°ê`Q²Ï{›,Jíai°(ßýý`Q²ñÇ9õ–‹RÅýè±ô¨n“E)L`ƒ°(]§‹r½Löäz|…¿¯Ðã3æÿ =Žßz‹’M­þ =Nÿ^¡Ç“M…x¿+ô¸˃Eé:ó‹ª*/,J6ã¿Bú,JzJ<À¢Ô?,ì®Ó°¦Ðã‘, =Ö2ÛdQ5û,JÏ?`Oèq_Ý‹ÒuX ,JË€÷EÕz‡?aQZ6¼,JËŠ÷Ei™Àê`QZ°:X”lâUkúƒ"½î¿Ê6Y”/;l×cµG`QZ6°MX”lÞ¥eÄz†Ei™Äu:Ùž¯‹Ò²€­Á¢´lî…EÉ&Þ`QÞ?¶wìËÖäzìýŸÛdQº6‹ª–vÒ?,JÏ÷ü+X”®ŸÉ¦?fÃò`QZ¶¬/X”/ë²MUd£°(—úç·Gü–(X”ËãAÅÂ`UäǪÕ{íU“‹’M-å2±°(Ùîï`Q~þÈÏØ/ƒE¹Ý·É¢Ô?µYX”ljÿ°¨ ˜,Jö‰ízìãõñÀ¢Ü¿°ôøˆó@°('®?°X”æŸ÷EIæã:z¬ëô‡›½/,Jós.,ÊešûÇŒØz,¤R·É¢$ËÔÎaQòg]X”Ëî¹M%™¥– ‹’ÌÞ°-ôXó{l“Ey{®£ÇgäÁ¢ä_ü‹zgGü„Û±ö‹r™OÛã'ò¯`Q‹¯¶âÇd½.,J6¬¥m€Z=,JÛˆç_Á¢´mx>,J6ñ‹òm`aQ5Ùx°(Év°$ôøŠbX°(]þÐcÙ°%¯gHæë¢t‹òö°-ôøŽßj‹ªÉæƒEib}¢tx€EÕdÓÁ¢¼?/,JÛì¥mã¢ôX6íÑc³‰OX”¶‰}ß&‹ªvÌbý¢tV‹ÒýÄ/,êi‹RûŠß‘Ï‹ªª2ªÐcÛ–ˆX”®ÃºaQê½EéùëÔ3î–,=¶öÁ’Ðc±¬…E)žŽ…E)¾[Ù&‹ÒuÖ,JëùEÕÉB`QŠ_Ö#,JiF°/êWìÇÁ¢<-ñûaQ,±?z û =žìŠzÆÙƒ%¢t?þ€E©Ö,JñžÁ¢ÈWƒE)ž+l‰zÆc£Ç«íë)Ê·Á¢dûzþÕXTP€É¢ÂþþÇljEW+‹*·ÏÁÇù‰ ?ïÚ>ÓÀAQ˜k•XæJ³´Û”‡fa® ´o<smÀ¨fƒe¿¬RÈ&ð/ƒF~,âT¾ôf¯¹qª_|³¾‚‰a_|3Ñé7Ÿ?}óO±.?ãwX×?~†u]-?ßøiÔE›?Cº~¦£e™DYŸÝäÀþ»ürÐ¥Ÿûœ“sµç+ÓwSe—'(W‹Â¹î±~o•ˆë„áj{´t Ë-¾¥?ãË+Ó§ûâ¸ÝÒ™ë[wH=lËFëWÐÖÇÈÖ‡FÀÖlé¤ÞÙqšëÕåi¨ÖÉP뎟´îH¬AZ)‹-MŒC)Ï õ„ø˜ÊÞVûê¾%Î’¼ú5ÏUS}`–N„¯ìm[üH”Õâ'}¬I8 ËÞÙÇ Çb_KŒÕ#ý‡bõ(¦±´C< «ÇA „¥Ó–÷éYßbÙÛªD喟ЭO ÀWÚUú–ôjä‡FÀ«Çô"؃šüh>Žw_Q‚ß\éu–o¨z–=ÁV9ˆ Vòîò•¸ß¾MfÕ²D²_]¾žZM ›üàGžÿ¥'WÍ/  U=~ˆ°ª¿gU-Q¨J =E²AöTiÌùTñÙ‰O¤üž“”ªÇz©„FE¦0•ŠÆ¾ÖTîÛT#ù |JŽõ1ƒ§&£Ì°š6H÷¦ÆDMžêͯD S#Á `J!²‚)M;OLi²Ê¦4—Ì`JÓÅG-€)MОM©0\â``ªda/À”Ç ú¿g¡0¥@c~SZ ªTHßGÚBUO¦~°'¸L;_û6ÁÔbSÝø¨ 0¥ëeSò«0Uò#SZTÇò‘”Úó‘ `Jí€)Ùø0õŒ0¥ëñQ¿Øï(ü¦tð˜Ò|â?À”ÆÃô¦dãoÀ”î4¦ü}<.Sz‹0¥ù.ËGRò? 0%¯`j±ùmÀcóã€=?ºLi~ùˆ0U¨ßO0õÄ`ê‰7À”ô‚øL©}€%~#Pâ``J’X¦›Ÿ ¨ÐÉøø@Í6!Râ 0%˜’]Óöõ4Œ¦Š¥4#?ŠRü”ü¨0UJ‚LÉŽþø¡@9Œ¦ÐËLM˜Ê,L飸ühÊýqÄøLÍù0µÚ?fLI/X¦æú0Uj‚°–ñ` 0µgá7ÀÔj»?vv S{L¥>%˜²ñ^€'ÀT‚¼SZï ªÆù¬ßSó›âtÄOKÙ;S%‡J 2ñABœ¿¯‡IõÈ3IH΂H)+X>Žê-y’ËpÏOKÀQ¹zL_<2¯m²(m¢¨‚$ªçG€(}ƒçc¦ÀÞk~ÒäêÛãWA¡zy÷AT‹3n0(=hAPJÄê6 ” ç lõsÙU•›ù9zŽ ú¤M:a“^?Y#ì©Gé*ÐÓ$}'–ÉO¹ïwzLWÛ¿2 êô<×£B+jaN™£rùµÄiÄO³8ù­”×7G~ÊnR‚ Mº"»Ø4¿›ƒ5Ù0@I ¦u¦ž  Ð49*œiÄ'Ë™ì}©ÊC™zü€7 S&®Á˜zü$"ÓØÓbCr±|ì4ù)|i®ðRþ:*èRæF—ÄbÛ6ÙÒüŒ´¤Y€$yœ÷w€¥¿‰®”ù\`%i*÷’¿ÎÆ™¿ÂÐ`JT’&Rêñ ‡ Jº@Dþz$/"½?€“z~™ MšK˜Ô[°X’ˆNß&JšË’¤UXrýÔÁ#¹ÒðTLFâ 4)R"l@¤Ì׃!õD¤ ¤¹¬pK<%Rßÿ?š8|4-=êWÀŽÓ“°£ÿsŠ@GÓ±£LЙI Á´i&FB6 B¨Ñô3Ш'ã‚ÍA‚ŒòlĈºÜF: å·L¾­ìù­’—sF|ЏÈLªÓТÑòC(©üíH°¢qG)T4⫾ E#~ò hÄvœH›6WG‚¥Ê$5Žë¦îås£ÔM*¨"Û6‘=—ßâ—™¦òmAi¹­Îo—b[¥¾ ’M=>¤m¹-|hnóÁ‡¤4‹ú(|¨äﱂéD·ò!µ§Þzg{š1mÒT6¼ >¤´±-ß*i|ð…#ÒÔiGšºØ´s§—SO+—o•–þISKó‚ùq6¿M:H;ùv>¤öð;ø®Ã;áC3­ >¤Sëµ|«äþ¢½ «žwËø½kMžòö×6ùÎÈ%yâ§¶ü–ÉÕUvð~ïšßòÒiv$’?Îø}Lð!Ùð:ølÆÒi˜Ðé±.|H§Kø|¨$ï>TòÛØàCÞ?|‡4U¼{áC…”aò!Ý_>¤ëÁø½«NÓ´'M=r}À‡t:Ç_ð!ÎLJd3~øl >¤ûkò ùã8Ãßð!Ùø >$~’ÿ‰øú‡‡–ëü~êˆÿwBð!õ‡4QGÓý¨|¨w~{DÙ`¶‡©¿²ð!ù{Úî¯σyµ›²ø.ü‰ßO)[Êo—?וß.QV¸¢„|H6þ¢êS,Ì¢~?u%/ƒéô~%/’¿æýð¡2¿ƒét^òÛ$Å¥Bð9øŸæ±)+<6z¼ÚƒöÄ'õçëýWãCÔÎ'ºâ×ö?¢E+LZéÐíÇÉó›2¦íÇ×}ÄZ.sRÉËqny./Æåe¬,Ùvq.` sm YÀR˜kÑç3©0ßÁ-£n=/øë€%óë/ûj¸¤ê—"©ê¤•›ï/½ùruú„g~ µÅ–%‡}Ò Íêç15Ð9ÙVU6Hóið®ÏçOƒõžQ?¯Òü÷Jú?ÔU~Ý÷û«½|õùO¹>׃è¥m‰ŸvѾ  Ïë>EûéQü,dSáÒ„Ê!Û7ß|îöå–¥'ùvè•Êiu÷‡ÿúø‡w·qûùÉínë‡z¶Qûˆï'à{û?ªsÓ½ endstream endobj 220 0 obj << /Type /ObjStm /N 100 /First 884 /Length 2063 /Filter /FlateDecode >> stream xÚåZÛ’·}Ÿ¯À£õ ÐÆ%¥r•´ÙªJb•¤*ÛÙÚ‡9»fÌ%^\Êßçô–IqµÃ59/Ù’¸g˜Ó7ô8>ãŒ/øm½¡”Ñ’ žÐ²ñhƒ‰*SÄdÖ6š’´MÆ{'è`©|)ÆsC·…´ƒñ1jfÐaQ;úÉú·‰ÐñÀQ;d(¸‚n é—€é‰ÙPt£ðÉ A— •È:™a鉼a Ú!Ü!ClX„@H©"X.qÐ+ɤÐÉdtD;@ÕjðRꔂÎxRéB1e+ˆÇTàAxÙd§QÛeر"D^V_B#«ö„`ÍpÂÏdQãkÔ àÂ;G\f„HNp#`r†»Ñ• 4Aø˜âB©õ–˜¢s0¢0ƈ·¢AÎé#ŒI‹Ài°¢)Qe`¢½ÐŒz†¿`XõüyU¿ÿïÇÖÔ/f³ùªªß­?¬ºïŸÌ~­ê—óŸ]\9$¦»®¿¯_×W¾ûRÕoÛÑÊ\!ÃlBxpŒ¶ cg‚M,{až?7õ;S7?7õ¥ùf¹þ°ÄàÉ|fÙ†gæÛo+ü?# .àâlAèq›r?9#b›á#f±NãÑ‘Å2—S¹ÊÇö o½¦_vVÛÙÛâû ¬Š+Ö뢪a¢!^lé‡B燂¼µÝJž¼ šn.!jJ?KKÒ­¯Öia)Ѻú±„óÇ,!]t­EÙDÌêZŠHü`ÐÊ&oYK'‹ºXH¡Ôk"jQEm@!F]°!i‰sÕªËaK X×y«U„puµËa â• DK‰u¬Ë‹¨œ•H]ø*eñHT9Â,„.¸«JQ;¢†ÕŸËf‰`IÅF°6ÊÅ&JðkÂvI`‰ÉFešL¨Š>eØ'õcÉ`¨¨d—2HBÑ}¦~,eË%&ÄRéÌ[Sÿôó¿Ìf CF]c3[O§×_aÅ´ò(aIl=¨âžð«ùlÕ¡~źMáͰWà~HƒíðtžcûŠ#o7_@>•zëÌW¿YÌGïZ˜ÊÔo._™ú}ûie®÷­ÿ¦¹m«úmg«%Ö î)jãå|½µËn+Ò]úG;ž4/çŸLç)Z\Àœƒ·Ykí›f) lÄ;¿.ñpÝó($ÝQmÚ DÝOmZÚ¶¼mö•m»OÛñôûø²iÙmÛÍ|×筢ɡ鿥#âÑɇ+è©ÕÝž’‰ˆÍ^Â2丗÷Å!*(h§Õ`À6[xëRìÇ2@X†àF2)´¬<§ ŸŸkÅ(šŒàZN¡i!¥% €­ <Ø~£rº‡£u€ÁŠžÄDÐMlð 5³ÿ˜ V(ݹ 6 ›•Üe€X8¥;/ÉR°~¢dÒf€í ¶ôÑw§LVƒ$ƒÞ¤~(=É‹±; Cþ$=x=Â,°=ÁF¥;"B)P+Rú± °ä ªMFý'¬+,X°Q‰.ôc€í 6*Yr·Añ¤GŠP9a—üÃÒSÖ=”J¾ŸaýÁƒÎNpò!Á‰†à쪕Ž$Ž_îèjp'B þHÜ'üUŽ»ã®]rºã.ÜÑ`þÌaƒÀŸäÈpèÈpª#ñŽÜáéÇèˆ/¢g '),| °ø:2¹’¡Ê£ Ê!Ô8H’ÅÃ$‹îD¨ñ1I‡K2=þê½÷“ÕwÞšïÍÇ_&£¥ùa½ú¸^=ët¯GíBï†îìnû¬¹^z¶¯¤Zu§6êE_Õÿlîô¡L6˶³|ý};ý­]MFMUÿm6š'³Û­FWõë‹ •ÕW¯wóûˆÍ„ŸÇ×?Nf/fËÉ·/'77-¬¥æ½2Aê»Él½4%ÖÿYÏWí´½YB=žCÿårRß.šßÚº­Wm=š,Fë»›iû©^M¦ã¶¾kF‹ù¬þ°h!ƒ!ÍhGÔã ±œ,k f0noêž]àÄé´ù|ñ—õì¶Y¬ï¦ÍzUÏoç³ö×zÔè|ËͨÝâtüÙÉŸì ë†O“e± ýµüÐ]~ì¯É?ýðáßÊ{tŠ×wúSæié’ÝAº¤üÄÓ%ç]Ž˜’ïò//çÓñýóî¦a÷“JÃTQy¾îÒô8'õ'²¹{¡B¤ýŸ FeJ'e^9dJÅ?ñÌ+åž açžZ†zD¡*ôˆBµ'‹n×5äm(CÖ)}å¤}»ƒ#}åIg‹¾§sO¶xzbÙ¢ï4]O„ª'û‰­¾må¢ÕwÞÎ[NÂÙŸ” >$ˆIöÿw ²ùîìDngÞÝÄÛüx]ý‘ëܽ endstream endobj 325 0 obj << /Alternate /DeviceRGB /N 3 /Length 2596 /Filter /FlateDecode >> stream xœ–wTSهϽ7½P’Š”ÐkhRH ½H‘.*1 JÀ"6DTpDQ‘¦2(à€£C‘±"Š…Q±ëDÔqp–Id­ß¼yïÍ›ß÷~kŸ½ÏÝgï}ÖºüƒÂLX € ¡Xáçň‹g` ðlàp³³BøF™|ØŒl™ø½º ùû*Ó?ŒÁÿŸ”¹Y"1P˜ŒçòøÙ\É8=Wœ%·Oɘ¶4MÎ0JÎ"Y‚2V“sò,[|ö™e9ó2„<ËsÎâeðäÜ'ã9¾Œ‘`çø¹2¾&cƒtI†@Æoä±|N6(’Ü.æsSdl-c’(2‚-ãyàHÉ_ðÒ/XÌÏËÅÎÌZ.$§ˆ&\S†“‹áÏÏMç‹ÅÌ07#â1Ø™YárfÏüYym²";Ø8980m-m¾(Ô]ü›’÷v–^„îDøÃöW~™ °¦eµÙú‡mi]ëP»ý‡Í`/в¾u}qº|^RÄâ,g+«ÜÜ\KŸk)/èïúŸC_|ÏR¾Ýïåaxó“8’t1C^7nfz¦DÄÈÎâpù 柇øþuü$¾ˆ/”ED˦L L–µ[Ȉ™B†@øŸšøÃþ¤Ù¹–‰ÚøЖX¥!@~(* {d+Ðï} ÆGù͋љ˜ûÏ‚þ}W¸LþÈ$ŽcGD2¸QÎìšüZ4 E@ê@èÀ¶À¸àA(ˆq`1à‚D €µ ”‚­`'¨u 4ƒ6ptcà48.Ë`ÜR0ž€)ð Ì@„…ÈR‡t CȲ…XäCP”%CBH@ë R¨ª†ê¡fè[è(tº C· Qhúz#0 ¦ÁZ°l³`O8Ž„ÁÉð28.‚·À•p|î„O×àX ?§€:¢‹0ÂFB‘x$ !«¤i@Ú¤¹ŠH‘§È[EE1PL” Ê…⢖¡V¡6£ªQP¨>ÔUÔ(j õMFk¢ÍÑÎèt,:‹.FW ›Ðè³èô8úƒ¡cŒ1ŽL&³³³ÓŽ9…ÆŒa¦±X¬:ÖëŠ År°bl1¶ {{{;Ž}ƒ#âtp¶8_\¡8áú"ãEy‹.,ÖXœ¾øøÅ%œ%Gщ1‰-‰ï9¡œÎôÒ€¥µK§¸lî.îžoo’ïÊ/çO$¹&•'=JvMÞž<™âžR‘òTÀT ž§ú§Ö¥¾N MÛŸö)=&½=—‘˜qTH¦ û2µ3ó2‡³Ì³Š³¤Ëœ—í\6% 5eCÙ‹²»Å4ÙÏÔ€ÄD²^2šã–S“ó&7:÷Hžrž0o`¹ÙòMË'ò}ó¿^ZÁ]Ñ[ [°¶`t¥çÊúUЪ¥«zWë¯.Z=¾Æo͵„µik(´.,/|¹.f]O‘VÑš¢±õ~ë[‹ŠEÅ76¸l¨ÛˆÚ(Ø8¸iMKx%K­K+Jßoæn¾ø•ÍW•_}Ú’´e°Ì¡lÏVÌVáÖëÛÜ·(W.Ï/Û²½scGÉŽ—;—ì¼PaWQ·‹°K²KZ\Ù]ePµµê}uJõHWM{­fí¦Ú×»y»¯ìñØÓV§UWZ÷n¯`ïÍz¿úΣ†Š}˜}9û6F7öÍúº¹I£©´éÃ~á~éˆ}ÍŽÍÍ-š-e­p«¤uò`ÂÁËßxÓÝÆl«o§·—‡$‡›øíõÃA‡{°Ž´}gø]mµ£¤ê\Þ9Õ•Ò%íŽë>x´·Ç¥§ã{Ëï÷Ó=Vs\åx٠‰¢ŸN柜>•uêééäÓc½Kz=s­/¼oðlÐÙóç|Ïé÷ì?yÞõü± ÎŽ^d]ìºäp©sÀ~ ãû:;‡‡º/;]îž7|âŠû•ÓW½¯ž»píÒÈü‘áëQ×oÞH¸!½É»ùèVú­ç·snÏÜYs}·äžÒ½Šûš÷~4ý±]ê =>ê=:ð`Áƒ;cܱ'?eÿô~¼è!ùaÅ„ÎDó#ÛGÇ&}'/?^øxüIÖ“™§Å?+ÿ\ûÌäÙw¿xü20;5þ\ôüÓ¯›_¨¿ØÿÒîeïtØôýW¯f^—¼Qsà-ëmÿ»˜w3¹ï±ï+?˜~èùôñî§ŒOŸ~÷„óû endstream endobj 330 0 obj << /Length 611 /Filter /FlateDecode >> stream xÚ•SÁnÛ0 ½ç+Œžd V%Y–íÝaØ:l‡ÃrÛvPÙ`[ž­¬ë¾~¢)7M‘†Àõø%R$KÚ„%7,®ï¶››»¼LjZ+¡’m“p!iÅD¢TI+ }òÜ¥UNÜ”fRä¤w“AËô–$;Fïî8ýNEEÌŒˆäfÒ=‚!TóÂ9vΣµ7³mcT½¤§I­ÙãÎF·‡sàÏ¥?·ŸCBç´. ¼ôÎuÇ~˜)ønîzJ20%§•Ì‘ù6Í QQO?XÁúfr·;0ù5x$áa³|gç¨suH4þ›Š*@¨ÍíUµªŸJÿa»áÁ` ª¹(Uyìúͯ -¹BÂÉZ«*7Ÿz•¼w›¯á·º²5bö,äëo-rFKÁã[ÛöuÏ«š¨7!“B‘í!í¤ÇC0kFæƒ[ê?£Ç¯”Nü˜Š’¸£G,Rqc#hg ´›Œöð¶6Ðe`Ìö§†¢i¦¸€Ë„û]xð¥]r¦Îcb+àæÁzHáΓ‰’Q¦‹U˜Ràu¦ñéanvÿd²í®ä饻qÞ\Çö†IJ¨ B£gÛ="6¦Ôu.Nì}g‡Áª÷zŒj­€õzj—‰ ölA÷×ÄÈK6‹ÉŠ´zDä>j~ª¤¥Nü “p^÷\>«{ŽC+ŒÅýºëìÐ"Ž• FsvÞº!nþ¼c+NY!ÃÁË!7$·uM«V$y!PMsU®×ëKq ªª'=›Þ×Ö$SFI*y‰b._Îþ?-½`ª endstream endobj 327 0 obj << /Type /XObject /Subtype /Form /FormType 1 /PTEX.FileName (/tmp/RtmpBlhU1W/Rbuild4444671013800/timeSeries/vignettes/timeSeriesPlot-twoColMultiplePlot.pdf) /PTEX.PageNumber 1 /PTEX.InfoDict 334 0 R /BBox [0 0 432 432] /Resources << /ProcSet [ /PDF /Text ] /Font << /F2 335 0 R/F3 336 0 R>> /ExtGState << >>/ColorSpace << /sRGB 337 0 R >>>> /Length 13698 /Filter /FlateDecode >> stream xœ­ËÎ%¹•çùg( ôwðN­†mHhmW4zÔØBVm ìÇ7×e3âTg)+¥¨J«‚q9\¼ìÜzýþ•^|ýû§ÿþú÷×õq]¯<ÚG)¯œ:TºÖGj¯ÿó/¯ÿùú7ªó£Ž(•êøhãG¥þæOÿã¿þöõ·ß}Âõ®×óŸßýíÛÿu´×ÿýôÿøº^ÿü)½~¿ÿ÷ÇO‰7ÿçV>V}•ÚPê‡O­~äëUÊú¸êë3dí[öl9ò«äýH ²}Ôµ¦6%÷5JÉsAî”váþ1Šd[î'ï’­ìÂ×Goã£$ž[Šäþ™ã’œ…Oµäü¸Ÿj^’¥ñ1*å¾ ×´$ —^$; 7ÜríûÖ —øü©_ ÷Ór5Ê„Â=}dN¸=åþ2¥fýü-÷…¼²äZ¼ò Ü?eð™Ï- ï÷Û$óâ¹cH¶}ßý±¯.9¿QãÑúÑÖ"9[|Á-¶”ñ±²d|æÚ%»îË·Ñ;¾$ν’dߟ»î-É}²k`á¹ã#Í-—߯¾ÿ®9íúȼÔüÈùMÖ]¸e}ß-Ç.¼_èä¥j_ÙïˆÕl˼ï‹sÇ…ÿü”eÞŸ¹gÉÖ)«ÎLÉw5ö%qßJ@9ŸWÎ)?ÎÝÕM…çìxÈý1¦äºøÌõjì4ùÌüCÆÙ…Ê8û¥”) ã N,Iç\jIý¢Ü$²ûÜŽT¶3Û’Ì“/–•ËVùt©}C½öš%› óÅn9ë]U†ŒßwÈ8ûR•÷•qZ’†Œ³åJ’0ÎCÂ8õøó§)ã Eh’0În/hç)ãlÉ–aÊ8j (aœ}4UI ~ï”qö<*㨽 „q¶ä/š2ι¯Œ³Ÿ™œÇ8ôà”qö»êx“SƉ—3mœKíÕ´q.4[[Ú8—ÞÆ–}°âçÞrš5v4EhëØnl¹_RÉMÍל¨P=IVf%Ü’jÈÅFu7_¬±SÆÙêÂ¥–Œ³Ûº™%aœh—Œ³F¾ö%ãìÆ|àÊKÆÙ’5gÉ8稌““¾à’qÒÔÛX2NZúKÆIKÞ_2Nʸ=%Œ“Š~Â’qRò3Ë8»{bË¿dœ}.·%ãìsS–„qÒ@Ó³¥Œ³{R6PKƹ†_ŽŒs¤ŒóM…S“„q®†qË ——«ª/[l˵ë" /Ô\HºlËý¾ó.4ºä¾FÞ·O.¼Õ¼ßBÂÏO×…V‡KµÞÖyhz'ïŸJ£Ac<°¿Þ ½Ÿ)ëãJïÚñZ5ý]u?(ïïËW×ó|Z(o²:Boe´:Ÿ&ÊÛ–l% 'ÊO5¸é¢ò¶žïGå]éG·î¸ŽëÓIy¿–9­ûx×ðR¼ëtÉLO 7m½T^vÚUº5ü´_ÃÖ£Ý C)8j¿ökXcœ_>a ÔÞ5F*çþ[c¨‚×X­1VÙ}Is´Pöe9J€Þrê{JrVj®ÉÖcAÓ[YôzX %¹+ê1ôRÍMÒò×ù½‰»ßo¢ÃP—®G‹áûÌËztÖ¡ßK“¡þÔe'Ëëý'Ú Ç»4}–ÕžH·I].ëí4Ô¿Z-ï–m&ëҟףÙÞ4Ýõ;ÙnEV…¦ßÂ?Ù~›°™ô‚—“†‹i7q9ó¸žgk dvýèÅ#™»|æPf×/=ÏÖcÞMEÊ…mò©?[}Õÿ¬.+hôY{_huZ§þdõZ·V·…j6¬ÑoíëdŽ ÕRåÕs%>¶4º.T«j ¿íjTt¾ü†jT­á·­Uÿ³ü 6t}+/¿íלu=ùm¿¦Ú¬9/h¿¥"¿ÅÄš3ƒ‰Ç”æÔÀ= 4çCãƒTä·³CO éUßöœ"•{òÍùg<Ðçì3;\hÎÅãÁa¿õk÷õÔ±íòjŸ‹z¶0¦¢®-ÆuÐEƒQµ×E[ö¾Ðo÷ú÷Ðog(IC “S¥ßÎXziøÎAXªIóØ¡«†ÙjOªü†ÉŸ·Êo˜©%kø-†Ö©Êo{$ªïQå·ó{ðb;®z?µhþ4ÎKøßÏXÖSå—Ž7M!<±F{½¿ÚÏÊŸñyªgŠÕ†5ü¶µüVå·}›¬ëËowyùí~>ùmÿ>õU~Ãü24ü¶'\]Ï/¿m]²5üÖ‡¯ßä·­9*„†ßöœD¿·Éo#Æ+M~Ü…ß§Éo˜Z$kømç˜,5ùmÏD®i ¿Ýדß0JÖ+=Ï—ßöø£]ÖðÛ­å·=ãPûÑä·ý<9ŽÃoûxåûÞ/®%>OùÅ}½Î ƈþq‹õ¼~g{éß/f"oåå·ã·&¿Ý¿G~Û“Ë~ÛßÇ÷?~Ó÷nö[†Í¥é7Ï;¡é·êþ­ËoǯÝ~ ?tû­º¿éò[Ì®¡á·˜"§~ü¦þ¾¿YËoOíÙºÚ£n¿5×ï^Þæë©Ûo=ûSËo1“†.óñ~ºüsË„F~´gˆiÈŸêÔ¨íŠñÑiOÖèöqzÇS’>ÔÝÞ÷©ö:üßå·­[³†ßb¢œ†ü“_hø-¦»ÐðÛþ=jÿ‡ü¶m¡ñÍßbÖ ¿ífBíëßžºhίú5Ü¿9Š͉|öïò[# 9•öfԘ˫þ ù-fþМÍM:Óß"šÍùüy~ùí´oC~‹)<4§ôçýÉo¥€noïchVîÇÀÝž#è1í÷`(¿WíñÖh¯#RöúÔ¿ÁPRQnkD>ÖÃoS~ÛÇÕ~Nù­g×Çù#¿Mû­»½šòÛyS~;~œ%‚cªÏS~;íÔßNû0å·ã—i¿ ÷Ó~s%M÷oñý§üvêç<~ÓxoÚo ¿¿Où-bliÊo§>Où-b4iÊoûøµ¬é·èO§üqÆ„ˆH~ø !‘ùЋ ÷›æø¨ûy0ñô ¿Çwºÿu!õ·ˆýûœ Z]ö‹gØMÅü_¯ïâ ˜ƒÔ×Fe;Uüõ…Es8ÊGŒ²w«,ŸÔLœn5tÓSàñ ¿ýÞ¯éŸþô…×ô§ú·Oó_ò+½¾ÿWމü†ÿ¤Þ-ê¥ÀÄþÐßÿðúÕš¿~}ÿÇOÿù{^úgœ‹øÓîÔyîúÖs+ÛYœ›®ë[O†»‹ON?y²æX¿Ñ¿ô_Ø«q2¤Ó¿ûíïâôö Ú}øaÏáF.ônôç.õ¨äs×gøÿ¬ùçs#ôN»u nÄΊЅAôÂ9Œ1@2Â+n„ž`–Wp#Hö ê!óx7b7⣭¼ØjóFŒ³Ñ·ÄÏÓ¼ŠÜˆ]ÊÍÜ7BLØ#n„ö¶#í~Íg¶ì(\ÜÍ9ŽÒ´âF8*$îÜM†râ•y®¸Ž^77B»*ºÃøšEIÎãØJÞÜ G+âFwaÆÐäŠH1”É®K]ez7¹ì(ÄðìgÅðÌëæFøµ‹ñ™ç+¸QR=¸nÄ!°¸;’›±â¹äFOY§~B}7ÂC ª-¹hi0-n„·Á7)nô”E…NáL«èMŠ%Ĺ ŠöI<:^Á(‰U¤K+é©Ä‚Ó–»KD»^‘ÖEɆ¸QBDy¾‚±0‚q–ƒòâFiy4%n”PnDY^Á u#F ³1RöCæWp#Ü7™ •Lɰ¯¸²XNžFA2ÎrÌDÜG97¢¼¹QrgdnÄKÝÜ(!üH"ã,G³ÄòåÉ„¸d1‚qð>ŠÈ÷åHƒ¸¤8ŠâÞ—i´¸QF´úæFùrP^ÜGƒ*!æ}ÙÝâFùrõ7Â¥Þû\¨Äx®"Ü} /ÅP˜UTÜ—¢ËÄð{9–7ÂQ†šÄ²èÁò5ýÈWÀ-†²¢ãÁ »å6Î¥5Äòîxeq#H™„ ùâF8W…i­ÆH³óF¬9âFÉ ¨L=Uz7¹Åré¾l‚ÄrJjsÄ2âï| Gé_q£Œ0yz7‚$ö7¢¯àFüE—‘AQÖ/š>ÚêC*žýgƒ:ôWp#<+¡¸îË®MÜ(#O©P6HØzn”Ü™!ÐjŒ„8vŠy‘¹Qr ÜèÖò1d~¿¹ çùé8&|O}?q#|ÿƱ3šg›Ç‡uS}¼Üß§>¸¾9™üv꧸ž÷ÊÖðÛ¶ü.n„÷™Ü(×Ã…ä·êy©¹Q®Q¿Åø}‹uuù8¿Õoˆáýê}‰=´üöÔð[®OâF¸ ¿Õ‰½éÙŸZ~ÛÕ"?¸t‰ãðÛ®væ\Œcç]Í5þ7ÊXv\ ÓpsÖq£Œ8÷ƒåñÅò~Wh´×Í+s#÷õä7èa ¿í×–ÜÇ{p¡âòÕº©|®¿Ž.n”[ÄÁÅp<‚ßG7âõ–5ü¶_Û ®¿õX·#nD½¬á·­U?Ų"8ŸƒåýZå_q#7W’ßúN#¿Ýå巾ܟˆAÏ7ÊãòøEÜ(ú n„ãê?Åp}s,r#Ü_ó E ù¶ ÆT7xêKCUq§áÅÂNÃËïD†á‚N»E»|¬ï·;üdœMÿEœ¦©N»EÎ7ošžH 7¡÷à59¼|¨}_„¨¬ö7^—¨ÍW¦Ûy/¨ýâi•çôΙõ¾÷&M<…˜âî"Lè3ïI@s×+øÒpü]xi Ÿ'„¢0Ò¨¯`KñÛ…–b™¿ÈÒô"Q¥ùÆ•Æ:ûv?‰ÑAyUÚO6o¨„ç´jüzœÏÄWQšÞà& „øæIDzWÒvÀV&ˆ&2øØ®a+ø€78^’•‰$=ÔvÀîÕúÍ‘V!: vkˆ"-ƒA$„ʉ‘ìñ ˆ„@nº!ãÊ<*úŠx{DBÔõòn#D¿“g‹‚H)‚u‚H¸T÷ö"„¾/‡®‘ðžFLK…ùn‘®¶DØ{nÞm”Q8š‚ÞWÓb1A¤[*äHÿ½ùˆû"1Î__‘Rì‹DU(÷æ#fôI …Éý‘ “¹BÝñÌ‚H ù GE`ç®05B˜[´# C›qä¾<ÉD¹lì‘øˆ¶Ὰl(ˆ„šîÍGÄå /ö‘n)úúM…µI¡ík*Ô.ˆ„ ËÒïŠ$ˆt*’ £ÿÞ‹ÔUXÀMAíƒxA$Äê5ÉDzh­\@P2 †‡9[ëµ"¶mè¢Å 9s "96þ9 ®gˆ%íáÇŠÍF´÷à"5h|ÙDtºŽ‹úcó~½;"ÅÛˆt6/"a{YlF‚•R‰ç——ÒÙ,%3=5ÝE M€‚ކHaÜ€H)Çû·£„Dº"HkˆôÔ€H€xÓÚ#¨úØ|tÚœ€Hû_z†Hº"áµ4¢µr@zëì…CZ±”Oiû@3v$!IzkźNñ£é1ñѺâÊôzðǦ£9â1è­[6„Ž¢+19º¥:¡M½P ,¥n(( ¨ÑŒ ‰ ö¶é™é­é1Ž‘Ñôú £™Þ6˜ŠáF\´[}Ñ)9b?•^Ž`Ñî²ôÙÄŠ4ãÿ¨è<•HzÙC»Ý=\PœhF˜I˜èP9Q"¬ÑcÐOњݒnš^×aBtß—^Ú7*>£ ã¡«nE‡–—<­³‰6Z±äZhhŸkòC­ÕCg?š¸º¦a¹àÔ:Q¡AA¡Ã<Å„Öužx(â&"4ca¬€P 2̓¦×*­ë–§”Ø´Ã`ÙabAÇ)BA±ôÈ$(Æ8AèNã(F+ù á+dK' [ ã\±W(ß#2# ¬Ó¹2Î),ã´à]2NuK"ü3c5‚èÎ ˜ã”›íTŽ.Õj‹iÌØñ(òs¬!ð³r=¸¢YŸû;‹úÀeÒ{wÂ8+ä`S0;…fL|bmÞ3g ÷[‰öÌ0…Á°é¥8f=½ õ²¯»âcŸôœ7)Î3 Á˜ç¼XQžaòl™ƒá쪋¡e Ÿmœñ#žû=Ë8‡GÑ8ç!…wb:bº³+°7È8#ö¨¸Û¹î=@Û9+¶8 ílé3tÎ °,°sÖyˆë¬î°Ÿ°ÎŠÝr¢:ËK uVt×b:ÛáT¶+vr·2:hƒû€pnlû¹ÊmBÑ 5‡eã}µöA,‡Ã×˺i„zöM:UÅr ûËáèàÁrîn¾yPWÝô6ê²ÙƒX‡b ÖE_$–sëÄrØqk¬UˆÕRf9èhž,å½gˆFzh:éMcì¿»1³%z }àxìÂóÉkb9oãÿ[ÓO8_.Ëa'úØô¸>cËì ƒí`/·8b9èíÕ˜‰å°ÏÖ˜$ç¹1˹‡µb9èÛcß—ÊkXöl'k<ƒÍ`X—cψXºßÇ1¬ËãÞ#åÃR4¬;{dÅrØ»ÁŠÌkYÄrÜv~–­çËq“÷9XÚžü`9håÔ׋å¤Ã¦ÅrÒÙs*–Ãëëyi5”Ï–ƒ¦½=öáüô`98îûÓnèdFìñßJøC,Ÿßè²Öƒå°ƒëÖ˜”èÚÅrp~vƒiA©ñ¾Ér ý¾Èr S°L J¬½ËÁõ½GE~»Ëo%rˆåàzjšÄrp¾Z=õ~è¯Ïžžë­¼üVÎù­¬[Ão[û~ò[MÁjä7tjbEò[»âþò[k÷ž ø­9Üg–­÷¥ñ\__~k‡mÉoh&ƒíÀo÷ùòÛÙ“%–ƒf'Åqøm7kfid9l¶Bcò}´XΛ.*¯ú©i®G¿ÿb,GqîƒršW±ÿ˜ì¼Ÿ'Éé\lóÃÙË¡Óë?g(õ8¬™Jö¼å>¼S‹ÃRO´»‹ú€@’Ï»¾]7²|¼¼·Y¾(þ¢DÝ?ð—@èèÚ_¶¹h±1>Êߊ2©¨Nîßzrcmÿ{êìÛ]¡^î1]¡ð¾Þ[eNÆp}ó¼îó-žW¼opx>ýýÈ÷ï¨Ê;À‘ž=_WÿÍ•~óåýQ_ºÛÿò~‰ñÓ—ø³( ‘¡Ý–eýîw_:ýqÊãJƒaY ó:áìïÿ÷ÿò$a¹×¯åãc1°¯%÷ûW{4C»ÙÏÚöóíÇÛŸéëÍ–C0 GB?|Ârfƒq¬»/±ŠsYc›ßåà"–K8{‡ÔÐÌžãBYÙsŠz hfÏñ¬iþÊÔýвçô¸cÉÌ’¬±Û«uœC^lô›¡‘²,–ÌãÅ¡Ê`ä6¬±Û9;%!tWöÎc ±Ûy´–žËS‘¤°ekìv¾5—_2ïÕeÍ}•ξÝαJzéþ]çOîvÞ¿‡Ñ ¤QÂnçìž»1RLÖØíœ=ƒ…F6Ä¥ø{æÅÝÎÉ«´¡§~ŸÞç$ˆgn±f]”]¥OkdƒÁªI>ßäò;¤ùâȺ+í×Õ¬‘]`ŸÏÀÒ:%g ›ÖHX’F&y22 Í(IÖ®Vd$[¡±Û¹x›5ÝFN2† ‘Ð,Ò3橽²ÐÍš)Í<Ó€nÚ­ïëuîv®­ ­²šU ±Û¹:ŸÓVef©É»12 ÍlI#Mè¥ÝÿM÷[Ê.P5Òƒfv®aVDÙô=–üV½Š‹i•2µêïJÚíììBÐCå9RFš%ìvÞïSõM›½˜‘sX#LvÌiŠ’¼q¤ÝÎÙÙ¼ ‘"Ÿç•ß°ÊwYÃo1ó@%øí¾¾ýÖãyí·¦‘6ôRýºtþñ[_ÖËåuÜ~«šIäe¿UE é·îï»ä·HAʤkNª÷+¿#lhøm×·6¬›² É?K~ËÞ`ˆ´Oð[öª>è©ò\ÅÄ4P‰ÇÙÞAÓo†òlHÕ>iù-y;'ÓF ¦¬kÅšÙ—\ÿݰB×lÍìKŽK ávö%Î\Ü£¼ïO^Îla*Ï)î?CÃo÷õå·4 á·Tãúò[ªñüòÛ­å·Ý>õd ¿EûÅŽ óyý>ä·Ë«Ø á·ËÑ0høíò*~vdÊÆ5C³ó®¾ˆ‹ýÛkhf;óLƒ³ ê¤lgEý 4³5õwL“¥òí²Æ*14+Õ«è1áQyúÍ͆´³±¾²ãRö²¢ãŒ¨åˆÏC3Û™Û+¦…«Êf6­±ªwÁ@cU/šÑláËt4¹u?ýÆìjÒôtÖUÙúj·ÆªÌéUÿÐØ%6 v¨¼ßý–c24VeN÷§ÐÌ.èH4v­LçÑDGÆì‚ŽÂ²ãnÌ>§ú¢…f8N`S´}!GoaRÕï,¿ÁFͺ+;š¯'¿]†%Ëo—Ç#ÐXEÙâÐa½'KÔXEeÙp5Þ¿ë8ýÆf;4ÇG—"}hȰªwy'"4Võ®®þˆ‘AÍö šÙÝߢá¹<>+ÖÈsÕxùíò®è¢ìt=[cUæŠö(Óol¦uúÝHh¬ÊœÞÕ¼¤—êô†ßÍ ¿MïraC}1›¤üXä·éUÈh¸á·È6 M¿õ7ÐMÙÕ>ûÍ»x á7ø±Z3»à%Á¬Ð³W®n ¿MgÇÂ@#éúìÿ «üéç‘ß–#‰ÐÎæ©ú^zdôót®ê]Þõˆ³ V·[W•W¢4v%v½!mÝ¥ì‚~ßSÙÅië˜0YµªÏEýÛe¶Íì‚^xÃÅRöÈd=•PíkUÿ†É´vvAù·ºóõRÝ¿•¸žû7/Ô*Õý[Žãîß¼‡ºj~¡çÕÀŠÝThöoήWªû7g7r&k6[É~K^¾‡²/™&ë¢$媿•FƒM[³žoÈŸU~+ŽŒ1íÞP¶BŸG2w¶Æ'j¯êTúq/VaÚ(eëVý®L+†ã#43{× ÓÞ$ÝoYWwäׯ†…9~ù>šÆ“û|Õ·¦ñdñø—iv2Ç×54Æ“ÅÙ¡›Çëü>MãÉâœL³4ù¼~>'±KI÷§ßÎø›i”GýO£ßp}ÏF¿Ç¨»²ýø÷¶HI®þ¯uå$®Ÿ~cŠg]Ÿ~ãñnÍìKÑ~6ù­z>Å´c×í~«^„´Kœ¬x¿ò[‰ña[ʱ<Ý¿`yzÖûáý;ÓT•Øeå4SÌnÉëõóù‹˜õ~9…îÊF¦þ¹Ëo%üÕå7D®›õrvL¾ÿ.¿Ávź¶Çûëò[uÚgèªl\ê/ºüÖ¼ ¥tù­y¡bÑryT[ºü)·¡‹²©>tù­94ôTvCµ·]~C¶Èj ¿5Ï_‘69ËÏû’ßZ´]~C$ºZ3ÛY´÷]~Ãg¨Öð[÷âß2ä·žü|C~ë×}~‹å³È„lgÝ»‚¡1é-ÎÏœtï2ƒÆ|¤Gû?²² õð/φü6¼4šÙ/ï!¿u¯Ø.ÊFf-¿u¯aƒ®J[¿ŠuWùåÍSõoÈo¨¦ÓšÙÎbþ1ä·æù&tU¶@®å€v6OùiÈo‘ šÙΜIi]0…fvÁæú¨³×5ë©ÜùzÿS~Cýº¬§²jü1Sd;ÓxkÊo-Æ{S~‹]4efe;kž?̬lg^½Ôžé})}Úõ³hþýçdCËìdÓº)û¨ê?â%~Rÿ1í7g£„fvA¯w)Z(ÀßÛ¬Kº³!B7•W{3å7ts|¿S~«ÞÕV4Ñ(±k³(Í7ÚG}¯)¿Õh´XˆºZ/=¯æ7S~ÆïÖÎ.¨övÉoCë*¿«½^WdÔxhÉoXR0¬]P¿wÉo-ú§e¿9qïw³ -¿õŸk»?ëÿeñQ÷®Õ²ª² ÆøbqA+ÿ„„ž‡‹ãàGÕg$öAöQ“Tè®ìªª?‹Œ~×ýX¡m“5ü6¼¸·,ùmDÿ°ä7Ïe ¿ ÇË’ß|‡5ü†%÷¬_K~þ(Ðð[dO…†ß†ãcÐðÛ0Y­—üvö(]òÛð©—üÙb¡Ë¼³£BÃoÃÙë%¿ “»ª4Ûh¦ùý¡ùW8ªW‰?Ãѳ5ÿ‡ÿìJ½ä7u+Òð[dk„*_õ¼œX³›ëÖu‡êÅí¯Ì¾º(ÛbmÖMÙJ9¾ýÈâ;2øáSMù}[˜ôÉ'èòoùãÓùOíy}©H[šÉÝ9㬔•CÐE¬ßŠ”¦ gQDú­HcËp‘~+¢Ç»‹<÷¯Å…Ø×ˆ TóÎFøó3 údÌRÚüægqv^÷~µŸ^?u6þ•¿WÆÙðgðÊò ° ÛáQÿÿ Îÿ»?üî ¸ìk }%§áϸÚ—íƒÈzýcswŸ?—qýͺqYžn—a}Ë =”„§J—aý‡ñq´ÂïÂeXïQe¥..;iöŒË¨çëà2êü:¸,Å>!ã²løÌë.K±xÖ¸ŒI}Ÿ1Y[¼Õ”­­¾ëJ×vž‡áÄ´^c8IpFh¦:\ÆUÂeHÒ´æë಴œ¼ß¸ Ç[à/, Z5Ês^ããËp?á%á2>o౦ßw=pÎÏǘt*û~Âe8_ßW¸,Å—0.ã~Øù:¸ I‹R'=öBã2hã"â2$õyâ2há5ÒùJq?â2hã8ÆEsüñã2¦ÀSymŽÔtÆeL¢Ô_—1‰R”ŸJ²$ü#\–/¯s4.CR 8ŒéܪŸÆeùàdŽ‘TèŠòÌèÖW*œxyu§qY¾Ÿ —1IÒõ:¸ì~?ÂeLÚxŒI§Šñ p¾¯ê«pÖÛäü:¸,>0.CÒ¢8 áÄädÒŽëDqÆeL¢¦óé7üÞÖ_—QëzMIƒ¼Éݸ Ç……ËxÿëupsÜéúôŸ'½.c†¼ÀcL~.£<6¼ü ¿.C’£õÀeذl|¦ð}:¸Ráûä…dÆeHBôÄeL$Ü£ð}öB2s&-^Rø>{a¢qYœm\†çUøM¸ Ï7¸ŒIœ²5’*$×Wã2/ÖU÷_\–c¡¨qYŽ?Nb\†çS8S¸Œå‹5ü AË >.C¥ø¬))R |¿åïK~ËNFm\†$KO\Æû5ë¢ò9tWR¦8m))“ÂéÂeHªäç•ßvùx,«¼ÂÍÆe»¼qÂ÷y^RøC•i=ô¼)ð“NEøÑ¸,ðmà²ìþ3pYŠð‡q–GÄq'ºž¸,ÏÀc ßg/ \–#¼a\†ú8I§zü~…ïc9Hà²ï˸, OË´ÕçóÁe)ÂãÆeûüz)é—߯ü–KÜO~‹…À˲ۋÀeðcà5&yóŸŽ \û·—Åþ-ã2f¦}à2ô×ëËØ¿=p3ÃÂ×Ù_ÑoL"Èï!\Æþ=ðYU²W_Ÿ~c.Ød]5>ÑtS¸ Ï£é²pYZβl\Æþ8ð×¥œÅ+ðYQbµÂe'±q×Ûêy†ÆG9ð~®ÿUù©¤Š—ñ’p×gOk,|U”ésà2^_÷_Jªø_¸ì¤z6.KÓÌÀ¸ìäQ6.ÃzY½á²û} —aüwðú·ÙWe%UôBp㲓¤Ðã«fÝ”¤PïS¸ŒõAףߨŸ>pÇ[¿°Qaµ‡UõÇÆkMã£ìöV¸ŒÏ«ûk ?_ —1é¶Ê%‡v’ã2Ô7µ÷ÂeX,Ü+\Æ÷S­±pzyüe\ÆôÊ¡‹~ŸÏ÷x²Æóx<9y<ø¸z<Ù}½j¿y×qYZÞÉk\–¬g<©ï+\ÆïóÀe¯ Éoûz5tURÒƒÃ"‰ièêö!YvߌËО¬ÐH:ùyËrìË6.cá8Þ”4RõG¸ŒI¥‡ÆGÎ.ãøOï‹áDŽ—•?ºkJß$@ÿ¯öB¸,§ÿ —aü”Uý¾ølh|¦öR¸ Ï÷Äe¿é¸ú·äìÆeÐÆAêß’“†—¡ýV8]¸,Ÿå4ÍãÉè?…ËrrÒ#ã2æx–4þÌ\ÆñÎeÍœÁQÿ„˘òË8žiÖËå‡a>‚-´Ãºª?6¾£ß8~.CTB~.cÒ>á&'±1dZ;©¢ñSޤŠzÿÂeÐ%ðÆ“¥y<#\–cy£q´¾¯p’÷ÉoÅ·ŒËr,w4.C’EÕgá2Ü_ãá2è8 ~+±üLqM\Oí»pY>8Y¸ çË_ÂeÈ94Ÿ1©ây^ù­úÏ —1 å—1é¤p’üVbù€pî¯ß'\†$ˆÂ!Âe9þ®q“Œ 'e%UŒñ¶pÇŸ\†ú§ç.c}ÖLª˜ü>„Ëx=/¿‡k&UÌ·ä·ËK„Ëø~ã8üVFà:ù­F}.CÒÄõÀeH’¨åHÂe'é¥q“À-k&UŒåRÂe(=p¾Ÿê£p“XsRÅÚI÷ä·êp·q´î/\†òz¿ÂeHZg|%¿5çƒ0.cÒ:ᣬ¤Š±@¸ çÀiK埸 QIõÂeHj§åÂeHbX¸ Içú—1 £p•ü†f­ZÃoÍyUŒËp=Õw᲌˘1püÖÎï“ß"iq´qžüÖ“ÛOá2$½;Ç™T±î“ßzàMá2èúÀe<.ü#¿¡yà2$%”¿„Ë Õß —!Œªú(\†´a.Ÿ#©¢qüÖ§ûá2èúÀeÔú) c}à2&u”&.;IËp¾Úwá2h¯…ËôpcRÅø®)©b,O.ƒò—¡=öýí7/¿6.Cù8m¸>ê|ù­%×á2& F¿9¿¿qY>Ë/–ýæÑÆe96—Áï%¿.ËÕñ(ã2øßϸ IW{\†ú8…³ä·æ, Æe(Ïúf\†ß“tû­Ë?Æe9þ8˜qÙI"i\Ƥ¢×ëಠԸŒIÇëà2è®çëJª8â÷t%U´ßŒË˜„ßã—Ãe'«¢ñؽ÷îGøì¯½á2lã]ëÆeÌEšßÕ]DˆÊú­r5<ˆšõt»¸œò†nÒoE´¢þ.âöÏ"•£¸»ˆô[ý‚»ÈãýBD{Áó_ ÅɸÒ7çaŒ³+ÿØÅ×1þ$ÖÂ8Ûùÿð÷_ÂZ_IˆhŽõ•”ˆ_¿ÖóuíºŠÁ6‚ý56Óê$êiE.@­î€‘V÷Fgó¬X½dœÕ"Ú4 Çfz7ËÊÊù›dõ ˆ «;9Öð~uc¬‘c“£êÃÉs ±FlÉÃ^Qb„5ZlXcD}´ØßÆß¨q#Æ÷F‹ŸÀð^d'4½n¼ ¯Æˆ­Zì$‡W]d‘½á}W£7cœaôØUÅ0Ã8›¶en ­F»%’&ö@4 1Ä‚«q ÕXq#v\Ó«›«æ¡I¤UÓYz «fŽ+W§^jªš9HIÕ,šÁ¸%ãæñÇÔŒ©æ¡vŒšÏ?ŸAóYbKƒa³ÜÈj2™Ì"•˜‚PG€jÞÄøÝœÇW˜±9ItjŽØëEãàɃ=e&“©\³i¡q"‹‹ÁÔ¼b›TrBÅØéRáõ:TJïä@©È i&…íI­OÅ®7þªÔò|ÇŦ-DÁ¯Ø„"ê´œvÖÐ S‹ Bʬ†ã±AkÅ~ Eú–y»yë~¨ÖSÄúó&¦u UÞÛÝŽ»?Mf}òf©ËÙâÖcorgKR¶¸;µ óI© ˆ4Íû¨ø©¦“ƒš3M§G6fŠ´V¦L3Þˆ ÓŠ%ØbLgG’ÓŒˆ—SüýH&ü½Ìj‰¤Wþ›LÆKÓ’Èt)þ¥áÒŒX¹ØÒ,÷Ϋ‹™¹ôE–fÔX¥ÃÄ•°Lè²ìõdæ2Uš±'SP)z83¥é¿Kf¤4½‚ÂDiö8 v‡údâIóàuK-èPv¶¸{­2³#Ù˜YSi¡¤äK$é šÐ-•x :(:c¤³éQ)þ0§!ÒÊÁÄèž;v„ð¹cƒ†,3'üsâ|Î{÷Õ²D’>Ó£­®à~o·üEÞiÆ©þ*Žt‰aßh€"' ®dãKÈ8þSV¦F«Q¯[ÅŒ€|âhWG£ Eê•b}¾€ÑŠõ $®Xþ \tè¯hÑ7Êl;ÌR¢OòÎ'÷Iº¨§OòS Í^þ{@FEñçÍLŠÐÒøˆÌ^A¶EŠ MnDf¯+~‰È,V>ÅF)¬\ÙÎÖX¹vºD‘¢t“.FÒØÅ=H»l½”Y­®H_Ö˜¬#p³ÆÊ54>)ÂõÕE7wIA¾š»¤ˆD6wI=ÈŠ»¤×/‘¸?HÏÖ‘86R%¥+1ÙqŸäo&Eé rØÜ'õ =rɤóNo„™M±^¤Ç©)âñ GN!׫4(?HQºI˜ÈlòèÚ¤éY¯ÐE)çZl¼jJÉæ÷ÇHZ:$K¤ˆ)á–õTy“Ÿ¤ré&Cå‘"Ϥ(E"“¢¾Ì¤ˆú±± )ÜL’DfslÔ)ÂõZ¡¬LÉWl¤ÂJˆ¤B¤ˆ¿çAŠø|Å:+óòˆRN!wÈSÈåx^ù-Ç÷)bÚÇÆ*^oYOgjŽRð[²)R„u96^Áo9š8‘"¾½ù Ùh¤‰‘êƒA{ãüvV‰ñüÇÆª”#’.R„çn¬ByoGH()R„Þ˜ÄÈuŠD0&E)þüœIÓ¸6k´×‡Ô‰¥øsr&Eηú9H´ü+R„‰)6RÁoÕŽÖ¤ˆ)Ïôûä·êŸ&EÌ«¥à·{#—üÖ‚¼Š!jŽXð[ ²-Rô¦á7Dîƒ ÁoÍ+qMŠ‚O×)Âqo<’ßÚ;)âù±‘ ~kN¥lR„Üej?DŠ˜âOäC~k^kR„”{Wh¬¤éñ>DŠNÊ?“"¤ìK±q*©|öÝx±îÊ@çO$Eélt)bJËií”56RÁo‡Œ‹!E™úMõOŠL“"ÜÏïG~«g£–üîΤ(•"E¸þŠXS÷"'ò[ÔO“"ÜßZ~‹Ñ&EL½­˜²1ß§˜²Ñý‘IÞ_í¯CŠRl¤6)Âñš)ýçÈLŠxýô:¤ˆõ'ÈüÖzlœb@öqœ«Ôjl$#):) MŠœY÷¢S9R9 †"gã¹Ñ;WzãDú:1»nã.§„°‹ÿ„Ï£ÀìJÍè’o)¥7rcýV¤te_Œ"Ò︊öÀU÷£þBü;¹ÿbü3¯“ƒñÛéORÒ oý3\qv>i¿åïpÅÙ3²ý!.¦Ñ¹“4º}$qüQwFLÂiC> <¯yßâç§qÌâuMGŒíÿú<ŽfpŒU1‘ã?üê?ýݯ1ãxýêû_ÿãëûßÿ¼ŒŽÌöSyzñéõG§W­µáZ¶GªŠÿï€ÀOÿ…q‹ endstream endobj 339 0 obj << /Alternate /DeviceRGB /N 3 /Length 2596 /Filter /FlateDecode >> stream xœ–wTSهϽ7½P’Š”ÐkhRH ½H‘.*1 JÀ"6DTpDQ‘¦2(à€£C‘±"Š…Q±ëDÔqp–Id­ß¼yïÍ›ß÷~kŸ½ÏÝgï}ÖºüƒÂLX € ¡Xáçň‹g` ðlàp³³BøF™|ØŒl™ø½º ùû*Ó?ŒÁÿŸ”¹Y"1P˜ŒçòøÙ\É8=Wœ%·Oɘ¶4MÎ0JÎ"Y‚2V“sò,[|ö™e9ó2„<ËsÎâeðäÜ'ã9¾Œ‘`çø¹2¾&cƒtI†@Æoä±|N6(’Ü.æsSdl-c’(2‚-ãyàHÉ_ðÒ/XÌÏËÅÎÌZ.$§ˆ&\S†“‹áÏÏMç‹ÅÌ07#â1Ø™YárfÏüYym²";Ø8980m-m¾(Ô]ü›’÷v–^„îDøÃöW~™ °¦eµÙú‡mi]ëP»ý‡Í`/в¾u}qº|^RÄâ,g+«ÜÜ\KŸk)/èïúŸC_|ÏR¾Ýïåaxó“8’t1C^7nfz¦DÄÈÎâpù 柇øþuü$¾ˆ/”ED˦L L–µ[Ȉ™B†@øŸšøÃþ¤Ù¹–‰ÚøЖX¥!@~(* {d+Ðï} ÆGù͋љ˜ûÏ‚þ}W¸LþÈ$ŽcGD2¸QÎìšüZ4 E@ê@èÀ¶À¸àA(ˆq`1à‚D €µ ”‚­`'¨u 4ƒ6ptcà48.Ë`ÜR0ž€)ð Ì@„…ÈR‡t CȲ…XäCP”%CBH@ë R¨ª†ê¡fè[è(tº C· Qhúz#0 ¦ÁZ°l³`O8Ž„ÁÉð28.‚·À•p|î„O×àX ?§€:¢‹0ÂFB‘x$ !«¤i@Ú¤¹ŠH‘§È[EE1PL” Ê…⢖¡V¡6£ªQP¨>ÔUÔ(j õMFk¢ÍÑÎèt,:‹.FW ›Ðè³èô8úƒ¡cŒ1ŽL&³³³ÓŽ9…ÆŒa¦±X¬:ÖëŠ År°bl1¶ {{{;Ž}ƒ#âtp¶8_\¡8áú"ãEy‹.,ÖXœ¾øøÅ%œ%Gщ1‰-‰ï9¡œÎôÒ€¥µK§¸lî.îžoo’ïÊ/çO$¹&•'=JvMÞž<™âžR‘òTÀT ž§ú§Ö¥¾N MÛŸö)=&½=—‘˜qTH¦ û2µ3ó2‡³Ì³Š³¤Ëœ—í\6% 5eCÙ‹²»Å4ÙÏÔ€ÄD²^2šã–S“ó&7:÷Hžrž0o`¹ÙòMË'ò}ó¿^ZÁ]Ñ[ [°¶`t¥çÊúUЪ¥«zWë¯.Z=¾Æo͵„µik(´.,/|¹.f]O‘VÑš¢±õ~ë[‹ŠEÅ76¸l¨ÛˆÚ(Ø8¸iMKx%K­K+Jßoæn¾ø•ÍW•_}Ú’´e°Ì¡lÏVÌVáÖëÛÜ·(W.Ï/Û²½scGÉŽ—;—ì¼PaWQ·‹°K²KZ\Ù]ePµµê}uJõHWM{­fí¦Ú×»y»¯ìñØÓV§UWZ÷n¯`ïÍz¿úΣ†Š}˜}9û6F7öÍúº¹I£©´éÃ~á~éˆ}ÍŽÍÍ-š-e­p«¤uò`ÂÁËßxÓÝÆl«o§·—‡$‡›øíõÃA‡{°Ž´}gø]mµ£¤ê\Þ9Õ•Ò%íŽë>x´·Ç¥§ã{Ëï÷Ó=Vs\åx٠‰¢ŸN柜>•uêééäÓc½Kz=s­/¼oðlÐÙóç|Ïé÷ì?yÞõü± ÎŽ^d]ìºäp©sÀ~ ãû:;‡‡º/;]îž7|âŠû•ÓW½¯ž»píÒÈü‘áëQ×oÞH¸!½É»ùèVú­ç·snÏÜYs}·äžÒ½Šûš÷~4ý±]ê =>ê=:ð`Áƒ;cܱ'?eÿô~¼è!ùaÅ„ÎDó#ÛGÇ&}'/?^øxüIÖ“™§Å?+ÿ\ûÌäÙw¿xü20;5þ\ôüÓ¯›_¨¿ØÿÒîeïtØôýW¯f^—¼Qsà-ëmÿ»˜w3¹ï±ï+?˜~èùôñî§ŒOŸ~÷„óû endstream endobj 343 0 obj << /Length 633 /Filter /FlateDecode >> stream xÚ…SKÓ0¾÷WD{J¤k;qœ •‚¢å‚½oã¶Ö&MI\Vûï{&Ý-Úª*óú2Ožìž|^pz?lËu¡“†5•¬’Í.²d5—IUiV PµÉôn—å…®Ó§LÖépF¡sA´AjRƒÊÖNnDüèü§Þt½9!¸Ïò@âÕ#ñ‰Ô…?X's´:Ñwº±nBNú’ÓÖP`?DÞÖíž²_›/Pl.k”‚/¨—kU¼¬]3^à}z3¢ÏujÖ€‚|Nf4½õv„¯Ê*õ¾î’òcHB§íyªdÐ<÷ ô!Ô©ìhQG}dfE‹(t‡*Î –åe)ÓuV—Ô4âÜ6V—äEÅ`”X92¦XW Š çµD½(úV V—F¿Ïr%U(û'W¼ßÃãj ¸ –2 Äÿk]Ÿ£»Á‡ÿ½ ¨ aþédW7ýÍ-zAû‘Z´‚rI­2‘2AµI²«¯_–üÓf!ðD\¶[–œUu™lûÅïÓ¢B‡g s)–w½N>‹oð›Mù̘¿ |ûª$¬®hk·?Ç™—"Õï`šªI¿I› à\Á¹óîÔQHè"¢7ֻѬÔú?ûÝÀ È×öx'ë½;îé#æµ­ñvÎ-îSª¹žþeë]?€ûx áú5]¼|)èòÌçŽî?6»cëþ¸ölˆz¼fôS¼žnÂiÜ„qõÄdð¡¦êðZ‡ûa´WKLS³ÝÃlà+x0—5{ëMDU±Z+ á%+…F¡þ]Ò¿1ä]Ç endstream endobj 340 0 obj << /Type /XObject /Subtype /Form /FormType 1 /PTEX.FileName (/tmp/RtmpBlhU1W/Rbuild4444671013800/timeSeries/vignettes/timeSeriesPlot-gapMultiplePlot.pdf) /PTEX.PageNumber 1 /PTEX.InfoDict 347 0 R /BBox [0 0 432 432] /Resources << /ProcSet [ /PDF /Text ] /Font << /F2 348 0 R/F3 349 0 R>> /ExtGState << >>/ColorSpace << /sRGB 350 0 R >>>> /Length 6969 /Filter /FlateDecode >> stream xœ­œM¯æ6r…÷÷Wh™,üŽHI¹õ <@‚$Ý@AVóÄp0ñ"ùùá9O‘âõ´=î‰}»«IQ"U<,Ö#Þ´ýfKÛ·ÛŸÞþeûÓ¶¿ö}Ë÷õ:Ží<²¬´·Wº¶ÿþýöoÛ]鬯óîµê+Õí(Çëº{­ãuÎZ¿úþ_ÿáëí×ÞÔÞ¾­??üúŸúÿÞ×ö?oÿþÛ¾ýî-m¿é¾}K¾ù?¾•ôjçvœç«Û§·’_W¿ÍQ_WÙ¾{ë·Ë¹›×«%ÌZ¶#÷Gj2¯×­Ê)Ìò:U9¿îCæýÚU¹¼ÚŽyïÝl¯l³¾Ž~ßcµ|ï¯\|­K»Ù/’y]2ÓëRå¦^w3{,Ž~¿³f?Æ‘ež¯[•W»eöá­6©\^;=ÚÌRÜrõ}ï×±»û‡zt7º¿JÅT÷Ïýué™kÿ»W>ûê¾5½ÒiÓ×vSƒÓ+'=s=ÜýþÌÅ•Owÿ<^¥a6UîÓcÔKµŽ³¼’ÞB-œþŽn7…+ôÒ+cªû¼ÁnöA*~æKMµ]Mê1jÁ¼¹oÕà´ìî÷k³šj‡»^¯š0í÷ëtS§}£;dréåÁ9»Ãº©›î?¦º%=M7«}ãꪷК}£›gÆÔà\}ØU9íÉýìì¸ú-rØr«èùdön_Íö麺&ÛŃð´W< Ïõ·}¤Û'õ«¤Øí°5LýzÚë“G#ÑŸû¥ä¡èõi¿Ûr”>?ÎÃv¶§”=ž?«§½‹áèu‡íñ¸4Ge{Ë•_ǶݥñSºñ—úÊÕvóxôòÃÏŸw<¦¾Z »æÇƒRö[šï=åŒÓT^^ʧÇãê“ôÛãщòË~³Úrœþò+íÆëŒþwÑx\‡º-»1GŒg·oê÷·«„½§Ot÷¯Ou×¥nÉΫûÀ®QÿÄ Þ'»­÷ïj`ÿñk•}3]Á¸eüþÎÙòØòŸÞíëOW¶vÉFiû0W÷çDj‹(¶ÅfÚ!¶ª.[ãÕ‡ÙÒœ®Û#žÿ ½ EMWn×°¶ü§¿Fîßòd]HØhîSŽèv·:ö°å?ÝMñ‡ Ùí6ï»Û/ÜV6ÂÛ§Íéñ)(o:cþ¤·O3ü¯ ½}Þ¾__Õ4^ýúâç)¨o¿žùUß>ªûWÐß>ͪßWA€ûÚŒ?¯i‹?6¼×˜7"ÜeÇ-[þÓmê÷õMãÕeëÄF‡»Ýñ¾³´¤ÛJœ»ÆzåI·¥8wEÞ[Ø}¼rݰ-Æ‹m5έ°(¤Ûrœûjà%Sv8Ù^œS_ézGTßkYªÖcÙøsµçvÄû¯ÖãåzëqÖ_GØ}¼r¿l§Üz¬rü¥Zs_¬ð¿j=Î}YÄ_«õ8÷¥®ùùû?Ýÿ ÿnèq_Fφ?6z¬ñ¦>z¼ÚòŸýŠ÷×Ðãn{µN =îõÑÛ†Ï÷ÕÐc-cîoxûO¼ÿ¼£Ç«mÿ‰ûç=îv¡=îË”ï'[þÓ—!ûsÞÑãáÿyGÓÅú&[þ“*z—wô¸Ï‡Ül߯1òŽ–}ó¼rBGsBÇøf&žü±Rߢ÷sûyÒ‰ÿdôDvøÏEý ÿ9 ;sŸˆòŸ^^nÛÖcÙõ [þ£ð%Û¶Ë?h?[swÚë¶üg¶—3óiÚó)ü[¶çÓ…>æl=žó'w!NÌÏ‹ëÑãþšJ ÛþÓõ˜û£Çz­%lûϬßðŸ“p0èñЋ|ä÷þsäá?^ßóq¼ÓŸ|¸!ù þu ÇÓŽÐãi‡çq?ô¸·çx#k{€¾z}Ígèq;CÖÙÖã=Ïgèñþxz!Vy´zúÏÐãY=î¯Ñú/[zÜ_£õ:÷õO㥰„òˆ‰c³‘/ô¸w#û^ÖÇ|¡Ç·××»}Ùä =î6ïÿ"2’|]”ìØ8Äõ…CDÿù":!|¾G”.ûÞŸ½D.;»‡ÏßmǃgÌÏB|<¶¹w›ùZˆÏ›°_¶ãÁ+ü£\Ä?%î_ˆŸöˆ{}ô¨x3—ö.”íxð"~ÍwÄÇ;ë}¾#>îÃìûÝÇö@ö½ÄÛ¹¯Ž#^ÎwÄÇ7ñì¶ÄÿùF5l~þ=Ö°Þa7ö—Ç¿¢Ç#>Ï=¬èñìOE»›Äõû‰=æOEÏþ\#>ÎÃŽø86K¹¢ÇýýàõÆ"¾·°ÕgC¨…òä}ð~ZþãªlûϬŸ¯ñ|-âã3Ư÷0jçzâãb™]HF”˜_ÂìýNáþÄÇÝæ}6âãþ=$„òvùØòŸÛÃ&;y¼zsQN|¬þ¶Æ£»õXÂ'ÿyÚC»›y¾ËÖx=×£ÇrÊÑãÕ–ÿt·ñ|ÖB(ÿéÏãù+¡Ôxõr¯o>G/÷ü”­ñ˜í%ôXnÊõèñl?‘²è¶ã3žå]}ôXa` [þ3û“Ðc%QxòJȸ½Œwÿ?kØn[/%œç²=2zÜçc¾Â¾÷g¾JX½¿Šù ‰ý'ög‘ó™ûõ#ßcîõÁökŽ·%̱?Ÿö}®6zÌ6A6zŒ c—òìÿ=~lôxµCx¾£Œý9ãsÜïö£Ú¨íË~W¶çÓÍ|òÆî|æ—lëqè©„ÚûóÐkOÌóÉÑèBëqè½ìºÌÿhH¶÷§N íÏþZ°çg¿,»,ûc 3ûÝ|„ûsÞß¹¿µíÖë¹.tng±oúçøÔ‘ŸÌÅíêO=v|©þc½Ñ‹~ç?Wèqä ,üè¥sKrÔ³<ù9êº?—}/ú¦¬©ý'öûG‰|Æ¿zù MœwãQÐãç~¡Ç¡çG‰|FŠþÜ‘¯¸X$D‘îrþAö½äƒ4‘å?zìv«Ë|»CöOÆÓó5ùýÝèñœo7z|Ñ¿ûãÅøÝm付?=ùÙ¥,úPÑã©•|Æœ/=ù–£¢Ç#ßrTôx¾ÿŠOÿ¬ä3”F°ÿTôxµ[]æ{EG¾îhèñôçF>cäsކ+Íáþ4ôx¬§G;‡ÿ0>lTžùÖÈg<6z¼Ú|PãþèqŸ/Žï>(‘ÿÿKÓ¿£ŸÞN…æç°¿¶^Œg$Õ§ùŸÛ‡ñ_l=?V8 !Öö™ }Žça®´9*O̵21+„j,¸é¬°<Ã×c˜~ûýg†éûßþñíWŸ·´}üð¤êÇWþi»+j8¯ÜÇöñÓö7­þíöñÛ·¿ûè¦ƵZK{ìkÛ—^«àíkÓ¾éÅšýW\œ~ôâdLôñ?ÎbñÒå¾þf\þ$å¤öãói²(%¶úÛþ ,ê§šZœ¼öÞ(£Ÿgþù,*)¬)“E)ùåU•º¶y‘…EÉôž•Æ„€E%ýÁ¢”³ Â¢d:¾€E¥ò‹RÒËjKT)Ó‹9,J-;6€E©t˜]i”³¢”ï¢Ô:’$‡nÊÛpe¿¼l¢Tjj‹R©7¡°(µ|çm°(µì-,J¥Ð&|Ê£yC‹Š4Ù`Q2½ÜÁ¢RMGû°¨§²c=¥ÜއEÉt(‹’éH%Ó),J]h‹Ò3[ÆaQ*u–ågøÝw¥–b„E©²G¥Ê~*XÔjÞ<¤G%Ó«-,J݉µ18äºaQ‹íܧRаXT¹ª`Q²É ¢[ªÉ¢d{ˆ‚E)åè½R°(¥õÛ)¥hª,Jv[XTR/`AÎ}ªü¨ÛdQJa¶`Qj?X“c;=O°«‹á8ÇõÎ}ÊN\_ð–c\ï¤ö`°(Ýÿ„u5Æã,hg¬­l‹Úƒuµ,j½ë`Qz ´wâ?Ë‹ÒóÆõ÷\Ï%·ƒÁ¢[ØÉ¢ì ‹²?ÖÒXŸœkåõfß&‹òzKB÷ýe}ãzô8Eî2XÔbO=æ}¡Ç‘kåõöƒï‘‹ e7LÛdQªb£Ç“¥Á¢ô¼°X”ݶ…‹•z<ÎÐãk´z\"W‹RùÊ¢¬—nÿ =Ö,J6×_¡Ç£¿°(µ¿/,Jv°­ÐãÊ."X”ÜÐú,*§Ðƒ`Q*g~À¢²RÆ ‹’ ¾BÛ`G¡Çwø,Jí·…EéùðïzÜЫ`Qš°zaQ*?‡-ÿ9ÆøÁ¢r[Y_`Q¾¾n“EI6õ‰•²ìIþs܃=Æ+öÁ¢<­)Gψg‚EI¼ž‹’`?Î}J6È¢,#÷6Y”ìvD||ƒ]Ymçm²(µ¿s?âã3ÖD|ÜíV¶É¢t}^X”Ÿ×íÁ¢<¾ƒ=y<Ö¯`Qö§…Eéýz<ƒE©~^X”ê“ ‡E¹|aQò—sß&‹Òûñü ¥÷±s¿ö΂EéyŸ‹²lÞÐã>ž;, =>Ï`°(ÙŒ?,*dx²(¿ßs›,Êõ±Ñã.ã°X”Æ÷„¡Ç«þ6zÜïwÆÐc!™¼M¥öƒ¡Ç¯¶üçŠo/‚EÙ¦ýÿi‘‹‡Eå3öOÁ¢¼¬À–Ðcm«Žm²¨‘%Ù‡­À¢Tîõ?X”ëöÐã²Çx¢´¬Ð?’ö^–`Aè±–!Xz¬m×±M•Ù†Måe†úÎghI ‹²í÷‹Šef²¨¬⢴,ñü°(•þ`Qºö ‹rýºM¥ú÷¢´ŒeX–óZ¶xŸ°(/cÔGË`…°(µ, =.‹rÿ¥ñ@O`Q +`ǰ(…)°VX”lô%B¯`Qš°XX”ê'lôXÛøÁ¦ì?¡ßÁ¢äÌoX”ʽþ‹ÒóÃz + =޼`°(Ý6‹R˜Äû…EÉÿñX”ükvøz‹Êb]\ë[{›,Êóƒrô¸·ÿ¢T~-,J6ó%½‚Eùþ ‹Òý™Ÿ°(‡Qö_XÔb£Ç«mÿ¹‚’h“¿øù1`²¨°¿{ØTf tæÃ¢œz8&‹R$¦%lûL'½Ã\+4L™0— [Û¬æZáô7׳æZ§š–‡üe`Ub&ÿuÐÈ9ü“‹Ó—^{_œ¿øâÃcùâã‹/Æ}ñùãÿëò¿Âºþù3¬KkÉœ•š¨«Ï¿.3týDCË êÕÛ¬=ÜïÿN?tU‡ŒƒsÝñq6˜««ì~nƒribŒe­º#ô„qõ²3ÎWõ¡ée-_uU¿ÇQ,kX¯iÉ…oi>ÆÉ«®Oªo¬äݽÖ×´èªå}l«D ÚË*d«ÄÀ֬鲜,è`-­rÕ*'bÔR+Ïù*}Dö¯*¶B´J•-ÝAÏÏ?³´<Úr´¨=£ž˜uG*–Õûe!eõ~9l„dÝ‘ôdÝM9B¼ÇÑ&Ç5>•‡bÕ8¨ÄjÁaX5>²aÕ› X‹Õ{«ûøîÞ¡™ÁWíˆó]Ž[|ðê1+UÆq ¬þê'é\µ<@”Á6΄À­´š ó>çCµê{Ã}@ªþÒ„.Û6™•2·_rˆ×ŒŒ,Äj5{WxÁ“W‘\%Î9NJõÞ×;>ÜV)Å;ØÔáyF@Uõ§¤ù !rˆË–Î89/ZëÀJÞ†—”ªŽ@*m}F%/¹·‰¨Ú>®õ¼Å†>Õø>øÔH?ž’ã Õ»¿šrŒ‹Üg°©6Ž)€¦”攓#¹´SA©42—A¦Ò~‘Ù 2¥·ÅA¦ô2OH–G]ï«Ðž3¡zCûB¦ÒÌôC¦¤µ÷B¦Ô^œbÿï±2%ׂT@¦T?/dJõy™²grŠŠoúõe!Sêd2µØdBû4à”dJå×rJJó¢-dJõÉôC¦4žô2¥úÈ”Ûçz¾д[ÈÔóü)] L©üvãþAÂø$@“RÄ7"“Ø|°ÇbdÊó|!S*SU|Ðïw,dJï’™ÒøCŽ!Sžûû6ÉÔbóqÀcóu€2­%>ØÇ)FÈT𧏠S¿A¦ƒLY0 O|#b'dJõï…LI3˜ñ©Åæ;%ô‰’ÿ¤Èü™²2@‚¼ó–40_ S*¿©ò|Šy)Ù×8%ÿIC S)E(dÊ6÷ãKtŒëùR ÷Ÿñ 25Ç#ÈT¯„™êåe9%¥ò²’©ù¾ƒL­¶ý'Ó SÒ Ès©9¿ƒL¥üî”Ô3ß!SÖÈ_ ¬¶Ç#HØ S"Ç”çá?A–òðN™êådƒLí‘)djÌß S-Àr€©ßý—Òg –êCBV*ÕG”R*ôÜ&“ê&)STKƒ Y‡k| @ªÖø¶5¢ÀQéj5’%£j|w,ªŽc' ¨þ$JûgJ-¿5Î$‡RéÀRÕ•óB¡j|Ja-[yë8.‚ê-—AœX¥¨~-=‚?ͧ?i•æ$’5·Ïà ðIçaEVÜïzªƒ„@žæ!4ÀÓcZm{åºzîë·Óâ#„€N#H æ¤ùto9µ<ˆ”uVrNŠÑ¨l•ÕÜ\p“‚JJ­±jùØ&lRÊ–ïˆàð:PS›§Žöè~€="¸•3Õø ™j'’ÞE®™Ú8ÏcB]Ž;)‘ÑrF³]ã0Uîçq¶iwåÀa#€mãdÓáûžu›p á™l©í£Ô‚Z#“d©ðX]$üÐ̉•žÊ#€eØJ5¾Á ¦T¯wÇ›ê `%í; Tïð x’(Ë‚“æÔ€&Õø9`RçŽîÀ2AIšÎ4eýÔΣm$iþÂu`ãÓÃÀHãDnP¤°DÒÛ‡9g9§©Æ]A´ºPÙ²Yïq¶éŽ} Î>ªeÐ+ËæIàQÜ\°£9° £¡9ª‘ˆ p¤-Ý0Ãö¹B7Ž…ÉUh4f4‹‚ŒQb4>À `¤°:9A©ï;à7ÖÍVþ±nޝC‚i%Ä´n¶yîÈûýÇ>éÑ©lÝl¼ŠÚäN-„ƒ4,˜h¨LP¢ñÑa@¢vDÎ> Ý ddÝÔN°l“µøò4‘–Q )ÂÔ3¨i†©qØ©Ö`µÖ×q8ÉaÆ<œD˜šF@”ÒHˆFÜÛD ÓŽͰ.‘÷†i›€Èa¶gªÂ¸¸?Ÿ¸¦ø8ˆÞÙ3¦M˜ªn ¤°Lßg×m"…}øËaê´#L]ìJضåÔa%‰]Ÿög˜Êa#‘ìàB˜šÂÙ©|_Q‡ÅͰ6‘6µ5m©<?YXý<#>xíå‘ë·m¢”qXŠ05—q½Õ5å;|@¤­/î Òî–ñi·Y@”ø6"í>×ÃJÚ}@DÚM"L=#L@¤Ý-Ï Jg€‰^Ïø1‘vËuDºž™ R}€€È»gÊùàõˆÃÿˆbë?Q¿œ#Q:âÃøD²÷É8ˆ´Ûß—ÃJn/mÉŽÃM–]g@$›ç)[@‰´ÁSÎT}šð¾DÎFpÒ ºžöIã0€è© r²Ã÷i¼ðw‘Æ€ ÒxËa%½ß¶"íþ‘Y‘vï.‘츞´ÁÛ¦Diî @$` Òî?•m¢çzQRˆÅa$Ò: P²§kÌ_‘Êy>Ñb£Ç«Ý¨p)›à â/ˆHžO>„ù݃‹BÿM óÁCÅ[™OóÐI¶žo^n0ƒµ³SűoyŠùÞ|Ç×ç YÒ÷WÏ!¦0× ]öÛÃ…Â\+œ^g̵=š–þ2d©80ü«NAñu L*)“Ê>úÀÅåK/¾œDøÐJ|½µÅ’¥ûA…a^^‚UAûä>«F…a>ÞµùÜâ©°vàyê§+:”¶®"¶ò¾—¯öôÕçÏr}®£yýY÷4qð™ô»&¤lN<&?|óÍç._.YZÒØ6}ñ#}×ÕÿëÓïß]~Äåç.·­/ïú6ãÜÓ¸þ'á{û?eÏÓL endstream endobj 352 0 obj << /Alternate /DeviceRGB /N 3 /Length 2596 /Filter /FlateDecode >> stream xœ–wTSهϽ7½P’Š”ÐkhRH ½H‘.*1 JÀ"6DTpDQ‘¦2(à€£C‘±"Š…Q±ëDÔqp–Id­ß¼yïÍ›ß÷~kŸ½ÏÝgï}ÖºüƒÂLX € ¡Xáçň‹g` ðlàp³³BøF™|ØŒl™ø½º ùû*Ó?ŒÁÿŸ”¹Y"1P˜ŒçòøÙ\É8=Wœ%·Oɘ¶4MÎ0JÎ"Y‚2V“sò,[|ö™e9ó2„<ËsÎâeðäÜ'ã9¾Œ‘`çø¹2¾&cƒtI†@Æoä±|N6(’Ü.æsSdl-c’(2‚-ãyàHÉ_ðÒ/XÌÏËÅÎÌZ.$§ˆ&\S†“‹áÏÏMç‹ÅÌ07#â1Ø™YárfÏüYym²";Ø8980m-m¾(Ô]ü›’÷v–^„îDøÃöW~™ °¦eµÙú‡mi]ëP»ý‡Í`/в¾u}qº|^RÄâ,g+«ÜÜ\KŸk)/èïúŸC_|ÏR¾Ýïåaxó“8’t1C^7nfz¦DÄÈÎâpù 柇øþuü$¾ˆ/”ED˦L L–µ[Ȉ™B†@øŸšøÃþ¤Ù¹–‰ÚøЖX¥!@~(* {d+Ðï} ÆGù͋љ˜ûÏ‚þ}W¸LþÈ$ŽcGD2¸QÎìšüZ4 E@ê@èÀ¶À¸àA(ˆq`1à‚D €µ ”‚­`'¨u 4ƒ6ptcà48.Ë`ÜR0ž€)ð Ì@„…ÈR‡t CȲ…XäCP”%CBH@ë R¨ª†ê¡fè[è(tº C· Qhúz#0 ¦ÁZ°l³`O8Ž„ÁÉð28.‚·À•p|î„O×àX ?§€:¢‹0ÂFB‘x$ !«¤i@Ú¤¹ŠH‘§È[EE1PL” Ê…⢖¡V¡6£ªQP¨>ÔUÔ(j õMFk¢ÍÑÎèt,:‹.FW ›Ðè³èô8úƒ¡cŒ1ŽL&³³³ÓŽ9…ÆŒa¦±X¬:ÖëŠ År°bl1¶ {{{;Ž}ƒ#âtp¶8_\¡8áú"ãEy‹.,ÖXœ¾øøÅ%œ%Gщ1‰-‰ï9¡œÎôÒ€¥µK§¸lî.îžoo’ïÊ/çO$¹&•'=JvMÞž<™âžR‘òTÀT ž§ú§Ö¥¾N MÛŸö)=&½=—‘˜qTH¦ û2µ3ó2‡³Ì³Š³¤Ëœ—í\6% 5eCÙ‹²»Å4ÙÏÔ€ÄD²^2šã–S“ó&7:÷Hžrž0o`¹ÙòMË'ò}ó¿^ZÁ]Ñ[ [°¶`t¥çÊúUЪ¥«zWë¯.Z=¾Æo͵„µik(´.,/|¹.f]O‘VÑš¢±õ~ë[‹ŠEÅ76¸l¨ÛˆÚ(Ø8¸iMKx%K­K+Jßoæn¾ø•ÍW•_}Ú’´e°Ì¡lÏVÌVáÖëÛÜ·(W.Ï/Û²½scGÉŽ—;—ì¼PaWQ·‹°K²KZ\Ù]ePµµê}uJõHWM{­fí¦Ú×»y»¯ìñØÓV§UWZ÷n¯`ïÍz¿úΣ†Š}˜}9û6F7öÍúº¹I£©´éÃ~á~éˆ}ÍŽÍÍ-š-e­p«¤uò`ÂÁËßxÓÝÆl«o§·—‡$‡›øíõÃA‡{°Ž´}gø]mµ£¤ê\Þ9Õ•Ò%íŽë>x´·Ç¥§ã{Ëï÷Ó=Vs\åx٠‰¢ŸN柜>•uêééäÓc½Kz=s­/¼oðlÐÙóç|Ïé÷ì?yÞõü± ÎŽ^d]ìºäp©sÀ~ ãû:;‡‡º/;]îž7|âŠû•ÓW½¯ž»píÒÈü‘áëQ×oÞH¸!½É»ùèVú­ç·snÏÜYs}·äžÒ½Šûš÷~4ý±]ê =>ê=:ð`Áƒ;cܱ'?eÿô~¼è!ùaÅ„ÎDó#ÛGÇ&}'/?^øxüIÖ“™§Å?+ÿ\ûÌäÙw¿xü20;5þ\ôüÓ¯›_¨¿ØÿÒîeïtØôýW¯f^—¼Qsà-ëmÿ»˜w3¹ï±ï+?˜~èùôñî§ŒOŸ~÷„óû endstream endobj 356 0 obj << /Length 881 /Filter /FlateDecode >> stream xÚ¥VKoÜ6¾ï¯Ð‘,šoQÜCÓºM.M‘½IZ™vHâFþ÷j¨}8Ú8@aœΛ3Ÿ–% KþذxþºÝ\ßê<áœZ‹d{Ÿp¡¨e"É™¡\Éö.ùDUiƹÐä­oS)È®îêDä!Íd®ÈDž㤠„Cñ‡ÆCúeûþúVæIA #LÂ’LZ*¹D÷ÿ¤V?¤*;$Êfð’¤ê]9:”>§Â‚nœOENž¢A®¦f¬÷CÃ}Ù¹/÷!šfš ò~ÆÌÏFPN´ê¡fë9ý0öÕJÆH¹œ}_?ÏÈú{<{LoX”ï¨|3µ]”N!"’㿉}Ù—­]ìàÆ”fNî}Õò¤£Â j%‡DçÛûÞ?­4^À£*³h]­8ÒÔ qt¹^r£ÏÜÀ‹*F ¥b§ Þ…¢¿ *M~ˆRö+1¸¦Æthô¢O4à‘(ÓPqÊò8ª¿ÀÜB ?3Íæ>ÜTWáFÌü¿ö°/¬!±™¿AÓÙÏΣoo8ÕWx©)Ôè÷7*†ô§ÁT§ãGñiÉïËÿON­¦¶¤|99óZrâÜC~ßnÂØ±„°CAuÁ“ªÝ|ÛМT8RóÅb×ïZ›üæ7Ãßr•-³—3hÊ—1jò*·õÃÔ‡…RšØ7i¦Œ%uQ€ Ä4DIPØ€ɰ›+s [Çxuë ªíAëâJ(+_Y‰ Y-:§(?¢DÈ×ãR ͨÔü|öMYA‰ k)7бÜ@º î/(†Ì.ÐÏ+¾GK7¤wrà˜?]ïÖÀG9:­Ÿ’QÅò÷3“"§Æ¨óÊ/ZA™´‹Gh^&ŠÈ¢àä«ßAæJ2ò®[®êïªrp(+ñx ]qÕ:øð58óÞ®“¥öˆŠó>w}Üß®ZïE檘«‹Fwøzй Ónpß&×á'Âäæ¼-Ÿ™Ta[ÂðˆwÎ2ž]ŸáSf]oN>xÀú¨ˆ_0 îÜc]Ez™@?}À @ƒh. õx†ÑÂ2ŽŠQ”¯ÖàÒßCÙ6ô¾~ º®y¿,è^]:n µ¹N2?wxtÊÍK°ûA6GÍ endstream endobj 353 0 obj << /Type /XObject /Subtype /Form /FormType 1 /PTEX.FileName (/tmp/RtmpBlhU1W/Rbuild4444671013800/timeSeries/vignettes/timeSeriesPlot-combineSinglePlot.pdf) /PTEX.PageNumber 1 /PTEX.InfoDict 359 0 R /BBox [0 0 432 432] /Resources << /ProcSet [ /PDF /Text ] /Font << /F2 360 0 R>> /ExtGState << >>/ColorSpace << /sRGB 361 0 R >>>> /Length 4933 /Filter /FlateDecode >> stream xœµšKÏ&7…÷߯è%,h|¿l ÌH,+.‚ˆA‚,øûÔ9§ìö;‰D&)™ÉI»m·]®*×óÆëWW¼¾¸þýö»ëßWÏw)WÊýnáÊiÜ9]1·{„ë?½þpýëí§_þþŸ]Ÿ¿{ wá:ÿ|÷ùoìÿözý÷íºÂõ—·xýÊþýâ-¢Áõë·QÕo½G»>¼vÇqåØïR¯Böf2ß“²ß9_9”;fÈy‡nrÜ1H¶jrÞ½˜œáNÉdºC–´)çPñŽÉxg(´{òi¾c仡H¶IY#d¹“ ÇÝ’ä°bÁx&ë]0«~öÜïˆ9sJ›N¶ïm|j hÅt÷!9l hãNÈy—ÎÏO(†x'›tlw®íµœlñô<ÝÙÆJ½HÏB¥ó]»¯•ºÞI3Ãõ°©'û¾LÝîb³KI =1^¿k ¶ÿhÜ­¦çóÎÏgwmkà»iÚ¾ºTÎ?p|[”9ÿ2]·Èñ‹Úç;jî ôLì?HL$§yÇBm»ióËA› ÈËm3_µM4ç¢}¶£ÕÆ+åîšul㕺Æ3[²ñJöïµ…ÅV?ÚŒËæ_â=§ë:©;¿'™Yuê4]ÃJÀ0Ðf:ãìϪï7<€Òf¯cÛ¤ûäüƒúŸÜo›b{[˜ؾ×ÍÆ«É÷Ë&5¶ŸÍõ(GÙìG󯜯-\ÄúN·/ÓÝæ—+º…®´‡œ`FÒCû“5~Ç Â~t=è:×½ödåzì¿é™Ù_e{›XkOö\:ʘöÒ#:Ë ÌZšö0ü<ÙBØÄ²¹·Æù¼lÓÎÉõhlߥ;œ#ÚgõÇŽ°>EÏ'œ_Ö°¦k ³ýÏÕõŒÔƒíkÄÁxÆ·‚=d èL'f^˜Î$ÖB/fë7‹ëy®W­wÍt\Cã5º93k}OíÜo˜¹æ70q´÷ùNxiø í§é*¯)Ñ}]~Þ4el‰þ n…ómþõEÃ옮_+ôwö<Ð^Zå~G.tÃÁÝ®6¶C…¯•?0Ý:„ì½ ú;sƒMó›ôgûyðgÉ<8ãKìì(Yp`6{H3a›¡rB´Pû‚…NƒñAÚ:NÔ¦ú¯gidl»´ì4¢û·Þ°±x_þµ82hÙ¿iógÉbWä÷÷‰…Ocøù1=l~}¸½{8t‚£yÑæÏRïîmb6ñdÓÒ~™6CIö ¿Ñ&bþ.ÙgË^žõˆ®m¡’m‹æ;xP’mk®m!’™ü‹MÜ6ÏõýˆÑñU›!&3ù Di¯2¤J›¡'3ãÆùÛÆTŒÇfÐû ­ýE¤Î|_öŒPÏ÷+w²c/{œ þ.™ÛÐùB´¶ù—æöpmëkÇ‘?"^Ûüàñ<™áÃìy®[ä÷Ó§À…Lf–¹»žéÔ†°×;™¡†ñªÖ?Ë';(öaÐs¸ží£æÓø÷~%¤J¯-M{ðýOˆ×åÔñíßtÔô§ÉÒ˜ˆñšâ´ù»dfX3uÁÁÞöŸ¯aÿ]þºÚw–nH|`ÿ!»†=¬ó¯ócÏ ñ:Ò^çÒmßk‰†-ü³¾¦ì5è|&ÄëÎýáyH‰ö¡þ¯e?ôoÉ“¬ýìÙµù»dnSßx¨³Þïðgx¿-Ý1^’¿IH§1^ŲIÏ|ô‡xÝNÍ@¹íz$~ý[Bà Ïù6ÃHit¡?3·¤+ýYkòwÐçsâ3¡›ìa·ïˆoÉÜ"ãmB l¿Hˆ×ù°ÄëHûdþ— ¿í›N{IÒ ñí±Îñª'쇟 ]`H°‡¨÷+ínëx]iUºÓŸâû‡x-‚ëŠñšÛ‹Åoø;¸=®Åkì÷òß©2±|Ú#^WÆÆ[hËß’¹•&iæ6d_ˆ×å¹…À˜¯7?ϵ)¾y|¤ãÂý *_€a¥úÜ`(ˆo¦å¯ÛsS¡Äò\¯ {b>.ÿGžû3óuÏç¡Ï|=Y¼Žó¹_@ã¾fýM=¯Êç«ïâõxîÐÈ×í˜=ùΧéñè–¡'ó7{Îx CD¾¾ûC¼l/ƒx=ž|“Ž¡>ù ù|ž¾>ˆ×Èw›Ÿ'ÄkäÃÃíñú¸@ÏðäÛ4ìñäË ñù]òõêSùºçÿ0ì¬ñd¦‘¿YU:*ŸÏ¾þˆ×íÉÏ¡‘¿e¦9ÐLÔžïãœÌÏ™?ÁБϛ[h\?Äkåë±»f¾Þ”ß Ñ(í¹/¥Áû1î_£¸f>Ü_ ^×ç~üÍöƒ7t$RUö¤³ìa·Ï+Ÿ×ü,>3_nÿˆ×÷í7âµgÇHñlò´Î+.ß¶8vz>™¯Û±ä÷ QE¾Ž|%¹F5¢Ý¿³Ý·aöœþ ù¼ùKúW$–È×Íßv=/Ì×Íì9?&šå쯲&aÇŒë |þy¿3_7³çyai#¾jÔ%p,:õdaiqv {hSöÄùz«º/¢ì€|}÷‡xñºìŽ&Ç£ÓCãÑßÂñ`¿Ÿö4Ü,7 úÄþÜ¿m<ä?ÃõÔþdéI†üŠóƒ#ªÔ<ÿи¿­û+ %ð<öîþnW8Þßü<БÍç~=ús_‡á¡>±îÛ0Tø;;_Z?ÄëvêAvjÔ'píäþ¦©û[WuÇ ûþG4â©ïo§žÇ}œŽªëƒûwzî£8ا?ǟÙ4´^ˆ×ñ9_а‡åO±Pñð×p3<õ8*Üß–¿g!¢>çºi<Æ?.D|î×p\³<÷e,\=îÇøPÔ'rS¼„F}ÂÞ—½À1Öç~ C€?öð{à˜û©YÂ÷ວ§>”k¿+Ê·àB~êÐ->þŽ#žöcºõr¥-ꯩåõhú³5Äëüø7èáõõ7^êpÜo¯o@#¾íþÌ‘¤s<Ü¿ÛãÏqYŸpÿ̓Úù½¬B³>áõ/8 Ö'–ýµªø6u?‚£)çyC¼N|îóŠoÝí¡MÅ·uÞpÿ®\?濹3±{Öñú¨A³åõ“ŒxÿÐHÿ`ºžþñ:>õè1ŸzKî]ñmí?îßå°Ï>ߘ¶˜Aû}hÖ£ÖyG¼ŽO½.["æaϦÛQÏɈך÷è9žxš¯zfF"zž7ܿ㩙z2¾á²´Wñ™o¼c…Ÿ•x\µì¯b¦ó(/ðöÞküþòkjü_þù_o?ýyºâõþoWäëaÿ!{’Må³ã÷®½ÿLJ¿þøzÿÅÛÏÞ³ûõþñÒO޾ã½™'Þ÷Ù/×ëö ß ADD™ºLJ˽´ë^×»¯4Ù:00¢IB,iO“­Ÿ&¯=M¾óR–JpQoZéBûIˆöÏ×­÷×w‚: î©÷Ò?©—4™½¼öÒ_çât©± óÁaÜ òi,ÈG ìï´åÙ`òŠ»H ׯ~t>ÂptïÐYï“OÐýÌkc?ÌDZ±ü‘æ#ì$)ìG$MìãÂpÂ~ü^ib?|ŸNœ°ÆÓ|…ýMì÷¢{àûŽñˆýè/—¯)þ8öƒÔÏ*„ýà#eŸÂ~˜oW{Ö#ð\ß#ìGŸªþ‡ì¡ùzû½è>5ž°yÖ×±ﻜ¯°±Ú«a?øaaxa?x^×\x<—–G‰ª&mì‡þ#ҧе±C•žûÍ)wàÔÏ:‰KÚ2L¯A8óÃÅk#?ÌX’Äoz€sàwÊÆTŽ–À¹Ì ÿÁ‹¥EÿHû¦ÃJ‡}s±±¾9º#ê{$IŸê·ôMßç|ó*Ç|£/*GÊgF6 ùº—3¾^_ßHª8áþ&|ÃŽó½±ÊãÂ{+²:ݳÅrøF¸7KÛ[±ÓÑžu¥J¸È>A’`åè%m>søjëÁߪL-¬ç ÕÆzsê¸8ÕCóxP½9|#õ˜¬ ’êÁ𯵙—ƒé!¬÷k#½é®Ð‰Þô@8гV½]:ÌC¢*{ÌC.ªó¨ë•ç¢æ1Ù $ÌC2*{ÌC{¹GÁ<ö/XG˜Çþõ>aÞÑ?aúË‚m LfõaÆ—ßÌÃs'˜uÌ6ÌC²›¼Ëš¿`©`^´c¯ó#˜Ç\{Á»èãÍkÃ<ô§õÌC²[¼C^WqV0É1ý‹Ã<ètÀ<$×ãó O˜Å6Ì‹ªño˜çÉõ†yèßáa´Šó‚y˜¯Š³‚yÐ]ó#Ìã|˜Çõ|ãMWËó`O}éž¹~ÌCæaÿëÒðwÙï@óø\°a×*û“˜û¨áÚ0oÛÃ<ì—à‚`.3qi¶º`†Ò/ìWZpþδ¾G0/®Û8Ìãþ¶kÃ<¶|#EÀeFë/˜‡õ‚…„y‡&Ì{Ñ–™árã0rÊüÇ N‘x9<ã¥ýÐL7_4â[«¾Ÿ‚yÔ‚}„y¸üh?óüò´a.Kó€yЩ]æárÿxõÅåÅû'ÌÃåCpR0—µÌãåd^æQë9a^47xÂ<<×z æÅ¾ìW0—‡…¼äãývÀ¼Øó‚…Lpê‚u„y¼ü,x‡øÖ‡Û§`.3M°0—½/˜G¯ ó¢¹Id\^´¾‚y¸ü8¬#ÌÃsÁ-töwÀ<¶,$ÌC{Á$Á<\~Ûópùá-Îa^ Π桽ÞÌóËÙ†y˜¿ì]0ól#ÌCû¸4ü™…a}¯`ž_Î7̃jÏrìMö*˜‡óàó!ÌÃyìÌãåü€y°·tÀ> stream xœ–wTSهϽ7½P’Š”ÐkhRH ½H‘.*1 JÀ"6DTpDQ‘¦2(à€£C‘±"Š…Q±ëDÔqp–Id­ß¼yïÍ›ß÷~kŸ½ÏÝgï}ÖºüƒÂLX € ¡Xáçň‹g` ðlàp³³BøF™|ØŒl™ø½º ùû*Ó?ŒÁÿŸ”¹Y"1P˜ŒçòøÙ\É8=Wœ%·Oɘ¶4MÎ0JÎ"Y‚2V“sò,[|ö™e9ó2„<ËsÎâeðäÜ'ã9¾Œ‘`çø¹2¾&cƒtI†@Æoä±|N6(’Ü.æsSdl-c’(2‚-ãyàHÉ_ðÒ/XÌÏËÅÎÌZ.$§ˆ&\S†“‹áÏÏMç‹ÅÌ07#â1Ø™YárfÏüYym²";Ø8980m-m¾(Ô]ü›’÷v–^„îDøÃöW~™ °¦eµÙú‡mi]ëP»ý‡Í`/в¾u}qº|^RÄâ,g+«ÜÜ\KŸk)/èïúŸC_|ÏR¾Ýïåaxó“8’t1C^7nfz¦DÄÈÎâpù 柇øþuü$¾ˆ/”ED˦L L–µ[Ȉ™B†@øŸšøÃþ¤Ù¹–‰ÚøЖX¥!@~(* {d+Ðï} ÆGù͋љ˜ûÏ‚þ}W¸LþÈ$ŽcGD2¸QÎìšüZ4 E@ê@èÀ¶À¸àA(ˆq`1à‚D €µ ”‚­`'¨u 4ƒ6ptcà48.Ë`ÜR0ž€)ð Ì@„…ÈR‡t CȲ…XäCP”%CBH@ë R¨ª†ê¡fè[è(tº C· Qhúz#0 ¦ÁZ°l³`O8Ž„ÁÉð28.‚·À•p|î„O×àX ?§€:¢‹0ÂFB‘x$ !«¤i@Ú¤¹ŠH‘§È[EE1PL” Ê…⢖¡V¡6£ªQP¨>ÔUÔ(j õMFk¢ÍÑÎèt,:‹.FW ›Ðè³èô8úƒ¡cŒ1ŽL&³³³ÓŽ9…ÆŒa¦±X¬:ÖëŠ År°bl1¶ {{{;Ž}ƒ#âtp¶8_\¡8áú"ãEy‹.,ÖXœ¾øøÅ%œ%Gщ1‰-‰ï9¡œÎôÒ€¥µK§¸lî.îžoo’ïÊ/çO$¹&•'=JvMÞž<™âžR‘òTÀT ž§ú§Ö¥¾N MÛŸö)=&½=—‘˜qTH¦ û2µ3ó2‡³Ì³Š³¤Ëœ—í\6% 5eCÙ‹²»Å4ÙÏÔ€ÄD²^2šã–S“ó&7:÷Hžrž0o`¹ÙòMË'ò}ó¿^ZÁ]Ñ[ [°¶`t¥çÊúUЪ¥«zWë¯.Z=¾Æo͵„µik(´.,/|¹.f]O‘VÑš¢±õ~ë[‹ŠEÅ76¸l¨ÛˆÚ(Ø8¸iMKx%K­K+Jßoæn¾ø•ÍW•_}Ú’´e°Ì¡lÏVÌVáÖëÛÜ·(W.Ï/Û²½scGÉŽ—;—ì¼PaWQ·‹°K²KZ\Ù]ePµµê}uJõHWM{­fí¦Ú×»y»¯ìñØÓV§UWZ÷n¯`ïÍz¿úΣ†Š}˜}9û6F7öÍúº¹I£©´éÃ~á~éˆ}ÍŽÍÍ-š-e­p«¤uò`ÂÁËßxÓÝÆl«o§·—‡$‡›øíõÃA‡{°Ž´}gø]mµ£¤ê\Þ9Õ•Ò%íŽë>x´·Ç¥§ã{Ëï÷Ó=Vs\åx٠‰¢ŸN柜>•uêééäÓc½Kz=s­/¼oðlÐÙóç|Ïé÷ì?yÞõü± ÎŽ^d]ìºäp©sÀ~ ãû:;‡‡º/;]îž7|âŠû•ÓW½¯ž»píÒÈü‘áëQ×oÞH¸!½É»ùèVú­ç·snÏÜYs}·äžÒ½Šûš÷~4ý±]ê =>ê=:ð`Áƒ;cܱ'?eÿô~¼è!ùaÅ„ÎDó#ÛGÇ&}'/?^øxüIÖ“™§Å?+ÿ\ûÌäÙw¿xü20;5þ\ôüÓ¯›_¨¿ØÿÒîeïtØôýW¯f^—¼Qsà-ëmÿ»˜w3¹ï±ï+?˜~èùôñî§ŒOŸ~÷„óû endstream endobj 367 0 obj << /Length 759 /Filter /FlateDecode >> stream xÚ¥TKoÛ0 ¾çWøhµª—_òðvì°µvh{P%5f[žì´Í¿eÊi³f¯CŠ¤È”ôÑ`ÐàíŒzùz1;>M²€1R$ «€qIrʃŒ¦„ñ"X,ƒ‹“$ŠãIøQE‚‡ÛH°Ðl†(YšÊóª]×õOµúèjñáøTdAAŠ”§.= b‘Á&^Üh ;dZ„URE,l¡Zh@nœ¦­så¡é†Ê´>ÜË3t —¥ÕjÐhëÜÞ]:]£µVÏ¡ž¹ú£(N([3`‚V—ºï•­êzû¸RªÖõã:ÀqaV—ƒj×›Z9TYë+ä"$QœÁÔÞ–[÷§}Š`ƒÑ|ÃåØO¡mv£ëµN­½Í{ ž‚V›¶3Ž;ÆÍ F8z[«­;µ§Ç"™œÂ`&2¡PÜŽÇ‚Ã(2ßxŠA¥i4™Â5#„ ûªéÆó“¾WnA<¦dÿ HJ–cžWp0¢v…òeì¤ôP/iB5Ìá³Õ½[•îað$ùnàœl×[kîP£Xœ}9ñÎÖºÛu7çnƒû­GÖ)‹0ì£l2\›a0Íœû´ ™ª¦› Šö7eàŸ”ù¸ðÙÕ“=é¿B¡McυƯþj4yÚ³§&vÐv#K1àAÓ.o8~ßÁ3û ¿ÉOãG)GbÝ{a»º©$”{ú;­ÖÇ‚añ^pë×Ê‘ž 9>pçúÍwiEÁÿüÀ%‘²˜Â¶Ž‰Ì³—@lc½¥¾­–¾ä®¶ðƒ»ôÖªEi#ÇÓÀAw=©v‰œÁ’œä’íZiêMÓöEt¨väæ%𯴠ñ¹£¾2¶A~YêK*d;_#ËŽ:Â…e×›F·Î5âÔ’‚îÆuÌŠ%$ͳ)†ìݤ_Iè“pyd§Œ¤þ|Yöó5üÀÍÔ¬ endstream endobj 364 0 obj << /Type /XObject /Subtype /Form /FormType 1 /PTEX.FileName (/tmp/RtmpBlhU1W/Rbuild4444671013800/timeSeries/vignettes/timeSeriesPlot-layoutSinglePlot.pdf) /PTEX.PageNumber 1 /PTEX.InfoDict 370 0 R /BBox [0 0 432 432] /Resources << /ProcSet [ /PDF /Text ] /Font << /F2 371 0 R>> /ExtGState << >>/ColorSpace << /sRGB 372 0 R >>>> /Length 7198 /Filter /FlateDecode >> stream xœµ|IÏe9rÝ>Å[Ú‹~ºœÉm ¶!6lW^^ÉäF•¹þûŽ3Ä}÷K7 Õ  †<d—C0‚qøÊëo_åõ§×?û/¯~Íë}æ«¶ó>׫õñ>ãUÆ|×þúßÿúo¯úöWþ¯ÿ᯿þáÛõ¾®ëõüïýŸâo×xýŸoÿý¼®×ÿüV^ÿþé[A×ü¶Ú{­Ð¥Êëço«¿Û~µVÞåzýxΫÕe8Þ#`ï}×»×^ïÞ÷ûjë»ÁÙ£p{wÂó®#àÄçîð¼/HwyÏh·ÞÔUWÄÇ·úî°½Ga¯zµû»¨š7‡¨Õn8ß#ªíÝãSâ{ëx/C|~h> pëó¯÷FCçzW4tPˆpGC­¿/J ¾»µ +þºµñn†3ºÑ¢¯èäéïŽv· ÷…†¶ïy¨Šxæ»a²ë{lÁS8G’î÷Іz{—xÞ­ç òóÚ-×ÅïNE\8±žX»\ xvãYØö5ˆû»ÆG÷èâݸG%…¾0 Àë]çW¼£½qaéïw/ŸõÝ1Wð¼ŒWôgt+p)hèë»D{ãà±±ž€c¢±ôŽ×bǶ‹w§¾óëY½¯B|%ÆxÌXó*¿9QP,»(ô_õÏÉò–—wï_±À—ñÆ÷·ÄKþ¡/v Ú×x>ÑÞˆU-9·Qð×À±ü0ý±°¶qìwÌÏÞXD˜&|P°õùÞÃã‘«©ÄÚï×cþÃ`<¢¼qÅ’E{‹íÇïhïx=`w̯x.öWë!öÆct_£mi£z~/|C·€c"õý-ñi,ïö£¡Îù-”÷‚ÞÂl®f|З%07°ôãÃÏ£ýÀûbÿ+¿7r6ŽÏáøtš^ŒçƯ`°´3ñ”Mnr‹ëo 7Ø{¬:©~aà0S\Àë<õÅþ¬Oû³}Ö7pØ{|OUýxïŸçé5¹?y>Uø’XÓë§ÑÅ…\ßÃQ®ñÃÁQžåæ_ãçg{¼Æcý„Ù´þÚ1Æx¤ý©q¾b)W|÷ÄÀ[ø`à(þ‹iàý‚qô·I®øîÖç)Æc³aŽGê/ŠÿÓß‡ãŒøîS^ñ–•ä‹ñÎý=ïÄ27VüóC{ËÛš‹ûþ3®\.í?ÆKÀ³~âW^ç4îǽŒïä~Å=Ë\Ÿý€›ÆwŽÏ€1¯Ã°b<2ÞöÝö×Vß}°â»‡¡…½}=ñÒ~¤C‹ñÈø†²œ¯x_OÜqþÝñ8 )ìý=>Mñ_Æ£0œÏx‹ñn×~žë³¿`ˆëþØS`ƶ׬ú¹¯A ù´÷Àç±ÿaˆ1ўƷ+þËøš7„û/#p‡½Ïø†ö>¾‡ö öǤêoŽGÆ·TÅ·;1⻦#ÌÏà÷ÇÄãüËû!`Úû+ËwÆ»yŸACSdoc<îõÇxä} yç÷Ò¾c"×ú܇`a´ýìÿ¡½Oû†‹ñˆ÷iè¯ÇøMÆw÷ýÆ#ÞçÂöf×ù·3þ»íw›ŒïÐmí¯IÇà¾ÿjá8Žçú‹ó”çßú`ž¹ßTÓúŒø´è¾Iû{Õ<ÿ4ž±ñ›ÖƒÆk1þûŒßb|÷ÙkÊÞûþ¤­¥ó¯0ìýmã¿Ï~ÙŒïîû`\mæ} q{Î?ÙòXŸ ,°>µ~6ã»/ø<÷; ÿüÜ×Áð–õXÏ›ñß}ŸCÊûÍ“ý9ºßôyJCü¸Ï„açýfî·Ãøïéˆ<0ã;ncî_Rº_£?ô/õyùŽÐ(þ×[} ßéÿñG_ëÿÝŸÿµþŸÿýÕ¿¯¯òúñ^ºÉÿüGqPÌ+Âá0·?þüú7?þ¯Ÿÿþß¾~üÓ·÷#ÕgýG¥?á·eJá)ï¬ÆÏ/[iãŸ^¯þŸ"7–ã†"µÒ©¸‹ÜøSä«âGCŸ"¿y¨7OD²M#]¯kþá*ñÏ_↓÷ˆ¿×²~‘z;å;-ëk_œPZtxv~ Ûñ仃'›á£<§°$YÀðY@gÄ]ÀGÆ£€½ <úð/X q°á¢÷‹Ÿzö/XÞªkÆØÝ¬}~yíÆ»JÔ.×õË«ã*r¹ú÷Óãû­_“÷Þ‰Û™ò§=’ ÿñõÃ#kNFu^_¿gÒ1cy…Ú8  fwÂàŠI óÉãa2ÝŸÆwr‚ÃôòhÃI8èØá@ Ù–›¨SÆ w¸«¤Ã„ư… ~E‚ÌáÛ¤¹‚ñg¾’±ÜðU"ÓmË eGnû¢ƒ»ì*:§H„2”±½8_+etÅi„ã¿x)£ô —#>Ö‹~þl¥Åð#GY§l*s¸xÂa]Sç€Î»ä€NcSõ†¿aZƱ$™ú2©³ê’aÑŒogˆ°èÑâkÙ3:èg꬧¤-ïê¦:DÒ%y†üî…’ÍHí!¦f_BR?yá:}ý¡ÛløLèKø}dGŽÖæ=ŽÆŠ(t._Zn&_ cö¸YFgsä5E}Œ[§ƒ/)BáÂâȇŸÇ.úÚÍëu¤¸-‹oÑl(²å 3ÝjAÖ<"ˆ YÃf#‚Φ5:§®¶§~nÊŽGwC¶/ῨŸ¼þÙÇß°,‹zÛ®çaˆ²=Gº‡G¶` Ag‘ÓŽ;…u'œ ëÙa=Õ²cY×·#~س.S9ýÃ×Ê[2ÆL–‡ ­¡Ð‰óq …ÎsÉÇmÔ;r6•FzÒ9Ü/~a!7êq½þí>n9™í+³Ã«â(É »Ãû»µÎÛÈmgû0é*“õr<™g=vsAæ(tž~ËeU s^ØGCÎÏ_”f:é{JO7ì”:Ï˵3µyQwio•«ºYe²/F–ÇV‰ÿ"é1ì¬ë´<ïcÎrÖíê¢sʪ†øhÌÄ%‹óØÊ‡ª×üÈ)yMÒÀtã.>ýYcjH”qºhaŸ0 Ä’qÙ¿ {eÇOâXÆhyMãywzý:’›MLyÕ)¯´H®ôtà®ú+1ëgºé–ú[ÿIºzŸÙ§R¥þrö¿TUO6@i’og¿J“üxÊK“z›4²!ªÊóóJOý4dÀhþîýðàÕfÚwsj©žã"&Aaæjûœ±"¤8ž·aH—ÃZÐ*B³’©‚¡ù £®RÕ N2•ÃíNgdËv»"@”ƒ© ¸›aؔݼè•Ç~†=î ãÈ_S0,ÂNµ"¼©Í ¤=‰¤?íTÒ_}€Ô öµÀ"î£Ü¹-`ʇ¹1I–›KýµúpèKõ·¹ }¡þò†í ûa>´Î: bÓ_)§þak€Ü©äæ>Éë7‰òæ9.é_îß`”±jŒú-×Q$¯ÿQ$ŸÉ-ªÔß–-ÌàÀa™ UU?¹mCóåe¯FKýæFµ[ÿÍjOýd·’R¦ò¼tƲò÷ ÉOö—wÿX¶â’ æ2¸Œ…gÖ×ú ¼U_ó1–ÚÏd,é_>I^,ߌ©¿zþ"’dûËÜͱ%?^ïºäŲ‰¡µmÌúÎM€›Eùð~SL ,35/éßn?bBêß L­_‡Ú¬Ù¾¹[UòeÃ6köÿ$l«¾¸;Jºr|/cŽ_rí¦çïRn ˜ãg10ÇÏa-0êW_)€;†Ç†\x‡·§€Æk’tAsœ¸-®‘\3ÖÏõÈbuªá’>ä=÷ÿ$וfãæ¢UÎ×,ƨ¯”ÊOä¢A>ÒžèBˆó›rè—}ŠIR·¸h—ôo˳ý—ïäEí7ïŸU¤?÷NQÈ‹í}x”?ðWySÿǹ†%|ŒYß\ˆv9äÃû?°åæÖuépé Þý%—Ó¢ý*Ò ‡áS~’˧ù[¦sCÿJßp-ÕOna`Ü—­´‹¤”—×.Ádùxb·GOÓÌ`Ü=Ê“›)>óJîõwåÎÉíƒÜ× À¨¿KÊ™Û,™ËÆ,×úA¼yKn^•~sÇ€›äŠÚ6I¿¨fî_Sûwù&ý-¹„Mú»ãÝ"Ø)ŸÉ’wn{¤~Ù_øƒj_öoë¾r›kESý7qj|/ŸG›A2̺öïf.Xó±™Û§™—\ó×÷‡»Hyrqá ®_¯›ñ,ÍÈ4Žƒf„ýÕÌŽü%û§õ §í¢½kYžú[r I 2…Wr˜ÙäBR¿ŸPSr™I1X?:oرM´ŸO“üÖפÛãžEú5ž§KÚ«Cnh™¹ÎH¹ö»HXl¿»}­'EÝØVæf’ñÀë;9UÎbèß¾ìyäΞ ´ß9s†¨/¶û©_8+âq2qöŸ¿Ä¶Êð½þÏ/hAæM¾Ê…ªîy™T»q~ê > <5Þ < üæá:#˜ÕýÕɲNÿ‹’¿˜åj¼’Ë$ööGÊr' ˆâÅÌFŠ…b8ö}ßrÃgAô]@ðY@=º <:ø rXH0ÿÚ$”\Ö.¿¼6/î4±m~yõÎë'Vo¿¢ºr¬Þ¯ïf^+XüëÜ?%´eýéžÃLŸUå*þµòg I†ŸätýL8xÔDY¼p ¯ˆŒ¹Åã ‘œšØF[°Rj—04®ëbÀÁº5á╸ºâ^âÅÈ6\LÖ­d^^Ìä‰È0”tÇò€Q·ûV4Ì:^½¥f²z{Ñ­*àÂ">8«ˆºÉ0ã„Ò¾ !-æk„æ–ÝXÔ<ò{ÉæYªBjÿŸ$jJEµÕ“ÄÞÍ4^¤`õuê"dd]åWð:¦BjÖ^Õ‘5íhV¥Ô¶á`þK`,Û -1(7Ýÿ•Ÿ€äÕÀÍ™øÓ›Îå:I%Ýt³)w·ô†!Ý5;I®þ2!4;! J8Ø Ã#¹9G8-5 –‹/OÙ&‹o;w¸˜jñh,J«)ß›O>Ý Q`Í$ÐrÀ×ð!tpª5¹éûÍáÙG>ë|¡yn/ÑM®×=‡qÖ*ff#÷sp-"òa*oÕä±’—?}‘NÚ,u5 «®fððÓÐçi¨vEy>L‰ÞSvè¾­$x‹ ¿KÖ%Ažüe(i¯†¡S£º\Ø¢<™›ËYhIÍÕÕdæBú€‹©2mÉCB4FLR;Å“‚{Mf\¶HÁ?U˰o%=R¼ïÍîâ8Í2ë@Îoãù¨7çè¼ô=8¿µñ«%†þRÌa»xì†ä%å-1åK~>0Û÷½XÔ™“{Uõ* qU}s†›äɹ Lý|”®÷êªßGSÞÌ™EE¥»®bLýW¶?¤ßo¿©þ”Ü—ÜÀçKÿWêÇ2°¿OC\°ª¾ûÇù;'‹kú.»ÙÀGÙºªê'Õ¯Ä]93N'(ÐÕI©Ä—–f>iÀðÄGI+Q¶ckýŠ¿Ê«ä¢‰R-¹Û«Òß½\ S#÷r#ź>–’`K×®ÒÏ]ŽþZWÿÍZf}?ùÀÛFÊòÚu- ýÕ™gP¶¡¿.SNñòmJgY)wý%¹ŸP[OLýÅ”O=IvÒLø¨ÿš¯r$O }Ñü•¤ŒâCês…û|À’ÒmØ™D4Éž3†{«, ÍæUà­rUб‚CcoŽ/I°¹.C°oŠŸ/´fºOχR}`#j%·‘ݳ’ªÞD…:Ù.×+.óRjb¤dCÁÓL©YÀм‹œúëÇÙ1Ç_Ùçm阆}|¡y'¼m3ŠÜ.ggW¹$|–õqßðJ ÒA£GÆG㥹‚‹íjÊ:¹+pÐT˜ä¨Ì¿JÚ$­©¹v~ïJ¸®Ïªëœ£]³.çhûEŸ¾Ó!ÕŠí$çÀÎ÷f©°hRö^;Ý‘ÍõÊw;[É· tXü½¤Jí|Ùѧù;n—d©íwꀽߞùùÔ<²°4‹Þ·ÉM×6›ª©!ÞWÇÚжêÉ™òh4¥«ßŸøþ¡÷Oáq™¦%¶ùàÕäòcU¾* ùK«n×ÕêØÊ—N£Z:»¡4—„Òìgͳ ‘¼Â[÷£‰nÍÚÎCsäh Ptµ™ÒCÞ™vp-ÿȉ‰úËÑyúûöÖùì­‘’d©ÜVzt»’š¯§ûÊÏ'—j-ïýÁ,ßrîuýÌïöc$™2[#'…„ªÏÀ’Q•î9Þ2^ßÔ„’jMNf{×òʙŚWÂÉ^•„§}:©7ÈÈì°OAĨ2ýP¤&=Ì™bTù;x4@óÙÍÕêùêä[&U¯¡ñTþб¦9šIuóŠë¶½ž§æhz™ÍiF›¬èdtx™I~Ã>&&ÉT{*Hìõ³'ø==¡S¼œ•iN2­äUM3©ÌùiËLªâ‡6ËLª’_®¤2í|ù}ÊëVà•Èk\dR1ù.,*U)vªõÃð²äõ-Q©n¯n‰J…dü1žöš²<ôW'oÛ• Œ¢cLùÇwò!ýÛnÃ"ÿƒÉõ”y}òºÖ”ü¿Ê—ôO{½úu&èóø¬ïôïü~¯˜puz~£'$ÿ=~äì ¼‚¬EÒäò2÷%yzµ›–â—z49^Úû’þi/~—¬?ò¡åÇóµIiBòvçC!ê7Ÿú›É.À¨Ÿ“ù¢t2Ùíþ0ËŒ[=÷‡Œ"âe ýxÏÝŒQœü^­<™‰§’SZŸ{H¿“;|˜„ú÷÷Mé¿¿IÞ¼~÷’<ÝʽÔ~F {IF {«¾øØß¯õ¹5=½ú­ùG#1õçC=Ña!—+êK~Èò '…퉮 ¬¨àˆÌÓMfýÜ?ú…€;™ÝôƒY_äGWýêÿi©_óyÚ]?ZQîJî׳|—~¸€y«ÛR?YX˜/™Ü3$_‰ÉÃBrNQÍ! É0÷—¬朳üQrKûñ,ÉW¶'2ÖšÙ_‘±VFaGd¬•öèìïꋌµübòL¾Cÿö-ôKd¬m{Œú\¾“—”s½vfæ€?É8çÞþÓqˆÖvÿ-é¸ ·‡¿Cæ‡lóûåãpk:®;_f#ýHÈ}WÀð‘PãæòÑ ø,ðÔy7ñ{¦äøã(õ7æäàTÇBùÿåäœUéü)¥Ÿ3ÉRì¨p´¾+Ÿj†ÏŠ.ï6}’»€à³@Ñ%È]ÂøK‘ÎHöSDøKyOŸ"Â_Šèó>EŸû/Y¾ƒÅ;¯H~ñ+7מ<ú~yŠÐÕ}«úýWÔ×™§úû»Õ‰º|ØfGÔðÎÌÝ£ùÌÔ}û¿Ÿó:¸ endstream endobj 374 0 obj << /Alternate /DeviceRGB /N 3 /Length 2596 /Filter /FlateDecode >> stream xœ–wTSهϽ7½P’Š”ÐkhRH ½H‘.*1 JÀ"6DTpDQ‘¦2(à€£C‘±"Š…Q±ëDÔqp–Id­ß¼yïÍ›ß÷~kŸ½ÏÝgï}ÖºüƒÂLX € ¡Xáçň‹g` ðlàp³³BøF™|ØŒl™ø½º ùû*Ó?ŒÁÿŸ”¹Y"1P˜ŒçòøÙ\É8=Wœ%·Oɘ¶4MÎ0JÎ"Y‚2V“sò,[|ö™e9ó2„<ËsÎâeðäÜ'ã9¾Œ‘`çø¹2¾&cƒtI†@Æoä±|N6(’Ü.æsSdl-c’(2‚-ãyàHÉ_ðÒ/XÌÏËÅÎÌZ.$§ˆ&\S†“‹áÏÏMç‹ÅÌ07#â1Ø™YárfÏüYym²";Ø8980m-m¾(Ô]ü›’÷v–^„îDøÃöW~™ °¦eµÙú‡mi]ëP»ý‡Í`/в¾u}qº|^RÄâ,g+«ÜÜ\KŸk)/èïúŸC_|ÏR¾Ýïåaxó“8’t1C^7nfz¦DÄÈÎâpù 柇øþuü$¾ˆ/”ED˦L L–µ[Ȉ™B†@øŸšøÃþ¤Ù¹–‰ÚøЖX¥!@~(* {d+Ðï} ÆGù͋љ˜ûÏ‚þ}W¸LþÈ$ŽcGD2¸QÎìšüZ4 E@ê@èÀ¶À¸àA(ˆq`1à‚D €µ ”‚­`'¨u 4ƒ6ptcà48.Ë`ÜR0ž€)ð Ì@„…ÈR‡t CȲ…XäCP”%CBH@ë R¨ª†ê¡fè[è(tº C· Qhúz#0 ¦ÁZ°l³`O8Ž„ÁÉð28.‚·À•p|î„O×àX ?§€:¢‹0ÂFB‘x$ !«¤i@Ú¤¹ŠH‘§È[EE1PL” Ê…⢖¡V¡6£ªQP¨>ÔUÔ(j õMFk¢ÍÑÎèt,:‹.FW ›Ðè³èô8úƒ¡cŒ1ŽL&³³³ÓŽ9…ÆŒa¦±X¬:ÖëŠ År°bl1¶ {{{;Ž}ƒ#âtp¶8_\¡8áú"ãEy‹.,ÖXœ¾øøÅ%œ%Gщ1‰-‰ï9¡œÎôÒ€¥µK§¸lî.îžoo’ïÊ/çO$¹&•'=JvMÞž<™âžR‘òTÀT ž§ú§Ö¥¾N MÛŸö)=&½=—‘˜qTH¦ û2µ3ó2‡³Ì³Š³¤Ëœ—í\6% 5eCÙ‹²»Å4ÙÏÔ€ÄD²^2šã–S“ó&7:÷Hžrž0o`¹ÙòMË'ò}ó¿^ZÁ]Ñ[ [°¶`t¥çÊúUЪ¥«zWë¯.Z=¾Æo͵„µik(´.,/|¹.f]O‘VÑš¢±õ~ë[‹ŠEÅ76¸l¨ÛˆÚ(Ø8¸iMKx%K­K+Jßoæn¾ø•ÍW•_}Ú’´e°Ì¡lÏVÌVáÖëÛÜ·(W.Ï/Û²½scGÉŽ—;—ì¼PaWQ·‹°K²KZ\Ù]ePµµê}uJõHWM{­fí¦Ú×»y»¯ìñØÓV§UWZ÷n¯`ïÍz¿úΣ†Š}˜}9û6F7öÍúº¹I£©´éÃ~á~éˆ}ÍŽÍÍ-š-e­p«¤uò`ÂÁËßxÓÝÆl«o§·—‡$‡›øíõÃA‡{°Ž´}gø]mµ£¤ê\Þ9Õ•Ò%íŽë>x´·Ç¥§ã{Ëï÷Ó=Vs\åx٠‰¢ŸN柜>•uêééäÓc½Kz=s­/¼oðlÐÙóç|Ïé÷ì?yÞõü± ÎŽ^d]ìºäp©sÀ~ ãû:;‡‡º/;]îž7|âŠû•ÓW½¯ž»píÒÈü‘áëQ×oÞH¸!½É»ùèVú­ç·snÏÜYs}·äžÒ½Šûš÷~4ý±]ê =>ê=:ð`Áƒ;cܱ'?eÿô~¼è!ùaÅ„ÎDó#ÛGÇ&}'/?^øxüIÖ“™§Å?+ÿ\ûÌäÙw¿xü20;5þ\ôüÓ¯›_¨¿ØÿÒîeïtØôýW¯f^—¼Qsà-ëmÿ»˜w3¹ï±ï+?˜~èùôñî§ŒOŸ~÷„óû endstream endobj 378 0 obj << /Length 669 /Filter /FlateDecode >> stream xÚ¥UKoÛ0 ¾çWèh‘bêe{XvÖÝm[†ÚœØI ÄVg;H»_?=´M€¢Càˆ"©E‰JÐ%èë$ñãçÅdvÍR”“\R‰k”“,¡HÊ”d U%ºnÚ3!£¢,ë¡V~v¨ËaÛ{K[!¶U½ÙÆ4‹oQk7ÛÊ »Â˜ŸbšF*†hcˆgZzcm®dYß%ŒU]ÕZ\ç±îTã¤NõÁGQGe¦ñýâ›Î\—æ:S]ŒyÆH+µÛ7­“ ÊQGbœC-LFÛ׫މEçuÕãCWõ}UºéÒ¥èÀº¢Ûì“Dj6G›] v²õ¦ßt¿½ÖïY‰'2Ú=ds rÂd|lQ†³XZ¤<øÙZ1æk¥…õ¾5åYÙ¢¿Ž¢ÏˆH²°zW<©ýp.–ŽAˆ'rJ2™!Ìdœ9ÏO1TDíÚ±¹s—ˆ¤)†¹þºúÑÌVæ¦Î9Œtê1m ºåS§óÅ_W^Ñš9=xذ¹…¤DLÁÍwî@yžEçHun>wXc©†A5sê ¸ êaÎ|TJÞf§lòÃOvëÂýÿScg©ÎÝÇx/9úšœ|/9~Ü»wŽäÆžxµ˜€c3„\êS™¡U3ù3!)Hçp”¬!¬òŠÙM ú¢&ßõ/Øp€Ä'˜—»0¤œäþ6\×›½i74ÓÅH>èžyô»¶Neï¬ô½t_f„Š7\XNhcWh=²mÏF(v½2‹ÊJ÷fFái°Îæi0¾[ÓJNžS( ’.òçõzч¸oĽӺ·F ¾ó÷äYý.¤ÞñT œê­óµ¡ {Yüö¡¾ endstream endobj 375 0 obj << /Type /XObject /Subtype /Form /FormType 1 /PTEX.FileName (/tmp/RtmpBlhU1W/Rbuild4444671013800/timeSeries/vignettes/timeSeriesPlot-layout2SinglePlot.pdf) /PTEX.PageNumber 1 /PTEX.InfoDict 382 0 R /BBox [0 0 432 432] /Resources << /ProcSet [ /PDF /Text ] /Font << /F2 383 0 R>> /ExtGState << >>/ColorSpace << /sRGB 384 0 R >>>> /Length 7131 /Filter /FlateDecode >> stream xœµ\KÏ¥ÇQÞϯ8KXø¤ï—­#@‰f¤,«–'RÈ‚¿O=—~Ï{&òØŽdÏ|ÏTwõ­ºº«ëy¿üøí#?¾{üéÿ<þôé¹Ç#·ù¬õQ[îþ(m=W~üÏ¿{üñïþü¯ÿðíã×?¤gJéqÿóã¯ÿ)þuöÇÿ~ø·¤Ç|È߯ÿß}È(ðøÇ³>ç|Ô4Ÿk>>˜íY×£ìòìóñ=àÞ²Æ3oÀþìÇzÖ8Ÿ­< þu®gª׳7ÁÑ¢p{ö ¸Ÿ%z>£Ý&¸FÀúl€+?G´ T­ò,›ªrÜ)zÕžÝX*ËŠæÑÉÕžYÝÈT¥) l7ú½++= ¥ó™gÀöLC0†_vWŸ×:窕¢¡ø«®hhO©ÚùÙbIRÂ8Œ¬ìý¬SpD7öxVô9úÞ&û¼û3­¨Û.#«)?ëF¥ô¨±F« îÌ5R7Ös¶fÍlg 1üèk*stãJigL@MýY%¯0„šþYx ±•nÏR¢µŠ± ¯uŽ¼Ôõœ¸„o8úUK…é¯g‹‘·‚!ot xãýiÉý‰aFC7kíÕh&c6êÀB×gíà ŠqìŸZ£ÿ*ߟsßõgK÷úó™2ë÷dŒùˆý§ñÇÀ0-{~KLt´×ê3wã1X¾±~ÉÏãïÓí^ÃÞ.϶ïúbfö§vãH­1lé‹ŽÅøã/ÍW°Õš·û8ö;Ög7âØŽ‹ëÑ8ÿe?3ð”Õc>Ž5åš8×ú×Ìùˆi¢YçèHeÅ0Ù~Ll‹ö0,•çÎzÃëßd¼9&óÍné›X-ÓóxF{ÑíÉñÖC­ÑÒweù$ùÆÖÅüÈc"+Ö{c„£¡ZiÆÀ…óÝ}báö­ýFCAÿ›ä±5aßÅým»ó)û Œù¸æ ßéQ²ä óìú¿ {ê †ßV(ï>Ž‚~x»Åú₃ Œ"¿’{ƒá•‘aæÂs°¼ö[ïX¸7¼ÃÅ…—¬Ò?èÓgÆ6^tê‹f ¼a˜—wŽù€{•?t¬elÀ ý™¶·è(<ûK^éÚ{˜Í0†oi^™˜çE‰i  Ü91Uå »Äz•ŸÏíÅzf•_˜¨fܳ1<|¬¯æ':õccàáH Ìx/´Wàæ€ ç#üE‘¼ò” ûèã˜{áNGÿÂ)±?×Á˜¸áÉùÈÍã ŒùßßÕ~r´û³²|,L,\IÉö8ü}û‘ù÷…8É´?âì Ç“Ãþ õÅá†zñ?Sàâý†›uä ‡¡'Éü}Žó¥ /TÞËx¡ý 3Þ˜W}Lë‘Ã^ÇÁáxh†ÂÜÈ0 ù¿]áï1mšÀa¨0 õ/óiËóÝœ*?Ä|DZ¦ó&NïØ87̉zÍwàð÷7¼á8Jì3:æ?WÙ0Îù(Oÿ\RÁùw­pœhýÁí¶þq~Ç»¼Ú/q0wá­ö'æ£ä©ùž°¿0;õgãêvÙ?;{_òÀáïK¸¹"œáïKø×Éöc é¶0Ðu³wjÃø»ö~Hý6ÞÀ1Q×üb`ë¿åÏKæE$ãÖCûp{ó³ÌïÂòj¿pbl¶Àfšªñž´?î‚‹Ç¢}a*æ6:xƒí†'6ÊeßÀáï1ž"¼àï¯ýSâ`ƒ¿Ç5‹òXˆû.Ý6÷¯ÖO;–û8ο[ùŠóË:„;ýýñÀ³ßìÙý)pÌÇåJåDbØO”a8/ûi¼8¾aøû²¬/ÎÓ"'{ ¼åZÿ0¬!ûÛ_\tÊ¢=¥aŒó/ð–>^´ÐM­_œ§ð÷1¬!ù„¢Ëã èí^~s>â/ÞgðÎ?ƒÄq¾âüI÷›ç'üý <€áïwÖ}ŠSyP„ùˆõîÛóqb‚Ž« b^¿K\ôÒ|'ÀãQ•8_áïà ƒwzEèhtäºÎã`Gàwîë<èÛ+®ÀEç_¸-Þ÷J8ÎXÜçÕßÁî -Ê` WÃ-MáùÀýÞúæ÷™,}¼áþÓ…ÇÝô1«q­×üE€¿î›¸X Ò {¡¿‹÷µÍý2éHØÊ?LFsËzð¬¯ø„;ç¾ ¼û뾌mø»$LüîÿÀ˜˜¡äú´>‹1âíÿŰ´—t¿/áh0×xc>p-fà‡ûrQyÇvÛöµÜEy·?ï„Ûrû‹ñÜì6fôçû= ¯WpCf|G3žŠ4e›Ç[yÅWÿ¶â»št?ÅÅñ]óù ÃÃ|„™æƒ1q-”¿ßŠÿ`öŠïb›È~·â»éóá3âi Œxg郡a>VÖ|פønÚß#¾›öÇhLäM/†uúþŒxçU_ñÝòý‰WÞ1æc ÝG±1bâÐÞ¤#þÃ2 +¾[¾aã ¾»ôÅFÃ|à £c>.ýYñßòý â»WyÅwÓç!9Ä;×x#Þ‰kïPûŠÿúö|Åw­+ÞÀÆD|·[ŒGyů5ÎÓR¹{1F¼sö+;Äwg? 0e|çø óqâu:æãÄÛ(ˆø/öWc‹â»V|wáJÃ>ÆŸÀ˜“ÜñŸãl|ÄwwŒøî…º+g`XnóSÿx ;ÞÝÂŒï°lcùÚ_DËzùS`Æö×â»ó^Ç‘›¿Þ·ýOGÕØ^Q}ÆŒ¯9þÆøîŠ—¹q÷+>ÆÂÅÛ«Ã߇¿áûDóóqâÛÚ“âÝìùèŒïn8ãü«ºv ×û0ü}i_œŸ=½Þ3jg|wù`œ—ýÀqÔ×{ðn|ÿèŒï®÷:¢uï?óË¿Áqõ[¼_GÖ|œùŒï®÷ àq‹÷kœ§ð÷W{áïáÏU¿ËßÛ×ÁøfÇ÷`œ÷çý µßírŒÓœŒg½í·ÉøÓ®ý­‡,³ægg¿ÅùZe²ÏY5gþ&ã»×~œŒï®÷8žÇ?†¿¿üÃdü÷Ú/‹ñÝõÞ<öë½޳Þ×?0οË>WÑù·Ž>Æwoxß÷ûâëóõ^GšçÍžã¿ë=§.ÆwpcÚ_kë}Óç)ðº½gV\ôêm¿mÆ7Ìøî†8áXÐzÆy gKà|Ôçã{Ž>†êV‹^`vßô¿ýägýßÿùžõÿüû?~øÕßG”òøôŸ½ä¿þF„¸=qz?}~üͧÿþü‡¿}|úîÃß}¢úSÿVé››®¢8æ¿Xÿã·¿9Õc?/ë3Oyg5>?쥿<>þE‘ Ó¿³ƒö*ráW‘wÅ·†^E~öT/žˆx˜ ÇLEp7¾I9þû¡ùþa%x׈sä/´Ì¯ÒÂÛNþBË|ï‹J@Ã`?;¿T· 'ä‹‘¢á½<õx¼ðq œ#ãV@^n}ø)62Þ{l {}…y«6¢Ãp“¬½¿º6‚¨8›Q;§ôõÕñÔÚ\ý‹å9û¡E çÅÙ×=tŸðLø_·¬a8E¼Å5ÞU#ä_"gˆWTôþ3.Kñ*Ö0?- ¡èFÄRô­ƒOûqòñ&0Ñ·"?Ç`a=¾² xZ(B¨×ôâã€Ìï‹83P·L:ûL†r­êeiÈ8%2eCZ&/1x¦ã-Îé~*ÇË -Fë¬m¹õsY¶-Ó4K‹:—Vz²¥(9ŒÔÑJÖÉg Æñe£ÐÙ“ÖOÙm+¶Š«ÆpPÈzV^dñ§WE}x= YÑÚš’1=ÌujBká:à‰‡Zu:“°øÐÙ}OŠkFœÝk²¦˜1{-ðƸx“À5—½fê<ÚÛNFCV+â ”2ZÏbÄHiëIM»cMëäW˜žç¬7û)-ÛýäkÜÚïje\w½`ÝeYhSFÛÅ“BȦÛ™õœÏDªœ2¶H²a¤~N—„Î¥{áæ9 8… s .=d#im®ÃÈî 9x}pV]õhgÊ¢¤›òînc‡ƒ\ñV¶yÉE½,GHßZÍ=!æ)l>ßõå\;#c||K.SFÛ „zYqøfšɯ&Y‘Ml¾åŽâ1lèåäãù^¯WÝÐ^¨|‰ rÃÃkÉ*ßðûöð+sÝ#† S Ãü1\=’â{ÛpºÐÒCó(Þ¥yHÊÜÝ8T•Ú-•Ë­LŒC4¨$&á\8ÒÁ^]uóaÉðȾòB:쮑üít#Ëš};’ªÏ›ÒãÜZÚ ¥Ùl—dÍ2Ñ–¬YÇSË–jÉ×\Ùš·'çŠR4À¸ÄG]гa(™Î*’µÓå̳WcÛbuåëFé¡T>'¶*¤ÓS§;_®ukÖAк5ï‹ Ò|ÈCÌÂ2Ò %­5(¤ÕF×ahn§“$"๎«.Òâ%ße£ðj†ÐÜMâÁ]zгӸ¸<5ÅHÜÍïÉ0ʬ›áš}Ó„æåyƻ㔿Â3#¥ë“BŠ;» y’ %ÃЬ=ÁA©Í^-•öz47Ck¾XMõ¦™´TQ[¾'‡ R_œIq¢t†Ó T»»3Aݬ{x J=|²+°tCi–ƒÂÁDÍ2ÝÁtQÝEi÷ú„tØ]‡·É”ж¤gêÕÏlUµš ‡€´ÙrF:Òb¸XWã 'ÍÃCùh–ªrÚ]†’êú1Šû,2¥2ÂQ=ÏýP·B±VapÀÑ݆М| æÉç>4±ÆºÓ¬œÁC|W0 ÌaÕåcóò0|(ÂÕ0ê^¶1—¬â#f01~…æuØU`‡•‡8Á‚¨;l„z{_Ndù¢5“mSó9bøž‹§uír’ð‚ù]J7¾–a2ç.ò0”fíÐð ’^ðMZ¡y;]š«á ´Hsc»Ûæ _TåÜàm(•ËclŸ¡¹Ú™O®Ñ>.hrö±œÉ5ÚÇ# Í€\ß0Ô¦Œëîv†!‹õ½Ï…y’ù·§#‘É÷û} IºÙÅ~›ÛR-™Xæ{*·Ò4;5GPHÙăbò[˜t`OcËuÅB xð…]×b0í$—E,1“ÒñZá(,—c‚ã~3ëªôg=b0Šã`@‰‚ü4«I~‚£Õ<&Ÿ†'ã^ýêþ/OÃá£ä>bsüþ@<§ñÎxæÁð$‚À÷Cb𯓠Û¯ðÕ!n—Ÿ ;_;òáN¦\êEÔKÉaN&r4žY,ÝJy7á÷ ”ŠÕ¬°©ù5Ë$BPM·ádúHÆÉË ˆ«‚…I)º'uV{Í"¢°‚`AÐU.6#ŸÙ_…é2ë670 êŠú:¹êaÎ"íÒ6ì¬+^ÜœÖìá“–Þ|—t»êÕ¶´fCµ+ ,nè ã#w’ÊÑœäÁRóŸ!¦wêZÝ|½,JMîLޤ$=ÇV5pñiª;3WŸ{ˆ‘‰Ù•~ãMöþ¢IJzAH‡Žß¢À¨O΀мLñU¬ƒ7§%M‡`™ÒõfX˜2H6æ/Ķ[„I‘Í¿b‰nðb8Û­³„PU -j®ºžFÝ‹—ŒKwcÚ«JzAhN&=/’–®ÙØÌ½GðÝåSíª›†ÝýA úìì ùŸJ9 CÕÕ^Øüz®ŸpgQfI…™4»–l3“л­]_bt?8ª¹~7Kó4ÜÌ÷ˆÅº™¼ê¾O‘dÊUh‡S*©¦]×ù;œ\A¡Bî Æ_gŽ‹¾:q ‡û Ý¦&ŸiBóÖäBó2É 1ƒ¤ÛR’¨vÅ Jd`Ž­'U@Ôµ‚ÿZXwoChÍ7¸™léRÕX×)@ÀͺbN%RP‡‰ð€m] òhY×½êG³X³¤÷à¾÷yZ³(tàJR*†Wš§®Ú]n·.ÃÉý-iÚþ¶æ1 “<âÞűU˜Ná«9Z zI£?x8p3¢!€½RÞà›´XóZ†Rq@3ÉëÓ‘—¿%<†DpyRæ‘7âXƺZ²L÷ôÊ€¨[O7˜¡ºCHyã6ñòw1‹¡Ù/÷€¨;æi©,6ó{†é¯2¥Y¤G‘ŽÁjf>ÎC±CÞ†}va2îfV¾°q¼åH5þv]CIGº qu,æ>’:®-ŒÌï¡ÄÒ¡ `HñŠs ¤ëМ¡9{‡FPš¯ [ªëÊžK;ÒÕ®\¾0¢´Âth>^…|iöY‹¼ ¥†¤ÚŒnòw!×æÚƒe88òɶÙ ZH·IQiÕ4Ýù@*™.“Ï)dÜt¿„‘î¬dš†@n Žòbi?šyiéNZ‚Ê[òuj>n0i5lLòˆ}Z¹F'ɸ™ã‹KÕGŸÝï¯þæâäÇ@í+L‰«“{><ñj¢‰,§V³I.¸(#ò*µY³;Ég­îOˆ¥¹ CSJ¤j˜o2‡!¤ítƒLœÃÏ0M÷\NL.ï~—¼¤ë”lý N¦¼Ò)¼ïí’Óý¸_×-|Þ¢v5uœÜ‚¶ád»¦„“•Ó#¶e·»ܬ«½ßŠ5Ë&›˜9çkŽ&jαºV-Wºiº·F«Ö,&x#-äÐIȘ·E¼ön"J:4x±[ôUH릷ìSXÜC‘tÎ'mXªÒ¦¥:5šx:ëðñEÔ™ºÈUq¥ P»ËíÊTÚ2GöÜDÖivŒí°u´éÚ6HDðžÌ9RaYc÷s,àæENæÝEÙ1=Š$}Õ,‡xt(ú’¶#µæl(ÍÚ½zôùI¯f4µÛŽæiØxq•gèÍšû‘ªn;ÌPmŠ÷ûPN¦“'rn×€`ðt³ö»(ÉåÁådîr%‡ùeÆÄ¹¯EÑ/˜]ÙQ|‡‘oWµI.Ϭ§!ryp)ª†Ð<½qfs]íÁI.Ï öw)=ätòp𨙜|˜ý|ÍA.ϾIÉåÁ£Ç4lº"6Ã7ÍËã]ç;IµÜ“<‘ÙO¯ÈQß›¤çHÏÕt‘Ë3ÏGúUÓ 1@hç{|ênCH§Bi|mér¼°ÈåYé¨bîkåó© ¹<Ë¿ŸäAÚ|ˆ,ryÖõ] ¹<ëê$¹> stream xœ–wTSهϽ7½P’Š”ÐkhRH ½H‘.*1 JÀ"6DTpDQ‘¦2(à€£C‘±"Š…Q±ëDÔqp–Id­ß¼yïÍ›ß÷~kŸ½ÏÝgï}ÖºüƒÂLX € ¡Xáçň‹g` ðlàp³³BøF™|ØŒl™ø½º ùû*Ó?ŒÁÿŸ”¹Y"1P˜ŒçòøÙ\É8=Wœ%·Oɘ¶4MÎ0JÎ"Y‚2V“sò,[|ö™e9ó2„<ËsÎâeðäÜ'ã9¾Œ‘`çø¹2¾&cƒtI†@Æoä±|N6(’Ü.æsSdl-c’(2‚-ãyàHÉ_ðÒ/XÌÏËÅÎÌZ.$§ˆ&\S†“‹áÏÏMç‹ÅÌ07#â1Ø™YárfÏüYym²";Ø8980m-m¾(Ô]ü›’÷v–^„îDøÃöW~™ °¦eµÙú‡mi]ëP»ý‡Í`/в¾u}qº|^RÄâ,g+«ÜÜ\KŸk)/èïúŸC_|ÏR¾Ýïåaxó“8’t1C^7nfz¦DÄÈÎâpù 柇øþuü$¾ˆ/”ED˦L L–µ[Ȉ™B†@øŸšøÃþ¤Ù¹–‰ÚøЖX¥!@~(* {d+Ðï} ÆGù͋љ˜ûÏ‚þ}W¸LþÈ$ŽcGD2¸QÎìšüZ4 E@ê@èÀ¶À¸àA(ˆq`1à‚D €µ ”‚­`'¨u 4ƒ6ptcà48.Ë`ÜR0ž€)ð Ì@„…ÈR‡t CȲ…XäCP”%CBH@ë R¨ª†ê¡fè[è(tº C· Qhúz#0 ¦ÁZ°l³`O8Ž„ÁÉð28.‚·À•p|î„O×àX ?§€:¢‹0ÂFB‘x$ !«¤i@Ú¤¹ŠH‘§È[EE1PL” Ê…⢖¡V¡6£ªQP¨>ÔUÔ(j õMFk¢ÍÑÎèt,:‹.FW ›Ðè³èô8úƒ¡cŒ1ŽL&³³³ÓŽ9…ÆŒa¦±X¬:ÖëŠ År°bl1¶ {{{;Ž}ƒ#âtp¶8_\¡8áú"ãEy‹.,ÖXœ¾øøÅ%œ%Gщ1‰-‰ï9¡œÎôÒ€¥µK§¸lî.îžoo’ïÊ/çO$¹&•'=JvMÞž<™âžR‘òTÀT ž§ú§Ö¥¾N MÛŸö)=&½=—‘˜qTH¦ û2µ3ó2‡³Ì³Š³¤Ëœ—í\6% 5eCÙ‹²»Å4ÙÏÔ€ÄD²^2šã–S“ó&7:÷Hžrž0o`¹ÙòMË'ò}ó¿^ZÁ]Ñ[ [°¶`t¥çÊúUЪ¥«zWë¯.Z=¾Æo͵„µik(´.,/|¹.f]O‘VÑš¢±õ~ë[‹ŠEÅ76¸l¨ÛˆÚ(Ø8¸iMKx%K­K+Jßoæn¾ø•ÍW•_}Ú’´e°Ì¡lÏVÌVáÖëÛÜ·(W.Ï/Û²½scGÉŽ—;—ì¼PaWQ·‹°K²KZ\Ù]ePµµê}uJõHWM{­fí¦Ú×»y»¯ìñØÓV§UWZ÷n¯`ïÍz¿úΣ†Š}˜}9û6F7öÍúº¹I£©´éÃ~á~éˆ}ÍŽÍÍ-š-e­p«¤uò`ÂÁËßxÓÝÆl«o§·—‡$‡›øíõÃA‡{°Ž´}gø]mµ£¤ê\Þ9Õ•Ò%íŽë>x´·Ç¥§ã{Ëï÷Ó=Vs\åx٠‰¢ŸN柜>•uêééäÓc½Kz=s­/¼oðlÐÙóç|Ïé÷ì?yÞõü± ÎŽ^d]ìºäp©sÀ~ ãû:;‡‡º/;]îž7|âŠû•ÓW½¯ž»píÒÈü‘áëQ×oÞH¸!½É»ùèVú­ç·snÏÜYs}·äžÒ½Šûš÷~4ý±]ê =>ê=:ð`Áƒ;cܱ'?eÿô~¼è!ùaÅ„ÎDó#ÛGÇ&}'/?^øxüIÖ“™§Å?+ÿ\ûÌäÙw¿xü20;5þ\ôüÓ¯›_¨¿ØÿÒîeïtØôýW¯f^—¼Qsà-ëmÿ»˜w3¹ï±ï+?˜~èùôñî§ŒOŸ~÷„óû endstream endobj 390 0 obj << /Length 650 /Filter /FlateDecode >> stream xÚ•TÁnÛ0 ½ç+„žd VEÉ’íÛ¡Ø:t§ É­ëÁpœF˜ce¶Ó¬?I”ݤÈC‘Ц(>’zOœ<N¾.x\ïÖ‹Û{•V*%ÈzK@d¬à‚ä\3%YoÈ#L')€Pôμ$ZÒª7ÕØ$©Ì]ÕÕ86=~|oí8$Oëo·÷2'%+µÐ>-'©,˜‰ ï“BRëÏhNwfmoêªÅᄅÛ Kÿ•ÓÖüJDA=˜j:Œw ÛcWÆvˆ©ä&š¹rØóàJcW‹“šižMq×!ôà ¸Ví`±ŒÞ8Öd}n’ !Y–—$űbÖÚuå'&JE«×ašœ(u(ÝÛÞîÑ}ç'ÿÏ¢ãØwô%Q:Üßzã ̾ÁtCÓ›f`IšI +?6 õµóykZ» »ÂåfìÍMh‡Ç>4öRʈàí,¿D÷iº-ç>™¶EoðƳÅ5\¤ Ù{?itnš±2ñÄÖF'Vëc7f8´±gÇï\]ò,Vd‘gŸ’T9Öªþ'W|ï&{úX{– PpVø]4ª.ºÎüq%—Þ•QxZâÞìST8Šd@¶W¤JÅ, ?†+êQL¸w"Æü c©«ns%¥WHù?)_ÏRNe;Ì£/2ÓYÈLÏGQz©v1`÷QjRäþõˆ"vž@Ù càÀ¤‚KVœS0p²Šb˜A÷^š .ük% ñJAŠ·çÊ÷Lú ’i7 endstream endobj 387 0 obj << /Type /XObject /Subtype /Form /FormType 1 /PTEX.FileName (/tmp/RtmpBlhU1W/Rbuild4444671013800/timeSeries/vignettes/timeSeriesPlot-scatterPlot.pdf) /PTEX.PageNumber 1 /PTEX.InfoDict 394 0 R /BBox [0 0 432 432] /Resources << /ProcSet [ /PDF /Text ] /Font << /F2 395 0 R>> /ExtGState << >>/ColorSpace << /sRGB 396 0 R >>>> /Length 21462 /Filter /FlateDecode >> stream xœí}K.½qÞþüŠY&‹Œxiv³·lÀþdax5NŠNþ}ºžK‘}Q²-ÎTÕû«»y-‹õã¯?êÇï>~ÿãï>~ÿ1îÏr|\ýó8>úÑ>¯öÑîûs´ÿñ_>þóÇ?ÿøÕþþ¯~ýñ›_~”ÏRÊÇþÿ/¿ùÛ‡{ùñÿøQ>þéGýøëçßï~ÔøÁÇßüøøh×õy\óøýã{cœŸå)ꚟçü˜×gëm–Ïz‰øŠŸÎúyŽ”¶Ï™8ÐÊŸ’Ñ?ËH\ýœK+üTeR*kˆÛmýúñKüxœŸýŽRŸÿ¿7Æŵq}žåc>ïð~ü!‚Îò9.KÏúy%NTñ§dŸwI\ÿ<—Vø©Ê””Ö·Û*óçÇõ㟳Ò|1®ÏþŽù9ÛÇ=?G}Ðå³W,6Yú¼¡Ä‰€Vþ”Œã³÷ÄõϺ´‚ÀOU&¥²†¸ÝV™ßz|í‡ÕôöÅ8?¯ç-¶#*ÆcÓý¼‰6¢î’@Aí©6%¥óóX8ÐÊŸ’Ñ?gI\ûK+þ”eJJk„Ûl•ù½}Þ×ÇÓœnÕ}2j©Q^ëÇgÕ>Ïçñûø}3kãu˜rÍ}êlÊGŒZ‰%EÝüµ8WÔcË隺Iñ×,ÛrY*ìn·ŸeDÏVˈ®à{ç<Õ/ªäÃH-3:„¨­¥šbyÏk-)?ž7¾°¦Fö;æè7}Õ¦u«lÉe—°»Ý~–3ZQ<ûðwgƃG‹º ž¿ ”Ž~ ”­CßEùÓnçš‚nw àŒh¦‰íü¦ÒÝ×7WÙ’Ë.b_v{ľ?{c[U¯kN‹ñ£k§¾§û;[ôÚ¢8w¾[É+ ºùkpžJUÐhµ©™DNº¥´I¸—ɹo¶nôß;g¢©=ëàGÃFc{å!yŒ%?ØÞ…Ýüµ8ƒ‹±O‹Ùtƒâ¯U6å¶‹Ø—ÝkìÏïëóµÎÁyFç¨^=:ŒZŸo¢‚–i*«óµäO…ذ¢ ;+LpŽËÛãS7(þZeK.»„Ýíöw©1¬O×=4’˜sc¦qótü-fX=æ{¢XùŸæ7–<Ÿ XQÐÍ_‹saÖ+l˜pH7)ž°WrÙ%ìn·¿Ëýy<]Á3.ew,NE½Œ¦ùÔËÖÐôíµšby=ú”1sK¬¨’ݱ8Ñ¥ ûXw,ݤ²Ki)—]¾ìöw9bV¶Üþ.â¨óÏ»›aëÀØôÌ5ñ•@å¼ôLùSÃËš‚n·€Â¯Œ:/lá7•î²s”-¹ìv·[Ïòô9-«…§Î>3˜>½N½M¹SŠ.Õr•.lÚ♣8O<ŽÄ>oº-ݤr¥R–œv»Ûíî•1ly&ß;g ^TÏúL#Zô„¨ž¢¾ÔyßwÊO4cMA·­§a=glùœ›nP^J¡lÉe—°»Ý~–A´¯§wøÞ9Wü‰^4¾¥j鳫§©\º%g­1Vtó×âñRÛ?Û¦”{ðs.¹ìv·Ûu¬Åø†Ò›ê˜83ЧÄ”'ÚXC‰A“ßô…I‡å“3aEA7-Î@Ÿ-lÅØiݤ\1)”\v »Ûíg¹b¿ïn/â`1¥_Ä”Xò—¶DÑ# :-y‹‡0Ötó×â4Ôc+Úšu×ÕU6å¶‹Ø—Ý~–‘ø©ï§údsfÌÚyb ùLÊŸ¡ ´Ç—%ÅòîxM–?ÖÕ…5Ý~òÊ^/FVcûç½éEÝ*[rÙ%ìn·Ÿ˼ú £_ÌQϧM}fæ1~<_=f¤²g:–üÂ<ÄXQ-Çs Ö–Â>ïáXºI}¹¶_K.»„Ýí^ó±ç¡ë³J¸œÓ9gâ›|lÅŒ®¡µ’rû|>AÊ9’+ º¿ÔG‚ƒ£±­mé&eOM=Sn»ˆ}Ù­g¹:\ ô|ï¸ Úu`~÷Lb…{XN“ú’ /懖O¬ùuäšÞœ†e®±ëv뮹¬wÙ’Ë.aw»]Çf¬Éñ­ô]ÌQ=¹±Ü‰:t }F¯NÊ­9ü–sò/¬)èþÒ{‡õDØÆÑOºI¹-FÙ’Û.Õ±Ýn=ˬŸCµT}²8&|³af˜ðX°’¢t„× å'œÆ’¢nþZœ+FkcŸö{-ݤì`²-—¥Âîv»Ž¡rDéEó1s*Ƥ+Ÿ_öæWoéyt–õå§¾±¦ ÛÖáKÌð\Ûè­”nR®‘Q¶å´KØ—Ýþ.t<Ïwi©/Nø ž:?;ÛX£ut;Ê7Ý–|Æ'O,)êæ¯Å™°NØÇÖ²t“â¯Y¶å²TØÝnø¸ãùæØòa]xaé0†ç¢6Ÿ|ÊÕƒkªd>³—}º_cc4ZºImžyËe—°/»=kür#û1sN´±§ß,7ÞMôSOŸªzà~lpİœžLcEìÇÌ‘ç]؆~ʺ[öc.[rÙ%ìn·û±#*\̤½®'fÓãYãê+ÖÏH|SžgÔ¹äwxKŠºÝ3œÅ—šØg;—nR‡1«‘\– »Û½ÆÔÉ–Ûælqƒ_~`ká:Y+Fî<\j’ß\1nÍ=uó×·WÔª¿UóÖMwÉý—-¹ìv·ÛÏ2¢3E­Ðœßœ‰yÏSB×|ëB-ÍTZWSþÔù±°¦Î\!˜3bÙ•Øo„u÷\!¸lÉe—°»ÝîÇJ,gㇾ‹9'…Ñ>+­«èYPƒg¾»É±ÙòÝJbEAwŽF\—Þma[ø1S7¨ìWê’Ë.aw»ý,s´Ç–K>%qž"œs6'úTÌ-kz‰æ‰åwõÜ1°¢¨¿6çŠ.ÀØçÉËÒMÊý½z!Èe©°»Ý~–É–>rniÎgáS^c­‰¹ã]ØögÎ-ïF€ämŸXS#ç–æ|òfïAÛt÷œ[ºlÊm×á·Z6Ý¿èM¡Ã7ø½sú¾§¼X5îsܵ/O$wCR®ÑOXQËiŽ6®…­è7¬»f¿â²%—]Âîvÿ9>Ëí5D×ÜRœŽik/}I‡×º쎋ŠòzéœGKŽ=òÄ’¢nþZœ+Ô}ÑÒMŠ¿fÙ䤥À¾íæ³ôçÁÂc3i®÷“7biðôêýQžFR(ïQV®”G£_XSÅ+xsÐ3°Ë™¯u“¢îÓsKÈi—±»Ý~–-ŒžãKr0 }g`0~ôÆ‘ºçøö ×£<>ÈšÊñ%9˜y$ãGêÎñ%Ë–\v »Ûíg©\q¤¯/9#<+=¶F‰i|(=}}xwcÉ/öÙŠJ__r°=‘XøòRwúú²lÉe—°»Ý†ÏR/x;\Qßç(Ñ­ôgòÞì£ÆˆÑŸ©Q¬æH±<¸DSÞ±6VuÛ:p®K}ÞCYºIá×*[r[JìËîõ,1 ?¦#r’ƒýøþLKÃ=jL0Bû3õ¥¯^-ÉëÎ…55½»ôàÆèÁ­›Ÿ…e[.»ˆ}Ùíg9±ëÁMžï3ã¥&öëž•bhýR´Ž=“ä Œ5Ý~rp°–Xl"¥nP_z«([rÙEìËn? ëd<ßгˆsc Wtá{'è‡Hey×’OÌ5íåLÎÄØxïK7)þše[.»„Ýí~·ý‡§q?97¾¬Úß(¬lŸ¤ö¶o9Û¶±¢¦ÇýäÀ³bìµu¹6rÙ–Ë.aw»j/=\ê¯ö‚!z«³×±×éëxµÉ݈5ݯö‡Pb1¼§nP¯ö"¹ì"öe·Ÿe`^óÔ‘Ùô,âôXF½ dzô ÚcÎDÊ}MQH‹ã…5Ýn]àÌ„}¾b]ºI}©åFÙ–Ë.aw»õ,OwžÄqÆÓ~ï„Mô§PƒqGgÚŸ#öµHq$çØlùN¾ÄŠ‚nþZ„M$¶£X7(þZeK.»„Ýív{阣Ñ%ò½sfx‰¢Ç ý±õ:1sˆù©lmÉ'æŽÆŠº¼ºJ\.‰…ƒ?uƒúÒÌeK.»„Ýíö³`ÑVc+Óã¾85ªCç2®ÆÆè[b׃TÎGʹ¼4ÖT±‡ØœçYï‘ØÇžké&å÷e[N»„}Ùí:VYƒGÎaÌÁÆg|g}¥˜£ÄÖD3å:VË’ëM +jä¦ç—ˆÜXìt¥î–s—-¹ìv·[ϱ9"|oœ§U=‹‹þt'Ë‹ž)Ái å=Àˆ¤üi˜×Š¢nüÚœ‰žIØã “­›u³lËe©°»Ý®côäŒnßErçLwL†¢'jzŒp‰6XÊ’wösĚʗäÀEžX84Rw]­KeSn»ˆ}Ùýnû1ÍWÛQèÊö÷<ÿ1³}’ÚÛ¾ålÛÆŠ‚î½íÇK- {pF#Ý ö¶o¹ìv·{ÍÇ¢_Œ™ÁÈù8“ýø…ߨ#B¿MyĘ3å1ó]XS§}ãÉ완=°r³îþñ,[rÙEìËn? Á+ú.æ\_5§Ÿñü1 ŠèPR_Y#6Âr,c+êÌ7mÎËáÄö¨ú©­²%—]ÂîvëY #Ð8ûüÞ9˜†¡±}†­…±l¤è_Hë!¿8+öòüÆÏbËЪÔÝòY\¶ä²KØÝnûa0¨Fû²ßÒyà0ÌFOï†YQôû`ˆO9†ÿÄŠªËo)NÁ^“°Oý,K7)û«¢lËe—°»ÝËoÞ12VÁœ+F?øI9~tzUcÅ1rÖµäÑ:šÊY|rŽpà'¶!Áº[Æ*¸lÊm±/»ý]Ç«3÷,Ìa€îSºæ(òÇÍbÊÞ»v/ùå¹°—çLÞ³0g„ÒÄ2@׺ô»»lÉe—°»Ý~`…-ž>r"µ30+·›Ý¢\bïÀò3:°Ä’¢n?yç,jôÄÆY”¥›TúªÇ’ËRaw»·½WY××ÞkÇ «ÕÜÿ<zÊGüµï½ZŽ@˜ÄŠªö]$§ÐÛMìÀhݤö½WËe—°»Ýïý—çiûµÿs¸+÷@ ±ƒ£=Rûþ‹åÜ_1VÔµvÅ9cÀ76}—nRûþ‹å²KØÝn? µÏ–q}æˆÍ»‰‰­Ãí3F7RY§Ï%ד +ªe\Ÿ9ðû¼÷¹t“úRmïwÊm±/»Ý^à–ÛŸÅœ_6ÚçX¶^ü²Û³p„œðf‰M*Ÿ%9•½,±~êÞŸ…e[.»„Ýíö³œøê¯©O6§‡jŒÖ¡ß¼±'eë"Æ‹œÁjb“jüO΋;0ÿ±î‘ó#—m¹ìöm÷OÏ‚ªùÓ³ÄAœõ,½îÏÒëÏϳüõ,µ,ª:b~{–Ú׳0¢ÑÏ2×ÞŽžezoçJìÛnÏaJL;£ŽÜš[šƒ}ºØ ïÜ9á£zfx²™>¬gæþv˹;d¬¨±¾¢8˜%$¶c…`Ý}}E•-¹ìv·ÛϹèëò™sbW¸góX7+ftèÕ.~1ÿ«eɹ;$¬©áÑ/9»GÆ"š9u7ï:gÙ”Û.b_vûYèÁ:½zì@>µÚ«cV¢Ö̱äœÙ«9öí~ÉÁTcc}½t“ò{ˆC¹-%öe·Ÿ¥Ã»þ¼WïWŠá·ß9úñ QTñ6¢5’r‹kùhcIQ·ëXcO(a#twé&å:†1DrY*ìn·×•8¶õýôºRœsëx[çS.­í̯mí§ö$ù ‚±¢ ÛëÊà<´’اžÜK7)÷+Q¶å´ËØÝîåçßÌ•q°ÉAF¬ÐcÀÀð&„ßçÊ8Øð8Ä Êr¸+*ã`“Ó¸ l¥ßCºëò‹¨lÉe—°»Ýkÿ%ÖC×~Kq&&Õñ¶Âû>oï5¢éfF¶Â¿0–üF””±¤®ŒlMÎÆ^'÷¨›”÷_î¹ä²TØÝn?Ë8ôÙ}„/9ÇöspwèÂW'•Öµ”7öLšêé½3§rwHXLÒRwɽP—-¹ìv·û=¾\Óçv“ƒ°T÷ñ¾ëÎ1€Ô>¾XÎñÃXQ3w7Í9Ùil1αkäî¦Ç6Ée—°»Ýk>Ö¨a¶œÃHÚûbc$í3Cº‹©Œó¸Í‰YǙؤFs°×¶#¶Ùº{Æ>»lÉe3 ¼ìöúGcâ½6Åõ™S#«§Šö€ÕÕ…¹!)¯Ϲä7";ŒUòtŽ8¼iÆF(üÒM*WnuÉi—±»ÝËwþ¿«d– s°4‚ç„o+b„ïá7éâçýD”®åøä‰UòÄ·8gÎ|ó¥›TzmÊ’Ó.cw»ßkäëö^’99r:á‡ò:v.—Ö¹–s l,©ky¼Ì¹dl„U.ݤö5²å²TØÝîuùf½ÓÜ29—s‚T¶—h=Ï”õ,¦PÞÉbËá¸N¬¨u‚Ñœƒ'²„mL)"Ýë£Ë¦Üvû²{å)ìµý]Ì™±úŒŸ?Þû™½º¿K,ŽÏ%×!¬¨ÜãKÎ@ 0{x©;÷ø²lÉe—°»Ýëì[øt#¤ÜgFÄ™ˆ7=y^.Ôy"ò>L¥u}É9F+êZ§@Ä<5Eìcëµt“â¯Y¶å²KØÝn×1ž/»pêö{çôü–ƒï†ç'Uz?òÝ…Ërž4ÖTÍHSspòÖØhÍK7©/×ö‘rÛEìËnŸ±B‡Ù§údsî_q–‰ý’ï`j$Šå5Ä?XÎô*š‚nþZœ3:ccŸªv,ݤðk•-¹í"öe·Óuð´À…ê÷½sú¢Æ*s$˜À ÊëÌbyçˆA¬)èæ¯É91/0öDJë&…_«lÉe—°/»<åÀ,<úTùaÌé8mP¼3¬ƒ¹KbO‘Ê‹¯’ò†À1cMÕŒ7§à„±°Ñ¯.ݤ¨›e[.»„Ýí^Ï'Qc^Ïœ»À§ô¬1"ùËáÐiR,¹‚RŽ¥QbIQ·Ÿì û :÷ÒMŠ¿fÙ–ËRaw»W‹±w®öbë|bºa]´‡^±‹}¯öòԚ؟²üÀN–±¢V{1‡u^ØÉö Ýsµ•-¹í"öe÷OÏ‚­Ìïõt©£YÞ³Û#ùnýnùý~„¾$L©»g*)—ÝKj.ìËn÷c®è®¿w:=dº°õæyÍèSI±gbîÉ㥅5uí§?'ëQŒ¬ÆY­ûHOºË–\vû²Ûß…éWXG¾wNî]ÃT®>Ó"nž- Îêðð”(×éð6Z~à d,©­§0‡éÛ„eûµî­§PÙ–ËRaw»ý,±-^#›Ãl9Mã³å4t)¢XÇfËQlbEµüŠæf0Óœâfv3ê&Å_g¹ìv·û§ïræœßœ óv¿œÔÈwwçœßïVr½waE9ç7‡»lÆvÌé­»çœßeK.»„ÝíÖ³”ÆÖp,\r.ÌJ Å—]õ28Ÿš™,³œômHއH¬¨ô¦%‡+cáãJÝéM˲%ož »ÛÍg‰ª ¿äé5rrèf¸1MD¯^P•Îiê ¿>é©”œ¹wŒuzœ)K…uw¯‘³lÉe—°»Ý~>{lHW=‹8ÝÖßl/á„¿ñIEÙº»/9ªZbEUï¢%1ÆÆFý\ºIñ×,ÛrÙ%ìn·Ÿe`»žE~Ä›õàÆÎp½'Þ)[wNsZ)|…À&U3’Bœ‰acç¤uÔMÊß\õrÚeìn·žåÂÆfÀ”*9ÓŽôXsG±Å‹mQ(ïÂ6kʱ›XQgÖspZ5±‡]Ð}øÜv–-¹ìv·ÛÏr!¦‘•õ{ç°2‡Kå\½ ÒŽÊ‹\ }ɲ>¸!ˆ:¼ÿ’5aUõ¥»zÿ%˦Üvõtoºù,…sÇéu¥8Q*Wᆈ”§|‘ʎˆ-?Ù Jºùkq.tYÄÆ“—Ô-Š¿nî"J]– »Û­g9/L ŸÒs•œµ±_Ñ^ÔË›c®bÁËoË2aMÇ\‰İH,¾iê…_³lËi—±/»W{™l´Ê×—œJwÕ@(ÍÓÕWnaéÎׇMƒ¶ä7ãÆŠ*Žh'ì¹/cÃÖ+u‹Êo>–v%v·ÛÏ2àÞ¥g‡‡ežšl¥›ðª¦l][IÛ> kªxÕ+ì¹…­©[”kdkK»»Û­g‰ìÑœ-v}s¸a=o®§†—õã6Å~³Ájy‡×TXSÍ^Õäp³[ØÉ#£ÒMŠO~{årÛEìËn?Ëáù”b­Í Ï ·ó1Ñ«O¦ô%ÅòÔ×HÎ¼ÃÆ’¢n[wÑЯÄNæW–nRüõð쮎e©°»Ýz–¡ö…úþ½s ãf-¡=+û¡áó6¬2Ë’¬ÄšZ­ËœJ—Ÿ°ÅM {µ.—]|Ðä\Ø—Ýy–?íg9íÑRNHsnv9 ΄†î PQ´ŽI¡i +Šºùkq°Ó`ì3×mK7)> ˶\–û²ÛÏRÙ¶g ±ß;çf*Þ]ô}“aÏí6Åòœâ–ŸôŽ+jzg89'¶aˆÅ +u‹â¯ó›mÙ%ìn·žå˜HTð{ç¬ã¯ak-*RYޱ䜚ªÎŠ•¦$6ž¼¤nQø5˶Üvû²ÛÏÒ9Èuerò M$G-y,s˜\Wâxȵ䓳aEåº29:¤#lË)·)þZeK.»„Ýíö³ÑÇãš'›3ðRâ@ë„u:ÌÕNSiÝXò;üÄŠ:rVmvSËÖlÝ5gÕ.»æaÚ »Û­gi7Ü ¬ïß;§amØ+œÏ»‰•YoH£A åu°œŒµZ9QkbuE¬ê¼tï­‹e[N»Œ}Ùíg©\YÇÌáô<áxïH °}R(¯qsÄrnŽ+êÈ:fNg‚aSŠHwË:æ²%—]ÂîvëYjckÆiÒïÃ)EínáT¬tH’Byõ„ÞrLi+ ºùkqL´Œe*1ëÎ{²lÉ›û`w»ý]š]GÚ¯LÎV$Â¥Ãçbê.ÌNGÎe〲äšG ›³êråw¹8FR(a ¦V-g¾Ý¿VÙ’Ë®ê„R}Óýg÷,‡ùÑóh|1‡‡#õrgŸû|Ù¦õsfGº²äZC_™Ê.uÛº“Ϫ¶x¨ÏÝtwGÍfÙ”Û.b_vëYBÅÁ&çB'Ã<þð¬tÞÐM¡¼rÓõDy-t=kjäŒÇœ”±7yHwËË–\v »Ûíg9°¸½§cG“Ãà‚s¿èû&´ÏÓËã¡7Ëo®fnߣ0Rw>9w8p¤/eøöƒ˜ž’úò[K.»„Ýívœ£`ú½sx|°2ä¨b-Šò—)Öš‚ÞGòÆÄ–šZ«^sxèMX^è`ݤÜë•{Ée±/»ý,8Ýjw¼er§Ò4µ•_ºR€”Ÿ%6^$„…5•‘sÉa¨¢± ³îŒœË²%—]ľì^í%ÆÏÇí¿$ç„[®(4àâ­Bã>)·€ZRþÔÖÔðþKr:¯O¶ñZénÞɲ%—]ľì~µ—x“î“ͼl‚í¥ž¼@ˆí…ÔÞ^,G{H¬¨#k9U[yƒÒéoæ>ÙeK.»š/¹¨›îx–‹yýj÷òÅ LQ¯ Õ»"ØôBQÖäé@2fuÍŒ½ª+€¦Vf`Åf\‰e‰µ¯Z\&¥²¸—­4ÿä ÿð:Ñ|3(1b¡ªjï½ êô–QzØ-8—=šf`QHÃótŽê¤_Å 8̸õ‡è²3®¦;« ¼Wæ‘ôâÑ.â.Ÿü’OÑ DN‡0=k¡¯¥S_!•5Äí¶ê(qHwf`ĺ™Ìã,º‰¬LxOLBÞi“ZC9®e)9ЩXIeq»¹å }çÈ ÎYÜ»Ožà¾¹Á¾ºF†Ã'!?yFHØÓ§4sd'/Öѹâ±tÌ3æ²-—¥—G•sÓgéù²gT½ïsúN‡[\`3‘(.¦d¤¸½Ðü,Ÿ¼3@XQ3çFæ\x$aû`buê&å•3²]K.»„Ýí~?Ëè¹›mƒd¤/N¿—,Ôþ,–ÓVcEõÜ—4‡—çË­Të.¹/é²%—]Âîv¿¼Aáðîœ9'¶5è‘ ÷ÆQí±µyƒRoObE¹;g c\ªÖÝrwÎeK.»„Ýíþs|–“ÛxÃWª&‡×ªtèÊ•ÉDчXý o-’<kj¤¿Å^Éblsèn˧§²%—]ľìö³ÐsÌ‹E¾w/‰7YÃ}¡tœÐ¾Ï4¬Ã™PÉqß±¦ÎìõÌN ,CA­ûÈ^ÏeN.s,ìËn? =ÇÌú÷½sÓ"¹ÚX…@ºc…‘(ò^rF=+*s šs` n,3ÿY÷Ê1è²-§]Æîv¿¼§áê×MÉáÑz0«‘ÐÃ)jóž¦ÞÑÄŠšés7‡ÑÂ\½jݤ6ïiÊe—°»Ýî“õÞoÏVÌ ëØ¹GÜÙÙ˜‚õd}_û /Íò›§J…%5Ö>¨9Ó£QÓ¦ÄÒMʾÖv,¹,v·û½srgÙLÎÉÄwü–'r€zwƒÔ¶s’rÖcOŸpèþµ8Jý#,¯Ÿ±îæ Y¶ä²+ÓM÷ŸÝ³¸Ÿ¾|Urpݱûø¸6.w7Díã‹å?„u¥Ìœ{/vö<¾éóø"¹ìv·{=‹ræzgÞäËhô”ƒþÐãä–å‘;ó.7HùÍÙ«°·söygޜ⴨•9©êÒ}ä•®.ÛrÙ%ìn·ëXC˜H/>!‘^ñ4:§‰¼¦ä©1pÊZs,9­3VTÉXqÚdzMb/.‘nRü5˶¼ù–/`w»aÔ™»;#‹Í9tÐ}`…tèüÉ ¹YŒ†r-ùíl«>$Ÿºùkqllgˆ®t÷Œ,vÙ–wg!?ÆOv¿#¿˜lp‹üŠ'VĶ›ª²ÅØ3O“£·,çö±§oÜpëºr{h‰mL[%ݤøëæ›3z_– »ÛíH–ŠÇ|–6Ê”œÆæ†êR‘£-QukÞ°Ñ&Ñ0-G¿ÄŠ*>¿aN¹}ü&°åÂB@ºIñ×,ÛrÚeìn·ŸeÒÅ‹|ß;QÛï¹±D½î=2ðÖ³ºŸ kªåþ—9…Ál²•×5I7)> Ë–Üvû²Ûu¬ù¦ ÏÇÄÑA’«û†‚?M¹Öè†ȧoñ±øÔÍ_OßrôÄÆ­•K7)þºû†V—¥Âîv¿¢ØpC‚Û‹8ÜwU$Y㾫"ÍH±¨}JWöLö)]熙¯ôíS:ºAù£æË_5ß>%ËeW÷•%Ǧûûê¸ärd±9ØjÆÅ¨´5q1j1EÜĪWòV8K8½_\S·÷¿Àé¼”U؆¥»u·Œ,vÙ’Ë®žºnº‘o°Ò»¡lôÉANöèÀ•©?|Õ‘‡éþزÑGyÊô9R†%VTf£OUËt/ÖÙè³lÉe—°»ÝkG uŸû{ç06vÁØp¹;.²å-,Y)¯Å J쥋º|GNrN8º…}ªÒ½t“òŽ–”n]ÀîvûY.ΧîŒ2'Ž%j_>jM¹¹þheP«³àÆ— +ŠºóMßh6ˆ ¶3=t“Ê¿²ä²”Ø—Ý«Ž]\KÝ3ë˜rÅâä–~z³1ýîUùî,¿01VTÉ8 {(¢‡º-â§¥Ÿf¦]Æîv¯ï¢`Rû.ÌÑToÚ£}|l,OSé‹?—¼s7#§ %uó×âГ.ì`G-ݤü]¢lÉm±/»ý,“^"d²üÞ8We ¹™ÏÙ,£<„çgÞLÔš±ä³$aE™739 •öDnaë&åýbä{ Ü–û²ÛÏÂîóª>}›Ű³½ô•ng#ò:6öAî”?oú^XSÕ§oÍ9óK(‡Îµt“òÎüÕ–œv û²[»aL7nN~30¿Òù—kYe-&M«ÉÔ\é–BS813uäBǬm ÅÜÊŠ«?¶‹¥ô°3&p»¹þÚíš¾ŸÕœ Y’jA·§¾Ñÿ!‡Du¦Üx+8ä#yò–uûý6£)‰=év‘nRü5˶\– »ÛýS‹ÇM¯ÏkÒÝâÅ¡æ×w‹—\-ZW¬gû·mõ1…2¶²ÍHwMšË¦|µñqþd÷OÏ2Óc!Î…øëc—·²ÍØËi«°¶<³Í$‡ƒázíØßSVÆ|!·¥Ä¾ì~ÅÅ›Ì8rÐÛÂ*ÎNº·8<•Ýz,ìËî5£¬túø|—ó44äÊx·p„œÐà'8"[ÎÈ:cIQ7-Ž¢ˆú¹t“Ê^_ò•ãñúÉîW|$n%©{|$îáÛRÖn,[é¿!Å7ÍYŸåô=ZRºùkqØžŒU´¤tgþá,{ˆ4öe·W-Å7xùl§8±9EçÄÅC +‡VMåÊâ^òA{„%EÝÞ{¸¹]S[a•AºIñ×,ÛrY*ìn·Ÿ…‹ØãtÞäpTíZ-êj•Â¥\žïÉãÕ–‘ÙÆš:ÓOkŽ.[©Þª›î#ý´.›rÛEìËîK‚£ùÞÜäðdx/tuî¥7nwfc\Æ×–üàMQÂŠÊ ÆÉa2|a;ë§t÷UU¶ä¶‹Ø—Ýþ.ÃA*Uã‹9¸=6 7gu“_阦¼¿¥½¼¹‚2„5µNÒ™3ÐA{p¹"ÝGÎs\¶ä¹Ïìn÷»Ž}íì‰CóyÒRì¼öìXõÄrÖ!aMõœ›Óx®GØ’nŽÃÔ^Ç$·]ľì^»ÇÊßí}!çÑ «ù”G^»™òîñQ–œ>c’÷³&ç²cã` Ϲt¼ŸÕe[.K…Ýí~‰‚óÏ^1qN6.žKºx†Wç–®uÆWçš,Ç™'cM­S­æt_ð,wk­{íæºìæý¦sa_v¿w)¯á{Ú’s1"¢9k;s'‘Ô¾Ki9k¸±§úÿZœƒé¨…m\ˆIwËó‰.»íû’Æîvû»,‡®wö†³Ùjï°sc@ûŠ8TüÞ«ë)ìy„u­½:s.^a(×ôI7u“rýíç’ËÒË×ÖM÷ëMEyH®u˜‰M*o(NÎé” ¬vîÌ¡àýY•m¹ìöm·ó1C [œW×SCÜsœÜ»™©ðž¦hžUrd™K¬¨’Ù2Å) r#8­8'ò>öêo9˜ö:L¡¼~°:I>Xk„uxö–œÎìæ‡«bÝt·•çVeK.»º3£—M7ó鶸+ï4‡·<œåÓ·lƒ @·ZóÌÿâ̼äga.bM­ZcOi ö,ݤ¨›e[>Üï»Û½å¡æ›V¾°ä `E¹ ÃVdÊejSR,ÖI>*3ÕkêÊ[Ìá¡la[Ï¥›~­²-?ÝS Ëîn·¿Kc ÛçºÍ©HÂÛFÇÅ«éz#·¤Q&•ïn.ùåÖ,)êæ¯ÅÁ^ÆF\ºIñ×,ÛrY*ìn·Ÿw<4^ö½qjåÝû"\<¥ë<²ó÷}7ɧÏ3ë^…™ºùkqpªÀØ‚“+Ö]òŒŒË¶\– »ÛígÁ†_”Þt'9È\*Ñ¥DeØ–eëª_òɈ•£Ÿ9…ƒ °…Þt—¼oÈe[N»ŒÝív{¹‘…X}ï\pu:ª /¿Š/‹£Õ÷ºQ¢£³üà)maE™!ßÄÊ&–÷=X÷ºÂeSn»ˆ}ÙíïÂÊXññ¿7N\w5X R%ö<å6åžI‰•ïT¹Vuó×â «¨±••KºIeý-K.K‰}ÙíûZ:Ò_ÇåjûæÜá$‰[IžÙ+.ޏqÃJd %Eëô¬’ãXEbEA7-WI,ݤøk–m¹ìv·[Ïrqè‰7©û'Ìá+¼^ÃÑ‘8æBÈ¡(”wq´|¢:+ªämâT>’°•=“t“â¯Y¶å´ËØÝn?ËÅËà˜þÞ9LpuM^÷§vÜ„4ýgæ¼çµi)Ç! cMùÍi±`L,ÜÜ©»æWtÙ”Û.b_v¯ïró½jΟÌÛÛ³•õ1¬]§-ל?ÊSâpÈo^Ž ¬¨Õ뙃y{b9Ù·îÕë¹lÉe—°»Ýn/•sÃéîæD 8qoϤ¾ƒ­'zR¬Ó#¹å¸:)±¤¨Û­«°Ÿkwbëé–©žÉ7Õ¨lËe©°»Ý~>ûÓïÉ“$´ŽÙÕ»LØ"íZÁ·3ûÆ_Ꙉ5Õ½‚ONE4§øSw±&Ë–\v »Ûí:†{Ý0×ÜÒlŽÄ—¿ÙÅ"\8Õ”kÍ]R>q¨±¦òìGrx[™°1Ù_ºIQ7˶\v »Ûíï2™¦8¿^r*^S¼»Šç/|ï‘Ñ‘Ëk\IÞ¹r#ÖTqdƒ9×þË0ë&•ß¼¦\v û²Ûß…‹Û¶žÅ¦÷¹ÔzÒV´ÖíY4þHž­gž‹ÚžE-HÆV=K÷rÎÏre ѳv·[Ï2‘,¶ø^vqšwÚGSkˆ Ž»£ê¶Œ°Ž› £™K~Õe¬©Œ°NNåýzÂ"‚:ug„u–-¹ìª¾›¯lºQ}oÉ}¿‘9ìü§žŸ]J¸ªš)–×Ù‚$Ç ‰•§÷Ì©8„­*¤»æé=—-¹ìöe·¿KGö´v8}r؉ðZ¸#¦—pŠby“34ÉYÕ7G'‡cn“±îæôY¶ä²KØÝîwÛ«ÂÚ«í7ìºýÅÅc-Û'©½í[ζ-¬©¹F?qp{¬±q}îÒMjoû’Û.b_v»ŽM^´2œ 9ð:Ç·œ¼Âãd=ˆªK*g%mÉ‘êÀXScÝ*.ñLlGö&ëîy#ŠË¦Üvû²[Ïrãnô¸žÇã¾9Iâ*‰†ë„¢Î2’V[3Û§åÊM¬¨ÌÈ•œb,¢Rwfäʲ%—]ÂîvûYNLiÜ«þ½s8×½q¯u\Øn„ЋŠòâ¾Øû2§‡û'±Im·¸“Ó1×5¶³­Iw_mQe[N»ˆ}ÛÍg‰{©c²Þ›÷+“Óã#ÆȱŽíðCÅ-ÂÑzz¼âÎáV–IH+*=^É)¼·Ø†…³u“â¯Y¶å²KØÝn=KÓ…;¸Çó{ã„£í±§ó žŽó½ã Q(¯óúËy5±º((o MÎ 5Æ6L8­»å­¡.[r[JìËn=KøOÔ í½&×¥ö®Ëq zïHÒ ŠåMŒ–ßte +ªzïÕœŽ+혈Y7)þZëIN»ŒÝíöwA²Ç¸ÎJ’3`]Ãuáv+q'6:¤¾ôa/åi)5uäôæ Ø*±ØAOÝ™{<Ë–\v »Ûíg¡KóÀ,ý{ç :¨7˃ ñDy×iÊÖE—%yg=aMŽÔKfê‰mñŠSws+±'Ö†Ö}æ*Ôe[.»„ÝíöwᥗΡg1Çf{ét©à®´^V)ŠïÉŸR>Ñë+ªúDVrpó•±áü_ºIñ×,ÛrÙ%ìn·×/8Äoòôú…œ¸Y;Ö/HHÖõ‚µTlÞ]9bÄ#üB–s%h,©ó^+qÑÃØ÷ÆY÷™7Ô¹lËe©°»Ý~.@ÏË™T’à ë[KÒÇÂ%©(¯sɹT6VÔ•·`›ÃÍnaŸЖnR_~«÷’Ë.aw»]ÇX'£OuÛçæ—å¬ãé§ðe¼¢¤XÔ$ç›6VÔÌžÂT.c£_]ºIñ×,ÛrÙ%ìn·¿ ò7ÞBÿ½sˆ*Ö±¨ÅH±ÕKe ¿í‰ ½.ù>×XQ§=‘ÉA ®Ä"éVêî>/–eƒ“vû¶Ûþ± î¦óÊ{ÙÍAF„ðaêÇ*ü޵™b=@õ”Ó³"¬©µÓe2"ýÔÒMʾ·³§Üvû²ÛÏ2¹éT|&19•~Ÿ›ÛPIÒÃsBÊÖaûLr^;.¬©’;‚âœÜåöäm®ÒMÊžÁYS.»„}Ùmÿ؉Ùé…póïƒtì]l©Mîty¹ß ¯V8|$ŸôÞ kjø$krx …±Í»hcšÊ=·²ä²«{wsnºíë+ìE§dNçPrÓÑvÐ#tÈ¢ÒºcÉ;ûsbMåi™äà¦cO„UZ÷™§e\¶ä¶‹Ø—Ý~Õ‘#÷ÅÌQ÷{ÀGÁÃdÓTî÷\K>ý³ûZÔ±öÅÄ¡·ÍØê¡»æ¾˜Ë®Y7ìn÷Ou¬çø²¾¾¬¾3×®g¶f×ÊW­:Ê^£²5‹ÃýScqýzê®9¾¸lÉe—ö^w»ßãþufÌ•98ò”ã>r°å¸gÌ•Ç}É5î +*O0&ç€!Æâ\aêÎŒY¶ä²KØÝn•77´ªïœKözŤQ[X1—Õ—µß§v·'ÈÖaךۿ§Ð™3Œ¶t“â¯Y¶ä¶‹Ø—Ýöõ©Åþ.ÉA&ÌX¹Þ¹½k\Ì‚6ßâÁ™€ä'çTŠ:ü]’ƒÛ—[é’îºüU*[rÙ%ìn·žå@¶ÌEå‡çB.¡þ,ä/ÖÙñ¬FcÛÍä=RÛ\KÎ `,)êæ¯ÅÁ¾ÆžHkeݤøk–-¹-%öe·Ÿ‰|ð­n=‹8ô©ÅpÎéÏgÖ9ͪ)–‡”C)ïÞD ¬©êˆ“äàÀº±Q‡–nR_z«£§Üvû²ÛÏ2ܧ­÷ÍAüg?ØÙN¸ªB¶Œ@ùÝipù¨ìü‰5•Q³ÉÓÑØ‹{Ò}­=•m¹ì"öe·Ÿå@‡7׺Ò„sSl®#ÿt5õ¥¯ˆé½ä7»faE­u¥9¯2vrÝ(Ýs­+U¶å²KØÝn=Ë…ôd˜ ©½˜ÓÂIÒ/ÞÉ©§Î^HX& å]¼ÓËrä×I¬¨ê`s&vŒ¸mÙºIñ×,ÛrÚeìn·žåÔï‡çüɹ0o?‘Ý+ôÅœþÄ-*¢PÞ)ë$G±ÄŠ9ç7Ç©Û0ç·î–s~—-¹ìv·Ûß…¡÷±ý¬ñÅx8âùoÄ ˆʈ⻞O~‰ºaE ßý’œ^­Äb$OÝ-ýˆ.[rÙ%ìn·Ÿ…;‘pÕÏ"Î{vý LôénR(oª<Éyë°¦ZŽ¬æ æ×Ø(bé&Åg¹Ý …Üvû²ÛuL¶f¢ä ÖRdË7âÈWz™by²NòÁ ðp è©Û_Qäð_ùd}Ó9ˆ²lÊm±/»ý]è-œ-ýÉæ £è<ŠÖ|7ª¥®‹!Ëã<ΚjéO6‡û™Æ"«wêάßY¶ä²‹Ø—Ý?µý{}õ\%þ¸ý=ýE¼wµOR{Û·œmÛXµô;¿ËêƹÚþÉ÷®¶æwqÛ—<û$`w»ý, õçULßçÆÁ(®.8:O‹â»ã¡Éy¦ØXQëÞ§ä\l¿ÄòZ&ë^W?¹lËe)±/»ÿU{Qn¨äœ±ÔÈ:‹€‰¬ÓWz"]ç%W{6Û‹=‘æÀ“X„P¤îæu–M¹í"öe·že"i”^ô,æ ©Aô<'Ç—gš×'¦y¢ØY$å<,b¬¨3wÑÌA|Yb‘V u÷œS¸lÉe—°»Ý?=Ë•þds|Îúnä¥sy¤ög±\¶ ›O6~z$„ËgÁ»ÍgY_ÑÏ"ù²¾þd·Ÿe08kù-ÍC'J8èð)|“pí.¿å„“Ïò»8Œ,°¦–ßÒœ{ôÂNú%¥{.¿¥Ê¶|8Ì ØÝn÷É·•·'9È¿ýf:˜¸#,F·r˜úÒWÔœ òáŒÀŠÊ[Ä’ƒlO‰ÅMa©;/˲)·]ľìöw99 ?|Ž/9HoK±Ê7)o˜×K7²¤X~#¨±¦Öº9ܵ3ûÚ©{í »lÉe±/»ý]øûgê=>spqNŒ`“~ÒØÁ‰eÊ4åÑﺖÓ@cMÕÜãgÜ|OÄŽ‰=<é&õ¥ÙT”-¹ìöe÷»íñsÛçÞöÇÜÛ>©½í[®CoçÞö©{oûãØÛ>Óó¤îösÛ·\v{Û·î_ô^O®¢›Ú‹9H2…7ÉuìÍöЧ©/õ ɆWš'VÔ•.æàFb‘d;u>'”eK.»„Ýí~÷cãN?Œ8gaÁ¾äÄ(÷5¤ö~LrõSŠ¢î½Ý7vàø«u“Úû1Ëe©°»Ý~VÇÑ-ÍaÌì…‰b²Ú¯fʵ¦Õ%ÇÍA‰Õ}Wr²X„,¤î’ó#—-¹ìv·û]ÇÆmr~½âžR_¢ÎUšýÉYO$W*{£î½ŽÅ×;±ƒQ!ÒMj¯c–ËRaw»Ýul•DkÕXi£é®ÃÇ 1[Ä5n¢—üôž°§÷®š}zo+zYaO ÖMŠ¿nŽu€\O&ìn÷ò[¢=lþ±é^8|\áËH‹ðÕyw²¤'RkÈ{³µücæÐÇ%¬â*ÆÚ½ô¯Y¶ä¶+}k÷¦ÛñcØSì+vTœÃñŸû/ˆ ­ˆ $EÿuÇY'ËÄ+ª¯ØQqp\<±…‘­Ò]ÒÓ᲋cKï…}Ù½üüX•ôÁšÓl}çñD|!už¨´n,ùðlXQ%}°âp·ÆXîÖX7)ûùû\rÚeìn·Ÿ…§NfËõ¾9¾”ÎØ¦y0RïðŠÃëýÎc¨–3ŠÊXQ™á+9…‘|Äò ¢u_™áËe[.»Š£ïM·c­c†ôL`ÇŠµ‡ñ9¼Š$¶¡Â³Â ™¢XS(HÎ|›Æš‚nzˆÅa|ް1I[ºIù=•sÉe—°»Ýïø{ù”Ì‘õŒC¿‡cá›X>%ű[ÎwcE-Ÿ’9Š‘–>#ë^>%—]Ã8öe÷;î¾|^,9pW9í¾Ìx5R܇c<›åŒuÖÔ•Qºæ Ù$öˆeUê>|^,˦Üvû²{ÅöL=8~L7E~þ‚ˆ¯QM}éÉKYò›!EŠª+Úìö~H¬H„0˺ç\ÑfÃurÚeìn·ãúxÚôΛ’s0Ž{œ7nvˆèçq™By‘­–s7UXSyóCrpé–±“ëéžk=¡²%·]ľì^Ï‚c>=ûdsðR":¡-'úÜG;B†Îì“=Ú’7ÕbMõì“Íá ÁØÂ³3Ò]ÖÙ•]|¶¦-ìËî«ý“Ù2ö¢—(|j S+ô Q¸G3EësoùÍ ;aIQ÷—úp.´aµÓ%ݤøk–m¹,v·û/Ïò'ý,'¢Õ<™œª+<Ïdz î׉9¤²KÞQµ…%%Ýnû“übîIl´®™ºE¹íŸWÊm)±/»Ý'ãjÑh»Ž7g"޽LŽôœGÔÂzÍ3je/-9†.cMe&Ñä ¬vŒEŠ„ÔÝ3ÞeSn»ˆ}Ù½ÎòT¡*F19z~Πn„ÄÛ˜Õß4HÞdÞ»¨á¨¦äpÐØU-u7G5eÙ’Ë.b_vÿåYþ¤Ÿ%Θ4N}|f„œZyNŠá ¼Ð(f{8ºÕò'Ýy‹d–-¹ìv·{åQTX©ó]˜ƒù؆b÷ 7ö@= …òFȞܮkVTž,5§1…‚°<‘oÝ-O–ºlËi—±»Ý¯œ]ñ{Ÿ‡÷¦+oVºÝ0••uúXr¹²„%ÕæÊÄsûf+‰mÈTmÝ-ïKrÙ–ËRaw»_ç_àÐyÃGç_”"!Ϩˆr?vÖ%ÇùcMµujJœÂé2±íæÄ†ºI¹;”Û.b_v¯>YÎUåèNNwè‹’?D`1‰‹¢uÅŽ±É…saMåÎSrx,lc¨±t·{õ‘,ÛòÓÕ'·M÷/ëÂAßó~±äÈ“ÃcOON)¦8úá|ZʆhcEåýbÉJ„Åýa©;ï˲%—]Âîv¯xþX7>Ïçy²9g%…mlpžŒô¢¢ì½›cÉF cEµœ'›S¹V#öyï÷ÒMÊó#µö».»ˆ}Ùí9u½sîs:OÍ7·—ðéòæ8Q¶Níer^>7ìéöâÈs¸ !lÔ¡¥›TúD%¯n/:%qlºQ‰ ­>}_RrnΩpÎ6œÚ§rá<Ö>\AÂQË™ÚÌXS3wÐÍ99#¶ÓO(Ý}ùU¶å²KØÝnÏ-ÈÜ‘½ù{ç JŽâ“.y¹(Sž-FXµå ¹&6©syˆÅ98%¶§ûã0ÅžBewº8d°o»W>¥X5´ÜGNÓçÉQ¬áj½M}iV=¯%ŸÝŽº¨ÜGNÓ[‹}âÔûÈY¶ä²KØÝn÷ÉHâƒÔ1_ÄØr›­=ƒíu°%OMáŤG+*wk’Ó2h”ãìØt×5¾¨lÉeWs°yÝt¿žeæ+sn†àQßQ¹­£#5ÏXÉËeë}©ûõ,'?õ,Œˆ š©ì™Ê’oÜ+sAMÝ÷+nµžØç=”¥›T®¿»9iWa¸÷n·ÇJ†G-o8r1,h0/Oí>æ¨ÃÊ$ËqenbE­¨CsxŠIØCyy¨ûXý½Ê¶\v »Ûý:“G·ÒŸ,ŽöÅP1è/ƹAQžÃD:0ËVT[þdq´·ElÔÏ¥›Tެ%嶋ؗݯýʰå|íWöAß#÷ ûÀ•¹ÚSµíW¦û‘‰52†ÄNWŒmÛ^¨¨m¿2å²KØÝîåç¿Ùš3w9¼Ïó êkœižÕ”ëA-KÎuaEQ·¿bgßPjbãÎÖ¥›TÎzÊm)±/»ý,H|ÜÇéõ~r¡_8¾ôL1 ŠLÊï.-çHn¬¨se”çà¼]X¤KLÝ}e”h>è¹ìv·{åTÁñÀê¼£ÉéÌú ö€»|àÂ9MåüïXr®žŒUW­§°×#ö¸ÙëQ7)÷‘jO­,»Š{Ô²éÞçcÇísG?͉Fs®˜3mÕ«7-ùt Ö}'u¬U¯9“lj=8úI7©}>f¹,v·ûuN<¾Uw®Nq˜dêV=`P7Ïr‹²u±œ°çÀ+êÌ#Ìa–zcµ¢–îåã¹³ž×²KØÝîµ~9 K¿rýW…ë%^ —{1•ûpsÉ™LÅXQWúùÍ9íîìáëÒM*Ûb]rÙ%ìn÷{½ÏÈÕ}½rGEknÞÝë5ùº%Økv˹ž7VTÞl£WÜQ‘î‘wɺlË5Ò »Ûí:Æ>ü¬þ.Ééö®Ç0±Û¬W7åZC<9Q‘ؤêú.â4!vð. éë® •m¹ìv·ûÝöãföóÕöLÙþN^÷£öIjoû–³m«ž`æíæ0LÙýÆpËD¿2òö ÷;’ËRaw»ß}rÒþ©O.˜$¸O®×Õ'×5î眫o}²°¤¨{ï“ãøÖ'Žëê“ÇêÁÕ'K.K…ÝíÞrCq^3¶ÜPœS•Ì ïÆù›¦)¾;Y?Ü'ŸVTÛûä“sª’¹¡¢_K7©/÷¨÷’Ë®ê0€¶éÞò)Å/fË9eOÙµ)űppìÝ”ß]¯KÎAÙXQ5cHÌ)8ñ[rŽS—nRn]ý0'í* RØí^yáÎâ4ß;‡ÝgÕ®r5¦\–ÇëšKÎÙ¢±¢Öº9ÜÔö`×+ÝÇÚAWÙ–Ë.aw»½Œ¡Géä¾wÎVSJ¹álã6•ûÖ3å;öÆšêy*ÜêÜ™Àò”¬u—<•á²%—]Âîvÿ´¿LÔûþ¾ž÷Øåú-®JÚSÌ=zɵß¹¥î×þ¾œÁÍɲKêµíï[n»ˆ}Ùí˜ÞÓ ²}΂LDi LÃæ\¿ÙZž³8ŠÓq‡üГ{¤­>ga' ]@KÝ¢øäÓqK.K‰}Ù½Åôrdz:VAœžYY8e˜tEk7u®X…ë\rzFÕVô8ƒƒcz‘w]ºE¹F^wÊm±/»ý].œ¥›müÞ9·¶7ÏVWºŽŽËß4óÑXÎ}aMͬ‘æ\t¢;^—t“â³L;ªBn»ˆ}Ù½bàÕÅù\9$èÈUŠçg¦´žu.©¶ É‘5±Ì‘z¯sIæ°;#a©[”ÛâK^×ìn÷+¦7úŠkÅÁNF¸ ËQ¦ìD–kN‡I±}ÒWb9½°¦JFiˆSnÆç4ÇA”¥›ûd–-¹ìöe÷ò))×gyÌÁ eØç ÿ+x¥™=ò,OÍ$Ð7»æsÊjÑhNáø#G«¾t“â¯Y¶å²«Ø;Ð6Ýûz¿µ5KëËkî6<’kg·/_Éy.yzîÕöõä=FzbëíùX½Líë}ËeWõ,áØto±Öxö–uLa¢É†y*#^?)ÖšÊýQÊKm¬(êv<ª¬ÜÕf™t“ò,!ʶ\–û²Û}26æá"Ôw1§û!†Frn… õ¬¸¨¦c’³™+ªe.s8ä [8hI7)?ù¸—\v »ÛýÓ¹¤‘ssNÎCt.éâEç’®œÃø\’ä:‰$¬¨±æ0âtŸ êÌË36Ý-ç0.[rÙ%ìn÷û\ÒK=·sI“¸W鎚Ü`;›)÷²µ,ùEW–°Š—¼ó„¼9ÊæÏsI÷É‘œç’Hy®}ÉeiÞpoº_1Š%óó›S¹_윺ù•ÎÛT–w-¹b…UÖ¬„gßž1B•ZÔ-*g¢%å:í&ìËî•ÓÝçá;R“ÃP›z¸E^l†ÇeêK=zRɹ3l¬¨#g‹æ4Îâ/;Åû¦»úŽÔ,[rÙ%ìn·Ÿ…GTë\ûûâÜÞ£¯êÕYúy˜JëZÊ›¢/‰55WvrqNîÑû<ù±t“òÚ¨Ž%—]§³Þ›î}ÿ¥ ï‰'G=!Çæ6÷=Rûþ‹å\Á+j䞸9 ¸0¶ù¦ènÞϲ%—]ÂîvoqÚŸ¿VÜEÇTo0BÿäÞª>)<ïÕ·9vÂr.úŒ%ÕÓË™žÊuÌÆÉP[êîéå̘Ée©°»Ý÷y)Á±æcæ YkŒÔ'À§NðŒ¬ùXa<ä•“JaM­ù˜8ýö´ ±žŠÜš©lËu½‚°»Ýïg‰ßùz–ÎÓŸÖ7pfËå剔=”ÛVbk^ ÑÎ÷³p ÌØâ fè.™#Âe5 »Ûý:3‚KîýÌHhÐ$u°so\Œë`¯ó6òÜGÊB¬(êv­¹¸´¯5±qÉÀÒM*}ÕGÊm©üc»Ýn/\Td úÞ8ÙÊÉÒÅ©“&R­™r?vÕ”·’q²gRÔí~ìæÔê¸[OO´¼a;³u骗6—¥Âîv¿c{Úù“ïü‡x®s-ÉÓòdžãs$WìNep;)êÎtöƒýJl»ò°Óaʾêë4'-öm÷ŠoÞ3NIœjÇ¢É c…búÝûŠS*>f 8¦3±I•§DÎcŽbu:XÎ¥›TúDÛ’Ó.bßvûYÿßæŠS"çù•rìÇ\5&œh)./óÎó´å ]3±¦Úº‘ÈÞ°"l;íh Ýí\qJ,›œ´ôrnÿ¾éÞr§£Ÿv÷÷Ω>$u:î¨ãÎ"Tú²Ž»C–#œ+±¢JZ'NŸ>b­ØÒsé&•ï©/¹"I§Ó†M÷Ó{Œs%Ι)Þ¸7 £ô1<2êÐq¹’+fWXScåµ§;% °1UÒÝV̕ʖ\vuÇkµM÷_žåOùYî1jÆó›Ó£1¦J)<´WäxþÉM^ËO¬Â•#kr ${`¼³î#GV—m¹ìv·ÛÏÒŠ[ù`“Óxt˜Ç¥FçíÍ'#¬2K ¢ë’3”ÄØéˆ¯Ó¿&çÐíÄxR÷‘¾—m9í2v·;žåï>~ÿãWøû¿úõÇo~ùñtï¥|ìÿÿò›¿ýyè¿üø‡ü(ÿô£~üõóïw?bîT>þæGå²ûBb³olÁö*ò¿|üòóDžXÆàçòü‘û‡™? ¹ýàUhÚ°ýà׿ÕC~ýáÿðøúç¿úOí£~üö¿~ЭSò?Ò•WlX«ÿöûãßÝóßüöw?þão¡ûß·‚p½ |ÿà )J¸–òÆ.r}¡«ÑAy&Qߤ”Ø /í'9Ü9¤6qȽj¹Èíq£m]?¹ÿ Žân? ¹ý@6ù»‰û Øžú?l/#¦ çÇ »ÿ×÷o¢ë§¼þñð¸þé¤oÿð‰ƒU€÷?A·àGùéã³Åü8 ~ªVkY²Ï‡Öw0ùß>Øoü·*ܤüèÞÂÀÿùG<]…5q ÷Ósøüw?þ7ßUo endstream endobj 398 0 obj << /Alternate /DeviceRGB /N 3 /Length 2596 /Filter /FlateDecode >> stream xœ–wTSهϽ7½P’Š”ÐkhRH ½H‘.*1 JÀ"6DTpDQ‘¦2(à€£C‘±"Š…Q±ëDÔqp–Id­ß¼yïÍ›ß÷~kŸ½ÏÝgï}ÖºüƒÂLX € ¡Xáçň‹g` ðlàp³³BøF™|ØŒl™ø½º ùû*Ó?ŒÁÿŸ”¹Y"1P˜ŒçòøÙ\É8=Wœ%·Oɘ¶4MÎ0JÎ"Y‚2V“sò,[|ö™e9ó2„<ËsÎâeðäÜ'ã9¾Œ‘`çø¹2¾&cƒtI†@Æoä±|N6(’Ü.æsSdl-c’(2‚-ãyàHÉ_ðÒ/XÌÏËÅÎÌZ.$§ˆ&\S†“‹áÏÏMç‹ÅÌ07#â1Ø™YárfÏüYym²";Ø8980m-m¾(Ô]ü›’÷v–^„îDøÃöW~™ °¦eµÙú‡mi]ëP»ý‡Í`/в¾u}qº|^RÄâ,g+«ÜÜ\KŸk)/èïúŸC_|ÏR¾Ýïåaxó“8’t1C^7nfz¦DÄÈÎâpù 柇øþuü$¾ˆ/”ED˦L L–µ[Ȉ™B†@øŸšøÃþ¤Ù¹–‰ÚøЖX¥!@~(* {d+Ðï} ÆGù͋љ˜ûÏ‚þ}W¸LþÈ$ŽcGD2¸QÎìšüZ4 E@ê@èÀ¶À¸àA(ˆq`1à‚D €µ ”‚­`'¨u 4ƒ6ptcà48.Ë`ÜR0ž€)ð Ì@„…ÈR‡t CȲ…XäCP”%CBH@ë R¨ª†ê¡fè[è(tº C· Qhúz#0 ¦ÁZ°l³`O8Ž„ÁÉð28.‚·À•p|î„O×àX ?§€:¢‹0ÂFB‘x$ !«¤i@Ú¤¹ŠH‘§È[EE1PL” Ê…⢖¡V¡6£ªQP¨>ÔUÔ(j õMFk¢ÍÑÎèt,:‹.FW ›Ðè³èô8úƒ¡cŒ1ŽL&³³³ÓŽ9…ÆŒa¦±X¬:ÖëŠ År°bl1¶ {{{;Ž}ƒ#âtp¶8_\¡8áú"ãEy‹.,ÖXœ¾øøÅ%œ%Gщ1‰-‰ï9¡œÎôÒ€¥µK§¸lî.îžoo’ïÊ/çO$¹&•'=JvMÞž<™âžR‘òTÀT ž§ú§Ö¥¾N MÛŸö)=&½=—‘˜qTH¦ û2µ3ó2‡³Ì³Š³¤Ëœ—í\6% 5eCÙ‹²»Å4ÙÏÔ€ÄD²^2šã–S“ó&7:÷Hžrž0o`¹ÙòMË'ò}ó¿^ZÁ]Ñ[ [°¶`t¥çÊúUЪ¥«zWë¯.Z=¾Æo͵„µik(´.,/|¹.f]O‘VÑš¢±õ~ë[‹ŠEÅ76¸l¨ÛˆÚ(Ø8¸iMKx%K­K+Jßoæn¾ø•ÍW•_}Ú’´e°Ì¡lÏVÌVáÖëÛÜ·(W.Ï/Û²½scGÉŽ—;—ì¼PaWQ·‹°K²KZ\Ù]ePµµê}uJõHWM{­fí¦Ú×»y»¯ìñØÓV§UWZ÷n¯`ïÍz¿úΣ†Š}˜}9û6F7öÍúº¹I£©´éÃ~á~éˆ}ÍŽÍÍ-š-e­p«¤uò`ÂÁËßxÓÝÆl«o§·—‡$‡›øíõÃA‡{°Ž´}gø]mµ£¤ê\Þ9Õ•Ò%íŽë>x´·Ç¥§ã{Ëï÷Ó=Vs\åx٠‰¢ŸN柜>•uêééäÓc½Kz=s­/¼oðlÐÙóç|Ïé÷ì?yÞõü± ÎŽ^d]ìºäp©sÀ~ ãû:;‡‡º/;]îž7|âŠû•ÓW½¯ž»píÒÈü‘áëQ×oÞH¸!½É»ùèVú­ç·snÏÜYs}·äžÒ½Šûš÷~4ý±]ê =>ê=:ð`Áƒ;cܱ'?eÿô~¼è!ùaÅ„ÎDó#ÛGÇ&}'/?^øxüIÖ“™§Å?+ÿ\ûÌäÙw¿xü20;5þ\ôüÓ¯›_¨¿ØÿÒîeïtØôýW¯f^—¼Qsà-ëmÿ»˜w3¹ï±ï+?˜~èùôñî§ŒOŸ~÷„óû endstream endobj 401 0 obj << /Length 19 /Filter /FlateDecode >> stream xÚ3PHW0Ppç2ÀAc(á endstream endobj 405 0 obj << /Length 19 /Filter /FlateDecode >> stream xÚ3PHW0Ppç2ÀAc(á endstream endobj 409 0 obj << /Length 1369 /Filter /FlateDecode >> stream xÚ•WKoã6¾çW9ÑhÌåS¢(К¢E¾µ=02m«‘%WMüï;ád+«¦›‹5‡3çe±:¬Äê—;¿Ÿ·wŸ­\IõIÔj»_Iex&Ô* —*_mw«?˜^o¤‚mË“_ot’°Ÿ^Ë©”ýîÖZ²ËZ Ö ýú¯íoŸuºÊyž¨5ŠÕÆHžCº¶GPaÁöC]ôeSÓ«oŽ$Š ©àd8q®š%½Êr;ŠÍÉw¤ö¥ìDõÇÖÇ»š3Þ$$ëbî|WêQ6JöÁE¤\p©Ê­U.ª]ü~ÁàM–r L<·–Œº?·¾ï/÷ ¶ ™Ö£õ -7vBà¾8–Å’-¹NÍUÏF¥`x½‹ Í-\†  ¢è½+«¦][É<þì~\²2癎ü)¬¨›ÐQÖ`ˆŽ²šXŽÖÀŒÛµd%RõØ®= '_#’=_°O Ðé¸QyÂMnæ¸wÇf¨vp«Vìï¡ë‰rOO­ÿRºà¬û£‹;/hSä¾ò—F ~Ëú<Än]ëŠÞ·Äêú¶¬-š=}÷CU]âííSÙ·1޾~m­5WWçÝR¼oF¡™³gП±¦+C€sX°Çu¦€·Ñ"£ÐÖ"‡Ô¦E8pÑ£)Áó†SŠ„ÝñLä.U\¢g3”$ª!\Hè°&~ƾö- b‹A°>!ëK¦ÐÁOL[ËÜÐ7§p,ìK#|àÛrsö‚fH†±בô¨–ÌD"Ó¨¬p!FãžwôžQ²,0žiÕ¼UýEV3s(>ÇSxÖš †R;à×áÖBöfÙµ*.ä¬à:¹V3¥ÃÔs‘Lµ€ê˜Vj4¨Û: !Qik!GØù½ª¸ÛÁ%&2.ö!°neoS{!¸•QõÝà–ê]6Š`É~%•¾ÝÄ/Åe6¼v2c ×ïw–Lr›OyFz Kä$F­Nà{bÅ‚è;Pⵎ>ÿgðuáIÞé08Òk—œ&Ó„gùôÖõwr ¥œ«tî ÎOؤÊÌÓ«;»M6ư¶ê@Z(o6a®BË„õ˱S%‹æZ[bQ{ÑÖÕO<¬³½¹ZÊBäG3öV`u½;»Þ¤žÀ¾ó ?K€Á…˜Þ)T'ˆ  ÿJ¿t…özí¿¯ š-W0ýÜÄc’Ñ袚yŠ÷¸6^jDZé| hF‹°ÙDÛ.¤d:$±šÍ-CzÁׯå‰\d„àIbç9ª0±Īž²±òLà öíeÆ/õ ô#™Ì ŽÉÆž`²,œ N¢>ðh/¶˜ ^A†×Ð9Æ®‹Ì©âà‘Pqægþ§â¤ð|&ûPÅ¡,‚€ÊÍ›,šÊP¬5ÊFK”ÍÞ«5yÎ1Áè^·eñÜ}¾„!úk{RÁíuâ۷͉.ˆ7åìL}ê9ÄÞÁ/\^[=©xí»%·3~i„Ëba ~5Ä [@E%0š™äMjc—»©wÚLñ»Î52ã„w±¡ø¤ÞørèÊÅ6Zuê•©þØøhá±å›†i§™ævðñ‹»•¿;â•}ì@ ­ã¸ÀÍVGpø¢Ü_ˆóDw©cyê|µ¿j ø§’'æÝ¹Ç>&t×c©|ˆÿtB¾À@“d®ØÒšp¹’¨ƒx7zÂs¡ô¤ðTÖ!)‘WƸ¤ ×>wK£*§øx ÃÂOHW26˜B‰ƒ¹<¾¸R3©Ÿ·wÿ`8Äi endstream endobj 414 0 obj << /Length 1190 /Filter /FlateDecode >> stream xÚ¥VKoã6¾ûW¾T"†OI,°-ºm·ØZ´ë[·F¦mm,É•äMòï;CR’Ûé¡r†Ãá<¿¶~Yа¾_-î?¨öï_Üü¿ R#Néó¸ã׫"eú?в YVÜ@Ë ¼õëj žS_–Nã–Ã-GÕ¦{ ð^(Â?Ïè¾u¹)͘™Á,ÁpúýžcüYlËÅ6ÇÃaš:Hp8“©«¦íFU!Ÿž4]Éh›µ Rk㦿Ãd øb°5ý5®ðÛ¨þ-Q(ôe„4 YÁ$ó|W: ‹×¸s°ôÒ{g^ZwŽÿô;xG3 dƒDxRÆ} c=> /ExtGState << >>/ColorSpace << /sRGB 419 0 R >>>> /Length 9968 /Filter /FlateDecode >> stream xœµKï%ÉQÅ÷ý)îÒ^øR•U•%F€°ÌH,,< Èb@`âÛ“ñÈÈs¢»ùÏ0cÉžîÓù»uëVeEDFfeœ¯_½Î×o_ÿþé/_ÿþ:ÞÇñ*g•?î«€úß¼þúõ¯ =ã}ܯÒÇû¾_×]Þ­¼úõ~Ê‚þàwõ§¿|ýÑ7ŸäpÇ ÿûÍýÅü×ö¼þûÓ¯ÿæu¼þáÓùúÕüÿo?úÝþ©ïvÍÃ^ïëz}÷©•÷5¿åêïs¼þEä9Ïé9¿Väülù¼¯gÊò¾»ÉQ§¼\Ö÷3eéïÖD¶÷Yõ³õ29ÿùºNoï§ëgõ‹úñ>MγQÙºžÕ8DžïkèYéM9†ž†Á×»SÞÞ:ÿ4iG~ÞÇ9åãðó®"‡ýÞ^ßåÔ/2xþY¦l~¨.ç.p“Ÿ?ïH‘KwøYw7yË/‡Þ¯ ëgGy—GϹ“ýÑ»yÉÕóš‰<åVOy˾îÛ[ïw­zô$G•oÖyêS¶÷ÑÖTY»ž³þÀÑߥëYé/š²wý^=Éó˜·cX¯ªªÏwzl=ÑóÇ]÷c?ò~Lëo>yN¢«t2Ñ÷{œ¨é¨×Ýí\Ï£¾Ï©Ÿ[NÃt=ÉIûiç7oÌͺ‰¾\Ïn|™¶ÏO=æ…|Š`¢Ï÷cÚÛçUªp¼©¥ÏîÏßï§)_õ÷€×S×ñí¶óó·^¯³i¿}žu<}p„÷ÏÏÞ/zøï=‡vÝÉ[{ÑÇp¯œï~ê÷÷¡zÞ(Ñçj¿´ûÞó0Åu•ëÛ–¾ß¥èõ·ûWªü0Ñ~¼ª]xj»¥½Ë½û’è~ÃýŸO¯Z*ÏãÔóñ•n<¿Ïø©{Õûkü¥' ºXOîþýSW¹~‡÷{Jå÷xûì]Ûí÷L-½yþ~ÿþjýs¬Ïë“v»žóÉmv=í~ÎÇï:ôzÚý™zØõµë9Ÿ‹ç„ïŸOàyêïs= `Ñþn¿g>eÏ¥í®gGºàzÍ¿43ú¸÷cötøó3?x>ªÕ ˆn{þæ…ºÌL\·kµ©çÒcUµÀçüá×¼žó¶âz˜Y½õüç}H_fX3»¢Õ²ÎÇ^¯ßì˜ÏPkéºj^¶U´W×ͬë±t×þ\Æ:þÐþíuZÅy=Ëãç_Oµ°EÍŠéjÚ~O-jcg7¿ôzV½Ð×|ìÆåºŠîK?ÚŸgûU]‹NÕ_ÕÐNþÐó«Ó¯óúM3Ó†k±·ç| ô÷´[ûó4Sãv-övš1oŸ†çTííó‡M-f²¹–þ<ͬë®ööЯ=ÔÞšžzzÒy¡@ŸÚŸ§Ùv]ÔÞN³u×Í´¯«G–ã=¿é?¥?ÏÏ{û#Yx?^U{»?_¥?—éÑŠ}¾Iÿ,Ó>zÿ¦_,í½¸¢‡ÜÖ©§W}D?rÙEŸÒ?Ëtkv>S‹½nªêõŸŽu†ëèÒDkÇ&ÝO¸ÞóÀ7éªöö<ýúNï:OL´·Oûx)ÿ¨=œ'z\p¿¦{;¿O?_¦¿Õþé÷¿Ì=ÖõÙß_æ….¦ûáZúçYìøåPÇqM·äü´m÷Ñ]ô°ç£ÌŽ£ýù´þ\扉½¼öOÑbo×óPæßc÷çržÖŸ‹¸ÓíØ¿·Ì† ®¯héÏó´«}þÖþ<f?U?b襔Ûõ0ííÕús±û[扊½•°Ät×U.£žÿü¡õWíe>¸³?Kÿ°ãu<×¼ Ú?˼boãxÓМ•µögïßeúW±·Çµ>«½]ÏO™†Mìí¼Ì}_“þ|ï‡ê¡í®»ÚÛy½ìþ Djo%ÌÑß3 u=·½(Ó0KÀýç*ÖŸO¹­¢/µ·Ëþ ƒ‹ö;¿é$hþ#15éfö¶®Ï7³··ßÿ鸊µoq¦ô'µŸ¢«ÙWã§u{ëí§õç!?C´¦¢Õ>—éO%|]ö[´Ä¯›ÔÞuc¢«öÏyÔ߉{;/»kýâv”éO%~½ÔŒšnçöE‘SÛ5ô=Î="(ÇD«?/Fá14-öv§Ê L‹4~.8Þ{P¦•þ¼‚ù2ýëóìx½Lÿêñƒ¿kž—Ùù¡öö.~üéO%~•Å㺙¶óþUúó¼Œ_‰]¿_ã‘R/oï{ïÑøuïÑþªo_QKÿ¬šžý¨½­}µ?joëXÇSC çÓ.Õêáx]ííü¼·µ·q|I˘¶óŸ†\úsðÓÐKü:¯kM˜ìß3µôçùØzû­övÞíïbH%~ý½w×ÕÆ·§}¾Yöñ«h±·O³û/†[ìíz^%0–øu=¢;ŒÏ<Žñºöç±ÇÛ¢}¼fß7ý©ôç­Ÿ•/­ù‚ËÆ‡×ÏJžßïíUííÿköéF=,_zÜ{<.•çë3…ØÛ5•Ѐñ­d¨¤?ß=¯20:Û~¾D·¶í©æÚ¶×êX [#icÛ{q<ÿš3ííÃì­¯Å0{¾@í‹è8÷øX´ÚÛïÿ<±bùµW¢{Ùã[ýa6¾µï›†§$ÝÍ_ ;~³|Á³´^(ùÃÎ_ÚÏÎgȉHüºìþ¥Bÿ‘¯;_ 7®˜}µß'ý¾ó!ÚQ:œÿ¼‘noí|åÆŽ=Þ—ŽìöÖu×øuå7D7V¾À¾o>Xž/°ó‘íÜöûšþTâ×ù3ìøóÁ\ù¯¥ÕÞ®þWo³·ÍûçÔã‚ç­VëÏÝ쫾GÛïǵÚÛõ¼MÃñØõöójoãúMÿ*ñk<Ó¿>mçO®VVþËŽ7uë`¦¿½:}Çú¸Y× ÿ+ݦb|¢ç»—ö˜øÑß”ˆ%‹žÐ<Ý\0 Ð)ž ¸à¸—4ZtÚh. $*Щ¥ ¸@'›6à’€é—P €fl6à’€ÁÀH€Ætp¹›ä `I-:¶— FŠÄÃp À%í\Ðè Ð ¸ ¸@§ä6à’€Ê@M€NÛmÀ%3¬A@%MÆ•p €zÜ ¸$ ^T~6mºp. è tlJ1€%8ÁbLÀ%•š¦Ü€K:=:•¹—èäæ\P¨ xdP³—ôF@çgÓ¦P7àTÝ€Kj' ò³i¯pI@g '@“<p¹™½=àé ÍHMH͈Îùâš‘q02Ž„è ®Qï ˆkFÚÉH;¢¹@\3222¢3 €¸FDçªqÍHKHˈÎhâš‘‘‘‘.A ®Ñ©r@\3ÒÒbêYš‘ñ02ž„è´; ®)G„f¤UFZMˆÎçâš‘‘‘õÄ5"šqÄ5#-!-#U§·6âš‘‘‘‘F‘EhD4á ˆkFZBZFÅ¡± Yš‘šš‘“¢ŒÐŒô“‘žži[,ˆkD.Š5B3R #5=Ó¶Ê׌ô„ôŒ<’ûÄ5":îÄ5#5!5#ºÈ׌ô„ôŒtŠ>B#¢ËqÍHMHMˆ­1ÙÈÒŒôÊHOÏ´­DÄ5"…"‘ÐŒÔÆHMÏôuIJ ׌ô„ôŒÜäìC#¢kcqÍHMHÍHeo¿4#=!=#:¡ˆkD:{û¥i iÑL8 ®!o¿4 ¶Ìg#K#¢+qÍyû¥)’hÄ5##!##{û¥Ñ…G€¸f¤%¤eäao¿4##!##•r¡iìí—f¤%¤e¤Sæ 4#äí—FdPö 4 ’oDdiFÈÛ/ÈI9„ÐŒŒ„ŒŒN–Fä¢LBhFZBZFnN–fó ¡y(£‘ÊÁÉÒŒ`V!4"ò ¡éœ,ÍæB#2(»šŠ¥–¤K-Èɧ»4#5!9–’%s„”Ïb©Z8–Z‘‹c©¥¹9–ZšŠ¥–FäáXjiFzBr,U+ÇRK#Ò8–Zš‘šKÕαÔÒŒô„äXªŽ¥–¤K-ÍHMHŽ¥ÚɱÔÒŒP,µ4"…c©¥¹8–ZšŠ¥–FäæXjiFzBr,ÕÊî„F¤r,µ4#5!9–jr<¡¡XjiD:ÇRK#28ѳ4#K- H?8–Zš‘‘œè‘%£øØ/HáXjiFZBr¢§_K-ÍÈHHŽ¥ú͉ž¥y8–Zš‘–KÉRWBêg‰ž^9–Z‘ƱÔÒˆtNô,ÍÅRK#28–Zš‘‘œè‘õ¸øØ/ÈɱÔÒŒ´„äDÏ(K-ÍÈHHŽ¥ÆÅ±ÔÒˆÜK-ÍHKHŽ¥ÆÃ±ÔÒŒP,µ4"•c©¥iK-ÍÅRK#Ò9–Z‘ÁÁÉÒŒP,µôFtý3 ¡é é9i¶&4"…ækB3RR3rÑœMhFzBzFn NB#òÐÌMhFjBjF*'¡Áù›Ðˆ4šÁ H§à$4#8‹‘Aó8¡é IÁ‰.Q‡g:4"'Íæ„f¤&¤f¤·ÍHOHÏÈE‰žÐˆÜäíC3RR3òP¢'4#èíC#RiZ'4"=¡Ao‘NÓ:¡ äíC"¯ ²4#-!-#'yûÐŒŒ„ŒŒJô„Fä"oš‘––‘›=¡Ao‘‡¦uB#R)Ñšôö¡i4­š‘‘‘‘NÁIhDMë„f¤%$%zÊuPpšœÖ ÈIÓ:¡)œ„fS¡¹(š‘‘‘‘›R¡y(š‘žž‘J©ˆÐˆ4JE„f¤&$'W§TDhFzBzF'Kr”ŠÍHMHÍÈÉÁÉÒŒ`*"4"…R¡¹88YšLE„Fä¦TDhFzBrp"+-ñ™^‘J©ˆÐŒÔ„äàän”ŠÍHOHÏHçàdiD¥"B3R’Ròb'K3‚©ˆÐˆœ”ŠHáàdiF0‘‹R¡ ÉÁÉsS*"4"¥"B3Ò’ƒ“§R*"4##!##ƒ“¥锊ÍHKHËÈààdiF0y©‘¥998YšLE„F¤P*"4##!98©{û¥¹)š‘–œÔ‡½ýÒŒŒ„ŒŒTJE„F¤±·_š‘––‘N©ˆÐŒ·_‘A©ˆÐ€´ƒR¡!o¿4"'¥"B#R(šòöK#rq*biFzBr*B^0ÄgziDNE,ÍHMHNE´ÊÞ~iFzBr*¢5NE,Hgo¿4#5!9ѧ"–f„¼ýÒ€È듈,ÈÉ©ˆ¥© ÉÁ‰üû¥é É©ˆ~qp²4"7§"–f¤&$§"úÃÁÉÒŒP*biD*§"–F¤qp²4#”ŠX‘Ωˆ¥é ÉÁÉl£TÄÒ€ŒƒSK3BîsiDÒØ~|>¶·÷` ÉîÓ^¤|æ>GÛÏÇöóŸÈ}.ÈÍîsiFFB²û´7sy>sŸö². õ3÷9ÒØ~|>¶·WziŸ¹ÏùO#!Ù}ŽÎîsiD»Ï¥i IîÓß($4#è>C#r’û H!÷štŸ¡¹È}†Fä&÷štŸ¡yÈ}†f¤'¤g¤’û H#÷š‘šš‘Nî34#=!=#ƒÜgh@NÛ‡fÝghDNrŸ¡é é)ä>C#r‘û ÍHMHÍÈMî34#=!=#<¶H%÷š‘ššÛ‡fÝghD:¹ÏЈðØ>4#è>Cb/ÙodiFzBzFNrŸ¡)ä>C3ÒÒ2rQ&?4##!##<¶ÈC™üÐŒ´„´ŒðØ>4#˜ÉH£L~hDxlšÌä‡FdP&?4##!)8ñ}6²4"'eòC3ÒÒ2R(“š‘‘‘Û‡Fä¦L~hFZBZFxlšÌä‡F¤R&?4"<¶ÍfòC#Ò)“š‘‘œØ&€ŒœÉ— +0“š‘žœØî€œ9“ïVRr&ß÷° $'¶­ WÎäûN„ôŒðØ>4"{û¥© ©á±}hFÈÛ/ˆî½ˆkDxlšòöK#2(“š‘ž4¶÷­?6²4"'eòC3RR3RØÛ/ÍHOHÏÈE™üЈÜìí—f¤&¤fä¡L~hFÈÛ/H¥L~hDeòC3BÞ~iD:eòC32222ØÛ/ ˆmµ²‘¥i i9ÙÛ/ÍÈHÈÈH¡L~hD.N–f„6|X‘›R¡ ÉÁI}(‘J©ˆÐŒ´„äàĶ£¤åT„ïPCÈÈHçàdiD¥"B3Ò’R²û 'K3‚©ˆÐˆœ”ŠHáàdiF0‘‹SK32’ƒ“vs*biDNE,ÍHKHNl¯@êg©ÛþöY*Âv"$'­s*biFzBr*¢ñË®¡ ɱTç—]C#Â/»†f¤'$ÇR_v ¿ìš‘šKu~Ù54#=!9–’”y>‹¥:¿ìš‘šKõƱÔÒŒô„äDOç—]C#28–Zz#_Þ[)¿Ëσ>ýà÷xÝÿ{,©û£IÜÇö.‚-šnˆ¥“©E·1*ÇQqœó_Ú éË‘E÷ÒóòQÚ:Ф¤|.Ï…Še|÷é>peù¿,-ÆuÈþ`¾‹Ô’ÿüúæ—ܨUöpÚÿÅí³ììçSw|y#­_Š)M®³ù?:"zã¯ÔùJG@ä¾x ׈¤M½Ö¥üú®^‹øú¶^‹øú¾^‹€½ö šÎí+÷'vY;¾Gi•¯æ'¨¬"oÒCeÙ“*«èv »²Šl<•Ud館"û<@eÝcWV‘ÏBeß}bUV‘ÏBe‘PYEÎ *«ÈYAeÝ¥BaÝÛé´<窬"*«è÷kUV‘Ý ²Š|TV‘M ²ÊY.¬¬"{&@e‘PYE`¨¬¢»JìÊ*² TV‘½ ²Šì]•UäAei…Ê*~We9g¨¬"g•Uä{±²Êi‹[£²Š+«ÈþXYE4VV‘ °² hݰF7$€Ê*ò =VV‘—á±²Š¾…•UH·[_äÇÊ*¢±²Šì€•UDce•}<Ûf>¯{ª•UäM|¬¬"ŸÇÊ*rþXYEx¬¬"¯Æce•ÓrWQYeÏö ñ½ ¢²Šì€•Udk¬¬"ÛF`e¹þXYEwž€Ê*¢±²Jô%ßQcßÛ>Cx¬¬rÚïQYE÷¤PÞö¯­›UÈXYEöÀÊ*ºÕTV‘ÆÊ*òVV+«(•U䬬"VV•Uö÷Û~ ò3°²Šn¢¿Çö8v¬¬²¯—í^ · *«ˆÆÊ*~Û¢²ŠÜf¬¬"Ý+«H·ÃÊ*bT±²Šts¬¬"¨šÊ–ºÛjïTK¥?TJE¼$TRé ©ˆ[„:*ý¡2*û³ZEE†¹PDE¢¨¡Ò:•P‘õ÷Z•ùC°€J/T?¥W*Ÿ2 UOAÙÏ}a­vÊ–Z:e,‡Aµ—C‘ Ë¡ìßcåP$Är(ËB9ÑXEx,‡ƒ/‡"÷Ë¡DPîåPb<çåPDc9éXx-‡²Ïχjì åPäþ`9”󾩊h,‡"Ë¡h¬ åPdï(MOùŠJU¡Š´c9‰=±ŠÄ®XEcW(‡Ç󕉢±J|ÞW j, åPHKÿ|.*‡"çƒåP¤Ë¡Èù`98ž.±“Ïc9”}|[[KöÍÄ ÝW ìëg“ù‘”ð™{8ÞXƒ|,‡öÜ'àÃ~û<¹ü>,‡¢I+(‡²ûŸMwëF‹÷+Ê¡ìçͦ %—€åP¤Ë¡ìçÍf’åû±ʾ~6»»ŸG›Ê¤‡ÏÛnû`“´Û>ØŒì~^lâ4’$>KIŸÝ÷ßæ?wÿ´ÉN¹ßXek+‡²Ÿw+‡I6/‡²û³•C‰$Œ—C9í5ò(‡þÔË¡DÒÑË¡ìçÍÊ¡€Ör( 5PçË¡ˆÎåP¾ž¨ÿ©Š¡hêá÷U eœX E“Q4)íêÀLA³DèP Å%fbp‹€}ep?U%”~ÿˆB(:kðƒ ¡Ø´LL¬šÔÄí¦jIM›i‰fUÜ<¨yp³ÎÄD³*lÖ™™hVÅÍ’™ÙÍSa³ÎÜD³*lÖ™œhVÅÍÓ{BóTج3=Ѭ šmæg5›âæFÍ›uf(šUqó æÁÍ:sͪ°Yg’¢Y6ëÌR4«âæJÍ•›uæ)šUa³ÎDE³*n®6Wêk6Sͪ¸¹Ss§f›ÉZͦ°Yg¶¢Y7wjîܬ3_Ѭ ›u&,šUqs¥æÊÍ‘E³*lÖ™³hVÅ͵cs¥¾f3kÑ¬Š›;5wjö™·Õۀʌƒ€q0  › ¨$@gï6 ’ÎÛ€J4÷»•è„ßT& 1Рi• ¨$@ç7 2Æ€Í"`2ã!`< è<ãT P™€ÁÀH€NMn@%:W¹• h ´4´Í. ÐéÍ ¨L@c %` …v‰€Íˆ`’"Ý€JtÎt* ¸ÐR»L@-Tî“6ͺ•hîj*P¨ Й٠¨L@g ' £Ýv‰€Mæ`2½йOÚtïTPÐ~»L@mTî“6C¼•hÆ|*P¨ ¨diM& 3РÓÎPI€ÎCo@eÐÒšDÀfª0I€N]o@%:—½•èäöT ³ÝP™€Æ@KÀC–Ö$:A¾• h ´tŒ}]&-­IÆ¿.°Y÷L&`00PȘ›$àÂ8Øe-:•¿•T2æ&ñ°KFÄ. èdÌM0È_˜DÀ6 À$'}…ÉTjŠŒs7 ’€›ü…É ¿0IÀCþÂd:=:÷•tò&ÐHþ¶wßÀÈþÂ6wÀd*5…ü…I.ò&€þÂ$ºûT& 3ÐPÉ_˜$ aäï2è/L ›§o@%ºuúT"`[ž`’€“ü…I ù “ h ´hBu* xÈ_˜L@c ù Ûž|5ÿ¶99œ(ñ­É7ÐRªÄ·ßÀÈþ¢ þM"`Û‰`’€3&.Ðh ¸È_˜$à&a2ä/lóï ¨LdO\ÐÈ_˜$ “¿0IÀ cn2Eq €oÕ½—̤¸L@e &àÂlŠËtzn4æ. ¨hÌ]&²*. h˜WqI@Gcî2[q‰€ma€IN̯¸L@e &  ¥u™€Î@OÀi—<ü»LXZ—TL³¸$ ašÅ%ƒ— L³¸DÀ¶‡Àd-'ZZ—\˜fq™€Æ@KÀÁ¿Ë€¥uIÀƒi—4L³¸LÀ``$ £1wIÀÀ4‹Ë48Íâ[&`’€‚ÆÜe lvIÀ…a³Ë F4æ. ¨˜fqI@ðÙe*ɘÛÖÆè)lö-‰0™€Ê@MÀIÆÜd lvI@Á°Ù%7¦Y\& 3Œ¹m¼'…;yðj ›}ë`’1·-7г1· 70RšÅ·ûE€Ãfßì7“ €°Ù%Ó,. ¸0Íâ2ƒdÌmsÞ Ü)lö­y7ð¤°Ù·ÔÝ@MißPw-sÛNw=…;™.-ƒŒ¹Il ÜL&Ò,. (6»LÀ` sÛúvW¶´¶eíž”fñ k ¨6»$ ‘¥5™€Æ@KÀÀ4‹KlƒÙL&-­IN ›]P0lvIÀ…9s—è¦1PIÀCa³ÉTRØl[Án fKk[¸n@e*5ƒÂf“ @KkÛº5“Y›·• è ô\dÌMpSØl2•6Û©¨)gî¤n ecnÛ£"ÂfÛu=‡Í¶5*ɘ۪ÕLÀéø,"µu­ðØßºn ¤±¿¯|ÝÀg1íàˆt|‘ÚÚØ <ÙˆÙbÙ Ô4ö÷Õ³´42ƒ& dÄL& 1ÀFÌWà.Àe \tIÀ‰fÐ%1—ÜhÄ\&ÂE—<8öw™€Î@O@C#æ’€ŽFÌe:=Çþ.8)¦uIE¤. (hÄ\páØße*57šA—T4b.P¨ h˜Èu™û»$ £t‰€-„Àd:='Žý]Ppìï2–ŠH]ðàØße-¹.0.÷I[²½žÆþ¾†{#ý}Q7lÌ}•w& 8qêÐ%Çþ.  ˆÔ%7Žý]& 1Ð@1­K("u™û»$ ãØßeɘÛÊæ Œ4eæ+Ö0I@Á±¿ËT’1·Eî¸ÒØßW½#ÐðàØß%‘ºLZZ“4û»$€bZ—PDê[l€INû»L@e & ¥5IÀá¢ËTjû»LZZ“Tœ2sI@DZ¿Ë FYZ“Ø‹˜L@c % àØß%s“ ÀeÇ& ¸1lv™€Á@2æµ’17I@ñ¿Ë F:s“ ›]"`/A`’€Çþ. (dÌM&Âf—\6›$àÁ±¿Ë4’1·w/6PsØl/cl å°ÙÞÎ@ óFKò\&`0ü…m€IN\bá2ž€‹ü…IhIžËt’¿°ÍÞ6ð¤ˆoõ¶¼¨Ï·hÛ@ËÁ§%y. ä/Lðåw?~䫬ÿþxí‡k?\8öá›×;|83üá$Û‡¦~ÛGi¶2þ> ´? I>ô?jtÒ7É“‹ç³¥»Ö|ûÝë×?û‹ûùkFë?û¯×qþüo^ßþêûm¶÷hl{7ØjïùþvõöiüÉÏçˆüõ³ßüÝ; Y­ÝÓyÔpÕâç}þ·v"ÿóÏc:Ú9bùÿž‡ÌL‹Üçñ‡vÿùO?ìÆ›AúÓ´ö~t㽤¬½„­ CòVö¡ßãN_ò#ë\¾æH°ýËž„ˆ/º"¾èKˆ°M¢È™¤cØ–HäMÒ1lor'Løv5äOø[|Wr(é¶“y”tŒvÑô³I"¾èSø²S!â‹^…ˆ/º"¾èWˆ°÷xɱ¤cØ›·äYüžª?_Qu=>_Su_Uuð®ê_~ú_ˆOr endstream endobj 421 0 obj << /Alternate /DeviceRGB /N 3 /Length 2596 /Filter /FlateDecode >> stream xœ–wTSهϽ7½P’Š”ÐkhRH ½H‘.*1 JÀ"6DTpDQ‘¦2(à€£C‘±"Š…Q±ëDÔqp–Id­ß¼yïÍ›ß÷~kŸ½ÏÝgï}ÖºüƒÂLX € ¡Xáçň‹g` ðlàp³³BøF™|ØŒl™ø½º ùû*Ó?ŒÁÿŸ”¹Y"1P˜ŒçòøÙ\É8=Wœ%·Oɘ¶4MÎ0JÎ"Y‚2V“sò,[|ö™e9ó2„<ËsÎâeðäÜ'ã9¾Œ‘`çø¹2¾&cƒtI†@Æoä±|N6(’Ü.æsSdl-c’(2‚-ãyàHÉ_ðÒ/XÌÏËÅÎÌZ.$§ˆ&\S†“‹áÏÏMç‹ÅÌ07#â1Ø™YárfÏüYym²";Ø8980m-m¾(Ô]ü›’÷v–^„îDøÃöW~™ °¦eµÙú‡mi]ëP»ý‡Í`/в¾u}qº|^RÄâ,g+«ÜÜ\KŸk)/èïúŸC_|ÏR¾Ýïåaxó“8’t1C^7nfz¦DÄÈÎâpù 柇øþuü$¾ˆ/”ED˦L L–µ[Ȉ™B†@øŸšøÃþ¤Ù¹–‰ÚøЖX¥!@~(* {d+Ðï} ÆGù͋љ˜ûÏ‚þ}W¸LþÈ$ŽcGD2¸QÎìšüZ4 E@ê@èÀ¶À¸àA(ˆq`1à‚D €µ ”‚­`'¨u 4ƒ6ptcà48.Ë`ÜR0ž€)ð Ì@„…ÈR‡t CȲ…XäCP”%CBH@ë R¨ª†ê¡fè[è(tº C· Qhúz#0 ¦ÁZ°l³`O8Ž„ÁÉð28.‚·À•p|î„O×àX ?§€:¢‹0ÂFB‘x$ !«¤i@Ú¤¹ŠH‘§È[EE1PL” Ê…⢖¡V¡6£ªQP¨>ÔUÔ(j õMFk¢ÍÑÎèt,:‹.FW ›Ðè³èô8úƒ¡cŒ1ŽL&³³³ÓŽ9…ÆŒa¦±X¬:ÖëŠ År°bl1¶ {{{;Ž}ƒ#âtp¶8_\¡8áú"ãEy‹.,ÖXœ¾øøÅ%œ%Gщ1‰-‰ï9¡œÎôÒ€¥µK§¸lî.îžoo’ïÊ/çO$¹&•'=JvMÞž<™âžR‘òTÀT ž§ú§Ö¥¾N MÛŸö)=&½=—‘˜qTH¦ û2µ3ó2‡³Ì³Š³¤Ëœ—í\6% 5eCÙ‹²»Å4ÙÏÔ€ÄD²^2šã–S“ó&7:÷Hžrž0o`¹ÙòMË'ò}ó¿^ZÁ]Ñ[ [°¶`t¥çÊúUЪ¥«zWë¯.Z=¾Æo͵„µik(´.,/|¹.f]O‘VÑš¢±õ~ë[‹ŠEÅ76¸l¨ÛˆÚ(Ø8¸iMKx%K­K+Jßoæn¾ø•ÍW•_}Ú’´e°Ì¡lÏVÌVáÖëÛÜ·(W.Ï/Û²½scGÉŽ—;—ì¼PaWQ·‹°K²KZ\Ù]ePµµê}uJõHWM{­fí¦Ú×»y»¯ìñØÓV§UWZ÷n¯`ïÍz¿úΣ†Š}˜}9û6F7öÍúº¹I£©´éÃ~á~éˆ}ÍŽÍÍ-š-e­p«¤uò`ÂÁËßxÓÝÆl«o§·—‡$‡›øíõÃA‡{°Ž´}gø]mµ£¤ê\Þ9Õ•Ò%íŽë>x´·Ç¥§ã{Ëï÷Ó=Vs\åx٠‰¢ŸN柜>•uêééäÓc½Kz=s­/¼oðlÐÙóç|Ïé÷ì?yÞõü± ÎŽ^d]ìºäp©sÀ~ ãû:;‡‡º/;]îž7|âŠû•ÓW½¯ž»píÒÈü‘áëQ×oÞH¸!½É»ùèVú­ç·snÏÜYs}·äžÒ½Šûš÷~4ý±]ê =>ê=:ð`Áƒ;cܱ'?eÿô~¼è!ùaÅ„ÎDó#ÛGÇ&}'/?^øxüIÖ“™§Å?+ÿ\ûÌäÙw¿xü20;5þ\ôüÓ¯›_¨¿ØÿÒîeïtØôýW¯f^—¼Qsà-ëmÿ»˜w3¹ï±ï+?˜~èùôñî§ŒOŸ~÷„óû endstream endobj 425 0 obj << /Length 681 /Filter /FlateDecode >> stream xÚTßoÓ0~ï_í)‘Ï¿“ Cð€„èãÁJÝÆRÇÙèÛÙ:6¨ª|½»ïsï|ßÁìÁìÃÆóívsuCª¬ Ç<Ûî3„)¨!Î8¯@œk—}Ë?ë×ù}Q ó^Zg &Ÿ§p{mCHÇà³çÞª»‚±\%¬ R¶ºŸ‡1ü²j8I£d¤S1*bH‡>Âb”=(JJaþæ R潿Oß·Ÿ\Q%B a,üñVGa<šÜv‹As=[ˆ^½ˆ:†«FÎÚÁáŽv¡»8iíé"$žõ2€2&Šq÷'VÂÃhem;Õ>ÇI(à [9ª)NÖ|ê%ˆÜì¨Ä 7YéΚ’~]” 3×@s öî®[oâK¡9r?–ïYù9Ú=´Çد$¢¼ØÓQ^_L—!KØëÔ§ßøØò-Jlëô¾ßn|a†Ö±Å^Ó¬6?6 B<$Á^´N´¸á.A,V•t´x[=Nj'MˆtÒD¿‡“x•$ÎW‰/tj(J”h‚°½d‰ï€‘2¶³¹[n›"mâŸ<¸ ûf .'ógfÒ¯†|Oí÷ƒazÜUŽÿ³¶òÒ{Y~ßÉ1äÎ~—óµWЩ˜ÿ0k^§´UŽñB»^6Ž3B­ÆªVôÁs0j׫ÑÕïk+1dbzþFëæ…1q»¶ûGñÞ‡qX6N'´ÿ™ê Eÿ¼ÌÎ×ͪ¾—Î¥ŒÜj0 LŸJ÷›U¤ endstream endobj 422 0 obj << /Type /XObject /Subtype /Form /FormType 1 /PTEX.FileName (/tmp/RtmpBlhU1W/Rbuild4444671013800/timeSeries/vignettes/timeSeriesPlot-chicUnivariateSinglePlot.pdf) /PTEX.PageNumber 1 /PTEX.InfoDict 429 0 R /BBox [0 0 432 432] /Resources << /ProcSet [ /PDF /Text ] /Font << /F2 430 0 R>> /ExtGState << >>/ColorSpace << /sRGB 431 0 R >>>> /Length 18245 /Filter /FlateDecode >> stream xœµßïíºqÝßï_±íŸH¤D› )` R郑‡Ôu[vŠÔH‹þ÷ÕÌZ3šá97Ç?Z ¹¾ëjm}µµ)røáˆs~~þ9?ÿôù—þîó/ŸãËq|Ú9ô®Þ’úŸ¿ùü§Ï?›é^_ŽëÓd}¹®O¿Ú—Ù>Ò¿ÜÍMñûÿøþòóW¿øAOw|ò?ñWûü×yþ÷¿ú‡Ïñù/?œŸŸ?ÿÿO?œö·ÿæ‡y~™ýÓÖür¯Ïï~˜íK¿Lòù­Ê¥r|‘©òù³Íäý¥ß&ïr “ÃäóŸ!ÏKåürBö9§ÉËŽ®/·àTzf9¾œã€œbWuÞ*Ï/}ÙÑuB.ÈÛÌý‹&§½¾\ÃÎ|9N|£rœ8³~_y¾äafùr4Èrà^5½æçiý†J5¯C¿™™M¶/ ÷j5H¹íèµTö/d*¯/Ç09䀼õ"õžM“ÓNeË_ÐäÀ­[&åKíkR í';Cÿ„énúüÒ Ç ^þÖ2Ýô&«–ÛtÿrB/|þÒß$é[j{þìÙLýÕó¢žÔø{S›‡jûµÎcië)zB[;9ŸfÜ©õº¡íïŸç—úÂñ®wø=ߣ×ÈŸ¿ôĦízÏûË ïóè =ÓíVô?«~¾è‚ÿ¤žªšÌy.4Ýçg°ã ]@œ¯zÀþ¾ù[ûrA_8ÞÑ|íÏ@´Ð}}iÐbß§=½ 庩ل­žÍÚr´%Õr¥ß¿‰·bѦwöÍØ hÁý;ÌßO½ÑI7´dÁ“«zàþY/pöçéÄýk8~kÇczQèn÷·´OÑGZ /\ÝHû}ñ÷Ÿû }MêÅûk÷ó:ôAxÿþu~9¡è¦7ÊîïêÚ°ìþA__Ξî×£Ùžö÷®ÛûSü^—uaö86ê mäy‰w©ë fŸŠßÿZÞ©ZuÞ§ÞÓƒz±[µöu7´çÐÝ;V¹¨'t·ß÷¾µ±Þz =³oUí+ôôÞµC Úó`{/9ÓñqèÕÖÍçeœèao>oÔö}FC{c 8Çeƒá£ñ}= OèíùF­ÚúÙ‹í} t´7Æ·sˆö‹öù“Z ÑžÆB_{³½Îí…ÁEõ(º¡=‡~ž×ezÜÔÏ÷¼Ðž/¶ÏG[{i³Qm£˜i·ÑE5¾ß£­=wö'SÐßv ç3®žÐb×ÿŒ¤«g}¢=wm¶ªúÛÎöòè Ý­ý>ƒk‡FÿùŒŸÖž;ÛË£çÀçq¾þ6>?ОÛL´ÏÆþúD»àø ^bŸoæFÕÇ1þ¬í³óy{ô„ÆýÕ Ãý<ñùKOT´œé~ë_ô@{ñ~hGØ q\ô‹šßÚËÓÑ=ý^ÐÖ¾Ú3ÞZûäïßžñô¸«÷û÷Û3¾6ê›ÚÚç…ñ¯=ãí-ð[hí_µ@Ûðßžñ•íÙ~ŸvúÛýê!ïóОñôZo{nÏxÊölq‹êy¼ß· ý-ï¯jkÏ]SÕÚsÇóÝžñ´£}^õ¢ÆñíçŸèo;ž—öŒ¯‹í׳¡vü>í_Ùž­P=Ñ^íynÏxÛït¾Ö¥&Íölí»µ ýmןzÍ÷ùiíF{¾´¨žhÏÚ¯ê…ûw@ úÛ ãck í9ü ýí… ²=ãë8ßþ¢=ãi;Sûy´·g»ÞÞÑß²ÿiÏx{@K3}#hõöóŒ§WÑýí…øAõ€¶È´=ã-Û³ÅGíOö¯z ?=ÌÿŒ¯Þß6jö·'Ž7oŸ·gã낾¡'âWÆëí–ˆ\3~hð/ô·ö±G?ãé‰ÉM,TOhßÚ3¾vÎ%„za~€ö=:ú[ ST߈_ã|7ÚóÔa[õ@Ëx³‰þ–ñ ja<†ó/´gA¼Úžñ´a>°&µ¤ùA{¦«5Ž7ô·Œ—Û3¾ ç öüÌ ñ+ãÿöÌQhÿTij6¿iÏxËøí{ŠÇ¯ó Ð蟟ñµÍô}-Ðx~Ÿñö‚ÆóþŒ§œŽ¡==šñ«ÅƒM:ú[ΗÚ3{=pq½žœÙ÷‘ø•SÃöŒ§'´ÍÁUOè Çýmøý­_ß3¾NÜOü¾ÏxÊù˜Åÿª­}.~¸©íïiG öŽÃ4Žßˆ_—~mÕýíbÿª%COmŸý8ø|>,~]|>Ÿ™Ðv?ÕØå=Ÿê…ù—Í7û3Ÿ½×ûù~ØÀ£6úasÑÏTm×ߟùlƒ>µðx7m‚o¦m¢’Îg Ï4Ž/m8ïùËð¸]ÿ3¾>úúŸñôÂý\Ðý­ŸGèãúÛ…ø¤Ÿñ«¸ø|ÌOÆW¶çuR æ_¸¿ÏxËùšµ¿þŒ§œY{P-i~ÖŸñõÆónãwæ³'õ æ|Íâ‹þŒ§œQßÁ \“XÿØŸùl‡Æ÷{ôb€¿/Á  Wð׋ýÿ™Ï’àþ<ãí mýkÆ×•æ·½w´ç‰ñ¦?ã+û[{¾T³¿µþ´?ã-û[žoú|l¹žëíïû3¾öôü«fkøª?óÙûgñCÆSò‹Ç’X¯šý­Åƒý™Ï²¿µxMµ'àøí¼÷ÿOÛ¦àïÏàÐâýíi×ÿŒ¯BcïOÉ ¬¿éÏü•ý-ÚÏ£½¿…nÞ>-ÞïÏxJ^`ñ€ê!éúŸñµÉÛ¿©ööi×÷Œ·ÞßB‹ó<¿&ÿâù–·Oü½g>ËþÏÇ£çùößýOÉ¿,ÞèÏxšù—jö·hÏ|ÖúÛ…øQ5Û3ž·g|è_ÑþÆDü*àªÙýyæ³lÏ×»Ðßúý{Æ×5ÓóøŒ¯ä_º9ÿBÿðè)©xÆÛ.éy™·ó¯kROhã}ðÿýŸù+ùÚç£Ùž>¿¼=CËøÕŸ÷GöÐgmÏÜ??ºtį~ÒŸñ”ü ÷W.oÏÏög>K0‹çío¢'â×Ђö<9þ<óWÂY›(£—'’zF©¦ÿ¼½Sx‹&^ ¿uÂ÷£‘¤ÛäVåAìþ4ɬI‚«´'^Ív ð*­½‚Âë(v¿Þ=½ZPx•6ƒÂë™ÁÙÂëQ Õ@áu¼^ ¯Ò*Pxþlœ…×Ñï ´Q–Q(¼µ ^OÕäãÞFÚ—ÂwèŽSxý,ð·QxF­ýƒÂ›¼>NáUZk…ÍFáuH¶Ø^¥um ðv”Øý˜öY{0Ñô2l…×/hÂ)ûú âFá;Öd‚ÂÛ§å^“Ò…×ã > ðªA@á“6¼¦Ÿ'E7 o{~‚Âkpj ¯Ç'(¿Qø¢§êN ߟÉ;©»QxÕ t ðvTÝ(¼j‹JIá5XÅ…×àÔ^ƒ‘q~‚«Æ*(¼'Âëq£&¤ðú÷ÇÂëùAYAáõ8V)@áM' ¯×· Â«‰Âëù@QAáíïC…·ëŸŸ ðz½ Ø ðªƒÊkÔwNRZPøþ\ö ê} '-Ã4¨5(¼žß¾7o_ ð꟠úFáõ8)ºáQÕÕ“ÂÛõâüFáõøJ^ƒ=P:Px îü}£ð½9…ïÍ)(¼‡^?U*Px=>@ÉÂëßÃ÷…×ã v ðúù ß(¼jqêþ ÷ªW¢ðz>úÂë÷……×ã=Qx½~?Aá#ˆ!…×ñ” ^Çëž(|ŒÇ¤ðvÿÆ'(¼W8nÞÆÛDáõ| ` ðö}@íâ‡Aê oã>oÞÆƒDá­?Å>?,^(¼úíù&…·ãvAáíøø…·ö}¡¿õû oñÎw#~àª!)|Œç¤ðúýAqAáíó8ÿB{^ü½Aá­=' ¯ãGE7 oãM¢ð6Ùù@á nÅ…·þ Ú(¼iPu£ðIOô·w¡ð6âzýmPpÃe6žÎOPxÕg¢ð6þÌOPxAÝúÛ ã )¼^Ïu‚Âkÿe³DRøèHáž´ù op~›|±ßÞŽË'(¼éDáUg opÔÝ(¼ê;Qxíß@IAáÖàû‚«F{…W Ê ¯ðF@é­#6˜-èo}• ^ÿV½@áõ8¨'(¼^_w­ñ«þ=Pr£ðz~´Px;nç…WxJ ¯ZœÊ/èŠo^aV%@á ¹Öö܇Þ(¼êªm^áÒÙ?AáU£ýÂ÷ëà*!(¼jRp£ðêǪ(¼êË©»¶çëðóY dp ”Ý(¼jRzÃma_PxƒSö}@áUËü…78Jn^uwê®íùòç ÞÎjnÞÎo÷þ=?(¼žTÝ(¼Â¯™(¼çù —éq\/(¼Á²Dá–‰ÂÛçíïÂëß'7 ¯Çñ<ƒÂ›Ú¶žý/(¼Â6ôŸ ð ×2…77?AáUc•”^KœÏ(¼Ǫ(¼Õ6 ¯çµ…7m^? G ³p ¯×{}^ Ÿ¤t¤ð~½Ná/ÎÂ_)+)üÅñÍ)üu»…~ÿ  ÿøñyRø‹Ï#)¼j\(¼þ=q*oíy þ"…öC ¯×‹û ¯÷ïL^5?o^¿?($(AáÕÏó…OÚ&ÆEk{wŒ—¤ðªyÜ(¼jPWPø¤Â­íùfº')¼iPoûáUà ¯°Ô^áîE7 « ð s´Qx…¹ ¶ ð ky~£ð}œ¤’ ðz¼»^Ô ôFáæÚxE ¯ÇƒºÐhO ðêõ…WMjo^aò‚6 ¯ŸÇ*(¼}^>AáMÃo^añ‰ë1 ¯p¸ÁoÞ4¨¹QxÕ ¨ ð ñ{‚Â|ÆqkxzÜúSRxƒÑý>ùm`R?žWPxÕ’(¼Âiü> ðªñ}@áUƒŠƒÂëù¯Dáõï‡žÐø}@áÍ*ßÑžÃo^/›ÔÜ(¼jPdPxûZ8>ОO_0Фíy¹^h¯¤ö‚ö¼Øß€Â«–DáUÛ|‘^¯ïtêníùà* (¼ý=PôŽþör¿Qxý¤ðGwê t¶/Røƒ³`§ð‡çú’Â쀜Â?zf ¯(8(üÁÜN§ðGã*)ü«AáÆöM 0Êr ¯0о)¼úS.¼-Ëâ8(<àáKá_… …?W=HáãúIáÏ…&…?|•^?ï¹ï×S.¼](>(üÑø{‘¾ªB 4æ6“ÂëßkŸ——çÖƒÂjœÂ«¤ð¯…5(üÁÁÈ)üÁAÂ)üqùqPøho¤ðÑÞHá}»^Ðè_Háµ=¦\x,‚“¿þ p ¯0·}^ ÿh¼KD ÿè;SøƒQ¦Søc8e…?œ¢“·çڃ¤NNá†Náõû€²¾Zyîû¹Òý …?8+u ¯Ç¡AáR>§ðñ{“¿þèžû t¾ C Ï7)üA íÞŸw§ð‡S7Rø¬u}\Ì&…×öçZÆÛ?9…×ßç…?ºçÖ“Â7æ‚:…çóëþé/AmÂsUÓ)¼ö·NÝä®ëóÛ7~1wÔsá"{.¼SpÏ…_¤@ž ﹩ž /¤Úž /þ÷˜ Z<— ÔÉsáùnXäÂOÏe?žß” Ê—ráA½™ ßÐ?8…×xÇAáFêK 8¥%…?øî–Sx/œÂk¼„ã ðßýs °ã¥ð³nœÂ{<éþÑ=Sx„)/…?©5)¼ÞVhPøWß¿†6^0HmIáQrá-ÞL¹ðIƒÂg½Ïâ~“ƒ÷›>ÎO ð]d§ðW¥œÂ£Y¼^ýø<(üá«0¤ðO;…W ‚ã ðwrp ðÝb§ð‡¯ºÂ|—Ù)üÁ͜Œ'œÂ{ʪSøÃß …?ŸRxÏq ¯Ø¹ôÓsOÑÞIáO_U …?›SmPxÍò@®úéù§ø>¤ðÏqæ®7Ï2YžûÎ,´/RxÍPU…?O¬Ú:…?¶?Rx`¤—žN 0KÃ)üãG{'…×Çöú¼þä*±Sxý<ü ðǪþà*±Søc9Å…W fߟ^ÿŽƒÂ¿ÇAáÈ¥…?Ï8…÷¬[§ð³®Â‡Ÿþ¨¹ðv¿‘û ïY?Náõ÷O¹ðöûBƒÂë´”þt N ¯Y:8ÿò¬¨3SxÅvòy)üé«B¤ðžäþý<(ü£ÑIáO²T§ðžå^³vñyPøWƒÂ¿þd–‰Sx=ŸÞŸ_üa[àüå/¹Áͯÿ n~ÿëþá/þº}ÎÏ/ÿ+ÎuÚ9~†ÿñÿ²õ4ÿ_þîó«ŸüýO?OOû“üí¿þæ÷?ý‡Ï/þÿÿ¥^ÍŸ·•7êÁüèwÜ·G[‘v¯ŸÏ/¾2œŸéøýt zî¸(³¡Û”> Ù€•à0pa8pUaHùçßï§ßÕy¢†[‡ÞÇñÓÏ/ÿIo²žýú¸Ž~ϰaoÂÇŸàþéÅíãW|\ºþÉÿLË>Ø`éwk§±á’Ý“Íá²YºŒ°TÊbXÕ°6–“Â@™ öŠøk ,†Y s3`‰* ”Å  É`2°ŒÊdÀÂV(‹Aç\É`2,ú~ ”Űªam,Ÿ…ò5pAÍ .‹aVÃÜ Xt e1¬jX›Á–R_e2 ”Å Õ ›‹}a L,ÿ…²F5ŒÍ€%Â0PƒNY“Ád2Ø‹è¯2°ìÊb½F}6¹4Êbjjàò¥\&ÙzŒÇ@Y £ÆfÀ’h(‹AªA6–MÃ@™ XH e1Œj›Á"×@Y 2‹Aê³ÉåÚ0P&pÃ@Y CŠaÔg“‹¼a ,©Ù d_åk8±$ŽÐÕ26ËØ-X_~-ÔÕ²ŽjYÇfÁ*ôk¡Î,L¿êj™gµÌs³Ø$ÉB]-k³¬Ý‚Eð×B-X-ÔÕ27ËÜ-X=-ÔÕ²6ËÚ-6ùKêlÁ²ük¡®–¹YæfáFvaq]-ë®–uolw÷Z¨³¥•8"tµÌQ-slä¼êjY›eíd¼êlAÒÁk¡®–¹Yæn±Ìßd¡®–µYÖn™%²-Èyx-ÔÕ27ËÜ-«Ä¡“…Éaq]-c³ŒÝr–(#tµÈY-²=ÓLÌx-ÔÙÒK¬ºZF«–±=ÓÌèx-ÔÕ"›Ev‹m~•,ÔÙ‚T‘×B]-c³ŒÝ‚„’×B]-²Yd·H‰>Bg‹½ø™,ÔÕ26ËØ,Ìg ‹ëj‘Q-²=ÓÌzy-ÔÙÒJ$ºZƬ–±=ÓÝrÍ’…ºZd³Èn¹Ê`:[°'åk¡®–±YÆnu´w]-²Yd·XÊj²Pg‹ÔÑÞuµÌÍ2w 6Î|-ÔÕRF{×ÉÂí5Ãâ:[°ãæk¡®–2Ú»ÎKÍNêjY›eí–^G{×Ù‚ ?_ uµÌÍ2wË]G{×Õ²6ËÚ-£0ƒÐÙ2ëhïºZæf™»E 9]-e´w-«ÐƒÐÉr…„®–2ڻΖ³0„ÐÕ²6ËÚ-­'®³¥’ºZæf™»åªÁ‰ëjÉ=Ó¡³å,«9¡«el–±[ZíCW‹lÙ-½€žÐÙr•Ñ>tµŒÍ2vË]@OèjÉ£}èleY't¶ÌzBWKíCg‹”eÐÕ²6ËÚ-«Œö¡“¥eY'tµÌÍ2wËYFûÐÕ²6ËÚ-­€žÐÙÒËhºZæf™»å* 'tµäÑ>t¶ÜeY't¶ŒzBWKíCgË,Ë:¡«em–µ[¤'¡³e•eÐÕ27ËzZ?JpºZò²Nèl9˲Nèli%8 ]-E„Ζ^PDèjY›eí–« ˆÐÙrºZd³ÈnE„ΖYPDèj›eNººZd³ÈnY58q,×QPDèj›eì–³'®«%£ˆÐÙÒ Š-½'®«%£ˆÐÙrºZd³ìÁ‰¾\”Ÿi×Ù2 Š]-c³ìÁÉ5 Š]-²Yd·H N\gË*("tµŒÍ²¡} ª'®«%£ˆÐÙr:[Z N\WKF¡³¥ºZÖfÙƒ“û*("t¶ÜE„®–¹YöàäE„®–µYÖn™58q-RPDèj™›eî–Uƒ×Õ’QDèdGA¡³å¬Á‰ëjÉ("t¶´‚"BWËÚ,{p2zí]gËUPDèj™›eNÆ]G{×Õ²6ËÚ-£ ˆÐÙ2ëhïºZæf™»E Š]-e´w-« ˆÐÉ2‚"BWKí]gËYPDèliE„®–2ڻΖ^Q„ëj‘Ͳ£ˆyÕÑÞu¶ÜE¸®–±Yv1Gí]W‹l–EÌYQ„ël‘:Ú»®–±Yv1WE®«¥Œö®“E_ÕÌ×ÙrVáºZÆfÙƒý÷üØ»®Ù,;Š^ƒ×ÙrUáºZÆfÙQ„Ü58q]-E¸Î–QQ„ël™58q]-E¸Î©(ÂuµÈfÙƒ“çXA®“eE¸®–2|ºÎ–mn¿¾žÛóÛlÙ‡O¾†ûZÚWÃçÚæöëë¹½n¨uWË>|¢(c²\_ Ÿk›Û¯¯çö¬ˆõZOÉz-ã«ásmsûõõܯ'ËüjøÄ«çŲŸØ)Yä«á“5º^ËújødÙ®lÙ†O¯äå–ÐÕ’‡ÏÐÙr–á3t¶´2|†®–<|†Î–^†ÏÐÙr•á3tµäá3t¶Üeø ]-²Yd·Œ2|†Î–Y†ÏÐÕ26ËØ-R†ÏÐÕ"›EvË*Ãgèd9ëÜ>tµäá3t¶œeø ]-²Yd·´2|†Î–^†ÏÐÕ26ËØ-W>CW‹lÙ-un:[F>CWËØ,c·Ô¹}èjÉÃgèl‘2|†Î–:·]-yø ,,n×Õ"›EvËY†ÏÐÙÒÊðºZæf™»¥’ºZÖfY»¥ÎíCgË]H~èj™›eî–:·]-™ä‡Î–YH~èl©sûÐÕ’I~èlY…䇮–µY¶àÄë †Åu¶œ…䇮–¹Yæni…䇮–µYÖn©sûÐÙr’ºZæf™»¥ÎíCWK&ù¡³e’:[êÜ>tµd’:[¤üÐÕ²6Ëœ°xãkY;É×-ò3É]-²Yöà„U!_˹“|/ùZÚNò½vd¶ìÁ ËI¾–¾“|¯0™-²[êÜ>t¶Üu´w]-c³ŒÝRçö¡«¥Œö®³5/_ u¶Ô¹}èj)£½ëlY…䇮Ù,ÛÜÞKn†Åu¶œ…䇮–±YÆniu´w]-²Yd·ôBòCgËUG{×Õ26ËØ-w!ù¡«¥Œö®³e’:[f!ù¡«¥Œö®³E É]-k³¬Ý²êhï:YXâ4,®«en–¹[Î:Ú»®–µYÖni…ä‡Î–^ƒ×ÕR6|p-WA¡«em–=8wA¡³eºZæfÙƒ–}-sG^6[Ön‘œ¸Î–UPDèj™›eCºÓN N\WKF¡³å,("t¶´œ¸®–Œ"BgK¯(Âuµ¬Í²'óª(Âu¶ÜE¸®–¹Yöà„5v_Ëø E°ìîk™_¡VâÍ–=8™RQ„ëj‘Ͳ£ˆY_v ]-k³ì±”Ô—]CgK}Ù5tµÈfÙc)©/»†Î–ú²kèj›e¥¤¾ìºZd³ì±”Ü5–r-õe×ÐÕ26ËKɬ±”ëj‘Ͳƒ©/»†Î–Uc)ׯåÛ›7íïò×IŸ}ðxÝÿH©ûf“y£´wQÚÒh¢õ°Íu£vãgÇùüß·6CúöI4é^[Þ~–ùGÅ*Qì×2ëµÜåGùá:rfùo]kçj{Ëú.T”ÿýƒÍÀÊY¬\SÓmž¿ÊÍÀÒþ\cèNï?óþ\¸z­.ôíº~ÖJL éWóo4„l±¾o ¡Z~¤!dËÕëÔÙ²íæ·öÇ· ó›ýãû†¹ãÇ7sÇï?é»uØûÎþc¿`ìùvüèŽoÈiþ¬Ú°—ØR€—yÑåK«ˆn;sÉ=þ° —pTì˜%¬·€í±tœ1e{a ÷2ÆÆWÂúiØåJ¸O 6¤ÖÞÁîSÂ=±Õ”°Îö•’ûb)ýÜ[_]¸÷+6wÖ÷ÆNNr`_hlÛ¤ç´2.¶G“pOBl§$¬޽“Ä·—tÖlÛIXÁ[ wšÅnEÂ}e±5‘œ,ýbû ÷TǦCºüÖr‘“µ[l3 aÍ ìü#¬]m~„;]bO9±/!6ðÁÛPQÅ{ùG9YèÈÉØ,GGyÔ5·qädYkl_óJÛªæéÅO/×ršÄîùØ‚F¼˜¶›‘æ×mS˜,§É;ÕOndË]Äë@`á¶õÜj%N…mUÞÏÚ*Ò¼Òºm—òH|l"4°‰JœÊ6+îGÉItç1û‚Ø„D«‹`Ñ8¶¯ -@ÄKe`»i¥R tj‘Óë°Xü¡GïO”i‘ó­Þ®h&Ü0#~_lŽ!,|Ç-,¤±¢¶«/˜­)^iÿ"^>[N<²§ê,Ò¼ºm¡2Õf/íÍôhª.ÍË«Ûv ú âïÚÖ âeH°‚4VqÀ– ñw±=‚4Ö¸Á&rz Û°@X'›ÄÍÁFr²Þ61ãÖØ44ª±ÈÉ’Ôxå_¸·-_ï×.-•b‘ƒ•Jð½pãu¾\¯fTK·fÿÊÎUð‚¼,J„רµgƒ´WÖ½äëéÚ BNvƒ=•A—ƒ õWâ(ʯÌÅÆê+“›Ç³øÊd­Ö^™ÜZŸ¥WômÐTye./”b…Wô¨WCº—ÉmòYve.VáAÕý¬a“Wª|>¹E?K®<§BE%T\y¥\y¥Õ[™Ü^åV&w«gµ•éÅPlezÅvÔZ™Ü ¥V4#éüD¥}¥Ò ­LÖ`•9YFeV^i=óœØEV4êüD•É-ŸYber‡zVX™,`Á+H¥Šú*ïg­¼Š~‘Tã\‡i˜­¸Ê|pÄj«ÌÉÒC(­2£>¹=#szyq>æäD]•Éâ­,«’¥œïEQ•WZMýPÝöꟂHU¦¬BA•øQPOEÿŠ’[;÷ß—ÕT²wü]ÖR™^J{šOñúã¶Eù/‡n…T¼=³ŽÊ\^}²‰¢jª¨ÌÅRµ(¢âÍ›5T¼‰²„ ^¼Ž *þY@Åï$ë§L/W‚ò)sz5«ž2'‹ xŠJÞDqf+2'+[ rÊœ~V8eNü ¬›‚.ʦLÖºdÕ”8Цd‰&:¼DÊóÕæd…TLñgSô)ƒ´z)ÓËG \ÊÖ€Fµ”)^lÅŠ¥¼f+ 2kÁrØY)%Ú ¥L¯[:)Þo°LÊô*&¨’mER^i5R¦”BåÓ+l BÊô‚(2½Þê£LÁdyt&Qe./aÞØêNÔJ±Ú(ÞDz4Êk¶Ê(s•òäšöØ>QE¸8Ë¢x´Îª(ÈnŒ¢(>Z±&ŠÔÂäO³"Šœü‚(ˆâñ<ë¡øÄƒåP„»»³ЇÑ,†âñ0k¡xÐËR(³Šœ,DƒB(rz <$gá®ô¬‚¢…|¢І¨Ln5Päd!t”@yOe¯RËÉ(€âëŸx¸Åò'Âj¬~"^²ÅO¤½µPä®YúÄWV>ñà“…O$ª‰[ÝŸYöD¢¶¸U=–†fÑaÍ7Ö<ñ(—%O„&Xñ$¾ žh¨†z$öB¬°¨°á»uËk#&l©Ø‰O.Xë;¶G©YéÄ'N,t"'ë ¡Î‰œoqñÞ’Ùž‘¸*¤a kÊ1åRK!½£pT8‘Î^i“ÒÙb‘Ü(^ô‰Œ:OM5Æ•sÊ'Š›H÷?dɈú­q*{®¥ó)Cz ô·I—8Óþ„eé˜âçŸe"ž°ˆ“î²ÔVwyÉK¦¯ƒ‹Ä9aA9¦·ÉÅúHe{Oeik*qÔÄ83ÒÑäBŤ±0#A »hD%“ø HüÖUc’våˆ2&ú“]Ÿ¨b"ÍKO6Ñ•j˜ À2uÊ:&8yóf2“Ïb˜¸ä“V&)ùÄ“ IÒXriC¯¼}²ì“eÔFê´R¸Ä翤f¯\>YNUK|ZÊ„š¸HžñiS\¼ b:‹>V(Hr±cDý¤©x×Ç”ïT™8âH‚I"Þ!3!ÄŸ_&`5!*•øL“é>ydê…Ï™f¡_Á®)ÒXté>ãc’ƒöÏ(,>ÈpBr²Œ¿;}² )ìO”;±ÔŸÂ3MÀ; ¦DÛÀò¿O–¹Ô/•A° ïÓ.¾Ç5c¡Ý{$.ªû„— èq¯°Ìí3z.i¿§Zlugª î].—¥½åâ±4 ô\(vžÃEáhWXÖ•¯M‚&Êß¶$+í ˯ÂB£\j˪z¯R1ð¸WXèŒÇ ‹šÎ ¸€O7+ãéÆÂd4~,:XàR¡ƒ. Æï‹%ÀhuXî“ÆZ7¨=¥GâEå‡N,<MO# –Qéµ¾Ásp'QtÄ™kŽÄƒƒ’#¯´Š#¯´‚#Ú¼Q¿d!Zt­¨Xž¨å;šþój¹è·Øæ¡ƒ;âÂàA¨ <ÜÏ(ÿ­ËÄCJ½©°^*+°°êÀÄr™X`øŒbË ÃE?M[5À*ذžßpø<¼)Û>ù ͆‡õ8¯ñan’€Ø®ç7Bl­*!bmV P`ÄÚŽT¼[[`(±®K¢=ëqøÁ‰õóX(Öã<×H±þ=| bk©×'X±~Å«‹UK*Þ­z%\¬ç£ßÐÒÅŒõxOÄX¯O ±= Ž4‹i74eï$5Vû l|zñ2rcÕâ¤ø„FS9ÖÇé@±oGzüLìØüÐíïƒÓ=VMl“|;~‚ëçEíú  ëß—ù „¬º;4Öö|²'DVöŠl[ó'Œ\´¶çóà: @²j`gdë]Rñî¤%'m0Y¿Ÿ$šlÕRñní€pÿÀ“µÂj €²g1n#Êúyq¼pϘòy²8#¡²GûUNÚ°òéÅÒÈ•Ù]XÖ¿‡ç sZõãyZ>Ï(ÖmlY5?opYus½FöZÔóãþ‚/¿í€Y5×@˜µÿÀýb¶!Éõ\Ö_àyd^“ ³v—$ÔF™½T,)ó+-fÑ?†› ̬gK•»õl§“å+¾ H³jÀU f;_bÍE¤Hˆ;úZ®ø6ë%£î¶Ñæ5‰A›«¼“6Ër³‘Õu8Ç6Ú¼.×ÂÐnA›×MÚüÜŒžh³»¤ÍKœ'[ôÅ\A›udM´Y!Æúm^ìpñ"\0k+øáe¹Ÿ†¦Zæ'¨5àÀÖª»ƒjmÏÍŸ'€k;?p³‘k;¿Ý ë÷ü`×z>òh I5Àž‰^ëqžßðµ^–ûé8, O[ð3lû<ÊdÂf ”`ØzÏ3&¿æ‡6Š­çCç Œ­=:Opl à3ȶ€~‚d«Æ‚ P¶ÖÁšX¶?¼l·Fªz Øn´žì 8Û4ÊbÏÖψh«¾Ѷë½>´õó f`Úq½„Úgç$T['H-•éÖö4] î'µmýý'óö<øy m›€•ÛVëÜÖ¿'»µ=w/s ¼í‡|[¯·{Ùîû{&­šŸ7ĭߥ2Ý6áò²Ü©öIÜ Êm¿o*Óm~;8·êK>ºÕÏó]hÏ¡­£-Zû[Äåíyúq£ÝªÁ?»“6Þ]´¶çËË ãÆØ¨.·jøÁ¼uB  è­È‘ÊtÛóúöÖ ã€6î­FPT€o-6ÂóùÖ "È!зï®5и…:aD™oÐo=¼û€F{ÿV?H,¸jâr#à:a]ІÀõó`ï`àöyù?±i\Pp+5‚ë1 ®Ð¿ ¦A¯ „«í ?og£@áV—Çí€GxÀp›ðöOÐðä7®~<¯àáª%qðâ÷Wï$®šeʼnëù¯ÅÏû|õ„î©L·ùÉ‹+PÈeºµ=g[ÇÇ2CÆU£Ì5ظõGø¼ÁqmÏËõB{%K´çÎþ|\µ$@®Úf†$ä~5psOI7Ÿü…ÁÍU¹6n®Ÿ¦ZÈä2ÆmÜ\³¾ô/€›Orzpóp7÷ psMò!)?,·È®Ü|Œ>àæ“}=¸¹ç›Ï‰~Ü|r”7Ÿœ³€›OÎÁÍ'ŸpóI¬ nŽ»ãÜ|.ŒÄàæúîÓ¦›­_L©¤¡…#‰škŽžDÝp/<‰ºãkxu#Øeu#(duH$Q3£À“¨Éò=‰ú"w`5±‚'Q'‰E7%&Q_Á¿Å—3€¿e0oô[&ŸnÀoÏÇ6t#ˆ¾Qj<È÷+ |¿Ò áAì-ìÁI½Eü¨AïhH`ÞÑ€¼Eø„‚x‹°gðÖf–’¨…Ãq÷+v §ä„Ýë`ê>X÷:øÂ:Ûup1¤{1¼#è^‡slûuÄ150·,OÏ6Ê-D=„ܱœŒ[¿öÁµí˳¦Ïõ~}ná,|[„$ø¶©oÇ ¼ýJÃÛ2=kÚžM™| p;žP°m!æ%Úö–d[œmlg©‰X Upm™ÀÚÞ«këï‹3ÛüS¦çck&%kû3H¬ýü4@¢ÀÚµAbm¹˜ ¬­Éc×'°¶tf0k‹ÓY`mq² ¬-lX;r#µ¥kû¨AªýJa"0˜¶¿ãC¤í¹$ÚÂìm m<;Ò³=‹‰4;³ý9²lÅŒ,[¸ŒB–-'Y*XväÜeû»c$ÙqU &žãDŽ­™9.å|“!A±=8!ÄÖ›Fm ÛGI"ìWÁŠØïß5~-Ž ¯=¢ ½Æëä¯#ëì:².®#Íä›§¸FŒÜZ:±#°µtt_žDí­ŽIÔ×Ì$j¾îëIÔÝ 3’¨™¢âIÔÎ_oû“‰Ð1asz½L.#‰ú*¬Zµ2‰ÚŸ&Q_oÖôyG$ãIÔÝ19’¨â1‰º;D>ùb¾>“¨=!™IÔžÛÌ$jO*fu˜‘DM\àIÔžTIÔ6¼IÔwbÓr¾YÓíM³|“¨{ÓñhD5?Û™ˆ…vÅ$jœ™DÍlŒ7‰KL¢öç7’¨Ø™Díü7’¨»ãiyßXô$j¬˜DÝøn“¨™îó&QœD͉¼'Q;çeµßI&Q3ŸÄ“¨ýÆ2‰Ú/2’¨ñ¢J$Q7ÏšFõ“¨ý>3‰š ùžDíIÔø L¢¾ø“1‰úæ“Â$êÛOåIÔÓAóiá4ºM&QßžêŒ$êëÍ©^6¦ ™IÔ\„}“¨‘IÔ L¢æª«'Q;¯auäc#‰šKÑo5`"“¨ù ¾IÔ@Yo5È.’¨žnO¢äâL¢ÄšL¢v É$j¾‰ãIÔÙ=‰Zˆ™DÍ·´<‰zX0/fŽx5C5O¢^¤L¢ô±„Ëë ‹eõ"jduÈ›1aHM2;È!Á•ue<'Q/&R2‰:$’¨“\"âÆ)kˆ˜ˆrœ@yñeOòäÅåO¢^žm4yÌ×eµ¯%€%{hJ”¼NfÆ‚$/¾ÕN¼øþ&9òòe`äÅEI‘ßs'D^ÊÉ=a’yyJ9òêlÞÈž®@~¼.âDàcÍüƒ4z¼†¯á,ÖbrMH@ªóÉ H|ãç(3Ÿ!–gM#Õ-ØX3$A¯‹Œ„Æëb»3^Ÿ ãå9Æ Æ‹ÙÆëb/^úââÅÕLÒbÔs X¼zaÅ‹K™DÅ«;i6R¬ }_€âué‘¿G ¯HÀ6J¼B{¢'ñb/q˜AˆWM¢^óÀÁ‡=ÿ„xXß”D½Øå?¿/° /G³@ÃK¸²2¼„O À°î±-ŸàÂË—)€…=…Tøý¬AaÚ€^Ä~DžC"¬I¢ø¬áWZ?öJëš³HƒõTz7~ñ‡ìß¡;ÊØö¿þý7¶çøý¯ÿùÝp&í2ó³´ù̉ƪúþòwŸ_ýäïúyúÅŸüãoÿõ7¿ÿé?|~ùsÝsæ¹”?ol2‚yÅï¸åˆ²ßÏd;~2 ªÛ®(+µmWpœ2NÁ®+4@f–"ÃÀ•ÉdÀ5…!]⟯/Ëã™6iÔ½}ž/ð­­þÍOkòËÓÝÛÇÛŸðñËæ™öñ+>®1•nîóþó]vÀÆ0±ù¤ÝzjŒØx ëqØT=¼ÊáUc%Ûʇ±´á‡MÕúÖñ~T>ŒÅ?l*Æjˆ6UëòÈ{øQù0ÖKü°©t˜ (< UÏrxÖÃXbñæêáU¯z‹0~ØT>ŒU?l*Æ26UrxÔÃXÈñæòa¬ìøaSõðèùð(mk?~ØT=,å°”Ã\âa¨|ËE~ØT=,å°ÔÃXPòæòa¬0ùaSõð(‡G=Œ5(?l*Æ¢”6UɇGikX¶ŠÃ¦êa)‡¥æÂ–§,[êz &7Ã:ŠaÕ`K0¯Ád1ØÙk0Y ¶döLƒ-¢½“Å`Ëj¯Áäf˜Õ07ƒ­;¼“Å`‹q¯Áäf˜Õ0« va€Ü ë.†uWƒ-꽓Å`Ë|¯ÁäfXÕ°6ƒ-¾“Å`‹ƒ¯Áäf˜Õ07ÃÌ}3e1XÔ÷Ln†Y s3¬ÜCSf"ÃY ¶4ùLƒ-V¾“ÅÐsOM¹F+†QÛ$–8_ƒÉb°yÖk0¹F5ŒÍ` £¯ÁäfjÍ ¹ß¦Ì,§†r3È(©mK®¯Ád1´ÜSn†1‹aÔ6‰…Ú×`²léö5˜Ü £Æf¥§…Ü R ²l ø5˜,[~ &7Cîi!³ Ça€,[J~ &‹Á8ök0Y ¶ÜüLƒ-@¿“›aVÃÜ wéi!‹Á–­_ƒÉÍ0«anɱ/åfÈ=-d1¬ÿRfÄùV5¬ÍÐJgY =ÇÁ”›aVÃÜ ¶ÔþLÃ(9äfHñ0e1ÌSƒ”βV/ ³ae¼€,†³ü ÈÍ0ªal[Ñx &‹á*ãäfÈãd1Üe¼€Ü R ²°»QLƒ”ñr3H5lãÅXe¼€Ì†‰]’ܹF5ŒÍÐÊxY ½Œ›!Å`k¯ÁäfjÍ0ÊxY 3Gþ”›!Å`)š¯Ád1X®ák0™ Øÿ0 Åp–ñ²Z/ 7솹lã—×`²î2^@n†Y Ûx¡«$Ù0öà_Fæ$”Å03)¡,†UÆ ÈͰªa þ•ü§F Y g&&”›aVÃÜ ½ŒÅp•ñr3ÌjØÆ Ý !Ln†DO(‹a–ñ²¤ŒŰJg¹E¡LK›y ”ÅÐ2I¡Ü £Æf虦Pn©Ù WîÌ)‹aäΜr3$ªBY 3sÊbÜ™Sn†ÄV(³á<2]¡,†3óÊÍ0ªal†–{ZÊÍ Õ ›áʘ…²îüSn†ÔÓRÃȘ…²fÆ,”Å 9ø§,†•1 e6hRS2@n†Y s3œ¹§¥,†ž1 åf˜Õ07ÕƒÊÍzZÊb¸3f¡,†™1 åfXÕ°6ƒäΜ²VÆ,”›aVCÅ,š¾•0 e1´Ü™Sn†6SCÏa3åfXÕ°6Ã;sÊb³PÃÌa3åfÕ°uæš`–-d6\GÆ,”›aTÃØ géÌ!7C ›)‹¡å°™²®ŒY(7ƒTÃÖ™_w›)‹aä°™r3ŒjØ:óKJgY +cÊÍ0ª¡†ÍšM—;sÈÍÂfÊbh³PCϘ…r3¬jØ:óûÊa3e1Ü9l¦,†‘1 e1ÌÒ™Cƒä°™r3Ìj˜›a•Î2ÆY:sÈÍ0 e1´6Sn†U [g>zéi!‹áΘ…r3¬jX›aä°™²féi!7솹VÆ,”Ù0ŒY(7Cîi!‹áÌa3e1´6SCÏÌœ²l·¨×`²î6Cn†Q [ج©˜©MBƒ¼+èj0¹F5ŒÍ°JØ ¹rO ™ r”°²ìM´×`r3H5Èfè¥3‡,†«„Í›aTÃ6ËÈÌœ²féÌ!7C›!‹AJØ ¹¤¶Î©ªa€,†‘®¯"R¤³fCû3Áõ5´mîÏ”××ðUL»jDº¾ŠH‘ûî½C¢ìkÛÜŸ©³Ù07Ã,Ý d1¬Ò‰An†Y µc ®(7C )‹áÌÝ e1ô܉QÕ;1ÊÍÂEÊb¸óÜŸr3H5Èf˜¹£,ÉåfjͰòÜŸ2ÎÓRC‰H)‹¡åNŒ²zžûSn†Q c3\¹¤,†‘;1ÊÍ0ªal†™A.åfHsÊbÜ Rf¤Ã¹¤d3œyîOY -Ïý)7솹JDJY wžûSn†Y s3Œ r)7ÃêŰj›DR÷kmîÏ4ï×°¶¹?¿³¡væLd1œyé²ZžûSC‰H)‹áÊsÊÍ0«an†ÓRC‰H)7CšûSƒä¹?åfXÕ°uæHh k[2cŠ{ ‹¡å¹?åfÕ°uæHŒ }›û3U>d3ÜyîOY %"¥Ü ¹§…,†™çþ”ÅPbZÊb()e6 Q? Åpæ¹?åfÕ06C+=-d1\9\¤Ü £Æf¸óÜŸr3äž²F^2£,ÉsÊͰªam†UzZÈlÀ a€Ü ³æfhyîOY ½tæ›!§CÕÃfÊͰªaëÌÇ(9d1Ì<÷§Ü «ÖfÒ™CÃÊa3e6àÅŠ0@Ùçþ”ÅÐJg¹RØLY ½„ÍÅpç¹?åf˜Õ°uæx­ã5Œ=lÆ‹¯aîa3^ýȆ­3Ÿ%%r3¬jØÆ  d1œ9Å‚r3H5Èfèe¼€,†’’G¹¤¶ñ©_ý1–£~ {RËH¿†¹ÿRRò(‹a•ñ2¾ýrÉŸù*Âwò¿¿Ÿ`ûÝ\Åï&Ž}7Åæ»ùß]þî"Ûw4¾‹~ç÷0Û÷ˆÅ÷'ß ´¿’|w|øþ£öFúýBÞ·m0N¾ló«Ÿüíÿøéç‰Öò¿>ǯUýç±Øöš©øýG%ÇN¹é2þú§ÏŒüó“ßüç?î2¸Yx¹ŽñG\Ç@üü^Çßü#.äÿü‘×1¬áŸzº2­cä{ÿ—ñ¯ÿí»]û=þŒë˜öýŸß<ßúï¢O×úÑ>l¢xÔ똵`=Ÿ&¯OÏÎÛ¥æ^¦rõ!k¹z|èÿcµúo#~-?6äãßIŠã›CIq|s,)ÖÄ̓ÉvV‚Í£Év–4ÍÃIux©Î<žÔ¿â)ó€²»‚”e;Çìeù²8¾9¦dÇ·•âøæ¨RßVŠã›ãJqàUá2°lçÀ;¾ed™ÅQ_„åÓñãoÂúãóã¯ÂºãÇß…uÇ¿ éû6ìßýð,’F endstream endobj 433 0 obj << /Alternate /DeviceRGB /N 3 /Length 2596 /Filter /FlateDecode >> stream xœ–wTSهϽ7½P’Š”ÐkhRH ½H‘.*1 JÀ"6DTpDQ‘¦2(à€£C‘±"Š…Q±ëDÔqp–Id­ß¼yïÍ›ß÷~kŸ½ÏÝgï}ÖºüƒÂLX € ¡Xáçň‹g` ðlàp³³BøF™|ØŒl™ø½º ùû*Ó?ŒÁÿŸ”¹Y"1P˜ŒçòøÙ\É8=Wœ%·Oɘ¶4MÎ0JÎ"Y‚2V“sò,[|ö™e9ó2„<ËsÎâeðäÜ'ã9¾Œ‘`çø¹2¾&cƒtI†@Æoä±|N6(’Ü.æsSdl-c’(2‚-ãyàHÉ_ðÒ/XÌÏËÅÎÌZ.$§ˆ&\S†“‹áÏÏMç‹ÅÌ07#â1Ø™YárfÏüYym²";Ø8980m-m¾(Ô]ü›’÷v–^„îDøÃöW~™ °¦eµÙú‡mi]ëP»ý‡Í`/в¾u}qº|^RÄâ,g+«ÜÜ\KŸk)/èïúŸC_|ÏR¾Ýïåaxó“8’t1C^7nfz¦DÄÈÎâpù 柇øþuü$¾ˆ/”ED˦L L–µ[Ȉ™B†@øŸšøÃþ¤Ù¹–‰ÚøЖX¥!@~(* {d+Ðï} ÆGù͋љ˜ûÏ‚þ}W¸LþÈ$ŽcGD2¸QÎìšüZ4 E@ê@èÀ¶À¸àA(ˆq`1à‚D €µ ”‚­`'¨u 4ƒ6ptcà48.Ë`ÜR0ž€)ð Ì@„…ÈR‡t CȲ…XäCP”%CBH@ë R¨ª†ê¡fè[è(tº C· Qhúz#0 ¦ÁZ°l³`O8Ž„ÁÉð28.‚·À•p|î„O×àX ?§€:¢‹0ÂFB‘x$ !«¤i@Ú¤¹ŠH‘§È[EE1PL” Ê…⢖¡V¡6£ªQP¨>ÔUÔ(j õMFk¢ÍÑÎèt,:‹.FW ›Ðè³èô8úƒ¡cŒ1ŽL&³³³ÓŽ9…ÆŒa¦±X¬:ÖëŠ År°bl1¶ {{{;Ž}ƒ#âtp¶8_\¡8áú"ãEy‹.,ÖXœ¾øøÅ%œ%Gщ1‰-‰ï9¡œÎôÒ€¥µK§¸lî.îžoo’ïÊ/çO$¹&•'=JvMÞž<™âžR‘òTÀT ž§ú§Ö¥¾N MÛŸö)=&½=—‘˜qTH¦ û2µ3ó2‡³Ì³Š³¤Ëœ—í\6% 5eCÙ‹²»Å4ÙÏÔ€ÄD²^2šã–S“ó&7:÷Hžrž0o`¹ÙòMË'ò}ó¿^ZÁ]Ñ[ [°¶`t¥çÊúUЪ¥«zWë¯.Z=¾Æo͵„µik(´.,/|¹.f]O‘VÑš¢±õ~ë[‹ŠEÅ76¸l¨ÛˆÚ(Ø8¸iMKx%K­K+Jßoæn¾ø•ÍW•_}Ú’´e°Ì¡lÏVÌVáÖëÛÜ·(W.Ï/Û²½scGÉŽ—;—ì¼PaWQ·‹°K²KZ\Ù]ePµµê}uJõHWM{­fí¦Ú×»y»¯ìñØÓV§UWZ÷n¯`ïÍz¿úΣ†Š}˜}9û6F7öÍúº¹I£©´éÃ~á~éˆ}ÍŽÍÍ-š-e­p«¤uò`ÂÁËßxÓÝÆl«o§·—‡$‡›øíõÃA‡{°Ž´}gø]mµ£¤ê\Þ9Õ•Ò%íŽë>x´·Ç¥§ã{Ëï÷Ó=Vs\åx٠‰¢ŸN柜>•uêééäÓc½Kz=s­/¼oðlÐÙóç|Ïé÷ì?yÞõü± ÎŽ^d]ìºäp©sÀ~ ãû:;‡‡º/;]îž7|âŠû•ÓW½¯ž»píÒÈü‘áëQ×oÞH¸!½É»ùèVú­ç·snÏÜYs}·äžÒ½Šûš÷~4ý±]ê =>ê=:ð`Áƒ;cܱ'?eÿô~¼è!ùaÅ„ÎDó#ÛGÇ&}'/?^øxüIÖ“™§Å?+ÿ\ûÌäÙw¿xü20;5þ\ôüÓ¯›_¨¿ØÿÒîeïtØôýW¯f^—¼Qsà-ëmÿ»˜w3¹ï±ï+?˜~èùôñî§ŒOŸ~÷„óû endstream endobj 437 0 obj << /Length 624 /Filter /FlateDecode >> stream xÚ­TËNÜ0ÝÏWX¬ _?ò@¢R«–ŠJ]TdWº03f&šF¾6ý0FnÝl3øW_õxœ²õ—³KQ ŠV9Ïý!"¢¤Dü|}0‘¤Ä T´àÂqçä~4Ö>¬¨II%ƒ™8ÙŒ—ø©uipUbݶƒ§¸¶Cw£pB‰ÞÅTæRqŒc „TÂtè“DsÄ0£é=Ïλº$5muk&êŒH.]µ%"±KÚ“/¶Xæ|¥²¢B,™ìôjþÜuRª™uº¢“SV‹Ì£1Ç5Ô5íY'ž¬ß­rE¥’‹b7ôö°*™Óœ-=¤II½`($­*…ˆZÊt#ÞeD¹Ûx¯Ç¦Xw7[?§>"1¸Eø…ò²TØüõîv°~½†´+t—ºÆ§‹PÍÓÈÔöb¾_iªÑ ¥ýߢ±ºo«.vÿToüea›s^:•hÛm~nhy$<ÏB`Þ•€³«$ú8l¾¹¿9FfIòB3<0¯:¾|ؽ/%@Ìó²Ù?ŒþªK†AžgDª_Ù„gFTΊ¼ÀÃhqéêÃý`MB:mÑØ{/;?†ÿ»ˆMþ…²÷¨gwN¾öг¯ý*ù:pôÑÌ´v§t´ƒoUœuþ¸Aê‘Ûp×3F`4z§o[C_ué­AîJæ H€¹ Šøa®þìño{qA endstream endobj 434 0 obj << /Type /XObject /Subtype /Form /FormType 1 /PTEX.FileName (/tmp/RtmpBlhU1W/Rbuild4444671013800/timeSeries/vignettes/timeSeriesPlot-minorTicksPlot.pdf) /PTEX.PageNumber 1 /PTEX.InfoDict 441 0 R /BBox [0 0 432 432] /Resources << /ProcSet [ /PDF /Text ] /Font << /F2 442 0 R>> /ExtGState << >>/ColorSpace << /sRGB 443 0 R >>>> /Length 10181 /Filter /FlateDecode >> stream xœµKÏõÈU…ç߯x‡0ÈÁ®²ë2MˆH A·Ä1  (JG ð÷©}­µ|úMI¾HÐÕ~^_ªÊ»v-{Ÿ¿ü8?~óñû/ÿúñûãue ù×U‹üë¼.ù×ÿþ÷Ç¿üN¡:^³ÔÚ^×úW¯¯c|Üíu·€þîÿö?ÿøÅw_dwÇþó»_üËú¯ýþø¿/ÿñŸÇÇ}9?~¹þÿ7_Nýíþr—Wk·E~ä‡/w}]×GmóuÍß~¹¯×y,¹Žð2¹öU[}Í!²½zYò~µ"²¿êÚUk¯zˆÔ?ø\K{‘ô¼îчmo6¤Gû^ZúÀµWŸ×Ðö¾·OÕW;\˰~{ç<ïC¸zÇý¹Oíwô‡õ4’ö~ë`iZÚûzîøöKÇö;Úûú2¸ßÑ––þpGû]O(iïwÜïu ÒÞ׿Nûû©üÂìx—–þÖn¾ô:é[mï[ë³®®gªµ?¹0]µõ¿uad˜_§Ùµý­ -ýa]»?ëFM·ë#D´´÷K‡ÑC‡ú«{û’ÓÚߺM½¸–þ°¶ë3ç\Ïiï[mï—? O{fÔuÛoÛ^uÀ_ÚùKûÃåOôsuikiï‹ozþ«cÉ ¿ÿ~訿š­ŸŒÊk5ú¯Œÿ²¿ÃB„s ”ÒÞ¯ºèÈ¿¶·ézÈñ{¶£jXçéõZ,iï«™Ùõ^(iï¨eøÏë=t =´?ÜþD?×cJÚûÒ÷éZÚûíÓs=·düÏû5‹Žÿë÷ô©.ºáý_.mï©okïñûKËøë°#ºéø¿xkß³[¸ä²ŠžÖÞ£ý/­í½ÛøUT¤}ëxZŽSÇÿÛŸ¢±?”õ<Ãö^ÖóKÛûiσ²ž_'œ¯è×·MûÃ¥—]ôÐö~ùxRÖóê˜Ú>®æZÆÿËÇ»²‚Rÿ/(Ëzž֞楺F{?Bk{÷ñ¬¬ç—Œÿ—6Ñ·nÒž4^­ý!÷×£½»ÙÞ§ëy©Ö¨´¬ç™ŒÿÑJ9¬? ëÏe=¿¤½_>¾‹–ö~ ?ÿõü’ñm¿íï/ÿ7¯¢Ú°(ºi{ñ¢¬ç×í©¬ç×iãÉY]ËøãO):J{±ö°ž_ÚÞ½ý™rtÖÝÆO}^—Zuü—aX¯ïzž¶?kU/Œ´'}>ŠÖö~ûï¯çW­Ú¾ÎÛµŽÿÍÏo ÚŸÓ&»ÃãwÑÝî—ïD{¿lÓÚûi÷¯ŸïúüU´Îw‹õ¯ºægúk]Ï/mïÞj±ùnÌÏD·¾gêuÍÏdüé¶èió5¯k±ùîÖ6ßÝzêøß}¼­óÝnãC]ó3í>÷¯Õ绩«µwУíùx­6ßÍë³ægçØóѺžWÒÞc<­óÝËâA d¯¶û—º2þÇxªïØãu]Ï/ÍŸynFÿu¼—@EÇïÿ¢»Ç:þj`Ý÷üZ•kÏ—%Ð?Ž=?Ýl|²ã½r¾k÷÷²ÔbÌo5ð´ñDã[ ,򼀿¯ñ¡çÎI pÛñ5ãomï‘ÏÐ@`îñFô€ù½®7ä tbfχåA¯ÉχˆîŽ=¿®{oÈøó}Ñò:®;¿!B¥ýÙ|wÿžÍwc<—À¡B’Kó†–|ê͵·wk pAû“Ôá¡ýÍŽ§ŸÖ¢¿u›ï®íS¿Û|wœ~¼’l» ¿-=íþWÛ³þׯÛ|7û£$F ¢Ò ƇaóÝÖÿ8oè/k ?Ëηˆîcç[d º*Üÿ5?;&´Ï¥»ÝëïkàîµÍw³¿›ïF¾NÚó8ëµó9u–hïÆO›ïÆó´®çWƒfW䬿ÍËúCêíÝu·ö^}üž6ß•Û&×û»¯Kòÿü{Oáÿê?’Âÿï~÷åïþ¡|œßÿíK÷ô3ý§kŸä­¶ôýóÝÏÿéo?¾ÿÍ—¿ÿ^~ÿ/[ðÅ™Á?ÄZÄzàH@úññÝpéˆk¦¡q&°A&s°MŽá/½€%ÓÇ­Wpޏ€²ë¯ù[ÉÜtýÛó8âåiZ?ðŸrÁ}ÅÅÖsÖéH£9Bëù<ˆ§fQ°Uš\`o. ž€Jn*&à€¦±C. Î €Jl½(—Xø€K¤ÿ [eJÀåšEì„$@¡¨ÀV®pIÀL>M_ÝJÀ%¶Þ•€KæMÀ|œ¦M$p À­™¥\0 óqš67IÀ%¶ò–€KÆ$`C¤ðkm¤<ƒ$ùO%¥F¤Rœ”‘‹¥ÐŒP ‘›¥Ðˆ4”B3BRhD:J¡(…f„â—ЈLMÐoÄ5#¿„ä>8~ ˆ96⚊_B#R8~ ˆY$6⚊_B#rqüó^lÄ5#¿„F¤qü3mlÄ5#¿„Fdpü]ÖÄ5#¿„¤¿„FÄl$qÍÅ/¡)¿„F¤êzÌF\3BñKhD.Ž_B#bÆ–¸f„â—Ј4Ž_B#bŽ˜¸f„â—Ј Ž_B#bV›¸f„â—Ѐôƒã—ÐŒÌÎÈ|œt?9~ Háø%4#óbd>NºWŽ_B#rqüšŠ_B#¢+¬€¸F¤qüšŠ_B#Òu…z#®¿„f„â—ЈL]ÚÞˆk@ÆÁñKhF(~ È©‘¸F¤püšŠ_B#bf«¸F„—¡R3BñKhDÌ¥µ׈ðbTjF(~ ˆÙ¿6â^’JÍÅ/¡1ߨF\2ya*5#”è È©†„¸f„=óm}JmÌxÒ¡©(…f„¥ù¶J¥þh:é·u*µLÓIßoÒ䥪ԈðbUjD:J¡¡@i¾­X“—¬R#29P ÍJómÝJ­ÞpÒ©9)PJÍJ©áå«ÔˆT ”R3‚RjDx+5"7J©Á@)5"¥Ôˆt ”R3‚RjDxA+5"“¥ÔŒôÂHœôÉËZ©9)PJÍ®®§F„·R#R)PJÍ®±§F„—¸R32;#óyÒ7­´§F„ºR32/Fæó¤Í(º׈ðrWjFæÉÈ|žô¤@)5 …½R3‚RjDN ”R#ÂK_©Á@)5"•¥ÔˆðXjFFadéÏ“î(¥F„ÃR3Ò#ýyÒ“¥Ô€T^KÍJ©9)PJ¯ˆ¥f¥ÔˆT ”R#Â+b©i‘ö<雥Ԍ` ”^KH§@)5#(¥F„WÄR#2)PJÍJ©¹xE,5"'J©Á@)5"¼"–‘JRjFpý>5"¼"–‘›¥ÐŒŒ‹‘ñ<éÆRhD:J¡'#ãyÒ¼"–‘ÉRhFú`䱤¯¯;a'¹ßVÄä' ”B3‚ÖÃÔˆðŠXjD*J¡¡@)4"¼"–‘›¥ÐŒP ^KHç@)4#(…F„WÄR3BRhD&J¡i¼"–š ”B#rr ^KÍJímEL_eÓ¯ˆ¥f„¥ö¶"¦ïÀÑIßoRã±ÔŒP ÔÞVÄJë(…F„WÄR3BR{[“·ò(P Hç±ÔŒP ÔßVÄJ?9P ¯ˆ¥f„¥þ¶"¦ï âI‡F„WÄR3BR[“ )P ¯ˆ¥f„¥þ¶"¦o.ÒI÷·@IÞÚ¢“Ï¥ýb/6ò ”úä@)4#s2òXÚ—%)P ÈÉRhFpi?5"…¥ÐˆT”B3‚Kû©¹8P ÈÍRhFæÁÈ|žtã@)4"¥ÐŒP ‘ÁRhD&J¡¡@)4 óà@)4"ÔÕ|O]ÍGêj¾§®æ#u5ßSW󑺚恵ùH]Í÷ÔÕ|¤®æ{êj>RWó=u5©«ùžºòl7ÒÞ"²ùH]Í÷ÔÕ|¤®æ{êj>RWó=u5©«ùžºšìÇN½‘xå7Ôˆpê*5#¸Æ—N]¥F„SW©Á5¾Ôˆpê*5#³32Ÿ'Í©«Ôˆpê*5#óbd>OšSW©áÔUjFæÉÈ|ž4§®Rrrê*5#‘¥F„SW©áÔUjF0"K§®R#©«ÔŒŒÂÈxž4§®R#©«ÔŒôÉHž4§®R#©«ÔŒôÆHž4§®RR8u•šŒÈR#©«Ôˆpê*5#‘¥F„SW©áÔUjFZg¤=OšSW©ÁÔUjD8u•N]¥fSW©áÔUjD8u•šL]¥¤rê*5"œºJͦ®R#©«Ôˆpê*5#¸Æ—N]¥F„SW©Á@)5"¥Ôˆpê*5#(¥F„SW©áÔUjFèÚú–º’Oˆa ”N]¥fSW©áÔUjD8u•š‘^链8u•N]¥f¤MFÚó¤9u•N]¥f¤5FÚó¤9u•š ”®·Ô•|L‹¥ë-u%Ÿ¹ @é~K]Å—0yJ7§®R#©«ÔŒP t¿¥®â«yK]ù¼yK]éGµ#Ï@éæÔUjD8u•š ”î·ÔU|çc#o©«øôÇFÞRWúÉÂÈ3Pº9u•Æ©«ÔŒP ÔÞRWñ呼¥®âc$yK]Å÷IyJSW©áÔUjF(Pjo©«øÊFÞRWñy”¼¥®â‹)ˆ<¥Æ©«Ôˆ°™;5#(µ·ÔU|e#o©«ø` "Ï@©sê*5"œºJÍJ¡áÔUjD8u•š ”B#©«Ôˆpê*5#ó`d>OšSW©áÔUjFFgdÏùôï~ò½þ¯ðÓ}ÅT?H´¿B´?M$o ÍŸ¬‡¨|¨GûÙq®ÿû±oýØ.ÄŽ,ÑãsýO؇äádP }t>*lñ×ëļËoSÏj_#u<ä¯?¾û“Ëc´&bÚÿÄï_ÙÁK†÷Ç¿„õ³ÒiY+´ÏçM¹å”w ÈgMë>) {á¯rÅ¥üü³\A|þ]® >ÿ0Wðe®}ƒÄ4þÉ Ê¤ùgÑ~¢ÊÙÓ7¨„"¾:¨„¢®À] E|„P Eì–P EL‘P EÜP ¥ØkèQ EL–P Eþ*¡ÈßB%ý²ô®„"G•P䨠Šz3å0ì#Mò·P E$TBQ宄¢Ç] E~*¡ˆ„J(C%16B%‘P E`¨„"Ç •Pä²C%õHêVýl8¡ŠÀP E¶B%¿ƒQ E~*¡¨AsWB*¡ˆ„J(ê Ü•PÄ •PÔ¹+¡ˆ *¡l©Ÿ¡w#TBk TB •PDB%”-uµ@œ‡P E$TBQë⮄"fG¨„²w¥9øý·úñ‘X E|…X Eþ+¡ˆÆJ(nUÌJ(bÄJ(²+¡Àþôà òûX Eþ‚J(âÿÄJ(r±ŠÜ¬„¢ÆT¨„"+¡dËñOgäýöïdÈþ°Šü=VB‘›Š•P@k"Ó?¨ž•Päø±Šh¬„"+¡Èùc%õ¿B%ÙŽ•Päzb%ÙŽ•Päúb%ø}͵©K*¡Èùc%ý€S +¡ˆÆJ(¢±ŠŒ'X Eú*VBQ#:TBV Eº VB‘ë•PDc%á±JN’¼Š\¬„’ñ½WBɉ WB‘û•PtX€J(ÀëKÖûøÌP*烕Päú`%é–X E»)TB•Pô*¨„"/a%é¦X Å_FÊJ(ÒM±ŠlÇJ(Ú­¡JîÏÝŒ¢± ü½: õ$¨„Úf¸ÖB%ù=¬„"Û±ŠlÇJ({fË•PöþÍp§ÃTB^MprüX eŸÙÛdÄJ(r?°ŠÜÏ *¡ˆÆJ(9uç˜ôG¬„’ýÕÝ\Ùܺ•ó3÷iåLÝMY9Ýv–>&  hŸï6ª„"•Pä÷°JÎý=kºZ{¿©JÎÇÝJ´¯ù†r>êöžÏÜË#ý+¡dÿr—NާnÉÉñÚ3™›q›LŽ÷î‰ÉþïÑX %ç×nJÉù²;Pr~ìv9¬„"•Pr~ë~9~¬„ºÄø•P2#ä Ù?VBÉ|†û&r¼q“Än?æˆÈ|ÛdX %ó!nIØÇoþƒßÜló}wìëg€Ìoøj?ìÏæ»û÷l¾ã¹/Úçøíkër~X %³^¾j¾ÛŸ-‘KÃJ(»¿Ù²µlÇJ(²+¡ìþf«ÏòûX e_?[ÞýÑ–3âk½{|°…Ý=>Ø*îî/¶Øšù_YÍ|‹/£îûok¦»}Ú©\_¬„Úæ»Ùß­Jæë¼ÊnÏV %ó9^ ¥Xš&+¡äóÔ+¡dÎÒ+¡ìþf•P@·hïX EúVBý¬„òÇ’üߤŠÎ}þZ•Pt³+¡È‡+¡l@Ë”¸DàìX Å%6È$àcö£ À1|›J(2 üs+¡ÈâO«„âë9¹^ëZÏçA¸´Îa€®´lÀ$¶~’€K4’Ü€Il­#—h¬·“غD.Ðac&Ð5„ ˜ÀVp‰€@0 €§ð“Mˆ€˜FÄíqMˆæÎ1ˆ¥Ã7âšfŽDL#bIë¸&DóЀ˜FÄRËqMˆ>=1Mˆ~9ÓˆØw67âšý*% ¦±o8nÄ5!úÅC@L#bß܈kBš¥01ˆ}{n#® Ñ@L#bß5ÛˆkBô+`€˜&D¿™ˆi@ü S‰„&D'˜€˜Fľ^´ׄè S€˜FľŒ³ׄèË8€˜Fľº²ׄhŠ ÓˆØ=6âšýþ ¦±¯ElÄ5!jPÄ4!jçÄ4"öÞþF\2,}žˆiDìð¸&Dß Ä4"ö¾ñF\¢G@L#bÙˆ¸&¤ñÕu ˆç öãÖ5!7]ÝЈØÌ~#® ¹èê†&dÒÕ ˆM©7âšAW74"6ñ݈kBt. ˆiDlzºׄt˰'b›DnÄ5!:/Ä4"6ÕÛˆkBtöˆiB&ÅE¡±9ÖF\¢Ó&@L#b3¡¸&dPäó/B¨hšŒw4 ȳöÁíÇ|];v*oc›Yù`@7M­Œ£Ð€|µO¾…û—ZûÞöñgXûx?jí‹“ +_\ÓвŠÖ¾”líû:¿×7°öå½qÇói@À­|ÔdÅOš"nåKÄ5í…§™q)?ŸgñùD3ˆÏgšyÃÊXû®ñ“ξã+|}ŸïæØúÆ…®>‰C·©oèéë7ZúzGG_hè3#[øùzC;_ïèæëÍ|} —OVF¶•Oæ†ÛÉ'.‚mäë}|} ¯Otñ™[ L|ãDŸ¸â¶…otðÉ*Ì6ðŠþ½Qо7tïIømÞ½{bÛÖ=)%¿{ãFãžÝ£ðí­ÿ¶=ùþôví‰Yo›öìS+áÙçöì‰u;öFCÃÞèè×K¥skY/Ún½YЬ'{õ,³V½T:7ï~õæ‰>=qLl›žìy»ôr/:KÏ¿SÞRhÑ›9ôÖ_¢AoòçÙ'—Óž7¹óÖV4çí]©7oždÍ“´€3Oò‘`Ì“×0À—·®7ÚòF'WÞ’hÊ‹Fáž¼¸ŸnÉ“[޼ÑÉ·núñ¶Ôå‰1È7&™ñÆ$/Þ’hÅ›9ñä‹Ý`Ä[[ч'/d€ OÞòÞ<É„·W×$Äy<1î€O|:`ÀË‹c‰ÝQÈ~·$ºïä!0ßÉ[Fན"à`½ëœwR’Œw ‚ïNÜO`»ÛRÛLŸdºë©4ÜõF~;}Àn··ªÛN^˜³]?Èk×O²Úõ“œvâ7£]ä³klv­‘Ë®u2ÙµA;YÉ‹ÕÀa'‘:ì¶TÝ–j¯k¹ëÚEæºVÉ[× YëZ%g];ÈXwOòÕÝlu÷$WÝ–:l݃ÈK·žûh¥»orÒ­­h¤»/òÑ­óE];ÉE‡r ÏyaÍC·¥ZèZ%]«d k…üsySÌ>×NrÏåý5óÜ–jâÉß5ë\+äœkçÌŽ’¾¹lÏf›k“\sý Ó\ë䙋æí–¹h¢î˜“8 sq‚î—‹+év¹{[îîd–»yåîFV¹»“Sînd”{øäîF6¹û"—Ü}“Inïª{F‹\´XwÈ݃ rÑÜ'Su°Çµ“Üqí sÜÚŠÞ¸ «5®rÆEgwc\´ ÷ÅÉp¶¸7Ü×™â¢m¸'e×±qí&C\kä‡kìpK¢® 2õA^¸6È c¬;á6#܈Ð× Ùàd¨\Äæn‚'yàâiå8LÀѳàdüo½»ý-¦î~“ܘß"hvï[D¿n}‹×o}»ñmò½‰; lo€»ëmI4½Fž·q“åMòïàxË]™ámÁèw‹@Îínn¹ÛÍòôiv›•¼nó"«[Õît‹ÀÕn|ºÏÍòŸis‹àÙ]nR{Ln³’ÇM¦Ö`q‹(×nó ƒ[ž‚ùÛÆ${ÛèänÌm–jIo[Ì(ÜÚ69Û"Bvc[L“Ü×6n²µ‹\mVS[•yÚæA–¶u5ÐÑ6Úäa~6y ìló&7›L9ÐÍ6'™ÙÎã 3Ûiµ4³Év4³öxI3[ìνls’• þZ;ŽLxÐÊZ§~:!+›üZÙd;ZÙd;ZÙöþÌÊ&­l{ÿfeV6à-~deÛçcV¶9ÈÉ6Ùf%Û¬dc‹iž»Øäë“`b‹~è¶hñž_‰;Øbë¶˜Šºm²¯m©sÂ-Õ¼6Oò®IÖµ˜{RlKÍ×£-gªn[Ë«a®µ˜¹¹i-F%ÏëKËZtw¬Åh膵gݯ9 ÏÿÇínµèÒnV“€W-&ŸnU‹ù¤¯ÄÒjc’Om ²©Å$Ð]jâ“ږŇl´¨E ÄjcA-fõ¾îc‡ÛÓ²m˜;-æÏnN“4 xÓ"#àëyÌæL‹AÊi1v_Z^+³¥Å$ß×1ö®tN¸HO-Fa·¤Å°ëŽ4 »Á‰÷£e»2;ڼȖÇÌhb‰/Ú¬dEËŽcN´y-¯•ùв[ÙúI¤Ü…–½ÛLhٻ̓–ß,h‘kðu–È5¸-ï¯ùϲՙýlLrŸm©sÂì°æ=‹<”[ϲ‰šó,²n<ƒ|gñ´sÛY¤ÞÜu–ÇLg[6oÀh9[GŽŽ3ù`òÃpöyîù›ØÍdUæ¯å6“Á6›­3d¯YnV˜)ØÜ+ÍLÁf'b³{³ýZl†ßþ6³5¢ü¹³Õ+þ4‡™­,ä·TL‚¿,¶»ªÛÙ¥1Rlvµ7Û*Plvµ7{>3¶‡À’x ¸Àjü&à+õ™€K¬œ_.ësé2€˜g$—XuŽ\`éOÀ%“®sÈ xL•÷Ñe}®®&à{ú%àòp 6žÝ–êW­.?l·ÀŸhC?y{~òÌ¿öƒ1ßà{1ßàs1?µ¤ìç+È~!sù¸qA9寿r½òÛ®'Ç=‰åe?šÏî:n÷Õc¼í²¸ Äßw$|í8ˆXZÆ}ÐSįá§ßþésÄ·ú ñíð$ù×/ÿ¦ú endstream endobj 445 0 obj << /Alternate /DeviceRGB /N 3 /Length 2596 /Filter /FlateDecode >> stream xœ–wTSهϽ7½P’Š”ÐkhRH ½H‘.*1 JÀ"6DTpDQ‘¦2(à€£C‘±"Š…Q±ëDÔqp–Id­ß¼yïÍ›ß÷~kŸ½ÏÝgï}ÖºüƒÂLX € ¡Xáçň‹g` ðlàp³³BøF™|ØŒl™ø½º ùû*Ó?ŒÁÿŸ”¹Y"1P˜ŒçòøÙ\É8=Wœ%·Oɘ¶4MÎ0JÎ"Y‚2V“sò,[|ö™e9ó2„<ËsÎâeðäÜ'ã9¾Œ‘`çø¹2¾&cƒtI†@Æoä±|N6(’Ü.æsSdl-c’(2‚-ãyàHÉ_ðÒ/XÌÏËÅÎÌZ.$§ˆ&\S†“‹áÏÏMç‹ÅÌ07#â1Ø™YárfÏüYym²";Ø8980m-m¾(Ô]ü›’÷v–^„îDøÃöW~™ °¦eµÙú‡mi]ëP»ý‡Í`/в¾u}qº|^RÄâ,g+«ÜÜ\KŸk)/èïúŸC_|ÏR¾Ýïåaxó“8’t1C^7nfz¦DÄÈÎâpù 柇øþuü$¾ˆ/”ED˦L L–µ[Ȉ™B†@øŸšøÃþ¤Ù¹–‰ÚøЖX¥!@~(* {d+Ðï} ÆGù͋љ˜ûÏ‚þ}W¸LþÈ$ŽcGD2¸QÎìšüZ4 E@ê@èÀ¶À¸àA(ˆq`1à‚D €µ ”‚­`'¨u 4ƒ6ptcà48.Ë`ÜR0ž€)ð Ì@„…ÈR‡t CȲ…XäCP”%CBH@ë R¨ª†ê¡fè[è(tº C· Qhúz#0 ¦ÁZ°l³`O8Ž„ÁÉð28.‚·À•p|î„O×àX ?§€:¢‹0ÂFB‘x$ !«¤i@Ú¤¹ŠH‘§È[EE1PL” Ê…⢖¡V¡6£ªQP¨>ÔUÔ(j õMFk¢ÍÑÎèt,:‹.FW ›Ðè³èô8úƒ¡cŒ1ŽL&³³³ÓŽ9…ÆŒa¦±X¬:ÖëŠ År°bl1¶ {{{;Ž}ƒ#âtp¶8_\¡8áú"ãEy‹.,ÖXœ¾øøÅ%œ%Gщ1‰-‰ï9¡œÎôÒ€¥µK§¸lî.îžoo’ïÊ/çO$¹&•'=JvMÞž<™âžR‘òTÀT ž§ú§Ö¥¾N MÛŸö)=&½=—‘˜qTH¦ û2µ3ó2‡³Ì³Š³¤Ëœ—í\6% 5eCÙ‹²»Å4ÙÏÔ€ÄD²^2šã–S“ó&7:÷Hžrž0o`¹ÙòMË'ò}ó¿^ZÁ]Ñ[ [°¶`t¥çÊúUЪ¥«zWë¯.Z=¾Æo͵„µik(´.,/|¹.f]O‘VÑš¢±õ~ë[‹ŠEÅ76¸l¨ÛˆÚ(Ø8¸iMKx%K­K+Jßoæn¾ø•ÍW•_}Ú’´e°Ì¡lÏVÌVáÖëÛÜ·(W.Ï/Û²½scGÉŽ—;—ì¼PaWQ·‹°K²KZ\Ù]ePµµê}uJõHWM{­fí¦Ú×»y»¯ìñØÓV§UWZ÷n¯`ïÍz¿úΣ†Š}˜}9û6F7öÍúº¹I£©´éÃ~á~éˆ}ÍŽÍÍ-š-e­p«¤uò`ÂÁËßxÓÝÆl«o§·—‡$‡›øíõÃA‡{°Ž´}gø]mµ£¤ê\Þ9Õ•Ò%íŽë>x´·Ç¥§ã{Ëï÷Ó=Vs\åx٠‰¢ŸN柜>•uêééäÓc½Kz=s­/¼oðlÐÙóç|Ïé÷ì?yÞõü± ÎŽ^d]ìºäp©sÀ~ ãû:;‡‡º/;]îž7|âŠû•ÓW½¯ž»píÒÈü‘áëQ×oÞH¸!½É»ùèVú­ç·snÏÜYs}·äžÒ½Šûš÷~4ý±]ê =>ê=:ð`Áƒ;cܱ'?eÿô~¼è!ùaÅ„ÎDó#ÛGÇ&}'/?^øxüIÖ“™§Å?+ÿ\ûÌäÙw¿xü20;5þ\ôüÓ¯›_¨¿ØÿÒîeïtØôýW¯f^—¼Qsà-ëmÿ»˜w3¹ï±ï+?˜~èùôñî§ŒOŸ~÷„óû endstream endobj 449 0 obj << /Length 725 /Filter /FlateDecode >> stream xÚuTYÓ0~﯈úäH×Gœi‘¸-¢ÀCÔ¦M¤\$)»ý÷Ìxœ¶)EUêñßÜÞÞÞû…pçëõbõ`bOJž£¼õΓ*ä‰P^,".Uê­·Þ¦¹ö)•aë²Îý@dž½z.¢7Ìž}ÉløxéwÛg¨þäÜdUÛìý@¥)ºÔð2 Õ“Ç¡Õl0Ž t8¹ÇãrÂ;Íâ»õB!”ûvKɤyá¡Ðl]àøÛ÷YWt(ZÛË®m““æÆí2磋ê R•Ãs¡[18Ìgpr‚»1\óí$²Ó®Ãöy“÷Ù˜oéºk{’:bŒí$88ÉhWyCÞ—ù€òw ¦Þ™Nç”Øß@Ãû|9‡9œ[‘voL 0€ÝÂjÙ=1 :hHD‘Y=FÀsˆWš™cئØjcjêÌ‹M¼Ò!mË¡»¨e¾…Ô ôâq$yé‚…ÚÝØ ¬$.¤Ã¦=}*Ê Þ ’eôˆ‚`°Q«ü#Í:,¾3¶â»‰èïÜrëË6šË(‚`è9oÜ6ð Lª0 7àWB^¢m ƒ%Ÿ­îÿÎ+oI¹Ö°ƒR„fš0Utý ü­µ¨ endstream endobj 446 0 obj << /Type /XObject /Subtype /Form /FormType 1 /PTEX.FileName (/tmp/RtmpBlhU1W/Rbuild4444671013800/timeSeries/vignettes/timeSeriesPlot-chicOneColMultiplePlot.pdf) /PTEX.PageNumber 1 /PTEX.InfoDict 452 0 R /BBox [0 0 432 432] /Resources << /ProcSet [ /PDF /Text ] /Font << /F2 453 0 R>> /ExtGState << >>/ColorSpace << /sRGB 454 0 R >>>> /Length 10410 /Filter /FlateDecode >> stream xœ½}Íï-9RåþýwÉ,ê’vúsÛh@ ª$£Yñ!ÔêB‚^ðï#Î g„ï«Wý˜Ò,(ú<ŸÌt:#ÂvÇý¥×ß¼Òë÷¯ÿøò÷¯ÿx]ïëzå^ß÷ý*w”®ùNõõŸÿòúÇ׿+©ŒwéÆºÛý®ý`ýùÿá¯~÷ú‹¿Èý®—ÿïñ·ë_{}ý×—ÿý^×럿¤×߬ÿûý—¤ÿ__ZzÏòºKÖÏ_Z~×ñºïù¾Êë_Öãr^°½3áh¯;¯.%ë"!'¹fÁö.BÎï1ö÷%äöî7`¿\=oÇû^Ͻ¯w« öë›^{߀óR¸®Yp=¡h¯f˜ßih¯Æ8²v£(,ï.äòNÒ 8!ßï¦wnïKÈë_`[Ï-×ûÖnô÷-Ïï¡pâõÇ;%@yý’Þ—Üy\ïºÈ%ãõGz§¢p=@¡ κsWxëë¯>O½¶èë—ò.p½¶Ë Žúnë¹EGpÁ¦ƒ³¾QÕÖ¡¯¿ZË (¯/¸à|ÉþžòÜy½Ó¥}. °ã¹:3ãõëû’œ·¾~Yƒ2Õ6Ö§Òk‹ÚF™Yupêúrz«®¯ï ¼~Íø¾s¨m´KFpÁ©¶±ŒOÍlAœº¾¯\›®¤ïÿà¬P‡ü3°˜ÇÂí·ÚÇÂ:^é*:Buu íMá¹_ÓQx®ïj#‹?ºâ¡F"Ö?ˆe˜Vïo±°´œGFbu_ß5¥¤C±øé"Ciz[ÁY-eñûT|ëX=÷«:«?¹ëx4»¾©µÔõš“XÌeµúCJö2í~ãqÉ /œ/ŒÇ…á<òcA)«uìïžr†Ñ,SÒçç¢ãQ¼D°ŒÇÂ3)®j7Oô÷Âý›ŽW­°ö´ˆŒG-ðö$A¡(“X§Á,Ó­/"íIû¿\]Ç«Á!ÒŠ:S>«àöÓáäiŵŸÎ÷[xúç¯h ã•à«i9ŽG‡³& —Ž_»ˆÕ‹l¼V`ºáëÛÒú2^ëÿU½a½8>eGYØCÙaö]g×k_h/:^kX5ूH»pÃýjöZkÌ`[iß Ëx­ažz¿Êp{‹Yf¼eDM•·ð{TDÜõ»Þ}è‚yöUsŸvÝefø¾Q7 ŽOEØ]f‹÷]XÆka ©"ðŠéø6DÞåFxŸ†Ð+n—‰Å~–›êô‘Ö¬&ãµ®×™¢¯¸a&–ñXn”—›i¤K x… Œ×Â2^F~0bðÂIŸ×„¯*¯ ,ö³Â”NiÍo2^+Œ]à#/ ÿìˆó ¢×k$γÙý5ç¯S'^ã%í·~®ÁØaÆyEpøg×pœ×ä0 ¯Œñ^3Ý-Ï_n¤ßgh<\:ñ~âxÇîzÇyM‰°×…×xåõ˜×k<Îë6ˆ/Cg–‰Nû34çu[>Oãq^4ŒÇÐÀ—e6ÓûÏ„ñXf3ˆu<6F<Þã==îø|Äãõ½t"LÈ{MC°¿‰x¼¿×D<¾–ÿ¢}À~øýó…xì±ÚŸŸ/Äã…uÙ•/Äã5m]†Õ~&VùB<6ûÏâñšFÔ«?u¬Ûò…x¼üáÆõjøÛò…xlö,¸£¿:¿å„xlï›S‚ýp|«ýÜXƒä¤ñX¾ÏÀõösÁÓ~ÊT\a?ãŸÅ‡¶7`ÇŸXìgá÷Ÿ°ŸÎñÉìgõ/‹ýìûå Úø†ýо«ýÜðל5oÿÉ+'øçÔ÷ɈÇë3êT°ŽÇ°þ ¯ÏÞnbµŸÍŸ°ŸÌþ܈Ç/ò£ýÜÙìGí?ßwˆ?YÖ—ÚË­÷¿·ý܈Çfβp¹žÕ®XÛ>W,e•^ˆ%þØ^"· »‡eÿ…X·•öÓ°>¶íDnX¯a®¸ë㢠`]6~Ÿ5ÿqýÁBÖÝëãÅ×ø,X׃\oæ6ÃzP°®âuî\3ÞçÎõñÍ÷é\s{ ¸»õv^󟮹^ÎëãõšàéÖÿ¹#Ë^Wß·#/Œþ.,㵆ßw Ûú\&.õñ=âñ~Ÿx¼Ìã³&*ÝO$¬óàúøÂzO0×Çí\s³$KìG–i™XígZmC(”ö›Éì'7bµŸÍÏ/ëßäú¸rþ˜X¯ï{›XËgÖþOÇ‚ïL¬ãÕíþXK@Çsb}¼–%ú¾:1.~çzEaÓv]/J ”ñZŸE¯×‰0k»®ÏËx¬Ïzõñs?Ç‚g"îÃ_õñZÔK1â±Çb?Ë .ôñXÌFÛ²«½ňÇbf†e<öýâqçü Lìgß?!e±ø:¾²ðlxܹ~,ö³ß'!÷ {–…³îGŸŸ×÷Ôï-Xý)ËgŒxlû×;#¯° ó›à~=þªüøƒäxÔ~ æÁÜŸë~æÎÝöç÷Ccù—Î÷˜Óq/#¯ë5I^èÂóÕ¾·öìÿe£R‡ÇLU8<Ú³—À]/7>wûQ än¿«·KýSçM•Ç¿t£7žx*Z÷çŒ×ˆË“£GÕxÌx/ÿc8ÿ—k<^ßSÇ·0ŸÁýµ4èxp¿,¸¹ý1‡¼OÍÄ2Ö7˜êxö·Ò óSG|•ijËáŽ÷ƒ}UÄcË É‡ÑñÊ|ßÊxÌ|†nÄóo4{û©ˆÇ–/}E¼Ôý¨îöäCôùý¹.ßİÕ~¸ßWÃÎnüã1ówkq<âñó<Äc‹çt´¿ïÎ|EC<¾;â±e¿w—ÇûYݪ†çpþÖ ãgg<δǎx¼ý­#¯ç#^õnã…ñëÓü þ8-"¸5âñŽùŒí/ñX¶ñúüxlù–{ ïï?ªåw`Ÿ£Y~§ãþˆÇÏáü}LËïèz히ÇÛž'ò–Ϲ'â±,ã´?ñØæÓ{³]_îÅùÛD>ãÁˆÇOäƒØÄãÒñ=~”Dþÿ[š>¨?)²Ô.†ÿ`X^3v oøo¯íÒµòú™²‚X¢˜øë+„[Ðd²XQÑ„ž€0± ŒŽ€‡n‚ëÃï~â0ýÓ¿2Lü§ÿòç™_éõÓ¿B<òŸô¿ŠWD½ºnÜÖ‡þéçןÍñ?^?ýþËÿüIoý'\+séšÔõÚù½×Ê>¨ëµéº¾÷bñî›§_¼{¬ðÿð/:‹¯\þãïþÚ.ÿn©­ ©ÿ¯·Lövýçë6úÃZ¶Hš‰Fú"6+”ﻆE ½P0ôè…’FÇó J}8½PÒäÐW J;õDä§/Fê…’f‡^½PžW K~ZÒôÐ+‘Ÿ–ë¡ß!?}ÝØ/P/>ìz¡ÜoB_D~ú2} z¡´Cß‚^(ýiÐÆcð} JÚú&ôBÁ°7è…‚›a¯A‚^(²î½Pp…^§W0ú ½P®'ÿÆx™¾ ½Pžû‚^(úôBíôG »rýmxâùг Š = z¡È°gè…Òÿ‡^˜EÞ§ùÁ|¾Šûkë…ú¾àWØÏ > ½PÚ‡é‡/âñØxb<ö7Ô ¥°Wè…ò|)©Šl‚ø½PdØ3õÂÄ)ÍôBɯãþÐ “éßÔ “Å;ê…†^(ÏC n}•z¡ôzôBÑŸ€¡¦N=ŒzaêØ›^(úôCè…‰ù=Ó ×ý‹× ·½P/¼,> ÆãIýyìx ýz¡Ä Ø;ôBùg¯JüE<™ŒÇ™þ3w<Þ¸#^AÏ…^(þ™^(ñã7;ãïd<îü^Ð å~Ð'.Æc‡5s?N½Pã;ôCÄã5LðÎ_—éˆÇ²íÖü8ôB~Æ­êgD;â±Lìšÿ…^¨Ëè—s9WcÔ eØ‘‡^¸çcê…:~×kë…:_AÏ»1œo©ÊõxôByŸ¸bþ*¦G6Œ—å«ÓŽÇÐ7¨^<@½PÆ7Cïc<¾˜‡^¨íÚ_ê…Ø¼l½Pç¯ûõè…ãõB]8½P¾_5}ÐæóòÚz¡¼?ú ½P¯7ýp¢¿ÐË ª=C¯K6?A»oÓÿ™_¡^(Ÿz0ôBoЧ鋌Ç3ê)7ã±éoÐ u¾‡x|ñ<õBÁ¥½¶^(|èÐ u>Ö÷ƒ^(ãQv<†Þ½P̈×#K<ʯ­Š™@¿…^(íÐ+ j¼4}PãqåxWÆãÂï_y‡z¡`è)•ñØÆ·2oýñxòý*ãñ Þ ½PÚñ½ fIë;½P0ú_'â õB‰§°è…rè•-ïñ€Èx.œ/©*¯­Êý‘?‡^(ú ô Ùfë…‚ °Æcí¯öz¡Ž/ô9Äã›ûê…jOh¿Í~ wA/”ï÷^(ažíˆÇÒŽû!/|9½P¾Oqz¡†i\?·ý@ŸC<Ù+¿¶^(aúôBÏìôB »åµõB ³È·C/”0Û ?"Ë÷½_[/T>Ú ×Ô e|1^Ð ¦ý#¯m ôQè…æ «ýpýeùÄ#{,ö³Âzvz¡`è±Ð e€ž½P¦]Q/”iC×CÔ Ã^ ê4àôÂlç¨Jئއx\™ £^(í¼â±`èšÏ0Ÿ^(íЋ *ú#âqãy:ê…ÙÎOP/”iþ ½PÚaÐ ³ ^¨÷ÓþB/”iú6ôB™6*îx,|Äã…aŸÐ eš¸®×Ö skô_è…Ò=z¡\û…^øð¡ ?#7®ç¨fÉ2BOD<^Óìz¡´ã<ôB¹?â ôByþ@;ò­›^‡x¼øÔûEotz¡ØÓíôB±ïž^[/”vøôBñ|è…yëUÐ Å~áÐ e™A}ùŒÊù˜z¡.Kôzè…Ò?è½ã‰ÇÐç·¾ˆ|FÔ{¡Êõè…røôB±Ä3è…ºLùŒfz,ô¼õUè…êN/”ûã}¡J;Æz¡à=ñ¸š=@/ÔçC?DéõÈM‰ú¤×#7%ê“^Ü”¨O~ÕŸÌeÙ¡Èø†GÑÍ¿áRd|ç,THÔTæ#j‚eÞ¡^q&ŒåGyW­8,y ñÑ:AíQFו*Šz{½¶òØMhð(*¹«SôpÁúøTuo#AÑq×ì@s<òMÉqDű›à ÁQ–y ëBwur8¹QúlÕˆI¿‹5™b‹Zã`),¥F‚ÒˆõÞÅ @V_“lþxm™qšJ•QVû ‘qkªHy¸>Ç2¤æƹC]°ïz,è‹Óä1È‹b"^^”ÏŽ'C^”•œ¼(ß_ò¢|.”A^”tYy¢¤÷Ó”“¥g)/ªm@îÃŽËä È‹bhø>Õ´Ln9䚆‘ÎU÷ÇAŽËäWÈ‹éª8JyÑa¤³¯›å›¥=9yQÆÞy1Y9åEqªÛ•# åd…¹ò¢`Œ/äŧÿ¥å8Õq5Ê7¥òäEùž?È‹Ò|~È‹‚1Þåz€¼¨ï£v yQž'†¼(ß;¹rDÈÙe¼½¼è0Nx<G<.+o„¼(ßåÊT˜-/>öyñ±7È‹/`ß…Oy'=·w”%d X@^t‡=$]þá´G¶òhèR `_ã8äEÁÙ°úÓ¤¼ y1­…é´òC±Ÿdå»Sb:ò¢`ÞÇ=Òmò&äEÄËG^ÜãAyÑÊ™L^”òS+OÔñ¸ÙÊ‹û{S^ôXí‡òåE‰ðʋۿ)/¦lrf7û¡< yñ²ô=åEu<.Ì@&/^–¦¼hñÉäÅÕß ùò¢É±&/Š¿›Ü8Ëã¿”wuìtòp/ÅÅõÎÐ: -ŠTˆÒAb«d²8¸Î °8¹8£®(«W†8º©‚†‡qAT´yˆšâÕyÖ×Vah[P Ô‡•AN”jWí3d’‘­xP£ïàÙj‰#…ÒÃÎL•Dye!–_[G\½Âà@F\ÞC•SîhxfCv¯ !Ê$m’¡¼¾)ÆP·^ ýn²åC›w©>P£íàY!j‡ÏsÕ*Ä£œrhk ‡ÓꪠN°£l8wU¢f©§ÍA4”&4ÁÊ\s’á®P…b¸ºA‚á¶:è…ÃäRÈ…[ ‡Z8ùã ×ûB[V8xTžR¡-\©l¡P8¯.ÛpáÊ · •p{ DB;ãFÐÖF”EQﯭî‚]„ò ª“_!VŸP´õÅA‰©¸ë×M¶õ+”P(ƒÈnapðœ uA¹2Ö¯·©~X¿6ŠH‡Õ@CÜ®Ipt*fPE—¯-nw†(^yPã§lTX¨|ÒÕ*ß^ÖRå“]kuU:^àk`•ç5¨~8µœM†Ê§üúÚ*Ÿì‘“©zb?¹[Õ FWÁTÙpjÙªæ©òÉnvšª'ãQxʉ*Ÿ`¨®Pù£Pùd7 U ò’ì³Sùdw  *_²S Tù’U¡SåÓûC¥Ã2UN-8•/aɰU>¹>9•OÚ©âáÔ²ì¦ÁÇ2õ6ÿ€Ê'»sŒT>ÙO§ò Fÿ¡ò FD‚Ê'×gSõd<îÂñ†Ê'ã•O0T`¨|2þ°¨|r¨ºPù\;NÁÝü•ª|r?„&äÑäzD=¨|énV凴ÁæCå“û%§òÉxo¬ã5ù<¨|šmFÚ@Tz¨ˆ8'«%«û©Õª‘V¨L!RåŒñBÖ'-3ãýq ®šê •OvïÕT?¯}=T¾´«4¡òÉîb0f „è&”î7Ð’÷ƒ‘à|Å 'dï-=áöþb0ú„~t²xŸ1è Õ{ÁHèW Žƒ£¡ŽƒÃžp8Ž'<„~:NÁq#Á;¡'Ìà8„Ž€C›@ Þq=!Ç!ô„‡0¼ãz‡ÐJpÂHðŽCè 58a$xÇ!ô„‡ÐzpÂHðŽCè #8¡'Ìà8„‘à‡ÐpÀd=!Ç!Œï8„žƒãF‚wBO¸ƒãzB ŽC Þq=¡Ç!ô„‡0¼ãzBŽCè #8a$xÇ!ô„‡ÐpDg#Á;¡'¤à8„‘à§œÛtzçF点UçA!O8§„ݺAOûuƒ‘à§œ[vFz禧“¹mçq%O8§„»ÁHðŽSÎÍ{*a÷nÐjØ¿Œï8õÜÂóÐÔC87ñ@Oh¢†<ÀH=ÆÑI­ ~€ž üCŒ„>¡œr6â!:B×_ŽßÂHè)zìdOÁq#Á;¡'äà8„žpÇ!Œï8„žP‚ãzB ŽC Þq=¡Ç!ô„‡0¼ãzÂŽCè 38a$xÇ!t9ñè:I Þq=!Ç!ô„‡0¼ãz‡ÐJpÂHðŽCè 58¡'´à8„‘à‡ÐzpÂHðŽCè #8¡'Ìà8„‘à‡ÐäD£ë$¡'¤à8„‘à‡ÐrpBO¸ƒãF‚wBO(Áq=¡Ç!Œï8„žÐ‚ãF‚wBOèÁq=aÇ!Œï8„ž0ƒã>= ûtÒ`$8Ç1è É;ŽAOÈÞq F‚sƒžp{Ç1 sÂ<:Y¼ãô„êÇ`$ÌóèdóŽcкwƒ‘0î@G'½(ÿ2è Ó;ŽÁHè5¢ã虹NzBòŽc0œãô„ìÇ`$8Ç1è ·wƒžP¼ãŒ„Ña¬Þq zB ŽC Þq=¡Ç!ô„‡0¼ãz ŽC Þq!_Áq=!Ç!Œï8„žƒãz‡0¼ãzB ŽCè 58a$xÇ!ô„‡ÐzpÂHðŽCè #8a$xÇ!ô„‡Ðî+8a$xÇ!ô„‡ÐrpÂHðŽCè wpBO(Áq#Á;¡'„ä€ÁHðŽsŸÉ-Eð<“Ràç>“ü O8çɃž’#Á;Î}&¤(Â;N9“Z&á:YÎä€NÔ@8§„ä€AOɃ‘ড়ɩÇðŽSÎä€lÔ@8§„ä€AOɃ‘à§œÉ-üð<“ò'ÞqÊ™Ðm™p8N ɃŽPCrÀ`$xÇ©gr ×0 Þqê™ПŽq¬gr@êX¼ãÔ39 ….)Ç©!9`ÐBrÀ`$xÇ©gr@ f¼ãÔ39 ¿Àä;y&ä_¼ãÔ39 E8Þqê™вßÉ39 ¿RÓápœ’#Á;N;“òÃMÞqÚ™à/G=„39 ÿâ§É-ò<“ZDä;y&ä_¼ã´39 …F¾“gr@~_É;N;“úƒP)Çi!9`0¼ã´39 N¾“gr@„ÊwòLä’¡‡ä€AOɃ‘à§ŸÉ)µòŽÓÏä€_¹Nö39 åX5Çé!9`ÐBrÀ`$xÇégr@‹¾|'Ï䀖Õ@8§‡ä€AOɃ‘à§ŸÉù#©ÞqúGr`ÄäÀøHŒ˜É“ã#90br`|$FLŒäÀˆÉñ‘190>’#&ÆGr`ÄäÀøHŒ˜É“ã#90br`|$FLŒäÀˆÉñ‘190>’#&ÆGr`ÆäÀüH̘˜É“ó#90cr`~$fLÌäÀŒÉù‘˜áä€ÁHp¨AO' zB89`0œjÐÂɃžNŒ„^¡ ' >IÁ`$¸“=!œ0 þüÿuž?TëNô„prÀ`$Œãèd89`ÐÂɃ‘ÐG ô£“áä€AOèþä€ÁHèW ô£“áä€ÁH˜#æÑÉprÀ #¤prÀ`$8Ç1è áä€AO' F‚sƒžNô„prÀ`$ôýèd¨0è ¡À`$xÇIg9 pÂYÀŠ\O8'…šƒžª F‚wœtÈßóŽ“ÏÒ–?„³8€uÁžp8Nõ=!TŒï8ù,¸s¨0 ÞqòY(Àêä‡p– °\ù!œÅ¬_ö„Ãqr¨0è ¡bÀ`$xÇÉgÑÀCÕ€AG¸CÝ€ÁHðŽsŸ¥,«~gñë¬ÂY>ÀÂkO8ç#Á;Î}°–û!œe,î~g!«½=ápœ;Ôô„PM`0¼ãÜgAüqï8÷YRÀó‡p°æÜÇ)¡¬À`$xÇ)gYËÔÂYV Ü;a$xÇ)gY+ßÂYVÀRø‡p–°6ÞÇ)¡¬À '„²ƒ‘à§œeòWÿ¼ã”³¬€ùá,+`…¾'ŽSBYÁHðŽSβõoB=Ë ä¦zÇ©gYü‹wœz–ðwÂYVÀxgYIÀÇ©¡¬À '„²ƒ‘à§žeúÇj}'ϲþ|'ŽSCYAOe#Á;N=Ë øá,+Ð?iï:Ùβý›Ä9Çi¡¬À '„²ƒ‘à§eü!†‡p–ð—ÂYV ÿâ§ew e#Á;N;Ë øëá,+àÏA<„³¬€¿á ‡ã´PV`ÐBYÁHðŽÓβþÅ&ô³¬€?JñβþJ…'ŽÓCYÁHðŽÓϲ‚»‡²ƒž’#Á;N?“ü­Œ‡p&øãáLð×4<ápœ’=!$ F‚wœ~&ø‹á#90br`|$FLŒäÀˆÉñ‘190>’#&ÆGr`ÄäÀøHŒ˜É“ã#90br`|$FLŒäÀˆÉñ‘190>’#&ÆGr`„“#ÁeÕ zB89`Ðf89`0\:Ú '„“=!œ0 .mÐÂɃžNŒ„ž¡ ' Fœ0N†“=!œ0 3Â<:N|_ÿŘ£N8ì”ô²_-$þÕCQ¿ºûò¿A‚ߪyþÜu°¶+3ëoäëj?\釯ÿåó¯ßCNÖ®ïzÞ¤×M$ÍWžô_îÉ7ÿæT3¯‰HÿæÁ_åo`Àþ?üÉû2ñ/ |ãë{ÿÀÿüÏß?øÆ÷÷ þycÄ¿~Füi!üVÑ/ÿ¶ÚùÇ…Ðþ_â!}ãç…ÈøÆï ‘ñ²Ÿ\z~aèï¿ü_Õ@ endstream endobj 326 0 obj << /Type /ObjStm /N 100 /First 880 /Length 1518 /Filter /FlateDecode >> stream xÚåZMo7½ï¯à1>tù5ä… ±ë6‡6A i6«µ³­¬u¥Uþû¾YåK’-KP Ù³äãpf8oHqí)£¼eå\T6+Ç Ÿˆ)Ñí²¢ä”÷¤BB›Š-p>*ÎòÌ*%WxŸT6‚³ÊZ%ÞA`Á+ëD-Z­‹h!ty˜XYbh!tEµ”!È<Á(Ë Å 9 ˜”3¢‡„AOTÎi±ZBgpÇÇ…Ž24G`‚Ì…n2A3"†Yy"•!ˆïÍb¿OÒ"––D Ç"c@”^'jðaQ!ª’8#!Í¿ä!<H‘3Ò `m<¦#B‹’½óȯ1þ¸™ìa˜÷Eï×ÌÃþwdÌ#·Ãþ· ~€yK`ìÑÉ9×”ŒäVÌÛzÏc9ÔîżטèÈ™Ý] ‰þØÂ{SØå„VNˆ,ß嬘rÀ#"¾L…ýŽˆ‘×èÑӅý]ta:6ºÄ¸Ã†² ~€.K`ÐÅ ÿ-‡2s:$]ä¶„ö¢ §5ºp> stream xœ–wTSهϽ7½P’Š”ÐkhRH ½H‘.*1 JÀ"6DTpDQ‘¦2(à€£C‘±"Š…Q±ëDÔqp–Id­ß¼yïÍ›ß÷~kŸ½ÏÝgï}ÖºüƒÂLX € ¡Xáçň‹g` ðlàp³³BøF™|ØŒl™ø½º ùû*Ó?ŒÁÿŸ”¹Y"1P˜ŒçòøÙ\É8=Wœ%·Oɘ¶4MÎ0JÎ"Y‚2V“sò,[|ö™e9ó2„<ËsÎâeðäÜ'ã9¾Œ‘`çø¹2¾&cƒtI†@Æoä±|N6(’Ü.æsSdl-c’(2‚-ãyàHÉ_ðÒ/XÌÏËÅÎÌZ.$§ˆ&\S†“‹áÏÏMç‹ÅÌ07#â1Ø™YárfÏüYym²";Ø8980m-m¾(Ô]ü›’÷v–^„îDøÃöW~™ °¦eµÙú‡mi]ëP»ý‡Í`/в¾u}qº|^RÄâ,g+«ÜÜ\KŸk)/èïúŸC_|ÏR¾Ýïåaxó“8’t1C^7nfz¦DÄÈÎâpù 柇øþuü$¾ˆ/”ED˦L L–µ[Ȉ™B†@øŸšøÃþ¤Ù¹–‰ÚøЖX¥!@~(* {d+Ðï} ÆGù͋љ˜ûÏ‚þ}W¸LþÈ$ŽcGD2¸QÎìšüZ4 E@ê@èÀ¶À¸àA(ˆq`1à‚D €µ ”‚­`'¨u 4ƒ6ptcà48.Ë`ÜR0ž€)ð Ì@„…ÈR‡t CȲ…XäCP”%CBH@ë R¨ª†ê¡fè[è(tº C· Qhúz#0 ¦ÁZ°l³`O8Ž„ÁÉð28.‚·À•p|î„O×àX ?§€:¢‹0ÂFB‘x$ !«¤i@Ú¤¹ŠH‘§È[EE1PL” Ê…⢖¡V¡6£ªQP¨>ÔUÔ(j õMFk¢ÍÑÎèt,:‹.FW ›Ðè³èô8úƒ¡cŒ1ŽL&³³³ÓŽ9…ÆŒa¦±X¬:ÖëŠ År°bl1¶ {{{;Ž}ƒ#âtp¶8_\¡8áú"ãEy‹.,ÖXœ¾øøÅ%œ%Gщ1‰-‰ï9¡œÎôÒ€¥µK§¸lî.îžoo’ïÊ/çO$¹&•'=JvMÞž<™âžR‘òTÀT ž§ú§Ö¥¾N MÛŸö)=&½=—‘˜qTH¦ û2µ3ó2‡³Ì³Š³¤Ëœ—í\6% 5eCÙ‹²»Å4ÙÏÔ€ÄD²^2šã–S“ó&7:÷Hžrž0o`¹ÙòMË'ò}ó¿^ZÁ]Ñ[ [°¶`t¥çÊúUЪ¥«zWë¯.Z=¾Æo͵„µik(´.,/|¹.f]O‘VÑš¢±õ~ë[‹ŠEÅ76¸l¨ÛˆÚ(Ø8¸iMKx%K­K+Jßoæn¾ø•ÍW•_}Ú’´e°Ì¡lÏVÌVáÖëÛÜ·(W.Ï/Û²½scGÉŽ—;—ì¼PaWQ·‹°K²KZ\Ù]ePµµê}uJõHWM{­fí¦Ú×»y»¯ìñØÓV§UWZ÷n¯`ïÍz¿úΣ†Š}˜}9û6F7öÍúº¹I£©´éÃ~á~éˆ}ÍŽÍÍ-š-e­p«¤uò`ÂÁËßxÓÝÆl«o§·—‡$‡›øíõÃA‡{°Ž´}gø]mµ£¤ê\Þ9Õ•Ò%íŽë>x´·Ç¥§ã{Ëï÷Ó=Vs\åx٠‰¢ŸN柜>•uêééäÓc½Kz=s­/¼oðlÐÙóç|Ïé÷ì?yÞõü± ÎŽ^d]ìºäp©sÀ~ ãû:;‡‡º/;]îž7|âŠû•ÓW½¯ž»píÒÈü‘áëQ×oÞH¸!½É»ùèVú­ç·snÏÜYs}·äžÒ½Šûš÷~4ý±]ê =>ê=:ð`Áƒ;cܱ'?eÿô~¼è!ùaÅ„ÎDó#ÛGÇ&}'/?^øxüIÖ“™§Å?+ÿ\ûÌäÙw¿xü20;5þ\ôüÓ¯›_¨¿ØÿÒîeïtØôýW¯f^—¼Qsà-ëmÿ»˜w3¹ï±ï+?˜~èùôñî§ŒOŸ~÷„óû endstream endobj 461 0 obj << /Length 588 /Filter /FlateDecode >> stream xÚuSQÓ0 ~߯ˆö”JkÖ$mÚ" †@€@Tâã¡·e]¤¶)mÇØ¿'‰ÓÝ6Ý4uvì|¶cŽP…"ôayù¶˜-×IŠ(%y’0Tìe1É"†ÒHÊrTlÑ/ÌI„”²ª‘AÈÓ¿ù§ÐB+b\9ÇÇ€S¬Áð ëCÓÂ/‡zT]í±ßj=‚ö¹´€“CÆàwñi¹æ)ÊI.˜°õD(äá”C%ºÝȾUmeðyŠÇ½e–áSízwã/M¹‹ Œ)·¶Þ[ÝŒluÀRS¼;lÕcĹ¹cRå)MpéRퟔíM|ÝN‰Ú­}†-Ú …?cmwxŒ7Sw8ÇôÃh[9¨ª5UQL &ÀU7bJ²Øwãu&f(]Ù?FIÔìz}\m¬JÖcjî»*I\£Mr‹²2ž:¹š7óÜ*ÇÕ|³W›ùíL¢÷ÅŒ%BôL–0"xŽ6ÍìÏŒ¤TÀ…gÍ9&”7,?6T wzöÝü&_8… /b:Ú^±äœ˜G$eÞ·VÕÁ Zd˜Š€âWæ)fþ?÷nzbš%èûÒþ^X h7eØéCïm° Æ6È^IK.žŠ‹«@ aù4tµ‹~òiU]ƒËë%ÂT²•}9Ê­aÕ‚¼æ‘1bÞ–Äø«¥«$ÇjôÈdçréaPO°‡&˜ÑÉl¡»“wìý£ÚŽ{PÝJ]úª²{©ð;Ëb(HÃò;Ë0+bÝ“ˆ A²4±iá©_–ÞÒò?l6ù endstream endobj 458 0 obj << /Type /XObject /Subtype /Form /FormType 1 /PTEX.FileName (/tmp/RtmpBlhU1W/Rbuild4444671013800/timeSeries/vignettes/timeSeriesPlot-chicTwoColMultiplePlot.pdf) /PTEX.PageNumber 1 /PTEX.InfoDict 464 0 R /BBox [0 0 432 432] /Resources << /ProcSet [ /PDF /Text ] /Font << /F2 465 0 R>> /ExtGState << >>/ColorSpace << /sRGB 466 0 R >>>> /Length 19790 /Filter /FlateDecode >> stream xœµ}Ë®%Irܾ¿â,»¼ÌxG,‡„$p@¤¦-\”D¨¾@ê÷ææžéžy«nUOk1·Ç*,OæÉæôúí+½þéõ¯?üíë__ÇÛq¼òho¥¼rê@éXo©½þí½þÇëBªó­c¥:ÞÚ¸±þôßÿûû³×Ÿÿî|Þñò÷ç½ÿu´×ÿýá÷÷:^ÿó‡ôúíþß?ýäæõC+o«¾Jm`}þ¡Õ·|¼JYoG}}¬}Ãþ–Žü*y?RlouíÒôÖ&áþŒRòÛ\€û ¥Mîo£æ±á~òNØÊ&o½Ž·’äÚR÷×á,òTkηcÉS̓°4yŒ*pßäú–a¹¼õBØAn¸ŸÀµï[|ħúñ–pßù6®&0ÜÓ[9áö÷/Sjæ×ßpQÀ#®%Ÿ<î¯2ä™—\[Þ÷ûm„yɵc¶}ßýcp&ùš”Ö·¶¤´ÂÙìܰáaKo+–!Ï\;aç}åmôŽ_׉°ïŸ»îÿ,Âýe×À"׎·47\ú6öýwÍiÇ[–šo9X7¹eþ¾ŽMÞ/tÊG-Ô¾²ß‘T³ ó¾/~ \;ü³‡e“÷ÏÜ3aë«–Î,PÞÕØ‰ûTÓr~KÙ]»«Ésv<äþ1&á:ä™ êÕØ5hÊ3Ë œMNRÊÀÙ/¥LBêÄ"DàœµI~£Ü²ëµ5¨ìÈl‹0Oy±Rù7lU~~Ô¾!_{Í„dy±ÎzU•ÁÀ±ßw0pöGU¹/§%ÆÂ`àl¸!ÇAN“züé‡ÉÀA‹Ð8»½pž œ ¥e˜ 68»4UB|ßÉÀÙ¿àRÛ œ åMÎy_Î~f‰ÁyŽÄàdàìwÕñ&'Ç^ÎÔÀ9Ø^M œÍÖ†8߯†}H%?÷†«H°KMÚ:i76Ü/©äÆækNT(Àž+ÉR 7”FÕà’Fu7_Rc'g7ª µ8»­›™c ãbàì†Q^ûbàìÆ|à“gC©9‹s–2prâ/¸8iòm,NZü‚‹“c1pRÆí"pRáWX œ”ô™8[ž¤å_ œ}­4n‹³¯M™“šž 8[I¥Z œcèËaàœã`#95BÎÑÐ n8åå¨Ô²%-b9v]òBÍ”(Ûp¿ï¼I£îÏÈûöIÉ»QÍû-$|ýthUQ\ªâ:KìäýU%Ѐ‘ì_oÞÏ”ùãïÚñZ—`‰àÊûIåýûÊ+ÎË_/!”wJuÞ1”Ñðz ¢¼ÃRZ à þdƒ› £¼COï'q”w¥]qÇý³}¾DRÞ¯eNÅ}DŒX²w“Lj¦ù §ýX¥+F<í×0‡âÑ® ©"j¿öc(Fžb¿|B¢Ô"F¦rÞc¤*xU1r•ýرd e¬d ÀûAÎúž#+5­ICk0Ç–ØÊàåB %F—ÕcàÅš›ˆ_ç÷M`×ûMa¨‹Ÿ'!†ßgŠG—ú1ø}%ÈPêRœ§ðùþ“„Ê;±ÄYf{Bܦàr(Þ‘†ú¡ïGB-ï–m&Å¥ûÏ“` X¢ÍêwÒp+ U`‰7‹Ÿ¬ñ6fÄ ±œ˜.¦ÝÄå,å|ž‘ÈìúÑ‹bd2?K*³ëŸgã1¯¦"å"mòY6.ü=Xÿ3%Ë`hÖ!ê LÑ:ëO¦j]˜²…j6C·öç¤Â…jI>•+ÉcCºP­ªbÄÛ®F…×3ÞPªbÄÛÆ¬ÿ™ñf 6p |ÆÛ~Í™ŸÇxÛ¯©6ÅÒ/hÌßRa¼YÇXzI,]U@`é æ©0Þ,gžLéYßvŸ"•«ó,ýíñ#ÏÙ ³.°ô4SOèH¬+ÝFªƒŒš×Wv4ï®LÀ§•K7¡iý)”4Ký¡iuW ^OQÛIxšŠ¡j;MáïS4/…ºv}…móÙ>*›%Œ©PÚ,¯.LFÙ^Š[·ö¾H¼!ïÕï#ñv¦òÀè4XšœªÄÛ™ë/¦ï’„¥šØoЄ¸2Íf{RoèlÉóVÆzjI1âÍRëTo;åïQoç÷Á‹í’¸òýÔÂþCbž—ðCò~ÆR<É_,oìBhÇíõþ}Ø~V¹ñ™Ÿ§zv±ÚPŒxÛ˜ñVoû6™ŸÏx»øŒ·ëùoûûQ?*ã ýKÈ·Ýáê|~ÆÛÆ%+F¼õ¡ŸßoKVŒxÛ}~߯x–¯4Æú,òû4ƺI1âm—KN–ãm÷DŽ©ñv}ã Ý©¤x%=ãmçíPŒx»0ãm÷8Ø~4ÆÛ~žl刷]^å}ï×’ãmXþÖo×÷a¼íÎe5ŒxÛ¿ÞÿŒ7þÞMã-#̉%Þ´ß ,ñVUß:ãíŒ×®ñfñÐ5ÞªêMg¼Yïñf]äÔÏx£Þ÷3Þ3Þ<ÖÞ:Û£®ñÖ´~÷úë©k¼9<»ÇŒ7ëI—éÞOg¼Yß2aH#»ö cŒOê5ª‹¯]ñ‘í)†5ºk¯1®Q¯ñ”Äêjïûd{mñßo·¦ñfå4oÖùF¼Ywñ¶¿ÛÿÁxÛaÁüf0Þ¬× ŒxÛÍÛ×Áxó¸°ÏÏú5TßtX:òY¿ï`¼Ù`°tå­½Õúò¬?ƒñf=`éÍv:Ó`¼Ùh°ôçÏçg¼íÛ`¼YXºôçûc¼Ù(p ïc°WÞO«=Ç Çpí÷¡T|_¶Ç£½¶‘*`´×gý2”T8·1F>–‹·ÉxÛål?'ã­g­óoSã­k{5oçû›Œ·3g±Á1ÖçÉx;Û‡Éx;Û‡Éx;ãej¼ Õƒ©ñ¦#(iª¾Ùï?ogýœg¼1ß›o#ÞÎxŸŒ7cK“ñvÖçÉx³1š4o»üXŠ%ÞLO'ãÍÆFD²‹7 ‰L‡—4ÜK~ÔõyÐñ/ò{üƒîÜz±ÿ¼ï ]AÅŸ —ýâeØôþŸ×ïìЩ¯Ïj”é¨â¯w…}8Ž€,{·ŠFPè l&N‚¶ŽÀ›ž÷ ö³¾¦ø÷w^Ó¿ÿÃ~øÓÿš_éõóÿ¦Ñ1ñçOä¯àÝ¢˜Ø?ôÏŸ_?®ùÓëçúá¿ü,ý ×büi‹º\»¾÷Ú*í,®MÇñ½#º‹^œ¾x1ûXÂÿð_DÕ¤3ÄË÷ga—·ÙÓw¿Îÿõ5SŸvÿùšícuô¥.ÖAüºt3N‚šŽÀAú“ cöžÀŽýI`äp= :þê8< :Žè6Xf Å‘"zì(ÔgOÑ!Š“B(ìu_í…{ ;ŠE;ŽžÂ¾ÏEѾP Húî(Lç=…èEÑŒÔS˜T]M²<…yÀEѼ PDê…Òç)l­/Š¶ÞžÂö¢hƒë(·ÖÌÌ/7gÖ„~¹=3Æ—4c¸íŠ@d‰;‹~7OçVI8·_û(ÌsÿÊP&ü­ùÛ[ä‡;¿0çVÒE±=e˜Ù Uå…Î-r±Y^æÜJËÜ0—9·’Èii+R诊#i—B|Í1Õ1çV’ºË¹ÕÌœ[¤@b·Ò¹EÆÓÕÈÝ™+˜¬°ƒ\O¸Ã ¥"›tnQJST|rWo6'ùd¹–Î-J˹EfCUFБ˜ÊHŠä)—s‹Š.ý:·YFóôЖÁ<@I™4 L/snq­¤jtnñ’éÒ¹Å3¯Ë¹Å”~3[yæù2ç6± 5ç7’N([Iå.çV2A¹Vœ[ëäW¨/snñ´µul±;KçoCÞ$UÀÃB2­KñÒnÇåMÒ¹MiV«v_$¥ãeέ@16e˜<­Ä§¢s›`)ÜI)Bºª‘»’ãÓ¹MðtæËœ[‰õf8Km1:·ii†Îm¢¥eέÀò2ç7’±:À¬FnÖ‡Ì/snqߤÞlÉÅx¡s‹‡, 'ÈSÍXÎÒQK:·(•N([—s›´ýTçV>êrn q#8KÇ“éÜæC»ótn‹z³<¤–"I8t¬Î- L:O‡Î¡s›á]Îm>Ô£s‹Róuá:Ýtnó¡Õ›Î-nÔ2‘Ø×Ê1[¹VKa8…<:· K¥s‹’(£s‹ï+½:·(•Á^:·™–9·ù˜úd$.f/SÉéO™s ØîÀÁµtnóVùd:·€Òi¡9(Iç×’,ƒÒªFîìr#©9tn“Zµeò©ÒËœ[\[.ÞWš MÀRb›Cç6ÓÇÀA©Ä/Û £*½Ì¹”‰'tnŽ—9·òÕª=ô©U›ù¦–¶ê Ó5‘_À÷ë/snñTR éÜâ¾"mtn3L04“àE¯×éÜ& unau¨‘ ')ÙÈ„:·I[(KF/ÌØÁ½’b¤9§S¢Î-žÔœ\„œ@bÆOæL2€`˜™S‹‚ShN.B(ÙH«:·)™«A4¢s{LÃÔŸý59²KçmG.Xíüé9T‚¢s‹–“Î[´†É9·ׯûñûj8:r@çͧ¾? (Ü?Ñ—ˆæÈ[|":·#SY62BçVšgsr‘«ì&`'¶¹‡9±2Ò&t)I»=åïI‹@ÅÊ!JᥪɦJYšš9©s ñ¤3BçöTOunåý™³«ÒÄ÷•T›T"Õ¹Åõz?U' _unåý΄ ííjÉ‘QundÏ«µ˜Û™s‹0ÈŠKg9Ÿ—Á†rçÜŠ™S‹p;’9áo¢âιÅïÇ‘7:·¢Åte¤ ß_Ki“ëÍÉ|^u.¥_.õy(S„"›3+mr1'¶°Q¶‘®¬’e#eY5kqRŒ:·Ò¦'Åo'VÙš¿Yu˜ЬÂ5t$1ŸÊÅ™Y¥«²…Uç|užU¼º¾Ï¬êu¾·¥í [´_Ã0,5i@çVº;EqRΑl:·Ò\š3[ËÍÉmCÊùûÓ¹Ey7§ö`ûÇ‘f:·¸_1'6ïœÜþÒïGçåt¾µCž,>éÜæ] ’snõù+Ûë¥í [´§ÙÊï¯Î±JZ×ö–έÜÏ9·yWSÆÛlÓ¢Ô¹Åý‹9µˆ·¬óIÕ¹E¹:±T¶]-ôû0Þð³OÅж\íû0ÞvµLæÌBÝv5Pg›ñ†ŸÝœ]èÛÆ¬tns¶÷¡£ûúFç•3%²Í4 s þ4gwÇ[Þ?gйæ÷£s›‹Å3Û\º•3ÞJ×™Ntns1½¢s›÷kéιf}£s+ŸoN-2!ÌÊ;#*6òOç×Ó¹¢s›ùš>™s‹ûשñ†×Êëo«óÍxÃk$f¼íëõý1ÞŠÍ$СŸ¢SHÕ¹~QŒx«6S‹Îm†sœ#Þàl5ň·s&[`ŽüÓ¹Íûg¨Î¹æû¦s+åM1â­š^ÒBlÎ.â­s^o«ÓÊxÛ|Îd s \ W}Þ¤xèûåóŠ“”‹¦ùêÜJ}b¹4Lø=ùûѹÅï¯ß‡ãfhVX.N’”ÅõñpÎ-~Ÿêœ[ü>êT3ÞÎúIçÏ{dň·Œw:·xŸÙ9·¹žÎ,ã­j¿TÛ\­~Ó¹•ß·(®Ê·rÄ[µ|ƒÎ-Þ/ß[‡o#ÞêÔúDç÷o†oÕò/dôxvo»ZdçÜ+G¼íj§N³8IyWsæ_tn3&>›³‹îf®H}¡s›á49ç6Ÿ3IèÜæýÃh¯›\©s‹rý<ÆðPŒxÛ¯-;çåݜ٢üª¸‘ßÍÙE¼3YèÜæfN[”gsfoçL:·òyK1âm¿¶eÎ.â­ÛÌ9:·‚—bÄÛÆ¬Ÿtn3Gp>™s›÷keüÒ¹E¹:»Œ·>Ì)e¼]|Æ[_ª'tn§snó84¡s›÷×f} s‹rê'[|¾:ÉâÜâþì_pÔPž‡ïGübN.ÚëV´}¤s‹úTœs‹úM½¢s‹rÆÓÔx«æ$j¼Í WÖßnÎ.âm7+ìѹæïMçõ“õ™Î-žÎûÔx+šO7çôNÞšS‹xÛ×ó}йÅç3ž¦ÆÛÐöŒÎ-êÏ2'ñÖMoéÜæÓéžoËœb7›¹85Þ–9×oKó!õšÕ:·rsr%ÞŠæ«tnQŸÙ s{a:·#?:g.©±ÐãùWsnuÄþtnz8¹Á­óÎ-š¢ý|>ýSº€íûbYÑþ×Á$Ÿ X¸óenð²E¯Òð;´ãÓ¿•AŒUüÞ•øÎhÙºVðN5%hÛÝé#¯½–ïÂ>?^æõÈh¡×1 I 7P_æÿÚw§ýk‹áèþN]JAówïw¬s•îÎe‘Á——9¿ûÉæeüâ95ùø&D íW¡ë;u8M_dÉ—ç ³@×îîØHRy:¾0µl×°e¦DÞàx™Ýkv6Ý^‡vì–´_^ï2÷V"ÀÖ4Òé]j^Òè…åŒ^q›ÆËŒ^˜-é2zÅû‘R΀'Ö_fôÂ9tM.ò‚¤#:4z“ ¨ÓèÅGu]„ {êP{‰F/L©©6ð"YÞ-^IšÚä¦kr3Èf{Ó˜:§TÓè½ Ó ¸q×]1×.£W¼¸ú2£7ÙêA½pþʵDd!¦Ñ ²xó4z“z·°£ì™iôŠ ˜_fô¢”.)½(XŠêì"Ÿ¢#iF/ƒKViD:A£×JcO£W¾‚ØÏt¡ŽÎ0¤Ñ‹4]KtŪ+/3zñbÑ{Af_6’¹Ú—Yä1i‡ÑèņÂÒ¯ŠD£÷¬H4zšӻdšâ4ž’u´iôÂOã@ˆæ–æì"fü"'ɶ$‰¯þ“£œ`”mÊ3^õ¯>™Ñ‹ÏS£™´»Ë–äJéJu5zá²›zhÙÀðqFQwKtñí¹$@^{fôžKüÔèÅ"l[²‹PJÅžŸ±”Î%Å &%šÌè¤Ñ‹&€Æ€½¸fô¦lï_#ê4Šiôf¤¨Ñë1Œ^íS±fPÕ-Ñ=Û3z÷ø>Ôè=q5zñÚÍØ•ÐÊfŒJl+Féó.›ðN›wÇGÕèò´%”ØZ¶úïÔœ@-ÞuØ'KlAÁÝÒÜ9ì1$¶¦-l¤½kR¢îî)BünªP1ë˜2dN=Ýi™4v±œÏ,±55ÇQ[wê(uug Ëq‡ÑÓÅœ¥»[}:ÈŒˆýT|94t·dñg£ŸËQ¹OfçžOE7*kæínwOïž^î´¡`Z¹§sN'³ØøO& Úû DÓÔ¹Wêâ^÷•XÚ7*ÎõìB-ÜekSèà.–¨î:WêJ-[˜Dûv_«î¬ѲÉ$4oÏUÛôn!MCá~€³Öѹ6Jãöœ—@ßvyJâÁQqº¶Ó–дµ$S=Û©ó‰Ô²]Ç‘§[Ú*ýâs>ýÚ3Rh×Úô@uk-ÇQ³rj¥ÈVr°jñ+d…3‹Îa+jó•‘©M‹9›¼–s’8Í‘ÍhT¿BãýVðue­’ÃI#÷ß2¦þ¯Ä4p¸•²x>ÆšÝyŒüÿÂO¸žQ@¿UDÔ­”uŸ/þ´ù¯èäC[ú­P{6fô[Eß³bô’î§~ë•ÖÒo…6·RVÞùLë0?Æü×Ì4xšŠ´.ÛÊJú­ßdåHëò¸VÒ"¾òéw2­;w’ ß*:bþ*fMW›oF¿UÛÎOç8hµFè·j“÷ÉüV´=Ùù­hå¨õô[Ó9„~k:wf ß*ŸÏç•P?;¿M{s+eq}r~+ÊõþýÝ"3l%,â­X|Ðo…øéû’ä’µœß*×£[PLÚé·âúlþ*º¥Úû¿X߉±Ÿù«è›K¿Ÿ¯+9oW9ã­ØÎ=ô[ñylšè·âz¶zT?èõ¹òõ|Æ[9WÂ2Þʺ0âmc½ã­&óSo5ú¹Œ·vØýo­]+goM‡ûÔoæûbþW?ŸñÖNÿ™ñ†fÒüWÄÛu=ãí\¹L¿ÍN²rÄÛnÖÔï¿Uš-Ãè|Ÿ˜~kÀ…|ÖO®×ù°¿šßÊqîÓnmºÒäî¾Ì»­]&Ä}>-2fL¯g¹ ‘+fOÅŠµßr/S³b"Bqm¾¸Æï-C®g1Q(vI°¢Pì’`E®xù$XQ(vB5ïBµ‚P­»P­ Të.T+Õº Õ BµîBµ‚P­»P­ä+“"Wœ}eRŠ]eRŠ]eRŠçòÅ3ü$˜0à>¼Ü*Ó*¾2)rÅÕW&E¡ØU&E¡x$_<â÷=ªuïQ­Ð£Z÷Õ =ªuïQ­Ð£Z÷Õ =ªuïQ­Ð£Z÷Õ =ªuïQ­Ð£Z÷Õ BµîBµ‚P­»P­ Të.T+Õº Õ BµîBµ–Ïz]Å2ÁâŠ"ƒ‘à{ð =!ùÔÇ`$ﵧ .ý1 kš‘} dЊO‚ FBžçÐj ´z'Ì#æí=TŸ Œ„YaÞn!;‹_ÂH(=J¿z„~{NÎE Œ„Þ¡÷;aå@X·[„Ž—ÁH¨5êí=„ΗÁH¨3ê¼F „q»E肌„Ü!Š›Lq·Hwy“¹2G D“É3=ÚãNã z‚̨½„‘àtΠ'¥3 eBw‚S;ƒ‘àôΠ'Å3 «ºÝ"¨žÁH¨-j»F „q{Aû FÂh0n·úg0ò„<î„V¡ÝÞCPAƒ‘ÐF ´q'Ìóv‹ …#¡´@(·÷ôÐ`$”eÝ ½BÜb¥@ˆý7ubOB¾÷àdºT „؇ï×Ý"ß{q2¥jBw‚ëÈŒו3è QóCs”ÅüÅe1?d1GYÌYÌQóCs”ÅüÅe1?d1GYÌYÌQóCs”ÅüÅe1?d1GYÌYÌQóCs”ÅüÅe1?d1GYÌYÌQóCs”ÅüÅe1?d1GYÌY,QËCK”ÅòÅe±u¯÷O™äéoqò”iŸ=n²X£,Ö‡,Ö(‹õ!‹5Êb}Èb²X²X£,Ö‡,Ö(‹õ!‹5Œ€ŒßFÕû ¨NLõ„[UÃ8¨ÁHðm”BGàtÖ“ 0|¥0|¥0|¥ÐRh£FÂ0o·È¡R ¾R ¾R ¾Rè 27ð"F‚o£zB m”ÂHðm”ÂHðm”BO ‡0|¥Ðzh£F‚o£F‚o£z‚Ìb¼„‘àS÷v÷÷t^±'ÜRwN4¾óžºsæ±'ÜÚ(NE¾„‘àÛ(…‘àÛ(…‘àÛ(…ŽÀéÌ'Aa$¬+Þ‚ž/Bº§îœí ·ÔS¢=á–ºsŽôEÈ÷Ô“¦=aÜnQBê®0|ê®0|ê®0|ê®ÐdfèE ŒŸº+ô„Rw…‘àSw…‘àSw…ž ÓM/a$øÔ]a$øQw…ž0¨»ÂHð£î =Aæ°^ÂHhG ´ãNðà =!ÊbÈb²Ø²8¢,އ,Ž(‹ã!‹#ÊâxÈ∲8²8¢,އ,Ž(‹ã!‹#ÊâxÈ∲8²8¢,އ,Ž(‹ã!‹#ÊâxÈ∲8²8¢,އ,Ž(‹ã!‹#ÊâxÈ∲8²8¢,އ,Ž(‹ã!‹#ÊâxÈ∲8²8¢,އ,r¶¿'ÜRwNÿ÷„[êÎõaÞSw.ð„Û¨;W \ÂH¨)jºü¨»ÂHðà «N‚ÂHð²8²8£,·,Î(‹ó!‹3Êâ|È⌲8²8£,·,Î(‹ó!‹3Êâ|È⌲8²8£,·,Î(‹ó!‹3Êâ|È⌲8²8£,·,Î(‹ó!‹q–LzL“IqžLzL”Iq¦LzL•Iq®LzL–Iq¶LzL—Iq¾LzL˜IqÆLzL™IqÎLzLšIqÖLzL›‘å!#n²¸¢,®‡,®(‹ë!‹+ÊâºËâûkJþÈéüÌ¡þhâê×g ~0Áëƒi3ß0Ïá#GøCsíC#ãÃ!߇×>©ø°Ó÷a‚ýq*ò¡.|bUN¿PåZr-Y© óP«¬–üùóë÷?þõ?ÿ´¿ëëÇÿ|é§¿{ýüÛûr—w?E1sñ7»äãhï-•yïÚ);õŸOðãoþåß^Gùæ««Ôéïüî"÷®•E·ËùßüôÚzóãüã÷|yøÓ8Ïí—=C“uÈî~ûÓk÷O~üû?|×34Y0ža|÷ÆŽ˜G“;7vü‹w6vd5ûÿ¿¯clm1Ý—šE_þ^»Êßi}ù{-£/_ÁH <^mc‹mc»1¢¤…ý¯Ö1ÇÖ1GFŽÚawÇ«}L±}¼Ý¥ÆŒ2ìíø•20Þm"ãÝ620Þm$#ã½V20fì ‡]¯vrÆv2þrq¦.ëüâ2L-ÿâ:L-ÿòBL[7úå•˜ÆøòRLc|y-æ¹:õZŒ‰hν~t‚§Ð¸ó£Ò¾¸ä7|šk’,zl»™Únÿ¯¿Å^Ý8÷“ÇN}þ{qãp^Û$ ‡ó∑¥§Àº«öòÆáó‡î%ŒÓ*Ý9'yÃ[”-Å[>ñù²p+ÂO¼_‘ð1áo)ÆaØI—0ã0lÉÁrÙëçÀNÃeßßÎs‹ÃaØØ²`(ÆaØ9qC%`z™u`œÙ¹£~ñùäì”’;·À†]XÎÁçÍC±œSyp‰60ŽMµ#<€ïßyýÝßG¶éÉC6Ê‘s-‡b†-’bõ™uëà™äûË’ó<9 ;éBÀ“ßïsʦ§%ë–äÀ8 ;ëuàŽûná§œ Q²æŒÃç7>šb>¿¯—©ò”­|¤|*®8<=qI~ž²%°l”yh)z60Ã.ºsKæ>3{ý Å[EQ^ø<<ø)ÆaØE·Øn<Ì]?¯ËaØ8‚å œ’b† 3–åSÃ.‹KÞq6¶D0ŒÃ°aÿNÅ‹‡Ã7ÞoñðùÊ-€åðùÎͱ2·òÂaôü=ã­êÀr,³IWâaØIãi%>¯[DdìÓ’å}²¾ñ$Büz½œÜy?9 DpQŒ³Yóâ6JÀ8´7ŸÏËxÃ4K1âͶÜÈ‹ñv}¾Æ[·çÕxkÜbx±~¼þŒ·¾/å³\ã­r ¼4Þ*·Å–xëúû.Æ[±x_Œ·¢G,äÅx+ºw+0âm×·6#ÞŠÅÏb¼e=ý²Œ·¬GNOòe‹}4”ozD°Ä›îF+ )Û§BÌxKzÖ(0ŸçJbœÀ›´þkà \³âŠöO7äAË´Ÿ²e‡6äàëýe£X´—‰|Ù÷Ÿ†o×ç3ÞÒàNzÀˆ·Tíóo©Úó3Þ.ÌxÛíSOŠoÖ~‰dy^}Œ·CXF¼º 0âíÐ#¦DÈÀ?OÀ¢oz䙼ˆCôKÚk`a°t‹ ¤®GÂn ,zŒ# V£Þ•$ñ&as(ÆhVªbñ„1$ò%Þ´Ù ÆŽ×+±¾ŠpUÁ…岕T¶é€qäÌÒö ? ŽœÁÖNS1Žœ™º÷ 0ŽœA3šãÈ™©ÛH¡b%Þ_ŸGâ ÍÔ$–x®S1ò‘©» ‹pùüq(Ɔø~¼¿Äøúþ$Þ² Œ#C¦ê)06,ŸºE0ŽTج(šiÞ_âMdÄ0N ˜zäB2õH1iHQ«Æ{a¼M=‚Mj¤Åzd!0âmê9h¸oCõ Xâ­Po€%Þt‹X¡Ä›1ŒxC?ñ†ýî–âÊøÔça¼-ÝB xiûÀï«ñfíÎŽbûÃߣȃ£~dÕ|êÉÆÈìHÆRdˆ¿¯¾ï‰†ø I{}v¿%íõ¡;CßÝTú†fZ¾¥¾zä0ôíÐmC%IR¨•ú†Å2‡bÑ7==¹TÕ·bŸ§ú¦;”—ªú–­\õM®ì_ðy™X‰L};4Þ«ê[×ï[O}cüVÆ[Ò}둘È7ŠÖÇ*ñ&ùÈTܘ/·¦x2ß`|VÆ[Ñ-¡€oÅÞÿîDJ~¤ûpæ'l¯ª$N¨ÔÓ*[þJ¾Éû-$F(†‘=Ò­´Cò£¢GWæ;Œ×& ª5ÛÏÆ|r_ÏúÖ˜OÍKÓ|RÃF>Yxîà¦ùºü>ùdÑÝ‘OîçÕçc>‰#ôx‰·3ÿ.Èg§êO“xÃmø{6‰·¢£°‚Ñiç÷•x“ëY.ñV8_xuÁ‹Ÿ/ñ&å]1ú#ÍÚÏÆx«ÚŸ*ñV«½Æ[Õ­°Kc¼•eï—ñV,?lÒ°áûS_pvRæû‘ûãl1òÙ>òl&ð™?ödýÆ'NØáû•þ+0ò£búÜoÅâ«3Þ°e[SŒx+ñ†°+Šoçû댷:5ß茷fzÑoMwr.ñÖt‡þ³œPm™tÆ[Õ#³ovD0â Õf(F¼µ¦ímg¼íûñ÷쌷¦ý×Òoí|_Œ·fíAg¼a ¶ªñÖ¬½ïŒ7ü U1â­ë©e0ÞzÒ猷~\åˆ7;7¢ 9±\^{UŒþÈ~-z}–þH×#ÑéÖþÙx³`˜D~þü‡ùÙ`¼ =“ñ6ý½ã­ëVÕÀˆ·^ 3ÞºnÞŒx³#ä€;ùÃøˆ·®G€—ÁxC5ŠoÝúƒñÖ´¿ Œx뺉10âóÆ€oÍâo0Þð{3Þºµ·ƒñÖšÖG&h/Ø Æ[Óñ†2o­h`2ÞšÑ\&ã­Y¾5oÍò½Éx³#Þ ÆK²`ö0^2¥~±¾MùáåyäûO9+í õ`öÿM?§4´ˆ7þ>ÜXíë?ÆKºÄõcj¼Zß'ã­éFÏ…;äÊ÷mŠ%Þ’êíÔxKÚÞLÆdNÞïd¼U=r±°£QìHÑ2o»}äï5oÕÚî’-¸*^|^öo&ã Óe»bÄ[3ýYŒ7 6Å•ñÎöz1Þšn^\ãm¿oæc‹ñÖ¬?±oÍôii¼úý¸Ñ­6»ÀŒ·nù9Ï¢–ú(F~ÔõHÕ²d¯RÔWÖç%'9 ¾©_ÜñÈúŒÝ 6è¢Àh¯‡õ/—ìý(ñÎûËÀŠ„mRŒxzªEYŒ·aú°o˜Ê³#Þ†Ž?–ÅxÃÄ­¡ñ†³f¤~-ÆÛ¾žõg1Þ¦ Œx:>Œxº¥h=oçzãm?ß f¼áù“âÂûK>ŒxÛͦäËõ`¼ ݲñÖµÿŒxë:UÆ›! ŒxÃÌÆ%˜ñFY!F¼íönuŃüÊ畎µÈ\WŒü¨k>T9›õ¡F~d§ £½¶#F…-5£eðù‡šr<³¸L=LHù'ƶšöØPŽÊÉá±I¯÷(m±'7Ïs·ìª$ƒ­'Eq ”Æó½ŒB(MZ†‹B(|¼‹â÷Ý'‡nât¿)㟲Sæúæ +õbôRÚüîãíê¼®ÃÓw_ÝdQí÷îÓiW#>m£Îwç|ÁûÆYͨÿIvüÀõùWÁüþ>ƒë±¿Õ겸x™õ¥õ^êÝš‹¢î§Lèç—žÂ1ó‹¢c莢ÃÂ'ņ‰e¹# ?¿ldÓSt°î¤ŠŽ/â@Ñ!’“B(ìõ_”éŽ4üü²Ž«§°ïuQ´/æ)ì>\íNx 3à‹¢±§0©»(šäÊtG‚¼ÄS(µE¥×QT-NŠ©G tw¤!(lð}}¹µ¤Ö`¥%µ6ü+-©Q¾Ò’å+-é)WKê¢ök‡‘:“[i™Üßx´iÚ…»úMw8wÒûÍ&·Lº_—Ɇ©É‰àÓ0¶YºÏ½šÜ˜¬¦´˜ÜÀ4ÍhrcVo1S;ή¯ÓäÆl×DSWFKÏ×ir ίÓäNv¬šÜ˜%ª&²ŒÞêvݧÉì¬5¹ÓÊv?1Ý’ë¦&w±+|>1Ýpd¦>´#i¦¸˜ié>jrcB(Mf6“ié »šÜ‰¹åir£¼™i}ì÷ý”/£(ÎäÆýh Óä–ç5S»ñûÎäÆõÙLmÌ-Bîn¦7ö±_zššÜ¸?MQšÜrdè|&wÂbVŽAÜC÷W“XM^1¹3LgrÓÄd°gž všÜÀj¢‹›þ‰woåjzSsޤ“,hrÓD¥É M"5¹7_MVšny™)LÓ Œ©xðy“™Ú0ݲ™jrÛ¤ 3¹³ê§™ÜÉ-Õ䯤&+Ÿ|ÞÛÜyš©MÓ-ë¤.3¹³ JªÉúh&8L7lfcå‹Ï«Xó{_š7§¡ƒ:jródºO§ÉÌÔR“{_Ÿ #Þ²™pjrçb÷c¼Ù¹5frgm/ÌäF<š)ŽxÛÕpš©x³ãÍä¶ãÕäFûWÉ ½^Îä}s&7Ö e3µqNËlf:K¼sŽ&·è»™Þ8¾&7?_â XMéÊühè Mn<¹ØéHhr_ï‹&7ò¿ÓÔ†¾Íf&³ÄÞß?MnÉçÌÄÆ,æe¦$MnÔ¾OšÜRøyo¢§Îä–|ËLkœ«µÚebW걚âùQÖö–&·Éß—&·ü>Îä–|•&/ãm^5,ñ6.úvXý¥É-íCR }³üMMn´'Ëð®xÈGª™Ú˜TbÇ«Éònå˜Trè9Cjr#¿£)H“üÃðÌÿø¾Ä|ÉLíBþ‰;ó)À±ƒ”Ì4—)ôŸíMîœÌ´¡Éü)›©]ùý†™ÞƒùÛKšÜx>orKþÆrê[ÒÃÖÕäV—ú†ãvÉö›&Mî|N‚kšOš~ÒäΔ©Ofrãû'3±óÏìLnÉwÅзdõ&7ôi8“[ò™¦x)ßLlôG°Øa(®Ôc5Ý%Þ$0Sû`þÀø É³ž³¥&wf3÷ÉLnàd¦7ò£b“‚hrgLj ©Ìñ˜ÜUñ$ŸíÝðiúÐäÆX"ã‘&70ë+Ý|óAšÜ¹Ø$šÜÙÎaS“¸˜é|²4Íghrg›”¬&70_¥ânjŸÌäf}¤Ém’²šÜ¹ê±½jrãþÌ?hrO3ÁoÅ&ÒÀç±}§ÉÏI 4¹q=ã‹&w®IÛº¹žÏËx«‡æ_4¹ñ¾ª3¹ñy|^šÜx5QoÅòKšÜ¹˜‰I“õ‰ù Mn`êMnðÙ¾Ñä–üәܨ|šÜR߆bÄdÔ™Üòy¼žñ†I,E1â­d3¥oÅ&…Ñä–÷k刷2Ìdg¼U«Ï4¹sµI†4¹sÕIçjrçšÍ4g¼qó¬OfrãóhòÒäÿp&7~?ÖGšÜ™Ûw}2“;W3¡irçÝ «)Ïx«jRÙx(šõ¥x’Ï÷K“;×e¦3ã­éñåjrã~ÔšÜÀÕLmôGöõÃLðE¾7¹á%PirgìÄûsµMÍWirçÝ,ugr3^hrëõŒ7Œcš‰xk6©˜&w¶sòÔäÎÝ&IÐä¿™©xkËLsÆ[³ø¢É{Òö“&wîÇUŽxÃÆ3C1â­Û¤šÜÀÕ™ÜRNÓ–ñ†fÄ™ÜÍÈPŒx릷4¹1ŒÊúH“;£Ù Ÿñ†O¦bÄ[Ÿª4¹«3¹Åèt«4¹Õt¯t3lR Mn\Ïö&70ókšÜ¹Ù¤EšÜ¨/jºsä½Ú¤bšÜ™»ô}2“í±Þ_ãMM¨É ~1|h}äõŒ·–´~ÐäF}?Ml‰·¬ùöR}³ISKãMÏñU“;Û9–jr#ÞK~&w®:¥&7âA~?5¹sSýU“õqÒ„f¼5=4\Mn𥾩Éï“x·ÎøQ“×ëóh¼éxšÜ¨Ïëx&7êïäó‰É \Çë4¹;ŸO¢PßôûˆÉ»Æ›y 0Íñ{üz&·Z§É}y3½£ÍLnl÷³Öerã1GvÈE¡±¬8Pp´¸óÁƒçIЗUN(\sQt]Œ§TÉâ. q ð\÷~%\6ÕÊ¿ÔÊÆHÆŽœÛÑß|õŽ»¡FxþòÕ_4£‘g´áÿêoÞ3£¿ÑˆúÌh«¿¯hN{3ú¢sÚ›Ñ%˜ÓÞŒ¾(ÁœöfôI‰æ´7£Å›ÓÞŒ¾(ÁœöfôE æ´7£/J0§½}Q‚9íÍhGñæ´7£/J0§½}Q‚9íÍè‹ÌioF_”`N{3ÚQ¼9íÍè‹ÌioFŸ”hN{3ÚQ¼9ýn‹w6¬_nñŒò•Ϛ㯴xFùJ‹g”¯´xFq-ž ì¯ì±àìj²>r«¿m¿†¹½,F;¾Ý¬î2Áíôª»v­Ôªî:¬Nu×·Õ¨¶ÉÄêS#m´µ×;Ü:—Úf©I=Ì£¢G=´K¯5¦Õ•×éPò¾b˜ =7]ýiÌAæCŠ]¶/IæVïlyØ YšÓC'xª7=š­«l4[n.ʨv#¸;‚ŒÛ›ØÆshV¦®ô¶rZ²ß¡óÕ“Ý,²ºìRiL0¸3Iìêæ]s µ4HC³u£G»à®£›÷*c‡6?S­èq:¯âDe7’Œtêb#õ¡çi‹ Í­ÃNzfûdy-Ón3®?lÝ2¦¤bÊJVŒ)©§$ÒN—…Íûam-`‘¬£ ‡ ÓÖ³'?™ŒÏS T†ÈG*?™ŒÏ§D7•$³´›J’Y M%©›eª’4íó9NqØ‚¦š4ÌÒUMš×ºfÑ$roªIMÛÛ¦šdSšjR7 W5I§$ªŒ~§®KæU²um:¼}øý8å"f)sʦعuÎØ2-; 8]7§\$Í®ÕÆþy‡a¤4É_ZÀr¬ÞPŒ”æ´¨iËytKñ$_-]"—ãèÌòEJ…³uÐHil_,µ€q®_ZÀ‚Ý:gœ=§1§\d[7K Ÿ×ÌòE¸ïغfLqÊfAêØ¾³€åùŠb„ÊmÝ2¦8å|Y¾ˆ7àªñ–í÷ ,ú¹uÎòyK1â-[f@ XŽÎ³u͈·¬=eµ€‹•/Þ_×3ÞöõÅYÀ8‚®: X×ñ2ÞÎ)´€åz·Î9e³Èhãùý:gðu°XRò{˜%Üø¾ŠY¼X”M iãþº.˜ñ–m_ZÀÀj13Þr3‹–ñ–ÕQ ˜S´8â gÓ-ň7È5ùŒ·’íyo%_ëšoÈl]3âm+xq0>o˜å‹x+¿´€¥ÜYÀ¸¾¹uÎ8鬘å‹x+§ÅÎx+íZ׌x+:F¡0!+¶ŽñVl(-`”ó÷¢,|ZºbI¥sŠ-`w½drÀ-W±¤°¦®K*Ù¾ljË UY1Úëz®SK*<-`75lÝ3â­ÚZÀÀŒ_ZÀ8Ø)ÙºfÄÛ–•i1â 7¿ã ûF¼Á·uˈ·k]5ã­Ù” Òm–ŽÐÆ×ÓÆï­–­ÆÛ°ïÏx;§¼ÐÆ¡jÔvõñyjQ3ÞªMi¥ ¬ë¦oû~œ‚°4ÞlJ-`|þ²uÑ“÷´DoV?ÕÆý3ÞlŸµ€å,+[÷Œx3‹\-`9 +¿N ï¯ö×i'Û×D-`”È·–9²ff>?½N XêYˆ7žçtZÀ®\«Ôª­ë 85gù"?jÃ,j:V= X·5ÇWwÂÑÚ pOÁÿ•Cz°NýTBO˜[Ó*0˜%)KVq ”έgB]y4çC_ú+»ØXåûºSNœú…¶nâ~Ùº_º:ŸÛsoÑüî«›ôÈäêz^&–ÆýE­TƒÆ¶;–šN¸ ^ûÆ«§sF‚;5É`$Œ#GBòÇCŒ·µAOÈ~/mƒ‘àŽ‡0 îÀSƒžPüQ#¡@hãN˜%æíÕŸšd0J „r{ÍŸšd0Ê „²î„Þ¡?n±R ¬Û{èþÀSƒ‘°Z ¬Û-†?5É`$ÔuÜ £Â(wÂZ°nïaúS“ zÂò§&Œ„¼!¯;¡µ@híN˜)â§êå…‘àö‡7è Éïso0Ü©I#¡—@èñ=Ð%¼ùv„ú†žT'Ñâa‚ê-^„r;LPÝFOˆžªÿxêíÔ$u$=¡Î;aÔ@·[´Ë°ÿl»‚Þ¹B¾½‡î50 å„rÜ ­B{Übæ@˜·÷0ü9à#aö@˜·[Lj’ÁH(3ʼz „^ï„uº½‡åo0 ^çCW”ÅõÅeq=dqEY\Y¤Ï{Òí0Au~=aÜn!…0ò „<ï„V¡ÝÞCñgTŒ„6¡Í;aÖ@˜·[DY\Y\Q×CW”ÅõÅeq=dqEY\Y\Q×CW”ÅõÅeq=dqEY\Y\Q×CW”ÅõÅeq=dqEY\Y\Q×CW”ÅõÅeq=dqEY\wYT§Þ#ÁÉ¢AO²h0œ,Œ'‹=!È¢ÁHè#ú¸V „u»Eñ'»Œ„Ú¡ÞÞCõÇNŒ„º¡®;a´@·[4ŸºŒ'‹=!È¢ÁHp²h0Z„ö¸…“EƒždÑ`$„*w?¿²3V¹ûù•2Qb½ÊÍX忣ÊÉ}O˜·kÅ*w?¿R&[”@¸U¹ί4 ¾Ê)Œ„‘aÄ÷BoÑ`$ø*—î½E™Òáoqï-ʱ·(“>J ´Û{½Eƒ‘ÐF ´q'Ìóv‹ ‹#¡´@(·÷dÑ`$”eÝ ½BÜÂeb=¡cÐõ"F‚ËÄ zB8ÖÙ`$ﵧ .3 kŠÑ±Î=aùLÌ`$äyÞ ­B«wÂ<aÆ÷ÃÑW#Áeb=!ù£ç F‚;zÞ`$ô=¾‡œýÑó#¡÷@èýNX9Öí¡·h0j „z{¡·h0ê „:ï„QaÜnz‹#ÁËb~Èb޲˜²˜£,æ‡,æ(‹ù!‹9Êb~È"&.¹VNa$xYÌYÌQóCs”ÅüÅe1?d1GYÌYÌQóCs”ÅüÅe±¦ÜÔ8å¦>¦ÜÔ8å¦>¦ÜÔ8å¦>¦ÜÔ8å¦>¦ÜÔ8å¦>¦ÜÔ8å¦>¦ÜÔ8å¦>¦ÜÔ8å¦>¦ÜÔ#È¢AO²h0J „’î„6!Êâû«l~õ M ÿp–íG¿aöØGól>œôð¡=ü¡Óö±«ñÑøï‡cm[|Øü8Ûþ(/ùH$¾!Þ>¨©~ÝεXçZÁ£[™V9 ëçϯßÿø×ÿüÓk«áÿù:ÒO÷úù·÷å?ï~L—å§å\:”£½·vèÝ‹—¬¥<ŸáÇßüË¿½Þ?ñ½Ë1C'Ñú{÷o¾8˶-îûÿæ§ý‰¯ÿã¿çû˱óí—>Æq††ßþôÚ–ÿþßõ 9â3ŒïßÔSöÈéú.þò'ìtðúñçó)¾{ã¾_asÏ[ƒiK¿ÜbÆ»Mf`¼ÛfzÆûfdD¿)lóy5›-6›-2rì†M>¯†3dž3GFßa‹Ï«éL±é¼Ý¥ÅÌ3lðù•Æ30Þm=ãÝæ32Þk?ãÝÔ3tynhAëÑý8]ÜÙSe;¬]Õ±_^»ª„¯¬]µ5µ_Y»j”¯¬]=Wæ^kWÿö‡ÿ«ö÷ endstream endobj 468 0 obj << /Alternate /DeviceRGB /N 3 /Length 2596 /Filter /FlateDecode >> stream xœ–wTSهϽ7½P’Š”ÐkhRH ½H‘.*1 JÀ"6DTpDQ‘¦2(à€£C‘±"Š…Q±ëDÔqp–Id­ß¼yïÍ›ß÷~kŸ½ÏÝgï}ÖºüƒÂLX € ¡Xáçň‹g` ðlàp³³BøF™|ØŒl™ø½º ùû*Ó?ŒÁÿŸ”¹Y"1P˜ŒçòøÙ\É8=Wœ%·Oɘ¶4MÎ0JÎ"Y‚2V“sò,[|ö™e9ó2„<ËsÎâeðäÜ'ã9¾Œ‘`çø¹2¾&cƒtI†@Æoä±|N6(’Ü.æsSdl-c’(2‚-ãyàHÉ_ðÒ/XÌÏËÅÎÌZ.$§ˆ&\S†“‹áÏÏMç‹ÅÌ07#â1Ø™YárfÏüYym²";Ø8980m-m¾(Ô]ü›’÷v–^„îDøÃöW~™ °¦eµÙú‡mi]ëP»ý‡Í`/в¾u}qº|^RÄâ,g+«ÜÜ\KŸk)/èïúŸC_|ÏR¾Ýïåaxó“8’t1C^7nfz¦DÄÈÎâpù 柇øþuü$¾ˆ/”ED˦L L–µ[Ȉ™B†@øŸšøÃþ¤Ù¹–‰ÚøЖX¥!@~(* {d+Ðï} ÆGù͋љ˜ûÏ‚þ}W¸LþÈ$ŽcGD2¸QÎìšüZ4 E@ê@èÀ¶À¸àA(ˆq`1à‚D €µ ”‚­`'¨u 4ƒ6ptcà48.Ë`ÜR0ž€)ð Ì@„…ÈR‡t CȲ…XäCP”%CBH@ë R¨ª†ê¡fè[è(tº C· Qhúz#0 ¦ÁZ°l³`O8Ž„ÁÉð28.‚·À•p|î„O×àX ?§€:¢‹0ÂFB‘x$ !«¤i@Ú¤¹ŠH‘§È[EE1PL” Ê…⢖¡V¡6£ªQP¨>ÔUÔ(j õMFk¢ÍÑÎèt,:‹.FW ›Ðè³èô8úƒ¡cŒ1ŽL&³³³ÓŽ9…ÆŒa¦±X¬:ÖëŠ År°bl1¶ {{{;Ž}ƒ#âtp¶8_\¡8áú"ãEy‹.,ÖXœ¾øøÅ%œ%Gщ1‰-‰ï9¡œÎôÒ€¥µK§¸lî.îžoo’ïÊ/çO$¹&•'=JvMÞž<™âžR‘òTÀT ž§ú§Ö¥¾N MÛŸö)=&½=—‘˜qTH¦ û2µ3ó2‡³Ì³Š³¤Ëœ—í\6% 5eCÙ‹²»Å4ÙÏÔ€ÄD²^2šã–S“ó&7:÷Hžrž0o`¹ÙòMË'ò}ó¿^ZÁ]Ñ[ [°¶`t¥çÊúUЪ¥«zWë¯.Z=¾Æo͵„µik(´.,/|¹.f]O‘VÑš¢±õ~ë[‹ŠEÅ76¸l¨ÛˆÚ(Ø8¸iMKx%K­K+Jßoæn¾ø•ÍW•_}Ú’´e°Ì¡lÏVÌVáÖëÛÜ·(W.Ï/Û²½scGÉŽ—;—ì¼PaWQ·‹°K²KZ\Ù]ePµµê}uJõHWM{­fí¦Ú×»y»¯ìñØÓV§UWZ÷n¯`ïÍz¿úΣ†Š}˜}9û6F7öÍúº¹I£©´éÃ~á~éˆ}ÍŽÍÍ-š-e­p«¤uò`ÂÁËßxÓÝÆl«o§·—‡$‡›øíõÃA‡{°Ž´}gø]mµ£¤ê\Þ9Õ•Ò%íŽë>x´·Ç¥§ã{Ëï÷Ó=Vs\åx٠‰¢ŸN柜>•uêééäÓc½Kz=s­/¼oðlÐÙóç|Ïé÷ì?yÞõü± ÎŽ^d]ìºäp©sÀ~ ãû:;‡‡º/;]îž7|âŠû•ÓW½¯ž»píÒÈü‘áëQ×oÞH¸!½É»ùèVú­ç·snÏÜYs}·äžÒ½Šûš÷~4ý±]ê =>ê=:ð`Áƒ;cܱ'?eÿô~¼è!ùaÅ„ÎDó#ÛGÇ&}'/?^øxüIÖ“™§Å?+ÿ\ûÌäÙw¿xü20;5þ\ôüÓ¯›_¨¿ØÿÒîeïtØôýW¯f^—¼Qsà-ëmÿ»˜w3¹ï±ï+?˜~èùôñî§ŒOŸ~÷„óû endstream endobj 472 0 obj << /Length 608 /Filter /FlateDecode >> stream xÚ­TÉnÛ0½û+( ’¹I”‚¸‡ uôRT—¢é‘i[¨,ªÝÄ_nò’¦·"p8Ë›7 5D` x?CἯf‹UÆÆi™eT€ K DGyŠI ª5øišÅ Æ$ƒUSÇ”ÀŸqByE·öÂ*.)TÃ^h¯Ž11Åðhÿ©ƒ¾“ !Xí¤ÜiÑ´jë7é¹½4j×ÊøGõi±¢”i™“܇@B‹”bêËrl¤Ì¡Þ5ÃÚŠ†¨Õrè„n~Ç„CéZyœá&…ãaAðÙ-R= Jå0¾Æ`;s­¥².6¡y+÷æàPnT7Ú6lá~ƾpQ×jX7ÝÖ´L©£¤”Á›1ôƒÜÈAvµÓ8aŒÁFõ Zíe€‰ÎŸòYìûVÞú‰™$Wc8-X˜˜™vfn±ÃÊÐ~3¨§emErc= b£¸ßUÙùU´íÓžw‰éŨ%²,A(ºñ^Gå “Å*, ‰¬Â/<øñS s ‹lA“×:ÿ*0»n¯UÚf×_i ±–T{¹ŒÆ)¿²e4¿Àæ"çߦœÆ(ôÿMñ¸˜_¥?­â»j†€>­ !EÊIêýì×,å8÷€³äST0,>î1oÕì‹ù›|ÉD™\pºå¿Z¯Sb³ûƾáU³=¸o¯(!æv3ó øOµ(àvýÎ{Dzëõ4zÕ$€ž0v‘¼4=aw¦8ïŒCø×Å8ýµÁ¾òQ†—ìA+‹lÍÖ\Îö_'À¹i”g†±”™¹9FR¼¼™?,ÀHf endstream endobj 469 0 obj << /Type /XObject /Subtype /Form /FormType 1 /PTEX.FileName (/tmp/RtmpBlhU1W/Rbuild4444671013800/timeSeries/vignettes/timeSeriesPlot-tailoredAxisPlot.pdf) /PTEX.PageNumber 1 /PTEX.InfoDict 475 0 R /BBox [0 0 432 432] /Resources << /ProcSet [ /PDF /Text ] /Font << /F2 476 0 R>> /ExtGState << >>/ColorSpace << /sRGB 477 0 R >>>> /Length 11676 /Filter /FlateDecode >> stream xœµMÏ/ÇqÝ÷÷SÌ’\øQ¿ÌtW/cà 1lðBð‘eةĒ…$ß>}êTõT]Ò¯(/Hð°{æ?/ýú«šóÔë—W½~{ýÛ§¿½þízÖG¹¯&ë㾯~·Ù.éO»~ÿ›ëï¯ß}úÅþî¿ýåõWß|*¥”+þû›¿úûÿÎçú?Ÿ~õW¹þéS½~¹ÿùí§Š ×ÿ4ëÇìW[óãY×÷Ÿfûè·Ê"×w r|È„Ü?KÙT>ýQùTÊ5T•ûSÖr~TÊÞ)çTykéúx„§Â™¥|TÊQ(§èUÕ²~ô¥¥«R.ÊG+÷)*§–Þ7åÐ3?¥òŽ å¨<3îWöQ­,¥QVÊÁgÕpÍû4ÊþPŠIT^w¦•U¶Ægµ¥ß>Ø>]Z¨o^>H}¿üýý¼©ïizÙóÕçyt„÷÷ïúQ© uÃÒç£ç»;–>?êû£öð¼¶¶á´êïݧ|_·aÚ›éI­ƒd½Å‡ÔULÛ˜Ê÷/Tu€ªOŃQ=L/Vµ}=íùèî«Ü¦'u×÷û<Et´¤lÏ6¶BûàJ=}tíÔÂö<¬½ïÉKj(/ª­Çú˨aëo[Óz?£qŒ}8Ôqëd¸5ïwëA]©¶ç‡#6´Ž³·µ÷18Ð>œßꌋz|5-ÔlOcq¬}¬½ÎÂÁöæä=’nlÏGïþºTÇô fÿž7Ûómískoo4hÅT³\ghÞßÖÚž»'S8ÞvNuÏ«•Zôú÷LºzÔ•í¹£ÙB7Ž·ÝÚËÖ“ºkûÝ“k§æø¹çOmÏÝÚËÖsðxžop¼=ǶçfíG&Ûg³ñzO¢]X>L/Ñã›Öß³êÃrÎ?«²}vëo[Oj>_\Ÿgåñ7N”´Ôð¼ñâ“oo{5Ë7ªõµ½ì¾ôð¾¶ÔÚ¾Úžoµ}Úûo{>-OÖãy¿íùµ™~Lkû¼9ÿµ=ßÞÔÂúº4;íZ¨uúo{~µö¬ï§áxûp¼ƒòö‡¶çÓ{½í¹íùÔÚ³®[ gyï·ÕÆñÖž/´¶çŽn }³=wöï¶çÓÎöyÓË4ËÇiÏ<ÿäxÛÙ_Úž_—µW^Ïâ µóý´=¿Z{Öñz²½jn{¾íO8_ë\¥míYÛwk7ÇÛŽ×J½æÛZ{Øžo4èÉö|³ýB/>¿B-ooέ-¶çSq¼½¹¨l{~õ/ÚžO[ ígkoÏz½½s¼µñ§íù¶PKSýpÑêígϧwÒ“ãíÍõô Ö•iÛó­µg]µ=Ÿ_›éÁñ´hý=¿úxÛLÛx[YÞ¼}êz¼íù´É;~C‹ÄúÇÛÇÚ×=Ø>»¿­ç/ö‡[¸~µmGÛó©­_Ùþ·žõÛS8Þ CÔ¶¾åùžÆõë´û}:ׯ¶5^\ûúA×mÏ·•ë¶ß=¿.ê‡zrýjëõöÈY?¸¶õCcýÅñVÛzϧ•›ÝX@OjßÚž_»í%Äôâþ€í{tŽ·ºL~¸~=ç{Øž'¦mèÁñÖÖ›mLŽ·¶„[ñü‹íY¸^m{>mܬiZÂþ ííêmšåã­­—Ûž_Åö ÚæÍõ«­ÿÛÞ£jÿ ׳º¿i{¾µõ-Û÷_¿ÎbzPs|Þók›á~¶jöß=ßÞÔìï{>µíÛÓÖ¶~Õõ`“ÎñÖöKmï^ Ÿ/¯wëiû/½\¿ÚÖ°íù´RëzRß,Ž·§¾p¼õëÛóëäóäûÝó©íÇtý­ísÙýcâ6­¿‡–ší›‡j–?\¿.Ü6ôàx»l|ÅÂÒÊ©'Úg/Åúç.Ðõë²þ¹ÿcRëóDÅ.ïù ÷_ºßì{?û¬÷ø^tâA5zÑ9éý ¡õúûÞÏ6ê2L‹•wÕÚ´¼©ÖJ8Ÿ6<Õ,_h8ïùe¬\¯ϯûBßú{>½ù<uçxë÷³õ ,¿9Þ.®Oz\¿Š×¾SÀÓ÷üjíyUÓÂýŸïžom¿¦í¯ïùÔöcÚ %ìÏúž_öw¿ûÞÏVÓôí×t}Ñ÷|jû1ÓÏá®èøØ÷~¶Sóþ¶^6ð÷åðêuxëeãžïgðùìù¶RëøÚ÷üºÂþ¶÷Îö<9ßô=¿Úx«ý ÚÆ[Oûžom¼µóMß-×s½ã}ßókýÚÆ[ÅW}ïgŸŸ®úžOèz ¸ÐxŽ÷Ð6Þêz°ïý¬·º^ƒã ,œðùïù´}¦ð÷çáÔâãmÕëßó«ÑßÛó©ñoúÞ¿ÚxËö³µ·ÔÍÛ§®÷ûžOèzzH¸þ=¿6yÇ7hoŸz}{¾õñ–Zœ°ÿnmüËη¼}ò÷ö~ÖÆ[ö­g}Çï¾çSã_ºÞè{>ü ÚÆ[¶¿½ŸÕñvqýmí™ýmϯ“ã+Ûߘ\¿ y´ó/}>{?kí¹ózÇ[~{~]3ôÇ=¿ÿÔÍùLJ­§„ñaÏ·]B™ó¯{šžÔÊúäþþ÷þÕøÛçÖÖž_Þž©¥pýêý}ëaãuÍíykáóÓù£KçúUÈOúžOñùÊííY׳}ïg ̲¿íù¶$=¹~=ZØž§Í?{ÿjpV÷ß(Ñ'óÃP\®„ì(#ú_Šýe/ÎöÄ×ðï»} ö×åË‹ý—yÄþ½4C÷º›†,Æùw›ÃÔ©}€ØR‡bTÖ'Bì©„ØÓæóbeñxˆý!uö'öÇ™ öû£T׆Äþ˜qïûCêÌHìùV'&bL·Å¤Në¶l$öG©®Úˆýqª&—cÚ_ìß±³—cKÞ®Øó¶v8b•÷åØR»±ÿ[Y±?ÖºØ$ö‡Ô±”Ø_Kó—©ÇêHÀ‡ËЉŸØ7¨ýœë7½}bxÅþ­á`=Z®ƒýQnaÅþ('f"ö‡&æ öZyŽ7l¯Ø_/¶^ûc5CLCìòɰ‚bÿ¤'t7ìIìßëã˜_±?4± ±¿–ã+ö‡Öe°a¬Žˆˆý±"f%öÇêgÔë`h†-ˆý±Òeˆa”+¦1ìß–+öÇù‰u‰ýQΰ±¿ê€ýq}‹Z±?ôØç#¶%ö×ß§Vì¯×?¯ƒýq½ÄæÄþÐ' €ef†…‰ýû¾ìJÌ^ØŒƒ–¡š˜œØçãýû÷æí‹Øõ'ÊýQnØ^y,´n# ûëõòüŠýQ¾öÇê’XØ«ÉÂßWìß›cHbÿÞËû£œõ‰ýq<ÃbÄþ(ÄòŠýñ{¼b”ûãø›aÅþÐ☯/ WÀþ8ŸÕWìû#ö&öGyØ×ÏþC쯫a,jbfbèFŒ¯ØšÏ›Ø_WÏÄôŠý¡Å1¥f{'öï»™è²Ä°?ÊkÀþZŸZ ý}bu}ÑІíûkùsì㉭‰ýõú¨ûã÷e^ûCwÇühÏÍF{ÃþÐlÿÄþºØ?i´ç6, FìÍ0±¿î.Úu°ÐíùhmX¸? Ø¿Ç0±?v|~ÄþØ}0LFìr^±?Ž,–³¿ûãx¶gb”³}û­Ø_OŸ±?žçó\ûã÷Ø¿ˆýQŸýØ¿sír°?´¯Øº¹^#ÖŸlŸbÏ—Øÿm/ÄþÐ £ûcüàó%öÇxu´Ž·bý…Ø_ç ¯<㥅 nŽ··ßÄþAŽ·þ|¹¿ÓÝ®¶_òœ¯:æ/_y?Äþº[V¬Bì¯ç Ø?éÁùÊ0}çxka;ÃþºPlBìû#æ%ö×ù¶\û+`}ÅþX³XXA/Tç÷yì1†ý1ÿëûc¾îûŸùذ¿>¿qì¯óËûë|°?ÎGìFì¯÷Ã0Áàúaæ%ö×ù‡Ç+ö×ù `O‰Í ×Ë®‡Øõµö×r}¾ÄþZ>®ƒýµ½Qßoýùûëz€ç{¸~°0¥aÿ3ŸöÇýûëñ<ÿb{^ö¾‰ýµ=ìù£Û+ö×ù&`ô|ÄþJSûëøF­Ø_51¾bÿ 'ÇÛ'ay=Âñö`wås:ŸÎë`è°¿Î?ó:Ø_çcbþÆñöæ|cØ×s?×Áþ¿t[jØÿŒG†ýAkÚ¼öWšÃúºÿTÚ£ï‡Ø_Ëå:Ø_uÀþÐû+ "æ×íô°?Æ7bYbÐ!Þ/±?4Û±?4±.±?h‘0, ±Ò#jáxëa-büÃlÄþ('f%öÇõu×X¿â÷ˆåûãülÄþZ®ç#ö­"&ö‡,êÆ°bÐ)†Aˆý•V¹F{îÃWì݈ÑûƒfÕ~ìÍöGìßïbaIbhÃîŠýQŸabèÛ1?Úó]ü|ºPRF¬¯ØÚÂÊ÷:—}û+ Óû!ö‡–yì¯4ŒX^±?twÌö|{"ö×óÓ+ö×óëó öÏOìóÆWìÚ6öG¹_ùÊy½ÄþJçö«ûëñú{Äþø}ÃîŠýQÎþLì¯õ©µaã|‰ýA÷8~ûƒæEì¯ôo^ûC3,kØÝ’çSìr†‘ˆýµœ]±?ÎOLHì¯Z±±?Ž'ö3ìÿØøàØ×{_/öªaAÃþ~½ŽýoÛ:ö¿oú†ýo›ßûßkbÿýþ'±;±ÿ®Ïã ûßÖ ûCózˆýñ{âamσë/Ãþ§ýöÇõòùûãùÕ€ý¡íxÅþ¸bOb£¯ûw {®…Ï[Áa­¯ç#ö‡¾å:Øõí|ŠýƒÖqÒoŸÎùÒ°?´•+ö‡&æ%öZ±ÒhÏå—öWMÌ®/šõ‰ýA—‰Y‰ýA“±½‚h†AˆýAµbÐcbbbÐa;¿bÿ>ªaPb”w×Ë4ÊýAu¾2ìòƒù 5Û±?ê3ûC[˜@±?èõ¢Vìãv ö×ãå:Ø_5ë+ö®¼Åþ Ñõû«&¦WìMlKìZÍ÷I쯴›åÚðP®ã©a¥ßý:Ø?Ô׉ õÙ_‰ý¡%`Ðp¾bhÞ±?41<±?Îìß?zRóýûk}†:Û󩝨—m˜^±?4±5±¿ÞËÛsõ°ƒ‚&´çåz±½Z˜@Øž—7Äþа?´î ûãúªc~mÏÅÂ2Äþú{Äöãííõûãx†…ˆýñu½i؉÷KìÛdƒØšÏ‡Ø_£jbíå:Ø__±~e{®öþ‰ý¡– ö×ã‰åûëï{@¨íüŠýû^æ0 Bì´bÿ ÅÛ³…)ûCëzŒ~_óµGÉý°¶$£“ïwkz)üà¬åÉ÷/ʓÝ;3Ãô•{ò½O¾·Ë“ï- Ç“ï¾>O¾·¤1O¾·—íÉ÷s£'ßw.Õ=ù¾[½%ßwvtO¾7úïÉ÷^Ù’ï-9Ë“ï;_’'ߣóäûÆ)É“ïmðä{ËóäûÎñÅ“ïmúôä{ÛÍxò½m¦=ùÞú–'ߊõäûÇrñ-ùþáDíÉ÷/Ò“ïoÁ“ïm–9É÷Œ=É÷ÂÇs’ïoê$ß‹QaO¾£Œž|´%ß[²ÆI¾·Sx$+ð÷¦I=ù>h >’J…Ü(×woò½íÒœÂ{2ÄI¾_Fõ<ùÞ¯çM¾g2ì›|ß=ß¾1ª>ý£‘éÉö–¬|´ÓI½Cò=©·%ßO£®!ùžÔÜ’ïýãO¾ŸŸ%ß{²¸'ß{r¹'ßûõ{ò½xr¾%ßûûôä{ÿ˜À“ï—µO¾·dš“|¿¬½xòý2ÊåÉ÷Ë’}=ù~Yû3 _œ…zP3jb¾£äFáK±¨Qx.¶_ Ø%×Ká‹'÷…/6²8…/Ök—Û(¶Qx,ÎIÅIá·æû1 _ºSwRøÒ­}…/¶ v _<¹Ø(|±È)üÖ3Rxt;RpRøbɤNáK³(QøW“—fíÛ(|±U–SxÀ@½£ð¨’ï5,ËrRxÂ×ÂB…/Í¢FáÏõ…/ž|m¾x”Í(<Ž÷dûbqà|¯×CŠO _š½/£ðÅ£*FáK³dj£ðø½v½¾ÜžÌO _lªq _ºE1ŒÂ¿šþÕ¤ðÅ&#§ðÅ& §ðåörRøÓތŸöf~ëÇõ¢æøbí1$ßc³HJlþÕ¤ðŨ€SxÀÜv½~k~¼d~ë'Røb«L§ðe8e'…/NÑ—ǓûIá‹Q'§ðÅ–Náq?¤ìÅ£õ·'Ûמ‡Qøb»R§ð(§&…/FùœÂŸ÷mþÕ¤ð¥{²=)|éöñQøÓ¿Â£ÐN὿;…/NÝŒÂG]t¹-YÙ(<ÚŸkïøäïŸç'…/Ý“ùÂ7K>u oý×)ü/ImÂ[TÓ)<Æ[§î…ɋ뾾{“ï—%«zò½SdO¾w îÉ÷Ë('ß{2¬'ß‹QmO¾ÿ=K¾?Z›<É÷:É÷Ó(°'ß{2¶'ßÛÇ'ùޯϓï-í$ß‹%K{ò½'¿zò½­—Nò½Øóñä{›ŸOòýÑ–|ïTÔ“ïÏï[ò½SOO¾O~·äûeÔÞ“ï=ÙÖ“ï=ÙÖ“ï=*åÉ÷ËÚ¯'ß/£Äž|¿Œ¢zòýâxx’ï½}zòý²ë÷ä{1JèÉ÷Ë©¹%ß[ÒÎI¾wªìÉ÷˓绯_›SùEmÔœ¾”Lá‹SdO¾÷þeÞ“sOòýrJoÉ÷Ë£¤ðž|’ï—Sòêòyxò½'¯{ò½'Ã{ò½'Ÿ{òý©oÉ÷FENò½'Ÿ¿É÷:ß„äû'PxížlßBrmH¾ï¿ýéM¾·ã»'ϱýyò½ž|oY)!ùžQO¾÷þÿ&ß3ªàÉ÷N½ßäûîT^ÂǺ'ùÞû£'ß‹} ãÉ÷–’ïGJ¾7Šq’ïr{ò½?_O¾·,›“|ïÏÛ“ïýzßä{~<õ&ß7O¶·äû'%ßûó÷ä{Ër8É÷~½oò=ïÇ(<`9©>)üžoØ¿ŒÂomç;É÷Ó©{å~€ã±QxÌW¤à¤ð¥¼Éø‹ó“Õ‹R‡ä{&«¿É÷¤(¤ðÚIÍŽ·N±HáC2ÿä~Ì¢ö!ùž”•þôß|OÊ’ïI½-ù¾q|p õ,ËIáK3êk¾8¥5 _ìc1§ð¾¾p õËIá‹}lèžËŽ—Â˺q ïëI§ð[÷Há¹Ly)|F­Âã±R“¿úñõëÑÊ †Q[£ðe¤ä{]o†äû Iá£^\Ïòy…/Þ·Qøs~£ðÅ>~v _,*åžÍâ¥ð¨ÏãIá‹GaŒÂûzÚ)<0ËIá‹YG8…/ö1³SøâQ£ðÅ>žv _ÌMÂ)|±õ„SxOYu _üã£ðeY0 ïY Ná]˜¼?=÷”íÝ(|õ¨‚QøÚœj“Â#˃ÉñÕóOy?Fáw¹%Ë7Ï2YžloY$l_Fá‘¡JªN _+£¶Nák±ögžé¥ðÅ“ÑÂËÒp ¿ë³½…G·½¯—ÂW‹;…Çñ¬O _V¦ðÅ¢ÄNáËrŠO  ¦÷o¿ÇrRø·œ¾œä}Røbë§ðžuë¾XÖµSøSß(|ÉÉ÷ú¼™lO ïY?NáñþCò½¾_jRxl HéIá«Sp£ðÈÒáù—gEÕHáíäz)|õ¨QxÏr ÿO ¿5Û£Qøj,Õ)¼gA9…GÖ.'…5)ü«Iá«e™8…Çùð|~bòý_~k©õ¿þÃ8êüá׿ûô‹ÿÚ®z}ûÏWÕÃËùWµÓ wÝ7ûýõÕ·ÿúýo¾¾¾ýí§¿þVOïLJƒþ"œ«rœcöŽÿ¿~ð¾Ÿg÷S9¸Øgß_x'`šÔß]×7?¨rtÓd ­2¸Ó9U\¿Uðf¨rt¨25]å­âú­âßoËuª¤;wôVùÙoT”ø¶©õ~!¿úê—__ûÝ|õ¿ûz?Ÿë«?þã￾þb^_ý¿¯ÿáúö—Ÿ¿è?-C9ºÌg;i¥Œk'?~x}4-^Öù߸Ž=}õ¯ß}Á…€l‚ÿ©W²ûпû’¢ÒÎu a†ëøêo~ýïÿëþæ÷_rŠ¥›â?õì{”?{[Á(Ö?o,ó ®kê*üÏÐX4jþù:WbŸ-‘Ù|o_1afÃ’ñó Õ÷ŒØS¼°8Þ³€W0+tÅŒ§e¬Àì”SÁ’UB^Õ©.ò' Ò{±8…=éZÊŒñ<Kî=pêáíO8|Toèõ.Ÿ½?ÝÅ®Ô0ˆi¬&»øÃ0ù/×7áû³Ùÿ]ç¤FÓ¹­‚眔h9'%:ÎI‰†sX…¼~sR¢Ýœ”è6'%šÍI‰^sR¢ÕœÔè4'%Íá¸÷ƒ3)ÑfNJt™Ã=¼&s8çë1'5ZÌIsXG½_šIþrR£½œÔè.'%šËí²à-'5ZËmœåð^c9©ÑWNj´•“]å¤FS9Ô žr nœ¨¶Ôä('5Êa¥üä¤&;¹Wj@a’`&Ç(É gKKVrÒ’“\”Seô‘“–lä¤%9iÉDaì÷XbKKr[F9iÉ?2ØÇIKîq»¡Eó8iÉ;ÑkiÉ9NZ2Ž“–B×lÐ'r-5¹Æ¡4˜ÆIMžqÞL,j}Þ/ƒÖR“aÜþÝè'-ÙŽRÿCZ2‹Û2zÅIKVqÒ’Sœ´d'-ùÄIK6qxƒÁ%N<.Ë8µ´äw~—QjiÉ!Nj2ˆ“šüáÎÃa„Zjr‡“šÌḋ:ái”k8)ÉCZˆMKI¾pR’-*W¸Wv£'œ”d ‡‘-8Âù h1i)Édžï¤$7¸SÊxô£Ü\É n®ä7W2‚›+…¢çJ6p( .pÓ¿vd¥ác0,à°÷ Ÿ‚Í• à¦$ÿ·Wjú•€ž’Ìߦ$ï·éÙ.Œ>Oÿ„Áç)Éø D<ø¾íGÛ·9“ëÛœÉôí•:2Ï™,ß¶ŒŽos&÷9“ßÛœÉî Ç··÷X7Ï‘¼Þ0M«·9’ÓÛœÉèmŽäó6g²yÃý—·)Éä-J©ïƒe”ù•dÆ[oS’¿Û”dïv^ Ìø¡`îæï×ÂËQŽçü®—§$c·)É×mJ²uóölå¹’©Û\ÉÓm®déæÍÛ‚ÊÞD-¦Œû vù ZDÙŸ¤”çLfns&/·9“•dpr›3¹Í™|ÜæL6ns&7.éNyÎäávNÅ(r”l¢ÑÀmÎäßæ}Á"ÈèeÁ½mJ2o›î•Äðñ”dÝöVÖ(éÌ_pyg·ÐñiŒOI®m>nXÜxJòl;mƒQãWjÐxJúrkJòk›’ìÚæJnms%³6&'\,5y³ù2ڢþ¶à°/z-6ìëq KM¶lR“+›/É-.,5y²aE,Ù°„ŽlR“!Û{* KMvl¾³€°/·,,-y±‰ÃÆh0Ö¢Á‰ÍW× ö…«…‚}ñi‘`iÉ…Í×Ï––<ؤ% 6iÉÍW¹––ü×Î-0Œ¥Zp_“–Ì×ð4‚÷þgˆþúæÂ‚¿DØ'öë+d ýúÆÉ"¿R“ëšÔdºöVÖ>r®ŠQ_iÉq ×b¾âÆ2ä ìÖ¤'·5éÉl ûÔðÑ•ôdµ&=9­á®ƒÑšôä³&=Ù¬ù©,Ð+=™¬ù±æ•;Y¬E‰Vw'ƒ5¹“¿šÜÉ^Mîä®öžJã»Á[휙Ñ]”gµS™±]Ü`ðU;·ÀÈ®ô䪆·LÕðÊ‚§dø¶Ê÷tÔ•– Õ¼ÓYH×›·Et}c]ß´Z<×7žÎEO Vj¯||³ŒÔ¤%5i)’ëû_£î¯\¾Ya\ß–Z÷< q}›f1\‚,„‹nìÓ¼§Xׇ>‹ßú já[G½õÙ‚·Þ-v B·¾Ó´È­o-p+-™¦á‚gš´d™æ;> Úb|†iQÚf9Ø¥9ý°€-ÎÌÒ| oáZ(,Z{Úƒµ¾Y¶X­´ä“æÛ‹Ôžkf ÖG$‹Óú†×´çY1Jë;z Ò¾§ZÖê¢?š¹¡õ1Ö´Ò’9šó ÏžvÅè,¢;ÁítÆfQ|Ѥ%[´Óq˜•–LÑγbXöt+FeXPöônÆdOïfHö4~Fd,X@ÖÁ‚ÅcÏûe8ö´:Fc¥%'´#‹=–¡X‡N‰=M”½À‘…Åa!ƒšOm…uæfAØÓqƒ}¥†`_©X4ïà~&õægå'XŸý$ôü§:ŸIÆg˜¹Þ/®dDÛ3ÑõLf4=“=ÏdDË3ÑñLV4<‰~g"Ñî »ä×íL$š­½ÎVVg«E§3|5õ­}ÎÜLèyŸ%¸œmLÎV‰gç‹(]˜¯':œ­'œ­'ú›š:©¯;º›­;š›­;z›¹É?ÑóêÑÙlµhl¶zò5swmCÏÇœžèyÝÉÔìXç=¯ž,Í^©èyÝÉÐìü™¢ç5’þÈAp3‹rUþ7f =#‰4X™¹Á£¡gO15ôì܆ž=ÑÐ3žB´1Ã}G3èhc¦‚T)~Fy´1ó|:ãÏ8}t1[+™˜­•<ÌÞ›@{î«èZJr0ÓŸfÕsAãR£ƒtt0Cýè`VÏ·.äÐIï¦ Ìp¾è`V‹7%²hÔf(fÐÑÁL¯78˜i« @Í*:˜¡!E3´¤è`†\Œè`†òè`†ã£ƒYuÓBãÒø½è`¦-58˜áøè`Ì £ƒÎÌêùö‚xåÑÁ ×Ì´/3èè`Ì £ƒYõÜ1£ÔÐÑÁ¬z.šqjt§è`†òè`VkIfúûÁÁ ::˜iyp0ÃñÑÁL¯/8˜á÷£ƒtt0«µ%3èè`Ì’F{®%9˜AG3]·SA+¹ZÑ5î/:˜á÷¢ƒ è`†(:˜¡<:˜áøè`†òè`†ã£ƒÊ£ƒYÐ ±«çªŶáò`lü^t0Cýè`VëùVJI6tt0ƒŽf¡¾‚Lœ?:˜½í…8::˜aüˆf:%3ŒÑÁÌÿ΀!m —ÑÀÌ3õi¿R×,ø±h_†ÎíËp¶h_VχšäÚÕ?<4°­ç d;é¡“Tt/Ói(¸—á’ùÙ“²í5“wÙº“u™¬ä\¶J2.{ÿü‰ØÒ Ú–­'¹–ásúÀ¶}Þ5¶íÎÇÆ¶O.=Ù6fÖÀ¶×H~ek&»2}ž,ž\{²+ÓÁ?àm<ƒ]êG»²êÞáÖò`W¦åáC©êÆäFy´+Óy?Ø•ïvˆ¹}â6̽$y•ÕR“W~,z•UÏz7Ò]=kÛP·Î,uë̼ʪg=íÖ‘,x•U÷½5Þ´oíixë̼Êtf ^eÕ³² zCG¯²êY¸†½uæ _IAG¯2\Oô*ÃH½ÊÎÈcìk±èUVwé·.í‚W™–¯2Õ€CG^Ý[Öxu/\càÉ¢WÖ‚Ñ« :~%½Ê°6Œ^eºV ^ez¾àU†ß‹^e(^e¸¾èU¦¿¼ÊpþèU¦åÁ« ‹ÓèU½Ê £W–§Ñ«¬º7«Qq,n£Wtô*Ã6z•AG¯2,h£Wtô*CýèUVÝ«Öè8êG¯2]¯2èèU†pô*«m%¯2èèUf àÉ¡£WêG¯2=ð*Óó¯²÷ü$å8_ô*Ã;z•¡&w2“ž¼Ça«$›°U’KØ*É$lù_æáÛ‘•,Âd%‡0‘d&î_HÆ-’ìÁÄóÙI¸Ïíp‹$o0‘d &’œÁÎ %Þ~¥âm™ÉLfr;=”l[$y‚y‡5²-39‚E‰´/I~`2“˜*†µE’˜Ìä&#Yy4¬½_M4“'ù€ÉlÀ¤'0éÉLzò“ž,À¤'°“‰I¬--ùù¬aTû•bi_ÑüË¿(2¤í™ F´¥&ç/©Éøë$Wg{ΔÑì“>I˜í_èËöÚŒeKK–_R“ã×Éð%Êö/ÕŒdŸ«"1ñŒ*ãØÈ f_'õ’Û'±ñpȨ•aû,iû•J°¥'›¯÷w•_KO&_¾¢0z-=Y|O²ë“ãIt}’:I®¥'{/éÉÝKz2÷Âeo¯Óê,e»'g/iÉØKÎ_Ã`ÊvO¶^âüõñ„Ÿhê%=yzÉùCLÙ¾«–;zžb)Ûw²óò•Œ§l÷dæå‰¨ž²Ý“•×IR¶”í–Œ¼N&µ¥l·dãõVfÊvK&^'…ù¤lG /iÉÁKj2ðò¤Î7e;Úw®qR¶£y—gbzÊvMÖ]R“s—'£yʶ÷ß“²m»N>ûIÙŽ¦]Ò’g×éV–²Ý’c—ôdØ…Ò˜²Ý’]—§×yÊvKf]ž>é)Û-Yu‹<)ÛѨKZòé±1e»%—.iɤë\äIÙŽ]ûÊ£C—<É  {ÓàÏ%w²ç’;¹sɓ̹äNÞ\ÁšKîäÌŒ¹Ðq‚/—ôdË%=¹r½ÙßLÙîÉ“KîdÉå}ðMÙŽ†\'¿ÛR¶G²ã’‘ܸd$3.É‹Kf²âò™ÝS¶%q‰$.ñ¿KÀ¼Jráò¥š§l¯äÁ%’,¸VI\²’×+[Fû-¬²‚û"ã1e{%ï­W2e{%ç­U’ñÖ*Éw뜙@y•亵J2Ý’•<·VI–[²’ã–/M %¯šü¶VIv[«$·­U’ÙÖ*ÉkkÕdµµjrÚò„ICÈ«%Ÿ-·’7€ìé ÆÝIßðñz’ÇÖz’ÅÖÉak=É` Ù Á_Ë-û ¯;¹k­;™k­;ykÁA&Xk­;9kmµVK¾Z«'[­u'W­u'S­u'O-,µÜvßXñêÉPkõä§å.üŠ×Ü´ÞRÅÄ«'/­Õ“•–'z#^=iÊ$Ä+§l¯;¹hyþ‰áa¼ß²½žd¡…?×´Î_“&FÒHðÏ:+œ`øü©prá5“yÖùCܤÂï± …ÏŸÉ&^+gyþa·E5 üJÇ^©C“{® ^ë–YÿqÊöŸË0«ÊÏò˲\¯ÿ¿,æ¢s)Êè–åLŽñ¹WÖ©@*˜3–W0+ÐëT  l°?I+ÄË>w*üìW¸ô/ñÝÓ,‚éÑ/Êø"'*MP='‚IÑ/¾ÌŠJW°?¼’?~÷eW‚½Ä¯äo~ýï_t%ö‡»ü™Ì/2\ÒTÏžÉgŽKÜ-»á`6\:åÕ–ùÙn €`·d2V¨í–LÆ °Ÿ oxM§B¸ÄŸlx4ëÏq[BçünKh›ÿ‘Û’u-7W²Â%kðZ:R½–>ýËç¢v endstream endobj 479 0 obj << /Alternate /DeviceRGB /N 3 /Length 2596 /Filter /FlateDecode >> stream xœ–wTSهϽ7½P’Š”ÐkhRH ½H‘.*1 JÀ"6DTpDQ‘¦2(à€£C‘±"Š…Q±ëDÔqp–Id­ß¼yïÍ›ß÷~kŸ½ÏÝgï}ÖºüƒÂLX € ¡Xáçň‹g` ðlàp³³BøF™|ØŒl™ø½º ùû*Ó?ŒÁÿŸ”¹Y"1P˜ŒçòøÙ\É8=Wœ%·Oɘ¶4MÎ0JÎ"Y‚2V“sò,[|ö™e9ó2„<ËsÎâeðäÜ'ã9¾Œ‘`çø¹2¾&cƒtI†@Æoä±|N6(’Ü.æsSdl-c’(2‚-ãyàHÉ_ðÒ/XÌÏËÅÎÌZ.$§ˆ&\S†“‹áÏÏMç‹ÅÌ07#â1Ø™YárfÏüYym²";Ø8980m-m¾(Ô]ü›’÷v–^„îDøÃöW~™ °¦eµÙú‡mi]ëP»ý‡Í`/в¾u}qº|^RÄâ,g+«ÜÜ\KŸk)/èïúŸC_|ÏR¾Ýïåaxó“8’t1C^7nfz¦DÄÈÎâpù 柇øþuü$¾ˆ/”ED˦L L–µ[Ȉ™B†@øŸšøÃþ¤Ù¹–‰ÚøЖX¥!@~(* {d+Ðï} ÆGù͋љ˜ûÏ‚þ}W¸LþÈ$ŽcGD2¸QÎìšüZ4 E@ê@èÀ¶À¸àA(ˆq`1à‚D €µ ”‚­`'¨u 4ƒ6ptcà48.Ë`ÜR0ž€)ð Ì@„…ÈR‡t CȲ…XäCP”%CBH@ë R¨ª†ê¡fè[è(tº C· Qhúz#0 ¦ÁZ°l³`O8Ž„ÁÉð28.‚·À•p|î„O×àX ?§€:¢‹0ÂFB‘x$ !«¤i@Ú¤¹ŠH‘§È[EE1PL” Ê…⢖¡V¡6£ªQP¨>ÔUÔ(j õMFk¢ÍÑÎèt,:‹.FW ›Ðè³èô8úƒ¡cŒ1ŽL&³³³ÓŽ9…ÆŒa¦±X¬:ÖëŠ År°bl1¶ {{{;Ž}ƒ#âtp¶8_\¡8áú"ãEy‹.,ÖXœ¾øøÅ%œ%Gщ1‰-‰ï9¡œÎôÒ€¥µK§¸lî.îžoo’ïÊ/çO$¹&•'=JvMÞž<™âžR‘òTÀT ž§ú§Ö¥¾N MÛŸö)=&½=—‘˜qTH¦ û2µ3ó2‡³Ì³Š³¤Ëœ—í\6% 5eCÙ‹²»Å4ÙÏÔ€ÄD²^2šã–S“ó&7:÷Hžrž0o`¹ÙòMË'ò}ó¿^ZÁ]Ñ[ [°¶`t¥çÊúUЪ¥«zWë¯.Z=¾Æo͵„µik(´.,/|¹.f]O‘VÑš¢±õ~ë[‹ŠEÅ76¸l¨ÛˆÚ(Ø8¸iMKx%K­K+Jßoæn¾ø•ÍW•_}Ú’´e°Ì¡lÏVÌVáÖëÛÜ·(W.Ï/Û²½scGÉŽ—;—ì¼PaWQ·‹°K²KZ\Ù]ePµµê}uJõHWM{­fí¦Ú×»y»¯ìñØÓV§UWZ÷n¯`ïÍz¿úΣ†Š}˜}9û6F7öÍúº¹I£©´éÃ~á~éˆ}ÍŽÍÍ-š-e­p«¤uò`ÂÁËßxÓÝÆl«o§·—‡$‡›øíõÃA‡{°Ž´}gø]mµ£¤ê\Þ9Õ•Ò%íŽë>x´·Ç¥§ã{Ëï÷Ó=Vs\åx٠‰¢ŸN柜>•uêééäÓc½Kz=s­/¼oðlÐÙóç|Ïé÷ì?yÞõü± ÎŽ^d]ìºäp©sÀ~ ãû:;‡‡º/;]îž7|âŠû•ÓW½¯ž»píÒÈü‘áëQ×oÞH¸!½É»ùèVú­ç·snÏÜYs}·äžÒ½Šûš÷~4ý±]ê =>ê=:ð`Áƒ;cܱ'?eÿô~¼è!ùaÅ„ÎDó#ÛGÇ&}'/?^øxüIÖ“™§Å?+ÿ\ûÌäÙw¿xü20;5þ\ôüÓ¯›_¨¿ØÿÒîeïtØôýW¯f^—¼Qsà-ëmÿ»˜w3¹ï±ï+?˜~èùôñî§ŒOŸ~÷„óû endstream endobj 482 0 obj << /Length 19 /Filter /FlateDecode >> stream xÚ3PHW0Ppç2ÀAc(á endstream endobj 486 0 obj << /Length 742 /Filter /FlateDecode >> stream xÚ…UKoœ0¾çWp)8ø1‡R)©TõP)ܪ`K,¬€m²ÿ¾3`³ iµ3ãy|óÍŒ7 öA|»Iæókqs÷˜ò€+&•A± ¸PÌ$"È͸ȃ¢ ~…*Š9O’ð¾ëúÉN®xØÑïâûݣ̂œåZhôN‚Xq–)E~?Û~Šb%EhOp… íPÓMݯ‹„ §ùê¥q%Ê ”¶#ýs‹,¬gÿªª+2˜zR!ÛHª~˜}ûñ­§º=“IåÆa#PÎYž¦´´„c`”š°ˆŒ !¦”ðŽÛ¶ø‘]Wƒ7gÄÛŸHh,J"‹š©'¿±žˆ´T¾!MɸΓÇÁ¿<ÞÿxzØà7M˜«¥ëæðÍœÇûÓa&Ó— „I–æêºÚÖБC•;> stream xÚ¥T[kÛ0~ϯy’¡V-ù>È £Íè…Ѽm{Pm%öæÛl¹!ÿ~G>r’¶F :>×ï\=²#ù¼ðìûi³¸^‡1ᜥa(ÈfK¸Xâ {ã"%›œ|§ãŽË¹ém9d²ÏËfç¸~RYUHÜ4M«¥.Ûfp~n¾\¯ý˜¤,Dd¼zÄõæsýÝ7Žð€J|rµ•c¥Í‡O»ªÕÈÞ;"¦ ¹y9t•4Œ2Ú¦: š.ÏN >9uN+ùä¸"¡ªúá…ÞÜz.ÊÌAaaôÖƒ–¿ [YxÛ¾­O1 +“Ö ó›R"iÛ㛵ÕX7H7²V³x‹ïv"Êú 5¨¾<tHç—Ê4 ¾ ›RWê ªí…tŸfã™ i4ùÉ£ûo„xO1Š&>!55Ÿ ­©À¾™ƒ: Ôµ¡™)­ì¡¶ù«|dסkÙË&Sh1g=MÉðlJ0M[*‰rl¾a Œv¼TõBÙóÔ)È܇ö´æõ¡€‰ –‘ CÃ’ýn¬¡³`¥qBCÿlB¹/X’DkŠ@W뛯w†9ˆ™‰Y“YgᙆËcøÃÃ’ÀNýGè(¬P'{3Ž5L×~•R\I@…™ÐiJ/d=[CBÆF?rku„únKßZÓ‡N­–Ãò µŽÞæo½ZvÐï^i}X¾;Dôß!ŽGên³à@x„“‹Ã„dõâÏ‚Å> /ExtGState << >>/ColorSpace << /sRGB 497 0 R >>>> /Length 25575 /Filter /FlateDecode >> stream xœ¤½Oï.;’¼¿Ÿâ·„Åœ)ÿ··ƒ ‰W‚n‰b5€ÐèöH0 ¾þ[ΈpÙΞf`¤îsNÞ´ÓñT¹²\Qé¨ðóï~ÂÏßýüÏßþÃÏÿüÉã×óüÄVúrø5žŸðÔ_Oþù_ÿíç?ýüýoýÿñßþÍÏ¿úÃoÏÛòùÙÿüÿúÿÞÿÚÊÏÿþí?ÿ—Ÿçç¿þ~þÝûÿ¿û-Ì?ÿþ·’Åú“rù•ÚÏŸ¦YÂOJýW/?¿O³½ÞT…<Íòëy½±ÿ* ¦yï¦YÍ›â¯Þ`ÎȱNÄfÎÈqüЦõ}Ñ™·aÜø+%˜æ¿ê˜fGä1ÿ«™¨j‡i}ïl}m„”21_åyÍô«>0Ûô–_q6®vR~~…Óúö_uþ„̛گQ`NïÛ¸Óœ‘süÌ9Û_¯9w˜6nýÕçO¨ Þd€i‘#'D~`†i‘˯`exÛ a&½É0an4#Pų`\`®ðæ_µÀ丱ôÈïÏ·¾ ˜ßƒ“`Zß÷€Ú¡ëð~æô–÷(t˜3ò<ÜæŒ<·…²9žÞsc§¬âÉl8Gyõš1›isã5ó0ÓfÎk¶é­¿ò„Ѽ àUß÷jBß0Qµh‘ßS•˜ì[ÌLð¾ã˜Óû6ŽæŒü²4`ÎÈoc›9-³Be[,T·àZxM7þ¦a~Ç-0gß÷,t·š÷=ìv•Í41Ì,ö{›EÖTyÍYç÷5ÙÞŽqßé`Ú¸áW*0 ³ÌÌï1Ê0­ï;çÏï<8 ‘?Ó"\ݽ£ïÀTyMó¾¿ÓÌq®…×ÌðâH‹ü&ó>`ξ¯i Çsx_Ó¼)h‹ÞñÌ9¼4˜Ö÷™Yû5qŽBúÌé WÙÀ9 ¼èFBäwšE˜oÈÙ7Y¨<¯¶iZ~~Mó6œ…×lÓûvzßI‘žŽÌ?ŠõÝÌÃ[áe’yÍù½ÏÚ=å5gäçÍž¦õM<8 ^Nï×|½q0Ž>#Ç÷¿âÐõ9fà×lðZöÞÏ4ï{ÿ}`Z䈻Fx„æ-gÚÖû½°ºÙþv¥Ñß&ñ´ÛÑ?Xü÷ÂxÐÞ’[|/ÐÞiÓß`'øÎÝ´gü÷Z±ƒ8íÿ“h~Ë•ÓÆ {˜q¦ýNÍÍ.󮵎ó´s=íìÀwŒ_á/˜ÃÓnèßm‹ÏÞ³mñ\‰Ó¶þ<ëáæßìsü®ñŸF{ ƒXÿÀÛÜ´§_s=LÇôÌßiÏøï|î™öŒÿ¶·»È´­?§x¯ŸsxÚ¿çëíÃö{çÀûñ}í·ÿœì4ýóüØe=mó‡ÏÕæñæÎeÌÇyMÃOüþw>EÚôçN{Æï\mNÛâΗ÷J¶þ+^b³ßó÷lóûµs1<=ÑnÏwùLÛúófÞüõúÓÃEÊ´é/ˆ?füô^%Ñ~ãoíúóÎâ?ÓĴͯùm90Ïí€øL:ÓžýWIáM·Ó¿æÏkÓ¿ìÿ=í¶ž¶ÅïHø!&ô¸M{úßÓ^l~Æ ¿-e`[üÄóñÚ?cÝ=mëŸq› ±hüiÓÏöÕâ¿?»Èžñ£Ý`Ïþo·ˆßoço=L{úS>;ëîæÍjØZ—xú\¬õzxï‹æï¸óLÛüUÇkÌuàzÚ™öȶXÆü›wìñ­Ò§m~.µÃ{g¥ßrù´g|­ù§=ã¿ñŠÚ[ÍO<߬eÿ´Ío§mÚ ñ×kgŒ?"í ?ñdøW¼ a¾}í±-C²'妛vÆâù>÷ÍU¬æÏkg,q|çß2~Úc["‡ÔŸëÜiOÿ|~œ?-Ö§mñÏ×|C|Ì××XFc¾½ë­Ú¿eõ´G²u¦-‡Â|¬ëÛï™'Ëà »bhO4óéÚüçcð'Ÿ_×ÿ{ güw-ž íÙ_kóðžHóóÁmÚæW¾zí?+ßâ[û ü _Æøº¿Í†ÝŽW—=ãW®‡¦=ãÏ'µA{ö¯\ …\ᯜï¯=ý-cåÞ‰5ã·ˆ„igø‘_{öo•÷×y àgû?—¶Ó¶ø+ÞP|Ìï×>úœ¿Æ5é´Í¿Ù~ŒÿÚŸ”C˜ ÿé¼?¿ö{áL?Îç{"߃-^œ1½ÏE5Ò®mÑ¿c1J‚µOðÎw"Züõ{2â¿¶õçkÀ…>Y‘ojÃøËîø}Zÿ¼v¾šiWà‹v¾êÀø$"¦]ë–o^{„mþ´øùÔ?më¹':?æßdcvü¯mñ•ß&¾Ç÷ð&†ãøµ¨ß|Òpþ¾xIãÙŒó?Y™°åïf¼×¼?à~4y™¥ ï…Ê6ÿ&3óØõ†õ÷kÏþëz{‹ù3óék›?N¶öؘ¬ÐŒxšÇëÅÉÏ<ûñëðëzœ Íóq!ÓæøÈ“£y¶üð&¾P¶ë¥ ŒOò$¼v¡|ìÉ´-¾ÎÿkײÍÏÉÔ¿¶õ'?ÞKú1¿^{ú×ýt26ÇÞÍP¶ë­§°Ùåòãüe{L„]ApZ>øƒîÆ6Æ÷QÃýE¾ýmôÏãÜ«­WHéÿé‡iöï??¸[È4ÆÌG­ùè´bÈÞša·a¾&óGþ¿ý‡?óCþáoÿþ·¿þ7ñ'üüñ¿ÿû÷ìl 9[7þñO?ÿ">Oý«'¼ÿû—?ü»ßþõmŒ¿d.{ßõÝ¥Qød>™…÷·ÀL íùSîi^‚4·)ZÊPš{$ÏÕ€¹tk€AWƒ ÃþS·ß÷WÛÏNvǰ'ìb?uô?w þboã»ôÿ÷½ç"»[ïð<×ñå\ù- »Ò5%e§ž°’á¡ù?~péD¼|øK/«¬ÙŸ»¸Ü5öO‰öϺ ç•ðÞ÷bo“û“ÙeÒËçi·j¶Ñiñ½==Ó_p»š¶ù –ïq®kÑÿ©´#üYvAK¯Óf£3â{{âøFÇL›ã?ðgÅ7ºnÚþ,?ãÛr&6{÷3ý–Þ¦=ÓÈ´í„'bÆï´Ù¿"~“ß–GÓ¦¿ÚŸo¦Íøý»ú÷H›~LÂnO°-~}4mÆÏ‰vƒøßÛ“ü6ýFwÆþB[ømöh5¾=ÞN[ñåg|{œ‹¸¦ùÑ“úÛã÷äEé_6ý£Ñ¶øËÍi[üÎóÑq=ôŽÛ½M¤²ÛU~þ>[טhçû¡Ýà·Çuûac×ägÿ6yRkG|¾?2ü ñ†ÆïmúG§ÝòGÏ0¾=ÎÆñ¨?âGþ;Èo Óf|\# ~ÃrÑxÌócÇg¾‰€¿UÚôÛòbf阷ù4âëüƒÇ¶xðgùq=Ì÷ð‡6ãÓ.Âë}á·åèä-éÇõ:ÖùÃõ<ÖùÃüUÇ'[ǧÐ~¼_ü^{{d§-ÒÎðgù[ÚÇïèß°´7 ó?Ò¦ßæïä™cúŽ×´-~Åñž¶õ¯˜“g¥ßòá´é7z`Ú~Ã;yX‹_ðø1mëOznò²ô§J›~›ö^ 6ãÛñ2ÞœñmúüEþih´¿VÚãà­—¿Êÿàx4ÄϘ_Ó¶øYÇ£¡F~0ÞþgЦ?FÚƒ~ØñÞïO›¯oŒî4ž9Â.´é7úgÚ þ,›¯‡*l¼~ÚíÓå·û‰½ßòû´üIíÙÇ7$ùc mþÈù2âGä {å?ŽGÈè1ç{ú—M?æo(Šó*âäïi³þ¦ñ‰¿Éãâ¯þxqøÚñûñúÍ^·ÒöCãc>ž?ÒKÓf|{|œ6ñÛãüäåùûìqtÚóõËg[ÑÈw¼çë‰zÚ þˆxQ~ËGÆó³¿ü£nç+&ĸÿN›ýqþcÖøË>ÇÏ+þ ÍøãõÇõñziÍÿˆ×K¯ë=VÄϸŸO»Áot•2Õíz˜¯'Â6Ÿçë þ~à럿·#þ:¾ýY@4_ãÓ:móÜ/í5?üv›ïIxül½2mö·ÇñùâŸ~䃖?Óf|»ßOÛâžWÿF{¾ûl{OñÍïùzþ({¶ë'%õG¾JYþ1hËxEñ‡lÆgû‚þ Å=ó½Žù•/R•ó'áõaOXÏM^/>[þIMýq?yŸ”èÇüI]þe3¾ÑCÓf|£ƒg9ûDÛü™Ç??ðgÜ_í½ü˜ßùAüÌë+?êÑ?¬ñmùÑ>*~O´-~½5më_x½æ$?î¿™ç¯ò÷dž?Ýçë‰avI´sþYÏäYÍî´éÇýb¾ž€÷¿ÉÃÂë#WôçóÀ´ÖSÄÛ䲿Gڌ㟛úGàïðóùbÚôãzÉCñq¿Ëñ×y¨¿­§­D+oñ&¯ ?æcy÷§<´3×köûK„¿óü‚µõv¡]¹Þî´Ç¶Þ¶÷\ÛzyÚôcýQ²ü9Ðæø8þ%+>ŽgÉêûI±×Kk}>íõ"æß|=Ñ÷ßcïÁm½ÛiW®'íü”ãõWšâã~8_OÀûå|=þ5ÐfÿŠã3äG~-Cþis|ÜŸÊP|´¯ú_}Öï3ü5è÷áú¯ñ×àeùØ{Àõë|=?æÓ|=1I+¾Nµ÷:ðJ;ÓhÏþãa~«Y~üžùz~Ì¿š5>ãÙëÃéí³•×s­ò/›ñ‘j~\ßµ©?®ïj¯— އ½^ÚâÙƒˆÙ‘vm{ü®þ˜¿uÈÏöC~Ì¿ùz¢m¿gRu(GÂzªíƒ×G›ÙYó6mó³ÊmÚ~Ü›½>´ë±Ñf\¯-Á¯ëa¾ž€ßžÏ¦Íøö¼>mÆG>mYý›lúi—Ë_dzÅÇz·•ß®§ùz¢öéoŠß+íóø4õÇú«áü­|Öpþf~”„íúš¯'ê–O›½~úòuà'_3í¶|ßmb}×ÿ|=?î7= ?Ÿ¯§=Pn„ûÂëùxÚþ ›ñ‡lÆÇó`OòçB›~Úyý¾N›øk¢­øv<&QH|‰v­[¾éEø1z~¬zU¬/Ø2í|àoù­·v¿Žß:¿o?ÆküJ›ñ‘ÏûPäïa¯—ì÷Úcã¿ÒóD|óoò;]oÀ7‚úãzqù+mùeK#)>î#¡¿ŽßÈòãzY~䳑W|ùùa”…ЦùTùñ¼2ªâãüºŽO¡ÍþXŽ&ÿ²éÇõ>úŠßiçg›Ï££?ùœ4ÆòGÚôã~Šº¹Å_æçA|^oÓfÚáòùm½1mÆÏxýö>è¶òóÞâü3G°ø‹§·å×ÇÓÏûxeðô³Ì<6xúŃƒ§™<yú‘Ƀ’§IíÁÓî O?Äû‘§ëHñô¯ ˜<ý`YªxúQÉ’§ÜÎBžÞè1ñös³ÄÃ×Îäé'ýÅ÷ÆóN<5xz+KÜyúyúÄÃ3þ#Þ¾À¿l‹Ÿ°®$OoeµégñôÓnOot×øY<ýÖ[nž$üCýÁ#‚§·²Ùú³xz+‹oŒm7ïïÁ{ðôFæŸÅÓÛñ@ûR¼$Ï žžtßâé§ï!ÀÓˆþYþe3þ?‹§Ÿx“x|‹¯÷( ðŒÜxúÍæùãË5òôVö)ÿÜ.ñpÝHžÞühmÓŽ?‹§Ÿôx^ÜP&Ýž7Òi‹§Ÿvoþ¤÷Cþ4~O?Ç+ùgñô3~/?û¿x‹xûü’§Ÿô&yþÿx{ó‹'O?ýIvFÿ"Ÿñq¼ÀÓ6ãc>€§ŸÇ“¼{Õñ Oo[QÅ»Oâ6òô)-Þû{^Ï àé§ýl<ýŒÏßÓ1¾æ xz£»+mÛ¶¤÷>àémƒT =°é ×xú9í°N‹Ü8ÀÓÏiC=`›Çf7n3ïm ³à¢ÓnئÂöQÛLp>ÀÓó4þ.ž~Æ#OžµeÙ6þ ž~ÝÈÓÛz6·ù4ñðþDÞ‰<ýÐsyú!Þ*~Û|6ž~­gÈÓÛ¶ @›ÛtÀóŵÍëâØ×ñ“Ím6cãé×ý–<ýì_Ú_qáøýƒÏ=àé§û xz^6¿‹§·ûq¤mñç#xúõž<ýœVä±Ígæ#ñöÜ^ <ýôã¹<½åËD[þAÛâë9<½MÓ‡öÜfѧ·=¦O?§Ñ#Û¶ÉD^àé§MÛD’Ž/xú9Mp¾ÁÓO<(xz‹·ñôsZµgü"^¾ýØ’ôž'Åç{nóIÌï™Û|2ßÛ€§ŸÓ¿<ý´q?Oo*àí±ÍgÊtÚÜfÄÛ¿~¼=·á~^¢¶=Oo{òÁKs›OàùOoÓñqþ²xÞ²¶ù€7+Üæ£÷ð%k›ò/xz;²¶©püªm,È_àéçaëâñ­ÿž¦m.äýÛŠ¿ñôÓŸÄË+þÆÓÏx˜oàéça žÞ¶)ÉÏm:]þm:ø=õQÿ,›~ÜŸÁÓÛ¾ïJ›ñ—ŸññöÖ?p~‚§Ÿ‡ ×#xúU6NžÞNK¡± ó<ý<­Y¼<·!gOoþA›Û|ðÞ <½Ùâíçé§õxzÛh?ò%xzN£ßÅÓ¯²zòô&?öØ’u¿OoÛdÛ6‹Â÷´àég{ð€àéÍßi›_×xúy~ooñõ|žþ›Ÿàé—"yúyY5ñöÛ\¢xyÛ¦R™/·‰èþžÞ¦©lúñ<½µhø±O?/Î/xúÍ.—¿h›L/Ïm0ŒWÖ6´¯ò/ûôc›O‹m>UïáÁÓÛ´‡óWÉ'§ŸþGþ¹M¢ê½ xú9-1?ÀÓÛ62ñîæ\O§ŸÓù<ýôgù-þàóxúyÚÈ[ùÉëGÅÏO?Û#ß‚§ŸÓ×+xú9‘¯ÁÓÛ4K´úãx€§7[¼½ÅÏÂÂé¦:3ðô¶­í¡m~­'ÁÓÏÃþˆ—èã žÞNð5ùÉ«7Åä·øzož~Úü}ØæÓúg›_ï¥@ˆ'Ðz¿‹§·m~¶ð‹×ü}à± ¤éúg1xM⥶yh½ž~Úì ?ÆQÛLÒÆÓÛ¶ñøÜƲóôs¾á=xzÓZm µ|v~¾mªäég¾CþO?ÇÃùOo·¡B›¿¯Š‡·øYÂÞMõô<®«ž>¨ÞõôAõ÷¬Ç^íYÍúªUOõž¢É_d3>ß4ÅϪzúˆzˆUOõž¢k|þž!ÏøC<~¿õô¬WZõôâýUOÏuŪ§/ªg=}Q=wÐ~òÚ¬§çûÕUOß?ÞÞâÕ³ž~ˆçNêßUÏz÷eGôÇüR=ýÏzúñ°¾™õôãž"Pý<ý‹—ŸõÔªGXõôCÇ õتoXõôâÁUO¿ð|õôø½]õÚqãé#n‹§ÿös¨ž~³ëŸ6žþ;>ª§gÞÚëéÁSù‹xxÆoãgñôö¾P¶ð«~žõè¬_ò§ÿΧêé»Þ#¤µï ²Ž_/OŠ?‹§·ó^»èüàzP=ýÞuþoŸÇióü Ù?è=êé_›ïPO?åtÄ»ÏzêÁ]Qª§mÔc¢ßƒzìùžpãé§Ÿï9PO?*ï ¬§ªŸc==„~_õô#ó¾ÇzúQxb=ýÈ⩃Ægý8êé§L¥á'zúùÞtЦÿ/ÏøËøýE¼<ÏP}=ýM<<ý|OPtüÀ3±ž~$Õ¿õÇ:’õô#<ý†¿)~ÙëéÇâùÛê¿ñôqp;±êé'ž‡6Ççñ+¾ì ¹#ܧYO?Ä{†%×TU_ß ×´ê濫°®`=ýnŸþ(Q½<¥¦°ne=ýÐ~ÖÓk¾©ž~Í7ÖÓ¿6Ö‘¬§Eõ樧UõúYý‡xxú—M­«¡úz‹¯ú(ÖÓÅû£ž~¨Þ‚õôÓ¯úyŠeáü°ž~òÄj?àÇsëéç{ú§7[õóô§§·ßŸió÷=âõÙ?‹—ßÕºTO?´Ne=ý:߬§ß톺_ÖÓÞOUO?2ëYXO?ô†õôëzg=ýàýYõô»M?æ+ëéGùêëGýò“êé‡Þ[±ž~úU/O¹'<牧×õËzúÏzú7Ÿò½F“ÜÔÎÓÛm ~ÖÓÖ©ž~è½ëé‡êý‡üM¼¼ä¦Žzú®zóGñÉ;?êO;,¿xùÌú)ñö-|õ-«žžò<«žžûÉV=}#¥zzÕ©ž^Ï}iÕc7ùÙŸõí¬§ïzOå'ÏÎzz½GR=}ãýCõô ëé;ç“êéõžMõôC¿—r\\/­zú!»ËÏ÷¨§ßí†õòêé×ø¬§äQTO¯÷Ôª§ŸÍñ‡xü†ñ£xûÁñÑ>hü*Þ^~´+þÆÓÛz¤ÓfÌOÕÓ«¾Tõôz.V==åSV==ÅJW=½Þ;©ž~è½CQ=xR½}…¿n<½ÕÇÂF==ß_õôëúb=ý ‡êéµ¾Zõôƒóõô«ÞWõôâiTO¯÷<ª§W=ºêéõܯzúÅ+?òÙô'ÙŒÿˆ—g|òÈ«žù^õôí«——_¼=ãGÙŒzúÁõ‘êéÛUO¯| zzíüêéƒx{ög½zYõð²YŽûùWOëUõôâ±UO¯ëQõô]ñ›üà9UO/RõôçWõôzϤzú~ðô_=ªêé×ñ«^~ãé¿úÛ¯ž>\õô;OÿÕÓsòª§Þ¯žùí«§ïª—·ztíwR=}ÍzúÀß«zúÀ|¤zúÀùÊzúAýëUO>ÞžýƒêãéO?ñ5ñòª×¯¯ýð¯zz䫯žžûV=}ºêéy|W=}¯¯zz´g=}Ò{ ÖÓë½­êé“xtÔÓí÷c==tW_õôk}ÁzzÞ‹‘§´>&Oÿ^¿äé½W'Oÿè=yúGÏŸäéÊEЧ´ß™<ýC>S<ý£ýšäéŠ:‹§´ß”<ý¬£i´oÿ`.xñ´âÚê¯zyú›xyŽÏöYñëVOoÇ<7xúUDž^uÃâéŸÂë‹<ý£õ2yú§óx“§´ž O?íN›þ ^Þâë=yúU'Džþë?äGyúðè=xzÕI‰§Ü%žþ³ÃåËÿü|<ý3p}ÿ៯T% <’üI’<‰[ʧè‘kRìñHO1[“ÉW<ákBûhò>²¼÷¢¯ ì£I±í)_ØGÀûšlpÿ/$¯þ¥X&g¶ ·ŽÒ?A†‰ýíi9¡üéßìõ›õÏ«ÿdÓÏþçþfFR`—Ø®8v7Yö§¦÷9{“\“vEiw¼õùšÐ>›œX‚Ç.,Ác –à±Ä KôXâ…%z,ñÂ=–xa‰Kº°$%]X’Ç’.,ÉcI–ä±ä KöXò…%{,ùÂ’=¼’Ù›˜½7ÙUÀfÚg“XÎ&ÑG)W”⣴+ŠÃR/,Õc©–ê±Ô KõXê…¥z,íÂÒ<–vaiK»°4¥]XšÇÒ/,Ýcé–î±ô K÷Xú…¥{,ãÂ2<–qa˸°8 F½.Ý›ÜXúsb‘}69°È>›”+Ê…¯f÷&K¸°%\X‚Ç.,Ác –à±Ä KôXâ…%z,ñÂ=–xa‰Kº°$%]X’Ç’.,ÉcI–ä±ä KöXò…%{,ùÂ’=–+ïvŸwû•w»Ï»ýÊ»ÝçÝ~åÝîón¿òn÷y·_y·û¼Ë—ó{³Ï&ó}ïÞÄì³I»¢4e\QÆ¥]XšÇÒ.,Íci–æ±´ KóXú…¥{,ýÂÒ=–~aéK¿°te\X†Ç2.,Ãc–᱌ ËpXX ±šÈ>›XdŸMÚåÆÂ*‹½‰Ã.,Ác –ౄ KðXÂ…%x,ñÂ=–xa‰K¼°D%^X¢Ç’.,ÉcI–䱤 KòXÒ…%y,ùÂ’=–|aÉK¾°d%_X²ÇR.,Åc)–â±” KñXÊ…¥x,WÞ>ïŽ+ïŸwÇ•w‡Ï»ãÊ»²÷&WÞ>ïŽ+ïŸwÊ@ö&fŸMfåÇÞÄì½ Š¾&´Ï&¹ŸM²Ò®(K¿°te\X†Ç2.,Ãc–᱌ ˸±¨ˆIM–}6Ù±,ûlÒ®(ÍGW‡%\X‚Ç.,Ác –ౄ KðXâ…%z,ñÂ=–xa‰K¼°D%]X’Ç’.,ÉcI–䱤 KòXò…%{,ùÂ’=–|aÉK¾°d¥\XŠÇR.,Åc)–â±” KñXê…¥z,õÂR=Ûs41ûl2ÚÙd´« ³¾&´Ï&¹M²Ò®(K»°4¥_XºÇÒ/,Ýcé–¬ÐÑdÔ«‰}ÿokBûl’ëÙ$û(íŠÒ|”qE¹±à[ƒ_Ùg“‹ì³I»¢ÜX𑣉Ã.,Ác –ౄ KðXÂ…%x,ñÂ=–xa‰K¼°D%^X¢Ç’.,ÉcI–䱤 KòXÒ…%y,ùÂ’=–|aÉK¾°¸Ï”Ìÿ4®(K¹°¥\XŠÇR.,Åc)–â±Ô KõXê…¥z,õÂR=–za©K»°4¥]XšÇÒ.,Íci–æ±ô K÷Xú…¥{,ýÂÒ=–+ïŸwÕwƒÏ»áÊ»ÁçÝpåÝàón¸ònðy7^y7ú¼‹Ï†MrqMÚs6ik2®(ãŽb•[Úg“KðXÂ…%x,áÂ<–xa‰K¼°D%^X¢Ç/,ÑcI–䱤 KòXÒ…%y,éÂ’<–|aÉK¾°d%_X²Ç’/,Ùc)–â±” KñXÊ…¥x,åÂR<–za©K½°T¥^XªÇR/,Õci–æ±´ KóXÚ…¥y,íÂÒ<–~aéK¿°t¥_XºÇÒ/,Ýc–᱌ ËðXÆ…ex,ãÂ2–ôœXdŸM,ÉçÝtåÝäónºò®ì½É•w“Ï»éÊ»ÉçÝtåÝäónºònòy7¡úkBûl’óÙ$gפ^Qª2®(㎒.,ÉcI–䱤 KòXÒ…%y,ùÂ’=–|aÉK¾°d%_X²ÇR.,Åc)–â±” KñXÊ…¥x,õÂR=–za©K½°T¥^XªÇÒ.,Íci–æ±´ KóXÚ…¥y,ýÂÒ=–~aéK¿°t¥_XºÇ2.,Ãc–᱌ ËðXÆ…e8,ù9±È>›XdŸMêåÆ2wï\Q–pa K¸°%\X‚Ç.,Ác¹ònöy7_y7û¼›¯¼›}ÞÍWÞÍ>ïæ+ïfŸwó•w³Ï»øzùѤv×d¤³É¸HJ|ã|kBûl’ÓÙ$û(õŠâ°ä KöXÊ…¥x,åÂR<–ra)K¹°¥^XªÇR/,Õc©–ê±Ô KõXÚ…¥y,íÂÒ<–vaiK»°4¥_XºÇÒ/,Ýcé–î±ô K÷XÆ…ex,ãÂ2<–qa˸° ‡¥<'Ùg“‹ì³I½¢ÜXæ®»+ŠÃ.,Ác –ౄ KðXÂ…%x,ñÂ=–xa‰K¼°D%^X¢Ç’.,ÉcI–ä±\y·ø¼[®¼[|Þ-WÞ->ï–+ïŸwË•w‹Ï»åÊ»ÅçÝråÝâónÁ^¬½IŽ®Éþ½ëeŸMÆeÜQL%xkBûlrb©K½°T¥^XªÇÒ.,Íci–æ±´ KóXÚ…¥y,ýÂÒ=–~aéK¿°t¥_XºÇ2.,Ãc–᱌ ËðXÆ…e8,õ9±È>›XdŸMêåÆR¯¼+{oråÝêón½ònõy·^y·ú¼[¯¼[}Þ­WÞ­>ïÖ+ïVŸwë•w«Ï»õÊ»ÕçÝzåÝêón½ònõy·^y·ú¼[¯¼[}Þ­WÞ­>ïÖ+ïVŸwë•w«Ï»õÊ»ÕçÝzåÝêón½Ö»Õ¯wëµÞ­~½[¯õnõëÝz­w«_ïÖk½[ýz·^ëÝê×»õZïÊÞ›\ëÝê×»õÊ»ÕçÝzåÝêón½ònõy·^y·ú¼[¯¼[}Þ­WÞ­>ïÖ+ïVŸwë•w«Ï»õÊ»ÕçÝzåÝêón½ònõy·]y·ù¼Û®¼Û|ÞmWÞm>ïÎ_Q–pa K¸°%\X‚Ç.,Ác‰–è±Ä KôXâ…%z,ñÂ=–taIKº°$%]X’Ç’.,ÉcÉ–ì±ä KöXò…%{,ùÂ’=–ra)˵Þm~½Û®õnóëÝv­w›_ï¶k½Ûüz·]ëÝæ×»íZï6¿Þm×zWöÞäZï6¿ÞmWÞm>ï¶+ï6ŸwÛ•w›Ï»íÊ»ÍçÝvåÝæón»ònóy—*{“qPØákBûl’ÃÙ$û(õŠR}”qE¹±PDb5‘}69°È>›Ô+Ê…ê{‡%\X‚Ç.,Ác –ౄ KðXâ…%z,ñÂ=–xa‰K¼°D%]X’Ç’.,ÉcI–䱤 KòXò…%{,ùÂ’=–|aÉK¾°d¥\XŠÇR.,Åc)–â±” KñXê…¥z,õÂR=–za©K½°T¥]XšÇÒ.,Íci–æ±´ KóXú…¥{,ýÂÒ=–~aéË•w»Ï»ýÊ»ÝçÝ~åÝîón¿òn÷y·_y·û¼;®¼;|ÞWÞ>ïò“«{“Z\“ñœMÆUfÀ³~MhŸMòs6É>J½¢8,áÂ<–xa‰K¼°D%^X¢Ç/,ÑcI–䱤 KòXÒ…%y,éÂ’<–|aÉK¾°d%_X²Ç’/,Ùc)–â±” KñXÊ…¥x,åÂR<–za©K½°T¥^XªÇR/,Õci–æ±´ KóXL?ôhR³kÒÇÙ¤_/|‡}ÍkkBûl’ÆÙ$ù(õŠâ°ô K÷XÆ…ex,ãÂ2<–qa˸°Œ‹‰2mQ–}6Ù±,ûlR¯(ÕGéW‡%\X‚Ç.,ÁcÙÅ61 ¯ÉŸ—Šº÷òŸ}ÖñŸ°ÝÿŸRR÷~šÜu’6m¤M2)òMH±Â¨)“Ÿ§þÕÞÿý9µ¥?e¾õ-ãŠÒÎ(RÀ"Ü÷ïWþï²S—.¼TªhÿSêú9ã„l÷ºð˜úôÿúo?ÿéçïw1¯Z§ÂÓ÷ç.æ…_0Ãÿ#²^iL‰Êï4Á¢¿p&÷&væÂu&ÃÙäϟɽIŽ&ÿüMGØ–KbLø/HŒé˜ÿ‰15ù cjò$ÆÖÉý$Ææ¹Ä·Åæ+šäLÎF›¦W¨Û_[“ÿcœ–¼[1ͦÉVÎﵓgjÔ ‹}õ£Q+¹Ø·+¿ÈõZ¯ïµšY•>S¹œ_Í2Ÿi&ûFTPD-•ýL?ò½@á3õÈׂϴ[ß#þS‚-¦ßè‚–Î~¦êWL»§ñÛmÅdzêÀ•W¬Íë3ýÕj¿zưÐÏ´Nk Ï”ûj ÏtôªIܼ–©ä×À˜6zìgŠ…5ÒgzÊ5Ñ7h!¦iV“‡iJžÕ”`¿X³||¦ùˆÙרðZ c6ú€ÓÔ «éN5~S¯VÆ ô!¦é@VŽj*е±ŸiøÖNŸ,øL¯°šô3!‹Æ/ µÈ˜†³Eõ³(‰>Ó¤m‰>SHm¦ 1YjZˆi_k™ý%k¼yZ¡ÏtE›I-4ªb6SUhüÆO3…øª|´à³¯e4Ó.Ð,h1íüµÆ~6w[§ÏT2[§Ï4'[gLXƒ1Mÿº þvûºIè3mðþ0¦)§wÓñìcÚ•Ú»idvÛØÞ¨8Úí|4~'»Ûvõ1çñÕ¡5žmBoW\Oô™.gOôÙÃ×t$ºmõnT0î¶«{^Ö¯Ðgj­ø Uã7ì»m¨ÆÜ{-Û;=óR‚õö«s©7ú,öFŸ)jwS†Ø,Ä4uæÞ3ÀÖÏrAôYéƒ8m5ÐcÚUÕû™"ìxv¾SSùÕÜaÚ•ßò&SPùÅÄaŠ•:àÃÎC¥ŠéˆôÙù÷N*¿Ð1lßÿkuZoö¯óeØÇ@¦•aÁgóz؇@ô}3èA¿Q€Ì¾V³Y»¯ÒgßÓöùײ,5lgº¾¹6¦ÌgY ÇÓʰ¦_k„Zr­¸V†mõ®üø°]ݕߩƒ>YðÙ7î†iú×eëðØ—\ð&zZÞOà–ঙÑWÞ¶÷ Œl¹#…&½MD®òÎÈÙ!`›òªNŸiØÖŒ ¶°®üþé4Û³.…i¢oæN/æsèôÚ/„ÁȽÑDd6¶­]sæ˜_ulr]sZ+¿º°yUÉ"`£jmø–FÀvÒ57°ut7¹šˆ\0Pb_»lÒ¬ *Þ2+?‚°ù²R£=`£eíi›*+ž^³hÜH“^4®ŒL•‘1Ÿ±U±ò#à µ–Ø<Øø)Ž€‚º…h›Sæ²±Ð|›iµðáðÆÏL^žÛ¢Ç‚ióbÜô°ï9òZ­†ä4Ùî³ÓDä&}1½±¥L æ€ícíÁSFÀV±ÆO5l kü²BÀ°ÆllÔúBezqR°«ñ« ›­´d ØX¥oilj‘W¶:5~y8`[“Á[˜´Ö Ø®¤EcÀ¦¢F5ö€ DZìljüVeÀÆ ªÑD_œîdÓ@‹Ói[w!«d+_?ßoüŠå4+–^vÊr å@o—9ÒzÙ–v-pæäȾ%zq_ȉ^$sliÕ¶†|3ûUÖ/¸…¿;6a4~‡-`ÃEKX²l®hü UÀˆ©®F\˜ñpº2R Œ<`ÆãX5#dï¦sÄPIã&šˆŒ”‹l³rl³Wn-òü6{½&Ö%4{•¶æU³×fó©4ÑN«ôs¥·É/ZcdL³f/ž¾cÕéÅeÕ:#gy9n£‰È¸ºÛæL^c B9ÒDdœßþèhšÃPamÖíònÒ‹¾Q‘Mü¢&}‚=Ñ‹[LOôâÖÖ#€´—«ëÂé™}i–Ó[赯¶L‘mU0‰ÜÞÊü¬vœæ"—T­P.ª6‘U[TmãG-AÕ6~"TmãbT-ßEÕ6~,TmpßUÛD¹6úl¡Fª– UÛùÉPµ=aäÎøUÛyçUÛùéIPµS·ºüˆªíül.¨Z½<UÛÓ¢qgLæoPµ½’æ4ªV›UÛI‚€ªÕ‡ê@Õ®–FÕö†{ ¨ÚùIGë—鳨Z}Î Tí‹ÚR¨Ú9O>ªvþZCfTmçó¨ÚÎ'DPµ½ñ×Úßô jç‹;fãÉŠÖ/ÐNû5¨ÚN2Tmï˜ ¥d”$ÐA—ésž jW¤ j§äìô%䤲0.ÇÁ5¨ÚQ0wAÕÎÒ’œ¾ŠÅ¨Úùñäö#ªv~jwüˆª"5œFÕæPµ#âºU;">Aªv0›€ªÕçyAÕŽ‚”ªvJä¶Qµ£’ 6ªv4ÌdPµƒt¨ÚÁOjƒªÉYøòGÕNÉZ’³3&?µ ªv¾o7jçaàªU;ø‘RPµúĨÚIe}Tíd«6®Öت¬Œ“|\D“&zèmðZ_K.K„ídœÂÇØÎ€)+²eZp¶_+i;½ácmÃÃ, ÚÖ6zßÈóe?™ÚgüˆÛ‰Â6˜[ã®â¨ÛiÚEîvšÞ¯ÝÐÀÞÑ•~DßNîJ-½¶ØkDWûƒ;½v-€Â&xÚGã>¤ts'oKÌv»r-àqÃÞ"~¿jð5,:O¿v .×8³ñ#2wšblßÛínš·âi|®ý"R¿£Ù@à[ ú~æá­ð’È‘™jÁêÎqmYZwòmÈnF“œqÝ™oÁì“7~Díξöøn×X@k<àýL󒨽;1ã9ü.éÇßEð’ü] ïdï’8]óg®q]L•ß~WÂmƒ,ïŒ%hÞ5)ÈóN{lDïüP Üox}‡šDïüŒ5¼v¶æWxe¾k󢱑ˆCk"½»ùþÂÁôO¢wt>—€èzVÑ;ím·ÈQE?7öÍÑ»›Ó«'<iÌÂßEôö¦ÆFôŽG¨½X™‚è}³ÉK#z_³oDïl\hξ•¼Ψ¾pJ¢w^Þˆ9.÷Aô=¥èÕ׬IôŽ@Doç;r½ƒ¯›IôޤPFôÎÂ-Q¹Ó;ÈЀè“0ÉÆI j=C®EÁôŽ~0½Ó ÆôŽŽû&™^(yÿ.¦wÞE Ͼz„Ó«;)™ÞÁ…+™ÞÁõ™Þ±ØxczçµûzÔ´Õk÷„ëµ[ÚÛjyÎe0`{mŠÐ( ¾÷°-¾xw0¾AŸ6$åk7‡J›ãã¤ï´‹x]‹Ï5)i_»[FÚÖ?s6€øxpÀüÎÙXÄõÎø!ðY ܯ͸A»Á_ѾÁÏ¢Ò¿æßøßiü~#g<ÑvN¼(`›Òƒ¶ù3Ÿw@Ï©DøÛFO?Æ<'fˆàiã|‚ ¶x…¶ÅO¼ÄÁÏøx<ñ‘YðÎEÐÁ3~=ÌøE¶õ×›0Âs ã9”°]²gü9-ßHá9íH?ô"o+Æ×u^xN³ ÿŒ“~oEüÄKÔð´ñÐnxößÉáic¾€žÓ 7 ÐÃ6mÚÖŸÏ„$ˆç´ m ±¶"žvÞ8b;-Iß@ü&ÿPÿ.F˜ããxž¿*Êôþ±1È^PÈv|í7o[š×i$²Í§Lû=óø‘5ªgžܲÀ#ÏöàÂA$›Ð6#­*ÙÒP¢mñus™üͰÉ/  “í²î´gü”™ïA(Ûeµ1Êó2!-Üàú} þ¦ãÛ¿ OC|­È@+ÏöÈ/à•7{\~›ˆvÙmÔò×7¸å!·ßE.öé7²Ô.“@Ûâ'žÌó²ëâÍ/ óô#ÿ‚cž6É^#0ç´'Ûk æœÖdafžÓŠtw†0?€h¶i˜iÏøE¯A5Û4Ë´éoÙlí7¶Ù¦a£mñ—¦à›í2Âïmòã-çÙ÷{PÎf‹‚¶ø…ù¤óœ6M¼²ùùiaÒÎsZ`¾ƒw¶iæõA|½pó<§ æ?¨çéïb—ÿÙÈçÙ>nìó´qÿýlÓ(Ñ6ÿà|=Û“Oú}8¾  çxxm¿ ®‘мÌ~ =ÓlïüÞØæ|Â|=ç7Ž'ˆèéÅ&ÚòQ¥m~½ƒmiñâ7Î'°Ñ–VawøëÇ8gàC~! où»é9ùëþšà¤í6bñ@JÏøäŽÄW~-=çO‰=û¿ië+Ó–vm‹¯ëÔôŒëÜôôÙìùvzúq=€ž¶ñEWs|’ÌFPÏ4Šù †z³ËåÇù›ç?Ѷø\_L–:õYhÜßiõþ;,µ­jÅRÏr5㞥®XPÌÒóCö,(ægîYPÜÈ£ ¸³Ü·2¦Í:7äsÅ –z¦‰ð#–zV´lÅ…eÉÆRWò,(.¸ï€¥VK°Ô•kP°ÔªÎK]ù<–ºüv°Ô³Šëc©g?òÒÕË€'ŽÏb&úÄD#æø Š+ç?XêYgbü²±Ô•\"XêÚ1³ÀROxüˆ¥®Y,ueYÅOÅåųÀ7±Ü·±À×r Š#Ž< Š?+Z¿¸“ÓeAqÆŠ‡Å|×Ì‚bZ,(æ{ó½ Š¹`²fêúPˆXü[Ž‚b[O² ˜, ŠÉ:² ˜\' Š?«¡à…|6 Š YfL›/« X }v÷aAñÃBd‹Ïf!²WU¨ûü¬‚b1ßMEÑ[A±Ê › Ÿ·‚b±÷¾‡¾a¾þ±ÔÓÊ?« ˜]ç!³ž¾ÏÂy¨d°‡ùlmÍ‚â‚ñXP\ =‹y³ »@TPܱÂdAqáÆ‚bnJ`Añ™Ç‚â; Š;Ž ŠI‡± ¸cž± ¸“.ï#¦çM'üˆ—žïà¬e£/°h¾Êbãó³†ù,€’n¼‚‘~-Ë` ¤ÛGOg;f*/FÌD®ýl 2ºUÜ•ÀE '¨èÆ7H`¢kâ@DkƒxèùÆñ+(n*¿< ·“bæqÐ Ë„ß9ܸ¢ÝG7J³©€ÙîÊ­³ØèçÍÚ}•¾@²9›o+(n|•æY3ijfxçùhòÌÑ|ñ+(n÷ÎýÁS&8çÍš¾€w8`œ»øÎ.ùæ®—Þ ›»ˆG°Í]ï¢A6ÏÀ‘æ°QIU[j˜/ke ž Dó ¿«¾¿,xfýpòÌm¨9Ó‹%xæÆm@ä™uÉ3ïf³—ÆQ%ÃÓÛ?bÞÅB#2Öºà™u’gž&"wyeb\Ö&wFn¢G]Y‚\Ū]ô2Ë`ұ–ƒ*(VÅ% ŠKÒXPœ9UXPœy4XP¬+…Åz¬cAqúXfDÎ"™9ˆ3† Š#ßȰ 8ræ° 8~ürýö© x5Îô.rÅàÂVA1íWA1’ Н…UP̨‚âg/(Ö¥± ŠY}Üè]œ0#Ëd‰ô ‰"h¤\ëžÂ‚â JíÁâÜ$vŹ˜í,(Ž* ~X`Kê2ЛU_ŒRå."…½˜ ,( Å‚bÝWA1IZwqˆ7ŠXka‹ÐbA±*ÅVAqŒÈ¬M.*¯N4™ UP\ö‚âLf€ÅE‡ÅڔÂâ"e¿C,1¼¸³ ¸pÖ± ¸ð„² 8s2¬‚b¤¯UPœäe©²èaA£f6°T¹odp³ ŠŸ½ X×à*(ÆËɯ QP¬·±,(n¼Å° X»CXP\… Å]) ŠuggAñPq. ŠY㤂âΙú·ë¢û«…Éßþ¨6¹1r5µøÊŠÚÏRÔ~`j£ö7%Pû{·–öC'9KZ•åÅü æ+ŒYHÎÆMo¡0f#9 ŸÈٲѱUZ F>ÿ9ª–T©ÚÄ#(ª6Q T-ßN:6T-޵¨Ú|Pµ…t,(Âg£jó*6EˆÒܰӇ‹ªE™p %is‰T-w?‘ªÍ$RÓA 'ú -Œ—Y^Œ˜FŒ‘ªeµ©Z–úª•¯ˆRþ Šçy q :¶Å›5¾ç©EÕª=ªÍ?‹ªå]’T­¢tú ‰[ÄD)÷ý[~6íÒàPÑ0õ2òϦý7ª–#ªM$(AÕ¦½œ¸± œT-ë%IÕòi™TmÄj“T-·Žªe±?©Ú¸ˆ[èI„Mû¯'HÕr ©ZéIlT-w’ªåRˆT-)zRµÜDª–D¤j™ÜIÕ*f—ϬA_fq0bæªý,ôuúÐö¡ïù´”½IÕ>,Ò½°(‘>°‘¾°Qµ|­Aí‡Áñ 40Ú,¦¡ö_=Sûá+$¦ òÞBÍË‘Ô~è$Ÿ+cƯLXÔ~å í‡;µò µø€ÚѺŒiýdz¬Aí‡óNí-©ýÐY Dí‡.eˆ@/x!j?,62P£©L‘Á*Pû¡«6Ò[7ª¶É*HƒLfÚƒ´TõIªöá:Tís”ë–.ªö!mBíJ*Iûa%ÁUï©©ý°dh? EngäÎq‘±ˆ¼¸YDnj Ì$ljyЛ·’à™T­ê‡©ý0øpDí‡ÁJíUŠQûapqEínj?t1™Ð~è*˜Mô’öLòîÚ]|rfdðÔ~èÜ#Hí‡.Ú³ÐÛ6ª¶v>£Qû¡ë'TaÞµxHûa|bÐÀ3µ†d3 ý0>±x±j¥öƒŠ¨ý0¾à M‚A$0¬HÕ>‡öƒ¤»DÕ>|8"Uûˆö ìÛDÆÂ»˜[xû¦ýÐÉ9DFn[!pS19©Zm%U«GcRµ«q&ù$ÈUûÀ‹'U«zKRµ*$U«§{Rµ¢ U‹DAª6ò}Á¢j«˜[ŒCê jŸ]ûAìÜ¢jƒˆ\±,"}ØwIAŒïº¨Ú$ŽTT-.ÉEÕ®ú_ôM'UUý /ž•HÕ&r;¤jW)m ,u‡üiz‰ª]?!3rÛ©Ú¬ª`PµYEË jË'‘ñ¢z×~wGªvõmŒŒ RµY"]¤·¸YŒ;äEä¶k?d«A*/îÚz@ª6‘&"U«ß»¨Z¤/Rµ*$U›T“ÔeTäªÕk Rµ"ùIÕꥩڤjÚDÊ”‚ ™Ôå¡ý Ò$Rµå¤jEªå®=QµYŒqå±¢$C%¡$ö€ÈxAª¶N_TmÛ*x'hÒ›i"r7K̪æe_Ѐ½«6Þ±•îªBT­t}HÕJ1ˆT­Ê4IÕŠ"UKõ+Qµ«F8’Œ {UË UQµàgUKÖWT-CeFŽRw€7‹¹…·ìTmU)neäg£j› ÷¨ýPTgÜèm{¨ðnTíª³/Î µtʨý°"FÎ*ÈEä Æè{h?ÞC©ýP$«‹j?µXa&í‡ÅdFzÓV—»`PûA‰‚Ú™7j?äO Þ!±‡jÞ® Þa^Ü­¨ýU?[8nêªÑ‘öC!)Jí‡Ì;µ²ô½XåRûA/&¨ýUgÛ‰y™è;DÆbÜ(³ª$)`.*¹梊ܽ/µŠ(Ä cµk?Þq¨ý ©Bí‡U[ªsD•œ#MBj?°ÜOÚEu¿Ð~ZµXï$ínE—öCãýˆÚí+Á­æÅë0j?p#²´D‰SûA”8µ$ˆEí‡Å¯vz£Lx«˜[DnR†`ä]û¡‰Oô&q³ðbòSûA˜©ý 6j?T¹ÇŠìk ÷_j?TqÂÐ~Ðv4j?ÔƒªmUŹ‰Þ´QµŠ‚Ò~XÞ̾4 ½‹›…÷‘"[R5ªö½fÞcŸUµý½ö6ªÖ$DUKqRµ¤»–öC#q í‡Lrö±òûQKûaЂöC'qÛPoc$«´dÁ'aÞ7fçcµ¸Ivi?Ø/_Ú™–×~àRii?tÖØBãÀn¤Kûr´š ª¸-Ð…H?›öCù¨Úù^fü,íî¥ö¥©ýÀ:gj?v¤öCf=l¦o“éíÜóMí‡ÌêßÂß©}‡J:¾öQµ»h@Õ>ô€ª|I ªvH†Ø¨Ú!‘`h? Ìj?ð‘Tí UDí‡ÁªáA]ˆN:¾¶i?<8º j;Åç¨ýÀÇkj?|¼Ð~h_UíàõT›¥ñQµ#.%ˆécu¨ÚÞ©;Æúí j;IPµ½`¶ÛWçŇÅYg"–},]ˆaX Õ ý6í‡LÑÞFýŠM¦wˆ6nÔ¨¤c©_‘~–ö_“SûóšÚ¼Rû;/©ý)5íÎò¥ýùh?¨6PO"}UµƒITíàÞ& ?<»Nï Yòê>€±üóº|z–îƒä'l]2Èh€°më|5JÑ®bAØŽF1‰zm2P‚^[Jó$ª4øÀ.Í Ió%šÒ|ˆKäZ¸™OÒ|`J“æk$¤ù@FXš\IóoE¤ùY7¥bÑÉçÒX{ËqóÇáƒ÷‘¸¦¿k>P©Tš‰,25˜¿¥ùÀ fÒ|H»Œï x»$Xg'Éò’|`J§äƒvfSòanôÞ%ø4Oɇ‡u””|xø Jw©¥;uQÂÏ|xXÔ!Á’Ýœíc|å¸8ðùzØ ê=LÍ’‡¶EW #ˆÝ¡Ý… vß“†Äî*ö5bwh+ˆÝ^E7Ûùèªá±Û—Ư £‰ÓˆÝ.Ö Änצ*»=Šª.Ô>à¸Fìv逨íÔà&±ÛUÂbw–š¹~/þAìöð‰;äúÕÓ€ØÅRï÷EìJ‰—Ä®ˆN»Z/b´‰]* ©W›ðXƒÛUXû ¬Ám*~wlZ«$ÄnÄÆ&jL0jpÅ\ƒØíÔƒ3ËyØíù+ºVœ‘Te›­(„’8Gå£}¡^A2ڈݞŨÚÚ·kO/ˆ]Éf‘ØíYúÀFvmàf .÷©wpÊÜ!TFìö¨£aÄnW ‰]ñé v;ï—$v»ä‘Aìv­¼A쮲»] v»ôŸAìÎ+%Ñlß"Äî\K‰ÉeßMàa Øíâ @ìj5Gb·÷CÜaÕØí:V v‡$AìjDbw¨”ÄîÌÓðBŽCjº vÇ*º…µ«HìmY±»ª@@ìÎT¦™¿µ‰Ý±ê†Ø\ç’Ø}“:žÙ@ì‘J v‡¶¦ƒØ™O¿ vµ”!±;TàbwˆŒ±;Štz##ØõÐp˜I~#v‡Þµ€Ø"\2‡ikQò׈Ý!¥e»c©-±;ô´bwH,ÄîÔœRÁëj!Ýð,ía£vgs˜8K’m¦póJx!Üðˆ¡pã˔ ÏÂá†GÄ…m|¥pã7Rnªb!Ü•˜B¸á‘Ä…Â# ßËJ¸ááF 7¥B 7i[R¸!HU’ a•ôB¸a~‘Föþ¡ZÛ|E6ß³ 7,å& 7¼øÚ.Üôr„ !ðB pCXµ½Mø•ïVüþ.¡?VûvÅ[®)?á÷B¸!¨´– Aog(ܰ„Z(ÜTñMᆠ’3 7¼ãcžS¸ái̧nxTôNá†'ŒpŽŸH˜„žJünx²ªl“ü$z!ÜðˆÏ¡pãb; 7`ðûnx–ð„mL§pãÈnXó“ !“m¡pCXÂn*¨¦pCÈ:^n˜wY1Ä~þ>7„tÂvþ`O² óîÙi[|qcnº“P¸áµ)ÞûÈßE[|-»(ܤ Fá†Ð¾Ò]ŽÏZVœ¿ ÏP¸!(yQ¸!.`lü*A¦pCԀ 1Šb…pCd-’„¢6FP¸!ª’œÂ Qoò(Ü%dDá†(î” Q÷8 7Dí¡pÃŒ¿UöÚ-x+í6ë !Üõê QÒZnˆ" )Üu=P¸áµq¼(ÜII¸!nB ô/™~bP¸!JX„ Q…nˆCEÁQþ¸úN{igøëVêË[ôïK¸!áƒpC_ñïÚÄGᆠ,P¸!”CAx^q«øµ%}¢]!TÅãá†á«A 7„¡xn]¿ A÷C 7HH^X4rÌ«šžòÂÏuÅÜXÿ»SÅ ˜#V¤˜¹wiÉ ΤRI#ƒJE¿DÚº~ÕÀƒßä"Å,q†Lœ ÆA1“X%ÅÌÕ å…õ½µB:Äx¡„pû„æ#GùY3ÅéH1³^‹³ÈoPÌ,í"ÅLÝHPÌFŽŸÅ1³n‘³¸þÁŽåSnÐóÏÒn䟛õ‹_9ðà;rÌL…䘥ˆ%Õ[¾zà>]4f¢I1^”Ì& Û¢ 8‰ä|>ýcÂ>ãÉÈAS]XT2ÉÕHÕ³V.ˆYð[tv•ïA5W<ŽòÂc§šùy"QÍÔÕÌ=Ÿ¼paQpÄ3N`pÆ#HbqÃ@%ó<¶(QÍ\oˆjf=*©æÀ`¤šg©æÀ ,¤š§fä§ë`O>}|0¡–CÄs @5¾"ÕÈŸ‰j¦>†¨æÎŠÞ%/Ü>}{âû¨f{@¡¦IìÊJaFŽ$—)/ü°ªx´m &Yã²sÍ oZÄ5ó-°¸æ²>+GžòC„pý ˆmæŠ&fÔgJ^8ëÃmˆù ú˜?9ËÎ@7ŇOM”òÂJ,$/ü¨H7’Ž^†›„s- qañ„Nÿ<̻Ԅ!.̪\ˆ ÇS\X*‚Nb‰¡[{~En¨fè– ÙâpùÓ€@ßG2¤‡Wa1Æe™ugä]\xtÖ¶CÎ#•Ľ“N ¸púP8ŠæÑØç‘DGç3Â÷81“qÄœd¢/Õ .¬§xÎCê^Ö—†(.\Å]K\ø¥ }`~ÌçhH?çhHÕbi c i d…ÊíH†¸ðPh‰ ×Mõa¨ƒâÂ… WŠ kÝNqa-‹).¬âŠ /*_âÂY3¼c+%ªXã<¤SÆy73~Rß(g&ÞŠ‰íMáâ¢êâQ¾—ˆKXX¤‘°ðV3ÌøIܲ„‘UbÜПUÄYÂÅ".&‘ aáGo (,üPPLÂÂOýü&>„…•²QXøiC=ÐŸÇ Â´ž·),üès~ÚGK›_›(,ü´§6þsðÔJXXSLÂÂ4(,¬½ÌV=7…… QJXXÏË~tþ%,ÌÕÝŽRB °ðÒM °pþê‹Í¯ ÑÖ§p(,<…ŽÑÂÂa‰TÅßå! ¯Èh 7GQ×N²)ŒÅFS9l…Ç&¼ »Ëÿl¥Çö¼.úÚ„…i[t #•›ðï:?à§cPÑ0øÍ(EòÓ1Jxüf,D?ýÚU¶ù•ÉOÏi¾ütÌâ_ÁOÇ,m ðÓ±ütlMáâ ºå‘6>ˆüt\’ Yã'ICÐßePµr%¾N›ý÷¯ÑÙó}§m~ 9“ŸŽõfü´ HX<Œßå?„…£nºä§£R'ùéÅŸŽâKÈOÇ&!`ž?½ !?½ø+òÓ±õÉŸ0´„…‹ø\ +ߟŽ–ý„…‹j–),L>––’°ðP¹ô&~ðÓQßÍ$?õ~FÂÂz›#aa}ÑRÂÂKx˜ÂÂIº.âó),\ÕÍöQÌ.^_¯£p1…•),¬ü.aá%¨Aaá"5Ž!áâ*M wñ×.*U–?Ц°pVu² KNŒüt_O~: ñ·QÂÊ,v¦°ðêOaa½â”°°^[JX˜U(â§³Êg%,¬÷åä§³Ô ÈOg]Ïä§ßiT×< LÅ7›¿ËOaa½¿’°°Þ÷Ÿ.†—°ðR¼ °°*E$,¬÷MNŸMá]~ìn /›ÂÂq+jžÓ ©Œ™Â¾Î¥°°î§Ÿ°pÙ4(LHX…ÎÂÁa+mE+> kÚ',Lõœ¿ªõùé*!0òÓUç—ütÕ×ÞÈOWé/Ÿ.ësw¦Õ‡$,<$œLaáªrã*áaŽOaa ƒKXXï$,ÜRgKcâ£9>O—ðð#% 7U?[|UÆHXXfÖü”°ðàü&?]t¿—°°Þ'JX8ó÷IXXëCòÓ¯¶ºçM8ütÑûP ëCä§‹>ü aa‰‡HX˜­ZÂÂËOaaÃKX¸«Â›ÂÂ]|;……ù8ò‡ßþÃÏÿüí¯ÿá?þÛ¿ùùW“¬~ž©2üý9Éj+zýß¿ýçÿòóüü×ßÂÏ¿{ÿÿw¿#ëþýo8”žûÓ,KKïïÿùùÃí·§3 Õ}îaï›ä†µ¹íýÝç§¹7Hö‚f5€¹7¢Õ`ø7äøÛø3àþöïûëó¦ØŸ?þwûO?øá…¿ìdI±Úwÿø§Ÿñ>ÎüËŸ?þÝoÿúüŸÔÛ^¹³{üè^ìYÕºçÕ}Òxí ?¿—ÕžÿôcS"Ñ´rúaìL¡{Ê7î×:ÝíèÝÎÞ¢–Û¬Ó½]î±Ë1v¹Ç.ÇØå»c×{ìzŒ]ï±ë1v½Ç®ÇØõ»c·{ìvŒÝî±Û1v»ÇnÇØí»c÷{ì~ŒÝï±û1v¿Ç¶×›ûµv·]~ËmÖéŽewÇ»w9z—»w;zŸcS0œnX§{Öé.Gïr÷nGïkìpŒî±Ã1v¸ÇÇØá;c‡{ìxŒï±ã1v¼ÇŽÇØñ;cÇ{ìtŒî±Ó1vºÇNÇØé;c§{ì|Œï±ó1v¾ÇÎÇØù;cç{ìrŒ]î±Ë1v¹Ç.ÇØå»c—{ìzŒ]ï±ë1v½Ç®ÇØõ»c×{ìvŒÝî±Û1v»ÇnÇØí»c·{ì~ŒÝï±û1v¿ÇîÇØýûÈkõÎkõÈkõÎkõÈkõÎkõÈkõÎkõÈkõÎkíÈkíÎkx±»¹_ët¿Ïæ›ûµNw;z·»÷8z³w8Æ÷Øá;Üc‡cìpޱÃ=v<ÆŽ÷Øñ;ÞcÇcìx±ã=v:ÆN÷Øé;Ýc§cìtޱÓ=v>ÆÎ÷Øù;ßcçcì|±ó=v9Æ.÷Øå»Üc—cìr]ޱË=v=Æ®÷Øõ»Þc×cìz]±ë=v;Æn÷Øí»Ýc·cìvÝŽ±Û=v?Æî÷Øý»ßc÷cì~ݱû=ö8Æ÷Øã{Üccìq=ޱÇ5v?òZ¿óZ?òZ¿óZ?òZ¿óZ?òZ¿óZ?òZ¿óZ?òZ¿óZ·×S›ûµN÷ûÏÍýZ»Ûj9–Û¬ÓûîÎwïvônwïqô¾ÆNÇØé;c§{ìtŒî±Ó1vºÇÎÇØù;cç{ì|Œï±ó1v¾Ç.ÇØå»c—{ìrŒ]î±Ë1v¹Ç®ÇØõ»c×{ìzŒ]ï±ë1v½ÇnÇØí»c·{ìvŒÝî±Û1v»ÇîÇØý»c÷{ì~ŒÝï±û1v¿ÇÇØã{c{ìqŒ=î±Ç1ö¸ÆÏ>6¬Ó½ ëtO%ºÏýZ§{*Ó}î×ÚÝÐϓ۬ÓÛîÎwïvônwïqô¾ÆŽÇØñ;cÇ{ìxŒï±mÓûæ©!àå6ët纻óÝ»½ÛÝ{½¯±ó1v¾ÇÎÇØù;cç{ì|Œï±Ë1v¹Ç.ÇØå»c—{ìrŒ]î±ë1v½Ç®ÇØõ»c×{ìzŒ]ï±Û1v»ÇnÇØí»c·{ìvŒÝî±û1v¿ÇîÇØý»c÷{ì~ŒÝï±Ç1ö¸ÇÇØã{c{ìqŒ=®±­îîëNój° OójÐÎÍEg„C81‡!œ‚ÃN Áa'†à0ÄCtâ‰!: ñĆxb¸ó!îîŒge‰õhpcH'†;ëYéâáÆO wæ³²ªr4ÈånО£A{îãŒ0®¶3ík`æÕàÀP†rb(C91‡¡žªÃPO Õa¨'†ê0ÔCuÚ‰¡9 íÄІvbhC;14‡¡ŸºÃÐO Ýaè'†î0ôCwƉa8 ãÄ0†qbÃ81ŒCx 0¯;˜WƒvFh.Â8#܉!8 áĆpbC81‡!ž¢ÃO Ñaˆ'†è0ÄCtÒ‰!9 éĆtbHC:1$‡!Ÿ²ÃpæÉàòd8ódpy2œy2¸<Î<\ž gž .O†3O—'Ù'ƒË“ÁŠ1¿f^ r>ä|7¨g„ê"Œ3¸"´CsÚ‰¡9 íÄІvbhC?1t‡¡ŸºÃÐO Ýaè'†î0ŒÃpƉa8 ãÄ0†qb7†ø`^ v 0¯õŒP]„qF¸1„Cp‰!8 áĆpbC<1D‡!ž¢ÃO Ñaˆ'†è0¤CrÒ‰!9 éĆtbHC>1d‡!Ÿ²ÃO ÙaÈ'†ì0”Cqʉ¡8 åÄP†rb(Ù'£Ë“ñÌ“ÑåÉxæÉèòd<ódty2žy2º<Ï<]žŒVZ½7¨ýn0ÒÑ`¤³ ò ̼ät4È.B=#Üú‰¡; ãÄ0†qbÃ81 ‡aœÆ!=˜WƒÌ«A=#Taœn áĆpbC81‡!œ‚ÃO Ñaˆ'†è0ÄCtâ‰!: éĆtbHC:1$‡!’ÃO ÙaÈ'†ì0äCvò‰!; åÄP†rb(C91‡¡œŠÃPO Õa¨'†ê0ÔCuꉡ: íÄІvbhÙ'“Ë“éÌ“ÉåÉtæÉäòd:ódry2y2¹<™Î<™\žLgžL.O&ÛY±7ÈñnPÛÑ ¶»Á8#Œ3B¶*óÕæÕ`ÇójPÏÕEg„C81‡!œ‚ÃN Áa'†à0ÄCtâ‰!: ñĆxbˆC:1$‡!’ÃN ÉaH'†ä0äCvò‰!; ùĆ3Of—'ó™'³Ë“ùÌ“ÙåÉ|æÉìòd>ódvy2Ÿy2»<™Ï<™]žÌgžÌ.Oæ3Of—'ó™'³Ë“ùÌ“ÙåÉ|æÉìòd>ódvy2Ÿy2»<™Ï<™]žÌgžÌ.Oæ3Of—'ó™'³Ë“ù\Of·žÌçz2»õd>ד٭'˹ž,n=YÎõdqëÉr®'‹[O–s=YÜz²œëÉâÖ“åÌ“ÅåÉræÉâòd9ódqy²œy²¸> stream xœ–wTSهϽ7½P’Š”ÐkhRH ½H‘.*1 JÀ"6DTpDQ‘¦2(à€£C‘±"Š…Q±ëDÔqp–Id­ß¼yïÍ›ß÷~kŸ½ÏÝgï}ÖºüƒÂLX € ¡Xáçň‹g` ðlàp³³BøF™|ØŒl™ø½º ùû*Ó?ŒÁÿŸ”¹Y"1P˜ŒçòøÙ\É8=Wœ%·Oɘ¶4MÎ0JÎ"Y‚2V“sò,[|ö™e9ó2„<ËsÎâeðäÜ'ã9¾Œ‘`çø¹2¾&cƒtI†@Æoä±|N6(’Ü.æsSdl-c’(2‚-ãyàHÉ_ðÒ/XÌÏËÅÎÌZ.$§ˆ&\S†“‹áÏÏMç‹ÅÌ07#â1Ø™YárfÏüYym²";Ø8980m-m¾(Ô]ü›’÷v–^„îDøÃöW~™ °¦eµÙú‡mi]ëP»ý‡Í`/в¾u}qº|^RÄâ,g+«ÜÜ\KŸk)/èïúŸC_|ÏR¾Ýïåaxó“8’t1C^7nfz¦DÄÈÎâpù 柇øþuü$¾ˆ/”ED˦L L–µ[Ȉ™B†@øŸšøÃþ¤Ù¹–‰ÚøЖX¥!@~(* {d+Ðï} ÆGù͋љ˜ûÏ‚þ}W¸LþÈ$ŽcGD2¸QÎìšüZ4 E@ê@èÀ¶À¸àA(ˆq`1à‚D €µ ”‚­`'¨u 4ƒ6ptcà48.Ë`ÜR0ž€)ð Ì@„…ÈR‡t CȲ…XäCP”%CBH@ë R¨ª†ê¡fè[è(tº C· Qhúz#0 ¦ÁZ°l³`O8Ž„ÁÉð28.‚·À•p|î„O×àX ?§€:¢‹0ÂFB‘x$ !«¤i@Ú¤¹ŠH‘§È[EE1PL” Ê…⢖¡V¡6£ªQP¨>ÔUÔ(j õMFk¢ÍÑÎèt,:‹.FW ›Ðè³èô8úƒ¡cŒ1ŽL&³³³ÓŽ9…ÆŒa¦±X¬:ÖëŠ År°bl1¶ {{{;Ž}ƒ#âtp¶8_\¡8áú"ãEy‹.,ÖXœ¾øøÅ%œ%Gщ1‰-‰ï9¡œÎôÒ€¥µK§¸lî.îžoo’ïÊ/çO$¹&•'=JvMÞž<™âžR‘òTÀT ž§ú§Ö¥¾N MÛŸö)=&½=—‘˜qTH¦ û2µ3ó2‡³Ì³Š³¤Ëœ—í\6% 5eCÙ‹²»Å4ÙÏÔ€ÄD²^2šã–S“ó&7:÷Hžrž0o`¹ÙòMË'ò}ó¿^ZÁ]Ñ[ [°¶`t¥çÊúUЪ¥«zWë¯.Z=¾Æo͵„µik(´.,/|¹.f]O‘VÑš¢±õ~ë[‹ŠEÅ76¸l¨ÛˆÚ(Ø8¸iMKx%K­K+Jßoæn¾ø•ÍW•_}Ú’´e°Ì¡lÏVÌVáÖëÛÜ·(W.Ï/Û²½scGÉŽ—;—ì¼PaWQ·‹°K²KZ\Ù]ePµµê}uJõHWM{­fí¦Ú×»y»¯ìñØÓV§UWZ÷n¯`ïÍz¿úΣ†Š}˜}9û6F7öÍúº¹I£©´éÃ~á~éˆ}ÍŽÍÍ-š-e­p«¤uò`ÂÁËßxÓÝÆl«o§·—‡$‡›øíõÃA‡{°Ž´}gø]mµ£¤ê\Þ9Õ•Ò%íŽë>x´·Ç¥§ã{Ëï÷Ó=Vs\åx٠‰¢ŸN柜>•uêééäÓc½Kz=s­/¼oðlÐÙóç|Ïé÷ì?yÞõü± ÎŽ^d]ìºäp©sÀ~ ãû:;‡‡º/;]îž7|âŠû•ÓW½¯ž»píÒÈü‘áëQ×oÞH¸!½É»ùèVú­ç·snÏÜYs}·äžÒ½Šûš÷~4ý±]ê =>ê=:ð`Áƒ;cܱ'?eÿô~¼è!ùaÅ„ÎDó#ÛGÇ&}'/?^øxüIÖ“™§Å?+ÿ\ûÌäÙw¿xü20;5þ\ôüÓ¯›_¨¿ØÿÒîeïtØôýW¯f^—¼Qsà-ëmÿ»˜w3¹ï±ï+?˜~èùôñî§ŒOŸ~÷„óû endstream endobj 503 0 obj << /Length 563 /Filter /FlateDecode >> stream xÚ½TMÓ0½çWX=9RíÆvì$ "–¢E\‘8·IÛHiR’”mÿ=¶Ç) l%N«ª7o>mw&B;¡Aä廑¼ñ Þ…ÄŒ¦±¿7!‘掺ûÉè°íÚ§åÆB>·–s£0ûµ¯Í¨itÝ6føâ<_C¤ëÔeu·l— fMQžgÿd•w² ß‹eèp9–ËY?»V’w+Å/_iäÙýnü ‡òÿûP/ÓÇufßç3 Bì:«œ)š$mÁÏ€&LÃä c”'–¡‡6øl>£Œ)ÉMN·%&ÿàÑ‹eFŽ7³ªv§ÎIa–½ I̜﫨]§{€EÕkmîân¶4ó̳ ¡ }r üÚ9,¾5ê®Ó7öm{ê MoVTí;²ÏÓSÓ”ø«Ý3žß»Ð_ö§|î½Í*íÖ0a0ÏŽ«Åpg»I€†mÚAÛåÐÓɛݓˆ)EÓD"’q+%ÿûÁãeg endstream endobj 500 0 obj << /Type /XObject /Subtype /Form /FormType 1 /PTEX.FileName (/tmp/RtmpBlhU1W/Rbuild4444671013800/timeSeries/vignettes/timeSeriesPlot-titleSinglePlot.pdf) /PTEX.PageNumber 1 /PTEX.InfoDict 506 0 R /BBox [0 0 432 432] /Resources << /ProcSet [ /PDF /Text ] /Font << /F2 507 0 R/F3 508 0 R>> /ExtGState << >>/ColorSpace << /sRGB 509 0 R >>>> /Length 25125 /Filter /FlateDecode >> stream xœµ½K-;r9¯_±‡ò@ÇÉGò1• èºU@  Y†-œà–öÏïd¬o1¹O—ä*• Ü{/&“\±¸"}þò“>ûùo¿ù??ÿíSçëúäÞ~ÌöI5ý˜×']íÇU?ÿÏß|þïÏßýæ_þýÿõ¿ýÅç_ýÕo®ç—×çüó¯þÕÿñüm¿?ÿïoþýø\Ÿÿô›ôùËçß¿ýMZ?øüëÜ>¥Þ?Jÿün™wú”2~Œûós™ýñ–ö#ÕeÞ?®Ç›Ç»È oúÑÒ2[xKþ1ºÌ9·uÆa®ÈyþÈ·Ìhûœ]x»Ž›”"3¼ùG›ËŠ<×߆™uVmÈŒ¶éG¶q„R*ç<Ü×c–í’Ù—÷þ‘×[܆R¯)ËŒ¶ãG[—ÐRxKÿ1o™Ëûüx`®È5ÿÈSæŠ\ã?™¹™qÜöc¬KhEÞç$“̈œùqQäç«Ìˆ|ÿHq *o_!ÂÄ[âœoŸsÇÌ:«œdÞ:®Î¹É[´[&ÇÍCfD~.?ÚvóssŠÌhûÜиuCÞ×\Þû¹ C押n÷-sE^·;BE/ϳ‰GÖôŒlv=£§OD¿zÌ\ÃŒ¾ñ˜u†=ç1ûò¶uFOò:T’×mŸ·ImÓ:«ž#òó¨ê%“¶w˜EÞçxIæò>?ÎYæŠüܲ2e®ÈÏ£çôªsv¨êãÞê–÷Ö»ð˜qÜüc™qÎÏqo™«íóF·…÷¹íñ–­ab†yÇõöˆì®ò˜+²ŸïcÒVÞ¡ã>Ý;ÉŒã¦å–çlsꜟ{TeFÛ§/®Ë<£ç¦$™xãÝ<£§ÇV™¹hTÉwc ™Ïm(ÏK>׋3òê2ëùƘ3âYæì2{9Ž›Õ6ýHá-á}îÆ}Ë ï¥.:jDöÍyÌùsf•m¯eÝ«q‡÷1[“¹¼Ïï!sE.M{´ˆüŒu©É\ms<ÈÇìá}̸íǽ¸9]‘_3"ßz»ÇPÛ©®ò˜á}®3Ì©ã&½ YåÕœùÌÇ”¹Ú>fœä¼¾¼Þ¬!h¦ˆœžã™+rzÐeFÛkÚ©g”ÊTd.oªz˦žQ⥛E‘Ÿn–e>!WÛ¡êzÛ–ãóc†·ë)EüçŸôûÜòó‚Ž¿Ë.òO=»e¯øÏ»7qÙ]þ«`ÇùÇX¹l=°‹gÙO×<ì{}µö}^vmßvOq㇎ßä¿Õ‡—ÝÕ¾'ìˆÏƒJÏÓŽø—ÞÄeG{žzz:GøûûøÃÇ¿:öTû.ÿŒö‰Ïܲ—ß}=-Çòßê¿Ë^ñŸþ<*öŠÿü>¾"ËŽötÿô¼%q|úð²§®_Ïëi˜Óq½ëÀçý}ì§ýê—ì²üëùÄk½ìð§×ž-úç[WüÕ—Õ×;-?çËÿô§Œ¿ì0Û\vÄOô—çMŽö;^[7â°Ÿçwýû±ëç3 v¿Þ×gÙÑžezƯÇ_.&)ËÆ+þ\ñËóZÜû‰ü~ª=_®”/ù&–~÷ŸÓõ<°“â3è,{µOÌ’Ò3Ü.ÿî?Û+þóØc"¼ìˆ?4à§\Ô>é;´ìåûý3Wùc*#;âžÇcGüªy÷²£}Õg.åÛÇ/?¿oÿ¹ìÛöŠŸã {µše]<¿½(Xöò—áó‹ç·¿~i}¬fÌu9Ÿ±f#{¾žžïbø‡¾<Ëóýšk¸W;Ëž5&Ëêë‹=ßYú²ÃÏT;=_Vü1–/{Å÷œÙ+þïöû§Ö7{Ú¿ìðÇc[vQüÎýzìªãÏŒÝäç|ªü;^•ÿf¼}ìyLS‰ÅžÓ-»jò¬ñ­ûÖ,Öý籫&Ⱥ¿ëFÎw¿ìyL‘SéŠÏ¿ªãûû¶~8â~ Û+~c>´ì­Ô&öjߘ ¥Úäoô÷Ç^þ^5sIOÇZñ{ÖaÙU~½Ú÷Æ÷uÝùùýŸ©í²#þŽ7_ýû±¿Úßz~9é²ÃØM~ÿ±#>CZÿåŸ|Ÿûyq–_ÏóyÏÂàˆ—WG,Ϻ¨eìÖÏøYí‡&ãé.òïßùoúÇÓ#þ¾žªøIKŒe¯ö­ó¾>vù›ÇÓÇ¿Ç÷;^ü½æLOÇ^ñ3ë¤é}_×¶ïÃc/¿WZË^ñ½²^öŠïåqz^Ôh?_;üÛž¿ø§âÞ·åÐñ[Â&~¬ÕÓóbsümû“®Uô²¿îϺ1ý]W¦¦ç·Ç³¦ç÷t;½ÏkàJÇûÕb xÇÓÇ®é¯[ ¿±”e‡ßãýc·v¼ÿñŸë—?^Ô½H^öò{ᛞ/üž¯=öŠ{~ùØ+þèÏíY±&½èkɪñ¦uÛC×çùÏcW_«ØMç—ãyµ©ãD,»µc¼y왎þÓ/?«þeGûÌ|üéè\¿úßBcÎóìˆïñmá1é]¾§g`øº=ûú5žt=¿7^ññ§mâëù/T&ãwÜk}ô=Z¸ÌR¥çEN÷Ñÿ2sÅû¦ù÷c¯öû}{–ðWÆÓÇ^h­ìy Y©ð´î¯æ‹ Ÿ¹Îû7ä÷û¸šëÅB–Íñ5>,Œæ:ƇgàK÷ñ¾ô©ãž¤ga—î=YvÄ÷óìvýs!5Wg«ý_ýÅ¿sóçþ´Œ@‹Ù ‡ß}¤±~>õë/lD?X Áµ°Û1l?ù {æýÉŸ|Ÿk|.ò³Úu›òuµ?¿ÒóÏï»ÙÿÀÃ*±ZúŽÒ¿££YëÆô\‹ÌÒµ X—òëÊ 0”š€yþ@Cûþ#ýñtÿà8‡? _•øžÅúÿŽKãè•jhÜ¥Öóo½–#Z§ëúåþÒW~“fŒCî’¶Ë(šgq+lþ—Ï?ã‹]è+ù—¾’uUu­Äêë¶ç"þýŸý»¿ûOÏ›ýŒBö?þÅøüö/yE?Yyšÿey½õ"=õ<úÂú~ö½°–¦µÁ²{ ;°Âü|{¯å¿õ-^vøo­Mòš´«ýÕ°³üÕö­öñíX6í«ÉÏ·—ãÖ´lŽÉ_?°Èegù«ýĹZî‘ØZþ»—½F¡eì.–_|ÅØ´oŠßí¹ß²ñ;â“JY6ñ§Ú·?÷G}xÄ’JvÄïÂÆ–MüZ°»ü:ÿçÛkÇÆXniŸÿíóŸØ´Ïú}öñcí¾lÇ·Ÿø±VÍz›–­þ1ŠÛ¶°@_üÛÆ?;vÄšK/;âžÇÐû0†æ2Ñ‘îÓnös}1i »`×)ûÂîò6ÏxÝ~Ú÷ÇïåŠOr,òwÅ›>þ¸°ñÏÝ닯ÿ!~¬Õó¼Ü^ñæeÿ-;Ùk¡e_ïßLŠß5vêùÄýYiù{ÃÆs§5Èçzô§YßÏ_ }Ä“¿Ú¯÷a%[äŸ6ñ±oŸ¿Þ÷yûüc®½@Yüz_ç~~zŸç~~êß³ùþ$ß÷çÆ~n|<_]o¤Æâ±eì*µ¿—óøCí»æº‘¾)êÿôߢçòÞ¯eGü¦û½ìhßÔ?ˆŒ?ÆÃeãìcÙSþ8ß2Gü[k«eG{°Ç:ã/ ô‡Hz”o›øq¿")@üŽ?ÉÛ?36çŸ:6ñ[Þ:þ­óm¿ø›ý—îGWüªþµìˆ_}?ºÚWjËMlü9cOü²§â‘–Mn*°Üѳì`[ËîòWÛ侚låÖNûÛŸíïI$}äñ}Ù]þâßÓ^÷7ûs¦¥ªøYãEäSå×ýHUí³úïJ2áß6~õßt;¾žOjŠŸ4~/›öEçß}|οۯûŸºâïöÊŠ>vRüñ•[Œ\2~ý~úøê‰çv¶lâÇÚxÙœ`+éÀõÅZ{Ù+·ôÚÁˆyï÷ʽ´o»ËŸ/ÛãQ$1hoÿlÇóÊEñ³¾¿Ë¦½ž®>þ¶¿_wü‰MüªãÝn¯÷;+w¶ûVîì±õ¾ç¦øUßóewù žV;Þ‡•{IG^¹®_ç7||®w(þ¾¿CíaG-Žþ4°ÃŸô½ ƒüñ}[I î_ÌW–MûÀ«¿Æƒ’¶¿b?¾÷ËŽø‰çQònß±Wîïµ# óöï•{‘?Û߯ãý)Åí5^•jÿœØö+ÞíøÓ6ñùý­öEÌ¥•´ ¿Ç‹ÒìWÿ)ÊŽ¢ùÜ-óuŒ?¥»½¾'ÏB ¿úOöo›ø}-›øu/® ígÁåþ×Kþªïk$ÅäWÿ®—âWÞ¯z¹}Vû´Ÿ°í×ï³ã‚ñoawËŽö7ïk-öëû[y~ë©ü³zÄ[ ­üê÷åøú>ÝÉñç…]™¯ÅõßYþÁóÈóí»1ߨó˜oGï˜//¿æwµ¿&lޝûWÇ×ý¼«Ûë{rGîlÏÏ—=5_Tÿ[¹—q^O$ùc¾;°óÉx>w—¿óþÝÝñõ=\¹ùõ½\¹µo ›öM÷gÚ¯ñõžö—ŒÍñõ}º§ãë÷ír{_»öõÅù·äëÓûß’âOæYöÈžòkþºr/ò«?­Ü˼ÈGÒJþÙ°+þ„½ÚÏ‹ñ­Uûu=+÷"¿ú_«>>ñ"7ºüWÁþnßì×ûÜšýÛ&¾Æ‡Ö|þz¿[w{½ß-rgq>º‘;;âÅB$ìŒÝú¸½úo›öóûi¿úßʽôãzÒ'®•æS]x꘼]`è€Ð·ìðCá[ö”_ßã¹Ñx;6íõ¾ö"¿ß‡•{‘?ÖgË&~¬×—M|§½º}·ûþÅ;¾îg¿_óÝ~ïøñ>­ÜKÿ¶¿ýÝñGÃþ¾?Ýí5ÿêz~{<ëz~k|´¦ãýZ¹—vŒ§=rkïx=.ùÁk–]Ó1ÞèXïû¿r/òë{3’Ú³¾^ö—JßM„÷úxÙUþd›øÓ6ñµÅþzcãÇ®ûú6çß ¶ãÇýX@!çW°[;Æ›qûüÕFóùk>0šÛk~¡mã!Ë®_çß}|o£ïø²Ç/÷o?¿¡ëÛÏxs¿a_ãù˜n¯ñ{Fî,®wbÏÿ*3­ñö¿…ï\ñ¾éüfr{½o3oößö<ð±2‹ãë{0‹ÚûþÍj¿ÞÇYí×x6ëŽo?ñ5>Ì{ŸÿÄÆ¯ñ6ûµ^™ÍñõügÛ÷çÆ¦½æ‡³Û¿müzßçØñv½Žþ<‡Úƒç”9·?cã×÷T¤À_ÖëR|Þ·eÓ;ýâOöÇ|cÙįonñ÷f¾“|r`ôûó|uòú³æ?*9s¾79°>–³•XÄMçJÌlð]ÉY×HÌ øJr`ÿ^ÉÉž'¦ÁF’³iòêäÀc |&90!ú:90à$ÉÉ!’É9Y°¶Ÿ\$òI,ÌäD;9°Î·8yñ¼j˜ä‘8lž A’A¤µm@¹˜¬’¿~¯1ËΟX˜¢Àe}ÅÆ'pZ_%0¼Xö0øþâäÇ´¿ÌÏN¬ãÝõ³“+þp2`µÎ÷v²`êüoÓÉþ39°ìËÉ€ˆŸô± 9¤Àì,?D’ë|I>h¯Sªœ¯’Ë9yÐåoN„ÿ°ñsþÚð”ªÁzíxJ7É>%£Ôõñüüü•Xvº?;9çëd”_É8%£ŸX£Þ‡É¾™K`0É…ùÛ±Ge’,Rr 0JÅŸÞáRœ,?D’IÚ®j?œ< ~q2àÜ@Cr`ÅL×&(a°?Ì5aÇù³3‘äÀµUr 0Ê‚矙Ì)9°lMÞ”XíI^Tùé$Â>’ËئäÀj¯É¶’y:ÀñIf´½½HíÙ•Yü)9°Ú ¼Wr ÎOñ†·Tõ»kO•&;J,Œ“û­Qy'kâÆï½ª$Âv² üÿH¦;°§üš|)9ðeû³ü…ëUr`/Ùñ»ú#É…©’\(ò7Y~ƒ3J,±]Õþvò€øº_J|ÙÄWPr`ÝOÀþæû“Žä@lî5Ø¿ü…5$JÙ`½vL=¶+J,û:’+>×3t|÷%coرÌÉ&%bËYžÚF¦÷IÉÕí49Ìl¬Iô%V·¼OÚ8sØ;Ûµ±fMävׯ~Ÿ½qGÏCÉãO'V<ÀùêAÛŽãOÀc%ö÷ˆä@Ìd³qªü¿å/€]$¦;$¦Á²ünœ:’{>Cr 6Z%l6> \Ì{ã”&ãyìûg›KóHìï-ÉÕ¾]Øßàh¹¼±JïgaãT¥)9°º•wJÄxZ°y~$´q*1Ÿ$9°ü€ïÚX“Ï«°±² Éå'¹P´1&3>6Þ\¼/JÄ÷üH¬ëOGr ^û„ño_o à€’Eû™~:9ß›†½âgæ7$V78¤ä@Œçj?ä¯,î•ø²»èjêÏJÄ÷Pç¯SyŸÿÔõO[J,[ß%xm~:9ßãŒñýQÉ’œPr`u+ÀnmœZã‘“l,˜¥äÀòk1­ä@Œ—Ûþ‰ñ½8Wr ºé…½6®dƒoJÄ®Ý#9°ºÑe;6eÞ%–Mò@oŠï¯’«›èy+9°l¯JD¼#9°ºuòï‰;Ðt~øµ±¦8¹T§ã“|`ãTa|¯lœª$‹”XÝRׯäÀ²õ=Sr t”,ÐÆ©%è0°Ù¸• þ‡¿½É6Vé{~go¬ºŽä@¨ gãTâù(9ÝNñõüªÁå{oœXw³qÊÉÿ»zã–Æ_%â1ÚžÚøÃñ›7iüRr`ݶáäA´Ÿ>ŸîC$úŽ$–¿8àøGr`ÅSSr`ÝVßJÄÆ/ûÙø4ìŸÚø¤ëi—ÛWÛøõ}Vr vÒ7lâo?ñ/' ¢}¢*9°n›ÞG%6Ÿä@<–»jc—úŸ’ë±V'ØØ%pGÉðOl6N)Ù¥ä@ØNÿL,[ó5%â| 6ç¯ñRɺÑO'öF’!è¡ßkcMõ÷JÉØHb;6®Ü$‡•X¿ø¨ä@øvøý¾)9°žÏND|¯/”xû§’[c‚äÀz­º“S‡²“±ñ§1^v6Þøû¨ä@tSÛø•üQr ~aOù5Pr`Ý_=_%ûþÅ{ãQs2€EÄ»÷Æ&ý¾Ù¿ío¿6NµìóÕÆ©æä¿’Ñíeëù5ð’ËÙ¿6ž4'{”XÝRýCÉØ˜g°?ü“ù”’«h|Vr`ù«ý²Qr`=6Àòd?É„ìøõH¬ßk¼Ur`u½¯J¬n¬ñZÉèf{ª½î‡’a;Yñ«Ï_dïnr›’±Qð¿ç“J¬Û~9Ô^÷WÉxl:¿n?`~wüËþˆïd’Ëæú´qª×¿“aBá‹`½ŸNÄÆÉíów2ar}Ÿµ±¦ûý‡ÀÞŠÁð¤3ž/)9°ìËv“_Ç›ÙwÊ‘ˆBN°1èL¬þ¦ä‹’¡^#[kÚýÚõz7þ’XãÆ?%Öñôü”ˆÏÐÍõ5ƒÿ¿:¹ÀÆ)WJ¬þ£ñAÉ5Ìè{©äÀê¿ÜÏ®ø;Ù1|þJ&+9°üÝþh?¾’ Ã?ˆã'lŽýäÀ¶ºíÕþµÓ/~=¿Õß,ˆø·¾ ÅÞáÏú\´…å'¡ø§qò ñßà®&ñWHª&ñƒ³l1Îí/&õgüóóâô&íl…$n?Ü6‰?k\Û$þüâòY~án&ñs_7‰?™d‰?™ô |ÿ8¤®MâÏÎStûoÛÄ'Ð_8«IüY$ŒMâÏÎS Ÿë™ögãðÄŸÆñoùû‰’Ô&ñ÷7‰ŸyÅ&ñß&åCâ¿M"OÞä® ‰Ÿ¤î&ñ·øÓ¤uHüÓ8wqûaR>$ûmgµWÿ2‰ç‰^ª!ñÏËçsÛŸLÚÇ¿qùEâ6 b“ø§ï—Hà&Ul¿qp“ø÷ù¼$~]ï0I<8}Öçbãôï&“ø»Cº:pú÷þ˜Äϸu’ø…S'ûoãðÄïó³qúHRÚöù›´ Ò|¶8ýûòcz>øNìßOùµ.‚Ä¿òôN¶IûøËÓÇõWl®ï2®Oûj\þÔ?3‰zž ‰?oHü§ÝÅÐý…Ä?ùžšÄ?+$HüÓyHüû}‡Ä?ù>›ÄÚøÕ_!ñÏû%õÏöŽO&ñOç­ ñ/¿IúhigœÞï/$þ™¿IüÏxJ^£[ÀëÄéã³%?$þ iÉ$þé¼$þéMÓþn\Þ^_$þa’ûåøàΗÛc§í7._!m·ïé%Õl?‚G›ÄÏ&¶MâïàX&ñ›´f¿×}e“À»ý´‡T‰8OPíg‡Äï<’Iüï‡Iüûü ñú“Iüγ™Ä?}½œ1_Ú$þi{ØOÞ@$þÓi<0‰ÿG1‰ßyj“øçksüi¿ëøÙ¸ýäøú}òñ›q{ûõû¼ã8}ÌG6íÕ?Mâ7©Õ$~¯‹MâGf“ø‘Ý$~çLâŸÎ;Ü&¡“ü›üíÀ郔+[$~-ßnÿ~¿ ñONLâ÷üj“ø'ýÿ&›ÄoœÆ$~çyLâ7 Þ$~¯ûMâ߸òeÿm±Mü˸<ñÁ‘7‰_ã½Iüý%éÛoÜžøÙ6ñó‰2?2‰¿ÿBâ÷x`¿7]¾$þdÜžöäïM· ^ßó—į÷Õ$~ãØ&ñû}4‰8~·_8§IüÆ!Mâ<_“øg2‰|áô/ Ö$þ}¿ç&é8ýKú}Iüéÿ‰Ó¿÷ß$~6Eo¿Ï÷%ñk|{IüÃ$ý Á{“•IüÙ86$þÄõšÄŸLâOôWHüEñMâO/nOûdR>þtàôëüºqyo0®ïMòo¿Æ«—ÄÏ&…Mâ/¿ø¹¿›Ä_ë›Ä¯ßCâ/Îc@âwÞÖ$þb]$þéM†ø¥dûs“ø÷ü¿–]?7‰ÞÌg ñ¯ù‰íªù^:Iü{> ‰ßZµ&ñOâLâŸÎãCâ~¯ñÿkß¿øo_ßgHüÓ$iHü³›„/ÿiûEâŸîïøçÞdЉ?ì?qú<÷&‘À'$`“ø§q^Hü“Mô&ñïù4$þiž$þu¿Lúøƒþ‰úýƒÄ?óCHüÓ<1púË›>ÀéÍs5No«qú+ãô›'N9Ï NU“ô…Ó_ú§¿œ÷§ì/œþ*ðhÀéŸöí ñG|¯púË›LÁé¯j\½ûøÕ¤~â+/Ny~ N%Þ_púËyupúËy,púËëOpú Nãô—7YƒÓ_à™Æé/o§¿É6Ny“+8ýâÑtì_ý®pñ²ãOl·7I7.Ïñù}uüvøã~ çN¿yAàôæ §¿nÞ/púËóepúkp¿Áé/Ï'Àé—=°ñ'ãòßy:púͧÛOûÅÓ§O—ó ÂéÍ“2NŸØfœþµÓ/þ´ý×çÅ鯩÷û&ñÿÉÒUk¥ø'I„eq׊=¥ÿñϦ"‡˜‡éC"ìןlû•÷ ™ßC$lÛÇO¾úg ËÅüUÂÖÌü¦æ]Zþλ< qÛ~ýÉ+c/`Ÿ,¨êJïO°¿~ò¬VŸiÈûÙ_?¹c;ÔûÙ_?Ñé½?9N÷釷>ëKš~ÕõúÃÛRÔ>ÿSÚ÷H­FûzýúD,,V¯ö%,†½•Äö-ùEYìŸiDøGµÅ‚\÷‡i‹‰†Ó®ÿ•ƒz’®˜ uvÁª^P‡ö¦rAk\>®ÔHí¨XP‡0¡ZA B¥‚ “*­TïÒ¥%Ý)-¡:AZ˜ÊõK'U ê,¥U$Hóo×ê—>ÓÒ6™A‚VæuùT hÅ×êФT¨3ÙWu þU¨ChVm ~iÚ¢Ò@&W¨2P'…¦Â@=郫º@+Jþ¸,P§Î‘ªu(o* d†juèÕêµÐB:ÔdêLU¨3}R9 ž´¸Q5 ÇŠ¥¤ŠõtÖRu—:,ùb £B@+¯Œµb¢™¡2@=k2¬*@¶T¨C`P  e•KõLqØÔÔ™¢©ÐŽ’ð©]dÛ:jéªþÓyµUü§CHUíŸÎv•þé®Ï9¶^(Tˆ€–êþì(ßQõ§£§£¢?õÕü餩Uò§'ªøÓ¡ª«àOOZ¨Þ{Êýøù©ÚO'•­b?Ä»jýttYUêg[“óŒ7N…~t¯\çÇ´ˆáçoÕðsPÁœ‹k…»jü,ßýq‰ŸNU UøélURŸžõv(±¾O v«ºÏêŸùãâ>뾬«ÁwB¥}ÄpeŸõ>„ïÆ£ˆýRú@e}:‹-UõY½t~\Ô§JU¯‘fŸ üôrZŠ©;1³\Χ3¹U5`*æÓ.T˧1MU)Ÿ¹\•|ìS*¶p©ŽOc:­2>÷ªâÓ @©fGCIW5|Ëf¥óäkUði SŸÆ„_õ{t•ïY#Bý¸zO¯y4€|%dëô5|*ú®ÖϺ= •íi]÷L‹øÖ]«hOcCÓñT²§5®6HD”— ö–|ªe4‰÷šj=íFlYÅzJjõ4v(Qª§±†J=oÛM:¦È‘¦\l‰W?.W"û*ÒÓØ:Ižv#1­=&ÿé =­¹ Oä¯^3ÒW¾¡”ç9Í^‰‹«8Oë®=ÔðîR=³í§AežÖ\h§Óö¬Ësš_ÇŽ<1YšÏªÉÓºK EÆÊ}—Š<}(äiÃõeb…҆˿\´•ºµªñ¸[RŒ§¹6‡jñì T)ž}'U‰§±ß‘B«¤NOT€PEO˜)¨³Ìx¾ª§ÓÉ&PNg·aª-Åd*^BU¼êf*¥ãY•thàÕ¤j6ûíV1›ýv«–Íîü*ec”€J6† (d³Ÿ¯êØì^§26Êÿt›Ó”W/¬jØ$¢„Í`c‚6ÊÂÿtýšýiSùãbT¯Ù/ŽŠ×¼æýí½ñêC¯Ê5·¾ ×üž"‡°Ôƒ ÿñ²R ÃE¢R ÃèÁ‚ £>*dXÙ|#ÃÍ7 Ãèô î\… w¶É  :B†[|… *O‚ ³=IÈð¸„z¤¬… „­„ èßB†­ +dx ! dØÕ,„ jÎ TI2<ʶVÌN]ô@†“y!ËŒó"ÃÖÛ2¼ȰµÏ„ [éNȰ·„ö !ÃÖ´2<Øä,dØúñB†×y¶‘áÑvÙx]C VL¡ÛûŸ]!Ç¥˜*/?8ÏjüÄœFá'מ_dØ–áyi@2ÆŠ×ić^`ñ¸T‡¾Ë 'K¯U güxÊÆ«­Ðë!/Yq˜/f¼LÝœ©È(< 5l  ˜ãªùåãÆš]ÀqÚõÑ“# æMŽŒÉ3¢£LžÑÔX)ôxii>N t^øq@m©€œ,ü"9Îy~ !§‹zšÂ—7&'‘OóËÛäe ±pädf‰^þ…é©} É µÝåÍY/ÐÀäå-˜Umo f"ëæLy_“Èñ¸…(¯[J|ùv¤S^a6NþêçIþö&…¿ß‚ðSþë–“uñ@–w§Z´¶a?ñ'¹°åÅ&L˜Oë ¹lyîâò-ÏDEØòÌ_¥ßO³§8Î+°åuù‚'.¯ms…FpÙ$>Àå‰Öàòi.ï.Yಿ €Ë \ž®?*py±pÙ›]—½7pÙTjÀeO —Çt¥õ—×¾A¢.Oש¸<³ÏÌÆO ^0î—ÓíÂ\‘ Æ \ž•Å€Àå‰H:àòê6¿õvÁt?& é€—×cj”u«…ä _^n=ÌórƒNz¹>w ói=©/Â,h`jËOãÔ˯¯®€êÕ­(;þöÿ2tío—oò»ì¹Ðêu[„` ®Ne—­ïŠï2Ù¬×c­†¨ÃÏ"È:ü;Ž?]~*¾?¡B­£[°õ²A[y~ì¸^¶ÆK!×t£Ÿ†®“ÕÇÀ®×k¬ç)ðzõ§f0»æxžÕv놅ŸÆ¯×ï)[^¶`‡ßï› ìõ|6¤ñY b¿ýS(v¼Æ7vøQÇ^÷3¹^ñŸç¥ñRHö²»Áêðà5~¥#f§Ê.Ðìek 8{Ý_=_áÙ‡=ñOÅÏÆû§âM¦¯•ÐäËþmû“Î5(pí°;v´¿¹ŸB¶“Õ¯€¶—ÿ²ÿù.¿ÎOàöê–êB·W7ºX/ÿ]\¦½ÊŸ]–=îdõ0î°ˆ{=6—¸£Û(~süz Üë÷os¯nB™ö¦ö¨tG7+ØSí¹ÃÇßÐwÄ>ÿ¡öP“@»—­ù€àîõX.ûWü– Ó^ŠïñCˆwjÎ× òŽx76ñϲîë÷¯…z/[×'Ø{=¶m‡ß™aÍ©QŠä{]¿/ûü ”O®O¨s€ßë÷í@¿×°¦¯àï¦m×+ì]ö½ÉÏññè€À×û°òˆß¾@ðÕß”O ¾l}?ƒ'«õƒÇ0}c¯øÅóE!áëx”…Ÿ>GÂÂcØ6Ü~J7€†¯þ•ŽÇý9ʼ¯ö7êûâ«ÿRv]@ñmT_ø:%8…‰/·?Úç/Tœaø§añ8~ÂæøêïÆ×°ÕmGûmß¿øõü¬†8ÃÖ?¦®\}I[Ipu›Š)¸º±ï"3ÈDæ!¹á+К³|ó³‰Ì±™Ù+#¸º±óSpu£™àêÖ6@ÃëDˆÌÜ;ÁÕ 3ÁÕy…àjÿRpµ™>‚«Í \Ý(Ý)¸ºÝzZ‚«›aàÀ%z zC—­YÇ‹³\ÝèS‚«ûW7JŸ ®6sFpu3èpucf ¸ºMèÉW7 ìBd€ÈLb"sÞàõ"Ì(ÛCdóƒÈüZùÞ„ˆÌ¬I 2WHÜ"2SJ"3Dæ ^DX¾\™Q‹‚ÈLJ"³!âéx`Í—‘™Dæ ŸˆÌß!2¿V¯›úú™Ø®‹ó Wo2öï’VÌøö¼Df,Îê²Â1+…È̘ ‘¹l€ZÄâÂuÜ¥Á})ÔòÅH‘¹ê»‘™$™Q>~¯ë<-=‡‰µb¢Ä ‘ùo‘uˆÌ ‡ÈÜA“Ed6AXDfv}Bdæk-z ° Œz@³‚È<õ¤!2S° "3 ³™‡îDf¾§B§;{ðNwæ¦;EõM/‘4 hÅ´5ãún€h]û„Ö,_w–O`øä¾ÄS&ÝÙIwù¾öy î&'ÎSHvr»ŒîMOSXô:úõ1Ý)¡($zÅÄzf_Ú`lºƒû †î¨D…îsSžg{G¸igëô5|7då*ßüîÆ?»‡Ètø<Öò‘Èì<Ìê<²Ò‡ÓøÂœ]gÈÙe^AœêÏðj% ¼y\n›¹–ÛÌeyË66ÿ6û›Jd€ÍkftaÎHk&°Ù°ù4{d!P7¼õ 2wêp5ò`Í~ Ášûôõ{"ó¼1{x7¯y¶=J€5w’*`Í} îнDÖì· ¬¹·/"sG½¬¹ß_XóúÈXs§´ XsgG1Xs§Š,Xs7’'¬¹{b(¬¹gÓ~+‘ábWÚbÞöZ®mo‘iÞü!ÍÖ³hö¾€æµL1s9_/“H8ó¦J%“d»½j+lK0sÏF¸^®7`æî‡`æž –fÞg%˜¹£- ÊÜÉü2w”ÜÀ˜=bîÅfÆ E:æÓì1u¾ xù=n ËimÀåM)¶ÜËkê¸ÓÈs¿^检ånH^€a'7 ¬Ü’ Uîñ*wcn”w¯¤¼y‚™½€ƒÈ\XÿAd.¾|“d…^@d.Ç'äÜbšs o; äŽ.¸‰ÌÕ´ß ¯ÞˆÌ•'‘¹šõœˆ¬~‘ÙôLˆÌ† 2;‘y“‚‹¯Èf{÷®™È¼\ñ›Š|+Vd½e›È ²Ý 9oª2^ãÈŠœm*r>‰ÌH˜Èœ¾‰Ì~!2Cíy‰ÌɲÚê¡@dv^ "³YޛȬ—"óÆR/Ó~ ‹æÜŒË+  "³q0ˆÌˆ/˜Èì,DæüoR!DfßXˆÌæBd6kr™Ó7‘ù„‡÷}†ÈÌŽG™÷IšÈ¬i™‡©Ê+²·ƒ@dnÆyEd¾}"2ß&Œ¼ê„™ocè“È"VÛdn²¼é„{5~i"s1| ùZ^™5Èl"3X¨‰Ìå›È¬;¹‰ÌÕÀ1DfýXDænØVDfg!2wƒ´"2{{DæÁx‘Ù_v!ÀZfüÜDfJ›È< /ü»@—ƒÈì‰àï€Ãö;.Céýg‚…üÊfü¾æüöNŽ«¯¤PßaJ©@ß‘LFÌ÷4¿¼øw`¾Ã‡È< 3Ú?8Nðèg%¾O­üÓ”sÔ:€€ŠpÍïþ{tsÎm¿ûM’*½ÔfjŸ¾ý[3çŸá%ÿGõr‚.Õ?iôT/ç茋m9Ñâ_ÿÇÿþ7Ç o§ÿUz;YŸç£H‡ZL úG6íMˆçt™ï.÷!Ù©ÿk‰Ø qPü£¹®ÛÞ;LéæÊ ªüѯ·`·rkÙ&ɵû³Ë~tX¥TýhÓE4nq’¼Íê5ó³K~t*UQñÊCüð¾cê}XÛƒr†r¨öÑç¥ØG'kK­îÒÝ’Aªp­J†r(ôÑ“ëZ\ö¶Ï.ó¡'¸«|tèWùè°5¨ñÑ!kPâ£'WÉDNö"ôeX=ˆò–ÚÕ;äÝæ!ÚCmÔöð^-*{t òRØã5Þr}vYž]½»Y3æþì¢^ÌSÓã ÕšŸ]Ñ£gDÔ©Ÿ]Ïà %Êy8/E5¾‹tOrCɦ"«¤û´” •7¬W“T yt×™Qo료‡± ªx"¤ˆG‡K o3§„Çî*Ú«²Ÿ¯æX†&©ß±æâù³ËwXÒ‡ê¯é¼\€5ÔîðfCJwx»(²Þ×FágL©Ûámn”íèÙÕ³;ú=gqm£Ôì0þBÉŽ÷¸½°*ØÑwò2>»Ê¶4p(>q!Ö#m]Õê°V¥::בÜìPâ(Ôa©2êt8ÑL™Ž6]5¤à•´Štœæ|•y(ѱ+¯©BGs!FèðÀH}ŽŽü$å9šÕÎU£M+ißÞ† %±;Ò>ܺŽ~ þ©0G³Žµêr4Ëpª,‡E{¨ÊÑ\dBE9 jr´AiZ•ä°ð9ÚpA‹ ‘êR_(òœe³OóË›ñRs;£ôCIêl9Ÿ†‰ÖO\¯ qX3„:ÍåU†£±Å†*m·¶Ž4hUƒã4åUå6Uà°P8ÚízÍJBº„nÍŸ SÊn£í´©ŽÞ_Êð¤—:ž™S&Ã2|TÉXSbäÀ˽ÖR¨Ib‰ÊÞ“Äwr¢êCÝæëÔ¢<ÆúqÁ$e·î¶N‘FQÕÆ°¶¥1œI¢2FwA]Æð욺ž¸RÓOªbt‹Œ«(†çÏÔÄpRŠ’ÎÉQ£[ývrHõ^n'‡4ª¨Æ{ íŠg4Òl»&µ¼¨; gSI8 f”y¨Û1hKù퉗ª#ïY£/PcÿX%0öY©†'(€Ñ³ë,'’*ÓœÞÓ¼NÕ/œ'¦øE‡%Ní “ó(}¡ ?]ùBàõO¾Xy¦¸Õ½pn›²Võ¡êÅê&²z{»¿Û6Ò]Ù&ZC6I•%L¥Ê¸¢N[½ÝúšC@±‹7Ô UÆÍ¤ÊŠMÒlj;ñòc©Ü—¡¸Pæ6¥ùCñé Íj-[å†V¹ÑÍ‘¶†×t¸03úû¥Sy‹Ý½¥æäU Å-¼h¥¶…ž”¶°î•-^óþöZ§HÓZ•µ°Ú U-¼þEÄü4¿¼Ö)âæôï»ÑY?\7gàÕp­jûMQ1‹=ô©–ÅTUÊ •,ö€¬BûýU‹ž¾ÊXx¥I /)báÕ"5,¬‘B ‹ž|ÖÀÑH8¬r£9Ò°Ê fõuÌúr€(^Ñ“kQÜÖÏ™˜Òü¡¢ômí]‚uŠ´¨QÂÉìÊVxùOÕŠ÷œ­S¤'8¬S¤Qc|ß+?#ÞÃψPÓÇ-˜Š¬!WÕ*ö;­rC-«ÜPº"¡c£³R¥ŠÎV UìGu*zæœU¦ÂìªTìGE*Ì ¡FžWJí×J*ŒP b¿ÝsëÅݘÖ)RçWu  §0°@mŠý|§uЍ4ÑÐÞÑÜL…)N¯ÚZ§ˆªƒ+ê6QõÑ­›x)å=­ÌS0狹QÂ/õ(^3}{ÞH…RŒb™ë^})Ïÿãª?@ýÇËþ ŽÁö¨‹QVÔM÷€ºS!€ºw£¬¡áN¨M* îlÜ ¨JP÷ 2¼—ÌJärÔƒB:Ôƒ5Ô‹(0? |û¨úºÔNõpIZÔ»‚€êQ^øzEæ;@í|6õ®æ,€z€ÒP»õûã‰$yI[ )_x…ü  vÙ4êq»ª²õ‹6|-a Õ@=XôPÖ­ÔƒäõhÆ` ÷ê^9î6¥”lꜩý\QR¦Cõ@6 €ú5"A·¡°Ã%ø9³+¶£PÏbl_j9ÉÇÇ1™ùPO6¨'ûs¨-;@=o'&ÇŒ,€z²K€z2PÏlà4áU¡sÔs#´ %¢tÔó6`›ÑR’HõlF­p$[8g*:+ùèÇa¡Ûˆ´¼õ¨zlDZšDÊ¿ ^âBúqC](‘–¼•®­uTçgÔsëîV:ê ¦u(8ÜŸQ‡‚ƒý]~ üáöyæ´P:pêÐ&z9>u•/ÅOÆ[/ÇOV.q.t.×m´z•3<åÎ\½NGeþ„Wo=c#’tì.²ʯôU¿Ô}­C¡È¸4~aÄ ô€[‡XPÁF<lºùø—‘ìy"]§]VìÚ’H\Ï«‰¤ë–*¬ÑÒ¬êÚ²HšÝ]è"M_¯Dw®ù¢Ô!ksØU²7„bÇõóYœ /§­%dûÛ/Y‹ û@ÙÁ÷¿°»Ž–•^„ŽW$Ûs¹Ìu±,eª·Jeª«E…´à¢Ôýþ¶ØÑ¶Ñ$=¿}þZݦf%¥nÛ¢E²»Žy`;Bv—¿Ù?%[Tl;¤™:vÿ~ËFsÿæoÏáËxû—¢Ò«&µ€Õ Ù¡ËæÒ»©ü€·Í’Þ>Í¥gÃWx{c´ÒÙ1B x{ì¼=ÙÔ ¼=7Ì,ÃDÁ'aJÏ(ðöèþñúgu[9HÇm¨i¶.x{13xÛ2CÀÛƒ¢ÀÛ»\©àm ïoû£ ¼=½6¼ý–¦žx…< ÞÓ·}¢gD怷wY{ÁÛkÎn[²A〷×hXlë!%ÿzJÞI(tæFŸøöò X|{²ß |{²} |{" ¾= \ßö|{2]ßž÷W¥è]¸]øöꣴC±'³–¢Î•\×¥¤ÌÚV÷@…¿”nÅŸiÛŠAÛ~Û(ÍçN»N¡€îø€4lŽ¯ï“ îeßF³«…Êvo…$Ðîe«7î^ç£Ç ¼{ H·îPJ®{œ­(¤º‚¼—_YVaÞ«½R-½Ã ÞËÖQ°w¸ìPÜY ð››&vøQ¸úE åSþ~€ß¡»V±Q Roü½l=OáßïÆnRDº —ŸºÙRÔIÆ“…Çñ䟎_ Šÿ¶Ý¤ˆ¤ç-|ÞôÁ;£l‡bÏåºÏRLÊð½ÁÂ×' ëlÅ£Ûðw•bQ²¿I±(-ø…WZ€x(UlNH|ÙÔµ®V4ÒH xì)º°§‰´h,¾ö À£¨ã¡MÀø²ëŒÇž_¶†ÑEËø~ßñ3¶ãðø8~2"Ž"‘†Ë{Ú߉7ùoÿE£jTÒ!RLZ×'Üz+ê4Û¡ÈS˜')æ8¥)ô}õoÝOÁï˯˜ð÷vøy»¯(~§? ƒßŠ@€ð±ùÏ8{Õùi| ãqÅž:?P{“Ü…ÄǾDÅkŽ¿ë@G|ãC!ÈÐ}(þ”¯:ѱ³¯cG|¿ïs8¾Þ¿‰bÒt‚b¸½úƒ0ùå×û P>Žožã­K1é®ê¯àò‡~ñëùÝ(ØÍÇN½ïR2RÓÚ”/ŸÐß  *@7¡,&êfg£Id,\ÂCÝÌnÔ…¤|…’)*7Š;¦r3ù)_ŸÄôÙHyk/p~¿Ã@ÊÛmZôm¢òÙHùûãf ¢öÙH¹iJ åí6…¼[†ÈÀ¹"÷)w 4rë”›âRÞÚ‹…#FtP¹­¶Rny'òfRHyÐW…”76Ü”·i¦s2±]p¯©Ü:î¦r3»%Kº@©îí³‘òŽ$¥©Ü‡)i¢|R¹7\-@¤P7ŠGÃ¦Ž‹Ùðvs·%Œ#ô*w„†Ê]}ùÝZ<×g#åïi˜Ê œ?L@>ò~™è=*ºLÖ–w›’*j†Õ¡r ½ˆ¬~µ©Ü:+¨Ü¦ÌCå¾L7•{ÃêÐÀ…ûfÓ¢¯ÏFÊûÆà]%¶ŸTn³Ñ§ëĦ“Êí¬Tîâs•»˜p}#‡¤> •»øœýŒ’s©mSϨG—&’P¹o·£`´qðÀ›MiõT£æÒn¨ÜhÏ›ÊíÍ%P¹'!Tn$8MåFõËTnãl—õ|4zCåF£“{€ã}˜pmÝ%})w3鄌w'ŒŸ¦ô€„$ ïþÈ ·® x?0ré mf7"DFÌÕVó]â½ñù.óžs'2Œòn%©‚I[Æà¸÷Aåîf: ïÍ@»¥˜4­Þ)  Þ=‰ n9@ðn ¹0pk.Ÿæ—7[>Éx·´ŽN*ww¶Cè·;à÷îH¾;Ån¾­·òݧ9ÔVÒ2W¸÷iJ›¨˜Ù-m"@ù†®ÑeSªGбò?…x³xäK°F—0Ð5ºl¢à¤O®h˜Ù­+‚?¹\þänh^/¬»{$¬»³ý ¬{?Paݧ)]£l²v }¡ n÷·JXwGø¬{¿°Âº—©ÈÕ^›:.@qµÚ’¡oé)5­Ô”Íì–W¸”°î~@ßµ½ï °î°î5c9°înÈNXw/¾“õ¡fSÚDÅÌn©i¹(¬ÛÅÁº7CSX÷æŽ ëîÊ•RÓ6“½SªGÂyu›!ÔÝ“YáÕ£lå"AÎÕ£l0[ÚD—‘oÑ„| móAåî‰1G8BßhõêÑfv×·ˆ=P÷{V Õ#u~!Ý}èÝ›’)œÛ“`îN}KPn%¹OS‘§yßó<ÜžQ€o[|x»£«—µ‰ücó0–-¯:° m dl¯ÈS‘“½‡b°öfJBåö¢ *w1&ošð0Ä ÙˆµÔx6€MIRãÛR*'•»ÒU rWß Q¹ý¦@åöÒ*wy‘nt t+r2n-/0º –V“µ¡EÔ†øl³½{Måö¡rçà•j³©Ü‚6•[ƒ Tn._*·.pS¹¯“ÊíWcS¹á}—º5.Md›Ó'¦èçro—œ#¾MǶ)Zt1B-Z´z;TîlüÜ*@ÀÏo5³[$ña0[”ju¨Ü†Â rû;¸©ÜÜIS¹‡qiEÎLíé´©ÜÕ r›£·©ÜÅ´"ë>7—œÕÍiVjÒIn*÷}R¹«ÑÛ‚úP1³[ªGÉØµt8É áz©–WŸc¨Ü·9Ô·u&&ºF2Må$7•»Ø IÜ5ôs†KÎŽîÆƒ6•û:©Ü~7•[³¯—Ê­OT8îDõ(›¬½"{_TîÆYAå†iªG—Qç¦ÉÉÄ”ê‘0J¨ÜÔè‚~é„@署¾­.ág*·ùºÃû'´r-@Àç×¼¿½·Õ‡ŒD£kd ZºFúî w>Í/oGOIoh·R¤øþyøz¦¼Ü8è…7¶7g‹„6ï©©Àæá—]XóÈ/­È «‰¶µªGùÀ™G6¬›‰ *Cñ(% úEåˆS1oƒæÁÒ€Ù\?ðåáõ‚àå%‚t Ë2\^Å[^z;fvYÈxG›hšÙ6Ñ+§ÀÊÖ&UNÚ Tþ, S·!q=£ö2»¥U@y˜Ð$<Ùµ“‡'®B“‡7ð L·±ÑD[ Õlm¢I>½Š¬£päám£‚‘MGEvK@ä÷ÇRjª†¬­Ô4y“R ëöþ4~<†ám)5M£Û»ªàñÜ ëŽ—Ëïx7w[ªGïqˆÍQnü¶xA©'ÚDÙ ²ô”â¡€O¦L@Ư™¾½ÉÞþÙxñd2¶àâô|{–ÄEþ¬g‹ŸóTþ€þlåz«/ o1uZ^«-A±ú2G;ÓvK}àµHo{¥`bµ©‘(¤WäN{㈕?’‘ç͉tÀÅ[u [U¢Äêò¡üq˜(¾ŸÊ ãVþÈÆÒ-ìžÌ³–ŠF7,o6“Z‘¯úÙp±wÔ½ÊÃø0 ®yÖ\‘¼‰È·ñá|jl$"wãÃhÑŸÊÎVà+ù÷Âņ‹ÑÞ|áâb!ÁÅ Ï@¸|ÃÅz .®ßpñm@XPäuÂÅõ¥] Šk¸Ø@冋áJw Põ:àâjö -Â'ó¦žxo›:n5ÏZ‘§Ò pužpq5Ñ8}{-Ã_bu§2Ç ƒX}šó]‘½pñm©àâúyáb>µ†‹w¨Š÷6zŒ ¿Nã6}^¸8›gmu–MFC¥~^¸Ø/Ý´,ÿ}*ò,ÀÅå‹VÝMœ.†j¸˜¸áb„… g‹—L"ƒ¦^hŠlôXš"éTþð–2àbï‹.ÞÜëdM‘.ö¦^àbO´€‹Íë.öÖ.àb¯¹€‹ýù.Þ‘«½2o¼ÕiE®'\|˜óU¸5\ìmaÀʼnÙ>pñõ’¥¥)Wº[E¡^à7pñæ™OÔJ†Í¥*1-È!åsˆPþpÊåƒR=C†sB5D0ÊÃ*™Èù Kou”?6ä+åiQâã6LŽ{ÀÅkA”1¥V2¬ Bdµ½9®æ0(LúÊž”¢üA "+ì+êx…M¡ü±/¿£’ÒM—Vd.aЖ»1ð¶.nÓTs«³5Ïï»1¹Wš #°o¸øú¢F{Fa¸øºAù/+Ì/jts¾åƒØ(ø‘ïÈ•ã^„yãÃý­Ûc¸ØzÀÅûœo¼õ F÷äÈãÞfJ+ò8àâ6y (¸·£üAÉ,+øq£üAm+P ÈÊ&» ü1èÏ( zÊfV ü1,A‘ð6ãÃòö.nƒu"ÊÀpö9ŸÊ~SPþðKW¬*¡u"Ê–&BùÃi”?¼å“;Pþ˜/ºÞ»(áb3逋¯/åkÅ.¾Œ‡w€èrÂÅ#0pñõ¢ÇòŽCù£_¾ƒÈý Dw“ꋽ=¸Ø£7p± \œxC‹¯o¸øúbBoX¸Øß2àbÓ@‹0Ùp±Šjaå,6\ÜŒ#)omÄÛOå#„.N“Co´ÝB óe..u ë•ÜpñæA«mù†‹³YÐòj½V-ìÉö?•? €~/¼ð/"÷.®fG[Ø¿NQ!h TeØOåã‡ÀÅ»mqÉ€ SÞf1€wãÃ:î´W‘û©üQ ßÀ‰ùTþp¸¸LnhNôSùÃÛË€‹Mž..>nwdÝÉáÈ'\ìÔ p± ÀÅNœ³´§…ß…_À§_ÊP´ ßßp±Á àbv@.®¦7gî‚P7YêC‘• .¾ô7\Ü&³eð_¸XCІ‹«ñaÎÙ¬fÚê¸ ïæ(Ë; ³™†‹­ê\l½(àbÓU‹ F[ÿ ¸xs¥€p²Ô‡àbÐrÃň6\ ¯Öp1´Û‹ÈÙÚòV£ÇòÞ'\ÜLIÎD¾¸¸›Àˆòûn­üá§\ì}<(ìP•¶z‚(ø‘¡ü±#ßD®&&+ròÕöKùã¶ÒIÃ[-õ¡Èp´»ÏÙè±ÚÂð>nÅ”·üä÷4¤*áåj͉ ï‘wZꣅw˜É<ë¯ÊÕ<âÄq³áÔ6¹ÈÊ·ÙLäÛeEF½£àÕ,å'GPþ¨W+ç¼Mµ„uÜl³µ]³ÍÊ·AìÆ9ßf&µí¾¢É½:•?n³®;‘©mÕE‡Ÿ÷YÏÈåf •Iäaï o:•?šÙ®RþèÆ-¥*Ñù¡üÑ_*r ¯Rr(°ŸÛʆåQþ0,ò‡åÐPþØoÅ›mÊÛŒ+r·.‘Oån`öÆ[ŒË«ÎòÇ>çæ+2÷xµõ^e”?šàŽWß_”?šqi)x[Êí .îÍ$剷pqGTÒÊx­ü±Í„wãÃò^QäT.~Þ™UUj±‹ÇóîpqÈоp±å`‹  mŽnôX:ÕøðDh[‡cÚ”Ç0zÜEþÆkŽmÊ»•¡ŸÈ®Ëfv¿:À¶Öá¨6s§W‡c˜l,m àqëpHüŽúro©t”φ‹¬±G·õ°J‡~ÊÁ.žÝÇ dGFÅÁ,áÅsZÿäÔà©þ48¼(·Çeͧ¡ƳÉEOg~àð Y ñì–÷È¿„.>t²~M8­¿±ÜâKK†Šiß¿ô7LEµþF~õ6ÐÿH¦"G|/`¬¿áU¦õ7L±þÆÆ¦ÑßðlËú¤u¶þUµ¶þ†g:Öߨê>~=pätynký o‡´þ†¥q­¿QxÖßðötëox{Ÿõ7Ê—ôœÖJF~Ã$CËol9 ä7¼,C~cïŽG~ãªFœ‹_Í2Îò vC~ãòX°ò4T*Xy)Û$̈nn‹Å7  Xžó¥%Gðä“í¾öËZáÏæZK{ã2•SàòôNOË«й¼É¤ç—§·… \f“ \æ2 \^¾fóÅuž—Çàrxƒ›À呟&t(ÐUpylæ—¢ñ€ËÃTNË‹‘0k{ —Gz…6j{yE—]ÌÕàòÖ‚¾á1·“‹ìYø—õŽ.#e.²7DÂEf¿–¹ÈÖ³‹Ü ÄŽ;ÝM¸<Èô.¿7gRS¶ÛÔS¸ÝaBàò¨/ù8I¥˜m\U¸+cöhÛpyX„Eàòðˆ pyxµÀek¦.j…ê.]Ø\dvd™‹Lùjs‘§Ï*Àe×x\æ2.Ó¸<ªŸíáQHàòðì_àò¦¿\^„\V ¸¼Þ”‚Ùß %àòš«M¦í!¶±‰S—‡qËž2.ñ%´±yT—‡ï•Àå¹U—\öŒ pyšR)pyÍBä•4ÊÖs.x«8æ½åÈ—§· \6pyRZpyn>q€Ësó§cý<««\~&+ Ø.O[—§÷·\žÕÝÈ·™ÊSÓ aÜ.Ï ˆ¼ÙŠЬ!Hàòl_z“ŠÑ€ËÓù˳™ pyZ+Aàòô%Ës+_¸<8\ž–+¸¼tÂL\ž’Aƒ_,˰‚—×Ïeê)Y8kS™%¢q’ADãòkŠˆÆµÏM"—Á?D4.oBFDãrV t™,t*—ˆÆe¹%D4’EÒѸœ&FDãòÆ!D4’‡BD4’gaˆh$ëš"¢‘Ì£DDcÉ^Úž:ÿiÎqÒùݶ¹¾ëÑØ*Zˆh\Ó`pöñ…Ë!¢‘(n´9ÎÅç™ÆÜtýâÜ?XÏÕñóÁU.¡ÞÑH›b,ä "[4dæ;"ÉÔ;D4žãë–ˆÆÕ œKDã2ù«¾¨t͇Š"Wóù·¿$úíoˆh\Æ”ѸL:DDC5ØnË"ˆh\ @DãrÝ?ÑHÕ¬\‰h$ÏjÑH&–#¢‘ªá`‰h¤Û$_‰h$_"©|Óñüdß>>€¹D4Ö„o`G|ãsˆh¤¨KD㱑nöÒßÓ.D4’UÚÑHý¥0s|_r¡ D4’/D4²%¨ÑȦb#¢‘ý%@D#güÑȪ,¢‘½AlF="ÙÙDD4²E¥ÑÈ¿•ˆFö7ìÝHˆh¬øÃ9Š çe#B!ìô"Ù2gˆhdƒ ˆhd¿ˆh<6’ÝÑÈÝØ¼D4ò!ŠcÙøEAD#[äl²"ÙDD4²÷â!¢‘ç«V]åoå™"é?·ˆFžœ"y¾$è%Ò¼™ä2ˆh¤ûKÃz½ù`>¯÷¢°D4Œ3‹h¤ò¥c½â£ä,ä²Oˆh$ïxED#ù{ˆˆÆe¥9D4R~Aì„(YÇž:ÿ˼fŽŸX{½·í¸?VGD#ûýGD#S±Ø"9Ù–ˆFòììLˆhd“ìÑÈ– DD#;×…ˆF ²ˆFµˆFFHÖ"™]¡Ñxìo,¢‘aù‡õó&-¡ÆÅîO/ÿfE?¿>YÑ‚œ€¹Ó«/}IŒ:}^˜»º  `îÛâÌ‚¹ï—$}D^ííÚ«P‚¹‡áõ‚éæ-7}ÀÜ}Z«Y0÷®-(˜;¿…ÞÕ{€à»¢€ìd©jëb\”6®·~^˜;›çݲFcd Aæ¾MΞ€Ñõ„¹«µK¦tKlæNƵåí+zlzn⊚55™ ˆ|Ùò£Þ÷µ÷oXnzš\…žÌ=æ«‹!¼È^mÞáÍ&IwmBÐIÞ@äÛ”—K°Üô²sÝ›,7}½‚¹»iÐ_Pµ`îlByº‡ˆÆ´fÊ–›Þ%ÝÒv ·ƒ=/KsOôrÀÜs£­‚¹7’+˜ÛÊ&ÈMïRƒ 0ZH=0ws¨Œ(4ìä dŒ”r2+zúµ憣f˜{ZÆ9úLà°óóâÜÃZ7ŠÒp¨o߇І—R¯Þt7 Þ£m>hÑÓy pîiÔ_8÷0ú>,×|¼è ×nµé ãÞH7”ƒt[DÝH÷†c/õýÓXñ§‘pÔ¦7˜ œ›-  œ›?›õœd=[Žùîn¬ö_¹éùw»—án«Eî¶:Þ+7}›µJfCW-tŠ©Ô] fø{ž 5ÃÝžØî6û¸;yïpw2¸;y×pwÊ/¼ÍñoKpLB–ÕÈZ(AWÜ\¸;²3Üm ÃÝÔæ-7Ý­Xhpw,Œ,¯œÞL˜&~6¾Üôe~õìçñŠå®ï/¼»Ã#5ÞíD¸ñî]}°/GÐã6$Ý"uô/âœ?ñ›ñtÊ6§ ’!l®¿Ú®:¿|¨o¼ê²ÈM_¦šXnú2¾?€ÄwyÄzw iĦU"6]^ |†w«KKlv²Ä¦ó·Ø´U%›.Fª¥cü]Kqš;õŠMÛ”Œõ zÏúêq¨íe[RÔ›`­ãª;!6]¾Ä¦7HˆØ´·€ ôžù‹Q½jRTLõÉjIBgcàÜôžÅx€Þ3½Õ%Ì­>ŒØ´×nˆM;†Ø´‘Þµ7‹M»ÞbÓ½¶ØôeX[zÑzWz¯©ðzO×o}µ¦äZkZ')Ð;>O‡Ç´ ý›n‡Ç4±éÛÒ)jJ„¦¯þ¢äSí©F(¡âËk~„¦/=Ehúê/4~oö@húê/Vì´P5BÓîbšv% M{_¹…¦ÍkGhú²0©…¦½fGhúòó·Ð4ÓÄ-4]¹¡ikXXhº¾<ëðo%ŠËBÖíÐéáka´šNÆ -4¾¤:â| ˆ#ä Ÿ#¤=m#”ˆ#”vqË®ö_;0Cè!4}Yg¤Z(û6í:„ ·°´0òœLžÆš­ŽFž³ÏWkvå0òÇn¶ÃïŒ<ßfr #ÏN]ƒ‘çjaä¹c乿:BÖÉüíYL Œ„¤…‘ßÎÉZhÚ…%ÀÈo3Û,4½…\š¦ÚšÆ¿…¦J¶Ð4Kï-4Þðšf9òü‹ßRáñ¯ÿþ7ëWú­ÿüû¿þ»ßüËûŒBŸßþçŠ:¾È^‹ÀçI±Iâ·¿ûüÙoÿëïþæ_|~û·¿ù7¿èn~´ùó#TÎA°_ó´çºW€ÿáÖÏœå'=¹? ü¤”oTÝòw†h™??Ÿ¿úõ˜·ö¬¬¬%ßÚë2Ïœ1÷!ŽüÉ78ßñ¡(1]^÷'_Wûó+=ÿü¾Ûü<¥õU¾‚ôï ”•êãï\t/k~wë—_x-†±uKž¥êÓoÕ<8´Ïÿ”öw Ѿ^¿< zÕónÇXEǵ™šQ_ßìÿòùg|ù Ý*ÿÒ­²^þÈȧ“âç"þýŸý»¿ûOÏëÿŒTÏ{ü>¿ýË?°æØsõŽ"ÿú?þ÷=ŠüŸ¿ùÿTÈj endstream endobj 511 0 obj << /Alternate /DeviceRGB /N 3 /Length 2596 /Filter /FlateDecode >> stream xœ–wTSهϽ7½P’Š”ÐkhRH ½H‘.*1 JÀ"6DTpDQ‘¦2(à€£C‘±"Š…Q±ëDÔqp–Id­ß¼yïÍ›ß÷~kŸ½ÏÝgï}ÖºüƒÂLX € ¡Xáçň‹g` ðlàp³³BøF™|ØŒl™ø½º ùû*Ó?ŒÁÿŸ”¹Y"1P˜ŒçòøÙ\É8=Wœ%·Oɘ¶4MÎ0JÎ"Y‚2V“sò,[|ö™e9ó2„<ËsÎâeðäÜ'ã9¾Œ‘`çø¹2¾&cƒtI†@Æoä±|N6(’Ü.æsSdl-c’(2‚-ãyàHÉ_ðÒ/XÌÏËÅÎÌZ.$§ˆ&\S†“‹áÏÏMç‹ÅÌ07#â1Ø™YárfÏüYym²";Ø8980m-m¾(Ô]ü›’÷v–^„îDøÃöW~™ °¦eµÙú‡mi]ëP»ý‡Í`/в¾u}qº|^RÄâ,g+«ÜÜ\KŸk)/èïúŸC_|ÏR¾Ýïåaxó“8’t1C^7nfz¦DÄÈÎâpù 柇øþuü$¾ˆ/”ED˦L L–µ[Ȉ™B†@øŸšøÃþ¤Ù¹–‰ÚøЖX¥!@~(* {d+Ðï} ÆGù͋љ˜ûÏ‚þ}W¸LþÈ$ŽcGD2¸QÎìšüZ4 E@ê@èÀ¶À¸àA(ˆq`1à‚D €µ ”‚­`'¨u 4ƒ6ptcà48.Ë`ÜR0ž€)ð Ì@„…ÈR‡t CȲ…XäCP”%CBH@ë R¨ª†ê¡fè[è(tº C· Qhúz#0 ¦ÁZ°l³`O8Ž„ÁÉð28.‚·À•p|î„O×àX ?§€:¢‹0ÂFB‘x$ !«¤i@Ú¤¹ŠH‘§È[EE1PL” Ê…⢖¡V¡6£ªQP¨>ÔUÔ(j õMFk¢ÍÑÎèt,:‹.FW ›Ðè³èô8úƒ¡cŒ1ŽL&³³³ÓŽ9…ÆŒa¦±X¬:ÖëŠ År°bl1¶ {{{;Ž}ƒ#âtp¶8_\¡8áú"ãEy‹.,ÖXœ¾øøÅ%œ%Gщ1‰-‰ï9¡œÎôÒ€¥µK§¸lî.îžoo’ïÊ/çO$¹&•'=JvMÞž<™âžR‘òTÀT ž§ú§Ö¥¾N MÛŸö)=&½=—‘˜qTH¦ û2µ3ó2‡³Ì³Š³¤Ëœ—í\6% 5eCÙ‹²»Å4ÙÏÔ€ÄD²^2šã–S“ó&7:÷Hžrž0o`¹ÙòMË'ò}ó¿^ZÁ]Ñ[ [°¶`t¥çÊúUЪ¥«zWë¯.Z=¾Æo͵„µik(´.,/|¹.f]O‘VÑš¢±õ~ë[‹ŠEÅ76¸l¨ÛˆÚ(Ø8¸iMKx%K­K+Jßoæn¾ø•ÍW•_}Ú’´e°Ì¡lÏVÌVáÖëÛÜ·(W.Ï/Û²½scGÉŽ—;—ì¼PaWQ·‹°K²KZ\Ù]ePµµê}uJõHWM{­fí¦Ú×»y»¯ìñØÓV§UWZ÷n¯`ïÍz¿úΣ†Š}˜}9û6F7öÍúº¹I£©´éÃ~á~éˆ}ÍŽÍÍ-š-e­p«¤uò`ÂÁËßxÓÝÆl«o§·—‡$‡›øíõÃA‡{°Ž´}gø]mµ£¤ê\Þ9Õ•Ò%íŽë>x´·Ç¥§ã{Ëï÷Ó=Vs\åx٠‰¢ŸN柜>•uêééäÓc½Kz=s­/¼oðlÐÙóç|Ïé÷ì?yÞõü± ÎŽ^d]ìºäp©sÀ~ ãû:;‡‡º/;]îž7|âŠû•ÓW½¯ž»píÒÈü‘áëQ×oÞH¸!½É»ùèVú­ç·snÏÜYs}·äžÒ½Šûš÷~4ý±]ê =>ê=:ð`Áƒ;cܱ'?eÿô~¼è!ùaÅ„ÎDó#ÛGÇ&}'/?^øxüIÖ“™§Å?+ÿ\ûÌäÙw¿xü20;5þ\ôüÓ¯›_¨¿ØÿÒîeïtØôýW¯f^—¼Qsà-ëmÿ»˜w3¹ï±ï+?˜~èùôñî§ŒOŸ~÷„óû endstream endobj 515 0 obj << /Length 788 /Filter /FlateDecode >> stream xÚ½UÉnÛ0½û+( b¸ˆZ ¸@·í­ˆ/EÛ#Ó¶m•ä$îחᜤNÑKQ‡³¼YÄyædG8ù°àá|³^\^éŒÁ ­%Yo‰ ˹$O™YoÈWš0ÅBHMßîM»«Ú]«LÓ×÷ÕˆÒUT$´k#%脚ëê§¾¯?]^©Œ¬He øœÄ*gJ(D¾î;UÁé´·(DvÒÖ¡ÊÜ£ºÛ¨^j ¨x¹‰bw –²kÇjc» X3x罺€Ô˜ºF±ÎxAk3ì,‹âDet½·-4åã°°ü#ÔÐ\€R´4- =Œ5ØŽúº›Ðæ@ -ùñhõh> /ExtGState << >>/ColorSpace << /sRGB 520 0 R >>>> /Length 26353 /Filter /FlateDecode >> stream xœ­½M³-9r¸¯_‘˪Ÿò _KJ33š‘cšn£2-zzZ¢ÑŠ-Š=­1ýûI„»"ò¾Çw«Z ¾¦WúɃ›‰@8cû›íØþqûï?ü‡í¿oû—}ßÎÞçÿÜ×9ÿç¸ïù?ÿò‡í?n4ÒÕ¿Œ¶]Wýr?ÿÓ®/{ßJýRªHÿæOÿ×ÿño·÷›æíö-þû›÷wÏmeûÿ~øOÿyÛ·ÿç‡cû›çÿþñ‡Ã¾ûo(ç—Ö·ë>¾Œ±ýÓåúrßë½ýüC¹¿»Á²>÷šð0X¿´ÓàY'l_.Üêº&|þ,Üj?ëÀgíêøráVç¼ZŸáV×8x«sÂóK½Ðª1áõåÄ­Ê8x«2aù2x+#?Š·j¶/'nuÀŽ[5kFÿRp«:ïÜö/·ª³Uíi{¹vܪùürãVÝÈ×—~³«åËq Í7`íÆüÛóãòVvµÍùºŸQnõ<”ŽÙX»:aßç_Æ_ðÏóF› aÅŸßæŸßÏ/o5[Õï/Z5 ਸólF/_êiÐ~²^¿œhÕQÇeðœÂÓ‹Çà˜=tÂkÞyìó?OhÍx`o€óOöKœŸ×ìn Þó '¬°{/î?Ù¨³±»sGÖ­ú—o5¾Ü¼Õ¼zìÏK{¸±]·áçÿáÝ†á øœhâ{öõë¾æo \q¿Žûôr¿_CG>ñ¶MŽýý¹®Wtésþð7ôésöà¾GÜ¿ÜüÑÑžNooƃÏCýú¾ˆîwÛßsÚj¿=¯óìÐþ[˜=¿ÃûÄÛ{œ½ûDߟxðùâz›¿{øþŽîbÔ9®Ý{øA¬.nÏç²—j=¯gÈ>1\´xàu²Þv\OWåË~A?·wrâ§³r ;‰;ÇW\oxöùZN<Ð×w¼®Çóÿ¸ß1ˆÛˆøÐh~Ûýæ†áýeÆ W'n×I»øˆŽëUCúq7Üýéîèï~} ¿ïó­¶a}çïSöÙñ'ÆïS¼;߇'YßÙÿ¿wÜïÄ÷5ô÷ýõ‰I÷Ãó‚Òç‹þøàŽûá÷®;ÞÇ'ú»ã ýÝb+ðå¯Qþ¨· óîöüŸP5߇c<Ÿ'ž•±ÞŸóÜõ>Ødã‰9þ_ö÷ÏáÀÇïs¢¯ÿ«=Ìò.„½‰¯ù"X|´ï{â׉xŒþò`ëïœÞŸOüâüþÆ÷µùÅ…9÷ùÿyÌwÄ«³ìÿ™ˆœÅŸgOs–oñï,§¦ùhß¿î}MÝÏ'^íuÍÎ'nœOWMõñ÷>ñlçýp=˜¸b>Ñíï©»æ;ø½<0?ÝŸðÛßûį2Âýžøubþd)ÝÄãXóÍó‰gšôãzÕ¬ýÿ‰_æO6žNÜq?<'~iâoí}ò/Îüñ÷>¸Ÿk¾|¶ýýdxâ×]Öäÿl·fÿ–¡MÌ鿯瓅ݜãz›9£ÏÏÏÆöDú;1çÿø{žLìªk~~>ñlçó°öt¦±'ß~¢¿ŸŸŸøuqþ\ˆ9ÿÇøøäcœÿ¸Ÿç²–žOü:q?¼ïݳÙû{:KþÀû öýýù{âŽù.Æ›Á”öâ÷?‰Ùÿ'®Äö>\ìOƒYí5‡ù‰™Ö^˜¯ŸOüÚq?üýƒ‰í…|ç|Ò³÷³ññÚ™Ú^hßõÄ«÷«Â6ÿçý®Ùí…ùÎõij}Ÿ¿Ñß/üÞ׿ê13Ü ïëÄ÷³þu=ùYÅý¬^»MÌìº}ßÁ,W÷{pçývÖý¯û?ùÙçÑðyfºÎ¿Ñß/Ì—®'~Ý=ü=“Ý ñhâ6Öïsžîvܨ¿w»~z¾kñ}bæ»6þ\ç¡÷ÁÞ×ë‰_ìïö>\ç­þnùÙÄÌw+øãÀ|°ç»ÄžïŒÿ'ûÃéù®×דŸÜÏÆóëò|—øRwÌ|×âåuy¾‹çóäg|lþ=ñê(k<›˜ùî~×ø?ОñŸã)'Š>^S9rm†‡÷öâÔõþ[G8Ã÷=ùÙ…çßû^ù®}߯vŽO…¸r|ÂçW¾+Ìñßæ×ÏÇ»9Ðߟ>þ_ÄÿÑßÊ…÷áD¼¾žøUØ>Ü¿jüGÿ-Uã?úÏÏJÐ ®'?ãøoRÞUw‰;6ÞNÌñí¯‡Æß®zjü·|iâô‰«Þêï–?]OüºÒýªÆ~_ÕøoãùõägW¹®'~±¿ã}­Ãû»á¶küGÿk‡Æ‹ßó}ÀûÖ.õwô¯v«¿wà¢ñï׃©ÁÙ|äzò3¾x~­«¿“?ÔßM?™¸ßa|è»ôŒO<ãû€÷¥_êïèÏýB§Þruä»þû?ùÙ>Bÿ|pãïÜÔ߉»ú;Þ÷Ž|÷¦^7q¡??ùß›^ãôþ~³¿[<»žøUƒ„yÛõŸ›˜ïqõþÜÔßwkïèšïXü›Š|þÀ9ýÿÞ'yiî×ìÍKs¿ðWKs/x餹ßèÒÜ B4÷‚Ž4÷‚Aš{¡šOͽQ §æ^¤¹W FÒÜ æ¦ÒÜ«Cë©#«4w&^ÒÜ™ÇIssÒÜÞ*iîL²¥¹wôiîª:g(w¦æþŒ[ÖÁ©¹?ÃÖE‘}Ê1…‹ ÔÜ W¨¹;š{Ù1÷¡æ^l†ìš{Ù©›cb2çû³£Ss®Z¿æNYEšûÀ¬€š»ÚLͽ0ç¡æ^v„@jî…»4÷VIs„æ^vïgí¡æþ Ðð¨¹ÏÔš94÷ße[š;Ž¥¹—‚ñEš{iÔ ¨¹—¦ëÐܧt Œ9È3¶ÜQsG¨^š{¡$Íýù<ž?5÷"ÍŒšûü>`hîEš'5÷ùyá9&ÏÔš94÷BIšûœJc2§Â÷Û¡¹Oé÷ƒæþŒŽ;4sÌAfê5÷9±ß—šûƒ¡!RsŸShàÐÜ+䤹WæøÒÜ+ç„ÒÜ+1iîUkÔÜë. ͽJc§æ^wÞŸš;Rý¥¹ÏöA#‡æ^9Ç—æ^9lHs04OjîO@Ø¥ÁÏ÷¡j Œš{½ôyÌAªÖH¨¹×C=4÷zð}¥æ¾04÷…¡¹W®+Ks¯Z¢æþüç ס¹×[š=4÷ù1ið³¿ÏÛBSǤj¼ æ^oiüÐܧT šû˜ƒLþµ-ͽjMš{å ¥4÷zK£‡æ^ Ÿ5÷ר¹?ý‹šûœÊîÛÒܫ֩¹{¡æ^µ&DÍ}Ž'Òà;Æ;¬áPs/4÷¢55jîsü½·¥¹—‹ý‰šûÂÐÜKÜIšûŸ¤Á7DŒÇÔÜg7†æ>ÛÓ·¥¹—ÆñŸšûÂEãÿ¶ñ_ñˆšûŒšÐࡹ?Ö0¨¹Ï »oAsoÔ¥¹7j0ÔÜg@ïÛÒÜþ¥¹¾ÒÜ‹46jîŠÄÒÜç¤÷ƒæ^Øß¤¹—‹š'5÷Âþ&ͽ0§–æŽ×ziîÏó¬QsŸ¯ñ½-Í}ާÐÄ¡¹ÏTš=4÷ÒcHsŸ×ñyhîó:¾š{i˜ZHs04pjî¥pš{¹©ASsŸñ\¼ÿ¤¹—ÂöPsŸóè¶-Í}öÜšûó÷A¤æ>ãMÔÜ ×Ô¤¹W®ÁHs×x.Í]ã½4÷ˆ9þ'Í}ÆChöÐÜg¼±¿š{Ys/ã›4÷Êy¬4÷ʹ©4÷)Cã†æ^iL‘æ^/ŒŸÒÜ5Is04TjîózÔÜ5^JsŸ×£æ>1>ÍýÁU¸c|mQs¯7æSÒÜ+ßiîSjÛÒÜgêŽëÐÜgJMšûÌI ÁCs¯œ?IsŸ÷Ã硹Ïñס¹?×Ñ¿¨¹×›ýƒš{­Ô<©¹Ï0slKsŸ×¡¹×ÆçCÍýÁ<4÷Úćæ^µEÍRÃÒÜ+çgÒÜkçßGͽi͇šû”&¢æÞö¬¹7އÒÜŸÿ ÍŸšûƒ/iìsüŸÒFÝ–æÞ4~QsoÔ䤹ÏÔMûìïíF +ÍýÁX#£æÞô{Ps0Ö¨¨¹Ï¯µß›š{“&MÍ}ÞÿÚ–æî÷§æ>sGhêÈe[Éš;þŒ¥¹7õGjî º4÷Æí~ÔÜ[áøAͽ1y’æþ`üþÔÜ'¿lKsŸÙ+®#«}Þojî­s͉š{ã|^š{k|¿©¹?5÷9­0M„šûü™ömiîÏý¡)Qso5÷Æñ_š{kÔ”¨¹ÏöâþÈpç´%jîj¯4w=_iî˜6-Í}ö§}[šûü=¡ÁCsøÐ©¹7z¨¤¹· ã‡4÷& Žšû”⤱Ïñ¿j´ÔÜÕ¤¹·‚÷Išûœ¶A#G¾Ûæ—ÒÜç4Í43jîiîS€€Ž|W¿¯4÷ÉÇ硹7ŠÒÜçï5÷…‘ï. ͽsÍWšûü~{¾ÔÜŒþAÍ}ahîÏñ¿Ób(ͽ»fÍ}NCqš{çüJš{§L#ͽӃ*ͽs~/Íýy¡áRsŸºŠ=jî†æGͽSA“æ>1®Cs†ôgjî½àý”æ>ùǶ4÷‡Ïû!ßíŒ?ÒÜûù¡4÷9mŽšûü<4pä»÷miîÏ0CÍš{§‡BšûÂÈwûàïKÍýÁx©¹wõ?jîÏu¬IPsŸÃع-ÍÝùÔÜ'=òݱK#‡æ>8ÿ•æ>vxZ¤¹]š<òÝçþxÞÔÜ;ç7ÒÜ;= ÒÜ'ß–æÞ¥1Ssï;â¡4÷ŸQspšû ™#ßíÔ礹wšb¥¹wª]ÒÜ'†æ|·kš{—fMÍ}ŽÇv?jîú‰4÷NŽ4÷~ò}¤æ>Ó2`ä»ýæ÷QsïœHsïÏ©¹÷ªëÈwçûëÈwû% š{gü“æþà5÷ùùº-ÍÝ¿Ÿš;ÂèÒÜg8¶¥¹/\Õ߉›÷wû=¨¹wz¦æþ<¬í>žn<ZÖÜ9ZFŸû5÷=ûÜé;‘æÎ)´4÷O\šûŽ\š;=‡ÒÜiY”æÎlšûEñŒšûű“šû´õÍ5w¹ß¨¹_¾¨¹_ÔÕ¨¹;³9£¨¹ËHEÍýêx'©¹_ŠwÚ‹äsßÉåsç@"ŸûŽqRš;õúås··L>w.„.Ÿ;$xúÜ/L–ÏÝFèåsïÙçޣωáë9ò¹_˜ ÈçÎõ{ùÜ}õ?jî2 Èç~¡Í×þí5—ÏY¿|îj†ûÜ¡Ù.Ÿû-Lß/|°ò¹ÒÔ/mÖp}§/éÞ~þ¶Ïý |îeû9úÜ¡ÙÉç~,>÷K¾w®ƒÂ'¼|îwÔܵn|îC¾÷|Dîs—&)Ÿû%_<}î5_ùÜ/>ùÜ¥aÉçN†ûÜù³»ÏÝñ)Ÿ |Üò¹sŽé>wÆ÷¹3'vŸ;s÷¹iðô¹3'vŸ»4;ùÜ«|çô¹WùÔés¯Òè¹î¯5ùÜ™c»Ï½rß|î…Ï[>w­1Èç^ùûËç^ôýô¹Kã“Ï]k,ò¹;¦Ï]š'5÷»p…|î̹Üç^^>÷BÍZ>wÆ8÷¹j¶ò¹þ½ò¹«ýò¹jÎò¹þ~ò¹sê>÷Â5ùÜ û£|î…k<ò¹h.îs×ß#Ÿ{Æç>w­AÉç^¹&"Ÿ{¡ÏX>wÇô¹;¦Ï½ð}£æî¿/5÷[kVÔܽ¿Qs÷þFÍý®Ô´©¹ÏöÊ×¾ã~Ôü¡¹Oéš:4÷…¹îÏTš;¤¥¥¹ß/5÷[k@Ôܧ†+ ~ð~Ðࡹßk(ÔÜï&¾ë}À>"jîwåxDÍý®/©¹ßÌ1¤¹ûó æ~3'æ>¯ÃÍýfÎ!ÍÝojî w½C¾ö÷þjîþ~SsŸ¿Wô¹ûûNÍý®ìÏÔÜ.2\Ñç~WîS¡æîã“|î•ã«|îÔh]s皈kî|Ýç~Só“ÏýÆ…ûÜoj¾ÔÜçx¼È燚ûÍ%liî7sNiî÷%_ýϾSjîòµIsW|’æ¾ð%Ÿ‹cîs‚žš»|'ÒÜå;“æ~sŸ—4w÷‘Rs—¯Kš»û©¹k¿¥4÷›û¥¹ßÒô¨¹ÏöÁw~iŸS‹>wm3”æîí£æ®x.ÍýfŽ#ÍÝ}¦ò¹_ú~úÜOŒOîsg|vŸ{À ÷£¯>w}¿|î—4ó*Ÿ#4^ùÜ/ùØésgæ>wùZåsçüÁ}îOùÜ/~¿|î}ÁÔÜoi¤ò¹«Êç.Ÿ®|îòUËçîß×äû‚¦.Ÿ;}^îsç„Ø}îÒPås¿¸æ!Ÿû…ù›ûÜ/ö_ùÜõ~Éç~ÉOŸ»ž|î—4ýæ¾ßäs×ï#Ÿ»|ÛËçyùÜé;wŸ;ù—öõ‘ï>÷"<Â>Ìèsšû-_úò¹Ÿ/Ÿ;|êËçMùÜ©‘»Ïû–Ïïò¹ŸÒØésľås‡f¼|îØW±|îø}—Ïýˆš»òƒàsGÿÏ]ïãò¹CS^>wìKX>÷* ^¾_\wŸ;ÞùÜõ|—Ï=iîþ¼ås×û»|îèOËç~×íçèsG’Ï]Ï_>w®É»Ï]í]>w¼ÿËçÞ£æ~kML>÷[Ÿ§Ïv*÷¹ßÔ4ås¿åc?4ÿ?£æ~ßÔØäs§ÇÀ}îô8Ÿ;5u÷¹Wið#øðƒÏkîs‡¦¶|î®ÁÓçþò¹Ó¿|îÐä™éiòô¹j”ò¹j|ò¹jšò¹sÍß}îœ_¸Ï½._ûŽù 4`ùÜ+5(ùÜ›4uúÜ9ŸtŸ{c{ås¯üûäs§ÇÈ}îš|ï:¦Ï½ó÷’Ïk€îso/Ÿ{{ùÜÛËçÞè;—ϽeÍ}ÝŸ>wiò¹7j¼ò¹7x ÜçÞ´&@Ÿ{“ïœ>wΧ¥¹Ë-ê>÷._;}î¿—|îó ÷¹;Ÿ>÷Î}ò¹S°rŸ;E(÷¹Óqê>÷Á¿Ÿš»<ÒÜ#ô„@£‡æ^Žìs/\Ó—æ^¸¯Lšûô€à:4÷™ÂÍÝïOͽУ#Í}6š{¡z)Í}zRê¶4÷™vB³g¾;¨ÙRs®ãùRs0|ùÔÜgØšûÄòµ_hß5÷y¿¨¹—š¯|îZSÏ}È|·PQuŸ»_§Ïbiî…ûT¥¹ËS#ͽpZšûâÓçNÏ¡4÷ù¼¢Ï]iî…ûÚ¥¹êeÒÜ‹ö=Ps/jîå’¦Í}b|?òÝÂù•4÷‰£Ï]žiîþyjî…Ú 4÷rgŸ»ècÓpURs?(dæÁJÍmBÖ9ØgJ 6‹bæ­Ð\Ç‘‚õûE!Δ²GÊ•‚ãH¹S°q)ÅÖ‚…8S¢hæ8Rj YŽ#¥§`ã8RF 6Ž3¥™Ò^Í=²|æ8R޲GÊ•‚ãL©5S꫹G–ÑgÊh™2ÞÍ-Iƒv)°8, q¤ôlgÊ82e¼› ïĢʙE5Ç‘r¦`ã8Sb°q)¶Ó Pˆ#%‹kŽ3¥Ÿ™ÒßÍ­)Ø8Ž”–‚ãHÉ"›ãLi5SÚ»¹#…,Çr)Ø8Ž”3Ç™ÓÇ‘cÉ¢GJVÚGJIÁÆq¤ÔlGJKù‘ãL‰ù‘ãHÉJ›ã@¹÷lGÊ‘‚ãL‰ù‘ãHÉJ›ãH¹RÈr)%áLéw¦ôwskÊGJKù‘ãLéG¦ôwsGZÚt(ÓW‡CáH9R~ä8Sâ§ãHÉJ›ãH¹s°Ž”’ƒp¦Ô‘)õÝܬ´9Ž”–C–p¤ôl„#eä`#(5+mŽ3%æGŽ#åÈ!K8R®l„#åÎÁF8SzÏ”þnnIù‘ãHÉJ›ãHé9ØgJ 6‘2R~ä8PZVÚgJ YíƒÒf6ÜØ\áH¹r°Ž”¬´9ΔveJ{7·ä%)-áHéiaÔq¦Ô–)õÝÜ‘ò#Ç™2F¦¼Dm3Çæ GÊ™ƒp¤\)?rœ)QÔv)wY‘Rs°Ž”–ƒp¦ô–)ýÝÜžó#áH9d Êx¥Pãc 5^)Ôø˜Bì8p)WŽj™’±ñ1¯j|L¡Æ+…Ž”–£šp¦DÕÏq¤¼±ñ!“[Ç‘’S(Ç™U?Ç‘’}Ž#%'bŽ#%§PŽ#%§PŽ#%ûgʸ3e¼››1Ç‘’S(ÇräÊq¦Ä¨æ8RŽÕGJNÄGJN¡gJ?3¥¿›[RTs)Ù¥à8SÚÈ”önnN¡GJN¡Ê™Í Ž3%F5Ç‘’1Ç‘’S(Ç‘’S(Ç™R[¦ÔwsKŠjŽ3%&bŽ#%§PŽ#%§PŽ#e¤¨æ8S¢êç8P®œˆ9Ž”œB9Ž”œB9Δ¨ú9Ž”lVp)9s)9…rœ)1Ø8Ž”lVp)#©~Ž3%¹P¯‰˜öE8E8Rr å8R®¤ú9ΔvfJ{5÷Ή˜ãHÉ)”ãHÉ)”ãL©5S껹٬à8SRȺ?$bÚÕá”ò!…ÒFEùBiïÇ¢|0+h;H¤¼T?íY”‰˜6,ʇJûHåC ¥­%‘ÒßÍmIõs)9s(5§PŽ3%›ú!…ÒΕE9ÞªŸ6³,ʳ‚ö·DÊ;dÕœB9Ž”œB9Ž”lVpœ)mÏ”önnNÄGJN¡gJ 6ÂÂ}7NŽ”#©~Ž3%…,áH¹r°Ž”œB9Ž”’T?Ç™2öLïææDÌq¤äÊq¤äÊq¦ô;S^ªŸ69E8R޲„#åÊÁF8SR°Ž”;åGŽ#¥¤üÈq¦¤%)-‰qŽ#¥'‡©ãHÉfÇ™’"‰p Œ=9LGÊ™¦Ž3%E’ñÁ¬`[ªbs…#åNSÇ™’"‰p¤d³‚ãHÉfǑғÃÔñ¢|}«Ôÿ‚­5ßÝ¥ð]Ã÷gü±×÷mˆŸ0a}Âëòý•þÏ,…~b9ébú'Éï+6ŸIi?‘|bRô‰Øó™—ó»]9n1[ûÊÖf³¹oÿùUÏ:…í$ü»ÿöÓÓ¶ÿǶ¾“ð»·±Ð3¾°EîÜ÷òµmn_ûðTkjÃßü´s7ã·ýütPf4·¡~¶ Ó }ÆFüø·¿û—ù>ýùÛÂþ¯üöjÇRÆGð·¿ûi{zÄÿó—ü Ó…7ç'¿®Óœ1­Þˆ?ÿ¼í×§[1ׯþ%æRåœ×¬_â7øç_ðKÌÈö뿽£ÞÐ_úBœ(šû+[1•œºë•ø|+¦ 3ûUjEût+*–~í;a…ìß¿Ä翽£ÒÊúö¿þçyâݧ??lKú7¾ýJ9ô÷ƒÐÏŽË"i1á?lØçîbuÕûÜÌúŒfÜçv_×:÷é®ãîk4{†Æ¯ïÃþúÄ‚­ùöÄ"¾>±„oM,"å‹Haù¾4±¨™¢“ØãÄ¢dŠ÷‰p øù×qbqe K]¥‰EË”r¾ìÀ‘ÒÎ×b(p¤|cb(ßšXDÊ7&‘ò‰E¤|cb),‘'×ë.¬j&g¦äÿza¾½õ_ŒoïýãÛ›ÿÅøöîq×öÿõ¢Î~ôÕ«6°NÃW«6|êNQÁ¼ÙÓ`:†—fž‡ÚNû Ciæ ­vѳ` J3Ox­ã'´:$ØoŸµ«¶ûÝ>;¯b¯»}vßTšÙ๩4³µj‡h­º6•f¶[Y•gsÜÚg×qˆWiæ ­:ö–Ohų°“ܾÈê)›Ö Ï?<È«4ó„VhC A#Û~o#¯Ò̲C•f¶Ç>ÿ@ìܶϮÒÌÇ` ìÊ6ò¬@‚=ØvÕ*&Ûr'A•f¶/Z¥™­«4³‘×qˆ˦ÒÌvg«Ôl» '´Ÿ {ž ÖM¥™¼¨*ͼ í^žðZÇ!NØX©¹7ÀUšÙàü,bÅ‚¶„7¡èÄŽâmÿ°A;ÿЖá&ÄQŠX·²åµõYÛ÷kp^å&߉Q:;zíÓ(µl¦Bá4óÄ=”fž>k”²Å.\»Žûôr¿_CGî< ›i­ñá8Äcðxn“µg\7/Íl¿J%èÍ¥A±ÛÕ°J1Ÿûê9ÜÇê¿77­ÚýÂqˆöùPšÙ~ÔcóÒÌÛÚa´g Ów•F>Ô¯q\ö‹Æñ„–eÛßoÏ ;Aíº0{>ŽÄO{žöýØÐi×Ãqˆö|qݶj†ïïèþ¥‹± Óþ~•jV¥™×óÂ^J{¹öÍK3¶öb—ä[ƒ—f¶ñD¥˜dçæ¥™mŒÄõ†¡ò¸4lc4ŒÒÉ;:{c)WlP øÐhŽR¾Øzh×uüᎡÇQbS¡¤ÜA®W é(ݽ6ćÒÌáú@ç!·,ÍlpÜ Iä¬@륙 £Tò…þ^ÙÿPšyâ ¥“oŒí,%ÇÒÌãù¡4³]Ç÷5ôw•ÚEifkµ¥™'FDiæ‰÷Pš9àýÝñ…þn逗fæ/Í<13,xÊ:þp¯¸~l^šyâJ3OŒþ‹Ò̶ E¥š+î‡RØè|¢¿ëøV”fž¥­QšÙ0ø7Þ‡ÂãQš9áûá8ÄðùŽQÿfGiæ‰ñ÷ 4óÄx_Pšy~¥†QšÙ®÷ÍK3Ûu”v¾ð>¨ô6J3OŒñ¥™n5¥™ «TsÃýДf^¿²û¾Pšyýþ(ÍpA×÷£4³Ý¥—+Æ•þÆLËú7>?ÔßÑÿQšÙúo8Ñú7J!ÿu¼J3ûûÀÒÌÞŸYšÙÚƒRÏVšÙÿ^–föçËÒÌÖ?€»ú;J¥¢4³õ²yifÃ(}¼cügiJ–f6ŽC4J3ÛçQjùÆø«”²u»¾o^š9ܯ©¿÷ÔßYšÙ0¾o`üçûÃÒÌö~êøÃ÷«:±ã~8¥™íz(Íø7Þ‡¢ã«ú;Ž7Cifï?,Íl×…ÛXãK3[Áq†ú;ûóÓ„ûS8Ñî‡RÍ7Þ–gifã‡ã­?©ó…û¡? 4³a”nîÿQšÙÇo–f×ÿ5‡hñ¥“mQÁâ£}j˜z’ÁÒÌ>½gif,ÍlñX˜3üŽC4ŒÒÊ;ú;–fö쉥™-þ£tò©i>Ú‡ÒÌ>ugifŸ³4³ç,Íl8”f6~(ÍìiK3F©ä]ó?¸cüï,]ŽÒÌ&Í^šyÝ¥™ŽCôù&K3û|¥™m>¦ãÌŸz(Ílóm|ÿð‰8ÑçÛ,Íìóe*#†QºÙöÌú䟥™íût<"§ÿ8Ž¥™íþá8DŸŸ³4³ÝOÇ#rþ¿¥™}~ÎÒÌö¸Î”vðûa'6\7/ÍlØ~¿Á¬v°Tù`ZË#†h>vyif»Žã;Þ‡ñ‘N^»n¥0aÛµëÂ6þó~4äF)å ïƒþF,µ _mÀÌpuܳö}8þлŽãÍ k×Qj™Y®î—«ah–Öuhr†ñyfºÎ¿ÑßæK´¡®¿ç`²ËãHi0õ߇nRû=qÿ¡þŽRµ§ç»^ª™ù.JÅÂêï+ížþ>ÐÛéùœž^Óµ™0߇ŽC ØóÝŽC´Ï×ÍK3Û÷¡”òŽ÷¡§ÒÌ_êï±4³çãô=®ç“£ç£t4úxFû¢½á8D¿hLôñ”.D¯i9tm†þBïi&ô÷ŸÎÁõ}° z~MO çË4z~L·Ÿý=:þPùn8Ñó[šö¬ýá8Ä€Oÿ¯ÍK3»"D£Ý?”fv=ƒ:oè—[ýæ8× è„³ûáxÃ]âÎJ3¯öÃÐæãÝkžïÓª¶ž_½Õßqü*Lhá~Uã?¿¯jüçñŠMïJ§Ã8fŸJ5«¿ã8Â]ã?K+ÿG8q½oíRGÿj·ú{Ç!®÷ -û~”^®zbiæÀêïíØ¼4óú.ý§‡ÒÌë}7Êõ¡\o¡ëiýþ°8­þ ?“=_à¦þNÜÕßñ¾wæ»ÔëXšyõg”fv=‡ë †ÏÍK3{¡ÒÍÝà:q~Ö+hîóªõ hî–-›Pn³ûó—æîm†ænŸ55ßré -BsŸÐþ^hîóV8hÑ„êMs·?ai¦ÎAsŸ9¹MÖ ¹Ï«ÊMs_Ðf"–À¯ãg~n 4÷™¾Û8ÍÝ®šoéƒÁuâ$ÛK†ÉÚ¼Úpü ÍAfªMšûÄÐP ¹ŸÏT 64÷y4w«›! ¾â~;4ò‚^Î#w©¹ÖñˆÖ“‡4y¼Îs.jîÖÞssÍݾÇšæníÇ!NŒãË ¹ÏöCƒƒæ>¯C³†æ>¯— ¹lš»áksÍÝîgíæ>¥hxÐÜM*fnšûÄwÙ\sŸø Ç!Z{MsŸ×¡AAsŸ˜×MsŸR ÚÍ}J+wÐÜÏCÇ BsŸ×±fÍ}~žÇšæÎ*!®¹Û÷ÛƒµûÛÄ>/<» šûÄ8^šûäÛdÞ¿wÜÇÏAsŸŸï¸ŸiîS*ÚÃqˆóïAsŸ˜Çšæ>14DhîóóД¡¹OܤÁ[gŽOÍ}büýÐÜ'æñŠïƒÖ ¹Û÷›æ>1g´9ˆ]Ç!RúrÍÝÚüB矚ûÄС¹O Íšû”Æviðö>h š»a|¾£¿kšûÄÔèMsŸ˜Ç îxŸèïŽ/ôw?Í}¶ï4÷ùýW8qJw»Ž?ÜÑ>×à­¿óˆcjîS³·9ˆ}~ß\s7iúþîØæ Æ×ñˆíƒfÍÝÚkÏšûä_:þpö÷óÐñ†6™¸Í}bô/h@sŸ÷Çš 4÷Õ_ ¹Ûóƒ&oš»'Òà;Æ;¬á@sŸãŽ „æN©×5wïÍ5wOñ}¦¹Üñ>ðø jî6>Iƒoˆ&%‡8ïß𻵧o®¹Ûý¬½ÐÜ.ÿ‡ðÿ=As·¨ Þ4÷ù÷a š»Yh³™‹š 4÷‰G8Ñzß\s÷ðOÍ}~ŽÏ‚æ>¿o—Æ~”‰©¹ÏöC³„ænÏ{Ç´†'DSsŸÿÇ!ÐÙ߃ã wŒÿ<¢s>Ï4w‹÷æš»§ÐÄoŒÿ'5#hîFÓñˆö>œú|Cçq¼|¬¿Í}bhàÐÜm>{ÇøÏsª©¹[<—?ûûÎñ‡š»}^Lj¯ÔØo¼:¾šûü³ Bs?!ŸænñèÞ\sŸïŽãƒæîã95w啕'Ìñ?jîöØ Ùßx*51ŽÃ„ænñ§m®¹[<Ç!NŒãÝ ¹Ïï‡ÍÝÆÃcsÍÝÇ#jî÷p¢]š»—ÔÜízÐÜ ãó¦¹O\…;Æ×4÷‰q\"4÷9¾áþÐÜ­èxÄ9þŸ:^šûÄøû ¹ÏnãD¡¹O|„ãí~ø¼iî6žâzÃø²As·çqn®¹Û÷AãÞ1þ_ÒÌí±ëÀÖ±çu<hîhð7ÆÿKüã¿Ö  ¹Ÿ'ÿ¥æ>»5Þghî,%æšûìæGÐÜ'®AsgÑ0×Üçkšû¼ÍšûÄ—4ö9þŸÏ šû|M0~AsŸ¸Í}¾6‡4öÙ߯ÇBsŸkdÐÜ­JŽ/4Í}b¬QAsŸ|¼¿ÐÜçýwxá~X³æ¾îÍ}Þíæ>qÔÜíµ>7×Ü'F„æ>q šû|Íñ~Cs·Ïß›kîóûmþBÍ}büþÐÜ_6×Üçý°æÍ}x¿¡¹OÜÂqˆsXÁû Í}b¼ß·&†Í}b?hÖ†%8ÇÿçþW8qbhdÐÜ'4÷óâñŒÔÜ­½¸?2ÜëF~@ÍÝÛKÍÝŸ/5÷ù{úñ‡'ûÓ¾¹æn¿'4xÓÜOHÉ®¹O>¾š»]›kîózÇ!ÎëCûÿ/ÿÍÝû5÷y½âxDÓÜçï5 ‘#ß}þ>›_RsŸ?Žûƒæ>ËÊAC†æ>1GD¾«ß—š»ññy C“ÅÀ`¿WÐÜF¾»°=( 3׿š»}8qbôhî_èïÏñÿ¢Åš;Ã’kîC„æ>¿¿ëxDëï”i¨¹O|èøÃ9þ_œßSsŸa .4÷Æ IBs·0ÍÜ&†VÑNüØ&ÆuÓÜgØC†ænaѾš»ñÃqˆ§ŽŸ¦æ~êxUjî³}¸iî§Ž‡¦æ~ê¸Wjî†ûæšû9'…fnšûéÇsBsù®ï ÍaÙ5÷y?´šû¼Ž5 hîÖÏÍ5÷Ňæn|hôÈwï*Üäʼn±fÍ}âÝ4÷‰©É#ß}îç ÍÝÚöX`9ýxRhîÆÇ!ÎiN Ç!ÎþT ™ÿ+ßhî6 š»GÐÌ‘ï^Ô稹ÏûãxRhîó÷Äq}ÐÜ CóG¾{išûüüŽC´ñ8‡hß|è}À4÷ùù¦ãg¿t¼&4w›ÖÍ}NÓ0>@sŸø Ç!ÎþÅëÈwïCÇ%"ß½†4|ÓÜçuô?hî— ¹Ûçëæšûú~hî§Ž¯¦æ>?¿Í=àªþNܼ¿ã8Fä»7=aSsžáöD§ϹWÖÜÛ-£ÏýXš»¥¢Ï¾ùÜ9…–Ͻâ‰Ëç^ñƒËçNÏ¡|î´,ÊçÎlA>wŠgò¹sì”Ͻ`ª.Ÿ;ÿRùÜ)|ÉçN]M>w‘é @Oc_"ùÜo¼“ò¹ßTäé (t£Ÿ2…á³ô¹ÌåsçÉç^(ÁWÂŽès/Ôúi ½H>÷ŠH.Ÿ;ùÜy̰|îÔë—ÏÝÞ2ùܹº|îàés˜-Ÿ»ÐËç޳ϽGŸ;-ÃÖsäs˜ ÈçÎõ{ùÜ}õ?hînÏ} Í×þí5ô¹3ë\øW3ÜçÍvùÜoaú~ვϽIS¿´YÃ5ö¾¤{ûùÛ>÷F <øÜËösô¹C³“Ͻ->÷K¾w®ƒÂ'¼|îwÐÜ}Ý4øÜ‡|ï=øˆÜç.MR>÷!_<}¯|îƒÏG>wiXò¹Ó‡á>wþìîsw|Êç·|îœcJsßC¤¹ï̉¥¹ïÌy¤¹?˜<4÷9±4÷]š5÷ý”ïšûüÙƒÏ}bjô˜ƒìZƒ¡æ¾3Ç–æ>¯C“‡æ>)íû©¹ïZc æ¾Ÿüý©¹ï‡¾šû.šû®5jî CsߥyRsßæ¾3ç’æ>¥¹às·Êöö÷Qsß㤹O~ð¹†FÍÝÛOÍ}?¨9SsŸ×¡©Csß9•æ¾\³¡æ¾ìÔÜgûàK‡æ¾Ð\¤¹ûßCÍ}¦jàc²k Ššû|þe[šûü}‚Ï=`hî CsŸ¿OÙ–æî¿/5÷]kVÔܽ¿Qs÷þFÍ}?©iSsŸí•¯}Çý¨ùCs†%ôjî c²3•æ©uiîûM—šû®5 jîSÕ?x?hðÐÜ÷‹k(ÔÜ÷K}×û€}DÔÜ÷“ã5÷ýäxIÍ}gŽ!ÍÝŸ5÷9kî'Çjî;s×Üõ{KswÜõ> ùÚwÜû"¨¹ûûMÍ}þ^Áç¾ÞwjîûÉþLÍ}á"óÀ|îÖÿ€©¹k|’æ~r|•æNÖ5w®‰¸æÎ÷×5÷šŸ4÷k®¹ïÔ|åsÔÌäsç‘íîsç¶ûÜ™sºÏ}ÈW?äsïT>wúÚÜçÎøä>wÇ—|.Ž¹Ï >zùÜé;qŸ»|¼ò¹sŸ—ûÜå#•Ͼ.÷¹Ë‡,Ÿ{“/}wß|ë§|.ÐôäsïÔ¸äs—ïY>wjîsWûäsg¹ÏñÙ}î7ܾvúÜõýò¹iæU>Gh¼ò¹ùØésgæ>wùZåsçüÁ}§|îƒß/Ÿû /X>wi¤ò¹«Êç.Ÿ®|îòUËçîß×äû‚¦.Ÿ;}^îsç„Ø}îÒPås\óÏ}`þæ>÷Áþ+Ÿ»Þ/ù܇|ñô¹ëùÈç>¤é7÷ý&Ÿ»~ùÜåÛ^>whÌËçNß¹ûÜÉ¿´¯|÷¹áöaFŸ{ÐÜíó¸ŸûÜÏ—Ï>õås‡¦¿|îÔÈÝçŽ}Ë玿wùÜOiìô9bßÎò¹C3^>wì«X>wü¾Ëç~ÍÝóƒàsGÿÏ]ïãò¹CS^>wìKX>÷* ^¾_\wŸ;ÞùÜõ|—Ï=jîëyËç®÷wùÜÑŸ–Ïý®ÛÏÑçŽþ$Ÿ»ž¿|î\“wŸ»Ú»|îxÿ—ϽGÍ}ך5÷9Çuhî;íTÒÜ'–ƾ#>ÑÇ~hþFÍ}ß©±Qsßé1æ¾Óã|îÔÔÝç^¥ÁàÃ>w®¸ÏšÚò¹»OŸ;øËçN_üò¹C“§»ì&Í}?¨QRsŸ×ƒÏÝâûØ–æ¾sÍ_š»æÒÜ5_’澟Ԁ©¹Ïù‰µšûó©©Cs×|Ršû~±½ÔÜ÷“5÷#iîûE šûÂô˜9†æ¾ßü½¨¹ï\”æ¾_Éç04÷ˆ;Úß95÷ýÊšûº?4÷]$5÷9M‚ïšûƒ |ñÐÜ'_|Ãýè;‡æ®ù´4w¹E¥¹ï·|íÐÜ÷‹¿5÷ùyû>jî‹Í}NÛ¢æ¾S°’æ.;ª4w9N¥¹ï…?5wy>¤¹G<è FÍ}oÉç>1|ÞÔÜwî+“æ>= ¸Í}oòÝCs÷ûSsßéÑ‘æ>_S`hî;ÕKiîÓ“R·¥¹?ï'5÷½P³¥æþ\Çó¥æþ`øò©¹Ïi.øÐÜwÖ*’æ¾WŽWÔÜçý¢æ¾Wj¾ÔÜw­)Psß‹|ðÌw©¨Js_ס¹Ë@,Í}ç>UiîòÔHsß¹F-Í}ñ¡¹ïôJsŸÏ+øÜÝã#Í}ç¾viî;õ2iî»ö=PsßjîSÖŠšû>Ø>jî;çWÒÜ'>w÷üHs÷ÏSsß© Js?öäswÏ“4w9t¥¹/Œ|wahî‡â5÷ó»ß|®˜Í¬Ÿe¥j~ÿ§¯”ªùÓïÿ¸ÊkÙ½lWÛ_u/²EŸ¸mÞ³ eÿÓÖf…²Ÿÿü‡?y…²§)YEœË7Åì(tŠõ‹îå}Þ„[“\‡h>.«Ca$œÊ‘ WA"#­rBhä_ú°Onå?æ+9k™ûþµBhÿʇ̓záÃç/þðmâ•}øöϸ=Ë™­ãÚΙŠR‡:©Î Äk׃DŒN±EÌ[3Z„ #«H"FוDŒNÓEÌ[{Z„ #«Q"‚Ö§H Ì„ÑaäFj KÀHખ€™0J"ŒW#¹ò%`$00¸:&`&ô‘ýÕH¦"‚ÖÔH Œ®²‰˜ ýJ„ž©•8#ks"FWëD̄֡½ÉÔGÀHàŸ€™ÐîDh¯FrPÀ@ÐÊ „‘ÀµB3¡öD¨¹‘ZO0¸Â(`&Øšã"L \…0˜µ‰˜ ãJ„‘ ­]Š \Í0´¾Ia&ô–=ÿZ0¸**`$pTÀLèG"ôü[h-UÀHàꪀ‘ÐS8 Ì„V¡½É5YÁWiÉ~QÚ•)ízQ¸–ëàD¹RTN®ø:øE©-S껹%EáDi):¿(£dÊx7— ÊNN®1;øEg¦¼¯D‹Bœ(gЉr… ‹WB}SzÍ”þj®Vµr¿CŽ/t;¥¾C†¯};¥½ƒ†/‡J7—+äNN”‘bp¤h]â%FáDáj»S€… ðN~QbN”’¢ˆp¢ÔG„_”12e¼~#Õ±s p¢ô„#EÕîD!~QbDNÖÄs p¢°l€S€_”qdÊx7—Åôœœ(%ÅáDa §¿(½dJ7·¥%œ(#ÅáHQù>Qˆ_”62¥½š«"N9Þ¹‹×ýsÊùW^ Ð)÷‡`£ê€ò6*è”òÎa¼† Sê;‹ñ²‚òY7WÔœœ(‹N~QR$!Ž”²çHBœ(\½s p¢pÁÎ)À/JŠ$ĉrçHBœ(\ t ð‹’" q¢´”y'JÏ‘„8QVQœü¢¤HB)uÏ‘„8QΔƒ'Ê…y§¿()’'Ê# q¢piÓ)À‰ÂÕL§¿()’'JÏ‘„8Q¸Lêà%EâHiGÊK„åÌ‘„øEw¦ŒWsÛ•# q¢Ü9’'JMŠð‹’" q¢4¬`98QzŽ$Ä/JŠ$đҹÔ+ q¢¨úäàD9s$!~QR$!N.+;8Q¸’ìàD©9’¿()’' —¨œ(YŽ”±ç´…øEIi q¢°w8øE‰²˜p¢œ9d'ʃ q¢”lˆ_””¶Œú˜ÝSs?(dæÁJÍmBÖ9Ø¿()Øй´Bs……ë÷N~QbÈN”+áD¹S°N”ï‹S€_”(š 'J–Í„¥§`#œ(#ᥙÒ^Í=²|&œ(G Y‰r¥`#ü¢Ôš)õÕÜ#ËhÂ/Êh™2ÞÍ-IƒNZœœ(=áe™2ÞÍ¥wÂ)À‘rfQM8QÎl„_”l„åB}5§'Jׄ_”~fJ7·¦`#œ(-áDÉ"›ð‹Òj¦´wsG Y‘r)Ø'Ê™‚ð‹ÓáDaE§'JVÚ„¥¤`#œ(5áDi)?~Qb~$œ(YiŽ”{OÁF8QŽl„_”˜ 'JVÚ„åJ!K8QJ6Ä/J¿3¥¿››•6áDi)?~Qú‘)ýÝÜ‘–6…#¥ì9Ø'Ê‘ò#á%.p 'Ê™Cq¢Ü9Ø'JÉÁ†øE©#S껹YiN”–Cq¢ôlˆeä`C)5+mÂ/JÌ„åÈ!‹8Q®lˆåÎÁ†øEé=Sú»¹%åG‰’•6áDé9Ø¿()Ø'ÊHù‘p¤´¬´ ¿()dµJOø ”óC°™ÎÜØ\âDÉJ›ð‹Ò®Liïæ–²ˆ¥å`Cœ(=-Œ ¿(µeJ}7w¤üHøE#S^¢¶™ˆcs‰åÌÁ†8Q®” ¿(QÔN”;‡,âD©9Ø'JËÁ†øEé-Sú»¹=çGĉ2rÈ"Ž”ñJ¡ÆÇj¼R¨ñ1…š×âÏHœ(WŽjÄ/JJÄÆÇDl¼R¨ñ1…¯j|L¡ToÜ)íCTS ò@y©~^•Ü)1/TNŠp¢äJøE‰ªŸp¢d‚p¢äDL8Qr %œ(9…N”ìG~QÆ)ãÝÜœˆ 'JN¡„#åÈ)”ð‹£šp¢)ª 'JNÄ„%§PÂ/J?3¥¿›[RTN”ìR~QÚÈ”önnN¡„%§P‘rf³‚ð‹£šp¢äDL8Qr %œ(9…~QjË”únnIQMøE‰‰˜p¢äJ8Qr %œ(#E5á%ª~‘råDL8Qr %œ(9…~Q¢ê'œ(Ù¬ œ(9N”œB ¿(1Ø'J6+'ÊHªŸð‹’\¨×‡DÌëÿ‹rH¡üH§|H¡ü”§\oÕÏ”öjî1áDÉ)”p¢äJøE©5S껹٬ ü¢¤uHÄüôQʇÊ4pʇÊÏ8pʳ‚{(/ÕÏOBpʇDÌGpʇÊÏKpʇÊP”þnnKªŸp¢äDL8RjN¡„_”lê‡ÊOhpÊñVýüЧ|0+ø9òY5§P‰’S(áDÉfá¥í™ÒÞÍ͉˜p¢äJøEIÁ†8Rt¾„(ĉr$ÕOøEI!‹8Q®lˆ%§P‰R’ê'ü¢Œ=SÆ»¹9N”œB 'JN¡„_”~gÊKõó3D!N”#‡,âD¹r°!~QR°!N”;åG‰RR~$ü¢¤Eœ(-‰q‰ғÃT8Q²YAøEI‘„8RÆž¦Â‰r&‡©ð‹’"Éø`V°-U±¹Ä‰r'‡©ð‹’" q¢d³‚p¢d³‚p¢ôä0”¯o•ú_°µæ»»¾køþŒ?ö6ÄO˜°>áuùþJÿg–B?±œô 1ý‚ä8¿§Ø|&¥ýDZð‰IÑ'bÏg^Îïvå¸Ålí+[›ÍP›ñèV*ÏvþÝûééÛÿcÛßIøÝÛÀ¡7•WlT;÷½|m›Û×>w6SÛ¼Š³þl,.7Ëî"6ªSp ¨~On÷œ· ç–›%k±³Ü¬ð‰-› ÚŠ¼X^ϸÜ*'|°£â„/l±,7ÃvÊr³¶+¶NOÀm’…§yrKä|tö½Øþ8¯†³ËÍ2ÏØÖ¸¾×Vf#íIb»bá9TÜš8ÿÞPÂØ¶¬âöÂr¡q+a¹T~ض žPÅ-‚åR±dSØ ÏïàÖ¿íi`›ß„¨,l*ù$‡ÂÅ mQæÛòæU ¸Û[ƒ±ÝNC·Ö­«•C.*ZcË\¹ØUP°x]µzÅ…¿²\ññ+ Zqa±4+.¬uÆ©U9Ù¯0´=ðB%a{O «±Pññ¬P§¸¨,5ÊUE•â¢"Á(R<§FuóÅåÔ±}¦¬.h´­@q±xêõ‰ËQ˜å‰‹ªK£:q9րπ\xžk—ƒÇÏ¡4ñÑ'Q™¸¨04 Õ™ÆsAûK‹N.EU⢢Î(J;ØÑŸQx¸œ¨;ǺÅe®Yv¸èä0TV÷fÑauQÖ.Kj¢ä°þ@VÖ“dÁáÂz߬7\xÄ놞HÄjÃ…ÅYl¸°Ö"k ïÁ¦°Ò"+ —ƒ…BQhx.¤âVVg¸èh,”^·jìÀ„=v`Öž_d%†õ.°Âð|Ëtˆßi·ª:ã¯Û­pÄ!Ê V7guáE¶*€(Iëµ…õ²³´°ú+ —cî׆¬+\tÔÊ «o0±‹°¡«„süæ­PbØ"QaÅkVÆÚ²ž]Eõƒ/»~n”žÕ†;Çg@ÖËZÂëêÁñ¹†ÓûÊÉÃÝPH¸œªl»…57gaMYEXÑŠE„gXÄ ¹†cûŠNÅDaÍÞY@Xië—Kå€ON“Ñ*Ì4ûeñ`MqY;X³o–.¬|ÉÊÁ“ kκÁ¢ŒïÎ OƳªÁs xhÂŒÀ6ÿö[¡dp¹uÊŸ-äh"Ç‚Ášn±^ðŒ•:’ï°¹JÕ‚çÄß;4qçóiâÊRÁš|R&(7n Öä™u‚ Nf™à£ÕY%¸Ü|’(¬Y.kÏ[é˜>ÌŸñ' B°f¹,\xJ;ë—›½å «w²:°2 .¬¥ÊÚÀš!³4°Ò$V.,äÏÂÀEg¯våw#Åç­‚vþ ÇæE ÏÏ¡š2S€P¸ðT5ZVKaW=µÖdžµðXÚNˆ¿SØ=¼ðÜÁ€óïë–â„M´ð4PZBu+Ú?KQ`[hZŸ5[gá¹ó´p.ˆ”OçÊÁš9¿Çì™ s^Å©€f¹œWQÐiŸn+eÑq°Mú!:ï„r‘-«šmF)`³9úŸKcáa—´/êW Uqþd¸ó`FÔS  Fˆ¤°ꥣPÝ›–Ae1´*Ѥ0BtïNØ[P ` çëÍÏÖÍKýÎ/Be^³ä)÷¥·àÅËü*-¥…ΟìrJÓhÓD\QE]XÞŠ6{›†>ZÙ4¨Ò¶&‚5 È´£éý¥õÌ¿63eš´”)y¤}LÙ"­bóOÐÑyLÃIzÊøh÷*·jôšµkÁSãóµyE_é´gÍ;‡z¾Jái»Ò@A‹•÷ Ø©”,Ó:Ut<&lRi‰ò6Ãþ¤‰V'%¼´5ù³‚…I=íJëV•ã3¿¨r|æ)|ݶa/š‘êù²ãȺã3«ïŸG80Ï_XæUw³÷pZž¿)°ïÌïE]ÞÊîËö.ò`nÇæE{ý통ÆßnØh¼óÃ2#aö ´Âøï Û‹÷:X\æ“lìÀ„/,JõŠN¬Ôë]…z%YPòžðܼL¯B«ôêl,Òë/jô.XÕ;0 0C8/<3o Ãýù“¦fþ{ŸÑÜ1GÓkI¿'æ9…©1zžAMé·!6Púm˜TPúeedJ¿r2¤_ž’Eé—5õ)ýòÄJ¿<„Òowôô¿Êó]!ýVf(~+sH¿•§2Aú­^H¿•é&¤ßºó:K«+O¸†ô;·ìÍ{Bú­¬4é·ò¬zH¿•b6¤ßJùÒ¯3Mú­<_ Òo=q¤ß‰Ö©wÐ3$ýÎaâÞ$ýV ~ç_»¤_µÒoeŽéwŽ.& Û< 2k…ô; C&ý:2é·žxf~+%&H¿•sgH¿•¹¤_G6èN frWy´"¤ßʺß~+O„ô;Ñ:á®òTRH¿µâo§ô[y¤,¥ßYCWMú}:TUH¿s€ÄYuùk]GÝU»Õ½Ö¤ßÊÃS)ýN¨sîf×,†-òWNm(ýÖJÝÒoÕ™z~g›Ã‘vU'èAú­<¿Ò/¤)—~«Ž÷ƒô» I¿•‡™Pú­WqL¤ß9>àV&ýÎõ¹phÝù‚ôÛtä¤ßBý‚ô;Ó5(²6mi\Ô¡ôÛ˜ãRúmœpQúm—¿³Èß$uCúm§ I¿MZ/¤ßvòÎ~›Î5ƒô;[½Ö¤ßÆ92¥ßÆWžÒoã¹À”~ŸAp—ütï¦H¿­è³ù›ôxH¿MÇ©Aúm—N³È¿ I¿ ÚPÙt¤ß¦H¿‡ SúmU²±½æsà–ü|ªñ¤YJ¿M/;¤ßùÙ}sé·$Ä”~´Èßx¨6¥ß¦Å<ðÆU-J¿­J'6é·5.g‘¿5ž é÷ù=Òokì ~›Ö¦ ýzg€ôÛ´àé·êTLH¿ó!Ø!ý>g´Aú­Zœô;ཹô;‡>|‘I¿ šô[yÜ¥ß9ªH n6Зpø\m|ì~+£ô[ÇgH¿ ŽÏCpŽÏ ~ëÅ_ÒïóA5‡ô;£Î ›Ò  EéW'\Aú­'EH¿Š¾”~+ß#J¿UZ¤_DJ¿µPfƒô[Ù‘(ýV¾Ké·²#Qú­L.)ýV}Mé÷yt5H¿uPÁ‚ôÛxæ¥ß©EA66éåá\úWñY“~+Ï7¥ô[u<¤ßB‘…ô[µmH¿•‡þRú!UJðóÿTŽ”~kc3 ý>Í ÖkIIÕ1x~çF~k¤_f.ý6.ÎPúmTú)ýjÈ¥ô«™Òo„Ÿ£ôÛxâ<¥ßùÊÛ_é·Iúm<*”ÒoãÌÒo;uI¿s0æjÒo£×€Òo+<… Ò¯†J¿gšPúWƒô«ÁÒï¼¤ß ñY“~X»„-H¿'1Súm|)ýÎaJGÉÍñY'Búm: élÓ¹‹~ç*”~ç­ðYûKå¡ô;«òA6é·Uþú~O‰§ôûÜ™ò­I¿Mç³Aúm:îÒï,a%Ø’ì6D6é·keonçѨ”~;gA”~ûοÒo×’¤ßÎc>)ýö3I¿Ã¥ßÎ39)ýΤ@Zï3>÷‹Òoטé·SX¢ôûŒð‡´Þ§÷ÊÓÔ ýv–Cé·ë±CúíëøÃg8ÀIm&ýv ¥~{岤_¿3¤ßçVh¤ßÞ’ô;#˹¹ôÛÕÍ ýv樔~»¨„ô;?{o.ýv&”~;O[¢ôÛÓƒôÛu ¤ßÁ£Ü(ý?éͤßÎ)1¥ß>ø†BúíÜ…±éÖ'ä[Lñ\ÿ÷C˹wv® euØ·vÅ:Ÿ[`'Š1ö»î§¤[lêÙ9·ÔNÖý„cAÛVçx1{TŸûã9sCª#×îS?\‘[M'?æ6¤†B 'Åà¹-'ˆÁã”dk)áÐ9•ƒe'ŠÁƒ–CŠÁƒ‚Åà !B[J8´â1xHX…Üu $ÄàAõ€bð ó‚bðóÙ¦ÓÜf—Öa€ƒGå!6 Î( ƒG×UK gÿÇUK G‘®lbð`„¢<—˜Ð<$.û÷B Ö »ƒMØ›«ÁWujâæ'ÆÁY½ÓÇ3áç¡m³Ö© I¾æÌÀGP„Ïd¦Í€Š0§©T„O<`*Â'~W*´€Q¦{ŒŠ0§àT„)QæøGEøÀ<˜f`þm4SࡘÊÍÀbbµXÚ1‹ùZÐ ¼ã%£x§ZŒ•âƒÆÝ“F|fà³0šù 4Ô‡+Mƒ"\*u#š¹ä,30—º—˜š¯ÌÀUñXåe¦0-30"7»@ 30Èn¦sØÍÀЋ‘ú5éÅ07Jm47*U47 s4s%Xf`Fv™ûrÿî6+€pI3p§¼B3ðæ 30§j26’fàοŠp¥eDfàAIˆf`‡HÚ=ëÎ…f`.,É <²xd3ðÈfàA.ÍÀ#)ÂëÎ0K>£xP‘¤x`ÉYfà!fà!®)ššR–;ŠpÝåþ…xðG"SubV¹±¢¿ÿÓWŠJüé÷\EpV雿Zõp`®µEVÈçïÚÚ,äóóŸÿð'/äó´ã/+]qiÀŽr€Vd¿½ÇëúÍ€Ÿ+…ÔŠB!¸ .·*!¸ .%Bp(*Akt=6î—>büÑu¬_î´<ÿ¶P: ûþµ*Eÿê§çŒeŠŽó—z˜ùÍ>}û§gdÕ†Ö¿ke5L¼h`(#¨ëD——tÃR„_ —m.ë—Òå§…Ë…˶zá—Âe[ÎðË@á²Í…ý2Pºü áòDá²­€øe uK"ºL”./Ô4,šøe pÙVQü2Pº^å6=q#Á·…6W¦üÌñòµÝ<_ÿôŒ#¶"œ»~þ‚Vt«K›ZñÕÓß¿þéã꜎¡?þõ?ÿ˶_¿à·ÕMýÕßßóê×cøó7Ÿÿƺmªúµí8ʨ¢vüõOÛ5Ûñ_Y;æ¢ðøõÏc.…@ãïñþþÿýáó7˜ë÷¯ÿþ‘ŸÃÿÛ~?À§o`ê^ûÕß?“Þ;½ÿ¿Ãþï_ö;Xæ:r;Ú/hêù¼Þ‹_ð;Øl¬}ãûãÙâÿôÏ1gÀœ[PD€› ÿaûÜ1ça“g­sSàú7nòD›g8ú×N*ϱ[mùVðŽ×¿½ãõ¯‡ïÄøjüN TÄJ¼fê@¥^2V7,Ô“bø•¨G“‚xËŒræbƒ‰Ñμ˜v¢pi`|5ŽGÆ×yb|5’'ÆWCyb|5–'v[Ç`~½îmÒ)šŸ™‘¶óÝøæ~b^ÿæ†b^ÿæŽb^ÿö–b9מâÿðÃÿsæpH endstream endobj 522 0 obj << /Alternate /DeviceRGB /N 3 /Length 2596 /Filter /FlateDecode >> stream xœ–wTSهϽ7½P’Š”ÐkhRH ½H‘.*1 JÀ"6DTpDQ‘¦2(à€£C‘±"Š…Q±ëDÔqp–Id­ß¼yïÍ›ß÷~kŸ½ÏÝgï}ÖºüƒÂLX € ¡Xáçň‹g` ðlàp³³BøF™|ØŒl™ø½º ùû*Ó?ŒÁÿŸ”¹Y"1P˜ŒçòøÙ\É8=Wœ%·Oɘ¶4MÎ0JÎ"Y‚2V“sò,[|ö™e9ó2„<ËsÎâeðäÜ'ã9¾Œ‘`çø¹2¾&cƒtI†@Æoä±|N6(’Ü.æsSdl-c’(2‚-ãyàHÉ_ðÒ/XÌÏËÅÎÌZ.$§ˆ&\S†“‹áÏÏMç‹ÅÌ07#â1Ø™YárfÏüYym²";Ø8980m-m¾(Ô]ü›’÷v–^„îDøÃöW~™ °¦eµÙú‡mi]ëP»ý‡Í`/в¾u}qº|^RÄâ,g+«ÜÜ\KŸk)/èïúŸC_|ÏR¾Ýïåaxó“8’t1C^7nfz¦DÄÈÎâpù 柇øþuü$¾ˆ/”ED˦L L–µ[Ȉ™B†@øŸšøÃþ¤Ù¹–‰ÚøЖX¥!@~(* {d+Ðï} ÆGù͋љ˜ûÏ‚þ}W¸LþÈ$ŽcGD2¸QÎìšüZ4 E@ê@èÀ¶À¸àA(ˆq`1à‚D €µ ”‚­`'¨u 4ƒ6ptcà48.Ë`ÜR0ž€)ð Ì@„…ÈR‡t CȲ…XäCP”%CBH@ë R¨ª†ê¡fè[è(tº C· Qhúz#0 ¦ÁZ°l³`O8Ž„ÁÉð28.‚·À•p|î„O×àX ?§€:¢‹0ÂFB‘x$ !«¤i@Ú¤¹ŠH‘§È[EE1PL” Ê…⢖¡V¡6£ªQP¨>ÔUÔ(j õMFk¢ÍÑÎèt,:‹.FW ›Ðè³èô8úƒ¡cŒ1ŽL&³³³ÓŽ9…ÆŒa¦±X¬:ÖëŠ År°bl1¶ {{{;Ž}ƒ#âtp¶8_\¡8áú"ãEy‹.,ÖXœ¾øøÅ%œ%Gщ1‰-‰ï9¡œÎôÒ€¥µK§¸lî.îžoo’ïÊ/çO$¹&•'=JvMÞž<™âžR‘òTÀT ž§ú§Ö¥¾N MÛŸö)=&½=—‘˜qTH¦ û2µ3ó2‡³Ì³Š³¤Ëœ—í\6% 5eCÙ‹²»Å4ÙÏÔ€ÄD²^2šã–S“ó&7:÷Hžrž0o`¹ÙòMË'ò}ó¿^ZÁ]Ñ[ [°¶`t¥çÊúUЪ¥«zWë¯.Z=¾Æo͵„µik(´.,/|¹.f]O‘VÑš¢±õ~ë[‹ŠEÅ76¸l¨ÛˆÚ(Ø8¸iMKx%K­K+Jßoæn¾ø•ÍW•_}Ú’´e°Ì¡lÏVÌVáÖëÛÜ·(W.Ï/Û²½scGÉŽ—;—ì¼PaWQ·‹°K²KZ\Ù]ePµµê}uJõHWM{­fí¦Ú×»y»¯ìñØÓV§UWZ÷n¯`ïÍz¿úΣ†Š}˜}9û6F7öÍúº¹I£©´éÃ~á~éˆ}ÍŽÍÍ-š-e­p«¤uò`ÂÁËßxÓÝÆl«o§·—‡$‡›øíõÃA‡{°Ž´}gø]mµ£¤ê\Þ9Õ•Ò%íŽë>x´·Ç¥§ã{Ëï÷Ó=Vs\åx٠‰¢ŸN柜>•uêééäÓc½Kz=s­/¼oðlÐÙóç|Ïé÷ì?yÞõü± ÎŽ^d]ìºäp©sÀ~ ãû:;‡‡º/;]îž7|âŠû•ÓW½¯ž»píÒÈü‘áëQ×oÞH¸!½É»ùèVú­ç·snÏÜYs}·äžÒ½Šûš÷~4ý±]ê =>ê=:ð`Áƒ;cܱ'?eÿô~¼è!ùaÅ„ÎDó#ÛGÇ&}'/?^øxüIÖ“™§Å?+ÿ\ûÌäÙw¿xü20;5þ\ôüÓ¯›_¨¿ØÿÒîeïtØôýW¯f^—¼Qsà-ëmÿ»˜w3¹ï±ï+?˜~èùôñî§ŒOŸ~÷„óû endstream endobj 526 0 obj << /Length 611 /Filter /FlateDecode >> stream xÚuSÛŽÓ0}ÏWXûäHëK®HE±Eð€^XBë\¤ÜHRÚý{<'Û.‹ªÔž‹ÏŒÏpRNÞ;Ü®oSg»""K‚@’4'Bú,æ’Di4ß\ôèþL?n÷*" KB$'žŠ™ Á®!Õ\S=<‚éÓQºöˆ¡ìR¸«³_®i5ÚUk3piâTOU_k„éënÂX>tѹõM®CUL¥…¨ŠÒ•µP.¢.í#?Ø~£³ÖtŸŸ e{Q>JŽC6ÚݹÔ6h¯¢®¯b4oI8Õ¤W¸fÔ5F†´šnhõ‹}Këk× ÌõÙðÀÞ˜{ŸwØŠ D|*Œ17· oOÞàÌôUÙSàaÓc¯wwÍݳ³ ËÍ+¢µÃ%uýòí~)°jì>u„Ùp"VmÉ@²P%äÐ8¿‰žvs`9eÛä]ç|6¿%æ-Þæ¬ê®…g‘AAVÅi¶…¤R¼r=ßО–ÖS Y_âv,;ÆyD3kqíZ›zèêS3;ÿ‘¢‰ØF ‡¥à´Ô±Ê¼ü)ô’îªãqÆäÊm\\f®P[Gô ˜GiÐÀŠ|svžƒëÐ0OAL'†l|ê& ½ódÉ1ldÍÒ…‡r}D"9ˆ¿}©ù¬;”»U?Yß×ÕÜ-(*ŸáϦN3ëWŽ•h“ýŒh¢qPVíýo%" YÐdÈÌÀc—ʮܿ\eL endstream endobj 523 0 obj << /Type /XObject /Subtype /Form /FormType 1 /PTEX.FileName (/tmp/RtmpBlhU1W/Rbuild4444671013800/timeSeries/vignettes/timeSeriesPlot-flipAxisOnePlot.pdf) /PTEX.PageNumber 1 /PTEX.InfoDict 529 0 R /BBox [0 0 432 432] /Resources << /ProcSet [ /PDF /Text ] /Font << /F2 530 0 R/F3 531 0 R>> /ExtGState << >>/ColorSpace << /sRGB 532 0 R >>>> /Length 7101 /Filter /FlateDecode >> stream xœ­œOÏf7RÅ÷笠KXä™ëÿö6#@ ¤%ˆÕ0¢ôHCðñ©sN•¯ßN'“f²H'§¯íë벫ìú=Nº~w¥ë»ëOoÿtýéº_÷}åÑ^¥\µd¨t¯Wj×ÿáú—ë,T竎(UJ{Ýý“R¿ùáŸÿîëë·ß¾¡½û:ÿüö·ÿ`;Úõ?oÿúo×}ýû[º~gÿ|÷–øò¿ë÷kΫԆRßzÂÛJY¯»^ß¿õŒ×•Ò_ÙeoWÉÖ¥Y_5ÛÓôjÒú–Læ×\’ý¶Âý5 d¥eÒzÞ%‡½·ÜhÑä|µÆº……­T¥·ä(ìÕ&Çý*™½š·äJìF¥´N¢åŠ÷™,¯Œ/*¯^$gç8|°O°÷VkݰQ¾ñÞùš”|dbáñ*ö 5½î%¹0tYŸ?ìå!ù¡hy@NëdaŸêN3û„Ê×›ÌxêŽ!iß]êÀè›´OX´QãSÎ<­EF‘MÒV¥Xç²ä,ìseSƒ&³÷r4æ¢Q*-G £Ôþêèä²QÁÍWé’ ï]•h²v¿2šZ…F9$ŒÒ²ì»ê«Ùa¶±©†!,6Û8ÍLÂdmaŒLeËI£´ùêYÒúÉÏ_ë•3%ÇÊäÄ{Ç+áiº99w[é¶‘OmèÑY~ê‚™Pº ùt½*;^njæÄžgÕïx€òév=ñe6UTpAYù±¨ÙÏ£½EûX2Æ)Ùº„Z÷ú)qQµòjË5V• 3—BJY\Þ^*4Rc3Ð5¬Ä‘N©qeÅäÆÒ “§ÔiFk¯êý“–jI š¦J0/ôâò:5Ö—õ÷fûù–-›&zÊYæªZèдW•ÓH¹`ãû9#¡×âóÄþç*{v­…dî¥h®Œêö2{Õ¯V¨õ}æ»Ò»÷OÚÓúÏešÌ!ÕÉñá:MæUîÁñë·ëÞñ2•å@èÖ g¥nlßÜPOÔs<ù¦+Ó|0mÿ¿Ðõ~~ñ^®ÍÐ%7ùºd¾ö2ÝÕÞ’/¼}¾ÂG¦C£#7ýæ·œÜðb{ærèÍ-òýµÐ^áL“y zÄêöç@j¿ÑžyúüªtpÇóA{åäö5g‚õgÓHãcëϦ¾¹QÓo$3 ìeÓ¼u×°—Ms}9¬?[êcô)¶Œ9’MDØÓêµ×h/«Ÿ²kØ+ |tÇÂ(¶ èä’MsçÅ–±Æ )zÑ^§†½L'¾Ïºu¼˜Ûèì¿9{Pm·˜›¹UÞz¢Öú´kŽ%/“´M¼¼z´ß`èlî6 ×60x^hsD½žzÂqe8àâÚ.¯Ûý£…Z‹íÐoÓ ï«A¦GBG +ßgÑÖì•Wòñ4½êQßâ­:[¼Ó|µ€k³EÇ¢ú öÊ3»±köÊsºÿ¶ kë/[ô÷ d¶8¦ñtœyeoßâìïYØÖH÷têÄõ·Ç{Ò1:Ëž±º†½¡i¯ìóoj_¶íeza¾Üè6t—=Ãþöay¾×sœï_ÔÐÜq%Èïï¾¾Lw¼iÓ‘ìÃk:æ¿M„ë¡ù|X ˜ßܲ%3\ÒzP|1=Ʊl¢´zÌçÅ@Šþ¦Ð#ßk³œãkzÝœÜ~dØËºIÿ•-Ó^·Ö#ô¬œ?uQg4ŒòÿŒÉ|Þ¥+ìÍD¶Ž•I]n×kp~4•yr[Í…y´7ÑÑC/ØkÏïlŽ$é{¸^¡¹þ|ýdx®?k†ß“Šì•´û„æú›Þ‹Ç¹p~ôâzæ³|£=ïìý1ÃÁ^á/ a¯˜?9qs{pþC¯öøŸlŽ´i¾¶oޏ¤gþ@¯ûÔ†À|âüÍoù·‘\÷Aÿ:UÞú[9ŸnŽ¿Åß»P¯êºgί¢úæh1_«æ74¶Ø¦9ÿ³¦6ÿ ëS¾pâÛVd¶gÊ[A·›k¬¿Ü´ÌØ÷¯çœ‘-aýYíÃ5Ö_ÄÇlÿõgÛç¡÷uÚ3ŽÙÀ^¦5ß ÏBû,’qrºŸtÓ™ñ8c£0Ÿ¶ëؽgœ&ª6è¬oñë/ŽÐX•˺ҞV_óÇâï=Ÿ“4ìeÕšêÛøwîéÓt=÷3²Ž›Új½4ôÓžiØËÊÓ?gl ׳߄^Çþ0[<†=-¬Ò_gl¼°Ÿvs‘…=}ÅcŒâdñÞoCãhûåÜx<†æ~-[ü½û³ÿ‡Æé¨'÷'q<Â97»ÆþÛþ%ûZÇ`¯ØŸglì'÷¯²‡éq~Og`À~WãcöĶEÏ;íeãÃý^îܘ¢üê®±þ✔-cýÕéþÓâ/íéûûl‡ÝÙž³`¶xL{5_o±þlZäîëï)_hÏÝ?‹ÇKßÇø<ìeàùfñëϺÅý.4ÖŸé¢çCöÑþ’½†ö‹Ð°—5«ïµxŒ³ìðý 4òöœûEl`O ëª?ñœû3hØ Û•WâiOIˆQ|þMe!žúƒgZëV»]ã¼ôh%"°P•‰À6ãvý·=¯oœ‚oöGóÃö×»½¥lĈø°”ŽØí›†=­¼ÆGá|–ï´×ðýô<¿g)'af®¡aOäSôþ%{eÙÇ×_«®}ý1¾#qýUÅ7"ÚÓ×+Í<Ö×_U¼A`âúóÃ:U{ÎÛØÈd§ïËMC¿Óý>µ2VŸþzèýœße<ÇžTßëQN­4EœÇ¡W:Æ')QçQÎó-ô”ÿd|àÆêX8Øà¼þº—Ç_#pá|éhœ—Âßã „óR¬¶¤Lk®Çq¾.8—缌À{Åùzè{šÚë\æoêpM{úù7f“†ü+й¾×SùÎ/ÖšŸ¤Ž·ò>ú^¤ðæ“ÏÀA“örÍ|EÌŸâù Ï 0ÓžEçQ&ÐŽ|²ÝGÿËÎWÈžÅó~ÞG`¦=cüjRrÉóÐÌ.E{Õóñ¾êù ÷ç¬õðß ÄŠôÇ ìýI|qb´cþUå+l½yûÊWìõ&Çç«»f~)û|lÊWìõÖ<_Ñ寠{?Ưqãþ¬Gœo#ÝÓášòÛ?4:êg½X ¥½†âNõÉ·@rØ¿)_±çgS¾Â^ËýÆíå:Ë^±Þ»ò‘¯Cǘ_ŠùÜkØ“ñ«à<¬ùÈüB±x {E<…^ëI[2Íc½õù~ýá<\=˜èÕŸ¡|…™‘öø9ü¿,Cÿ||+S'jéïCBŒ°ŠoùŸ×·ÑÎ õúèD=Å¿>S€{L—G8›|QÀåY@nbp¯qÐKw£_ðaúýŸ¦~ÿÇ·ßüm¾Òõá?ÄM&þøŠR% ìàf†þðñú«5ÿúúðÝÛß|`Ó¿ .|µuÖ]_Zy¶Áºé¾¿´rç&X•ÓOVÖë+ýK£dræäEõo¿þ&ª?ì(½Ìϳ£¡¼ÐŸÁP?×Ô1ɧÍg0üYó/ÇPÉV“yÛÀPBIÄPȉÑÙ2•D*Å- 3I|Ê $#¨0dƆ‚d<†Jcéø$ …$“ÞËÍsLõ …U~0ž—æÉP;âÆù$a¨„í¬KscÈ&Iò8ˆ§\´ÂPxJ'- …–™†B˪ËžÞ†BŠV*Mߟ CAÖC! ÅÝŠ0ÔS˜Š),´, ÉÐ% E™®ÀP¨Ë@! …n0Î C¡ÏëÁPL®ñE~öy^¡ š¸Å˸†bâíÁPhŠƒ# uJ¥+œ C¡“dtÂPȹe—4ÙÔH C=rÊ(!…¡_ãH C!7JYØáSa%n먙f†Jè|½6†b6Nš Ù¸XÊ´ÒâÂPÈÆ)í& …çJ£ C¡=¥í…¡Ò # …î®CQ q[­÷ CAçÀRXUè¯01Þ/L# Ål"ÓœÂPè¯Ò¨ÂP̶kc¨´<â Ï»Úã¶ŽúÀPÉCT`(ÏFn •Ö ¬D ­´©0²“u]CeŽ6†J:]m Å쥰1´ÒÎÂPÈ^úû¸­C¶r ÙH¥í…¡ðÜ11²ÂnÂPÈf CCá}-4ÒªVŸi,ÇP¬Ÿ®¡²¦ÍÆP(«}nëÐÞêׯPø~aa(<6†Bº°·uÈžê{„¡]ulÆm´æ›0´ì' Åì«°·uȾª}a(fcUžÛ:hõW õ½|“=[ Cá}š_ÂPÌþvJCýKׯP¨/{CA “C!Û«´º0²ÁšÏÂPx>˵1TFš]‰†…®*O E=®¡<û¼1TÖixc(<Ÿ¥hÏ­—ìuhÚkjÛì ýÓ|†Âû$C!-ÿ$ …ìµæ³c¨äÑ,0Ò¶j_*Vu •Âß9†z´0Þ—®Cmlç*%·¯c(¸ a!a¨4³8†2=ûõ`¨4ü}Ž¡’§C¥XIjÏÇPwø'a(øaMa(ø+a]a¨´b¾ C!Xœ þWþD þ4êÐä3|Ÿ°ÏÇXŸùÀPð¿?a(Òšzm -{ C¡=ù a¨wzÆ'aa(úwöG ÍÌÐï¿+Cá¹c!b¨¤ì߯PÍa(Æôum Å¿O ZóO Ý\¡G~‚±;~Žß}m Åx%LtË^oC¡~ëׯPøž"M ÅñeYŠñG˜JêNž6w u;æv …ñåV10²mÂbä3|.l% …ç†bü ì”ä¯S`©®xÛ åü1ãy½6†Â÷«¿ÂP¬Xjf¾OFŠóYï#†Â0»CA + CqXîkc(|vQûÄPü,öWŠþ>]Cš â´¦0T¾ëC1ÞHCáýY˜‹ ºökc(”F†b<ׯPÐa(ôGA Ã8B&Äþ CåÔ Cá¹ÒØÂPô—è/›·0žËþÂPx®ñ†‚Vš^ ïÓø CÑ †Â°ëû„¡ð\O Ïe/a(˜! Zý†b{¥"HÃçƒ0Ú†âxÌkc(¾ïÀP9ß¾ž„¡rüìÃ1§I`'Ø+ûoíCá¹c%b¨¬lçÆP0»ÆS*gß;†Â4Ò÷CqZ¨<χ¿tf"†‚Öü†Â4ñúÊ `›§ö‰¡ GèÞX^þA*cÛV¯¡ õ}ÂP˜6ZÏÂP˜þœ¼ šûuÇP˜ÂjÂP˜&ýÀPКoÂPl?4Ö~†$ìE ÅöÕ?b(N;ab(L+µ/ ÅiÈþ Cáy ,…õ‡ì+û+ Åi(͉ú?a(,aQa(¼_ó]ŠåUŸ íÉŸ CaZ “(e¤éÙž0TÞXT ZXK Ë  Zã- Åçj'¼Oý†¢ž×ÆPh?v*ƒZØH å…e…¡ 5žÂPìoº6†âø ûCÁk Cq>åkc(ØSö†‚ýõ=ÂPp[þ¼É^EñÕ1ô}`(º©CÁ>ÂnÂPÏü†¢[ºe¯äisa(Œg>0ìå˜)Ëž~¾u ·Ñ…µªìÕ4C±|¿6†‚.ªßeO/ÇPï4íµ53pK9_CáýÂ*ÂPÐIX(Ë^‡†½͉·” ]ôœ‰ ¸1¥é…¡2²ÿíÚ np©>7žY4`c(¸±|`¨XÜ1ÜÎûëæy0ÇPx®ö„¡¨Çµ1ÜT>0ž CC±|½6†Bù.LE •Ë;†‚uÌŃ>žk>CåÀÒŽ¡Øž0óÙŽK؈+wÿc(j•'†¢Û+ׯPtsÂHÌWd¥7†ÂsÇTÜø£¾æ¯0ÔQžù ”Ï¡±þºïçCÁ:Ö!†‚Õ|†ÂsÆOÇPh_XL ïŸz^dOÇŽ¡P¾–‚½€± …ùT …ùÍxå ϵžÄK°Z`©©õ ­‰‡ù«õ( …09C#_Ñ<;†ÂüÔ|†Bÿ„…¡èU_ùŠº±UÓú›Ž…¡P_ã! …öµž„¡0ÿW`'¬¿ž£?ÊWôÀ|ÂPyc;a(®C¡}}¯0ü°“ò¦§01´æƒ0ßX ëͨ=å+ºg­CšêÑÂP9~†á šëùWÃP6†rýý°Ô;j% µ«Ð¯]E¿íº>[DçTéwEÊ­Gé³w³é)âú]ãE®ßQ÷ž"GwÿRfUo£;½ø¥ð(jO²PÖN_\›)gOùËkÃoyÏË—×ÖteíúÓµ̽*Î •és_ÿøðå¿ÝþYî¥2{ýLCÇš*Ý›æ ì¿Ó/§^ƒÐkoO‘quOX quߊpuÿ ªW÷­¶øV8Já-¸m‚#î"{Ò&@p ž˜@Š;H8\¿peû‘ÞtœÙêK›W­á¿X×þû>a­áÛrQ-| Ÿ1Ùh½ÕM+n¥¦„´¦§WE´Ö-K hM?Z‰g™¢cÎ:”õp.9yÁ¬u;q"ËšW ¬å¿2QZr+î #&޵Êy›j%™(ÎÇ]ª9P1¬x¿#, ×q‘ cy_`È׋_³·¨Ni» ³¦’™‚W1ήö¡«é?Hur5߃«yBq+Þ6¶šM?Vrj5[Ü•ªnŠ}UÊúvrd5ýŽž«¹üwûVÈWÆ5*³²¬*Ìí~Ä>¯M«VÀÁ* ¬®8qï·ÑœPÕ#¹ó[5î7T­ÍÈ©ömaª”E” v=)Uºk¼™” CšJÈ¢TTQ$®X ã—§uM~ÊtJ•"Ëç” íùe*îúð\YoQ*N‡zmJå`S*”ßY×»%ã®ó§•U´É×îkSªG‹Ra®¶ã²ž§ƒRa|DE©Ò¾¬!J…•PŽËR(¯Ë.¢T(/ê(J­ñ¥zú/J…ç~9‹” ÏED©ðü*{Úµ¢½‚*ˆRAk¼E©P_ÔY”ŠßÃy)J…÷i‹RÁÞé¸,…ñUœÄxŸ”êÐKöï(´._‰RÁ¾Êâ+Žà}#ô¨Ç|¥zæ›(VuËSÈ’ƒòˆ2‘R¡¼Ö(V²œ…(Õ¡I©°=ÇäLÉêŽãfÓôŒ¬¥Ø¯;PšÁKÅ“ö²Nš¾Oqš4ýÇ@“‚í;KŠŸ8Jš-HIÒI¤éÿ çH{`…‘bƒîi6ŸH‚HVWÌ\ iz‚ÊÒg¤ÀKiwRü(Î6ŽVÕ¾Äé~ÜiÂWqçWp†ùW²ÄŽp"[×FG+ȵ΄ñCGàòýÚÜhÅí>a£å?}tj´<\;4²µ±Qâ9oÅM¦Q/#btÜ[BÝvm^´¡pþr\›á/u‡‰°ˆÛZÝ9",â‘/î0Mm;5‹ u‡W°ay°h‡y‡Eܶ¬kÃ"l#7‹Ò½¢¼~Æš"ù*X´·u‹°ÍR²T°(ųá uÂ"”÷;N„Eï4~ ùhýœÕ‰Ã+ýžÕ¶ã¸³„þ 6½Ó£œZ¿iÍ·VÃ"n+;KGû„E¨ßáØbÏ“î(釭Éïˆ9,Bù𝠋ð¼ Öím­Ã¢d!¯w–8^*¯·î<òm°îp ±¼`aΪ)î0áwÇyÄ&Â"hÁÁ¢wz¥RâÎá8½é{‹ ï‹ Õ?Á"œæG‹p:Ë,ÂiN0B°(üvX”⎬âT=d;,Bù|À"?mnX„úé€Exîï×[qšÕ$¦ñýšïzNÇ/Á¢{À"hõ_°ZðL°ˆ§í~mX„Ó¸Æ[°ˆ§óymX-˜(X„ñ×ü,Bû9àŽ-û¹`Oÿ‚1J`;R¯ ‹P_0R°§{¿ƒDXt”WÚ ì;IJ”õhØÓ´¿oÈ^)`Д½ªÏ'Á¢dÝô÷+mÐÚs‡ ölž=tX­ñ,JûãÚ°Úï)m€nö(mðÔ',Jû™`Q«rmX„Ó²``OÇÒJ<š°èÑ‚EКŸ‚EhëýWƒEJlVÔüW÷Ÿ¢£wdé¼±Ôé>î»'Ú1]?~Î(u<ÖI%û¹åy¼˜Œ‹ÇRÇc„‹ú܆ry3HOɳèès]ÊåY@_´ øë܆êµ?ü×Ç?¼«^¼zý¤zÕO¹y«w´÷¿ì½ýbÔª endstream endobj 534 0 obj << /Alternate /DeviceRGB /N 3 /Length 2596 /Filter /FlateDecode >> stream xœ–wTSهϽ7½P’Š”ÐkhRH ½H‘.*1 JÀ"6DTpDQ‘¦2(à€£C‘±"Š…Q±ëDÔqp–Id­ß¼yïÍ›ß÷~kŸ½ÏÝgï}ÖºüƒÂLX € ¡Xáçň‹g` ðlàp³³BøF™|ØŒl™ø½º ùû*Ó?ŒÁÿŸ”¹Y"1P˜ŒçòøÙ\É8=Wœ%·Oɘ¶4MÎ0JÎ"Y‚2V“sò,[|ö™e9ó2„<ËsÎâeðäÜ'ã9¾Œ‘`çø¹2¾&cƒtI†@Æoä±|N6(’Ü.æsSdl-c’(2‚-ãyàHÉ_ðÒ/XÌÏËÅÎÌZ.$§ˆ&\S†“‹áÏÏMç‹ÅÌ07#â1Ø™YárfÏüYym²";Ø8980m-m¾(Ô]ü›’÷v–^„îDøÃöW~™ °¦eµÙú‡mi]ëP»ý‡Í`/в¾u}qº|^RÄâ,g+«ÜÜ\KŸk)/èïúŸC_|ÏR¾Ýïåaxó“8’t1C^7nfz¦DÄÈÎâpù 柇øþuü$¾ˆ/”ED˦L L–µ[Ȉ™B†@øŸšøÃþ¤Ù¹–‰ÚøЖX¥!@~(* {d+Ðï} ÆGù͋љ˜ûÏ‚þ}W¸LþÈ$ŽcGD2¸QÎìšüZ4 E@ê@èÀ¶À¸àA(ˆq`1à‚D €µ ”‚­`'¨u 4ƒ6ptcà48.Ë`ÜR0ž€)ð Ì@„…ÈR‡t CȲ…XäCP”%CBH@ë R¨ª†ê¡fè[è(tº C· Qhúz#0 ¦ÁZ°l³`O8Ž„ÁÉð28.‚·À•p|î„O×àX ?§€:¢‹0ÂFB‘x$ !«¤i@Ú¤¹ŠH‘§È[EE1PL” Ê…⢖¡V¡6£ªQP¨>ÔUÔ(j õMFk¢ÍÑÎèt,:‹.FW ›Ðè³èô8úƒ¡cŒ1ŽL&³³³ÓŽ9…ÆŒa¦±X¬:ÖëŠ År°bl1¶ {{{;Ž}ƒ#âtp¶8_\¡8áú"ãEy‹.,ÖXœ¾øøÅ%œ%Gщ1‰-‰ï9¡œÎôÒ€¥µK§¸lî.îžoo’ïÊ/çO$¹&•'=JvMÞž<™âžR‘òTÀT ž§ú§Ö¥¾N MÛŸö)=&½=—‘˜qTH¦ û2µ3ó2‡³Ì³Š³¤Ëœ—í\6% 5eCÙ‹²»Å4ÙÏÔ€ÄD²^2šã–S“ó&7:÷Hžrž0o`¹ÙòMË'ò}ó¿^ZÁ]Ñ[ [°¶`t¥çÊúUЪ¥«zWë¯.Z=¾Æo͵„µik(´.,/|¹.f]O‘VÑš¢±õ~ë[‹ŠEÅ76¸l¨ÛˆÚ(Ø8¸iMKx%K­K+Jßoæn¾ø•ÍW•_}Ú’´e°Ì¡lÏVÌVáÖëÛÜ·(W.Ï/Û²½scGÉŽ—;—ì¼PaWQ·‹°K²KZ\Ù]ePµµê}uJõHWM{­fí¦Ú×»y»¯ìñØÓV§UWZ÷n¯`ïÍz¿úΣ†Š}˜}9û6F7öÍúº¹I£©´éÃ~á~éˆ}ÍŽÍÍ-š-e­p«¤uò`ÂÁËßxÓÝÆl«o§·—‡$‡›øíõÃA‡{°Ž´}gø]mµ£¤ê\Þ9Õ•Ò%íŽë>x´·Ç¥§ã{Ëï÷Ó=Vs\åx٠‰¢ŸN柜>•uêééäÓc½Kz=s­/¼oðlÐÙóç|Ïé÷ì?yÞõü± ÎŽ^d]ìºäp©sÀ~ ãû:;‡‡º/;]îž7|âŠû•ÓW½¯ž»píÒÈü‘áëQ×oÞH¸!½É»ùèVú­ç·snÏÜYs}·äžÒ½Šûš÷~4ý±]ê =>ê=:ð`Áƒ;cܱ'?eÿô~¼è!ùaÅ„ÎDó#ÛGÇ&}'/?^øxüIÖ“™§Å?+ÿ\ûÌäÙw¿xü20;5þ\ôüÓ¯›_¨¿ØÿÒîeïtØôýW¯f^—¼Qsà-ëmÿ»˜w3¹ï±ï+?˜~èùôñî§ŒOŸ~÷„óû endstream endobj 537 0 obj << /Length 19 /Filter /FlateDecode >> stream xÚ3PHW0Ppç2ÀAc(á endstream endobj 541 0 obj << /Length 723 /Filter /FlateDecode >> stream xÚÍVKoÛ0 ¾÷Wø(µkY~^ۮņn(P;,;¨±’p¬Àvé¯)ÊId¶!ÃNÖƒäGRäGGÞÒ‹¼Û³È}/«³‹›”{< E’Å^µðxœ„E{y”…<.½ªö¾±Ô8"v­æ¦“ƒ6mï¯>]܈Ü+Ã2‹3T¼ áaž$¤T­T§ü .b¦^u?вWÏ~\0ÕɆ̆ìÙÍ`ðËY ŠœY0Ÿ3geÓ˜ÁÉé–¾µžEBN‹FÄ ®¥çlÛ‡~ÀâT^n7¸aª'´Y”F š¨þ#ÃX8Ë4¥XHÞh‚™@$[™N¿:ÃpD’²GÙÑuÎÔ0Á4ò’Íeë$›6k 8§8U;ÃsÓ˜®'‰‰^>êÍt%ÛåNPïTޱ‚€‹0>Úäà:a=eªqò²u2½M“~Sç´?H ’Ó`eï4šTšGÁ¶kÜ‘‡¦9„´pŒHð°(8Eô±%aå¦i ZzÑí’îl5¸ëÝ8ÛKýìï“ac2PikªW¹Þ4ÊVú®c ]²w%S&©­û"äÔ,æÑΤMì .¬ðçñÛÇÕ´D­€Ã7Nÿþ Kh|5–}¸ñuû"×ÿ…öœ–÷¶ÇKÃðÞeíÝ4ùö0¶Èi“s¬FF'ûªëaõ/_‡¸ÔÆ¿µ- >6îißáX"Foxs„C°@€>N› kÝÏeWítM)º³¬|RìNN Oò¥åÁWK› õ2ʧ<…³V¤ëWĘ´Cí4e åöfA_¢X¸Yµãh¥Ýá8Ý«ÀšNq=’¬´8™SÎáýäØáˆÞ™'ËúˆhÈŒ¤Y§ñÇF nqmžºŸkÕ-`ØPJF Ôž,k„“{<ËÂ".3rî’$²‰Ô‡êì ˜? endstream endobj 546 0 obj << /Length 448 /Filter /FlateDecode >> stream xÚuSËŽœ0¼ó–OFZ¼~`‘&‡(Ùh#å-·$Ę^¯Fü}Ú4ìÌD‰r¹»ªŒ»ANDÏØÖEðødR"%ÏQ¤¨‰T1Ï„"©H¸T9)Žä;3\†‘”ʰ¯CiÉŽM½4ý pjXBdׄßÅ05$çy¢ï)H¤3®¥F·÷adÀj,§ˆ®ž†Ë¡òP>øLÌ$lÖ×{y5~ß½ºœ×¸½©|„»e´ÚÑd­ÛÕ›¶{ŒŽtSÐ3õ§lñÒè8Y纟ýV¨OE "ß ¤¡@±NIÕ¿žÊ W´&vÕx|î”"‡à<{.Ú-£ϵ5¯UÜY*‹¹‘[gžšÓëd¡ôIÆ”z7‘š=׸„*c[²m~Ýìœoc’² ÚʆP²Þƒ¹9Ú MÄ—Îk_[׌íæà«¨6-«P¥ìŒ»±ìm‹ŒÆÍoséoþÑàÙyÖ²¤cæÎÖ ´¾^pUö˜ž­ÃðÀ\•upœD}[C“sGùñ@Ö]¥•à¹Lv¿›„ÿ­pÍUÚ)øsëôï9ú)±Óí endstream endobj 543 0 obj << /Type /XObject /Subtype /Form /FormType 1 /PTEX.FileName (/tmp/RtmpBlhU1W/Rbuild4444671013800/timeSeries/vignettes/timeSeriesPlot-typeMultiplePlot.pdf) /PTEX.PageNumber 1 /PTEX.InfoDict 550 0 R /BBox [0 0 432 432] /Resources << /ProcSet [ /PDF /Text ] /Font << /F2 551 0 R>> /ExtGState << >>/ColorSpace << /sRGB 552 0 R >>>> /Length 30193 /Filter /FlateDecode >> stream xœ¤}K¯.ËmÝüüŠ=L:®÷c*#1lÀA’{‚Œ” mÀÑÀ?M®µXÕ_dÙB÷ìK²X\ìU]Ovþú»¯üõû¯úñ_¾þé+ýLé«Ìþ³Ö¯V‹Q9ퟹýŸÿùõ_ÿè…ÚúÙ¦JÕQöùQê¯þø_ÿæ·_ýË«/}Ýÿþò×ÿéáÎþõÏ?þÛÿJ_ÿãGþú»ç¿ßÿÈnüïŒüs·¯Úº•úþ1ÊϾ¾jÝ?SûúÃÇ\)9~’k|Õò@ÊF>JV8›ÎCŽŸÍ —Ÿk9&+<~Î r¦‡|#×ÏúØ­éçè9ÓÏ2\·V;9ùè<äc¡9ª=,?órT+\Åa4'ÛÏi…ÛÏl0ìÁYáúsxÍãg²Â7ƒÝ–~V‡1V³»~.'7Ü_?siî·ü3YÍ+ýìOáVàþÊ?ssò1à¤=œ§æédu÷ÌÛu›»ßÚÏÖAîåºÓ\ýçxì6‚9üá<¿Qwér÷i« Í}ü‚¹6{’óç6»;ýÌÉ1·r®?]à~ÿ™ÌÁ]Ýýö<” Òcãù©\·yl´§±»?œþür^Õt÷/ÒÜï¿ï^#Ù|Èí±ñŸ‡ÙCÚÃéÏïkº9e÷ÿÐÅ@_ÆmáñÐ òêññÐþ¼rjþ„ú òááÔ7ü)ýé1ò”_ÓéåAbÑ¿HÛczÐW‹°ü4{|÷5çìâ)Ÿi ”áÕ]å¯ }í¡ÙÙvÆ÷CÛózóöú:»Ûjaa4û[ö¨¹³Ãmü=:zÜçgœ^ÿóC7¼_}î‘£Ó} ¿oG¯›ŸOG·û„-ü}h{^íGîèx­ùóèyŸfº^kv…´ÅÏÓLýõ‘Ÿ·š=¯Gß»È<ÐûZ3,¤íy<ͨ€Fÿû43ïéò@ütx^mÏëÐè‚>ø¡³Û›è„S7·A[ü<Ý”¿óó~³çõtc åÑ?4Úçôޏ<脾÷ÄeÕï]qyúë@{\𳂶øyèŠú7âgòù”„øyðeÒ?Q_)hOAWÄãÛhŸŠöZŠ÷ÇÑ~ÊÓg´ÏíþôÇÏÏèCP£ýy,áAüü죒öø‰òñSˆ§¢?VQjyÇO-ŠÿRë«ÿ)6N/Õë¯è#~*úãC³?Έ_£7âa¢þ…øIü½ûcŸÂÍþ¸˜hï+ÞÇ¥±?nŒïç}gÏë¡ÿý1ûïÒØGyôÇÏÏX2i럟±Aúó*ãÁÒ8&æd£tôÇc’ž×û±tôÇÛ>ž(½`ÒÀy@é?4â­cd¬ Iéke4'þ>.6pIgô_:FÇ—Žá±Òië4—(#aöðÄ#íÓ‡Îøk:QÆÇÏcîÐÇø¸ùD´ŸçýÇñÚ ²W}?å½6Úǃo–±_ãA£}<8Ð_—Éñ1ûû29>®ôgr|ÌéÑóo—çýçãAŽ—Ëäøxa¼fô¾Æÿe¢?¶¹®û;Ñ?4ð>´=¯ç1â÷]è5>·—=çgÄï±Ð‡? ýñx>Ï‹ÊçãÁ²8>NïÍññ†œãcN–¬ã²ø±aZ!íñ³…ý±&„Ö‘2~ÐÞvVü”AÚã'Ê]´_Ð?¿§ÿÞF{{*ö³þXó×ZÐ?Ý‚¿ßŒžé´WŸx”ÓlÇã§á}c4çç>Ÿ©ej~ÞPV0žöåï{ë˜szÓ³Ý4úãGßû#[J°ïñmôgþo•¾nšK½Æ™[ÇÝÓõ|ê|ÍG½#¿æ»>qKÞ>ýýàËGí´/Ÿè­ÓŸZGíósö×Þ·³Fc Õûcö÷ö?ëjÿV±÷ÇÏïéÏ·q=ƒókøóà|ÙèqÍÙq˜?½öþ§`|cS_g~k?Mô¯fÈ—¶.zÂ?ÄWG¬•!ûaüyúÛÙs=Ã'âåô7Þ‘ßñÓÑk½À&öý¥ÏG½ãg=ĸk~î×Õ¿Y`{üp¾ï]®ç7Øs}£Žñ~ýñ±‡þXý9Zôßur½b ?®ý±V¿Œž×z5\‹ŸV½×ÕÞ&ûãÆþs²?.ŒÇ‰þ8ÚÛDüØG5§žžßÜjOh ý±ÖOŒãêúãèÖ3¢½,ôÇ6wû ý±Ö[êB¿ÿêZßA|®¡õ‰úÑßô^W{_[ë;>^«ýqÄóÆz†ÖsêFlÃ8dzÑë}ZwSüøøÂèÙ®ö¶±žqhôÇ7½±D<èÛÄïñ‹-äÿÿ-Ó¿v¾4j7ÑmÞWìP<Èÿýõ‹j0híë›Û ‰â_¢@µ‡Gò*`/‹§WT’wtQ€½ÆUF£À…á·¿ò1ýîâ1ýñwÿøã¯þcùÊ_¿þ/lž,ûç7þ¯ÓOš¦OÜžú×ﯷ׿ÿúõ÷?þï^õ¿A×Þ¥ÏKÝu÷_ªkó éº9¥¿TÙZw¥rþ•1Çú þ€³°ÏâÁkê¿üöo¥þo gBzÿ{G&Ñ>ÿüéýÍ3l±e&éiE¡ý¾Ïc±–EHßEl²ï‹C,Bú.Â÷{Ñûþ*Ò0‰"¤_E>âMêÏ<ÙŸ‰x–ø3!ÏWÌŸßÈÖ}\ô'~£Ø/d¡e¿ðÏUuµ°õü²Ïإؿ­°…høÿëýÔ××ð¶Äù¼2¿_Œ§s>¶Ï–¿°÷HâwV´Ø PR_’^O­^ŒgfùøG½‡xÞ笄…MJ†z7Ößýø- × «ðÉÈÖ×[Ý£ØÄ{¥$ˆéi‚’V›J„×Ê¢d”&½Ó̪áïÒ€gz7VÂ/æ¤-û{âfl 6«ÝÆØÛ%ALÏ“–´ÙXAzAT>R2ª-PIωªÕ ý¦#¤D½kÀÓYOáïã)YÝÉ> Ø‹&AL=‡´[3“^ÂDFµÎ@zÅ&[ªÕ ý¦)¤D½+á7Äï3®åÓ'c¸BG Ola6“tØHOz$¼V%#¯Ðó¹ƒju‚žºMH^^o¬„ß¿Ýîûbø8Íêðäc¯Ÿ„ò—˜¤Óz7é‘ðZY”Œ2CÏ—6U«„ï6! xe¾±|Æï#ÿ~1¶?ÒLÏÐgHÓÌ!]Öº¤ÄS«%£ÙàNzÍ›>ÆÙm?$Ϊ0CÓ÷u%Í6~¤žˆ©~ÿ0ž’žÿjªõü¦[È~Ó€WÚkÀ÷8ôØü~1ž™^ðé*p¶†1õRyI/ˆ‚îDŒi}‡ô¦­Ú«V'øPJCh\ï…ðgRü²éÆÓgBÈN[ÀY Ä”wH}ÉRzA°éŠáûæÒëüÕzAøl7øÅ ôn¬ŸqX¾3|Èau#†Ÿ!Î.‘ ¦UCZm‡Qz$¼V/JÆ4/¤7­>Õê„~ÓR¢Þ•ð³íCågV¿:à“Q¬•OŸåÛžîÓଠb²á&¥ è‘ðZ½h0ž7ªô|¥Aµ:¡ßt„4à™Þ•ð«ÇácՆأÚ(¯ÏHgÃH¸¡îC(J½Ã— ¯•EáÃKéùHUµÆ8V6!%èÝX ߪ¹UÂÆrø²v^ÌÖ% BQ:|¤E½ JRQgøšŽôŠ-ªV'– ôn¬ÂOÆ3’²ºÍj'ÀTD“ ¡(>Ò¢^‚O†??éUÈZkÀ§ÍJøŽz7VÂïŠßg¤õ}1šíYÝ€ñØÃYAÄTsH§½¯¤GÂkeQgx£•^±ãªÕ ý¦)¤D½+áû³Ú0â£Û ˆÕÝœx†8ÛH‚˜vé²J¥G¢Å+‚ _÷•žŸNP­5^´ )Ñ@ïÆJø qèïßÃÏÚ;Æ"Â÷4H¸¡íC(0l+&IO„×jEã‰éùæ‚ju‚žöÒ€—ÊkÀ€ ØãÑT7´†bêYŒå;ò%ƾþ¾•^ø2Dð¡”t!v4®÷ÂJøq˜­·ú~1žá6`<냳®$ˆ)÷fŒî\ï|•Šá+0Գʷj¡ß´‰!4®÷Âð¿} ¾3†ý¦¦¶œx¦ø8›K‚˜æi±uké‘ðZ½(ÍkÒkÖeªÖ¦q¬l:Ch\ï…ðÍy«j ¾ËaøY*#¦?_›Ö &›ôQZ|:H=ó‚ï Ioþ\"ô›– ôn¬„_‡ÓÊ÷ÅXöc<àµÖÎ>“pC͇P”6iQÄT{cX`HÏ÷ÕT«^”6!%èÝX ¿"~—þ~1ö4ÂCÖ7pqV›1ùtRŸI/a"cZ'/½iYµ:Aøc…”h wc øóõ¬õOS#ª+Ð@ïÆJøMqØÑóˆ1mV¿šbØúë‰1•R?r#=^+‹.¼òv =l¨ÖŠÈ&¤D½+áwŸ€YïÁØ'cÚìÎê¦ç áì> bÚ9¤® =CƒÓÃxºéy媵kp*›¼ÜÞX  }fù}1¦ œìwÂ&t¸k@ - ¡ ÝiADL`#Ðóö ZcQB6! xi¼±þDNÛ´û~1¬3_Ðë w#H“¡œ±¹âz‡ÈðTŒá¿ õº4ku‚%N5ü¦+á/ÅaÂí0§xù¹5Ä”³¡Ñݺ®z‹áï"éù+Õê„~ÓR¢ÁîÆð7¯O{ù¾~øÐêæ(ØiN"ˆiöbYz"¼V/Œ§Ï“^¶óUªÕ ý¦MŒ¯æ7VÂߘ€5;*ôýb,¤ ùD.îÊ &ŸôAŠeyê‘§Šr*Ùsèù®¾j-êdÓBãz/¬€oGdñƒ°ãcX¸‹Böéq·‡„ªBAZ1ºƒ‰®ŽSŒf^HÏ'™ª5¦ ² iÖäi—7VÂ/šdô<‡a¿lAÈ›Rà. bâèΤMÓÓ ‚‹Ñb4 téUÛ^U­N>§+»èÝXþ$„&ød<Ô®Ùi·pwŠ1iõH»FZ¦DE{ ›zÝÛ kíÑ¢hR¢Þ•ðkÄ/Ÿ~06ž`t¾}‰ ¦RCŠeyêÁ1ÍNÃH¯™KªµiÌ#› ôn¬„ß´ØÁÉ¢¾ãcuÆrL¥ˆ ¦5CŠeyê‘hœÆ3…’^µ‘£j­œÊ&¤/•7VÂïš~ל€0Vœ˜XøC(H—f(¦·4;ü` ê k=ð‡fõpŽðàöÁJøCóÎØ 茺9J«_¸ûG‚˜8º›Ž˜£;Ó[šô¦¢‡­z݆ýªÕ >6“5Ó»±þDúöÚ÷‹ñL‹÷DÈúOÜU$ᆰÞNéÖèÎô‚àü[Œá¯êù›j]<Ù„tªyÕñÆð=·vÕãùcu?VípþÂÝJÄdëí`؉‹Ò¤xQaÇ×A¨º|郃ÐÏšBJ@лáÒƒ¥é!c¢nâð§ê3»­u{å?çÅã£yQ\oŽ_o Ýᑱ5i䊃 Æ›æ‰õ >X]œwÓ‰‹C,½Ä™1ɸü(OQ^0î¢nPáGÑä¦Õ£ëH£îp$lSžÔäø nù’~©úûâØÈ±À‚•¢ä\RBgƒ+É«¤ u£tp¬w¢îò ‚ªÔïˆd¤#¤µ|à>¾xˆW-FÇwçÝBsjÀžíUíè3ýq?r,ßK—Tt›Áñã­¡[|d¡º‹†-a›râ¢î[¾ø©O/Qé 9¾îëQt)ñê3)¡³5zɱ–/]REÐÅÉóèf©î¬Óa›ò@šçîã‹ÍÞ¶oö|ß_&w Ï}|ñÚ6)¡³Y£äXØ—.)¯¥ÉÁn’t}=êŽeö°M9qQ÷Æ-_â½Z?þ}sõƳçîG´IÁž¯5†|úÀMº¤¼n”&ǯ܄nñƒêvJž·väÄEÝ·|éˆÙ¬—yp°)j7 iöf%tu¹Ÿc]RY¯ôà$ôLÔE·¬º“Þêa›râ¢î[¾ Åû”/ä`Î,§6°Úf(ØÃº¾äXÿ—.)¯[¥ÉIûè¢ PÝNÅo^Ž<¦ýûøâ1;N{!c4áÓ†ÈLS@Jèl‘_rlH—Ô8í…œn×*B·{à©î®•“°M9qQ÷Æ-_¦OÿÞèô…l%?—a”÷„~e”ÐÙ´Srì H—Ô8ž‡§"]×ïMDÝ]§}Â6åÔuoÜòe#f§uß7ǯ°åŽÒCr{Ùo®…<ûȺ¢¼n•&§Ô£ëÉ¢n§ä¹Û†ü -õ7}±£š¨/ñ]y8ÖÉä„G„Ô¤„nÍ#/zR7¨Dtâ ƒ’.º$ÕÝ´Ž¶)Oj™®{ã>¾Œ©wÝ÷›3ð¤áöi9H ]ÏGŽ é¥7«88†!]œÃPÝçÍ*Û”uoÜò%G̲½ˆƒî>çˆùöÅ”"¤„Îv $Çn‚tIuš Ž ]¿u7mö…mʉ‹º7îãËÆÛd§ðec Óa¡­è ‘…”ÐÍÓSfl-H—TÑ’ôÅ)ýèúå¨Û©ˆÈuäÔuoÜòÇ[Çñ˜8Õ›bÆYØíkLåB özŒDM>0¤îÐØPo£Ã±Á¾t³¿$UwŽ·‘lSHËüÀ-_šbV}²8XË]î1ëiH G¢. R—T‰>Yœìƒ)éúÝ­¨;GŸ,Û”uoÜò¥#f‹õCßoÎÆÓSÍ/¦Ð¹G‹y\cKø29†!zŽa`o/QB7ê‘c;Bº¤ÚiÁ±p’nÅÛˆuW­[†mÊi¸å $ï¡3Üg"6Þ/V-R‘º2{Ò *·(½ø~YG·y3VÝM‡¹Ã6åÄEÝ·|ÁùØíg§¾oδÔn}9åíoc¾4£}Zâ¥ò’0„®¨¡“€ÁÁ¯(]þЬ»ë0`ØŽ_ÙqEÜÇ— ,õø²1†±¡^ÆÉÚíW™2Š”ÐùòŒÑ tEMm•Ç÷íBwøLQuí–„mÈ… º/Üòe#fÝÍqI*!ÂñÐÐùrlaH—ÔŠ‰88-Ýé&T÷ŒuÙ†\¸ ûÂM_ìâÆ›‹ïJqüÖ¦[€u{ HÕEJèr>rìgH—”×­ÒäX'$]솫î8!¶)¤¹à>¾ø\s™‘ï›ãW-ÝBsÊÂiÆH Ïs)Çæ†tI­ñˆžÏ~{ž“¨øÍÛýT÷õÔnÜò¥(Þõ®'ðTĘ "ìa)9†˜Ò%µâ])ñ4ÍãkÓ‡ƒ[¾à%Ç»n7ÇÀ» ³ù<Ò»‘ºš¼c4H]Rë´®®¹ÚÚGwz—¤ºçi]M­ËåU­ËuoÜò¥!fãäÎÅÙxZè:žtM¢„n÷#Ljtƒâ^Vp0£–.fÔª;Žð„mÊ›Zæè¸/Š÷¾<ËÕ5ëËòAÙ’Òꑺ¾Ü͆.@ì8\wqlˆ ]Ã3“ê&¹Ž<–ö[¾`ÉdomM“S¸£Pü4Q¶*…”€¤„†ÜwGB{%¨[¥9‡¶Uèú³Ýª›TüæåÈ»æî»~à–/~B׬k=Yù+¾(a” óÎìù}Ëoûº¤âÈ)9ÑSlŒÙS,ÌÅv¬'ÃvȧZæj¸/U[øBŽ÷~¶×ÐÙø©I ÝÜ!¯v4tÅ­kr =Ÿx¶þÆØ˜‹mí^ÓvȧZæX¸åËBÌúâ÷ÍYvÍÄ-p CëK”еòŠꊚ5â`‰RºžT!ê±– Û”t_¸åËF¼ûòÎ÷›³ˆÞZ€çfa LR@çû !÷éJ+Äâ`ùHºž4êNºÔ¶!®¤§zp_ÖD¼çðe ªÀ.ÜÐBúNRB7ó‘ûŽIè’ʱ’.~é&=‡4DÅožB.\Ð}á¦/5#ÞKôÉâ`;¤Ä«ŸŒòü€û‹iSI ]™GÞìç ]R^·J“c­Yº^iÔ]cl)Û”R×½qË—Š˜õÑç÷›ãQSáÓ'ãHùJJèv>r_T Ý Ô3‰Ó½û•.‰ªî¦Ãja›râ¢îûø2QŸÆ0‡³',¼­'QB×û‘ûŽIè¥1Œ8Õ‡^ÒÅzÕ]c #Û”uoÜòÅe[ Þ  öO«Ÿàö•ó‹©vI ]ÞGî;&¡K*îу\éboUuŸ½WÙ¦œ¸¨{ã–/Cñžõ»džÈðg¿¥×çÃoR°çCîӉР*ç(=ð,Z?º8¥º«nM‡mʉ‹º7îãËÆˆ¥_6zY›™Ö‰/èKR”ÐÍ~ä#QênK»|!'Ùå³ÐÅ`@u§Xã‘mʉ‹º7nù2³#ß7§Ë‚G¸ŸbzfRBç#QÊ7F¢Ô%uEdpl]ºØAWÝWD.µ.—R×½qË—…˜­vçûæ`MÍ,ž{]¢Ü^ó}É›ï˜HWTÕÙŸà`r,Ýâ/ª»ÄáÙ¦|©uÍþûø²*b²…/ èlÀSý¸ÇeùbZlRB7æ‘ûމtEyÝ(›˜H÷xVDÅo>nôDZÖnúÒb¶ÅٞñY/²wVÛAJoR@W0£¼b.FÝ 4JÇwåC×ßÝQ÷%È6åÄEÝ·|Ɉ٬CÚÁ)þ¶mŽm¤#'%t¥yÕHÔuIåxûŽME¤‹yœê>ó<Ù¦<ºî[¾Ĭ'ƒý¾9ž®Í-Àº|‘J”ÐqŒçò†Ñ u›Æ†êõÇF¾{(Û·P7©øÍÇ‘R×½q_°Ì¾0Š- Þ´>D ]ÏGî;&¡K*G 8[ “®ç¨Šº“®ú„mÊ©ëÞ¸åKÅ\Óo}ßœá'jZÅÙÓ71…=)¡ó92å³bê’òºUzpŒ³Ž.Þ~ª»Åî¦lSN\Ô½qË—Ž˜]1¶<¶Žß>vDú}R°‡¼c’OŒ©T´®©‘jÛG[ª{œÖEÛ”uoÜÇw_ ü¾9>Üp ɱÚZ;>@Jè|ŒGùÂhº¤ÎêóáÔzt‡¾lD”Æ0äØá™ †«ªøD )¡³1žä¾cº P7J§¦Ð}ÐÙ…uƒŠß¼y ­é·|ɈÙ1&ŽO›Ü¬3†V%tm¹ï˜„.©Óë‰3cÔÅeRÕ=OŒÑ6åY­‹ñypË—‚¹æŽù>9Öš+, …Ø|Ÿ†!%t9¹ï˜„.(Ô­Òƒý` ݇òÖ…ºAÅožŽœH©{ã–/ 1;cYO=n#ÜVïðYR°70Æ£|`$J]Rg§Kœá3jébÏ[uŸ=qÙ¦œ¸¨{ã>¾,ür¹†/äØ;©wD8zB|’‡”Ðyä£Aê•cä`{LºØDRÝ=Æ­²M9qQ÷Æ-_zÄ,çÈâ,?AÙGÄ<žä¢„®æ#çn uI͘#‹ã#C×oFFÝ#VÒe›râ¢î[¾ Ä쎵¾ÃÙð…- kM¢„n÷#çn uƒÒZŸ88òJݴкP7¨øÍÛ‘µ.×½q_&úéTÃrlϦû¡Pc †v%t}ùƘºA%¡#gù±jê&l±îtÎ Ó¶äÄEÝ·|ñóÎþ~àæpÐY„g\kÀ'¨H¹½á‰X$Ç·*¤{(µfq½…ºö+®¨”ˆäØ1‘nPjûâd?o ]$uQÝ9ÖÞd›râ¢î[¾ø†Ÿ?IƘ85, §d½ˆ‚=ìƒHŽé’*1#'ûýé2“/ëÎq:S¶)'.êÞ¸åKE¼—¸g!ÎÁƒ_ÂzB|.Žì5ŒD)ï‰R—T‰“¦â°o .±²îË—ªÖçòª–¹Êîã g^Zç'ÇÆT0Š·‘>uGJè8s¹g| Ý¡q©V¼ÄY¾’N]ëekÔ JÏ©–#¯š}¹î[¾4ìû-¥ò ÎöóÙø‚÷û‹Ÿé#%t㹜;&Ôj” ŽH×ßÝQ÷%È6åÄEÝ·|éZÒÞ+9öõ4>;¼é7°–!Jèv>rì˜H—û';ö^'÷ÐMþAÕ *~ótäÔuoÜÇÎ5/_8Gv“Fƒžä<(­x‰3}-]$pVÝ3î<É6åK­Ët_¸/#!ç@ÆF À|R“”ÐÍ~äØ1¡®¨s†Dœ‘.Žø¨î¤ô‡ará‚î ·|Ùˆ÷¢LÚÁA>þ™Ðü»#ü()¡³}ɱc"]R%v†Ç3SדñEÝ9ÎrÊ6ä©é¾pÓ—™°&ÔâÞ«8É€V‰:¢Â?ÕD ö°"9vk¤KªÅiqüSj¡ËÄú¬»Æ\L¶)'.êÞ¸åKÁÚC³=â4ä2.˜‹]Å\,’û‡Xû‘7Œ©Û4.Õ®Ýáx&äªQ¬g?fÝEÙ²Â6åÔuoÜÇƻޕâxþZ³P`}—ÝD ]ßGÞ5u]R‘ü68üdCÓh±åSwä¿ Û”uoÜò¥*Þû±ÃáÓÚˆ1¦^ïK”ЕzäØ1‘nPiDiŽT=;{×øéÙû¿y9râªúÂÁ-_b6>ÁpqüÑÔÝ›#šgäŸGŽé¥–8¸Î']\zSÝñ-†°MySëë÷ñe ¾hûä4¤ ï˜#7eô_C”Ðõ|äØ1‘.©sç]œ‚¤ÿCók&úçü;Ú~×üÜåÄUôÁ€ƒ[¾ Å{øBNàñ¬s½}Y˜OQ¾0×jÊ¿ÇÛ—¥Ù™ã¡.±²îË—©ö¦ï-ì×sØŸ¾à¬´õm|WŠƒ+‚§§sÖVDÁÞÆH´+­ýHG—T;­yk®6ÇÑå‡>–æyåüŠl].Ÿš#òã÷ñea<¥ý}q²òŸ7Î÷—>U Jèfùân tWìÝh¶s8væwÆ 3´s/´Ÿ_‘{¥Ì¦N¤®{ã–/ 1›c_d™˜[¾`o&QBW{Èwk +*Ç:ÿŠÝOÅ;=­ŸºÓëm­@¹œ¸ ûÂ-_p†Ý®+Ê—àøçØy=Ÿ%t>Ï¥œ»5Ð=”V"Åa涤ùµ§;Kšk=Y¶!.è¾pÓ—…óÎÙÇ¢ßoÎ:ð…D4ø¼y>#ßU0Æ£¼b4HÝ 4ògøÛOºI‰Xwó)²M9qQ÷Æ}|ñx÷o~ß&,Âéè¼=JùivPBçc<ÊFƒÔ%5ãäÜá”|t‡rI{Ý#övd{(ñ4‘–ü[¾ÄìŒ>Yœ¥¤ˆ è­7QBçc<ʱc"]R3údq†¥‹ ת{ĘB¶)'.êÞ¸/³3ÎŽ´WE„G~ͼD ïWFÊM߯¤nP‰Šƒ/æH·£dÝ=úHÙ¦¼¨eºî[¾àzα>&NAÎ5œZÏ~›90I ]›GŽé’ʱ>&Nòõ:é&d6lšk}L¶S¤­G÷Æ-_xÞùìñ‰³”-3Å®žgÇl¯=¾5q&ò‰ÓkKÙbwyíñ‰ƒ}:éFåÙD…çëȉ‹º7nù2b]¾³}µ{Ø£ÖºDÁÞž#å û‘Ô]Ú½ _–v0™¾;qŸnžºg¬óË6åC;”Ì·vp_x›r®ðeáÄ“"WœPÛÈ+—Š(¡óÈ·N¯1Ío[j¶s8¹J×ïH&ÕM*~óqä4×Üò%rÆlŽÇęȹt§8ÃÞ¢„®æ#\7®™m”%åpÊ8ºo£Èu£Ûþ+rݸ<2-õÁ-_–n-êÌ•8ø³p weÝzzÝÊ]HuOùNºQ™ó¡Î™+q˜©›ºCiòj¿y;rV:”8ïà¦/9cì1ÆÈyþtþAF O|Ç|49Þä›ùj(/J³ØkP¨¥ƒã—³"Žuƒ’ç ±o¤¥~à>¾0?“3”8ƒù: ¯!Jèz>ræ²Jø‘šêFéÈ~3ëÑm0Áºãã,a›òÈn3ënùœ1%Î[΢fÚ¡õ$Jèr?r没nP:o)Nö]4éòrëNqH¶)'.êÞ¸/ÌsùBŽãa¾™ÀšÛ§/syä²Yé¦Âr—¹VÖ}ùBÛáËî·/·|Á‡4qÏûûpü¦Ÿ{a¤™K”еzäÜ1¡..G]·Ê‡éu7³«î;ÿl‡<2QÝÁ-_â¼³ÖÆg#ÍOG7$ΘIì œI£|àôZR²-Rš!læºÙ>ÝÌuƒ|_›¹nN>°Í\7”3» uoÜò¥+?“æûâ0%PW~§. 6QB·÷‘3— u§r0j¾/NÅ%ïÈ“SÊ©ûÌ÷wäÙ)åà¢îûø2qVùa‡É]*ïjãºí^¢„N94ð‡Ù6Vº)­x‰3ó'OÎ8u÷Ó^NžqpQ÷Æ-_˜3fƬ8Ë×Q÷Ð cªŽ"JèÊ<ò%'J?Ô<¿bd¿áU}æßhùÔ=ί8ädËuoÜò5…óï©[µ¨°ßÒ‚Keˆ:ÝâB£¾nxbÝ(½˜ƒƒ…2DÅožŽ<2-ÆÁ-_˜3æò…' O/Âjòº}ñÿ­Gž5åå[æý /‡CTæßhKu¿|‰<;J<±_Ïaú²•Ÿ)+Æ‚ÃKÚÌæTàà*¢„®Í#g.›¸ÒCŠ#ÑàëõB7®ì¬"JÏ)qWAyã†/Þ©qd€wepº®Ý0ß ]0 ×}üúa>ræ²éº\>šêVirxµ1#Ó¯ µ!*~ótä”WîãËÆ”k_øi¦ƒh˜dñ’‡þéøâ ú)?8WÜP7J‡„”霺»Î§„mÊ)§H·|ÉʲÊ÷Kpâª~ÂjÐz%tuyUØ4ùÈÅYL2ÀLÿ·P÷IH.Û’—toÜò¥ê‹ü$^p¦eô‹r˜š^³¢`ùj$G.év}5ƒ;ÃÁé Q·[u;…Ò´MyÕ2æúÀ-_3‹¹ß‡‹½ ùf–gÇuëI”ÐyʇWy±…Ë¥S¿bpR ]. ³nPòÅlKHSûÀ}|™ÀRkøBÎæÅʳ4̣ „Γúg]lÉåèUõ»€Ã…bêN&7é±lÜâ9•uäÀ%Ý·|áç?‡æbÁ™ñì0Ùî´¾D 3üw^^G—ÔÐ\,8ËGÔí:€íuGNÓ°My×w4x%úà–/C_Ñ`Ž»à [·u \¼ÂrT)¢„nÍ#ç7¨»ô¥èe—¾®Ád ‹¡užº›rÜ…mÊãk§;à–/ü4e|Å98K×Qú0j¢`Ç(ÏIŸQã5™ZT·—>œÒŽîÀDbÇÓñ|y Õ…º~êþ-Ëós€[¾³mUÕ- _Í<Œ»D ]«!Ïñe‚ѵtªéâÔsÐÅð䨔ž?÷±ò”WÖnù‚eó­qð½·€qDæVt±@ÃÃ#….©ó­‹£#ÐX‡™+ê>ßmÊRS>¸/Ì«šSøBÎÄÂtÃ8¢àˆD¢„Ž9ÿ 6y™ïuŽ›ÒxLŸ…†nÒ‘#~éƒ+ƒará‚î 7}á‘ò]4G¾8²€Ùž¬'QB§/pÛrÝ 8GŽŸ” ݬ£½Ì›Î¯Èe­t¿K÷Æ-_ðmË“Ó>8¼ÔV´à3y衈‚½†1åM‹7@ºãsËÁñï&{Tp†‡zš(¡cN×ÁÈrtIÅG—ƒƒe6éVl±²îøîrئ¼©eòËÁ}|aö×±ÃÎ>+-`RÇmмD ÷A6ˆ9®íP±Ú}qR;º˜í¨î©;Oa›ò@ÊãC·|‰œ1¼gqq66N™ë¦a»˜Â&t‘ëÆå‘ÝÆuƒªêe#×ÍèG7áOäºÉ%<çJH.uoÜò…çsŒaÄ)h‘o†÷ÌG£1LŽ|5.g.›¢Mÿ]T·J“ÃCÌØ8Ó©;NÏ„mÊii¸åËBÌú÷Ž¾ßœ§5±°Ð€®.Qn¯$åÐ0yIʶaº‡âîPpü4u=j–ê&¿y9râ‚î ÷ñeÅÚö÷ÍÉ82ùf2­QBÇL.g.芊•ô‹c¥KR.Y{7¡îk%¶%?HýZî[¾leå‡Ë/ΓNÌØ´`}ˆ:晬@ÏŒ”®”f$â`]²ö:SVݤâ7O!.ŽÁoÜô¥dÅìÆ_ppy¹dµëã óÑ”xæ«¡pË—¡ŽÆ0‡cã21ò k·žD ¿¿æòßÞ7¥1Œ8èÚ¥›ôœf¥ç”Ú‘uoÜòeÆ·hÔ'Oµ–©`ÝUYØSœ:soæüwùÖhÐuã[Cë*¥Ûõ¸Ïiªuñ9©ëÞ¸/ÌaœzøBŽÍÊRdæ£I1./ÌWy/˜î¡x:388x\âë|N{}>'n͹|j±¥Üòei߯©O^Ú¯Ì|vìã¬QBÇýJ“×›®õPSçú‚ã¾Bw ×ÛÚ¯ÔºˆlS¾´_¹ÊnúR“òw¶—ÃYD‡å[{5+ïhcÔ¢¬‹./Ê4êºA•¥çGK·ø{Vu—xË6åIyG]÷Æ}|Ù³k=Yœæ¯µÊEo\N©ÌGÓb=¹–Ⱥhr没.©ëÉâøVKè«î£7Ù¦œ¸¨{ã–/Yùµg!ލ×- ˆíIT棉³ª†®Í#g.ê’j1#9œÔ®>=ê®±g!Û”R×½qËæŒ6Îý¾9X«¯Ewû„õ!JèxGÒåM÷']—ÔˆQµ8ÝGñÒÅ:¾ê>ëü²Ýõ»)OmýÀ}|á¢`¯á ï1Z0ת;Žèµéä,Žw$]ÞuÒuù‚­¬ñÁAJ5ê&¬³nPñ›·#/º½¼Önùœ1[wEÅɘóÔ®lOöˆ*òÑ€‚½¡Œ.G.éNåÞŠrp¥§Fž;êT#N;ž3ÏN«)uoÜò¥+·SørN@OXH·/c¼}™Ê¡A_V»}aîËæÂq<‘'‡¾Œñö%òì¸òù^Û¡Î\ìpl>%]̵T÷™‹É6å4ÕÜò%rÆdÅXpìO‹\7ö§E®›ª‹\7.ì6®TVŒE®ï)zdFìQ7(yμ£.\7®{ã–/M¹¢½4Ý0ö˜oºal߯-£øV®¡+óÈ‘ËFº¤êi/Á±à’nA{`Ýå´—¦Û˽ÝHÓüÀ-_ºîg¾÷gNXÀFOÖ2D ÝÎGÎ\7Ô J«â4€H·Úל¢îª/ª†mʉ‹º7îãËäÖb _Èx¶Üî(ˆ¡D ]ïGÎíê•„Ž¤”nó׈ênÊq¶)ïÚä¬ë·|Áv»ÙͱåáØP¯¸ã˜`½‰:Ý‘LN)ÓǸ)­Ü‹ƒkH·¢×cÝ5ÖDe›râ¢î[¾,Å{RŸœ‰'½pÖÆ[ ùhò9{ב¯†òŽ\6Ô=”Nˆã_;]\ UÝ%öBe›râ‚î ÷ñ…w‚µ¦t8¶³Ö¶n[wÞ™&rPgö#g.èJkJâ4LI+ª»ÆÙgÙ†¼Å¡Ó}á–/;6îõ~!fOŠyÛ_éÌGsÞ~ùj(ÏÊô1ê¡ÎÛïpj=ºè^UwýÙ†ü 5ÝnúÒ#³æbâ [éI7ˆžt]¢`ùj(g.êFæÍvzd¿Ùãèz¾š¨;2ã„mÊ#»Í¸/º\—t OZwŠ3¬QB7æ‘c·Fº¤z´æÃñ_±(KˆÿЬ»Å‡l7E‘2BnùRïZ‡k)ùf°ÎÒ™æ¬Ãtæ«¡œ¹l¨Ûµ1¯uq°–"]¬³¨î³#Û”¹™õ·|aÎÏÜþýæØÈ¡3ßÌö]»Þ´¹¬³ ùj(g.êUS”žø•G?ºÓ»3Õ=cZ¶)'.êÞ¸å sÆì'Îâ³¾¥k?wG>šrNëtä«‘œ¹l¨Tôä,ëR7{¦jÕ *~óqäÄEÝ÷ñe`»|æðÅ9;н”;óÑl}ƒÛÐõ|äÌeC]RKÙž.Ž­=Jw†gCTüæéÈ©ëÞ¸åKÇšÐ97NNÁâs÷SëFÙ«YO¢„.÷#Çפ *Ÿsã‡ãí ºÈ(¡ºó97NÛ’R¶Åƒ[¾`Á¦dݯ¼8 OÚ"¼Ÿ‘tä£{X>’¹l¤”V:ÄÁÔžºy£—e®›È/Û’uoÜÇ÷ã±Ã±±sëkíùhÊY‹ïÜr‚|pË º‡R.[µ±u1Î9 @Û’ÏW˼qË—¥xç=¾‹ãocü¤%áM|4åœÐ'ÊCB÷P:¡/æSÔ͘k±î|æb´-9qA÷…[¾0gÌŒ½¤ÃYxvƒÖñÜk%t{9sÙ@÷PÚKéो…dÕ=b/I¶!.è¾p_&°¬¾Lk³ST“_‹Y<é½D ݨGŽé’Z1â9"]ÏòuÏñÈ6äiî¸éËÈ1§á{ÿpìý:JÌŦSš‹ñ1jÌÅLÞ4uÝ t"Lœî\é6ïUw‹>R¶)Ïš‹¹î[¾0g̶aÜ÷Å)8E50yÀ§ÈÝz%t6Æ“é2³ÍŽÝšÃI3t3NQÈ££ùþˆ<;.¤i~à>¾l`ÑÙQqü»ZnGœÐå%JèV=rì˜H—Tг£‡cšºÙ¿ß­ºs|ß[¶%¤©}à–/UñÎïòˆcÏ–`}!jV%tm9vL¤Ë½›§¨ÄÁuTêZ¿1¢nPñ›#'RêÞ¸å ¾¹jO’ã1q0¹øB+‘ ÿ+)¡Ëùȱc"]R9n爃‰‰tqßDuŸû(²MyÓÁF×½qËäŒÁ¡ºï›ƒÎùfŠgû,ùh@ÁòÕHŽ\6Ò%Ub]œì«DÒÍèϧŽ9FOÛ”uoÜÇÆûá‹spÒi`¡ø¡ì}5ë”ÐyäÈu#]R^7JÇ~éúwÝ¢îøî[ئ<ºî[¾LÄ{±¥8X´ -Ë;céP´}ìƒHŽé’ê1¶<o¿ÔÅÑ@ÕÝbl)Û”R×½qË|sµŒX»gzh|¡µ,Ÿ™ÏGCJèì¡|bÇ„º¢FÌHÇG<ÔíÞ,Tw?oVÚ¦<Öúûø2q|OcËÃ@Ï£göx Ocˉ}Ê'vL¨{(-ÅÁQéâ(‰ê.§¤mʉ º/Üôeâ¬t©1¶<$LœŽ.XÉ™ž†ÐuyÅH”ºAil)NÿK×Ó£DÝ9Ú¢lSžÔ2]÷Æ-_pVºø–í÷›³‰1fá9ãøžNQMäü—_nPeEéõè¿a¢º’/l}i\Ô½q_ïÌ œí9 "|£øc %ts9vL¤KjÅéq<·GèN_MSÝ3n—É6åÄEÝ·|):æ©}1qp‡e"× ??q‘”ÐÙPOr,ÄI·éØ¥öÅÇ^,ÒˆÖ=cG¶gDä>º7nù‚3ìåÌÅGÏGâ*­Q°‡}ɱc"Ý ¢½³€ºs-Ö]Î\Œ¶%'®%Ïnù‚o{>åù=‹à 9þÄ—@Ê&}sàPèŠ7ÆãQŽ%é’šñÆgx3—®ZŽº#§iئ¼«u­ñûøÂ˜Ç— tv£yÅü†/{‰ºQNH—ÔŒ5¥ÃIãèwPuXS’mÊi¸åˈx瘟œ'jlÖ0Žá¾æDú„zîsNìƒHŽé‚BÝ*MŽuÏo:Êÿó›;&ˆÔuoÜò™mªOá¾ßœM ˆäXî—.©÷ŧ¬£Ûý«î÷Åd›ò@ZÖîãËàï¾ ”°€£š Qã[œºž;&Ò%u2¾޽¥;| ¦ºGä9mÊ©ëÞ¸å Î;?X¢§aÃ`átôCy?åùhH¹½íùj$GºSé’BÝ*MŽõEÔåarÖ]#_ŸlKH½»qË ;Qþûæ »S—UÏñçÖ‹( ÃYr¤T£®¨ƒNœé++ÒÅÍRÕ=ã4³lC.\Ð}á>¾,`i-|ñc ÉW ¶ç›1jÐú%t39vL¤ u£49ž SºÕ³aªnPñ›§ )t_¸éËNŠ÷Â1Ìá,ØK@gmgÄJqþfcDrì˜H7(½ÄÁqwêVd°`Ýõä¤mɉ‹º7nù‚sè-Å9ØÃ±ðÝ8µÞ°„³±õÝÎíÏ}ɱc"Ý t–œŠ×Q÷Ag½,빎<ëÊ@›¸åKÕõÍ÷ÅAV–]Õì¡í†ùÒ9}°qVr\ƒ–n׸Tó}qÐeIw¢§`Ý3æû²MyUërÝ÷ñefõáß7dz°º…¦~Ü­¿ß9ÿ%ÇפKê¼1'§£Û|_u·Xç—mÊ©ëÞ¸å ŽbÖg®Äa”¶ˆp<éÞD G¢.‰¦v¨g®ÄÁÞ¬tqîTuŸs©²M9qQ÷Æ}|aÌêî›8Û í®7ÛóѺU;&Ò%µâî›8ÓAJ׿ãuÇ#Â6åÄEÝ·|ÁÑÈ–"7”8ÙsîmœZ·kxÒº°Ð}¦;&Ò%•bGœŠSMÔµ«,3ê¿ù8ò®‹/;}à–/8ïl—¸'~8î?NG7Œï6òÑ´3þÛÈW#9ÃH7(̧""©[}h¥ºkœÌ–mʉ‹º7nù‚óέǘ_F)NG7|doÔçïRrÝ€cTÒ ªkÌ?}ºÍOu·óË6åÄ…“ /ÜÇ‹Ùæÿ÷ÍièWq¬­ùöÜz%tc9vL TÕJdpü {¡[l-:ê.±V-ÛÎ \®ûÆ-_ðêyxU¾gÃ^º_Æ%t~)rì˜H7¨z|qŽ€ Ýbﻨ»èͶ¸pxö…¾Xy^íbžÞàøÕ·<#Áz%t¼œâò¢Ë)®K*²qÇwC×?dugµ®°M9qQ÷Æ-_p¬ ­)§Ú¸Ç(Fx¶úë¦EîŽÊË’ã¿tI­)]œ²ŽnF[cÝù´EÚ¦<–õûøÂ˜å¾Xpü& [à/Qa}ˆºžÖÒ%U´/¿ÀºÙ/QwÖ¾Xئœ¸¨{ã–/Uñ>å 9žgÛ- Þ Ö“(¡ó1åØ1‘.©ÈÑ}qèkf/;NÝ‘£;lSHùŒîã /kñCÁñsZ@ç­ÙóѺ¹;&ÒºdÆ•Ž‹“ëÑõ5á¨;VŸÃ6å4×ÜòçÐ[Uή‹óÌzÝûxØ›K”еzäØ1‘nP%E龪÷£[lÖuÛ”uoÜòeD¼7úBŽßŽ­L‘Ðü¨WÅG¤HÁÞÂ\Œr$ .©ª]çàÛ ]ù¨ûj]ójo‹º7nù‚óÎÍ¢}¿9«9ÅP'¬7QB·÷‘ãËÒ *+ƶ.‰özt«½F¢îª5ž°MùŒÖU?p_x²_Ørƒ´€_ö%t>ƃuÿ–åß&zW.µ€LôÃ$X/¢„®Ì3½uEEf‘àë~C×/\GÝ化Z—Ë—Z—é¾pË—­1ó*ˆÓ|;Î-p&Ãú%t;9vL¨K u£tìæÌºÕ·òT7¨øÍSÈO²†9?pÓ—ŒóεéÛÁé>âË8µö𑿬ÅgÕ#Ç!"é’j‘‡ó #C·Ú部»êÜEئ<ºî[¾dírœœŠ8ÁéèêÓëŠÏº’:ŸOQÞ´CéºM»“'‹ã£C7ÃsÖ5NÛYOÕqQ÷Æ-_Šöý¸žœikÂn«V Ö‡(¡ËùÈ›ö+]·i¿rå(MNG×/"GÝ‘77lSHóøÀ}|áþ¼Æ–âø•'·€ý;æ¦=Å|ž4÷]»5®K*n_æØQ±±£ê.1¶”mÊ©ëÞ¸åKÕ{ü.UI¨øl¹G¿7¬7QBW÷‘sÇ„º¤òù]‚ó¼B7á¹7%}WD®#¤®{ã–/]ñ¾Õö»vìmä›q:Úžûpj Q°Ç´ ”#—tIÕ½Žõ²Ò-6x‰ºKŒÞd›ò@ZËnù‚óÎÕ~¿9cÃР«I”Ðí~äØ^nPIQCNñÑ›t³ÞTwÖ~Hئœ¸¨{ã>¾0Þõ~gà1µ#ïïä£%t}9wL¨KªŸ÷ËÖ~¦¿_–ö:ùœvû|Nl}¥\Ô½qË—©ÝÉ­öBŽñv Xµ²YCÆ";(¡ÓnMrjŽ£û';Gií¨Œ£ë‹™Q÷Ð:bئ<–ñ[¾¬Ø÷S?¶®ýJ·û•nýµ_iœ³_YKºö+ƒŠs§¬£ÛÑOíØ¯<©ýÊv#uÝ·|Áyçê_ºú~sl´X’ö”'† òÑ€:ä«‘¹l¤oMÇÏÊI;]ªTx¾B.\Ð}á¦/ç-9çÈâ–¬ùâKÖ|‰y F|Í)Ó¡²îŒ\œ”Žnò\u'Ý Û”R×½qËäŒi%敇c½ÿû¸{Ãz{L B9vL¤”æ•âøa–Ðõ{…QwÜ` Û”uoÜÇ_×é1¯gèÙú*Ð@  (¡óýJʱc"]R‘})8¾Hº Ïu·ó»ÐvÓïÆÕ+×½qËœCo~6ûûͱWpj½y÷èÖ“(¡óy.åX—nPiGéÅušvt})ên:—¶)'.êÞ¸å Î;·8§ŸÔå¾5ÈGÓâœR-ÈW#9Ò+H—TœSº85ÔõsHQwœS Û”R×½qËœw¶õröÉâøÖŽ[@ŒM +C”Ð1aË‘ËFº¤ºö’‚ã?Oè6›èGÝMwÃ6å] {ÖúÀ}|a¼§¾ÌüBÏÃsßI”Ðõ~äÜ1¡nPI¿Ë½›ºÇ³$*~óv£ŸùèÞ¸åˈÔ1j/C- áiy„û7·Üz%t¾B9wL¨{7Ñë‘ãyB×ïÎDÝýôzC­ËåÄEÝ÷ñec=Csdq:b§Öjá¹ç%JèV=rî˜P7ön4G>ùØCiëÔ]cÕF¶)¤lk·|YŠÙðe)¥ñ0æ kE>šË—Š|5’#— uE_ÇðHXU÷å m‡¯DÊçppË—­˜ _È!ž¤§/³½}ÉÚ¯¤/»Ü¾p¿òò…÷%k¿’¾ÌöòE¶éK u_nÜô¥"gŒ¥'âxìp-˜õí-®" (Øã>åÜ1¡nPÚÇ3°„î´?Q÷ÔÞkئ—×yûÓ—¦˜ _‚ãxšbžXKùôeÍ#G.é¾ Té Ç‡Ž•u_¾4 ¯~_,8ÅFÓÀ3€57QBç©W(ÇŽ‰tIeÝb ŽŸû Ýäc9Õb¬'Û”uoÜò… C·¾,N÷f½áiÙ«" (ØC¾ÊrÙP—êöÒ‡c#,ê6¿ ®º[|Ç@¶%¤®{ã–/LV”ô-ÎàøLÚ-à—°Q<"‚ºÚCÞˆº¢R¬ØNm¡kxrÔ JÏ)µ#¤¦ûÂ-_3¦§ØãÇ—EŒR„OXo¢„ÎÆx’cÇ„º¢Rôä4Ï*$]{Ò;ê%_Æ 9qQ÷…ûøâ1Û•·çâØî€#q˜[_¢„ÎÐQŽé¥(qš¯?H»Cª»Å ”lC.\Ð}á¦/ çÐ-}ÛþáLD “PÙhѬQBgó\ÉK$ÇZ7¥“Gâx£]œÙPÝçL‡lSN\Ô½qËœwI9T/Î žåirì¬PC>šqÎ5ä«‘¹l¤”F‹ät?u(Ýç9ØÚ랯#.éÞ¸/ï~ÚóûÍ‘à©@çéMâ[i‡BÞ•ŒËuƒÊjûäx&év쩱î~öÜh[râ¢î[¾ gÌ8m_œìçNòÍ ¶mä£WÛÇ>ˆäØ1‘.©«íƒÓÙö¡ÛÙöQw?mŸ¶%.éÞ¸å rÆs1q|Ô-õñ ùh@ §8¡;&Ò%•b•ˆœŽ^–ºÝÓZ¨nPñ›§#.éÞ¸/žÔ©ènÂÅY´Ð<åO:›Ç‚:›çJŽé¥q²Ï§¤Ë÷,ë>ïaÙ¦œ¸¨{ã–/Cñ>8'ãiàtôÈxÈG3ÎdC¾É‘ËFº[‰ï4â!§ûš¥Û}Z¯º{Ü0‘mɇRáíù[¾à¼³ýV[ŠƒÑi[j¶‰á)¡«ùÈ‘ËFº¤ÒA¡I×b¨GÝ ôœØº\>Õº\÷Æ-_3æù­˜Û68Õ÷ŸòÍ ÏðïÖ“(¡Û=äx KWT‰AqüÐhèfÄ'ëÎ'~—ZŸË—Zæî¸/ŒÙ½Âçø5h  ³|C>PBgû ”wì˜PWTVÞž‹c37é&=§ÝDED®#¤µ~à–/8‡n½6ÇÉâ`<ÚpjÝ~ÙëK”ЕzäØ1¡®¨çÆÅ™¾r/]oQw|Ç lC.\Ð}á¦/=+Þµ—t86¦ê8=°Ý‹R´iUµ#_äÈe#Ý ´ª*Nò1•týÀxÔb<&Û”uoÜÇ— ,Ìלêc…^Ôž.Э7QB7÷‘#—tI•Ø8œ4Žnö“zª;Ç®€lSHÓøÀ-_3Æ"œcqª xÜÐÙ£# (¡cJD—cÇDº¤Šòõ]{cH7ûÌMuç89'Û”R×½qˤ¦>åü¾98}БoføÖ­Q°‡}ɱc"]RgeåplPº8} º#ÿDئ<ºîûø²€…9"‚ƒÓt§ÖNÓuÿ¤)¡³y®äCÉí\—ÔÙáÇwåCsÕ}æF²M9qQ÷Æ-_pÞyLåîŽßÐ4ëáËg$ùh@ÁòÕHŽ\6Ò%5ãô´8~S.tý¦\Ô=b­Z¶)'.êÞ¸å Î;ç.Äñ#nÁ¬{æ*·^D ñ(_JáÖסfœ»8œ2.vjU÷¹'$Û”Ò2?pËäŒ~Ïûûͱ‘oG¾™éËên}ˆ:ãIŽé¥‰8 }t‡ßõVÝ#n•˶äÄEÝ÷ñ…1«óüâL?“ß—b~ãIî$Jèl„òꊊ/S]›H·ûéÕÝïˆtÛ”R×½qË—¥”‡YýXpø´lž:±’Ó‘n”ÐÙ<—òÁDšÐ=”V"ÅYöZ”®ž꾟lŸçÈÄŽ¦ûÂM_Î;çaÄYöb­ e³Þ|4ãÌÉGQªJ—%_t]R3ÎÃŽÍz¥;ü\…êqîB¶)¤)}à>¾ìwŒ‰ã[ñnáÄØ@>šëÙ ä«‘¹l¤KꊱàØZºˆ!Õ}bL¶)¤¹~à–/ȃô‹ßoÎÆ“Nˆ±_f%tu9vL¤T9¾lĘÍv¨k©*SÔ JÏ)­#'.êÞ¸å rÆŒø6º8H«Y1à±T6¾ÈG3ãÛèu`Drì˜HÔˆlè'¯ÐEJNÕ *~órä4¯ÜÇ›kΤïñ'û8d0­fñ1Êð%DRBçi9)”®K*)KýűqȈt 6f`Ý â7GHkúÀ-_pÞÙÒbò½8›èÒ3N§l¾ ö¯Fr䲑nPZ%'!N¨›C¬;£mʉ‹º7nùÒïIí%8vVcàt´éLX_¢„®Ô#G.é•V”žxÒ­†®õ;ê%_غ\\Ò½qË—¡´Íº“(ÎÄÓ`úù‰˜E>PBÇ1žË—R!»îб¡údr<[mèv_PÝ=î$Ê6åÄEÝ÷ñeËZáËÀû¥óÙ%Œ`o QBç )ÇŽ‰t·Òk¶s8¶ú,Ýá'³U÷ˆÙŽlSH]÷Æ-_p}ÌXgù×À©uû3¬'QB—û‘cÇDº¤f¬‰3ð6¢î@Ô°î;‚²=‘5Ý·|ÙšyUÅ9|¿nÍÅìIϤ¹˜æ±3k.æò¬Ñ éŠÊ”œîOZº¸  ºÏ]Ù†\¸ ûÂ}|ñÝɪ¼ Áñ[þn!9õtn}ˆ:߯¤¹l¤KêÜdÇ/|„nñÕÕ]⎠lC.\Ð}á¦/9c°Dó}qºçÁw ÜM]°žD ]íG^´éºñ4­Úˆã)¤kçsRÔ *~óväDJÝ·|aΘø.OpüØšGðتÔ,ÚSÔYÕY´çèòªýH×%uNŽõÒõó9Q÷9 $Û”Ò4>p_ô!²¾p×ÝfW³ÄŽ<ìí%JèøÁ,—sÇ„º Ú>­‹œÉ¢nóK¢ª»Å7¸e[r"¥î[¾Äyg­[ŠƒSŽ3ÎKÛÛo" (Øë:9çò¡“s®KªÅº¥8Õß~Ò­øYwuKÙ®ú•g>º7nùŸ, _švÝé cÞ£ùhÚYƒÈW#ùÐÉ9úÂÏ^¾ðtÛNGk¬ª{Þ¾ðÜ*}å™Ö>p_6Ï€ŒðÅ9~¸Ë-§°úžb¹Øä®åÜ1¡.©»h‡“ÒÑõ£`QwޱžlSHSúÀ-_ºöç—|!dz,»¬o§ëE”еyäÜ1¡.©7K§ä£‹»ÌªûÜu–mÊiɸå Ρ÷øÖ@p|ñÊ-$Çêﳉ=Åﳉ}ɱc"]Rñ­‹c§¤ëK]Qw×wÃ6åÔuoÜòç{³=â =Û‰}c  (··¯†ò…\6ÔuNÍ޵'é6穹ØáØ8Zº¾uŸ'Ù¦<–ò[¾à¼³}vûû‡³i½¨£C>PBçgÒ†ÐûÉ9꥖8 è¨Ûül¨ên±b+Û”W“ç÷ñÅÏ`θ[-ÖÔòÍØy <÷ÜD ?Ûçrì˜H—ÔYå<{GHwøûCux¿È6åÔuoÜò¥ißOïJq!i!ßÌ@6Ž…|4 „Î÷A(Ú¯¤/ÜÔ»òpψO¸íSw>¾tíWº<Öò[¾àúˆœöÁiè=zì.X/¢`oÆžãrÊçÀÔ%u² ‰S|ýLº~õ0ꎜöa›râ¢î[¾Lí±k<&voNGÛÒ®Å,òÑ€‚½¥ º¹l¤»uŽNã±Ã±1u;nü²îyse[ò@Zòîãˉ÷ï7gÁ^å¹>ø²—(¡ã™4“ï¤Ók¦{(µ.q–|å(vϨûn]ëjokéÜò9cz1¿8ÃÇí ùfl<Ú`½ˆ:ŽñL¾“ΆÖ|¨cþñq»tÞF¬»Å˜_¶)¤¦ûÂ-_3¦ïX#›5n';ø¤}O1ÅúØæ>åÜ1.©~F â,ÒÔíKu÷k”Û” )t_¸/g¸ø-Nqìd¢¦e—2ëI”Ðõ~äÜ1¡.(ÔÒÁ±]êZž£nPñ›·¤¹}à¦/;ëD¦ö’ÇF§§£q®Ñ¨UDÁ^3i&o:½æºAi/Iœå£7êÚ‡ƒûøÂýù~|™ØQ± 3›§£—g.ÝÈGJèú>r䲑.©©,ØÁòµ#>ë:u¸+ Û”×Ð3>¸å rÆØ®œú±àØ ã=µgèõ-í)†½­=G—Çn륓@â ¿_°cÅG¬{ÄI Ù¦|ªeºîÛ|ù/_ÿôã¯þø_ÿæ·_ýË[(H_÷¿¿üõúáSÑþñßþûWúú?ò×ß=ÿýþGöU…¿ÿÑüóˆy dalÙ’þƒhû°ÙÓþŃüß_¿D ˜?}©„´ÿðõ' TkC$ïOz|Œ ¯ü¸ª ¼ Ø\ü˜ yª(püí¯|Ž¿ûãŸxŽüÝ?þø«ÿø€úúõ9ëË—<ãÿ&,ÃÚ(&_òøõûëßå”þýׯ¿ÿñ~õºÿ-ÊøÒ‰+ç¿TÙ¾§ýŒ­\¹üÅÊHsãÊõ/VFËråö/+g_­þ þ€ãû=%gë€Lý—ÿü·Râúëšv¼â±’“ïÄþŸÿùõ_ÿxGþxÆ*÷¿wäíóÏŸn¿É~öGà‹´¢ÜÂãù5m(EHßEì«å«œ"¤ï"¼MEt»ú*ÒºoÈFÒ¯"íIMö_nP,ñgZ›ùŸiR,ñgÚ”ºŠÓ¨Î¯h+ÖåOÿˆVÈ”i¸+ÿÿ–ùW+º¢¡.‹±õŒmŸÿÏÿ–~pøúÞofðôô®º/ÿ·wpzŽpvørÙUÀi;3£À3‹~xhl뢀¯™\œÛ ¡€/D\œÆIðÏP^œ¾½¦3½ <ôLÇ„¯\œÆÁ%/0}rw €ž~»YvzxhæFlaxpŸtG¿î{pzøâº ¬ò.ðж:¢|zp? øÐî*àôð­ggW§ožñUà¡-lø[Lq]œ¶a;[ØÜï€=üv³ ¼ôÈÁ6xn ‡g`F¿^|pSx…èQãA­úиb„í0 ‡OCU`¯w‡åÔà©¥®N_ôEñ9Ðvœ’Or­wÀ€ž>_RWÀ€ž¾Ù„ÛÉUÀéé˯^`ûQ¸S4¾á«¯-C(ï€/Ç¡@} èå—%TàÕGÆz ´wzù)qèï½ü< ÌwÀ€~x h[qTõî£@oOØ‚ûÝGÆwŸTàr ·¯óeœßCÆ«HyÇ ;Ç+Ð8¯ŽŠŒí§e‘úîªÈØ9¢;§öŽ=2ì×ZÆ;vÈÀ'Ó¢ÈlEf»[NóÝe‘±z¼sZïN‹ŒÕ/¸ë…dÌuàæôŽ"2æ ï×d«} îɰz¦lµñ=ÖGÛmC|2¼›§Ì»ßÃl2í#JÁÈøLË|ŒõÉð^?üúî“‘S·ê¢Ì»ïã]æcÐO†¿íæ)óU0¬ž¬2ýcèO†Åa¼uûÇèŸ ³Nÿ˜a/èšO™w†Ùª§~Ä*f«éö™V&ÚEÿ˜ ñú½úÇ|€ «g•ù˜aõ”qʼc «'…_2¬Oˆxs2ìF÷3>¦dX™h;ãc†@†áѤ2ò«`øoªþ}|ÌÈ0ßcp2>¦ dXß’õ ÇÇl k_%|ÿ˜0am'úÌ1>bŒw™iï232¬Læù1y ÃúœN™w†ù>£LþˆU0^}ïü˜Eñê{çÇD‚ Å{g~Ì%È0[šÀæù1 ÃlÅ{}öXÆ`1øš“ 2Þe>ædd$Åg™©68ªé”y÷‡`Øè(úÕ•>b +3ek}N0À°2å*óŽ10 s,,®Ï)9¯˜Þæõ9ÇàÑNWûˆU0rö¢,ó9ËÃÊÌŸÓ 0ì7ØXŸó 08~2ïþŒWŒ­ý«`ž¤ß}N5À°zf>eÞ1†aŽßkN6Àxõ-ûs¶Æg™w¬‚á}‚~‹ý9ßÃÆZñŽÛŸ0^ã–ý9ãÃ|öµ?§`¼Æ6{}Ä*>Žâs.écÒAÆg™WŒ‘a~i&[ÒÇ´ƒ «§5•ù˜wq3õŠU2ì·PŒ•ô1ó #ãSe,ó1õ ÃÇ6õ”Ùí£Ìn¯ñFI“2°÷{;›Yn#ï} _À@«Š?GðÒ÷°î¿õIFDKO Dò{Ó왊êÎ,& ¤é¥ŠÀhû[xùXòÀx÷‡òŒ´nH[…¬°–Ë’æ)Ñ5K>è§hVâ“E‘Yh9ÅàrYòÀ,9åßà²ÜÞίî²Ô^w}®\–y @ùì|œ¢h±¼¾ÌnD/«ÄMˆeôóÅ©K:è×ΊgD¯Íüü”;ÿFÅRú)–b½ì/V3Ò«!³Ðr¾ –bùmœ?@yíÂŒ@_MØ@,Û@`ÖsËAô’ŠÀ\X½ß´åôϱ~±TŸwôì7=ìÂŒÿvoK4˜…–|G·åÄJÖè-/}œßËmYTjœÇîè…øØb9TÿXÇUCjUÄ2 æg/!z³‹€ü^º]˜èãüvK/ ÚÝ|¨¬—Tfa|Dïuë‘0Ë-e§ 8ˆ—‘×ÎÜ׉úB+0«øùx,±@ –Á±¼ñ9€ÇÒ b‘#fføîýXR@ç3뵜~¾†¬vÇÇ(*ïz-£@ ÷L­63ù½D`.Þ/dùôdb5*è =¸¤>û…,›@`|Ñ{]æ×´¼^ÞjfÆs<ýZ*¾–I ë)7¢÷º7ŸSäw³µÆY~/øyGY¢ÀUEÆú´ß·æjYÓyíVK"ø¹‘Õ=Ë!øA>×Aô^ª;ÏazaF ×ýE{.ÑK*òB–? ÐÏ3âµ%H5$—íoÕ²æÈû zaF`Î%ÿÒÍr~>²žXê€À| ™oºYæ€@?űÑ{]øã³=vaF ¯4Kèå|o–5 À_qš% ô²k^¥Y΀À|^9¢fzÙÏÀK÷Œ!ñˆ' èe×xK÷|!ô\kFô^¹è?/†~Û…þ(ïž,D Ÿ:ߌè%ùÈ9¯—î©Bz݆Ò=UˆÀüJüD/ÌôUžâ©BØÃS…ôç|ª O"À&ž*D€Z•QìÂŒÀì†Ù?ÅS…ôg?@›½¤F¦ û?ŽxòÒ¼,Y@`2ã=ÌxŒAi$oò8¯“™ÎÊ~Œs0™ùè,oÐ8_’˜py ç6*³JÅÏN_p"3ñ8bžxœE±œë3èµlí³|›_Îp.3ñ8‚J×8oŽ™xñó'ÉÇk8ÇM™U*.{ÎGcfæSû{¿w[ …À|­ü|Ây^ÄDÀ½Än<Ž`Æ’f ùá‹sI”Y#>û›#Îû &óA~Æ9ÊŒøýäò1œOÁÌ×jßtpî3ñ8‚}à<fâqDy÷7LœS Ì,—SRÅþÿ̬À|ï™¶`_}fV`ΧïŸcéëµÞÃè5¹VåÚ?Ç ®ÒÎçðc“Ée Øœ˜Ì%"ûžðZNÀ§k±¤¹za_¯eL¦ìŸci“Ù¿ç×VQ!0W/¼y=¿–X 0W&ädì»ËL<Ž˜GkîŸc©suG%F«2Ú~ï¶” ë9%ìkJLækå÷RìÊL<Žˆ’Öfô~9ŸüfŠý-™Yùsöï§Úz*Öê—ýZ–b 0¯ŸýÙT-Ç@`®ØÙ×jµ$¹bg¾WË2˜Ìþ{U[T…Àdö7Ëfys>ûžÙ,Ñ@`­Dº£÷ÃLf_«ÍR &s݇Ñk5Ó_û~Ø,Ù@`¾¯²Žeèϱt¹˜+ü`$eæýðgÎû+f³¥UÌùìoÍÍ2®»ž”£{Êy­î9Gæœw^Ò=çˆÀô3z­F`2û½wÏ:"°VamÆÓŽÌûƾgvÏ;"pÅFn›Ñûa°ªŒ-­B`2ûëžzD`2ûZíž{Dàšûøï÷åÉG®÷t3 _ž˜Ì•?ûÞ;li2ŸáùGæJ¤$O@"0Wìlï Ï@"0Wå#wôù*3ï‡õ"Æ–V! Œç pF¯±\•îóÃsÌ9çw›¿©W2:v«dÈÿ~wNr÷wJÖµà·ÝÞvJ„6¾²ÜP4þr˜ÆðX«nsxœ5¸x¹kJ´›,C20¿ŸŸ&KHžÕ‹» Ä;:žç þ=M–ó´‘w5 Žþ{Çú´ŽöÌòWÛ3ËÚ5(þqý«ÿø]™ÖÛ978ž ‹çÿó"ŒÖ3\fq!¿y™ÍߢŽC•ýÜ-ºœöp(^E¡=J†®žŠ†×]y‡¢áuCÞád¸un:µuÞáhx=nÞád¸}x¸éÔÖý{‡’áÑyxèÔÖ]{‡:ÃÑ—ÃP2<>< öp(&@Ñpg@Ñð`@É0Ùê G£]CÑðÅ6€’a² ¶ ßl(&@ÑðÃ6€’a² ¿l(®l(&@Ñpc@Ñpg@É0ÙŠ†Ûê G;_CÉ0ÙŠ†/¶ ¶” “  høf@É0ÙŠ†¶ ¿l(&@Ñpe@Ñpc@É0ÙŠ†;ÛŠ†ÛJ†ÉPgx>[!¤T€œ’‹½’ÂnH©ù!%7;"%{"¥½Ðm’/û"%•‘RöÐl’Ý‘’ÎþH©@­T›ä`¤T`<¨MÐã¸H.vJJÈ+)(ì–” Üì—” ´.@³I>ì™” ¼ìš” ´fœè‹<@uãDW$fœè‰<@sã\]Œ©’!Æ$ :*7©’KŒÉ@ã@*ÀÆdàã@2ðˆq `ã@2ðŠq `ã@2PÅ8 41¤lHº’!ÆT€I@ton’KŒ©’"ÆT€ÉÀ-Ædàã@*ÀÆdàã@2PÅ8 °q hbHºR6$CŒI@ôˆnR6$—R6Îãi:úKà‰:šKà©::K0ã<’­§d@òõ” °qOÙÑ•zOÚÑ’zOÛÑÊ€ç‘Ì=¥lœÇ“÷ë‘ì=%¯äï)`㼞£‹õžÄ£…õžÆ£•3Î+™|J$—O©çõt~UÉ_B*0†S2PgMö!X+ÄRq 0%TûíŸ)X+úR~ Ðu’±Ùê Xe ¤í é$ã¬æ„dàžëžRÚ¨6Ég>k?@HÆ+À°I®’:• ¤½ Ðm’ëT‘„d`­¬<@HÚ Ù$×aoIÀ<ÿ& ©@»h:ÉÙˆK“„T€É@ã@2p‹q `ã@2ðˆq xÅ8 °q ¨bHšR6$]ŒÉÀã@*ÀÆ$`¶ûÒ$!`ã@2p‰q (bHØ8 ÜbH1¤lH^1$UŒ©’&ÆT€É@ã@20Ä8 °q 1$—R6$EŒÉÀ-ÆT€ÉÀ#Ædàã@*ÀÆd Šq `ã@2ÐÄ8 t1¤lH†ò«óùL2¥dœ” \lœ” 6NJÈ8)¸Ù8)C€a“|Ø8)xÙ8)—Ã&YÙ8)hlœ” ô[€n“ä‡òÿLÉÀ`ã¤T ½¨qfw5'%'¥dœ” 6NJÈ8)¸Ù8)xØ8)èM€n“|Ù8)¨bHØ8 41$]Œ©’!ÆT€I@ùˆq ¸Ä8 °q (bHn1¤lH1$¯R6$UŒÉ@ã@*ÀÆd ‹q `ã@20Ä8Ü1¤lH.1$EŒ©’[ŒÉÀ#ÆT€É€R*Àƹ½8°úÕy’^˜­èlœÛ‹«WýÀŒsKq %RH©çöâÀìtgã<^X}î4ÉÇ‹«ËýÀŒóHq %RH©çñâÀl€gã<^Xò¯fœGŠ)â@JØ8VgçõâÀ<·‹ózq`FØ8¯f'?çõâÀêãçIzqç 1`Æ©RH©§zq`ÜF“¬^XçþÐ$«ÖQ@M3N•â@J¤8R6NõâÀêüçIzq``“ôâÀŒ°qªÖΟú¯Ïõóßïícòû?c®¬ýù»úié‡Ì2ßk3i<“_ž?»™>ˆÖÉñÿþ“ããö88>ÿ2zŽü/þúLà˜xþóŸSäñ÷g‡Ä'¡gÈ¡[ Å^E¼·PŒÿñæB1þ‹Ý…°Ò/¶ñ‹ý…@übƒ¡Üréì0ôŸüecM endstream endobj 554 0 obj << /Alternate /DeviceRGB /N 3 /Length 2596 /Filter /FlateDecode >> stream xœ–wTSهϽ7½P’Š”ÐkhRH ½H‘.*1 JÀ"6DTpDQ‘¦2(à€£C‘±"Š…Q±ëDÔqp–Id­ß¼yïÍ›ß÷~kŸ½ÏÝgï}ÖºüƒÂLX € ¡Xáçň‹g` ðlàp³³BøF™|ØŒl™ø½º ùû*Ó?ŒÁÿŸ”¹Y"1P˜ŒçòøÙ\É8=Wœ%·Oɘ¶4MÎ0JÎ"Y‚2V“sò,[|ö™e9ó2„<ËsÎâeðäÜ'ã9¾Œ‘`çø¹2¾&cƒtI†@Æoä±|N6(’Ü.æsSdl-c’(2‚-ãyàHÉ_ðÒ/XÌÏËÅÎÌZ.$§ˆ&\S†“‹áÏÏMç‹ÅÌ07#â1Ø™YárfÏüYym²";Ø8980m-m¾(Ô]ü›’÷v–^„îDøÃöW~™ °¦eµÙú‡mi]ëP»ý‡Í`/в¾u}qº|^RÄâ,g+«ÜÜ\KŸk)/èïúŸC_|ÏR¾Ýïåaxó“8’t1C^7nfz¦DÄÈÎâpù 柇øþuü$¾ˆ/”ED˦L L–µ[Ȉ™B†@øŸšøÃþ¤Ù¹–‰ÚøЖX¥!@~(* {d+Ðï} ÆGù͋љ˜ûÏ‚þ}W¸LþÈ$ŽcGD2¸QÎìšüZ4 E@ê@èÀ¶À¸àA(ˆq`1à‚D €µ ”‚­`'¨u 4ƒ6ptcà48.Ë`ÜR0ž€)ð Ì@„…ÈR‡t CȲ…XäCP”%CBH@ë R¨ª†ê¡fè[è(tº C· Qhúz#0 ¦ÁZ°l³`O8Ž„ÁÉð28.‚·À•p|î„O×àX ?§€:¢‹0ÂFB‘x$ !«¤i@Ú¤¹ŠH‘§È[EE1PL” Ê…⢖¡V¡6£ªQP¨>ÔUÔ(j õMFk¢ÍÑÎèt,:‹.FW ›Ðè³èô8úƒ¡cŒ1ŽL&³³³ÓŽ9…ÆŒa¦±X¬:ÖëŠ År°bl1¶ {{{;Ž}ƒ#âtp¶8_\¡8áú"ãEy‹.,ÖXœ¾øøÅ%œ%Gщ1‰-‰ï9¡œÎôÒ€¥µK§¸lî.îžoo’ïÊ/çO$¹&•'=JvMÞž<™âžR‘òTÀT ž§ú§Ö¥¾N MÛŸö)=&½=—‘˜qTH¦ û2µ3ó2‡³Ì³Š³¤Ëœ—í\6% 5eCÙ‹²»Å4ÙÏÔ€ÄD²^2šã–S“ó&7:÷Hžrž0o`¹ÙòMË'ò}ó¿^ZÁ]Ñ[ [°¶`t¥çÊúUЪ¥«zWë¯.Z=¾Æo͵„µik(´.,/|¹.f]O‘VÑš¢±õ~ë[‹ŠEÅ76¸l¨ÛˆÚ(Ø8¸iMKx%K­K+Jßoæn¾ø•ÍW•_}Ú’´e°Ì¡lÏVÌVáÖëÛÜ·(W.Ï/Û²½scGÉŽ—;—ì¼PaWQ·‹°K²KZ\Ù]ePµµê}uJõHWM{­fí¦Ú×»y»¯ìñØÓV§UWZ÷n¯`ïÍz¿úΣ†Š}˜}9û6F7öÍúº¹I£©´éÃ~á~éˆ}ÍŽÍÍ-š-e­p«¤uò`ÂÁËßxÓÝÆl«o§·—‡$‡›øíõÃA‡{°Ž´}gø]mµ£¤ê\Þ9Õ•Ò%íŽë>x´·Ç¥§ã{Ëï÷Ó=Vs\åx٠‰¢ŸN柜>•uêééäÓc½Kz=s­/¼oðlÐÙóç|Ïé÷ì?yÞõü± ÎŽ^d]ìºäp©sÀ~ ãû:;‡‡º/;]îž7|âŠû•ÓW½¯ž»píÒÈü‘áëQ×oÞH¸!½É»ùèVú­ç·snÏÜYs}·äžÒ½Šûš÷~4ý±]ê =>ê=:ð`Áƒ;cܱ'?eÿô~¼è!ùaÅ„ÎDó#ÛGÇ&}'/?^øxüIÖ“™§Å?+ÿ\ûÌäÙw¿xü20;5þ\ôüÓ¯›_¨¿ØÿÒîeïtØôýW¯f^—¼Qsà-ëmÿ»˜w3¹ï±ï+?˜~èùôñî§ŒOŸ~÷„óû endstream endobj 558 0 obj << /Length 728 /Filter /FlateDecode >> stream xÚuTÉnÛ0½û+( b¸hsôÐ%Es(PÔ—¢í±iYˆW”øïËáPªR¸0`>ÎΙyâ¤"œ|Úðp¾ÛmnﳂÁ¶Y&ÉîH„LYÉ%)x΄ܒÝü “q"„Ìèû“«âDîÖ7ý`?ÆJÒ â/º56þµ{¸½WÙ²m.sÎI¢J¦„°‹»t¯;ÜÅIdIMÆCJs@A¬¬y•tƒŠ¸êX®A]PAYœ¤)§“çÐ^íñx2õ€®¸N-ü‡äƒ½•¢†U!Š@ÛŸ<ãöu=¹‹†^ ïÃVâûÀx0‡ÙLåTÁ‘yE5Ó 5fܳ`ÔhõI.(ë\Gƒ:Ô” -•DA¹t öæÄ/Âç«ð,Ìtý„ŸàÛjh²JéÁغêü,”¢{³nÌ8Ëp讯†ž V¦aèoã$s‹tÖt¡=ýËÝ ô(“)Ðߘ+-½›~Ÿñ› ^ aãålî¢6ºA+W †všÌ,z· ìÔ"9èá »µÎ½Ðäãn#àD,ô)gy™’}»ù½a…ÈÑà/òŠÙ+n?·R‘ýæ«ûͺd™¬bzb¾¢Î’X”¬ÈÃtîëj ,hI¥z'y‘ÒÝ©¶(ª}>!´§&ù4á—-{8 ª=k`Âh´ô> /ExtGState << >>/ColorSpace << /sRGB 564 0 R >>>> /Length 6914 /Filter /FlateDecode >> stream xœ­œM¯&·q…÷÷Wô2Yèu“ýAr+# d A àE•ã 4-’ŸžóÙ¼£‘¬1´ÐH5Íf³‹ÅÃb=o+m¿ÛÒöÝöç·Ùþ¼í¯}ßr¹^DZG–•ööJ×ö?Ü~¿ýÉÎú:ËhuÜÇë*Ÿ´úÍÿú_o¿ýöMýí&ÿùíoÿ©ÿm¹¶ÿ}û·ßöí?ÞÒö»þÏwoÉÿÇ·;½Ú¹ç¥VßîüºêvíµŸÛ÷oýq9wó~å0ë½¹)Éì7©qÒ=ݼ_§çWm2ËkWãûU̲w³ü–Y_GeåÛ÷fÛmö{ºÙŸpzT­È̯T=ªºcÖìaœ6ÏWQãó•4 9N×ížï×®ÆýoæÝŸ{î¯ÃÃ(¯CÏ­¯j³ñúõ•¦^ÿì¾UÏu]½ñ™yýš^é´Ù`SÎé=›‡_¿¹ùÞÓ¯ž¯óÂlÕ÷½`½^wîivó¶sú]¾Zýúýêy`êõ™Án¶×)O–WÓsÛþJ»Ç|Þ˜…çÚ-óú×k× ¶Ã¯v§4LÇFŸ*ß{:6Ά7Úeç\}æÜUñë/¦^ÿÊÌo«Ž{—»Ù=øfÝ”s®>¿º7íÉïÿØÙ¸ªþ[áÑí“ë‡ã£ÛöWÚO{èêCâúm'<ýÝöÂsqŒôöµØ®E [nê£?a©/y¢ßïšR²+zû´‡­@¹Ý­ììHéíK³}ØWO—ÝÑÇ“¯°í{Ü;Z®þš-l…K÷µ×CJ…xi£¿†?v9½ÛyÇ;î–]óA);:æ¼§œ šJ~~>í+±JdËÝnÉöå¸YíÆxwú¿í¯ë"ÚSùã:YíI¢pÚ®-lÏMX¦Ã/¢ëÉãïKÝþºY©ë„ýÑ4­²â§°ÈS×ÇO‰÷ëv[ŸßÕÀþJ¬ÕÔýQX¬I°Û÷¶WÑðW¦ƒµnmKý?ä¯þ¯ËýŸ!²{øçœ*K<œSf‰ïí¯½sý´¿º[-xéDi»}ÓRÛÛ¯'ZûØ!¶WÄw·å¯îææþ®ÛCa!;ô65]!¸gÌÇ…âöi,î¿Oôɾ@|]hîsÑíaÆü^¨nªáŸ ÙíaËûv[þê¶Å#]¯–‘ý{£¼}ñ>7Ò«e—ÃVüôeêí#õ]Mþê÷["Óújæ°å¾Œ26úÛ—™•.Ýp— üÕmù뱑àÇFƒ»ü¼‚ï—^[ñÓeÊ[cêû›üÕel§=:ÜmÖg±ç.¢…û­Ä¹Ý£KqîzJØÝ_º~x>ŠÅx±­Æ¹+8ë³XŽsßÚ°»ãdãï¾Óz~_FžŸj=–}–°›Ú§ðgµ/÷[s߉×nwåþ˜ƒû­Ç¹wƒ¾T gÖFçñTëqîÝÆó¬Ç¹7ÃÕ—µ›¹ÿ–ðG›¶ý1môxú»¡Ç«]˜F{ô¸Ï—7„#¾  =žóÕÐ㽯_®Wâ'æ?ïèñj;~âùyG»í´+ïèqß¶öa;~™GÞÑãÿyGû6âxíõTÈÛòŽ÷õpp¿®‡¼£Ç#žeÆëý-'ôx¼oN‰ø ÿÊvüä 9Y5?•ûOâgg=ÊŽø9›í‹øÉø?k!V_¿±­Ç™iÅVütû ÿFü”ðOÞ‰Ÿ>¾¶âgö—3ëiÚññ-Ûñs°^s¶Ïõ“»'Ögóûdô¸O£SPÙöGãAû´ßGØŽŸÙ¾?9Æs ÇC/ò‘ßÇÏ‘Gü8þóq¼ÓŸ¬\xw¼îÿ@güèñc‡'âWv# ýWâgù>C}„‘zœu¶õø`?ÎgèññÝ÷;ù«ÛÄÿzúÏÐãÙ=îÓ˜SØÒã>'÷û+_äƒùŒœ8ùBûkÜ%ì²ìùBûk;ŸÈWæÐç€|‘w›x»ÈŒÇ$_¤Æã%;Þ³—ýÉþóEv—ÈxŸùqd—%ßÎ}ÿs>ùr.‘Wò5ÙmÉÿsAuÖõûô¸ÛŒ·ÛòWw#ó[Ñ㑟kã’?ú42=žïSÑãø§oT>O$òÁ\#?ÞÉ÷dG~ܸùq–$\Š¥i9lÇOãAÇPBñÃzkiÄO¾ÃvüÌöñµÈ¯Ø?ùqŸâ­‘kš=þf=–}ä°í¯2ú'?VÀþläÇ=-ñûzcìíKä+ÂÛ×/Jå¯>-¾ßaöuçg²å>­;íÉŸþ¬Ç²[ »Ôõ~òãž\»môxµ?= vÆ‹+l|=Q­è×ÏÓ6z¬0¶ü1ûKèq‰ýAB¦ø™ý'J½½ý«Äó~×=.‘¿ÉVüÌ÷IèqÉijgŸGk=ß²½ž²¦U6z<ίGF»,x“]ög½úà‘Ÿõ ãçd¿‘çsŸgŽ\Æùü¤?*}}y¿—0§ý½]ÎÕFûýÖ#Õ…vžïø–}ßÏù_•«®v”*»ÞÏy\Â}틎òî¸í^ŸÞ\>:Ÿõåƒ^}ôTBíóyèµ…ø|j4Z¨ÖãÐ{ýG]Ö¿:¶÷ù´ϨgÄùZì8/˾—óq‡ÞçÊa[2ù„éªÏùV¼?ôUrik± ïG|]èñ¨ ibì¯ï{…G=Ãñüè…|Ÿ =õì/ôÒçQ ÷ýÔC z<ª_²ËRÒÂUüôa]ÃnuYo%ôø ý,¡Ç9â± Çs½ô¸?½*eø ÿ•6Ö뱢ǣ~"û¾}¨èñÔ‡J=c®—ŠëïçWôxÔ[ŽŠÏù¯×¨ïŸõõBÿèñj·º¬÷ÚF}ÇùÚÑÐãÏzƨç =Vçñ4ôxì§G;Gü8¿]Îe½5ê¯v£ãAÏÂ||«BþçËôû/,Ó¿£ßN¥Úç°¿¶ïŠͧù_Û·£ íÜ>VP$*Ä·Ï48ä¼0—Ú,º*Ža® ‰Ù TciÀCgƒe _7ýá‡Ï¸é‡?üéí7Ÿ·´}øOàIÕ_ùOÛ]Q÷âƒ[Ÿè·¿iõo·ß½ýÝwý îÕ^Ú7ußÛ¾ô^ƒŠïMûþ¥7kuqsúÉ›9c}Å¿ø› gqðêöo¿þfÜþ$Õ¬¼ç~ Mþ‹ú¹®f§˜·»ßðKƒ¥êXWÛÁ¢d“œªöå‡EÉtMåÊ—¯ZQdz…E¹NV¶Á¢dz?„E©ê1²š¨hVÃlj\9|¢R—Øü°(]=ÂìJ“º@,$ªy#E© ‹rµðaQ.êA°(Y‚EÉL¦5R`X”®‹RWv,j5 ƒôv‹Ò ê`Qª!æ0휊'ƒE-vÆÁ"ƒE©dhg‹R‰±Â–œÛù:í»ÙÎÛdQ*QzpÁ¢[ÐdQ*1žƒMÉMݦ6‹RÉ‘Ú&,Jש¥Ã¢Ôµ{X”Jš°X”ÆÛeûØ&‹’Ïó È΃Mµ¯ß¥çÃj`Q²©â4ÞcØ ™>)Á–œÛ©=¬¥ë7ìêÄ5Æ‹J±O åþ•T›„-Ýø+j]Á¢T‚={R-oߣV‹òxa=ÎíT¢…ýÁ¢dS{†E©DËó`Q*ÑÖ…E©Kí¥ëÁª¨}î¡Á¢TÂ…EÀ¢ô¼kت}ª £ö©ûaCÔ>÷ƒ\4X”Úϰ(õ×`WÔ>÷ÁN`Qº;Ei<7lëÆ5Þ¥’2ì %›xƒEɾ‡mÕXO°(•œé%û‚Ype3^X”îöþì¥ç_°(Ù°X”ÇÛ²ìêþcØçÃJ`Q*qÃZ`Q*‰ϰ(]gýâ²jí°$ŸµeŸ´·úÚ.ÛdQ~_Ú_ÄO ö‹Òõ:ØTø+ìŠ?¦ÝðG%w¥ñ¯°(=ß;e°(•äÑ'X”JøÄs°¨[Ú`QªÝÒ?,* ¶,* ½ õذ(=ñÀ¢&» ¥ñ‚`Qbذ¨T‚µ‹J…³Ö`Qb°)XTŠÚÑ`Q½ÿseQ3^‚EíCŸjèq ¶Y§ÃvaQÒ â¥¿^Y”ô=i¡Ç9ÖO›z<í‚^Á aQZŸyaQÒ_ü×BKèo =.1_°(õGí{=^lëqœõ‚EYßaSèqwS¶÷¯}°%ôXG:×^aQ1“Ey¹Žkcwmå46ÖØË# %·Së…EÍý8X”ý·o“Ey¿‚ø#öÛ`QºŸçÁ¢ô>öÅþuÖuã¯Q MS©‹Úƒu‹’3,)ôxÚ.,Ê×=Þ`QûµjX”÷¯c{XÔú,ÊùÀ¢4×`Oc??·É¢ôþŒåû›jŒ‹r<Ã‚ÒØŸ`/Gèñ1ØRèqœÝƒEiša°(ë,+ô¸ vz<íÐãµú#ôx°X”÷Xz¼«%û¼·É¢Ô–‹ò~ì÷ƒEÉÆçÔcX,Ja÷£ÇÒ£¼M¥0 ¢tZ8,Êz9Ø“õø _¡ÇgÌÿz¿õ%›Zýz<ü{…O6zÜâý®Ðã,¥ëÌ,*«d¼°(ÙŒÿ =nèK°(é)ñ‹Rÿ°°;OÀšBÛ`Yè±–AÛ&‹Êã·Á¢ôüö„çøÕ]°(]‡ÅÀ¢´ xXTÎwø¥eÃûÀ¢´¬xX”– ¬¥e«ƒEÉ&>`Q9P¤×ýWÚ&‹ò²Ã¶«=ú‹Ò²m¢dó~°(-#Ö3,JË$®[èd;_¥e[ƒEiÙÜ ‹’M¼Á¢Ü?¶;ö2„5YÝÿ¹M¥ûaƒ°¨ÜÓNú‡Eéùο‚Eéú9Ø”â§Û°®£ÇºNèq·÷…Ei~Î…EY¦¹¿Íøý ÇB*y›,J²Lí%æ…EYvÏm²(É,µ\X”dö†m¡Çšßc›,Êí¹ŽŸ‘o‹’ñ,êñ6zÜ-°7X”e~ØŽŸÈ¿‚E-6z¼ÚŠŸ.ëyaQ²a}°(mÔêaQÚFœ‹Ò¶á|(X”lâåm`aQy°ñ`Q’í`IèñŰ`Qºý¡Ç²aK®gHæó¢t‹r{Øz|Çoµ‚EåÁæƒEib}¢tx€EåÁ¦ƒE¹?¥mv ‹Ò¶qÑ?z,›öèq·‰OX”¶‰}ß&‹Êý˜Åú…Eé:¬ ¥û‰_XÔÓ¥ö=¾#Ÿ •Ue„U¡Ç}["`Qºë†E©ô¥çW®SϸË`Aèqo, =ËZX”âéXX”⻤m²(]g=Á¢´˜XTž,¥øe=¢”fû¢žqÅ~,Êi‰ï‡Ei|°Äúè1ì'ôx²+êg –‹Òýø¥þYO°(Å?z‹ršB{ê÷`}°¨<Ù,ÊëcaQêŸ÷…Eé:þ‡EÉ®°&ôøñ‹òóaSèñ}¯‹RȺ€*y|#•<;ñ‰”ác²‚RÕø@/ •â¨È&¢R@ÑØkMuàºM@Õ_Oɱ3xjÒ8Ê «Ù§£Ò½°©6Q“S½ù•dª °˜Rˆ¬`JÓΓS𬴀)Í%3˜ÒtñQ `J´¦TNq° 0•Fa/À”cPýßG¡0¥@c~S­ªTHßÛ°)„ªžLÿü`à0•úùûÚ· ¦›Bè~ÄGe€)]O ˜’X=€©4>Ò0¥Eu,I©=¹¦ÔИ’SÏøSºeñ{€ýŽÂ?`J×€)Í'þLii²úÖøÕGP¨šÞ}U⌠JZ”±¼MÕG…sP}õ³ìªÊ̓|Žž£‚>i“°I¯?X#ì©Fé*ÐÓ$}'–ÉOcß îô˜VÛ¿2 êô<×Q¡µ0§‘£rjãkˆS‹Ÿfpjó[)×7Ûø”ܤštEw/°i~7kêÃ%šfÔAšêm€¦ÉQáL->YÌÔß—ª<”©Æx2Ä5SŸDbjûcöØ\,;M~ _š+¼4~tiäF—ÄbË6ÙÒüŒ´¤Y€$9Îëw€¥¿‰®4ò¹ÀJÒTî%Gþ Cƒ)QIšH©Æ/‚(é^ùë1xùëøœTÇ—™Ð¤¹4€Iµk%‰èÔm¢¤¹œ!IZe€%맃+5§Â`r0¥I‘ja"|=Rˆ„4—n©‘§@ªñûŸàGçƒæ¯ Gõ xÃqbzvTãNèh:r4ôGÝ$àFÚ4FB6 B¨Ñô3ШÆ3šƒ³M#êré€6¾eò¶²o•\Îiñ)Và¢nR†µ2>„rHߎ+jw”AE-¾ê RÔâ'ŠZl×Á‰´isµ…pð (ÑP™€D-†cÝÔ½|n4t“ *„HÁ²M@ÔŸËoñÓLSù¶ •±­Îo—b[¥¾ ’M=>¤m¹,|hnóÁ‡œ¶ÀkHSûáð¡´·Ñž·¦Qo…Í´.øÒ,ê£ð¡4~|H'º•©=õ~øÐ;ÛiÆ´ISû°áUð!¥eùVIãƒ/‘¦N;ÒÔÅ®¤;ý[NV.ß*-ý“¦¦8æòqv|›tvòí|Híáwð!]‡w‡fZ|H§ÖkùVÉþ¢½…UÏ»áEüÞ5žrûk›|Hgä4xâ'—ñ-“ÕUvð~ï:¾å >¤Óläþá;¤©âÝ J¤ “éþ´ð!]þÃï]uš¦=iê1Ö|H§süÒé¼-|H6ã‡ÉF‘àCº?$gø>$Á‡dÃáCò?ñRÿð@øÐrßOñÿN>¤þ&êhºÕƒ¥ãßQ6˜íáCê/-|Hþž¶ýÕâyð!W°)ˆïŸøý”²¥ñí’âçºÆ·K”®(!’¿¨ú¤fÑ?¿Ÿº/ƒéô~ ^$ÍûáCi~;Òé> stream xœ–wTSهϽ7½P’Š”ÐkhRH ½H‘.*1 JÀ"6DTpDQ‘¦2(à€£C‘±"Š…Q±ëDÔqp–Id­ß¼yïÍ›ß÷~kŸ½ÏÝgï}ÖºüƒÂLX € ¡Xáçň‹g` ðlàp³³BøF™|ØŒl™ø½º ùû*Ó?ŒÁÿŸ”¹Y"1P˜ŒçòøÙ\É8=Wœ%·Oɘ¶4MÎ0JÎ"Y‚2V“sò,[|ö™e9ó2„<ËsÎâeðäÜ'ã9¾Œ‘`çø¹2¾&cƒtI†@Æoä±|N6(’Ü.æsSdl-c’(2‚-ãyàHÉ_ðÒ/XÌÏËÅÎÌZ.$§ˆ&\S†“‹áÏÏMç‹ÅÌ07#â1Ø™YárfÏüYym²";Ø8980m-m¾(Ô]ü›’÷v–^„îDøÃöW~™ °¦eµÙú‡mi]ëP»ý‡Í`/в¾u}qº|^RÄâ,g+«ÜÜ\KŸk)/èïúŸC_|ÏR¾Ýïåaxó“8’t1C^7nfz¦DÄÈÎâpù 柇øþuü$¾ˆ/”ED˦L L–µ[Ȉ™B†@øŸšøÃþ¤Ù¹–‰ÚøЖX¥!@~(* {d+Ðï} ÆGù͋љ˜ûÏ‚þ}W¸LþÈ$ŽcGD2¸QÎìšüZ4 E@ê@èÀ¶À¸àA(ˆq`1à‚D €µ ”‚­`'¨u 4ƒ6ptcà48.Ë`ÜR0ž€)ð Ì@„…ÈR‡t CȲ…XäCP”%CBH@ë R¨ª†ê¡fè[è(tº C· Qhúz#0 ¦ÁZ°l³`O8Ž„ÁÉð28.‚·À•p|î„O×àX ?§€:¢‹0ÂFB‘x$ !«¤i@Ú¤¹ŠH‘§È[EE1PL” Ê…⢖¡V¡6£ªQP¨>ÔUÔ(j õMFk¢ÍÑÎèt,:‹.FW ›Ðè³èô8úƒ¡cŒ1ŽL&³³³ÓŽ9…ÆŒa¦±X¬:ÖëŠ År°bl1¶ {{{;Ž}ƒ#âtp¶8_\¡8áú"ãEy‹.,ÖXœ¾øøÅ%œ%Gщ1‰-‰ï9¡œÎôÒ€¥µK§¸lî.îžoo’ïÊ/çO$¹&•'=JvMÞž<™âžR‘òTÀT ž§ú§Ö¥¾N MÛŸö)=&½=—‘˜qTH¦ û2µ3ó2‡³Ì³Š³¤Ëœ—í\6% 5eCÙ‹²»Å4ÙÏÔ€ÄD²^2šã–S“ó&7:÷Hžrž0o`¹ÙòMË'ò}ó¿^ZÁ]Ñ[ [°¶`t¥çÊúUЪ¥«zWë¯.Z=¾Æo͵„µik(´.,/|¹.f]O‘VÑš¢±õ~ë[‹ŠEÅ76¸l¨ÛˆÚ(Ø8¸iMKx%K­K+Jßoæn¾ø•ÍW•_}Ú’´e°Ì¡lÏVÌVáÖëÛÜ·(W.Ï/Û²½scGÉŽ—;—ì¼PaWQ·‹°K²KZ\Ù]ePµµê}uJõHWM{­fí¦Ú×»y»¯ìñØÓV§UWZ÷n¯`ïÍz¿úΣ†Š}˜}9û6F7öÍúº¹I£©´éÃ~á~éˆ}ÍŽÍÍ-š-e­p«¤uò`ÂÁËßxÓÝÆl«o§·—‡$‡›øíõÃA‡{°Ž´}gø]mµ£¤ê\Þ9Õ•Ò%íŽë>x´·Ç¥§ã{Ëï÷Ó=Vs\åx٠‰¢ŸN柜>•uêééäÓc½Kz=s­/¼oðlÐÙóç|Ïé÷ì?yÞõü± ÎŽ^d]ìºäp©sÀ~ ãû:;‡‡º/;]îž7|âŠû•ÓW½¯ž»píÒÈü‘áëQ×oÞH¸!½É»ùèVú­ç·snÏÜYs}·äžÒ½Šûš÷~4ý±]ê =>ê=:ð`Áƒ;cܱ'?eÿô~¼è!ùaÅ„ÎDó#ÛGÇ&}'/?^øxüIÖ“™§Å?+ÿ\ûÌäÙw¿xü20;5þ\ôüÓ¯›_¨¿ØÿÒîeïtØôýW¯f^—¼Qsà-ëmÿ»˜w3¹ï±ï+?˜~èùôñî§ŒOŸ~÷„óû endstream endobj 570 0 obj << /Length 678 /Filter /FlateDecode >> stream xÚuTÉnÛ0½û+(Ôb¸i+àÒÖEzJQEÑôÀд%@‹#QMò÷%9Tb.‚˜ÃáÌ›7›HtˆHôeAÂy³]\oÒ<¢—iÊ¢í>¢Là‚°('¦¬Œ¶»èJ1JYŠ>V²;ÔÝ!Nxno}Ó#È1gèåDâ]Ì)’6Fñïí×ë Ï£—Ë\8%¼Àœrô£Òõ£92•¡‹Y¦Öý>ĉýÕ<ô{8Õ4üq¯z„{Ý©AËQ+{gÄ!ÌÚéÙ ¨ú6{œjÄ]}Ox¦¦&8™|¬Vt;—Š#•ò Fí=Ò@0C]­4HÊ× Ç‰àº $ÆI9úU0qÌAºöOÙ >¦®$9‚œ»Zûә䨭 S9?0æŽY£ýз'¼ÎRÊ ¥cè†ÖÙLÏZ'(.Dh݇8Ií€åpORÒZð§µr"]¹¨½øÿKµ›½›Þ8ó= ^NƒÍËQ¯—ãrV–îºÒÒ`ÈÆ9tëÙA6ÇJ‚Ýtu)ê;[™Ô©4륭«ªj5ÇØ÷C+ÍÎòêæžÚæ_ý\Îi¼nÒçí‚ZDôuƒ+qš‘j œÓ Þ$ÿ0{ÅõmËDô©_|³ó[2C&'˜~wÏvé50Éq&(¤¸©ÓàšOJÄÄ{7zm+?SVuä±q¬z7IO~:ò;bÕúY¶Ç& ø‘v¶õ3°|9,Ÿ½ïêc#Ì‹Ï`ÕKoý8éN½a^˜¼Aïü¢6qÁ‘ÿžXê¨sž|Ð*Ù4>˜Õ×Ã*÷S§LÝwavùi©2Š9+lP̺ƒI¿ð‰Jf%MñYÛÿwÚÕÈp.„͉¤˜‰œyùïÐü¯<`” endstream endobj 567 0 obj << /Type /XObject /Subtype /Form /FormType 1 /PTEX.FileName (/tmp/RtmpBlhU1W/Rbuild4444671013800/timeSeries/vignettes/timeSeriesPlot-palettesMultiplePlot.pdf) /PTEX.PageNumber 1 /PTEX.InfoDict 573 0 R /BBox [0 0 432 432] /Resources << /ProcSet [ /PDF /Text ] /Font << /F2 574 0 R>> /ExtGState << >>/ColorSpace << /sRGB 575 0 R >>>> /Length 15245 /Filter /FlateDecode >> stream xœ¥Kï%¹uä÷õ)rÙZø¯ä›\Ž„ È€ÜñBÐB#·a R-ǘo?<'"˜dvIå–]]Q<7o>˜|üx7\__áúÝõoŸ~‰ÿ®2>î|µô‘ó•rühñŠc|”xýñ»ë®ï?ýôOÿ·?»~þͧðqß÷uo~óó¿ût´rýǧ_ýúº¯ü®¯ç¿û,àúŧ>ZºÂ?îqýáS‹)Oy¤pýÞäÈ—}[7•>ÊTí£Ü¦ÊG*×èöW£šªÃTý(Õ"ïjª}Wí†jÍ"ƒ+?öTÅŽÒïàªRµî'Sì0=|¤a'31,4ÚöôÑo+»“©ü‘o+ëvÚ½|ÜÁÊR‚ªÁZý õ#BÆf²ÜÑåMY“ËîÁã#š ±@vH¿”qÛ#²àfÁ#Z”nƒêÅc›}tÌÇ ™ü£Ùî”=…œ!+äí²~äæ²ØØmmzd.+îÒm÷Åî䨽ã{-د{¸îÐÁn Ì£éq»nÝu´§mÿÐÉ.Öþç÷+Üùc„]«™¦Gp]?‚éúqwê’ëfÅ>Öñýã#äS7èìç;ëm¢ÎÔ£¸NÅuø(ÐþLBH©nÇ›zÔýóù£4|ñóƒÍϯ%ê†òøfõÕÏ߯'Ìz<\Wéfº}t¿þ0¬ÒZ¹×¾ý½{Žƒú÷ûõÅYLg{ ¦“U^Ó¡RWÜßì÷;æ}ãøÕþb:Eê ¡ÛGÌO]2ÝóöücG%N|^é¶Zlº4ê^]û[RøÈ‡Ž¨ÉÙNº6×ÕŸOš¯fwüzR±/2]u…î~ý©¢~f4H¦;ôÀñºµ*vÿP¿Ó°éÏ¿PÜ_eC¾í‹žïÏóåEý­~}³ÑïžgNv£ü~¢<[Å}î×Ô -DõóËE hƒnv"Þ‚H7h\oîÞŠ}}÷f´ñêòðv´òÃ%ØYLé ŒÉá-éí·ªD{FLÞ–»ßó¶#£t¾§Þ|¯¥Z5Vkj²{Š—¾4oOgCƒÏúWL™qVÃNo•ÖÙ Æk6 5X»4[4´7SV/õ–-Ôh-ꔥÙþ}6ŽxØSV—ÞT‡Z¬îNålR­aõšW«5©#ñÁÖy-Õd–œõvžFÁ÷{¦D­hÞÞÏC¡ÒOYwí>2YÃ;z“Õ%¨–íN3å¼á³k@užã¬Sâ)̾p^éìGpSÎÊ:;á•¡ukK§DS3»ÊÙ”Î`oçÃìç³{d°Šj–÷híhoö”!›Kœäì.g-Á¨Ýß–)Ñ&L9+Ø ö^8Ì.2Õí³Õª¨}ÁÍjÝìqùÙùþu+Eó3å¬Bó#Ýïó¼ðâ¥ÕKç]šÏ}–¶B9_bë‚ýPöÜo»@ïÈ‚=¬SöðÜØY…ò.«÷ùÕlVÎYIìë ç…' æ÷ú°a={]z mh×óÖí–CΆMß­…,”VëžBœ­Î¬³"•è²Z§©úl²{¼q¨Æ*ÊÏv«~38VÊÚWõ޳œç£*g/ˆ*ê”Év¯ Œ!Z©;irþû¼í ŸÍVE{סüQÚSΔÃeƒ¬ª¢Ée³¿X©Ÿóì ‡×ºŽàa±z女+´*Z1,49Ç,MÁÆu¨Ùï…zH«¢¬±qö‚³a´Km½ 1¯¢Mrœ]Þ¼Òy7øEÍ’ö¢´{Ãx£¿qxø‚‡7Œ7Ïyö~5¬—=ÎÎ.†§nL‰*:Pš¼ad»gOh cDGgÇ7“«nÌ~.ï²yÃèduéoJœ`tÙýP³»½%Ä%dêZUñ œ=Æ"Ù]6ûù˜Di¶Ñ¤ÚX“½oÁÅÆÂÛž«×º‚>Ť5Œ/{œ][¹×¸?ήÍ’7&¦[XÝU,>™Ò›B“#<ãòX"†‘·5‰¦“ƒæ&Gz¦1±dôâ7F¥qö~>Êäh<ÎþnägHg‡WÊ3j޳Çc/Ž Ÿº¡üÆçF™zör>ŠäðÞtƒ.ÐÑꫲõè®Q‘jÂ(3ZKdº`¹ŽW¬ÎZ¹÷›qö}õ~F}±6k+רÌtǨmK˜UÞívcYYƒ¦îÛ(=ÎYbŽÏ¨7¶ˆQ&G­qö£r4V³×Ëù…Çæý½‚;u-®#Ž_1Ê,%ÄÙóÝõ%›®Uâ½›]alÛõLÝ1ÊŒþ|gßè3¦Ä×gv>)ò7š£H´¦³?̳”g«>ëIÔí~FÙq6Z>Šä-Îv)@éPüü¬eŠ{|Ç(Sçg JÂõy}²–;ùýñ‰·i¯Ÿ¯´µ Ð7tB}n˜§Æ‘1+j|ç;9Ç‘6YB£>_Ê`÷s°ƒÀÒËýúfiõsj¼»óÅ ÝãÑvֽًܟgºý/ëx¦½~ôéöp}>Ùƒ˜÷3 ^ÒíÞ¡­~Ní×›løp>º£<¢¼ù¬h–|¾ûDý9^÷™ú,ïøü°Öõ9>.Üã½|þÅêóŠŸ]¤M×ÃúŸæ,ò.ÛõL]¡ýù§àËŸWp]1e÷FºBè†úÌY¤éŽY¦×Ç:fM|_Óì+9+òúeºo³¤fíœ5§9« ÷3ë5ÍYÓ@yF}^º`Ö¾éç9g•^Ÿ3:MÓ#=³ð4;QÎÚ©‡fíKüÌŠÓœU–²ÝŸÙ¯z{ËYaš=éØf™iv¥€÷ö¥¡=ï—éÖžö4ÍÎ5µ§½N³ÿ 31ÝÆÓÞ§Ù¡¦íý7Íö¶C´·œå¦Ù‰rÖîÃ@Ãtœµû<Ît…ΈOšµ£~MÝã3ËL³c­˜eâyç ð´iÎÚ}¾–æ¬2C”w´· C¼4;׎ûé#À4»Ó\Ÿö&Í™$gí¨?S³½­(¨Ÿ™õyö§œµãý™ºöíügÿÊöïc)«~"¾¢½Õý›ýiBûZõ¸÷ã ÍÚñ}sŽÉY»·ç¦[xÚïT#(Táóžý©(Ô ni«s¢éímãû?õHÛû6û׆ö5 ¼YEö÷5Q“Bá}›³Í‚ûÝñýí­îßì_GÛÞÇÙ¿’B¡þÍþ5ìíC‹ ¡jf›úö¾´ EêaºAGèêíízþs¢yß[ýlMõÙ§iö§¬ÏÐýÕûÞoP(R³4'ŸG}žºƒš ½žýkE}lЊý©éQ^€Gõ¾Íþö>t…Zº£>GŒG’ÍŠILí~=h<Îjô qÁïfOûßµðÛˆŸJ@¿Mú=þ6~W(­…ŠQ€íó nÒŸ/x˜­ ?N0pCu~(@ð€iÊ¢à³Éö±(¸•zÓ nXÌGjààa¶g>p7–=ØI¸1¯¾¡ðÌQQxæM$ ŸM'¾(|~‘71Dá)(<“ …—ã3 p£YαÈ [[²ðYê Yx&º Ïœ7…熑}5hGdîPÜ™žôl\âa­ Xnh€<ÞB¦ äÆ Ñ^‘»Þ¹_/>ïÜè¿›—“^ûtlÓ><Ý´ƒrû<ÚWr;¿¸¡r£“ycå†ÃËcPý-š—°Œ(w^nºoÀÜø&Ú[óM{ßmßç³h2ó¸Ö*Íí|±ÖjnÌçln¤²HÏ†ÇØd wpcd{ r5d :~,vÖêà¹Ý?¼O çvYXi>·Û^Óµø¹Ýf"rè¬Ö‹ ÛcÆñн¢¼ ½]Ú!º}Þ'Pt»>Š#F·j…µAptû~<€t{¬i#év<ðB ôCÛ,ºŠT¦[ûž×;†t݇§þZ:•O·f 8ËšiP?uk†3Êq{³$í£‡B¨¨n£¿^Ruk6¼½#V÷þxãêöš~-°n÷3áóNÖWK´æä©§k±u»P Àu»¿u£ëöZÜ;^÷j‰ïwÜbí%¨»½þ¾“°[µÆù±[9¨»•ƒR²Ûóie·r|?0»õ¯Ô㇌úIÐnÏóÞH»]?¨P»'õê³Ö Û­þ`%´Ý^9´'6^(|Ôú+)¼U;PfPøM;…?ô¬ÏÉV@ÙÂÛc$w oÕ€ÔÛ;~V«Eá­ùû@ Ÿn.Z“ÂÛcÁó…·Ûj o—Uëµ(|²i½¨{2]u|ïØ\oÞNç Ÿbãó…·x<Px»,PbPxûü¢ò³á´Çrƒ¢;…·Ûˆë…·ÛƵ(¼k?Pø”¹ŠF o‡%•÷Ž2efx›î¢î·iÎIáý2üx ðVŽú oŸõ…÷x¯ ðIèŒÞNÔÞKŠïé!=Lëz@áí6ãý…·ïGû ï×jîÁî®Þ^û±Qxkƺ¨»µ·£ëøNá“RHᣲMHáí}ðñ")¼Õß¶Qø¤ñ )¼Õ?PfPø´ª oÍ)¹SxkïHÑÂÛ÷1Þ)¼/Ú'PxkÆÚFá­%…·f‚ß)|°uQx{m?TŸ oïG•ŸímÒü•Þš‘ºQxoF@éÂÛçq¿@áýû7 o¯5î(¼¿†8žSøM;…ßtG}N\U…7íõç¡ðe^Æ)ümÕR~ð!2=cˆÀ t68Ì@莘Î/gz$ŽGzÂÀ’艹êÈ@O˜¾Ý—ØûT™)çÉóÓ¼»yï|¼÷†©¸{¯Ì"wì¾"}:Ò+ÓØºÛt"ïÌ}þ› €Üí(à¸ÏÈ›ü½»ò®¼½4·÷Šëmï °½7ÜO°öy¬ µ.ª‚´N†Ú§òWœ}pù ˜}pÊ>:!´ÎÙ鬄s6Z+á\PN çU èL8¯„TJ8_š ç]ÇcÂùP‚9 {¸ }•p.h¨„óM7h&t²‡Àã+á| õ@=pÒ·ÎûÝGÝÎçI8VÂ9'Í„ì†NðuÎØµ±ˆ}—¾Îw“˜nÙæ àÌ6ç }Ë6Ï;a_ÙþÊ6¿Qu¶lsjf›+›ZÙæ™JÙæ•ÄHÙæz˜Ê6¯$ŒÊ6ç pe›sqke›+·XÙæƒÙóÊ6"òÌ6$Ž$ìA+$ì»6⢈9ûÔÈ–'aÚÝ@Ân „}>$¬x°Ûmß {亯{\Ùå ìQé²$ìó6‚“°G¾ô"ìQÙö$ì‘„E„=&töHèHØ#Gh"ì‘I„ÝÀЖpnzˆ¸ñ‰ªì$ìaªŸ ìV7Ânåm#ì^Ûµ»iõ†¨ ì›v¾i'ìþZo„ÝŸ/òÚ°{ý’¶ÆvÕ7ö§¾°ûk½öÕX°{}±w´â¯1¸£®M;ê¶×õ„Ý_Ût-Ân+0 ìaŽ@‚2Ñ­>'­h‚°Ûñ°"Âî„·\‹°ÛÂzÜ»7C8>{d’1 »]n׋úE¾î {dˆ{Ô " {äŒJ„}=oöGƒ°Ç(µ¢A¾Þoöy|t>$ìzßEØ­}!aßµöÈöZ„ÝF”ãz»Ú'öÈý"ì‘ÏC„ÝÞ÷-g}½¿"ìy)"ìA„˜„=0¯I„=Ü$´$ì÷`ž) ûÍú%Â~s‹ûÍ÷Y„ÝúÃq=„ýæJ„]ý“û£»òÔpIØo¥Ý“°+ïD„݈-ÊAØo¾"ìw"Ñ&aW^—ûEÈAصR„ÝâAøAØonka·1?ûÍöG„]+ž"ìëüHØoÎ DØoUö•gJ®Á »ç-âxy@"Ä ì›nÈSkq÷<¡õý ìwÑa×øF„ýJ*a·ú¯‡°öÇ"ì÷à »ÕäÙ‚°ûÐÏ„Ý4ž»O¤ëØê'»F•ÔÎÁ+®„Ýë'ˆ¸vÏ+“æà[$”æÎþ„Ý¿?]¿òÜ; ?óÜÛCå¹Ï×ïóÜ×ûÅóÜSPÞ;òÜSP:òÜçaª´µ·‰ý”çžô¼™ç®ñ´òÜ-[Ô¯—yî‰íòÜçÊsOÌRž{J\bž{Ê"ðÈsOZÁaž{jʃGž{Výež{ÖûÁdøRðö3Ëýæò‰_ ._Ž/Ür#Çîx”ã ©¸_øÞÈñ…‰]r|ánL9¾BÈñEŽ$ðUYéð•ÌMžsâ? àÅÄàeðxd9 À/ _5‡ƒüZ €ïd²ðb|ð·ZàûÀÀ# =iŽ‚ë{<™ü8³Üíó€÷½…÷9â‘å‚bÀàC ¼íܳÜMƒ™ƒÁã²îÇáåÆ1íYîw%3ƒ¯r©š£ŒƒÁ·'« >ŠÉ“ÁóûÈà+Ÿ‡¼Ž/_ÅÀÉ૲ÎÉàUŸÄà+ _VV<_ \äøÂ÷s9¾è|åøÂ>w9¾t}?_¸„¶_h)µ_È–ãKc–«_˜U¶_ë«_*´_d€!Ç—,fOÇ9 Éñ%éó`ð·LNÈàMož/>‡TV;÷.âyÁßÌRƒ¿ÅHÉà—üXßÈà­¥•×K€&³ƒ¿ïü£»öŽÅ=Ë}-è‘ÁÛùn0ÞÛ(ë½bë>‘Èào­!ІHE0Zn£Ì°‡ WàåÓ¸¼Aæ_Ɇ{£äÓøEô€a2–<` E˜Æá£6’0J §L•‡Œ¶Ñ†K°dî¶²ºyÀ('”ÄÝÎ )ñ܇ÚðöQ7ˆÛ-Òiû`úaû”qcíc%»û˜mô‘´¢ÔvïMÞ×âìC¦NÀì¶ô¾QöQtd‡ìó‹ÊÆØ‡–$€Øínl0£)Å€ýÖ–ö›·åÃá‰\`†Ž ŽåÓûáÃå|¹À´GV—$Û7ë]ÚÀºÖaå£DjºÀ0E.0ƒˆ.0”û’>”znÕv3‚J¼F·Ú„¼r§èƒC?Bô‘Uê Ý\E¶$õ!"‚®7}ˆ/‚ŸË©ŠøÜž8#˜N8x>¸ïPF0BŸ4‚$U4‚e4‚Q¦<`„¡i3HñEÍ9k]Ô\y΢æ\Å\Ô<0ÏUÔ<àõ[Ôœ#”EÍ#)­¨y$e5'åXÔ|Åí®e5¬À¢æq-ju¸Ê!j®ë5Wë-j>DµIÍ9ÈXÔ|pÕGÔœ«þ‹šk“¨¹VeD͹ïwQsîÊ5—Ûˆ¨ùº¤æAûNHÍ÷ ‹škßœ¨yà>EQóEÕA̓܃DÍ“ÜfH͹ÏrQó¬xRó•§Nj®UQó¢5Ìò5WЍy…$5·ëß©yનy(ÊC5‰š‡"7PóÀ˜¨y$5_Ï“ÔRóD¹£ÚÛ 7˜ˆ÷õ‰Ô\ûdEÍí<ö*·#ºÇTµ·i§æ¦ájäžCj’¨6¨y颏ʾ!“‚’š‡ÌU/Rsû<ÊAÍCõÎʲ5%5Äw¢æ“ QóP˜÷OjŠÜh@̓V}HÍçû÷‘Ô©¹eÙ¡Ô<î# 5ÚCj.óQó@¤+jo}Ô¸ùhr£¡;ŒÞWró.w(¹Ã¨>“›¿¬Óq‚Œg¹Diw+ÐûÏÜõ¿Üb¬Ì-’;Ì-WlºÃÜáq‹q·‚¬\zä®Ë A¹ëCëÌ]×f-å®÷¤x䮣3X ÝŽÏ\sä®q8©ÇµNÀÜõQXŸ™».Ÿcå®Ïûóeîúкs×;ù¦r×û-7ä®÷¢rä®/7"æ®,€{ r×»ö 0w}Då¢#w½k/s×»rÉ™»ÞºØ>r׫Ú?æ®—~X«[µ‹»;L¦g¬rדܱ˜»ž“rÇ™»ukæèæÂÜõνDÌ]O˼¹ë³ýÆ‚ s×ó» r×í¯•»žTÿ˜»n ä–#w=Ë™¹ë)Š‹3w]¹~Ê]çÞ›•»ÎÜ©•».]rö°âÉÙùügOŒÏbäFÀÜõy|º± w=ŠR1w=ÊëQÔ„¹ë±“’1w=rór×Óúä®Gþ@ƒr×#Ç#Ê]Ÿ{ ˜»žèn¥Üõ”˜ËÌÜuƒ  Þa¹n Þηïî0–;Ê Ÿ¹.* Ÿ9’…·É24(|®‡%»bëõPøYÿ#Ž Ÿùs¢ðYî¤ð9+·Þ«åz(|–ç2)¼ÜEá w‹ÂæÊ‰Â×åF _£rçAág7ÊL _ä®D _Ë“ËÞèÆ·ç®O]åcímå:°(|•›)|­gîzåîeQxƒÓРð-Èò¾iï )|#…¯ÜÝ. _ HᫌþIákdý …o·Ü[@ám&W¯‡Â·•k ßÙÿ‹ÂÏö›žë ðMžé¤ð­¾‰“ÂתUPx»Qww7¤;‡(|_¾ë|Iá;ó~Dá{`n>)|O®ú ·¾¸ç®Ý_Rø¡½ ¤ðZ0•;Ì­Ÿ ;ŒM‹¤;ÜΘ[w£f[îº5ƒu£ðq­ÊÐ&0—Uî07÷Ê&ܼt‡ +×î0¡íîf„ï‡;LЪ Ýa‚VÁèsw¶Ït‡Y«.t‡¹åÞDw˜›)r‡Y«t‡ ƒÏ‹î0¡î0Öÿìî0ã?¹Ã„px´Ûý©…·Ï#×’î0Qnt‡‰¢rt‡Y”‹î0rƒ’;LàÞ\¹ÃyÓ&Ð Iî0˳™~QyPøå^Bw¹ÊF郢ðƒ›ã壽Nr‡¹3)Ýa”'wåIÉÆž(=Üa¬Û×¢ð~Aåáøƒ,r‡‰Êí¤;LìòL‡;L¢âp‡‰Ëf¹“Ð&.Ow¸ÃÄ(Êw˜¨\\ºÃX7›¯Eá£úG¹Ã$æáÈ&rZî0Qnt‡Iáp‡±n±nÞºÁÂÛ÷Á Þô½å®[77 oå Ä ðìö…÷njË]÷ng\‹Â[7j oîVET~@ïî0±¹¿ø…¹{ÜYœÂ[|”¶ú\ä. 3÷ª‘Â{7‡r§ð~üÂ[7†UPxëævvë¶ÆæcÝNÝ(¼»a‚;…÷n Ô;È‹¹æn\r+…·n‰žéÞQ™[¨/(¼¹3-7wãj…·n «  ð¦A…AáMgå²Û|Ìà6¾Ï)¼Tû†Wx¨¼µ·µ*Ý)¼ÅƒBƒÂÛ°ˆïp‡I‘ïÝa"ûKRx¦bï Ýaî&*w˜¨½ ð7 oõ‘î5Nám‡÷Þ‡}[¿X5¡;Œ~#Cî0að|è˜H oï{”npC{ oïCÛ(¼»Çm¹ëVÿ°* ïõî+Nám8¤Í+i/(¼½_mË]·ÏÓ³Ý)¼AÉÂÛ0,‹Ê{}æø†Þ‡] ò>p17wÝ…·ú‰û oÚÇ×o o1§;LOÇ”†Âë‡oHáo¦Z‰Âs{+)¼v{‘Â[æŒ>Vž /Gûž½ž9`…'/…g÷G Ÿ˜5- Ï—S¾¬dv›Ê$.`‰Âs×)|Ì‹…OÌ»…g3% / # ÏŸ3]^¿( /G Qxæ¢, ϵâE᫲ÑIá×ñHá¹Ú²(¼Dá—…¯JP…×#ŠÂwy¢“ÂËáD^ÙôGû½g¯We‹Â+åóñhG¶¹(¼RDá9+…׬gQx9ÂÈ£}9ÆÐ£}e«ƒÂ—›Ô˜Þ:hz´+›^í™TXíüe‰Eá›kèѮݤðÖ˜Ý×CáQø~‹jƒÂ÷¤ìqPø®_z$…ï\›…ïňÂ÷¨ß…oœ•.öåÁ ?_ñºSø.O{y´s»(üµ…ï…ÙÔòhE–G{5…7j *ï>®lpRøQOöFªJ oÔÔ~pÔ±<Ú‹c@áGÒï§Ò£}œ2+_þñ …wõr- ×ÂÛ¨>ÞˆíÚ- ö"ªOví^ G{Ð* =ÚƒV­èѾ~@á-~l2ûQîÞfAqË^·ÏãüéÑ”ÍM dGEö U z´«Ó‘G{ eZíå.vý†ƒ<Úå#vQdy´ý¤(=Ú«²ÍAáeݵ<ÚI1—G;gÉòh¿ßQø&Owx´ßºŸôh¿µ*Eö[[~(U~9¸€Â ö úŒï'…_yù¤ðC«ôh×Þ6y´ßZE¥GûÍtòh—Dz(ü¨¢ò ð]»YHá›(7)|«/vý0)|•#)|å/.v­òÉ£]ŽLòh×îy´“j-výþ1)|Ë¢üË£Ô¾’‹ÂWQEy´ó—s–G{Ԝң]¹¸òhÏŠ…/\E…¯ëwKAáåé* _äPA _˜»" _™ % _åpB _WZ:(|•c )|½”w‹G®¥<Ú³(;(|åî QøÊß|…ÇkûPø¶²äéÑÎtQx­ê,vQSRø¾avPvz´+—Wíù¡ò¾ªäù¾<Ú™Ovn¤…oÜ‘¾<Ú•;K ¿<ŽåÑÎäŒåÑ. &våzË£]žÔòhç/{‰Âwy˜“Âwý2#)|¯Êe…ïý̅屢Âw9ÄÂkÕV¾æ2“Âwm …ïr !…ï¢zòhúTz´“bˆÂ9JÂå Ó£]{båÑn¯Ñ}- µÛK~ÈcíËqƒíËá‡íwx<ÛÙÞҳ햋 öåÐCö›ÆŽòh_¿Iö°¨?<ÚCVî;<ÚC•C <ÚÃZ€G»Í’¡áÑn””œírì G»årm>9ìУ]¿Â%ö˜x?éÑo}íAû6èÑ×/µÂ£=ªþÒ£=pü#öÇ›E­²Ð£=¦Ó£=Vy¤Ã£Ý( 4<Ú£©èÑ•KNö¤÷…íY«ôhÏô”–G{¾OöÌñ¤(|fV†(|Ö/'“Â9.‘Âm"…·jª ¯ù“(¼Dá5ß…¯+·¾ÞZ…·aŽ /ê% _Ø_‹ÂR]Qx«Æ¢òV? ç¢ð…¿”( _Ö/­‚—ÎUFRøµŠF ¯¬%QøR;^ßd¬œç ¯,*Qøš”Û ¿¨)|]¿¬ _7ǘ†ÞÆÈe…¯œ‰ÂWþF“<Ú 7.ö•{OöµJ@ö \tz´ó'Õ–G{=ráíû‘,vyŠ“ÂWQ)y´×'÷ýÆo¬Üø MPøÇþ 9Ê€Âwå.“¬Ú)|Sî/)|g–†(|—G³<Ú9ž\í¢pòhç*–(|_Tíòl–G»~Qí\_íœO‰ÂQ7RxßEá•$ ¯¬Qøçó ðƒý¡<Ú×* =Úí7¦öåpDö°~™í¿ù ö ŠLöÈUÁåÑy>òhWî±<ÚoyªÃ£=VQvz´7퉩´Ë£¿Ñ³<Úù.òhOU”žíú y´+w^íÌ\íýѺn2ö5x~òh—ã‰<Úé *ö¢_¦G{áo¶É£]Y›Gûž oaX…¢G{‘ã=Ú«~³€íe¹ð¿Sx»Ÿ¤êNáÓrp…OúM+y´úXˆÂ&íÞšÔoz´¯_r¦G»úky´[V¨8<Ú£¨4=Ú­YcŒÿæ@8(¼5CXU…·ïßsá½~¦kQxíAùéÑ®\uy´3 by´7ýr+<Ú—c=ړ·í;<Ú“öbУ=qî<ÚSÓ/¯Â£=¯_F…G{&’G{Ö^z´¯Uz´g9hÑ£½ÜÊÕ§G;³¼¾ùôËëß>ýìÛO?ýÓßÿíÏ®ßþéÓ½eÅãÏ?ýöûO?ýs~}ûOð`´?ÿÿÓ¿X3t{íþöׯ¾úŸ?¹fCúÕo~ÿïßýé'¿¾¾ýúÓÿÖ¿ ßôóo>óMÆÿ}>ôŸ~õëë¾þñS¸¾žÿýîSðoùŧ2d¸g9ú¤Wúy÷¯ë›øYó½Tçt+r °‰Í|ñ@¹`ëü àNú'€g¥€ý$ÿë÷Ûž·[·g¿Ý_…ûþÉõíïìÛÁÿSŸf*¸<üøseÍ?ÿŠwÿ¡Yÿxúñ·=ß`ÿx^·Ábºö?Ÿ%¥æ?óõ‡Ë_Léã,‡ªX€ñb_‚ZÅ®Îâq³ØIà*vµû’Õ*vuÏ÷j+žj/ö%­Uìj/ö^s»:‹çX`+žj/ö%°Uìj+Æ’˜Š¡Îâv·³Ø—ÌV±«³xÅã,ö%µUìj/ö%¶Uìj/ö%·Uìê,®Gq=‹}In»Ú‹}‰n»:‹kÚ‹ëQ×°„·Š]Åý(îG1–øT µû’ß*vu÷£¸ŸÅ>ÄZÅ®öb_"\Å®Îâz׳Øyõ*vµû’â*vu×¾×£®aÉq»:‹ûQÜb.Iªœòð†ð pù ÷0î3ÀW1Ÿ—G€/k>._ç|\NП—G€¯„>._í h¯‡ÌO€Ë#ÀOŸ—¯€v´3Ë«+ò0Ê0Êà °O€Ë#À§fO€ËWÀ8Æ+À×lŸ—G€/â>._í h¯€¶·Í”G€¯û>._í h¯€±·Ð”{–ŠWäàÐê pyøbòàòH{KMù ¨ñ¨gÄúóàòðé'Àå+ žõàKÖO€ËW@?ú+ ïí6å€Uîù èõègÄ:øàòˆ{ûMù ¨í¨gÄÒùàòðµô'Àå+ žõP–òÐÏ€þ ðõø'Àåà ôO€ËWÀÞÒBîXÂ_G€¯é?._ä\¾êÿ¸<< à pù hg@{”£¥…<.€rô¯€v¼ú K¬Ùê{ðßëÎI(€¶“Ê#`ýä+`œ¯Á?~Šy@a'&”¯€v´W@:ú È# ýä+ ¯þb¸ÑÍàò°ÑÊ# ýäÐþòGcù Ø( åà™GOåw’Bù ¨g@}¤¦P¾úÐ_yoÌ)€º7支€ªPmç*”G@ßsÊWÀÆV(÷€pït…ò;_¡|Ô3 ¾âÞÒR¾úÐ_yÇ,”G@Ùÿ”¯€­¥¥<êŽY(€¶cÊ# ïƒÊ#`옅rˆ÷ŽY(_í h¯€°·´”G@Ú1 å+ í÷Á?å+`ki)€²cÊ# í˜…ò0΀ñ è{cNyŒ³P¾Úpb[õÙ0 å÷Æœò° ›)€´›)_ã ¯€²7æ”G@Ý1 åÐöa3å+ ž¯ÆÜÒî¶J ¹`›þ €|Ô3 ¾ÂјC¾¶a3å÷a3åwÌBù ègÀ«1Ïe6Su6S¾êðjÌs?sÈ#`옅òPÏ€sØlI…{cù ؆͔G@Ü1 åvÌBù gÀ«1/y6Se6SuÇ,”G@;sÈ# ïÃfÊW@;Ú+`9ä` “[¥…|l˜…òˆû°™ò0΀Wc^ÓÑÒBeÇ,”¯€qŒW@͔݇G@;ZZÈW@;Ú+`옅rh÷ŽY(_{K y„}ØLyÄ}ØLy¤™Sž¹þ¸<Ê1l†|Ô3à5l¶œÓ­NBýYA·—¯€zÔWÀ8†Í¯€½¥…Üú} ›!O z\¾úÐ_éhÌ!€| ›!_õ x ›mŸÉVi!€v4毀}Ø yôcØ ù ègÀ«1Gî €<ÎéøÁˆYº{À9÷gÚî_sæñ>?ÓŽsD:~0"E¦ïPÞRŸ€úšû3xh¯€v4ƒGÀ81ÈW@;ÎFŒùÄ  |lÃEÊ# ìÍ åöFŒòÈ{#Fù ؆‹”G@Ùçþ”¯€~ôW@Û1Ê# ïå+ Ÿý0ö¹?åŽ1-åpŒH)€¸7b”G@Úçþ”¯€zÔW@Þ›AÊ# îå+ žõÐvKù Øæþ”G@ß›AÊ=iÝ+òÐÏ€þ ûÜŸòˆûÜŸòÐ΀ö 8F¤”G@Ùçþ”¯€v´W@ÝA.å+`¤#`œu èO@Íý™‘þŒ×ÜŸ)ê{ÀÙ˜3g}@a_:¤<â>÷§<Ž)å÷¹?å+ ípŒi)€cDJù Øæþ”G@ßçþ”¯€q¼s¤Û?ãµdÆüûyÄ}îOù ¨gÀ«1GÊþ^sæðïýPö¹?åpŒH)_{K y´}îOycZÊ#à‘RîØ:° €°Ïý)_õ ¨¯€x´´G@Þ‡‹”¯€zÔW@Ùçþ”¯€½¥…<ê¾dFyô}îOù gÀxŒ£¥…ܰ b@¾ÚÐ^qŸûSéhÌ!_{Ú1ä÷a3å+`œ¯Æ¼Ö£1‡<Ú>÷§|Œ3`¼úјCc6SîØÒ± €°Ïý)€x4毀mØLy¤cØ y”}îOù hgÀ«1ÇN’' ¾‡ÍØZò´÷°{Mö€WcÞŽ”<ÊWÀ8^ý|UVäö ÊW@?ú+ ýäp¤äQ¾úðê/zÙåp$õQíüCGJå0Žþr øüî–ÿâV„/ä9Áö‹¹Š_LûbŠÍó¾¸2üÅE¶/.h|ý¶/a¶/‹/Oþ¾8Ðþâä‹ý×_µ/TÒ}»Í¶ÅfÛycöÐóñC㾯ëïþ÷O®9ZÿêÿþdÞ×ë«ïþð¿¾ûãÚßõåããf'é¶y'ÞwùÜæŸÏÚ*¬ ªt:_ýâ7üí?ÿˆÏ'߇r|}ýïÈyîÆ×ó.Ü×Wÿþýw?âØaæ$ü¯= [LmûMøæ»ý?xÿùcd÷]økOÁxK¾?s#~óý¬vC~3OæofÍùêÿýˆcÛJßµ£ýˆ³²‘ÃñxþÛ¿ÚiXuý—ßÿ˜óhþSMŸ?²¿SŸò½%ý^ÒF î§Æ­€’ÿ|ù†Ëk?†Í¨l ùðT?~wýÃõý¾I²VÛËöü¹o’ÄI§ùÇç·K~®;ÑÉü¹þd/ÿ|‡rD|¶G9">Û¥0Ñ:ú”×1`ut*¯cÀØèèUÎZóÝÊù-4›9ú•×1`rt,¯c´t¬BCŸíZöˆÏ÷-GÄg;—#â³½ËñÙîåˆÀæä£yÛ‰¦¯ý¸|?þ†\EÔ?»#—aK®"þüž\½§Û¦Ü_~úÿåÚ9Œ endstream endobj 455 0 obj << /Type /ObjStm /N 100 /First 874 /Length 1528 /Filter /FlateDecode >> stream xÚåZ[oÛ6~ׯàcó0ñzx‚k²l}ØZ´Ö-èƒ*+©6ÇÊl¹Hÿý¾cçbÙ®cW5Ì(ŠPäÇÃsûŽHÊŽ¬P‘ÑâZñ_%¬IÂaƒÃ£NãÏÎãÙ+AqÞöýžDÔ>sÞ‹˜ø9ˆÄxo„ÖŽæk°Œ·BK˜Ðˆè ²<Hh— ÐÁóì„Êd.D4 azrQ°)AO(g”h(4Ø‚¨…Ñ<„c–Â*Æ:¬ VªèšA C¶Eˆ!Øî ˆñ–a¢ J$/Lô<„IÉqODÃsë„QÖð‰‘2ŸXká$ˆƒ‡Iya j“‚K}4hDøÖsÀ‘1NÁÐQÁp£ÓΡfè˜2RPÆ ˆ¥€Œgo{Œ¢dØÏÐ ÀÒ¬¾&_è¥1Q)ŽËl56ü –á‰p+±úë‘ÅöY c,fQb0äDh€ØaiôÄ&Á22À'²Y/¼â!DÉk¶á÷FϤ£ÁÆ8`²¼$ÎÀäŽg—Ò{xŠJ>XÄŽ0)jî±h`!;ƒâT´°t¶4Óìtø0¥X}¬6^‹àØh¤h`Yƒ·”2$„ nf+TƒG‘7QÁóFÅNGÎD­¸£†ÝøE¦Á‡‘ùDp›²ããL¾ûr] yÖŒÚL¾~lgÜ©3ù{qÅc&“/ŠIÅ!­†Ÿ«¶.‹Lþ<*›A=ºœ‘T‰7ÙóçÙ¹|yrÂèz÷~XXænÆ\àý|ùG=úi4©†Oë‹‹j\Êj"Î!I^Õ£éD$/ÿ6m5¬.ZpÓÉAӫɤ–—ãâs%‹rÚV²¬ÇåôêbXÝȶ*yU”ãf$?Ž+`0¥(ËjÔÊA%&õD棦TrŒµeY êá°¸ïü4]ãéÕ°˜¶²¹lFÕ?²,XÞäº(«l5[x*Π‹…|ÿç_(&.‰3ÑæÈ;1š‡ÁÔ Úç y¼L® œw&÷¨Mð,JÇÇÜoP„fÓÎPAŒ¾•q†4t7Õ ²‘ B¾õñ煮‹xyŠ:çï_›òmÕ"$òõéò¤ºiÅýÒó¿..«Lž@ øx¾ëYnÈ7Õ¤™Ž9 \f]¿ÁÕÅ‹æFœ³2`gn`Nt:éV+-PwÙÅ«œŒ«¢­›ÑiÑVâÙéF¡bþçPhŽ µ|uì]|ÏÞˆ_ÆÅõ§ºœˆWÓözÚÍlLËjÌ£.‡*G·k5ÜuÔ5²'_|XÇ/^-RÀÎ)ð5¾tÁð¥.ÇÛë»ðe E<¿åzQo°eŠÌ9`Š„¸Ž"!E‚Þᕲ ÞH‘8P®°cAÎçÁwŠðÆ<õ¢H\¥HìCv@Ô[VŸ%…SÕ¯ªêúªJ;dÁ2ø«Y°X>£øµín¾%viuwÛ$·uyÓÊ›~:å-­-oéàÊ[ÚevÙtÀÁæ8#­¨ó-åíÕ:œ~c¯í)»Ì>eï‰/f_Ì“á ßD,X´…H»Vä/šá`½Üò Ìañ¯…¶~Á¬€7ñ° ÆÉ5ò¥—òy„“÷ÉÃÄJ½x¨i…‡ÚvíÓá¡ÑëøbÌ¡ñE»íß[KàGøÒƒ/ÎöØ•oMÃw°¦]Ìʾ—ï\÷D·.îéÐÅÚý¼¶ä.ÒкC£¡Ùá\´ÞHÃÛG…ãò~.PRQâ{QφêYêA=v€õû8“Ó«ª¦žª:µC8õ=OÇ^ßëtLnµlºÊ&¹ô+›´¶¼ÑÁ•7G;ì2Ü.å­æûqä·I>çï×Û”·Î'¤MFKµÎ ]ûÕºù]{‡/·Ÿ—/žö³Íx»ÈCï‡ó/Ù[¾`–ÁyØ>óOBTŒÞçw]þ1Q/Þ}¡Zà¡ûâ!mà!=†u¿ƒ pû;ˆÿ+è÷[ endstream endobj 577 0 obj << /Alternate /DeviceRGB /N 3 /Length 2596 /Filter /FlateDecode >> stream xœ–wTSهϽ7½P’Š”ÐkhRH ½H‘.*1 JÀ"6DTpDQ‘¦2(à€£C‘±"Š…Q±ëDÔqp–Id­ß¼yïÍ›ß÷~kŸ½ÏÝgï}ÖºüƒÂLX € ¡Xáçň‹g` ðlàp³³BøF™|ØŒl™ø½º ùû*Ó?ŒÁÿŸ”¹Y"1P˜ŒçòøÙ\É8=Wœ%·Oɘ¶4MÎ0JÎ"Y‚2V“sò,[|ö™e9ó2„<ËsÎâeðäÜ'ã9¾Œ‘`çø¹2¾&cƒtI†@Æoä±|N6(’Ü.æsSdl-c’(2‚-ãyàHÉ_ðÒ/XÌÏËÅÎÌZ.$§ˆ&\S†“‹áÏÏMç‹ÅÌ07#â1Ø™YárfÏüYym²";Ø8980m-m¾(Ô]ü›’÷v–^„îDøÃöW~™ °¦eµÙú‡mi]ëP»ý‡Í`/в¾u}qº|^RÄâ,g+«ÜÜ\KŸk)/èïúŸC_|ÏR¾Ýïåaxó“8’t1C^7nfz¦DÄÈÎâpù 柇øþuü$¾ˆ/”ED˦L L–µ[Ȉ™B†@øŸšøÃþ¤Ù¹–‰ÚøЖX¥!@~(* {d+Ðï} ÆGù͋љ˜ûÏ‚þ}W¸LþÈ$ŽcGD2¸QÎìšüZ4 E@ê@èÀ¶À¸àA(ˆq`1à‚D €µ ”‚­`'¨u 4ƒ6ptcà48.Ë`ÜR0ž€)ð Ì@„…ÈR‡t CȲ…XäCP”%CBH@ë R¨ª†ê¡fè[è(tº C· Qhúz#0 ¦ÁZ°l³`O8Ž„ÁÉð28.‚·À•p|î„O×àX ?§€:¢‹0ÂFB‘x$ !«¤i@Ú¤¹ŠH‘§È[EE1PL” Ê…⢖¡V¡6£ªQP¨>ÔUÔ(j õMFk¢ÍÑÎèt,:‹.FW ›Ðè³èô8úƒ¡cŒ1ŽL&³³³ÓŽ9…ÆŒa¦±X¬:ÖëŠ År°bl1¶ {{{;Ž}ƒ#âtp¶8_\¡8áú"ãEy‹.,ÖXœ¾øøÅ%œ%Gщ1‰-‰ï9¡œÎôÒ€¥µK§¸lî.îžoo’ïÊ/çO$¹&•'=JvMÞž<™âžR‘òTÀT ž§ú§Ö¥¾N MÛŸö)=&½=—‘˜qTH¦ û2µ3ó2‡³Ì³Š³¤Ëœ—í\6% 5eCÙ‹²»Å4ÙÏÔ€ÄD²^2šã–S“ó&7:÷Hžrž0o`¹ÙòMË'ò}ó¿^ZÁ]Ñ[ [°¶`t¥çÊúUЪ¥«zWë¯.Z=¾Æo͵„µik(´.,/|¹.f]O‘VÑš¢±õ~ë[‹ŠEÅ76¸l¨ÛˆÚ(Ø8¸iMKx%K­K+Jßoæn¾ø•ÍW•_}Ú’´e°Ì¡lÏVÌVáÖëÛÜ·(W.Ï/Û²½scGÉŽ—;—ì¼PaWQ·‹°K²KZ\Ù]ePµµê}uJõHWM{­fí¦Ú×»y»¯ìñØÓV§UWZ÷n¯`ïÍz¿úΣ†Š}˜}9û6F7öÍúº¹I£©´éÃ~á~éˆ}ÍŽÍÍ-š-e­p«¤uò`ÂÁËßxÓÝÆl«o§·—‡$‡›øíõÃA‡{°Ž´}gø]mµ£¤ê\Þ9Õ•Ò%íŽë>x´·Ç¥§ã{Ëï÷Ó=Vs\åx٠‰¢ŸN柜>•uêééäÓc½Kz=s­/¼oðlÐÙóç|Ïé÷ì?yÞõü± ÎŽ^d]ìºäp©sÀ~ ãû:;‡‡º/;]îž7|âŠû•ÓW½¯ž»píÒÈü‘áëQ×oÞH¸!½É»ùèVú­ç·snÏÜYs}·äžÒ½Šûš÷~4ý±]ê =>ê=:ð`Áƒ;cܱ'?eÿô~¼è!ùaÅ„ÎDó#ÛGÇ&}'/?^øxüIÖ“™§Å?+ÿ\ûÌäÙw¿xü20;5þ\ôüÓ¯›_¨¿ØÿÒîeïtØôýW¯f^—¼Qsà-ëmÿ»˜w3¹ï±ï+?˜~èùôñî§ŒOŸ~÷„óû endstream endobj 581 0 obj << /Length 479 /Filter /FlateDecode >> stream xÚ”Ír›0…÷~ í 3•‚@`ÔãÚvÜ8u•U¦ ËTS~l’ÉÛGD2Øaœ®Á=ß=Ò=Â)ðÀ·‘÷Îóš®æa(¢‘¶Ø'(ö|0ö"„} ØÜ;ó»›)û¾¼ùâB‚)u¦ËÅònåÂÐ'Îíd1clæþa?@ˆ|-~€(6JÅe±.oy&ªJ¸‡câLË¢’i]Ö‡‹Ì")3;e­Ìrg• ÞcDÃȰÿ ^µàzn*ŒKMD†Y Õ˜¶k‹.¶{d—»rØnSñ!»IÞÍ\z@É[“—àR%žúWM™NHÜI%:Dk_4ŸlE7C&¶Àî-åyÎaR*%’JlŒ¿Tñ§SWRh`oØ•|Ýké©0™Ÿ1‡=‚ÂØÿ¨»Ÿ¶˜\ÃLþÀأ(ƸpŠç»·£}Õ7_ Õ’çÿ öý þûZèyòìÔÒZ‰GÑ·wº‘}èWù T*‹ôÿ™ûšg²¥ú‘Æüj^m¥k7näbê¶LêCŸº›Ï6> stream xÚuTÉnÛ0½û+( b¸ˆ”À=tI‘¢— zhzPdÅ"¢Å•èþûr¸xAS‡oΈ¢-¢è낆õãzq{/ ÄYJÉÑú1ž“’rTPE_¢õýÄ’äiÆ—øS[ [=lÓL×Cã­'“ Ž]3§¿ÖßnïE–d©¸MŠ2QÁ„W{ÒŒ/ ܧ¼ÄûÎè]×xd×,…Í葦ªÕú]½ŸþÀ6Ы 4v›ÎųIy1ù¤¸‰Cz6sêCêÌñ¸YIT."§Žî^ìRœb˜g½šÍ]šå\aêÑ䥫†·äDQÆ¡¦(óUöbÌ–MœÌc§7–gw9æÜTsÛT`ÑјæÄÍÏ(Ð#Y¸‡íÏñ8ùÎ+ÔgtÙ8. …’×o—3Ræáí>¤™´m°«¦g*iÿ:‡U &»“3»q—: Y«ëÛö™áŽyá ÄwÍ*é“ϪÇnÅ‚mßgÅî"»2«¤nuD?§†þ²^0kPÄNÌ%'J,QÝ/~/HÁ”'œ-woàö¡ç}öϲ(™]hººîšèXPRpæ3¿×Ûý³B)æÒ5K‰´Íà!÷á¸Óo¡Çaƒ (ûICÁfoj;š8èØ¶sóòæ·Õ°ñ·­6 ´µ£ÕÂ…èYG™¾òngÃg¾Ë§ <7à !æ!D»î#°ÑÏTˆf²>lZƃ~.D;ÛwÁÅæ aÚ*ð±Öv£ç•gc¿>{=·‘)îÓPœÇèͶhÿ^ç/‹›+4‘«^úߊ˜R¤,$È)"Š0ù?øwOý endstream endobj 584 0 obj << /Type /XObject /Subtype /Form /FormType 1 /PTEX.FileName (/tmp/RtmpBlhU1W/Rbuild4444671013800/timeSeries/vignettes/timeSeriesPlot-ltyMultiplePlot.pdf) /PTEX.PageNumber 1 /PTEX.InfoDict 590 0 R /BBox [0 0 432 432] /Resources << /ProcSet [ /PDF /Text ] /Font << /F2 591 0 R>> /ExtGState << >>/ColorSpace << /sRGB 592 0 R >>>> /Length 9897 /Filter /FlateDecode >> stream xœµM¯&Éq÷ý+j9\èª2ë#3·lC(Xž´´ d Á¡-¶áïÞ®öâúë?ý·ÿòëío¾ÿ‚ýí[üóû¿ù»ù·íÚþÏ—ü§mßþû—²ýfþï÷_Šü·_îò6Îí8/¸~ür×·«oÇ1Þösû×y¸Z§¼ßªd¿·£ÎS*s#˜ ¶™ò~;a®o}@¶·æû­”mŸržù ÙߎyÜc»¯)ÛþVoÛö8(Çnrn3å<Âig5d}+ÝΪÚiœ&Ï·óùVpè8˜·Ûö|¿í0Ï¿-”÷<vííÀqû[79xùý­J\þYÞvì¹ïo×4Ÿ•—ßË[9MΘDçÌ=7“‡]þ<çaÛžvùçùv^”£Û¶ د·{÷´œò¶Î™÷è²Ön—?[σ—Ï;8åx;Ñ“ímà¸c+»óyS6×zcT^þõ¶ãÇa—ÎN”óVÙ¶§ÅÆ9Øã²Î¹æ³]5»ü qùWåýÝbãÞуS‹|fS¢s®y±mÙ‹]ÿ£«uÀÕñ×Ô©O¶S[•ý´ºæ)±ý¶Nxöw[/<Û7‹‘éïÍt· AôwitÓ<ûVæàAOÌÓ·k-¥XWLÙ¥(·íºZ¤L¦ë«g—uÇ<ŸzI[ܾýmÑrÍËÒ—Ù×6JiŒ—áûì>uÝÙ;»º×'‚JµèX÷½ÔÊ ™¡dǯ§õÇU8J ÑSbú²¸‰zð|wîÿ¶þº.F{™ ýqíIá4݇´ÏͰ,‡]Ú‹ÿêÖ_7D™yÂúcà¶BŒŸÆA^f.°øiº¾©G<þÌÖ_…cµÌÁcýÑ8X ÀnýwïÒ6м¿fb:8Ö-·•ùè¯ù—íÿT’ÝÕ?çʲŒ‡s¥YÆ÷É<;/{gûiý5»Õ^9™i§¾¹?¦Úég¼žÌµV²½ßS£¿f7Ûߥt{ , •o•QË¥„{ê~\̸ó66Ûÿ¼Ñ'Ÿ Œ¯‹9÷igÒaÆû{1ë–®þ¹˜vgØòz§FMmÉ£\L¼FÖ¿73ïF¼ž›©îJ#~æ0µÇG™O5ô×ÜÞRd¹™}1 «4úc£JÍü;‡™eºr3Ï4Áþšýõh¦àG3O]ìxIx¿pÙÔˆŸ™¦ìÑXæó ý5ÓØN?óðÔŸÍqI´q{ËÄuܾKÅuæëÒ¤g¡ý°ûÑ,mٸΠÎñÙ,×ùp®gÇA³¿ç“îÀñç0²ûÓ-CŸMzÀ_ÔŸÝòqØÞòqDÆëÔ³¿ê<ÌÁí-×¹æ—n‰³âAgçÓ-×¹[Ïòq6öG·ÄWñ4³ýÂþ˜aÓ¥­?–f>^ý=˜£n¼ƒ~æãy¿ìAXØ‘Ç| 1þóñº_ƒùxŸã—íñ£û_wæã¨-~tüº3OmeWÝ™çckwmñ3XyÔùØã¿îÌÇó1bñm㩱n«;óñ··À_ã¡îÌÇÏÐçkÏ·Z˜ýzk)Œõ/´ÅÏÁ¤ËǸ?ÛŸŒŸãZñsÓã§²ÿ+b·ö›Úòqåm¥FüL}pÿƒñÓÔ?ugüÌó+ÒˆŸµ¿Z9ž–>?Šoh‹ŸƒãµVËÇküÔ™ˆ Çç°ë©ÌÇó6Z mýÑý|˜çm¿i‹ŸåŒŸªó9˜=_Ô£æø9ªÇÅ=Ž”*jáÝâå°ýÌÇ+~æãG+Æ/ô`<4î¿3~vÝïSùØ^a •+6£¶||ðy\OåãSñ=Ÿw诩ÿ§ò±òw=•—ŸùxÞÆZ¤‘çm<¹}³þªëÁzª&ÖËF½˜çeÜMº…çc½˜çe[=Q¯Ê—½Ô‹…ñÔŒ·‹•±¿Ô‹¥±¿EAëÅÁžÇ…ËþTÿõbuì%|½X£J?¥‘ü]¢Þ;ßfüŸÒöúp)~nÖÇþ:QoÖdz›/nÏúø´ j«oÝŸùüSýÃñÂB6ìõñô[~†¶zPõf½Gª¡­¼™¯kS}¬|_›êãC×ÓTëõº…z»ÎçŸÕƒª—kS}ÜY¯APÿׯ|Œw]»ÞÆ|<5Ïwjô×ìFÞßÎ|ìõ9\èyy?:óñºžÎ|<Âý3Tö>QXÖ®úxg½­úx°]õ±^–¸?(Óª´ÅÏðóa>öB$RÅÇÛ(?õ–¶øYþÊþòóª/=?ëãyoƒõ1n³ÿ°| }Tië¯æûg}ŒëÏÁúx–%v½ö`œþ¦zÅámíV/"¢¿æm±ííAX­Ýê3hôǼ­;ý¬ŸýY>†Eºõ¸=ëãY\»iæã¨?3 vž/ó1ÂÆÚ g+fûyšf>F˜¹F¬ýæã¦çâgí¿pÊbú­QxÞÉÏ|ÜT¿A#~ÖõæãVÏ(œí}´ëø•ùxÞO»ßÐ6ž*n+4󱿿•ùx¦{¾A·ý¯öâQŸñ€9‹Ÿ“Ïh½ŸÛûÌQ›¿ŸŸÜg0æø²ç=sÙ³ngÔÌÇs{ËG˜Úy|‹oèû~Þÿñ¢rõ¨5Ut¿Ÿ÷q$îkýs´ô>j‰<¼ïÚ‹ÛnãÓž6}t>ãË^ôú“O‘¨íý\ùÚñùÌÑ` Z>V¾Çô0þ±cËÇó~ZÿžšÏÐû5¬?ô¾ }‡÷c%\ÏU¥-ÿTÖ7HLWÞoÑ`ϧÆüŠÙÔVÐ×Çøº˜}f7Æú«êz/åcÍgØ‹x}ò%ò?ó±ÏàÅþb¾´÷QKÜ÷3b7.¼Ÿ[â ù mñ£÷} ìúïV>ÖüÆqß¹?næãçxÌÇžÏ5ÐVþ>šæ+næã£1ûìt óA¸ˆŸyZ—ëÑÃxkÊǧògS>®ŠÇÆ|¼Æ[c>žÇg¾jÍû‹ý׆'ŽÇÎ|ìó'Ð÷òCg>^ù¡s>c—Î|Œ×x;~g>öù–£3¯ûß/Ÿßa|öÛçw÷Ï|õèa¼÷áó;V¯ƒùxÅóà|†ÏçƒùeœÏ`>öçé1N«/ ÛÆÛà|Æ£™£œÒù0Ÿ÷ã{LäÿyÓô‰üøåD©}ºþƒkt¼ÍØÑ¾ä¿mßûpjçö£°"!¾}`8Ðy’Á€‡ÅÌŠnŒ¦‰ePÖtÂ9üúuÓ?ÿéƒnúÓ?ÿñË_ÿ纕í‡%<éøã¯ìOÓ3£îÍ^ÜæþáÇí»Ñµýðû/ÿéÛõÏØÏÒùP·mÇ·n‹÷ fÛ–}ÿÖ1ºm\~rc¾cýÿÓÉY,x±ù÷¿þ[ßü›Ò=_HãŸ12u¶ó¯¡$ÑMdI1ˆ»KB² &Á ‰ÿåp-œ»~,šËŽNÏ>M×F g‹f£E“fËB,œçy,š÷ Ÿªp‹t²ðíû±èml³ÑÖjOyH«jɇmîºm·!­F%ÆL4)®=á1»ä€¹sBˆ|¸Ì¯Fë!9ã¦ÌtC¨k!9k+îȇ1emµù0f¨]αˆV{’£Õ 'òaìÙæéȇmvÛ’£uøpéNqí SÝ”öŠy>|ÝöA>ü˜íý«pÚÀù°Í¢Û¶öve²l·±­oäÃ8 «}ɇmÿáÃ6a‘Û9ã@äÃE¸±ÑõZJ>ŒÖãáÃØ•uùp”'i%&ù0NÒà9ù0æõ«¤uNgOê±teh}€ø0¦ñ­3Ň1íßÉ{í}ËÚé·÷)Óu[|ØÀNN|¸°,\|Óþ§óbtÓÔäUäÃx7£|‹|û#O#f %ÆùŽÀ‡MÛâÃÐ:žÝèê¼xè|ízȇq|òSòahòòaœïá!3oŠx¯½oÁOþH>Œö›<ùdtùpQ’u>lû |¸€÷Þì/Í?‹‹œÎƒQ8ì»øù°/ù«½o›Ç“C“‘›ðxäÃÀ&=ða`ò4òa´‹óÁ¹+ ˆ«’ãx—k Äý“C_ä³–p¡y¾äÃØ^þƒýåë ȇq<Æù04yù°y³¥]l¸<>ù¥ª¹9¬È?ɇ©ÏäÃhçø'®à_ä»6ÿ}ÒoÙ×tÛ¶ë¥ÿbüt­ F{w^¬þ’îì¥û£ó}V|çÇx%ÆñíI)> LÆüD> ¬Æx.z¤9Oáþɇ‹¯w.žïTê>š|Çãù/ž.>Œó%Ÿ%o¤&.MüS|¸4Î8$/&.šÏu><÷F>¼âE|x÷üÔ•‡Öô•¹Þ‚|ù‚ñN>Œ¿Ž|ù—ùd(WŸ±òñÒùŠüž|ã³>ŒüËþÊÇMùw(7Ý/òaìÚò±æ_ć-¿“3Ïnê®íùµ;ïe>Æ4‹ñòaÝÆÅ‡í6²ùv›ï'¶2€¼zðY®jL|ÝNþB>¼žÇâÃÖû¶ø°=¯Èoö‡ž·âÃØžÇ#ÆõÔŸ_§óç›ýå|¢¬|Lž%>¼ký‰ø0ú·’ï*ïâ-äÃÖnç+>¼ïâGäÃöü:¶‡ïÊ?âÃV>Œûw9öçù¹->Œëçù’Ûö΋Ï—|”|Øâ™|¶øó‰<ôP>>œ÷*k>M|·™üŸ|Øòù²òñpž¬|¼´òq?;”·’Ûó†ü•ùx×úñaèóÞ†Ÿ|›|ØžÇv}äÃÐìsåcò=òa„‘¶g>F>ªÛâÃòzòa´“O‘[¾tlùøR_Êǧîÿ¥|¬õWâÃÐäg—ò±÷ï¥|¼x±òñÐõ]ÊÇ]|]¯ûÅÇ'ùpÆ |šç)æñaäSÆù0öO>}×Õä¿ÊÇÃù2ó1†ÁØ®¾K|Ç?ȃ™«VŠ£|”|À×C>\ë­þ$ưáõcXñzȇ1LÈÏɇ1,ÈÏɇ¡äõzhnªl‹Û°£æŒaÃõäÃм>òa #Žgòa µ[¢ƒ¶z]|¼›|Ãæ|šñF>lû§¶Û0$ÿµ|lû?·Å‡±=y=ùpe'÷O>Œã[ý%>ŒöÓy1âgjòuòa [Ž/òaÖe[|¸¾^AK‡Ö»Š›ÿض4Àóa>Ÿ&?f} ~fóéäÃÕ×+ˆC“/[š|Úú[|ØÚ¹?Öǧž—âæû¶ø0öO^B> MG>\‰é†>©-ÛùÚù[ÿ’Ç2z¶xbûáñC¾I>ŒûÏëѬÜLójg>F;÷Ç|<õø0îÏø°¥in?VüÇ2sÖmña¤eò,òaôg |ØÒî¹->Œ4K¾B>Œ4{“73ãþÛâÃæg;óñ©zC|ýËþ"NZñ#Í|<_[ÈÃɇ-Í»¶øQýåS˜f>Žñ3Óz |šü|ò3òaŒÇÇ'ù0ÚäÃÕ׋ˆÛþì|5 ÌÊí­PÀcãâþ™¡ég>žšñI>ŒÇľo‹×ùšÅñK>ŒvòcòalÏø%~üäÃðWjæã[õœøpÅ,#ù1óñ|,1ȇÑÎõ'äÃØ?ó ù0ŽßÙÎùŒ»9Ÿe>ž~ñ]æcðåÀ‡OGàÈïV¶Å‡ÑÎñD>ŒñÀûC>\Ÿ$Für<’£Ìæ|Æ¥ç±ø°•%¶=ù0Î|¿?ù˜ãvþN>\O'¶ñø0öÏë%F;ûŸ|º“ÿ2_äÃv|òbæãû`½*>Œx®ä½œÏx4óqÔ6ž4}ëØbjÏ1>,°ø°ôÞóâÈ#.·Ýƒ5ä’ËíƒÁ[ÉhÀ4ð%#‹ÁÓ¦<„™2ðÜx! <«e'ù—Ș š7ð—\{-.ߺ±Í¹ˆ×oÞ_¦é´oÞ˜ÑiŸ?½ñOñg{ÇïäÏÿõþü3QÕŸÏŸ} l Gþ¼ GGþ¼™GGþüXŽüù±$ùócI<:òçÇ’xtäÏ%ñèÈŸ—%óèÈŸKâÑ‘??–Ä£#~,‰GGþüXŽüyY2Žüù±$ùócI<:òçÇ’xtäÏ%ñèÈŸƒ¥¿žËkÖôÄüÓiSޝäM%ó¯$N9¾’9ýð¤Îg¬^Í?üi@MÏ'|ú+;ú¹xúgy,œ=nÏ×ʳ*›µ”dÓMKʉ¦ï¿\öòŽ`úÖb8ré¶Ëi•‡†¤ÒøSß0Ϫâ¾8ÉC&™’Iß*ÐH¤çÙÚtô­Éòè[S=ÄÑËi#×lܪŒ[P‰,úÖ+Qô­8H¢o½3ïy1C[d(ÙÞäp}–<¯Õð¾9„FQdmöŽò hÌã9Ï«mú܇ºiq<ùsÓ«3ñó¼f;OÒgV£Ÿ»^ÚÉž»Ñ3꺇uZød™õýË(šm¬ÞômaåáT”TkçL¨¼:'£œ·cÒˆòX€Ø^ÐÖ÷–äÉÃq(q2B$âdÜv™87«œŒ{É;AœŒÛÅÏC‰“qƒvÿüÅ¢é áäâÓñÂÉÄ»,„vÇSÄÉ4Þâd -ÇË(FöášøˆûçÂÝq;qrÙ/.ÿNšøb?ôy6q2ÚKÀÉèŽâäâŸ; 'cPáscøù¹(q2ü\@œ Íþ%N~Ο8íú¼™«xö[¸Ž8íÄ•*΀׉‹¹gwüGœ Íþ&NÆö\Bœl×cqIœŒãq'ã~—ð¹1úŸËˆ“Ñß'Í=æ’žÝ?_&NÆýåÏ'R·…“Ÿx#N~â8ù‚ñMœ ¿p0Wö½Î '#e0Y¨r}4÷Oðü¸º§úÏC"0¾ˆ“¡¹|…8ºº¶ñ4„³‰“Ë,m‡^Œø)þy>qr)š¾N†Öþ¸¼§޳‰“™/œ¼úC8Ù?WtœŒÏËýócëCç/œ¼î·prÔ?©ÂÉÈÂÉk| '—êøºyüG'ïŽk„“£¶þØùrœ¼;Nöüä8yžïE\LœìøÝq2Æ»ãåq>ãW8y}ýÊ8Z¼/˜<¯™l‹,yT}ÚG”<üK5’ä®:C y¨8GFU>3îÍ)°¥áîi"ûsH ù‘6x ¯mdÚÈ] Aü¸ûç…ÄÇøšÝΙX¬Wÿ8زo×Z-±ã^Ò§ÅM3S"Ç8PÇ(Ä궸ñ<+v±ñ=¢Ú–vÁ¦x ›ýZgEfŒ‡´#b\¾¯ 1îšp0^|ž¼˜Ãdábîêýñ‘–m»Ö†‰?ǵ¨Àˆ ¤ØkâáßM’-¨&ë«c£Ã?Š%$FI|©€»"^_ “ÏÓ & ^QG>ܯդÃC?þ!8<¯—,l¸ëS¡a/\E†»2 ý‘36.ÂgÃkÕ©ð)„¾¦QLØk#!a¬ hÛ"Âëƒ|aÜò_‹óî¸8¸ëë2Ñ`¯çƒ‘S¹-ë×eöú•ä›$˜ó¿ w­Kƶĺ¬_§¼¬_oACBàî¿q@¼†po"¤œ‡íÛÀk8“ÿb”[þÄ‹‡Óàa¥0·þòEi±ß.t"ôëõºÈo÷… ¿kX±[ºêaß®U{¢¾k¡ïZãCæÛ/!W¾N¬ž$ñíú™'ßÕ±ä½^  ÷NÉ@"íÅCÓá/Ó&ƒ“6«Ÿ‰z»“i’Þu’½þn#ÎËÙô…yñ‚æ_Ûce÷¯~mvè£fAÞ)É”ÈxGóOŠ-¤|Å—ï¸x‡¾ßZ¨,¼;ô¸ÝÅC›­C‰ƒ"Ûõ,#´;ª“W˛ؖîzÞ$÷ ×Å‹`ÛÖÇåWme•©üJ¯4¬®¯€õX%• Õ…&!ÕÅc¹ª»ó¢ºV¶²²L§L Iª[öá~.I/NIHuWY'ª‹2‹TƒT·ø*JQ]¼ÑEª ?)©nÒVf,Í2už6)3©.ÊÆ¾úÅù‘ jbðÑ*Sƒî,;wš(+ÃW¿aÿ,S‹^óDuíuÖ¿ò=Xvò+XR]øIÝIuÑÎU ¤º«¬ÕÅ[ë¾úµþ¢ß+Žw“òr•zõU¤ºæ¿¶EuñŽ\œâ"~jó¯‚-»B‹ªr•ºÿ*†¨.Þf‡S\ôÇ©Um¢ºÐ¤ìše=õõƒ¨.Þ&I1‰ñöXÕÅÛ%©!©nñU*¢ºÅeBT×öO*Ë2«TÕ-,ÕÅö%P]´‹Úr•:Þ¦ég™zøø ÕÅÛ9û‹Toç#P]hž?©.43©.¶¯NqÑÇ©þ&Õ…fi ÅÔ±-ª‹þgüêbÿ¤ø¤º¡«ý ‘¨.öÇÔÄy4lϬGª[ŽÛ¿âå´Áò“êb%P]ô÷ÒÖ_CÇ#ÕµÙjN`U©1W=¢Zò¯€?×å_sZáÒ¢¨.4û‹³>e†™öÏU—SnR]¼½_NyÑ_k{Í߯¯°Iuñv^ü+_C#»¨ú{(®9­ðhæã¨ýŒO‡Zuû£ºœE_P÷Ò72ïoäl‘éÞö:ùãq¬˜¶÷íö¤ Í|Sñf½·<ÍÃ&ã¼™*rù¸8¦Œ¤õKF–1<K&¸bWô éçÿ28ë\®_ö9ñ°dD\¿$W[ÁïoÝø²ìôB¡g¸@«ÏŸBB…!Öåa†^ÌíRÕ^ѬÙÞoW3Uh¶ºm5S¥æ1¡*4[ÒXÍT¡ÙrÈj¦ Íö¾¼š©RsÛc3Th¶„´š©žfÒo–JÍcÍ#íœ|h5S…f œÕL•š{Í=]7‘Òj¦ Í–ÙV3Uh¶÷ùÕL•šï;6ßùº-M®fªÐïý?n.£ÁøÉc Ì†r’ÑÐRÀHFCO# †Je6Ü-îÜX]úA2j Élˆ# ö!àc Œû¥ËÇ@™ 1ä$£áN# -Œd6Ä% =å(Éh)ä$ƒa”0’ÙF2jÊQ’Ñp¤%™ 1ä$£áJ# w Élˆ9J2ZÊQ’ÑÐSÈI>[ÂóÂe6„€q %æ(—ÑPcŽr™ !ä\FÃÆe4\1`\fÃ(É0^qÇå2Z 9—Ñ0bÀ¸Ì†v%CûY™pÉh(1G¹Ì†r.£áˆã2Î0.³¡·dè/‡¸bŽr w 9Éhè)`$³!Œd4Œ˜£\fÃÉs”-µ ý  5Œd4)`$³¡ÉÐ_qÆå2®r’ÑÐRÀHfC Éhè1G¹Ì†q%Ãx金BN2Ž’F2j Élˆ9J2Ž”£$£áL!' ©ðv ©ðv ©ðv™ 1G¯…·-i‹ýÐ_CîL…·ËhH…·Ëlˆ9ê|-¼±4.æ¨óµð¶År¡$£!Þ.£!Þ.£!Þ.³!æ¨óµð¶ex±ÚkÈ©ðv W*¼]fCÌQ×ká]¯Tx»Ì†r’Ñ o—Ñ o—Ñ o—ÙsÔõZxcé` 9ÉhH…·ËhH…·Ëlˆ9êz-¼m bè‡ûµð¶E‰W2¼„Ü o—Ñ o—Ñ o—ÙsÔýZxÛrÇÐ’Ñ o—Ùæ~-¼m‘d<Äkám?\ûáµð®ü+^B®¥ÂÛe4¤ÂÛe4¤ÂÛe6ÄÕ^ o,ÑŒ!' ©ðv™ 1`Úká…1Gµ×ÂÛ–zÆ~xWxÏ¿‰!' =Þý]áÝsáÝßÞ=Þý]áÝsáÝßÞXVúA2ráÝßÞ=Þý]áÝsáÝßÞ=Þý]áÝ[ 9ÉhÈ…wWx\xw…÷È…÷xWx\xw…÷¨)ä$£!Íx»Ì†0é2ÒŒ·Ëh¸ã¦Ël˜.£!Íx»Œ†4ãíò1hi®\fC€,.£¡DÈâ2ÒŒ·Ëlèw2ô—C¤o—ÑpEÈâ2dq iÆÛe4¤o—Ù0z2Œ—CŒY\CIëL\FCšñv™ !`\FCšñv g„,.³!„œËhH«F\FCZ7â2â2%Éh貸Œ†´öÄe0Ô´~Äe6Ä€©¯KH´¨ú1ÔÈ¢UÖáx,Zv /!WÓJ—ÑÖ’¸Ì†±'Ãx9D‹9Êe4¤õ(.ƒáHkJ\fC ˜ãuY‰–?†òš£¸ü1Ô×u¤•).£!­.q i}‰Ëlˆ9J2$£!­Qq i‰Ëlˆs¼.5ÑBõĕ˜£$£!-Vq i©‰ËhHKM\fCÌQ’Ñp¥% i±ŠËhHKM\fC ˜óu©‰–Ô?†ñ𣏯>^rÔ•«¸Œ†´ÔÄe4¤¥&.³!æ(Éh8SŽ’Œ†´XÅe4¤¥&.£!-5q =å(Élˆ9J2Òb—Áp§¥&.£!-5q™ 1GIFÑr”d4¤Å*.£!-5q i©‰Ëhh)GIfCÌQ’Ñ«¸ †––š¸Œ†´ÔÄe6Ä% 5å(Élˆ!×^«ès‰ÇðZxëû‰ÇðZx냊ÇðZxë ‹hxÉQ--Vq ©ðv =Þý]áÝsáÝßÞ=Þý]áÝórïþºXEv<†w…wÏ…wWx÷\x÷w…wÏ…wWx÷´XÅe4¤o—Ù&\FCšñv c“.³!L¸Œ†4ãí2ÒŒ·ËhH3Þ.³!L¸Œ†´XÅe4¤o—Ù0J2Œ—C¤o—áão_þÌÏ>YëýùÛO×*~ºpìÓ%6Ÿ®wøŒ Ù>ŸNý~:ÍöÙŒÅç/ŸÚŸ–$Ÿ>>jŸiü°æùšæùÄæ´¯ãOûºó‡·üîïþǯæ=ß¾ûßÛ^~õOÛ¿yý<磽\öͳF¶—ïê¾_}ÚóѶÝþ}àp¿ùÕV÷í»ßýqÛëÏ>ƒa¿Ò›ÎàË>Ú+]k8…ï~û»ÿØ>þ× ?Þ|à[Ã_xìÛ~ ,\þo÷«m>Û¾û¿ßrì×s®_xöOÒônÁÿúö?ûlÕAû¥ç€ÏÜâ=øþ_þç7Ü£h¿øØÝþIš?w`úøþ¥q€9•ûú‰aðóÏÁæMz>‡ö³Ïá²*à—Ž¼(œçÏ8öWrßkÌ—& ¿ýà'W™êþÿÿâj~Bû¨?õˆŽí?£“ãÇtr|ø”NŽ+}S–{õyN÷üœîÙ1ò·é—Wד:¼Mæ^}Õw~TçóÐ/¼Ägõõ²–¾É¿ºú•§ut|ü¸NŽŸ×Éñá;9>|b'ÇÈÓÒé÷VŸgvÏÏì|/_0ó“èŸþ„™í?ý 3Û¿ò³¾¹þÊWÌr|å3f9¾ò³Ùý|Èü÷_þ§”º endstream endobj 594 0 obj << /Alternate /DeviceRGB /N 3 /Length 2596 /Filter /FlateDecode >> stream xœ–wTSهϽ7½P’Š”ÐkhRH ½H‘.*1 JÀ"6DTpDQ‘¦2(à€£C‘±"Š…Q±ëDÔqp–Id­ß¼yïÍ›ß÷~kŸ½ÏÝgï}ÖºüƒÂLX € ¡Xáçň‹g` ðlàp³³BøF™|ØŒl™ø½º ùû*Ó?ŒÁÿŸ”¹Y"1P˜ŒçòøÙ\É8=Wœ%·Oɘ¶4MÎ0JÎ"Y‚2V“sò,[|ö™e9ó2„<ËsÎâeðäÜ'ã9¾Œ‘`çø¹2¾&cƒtI†@Æoä±|N6(’Ü.æsSdl-c’(2‚-ãyàHÉ_ðÒ/XÌÏËÅÎÌZ.$§ˆ&\S†“‹áÏÏMç‹ÅÌ07#â1Ø™YárfÏüYym²";Ø8980m-m¾(Ô]ü›’÷v–^„îDøÃöW~™ °¦eµÙú‡mi]ëP»ý‡Í`/в¾u}qº|^RÄâ,g+«ÜÜ\KŸk)/èïúŸC_|ÏR¾Ýïåaxó“8’t1C^7nfz¦DÄÈÎâpù 柇øþuü$¾ˆ/”ED˦L L–µ[Ȉ™B†@øŸšøÃþ¤Ù¹–‰ÚøЖX¥!@~(* {d+Ðï} ÆGù͋љ˜ûÏ‚þ}W¸LþÈ$ŽcGD2¸QÎìšüZ4 E@ê@èÀ¶À¸àA(ˆq`1à‚D €µ ”‚­`'¨u 4ƒ6ptcà48.Ë`ÜR0ž€)ð Ì@„…ÈR‡t CȲ…XäCP”%CBH@ë R¨ª†ê¡fè[è(tº C· Qhúz#0 ¦ÁZ°l³`O8Ž„ÁÉð28.‚·À•p|î„O×àX ?§€:¢‹0ÂFB‘x$ !«¤i@Ú¤¹ŠH‘§È[EE1PL” Ê…⢖¡V¡6£ªQP¨>ÔUÔ(j õMFk¢ÍÑÎèt,:‹.FW ›Ðè³èô8úƒ¡cŒ1ŽL&³³³ÓŽ9…ÆŒa¦±X¬:ÖëŠ År°bl1¶ {{{;Ž}ƒ#âtp¶8_\¡8áú"ãEy‹.,ÖXœ¾øøÅ%œ%Gщ1‰-‰ï9¡œÎôÒ€¥µK§¸lî.îžoo’ïÊ/çO$¹&•'=JvMÞž<™âžR‘òTÀT ž§ú§Ö¥¾N MÛŸö)=&½=—‘˜qTH¦ û2µ3ó2‡³Ì³Š³¤Ëœ—í\6% 5eCÙ‹²»Å4ÙÏÔ€ÄD²^2šã–S“ó&7:÷Hžrž0o`¹ÙòMË'ò}ó¿^ZÁ]Ñ[ [°¶`t¥çÊúUЪ¥«zWë¯.Z=¾Æo͵„µik(´.,/|¹.f]O‘VÑš¢±õ~ë[‹ŠEÅ76¸l¨ÛˆÚ(Ø8¸iMKx%K­K+Jßoæn¾ø•ÍW•_}Ú’´e°Ì¡lÏVÌVáÖëÛÜ·(W.Ï/Û²½scGÉŽ—;—ì¼PaWQ·‹°K²KZ\Ù]ePµµê}uJõHWM{­fí¦Ú×»y»¯ìñØÓV§UWZ÷n¯`ïÍz¿úΣ†Š}˜}9û6F7öÍúº¹I£©´éÃ~á~éˆ}ÍŽÍÍ-š-e­p«¤uò`ÂÁËßxÓÝÆl«o§·—‡$‡›øíõÃA‡{°Ž´}gø]mµ£¤ê\Þ9Õ•Ò%íŽë>x´·Ç¥§ã{Ëï÷Ó=Vs\åx٠‰¢ŸN柜>•uêééäÓc½Kz=s­/¼oðlÐÙóç|Ïé÷ì?yÞõü± ÎŽ^d]ìºäp©sÀ~ ãû:;‡‡º/;]îž7|âŠû•ÓW½¯ž»píÒÈü‘áëQ×oÞH¸!½É»ùèVú­ç·snÏÜYs}·äžÒ½Šûš÷~4ý±]ê =>ê=:ð`Áƒ;cܱ'?eÿô~¼è!ùaÅ„ÎDó#ÛGÇ&}'/?^øxüIÖ“™§Å?+ÿ\ûÌäÙw¿xü20;5þ\ôüÓ¯›_¨¿ØÿÒîeïtØôýW¯f^—¼Qsà-ëmÿ»˜w3¹ï±ï+?˜~èùôñî§ŒOŸ~÷„óû endstream endobj 598 0 obj << /Length 557 /Filter /FlateDecode >> stream xÚ…SM›0½ó+¬\ÖHÁñX)=TmªVm¥ªH=t{`k§|l”_›1Ù¤ÚªBÈÏ3~of̃¢¢èƒGÝú6ó6»(FŒ‘4Š8Êöˆñ$”£˜JÂxвýĉü€1á/ÚÃ¥ÚŸUw08ŽðgÕU€~¨r¬ÿWöi³1JI*¹´ª"!‚ ÐûªGC Ö]s¶(Æc]A¨5ƒFŸ'ø|ô³Tk[AâÇÉ1ófÐî0ã+æÉö°È;È>‚D[=ïJõ@EX•ÜëŽVyaK/ SÿìÏT3—n &QV]yŸyÌŠØÅ<âDŠ­÷Û#1“pà͉…å›­ÿö¾™gÉ‹dp¥9;óÆB—‚’˜3˜|§So¿)M1—÷~ ޳ÙŒâCŸkHµ¶ßð4Àv¬ûÊÑœk :ãÙÁÖl,tÎ2™éè\Øß9SÕÎ@u)¡Œ‹bü´~Í7³4— ·P´l*+m#WÒ\òEÚfÔ¡¶*ÕÔš&¹ù#ò®„¬“ä¸Qga¼s¤ÿË^z2­w¡¾Ö¸î*rã­ˆII’8²tIDìœò¿õq= c endstream endobj 595 0 obj << /Type /XObject /Subtype /Form /FormType 1 /PTEX.FileName (/tmp/RtmpBlhU1W/Rbuild4444671013800/timeSeries/vignettes/timeSeriesPlot-lwdMultiplePlot.pdf) /PTEX.PageNumber 1 /PTEX.InfoDict 601 0 R /BBox [0 0 432 432] /Resources << /ProcSet [ /PDF /Text ] /Font << /F2 602 0 R>> /ExtGState << >>/ColorSpace << /sRGB 603 0 R >>>> /Length 9898 /Filter /FlateDecode >> stream xœµM¯7¹qÝ÷ϧèåháë&û…äVFX€Œ83€†‚cÃ4Jl! òíÃSç»êïóÜ™‘²ðXgxúåÏ.Vùcß)Ûo¶²ý~û÷/¿ýû¶¿íûVÛõvÛyT¨²·rmÿñ/Û?l4ÓÙßÎæ®ã>Þ®öâúë?ý·ÿòëío¾ÿ‚óí[üç÷ów_ê[½¶ÿóåÿiÛ·ÿþ¥l¿™ÿ÷û/Å.þÛ/wyçvœ×[»¶¿ÜõíêÛqŒ·ýÜþðe^®Ö)ï·*Ùïí¨ó– ä<æ‚c¦¼ßN˜ë[ím‡ù~keÛ§œw~Cö·c^÷Øßîkʶ¿ÕÛŽ=ʱ›œÇL9¯pÚ]YßJ·»ê;e¯v§Éó­Á|¾Ü:æãí¶3ßo;ÌóßÊ{^÷Üß»övàºý­›üùý­Jüü³¼í8sßß®i>+~/oå49/`3ÏÜLöóç=;ö´ŸžoçE9ºÛðûõvÏëžÖƒSÞÖ9ó]ÖÚíçÏÖó ÄÏçœr¼èÉö6pݱ¿•Ýîù¼)¯k½1*þõ¶ãŽÃ~þ9;ePZlÌGeÇžç`oŒË:çšOÎNÕì牟U>ßÑ-6î=8å°Ø˜Ága6%:çšÏÇ–½Øïtµ¸:þ55Âcê“í‡ÅÇÔÖ_e?­‡®yKl¿­žóÝÖ ÏñÍbdú{3Ý-Hý]Ý4ïþ@„•9xÐóöí·–R¬+¦¿ìÒ”ÛN ]-R¦¿ Ó‡õÕs¾ËºcÞO½¤­?n?þ¶h¹æÏÒ—Ù×6JiŒ—áçì>uÝÙ;»º×'‚JµèXϽÔÊ ™¡dׯ§õÇU8J ÑSbú²¸‰zð~wžÿ¶þº.F{™ ýqíIá4݇´ÏͰ,‡ý´»ÿ9Ô­¿nˆ2ó„õÇÀc…>?ƒ¼Ì\`ñÓôû¦ñú3XŽÕ2õGã`-H»õß½KÛ(òþš‰éàX·ÜVæÿ@ÍÿwÙùO%Ù]ýs®,Ëx8Wše|ŸÌ³ógïl?­¿f·ZÂ+'3íÔ7ÏÇT;ýŒ×“¹öÑJ¶—â{jô×ìæaç»”n„´ò­2j¹”pO=‹w>ÆfçŸúä{ñu1ç>íLº3Ìø|/fÝÒÕ?Óî [þÞ©Ñ_S[ò(/†‘õïÍÌ;‡ÏÍÔ‹aW¥?s˜Úë£Ì·úko)²Ü̾†Uý1‡Q¥fþÃÌ2]¹™€gš`Mþz4S𣙃§.v½Æ$¼_øÙÔˆŸ™¦ìÕXæû ý5ÓØN?óðÔŸÍqI´ñxËÄuÜ~~KÅuæëÒ¤g¡ý°çÑ,mٸΠÎñÙ,×ùr®gÇA³¿ç›îÀõç0²çÓ-CŸMzÀ_ÔŸÝòq8Þòq¯DÆëÔ³¿ê¼ÌÁã-×yæ—n‰³âEg÷Ó-×yZ]Ïòq6öG·ÄWñ6³óÂþ˜aÓ¥­?–f>^ý=˜£n|ƒ~æãù¼ìEXØ‘Ç| 1þóñz^ƒùxŸã—íñ£ç_wæã¨-~týº3OmeWÝ™çkkwmñ3XyÔùØã¿îÌÇó5bñm㩱n«;óñ·À_ã¡îÌÇÏÐ÷kï·Z˜ý÷ÖR?ê_h‹Ÿƒ5H-–ñ|:??;Ç#´âç¦/ÆOeÿW Äní7µåãÊÇJø™úàùã§©êÎø™÷W¤?ë|µr<-}0~ßÐ?Çk­–×ø©3ŽÏa¿§2ÏÇh%(´õG÷ûa>žý>¤-~–0~ªîç`>ö|Qšãç¨?ÿõ8Rþ©¨…w‹—ÃÎ0¯ø9˜­|\¿ÐƒñÐxþÎøÙõ¼Oåc›Â@+WFmùøàû¸žÊǧâ{¾ïÐ_S3þOåcåïz*/?óñ|ŒµH#ÏÇxòøfýU/ÖƒõTM¬ÉF½˜çϸ›t ïÇz1ÏŸmõD½*' šÔ‹…ñÔŒ·‹•±OHêÅÒØgQК8Øû¸¢pÙŸê¿^¬Ž½„¯ËcTé§4òÏ%ê½sö0ãÿ”¶éÃ¥ø¹Yût¢Þ¬g7_<žõñi j«o=ŸùþSýÃñÂB6œõñô[~†¶zPõf½Gª¡­¼™¯kS}¬|_›êãC¿§©>Öôº…z»Î÷ŸÕƒª—kS}ÜY¯APÿׯ|Œ¹®ýÞÆ|<5ïwjô×ìF>ßÎ|ìõ9^\èùù<:óñú=ùx†ûg¾¨l>QXÖ®úxg½­úx°]õ±&KH\ˆ”iUÚâgøý0û„‰TñÃñ6ŠÇO½¥-~–¿²¿üþ†êãKïÁúx>ÆÛ`}ŒÇl÷?,CUÚú«ùùYcÀús°>že‰ý^{1NS½b‰ð¶v«‘Ñ_ó±Øñö"¬Önõ4úc>Ö~ÖÇÏù,C"Ýz<žõñ¬?®Ý4óqÔˆŸ;ï—ùací…«³ýnªß ?ë÷æãVÏ(œm>ÚuýÊ|<Ÿ§=ohOšùØç¯Ge>žiÁÞoÐmÆ«M<ê3°Æcñsò}­ù¹ÍgŽÚ|~~ò|\Á˜ãËÞ÷HÌeϺQ3Ïã-a]hçõ-¾¡ïû™ÿc¢rõ¨µTt¿Ÿù8÷µ‡þ9ZšZ"ó]›¸í6>íý`ËGç3¾l¢×Ÿ|ŠDmósåkKÄç³FƒjùXùÿ£‡ñ[>žÏÓú÷Ôz†æ×h°þÐ|úóc%üž«J[þ©¬o˜®þÌoÑ`ï§ÆüŠ ÙÒVпñu1ûÊŒõWÕï½”µžañúäKä1~.æc_/ÀÄþb¾´ù¨%îûY±æç–¸B~C`[üh¾o]CÿÝÊÇZß8î;÷ÇÍ|ü\ùØó¹ÚÊßGÓzÅÍ||4æc_ý‚na=ñ3oër=zoMùøTþlÊÇUñؘ×xkÌÇóúÌW­y±ÿÚðñÄñØ™}ýú¾C~èÌÇ+?t®g¬ñÒ™1·ëwæc_o9:óñzþýòõÆg¿}}§ñüÌÇQÆ{¾¾cõÚ1˜W<®gøzÎ1˜QÆÙý æcŸãôø±úºa¼ ®g<šù8êÁõ ÝóñÙø<¾ÇBþgËô6úê2}¢?~9QjŸ®ÿào+v´/ùoÛ÷~ÜÚ¹ý(¬€HDˆotžd0àe1³¢$£ib”5‚]†p¿þAÝôÏú ›þôÏüò×ÿ¹neûá_ O:þñWöOÓ3£îÍ&nóAÿðãöÝè¿Ú~øý—ÿôƒú'‹wé|©Û±ãç‹yP³c˾ÿ܃1º\¾z0çXÅÿÇÓÉY,xqø÷¿þ[?üg¤{NHã?cdênç?¾…’yÝÐ'TëÔ³îq* I²kÓ1¬BÛˇTÒè ©°­A[«½Û!­–%¶ë¶9†´Ê”TëÏd·ö^ÇÈë’æÎe Rá2½>T­‡äŒ–2“ Q®2Vª­¤#ÆBµUH¤ÂX—v9G ZíõI*ŒV+—H…qf[#¶5qK*ŒÖý¡Â¥;»µ™¸)mby>TËÛ6o ~Ì6ë*\,p*lkçv¬Í©L–Í©0޵’T·a/©°­Û?TØ–éq!Ra»g\ˆT²#6º&£¤Âh=*ŒSYç GÙx“VX’ ã& ™“ c5¿JZçtö¤^AWv€vˆ cñÞ:ST‹ý”×fYÖN¿Í¢L×mQaÀ»9QáÂbpQa,öŸN‰ÑMS“R‘ ã%@Ê@*ŒvR-RaœTpT”T÷;6}l‹ Cëzö «Sâ¡ûµßC*Œë“š’ C“Ê ã~×™ùPDym–?©#©0ÚoRä“ýÑu?¤ÂE©Õ©°/PáJ@Ê{³¿´ê,* r:F¹°ï¢V¤Âv¿¤®6Ë,!…'†&",áõH…Kz Â€!¤h¤Âh5æërWL!$Æõ.×xwÆJ“BàxRZRˆýà¬PT~Æ3©0Î7H‘I!v§˜¤Âh'Å$ÆýܤÌ7û£ë÷ îb“ C3ÞH…¡o×Ö_]ã‰Tð‡ç'†¾He-áBó~I…q¼üûËw ãzŒ/RahR>Ra»?RfK»8þp=x}RKÕpsX‘z’ N1žI…ÑÎñO*\A½HumÕ ú¤ß²¯é¶-*l¿—þ‹ñÓµ €TíÝ)±úKº³?–ìÎY¬¨0îñJ*ŒëÛ›RTpŒù‰T0ñ,*\ôJs* ŠÂó“ ßå *\<ß©À}4©0®Çû!^]T÷K*K* ÊHM*\š¨§¨pi\õp* IJL*\´ŠëTxžÿŒTxÅ‹¨ðîù©+í2è+s—©0òãTÿ:Raä_æ“¡|\5~ÆÊÇK7æ+R{RaŒÏ¨0ò/ûo(7åß¡|Üô¼H…q>R¨]ù8hËÇZu¶üNJÌ|<»©»¶÷×î”—ù‹+FAH…õ¶ÇÈvæc¼Øm•ŸTØÊRêÁw¹ª1Qat;© ©ðz‹ [ÿíÛ¢Âö¾"µ=Øzߊ ãx^T¿ç ¾øþ::ßì/§eåcR,Qá]»ND…Ñ¿•TWùxe!¶v»_Qá}5"¶÷×±=TxWþ¶z Pa<¿Ë)°¿ÏÏmQaü~Þ/©°ï”xð~IEI…-žIe‹¿ŸHAåãÃ)¯ò±VÑD…ñ˜IýI…-¿‘*+§ÈÊÇK+wQ³CùØ)+©°½oH]™wí†>ïmQaøIµI…í}l¿Tšýq®|LªG*Œ0ÒñÌÇÈGu[TaBJO*ŒvR)RaË—N-_êïKùøÔó¿”µëJTšÔìR>öþ½”%V>ú}—òqU×$¿øø$®€7 Cóþ/åãÁü"*Œ|Êx ÆùI¥ïºúƒÔWùx8Uf>Æ0Û¢ÂÕwa‰ ãú)0óqÕþWQa´“Š’ cð÷ ×z«?I…1lø{H…1¬ø{H…1LHÍI…1,HÍI…¡¤ÂµzhEð©l‹ Û°£æêMU=**ŒaÃ]¤ÂÐü}¤ÂFϤÂ&j·Dmõº¨0†)7©0†Í¨04ãTØÎOm'¶aHêkùØÎn‹ ãxRzRá:ËNžŸT×·úKTí§SbÄÏÔ¤ê¤Â¶_¤Â6¬Ë¶¨p=|—‚–“ír6ÿ±-*li€÷Ã| *MjÌúÔÌVÑI…«ïR†&U ¶4¨0´õ·¨°µó|¬O½/E…M÷mQaœŸ”„TšŽT¸Î-* }R[>¶ûµû!¶þ%…e>>4¶xbûáñCªI*ŒçÏߣµ¸™æÕÎ|Œvžùxê=Pa<Ÿ3PaKÓ<~¬ø!…e>ܬۢÂHˤX¤ÂèϨ°¥Ýs[Ti–T…Tiö&ef>Æó=¶E…ÍÏvæãSõ†¨0ú—ýE*œ´âGšùxN[HÁI…-Í»¶øQýå —f>Žñ3Óz TšÔT¯R3Ra¼F¬þÆkÃê!QahÆ ©°½®¾KETi[T—ùøÒb˜¨0Úu>æchR^[Ï@𝠣TTØü¤ÌÌÇ·vMŠ Wß%#*Œ×Ç'©0Ú¤ÂÕw‰ˆ Ûùì~µ ¸Êã­PÀkãâù™¡ég>žšñI*Œ×ľo‹ ×9Íâø%F;©1©0Žgü’ ?~Raø+5óñ­zNT¸b•‘Ô˜ùx¾–¤Âhç®RaœŸù†T×ïlçzÆÝœÊ2O¿¨.ó1¨r Âˆ§#PaÄw+Û¢Âhçx"Æxàó!®‹J’ #~9I…QfˆBs=ãÒûXTØÊ;žT÷GªßŸ|L «|¼(2×3Î.ªO*ŒãÙ¤Â8?Ç©0âŸùŒTØÊú¹žq;u'®‹¢“ ÛøTççï%F;ûŸTº“ú2_¤Âv}Rbæãû`½**Œx®¤¼\Ïx4óqÔ6ž´|ë°bjÏ1*, °¨°ôÞSâH#.·=ƒ+äFËíƒ![ÉhÀ2ðƒ%#ÁÛ¦<\™2ðÞx.! ¼«e7ù—ÁÆX šð—á[›‹ý–Ÿ{°­¹ˆýÖŸ}0¾GÓm?û`F§|~ýà¯Qg›ãwRçÿúuþ‰€êϧÎ>¶¡u^†¡#u^ŽL¡#u~,‰BGêüX…ŽÔù±$ ©ócI:RçÇ’(t¤ÎË’)t¤Î%QèHK¢Ð‘:?–D¡#u~,‰BGê¼,™BGêüX…ŽÔù±$ ©ócI:RçÇ’(t¤ÎÁÒ_ïå5kzbþzÚ”ãySÉü‰SŽodN!<©ó«Wó¿Ž¥éù„JãDÖÛŽC›dÏŤÛóeò¬Åfe¥‰tÓöqé{ᔽ¨#޾µñ4ºírZ½áå Y4þ©ï•g-q_\Ú!‰ÆúÈ¢o•eäÐónm‘‰úÖ’)ô­Bèå´ñˆßlèÛj‹[(‰úÖDúÖÇäÏ·&ÁÌv^Â>ãÁ@¶ù® Oç¯E ¼oŽžQ Y›ÍìÀ?ðŒÕæùk›>í!vnÚOêÜ4a&tž¿Ùî“Ì™5¨#箩:‰sø pF5÷ðæ®Eâf¬ŒØ9m†ÔüµXN6e«ió,Vì5cl÷ÍIóðÏs™ÿi Ö¶e´q¤oGa¿ 2ãç„/»# "f¿ fônøìŒ~ß_nŽÓˆ—±"|så ‚ùðÅzm®æ=!´¼¾¿#Yîú|C`¹g®Ü«+£˜§ÙÞ6ó&{€Ê¸gÿ²¸ØÓчÅöšô‡%¢ÜõY»€2pàɬêNF@Ñlc ̦o &g¡dÉèX»g¢äEι$å|3îÀ‘ÇÂÂ6-[ßV’"‡ „È‘‘ñØyeBd<¬ 2ž%Ÿ!2?%DÆÚýSc¼ ‹‘‹/ "[lê²üÙJ"#Ðø|‘-´*£Ù‡kB °žŸÛuv‡ì„Èe¿¸Õ[9hB‹ýЧ؄Èh/"£8z‘‹Ú(ˆŒAu„O‹áç§¡„ÈðsS!24û—ù¹Bd´ëSfîÝÙoA:Bd´Rª$T'$æöÝ¡!24û›ÇsS!²ý‹KBd\ƒ˜Ï»„O‹ÑÿÜ´@ˆŒþŽ9hîãy47òìþ©2!2ž/ÿô!r!k[ù‰7Bä'Þ‘‘/ß„Èð s?OÑ$^)ƒÉBõ꣹¥P‚÷Ç==ÕÿÔé#2㋚›V‘¡«kOC›¹Ì‚vø§ÄˆŸâŸâ"—¢E[Adh›zÊá›™ùòÈ«?‘ýÓD‡Èø”Ü?5¶þ8tÿ‚Èëy "Gmñ#ˆ*ˆŒ|Áñ ˆ¼Æ· r©­›Ç 4!òîF9jëo ‡È»CAdÏO‘Ka8DvèîãÝ¡ò8Ÿñ+ˆ¼¾teœmÔBž¿™D‹yT}ÆG€<ü«4òã®:Cøx¨8=FU>)îÍÙ¯¥áîdû{Häø‘6x ¯mqcÚÂÆ] AÔ¸û§„„ÆørÝî™0¬Wÿزo×-ã^ÒgÄMëQâŸPÀÅ(Äê¶hñ¼+vañ=bÙ–vA¤x![óZwERŒ—´ƒaü|ß@NܵÌ,L¼¨<)1‡É‚ÄþÞÕ¬ñ‘–m»v„‰?×µ¨Àˆ |ØkáááßH’m£ë ccÃ?€%FIò{©€»^_›“ ÏÛ ö%^QG*ÜŠ ¯=dÂCèCHxþ^4á®Ï^„½pîÚ¾$<öGÎØ@ºŸ¯½dÁk¤ûNF‘`¯‚±o¢m‹¯ï‰ñH}-λCvBà®/ÉÄ€½žFN屬_—ÙëWònò_®ú.üÛµIôÇæ²~=œí²~½… ‰~»ÿ=’ß54~{åèkßö]ÙÔ£ŒØò'&΀‡•ÂÜÒBäˉÒ"¾]ÀDÀ×ëuñÞîÛˆ{×°b·tÕ)‚½]{õÄz×Ö¢Þµ³‡¤·_­œN¬ž$çíú“N¼«cIy½@ä’DÆ‹—¦#_¦M!—jV?ðvçÑä»ë&‰w}n#ºË5ôw1Aó/€íµ²û¾¶ô:ô³Ðî”$I$»£ùçÃR¾ÏK\wÜZö'Öú^Twh{² îÐëZL/m¶%^ˆD׳Œ€î¨Î[-oâX~¤ëy“´ƒ4Á¶-˜;¯Ë/ØÊ*SùE^iþZ]_üêµJA– MöA–‹×r ,w½æÅr­l![e™:o™ì‘,·ìÃý܈^œå®²N,eYYnñ½“b¹˜ÑE– ?ÙYnÒVf,Í2uÞ6Ù2Y.ÊÆ¾ðÅý‘j9ðÑ*Sƒî,;wžŸK™(+þáü,S‹¦yb¹6õ/z–üâ•,~²v²\´soYî*kÅr1k½Â¾Ö_ô[bÅõn²]îM¯ÎþÉrÍm‹åbŽ\œÝ"~jó/€-»B‹¥roºÿ ±\Ìf‡³[ôÇ©½lb¹ÐdëZ[=õ̓X.f“d—„ˆ˜=ÖÀr1»$+$Ë-¾7E,·ø_”˵ó“ŲLÅÞ”Àr K†Årq| ,íbµÜ›ŽÙ4ý,Sd¹˜³¿Èr1;åBóþÉr¡™‘Èrq|uv‹þ8Nõ7Y.4ûK Ï(¦Žm±\ô?ã‡,ç'»'Ë íÜëxè/‰åâ|LM\GÃñÌzd¹å¸ý‹].,?Y.ÎWËE/mý5t=²\[m æ²öbs¯#ª%ÿâñs]þÅ/—.-!ŠåB³¿¸êSf˜éüÜëx9Û&ËÅìýr¶‹þZÇkÕ~}qM–‹Ùyñ/z ˆìbéCÄ¡¸æ²Â£™£ô3>h¯í_Œårí|¡ÜK_Ƽ#»‘®E’{ÛtòÇ…ßX1mïÛíHš9SñfÍ[žæa‹qÞLiÆ|]œSFÒú%£›ž‹%R±_ô€èçþe 0v·\¿ìÓáaɈø¸þ\|\mW¾îÁ—e§ö<ÃíP}þ‰ $DQ˜]]fèÅÜ.UmŠfÍ6¿]ÍT¡Ùê¶ÕL•šgÄ„f¨ÐlIc5S…fË!«™*4Û|y5S¥æ¶Çf¨Ðl i5S=ͤ?Þ,•šÇ›G:9ùÐj¦ Í8«™*5÷›{úÝDJ«™*4[f[ÍT¡Ùæó«™*5ßwl¾óï¶4¹š©Bóˆá •šC8H=Í$YÞ,š­ä]ÍT©9“Th>b8H…æ3†ƒTj=6|r«žW3Uh¾c0I…æÃA*5‡p Ͷ~±š©žfB8o–JÍ!˜¤Bsá šR©9ä©Ð|ÆÜ"š¯LR¡¹ÅpJÍ!¤Bs¹E*4˜[¤Rs&©§Y@ÑÛ]FCá2B‚q GL1.£áŒQå2î.³¡ÉÐ^.Ñbªq =&—ÙpßÉLt$£¡Ä(q™ !鸌†ÓŽËh8b¤¹Œ†+F‹Ëlh{2¼ ìc¸_ò l4Œ—~h)ä$£a¤€‘ ¢Üĕˆ\FC‰©Èe4Ôr’Ñp¦€‘̆0’ÑpÅ”ä2BRr w 9Éhè)`$£a¤€‘̆֓!g'!êeŒ†’BN2j Éh8RÀHFÙr”d6Ä% W 9Éhh)`$£¡§€‘̆˜£$£a¤% ìË  ©Bv ©Fv ©Jv™ 1G¯…²¨ýc8_CîLµ²ËhHÕ²Ëlˆ9ê|-˜EþÃkɬ­ÑðrWªš]FCª›]FCªœ]fCÌQ×kñ¬ýáx 9,a\Á@™ c$d4Ø¡2 =Êl% £Á¾ax ”ÁÀD–A2l[ác Ì†v%CËýpÛÚïc Œûà 2Æ• ãå–e4ÜX?{ ”ÙÐ[2ô—~è A2XØc  †fõ{$³¡•dh¹ZI!' G Élˆ# ç3ïÿqs ÆOe6Ä“Œ†–F2z Éh0Tú(³ánÉpç~Àî’Ð’ÑPSÀHfC Éh°Ïÿe4Ø_µ| ”ÙCN2î0’ÑÐRÀHfCÌQ’ÑÐSŽ’Œ†‘BN2FI#™ 1`$£¡¦% GÊQ’ÙCN2®0’Ñp§€‘̆˜£$£¡¥% =…œäc°-<Ï%\fC—ÑPbŽr 5æ(—ÙBÎe4œ1`\FÃÆe6Œ’ ãåwÌQ.£¡Ås #ŒËlhW2䀱?&.! %æ(—ÙBÎe41`\FÃÆe6ô– ýåWÌQ.£áN!' =Œd6Ä€‘Œ†s”Ël#r޲­V¡$£¡¦€‘Œ†#Œd6ô#úË%Θ£\FÕBN2Z Élˆ# =æ(—Ù0®d/ý0RÈIÃQRÀHFCM#™ 1GIFÑr”d4œ)ä$£!Þ.£!Þ.£!Þ.³!æ¨ãµð¶-m±úkÈ©ðv ©ðv™ 1G¯…7¶ÆÅu¾Þ¶Y.ôƒd4¤ÂÛe4¤ÂÛe4¤ÂÛe6Äu¾Þ¶ /öC{ ¹3Þ.ƒáJ…·Ëlˆ9êz-¼ë• o—ÙCN2Ráí2Ráí2Ráí2bŽº^ olŒ!' ©ðv ©ðv™ 1G]¯…·mA ýp¿Þ¶)ñJ†—»Sáí2Ráí2Ráí2bŽº_ oÛîúA2Ráí2bÀܯ…·m’Œ—x-¼íÏÄ~x-¼+?ÄŠ†—k©ðv ©ðv ©ðv™ 1Gµ×ÂÈIFC*¼]fC ˜öZxccgÌQíµð¶­ž±ÞÞóßÄ“ †ž ïþ®ðî¹ðîï ïž ïþ®ðî¹ðîï ol+ ý  ¹ðîï ïž ïþ®ðî¹ðîï ïž ïþ®ðî-…œd4仿+¼G.¼Ç»Â{äÂ{¼+¼G.¼Ç»Â{Ôr’ÑV¼]fCXÀt iÅÛe4ÜqÓe6„L—ÑV¼]FCZñvù´5× .³!@—ÑP"dq iÅÛe6ô;úË%ÒŠ·Ëh¸"dq™ ²¸Œ†´âí2ÒŠ·Ël=ÆË%F„,.ƒ¡¤}&.£!­x»Ì†0.£!­x»Œ†3B—ÙBÎe4¤]#.£!íq™ q›’d4ôY\FCÚ{â2jÚ?â2bÀÔ×-$ÚTýê dÑ.ëÇp¼@m»Ž†—«i'‰ËhH{I\fÃØ“a¼\¢Åå2Ò~—Áp¤=%.³!Ìñº­DÛ¿CyÍQÜþêkŽ:ÒΗÑv—¸Œ†´¿Äe6Ä% wÊQ’Ñö¨¸Œ†´ÏÄe6Ä€9^·šh£ú2HfCÌQ’Ñ6«¸Œ†´ÕÄe4¤­&.³!æ(Éh¸RŽ’Œ†´YÅe4¤­&.³!ÌùºÕD[êÃxÍQÜc /9êJ›U\FCÚjâ2ÒV—Ùs”d4œ)GIFCÚ¬â2ÒV—Ñ¶š¸Œ†žr”d6Ä% i³ŠË`¸ÓV—Ѷš¸Ì†˜£$£áH9J2Òf—Ñ¶š¸Œ†´ÕÄe4´”£$³!æ(ÉhH›U\CK[M\FCÚjâ2bŽ’Œ†šr”d6Äk¯›Uô¹Äcx-¼õýÄcx-¼õAÅcx-¼õ…E4¼ä¨–6«¸Œ†Tx» †ž ïþ®ðî¹ðîï ïž ïþ®ðîy»wݬ¢;û»ç»¿+¼{.¼û»Â»ç»¿+¼{Ú¬â2ÒŠ·Ël‹.£!­x» †±ÇÅ—Ù\FCZñv iÅÛe4¤o—Ù\FCÚ¬â2ÒŠ·Ël%ÆË%ÒŠ·ËÇðñ·/ægŸìõþ|ƒí§{?Ý8öé›O÷;|F†?‡lŸO—~?]fûlÅâóÉß§…ö§%ɧï‡Ï‡Ú'A?¬y¾¦y>±9íëøÓ¾îüáÇí¿û»ÿñ«ùÌ·ïþ÷¶—_ýÓöÃo^?Ïùè,—ýeæY£;Ëwu߯>íùèØnÿ-àp¿ùÕV÷í»ßýqÛëO¾ƒa›7ÝÁ‡}t,vºÖp ßýöwÿ±}ü_&üøðo áµoûK`áçÿöw¿Úæ»í»ÿûs€ýõœëÞƒý‡húàýaÛŸ|¶ë ýÒ{Àgnñ|ÿ/ÿóg<£h¿øÚÝþC4î ÀòñýKãk*÷õ•aðÓïÁÖMz¾‡ö“ïá²ÿ@À/˜(œçO¸ö7ÿÐ*¾×˜“& ¿ýà­2Õýÿÿ;«ù í ~íÛ?~G'LJ/éäøð-Wú¦,ÿÅÕç=Ýó{ºgÇÈßN¤¿·ºÞÔa6™ÿÜêóª¾ó«:߇þÂK|W_/çhé+ü·V¿ñ¶ŽŽ_×Éñáû:9>|a'LJoìäyY:ý•ÕçÝó;;ßÇËÌü$úëŸ0³ýëß0³ý1ë›ëo|Å,Ç7>c–ãß1û—ÝχÌÿåÿ-$‘ endstream endobj 605 0 obj << /Alternate /DeviceRGB /N 3 /Length 2596 /Filter /FlateDecode >> stream xœ–wTSهϽ7½P’Š”ÐkhRH ½H‘.*1 JÀ"6DTpDQ‘¦2(à€£C‘±"Š…Q±ëDÔqp–Id­ß¼yïÍ›ß÷~kŸ½ÏÝgï}ÖºüƒÂLX € ¡Xáçň‹g` ðlàp³³BøF™|ØŒl™ø½º ùû*Ó?ŒÁÿŸ”¹Y"1P˜ŒçòøÙ\É8=Wœ%·Oɘ¶4MÎ0JÎ"Y‚2V“sò,[|ö™e9ó2„<ËsÎâeðäÜ'ã9¾Œ‘`çø¹2¾&cƒtI†@Æoä±|N6(’Ü.æsSdl-c’(2‚-ãyàHÉ_ðÒ/XÌÏËÅÎÌZ.$§ˆ&\S†“‹áÏÏMç‹ÅÌ07#â1Ø™YárfÏüYym²";Ø8980m-m¾(Ô]ü›’÷v–^„îDøÃöW~™ °¦eµÙú‡mi]ëP»ý‡Í`/в¾u}qº|^RÄâ,g+«ÜÜ\KŸk)/èïúŸC_|ÏR¾Ýïåaxó“8’t1C^7nfz¦DÄÈÎâpù 柇øþuü$¾ˆ/”ED˦L L–µ[Ȉ™B†@øŸšøÃþ¤Ù¹–‰ÚøЖX¥!@~(* {d+Ðï} ÆGù͋љ˜ûÏ‚þ}W¸LþÈ$ŽcGD2¸QÎìšüZ4 E@ê@èÀ¶À¸àA(ˆq`1à‚D €µ ”‚­`'¨u 4ƒ6ptcà48.Ë`ÜR0ž€)ð Ì@„…ÈR‡t CȲ…XäCP”%CBH@ë R¨ª†ê¡fè[è(tº C· Qhúz#0 ¦ÁZ°l³`O8Ž„ÁÉð28.‚·À•p|î„O×àX ?§€:¢‹0ÂFB‘x$ !«¤i@Ú¤¹ŠH‘§È[EE1PL” Ê…⢖¡V¡6£ªQP¨>ÔUÔ(j õMFk¢ÍÑÎèt,:‹.FW ›Ðè³èô8úƒ¡cŒ1ŽL&³³³ÓŽ9…ÆŒa¦±X¬:ÖëŠ År°bl1¶ {{{;Ž}ƒ#âtp¶8_\¡8áú"ãEy‹.,ÖXœ¾øøÅ%œ%Gщ1‰-‰ï9¡œÎôÒ€¥µK§¸lî.îžoo’ïÊ/çO$¹&•'=JvMÞž<™âžR‘òTÀT ž§ú§Ö¥¾N MÛŸö)=&½=—‘˜qTH¦ û2µ3ó2‡³Ì³Š³¤Ëœ—í\6% 5eCÙ‹²»Å4ÙÏÔ€ÄD²^2šã–S“ó&7:÷Hžrž0o`¹ÙòMË'ò}ó¿^ZÁ]Ñ[ [°¶`t¥çÊúUЪ¥«zWë¯.Z=¾Æo͵„µik(´.,/|¹.f]O‘VÑš¢±õ~ë[‹ŠEÅ76¸l¨ÛˆÚ(Ø8¸iMKx%K­K+Jßoæn¾ø•ÍW•_}Ú’´e°Ì¡lÏVÌVáÖëÛÜ·(W.Ï/Û²½scGÉŽ—;—ì¼PaWQ·‹°K²KZ\Ù]ePµµê}uJõHWM{­fí¦Ú×»y»¯ìñØÓV§UWZ÷n¯`ïÍz¿úΣ†Š}˜}9û6F7öÍúº¹I£©´éÃ~á~éˆ}ÍŽÍÍ-š-e­p«¤uò`ÂÁËßxÓÝÆl«o§·—‡$‡›øíõÃA‡{°Ž´}gø]mµ£¤ê\Þ9Õ•Ò%íŽë>x´·Ç¥§ã{Ëï÷Ó=Vs\åx٠‰¢ŸN柜>•uêééäÓc½Kz=s­/¼oðlÐÙóç|Ïé÷ì?yÞõü± ÎŽ^d]ìºäp©sÀ~ ãû:;‡‡º/;]îž7|âŠû•ÓW½¯ž»píÒÈü‘áëQ×oÞH¸!½É»ùèVú­ç·snÏÜYs}·äžÒ½Šûš÷~4ý±]ê =>ê=:ð`Áƒ;cܱ'?eÿô~¼è!ùaÅ„ÎDó#ÛGÇ&}'/?^øxüIÖ“™§Å?+ÿ\ûÌäÙw¿xü20;5þ\ôüÓ¯›_¨¿ØÿÒîeïtØôýW¯f^—¼Qsà-ëmÿ»˜w3¹ï±ï+?˜~èùôñî§ŒOŸ~÷„óû endstream endobj 608 0 obj << /Length 319 /Filter /FlateDecode >> stream xÚmQ=oà Üó+P–Â`ïÁC—~¤R§JõÖvpbGJlËPUù÷ÅÆM[)‹9žOw÷Aj"ÈÓBÌç]¹X­µ!RòBk åžHPÜ Fä\BAÊy£šç,“4½oª¶>´5ËÐhúrìBB¯çC ›xÚ=û(ŸWk4¤àEù¨,H†–£Ä¤Y2‹´‹|DúéÝ€îï1^$˜¤hX±O†‘é£!˜Ñ0~GÃéÿ×8›µ¶U›†•÷‡zÆ¡q§„6£ìù2õ«¡:¹à†”_ãŸüPä\˜¸Æ¿ß6Wv”±){áp–)¥èC×Þ„‘ ˆM›Xx–O´}7Ôn^°:ú¹“0Šz®„19À£pîÝí²_^I¤$·ˆ¿‰"ãòüDYneA2k®`~…ÿHåâ³§ˆM endstream endobj 614 0 obj << /Length 701 /Filter /FlateDecode >> stream xÚ…TKÓ0¾÷WX{JÄÆõ3•ÊÁ"@ Í 8„Ôm#¥MH\JùõÌØN¶e¡*õxÆ3óÍø3²%Œ¼]°°¾*Ë{Îi¡µ å†p¡hÎÉXJ¹(H¹&_"M³8á\èèC'’Gëfsn[3}j;륇ó>ëww¦kƒ²ùmÆø[ù~y/3RÐ")fb$‘9•\úåÎĉ’2ª†íqo±È#ëÝ´¼pK3ÊDÞΫ6¿h_ïž ¯-¸žÎUmÛaÄÓè“ØÎ¯Í¡L5†ÔÝ€«ˆÖæRÛ»úP¡>õÁ?Öç”®>'žë „³ŒJ‚àZëA fì«©±a†[±x)UT‡aªÝY|r4=I.Crº¤¯›«8ÍUhîË8Ñp{}5|eší7CwZÕ(ò[´¨ˆÃÆ}> ¸Óko€‚>öA/ÔP{îÍêf¼¹õ§ü¶‡íe¤Ð@# œ¨»vóˆßÉàw¸üNLûp¯$£"$œ$ »ÊBÊÁX{¾™Š˜Iþ¦\pá3¹#Í©÷‹ šñÔx”œaò Šå»½ÈÈënñ~“-™B&1ÝX]‘qNÌs𥡭÷Íö8 ǘŠDv•䯠A2±™v`wž»~w± <eº]u؆xŽPx‚ñHâwÝÆ›§ØO(=z{sðÖ*€YoÍsê‰ÎÌd-\kƒ¾¨wHP˜ç:{v®áyqŒûï`Ë”¦LMç( ä;k€ ¢PѧX@+Må»âÓÃdýÄÜMcG/t¨<B®Sè\WAå;c¦"- ® h¯æ¤ôS M­öaž«¾ºÚ\`æÀxî8šµWWíW {„xlmÓ·æñUéÍÿµž¦4Ï4¾IŠ*žy¬Jý=$ÿu€& endstream endobj 611 0 obj << /Type /XObject /Subtype /Form /FormType 1 /PTEX.FileName (/tmp/RtmpBlhU1W/Rbuild4444671013800/timeSeries/vignettes/timeSeriesPlot-symbolsSizeMultiplePlot.pdf) /PTEX.PageNumber 1 /PTEX.InfoDict 617 0 R /BBox [0 0 432 432] /Resources << /ProcSet [ /PDF /Text ] /Font << /F2 618 0 R>> /ExtGState << >>/ColorSpace << /sRGB 619 0 R >>>> /Length 34372 /Filter /FlateDecode >> stream xœ|½ËÎ%?î$¶ï§¨'(ë~Ùzðv/¼0¼j` ª x¼ðë;Œ tΗý_ô¯‹ŒT(O¦®•ýç_ù×ûõÿüãñÿýêûwj¿fýÝÚ¯ÚÊïY~•½÷òë¿ÿ×_ÿÛ¯ÿûÿÃÿû¿þOÿã¯ÿôϤß)¥_÷ÿùŸþË£ý×ÿ÷ÿýÿø•~ýŸÿÈ¿þóó¿ÿölðëþǯ_3ÿÎûWNå÷˜¿þ~(ö4á)7§ú»df•ð/ûÓò;…âÊ »¯üS*ê »ò;×ð á_Ne %³»¹þëÿô?^éQ¥ß«‰>ÏŸ6Ô x©5rõB׺ì À+ÿ”ŠÞÂ.ý®)¼BÐC Å¡gv7WÒ/¿›«Ÿ~(*žâpWÏw‡€‚žÇ~ÐçYaBšúÓGñ¼6sÑÌ^¡JŸþo=‘ {˜Ðæ¦IæÕÞ =ïêÄ]^¿KEÝþÝÀ婺ÿ›T–äÙ~§%ÿ7üáï$&›ù»nùÿõúÁœ“Û?2n¿W¶Òžáï‘÷ïüüNýwöïç·|þ];ÿúïѶö±±ÃÿÎåždóÔlÈþÍ¿³²'³¹ø‘qÿÝ JÎXrë(Œ'JÛú7Jx&Ž óMý;ý]Ço¶†lÖïQäÿÖ3nsñ#ãñ;{êÏëö÷–çD ðòh¬dý›LV`Óž.môïÆOVr^²ö Óþ­'paÎÁm‚_0¨Onbìrƒåš¨÷BɃÿ&“66“lôïÜôwŒW“Íó~&ùÿõ¦0çà6?2žö½Ø;Ó±d–0íI%ëß(añ7lý®Y6ú÷ãçòó>Ðæùo–?ü[O`Ì9˜ÍŌчYÉüò$[ ¹4MÖ@>ÿÕ¿ÿå·&å•~çB›øwògGyýžY6Ëž4ýáßôWÖÁR;6Á/{C˜³»ÜÀxåó…[Éú7™´ ›…6ñïœõwÓ¿¢,›çyMùÿõ$“ln~Îø±,Þ»=?ïßÅÓ2ZÂÓsYáKØdkk…>%ìBàK$E²VJvÉ~jyMzT¦PvV+}r ú«/Uô©x†: iÂó.0£@Nc]è®—…5}ÏsÛ•fö[múôë‰Hv&´¹i’y¶¦Õ^$ÿ)?/SGpSPÄÖ¿QD±+ÖãÒÆÿ ü;—[—Ͳñý- ÆX–0çÔú?2®hŸÃ_îíe¬Ö% §ï&¬PF³æWh³‘fúwÏúC—K¡ÑCàºGÿ÷¿¼è~aNÃl.ŠAzy=DÚå§g²ГïdÿöaËNb2ÓÁ|¸ã6þï:ôw.§!›çK+ò‡ëdaâ`6?2nl‹Ÿ¿ü²=¼ ·OÃñÚcÛ­ ¥€Bº5sí=ì ¸Sþ¥Ë½Ðj£‘s—þo=ÉA¬—’¤ÝmggñÖú(žÏÙÊÈf3a„@6û ãwîaBáïBEÂC»„_†^!èiÜhš—Ýáô‡ãs‹þð¿Ix,›[5 %K §ž/tö°£¯üS*¼rÝ >ÝŸ¼BÐC)=˜‡+éO Ý» ŽâéaúŒÅ^Œ–(  ¥ÆÚÑgD$»úÒŸRa­ÏR›þô‚ò á_N`Ö@ƒÙÝ\IyCí£ùû¡x¾(+Èéo4B 'þ6†>]Û »_žPävÉ>@y… ‡R/”lÜîp úÃU+‹þð' ò™ê3ž(”È©ßèœaG^ù§T<¯ì²Må‚ÊAƒžÙÝ\I úqöE¡ØàÔ9¯Þ&¬*Á ÚɦÛTlL#iwrŽâid—­Y”×ã–)”l¬U»¹ýíS÷šDŸŠNÙ]Ù\0ùAÖýQðËpt¯ËŽ'èGaĤ·{Žð A%‡MJŸ\þÎjÒ‹ºGa£Pˆs»¸¨)E±Rví½B ×|Ю>Çìn®¤?Ôëpùö(ö¼>C£9Õ9ìBPMCQWØaV^cùöT.׋ÙÝ\Iªí|ú¡Xð]ÙR;§ÈiÏ@—Md×5¢4ÚYc¸éÔÿ­G²F*ntˆ÷é}POâNÅò•Çå/!&í%ê7ºJ؅Гþ”Š’Â.q®„ 'rÐ`SÒ'WÒ_¾p]ìÇüû¡ðÅ>Ü>Û m[¦ºmPv!Ô©?¥"ͰËü•V÷CJ6þë®Nó!6aÎÿÒLU£·©š[þſޞ5œãÈŒW­/MžÇ6–FHò]?pò¢íá}êÂ^ˆ«é¡©þâb̶…ì¶$±ëûÆW½m9Ò v¡á¼³›ÞÇwÑúz”]bÕ¥Û›·ê’Õâ×ͺ„†0;&lwH^^ñ†¾ªáãÔX·ìB“ö±áÄIu8y¥ýÅûÔ…ýS?u¡f]å5{Hb·.¼úÎmCêªKhø,vàýø®ZйžSê7/ øÇñýOþ}wÝN¬ 5M»j+CâÍI’صtãØš¢-¥ªU—Kûqd2ŽïªåŽ(»ªã&SþX‡·êÒÔsE]¨!Ÿf|pm>æºêÒ}ŽN¼ûð¶”®º„|hK®ô}Õ…eN¦#}ñ>uA£Ý¬š?5kxyÿ—½ô’ØáÙ>–£mH]ìBSƱm>ç§ï¦]™(›xð*ã‹·êÒ½Úc Í{ôeÓ›óᣱ©6Ó´»©š!ßúkjz:¶ÃÙÑ÷Ю[”=Ts2íé‹·ê2¼¯ÖVü½5Óða=pÝf‘äåMhħKÛ)’Ñ2…¦÷c;|®Cßâ9Õ'ÓÞ¿xŸºÌ­¿øû©YÍËóº$çZC»Qn|ÍÛ–R?u¡¦ôc;¼Ÿ¥ïqúa–MuYÔµ¨ 5ÍÙ%ÿIí>’>ñìF»ñµn[JÜU MÇ—mÇ#–﮽Õ(ÛqñêúÍoÖåùûæ/cb]ަ:ŸáuéÎu‡äåeßà"žýǤmH©Å_7ïxì'mGË$ß]Ó(;ð–íÍ[uÉþ2v[Jù{kvï¬ðnÛ}}Ÿ¿n‘”­Ûò}¾}•M¾ýÐàûEx ¾í’|•ç|û%é{"Þ[ØIßþÑX]eëß¶|_ß>Ëæ·¼øœoÖ¥dõ nº4¶B\²oÀ4%û¬v(¾)—SÈ ©öc’F Òt¼N²õ( ùî1JPÙ³·íÍûÔeù_è{9û?+“º©…$vóçø¶-®økjÊ:¶߃|÷ø^T6ñàUÖoÕ¥xÕ´_ï¢Kñ™k÷·¢jDWõìª÷s|[JM»æ—ƾÙz÷.ßMçQ6ñ`ÚËoÕ¥z0¢•þ~jlñÊÊs6Õx¤’Øq• x³-­° I«6Òx„•l+–ºä»ÆªÊ¼Âöð>uþ£E]¨ÙÎ~;{[*x¸”Ä®àÜ‚Ü햢桩íØú"Ÿ|×Ss–MÒövÕŠ­ÉGtM!0¦éýÆçÛ#1"14>í—­Oûå»*(1Êv\¼Üöƒ7ëòx`ÿQùíµVWî7¤’³ËúÝ€gíÆÀ6$õ¬Gc‹²­¼ÈwžUeN^i|ñ>uÙ?ê²ËÍ'®?ê2/¼øø¶!E]Bú̋+}_uÉѳæ›ë²Ô%«¯óÍfy%ç2$‰]7Î؆¤·æhê8¶–"ß-Æz*›xðªã‹·êR|£±+ëÒ /¡r¤Iv!‰W‚€Wßé¢mHZ<ëeÛÐPËw‹•A•8yÑöðV]|¸a¡º„Æz¨êƒH-$/¯éwÞµÅÛ´Ó%MàQ¶îT¾›N!EÙÄÉ‹¶7oÕ¥yŽ¿Ÿë1ª?”ä‹AÆ%$±«ø(Ç6¤<ã¯96]óØúò|·˜©lâäEÛ›·êÒ½¿ê:ãui&Ëã qq.K’Øíuð¡ÝM؆O:4öÀe‹XýðÝAeN^´=¼O]¦·©{F]&çKþÖl_Ͷ)žq IìúºñU-¥¦³—fÌcÛ0â‘oHñœöÁƒé˜_¼U—¡Yëàæh¶—Ð9'÷ÒWH^ÞÔJðé;]´ )ÞšÐÔzl;†‰òÝcF­²'¯Z¿x«.ËÛpìdýýÔXýHÍW+Ðz,­d$±ÛZ;µÛ4C8´à´í˜8Ëw‚Êœ¼`{ó>u™lSkÔer‹ÚÙq7ƚǺ}D7?eš‘nœ;Œ´]M¾õ×ÔØ‡+ÛŽ—K¾»Ž EÙă)loÞªËVÿ¡q²4£E+ÏëÒœë åµ»›Rëa+©Ç8ùhlF-[ÿÄå»Ç8YeN¦£}ñ>uÙó³ õV^Žþã‘zHb·.Ê&¼Rþâ­º”˜=±9k0[ÑÌÕܶ¢Õ„¥']µâ¼j|ÛÊŠ¿nrd­l æ ò]b>¡²×Ìnñ>uažRÔ…šÂò¼G°/î‘zHbÇß xóÚ†¤'i*š,Ùzå»ÆÎ“Ê&^ Û›·êR'¨o_hVýMpÉ!‰]ûÀÇ<¶”ÎùhìËÖçÀ-Âíôí«lâÁ¶7oÕ…¡^9ö^¦ù“æñ`<Ï–hïµù°Ux÷U^Ú†¤½Wiüì¨l“]MçOÆùy…xÞÇöæ}ê„¥G]¨é·90¬÷{¤’Ø1 ”8ƒriK‰gÙBãa ²M—ÊwŠq«Ê&Þtœt÷/ÞªËÐBÍ+Æšœ6¸iŒ6uÊBóÊ6ãXãŒË…mHšWMêÇÖ›fùN1¯TÙăWê_¼U—©Ó…Š!9{LVž³GË4užD+÷m)°øŠ³Sí–´r/äÊ6a¥C¾SĨìÀybqö/Þ§.Ëÿ"¯¨ 5øš£<ônËGtÄn|àkß¶”ò©¹'ÙXK¦{û`15Çh÷C:™Ðc­/ʪÆbî E&‡ÂÞ+ ï6â}„-ÁKÚÌtóØ2ì$Dk$…-pÓnú÷»™¯%Z–¨³ÝÅUô7“WXBa VGý§ AœV =1‹†Ù…PøcQá1´ó z=1(,3PcvÁ•ô»Îth { ºyXKñU‚sÊðYŽmenÍÓC1–,·~ºcâÉTÇú$¬:xæ%œeùû¡- òÀ£ Á Rª$  ç i'AAÔ¡H-ì&úZzAÍ2u6©}rýζV1+¡ÀGÙ™ÎÂúöGèþÅ?]ÅäUv´§ŠœÃnùGé^W„ª°Ì@ í‚ë¡?î<R é† ;1(Ü©:LÑö…Îv.D¶Ž£h=ì“C^#a‡Ê$*z­rýÁŒŠJ Zôi;¤’5•@ødv˜JÐÄ ¶ä†ÛØ»_TÙž.Ô Áî¢{jÀO-jà [ê“YBl´óM‚8ÍÝí²Ûzkõ§»éÀ[Wö¥žÂëŒaPW6©ž‚ í.®¢¯äMZÆéÊ•ÆD»gë }1šÖpEMêÑ`·˜ÎL 8¡°ñ7í<ºŒ^çy+½L¢¢×Û'WÑßì‚âéK§ˆd z¾[IÉøôGb£nè#x:3³ AOŸ Š´ãóu¯×Ó÷2õžOÿâzè/œ Í=è»Â¾—á  ÆcÐ$ˆÓ̺Çeç‚6(©èÑÎÓ‘ÐkWö=•é(Ù¸ÝÍ•ô‡çxêÊntÝŸâ„+[#§®ìF¦¨7ÚGØIÐÞ-¾üM»æÕv¯íã¡ÔÙÐîâ*úH‚²{ œCa]Ý@¾N~„Q(ˆÓ.žô† œC‘vØ5ÿ)Ük‹3Ë ÔÙÐ.¸úG©{ ú®Xþœ6~É9 âÔË…Z 4ÚIÐjM(J »Ž—^{´Ç,“¨Ø”öÉUô=IŽýýPØÈé,ÃŒ†Å-ÖϽ;j%h'A ¡°LÚyL;½Öˆ\d™DÅvWÑ÷üO2ÿ{)<}x¦(ß“žrAœöA±‘#;ªÎ>Åaç¡êô !Ê:=Ú×Czâˆô]1ñö\ J£ N½]è\a'¡¥øSWävÈ©!¯‘qCe›œ>¹Š>V¤wÈÉPØRÙðôQ3 æË/1þBR¤ÑN‚vx¨ð3«´óôZ"`’eZsØ]\ý •‚XCa#É6vûªØ@›î‚8ÍÝû²sAá«¡°uÚa&#¯5bWY&Q±©ã“«èO´²Xûþ{)°Ñ\Û¶Ì@BÐ}–ÙÇbcNt¥°sᬱ‡Â–riç+«ôZcvÏ2‰Šì.®¢¿Ø…,.ûPáÒîîŸ2‚ºàm6æ@‘Qv.´˜Ü„ã‹ÍT!¶ÐE¯-&7,3P§»‹ë¡¿Ñøõô]±œF}üyÐúù™1:&:²©¹]ÌÁre†,¡×“,“¨Ø”ùÉUô‘j#sÉßÅpßÖÛ”à$Aœêz³ IÏŽ"Õ°óÅ`z=kÅ,ÓÑ`ÚWÒŸÈ uÑiÔuü¦¿ýŒ%´“ô¥ ý}ÓôYf ¹^vù›>òÝô¥ Ï“E‚#¦‹~QV#C+óÁNBЗ"Õ°#A÷zÑ÷2‰ŠMªŸ\E¿°•Utf(ì{œžDʃt&ÜÉ^Lá¿ ÑQÃNB>ôMá%hç %è5GP&Ë$êlhwq}„Z]¹ÖIEÁ¾á¬ìml$01è‚8íƒ"ŽQv.äX˜+·AÐè5Ǫ4Ë ÔéÑ.¸úÓñô]1“Íä<¬mb;ÏqêûBW½ì\ÐT=Ö¬ÒÎCÜè5ÅTe›BÌvŽmËPTçôŒî¶o=–àu¾¥@»­áÉNBÔÔ¾;D»„¾ˆ^Ó]Ó‘.Ô3ÏÍüÉUôò³²÷…En¤XÈÁjÂμ O_KÔó×ÒN‚b#©ðìH´ó z=ë,3Ð:ÃîâzèodÌT(E(l?v"“ÔòsþÐ%ˆÓÊz^XÚIÐè.h—ÜÎwgèõìݰL¢b“ò'WÑG-=Aæß…í–LÏ[ëK¯ÕvAœÚŽv´ËB…/½ÒÎSoÒëŠYË$êlhwq%ý…ÌIýPG° ÕNðv½é/ähšÍBvDŸ §A;'H¯‡>Ë ´î°»¸úÛñô·ÿs*HmjÛ£Ð+qZ5Pä½’ +ö¢Cax´[FÒëŠcP,“¨èÁîâ*úÈ´f……ƒ,$×\¸®Á(d âÔê…Žv´ö kViçûíô:cAŽeØ]\E)‘•²5…b9ÆÙ N ^Peö[ â‘ dBQvØMLÌèuF’&–¨³)û“ë¡¿evä ï u[HôdoD‡PqíB=é/í\ÐÂe(j»…È!z]ʰ§2‰ŠMÍŸ\E¿¡ YG ‹ _8¦¿¹Š~W+ËEB*|?Ì ‚+; ùS‚4øœ€ÜQ@;f„…ÂÖŸi7Umó:?ʸP§»‹ë¡¿ÊÇ»/ÞáÁÞo7Ž©ßïþ`cNt§Ën§w_Š²ÃŽo·{½Þ}/“¨ØÀîâ*úÈÞ´zd…¢ùS|¦"˗ à!w’Pì'ÈN‚–C©ðecÚy˜#½¶Xe™¶vWÑG¤ñƒ3åQØüÆ šJVn$ˆÓмæö#ä°“ <¡(-ìüd½ÖH’Á2u6´ ®¢Ëêv ?Ÿ¥§NDZmŽç‚´ùÛÝå²s!z8)R;Á•×sÖ—eØ]\E©ŠÖˆÈD*¦÷E[½Ít PÐNlÌ Ý‰C/³£0b'#£†–=åuÄNË ÔéúÉ•ô·§Vï±Æ [«Üžk›Ç °(ˆÓZf^; šÙ„Â@Úùò½žÅ/–¨³¡]p=ôÒÞkºBÅD³k!U¼MH6.ðrAœÚºÐYÃÎ…Ó•PØÎíüÐ½Ž˜®°L¢¢×ë'WÑGâZ±³ŠæO±måZ÷¥¼ d;ŠåÙIPM…GzÑΓêÒ댚eZzØ]\E›0ž¨üï‡Â@[™Æ¬ÔšFß•qá_üÓ}PìÉN‚NW,?]àv˸WäµÞh—]p=ôÇ7ýqÓßù"X>éc[è ó¦?¿éÏCv"¯Ÿô9'MbsèoúX¼½JBQÝwÏZ Ù~½×Y(ÙÈØ(Ô/ £-”Pá ´ó¥z= %,3Ð2Ãîâzè¯oúk^4r¾¶oúóF÷MÓß7ýyv®‹}/óT¾éãœ}q†¢»oÛÏðK÷6Òr¹à!YÑA}5v´žHBd瑬ôz]Y&QgC»‹«èOÀe]is¶ú±qÆ}{[ å(¼ j…..ÁN‚’…"°ó3ô·¨Ì@MŸ\ýåKŒ=è/¬üÙIioñ¦Ì„&Aœü·!ê‹ö´Û“^õ§®`åüùÚ‘zM:Æ2‰Šfp}äº CÑü‘Ϊe@‹õ’à! ÑA{,.†]©+|±v¾ H¯g‘eu6´»¸Š>RYcÄñ~(ðHU.°ˆD â´¥(!=iwŽ/¨°pËnmU{H¸ÊAÓ¾ì‚ë¡?Ó½¾O…¯Ó[A+ø… Øc}¿xöƒúVËc'!Ö÷â°Ë+øòzÖ÷Y¦¯ï=[z¼¹:ýâW°úRFÆŠ: „"^ôqcèA¹ØŸC¸èKúnGúî5è«L¢¢»‹«è¶ìl÷âi¿QPgËn Nû •‹ý°“Àv_ ´ß²CË.¯Ñî«Ì@½ ]p=ô½ëa–®£x|‚Ã9 âÔoÔ·¤`'!øÓéôGØa ¯IɹNåz 6´»¸Š>²ùX×£—§òžì¾ýžê¥. ^Pã6P¿›Œv.d5æGÑKØekÅå5kýBeêôXÓàzèo]Ró÷R  ò‹`ªS Nó ’]çžØTË#Ú%·Ã7òZt(Ie=Ø]\E¿ó"“¦w_Š™ Xoƒˆb'S%xAÈtЕÃNBKñ§®È)ì±#¯Ï£2‰ŠMNŸ\E0 «êå‘bä(¨© %Á šŒ>Š$~²“PKü©+R ;DËkUb‡óPˆ:Ø]\}sàT] D¢ Äî=C%P˜ÄiÎ@±"»ÅŒ­÷LŠQÃI“ä5r*©L¢¢»‹«èOt1ÿìQ¬îÁUÚNA‚8Õ%ì$ôê tnW½‹p¯q…Ê$*6e|rýÅ€®©†Ó+:јãfÛŽdŠ}PÜÒ(;"é(Ú;Ék„#Êuz´ ®¢¿øVõòHa?WN'$Ä&åĈ C/ÚIP#ŠTÃsty •éh°1»›+éç¤Y†ïm…}W£nôv °)ˆSÝÊ ‹q }ÆŸº¢Ì°Ë^—ÌŒTƒþJŠM™Ÿ\E‰u΀ù(žÖÉ„¦}s¦qòÐ  …C/ØIˆšº_ÙaH,¯é®iKšÇe\ýy¯4Ek(5i-Á„z¯4˜bÜhÜ@w®4Pá+´óµz=+ ,Shæs°»¸Š~á¢}×Ó—b¹oß¾Oþ|}y&ééW®q­Üƒ„žãO]QrØá¾$y:&¥2u6°»¸Š~ò߬©ÄÕR õ2 òeã Y‚8ù—At̰s!ÎSš“É·Ûæ ô:cÁ2‰Šì.®¢„7kjGŠeí´ ¾lÃíGؼ ÍßèÆ¢=í6Wþw?uºˆÍçÛrx„Ö*3P§»‹«è#ŸýTœ®PÛ_QPã%±&t â´µ4ÆKvf¼g¡@»´µPzxç=ó2uz´ ®‡¾u!C·7Hk÷PÚ»§q0 <¡A|IÜ#êÛ`fGaÄ >Ïo »Š^‡.mP™C?£Ó3»›+éd²YS'‡Žb° o®ýùN Î)3´hæ•ð“ F*ømÜùä5nÚQ™:þ¦ÁõÐ÷À,}ºTø'X2·ýíã,XE¸>Ý‚ÑÎA×¾ìÖùÈ~] ûiç'½žO—e=Ø]\E¿°• ú…M=h†G€ îà¼éW6õDýþfعpÑ—4*;t¯ý¢n\ô`wqýÊ®‡%G±Ý·u/uŽ+¢¤”Æ' !°“ ‘@(ê;,“ËkD”¨Ì@Ù3ŽO®‡>[öCß³{A ŸHP‚8ùÆQh¤ íÐwEa7­»•ש `•ITlòøä*ú]Œe“1i(h2Z $xA·Yí¼çv—Ws§ÿ(l¹‰vˆV“׈eS™:½^>¹Š>ò쬭³+Tl$AA ƒx/Õ×è[ Nk:¸ »¡{®ÕpJÑ·ì¬ÚM^]ˆ‡².ÔéÑ.¸ŠþàÄ¢ëÓ•Â2ej&× xØ}RA~«i »\v.ôê |&°[Û?xuAêKú@ÅvWÑŸ èR·åŠ^ù-ãÅ)Hð‚ ‹[R°[Œ™ŒnKжd·¶w[“—åz(ûBZ¼>¹Š¾ç•IJÕp£yA¾7CNÄitsK vâÂ&fn·p½ºåF í‚ë¡ïËëñòH—`1d?‘Óþxy6×8ˆú–ì$ÄË#^Øéõؼd>^žÅ9)P±)é“«è{Z™tº-WdÔ}oÆ . (¨&~†ÖÄ-³£¢Û …­€Ò.ycî^Óé¶6C ˆ:=³»¹’~Í<6¥v?¶!ð~âÑZöGþÅ?Ý)Тƒ%ãÔî‡â™¬ÊÇ;ä5¨Ì@ í‚ë¡ÏcS%èûé,[b¬ž^Çw¯ E‚8õ=ìŠÀ”øÓ©#4²óÝ+z={[,“¨èµòÉUô òž¾ÏGŽ‚°Ÿ1ýùzHÆÔÓ¯ÜkZ¹ ; yÄŸú†•­ÁÑ.ÙF^“n.W™zHÆŸ\ýµ®s[GúVlßãd– M‚8Í|¡{\v.0äS „ŽË'³ä5Îm©L¢Î†vWѯ< Ç(Z)pa< òumû!±I°#£)êú)VØ5î‰iI(øÊ·Ïð•7ž§+ç7=Ø]\E¿ó0–&‹TøUEÚî^U$F½ö¶ª'®!Št­²<Œ©Éb(lZL;ß½¢×³·Å2uz°»¸úë›þÚ _Q'ÁöMÞô÷MÓß7ýyº×‹¾Ž‚«r7ýõM0y€ C¡‚|CÀ~»É”ñFL&, ê© `'!ÞW`=Sv¸¼\^KL X&QgC»‹«è{:™¢Ä¥GÑ—äÇø „!AœöA±V(; ñ™¸"Û:“첪=$ÄC¹Q7£]p=ô™I }fpkOt0œ“':‡SoÊÜí´@N®à––ÉåµÄ9Ë$: »‹«èot!Õšë¿ ›5ÕÍçgPXN;‚¥MQû…ö-»xàQävËÆô á_$à›Ö†›Ü?¹’~ót2h‰ÿ^ ÛDA8U^¼Ô!Aœv£Ù¹Pcá2-‡]ÅH€^«ÎH©Ì@í‚ë¡ï-û:ô=ÑAvúLt@‚ĩߨ':È)„¸Üá(z»Šé ½Ö˜,²L¢¢×ó'WÑW®E”„Â|-sW}Ó à&,Z˜ÊvQBVdW1¥×%,3ÐZÂîâzèûNuÊAß¶¼n¾­z·Âýoí5? OX´rßv´×L~wÙaAD^#Û¯Ê$êlhwqýH'Ãn+¶çð¾l›µ­an9JLáωè*a'AðTàä’ìŠWÛ½–óP¼Ì¢‡âì.®¢¯6ñòH—ÀÓêðõ@zŠûåQN H–!; ñò¸‚/²Ýàõp¯×Ë£T>Dk »‹ë¡o]Hס¿£@kèDz±ß ‹‚8Íè`æ*ØIzúR´vÝ[Ñ΄S¹Ÿ‡²›–>¹Š¾§“:¯{|¤ôm[µá+Ä©® 5ì$(ü!6™¡]÷Gê^{„?°Ì®ÊÙÀîâ*úžNf* ýQT´‘#Í Äi§Vd'!é˜Ìk5KØù¢=½%ŸW™æ~ÙWÑŸÌ“«€˜Pà‘bÕÃ?Mè¼ ÏZCtcèE; ñ=Jn1 !éb Âh¤¼L¢b»‹«è/¿ø(Å|E‰h˳{d\ãœ'p®m¿ "ðÑŽíV†R ÇŽ¦µcëË òb˜ª²‰ÓÖ¾x«.ž[&R¤RÁ4§(.+ªI3ß)RKç-ŽwÞwå¶.©Gaã=·ô$¨t|R¤²Üƒ:DZ> Ÿ:L§ÌËe.ÍdQLN] ÕÄk|àLÙM[JzÓÆ¶5ÜÖÀéÚ…x<Ry‘fEºg`HI·|^šådpGÎd™´³$/.+ 7ð¬|ݰ ©¯økjÊ>¶8ª¾S¬ð©ìÀÉ«ì/ÞªKQ:]E?mÍuÏ “|}¦óÞͳ|Ó«ß­D¼úÝã´ I1GcËä²ENêð" BeN^°½yŸºLO„ÊLŒ—Æ:Ë^•NH5$±ã³#¾æmKIÝ£kuÙ¼z¡G]¨±)ØájãŠÞü"‹“·î‡e…û9ZÙ†¤ùÛÑä~lñ„ïS¸SWàÁ ¶7oÕÅ [4;»ó£^¯w´å¿Þuõ£"t /º >Ö± I%GƒÖ޶Ù[;úαW¢²‰/ØÞ¼U—áý–ÿ~j,ä«Oê[àLâuŸEÏ.®ü>=7mCÒÚåÑàŒ+=s=¾Ïò¥Êœ¼`{óV]¦÷¸ƒîï§Æ–vúôîÎçê}úÕg*ßýü¥ðåÙ”iRU]B“æ±õ5SùÎ:ØeN^´=¼O]tÙDŠºPcK´Wy \rHb×>p^‚Ûâk­'ÈÖÏ6ÈwŠ¡¯Ê&¼`{óV]–º¹¡º„f³<¿ž';×Ù%yyž…[øÖµ° I“­£±1²lq4#|Ÿã*;p]Þ‘¿xŸº,¿.¨—¨ 5¶ª×·_ïXÝßö«ë)ϳ|§Û–’¦xGSʱõÅ=ùÎÊàe^°½y³.#©ÿXœÓJƒM'“–_Ïc³²á‰Ä\By#{øÌÇ–RŽVïhl"#[,s„ï­žÊ&La{óV]²·á%æçGSÇÍukH^^ñƒxñ1'mCÒ¯(oÚÉÖÓÈwŽ_ñÔ•8{º9¾xŸºð’%mL-€Xy¼|9—)IìÆ¼q^;N[JÚž>(ÊÖ×Aä»ÄµÊ&¼`{óV]Š_¸S”8 4ØpBy¼|:—¼¼ê×*¯qíx9R‰Vöhz=¶ƒ^ù.Ê eN¦½~ñV]ª_tTcXy4*ÏÙOr IìÖ…û}¯² IÃJi &#²-:Êw‰‘¥Ê<µÛöðV]Zôú^šz?|¿]½¾m^¹y}ûÝ{ŒÀw½m÷õ}þº5ø~{ô?åø¾¾}–M<˜Âöæ­ºtï¯JÄ¡HSÑ¿ZÖe¿tšåñ´(×rºD ¶C—k„u4¶t"[œgß%RTvàd Û›·ê2Ô(Wšæ†_îM!‰ÝÚŸ>>¤íÔæ Ç=‹«•­¯ÿËwˆ\•8™Òöð>uμžºPC›íì—s-!‰]ÿÀye9lCª§.ÔXÏ*[¬ÿ‡ï³Ç ²‰/ØÞ¼U—éV Æÿ~j¬·µòüLëm‡_¶â’—ç> _~‰mCÒNÛÑÔul1Øß‘ï(Êœ¼êúâ­ºlï¯pØßO- =.Çb&aD7uû‰ivhŠeÕía{$ͤUËy—Â÷ˆ‚Ê<ÍÛöð>uAŽä?5*ÏüyäÃ¥„$vxvã:T·=’‚<ŽÆZ ÙNl Ë÷Ô­Q¶ã‡Z™›7ë2“·á#NpHƒÌx(ÏýÙÎÇä•›'>zfï1ˆgÒ–Ò ‘>šÞ-_„ïG9TvàdÚûïS—å:…wIƒOåUH¶Ò9ý ¾Kb7ËïqÛî!ßúkjl-[ä* ß#â¼T6ñ`:òoÕÅ3X¤[WGcߨÌ~½#6ÊôlÍ)n 3ÞiÇû<¶!iÁ\ì/„m÷'Mß=ÖÌUvׯØö±½y«.UWÕ%467|¤îWÛ<õ‘VH^^Ó³Þü:TÚ†¤–4¾[$ÛîíFÕ%ÆjWTvà¼Èxï/Þ§.ÛÿBý¾4ÿH¿ÞtÔB»ùëÊòu¤ýþÑ´ulqmCøîÑï«lâÁ¶7oÕÅo¿°¯•mòÑØËÊóvÕz¿É+7gœ½œ¼–3ð¾ŽmH:~) ޝ‡-Žx‡ï'0U6qò¢íÍ[uñ”bö ©£ÆOÃÌîWGNŒQ¦ç‘sIìv;¸§=“íÐæÃ­ÓÈ©˜Ã÷ˆ1ŒÊœLi{xŸºLþEŠºP³œývöÖ÷>\JHb×?ð¹mHš…­ªÊC«ð=bª²‰¯š¾x«.SmøÐï›'Ì©ÞÍ6<æòÝŒ41sEâ8¯,‡mHñF†Æ">d‹@úð=Î9ãºô|ó‚íÍ[uñ|Ÿ6ÊáZßÑØÈa®¸ ÜËC’Øíqð­kÇa’f¡Gƒ¶¶ÍÛÀ¥Ñb´‘,;pò¢íá}êÂ6\cË£Ù,ׯç’Øõqã¼²¶!ily4u[;Ê÷[ªlâÁ«®/ÞªËöÍ”óJiº°¶z7´LÛWyzÌ+Wò• Ç—gy¢­¤“ëâh¬e’móöœ¾Oº •8™öùÅûÔe».÷¨ 5.¼’o=áz-p™’Än΃g_å¥mHŠ‡Æ³EȶbT"ßUé£lÇÅËm?x³.+Çìˆu9šê|x¸õ䫸ˆ®E|ð*ÞcÎ+Ëa’΢Kãáu²õÀ,ù®±)­²‰“moÞªKñ6¼Ùðúï­éØ…XÅ/Gf]p ÉËã͡īï¨Ð–R‹°ú£±ÈnÙb`¾[칩ìÀɶ7ïS—íº>¢.Ô,g—½m}®ªñ¶FWõƒ¸ß9#ÛKr4e[Ïã"ß-öÙU6ñàUÆoÕ¥zUuÛ¥±]ËIíóyk WóUžó¥Õ|%(ðQmHJQu4¥ÛŠ]ù®qäBe^¥}ñV]mß—êšíå5¯Kv®3$±ã÷¼k— ¶!i;ühj;¶U¿)W¯â7gÙ“moÕ¥{^bŽ|4vVÄNùz˃Vvø¬¶Æy =;àS»1° Is䣱sÙÿM黜ߜe^°½y«.ÃÛðW¤©þ–ïÝ<;Κ>¢«qvÅÏ“_8w7S9R‰ã+Gcq-²-þ›Òw9¿9Ë&L[ýâ­ºLí¨Žaަ;»Æõí ‰û+ZÃZKû†À—v7a’Be¥ÁÓa‹écøÎÊeÎýØÞ¼O]æú|ÇBƒ÷dyˆß¡å³Úë[¾ø·-¥xÇBƒ÷„¶|‡èûzÇX6ñàUëoÕeù¬õôûGƒ¾{yxûu¿÷î÷·¯ß¾ÃHۢߧ†ý>mÙïÓ÷Õï³ìÀë:¶7oÖÅó1¢¯ãïr4ígÇ_9ÒfH(ÏÓž5>„mH:R{4¶c/[®†ï¸.Ê\=]ýâ}ê2}&XsÔ…{~œÑ${;ûˆîd.ÙÙ{ŒÀ×¼m)iÅëhR>¶¸ '|ØÛQÙăloÞª‹gMÈzñ÷ÖøÙU+¯A²ƒ}V»#ªmß7$^<®‚¶”VĵÍhÇi4Â÷Š1¸ÊœLGûâ­ºø-ViEœ’4³†]¼wCúw“zHb·.¼ú#m)Åõ;—¦·c‹ÛqÂwÜÀeN¦´=¼O]k[£.ÔXœ–M» .¹I»Ön|¬c’V¦¶cëÉGä{ÅÊ Ê&¼jûâ­º4ï¯VÄYEíæ3×Þo7Ñíÿíî=ñîãCÚ†odhò:¶žI¾çy#Yvàä•×oÕ™rN‘SPšŒY¯•7 ÙY»Y7$±³g'|`GE¶”R$%9[±•-n ß)ÖÝUvàdJÛÃûÔe¸N§WÆf$ ¢L²ªÆ%$±køLÇ6$õ¬Ô$L‡eûH£„o—â9]¸ó’íÍ[uÁ¢ƒé”A?zlåMî^Þ’¼<¤c |b•W¶”Ωû£±Cs²õ@Pù>ïUvàd Û›÷©‹Å/d,›ÿýÔØzúFZœ=÷ßöû \;{§n;]Ç–’V9&ïc‹ÃZá;dGÙăloÞª šk¼#ê÷©ñ¨i‹Á_þØ[³1«Í‘Ý4ü݈Ïrl)EFôKcc]Ùz2 ùN1NVÙă)loÞª º5pÑ·šÎòœ=ú^¤x dåÕ„¼Ô˜T‹l/‰q¡IÖ2…mÂXN¾SŒõTvà¥Éö“÷©ËjŸß~hžåm}Ûà²îoß4cÝø.·í¾¾Ï_¡ñïW¶þmË÷ùöU¶¾ý­^yÍ/Þ^ûûÆvÑç/—f8Ÿáuy^tp ÉËÊÀñƒ„mHuÆ_S“fØ&ÜI(ß)î=TÙ'¯4¿x«.ȸt}/¡Á;ò¶¾‡ê”×÷bšuáY [Jñ½\šV-¾‡ðßK”8™ÒöðV]üÒðœµ÷Ü-[}áÀ¤Ö µ¼<¿½üà6><¶»É·þšš‘Ž-6—ÃwÜSe¦°½y«.~mõ]j‚Í\Åb?êâ7k¼_u¡tÕ%4àÓÔ+}Ôe溎û9´uiÑtÖ%4µ{y^—îìFHb·ÓÁqr.lCâlh#¶H¾³Ö`£ìÀÓ¼mïS—áÁµ¾K³üIogŸ¸”Ä®à³Ûz‹¿¦¦´c‹ˆãðé£lâÁ«´/Þª òQá­ÐïšÖo®ÃŸû ÉËæUàØ´ Ûr¿®¬k?¶Hæ¾³æûW]‰—yloÞªËT_Ç\|—f,Hìïv…4Búÿz_øò±'mCªªyhÒ>¶ÉÙMWßõ'/ÚÞ§.c~ûÔðûEÒÀø¶ýRÊûÛ÷+0>ç±¥t}û¡Á÷K[~Ûô}}û,›x0…íÍ[uYê?¦¾jŠ- ö¤-¥ó޽'²õwH¾Ï;¦²'SØÞ¼O]Ԇר 5ÏC«ÞèáwžZHb7?ð½o[Jœ¹…K”a›ü7--Æo≓moÞªKñ6üŒ-¥ññáájcÇ\5îÔØ2WõÄ9ö„mÕhQcË£±ñ¡l}ì(ßglyêš×Í´Õ/ÞªKõ]äxû{iž¿"»êóØž .Iìv?xÃ*¯l]J‘PîÒŒ¶ QË·KñœÆ“)mïS—éÑ kG]¦Ï—¬ÈÕã<¶?æ‘´qèÙ4ý_éØRZŠf¾4}Û…VO¾—¢™£lâÁ´ï/ÞªK÷¨ô©yå¥Á7Ö=ž)ujФռ2ÅögÌ/lCÒ¼Ršaö°˜WÊ÷ˆy¥Ê&N^´½y«.C±¡Eu MgyÎ~‘kHb·/|ú#mCН‹\ ¶È…¾ÇùºXvàŒ†¤íá}ê2ù+ä¨ 53{yŒVîÎeJ»>o|åc’zŒ£ÉùØâ.—ð=t[F”M3bá. u  ¶”Šà/-sȶ؊Eø. €²'Ó¾¾xŸº0)VßQ¥ÉÚ^ž÷n¶ÉÖ¦/ñ´Ø„k‘‚‹8“ùÑ–’Â€Ž¦ìc[½Ý˜JíJ¤*¼`{óV]"yØP;-¶©Í§lþ––x¦×"q‡mHÑ‚‡¦æc‹¸ÉðÝN β‰¯š¿x«.[ýU×;[ h‘ÂÆmûˆnÄ8£G .Ã{Ò¢ÙIKiGca@²EV˜ðÝc)­E âäe¶¼O]Øð@⥙Î.s){@j!‰7àÙ·Sh’¾®£±7R¶8p¾ã@b”íøá…·ùæÍºô¤þC›•Òø†cgº0ߌìÙGtg³²3Wà{Ž#ÍÊ£± GÙúf¤|ŸÍJ•M<˜ŽùÅ[uÉ‘˜„¿ËÑ ¶L¨²ÈnH»=^b{±ÜRÕïšT-î´ß-6CTvàJ¨R¿xŸºð¨éQlMΞIM¬'ïE‡VyhÐ4|§‰3aJGjJ‡|il!P¶Øû ßMÉZ¢lâÁ´Í/Þª “b5]ºd+2‰›¶<Ü›Ïj{,Ýõ¦ß8·b`Û´¹¢¥»£±åaÙ6«Døn±U£²‰SØÞ¼U—¦þCã±£Á“n:Àl}¯q Iìö…w³†mHIãc*ÙúxK¾ÏxLeÎÞ†¶‡÷©‹úºu¡¦ù“fàƒ…÷®‚Æ{>ÄqŽ=a’z#ipÐ;lq<|Ç!ñ(›xSϺúoÕ¥+Ä"éÛMõ b!T½GÐ…žÝÐ"𡠠؆¤Y¨4ÍÛì¡„*mß-f¡*;p†YÀöæ}êÂÄ$šïÍvvùÌçûPBÍ÷ûPàð©íE؆¤ùþÑÔql}>/ßg¾¯²‰¯:¾x«.3fGú^Bc[Q–‚Þ{ÛŠêK« ÑíK=ñÕn[JQó¥±éNǶb5M¾ë©9Ë&N^´½y«.ëš/ýýÔÔtsíÎu‡äåm¥‡¾5ö„mHñíog:¶ü¶—F‹m|>§À¯yÜïS¶áÑ&Ÿ4YÙË˧ÍÝšG›¼µ…høHTá¶’N›|4hW·’è Í¥ï«MfÙă)loÞªK$ÅR0ÿÑ={7 =}¸äÄ®}ࣇí‘4Š—ÉÂÓþð]Ïøh+™Kχ—Û~ðf]F$c2¥Kc£’qÒ‰%H3$g—µ<û*/mCR«w4e[$K ß‘LézNÄÉ«Œ/ÞªKŽ”\“u"Øï<ŠRäé=E+À«îÁ¶j$ª0Š£iõØâ†¶ðÝ#ÔLe¦°½y«.%ÒMòÛ—fú“.¾É8Mã¢4¶-صrã ìmU¦§c;üIWö(üWe¦=}ñV]ªÒQjsÿhðÎ3]˜oÞª0Zm ¦à"Þð Û´5$oÐËÖ7ïåûlî«ìÀ™sŽ/Þ§.LL²JÔe2‰C9å¡ 4©†$vã_ó¶eú”U⯩éåØNÌå{Æa”óœ€SÕ|ßÿäßW×) ÷hк4ßò€ÝGÚU’—×}%ˆx÷U^Ú†¤€^i<(W¶°+ß' WeÎPž¿x«.Ãû«©w¡Y˜5v4”{ʳ®1•ž5ZÙ¥€àKé™aR´²KcÓµ­ìÊ÷ èUÙ³§[û‹·ê²Ô×iùhð4–‚ mj¬HE;ƒÝêß¾ÊKÛ´ q4¥[LSÂw‹md•8yÑöð>uQàˆºpž‹ßr)ÈÐF‹—’Ø1°‡øLÇvkßKóý£ióØú|^¾Ï|_e¦m~ñf]fR®@룱`éGâ‘ ¤~¤Ê›Y¿ð¬ \؆¤°iuQ5¢.ãJ²ò¼µ®Yt\E£’YÔ‡ŸçPÒ‘4*‘ÆÉÖÉ÷9”¤²‰Gª²=¾x«.E†:”t4Ú6‹‚ ñlŠŽºÄ³«:>¼Æuù–t(éhr>¶~èH¾Ï¡$•8yåüÅûÔ…† P<šæìê$ç’ØœW6Ж’³¤ñ CÙz¢|ŸE•M¼D2ýñÅ[uijÃp% N–™´ªlϦk|§—ÙãZÇ9>„m× ¨"\ŽÆfÔ²íØQ‘ïW*›x0…íÍ[ué꯶êÒÕûçÃã.\·$/o(øPÂ=Ø¥”VÚ‚£íØâf¡ðÝ#mÊ\=kûâ}ê¢õu¡f9;w±‘ï ªÕºÏ¾oøn·-%í¿|eëû+ò}ö_T6ñàÛ›·ê2´£¢¸‹£Ùþlu$ÅØÍ8®¢ßeê"â\‹†mHŠ»8šZ­ÇUÈ÷‰»PÙăW­_¼U—©Ykæ8ùhl7uNí?ÙhqNí¯h kN]Ä|éÊ؆¿âÒ*ð*Çg?Âw;¿"Ëœû3´=¼U—3ªKhÀgÇnÌ€Ôòg]¶V3ˆ+š§ÜRÔeG’érlÉuNJΊ¿VÄ:¼h{óV]<)V:FŽÆ}Ì­#) ¼’Ž«hÅk¥ØÝt¼Ÿ#GÒJ‡4~èC¶~ D¾Ï•í¸x¹íoÖe%í¨lŽa¤øÆVÒå.ÝË!‰Ý¾ð¬#\°Í±×É/àhlW@¶¸',|w]seN¦´=¼O]¾b{ŽÆF%+éHŠí±¯üÛ³²V‚ˆ_±=GÒêÝÑX|Žl=vG¾OlÊ®ú¯ØžÞªKÖQS^zi†óé¾2o£ÓÅ« ›ÒˆÖUôN/Úé‚mHš!MÊÇÁKá»Æ AeN^)ñV]<)Vj1;•¬ªxo­ê« -Æc«*á?ð¦؆¤ñØÑؘJ¶ˆ ß5Æc*;pò¢íá}êÂØP&N¿4¸<]X ãRB»þÏulC-þššÚŽ-h…ïHœe^µ}ñV]šv=”€Hä¥@yÞŠ6/;*Šl]]=ðé™÷‘j¬t ÞÁ®H[[“ï+*;píϤ/Þ§.¼æS1½Gƒ–ÒÓ…1fwÅŠŠé]]»ˆÄ¹ÃH[JW͕¾[Ù•ïr×<÷ƒ¯Ü¾x«.]q‚Q—Ðϼê2|•窋§à:x¿êRÔ%4àC[r‘ ÿÔeö»®=ßÏ¡ÿ¨ËT*ô¨KhÀgzïF®ÓGtW]V\K° ñj؆u øÐ–\g…Q]f‡I7/ØÞ¼O]Ð_á¦Õ¿·¦z·gh«¸k騋֯ÍßÚa„-¥rjš±Ž­'ûZq¥ôUs]_Ün¦c}ñV]VD¥«.¡i,»Qä’ØiÑq¦·oë–ò© SØÛzlq >|—Óê­¸¾¸^´½y[],öºÿ²‘‚ý·•ôëŸÿé¿ücró ‰ÍpÞÿùïÿõ¿þi “?åX<³!¤ý})#äíèù¥~€7ðùD~€¸”-dë}mtPò«åDw[‘/õ¸ÑÌö_@ë ®ømäe Ž6½€Öd=–ã§Ûå+nu¾ZúR‰øù«,_{hÉÓ ⻳E¿”‰¶Üò[=—/X„òK™>«ãíMX~3|]¯eúUë3àF#Ú°!òZ4XÇùÉopûíÚ -~€>Jë¸ÏåÜn9~¾CÛ/Tnøú€©!õÜÐ{º†C,?@ï:zzeë—Òzðô hÛü~žäè­xC”ð7ˆWÙîÏç‡s^¥¾ðµãN¶[Úñ-˜M5œyCm¥±a´óõ+ÿê—>O1ÿü†¹4c±¾±ò‹ñV{’/íI. çDú7ÔF2s½~¿’æùím/Ȧ•'RÒýD7Ϲ_ßê Áç~mUÔâ:æz}5£ý\å­åÍ9W³-¼¼íÕ+¯öúûVLɳMLßX!LOò•§ÓµÉÈ˯Pqh^Xÿ‰b»<ïdš7ÔZÑÍOÔÓ’î×Á•µé¶»´qâú µVtç×'Ù0|Í~ÃOÔÓ;®ñÚz7O˜¸Ûk{õ4ÌÖŠn„•¿¡6"Üå­ód?n5Jo5òÄsk¾[ µVtáÆŸ¨'G[óõKyº n,˜ÿ@;x›?¼°ê¸-¡ø!ñŸ(’L•„Ã~oètt¿<ޫЋï”üDÑP•„KfßP»Y)×gÕý¦â„ðÊ7ÔnËJï­÷Ó‰Ù] i¼>玤'V£—¡œ¡vŸçÔÿŽ„ÛtR~mcR]X¹ù…ó@f^{VoœÒ1KýæWÝÏÛü†Úmž½õ'ŠÃ•Åsˆ¾¡vÏY~o‘® úV#œ¾¶úŽwÔn³NímôÏ®Æ~ßúòœíšõôïQœbýP»åÊgÖ?ÑŠ»/-wí;j÷Ëeœåý‰6ÜÜb]øË³šØ°+Ïàà­­›~‹xFèýOtâF÷¹¤uDÚЗA9:OCÇk?¸pÄ¿Eq¥BñAÉjwE§ý:ú²ËM‡?É—ßwႃRP÷'ŠÃ9¥`·î'Š-½R°$ó†Úje¼¶W ÃLóüÖ/Ø…œßzœ…hi´ oõÝ6t*“ûµKÜF/~C1bDö‹Ÿ(ò_Ø+òöýnãìóµ¾ÛäzˆÇOÔc ÇûÈm{ôÞ¿C=]í*¯­ÙöD£«¾Žê·Çlõ:rÛ5ûë8Ç.ÑñãzåÚµ4¬ÒÏß×~:‹}ùw¨G“Œþöëj#F›MüdUüÈ­uÃëg›S|I2÷õÖz[“o»ò ‡~¢žî¬¾~GÅsúåú:Ï/Zkž_Zë.0ë~mŸ­«±è²Žtîo¨íÀuœÐû‰úžVŸo½¤¡6b¯ó#tSþÖ½ôƒÖ@ÚNÆÈo­¨¡¶7ÐÇÛØÌšm1Ú‡—'™}ýz¬·…5CmÄh÷f¿±ò5Ö…„Ÿ¨ç¯Xóm¼a¨m¥ìåYYÙ‰qûË⇡Ëm_¾…R<Àêoß~)80lßB{³ÅQåìKø?цy÷lo=¬5®ÖÖñ6DZ†Ù¢â~¢˜RÛïû2¢(eù¼ûuÜn¨µ¢ ‡Y¢ Šs¼Íp‹¥!«˜ã¼Ì€ >yY-y£0›xy–¬aôÒÖ•Z}Ä8ÞÖUJű˜âñÑoèþУ5=o(‚­Ðˆ¿£süb“÷õó&¶Pø‚6¿ŽýAßÞg[²]ðœßÐŒVÔf‹/oNóËÑ÷~/×Z£1}y’ ËÅ3œÿD;îÂ.¯Kã†Úˆ±”·9Ni¸¶Ùl_VlJÃ…»¹½¼W Q¯fûö+4¬}Z_¾ý†û¼Š/ëü@»ß!W^Çü¥#zùË“ì¸\Ó>§·~á™ÓوѳôýD1Q°í­åï8ÒgŸÄ˺Jé²ÙkýÖKv\ûgèÛ×Ý‘ÑÜX½ÌK_>bìïÏjùˆñu§ ¿Ï®Ô×¶} †Ájô2³6ÔFŒµ½>I;S“`ûÖ¿Ó¼Ž·Uˆ2°lŸâ[ lC˜ç½ªóõ;+–FâÍ¡³xÞžÆÄ]Ñíý[~SmÃ-¿oè3•/=¿Ëô»);mßPkëz{í»'ìå}Ý»L?Rþ†nCËÛ¨¾L,Æ”3’™ÈCi?Õ[Ë?‘NÊ~ª—¹U™HÓoûe¶X,Âîy¯Þן µ›4ŸÞ~߉—Âþèe­ÏP›wÏùú .$7Ï/«jÅ Æù­—|û6ïžíuĸüžîÙßVòË¡€âA’?QÖ³â_æÝ†Úý²s¿­ŒÙµæÖŠÚdõ}FŒ†¾}Ý ©{‹þü@7’®¢Ü—ún4e–·=.~Þºù:KÅuÌžßžÕFZ?{do_èn~¯o}›áâ^¿óõ­­³ÅT¿cûm&²ª‡¿ßÑåw¼¿õt{ù ½éµ5ÛÈþf¶/3MC­µéèOÎv…eÏŽþüìRÈ÷¡ÿD‘EËèËX´ú‘=É—£]Ÿ‡ÛÙÛÛ:aõ J<É7ÎØè´·îe}Ã.M³VÔfšï¨­ÚqÈ·úâ–ûÊ^ÖŸ µVtÌwVÈ_cÝÒË÷‹«©&lß~_æAÇóf[ЊÎ×u\H´ðí¿ô¸âgý»¶—æØ[—ÞÖ‚p ¡ó­Ç1ÔÚ:ù‰N¿óýߨâÌ¿½Ö/s+»ÎÃÂuSyk‘ªñÁ¼ü¾%n%~©Êi_öíZ„âïä|³mX'ü÷èøÐîwÀÕ·VÔPÜc˜ßÆ„–°÷祷þ)ÐÇ„®ÿÅõÊöмÌ‘øÚÑ·7¶&¿&¿ÍœÙïˆ|™ ݱÝÚòþÖYa¿Íïeƒ”¼~+I~cåY¨ßGª–9¹ÃëÛÈÍò—VÏÆý2‚2t{Ý—9¬å 펾µ*u{ZÎõú4žáÞö\ÃoíUËW&âŸ(š^$}ystFÂØŸ¨‡«–ýÚ^5OWVÇÛʉ¡v¸ù)÷í9·IK_¾îæÇÉlj÷fëÙzyý}®£úÆj#t±¿Ž®‘«Ê¿¼Œ‘ý‰IÊ^~£ŽKp,íÊË>Že(Lÿôò${ó0é-®ÀÐåéc^öqŒ¬ÝØØ_w“nhzó<ý¾ÛÜ_¿²™}ĘßfOvf°úÆo#·éaºé5ÆÎèñθ—õI;)V Úç—•C-ÿ|y݉³“Y®ÛÒë¸núl­¼³Zì¯ýõõ…g´í/­ÊBlºÿV+y[÷ºSc§0*oIxaµ˜}[PoèôäÏ/s ÄÞ{:å·ç¼ü Q¯ýÑò#NÿJpúoP¶¢o½Æò›#{~{s,îÇj²ð¤ž^Â8šì§ù‹°ß ñ±û(‚Ð âDÑÿë℟ŽjOÏÅ31Möˆ˜?–uÎ0~F#Âhû¼ï9lŸñ×F“ûGÂ…!=)a°Åâò3ÁØSì½ú? ¶Ï7( ¶˜§ïâ9D&&àÁx„!$áù}ša´}ê¾™;ubz¾‘ßç0䯆ô% ƒ-Æê®í‚í3û# ¯$<= “0ÚΉg[Lü} û0¬=AJ;0ØnûÞ7îýk1ÏO§³c¸iãÏ?|MF˜Ù.,—{;lŸáîB"…?Älë¸ÒS Œ¶¶Û~£…%……À‰?Ävò'»Ðw ƒ-zU? ðØbÁa!Åa6tO90Ú>u_ÃÏ<¶Ïsñر?°Žãžr`°Å2½$ùk±ÚÏ»»&Y:6ýÝXØØ[,â¯îçC–23ÿþö/œúÛn_]pÆBGpvLœ±)þÁîí±E„ÝBî…?ÂÐ…Âv×À`‹íÅ\ ‹$QCÇ–y€§§|a°ÅæÁÂäê/ÂÈýÉ¡¾Ž!pú§?_¤0Ú>_¤ú‹(sئî¶ÃYV·Ý=0Øbãa1W…¡£ÏÊ1|[ðôôûÂh»€³ÝXœ±¦Û†Ã+afûüuÃhvÆlcéÆêi81„ÒÃvŽÀ`[ì«[Ãóho,ì,„þ†DÊÌoŸÑvG~1k·À²e·5 I[ =ß‹0Øb;Ä׿ÿðvÚwzÿ[Ö†Áv®À`‹övmrÆøkm¯¡cÛ8ðôüŽÂh;ÐNâþƒÕ‘H?ÂpŒ¶{[¬:zOñ×âëD½±AOM[lÃltϰ}m„ÿ†ÛÜàéiE„ÑÖz æsÙXªÚÈ8ôGR>»§l±…³‡_&dýå°¿.øUã›´ô Œ¶ÖS,Ïﳱ̵‘ð0Äm¸§˜ÙfõÃ)Uû…qMÜðçàÝ{R“u‡Â qÒ-nËá¹ñ›¬‹h[ø,—íy¯ð«!yùÄÉ$Ú8ž“ÿrðYî>Š2M EáÑ(æûsðå­ ®|)wUpBǺx Û† ñéߎ™Õƒ»žd Ìl>ˆ‡‹ÝOQ/oY™ÒÉäí¿ñí­+7î>†Ÿ^Þ~:ŸéípÚËŸ¡ãìâ>‘K“¸|Lÿ›í¿-š/;ÖÐ7}Gúc—y©}?uÁ®šùÀ•F2%]7ÿçàˆ+u¼L~žß{o—Ïy|³òíczý|d¬TâŠzù˜î#oÊi>2öñìoå ç;¼þÉZ…À‘ÖÇ}Xü‹pùÐõö‰>†óH‹>6ë?([”Œp÷ÅfÖï/N’†Ü…~ÇÙci„»ê§—½ÂýD‰?Œæßö üA8O\>ö¸Þ±Ì xzLjÇ;–=.çãËM9›²óèþ= ¨ÿÏÁ‡·ÓæƒjV´c¹GfÍMÌ_ïï¥ðÎï'·È‚3/6ÞÞéäÌxjrt»}òŽ.ÓÁݶ=÷Ò†Ïw#wë¡°ð ±-‚ÝvFoëv°¼ˆ'úv0ýO†?ˆí)¥ðÂþ98Öyè2Ü}l%.ô¬$¥òL¸0óY¾0³Ið»&}ð^5 CÇÝ/ôYî>²²w|(%RFe¼‡ÞMÎõàîƒÓY|qœç\¦òçàœšYî>ªwz¸˜Ò} ¿2 ÙÀ³OsÍ'R)ä3Ñ…î£EzùP"»DLŸP¦DÞ)<2ÓÚøK}îaö­DùÓ)œ{t1ÔÀ!$&-örãŽßíÏç$O—Ý‹·´0Îäo)™sÜÇwS÷—zg]¦:EoŒ…«³.èßu™J´UœÇRrÜ-L½åU™JʲÞ_;=í‹ÊÞñb:8ÎM¸9×ݨðQ“§ÚX¼GõeôupùX^?ÿn+S‹,~·Â7¿ÛêhÂÝGV§—ñ®×¢N¶ÀñîÃ7ÂÝGQ§—äcºá>ˆ«=©èQ—áƒ7¤l±øÝÊNaÑÇðmÈÇÞw˜;³/ü‹#^Ùß‹pÜðõe¬tp÷á÷-Þ>æ¸}Mƒ›ƒƒoÎ#–¬âï@¼r R1ÛÜ}`Nm>Ú¤í>ü}^}BŸùàò±üoü›«˜iY?æßœðÆo®bö¸ûð+õžn§º€Ä§I^âS¿õòÁ q÷áWÙelZ¹ þ΋}ðíÓQóampùÀ¹ùĺøŠ²5½¬ ñ¬º`V8|4̽­Éò”y·®±tŸR>8˜©˜î>ü*»R9xiØF2ÙÛSáíiÃ,=p÷áW•¦ÂỄr½ýŽ% —­-î>0'GóäÏ[Rh¾ð»GžX÷aƒáîssêÜÇÊ· ñí‹æÓF7ÂåÃú¾§¾þ~4loYÝR¡éÏK”&[[ Ü}`®Žî ß~Ã&º&|û›/ ›;˜-Ü}`Înºæ¿-¶Ê¬œì<ˆwN0fõˇ¥%(…“Ò†Q®É´!^9¨l˜Ýî>0‡Ïù×!VÈ> Þ|ÑÒ| Aqù°o½T_Æ4ßq—íϰ Ê–¤Y¸ûð«—Jf_Ùýîo?ÕüçàÕ'ö8k±¼Z§_¦·“Éë†å›À+Ç0Ý'þÂåÃ&ö%ûæÃ&j6d*ô±½ Ÿ„uŸø w>±?>ªáƒøåÃÆ8Ÿ>|bŸ7Ç…½ú÷8.$þÿ—v5½ÖôFqÿüŠ»L3öŒ?¶ @Bb¼‚b,нH!þ>×]Uvwç ! %¯TOÕé{Μ3ã.·Ý.óÂÆ_¼bthºeû/ÎTFaìóðÓÓó:7¿V bËq¦{6û}¿4œ6¿ïñº_^ÿp¿4{kƒï¾<~LjiϵËù†c¿¾¼‚f2…9¿øÊñ·Áø‹G ûÉgP+˜³þŸ›/·ÆŸÿt Æ1&b O&_丌ÿí'ùŒý=ÑÖ”]9ìºßŒÁg°l'N?ªáxÅ{rÜ'_4iÑàüÅ#Z—ãyð… ¿{^ˆ×xÓàýÅ+ÆÀ³Ïäfeð•Å0¾ñYÐ`ÿÅ#ü=V¿}Ùæáã+ž§â_>Oü¿x‹Ñ/}ÖþnaŽ6™¿ù“Ý+f‡WŒbÜ;wŒæctøÿ£¨5cXyÞÅ ¿cÀÿÇU÷:žAÏb ÿ<¼Æp–»Å+Ç><ƒú£±Ï ñŸ…¨yo1àïa§ÃÆÆÆ{N|çóµïÍ+†}Ê-û‹±Q¹¥xå–¨ÇܲÃßïܲã`Ñ[ŠWžˆZxÌ-Ùâ¶4”qžxïÈñÅæg¨‰o^1&rIÜûè/rc#óçá;ï}ÔÆ7ð÷¥sÌ^L&Þ;^C~òD|óˆ_&ŽvXxÅÃ}n¾^œ§B­|óƒ-LwŒµúþv1È神§ð÷õF =Û;½Ú…zbñ•ó¨o1àï×¼Êë~¨–{^¹7jè›WŒ5öÁ>¯8Föúóð/¿ÔÒ7ð÷õÁ±ØÖá1:^CþÅ€5õÍ#ü}µö/_Øè^MÃÏO^^µõÍ+FÇ5²£úl;|5Œ[ñ•Ó©¨±o1àïë£rlÓ<®1îñ•¹jí›WŒ5öÕ‹÷þwŒ56Ö‹c£xMÖ£æ¾yÄ€¿/ƒó0¨³Z¯9¼†¼ÆlÔh7ð÷+†]‰ÆÊàØ ~rl@ ~óŠ1ñ¼°–è+†ùµÁg°ø²èZüæ-Ƥ¿oün'ŽŸ(ß­ø¡ïvêYp¾ÛykìëŠ1š{‰×3hÒÿûgФ¿ïôbÇ-”ÁùAñ“EƒIÿ?\1knúùÀ¯uþ¦ÄNÑÏíÿÏýÜþ¾)ÆÀç¯xïä'ç‚çöÿµ¸ãv¹Ãd[Eåâ•;LúŸ;ÌíïíôO ò.Fÿ­ô÷Gã.̱³á{ÙþŸŸíÌœï…þþa¾¾jÙ÷¯‹oœ žÛÿŸ|}ÒßkÞa²å£ž}â5ï0éÿý¼Ã¤¿oú,Sc§-éØüþžèÿÉ+ý½å…«—NÅ5æï’¼æ.æöÿ{îb½¦ ‡³å- sì´Åæ_,q¹'ýÿ{¸­=È×ëcøµƒÐbô¾ø{ _üݪðÅ4ö,0±¥8 ÕüÄbÒÂÂÿ5cŒ åR«sÕ‹¶G<>°IG1àï¯c²VkŠúʃôÿž9åþŸãÕqÏÃ5_¾æÕa?ö+÷>vï<ÇîWG"\ˆÑpxFC~±ùŽ<¨¨ðßNþ±^Úž Öãà:uÀͳNXTøwuÂõÖ…wŒbß1öÑw.üýeÛÚ¾ÐØdìôyx.n.76#Smëñ;]‡<ÊŸª%>§ûH±ó;Ń`Õ³­¶Vð `KëÏÃWø„¢ú9µ5ëÖå¶óÖ ×ãKAÃÛ‹óƒ›çÂî¢úuã &xð—µá4øm‹ù :õÿr¹jê3Fã5œŒÁ#ÏŸ¦˜÷áþþºà÷ ’ê?_8î©þ¿_`°íš5Æ»µûçá+ŸIªÿ÷+?Äެÿ‹· þþ²´ôË07.`J¼îAÖÿÅ+Fß‹¹¿¬Õ؃q¯ÝŒÑyŒû$æ£mÿÆZÑFø†V5vâ^ß8V°þ/^1&Ö#ãYتŽ0·uj›ç:ØÂú¿_kݵx|Ÿ•íÙÇ:ãï’ïô¬ÿ‹G úûýYšŽÞågyµž›ŸåÑ¡Ãî³¼ÊéíHÚƒG@ãÞϵՅõñˆÑt8¦ÕÅV7Â@ö»ÿòYÈú¿xÄ ¿P³°n><ä¿ò¯~ôÿÿ}Ðß¿¨—6öºñ›1ç!&±ÖŸßÇ>ºÉÖ;¬m:ÜßÔ'è¸>Cëµ÷Z8{ ¯Qg æü¶¦jó\\Xÿ¯c¾/4ƒ»ô]^ß5ëÿâƒþ^ž°%‹ûT¼ÂúðÈcû{äR£À¯qÒæ¹1¸ŒíÿÏïtÐßw¬?]]ö¸ÏñÜPÆöÿgÌU›”à=Öž¸>ïƒ|gM‡õñˆñhìÃ|òx06j,ßéXÿ¯ÏdÛ\Æ«±óíŒÁ±±ì˜åðˆ¿Ÿë£ïÃKñwÉë¹Îúx®øûëa=jtìez°5vó/çíXÿ¯œïÂØ0†ÆFŒ â56°þƆAÏ=4 Oì†÷Úügpeý_ZUÿ¿Ïþ¨õúûR£ó»´×ˆçs«ªþŸyºÕrpmy³EëµmœøÂú•õñˆQ´^Ûò1k©Š–o‰/7ò±Êú¿xÄ€¿ÇztÄèXóÝñYÀký~Uýžõ¸§HÙ)†ˆfîÉßùra½Ceý_Xÿð÷ØBýeMoo\Êä9fWÖÿÅ#ü}}øL.Ö›ÇÎPÄkÈë÷Âú¿xÅèXƒˆ±¡à zql _¹—©²þ/1àïOŒ×üÚ‰þÄ€ÿ1^­×Þ1zˆñj½6cÀÿçë‘/{~¬,¯Ï`ŒŽÏ_ñõ1àïkź‹ZöÑD×Ä?ðæ•õñŠ1±æÛÖ¬ë~¨5›ÍsÏ]eý_6ï´ùóR•õñ£Âß× »ú0\C<ƒÅ¿È%+ëÿâãÖºñª£ºïE¼¾ÖÿÃ÷Ráï×I7ö«ëµ+|õæ+æè+ëÿâþkÉ¿Ð1,—ÜüƒùÓÊú¿xÄ€¿_Ûôc \c< Ås½zeý_¼bth0f¯E÷ø aÌÏ=3•õñˆ_o̱­.ÃbØÚæ æØ*ëÿâþ^{í¬ùß<÷TÖÿýž€¥±{½cÎê”lû¸7eóÜ»RYÿ¯nïÊz0ö`Ž~u=^cãzÖáï’áo+ëÿâcâ»*•1C7cLþ¡˜åðˆ¿¾B{šúÔGŸ…ü«Ïÿ/Þb<ð÷ø;_Ö@úâsÝ®©økñ+ëÿâþ¾rOÑÂ˯UîÚ<×ÞWÖÿ«ÛSd¯¹íºWû}ìòù‚šoeý_ ëÿá³tøû§`¿ØÂרj4›¯ØgQYÿ¯críŽÞ>Û o~2×fý_­`ðæ‹®ü¿xÄ€¿o7ês븃å×ZAýmólP_Yÿo1ü}»°Ž~á•ó7Í‹/œ·cý¿ù¹à߸¿ÁVÀgÃü±ø}}àÿ›Ûß°^SðwlíùÂvX.×àmþásõñбîõs=ÐÛg_ñº¬ÿÇë߸ÏÂŽr¸qd3bç>‹Êúsû,Ökì¨f³­ˆ±üZëX7¿y®ó¬¬ÿ‹WŒ5öµµ5;¢XLøñÜUYÿð÷­¡¶VÑÇ}aä_â;k'¬ÿ‹G íb¬±ñÄ ¿cÀÿÇð÷'†-xu1Èïðÿ1ü}k¬{ ôöÁQËŸ‡çzÊú¿xÄ€¿ol&»pÇ5Ƹ&žk5*ëÿÍ5“ý«ÿñÇ?ýø£ßüõŸÿñǯóc=òֱ翿ùõ¿ýø£?û¾Ì?ýË™¬ÿþêæÙëø—ÙìÄÞï{þ§¯¿ÿÅßþòãû‘ù‹üüÏþÍ/ÿáã§¿øñ§?ÙÃ_ú“¿ù™¿´è°Îýøûø¸>þéÇýñßÿÿ×·ý•¿üñN-VZ…¯@V¿ïÃï“·6Y­‰9'hh©# ¬m 뻥€Ð Xc”€Ð ð®¶À½Éÿÿõ~l—Ð:£¯Ëý‹ûº~ùñÓ¿®k¼‚ÿ¯^½:A}?Åìå÷þòµ™êæ_/ÿ‡—OkRk/¯øËWJÿ:ÚËŸýòå!×I0ç¿çÌ—n§Ð}ØMñÚ×y †e‰FÛ“sÓ†"==#m=,7mÈÓVžß´¡H÷@÷H› Ø´¡Hß•ŽþFž6'ºiCž¶ rÓ†"ýýluô7ò´í‡Ú´¡HÏ@ÏH›±Ü´!Gã4Ñ@‘îî‘6§·iC‘žž‘¶ ¶Mò´YÀMŠôôˆ´5eÝ´!O›'Û´¡H·@·HÛ2¦MŠô·;tô7ò´ù°Mò´Y¬MŠt«žnáÃQ$›6éèh("ÈÓ÷¹á¿iC‘nn‘6Ÿ³iC‘‘6w²iCž6ã±iC‘nn‘¶>š›6éÑ==Â=†ãA6mÈÓæ#6m(Òmxº…{ çxlÚP¤G G¤­-ý¦ 9Úæ8÷ a´(hI`KrŽÀ`Ì+æ–‹Á °.kG`0 úýŽKÕÀ`Ì(˜I`Iö Ë À`ô(èI`}-À`Ì(˜I`[îŽÀ`Xj|“ GAœT±€I0ß ˜oX«É#0Åä„IÐ[ôÖ‰ò &ÁŒ‚™ÖCò 5ˆ<ƒIУ '­·>ƒI0£`&A÷c;aX;©#0˜= zL?ÂzdØÀ$hQÐ’àöã‡ô‚ÕcÝ ƒào0 Z¤¦•Ã&Ïaƒ †0žÃ&Ïaƒà 9 `Œ(H9Lk!‡ ‚rÀ$hQr˜6B˜# RÓfÈa½ _!‡L‚)‡éwÈa“Àç0€APB5ä0€IàsÀ xB˜# RÓ_?¯A-ä0€IТ å0«_·ô4½a͸g¤¦ÃÁ S€Iàs@/WÈa“`FAšâXm¹ÝÍ %ä0€IУ MqŒrÀ$˜Qr˜ñ„)À xC˜= R³Ú`{AËS£…0zÈaƒ`„)À$ð9 `ÌÃ&ÁŒ‚4Å1¯ÃÁrÀ$èQ¦8f 9 `Ì(H9̬!‡ ‚'ä0€IУ å0ó 9 `ø0ZÈaƒ ‡0 |#ä0€A0Cz˜>‡të¼|„I0¢`$Áík„AP|Õ€0 Z´$¨¾r@˜# F<>= ‚×ד EAK‚æÓÂ$pU 辎@ç„Iàj „A0}50 FÄôÀZ Ÿ{“0n_S L‚- Šy “`DÁH‚ê§8ƒàñ#/a´(hIðú)Â$p#/a4_\ ‚î§8“À¼„A0|q0 fÌ$˜~ä%ô‚rùâaô(èIpû‘—0 fÌ$(~Šƒ0ªy “ GAO‚ÇOq&y ƒàõÅ h~Šƒ0 ÜÈKÝ“`FÁL‚áÓ ˜¾¸@˜= âÇê%ëÒÂ$pÅ ¸}q0ŠO“Às ¨Þ˜&ÁŒ‚™7æ„AðzcN˜# F4oÌ ƒ {cN˜- Rz°š´º›0 FŒ$˜!=ô‚çòÆœ0 Z´$¸Cz˜ΘAñÆœ0jH“Às x¼1'L‚)=X;,ܽ ÍsÂ$hQÒƒ§{cN˜# FŒÁôÆœ0 ZDcnÍB0 œ1' ‚Ûs (!=LgÌ ƒ zcN˜3 Rzð>Þ˜Áë9aô(HéÁÛ¼1'L‚3 zHƒ`xcN˜= zÌ&3æ„^Ð.oÌ ƒàé`8cNÅsÂ$˜QÒƒVÃÈ 7æ„IУ ¥X–}oyÛë9a4oÌ ƒ ‡‘0 zô$Þ˜&yƒ`zcNè«£¤&yƒàöÆœ0Š7æ„IàG^À ¨Á˜&Áˆ‚dÌûF^À xƒ1L‚ɘ¯~ŽîÞL‚ɘ÷Œ9`Œ0ò&A‹‚dÌû Æ0 üÈ èã Æ0î`Ì“ EAJV·Dwó&Áˆ‚dÌWŸC/¨9=O0æ€IТ óñ†ô0 ¼1 ‚Œ9`ô&7æ€A0‚1L‚)=3s@/˜W0æ€Ià5À ˆžwþ–çE‡A/HƒÚAɃڌžwþ–çýþ?¨Á5À$˜Q5ô<‚7jhúw-j3zÞù[žíúŽ çAíû_f¤Am5çó‚‘5tó;‚™5´êó‚8¨±Ÿ„Ià5 ¸ý FÅj„Ià5 ¨~P# ‚Çj„Ià5 xý F˜# F4?¨A÷ƒa´(hI0ü F˜# FL?¨zÁ> stream xœ–wTSهϽ7½P’Š”ÐkhRH ½H‘.*1 JÀ"6DTpDQ‘¦2(à€£C‘±"Š…Q±ëDÔqp–Id­ß¼yïÍ›ß÷~kŸ½ÏÝgï}ÖºüƒÂLX € ¡Xáçň‹g` ðlàp³³BøF™|ØŒl™ø½º ùû*Ó?ŒÁÿŸ”¹Y"1P˜ŒçòøÙ\É8=Wœ%·Oɘ¶4MÎ0JÎ"Y‚2V“sò,[|ö™e9ó2„<ËsÎâeðäÜ'ã9¾Œ‘`çø¹2¾&cƒtI†@Æoä±|N6(’Ü.æsSdl-c’(2‚-ãyàHÉ_ðÒ/XÌÏËÅÎÌZ.$§ˆ&\S†“‹áÏÏMç‹ÅÌ07#â1Ø™YárfÏüYym²";Ø8980m-m¾(Ô]ü›’÷v–^„îDøÃöW~™ °¦eµÙú‡mi]ëP»ý‡Í`/в¾u}qº|^RÄâ,g+«ÜÜ\KŸk)/èïúŸC_|ÏR¾Ýïåaxó“8’t1C^7nfz¦DÄÈÎâpù 柇øþuü$¾ˆ/”ED˦L L–µ[Ȉ™B†@øŸšøÃþ¤Ù¹–‰ÚøЖX¥!@~(* {d+Ðï} ÆGù͋љ˜ûÏ‚þ}W¸LþÈ$ŽcGD2¸QÎìšüZ4 E@ê@èÀ¶À¸àA(ˆq`1à‚D €µ ”‚­`'¨u 4ƒ6ptcà48.Ë`ÜR0ž€)ð Ì@„…ÈR‡t CȲ…XäCP”%CBH@ë R¨ª†ê¡fè[è(tº C· Qhúz#0 ¦ÁZ°l³`O8Ž„ÁÉð28.‚·À•p|î„O×àX ?§€:¢‹0ÂFB‘x$ !«¤i@Ú¤¹ŠH‘§È[EE1PL” Ê…⢖¡V¡6£ªQP¨>ÔUÔ(j õMFk¢ÍÑÎèt,:‹.FW ›Ðè³èô8úƒ¡cŒ1ŽL&³³³ÓŽ9…ÆŒa¦±X¬:ÖëŠ År°bl1¶ {{{;Ž}ƒ#âtp¶8_\¡8áú"ãEy‹.,ÖXœ¾øøÅ%œ%Gщ1‰-‰ï9¡œÎôÒ€¥µK§¸lî.îžoo’ïÊ/çO$¹&•'=JvMÞž<™âžR‘òTÀT ž§ú§Ö¥¾N MÛŸö)=&½=—‘˜qTH¦ û2µ3ó2‡³Ì³Š³¤Ëœ—í\6% 5eCÙ‹²»Å4ÙÏÔ€ÄD²^2šã–S“ó&7:÷Hžrž0o`¹ÙòMË'ò}ó¿^ZÁ]Ñ[ [°¶`t¥çÊúUЪ¥«zWë¯.Z=¾Æo͵„µik(´.,/|¹.f]O‘VÑš¢±õ~ë[‹ŠEÅ76¸l¨ÛˆÚ(Ø8¸iMKx%K­K+Jßoæn¾ø•ÍW•_}Ú’´e°Ì¡lÏVÌVáÖëÛÜ·(W.Ï/Û²½scGÉŽ—;—ì¼PaWQ·‹°K²KZ\Ù]ePµµê}uJõHWM{­fí¦Ú×»y»¯ìñØÓV§UWZ÷n¯`ïÍz¿úΣ†Š}˜}9û6F7öÍúº¹I£©´éÃ~á~éˆ}ÍŽÍÍ-š-e­p«¤uò`ÂÁËßxÓÝÆl«o§·—‡$‡›øíõÃA‡{°Ž´}gø]mµ£¤ê\Þ9Õ•Ò%íŽë>x´·Ç¥§ã{Ëï÷Ó=Vs\åx٠‰¢ŸN柜>•uêééäÓc½Kz=s­/¼oðlÐÙóç|Ïé÷ì?yÞõü± ÎŽ^d]ìºäp©sÀ~ ãû:;‡‡º/;]îž7|âŠû•ÓW½¯ž»píÒÈü‘áëQ×oÞH¸!½É»ùèVú­ç·snÏÜYs}·äžÒ½Šûš÷~4ý±]ê =>ê=:ð`Áƒ;cܱ'?eÿô~¼è!ùaÅ„ÎDó#ÛGÇ&}'/?^øxüIÖ“™§Å?+ÿ\ûÌäÙw¿xü20;5þ\ôüÓ¯›_¨¿ØÿÒîeïtØôýW¯f^—¼Qsà-ëmÿ»˜w3¹ï±ï+?˜~èùôñî§ŒOŸ~÷„óû endstream endobj 625 0 obj << /Length 522 /Filter /FlateDecode >> stream xÚ}SM›0½ó+¬=iqü TJ¥®v³jµ—j¹Tm4,R)!]åßwì1äCi…ÀãñÌ›yã'kÂÉsÄÃúPD³…I‰,7F’bE„Ô,ã’¤Ü2!sRTä;5,‹!¤¡Í~YöU³]ljJ }î› ­—f[ïãŸÅ—ÙB¥$g¹•Ör’¨Œ)¡êááVЪDº*cAÎÚ è_# •tã½³ìkôUÍ~·)c™Ñ£ûÔ‹£8-âLÓnŠq-âfx«ÿƒ»ê»ö*n·étíëÁÑqp>H#•‚¦Ö‡¶ÞÆ2¥C nÔqk˜5Ò}š«?_|zy}º1##™ÌÅʘ9‹€R–kC-X¦Ã0?{¸”]Ùÿà†·@ç}¾t¦¸w'š Øø÷‚‰½ÌÆ.gxU!ËyØpÜÕó»ýÝ=Fúá&=‘ë1©G#«r²l£ßK…Å€“åƬà˜}neF»è+<ãY2B&g˜^·œ +ÎR)à¢Y¼rdNeö!N´V(@pTõª> /ExtGState << >>/ColorSpace << /sRGB 630 0 R >>>> /Length 6675 /Filter /FlateDecode >> stream xœ¥œMï.¹QÅ÷÷Sôyâ÷—m"@ŠR˜‘X ¬BD™H! øøÔ©sÊíÿ( †HÉÜ9·»Ýn»\¶>ÏäçWO~~ÿüéÛ¯Ÿ?=}R{fý´öÔV>³c>?|›åS›ÉôYíùä¦L²~z{öüì Õ?µ?{}V¢Ú㱊å5>}àζ &þÖîÌjN¨Þ¡ìSžK(e¥Ov•3Õ\öúüI2êöÚ”J¹7Ê™(gUÔ ãO¶OsUQ›Õ?)C­B5²—Zü•ãS²—:ý%듊˾(Gõ››ß¼ñnÈ•)å@vB'AVܼ˧tÖwQ®îM]Qm=ÚYê¸Û' “õ“*åì¿y|Úô«Ù¯NtˆzÍåX¬³_]x!Jî‰r-¾7ã¶±]7jkjïVèü]u».(ºS[%¨wrÝ>;ߺ#8ñ×}¸Ÿ =(ø—)Ý]ÏO­®;õFœ|Ñ“:ûû-t«t“ÞÝu¢ÎxÕ˜ËuE¼å™Þã~Þ*:ýþÍòû'C[ðé9ýþÁç'Bšß›*æ÷gé ½A9o†íD›.>ôÞòŠ“×ßß_ òfôçKEô¢==æ Û7ûûKû”âí?ü{Ê@ytéAÍö.óSÚKЫ]ý_£¸"LL×Ä0n/Ðk¸®^^Íh¨KF²G0õ`ûzÜçÚ µý užÒƒÚ“G®ƒñia6¥×öëÃë[­½ÙžõÝ~¸¾ªôf{òûZBŽxßß2²×ß¿Ïrìf{ú°Í­bXázâõ††{ÛËôd`<µÎøÌh6hË~è5ô¤f¼¶… –yIoÏ–~m+•¯mÏÆ&ÙX&·'Óæeõ‚X~eU:eË™´!’yµcDÛUÆm÷ù’ •£ OϨK•î ­aÕ`Û$eÁy®K„層•½¨‘ÑÔ»3oC—üÀQð÷&;Ÿm(IJãH’ÃåöjŒŽàEîL’Ô&9tÆ@Nų|¯%Â9‡¤®eÖåí<6àöÑorú$gIºVÉqË‚h|¥¿Ñdiceg$6ȆˆÝYMgÒ2…IÆÍ3Cú—m:¬ùáLB¹]fµLmblNÿ{\õ:Ûi1øÊŒHµ›™(VAD®©qlrºd· ÓÂóoîè,È!if7Oï›$븞è<«mhs®O?ÙêS®2ÞMn“CÃeûÌŠIد¢6®òóцÕ`ä0 ì¾h{ºåÊoÃZµ[Ÿõ3brbú7Y)mŒTÅ$‰H};Åäð«>åLáíôo±¡Ÿú9úyoAâ ’ˆºÌÅG±4bQa‘3Y²%·yâryLVÞ<¢ÞtŦË”v³Odcð.6¶}B´d_5Ù{=¼!g:XrADEKBZˆZ³w^mQ“žñKöÔ@j’Û¥Ï'%…h¡œvuóÙ…%$$¯n sÄ•~ñ XïûT iÉÇ®V^õ”yв‰//!ªˆ-6 Zb4ÙBîyÆB)ÝCtsì›ó,DŠIÒæ~»ªj,OŒ‰y²”í‰ñܼ=1&Uæ¿‘Ï`/6Û•üƆI†hæÕê‰Qy£ØT˜\ú(+Õ§¬6ѵ[N%FvJõîÀÓël³ #;Ô&¹ä™°uÉÁLè­Ñ²ã ÉÄȈmEQ—xµá‘c!­ ß›}š¶¡7xÕW16ô<_A"1NNµÅ¦¶žÎÒ¿ØÔæ+ɤï7=ó™®JOÈŒ¸ì èߥyé… É¤¸´éÍ’Ú@ïúnfJoœÈ³¥w.4µ /6ãíö®ª‹My½¿ çÒיȓ´&òA½¹Ð,¦mžó…¤VøÐ“«v6Ýð‚}¡Ý¤7êž Ë¨\hV}Ïè\Hžò|ÅŠû¶6ûô.üʘ•³0ƒ^\ Þ¿¹šˆXÓ6åùBrrϽ®…z±½b+ï·؜˜ê»p-6 ..Ì uãBR ñb[ÄÔ¼|æ és€—ÇòšCíis_ïBÚ÷D]ýa“a™×÷˜^“ eo›µO&eõÒÂÒ¿×fĶßK± -mn|šôLïB»XÚò…¤öheM.$‹Æ>²uñúb/÷ý‹ ͨŸ¥_Hv43tf<ûÚãÓ3*tAÅ ßüо0.˜7ºkŽ,±¬ýrÒt‚õ5““éIÍ$iÃñi3‡/¬Al5‰ëü¾íù×=>« ´ºÞò ŸØ¯M×iõ<_“ÏzxÞã©r…ýE#>saUkìÔ±¿kÒ‹×3¯{àâºOÕ>›õ·¼å»uÌ„|~#¿¾åƒ‰è~êìñ|î·±cÇÄêßkÉÔ¯ïÉ•ñ¼¹V­6‹–ÁþñúØD™¸±dû˜Ô…z2žµ‘„öûd>ªyqã¤ñZm¶,ûÐëÚ(Õ’¹qׯ¹ÚÆ2§wã ­Óôö±)³ç[÷ظ­{áóƒñÜßл¾ñjÓ¨6îÒ;6îGïönŒ«m,=ßFûØÌšû»1¬6—îk£Ym2íD&ŸÕfÓ<ßñ­|ëù´ÖÎ|«|]mÍ6žûÍ÷µ.æ[èMݨ7ó­6ºÕ¦QmÜ}¼Ö¥ünT¡‡6ž^¾m,‹@I–^åÝhÖÖcãžXÞ |º´6î‹zÆÆÝWéµ-æÛÆ%~µéuõ,T›OÛxóMí96îŒÓ‘oy½0>œÕæÓÄ|êËè±®úÛüª|ËñiZ÷ÄòFä[¶__ܸ 4@ït—·Ÿñ>ÛejãÎñkzæ7×Q¢:Ò*tý¢ g½â϶šÊ·¾T‡Þõo6¿NÆëæõÉûä–Z Šãm¬QŒ¯ápôm?›_÷¼ÆãÌQÕæ×|ç‡YˆC#?Ø|[×5^l>Íl_Æ»éI½©‡çÛÓÿ¶ÕLéŠOÓƒÚ÷žuîˆgj›OÓ=ÞMjgÕ¶Ÿ_âÙô"8ñõR]>qB³þ6Ÿ Dù| ½¨™¯mè„4ƛͷ鋞QG/Ƴ/£ 7óm%Xù8ý'#õeK<›À þÙÊOAꎥRÇîÌ99‘zð&R3ô5#™: ‡¡:¤·0©:€¡±:$¯:WŽóÏ%XÝóÞ'YÜ[툥"((´ŽÎ‹ÖsÕl m¾pܬˆ§#͵Êa&¼nWù^âuÛ5ø"Fx½Nñtâu ´…×Mú–Gxûõ¼néx‰§#­µ$4O¼Þ: ¯· tÂëm°ìÀëVÏâ×íz''^oZ•^5ñzÛÂÃÂë= w ¯wmÁ¯w’Àë·FVÃæ†¸šx}Laául>œÐ¯!"'¼>‚à¯[ò%Á^·d'M¼ŽÍA^¼>ý3_¼>gàxâu ”ò¼xÝ’kåýÄëS«¼ÀëCY7ðúèbWÂë#0 ðºM;4²ÚÔ™Wàu >–G¼>#)ðúÔ*)ðú ('¼Ž‘ÍëÄësë{…סËóâuÓÄùÂës«>Âë6&…ωױ¹!n'^?4Nx}i¼ŽúNG|‚ðnÇ, Ã÷¯P¯ï׉×Áý{„×m„¥ ¯ïÂqx›âtâu{ǃðº•p;ÒéÎÜå^ß•;ð:¨X~^¼E¼O¼Ž,ÕŸƒ×óÁ¢äëÀƒÂàØífèMÍx#b/I»1v@@r‡ì%uµ/)»£<¢r_@ èûr0¯¬{j,±+iÇý¬?Q;Þ?IÓµC3ÞÛ¡…¹¶ƒ,2žˆÛQ?–OÞ>Xæs€{±´Àã w'„õ9Ȉì›ÌšñFèîº=‡º£þºß±;¾ÇQh¸¾‚Ã/ÚÉû¥D•sCöî@Óû“ðr\ôà‘íOü.yówÔWõqëŒ'x<_¨Áƒzrü’Á_Ú!¼¿ÏëG :ËüG _¸™<¬“Çkñiæ"ñ`’éBñ¸Îø#‹÷û½¿ ãQ~ºh¼ÇKyŽGû•‹Ç{µÆ3óm×ñ-‰<šñB$09÷å:òï ½˜O™/Iå/í!ÞÇx$—G3ì Ì#L˜/Iæñþ¤Þóíe"›Gy¤„ó_ôà|•Ñ+ómu!ŸÇüIªD@aF°JB4!&ïˆi—‘Œi7]÷“ª‘Ò{š"—wL4²̃2Yš %¨÷ù˜×Ô£ýH‰ê}¾¢vVïó-Ù½o×Ð?¤:¤õøž|ázo_^÷Še.î°GXéàÀ‰=ò)©‘=Â\ =1ßvî2íqýCjïaÜŸƒí3áÛáöx)'Á=†­w‘{¬G ßçèiHïó…Ò)á½{–ïô^Ãôà{g‚sç÷sRX|ŸoÒs¾ÏG^"|„!)0¾ç7–çÛ/èzQüK;ÆG˜]‘•¿/¯0=$Ã*ÙÇÒu7µ?Y¾‡yhÄ3æã‹æc˜°ÿˆóƬ/y>€•@ݨëŽwÑm:Jp¤n'&Ó÷|ß&v¿¾Ÿõ¡yFª_¸ ;TšýCªïaAJïTß»1(?òmÍ:ç"Õ/5(&©>ºÔ˜TÝÀñLªn"E%ÕGy%ôdþUý|  |Ñ‘ê£=x Aª÷ñl…Tåç‹êã:ÏÖHõQR[RýR·âTaÁÃRýÒDEõ½ÙÒs¨~±eãTß”ܩ~ÌiÏ¡úÞl¼îTß›”Ý'ŽûQ}t‹N œê«›Õ/'_‘ê,+¨êCo>ïTÕöùXTÅ2Ÿ‘꣘zAo®OEõËŒÓHR}¼Æ×“¢ú(Ÿã™TßË'…wªïå×çP}¯–×TÅp<‘êãý3=‡êãºÞçT¿`Ú_Ï¡úx-ëOªïÏûûIõÑÌ<¥ ÕGýoªïŸMJïTÕd¾$ÕÇé¢ú€õŒOR}ÃùŠT¿œhRý²¶Æ+©>ª¹Bcýº¶¨©>^ÓÒs¨¾ë‹ê£|RR}¼ŸÔ‘ 5|© q$êO*GªïõMÏ¡úh_R9R}„­Nœê#žæ|ÕGêý¾Ð+X†’º;Õ÷aHŠî \ç÷p!†aF*Kª¯ap¨>â£\Tÿħ¨>úŸT›TaÂö#ÕG{¦‹ê£H½HõKønDõ+òù~Õ÷nãu§ú¸\TßùUzÕGÿˆú;Ž»´ã¼/Ú²wµS}¼ßó«¨>ºÙÇŸ¨þ¥êÑÈÞL¤òNõ¡ÙÞ¤úhvROR}t£Ï7¢úè&¶'©>º•Ô”TÍ ŠïT¿F¾ÕG1<5!ÕÇkûEõQ )0©¾Š=T°>¤úx ãTßïÏÏ¡úÕ¦åLÊïTÅö ü(Þ,ÔŽ[«åžRêãyÖŸTšýMª˜¯S߯óûIõ]“ª;Õ¯]óµ¨>`{= “êGªë¤Ô¤úøk¶©¾ßïýIªû×EõëËEõëˆþ#Õ‡^ó9Tèy§úø ö/©~å´¨¾©ºS}ÜOJNªaÅ|Dª´‘‚âWè8Å ÕÇuÆ+©>òOéHõkì¿Eõ«-£y*AªïÃæ¢úµh!ª_m}ÀSR}¤ö'©¾§ýüª_Ï) ©>êËö&ÕGš(AùÏ%¡ú–ÔÞÁ–ù“TaªòvÄ3ó þlOR}O#Añ=žKPz§ú_̤úx~R;Õ÷÷¯çP}¼Ÿõ'ÕG˜ë~§ú—vªéÅx.ŠWR}cÔŒÞêüX–´nÀÚ’Œ>(|Bv ¿7„ð[)@¶v9Zdkסšlí•—­]èM¶vùdek¯‚ñ´µW­¾[Í}H’½c~]ík¿i‡s㺃÷¥­¹û ÄîçNß-5¡;œRè22÷5 Dîlõ îK›Ew<ç€Ýy;ž~qû¦¤íèæžlH‚µÃMÕŸ@íXŸrÒ¾uDJо·:ä즖¸:lGÚæ“²ï€0áaצìxØ[xÌåaßáq—‡} "‡‡}Ê8ö£åa—ÕâxØ· ‡ {Îá‰'d+@@ö[OjAlBöiÙ±V!„'d+Ãñ°oAÓð°G}^;­Ÿáa×QÝñ° z²Ç¯%ÄØo9i4¢Wòr°¡ËÁ^„ø^;ÂÁ^…”ÃÁ.£Èå`ç‘B8ص%;öƒÌå`döèÍp°¢ ûâà8Ø—¢!ìKß'Äžõ@ì9…^ö­#!vëq#ö[ù`%IDNÄžeÎ Äž›¾GˆÝ:…GDBìY[ø@ì%Ò&b·)ˆHSˆÝš•í'Ä^†¾_ˆ½hKˆÍ´Ÿ±—ãˆ'b/-ðDìEÁØ­›9Ú„ØKÕ”;VžÔDìv?1;îòvÄŽïãha®¿tåÙe8䉨q•ˆ×wX×|r±±CË4ï{¥HBˆÝËçý›Æ¢­ö"bëOÄŽûë…ØqñLÄŽçg õÄCÞyyÚý 8z¡f|±{ù|ÿ ñh«‰Øñ¾Èñ\Fì½ ´¿üèŽØÑÞÇê>¾hg¥—ö¹:_ˆÝ‹‰À}m÷ñ{ˆØßx#b㈚GŽD츟ßKÄîñHÑë²¹_Ú»K"rßKä¿ b×0=ˆšˆšˆšGXDì(G˜DìŽx/»;Ò„8Ñ¡¾rÖ;›€¾;4P„ØK© ±Ÿöb'¹|»]g>b/K¿ b?ý-Äþj"öR¿"öG¢Bìg| ±ÛûÆØc¼bG~ ¥ˆýÖƒz‡åù¶²büˆÝêK!Ä^Âq,Ä^Â.Äã7»-©ö|Ûeb·|K„'Äžõ;Â@ìY§ÿØÓ~µVBòDìXM„¾Ò(Ëöbç¢àEì1?bõ ã¦Bì±ÄF–@ì©Èˆ+Äž´¦ ÄžªŒ¥Bìa Äž´„ÄŽ#C:Ú‰ØSUû ±'my±ãH”õ#b‡‘šˆžˆG a˜_骟{ê_{ Ä*Ä~Œ«Bì±Zbw#$‘y§‘3…4r=i|[_¬ò×û×KHNˆ=Ö7Ø“æ!v7¶öçEì9)¾‰Ø}ýp!v<Ï#%"vÿÞßiäŒ_#±û/ësûŸDì¾%rß±zíb÷ø ¤^¹š%b ãü”±8ŒóšoŽq~éaœß2Ë8Ÿ³¤Œó6Ld¬§qþŒ/籺½ó±¾:ÆùF~çsÖ÷‡q~q‹}ŒóCG>aœ×‘ç1Îk}xŒóaãü¹_Æù®ï ã|Ç_ã¼Þ/ãüÐ[盎ÌÂ8/cì1Î ™ã|Œ§0Σ} ã,Ã8ù Œó%Œò2Îwß0Î××(/#'Û#ŒóUG*¯q¾„‘~]¿x=Æùaœª_çãWÚ;ŒóÚã¼ôeœçkç‡ÆOç£ýÃ8¯_ã|Ô7ŒóÚ]ã¼Ä1Îwù†q~r—q>~hÆy™íŽq¾ Q†q¾É¸Æù&¤Æù@zaœ×ÁŽq^?48ÆyÁã|r ã¼òÕ1Î'!¯×8OÆyßcœÏÜÜÆy"uç±M¼óE?ä9Æy©„q>o!Mç‹~ÆùX_„që/^§q¾è‡%aœ/leœ¯úéTçc=Æy[ßÁÉ8_ôË0ÎÛ´S¹#ž±­äó4οšÆù[#ßVYÆÂ8oõÙ·qG„^_ç_Mãü­7×»Bê4Îc=K„Nãü)_Æy ;"tçñŸ§q¾–0ÞÓ8_‹úSÆyl‹‰ôiœõtçãÈ2Œóh¯òÄîõáuçk¹eœÇýDÞ4ÎWí'Â8Ïbbw?j wäÛHUÆù& Rç1-Žç vh¶ŸŒó°Pò}4Îwý¶.Œó6 1Ë8ߋƋŒómAì9Ž\Ã8ß“ê+ã|[_óMÿŠ0ÎwYºÂ8ó*˧qÛnùiœG³•ç vlãy¤ ã|›ú>çMóÈMÆù&~Æy4#8óM¿è ã|Ó‘qç[‰È8Ìàß/ã|›Ðiœ¯Ó8ñ…ØQ>Û_ÆùvŒî4Î7ýÐ,Œóç~ç›þ aœoqÄ&ã|XzÂ8ßõ Þ0Îw­_Ã8ß§ò“Œó\–ÄŽm?ødœ:2ãüˆ|)ãüÐú;Œóañ ãüû<óCëó0Î[³å@îˆç?¬‘q~ÄŒó¯¦qþÕ4Îý 7ŒóC4‘Æù_|/[üoÿüþK3þí¿ýüïí#žïÿýÁÂøjlƒP¬ï’¿ÿáù›ïÿó‡ßýíóýï¿ýÝ÷^z<~=󳫨\htKˆ"<ÿ?ñ0ª÷WýWp´ôæ/~x”€)ÿð<ßýøÉÁnÀÇÍ÷Éë†/ežW\7üÕí;= šo2Ñ<¶m?KÙþ÷—ù/—›°Må?.dþ¤B°^J?ªÉüZþC;Ôôƒ ŒsL˜hßc…ŒüuÃpTqn ¼nÀÞÐrAÜ yÝPùs͸Aò¾µ:7\•ü?­ÍI“'FÓ›"§ôBžOƒG¶¤­Á~òã°2e½½ü?ß~èןþ8&[Ìùã-}í †o-ù­o!‘¶}½§~ùÖò¿þö¿zБH endstream endobj 632 0 obj << /Alternate /DeviceRGB /N 3 /Length 2596 /Filter /FlateDecode >> stream xœ–wTSهϽ7½P’Š”ÐkhRH ½H‘.*1 JÀ"6DTpDQ‘¦2(à€£C‘±"Š…Q±ëDÔqp–Id­ß¼yïÍ›ß÷~kŸ½ÏÝgï}ÖºüƒÂLX € ¡Xáçň‹g` ðlàp³³BøF™|ØŒl™ø½º ùû*Ó?ŒÁÿŸ”¹Y"1P˜ŒçòøÙ\É8=Wœ%·Oɘ¶4MÎ0JÎ"Y‚2V“sò,[|ö™e9ó2„<ËsÎâeðäÜ'ã9¾Œ‘`çø¹2¾&cƒtI†@Æoä±|N6(’Ü.æsSdl-c’(2‚-ãyàHÉ_ðÒ/XÌÏËÅÎÌZ.$§ˆ&\S†“‹áÏÏMç‹ÅÌ07#â1Ø™YárfÏüYym²";Ø8980m-m¾(Ô]ü›’÷v–^„îDøÃöW~™ °¦eµÙú‡mi]ëP»ý‡Í`/в¾u}qº|^RÄâ,g+«ÜÜ\KŸk)/èïúŸC_|ÏR¾Ýïåaxó“8’t1C^7nfz¦DÄÈÎâpù 柇øþuü$¾ˆ/”ED˦L L–µ[Ȉ™B†@øŸšøÃþ¤Ù¹–‰ÚøЖX¥!@~(* {d+Ðï} ÆGù͋љ˜ûÏ‚þ}W¸LþÈ$ŽcGD2¸QÎìšüZ4 E@ê@èÀ¶À¸àA(ˆq`1à‚D €µ ”‚­`'¨u 4ƒ6ptcà48.Ë`ÜR0ž€)ð Ì@„…ÈR‡t CȲ…XäCP”%CBH@ë R¨ª†ê¡fè[è(tº C· Qhúz#0 ¦ÁZ°l³`O8Ž„ÁÉð28.‚·À•p|î„O×àX ?§€:¢‹0ÂFB‘x$ !«¤i@Ú¤¹ŠH‘§È[EE1PL” Ê…⢖¡V¡6£ªQP¨>ÔUÔ(j õMFk¢ÍÑÎèt,:‹.FW ›Ðè³èô8úƒ¡cŒ1ŽL&³³³ÓŽ9…ÆŒa¦±X¬:ÖëŠ År°bl1¶ {{{;Ž}ƒ#âtp¶8_\¡8áú"ãEy‹.,ÖXœ¾øøÅ%œ%Gщ1‰-‰ï9¡œÎôÒ€¥µK§¸lî.îžoo’ïÊ/çO$¹&•'=JvMÞž<™âžR‘òTÀT ž§ú§Ö¥¾N MÛŸö)=&½=—‘˜qTH¦ û2µ3ó2‡³Ì³Š³¤Ëœ—í\6% 5eCÙ‹²»Å4ÙÏÔ€ÄD²^2šã–S“ó&7:÷Hžrž0o`¹ÙòMË'ò}ó¿^ZÁ]Ñ[ [°¶`t¥çÊúUЪ¥«zWë¯.Z=¾Æo͵„µik(´.,/|¹.f]O‘VÑš¢±õ~ë[‹ŠEÅ76¸l¨ÛˆÚ(Ø8¸iMKx%K­K+Jßoæn¾ø•ÍW•_}Ú’´e°Ì¡lÏVÌVáÖëÛÜ·(W.Ï/Û²½scGÉŽ—;—ì¼PaWQ·‹°K²KZ\Ù]ePµµê}uJõHWM{­fí¦Ú×»y»¯ìñØÓV§UWZ÷n¯`ïÍz¿úΣ†Š}˜}9û6F7öÍúº¹I£©´éÃ~á~éˆ}ÍŽÍÍ-š-e­p«¤uò`ÂÁËßxÓÝÆl«o§·—‡$‡›øíõÃA‡{°Ž´}gø]mµ£¤ê\Þ9Õ•Ò%íŽë>x´·Ç¥§ã{Ëï÷Ó=Vs\åx٠‰¢ŸN柜>•uêééäÓc½Kz=s­/¼oðlÐÙóç|Ïé÷ì?yÞõü± ÎŽ^d]ìºäp©sÀ~ ãû:;‡‡º/;]îž7|âŠû•ÓW½¯ž»píÒÈü‘áëQ×oÞH¸!½É»ùèVú­ç·snÏÜYs}·äžÒ½Šûš÷~4ý±]ê =>ê=:ð`Áƒ;cܱ'?eÿô~¼è!ùaÅ„ÎDó#ÛGÇ&}'/?^øxüIÖ“™§Å?+ÿ\ûÌäÙw¿xü20;5þ\ôüÓ¯›_¨¿ØÿÒîeïtØôýW¯f^—¼Qsà-ëmÿ»˜w3¹ï±ï+?˜~èùôñî§ŒOŸ~÷„óû endstream endobj 635 0 obj << /Length 549 /Filter /FlateDecode >> stream xÚ…T[o›0~çWXÙ‹‘Šë;0)“µ™6E•¦²§mN -„Ì¥ù÷µ±ÉHÚnŠ"›sÎw1ç J€Áçûu–·sBP*ÙÊQ‚)ˆ±D„¦ ËÁ(PF„Pïµ …ÇjW†‹Tn™5!#ð9ü•}5Œ¤(•TZB "– F˜£úFÂðì•þ‰®×º9NWvKnl†Cbú¿å²hgî½m:‹éž˜GÙêNûb:i'7®j­U] ›™ÎïO¾´ÔUî¯Tä…ʲy¶"ÿñâ«–ÝÉ=OÊ$l¬ší(#à丩ºbÒÓö¥ÎD‹èXÛVèñÎó>†~_,ÞSÉ•þ]êâôZhc©¬Òy²€˜ äÜc$bVuð'@1‘®àï®O (¸ýRÓÜ7Á7órÑ@8ûÉcñhNÎÂÆ¦Wj¿1ÅÔ¸uL°=hÝv¹U¶‰¥ó⊆Pd¬5Öß³1À÷®“Žq¥vã”×1])}–i‹®s·û7d£öŒo ›Òå¡.vV¤ó—™šÄ¹i 5 ¾øú‚9ÀEW†¤”]LÝ{+0ß iX"Â8bØ;ãòzd_ìŽ)Q endstream endobj 640 0 obj << /Length 19 /Filter /FlateDecode >> stream xÚ3PHW0Ppç2ÀAc(á endstream endobj 644 0 obj << /Length 523 /Filter /FlateDecode >> stream xÚmSMoÜ ½ï¯à¾ŒÍ5R6j¥J=øR5=5ëE"xå¤ûï v›H{f`æ½y304 †žw¬ìÝîa_sÄ•J ÔжL †iÊ…A]~cM*ÎÃÝÉ‘Jjɰ.€¹'†ã5?Fò§ûþ°— 2Ôh¡sN†*Åi£dû±†ÅŸCJ¥˜Âç0.s>ÖØNÅ÷ND‹Ýt)/H•Ì1û>ˆhÒÅq ÿƒ°z7û!ΔTºnñ“=äSy l3Œà²° “=Ÿàæ`7ú™0çÔÔ5ö±÷ï¾_m‰’”/Ö‡qr=X¯çz³I”ëì¦ÄD)…Ë]ïŽ6Õ†»ÑËÖa èlŠbv[Ø ù|ô3샋n²K¢p‡ði !Óàã—ÕòSƒšËeʲEÝ|§"M„Ò×WYeÙà_¤Ux\+ë·aõî…I ò%wm\'° ‘"®•}â÷Âj6§…ƒû5_à<;èv.¹2-eµùZxQ_nƒ‘O©ËÓ@R7Òã73örGVPn̵6`uGC•¾)°Œ€ea[çŒá&°z ëZ[šMà®i#8ª$§mË!Ý·oK Ç@¬mÂóÝÇmFTò†ÞùT>Çæ_N“+*¸¿ö-}4@½ýzÄë$™Lðuúä‚|Õ~yõÔíþÀu” endstream endobj 649 0 obj << /Length 664 /Filter /FlateDecode >> stream xÚ}TMÓ0½÷WX=9bãoD@°°ˆˆX>ÞÔÝDr“’ºÛvÿÇiéRPUùÍxæyüœ™ˆÜ‘ˆ¼™D~}9›\^¥9᜕i*ÈlA¸HX ’Gã¢$³9ùJ- BÎEJ_± ó¹µhÛÝaœ'TÂ’Ò·ýÐ>¨.ˆ95R£ï‹zDïÛNßgï.¯âœ”¬ÌDÇE$Œ óºî :§¦i׈ÔN.WZ¡± DA=^7} Àå,0ŠÑ0½Zã£%.+Ù)p±éjÓöþÀmÓÖÀÐøp­‘p}Ê(áêÈgïÕ£tX} XF›ˆ Ä1}©PkiPÁ!ã=JÖp­•îGP)CÅ,ÿ‰b gEâ{„©}—•¾Ei´\ ý¶ªò ØI(·†ûŸTœdCYkøYˆy£>Àõù÷nüjö+åáNœÊ[oíõÖ½öhU7i³·Ñ£±¡jç tÆÏsÊ>±*¦9jÖPÔ®ËÚW7ÇC+ûÿëºhä­n-M§€§©¢c6‚ —é­U³›z!m½•æ_ç¨Çw°¤ƒ2›¡su{Ò§/Ž1 $¬¦ËéÙ£ʬ£¶RúƒÔÊWªgùÏÍà³±çé ŸÔ³KSMW¶$³ŸŽÒfÀëÙ„[~è}!"–§©—“–ó ŽÈmŒYÞqy½´ãâU?ùhã^8R†pº©s2Æ(^öËÆ]µw›Á6‹(3GOƒ0±™¹¹®%tÙF›Ö ð`ïˆ2=Œ†5š®•|Ú¶5 ¢z3Ü»qâw Ö½›j%½ûQOƒïúùÌ# j¡ÕÕêØììDë­„g+ò”„eÆ’$Gº¤|üP¿™_oR endstream endobj 646 0 obj << /Type /XObject /Subtype /Form /FormType 1 /PTEX.FileName (/tmp/RtmpBlhU1W/Rbuild4444671013800/timeSeries/vignettes/timeSeriesPlot-horizMultiplePlot.pdf) /PTEX.PageNumber 1 /PTEX.InfoDict 653 0 R /BBox [0 0 432 432] /Resources << /ProcSet [ /PDF /Text ] /Font << /F2 654 0 R>> /ExtGState << >>/ColorSpace << /sRGB 655 0 R >>>> /Length 10759 /Filter /FlateDecode >> stream xœ½ËÎ%9’œ÷ù±”u&ȸÜö@f uZZ$®F½Ðë‹fæÎpÿ#+« (hÑYmI;q¼8Iÿ"N–í·²ýyû×oÿ´ýë¶ö}«íúÇvªìãS®íÿüÏí¿nÿBÓÙ?gs×qŸ«}qýÝ_ÿËøÃö÷ü¶F?çUï»áÚ£oüûÿ4ÿ¶]Ûÿýößþû¶oÿã[ÙþqþïÏß ¿ü?~»Ëgœ[ǧÔíçowý\};ÊõÙÛö—oóëjÝj¿ç5!¯O9·cVéî’mšŽÊLyÎi÷ggiÃ5Žóüœ&[™æñ©r|îy?çþ©¸r›ÿæÙc—ÓÜ >3eù\׬Õ,Å¥ZýÔ};öýsœ'ê~œ÷çº%Û½µ}nÔj6ܹë‹(gíæ-\³‹ä¼•£ÖÏI9ïl~oËj |è8›]y|úà-4Nß?ס/Bi/øë£è¯)g‹¢’ó3§d›ß;«~Èósà{ þ3ålïy ×iæ —d­(;å,ŸJÙ?õÐg‡doì£~@ŽÏ5oášÝŽ[;:]vá‹Fe§ÌvîC‚Æ¡ùøw'ÆÈqœŸÑ$ïy ÇìWÜï`{Çþé47vÊìîã’ì¸ßãuÊ9ŠÑtE•³ q §¶ìå³_ϵ g3¡-¯J]9úë@×J´uÿ4•Ÿ{¶_½Ôoe¿>×É!Á&*ûý)ÖÍÝt¯ê:éö9ç½ÜsØézWN|ljÌ–ùýÇõÃôÝØ#V>û¢°ÿîÓô¸æTvý9r¯¡ï×çOÜØQÚzN,tøŽ±*ÝwNC»þ‘pÌÿT•7Ì1 Û2/Äž²®žƒŸíÃòyá:øy]oêqhœó~ç…®Æö:ùy â¡wPŸì¯ã°ö™š3ßûk6üœúüþ‹úFE8س¡GW«¼³¿žë „~þ4ù?«©þ›_„€ë±>süž£šs­Ì Íé|Ìf©Ýt»4Ì¥O\Dí3;ý‰ödû÷gÜœ¬C×oHÇyYûÎ7§Þ1‡…]ÿ§3$ ?}Z•Ùpçõüâ¡û/¦Û¡€ÆúŸsâ5^oç÷ÏÝçýÌaÊY[fEf”æøgûœ,8®†ÈݧŸþ˜ºc Þk ä«~s]C|˜ÓZóëÆsCX€˜8ˆ\Ê ‡Æ‡î®m쯂aÍ@†þ¼T~(ž7ë/¬vØ´ kè ãk°ÿæb…x~6-èÐè¯Ù-;ïg®x·î_ã KÞ¼Ÿ9¬Ô¿SÏ}Âqu‹÷s  žÏfTüœïŒ÷?×9ÄóÙ>šX÷ÎX~0>Ì0À]Aãùê©ÇÅþT{ |ŽWéÆ%y¶‡â?V¿Æù§ø;Äsÿ¾:·.Œç—Ú£býëOûB7mi8ß+Ö¿›‹:çG ƒx~Úúx~ÿü¥x~h½©;Ý1/S«é~i¾Ôñ|~mQýãÃ\¦t=¬÷3^¡æó®þ¯XÿNnýnéƒýUOÅohô–)ÞOáBÀï—Î?í¬¸y€î‡Æ+ë;ãyÑø¯¥³¿|¼U\ø`üfÿC#>ÌûgT,$•ë™Úúºw¶wç÷iãÁùÌûÅúw+þ4ÓëýjCߌçèNÝ0ð9_¥;ãùŒGê/L´ë?ëŸÝ?ÇüQÄsøwÓˆç>>ëÁÆ?•ܘc| ÓM÷¯úφææÛÇ×¼ÐâôÐ÷sý¬Xÿ4ž›Êyãœ?¼ÞÜ!²¿šEšñáÆ2]ÏmþÔy#Ø¢?åU{t[Ïê\ïÏ‹âY úëÚµC[|PÎŽB|8m¯]±þ]Z¯TnѹþuÓè¯v)>V¬×sÌ©WQ|(šßÐŒ¶~×Ùшçh?ŽÏ¹p0žÛ±ñË8¿ëŸÚÿÐõoÆ߅׋G&žœŠéfûÞ?Ö¿Cû j¬Mû£nšë3QЕñ¼4í+Îm];÷nšñü¶þ¸¹ñæþŠ÷3×;¬ÏÇÐI®ÎÃâùŒšß8¼5}Ÿ>ßÏ/Nè¡ý»­ÇÐØ¿_Íæë<À¡¿´,CWí÷|ü¶ªøpY|Áúw>Ç…Ú¸r?®Ïßì/ÜßmýåûçŠõϾŸõÅúw<û}è[g5;+ž¿jg Ãú£ùÑ‹ÖgÛŸÖ^µ?í~±þÝÏ~²â<ןý74ãy·þÅúwëûuýÆõûÏÓôö¯‡º¦r^o®wµ„ñ‰õïzŽ„Øxñ¸Õµ?¬XÿÎÓ'ã¹¶aÒˆç÷Ð!¹bý;™mPaý³ûa{[ç­[gì:wˆçh_}ÿ`|è»ÖkhÄóÙ¿ÜáèÎýû¥Ó?ÍgîwŽyžÛÍ_Mß:é“ña·S<4öïÇ©xqì<âþwéÆþºNõ7&V ûN´ÂxÌöäBriß6±þµ'ÿqÌõÏâƒâicC¢™™^…ÊÜ*^Ì@}뼡ùõïñaê¡óë-}0žïÜfBŸŒç¾^s½C<¯‡µ?Ö¿+ô_¿µ·üÎ1ÏsGÈ_@8žçúgùµ&êÉ °êƒõ¯‡þ›ë]/¡>3pÙ~û›ëŸÎ{ïXÿ”RU<ÁúãÓhÚŸ=’Ÿ»•)E*þ>‘„/s>èO¤â †ßJÅ+{eýy;1ûžCɶýùÿ˜Ýß瘊þzv?ÁƒŸ¿ÙW˜þ‹kvÚÉjäòÏ*ø¸ýÙh6¢Ûw LI˜ Œ•9ÅÜ`2V;Èš!}é2„:üáOÖLÿü×ï4Ó_ÿù_¾ýÝ¿¯[Ùþô¿Ä\˜ ù‰RDUY¢?ý¼ý›Ÿð×ç¿ÝþôçoÿîO¼üßðy ç¦Ïó‹ëÇOÎFÿxýÅ’¢ŸôýM×ñœ«.ðÇ?üƒü73¤{N¡øgeVßùÇ÷ÇÛO…¨ÂÜfÚGúª`ïW‹éh©Hw?Ãv3-¶â/‹ï‚åÄê¾Èt²|»>=~yðÚ„ùÁè}Ïâ¯Ã×aü>}„L#VÆïõÑB†fúdø£K…î>9ˆÊÜ§Íæ™‹üߌ gpžÁÓ‘!ŽØ<2ÄÖ—’ȰC;'!Cä;¸ð2¬¶o2¬‡- B†8¬s)dX«A!C˜‡ĹVÕbGd!êD¼#CTò|!n¡šœß€¬ ¿WȰ§nAÈ•äáQÈ¥¼!Ã:;•[_!C$l†ÉR˜oB5„ ‘à¹WÈÙnã„ ‘:dˆJr%dXý dˆL·$B†h ]Yó°vURȵâùÄáœVãA†‚sD†Èqñ2¬Îv„ ‘ñ Èfc„`ÚÕ?B†uîx.2D­¸, bl0ë,dˆJ2É#dˆä™YÈWæÉẖ!C\¬ V¤¬Îm!CT…[zC†U'‰… q½+ Cdë¹-dˆÌ“!B»TÞÕyvD7dÈÌ[Ý2Äõ„˜„ 1^kÝdxøõ… «NŠ âúJ‰k/Šy¦”´!úE÷+dˆL›}ž×ïú~"CË4.dˆÑ¦”½!†¹aÕ_/dX=…cÈý¡òSýÕ É ¢¾w@†Ì4 Q¢¼èódÕä† «N’ bÞéþÔðB:B† Bˆ<«¢?˜Â3dˆús‹mÈíSTÎÜ5ûKåÌ•cú©}„ Y]Ÿ7†ÌybäÀÁx¢3d8ǯ‡!Ú_H[‘ ý{ b|¡ "ú ñ2<,*2äý ù1׉þ?…oVÞ§íÈoȰêdþ CLl!¿]ýUud3dÈãqaÞÏîˆð¸X_C~D†èOóŸ¯…Ä .$/dˆþ;!ö°2d¦ýeÈðÜ•¢7dˆþÒø2DÿêúB†¨¯ÚG¹IÜ¿æ«6ƒ¸¿!ÄÇÜH†!A"CÄ_/C†‡Ù >ñIÈ0è¡ø½‚2ÄxáÒ!ãGÛ2äüòcîó]óOÈãKˆïTwD+dȵUåD†h!{!ç~B†_BbB†ß*çÿáü<¶… ‘‰×ý ²¿„ðˆ m×°aõGmV[¿ ‚œ)þ’\m!CÔOý-dˆ¯Ñü2D|ra’#2Äeÿ„ ™iW9‘!H—!="ç}„ ‘Ù6$HdÈùìzÆsBµ‡!H‡®!Çß¾-dˆñ«ñ,d¸¾Ï!ªQ…‹ây×zfÈÍ8˜’2ÄŽ§dˆöB2,Zû2Äü¾¥™Ë)°Ïr¼èóD†ˆ/BtB†¯J r›!–ó]HOÈó- C\¿dˆø Ä!dÈø¡Ï‚Ô ×[«ê~"Ãz†t„ qÅ!ây9ýzCñÁâ£!Ãêè2Dy Èÿ‚2Ä|b2¬ž"1dˆÛ‚2ä~DšÈЪ±!Hê+dˆö‚2D5•Ò×BŽñË ¯!CØ„`„ «§  ò~!²?/«¯!>¯þ2|Æ£aõ°!CtsÈãý ÈÃDý/dÈþȰúS|† q†øªöèE›EC†lG„è¯ë0„!dÈþ` â6 !Ö…€„ Ï]3žÛ#9† ¹?”&2äx Èp­Ÿ† ëBXB†h–…íeñÒú|Ûý òûZ<×x2D}Š"‘!Æê#dˆa¯ñ&dˆn2ÄGdˆñ/ä*dˆf鮇ŽHêo!C´Ï)}i}n6^„ 1l„x… y\Ñ÷b?®ö2„_ÈNÈÓ^ãGÈŸ×ü2D· òÆï2Äú¦ù!dÈ#ÊoíÏw«!û+ C¬WöýdG¦/B†¸þ%DGdø´!â©!üŠGB†ÜdˆùoÈZý%dXý‘C†¸þȰj›»!§µ>?Ï›g!Cž!vùõy!C„!Å !C‘õùSûsßB†»B†ÜŸôm!CôîOÈñSŸï: Û¢† ¹~ÛB†ÏLÙ ¢~J r|Ù:oÙµ† «?’eÈý;\ÅÇ]×#2ÄýU]¯©¿ìü`È׿2ä~JŸŠ§â!CÄ»"DG¶Âevl ò|!÷ï×¶!æç-?7²\Æ„oÅs;/2ä÷ßÛB†¿ºS\u½¡þ âûF@†¬®w+ãˆKÈç !z!CÆ3ÖOÈyQ¶§!ã±#B$s‘Õ¬ÛB†ñDÈñ­n V$Ëá?„ “fY~È!ó]пÚE`a_ò;"Cƒ šþË!F´‘!óÖ[Ïnß1癌†Á”à2HFpSxøXÐQ2VCÉÚÉ '§ç2HFƒª½ á.~舼Ïu,èxüFhÈcþµ>^~ëÇçæø2æøËŸþ%äˆíéþDŽÿù;ÈñodP¿rô»e‘ã²d‘ã²d‘ã²d‘ã²dùݱoÓëƒß?ý6%0üßqâëø7Ç&€9 xúùâfèÇÔRž_–?¸P/×\Ùæø ³œýof– Å‘e³P,b9ã1––3šsY¯|!¡¯—Ø—peñ”¨p%@ÑõàJŒ5>#*\ÙŒü‰V:1lŠVŽ®³—`%°‡U‚7ÉJV9 cU¢ ýA•`[\´„*’úƒ*çÂÖímƹaFýä%©,ÎSD*A­¸©D}¹º‹T‚îÕÍA%ˆ·^•Þm§>ºLÎý h ─I’ÜõšsJ 5"@å*ÑÜbŠS–ÒUEqJpnˆÄ)ý·S‚ÒN9,.L „È\0eÙíí5aÊâ™HaÊâï¾SâæËófã°ÙŒR®K¥D­”Å¥,þ`™QÊáï½ R¼ç ål411JvRÛ£œ#DHMˆ’ÑNv"Ê9L@(‡m€ P>U ,ÅZÛ%¸7p(1rõÊ!籛켥¾Þ $žìöÀ«ÑI@?ÑÑI¸ìKÒÉaÏœDW ΉBrªÕmÁÉ1Œe‰MâK”ë›|Z]l’­[¶Å&ñ±ؤµþb“ÃчÐäêrC“OË MO-šäädΦøÓ–†&Y«ð6#®ßšDõn•Mâ¯õ6˜Ðäá"“¼i}œd_g/O’L"fì®çª0'¼îF`rXÞŸ$FG \²Û±Ñ°$‡°­°äšÁ†%»¿X)*‰ïÖ‹q¢’ã°1((ù´¤ äpf(&‰é`ÌoW?í6DÅ$‡=emHrØCâF$9ÆÚ¶ˆäL’$zÇV@Í(À/ 9ï¤8ŸdèÆ«Ä#»ãáÈqÛÄ€5ºHɪr;ÙÊN'GcD²ÏŠÎÃæ‹h$GŠÊI#ÇaÓK0r±'±È§YÅ"ƒfŒÁêØ‹|zI,²ø£'Æ"ñycd‘Ï‹díõ:!Yäh6[…"9›…&™C*ëmk¡È§zB‘è {Û‘(ÕýmÅjaÐßf•­žŠDuôv¦P¤¯ƒF"Ññ‹LΓñ°'x DîA•³Woï)MnqÈaXÅ0$ç~Àl*aAbÈáOeˆB¢åõå¢sÐi€ B>-#9«PƒD0ÕŠAb/dñ¯!Èùé$7*f*|}™Èb¿,`Uw9»gØs_Æ‹¿roü±û‚ÂÍ^`6ú8?,x%øØ{½Fìó¼öØ»¡¡ÇÞœ$’<›˜ñ곸$¹#ÖB½*%îˆÞ/Î!gõæo2;¢{ c;¢JxSñi$aG};±£¿kÔ³l :¢É 2r»ÅuMïV…ê¦ioБ+º4¡#7.þ^ba¼,Î {áD3sÄj©vsþ“#¶dǶˆ#B»•ˆcQ¢{Ççj"ŽÚöR"‰#Êð’"F§ˆˆc?í<G¬½z§LÀñiYÇ9€ô §x#æªÞh$n,5àÅÙO£: ä.—LŤhh£‡U›ìáti5vŒ^ $mv 0؈…HpN°Ñ7Ƈ¥î 5rÏÆj§½ì%Ò©wÿv-¸Íß%$hÎaÄÑå¿âŒÜ0‰’3{lÈ0#Ç«>~iÃm:fänV~n±Æzѱ+T°¢Œ~2ȈӶŹ#W1c?ý-C"Ænë±Ƶ=5ÂÈ}¥®ÆoᬑlŠÓA·5 £ï 0² 8bë]N{ @€±—ä‹­÷¶øâ°Ç× /r )ÍÅŠ[FiâÅf?ð`tQF´Ktgx½¿H¸ˆNo.ßzOlŸ [DD hq­Ÿ†Qu¡W¡EûZì=‘E*D.Iý·, ,"æìo."=pÅáoB +r"ÛŠv8XXqø‹¢Š<Lj:’*b4Û‹¤ŠO Uö»&чöm·vÞ—A`AE¬pê5AEììr<'£×Iy8eRYP‡]½–H¦ÈCÙ¾-¦ˆ©i¯-’)Î/=\Ž;Ú‰‡=AgDqŒ(b.ÕmÅÑŒ—)Ìržž¸Î$ÆíD¸x"ºMù}ñDή±-žˆ1¥WzĹ´ˆO2ñ]îjŸf†'éfX~rDDÔÈ׿Ô8âp,'ŒÈìcÃÊ, !ŠÈ–P1)âð÷JÑê-@DžoDô5Õ"F¤.F„ÈÄ@@ˆ¬Kxë³QÈ’q-ø†ËîHIQïÐ[†DˆÅŽ7„ˆ#¤Þ‚B\[mCˆÌrœÛBˆ¥Ø¯ BäR(M„8ü¥@Äæ@±@#€8œÇ‹S‡<çºF¶¤ØÑÔðá°§%zÑàáílKìa*²C6œ¿~ÈwÑÎÊØ!Ó=b}\¤†!C‡x[¿l Z.i¡Ãâ¿Þcè°ìöv¶¡Ãâ¢:ìöã"FÃÇI™àÑ×1Dÿñ #‡È‹…w ‡qC$7…ñˆ ‘FÐc†ܞ‡7 ñezVØk_Fd-0Y¶„ ‘Rѽ"L³ðP6öÔ”QCžDI Ñ3êfQCôkxÏ#¼hXŽb™" зŠ¹ËäÍ‹>±BÌ0é¡<•·˜!Îi¿Ú5œ`ßñ;"C¥î1”üË F0ag‚Üy!²3ù5­UÎ4€T&ëE à š¡r¸ ’Ñ ¯\†Pƒß‡õÝŒŸëýÂë7Â: úsÁºçã ì gÏŸè2ë”íÐÎÈ:·”ËMÕÕ¨B$«X*“™¬b©TDÿóf*ßg,¾S›ãÄ𴹩T@ñ$³¡íÉÐr_TÇ ™ £'ÃøRɞ Éh) šÌ†Q’!ïÀíÁŽe0 |Lâ1HfC?’¡ç¾Ð³"A2øðÈc̆v%CûRÉ3…A“Ñp¥0h2–Üe4ðÉ„Ç ™ ãN†‘ûBµ<Éh ô| ’ÙÐ{2ô/•) š †sOaÐd6„-ºËhàÃ5A2ø´Íc̆v$CË}¡rƒd4ð Ç ™ 1 šŒ†+…A“Ù¶ì.£áŽ›v—ÑÐb¾Áe6ô;ú—¾è1çà2FÌ:¸Ì†MƒGZ“ÑPâÞe6„M¼Ëh¨1ÿà2BÂe41á2øÆ÷c̆>’¡ùŠ+žk]FÃO¶.³!N“ÑÐÒÄ1 =M“Ù'ŽÉhi☠ÀïÐÝ&³!œs]FC‰']—Ù'Î+KWrš®¼òt%'êÊ+SWrª®¼ru%'ëÊ+[ÇgÏB_˜Ì†pîu w<ùºŒ†–&Î+kWrÚ®¼òv%'îÊ+sWrꮼrw%'ïÊ+{ÇW·B_˜Ì†pv %ž„]FCgáòÊ╜Æ+¯<^ɉ¼òÊ䕜Ê+¯\^ÉɼòÊæñq¾Ð&£áN‡b“ÙÅ&£!%õÊ+«WrZ¯¼òz%'öÊ+³Wrj¯¼r{%'÷Ê+»Wrz¯¼ò{x.1ŠMfC<›Œ†”ä+¯,_Éi¾òÊó•œè+¯L_É©¾òÊõ•œì+¯l_Éé¾òÊ÷ñ‘ÊÐ&³!ŠMFCK‡b“ÑÒ~å•÷+9ñW^™¿’Så•û+9ùW^Ù¿’Óå•ÿãß¡/LFCM‡b“ÙÅ&£!¥Ë+Xr"°¼2%§Ë+Xr2°¼²%§Ë+ÈçTC_˜Œ†–Å&³!ŠMFCJ –W^°äÄ`yeKN –Wn°æÜ`}åkÎ ÖWnOÔîÉÅ|Æö †úåPŒ¿ ‡b—Ñrƒõ•¬97X_¹Ášsƒõ•¬97X_¹Ášsƒõ•¬97X_¹ÁŠd` †öåPŒ†Kì‹þåPÌ'Нdh_ú"åë+7Xsn°¾rƒ5çë+7Xsn°¾rƒ5çë+7h/Ÿ?†úåṖC_˜Ì†p(v )7X_¹Ášsƒõ•¬97X_¹Ášsƒõ•¬97X_¹A>„}$CûÒ=îí]Fȇb—Ùp_ÉÅ5çë+7Xsn°¾rƒ5çë+7Xsn°¾rƒ|\üN†¼·¯úAôÇp|ÙÛóòÐ&³!Šë+7Xsn°¾rƒ5çë+7Xsn°¾rƒ5çë+7ÈÉ0¾ôE{{—Ñ0â¡Øe6Œ’ 97Xsn°¾rƒ5çë+7Xsn°¾rƒ5çë+7Xsn°¾rƒ|$ÿN†¼·çϹ…¾0 W„õ.³!äë+7Xsn°¾rƒ5çë+7Xsn°¾rƒ5çë+7È_éÉпôň{{—Á€d`è “ÙRõ•¬97X_¹Ášsƒõ•¬97X_¹Ášsƒõ•Äkaoï2ÂÞÞe4\1Åá2BŠ£¾rƒ5çë+7Xsn°¾rƒ5çë+7Xsn°¾rƒ|c#öÅø²·ç¯_dÈ{{¾ÕúÂd4¤Ü`}åkÎ ÖWn?­R.³!†Áëkn°æÜ`}åkÎ ÖWn¯›ÔdÈ{{¾úÂd4Ü1Åá2BŠÃe4´˜âp =¦8\fC ƒ&£a¤0h2î=ííMfCHq¸Œ†S.³!¤8\FC)—ÑpćËlˆaÐd4œ) šŒ†+ííMfCÜÛ›Œ†;¦8\FC‹)—ÙR.£¡Ç‡Ëh) šÌ†MžØ}a2âÞÞd4”˜âp 5¦8\fCHq¸Œ†#¦8\FÙ ÉlˆaÐd4\ioo2î´·7™ !Åá2ZLq¸Œ†S.³á>“áþRÉS.³!†A“Á€d`¨¤Éh(ioo2BŠÃe4Ô˜âp GLq¸Ì†âp gJq˜Œ†+…A“ÙàÉh¸ÓÞÞd6ćÉhh)Åa2zJq˜Ì†˜â0 #¥8L’¡’&³!†A“ÑPÒÞÞd4Ô´·7™ 1Åa2Ž”â0 g:©™Ì†xR3 WêM“Ù{Ód4Ü©7MFCK½i2bošŒ†žNj&£a¤“šÉlˆ'5“ÁÞ;tƒËh(±7]fCèM—ÑPcoºŒ†#&¬\fC8©¹Œ†3žÔ\fC8©¹Œ†+žÔ\FÃOj.³¡÷dè_*ÙbÂÊe4ô˜°r™ ½$CÿÒ#žÔ\ƒÞÝ\“ÙNj.£¡Ä“šËh¨1aå2BÂÊe41aå2â%&£áŒ'5—ÑpÅ“šËl'5—ÑpÇ“šËhh1aå2ZO†ö¥’=&¬\FÈaÐe6´’ ù¤fÿîî2˜Ì†pRs %žÔ\FC'5—ÙV.£áˆaÐe4œ1 ºÌ†pRs W<©¹Œ†;žÔ\fC8©¹Œ†Oj.£¡Ç„•ËlˆaÐd4ŒMfÃhÉOjöò2˜Œ†Oj.³!œÔ\FC'5—ÑpÄ„•ËlˆaÐd4œ) šŒ†+žÔ\fC8©¹Œ†;žÔ\FC‹'5—ÙNj.£¡Ç„•ËlˆaÐd4ŒMƒÞá^“ÙNj.£¡Ä“šËh¨ñ¤æ2ÂIÍe41aå2Θ°r™ 1 šŒ†+…A“ÑpÇ“šËl'5—ÑÐâIÍe6„“šËhèñ¤æ2FLX¹Ì†MƒÞ‹_“ÑPâIÍe6„“šËh¨ñ¤æ2ŽxRs™ á¤æ2Θ°r™ 1 šŒ†+…A“Ñpǽ½Ël{{—ÑÐâIÍe4ôxRs™ á¤æ2FLX¹ †{OaÐd6Ä0h2JÚÛ›Œ†šöö&³!Àh—ÑpÄ„•Ël +—ÑpÆ„•Ëh¸R4™ 1 šŒ†;ííMFCK{{“ÙÐ÷dè_ú¢Ç„•Ëh1aå2ZM†œ°²_sX“ÑPR4™ 1 šŒ†šöö&³!Àh—ÑpDí2Θ°r™ !aå2®˜°r w ƒ&³!†A“ÑÐÒÞÞd4ô´·7™ mO†ö¥/FLq¸Ì†âp ú…Œe0 %…A“ÙàÉh¨ioo2Ž´·7™ !Åá2Δâ0 WJq˜Ì†˜â0 w ƒ&£¡¥0h2âÞÞd4ô4qLfCœ8&£a¤‰c2ô ZË`2âÄ1 %ŠMFCM‡b“ÙÅ&£áHÇd4¤ÜàñÊ 97x¼rƒGÎ ¯Üà‘sƒÇ+7xŒ;ŠMFCK‡b“ÙÅ&£!åWnðû?Ÿòå=átRâÇ~õEâ_}(êWO_ñGFôÃ-Ïүä¤îûýÓ^~úþ¿ ðýkàÁÙ¹z}½HûMAïhù"í—kòáÀÞfæ¿Pðßù Ô`ÿþï™üïü ÷£Ãþ9‚Ôý-9¾ßÿÑaÿ;ò¿U GþýpÏ~hçýSB_iÇ?ø©s\ÏoíüÓ·ÿ%ˆàm endstream endobj 657 0 obj << /Alternate /DeviceRGB /N 3 /Length 2596 /Filter /FlateDecode >> stream xœ–wTSهϽ7½P’Š”ÐkhRH ½H‘.*1 JÀ"6DTpDQ‘¦2(à€£C‘±"Š…Q±ëDÔqp–Id­ß¼yïÍ›ß÷~kŸ½ÏÝgï}ÖºüƒÂLX € ¡Xáçň‹g` ðlàp³³BøF™|ØŒl™ø½º ùû*Ó?ŒÁÿŸ”¹Y"1P˜ŒçòøÙ\É8=Wœ%·Oɘ¶4MÎ0JÎ"Y‚2V“sò,[|ö™e9ó2„<ËsÎâeðäÜ'ã9¾Œ‘`çø¹2¾&cƒtI†@Æoä±|N6(’Ü.æsSdl-c’(2‚-ãyàHÉ_ðÒ/XÌÏËÅÎÌZ.$§ˆ&\S†“‹áÏÏMç‹ÅÌ07#â1Ø™YárfÏüYym²";Ø8980m-m¾(Ô]ü›’÷v–^„îDøÃöW~™ °¦eµÙú‡mi]ëP»ý‡Í`/в¾u}qº|^RÄâ,g+«ÜÜ\KŸk)/èïúŸC_|ÏR¾Ýïåaxó“8’t1C^7nfz¦DÄÈÎâpù 柇øþuü$¾ˆ/”ED˦L L–µ[Ȉ™B†@øŸšøÃþ¤Ù¹–‰ÚøЖX¥!@~(* {d+Ðï} ÆGù͋љ˜ûÏ‚þ}W¸LþÈ$ŽcGD2¸QÎìšüZ4 E@ê@èÀ¶À¸àA(ˆq`1à‚D €µ ”‚­`'¨u 4ƒ6ptcà48.Ë`ÜR0ž€)ð Ì@„…ÈR‡t CȲ…XäCP”%CBH@ë R¨ª†ê¡fè[è(tº C· Qhúz#0 ¦ÁZ°l³`O8Ž„ÁÉð28.‚·À•p|î„O×àX ?§€:¢‹0ÂFB‘x$ !«¤i@Ú¤¹ŠH‘§È[EE1PL” Ê…⢖¡V¡6£ªQP¨>ÔUÔ(j õMFk¢ÍÑÎèt,:‹.FW ›Ðè³èô8úƒ¡cŒ1ŽL&³³³ÓŽ9…ÆŒa¦±X¬:ÖëŠ År°bl1¶ {{{;Ž}ƒ#âtp¶8_\¡8áú"ãEy‹.,ÖXœ¾øøÅ%œ%Gщ1‰-‰ï9¡œÎôÒ€¥µK§¸lî.îžoo’ïÊ/çO$¹&•'=JvMÞž<™âžR‘òTÀT ž§ú§Ö¥¾N MÛŸö)=&½=—‘˜qTH¦ û2µ3ó2‡³Ì³Š³¤Ëœ—í\6% 5eCÙ‹²»Å4ÙÏÔ€ÄD²^2šã–S“ó&7:÷Hžrž0o`¹ÙòMË'ò}ó¿^ZÁ]Ñ[ [°¶`t¥çÊúUЪ¥«zWë¯.Z=¾Æo͵„µik(´.,/|¹.f]O‘VÑš¢±õ~ë[‹ŠEÅ76¸l¨ÛˆÚ(Ø8¸iMKx%K­K+Jßoæn¾ø•ÍW•_}Ú’´e°Ì¡lÏVÌVáÖëÛÜ·(W.Ï/Û²½scGÉŽ—;—ì¼PaWQ·‹°K²KZ\Ù]ePµµê}uJõHWM{­fí¦Ú×»y»¯ìñØÓV§UWZ÷n¯`ïÍz¿úΣ†Š}˜}9û6F7öÍúº¹I£©´éÃ~á~éˆ}ÍŽÍÍ-š-e­p«¤uò`ÂÁËßxÓÝÆl«o§·—‡$‡›øíõÃA‡{°Ž´}gø]mµ£¤ê\Þ9Õ•Ò%íŽë>x´·Ç¥§ã{Ëï÷Ó=Vs\åx٠‰¢ŸN柜>•uêééäÓc½Kz=s­/¼oðlÐÙóç|Ïé÷ì?yÞõü± ÎŽ^d]ìºäp©sÀ~ ãû:;‡‡º/;]îž7|âŠû•ÓW½¯ž»píÒÈü‘áëQ×oÞH¸!½É»ùèVú­ç·snÏÜYs}·äžÒ½Šûš÷~4ý±]ê =>ê=:ð`Áƒ;cܱ'?eÿô~¼è!ùaÅ„ÎDó#ÛGÇ&}'/?^øxüIÖ“™§Å?+ÿ\ûÌäÙw¿xü20;5þ\ôüÓ¯›_¨¿ØÿÒîeïtØôýW¯f^—¼Qsà-ëmÿ»˜w3¹ï±ï+?˜~èùôñî§ŒOŸ~÷„óû endstream endobj 661 0 obj << /Length 621 /Filter /FlateDecode >> stream xÚ…TQ¯“0~߯höTâèhKa3b¢ÑML®W¼Q¸¬$ ”l‹ñ¿{JË6nâ5ËÒï´ç|ýzÚíQ€>Ì7¾Mgˈ¥d-CéQ’UÀPD„²5J·è;Žó|J™Ào<Îðv .«½çó8Ä™‰*‹ï{¸]›QàϽÒe£¤î¥îÛÊâ;UëÎû™~ZnxŒÖd±È¨ÏW„Sn÷O‹²ƒαæø#µÃ=|(&¥<"qH£‰×îlÊ}ß:ÏpúÒóÃPܘ÷`Œwý€LíÚ/7Æ_™7|W(–ÊMKíŒ{ukíŒ=u+p9fcÙ²#“NþoDT„dÍ#Ó€p±²çƒøä>þMD endstream endobj 658 0 obj << /Type /XObject /Subtype /Form /FormType 1 /PTEX.FileName (/tmp/RtmpBlhU1W/Rbuild4444671013800/timeSeries/vignettes/timeSeriesPlot-rugMultiplePlot.pdf) /PTEX.PageNumber 1 /PTEX.InfoDict 664 0 R /BBox [0 0 432 432] /Resources << /ProcSet [ /PDF /Text ] /Font << /F2 665 0 R>> /ExtGState << >>/ColorSpace << /sRGB 666 0 R >>>> /Length 38953 /Filter /FlateDecode >> stream xœì½KÏ/»rÞ7?Ÿb “VºyçÔF؈ƒ8àA‘âÀ0$²ùúébYÏSÜ[gm[Ž“@€´öyš¿÷ß76«Š·züËïÿãïþô¯üÝççóüH½þÌùGz›¨÷™?ßúã?þÛÿæÇXP?KßÔ[úÏÚõßýýÿú?þ³ÿü/ÿôüœ£|¿ÚZ—ßžãÇ_þóÿù;ÚëÿëOÿÛÿþãùñüéýñ/¿ÿÿ÷z×ÉÿÕŸjþ9ËœŸŸãùñ·?T¦™¾éÇßüøñ—ªågzPùÖŸO?@i|2ö]†õgÂS¨üî«Ô‡€%‡Üãz#à“i~OlÿÂ÷?*Ëw)¤AÀ’ógz è?<…ÊòüLí9°dú9ŸÔA€Èþþì~ŠQù~¿°/ò»c|*Ÿçg.ƒ‘¥ý¬û"¿z§P™úÏVPDÊm:Ð"å‡ÞÌD€È”~ÚWáà&ÓWžÌF€H©Ùvíý™à[~×ÐP"å6ýz!@dÊò))~V<…Êï³å} ²§Ÿm>•éý™ÇòK€Èïó8§Èò-# RžäÊÏŒ§PY^ù~6Ð^DÊ5ÌŒNÀ'åÛÙ€úóͨ¬ëm OD~oÝ·Ùäµ8`2ýó% RªÜs€6 ÉÀ¬,Y~Îý6»|ǘ\ßðæ$À¥C¾T–õÄ7P_Dö&ïl½°äëzþ|ð9¨LÅ«ýWû^\. ?ÒèÀäW«k:@ΈLSšË ÔI€Èô]«ÿ¨ˆLõ|¼_{Öð*¿&hW¹ïȘˆ”Zmæû†^<…Jùx(/"¥™Hhƒ‘oùùî_Èd’¶¬òÖ7Ð"¿/ ~aD~o³O¯2j*Óœ~…ŒšIyû*™$“ïü™Ûj!@dz¤ÉÞÀxjšÏ]42j&?OcYß4j&¿d7潓IÚÒ¿¬ïHnˆ”å¿Ð"¿k8Ùɨ™{±_÷ £fò«r¥M’Ii'íÓëSÎæ€Éì5jJ£‹€Ë  QÛ²K£¿4j&¥± 32I&åó·_¯xd˜ô‹üޤFÀ’ß5” Q3ùU˜ým~Gú @¤\¤=¨‘È$™Tt# òíRwøì |þ[&y©@£fòó‚vúŽ Q3)•ÖjÔ(d’L~¶{æ´J€ÈRO­þŽÌ—‘Ïð‹¬dÔT¦é-ÌwÚ–nõF#“´%¥ RêÃ{€ž )îb?5•r‘Ï~ŒšÉ§Û=™$“b/ÊÒ$@äç¹îèà;R+.7€FÍäçNå}›“ŒšÊ4»?êI&ÉdMâÊ,`>âœÀäW'ßr€Ü ùÕ‡ý¨¿#hÔL>ïiŠ¿#hÔL~mTµ·9?;ˆ§0ùEIïf&à“éûÏ~P3Qœeò}Åïܵ-ó©ß4j[É$©Lóñ»Ègm9Χ'• O¡òþ¨ 5“_­. Q3YÒiÌg%“´e:Íàwã,“ŸiÞÁàwã,“r ÖÒ~#5•ékv|ñA£FRN&‰ä0Î2)¦ã,“Ÿ{ýШ™ü<±íGÍAFÍdr«÷A“´%<ÉIq–ÉÇ[¹ïÆY&?ã|z“ŒšÉ7ò}²j[&eGÐ*±6ä#ˆêÏõÝ–Kå‡ÑO—&ë µ1RÕYÙq£Bû¶õ×Úìºñ>‰ìÓÑÙÁ°këïc®çW2^[npŽ 3>×"ï ¥Ûúkû滑B–jë/ÜÞÝr¨MFD·õudVFDÿyΉ*Y¼­k=ß¾*Ñuœðâý¬S¢©– ú2RßP_c[µnœ§ÛÉö±>HšŒ,ÝNgÈûù È´dr(gFVT– b ËŽÎ'`‘C£2"ú«Rí<ÝIvpkéºx“‘Áðû>žmý}ô§J}‡0@;¾€ïÚí?§}»r-âÖõ9öêë—Q]<â•C¨m]&\K¢PmëïWv(&‡Ð2ný}·)9‚¶ñèzÜ­÷k÷*Hõ׆¼#ãadÅl.·Pж54ìr('FDOð´/ß¡:ù´ô™¶ƒT²r[§râ9Ô#M þSƒ‘¥½ƒø•`ž‹éyzWäPyY3ËÒÉÞýk$‡Ja¤nT¿CýaDô3½}‘Ö³1²b¹ìŸÚ;Èrný…œ^¥&>ÒIÑßG2’#51²bº¯ÉËŽùÜú»¤ý\¾̧éúÙœéÈHŒ ûÔ¶™øjàƒ'2­/ü Üm]’°ß!2Ÿ[C[—^6Ÿ[Ï4¼_ëI†ÏtYßíAæËÈÒÐzÕ륩öq„̧ii¥¦#d>MË£ÛïHœO:‘ih1¿C=3Òƒ=úÍÉÈÒóDßïç’ù4- çã™OÓbÏå66|[_\s¤VF–nïd¼ŒŒ—mã÷e’ùÜ¥³ù4 Aêû9pdøL«µ8H®Ïé4C-3"ºyŸŸ"óiºø¸Â›&›OÓl6GÈð™–˜x¿Æ¯ìÅ™îÕ½æïP©Œ”Jsr¨¿Œˆ–ޱéÈìŒLJJ†oëWC……[àÂÀpëïÑùM'6Ÿ¦éŽù4 ÃoÎlø¶þÙ‡FaÄ"ÄÓJåB!âÖâuBæsë~Q9DæÓô眴]ë>;I†Ï´ô¨=Ž´ÆÈÒÅ=xéÚNŒˆ®Í?µÜØ|š–ÞÁê™Ï­çˆ}¿»#ÃgúIîy|‡Ê`¤¨GÖ’#½0"ZÆÆÎ;l>·ö®9DæÓ´Œ«žk™løL·×}L±*#ÙNô8Bq£éïS;ÙwˆÌçÖ>üó–‡Í§é׻侭‹8 †¼7š†Q9Dq£iy.É2Ÿ¦aF‘ù4Àª•Ćoë÷ qÊ!ŠMqã±jß÷MqãÖÞ¯ýjKˆhyt€ù4 ÝÆo)løLÃX…¢¸Ñ´´/çÑUŽM§ìžÇwˆÌ§ié¡.ŽùÜÚûÉ_S£©–_y¡¸qkh½Å™¬ŒŒJcÄ2§„ÌçÖãôòÊ!2Ÿ¦?“åÈ`Ãgúk‚¼2 ŽM·yfÈ!ŠM7ïÄ”Cd>MËk<s²ù4-cJÕ2|¦å ØÏåsžÈôx¼Cã;T&#¢%’z!óiZêÄþ¦ëËæÓôW…wGœ"Ãgú­ÕÄq£ii ¡¸Ñôã³;ä™Ï­‹;JÒõ^Yzx$üEdøL |„âFÒ†Ž·Î^½e¶ÑÃÈŠÄdж1Ò4œ;í®öQ¢ú­Þ½(sŽFDËÓ}¡¸Ñtêî{˼#z.¦››òï™OÓR½O­ëløL'ˆíeöQb¤ê°ë±öß!ŠM^Ð錫ƒÍ§iªÞƒÍ§égºZ'>ÕiÖÓ.‡radiä‘Cía¤ÙøèëÈhŒ ÷ßO·=ò58bZF¼›#døTË”¸ã©~þŦÅßíŽPÜhZª d>MÃt 9DæÓôgEç!Ý{t¢tÅß!ŠI’9n4-Ï¥8BæÓôeÏþ;DæÓti†ôA(n$mHå¸Ñô×*ž8 U6Ÿ[C•ú‘ùT-bǵƆϴxÙŠM¿ùŒhË!ŠM ržngó¹58ùß!2Ÿ¦ŸêíKløLKØŦå?ÓŠM‹{ž!ói})1ëô\T_Àq‰ežÈôç]œ¯ñ;DqãÖ>-BQÜhú…îÅþ°ù4ýÎ3=ãí/›OÓéñ賿løLËÜ…}Ó2›‰N„ú 7š† /rˆÌ§i™Á™OÓõu§g6|¦?ä|$=sÜhZ¼ sÓ…ãFÓiµg!ó¹õð^œï™OÓô*¾­!šøQÜhZ¾Æó\Ç[g÷_¾Cd>M—qæÈ!2Ÿ¦ÑÉ—™Nt"Õò\’#7šNÍÃÿÞ9n4]úæ}û`óiZ¦y5GÈ|š.¯_Èœ':‘êï›îÕ‘<ý¹w~¹S:ÃYÚgÖÉ!2Ÿ¦K;“)Þσ&óiúþÁ~‡Èð™.àïÊü'<‘i©RÓŠM?Í}™•é<£I‘ù4 óìÞ‘Øð™þœ¡=;ü•™Pt"ÓÃ¥ïŦåÑ GÈ|n ^ÐwˆÌ§i™Ãy†Ïô“Å<Å[C€*³¢èDªSöïè;Dæskø¦¿Cd>MìµWæFщP„âÆ­ÁLÈü¨ÌˆèR¡bv6Ÿ¦Ÿ]gói¿i™%E'2npÜhZªTu¤WFºu°Ÿk™l>MKô8’:#®ùÜ2|¤Bq£i<Ñ|8nÜÚ'iÿë‡KUÒw7óYG™lÞl©ÊWÑw©Ê+ >Êž!çX_Mˆ0Dçò8s{²¨â Vù\BŽëbˆúŠŽ ïèÈ÷qrF#7òî§fÈ ÏÞµ"_¡È÷ø9íÆFÖä@@|² ï`俲"7@ ’ÛÈZ›ˆ¯Õ¤uFZxG: ~_Ëšq ˆÏÀ6ÄÐÔò}ᄜ/~#kÕw$7Fr ÈšQÌ0p¤vFjÈj¨†{#kf 0SÇ‘w0òÞ¿2FF¼é~áÈFÖŒS@|* óedÆË]³o™]árÇ}¹k‡‘øÖ<`Fâå®î @ »Ã‘÷eäw´&Râå®I„ŒXwgxtó~tk„‘‘|#áD3œH§—8‚ÓM6òRûBŽð¯¼¿õ+­0Òâµ¼\ëp€t#3 ¡JipHº¾#»ÎH»Ù™ñ¦×:@FB­Óþ@ ÿx-9<—|?—BƆ,@r@â›^ݵŒÄËe;Mƒa€Œ€Äç²fO2âMWþ`q˜ü!oeä­7Òr_ ßQ½ïè—”Iñ¬~ZBrüH~ I‰•aõ­1r_ËOwÜOw„›Ží®ÎB$$¶uº²v?ºµŠoz2rŸè\ɱ¾ôð‘ôû#éárû}¹+\e$Þô/!ütûýtG¸£qßÑ ­Lt„|)œpHø•ë¹Löë°3é!5 ÷‘_kô™ –DgZ8‚3/éé72ò'úóy8}à!áD%žèåV g±þ!#\eÈ È}-¿€ðM¿÷Mÿò佑t#á¹”û¹”r#- ¡îêLt@ÒåXëºB¢™ÐÉ^„\7ýkH H¼£5ÊÆHüHÖjcFb}I¡î¦»îf6|8øŽr¸Ü|_îêV&¤Æ§ûkÈøt×ÄyFî;ªáŽj¼£¾£rG%¼Ær¿Æµ‹‘Ø–pGå¾£î¨ÜwT¨·—Ü"Ò?’5,NH‹/ †ZWïZWïÔßúêŠÀ~Û´ðŽÚýŽZ¸évßt£~)\Í_n»/w [1_À¯!áŽÚ}G¿‚„›n÷M·ø5öð\úý\~ áké÷µtêÄýKé¹/·€ÄZ÷kH¸£vß…Ü8¯e##ÜѸïhÒcõþGCR@b­[SƉUjM»a$6d3 ó?i‰îÏ#Þý®uâÿòOÈÿÇ‘—dhzõCJ@Ê)¤¤ÞH Hü¦ÿÑ`Itj é?ÿ‘ùÿjd>ŒÌø¬ÅSŒ¤ÿZHw”ï;ÊÜ£„óþ’r_ËÿsHHl_2wöà OwþÆÓýsH —[îË]«K‰G —[îËý5d$~°¿€T=Vɉ7:ûóÝÙ¯Ó† ùkáPïð H wÔî;j2•ޑ߸•?¼áDqHò×~.í~.kí °–ç!5 Ñ|vî‚Éþ 2rßÑ~%ëèú@Æ]ëF¨uã®u#¼Æq¿ÆÞѸßÑà<\ËH H|ºƒG8`# DÂ¥xG¡³?ßý9tö绳_!2/wò`"l ßѼïhò8 ®®v$‡_É÷¯äp¹±³_7¸t6¼üƒH Èoœ¨$Ô]]ÆÈ{U)]GÄHº‘q!)üJœÿRÂx@¹Çt TFÊÌ€Ì )‰]ÀºÐt_KâvØÊ ê¼Å] I‰Ï%sõÆL€Œ€Äyˆ WW~%޶”Щ]îNmÝ^…‘øtCwt¹»£KèŽ.ww´nQÁÈ}¹4¡·òØHáž|Ø‘ßø•ø5î%†MªRÃ[ï6 Ê—{P^'¶2rÿ ÈÀ‰ˆ„Å*ÝHØM¯Û0¯¥…Ëm÷å¶P1Û]1MÀÆ´€Ð®N$\ËõM·pGí·îˆzýpó#Gh6ný²‘®¥ß×ÒC•êw•êárû}¹ý:Ü!l##|öãþìG¨ ã® #|öãþìGø¦ÇýMÿÒû½u×Bbw‘nÆH¬Rƒ'Ïáîv™<‚Û!’¯erGnseˆ.ïq—û8B=ù°Ý7 7âF)€ô€ô™ N—ìòÞ—ûòP ®qÚHbÛˆûõ9BO—µ82¯ÄÞ-Ý2 ØBi#…£ Ø™²j¸.Ú2Y°õ4 ½2ÒãsYK' 豈¤ ×»:uÅ4 õðÕ¥¯„ôøt+¸Vp#-\K»¯¥q/‘ÎÎ,ìh ÈÈõ\z¨/ý®/ƒGPq«CGf@b¯_ s‰ë=—X·Gw¶K?ÈË,‘ÄÏ7xp„‚ØÊ‘q!=üJÀk¡¬Ý}dºC% °c¥#ôµû;Ò¥¯€ŒËSÕ-*™W”¥‰>ÄIìyখŽÐ8,$œ8Hecƒ›Bm¤ñkHS°ÝÅÂØÕB–ÛÉ›ú¾ÈZP‰€l)‰?ašÝŒÀÛœôÆ ¹®$ÔÛóiünÅÝËï×ÜÓ«ô»UŒÉ®»þ~d¿wüöû9y¨ ú3y¨þ¡ŸÂ?ßïÿjª”líÉœOW¸&žr@åçï)–x €%»+ñ”*eK0OTU–œ'âµÄS,郚xÊ•Ù'àjâ)Dêž@ ¬îiLzOš&žB ë)¦ÿBˆ|½O[O! Rz¿v¨•xÊ•e ¡‰§ù½¬Ý §‰§P™“?‡µÔ ‘¹øËZ‰§hú¨OÒ°•x ‘ß/ì—¥‰§`òMÇëÔÄSˆÌîiâ)L¦cû5ñ"¿j±;¸5ñ"ËsBxM<åÀ‘'T’¦‘Ùû¸4ñ”&}z¢&žB`É|04ñ"¿uîbõb9päyÔ+ñ"³w"[â)D¾í•šxÊ•/%ªÊ“‘ºÏ¡+ñ”*aÌHO!°d y¨hvŠ~€Y XÒGÆ5ñ”&ëdÐÄS¸T`%žr@ežžÙlõ§ PõQo¯VO! ²¼Ç¶hâ)L&OuµO!àršxê&ó<½šx ‘ÒqRP'KÖ³†LO! RvÝÀJ<åÀ‘{"¦&žB@¤lÔ¼¯a%žr@eñ`QO! 2Ù]fm°¤¯¿ÔÄS¨üjÔI·¥N-"ßq¾ M<…ÀÔÆ|šX⩨Ìp…ŒšIÝ\J&iËy,¯&žB`I_~«‰§XÒ#sM<å€Ê’ü5“Ù?M<å€Êì£Gšx ‘ÙG74ñMíÅI·Õɨmùøm2j&a8SO!°åþ…•xÊ#÷£&žB è£>é×&5“_+·çˆkâ)¦zA»›JOÀ$ÌÝÑÄSùxš©ÔùúÛÔÄSˆ¬Ïñ@4ñ"³÷jâ)T~Ξ?§‰§)£¬;1ÑJ<å€J™ÎP€FÍä;<ÝV&£¶e95JO9 2ûÌiM<…€ÈgžÑM<…€È¯ :é•*µ-ÓéæÐÄSˆÔ-šhd’¶ô®eM<…€È¯…9‰ËVâ)–|O¯ƒ%ž@dvWMO9 ²>§…ÑÄS¨”‘TO3•&&áj%`IŸ²£‰§úN6­IFÍd®Ç¯ÖÄS˜IÚ«²4ñÔ\;"ó{M<…@ÓG}r@=dÔLæì§X‰§Pùú€%ž`I_0ÅY&³¯þ›‰ŒšI£¤G)°d?†%ž:€J™–à‰ª0ÎÚÒm–&žr@å÷u7Ш™Ì¾ÊNO!°ä<¦YO9päîœÕÄS¬8ËGå5ñKú$½ÙȨ™Ì>”;5’ t2I$7€qÖ–Ï|ÒÄSˆ”¡JOT…FÍdöÎ{M<å€Êì#™šx %Û±›šxʹã¬=JO!Pô'qÙ$£f2?ê!«vtñ9ešwŠ׆¬¼S€¨–Ýá_GòÈè¦iÞ)Bª:+g´CóN"ºtæ×¼S€¨ÎR2% »¶†^Ë;ˆiXéey§YfÉiÞ)B>ýNX(­y§Qý4HÉT(Ûú{ºgN™æ"äÓ²ßÚ™!¡y§Q-ûip*BŠ¶Äž6«‘Í2-›†ùJšwŠÑí§Û(ÛZ²!kédûX$MFD÷—2H=t"Õ64ѼS„H§ílP¥…e[ØrCóN²Ì ¬‚ÖnQ]`½»æ"dp ly§Q-Ãgª—æ"Ä"´³‰æ"äÓ²'Þ¬Ž EܺL¯uºu ª¿·y¾Í;Eȧe¿µ3ýAóN¢:'_à©y§Ýà мS„˜qôkÉdÛ¶. ’)e Ù¶ÎÞUay§Q‹/C×¼S„ˆ.¯ÃYÞ)Dª†—gðWóNbºxÝÕ¼S„ˆþüš3†¯»…2´SòÌWÒ¼S€¨®¯•jÞ)BDãdÍ;ˆê’ýkÔ¼S„}Óž¬S ·õ[àr;ÙÍ­³{½–w Õ¸A…æDõ÷+g·æ"déi³&…t[gh5ï!¢qo2Í;åˆéÓ =â7²4l££y§QýÂÎošwŠÑ;½l>MÉŽs-ï!K?>(®y§QÝ_Ì®y§™zœÉšw Õ¸V@óN²"½5Øw2Ÿ¦K…PØðmíq³å"¤{¤y§Y6bмS€¨~aMŸæ"Dt¹0šw Õ²ÛZr¤VF–žÞxhÞ)B††Ùž’©³ùÜú;êl>Mç×—8§Á†ÏtqGÖòN"ºfŸO[gØ^HÊ&#zªªÉæskLJ6Ùðm +šòÊ;åˆé ÛFæUÕ 1]é/#¢¥9äM«KeÈd÷5X{Í;EHÖQדhÞ)Bªzd§­Ó¼S„ >VMóN9bZFë¦#dø¶~üÑiÞ)@LOŸ¼¦y§Y¤ÓŸ‹æ"DtøHóN":?gÆòN¢kæ"DtÉ> WóN¢:?îúé2BŠõKs*B–®ª°áÛºú«y§Yq#l„©y§QýÏ_@eóiZ]u„̧éì£Ã–w Õß·rÌ„æ"déìÝ"šwŠ¡'òkél>·†Y•šwЬgYŽæÄ4Ä$šwŠÑ/,мS„ˆ–^­á™OÓ¹À›žl>MgØŠDóN"ºÀŒ=Í;åˆé >jÞ)BD¿°HNç˜Òudè?šw Ó’)½løLgØMóNbv Ò¼S„¬ñ¾éQ¹æ"ÄâÆó5ï!KC·ˆæD5Œ4YÞ)B†6‡ç5jÞ)@Tç’o6Ÿ[wÚ4ï!¢ˆItÖ4 ªŸê˜æ"dŰ4SóN"ZÆ[ÎÓml>M¿²Ÿ56Ÿ¦ß9±:>ÓÖ-hÞ)BªV)Ï~Ö9n4-6à\î`óiZú¦#d>·.€L6|¦óôžÍ;EHÖùµ¬¼S„4ý<µÙZ•AˆèR½çDóN9b:¿ÎYÞ)D–ák¾`OóN¢ú…öEóNb#„~-/›OÓâÈïùÜì´æ$]q£æ"$L±¼S€¨.`a5ï!f>+ d>·î¾ÜÚ¦z:‚ú 7’6¤rÜhZ¹ä™Ï­§WLË;…ÈÒ0U]óNb_cã¸Ñt†OMóN²4D6šw Õ+fgóiÝsÍ;ˆjÊ[68nܺº§ªy§Yq#ø»šwŠ­Ï‰&›OÓ0EÆòN9²õë&KóNbú„âFÓyxW§æ"Ħʜ›Ö¼S€¨~³wjkÞ)BlçÙÙ@óN’öØâyGšwŠÓ~¹‰Í§é’!ÉTbói;5ï GO@(n4w+4ï ª?×ïôcjÞ)BDK$Ìy¨Yh&¼éʆoëä¾”æ"¤k8wvNѼS€¨.>«ÒòN’´C㸚wŠªñôqò5ï ª3Œ¶hÞ)B–† B5ï!¢ßäÀšw Õ¹º»¥y§É:é,|Õ¼S€˜Æê½òNòéõÅA6«VýÀ˜æ"dª™81¬ærÄtN¾¯žæ"Dôƒ‰z^ŽMËòÙîŦËðæPóNÒuÎd‘ùÜÚç§[Þ)@Lgß6RóN¢úõIG–wŠOË7TóN"ºf·jšwŠ¡ñôÙdQóN¢º&o_4ï!+n„,#šw ÕÒvGÈ|š®gGóNÒ¬÷üJcÃgúmn„5ï!Míôñ15ï!¢ßäÚw ÕÒßhÞ)BŠö{^óNbºyd£y§)“Ý-Í;EH×߈iÞ)@Tã6:šwŠ׊hÞ)GH„âFÓyÂþÇ[û~=ÿXy¨¬AúÛ½„Ž(Ct^‚#8OÁÜK–¾²¬Ñj@`ôz#¯ç)ldÒÊ|š6D×ÁÁö4Úx‚¦l„·Ë§Y ¶*$0Jw#kb¹rmX\LH̬¥Cô€´+•Î@!dÄ_™´Î” Á]Ž(¬w+NE¤¤ßÈH¸#^âN7q„2޽×f›)dáIw¸%$lG`Ó‚Iq}¿9„´x-‰³|acé./Ç…>€ÌÂÈŒw”)­ ¹Ý€ô€Äåðtóýt í«BÿŽð£+÷£+´\˜:péáD=žˆwŠ¡ŽÐ4N?‡î‚#%3R⣠õÞ;¡ž® "dF„·kÃ¥m€¤ÄHŠ—Ûi7œñ H '*÷‰Zf¤ÅËå]Ô¨«x-ƒö'¢~8GøM_;^Û  !×;¸9ÄèÉ‘N4ïÍp¹×käݨ³x-3ÔÝÛØ¼!Eà{¥´)ŽàGpC1\nCÓiáDí>µ»én½u Ž‘pÓ:ÞÈ{™,uJ‰Ï…wtÃu¨€ðs¹6ZK!“Iº3™XW !1¥dâdm8-jàa‘ÂARx.é~.‰“dÂDd@¨îbç€#) 13¨®¹#$ßW©+Ù‰MÓ"$zA‰7óÅ%‹É´ÅÝ’ŸK¦m2q•) Ô¨âÀ¿#5\K½¯¥†k©÷µP~Q ‚™áWb+•x]œ¾Bû”Òœr@F@âMÚø“F.!K‚£K€´€ÄP8}.¬h„œ“t;':(ÈÈ}¹5Ütô_BV\¶ŒH Hü¦KhËݶ°îèj_x«dêð$œhÆ­.n@ ËÛþÔêý©Õð©]‰má1!W}©´5%ÍS$\n½/·…k¹^@eÏg;8ÂîÚpÙ:Êi÷£kˆÁ:@R@Òp»Ûîv÷—Nt=Ý ß•¨'…,<´6ўˈϥÓÞÃ4ÈH|=Ôºkd[kÊHüŽzð®T¾¶\ŒË6öð\úý\zøì¯Ì7¶A!=~j6r¤þlGFxºã~ºl„ûm„ínúBJ@ⵌ`ÆmFðëÆí× îgHãêgH#˜‰q›‰\¿q»~ƒv¢2‘~å²ö#xAãö‚÷àH—##œ(¨Þ³èÈý\þ ùÿªTŒ„ïÝgúBÂGr}ö3|öóþìCdº{ uF#ñr'uÞÒ¼GJ@®oú—êI×V¦6RÁÈ}¹=œ(vhè:"Fâ7=ƒ_7o¿n†Ö{Þ­÷ ozÞoš±ãF ˆ„Íß8ÑŸAt6¯#8»×ò‚`7@¨2àt6@r@òÔ€Ô722/¤„;*÷•pG±bê´aB¢3«Óc¹ï¨…ç«·sÝ Ý³‹MdÞ@—&.þdMÞ&ó’ÝK‰4pß@¨!_ilš9#÷å¦p-±óVçHrÕÝ7Ôº+g°-Z%äªRo¨RWž^›JÇH|Ó/Ç8¹ð+ÑÅÉa@&ß2º‰‘ûQ·p;ÒïôûWzx±Q ™Øh¡Ž#3Ô—ØbfÞ´·g;H m]ºÛºj]ºk] MPº› Þtºßt ¯1ݯ1Q‚WZÿë¿€t¿€Ì>®éÿC…sùî¯Ó¹õ„¤xG™‡/`#@@¨Ó)_i'l !å¾\þóý5æðŽòýŽrhàóÝÀsN Üonúú3í³NsÕár¯$³§ŠËZþ2«wfg™m¤„*Uî*U¸÷× :ò†_¹ÌDèÇÌw?¦n ÁH¬R…‡`·/@¸JÝ]ºâ‘ûޏ/wz óÝ©Û_rù ¡“2ß”¿‚TpÙœ#Ôw˜ï¾C]€ÂH¼ÜÊ=8YÐ6åW&[lGÈe§køÔêý©UŽ&`>Dz@b•ªQ„ÄD·æ$$zÍ%Lü,÷ÄOÝ2 |y‡º@Ÿ8_JW#1]ˆlÊÙèg€”kD¬pÖ&Ú©f#•C\ò陬r;%8åv  S6Ë=e3$±Kw;[ˆÎHüHå¡ÂÍ#™áZ¢s¢‹é÷ è¡bö»brÖ&ÚœÄr‰q¡ÎFFøHÆý‘ŒðÙû³áûp-ã¾–0%¼ÜSÂ53!qUódCˆ/Ê=|¡[ö:[ø"RRodd\u€A‚Œƒ„NízwjëòB¢ùԽ߉3$ŽKwâ8[°ÄH¼éÌ–WJ9BÁ®"¤$ž¨²µ‡l ¾T½}©Þt»ß´îqFHüZè¢iwMÓGaSË_ÏCeYiN*Óãg±¼T”¯óP­„r7FH¦Ê´ãÀÚ£ß$rªæ€Jd¹Áš0d™¬^ŸÊiÀ“.˜bÝ)ïM¡Ë>ÜÅ?N&+Y<]óÉd•ÿ`&ªô¬®ûÏß?úçßê³Yýþ_ÿ^+YŸòµ4+Õÿòy¬~1¹Ñ~«]_PZ«õž4qÕ(­•~8š·êœ×JÝÌÀÊkµË[r]I¨÷ûÓúýŠ¿‰ß¯ùûsüýª¿‰ß¯ûàýNå?­×~Ç’P)ý™TXÊü™LXÿÀ]uå­’+÷üˉ°dcãµ9_s]!ðÉéó4Õ–‹|ó‰×4Õ•KŽ~†~4Ó–‹|Ï„´+9°d/žBh%ºÂò%½3O]!ðÉï}î<ÀšçÊË—œ>¿OÓ\a¹Ë ÌA€È×?Mså€ÊÏ%Ø®‰¦¹Bà“ý=&F³\y¹Êü`V¬Z¨Ë ¿^>*ÿ¤ì@)¯¼\eò®Íq…€È×ß4Ç•KNÛ5Å–‹|ÇÑW,ÙN»£)®øäw†½êB3\y¹JY‘ûmЖ)$¼rÀdÆtU¥ 2¹ç¦®XÒ»5ÃsU¶ímh‚«SnÚMpåÊ ¤F€HX°­ ®X²7X\! R⓾(‘2™ÊYY® ®ùÝÅùÑW¨L>¹K\! R¦8€vLåç/œüG…ŒÉäSp4¿"¿»Ü&Bó[!°ä<ýØšßÊ%eW/G+¦rúºaÍnåå*ßçôÞiv+>9|}„&·Âr‘¯1Mn…Àœdì5¹•KŽyÆ5·•—›\›ýn M>9³'[©­°ÜåІ©œ> J3[y¹Ê7ŸáÍl…À’Ïi}5³ÕT~A×ôòÜ©ü“ÓsÖk^+,oj#·¯¡y­ø$l#¢i­¼Üd:1ˆ¦µB@dÊg¸u&Š¢TŽyš÷™È„™|=»²tW%>9|3MjååKÎvZoÍi…åKú­9­0Ù=ÕW!fò}N˧9­Xòõk¨d‚LJÞ†zŒ£Lf_÷«9­8*+8Ù0•žR#FRN&ˆä0ÚòõÜQ©-ýƒÓŒVˆ„•¨šÑÊ#_Й„†O3Z9`Ò§ÕjF+>9û±ÔšÐ Ë—>‹UóYbëS÷=l'Mã¾Mš¸ŠÑÉ{±-q KÏBm¦¿ªâI|^¶’¦¿Oõ,žÐ´U„ ²ÀUdáLÓÅ& Þ¶~a±¦­Dõ÷+2”¦ßîS#4m!Ÿþ‚Å39K³V¡Z¶”x陑lf­"d.Ûsö1ѤU@,=²ÏEМUDˆÆÕ¹š³ ¥'ì¤[>!:y7¸¥¬"d陥:Ê­'ä„êl(·Î¾2. 6rª§þXÆ*"²ÆÇ£Jƒâ;ÓÐ'c «ˆv¯Ó|U@¨Ö6ì dãT?rö|ÒtUN¨"9Q*E;Ï#ÑlU„ôPŸ)Aš¬Šˆ¥_7 º„ÕïôZ­ÉªYz¶tš«Š ùÎDcÍUEˆè‹Ù4W!¢¥ýœ(³Sý¹ÄžF+Sà·õ÷#§Ñ×TU€, …–©Šˆ¼†!ÔDUDˆ~‡·(º¥¥w R[µÆÄ§‡÷GXš*"–†ù«š¦ Õor_ÓT²4l «iª1ÍHŒ,ÍH/Œ|º†-K¦aÕf©"ĺ5OT£YªYZ¶î‚‚AÓÒâ7G(T=a7ÍQEİnä“kçaKi:½îjŽ*B>Ý‹o¦)ª€0Ùx^ŽUË|‚îYJÓ/ç½"K©z‚;MP„éá;i‚*B,ä|U@¨ÆNÍOEHQƒüB†Òô› IWa#§zŒ˜®Šˆ®6Á_Oå`Põ„î ÐAÄÒÝÃ_ÍNEȧ'&ÎjlâTKmëNP,¸uóZsS2´'û¬ÒÜT€,=}L×RS±t…„WƒmÜÖ° ZSSR‚×¥©©Ñ`ðô+hf*"DKtny²¥ÜºúÚAÍLEˆhÙ»é¤ÏY=페?ðq¤LFŠTœÎìIˆh´?)±^¶•ªÇë«—u®'K'ÿN51 ªß¹‰‡„ª¿×œ S¹5Ÿ‡l¥ê ‹õ4-¦}ÉÒR"ƒuÕ­‚f¥béÙ½ÉפTDXgiHREˆÍ;9¾¨&¥Ä4l §I©é©yƲÊ!áÖäkl,·ö¹L–”Š¢qNoŠ&¥DuKî4iR*Bªõµ'G($T=aÓ ÍI„é ɰ[KÓ¶ÖœT€¨Ö€ƒäÂHVÏkÜÊIEH[s:üÈhL|ZFbNfµ®¦l£©ù´ŒBj)Š·ž~ÇšŠO?‚Y®ÈZš®°aæ£"äÓmz­ÕtT@¤+ ÔtT„„*–Ž ÕŸ[ê)¡2[KÕңߜ k©Z†gQØÎ‘>E„¤ ©š–p¡9BæÒôûÆäT„Tí·>vL“Q²ô„mç4M§8ù[nªþ¸ãðh** LOÊ^EæRõôi–‰ Õ)¹ó¬™¨ª¯ªÐLT„|û4Ÿ–öú\ëdciú…¡5•#[w·?š‡ŠÂ³š‡ŠB·Œæ¡"dè|ÄÓj*@TË~Û¯#dèLãÎÇš‡ ÕŸ×y–éh*BLó¯y¨±©-§IóP2ô¹x¢§Ì¶Î´Ää€P`¨zÂ6Áš† ˆ¥;Ø(ÍBE„Íq9Ÿ¡f¡"DzPa£xMBÄÒZIÍAED׈üôköÆQ¡é¶HÑT„|zš‚Š™³éˆ-K7X]© ¨ˆý¹’!!!Ö}zÌ& Äôë–š€Š¥<ûÉvNõ„ÐòO!‘ÕÅó«]½l„,|¼LóO2u8ó|˚ʑ­ox4ÿ!SÛ¦sCš ¥%ASr‚­ñR^6—¦ßêAÓO²æÂtüš~ ¥¿z}úð4ûKOðˆ,ù2D8)£YKÓ¶^ÒÜS„, _˜æžDõwÇŠ M¿°r^sO¢Zú¾²#d-MË–TÍ2—[Wwñ4÷ ¦+d‘jªžCASO1utüÄkšz Õ’Ú ;BöRõ¬îøjæ) LÊwÍ®ÝÀuÖ:awshAÀˆnî]ÃLÚñ Ç^ Œà:a„8î,‹µè ôt¾†x›“¶‚Y½LÎ4¯A“7Ô×~º“7Â×>¸ÚEƒ@Ø6iò–%óÚ±dr 3¯FÇùMÞ“s^[rNÎ}3¯Ô7:—ÂóÉ»NÎkÓÉÉû<Îk›G2@|P™ª=ÌZ`00"€Õçþ€/r\9ù6c•ËôùÃ|ý ðf‹óÚkQç›P.`00"€ûþàlTê +˜ ÌLþ…yýÂäÛ ©¡´«É÷Ò¡kBÖ_øé>Eg G ñ/ÄUi· ôÂàS´û“ù‡j@®Í ''ƒ™W.˜É;ÎkcC’u EE§Ÿ!œÞÉþüü¬@Ø1E#FpÿœÁéŸb\§˜|ŠG‡ùèq—+¾ˆ@üü9â¼Ò ê=¸Îþt`0pÝzƒ0ñÝzWŽ«ˆ )ïñ7¯-þth€t•zƒq“ë"éeëeq.Éy¥’Ô‘núðE¦ë"qMë<@á'Y®'YùIÆ·Éç¼N]‰À¸îbð]Œë.&?¨ NöHçå‘NNO «Ì˜ ÌУ¾õþ @ïâÊm©ÃJDËËY6æ•dcòÆPóÚêW€ÉwÚ(۹ḓ ”¿ú¹siÿã!# ÷å–p¹å¾\lòh‚1 - í? ™™ÒÃõûŽF¸£ïè¥Ýq"- ñrßð¦ßûM¿”>™&^2oú%W‹æZòä½î(ßwô H ®Ü®VFj¬/¿„ôp¢~Ÿ¨‡Ëí÷åÎð\f|.‰vÿ¢åR޼#o|)¼ét¿é^cº_c"sƒ{VRÃåÆÌö:™?’D..cÿcÈ —;ïËý$<º]3ß““.ÐôO@F@âsÉÔu‹«÷I‰íî¯!9 ±zçÐå»!Ëä;Ðú,@f@âk,¡z—»zrœy|àŸBþK!µ3Rcõþ5d$~öœ¥–Ö˜Ò› _Cf@â×X©Ûwò¤†Ë­÷å²…­·…­´ç? §:ÒÃåöûr¹¯wß‚»Õnw«…ÐîÐh¸‰æ58ÂöÊuk;ÂRcÓÜhƒ–宥Þ×ÒÉú}¢nújà;Eò4AÜ‘”Iñr{¨Rý®R=XØ~[Ø,ìÕ9b³H )ñ¦{p ûí@vi¢®€Ô€ÄêÝC¨p¥K°µ¬€Œ»¾ŒàX_½¶’ŸËµîê&°•„\õegö ôm@æý©Í^‘¸mAHŽÏå—® WêL[–ÉH|3<Ýy?ÝI]n4ãÖ‘ViñSã!3šHáH7}UÌI½œ¸Ë! 3œ(ú»:ëÁØsç!8‚Okç©©7ÒÒ.$…Å&è ]ïÝñrŽNÜ[‘ûÑá "mÄáH 'Šî¹nQ@H|Óº !#>ž¤©ÆŽPÜøÞ=oè!xï]ŠHH°EÒ„”!·WI.7º[oˆ§ß;ž~C<ýÞñô¯!\_®Ô ¶- #±bþÂ7~ë¦ÉÇ|¯¶¢Š‘ø¡ÐÂ4@ÂMxÓ9¼€+¢m€EHŽŸ=9ÓÄYGz@bÍËÃÊ´Jn#…]?\©ÈÈý+o@ÞI“‘_'ÈÃ}ïi‰Ÿ}aßçao¤R<Í5¤$žhÍ×$¤ß¿B}A¸¸Æ²Gï•6Ívjd$>ºŒM»Í/!Ô†Kàá§Ûî§|ï÷ö½ucGB¢/¥“{éWÿ®.Ÿ#$ÅwÔ¹Ó WÛ;ÂwÔï;êÜ]ÅÒÉ®O­sÀ;Qld„Oíس]7 ¹|†LÖ¸MÖ/!©0’îË%7Ë’+§£IΙ4 J 'é‰ßÑ vzÞvzrd;ÔB!÷{¥g·UÏŽà*hG¨z§{ ê×z¸Ðæ!9 ùFZ@Ú…”ð+ÑdéfØ„ÄÞs]zËHx‰g¾ÒòGr@®ËåäÔ¸) ä†Â^±ˆŒ€Œ ¡˜$]É©mÙ ×,6ÛVƒèXëŽ[„Ä@Lמ}†† Ò=d ‹¬É—w˜8}2îÝ ù»°e& =üJ´$ºq- åòàuEBZ¼\žFâl$8évtÙ!q4W7"dÄ/€s\áFÎéÎáÂxGøSë÷§Öyø÷¨ÛȤ™*´Û#ü©]3l¯kG`ï냼”[cl$ñ ÀU,ÉìâÀö¡€ÐMç{ÌFWÐRî_)ì×áªÜðL$Zzí¹~ùJf+‹)W’w[ÐH¾bÝ­“FÙþ€´k4·òZmH –¤Ý–DWÃâ«ã= “fD9I˜TþŸÃr2Q®ÌÁ4, ¥Õ[ÊP¾œá)*íöèI¹ö“7 ÷ P‰@µÆÚ¸‚œôImí-»Ó=õæ?'¬œüGþçßËÈ’fÆÿ•W¤™cì¥üXëö;Z·Äå¦Òy¨šXæ«¢â/ƃbQTü=A(EÅs`±((^IiN±**®×'÷†Å/­IÁ)VEÅi`q¡¸,®%‹_ÚšŸyŠUQqNXœS(®‹ëÅ£`ñàK[IlN±**Î ‹s Å-aqK¡x ,|i+ÿÍ)VEÅåÁâò„âÖ°¸µP<Oº4Ù¸Â/Í—‚Å¥„âþ`qBñlX<éÒ4gÎ)VEÅe`q¡¸,î|iÉ“U­Ü8ÏŠSÃâÔBqMX\S(î‹EAñJ´sŠUQñÄA±(*>mÑιÃÅŸ3Å¢ ¸HàvŠUQq®Xœk(n/·7ŽÅƒ/måæ9Ū¨8O,Î3·ŠÅ­†âùbñäK[Y}N±**.‹KÅmbq›¡xV,ž|i+Ï)VEÅ¥cq顸g,î9ωœ/m%:Ū¨¸¾X\ßPÜ;w¾´5xŠUQqšXœf(®‹k ÅãÅâA—¦Ùƒv±)*΋sÅubq¡xT,tiý•ÞÕS¬ŠŠsÇâÜCqËXÜr(‹_ÚÊ8tŠUQqy±¸¼¡¸ ,n#ƒ[c Š3º5¦¨ÜST n)*·ÆtkLQ1¸5¦¨ÜSP\Ñ­1EÅàÖ˜¢bpkLQ1¸5¦ ¸¡[cŠŠÁ­1EÅàÖ˜¢bpkLAqG·Æƒ[cŠŠÁ­1EÅàÖ˜‚ân)*·Æƒ[cŠŠÁ­1ÅÝST n)*·Æƒ[cʋǃn)*·Æƒ[c Š_tkLQ1¸5¦¨ÜSTÜw¾´ä‘ëʲô”P n)*·Æƒ[c Š3º5¦¨ÜST n)*·ÆtkLQ1¸5¦¨ÜST n)(®èÖ˜¢bpkLQ1¸5¦¨ÜSPÜЭ1EÅàÖ˜¢bpkLQ1¸5¦ ¸£[cŠŠÁ­1EÅàÖ˜‚ân)*·Æƒ[cŠŠÁ­1ÅÝST n)*·Æƒ[cÊ‹5Õ.6EÅàÖ˜¢bpkLQ1¸5¦ øE·Æƒ[cŠŠÁ˜¢b°¦ 8¡50EÅ` LQ1XST<;O~jkqþ)VEÅebq™¡¸W,îüÔŠÌÔ8Ū¨8u,N=׌Å5‡brMAqŠ׃50EÅ` LQ1XSPÜИ¢b°¦¨¬)*k` Š;ZST ÖÀƒ50EÅ` LAñ@k`ŠŠÁ˜¢b°¦¨¬)(žh LQ1XST ÖÀƒ50åÅk!¢_Û– €AØ’0 ["ð¢QØ’0 [2P+µF`¼Œp‘ Ö äL@Îhí‰ÀhŒÆÀÚnÄ• äA@h…€V"0f¸È"e¨d $JŠ@´0‰@ÅXxKJ# ´ôD@O˜ƒ€.²aL¼%õ! >è€.²cd¼%iFj! –Œ‡€.r`„¼%9Sê  ŽŒBÀ91RÞ’ÜÈ--ÐRÆ €æ5S.Ò$3oÉøI[2žÒ–¼è+mÉ@)”þПÌFÀ ™0‚Þ’ðš¶dü¦-Èè9mÉøN[2ÞÓ– ôI@YЃڒüßÔN@홀.²¢'µ%¹kÚK@{#0:#\dCjKò$ Ï´J@«˜/3\dGÏjKJ& ä´I@›˜•€.r ‡µ%¥Pzz& çÌI@p´$'^䌎–d„{ Ž–Ì?ìGK¦ÂEšd-“  £e’t´L"ð’£e’t´L2P'uF`TF¸ÈDŽ–IÐÑ2É:Z&@GË$™-“  £e’t´L2€ŽVŠS)dæ:Z)N¦XìÁÑJ4ŸbKÐÑJqJÅJ^‡'U¬ÜuÁÑJ4¯bKhfÅ–  £•â䊕ú®­Dó+¶D “£e’t´Rœd±&å ‚£•hžÅ–ÐL‹-@G+ÅÉ+c^" 8Z‰æ[l‰Í¸Ø’t´Rœt!GÐÑJqÚÅÊ·—ŽV¦™[2€ŽVŽ“/V®½‡€àheš±%4cKÊ  ŒôB@™ÈÑ2É:Z&@GË$èhå8CfL¢£•ã„ 9‚ŽVŽS2VŽ¿N@p´2ÍÊØš—±%èhå85c¥÷{ ŽV¦Ù["@ó3¶d­§h¬ì€•€àheš¥±%4OcKÐÑÊqª†AG+ÇÉr­§k¬Ä‚x‘qÂÆšùÚ ŽV¦9[2€ŽVŽÓ6VFA¼È8qCŽ £•ãÔ 9‚ŽVŽ“7VFB¼È8}CŽ £•ãޕް­Ls8¶ Ð,Ž-@G«Ä‰r­§r¬\†•€àhšÍ±%¹{ÐÑ2É:Z&Häh™d-“  £UâÔŽ•±­B³;¶d­'x¼…fxlÉ:Z%Nòä‡èh•8ÍcåO|ŽV¡™["@s=¶d­§{¬Ì‰…€àhšñ±%4çcKÐÑ*qÚÇʼ8ŽV¡™["@s?¶d­§Èt´Jœ"GÐÑ*q ˆ%mt`„a?KÙˆ@y#Ð:­G`ffh@ÖÞ“¨d TJ@ èof'€-×ãL2€=Z&Àjo—ª½I°Ú›d«½I°Ú›D Qµ7ÉV{“ `µ7ÉV{“dªö&Àjo’¬ö&ÀjoBñ…I0¾0ÉÆ&ÀøÂ$•â “ `|a’Œ/L2€ñ…IÅ&ÀøÂ$_˜D ã„¨-H€Ô"P5E z¸È£¶d ?ä'µP[F"`„‹\¹0PÉ@.ä:4Éš@³`À$_˜dã “ `|a—â “ `|a’Œ/L2€ñ…IÅ&ÀøÂ$_˜dã “dŠ/L2€ñ…I0¾0‰@¡øÂ$_˜dã “ `|aJñ…I0¾0ÉÆ&ÀøÂ$â “ `|a’– h9c0ÂEvr´L2€Ž–IÐÑ2É:Z&äh™d-“  £e’t´L"0ÉÑ2É:Z&À¡C“hžÑ˜d-“  £e’t´L"ð’£e’t´L2€Ž–IÐÑ2‰@"GË$èh™d-“  £eLŽ–IÐÑ2É:Z&@GË$…-“  £e’t´L2€Ž–I*9Z&@GË$èh™D ‘£e’t´L2€Ž–IÐÑ2‰@'GË$èh™d-“  £eAŽ–IÐÑ2É:Z&@GË$“-“ àСI°GË$èh™@ó¯À$èh™d-“  £e—-“  £e’t´L2€Ž–I9Z&@GË$èh™D “£e’t´L2€Ž–IÐÑ2‰@!GË$èh™d-“  £eJŽ–IÐÑ2É:Z&@GË$-“  Å1ÉZ“  Å1‰@'‹c’´8&@‹c’´8&dqL2€Ç$hqL"0Éâ˜d-ŽIÐâ˜d-ŽI$½-ï,Î|Èâ˜d‡M:ðÛ[ˆðÚY ”Ö_ý™µµ~FÔŸ ½p× Ý*ÖÿQ]VcšçÞµ#=Oû‹çýþï·vîøíßXÃ{áGúïÿˆþÍZËÿú=2vâïý˯üå¿øûÏÿõ¿û;°H'uí:Á³þøÿöÇ¿ùñ0Ópk²Mˆÿ‹û¹Øv#ß?¿s8Ñ*U{+—ß{iX¾^Sã·Æ¿ðÛ¯ ‰ô†‹¸®‚÷‹± h~ؽCÍïïsöUúÝ-cÎ~<¾gŒ¼—ô]þ‡¥¾‚òß|3 {—¯i˜©ßÀþü¯Á{Îßã‘)EòJ~ǽoÏõv¿6ËœºnM´õë)…’Äz ¦e/ÔבÒýzz£ôÅZ Od:ûÖ­r¨>Œˆ–üÖÅ‘Þùt’œes#Ij ª¿·—_G¾è•ѲçÎA²|~€¨~=]¯ʉÑÒ9?©ƒÑ­ŸÉäÐ(ŒˆÎ.w¥"dé$Yõš#c0"ºù†{R…^:‘êâ{CÉ¡ò0²´ïß%‡ZcDtMgûK94#¢3ÞôÊ› ˆk@fcdÊ›žgûËôÅ1‰NdÚóÉ¡2œ¨‹&diÏCúÚ©ÕOG7d‚!¢ë8Ù»R_»e¢¶ú•Cß['Dtõ-åÐ×òé$ŽÄþ•¯ÝËx"Óù=j/#¢³ï+‡FgDtgØ$ÙÎ3"¦›?]Ig>-“*ös+K ªs9IäPÉŒ,Ͼ¬r¨MFDMÐÞ©MÍʈèRNŠÝ$Q=ÕÅ÷”Cs2²ô8;û¥ÏËOt"Õ%ïÁ };f9Ô;#Kç“Í }®üCÏEuö$Irh¼Œ˜öÐ$îDõ÷‘œöE’­gFDו¡IìCˆèìyyåШŒ,Ý๬=}Q=Ú“ѧMcÉ¢úmg—A9T^FŠ~ju:Ò#¢‹g†’ÏŽ ßÖõl.*‡zbDtöýjåÐŒ,í x’dÇVJušÍŸ‹xá#KûÆ•i¾løLË6ÑZYÚ·•CãaäÓòö¾Ïi&6ŸªÓ¬g?I9DæskOù‘ffÃ纒#¢eWÊב–ýLoMKÞMsÖü”„ˆ~ß³}~¾­_±äPéŒ,ýž}ÂåPÏŒ,ýœ–AÍɈ™Ï|LæskY)zNTÈðmýöã(É¡4Ý×§eH…âÆ£‹›T(nÜZö¦x!óiZVáï$U6Ÿ¦‡gr_AÙddéç„Übá^:‘j±9Õ‘ò2²ô8îùêÿ茈–Ùªp"2Ÿ¦sóÖû+#Ãg:oR§¸qëöúw”Ŧ%8.Žô\<ŒÔG;-àWÈ|š–Ïþ\ËdÃgúõ$rh<Œˆ~|üœŠ~Oÿ®"óiZҴïù4ýxÿîê £©–ÕåÑ–iÚy*Ãwh F†¶Þ»rE¢ø\LWÏ@½:ZF–ùô ŽÒ£C†Ï´´Ð¯#¥0"úûÏlŽô‡‘þh d>M—ä¡‚ôQÑsQÝàSûB;2|[o¥¾C©1"ºÌÛË¡šYÚ{¬W 9]ÆI¡)ß™OÓuxÝýÄÈÐy}éÒ[ˆjl¤Ë¾2"ºo¿Cd>M—â^Pîl>Mç¿2ØðmozpÜhZ͵!“ãFÓ9Ÿ|ÍrˆÌ§éÓÙ“öÉ!ŠMgˆ¿C7š~ÁùÜc2Ÿ¦ß.³ù4ý>þ5Jo$¨ì¸ñxdß!ŠM·çôÉ!ŠM·äoú;DæÓtöÞsñÉ|š–®Îê>Ó¥ŸtX¹4±“€¨–JÅ‘RYڳʡþ2"ZZÅêÈ쌈.Ó=²ïºÈð™þÞôqN¾¶†âFÓ¯IÊ!ŠM—z2‰É!2Ÿ¦köVê;DæÓt®P&¾­T†Éq£é§{°\ŽM¿¯‡ õaóiZW>„̧éìU¹¾lø¶öñF9DqãÖÅën}9n4]|d OâsÙúq{T›OÓ_+u<²šÙð™–˜d:Bq£iY U¡¸Ñty<†“AÏEµ¼ªä™OÓ­RÙð™ÖUv¡¸qër2 É!ŠM¿Ã{q¾Cd>Mc»+ýeô\TK:!Ãg:'ïó¨ãÆ­\nç¸Ñt}Ïø´"óiZ·‰:™OÓ²ãytƒ ßÖÙ]¿:8nT½ú“Ïs™7š–tž¯#d>Mgˆ&êdóiú­nIÚÆϴX5@(n4‹ÛÆörÜh:?~ÓíeóiZöù„̧iãÛï¨%6|¦!5º¢¸Ñôw-{(EQÜhºxrºÜ2›OÓíq/H’™tFDWÏ1˜[aÃg:w¯»­pÜhº%÷š%5Ig¤é‰ÎwÔ ›OÓ8Ð*›OÕËÉÏŽá3ýNoà[“ž.@TK!@ÊdD´8!óiú3õ¼€ÎæÓtpÓ ßÖp´Áq£iÙË#9Bq£é7”ˆrˆÌ§iMÂu2Ÿ¦K÷¾ 6Ùð™–Í ²#7šÎÐÑŽMgÏž.‡È|š†ô¯rˆÌ§éâÃÆ¹¿lø¶.Þ4÷—ãFÓ¥úpW9n4ýf¯˜2Ë Ÿ‹ii~»#d>M×êÃ’ÀƒN”÷xãq¬e–Ub¤ê¼ óôÌq£iÈ(,}wd>MÓ6Ÿ¦ŸÏ¥²á3-á\u$FraGIfY=Œˆ~<¤‘¡SðŽ§Ú›,©Ätön´ÞØðm<@íãFÓ/t/öÎq£i{2Ÿ¦eÛšì™OÓ¯ÏiË}°áÛbû>8n4=5zî“ãFÓbí“#d>M¿/ý ™OÓ¹xs86|[77Yãá¸Ñô¿¿ûZÆËq£éœ¼ /›OÓ_tB¨ñ²ù4ýL#±áÛzø¨ÂH7š–=¡¸ÑôÛ=p™Í§é·{ݙͧ駺ÿ"I,èDª¥ƒýu„âFÓÙ³aË!Š·®Þ4Âæsëq2¡æQÙ|šÎàyÈ,+:‘ê’Ïä99Dq£é·Aeh7n]=øͧê5gáÔ—ÎæÓ´4‡Å2|¦ßnpÜhZ:¹¡¸Ñ´î>r2Ÿ®½Ö 6Ÿ¦óôvwL6|¦Kƒ*59n4‡¿éùpÜhZ:ÿ²#d>·ÞÀχͧé§{s8_6|¦ßáMÐ|9nÜzÍÆ6$qÜhºNoà¿Cd>McP8›OÓØ1(³¬èD¦«ñ}‡(n4‹÷©ÎÌq£é Ã’þž‹ê÷õy³°ù4]†<È,+:‘ê ½’Ìa0"º·ö2fRi:1«Â¯ùt}&NÌÆæÓôW¥ÎˆØlløL£»5;Ç[C‡Æì7šÎ0ò3;›Ï­«O(™Í§ê¤¹ løLã99n4]¦Oš“ãFÓ¹ÃMO6Ÿ¦ß×½ 9Ù|n +Ež‡ ßÖ2|ÿ:‚qãÖÒÓöl䥸që\O•’Ch>·~ßÓ'‡Ð|ný”ã”'‘á;Ú{CåP댈~ëùHäÐÌŒ,íÓjË“É|í³"äP©Œx8¿RÈðíÝEr¨LFо€^ÝØ”§’ùÜú'C©3bºžçÒÈðmý¦c>寠ü ÆG·3$ù¯ü.ÔIßÝHŒ¬õêß u¡Î×àüîBò® O6ÁJα¾2œpµ¸ur²$@¼:Èš%ˆÏ:ÈŠPñˆžÙ1"- áZtfŽ#0Sç kº# >ýñ U§Ä[B@fed\d…΀x(}5‚ˆè2#3¼#]Óˆ(dM»ħá’#çr¤tFJ¿ÏW$dûކX´tŒžiiÒFz<ÑèÄþ“‘1²æÂ⓲º÷ñ ÀY‘> ù’3#9_ÈȈH¥¯gÇÒ*#->—JŸFO€Œp¢O´&pâ:ÿ²&câ“3áwÔïwÔ©î¢O HÏŒôøt}G8x HÉŒ”ûWjø•zÿJ‰sÍ( ¤ÇG÷ ÈÔ¥²ñž"DF@Æ…¤ÌHŠ7½zä )÷µ´p¢vŸèÏ!Ú‘åtl!’’/›f\¡ H ¿Rï_©# ÷墱Á9å€ôð+ýþ•~eÆ_yùMƒ7 È;yã‰^þÐIu$7Fr|tkB!½_ÈxÏ„Ë÷åòsyïç²æâsÊ))¿¤€Ä§›ÂkL÷kLÜÀÃrDJ@â Èáró}¹™¿#è¬þ#H!¿W¨’Fr|ËŸü{@f@âk\ëey㣫º¨/·’׌³äá–¡Þ-à  éñÔP½ë]½Wï Þ›‡ÈH|Gü:쇤<Œ”ûZêËHw´&¯Òâ£ká h÷°æ2îËý$\˸¯e„k¹5…™ëKÆ¦ßÆ¦‡úr9ù6»híuÀ‰«÷ê„eä¾\~Gý~G«»Ÿnu·ßu·³wóO™ƒ‘_À`ïæ’Âí˸ÛŽp %üÊõ [ Ç‘øÖ¼JBZ|.#|$ãþHF¨˜ã®˜3Øé+â³IÙŒÄʰ–xRî_©¹nz†º;ïº;Ã×8¯¯Q×L:k(yò^H ¿’î_Á>\”ˆÈ ȼ ç`22 %\n´Ó¿ˆ”€” áŽF¼£—?˜HE6Ø'èH ¿2]JÈß´"’®xZ'*2o:ñ7 »}R;#µ_˰ºŠaú 3œhÆåPwó]w×âFâ‰2Çj°ñ oø•è(ý’ÃåF¯Yg¸r½€µ”‘‘X_2·/°¿ MÀ<@f¸£yßÑl‰Ia_ –·Âl¹?ØÂq#¬qBd$>—Â*Lt„úÈ`—DÂEk¯s< ¹ên u·Þu·r¨[V ÒrŸˆÛÝz·»5TÌzWÌZÌz·˜•1ا‘ð+1KÁ=O·{®K¥ ¹ŒMã~L-„\?ØX~%öéö*„D§Mg 0ŸK Ï¥ÝÏ¥s8—úÎé¨ !—îáÑ]cëÐH|=T©~W©Îq#Ì0¤†Ë­÷å’™úå@¦àÁ§Ûƒ—Cá¦Û}Ó#ÜQìÔÖI€Œ+äÖI*„¼ñD#ØéqÛéàÁ§Ûƒ×9XŒÄÏ~„0î0¸Ó ¶‰¤„Å.`Ý›‰æ¦àä§ÛÉ×%ž„\>Ãíî¸ÛÝÁ1,,o?È -æ¼[Ì5üKH—›C‡i¾;LsðwóíïêÔnB¢µÏ¡«3ß]:)›Ø¾èÄV@Þ«ñÐeþ„¼ñŽ^n`. Të`[4@È%ηK¬“Õ©2õÄHX·$]·ºÑ#ñrSx.é~.kR6#¿ñ+# ÷åR7,Ž„ºE`1@Z¸–Øê\FB¢‹£Ë‰õ%qì5y5© ŸäôÇ® ù® ™‡Þ`á"# ñd6°Ñ 3œ(ú»: rùu:k•«¾¬¹ŒŒÄÊPB•*w•*¡•*w+UB+UîVªp›BÒ;ou+Fâg¿¶ô"dÆ*U8&’ˆ„çrµ 5<Ýz?ݵ°ƒ‘ø\ÖúBZ¼ÜZïkV„­±%$Z5ÝË‹‘xÓkö!3~Ó•‡u`iãA»80‘‘ûWøé¶ûé†ö|w°ëÖ@ŒÄO­…³Ý-fcÿfÉR;4tçNB®wÔ¹§ ¦Ö!ò$~${+`., üÙ÷û³ïWIHŒÊu~2!×GÒ9Ê‚= áZ®¦yðÈLê„Ûºq·u#TÌqWÌÁã°gÈAf¨ ó® <½ ¤ü1„øy7ð3¼€y¿€É^3ìœ†È H|t34Aón‚&Íõëщ­ŽÀD×?†Ó롾ær÷5ëö“„Äž¶FÿË=ú¯KÓIW§“.!$ºG!±õÖmÉWŸ‡nFHì#Ó%„DR·ÂdäþêÝy 'Šß´n H¹æ(•à(•ÛQÒ @‰!w nE¹Ý H½¾é_CÈõƒ] !ßv„Ÿî=L×bÒ®–¡ðdK\·xÎ#…°“ = 1šÐÝá—±)aX¹g€é¶3ŒÄê=xê,S„ÂX {ÉQl·‚ÈH<Ñä®+Ø{òša¡"9 ñ¹„ÞŠr÷VèÞ„üÆMÓ¬؈.cq–µüdmˆoÅj°º#XÆ}N”î%ö_`›NDf@æ…ôð+ñéó„ÄÑÍlH¾ݯ„ØëWÃ4¦zOcÒͱ‰¦\wU¤\V7Ý $~5„Põ¡tí1!±gV7ƒ¤]½85õ8tWHBb_³n·NÈŒ.L©÷T]Ãȸ‚:oëÝy«™ ‰ž‡îÞ È¼ÝZšFH'šá¦çuÓº[ƒ#°{ *´{8]·|$$~Gºe!ÑQÒ­¤‰±½&4ÄœBý˜íîÇla>f»çcê^Ç€¤+TÐý'¹žKb÷6…:HáI…°lî !Vkw¬¦›×[LÝPÓØ`ó gÑÀ2À.‘¿¹Š“èpRž¿Ý?kúoü4e™ÜÍýï¾óìßøŽ­™C›éÞy~!ue0MÈÚé·ÕˆÈ<Ïå'bš½~i#ª ù|úE5!ÕÆ57R}\“oÒ¸iÉŒµÖIýõßÿÆ»øû¿þ!qÖXy³ÆI›•êÚEm¥ôé+oÖ_Èñü»™·~çlšüÀ:õþûÏUÍþ÷édþJÒ«»k~u[~áúW”úë%…úÏJþ•×8ä®é?Lïš¼Þ騲 ÓÕˆ¬~øÓ„èVŽØÖ€”Ä×bš®%|1ûÃü¾˜ü_Ìþœÿ/f#ÿÀQòï}1`æï‹9œ1P#,#ÚŸK:fØŸK:öýÔ/ý:Kžòï×ÒþrÒ±”¬kôÉ£IÆ1­×r/è!DtñÞ#K2ˆj©ŽÔ‡Ó˜—¬7FDçt|8K2ˆéáùq4É!¢¥>œkYCK€¸ÎŽäÄH¶Á¯ÔÁˆhéLŽŒÂˆèìÃø;ɘ#ªó„\`+É!¢alÑ’Œ¢:ûK2Fˆèìiõ,É!¢³÷ªY’1BDoú|ùšd Õ²A<'#D´ ùqÒ1@L¿0o%#DtõIX–dŒѹy¾M2ˆé^À~B–npÓ+É ª³{‚–dŒѺÏÿA$É"¢óÊ—af`%sdëù·V’1BD—ä9õ4É!¢eùI–´’Œ¢ú»Ïµf¹":¿ÞÆk’1@ŽnÑ’]|êŸ%#Dt©k%#D´Ì€8w´’Œ¢:¿žrI“Œ2õŽN}Ñ$c€¨/2ŠÕ—‘¥}Ѥ%#D´ ›@F±‡ž‹iø4É!¢¿›>©5É ¦äßZIÆ!©¡&#¤êà¹ÀV’1BDgïù´$c€¨./¤Q[³­YºBÝ]IÆQ-ƒR¯#åeD´8|É‘6YÚç^Z’1@L÷ÓfIÆýNOÓ¨IÆýtO¥iIÆbZ7ž=™OÓo‚D_/>Ó¯Ù³$c„Tm; ãaD´L’8Hbóiº H‹•Ø|š®Ò?e6|¦ËÏe%#dérº5-É!¢³ïÅ`IÆYú4j…Í'èê>Ó¾#M2ˆéÇë®&#di_fnIÆéê—W“Œ2ÕNûM76|['o15É ªK$ukN(![Ïÿ»µ³ç¹]G²sî_qÙ`Iü6àp`8t60`ÿÿÀ/U%r=ÅÛ·Ïtrëíçì-mI¬"U¬µ„O×ó™–/jÈ«û6f2&ˆë´Ÿ™Œé–Û¦™Œ b:ïµ\7òê*îfƒáÓõL —åÒ…À÷éŸ{w5ö4“1 S_Öeã&2õ|‹¼¢n„Ï¥÷vj72õÜk´œ³¾¥ŸÝ9ÈLÆ€¸^ìÌd È«/|ÊDÞð)=ºÍdLÓ?¡|uâ5“1AL§º;“™ÉW7± {“& ÅâÑj’n&c@^wK3Äôô…¨é…ˆëÕ™ÌLÆ1=óÝg#©™zfðy#5™úgdX}HÍd ˆëÕ°ÝLÆqįÍLÆ™úÎÛãê˜7~:KǶ«#|.½³f72µ9€82ø>=çGâKÖ*‘©§³»Ø…é¼ñÓÚÄØLÆ€LvJì&c@Þðyïþff2&ˆé$f2dêŸP¾3òê¼ûxÝÂç§Ó~ä&c@^ÝåSß§/11“1 SO·€k#µyç}uf2dX˜Xw™Œ b:=»©›™Œ™ú>LÇqÄ®`Þøé$ÝLÍd È«ïݳÐå7búÚk`¾AÈ«Ÿmfb&c‚˜žõ%b–."¯?3òê²[æ™Én¹÷ö±ë|ŸNcÍf2¤Ûà±{3ÄuЦc@¦žmOh:äGß?Yújkh&cqýCns­ óÆ¥óîRo&c@¦¾Ô.ìføtý3ÎA>]Ï9ìwיɘ ¯¾[Ýc™Œ™:Ë]ç&cмóFEçë,2Íd ÈÔµÁ. ïÓmߘf2d®“÷ HDŠedC¾áÓuçO3ĵ¸šÉ©¯´ó3Äõµ3ò겇f32õvvh&c@†eªËŽði |®oiý4Ì]ß?ÓÇ}:æ¢WŠã&cŠLÝô~é Ÿ®gñëú”ÁÀçznƸ7Ò/"¯û3ÛˆëYäÙ6‚ðézN ¯ |ºžab¹UÝ |®çÖñ%«‘©ï&æZ7æŸN%šŽ bzÚ<Aøütû§ÄÀç:ï霛Œɶ¾«ŸÒ."ï²k´ÄðéZ=^ÍdLÓ?ñoZ>×ùÙi…™Œylâ¾3òê¼v\»É}!Heøt=»×Ôô‡ÈÔé‘ËøšŒ ²ôJÚÌd Èԥ™Œ©6xtA>]_C>¥3ð¹®b`&c@†…‰52˜É˜ ¦Ó-`0|~ZìqÍd Hõùôòvºø>ݶGš™ŒyuÙ½îÍd È;oÌ»/¶™Œ b:]{ð0“1 ¯3Ä´ºùÚÙñyãš}šÉW÷}cšÉ˜ ¦µ3¼™ŒyuÝEf2&ˆé$m¼Íd H²/ÏF0otýóëvA>]Ï1DÅ>]'ñ5“1 ï¼QL ÍdLÓ¹î±ÎLÆ€¼Zæf2¤Ùº÷ÊÌd ÈÔé´ÆÀ÷é¶¶›É˜ ¦ÓÞNí&c@_H_2„O×I̵l­È»ì*žWf2&ˆé”ÄÒmpÞèZöÔ¸ÉØF\ßi_#32õ-ë f2ä]v{d0“1ALßÏnµn&c@¦Îmg‡f2Äçk®f&c‚¸®ûi4“1 SçݼÖMÆq-¦Ìf2äÕÒ°ÝLÆ€¼ïŸ>ÍdLÓéÞ¾Íd ȾgÃïu Ÿë¤F_…óF×óþ_2Ì]ÿ ¿kÜ5“1 Ýž€•›É˜ ¦g#¾d|®ï G1Ì?½·ß»ÉwÙµï§ÑLÆ€L='by#Ÿ®Ó#—±3ð¹N·˜±uÎ]ÏUâuïÎ?-Ó3òÎïÝ<ÞLÆ€Lw»L7Ûˆë”öDÌLÆ€4»+=7“1ALÿ AkÕÏLÆ€¼ú‡¨›ás땚ɘ ¦³dðf2dêù]Á¼ÑµÎÌdL´æc#Ÿ®ÕúÃLÆq-6öf2$[æ±¼Ìd È« ÜÍ>?ÝÅÅ«0|ºžÃ!MÇ€Le1ÎLÆq-³ 3’íÜ‚ |~úï¸Æðùiqž0“1 þ¾±¯“îœ7ºÎê×9ot$’˜É7|Êâ­™ŒyµÌ>ÍdL×i¯†šÉ¯\gÙ]œ7~Z ­.†ÏO§h:dêkwºp“1AL_êâusÞ¸õ%Ÿ‚y£ë¹b9D= ŸŸ¾ä¤†O×ðK |®ó~)ï&c@Š ­•632õ¬ŠX‡›>]'™ÃšÉW·V˜É˜ ¦ó.©r“1 ®÷ïòšŒ™Z_T™Én`»›½#– ¦ÓØvíf2Äõz b&c‚˜¾åµŽ™Œ™zÎ߯ |~zf¹ÉaeL+”›É˜ ®»ÜuóF×s‰FÅ0otÅ•ÉLÆ€{ ¸Ÿ™Œyµ,ôxeíB¶^Ÿb&c@ºeëpÍdLÓI"‰™ŒyËuäi4“1 ¯~öJ¾™Œ âzל¸ÉŸ7®ÜÛLÆ€x¹ÎÊšÍdLÓYŒÖÍd H¶iîŠÓf2&ˆé$ËÑf2$Û«ýõVÁLÆ€4Knù"„Ï­×(e&c‚˜žÁFìÂ.|‘kõH«œ7~Zæ$f2dêrÁ— áÓõ-Ëhf2&ˆé¹âyoÏuºöK3Ä´."˜Édóé5á0“1 >o\³ 3Ò;'f2&ˆë.hƒóÆ­—až™ŒmäÓbÆf&c@²…‰õ˜Évqª`&c‚Üß¼ñ¹7‚y£k)Ìr“1ALÏ6â(†ðùiY—2“1 ¯nò»$¾­×0“1 ¥r2“1 Ý ¨×½k&c‚˜NM Ð2çë[®´™Œ búçÞÝgôšŒùÑ÷¬u—O©™ÈÔ÷³“Y32õ=vT3“1ALß’5›É=çöÛ\«qÞèZ=Íd H¶Õ­íúÖ>]Ï9³| §ë$f2&ˆku7œ7º¾ä½š™ŒùÑó¤—%­™Œ™zÖ |º¾wBà&c ùt©»½‡™Œé6q_­¤ÍdLÓEŒ#Ìd Hjx—å&c@ªMÜ·ÿÖƒÀ·tÚ]ZÌd È«Ÿ½ÝÑLÆ€ø²ëÚh&c‚˜Îâe&c@¦–Z7ĵ4Ð2“1 SÏÅ?ù”VˆL=)—ÑWAø\Zz½˜É©e ØMÆ1=;]ˆ£˜Î—®bRW1o\zgÿ(Ó1Ö~I R±ITË>ÄÞ¿oDÞÇ/¤£ÿ–U,d`C¼¾wþ³6"c˜ ºMZŸxA´k„®r.„:úðÛWf£ÍBtôYÍL´0z! ´|`!ìP¬¥R‚Ô€Ô_Ûu´@óòAJ¼^L*È..]H£»™”/„-9uq@m ¤K ôH“bAJ@âY¤ÜˆDNApFR7'ˆv$Ð(.ˆöäÐׂèfkŒ§á‹¢ÝÍn=ú2l! }Hµ–Dm£›†iáSÚù)<–tKÆ£¦ A´åŒ–… 22‘·„+]Î+M7™çt“ñº ñQ >0º´«ÈHü]*Úx뛋…°c›.š’3‘:•<§S‰—;‰cmäöï{y¬ =vöðÌ H‰×¨#if.H_Ôã 4hÑý3‚¤‹HŠg4`"¥%ð‚´€´?Az@â0Âs4Žçèag2­’7¦¬•*’’£§Ý†¯_‰?-Ú‰#æCK7݃ ˆF{Ýù"îÝçŒÓ{rèÛEF@â¯{óQ“wT‚”ð)Ñîôaÿ!Ý|÷;Èn†ç¼žpŸó2>h¾ª«y‚´B¤ÅËøÐÀUv)Òψ^Z¬H H<£„¦núEç&òÄÃMèq‰Ù“ = ç¥p,ÇC’èñ*[&ɃHŽWúÝÓ¤Äk”8JIñº"ጎ».Xõ>§UïC_U}¥H H|¦CÎðœ9ƒ-”9ƺÌ EÚ‚<ápŸóp‘5Ÿî ¾½‡HØÄX×m gt\£ÌÙ„ì[¤‡/й”ÕC‰¿ Dt«"5 ñiü5d$ÞÞlõ¥À‚<ápgº`&¬Å‚‚¤ðEq`û€”xʲ‚ŸõsúYÛ[| ÇÓÈ^ŽúŠä÷†ÏÃ5ÅË9:7{i‘x¸mSµ*QÌøäE‹"áXžóXJø¢#ªUôrÔ² Ajø”8ø5¤äxì+'î²~! Ë"º)Q‘xF-@ aÜç¸;B.uÎxaãïŽ3ßaÜç¸;ÂjœS¨"ì8#,½)õ5ùo"= ñÞ!—:ZÛz¥#—ú5$\€~^€ΨŸgÔÃ8nÌWI×á¤Ác„óè£î¯D6¢¯H~‚%=)¸R$|JŒjÖÅHœ«%¶ÕÝÕ‚´€Ä8m[Gˆ´Á$Ý„ _o{ $^FÛ˜#È}Œ V¨äþ!¤Ná7‘xÙ6U» ‚W)²P‘pF1±öM@âtÎJ]€ÄÔÏ^s‰OÀ¯!á¤ëyÒ# 1ÚÛ[|Ažc:ܪt Ÿ"5 ç!ØH‰ª"á‹â‹‡ÞÙ¤óµð#/ãè&uÜ¿‰„c‰)±ï9%…ß%.ô$ÚãêvBE§Äåhk¾G$Žu32©±ù=¤†Ãé¹íQ!rÞu- q~”ž0bíͽȖH_Â{µt¾W³Þ)‚¤3d¥ð¤ó!Iá HçÂíÎÛ;¬¦síÐê“7C 7C:o†®Q:¯Qâ°láSd$ŽÞ‰/|¥_¾è¸F9\£|^£žÆ|>9$ùL2¼µ)” 9ÉqðÈè ¯ö)9Oº†/:’“Ì™°ìs¤‡Ÿ.¦¡¿‚ІD›ñ)Roï°^—Îõ:kSD$žQ 9Ãa>ì{l—±„q·œãn Z9µ®t9¯t ·w9oï_Cz@â½[ÃàQÏÁ£†xtx¤ys,"ñ§«|'•£‚ðö®çíãÒ¹gÝUYP CP=‡ _@Z¸wÛyïÒYwî ‚in:Kd~ a2ÛÎd6,£¥sÍú9’ÙÆÅÙò)g6R•ø[È?]\9±’w"ñÆlðSÒ-Y éa”êç(ÕÜä0BööMDâõpûyÃÒU:—®~ a èg è\Šm–Š„“>¥Ái­HH¼¥BmE:k+l‘óXøëžËEÖJ H\ì·-äDÎ/ª©'Ò™*}ìt·É‡X©îF´tWz È_ò¹ ‘C…F>+4¬/*‘ópsø¢8¾Ø®a ñõ¨mþ“ës ¤Ç/º9W“ªŠ´€´‰'}36JG9EÂÅ:2ëäø]n¾”=Ú‚à™–&¨Šä€Ä_÷am…l)W¤$Þ˜W”¤}© Oø¢ã… j>'¨ù ?Ýáœå cˆœ‡‹™M>gŸÖ¿œÈyFX%>ݽM8¨Zï 1ªY3"ñ¤sL-;ß^<ä³B#‡rã|–[“9"ñp3‹¡¤‚ ÈTóá•ìÛp9Ë ¬+8À[Óv ÇIÓèK{Á)2ŸÆÊê"éX)‡æzÍ¡ª3ŸUf–$¾Ë! Êg”C”Ï,ÈZø‰÷ng¾+½1©‰!«3ƒ—]8ŠŒ€Ä ^àåóžmÃdëR™®µê;"H«D‚× ï™ÜH9‹-m{XÄaÛX¹G­„¡Yöh ‚õºr®×•P]TÎê"ÛÓ$H=b£u'g¶L½=l!ô±{N;ïë$f‡5T€Õ³Ìzœ‰‰u¥Í´v¬üriuø¦cnʳLÇ\ÿûþ7!£ILÇææœ÷Žú˜yÎ4?ÚˆYt¹2²=¯bZ‘ç¾ím×çKfÈ·éCLùýÔ.Ì5;ƒÈýƒÅæý3Ö|Žb¿m(6·õüdß¿¿ûß§þÙýå?Ÿ·ÁŸú‰=sWW?à¿ý÷?óûE¿§€ŸØwÿA1»¢æ¶ø‹½ˆû‡-„þb†X—ƒÀ_̞ˇÇB±?}¾gî/†ù‹‡á{Rÿâaø¿x$Uø[ƒq~,r¹Ë[þõ÷¼ÂŒú{VañYrç”<=ÂÚOœ>"í×Âîç3—ûô³^f»3ØF\Ër¥;ƒ™:ïÚAwÄt*ÛÈœÁ€¼z¯Ü¹3©S¯çý©6b:íÙ¬;ƒ™ZmWÌL×IÌ¡Þ*p ¯~¶9ƒqý½šqg0 SÏŽõ»¼Î`‚˜þAnAz'òê!®e¯3˜ ¦S] ©;ƒ™:íkî äÕ·Xo½Î`@†]€¶~Ý×LÓwZžî dêyÒëS^g0ALÏ–ím#¹Év¥×àaÎ`@^=Ö>/wÄ´ùG.¤ "Sß{)ÌÁq½§î dêû–çèu2u*ÛÇœÁ6ò齜àÎ`@¦žŽédê¹ÿm¹P½Î`‚˜žÇòl$ "¯®ÞšA–Îy#9™:oÛjw2uÎû~1g0 ¯Î°{𻘞5,b&6‘©ÓN÷ÝL×u­s¹3W§}cš3©’í}ö:ƒ ²õ³‘~yuײ×LÓwƒ™XJD^½÷úº3W?ۦǜÁ€¸Þ¦Yï®GALç½ÔÁ€¼:Ëíý:ƒ bz¾Ì®É7‘©ó%÷Ëë äÕ÷ZÏqg0A\ïmFî d꫈kÙë dêë[§‹áÓõÏï²¼8Í È«Õêfà3=ö{R71u’<ÉŒÁ€Leì0c0ALÏ·!×F=]Oƒøõ)‰qÏuÚ›Ü È«»œÑk dêù¼Ò( È«/ùq3£§ëŸ ¶®¢ƒy㞤 f &ˆë¾Ó32õO¦¹n:3ÒìwÙ–p…ÑóÓ{'»ƒ bú’¸gÆ`‚˜Î{·žƒyõµ33òê±Ü ÈÔ·˜Eš1˜ ¦ï]líÆ`@¦Îyûlš1˜ ¦óÞkíÆ`@¦žÅu#ˆž®­Á¼MÌl!Ÿþù”բьÁ€L=ɼ‘qŽÔL£ç§ï¾Ì ÈÔÓ gùT=ˆ{Ÿž/‡ÉHŽ_ôƒ™úÎâ™õ z~:ïbH7Ätƒ#3ĵX6™1Wß» ’ƒ™:I§*32õwƒ:3Äôϧ¬þ®f ¤[B¶=ÖÞ©’ ¦çøB£0 É2¿ýëVDÏO§º[š1W_ëUªƒ âZz›1©ï.HÇ´ñÓ¹íÞ[WGôüôϯ»zε¼‡ÈÔi n &Èø¦éÞH«D¦¾w#7Ûˆëtí®|f dê9=¢QW×}×™1˜ ¦§ùyÙHéD¦þù‘×iÆ`@ºÍÛ¿p7ĵxÞÂçÒmw+´†#‚¼zqÕJ˜5~:§ÝµÌ|Á€L¤Õ¬ù‚y£ç½¯¢ù‚ bzv{dT"Sß—˜£Ì?mn2 ÉH¶/Z]Í ÈÔw—O©ˆžŸ¾“üþqïÓ·´„7_0ALË´Ý}Á€¼zϱÜ È«ëî¸g¾`@ÞèymAóÄtλWžù‚ygE®ÑÀ¬ñÓ·t£µi3©€íÂ6=MϾ{ËÓébØs}•¥Í ÈÔwÞV(f dêKúí›-˜ ¦¦ÕÔlÁ€d»×xé¶`yuWÛ­sÆO›}ÈBZ#òê´oKëØ ˆéYQ|m±Óui»û¥uÄõnüñ¹‚)2õOö±ÿŒ9ãÒm·«µ]ï@^=V¯»‚ bzVÙÑ% H·eˆÕwÛ\Á1={x•ä›È«¯ÝªÐ\Á€LýsÓé§ vºžÚº£Þ§Ÿ%¹+˜"?z6þ\'Ô1eüô=Öò˜›‚™º>kÊâ¦`@^=¶³È3ô>Ý÷àò Ì?=´åètaθtÚ™‚™:_ûA³aÈ«ÓnÑk’1­€íð2õXëFz'â+®ûŒ†N×yïÉtS0 ïŠëµ3ÄôOXÍuÍ H¶ÑäSÚE¤YŒX‘™‚¾–±~—ÌØézöçZÇRõ\«³ˆ™‚™ú'T¬¶¸f äÕ}G`7SäGñª1O0!Lÿ\ÅÕ[צl@¦þ9”]ÍL×] 㦌ŸNÒÂÔ<Á€T£.ù"„N××®ìtO0ALç½(îž`@Þ)cÚy‚ â:‹_Ù`ì]6‚Øé:¥Ý¡×<Á6âúçQY#y‚yõ½ ›'W÷êÍL×mOÌ HñuPz„ bú––ýVÀÄõºÌ È«ëžÉ™'˜ ¦í¶Y‚§ë¹=tÙee†=×éÙ}çÍ ˆOWönž`@^Ý÷Í`ž`@¦–-¼î &ˆë¼§iæ ä2&9Ü·ýž ¦¥°Ð=Á€¼ú^o%Ü H³(ýȧŒFäÕCNº1î}Z’ óÄtÊâÀÖ9eüôÞéž`@н\9¨½˜2µì uO0ALÏMÉÏF0et-[Ýl#®oq5O0 Sße?æ ä]q­{âcž`‚˜žó±Üѵšcš'aK»ëö6O0AžoθòTó2ucWóÄô-7ƒy‚Éþ²àÙ挮e“©{‚ bzn…1DO×׳ÝÍL×Ï6;4O0 É^êoë®Â9£ëù*@ÌÈ>]ÏUÎu*çkÙìž`@º½ZSóÄô|ï/î^˜3ºžQT>áÓu;>óòj=éÎÀ÷é[ܽ:'®Ó®ŽsO0A\ß{2gž`@²­,ù„O×?#æúéÌl#Ÿ–32O0 S_ußuæ &ˆi)óuO0 oøL;ó3O0 ïŠë½SóÄôŒ÷F0k]7‚Yã§³|Qbøüta@¦žŸ×e¾O÷=™'lWz-‰˜'W‹!¥y‚ñ×å¾cž`‚˜žoºÆFø\kÈ2O0A–Þî^¯'lÙ21O0 øŠš'˜ ®Å+Ø<Á€¼º‹¿]ç´Ñµ%Ï Á´Ñµì[vO0 ®õS>]ÿ¾õØ›'˜ ®%|š'©ç¤eºuqÚøé¼Ã„y‚yõ=€T‹ÓËnÂ<Á1}ÉÔÞ<Á€¼Zžió2u–W$æ &ˆkY 5O0 ï’kÛ¯HÌLÓYŒÆÍ H±[jÅFóÒlÞ¾—Ìðùiñd1O0 ¯–ÑÛ<Á1=7^‰XÊD’]éõZÂ<Á€L$k6O0 Ý^¶ïŸî 나N² gž`@ÆÍgÚ<Á1}eñ”kœ7ºžÕle#ŸŸ¾÷ëó2¼~fKgàû´¬ó˜'·PçÞv6æ &ˆé,ë¿æ dê$†Næ Äõv%ºø>½ ¸'nqz{aÝœ7ºžûÿ¯ |ºž èy#ŸŸ¾Å¢êaàs=ßõÒ# È[¨³«@Ü H·ÕݵŠož`‚˜Îò6Ò<Á€d{3º½°2ŸkKæ¼ñÓ²Òlž`@šÅéõ"×<Á€ ›O¯Þ<Á1=W$ÖO÷z‚ â:ï7£æ ä±iËšü˜'©Ë%öj•áÓõÏ,k{§5†ÏOï¢g÷Ò­rw[½uÎ]ÏšÛ¾Ì]ßbdž`@ª½ë]oÃÌ H·Au­(™'˜ [ßÁ¼Ñõ\Yw/Ì]ëÔÒ<Á€¸ÞÆEÃç§ïý™'˜ ¦ïw­r!˜7ºNÍLÓéÞ··y‚ylAc[w= ŸŸ¾Äó)1ð¹VSóòÎÅUÑ<Á€¼ºîÀgž`‚˜––î $Ù Žõ¨™'˜ ¦o1õsO0E~tÝ}ØÜ DµÌo ˜f dêiè±N¨2zº¾ÄeÙ-ÁùÑM]¹g®‹ @æäÕ}{ˆ›#©‹¼k7G0 ×âè&ˆéù~i!ƒ³F×W·²ÁY£éŸ‘j{ñ ÆN×EêÌ È[¦³×gÜl!ŸÎÒ_Ú Á€t›µ¯­ff&ˆéYÈpmDcç§us®‚©¶¸»vÆ™!˜ ¦oÙl†`@B™Ž‚™ú’V¨f&ˆé, ¦Ì ÈÔ©ÁýëÁ-½4˜!©Ó¾]Ü ÈÔé‘kT;?-Uhnä±ßeõ«1C0ALçýjÚ Á€$«Z\¨Í H±%¿ï¡ÿG„Uv‰¬G“ÈÆŽ!íhbÔäùúö¨éG‹{µ¼yÓül¿4ŽîKV4¤@ØÚjÕ¨±aÉ`¿’q´+ñêÉEh5åBn´ÏÒ‚„…RZgý!7›ÎjÕÚBnÞ˜²3H휪Åd‚¨‘ÖA.äA_7-f¤\DJ<Ü„f š ëCÙ&ˆn2×RIAj&Rãá²?¢.E ‚ûEÞM*Òû;£o¤–? ‚§Qú = çåøëf4mÓÒZAzø”?¥ÐïQ¹3‘;^€‚V^Z.HªDRüéÂ@vŸ™%æ@ÚŸ - ñ!)ái,çÓøNÒÙ“6Axëyk¸1ëycVôhÑMx ¡m„îÞ$ÝDRü]Z¸í¼ ‘dÿÒ2‘º†>€Z·³`DzŸF¤7[§è»lA²¤IžQ?Ϩ‡³Ÿ7æ? )áXŽq·‡Ç¾Ÿ=Û¸hé¾ Ú»F«¢2¯{xxù ímÆä‰g4˜nÉJ˜ HýdÍS‘xÒ#\€q^èj‚"- q|¡÷nv¤…/jçµpŽÁc„±n€ ÒÃõó‹F¸F1Q² ›HÅ"= ý@`t,»ð))'RRO¤¤ÈÈ8Ž%^€‡]ótÆBn&J2‡äîDîóSR"’âø5$|Q:¿á,h*rä>>%dV>C$Þ 7š*ié "áŒâpøÜáJMþ~iá¤cæaˆœgÔÂá¶óp{øébrb%q@bÎ`yb‹3¯TrLJäaæ! 0I‘8q·}âDò‰„cIç±`P}ëfßu$f¶‹ŠH¼¥Ž˜Ò0E>eÄOIœZJ{A¥[‘ó‹8î>˜¾GÈñ„YÖsβ¬\Èñ»„)ÔsN¡ì5.’_@r¸½óy{ÓäR‹OÁ”[Š  _t\ÆÌi®¼ì$$."<9Œ˜G£ú;˜\jw Aøäó!ÉèVªµá‚`AªRàìé:"˜“Èk:Ež€Äߥp…ÀôԀ椠Y¦ëÿŽ%ŸÇRÃ)NXŒ{ÎÅ8{ÑHä<£>åHN \©hP©»¦©‰ÇŸsaФ9…ÙѸÓ÷=9Rœ_CÂYPÅ‚·–ø+¾(Ÿ_Ä´¢ži;âk%’ # ÇÓØ¸.%»#a°9šæ{õ‘ó‹øLŸë OX!xÎ+ÍRÎOù$œô‘ÒÁR«.iዎ¼ŽMóµ"% ñilá2¶ó2†ç\аwDâIw®×ÉnA8ëçt®‡' ŸO@#f?GÌÎ52)ï¤ÞDj¼¿„´‹H‹×¨‡ô¼Ÿéyç¬\6¥ ÒÃIá³sÙUêŽ2¸º%5Š‚ð=WN~ɉ‡Þp<çkèB$^ƆÃq‡#Ü/ã¼_F¸_Æy¿ŒpÆyF¸‡ À<µž"- !d%v ÖÊ5EF@Æ |Ê–Ar@â”Ûú‰9¥„O‰ƒªmª%ÒSi¿¨Hø”˜†ZÏ@ ñµN@â+IëádÄ3ºa¢«ûìáe¼ÏËxs ûÁLX:Ë)rä<\¬cJM« x³,=ƒ)ዎ+ýnÕÒâí}3”‚‚Œp,q&líóyލf»ˆÄkôpM¶>ÂËxXÞÁ´P÷* ò„O‰Ó¹à%¨Û¼ÉáŒâ@f‡@b<ú5/6e¨ È_¤«"á×ÉI é\а*K"ñ×MÌ<¤½«"9 q|IÌëdƒÿo"O@⯛¸'{­¹/@‚-°6+ñètÞóæx‚ä#Ú[é:˜ÌZE9ãŒr8£|žQãn>ÇÝÌUÙˆô›H Hü]J_Ê9¾Τ±– 9 Ç ZBT+gT+œñIAøLµR¿Šä€Ä‡¤pj)UÝ‚p€?WlÃ'‘xÒ5Œ»õwkê92T.uJ Aa[½;øÔݧO÷Îr<$5dõÌ]ݼY+˜@Z AÒñ4Z£a ǽ›²¤ë¤ <étžtâûi©'H Ÿ—­=ã–ÊáŒòyF™+³Ò¾X¬âœÆoÞ“‘H¼F¡|4Ÿå£™–ÖÚ)C>å8éÂÕ-Ù±-æÓº-g#xû/-áÁ‚ÆiBç ÓÄ霹–R¾9äRùÌ¥¬Ï-ãŒZøuÛùëÒ²í>-Ûî`¶vŸfkÞqNqTÞÚ¶ÑÈ6Ò…„u©r®K•ðØ—ó±/a"VΉ˜õ€ç%¤¡åLC­‰áF¤©áoØ™ñÎr3ùïûKÜŒO0+ÃRЙ7Â׎<æ­eRî…Rt1 ð½’-ý~Va¦|Û”>Ä4;´È¡þƒL¾òëðöøo#Òÿ°Å×›ˆÕÃâk¶œ›f[û¿óvp+$¿þxww}÷ƒÿj>ùl“sJZ€K?Ç£À”æ!À”F0¥¯ÏÒL(€rE U-ä3ÿ¯ ˜$ðtO@ÉJŽ@¿ôp¯?ÓLH€ôD t¥G g=äÌ7`’@ªR@}Ô'½èá ß}0I _òZÔñá ßAp& ä ç´ @»"0*€ò ·0I w¹G e-dßþmæøt]x*€§F <Ê6L©À[o¿“~‚´SXÃÚ²“ @O¦ ¿©Â\H@*¨7€zG 7iÎS0I iD µD`ÜF8È÷5ÑLÈ @N¨@ÀùúUmÀ$Üä–´1Œp¯ÛÕL(7€rG 5-äësµ“žà(@)è7€òuÉÚ€I)H)e(#½èá ÛœšlÀ$Ô¤šÔ>ôp¯·ÖLÈ7€|G vµG@-— $Z. h¢å’€&Z. h¢åRsäZ€Kšh¹$ ‰–Kn$Z. h¢å’€&Z. h¢åR‰–Kšh¹$ ‰–Kšh¹T !ÑrI@-—4ÑrI@-— d$Z. h¢å’€&Z. h¢åR‚DË%M´\ÐDË%M´\*P‘h¹$ ‰–Kšh¹T !ÑrI@-—4ÑrI u-dßÓ³»r4ÑrI@-—4Ñr©À@¢å’€&Z. h¢å’€&Z.70È$Ñú$I´>I@­ODë“ Üšh}’@NrŠ@êˆÀ(F8ÈG­OÈ @nh @KÀ™4Ñú$r(wZÐÂAfM´>IàžR”~èá ‹&ZŸ$€”"P€2"Ð €²j¢õI©H-5¨)}èá ›&ZŸ$oùŽ€DœOˆóIºFœOˆóIq>I`4#ü’cVƒmÀ$<äV4þ’³ý’ü’. < ÀÓ"P€’" SûO*pëÔþ“Ò Ý( @iè @ùhÄù$T¤z¨wz"Îq\Ј㒀F—4â¸T #â¸$ Ç%8. hÄq©@AÄqI@#ŽKq\ЈãRŠˆã’€F—4â¸T !â¸$ Ç%8. hÄq©@GÄqI@#ŽKõP¯ô  s(¶60I u©G f5G`\ò¹fä\È€üD@¦öŸ$ SûO*pëÔþ“rkÚ =ÀùèÔþ“Ê \h@ ™tjÿIOðô”  äô @™ujÿIéž” ôô  ‡ƒ,:µÿ$T¤ú¨OzÐÃAVÚ’@¾ä+šh¹$ ‰–K-—4ÑrI@-—F0ÂAvÚ’€&Z. h¢åRDË%M´\ÐDË%M´\ 0å ]ÐDË%M´\ÐDË¥7-—4ÑrI@-—4Ñr©ÀƒDË%M´\ÐDË%M´\*h¹$ ‰–Kšh¹$ ‰–K2-—4ÑrI@-—4Ñr©@A¢å’€&Z. h¢åRŠDË%M´\ÐDË%M´\*Ðh¹$ ‰–K:µwI@§ö.èH´\ÐDË%M´\ÐDË¥‰–Kšh¹$ ‰–Kšh¥X¬2m5ÑʱXåuo¬B¢•Q¬òIšhåX¬òú6ÊAæX¬òº6^B¢•Q¬òIP¬òIšh¹$ ‰VŽÅ*)£Xå“  Xå“4ÑʱXeþE­‹Uæ_4ÑʱX%e«|’€&Z9«¼N‘€he«|R«|’€&Z9«Ì¿h¢•c±Êë2ù‰VF±Ê' h¢•c±Êë/y‰VF±Ê'@±Ê' ä ÷´  …ƒìH´\ÐDË%M´\ÐD+Çb•i8©‰VŽÅ*ó/šhåX¬òºZ6!ÑÊ(Vù¤Å*Ÿ$ ‰V‰Å*¯›å $ZÅ*ŸTÅ*Ÿ$ ‰V‰Å*¯f­‚b•O*€b•OÐD«Äb•ùM´J,V™ÑD«Äb•×ES2«¼š @H´ ŠU>I@­‹U^÷L=ÈX¬2ÿ¢‰V‰Å*ó/šh•X¬òºoêAÆb•ùM´J,Vy7 €h«|R«|’€&Z%«Ì¿h¢Ub±ÊëÚY„D« Xå“ôJ‰Å*¯_g­Òh¹T #ÑrI@-—4Ñ*±XåõúÌB¢UP¬òIšh•X¬’ ŠU>I@­‹U¦Ã§&Z5«¼.¡€hU«|R«|’€&Z5«¼î @H´*ŠU>©ŠU>I@­‹U^oÑ $ZÅ*ŸTÅ*Ÿ$ ‰VÅ*ó/šhÕX¬2ÿ¢‰VÅ*îJº_š'©áÕ¡9’*P[F08€˜éLÈ@.h7€vG@_ºT âÕ¡Kº¢å’nûzÜö·};nûÆÛ¾·}ãmߎ۾ñ¶oÇmßyÛ÷ã¶ï¼íûqÛwÞöý¸í;oû~Üöƒ·ý8nûÁÛ~·ýàm?ŽÛ~ð¶ñ¶7çÔ¸$ ó —t~á’€Î/\*pc~á’€Î/\Ðù…K:¿p©Àƒù…K:¿pI@ç.HZ£õIOðÔ”@y"Ð:€2kÖ' ¤ @º"P*€R#Ð=äëòº“RrôÕ¡KúêÐ¥+Z. èüÂ%_¸$ ó — 4Ì/\Ðù…K:¿pI@ç.è˜_¸$ ó —t~á’€Î/\*00¿pI@ç. èüÂ¥sÃ…¤K:¿pI@ç. èüÂ¥7æ. èüÂ%_¸$ ó — <˜_¸$ ó —´FË%­Ñr©@B¢å’€&Z. h¢å’€&Z.ÈH´\ÐDË%M´\ÐDË¥‰–Kšh¹$ ¯]*P±¢å’€&Z. h¢å’€&Z.hH´\ÐDË%M´\ÐDË¥‰–Kšh¹$ ‰–Kšh¹T` ÑrI@-—4ÑrI@-—˜Cï\ÐDË%M´\ÐDË¥7-—4ÑrI@-— ôp&ZŸ$ ç“$â|’€DœO*4â|’€DœOh€vE`T#dÖˆóI¹È=-há ‹FœOx*€§F <ÊÖ´pU#Î' ¤ @º"P¾:üóž0aŸ0fJï?û»‰ÿ~QÔß›}iwó…Þÿ¹½½É; ¦7'šÝMžëªÿrÝÿòç-RþüCÞW|áCÚßþû—oöñ/ö?v(é­¦ÊÏÛiåþÓþ¯ÿ<½lÿø§ûçÿõÇ¿ý—ùAÿúÇÿý vf«™ÒÌ[·üñÿþ÷ÿãÿ£ÎÙµÎÖ,û¿Ú¯Ç¾x.8ý¹‡ö“°íÅåתço_D%ÞËVyù~•ÈŽÃ%Žƒý€¾žC³ÑèëSôý€>ä/úiO+ïô¯ÿéÿßÜ¿ endstream endobj 668 0 obj << /Alternate /DeviceRGB /N 3 /Length 2596 /Filter /FlateDecode >> stream xœ–wTSهϽ7½P’Š”ÐkhRH ½H‘.*1 JÀ"6DTpDQ‘¦2(à€£C‘±"Š…Q±ëDÔqp–Id­ß¼yïÍ›ß÷~kŸ½ÏÝgï}ÖºüƒÂLX € ¡Xáçň‹g` ðlàp³³BøF™|ØŒl™ø½º ùû*Ó?ŒÁÿŸ”¹Y"1P˜ŒçòøÙ\É8=Wœ%·Oɘ¶4MÎ0JÎ"Y‚2V“sò,[|ö™e9ó2„<ËsÎâeðäÜ'ã9¾Œ‘`çø¹2¾&cƒtI†@Æoä±|N6(’Ü.æsSdl-c’(2‚-ãyàHÉ_ðÒ/XÌÏËÅÎÌZ.$§ˆ&\S†“‹áÏÏMç‹ÅÌ07#â1Ø™YárfÏüYym²";Ø8980m-m¾(Ô]ü›’÷v–^„îDøÃöW~™ °¦eµÙú‡mi]ëP»ý‡Í`/в¾u}qº|^RÄâ,g+«ÜÜ\KŸk)/èïúŸC_|ÏR¾Ýïåaxó“8’t1C^7nfz¦DÄÈÎâpù 柇øþuü$¾ˆ/”ED˦L L–µ[Ȉ™B†@øŸšøÃþ¤Ù¹–‰ÚøЖX¥!@~(* {d+Ðï} ÆGù͋љ˜ûÏ‚þ}W¸LþÈ$ŽcGD2¸QÎìšüZ4 E@ê@èÀ¶À¸àA(ˆq`1à‚D €µ ”‚­`'¨u 4ƒ6ptcà48.Ë`ÜR0ž€)ð Ì@„…ÈR‡t CȲ…XäCP”%CBH@ë R¨ª†ê¡fè[è(tº C· Qhúz#0 ¦ÁZ°l³`O8Ž„ÁÉð28.‚·À•p|î„O×àX ?§€:¢‹0ÂFB‘x$ !«¤i@Ú¤¹ŠH‘§È[EE1PL” Ê…⢖¡V¡6£ªQP¨>ÔUÔ(j õMFk¢ÍÑÎèt,:‹.FW ›Ðè³èô8úƒ¡cŒ1ŽL&³³³ÓŽ9…ÆŒa¦±X¬:ÖëŠ År°bl1¶ {{{;Ž}ƒ#âtp¶8_\¡8áú"ãEy‹.,ÖXœ¾øøÅ%œ%Gщ1‰-‰ï9¡œÎôÒ€¥µK§¸lî.îžoo’ïÊ/çO$¹&•'=JvMÞž<™âžR‘òTÀT ž§ú§Ö¥¾N MÛŸö)=&½=—‘˜qTH¦ û2µ3ó2‡³Ì³Š³¤Ëœ—í\6% 5eCÙ‹²»Å4ÙÏÔ€ÄD²^2šã–S“ó&7:÷Hžrž0o`¹ÙòMË'ò}ó¿^ZÁ]Ñ[ [°¶`t¥çÊúUЪ¥«zWë¯.Z=¾Æo͵„µik(´.,/|¹.f]O‘VÑš¢±õ~ë[‹ŠEÅ76¸l¨ÛˆÚ(Ø8¸iMKx%K­K+Jßoæn¾ø•ÍW•_}Ú’´e°Ì¡lÏVÌVáÖëÛÜ·(W.Ï/Û²½scGÉŽ—;—ì¼PaWQ·‹°K²KZ\Ù]ePµµê}uJõHWM{­fí¦Ú×»y»¯ìñØÓV§UWZ÷n¯`ïÍz¿úΣ†Š}˜}9û6F7öÍúº¹I£©´éÃ~á~éˆ}ÍŽÍÍ-š-e­p«¤uò`ÂÁËßxÓÝÆl«o§·—‡$‡›øíõÃA‡{°Ž´}gø]mµ£¤ê\Þ9Õ•Ò%íŽë>x´·Ç¥§ã{Ëï÷Ó=Vs\åx٠‰¢ŸN柜>•uêééäÓc½Kz=s­/¼oðlÐÙóç|Ïé÷ì?yÞõü± ÎŽ^d]ìºäp©sÀ~ ãû:;‡‡º/;]îž7|âŠû•ÓW½¯ž»píÒÈü‘áëQ×oÞH¸!½É»ùèVú­ç·snÏÜYs}·äžÒ½Šûš÷~4ý±]ê =>ê=:ð`Áƒ;cܱ'?eÿô~¼è!ùaÅ„ÎDó#ÛGÇ&}'/?^øxüIÖ“™§Å?+ÿ\ûÌäÙw¿xü20;5þ\ôüÓ¯›_¨¿ØÿÒîeïtØôýW¯f^—¼Qsà-ëmÿ»˜w3¹ï±ï+?˜~èùôñî§ŒOŸ~÷„óû endstream endobj 672 0 obj << /Length 702 /Filter /FlateDecode >> stream xÚTmoÓ0þÞ_aõ“#/Žã¼ ‚4D‡†4i@˜€^â5ÑÒ$¤m…øïœsN_´ PUù9ûîÉsgßydI<ònæÙõM6;¿”áœ%Rú$»'ÜXìù$òBÆý„dùJC&—s_Ò Gø´(À¢U³t\T«A¼0øÚáඤC‹ëõc=T]­Ñºj ½ExS·ÃÚùž½?¿IXú¡ÑáWÄLp ²²Z;n ª·j5c]¶ŽÓ9 âW.ÆM51\_Îðì֮îÓnkxuêÎZ»z󶶨ËK‹êaÞ“±)&W½ŽÆ¯ö:_À½Ë…ÃÚˆÚ¦“¬]z,,×óƒ†4ÿ¶~R€ÿ$Þ¿£[ÃpD;¿«Uþ0Ÿtô÷sôÓíÀc6$Ã'aoÅì0T»:R‹ Å…Á4 §›RëúFÕz´¡GZÆîHñÏþ–Ჯ àJ³Ÿ6V é¼ëu7Ÿê³ŸQ‹lÆx„ïg“o;’1ÉW³3ñh<˜¢ìÆùÕJpò¶}€ßD>ù¸µ{Ä=NÇ“VáR²º•'1<|Lë²Z>öÛRø/Í ’ãô{eZé0ù`gÙ«®Ä¾ÝTC‰›ã@~H¶ñ+;i8‡Ž‹ô¯•p- ]îr/f$:jZê?Z— endstream endobj 669 0 obj << /Type /XObject /Subtype /Form /FormType 1 /PTEX.FileName (/tmp/RtmpBlhU1W/Rbuild4444671013800/timeSeries/vignettes/timeSeriesPlot-emaMultiplePlot.pdf) /PTEX.PageNumber 1 /PTEX.InfoDict 675 0 R /BBox [0 0 432 432] /Resources << /ProcSet [ /PDF /Text ] /Font << /F2 676 0 R>> /ExtGState << >>/ColorSpace << /sRGB 677 0 R >>>> /Length 16054 /Filter /FlateDecode >> stream xœ½AÏõ8r÷ý+î2YÌk‘Er;FØ@‚83@AVN‚Àpp¼Èß«Î9T•Þîo¦Aûë©Wuuu)V‰|‹*Ÿ¿ý”Ï?|þù§¿ûüóçø:ŽOííëîà2ç×e-Ù¿¦}ï<¾Êá×|Ý0;¾×[cVüüöuØœ§ÿük5Ê„é}cÝ*ÿìå}ãšhÙ¼qÚºs~ªî??˜öó[ÅýÃûÆ}X .szßXÏ»Ù2­qÚº¿öÙrÿý]½Ú°?öî±ì ÇOïËöö*Çå-ÔÖ%áøíðœïöVx>ß½,ÿÑÝÞI¬÷ÚÖLëêOëaeµÄº|ÿ­¥oŠå_ÚÖQn?­ÙÕ{ÊòïÓíÓÛê9_óæX×Smo[Ÿ¿½·´õ3'më.«­=Jéè/Sç›hÃ}Ùõ@{hn³G}zP©Þ;ö}/µ¢Ó¬®äß_/oV%f[{,{·›÷›hO\ïóßÞ^­¡·—•@¬=Ú…h/–.·Ç¤íçF·,§ÿ;^üúW¨{{݈²ò„·Ç´Ûjö‰þÓäeåï?¿oÙ3~ÿÊÞ^±ZVðx{tk±pxûÝm"µ×JL'bÝs[YÿÃÚký§ùù/&Ùƒísí,‹þpí4‹þ}!ÏžMYájíµšÕ^¹i—}ã|HµËýõB®}l&ÛÆþ½lk¯ÕÌÓÏטnOëf3ß2£–Æ„{ñ~4dÜu»ŸÝè Ïô¯†œûGÒ]Ý ÷·!ë–ÁöiH»«Ûâ÷.ÛÚkÙžï)²ÜȾ†•¶µÇ £ ùw…™gºr#¯4öZ¶µ×c#?6rð²‹_G>šýlØÖVšòGcYÏ7k¯•Æø#/ñÙ=וD;>Î[ç÷T\W¾.öj/;~úý螌ƒíÙ¸® ŽøìžŽëz8LÙ«áÌF{¯'Ýiß¿ÂÈïÏð|löÕiOó/lÏáù8|Þóq]Dô×e¯öªëkN|Þóq]§A~ž8«=èüz†çãºNËïó|\—Úcxâ«ö4óóÏ‚öXÝfÐööØ6òñnï‰|íŽû1á|¼î—? ò\!ô¿‰|¼ï×D>>Vüâø@ÿáý¯òq´½ÿðûë|¼lvÕùx=¶ÙÞ&Fõ@>Vÿ¯òñzŒx0Ûã©cÜVäã'>ïÇC=ÕŸÍî¸^¾Õ‚|¬ß[KAÿaûšíýçĤÏÇv>¡ÿˆG³Ù®évCÿ©hÿj8üø ÛóqÅm…mýgÙ'Î?Ñ:Û§è?ëú më?û|µ"ž¶}¢ÿ°›íýçD¼ÖêùxÇO]‰¸ >§ÿžŠ|¼n£AÍööºäãuÛï“¶÷Ÿí?Ñ*¯çD>V¾¨gÍýç¬ê?Þÿëy¦üSm,|x9ýü'òñî?'òñc3ô_³'úCÇùúÏÁû}1ûÆlæãjƒíùøÄó¸^ÌÇû÷zÞY{-ýÿb>fþ®óñöG>^·±Ú–×m¼ðùîíUƃõ☘“Ú×ϸ;ížµ!¯Ÿíã‰Ú*& œÔ†ñ²ÑßFÆšÔ†¡±fQfsâàÏãj—ãý׆ѱ†ðµaxl£ô‹¶åÍ%ê}`ö°úÿEÛ§ýçÆøXÓ‰zc|¼š¹áó_>‘€íãÁ›÷g=ÿ8þA¼` ·ññò÷ül¶9Þ¬÷LãA³}^6®wÙÖ^«qò±Æçöà²öX·÷c ïß3W·@û¬•Ï' ƃup||`¼g6ÇÇÇ9>ædÉ—õ¦UÚÞ¦®ùXBK¤ì?ˆ·YÔêMÛûÏö¯h/]ßäø¸ñù11>^÷ýmb|l·Ù¯z>6û¬´½½ºÎñ±oωññ–øïõãòï¯x"¼ý¸-Z{­ÛâŸ÷aõã>>3ÛÚcÝÖþ?çó|lö,´ûˆŸÇøx?Úá6òq´­ÿ¬npàz‘­ÛøñZ±Ž_—ÛÈÇÖÍd[{ìóäãÎçƒ%2ë?ûüÈbù{ûÚÀóNþÈÇã7³­ÿìßS{E¶³ÏG¿¿"¯ûé÷Ûl§j·ÕläcÍ_ÏŠ|¼Ò‚?ßÌîǯ>ñ¨O<ãñþsáyc6çç>Ÿ9k×üüÂù@0V|ùóÞs9²Ý¯h#¯Ï{>2.tàû½›}ßÏüß&*mD›¨"Øã~æã–¸ÛÚçìi>ê‰<Ìw}âvx|úóÁñÑõÄ—OôÆ“O-QûüœùÚñõ0 TÏÇÌ÷ö?Fˆ;±çãu?½}/ò ί퀷çËfßa~ÌÄa¿§UÚž*Æ7–˜Úxæ·vÀŸOùÕ¾ÈÑV°;~úWC>²ãíUù{ó1y†OÄë“o<‘ÇþÓÅ lbß/}>ê‰û~xˆß¸0?÷Äò›ulï?œï{Ç®¡ýnæcòó¾s{ÜÈÇÏ÷!+Ÿ3Ðvþ>;yÅ||väcÑ/³{àA¸ÖÖe5Ùs„xëÌÇógg>®ìùxÇ[G>^ß|Õ»Ú í×§â ñ8ÅO̾ïòñζi¼ÿ@>o9òñ¾ÿ£‰ï Ž[|§ãüÈÇÑž#Äû˜â;>^;'òñîÏËNÞ/±÷¹t³w±+±÷“í!öNö±Ù»X°Ø;Ÿ]›½_xÖmöÞÄî/=Û¡Õˆ½‹…’½7²€ÍÞO°±w{vÞšk¡ýÈÞ7«{Ÿ`-bï6Dòó“½›"{@Ã÷‘½_º?dï—´²÷‹,k³÷Iö,ö>ñl{7và÷KìcÁÍÞÕ¿Ú;çõyØû²ÁZÉÞMЄ͜«xj;éž‘½¯ÛHöÏ´{P{ {_ÝñEö¾†ðÃÛì}u+ô²÷Õ-§X¼õŸzbì önS °{dßÕíÁþÉÞmŠvŽü[uÿÈÞ éŸ‡½ôßCö¾ÂÚ Ù»!C±öBZ>{7ÖV޹Eá³QìÝXVùlönS>h=`ï6%¬`éHÅ+¡½«E|ƒ½ÛZØ»MY‘OÁÞýÎÏfï6F¾{7Ûçºdï6e†¶'ö>1WÚì}°ýÈÞçÍûMö>dïSZ ÙûTþ!{7dP>{{Ýì½P;{×ó@ìýäï{w¶ê×ön׃þöîˆþ ìôÆØ—ì½®g\kŸÍÞí÷Ò¿ƒ5ö°wk°L°w³Á6ÄÞ9ßìý'{?ÈVÄÞ ÙÙ»i°‘ןÉꑦó!ËìÝ‹°ÉÞï{¿y½bï7Y.Ø»#Žë³Ù»#°uäããä÷ƒ½Ûõ‚õƒ½{ÿ‚?òñú½`kbïbÇbïƒ,Lì]ZÙûäXXì}27{oü=bï\±Ù{#ë{ol¯ÀÞqþ¦þsáüZÄ-6ßÕ*Øú@ÿQ{‚½ÛõC+{7ÇÁÞ+Q›½[{­WÄÓÄóo³w®(ÙìCÄÍÞ9Ùì½"¿Š½Ë¿?½_ìŸd呂u#[~ñë'{_ýÃÙØ»õ'??Ù»å+°q°žC,›ì}Ù ¬ùøàÜ^ìÝXý<ì½p|(ö^¤õ½ŽÄÞW¾†V@ö¾º´²÷ÕMÀRÉÞkE>{·aü‘mvŽ|\uÉÞ«X7Ù{•Dö.­[ìýû${?7;G>^·ßGö¾ž¿øýdïö|ŽìÝžß`áÛìãü<ìÝ–ùý {·ñ쮵¸ÿbïïÙ»!&|ÿÔ<ÚÙû¥ö"{7Vì¿·oÖ­…ì}Ý6\Ù{S>{W¾{/X2µÙ{ÅøFì}ݶç{XÏõyØ{»˜¿ÈÞ[ûÆÞñy±wõw±÷í¿YÏk'ûòçïfïµK±÷‹¿olÖÓ#{·ñ2¾ojaú#Ùûþýdï‹ÄÄÞ ±Í“½s|³Ù;µs±÷u~hEdïMZ*Ù»ýÞûó°÷vS»#{oˆ½·‰ü(ön,êü<ìÝXØøf=`Ÿdï7µN±w–ÁÆøØ†eø¾™Ÿ6{¿È&ÅÞ9Ýìý"+#{ÇÂÓ‡½±>ì}ÙÇ/­óx{7dÛ?‰½óûn­ãù»ÖÆMŸb§‡lÎχX;ççM,ž¬ZÙ{;%{×üWìÝØáñyØ»=vÀÚ‘1Ífï…ñ#ö^ÉÆÅÞ©Ýoö^u½›½ãóbï5³wû~°yòŠB6LöÞ8{oÔâ7{ßÇ™¹öf³÷Êø{¯˜ÏlöεV›½Wjbïºÿbï•ñ%ö^1^Üì«d7{¯ÈO›½3ŸnöÎùÆfï•ýWì]ýíaïð{§V-önì~|öÞ.±pæãÆþBöÞ¸öKì½q¼"öný ¬þÖZÓ[ö¨¡?нS‹Þ콋e“WtÆ?Ù{“öIönüª~öÞ8^ÚìÎ>/ƒó/è…NUý³>Úv³|¤ÚgvC/´Ëð±ôB'ºO­Ž\—+~~ÃA/4³°tÇúÆà!è…vô|juìTÞ8Ð £Ùq‘>d겋t1z¡qÞJÓgGC/ vE°V‹z¡a]ðiè…†GÐ ýxÐ Ý®Ÿ­F†ž½°Lòê…†/ÕîX3-úôBÃÂCú`…?x;ôB;x?ôBÃÎÔÿ|üm×;C­ŽÛÒ­=fÕ÷ù0»ªvgòz¡Wúø»l¾ ½Ðlè“Ð ízOÙÖeŒg£vÆÇßæZè…E<–z¡Û¡V§0µJ/ôó…Zbk·ƒ^hvƒ¾èãkÃä—jsŒ·k  úõBÿCò?T½Ðì;è…†ÑGÐ £P«c˜µ Ð í8ky°6ü` ^h˜½H4>}p>M½Ð1=j…À§íóÐïÀ§“úôBóG†^hçƒþ½Ð~?õ=ðéuœúø´­Í†Þx£=ôBÃþ¨-x3›ú#ø´ñYÙÞ^ƒñ½Ðdœz¡ÙÐC šë…^hŸ§ÿ‰öRmôBû>ô/è…f£öz¡_j<íÚçOÙßZè…&C@ÿ‡^h²ú3ôB;>¤ÚÀ¥H_‚^hvÔ Ýúï…CÿI/´ãCµ;l/Úí±í‰öÔáÚõÝA/´ï‡> ½Ðdä'è…&³ ?³V§ð‘¦ZB~¯Z¢Ú3Öêå;Öê<6juìûp=¨ÕÙµM¬Õ±ëE­ juLï„ZÒ©wR/,]úôB« ž½°téÐ KQí ôÂÝ_¨ÊOƒùx²ökì| ½z¡å ôwè…öçX«cùùd2WÆÏÜùxÛùŠz#ôÂãàýœÌÇj¿É|Ü“^èÿéÁÎw½0Ùž¹ƒz¡çwÔî ¯f²ýùu¨öùxP?§^ÈÛ¸õB¿8Ž|<¤÷A/ôaôʉg9GcÔ ­Ù±zá~S/ôö;>[/ôçjiN´Ÿ·Ô íóø>è…ö{ÀŸ¡zû¢èF{i­xÙùúõƒµ€Ô ­}kÔ mrR?[/ôã~½Ô 1yÙz¡?¿¢^xéyúÏ­Z›†þCž@½ÐŸç×gë…öû©GbðkŸWíÎÄõBÿ€^èýµ2EÏ'Ô¦P/ê…fC?…^hþ=è…þ<†¾xâù¥ö¸v>F­ôBëFü<ò±å£ Z7¾½ÐŽ£VzaÕú ê…~\µ;ž/ÞÿÆ|ÌZXê…f£–¡1«}óñ®Ýa>žü}ùx°Ö z¡Çý‚^X ë‡Z³qýùxRߺ™;ûôB;?ôÑ»îö@-ó±Ö'@/ô0˜Ÿ­VÕÆR/´ï‡Þ½a²õB;Þƒ^ha€ß½°Ö›í ½Ð¿z¡…õ:¬ßXa} z¡…j™ šþ½°VµôBû|+Ÿ­zØÁÆúÊñ(õB ›ôB³ñû V-9¥^haÂãžèÌžÒ­ÿœ|^Q/´°¹C­ŽÙèoÐ ýü°ýĆÐ=W­Ï¢^hŸ‡¾ ½°®a'νоz$ôB;~©vÇúϲ±þz¡…-â z¡‡uùl½°žªƒ^èiçÇú -o¥^èi׃|lµBÐ1>¶Å£ÐßU;F½Ðì¨zšµ:fÏ úqœãã‹ÏKê…nÏÖ íüX»½ÐlèÐ +d›­š}ÁÆú »^èqÚkª6ùøä|€z¡÷'?Õ ßA/´û?¥zÿ):Ž|lÇq>äãe¡VÇîÏjuXá½z¡ûã8òñÅñõBk_´ôÂd³ÿÐF>¾¤/@/ô4/ÛûÇ_â‰|më?¦÷„Z³Q ½ÐСÚc„ú#ò±M›üóÐ ÍF^èP«S·^½ÐÒ6kmaõB;Îó!› ½Ïy†¥ùôB;}z¡ûC¿D>¾YËN½°ªv‘z¡=†ŸÎì8úôªÚ=ê…~>è‡ÈÇ&»áó>P°ÇF z¡ÛðG>^ö z¡=& ‡A/¬7ׯQ/´ãÐo ÚçÑ¡>þÐ ÍzôB³G¨Õ©FQ˃|¼KèÐ í8ôNè…v~äè…öýЧ Ö½>z¡ù³ÖùØô¦P«cýé µ:Ö¿{ùl½ÐŽ#ž Z<àþ@/¬»Vz¡õ_Ä#ôBf°6<£ñyL½Ð‡%Ðãí6½Ðó1ô;æã]Ûžq ÖZA/´ÏS?D>6 €Zäc­w§^èÃøƒgÜÒã Ö]Û½Ðã#ÔêØùñ{¡Úq´?ôB³jq›úôBÿ~è}ÈÇ÷)½ùø&º¦^läãh{<ßR/4Ûãùǵ:6_wïaïœÅн÷]ºãìýÆ^±÷ÈRì„Rì bï\¿ ö~ïâœÊ3—ÏÃÞ¹8öaï ódï"²÷Þc­Žý"Oldïœì½sÙÙ{'e{gR{?êÉÞHÎbï̈bïœÀ‰½sýšØ{ÅãCì´XìýÄà]ì{{‰½ŸHmbïÙˆ½_ˆ½_ˆs±w. xØ»¢ÄÞ¹HFì]Ÿ%{'’{¿Ð£öÞ3{w½[ìýÂ5‹½7DØ;'ûbïìJìû‚lö®µùdïC¬ƒì}p¬-ö¾Z†û\½ÎMÄÞ×N‹½[H'ö~°ö@ìýÀÙ콈“½×{߬|³°˜‡½ß¡V§LæÎÈÞaoöŽZ±wi bï'kÄÞOÕâÒ"ÈÂÉzö>U›õ“½_d¹bï-í“eö­ÚœÚ‹Ÿ` gØ'ËYØÿ@{tíÛÖc)­2{Ûë™Úç$ëÑÚx°wg3ØçЬGµ `ïfCÛ{?t~²÷CµAdïÚ÷çaïhßs³žjuœ•ƒ“õ²U±wÕ–‰½s­úfï öN6Oö^UkCö^S­N`ÿdïÚ÷Jì‚ãfïÔÉ6{ç¾:›½k.±wÅ£ØûÍZ·¶YÏjuÜVmÎh[bïÌö›½s­ßfï¬Ë{ßÚÙû¡Ú"²÷CÚÙûÁ±ŒØ»±|ÿ½dïçN½³ö‡ì}²¿?ììöaïôßì,J‹8Ã>YUûЈ½›–öMÖ#-Aìû¢lö僛½îë%ö®Ú#±wÕŽ‰½kŸ2±wõG±wîsØûŒûdå3±÷ª}ªÈz¸–x³wÕÚ‰½_˜ûmö΂Y±÷½oÙ{‘öÈZu=hÖê\;ªZƒûÞ¨Vç X­ZëÏ`ëU¬ðŒµ:V«4>›½;â†?Ù;×ò©VÇSó³Ù»kƒ±VÇzØ'˵5°sÔê¬EÝì]lHì}¦ZÏwõØ;µ ±÷y“u“½¯ü}Gön?3Ôê°önͤڜmšlŸùXì™ì}îÚæcí»"ö^Ⱦö6Kö®}Õ6{×¾8bïEµ=d`ïö{ÀZÁÞ˳/×”–vön·Ú€Ø;×6*ºvŽZ›SZúØûÖz{Gm‡ØûM–&ö~§}²Ø{羇›½kß!±wÝO°÷¢Z1²÷­í½oí›ìÝúÚWìû8nöÎÚÄÍÞ ÷ù{¯:×ó ÚØ;çf›½ŸÚ§ª(žšìh5bïÜÊv³÷Kµ6—žOho²wChð¿¥¥÷ÈÞõ|{·çü·ŠökO>ûdY·æ>X;ƒmн«ÿ‹½3þ7{¯l±÷3í“åaÖNö~‘·­…N±öÚB µ:ÎþÁ²™¹/Êfïª{Ø{ µ:fß‘½®„Ùì½ëó`=¥‹Í3ï}µ˜UKEönÈ,žìýP-ÙûÁx{×>hbïÌs­øf﬽ÜìýT-ÙûÉZC±w²ÔÍÞ¥e’½¯°Eþ#{¯¬-{¯›}ƒõT²M±÷:ÄÂÁzªj›ÄÞ±u²w²¬ÍÞU[&ö®ø{×¾obïd›½Ÿ/ö®Ú#±w²ËÍÞ¥U‘½ŸýAì½ñ÷=ìµTbï7󽨻úÙû©x${?¥’½Ÿœ‰½Ÿœ‰½›6vÖs2>ÅÞÏ©}¯ÈÞ™Ï6{{{çód³÷¢ó“½—T«ãiêþö®Z±÷J6Oö~±Ö\ìÝÒÞø<ìýâüq³÷©Ú²÷I¶%ö>ÈFÅÞ9¿ÙìµO›½síJ`ï`Gbï:.ö.6Döniݯ—ìýÔZz²÷“ϱ÷eP«ãí ÿ[íQdO´/XšØ»j?ÄÞ¹+úfïUµ2dï•,Uì]ÚŒØû™juªj7{¿È6ÅÞ¹w€Øû%í‚ìýâÞbïIÖfï7Ù!ÙûÅù©Ø»i‘½oVIö¾÷å!{o¬ÅÞì½<µ;Î ÕßÈÞµ7„Ø{“vCöÞ8Þ{·½)B­ŽÙôG>¶ÇP¨Õ1µdïÚ{Dì½uG>6dù¸mvöŽi[`ïÜt³wæûÍÞU 'ö^ÅêÉÞOƯØûIö/ö~ñûÅÞU'öεbïw»G>Þµedï÷öɪ÷­Z²w®~Ýìk{ïbí®E¨VìÝp*Ø:òñ®U${×>‘bïm³zäã6TDöÎ}=7{—6!ö¾k{ÈÞ•ÄÞÅæÉÞ[S­ òq»˜ïÈÞ×ZlöÎZÓÍÞ/±{²w®{oÒbÉÞ›´ ²÷¦ö{'/Úì{ löÎZ÷ÍÞ¹üfïÒFÈÞWû±v'®í{·öFmxF›ªõ!{—&ö®ü/ö^¿þbµ:\¿kuhÿã÷ÚXŸkuÊíúÇÏ»‚[Ï~~ÁÁ ihF£„O1ÍXý`O›òTûÀŒ–'Ÿ¯ pUÛ!\ä_¦˜ÇѺÿº¢_’ÆŠœò[?ìëY‘Só‡m@ÃË>ó‡¡ ù‡¯_ÿð¯Õù è?ÿB-ПYò¨R|rmP¬Ú.¹6(Öm—\k¶K® е@Û%×ýb<)d= èñƒˆb˜ÿ ¤èñƒ˜Rªx‚깋­ë!¿^,Ÿ?Q+ôƒ…ÞÐÖðÛöðR¡uÉv©Pw¡FjeÞⳞÒëÙŠÍý™Ôù…TyÏøFIí*on ²ôô'‘Dz¨”[F@¤¼¹A4Ê›Û7@¢¼)–C¡¼9ü…@ys¡ôÉ›Ëî OnOŸØovÙÓŸ:7!?´É›¤É››C™¼9}„0)aºäͲaÈ’7'žP%5Œ‚(i,Áù`À†LO9ÐÍ ÉέQ HvÂ]è‘˘ Gvnr5²sÚ¹kp’-rp9:¤H{Î?JäàR4‘ƒ‹A‡ ÖúµƒI¨“[D@„´±ÁøHƒœz• $ÈÇtâýë8`™gzOÏ,Äç·ºõqhá8T]µÇ!) Ò£UNŸ­Š{C?ÄïwU@sDŽ”G®öé3m8Τ7š\J}ìš¡æ9Ýû%<St³¨5I=‡”:(ïm¡qpꌆ©ÇgËŒS*P§ ;DÆ]Ï1šëv!Õ=sëø€}¿‡úâTi åEm•%yñàŸ”‹p"åE»—¸˜§Ûíº%'Î.Ú* ò¢Ù”+³µ4šò¢÷ ”Ú` Ç¡RÈ‹ÖÑp /z×R©áìcÊÎ6UçÇBŽC¥OËA\Fy1ØÀÙ·Ò ¼X9«9Ž‹Ñy±è5 ”-¨NÉö„6yŸÎ>TjyÑl´/äÅçú!/–-B^´ã5È‹vò?æ©v?Ñ~ízpû!/šöƸÞ>ßÃV€þ{‚¼h߇ †¼h÷›¯åÁòŽƒKa)/Z{ÇÒž`c…Çcc‰Ç¡×ú@^´û‹­!/–"9òâÓß />ý ò¢å nõ‡áO)*ÍÁJÂ¥U”-e Y@^ 6{â#Ê‹–Î /Z&@ÿ‚¼hv×ÖÖUò&äŲ·J„¼XÖÀ”¥7XîQ$7C^,…ø—ò¢Ù5È‹ö{XZ„åEòK{v{°´G¯ñPiÉŸå³åE;®Ÿ¥=û~³´'ÚÞXÚB]ÈòåC,÷ØñÍÒžRUJÔÕX„ÒžCKçYÚmoC[ñA^<´4›ò¢ò“äÅR$B^T)”äE‹w•úÌë‰_Ê‹û­0è§“›ZS\4-®}¶¶8«¶åC"J¢=¥8Π°898£®8¹<‹²âèªÈñ4<ôòˆŠzQS|L3Ûg+ŠãT±gàÁ„@=q赇Ԩ‰£ê¥9ž}‡ö½‚–8JzåNŸR”Ø…"ˆÕÏÖÇ©-=ïÚê´þÙ*¢iø"‡$ûª !ÚCZå:öó¥pBA\üKq×JA?D˜lùPÏ]ª‡éÙv°N—Úáó½Þ+ÆLÊ¡Æ(§Þ'Ýp²¸²áÜoãqJ=õ²ˆ†6À„&Ø8€»C¹Î~3C-£`¸{ô¡R%È…» jᔘ ±p…R+’ÎZ¸R)Ú„Bá<sõ-ÓC&ÜhP w¤@$T}95B(ÎCÕ3>Þ/ª‚@8¸w/õÁ¡Ò'ȃc«‹p¸ ˆƒCZ´ÁÇYãWT!AÔúB ƒƒëΨ ÉJ‡Td¨‚C>Bz÷4ÁG§bEpÔ$îp†8´œr Mõ>mÅJ¹oòqMµÏÚ8:™8¨Õœùá•úfUŒçMû¬ƒë¢¼É=öü—Ϫ’ŸøOíÐVö0ª[éz¬î·ãð±ŠâPùÌAå³Çr6û1O•χ-Pá0LµU\aG¾rLùc‰rÑŠu¨|{XG•φYT1LUE;U>›ÑÅ óGÅT¾dû0cÛ¦V®˜¦ÊgÃF¨(Pùìú¸c‡©Ûæ05ØÃNìx•χ•8V,?çÇ0µpšG•ϧ³Aå³a'vtƒÊgþqG>;U*ßÖRå³Y+wôÃ0µj‡7¨|ö}T±j¹ª" *Ÿû·ÏVùlŽ\TQcý§v©„ž]ÍžAå+z[U>›ÍNUÔX{\\åB•ÏlT³¹#ð©··@åóÙÿùÙ*ŸÙh/¨|Eï2¡ÊWôª|v~¨ªPùÂq¬‚;µã"T>;ß;òÙçKPùÊyëí6ÀÛ*Ÿ¯„ kïm{{M~T>§ ° LU‡j‡Up6ZšŸ­òq¶¾U>³ÑPùÊV¥A}J»t~¬‚kª8‚Êg³÷¦Šk¯ýy¨|e¿*_Ùª2žC6»‡j •Ïgó°ù8Úþ蟘àØù<Þ\aƒ:4!ë{`êd}s õÍ É¬Yß\A°‘õù 뛫¬oÖ!Y›5>Yϸ¥ÕMºFdÍ5 DÖ¯¢²æ0Èš["ë;nfuóu6DÖÌxDÖ¬!²&î%²æ›¼ˆ¬¹·‘5Ç:DÖ|ª=Èz|6²æ:C"k®Š"²¦ d}³ZÈZµr@Ö7Ç)@Ö7³õ= <Ywî!dÝùô²îeYwî^d­÷ÞYw°¬ûˆ/˜ïŒA ëÁ d=XIdm³Ã€¬‡Ö\YnKd­Âˆ¬ Yk=?õP¹Æ¡!?ð ‘5'DÖƒkɈ¬G{^!_}  ´W4äga †ü—êf0äß[X5ΆkDÖzáõ8Ÿš ùùú›ÎÆr!²Ö™‰¬/Õ·`ÈÏ­,„¬/¡c ù¹‘ÕÅ` ‘µJ%ª†ühI ë]¸d=¸g‘µÍ³qNJÆÞájRË ”ÆêÝî@Ö*! ²¦ÄEd=Y\Lb­½B¬''äÕZÍL\=EA«'÷M¬>$~Vë=‚Õ|çÚ†ÕzO`õù¼#¾þfXÍš´ «/ÖV7öóV£[<°°X°Z XÍ}7¬ÆÝ¬Ö÷ V_z̆Õ-Ô˜ÍwÌV_zï aõöç,€³ü «/ÕÞpÀ}L6¬¾ôžÂj½Ó[°úÄ”,Àêñ‚ÕP¶« „Õ‡öu¬Ì¬nª}!¬–€%X͵ÊVß¼?‚ÕÜg]°ÚRŒž°úèÚ· °ú ™¬>øŽVÁê½aõ1õžœ&±ð\°ú |¬.ú>ΪÞ9YÀ†å„ÕE)‹°º¨–J°z¿‡†°Zïý¬æ¾V7½ç†°ºéò„ÕœËnX}©ö…°ZJÖ«Ñ‚Õz/`µ2°`5ßë²aõ©ÚÌŠj «Ã;âOˆÜ÷jÃjüÂêÂÂWÁjÕ VQÁj½÷°ºèÆ„ÕEµ ¬ÆÚgÂêºmÁê¢}F«§`ö­þCøYÀÁQ¸jaÔU spÔ«Z˜ãÔ;Ú1 Pížjað³f­¥â`ÖS»ž€YÛÈ3ÂÌýNŸLZȬç¥S â…;0ëÉû@f=µäÌÚH:0t%—ÄËIȬ…)ɬµµ˜õ誮³¾H}>Hf} ޲'pX2k½ƒÌZ504˜5WµŠY7½Üý$—$³Ö÷‚Y½}Ìzh32kíÝCf-*MfÝ Ö7³ÞKaÁAdÖC¥Z`ÖC÷ÌÚ†›Y¡ºϵC[ä€YUmf}†"— ©™YOò_0kØ‚rÉ32ë)¢í)vhç²Í¬ÉûØ1˜µõœ°­Ô<„°µâ˜ÌZudÖ Í¬{dÖ|Ë$™µ94cÍkñŬ¹Â^ÌZ›[‘YOÖ³‘Y‹þ“Ykg72k%2ë.°>Ù7àLf­®Òµæ¢„B–¡º-2ëýºx0kU‘‘YsÃW1kõç®5h2k>uŬ[ƒÌZïš{ëÎdÖÁì“YïwÂcÛD´å~VÌô3ë¢=¤ÆÂø,°|ȘµÎLfÝØ8›YóÕìbÖwdÖûÅï>ñwÓ䧺™7n™hh`Ö6õ ©ÛJ’Yk`2k…‘õ˜Y"É@ÖâDÖ…EDÖ•5DÖZòNd-ÖCdMMñAÖ3"몭›€¬)O?Șf#k¾ÓÈZ¯ÅØÈºEd½!õ‰œƒXkËbë©·…ƒWëeÄÕSï­ž„‚ÕÇ!xÌaª^Ÿ"X½á2`õ!˜V÷0%F„Õë±Í˜½¦°°úÐö5„ÕÚþuÃêýzÂêƒ%:ç^SF&¬.Ü¢aÃjÁ~Áj-ù¬æ”S°ZÃ:Áê¢%ò„ÕVò§”0~oÕׇàô ÿ)xíÃ2®Ù°š# ÁêeÆV—ýjvS‡JH«m¾°ºL•Äp˜ÊíE7¬>^°úÐõVn§EXmÝ ”¤”ª×VWmïCX]µÝauUIauÕ놫m„Žaê¹__CX­íá«O£«Ï¦W±V«óVŸÚ~Œ°ú¤Æ*X}ª¤A°ºs»+ÁjnŸ½aµú³`õ>NX}ëu6„Õ7·£¬VI‹`µú`õMØ.X}«…°šYsÃjÁWÁj>6¬n*Y!¬Öý¬æ´hÃjŽæ6¬néõ1ÞހÄÕjoÁj®¹¬Þ¿—°ÚXlÀêsFV_‡JPn‰#låtB¶‹œ– V_íyU»õŸK÷›°úbɶ`õÅí «/n Xݸ›`õ~}aµ•„¡¤Ø éõf„Õzý´`µ Þ¬ýÿb%)Xè½+RKŠ¿¨Ä"Xrûtòç]E€ûûù~ÜŸ9°Âa,«Ña.²yãñ¢Ã|Ø„¥÷Å+²v- Ìèpúër·Ìè`7ðys Í´þßÑSOóüÀ¿L-ËíE­ÿª÷ÒL  ¦þÖ*d^|øþ­nž^%4«»VòüûÈ'ìfïÇMÝÌZ1§…—hùaŸØîðÂa±íðÒáÕcÂa³ÂaO*û0¬pØsÌ> +î#îùÒ<íðÂaŸfïðÒá~ÄÃ=_š§³}V:"\äõ†^1Ñ’Ã+p®dF‡df‡8×øVññ"ßpÀ+5ZrxΕà€Ìèà€Ììçzïøˆù†¶á| œë üíž39¼çJp@fph ÈÌ1pÚxíI¸Èö†ö—8í ü£á"ÛXK œö†^áR’Ã+pZ‚2£C‚2³C œö†V)§½á€og/ò |¿÷–^Ó™bà´7ðšp‘÷xOK¯À¹™bàÜo8àµBñ"ßpÀ«‡âE¾á€ý%Îý†^a/ò ìe1pî7àÛ¢Ã+pîdf‡8÷xeS¼È7ð·Ç‹|Ãz'8 38ôdF‡df‡8ý ¬Æ*Nïº ÙßpÀë°ZrxNOp@ftHp@fvˆÓßpÀ«½âE¾á€×µä𠜞à€Ìèà€Ìì§¿á@í È #Ãñ Œ Æ7802ßàÀÈp`|ƒ#Ãñ Œ Æ7802ßàÀÈp`|ƒ#Ãñ Œ Æ7802ßàÀÈp`|ƒ#Ãñ Œ Æ7802ßàÀÈp`|ƒ3Ãù Ì æ7803˜ßàÀÌp`~ƒ3Ãù Ì æ780ÓÊ™Ù! 2£CZ9 3:¤•2³C@eF‡´r@ftH+df‡~%‡þºÈ´r@æãÀêH9ÈÌaå€ÌèVÈÌqýÿñ^9piå€ÌèVÈÌãNãu‘iå€ÌèVÈÌ}$‡þºÈ´r@ftèqå€ÌìÐäÐ_™VÈÌs$‡ùºÈ´r@fp(iå€ÌìGftH+dF‡´r@fv#3:¤•2£CZ9 3;ô+9ô×E¦J™Ñ!ÕÈÌ1pÊ»€µ·Ã» €Õ¸Ñá8%ÕÈŒ©*@fvˆSÞ…gM•2£Cª ™bàÔwy …‡w+‡‡w‰K‰£Ã+pjª™bàÔw¡«“‡w©Ë•‡w±ë—£Ã+pjªRÅ€Ìì§¾‹Κªd‡3Õ ÈÌ1pÎwé˪‡wñ묇wù ¯£Ã+pÎTA 3;ÄÀ9ßE¬å~Þe,î~Þ…¬öޝÀ9S-Ìèª df‡8ç» à&¿|[Y»îëû$ý7Ä0_{]Iÿõ+ùá z}‡á/èý›_xA/ìÿÃûyugòëzp÷£߯oÿó²ÞÜÿèÁwñÊ#¿ªyk!ìUôë{ áø¯o.„ã?Ø]ˆ›!ý`{!zü`!zü`ƒ!m¹ôì0ôw?ý?™r) endstream endobj 679 0 obj << /Alternate /DeviceRGB /N 3 /Length 2596 /Filter /FlateDecode >> stream xœ–wTSهϽ7½P’Š”ÐkhRH ½H‘.*1 JÀ"6DTpDQ‘¦2(à€£C‘±"Š…Q±ëDÔqp–Id­ß¼yïÍ›ß÷~kŸ½ÏÝgï}ÖºüƒÂLX € ¡Xáçň‹g` ðlàp³³BøF™|ØŒl™ø½º ùû*Ó?ŒÁÿŸ”¹Y"1P˜ŒçòøÙ\É8=Wœ%·Oɘ¶4MÎ0JÎ"Y‚2V“sò,[|ö™e9ó2„<ËsÎâeðäÜ'ã9¾Œ‘`çø¹2¾&cƒtI†@Æoä±|N6(’Ü.æsSdl-c’(2‚-ãyàHÉ_ðÒ/XÌÏËÅÎÌZ.$§ˆ&\S†“‹áÏÏMç‹ÅÌ07#â1Ø™YárfÏüYym²";Ø8980m-m¾(Ô]ü›’÷v–^„îDøÃöW~™ °¦eµÙú‡mi]ëP»ý‡Í`/в¾u}qº|^RÄâ,g+«ÜÜ\KŸk)/èïúŸC_|ÏR¾Ýïåaxó“8’t1C^7nfz¦DÄÈÎâpù 柇øþuü$¾ˆ/”ED˦L L–µ[Ȉ™B†@øŸšøÃþ¤Ù¹–‰ÚøЖX¥!@~(* {d+Ðï} ÆGù͋љ˜ûÏ‚þ}W¸LþÈ$ŽcGD2¸QÎìšüZ4 E@ê@èÀ¶À¸àA(ˆq`1à‚D €µ ”‚­`'¨u 4ƒ6ptcà48.Ë`ÜR0ž€)ð Ì@„…ÈR‡t CȲ…XäCP”%CBH@ë R¨ª†ê¡fè[è(tº C· Qhúz#0 ¦ÁZ°l³`O8Ž„ÁÉð28.‚·À•p|î„O×àX ?§€:¢‹0ÂFB‘x$ !«¤i@Ú¤¹ŠH‘§È[EE1PL” Ê…⢖¡V¡6£ªQP¨>ÔUÔ(j õMFk¢ÍÑÎèt,:‹.FW ›Ðè³èô8úƒ¡cŒ1ŽL&³³³ÓŽ9…ÆŒa¦±X¬:ÖëŠ År°bl1¶ {{{;Ž}ƒ#âtp¶8_\¡8áú"ãEy‹.,ÖXœ¾øøÅ%œ%Gщ1‰-‰ï9¡œÎôÒ€¥µK§¸lî.îžoo’ïÊ/çO$¹&•'=JvMÞž<™âžR‘òTÀT ž§ú§Ö¥¾N MÛŸö)=&½=—‘˜qTH¦ û2µ3ó2‡³Ì³Š³¤Ëœ—í\6% 5eCÙ‹²»Å4ÙÏÔ€ÄD²^2šã–S“ó&7:÷Hžrž0o`¹ÙòMË'ò}ó¿^ZÁ]Ñ[ [°¶`t¥çÊúUЪ¥«zWë¯.Z=¾Æo͵„µik(´.,/|¹.f]O‘VÑš¢±õ~ë[‹ŠEÅ76¸l¨ÛˆÚ(Ø8¸iMKx%K­K+Jßoæn¾ø•ÍW•_}Ú’´e°Ì¡lÏVÌVáÖëÛÜ·(W.Ï/Û²½scGÉŽ—;—ì¼PaWQ·‹°K²KZ\Ù]ePµµê}uJõHWM{­fí¦Ú×»y»¯ìñØÓV§UWZ÷n¯`ïÍz¿úΣ†Š}˜}9û6F7öÍúº¹I£©´éÃ~á~éˆ}ÍŽÍÍ-š-e­p«¤uò`ÂÁËßxÓÝÆl«o§·—‡$‡›øíõÃA‡{°Ž´}gø]mµ£¤ê\Þ9Õ•Ò%íŽë>x´·Ç¥§ã{Ëï÷Ó=Vs\åx٠‰¢ŸN柜>•uêééäÓc½Kz=s­/¼oðlÐÙóç|Ïé÷ì?yÞõü± ÎŽ^d]ìºäp©sÀ~ ãû:;‡‡º/;]îž7|âŠû•ÓW½¯ž»píÒÈü‘áëQ×oÞH¸!½É»ùèVú­ç·snÏÜYs}·äžÒ½Šûš÷~4ý±]ê =>ê=:ð`Áƒ;cܱ'?eÿô~¼è!ùaÅ„ÎDó#ÛGÇ&}'/?^øxüIÖ“™§Å?+ÿ\ûÌäÙw¿xü20;5þ\ôüÓ¯›_¨¿ØÿÒîeïtØôýW¯f^—¼Qsà-ëmÿ»˜w3¹ï±ï+?˜~èùôñî§ŒOŸ~÷„óû endstream endobj 682 0 obj << /Length 19 /Filter /FlateDecode >> stream xÚ3PHW0Ppç2ÀAc(á endstream endobj 686 0 obj << /Length 19 /Filter /FlateDecode >> stream xÚ3PHW0Ppç2ÀAc(á endstream endobj 690 0 obj << /Length 518 /Filter /FlateDecode >> stream xÚmTMÓ0½÷WøhK$ëï4W‹Ä•Ü€ƒIœ®!µ«$]è¿Çc;%‹zò|¼ñ{3c™¢¢èÓ–ó}wxzV 1Y ©9êFĸ¬”£†êšñuúŠR1F)þ|?]G~!ß»ÏOÏ¢AmÝj®¡š¢J²º‘2×u/6ƒ”Ø8­¥Ð›0—)¬nŠ2X{Ü@ãÕ÷« žT‚·ØÁ)(^^¯›Z7[áêÎö‹}¤V©Z5â.Äô„ñ/¢6'[¨–Le²û ;ß²w!UtÄ~ÞÄÄxrj 9·sáš~¶fM¢QÅ¢ÐcÓDƒÕ­Rÿ¦)%xœÃ9[ÐD¶–ÜJ²ÃRE©?m פÒJÄ™»p°¬aƒƒ8·\&šo9põî•(Íì@[Š?dã }]§ÕUI4}«ö¿:}WªwJUÚWŠ9šJ¾0(`8&† 4‘Ó*.ÃÛiy.‹t…S›vŠ g›Pä-;€mdª¹MY‚Æc˜5/¨âJÂ+óFnD§Óæ…û å4nÚå ,žg©N½YÏfÝ¢}æ!Ž`™Ä/ÐÔ7õQè¼µCÁ^¶Î²¢¼+‰Ï鲸ÛoTÈ[ŽïÃjŠ,ß«•ì3aÌ%éõÕÀxÿ"Ó:>S…*ÅáG( ”|ƒúØþ•&# endstream endobj 694 0 obj << /Length 19 /Filter /FlateDecode >> stream xÚ3PHW0Ppç2ÀAc(á endstream endobj 578 0 obj << /Type /ObjStm /N 100 /First 884 /Length 1522 /Filter /FlateDecode >> stream xÚåYßoÛ6~×_ÁÇæEü}$‡ Àš,[¶mu ú ÊJªÍ‘3[.²ÿ~ßÉéE©cE ÌSwGòîã}GRö„>ha ‰„/4}0Âjž¢°„稄‹ßÈCduײ¤DÔ.óèŸäÉ­ :$‹†ãžQh”H^h«= ÂÔ§ IAho¢ ¥…&«ÐÀ0Á‡Œ†‰Ž% î•’0 Óó7„F¡`|"„bX„± 6β'À3ÀÆ›‘†„% ã1é(L„1iôJ FN–UžQáЂ°ÿj‹Ð`‚¶Î¨Œˆõ*“€¡cÀ”°Áš9¸· þh­…S“š€P'<8 ïÈ:á ÏŽ‚ˆ™†ÎFVÁØ1`p×¹À’$@XG‰UQ¸à ÂÇ‚‡Ý#}è’FìΠáSF. ¯fo…דÇÒ“ÇYöÙb¸Uð‰—Æ<?:bëE–¾Û£šiDMèßQzÆÁ ¡ rÜä¼GŒ¯ =~|)·Ô>͸I|IqÀ—DÅÚÂz4|áSÙ/¤ü¾ñ%…kûmÆ[ùÒ3îöP?/[)Âçr?…"¤áSüŠV7¶M¾!ì\Šnovdu¹/ Ú Á-äþ€l­aKͧfu[Í0zÏj_kw®ãm5£o|î"ß²UòCî±|IÔ“øbü€/Æî9_¬¾/Öì_Œ±ôïàKÏ{¬´ÇîÎ~…d&ñÅùbíÄ ×ºuÉŽÁ¹g\npP4¤r~ù· νWÛ¶êSCœãTœw}ïrÃá»\9œ™èª³U9;þ ¦±–ô@g°ë[W0 oþo{ЧÛöömOqqÄžÒ7¾£ÖõŒù=Î1Fã –¾û½- ~Û>…"4xÝÉoóˆ"i EÒã¡Èæ òMŠPÜ7ŠQûoo¥HÏ8úͱ˘1‡ »ÈÎ7þAþúÇ»ûò%ù§ð…шzÇR4îÄi誛êªÃ}ÞCbœêjQàcz\‡û4¼í¤q·D7IvÂÚý±­pm endstream endobj 699 0 obj << /Length 466 /Filter /FlateDecode >> stream xÚmS=Û0 Ýó+4ÊÀE§K–ÇÐ+Úñ CÑAq䨍’Ò´ýõ5M9H7‘~|$)š“#áäˆgû²Û<¿jADÉTi$ÙµDÈ’Y.IÅ ²&»ùAm±‚súišŠ­âÔŸýŸâçîÛó«ªHÍj# $s²-«ÊÓ¾žg¶®iê<:òe•óèZHKs¸sðñ» –ÆËéäBÿÏq nêb.vÎ`rûÁGL¸ÎJi×7P¨Ë¼‹]¢o/CÎg!–ŽC„1@¸¬Ö…C´4š6Á»äÑŸ†1EVl­ªé÷Â*šñ›h»"ÁO¡?@7@ËÀ9øÖ]†”ËÃ2f@· ã ½·'´×»âí>~¿1Yq=rƒÈM¥©é䟬nµØ@êúe!ŒG3; ¢W8µ1ëÍþµOVks Äñ¬\pMò!"ãV½ø÷Äýzb f¸ýtDÃl¥ÉV+˸•8Œ6¬Ï»Í¾ÎòB endstream endobj 704 0 obj << /Length 1042 /Filter /FlateDecode >> stream xÚ½X[oÛ6~ϯ´‡Ú¨Å’”¨Kh¦@ÑvÅ Ö=0‘l«•-ƒV§¿~‡")ÉŽ.ö°ìI”CžïûÎM‡ÁÖÊÂÖû+¬Ÿo“«W7,°AcÔJ–¡ 1µì#B#+I­¿f!"s‡ÊfŸ¸XåÛýõÜaÏ6\Ì7`3¾MÕ¢ÜÌÉŒÏÿN>€afE(ò©/íbËqCäWY| ÀÜ/êñ¥(«kyHnSdú¶9ê¡1êõ¾è½Z/K¡£0Á ˵Å=Ü¢ Toøõ¾Ê„2¨ÄªõÀïCFl̾C±‹"—imþˆ¶ —8j-²ÈöÙ¶ÒúªuÖ§ÏÅ( áÙú–ùê^d]û«‹(&DEˆøÞKÖ JC¦Ù’ß•Ž^þSÿœkw_1ÃláÁ…· ðFÔ^ª†L<7Oº°ü/æw߯ÅwýãC^råÍV¥&´m®²+{M»È·˜Þ0Ùq!•l–¢|ˆkQd¡ˆÔTëš<]n¸:ë.Ü£9ìøÝ抽thªtz³5ÿ¡™»-k-·\êí;~—¡‚‹ØFòÌQlj¾ƒ"—F1¹&X{ã±}_?î²ØÞÚúõPðÛØþÃ6[ë×?í ßi—¼ISu,É:lUYÄTö½â¢RûS|éHF•C˜fgK[†ù]YÄ6XhÞ³CL'x·&=cò…;׫‘êE­~Dù„=cû§ò¶;¸; þ߯Ü4h5ÝÙÓý«wÕúr^Ùñh$' ªw6‚íë\¤ÙH §TUîÔv3~4Y ¾[£±£äS Lj^;ç5èÒógŸËªoœö„}zö4]ÊÛBœüöû»[Œ"Þ<Fkf÷MùÃLh÷æN#à¶£}Ü ¢íßùjj§C&µ˜úò)Ï‚/ÂÞDî6×ïøúwŸ£\&M.âé‡ …Ýü½…xkR²uØ¥ŸÔ²¾µqg`Ö”¸÷´¨JÁ·«ÌT¬ÀüEĆÚ+j¯ø´Îñ±§º¤F]å_ØsÉPÏÅ£çÔI°ùâè ub2Ô‰Ÿ‹’i‘µ[:¤¶iÎ7ÿyuãÝbõ}òR²@YgÁÑ™wÉÕ?ÍÔLr endstream endobj 710 0 obj << /Length 127 /Filter /FlateDecode >> stream xÚuŒ½ A „û<Å”Z\n“]w7­øƒvB:±R´:Ä÷o\ÔSd 3L’/à‚€5…?.m$ [5ÄlœTqèF\$?>鱿^E¿¢bq¥]Ó/¹ÑÝ{îÔ¯b±eÍð3Ô„siMà+ü„ýdV§ß¾‰K§;&–(Æ endstream endobj 707 0 obj << /Type /XObject /Subtype /Form /FormType 1 /PTEX.FileName (/tmp/RtmpBlhU1W/Rbuild4444671013800/timeSeries/vignettes/timeSeriesPlot-marginsPlot.pdf) /PTEX.PageNumber 1 /PTEX.InfoDict 713 0 R /BBox [0 0 432 432] /Resources << /ProcSet [ /PDF /Text ] /Font << /F2 714 0 R/F3 715 0 R>> /ExtGState << >>/ColorSpace << /sRGB 716 0 R >>>> /Length 833 /Filter /FlateDecode >> stream xœ­WÛnÚ@}÷WŒòj2Ùûå!MÔVŠz¥¶ y@„6D†´@Õ~~gñ¯Mp°k†œÏž™9ëp¸÷ð+ûDŸÓÕç7çpq•1dŒAº^]¼§_­†?Ùõ 0¸Í8\Òç>ãï2.rœ4 æ •Gf+»¸ÚT6ó¨*ˆgèT‰vJ¢ö5¤²Sˆ7(e‰vi¦W§›@ÎóÈÈdõ#«É";}-ˆ¾ü;pñåRÚ\*´h V@>‡B~Ÿ½Ê7¡Ÿðu5O|Õá¾B1:ñ5=|½@ï_w¸¯TeêËYåÌ™@AuT• u-m)8:Kß‚xlk6tÉkDi¦AÄó:F4ëÕ1¢™"bj"Í4e"ÙþIŠSè %/‘w”¾ÛÙôní;…¤¹‘û‹ßíl·¿úÎ’kä~Oùã´eR;Ôf+•-…GK…ªkí;b ˆOÁâ*•F³½,§ðÿ?³BP¿10}Y¸¯=Ð&ô™à>䜿UÎ!ù& ‚ÀRxÓ"¤Ï»Óª™~isR:MÁ™D[ácñ°î!;Ô7Tú >´ò¿¬§C ×£>ÖÇpBÃä¡8;ZNoÃ× s&FÃá ä—qÓõ™²»p¦pÔ¬ãLy†¶iv„UÒµÙ®E‰+)u¡#$µ ÒwãåÙbÕƒU#QZjU¤$„ø9^ŽóñòlHUô&âà9q"ðpÅ:¨’”"¿-¦ÀzDOSƒðâýs¨ùÙäÐ㬫"XÎúmÙcò¼GI“çm8Ä»yè–¾V„GyØÁ²ÐŒí}„?êG+–‡ÝöÝ},K’Æ9t•YTf¼¼5ŸUee¬xK¦J›Û²ðZ…•ÌäëÙº˜Â äw³å ·öß“æ²abŒŽkS [½VÚœzMSnEyÂ}ø½ž.¡ix¹œŽ“zUšUÞçЧ>#Б°£sõ¨?ÌÇaÔåñæ=îrÖ ¸Yc!xu&÷ñÒ¿ÞñþÆîòßî§ÙÕ;ú^sõôÿ õæÔ‹õ͋ʊ—"c kÓ¨Ù?ÁËž endstream endobj 718 0 obj << /Alternate /DeviceRGB /N 3 /Length 2596 /Filter /FlateDecode >> stream xœ–wTSهϽ7½P’Š”ÐkhRH ½H‘.*1 JÀ"6DTpDQ‘¦2(à€£C‘±"Š…Q±ëDÔqp–Id­ß¼yïÍ›ß÷~kŸ½ÏÝgï}ÖºüƒÂLX € ¡Xáçň‹g` ðlàp³³BøF™|ØŒl™ø½º ùû*Ó?ŒÁÿŸ”¹Y"1P˜ŒçòøÙ\É8=Wœ%·Oɘ¶4MÎ0JÎ"Y‚2V“sò,[|ö™e9ó2„<ËsÎâeðäÜ'ã9¾Œ‘`çø¹2¾&cƒtI†@Æoä±|N6(’Ü.æsSdl-c’(2‚-ãyàHÉ_ðÒ/XÌÏËÅÎÌZ.$§ˆ&\S†“‹áÏÏMç‹ÅÌ07#â1Ø™YárfÏüYym²";Ø8980m-m¾(Ô]ü›’÷v–^„îDøÃöW~™ °¦eµÙú‡mi]ëP»ý‡Í`/в¾u}qº|^RÄâ,g+«ÜÜ\KŸk)/èïúŸC_|ÏR¾Ýïåaxó“8’t1C^7nfz¦DÄÈÎâpù 柇øþuü$¾ˆ/”ED˦L L–µ[Ȉ™B†@øŸšøÃþ¤Ù¹–‰ÚøЖX¥!@~(* {d+Ðï} ÆGù͋љ˜ûÏ‚þ}W¸LþÈ$ŽcGD2¸QÎìšüZ4 E@ê@èÀ¶À¸àA(ˆq`1à‚D €µ ”‚­`'¨u 4ƒ6ptcà48.Ë`ÜR0ž€)ð Ì@„…ÈR‡t CȲ…XäCP”%CBH@ë R¨ª†ê¡fè[è(tº C· Qhúz#0 ¦ÁZ°l³`O8Ž„ÁÉð28.‚·À•p|î„O×àX ?§€:¢‹0ÂFB‘x$ !«¤i@Ú¤¹ŠH‘§È[EE1PL” Ê…⢖¡V¡6£ªQP¨>ÔUÔ(j õMFk¢ÍÑÎèt,:‹.FW ›Ðè³èô8úƒ¡cŒ1ŽL&³³³ÓŽ9…ÆŒa¦±X¬:ÖëŠ År°bl1¶ {{{;Ž}ƒ#âtp¶8_\¡8áú"ãEy‹.,ÖXœ¾øøÅ%œ%Gщ1‰-‰ï9¡œÎôÒ€¥µK§¸lî.îžoo’ïÊ/çO$¹&•'=JvMÞž<™âžR‘òTÀT ž§ú§Ö¥¾N MÛŸö)=&½=—‘˜qTH¦ û2µ3ó2‡³Ì³Š³¤Ëœ—í\6% 5eCÙ‹²»Å4ÙÏÔ€ÄD²^2šã–S“ó&7:÷Hžrž0o`¹ÙòMË'ò}ó¿^ZÁ]Ñ[ [°¶`t¥çÊúUЪ¥«zWë¯.Z=¾Æo͵„µik(´.,/|¹.f]O‘VÑš¢±õ~ë[‹ŠEÅ76¸l¨ÛˆÚ(Ø8¸iMKx%K­K+Jßoæn¾ø•ÍW•_}Ú’´e°Ì¡lÏVÌVáÖëÛÜ·(W.Ï/Û²½scGÉŽ—;—ì¼PaWQ·‹°K²KZ\Ù]ePµµê}uJõHWM{­fí¦Ú×»y»¯ìñØÓV§UWZ÷n¯`ïÍz¿úΣ†Š}˜}9û6F7öÍúº¹I£©´éÃ~á~éˆ}ÍŽÍÍ-š-e­p«¤uò`ÂÁËßxÓÝÆl«o§·—‡$‡›øíõÃA‡{°Ž´}gø]mµ£¤ê\Þ9Õ•Ò%íŽë>x´·Ç¥§ã{Ëï÷Ó=Vs\åx٠‰¢ŸN柜>•uêééäÓc½Kz=s­/¼oðlÐÙóç|Ïé÷ì?yÞõü± ÎŽ^d]ìºäp©sÀ~ ãû:;‡‡º/;]îž7|âŠû•ÓW½¯ž»píÒÈü‘áëQ×oÞH¸!½É»ùèVú­ç·snÏÜYs}·äžÒ½Šûš÷~4ý±]ê =>ê=:ð`Áƒ;cܱ'?eÿô~¼è!ùaÅ„ÎDó#ÛGÇ&}'/?^øxüIÖ“™§Å?+ÿ\ûÌäÙw¿xü20;5þ\ôüÓ¯›_¨¿ØÿÒîeïtØôýW¯f^—¼Qsà-ëmÿ»˜w3¹ï±ï+?˜~èùôñî§ŒOŸ~÷„óû endstream endobj 722 0 obj << /Length 178 /Filter /FlateDecode >> stream xÚ]»‚@EûýŠ)ÙbÇ™}²­Ïhg²±@D-$FâÿGD „Lq'wž‡à;A½.“Xl]fŒÎiH7`m1' <²Ž®pÊrÔR1k—­ES”Ÿª‘Ê—%mV\ž•<§Ã¸v“· 댵Hl ¬Å[`ˆÎ‡®c’v¥a®7ûÚX¿Ä± š}?×aV GÕäj‡jDŒ^û©ŽŒ>´¡5ùÔÅ9Èà ?k endstream endobj 719 0 obj << /Type /XObject /Subtype /Form /FormType 1 /PTEX.FileName (/tmp/RtmpBlhU1W/Rbuild4444671013800/timeSeries/vignettes/characterTableFig.pdf) /PTEX.PageNumber 1 /PTEX.InfoDict 724 0 R /BBox [0 0 432 432] /Resources << /ProcSet [ /PDF /Text ] /Font << /F3 725 0 R/F4 726 0 R/F6 727 0 R>> /ExtGState << >>>> /Length 29175 >> stream q Q q BT 0.000 0.000 0.000 rg /F3 1 Tf 14.00 0.00 -0.00 14.00 172.98 397.45 Tm (Table of Characters) Tj ET Q q 73.44 73.44 328.32 299.52 re W n BT 0.000 0.000 0.000 rg /F6 1 Tf 8.00 0.00 -0.00 8.00 122.60 342.74 Tm ( ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 122.60 342.74 Tm ( ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 160.27 340.12 Tm (!) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 160.27 340.12 Tm (!) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 196.75 339.92 Tm (") Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 196.75 339.92 Tm (") Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.60 340.11 Tm (#) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.60 340.11 Tm (#) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.40 339.91 Tm ($) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.40 339.91 Tm ($) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.27 340.26 Tm (%) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.27 340.26 Tm (%) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 348.49 340.17 Tm (&) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 348.49 340.17 Tm (&) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.84 340.81 Tm (') Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.84 340.81 Tm (') Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 122.27 331.25 Tm (\() Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 122.27 331.25 Tm (\() Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 160.27 331.25 Tm (\)) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 160.27 331.25 Tm (\)) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.60 330.44 Tm (*) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.60 330.44 Tm (*) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.40 331.05 Tm (+) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.40 331.05 Tm (+) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 274.60 333.37 Tm (,) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 274.60 333.37 Tm (,) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.40 331.09 Tm (-) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.40 331.09 Tm (-) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 350.60 332.87 Tm (.) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 350.60 332.87 Tm (.) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 388.49 330.67 Tm (/) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 388.49 330.67 Tm (/) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.60 320.93 Tm (0) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.60 320.93 Tm (0) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 159.60 320.92 Tm (1) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 159.60 320.92 Tm (1) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.60 320.87 Tm (2) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.60 320.87 Tm (2) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.60 320.93 Tm (3) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.60 320.93 Tm (3) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.60 320.87 Tm (4) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.60 320.87 Tm (4) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.60 320.91 Tm (5) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.60 320.91 Tm (5) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.60 320.93 Tm (6) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.60 320.93 Tm (6) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.60 320.99 Tm (7) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.60 320.99 Tm (7) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.60 311.37 Tm (8) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.60 311.37 Tm (8) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 159.60 311.38 Tm (9) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 159.60 311.38 Tm (9) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 198.49 312.28 Tm (:) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 198.49 312.28 Tm (:) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 236.49 312.82 Tm (;) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 236.49 312.82 Tm (;) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.40 311.96 Tm (<) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.40 311.96 Tm (<) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.40 311.93 Tm (=) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.40 311.93 Tm (=) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.40 311.96 Tm (>) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.40 311.96 Tm (>) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.82 311.37 Tm (?) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.82 311.37 Tm (?) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.40 302.59 Tm (@) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.40 302.59 Tm (@) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 158.71 301.80 Tm (A) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 158.71 301.80 Tm (A) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 196.93 301.80 Tm (B) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 196.93 301.80 Tm (B) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 234.71 301.80 Tm (C) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 234.71 301.80 Tm (C) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.15 301.74 Tm (D) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.15 301.74 Tm (D) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.16 301.80 Tm (E) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.16 301.80 Tm (E) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 348.55 301.80 Tm (F) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 348.55 301.80 Tm (F) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.19 301.80 Tm (G) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.19 301.80 Tm (G) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 120.71 292.23 Tm (H) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 120.71 292.23 Tm (H) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 160.27 292.23 Tm (I) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 160.27 292.23 Tm (I) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.08 292.24 Tm (J) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.08 292.24 Tm (J) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 234.71 292.23 Tm (K) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 234.71 292.23 Tm (K) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 272.86 292.17 Tm (L) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 272.86 292.17 Tm (L) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.04 292.23 Tm (M) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.04 292.23 Tm (M) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 348.71 292.26 Tm (N) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 348.71 292.26 Tm (N) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 386.71 292.25 Tm (O) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 386.71 292.25 Tm (O) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 120.53 282.67 Tm (P) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 120.53 282.67 Tm (P) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 158.64 282.69 Tm (Q) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 158.64 282.69 Tm (Q) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.38 282.67 Tm (R) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.38 282.67 Tm (R) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.23 282.67 Tm (S) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.23 282.67 Tm (S) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.16 282.67 Tm (T) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.16 282.67 Tm (T) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.84 282.67 Tm (U) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.84 282.67 Tm (U) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.84 284.29 Tm (V) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.84 284.29 Tm (V) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 386.53 282.61 Tm (W) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 386.53 282.61 Tm (W) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.02 273.11 Tm (X) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.02 273.11 Tm (X) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 158.42 273.06 Tm (Y) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 158.42 273.06 Tm (Y) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.16 273.11 Tm (Z) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.16 273.11 Tm (Z) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 236.27 273.72 Tm ([) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 236.27 273.72 Tm ([) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 272.15 273.85 Tm (\\) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 272.15 273.85 Tm (\\) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 312.27 273.72 Tm (]) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 312.27 273.72 Tm (]) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 348.97 273.10 Tm (^) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 348.97 273.10 Tm (^) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.60 276.60 Tm (_) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.60 276.60 Tm (_) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.60 259.04 Tm (`) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.60 259.04 Tm (`) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 159.08 264.31 Tm (a) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 159.08 264.31 Tm (a) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.40 264.16 Tm (b) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.40 264.16 Tm (b) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.40 265.16 Tm (c) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.40 265.16 Tm (c) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.62 263.35 Tm (d) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.62 263.35 Tm (d) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.84 264.30 Tm (e) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.84 264.30 Tm (e) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.52 264.44 Tm (f) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.52 264.44 Tm (f) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.96 265.14 Tm (g) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.96 265.14 Tm (g) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.19 255.42 Tm (h) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.19 255.42 Tm (h) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 160.28 254.73 Tm (i) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 160.28 254.73 Tm (i) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.19 255.57 Tm (j) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.19 255.57 Tm (j) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.40 254.67 Tm (k) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.40 254.67 Tm (k) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.40 253.78 Tm (l) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.40 253.78 Tm (l) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.30 255.56 Tm (m) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.30 255.56 Tm (m) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.52 254.71 Tm (n) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.52 254.71 Tm (n) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.40 254.75 Tm (o) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.40 254.75 Tm (o) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.40 245.24 Tm (p) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.40 245.24 Tm (p) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 159.52 244.42 Tm (q) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 159.52 244.42 Tm (q) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.40 246.03 Tm (r) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.40 246.03 Tm (r) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.19 245.19 Tm (s) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.19 245.19 Tm (s) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.84 245.18 Tm (t) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.84 245.18 Tm (t) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.30 245.15 Tm (u) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.30 245.15 Tm (u) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 348.75 244.85 Tm (v) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 348.75 244.85 Tm (v) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 386.86 245.18 Tm (w) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 386.86 245.18 Tm (w) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.63 235.38 Tm (x) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.63 235.38 Tm (x) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 158.86 236.46 Tm (y) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 158.86 236.46 Tm (y) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.62 235.42 Tm (z) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.62 235.42 Tm (z) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.68 235.58 Tm ({) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.68 235.58 Tm ({) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 274.80 235.89 Tm (|) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 274.80 235.89 Tm (|) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.68 235.58 Tm (}) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.68 235.58 Tm (}) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.40 235.50 Tm (~) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.40 235.50 Tm (~) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 389.60 237.54 Tm () Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 389.60 237.54 Tm () Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 123.60 227.98 Tm (€) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 123.60 227.98 Tm (€) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 161.60 227.98 Tm () Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 161.60 227.98 Tm () Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 199.60 227.98 Tm (‚) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 199.60 227.98 Tm (‚) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 237.60 227.98 Tm (ƒ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 237.60 227.98 Tm (ƒ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 275.60 227.98 Tm („) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 275.60 227.98 Tm („) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 313.60 227.98 Tm (…) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 313.60 227.98 Tm (…) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 351.60 227.98 Tm (†) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 351.60 227.98 Tm (†) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 389.60 227.98 Tm (‡) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 389.60 227.98 Tm (‡) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 123.60 218.42 Tm (ˆ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 123.60 218.42 Tm (ˆ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 161.60 218.42 Tm (‰) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 161.60 218.42 Tm (‰) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 199.60 218.42 Tm (Š) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 199.60 218.42 Tm (Š) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 237.60 218.42 Tm (‹) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 237.60 218.42 Tm (‹) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 275.60 218.42 Tm (Œ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 275.60 218.42 Tm (Œ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 313.60 218.42 Tm () Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 313.60 218.42 Tm () Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 351.60 218.42 Tm (Ž) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 351.60 218.42 Tm (Ž) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 389.60 218.42 Tm () Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 389.60 218.42 Tm () Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 123.60 208.86 Tm () Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 123.60 208.86 Tm () Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 161.60 208.86 Tm (‘) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 161.60 208.86 Tm (‘) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 199.60 208.86 Tm (’) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 199.60 208.86 Tm (’) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 237.60 208.86 Tm (“) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 237.60 208.86 Tm (“) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 275.60 208.86 Tm (”) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 275.60 208.86 Tm (”) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 313.60 208.86 Tm (•) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 313.60 208.86 Tm (•) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 351.60 208.86 Tm (–) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 351.60 208.86 Tm (–) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 389.60 208.86 Tm (—) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 389.60 208.86 Tm (—) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 123.60 199.29 Tm (˜) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 123.60 199.29 Tm (˜) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 161.60 199.29 Tm (™) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 161.60 199.29 Tm (™) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 199.60 199.29 Tm (š) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 199.60 199.29 Tm (š) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 237.60 199.29 Tm (›) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 237.60 199.29 Tm (›) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 275.60 199.29 Tm (œ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 275.60 199.29 Tm (œ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 313.60 199.29 Tm () Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 313.60 199.29 Tm () Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 351.60 199.29 Tm (ž) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 351.60 199.29 Tm (ž) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 389.60 199.29 Tm (Ÿ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 389.60 199.29 Tm (Ÿ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 120.60 187.04 Tm ( ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 120.60 187.04 Tm ( ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 159.12 186.99 Tm (¡) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 159.12 186.99 Tm (¡) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 198.61 184.95 Tm (¢) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 198.61 184.95 Tm (¢) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.40 187.17 Tm (£) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.40 187.17 Tm (£) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 274.93 187.07 Tm (¤) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 274.93 187.07 Tm (¤) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.75 187.62 Tm (¥) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.75 187.62 Tm (¥) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.60 187.76 Tm (¦) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.60 187.76 Tm (¦) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 386.59 187.70 Tm (§) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 386.59 187.70 Tm (§) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 120.59 178.11 Tm (¨) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 120.59 178.11 Tm (¨) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 158.59 178.17 Tm (©) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 158.59 178.17 Tm (©) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 196.59 178.12 Tm (ª) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 196.59 178.12 Tm (ª) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 233.43 178.18 Tm («) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 233.43 178.18 Tm («) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 271.65 178.18 Tm (¬) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 271.65 178.18 Tm (¬) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.19 176.53 Tm (­) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.19 176.53 Tm (­) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 347.65 178.18 Tm (®) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 347.65 178.18 Tm (®) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.19 176.70 Tm (¯) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.19 176.70 Tm (¯) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 122.00 166.32 Tm (°) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 122.00 166.32 Tm (°) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 159.40 168.02 Tm (±) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 159.40 168.02 Tm (±) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.96 165.82 Tm (²) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.96 165.82 Tm (²) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.40 168.05 Tm (³) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.40 168.05 Tm (³) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.40 168.47 Tm (´) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.40 168.47 Tm (´) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.75 168.49 Tm (µ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.75 168.49 Tm (µ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.62 167.70 Tm (¶) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.62 167.70 Tm (¶) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.76 168.26 Tm (·) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.76 168.26 Tm (·) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.40 158.93 Tm (¸) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.40 158.93 Tm (¸) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 159.40 158.94 Tm (¹) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 159.40 158.94 Tm (¹) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.40 158.94 Tm (º) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.40 158.94 Tm (º) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.40 158.92 Tm (») Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.40 158.92 Tm (») Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 271.60 160.73 Tm (¼) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 271.60 160.73 Tm (¼) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.19 157.48 Tm (½) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.19 157.48 Tm (½) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 347.60 159.06 Tm (¾) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 347.60 159.06 Tm (¾) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 386.97 158.59 Tm (¿) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 386.97 158.59 Tm (¿) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 120.31 148.92 Tm (À) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 120.31 148.92 Tm (À) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 158.86 148.73 Tm (Á) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 158.86 148.73 Tm (Á) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 196.42 148.60 Tm (Â) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 196.42 148.60 Tm (Â) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 233.65 150.03 Tm (Ã) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 233.65 150.03 Tm (Ã) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 272.53 148.85 Tm (Ä) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 272.53 148.85 Tm (Ä) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.53 148.84 Tm (Å) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.53 148.84 Tm (Å) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 348.31 148.70 Tm (Æ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 348.31 148.70 Tm (Æ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 386.53 149.44 Tm (Ç) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 386.53 149.44 Tm (Ç) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 120.53 140.01 Tm (È) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 120.53 140.01 Tm (È) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 158.75 140.03 Tm (É) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 158.75 140.03 Tm (É) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 196.75 140.53 Tm (Ê) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 196.75 140.53 Tm (Ê) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 234.75 140.03 Tm (Ë) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 234.75 140.03 Tm (Ë) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 272.75 140.03 Tm (Ì) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 272.75 140.03 Tm (Ì) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.75 140.53 Tm (Í) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.75 140.53 Tm (Í) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 348.75 140.04 Tm (Î) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 348.75 140.04 Tm (Î) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 386.75 139.92 Tm (Ï) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 386.75 139.92 Tm (Ï) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 120.53 129.66 Tm (Ð) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 120.53 129.66 Tm (Ð) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 158.75 129.55 Tm (Ñ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 158.75 129.55 Tm (Ñ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 196.44 129.73 Tm (Ò) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 196.44 129.73 Tm (Ò) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 234.44 129.71 Tm (Ó) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 234.44 129.71 Tm (Ó) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 272.04 128.49 Tm (Ô) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 272.04 128.49 Tm (Ô) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.31 129.75 Tm (Õ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.31 129.75 Tm (Õ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.40 128.83 Tm (Ö) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.40 128.83 Tm (Ö) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 388.60 130.27 Tm (×) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 388.60 130.27 Tm (×) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 120.75 121.63 Tm (Ø) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 120.75 121.63 Tm (Ø) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 159.19 120.97 Tm (Ù) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 159.19 120.97 Tm (Ù) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.19 120.88 Tm (Ú) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.19 120.88 Tm (Ú) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 233.43 120.83 Tm (Û) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 233.43 120.83 Tm (Û) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 271.65 120.79 Tm (Ü) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 271.65 120.79 Tm (Ü) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.19 119.13 Tm (Ý) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.19 119.13 Tm (Ý) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 347.65 120.83 Tm (Þ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 347.65 120.83 Tm (Þ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.19 119.30 Tm (ß) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.19 119.30 Tm (ß) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.62 110.24 Tm (à) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.62 110.24 Tm (à) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 160.28 111.03 Tm (á) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 160.28 111.03 Tm (á) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 196.44 110.62 Tm (â) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 196.44 110.62 Tm (â) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 234.44 110.58 Tm (ã) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 234.44 110.58 Tm (ã) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 272.46 109.36 Tm (ä) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 272.46 109.36 Tm (ä) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.75 110.65 Tm (å) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.75 110.65 Tm (å) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 350.06 110.69 Tm (æ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 350.06 110.69 Tm (æ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 388.06 109.86 Tm (ç) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 388.06 109.86 Tm (ç) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 122.06 101.13 Tm (è) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 122.06 101.13 Tm (è) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 160.06 100.28 Tm (é) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 160.06 100.28 Tm (é) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 198.06 100.28 Tm (ê) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 198.06 100.28 Tm (ê) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 236.06 100.28 Tm (ë) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 236.06 100.28 Tm (ë) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.62 100.30 Tm (ì) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.62 100.30 Tm (ì) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.62 100.26 Tm (í) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.62 100.26 Tm (í) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.62 100.22 Tm (î) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.62 100.22 Tm (î) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.62 100.26 Tm (ï) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.62 100.26 Tm (ï) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 123.60 94.10 Tm (ð) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 123.60 94.10 Tm (ð) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 160.28 91.90 Tm (ñ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 160.28 91.90 Tm (ñ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 198.50 90.86 Tm (ò) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 198.50 90.86 Tm (ò) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 234.86 90.77 Tm (ó) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 234.86 90.77 Tm (ó) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 272.86 90.55 Tm (ô) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 272.86 90.55 Tm (ô) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.86 90.76 Tm (õ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.86 90.76 Tm (õ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 350.06 91.56 Tm (ö) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 350.06 91.56 Tm (ö) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 388.06 90.74 Tm (÷) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 388.06 90.74 Tm (÷) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 122.06 82.00 Tm (ø) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 122.06 82.00 Tm (ø) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 160.06 81.15 Tm (ù) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 160.06 81.15 Tm (ù) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 198.06 81.15 Tm (ú) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 198.06 81.15 Tm (ú) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 236.06 81.15 Tm (û) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 236.06 81.15 Tm (û) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.62 81.17 Tm (ü) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.62 81.17 Tm (ü) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.62 81.13 Tm (ý) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.62 81.13 Tm (ý) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.62 81.09 Tm (þ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.62 81.09 Tm (þ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 389.60 84.53 Tm (ÿ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 389.60 84.53 Tm (ÿ) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 121.38 359.13 Tm (0) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 159.38 359.05 Tm (1) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 197.38 359.05 Tm (2) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 235.38 359.13 Tm (3) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 273.38 359.05 Tm (4) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 311.38 359.19 Tm (5) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 349.38 359.13 Tm (6) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 387.38 359.11 Tm (7) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 83.38 339.93 Tm (4) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 83.38 330.50 Tm (5) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 83.38 320.88 Tm (6) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 83.38 311.30 Tm (7) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 81.15 301.75 Tm (10) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 81.15 292.11 Tm (11) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 81.15 282.55 Tm (12) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 81.15 273.06 Tm (13) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 81.15 263.42 Tm (14) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 81.15 253.94 Tm (15) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 81.15 244.37 Tm (16) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 81.15 234.73 Tm (17) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 81.15 225.25 Tm (20) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 81.15 215.61 Tm (21) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 81.15 206.04 Tm (22) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 81.15 196.56 Tm (23) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 81.15 186.92 Tm (24) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 81.15 177.43 Tm (25) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 81.15 167.87 Tm (26) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 81.15 158.23 Tm (27) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 81.15 148.74 Tm (30) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 81.15 139.18 Tm (31) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 81.15 129.61 Tm (32) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 81.15 120.05 Tm (33) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 81.15 110.49 Tm (34) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 81.15 100.92 Tm (35) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 81.15 91.36 Tm (36) Tj ET BT /F4 1 Tf 8.00 0.00 -0.00 8.00 81.15 81.80 Tm (37) Tj ET Q endstream endobj 732 0 obj << /Length 173 /Filter /FlateDecode >> stream xÚ]¹Â0 †÷<…Çfˆ±ãÍÊ)ز!î¥Uï?PJ[U•‡ßú}~/ Ø)êu™Õbë#0còÞB~[‡%Yˆm‚|‡SQ¢hÃl}±jªæ£DWd¤¸\«‡>çør“· «Ä9$¸Õê­0&b×1I»Ò0׋}-Ö:¶A³Ïç:̚ᨙ\í0%BÂløQÚÄbë:)ÿæ _W=Õ endstream endobj 729 0 obj << /Type /XObject /Subtype /Form /FormType 1 /PTEX.FileName (/tmp/RtmpBlhU1W/Rbuild4444671013800/timeSeries/vignettes/colorTableFig.pdf) /PTEX.PageNumber 1 /PTEX.InfoDict 734 0 R /BBox [0 0 432 432] /Resources << /ProcSet [ /PDF /Text ] /Font << /F2 735 0 R/F3 736 0 R>> /ExtGState << >>>> /Length 17300 >> stream 1 J 1 j q Q q BT 0.000 0.000 0.000 rg /F3 1 Tf 14.00 0.00 -0.00 14.00 160.63 397.45 Tm [(T) 80 (ab) 10 (le of Color Codes)] TJ ET Q q 59.04 73.44 342.72 299.52 re W n BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 82.49 331.40 Tm (0) Tj ET 66.33 301.00 m 77.13 301.00 l 77.13 311.80 l 66.33 311.80 l h f BT /F2 1 Tf 8.00 0.00 -0.00 8.00 82.49 303.59 Tm (1) Tj ET 1.000 0.000 0.000 rg 66.33 273.27 m 77.13 273.27 l 77.13 284.07 l 66.33 284.07 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 82.49 275.85 Tm (2) Tj ET 0.000 0.804 0.000 rg 66.33 245.53 m 77.13 245.53 l 77.13 256.33 l 66.33 256.33 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 82.49 248.20 Tm (3) Tj ET 0.000 0.000 1.000 rg 66.33 217.80 m 77.13 217.80 l 77.13 228.60 l 66.33 228.60 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 82.49 220.39 Tm (4) Tj ET 0.000 1.000 1.000 rg 66.33 190.07 m 77.13 190.07 l 77.13 200.87 l 66.33 200.87 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 82.49 192.79 Tm (5) Tj ET 1.000 0.000 1.000 rg 66.33 162.33 m 77.13 162.33 l 77.13 173.13 l 66.33 173.13 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 82.49 165.00 Tm (6) Tj ET 1.000 1.000 0.000 rg 66.33 134.60 m 77.13 134.60 l 77.13 145.40 l 66.33 145.40 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 82.49 137.25 Tm (7) Tj ET 0.745 0.745 0.745 rg 66.33 106.87 m 77.13 106.87 l 77.13 117.67 l 66.33 117.67 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 82.49 109.53 Tm (8) Tj ET 66.33 79.13 m 77.13 79.13 l 77.13 89.93 l 66.33 89.93 l h f BT /F2 1 Tf 8.00 0.00 -0.00 8.00 82.49 81.80 Tm (9) Tj ET 1.000 0.000 0.000 rg 95.18 328.73 m 105.98 328.73 l 105.98 339.53 l 95.18 339.53 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 109.12 331.40 Tm (10) Tj ET 0.000 0.804 0.000 rg 95.18 301.00 m 105.98 301.00 l 105.98 311.80 l 95.18 311.80 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 109.12 303.59 Tm (11) Tj ET 0.000 0.000 1.000 rg 95.18 273.27 m 105.98 273.27 l 105.98 284.07 l 95.18 284.07 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 109.12 275.85 Tm (12) Tj ET 0.000 1.000 1.000 rg 95.18 245.53 m 105.98 245.53 l 105.98 256.33 l 95.18 256.33 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 109.12 248.20 Tm (13) Tj ET 1.000 0.000 1.000 rg 95.18 217.80 m 105.98 217.80 l 105.98 228.60 l 95.18 228.60 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 109.12 220.39 Tm (14) Tj ET 1.000 1.000 0.000 rg 95.18 190.07 m 105.98 190.07 l 105.98 200.87 l 95.18 200.87 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 109.12 192.73 Tm (15) Tj ET 0.745 0.745 0.745 rg 95.18 162.33 m 105.98 162.33 l 105.98 173.13 l 95.18 173.13 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 109.12 165.00 Tm (16) Tj ET 95.18 134.60 m 105.98 134.60 l 105.98 145.40 l 95.18 145.40 l h f BT /F2 1 Tf 8.00 0.00 -0.00 8.00 109.12 137.19 Tm (17) Tj ET 1.000 0.000 0.000 rg 95.18 106.87 m 105.98 106.87 l 105.98 117.67 l 95.18 117.67 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 109.12 109.53 Tm (18) Tj ET 0.000 0.804 0.000 rg 95.18 79.13 m 105.98 79.13 l 105.98 89.93 l 95.18 89.93 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 109.12 81.80 Tm (19) Tj ET 0.000 0.000 1.000 rg 124.03 328.73 m 134.83 328.73 l 134.83 339.53 l 124.03 339.53 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 137.96 331.40 Tm (20) Tj ET 0.000 1.000 1.000 rg 124.03 301.00 m 134.83 301.00 l 134.83 311.80 l 124.03 311.80 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 137.96 303.59 Tm (21) Tj ET 1.000 0.000 1.000 rg 124.03 273.27 m 134.83 273.27 l 134.83 284.07 l 124.03 284.07 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 137.96 275.85 Tm (22) Tj ET 1.000 1.000 0.000 rg 124.03 245.53 m 134.83 245.53 l 134.83 256.33 l 124.03 256.33 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 137.96 248.20 Tm (23) Tj ET 0.745 0.745 0.745 rg 124.03 217.80 m 134.83 217.80 l 134.83 228.60 l 124.03 228.60 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 137.96 220.39 Tm (24) Tj ET 124.03 190.07 m 134.83 190.07 l 134.83 200.87 l 124.03 200.87 l h f BT /F2 1 Tf 8.00 0.00 -0.00 8.00 137.96 192.73 Tm (25) Tj ET 1.000 0.000 0.000 rg 124.03 162.33 m 134.83 162.33 l 134.83 173.13 l 124.03 173.13 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 137.96 165.00 Tm (26) Tj ET 0.000 0.804 0.000 rg 124.03 134.60 m 134.83 134.60 l 134.83 145.40 l 124.03 145.40 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 137.96 137.19 Tm (27) Tj ET 0.000 0.000 1.000 rg 124.03 106.87 m 134.83 106.87 l 134.83 117.67 l 124.03 117.67 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 137.96 109.53 Tm (28) Tj ET 0.000 1.000 1.000 rg 124.03 79.13 m 134.83 79.13 l 134.83 89.93 l 124.03 89.93 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 137.96 81.80 Tm (29) Tj ET 1.000 0.000 1.000 rg 152.88 328.73 m 163.68 328.73 l 163.68 339.53 l 152.88 339.53 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 166.81 331.40 Tm (30) Tj ET 1.000 1.000 0.000 rg 152.88 301.00 m 163.68 301.00 l 163.68 311.80 l 152.88 311.80 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 166.81 303.66 Tm (31) Tj ET 0.745 0.745 0.745 rg 152.88 273.27 m 163.68 273.27 l 163.68 284.07 l 152.88 284.07 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 166.81 275.93 Tm (32) Tj ET 152.88 245.53 m 163.68 245.53 l 163.68 256.33 l 152.88 256.33 l h f BT /F2 1 Tf 8.00 0.00 -0.00 8.00 166.81 248.20 Tm (33) Tj ET 1.000 0.000 0.000 rg 152.88 217.80 m 163.68 217.80 l 163.68 228.60 l 152.88 228.60 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 166.81 220.46 Tm (34) Tj ET 0.000 0.804 0.000 rg 152.88 190.07 m 163.68 190.07 l 163.68 200.87 l 152.88 200.87 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 166.81 192.73 Tm (35) Tj ET 0.000 0.000 1.000 rg 152.88 162.33 m 163.68 162.33 l 163.68 173.13 l 152.88 173.13 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 166.81 165.00 Tm (36) Tj ET 0.000 1.000 1.000 rg 152.88 134.60 m 163.68 134.60 l 163.68 145.40 l 152.88 145.40 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 166.81 137.26 Tm (37) Tj ET 1.000 0.000 1.000 rg 152.88 106.87 m 163.68 106.87 l 163.68 117.67 l 152.88 117.67 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 166.81 109.53 Tm (38) Tj ET 1.000 1.000 0.000 rg 152.88 79.13 m 163.68 79.13 l 163.68 89.93 l 152.88 89.93 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 166.81 81.80 Tm (39) Tj ET 0.745 0.745 0.745 rg 181.73 328.73 m 192.53 328.73 l 192.53 339.53 l 181.73 339.53 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 195.66 331.40 Tm (40) Tj ET 181.73 301.00 m 192.53 301.00 l 192.53 311.80 l 181.73 311.80 l h f BT /F2 1 Tf 8.00 0.00 -0.00 8.00 195.66 303.59 Tm (41) Tj ET 1.000 0.000 0.000 rg 181.73 273.27 m 192.53 273.27 l 192.53 284.07 l 181.73 284.07 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 195.66 275.85 Tm (42) Tj ET 0.000 0.804 0.000 rg 181.73 245.53 m 192.53 245.53 l 192.53 256.33 l 181.73 256.33 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 195.66 248.20 Tm (43) Tj ET 0.000 0.000 1.000 rg 181.73 217.80 m 192.53 217.80 l 192.53 228.60 l 181.73 228.60 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 195.66 220.39 Tm (44) Tj ET 0.000 1.000 1.000 rg 181.73 190.07 m 192.53 190.07 l 192.53 200.87 l 181.73 200.87 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 195.66 192.73 Tm (45) Tj ET 1.000 0.000 1.000 rg 181.73 162.33 m 192.53 162.33 l 192.53 173.13 l 181.73 173.13 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 195.66 165.00 Tm (46) Tj ET 1.000 1.000 0.000 rg 181.73 134.60 m 192.53 134.60 l 192.53 145.40 l 181.73 145.40 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 195.66 137.19 Tm (47) Tj ET 0.745 0.745 0.745 rg 181.73 106.87 m 192.53 106.87 l 192.53 117.67 l 181.73 117.67 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 195.66 109.53 Tm (48) Tj ET 181.73 79.13 m 192.53 79.13 l 192.53 89.93 l 181.73 89.93 l h f BT /F2 1 Tf 8.00 0.00 -0.00 8.00 195.66 81.80 Tm (49) Tj ET 1.000 0.000 0.000 rg 210.58 328.73 m 221.38 328.73 l 221.38 339.53 l 210.58 339.53 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 224.51 331.40 Tm (50) Tj ET 0.000 0.804 0.000 rg 210.58 301.00 m 221.38 301.00 l 221.38 311.80 l 210.58 311.80 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 224.51 303.66 Tm (51) Tj ET 0.000 0.000 1.000 rg 210.58 273.27 m 221.38 273.27 l 221.38 284.07 l 210.58 284.07 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 224.51 275.93 Tm (52) Tj ET 0.000 1.000 1.000 rg 210.58 245.53 m 221.38 245.53 l 221.38 256.33 l 210.58 256.33 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 224.51 248.20 Tm (53) Tj ET 1.000 0.000 1.000 rg 210.58 217.80 m 221.38 217.80 l 221.38 228.60 l 210.58 228.60 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 224.51 220.46 Tm (54) Tj ET 1.000 1.000 0.000 rg 210.58 190.07 m 221.38 190.07 l 221.38 200.87 l 210.58 200.87 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 224.51 192.79 Tm (55) Tj ET 0.745 0.745 0.745 rg 210.58 162.33 m 221.38 162.33 l 221.38 173.13 l 210.58 173.13 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 224.51 165.00 Tm (56) Tj ET 210.58 134.60 m 221.38 134.60 l 221.38 145.40 l 210.58 145.40 l h f BT /F2 1 Tf 8.00 0.00 -0.00 8.00 224.51 137.32 Tm (57) Tj ET 1.000 0.000 0.000 rg 210.58 106.87 m 221.38 106.87 l 221.38 117.67 l 210.58 117.67 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 224.51 109.53 Tm (58) Tj ET 0.000 0.804 0.000 rg 210.58 79.13 m 221.38 79.13 l 221.38 89.93 l 210.58 89.93 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 224.51 81.80 Tm (59) Tj ET 0.000 0.000 1.000 rg 239.42 328.73 m 250.22 328.73 l 250.22 339.53 l 239.42 339.53 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 253.36 331.40 Tm (60) Tj ET 0.000 1.000 1.000 rg 239.42 301.00 m 250.22 301.00 l 250.22 311.80 l 239.42 311.80 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 253.36 303.66 Tm (61) Tj ET 1.000 0.000 1.000 rg 239.42 273.27 m 250.22 273.27 l 250.22 284.07 l 239.42 284.07 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 253.36 275.93 Tm (62) Tj ET 1.000 1.000 0.000 rg 239.42 245.53 m 250.22 245.53 l 250.22 256.33 l 239.42 256.33 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 253.36 248.20 Tm (63) Tj ET 0.745 0.745 0.745 rg 239.42 217.80 m 250.22 217.80 l 250.22 228.60 l 239.42 228.60 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 253.36 220.46 Tm (64) Tj ET 239.42 190.07 m 250.22 190.07 l 250.22 200.87 l 239.42 200.87 l h f BT /F2 1 Tf 8.00 0.00 -0.00 8.00 253.36 192.73 Tm (65) Tj ET 1.000 0.000 0.000 rg 239.42 162.33 m 250.22 162.33 l 250.22 173.13 l 239.42 173.13 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 253.36 165.00 Tm (66) Tj ET 0.000 0.804 0.000 rg 239.42 134.60 m 250.22 134.60 l 250.22 145.40 l 239.42 145.40 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 253.36 137.26 Tm (67) Tj ET 0.000 0.000 1.000 rg 239.42 106.87 m 250.22 106.87 l 250.22 117.67 l 239.42 117.67 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 253.36 109.53 Tm (68) Tj ET 0.000 1.000 1.000 rg 239.42 79.13 m 250.22 79.13 l 250.22 89.93 l 239.42 89.93 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 253.36 81.80 Tm (69) Tj ET 1.000 0.000 1.000 rg 268.27 328.73 m 279.07 328.73 l 279.07 339.53 l 268.27 339.53 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 282.21 331.40 Tm (70) Tj ET 1.000 1.000 0.000 rg 268.27 301.00 m 279.07 301.00 l 279.07 311.80 l 268.27 311.80 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 282.21 303.59 Tm (71) Tj ET 0.745 0.745 0.745 rg 268.27 273.27 m 279.07 273.27 l 279.07 284.07 l 268.27 284.07 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 282.21 275.85 Tm (72) Tj ET 268.27 245.53 m 279.07 245.53 l 279.07 256.33 l 268.27 256.33 l h f BT /F2 1 Tf 8.00 0.00 -0.00 8.00 282.21 248.20 Tm (73) Tj ET 1.000 0.000 0.000 rg 268.27 217.80 m 279.07 217.80 l 279.07 228.60 l 268.27 228.60 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 282.21 220.39 Tm (74) Tj ET 0.000 0.804 0.000 rg 268.27 190.07 m 279.07 190.07 l 279.07 200.87 l 268.27 200.87 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 282.21 192.79 Tm (75) Tj ET 0.000 0.000 1.000 rg 268.27 162.33 m 279.07 162.33 l 279.07 173.13 l 268.27 173.13 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 282.21 165.00 Tm (76) Tj ET 0.000 1.000 1.000 rg 268.27 134.60 m 279.07 134.60 l 279.07 145.40 l 268.27 145.40 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 282.21 137.25 Tm (77) Tj ET 1.000 0.000 1.000 rg 268.27 106.87 m 279.07 106.87 l 279.07 117.67 l 268.27 117.67 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 282.21 109.53 Tm (78) Tj ET 1.000 1.000 0.000 rg 268.27 79.13 m 279.07 79.13 l 279.07 89.93 l 268.27 89.93 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 282.21 81.80 Tm (79) Tj ET 0.745 0.745 0.745 rg 297.12 328.73 m 307.92 328.73 l 307.92 339.53 l 297.12 339.53 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 311.06 331.40 Tm (80) Tj ET 297.12 301.00 m 307.92 301.00 l 307.92 311.80 l 297.12 311.80 l h f BT /F2 1 Tf 8.00 0.00 -0.00 8.00 311.06 303.66 Tm (81) Tj ET 1.000 0.000 0.000 rg 297.12 273.27 m 307.92 273.27 l 307.92 284.07 l 297.12 284.07 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 311.06 275.93 Tm (82) Tj ET 0.000 0.804 0.000 rg 297.12 245.53 m 307.92 245.53 l 307.92 256.33 l 297.12 256.33 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 311.06 248.20 Tm (83) Tj ET 0.000 0.000 1.000 rg 297.12 217.80 m 307.92 217.80 l 307.92 228.60 l 297.12 228.60 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 311.06 220.46 Tm (84) Tj ET 0.000 1.000 1.000 rg 297.12 190.07 m 307.92 190.07 l 307.92 200.87 l 297.12 200.87 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 311.06 192.73 Tm (85) Tj ET 1.000 0.000 1.000 rg 297.12 162.33 m 307.92 162.33 l 307.92 173.13 l 297.12 173.13 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 311.06 165.00 Tm (86) Tj ET 1.000 1.000 0.000 rg 297.12 134.60 m 307.92 134.60 l 307.92 145.40 l 297.12 145.40 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 311.06 137.26 Tm (87) Tj ET 0.745 0.745 0.745 rg 297.12 106.87 m 307.92 106.87 l 307.92 117.67 l 297.12 117.67 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 311.06 109.53 Tm (88) Tj ET 297.12 79.13 m 307.92 79.13 l 307.92 89.93 l 297.12 89.93 l h f BT /F2 1 Tf 8.00 0.00 -0.00 8.00 311.06 81.80 Tm (89) Tj ET 1.000 0.000 0.000 rg 325.97 328.73 m 336.77 328.73 l 336.77 339.53 l 325.97 339.53 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 339.90 331.40 Tm (90) Tj ET 0.000 0.804 0.000 rg 325.97 301.00 m 336.77 301.00 l 336.77 311.80 l 325.97 311.80 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 339.90 303.66 Tm (91) Tj ET 0.000 0.000 1.000 rg 325.97 273.27 m 336.77 273.27 l 336.77 284.07 l 325.97 284.07 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 339.90 275.93 Tm (92) Tj ET 0.000 1.000 1.000 rg 325.97 245.53 m 336.77 245.53 l 336.77 256.33 l 325.97 256.33 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 339.90 248.20 Tm (93) Tj ET 1.000 0.000 1.000 rg 325.97 217.80 m 336.77 217.80 l 336.77 228.60 l 325.97 228.60 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 339.90 220.46 Tm (94) Tj ET 1.000 1.000 0.000 rg 325.97 190.07 m 336.77 190.07 l 336.77 200.87 l 325.97 200.87 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 339.90 192.73 Tm (95) Tj ET 0.745 0.745 0.745 rg 325.97 162.33 m 336.77 162.33 l 336.77 173.13 l 325.97 173.13 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 339.90 165.00 Tm (96) Tj ET 325.97 134.60 m 336.77 134.60 l 336.77 145.40 l 325.97 145.40 l h f BT /F2 1 Tf 8.00 0.00 -0.00 8.00 339.90 137.26 Tm (97) Tj ET 1.000 0.000 0.000 rg 325.97 106.87 m 336.77 106.87 l 336.77 117.67 l 325.97 117.67 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 339.90 109.53 Tm (98) Tj ET 0.000 0.804 0.000 rg 325.97 79.13 m 336.77 79.13 l 336.77 89.93 l 325.97 89.93 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 339.90 81.80 Tm (99) Tj ET Q endstream endobj 741 0 obj << /Length 182 /Filter /FlateDecode >> stream xÚ]=‚@ †÷ûÚÞq_«ŸÁI“ÛŒƒQt•ÿÒ¡ÍÛ¾oó¼€à hèë(V{〃1 âXeèI#‹¬Ä\¤Ç,I™•‘›º¬?Iª]&O‰fy+‹¦)¾bdž\ãq ßEÁí@ÀS¨ÖI¸Wâ-Ðc]1ûÕè„U^µ¦m-ÎmÑ‚aÙGo:>Mg_{`í `°Êv¼*0Z×*„™ö\ËK>@ž endstream endobj 738 0 obj << /Type /XObject /Subtype /Form /FormType 1 /PTEX.FileName (/tmp/RtmpBlhU1W/Rbuild4444671013800/timeSeries/vignettes/colorPalettes1Fig.pdf) /PTEX.PageNumber 1 /PTEX.InfoDict 744 0 R /BBox [0 0 432 432] /Resources << /ProcSet [ /PDF /Text ] /Font << /F2 745 0 R/F3 746 0 R>> /ExtGState << >>/ColorSpace << /sRGB 747 0 R >>>> /Length 37256 /Filter /FlateDecode >> stream xœ¼½K-9z$¸Ï_ËÌ…îøû±­Æ´€zZ#Ð A‹’”£n¡¼TÊRAп’öøèÇÏ ô㋾[¤~ö™‘´öí·oíÛ¿¾ýûÿïÛ¿¿ßÖæ­Ÿæoýð6Ï߆ùm\¾ÍãÛ/?¿ý¯·?þðýùoÿú7oÿôçÚoMÓ¼5Õ¿þ'ýŸÿÛßýÐ|ø?ÿÝûÒÿoú/ýçÿoÍÛ?ÿоý6ý¿ý¡Í_ðö?~˜¦oCúÙëômîÞ¶<ì×4L¿Äòö‡2ÓpùÖ vK®+†mþâ5ÿ„2læ·¡i¾M¦¿ªKÖß;æ¯ÊÃuÁpœÒ°Ë?· û> ûo¿¸YÓpøÖNy8|[º2\f Çýßÿ®¬O™«~*ÿc)6wùçúüæw±D× ,ÑïÒrÿîÿ{[¾ñÿÿ”aZ÷y*óÖ6o¿ÛÞ~lzûÝ¿þðÿîKØ›î2lÔS·åaY”†²ÜiŠ–2\V Ó:Íúmló°ý6§/Nÿ©¶Ã°oËp.ÿ×æÛ’‡mÆSvk.yÉÆµLH›–dÆ0¯o–”þü´4CÛg¤–a3”aÁƘ¦(ÿ_‡o}þ¹ã„4äÿb¶ ¢í˜ÿGŽHmÁâ#l¤‰èËOnÛ<é·æ/²Ü…6c<ÏÛÒlt_ÇFûþÿlì¦nûaLÍ© ×ü±IÃ6a£°¾cÒZ†å38vÀÆ”ý2lòÏ\”•© ‡ •±É‹’‡SÆÆ°æoÊåǰÍ_\&+ —oCW†m†è¾i.Ã.ÿ—‡ôÛ2ì' ù½CùOyñÊp6Êpy„ô{usùEæ!O@úäåËçõ&l¤¹`f¸oô_ÁvŒK±±›ºí‡¡ÏŸ—˜Ø>o 1íåS‹Ò~›ÚjÉàJ Úè{Ër÷ë·~ 0ôË*ý\ €”†]0ë'a²€°…ɉö ÷pEx÷=6¨£}#ý^MØH¿×Üß‹ô 7øcìÃ×°qù¾±›º-Û66ä¾Ë¿Œ·ë¾-¥\›y_¨„·ú>í c‚4,ÛËD·bƒbé gq‰éü  .AeŽò”†CU¼Ò°©J[W0âÂ×䲨¥Hòô{ucÔ”®|Hn­)]«ÉsÆÆøë±‘÷_ÝÔmy˜K¹ˆ\æõÍë°&’À4¦ ˆiØV2ýÉëô2 ç.ÈgŽ}PÓ4ÌHÄ5OW´¶+,Ȥ7› Ä]—w殀Ñtº+«l0|ÄFÿmê‚‹¦/ÏÅÿN.š7 pøT¿36¦_‹öš}c7u›'6ýFS5ícÞâcQ&ÆjÉÒì®^Ð~툫²Üy¨ïóªÍxTúµ1TÖ± KÌúeå6R@Ø/‹19çáLLça»`˜àݧ5® øûeÄu€ü{¥¿EØÈ¿ÈÒÞŠüûÏØ7æ¯c£f휺r¶«·ë~2AÓfÞ/=J9¶ú<ì;‚~érÅV™ÈCl#åSÞ/-öv”˜·rÑ~.sÆÆúõ>åRlì¦nc[ÚØöÒ<Æ¢”Ö2–l®´ «¾wˆ¦Õ`@Kk¨ á5Ðfh– B´Ò†(mm¸á&ýxß@kl ‡½èa+l´Í¯¿.Çnî6*GÞ‘¡+y¿†êäÝš”÷z(V®г\' v¹Š@ sRæ Íõ *›«48×6(t®|Ðï\¡îˆ_.*¿n-*¿2ÖWÑöW)£v*»¹Û(9›ÊA6у\m1Û$R·)$„pLÈ䦟ÑMN!±›ºB€7±•²zN#¦K‹ubêÜʸ«Üž6µ3ce÷´©çX*1=ÛJko{Šž”âÛ¨”úmóX6@ÛÖKÇMåû´© ì+ã§MÝöX9?Keý!ö~ &ýFSåþäñúÁþy5brCzlFL"Z—!f7ƒy~š ˜a©óZnÉ¢R¬P7• Ôv3§Nµ{¬Œ ´=~›+'(wêÊfËjZeµÐ¡iÜÖîÚ\á]ÄéÆmå=@L–û*O(ۦЫ“~ÍáØ:˜ Ïìg0ÍO[ÊMìâi¢–ÊÊã®ò†Z,uT‰ü7VîP›–n¨ì¡¶ÅñW¡4îë*ÕøyªbùUQ›P]ÛBx¢J¶EƒŽ*ÚžU¶ípjå˜Ç¤ßhªœ¢ò}°Š^˜ô÷LÇfÑiÄLiú 1»ÌóÓ^`¦˜ÆCeåq[9Fm oÙL4Çš©¦q_™F-–:˜nššµ²òx®|£2u•q”ÇCʹ󸲎ò¸¯¼£<îj&߬hàˆGˆISSùGmÛúÁ@z9bz5,¤Óˆ™Óœ]…˜Ý n1Ãlz½쉽Bl™½‚쨹Ân¸‰÷ãDˆÛu"ÈÝ<æfŸ´@„Z* ‚­$áø °ÁOˆ%žCĸ'bÞ5ò÷ æÈX:˜e¾®WÚÍà¦]ÜwyKl¬VàX%,бŠX¿c•±¼Ç*dõUÊâ «˜µCV9K‹¬‚VY%-L²ŠZ·d•µ¬É*lÅø°*YdUz§ ÞS•Žì¦“ˆ¹ô|Ë~71EKòd’VìÉ4-蓉Zï'Sµ@&k·€L×f™°½2e[dÒv*È´md‰Ûç S· B&o—D‘ÿtÈ|í2ù¾³îa¾G&ÔÓ¦?àª^É6»Ñj†K·Z­@éfc…ÐíÆ ¢ŽF·@7A·B7C·D7E·†‡ZŸ¨ Ïöt×t×7#Ýõ¡5õEÈ\b?ì§p“äÛ8$±Øæ!™E€¤e’[”HrQfè…º AÒ‹2É/Ê$Á(s £ BRŒ2 É1Ê($É(³,ŸHxQ– áÝ\– áV_„ÌÒ_±Ëì¦p“®\º{pIèòÁ5¡Û…®\ºpYøÁuᆯ\¾CpiùâÚò-ÄÅåkŒñýsÍåw¾ÈCÈÀ'î Ÿàfî ŸàK6V¡ ]åcýø¿þýÔ€kÓ0Ͻúî}øû¦ë¤å®‡îs},.>1™Ç•}•ÇMe_u=ŽdÅqLÈDqZsÁQOæ\Ê9ý8ë¹à°§‚.E–‹“¢s‘yâ éŒ#š:gšÆ}e_åqSÙW].e•}•Ç]e_uY‘|wÎoÁIVí!„í›øv¼_vžkhp?ë±u3©Xå6þ:©p?‡y†F¬‘N`§ñPXy¼;¿ÒXïNã¶2°ºD°¦ÊÀÊã¦>;.û@GËû2ñqòó¸¯ ¬G˜é`‰éPyßÛR[îÃLo‰ô‘ƒu3]úp^*ýìç0ŸËµ? ø]Wj¶þ<î* KMËX]G1vÖÁï•ÁÐuƒe@ti¯ž+ «ë`›ËÀÈ㮲°ºÜ¯TV—õ¹²°ºô¿¬•…•Ç]ea#¶°`&ŸŠ®,¬<î?XX/ÇLno=¬3˜挙åºV~?‡i†2–zŽi!x FÜ.ô °|¼†=0§5Nϵ2±ò¸­L¬.Ë{5†ÚÂ¥cøEƒ3£ik § s­L¬<î*«KEj¬L¬˜É[`ebåñøÁÄz9fÒï¹»Xg033ò™Ý¦JÄ`­l¬<î*«k˜<ªi\_gí2©l¬®éliÜ×לÞ»U­Êħ¾è” ú\ßtjxõV6V"RKmc­E Ä+l¬4žš§|&ý†M}á©iaßZ›Ú—Ó|¬3˜Y “Y/´>ws¸•qWYëŒ;2²Òx¬¬測ô·®µ‘•£6²V˜ç6²Ò¸¯¬tld幫¬uÄåY+î_ØÈJã¹6²Òxª¬<®Œ¬˜É?q¨¬ôý#ëå˜I½írìdÂL[¬¬¥ˆ†iǼ3õn1ÇjµjµFjŸµ†j¯±ÆÑ~Ñž#ѾCÑÞcÑþƒ!£!Ã!/ã!?à3ò>#V€1í<0ó®¿ 3G^Ö9ÌÜfüo¦Ëö™€V^g’ʆ½>T8Ô‚¸*ÕðhºîJ¡–Äe)Ôš¸-Õðtº®K¡VYEd-³ÊÈZg’µÐ*%k¥ULÖR«œ¬µVAY‹-4Ö¦¸8…ÚôN5¼©6¹Y§0SމNkÆÌ0]°Ïìçpg´˜ONi±ŸœÓf9©ÍrV› ä´6ÈymFÛ¬ g¶™ANm³ƒœÛf9¹Írv›)äô6[„ÙY‡Øæ9ð;sâ&|ägÂL‚~^„‘Çs®ê›lh±7­æ¸ô®Õ”Þ6Ö½o¬!zãXcôÎôÖôÞ!ôæ1ôîAôöQôþahqhñ€¶ðlŸA¯˜A¯}7fÐk:Z§@SD½¥í ­¹4»IÜ$‚Åf‘,6{ˆhQ zÞ$S±€Å"]ºª.F=oG©XAŒb‘0ŠDÄ(†£XB„ŒkÇ)ãV2DÌ'„†¢^'ˆz7'Šz‡žÖ9Ðd=/þ½J Ž›ßPÛãN=Ôø¸rµ>näCÍ ûPûã>?Ü€¸î· z  ¸ Õc ^ ð[8nO ЭðKt3üPÝ¿c@7DÏìÜ’Ç )îA<÷àfL÷àk—³ËËÝk¸Zÿñó/ù~VºnÂÅí§ÆÖw¿èƒà®ë¯¿wÞÁBWƒÖ³È¨ .ãÊØÊã¡2¶ò¸¯šì<î*c+ÛÊØêSQ[+c«×û(lòóx¬Œ­<î*‘ ›ÊØêõF E†<*c+›ÊØê;>G!¨O?hÙ_DïðF˜šúü¬Œ­Ž·c_v=óccë føSÓt_†™z·2î*±­OÄg©Œ­<®Ïœ÷‰8­•˜—ÇcelåqS[}‹›_ {™ óx¨Œ­Êt¼¸ÔÎû™2™®Œ­/Velõiê¦ú¡³´økýZKyVϤµ0‚ýŠZ³êÕ9<²–Æmelõ u?ÑÖÀ0—éÑçÆ¥2¶ò¸­Œ­>m8}elõ¹é¯Œ-bÄÆÖÌäSÚ•±•Çócëå˜É tllÇÌrá>³›Ã­Œ›¥žcš ^¾Bé5âó^Ø“±•ŠV_[©¨µ±•Å¾ÚØJã¶6¶ÖI×”ÐBÜ ck„ÙIã¾6¶V¼£ec+ó¯ÊØúˆ™üvµ±•~£õƒ±õjÌäß»;6¶ÎbÆ÷ú.y›m7‡[÷µ±•çT[™ÖÆVb õ-Þ. ¥µ±ÎÆÖ‚ƒ;ñ~oû?¾í)ck™a¾ÊØZxëX‡<Ò¸~Ÿ¶[øØœ‰,¸ÿåC$iÜ4ÏøLþ†ÝK~3ìá[kS·ðÉÄÇÆÖÌ´W¿ºŸÃ •Æ]ý¤_nžë7ýæ>lµôX#ÆJã¶~Öo¡îÃ\hÖã°×‚ƒ;> –ç®~ÚoépH‡ÉÜþða³,Ô¯û¥qSV[ðt0ò38²çÃ]©¹Ÿ?[/Ç ”¯#cë f.;v?‡[̱Z`­Zd­‘Zh­¡Zl¬q´àÀ@´èÀH´ðÀP´øÀXHÀ`HÀhHÀpH ÀxHø „Dψu CÌDK̼kéoÂÌ‘±u3ë‹ö™Þ¢’Ö¶ð@iq¨¡Õ-<Î.-µ¤z}Å3€<Î.­µ*´D€#´FÔºÐ"Q C«D­ -µ4´NÔÚÐBQ‹-8Ö¦êEÀOAí´Ã›jÓ‘±õy̼àžÖ~7qÆõÁ)Côç S€†»MpÖ0ÀiÃtç Sœ8L pf›äÔ6=ȹmŠ“Û4!g·©BNoÓE‘±uÈä~gRÜÄŒ­³˜Aß”­‚ö²¾ÉÆ{ÓjŽG¼äç5˜ðŒ§×hÂíK¯!äçXãEß?T½µ1ÂÞ;Þ˜DoOP‚ Ç •èíãKôþñ¾%´xþÚA¼Ž máÉ>Ã^;^—D¯}3fØk[_͸}¾L ‰H!‚ÅÛ®ÉâéWˆhñ2l;¿{8v†-Ôè]Ù§6ôì,ÝU¿JÛò.‹E@3Š„.v] )2ºXR„t1¥HébKó ¡¡¨çâDQïæâDQïÐØúhÊaÒùŠn{?‰›Ôv³Fªñf•TëÍ:©æ›•Rí7k¥`VK·À¬—n‚Y1ݳfºfÕv+Ⱥíf•ÛíãûÇŠÕïÝ’'îY0݃›Y0݃/¾:8–ãÝ•±õoú·rýˆ»áO]­ï~ÑÃîìªs[¸èêÎVuÀe\¹ZE®©\­<ž«;§ÊÕÊã±rµò¸¯\­Ao¸°Ã/²GåjåñT)yA’ÆCóŒÏäß`©\­ò}pµ^Ž™‰¯=>vµÎ`&_£¸3õneÜT®V–!¦ÊÕÊã¾rµò¸©\­~Z±F:‰•Æcåjåq_¹ZyÜV®Vž«µrµÊÜU®VOõI² ×@|Ò,ÇÊÕÊã¡>©6áý÷f1“~£®rµòw´\­×c¦S/ñÈÕúŒ†~ ‡¾Œ‡þ€Ï@èøŒ„t„™èç™wýüM˜9rµÎc&*ÔUûLh@3¯SIh›ù6¡„8Ô‚êfžh—‡ZBjM3O´K(D­ !µ,„FÔº"Q C¨D­ !µ4„NÔÚBQ‹Cm>ªM!¢6½oªMG®Öy̾ŠÏ„Ø ÎŠ>8e(þàœáÐm·cÎŽ8mz5@œ–#NŽ8s8àÔáx€s‡#²ð-9&tVí¨€Ó‡ãBŒØÕ:âÀUþUáÀ8ð‘«u3Ñq_Õ7…«…Þ´šãïj ÐÛÆ­½R¤Ø[C{ö£w  ·Œ ÷®’Ó ±cè݃èí£èýÃÐŒqjþ P[x¶Ï ×Ì ×¾3èµ]­ó  iï*Æ E0oæɼÙSDs1 ÈæbAÎÅ„"‹ E<#Š|.V]Ì(ºØQDt1¤ÈèbIÒÅ”"¥‹-EÌ'„†¢ž‹E½›‹E½CWë  ¹î¬ú~7©ífTãÍ*ÙæšuRÍ7+¥ÚoÖJ7À¬–nY/ݳbº fÍt#̪íVuÛÍ +·Û1Æ÷¯«ß»%Oܳ`º7³`º_rµò'f¨Ã´~ü§íCH[¶¾Ž­ï|Iõ—õåÑ•ø÷×ç¸á3Ú hË‚óÒi݄ƘjÝ„¾™ñjÝŒ¶šákÝ 8Ü3ÏɬÛƒÛÒpeÊ[>¸ÑG¬[æ$}‹ ÉH¸^™xÏ7_ß'×÷ÌCØ\?@m`]"Auý¸K#Çß[å¸õ :wæ¸åèÚw‰ÓùDÙKÀÇ·4oœ«Ïc#UŒ·úß«2þ0u›Òû J³Ó°mL^ìÉ1˜Ë˜¯voNmì¨xÌtìù‡g5ú•‘žH„t´sblþAÌ’ZŒLšL æPªS*Ó°¨—̰Ìy²å‹‘p™†ãôM)Ù9º¶Èü‹,wagÜò-ÚÕ l,yLj¯ ÅÔmŠ]H&Ê: X_F¶>ƒ|¨6 WsMÈž;Œ0 › @$›†År`Ìì0±Þ!„v˜`X0¢v of€múÅîà“Îx7ù ¿ÍÃ9À0LôõßcƒÉ±é»Š³ÀäØô啱ðZl09v:QŸÇÆX®£Ç¿W¥ cê6Å5{bæìiGÔ³AÐ^2äxA}oSEL  TO- 1¼Z0c´µ@ÈàkA”±Ø0C³oFjîLœ6˜8}'6˜8WÍ?8NŸÇÆÔä]ñïUiäÚ7ó® ™!ðÚ®¯ÍœòÚê/¯BÀðy• ¦6¨ˆ0¸^%†±ö*@ ½WyJá*^âTÚºbOºðu`A*‹NÚò &Õ«¦0©þΚ¤ú~yè,ÀF9"ÿþúéõÔmy˜K¹ˆ\WÔÓ<ž× ì 7EÌÞT2ýɹM½äYA‘Ï4ÌÖƒ¨i‡^@ĵC’€hm$ÒÛ!þF”¸ëJÿ%ÂÌ(ÑéÏ} ±øâ¢2 îä¢Ù¢‡ä }s““ãß ^Ç©¦nóÄö¼Åiïõj¥Ó£"X²VoŽ`A=I‚ånü½ ùÌðPiC%É÷z³|@y ¶“1™!ÚŽÄ$ÜŽ¼x·qð·ý.Œü6Ú•w €vù7ýjl¤ßÿ‘St…iÄ¿W혺Mr¶kl×9·jŒÍ< }lõYé¢äÓ}”‰¶Ñµ§RDšU·®K‰ÉšÎ¨Yè¡<¥!O©—âÕÌ:Ä^J[3롊òƒš‰ÎÊb#ÁE3+~¹(k ÂÛ\Sò/2Ý„ Ö”ô>r„N`c-ïiùß«ø¦n‘Ë'Ú yi3$0+¯CPÄ4‡ Y§ƒ^æ'+ò™mWÔ´é!ñ“¸ú”.hmvkÞ†,ˆ”÷—L˜ó°Å0ï„Ms‚`h:*n\4}7ÝŸÂEóÁ„ñ&l€‹æËUœŸÏccIÓ^ÿ{UŸ‚©ÛÔzbÑzÚÑÓÇÚâÓ’ÌóaJf…ù¬%³Äâ(&ømœÔÄÕé8ȉ¬²8ç‰Çfã(²Îâ”(²Ðâ)«õSf©ù*³Ö|B•Yl>Àʬ6©>Ê.«Ïø!»Ì@™]¶ïá™]öº³\Ì.;°¯Îc¦].ÄL=‡›C}úš¡ƒ>œÍPBŸÝfh¡v3ÔÐ'¿zèƒá Eô¹q¹:VÎPEŸ:g袔G…2J™Th£”K…:JÙT裔O…BJ[~„„$ú@9C÷JáË1Ó[!}d`ÇL?]æGìçpsz©ô{¥›JßWú©ô¥£ÊPzªü¥«Ê_Púªü¥³ÊŸPz«ü ¥»ÊßPú«ü¥ÃÊQz¬ü¥ËÊ_Fä`=À ÓXåG(uïG¼3Lc=°°Îcf®ÃÌn7§$ÇÓAð e9Ö)̱†Éð3ÅÙ`ʳ1Âhcˆ)ÑÆS¤Aª™Æ(S¨a¦TãL±ög€)×Oö¦>3L}¾3L}>0±Îc&§>_…™ÝnNc÷^δvïõLsw-`Ú»kÓà]K˜ïZÃÀ×"¦Í»V1ÞµŒiõ®uL³·‹Å´{»X+p»X+ïPÉÅZñD×>Ãty×&¦Ëß[›”.`cÇÌÚ^Ægös¸•qWûXëŒó:ò±Òx¬}¬Šž}¬uÂÉÇÊ/-Ô>Ö EÐ>V÷µµ¢Ù·µâm~ûX+ø–}¬w/ìc¥ñ\ûX ±µâ -aä#fòOjkEZÀ½¸Í5ÇFÖç1ÓV˜¹ª›ßÍás¬îWk îXk¤îYk¨îZk¬î[PwNŒ¸{'†ÜÝcîþ‰A«ĨÕbØê1nõŸ«üŒH:ÆŒ»ybfßÍß…™+ë2º¾Š™LÁ/ ÚôãZL3õ³eL;õ«fLCõ£g|½Õo¢1MÕO¦1mÕ/ª1Õ®1­Õï±1ÍÕϵ1íU&ˆÒ`e’(-V&ŠÒde²(mV&Œ0"£ëf˜Îê—Ø˜Îº7-^ަ³]_ÅLnõ/Kô•Ñ¥Ôä˜cš ^\Ê5±† 0'£‹©Î6º˜úl£‹©Ð6º˜m£‹©Ò6º(vÚèb*µ.¦VÛèbªµ.¦^ï3J¶ÑÅè{1£è£ë«˜É’âeÉá6º˜În£‹éí6º˜în£‹éï6º˜o£‹éñ6º˜oùñ²óÃc,6º˜^o£‹éö>ô±L»·j»…ÏéÐÈÂë`:T’ÆMóŒÏ(m>^õËý÷Ö&¥Í]_ÅL¶.~=fvs˜gh(üÀ‡£–{txjéñ´€W-û|øjé±F:œ•ÆmýÄ߱Ї»ð~þZ øpØ‚çú}xlßòá²—A|ø,¿³_¿ôÇÜ^[ðŒ0ò3^µÖa¯÷rà¡GF×ç1³oñ/ÂL=‡[̱Zb­Zf­‘Zj­¡Zn­±Zra ‰ïŸë·ÿˆ!·üĘ%bÐ’1jI¶ä@Œ[’àgÀ’?#Ò…Ž1㟘ٷøwaæÀèú*f®©M»9ܼ—[{ã^om޵ÀÚk…µ=Ö’xµ&žD-²vÈZem‘µÌÚ#kµIÖBk—¬•Ö6YK­}²ÖZe-–}\›âu@Ô¦½–xWm:0º¾Š™K8ð~7sF‹üä”6È9m“ÚD gµÉ@Nk‚œ×&9±M rfç’SËç–I"N.Eœ]&‹8½LaDF×1¶iA¼7-nâÀGF×W1sM¯½›ÃͽiÌ1z×Xô¶±Fè}c ÑÇ/úþ¡î­…õÞ~o’½¹Ÿ£dïî×*ÙÛû1Köþ~ë’Ú€ŸÂ¤và—2©-ï3êµýÒ${í{1Ã^ûÐèú*h®õv“¸Yó;¯Éü ,E4¿K‘-‘…oÌB¤ó´r[õB-E>+‰€*f Uì$"ªJdT±”©b*‘RÅV"æ1¡‘¨§â$QïÞâ$QïÈèú*h.rêIܬ¶‹5J«”Z/Ö)5_¬Tj¿X«Ü§zëÙê%Ü‚µ2ºì65Ûh¤þÓ­ ë¶›AVn·£ï+V¿wKž¹bÁrîeÁr¾btu3.ŠÇí-ú\ÿòËÏ?ÿ±X\#Ëž;\ßý¢W^åRΗ:5倩VN˜ºe利›VΘºmå©WN™ºu嘩›WΙº}å I PNšÔå¨IMPΚÔå°IPN›Ô å¸Yb®Y`T¹fêî•k¶ïî™kö2Œ*×ìÀá:ƒ™8ïqqž!JuS ¡T9öN±D ¡T=JõS ¢TA&J5T ¢TE.JuT £TI6JµT £TM>JõT ¤TQFZyþˆ(JET€â^E|9f«§®3˜‰ƒe‡nJyV²©.&ŸÚáb2ª.&§Úáb²ª.&¯Úáb2«.&·Úáb²k¡ÖÚáb2¬Ý&ÇÚ=a²¬Ý&ÏÚ}!Fìp}ÀŒ’Zíp1©uïV¼3Jj=p¸Î`&°^œîk‡‹ Êžc¹ Z&0{˜Ðì5d‚³×˜ ÏÆ #•jØcˆ ÒÆ¦AªœÆ(ªa&XãL¸ög€ ØÇûŒ¡#Ÿ ‰Ð7c†‰Ð×ÌÄAù‹SÄí¤3©Ý§˜äîÓ Lz÷i&Áû4“â}Z‚Iò>MÁ¤yŸ¶`½Oc0©Þ§5˜dïÓLº÷i´Põ³½yÜUW?óª˜N“¤ñÐ<ã3Jž÷é &Ïß[›”<àpÁL\ȹ 3õneÜTWޱž*‡+ûÊáÊã¦r¸úiÅéTV•ÕÇ}åpåq[9\y®ÖÊá*sW9\y<Õ§Ê&\ñ©³4+‡+‡úÔÚ„§Ü§}ÄLúºÊáÊßÑ~p¸^™N½Ä#‡ë,f®=¸›Ã-æX½°Ö@½²ÖH½´ÖP½¶ÖX½¸0 ^q/O ¹×'ƬƒÖ ˆQk ݵbÜZ?Ö*ø± tˆ÷öÄ̾·¿ 3×ÙÚt-fvs¸y/·èƽޢkE;Ö ‹z¬%ýXk, ²Y4d­²¨ÈZfÑ‘µÎ¢$k¡EKÖJ‹š¬¥=Yk-в[y>¬MY›ö"â]µéÀá:Ë/ÆL=‡›9£Õ}rJ«ÿäœvÈIí³Ú] §0,p^»äÄv/È™ínSÛý ç¶;BNn÷D.«Ürz»/Ĉ®C¹XàÀ{·â&|äpíµ¯ÅÌn7÷¦1Çè]½ìm½Fì}½†ì½ÆìöÖÆ{ïHTConŒ±w7ÙÛ£ìýaj¸´}¤-<ÙgØk3ìµoÆ zíC‡ë¬¨w)hö“¸YÓf.‘L›½D4‰l*áTL$Ò©ØHÄS1’ȧb%PÅL"¡ŠDDC‰Œ*–!UL%RªØJÄ<&4õTœ$êÝ[œ$ê9\g݃‹ASOâfµ]¬Qj¼X¥Ôz±N©ùb¥RûÅZåˆÕÊ-ë•› Vl·¬ÙnYµÝ ²n»dåv;úøþµbõ{·ä™{ ,÷à^,÷àK[#ÎC<½ÊUÜ®Ýe.dºå+²‡F×w¾äá;šµøHEëòk“RÏÒÞÑ=b$ªuã€~yky¸2~­Í_<¢ÛFV[7NŒŒ+WÓ»qæ÷–¯ÊÕ¡pi¯èÆHK[Iö7~qI7)"°¼œ‡Š›ó°eÜ\ɦërR`ùâ’\—‡ˆŒ+J7õux9ÿÞˆ}Ë¿HièûÖ!ßpûÖN¯Á$bßò¼=2´NccÍÿíK«î¦ncØb—§»,wñðó°èojÌÇ õ@Œc—!Ò½)ä±Ãì*²›”X^âÈC$€–Ø.5E9¤é{‘üƒ°4]nó9̇c¦Ø@le6£KñC¨eQ‘—]j/ C fŽÓCl4¹’çŸ\BäE–_ä}°ý˰Q0žçí‘qu0<—îlì¦ncJkŽ-²Ëm{É- ¯yX>ƒxÚ6W›üÅ$ÕÈŽÍÃ!’eópŠÜÙ<,NRi»y`,™µyX| $Úæ!˜Z!JyX\<‡Å$AVnΆ<\aA³ù)†‚fó—W~Ãk±Á ÙSR4q¾bR–{Xü½ ‰:ð ”•|Ž  ùê/`–¨¶€0­«0™!šëßÎõoÁ0Ã;G]4±od{}x‚ô{áš°1Lã©_Œôû?2Îb#W“‹..ì¦nÓ†œ†íÛõÐð¶6ó~åå(lõýÊ»Q(¨Ø.ù ÂE$?³ÓD‰égºF(@‰XÀ4ByJCd/Å+‡×ÌQÚúQoYà 4ŒP{éÀ(š}"qTSòs@}Ô”~ú˜kÿâš’_z`Å¿dFzßÀÔm"rù€B4/O÷$0W‚"æ§µ† iH¯ÐËüWE>vdjÚ·PþI\}x´6cb ÒÛ“‘ç„%s¶æ0Ák­ú”<|ÆEÓwÓ*\༕‹¦Ïá#Cè,6r÷zÑÝ•ÝÔmj=±h=íh½(h-½dh<½ «¾w¨šV-­ Â†W@b;,˜±YÙJ ¢l´`¶á‚7›ôÃ}ƒ=¬°ÁöNl°‡ l쟳àà ¨+Â5ws·I9ÒŽL]Iû5U'íæÔ¤´×S±R% ž¥:AµKU„Z˜j •2U êhªOTÙT½¨Á©¶Q¡Så£~§ºHuïpPüRQ¡øugQ¡ø•°þÐà9ŽKíÝÜm’œEå(H‹èQ® ¤˜-’H©[’B¸&erÑOŠè"§”ØE])À‹ØRží•xOR,i¿õ÷ö¡ÞÙǪ¹Ø(Uó;Ù(UóÌFÏ9CþËK »‹JúË/úƒœ1gOœï}Í+o*1̽£ÃÜu2ZÌM)£Çܳ2šÌ--£ËÜñ2ÚÌ 1£ÏÜ/3Íí4£ÓÜm3ZÍÍ8£×Ü«3šÍ­<£ÛÜé3ÚÍB£ß,±0 ­B'£ÐÜH3 í]'Í(´—µKˆB;òuÎ fUU»ÈØÙÏà¦|BkXÌ/´ÄÅ|C+`Ì?´@Æ|DëgÌO´¼Æ|E«oÌ_´8Ç|FkwÌo´´Ç|G+Ì´0È|Hë†Ì´¬È|I ¶ƒ«–嘷øN—{5bËÜžˆÑs¡Ý³ŸÁM!¨VÄ’jÁœ!ªÖÓ²j¹!¬VãÒj±ž!®Öòòj©Ÿ!°vk£€!²ö2k›!´v!Rk“B‘ýó1uµÈÏP×w*ÿ«ÃP×èb`ÿ\hígpSÐrÌ0Õ¯‚šc…ä+ˆ çXaAAšd AÓ0Q))¡²#è:Ž ìø (ûÙƒàè@ ‚£oF ƒ£œ¡ˆ)t¡5´ŸÁMa#ì=vy„ÁG@X|T „ÉGAØ|T„'DBX}T)„ÙGCØ}T¹|¸«®‚m*¥*ÙB†vmÁ³\eÛ5æ˜Ç œ>ªÂéo®J§?ò‹N VÑ…†Ñ~óü´…˜)æøÊ3jqJ.˜f ÎL4¿8S3Õ¶A“$&‹SzÁt›¾ƒ˜pƒ^ÞL¹)¯ü“nÀ³Ì´›m–˜8NTSGÜH0ùfE×íB€wˆiò‰ä`¾M¹Vv3óÍçŸ]¤ˆt¡´ŸÁ-f˜}¯W€m±Wˆ]³VPMµVX=· –\QÇ.©¡ÂÔï ’„P©B°Ä!\Zƒ>’"ô ‘ÊsŒµòB̾—¿ 1ÞÒ ÄÀVºÐ\ÚÏàæ]\2švy©lªáT%¤Ñ©ŠHÂS•‘§*$PUJú ª˜äCU9©‹ª‚U%¥MªŠJºT•íͳP…%W%)ƒªJ{ið¦ªtà8@ ̦ -§ý nfŠRåÅ$%Ú‹iJÓ•ä/¦*G@LV†˜®ü1aÙ bÊL$“–Y!¦-/CL\V‡˜ºœ1y%Bˆ,¨cæ+£AÌwï4ÜÄ||¨ˆu¡µŸÁÍÝhÌ0ºÕXt³^!v»^AvÃ^avËF»i#„ݶÄnÜc·n²›7BÙíÁTŒpªþPMx²Ç°»6bØ]ß‹v×GîÔ ÈÀ˜ºÐžÚOáfÉËÛ8%1oó”Ì\(©¹LPrs¡$ç2#;Teˆ’žË%?—1J‚.s” ]))ºLRrt¥$é2KÉò ‘¡„ç²D ïÞ²D ïȳú4dà \jZí§p³®n®HÝÝ\’º¼¹&u{sQêúæªÔýÍeé ˜ëÒ70¦¯`®LßÁ\𾄹¶| qqùm|ÿ\sù/ò2ô Ì}éÜË}é|ÅÉZñ­¶²ÝHú/ü¯?ÿáÿ–þ·ñíÇÿÜ]ORâÙS{ë»_ôð.ÝU!äˆ ‹³¬>>U‰˜±8t‰²8“‰˜²êÈ&pœè\pÔ>ËMÌê<è‚¡ULZ[ù[ŒY«›–woã,*bÚâ¨*bÜâ$+bÞâ +bଠ1mw°Ä¢ÅAQÄ¢½ëò‹ö²3_ˆE;2¸NaÆÞÖe˜©çpS^aœÒFžaâFÞaœñFbG^bœGžb ïy9JçËÉØ|üyq:yŽÖ&™÷hí’yÖ6™ií“y’ÖF™7iõùf:hc:xŽüÅwZâË1Ó[C}dqÁL¸[Waf7‡›‚Q­ð38ÕƒUí0xÕƒYí00¸Õƒ]íP0øÕƒaíp08Öƒeí0xÖ ƒií°0¸ÖŒ0"ëfôjÇ‚A¯ï‹—c†A¯&×Ì„¿ufvs¸)€¹šc¨7±#n zxB^ØÓ# :0€éÀ¦C Œá Rï4FpFv`Ùñ@€ö³}ÒJßJØ\g0×U˜ÙÍᦠ÷ØË{=‚⣠H>j‚棖 ˆ>j ²¢5¼›«Z… û¨eMy:× Ü>×Ê»Vò¹Ö•æ˜Ï ¸>j‚ëo®M ®?2ºÎ`&<®«Îxíæp+ã®vºrþFít¥ñX;]+4?;]ë„5’Ó•Æsít­Ð ít¥q_;]+ä;]kyæ?œ®|ËN׊;vºÒx®.ä„ÓµŽèõº]4pÝ»å7cj§k-Á7sàvYÕK<²ºÎ`&\®Ë0SÏás¬þXk þYk¤þškèþ›kìþœpÿNŒ¸¿'†ÜÿcÖˆAëĨõbØú1n}‚ŸëüŒH":ÆŒû}bfßïß…™³ë fÂçºxŸ±FĽÜ2÷zËt¬¾NÅZáûT¬%¾PÅZãU¬E¾RÅZ%™QµL2¤jdJÕBɘª•’9UK%ƒªÖJ&U-–}\›|¹Šµi/+ÞU›ì®3˜ §ëb>#)ZœQj¿8¥ÜqN¹â¤rÄYå6ˆÓÊç•[!N,7CœÙ¹‰äÔrCĹ喈“ËMg—Û"N/7F‘ßuÌå^ˆïÝ‹»8ðáu3áu]Ü7ÉñRosŒÞ5Ö½­×ˆ½¯×½±×˜½³1ÀÞÚaïm ±77ÆØ»ƒìíQöþÆ0µcœÚ?Ôžì3ìµöÚ7c†½ö‘åu4áv],Ðx£¡æÍœ"™7{Šh.Ù\,(¹˜P¤s±‘íªbD‘ÏÅŠ" ‹EB;Šˆ.†],)Búj2EJß\¦ˆyLh$ê¹8QÔ»·8IÔ;2½>šÚïºX öípªí¾wO5Þ×ò©ÖûÖ>Õ|_ê§Úï;ÿtü$Ý‚x1nB<(·!Þ€Ïqùµº~Ì€n‡ß: ¢§vnÉcÐÀ=ðStîeÁr¾b{µóXvÿ¾×?Áìú¯ŸÞúô?~ÿÇnû=þþå§·¿*6Øÿã÷;ÿKAhOý¯ï~Q½í¥eKÿ&°]õ ?ÒÃÜÇ1]̽2ÓÇÜKó9{÷ÚL/s/Ît3÷êL?s/Ït4÷úLO³Àt5kL_³–Àt6k Lo³Át7kL³–Át8ëELK«ï´#-ͽ?ÓÒÞõþJK{Ù½v¤¥ù_g0³¤?ü/äˇÿ…üùð¿Oþ’ª§y{Ùoâ-û_KyX<ü¯…·—ud™vOÝv ß­ÓY’…÷ÈtÖ$›æŸaž}õ(àŒê}kmbžý‘ÿu3C9Ï¥q¹äù·Ýæð °ÎL-Ö\gª–üCg®h€>“•Æ}}f+Ûú…À¢Ï|¥ñ\¿¸@ð™±¥„Ä™²|ËgÎÜ"ñ™´ÜºÔ"$δ-xHy„™®œñö°¥ÄÜÌ»yR/ñÈÿ:ƒ™~ÌUiú·‹î¬ïçp‹9V§¬5P'­5R§Í5t'Î5v§N ¸“'FÜéCVˆ1+Ä •bÔJ1l%‚·RÁÏ€• ~F$cÆ?1³ïüïÂÌÿu3]a2ã”™ð<]Àgös¸y/·$ǽޒk%=Ö K~¬%ñ  ß ó‹‚</I‘µÊ’#k™%IÖ:K–¬…–4Y+-y²–Ze­µdÊZ,]ú¸6ÅエM{‰ñ®ÚtàÁL[8ð0Í¥Bu×ñËÒäŒÖþÉ)í sÚ; 'µ·@ÎjïœÖÞ9¯½ rb{äÌNU$§–7"Î-ïDœ\ÞŠ8»¼qzy3ˆü¯cl/ƒxïeÜÅü¯3˜i Zú9#gJ{îe}“ü/õ¦1Çè]c ÐÛÆ¡÷5Dok¼èû‡º·FÔ{û¹Jöæ~Í’½»»doï·0Ùûû©Lj~I“ÚÚ¤¶p¼Ï¨×öC•ìµïÅŒzí#ÿëhŠišv®\¢æ+@³›ÄÍ"˜Ÿ‰¥HæWd)¢ù‘YŠlñ-D¸x¢"_°• «n)ò©XIT1“H¨b'QÅP"£Š¥DHS‰”*¶1 D='‰z÷'‰zGþ×)ÐU¯MÓ’ .qêIܬ¶‹5J«”Z/Ö)5_¬Tj¿X«Üg…ëÕë)Ü‚µò¿ä6ˆ5Ë«–[!Öm7ƒ¬ÜnGß?V¬~ï–`†Ñ¯an!úõQñjÌ(úõÀÜ:ƒ™;L;£â‚Ú´ŸÃM‘Ì1Çk¼ˆtŽ5Bäs¬!"¡c@¤t•g5ì1„HêÀ"«ƒ8QD^†‰Gdv|©ýdŸaÄt•lU"¦ïÆ #¦Ì­3˜™ð”Ë(‹ëªXò0Ñý -!=%M)]-f^\ÖAŒOÃø Fš´±2·ò¸©zd°ÔAf>±§ƒ"3/é IÍ3>Ã(û8x(û›k£ìÌ­3˜¡tX8ð´^Àgös¸•qS™[}ÇT™[yÜWæV7•¹Õ'°Ìõ¬4+s+ûÊÜÊã¶2·ò\­•¹Uæ®2·òxª”M¸4âgiN–« ËzÌnê¶<,¥œD.§‡,Aóp|Ï$0 ‡)(b~&§"éO^Ç —8:hò™†cÔ´+=‰kW Lk;° ‘Þ®¤ê˜w]Þ!L˜‘b:Ý•G †Ø(òÍE»•p+mgqøGNÒ l  ‘5WD¨í¦nÓÄÎ+¯HaÚçUoàoYôIY²ôó¥’² ó¤‡LÊrgM¿ 0ä—;¦€Ê<*H¾æ ˜Í=·€pîŒÉ ѹ%&àì°/fxÏ qðOksþóÈ+QÀFöÚ{±1·£3Ø(:n“étÉEÝzê6mÈÓÊ«PØ®§…7“°™O3/Ba«ŸfÞƒB!È]`ebšt ª‘iÔ%ìRbr³:GÊÝéå) yh½¯©×™öRÚ¦^ïVàutˆP' ¿(šS‹"qTSÐD»¦ä_dº ¬)é7|ä ÀFqž»„ p«ø¦n‘ËmUkš—‡0… l§†Rçl4”Û ÌCºvEß@(òÙŽ«´üLMópjM\[ŸÖ-´6»Õ¤7q޲Pâ<,6s¶æp\`R ãBåí1ÍßM(#;y%èßÁE[´ˆ Øèò¾Ñ¢²Œô°»©ÛØÆÄ–}L{icQJkKVÏXÐUß;DÓj0 ¥5TÐðHh‡ 34Ë!ZiC¶Ì6\ðf“~¸o ‡56ÐÃÞŠ ô°6öNÏçÁ1”óq•iè/hTvs·I9ÒŽL]Iû5U'íæÔ¤´×S±R% ž¥:AµKU„Z˜j •2U êhªOTÙT½¨Á©¶Q¡Så£~§ºHuïpPüRQ¡øugQ¡ø55σ#Ÿ0ÐOî®P8vs·Ir•£ -¢G¹Z4b¶H"¥nQH á"˜”ÉE?)¢‹œRbu¥/bKy^´—â½H1¥}Qf ÿ"Ô;[àX5¥j~'¥jžÙèékI]Öeòs½ïŒ›¿ùËÇ ´çÎÍ÷¾æÈº. 6Ì sOÄ\1w›Ìs3Ê\2÷ªÌ-s+Ë\3wºÌ=s#Ì\4÷ÉÌMsÍ\5wÙÌ]sÎ\6÷èÌms Ï\7wøÌ}³À\8K+ÌI«PÉœ47ÐÌI{×A‹¯k““väçœ@̲âBRûvÑEý n /´vÅpCK[ ?´òÅpD c O´nÆpEËj _´êÆpF‹r o´fÇpGKz ´âÇpH ‚ ´^ÈpIˉ Ÿ´Pû1c´Ç0ÆwzÜ«3X~|äòœA ^L+‡x/y›f?ƒ›R­„+AUB¹V¥£+U2»Z¥Â+ÁU"½^¥á+V¿bå(AVfå(Vö‚jå>(ÁV愲eû<@ _%î+ñu¯î¿1L|=ð~N f.éd¸Œ4_q>j?ƒ›S˜=ÃLiö 0ÅÙ+Ä”g¯ S ½ÂL‰6˜"m„0eÚb µÆ”j#)ÖF(S®`¦`áHÉŽOR´Ÿí1L•6b˜*}/b˜*}à@ÌT:­¥÷žÛ+ö˜Ý nJz]Ið±Ë#)>ª’ä£J i>ª’è£Ê 7!ª’ì£J!é>ªX[ž *×öE|vl{T)UÉò³«h žå*Ûv¨1Ç<ÉõQ•\sUBrý‘Ot1|—®\Gê¯Øcv3˜ç§/Sl[¬¸˜$ŽÃÓl¡¿™‰æWej¦Ú68÷$&‹ãxÁt›6˜pƒÞL¹)JO0é<ËL»YÑ^‰‰§q_ÙF-’F‚É7+^5»„àb²øQ1ߦ¨X73ßzsì@ÌÐfe/¹î¸ŸÁÍ3¬~W+ vX+¤nY+¨fZ+¬^[P+.„¨S‚ÔÈ aêó…@ÉB¨T!X"‚.AŸIú„HÝ9FŒZx!fßÃß„˜Oébºò¾ôTÞó̇.Ûc¤ñh—|¦]^ꚪ€Ä7U isª"’îTe¤ì© IøS•’.¨*&ÙPUNª¢ª DGUIi’ª¢’,Ue'ó,Ta‰ÅÇUIŠ ªÒ^¼©*8M'Ó®xð ;ÍzÌn73E©ñd’ëÉ4­å“‰Zê'Sµ@&k£€L×>™°m2e»dÒ6)È´ía‰Ûâ S·B&oƒD‘õtÈ|m0ù¾sîa¾GþÓ Ä4Å–À|¯¸°ŸÁMÝh5Ã¥[­V t³±BèvcÑ Ç £[ ›„ Û¡„õt’…@tóPtû`¨pªþPMx²Ç »Ä »¾1è®]©3)§*ûBd¦+îÆî§p³äåmœ’˜·yJf.”Ô\&(¹¹ŒP’s™¡dç2DIÏeŠ’ŸË%A—9J†.ƒ”]&)9ºŒR’t™¥dù„ÈPÂsY¢„woY¢„wäUL9rÙå‚ì%»Ìn 7ëêæŠÔÝÍ%©Ë›kR·7¥®o®JÝß\–¾€¹.}saú æÊôÌ¥éK˜kÓ·0§¯a®NßÃ\~ç‹<„ }s_ú÷r_ú_q°2YÏ·™ß[XýÇßì,,&š=÷°¾ûEG&Ö¤‰1`q°%ÆG&#'*3.CVÇÍãš Îzú4gÙhªÃž N{V1hmeb1F­:I:㌦š"†-Ρ"¦-Ž©"Æ-N±"æÍâcÏvýeÕ)PÄž½kå{öº]ˆ=;r±Na¦+·N€œ+6´ýnÊ#Œ#ØÈ+ŒÚÈ3ŒÜÈ;ŒóÝÈCŒãßÈKŒÓá=o>éð8i™Ï–#1Žž#¯Ñ$ó-P2ïÑ&ó -p2/Ò(ó$-1?ÂLL§Ê‘¯øN0|9fz ¥|¬3˜YJù“‘uA;¿ŸÃMÁ§–ñŒ*™_Á©²¬*›@Á«²Ì*›AÁ­²!ì*›BÁ¯²1 +›CÁ±²A,+›DÁ³²QL+›EqŠ2²`†A®²%亷%^޹8Yg03—`޵œõ™¯ÚÏáæ€å˜c0Ç  9Öα†Îð3Ú`@´1ÂicˆÓƨAT£ °6†pmŒ#;>È~¶Ï00Ú˜a`ô͘a`ô—u3¼½R^™.Ùgvs¸)È=ör½Ç^ ø¨ŠZ ù¨%šZƒ„¨E /ÞªV!È>j‚î£Ö5¼{+3k]Q«df­Âmf­¼H%3k]PiŽù ‚é£6!˜þæÚÄ`ú#7ë fp«Acã%èn·2îj;+ƒ¡¶³Òx¬í¬ží¬üŽLmg¥ñ\ÛY+„AÛY<µµ¢ç·µ–÷úÃÎZÁ·lg­¸€a;+çÚÎBpHØY눃hÍ.ú·nÐò 0µµ–›9pžàåØÏ:ƒ™2s¹Ý0\bOìæpó» æ¸I湉æºÉæ» 'ܤ#nâ‰!7ùĘEbÐ"1j¶È@Œ[„àgÀ"?#ÒŽ1㦞˜Ù7õwaæÀÑ:ƒ¾«WjSÉÑœÝnÞË­µq¯·ÇZà»R¬¾,ÅZâÛR¬5¾.ÅZäûR¬UÒUˤ5ªÖI‹T-”V©Z)-SµTZ§j­´PÕbÉÍǵÉ7§X›öÚá]µéÀÓ:ƒ™vDÐXÆL·^N·›ÃÍœQ’>9¥%rN[䤶 ÈYm)ÓÚr çµ%ANlË‚œÙ–9µ-rn["ää¶LÈÙm©ÓÛrFdjr`[äÀï,Š›8ð‘«u3|W¯¼ ’¦ã²¾É¶{ÓjŽËÿ¡ZƒÒÛÆ¡÷5DokŒÞ90€Þ:0‚Þ;0„Þ<0ÖѸÑÛFÑû†¡ Æ©ø3@máÉ>ƒ^;0ƒ^ûnÌ ×>ôµN¦ü—ù®ÞÁûIÜ,‚y3§HæÍž"š‹E6 Šp.&é\l(â¹Qäs±¢èbF‘ÐÅŽ"¢‹!EFKоwL‘Ò×’)b‰z.Nõî-NõŽœ­ó ¹ð]½ý$nVÛ}©žj¼ïÜS­÷•|ªù¾±Oµßúéø¾?Ý‚xnB¼·!€o €qÅSp3⥸ñÜ¿s°sKƒîß  {p/ –{ð%okÅëμ­_~þù»Ô(æ•=w·¾ûEþ;Ûò·Õÿ^j7]]CÀÜ 3$Ì2CÄÜI3dÌ6CÈ܉3¤Ì:CÌÜÉ÷z%¥ 9+w‹!iV ¢f%¡×K)«BÕz¨AT"Òf¥‚!nVƒjV_GG¨™;{†š½ëìjöº+é5;r·>]ÏŠ¯ ³Ä´A+nL#´"Ç´B+vL3´¢Ç´C+~LC´"È´D+†LS´¢È´E+ŽLcŒw—ÖÏ2!ͱzµ ÊB<ꄾÜo>!-ÒªóÌ =Ñ "Óß)ˆ¯ÇŒ•ÓGîÖ)Ì"þ½*q3žÕB¬i9æñʱˆ_Ž5BÑ•Æsý¼ß‚Æß'–ò`œ[À·|¢lÁEŸ8Ë~×oü!9$N¬-xÀGy„™ïYë„×RÌ‚›9p—›Ôcwë fÆ>ï0ñçp󻿏Oæ¹æºÏæ»'ܧ#îã‰!÷ùĘubÐ:1j¶Î@Œ[‡àgÀ:?#ƒŽ1㾞˜Ù÷õwaæÀÝ:ƒ™üHSýïeûŒµ îåܸ×[c-°`ÇZaAµ$ÞD­‰Çy²]‚!k•EÖ2 ެu$Y -X²VZÐd-µàÉZkA”µXªóqmŠwQ›öâ]µéÀÝ:ƒ™¾\$Ž/ã3É­ìƒS†òÎÎ]w;à¬á,€Ó†óÎÎ8q8àÌv6È©í|sÛ!'·sBÎng…œÞ΋0"wë‡SüΩ¸‰¹[g0Ó•kçñïU}“Ý-ö¦Õ—ÞµZƒB «5*½oµ†Ð c}ÿPõÖÆ{ïxi½y+‰€*f Uì$"ªJdT±”©b*‘RÅV"æ1¡‘¨§â$QïÞâ$QïÈÝ:šG?ü %XŒFj»X£Ôx±J©õbRóÅJ¥ö‹µÊ p®·¬Â-X+wKnƒX³Ü±j¹bÝr3ÄÊåvˆµË «ß»%Ïܱ`¹÷²`¹_z{0ñö¬tÚÝúû°·~J8{ûñ¿þüÓ?¼ýî·ïrËž›\ßý¢w&— ‹îñ™a_nÖ憘aan˜&憚acn¸F憜aenØfæ†~Ðë.;kð: †¥Y0`˜š…A¯»¬Jä[! Q`X› †¹…(„p³ ¦ 7sƒÏp³w >ÃÍ^S†›™\ŸÇL.ùøêÚæz¸\qy?‡›R-¼1•ÐÂS -Ü1ÕÐÂS-ü1Ñ S-2UÑÂ"S-<2•ÑÂ$S-\2ÕÑÂ&S-|2ÒÂ(S#C|þˆ¤(ZHdŠâ;!ñå˜i, >2¹Îcf™.{õi?‡›âMÃäbü©M.Æ£Úäb|ªM.Æ«ÚäbüªM.ƳÚäb|«M.Æ»Fâ_mr1Ö ãcm 0^Ö ãgmÀ04Ñ&×Ì(®Õ&ãZ÷†Å«1£¸Ö“ëfÊå-¼Ì²^p@h?‡›c”=ÇŒYö0†ÙkĘf¯!cœ½ÆŒy6‘TÃCŒ‘6Æ3m 2†ÚeLµ1Ìkc1×ñ@ ö“}F±ÐN…Xè›1ÃXè“ëM8qàÌap€S‡Î 8y(4Zm°€Ó‡CŒØä:âÀU(VáÀï ‹›8ð‘Éu3h튙Þ4\/ÞÏá¦Þ´šãһƠ·5BïkˆÞ8Ö½s`½u`½w§ÉÕ[ie ƒèí£èýÃÐŒqiú H[x²Ï ×Ì ×¾3èµM®3  nË¿Í÷þö“¸YÓf.‘L›½D4‰l*áTL$Ò©ØHÄS1’ȧb%PÅL"¡ŠDDC‰Œ*–!UL%RªØJÄ<&4õTœ$êÝ[œ$ê™\g@3O´-GJ/M=‰›Õv±F©ñb•RëÅ:¥æ‹•Jík• V+·@¬Wn‚X±Ü±f¹bÕr+ĺåfˆ•Ëík—"V¿wKž¹bÁrîeÁr¾breU®&×ÿüå÷ü—ŸÞþªmÞ~üyïs•4·ÌTM®ç_ñÀá*Îu„3 ­YШ1+­YÑ 3I­mÐ*3g­mÐI3…-£†±k™ïæÇÄ™ï–Ϧ¹Æ÷"ß- W†Áå“m¤¿e–ÒŽ¨™LŽk ˆ·ÛÉ1sÆí̘9dÒ囨勑X—O oÎl×]h9þÞ*î-?a>FÜ[;|¦_–ö–x`e}ö±Jè%—(v·)b±k ¬1€1¿¾¾9ž±#ç`xc~¼{s´c¾Ë^þ¯8éÕ)Oot=s?7Ð ixgw<ÿ NÂŽwe7 «ì&T=FY¦aQ3tÙÍ nŒÁìf¬È(„,ÿä¢ 2%ìïANÀuÝCÃê2r¿…Þk¾âJÖnâ6%³v¤Ìmí¬.S]»Ÿ?>h›oÀs˜+dG.ͼØ4ª4Ù|=¾ÊšMÃb@0‰¶oXýSÛ7°/˜bÛ“E3ã¶o`~ðéç¾7Â|ܾuB(äáò —M¿Hñ.Û7µÍðRd0[¶[ÚRg‘ÑJ1¾*v·)ÏÙÓŠ´gO:² ½$HŠö‚!%ÄËÙè{›*ƒZP`Bµ€ÂüjÁˆéÖ³¯A&c  ÌÍ|™ª-p3sûpÏ`$µÁHê‘ÁDêô <2ŸN"cF õNî&nS ¼¶bfÄk£f‚¼¶qæËk“gú¼J³éU ˜ì òÁ\{¦Þ«ôt_T˜Òp¨ÊV‡µôëdVª’×ý¨ v8‚È3d¯j û« sìÓ÷Èb:‰Œ iÔWè1»‰Ûò0—pÑ·²¹ã1>Q¿4ÌR‡ˆažÜŠ6¦¿8·+"•ŒòàPÜêþŠÓw»‰Û<­3/JaÒ›Y?%«c,X3ê5’²œÍ ÇJÊb7ƒ¿7C!¿Ñ6Pò±.`ä˾žS3›ÆˆLÓ°®dAov׌´ó½ˆÆnM£¢éy) ¨ÈÏʵw¢"‹‹œ¢Ïƒ¢4$}1»ùŠWõ«iÛ¸ ç«(«wèü|ãèí{yªìíùáÄÎVj{W…uÐí§ü©^{ݽÎõdíi •b“áÑ•hítP=ü×VÇØs [[½TQ~BC+¨T¿Uo)ËŠJpP8Ò/À‹NySY»Qõ¯-é·{dþ|Ë”U5\Ui¯¸®]OÚF–¶¬t} …[Vz>…ßåÓƒÉ_ƒ™aё˴1šà”Ë,•>Î|ˬ5õ)ÜBU³µ³šÇ.d6 ¹iT¼0àÜ Ðz è+MݵÒÓÔ|+]M½¹Òפy(,©42õ¶J#Û7·5_Fv`°|/ùÄ›\·‹žÃÛÏßæ€@IJ ”⤀ÁΙ“x¦Az• %g)ÀPj—%†)QZ™%¥)@QJ›%Ä)€Q:%ã)ÀQ*Ÿ¥ž>À ¥’)ðp/“½/ƒEÁG¶Ëçñ²”g§qKh½BCÝÏßæ ÒΓÈ(•v­ SIÛÊ8•ò­ T ãÊH•n® UÉêÊX•ê® V‰òÊh•f¯ WIúÊx•⯠Xʈ•_ |ȇy€fªJoW¦ê^p1^©z`Æ|/s‰\š¬Ÿ-ëõh7›SŽ=¿ÔÊ=ÿLIöú0EÙëÇ”e¯/S˜½þLi6>˜âlü0åÙøb ´ñG…ÏødŠ´ñË”iã›)ÔÆ?SªŸì/Lm6^˜Ú|+^Ú|`Ñ|/î–ª´\q{?›sÔ½3gÝû;sؽÿ3§Ýõ9î®Ìyw}a`ësâ]Ÿ˜#ïúÅœy×7æÐ»þ1§Þõ‘9ö®ŸÌ¹w}mq¢ü a.¼ësáo­GŒ…?0n>NË€ó÷Ï^ ðÒ”³Àæ» ^ú¿•ïæFôØÎù<^í]žeÍ÷V=^vó·Åü²¿Õü«ýÕú¨;Öú©yÖúª·Öú«õ>Ô™ ?jÜ…/õõŸÚ~áSªñ+Í€ð– @ôKmà‡CÎ1VÔ® +û~ý¬˜<Ÿ‡J[¨Ëˆ×X¯x½l7{›vnicÜØ%œqß—ªÆ² ÉUCz‹ŠÄ:Ö)y,I’ùX±¤² I d½“zÈr(i‘ÕRº#‹éj.Uj­tÞÃÒ#…•g/ñÝRx¬ŸÏƒ£ÁCˆ%Šg¾$õ½ž»M4P:Y¢Ôu’HIïä˜ÒåIA%Ú“¡JÑ'•ÜO~+/€ô×у`ÇrHže1[Ë õ–9Af.ç‚htÈceÆî}€[Xì!t¥Ýéy¨äŠ$¸zî6õ”1³åó^:R¯ V¯úY¯(Ú]¯7ºa£Ͳ±‚^ÚHB«mœ¡7 Ѩ£èã`´ùÆ7T€';šbƒ=ñ­à@K|d@G‡Ã$åÚGWw“·I¡ò¦ Ë[6ô-o迼ÝCs1€xæRmÍ…Ò›Ë ”9!w.QÐõ\À û¹¼AtñƒhèÒMñ í€Ä溅íÖºíÈ<:³w,åPAþ÷×!v“·Iï6£ƒn¾µÜlbºÉ"Åv“IŠñ&›ëMF)曬Rì7™¥`²K³Àd˜f‚É2Í“i™"Û;³â!R(Þ› R¼¿• B¼ÿŠ¥”éK;|ô”þæ/¿ù˃”¯§¦Ò÷¾æ•®’‚±| Ž*®2X˧¼åÉ 抳‹Pqâh#îÇÉGÅÁH<ÚÚVÁ`må*)X,N]âÑWÊd0™Ïl2¸ÌG:læŸ >“*£ °úh‚À|b’A`û.›A`/;Ű[éÓˆ±¯Ô”û;—DYìfps>Ÿ*3¿Ï'™™ïçƒÎÌÿó9hæú˜4ó}Ššù‚>dM]ßg°™Oè#ÚÌ/”.¨|Cé†Ê?”®¨|DéŽÊO”.©|E龃¼AŸ¾fÞà^Ç{5bzË—Œ¥ˆYæâ”C4—¼s¹ŸÁÍ1 RÖ*å]1¢Ræ3*å^1¤RöS*å_1¦rs*ç@1¨r“*çA1ªr&³*çB1¬r6Ó*çC‘³ô1Œ5•S XÓ½SðjÄ0ÕôÀZ:¼;·Ât‰·´ŸÁÍaÃ1ÃÐöcV+„0ãXAÄLx…†l0,Ùa˜²İe#ŒaÌF µF#”aÎF0Þp†AûÀ°è'{ Ó†'ß‹f'˜K'3ÍíµîÒ~7Gš{gä¹wyF¢» 02ÝU‚‘ê®"Œ\w•a`€«#Û]¥éî*ÆÈwW9FÂÛ]bd¼Ý%FÊÛ]bä¼Ý%FÒ?á1ŒhwUbDû­UI íöÒ Ä kþÒ%RÎn·2îj)7jµ¿´Î dò—VøÕö—òK+µ¿”Æsí/­ð³í/åg kiÅyûK+^¶·¿´‚gÙ_ZyµIþRϵ¿Äˆ ûK+^œB>"&ÿÄ¡ö—V¼µ+óÍ/û-ÇÓ Äôp$Ë­€¥¹àøç~·˜a5½\7Å\!7Í\A7Õ\a7ÝD€›r"ÄM;䦞sÓOZ B-ÁÖp+À¿õ|<$õ£Å <ѲoàïAËÅt,-žÎqèá ûºž¿Mû·…4lï–Ù°ûû6Šƒ¯¡vø>J‹/¡òøF “¤AÖ- ‡,k’Yõ$:²(J’dÍ”`É’*9“WJñaòÝ"Àr¯ÞS~L¦ðhú"—wÜÇKt—zö6B©ðä‹ÒèI'¥à“mJß'•úO®*o€TVΙ®|aG‚'Ë“ –cA–-?ƒ$\n9º¼âA.Ó!£•‘@B»7îá³6Óx”ÿìPŽL W“Ýìmê0cnK3_úS¯ ÚW¯º[¯)š_¯8zc㭳тÎÚXw4ÒЗ‡hÛRtõÆ0š~#šÀ“Ý-²áù^x A>2šÎà£<‚²¿Â¤ÞMß&ÍÊ[3$-oÜP¼¼­Có¦½Ì%rš Ô6—ˆq.6Ðê\Š å¹PAésƒè"Ð÷i!#ú²-TÆcúAÑÍÕšÛ­Õ…’Û‘ÕtjÿÈmMWn.¦-ù2‘Ö™¡‚ûr8Dr߇†îk唨}霼ï¤S¢÷•uJøq£\x‡÷áaÄuy´¶q›C\¶‡wñiQèªþÎÂxŒHú¾îNIÿVªJIÿKfSúœ¯.0e³é¯ÿø vë©Ýô½¯©ÍM…ç.HæÂ%µPJ²R“ª¤+5±JÂR“«¤,5ÁJÒR“¬¤-5ÑJâR“­¤®¶Jê+»II_­ãÝðüªš|%…IP’˜D%IDP™d%sU7¯™Ì¥¦[É\û¦›É\/»Í`®»é bp©®²›ö3¸90OB˜õ$”)pOBšù$´)°OBœý$Ô)ðOBž%ô)0PB ý`ýž ã¹!4ýñÚf?VÄÀCIÁÃ@ { Ü {/GŒõÌGvÓ Äàé8<¶^q g?ƒ›s9ýÊs;ýs=ýFõ^Ìõ _Ì õ`Ìõû`ÌõóaÌ%õëbÌ-•¡\S™Ê=•™¡\T™ÊM•"„Ènz€æŒúá0æŒî̓W#†1£vÓÄàI¹r»{¹$¸f7ƒ›Óc†!öÇ àíÄX!¼9+ØÄÙnb:±í&¦Ûnbº±í&¦Ûnb:²í& ¶›˜®l»‰é˶›˜Îl»‰éÍÇ{ŒÒŒm71ÍøVÄ(ÌøÀn:\~ñìÜrA4À~7gŒÛnb¹í&f”Ûnb†¹í&fœÛnbºí&>ÝÏñr®_žÃƒ”¶›˜Án»‰í>tÁ wÊ`Æ»m0Þ‡:˜Ìc”™OÐá¡ù[«’"Óì¦3{ Ž•ãYÓ+žBÞÏ`žŸBðâ`R¨ß¢[zP2lZ`aûàS÷õÁ¨4në÷èXÜ>X•Æsý"Ý‚c4>˜µàyÜZÀ³|°káu,üJã¶~–Ža>8¶àñU!äb:<¼¬ƒV ^½¿•ùvù¹òc»é bJ=êËA«i¸BÜÍà3¬¾—+྘+ä¾™+辚+ì¾›p_N„¸o'‚Ü×aîû‰@ëD¨u زnÑø·$‡ôžc´¸‡'Zö=ü=h9°›Î´IHÃâ ¨W>õümÚ¿­¦a{·Ö†ÝßJŠƒu:ÔŽx¾®”–x¿ÇÇ¥¢0YDݲzˆ²fmUÏÊ#Š¢uIÔL«–(©Ö4Qq% xʮԟ½ xOù9°›NUŸò‚Uyâ!_”¼Š¯X/!´¾h©tÒB>ئe~Q›àª¶€@`º¶@„Æž,k‚4ZÆY¶l ’p™äè²DˆÙM‡ŒÖ~íÞO¸‡ÏØM'àÖ§)wgÓ‡÷²Hv;̘ÛÒ€ÆÌ—þ4Ö¥0’XµÒÝÆš.üÞ¡ê…¶Î~µßHDãíÑ—ûuE´í~z]½ßeDÓïGAƒŽw¶È~ò­ð`ƒ|d7ÀÇØÏþÑÝ!î¦o“få'F!iùýQ(^~œ‚X¼\Zô²xÖ´Èi~ój›D…§bC­N¥ˆRž •>•1 *rÔ U)#ª@Re<¦ÝT]¨¹ÝZ](¹ÙM'ð1» ?º½‚}ì¦o“ .nG‘\̺x¡$vÑFI𢕒èM _´T¿h«,ÑZY¢½²D‹e1ˆ6Ë‚­¶EAÚ½·0žIú¢ª’ôo¥ª”ô¿ô\^¢ÖëûMû÷òõÔlúÞ×Ôœ¸ˆ4ùVÞÿõÏ·21J ”¥Ô¢*qJ-¬©Ôâ*±J-°­Ô"+ñJ-´±Ôb+1«ubµÔ¢+qK-¼¹Ôâ+±K€½$(ñK‚Á,Ò0!+©„,µÜJÈÚ·ÜLÈz2u`6ALybðfÞ4y?ƒ›ƒë$ƒ)ØN2™‚ï:§!"O2›‚ó$Ã)XO2‚÷$ã)˜O2Ÿ‚û$*ØO2¡‚ÿ$#*P2£‚%C*XP2¥‚-D ƒø$ë)ˆo/ë½1ÕÌGfÓÄyRÍzÅ¥Þý nÎÇ´ÙÄüL›MÌ×´ÙÄüM›MÌç´ÙÄüN›MÌ÷´ÙÄüO›MÌ(TW›M̵UÁüQ[Ì'µÕÁüR[!DˆÍ¦ˆQÞ§Í&æ}î­ƒ#FqŸfÓ Ä ‡œmíËbm61…×3L©ß+À_¯S~½‚Lö 3%Ø`Šp¤ {1…ØcJ±HÙÑeʱÌd#œ)Éþ0EùxQªpä!Uø^Ä0TøÀl:ÜjšKmZ®¥ØÏàæ¬o`¸0+ÜG˜%î# Ì÷‘f‘ûH³Ê}äYæ>Á¬s™`ºT0+ÝG.˜¥î#ÌZ÷‘ f±ûH³ÚyŒ²Ë}‚Ùå·V%E—˜M'3•üŠilËAˆ+ú±Ý "í¾©Ì¦~np¨FÇ–æ”LÇšæ/+ëØÓ´‚ ëXT•Ù”Ç}e6åq[™MyªÖÊl*SW™My<ÕǺ&ÞÍÒ±¯œ$\™My<ÔÇÆ&¼5ëžì#bÒoÔUfSþŽöƒÙôrÄtj ™M'3–4¤q*öätÙÏà3¬®—+஘+ä®™+讚+ì®›pWN„¸k'‚ÜÕaîú‰@«D¨U Ø¢nÉø· ‡ÕžC´¸ƒ'Zöü=h90›N€¡YC¹Ö?_"÷Ôó·iÿ¶–†íÝJvëp(VéP;¬á¡´XáCå±þ‡ÂduuËÚ!Êš•ET=ëŽ(ŠV%Q3­Y¢¤ZÑDŵ\|T€,¢þìåÀ{ÊÏÙtÅGè kÉÑ)Wñ«Å „–âÁ-ÔƒNZÆÛ´È2j \5B” •µ}¦ksDØÖx² ÐhÛ`Ù65@Âmy€£Ûl61ÚÈS*„vï&ÜÃg̦ðÀC­]y+m¼B–ÛÍÞ¦3æ¶4 žyô§^´¯^5t·^S4¿^qôÆÆZçÈÜ*¿€±„ÆÛHC_n¢m7JÑÕ Ãlú…pjOv´È†:ä{áùÈl:ƒ"©$j–ñqÅÍÙÝômÒ¬´5SÒÒÆMÅKÛ:1múÔËT(§©`PmS9¡§bC­N¥ˆRž •>•1 *rÔ U)#ª@Re<¦ÝT]¨¹ÝZ](¹™M'ðÑ aFæG®iÅ>¨‚‹ÛQ$ó£†.^(‰]´Q¼h¥$zÑNIø¢¥’øE[eˆÖÊ"í•… Z,‹A´Y„hµ- Òî½…ñLÒU•¤+U¥¤ÿ³©Ÿdo0›~ùÓ~þóÇà¯'fÓÓ/8zEïŠK*ÈÎê'ôNÖêgô¦HÝJ^Ç„®4ÜxS^W¿¢ëE˜W¾&ưTm‡–ßWbÀòû¢Œ K[ÁaiŸzT=¤‹ Š‹+̃ƒÈZFŽè† âÌrá𦀸dyÓøû" ,§OŽŽK£÷‘ÂÍkІ °œ’ùÀ>ú”4sxÙ¸(3¶1noX h!Š/ŠÞ…˜¾l~cƒ†x¿4*J¢ÿFÅ•W!Ò1å9ú±ƒF3yìð58öÐ÷C˜Fˆ(.ÄŽ”CDŽ#ØbDZN£¯0Ðä›ßm8vÇ_…Rás`äCè¨ð7` ž±Ñ›#9b9Ç k‰ÈÎqÂç ¯§¦ÑÊQúeFr\Ä€æU‹ˆÐq†vøÐq†²hÑqaé*±£iT\D’Žd·ˆ+8 xL8Šß€˜ÓqU:æï0€¼Ðôó‹n¼Ð<Ùë-@\hž§ϧ1PNP³¿„¾V3¶1…׳Yz=Óå³ëU(ɾ^¡áÕkø}M¤kÕ‘$,D eXhA±„tb¡ ÉÅB R…N$ ¹HC>Ú, :ø> 98ýôG¦Íç1ÿ“SñøæK<¾jÆ6rkWEX·v\yk7FÈ·vj€kG8¸vx¼ð¯Ý¡âª WÕ@¹* ‚ÊUmb®J„€sU)„Ÿ«‚!ýˆ E\µ)â÷Õ„ˆç³Ø0ç00,åúÆxÅ‘ëjƶ—‘W¶ïü}iÕó.“Ñ7FDB‹/$u+w…‚²ôIæÓnÌtfwyÁ(!7§‘4Þò¿Ã!ò[ï«1Ð7‚_Šô{?òK>‹¥¸°m9…xÍK_ÕŒmÜUóÕÕ;n>à:z7ΫÖ{§Î«Ýyï:ÝÉÉBö×'ïþ]«Ë¿¹2¤Q3»jt -’RQòÙåÑÕ&'Í®DiÄgÊEÊ…öÞæ—6Zª[;×!òïkAúù¼†“÷Ž®ùþÒZ‘ðÀù,æòÎ[S^ŽÛ $«zÆ6²«ìµ·f^i;¤°²v¢4X[ƒÙ\;É£*·ó&Y!åˆÅ(m»¼p0Bø{ô©SÄŽÌÁ:[r0Òv„ضšG-FY¹ Ó3| Lõ¦ï¤ýQîµ¼ýzNˆxµÎÇg10Î}È–Wœâ©glc—åÙÄßšiÕ*àf·V¨tu^½•ß7D7¨UG§(D ‹ZÐa IÌî ÊЙ èZ…Nt´B.ºÝ£}½¡0€Þð>  7 ìíÏ‚`(®^o® …õ”m”[´­BŠÑ– ™FÛ1$mÕw´CúÑYHÛ?$#•ÈI*šTR C©Ü@¢R)‚|¥2iK% ²×!€H¤b‘è¾b‘¨{ŸAWzîW°ÂzÊ6ê®âWÐdŽ ×Š—AËgƒÎ+> X\ú°x ´cqDèÊâМÅ-©G“wR«&'¥Ž=úûúವþ}¤‹B-¾B-îæ/܃鲦‘ç¤6&þöŸÿf—ðÃD©'¶Äó¯xàKdº^ò1b˜Ô€0¥IýCœÔì1ãI # Ô&2!J=$¤Ô`2_JÝ'ã§§O•t*õ­ ¯RSËl+u¼L¾R;Ì\,õÊLÍR#ÍL-錙 è1eJ])C¦öm) ÷ªžSfÅYd“}™®xý¾ž¸M9oR{˜')ˆqƒ3Ë‘˜/'…‰és’Ÿ˜M'mŠÉu®˜k'U‹©w’¼˜‰'=Œ‰y˘§'%i{’Ù˜Å'Õò#2T'ÍŠIu{ÑêµÈ$Ì=²0>‰ {…¢\ópy=q›ò!Gç–ôH ÅÌ–”ŠÌäIIÌÌ¥”þÌÔJ‰ÓÌ´”rÍÄKÉÚÌÔæÍ´L âÌÒ”ZΤMIéÿoç®$ÇqQ__³ïÙuéÈ”-ŸòÔ2øÿ†vêdžîæn‘3ˆÞvD€«uoæ­®gsöP:ÂG i|U„Ë&Ú!\î#íŸU€ËÉ`ãve0Ù×s^?Ž ãnn)VÖeM$ÝEIë–¬uÃÀÙºÀnÝlP¸JP®B£«Œ€ì*2R6% W‚ïU¾À}7èßù™WeÆ=Spq'ãŽ;•ñ:~|9‚¾³[¸¥4jbXÕÔ¬=Æá\{ÈCÁ¶Àȶ@ðæ¼å¾¶Åú¶¥6·… r·e ®·E ê·%&¸bø¼Ï€§m5§}f5§=‚Ü© ž=;zº]¸¥{Û7 ÷6wܶ²õþÛ^_Ù´Ÿ?}^‡!\ô²åü\urýée‡ŒÕZ"ã;’M«û0à6Ÿ?}^g"Ðl¢†«¾QÆ5I\;ЇñRû™èõ®Út4r»2Æ'Ü?äM‘ÝÂ-.ke=v³[/Ú ‹QÍvÖÅf³kq#…úߥæ82ªsŽÈj«#Ázî´†<ò­[¸kå#ýF#3eÔG{c|†2&“Û•ñ441:ÐÇ#¾EÚ-ÜÒ£¸Sê¦Q9ÆUåoŽ•Ð+¢ XÊGã±—fg)= ÖR˜šº¥l5’KQk^—’×0/ñÑîg”Ë&§³jÒØ,ÕdŸ›QM&c”›•ñ>âS®†òÎÌná–¶oÍ£ÓÜ5¬Në×$;acî´ÍÀÓT6 OËÙô< i£õ´«’ÝhfʧÕmbŸF¸q~Úäfýi¢;(';s•YÚT=è>V?£ WnVF&l˜óp1t·pK-ߺ¬?3p­*×z¦2p­³‘ËÍÒ¸ZÕë{ A.G”“íÊ-͉<‹I‘<©É˜<ÇI <åɧ¬¤WV²-ëÉ—Õ…\ÌÚCjfe"S³n‘¸YÕÈã¬y¤uVD²¼y£AÔe9!ê:³œuÍ17KcûnÙU G¥ v„Ë6pD϶wÓ6ÄÖ¶†„Ú6ŽDÞ¶•â6Ä嶤„é6¬Dí¶³ âÛì&¦o+œÿuý½—M½|' òq{Pòñ3{Pòñ_Ï\›”Ï¿æ—ùÌ~ß}5R Ó_ hþæ—ü)‡oÈWyEŒ á;ÐG^ǃŒä]=þc½È7zÎõ–ßøFh½8¨LëýÀ̽l˜MÏ››…ù´^;ozz'^”¡Iy›Ö”W!Q59 œi{el°™¼7šiïCfú! Ì4Ñܪ ߯åëòvÀé´[º¥„4oäÂOóº.t5ïòÂ^ó¢/d6oÃmóŠ0T7ï'>ïåbˆpÞ<†×€.4¹ÆwaÍ5Ü ‰®Ñ_8u C±kÞú6ƃ_Þ+†ñ¶OÕ~XÏ¿ÒÜ¡ °HãNÑÛûï}o—n)]±AvØ‹¹Cflnc#òP ‡ùØx=DȆïáE6šM²Á}X“õC¢lèNeG¡Xv`ÆeÇ C§4_µ²Y|øû,þ‡µr2¦¹]¸dR”·§#nœn—n)˜u]X"t—}üc\7e _×-oú»¡àbÝn²Q  f• Z…Ä_O™‘÷)B·J®«¼çÎÏ Ð²j´ì©Ú€,;ÔÜ®ðîGÿzD¶¶[º¥LgdˆÏ×ð =Ì¡E{ÔÃ’¶@š¶Lð\»EJµ%†µµå þµ“èØNj`g;©¬í¤îö¼ß€JmMáËï3kJ Ô“QÍíÚxYüõ½°kÇwÄ|w»tKðÎjÀÅ;«&ï¬Ö¼³PôÎj Õ;«dÿ²áØ?of5ÃF;«ù¯‹;«ù]³š‘¼8«ùüée3«]à¬æc<T1|ÑFöÎj ÚŸÙ‹†g?ÖÜ®çñýÂõ>Èõ}Ÿ.®î–nqak3³ì5¡Ù”ZÔlY ,ª½e»5¿ˆAkŒT4ÎI[Ì4݈PKŽD5ìX;¼5ûˆ¿!ÊL:e´±wʧhc2®¹]Oϼôtýñùˆ×vK·ô@6â¸6»â0÷+Žz?s¡ø eÂ]("~éB‰iÞ–Ô4.å©Y]ŠW“¼”¶æ|)|MS›¦h6{Õ¿y¡¦ì¶SjÊd`s»6ê÷u|÷t9"ߨ.ÝÒF®¡vÚ¼FÞiˆ§El\ž²azÚËFíi>ħ5mLŸÆU8mm#þ4½¤%îx s‡i§;Z(m:›Y/Ú\>½è>—?¥ŒlnׯÃ81^^‡6žÈ7¶K·Ô® Ë‹‘.û0n ÖÒ-Ãxº¡ØR·Óª°´Jë°ÃÊ,ø‡Š+­D1Ú ®¼1éós«6ð°§j;ÚÜ!ŽñÇ>K"Ÿgëa‡É‘'2¹’ç5©“§9™”g=‰••€<Ë:AÚe! ³Æ”YÈѬO¤lV/28k Ÿw’ßùí'éÞ´áHøeQ!ü:³¨$üšmîÇÓe|Y{`2êGµDÎ~£L íÌÄÕ~ÝL˜í§ÏDÝ~MîGÓÄäëÕ#D_?·ûú-öà×µ‰çýŠ›ðÞO¼‰öýþ›à¿‡oÇߊc¤æ~iMj~f7šÔü—7c»þ<¸ù÷ÿù¿ï€F1¸ù›_2Ü1: ¨†(” :Í@„êCêK ‚¨6„¢:ÜŒêÃ7ª;þèeC?z]7'½HÈïcÖ•‡¼TÏ.S}¨Mõûa:5H æhó/”£šå@Žöf9Œ£’`G“ÁÍ­Ú#3þ­óüËv閲ƚP…DÖü*œ²¦[¡˜5û ã¬ÉXhÍÍÂGkªzZ3·°ÕšÈ…¼æ{2pÙ|ljÛúÍ0éë35Ãîú† ½7€ýª hi ÙBKÛ‡l?­&Šß nnׯûx5…¼ñzåné–r }j Š¡ï0Á8ô‘&^‡ô'øˆ>ï=Ñ·Ÿ`+ú0äE_‚Ëè“RP›ó‡éØ)@ˆ„Ù Bh‘/D Ü|Õ¤EŒ‚´¸çX€'ƒ›Ûµq˜Å÷Ñ_y&b»tK§ëÂ’¨»ì<ç¦ðV [ö0tÕÁ àU7`YÜ𖪃®n¾:¸!úsp,ÖÁ (Y7€fÜ€¡ž´:¸Òz¦6Âh nnׯëøÎ_žŽ#ø:¸Žìàv²ƒÈÊnà.;¸Êìàf³ƒž>_ŸãcOßã!A™ßCn I{ëδw P{cFµ÷ XOûð×GÆÆ{ÝgÖ”à'ƒ›ÛµÁSÅBŠß,ÝR´º7p¯{?,»·w ¶{·¨»7`¾{/$¼·† Æ{§ ¼7ŽàÍ{ ½·• Õ{— ˜½7Àxê}œ?¥ nîÐmŽšòötœOéà&p]X ¶.û0ë¦ k¹nÙ0žë†¾ç÷¾lLkÅKë+w^ŸÀÃû>^P }<+íËzmŸÝÆû&&}znÄÃúÀöLmÄÃÎ7÷Wq<Ž›©‡à[vk·49òíHr%–$uòÕI2©õIÊaP×÷*Gžåc–¤]¾tIÖ“¤¬(9ZëSR¶V¯dp­mIèZù’ßµ.&Ý›6 ¿ZT~YT~Í7·‹ãa Á¯Uåçvk·4rn+—@º^âê¶ ³Û$&ên ™ \ê-1yÛÏ„èmN±·uM߯¶ñ|ÚÞ†÷iŠí¿®¿÷um¨wcyjÞn4©ù™ÝhRó_zíS-Ÿÿß n~Û½‰V<Ð_Œnþæ—üä77êÔ…¹S¯$Ohˆ=õ©úÔņ÷SPphAõÇ ½È¬¡zë ˆê¼C*ª/Ǩ®=”£zú0êøCH2J´Š0Ì Úå ƒöv9Ä a€A“ÑÍ}Úpts9àìné–’»šQ…ëÕ+Ô¯¹oƒ Öô+İfcá‰59 m¬¹ZXdMÝB*k&ŽY»PΚç…Ö´/„´fá§5) ]Íö‹6`5f {l³ý°6š)~7º¹[#¤;äßíÒ-¥þ9º èèb £x‚Žn  :ºEèèR¡£8†Žn ®4QÝ@Ht?Ñ)tEg°0 G7ÖF¸…ŽnàîãùŸÕF°…“ÑÍ­ÚèWzïÃ]£ú£ –Žn†º°ÉÔ»ìÀFÝP¤n R7Œ©Û ät¥¿¼ì¤ U!OUf„Šôª̪€Á¶*o ®Ós#ÈÓ•3§§jâédts¯6.£¦<1ºÙ-ÝRÖ°³tHÄNÚá;‡‡bì”Ʊ3|ÈNøá#;ÿ‡žìíØÊÞ€¼ì͸ÌÞ;€Úì­˜ÎÞY€øìxÐÓ~#´d¯@K>³¦–<ÝÜ[SÞÞ®nùéáˆ(e»tKAåÞÁcî  çÞßîíéÞý îÍ ë/¾úã:º žÝ;GÐÛ½‘ÜÝûJ°ß½ÍÞ»Nã½ X^CôE Ö½FiýÌ^4œõÉèæŽscœ¯ã››ÇCÎíÒ-.lf–½>4›R—š-«‡eCu¸l·þ1莑ŠÞ!鬑™¾êÊ‘¨žëè‘·~ñ›¤L´¡YF{³|Š6&£›;´1Þ; Nàˆ^t·tKd#*Žk,sã-ŽzÃ/ ÑeÂàŒ"b¬F‰1t£ÉQž ì(^Æy”6Ã> ŸQ eÑ ¢i;©)¦lÔ”}ÊvJM™ŒnnׯûàÌ?7ð°j{ª6ð°³ÑÍíâH«1>õ}8âáˆÝÚ-MŽz"'WêyÔ©§y2©žõI¬Z ’gµN$íjIÖ“¤¬(9ZëSR¶V¯dp­mIèZù’ßµ.&Ý›6 ¿ZT~YT~ÍF7·‹ãeü±ãK΃ıY»¥‘s[¹ÒmôW· L˜Ý&1Qw[Èám0“·ýLˆÞæ4{[×ðmlϧímxŸ¦¸Ñþëú{?Ö†z7˜§æíF“šŸÙ&5ÿ•ÑÍóÛÀ}ÝüëßþPaÿø?° îè endstream endobj 749 0 obj << /Alternate /DeviceRGB /N 3 /Length 2596 /Filter /FlateDecode >> stream xœ–wTSهϽ7½P’Š”ÐkhRH ½H‘.*1 JÀ"6DTpDQ‘¦2(à€£C‘±"Š…Q±ëDÔqp–Id­ß¼yïÍ›ß÷~kŸ½ÏÝgï}ÖºüƒÂLX € ¡Xáçň‹g` ðlàp³³BøF™|ØŒl™ø½º ùû*Ó?ŒÁÿŸ”¹Y"1P˜ŒçòøÙ\É8=Wœ%·Oɘ¶4MÎ0JÎ"Y‚2V“sò,[|ö™e9ó2„<ËsÎâeðäÜ'ã9¾Œ‘`çø¹2¾&cƒtI†@Æoä±|N6(’Ü.æsSdl-c’(2‚-ãyàHÉ_ðÒ/XÌÏËÅÎÌZ.$§ˆ&\S†“‹áÏÏMç‹ÅÌ07#â1Ø™YárfÏüYym²";Ø8980m-m¾(Ô]ü›’÷v–^„îDøÃöW~™ °¦eµÙú‡mi]ëP»ý‡Í`/в¾u}qº|^RÄâ,g+«ÜÜ\KŸk)/èïúŸC_|ÏR¾Ýïåaxó“8’t1C^7nfz¦DÄÈÎâpù 柇øþuü$¾ˆ/”ED˦L L–µ[Ȉ™B†@øŸšøÃþ¤Ù¹–‰ÚøЖX¥!@~(* {d+Ðï} ÆGù͋љ˜ûÏ‚þ}W¸LþÈ$ŽcGD2¸QÎìšüZ4 E@ê@èÀ¶À¸àA(ˆq`1à‚D €µ ”‚­`'¨u 4ƒ6ptcà48.Ë`ÜR0ž€)ð Ì@„…ÈR‡t CȲ…XäCP”%CBH@ë R¨ª†ê¡fè[è(tº C· Qhúz#0 ¦ÁZ°l³`O8Ž„ÁÉð28.‚·À•p|î„O×àX ?§€:¢‹0ÂFB‘x$ !«¤i@Ú¤¹ŠH‘§È[EE1PL” Ê…⢖¡V¡6£ªQP¨>ÔUÔ(j õMFk¢ÍÑÎèt,:‹.FW ›Ðè³èô8úƒ¡cŒ1ŽL&³³³ÓŽ9…ÆŒa¦±X¬:ÖëŠ År°bl1¶ {{{;Ž}ƒ#âtp¶8_\¡8áú"ãEy‹.,ÖXœ¾øøÅ%œ%Gщ1‰-‰ï9¡œÎôÒ€¥µK§¸lî.îžoo’ïÊ/çO$¹&•'=JvMÞž<™âžR‘òTÀT ž§ú§Ö¥¾N MÛŸö)=&½=—‘˜qTH¦ û2µ3ó2‡³Ì³Š³¤Ëœ—í\6% 5eCÙ‹²»Å4ÙÏÔ€ÄD²^2šã–S“ó&7:÷Hžrž0o`¹ÙòMË'ò}ó¿^ZÁ]Ñ[ [°¶`t¥çÊúUЪ¥«zWë¯.Z=¾Æo͵„µik(´.,/|¹.f]O‘VÑš¢±õ~ë[‹ŠEÅ76¸l¨ÛˆÚ(Ø8¸iMKx%K­K+Jßoæn¾ø•ÍW•_}Ú’´e°Ì¡lÏVÌVáÖëÛÜ·(W.Ï/Û²½scGÉŽ—;—ì¼PaWQ·‹°K²KZ\Ù]ePµµê}uJõHWM{­fí¦Ú×»y»¯ìñØÓV§UWZ÷n¯`ïÍz¿úΣ†Š}˜}9û6F7öÍúº¹I£©´éÃ~á~éˆ}ÍŽÍÍ-š-e­p«¤uò`ÂÁËßxÓÝÆl«o§·—‡$‡›øíõÃA‡{°Ž´}gø]mµ£¤ê\Þ9Õ•Ò%íŽë>x´·Ç¥§ã{Ëï÷Ó=Vs\åx٠‰¢ŸN柜>•uêééäÓc½Kz=s­/¼oðlÐÙóç|Ïé÷ì?yÞõü± ÎŽ^d]ìºäp©sÀ~ ãû:;‡‡º/;]îž7|âŠû•ÓW½¯ž»píÒÈü‘áëQ×oÞH¸!½É»ùèVú­ç·snÏÜYs}·äžÒ½Šûš÷~4ý±]ê =>ê=:ð`Áƒ;cܱ'?eÿô~¼è!ùaÅ„ÎDó#ÛGÇ&}'/?^øxüIÖ“™§Å?+ÿ\ûÌäÙw¿xü20;5þ\ôüÓ¯›_¨¿ØÿÒîeïtØôýW¯f^—¼Qsà-ëmÿ»˜w3¹ï±ï+?˜~èùôñî§ŒOŸ~÷„óû endstream endobj 753 0 obj << /Length 186 /Filter /FlateDecode >> stream xÚ]O‚0 Åïû=ŽµÝØÆ®þ ž4ÙÍx0Š^ DåûÇ@YÒ×¼®}ù¼€à hÐu«½qÀŒÞá ¬2ÌI#‹¬<„\dŽ&I™•‘›¦j>Iª]&O‰fy«Ê¶-¿cd…c½†ã± ‚cCÀÓi­s$mà^‹·@çuýYÛÆ½ÁXµ¶°mÄ9>Z,uÜMÇÐt–Úck½U¶£VžÑºèf:ÿC[µùÂýAÌ endstream endobj 750 0 obj << /Type /XObject /Subtype /Form /FormType 1 /PTEX.FileName (/tmp/RtmpBlhU1W/Rbuild4444671013800/timeSeries/vignettes/colorPalettes2Fig.pdf) /PTEX.PageNumber 1 /PTEX.InfoDict 755 0 R /BBox [0 0 432 432] /Resources << /ProcSet [ /PDF /Text ] /Font << /F2 756 0 R/F3 757 0 R>> /ExtGState << >>/ColorSpace << /sRGB 758 0 R >>>> /Length 30481 /Filter /FlateDecode >> stream xœ½½K“&;v$¶¿¿"—ä‚¥x?–âHC³1“fD¶Ù6 NwŒTGSì&%Ó¿ ò‹¬Œº‘±˜š vfU&àŽwÞ¿ý§·þíŸßþõ—ÿãí_ßæo{÷6.ë·qz[×oÓú6oßÖùíO¿û¯oüåúóßþÍ_¿ýöÏ¿ôߺ®{ßݷ-ýùçßêþ÷KÇÿ!þü»ÿð¿§ÿkú›þß_þ´uo¿û¥ûOéÿýó/}þ‚·ÿí—eù6¥{_¾­ÃÛ‘‡ãž†é‡ØÞþP†sn߆ Ãa+Ã}ǰÏ_¼ç¡ »õmêºos—‡é·Ò°ç÷Îù«òpß0œ—4ò¿[†ã˜†ã·‰_Üíi8}ë—<œ¾mCn+†s¦¿‚áKÃôÕ/¿msî†éW™ºõÛ’ªiú¶Lú}ÿðËÏÿÔ‚$ý¦eÖ¾ —1ýïWÖ§ÌÕ¸”ÿo+¶ù¿¹>ý›X¢÷kP–è?i¹ó?Þ¶oü¿ã2Lë¾.eÞúîí7ÇÛ_ôùö›þåýÍO`£K¿ôœþ\Ò/ñë±QOÝ‘‡eQÊr§)ÚÊpÛ1LëO@þÌáÙžÂFŸ1žçm 6†ŸÁFBždltCúsžÇ_fêŽ_æôÑ\ÊpÏ›4ì6úë;' íeX>ƒól,ùÇ/Ã.ñÊEIPYÊpÊP™»¼(y¸dlL{þ¦<ÜF ûüÅe²Òpû6 eØgˆNé›Ö2òß<¥°/ÃqÁß;•¿j΋W†«ÁP†Û+l¤ŸkX˲Nyò¼–/_÷‡°‘þÉ 3Ã}cü5ØØÒoÓ¥™¸aßh¦îøeóç%&vÌ[BL{ù”Ç¢ôß–¾Z²¸Ò‚vúÞ²ÜãþmÜ ãÖ@e\KÒpèfã"L޳0¹pØwàq®ïqÄu¶o¤Ÿ«ëéçZÇg±‘~à¿l‡}cú5ØXûüwý |£™º#û>6äqÈ?Œ·ë±/¥\›ùX¨„·ú1í s‚4,ÛËİcƒb gq‰6üC,@C‚Êå) §ªx¥aW•¶¡`Ä…o(,Èeq˜K‘8åéçæ¨)CùãIP÷ÎPÙç2,0·ÛHá¸mÆäš‡+1Yœ‡ý†a‚÷˜Ö¸*à·Ô 6òÏ•~a#ÿ [ÿ(6òÏ¿bßXéL¨Øò¾Ñ§_â¦}ƒSwpCÎÃ~÷v=nS&hÚÌÇmD)ÇVŸ‡ãàB0nC®Ø*yˆm¤|ÊÇ­ÇÞŽ“‡Ýê4nþ!”§<ìf¯qÝ ”¶<,%…o\·ÜSýe1 BÑÓ´n/¹(jJþAR­šR~åÑš’Ân)ØØ~}ÆFŸ~ôgúï›ø§î ‘ËÃRÊAóò°¬/Hà¸.ø ‚"æá<™@æ!¥ÐË<ì‚|ŽkÁ¢¨i.½‰k–Z›‡ÃnÒ›‡…ça·™0—aaÞ ×)#Õ`Èøhþîµ3Í_¾ÌrÑq-¿sÆÆþsØØú\Sº1ë}ÂûM} §î`[ÚØöÒ<Æ¢”Ö2–l®´ »¾wЦÕ`@Kk¨ á5Ðfh– B´Ò†(mm¸á&ý|ß@kl ‡}èa+lôÝÏcÙÿËýrmæî r互÷k¨NÞÍ¡Iy¯‡båJ=Ëuj—«´0×(e®@ÐÑ\Ÿ ²¹zAƒsmƒBçÊýÎuêÞ9á€øå¢ñëÑ¢ñ+c}íJÍÄ}p¤ÏMʨ$gS9Ò&z«M!f›$Bê6…„n‚ ™Üô"ºÉ)$vSWð&¶’çI{%Þ“KÚŸý½cE¨[àT57…jþ(…j^ØhpXÍó? ²{V„¦Ì«?ÿþ_ßþêíïÿðŸÿô·¿«–frübŸùšêwÓ;7V[þY†Ä”>‡­roz õ›e\ù7y¼TNÏU3›ÇSeáäñXy8yÜW&NŸJ×^¹8y¼V6NOUžÇCeäôiûß+''—ÊÊÉã¡òrÒg·H‰WúÄ¢»—ù'ì*;§Ÿ!0µ=4ÐøuÒ\~ß3Gçb悘±ñùõˆifð(ã©R¯ò¸«\~‚¦*í+ûJë§òù³r–ÇCåìô‰6-•îÖKt§,×'ÒµTæNw•»Ó§gª4¿>³ßÊßÉã¡2xú š‰Å>Që½²x^ &ýDSeò”ßàËóÕˆ™,@¾òy. fíóî2-¥oh®Û<Êx¨¬ž>õ2såõô©áØ*%=ûJhïG*žÔáÓ E˜2}w•Šß§.l¬Ÿ~„s, aË"Èã®2}R/ ë‘CŸZí¹²}2"¶Ê÷!Blü¼@Lú‰–ÊúÉãý÷óÕˆÉÝè¹ûs1Û”±²ì¹ñžº;ªR3ƒy~²ÚSͰ¤y­À°eE)V(»Êê‡ˆÓ §Â=W.Pê¿­• ”Ç ‚†²Y²”V9A=ÄÄ@h÷5‚‡>—Ã@øÐqú ö´ó=&k}•!”Çý;Gè«3t08_{B³Ù*ÜŠ¸{ËAƒvÓüô¥ÜÄ.ž&j«|¡<*c¨ÇRG•È¿ce õié¦Êê{œMqJã±®Rý„OU,ÿC•?Ô種‚}!õ¥.@ÿêXÎ<Œwœ¼m§ðäÛ8$±Øæ!™E€¤e’[”HrQfh„º AÒ‹2É/Ê$Á(s £ BRŒ2 É1Ê($É(³,?"2ð¢,AÂ{¸,AÂ;u«.Cf€Ñ~õ»Tß`2ÐÕƒ+Bw. ]>¸&tûà¢Ðõƒ«B÷. _ ¸.|ƒàÂð‚+Ãw.-_B\[¾…¸¸|9¾­¹|㋼„ |‚à¾ð æ¾ð ~ÎÃJk›E±0±~÷OÿÏÛ_½ýõŸþúojØõiXäaýð‹jKb^ËQö³áŽ“}#üpíc‰ñ¡É<®L¬<î*kq*+NdB,Š›N{ú<çVŽêÇqÏ ç=}t+â\]‹ØgIWœÒÔQÓ4++»ÊÄrA«L¬<*kȺäwGý6fÕ9ÐrXÛÊ÷+@þeGº¦W´^»XW0³ÚÔ­ÅšXo Míæš±F:„ÆSecåqs„;Ѥ¹>áÆ}ec ‰f-••Ç]}|\&‚N—eâãðyÚp¦ÊÆFÞÕ…™¼¿‡€9 ‹$pÄ  y]Yy<¾3²¾3¹É=w²®`fÊÝǹÈÌý };‡i†2¶zŽi$x f\0ôM0~¼†#0§5NϽ²²ò¸¯¬¬!‹|5†ú¨cøAƒ5£iœk § s¯¬¬<*+kHEj®¬¬˜É[`eeåñüÎÊúr̤Ÿs;÷².a¦X¸ý¶ßqâ¸Ã4C‰ì•™•ÇCef Ý«Gµ ë­C&"•™5tƒ­ Çú¦SÇ«·ªU™øÔwRA_ëËNoßÊÌJDj«Í¬½áafx…™•ÆK÷!ŸI?aWßyêzÂÖ¦~Çýô7ëf¦bNdV³/7œ'lçð(ã¡¶³öÇvdg¥ñ\ÛY;,rÛYéwÝk;+/Fmgí°Ðmg¥ñXÛY;ŽéØÎÊsWÛYûŒû7²³v\Á°•Ækmg¥ñRÛYy\ÙYï1“ÿÅ©¶³ÒwŒïì¬/ÇLêp·s?ëf`e­åö-Òa3‡G̱š`­šd­‘šh­¡šl¬q4áÀ@4éÀH4ñÀP4ùÀXˆÀ`ˆÀhˆÀpˆ Àxˆø „Hψu SÌDSÌ|×Ô?„™3GëžVï3ù`×]ûLA;o4IkÃ^ZjAÜ–êx:]×¥PKâ¾jM\˜êx@]7¦P«¬%²–Ykd­³ÉZh­’µÒZ&k©µNÖZk¡¬Å–›OkSÜBmúN;|¨6yZW0³”£@Î4Ý 7·sxˆ3ZÒ'§´äOÎiK€œÔ–9«-rZ[ä¼¶$ȉmY3ÛÒ §¶åAÎmK„œÜ– 9»-rz[.ˆL­Sl‹‚ø;‹â!|æj]ÁÌT:&Üòî8˜ÞÎá¡Þ´šãÒ»VkPzÛX#ô¾±†ècÑ;Ð[FÐ{†Ð›ÆÐ»ÑÛFÑû†¡ Æ¡ÄgÚÂGû zíÀ zí§1ƒ^ûÔ׺š¡œ´€|Ëu¼v‰`±™C$‹Í"Zƒ‘—ÉT, ÂE1Hņު‹ÑÈ R*V£˜A$Œb1Š!DÆ(–!ãæ1Dʸ˜ óBCQ/ŠD½‡‹E½Sgë húbååˆn¿Ñ´“xHmkõPããÖ=Ôú¸”5?îìCí+ýpâÆ?Ü‚êA€â&Tïlx0ÀÏ àÐM¼6@·ÂÐÍð[t;ü”ݽtи%¯AS܃x)îÁÃ,˜îÁOÞÏZó6üÎÛú/ÿô÷·5,¸¾ý¡·õÃ/ª·´¹÷o9ÜqLu५GYgÔ—qåmåñTy[y‡OuzZ¢Ãazê}yÜUÞÖØã ˜ôÂQ¾õÄ<ž*ok„8ï.õkû,SæÓ•·•Ç]åm=”¿ù”ÿ‡ÊÛz™ô•·U~ƒwÞÖ×cƺé+oë f梡 .wôhíeÜUÞÖ˜«ò¶Æ4uKýÜYZü½~ ­§B«ÇÒzxÁ~K­ÛõöžZKã¾ò¶Æžºjëà™Ë÷sïRy[yÜWÞÖ˜6œ±ò¶ÆÜ÷WÞ1boëfòqíÊÛÊãõ·õå˜É tîm]Á .öÍxm½ÁÛjçð(ãn«ç˜¾‚×€oQzøˆ§×°æäm¥¢5ÖÞV*jsíme½¯ö¶Ò¸¯½­}Ñ}%ô€Ð7ÃÛJ”i¯½­4kokÇkZö¶2«¼­÷˜É?áP{[é'Úßy[_™üsçÞÖÌlèë—ìŠn·œ»hæð(ã±ö¶é\jo+“ÐÚÛÚ»æ:ïµÒÚÛíokÃÙxÅ×ýŒ_ø”·µ­ð_åmm¼~¬si\¿R;l|rNçD6\ó9’4îºøLþ‰¦æ=¿ñ£µiØøpâkoë förVg†7ü÷¯ÇL3‡y†¦Â|*‡úa¿Ü?×/ûmðÌ}Þj±F:•Æ}ý¸ßOÝç¹Ð¯Çy¯ gw|,Ï]ýÀß6àbΓm¸âófY¨ßøKã®>¯¶áaäf¼g­ó]©¿_ßy[_Žˆ_gÞÖ%Ì”÷ºöu$rnÀL=‡G̱º`­ºd­‘ºh­¡ºl¬qtáÀ@téÀHtñÀPtùÀX¨À`¨Àh¨Àp¨ Àx¨ø „Jψ¥ SÌDWÌ|×Õ?„™3oëŸ)Ï ì…Õ¬7î3¡m¼N%¹mã3’ãP B®Ûx®]rjIõ žüŠÇy®]r!jUȉGȨu!G¢†\‰Zr&jiȨµ!‡¢[s>­MÕ»€+„jäÇjÓ™·u½oÚʵ­ùÌ4sxˆ3†®Nº?8gøôÜ퀳†¯N¾8oøàÄá[€3Û× §¶ïAÎm_„œÜ¾ 9»}rzû.ˆ¼­S>8ðw>ÅCøÌÛº‚™±S—þ{º3íêM«9žñžŸ×`Ácž^£×0½†P c7}ÿTõÖÆ{ïxi½yEyPc‹ÃuKÊG;]=ÚÄ:£>¸Œ+o«ˆ6•·•ÇkÕgçñRy[y¯¶à9 whï1“~¢¡ò¶òwôï¼­¯ÇÌ ^â•·u 3pBË“§¹NýzÌ4sxÄ« Ö¨KÖ©‹ÖªËÆG D—ŒD E—Œ… †JŒ†Š ‡ÊŒ‡ Ï@¨øŒ„t†™èê™ïºú‡0sæm]ÁÌŠS:e·É޵τ´ò^•ä¶•OJŽC-¹nåÑvÉy¨%!÷¡Ö„¸òh»äBÔªQËBnD­ 9µ0äJÔÊ3QKCîD­ 9µ84ç³Úò!jÓwòáCµéÌÛºÔk—s¤K/äÜÅgBrg ]œ2tpÎðè¹Û7g _œ¶ Àš QË—'ßœ9| pêð=À¹ÃÙø –|ú«öUÀéÃw!Fìmqà* «pàï|Ї8ð™·uIÓ+ÓTþúåŽ^»ÃC½i5Ç=žÔ ·5ÚG%J±·†í5Fï@oAï]¥¨Ai5ÆÐ»ÑÛFÑû†¡ ãÔü ¶ðÑ>ƒ^;0ƒ^ûiÌ ×>õ¶®€fDÄ^I™ï¸TÜNâ!Ì›9E2oöÑ\ (²¹XP„s1¡HçbCÏň"Ÿ‹E@3Š„.v] )2ºXR„t1¥HébKóBCQÏʼn¢ÞÃʼn¢Þ©·u4ýñj·ÜŒi'ñÚnÖH5Þ¬’m®Y'Õ|³Rªýf­tÌjé˜õÒM0+¦Û`ÖL7¬ÚnY·Ý ²r»s|ÿ^±úÖ-ùÀ=0 ¦{ð0 ¦{ðSÞVÞž²¢ñÎÛú÷ÿü§wÁmùè̹³õƒ/©k`y! ¿aÇÙCFŸ Ú3£ÁÊtlÚ° Af¨Ú° fäÚ°¢½f Û°1ÏsZëôæ0·4Ü™ü–qŒõ–¹ÉØ£R2&nT žùÍ÷C×7GÌ#3å@7NPO—†È…CxÝ87 åø}«l·±CÏl·gû] u>_ö% d¶[š·WÖç±±”»âEÜ ÙLÝ¡DÅq¢Æ¼Å4,‚ÓGr f5æû¤Ã›“Ç jsGÅâqgÌ'²¦R!ýíœ"›ÿ!æKN=„F¦O¦aÁ³)§ÕŽÉ•iXTLæZæŒÙòÅH½LÃyy‰®”îgÛG(dþA¶§°oùZí §êóØ@šìXÔ᎛fêE±N$ j&¬/c\§ ŸA¾_›†;‡¹6NdÑ ˆf˜ŒÍ…  —MÃb=0zvZX÷L;-0.[;‘?3Ô6ýÅöàKÏxNù ÄÍÃ5À0-ô÷¿ÇÓdÓw‡i²éË+ƒák±Á4Ùi~éH};Èqyî–7Tš©;áì‰EÀ³§ñÏ^„C{É âíô½];-00”ZPadµ€Ä@kÁŒq×!ðQFe À Ò¼³}ºo0…ZØ` õ“Ø` 5;ç<]ÀFQõ í-·xõÔŠ~׆Ì`xm׌×fÎPymõŒœW!` ½ÊÃTDf¯è{ â‹ÊSNUñ Ò6›Ò…o RYpîþ”o0½^5…éõOÖ¦×ÛK‡é6Ê^¾ xÇ ÀfêŽ<Ì¥\Dn(ª‹iOa–:Dó 8L¿rnWD/yrPä3 ³!j: 'q0 Z;€‰ôHÅ%†Ò‡‰03áDtzÀû_Ã{là<¾¸è€¨ƒ'¹h¶ªÁá_9I°Qd—}ÖM©žË©¦îðÄŽ¼!ÅiõŒeÐ+#X²^`A;½Q‚åîü½ ùñPé7C%É·|³|\y ö‹1™!ÚÏÄ$œö à ï~"®þ~lÊßa£ßy# Øè·wÔ_ôó¿rŒ>µì¸­»ß!É5SwhCNÃ~í:ÇYͱ™gÁbŒ­>ë#C‚|ê`Œ2ÑwºUŠH·ëv)1YÛY£u]"”§4ä™õR¼ºUGÚKiëV=[Qþ¡n¡C„²ØIøEÑÌÊßK.Êš‚L7×”üƒ,aƒ5%ý„¯œ¡ úF9ƒ¹–>e»å­ˆzê¹|"¡š—†0…@³;ELÃy ™õÚ5èe~³±"ŸùwEM»R?‰«Oë‚Ö¦á°éíÈ‚H‰q›É„9{ óNØ 0)†n òvÂEÓwÓ*\4P˜Â¸h¾jõÊú<6†!×”ïƒN7¼CÜLݡЋöÐÓŽæÑ‹‚ÖÒK†ÆÓ ºë{§ªiØÒ *lx$¶Ã‚›e­´ ÊF[f.x³I?Ý7ØÃ ìaŸÄzØ ­ÓóypôeË@ðT~ÿ.C•#íÈÔ•´_SuÒnNMJ{=+UêYªT»TE¨…©ÆP)S¢Ž¦úD•MÕ‹œj:U>êwª‹T÷N Å/Š_OŠ_ÝôÚÑù<8ºr‡¶/]ì¸Ýpâ¶™»C’³¨i=ÊÕ¢³E)u‹BRÁ¤L.úI]äT;©«x[Éó¤½ïIŠ%íþÞ±"Ô-p®š‹R5’R5Ïlôò­$8°Ø;øùÛßýõ¿¿Ï0ûعùÑ×|¥u£Ü/õDÊS·©Ü05£ÊS¯ªÜ1µ²Ê%S§«Ü25ÂÊ5SŸ¬Ü3µÑÊES—­Ü45áÊUS®Ü5µðÊeS‡¯Ü6 Êu“´¢œ³@¥rÎÔ@+ç¬í Å¯k“˜svâç\@Ìí†N;ƒ‡Ã¥])œPÒ–Â GçYâ c ?”n¦pDÉj O”ê¦pE‰r _”f§pFIz o”â§pG ‚ ”^¨pHɉ ”Pû1 S”§0ÅVûjÄL–_¹<s»ÍÓÎàá„ÓÉq–H@•P®„TéèJP•Ì®„U©ðJ`•H¯„ViøJp•į„W9J€•A „XùJ•½ „Y¹J •9!„Èöy&¶JÜWbk«î5b˜Øzâý\@LkþÜ‘<ßÌàáeϰ„y­S˜½BLiö 2ÅÙ+Ì”g#€)ÐFS¢ ¦HaL™6)%¡L©6‚™bm„3åÚŸ¦`°Ç0Úˆa*ô³ˆa*ô‰#t1¥ÇZÑiÝñÌ^;ƒ‡“Ú½‹3ÉÝ»<“Þ]˜ï*Á¤xW&É»Ê0ÁUˆIô®RLªwc’½«“î]{<¿ï*ÙC~víÁ³\e{_ÿ€Ç0yÞU‰ÉóÏV%&ÏŸøDŸFŒ.¯ñ©½ñŽPèfóüô…˜)æPÊ+êyÎL³‡þf&š•©™j_d0YÇ3ÓMS³WŽQ¯•eT¦®òŒòxª™v‡à3q4ÙÁÔ"b&ßáq-!äbºrôØÌ·CšÀ³Ì7ë]çîÑ•=¦ä,ìå)Ï[ì£v˜aö»^¶Ã^!vË^A6Ó^aöÚF@ß¿W6’¤F^SŸ/JB¥Á„pi úH‚Ð'DêÎ9bÔ 1mÿbN<¥+Ì·\ÝŠDœÿ¼m‘Æ£]\ò™vy©kªßT%¤Í©ŠHºS•‘²§*$áOUJº ª˜dCU9©Šª‚U%¥IªŠJ²T•í̳P…%ŸW%)‚ªJ­$øPU:qš®t×ÅjŠ?oã1’ŒÅ¥Æ‹IJ¬Ó”–/&*©_LUN€˜¬Œ1]ùb²Ä”ƒH&-“BL[†˜¸,1u9 bò2H„YOçÌWƒ˜oë0<Ã|Ïü§+ ÞIóŸ·õJr ÔÆ £[@7ëb·ëd7ìf·l°›6BØmAìÆ0vëF »y#”ݾL5À§ZàOÕ„öv×F »ëgƒîúÔ•º™¡l/ñçm‚Œ7J^ÞÆ)‰y›§dæ2@IÍe‚’›Ë%9—Ù *C”ô\¦(ù¹ŒQt™£dè2HIÑe’’£Ë(%I—YJ–Jx.K”ðž-K”ðμª éËžñçmª¯™ uusEêîæ’ÔåÍ5©Û›‹R×7W¥îo.K_À\—¾¹°|qeùâÒò%ĵå[ˆ‹Ë×ãûךË7¾ÈKÈÐ'0÷¥Oð,÷¥Oð3V!ïã+ ëoþ¿ae{X?ü¢w÷¶ibÝqC _>ØÇã#“Ló‰J&ˆùÀ%Æâ<&hn×Ä=ê8͉³8ì‰÷gã,(Ðâ¨(Òâ$)Þ¯õAS&¬ù*Ø|L• m>ÅÊ7‰?J4«ú!Ņ̃@™hÖ¶òL4ûº]L4;q±®`­Y¿ì3wär4sx8jÐG°EèÚŒ*ônFú|7£}ü›Qˆ>ΨD—‰ ³åŒZôÑsF1J€TT£JE9JÀTÔ£NEAJUT¤$æW˜At¢O•3:± ¿3£…ÒW>ÖÌàTùXl‰á–\ßfgšJÆWæ©d~e¢ÊPfªleªÊFPæªle²Ê†Pf«l eºÊÆPæ«leÂÊQf¬leÊÊFQæ¬laDFÖ Ì0£U¶„2Z[[âË1ÃÃ'NÖ%Ìà– ,Ð;Þ>oçðpvrÌ1¯²—cÍkˆÜ ¯1³f?#̆6†˜mŒ1[Ú¤¨iŒ2›Úfvµ1Îlk˜}ýÁ>Ã,hc†YÐc†YÐ'^Ö%Ì”;,[?”–þÙ°ÃÃíÞË™áï®Ì€w­`F¼k 3ä]k˜àZÄ z×*fÔ»–1ÃÞµŽ÷6³v¼·o3k‡n3kçE*™Y;Þëú€Ï0sÞµ‰™óÏÖ&eΟ¸YŸÇLkgÝrԴ㌇ÚÎÚWÛ‘•ck;k‡°g;k_°F²³ò³ µµC´•Æcmgíèùmgíx®ßvÖ¾e;kÇ ÛYi¼ÖvsClgíxOKy™ü/Nµµ#@àYÜç ›s?ëÒ>ó³¼”¶-w³hæðˆ9V¬5P“¬5R­5T“­5V. ¨I'FÜÄCnò‰1‹Ä EbÔ"1l‘·ÁÏ€E ~F¤cÆM=1Ó6õOaæÄѺÔ7uåýN¼ÈxGf;‡‡÷rkmÜë­Å±ø®k…/K±–ø¶k¯K±ù¾k•´DÕ2iªuÒ"U ¥UªVJËT-•Ö©Z+-TµXróymòÍ)Ö¦V;|ª6xZ—ô™>øÌ-o1¶sx˜3JÒ§”ä/Î)K@œT–8«,qZYâ¼²$ĉeYˆ3;ÿœZ–‡8·,qrY&âì²TÄée¹#2µÎ9°, qàÖ¢xˆŸ¹ZW03•Ži³>³u7ØZíîMcŽÑ»Æ ·õ±÷õ²7ö³w6Ø[#ì½!öæÆ{wc½½1ÊÞߦ6`ŒS;ðg€ÚÂû {mc†½öØA¯}êk]ÍP®ïN4Ë-S3‰‡E0oæɼÙSDs1 ÈæbAÎÅ„"‹¼U#Š|.V]Ì(ºØQDt1¤ÈèbIÒ÷Ž)RúZ2EÌsB#QÏʼn¢Þ³ÅI¢Þ™³u4}鶇RæíÐ4“xXm÷¥zªñ¾sOµÞWò©æûÆ>Õ~_è§àûþt â9¸ ñZÝ?&@7Âo ЭðSt3üRÝ?d@7Dï4nÉkÐÀ=ð;tžeÁr~ÊÛÚ‡²»¾÷¶þþí-Æ•}ìnýð‹¾ÒÝRÆ—º4e€©VF˜:eeˆ©“VƘ:me©WF™:ue˜©“WÆ™:}e I PFÚè`<¼`+%AkR”Á&%BmR*”á&5H™fÕutfš©³W¦YÛÙ3Óì뮤3ÓìÄݺ‚™ÛÝ­v‡ JqS¡9…J±S˜¡=…JñS¢A…%J1T˜¢E…-JqT£ß]bX£Ÿeb˜c¼Úe!uB_î7Ÿ)Õùfž(Qቭ‚øõ˜±rúÊݺ‚™ÆÝïØgš9<œjê˘zê÷̘ŠêçÎø~«_CcªªKcêªßRc*«ŸZcj«_bcª«jcꫜ¥ÂÊQj¬œ¥ÊÊYQ꬜aDîÖ Ì0¥Õo°1¥µu*¾3Li=q·.a¦q·îÀL3‡‡Ó“cŽé,x p?ÖOÄvÀœÜ-¦;ÛÝbú³Ý-¦CÛÝbz´Ý-¦KÛÝ¢Âiw‹éÔv·˜^mw‹éÖv·˜~}¾Ï( ÚîÓ ŸÅŒÒ OÜ­K˜©Ý­;®ƒ¶sx8¥ÝîSÜín1åÝîSàín1%ÞîSäín1!^ñãd?ã‡gXìn1ÅÞîSî}Òc[šWj‡OÎé¤ÈÆ«`:I’Æ]÷ŸQê|¼ç‡7ûŸ­MJ?q·>™p·æÒØß¡5s˜gh*üÀ'¢¶ §ytbjñ¬€NTmPø|âj±F:‘•Æ}ý¸ß…Ð'ºð~œøÚÐøûD؆û}blßò‰² A|â,¿´_¿ñÇäŸXÛð€0ò 3Þ³Ö ¯ Ïrà±gîÖ5>SÞrã+¡w¨ÎÍ1ÇꃵꓵFꣵ†ê³µÆêÃ….¾­_ý#†ÜçcÖˆAëĨubØ:1n‚ŸëüŒH :ÇŒûzb¦íëŸẨ»u3ka¿ùÝPä§Þ¶ÏX â^nÁ{½9Ö v¬ôXKâ-@Ôšx µÈ‚!k•EÖ2 ެu$Y -X²VZÐd-µàÉZkA”µXªóymŠwQ›Zñ©Útân]ÁÌŒŒ±rÉoêîx9²™ÃÜÑÊ>9¥•rN;ä¤vÈYí,ÓÚy çµ3ANl炜٠ˆäÔr>Ĺ匈“Ë9g—³"N/çE‘»uÎíT·NÅCøÌݺ‚™±ä_®ýmoµsx¸79Fïk€Þ6Ö½o¬!zãXãMß?Õ½µ0¢ÞÛ/M²7÷C”ìÝýN%{{?cÉÞ߯\Rð#˜ÔüF&µ…ó}F½¶ß˜d¯ý,fØkŸº[W@Óm°(òß?Þñ´u;‰‡E0¿ðJ‘ÌÀRDóû°ÙâùXˆpñº,D:?>+‹UoÓRäS±’¨b&‘PÅN"¢Š¡DFK‰*¦)Ul%bž‰z*Nõž-NõÎÜ­K )óTNí ý i&ñ°Ú.Ö(5^¬Rj½X§Ô|±R©ýb­rœë­«·p öÊݲÛ@Öl7¢“úO·‚¬ÛnY¹ÝŽ1¾®X}ë–|äˆË=x–Ë=ø©·×"¿t·þæ/Ë>v·~øEµ»5àÉìi¸ãܪR¾Ô¥)L°RÂÔ)+EL´RÆÔi+…L¸RÊÔ©+ÅL¼RÎÔé+MJ€RÒ¤(EMJ‚RÖ¤4(…MJ„RÚ¤T(ÅÍjSÍŸJ5Sg¯T³¶³gªÙ—áS©f'îÖÌl3Cñ¸r”âmIxRƒ7(ÅMq„RäW8:Ãq†Rôw(ÅOqˆR—(ÅPqŠR·(ÅQqŒR$×(ÅRqŽR4÷(ÅSqRDiÕù=fŸ(Qñ‰­‚øå˜é¬œ¾r·®`FîV©ƒËyBÍÎ5µ»ÅÜS»[ÌEµ»ÅÜT»[ÌUµ»ÅÜU»[Ìeµ»ÅÜV»[Ìu"ÔY»[Ì…µ3ÂÜX;'Ì•µ³ÂÜY;/Ĉݭw˜QN«Ý-æ´¶NÅWcF9­'îÖ%ÌÐÝ*xßqw«ÃÃùÉžc9 Zæ/{˜Ïì5d~³×˜ùÎÆóŸ#‹jj1ÄühcŒùÒÆ Nc”ùÔÆ0ó«qæ[û3Àüëó}FyБJ…<è‡1Ã<èwëfèn-w¹[íÎi÷Iæ¸û$sÞ}Ò9ð> ÁœxŸ”`޼OR0gÞ'-˜Cï“Ì©÷I æØû$sî}Ò#-Tý\o•»5®¼¦“$i™ïînÝáT´sx”qW¹[9%v©Ü­<+w+»ÊÝ—k¤YiXk >Yk¤>Zk¨>[k¬>\PŸNŒ¸'†ÜçcÖˆAëĨubØ:1n‚ŸëüŒX :ÅŒûzb¦íëŸẨ»u­o*XY{ž÷;r›9<¼—[pã^oA޵À‚k…=Ö ~¬5Y‹,²VYPd-³àÈZgA’µÐ‚%k¥MÖR ž¬µDY‹­:ŸÖ& ˆ¬M­€øTm:q·®`fÆíÐ)gæ-w<ÅÝÎáaÎheŸœÒÊ?9§rR;ä¬vÈi# œ×Î9± rf;äÔv>ȹ팓Û9‘Ã*g…œÞÎ 1bwë”G8pëT<ÄÏÜ­+˜KÂâZq¿å.z;‡‡{Ó˜cô®^ö¶^#ö¾^CöÆ^cöÎÆ{kc„½w䨡77ÆØ»ƒìíQöþÆ0µa\Ú>Ò>ØgØk3ìµÆ zíSwë hp gîóß¿Þqd½ÄÃ"˜6s‰dÚì%¢©HdS±§b"‘NÅF"žŠ‘D>+‰€*f Uì$"ªJdT±”©b*‘RÅV"æ9¡‘¨§â$QïÙâ$QïÌݺ´Ó”cÍ2Þšz«íbRãÅ*¥Ö‹uJÍ+•Ú/Ö*7@¬VnX¯Ü±b» dÍv#ȪíVuÛÍ +·Û1Æ÷ï«oݒܱ`¹ϲ`¹?ãnËTÖè{wëïþïßÿößþôxß–/žº[?ø’æiÞÈ)ÏÿýëÞðøc~Zr@ÀYÚ0†yDŒð´ažÐ$#Z-w&­õù‹g´Øˆeæ…épåú0¯üÞòUy¸3ÿ-mÃoiÿȆæÄ/NÈ¥–ç~óPÉrsöL–+1tC,_\BêòépåW–±Î)çï où)]<ÞD¶ oýò5@DÂ[ž·W.Öç±±Ì%1´ì\Û¼º™ºƒ¹ŠCžî²ÜÅ´ÏÃ"º!“1Ÿ#(|‰C†Èð¦<dz«´ÇaQ`yo#öY2†Ôq¹.wn@ò?„¥roÏa> ³ìÀ*‡Ü¹—„Ê’_™‡EÉDºåzÊBÚ}™‡óò].ßù_.ª ¢!Ëò}†ý—a£`<ÏÛ+·êóØÀ݈e-™oÀF3uYó° ì‹CîÕKlh sÍÃòÄ;¶y¸sØå/&“FLlN"›‡KDÌæa±@;¬k_‰§ÍÃb^ ¼6AÏ ;ÊÃb}àÅç<,ÎbqËp 0äáö È”Í?Hq)›¿¼2¾Ì”Íóö•ú<6p½Õ| M®§î`sLl‰yŽi/!б(%":–¬®´ ¾·‹ðiƒÑÔ† ‚« $ÄZf½6‰mˆ"0ÛFœ¶á°íó}YÔÆ²¨Å³¨×ñ¥ûôYlØG(gKçáŽ[¿õÔ €÷†Œxxo×÷fŽhyoõžw!@,½ËB\DiïƒÀ{ ¡0.Oi8UÅkXQbtXz)¼T…o RYf‰3¾ {×dØ?ZSaŸ»W.Ó…š2—Ø¢¢Ì,à 5¥™º#s)‘Ë) [Ð<œá3 LÃi Š˜_©dîMæ —8?hò™†óÔt(}‰ëP‚Lk° ‘Þ¡¤ã˜§&iƒ0#éÄtz(ï€ ï±QN囋%òàQ.šÃvÀá_¹IŸÇÆP˜Æ\â­î¹]WOÝá‰íyOŠÓÞë9,J§×FÊ’%®ÀÇHÊ‚&®À·JÊrO›¿7ƒ!Q¾tR ’ $ßõÌuÀ6¦u&3Dsý[À¹þmfxç\‹.öì©O`#ý\¸lLËû$ê/ÆFúù_¹FzØ‚¾Ô”[ôÿfêmÈiØï±]O¯'a3wÞ†ÂV?î¼ …B€Ší2‘,QDòc:]”˜q¥U„”ˆœ"”§4äÉõR¼rRÍ¥mœõxþ¡‰.Êâ(ñEsQ$ÎjJ~ôgŒš2.ï#쿸¦ä·¥^¸C¸h¹ŠÐá¬Ëgêš©;Däò©„>h^žî%H`>£0EÌhMA ÓÎ]¡—ù¹­Š|"ÙÈÔtì!÷“¸úÄ.hmÆÄ¤w$ "%Îq[æ<ì1Ì;áØÁ¨ òð#.š¾›NPá¢ç£\4}_¹@øF ¤J™¼{ÜñNh3u‡@O,ÚCO;šG/ ZK/O/è®ïª¦U``K+¨°áØ fl–B¶Ò‚(m˜m¸àÍ&ýtß`+l°‡}ìa­ÛóypÌE¶ÏlÓ_~ÇÃÃÍÜRŽ´#SWÒ~MÕI»95)íõT¬T ¨g©NPíR¡¦C¥Lˆ:šêU6U/jpªmTèTù¨ß©.RÝ;%¿TT(~=YT(~%¬¿tu.0ŽòׯŸw)£b”œEå(H‹èQ® ¤˜-’H©[’B¸&erÑOŠè"§”ØE])À‹ØRží•xOR,i¿÷÷Ž¡nlsÕ\l”ªù“l”ªyf£—_Þ[³"TÞV®þõßÿñÿôöWoÿóoûû?þÛ‹<³Ý›}MíH—gpAiºåytd€¹/bF˜;Nfˆ¹!eƘûUf¹eF™»]f˜¹fÆ™{ef ¹•fFš;mf¨¹gÆšûtf°¹gF›»|f¸Y`Æ›åfžUÈdæ™›hfž}×E3óìËZ%džy:³•§Çu©ÿé_…˜fZ¿bP¡å-ZýbСÅ1!Z;cP¢¥5)ZycТ…91Z·cP£e=9ZõcУEAAZ3dP¤%EIZ¬}ܰ$Ç`Åï4¹¯FÌd ò•Ós1òq=·ßn°ˆÛ<”vj5œi¨Ë™–j-iª–Ú™¶j%ži¬ê™ÖjŸi®–ù™öj€i°6 ˜ki²¶˜6k‚i´6(„Y?/ƒôV üLoýNáÿjÄ0½õÄÿ¹€˜©¹øs¹£éngðP¢rÌ0•¯™c…Ø+ˆDçXa$>Abt ‰Ò0$N)'¡H¬#Ñ:ŽÄëø û£= Ñ$D?Œ&DŸ¸BWö˜1W¥¡áÍOŽÜ•){ RÛcGª{ìòH}*€Tø¨H*‚Tù¨2ˆGˆ*„Tú¨RH­*†Tû¨rH½*بRª’=$hWÑ<ËU¶PcÎy Rè£*!…þ᪄ú3¯è )»ËPªRÞi~=bšÌóÓ^`¦˜6*¿¨Ç±¸`š=483Ñü¼LÍTû ’˜,ŽåÓívxbÂúx3宼ãLºÏ2Óîv´Xbâ8BL"Áä»ýÛФý6ˆéòä`¾]¹Gö0ó͞ϤKU©XHå Û-r_;ƒGÌ0{^¯[b¯;f­ j­°úm!@í¸¢n]R3/„©×%¡R „` B¸t}$Cè"…ç1jã…˜¶1'¾ÒÄ@Dmêî0¤Û<¼‹KBÓ./…MU@œª„ô9UÉwª2R÷T…$þ©JIT“t¨*'eQUP£ª¤tIUQÉ–ª²£yª°ãóª$UPU©•ªJ'nÓÄàâNvýôÍ fŠRäÅ$%Ø‹iJÏ•Ü/¦*7@LVf˜®¼1aY bÊŽD$“–Q!¦-CL\6‡˜º\1y™$Bˆì§sæ+“AÌ·ub¾'Ôæ»åg…æòÌÞ->C;ƒ‡»Ñ˜at«±èf½Bìv½‚솽Âì–vÓF»m#ˆÝ¸ÆnÝd7o„²Û7‚©áT ü  šðÁÃîÚˆawý,bØ]Ÿ9SWˆÌ®w<ó•¤;ˆL3…‡%/oã”ļÍS2s ¤æ2AÉÍe„’œËŒ¬P•!Jz.S”ü\Æ( ºÌQ2t¤¤è2IÉÑe”’¤Ë,%ˈ %<—%JxÏ–%Jxg~ÕÈt7VíÖÕÍ©»›KR—7פno.J]ß\•º¿¹,}s]úæÂôÌ•é;˜KÓ—0×–o!.._£ï_k.ßø"/!CŸÀÜ—>Á³Ü—>ÁO¹XÝR>µïm¬ÿåÿô5ê±²Ë>´±~øEﮚ ¸u»åx¿â|«ŒON"0,V"P,Î]"p¬:– ¦§67ùô¡ÎrŲ:ó¹áÐgxÖW>Óª¥å1Û8oŠÀµ8ŽŠ@¶8­ŠÀ¶8ÌŠ@7ë? 8kÎû•€³8 Š€³ïºyœ}Ù¹.œYW0ÓŠçÈ·ñïx<¿™ÃCɃqÉ„qPÉ…qŽɆqÌɇq ɈqH|ä(!'3ós$/Æ t$3Zƒdr£5J&;ZÃdò£5N&CZer¤UæW˜ ép9’¿Ó ¿3£µÒWVÖ¥}¦\wÜÊ©žùŽçÒÚ9<qj%Ÿ¨Vú‘j'€ªv ±j'¬vÑj'‚®v*ñj'ƒ°v:k'„²vJ1k'…´vZ„yY/0ƒÈV;ŒlýΙørÌ0²õÄ̺‚™¡üÅ âêÖܬvE)Ws •&Ö`Æ-C¯ÑïÇk8sZcD=ATt`QÒ1ü AêšÆ(¢ªȲŒ#ê:>ˆÂþhŸA4t`ÑÐOc†ÑÐ'vÖÌ쥫_:üyŸiæðPd{ìåˆt½‘ïQ  µ‘ñQK)µQ‹:Þ¿U­Bd}Ô2DÚG­ëxW~Ö¾£VÉÏÚ¡…ÛÏÚyŸJ~Ö¾¡ÒœóDÐGmBýõ‰ôg†ÖÌà8rßeVÓ÷wôhÍe<ÔŽVÕ¨­4žkGk‡¶gGk_°Fr´Òx­­Ú ­4kGkGÛoGk/o÷‡£µƒoÙÑÚqÃŽV¯µ£…‘p´ö=ÝЄüÖ=Z~ ¦v´ö’&ð0î·]½Ä+KëÒ>S^¤Y'dŠßá‚6sxī֨OÖ©æºÏæ»'ܧ#îã‰!÷ùĘubÐ:1j¶Î@Œ[‡àgÀ:?#’‚Î1㾞˜iûú§0sbj}3º‚‹‡?î9ØÌáá½Ür÷zËq¬¾2ÅZá;S¬%¾4ÅZã[S¬E¾6ÅZ%9QµLr£jäHÕBÉ•ª•’3UK%wªÖJU-–â|^›|е©•ŸªM'¶Ö•}f-û »§;‚wÛ9<Ì¥ê‹SJõç”+ N*×@œU®‚8­\q^¹âÄr-Ä™†HN-×Cœ[®ˆ8¹\qv¹*âôr]„ùZçX.…8pëR<ÅOŒ­+˜™Ê¥»Ó &|[ß$gK½iÌ1z×Xô¶^#ö¾^CöÆ^cöÎÆ{kc„½·1ÄÞÜcïn ²·7FÙûÃÔŒqjþ P[ø`Ÿa¯m̰×~3ìµÏ¬­«ÅÉoßÒ85“xXófN‘Ì›=E4Šl.á\L(Ò¹ØÈ^U1¢ÈçbEÐÅŒ"¡‹EDCŠŒ.–!}ý˜"¥o'SÄ<'4õ\œ(ê=[œ$ê™[×T½Üg…÷wB§Ãç¢ÖiŸ ¡Õás!Ô:|.„^‡Ï…Pìö†D‡Ï…è‡1£èŸëf–²ÏŸë–ÚÔÎá¡ðöð¹î>ÂßÃçB8|ø\Ÿ áòás!¡zÖ·‘ý®Þe±Ï…pûð¹6ÞFÖ™miž­6¾A§3#ï…éLIwÝG|†aôÕ+ª÷£µ‰aôg>×%<3eŸ¹ã Èvó MxÑWg£¶ k®³SÛþ¡³U´>Ÿ½Jã±>›•Æ}ýÚß­Ðg»Òx­ßûÛ ølØV^ñ³cø–Ï–m¸â³g¹E©ýCœHœ]Ûð¢0ò 3C9³í³^[Éx˜ë¢^â•ÏõyÌøÙPÜ®XïØgš9×%Ì”û¡®»§×næðposŒÞ5Ö½m¬zßXCôƱƛ¾ª{kaD½·Ÿždoî—)Ù»ûáJöö~×’½¿Ÿ½¤6àW1©øÑLj çûŒzm?:É^ûY̨×>󹮚m͆ú~Ç¡Òv‹`~ò•"™_„¥ˆæc)²Å{²áâ¹Yˆt~Vf««¥È§b%PÅL"¡ŠDDC‰Œ*–!UL%RªØJÄ<'4õTœ$ê=[œ$êù\—všâpÅŸ·)Áb4RÛťƋUJ­딚/V*µ_¬Un€Ã¾õ‚õnÁ^ù\rÄšåFˆUË­ë¶›AVn·£ïŸ+Vߺ%¹bÁržeÁr~ÆçÊ :ï®ø\þ·ßÿ¡oŒ.š}htýð‹jºcݶÛÈ7SÀܰ1%ÌM1SÄÜ43eÌM5SÈÜt3¥ÌM9SÌÜ´3åÌM=SÐÜô3%Í¢SÔ,0eÍ¢SØ,:0¥Í¢SÜ,Z0å-„!¤žUPeê™›|¦ž}×ä+õì« ÊÔ³3£ëó˜qM,——醚ØÎá¡8B‹oŒ+´8Ç8C‹wŒ;´¸Ç8D‹ŒK´8È8E‹‡Œ[´¸È8F‹Œk´8É8G‹—Œ{´¸É8H‹ŸŒ‹´8Ê8É ßcñНø˜øå˜é,¢¾2º®ì3$ßźÈµÜÉiš¹§at!5Œ.䦆х\Õ0º»FrYÃèBnk]Èu £ ¹¯UDJ®.䯆I‚\Ù0Q;& riÄ!Flt½Ã s\ÃèBŽëw¦ÅWcF9®'F×Ì@zÞÊÁÄí–\úfå+ÇS¸ñ Ÿ9Öùͱ†ÈwŽ5Fþs`ùÐUNÕÔbùÒ1äO)v£È¯ #ß:0Žüëø ûƒ}†yÑUbUÉ‹~3Ì‹>1º.a’P÷áêÝÌá¡÷8´€œ÷8Ô€ø8ô€œø8ù84œù8Tú8t±ò²²e Ç>m ç>u¬¼¬¬C,õ¡•ÏçéÐÈÊ c:T’ÆS÷Ÿa.}Â@.ýõ‰¹ôgF×¥Ú„#;%)q»Ã´hçð(ã®2ºÆµÃšëðTKetåqW]cËZÎJã¹2ºòx¬Œ®<î+£+ÏÕ^]eî*£+—úpÙ‚‹">|–ÆsetåñT^[ðTÛµ÷˜I?ÑP]ù;úwF××cfP/ñÊ躄™rübÇñ;kçðˆ9VK¬5Pˬ5RKÍ5tËÍ5vKN ¸e'FÜÒCnù‰1KÄ %bÔ’1lÉ·$ÁÏ€% ~F¬ bÆ->1Ó¶øOaæÄèºÎ‘t” Þ¶ÏXâ^ní{½µ9Ökw¬ÖöXK¬ý±ÖXd-²vÈZem‘µÌÚ#kµIÖBk—¬•Ö6YK­}²ÖZe-¶}Z›¬%²6µZâSµéÄèºÞkïS®PËvÃã)íæŒùÉ)msÚ$ 'µ‰@Îj“œ6â±ÀymRÛÄ g¶ÉANm„œÛ& 9¹M™­2YÈémÂ#6ºN9p$e·¦ÅSøÄ躴ϔÐX¤ù8òm}“.ö¦1Çè]½ìm½Fì}½†ì½ÆìöÖÆ{ïÈXConŒ±w7ÙÛ£ìýaj¸´}¤-|°Ï°×6fØk?ŒöÚgFץƩÜÍÙæÒlßqC½ÄÃ"˜6s‰dÚì%¢©HdS±§b"‘NÅF"žŠ‘D>+‰€*f Uì$"ªJdT±”©b*‘RÅV"æ9¡‘¨§â$QïÙâ$QïÌèú …ÆÞ¦‹ÑHmk”/V)µ^¬Sj¾X©Ô~±V¹bµr Äzå&ˆËmk–!V-·B¬ÛnY¹ÝŽ>¾¯X}ë–|äˆË=x–Ë=ø£+3èéÇF×Ð]%ã-?&ynsýàKÞím7¾Ø‚”´~ëЫ!C­ßz´ÃHXë·Ý2ò×òpg[Ÿ¿xD¯ì¶>qŽ™¹o{Z‡DŠð½¥#ÏÃ!qi§è·H…KI¿­(›H”ë7E–÷€óPñsóPÞ§˜9òe»’ìòr¥³ÏWñª0sþ¾—ÒÎC|ÊÃï«ó“³_‚HÄÀåy{eg}Cy+aDSvYj¦î`øbŸQ‘×͘‡E}Cpc¿“x Ö±Ot¨”@„>æa‘ö ™ß5Ab`y£<äRAKAy§e}SØd–BK~ee䰛ʰ`1–ù=Rúr™‡EÒDf~ ¤°7dæá¼¼ÄF—ëxy@¦w~dùA¶§°Qt´¸ˆ ÷Þ%&ç˯Q€†¢Ä¸<¥áT¯aE‰Ñê%cÄ…o RYf‰3¾ {×Ý?ZStŸ»WvÓ.ZÞAË ™áެšfêŽ<,¥œD.Ç„lAóp®Ï$0 §%(b~'§"éWÞç —8Shò™†óÔt( ‚‰ëP’Lk° ‘Þ¡Äç˜CÞ!L˜…b:=”W †÷Ø('õÍE‡’‰ð(íWqøW¶Òg±!±w×úþuêTSwhb×w§0íë®ÇFð»lz‹¤,Yú‰ùTIYÐuÑK&e¹³ÀßÖYï ¨¬“¡’äû¿€Ù:r×Á˜Ì]{bÎvû†a†÷ÚWÿ²×Yæï±±Î¼+ld“ kÿÒ>úü¾±—Œ+H»9Mï®}SwhC^vÞ‘Âv½l¼²„Í|YyC [ý²ò‚ AîÇ(Ë¢ëQ¥ˆ,³ng—“›Õ5 PîNç(OiÈÓì¥x-£»—Ò¶ŒzÐÿÐ@»eq‘ Œ¢¹ô(g5M´kJþA–‡°Áš’~ÂW6Ñç±±”C/SIåìïà¢ÍÔ"r¹­êMóòQ!ýÒQ÷\€ŽÚ{!yH ¯èèE>ûy—°Ÿ©i.½‰k…Öæá°›ôæ!UJœ‡Åsa.ÃüÎ ‚aÞ(ýæ¢ù»i edç/¯Ôý'¸hñôylàEHpÑiºãàe=uÀ˜Øò¡i/Íc,Ji-cÉJã ºë{§hZ ´´† ^ í°a†fÙ D+mˆ¢Ñ6€Ù† ÞlÒO÷ ô°ÆzØG±¶ÂFkû\%Kù3[?w Ú8¨iG¦®¤ýšª“vsjRÚë©X©PÏR Ú¥*B-L5†J™*u4Õ'ªlª^ÔàTۨЩòQ¿S]¤ºwJ8(~©¨Püz²¨PüZº×öÎ…ªRã–'›¹;$9‹ÊQÑ£\-H1[$‘R·($…pLÊ䢟ÑEN)±‹ºR€±¥¯JÒU•ZQð¡ªtâ5]î®·b6 w¼ÓÎàa¦(=žLÒr=™¦Õ|2Q‹ýdªöÈdméÚI ¶Ñ@¦l‚LÚ6™¶] 2q›dêö@Èäm‘!2ŸN™¯-2ßï<†g˜ï™uau½n¹6Mw¸ í êF«.Ýjµ¥›B·+ˆn8VÝr Ýt Ýv Ýx l¤—,¢›„¢ÛC „S-ð'€jÂ{ ºë@ ºë‡ƒîúÔ—ºB}ËKåët[f|;…‡%/oã”ļÍS2s ¤æ2AÉÍe„’œË %;—!Jz.S”ü\Æ( ºÌQ2t¤¤è2IÉÑe”’¤Ë,%ˈ %<—%JxÏ–%JxgnÕ&S®wÄŸ·©¾f2ÔÕÍ©»›KR—7פno.J]ß\•º¿¹,}s]úæÂôÌ•é;˜KÓ—0צoa.N_Ã\¾‡¹|㋼„ }s_úÏr_ú?ãae²¾ž™XxÌX³]¬~Q-â5ýòúìºÝq¶Y`q¸EÆÇ&‘%§*‘5‡.‘EVÉÑ#›Î{úDgÙjªŸN|VYh}ec1K­:Mºâœ¦›"‹-΢"«-Žª"Ë-N²"ëÍò³ÏšÃ~޳ê$(²Ï¾kæ™}öu‡º}væc}3mÈâ-[Z;‡‡B ã6B ã”6B ã7BãŒ7Bã8Bã„øÈÛO:@NbæóåeŒãçm´ÉPGK” }´„ÉPHKœ ´ÊPI‹Ì¯03@ÓÉr„,~'~9fFK¥¯œ¬+û dæuØï’™Û9<”~j!_é¨ú•ž*#@éª2 ”¾*#Aé¬2”Þ*#Bé®2*”þ*#Cé°2:”+#Dé²2J”>+#Eé´2Z”©(+ëf˜æ*cBi®­1ñå˜ašë‰—u3H;œ uºåŒ`;‡‡S–cŽ‘Âk€”æX#¤8Ç"UÃkÌhc€)ÑÆS¤!¦LcL¡6™RmŒ2ÅÚfʵ1Žìø %û£}†©ÑÆ S£Æ S£OܬK|¦CÒøDVsWÒxì3Hs½iï±×# >jÒâ£V M>j Òæ£Ö !!jQÇË·ªUH³Z†´û¨uïßÊÎÚwÔ*ÙY;¤pÛY;/SÉÎÚ7Tšs>ƒtú¨MH§¸61þÌϺ´Ïìûù|ãú鰙㌇ÚÐÊ`¨ ­4žkCk‡´gC+?,SZi¼Ö†ÖiІVOmhíèúmhíå1ÿ0´vð-Z;.aØÐJãµ6´*†Ö>ã(Z×äÿÖ-Z~¦6´ö/ð0μ;Z—ø (/·æ¦;Úkçðð» æ¸Mæ¹æºÍæ» 'ܦ#nã‰!·ùĘebÐ21j¶Ì@Œ[†àgÀ2?#R‚Î1ã¶ž˜iÛú§0sâi]ã3Yj¾1í°ÃÃ{¹Õ6îõVãX |_е¦XK|cеÆW¦X‹|gеJj¢j™ÔFÕ:©‘ª…R+U+¥fª–JíT­•ªZ,Áù¼6ùökS«>U›N\­+˜ÙÊ+ñçm|Fг8£D}rJ‹þäœ6ÈIm³ÚT §µé@ÎkS‚œØ¦9³M rj›äÜ6EÈÉmš³ÛT!§·é"ŒÈÖ:åÀ6)È¿3)âÀg¾Ö¥ÚTôÎ^Å]}“-ö¦Õ—ÿ¡ZƒÒÛÆ¡÷5DokŒÞ90€Þ:0‚Þ;0„Þ<06кÑÛFÑû†¡ Æ©ø3@máƒ}½v`½öÓ˜A¯}êl]ÚhÊß¼#,þŽD©v‹`ÞÌ)’y³§ˆæb@‘ÍÅ‚"œ‹ E:Šx.Fù\¬(º˜Q$t±£ˆèbH‘ÑÅ’"¤ïS¤ôÕdŠ˜ç„F¢ž‹E½g‹“D½3o몌íÓüŽ×ÛI<¬¶ûb=Õxß»§ZïkùTó}kŸj¿/õÓ ðºñ$Ü„x1nC<(7"ÞãŠçàfÄkp;â1¸!~ë qK^ƒîß  {ð, –{ðSîÖŽ÷w^º[¯âæ?v·~øEÍ-ãÜÓwxg¯¿#ª `îÒ˜æN˜ bî”™0æNš dî´™PæNœ fîÔ™pæN~ÔK)UÚ\¹[LP³RÀ„5+ £^KÙ•¸6B ¢Á7+Lx³ÄijúJ:ÏÜÙ3ñì»Îž‰g_w-‰ggîÖ̤}ýͯ2ÞqÆ£ÃCQ„VÜUhEŽQ†VìuhEQˆVü•hEQŠV µhE‘QŒVÕo/!Ê1žfBÔcõr”…xØ }¹ß}B”¤Uç˜A´¢DF+~§ ~=f¬œ¾r·®`¦/×'úÒßç?ïŠãŒ§µy–1Õoš13ÕOžñAW¿ˆÆÌU?˜ÆLV¿§ÆÌV?·ÆLW¿ÆÆÌW?ÖÆLX9ÊŒ•3¢LY9'Êœ•³¢LZ9/JR”»õ3Ìpõ;lÌpmŠ/Ç 3\OÜ­+˜Ë3Bu¾eŸiæðp¶rÌ1²—c Ík„ìæXîDiØÝbö³Ý-fCÛÝbv´Ý-fKÛÝbö´Ý-fSÛÝbvµÝ-f[ÛÝBöu¸[ÈÆþ`ŸQV´Ý-fE?‹eEŸ¸[W03íp,¬&ÿyW¾x¸[Èpw ïán!>Ü-dć»… ùp·1îbª—üxÙOùá)»[ȸwkãdôØ–æ¥Úaã³s:)²ñ2˜N’¤qW¹[/0ƒLúêM¿ÕûÑÚÄLú3wëfàQà-¦õŽ­™ÃqO ¹Ï'ƬƒÖ ˆQëİubÜ:?Ö)ø‘tŽ÷õÄLÛ×?…™wë f–òXAüyÛ>c-ˆ{¹7îõäX ,رVXÐc-‰÷QkâA@žm—`ÈZeA‘µÌ‚#kIÖB –¬•4YK-x²ÖZe-–ê|^›âm@Ô¦V@|ª6¸[—ô™rú8þ¼ÏXt&g´²NÊ?8g8tÝ퀳†³NÎ8o8àÄá\€3ÛÙ §¶óAÎmg„œÜÎ 9»rz;/ˆܭSN8ðwNÅCøÌݺ‚™mÀÙ.ýyWßdw‹½i5Ç¥w­Ö PÃjJï[­!4èXãMß?U½µ1ÂÞ;^›DoQöô°õV%zûxʽ¼t m ¤vàw2©-œï3ìµãIôÚc†½ö©»u 4[9®ã?ohü)E0¿òJ‘ÌÀRDó±Ùâ YˆpñÂ,D:?@KÏïÓRäS±’¨b&‘PÅN"¢Š¡DFK‰*¦)Ul%bž‰z*Nõž-NõÎÜ­+ A¼aüy›,F#µ]¬Qj¼X¥Ôz±N©ùb¥RûÅZå8è[VOáì•»%·A¬Yn„XµÜ ±n¹bår;ÄÚ冈շnÉGîX°ÜƒgY°ÜƒŸzpE´–Ý­ø‹ÿñ—oé§ý‹ùí¿ÿùí¯Òø»êÿФI1Óìc¿ë‡_T{sIÿÀ[þw<ÿÄ 0÷m soÌ 1÷Î soÍ 2÷Þ *soÎ 3÷î :so?é©¡uxª…ÚƒÔ¬0hÍÚ¤§^v¥õíЇ¨M0ÈÍÚƒÞBBðY…XŸ¹×gðÙw½>ƒÏ¾ ± >;ó»®`¦/o0%f¼¿ƒƒ·sx(‘Ð ­Ñ1ÑЭñ1Ñ ­2QÑ"­12‘Ñ$­Q2ÑÑ&­q2Ò(#­‘2Q2tè÷˜A¢5E&,~§)~9f:k©¯ü®K˜)¹d}чRµ¸-•Ó:4£OÃïb4ªý.F§Úïb´ªý.F¯Úïb4«ý.F·Úïb´«ý.F¿F8¢aíw1:Ö^ £eí¥0zÖ^ £iíÅ0PÑ~×;Ì(ÊÕ~£\[ïâ«1£(׿ë fätÉõº-þ×~#–=ÇŒ`ö0¢ÙkÄg¯!#ž½ÆŒ€6 US‹!FLcŒ 6QmŒ2ÂÚfĵ1Žìø "ûƒ}F‘Ñ‘U…Èè‡1ÃÈè¿ë fðÈ2\¯©»ávz;‡‡¢Üãì¢Þãl¢àãì¢âãl¢äãì¢æãl¢èãìÅÊ›Ê:›(û8»¨û8Û±ò¦²Î~¤…ªŸðÍã¡ò»Æ•·Åt¶$§ÚïzDÓÇY DÓ?\›Mæw] .¹^7`¦žÃ£Œ»ÊïSû¶T~W•ß•Ç]åw©ý[ë3ZiÒ>ØgÐkfÐk?ôÚ§~×%ÐÜíwµ“xXÓf.‘L›½D4‰l*áTL$Ò©ØHÄS1’ȧb%PÅL"¡ŠDDC‰Œ*–!UL%RªØJÄ<'4õTœ$ê=[œ$êù]W@s»ßÕNâaµ]¬Qj¼X¥Ôz±N©ùb¥RûÅZåˆÕÊ-ë•› V,·A¬Yn„XµÜ ±n¹bår;ÄÚ冈շnÉGîX°ÜƒgY°ÜƒŸñ»Æ¹\£<÷»þÏ?ýþ÷|˜fß‹à·4Ü™—{ô —ùJ?£z2R®Wf žîçÏe@÷+óçV—¯g—/F”]>:0½9$°ß›4sü¾U\~Ù|ޏ~z—X ,~Y \~!à…©õyd”²×—«ÌÃzÃ>ÖLÜ¡ìÅ¡ƒÆÆdÆü<øþæÜÆìƒ©ŽùqðáÍ™ù‚{ù_qükP` ÞãF‚"…`˜ ÒÏ–yþ‡˜D Þ9•à d0ÅrXPÿ˜q™†E×dæ°‚Â1sXQºÞ#£P³ü/ñ‘¸uÿ 2p,n^ZWŸFF_nNôå]¹~ºAl&îPdë@’Á@×aÃê2îuØðùã;·ùZ<‡¹VdÕ ’MÃ©Š™Íwæ«Ú4,V#jÇŽu¶c#ƒñ¶#ù4ÃoÇ6_„;¸$ Î;˜(„Bn¯ÁÔÙôƒÇ©³cW_Š †ÎûKƒêÓȈ{Xù#xà ¾fâ={ZíIGH´—Ò^0„‡x9;}oW…S Œ®Pl-1öZ c(¶ ÈÈl”Ú‚/ã¶n†qŸî̪2˜Uý 2U~€W6Ôç‘ÁØ8QwÜí¬'îP<¼¶b†Çk£f´¼¶qÏk“g,½JCëU ø òÁÀ{—,¿Fé Ĩ0¥áT•­É*jéÇÉüT%oûQAp.ÿ”g0á^Õ„ ÷Vܧ_î•ÙôidÄ*øM¿þôz=qGæ.ú–óA¶ w<Û'ê—†Yô1Ì“[ÑÆôçÆE¤’ç {¿zQÌÒÝèꀑÙìGTw@nŽˆpžÓ1h23PD¢¼&(¼GNë‹Cxæ÷W–Ò§‘qÿý©zâOëÊÛS˜ônÕ##åWÉzÄ ÖÍz¢¤,g7铲ØÝäïÍPÈ·-”|€`ù0@†7Ö Á®3"@÷€,èÍ>û†Q‚v¾,ÑyÃÀUŠsTt#oJù­¹þITd™ñ…gôiPÜAªš¶ƒ›p¾Ÿ²{‡Îo:ÎÞ¾÷™£ÊÞž_S¼ñgÍvtUØ']‰ÊŸê}Ô…ì\Oö‘öP)6Yž]‰öA§×óÇïu¶=×°½×óå_èh •ê·Kê-¥qÛQ N Gúxû)o*ûð.ÃþkËFúé^Ù@Ÿ†ÁýP5iYÚ¶Óÿ)nÛéþ~—Ï!L&i4Of†iDo.ÓÆ4ê‚Sn«ôúL8óÕ³ÞlÔçq UÍ&Ïn»‘Ù€ä¦QqÀ€ó¨Ç(ýôiÉöh:¶…ªÚkf™¾•OFî¶ÔRý¼2e/œOÃàv[§ž´ƒmœ'´ÐOvi½¥;ô"•ÖÑ ¸óû¦h:µðèH ´« zY ®€†.X D‹,€¢xÑ\Ÿîh=tžÂgÀ 5k>ƒÆ©¹áXd=k…í¯P´÷B"Ò¾ ýH{6Ä%íçPž´×C–R€f¥AKõj—j ¤0ÕèdªIÑT¯ °©–A~;¥§T M=X M%<¿ô_>ƒÛÍ—zÖJÁ"\ЉEÆ "‹¨Qa‹£þ,ŽGuZ Úµø!•m±GêÞâ–TÅÅ<©™‹—RQk¥Þ.N+5žŒ·ÑêÏ¥lQFJÙRFHÙûþ3aP…rç‡ _ú+ÿý/Ó—¼ýÅþý÷g& ÒÉ>tY~ð%5k­ó; Vž—zå}©GT˜ZHå…©ÃTž˜På©?U™ÚWå•©»UžYï<3ä©7VšZg奩³Vžšoå­©/W›ÚvåµIQ~Y€Uùej{•_Öö½5F¿.¾ìÄ{ù<^ñÓ•‹hùáÅÛò(RPj“"%F)’ppJ%žu”¥HC)]Š<”¦HDédŠL”Œ¦HE©lŠ\”§HFitŠl”„§HG)|Š|”¨HH «/ðˆD hŠHl´/ÆËd½ð•#óy¼ ±7Íÿ¼-RSúªRKGT"ÕT²¶RO¥z+U¢¸RS¥™+UU’ºRW¥¸+•U‚¼R[¥×+ÕUr¾R_¥ö+Vf€Rcå(UVV‚ð!‹æ^˜Â*)^)¬­ÿÅxaë‰Oóy¼à±á¡/FÞW\Ûù;œ‹ìù¥Œîùg®²×‡¹Ë^?æ2{}™Ûìõg®³ñÁÜgã‡¹ÐÆs£?ŠÆ's§_æRßÌ­6þ™kýÁþœgã…9Ïâ…1Ï'îͼ”$ù—\Ç;øK3‡“×½3™Ýû;“Û½ÿ3ÙÝõÉï®L†w}aÀë“å]Ÿ˜<ïúÅdz×7&×»þ1ÙÞõ±5áü÷À¯\_{;ÿ€¿0IÞõˆIòÖ#ÉŸx:ð‚<ð)?è:öw„´4ó—g§Ü ~Ø÷8]"þÈClæ—=ì_óÏüjœµ~ꫵ¾j»µþêÊ…5íÂzzáK-¿ð'E@ø”`@üJN ¼¥5ý"øá¶sŽuòÂJÛÊ?‚•ÿç*T–Bxçᨙ½C;·d3nìÒÔ¸ïKpcYǪ!©ŽEE:kŽD>–$)€¬X’YФ²ÞIXd9”êÈj)I’Åt7—*µVði鑸ÇÊÓªžWè*8Ö îà}›È¬.]d–Wœl„q79ÎE"+,ŽMâ×¾Êë+cIYdq&ïÄúÈ&³Ì|¢“Yg>ðÉ,4ŸeVš„e‡Õçæó”Ìkmf‡}Ù©)F‡8KW³if+gôÇ;Ð53x8ÒÏ™ùçsÎŒô1hFú”4#}ˆš‘ƒ>cÍHBÁ¦´ïÚŒ4ônFJT$¢¤CE&JZT¤¢¤GE.JšT$£¤ßWˆAD¡Ïf3¢°•ò¾1£ÌWÞÒÄdƒ ³Ä|Èj¸ÃŒlgðpr¨Äu%‹J|Wò¨Äy%“J¼Wr©Ä}%›JüWò©Ì%£ÊÛ<Êx¨-¦Ü«ÕÓ¾‚’ÉbÚaYÛbÊ/²ÔS¯µÅ´ÃÒ¶Å”Ÿ7¬-¦Gfl1íx ßÓže‹içÅ'YLi¼ÖS9l1íx™Jy˜ü/NµÅ´ãyþG™o~p;÷˜. f)/`n¥6ÍÛ o´3xÄ «ïå ¸/æ ¹oæ º¯æ »ï&Ü—!îÛ‰ ÷õD˜û~"кjݶ¬€[tþ-Iàã!µç-îቖ¶‡-'.Ó°¬]œ(_îx³™¿Cû·µ4lïVÚ°ûû®Šƒ/¡vø¶J‹¯¡òø¾ “ÔAÖ-i‡,kRYõ¤;²(J•dÍ”fÉ’*E“WbñiòÍ#À²•Ÿ)?'>Óx/jñzǪföB ñä‹’éI'%â“mJâ'•@®*{€TVæ™®¬a§‚'Ë– –iA–-Kƒ$\†9ºìâAFÓ)£•—@BÛz ÏðÙ§é<¶Bñ^ §û YMì0cnK3_úS¯ ÚW¯º[¯)š_¯8zc㭳тÎÚXw4ÒЗ‡hÛRtõÆ0š~#šÀ»ZdÃò³ð@ƒ|æ5]ÀÎ_âTÝÞß'©xû€få­’–7n(^ÞÖ!ˆyÓ‡^æ’9Íj›Ë Ä8hu.Eò\¨ ô¹ŒAt‘ƒNèÛ¶}*ã9ý èæêÍíÑêBÉíÌmº‚¥à£œfØo¹ú^Oß!ÜWÇ!’û^94t_:§Äî+é”à}c½/´SÂûîøã:<,€¸-‹ .Ó£µ»ö°â*>,ˆ¸©O‹Bù ã5V éû2<%ýG©*%ýŸºÝ´uåöÃ;é<÷½åÄ´®-§}Mý‹Ž¹¶ ¥ÂÝÒF)àJm”°Ô¨* K¬´Ôè*`K°¸Ô(+ K´¼Ôh+૯¾æÊrR@XïT8<ÕªF_c@&¡@e`&©F^ÕÝlz©ñV WÛx3ÐëËnh3ÏëÄrº‚˜"ÕŒ8¸wK˜@3ƒ‡sö$†)‡Ob™rú$¦)ÇOb›rþ$Æ)Pbr%æ)GPbŸr%*‡ÐO 1§Ð/1Ç0$Bãï¡uösFÌI”ü1Ì ”¸§ÜÀVÜûrÄXÓ|e9]@ B‘¦Ò}¯wòlgðpœ§ßábܧŸéb¨_ñâ#¥~ä‹q¢~Œq£~"Œq¤~AŒq¥~`Œq¦~Œq§2$‡*ÃBq©24§*ÃCq«2D„YN/ÃxR?-ÆxÒÖ@øjÄ0ôÄrº€˜¡Ü5ÀcAëzǃ Í  ކà+€×c…ð*e¬`Wgˉ¡Æ¶œzlˉ¡È¶œšlˉ¡Ê¶œ(>Úrb(³-'†6Ûrb¨³-'†>Ÿï1 A¶åÄäG£ äËébÆòJÀRØÌÖßðºw;ƒ‡£Ém91ºÜ–£Ím91úÜ–£Ñm91:Ý–Ÿù§éxG×oÓáÉJ[NŒn·åÄhw¼`ô»f0Þ7–?ç1ŠZGêð(ý£UIIë'–ÓÄLå>6•á[Tfóü‚‡“rPDýZÝ6‚’épÓÛ‡ŸÒx¬G¥q_¿X·Áæöáª4^ë7ë6¥ñᬠïÑûðÖžåÃ]oeéðW÷õÃu Æðᱠϳ !¯3àif¶ÚðBþ£ÌwÈO›Ÿ[NƒC3ù°yÖ> stream xœ–wTSهϽ7½P’Š”ÐkhRH ½H‘.*1 JÀ"6DTpDQ‘¦2(à€£C‘±"Š…Q±ëDÔqp–Id­ß¼yïÍ›ß÷~kŸ½ÏÝgï}ÖºüƒÂLX € ¡Xáçň‹g` ðlàp³³BøF™|ØŒl™ø½º ùû*Ó?ŒÁÿŸ”¹Y"1P˜ŒçòøÙ\É8=Wœ%·Oɘ¶4MÎ0JÎ"Y‚2V“sò,[|ö™e9ó2„<ËsÎâeðäÜ'ã9¾Œ‘`çø¹2¾&cƒtI†@Æoä±|N6(’Ü.æsSdl-c’(2‚-ãyàHÉ_ðÒ/XÌÏËÅÎÌZ.$§ˆ&\S†“‹áÏÏMç‹ÅÌ07#â1Ø™YárfÏüYym²";Ø8980m-m¾(Ô]ü›’÷v–^„îDøÃöW~™ °¦eµÙú‡mi]ëP»ý‡Í`/в¾u}qº|^RÄâ,g+«ÜÜ\KŸk)/èïúŸC_|ÏR¾Ýïåaxó“8’t1C^7nfz¦DÄÈÎâpù 柇øþuü$¾ˆ/”ED˦L L–µ[Ȉ™B†@øŸšøÃþ¤Ù¹–‰ÚøЖX¥!@~(* {d+Ðï} ÆGù͋љ˜ûÏ‚þ}W¸LþÈ$ŽcGD2¸QÎìšüZ4 E@ê@èÀ¶À¸àA(ˆq`1à‚D €µ ”‚­`'¨u 4ƒ6ptcà48.Ë`ÜR0ž€)ð Ì@„…ÈR‡t CȲ…XäCP”%CBH@ë R¨ª†ê¡fè[è(tº C· Qhúz#0 ¦ÁZ°l³`O8Ž„ÁÉð28.‚·À•p|î„O×àX ?§€:¢‹0ÂFB‘x$ !«¤i@Ú¤¹ŠH‘§È[EE1PL” Ê…⢖¡V¡6£ªQP¨>ÔUÔ(j õMFk¢ÍÑÎèt,:‹.FW ›Ðè³èô8úƒ¡cŒ1ŽL&³³³ÓŽ9…ÆŒa¦±X¬:ÖëŠ År°bl1¶ {{{;Ž}ƒ#âtp¶8_\¡8áú"ãEy‹.,ÖXœ¾øøÅ%œ%Gщ1‰-‰ï9¡œÎôÒ€¥µK§¸lî.îžoo’ïÊ/çO$¹&•'=JvMÞž<™âžR‘òTÀT ž§ú§Ö¥¾N MÛŸö)=&½=—‘˜qTH¦ û2µ3ó2‡³Ì³Š³¤Ëœ—í\6% 5eCÙ‹²»Å4ÙÏÔ€ÄD²^2šã–S“ó&7:÷Hžrž0o`¹ÙòMË'ò}ó¿^ZÁ]Ñ[ [°¶`t¥çÊúUЪ¥«zWë¯.Z=¾Æo͵„µik(´.,/|¹.f]O‘VÑš¢±õ~ë[‹ŠEÅ76¸l¨ÛˆÚ(Ø8¸iMKx%K­K+Jßoæn¾ø•ÍW•_}Ú’´e°Ì¡lÏVÌVáÖëÛÜ·(W.Ï/Û²½scGÉŽ—;—ì¼PaWQ·‹°K²KZ\Ù]ePµµê}uJõHWM{­fí¦Ú×»y»¯ìñØÓV§UWZ÷n¯`ïÍz¿úΣ†Š}˜}9û6F7öÍúº¹I£©´éÃ~á~éˆ}ÍŽÍÍ-š-e­p«¤uò`ÂÁËßxÓÝÆl«o§·—‡$‡›øíõÃA‡{°Ž´}gø]mµ£¤ê\Þ9Õ•Ò%íŽë>x´·Ç¥§ã{Ëï÷Ó=Vs\åx٠‰¢ŸN柜>•uêééäÓc½Kz=s­/¼oðlÐÙóç|Ïé÷ì?yÞõü± ÎŽ^d]ìºäp©sÀ~ ãû:;‡‡º/;]îž7|âŠû•ÓW½¯ž»píÒÈü‘áëQ×oÞH¸!½É»ùèVú­ç·snÏÜYs}·äžÒ½Šûš÷~4ý±]ê =>ê=:ð`Áƒ;cܱ'?eÿô~¼è!ùaÅ„ÎDó#ÛGÇ&}'/?^øxüIÖ“™§Å?+ÿ\ûÌäÙw¿xü20;5þ\ôüÓ¯›_¨¿ØÿÒîeïtØôýW¯f^—¼Qsà-ëmÿ»˜w3¹ï±ï+?˜~èùôñî§ŒOŸ~÷„óû endstream endobj 764 0 obj << /Length 180 /Filter /FlateDecode >> stream xÚ]¹Â0 †÷<…ÇdH;÷Š8"b \K«Š‘·'”¶ª*Kþ-¿õix†Óƒ®2«¶.¢JÎä' Y5AÐ^!%Èw8󨼈äøéÓ C¼²ä®)Ï"Y~­›‡¸äÃd¾É K¡'ScQù˜àÖ²7S!9úYÙÆ»¡Qí[`ݱc ½`Xêx+ǧröµ.^I%OþÇK© CéheMüãz³ù­@ endstream endobj 761 0 obj << /Type /XObject /Subtype /Form /FormType 1 /PTEX.FileName (/tmp/RtmpBlhU1W/Rbuild4444671013800/timeSeries/vignettes/symbolTableFig.pdf) /PTEX.PageNumber 1 /PTEX.InfoDict 766 0 R /BBox [0 0 432 432] /Resources << /ProcSet [ /PDF /Text ] /Font << /F1 767 0 R/F2 768 0 R/F3 769 0 R>> /ExtGState << >>/ColorSpace << /sRGB 770 0 R >>>> /Length 3433 /Filter /FlateDecode >> stream xœµ\i£Ý6ý~…ÙW3²6vÚB ¥ihZHÓ„^šÊÎ_G«g|Ó¤R4|ÈÍóµG>–ŽæèHW†åú˃åãÓøïÕ‹Ó+OßyãÕåÎÓ“Z•R ÿ|zçÑé•×u¼þâÞÛZO”r F­ܺ™åâáòÞ•‹«‹WË•Û^] þywy|oyûòñ'˵û·ŸÜ¾óÉÝ'ñŒY®<½úþrqýôÚE‚²˜°ªmqzݶEo¸:\0„Õàòäîòîò¨½vóϽyí­ß™åÓÓ{ï/jùèËõøïÁ 2Ö7OÁ®b¹aÕíê|ùˆEßìª,•àuA…by›_7›ªàŠºº\¼6åc(an­‰xÌ£3£Qµ+™x©2|˜ ʧÇË=+ö¸gÎæà™L­nÏ2zúܨOÇèôÞ¥÷#véÀmîõJ ;V¶ÅlËh=Z*ˆ–$žòðWåù1¿¾ëæZ§<Äì)šŸ:bõžÑŽÆ2èFcÉã¡U{/Ç0›]Uá‡V£±ä‹4°ØÏN0/.1Ñ DC“­r*qa¾Lj©£CªU£Q¢Å5jA"Ù ­ç!b20Ñô—¬øEˆäoô6 s‰ÑwTçú%ˆdd´‘ –vù² ÄX`kh; бQ­]¾"‘ ÔnbuU ¿*‘,ŠöóÝ%Š—Ô-•ø5ˆÉ®´†=ºø(}ëŠ F2*›’Á˜¦³ÊSߺ*ƒÑí³½›€¾$;`ãàÑå¿.‘üÊ6­/X K#®Tâ7d ’SÙô%J Äñ•+lü¦ Äæ˜ô%Õb´¾´Ë·d ’GÙ>ÇØ§q+ÄѦZ‚\Húˆâ¥Mgû€k›VҔѨTC÷ŠL=‘óÙ¦$ë0s0J"¸M+H†HnD†}¸µÍêGìª~ËŽ¨ù±I„&´+D3+"/-‘ 4³êQ!’¿Ûd &\˜hfÕ£BLkx\$›ƒH¾Ô̪G…HnÕÊ@¤ͬzˆÙÃq]m "_»œu'"yo/‘ 4³ÚR!’÷2S⬶l!»»Cü¶ D2ófV[*D³ êwd ’™7ê TZ…4¥»|W"™y+ .®,7Öñà÷d ’™·ê’ FÃ\þß—HfÞʨKòÞ¶<ôd ’™·ÓêÅ9hƒoÓs?È̼P—j½u)ñG2ÉË[u©Î»Ú‚We ÒŠ£P—¼Òg[C_“h÷Á˜V—ºÚh[CÿX"Yy;­.¾9ïÚЯÉ@¤G+ .6ž×[kè×e ’•wÓêâöeÄRâ2i]Ò ¨Kâ"ýŸÈ@$£ï¦Õ%”¥Ê}eå§2Éè»iu iþ0d»Ÿ ¼.‚&ܬ¶¸m7úå‘6‰Ðןûf(ݬ¶8“KÔ»¶ü\"}7«-±óOtwmySb2úÕPÎj‹+«â°kË[2Éè»Ym‰ë²tmè_È@$£ïfµ¥t »¶¼-‘-§ûYm)µ˜}y±7d ²™ƒYm)\L¾¼6ô;2iæÀÏjKmhZµ¿)‘ýxyV[b^L‹¹Ñ—×}!‘Œ¾P—ÔÐiÕ¾¬”þR"}?­.¥¡ÓïKCÿJ"}?­.:U_þp)ñ]ˆdô½€º4£_Jüµ D2ú^@]²/ßþoD 2£ï§ÕÅ”_ßÊ@$£¦ÕÅ£¿/Ú¿'‘Œ~˜V—‘Œþ­[2Éé‡iy)Ó¢}yê÷e ’Óòb“1ßÒ,c*ñˆäôô¼˜²hŸ7j¥'‘œ~˜–›'#Ò‚o±C¿—HKúaZ^J-¦] E¤oË@ZÇ™–—1óºú¡ DrúaZ^LZ̺¶;"iI?LŠ *H™-é4‡0mŠOÌ–ôAMªKÃ;t¥â]ŒdõAMÊ *,kðùë©È{2iQÔ¤¼´z—6×¥"ÿ ƒ‘Ì~ì:2Éíß—ÁHnÔ¤À¤íhÉb‘Ýÿ£Ff÷AM*LÃèMÚ‘Š| ƒ‘ü>¨I‰Im,7ü’ÁH†Ԥƴz$Ç)ƒ‘?¨I‘A¥²Ÿ6»>”ÁH–”ŒÎ¤ÅýºÅê‘ F¶ådt&ytWúÌcŒ|sñ´Î@[Þ÷¥ÿ,ƒ‘í3A€˨çcŒl?1Lë æx´é®`|"‚‘o.†iÉù1ÿ\¿`|*ƒ‘m+†iÁ}E¾ÐçŒlS1èLÚ™º/)÷/2Ùöb˜Ö™ÒÖf_Úù« F¶³¦u&ïJ›‹ë8üSŒl_1èLªÇèÕkÿ› F2ÿ€:“0¢Iÿ¥"ÿ.ƒ‘Öùt&a$ûÿŒ´Ð8­3%‡—·C¤"ÿ)ƒ‘ü? €Î„¼Z†ÿK"Û#€³*ƒQ±l~ÃWtü÷$D=0ŸÀY•AŸ1jß~úŒlgUF«²m;¤=ö©ÈÿÊ`¤}п¼3kÞ¿¼3ÏÜ¿¼s3Û¿¼3—yØ ÞÌìŸÆÑ`æË¨»‚™a:lmî fNFw¿h Óº"èî7 ´`Zñ=ʰÃK~FÆVÉ@2Œc·Q†±Ý °2Œü¶Q†ñÙ6Ê0¶·¶Q†ñ1Ì6Ê0¶´Û(øêo£ ãr¼2ŒfÊaë'XL:Ìô¬3i1ý«ÁlÖ×ô¬³œoú VƒYÎ7ý+Á<ç›~‚Õ`–óM?Áj0Ëù¦Ÿ`5˜å|ÓO°Ìr¾eÏùv”a<çÛQ†ñœoGÆs¾eÏùv”a<çÛQ†^T6Ê0¶Ìv”a<çÛQ†q{lGÆs¾e7”n”a<ç»Q†ñœïFÆÌ‘ë'˜?ߊ ®Ÿ`þ|“0¸~‚ù}ûîþ²º~‚ùóµàú æÏ·¼‚ë'˜§Í¨µ¥|?Áüù6Qðýó´³Âöýóç[+Á÷ÌŸoz?Ê0žóý(ÃxÎ÷£ ã9ß2Œç|?Ê0žóý(ÃxΣ ã9?Œ2Œçü0Ê0žóÃ(ïZeÏùa”a|œFÆ~Üa”a<ç‡Q†±q~è&XÊšxx=ªn‚µ`öŠ?ÕM°Lã|TÝkÁì%ª›`-˜½åOu¬³œª›`-˜r>ªn‚µ`Êù¨º Ö‚)ç£ê&X ¦œj”a,ç#Œ2Œ-#Œ2Œ­ë"Œ2Œå|„Q†±•P„Q†±œ0Ê0–óFÆævFÆr>Â(ÃXÎGeë-Ž2Œ¿¸Gvx;í(Ãø eq”aìµ°ØO°ô’|}Èùý¯umÁ,ç÷¿×µ³÷ºöÏç·`–óûçó[0Ëùýóù5˜çüþùüÌr~ÿ|~ f9¿>¿³œß?Ÿß‚YÎïŸÏoÁ,ç÷Ïç·`çcÿ|~ ¦q>öÏç·`–óûçók0çcÿ|~ f9¿>¿Ó|>öÏç·`–óûçó[0Ëùýóù-˜æó±>¿³œß?Ÿß‚iœýóù-˜åüþùüÌr~ÿ|~ ¦q>öÏçkò»Áù›¼û VƒYÎïŸÏoÁlœß?Ÿß‚YÎïŸÏoÁ,ç÷Ïç×`žói>ÿÆér Å> endstream endobj 772 0 obj << /Alternate /DeviceRGB /N 3 /Length 2596 /Filter /FlateDecode >> stream xœ–wTSهϽ7½P’Š”ÐkhRH ½H‘.*1 JÀ"6DTpDQ‘¦2(à€£C‘±"Š…Q±ëDÔqp–Id­ß¼yïÍ›ß÷~kŸ½ÏÝgï}ÖºüƒÂLX € ¡Xáçň‹g` ðlàp³³BøF™|ØŒl™ø½º ùû*Ó?ŒÁÿŸ”¹Y"1P˜ŒçòøÙ\É8=Wœ%·Oɘ¶4MÎ0JÎ"Y‚2V“sò,[|ö™e9ó2„<ËsÎâeðäÜ'ã9¾Œ‘`çø¹2¾&cƒtI†@Æoä±|N6(’Ü.æsSdl-c’(2‚-ãyàHÉ_ðÒ/XÌÏËÅÎÌZ.$§ˆ&\S†“‹áÏÏMç‹ÅÌ07#â1Ø™YárfÏüYym²";Ø8980m-m¾(Ô]ü›’÷v–^„îDøÃöW~™ °¦eµÙú‡mi]ëP»ý‡Í`/в¾u}qº|^RÄâ,g+«ÜÜ\KŸk)/èïúŸC_|ÏR¾Ýïåaxó“8’t1C^7nfz¦DÄÈÎâpù 柇øþuü$¾ˆ/”ED˦L L–µ[Ȉ™B†@øŸšøÃþ¤Ù¹–‰ÚøЖX¥!@~(* {d+Ðï} ÆGù͋љ˜ûÏ‚þ}W¸LþÈ$ŽcGD2¸QÎìšüZ4 E@ê@èÀ¶À¸àA(ˆq`1à‚D €µ ”‚­`'¨u 4ƒ6ptcà48.Ë`ÜR0ž€)ð Ì@„…ÈR‡t CȲ…XäCP”%CBH@ë R¨ª†ê¡fè[è(tº C· Qhúz#0 ¦ÁZ°l³`O8Ž„ÁÉð28.‚·À•p|î„O×àX ?§€:¢‹0ÂFB‘x$ !«¤i@Ú¤¹ŠH‘§È[EE1PL” Ê…⢖¡V¡6£ªQP¨>ÔUÔ(j õMFk¢ÍÑÎèt,:‹.FW ›Ðè³èô8úƒ¡cŒ1ŽL&³³³ÓŽ9…ÆŒa¦±X¬:ÖëŠ År°bl1¶ {{{;Ž}ƒ#âtp¶8_\¡8áú"ãEy‹.,ÖXœ¾øøÅ%œ%Gщ1‰-‰ï9¡œÎôÒ€¥µK§¸lî.îžoo’ïÊ/çO$¹&•'=JvMÞž<™âžR‘òTÀT ž§ú§Ö¥¾N MÛŸö)=&½=—‘˜qTH¦ û2µ3ó2‡³Ì³Š³¤Ëœ—í\6% 5eCÙ‹²»Å4ÙÏÔ€ÄD²^2šã–S“ó&7:÷Hžrž0o`¹ÙòMË'ò}ó¿^ZÁ]Ñ[ [°¶`t¥çÊúUЪ¥«zWë¯.Z=¾Æo͵„µik(´.,/|¹.f]O‘VÑš¢±õ~ë[‹ŠEÅ76¸l¨ÛˆÚ(Ø8¸iMKx%K­K+Jßoæn¾ø•ÍW•_}Ú’´e°Ì¡lÏVÌVáÖëÛÜ·(W.Ï/Û²½scGÉŽ—;—ì¼PaWQ·‹°K²KZ\Ù]ePµµê}uJõHWM{­fí¦Ú×»y»¯ìñØÓV§UWZ÷n¯`ïÍz¿úΣ†Š}˜}9û6F7öÍúº¹I£©´éÃ~á~éˆ}ÍŽÍÍ-š-e­p«¤uò`ÂÁËßxÓÝÆl«o§·—‡$‡›øíõÃA‡{°Ž´}gø]mµ£¤ê\Þ9Õ•Ò%íŽë>x´·Ç¥§ã{Ëï÷Ó=Vs\åx٠‰¢ŸN柜>•uêééäÓc½Kz=s­/¼oðlÐÙóç|Ïé÷ì?yÞõü± ÎŽ^d]ìºäp©sÀ~ ãû:;‡‡º/;]îž7|âŠû•ÓW½¯ž»píÒÈü‘áëQ×oÞH¸!½É»ùèVú­ç·snÏÜYs}·äžÒ½Šûš÷~4ý±]ê =>ê=:ð`Áƒ;cܱ'?eÿô~¼è!ùaÅ„ÎDó#ÛGÇ&}'/?^øxüIÖ“™§Å?+ÿ\ûÌäÙw¿xü20;5þ\ôüÓ¯›_¨¿ØÿÒîeïtØôýW¯f^—¼Qsà-ëmÿ»˜w3¹ï±ï+?˜~èùôñî§ŒOŸ~÷„óû endstream endobj 775 0 obj << /Length 306 /Filter /FlateDecode >> stream xÚÍSMKÃ@½÷W,Àæ°Û™ýL@ÄšRi˜½HíAh+‚E©9˜ï&ÛV£­âÍÓì<æ½y;; ä÷`‡®×ϵ%ˆ<ÓZ·&(OA †£Èˆ[’9M¹M¢Ðtðöøš0i5-«D Z?­B½lW•‡ÖQ²p—^W“ŒgF˜F“)—(ƒày´WË‹›ÙÀ…ók¢¢U‰ëçíæ¾jdbpwŒúÈn…s\Bß²xÃâå s-Iuz\åÄMŠ«2ø þ4mïYßïÔüÁæ‡Ä¯NÇ3wLèp¹Ð ,ƒ€¨1>Ý#z|©±ßjì¾æô(ºï4 GÓ²ûN™ÃŽÿo‡pøý\ÚOöОZíwüò¦6Ø1ªÃ¹Þ;øÎÁ. endstream endobj 779 0 obj << /Length 1133 /Filter /FlateDecode >> stream xÚµXÛnÛ8}ÏWF]Èh¬ŠºÙÒ t±Èv÷¡pý ÈtLX7Htc¡é¿/)’)[´l ™âpæÌÌ™áØ¶ñdØÆç ›?oæïïü‰€5ó}ǘ¯ àxÖÔvŒ‰XÀ™ó•±0§Öt4ÀñÍO{TŽÆîÄ7ðÈuÌ*†l9ˆF.07(Œ–ó?‰VߘY³À ¨RÛ»SË.S÷q4ö‰®p?GѶ¼©æ(ô•g^éi*Ï 1ùwL~½K#Œ²”­¾Û¾=æþ’-1Ue‰Íì1w8p‰-3Á·‡0.•Só¿ÞÍ\`Iˆ­¹KoÐàub ¦«c'ê¯OXÚñLGÀÁÊ×&nML~öìÕöѺ &1¾ K¸¢‹=ÕZk®÷÷ìqMœó7¸¤rÌ飉LŸðFQÀwö˜5š+Ë1Ç5·Qeƒ †EÉÒ䙃$Kñ¦Y¨‚†c_î_Ù`ÔÜžc£fÆ’“@ßžÎl»ˆbˆcú5ŠÕ;µak{©~ùzßm|‡¤&òGxÅâËVS¢ér“0FlñW爛Ù{ë·Ü÷„Oò`¿æÆbóÔ©Ú{¡“‰8{"þ (¬ó¡Œmm^^¹­hä@aX=qªÁj{á.E{5ÚhMÓN-YÿÄ!¦æÞû?X¸Ê9>ˆÁ•“L35zæÝ§û‡[ jÓtÔ³J¨ÈI±d+!\±[†qo˜¨Æ—u‚û\¡8›bæ0>’Ï·f>ò‘äÛàdÛíL«² M\ž)gá6®xi¿ˆK±GžVSˆˆ¸Æútvè£×ãcãϪuÓ“_ÿ¿î&ô¶¤cíùîÒ¡ø7ùûÇÕð¯~—ëÝSÜ<Ëë’&¹Þ›WÃúÃàp_r†È¼*‹¼§k(ÄÖÉžrÜõç§IsËËŠ(X¦¥™®à^.ÿÌŸDŒŸšÿ:“»¦ :³©SØ EFÐ+ÈNõà¾«Ú &½ïÈnóoÇû;w"ý)‚ÀšN|cìÎ,wÆý |åÈíüâ?À¡6[ endstream endobj 783 0 obj << /Length 389 /Filter /FlateDecode >> stream xÚ}”ËNÃ0E÷ý /]¡¸¿-!¤VP¤H…ÙQ¡„ªJESÔ.øwœØî#Mºˆ<šãkωLÑQtߣae½ÁXJd‰UL¡ìÄP†4U˜EÙzÆWý„Qkñ_ÿ%K0 ÖÉ"”L˜b÷ìÆ³r1ûXûð:©VómV%G»lñUL©¤ùšlËu•Oà–ú;¹Oµœé¸3À 4*xøþõû)L‹%£Tú n‹Y+‘8Í—°H”׌‹×¦FùÄ$ÿi>à‡«ùAVY“šHvÚNp;µ˜ñ:àXÈQ:dŒ®«±€ÖÇ$-çLò]k©§›e‡‰tóy^g­næš§bÕmüqVžw²ªXhBUÓ9§Ñ—äud0hsV…ˆ ”‰à¿í¸–É«ÎÉ«æä÷DwN^_š¼nNždg 6S,Z4"Xä46€3#a"Õ°¿àÂÝa3×5Û¿ ƒ1×ÇÏXb€»‹Üû ¨ñ"¥N4wYï:+ø© endstream endobj 787 0 obj << /Length 834 /Filter /FlateDecode >> stream xÚuUKoã6¾çWèH‘*’’(“]§» ,lh·E¦-¶6iHÔé¯ß•Ø…{1g>Íãã<è"Ù'EòëMÏûîæ—‡Š'¼ÌeY‹¤Û%\”ySˆDuÎE›tÛäOv÷œf’3—r¶xkÅü¨I¸[ü¸›æô¯î „I›·µ¨1Z‘d%ÏUYRœFƒßá˜feÛ°ß¿KUNþ_ò“ê̯Qà¤À=¸™¤d§Éíô<»‰ÔÞã)ˆ Ÿíì_|Tw«a7j7io†þ@c*{Í0ßR)ö¹{LÉn‘K’Åôçy[UD¢·[¸0ÐaQ €ða™&3,‡åuw<-¾÷ÆŸÐÓ`´‚}ûjc÷Æj=»G¼dž6·ô o·fARÅ%§3cYDÍÒ¦dš³­žÂEuQ °q;‚;¼¥RѰ=¬;`W÷¯df,,p¡`”§™œ}Šq¼î ×1ùfpÖFÄB= òW8c‘3Y4±…bórJ3pÕÓ3cHü:룱ý5¤„ç`v0¡ àyVº›ZŸ Wp{ fMhȧ~òÙ±šˆYJ†2áIÉ4)¿Ù¤Â¶4¨¸é>‘0ŸŒÍÜ÷BJÒh>a=0Ñ㽦!”ŒU.ÙWh±±‰ $s¥ˆ4u0ÕߎÚO¡âR–ìö"”J„ƒG6&žçý¼¾¥2odÌÐ¥- ̳鱙\²'}uGkž Þ®;:¢µ¬`¸é¸_çäª_­SÂ×~ö:‚ œÿ]€ÞW”ÝäŽ$aáÃ’ÆüÄp–æý¾¤‡ÜV/ÐDÆ¿CŸaI¶¾Fíº°&z^uxƒÒ¬ªØßzðr–b¿`À~à>h¿fï¯>´j%s¥±÷= ºÎ&ÌWlÀ—jìí^“Nûâ ±'ì,>ªÞÅ3\%ðß>“#ýþv¦ k«¡uö%†¥ãqüHÂì—­¶È'æÿ7 ¢={Ôú4 ºŸ\XúV…'.â.¡Û¦¨mõ›A ©Î_5ÄÃSŒøÅ _Î^ø0ñoy ¯ë¼QŽ|™Ã#8×êÂhÓÝüo*è? endstream endobj 793 0 obj << /Length 2102 /Filter /FlateDecode >> stream xÚË’Û¸ñî¯Ð-T•%óMé8™¯Šc×HYWe7ˆ„$ìPC‚–g¿>ý"Ei¸ë=èn4úÝ ú³ÃÌŸýôÆ—ïß·oÞ½O‚Y/£8 gÛý,ãåÊg™Ÿ.ƒp=Û³_¼»Ý|ž^ç`™fÞÓI»Æäíü¿Ûx¶^®Ó0EþlË,ŽùðÓ_$KX²FÕ,Á…—S¼¯ë,‰úR›%!ò¶hD?½»˜!‚P©UŽ<Ï“ÔS-PRÉ5ëǦ* ]0ÞTÂí-Ñ5ÅbJRÒ$uU…ß ¼¾™ÆV'^;Fœ†D9ò6o´r(fد~7­cX^ª¶exûÒ:”À{ÛÈ™º.Á¸@Hf*Æ8JgÞ…›yð Zc’h÷WªÊ5X5ÂQD!6·¤rÖ Áo‹amNšwÛ²ÓàB Ñcn+{"œ#dNÉ€qÞ‡ŽT Ñ3%ðv¾Š€›}Ð!ñÚûéî …¹ÿ€ÛÔûtZc­`ñó|{¶KÃðÄ{\m£s5æüøÝ5šâ£`貓-DÆý„„|Yšx÷¶îJ¥‘YAù09 ”¦”öHò óâÑÁ¯%òØû‚;Û¸½-eâ»KR!Ã0Ñ¡b”Ü™Bsæd~W×*IŽñAQÉ(ö(è»>u;,§K訰q–AY|]JïÚÖR4 <ì(,l5Q/oó§e³+þT¶Z@†c :†8õ,."¼ · ^ =³Õ.d”½‰Ï•u·+1è‰9©ˆ',ëPÔŠ×W½äVлWw‡à•Ó½ T¶]-ñÜ8¦áŒ……©*I¡$ñ†Ä%ûü3X2‘ÔߺsRæÂØûJ‰ÀÄR´J£9ˆ&T$Dƶ³Õ(§q÷eͨâv#O´èCì´·‘G% DŠœò1Šáàe§v¥\¬‹Ž«+"±=mù‡ ®ý^…5Ä“c† îÅïØkq««¶k¸#‘*¨=äpö{§ bµÒÖ£2 ˆÑÅú¤Ð’ï;×Q{A‘§<ÎþrÛ]¬‹PŠB{hÔKAÌrd öDD´VÍÕ£[0®&»W_ìã({?ãÀÎúÀFˆâO£÷;FIä¯<‰uÓÛIX|@šª0À¡SeσćJ†ê×õVñ3öõ„˜l Â+„b'‚tm¡»á;ä¹Ês¦± W\€ÍÓ‰úT¯7@ÈÙÙ³ñ œËfeÈc/˜Š4}pŸœ´¯ð…Ö¡IáòKæ4^Ò8ˆ¨Öu…\EÎ ÄԪߛFC㋯þÆ`Pì쌎=™ÀlHýÝ1à&t¥\l.’p£øØ±j¦nÜ!^7† DøY5£™úñÔ÷—ºÃGœ”Ó5|b;Þ”æ™ íÐVqæ{퀃~ͩvKUQVØòè ‡[ó»îO¼øDí0.LÕNÜJ휈‚QÉMÖ0ØC£N¼9ªËLÅÅŸRŠã3o©ß$r9.>K{äå5ûÍ¥¢dR¯ú8Ôc4 ~á)Ài†Àô *¥eÂCÈž¿›ît‚g?¬8(-¶µ0”Á‰¾ÒS£yn¶ÜH^Œ© u/6®{±§Âz‹¯œÔ¿ÿ}°Á± ×Å<à¡Ípùž{,ŽãÜ{„öƒ.kæ'ßû¤Œ¼›`óO8„ÿUw•2Z0—]ÿÂ’³ßðÑ¿bî(Iz¹!P0+4ë·s7{@´P\8¢åü«HF1þ]™þ™Ú7k‚Ó hùAúƒ¾¡_-±÷³ÑU¥ð‘ …àþéî_ îߢ¼ãv‹°ZŸÜ Ù Róž¦"\<ñçvô“›nuCRÑmBXÖtÕkÊÍŠû;}/šÒö¢S&^@èƒÞêšñ ¼ÏsòwFõSt,Ñ,Î^‹° ¯ªBä×DáúÕkJ~ã|â!ï…‰`¬ž[>Ì´ÇRþ]m¢¦:%ÿˆ†ªÑ2öŒ9‹¸Cßqi‚@H7ãwCæu¶“p3qÄ^67­¦T­½^5> stream xÚVMsÛ6½çWðΘ¾Aúæ|8uš6S[mgjçÀJÌX"U²'þõÝ@ERä´X‹Å{»`Ù2cÙûW,µ¯§¯&—šg\Q©ŒÈ¦‹Œ EK&2Ë å¢Ê¦óì–\»…ó®¹>ÿ<ý°[>¹”6«he„Á¥ŠVeVHNË’Çu·üóÁ‚ì¶ÐŒ‘‹\”d– Kî›u^H!È_®Y¹¦ƒºÇÎûúï¼€¹Ocß ÷®7./8z(ÉCüsÇ4Œihù9î çR{± a©%D{îrÍIwžJkr#Á‰²äª]x´×ýà·³aÿ•d1€Û¬\P!eVpN+­@q&|–ÛU Ñ*Æã1°såÁ“øîÿ´Y»Ø»q¾q=q`Ê5Ä­«1îÝÖ·õ B*éØÂê¡Î9ĤšÙøÿ¦[ Ñ~jïÎÀ\ ‚e©³x¤q“ƒ#ñ ƒR’߯?bG‘{ô4 ›óÉäéé‰~é‡zèaÚùåä‘«IÃÌäqpNU¥¡…C‰äûV¼œsßÔ-À™æ¥"¾Þ¬Ü€§CSÄ”sòóÖÑôSçÛæ!ZSØÓ€YZr>Â9ôxHÃÔȇ¨З!(Có¯}Ó'¸*A%7‡pÅœÕ%yÓ­7[À¥é"Qڒ˦­ÛYH‘“ÐÆÊ1¨ëú¦òkCꥋÆGDÈùœGFå ã8]IÊM™P7 uyõ·ƒ¢ZA!–F“?ï¤U~xŽ#7`«H½¢Ñð_óÊ¿âßÎà­Ýà›¢ a•6p uWÄ&ÂÔjfc¨¬Ž`~×.›6PÔ.óÂ*±·Jc¦{ñöUy„½Õ'°·Zbah “"::ˆö¨^|:5–Ëÿ©u’¯«6xu ãAPôÉ/ë¶y¶hAÉ‚ŽDh絟7Ï(ßÄ¢ƒ^š›xU/މ&œ®n>Á:[’Ò€K ‹©Tq‡¨”šÂáVníÚU^«±b$iZ8 R:»‰®CìëzœyÇ„‰SA¡Ã˜ÿðkm;.þ6?êò& yïÚ"D6†•êbØù+£¦BÉÎëÁõÑ´«ìõáD ±’Vrwµ$±”H¾Ý#¿é»S¼Ãu«Âm²O¼>I< ”Î[÷ˆžÝªÛš!â7#§q€) Ÿ’¸z}²PƒBU»Ä?Ç3rP;hù®Áv¹M„ðøØ<»ÉŒÆj¸ðÁ„K¨HU!4ÔðÑÍ”0h£­RÉb)¬´”»ÚÄëËÈ€j*Þ Ýòz$ÏŽQØ]³Qw»$'4®më4¸ØÂ}ßÔ©Ó¦_ݼþ5’*‹ŠÁ3ƒÌììÇ×ãu±ñ]^Àã‹›!7pCw$ýPFÉ6'3჻cRz÷¼W–\ÐØ^ 7}G©¤Ë¥Z†z—ÿlëvX‡wÑ’¡íü M bùèðŠd{ CN~éPX熰l•XDì`›ãÅen-‰ï«µ{êüÃ)=Ö†Úr_Žq› ÇøÔ(CB¢1Ê1¢éÍ;!ð£²Ð4ö;†‰cvð¼ÁgÆ7æœÎºu ?qý©²öŶ†¢vÔ'® rßC{ä!¼ LŽ»Ú´+èÌ!½\ª2¬$¸LlÜÙTsÞM_ý R‚ûË endstream endobj 701 0 obj << /Type /ObjStm /N 100 /First 885 /Length 2556 /Filter /FlateDecode >> stream xÚí[[“Z¹~çWè1~È9º·”rmUÖ'ûxËvU6;å†9ã%aÀËeËûïóµúf&`˜¼Øå¢­Vwëë‹ÄasRZ‘ÖÊh«ŒËÊxR1“2Ù‚ï”%‡÷ Ì9å-Ë[åcÀ{V!dEƩ̀ŒW”>•Èà=ª\æIeâw£Œ…220fkJdp,¦ËX; ‹- Fd#9‚·(X•< ;v”Š×&\†{VÃ9£¬a‡œWÖÂrAY癕õ–äHÙgÉa£!CYÀªè°=1aïJp+mJ0á†ÆÖÉG 'Xw¦È$å¬ÖòV9ÏŽ{ÆغÀnLE F<ÅPQ€ž„ '[æ$åu‘É8x@òÀ0Çiì®zÇ DDÅÊQù(⃀Q„BÔ(bU‚?DZṵ̀À'Ÿ !ðK ô„h1àèWÞcUð°e 9øÌË!°'¢¤Bd±ÿÀh1‚p²a@@-d ëÉ«¨sB€£¼**€!y­c™Œ'@ *:öG¯á ÇÀÈ#cb~@zBfÒ6²Q|ˆl0!©cBn$ ™DÌžl˜ƒ|Í‘9ž“Ÿ9œÍ9‘Ó¹Á g`@zzNØ|@é úRtŒ>D'Ϋy7\ŽgÓ«á²S¸ú“Õ莖ÿùàô hÝ>:÷~¼œ`æ­úë|øé—ñh¡Þ¬–ŸVËeo·«Q7çYßÀ•½­³^ìn’´ïV7Ëò‘™fÐþcxÏsvÐ~?\tìöoÝä·n9 í_¦£Ùíxú±5ÕŽŽPéTùÇïg“Û‡õ^·?¼zÅKnÁ-p¨l­WˆÖÍúöŸã韧‹ñvúj|w×! ¶kôíö~<]-TŽí¯«Ù²›twKk¾½×ÅbÜ~œëÚáhµìÚÑx>ZÝßMºÏír<¹íÚûáh>›¶7ó2X2àöv ‹ñ¢m¦³åmw×Îa»!9&“á†ùËjúq8_ÝO†«e;û8›vÿiGCÖ·ø4u»‘o'Â¥z¬n4N†Gªg¯,~zsóïn$s?Ü;¾“ÐY5cíAÍX}ÑšÑY;¾UÜû5³7wdÍØ&5æ‹kæÈ·éÄÂñë}s3ÿºêÎRkÕï~¿¿™MvWŸYe;IŠ›çñçÁ®ðéçÁÓé[„Ég¥·;LowÙô6ÚX\Ï,¾øýôÞ›;:½=#¿ÿ÷™àžéLxHï%3Ó™z­{ÖÌÄ=Ùž—™Þff>%3ù{ÌÓ™é5.ÚV;oÂAfîΙ™®ÑÏzYñéys«·¾¬øüµ]V¼>¡µï _º€ø!Ãy·ýà (˜¯¼€B~žÚê­ (꯭€‚=áÚ¾lñ“3}VÅÇ Ñ^¼€ Fc* jîÿR@¦ÎöŸ‡Ÿî®7Ãåâ¢EHæyŠp«·.B²_[ÆSXE÷œEH(sVR8(Brg!ï™ü ]j_ø¼'zÛGœy¢Gù:tBúPº :$^z’9tì¡Víñ9ƒžèpá¤=f}°ÇxBÐw…×é|£ãqOýqð[y>z@òaг9|ÊM&Û3ñÈpA@Ò! ñ\@è„ Ù~Ç„cÖIùHawâ‘nDc›€ÛáQÂ!¦†+>NïΘã„}Ò§ø¿’msåéô¸fÓí{´WÙQãT=N—”3Ìñ଩ÈÔ³ãÜeöÚë4[+5 Î4fÍñ~RÎMäQÙghàÉS#øSb™}7 >À›ÁÕX)@‡Ñ12«%ŠæŸ»YU'ÇË"GíÔBmоH²ic¢Ùø!4&Æ®ÆBÆÂ¯Q >€¨öØÃÚL0®áŸýfEƒð{\„Ÿv ‰V¿¶’Êî}—øÕ³‚šŒ{yöY±~/Y¢¾ÑoôIŠL1ºü(æuâdÕ‘Ôq9ƒС”f@ í’i‚JÙ3­Q¤y” íŒÕúyÑ,”%ê9e±Õsì«_]¨è«éÖ î¥¥pñ‘ ÿá —Q$n51X^OŽùEÒ¥Àë @ÜO¸Ð‰W®´Í¢¡·bsß„ÍÚŠHšsb+GÝîZF­Ñá9n¹§ÛÏ=²n‡pmàôq‘öØõ­ÙÄFâc+d6¢[:å£ãÈ%ÞQ‘üÖX¾ÑGÚHH¥[ΡÉ9®œBå ´ÄÕ.ãš/c|×EFŠŒK%/ ™YÈ»mçPÑ#T¬ÔÔ“ÝøF¸*$þ+®S˵Ê}%òÝoÝK’/}‚å\4åRúÕr€g|ökMÂ/ë{ .0¦¿bAä E£¦r¡å\à cæÖpÉœ(õž »;Í¢–éAÖÒ ·àH˜úµ•¼È¸Ì Å墳®žåÍÈH¨Ø>L öÝĸ[¨}p­.ÐÖtguæc«¸†¹¦;õê ÒëWŠ|-á­pÜI,áËݱæììZÛ’ÐñoDnk…nM«BÙÑP£\Q)ÓÞvIíèø˜;Üñ!ͺ¤8ð©#PSƤ~I¼êdŒmÜ–õ¤`cŒÞ4‡§){|ˆDMëP·˜ÊyÚ†4‹º*ö)7Éòë˨3¹|+)_xʬ¦Á[n¤Â‰™)Ž~n­¾¸ÿÉqÉq¾îd\‰Xžõœ}%›²6Mù+YÔ‹Ñxó½H’@&.óW6·N‘)¥(ÆI|éé=)”JöˆÿõŽ„/ž÷³¡|y+ªùÂÒ,ñŸPs—ÕÂíQ)üˆëOê9:6++y‘ñ¸òñU§POI™Ìd,´\*Ÿe,ž›R™õN{Ì‘/TŒkÚ]h6ñèîš ô;¨Â ÖuÆÍ‹C¾†5÷€÷´¿%&È•–-¯zóšïÉ|Šº#r=¼5ÈaðØ+ðë[~$Í!ö¥¨J‚“Ûnîij´7›(Ôø×”a¨_­:壯ˆ-)¸øÃ XþïÄg÷ÐPñ¸@°¿Õ §réªòûéÒ÷Šºöé‡Ák¹¾ endstream endobj 822 0 obj << /Length1 727 /Length2 20894 /Length3 0 /Length 21453 /Filter /FlateDecode >> stream xÚl»spfÝÒ>ÛvîØ¶mÛÎÛöÄv&N&¶íL2q&¶mßsÎyÏû«·ê«ýO¯FõÕW÷^{×Úµ)H$ÜÔ½€, ,ŒÌ¼5IQmfff3#…˜ ÐÄÍÚÑAÜÄ È ÐšÔ€NV+33 @ÌÑÉÛÅÚÒÊ @mFóo%@ÓÎÄÜÚÞÚ éhçèamfà÷ððöpugtqdü'H ¸YÖv@€˜’²ŽŒ¢€ZJQ tº˜Ø”ÝMí¬ÍòÖf@W ÀÂÑ`÷ŸÀÌÑÁÜú_˜\ÿ ÀÁèâö0 G{€‚„ºˆ¤’¢:@BŒI] `â`—þ'ÜÁÍ•÷o ™©×¿ª£¸ý¯dgò¿’éÿJÿkuñúÄÇÂ0·6s˜-­à˜þÅ›Œƒ…#€ó?jsw§ÿšþäú>õ?ÒÌÿx»ÛÙ)šØÔbŽöNîn@€‚£9ÐÅ êhgðr:˜Íÿëmbomçýÿëÿ?2n&ÿð!â`ùƒÌÿQY»JZ{Í•­Ýþ¡ÜÂÄîªþ­×þ§?ÿJôŸèRþ髹£ƒ÷ÿ$ü§˜§c’Ó—‘§ûß1ø·YÂÁÌÑÜÚÁ æö£&.æÿ«ø·YÙÄú?3ô_(ÿ)Ëÿ[+˜¸¹X{ô˜ÿ5$Ìÿºþ+ü?/QQG/_VV '€…‹ ÀÃÆãÿàš¹»¸ÜþMû?µüwýïq½€fp«KŽf|a6­áų•t³ÓÆtK, ËS½×z¨ÖÃVæF—xwêxž„3Á¦;Û2w-¤& @„£‰ÉX’Hƒ%¼—²JË€ÚÍ7•Â׆HÃ+M\{3Eˆë¨[·¬µÕeÚIöì¼³ZYç2T)–XãòÎH”­Ä•ù)*´Ÿº_À=º”Y«EW¦B/»«`¡;׸FVEÁNº… —6bú`CZ‹T}zµù,f/?E-o]=K9L±š¬¨ò»Ë³ ïó;ÚÒÉ?¤28 ™ éoæ©¡–*èd0 ¼L®?kì‘ėﺕÁSª Ò~Òë¸m±2ïVçm—.ìÔ£þmKé«Ê±!Ú{+n“ùV¥ ´Â­ÓJ™c,7mzy^kÜÁ׸…ä±lê|ºµÌ…ÒÏ–jûb«%g|­@_¢ù3Á+àp„Ä ð_¹¡ü¾ìãQ 'òË,œÊX™y,_$‚sìþ¹¸ýcAé/ë•Ë„aTÉΑ¦Ø´N¥?)9…˜£%תx€Ù…ê/C¢þ¹ø{ŠÃØ×—Zhܜʀl³óæ†y5^ÆÂ aω§iG\‰ Ì/ªÒB$Æ?Ž©&!¡“ÛבÓáv=)pÓ)¹šjÕ¡Â]¼×’ÔSi&&ÜÎj.óIË=JlLÑ0<\)æðùsó’ˆ7_â‘xþí¹üHã¬gbáW{4Œ(šS AAe²8¯4G(­7¶i±L¹.y¾»}ÝyCõuAsÇeNÜ’¨~W÷ܼ—‹ĥí)n! ¿#wÔã†âT·áÊ0€;c ÊVCÁ˜±l§=HfÜÜ‘I±Ú ,Ú®Ç9ÂBäÒ ¨.íÙÚסoz…b±CYì}¯YÒQzÛË#›†k_.³I®ç8È]ç™ü™ï¦‹Ksñg‡_T• ›ÃÄéÈw0[ÈsZrú(¤‘yr$íä9%‚¶Ñ‡6ñ±¦Ói,ÙÔ‘5ÌßN&ÊÞ zêL.y]Ür‘Ñ»:Fc&K2>kJ%šªg*Ã2;­¥Í{9ùêÝ‚cùû6Ž;zšñ§N—‹’9=UjXoå®\­’‘؆Š•§4mÚ r=Ua¦ø)^F?ŠçfÛP–ƒ­ÝÛBpî,ÜRšÄf>T/Lˆ/³-A Ù¿2jü¸JÆ4¶ Óç&Åîì¼Òݸ û¯?J­‡|zÉ“Âh]³ª”Oær¡9›Ñ¦Vvk9Ð˵»[««ùÒ_S°‡2ñº8?ÌGk„™}lÑ÷¹¾SµÆêf£Ð6~äãÝuHâ8ÜS&jÞ×ÞÆ(δ‘щ=ú‚JàÛçX"mß 4uö¯oÆ<Ø:„[…Þq¢‘EOÂͺp @¨ïyZÿnY{úùxÕ4Œ_(9Fo$)¸a*þ ‹ëÙBU>bñ‰ˆfpQeÖ´ Ír€±Óþ°Àî×J‘¡LZï\g²1Ë7³ëcÃ.E-^Ù!rw¤Uhm»³Û’ªÏìÕ:*Ê!»úZŸÁ™dÇ_Fu‹³ÉDc]À/úIûÑà°6Ã>ÙŽÇž… XÆ U„ X—×p?”SoˆÞ®Ëî«U{ègIO³qÜ0«;µBª é{S1¸O— ó‚CšËjœéÆô­Ò–*úµ0ýy«0å<”I ƺ<ŽX‰Ðžx+ËÕ¥Šº®Ê{+ÞlY›˜z_…·šGÓ¶oøÕ º¸^ðò©y€?å׉UHaÓ.i-G3ˆ„’P ì;A»ö×™$ØS¸ç3ﻳ»±¢¸ >ˆo¸î¸øþFê_Ïg"•Å#»Vy[p1ÖçVá>ò˘)G¸ìÅzç:]y#!µåþX3ªv¢ÈÞ¦Xþ¥+ü>*ogéƒcO⤗TŒÏ-Ñ*׋џ¶Ì$7ŠŠÈ<ÞäEiä2Puë,ß¿ÈIs.HßMÁlé…ññak^ó2#ž â0…¾ÄÖe¥tØóçuƒJ&8^'/eÖýb°‰¹Ímó‰ÎŠ´Š#¶z-ê eÉAa?ÏÒØíþPpà\‡H&:…S*øma/¾E9çB|_Ͻȇ9…Ó–<Óâ“€V-´zX<â©Ë<ÓC}iãüòesx.µ¡ÀŽ©>ÒqásÓ QQkL³,Ò°ÍÜÉsê€êÓ¸»5HœísÜÓºÌÅpe—–Ç ¯yì›Xø÷ðU«õ½Ò:¢ ¸V‹ÚÏpú±†XE…V“‘œßô5Eö”§‡ ­n¥b¾¯ÓÝì ÞgÙ ®Èù×8Iéû 6îW3rs‰|4¯,ÇDï·ÔÙc 1³¼üÔºíCc„t·/”/Í8ΰ!Ž6¸ì;¤cíHV-Í2¾¿N<²ƒ¡{)EÍ4ØG±Â˜ò1D KeLŽ+íÖéji'ãbP¸_ÝV#k°ØT±‘ÛÓ‹ˆíF Ý¿ªæé°mL’ø[Û7“&èáÎ|¸0ÒA˜˜ÑuaFü¬ÃN Y~>|Ëø«‡HÂæs-úÂ[–‹!>y;¨áÿÕÀ!¾¤óe§¤Ÿ{àEø ò§”.Ciâj¸Bò²IÖ0C\X!åím÷ýh‚ÄÃF¦°OôÄÿ ~ا)߸’² ßêf…¹h‘8ÊW˜Ðú@ôOÿóÃÔsÀ¦¨†V…àeÅcƒHâåDÉñ"à5s ¿0X*¦ÄUP ÁoÕÌyÝ—ýA¹ÿâ~ çÇwuš§Ð©ïH DA&j0.m —€¸‚påhxfÌ6Ù1²søöaÛÑõšütù´宽 Ïà3^,7%©®o8,*k’J¾[ZŠ›La–#:¢JGèöý*p¿À_p/QҶhøÏåt1sVVÑê”ç`ðËWž¾VöÏXý èúŽò =Ÿ˜¥bBïMÎ%ÎdÞ×ëYe‚Ý£§äyñÇ–û_|´§å’oÔJšœü[¯€Éù|`"jñFaæCº“ ã„ÒS ¾î·p†ÐñɪNñ’Õ 8Sø"äQuvÓP·«Q 5Ô„§%ô;@»ès@swž'üÃÃû^|§=.¨}9þ:o¤²²Åý; íÉ©/ÚD휈ªÆÂÑûÙÀ-Ùi黩ޞl™_ Ç·ÄÆ¤0‹Õ^®ãBœ¶ºê0106C¹çl¸Öæ"†{‚iðàDŽ÷ÔG ¼-žB>ÍsÒëì¹áÔç`Þh¦` ®ö¬âùRZÖ}"Ÿ›¤,wì7ÏÝ…—ôr·«w{Þ¥±ËlÏ$­ËRÖ®ñè_ìõ(O¶ZÉ ¼ßaçq G05Í¢°Þªw¶ßQ¤³Ýj·|håt¥Uå‡ûBãLró"£n£ûÃ+éJzÊš‹+$w²‰æëá wf wíjŠ©¯xO –ux‘Ì”8…{Š·†zcÃÔZ¿oAÖ`vÃW,Öø”íN8j²ÿ’n–\v;«Zg¡ÜàÏúôVÙÔéÝÞ|.ߣŸÌ35Skd'M+kù!T%çâÂ’XdŸ> #„™‹F¨µºWç ™]³œ„HÑÖÜA=#;py‚ÆåK_—ò,ž#4Ûpp [ÎçLóòØ \c¹ =¤ÝeÂGïªéþâû¬tƒ⨮j}Mþ¿ÍA0Ê"nžÏ˜¾õïVìºFÎê–švÐ?ÊzYÌl"‘ú°)'Œ /ŒZiç±ÿúä•-i°§^V¯üàÁ›ÁzË y,ÓÌLèM<—#kIñ2ñH!a6‰ò*¢—ê/¢r]6 XV ç8£ÍJ¨fïÞucÏøÎ §~¢­I¥ ³[ ãïÞ2óNð„cAIµÌÚ˜ç„çJFG½Ò““ÆrÕUÈÊI€ññ¬#HfHµ¢ÔërnÍÿT“Ÿ[(ÞØºÒâ#¸,(Ã~ż íD]dzý¨¡(Q¸ÃÔðC þxšŠH™9‡=·ÉºÚ Yerƒ8øÀó‡ZÌ ‡ÖëÂòZÒ¼•ST6ä*¶ÚÇ•ÑQ:°Þ8ˆQêÆ?1WCy¬Ë ¿0z(‡fóhPNk÷1^5yÐìŠFQ±73[[þ=š7[R%È Ù¯Câ<÷Xñ-žšÚ #bœ‘ ÂÈE5 ½ß O'}{‘‰óŠÐFݤw¯Æ0E†M|DžÓïÕÏÀndFýàM§ æ$ZKM£RäfîÍ|„ì# Wö(Sv¹mI@Ç ^îOh‚ÒÎãY‰Ÿxˆ#ma=äÖ’.­3Š++F?·%xu®ò©)£?·d*7K/ƒ)ØB42¤ ‘¼Íœ)¸Ù¼Ë)9¸ †Ç”&(vŽŽ±pÕJð¡ðLP¼S£sZÞ¶o°Z(bvKæü¸apËv‰«ÑÍÇîæKÿòtðžlšñUx7Ü­šXmuƶghîvæ6Qïp¿VÀ›ˆqæôEË÷ 4ÕK§Ãòì­…¾WÁsw@,mÁ‰ M@=‘ž,$ n<¨“;l83RšÛ‚øü¼À¼ƒ¶ 0†éµ*”--gðw7mçûÝ\V»ºuí.±»öópv÷ë‡ ‡$d¢t'<ãHÉikëbýºµÁúù¾ègd{ðç?‚"฾@^ɲv QIlY,Dd`10"vÉÇöB”ÓÏfНÂc²W.¬CjÇi2‰ðW L¦çWœ*‡È¸¿®G¸ª~4BjLô”x›}€Ã¹“©Jö;—þ¸ûWí«0G Føm¢–„¡lŠ/øÚÀªˆÕ©KØô&ÞXè µìêVü‹z«oBZƒ°€ ÁX˜õöÂ%'Ô†äók“ž>øÝ¬#^u²E$6øø¶*ò^~׳² ’F½´!"Úr8²AÊÜâªzèøÖÃOïš^·ØkÌ–q¦ŸÒ¢{ÙVÜÙ}‰]9V% ÎŒe‡‡®þ3ëïúRóa¡AAnŸ•Hé­ü Nu¶ÄEßÇ2B"¥®_d–™©5„½ÝØŸTùŸ[9*Ü\;Àî½?YÚU^*͘М._aßJÀ¦š–ÎÑ:ßä@T&ªµ%¾ bœ™çÝThzé$ì³mBÖUc¦¿bysuîû­J|1 ²ÃïûZ ðÞÀlË÷T-ý”îU>ôÐjòL†ª¤fOK´«0jZ‰"À‰ÁÃ`gu”Óœ7‹ÍmyÝ™)lX³Ñ­3îì05ÇÉ©j6xˆ¾¬¦Ó¤Z;ß_an~Ì2H©=k£ÅӳǂOBm7×Þ Ã¹¡Þ®öyd5${™>ú§ª¢`;ü}ޏzñ!ë1„+´wJZ%–ÞB)7°6voN…ÇóeîúE,Tª¨ÐPHKÍæ[!cÂ̈́П•³~à’MÉ ƒ#èåÎ HD­a5þœlE5]ÜÓUq9oô"Ëo^þ¢á8b<%YÇ–È߸õ„tfž¿»B€Ÿ(«²d35‡`M¶zyÍkìÝ‹RÒ2æó, ðͺA[aóHlòâ*ë½­LzºYÆ([P'q/l¸W—¸ì¤Ï—ùËÔ;¢[7OÇ€þåììIì%ѳ†7˜ú§R"V †èÀG7©Ýý†ci@4Q@JÞß¹´ó³ééL[©xæå/­Ëä¢o~ ›XRø÷aºè«±âó'pÑá¬M¬³ò… $NX ™{ú©îBåk­&Ç+$;„¯Òëw’àÆ <êgý-Q-ˆJñ:ÒÞëõòuñS¢ X§+œØ"Úyt©>µA©CnêJFlù"-Æî’è¬ÈoYs’V>=‡,5Ì̯6Téœm—ÄþlÛRæÉÙü†Ã8´ ),1åIUì¹ß½¿“Ù6ÞÚ/ǦcJ©@ÖYGÞ¸¯^XÝNsd9˜{â°¼‹ dDÄO> SI ²èfnݵӽ(¼ŒÌÍ´aÀBÄ ¶Ï{rD«3\É&œö¸´‰¸GÏ¿kËÅ8÷¥€tMïçœj“¶Þðy³.ɸ»8—<ò‚m˜ïäß\QÆIFZL'’äìF>ƒh÷tSìëT;ÙJ%ú$š‡‡Ý~fÝîŒqIÝf…Ùƒ\ø$YR©0Ϻµº»qãŒ"ç;ðáâqÚa0ã#úδ›n$»»(t«m놪=¶¯ÿWEÓU;׿—‰Ƚh´?#»Ê5R@¿øÔŸÔ@dW´rÀæå% ú¤o9Ø©„1¥.œÉ]¢U—Ü…ÈÔgͰ8gzlÜé[ŸÈçÎäͼ)¸Ô&Þµ0ËìZÉ?íxѹïˆ4îç2Ü#Y¹Ç™V¯ÙŽSÕ(AΘ°1‡;kÁž÷dWâogT öY×ä:“–b£ÅHûÝ*/?Òħ½|;´öe»†=(Rƒ¶Šë ´œ½#¤[ªp¨Gïv Õ÷‚“z°ö8˜ÿJ´h¡F¼_Ÿ"jó¹6ß½_1Åê#ìþȘ07‰'BÖÙ˜)¬Ë¸œæopj`õÂné ÅèþeêDet~A¸C0ú切[R_'B•²ê=SìGaØÖ¡75ëàØÈh®ë—³/Yõ.Þ¡jë­óuÐÁüäˆÀ#xÆ0ݱE€7ÁY7güãQcgr‡Æñ‚!Šl™Áýs ú¤ÕH#Í£W>Ml5}RPÚÉ7Ê>u•|GãkXËÖÝGoD=Ü¥[-~í-#Üì,tB¾Øcƒ%yöœݱi~µáí¾³KÞ8™ÃßíÕ¼º=H,/ØÎþÖv¬œõ³ÒÔlCs‰PDƒÍbX6ýœpUB&°ƒvîÍu”LVG“ÎÃÛ]ßQ{oSåQ!!Á…ÛºÃ'ImFƒLòÖÄyÑl–FøF5E™»ç5óZMðÓ2·MØØjBîy‹+`õòHµÈÉsBÅ ”P°”ì~8ókù§E±ßQ†¼*¡GyÉ_«zzô•û½UÚl®F’¢V±³y~‚[®š™ö¸`ùêãšÉk?¡E²RâÝ¢²o•»œi²Ž ûú“å¸úF~ !´±iŽ9£ea×oœ& ‹Œß¡\¿Á»ú¯ÜÖWH{ñÑF¤À CÒ[²Öƒÿ˜aµþƒ¿ÕIð¡JBÙœËe^ßek¼TVÅ9ç'/Hj/a•ëÚÁ;¯ê Nʃ¼ßä Д˜UhÎõHt€ŠjÑ:f ÿµŽ×«à×<»º†û²ýqlâx¶¡ÿwY+q¾ô^Õârázl>«/±ì'àÆã"$­Èµâ/Cåp¤'ovìŒ^¹=Su&kíTcŒý™»ÉšÊ{¥ñvtÊ-VšÙ²õ²:µ“$L˜¸*¥_pu®eôU “1ʨÊVÎ iŽ`€ÊBïKJ±v|f­\Fð—Žiíù¡'ƆYª> r×Ò9Þ’\K»zÇæDç¶œþÆö¬–S8Åì×o…r”½Ë ]f,vWÓ÷^ ’ê×$&XíÁ§¨»å§”Gô ÷2ÞPy+SΜ9iÜ­¿\øét“´N~¬ƒ."®Éæè¼$%KG$jЮ+vŸÉSK{Ë¿5^d&$¯#o¼¸K&esx2& ³òâº~Ž!ÀÉvÓIö»¤0ŸŽJ“‡@ÜúbH>Y}fAfÅÆO/”åLä̧a—¢Ö2•šg¨±0‰+úÏ¿¾ÂòÛ´¯$uážDú½„ÐwȉªSžÙºgÕLAqX|‡‚TZAMî¼½‚Çm‘4»¾y‡YýØþö/ÿ[mèda«NÍãŠSÕq¢Äî‹ì‘V»‹Î9dZ»&«€|pMí´S–±sÝ—?(DXç !Ç—Ò_ùæ+£‰IÀpò[™š8Û}¤W(Jú°|Lî·Å½Ö'+SðØ3T4Öþ}Küõ-%êÈE[f¢#êS ‚Ë¥e¿q)wQ÷ù}‰ RW—íó&q$FËé×(hÞhó5SµÃŸc¯ÇCsùvÏ?Y"xA^Ü‘: ’8÷zß/Å:¯lH×fKÁd/Y·XJU¤…\«‘Aê‚æò¹=@/NSL¯‰6ü¾`ó§sP/Ð0‹u÷»ü:†ÏÂÙ^&sÊÍkÏ—™%//ïêô«ŽÅMUg™&a ŸO{S‘ûT>3¦"&‚búºwÜßë" _iŸÜÇÁ6¼f×”ã®ß4ÿøÔß“'t¡§ñY'(&Ak£Ñ <ÙÀZm³¡F¼ð‡95“(¶Rc—üÅ´;¤s (4™ó‰ @òkLMtøe:vÃŒÈrÇÄô‚ûÿ"ûìxìö‹ûÌm6#‹ã×ê½’[¯“&§Hä:OT[§‹î \Bÿ½GiÛh— :~üùªOF;.OØ+wÎ>Žm3,æäp¶áìê¹ö˜K;'ÿÚˆ¬sG¿Ø{Øéû‹ôÞ¸T¿ÀXê‘'762„SiJ5x™U1¼+V`Dç>c¡KuÊÈæ´ÙŒ/ï¡ÄÊ"º½²-ÿ•… dÒÐÌ š†Bw>j–O‚(¥SÞ:»–Ú×¢¯u»´;ØÔ«Ýæ'¬‰jrúiWÄYÄ·CpP^Cí¼wdñ7A¯«°øáY/¡ÛölN|83´Vëy$;ኲäÊ@ÖäPæ4«Ò™q6)~("ê›úAb¸oïœ= Ñ“†=}¤êO;é/ª¨gJ_Líë+ZšùìQˆ—¬þÐ;vßàzôÍáLœÑàzüêf¡*s+ÙAÍE0nÌáGF5xCfs])OéZÒÕ:€u`¤­¥‘xRö­izüút‘ ÁîY¶×Ðó‰h8ëüT¬j¥IkqÄ-Ëñ´ÎÒå/•FÃAlJ¥Á´gpÅ."¢dáMŠg»ji# Kݬú~ê™²ãØ¯tæ"/°«á9ðܤ¯µp?ýÏ»O„ºÎ>N•(5þùËp^p3Šà7ñ¬tW¢çý¦ì‚³U±t¼B)lÕV2¸6–D–Vp½åµëO'“*ÆÃj&Kk±¦Ž¡Åk¥ó (­3¾Y)ûšà5Ý<‡™?Î×tÄ-~ ð‘OaS’‘”!¶›4ÏÙnâßõeKQH{ÙÝûˆZ¼•4a5â>C„l°‚õxùêêu6&±WÝÙËâr¢Ñ+NõíP’â‰Àu;‰ÞBÞá¿[7¾|¡`ðWƒ$»N†î‚Ì~ýê©b(È6&7º•¿C×fÞè!Ô´õãë5Íøþµ§œ¬†O[™(ר—6ç4õp BOÄâ(¨R#öP"t_ºt¼OÏLr­šdA%óù§¹Œ¼»˜maw¤,î võŠé¼ ÂX&:æ.bê•Mˆò¿3BÆc­Î¶Fš§ŸSê64!Õ Ydd€ ƒŽ¾;œr1ÖwÂÎ÷y JÓRs¦jÅ Ç^°Xèë¶…Õ“ËÛ HHyŠ'#ζE·ÓÒ(Ïš\v[Z á ʵ`|‚÷>=ï+¤Ð›@ãÚÊÑ…@ýÂN6~.kžå§7’šLe­{ÈZEQýŒH³‚ù Ü¾™=|É»¸„M«cÖ–>/G½<ž0%ö(þÙÕ»o/h>éÓ~+øV•àQW°ÆÖ_mZ­îª;ÄœpŠÃ‡šN+|Çn¿ÞxúW”F™¾6³E¿®¤ÅÃk¥Ñ—¬[”ÒË›° 2Šü EBaÀÛÇ9²–!Æê÷<Ü…¡È¥Á&ÙÎóíÎØß*†i®Tžà¤Ž4pxüÛ+£G.¹Ïx&¯S]+=xwî—25¡¨ud*]¥ÿ{Hù@­ë£h~ß"e³€Ïueq¦"»ðÖ* £æâ9oÝJq5ÖsѨÁÄé"ÄTÅ•UM¥ÏNêøµö÷Þ€’¹mKšáɵ¹o ÖŒrê3Þå…µ©ÊiŒ mïÙeÇo6˜‰Ó:O+¥îüãTÁš¨„‰F D|`âúÇ 1/îZ?<»±´ÊÙÏìȳdáÄ¿ ã5póÎu¸«µ,•—û€U8X]¥†Š3 ½Ï-¿5Ü8Ã@ÌSCu}ÆBнuAc˜ا£æ„Ó^G:ƒä^€`cWLœÑ×2­‘ÄÏ후±eªÜœp|e] åd^ÍšáøýlAÊtE©ÁxA¸iýg½™m„Bq?Õb„Ÿš*FÔ”`ŸJ2†.hÅ‘ÕÓí4Ë>Š?$â«&ù6.oú—¶Tõ¬QŽÃ}¶~Ñ»O "‘>sV«A-›]zUh)aÖ„@o!'­ÄꌒpûQJe±Õz¡ÂŸ´ä#4D<À^òÇdU%þT‰9óÝ:ALJ‚)Œ¸@ì×öCŒ§}±H·h·ŸÙV-IB2ÆD€Ö…é!¨XåPº†N0ÖgýañGÞ†„ ʇºÏ.N'g´m¼ ´`ò' T/ÙYÕËß•Ea~yÝGRîŸë&¹ˆ/>WË>)CðW}wÝuí˜ìðQ Üx¦aš¢÷}J§S²ÓD÷ç4µtÙG^; ½›)ðdá¡h‚b¯ÇÎ"c"þ™¦¨áê1-57ÛçÆ­5²ù{F4ïhÔ5•zÁ)GLïç`NLžâ\‹]åâÇVÀÐQð1Aš0HÞ$o@ö™—Ùº,¼H~qpËÅ!ŒÓ˜ fE‚\|–.òÃåïʵÑôy¡ÒdC…yðw5(Ÿ¦[Ýwh‚Ã<ň}Ô–†¦z&»©Pµ=µë,L5ä{ž ÔƒEq ฺ|´Œ[±¥“Tg¢ýô†O1sê<¬ü*¸›óîJyvz1ä¬|Á†Ñš¬VëoïÄtZÍV"bå¿›TH’M¯á­tñX‚2®$Cyï® ñ¢{à |söþ†cÖÎÔwÓ Ù@rZìŠlÀ´EuÎÖØxn› ![½•âÉfžC‘x¬íËA„P²n·±– [– eîµÇ uÀêܸ,(Õc+ÙsÉ⺜ÈRLB1D dšg‹I²XìLƒÄÞ ¬HòÜ1ÇŸ£ÚðGQ…ÀÌÅ©´dNä j‡¥47bx„%ž,,-ë¶ä«§ðÓ2‰ ¢…#iè¶UKŠ*L¦ñ4vµ§Ú૾HÙza  G–½—¤Ç?‘[Ê3`iU¬û Iƒñ%=ã'}H³ —à ±á!¥¸TIKí½X†’-æ ƒÆ‹c"éÒ7žæÒJÙìÑòE?a ~Ùq2I' hùQ÷Ъá™I¼çô™‹Yîûp(Œ÷¨¿TÔåš÷§6ÆE4Ïyò·pûYëV½á7ëP'<_ó§f’YË6*[ßðXÆ•·.(;ÏMI~+𤠀ûŸZ—ãw¹çoØ£Ì.´u^_´È”ÙUæ¬ #øÖéõSrýÓ,”ï£6AD9ÿQH·ß9ìü1ˆ0«wºŸÂʱfú‚E"ãøÇ ·GnªÄoDÕÈGÁÊ7— C¨O!´þ…P 4vdȡ̒DD1—Tgøá,v}•‡ºWS 66V²ܾ‘ Èœ,^ÈÎj™D“óè„?pÝ)Ífèý©µÅ9i³÷Õ–À§ß"*WÝ!v·ÕÅ;o›5y‚Ooº'¥©Èªr K=–Ås+µë/i…Mb#hö+)öSSKÅã°öžï('}¼|IŽäu5Û0Ø›t˜_k+wÒ•²thí~Ç^#âìʼn‹ÐñxPòîk 'ëSö´à%ûÛpŒÏ“cÇo£šo˜†u±¢Z(ò·ƒ;Ààn$ÞI¼ûò0xO$¶6‡v#Ÿ;BtÈöë&Úqâ8ô!…cÕࡌQÿÇ#èÃÖQƒ‰R¿9.ÁÒx,M‘_ÛTÌ”e1Ó¡$n¾a$^Ò¦|BOd‡ƒY°yˆpÜ šç&wwe`sò¢eÝ…äRAN‘3\z—rFPÞ’âjµCäÚ1Ïx1ë£ CïÁK\ ߺÄ'ÕUP*êV¦Tuª¼3*çFž“DbLµr쪵‰ª ®YÔÉÓ›³éüŒF2Mó$hàS‹„_½ðøCÓ 9Ö<ò¤ÛË®ºšáqâÛxîz+ŠôlòÆo Æ=ªðçMw“ýØpîPÉHLf28@Œè1da¤"fuqÏ­|˜ÜŠºÆÎÂ0J$ÏÇJžö«”@z;ÿ·£÷És ‹»€ûþ|ÏÚy¾›²ÀwËFòš2ÏÌ_ÁtiNªrJ•¯2ûTÕ‘gØÑZ-꿈Õ3¯¬©Âö¬à‡å2ˆ§Ä›« bâP¼êß^ŽÓsnÖ4Ðü6¡ƒ¨)¶:ÖŸ ™+†ýˆÕ‹¾ŽßxšyÎì©o.VÈ ý»ªùÎTèÏÇŸv0]â3]þNŒ¯^m$¦ð¾Ü8”a•$äBîCÙ"&Ä­öÀi£,»¬_˳CÇ©/׃ àÆ’£ÁœîÛLfÎíñz¨ðR4z3lgxGJ°Á]TÔÜ©Ó8¨0þ¼C¼˜N¦˜@$}uóHŠì7Mó‡œ.ütì;šç'[Ó!#´Ž[óÕì@EžFWûa6RÍòòÎ<%h:¹ÝvMûm°_×zíÌõϪz7KûÎoØ#„9½¬R#qk}UL‹ûR–:!µˆk•B¹¤2à×çEݺn@e”¼áÓØíÈ[Ä1òL$àt*1Þð‚ߘP`æƒn¯kV8£ÝjŒK´¹ö<¶êߟíÁ/^× {žºÄË\~ΔÕ`mÖPÌà¦*ëXe–ë™Yê+D¢ÂñjßæU ,QžaO=I>ÛQ\ÁÝ.[O®!ÅØ>ã€cœŽvø„däHœ·Ô䨵QŸÚ·ö6äl[òŽ»† jikÖf¢eI'û>÷o¶Ôˆ²á¦/1ë7¼Àía"U·$¯Å$ølr—¸ ~|(ø(‰ãÍ0O 9~+äÄ2iû YçiìÂxÐcs2g`Õ‚È]eâј‚ÐËÕ­¹Ù¹ƒ {¥í:Ò±Õ¼x—_ÚˆþωÃ7*Ã1öô7½6õ6þ‹Ë’…Æ›/¡p•GMˆóÚã|]Øø†ª³(Ÿê|Dþ|ð¢¥÷4ë7Z£6‹˜*†{¥ßöþa‹ ¨Yß_e&SAiàŠDعñ'‡„õƒ¶BzØ#Ø ðÑ/“·›¶b9í†ÙÏÓŽêëøøŠ[8xB()7ÑDød ÆÿZ6h/"S²“«V*Wp€Ø5m’†ËÞíß„§&AƒÜ[£1(FÎÎn‰àáñ•K–´˜¹ºŸKÒÁÓr°?S¶ô ‚„M¹^E÷î·“K8o,¼\6 i5ªÞ'Æ9xŽ\8¦²·‰f$¯"Þ©NVöÛqýŸïx½À]$ ©kr:™ 1‚1Ûo–KRÏò]ê®Úõ"3–eËCíÎÐÑLÿ ©pa¨€íA|Çá,J I7¥ŒtÈ.¨“$»-êx›â¡ßoýp"±ÒIåt8ãZïÉö±8SÞ~aœM&‰*eÊݲXb8zù+/¢ã«¢öZéÕó ãwVzT÷bH[½å÷I ÿŒ’ ¨Þ@*êã@±w@ÒO2… ÙÌDäï¥w ö£È¡FÉÁ#x°ëpê@W»G'vå›6wÚ˜y2â=Ìw6DãáÝÇ“Ù$**%z›S˜­Ú±1ôÓM‹™›ª×êœOÄ~Uu¿¦Rµ ²¬×ÿí%»€·¢†ô©÷’þêû°æŠÛó¯êD%;Œ²°¬#ÑèYÎ6HýâØyøÍzáÂn÷ŒhD5ÀFÂößàñK¸±¤Á.³ÆŽ­™À¡`âDüpŠSÕ¨àÒý"Iç}Æ6¥K­SfÚ§[;ï· ½™¼/(*”*ðöíÙ9ó<«ƒc\côôüŠ‡ÍŠÇ;§¯”<Uõñ¡ÄS‹øœWM¶ßx¶þb½;7`;§få\‘)s¶»¯±‘µ|îs ÝWw¡äfF÷¬ù݈ÑýìwË6ü´LÆ­?&îãæËŒ@U”ÅïÒ;:Ë}F!Þ/6ôÌúb®ì›þ²» ãÂñÞ^Øßr«;3­ƒ—0ÏqŽîV{QÚ­á»]Œ&‡M³"ÞøŸE\T廪Åå›Ù{ÆÕÑèºj- `ˆdnÉÍÓ}•¨~òHÉÊͲïš*+Ì• LTTXý*lO¦n{þÍ9¬?ÌØ•§ÿ²ÞþÃùÿÏ»LŽÎ@H§|e¹Âׄe•è¹õ€tâ$0iËš8ÀLVJè îlS¸Mô9qx$û`4J]Ï~‰AÙRñX§ GÿE‹÷­p2sÅ„¨ûÕÑ™} èM«@Ú…m¶k¨øg"•raZƒëìý÷ Óò7ðª4ÍQÝ\½MJtnrŒ•J ×^sÑ‘ý-I£1ºÈÑ¿xnSìˆ ½W÷o¥àï'¥Ô)|òûí¬ìßùÿl€d3΂ð#tà’aL¾ŠëÉYl³«ðÍ[烔†6¨aôk_ëœÔ9¿ ´¯rŠ|bs¨u™t£?eÙÌy0À„þß ’ˆr˜×ŒafÆ?öéüà)@÷Ï`Lö±r¸*ヶ„Ï7ì®Ò»Hœð²a?o/°§ï’-½DX橆౸Ù>dŽ‘îëäýìsX%& |›`Ð7ÏÉx¨¦ÏÃß#8ÚøÏØ)wˆOV›FÑþ`á>>i‚†’-iéÐÛ3êxºÂº®Âï¿¿£uUILíë{èªAøÅËjšáÿj(ã| ÊãºÊã ¢¸KŸh7‚t¹M1±gaÛV˜ÇöalÄîDÄû+ßÐ Í×—dÞžhºÛ õ¹b6 9åùÏtƒFOH+¥Õ\Lª?}AªÈ—Ñ"Ò¯ù®Äç¦=Îo!yÐ+æBbÊš™l„›–È&ú¥º¹†Ÿãq¤:£óÜëpYDï‡/XY³«./Ìœå%÷ƒÇ¾@û øW:Ý(yIÇÙꀃ9x";~J¦­»í( MD‘®Ü›ü¡»´sP2<–Z!Ô4ßá»Î¡É{RCñ9«îp±wÄåG¸nÃ.“¥‹žÊ«®" ãsHùOÇðžQàqø„8C­ó¼Î&.aC°ð+ö”f‰Æˆo¬wS”'”;ýŽ:ŽDû“«#c·ç,àÕ_’_?Ž£÷ 6¦.-’ÀÓÎÆTæ 1/•)­<õÄu–ë¿_£Žˆ±;D³qcÜ·f!S'õ²_àƒÖy´G…¾ÑÈ˽çë–ÑõKaC»môåN¶#"Zùf{ oZ¨H!m7l~,`oLì_®ø|Íõä^ ÃÓ€ÉzÜè³X*ÄØ„>éxÇþî‹à_Òä-/`â#䨿8;Ò¶XÈéXKIÜtv|t¿Oá!±¹/”¿+Væø˜û…Gi1}[Ó±–½Q›w=Úà=àý[\f‘‰ ‘¶(3÷‡¯ÆU;#ñ›0bÆä§!qöäf‰ƒ6òX5Û!mS…Ô²ûî¼ò¬ ÇœYüLRÈJÔaeA™Ã#ªGø²mk]œ0κ} ˆûا6ô(›ýŸjäè¿Ü"Ö˜Ærx .S~ºK»äY“kr¨éÜaÅUQj(oÏBWgðû2CóÜó}°bl]7žˆåsÞ´4Ó4ÒkuDWå²+ˆƒŒ,GjÅR Þ¯§WFÙ%þ™@¾šWÙÍ€À•{ÂaÛè½Ùg•§ù¬Jì‰7·ô“)“BÿsÜ ž{uø–ž:ž%£Bðã-S!ËÆ¥úÑÇp1ôNÍL5Á7=îŽ-³ÅLó”K÷•ôïé#HΫÂU´ÃB‚Ú«lŠùŠÀÂBÁºx7¬ÕDr´pWz%l%\4LUÆ´äÍ©-ì8ݔټåÏ|PG%÷ +GC$¿Â«Åˆùv]’Ûuÿ¬ùˆœ¼åÖMØ}ÿè34‡iÛ‰Ç÷¦á}F>»ÉÊ!šKTw_¿Ã-`doBMóþ«P|ó¯M2SpÀ•ûPP§×´ ^•ARÍ®¢Ž”³ÅºOçeX+Ig\&| ÏHíËÈ“ŒD9QY£µd[ÆÑ ½cŸ­ù{ê²Å#ï>ªmßyè5'¤1Ê«Ý%ƒæ×[oœµ¼­;>œÉÈ!ƒÝY½ñOÇ<&&”´°ëš¡“;ì³X?ÚÕÇÒ¶£¾T™î®nØd'[b;´Z(¹€@vðA—Xׇ(ö.À.@ Éd\dÑ> e¢”S° ®4RÕ"‘!™õÓ?~~=0C#öIoÓîw×QE‡Õvƒä,-´>aM$ìu°+L·yñì™’ÇMv'/¨oV*ÿÇ£2JÓ†\·dÌÿôoÆLfe‚xÞ³dä}é™á®4—ž«¬‘9Oû÷dYI‘zâðùvò4œ€I’´Ñà’«€æ&ä aà@ñ‰š¯¨â³uf;7f Z'‡Dó¤xgö~UÿF§ºíd¨ˆv6LD×Ïø±NŨ•Í~Ø.ðùíD”BÚ¯×À0luÇÁònº¡ø ½ÐKU  ªµ…îÏFÏÆÉ1ÿqÎþÅ‚ºvŒÂ¡“¶t8ËŸõ FìÝ]¹‚9È äßû[oÁ´ÞÂ…ªD‰]Ã@{ÃjÉ_Ø\”­ß×£#i{f±ÝýãÁüØëœÛl£ëï1ï‰ãüúzr–Sö)|›Pà˜Ö&Ò[NKåTQÌ}ž.AÕ¤Ùá¹£òÎOÒÔ§!_¦¦:¢ÌF&¶Zùt“ÈÉy‚sÖÕÛ|´ÔªÇ'RÙÚ4æè6LA<¾ÌP($ƒcÜsq˜ðÃ1ãJD©˜´~¼fþ¢‹ºîcÎ5óp!Ш»Zµ^EKEÇ­/ú6 `·&ôç Çƒòl¨îu·H™¹Ú=œ€oÂx¨òA|´VÉÙê:.ÈaDóñXé XªÆ ³ôâìJŽ‹1ÙʱҲ8C˜Ì°4 Nl¢”šÙ3ù¡T86#u‡ŽÃ°*¹¥ÂFÛÝ–ö÷²3‚Î8¡À7h ^±½ÊÈ,ÖùðR„tºÁIúÙŸÜvŒþ Ó?ç£á"q¯Pœ‘4%íC¢4anæ€ßx©DŸ¾¥Ä«õšÚâVÐQfF^Kõ¬ãP,=ÚÒ…Žú¬ ýñþÎSXh²<žbªî¿G—nNd»w|þ°ŠÍê€ÑæØoÒÆ¶•ꄹ‚ò4`sç8ÃaÈ=Iz@ZìËœ‰èw6^ ßD¯mΣ™ç.š+þ"GÕžÂá Î+À{÷©éÁNŠ£²o·N ¬h Ú‚×Ò<¸»¤ŽLpè:‹ß X{f8 MD²Á‹»).”‡@…Zî'òù½”¡%3BYi˄ᴳWO p!÷ÏÐH=`/ÓO°¦˜9÷~V<× ÁÏ…'­©¶Ÿ:¬³Ó'WKÖy×߬×AñË_Uît¸öâ‡9®Œ‘üí|h\ç0êð“?®ö[1²a'óh  ô7gœ#fÉcB#¼À©b›Oé#âÅ@&š8®Á£°Ù-íQzróÉ>*ž;œ`ÕóÈÚÝ Ä‡:’s0ÞÎÊ95¤R$i y ÍaI>•ë#ßMõߦʳ‚>â÷öaÂE.‡Ó×,NKð‰R Ž ±&ÿ¢~{ü£\´»eÆ‹>ßrúÏ¢I³ï'Á[[SúIØI¸!Q¥Vƒ]ÄI­w£ÄÙ_%˜ «L0"iðÍ™†ï/­(’MѶħ®ÍªÚØó?j¥ï.ŸÆ£=ÏÛè@õ(ÒLÂGŸïk˜™ž†5ÿnÊ_9“1øñšcð!Qƒ½…ý‚Qw(ù†ñ¤i¹®RfMÀ:û+‚\öì4ã&ù€„gæ:|ó#Ž%I,••h „wÈ:¦¬Z¦ã;ƒ¸We³¥y½•)jWg%ŸÇFûí"¨®Âr Ò¶Òƒb` { s¾ÿÜ%ñgMPóævU ?ÌéÿºÄHúðŒDÌÆU¡žÕ£Ïˆ¥^O1LùÍΔvá™#îRzÃðb”â„’¦âÈíªÎµ2l:ó·ó¨??«d]O?í1:ù9.Ii7iZà'YRõó_#4 ùhܰr ä©º/@}¤}•XG¸¿úð§Ì»© ñ:‘Ú›Eyuÿ‹Ä8â7’4h†µvÕªk‚h'Îòƒ·ÐsËS$¨¸‹+ œÏõ4!Ál=à‡5,ØaÖ¸ºp>î…U")‹ª‚­‰ÆS×0­³DÐ åJrøüƒ³¨¬rzQkÌzhw.²¶þdÄY<‚½ïnT%‚>Öp‰Ny®{YL2Eéé‹ÌM?­¹iÿaP¥†|™™¯Ðâ–íµCªO*!§ÛÅ$‰nú;å°2ÀÖ ï>ó þuh궈¨º.Oc»è&Aä×.n}¥aI£òiº1'Eg‹íZ¯;ìk$+ÒCí³9Ù3zPÔ5‡Ò€e©Ð„ûO­¹…ýx©gˆ'x 嵇¶†ÛÒ.¼!¿õ½<®lЏ]ýía1 ³DÚÜ’îVø¹†[›òVuCØŠØ[Õì2&÷Gà•„-uliIÇ)ä0¢è•ƒÉÎïjòuÃ1¶÷D› ùªeÄK "{ç¹>õmª+ÖÜ›œ¬/³*Ì“10XM¤pw*Odæ*¢ûæT·Y.2¶¨,! foSuÖK†·dbÃméFJ?†/IdÈ»·Ã»0dþàb¯êg&8¥/ôúɆab~ÂSŒ™£F<}ûžèt-•؈Ê ÿˆÛ!$š±27ð+I®±ýÌ?¸0¿yE p“˜äÑÌÆ¸ü?ÇX>5ððfGýaä‚—áLVo±™[?ùöÌWKE­ ©_™öÅ”ØÔÚD|ÚÓîLÚ÷:“¬¾™_IE$3DƒZà_+´v«™SpEh «QjÍP­DYyÞÅDò¡ÜΛw*‡ã“cƒ·I8ƒ7!R>J HBl§­þI!}”îϩγQÔNòöa—ðÐ"ª›“ ´’mâÒ{‡w- ÌG¥ž5CÝÉLµÑEþÃC¤0äìÙE¡wj¾yd$¾>ÌEõfŸâÈçàд٭UO½ ãÈ›Òù#BÔ‹›d%I,x©Ý%UB»ÞâØ?óë‘hN*mâŠ"@‰IaסUD‚f…Þl“v¡âôìS£gDróS\r*ãŸÊÛy0¼™†©`ÛHR™õü‰çHŸUŸ7™À‘Ü_ êC;¤Z9ûšŠ=rhcy7hMVy±^%yÁ0]QÒ»ÁH(tö,åQo¦]åPþâáD‰„*¦–×ߘv*Ï^Ù º«ŒŽÝkÕª &-=êwHú³&Q6²X)‰ïå£ß‘Áš´A ^mçâÃä„zK)DÎØÅñ¦ntœ±‘l®÷* cpþøÍå^Î-7õ@_8x+! PngŸIÀöswßP/LâÛ¹GÒf˜ÑÔh÷|qÚìDhϵ€ÍÐk³ºðÆK[\¸¹Äý±H5$È“O&¸|èÚXÛ.¢h¯ýG{7^«¿‡4Vs<:±‚¦"rO›K°b³ö~ÛäC˜"àoŠ»YL6E¦cMƒkºÊÏ}#“`C´‘Ì1Þ“ ÿ”(ï…>Ç]»0Öþ.ÇÍË,øÁ*U‹Än!ÃÜæ¥ ìJ‰OÓŸü£&È…ºìU_Jº³%åeÜ¢òKq$Œ (…ìsÕM{ZwþÆ%kÒªE.6Ü‘ÚBü8á1ÎÒ-:‘Ý ¤ûª{Æ“7A8.‘¦ŒEO”ùO>jr °ê•¦ Ê•/ý¼wòƒ“—‚MCYDaläð…]|]AÊ`’ü~/çw…Û9 5B׊ºcŠT”Þ”¶[Ÿ ¾Le¢ÑŠÞÕTYkDž¹r—ÁǃqÍù<^t2• 5 HY¯ð!*O7PÊZG8[Ñø” ÷žD¡Ö¯”’»tǦYÛQ¬²tCÿÛ%oxí/GxˆÅçŸ~Q?ñ¾ðƒïÜ„ÖCÖËUÛ¾<®±Y 8¸0ÝÐM2S>KÈìŽQË–åÆ 1$W)øC GÂ~gÞTÐ/ðYÕía0É1Õ)ÈC޾®€¥›ìBÃò=zã7m…@4ÑòëØ¯ã÷IUâ7IP퉞£äjv¸e©"²¬‹  JAô#G~ós½ˆ‚ c¿þ~ðÏ=€¢,žÏÂv,P_r¯"˜jÏŒr^—®A4çeöfn†õ4~3¡a¼Usy1ȶé?†40Ü ,%N‘îÞßÛÙ)¬Ë—Ò©6õŸmøô®;â0¨XHEõëÚ‘ã¦A蚬áwˆøy?׆ô{~sÐ*¢¯ð/Ô ëµÏ‹Þé$½‡gÍPp´.0ýš.ö“Ü'¤žQß5'ÁQÏuaýQÿˆWQ¨È-3R¬û•ƒò”bÀù²#$umrcK7Gôz¯XÜ•eÐwgzõy¢|q¼­"Âåª.é¢êJ¤‰¤E®ê$@z¦1gþ¶›çå ä‹9¶Žªí³4CGúXâºz:#¼YÞcj¨—>yæ\™-T”jØädòîß'æVæëJ8]ë9x2W*ÂcÁZ“ª…êøO6?¤q1›gØ{˜¸YKh=öÌ3µörl\ró.bF¨&Ƕ³*Âf«¿cƒ¡N)ô¥h%õùï_œÆ7¼ÛåKõ©¤Âeð˜;A^½"f&ØägLè®FW¯>h¦:£OÁMäšmILw€²ÉGŸÚƒe1¡Ê¤²s·ô÷Ô¨<0SüÁý876ØØì=!ÊâÚ ÀT{V^t¤ufys ŘTý–—_\,u(ϼíÆ;ú@ÕkÓO Á;ðÅA››;L!O[Öj¹×s ¾’ᬃ bC"×í!˜ Ù¾˜}”y<†hÉÒµø2÷›.lTê_õ©óLµf)ôÅiÀ6߉FM¼qòñ ׌{™½nG{Ârwá-B½êGkVÐ£Ì q}§œB—)Ñ3[ ºJã¿3Á¿=&ýh ðLg .w¹#N"q ìâ]M v ®*îИ»å’˜,ý®BÒêa¢ß€ðïW¯ù¢­µq›BƦTèþQ–!ÂgÐÖ7ƒskxuŠÐ[œM¿¯$‹Ã‰œ‰O+•yärŽÅ9Óa”Œ¡öû²tðÚ†¾áoovaÄ$£5 åMíº;rÞ ú_¥ZÝã1ð¹ø.Tš ÛPÅ>2q#(Hóøï~ü•òKó´àúUUÁ¬Ô{[Ɇ S­–Â#‰xòý¡v^½2ü¯.X"g0ÍÁP4ͯƒ™› endstream endobj 824 0 obj << /Length1 727 /Length2 21996 /Length3 0 /Length 22557 /Filter /FlateDecode >> stream xÚl¹sfͲ=ܶ§Í§mOÛ¶m>mÛ¶§mÛ6§íîiÛ6§Ýß{Îù{ãF|±ÿÉÊ\¹rUîŠÚµÉ‰Åíí\T=€ÌôÌ LÜqaMf&&+9¹ˆÐÈÅÒÞNÔÈÈ ÐšT€fÀ?f8r€ˆ½ƒ§“¥¹… €Ê„úßN€º‘©¥­¥@ÝÞÆÞÍÒÄÀëææ&èæìÊàäÊÏðÏ$ àb˜YÚ" ŠZRò* y5€ÐèddPt5¶±4ÈZšíœÔ3{'€Í{;SËqrfø7;7 “Ë?ÄÌœìmrbªBâ òª1FU€‘)@VòŸév.ÎÜÿ &ÆÿªŽàò?–ÑÿXÆÿcýOÔÉã?33ÀÔÒÄ` 4·´ƒcü—nRvfööÿ¸M]þú‡ó?üTÿhH 0šýƒvµ±‘7²¨Dìm\]€N9{S “@ØÞÆ æá´3šþmdkiãùÿ‹ÿ)£ô²3ÿGA¦ÿ¸,Å-=€¦Š–.ÿHnfdóTÿökÿ³>ÿJôŸÙÿ¤ü³®¦öv6žÿ/á?Åü;£¨’ˆ˜†,íÿ´Á¿Ãbv&ö¦–væ—5r2ýǿÊF–ÿé¡ÿRùoO˜ÿw,gäâdéÐabøW“0ýëù¯¥÷¿(aa{ozV=+3;€™ë'€‹…Í÷ÿÐ5qurÚ¹ü[öjùïøßízMàÖ–íMxB¬ÒZÃ*üÄŠæ*!içf Õh—™AW¦{otP-‡,L ¯pïUqóÝfùv/w¤î[HŒâÇ“S1Äz˸/%¤•æ~µ[oJ¯ ú×ê8¶&ò7‘w.ëkAŠ4S?³rÏk¥ËøP%˜c Ë; Q¶Ví$¨õ(Ñ2µ¿€û´5(s¡…ׯ/{k`áùÁ»7ä8…‡áÚùûF˜ç,!´¶ £öQ¬ZÙÓ¥üUb ß;²»k›yÐxÊálÄ©ó«‘W[í58ùÚéĺ¸4wgÑ¥ôùÏÈ´Ôe0sOéÊr»ïã.“nËxü¼Ìp Wî41R“XÖ¦pHŒê¤Yè;æ°Ë³Ç‰}ƒ¢Í?½u©Y­þ$0VTÎãÆë,3O6m×xt±ÑÜY‹ª{'ÞefF©@}EÀÃ,µXmšRÀ•½±T¨HÕEÕ4Ôûõb‚ ຀Æê£>‡‡'ÛôÓÞ³|P±èš?§ð*JgmÍñVØJŠ©Œ‘?ø$afC-øîëauÿžZ(ý&à+vÄ©>8rÙ•aßÃý(´ŒUC¡)Yê SŠüy(ÁKn;#Ÿßm›`—G˜Nø•ÈÄC“[ã*‡¸¿:_Õ'¬GÍãYpMÑm÷™½LjÙ ßâf¢Aû:J7ŸÁ«a‘?°m2ñ¡<,Ü˦z%»—q›&²_½ëÍY•–&šÃdÒa˃Íq°µÈrÛ%×àŠÉzDæVÏb º¾Â\ã´|›÷.ÿûU˘ó¤Ô R…'(Aà&8Etßí±Û"ÿÊK ->‡Î17†yÖäÃbÝœ‘•(µ…Ëg9ÆXÉ%–[WÉѬ=Í7îø®]B¢Zù6¶‰€‚ø,° È8)¡A²é‘vð ®æUCËûQ ¥#w˜½˜÷º«üƒÛØ^sa;»ûeMWàÚ•a±[ˆü⸿=¡-g€lÄï'N¯M÷¡œéAï^Fù~W}fLO&ö˜.úC uÁ¦9ˆCq”Œ¹3Á$=6ÿÏ©4%ˆ ‘°?nŽæ{ê/Iû¦—ѱ‡$âoÎis$>6%Ûèu:ÐÄfÒéXÐPihñ]s%FœÛHnßÁH6Ç‚í²ç:#X©ó¦Ø õ7+ŸýëBˆo Ìσ9KM)jïö4Q<„ó›BH†õ ¯ë J•|à\‚òpa²Ð¨óµ¨Ì )©UrU{1ÁÍjÅ÷T d@Òä¾DÐå¡·®k¸9Vþ})¹k¡|ýTìス‰ º~ +&„ÿ]†-F÷w•jLñ*Â=úê‚<>hžô}q%ïÌe3“”û–æžWhí²ð§’ªŒëô,£ÕTú}zÊ‚~9Úx‰&[kF²Eb{¾±38âê›’+’i;ªYÎ71äʯÈ©u|Å\û[ ~@Ì2”¾#gtž:õ<í,2]¶>»AçE¯2ÓT©¸-Ï,J¾‚AÂ諾TáSØ ã¸¹E.Féù¶ê&œÊºñÀô;‘ráb]ž9’ÆPß¾Q„)Þ´3Û\;D"ßK¤#*ý®:Ê^T2}ƒˆ¿h!÷h[9Jö9?k½)ÎXàì$îÆåç5ø ’щX;Òf²™–Q((·®•DVw*”rô$CWåoF@eˆXHìoÑÍ%ëß9aÕl¶“6euôgê­,r冪6¤Áˆð•­ qï3–·?,}T¸‚ÚöuK7ô2’U ²·UŸÓ'†¾ÙN¸ùki° -DPW¡P»E¦‰€þ‘ñ‚漡äèÓ³Zì'Ï#Ää< ô¸­µ»æ÷²ݬ s_3yž#oÆ”àö6Ph©‚T žË¢ô‘ê<€ÝŒÂ¢:ˤ–/t’J±“Š®ìDõ¹'ðæ†÷š.aùØ‹Ìwm¬âK“°XF!:3ÐF¬å C^œ‡ê#E‚xV`µlkSzˆùpÕ,xPû©h³äÃ9=§ômŸì÷W ô•¡,Oœ™D¢F~V¼ú9ºî<>chÀ•Ó}6ùô¢-O{¡C+q¬bþ?v&{f¤on1‹Yô:‹3懻`+}}¨Ås‰ùï@i…½Cƒ{UÚ1½ˆÌ& ë™V^xC]`ž¹4F)°ô˜™ïÁÅøB:¬‰ÉÑ=¯Ê7PǤ1\%U/l×3WúNÝ®ñ'Pää,ße +“Å#q/ô ÅÐC{õà¶5º(Žò7[V½!n¦¬¥¶`ÄjŸ þ‡³G+#ØÞ¨YaŽ‘ {ã_0êäùëW¾œõ »;Wñ³::ËüågOf1Ê 8Y´dúot#¨5LñÍêÂM£óËDç0yÜå.g¨ì}Ñ$k(jÖ“´÷B!†š@Aó1)8xÆ…ù b‚‚óÔJgª1¢¦¥åvjÒt¡ÛYNT¬}UíÙ§Â×Ýö§A2'ÎwASh ïìp T¯7òò ötì(ì3w4o«îc/˜Aw]¶£ii̹¡y o1Ž&_â‰-Wh1È~Ô…„É¿ Çg‘¢ZG©o‰mÝZñ}'–(Ë›¿ÏòGt¨ßÉí½Ö†H¡w¥×r®º ÄfʪöV_ÆJÜs—è`-P²õ¦ÈÇþW-‚ÓÈGã6T³7Cë veû×.ȱ#ÕLšò¿Ò„ÜÓvx¡M]«“mr‘æ“[£-£`Xª‚ü1X~¶¦á“×±0m¶‡Àê=køðýß=àíágOÁK',…Óß¿Ž­›-#­Ü¤•JÌÓñWe#®4-ŽÛ¿V]˜z÷Ô—0/|›±ïŽýµŠgäÎÈÌ34ét[_×øJ9ÆL )Ù]…´ ‡½¡!ÇxSôEzkô1»æÃoôäZZ–,ôØin•éì7/Í×Fè!<ᕈD^W¨Å^Â+hZ*²7±D$oË…ÑHò·$Qœ#éˆ}ÉÄnEƒW÷Õ´$ö ‡ªì„‰ïjð´~ MŠñ~þNЀ×|ž±çínoó6Ø]ì/ÏåiT%ì|¹°/o•UšÑOÚ˜n vʽ>KþìŽ2!™|++Ûc±ÉÔs4íM8¢0ïLaøî[æXYTÑüŸ±§ýª†6.$q=—X>«ÍýOΣ*º„Š·¥‘:ñ+ƒ­ìwßô!ÌãèFñb“"üÚÒ#%U&¼UpêÖß]ë !Ø-Ùœ¸ ¦×¨®OJ |TìkŒ™º‹ÕòÓçQ¼}Åô•*:3'FrVJ 2ÌÉð›òo ‘:& èðbnÂÔE(oÞÌ óWvDÏhÑÈð/vS–[hA#„§ç™ÕYÕF{Îq5~ànÔO6 °$ïÐÈÓÉîóíóN5íÑõŽU×RzðkòŠ'ÏdùðΜVló7å8ºUû:T¯Nx¦‹“Ô;rbâ.5µ›ønG²aßøzøîodbö}M£^ƒƒ_*É®R$è¿R(qÒÍkY*k´Ã­ÝÂ4ÿ,"›ƒ ýðêXÄ °ìÀ!…‚øòï•Å ÎàòþaËÀÈPÇ÷hÿ(öø2Ç,ñ ¯JºÕÀç¶øÖ{¶‹Un~Ô?|û¯ð\ñ/TõæQÈ=} DMž¾yÅ.ÿ2œ¶ñ»¥ç÷f^{³†wi¾¼¥õìCqk‘ËèÅ$V…â߯ª)>,,Egåj æ\7¤ò™§ˆ7z84¥ —Ü7ø[æ¥ï[C˜ê«àT>ˆÊÏ«ºÆ—”[†jß`õ¾já;ÎìÆfzØNš$—ôÙe‡ÈV5é ³ÑÄK›˜…GåQÙ(…Þ,@¿\^åùy"F>‹‹gÑ_Úrs"“LLAFWB^¥ ·À†“ÚšÀýQgüº½ ÕQЮ°¥¿ªgéëCþu)ºÓÀƒ©½ ‚7UÎÂãcËÉ´2¸â¹­ÑN®M.r4° ö63.Cyá²]e¿„õìly%W-sîß<‰“…Qd-‰>Ó¼µƒÕk°Ô®é/¬Û‰Mt­Ã¹"qgZ#sæ½z[ù[X£~懥áöC÷õ-Ýî.êiÍZv#íþÚI¥òû`>°¸ãâjí* £äÖáºIЉ¢Xʣ߬æ Ã]Ü?•Íé_ æz„¼7ÂJ¬FãmÚM=ç8L“ètP>[×ãÂýÆÀ7ϸwS˺-ë<Øu¼1¸³­žò6ZÖ-Éd»uËæWšÙ‚¬Ò¨âjºå å·âÔ¡õµááb›ù"„ð(®‹cóç©-ï9"ãÄËo†¥ÛÐ{Iˆ½léÕ;7/¹”ùFÞˆtÖŠ½ Õ¾—ñZmÞÔ‰Áä ù<…Ó³¹ƒÍ ¾ÑƉû8½”OA,oyHñù]©qú2 =•XÇIføŽegŒEÍ#ÝÂÂkìéL èÓ\æg4ŽÐݵw•`¨,B+—&ÔÅ(5id&r[^à A 8¨Œ¹âx+âà ©‹2ûst¿FWmk²ŸÝG £(J2fU)Qt*É„‡Ê”!J¸ÿ†ið†òû%%Õ7pp?#¬Hçï SXWL:©šyÿþ©ÿ&E_üÚ-,Ù¼ *¦m3Á/žÜ¿Ë»y”HIÉêoŒÈfĨnß0ηç~'¥žÝµp}¥^ëТðçÉ&iù&$­ÖôpLN‚ÝêྦzÃÔÅ·KЫYD¡°ˆ¸Q{¾ê­ŽÓ캿ƒé´vÅGc·h{‚)5SG®Û%åjÿLmã‡4Ká+ûÇ.Óìy|þD­ÍËS…Ï ˆº“# ¢>é•A ¸ñ™ 8Œço“'NÖaDzôé ²cØÎQÛñͺìáã ¬Éîþ©£?ÂÉYÓ–”n:MJ© ¹¾Õ/o1xóŠç{åÌHIÐ^Lº¬`=£ûÛ: 4¡q»ÒSâÚúÖ y•ÇépÓàÊD2<—ÞÚ-ì1…–¦«GÖDqxu‹HîÎF:/„ ‘JÄÝÈÂìn&ËÁýª¼Þ­E‚\)i¬b Nö›üÂD\íh:Ò¬Wnk:x¨Ô…¢7‰eùÙào± åi„2©Ž­A`É/Ô(l¯ô»YIX4pëô]v8©€®˜ìúå¶‚Ç"ãH¨Q‚úa„z/¸‰‹½Ùvz%á³(QÁ1âù½ˆ€;ô¬ü“bÁSøÞËs‡¾^2˜r¤Y9DpmŠTQ;ÔpŸ£Ð9=¬}Ž ¶b˜…'ú;j7âñÇëKfgàuòPÓm«që²¼÷Á¾¤|N›»xB îžn(dV«ûìïvœAèÝĽ6‚h³âåbuç´u6u6²bÒÿeÑNN?ç1&%Ò¸®^+C4V5Âè2âx‡lQw3¡©PºŒÿ]íÒz‘©çÄX6š ku$Év·yò QPYùzI7À°—“Íõ~ûµ¶-XÿÑŸ ªê`´³æqk<.VƧhd+¿àpSàUŠÌSL›•&0Òåî…mw¢¥4»}Kssÿø8DäÂ[ L±×â’Í߆/ÿ×¥d%Ý¥5½ãp‡3þ¤ä!‰ÏÛ%‚ÛýbyW²ÌG?£ý²C•êÔB …lürQÃ/ÿú5ü<ù`àDk–þ ªŒ2|ëð‰¯€3û+ö韶0oËäý§XJϾq˜ëo¡ZmÙ®¡MãÕ`Ñæë– ˆ•¯ÝsÏ/ÒÈb¡J¬'FR¤sÆÏ𮯗odŽ*taáãJ¤}t×2K ¤l¸ü)£5Î>E܄nj_`­GèÍVJ)·˜; QÓ¯xŸçÝ/"~-š€ÿÑ—= ÀE¿%rpUÐu„ÈF5„ú¤•“ÑÌŠîUdaö휼»=æpÊÊÕ‰ë<~`Ψ˜†]q&+ô~ö¤sû#Ã#”ìîW,J_êFm Ÿ}eQ[>~…cÂÔS¾Õ®!ñê[ô·%-3áW ôVh¿Œ~uÑÏ ƒgoèQx?W+]¡Ü@óÆáÐ š½Öl}0À{üvv¥hœªNéßÑtŒÞ—M00Þm5Œ÷C(<ÈF†˜¯µ7#çÊÂ<kÇ¿·kþŽÅÌàÆJ˜eæéª«„¢X¿eî+Ê‚oz‰=Û®žÔ-$¨”ž6žÇA¾t]R3‰ÆKÌ¡t‰¬3NicJ&·`¦ãþ„a›€m›èÁÍ^ÅcQ+±?•»ÈžURzÇ.ñ¥âõ® ·+84@ÀÜ= ˆSõ×b¡¤°2†€›õ¼.‚2´?£70k•ÐÐãI<Ы`ìyYÄÉÃòîsÂlvå1‹x¢• zza¯@PñæÁa 3h k@Š?Ïó;·uŠySè æ]¨ °þÁ¹ÊFO¥f‰ƒjô(‡Í¥u„7©ÇÞ w€Ê:{“4 š¯yÑû¸˜e½ÎmOç̧¥øÀô=#'ã;•¾N+¨Ãë¼_[vc–Ä&dl¸nœrt'Ü*ä-(­.â¿Oòò…h¦{ñéé·î§G0¯Zëø™@›@4¢‘¶t ?\§b'Œ¨Á8 VˆK‡E)MÙuúÙÒ¦+x®Å³`PšóäiY]eÄP%” ­]ÀQÛ‡ ÓÏÕ@„§i•öHåˆF„zýáslKHнæßà¢Ì¹m¡Ir®ÈÅ:½K²ÁÀ¯oÚY?ç¯èìò^ÔwuhOºç¢Ö62(н*YHØß„–6ì^Ü’ÈÐ8ÊÉ>Jä<™-åw×ó{øÜ„ ¡ïàf])Ûœâr™z<º¨&q ^U¢|ö»–îfË|n>‚X¼6L?âÞñreçÈ.†ÒlVÏê³`²¿(±¼jîÓu7W'xÞĶ”e¥ŒÎ„ˆšã«TI†Kìp {u¥ç{¼º /\z*ÁÛwæ:dLs-Op $Š+ÓQbn)]J’ꌘ ÚQnkð¦×k«ÎäôíÕ²oõ(ÂÔ *ÕžD¾wÑ]H“ŠÚCa‹yߦ~øÕ7tu ó­sÝŸù€¨Æ‰AðjE‰ì‘í,XO)sôW0 '`ré¹´R| £ö¦•”Å0fEtpß߯W‹·«¯¬bçí“ý³%ðm®ÆãC5då!ñ%¦Ðä÷ºúï†Is¢O¼'ÇüÚžù”óÐ!e¾£Ž×kþÙv©„¾ŠêqÂF^Ò3k&¬Ê–)yøeî³<ù~²÷œõ¡çMº  ATÕPfi¹<±µ}5/ur¶>çúv®W:˜†7ÿÅigë©ÐíM_BðtàÞ5`\“3ˆª<ªHA2FQCqq¨q§bšÛ²½³<äÞK+TQKR>G–°ãzž9óBÖð÷êM“1½B×`‚͉‰)E칣âù)YÝ[ø»óV!ùÕ%*TäÝ”HHmøq3¥þNóñáö¡?ÿ#cÀ§ ÅH>m‡Ou¬jp»'"Тr¥VÖ/ZgÒŒñDrtßù¸:Õm)¥1R0€báqÊ6™³ä¡µ•Œ7ÌÞæüz›µäü‰ƒ ÿ=#&º…¼jH=Ó”—a‚*7È’äàù#G;.Û»nûóX¬<¾mÕ ºPý¼Î(Ø_áuö7-ÊÌ£x¥'ŸbûŒ7Ÿ‡*j¯%#{Ý$€Rñ¦s=û¶îÍóPÄh:\=þ¯V¨i|ÉŠ"£.ت_ì÷‚xFøù»Žšö¸GĪJýòŠ\Ó3\³Û­sëO„;ÎøÑÛÌ&"†öº­U†zìU9vB¯Òâ*ãá‹æ¨Èb©ßuQñ½ƒa©†É&µ…Ú (†´*£âT»´G~^È\¬š¹Õ¡ñ&¡zÿ±i—^‰ðó4f«~ë²/Ž{ýWcm&§,“—Qÿ×%1,•G‰Øu.{ãõ§nÚŸY ˜&…Ù€îÊÏ8wÃC¼‚è "§q&Io3¶¡Aö8±SªJ£¹¿àê€À"øcr\„}KÈ¢T¾>1°t(üaÚ©=øïfÜ»( ÷xŠÜ£:bî´ÆØq?5$ ])‡ë©ÔYÕLŸÀVõ¡#>z î,O .ìŸRBúY'Ž|éàÀUϬHÈ8ÿþóJÅ;¦‰€o¡ö›ék ‘ŽÍcÕ½Òægܘ;()Mæv§*%S .û!£Ú馷„UP ŠGüÉ|Ú1 §‘ ½{`"m’ý’¾¬9ä}‡ñDÄä6:¸z¤ÉÚä&W:§wêža±¹MWth¡¢wM8÷r»Ú”×bŒ —îÙ  ~jz‡““•ËüÚ×>‡€œY¤+óšÛŽ Ò|°Ú‹ ;º~3ï{:¼þZºu±Þá×1ŒÏ©ÿÓ>BÄèiÎ}hM“sbÑÑ.ŸK¹›Éª;IdŒ fèk GE 8Þsž1kÌœïò¹íÈçP3oæY.,±é­|åPg@‘ ËTë†D±h§bŸu„÷ ×Ü1Ìm!~s(c4•^5B}ІeåÁÂ:ÿv5 ØEö.I$Í7Úk˜,ÍR‘¬Òoù˼adsþÞx+çrOSšdfy\kµÂ³û½¤ŒÉòÀïE-¤Ó}DDk¦~Í®‘Aµ‡ÎSø>MÓ.Î"qw˜+ÖâH,?Áv9‚–Üô7 ûaÙrÑŠ—¬çŽþ„Ÿ12ÿ·VÍ¿¶"¥>™BNÆÚæìJ¬?F÷N°^Túw¯ÝÆ@$°ä5±2_9=#š=|¯•}L,$\&$Ì•&Í?Žo!.2¢O<Ä$å:1—™‰§÷ÓÇp×Jô^ ó<Ä¡ XÂì ±ÿ’¬’²ö=#v*È»™àŠg¢êÌöÛü‚ž}!È.O¹v¨'[«üvëÏm¹[…_ ™›öƒÐB/î‚ýSÜáˆhš48ó ¿…ÆÆÊCª£$#,Rêg4[„MÕÛݰjyb£ö†ªg’ OÖΓík *á~ÒÉc› 0`‹ ËrÇ$<¸–ö-X–k!î‰È¿BaÅh‘ßS^‘픹¿’ÎFfÝ`­UçÌ?Yš‚{[•(÷N‡téè±)‰eŽâ·ÈZæ8ÅF»Ñ씀BeÆ?n?Jšv‚y;­2QÁütþUñ2P¨ü¬¼ˆÅÊEëO)Ö.šéš©½kaÞ¦Î1”ìKš© ƒØQѦH6Ê—r¨ªÂ–úPúÝýUyà¡©Tƒ?ã0‹)Ê\ׂ.¼Š7gHÒ~Íî‹s<×ÎN‘­Lé³ßÒÑ0‚b “H4VXÙ9)2>S`¼½Éß?¡‰ÛÇM—BàRרþJÁ¥rÙè ?+“Õ–é—Êÿ[k›ý=J/½kI|éÄ=QÉ!²w8vAºiOøIÀƒ¯ˆCÍÁ'*'X9±Ó>9HŽºlñ¢•I!p™ âS‰¿•N:›w·æ™K†W&³öšºoBWØ §êkU<¿,³àAt¹–+Z/!(=6¤­«²D ùØT×ên%¬ÊFÝOg¬l튚O vèçm-—–ì&ë%qz™±P¤›õè{ºèû[X„“8zCxhX.–‰U…ö~Ò'f%Kns‹yƤW:Ðqô‘ÀCjm¸Ü«?ÍŸë•ÅÜåرˆEmLâb¿4Zê`/Vw‹Òdp¨&‡ È2>¬†=aë¼:â:õ 6P›<®†y|eCwÌÏ ¾ý8·àDt,ei­×¤Ôr¯v𚛑3~eÛb&h0øª1ŒÇ&yU‹¸öc!VùDöYÜî²þÌPUZýÊšHÄ½Šž<Èî¡uÈíGjª?ªÖ€8”“k:—ä• ÄŽ­ vX0*âtKÅFþ“©ÎFÙÄ£‰(á`ÄÄ%è(0£ÂµçFMg{,ð•±-_lBvVMt7YÛxJâhtõ!…TIÁMH€Æå¬ë„ÄV¡HE#*qå\éÝ-ÉÐw¢Œ1’ÈÐCÏ’±ùö#Ûü©hŒ?üÝðy‘Ö•¾Ì¢e#2òemz7UmH/°Î’6±É˜\_$8ÕÞ#í‰Y*°¿™Ú-‹-ÊÌúVt§ :ir—„X™l¹f3«aæ{}wgáOдP×áö"hIèßdM«žBxD$\óXÈCÌì–A¾M³®Y´œ*e®Ôò?s¢û®óÿ¦Vm¯cfL¾M¥Û¬‰0žÉ›JK+ådÃï“ qoú_ÔWÆÞM;–--e¬Ø‰òÒBŠeЈ½ä¢M®sÓç€ fð!ÈÔ“‘X.TëmçãBÜ#œEHá!jÆ”ðŽ~ƒ§Õ-tŒgAQüù¿g0K8DeBJWGÚŧ "Ï0—á 0Ñ­;ñ`ˆhX²ÿ*Sä×l-åc®=œrõMwµÓ†?:ý=Ø^K0¯oíz§„Ѓµ¸¸c8DTOŒpõù€ ,2G¡Øk­~3«¦5Nná*ÜÆ\n¤Êxf¼k¡ÈaàŽ‰aO»…e»ÆÛ‘»9Ũ…ÔÇž¯D9> Þ¤šìE&•ŒW=e2Z¦ãEF äag¬ØwÒVÀDϪ¿ôçr²wðÅÚ†kÿ .%.ÒEWÊî0‘*Gì¤ ÿ!>ðK¨¶ã:°Ô™ã‹äºx¦5=åPa0³",åyóo™ž´ÀüPäêGé§¶ér7…G‘YÅP=Å\P¿aäÎg‚Žå@æt¼¦Ad˜’i b ÝÕ;îí½ôÒ_sêÒ`E6Þï‘gÝùׇŒ´$¾–ÃÏ œ×Êkœ:Gö:Œ/Ç䨳ã3Ÿú@›MÎÿý€ê«q6`  ©2‡£Wãói`§Kh/tóBé$#¯ŽôÅÙqFzó þé1¢xp$¿ $f¥«ó›©…=­(mFÇ9ÖDá»L~"Áé Öá GDþ°d¤ËÜVSìÁ“É(6½ X}lGHÊÿOjAÕÅ«HC„}ôއÁ¾ë y aow¯C7âî å7m“àDœ:¦YsG“Êñáâcÿ¿?'Ê=°29°a×ìBZkžÄÔbøç  ×ÕŠ·Ûžà¶®=óÜ„ˆ•6ñ#6GT¶º<€7•ùÙ2äèC¢½ß}‘Ô¯ *[¥Õ•»]i…LXÿeî¶Í'«¤'R·5zQu)ž“yïÕÖqɸgšGƒWØ2ÎÕJø›å½ÅHø›•bn5Ç:~MU×S]Tݽm¶^'3«"ÑóAãˆt1«Úº ¶|Û ±ÈIªêç²>¯ŧǜ ÷8ÍV¯K.»$Ú¶2uŒ†^]xðf÷ì¢ÓÁ›qmv¯7”j˜¢zvtë|íqÄ’¨n[Ì*©:ª@ŽUáVwb0÷Åž_µ5¼µÿ.ú4äù­öÖ¦Ç 6k8ù½KM}{‹{Íœw¸ ÏÛ­yÕ­l™ù ¢½nüöÚ=^Øû·´RF ÏéçÞ’Z³¨©ó¶MŒu~‚`Âwí’ÂZ¼Æ¶Ÿæ |}:bäÜ.9>—ÆÁSl[Uûé!…È÷ERצÞN9b² 2êgîï‹D’,—©½¥qLìÖ9¥šDÛ=Sg?è ˜Æ[¢âjÔÐÎ5á4qsï’X<YÄæ‹Â©&“(ßÛcµý_˜h^çùÀmD‹ä ë]výð„y.Ð8ÜTÄeGm2²^Ák„y?êg²]RÛKú;à ©&G|‹tk†­_Aåwb5Ö–~Ï3¤¶úËÕÎäunÏ=Ÿ—{¥é=Û‰SµŒÓ»”þsÛ±nŸZSðYŽÛi·Àb %¿¸ÞÇ+h8:l,™ßª’êL]Ï.÷}IwøYoK»4‡E¼ »Ô¹%÷SŽÇ‚â.IU;Õ㋪[“-žk&ÖÆ¼qÐcè¹}uΑޓ¬2ïàó–læm$t³g÷”-]©Ê®ió&y›ë?MaÊAß?vnÂÀšûŽ‹~ñó²Í›¡5O¼EÒAÇÂ×’à·‹rhðÙL^^®Œœð …‹+NŒ°÷¹òët,çë˜4Ãî‚j3žš—#™’žNÕ™;’”:Gqt+fÏbC‡Q¨›Ýj½¨o‚{Ô•æ’mϚʽÒGÇx9;Åw È´IhûMmƒÀÄgûô¡ZzæÞ¥ïÜ1ù¾¯Uýûð7˜Þœõ›÷ê,2I6%ˇbÿ÷'‰Uév_ ÊGU³d´…Ž`¦[”Ò,aù®ÃÑé«GÁ«Î㫱'o:` ÄÈ ¡ívôB,›+m’ú(#7¶þJ6©›.¹Ê›çg¤Ümÿu¾899iZû0T_=p°¤~ÞTå~q“÷²õwæn¡œ‹ÁLJ¯Íë]nÞ©Ð-¹UjU+Ž]œðJڢ܌Ç'u*"ªܸwVMûÁߢ]r«ð‚xÏe:ˆ¤ÚKÆH1n.YÈãØoî8ý#Ÿ¨ÆÕ›£(Τ½Ë©_Øpœß¤ 5Ýoµ" RÌÍóêÖqˆ¹¶[,jGNš±yû¿µI⬆¬¬æã%2â ¼8‚#çî?º„l™gb+®§ç¦rHßìÜÂ!e’V=¶Èðó>6­‡¼¥¯N®(P*Ê„ÞÞYFžLß×½Pëí° »ÑC÷D1Séû§ˆòwñzBŽ„-_”å†Z?ãªhÚÓëœúö hí"·&Žáèžü d$EàXX‡Ó¢‰4²u“_--ˆ!É+ç–¸˜'ð\”:Í‘i¸c PêVÃWî1Þ¸¿Û‰¿þd&ö¯k¡sC eúœ‰ô6T]Jpo뻺þá8"óHÙ¼áÌj} Ö~$¢4W kÇ."ÆUzù{€’ù<—é­°©eGW‹·¦:õûsÆ_àø!D¸D£c<ÖŒVËlzƒü!9Vžo0eqËð-ob_^×h9Ÿ’ÿšƒšåÇ_¹}óK÷²–Û¡¶&ŽÅ Ævá"q†€“×ôXÿ-R0 ˜#ÐÿKLHÖŽW2^ E`Ài‰Џv(ø9™"ª·GÛ+Ë)ºào躔KL*†­áÅ3­´òçûb´ï1ÕmfÚQ$)érxÊ©dp²Ø9ÕÅ6Kt˘±§¯#‰Òk^5S¨oPв"pv©ÖÌ;[ž;zÏ\·IR¬‰æçžWÍ?=Fìí9às´`…H×ÊwêGc””ØFæ$ÍPcÞÜ9uûvHÁE'%Û*‰†PNT…ÄûÔݶ»òµÇzâÖr¹˜|ÁcQ¨í êÞ’(ƒ#® a½ ¾y}áZ^?ý±â¶õ;'¨ƒ¦R)Œn§-÷7FZósi¡ø.Ùè1§à­êÝôlùXÇäPX‡ÙXØ)ç5+?l!Ž­oW€Ó‘tœb¨ql`>x–;ÝÊh` òúGO› [Â¹Š¶¸b ,Oÿ›Òᨤt­ÕÜÕ6P;õ‡¥ÿz(9üÖY®»9ò‰ßÐÉ­‘VürÖ³³Ä¼u¶Ø½ŸÜA>¢66ذ]¡¥@3ÑÇÓ*˺hÁ•r¡À&Jm"nòóöÏ·ìª%7‹Lpø|¼ƒ‡ô«p‚é«›ÿ—}™ÑšË¦ÅŠs@àJYÄJþoÆÜeÝ/4í¿ÇX´å–_w% /‰£æ‘2Z#ÐÇW­‚þC…ÜþÖ)ꔾ©ö¿ à‘™õSStçéD[¯ÍãK”à–­rñízâ’/—·ƒðp5¿âB嵿 H)¹$ñÀÎø¬Uà2D í†I¨'‰BŒ¡;#Êá° â¡3e b’óF(F/V@1‰KÖúý9ª¡5F„…Ï#!)ûæ8Ây Œ»t…G[`âDœÝ7™û#rc%~Ù”OèѦ:ÿä Š0Ä] îãrú'Á[qÙ©?;¢@w Õ!W¹ÄÅJA</ŠìnVôHÌG¯Nõ˜éÏf±XV§JÂ>]¿|®$†kG^)¼Éžyå—O— çš:«4ª˜±r¯êæªs¼œŒ›Î>g^èóç…ãÒIâJé&¿«…IáwXI\·é®(eûŠÏ·w¥Ug†º~-㵪ˆ@°õìaù$q7Ñcv¹v¤F‡‰«RCá.‹&¼$Û›MØJÛ(S. {hÑfp• À`÷Á$¦V/(ùTØWešeö»vÆÔSLü"F2L”cõÖ–~ ð †TÁÄîDéóDß}zH÷*ÇÆÃ6iSHBëöŽ\)iŽUëtwÏe4ò$À‘Ún3Z¨øÃp®W¸=˜3™$,ù\xNzŽéJbl&0UF·O0Sd_§L‚ë93RõÌK濯š­P[‹bH%pB û€ÂHÍ_½0ü0.ÃŒ¢¡ðj æ/ŠøU& >AÕÇ#V=¯õy$ºÁT?éÍÇ$'ˆŸôtì¦b\EB/6~ƒ !¤ºX6ã2D¤*êÉH>ç7ÏÝæÇ¯ñ±Ÿ°ÿ^yÚE®â‚‰Ì™M­ú¶&pu¢¦RÖ´ÄtÂN»†Â¹‹w2|Š¥r¨¦æƒøk¡Á¸{H1¢è3ÁË4VëJ¥U©do]4ï¬È®é%L® ¹»U¤hl¹uªk¾7a|D†˜wÇà«2B·°&AÎ@1Øn÷±—L'Xï¸×¡×ßvrñ틉y#¢#?­-‡¼Hߎ1ÓqLŒ­-ó‚gCÑÓ¡z„tÛ+q¹‘LC,ŽI Gßé2{À>ýˆ‹$I>ý;äªaÛ€…,ü‹nÀ$?]-°Að9mI\®Öv:¿kÑ ¢íu’f&¡Kë‰B gDW‰ÿé.7i1ñ¥¹]6Lâ»4Ô÷„\ç7Þ "leQÞ#½Å±Å#bŽÒÐÑ0Ô†:2™ý+e¶iÀ ¤‘h3-ÏHÄ¢$ñ~ЙÀŒªO:IØo f“Ø05=Ö»ú²åCâo¦R¡Ÿ5Ö;,ò¿\”88\Gïù^äNå-ƒ6LyĵDŒ  Ä2¤ÑÁŒÓôc/6ßQq¼kc}]–¨¹åàÌJBüÛ(KÕ¹ÙËô»þ÷é(‡iÜãpˆŠ;7€‰–((ZŽftÈÜÉN7 Ðý`@/…0üY‰ÏBâ§fÆÐý\€–f²ïã±ÆÐ¿ùìôO=üû¶zé Ä)]ÉâèúXH$‰Ml a-,__Úû¥~?Áê“ÜUõø)’ù =OòÆ šââSç0DH`³ñ5´Û@URâRiHånǃ{ÔÈÒ3w­ÄìqEرUz~F¥¬çdµcvm½yõoË×S—°äÙG9øì¼ê«nÞ¤“^¬Š‹ îet“霩‹hjB¹ça†U{(‚™À?ÅÒ8kc)ûF³Hêã¶e–á8”ºËhÙyÞ©edEL¥t "m½ü%—J¸¾»²tá\9½Qþæwü.™`¤Òro@Á©l2qêâ@ Gš4óxU!¶ÏCxÌÊ™¶­ôF^`X4õ!ðG^_¤Œq!$:&¾ µ²Pp¸Ñˆ®ÈK|3wù…ŽlÜ‹/«m£ÃÛ¯ €™}¤£X¿ñ@Nç?¥ë"=ðrÍ;ï¨È5¤Ò¢9æú[$/idÄEŒfÞcø?*W+0%Ó~ÐyÉFª‡¬`xKz ùx#:ŠÉž¤ Õ°™ÃCXE.èr]" Ÿè¢pª‡ÔÞ2+bºå54Œ.Eƒ_å“–85ãµ?P[v•ϘEöRóÜ>Q€Ÿ´9Tâxï¿*Sï©ÃëÔù“ Uª2+oÒBØÓT2mUrx£áö•éÅÈ&:!úeî˜1E’(òu/Ï<—$6yûïêºQ¸âµqs5²›®È¨YcC…¤ÿ¤ë‰YPÕt3Óߣw EëJ“Ϥ> 5-xÍà A'UÚY`xPºÂqÁ 4ú•:«+>¬ìNT„Ï|&¦&hç7W“E$Ç.h=áî é8”°@òdÀªevK¬ë´¿½|ͧ¶P‚'È¡[Q¡‰ÏBñõ^ñ¦’ÉÖäkô ÉÏU-ü Ug¸åÓˆ¨—œ·’n«ÌÏ5ìæž$µjëô½Mˆ¬7$dœª´Fî&“`TXŽÕÆÎ5i?t¥¤™Î…´ÄÁÄ…âø/RgºKDà^Œh ã)Z9ߤC0Èt“ìÞ¸•‹YÇšvÎwzIá©£d+±qöënsÉ‹d1b½X)Ô X<Šï•CSþ!Òø´ˆ¦§-OXXìæ¢gŠ&Yðl±Õ83vC23œ¬Áï¾ÊÉçtÇ:xp#Ëc¬Ív1nhAÇÞGl棛ïnªÝeSåXÑÍÉr,&Ê—:‚#ðü±ÃÈ6ë‚ukŸ½„\‡s×MƒñX¡ÀÅYÓ\­"ô)p­•¾ñÂÒu†ædÄýqÈGPÁy(4K‘ÀO«o;Uív•ào¥ÄõùV·'w¥5´Ã&±“}¦#®‘⺳ ÅþT9¬ÞPC‹Ek;àxß`j®®»<î @ …K¿œ‹L®³0£ÎÇî´iÌ[5ÃÍ&¢ú£æË>°þðvÜ«á]d©G3TÒeÓ÷YÈp ˜ µž8Þ[r2Õ=zÍ}é¥köa2¤‚|‘¿TòêEt0’Ÿƒùª»Ø0²¾è<Ñ*Ó]ÓY^Ö`6p¢úì›Ç5ÚöqÈ(mbßÌÖU 0IBS×.cÔ<Îè6(Ãf²J|6’mxf±UAͶ™ÝÊ$APéÙÿ+_!ð—Q0½8Α&\{IõT]@p5[q/¶ülõ‘©P$ ôc(ð^òȺF×4‘u'Kï{›œDqèŠÚË5Hܾ5—ë!B·w"¦ÝÓ7æÇg¹?Q;È‹éóüè$üMüg 6PéA‹.y еޱ+P† ìÀÊù[RÑ;)LôUJo›Åþ×i`J$& ÉL€`Ž…üïëðcï[¤ˆ3Oï·ô$žéDëb_Š.ä­¬öZ¦³‘Iý4'”IC$“Û!¸_fG­jíÓ¹M á1»N=Êß'ø{£P·h?ç¼åEÏrSzIÉd–T6ZÆSÄ@ƒö´³Ípâä9Š9°‹¯=açj6º&5Hz ½î¦:Ûý|„¶±¤Tá„´º>„ȀР9ÏëíDô“>¨ç¬iwðãr„þíOµlE†úÍø13TMþn# „qªwkÔü„3ÿ6íñh#&öMÔ\ÖÍf:_Cá95?CßBj9òà#©†Ö{ÚT¡1(¢÷ˆЂ=Àn|#ÍvÓÜK„øuHE*­òÊ×µÙaðê3ùžÍì¤tOÕ›haoÞÌ6cÔÜœZk:(ÇEûø“=öWßÔ½ÖQI¿îÿ g˜èq&19)¯JÄ'*hù\žoü¯6róê±Ê]ÿÑ—N’ÚΛÚy¬ëe|™>(óͽ(Ô—æH<Ï´VmAÑ?»ù¢½hHâßO#–­)Û ôlîµsõÇ6ð”Ð Ò8ô\¼‰_"áLR˜D×9÷Ëðvb䇉3O•P¹OƧÜ2—Ž¢úôáÔÐe-¢Ò„>cŠÄͤȘaž0òx‹Sƒ>À74”êú™túÜéŠ9>«Gï$–¥œM±ö?$˜hÕh]—¤ó˜”T^vu%Ë:ƒ@çÑÈæ)ãqsÆTç0=ÖȶL|:'ùd§P,gëk–i}Ò¡õX€BwÇòB[¡jâKÍo@ðÈþ&yW$O,ÁÖoÛ$„æz25+Õý·÷Vt®ç†û2-Cólâü¡-6-í]£Ø¢}+Ƶù²$¿QÍÏœ†O Á‡÷Åð¶ÿ¹©¥PpÁä;Í“[#íVNæþ¦ @Ÿ^9QŒC‚Ú÷Ò&ÍýêsëH@³}–rÛ}¹ö:Šž'Ô-9bœ›*èëJ iÐ.ãô‚Pw‘ßù=2Ä£'>üÌÐø–*`8Õ\œyó³>îáØØ!2|»»LÐñÌÿãp¢%.aû.lÌ=5>›÷lÅòˆÍx ʧ_µ8ô×ÊCµœå£¨ÓŸ®*÷ˆ˜²²$uÉê*KfO†žÌ[(03¥|+ƒT½®±æùÈë÷¨TšÈaf ð…œ¶©<óù,S<›ÀBÊîļ°ÇSšmõXˆŒîÉ ŽKÐ šýÿêà·z¹È"©GïùÖ/¶ ïówÉ××P¹ÑûM†¥Ãª=1çTFÀêZ Ú.‘ß:¯€€Æ}z]5 ±¢]æ_’ez E¢ÔÓÐYT ù($“–™›Û»Ý™"Í|îs¦7Ú4ñC)ô­©ÕúɆÖn¢`»•_§ãϘ²õ†ë,6¾ä×õç½_/DµGàgXü&´˜]É¢"Žq>C)¦&nçø¾—fl±x±w &ç)ƒÃI¼ì“P%F;˜UæëOðÌrT^½Þ5Ñ`­VBÏ(\‡á=(²B³r5ñó}ìCZß=ÐŒáYóŽÿd~BÒL¨j)ô—§¶X¬²ZgµºáÝJ¶æ¦¶Pw8Oe_Ak¿'ƒ(òe^¯Û™¶Ãîìk+œ¿ñMÎÒéõÃé»sÞ,·S`ø9'ßsP*ŒÔ\€ |òNyº-ŽUB`¬¹.,–W…DÜ«\;!}¤ ˜¶ nv$©Óž!K»ŽÜ9Iü›ß4HüQz075pž? ň€æ\a¦UI·Ÿ4@­ Œ°³»žQ^GÔ%gŽÍÐØdÌß [±jQ¢üé6ý9¼ ™ ¡˜©¶•[HLØ÷OÁÓ)éÞ‘•Ž^ÙPžw*77ß º“Šžºï ±4ªÀš¨[*ª^š&[3U4öñþc̱•’ãI?@ªÀ¸Äãý_ÿÍé°Ìú ¤þæ>#7[ÉÑ_|7D³T´-àë…îé!´t ®¾ÁÇRñåsÙBÂF"ÅÇä‰óÖA‡NÞì>V€™^/_1¤B<“ÔS› « ö¸ž*“™;g·Ó¹ÍÉ[Û 1 ÖÛh£2»Fg`Ædˆ4'DOXƒzŠXš ÷ïæÁÜžì)ý*Á“çAQ ¼Ë)ÎÿnDp"èWÖ8¨œ¦`¥ØY/ØAM7À¼­°g±g»ŸC²@ö ½4W¥¾ßÔ’õáíô{ÄÌ7Ðâ@[惡GÈ,¨üYóÑ,Ì“í–~dÞ³€È2ˆØÜqã™nmJ¼ØÜƒ–o|¼W†|ò„Ìåͪ]ò«ºmÚìÁÄ+ly¿?IZÍÚº©[Ê+åeŠ3•±ƒ ÌAípí‡ÝZ¨ª/Õ?zí¡÷fWÜåˆÜð’)ÒÓ(ïÜ*l§ÐvØ$Ód˜ÂïzñãÜS#ÖOS6ö‹ .j&>KŒWÈΊnÒ”,} J—cºõ“B?¨‚‹Q°À»££KŠè=ÑGZw¼O:9Èjú•Éø9š°ÖÕ“Ž\ÙªExÆút°%1ߊòIb ~½¹Ýö*¥Í¦r]Aq}µÆÚ áªä½.Ã@…K,äYÓ,“.Ëm‰ Sâ2à†ÈH›g%NhFçÌó’aÿ£üQŽV¦Âº5>?ÄÛˆœµ¢ä%ɬù¤¶Ö©<ýz 3¸âdrºíVå盟Ћ]Yï„+};ë?ÑÕÛ2¶ZÎØŸO`Å]ˆH½&CÔê%½-ÚÙ+Eºr1ãÕþØð|ÈH{>ìz*ªÐ×ýÙÓÿOyÎd‰V†Æ#·2r,stÓO./>ˆ/ZÖ…}übN‘(»ùpŒüh°„é…qÝ¡}u¢Zzr¦ ¾LÆW¶ØD›·3A„0²9×È€eUw”>)MStÙù(Y‚=±©pÛ,å$rúì|QE‡M|ÈT1kqÊ?¸ écÙ>*ù™®Åxí©ØÒÜÉ¥X®ƒ´˜¼ü íY–sª¯&««"oO¡¿“7×EŽrK@•‘Fµ„Zâ®pLß¹îɦ;ÇTßšù2Rc¥‡†”T;è9ƒȄÄPc¶ó f3‘—• ÔbwmgÈæLèkž[ëÝ›‘ÝHƒ7‹ÝºÜ)šæÌUÇ«i0e¿›4ÿ^94肋sÄßшFæøÖû‹ ×ìéÞ€QQ,–žÚ„>bvfÇj³VÍ„ÕYÓ¥óYÝ:’(ÖO·e_ €ðq ÝeµOYߺ¦_ +x¼…/B†Ìã# Ô”ÔµŠ°æÊn7‹äºöãGûh.ãUm{+åµ´„!Wß›ªXï4Ldý“!i@–¼¨rcþ/ŒïT^€jF1’”NݹÈK” v|ûç3@;OLRÙ-“£ãÜw’CA«Œ1åÂîgk`®àï,£—níÆ|oâP#Ã.•–´þWg%¶ª¶î¹ÄXÏ-¥®4Æ£†ÎÚ³ŸJòØ·ÂQ„ísk<Ÿlî„Æ(„áYIíM &èÖòmø?ŒW®ùÓÁ'Hôà=·š†aN‚¥?oJ»+¼UƒáŒeT“MâÞ«¸±Žø~E&16Æ¡˜JðÅ5ÖuÞzñºJÐëj‚%9 »¥ðv‹"5ÙÒ, øþª ÒXÄ]æ1ªáýWò½E×.t‰a˜Óí“+?˜Áý^¡«G!ç ÔÖ¦%j€pº‰¹ž}üݳ.Wf¯ Î—L0|â+EøÏØ:~³Ú—Q…paõÇù>§ô§oâ™u&)*m<ÒÑIÕ¼(¡\hHVœ•U<-YJ^6‹›kl½€*–ÆäP7fUvz9A—*”>˜ìO”s(«ÇÈ1=Åë(žË&Ù¶îõCdÓ¿¿c=ž´:OîGšÍ,µÆ4-²\ ²ÞJ®`ó‘Y§á7wÀ½síCÏPç„,î ]tC5ò¡BB_DftG®‡4+BÝö¥p’ ÿøJéD¢3 Ú‘‡Ì^I.CªÔÉÒ¦ƒ¸ÓC©Úç8B»4’”8uÚ;[ödí™ ¶•¦˜Ý¹ŽRíᡨ?&’‚ F¦Ÿ]Åþ/ŽëúµÆ™Éou*í±l>Õ? ªhƒ•„ÈŠ&ا¿\džo…_+‡“/§C4d['R©,ñÁ_ËÂ+ š,ü7º¢ œª—C•»aI×d–÷ô%°‹Ò@÷^ª6£úºéÍË‘ºÅ>çÈ(»  +5pB.—ÝÏUéU«h9 ó0³9Ù¼P5“ñâ(¯˜Á~-e._E®U”×TÕ\µ_h‰ûwÛ‡ÙXån°XçJn»x Zóž„åÇ„)ó  {ˆ:yÓq¥• Êê2zù„Å:ý÷s¼ Àë4x·Žt v²–TÙ¿eòWÚˆÛF[û+èþ£¿ÞÜœ­E>#λ]d‰šG¯°•ëÆ•a%éå–ãßøLOî]øâ Êi‘Ðg¹= ÝÀÚÖO¾ )´ý«7ÿÜþ…A5XÇ$CäRI!}f‡X󴀋Óß¼=%b£¸ÌC&Û|°ø²SzK¹Is‘šô*Œ6ÁbÁJó˜àÊÂ@¡7ŒÕß}¨úlÍ„ѽЛ€Yî¼Á¹8ÑDJxkÒ µGû›Pe_tç¶Æhàáè®Pú*¯›;"Œ-$ÑKmldÞ À†¤rÑq ?“@y9Ù"qä#Go8I`Î=Kʾajö;hœ¹Ø†á•7ÚŒ!¢Æ¡TÂÒDÌ¢èΟSQãæ>¨èË‚‡ÆÎ],)Û"PNF©göû<Ì÷õøŽNìQë¼Ò©ŸVK6j$,Ê·æÎ®?#Ú[7½ 2TÌgWÖÒîíj%QÄÌlúÝ!L Ø#"h„lõ0o^÷-³ê‘ʲ,t‹l¼=Ü\*dÞñ7h50ÕwäRåÚèþ¡”¬ÖÃ|•ÜÓÜÇq_ZR´ë\¦Ü6ðoÿý$c5ÆÄVb2&ŸÀ|à14õÇ‹¾‰Y©æ4°²:šæ†+‡q]]6;Zª£ÞåÖgC` ó×@«¸ƒæv¶(0à?®Fqj3lçꉃ‹1, ø“Gþ½ó9½ò1“‰ Ü¦*ݰi¹9±ftOҾ̞᷸_NÓ4êsƒÇ%ÉžÐÛFëWÙÊÿ«N“"Vº”>lUq"ƒ¼_ޱÚKºui§[Ö/zñýòô¸#CΧÐÿݳEu†ŒG…SÔWò7})5$œŽD÷C&{òÿú ”¯¯¼„—!¶ Rà¹çl§ÑGJÁØôãÐ>p“žD¸O9 9"=M/^>î“KZæÀ;ýv{‰& EèQ=Îþ¼Hm²ÜFœ¢Neg$L[:Ê2¾·j1ò $§ËJ=u¥.ü°ê>Ö³éi¡)?ý”¢/Y ô²|]èl7ãyÒ ŽŒ¬%#ùÎdް‘›c»È~Ö`o;üÂ0£ –7miF×jë`Ü—z5†·É+öëUvä²>'Ég:³L'xÌèr𨄨_Àê¡ibLOÙçíÆ^•JÊe™½ßÄ@pc˜×"5ef\&E-Áî¥é±Î bn‘w¤Š¢z^Ý÷RàÏž¨Þm`JIŠÅä½D ¢¶ó²œ‚ÔF׉·ò—bG!í’gŽ}ˆ‡á*›‹åÅã–™ztY‰*¸^;ò¿v4jŒEº™ßÖEÛ«<ýIkñb©¢J c½Ü\ËùÉ‹¡…º`ÎJ2½‘öÀb?øªD˜ð6ïúÁ%xÒ”×tæ bE޾Û½SÑÄdÆT¡o½B®ƒù ÊC¾pbQT¬l§×RÂ;öx›€¹œä9åÇ5wßévñîí¤¦®çÎW#>µŒjêþFÏ© ú\ЩvÅÑzÔ CÂS¼Nó!ؿɛÜ¿¬w4§7o¤×tÆïTÔ õ²Éf/~ûº¦h Áü{ÿÜ´j¼2衉ûñ5îÌÒºCLϪ†Q—«ü!w'9Äsº<0þcÜ5¿–vlÈf%ÆÞôæï &RÅð8õïüNƒY3xz/YGáS+v(cá³@ÄÓ%Eå‹Ñ¯IÜä»Ã Þ´f$8ùìeyªÚÌ¡Ýaü{BAwÌjbBƒŸUʶhzb…ÇEð‚Ù›|tüz=•(Ã?[bŸT*¬RÒOÃ>O!w¦æ<‘¦GŒ HìÁU¾Nè=¹HP!ɳ¡´=<»GŽ|±? £x›ß¡…¹#h<ÁÓ«^£dx˜Œ²<·…LĽó°ìIûû ó3ýÈ(ˆ 邾¼‡ÂÔ¤ÔDÂ’ˆޤ,Ï•M)<%E½ý štSdm ;RÓãJÂúeâÔœÈ^Fà&qo‹«>×Q;LÍ5§Ç.:ÆæCi2§·Í²=T}–½}Ó§’¸gjÅnÁ ™ÍZ& d¼¥þ‹~evo”žïkÞ;†yN€…ÁÚ«8E`.¨›\Ù@j‰•˜›ÖÇÊ—¦î§‹° i?Û¨LoŽ© pE2ªz-m¦» Ç’J³—"j¨S![퉧èc1.1ínþ?ÜúÚ6($™âÚE­ñÑšê”›‡½Û¯Nñæp(J‚²2`ÈA¡‡Ì¬w¬gÒ>ïkT~ãq27r`˜PjgnnlSÏ#Žþ@—•¹“ö?cßO‚ X˜‘ke„O j+ÆØË Œ†ï€Ëp|ŠF|hÚØÒ1Âý¥5iŸ<%2ámI;X T¹³)zõè9Ós2nYåŒÈ9âý°1êöƒRf4PpXŽH=ÚÅ8hwÕËÇ «E¾|JYqBGg0jžsië'(ŸfÄä÷'8 Ì]é\¶,­•«Y§˜G&kÆ/é ”YCÒy­…\©yˆ¨4Ëènù)˜ ϹôA¸@H¦ìâÕËSmæÊ8;úè•ò¾‰¾¦´¯ûH£N5ÐØ•¥«Nñ”1uD{S•¢ýzÈíc lÏyg÷Âa(9s¢ž¤çì‰ÑÿM"Q=Éѵ;©6 È”ã¤))xç}©µŽÿ´Fž7Lî`?p »K*Ù[ åþ?Oiß‚ŸÈÃ9@B×#: /ñ;É£HEƒ[Ï’Ôt¦Æ®ƒöd›„Ÿ92¿Ý4ÂN‘ó÷’ýst†Ñä‡%º ùíû'lÐëAyöÃH1 oزïÕÐþ1©`z»#H¢µœú vÊ™ÀÇÄœhªç ¡³$õQ\æ|}Gô ¬³srabV` Wg(¦¾¦·,^õzŽõhšl;8;áãnkƒŸ¾¦úuM—:¬¥¸Ss2wDUnÿÉЙç÷Ñ–ðÚ’YŒàS'^ïv¹sçêú¯°èÓ 9©'CP¬Y^äìޤW°‡Rcâvõâ`Ü|p I  ê±Î} q™Î8vjßY»Õ$à ‚ä}‰¹´ñ(Ëd ^"7Æ &”ÖÜݪòDÍú Iþ \¿ÿk„†Èœ­œ‚ Å?:w«7û¢NÀÌYgEj‹Ø¥­Íæ+b0õi7û‚‹°3 œ_dzòV¾¾0[Úv‘íÊz* ûs^ƒ·g¦t^vüÿË>}›AnèHEo÷–þQ"D(»-,AŽtÅ#TUüz%V4O(´QSwà ¾rï[÷ßA¿`Ð\AR‹ gòGO•‹‡üзfž½—óñ(‘®d-Ö0'!¢ûÐü^¹ÙD§ß²§W¬ÜùŸiÅ6e[mc¯cÖÅf¯ ÃïÌÄGfó+þG0|’ùÒÌíòþ“ÿ„¦º§¬Óie‡°»;TáÞÀtG9ºÔÛèIáI€ø ¥I’ô@I ¯¡”’®áLä6ðBÎáI”zAGß¾‹£SZ$±ûÞ=RŒ¶^¾£°·J‚O¸ÝuðO)XaµÅ-ÒK­ó]3^ìZa.E‘5¢×”ÑÄ?n¾¦ }ßw’²·9¥!f¼ gÃ*Æžì$öbeuàù? ø»…B>hí4”.ì³A¾&†2ßàJk£´ôÞìÏÞ0ÿ,gW³Wk­ë endstream endobj 826 0 obj << /Length1 727 /Length2 15035 /Length3 0 /Length 15637 /Filter /FlateDecode >> stream xÚm¶ct&\-£c;yb;éŽmÛNžØ¶mÛ¶m›§cÛé8¹ïÌÜ™»¾µ¾uþv­Úµ«~rb1;[g{ 3=3@YLHƒ™ ÀÄÀúƒœ\Øhèlag+bè ä¨MÊ@{3 €…‰‰ù9@ØÎÞÃÑÂÌÜ@eLýï @ÍÚÐÄÂÆÂ fgmçjalàquupuraptácø§H8›¦Ö@€°¼‚¦¤œ8€J\N ´:Z\Œ¬-Œ2Æ@[' 5ÀÔÎ`ý`lgkbñ/NN ÿ&`ë ttþ‡˜©£ @VTEPL^N *̨" 0´5ÈHüSnëìÄõhläþ¯éèÎÿ³¬ ÿgýÏú_ÖÑý?Ãff€‰…±3Àhfaûƒñ_ºIÚšÚ~þ'lâbÿßÔ?„œþá úGCj€ Ðô´‹µµœ¡ @%lgcïâ tÈÚ™mBvÖ&Qwg ­ Ðä¿hC kÿ_üÿEH:þ£‡ ­Ù? 2ý'dá$fá4Q°pþGrSCë¤úw\øŸýü«Ñªÿiøg¯&v¶Öÿ·á?Ãü»£¸Œ¨¤‚(íÿÎàßiQ[c; [3€²ó?Š:šü/ðï´‚¡Ånè¿Tþ{SæÿçË:;Z¸´™þu$LÿzÿµtÿJHÈÎÝ‹ž…™@ÏÊüÀü“ÀÉÌîóÿ¡kìâè´uþ·ìÿÌò_ÿß纬¯Øs[¦µ†VøŠÍWBÒÎϨҮ0ƒ®ÎôÞh£X ››è^áÞ«àæ»ÁÌñî^îHÞ·Æ᧦£‰ÃuWp_JH+Í|k·Þ ^Âõ®!ÔplŒå n"îœ36Öh¦Ù²rÏk¥ÊxQÄ™£ Ê; ‘·ã×mÅ©u)Q3µ¾àû´5Èóæ!…×Fü/{ë`aùA»ð7ä8úæ…ö‡aZùû†˜çX‚im@Æl? ù¬E:`×kdñ ©¨×2ÿxç 3ä(ÉÓXsÛg"É/Ô¨„iw—©š&'¬ŸeॲÁÈÅÒÚ¾@6â,ÄhmgÆt¶U¹b›ôµúj á³Ìª¥’­3E²4S|¼é\„³‰pò¬–Äe/“t:í9'nUµíÂ1Zúièµ}þùf½k9S sðÁðGÖ~H̆½4¿N…º”\…ÄAIÒ;¡*ü…OºVI@r˜ %é˜DÍ3ëÛ«×” !Úôˆ¸µÅ 9èŠ:Çs5G4~¦]ø k*[ÏŽ9ìbþýÑ)yGkQÆô¦EŠŽ 9èíá]@ûbª§ƒX¿‚q€n˜×ÎÂÌWiÒX[`4hñ‘IÌ›õ´g¡gð¸¨H#樖ißQVEæÜÃó"š. îL?BëätÇ{ªüK€Ë;9J;²þ(@¬³2qÏ*HJWÿ¶Làºt gΟ kÕè½8¬˜Eøã uü«ø@”=ò·Gz²¯xCëÏ9Æl«ÍWUX^0sßÏ­.’çnÒ )D§ÄÅ»xp«Ik»_QჂœxÛÓƒÞTsÑG(iºVpNröV<&c (x>ÕE“,)·ìÛÍP&–©}ÑXpPœQÚ~Ñê£)¨ºÝÝx¸§õ:jyøiòC-¾¢$YQˆvY$‘?mQšL‚¡è“TÎkÄÒ†¡^ÇÈ} s‹ÝE¼\²ˆæþ Ö¼ÑÜiz²M”,Ç!Èhâ§­__¢jL®’Ù½ø“ ÚG’–|©¿S‰OÑ+ ôÒà·sVW>Tîµ Qœ1šÅy”çZ,^;xJ]*àÃAFçóKó6ëµ ¿µÈÁޱü´¸ã™ë§â-«g ÞÍ£y¤×ý#WÂ@6AÂÊLK‘ŒÓ¢NÔO¾1áËD^‰ñÒCûC"„·X¤TÉlÞ„yÝhžÕi„š³ç«_ðÙ!—†ha8Aïv9,ÑWpqKÖ.®Ü(j?â©obMÂõ¸ó†jKˆ…‚• ÀÜë#J^ý§ÑšBº§µ,ý?¨`KãÕ˜÷<\ÆèU 3-ò³™rÖïš~•“ ÿ¾#7baùe-¦5ññ‚Ú3„’¾ˆpS ÅÚŽ°T[úÜ»UÞ2†.¼¤Sgô÷ܳEO”€o#pÇvžãbБéƒ('K!YBLzÌJcœEL/Ñ̵ê=¡æ¬1/ŒòB6ÚŠ7I¤Xy«Î~à½;…v#^R¾:füw"&ª“šœ;Lñ&êÏÊúó’5ÔlŸ½ß´¯G  ï/|âPœZ4Öh¥< lÀ'} Z)ïAÌà>>ookéÒÉÄÖE:Ó†ÌÜäÇ­79èÁµ°ÍxŒÊxUþ´¿w¬¸¶“X }G¯—?ÙwËŸPF‡É.Meøc#ÈnXÈ”%§jŒ¼)pÑ „Éɾ³>²G›Š¸«‡"‘µÏ‰a¡6WŸeI´q|%–H÷í4˜`¶‡ÊS`fu|tpŠURšsy&0DJn7$wú„„gUåÔµ ´8BËY2w%oÞjà #!Jw3\ ¡ö4æŒ -XŸ9ìôÌET¬ºð°e_UrñúÜlâ7–ÇvL…]ê÷¿ ­4\¹?§ÍÜ“QÆn±W¿K}|¯V32´#¡ô®Öò˜òþ\{á1pùЦ3’/SßËVSy®šéA£¥¶j”O5/vNŒ¼UÙcf†ûÈûM`D¬³ù24¸5JXtVà¦S¾ê؆ièVH"ë L ÿ˜:áïnÏ—Vµ9œéìA3èl¸ñ´<ü° ACq+µCÞ¸×8 E>Ä—PƒNƒRÚFUi'×"—J9X{›¦¼:Sdäê·÷®; ¥ñYž"{›±«dÉ‹ÞC÷¨‚­ÿTòüäŽ\ü‚,ß™—(}W!}VÒšˆA)êW‰A´;sÄ`ͤ‰ßß^h”ÿt´­eñ3ˆ21¯ÊÕón›/Ô‘ušdĉîœìˆÿžÏï!VËQ·GÇ”8½hÙ5ªâ‰É_U¯Á $[õ ùfî#†•Ü©¯VÌ9°òK¶ JöåÀÎÅhãÚÂöš€:\ÓÙ±êOLôš ÖX–çHˆ×Ùß'˜ÕÚŸçÇp¡×Šœ„=„ª#*Ý}ù0nè Ác’úÒÞu]¶•0ñ€ÜÿxW¶Iȶ- s–ÐÅ;ÞÜÞÝf&g?ù;,¿æ£VQØuT”ZóÖXmû­Èa©Ð =¶Œå<ê’ã—äf©Ç³Åêzf¶ 4cÌ¢H„" =øV§—.¾Ÿ"+ù]’ý5"As2 …?]Ok6æÀ-¾Ñn“ä©4âÀ "dº>þFé_«âS’h8pk¸¤ U¨jQrrÿ™HåRÿöˆ¦{Š á)UrE¼,§fúf–Né™Å.fdæ8ÍQÇñÞËÒs.ÿ5Eª‰-H/Q+$-òWô2ùð¼pèyXzg<º1p756X¸ànÅVHÑî0¨:0¡Ä–•ª80bÿà4ÐI½)ž¤ mVƒ\Ÿç=é~8+‡Üf$Ô¬ü“d#ž;EÿwaFø(9ð…ŠÒÊ;ë¨ç²Æ&Çâñ™b¡Ã¤£(·‡úò»S’QÖ—9˜âÓ{«n±Áîê{WmŠÍÅY¦‡èJß#Fòè7Ð×Ô²M î…‚É9 #ç-²\c®ü€Ñ­Ìól.ë%R —_"C_îI„½‹ŒT)äZ.™÷Ëór¨Ýåš96jÑÂŽÉÑšÕ|eçiªæà\ BÜ*e7ij×5¦jqÑŒ³á y¢a¯ÒÁÙ?ƱÐ?ópÙ3H‹á•_¸YI7ËzöÿìW'˜Î€7xÏ3‘2ÂpŒôv­9j#®É±øö¹„Ôí÷úÎu—Ï(1‹IïÙÀ wSaí{Â:¦àˆÎ:Ézš#¿ØØ¯®‹®Ù_G¡ÑÞŒH³%IнI“®)ë®Ñ¹Î€î×h§Ï­0V8¥vÉçÔÞä°ûjt=¼ÐÀ¨!j+5Æ9¦&ŸF½k Ã*ƒq¼'1@C«qÃ,Q‚öåeâ¹ÓìÈô?97uBÀ¼Sž³õiúB26ã1…áŽß¬Iw?‡Øünq¸Ã±ÃËœ·8ìîphV·Ìò´E48çvU˜ÃډߕkKc6íSñ—bcS  /a–g^ZÏÑ;¾å®·e7˜úRJl8ÂÞÑ«ýT©hš³±3dUÆh›–½¶§Ïß¡¤\ß«¤ÏÄÊ@Œè¸g„ÏZ«ÿ-4ZÝ=ÄÙS¶äš:X­|øQ”÷âØQC;[Ïû0¾Í~ëɰÀ–nD-DNaEù”{ÏÉB0BÊ Ä®±ÉWòÈÁŠZ,¾“Ö†ð·6J!'Ué{õÎ.јßúŠÙí×ÝhkÛ~îFêÊmsÀµÊß ;F|/àê—9…¶[¶Ð’“˜¶@+8)¢HëJº©Cœ—™iÂX;“òTØ ïndñóSã “åøqâæ<‰µT¦3þ-†TG×úºÜÓÏ _ÌßM”¬á㘵¬Â1S_èNÍV"!.ÖÀà^ÔËöàoEè×ryÆØ¸•øçŠAø˜#Ì´w[Ÿùp(BpºÒ]òP:8°EŸŸeŽ;˜ù°§Ç'¥ Ù>rÂUµƒÍ}Ÿ‡\è€5 k$wA_NnŸ¶ØÐ#É”/ ¶K©¹ Ž.6;i¶šê(”ïÏY?þã‡`Uâä<+%€ÍKª_ÕÝ<ïU‹˜/ÞñäX Ã{ígöV«±ß_Óÿé)æ¸/ÚÒã æ“ÕQÚ„éá–ùB‚ù‘~ÝPr%ƒ£Ð£¾„ ­Ó‹_yö9p|OÝÎÜÞÎ_qîHûºÀÜ]š{²C]¢«F3扂àj5^¦'Ô*Ý_µÆÚa”ðÃrð<öPžoSd¼|ºL§P5þiP±îîÑ&J)ÑKN¹.C÷[‘5β{ky…a‹˜ó&8uSÑ¡#ÒÞ|”­ƒÄM(ŠmÇ"2ô×÷ØÃxàa,q¾¿(ÁZ„xð#ÔTx§Ð¥ºö'-½púu>‚.,±ˆ¶± )f4ÏšÐ"ãk "Ÿ¨Ó^º(hœ`ëŽÓ1~²!ɨ„ƒ#K%=Õïõ ­”ã ͉,k­ÄhÍÝ¡_5ÛªÐ{ó¼IT ½ø–óöÚ|ï&¢ÍG¾QM‚©C«±8>ÚËZo*Y× a´†@DÂÈJ­PÈí‘R7ên¾äjF½¤ Áƒ09Uîwâ\¦§Æ°sÛÊnTô˜Â7/¬o ÁÚ¦H)zdÁ…?†Þ—ôºy»”_ˆX´(µ!Ïšµê‹mðEmëãðLÛ“|ŒE!±ñý¼óÞ«8ÔÛÌì¡PèºåNu¹Ç{%Y —r8ñ“ãÝÓ\XDb+Š:½AYnTß;Š/Î…{I4@¹8;ƒD–1*Ôœ(€¨ÕÎ:Rõê¥DŠ"ÚÖšš‹Øí%ŽáKeªŠü RŒ ×sžr™‡Û¾BÓ»ÕNgš™\º4¥¬K+éáÂC¯+äGÆÉ„ ÚIíC„aæ¥å(¢YÜ6J¸AÁ½ 4vF½¦·V¡ÇÇdŸÜ'Ml¯ª'ˬ Çc!òˆ_…à ±Ö¾Ï)§Ý£óx×G‚±‹y:–½¨»‡.}Å0‰©Zbœ/8¶ÞFM`}jÊsîm_1¼¾„¾þ+Pݹ’Ûw}MM/ÛsjBë~û®hýµŽac *±)éÃ'³žTOH*D§‡&Ôvíš¿¦†'vZ2ßž?®¯$`ºä–­3ÓUzˆDQp™ät“$'c¸½‚„T¯ê·Ÿ¯~/¿}ÞK¶^„%\åÃ.&lg5Çb^CiÆ”‘†¿bÞ@ÆdäÆc„G>‰?»WÁrß NŸ~}½½žÖÛ)G»‘cé»8qJ‚7ß>kC¿‡ªÈ‚R$°j#–Ãâ›Xh(3 Í,8Ž0ÐÎÑ@ /Æ`.þÕuÊcÞüñÍ€ýýA¹´{ æÁéÜaìéç/f“zež;<Ê^ÚË"ÞS¼_\‚õe—­ݪ„&«EæN/;VmŠ&eÿ%Ÿuï¥ÞÆ&Ú‹9±/ìL×Ïüó׆á#,dycZ¯.©S[tÇ@ÒxSC´ÇÑRø¼èí´ßØ„DÒW†\MpF‰^À!H"é ÛqýÁ ~çu‘Üx»Ã|§Íµ”rIÈøG”zš[£ã×Úú‰Ïä ×(uƒGçŠ=F„Úc7`7£d‚ukˆ¥tÌžoª Iâ³¤îø¦¾ { +û‹Þ¼ewl?ÄVÈì^p‹ú¾ßµ`ÍÅAî öR#¬lsäH#8ú?Û`€P ‚ ¾ÊåÉ›k,ÓI¨OèͼĞ¢È÷‘¥ˆRÈh$+³2šKm5­ Å!Ñéô}{¿B«§êŽåL¬í˜Ð¬æÌYvá×¶C Áƒø½39•´W”ª¾¶â?ˆ³ŽŸ8ÂÚÁ;9Íààg$ Q“Sf”j¡}´¥Ó¸. ·Ú£ør±°"=¹*ýðgqa*¬;]ÿ Kev€]d£*¿l=)ReS£Ga6뢙`ÌÿM–9žpx!" }³ÿ è]óº{x ÆO·(›Ð[Ó“—r÷ 9$(ÏEûþzEñÍC( 1ù††Îá•^x5+ Ìë΀Ö[U¥”ƒY:êWþv šéD#E¡>Qø r—Œ~þƒùÍ)øÎ]–£k¼ý—/¼Y ãD[WÁîD†¾8´9o¨cÀJ=¿|ÊŵÍÁ<b2îÜçVΓìºèÑD"0¿9™€ø¦b‚N,d–Ÿm£Á$wW ¹°GÁŸ]OÒw˜ŒCÎbãÁÀZ<8pN}.r?Ë$°OãP{W-÷#”ßé 9aVŠp‚œ#ˆø[¼hF^£2Ÿ7yl"Ê ­ M)O3DØ¿=Úéã.¤hÇ:‚†G·.ª«¿ž¨4q"rFHÁf›àÒLý#.!ÙŽû†ä Ä~ÔNîÞÐw.$ùØæ1¥{ TV™MÓIwoå/ašÏ«)±ñꉫJV‡\‘{z=`VH§ñ(í‹dd)ZX;̧eD*ÜV2Ô=«Y¹W÷’VY¹ŠA³Ý†•eŠö‡Í2?æ¬Õü5[§/U3õ”®’€pí÷\¯g,‘.èÎËÞôS ²YÌžtÐ`ý#îR¼"#+ %-Æ]^$ÔΟ ¢„ß™Úü:a¤OÉä9žüƒó#8ÏšW]«Ûу¢f#­´þ¶·hØm…ä üºRÁÚOáÆ\¹²­UIŽ™Qf¾'`÷n7ø];ÿ:çSZ4;˜×FmÄ´Ï;—™ÐkW^†uÒ©ü”œT-ÛQ§Ã.Ú‡JNä#'Rb× ðxı+A»/wKáIåä#+×!¬nmõ°ƒ½àòKt)ùn©üáÊiN†º 4áÿ‘/´~WlÄÆbè!oZ­ö…‰‡8L0êyUð”¾YؕǿJîœPЮçR×ZΘ%C.aîÙÇ2Q:@ ;‰#®1†¯à¼0¸ óÖæo^h®¥$,1´%ä5yáå̸XR¦\ã¥Æ± üEî‰)S”áÕD¿8ßgÙPçÎV.‚¶ì’p>yyù¤ËPô>]š•‹Ÿ¦ï¼ãqtNÜ_ÒV| ‡‹öI?9uß>!²!P9™¢eÞ¹cÝQÊžI¥!µH.عc¶IÐ]†· Œ¤þ·æÔ,ÖBé€"4è…Ï‚u‡‹˜ødÍ=a‰#ìÕä2¬ „}ÿÎ àÉõÞuƒ‹ñ°éÚnòŒlLr¡m)‘¥;©¤Ò@)éaoŸWwíû…V|Öêð£Êh›[Ð2âÒ;±“ÙÆ®¶ºvÝ¥ H{´³WkLK5š~‡Y4ª;ºE/F8]óÅ„v;H¢Ñ‰`Ö~i»Ä¿fÖqÓÚï˜àáªa´Yûô J‹rXeÑ CÐ}ƒ«o×¢Ú¾p †Ê¦`(vææ8±éh|ò&Lžê>ËÏð¯Jâ³*|@Xµ]‰Î±RÓï¢çyLn7ÙËY*;Ì ®Iè'³{ßíÇɛӶͽrLo5 ÑàïXÒÁî~†dê5ü"¥Ÿrµv©¿Ñ„*'æïÄÍQÞÛ[|Ç˜ÛØ)ÉX~ÂôB2"îå °÷Wn#G«_;Ü­îVAe…£#ðð\æt°ë²‰ÁÉÔ#øà8U<54›™Ã;•I©dß@#ÆWsƾ™¾V€ÊÕYÃÐ@°Æ?HGŠHVÑiíÌçVkܤ÷iä\˜Ø£3 йó |`.iËÏëµÏ¾úÁä*®ÿö*£z*€VýMC]|‚ÍHj±n*#샠ø¾Ê|Ú¨÷’‡!{ þÍÛTý§®X™â…½s${©M«)\ìP{<“tpZ 6@ åö¦†ûÊ6:êº~HüHñÀò:“l'h׃òµEQòRõíxÖå´Xoãz¸n”36cã¶î(=0 &þ[‡¨—r’ÉHô&›h=Ç’.^XY”Öè¤W.¯Kº„eêó=¯q4×~í…_ljÍäôÄ¥‹MpRÿOÍìÔ Îš™ì÷ØŸ’’PÈ=}FSâh}M»Å=ýnõYšG1;1Hh~óY¿nàé¿\é}ènnéXÚ;ðÙÖѲfC+×ú“ÃïÄ(ÌÍ4#TD _=å¥hËÜ>›Ë¸Édw¯Y” VðÝ?tŽçæá|?4{Uï£ë¨Ï6èy¸+6„R c`D+;AD—Hâ=#ƺ¯údÊÇ‹­¬E Ô‚†Ÿ0ÙÑysÿ¶ïfÂÒÖ³ÐðÜ h×rÇv¯Ã?¬ûŽÁ0jf+¹FÉP[Õ¼"áœþ0)¬Þñ2í­€X¦Þ…ñecM—|h”JhóYš&9™ԹD[8‘X®kYÝÏ»Z‘Ÿc³Oá$¹E‹€u(§ðÔû±{T±¾µêxÚù·XxÉfÊÜëÏkߊÍ(òthâ¬jAœaa  2þËšë&¡èç<®9m¹°ëê 9¯/›Gý:8‹"!L»Ö€Ó¦'Ü“ìÇf å˜m9ŠÔšbY›XþÆWVOýJ(wŸô•«QèlLŠÏ,7ËR_GW…1ÕÍ«¿’!œ dÉn±Æ†Îç±)›„ÿ´]H顇™`}ŠE/öŠÍœö×dý*-6Ìîƒ3àÊWÑõD$šÇ¯GòévaE!e}Å®š«ÐLà´Ö©w̆ˆYÒ «ÅM ‡­Ô®@ðœxç x¡§:çòêEÛFóÙ™Mùï ¸x?)“ÝÙØ”Xð„ÿ=@Â2Õ:„X¦ëFæœÛ¨D¾û›´0) äê1ø²ŒTq@%£ðù;_@…5K,¤'Šü/+sÌ·kJìRͯŸQ§ôqúÞ²Õ÷‰éxØ\‰¿b£œ$ë °;ü¾fYÇGÞ\Z°»g}ÿ ißnágéÎiû.˜S_qvðíãlå6âõ$Á¿E’|$03É>CyÕä¸8|mXËœ#ÊÓÙ˜qL…™¬ã’£>DõP¯³•­9*¦DT· ­fî“—…ô÷œ²A!å8É«yA^.b”" Ê«ƒ›€&áÀÚ­¥çô'•“­]3>äp1(7 ’jÎ tx–z€\ˆã°Ážñƒ*‹“raéÄÄœæRÃKòºs<¡G_³’‡dõ‰ÿèÎ0'?‚É\^oš÷Ƣĭ…jÀf-]ŒW©Œ‚45–£Y %zê:ÿf µÎ/¹dy†HÊLT|:c[î AšTÝCp¾f?%(a†ëš†X$˜Qšíÿì¥lüÐMTU1u{OsĤLæZîzøÖ»ç±°er€e‘/“CTn²£;ŠPÃ8œvMîßÉ@.„UÐ;XÇŸÒÏN§z§Üc·½&f‡™Ô¨.TÖYâ‡fËÛ7g0ÐÄaóªxG?>ù¾i¾f›T³Två‡Cu.÷ çUŸîö dñ¼Ø¥h~ âôª·¾,åHÛFP'.¿Ø^ð0Cb%òƶ Á³õÕvÏÜp9Ø@)¤Ô̓Aþ Ò*— mi Æü!]½>haü/äýžÓv :—tæ/»’Ìy檦؟ÏçGÅûHìó1(ziò1¯÷0ÅÆ%ÈW-‡®ÙŸÙ£V¾8È­Lí!hnuS^Uü0Ý%¿Ðg§tiÏ:.Ë\APö%ÂíÏFB°¶7V<](£æ/Þ–¥ÁpÑH”9ÓÎÝÍcýïo,I4KZ®óØÓ™Š?0fžB×Ñm)£Èá%’ûÖñÿ|פ¥Ñ às¨¬ä”ÛYxÐzòï¥n5à(ˆOvzˆ°ÞðؾÈÄäRÕŒÔ3@ö"®lëÕy¡1íRØAz­‚Æf<çgÃ¥QT)g)yZÃì­`¡^!wÔ9Gó¼‹v@WObF¬h…Eu¹eI$³Û?±ˆ]ô4\R:AT§Ò1ƒ¦köÕήé™+Ùß» ë™aüµÅ+ pѵ@Wå…­Ìʼn¢m¯L‘„Ýq%„*½ßAX)ý#²Æ–žš!€C Ä(…°8c£ÅŸX òœŸ[ê`çZŽX,ÆO}"¥ìMyP~I憸Ê.„øEG“Õÿì¡®A²r‰ÑR¶¬é\ÂWòø–ŒWìj™k³–? ¸D|Bý)È:ÑGë ê?`Ã31êˆåý7<ûÉÚQäþüãm5¼‡EœU®ž›e:´H+ÜüFïtó\²nlaÜÚÑ1Ó‹LÂ5fÐÁâ?~ˆºæþwjŽ‚¸>`Øän©À+Ätdl»›ÈäìÇ–wû6p±åy'éõ?º¶6SóF —|üOLü'÷ûTI3^âÊŠ“œã¹Yv©8s‰p¤à·Ù R¾©Ró*¨3ÐUãvƒH¶cu Õ–8Hx’df‰óoÄ׆ï±cQèÁ|í¿fdíRÒ)‹ìMœ«àÙGVÈ ™x Yº1µ2 â+Q4ñ ÙÍ æ[Ð}³%Œi>u‚h¨ÊÖ{²¯³J7Lå†YÿÃÉ¡û \£J¢­ÈŽ~J;íX{Æç Q+#ì(ŽÜ̶á 6‰0ÿ &Ó Ä—Ës'$‰â‘Pî<’³Œv^ZS¶+sBÙ&ôÏÎËžï´ÄÑß#‰Ì -Í3©èƒk½órÙúüQÁôÂë÷92>¢ÆJ†¢¾Ž¹°ÃêUw·C¼ª3j(¦‰ýx_8-*K(`©•áÄà8F(îÕ¥÷±ø !Ï£‰†5õÄ>€³ü;ØÙŸCSµ{ ãî’èJx¦ëPÇôóP+ôíácÝ ••ÛUu©$"ÐÅš†Í³êÒyø‚71­þ·Ôu“ôntCÇn2kbhoûº:&¡‹c}¶Ê]CÅ4¤¤ã‹aÉ1ÈíãžBꡉk1ùüÕÁÁ' EÞZ9û¨7#„-Ÿšs¨Éèê³…Ó]—n¯¤k«ò”¸Åç5t7Dh?øÿ>°DfÞÞ5º­F\É{MÛ|„`)ëÚ×;Út\“KÊ8k„¥ÊK¨å< õp³Ý>Æù[’#Í¿œ;Q>˜L‰-¥û¦MÞÖò‚5ý@~ԼżË)݃EòŽ¿*%,9ZFñL1·² ßþÃM8ÃиÖ*í­Ô÷Ý´"µœöå¥VxÒ—ëq‹Y¿0çs©êÏ;ŠE—'M3‰“ŒÞ†v¦3RP³ƒŠÀpHÁy»ŸŠp±S¢Û‘Êâ3ZŒà´\ÔãÞÍ 2¨{á/ÄòØ·˜±Â%þQ£ žî´Šù—˜ÿS+5ÀJðMÆðÀR<|Wj³à[ÀX<Æ^#t];æ¦jŠZÕ‘\ýËã©zó®.Òšr€`l* …pÔý-Hü°±°ÍÓ¬ïþ½Sp¢VÆjÏ¢jÆ ÈfµTòÜnn§es§W|saê&hˆ”«¶YÉÌ|0`˜Ì.uªPú°S›Å}qæ>±rqo/Ë:ßd©WÝÌ3|¬ Q?WwqÇ-ðÏ±Ò pa¸iÎÇŋĠ+Ô9ãÍ{Ÿv‰T²cß~µ£Ïî®óaX§õM`Pާ•ðx0Ѥ$R¸b8öO±ìe¢>0¦R×(ªÐh>þ%Ñb#p¹bÇ*Õ ë”5L'R„=¨ €·v7 LúØ•¿æ7ÜSî"Ï'lõ{ˆéJÅp+ÇKØñUø\ÒÙO?¼h÷×#ÐLÕõÑõ/˜ áW þýøh‡õCK;¿ÇÔ¥½Ýq­ǽóçõ«“gòÊ­¶DjzƲm°Í¨±QÌð±Ƶ}Ç£º"vÕ—òA…g8@ÊÝ4 öI…mÅ™O瘽]sËÿt#õ½‡¤i¶¶Ôõlwòá?†²D £^Ë‘I™¥£L{—%êu˜¨Sh(êÚ‰€¾€Û‚‰Edƒ¡NŠ3 Œ+¨´2ùEËf1Š|fߤ>S ¦Ÿß7Ž÷Yˆ¬\øÇäÛ%©?Bú… …œ©÷B†¸B¡¿Ë¹fÆä~coá'Šx—Âߣ¶ó{“ýæÑg:Vöj©ɯl¥Ù‡¯¤…=åãhü¤þæ]GÚë]WsbcX”σ8ß»{š _Å­nçzuýfûŒe|uô~*x9þ‘¨˜ÌÚßE.D7ÿ|Üßè´¿ùwþš¿+/Fn'^&|áKf€¢Õ­5­W+2,©CºÍ¢Ã* YgSrÿZQ¤­ ëºÓ'†ºkOR]}èØ­4¤åd„YX8Ø­¸“¶‡nÀƒHÇÞõxÛ,%½µŒí\È+Ÿá¿šÆÀ}<‰ÄmîI¶½ö¡/MMZ–»S_‘g×0²êž¾Éíö9⣉[,oÒ$N^šCã_² ÿ‚y%ÆùƒU}µæfÐUʆwÖ"j\Û—Ì% æ!$×Kë ½E.<<ƒ­+” ¿eãJ %%›­¿ÓÃú˜t'E3E™.æØ¨ûÆU@7awô”‡—eQ»ÇÞ6£°àÚ”h ÉXݬæ 뽘°4¾-g©$ ÷'óŒš5¬¸" ÆâËŽ8:è†)²üÙ‹0Ô+\/(Z!rÕ¸B<†¡AfT#0ëQHÑÚ°µôí¤üm½¸Ë>M’ëR°š{ªŸå_kø‘")nX)rIJoñær{À˜¦Ù¸9ΦùU¤(î>LÍ0§ŠT¯ GCÍq·rBCµ!îó’×nAÿ|ñÓ õN¬¦ºbäDC!á§Ò°^†ÿo£üŠ…Þ"‹¢¿³Ïê–TŒ[¸ çm½¿m*Âzñí Ö¡smÛ~,|G ú|f)þa­»5¨½ô4ò <`´,†åà jwÌσ¤ºFþxŸ¡Ï… ö7¯ÕqF‹sñÓ=¾õKâ!«hC¼iÂdÚÕ„60•çÈrڱؗªmϯó#¾z¼ngÚi?ñSEÏÍÛ²R>6òíǬ}*a ±oîh©9Í­ß‘–DLmÅÌÊj2Y7nóƒ¿[AŸ$`rD|ƒòâ<ÀÚí/Ò+vUºÿœn;ylOÁ5®ê{ÿo è÷á.[1ÏØÉÙ|H #my×ÝÛÇý,?Z]ÕX¸Ìû&ÝåD{2?nçæÔ¤ôf6¤•¾{Éu’€Ig;`Éè0ÃíF( »Ø\s¤í^$R§™…]œÎn.+ïª:ÙcáR"ÐòÑ>Ýn!Ÿ©ÙZ.*\ ¸^uˆ³â)‡ÆÙ7mPgŒÝ£Ýì8¾6Û?f“JR57å–Ð ¬ðà3òTöæÍÏÓì!æ þœ”ÂG“Ñ௾A%<œ8ٌάSÞB´WíáÙùqÁ+qÌ^ rÅî¤ÿà´^g¤&¦õÔ +TM·€„|½kä£x™ø×ê”e+ó¸9~¡Óü(lp4¯ÎÄ‹¤Œ‚¥üù¥KÝ!¾·e§zH2j"´SJ09l›ÓÍn¥å¹X>w¥T±«:jíï²ÞËÞVñúd’…Sú›ƒê/ïoår  ·tÅ#vµço‚ižÙIýÕD|¯´»ÚÃâ©î‰<|ÚÂû[|=ôX6Ãq¯;o=9# 颿¸ ²åÊÖè‰GOÛ`y¨»ûº%|œtà„8 Ó»dHmäÖ©çXbˆöߘõE˜<ýž_ôßäLÉhºDoOÒ¸:Hô}(|¤î8¦ž=%éUs]µ0Ö7$ãu´ÝtàÓ eÉçæO?¤ÐÛª£‡Z€Iu lVßÐFÒ¤M÷r7DM·“³pÇ5P¬ Áà0ÖLœ±þåú€!˜C}Ú8o Á fèx!Ùº«køJ¡_ ÉÄzÉÞ=ã°ä§…vqgø¦ŒÏiuh¿müf“ïË:k q°eCA~vñ¾­l)IÄ5(z½ËÁŽúÂál‹•¢ ^3Œt”$Oô±G¯ÖÔv¥®Öx+ÈÞz½Z#»„ÀÊl}AT†õ_êð>m?`Éߨöã„d[ä¹Gàæ9@™¨–äÜ ÚÁ¿®õ•lÒžHDh”ãDvìxѰ:4rƒÐ1ï7Ãò<³ùØG…‰‚r¹:ˆqˆT±ãtØEliXTÄñäù÷t²óøaZ+ê%“ÎQ75oÑ8HlªX]ý|SÌ ~z/áÀ{ïí2` pl´ê³Ø'Eôy §kª‡ÃAiζÂùbkIþE2%¨Ä}æÁÇËÐ7džqàJ[æöcƒýp]ynU‰‘Ÿ«­´!»ÖCû˜Ïò@§,Uá†êɽ ÒˆÆýÖê^cV|#ª¬k6îØa¶@‰“Ü è89 ‰žUž±¹¥\ãQñüÛ‡âx+Þ‹±éjByX,W:ƒö‚4îê\†")ï ¿…úXeÛŸ¨n_åàå…㨂’‘dxeÀÓƒÒ§ !ñG -N9˜–)‘«lçhº\—8‰Uq‰”„Öÿü+XO©kŒæPZsÂXœ² ™Š3®ÔŸXVIÕÁå@lLÉïxŒ!{rLˆ¬ý®J?"ׇhÛS]Ãf?DI¸Fˆ¿ü˜SiÚL:fM¥[ …-Ɖ¸yE?C¼\µšbMoù"?nóÁVÜéè5=«1Á£2lE1 %'j^Þm=Ÿ a?fàNouñÔLLÜêÚ]`g×h]_Gi$æpzæ•·ÛÇE¬mk,,WÖ¸§„*³„)ßé&®âsq”£¾ï"LöCoä!ºÈ¢õ?·ø‚H—5çgFzbuÈ'ˆ€ˆà?šgxjÑŒˆKFHùDx8 :±߈ÅSjaöÆbðÉçÒ-ää›åKœƒKç±u' ¡ˆdà`f×üÁþøÀ%­¦Ÿ¯»÷’0•ºÃžT¾˜8À{dY~ãÔ¢øõ‰VVÜò¤žcÈøÖ9gA"ùàßê亯XÝÂÕîçì?ÑC ¡ò¯Ô5"ޤ}·…Ÿm“ ^lÆ{ ªìêºt·lM‹¯y.½U²äàŸ¥šj¢ÈûXWúoŸkʼ VëÑ¡¾7„4âÄØw˜š$8&ž#jª&9ÅCjXYȪ¨ýæ™±8.Hü³'u:û‰IÕ„PxÏñ6§ÒnÂÊA1Ë¿UÉUŠ©b÷-z»µú$ mÀJ~?_óPªCîÝL¼³€ý%þÔùĨú1ßkÓç|ü–ïÔ?<×:fÎá2k9•¯«øZ5ÛpZ% Ü}¬ ÀÈsŸñþ3Ù¹Ž6Å®}ŽßG0¤‡Îm)+ñìõßõë3Ù3¥¤¥06pwmµ²Õ̤¶b8SFç®Õ7Ý>³ÎnÆúÔBiA¥ 7ßÄ]öšÑwÞ,l¦Uã®Ó^©ž uüÂöç359qYî¼A÷Ü$†é>F;…sgûõ'AXõ;—S)py%ÀêÓOWÙÇ·£ob–•ìi¶€`ùòÞtczùݸÐsxa†1ÔÍèð”|aâàD}î ¸I.Ÿ`òs±aõµ/¡ƒ‘Aö4ÉÖÞG2Áê÷ê…õ˜èOjõÀ©¿Ò¯ýˆ&kGºÜshî•Ô¿µ}êµâVLA{óf ·,=¸ØÛ©-ÍnÉ,<ÞrÅÊ]Ô¥M§ â¸Q6.bÅd}ÖN’n}Ÿ^8¡kQîu,ç7gÿ4`".¼úŠ¥a™n j¶Aœ™Âíä ™œü°•ñ E7f.  î²–͹ 2¤×qÇOE5IþiW“°ð¢}ÿRýRoÆn‹†¢— ò»(Q¶à—s!¶4©è§/Ø¿Gt%Õ-ØGáñ“Äçc#¬uåÆt8«žõϬq°{V&øª¸„6k´Ë‡í¤½„Ciãåéòƒé] C{†ž%ÏÚ ôéWô‹e€"/‘ŒPA±s‰N7¿ìü“·@ ²Ûï¿_Ù.šá,IÔ9ÝçÊ:º¼øØ–?3~ò‹EX™zŸ9 1Ø‚%Qèc­s­Ûªt¬Õ׿ÎP~ö„‚9Ò‹¬!l7ÞÑÏZ+Ì1Û݉R†FÊôT2>âÌSÛ¸/°ðL²UrÌÍ%ON¥ÕÉö`ßaÌEYýì•Î8i;0Q'NîWòAè1⟜ÎÏЭÆ÷˶¤fq¹/î%~Ö?W´æDvj©€Cu­Dju¿—O3X¸ÜmWG‰¼]b«ŸÚÜH?΃…[mwuW9d –`Øl4/É*øÅ™Ó»µxĹÌnJ·ûð@çë¹7±ÊŽí³Ôý d‡ñ/˳¨^Ù{«Óe`‰&ršú³âîâˆû®DÊ~óûÎß'Ø65WïqrT-œ¤¿+ìÑŸ~³W žRšÜ}¥—K ünôÅÄ {EŸ&Ÿû+^Í n-n!Í*jVÛ°CëŽ,çÌÄýÎÚP@~}ª‹ëWc!]Ö” #Ã…ž&+ʈ´åXWa©OßBž€sV=HÐÉê>±[ãÂìkä“èÊü?v1' endstream endobj 828 0 obj << /Length1 727 /Length2 12316 /Length3 0 /Length 12888 /Filter /FlateDecode >> stream xÚmzcp¥Ý¶ul›ÝéØVǶmí$;¶íŽ“ŽmÛvÇîØIǶñõyÏ=çÖ­úêù31fÍ1Çšµþ<‹ü«¸­³ª‡=™ž™‰ ".¬ÉÌÉÂ`b`E 'q9ƒìlEœ<  )@h`f°011#Dìì=AæÎ*ê‚uk#S È ngmç 2±ðººº º:¹08ºð3ü-RÎ@€ÈQPÔ’’—PIÈ«$€¶@G#k€¢‹±5È 2Ú:©fvŽë;;[Sп891üCÀÖèèü—˜™£ @NLUH\A^ &¨*0²5ÈJþ-·uvâù‹š»ÿk::€ó-k£ÿZÆÿµþ›utÿ·Å€ÀÌ 0™8Œæ [Æé&ekfàøwØÔÅþ?©¿„œþòPýÕ` 4û‹v±¶–7²¨Dìlì]œŽ9;S £-@ØÎÚ æî ´5šþmd²öøÿâÿ!ålôW![ó¿ 2ý;r¹MAÎ%73²þ+Õ?q à¿Ïç_þ]ý·à﹚ÚÙZ{üOÿÃüÓŽQEMASA…ö¿kðOZÌÖÄÎdkPqþ«¨‘£éÿ¤@ÿÞ¡ÿPùÏN˜ÿ×—3rv¹t˜þµ$Lÿúþcéý/JXØÎÝ‹ž…™@ÏÊÌ`æ`æp3sùüº&.ŽŽ@[çdÿ;ËüÖ tš ¬,Ú™|¶Li-ó+˜)‡¦™2T£]d_šì¾ÔA Z˜ìܨäº!OómŸmIÝ4‘Å‘Æ'¢¾†ë-<}+7÷­ÞxQÊ{® ׿€RÇ·1‘‡ºüq휶º¨H3Á–‘}R-íP‡.ÁeXÚÞ¶·üûÎV‚Z#]û ¸K[…6c’a,ð´³–´tIŽo`‘_`¿¦»{gÔŽs²ÆLk6lûf$°oÍá€WGâÊ&ïÀ;gsÅätr´F6æ°ýÂF„ѽS›ëü6÷ˆ|s»4œ4 ðC22å{' R@ü N`ÐÍvÀmBT¯ZiåÄWØËc»Ôi…ìs‡êºÎ›…n·øüU²=Ímt–O¦\‚©6˜€Dê=žÙàÀæc'ª¡ Ÿà«v¸’GJ¢JÖ.…à íR˜íQHƒ‚v@“Ð ŽšKœXD”ŒHq猪3;*¿Xf—²¾\”öÑ\´Qˆìç›x^X˜Ç¸)—ï«UiOþGr=Ø‹æà‚EÌ]ôËUÊÓ‡®áïÅÍÆn¸Gü‹±þ{D“ª­`µ2”ôþvûä¨QZƒ‘ ¶›¬µ›&mÙ˜§\’…¤ù<”6ûÇ%ÝuÉžšêu(´:‹ÕjÑï.4°È©ŽO59;ëž‹Ò)~v>ìU8+¦ìK!“ÏÇíÍ·°r-.W×sŠ[nsƒê³×?¾U úûžé(S> ’çW¬­ã®BA0®R¥ò¼Ä-CÀÌþ‹——òsƒ–LJÒÕèb¯úØEõ¢"¯¯Ñ¿KîùÞ—Þ®m(øÅEMGÊ«t­ 7åߔۭv7(:Ä Áà¿¢.%§-¬¸nÑPPk¼â*¼xv&i™5¼°ÄôI !/c|†onz¡\!·ü*­îxë2Ê9ÏÁÄ¢ö,l~ùÂÈ8"{jC4_õ=<©c7“lèÞ—ñpêBošáØasB(ȉ§LVg^0ˆtY5êug u¯î§¹ÂÝòYø‰i§ådåm'†VVËvÈC϶=h]åÍká½|Ë0ä-;ù¼€Ì\ ІKùjØö^DŒèÉÃÓWª.×ã Öi ÕÈ­0Ñ…‹ue/ÿ+>ÕrÌÜœ(óˆ\dÊ¿à áÜ…cÞï@FìsWóÓSHÎnåyªOÕQtCÄ;kŒ€k"nI:›zð1&w áޘ­¼ì< ·à‡Ê„ŠŸ=Órnu>†Ú騲P­ßS¾Ñ<¹áPÙVvàf¶ ênL…ˆACjRñ†"çëtÌÎZ†Á¼ÊO ʬ­{¸Hl—j5úÌÏ·ü  Žß±ÑdP“I|Zq$Ÿ‹KW’¸M?@­9ªÿZ,×z' ÖŽ¨ !‡$e½:i™ ¨ˆõ/5z-ó"͆=ˆ|`óëŒhÈbÍ¬É {Oú—ŒØäW̳ÄT×É8ðM’ÄçvˆøL ©Ñ·=TjLò< þTÄÂÇÂ_fC$ÐrQÂ9]¥ ueȬâ˜gÑ3Ü©Ëî9r(Î?}Í¿W ãU V÷­x¿(‘„ÌÍ‘d¦ùÈ;Í74ØŽÝc\T)ÍÎÕ„‘î^l /¾Ø@Ô\ÁeNû‘ôõä_öD?Î&$0ñê{Ü{Zwë”qBÇÚ~‡?èí!ôåÛ\a~ŸµÀÜ?&sJâÈ•pž ðߤNvA *ÑõrÚiãgA ¤=B™›j3z·ƒü8ñˤμ¦HuQ7嫞Ý 8åhw$i¹^I?Sõ¹9¼ ÚO[x•¿3M I½k*Ú½Ž*úl¸ËR“Måc[óðu,ÇÜr±µâf%3†óøú`4^?•¼¾ ï`%¦±4U‹íò‘Ýü’O/äaÀxx}ö&¹»gqgSðKÖ_jŽÎLÖÓÜíJ[çÔeY"W]ǃæîÚ÷„Šþ‘ŽV€ãu¤‘Ó¦Â)&*vÇ Ç\€áÏG‹Ì¨Ò>tõKâ½HÕ¾ºB!ð37!{R_žžÀ*àí´øÎ&ȪŠP;½?ò{ÏUÏO’§ð*C,¿>È…SÅ™þ¹Æ2Þ°KŒŽò]ó²›iaÙè{7…$/ÏRhÄ£j˜šÓ^þ`‰1µ6þ#óΣ·k4®W¯îƒ°Yh’lÉœûk2Æz úB—*J݈¥.îD¥¢›)'øVÓù¶?}0é•N ç£sY¶óצÖsøýé‡rNPóý剄FK¤Â₽ò(‡ß´C*ÄÐLqaarñCGø‹>a¦o~࣠YÞÈÊsg,rù!£>ƒ÷XÙn°œCn”æq `j¨“m×7X(äžù¡Õ'ÚªûC¶v‘ɽšM™n˜Bÿ4±â}|,~¼—ÞÿltyëÃÇ·µÙ¬½³e.}åU:!ÏWIÛ…£]F¹;Èdz5ŸÎäzä;å†:LÏ@õ{Ûcü!%â…`š‚Nb{7“* 'Æ¢¿JëøÕÝŠ+ž=Ø+‡gsü9†ˆ¾ƒ’˜[.û;õèõ˜¤ï@¯ô¿ûE–M{,kI~‘nØ`¿ÕÆNCô;ê2T]„òòÅ 6wi‰¥H”NXÕò!øQó­â„9ãÊaG5±Í“rc?~œÅýºÊÙÓ0\ê¡°Ë¢ú‘jh¦ïEGL÷®r}°oDZߥñ®"*(\:’|fî»Ð·îÎòÂOÕÆ“!÷§‹G ?¯è<ÐÊÓM¸çÛ˜cäæí03þ"Ëd1nqÿMœ/Ëú2,i´g©hæ^ðˆ,^)†l¤åÐeœ©°ÒX¡}Óã?ö¯†!Y”|¿¡79q:_vÙ7ǃé& šr,ÕvpÕ±’…˜² \Ë­>T¨o¦7PÜÏiV>½'›ñ€£íAZ8³-o2 ãEË‚Á}Ô³*2R²Ž=qm¿ÙÝéÍŠÞ©c˜eŽRÀ¤dZ®à‡ Gª»ªŽg™éÍRª˜ž)›”„2hLþ+g²Ál>V;Í}`îàzôǘÚµ>þ§dœèô|2ëï3ÊßÍ1˜7‰’1Ó¤Õ^ÐÑ©nê>úБ|ïK·Õ² ´¸ƒbñœüfÞ°9M ,cÝí]JiX–ö,ò¼ŸnHépØGîÐ4’n†YÞèÛžèÝÆ`ƒ© Ë‘¦?jX¤× ¾çz¥›“…*¿±²Äà;§£¼¢(NJäYãÂòŒ.ÒΣus‡ù½æ lÔæ“r¥„æ^ÖÞ6+®a|ldu×ð€…· çKYé/)ªfBp%XÞj¸î>½†-5Àgý\Zº Òt¨Iûlò(2aÇ”T«—| }¯’ûiãíñà$‹o÷E+¯É:κ¿#VŒä@[bÁ­k‚Ï¡‰XÒwQ³8øäëÞ7 ^hlüj–7nFñæŽDpqÈÊß*XÃln×cjj5 Áû#Í»éØÏû06žæ 6éò»|ö#CÏYx<(ýµ®oàxbþ¯A¹h÷<²íFÈQh8¾³d½æ4‡G=ƒý¾Tñ‹gÁË^0ö™¢Wè‹ä† *h$ZË?jñU™(aG ÷ézR|M"*\7 ;J?¶²s 鮬,M`zÄo?ÎØÒ©×p –š-¾[) mb÷}.j®'['b¥ §|I ãߦ £·9¾ã•åoh´KÿHÈ¡®†Ï©UÞ¢[öp¥!Î?H+_§½Žæ&Åe) Õp,è Q<»LÌWi_—Uíî`¾Ý¦Òbùþ|’%KN-m÷bâĈAFÅyïy¬0³Œ ÿý>ëqcÅG±tß’Á&n´¥Ý¥ô)0|*ü5_1ÂE²^'ªì%w 1²ó‡ÒÊ&• à­ «Å(¾Öî,¥Ú%‰+Eyª]ÕƒT AAZëlMÞ‡RQ¿f]÷q-€ŸÃ€gÈÒ~ø¬¬V8èVr‹¿Œ— DŽ_K3ûËýgãt4"]Zÿ…ÌpÑæÅ´M•'ã¤ï=IçñÃàðkß!’è[þGº~Ö~å³ôõz Ô˜r»÷\¢mƒŽîD"ùhŠÄB{ß‹¸ nh!¬ÙæÇ\6ö˜=Ôö?ú#X錌j´HIÛFà` ±„—aoÁêÜjHЬÉ:=ÏU¯%58Ó]CN̶òdŠà™DíßgYBuAÑOmfOSZÈ«‹³rq’Ò­µcdXýuùÞòÉ÷¼llYá™±ÀÜÈGËÃÙÐ(®»é\A¤ÿ+Õa{Ì섾Ð8FJV6¢ó¬³R+9"¦„D./½ï½ ueeíþÑõì‡ëª@×½¢"Rñœu%„§cvpyɽò¼ú lAë3A=ïL'$/&öægÄâ<‚Ì’_,?‰2eús}]ª”±/íb*Ú䣤U$÷ë´¸–F !> KÀ?÷3Ù燣[DÜÐ5ï•å„Dtîá—™9,å>±¡ðËùTn@|…ûGCQýqBi'cÂ(¶Ì†Ä[ðY%† ÿMÇÒËq·î^[9XÂH ø½J‡x?~{•G† ˆ ·ë8ÏÀ*#·õì6u‹ÕÀìðÊ©L¯®–ÌyÄuÓ²¸öbb|è¨ÓÍÆU~¼8/Š4§”GâtA·ƒß¡ll(~}JèEp¬o!á .丟 vLΛƒLjPº„ôÛ/à†¡¾¦„øž }_{èà¾3°ŽzÄßEìbªœ\V|v[Ilz@Û ¾5HÏ3ýš9oLÎÝpc÷[ÑZÞœ•;e‡ÍÚ…ë§ÑòGè3n_›šo@“Í {…–Á\ïÝ®Ôý_bÇŒ1nM(þ¿::¾äã9ü¾Ï‡–jOŸ¸œ8B00Wg›–k\¦é w0СL’26vS̾³oOP¥íFzæ,„"Šw=“XÒ†gæêØr§±ÈwEcô%æG…’XF!¯©!¥šØs ’ÿüìñäj ÈîÚá¼ÖLòÓ|PMÑÎ;ëûf[C#®lÞ¤KãäñЗæTÙÐÞ•·QŠÂEƒÙ«É£„$é$A–¬w9ÄÆO—°ôx¡òÖréuÜFq±ž¬éþP>Ê÷î\M…@nk…ŠVÞÆØ·¶¾|2N¤¯Lq§…Pª©Aâ1‘•/¿ç¢òÇ”hú¤Ë”µŽ3)ÒÅq3çÉEÈ|µ‘FFÉ}¿%‚IÔN|ÚjüGl;Nëºæ±½ÕþgÓ&Õ°ÀÆZ 8vê§”Ú®jØ%g×¥Ãíø`ÌueêW©ˆ¤ *œZ£Òખ¡¯›ýãt$D”_ÆX£å›¥êÐ|CÂÆšøk"'_>ÜPçö¢~hÞɃ´E_–'zýݶ±f¥uBŠ’tO{ת¼4ÚüwÒ‰‡Æ…0@‰R%Q” ƒÆj̳Qc †K£¥q±‚ÙÈ=‰m_] Îo*iI#Ÿ¶> ˜Ð¹.VÕmE£Bµûú֥˛•/âæ~xìƒ_Í™ŽPq\?øM½dÖîR;íŸ[nmP¬°`5!p°%¨Cy]·\@8ì~p?óøÎ­¬³¨\ñ±ëøö5ÓÙÁfu}ÏM ûRû±‡ùļ-DbV-VÁ@L¢‡- •2CÜÞòË©÷G›Ë¡½÷x ¯0Íb1øSB§®øˆ6Ô¥_˜£f‰ÁzcW }Ƥâ}šI—ªÔD°Ð«Œ)Årâ!ú!¸Ê«òfgÖ{2ÔNʃŠOFÖÆâ¨;qÁŸRuƒXX–»r°¯X„Çú«ôTsW·‡v•Û, .µ3ŽýI6‰c ÑèâÇÖ)¢Xj¦.´Ë™v{Møm±®÷0â7Šf= ò,3o%”Š6°¾îò)MÆ›f~¶69üN+ÈvÛ î"Wx†0ü£a÷ùhYö•,/ ŒZ%¼'BòCZ‹D¦=ÂógzÔ¤lG¤­Hôi?³>—îŒóý¨&"?ƒAì…u˜I¯€Îà uHz3ï–v“UM??wDõ>Èüí2ð¤ÇŸ+ò÷µÛ¸ÓpW­üÓ3Bÿlu[ÂQÜiß„›øÛR ˆÌŸã›…C m -¯©9%’¸:´µR¤×È1¾•¥õ«ã˜ÐÙ.¥rÚøx¦X¡¸ÛŒj?kr@üaËS3)p'zó1£ÚxŸ†i"þŒiMçÙ‰{ÓDfŒk¶m HöyÍó9ÙXÿE’⤦¶íwY/,ç.‰ ŒÍ‰~SÞkU$c¹mEö|ÜÔÝ“9ªà„.ªN ASÀù;}¤QAè× á𠋤q˜§gÜFH*œugO&®‡ÄW ÉVQö/·þ÷q±C2w”kN|A÷sËEB¬ŠÃ³äl7²Â4îŠ$ežD®in òñ¦Lv˜sT`LŠ—ÆE‰IìÕ¾»EGÞó>–pÖË0my­ [,Ä ö!CVž—ÉwøÅÞv [„7¿„¡åS;Ý<°ñ~Ÿ7~ÿU6×vsN?DŽ̧©urÞ„ÄåÕúñ´ ¯È‰AÉ9_:®3w9° ý#˜†¹w¢Úµ›1Ø\¤ã©ª÷ô' '¬åW…Û£µ\m0ßR”މ©Jä0Zpw>ÊÚö{ï7ˆø¡V½ýñâvCq°…«ocHÂÚøÑìÚÑ™ã)ÖÀ7>‚~l÷Õ¾"¤a=ÝR¹÷Y/XK+Kt%4hœÝ±`¡àɤBšúØð-å¡zžf­Ã—(•~¶· Tk=×å@?š¢ç鱃Q46¥ÁØ0â¸æªïàŒXŽçn;©r¬´ˆg= 7ŸUáLt× ¦Ãö‘4³Ðßæ„“áßvqÙ‘iÚ-)t(aܲ(5°J…ÞËCIJU›¡–óhü±-š2À·ü‰ê«|TÏßë^–©è±ùÈÆ ®økÄe> µPâÊ5‰NŽÌJaÓs{º¯K0Ç Í'À„°PÛûÙ¢ý@ÙËn³†ƒQ•B™¦@ˆ6æUŽæy­y÷޳­vD™$Ô×W‰×1-™ ã\^”è‹ ?4TíÊ[HºÄÉBí¥|a»Øf-]Jø±Æèú±•ö~}êV½ù±Zey¦¥¬ÎqÙÌ ïÃ9 ×Ó€$iu{9ÓK‘ë3‚uØb‹|rÐŪŠC2Þq{¸¤hp°ê-»šŠÚfž#³º}òø…rüíZ€B6 P¡o-£uêÕ”E÷ÍbÙ¼"-Ë ‰–ýRÁAäbiù[šÎ÷ä(¹Å'ËþP‰nÚLÊNGœ‚)yÓvÖîi içýì>‚Œ»þ{‹bØÄ°«ôÔ9}Lì@N¥{sܻ̅ËFvVbe²ÄkÒÔÂÊ`x:%ýϸÕ@¯§y°JðÐb•ö§e²Û^uÊDõ#/˜ŠVÈNÿ`ÙìgËÏvøÃk½ õ£ ß8Óýn'`F˜Óø‚÷.‰ùÅš›{±çr×ÚYÀTÞU 70æ†JÇÜ?EOUþÈAÜîB6øÖùvºsr˜ûyÚùœ~£²þôî#[7ôÖTiÒZ_°ºšK3ßJu^Ùú§(“Ek|9•½@‰+åØ4U0¦?¾Ã'jí¸õt¤ß·“v˜ìÍ’•Zœþ¬¶‚5¿|cJ²ü¹à­ ¥ÈyÒ±¼ŸÂQŽPC éͤ7 ûPNQuK·ì6ªŸ¼ÕIW,“ˆA…Êo¼%£m}#`#ÿìÿÊ’÷Š$g2Ÿ1,·ju'¡)ª“Áõcÿ­Š`ÏŸ hÆÛÛvëZ{³\ÿ8ÏX¯’è¬rCûZµ„9²«H`RñêgÉÌýà7É™h~¿dšòž u©Z0¬SÔWža2 zîEelÎÊ»[5ÓDs%Æd½8#ISfmñ°uHiÒ`åÐ M;“1Ö aòGŒÕ7Wãz)¹`½gÝ©µÌÜ&P©NóY ¤+œ…ÐR\ÜŠ–Íjó#*cÐûºa¿\²mžÃ«î¹냎üÕ­{9Y’§Õñº™;•çiµ²î,õ\òÌ(ºV~á‚ð ë’Àä:c< Eår˜{I¥Ç:Fæ ‘ Vn¹3«†njNt$‚.°BOYüÛ¥VnuÞØ×xFÔq„ãÜoçû~pÕMteŽ™¹Mý[bh>åeØÿ(Ü[%m7 DÙ$ÒàâÜÚû†¥˜Ÿ– Òâ¸Eu¸'×Rå‡<\Ž?6d¹ Z.äñP6оœàgýÎü¤RýVGcÝãìKíasníZ‰_ªŸ|g̱ Vd5&’ ½…Áƒõ+…ÉMh[S*æ%¸s^?m ü!‰R®ÁŠ:*\võ£¼­r| mÄBÄ@mÿª/Ü^UOúñUÕ‹­Îg‰. ¬êWeôJ±¡R£ÐÒ®)Ö=KìySßX¸€lêѵ2TÓ¨›çL9Áê ä†ÞïöëR2êüÇlU6¤V­!8ø²Ç±n6xÿæ~UeíDØa§ƒ\%Â8P¥ ô^*عe$SEÀƒ£Vg·l¥Š8Nð¬ÂvÕ-£¦š4¦A`{Ž^­÷µ@;Åâ%-ÎgaÔ‚è˜Õ%©0º(]_Ñ€ÖT7Bf„Ùò?‚Ýí§ê2´¦f†e£‘ ËÌéé‰ë˜ ŸSS—yíõçià’K¥JÞ[ O‡ú«UFHó73uã|)ƒJv¸žÒ äa_:%ª4v¢è«{“aH£žÉ5ý‘æÏiCh¤, ÝLÙ£Ýý~¯c'ÀÔË[·‹#M9-PÇ—»(?ÏVY“Œ°Úº‰@(Û¨Ž°*„¢Bèùõ–¡’¥Ss¿ŒU5‡h#§Í›q³xÀ‘Î}³` `ê^¤ïx)|CÕ™—tÛÁ{fdaâvá±î牲y¢'ÕqàÉ>ÉHV¥N¹!Ј¿ÜSƒ qó`„_mª_›º TYˆ2›`á¬w|"5 λ»Ÿ*iñ.³ÀÔ·/…×kÒåUL0Îù<¶zT¼ÿ~§Ïº¨»°Oº iÓrEIÚìÀ„!&«52YJÙ´ôzXÈIïÁ©m+Oáp–ýp@æ‰zþÞ-3ëÅûÊoýmX\ñµï+"½ªc¡4$^ú"J¥÷„WÄé Zä«,‹ëWo9 ½ÅEÝá¶M%½ŸðõMÆÅµ/2ÏÝ?îW…Áú%põ¾—"MÎXÛµFÇx(wn;Å~fÜè KKKݰÙÿ<`í›ñ‘É<ï1IÔÑ´[ÊsÜi˜¸@(ˆÖGkªü]þegñÓ—Íž?©x¦üž!g<êN$FÌ4Çd£ŽÌ ásTÉg8in2^7œzÕ¼¨`% yi,Yvyª[*ÖË9SÖÆ‚ÓxnS2j »ïQJcŽNÅþÎ.…ƒE{…¤Ô¼È•¸¢®Œ ·¬s_²µŽÐJÝÚèX¡ÕàFÈxYê”e®ÿâ÷’GKn{”Aª Ÿ8÷}Ò™´…ÌÉ¡2Ö£I™êgõã1w®x§×ßeà3h©êuDÃø €ÌÕÙþU}ƒGTÄû`¡ãÔ} ·Dˆ¾àÂD<ôSiu‚©Ý¾£%`ð½ÖÑzc[çH¬8: å tè]Î 1“Íp½]®¸¿tˆ<Æ&,}÷ S^8>kXê~›¾×¯ÐöŽ¡Ý„´ˆmÃWS"ï’n¤[YѰîÙ¦ ºâNŽ¨Ü Á·ë¨W»,[Ì ÷wµ×Ÿý‰˜s;v ê;9äþþâÜÀÊ ,³=M׃¼ͨ+ˆØNŒË.%qöK½DK-,—AJ=ùÂ[±0"¶­.–Ìèê…í©~ƒ†ÙPº þ5]ÒPÑcÚrëfHL¢Û*\©ÇJÁˆ4Þ©æªN”÷—M3'òT}¹¹Îâ\’JÊøE$¾STä©ÚñtÂV¶F³û£‚~7˜"1è!žw{;J6xi ýRÚòC‰8QžzÊ» | ”‘4 Mý’´RðÈ™ÙÁÀùº“ÝJZÌÚ§Ê8SØàzc£¹PêªØŠZÐÍÈ Í—¾±\™ÖZ“ºËèç7½!Ykîdô£ÂÓÖÊQèHÿšþ¶Û΂ï͈©¡aËù?ã ‹]½~ˆ¶’N!Ø—y‡XëÍOTy¿ÏI/×û6õw:ŽÞ39œ%3 hDÕÍI6D×D&âÄSÝr›¾|0}òIQx›^@ï>»³æ¢¯YÇ-—õôÏ‘¹ôÙRþá±Qæ¯tÚÊ!¾à¢û1ŒJ¹‰çvàHŽ~w[©‚0n×m¯ˆ ³‹‰ˆ3†ùBÅéÂÒIƼûøÇ)Z?õ×ô¯Ë* Ñ´©mŒ×œ/oõŒ•¼Pî¿ð*™Á›âѯAÕ²wUÆüÛ+âD‘Ð2†õ'ì5Mcž§ü97Øþ]:¢_ã|IøST·†ÍzßVÒ)¦¯ý 6C9ÅQPè¦.¶¹ij–ÚÌ¢êbˆÈ)éѬßè¥Çæ?âN7 CÏùë ª•€Îô›sQÕ6êRaïÒ‚©µ½uw½ÍqLM¡8ÙΠá4¨`ÒŠô©n`G(«î³[2ˆ)HSVˆJÂÉ_ÆÇGµE‘D§ „C¦ ù\0;„1qúD}D*Ÿš±÷Oà _5Czbð×ÏW'm±IºiÏ[,Šqn™`>ãJ]žé舡$vD˜’åý¼¾¿…éÎw?ÈC<ãu²Ü iëˆs;ˆa?ÇϺû6Ýä7B/+ÃôÔžû…b´@¢×)qÛ—l7׋V^¢«)w¼°Q ‚øx÷KûÒª”3•_ºS ÑØ }6ÉD©Ú1uÖ)7ž‚6´t·:^ñ²ŒGÌŒ^¤ÄÔ©m»Êú€è_¯LQÏ'ŸÒ’ã3ÃŒj´#<ÛnhôwûêOB6'á`-úÿHïMpÙ›%è ëÑáBuÿü™5´¬¶œú‹fhe‚½Ø!âtòå²ü“Ýß}Q ^Ýo“öÙy¿Ô íºdÑgþ–]a-ý]â3}gêÄWc¸Ù®…u*Chè‰91­¦Ý¨,¯åæÊ7¼;Û÷MØbs&™x݃§C&Íâ[ñ¡qü—™Z‡ ÙÒ©®ãaa…œ‚äušÂ?ͦ-Œh«Ho±e„'>£¯![4š«l’ÚØžEd4È‘ØEµTjërÚýÇÒ/ø.ð¼›/ÚÖÖZÎú6¸Ë!Š–8ൃì=”2ÕûÑ:4l r#8ÙWO?=­ò;Êtã&û×.œŽw,·Hzë™Ä1 –ÝÔ8ÖqËïìŽÐ"B Öв6ÝAïì¼óÚzÐ]ÑiŠFdÆ·’ƒj¨ÑŸ¡Cg"žK^¼*QÉdÖ¢‹mèì$ï™3–Ür‰U¢¬ƒ‡ øê'S;C®‹¯ ¡Úý†¶ø•ôG+#’; ”:_‰$´$jF ;ºxN¾Œ*(y)#3ÉÎ £—ÉSTÿâ!üxà G7…üªŸM/p郠ǣAËž\ÉïOœï)¿ôéÌ1yÍ>Ùîþ®v«Ëå‚=}©†Å;½é€øì¥ôânù†Ïx©¤£ª;”gPËê&¤Yt‰zÞ‹—I\©gp›¹šŵX ½–Ì0@rB­§§ƒ1ÕæŽˆ(þl´øtv*V`®¬SÖä) #¤ ð´N¼j8ê£êÂÌ„Ûp¼ÆóÁ¿”ìƒ9þCŽ k™·¦G©tç\Dq¨`Ž—ã­ÉüJ iVE^‡³Ô?Ö0äÝ* xÚ=ÒDYõý<µE£A…lm¹/I»9ÇkV×jè´»ê<ü$5 ƒ„ZÒO.Ľ@ë ­N˜Ë“€¹6¼GË÷ÂWårÚD]æ,zIZj?Çqñvh4®©½,AÝ¡òðêþ.?Êè-Íx°qC yu¬…kÔjŒnìGÈ- î‹C×N2ÎÔ܆œX=åj¥V”äøà "g¿´å=êôD§µÚ¶>ßWô*ÆÇ¢ä¨ ÿÙ Ö—y].Ñ)Q¶*„T¤4îî烡ü¯z‡i<\!"å {á¬EFß7Ë»/óx„ÑñäšÚ™©+âDiƒmUé2òTÚ‰!Ž:=í®ÍL;òH¿‰õèßo·r¼#dÙÂé¨d·•z±ñ)é':¢6ŽÍƒ‹Ñ–~€š^V‘&Zcè¨S½ÊiUR§%Ò-Oþ³äF úJ󆩗 Àï„í3çý*Г`«Sö|ör¸ýx¦ žõ%AÖœ[S4Jõ4Y¹û×\@òå|œî#çyÓAö­²RD"lYßúÓƒ«ÿt˜Pã!IV³;JUÐy}àã£Â‹dcÆ¢}L±ýÐQ×›ç;Õ(ï¸Z¨º ·ÎP ÕVülí1«gÝ(ënã„ÙÔ9ÃkKÌQ¸žfS•¸è#µXÐ%ÇF›=Ü2Y†çÿéUtGéêÙŽˆKu+lƒÍ3øû 5n½©|gWþmtG‰‹Áîû³™âòÉ÷>nñ­zt†H•i%oš*Ë®uÕ ·Öê r1)ïOæY*õ…¹ <†2Õm'á—4`šßŒ'fGø|ˆíXîÈD#+œÑ…Ü7§åž5Ñ€2AåRž†ž« ~É$¡Ç‡¹Í>_¸ï½’XÅX÷%Nyþ—¦ßòå)õk¥—4¼z®Žèìï+ÕLqä³FI^óÛÈ‘/R¥z¢ŸÕv]//:¾Á+S8&?¼æ÷¥tS¸îèĸúœ®mÔ}хΙ;-(<­:‚“L?|åkXpûVØI7ÆIÐ'$ÓôÀ¸CÔ®éŽÑ²=U‰zR§Ø€f 'î,YÏšÿnÏ-º&öíÚóÅ)?¼Q÷ƒnK?uÛýN×M˜Í—$EÌÓJd\Ï6_éÛ“{o¯ÙŽ+QSF jUû£­I(‡QçåGdÙq“]»_«_*ùFïwè9†. 8äû´)åw&2›«c,Ž~~ñtžÒÒ´ù²7kâòSPÓ°Û”ð{w]öTûb’Æ÷+)6o>ò îÒï]¿Uˆ¦qàTˆ+!Dl¿¢‘mû¤Ub"cf9(ó[ˆ&äB9–6Çœ·m]”¯{õ§~ ¹nÅÆ6‰fü;Þ4rû*‰ÄDŸ³w¤$¦©Ak¹YŽK¶ïN ðÊm÷<{¸È°ú›IMî#õ{ãÈvü^?+Y`¬Í“u®R»žO[Oea7ËéþΗý³ endstream endobj 830 0 obj << /Length1 721 /Length2 25031 /Length3 0 /Length 25620 /Filter /FlateDecode >> stream xÚl¸cp.L-ÛvžØvrbÛ¶mÛ¶mÛ9±mŸØ8±­ï™;sëV}Õ6Ö®^{õþÑÝdDböv.*ž¦LtLôŒÜe1%Y&FFF#= ™°“©¡‹¥½ˆ¡‹)7@ÝÔ lê`b0322Á„í<,Í-\”ÆTÿÔl M,m-jö6ön–Æ€_nnnnήôN®|ôÿ)›š\,Lf–6¦ayMI9q¥¸œ*@ÜÔÎÔÉРàjdci ±46µs6¥˜Ù;lþãŒííL,ÿÅÉ™þßìÜL\þ!fædo U“—Sˆ 3¨ íL2ÿ”Û¹8sÿƒ65v²ýWw´—ÿY6†ÿ³Œþgý/ëäñ‹†‰ `biì025·´ƒaø—n’vfööÿ„M\þ›ú‡ó?ü”ÿhH015ûíjc#ghk  ¶·upu1uÈÚ›˜:Ù”ìm íþ‹2´µ´ñüÿÅý„¤‹á?:Ú™ÿ£ãB–Îb–¦& –.ÿHmfhóDÿŽ«›þç\dMM,]mÿSoúÿœ¨‰½çÿÙòŸ6þ½!ƒ†– ’² ÍÿàßiQ;c{K;s€²Ë?Z:™ü/ðï´‚¡å¦ç¿dþ;M¦ÿë˺8Yz´éÿ5ŒÿZÿµtÿ/JHÈÞÃ›Ž‰“ @ÇÂÌ`becp±pøþ?t]œLí\þ-ø?½ü×ÿ÷ ™šz˜ì¯Úó„X¥·…Uú‰ÏWÓÌϨҬ2ÿ™é½ÑF¶¶0Ñ?ºÂ¹WÁ)p‡ÃŸ 4âݻܕ¼o%6Œ7…;™šŽ!ŠÐ]Åy-%©2÷«Û~W,|kŒÐ»Sö5–»‰¼sÉÜXR žfÍÎ;¯“r,çEgŠ1¨èGÚIX[z´§Ò¥@ÉÒú†3= ©Eš·-º6âÝ_ /Þƒ»!ÃÖ·(ª t8 ×¼7ÿfUß5²ës^tÏ’²£>9Æ>ä*‰¸ÿFJ)·$›!ÝXVç^šc="wˆ¹µóëûì}ÒTB²#Š|ÄZðZAºÒ¶RŠ‹©âÖG|¢>Ü©†d¾µ6>ˆn¢Ú{ü‰b³îÙ Ú¥Æö§'ŸZÑ'+ ®Èb—ð«Ú¯a¢MÊÙ:†$~·Ñ´!'õX­~ñ×™EDø#^pŒ†›æR^£¡û«Ûiºôm¶ªn“»X§öÑñ¥z½á¶Þ¹‚B~Y°;¢ 33ÔNÜ]YpUÑfr°n£f[þ9@¢à$¡ÉC]¾ Öej÷ìñ0—iê¡Z©\Ñù±À{`‚j¼£±L2fD Ó {ß\Ud}ዎ~ˆšbLÛÖD Jò®‡Ð[³­ 3`Ù«§¼+ ô§ÿÕ¬.߈‡)–˜Ùc<'"á½Õ~Rz³92×6‘^›+‹·óC`aË>˜*Œàu4šý'">àü,̲/þxR}ñÃ*;†77— Jo`9ò¢6œÒñ¾®µoèrR^…Á ìW"âžã"È™œKŒÝ|ãÉDwÄ:–Y®ÏójÌGδʰ- í(Ý? wšÚœ7‘Ö+:ÐÞS£ì§3F‘³®8D»ñ"ÐŒŒü/4p4ç8ÈE8!þç\ḭj,ûŒ‘mŽGúgN$ { ›Nø]Ð ?¹¾Øƒîd‘ $¨wéb•;u±ª!VçÔ±R5M”R„9Ä+<×óØië°/ТBà+ß· }ǬŸ»B”à1òúÏÒw-"ª‰sÊw ÅÝ}FN{ vgøs¾¹÷âtJù+ˆØãt¸[:ÀÀ?üyª»º&ÒAÐ’Š¡Ç ÆxØMfZÉCQœ6–ä~|ÀÛÚö$ Ôvj3Ce5™oªâu~&÷Ò\Ü´fìÆqõ+L1ZIÈx#ÙŸX‰«(ªý$kòuoªv!ÉÞÛëÃϱ§s4´nûWù× * ×$Ò„’^l¦ ªM™†„ÖÝ!2ï|dŸ07C™Ò¹:˜w§·wTÛu¤ç¸ê—z'ºzî£%ð(#&,þ*…c9#ÎtÓ*Ù‘’Õ§^v‰|)Þ?ƒ œ©l~~=·wšÜVɪÙë(Sð%ïYOæA;m#0bb„Ò¶ÁÊ"ÎINå¶$X=ÅÝ΃¿Ø«·¹á”'ºYüš@ÈjRÆ#Éñy|WÈf/±èâ­8VeÔ›kç>¨7Åò"`§gŸël¿ñ&åê·ß’êÛ2'ìf§Óq)È?ì©¶Þd4{àg‰C¼IðßèÎö€óHÞàzm‰åð÷%ˆPdlŒ·ÍH0÷› W{Î6F©ÀÅÃ@@NLÍ£=‰¨«}WŽØ²¼pûb5­Í¡ÇS¡æU°eW¬âg<öIÂõCå¿XR%”àøx”Á°»Íê„„ÅýðDV=±v„†‡‹‹&ýFR“TNäÔDuêÜü!I8)J¤kqÅxG'4o6<¾W„VDìK°¥6E?ëŸ`I,X ÈA¯Jîp?aœþî…«î|¿ :³è!@Åb:´ Æ{xßÑt¢ŒD:ŽW^Ðöá!óå!ˆ©^òSßñ¹~í•cëLƒ­~å̶yÿ5ƒ×} i½ÙUƒìéiÀ°î—¤:·²Ó!nñ×»þ°6:·3›¹Åœè9Ò6Mö«ç»Ö3|ìšµfa=2)²‰³³Î>ÿ>(JÄ„sÌœ IsU¤GôfÚ;è7×er„®ˆTÍêõµ”ü£²X%úTq昖k´0[ Yhr·DGýÞ(Í;·0d¶#!"z²ØÍï8> #»^ùÝEóÅ­üywPìŒPk4õýcÊšòG§î©Œoƒ-Cê¯Èl ¹I]3^éd ˆQ4¡/\5õV\cëc.Ng›2˃®î•B •ehùBMÝwv¹K¶o‘=KoqæÁ ù—–QNNïu¥õòµ¤Ó«´‹­˜?&Ò¿ úíï·Ç!ªlZ®F ߪlˆ>®m7ÞkTO7xªw\9^P&Sg–zñ3I ñÁì?é#tÔGfIõŒèÄåû»=¾¤6‚K¦Z5s<]ÔCøYâU,lŽGô»W’h4w€ÉÜÛÌ“HÆðÈîû|·lÒ& Mц AtŒ°(]$ª•¯'—T xr¼¨B$eÁÆoµ¨±¢0¤0!ê?³qIÀ”bAšd­Eìñ&U'úæÿ¸‰§Œ.-›Û9÷˜ ½¬·ÇLüŠY§iOêwœ øþ»àë5ÀVŠYTúýR Šó¤Þß9•HÐÏÉa;álÅ­ýZΪÆÍíŠc¯°y[d…gíëYŸ‰ ³•…T¶æJ òz®YæOk›Àf#v°¢’Ö>•ถ‚ÆŸÐrÌÝ.°8NßìúL†Ê$d±0ëWÅð·'>£{tB‡,à-nHÏݺ×n\Ûܰî¬ÙØ£)ºj2ÙõÐKœÍÉ“ÍÒæ ‰ßrŤòÎ&’>„‹}O¶_+QŠÍnrð¦w–ó„ÎÉA±H>„¦ Úk­ðÃâgl¦OêëÔüu(ŽMâ÷8]އŠ@ ’ô¼t Wñ‰ÙÏÜ…ý`(¨1Þn@?ßi»O%¥\:þƒæÇe³opŽ¥Š\â¸ï2Ϧº#¼×^¿"÷ͼwVèSÄXÓTüDs9w–Ôþr &™ù²oÙZUQÑžˆ¦CÈ·JH³¬6[Æ ×®°3ý E6°ž"•_ qÀªHÁ³=áÚý1êêõ‚PwÒ%yF%áº- ¹%r4ì––©I­ÊŠƒ„Ei†‰AOX³¶"ëV…™ÓŠñ‡iƒ7•ÚÜ8ßV ²¢§ãG¤ñMru7Ú›ö»óoq´7ÝR½ÖËcÕ²¢Ê€ZÁïPp9NþOQ;Æ–J‚+´´_OÄÄ“›Üªî¦ÒL–Ïc3´PΩÚ×½Ó‡«ÔÖü'J¡?«c_>ÑoîTÜpªËž 0Šþr¥7'4žó|•̆ß+·}ÒÔ:5™Û÷^Læ×Ý…{ÝÎ÷FcŒø˜û¾gìk˜X€—ø•uæèÖ˜¿Çn÷c“B*Ba½…=¯€×î¦åYƒŠ{ú}KÈçuÓµóTûúR§r8¿ŸTĘ̂Ì¡®/t†Ýzp26:kOa±Ö0AŽÈ$úÕÁ çí«Õ_;°opÇß‘` þ&AD.¨ iãê¾™ä¥øöYkn3°Hrþ‰á³Œµs~ŽÔ±žíí0˜Éæ/»Œñ(=9rÇt&jtkˆ®NX–=L%– Se=æ±Ù(Ò r{èQܹ¢“â×épàel ¹7ùà3"™Iœ´ Gýc¤œë¥‚‡j *‰U¨Þðj>5t¦C¿£V7 Ay:`WO÷0K¨mk+l Ë(~–ÙŠ­øõƒoƒpŽóÝÄÃbÖ´’5Œë­c’DÏsNŸÙF‡SýûCú‡åý›Â Ð0çÒ2é±C±,_5·uÝžD=Ìü¥yvÿX*tÁ¡ Ô"Ѫ}¡føZôÊ÷h@vK°êöÚC­æÇ Pä-*K¥o?@iÞI\WöåqgÜ®u‹öjÜÂònÀ ‘›pîDpŠ]N}8mq•Õ¡"pÖÍÁÝ(±íŸï¬~d¼`€¥°¼‚æ$¯¦$¡!»zŠ”ù dÏNžAÀ8\s@Tí¹Û!¤ª†›¼3aÅUo !Ù€s*–…Ç8›âH­FV—\?SviÓ q´‡”·“æUš1”jmûÔŸ½X^}fIÄ^‰hb‡¬ÿý;Ã*ˆ”bž¹µÄ&Ë‘„Š|£ÄiÒ—X3×Höt¼h‘vp*Ƴ¾âŒ/ž)ÅiD&!ñdIȽX~›âv¹S)˜ƒÍȸ Íãwúza¯RF{¨¡z|z¢F-7 $Å®¶èQ ‰Æü S…  5!†À?ÌV´:|Ø J4â Œ6<­Y ÷©w׃EbàæÃšà€ò ]ŒÎdŽ(Eš•$Z®Íý&­¹–®âåºd#:‡a/丹‰?w4ìËàô·(*£„ ²D'3säÎíú©ò˜Ü'i&í3퉅Tx#§NÑZ-¤ã*¤òÈô§_ƒÉ; ´þBÍëÜñ :ŒDô.|àÞïýoÛWÏ]3F:dI::¤LpªCÑ^/‘Òl3’[/6ù¹€ò5ê¼ÄÌÓí?%Ë·ƒã{´sxbIvãC{—"6³Kƒ5¹s“V Zñ‰YÒ¿_R7¸ÐG¥¹‡¡¢ñ­kQ ~ñÒÅnú ÓåÀTÄz¹ª±¥ ²•â¶)÷D5iÐlÓ7­­«‹û¤êDqW\ªn0Š{˜«oÄâ Ô€ŸþzQW\þ¶pÆ{,4‹Iî.ñš7J®59]üTÔN½-×È!3K”w®†ö™k¤"oä~â`ºr´"k"ü°ˆìÿ{SNOÈ„.äµÂÁ`U±¯lR·Ï…Ùx’ þ÷Á ¶þA¦Ãd¯…ÅÓ Îñª[6X­!µ\>Œœ`~;²)ä[‹®ŽÓ²ÌcÄŽ 2„cn@¼®ˆõ¹yÒHõË¢~„F+ lÛu ž.»Í¸ëGä¥ÃÕS¹m=½#Q?(Ò®WÓ+cÖ!óÍOª«^Ö¢xIM«`6ÖåÚv¾ë¬ôì‰_5'âèDÖ2ñ¢cº5j}x:ªÆNê⥺c6›³ @§»ß¿¤eå\n;(ŠkÍÈ8ôt%Óü¯ÛÈú¢*ÿuÑ’Ò^áÉ.y—”ÞÅõm’o)ž4×ÀÅpV.@Íã× ˜Å‘"â‹|êÞ‰'—)ÈÖJ|i½§¦æ{˜;PþY€Ãº&¡DPªZ\|©©T±Q“Ö¥êHGžh¸u¨¶ˆ¬„VñÏñy@~ ÂùÈg¥M€__%Ú(þ‘5YŒ¨ÌåÚï*'i•—JÉvFÁ‰ òÊ‚7„·}’›÷×Ïà³. òH´½õàu<™ºªú£ëæ{OXÃNªöÃyåã÷¯_ƒî â1^“Éæm$7ú—/xèØþÌGÜVðK£¹¹!4¸}’™fú!¬Vz“»~¸ƒþýâ/½zü‚ sÍ_!å µ…¶cÊ Þé=.;ik‚¬UVG,^ÐÀ‡‘P%÷"þ­"C»˜G÷êÒû)ì 8ÛÐHŰ䢬G|¼Ìzbü8âü¦ÿRÒµ´nu-ÞúZ¶xJªWð+YòØ<ã§Ü2)^äƒåý¿wnESüXºh1ïn ^!Xiµ±çfI˜ìÜ™ê&8•:Û×·•S%~¯}Ïh8ÌNiµ%©e $AK«M¨ð.=3aú˜Uu Ò7¸¯EÿÌAC,óI…„§w¼j,f…ÎTVõëfåN«\ë‘WK;ÒlwÓpY‚+ý¢x¤RýÌO{Ór\µ£z¥Wõ’Ý Nj â&LÌýn./i¾^ž®|Çp‚¼ò¤Í³;e›Jˆ/»ÞÑÆÄ‡R–[åÑSaaÛ—”î`5#d»F}½©–î±¾wéi0è`Û©’ó]Aì[IŠ'>ÁCà±}^´Ø.„„|¤g%á—~׊õÑ›@’àEæ­G«fº BiÕXtE50. 7¸Ï zd¡?&¯d²9µîØUPL„åxšefcÆ{:Û qÖdœ¹Ùºõ–Çç0Núáñm»gzdpDÿàmýfÛ†êÝ=‚ËÓ\]û¸ö)•á$ÜðPqx¶¤.Ö˜5Ûéð#À×£žÀ\ü!u>¢{Du¾BÖ±ÿ=Ó×xm„llÏvŠÚ<¾:ãìï¾F*Hþø`S‡4ג怑EÛ‚|ƒ•Š—×6.¸L{×$J5]Û ¯6Ì·ã¼|§ôòY±ÏÒ(# ª5c°cãþ4s»‡H8X¹Z³ÌX)skc½>›¦›AÌ îÌŸzêÈeœêÖR'6õ‡úÑ=È{p]ývþÖ`Q4Íæÿý±ý¬‰ú^¸\в:dˆ~¡Âl:͇eÒÅÕòbQü%t?k#îÒ]N¨ˆ•Ö•@>Áá,'‹º¾ë‹Ö1«¿ ­fÌX4Î*Ø›” lÂZXÒ(“})šÝ…jIƒÜO{óÚú[c×'ƒ¨êšÖ4[ÕÍÌÄý&(1‡Q–ÃBøò0tQÜ=—p”*WÖ’6½W½ç`ÈSÿ\‡©LÄŦpÿb;phÆø²Ô}.ží‡dÿbš?€‘‘E¦CÒóàwÌ[y—Y">™0ØÊŒ…¯4§£¾áÈ”PUܧ$[â.åóXzM…‡7$ÉÞÐÅyøWÃŒ`ŠWÜçËÍX~±ïRqr;(Nï~åO¬‡=xG»ÆÜ1f´®ø¦C zºHF‡Ðij©"7ƒ¼i–<@?를ÜRzv˜Ý@?]áI-BDÇ*ÐN‰®oçâ³_/ò‡öQ’‹¿Ö qZ”TøÐ¸hT8ŒÂÀ{:Ï"£bäëë$àÐÆøØàÎÐE¥ Ûœ¼ºwQk`%ˆI$ȵZ1ÛVËÎŽ„…‰¥—Z>¦aì;ŸØ–ð±u EX”5Ôb ré]j^QÖìKý2™YQÄ RÝUÆ{v¤KÏ®4éu´7̱à|Q@רg2š#e˜Ä0u"]NÍøSl%;Äe%]Š_®àCKÀÁñ8?CÝ“[5ó{)œ¹ 0 @ªDUÊ(-°§ÌRLuˆßµÍêØE¿Û‰>¯¡³OÜòÔ’ÔAƒ: Ž]àé±ð"\á~-r„\xý!T€/.—IZzêõ§,8à˜ øl¿™¹¶Fpl™sɈ¨Î¤ÞxF–ÕŸG¡éȸ°¾ävZáì³hcë}‘㢩ûÇ‘¬ßJÄÎ…ºß&%;“ŽŠ@” ±k³HÛ¹¢ùFuäC6.ÍqŒœž)VA æ¶æÈN¤ÄþRt´uY ϲAØT“MŠÒ ܯïñ™˜ K·­šz•¢+TT²ØZã.‰~âšïͪ¡þ>Ï7¬b¡¥ªÛî䇗¢Ùr/:t°àlGÕ“ô©%åÜ«6øB;ý2œêãƒÅü¸B¨ I€cësP»ÛI!Ëgø[í•‹<ï*š‹BšÂÈ-WŸP¢y Í>¾3R ÅHBä&‹Á>J)¡ešV2äK2ßÊK/]ãIdúˆ£Ö2Yëß? úîŒ/gw~‘= ዠħ2 Ýú4ó5„.¨GÃL¿_èÝòç¾nÚe?ä "§ë AÈ‚^s£›+3“Ëe[ãêQiDòûLò¬5Œì–dçÏÊkÓXrƦ®úFC¢à9oÐ×£:ã7Ávò:Bô„|& 9˜«° jv)Õ^Ž”.d‰ÎxjF¥‹d«e«ïà7I•>©1niúP(·S½7¿à~òé5%Ùø‹´ôDZÛtÃM† ™|••1èØó1œwo~ÊqYåöìe;¨9EÛ l/¶Z6¦â,0šçlH£’«3]–\Ùíg(˜'%2ÚVî5_âQó|¦Qû³üë1@SJÌç£=«8ð˜…G‡ÍYrÈsgGÝoÙˆÌyZ õ=À4/‰n9½»^mRsØ,±ÆVTÈK•øÊA D4¼ØåtýaÝ¡Ì@Cö ¥/”SÆŒu}ÿhq‹)1ëPˆÈèÐïßù3•ÚŸ\œe>ò 6v‡b_‡ q@{¯mîb“ ÚÝ^nÓèrúä…FfÜ^ÿTU"‡;íQ ýúmM®•¯c$(êÁøºžôíÙ#h=œ1Ü} ß²dÈ'*ø´úmUAU˜S¯È­ÚÔþè6Ò±+&Û~ ƒÑnÑ P¹ÄŒµ¥ö5$÷ OvÚ>\'lŠ‘ÈÇ·À‰…gNÁ¾ás¨@Ä‘„ß’âP3Ãedh(¬ð¶# öÐæT³ |p®¾å”R:È®VS2 „îíxÌ]gpˆ¾]-ygæaj¾Ô¾ü®"¿»ñ#ˆú.¸Q^8MjÚQE½ƒÈ”àvºŸ5:c–¾ŸáM|º¾9Õtryr)(*)bhïdëW}·U4ia·?È*H¤ª8ßëG(6ô8µ$dŸZŒ#¢;?ôdkon›‘KÃ`$ŸÔÛ-3ÚNëô£ÊXš! æê½³¼ˆÁ¨SªYF²Ôá¯ÐØq º»šGÙPNJ_ªü^}.²Æi¼Î<¥|nÕܺòåÚ·WÜq›ÞÚ¹¤°ºP6GÚa Rþ}„¡*6E¼ä¥¿á}B”$—šQsKþ5 {+v,,k/ºéù‹—$B½8L‚úªh&¸Ê£‚ËÆ1b¨k¬Ò–h†7B†ìžWdÙÉ[¯è›ëæ2Óþ(6®ðíy?Íf…–¬7źXPâö -€ gâ/,œ­¹îušYžÏ¨ ’Ó¶.NÅ$sÅx¬:”¼¤ToXJO ×5S>­Ö®‹CB2Žê]Î[N.WS´6O‹/9 ¥Q,úßž=ß ¬ j5pã–°¼]%˜aÁ—­µVŒöK PÏ\lÕioáÑ%|5¾Bó tƒ”‚îjm‡NÁbišºj¼è\—ÌZÅUÉ©£Â4Á:–éºMxèXF‡†Ö!s± ½œÅ®Áàß5Ø +×±’‘"¥ðü{sœí:Ê’Z?³ˆ?…“…PÑÇyÍR¢XR™ÈòDjï]ªKê§Û89á¶rÝ*žÉg8oãÚ¦‚ëú7ÈeC†÷Êh”Åwh$û¿H91\#.ÕaÄœˆÙÓTlï£]/JÚä*ý˜äi W.Ò÷9iB6úÙÑÿdFzQJ›Í2o4¿…ó —¢ê-ÊÆ³9 òÅt#öÐ¤ç· ö„OÝžÉüÂpÊG§[—G´X<ϦÑo|Ò‰@»råö9d:1êØ6µe︗k]Js䇳Ï`„ý­.%f{Ç!»zÔ<«’$Y(> ¾? æa^ò¡ÍÝ­³¬<mîÀ(ác¹¨‰3wœž™ãÃÓ1}moä9ì(Án(᫇“÷xUgnÞª;F°ÖÈàÉVp|pçÅá˜@1 šÙÉ’ÝDû¬©vµ~N6ƒÏ 7¸‹GÛ¦¤ÜÛö¥OK”¼„••~a0¬‚Êg3^¬¿ö¼é÷t̂ǒ¼“ ccæ¯×ÛjiS­ƒpO›~Gÿ.‚tââ…;ÞÌ3î‡{èô §iõþHo m˳âô\Fè^)ß›p¡ì»ÀJãwæŸy,°ú£ªXÍF×÷½ô-35×}Š…füV^ü±(¢,Æj½m={™oKçjÓ <¦ÿ˜t1VRÂ-ÃÿSݶ”‹eºn\’öJGÅÇ.÷Ó†jJ¾á:¾AŒð/ËJ9ÞØnªðœ÷ÄÙ¢ ¨-ÿJðm>"È9”Zyô==®»`žk=kÎâ­Í†3 W$^Ê!#ºp|8S£Ñ»â¾¤6NhÉN÷þµÖ9¨?tæÌõû‘¤·G,Šçî¨x‹½cymGƒ)+ ŸìKLï«sÓ¤HR|{*åö@I >à­‡û,®>ÈÓå*¨UV©±çC¥;£UK ’–T±Ñ^"õBCFoþ(H)Žyæ”ün+FH¯iÇÝ0R1€8J,ì“Ä3Èm'„ÿPä³G3-"É W2ÊâWxy©Ö)Ñ!%±Ÿ\ùÞÁ¿Ù¸dו†>,ß¡}5Íèû&SyÈ'†€«/A“cP»‘¸(EÏ"$VP\–jÅ?ÝNã‹ØûoÙ8( Jß7‚·BÁ4ÕúíI"+`\IäXO»‘)¶E뻯–®Ðž~=ìŒé‚Þ]‰ †åÄùûUÊJ{“£Ÿ-"bŠÁ>¿w¾}É4ÀÕÄfáײ?í˜uS`áÝDÿV©Â²XÖþübÝ%ˆó»ÌîˆtG[ºÏÕ7ä»gé“‚ ªæK}ÅÉŠ‹½ Qí´Þ–z(í{¾ÞðEM•èãÀ?À ]ÔÃ3~oå–os$v“1â¼$¥<Þ4I€ ¾¤`ŸR]dz(ƒxAáOýQêQê£Ç1­¹ L†øÛúЇX™8-.„‰Ãv8|ã'"ÜDsËIÛ8ÁIyWø67™ Z¹´Ë¯‹þ°}’´á-Ãæù›ÎÉ›€L/.0rJ>ÞI5„sÖ£œ¤Y†œƒëãÒd”ïÑ$l›½U6ñÕFT%( l=Ë4ñi.s7ª8Èð D<Š2Ž×]íÞ–9p>Ü#ykø:Ñl„ÖÙD(qÇú;36ˆD!¨3 s,h¾x°VT*wª[8Á>ÎLÓeýdUºŠÔŠ+¾^i¤Èø£yú2ê±è¬“²F LLô§5ר~͸«Â« 3“zœºðì 3Ž–_Ἢ÷£t/]0ØP< íÒw_‹Q”%0¡_ÕhÙ„ù¹¤#Àe‰÷ÜV:##k+š´™™ï(Â,šÑ|u¬u½XUw3*-~ZFÒ1µžÃ HÝ˾¡Ç¼«¹$‹ Ä€É€÷‹;·Ÿnð< Œûþ !ðɬ?°1ŽêÞh*Ikê\iKw%ônmž·ec EKÙ©’Fs&l‚㇔Mv ˜oòÊÀHsºIøerÙÅñ…çz­[]âti„+EVŸœôpäÀqKòWoc>C9æË¶ö39(RŒê‰àº\mmµõÐʲÁå1ÅEìÅEkÿ"™o¦˜ÆN±W ­o{,ôÚÐoé@Š@¦t(ÎV%RîÅí!T›E·«'¬ÉPù°¢Éö_þöüø‹þûR¶Á¸ûI(ß6d31Aa¾úžÏ.¢‰_]Ód(’G2_¯27"ü­aø­Å ¿nÉeU^ Å»$5HŠn‚†Ij =×#á+ã¼rk"Òä“ñW‡&Ò±šªøÅ³“ω Aâ¹§[ho<êØ­ÄãÞíaim郵±“;n†p«ª€šôùYO(ê¶Ï¼þpÇÊk@Œ3¡²ª ¡gÝ8ÂØéû7>áN÷­R#UÒ°s¥ÌHz¤ëáCµ|Xù÷) Ge+euâÝ“ö3¡ná‰%SáY›{!Û"­èø=díÜIiS¹Xû€™{A=#‘&rX.Vˆþ@ª`Öä©.¯#°Þ{Íw'˜1­¢@6•ÅfÒ]0dœ÷ù&ÔÔ*1üb˜3ŒH° vCÞ¢é1ë_Š6³ˆ»B%Ðf¨÷1Db'ñCðô(øUtLZt”­deu ¤A#âÛ4YY“Ó-Чv^2ŒJÅeûÍï>?t4;6àæ< WJ³îÏo)?£ñ!¿i+ÛVYíR'kÀ!qJ A OÔ“Ô{.—CÉ`µ¥þ˜¶èç®·é~[ >m"÷R4'šc:Pîkò רô{·Ê€`NùÊþ%­_{O{W~ˆ<ýrFMŸþ¿Na{è}YÕ&8ª ¥”Æš–j¹æÞL’´Ì‰‰ì¼vÓÈLð­À&í2ï“%C1Û粩¨Ã1cn·¼:ývUÇ®BHö]€ [ŠƒÆý³Ì…äÆòI¥&ÁéÚš¡lCt!•“’^ý¯B56Ä—#‰úx©´’Äë_ê"Hµçóxô䜔‡á ×j4𠇺] ú¸íÚëÑðÍ7ô7—¼¯¨¼¡£ïSÒ6ÉÆÈ£iE+ž¤»ë¦èŸŒT. µ ‚0>ç톄 lš-‹Ñi±öâ«oÒR°ÎxèHå‘>-ü‡Þ4#©8Ïç!àã^˜2AWÑVraVHÔL}¬,¸® NnAz¬Ý®'x¿k/ì&$Z£4¥wi?, ËÓWäÍêÉÀðʱ­T…dÕQ<ߘ·î``÷p»ë>†›ʇÆåìuÐë7„ä×±M2²YÅ8 à’Ü(½ÓWDBÈ]Ü…ƒûø… íj< ·K´F•ó뚨Ï¢™@”©[Þz ï¾Ô4 ¸»Ä¸Üü}Òùÿ^efò’áv‡VMPbÓÆÃ*<ÀAàá_ðI 4'¼¥‘¶‘I|ç­ò‘ØAögzbgIMb°Lèú"–^çtlÞ”¿¨_ú~Ñp-@@eu&´ºè(Oäñ¤}r’¤™b»($)ÓÅ…±d™Â$Ù;EjìzS¼Ãv$Ó´i»€o$¡^e&¥û@ f»çÖÞžnIˆö‰5EØÅŠcX6î4¶r$D>6¿fâ =E€T³¥œ'p¿à 5`µC_Ê:š9X ‹ïHsev5îPµ§Ì²{ô›~F{Ü0 -úA2A”%¶®Å¤äùúÞnªõW!AêMÓ˜'8wÃÉ÷*¼¸Ò„}ã“n¨OÎ×OÌæ™­e†RáÏ[öKF€üI†\z*Ϲ°ÐŽê‘aßä9›^É·â¾\£&5(ˆGÜsp«Bí:nM£(XçM·}.Ÿ†[·¨D&†Oq™¬·Z’zH|¨Oºè'eµyÄI;7w5/ÌF5wBˆaê&òˆÅSÎSZ|‘‘¸`f›¡Ò-~MOPƘš%âK¶ [–ÇÏÉÌ÷ªxo³AS1\¿ 2¼öÈ÷IÔ# gÙO½Gd¤‘óV5\¯¬š³úZç—Ht€­[ñ)eW@qZ4m½„ÜO©ÎN¬ ¸3o³Hå@ ÐXÏoGQóº½8q©þ"°b8úÍ ÉWÜÓ'j©âðêºeóC52X$ŒµÈjõ–픑RTµÙ®÷l÷gÂ4ݰvôÏÙì(=HÖuçýZ3T?Á˜Ù9à—â¸Rv¥yb0SVãn"Ð@+-F³ùC¯ø[éçÍÿÞWpÃwÛ`Cé-L©{Ðéï)¤iy‘˜Ì£©iBÀº'Õ»®Ä..âÃHuO)Žª¨ØY¹B5kðeiâÐvá,‹Äã>l9zJ“99xTÏ\!†,/E.LyVÎïoÐ b"ƒˆ½°oÄ­ÎØŽß$.,Vœxñ½ƒ=DT¼MOF?TÞ½F’Æäãàß‹Ur;%†! ½" ö©[N)žr‡•“:Õ“Hÿ¤: õEv6ð6Ç£ˆÌ9¢jÖþ¿¦Š’èqî«`šy´œ’o?ôñôjîþ´¼Jÿ‡«°ž…I08L’—]u& çÜ =ºò7BeÌà «…'ºÌakl.Vu馿'3.à¯<†õ*)«10syE?Wù5Çi`‹PN'ïœüçævœ}«ŒÃ,îÓ‚ûIŠ BT`UêBí„ÓW#65ªnúµa‡Lº<§_{IþÛ=ÇGÊ4”lÉsÉÞK2Ùî÷ˆfL¹æNêp™øbVf´Ò^Ø!> ˆ0;¯p+2a+#11©:ٺ؊^š_ŽyýáœUž£ÜB 5=¿+Ÿ1{¶¬ŒOl.=:˜ÏïÖÒ† =Ü·K©qÖøÐçºwÑCÛª’ªgÜÕLSýK×%o·Ù€òšóÆtÝJ©WðMû WIÇß")‚Åne¹k6I³%+›Àxù{LX‹ÒJZ‚Wùôr µÅ¸ {'‰Ô'µ­r³’PALrQŽîô°4åZè€Íö­q£\·8EìK n„'&ú <{¬ëHÜ?þÕéþŽQ˶6AYüÐÎÄï>Û5ó¬G‘}ªœ Ä‚ÄóMæcYIEµz …2ó-aü<NŽhš{T2ÓuýV~¨èï #2Š+ƒÏ<ÕˆÍî%aZséc«N‡R>QSóMU“«á ãžàí{øÊÊ«äðæóÝ¢_´i˜lœQÆ-²²ÿúïÖJ“±ˆˆUDI“)C¹ÀÝ©&(æ`1aÂDOˆ²õRåÿ v#‰Ü}Ct–J.€ˆ&TQ£7°ìm­ùé¼Joq˜cªÕ7á”ò‚u?·² [0gɨ}Õ—Èîôé¶ôð¯úË¢›+¨+ÉŠ~Qˆ`M9¥ç˜w•çú½ŒÈÉÿ80§cx%Ï|zñ…½ ´¹9À €‹æÊ$6=€Ý÷ßwךÙ{¿®Xá|Õ³±˜êÔæž-׊r˜ïò&ä_ÆÌeÒœjBº»ú>ůðÖ%â‡?¨#B<ÏHÚ’1/üà:ų.‡)-U @šf­í!G:ªýÜäv\=l,7»°÷¨¸”Ø—o"ä—'ËkÂÊH 8-žmm†œ9ý1®5(&"ÑÇ(w–ª~]Bè ™–þ6Ê>Ë«ŠF`–£ç”ùSÙMä(˜Öòò܃ú»6sTȶ§kÑBºÜ¼/"qQË8a#ó´òV 5-þæ0fÈÊöC‘!¢»P¨rïnc€Ê.‹]’@ÏDZ ¬@wbüÃöZ{ŸQm0ÿëˆ?æ™Ü{0/l¯ÅzmÄŒïeýc‘SIÚµ£µD¾³àJâàÂÙ·$Ì~~G·p½~v´…Ïçïàþ?ý!«Å¾•2 ÇñdâQöŠÏ@–¤òZoEÞuÑØ¹XNZ¤‡ÊÒ’·þÙF"©¾<2ǘ“Q!-ž¬á 0óLÇ,¸ÜÏßbäl dÃ$$ ©zQÃSüÃ74¶çR9 yIï^è-‡IŽ (‰ç¼™H¤ô?ôïßáVöŸlœèó$çmè1΀õN©…´èPdMlfç+F»Ïà9^™>õxÛ9ìä{³gèÉœŒJ5ºC§Ãékð´©tÄCh–ã¼xöG™a×USj84 * ŸÞ$5G÷¤ºØ o_›]M®U ú(ø”,±)íêéãòÒÙ¹ÓŸå…ÏÙÒ Öù4N¿/´eyŠ6§eÀÔh› #[‚–üXµÛ#‘©ùta¿WÑÙ$Ì×᤽RŒ~Dá·}"6ÅkÍ0W ¦A[Ò®8)6z±).KÊbg©ù}¢œƒçŠ‘‡›šIÇyü8V©#µ£t\­tà”šmš©%ŠPÚ Œñʃfœgj©QÆ Öð†ézY:ëü“Õôuºr‚DGåß ¾qœH&l¾Xõ|ô|£ù:ç¹LÛÚSó6hm äTç:Æ9á©O¯ùâ5|€ùAÌ !?ñ˜É˜KÞ“Ç‚*õN3t¦ƒ+ÅÕŒœƒJ;5ª;Jª`1g•{µ?¸ÚìXB 4³ÔwÃbÜ=Pš’†]mÖXéTÎ'âèfµâLùû¾†eÚ^,‹LXxÝáÝθ~’|ÄÀÈwÊpÊíäþ›Õ‚G½Ü<ÿ/ôŒaÌ¡Ò"Vb’åû!}XýFâúêâÜò¸‰¡'²Ü›þîr™ã\ø)H’N]Ù¸€êˆR’W¼‚ª¤¼?‡ Á‚öå`➊ÖA—òOš”´ô¼V··rä,¡Ópq†k""ô½q…A ØÐ1ƒ`Ái1>ÃÈ IUM4b9§•ó‡oRÄø¥•Äú‡ÇQâ%“y†Få³ Þá>ýÅÀß°k$ø½+ê-×ë;è?Áî™â7æ?:±½ƒ6aÛ|ÛžìQû÷kVß”Mao„è@ÜRg‰4¹÷^ŠÞóp,•áéå·Z?@a%BÆg«ãiðäéªAHº¢7Ø*íd”¼êÀÒ!-éÁý5 y× S²`!Ž¥k‘Š:71äÖ}`AŸ‹ÙS4ƒMOôyÄ‘ö±m+&6ðs1¯^CPq™<¶ž‹µCÁÝJ˜r+î4‰æ¯“²^á´ $9ÅÁG¼»ý£uÔ{óLnJmè0U}4gŸ¤ HÁœC?dŸ¾j¨ö:‹%@-Céûñ•ó—‘ïþ•óÛÕ²Àjæ 8¾‰;ƒÄÄòtç6ì3ü7ÛË×Ö„0 P7Ð6S« d.‡òuá@«#(ùâò2•ÉÉgöŸWôt|‘Ý6†çßIzWMœ )4åXOã<‚P0®ŸœÝ s¤ñÛ¥?È7«+O®Œzƒ~&1õ-Nš û@Ͼ6l” † ”=E0Zí1Ñ]çÉå».«îý™'†áSñ®x9Î0)p±.…¢4Ê.ÒØÉ{åõ®iŽkùò-°ø5 z7*ð~á(Z ­“S-æbjMÉóüEƒ¨vá3îæ…¯N1†‚è›ZÆÁÈM¦oÄ$µë{›'§£t_ûTß;œî–ÇÌ5µB ŸÇ«w¸"‡&‡œ˜ÞÑ•;ì—ètØk#?ÁLlä/8gv0Ái8E îŒ·Åç+52ý·žàþsZŒ_‰ªû¿&yÿ¾âúÉ«ïBö¨´/Ÿ§öÅc¯µã;̪¸lÚ‚ZGöå&[—¸\ÇΌӸxNø» YBW%FF×BŠXYqû¡†.žþÙòSi¦‚ÇYítØ“ùëÕ ˜@ÂÏC`㯊B+ ‹öO——’"qAëgÞ× Ó¬öæàK‹­¡’éO—¡SLÏG”Nªï”\´1Òj«è'#7±¸ª}\°s–;^`R,ú‹¾wÛ@Ž÷Æ­šÞbŽMÙs€9%… wø‰ô· “ÛLâNml.À£çÈšÒ.%Pë‡.Ó᱄²—à禜[ß_×E¾-ï°¡ç-a§½?­¦ ­¬y4VˆÞgf :îk0¶8qÓL2=Lh”¹°ÅxΞ\FÅm)6-”%ß'ºî\pâ…äB %ú½êÏ'2~mÄ!T ¬}‹[äŒAn:ð_³~Ë1ô¥t …ʼn朾­Ã–ür_Åu[åvµ·à¢ýh±d`ÊG¿Ý^êg‡D ™±ZÒ5ï›Håc GS§øÃMù‹»°JÑ ¯ºî³qr´ð‘³±¢Òº¡KÈ>æÂ=óKU.ÎC¹Ú.-Îs¸þm“ú#„ÝOêÓó’—4e}pS¥…žèi]‚†-@2õ‡â¢†SeÉ1EáÀ2ltu-©U•‚ÑGn¹¹ -šKK:Õ5Ux¶îQå|d£§p£·_ «ëmj©äžášÈ§2IÅÿ¢%‡¾´QCCfØ;be«T7æj4uä 1ͧØásñtIS*¨š}s)±Žé¤Aó ›ð  ”V$딸w«QÚËb XÓÛ‘öíΑøã&Þk“üF`€]¨ZW¼`n1ðÙÑ1õ鯦÷  ù# ‰£è`ÝP0ê!à sÔ_ÉëÇ.Ȧ‘•÷ò¶EÙ ià÷¦ÇJ—˜ ‚Öò'ˆ6qm´”fK‚–'¦}Æ»ÀlòuåŸâ[Â|ûâæŸ8±ff ÃEc~¿gàL+){:¬n_ʾ¥$S@o\ú:ì‹9U´ä¶àDS=µ¹‚ÞdÊÓ½Äâk¦ÆÅ{ùœ^†Eå#ÎÉø³±âø A݇–ép:R=ñ;gx¶!9^³eZiûrÁÛËü1_ò©Híø¢J÷ÚWj³üΠƒ­\[‘äЧuÝ„’YĺފDÉø°¯’&’œq0ósS(6) †¨Â¥>wœå„p-º&™w:5¸)c›Ž×‚µÌŸ“3äÌk”Јn<ýc¢;mG#]ÏøÅj¥}Œ|í²V@%«–ìÉÒ?œIç^BHáŸ*㤙.8‡½&Á:ºuñî¶,8& §Žbã1a3s› ,·n¨GqŠ=²*dhêi¨T5ùÄç…q(l>²Š¾`”Å)®ßí&³kX0Çh÷—£ô²n¥á8XÑÄv¢˜ßûŸ¥¯ÅG00Îûþ¢,{¨‹^Uª1øW–L`½9O‘[Fj¼o͵ÆiBÕY•îãÁç3xè¡‚L†ÂàD—ÔÇ–O•d—4[ÿ±ÁD€a…¸K÷z’þLº¯No0_T æö´ÛÕÉ€˜ñ õñ:-'Tí²M…?…Y¡½d÷ªàlÑÖJònåtiÜ“Ñy[Ø^ÌžÚrvï¨gµ^B0±c‡„JÈ@†M>2Q•ËìËH¯Ú5eíŠN ¡Ü*‰aèl‚N5´ãOª¥ð«Ýq‡a\ÿÌ® ïêÝ3¹Qz~¿Ñ¦<+eu)1 ÿšVG“kj‡•îèWú¢”‡ªSÚ0ªÁIÊÊÜìr,6q]{ÕÕdÀµNòoøæ-=ù[­{Ü[éJwÄüÁšw<¬òÎÒâ1-`ì#GÓ•¨Þ©o·r%:h›bRZü¶ç«^VÇæb æƒ T¢%ä«ñü.œGôc¿”YýJ€ê@Bøº¾+0+ZAsJšM9Äéh¹Ì3Y±PnÉû? hãG„÷ƒ:jÍH}ì0áš—Ú>™ü#Çê_'”¶,sÜ+µÞäpct´‰ÆygƒÈ™s݃«Hùö=è,=Ú¶HÎ5ƒqNç$­\°Ú²KñøóÉóckOÍÅòîå‘,àùTáh”‚yÛ›ØPå{_šðX¨S¯À7eóöýø+áås=µû“YÈÌ8½’©D+~ùŽUÿ €[|È´:/Ñ^K úKƒºÑZnÁÎ1Û<ßs_gEP/ „'Û 8|•”æi¯±‰Dšs{™OÚæBŸ…ðÓI×åó=£=v“Χ×4kA¶’®Ç};R§~¹¹iÇ¥zŸ„››Êc/áæ¥Ì¯ì`ÅjWåÜ‘Á%Šl:€ šóX\î¯Ó–¨:ù7Á†Û–°,Çíö‡©2ë_½8Ðo¹'J1&zãÂp¢Ak³ÈÀ`ŠÞÇlYv/Z»!¦Ùó∾ô-÷>‰ô#QYŽLZ2¯N3qj(öÙ^ J¿´q?JSº¢µk½Â’ùøm“ÙÆ_YöPÄ SÉ´n‚ú½'0"Í2æHËÏè˜aœ…R¸ø%r¶csºõûÄó NSEÀ¨Y']æ*ïä>M·ÚYøà9Óv]}iXÜm¶4 àYå{–Jþº1ŸÙ#O…h¸Svm†ÔtòEÛhÕŒˆÔ 4[ê!¯j%Õ¹Ë'b®AŠö`àùe%Šã<úøÝq\z-ÓºF ÆUßÖ¹Ý÷öµ«(]Ô¾µ]ñÜR“ö¬¢0©ºìS7I¨‘¿9ØêÂî`7 Kü J{$"¨Xí|—/7Q{+ÙœlN>Ôª5ò!Ydº^jH¿¯:¢uÊE{爠Mshí߉™î¦Z†åÿ14ðQò[n_ô‰>ëY²Wæ­-ՋËnjƖ¸áœÕR¤¾LR[þòا݀ž¹Õw Õ}¢Ù¬{™¢)Í|œË÷|°p¬6ƒÒG¡zFO§>A…E1cJ€ ’![úqÿñí ¬5Ý‘CÜø‡‚éÌ4‹O=®ÕVå0Þ§ÙÏGÑ©ÈÕÒ½ ß³£;¬Ãdî¥0)ïKÝpÑ©½í!`̆¿Aðš×wØtœ™ì‰Â`‡‹¤%#O¶>x›Ÿ úëÑFÔV-w«1ŒT ‚krú%k»&ÎÍbž«2ì*>Y¬¸ûuÑÑÝnpô¤boTÿŽ C Ü$e“ÓBØÛOÏ,$]gwhˆM\41e‘®ˆIyçÕÊj³rѸP6ªù“üÇg,¡ ýFk³utš¦û”3¿|$BÛ¥pŽÐ.Þs ªb^iàCà=8âÍB€Õ¡þä }Hƒw åÊN*afUo”áÊèÆ­€$µwØé¥qHã—Æó£ÊèÈæÔðp*…Ûd¼œ¡Ä½$:g,xÛàîêT 9!Ÿ±é†‚ž[£ãUž˜¡D央þžˆ'æxðLIzMÏ-†™êMFÏ5’Ô¢ÿTháÐádu¥bÆùÝ_f,Ž«¯1õg™¢¶í¢-Ч¤ÝZÁ¿K9nÌ´nœUzw+!SnÕ™‘†’ÑwcVô–ÔÝ6Ò/$f"R¬¡Ì**,Çéš'nOý­ÖiLPÓ׺敱, Œ‡,™ZnF4aO$;@”µö½<¬©xKèÚÒ/©Jïy7 É‰ý°Ð»E%ÿP÷몼ÑËÊ ¯¤ªb¼eÃ-5!Ø@°¾.Ÿ(ùù½—þ"щü´eÖ¶²›½ýá/Hg§þU7ñ÷Bdõú‰ÒÀô@x÷ãÆàû‰ØœÑHï3Êe?N)›"ØtšATs´wÏD[ÿ»vzÌ5.¤Ë®a£þgs‡ÁÂ8§Hxj †xº©íJQøÃrWLÔñ†íK8Ò(˜ö%×åÑO7ï`UŒê•ÛËõ1«LÀ x¨>…èq ‘T#ãX¢;H½²ãR¤ÁÛœxuTQ´HŽ?=°J˜[½ pÞZI¶³50h Bß¿퀽Ò^l•eÁ§l稴ÜJd©ñKÃÏà™¥¬Ät¾ U 8D‘ð0š£H€¬#¹ßï,j؃q—oÜg¼åÁå`dkš7:÷0Wt ’ÉèFCèàð$Åðbà ×)þiàhŠ*ù&p|…-à¶ÛAR ާx"íå8Èn è|ྠaÃÙXöyõ|ôLâ¨1 ªÞKU[Ô„ãEuœý8ÖÆ©§Žü å¿Ä- ?½õ3î]ø$5jwÊ+©¨&ìYe@Ï,ÒòF¹Ü&£ê*¦Ï…8³ÌŠÙî&ºžŒ =Zsf‘ªÇuhqc(Ú¢ç'Žå®2~ÈÚ«}!ÔEù„ìêOGŽuøÒžšm^MñáׯáðNÆß£7ëSoù ¡ŒŒ™%®ÞâäoBó. Ö×àx*þ"DÒÂWy‰Æä°ÁGâÊ«'†çø,V”_…B¦ñBpä¢ÞÂÝ„AßP\ޟΦcMù«]ŽZ“|õ‚>7lûaÄ»àìùæ<÷ŸÉ$Y{f¶ü¡Ðiîê µ Sü»EÐ8˜!ìpÚYÆ—W(dŠÕϵm¸U>éýé±(RÃÑ}ñzE–øÝ û}.Ç”Kêg=[xkv9ÎRoÞ7a¢%j¾ƒMÔ>:R’L¦â•^ ÜžåS"k!¥ä|¦Àff¢¨{®£´Ť¯e?O'Rß TecììÇH›J¯–Ú4Á°öÞü’œ´rê,–âöâ/œªWµý3\N-ÀÜÒ}G² —Ö ÌAP@~…öüú ú‚ñë_ʱî³UÄåxˆô†N¶ ’¨›Oï3×ô2P¬o× ñÈiD’ìýÜkiø.Âa튞գ2P~·Y;š3JTnWÛaˆÃ|:£ZP±ª–’«ª‡L3\HPtÌ ¯Eb‚3Ó?ºì„ßs).Éá€Y-_ <uÎx#¶¿²¼Œ´êYõ#`.Ι9 ) 7å2É¡8Êãz*rJ½' O «t–Ò=³£9X#ÑÀQž2d/Jø6ÿ‹`‘–¼¢û%2_·Òè“kÍ9×™]ú¢ò=¡ÞƲk?øPl©›™}³/!nSew'¤GßU›‹‡Þnjµœ>y÷Ú¶ iúڛ¤ ðS“ŠZ¢~ï*+pM(tµ"ý4M^Ç7^;›{k:Sò;ìSøˆ“5u”õ”¡§xÊì‡eÇXgP³ÒïуÌÅÑ•†àd¶¡âh¹’Âñ_/T° ŸK@v„’²æ/ÚsZ03TÝ?µ¶õ>¬JÆ÷¼}äÎ5³ƒì·Û_$‚þ’òî¯2­d“òð`’­—3 Ðc[ Ž¥®Îòc­€ìJô †Ó½/¸2C/39boÈ—43xù® Øô½–Zö ­§±38qëQF^þfNΰî'˜ìBcRЉb™t­0(PÎ9“q¯‹® ¾­úGÆ0çTƒšpâ±Z¯@ùv¤»Á6¾&`ËF_ìäru§ˆÒǽI‹ïȼòµoòó~cc&ÌnåžXÊ–tÅwç(sšú‘ƒƒ|Ú¨æ“ûÓùU-fU°x.µ<@³$¥7ÌXzÀRò6i$a9kHŒ•ŒDQ@Ýi=K·ÜƒÃÁ¬ÆßêRUy®‘-tȧØÈ¶—Ôô ð:OrW`+âXn¡D dg‚âÀÐZ”GQ )½·h}¥æŽöó\—±oÇ\ÝBѹ1š w³ßºhÐH@äÏàîMøÆóãõK³Ç蔡#K}˜n?wä4{Eçu¦´MŠš0e,ˆÊW4j¹ËŸ‹’±­?áÐu?’ù ­)=€NJ”úV¶aë“G§µãBr7u<ÕMÍ3 êS5pïîÄ]ÝľdÖÉáP¹ ‹RÃRnnq ´2æégŒSCD…¿ŽLˆöŽ6t Ì­’u\¤u*0€2eƒù5>ýìÛËo~µOl)ƒGá¥M¿ÕÑ€Á!Ð Û[ …4>QÕ÷~‰wY¸º^V°Ê™¾ ×U-‹D‰zÁWUˆxRŠ+yâ”oŸö:àÌõˆäî–zÄXÂx°Vpˆ.Ê7bSD¹g] „ÐWر"žLº½Š2…Ÿ*ÎQ Gñ:Ñá˜3Ì¬Š¥žcOŽ?"?Ÿ"….Ëð9`)"åQ2™ð4&Xdê}£M8F’Û-÷~hO[Vp¥U=´ÕáЩöÅx¼.$¬êgâécêvWÀ ‡_›¬ñ}ŸÛFÜrcodßI‘×q<»HÖEð|RФ¾‹•WaOϰ ÀE‹xn‚gd¹~J¢ü¬˜QE¾ 7':=A !yÒÌtÇšêâ!xÙ;'v™÷'β&kƒYV}Ôªáùì›ãñ¥ê›YË{r™Ça¨J²¨16g¡ˆò-ÅÞ¹¥ÉSt‡[”µåë¨Ç}̈`lB÷ÇN–8ƒr0¼ê´ØØì‘ÂÐg}ø LXŸjïÉ!ÞÒ³u Ŭ#Ø}VØw;ºjø ì½tµR1„£—7^F|yÍQsÉãz¼½#%\‘)Ovß#v(ïOdbK¨«L–ÀΜÁVЦ—NÒÑ HóåkÄ¥ÔŽ.B;sÕÁâøH!ÝÙÓÒ kÓß’ìé°§›¡‰œ¾.Ðd{|)B¾ËÁÑŠL[c«Ã[WKrúЛ’ƒ l£O…5©þ˜xx°²CÌõPÛâ; ÐÇY‚ö8u ßž–ëÍÆøâiÜ£¥3 ëúÌg%ýgñN¹3:{ë«ûö[1Ñ}rd•cü:™'%NsC0¿ñÝU²¶;¶õîÑS‰sÏ!2ƒK%î ŠÂeW8îfs°l_[=±¬°éL×Ä¢1 ò㬠ŽI¡¾Že+—À“[9'·;û}_&VÆÀw$×!-²FŒíÂ’ Êþz\6ÿŠÜŒ‡´;ÑêÜ)Ó—4ÀÀï Øú[)˜ú0ðbñÞdäQT~÷FVñ襴R,T ÉWð…—nIä½õ<ÄŽH]>ηGáEW~‰úk™Ò+ò6&cI>Zá!½ýúDvg^± ÜÏ^õ æg,ôIºT=÷‘òHX)ÇÊñ8îµ­j¼ø—êçûM^¤­£ÖÑØ“|Þ(å ÇØÿ†âÁÍcñ޳0°•,„ŒŽ ¹£¥Vî*Åà ëk¤PÃÛ•žùy>O)ýÜÝ¥ÿ©qn$—áù´' #û©6í7š‰Môe ˆ¦kYo>Û‡)þá‡äµ²4¿(t5ȧte£ Û.ÀþWkÌ‘TWÃø/LeL/5ëÒ²ï*‰‡¥ÑQZšØ?âÿ©¹;R¹bä){Ë)t…,H¾£fØ©¥jæÕlw}üÍ•S.Ýí#iV›]DÄÎÚA£îK覘}Íh½º(iäÍ`쌋¢9à†âϪ7®ˆ>3vQ¥Â8ܨÈD}iô>Õ[˜®'ƒ´Ð™c¦á 4ð'Ë›ë†þwª9`Qüxw )1m­ýÁ[örxlto;´ á qZ}„Ê)¯)_zß"‰$ÎQÏÜ™-¹Åí×JyÒu{×zHڟ褈’¥÷î4X9U] ¹å"ÊËh¯ÇÒ‰HT:Ø e£U?öM9VI!”¥Æñï‰d³ß—‚ó —$¨( ïgÐ9ü“yRì'§Y›½æ¼ôd°©"0òx¿äØö½ãk"ÈæÈè2yðaþ…Kÿ¢F‡Azm†ÇV3ø Ë¿ãu¤ß Q¾š´!3àyxàÅ%;ºÓQ½ÌÈŸÆåq}–cÌéÿ¸¢p5g¾nÚ|ø»|ŠÞ¨à×Ì-C-þýã-‡ôåáż»¦å%”Fmä Ü—'6iâ¨Tü‡æ—%ã[/yÃÝAi:‚#ö‡ ûŠÅ¢1:¹§”W›µ¹' ï¶w«a€òÎjøÊtawo¢0V_@ËûIQõÓEÓ•Ý1DÈØžnÛ)þÀÕìWÿ¾¦§q3„=íEˬg0ò·0ë$c­ô¹-€³wñ…ZëÃ]Ìñ)BŒ3ÒE`.Ñ,ÞÙÏys­Ë :4Õ÷x+ÁQ`×ê}*ž”ºö¬ýpÊ4—œ˜Tq_Î6·LÎg¦J•-<38,BÒ¹°d‡ÑÞÛ}/uëã¥,ñ©Ì­CŠèj›÷¯]Æ¢Jlû«Í‚¯øû;ŒÜ/÷MŠä’CÚ½®ð%åUÛÁvµmo_ñ:R"|1Üç²æ³OWèž¿ ²ç5–‹‘O¸™CPî"TLu]ró¦ç™2†mCŠÐkÈå22™ÀÛ ¯ ›KǼRyðyËûÁã×–Ç+êñ—~Ã?¥Ès¼q¸Äs÷Ç—¾èÔ¿26tànî…õPÓAýf< 1ñÃ&XS´œòs¸ñxÇõˆÙÛñÐÁ×ûu÷&F‘)ã5«»Êõ´ .'5Ì’±‚ endstream endobj 832 0 obj << /Length1 721 /Length2 15130 /Length3 0 /Length 15687 /Filter /FlateDecode >> stream xÚm¸pfÝÖ-ÛvžØ¶­NÇNwÌ'¶íŽmÛ¶mÛ¶“Žù¿ß9÷œ[ß­¿vÕ®‰1k5Ö¬µÖÞ$’¶6Nªîv&, ,ŒÌ¼Ie9Vff3#…˜ƒ‰ÐÖFÜÀÉ„ ab P1±°°þ°ÀQÄlíÜ€fæNj#šêVÆ@k @ÝÖÊÖhdàwqqvqtftpdü§HÅÄàdn0Z™Äµdä¥ÔRòj)+€¢³¡ÐðhdbãhB0µuXýÛÙÚÿ‡“#ã¿ظ˜88ýCÌÔÁÖ '¡*"© ¯ cRØ~JÿSnãäÈûÚÄÈÁúfGpú¯eeð_Ëð¿Ö³nÿ¶áXXÆ@#'€¡‰ÐŽét“±1µpþ;lìl÷ŸÔ?„ÿá þGC€±‰é?hg++ykµ˜­µ³“‰@ÎÖØÄÁ lkm`ó”5ÐÊýÿ÷2Nÿè bcörÌÿ%n&ÆŠ@§¤65°úG¢Å5Lþ½.r&Æ@gë×›ü£Ç?+jlkcåþ†ügÿIYRR[Q–î¿ ð¯´„‘­1ÐÆ  âô–Æÿ ü+­hüw÷ü‡Ìº Àò}9' à3ãÿ´óÿ<ÿ±tþ/JTÔÖÍ“…›ÀÀÆÊ `aÿçÅÃÎêý¿è9;8˜Ø8ýKðæòÿ_dbâfb·¶lkÄd‘ÒRæ#Q0[I7;­¯F·Ìº2Õ}ó 8hn¬wx…w¯Š—ëŠ@8ão(°{¹#sßDjc‚p<1I¦³Œ÷RDVnæS½õ¦”÷Z¦{ ¡Žkm$qóçÎ)m}-@‘v’=#û¼ú‡}‰ªK¤~i{$ÊvìêƒZºö‚É>]ʬypþµ¡ÐËÞXhnà. ®žy~™¿Ýa¨Ö_êVžÅ6†[™{ëv×7@³’õ¯£°ØH¾* ü{dAM_tWù¿„¦õ\Uúö úo?‡^Â-%]8,:&ÛebD¤€dnÉZãaœ÷ŸerÜTÄùã®+°)úL"0z¦N-åˆCac“]q˜úÄSïSÑüDöцª·z›üM—œNñbÕK.¢Ç26°õkǧ«…Ž¿ï샻ãÍ*»ô0õXH©â÷ìHïùˆ%¿Þ¡’ú[¢v¥r`›•mƒat†©Ê?ª­­dãÑ»õ^äÙmΉÎWc*3éOþžƒ]r¤)bk‡:'BholFò:ó9XA¿hHë},ñﹺûœñB¿TÞ¯''炪UùNg„ò <Âtxš»Ì»f寑ªY ÕÍÆƒP¦…‚åPË|Ÿß¶3aÑážò´âyojî²AaöB•UÓ^š«$(f­/¸,khÏüœ(=Ÿé<§ÆµúÓ7=ße†‰Þ‘¡·¶˜æ)Œ…Éâïm¯ížVÐ|­xg§ßm¿ì‚hÍškÖl%dÅþMÅ“:1yº{; à–´X3IÆÃ2vÆÔÂó…XmTb6¾ñ„ú%VIè–(=9šÛhUÕM‹Ãùp\ɰ\Ž¿ª)@ ¢{Îöý\(ê(Ú/eÛFE~¼H²Ë›Õ5.ô¦¾¾´ûÁxvo,ÝÓ0ñáeÆÑñüÙCÉ4ų åÍ¢I—×ÏÓtÆö9\–äÓWKsËQ!vSóÞÈÉîGôh|<·)ºûB·ü«˜bA¥ôEýq•ôvYâMWeHZ®† ‘‚›‹ˆµÉßQ‚µK'èúï,²º2U÷ÛU1¯„ès_©–m˜]ä\mâ¾S{ñðÏíÐØT»+î|R C©ŠF±u6È’³—î[@jáU)<%¥XqŠHÄnÑhƒþ½×⳿½ÏhÿŒWâõN˜(é´†íoç2 ÑR'_DýÓ#o¿ùÎÄëÏÒr‰›Wz)cÒTºðY1‰§u#BŸ1Nw©f4Ï*µê…ëQGÍÞg$³õ Ó1pä¹pˆk˹g½µ(É) ’· òv4WÚmÌ|9WÑüØž-éçøy §Ä Z?ï0¤N9ú‰,BÖ;0•€ CûÍ):8}‹¥ S7ãàZÍÂ!v*œRå…ž¶ä1D85o<ä”Yøf•~·ÏÕŒà˜–)E±,ѧ„Œ9~8®µ£$LÂdžÈÇV4_ ¹ff"ëïz}΀s`Ê}Ùmyˆ5muW2âõ'ùý²òCÍŸplL0ž“l‘ô_ ^ß,|WÉ2¶;e0JÏâø?~£M´Rø2ì¼ò‡ÌGF _H ~›o¨ƒ¿-exÐû:æ´“ MÇG8:m˜ÜÁ|Un?k£‚w —Ãß±ec:;qW 4Hlš:TMˆ^æœéP&–´–ÊSí‚óH—7=©©Hîp¡q}Ö|@öŒóMºq‰}‡3zQºjYräu꯽õ˜ù§%¹Lc„ºXÈ<ò×ÜÀu¸ 4Ef›LØw„½>©ÀK=§K¸.ìŒ8¼;hqkÞ½¼ÑüœÌ]]‰ïë9$VhâÜÒd£JŠÄmôw¶&.òÎÞ¼üoš/º"òUY—΋Q~áÆ¥û£x;X³ |Rÿ„hq³¢jÄ 6lï­ ¯Ø—𣠨‡áZCÚ}vH¹c°£äüpÕ£¦\…Öšã]c®‘qˆ”V4y`jÔ1 ð²Ì‡ÈŠSðh–xÐ[*ç˜o.ù* crµ/gCÆ „)MäÙ~ݳµéˆ†.€!×o´Ná3Þ‘1`u¼qb&L[àQwCr ²“Ô@ƒ<ð1Âb|1¶Æ“iæ!Hôá8™'! ýB¦d‹=ÎZ‹ ®”ž¶ë²}ÆÏø iꡎÅþ6ÑÓgòc_˜QÐb±4å“W¿sŽ%|`Šc ´°Ð¼,·ÍÀH90|K#åq³_лÐGëÒjƒT× )<]V¿½“š“gþixöå +=qsé”S¶‘9?–ú¤îæõ´2¦–*y(ü¾·|tEoMgt*¾Àßâ»Ó%¹FòŠ$Ê?‡„”ºEG«[ ¯ðþëoø,—FTÅßRÊ¡mkZŽè!Î¥`\,T†CnÎ,ÑxŠKý°ŸáÕiœå˸ʕ0&ŸTüÏõ#x(k‘¢¿Ò½©¦40kü †ØqÞ[c‡FHi›tkàò«±Ä«Ío»yª£Kân'%EßM<Ïÿ´ÐŸõyw›±N¶ ‡ÑGñ·‡Hîºt<©2N ’Ú̧a¥íf¨\&íÏ 7qitã¶‘ÎY˜iHëÀ‚Ü#¬ OàzŒ nMk ÂfjJZ„Øž¡ú’ýbÿ†»Ü*^ØC~7Úª”œ²)顊=@}˜6â=Àùxتõ9ò[>â{åa—^¬’%öT§?¬Ì­À->‰n…‡YK.ãé“yb:“×_Ót DSháÙ˜^ I›ZÁ€¸ zIÖ«mëw )Á¡ï\˜ÄLªmU’ÉòÒ¬Þ *$—`¾¹g‘ß×[ܾL[qÒ÷&-à$Н·j÷™êÃ2®ï¦eüÍœ“²‹ Õ¸ï]÷OáÎv™«eg¹CÑÝ~Ë~QR¸÷c‚¨)Î2ê–99(×Ä‘{½µÍ@µŽ Ü1îÁÅ‚\–z©åü…'e”FÍ’‚ÙD¯qGjÑýMûmJ¤ð ¹¢ÅK‰:§Šz`§øu›K é¼@d–ôq1çOí Cè BDOÑÐd´: yn ´¨ r›Ñãd71wúLPË×·ÁycQ}s/uªS'nÁ65ãÇÏ9Šª„“+ò婲J;òéF3øçUÝñ`= Ã¦g¤á&Zæ'é²XwÚ–Ë¥f2°ìëvJ˜`’!Z“ý¹!s«¿Å¾£U8ŽÜ¸Sd~þ(vÆøà¾¦në]Rk•M-^zŒ·Ý¦d­fµõ”Ã*´7Ú»†ãÒIxùnç “çÏhsß¹iý¬w§!Š Ý¯2ŒýîaäŸ@A@ ,¾×ª‹ØÏº\ÙÌذ'Lìn#‹áÆÑ&M‹c_M# ©±iå­w3Ý›p­8èa0}ó©²7Ô~£‰'jÍçŸ-òÙœê¯hõµØ}ú+x_ÐæŽzŸŒ²hÝøÑfš@ÉfuDžëK…+DüÕ…p‘°¹„CsýăڊÿÞ>p¼&!ߌ]Jü‚‡”ýÝSÆ‚bánWzPÞ õ´!z~”˜¯úÑ€Pµ¨­.`‘5±w[å5ØkºŸB®t«5äû£FŽ7Â5MÁ¹a?îyëÚwÅnFŒBòY”ƒ &Ò¡F6çÙËx ‰»µ(…sÃ|þŽºËˆÆ* Çj(ÿ7lÎ95çôJhjlí…ÙÈ(×þŒiâ`„=µ^Ž EaukÐ.çNwœYxŸÖ¾ú®R.DÈù Ù¨ÒªP{-ÓV=GXÂ3ÌÝÏåYkpP–G Ê—^§‡•X¦W• “_G/œÆ#^ƒu%Œ•\½Ô ÷î.Er‚%ûÒCšÄpÛDY]>æ5Eu*U+²é½5åÅÂgް=J:Âû’J&ÞŸþuÙîîÝ\ÐVºfœ`C•˜íÍR™â¸Åul¹×xÒªAé5 ‰ š|Ö«Ÿ|çFç ým¬—ñÍÊ=äQ¨>™íC³iv“]<û ¿–GÌc«Z›H$r`làu-øoÑV¥ŠÜu¾à»é˜ZÒ²<䧃CVùWÉÚ–;º¢¹¶þ~ø#l«Ì¦•nÂh:™®í.—¤l 7™9T@NøJæ–mi†/rögFám$¹ZµÍ#¬ÒuoŠcJ[f4¥ŠÖÖ1H³ö1®äórÊ”¹‡uU\sñˆ£ª<5q=òŒle„ë絿öJår‘T´/ôñ@ØÑDQÇFõ‹<=QsƧrOÝó ÓÓØÅÞR"/Ÿ «»½NÓDVQ_rýÁʉ X²Î˜äû¿mÒçÕ¤[Â&Ö Ë„ã¤*1öû‹—µ™Gˆ¢:: ùzºÔÞ?Ó:ýatŠœ–ˆƒ_ÃnÚ”o£c´êëú™©ç¤rUñ¬ä¤IT‹µ¿pu¥õ!xò'kÚ–F¸‹£ßÚ++\gf­3Ê•X;J¶Š¹q±Ý¬UÞ×:}ðôò¯®dýYêôä’§rÁ¢­p3’ç³süºÑÕ.Hãkƒ¹¸O›šüðxÛB<ß´I‚£hwTâ¸Ê:—õ}(6Y¸¨k7³.AÃS;9±Q†gBh͹Øu$‰„^‹–-']i- /mƒÈþT–‰¼ßú)PmÉ]ÐZq©„à[äk÷³Áà™Y®v9äŸ5{¼‘¾¢\ånTµR«/oýü-í2ÅdÉ‹bÈ— áx+}ýÙðùSd†ÆúÃÐ9Ž•¿EÛŸ³™=§*÷ay|Tã&–ç6‚‰ QF‹BŸ5ë B,dbV±±÷·\WjýŒº[ÉÍà1x®²özÀ––Sé TÍÔaaùÙ…ïn‰#øšˆ”âØ”?øØþaáÈqy-êq}B® ßõè¡<ÒèØ~PTÂÓd«Í yÅùMx8 +ý$ÐO…¦"Þæó'ÇLûl¾Ÿ…VTàÇ1Æ>R!.ÆÆõšL"&9Õdvô~ì…vÿ”‡§3饥¾Õ]ÈRÀV í#rÚvT£cð#ƒ1!/q®Ð*¥§µáG›|¼°‹cã¨o*¹¥D‚ò )çÂOû¿g?G ËkÝÈqFÈ“H¿ç‚âÑk+j¨ºä¿­¥hFeuìØæ(Ý.iߏЯ±£Tb³2g÷èX?æ©Iº`D%6{',Kó'aüñSsöºZïíW?wž3—åÃuZ¤m¿Z ^àr6h~©Añæ½U䎵„²ïb¤@•͵ÄU=ÇuäC·nò¡ ¿õóŒð=¥@!hKÔ^ÐK#ç—¡2ÄpY¯çº´ÀxCøÞ¶èÌ*n–˜Y£xñ#noßA¾¸Ø›EÆh§ý,Šïã°ã¯y…ÉôzÈ~•ÓÉ3;&$)Ša~O ãl]þþ¹ äe• ‹±sûñ™# j:òŠu÷‘ÁCZfRŠŽÛòVçZLIwÖ1†$è q2Á`Óî@ç’Ð .FÚ«ŒF•ã\Û¢å´9uñ¶,y‰½ Ü„éëã+T“Ø· ÚºòÓCoY•P¾ëywÔMd2M³¶’蟺N°’2=¾:4÷¶ZbS¿ä†Ï…óu§ÃÖ%ª *Ñj´‰]b’²átBÇ9˜ûu3¥´CÃwúÎÐQ¶78NP¿k¥lLòã–i5ó¢ ħØMèOÀK‰îúkO±¤JÿV ´¶©ÎÏÔ·x• tÿöd›`÷þËôè­J Á)õ¨3ïëE³™oë÷c/×oÈ­†€0Ç´Ç‘o`ïÖÔUtFu¸Ë8u%[–ÿûÆýëQ…&†VÓ‘Ž]ï^9³Æ«ê‘3hTfË”î‡Íã‹ÄèþÏx,«X ySÆø­}á˰è—nš‘ìgÅð~½¼f'ô$öÓy@aoVERùs[Ê̯æÏÐc.šhz ˆ%h'‡ƒ’× °^uáóq^RgokCÌŒq'>•d“䇸ñ¼W,s·røázM&h$âBá†ïU*g°µ7pR³Õ{rF÷N;WŠ>>?ø¤%Co¢sÝF.žÌ~«œ¦b.û_¯»’z¤“™(ZÐHýU§Œ¦» …u ùÕ?gȰñ‘b ÎV.%½aR¸7ì ¿¸üú‘pšå*Þ²6 FWS«üª~3^¿qö}l$X­®ø'xMEJ<î ¶st£”à·Á˜@ʼnÔù¨”&n®²N& €(Ù!ovÚªáGø‚àˆÍñî/ïJ—x©¤µ^¶©RŸƒûQ€9=öQÜö8© €R@FÔð…ÖýÀ-{Úö^ú«ÊóõøKtŽ‚ûÜp„M2Ý®ËÊúW›¥“òVomgòaw,y{‚Ë^ºò°“AÀÄEsÉŠ'dðÏ~¥{‰¾[üëO–öÉc\H´)¹ZÝðûé+èb3ÑoÛ°Xa0²Çæš[¬Í!˺¹–©ý¬|&uÇeæD=ì‡Ï&Ç2úüœä(Žà“ÛÂÙCµw$¶%Ï¢¿Ž Ì„Ø6RýQ¥fC"Eùo+ÆäîhCnÃï>ðVýP~{8 nô˜]Ò|ƒýÈZM¡—A°`kÝý)…¸ïدôÒó l‘e¼Çª<´¹†%ˇˆÕÊvˆH¯ gaš³Å8„÷(û ‘IlZÜ 9µ<í ¨KÒg—ËE—Ÿ¦ßÏz²™v…oíHV^1é{ô(}J­Þ—Bžê²î69>Ü·¦þz Ùr"HéB8(ùâêgº©¨§S®)@›ë ÜirOÊà¿DbÎ%¶wò‚Wó8úÀâõ/ÓI¿<„œ}€/üy;vDü:2í“,¨ìÈŽB~é"/âK¼º1ýÀ#»Vzö=jìEÒ Ý1ÃÒÉÎKa¡“:3ƒ)nb¼6EYÕã¡jÔ]W9€œº"[Q=A~h\‹]^Úi”.JŸ¿Ÿéòèꆩ‚\; AA·QÊÙ}-·úe`}[‘_Ô°-Š6=1«ûe ÙJK1Ž8sõŸTwÚ=ipãöFîK.9T¹0ÒWjXÞ28æIò”€~üy“byæ¢Î®áœ™ûET»†ëåk¼R² ÿÜ™›¦ Å•6î]HÏ ·E×€¾ßεZ„GQ;QÈè¾³óǃ[ùÚ«dçï¥6Øs íà¾YˆH/m›KbH©¿¡ÆKøR•m‡§à¨KsDïË*-ÏïÊAgÊŒ9sÓMË(ã/[Õe4Û*µÚ–“²¹¹ùY©4¥8n:˜{—:ã!vä•£›:.—.UÉn2EeØIùëÝ)¯ªÊî_ÛÃãvL4êŒ-l£Üõc[þ8¿D0]}†òuýX“H1·'ºÞžaò ˆ=ŸÏOƒSœNCÃÁ¢y-Ÿ”ÆFKj?VG|ÄTíöö‡`ktÜ·Ÿ<£±Mf üõ1G‘£Û‡ºÄnÍ-£ ºô¤õ9ôš“FÃK:1C!ï‘•æ¹9J¨ôNÕ¨ùx Sͼ ä—£”6Ñjr“oE£È¥Ó ¤¿+ð‘ýàS${âÚˆ5…A]‘ðH++‹y­\óv›¿/£ëP¡¿[²ìTY7ÊÖö>FÝJûæyªP/^S#@c–ß›±OÛKz¾ê¬‡ÇhR7gÈÎOþ(®Ì”Rú0Òí–ÓwºMC<ˆÃž”$™t {/,ƒ­¾ëxÔ9ÿ¾él$;öÄ1æéÃIܰûŒÅLà_©Z;¢vþ’_üvŸkÈå-“4RÃÝ5½)ÄíÕ'¢ nÞžãÙüÇZ]£ºl¾wµÂ…äœëŠ·ï„yÑ(ç+þ·jçbUÅE±Nί17á‹;ÛrŸÿýƒªír"8ljPÚ”æNÕFŠ',®ÿ"ÃE²JâU½Ê­™çWè—^ŸHZý¯a¨ÞV†l.®»°wYZøQÌ%¤£Ç˜É*á(ÛÓ͇±MöŸ4@5¹%Ûò@ÏÀž¯°ƒ ß°X¼–Ê|Tù× ;4¯ÈÍ}!i0°6÷¥ &wÄw³fwÊQ\¶)GÓ^œâBVðjVзZFý~z7Pµ˜]çUH{(¨}v#åŠNøô;•ÇP½˜÷R°Î©O:mg[?š9ÏÖÙA±ë£y’Š|?iï3FÛ°´|o~¶"`yûéÃ'þBå7$+E~–¼3‚`é©ÉÈ–8 o¹hk~@âÁiÑý¤Û­¢t¤[·³õ1¼…Ѷ¢Qg]°‹úFçì vcèšãû ¼h韎ïd}X9jÿÔ8d›ºpŠïIbîþz:‹Ì¨†ò â,‘(–½ËŒ+Ñåg~Ÿ÷bÌ/!Í(R?‘Ú6ÿŽœÉ[¡CpÖ¢oà°©´®”†äÊiæ}à»hä:= (Þ"‚·UŸ é+|ÀÔuäúÄÂFËÅL>ƒò¸Í=òiý̤^à˜#ËXF¬Aùs–ɩhµM)…žv‹a´9= wš@k¬²¬ zµªyóÛÆWżgRÄ; åú+‹}Ž_Å„œ˜³b¿Ògþ¼ /òÃfb^'Ôç£=™âãÉ©(1Ϊ葧;yE:cv°2rN² 9Y’Ad³Z"¯*G1ü ŬW²fÀ*e^¡çÇ’g—J®VH'}`§¯˜¤ÇéŽÉÝÍtzdÒ¡Ô',÷.=^–÷8Á”¤¬¥‚éfÂ@÷»êc-¢Þ V$’@!HÊúmÒÂsÀË4~qVkPAɦ8‚ ½øUm¦Ëeà FÑ>¨7(àK¹ß_Ÿt’x¼¦ÑYÍH1#_ÐgUÊOÚ²i$Þ¸áj>"ŒŒÔÞD3K@²'È1‘‡ñ)BÑRJdTxŸÇ¿ïçò7¼pKHnPAH.׫ÀB*Zp¸ÁšŽº×&UÌ_µš–]Ü©»›Ø,Mr}8áoÇ Yôv™,(ZŲ?ÌÀ¾]̸9÷‚îwèR¿ — ÓOâ9€7»MïÜ„·*ŠTtêœi R%=Ò‘°GôÃ1œ¡b™k-ðÜR±‘þ$”جQkJ>&žOîÒMRÆÉ¿®.--„Á k“nîÌó¡zdr–2#ˆ Š"ÌTΠê’ãn$ü“Óð ÄÐ>çT§2XtˆÕ.…¶Q쾿Hú4+J±ÀÕ–1¼n‹ø‰ù»2Û-<2”-K¾¶åW„,ºÎÁðÿ7Ãy;àYa¦ö6# _8!¬%x>| ëíÛN¦K«* ˆU¿^ ”ò=ÞÍï"Û;Ä©…¬ÇïÀߥ\­®x–|ËgÈ+q> Yw¾O©1bsß3†^àò/5¿â¼g2÷&ÉòßQ½„ÑŠ°ç4ß­¥?“ åAl›z׋"çcƒ½ ^Á÷—{tBe¿òrJ`ÕÓFíhÁ]Úe š‹Ø4Ý}I¥C8{€!Øsö à´p¬4îðn,ù×dç4FÇ»öCd:ß{T?QÕt¡L÷Û0½Š“á÷^”Bxäá0š?´–nNÄ7 Ñ.ªHÔ©û …BUOБmn[ÙESš"í¬°EŠ YïM›Ï7¡ Ÿ1¯YDÄ6þ`d”†‹ÅË:Y¤lR¹–­çå¶#«û¼‚®Åd\6Ù Ex=0o¼…Ãîa4àGÎŽi5°µ¬é˜D«è¿v}3x•ãÞäf'Q÷ÄÔƒÐWÕªC‹°‹l¿W°ïo´Ö6+õþ÷‡ÃNþVXNç ÈÓÙ½öØ·‡Ù¼Š×?¼ ÕO›ü˜RÓiÚ¼ÚƒkìiEß==}¸·½â¼™Ž®8ò+$’)à:™„–ØÞ :î2åö*?’®HüÔÁï7Ÿãµw)}:š1;' …ƒ<[~šGOö·ÊLÅcínˆÖiù¶Mµ«„bËPMâã‹I °t JÁÊŸB˜žM˜œ=œ»›SÔüã8 NwHbRpõÇ^WîÎsa9×ìz=I1Jé>e'×Ñí—pKýZÓdº˜ÄÀfÉDCõö£ «œW£Šõ"ÂÛ‘srš:ßOšû.‰îñ i´ÙqŸYöžex\ê 1cËXÉl¹?4v+ôê@øaÓ-¢W–ŽûM+Ç@&î|Ba·Á×Õ³V°qZc­„¹ù³’uøm@»úÕýˆ)€Ò¸ç9[^¶rÐ{=gõ¦Š çFM «¸˜àû¬ÛmÄIlbK^Ÿ?G³àO»6}·œ¥#–Ìx“”5PQOF ÛS±n¾žGŸ@Å«À"n ¢=¬l€ÛûT3×m…D!îüºL”œžˆ'Ì>¬ª…,…uí+Ó¤š—#==Ž©>–²Gn]‚r#ñå!ÂiR¦FN¬Þž»ÒHÏÆM¢®>*^ “—ýå0¹ ´øL&O¾6yË­<¢'DºÂhÆûÁ+lz¶î΄R%áÙ´áo‘ÛÍtaåW›‡¡.PŸŠ›)´Žål^¡;““&燑bÿµ]îh–3ñh^²SÙë"<®FÖ"³ðV¤áУH¿NUŽFc’‰r;˜e‘Lê*ÆjfÏ6ù±øzPv“¥t¢ÁÜ Ê‚õ…‘^ÇŠaÅÛç\!òcP‘’ÂNŽ;1í¨V´Ú,×Î~ûÓ”DœÕ“ü÷÷ZK|Xï_6…ƒÉìçmðEÐધœ7õÌÞDdA­D•Á«_ ÒV‡à®`Þ€ÒöÁœ‹9‫H(b%}‹^ÍZn²-¯ÆãâzF(+«ú ODY8ÕªÕ]>¯µØ,ÞäÁî"½)Hĵ×Þfjö·½Û_¶ý7ýv΢ÿ%nóz¼án¿%– LÉæoAø ˜D ­Í)E‘ÙÎMõZ#ÔöVϰû»¬s4Z—ÒE)åçy”qñ v)æ ;Zƒz"læRŠžjÖÐõH,Õ#NúUô˜iž]™˜pÃf°¾æ\ïΏsɼ_–­&˜©ïÔWm«{n{\¢†´°ûf4T>EÝ"~Ì¡TO”žÿ¦ù$8HÛƒ b`¡…ÏJZÿåEŽÇÍGŸlH \¯ù–¥|ŸèVò־Ě’†,;ù"ɇZZ¢¹ ùë˜îv¯ðìš^+†Y_eÁOât—?gÊgpB:bvSv‡KS(°„gº®£ÏeNËA//l¨qR­þ\=ä.uºßk´&‹>)ð¬Ôd=XN?Sþ¢ué–äâøv+»@÷ñ Kú.¸AÇãÈ–“1ÚZеh¢èVa±ý sÿKšùe±ݧé¢ÌlhôÇ` çc±96ê•/¤1,qö7|jy–Æ|ôõ¶I‚á<_Ow—{«õaðýÆ™?³Gߙ֫··ï•p«VÙf0¡noŒ C+Õél0Fs>`Æ£Æ~¹T¥±šf/¢ÂvÅè¯pÎ,ƒ“ŽÁêÛДÎï,«ôG๳íåÞùu8»çÔœuz 4}ò%–ùyÛëÝZ¢„W<¾ÅÿH'« >IGY¤WàtµSd0qutv]ôt%ЂuñóEŸ#ýF$ºŒÒ°sHn_jbêD~¨CеEN3¬þœ[¼V[Ä$ޝöŽ@$à% 7vuï×søü†Øèé2ä2ÃÃÁ¶m#Ï-øjL~ÜÂýŒÊŸyçËw«žh,Î µ‰N¬Ü…RÀÆkÞ»D:[m¼­÷Sœ#¹úøî#jm .#O .vÉ0 .€‡=ªDÆy¼ªµ±^Ÿ%ǺÎùhù: Ð+˜ Óš¢Q œ]Œ(«cÓÉ—4' ëAO´)ž©*H¢ÿå^,—jC lU‰.,sïùI…Ú¼ËAqÉÑ®_ HsˆˆMߘ‹ t~2m¸Eç6ÈŽÆJD—;I‚R¶ˆ)ƒÕ=’cÚ^Ñco^Úãã·ä‚À§‰H§·à÷mš6és5¶ÕÓü²ƒ3!Å,Vú!/×–. Z.$YŒ’hÎïühZR9ˆÛ1¼dÊK昂iާ‡È=e.ø˜áFþöøKóñÓ'ÛÜ5MZä° !„íINþë;ãÀ€ŽwÖˆO¼šµÇFÃîé<¬€Þ¼^m÷ ³ì-y–šžo…¡‹ª*ô(X'W!:LOhƒ§>[[³ƒÿ€¼í©† òT7·,§S“æ êǶ±†%Ž®ƒ‰Â#ÖqÜæ'Wq©Õ2H”dKðN_‹ë‘” f [à:PÚîn3A¶· uÕ‹{míQòuÇt-âo–[“‰‚J”¤®Ã Ä?çÛŠÙ Ç~mŸ†ßòÃCÒèÙIßî,ZEéÕkXÃøg=c(Ü@`t0ü'ÈN£Ž)t[Ø´ñ2opçõ³~³ÜÏßÄÅv£}#·,Ùì¼8YʲQ¹Éâ‚ÇŠ5ûšªÙï³:³ª–¥‹¯9ã ¯pÃ,oNƒÄ ìv¡Ê69<„‡¾R¡OKÄM:NeÕ†ßqžw)’ß!Nå|ä«­blúÆ VßàKŠA_ñ³ä®…ªÚ}-yzì}X¢”_1MÚ£ ±~÷®ñý á5Ê7¸£…Wï÷¤‹’•3QÅ R µèy™²[¬'g4û2 «„ôÓƒ:nÁ ùæ‚X&xSà†ŠGzû‹ ¡DÔ3¹— _®í8%ÿ Ï>$3„ÃhõЍ¥² ÷ Lcíœ=CüØJxÎåBi˜:®!20DÈYb“1"uÂl^IT|>${sV¥ÿ’P%é¿«Býóž±3,FÈ 1’+| fq0ÆVÖ?ÁÎêuQŽÁ¬Ä*îg+2?çýz/-À”I!Ê‘Z9Wû7›¯*-OãÖÌÃ8Ül´ñïå)ÂèÞ×ï¥?jKûuê—^JtZï¡Ùƒ,ÿ¼Js‡8¯aÂGj<\!=W½éú$Î!Wbëyá71 GÔø†j/%ŸÅÒÃ" §kÄò7MKÊÞ5ŽªòòA¾²õqYÛ`±_sNæÇyß½1y2oÚ<ÕžUŽw‰¼$Ôl3 î§³²fX&/qâ§Á­Ø•ÈŽ OÊ?mƒœÅîøÐîÌ{KrZÚ»ù–Ÿ OJ:š]tzÞMƒ È’|ÈÜÁ~É(÷ Öf §a™Z½ÜI¨Ó¤#*MÕ¶Ÿ"Ajå?ÑöýàÈ$>r¹L <í5¨ïý™òFügÓ䚇 ÙúwÞSÜ)õ(~*bŸ¨÷ þ9 c=7u‡E™ #¸›= mŒ”Ñ#C‰HS–à÷vä5Io3‘s‡ïndzJ` ­*É«:ó>GÌj³—2°À-k‰UryUµ@âøf÷¥Z‡cÈÑÂRÐc{+ûL-nôìβ×4ì2½ã o}y: `a¾Hà8ȸÑÚÇ(˜½þvïþ3s7GÈx]Â!ëc!ϦñÃ’ÁÁÃpAùì¢5õ¬MNH×ó„Ìÿn=9~QufÖ‚­âêeDÇ×½EìMŸ’¬ žð¢ÕPRHý²ý$>Ý&.Jy¶¨Š/iÖiR:I ®¦©Û ´¿Ñ¦q)pí¸ÃœS¢;ãüðÏ6aå –&êà#x!èšÅ õušü×ÂmÚ&_Bhÿ|(,´7H•ø¬õãð@dzxg5'eÊ–==´ÉY꥚”;nÇ0f3WšáÍôXíËÀýÍ ‡h2 "‚pÉÃÚVr $Ế6ûÐëq„ÚŸT‹‚åêùÏV1ÃÎlL#y_gß,½ÒرTgó7Ö«ïÔ"I"y2Vÿ§ÕL^ü?dÃJê!ª1§ âŽøfù?#ÖŠ-×w¸!)XŠÝQEi‹ÙÔùÛv'Œmüð­­’L©œîTqv ‰â¼z³ ‡/Q²C…{¯ üXÀ“ÎF¨?ŠË÷§Iêµ…šzŠ5u²»¦±—·ã¨}Þƒ²Eò.§@ TlJcGûA—×f×ïr27A§¦K³-î»Tanë=òÛP›×:¨/³èþží•ˆJÅw,† ˜rùø3ƒ"¨ÈCÌãh$Ö¥³”3Ú\v9D6©›ìkOÚØÊZ1¡„ôOHQ9‘UVSƒÒÕ§±$;¸bˆ7G¾{ïwíÿ° žÕ’TÔoûÍH»è²ÉBR!öˆˆÚnG£×D |”i½™^î^_¥¹ÿm©ün9 w%-MçŸOÈ¿°tdJ2º>"ˆ%dE«ÍßåýþõºîÝ9bḠPL¡¿“,æÏB²p—÷— pO£Í\èÒTI©ƒHh}_}L€”ǧ^?kêTGñFZý+!™·ÒP•ãaT¡43mêQYwÚ?TÎÛÜÿ€éhžÑß~ü’68Ù˜ëk»:Ð¥]À§ß/P<ÉÈŽöò'VíO8a;x˜Rp«‚»—°3ú䩎pÉPÔ¯åï›»]XömÇœ³•…þuûÜY3ñ¢xiÊ:øÇS¾[Q^b"ˆjHµç(Äž;"í¡e¾ ,G…ݺC´ˆØä‡j}¾®îìÁþ'•©U¦5U;×6`ñ¹NøzÜRòÁîßÑê ǹDP*Î…Öò~²‰¼xl¯hº>JÞÐL™÷j¤rî ÁÒ³’Z]Š¡kö.*ÒË ›¼ËJ:ÝÕx­ºòL{ðÆÚµêŽ~jÖL†þM~;­ ñzGϵó¬â}ª×Mã%H?AÞ?ÖO†ÿ1rˆRí ­c©ñÑTh «Üç¯Á#<ýô.à;y${ƒ#qJJáLÒ{¢&Õ8Ìœÿ‡Ê3Qû»"n•;Ÿâ†¡7-)ºæ¥ldŽk{åNg€[ +æ®ÆÇHÝ.Š)¹’ÁMWŸÁšµßUÑ%ã¡o’;?2:Ë0OVë•<[âËb<œþìW9æÂ¥g›žD„…_Áüåä&@ïT©‚È©~#OÂs¨™ÐB·~Gý$À=„‹CÍÄàI2sn¥Ø†bU÷M©ôæÝ®´‘ÇëÛÍ Ç ÷‘!³èUms{òë.»äªHwÊÄ0v‘¾´9ÃcVK6 F£¨uõ:àX Òwt)ZXÉ9š~jäµÈÈÑ¥K«ÍqÂ8×ór÷n¼(]Âñ >ñðTD%<,&n{ž) Ì õÙˆãfÀšMÑô™ƒyrOqæWCdÁ¤S‡&Š?”ÜÚß•5`‘#½`í'rMÉHÃ÷–+±Kj”åFl¥©4 âE°G¢†ÝÚB£ËÚ¬Ô»H2„vwίD*‡C t»,‹ƒ7M˜7†Ï'qD“¸=n‡¤úà Y¸ÚŠ*Ëx˜gV†NuS}]u[Ùºø9y}ÝÖ PJ{ÒªÍø¸Œ0;ŸÆFú›I¡ÙãVÜÉž«©Ù#÷•9q'؉ÁïnÔYZkÙÇçKE—öþ,ñŸr0H­³5ǽaǹ{:Ÿ`Бq^÷ ÆRÜ~ª¡i-¸–±™©3èG†–ý' Ƴµ®+/1ţljû?ì/œ?ï·KÒú~ßɉb¥ùò? ¥H'G ½'³.sª'ôyl¿Gà`ýh™›Yße˜FtÙ¤[Üò\ÁÑ7\‡d j³éôÈîü]HÔ" g¶èè¶>œåm®g ü«˜vâs¹÷K˜”N™ŽÎ¼ïY[ô ³<\¨ÐÌâÁÅ‚Hx ûA5VȦnR`Eª¬RÙØCe½]·Çn¨"„M#—•Ó´k’*¬$11ÃâŦS‘À3 ßÜ1‰†‚× Sï.EV”ì÷n­y]d1!?³ë„ÇcÀÓ,•"=lª%ÿGõò¦fýÎä ˆÏ “„´¥[éÖ‚'JÅi!¸ñ+fî‰V˜b˜èÝú'»çê@ywœtcûnÒ†oõY¼o^ö-t®@#z7rǶù‚ü?Ür1õšh6žóÊ”Á¼fËõ’×òdžsOI”ÒâõþqËšs8µL¾ø…¶DÒ1½ 8Dî/ЪR(ÉôʘÕ,7ðœ6* j³êc+ýC’pmì%—üÓÊÚâðè)ăuªféu×$|…‹·ã½K·¶i’Ç0ëÚ·rºXã?ÅòÃ`Š2ùAK®ªPiäÉå1{f~µ¡¿\þ§I9k'/³PЙs®Ù™>ê³Ûæ,@žq‹ ~•¯qÕu3Ö„ ?Ë>é¾ ™fFUtIú)1£og‹µ‹»V›­?ë1ÍXE|ÓË-qK!czÒØÈ•æÏ²¶\³²T›ÙgÛÜžX¢Êê„i±7±Ñvƒ5¥³aZƒÉµã’ب»ÑŸ¥2ÆXµ”“PIF8… ^À7«èåqEsƒQb%yEÈ­šY<ækE_žœ9j ÖB7Hyó(é3cªwTÀí–âÉWÈ‹ô›|ņKó̲*ÅÂe ËD ²ðtƒeÂŽ‰@m“Œb0ZÏMå-Í[þ¨4ÐGòÕb¹ºný¬¨¯¸?™I÷ÅæBÇ2i%‚[É”Í8+Â.! ,&Y¿E%íÈÚÒf|} ÚdlÐ IÌAN€Hw0{£ý*TÝ%ÖK0ýáÁ{Ý¡¾KÐyÃ.éëÏœ;å¹g ùë½w/}^ú19Ñ”T†7?À߸­êÜüo„LùøÑ§î<ÞÇi«±Ìy3¨Åü˜³{n«Ú_Ǫ'=ò ÕëS ÈwØ.œÙ¹j:²ö?Á¦—Øý&Ü:€õ?EÁi_ˆ‡-ïD%éÛ*š4(­½†xЬŸOøûaÞ.Á±f—)ÙD#ì_)ÈPmE¡Ì[Ý''&ÃøK—gë0 ~cUúO5Ž`Zï ’ ”$ Óbâ"dõFþÉ„MΚþg'^#fÇLk•cûÿ­Ç‡" endstream endobj 834 0 obj << /Length1 737 /Length2 34314 /Length3 0 /Length 34871 /Filter /FlateDecode >> stream xÚlºpfÍÚ6ÛvžØ¶“‰mO4±m;™'Û¶‰mÛ<ïÞûÿö¾S§VÕª›Ý×}õ]ÕÝ«‰„½‹š§……‘™ *¡ªÆÂÌÌ `fdƒ£ u»XÙÛ‰»yš@3€*ÐÀ `eff£ˆÚ;x:YYXº¨MiþmhØ›YÙZ94ìmìݬL-ünnnÂnήŒN®‚Œÿ$©K ÀÜÊUTÒ–VPK*¨$v@'c€’«‰•)@ÎÊhç ¤˜Û;lþ£Líí̬þ…É™ñßìÜ€N.ÿ3w²·È‹«‰H(*¨ÄE™ÔDÆvf9©Òí\œyÿ‰š:»ü«:z€Ë%ãÿJ&ÿ•þëuòøÄÇÂ0³2u˜-¬ìà˜þÅ›´¹=€ó?f3W‡ÿqýÈù|ê8¤˜Íÿ‰vµ±Q0¶¨Eím\]€Ny{3 “à_Ü»;YýË¢jcl÷O%ÿ“blkeãùÿ›ô"¤]Œÿ!EÄÎâxÙ9ÿc¶r–°òš)Y¹üý‹“+ð?fMàÖIhfåjûŸ!€ÿðóÏ ›ÙÛÙxþŸYÿ)ëßs2ɨIÉKÊÐý·!þí·3µ7³²³¨ºüí±“Ù ÿv+[ý§›Ìÿw¸ë,ÿW—7vq²òè23þ«]˜ÿõü¤ÿ£~ü°÷ðf`ád0°ýóbaãæp³qûþ/¸¦®NN@;—/À?µüþïÆ=€¦p«Kö¦|!ÖiÍae~â…³åt³ÓFêtK, ËSÝ׺¨Vƒ–f†—xwjxyî„3&;ÛÒwM¤Æñ@„£‰É’ý%¼—b²r ¿êÍ7åü׺ƒ+ \[SˆëÈ[—ŒµÕ %ÚIö¬ßgÕ2Ž¥¨’,1FÚû Q¶Vì$iô©Ð2u¾€{tU(³–¡W&B/»«`áyÁ;׸†–eá:ùu—Ö¢ºÚJs,Rعºû¸¸b›a”[nåŸqƒA"w—l»Péî"KzyLÃóPQ×üŠ[Bò„Š7¼ÐÙ‹²üêQñ›ì徑;3nÏ~ÓššzTŽQ.ÓTú[2óâ¤pAœÂðÞ#¶»¼¿×dÿºÈ9® {ó¬­h¢Ñ–YÍ&n`‘VD·Fýš¿r:-xsư٦ø¡’å²n¦pÎYn±n–hwŒ 'z;zÒÚ*“„PÆöò@Ì40jdÀ oo)”ˆ´p œvß‹!âéhræØK¡’µbËlŒðáùµÌ¹w¦f^AÁtî˰‚*u®ˆHW–éqZ~0\ù tËÐH[/ÖòÄ¥,íË+üÅ'²¢F|“۰ǯYT›Ð<  =q‚x$b`ùÆÛ~tûÕõ<)+Ñ»4{®lö“Ù:;døÖTåH¥ µ¼Œw‚ëT×þùÚݺ&ƒÕ«†×~ïwKÅbx‹šîg´\~Æ+¤%êpÑS¯ü0%¾°a6Ó@Í“¨É3AòdnV Ω¤P’øL‚u~d‡4 G·æ^Öw\jˆoúô3byE.´blž‹I…îÍ ê³›ƒhg6œŸe;D6àóÏAŠ‚/©™îÐûÓˆy°–S#£¬–»¨iéAGåÚ{´øívÙyÑû¾kêÚð÷ƒó«}|s×P†mñç=]¦®¾kŸ8%Ý?Oe™&w$?ô„¤µûÅ_Ò-ÀG<¿½¡Ûf ž‡5YDLdð2h+ÕØ§6 ÖA^tt¿ét¬Â+é¤Ôñ*À ÿ/y¯$ò{¶ËnTC [©e=W@Çù‘©1O / îì°ÁÆÓž|oèÅåÏÛ©SxÙÇ‹qÈ¢®žiWÌvõSþ:¢÷G7f­BÁ¢,jmw}yâÙ ƒÞ?ª­ÌhÚ˜2¹¸TÚ:õsËvS| ¦3¡8XÝ+2µ –ï‚÷Á/Lacƒ:Ò„^çðHô>Øn¡èfF3µƒ.Vã*Gä 6G)öyÙ#¥a·×oœ/ ¡ú?§•Eµ#^æby£u4KWªF$i³cÐ$l,Ÿ0pÿÀµáãPif“ÂÚçÞ§86[ÿ6Ô,AU%CƒŸÎ&8\~“lN‘K–ÆJ]@ #Hâ,²Ôͯxvpb“×3K?'^i¦ƒþðº`ˆÑq¥%>‡ÀAÞT3ôáÁý‹û,þW¡Ví…ÍP}FRágY6{õûœî|Æv] „ȉȰNç3»žòR’°¡Ò'­¯6§œU¨´‚Öœªød_íÜ$1 ™mùŸ¸#ߺéÒö>>D’òR‡qˆ¨·Úš¶ÚdÈÔÓǤpäÃ*Ö~#p°®Ö `ÖºáÛÇ1²ojáÞ<Dm€fCUÇ“†ƒ£àiÌ{†•CÂvãp$Á]YEbüÑÉt»в³×µEõäвü €!jû /5±èù:n”?p“+1 †×ôëh.¶äyÆp;8œ´×³BQQL–>ÙH$Ï^BAÚ3€º”܈â@Ûý Ú´Qº åÃ@í*ÇÂ3;ô¥F”—+£—ÈFè™?–ÑsÉmâh÷XyãßãIÜbbCɧ \ò}]»£d·§ßKÇp&©/ÐêqƒÚÌîY¾r ¼l1òÆ«ÙYóÜñ4qÍ8ð³~ï5¶¾žölu>ÆVá¯ôÖ7éè¾sËëðe^æ’CpðÑGÖÕÊèƒäW½üâÔðZd¡ î:Q)ž’’86ñQPžGùºxàR®-_®-Ëç™ê4Òiu‹Aéw7Oªµ±l´Ùg*ó½¦Cq‡jܳ²Ð•¿ýV" Ðû *Y©†ÆÜ×Ö2Ú>{ÅŠuùÝum¡¼Ñ ÜIAæó}È`ú"†”&™RšX‰1F¹Ì?“àLœ·…Çq[ÈJG 6€ßò#¹:Kßùˆeª4¬%a‰>aPßMeg}âm‰+AÔ’#뜟£Ôˆ€¨U1åKIö±×j-¹ ÿº„nf‹˜vš‡,T›åß…ˆdÒŠÔ6º9¥‘RëÎ#h|ÆÏ=|a5Á”îCx#ÈÞ_.¢Ê`hñ3ïL…H1fÑ›™QÃF¥~mÖÖxò>VO¾z´d£[r¼äçÏVÏåЮ“ƒuÃd6?©YÏ6Çã67Þv}•£‚«ÃÜñ÷ÓäÚAÃ{šwâ×eQòZ<§¬8¶íÉP¥¨Ÿ¯-}fó˜å±"–´þúÍ—±vSs˜ô &á[¸óGí˜^a.çÊëëùD—6ßÞtœÅù5@ªž}§ñOW‚4`âUîO¹øõ+#C£ÿ{#V–s¥¹+ž7t<ŒJáþW7|:MÆøñ¸ÄÔµåRÎÍ È®ô« V¤°íÞ”Q·­É=÷ª6ƒ§7Ft¤Äаƒ¨%ìïaJ½ñpµè0 > ,fnÖ:+{—\ÉTi¦¸ø¥„ç²~„ØC¬¹ôïÒVšýºqÔVþËÒ+²Þj™•ƒú–’R˜7óæ,QòñTÉ«²²:¾%ˆéQQ P%|Qd¼]sC'†2i­S ²8´Sé»Úg’¾ÓwZ£²¤©ÝÕˆ´ÈBÁ€Ò™‡É“FZ{î‹s¸¹G‹iz:'(9º:·>Û¸ÄÕ=øõ›šzÀ±Øñy£Æ-XÙPÎ7&à3ÒYàá–µ¬2×çRZù^¼žŽ6_¬S.u\•]v¸rþÆÃ¤V^üøO}ÙCû`7E ¾d êHÁÆ}FójŽî›6¾EÞµN$ÔæÅ²äBг>ж£å‚á>(¿^ÈüÇ.¯+4’ÎÙÀG­ãË.v9ß!f: uÎÉ<œ~ÂB6eF©4,œ´âU•S—ô2Jžy¦^ëœjIo®6ŸYŠùbÜ?“;Œ&F­[`È[}$¯ª:N¢^¨6§¿õx >«ì„ë&y[j:.®3M›Gç…ZXº“E¸ˆ‰ “Nš†ôsu㟎¡Ú&g‘𢿲Èkïîòö—S#¦—°mkyZ–ZË›ïh í5’€°úVÙºf#XpO*¸z Wÿ|?[—‘Z*󳿙Ú ðüuZOÍiqúÖ ‘ì@¥Ìd‰MÇäãFø+ÛôÕ¿zaÖ«NZ‡}ŽäRfŒÃUÇô±ÔüeÉAÎ_³èDTÞ¹’s˜vBFjÍÎåTHiK í."@Ò¨ué°úeH£ˆÛXx<á\s±¦žEõÉ'ù2ÇžðxeÍÆwîŠìEÙ ]M!&Ï@/0¢™²”ÑÒ£] ²¹ |¨Œš5F^¬ÀšuÇ …ŒÉºxwd*õD†³üÖ¸Š|Ûvµ#I¾¨* ÖØè¾õÎRÁ“ù#ÏHTWVä÷\:‚\ƒ ÏE‘;I1ê;n˜[Öl>&Fíb5µ2ô ¾…”=Ý…æõÏ­‰hD¼ º~ù”³²b2‰pO·>º6µ.Òƒ³8ÞÂ)‡ ˆêû¸fŠ"©mf~žtÕý×A×J»ÑÍ>¡e»i8úÒ!Ï3»WvžRËÛüë#š%| Ê©×%vøïž¿'kšc£Jýv¿q=¯Êüú§!ÊfñïÏEPÜLð+Œ°éRêˆAóxûpqó— Šü­‹LÊ’dðï°‘àš~h•Z¹M;ÛÈMŒ§µ±ç öñ¥±1ïˆS£ÓqGãl9ÓÅÃ)"â¢ôÆþ'=!ޱî1 ‰AyévÉÛ[S´^)Û&A‘Zâ³ëPQ^>LiŒ§Þß/ ê®k¥ãV‚ì¬jÖî醩£—H|ÅÈ7>¿Ú{ØŠoîñ¿’^¸ÁêO~A4è‡e?ÃÚïü°¾*;2uPJù 'ýDkƒN—à°À3Oþ¸¹÷2ŸË3Uè>x•GŽ¢bì ‹$k™†§Ó£²ÉЈHœ. ìT8“ÇÖ:˜!N]àe†czÒÔÇ&³;ÖfÝ/±­ï°V-ˆ(-$ý!¢qOóžMšÞ²ã‰ ˧4ͳà‹´Æ€Ú&–ÓávG>ˆþ–‚—i—(¨’î‹<¢m/Ýîœ:*Ê>!‘ÄÇÓȪøFÕ –j߯ÎäbÎõA_9nXíò|éw3–Dæ´ô–b“%`>"z¶¿qHŸû>å6VgÂÏö}ëEýµ?$¿úªû 3|ôJz™êÈñÉÇuŽÚ _®ðϨš‡,)ØhH~ƒ}ÕxV@,„ú¤ºãk3Qææ!âwøƒ¾L,Ä/J0Ž3AÒ´EzEOå…âB‹™•>­—n-Šý°ŠËG²(pØSåC¨^„) *úmÅËÅäbí_–À’æÅS‘‘a¡$‹ïR¥ˆô±² ¹wî*ó¦V&6—ÁÚ IÛ.kÍ衳¡×)y O¤5›³m'E²Çah'Mxäìÿžn<ñªC!ñ©’ ›õ¯_IþbVqP¬ÓMR9¼&ÈÛߣíl2Dyº>È] ÙÇöŽV÷CbVãÈ8W´€ËòúªæWÁPöI¤nzý]KáÿÈGêòxKú›5jua€µÆH¼ª¿sñÎÏ}®3ijŠr im©ŠÉŽn¥.aéç•hÅ3¹I›ÿYíY'ÁƒW• ¯ h*`ÍŽzz¸»²á°Óx5°kÆÀðL?Oý‡Íæ½(RÖÝ ½C;0å¬r¾»±[¦yr†À%¸÷¹‚<^Ê›SÙ}ý[qiDftþ0Šw9!9ƒtË*­âè..εž‘õˆ²¡ºÃDô9N;A<ƒ#ÿöž;0Hg¡F‰›-føkÚ&Í]Ý'êX¥l­fKη±Ž Ì(wog ’¸Ø ¶£\# z¨¬ÚqÀšS#ͳ*A;h=³ Í^P¨—W;:ú>iÚù•‡¢ªìÏ&U&0Y’„6Ñ›|ÙÌO}Ò»ê…òŠL*åÒÙÑ·‘…TLWW.ÇÈMl8!r§Æ°Ö{*n+S È+y˜öºÖùŒ&•3vmŠÎ?–0Œ`Y†ö+]L8‡WçÊöÍ€jâyAöN{§0cóø=߬Ò_µIѽ%Ã÷‘ë/9ºcø˜‚„±þ¤ÀϘâá_ +ɹÆ?1ÄÍSXnOìVÛ—®…|ƒü¨öµIFÞÌ/ùãzœ!ÊÖ?ãÊ×àìe?Ì~–Á%Ÿa»&}dwHêD~ ²GóÔ¡p”Œ m¨Ã¡²¶L7‹÷*÷8ä¤)4ùƒîÀn¶æ¨ YTLy ÁÙZF³Äü@ á « €p½êTø-û¤Ë=›çuíÓ¼’1†½™Ãþ×ufó°Òu)×arD}ú³r¸KUlµ%3_§üìOgË^ƒl¹ÆÛ¶ qoôƒ¥ÕãŸßf:#“ŠA–VÍŸáG\4q´XèD_ÉIž+ÆLyf‰GUði&YÕ^f^þT*œYüÙ}“ª/RY[ªÛ&_4ELç8\ Rìi°Å "ÑâÄ´àƒ½èÝL^þë‹®‚U•h îéð»úãM÷õнæŒšà½Yä8`¢4P;p/ØÝq®2e[oë’T¼±×±UêÏ8Ùo@*PmO§`’L¾NÚMCœ•‡ãóGnýpÈA½‘9yv¢’äó$NíÚœlÐÓcâÚ„éŸ}å ž~ýÁÙmÍ2%Fß}ŸTèãßnñ†pH-e"£¨âo©EV¤†ëšŠ#jªŽgWß2¬~å”]œãÆzÈñpÝ}9AFêÔÊ(Ç ¥Ÿø÷UµSN Ú  Ò"{VŠL>" \1¯.>¬2ùÃ¾Ñ ƒ‚ xµê>¿&Rz ¡Rퟢ‰¤Ò jb1ÃÏÒlÁ ôy,Ù“[™vî !Ñ!šeö¹Ä"×…ëð…Ö‘]–ºÁãÓÓþÖ_J¿j±¨‡Ï»@~&"DtP¯ú5`RèiØl³¼‡zÃÉ Ž6 3Sbœ•¦§"›ÚÏaœÕ›°FGùª,f ý¼ƒ¬”ì(umôtÝ€Ç}Î9ûÅa-@ÅöŒ¶‚g^ĨGûò˜ß7çFn‘‡»«.R¡áj:›ð–R«0Žã~èM¦8ø%Ù1+ƧïfÈ”\n(mÔÕ§º[Ðmº:ÅÌp€öCŠLî•v¤#såÁ=¡šøªßdxš±íÙIxŽóîà“Èhwû|86‹hj|$Õ‚èl%á,ÆLw‚“Â*H;s(3¨9OzDõ„cÂûX¸ÝF´”3BxùìTö‹fäÐxÞá7A-ì5Ó2O­“\µ£ßP® èÕ5ñ¸ZåFÿ§>æ#ŸÉ”îš6µ°ôRVf'ê”ÀÐò/žè >bU”çÑÞ \Â$zts69W–ÄHù>~X7j?.!u ãè¹]8•[¹ œ ‹ÛøµìðìTUzÚ'#a Psü­ø¥±T?³Tˆ´•T/×ýZ(h3DÙko|Ý^¢\]™Má"„±}üxsËÑ:xû‘zû<‰™P´th†Ö©vZÇ:n˜z×ðD\üT³ypü·Ø×97͉ Dï#ÔØm\v¥S¶¡6Ù–úðJð<ò³Ä•¡OCµÜKÇžÃ#²ðT>úñÚÃÃüÆÏ/?ž*¾€Â2éÞ¯&š[ ~¥$™¦ã°R>˜olÓiÜ5âûÚ)Ù$Á“JmGDò@¿þî®=¹uŠ`(9wXå?‘ÈÛŠ®ÔÒÊWæô­L›n;qó6? pe9›âd›[³g9œ"2 T &a ÷+3d§"_ó{I%ôOõÆ µ'sÙhxõUWU0ƒ/rMdÞ¦À¦ŸŠ†×Y0)Šmǹã̽>r,5è_½á¢ðR„ºãE,‹Ùö(wíÖ%€¼¢¤´ [ØRh'ÛÁ’JÃúÃc¢ŽyŸ*Ïî­{ɦ¦½ÓÎqÜù}ÝO!í{uW}9ÞÛª*.3ð†ôÚôï“'>ŒŸà&ÀŸÏ¤~¢gй¼ý[çé "êëà²QŸ0Á”Ã]§_ÌûTä°éðFôøs<”à ÌÃÙÙÛÕnOK][è‚[ÀŒ4Ú c ð2õÖпv¾'&£’ýj/¢š¦fWd?Ænñe òw~.Õ°HÑ 9ÜÌݘÎü·» u8eH8 ³¬½³?û¢ánué*L\Ä>~WØó1ÁEëÝOY#Æ&œŸÄ]¹JÔ„ÝŽËhD¥¡e^ÞE7õ“V€šƒâñ2¿?žÍ'ÑÎ%f´1Ú™vt%ÜÝþèÉÙñkYLG¦gú¢ä¢á1¾­T ú}&OHk¦«G¾Î¾~¼À»¢g­‰ë{¿Yï!<Ò´ó·%`Ìäïr‚þíºKõ)ÜËÞÂ.â]Ñ7Õð¸ïƒSsE&“ZôdØB9|ÀPÀ-†•ÁåFá&Î7º”3ÐøMiaˆÜ¨Wùˆ5•0×Ož*.Ðû¾à¢æ[«1/Éíá³îtLp6§Þ `r.„H´Ïûw|É2·žä¡q,‹¦…ª.ëV0Èh’YÍ Åó*LóJ$j“￘`Ü9ÀödûÃÃDEN1ö6 +¯¥ùóIï6h€)»˜ŸŸU±Îš­£ Roš–‹­ë£ìåŽtÞ›Ù=!ݽ>•øV^?®s"M”œîð#8ÁB_Ì ä¤PÄG ÌÉé]¨“ áuÄ™êôì•=«¹B]g×iÊð…ÙZ•4ß¶HeeAsòʤ$=1!o.ý»ÁˆÓØÀ`p X;ÉýeOÎØVS‘˜Ž•—’»¼Rñ>¸|;³`h‚.-‰è‰¿çÖ5ë ·×w‡§!Ñ­¥ðá¿Ýã|ìhgìkƒÖSqÍUÞ»¨š}!“&’$@é­mB™”Õn+wâ¶“äø-çíÖA4_«‹+´+%ñªC»ä>XÌêÿúPÙ6 ®9‚4`j¶µmXß9ŒxG¹«‹õóÁ|ÆowCþ˜Ó Yï¾Ñß»¤{I¼/Ú·Ì£©“ßS­iAïþJ‹D*!2 Ãw ­¶Î„Ü’¥¶ôÓ…ÇÙ{~YŽßâkëKÝת¼{dw®ðˆ…Ó),éWšpá}ß)—N@²¹<Œã¢ï<¹õ`¥;ž·.f®a=5Þ”cEºóAP-6Ò×Þ\öâ¾³)¤Ssæh¨Àø×Æ1E2 Ö|ڈɆUÝGÄÒIª'ÒO'Ò¨>Ýä'`M²©Õˆ]Ú"l6"ÅÓÑmžÁS«“öË]{€¥ó]Liþ†Ë, !/•Pùàfg†¶“äf¶ÃCá§blj¥! ±ÿìø¨æuÏM4>¢´ ,*#q^öÊ_y·rìì&¬eºÝ*ý:çæ7ÒuüaÛNùëÜ y¡ÔÌ‚Á€ª`¿óÚ5ê9ñîiYw2ý‰Ü\rTdDNùTdGÅüh*ŒÃàoÏÔaSŸ Vt¦.¾æé̦°°œIHÖ£¤÷r¯·ì\ûV¹ðbš8‡¹k 6»)±OµTü?{Æ!K±:÷*¸é[MÇÉ^¯•@YšníѨ­þÏXÊ™ãÊT*¾‚£¾üÖ•Z¼ÍÉm©ìán¢K½Öö¸|#õÈĸ*r_D+e„UÒ^·ôGä }ÁÄÓIWMýfãÿ÷A1Uoº3ŒcÁí}CIöùc`žÂaƒ¥imeŠ¿‘êRïós¡&¾1]‹ýPˆºW…nm÷Æ2tàió/ýK®:Ô[þy út–¯gB þZûïE> y/õ™ ¼¿úâR±L‡ó“÷þ¥jô #ü3 ˜4œ£ jÝÊû;w”qéÊñë®™‹oYœX&Ôc_Âc$€³^½G~gæ»VÉBVkG#ä´4w÷MYyý܇£º`\a¦^zà„^Fü œªü†ÔµRÍgVEŒ‰ØÑÛ2v›OÔÔ@Y:i_/žWíâå\|'žB´ov® vÑnº2’_ø‹¿ 3  ÖŒ\"Œhì]SZfBb1ôfh¯¬AMqâ;JÑ®û:ˆNTxðCö ×\Ù²¯r÷áÒydý‡¡óá×ð²v–§^o<Ñ»ºrîm(<ê:3;¥µ¯Wð¨M‰–o™—ÉüäjÊíE¤–sVø££`æYLáKä êo(Ħ.™ô+’&¸`*n‚–Tò6à-ý ™Ëãæ ~Æû8èzÁ®l|ÐFóév°—Êåf†o*%ìüS '¤ï¬ÔV¿Œ¤äv–²ƒÇ J&Nñ=E=l~G+eg‰·ï„lè¤@±"¤¹£`Þ©ÎØ{´ý ™ñÙ·âcrRg—ÿŸ2¨lÁ&/Ûpår“”æŒ ñÜ]xH-÷¶R½V)¤T~„5 ¦5jçbê,£`Ι0ÅØðŽ#ã܉ԗ6 ¶q±r¤ Ðô“a/¹¶[~&iÃù{‰øÉ bÄðÙ.dî“ä?½íÆ•.úYAÕæÐ †Ž‚ µv$œo®cÚeÐØ÷VÃpÔ­ñNp<«¸/#¿á¢*,¦Ê=f·PÙ™¬è%UÜ4ç2¦Fñ¿dMîã3 fP‘'ÿÄÒ¤‚´A”9ÇPƒ~•<ã'ÇtÏTÒ.32FZïFQ†FýüÚÙÏL’+ÕwÈÕ™r¶"‚@¦³c±í¨ó‹“ ±`Êeß;ž¬ oÑvk‡"4k<¿+]]d‰y0Ci|Mó&ëqOO’›Š ÑX> û/(8¼=\ÌFWLj‘˘Z¸Ö‡¼ôk.ë’oi‘¬©Ú•à3K?ÄÀ•N'2k¥ïÉ_ÂH{²U­ð“8Hfxº‘ÕÔ8¾àg†´ªÆQÕÃ&Þ¢ ‘ùYÃ’#cӻЀá+ä'ægÒÃ$‡A§Ör@¼„ð#_8EÔz©ïpK+åÅ8#¼€µKôZˆR·ÉÞŸ<ÏE½Äc‚‰ê ЙÜ.f ûîä,žóÜ©ÿDCÆ½×ØÌ»ÖÕ€ýž¼Ã)³Á³÷W]/ÿpHõNø];Zuä“ï°xÄnòZáL‰WÆ {Á—{*šq S¿ñîsT;í›Î“-¯þ‹ûÛN´ 'Ô®dÈü°Úôd:Ìó¯F>ý!ÉvÜgåð|-¶ž³" é‰Û+½L"øW;•§) â]Ås % !tõ»'0bû•F…ri/¡ÄúV!®Coˆ1xq×záY¶IöÚ’zÉa\¤ˆV_…¿äûbü§Ä‹ðpš•†Ñ¢IÔçÄUø„„—eðÊ~þmh·âòHè¿ÛñUž¹³.Ó¯¥—ŽGRŠïÀ·OôŽþ:Æ6$2fl…G÷@‹ƒr3ˆÓ“ 2-QÊzÂãû;’Ó%?„m+’ô÷§:›ßá‡+ŠV Ž…·¦6ƒºÐƽ×mâH!ž*ŠM*†!]ILfW™ÈÂWãÖeý4ªg\6¢QÄ#¯¹ƒ¦á}ÏÚ9Ï àrÜ!ÔÖE^Þµç.îh¤„…F¤2áS®æ2Ä$hô°‘€+ŽdÛ¥|V'Ï¿7‰×εæíÎïr¶=¼.ˆèANßAë$¦'ËÍ[Ðf—þ¬}°µ3‡£ïñÜu°‚Ä rºe;[t@y‹ÅÇ!à 'Ñ[§…ÈÛñMÁð¯~C¼PÁL{Q–ò7M«ApºAñ(¼]5p$¥„)@þDsôׇ‘/„²« Ùœ/oµ%Huä¦ï±Èìþ•‘±»8ôcž£Œg+Õ˜ .ú;!nË=tü 9™×(¯£FlAô\dæRÝáÐ]QrTp|KC¦wP0cÞ\¦]ÃGÓfA=ºj[¢¿ã—”©’¿neâå°ÿk›#›gÁçŠa%Iâ|;U¨¯8ñïìRÄ&p›ÌjIŒ‘‹j‰Z(¾Jo[“ 6…$a7çÆ™q I7uO›g¿ßúÒÂÌÃØúÝÀѵMÜFDp&°Š…$ap̼GÙ3.>CcÝš–7!-'½úÏß ²p^›i©FÍm[4Š÷?vC:ûå…i>ÜÀà²wrºˆêý0ÎÌÄå´ÔGÎ÷=†é_hG«‚‰÷ƒoCTGian¿›spù:±evJBÏ€¡/oo‚öݧGu.u©yÛTÂÐá>×¾oßkTš“¢Í—ÞÊ$KŽÆH^·Z–X”fV‰zl^ª©+ÂWõ­ì).±¬ÇëlŸ~r}ñöh1»€†&8WØbQœ^_2•šMtÞÿ¾20)Râ8ÐS2©"ø§vÁ«æè'9§Â8Ç­!F³ðÊ3Á׸  Ñø×ƒx^¡éÑV5áÔß³>/èˆY’–ä_A¼³kBŸ/}y² ‡™An¯ÙV£`kCËjñ-üXF5Y‚ë®N´ ë•©æ÷¶ŠªÐOP­ )å¢ÔkjSÿ¬0t1bâ$ J«ÿ²;ÿ[ûè´«ée_Ë•xp/ÖL2¥´`æöÑ¡„? Ø ”zNš©ÑtI^­'el’Ëïs¬˜De8fƒ€“û»! Áy:Šy¦¶uçëÏÍÚáŠLKfLǯ A…Í›h±4§êƒbZÃü*Ð r\ãFŒJ§Gé'Ÿ[<°*©—wu®iø•§ÖOnV´+¯_wð˰Ù«RÑOTŠ*’@ƒ^a½+w½®†C;J^CÐ< ;P0¥JoÃÉ ŠlULTæÜOÞ3§ãÉ#h3œ9Ü}ƒÐª¤0b^iÃ:tàZ.¬ubdûN?ÃûGîXm†¶)ðôå˜È£E wD~/Bj3Á¸yâ©ÄH >úÊæ»1ÝÀˆf¦ÓÒA•ói'#ËóÀg}U›F›Ô ½cýu(9©œÃkwr÷Á4«w~c‘'HZ>¹ÌD0Yo2}¸a{á´‚Ø\}à‰ÒY’ÔÐi£«²>DnÞÓM­ðŽb¡vÜ$åMý…-i:XV†‚¤1ò‘ªµg³Y«8PÛ·ÿtR?—ÔT»vvP¢:Œ†íÔÛ΂ kôA 7õ™è/trPñ€÷à<”Ç@^¹ö£A,¥º—­Qñ¼F•ª`BBRú9ï‹y!S?r].­¾ç47ˆÛ îã$ÅPõ†bwSdkîμÚ2Q‰¹€Kà|:G1Ü”š,ÈW2±ô.p!óOaJ «“”ð¯¬ã Djfr]¿:ˆ¾à1Äœ+·aûä„ÓÌõ*K/7[JÈõùë2îld´özÜÀó8ÂÎ6Ä«»½ òºÏ2ØaÍyìœ4ù"EÙ0&»XA°b—DÈ¢ûËèQP„$0ÆÞŽÍ½5MŽ”PÎ/Ör"U0“ºx¤ø6gpè×Éj>aáízÉM0±”þKVŠšíKÖË~„?PÙ?¶p+Ó J]„”nàO²P­Jƒ1ÛOåæub‰"XW$72e-¡ „ÈÕå»Û¾Éóþ&è÷! ƒ™Á0¾†…µß¸¯v±+Gú¡ŠW&4¹ˆ à L{NྒྷŢßà¿J¾p‚Ëq8Jk†)Úçúz×n¶b /Ÿæå›wB5–ÜÇNA»ƒ«'Ï *#bGSB»¥ÚÿΩ7aæz!• w_C©à ‘7[U"#i (´5üt°L$³¼¡´^‹Ö¡î×T·¯Èš4 ¦ß§Ù+ïº-ãú«Éì³BD‡æ¦wêXé/R40»J)a}F1ÄÕ•n—Äó ô*É2<ÈÊ–Á˜6ˆ–7üV§—ï«Ïê³éGxí|¶N6)Uݬ¹–[q]îÖZ¼‹r20å)wýÞx*2½"ä¯0ÏÇHG3—ÃAð#Ò øVqáù’¥(ìt†g´ŽjZ;°Ä*¤cŽš¨H8_̬`{ÅÛ6l~ô·ÕÈ«Må*~±©xè’2êïÙË] ¸eÑ©M¹É!oÖÊ¿µe †B²QΙ¬ÝéÞ¥jsOMK'\±*\LWÉ™ùú0QÌáÍ+ûå"®Qïô²Nn0VáõÖb(xIi5?(¤†ÄÄë|~»÷„ë;„¬¦­{ 0n­\ïw]xe=v¬7¼ ®|A3„ªQ?úƒá*J’ˆ°ll{í4;¯oî!Øâ9"Ëržàg¸þ&·1Pzñµ„Õ¤ìűÍýmâ{µ]•m0&,HZšËÞJZܰr-†xHÑÐDÿäðê?o@ÃÆ —ÇÆ Ç\m¬˜z„&íŽ<Ū9­½4Ÿ´AU^õÍa…w¸æ8€à÷VÞP±Ì÷0QpøÉoöC}^,©9™ÑC5Z‹Œª|&Kº©Dƒ»]rØ‹’{Ë©ê±åñ;ÁEèÕÍ!X(h© ÛpL§Øo`t¯î÷¸Œì^ögʤõv·Q‹Xmo:2S'¬–ÜÛ@_£¢ÌòTrÿÓÝ;ZzŒ8haÍXŸ[8.éõªù9Z¥ÌÈ6¦¾õ•³éê ~ˆ+ ½ùA ÃÑÏáVüÊqV”Ò0,oEõÈ–UQÑ@5èŽ µÅ!ÝÎF*ÍÌúHŸ{Y‡±8—+†Ím(éu_ÒözATçh~hBïœ*,›®#¬Š°XP¨áù³À8|þô'Gy‘‚¹é2†í‹‰$rn–R&!ÞÚÄÙ#ò'T“ˤëíÜØ Õ‹ÐÝ[Í,zŸ¸#AódшPŠáµ-˜O“‚Q‰YÈxÊ4ã£Ý“ë.yx¼ 9.9ŠÏ&`˜± ì)7˜Š_p¢)hšð44ù¬O?ùöµþúG‘)î”þïü4:ÉS ˜’&N'³ÿýKp(’uM‰ôPá†Ë 4¾>aŒj@;É럌5úÎ_”ÐäP#•&¡Wžs¨?lò3+vûŒl/ûòÞN{ßò•BHÚ$^Àý°€g~’8.7˜m)ó-„5=¶LÏ ~‡€1$”Eö-Êêk|‰YíÌIã{ÉF‰)¥RôÈß\‰Xù2†ÇjU"ßéQ@ù©›˜ÙÓ?~‡?:j›î|%OBž‡Õ'ÌÛ8:•¶+;†¹([F6 Ï§˜®ÄßNÃzß ´%²ºT[Æ‹àÍóPGQA±,„<öµV?ž`½+™ÒpêM¡Ïî7œ#­*Ùc«Us¡ö[ä÷ò, IáqXÍ™ymxúÎŒWËZ¨·PeÁËíÚŽáÔ÷+ S¢“[‹Ye;vðÎ+Ú@#qÝ.4Ké)•Edâˆhã­ÇRNtàa$úгÄç'”vÚÈçíc'Ò«h­–gšÊêΔKÃ!fEajPyƒÞ3ou5‡ÀÒ°Óš´mÛóÌ‹ïlšWŽqbƒj8çÓ>erŠcìˆ2 ÓéŸæ[€»ìÒxAÇ*ÐÖ}Ãkz7k¨_xG¼a­hþV–ZÙÉP¬zÑíÁ¦Z!y3(,·¸ô–…"Ùþ4å |†0H¬RBYãÇæÄ›Ða͹áÓjª´UÕR t},M,þƒ“ÃU=ä…]6ë‹ ·1RT½‰È ÷¿ç‹Èª•¬­F“XÕXƒñS¬L³±Ö˜—TΟ ûfõKd v{æ1ë÷Ó¿!yJÖ§6Z[­妔ù]·.ˆ<¦Ï¶ÚEpì=?çÐóõp½[ûš-˜ŠÌU[(snÍ%žò¥î—£A4¶V,—Ó{ ›\è‚ylÏ>È¿û É]ÓÞ*EÕ»èx#׉b³ ÓÚ¡-jkÛ®;þÌ$`¼æ×ðt‘à@Až¶2óH!z4ëQíôtü?ý‚%"'”`ÊPªcŽÕê ã·aØy—\IeFµ¥”]`{!–hË>—6‡ýruvùw`/rOCé”ùøÄ$\Ìj¢ ¤‰d‚ñZÝê¥ÎO eO|;›îc&cÆæÇö\Û¿ölY5Ýé|q}‹¿±Ewbï»^ƒ~¦þþù!¯ôAFYŽ‹49loÓù5]2J#­ÿ°$Q#õUÑö*Xvh„åd1#ÓNtj¿—9es©M%LѼŸ˜Ä­‹ø/¶Át@WYN80=HÏ{ü¡jG fPdÅÖhO–§û)›™pî» H&` FZn]˜ø€t 뜂ÿ,‚óv|â3®Ûæ7³ãaEå˜e°'E×yßGµ²Ô;\¬Ú…pù?áâàJFkÞGx‹3ˆœ!˜¨´Wø†„P9.ãÉ®N¶õTšP‰J«ª¥!ŸˆE}Ø)NmßùhBPâµQí#ø‡¶ðý1*ÉX ÷±ìv,óeáKu‡aâ*Њl‘K ~(¤!@ÚÜ­)Œš‹ÄºI¡˜q7ŸÚµ½uAß-© ßéËS’{€É\ÆLÁ:š”Ný ç¤9XÙ{I,³ŸSb¯tYðO¹Üƒšçôñí:ã;_Yü»Ë&ÏWÊ5h®þŸ]ŒôÿïÜyà™³_jªw„€ô;cê¥Ú°ü‰Ý~¶ö}Éù ú/¹{ûÓÀ©²0OÎÊáí~ÃÏ¡ö9Šð)¬o ƒ*H ]”Îæ0|‘@ªsQ­žIyrŒî` ŒfǤgÒ”–.;N2‹‘$¸œ)ÑüYê,{‰>ØÙ{òr¥˜63ÛJui…~N¹•žÌMJþÛ€’jÿêŒEM/ÈdqÕÝܦëÄMlxmò­’J p¯b1S’¶²—ñŸœÁ†³‚ ÁÒœC×Ä'AŒÎÑÔdBÁw M5‡›6¹ÎlmVbÚϹcü·ŽŠÏä%¾ÆVAA¼­mWŒ(ºŒºâ$‰«Å+'Áu¯ÖVéL‰W-¨qA…KÄKtæU<«b*uÚY ®x?/äî´o_ÿ K—Ý ±Óò —;ƒOW.ž–²ZAä–¸’ÄÆv‘€©±#5-«éÐ?úQ ÜÐÕ(¸©R;Pß–ŠU]¤ŠúøÃ¶¾NŒ nR‚t䑺9÷cO+{Çø~‡÷)åAÏÃ)¯9X…ÆÓ3Ž‹6RšiñiÎ{ Oê åýc Íõ/ X‡{Æ|þh‘\ÑNW9­+çÅ^a19„4Ë4ªuyeóFOä©ÂßzéA9Ä‚ìeñ¯œ&AÒø÷a2³ðÃÝ þí7èþan!Ü/ kµ9Ū%®#ŠõŽQnu‹ÛM Ù”9có_T,ùˆc[xºz HÚx[#Ýz:@ ÿÄ…3%ÿô]x`Ýgfýs‰ë(öTK+"ð¼4”<:n2â¦bÿ·FjïB #èÔoÓÀ®=»‰G@—ÀZ)˜¥~Ó'å'y¼«Ãô@ Më{Ùtn–ô°V°øÛ×9ÌÕ]ÛÖþrA&òKu™¼¶t*ݨõþÌ}“&+ÚJ‡Fª]$Ægõì*%”M“Ó í*qžL©½<æ” þ.㵎g_Rï ïL§qt ·íý׿QÝ5gÄ_IDÅRõ è‚À"{›!ÿ WÙNFæÔ±¯ŠiÁÒX¦Ü}¨ÜÀªÏjyK ðÛÒ:4–èsvaJ­õ .7e…ZmgÆP…“+<÷Ï·ÍLŒ)VêUU±ÿ‹© g÷:ðEéûéÛ)¼wƒt¯imãO‹õo\²OHÁÛ?±±£Åºó=°B'Û’]2ë!A…’® b"ÝB’Ÿ•Wäen¹+4@-`ÒõÆð§~סgôw‘ù‹nÞÉÖ÷;YBÅJP •øÐ‘Ð"ÐíËÚ7›¬ŠÁ#\ê»ÿ(›C—9Û~±“P“­.m…H~Æ6”ü÷r$(òòJ¬â}d%¬š¯áÁà°]ú«NJo_MœÍ³"íàGÚ[ZÍŸ3­M3L†’¿äžÄ»ký–DrÓ•ßñ '%!¯ð´Z¨®—d ;8k•G–!—™µ×Ðï46žï |?¿H]óÑý¾[7þdm…;¦€$ÜÂ4BRG×E”=¿žÊ¥õœ¿ÜÑÕܱbå›èàë/‚Ѧ†¤ÝãÑgïzIAüÙZ—6ј·/ Š$í‡f$莗aBâµO$@i¸W€Áp~º™pžWŠÍîuů1WÀŠhJøçƒü‚+¨:Þtº™J#€ýã,OŘ™–­„*‘lY‰À&̰|9í˜ Åµ˜ý9|rJÙÚ ˜ŒðkEäÔ²Ñ3¶Ý0ÆÐÔnp¼Ì²âD,™ëõÛN|ÊÎ!þ·”i€>Òq ±ó;é \jdmÖfôÔÏ—â*©Æ2¹Æ=s3Óu¹ˆg•$h©0axø'½üåŽaÕIË –ÿ…hSï"ŸÖ¡î©Ÿbe'7ŠÐ7ÉF C{»©Ê¤éÉýWF¥!" ÐåËÓÓ¬³„€ ÊÿA@ ¨µÿ.Û5AHæ›é?A)íˆtÔ¡zÇ"[‡ÜûaËÞYžo¯‹Â}Aš‹]½’Ñâ–„o4ªríÜÿ[±µnyÆÁRZOwÿ– 9Ù½÷é]ŒÎþ‡-¥ ]ÜFþ…½7ë(¡ô8m“jŽ·^i¨ô=««½Ì&‰:ÀqmQU‹tйæ£íAnIÎó®›ÒŸ•¤Æ kï-v:?´Í·Û`_·<¯ÚÇt‰\&žû!ă$H¸uöáLÈuÕ'ÚNø×:ÿó;}Ëš,ýˆYλ %ËšŠLÛ­·ýÖC¶sk§‡þøZKŒ#nßõ2=ˆÃµAñ¸ŒœÚP-qÚLy9|—!rï/›°XE´•I§¨ è¤Îsز)Šÿ¥3Gy&9šŒv¼þB§­Ø ¨ÅÙz6ò–·­¥¼;z´¼Í¬³r^”«Âišçì‘– Sy;á=¸”Ý3\W%—8瀿ÝÙî6YwÚâ1#F@ÖcCm¼„ µHœŽýÇ| ‡Å¬¿Kf‰áá幊×ÅØV¶à$F},Z€.s?ãT™hÒVXÄ$ÕÚè6øi‚”{@wjHÍéä\ t|Xö€4o(N´~q4 $¬œ9ˆX Ä… ´RÀ©vI[¥Ë¿TH·uܳ÷Cð—f¢Å€Œ½½“°cš^'ÿ—orÔ -ä”\šdiBæ°-{O÷Í6,—?úŒ{g;¡‰g©…«YMëI~)&l\|/qŸÊåÓçV5Ãï ^ÅËZ«!ãc è9.<ôåi”y³Y?–9«àêǺøºàò­|æ½aQðxƆëÒãECkÐ7™T¤éƒÆûgšŒSpÇÃl?íëT‡,¤6_éÇW[Á:{ŽTy̤—NNò•_áÃq‹£3ƒM¥{ßÙ{^{q÷½IR á?úNbnlÎÂ5!³D7”vGéÚ2$[êeQØ'1ý!Rt•Þ¼ UßL[Vøw=—Vg® ª§ÿ~µ1hYÒŸ Vò°UFy=¿ öë5ÅAJ/+ñO\¨ QÑŒÈà·á}$oý{ìrüŒÖ•¸ïA'@ÝðH¹6Ñ8j¹¥ÛóŸt· –®&5©ðLLà;Ô5ˆÊVÜ]®°r4ž"‹#v¥ +—ˆYfõ[s«0ð‚°G4܃A‡vS±ºÕ"Ç\ž¡”ý»¾hŸš3ÁŽ­3tÄ·Æ‹<´Ô•>å’­v§Qã·EM½ÚÄÎW ´ æm8ƒ^Êpdñl‘ÌðzVÝàÓ¿q°ønÏò+ÞóºÞñ°ÜƒEH Ó«É„f»¼^ÇG’í™F&}VV1 åó±˜ñ¬å àSv~EWäÓÓU~½µžxøô}„ ½?®ï§ 6­¦…TSÊßb“ù±¤7üð-f/ÙÖþ×Ñ ©C‰Ã•ª ?!µÞÌ]L}Ý´ Éã×ðݶ}Ù¤sѬ²uS8 _‰ü²ó°«Ä,ÄîpºR”;\Îó[J»hzóÈq@É/þ" |´e¾w £èNE6eÄPèoX†šüÐ÷¯¡èk‚8›Õ{÷æ|€Å6'üö¶o½ÞͣߔâÚéö‰@Òmžà¿Äá’•' 8Hþð CÀφN&dŒðnšŽËÎôév¢óê±¶l"™×ê›îÔÖ‘™KÉó’Éj $ûtor´ÄƒƒNW)Êçºetfwcc•ÑghÄzÀ°)JvGªÌ†{›/¼’®ø ó ûäJ)¿‰@¶9˜üXgߦ?uhÚˆc¹ÂG¨i««6Ú—^l¯t~£†òp’ë—¢j¥Õ·Ôî¢JÐHϽ|i—žW²GFWš÷Ò¹†Œ8#®~\L«!“Þ&"ÊÒß‚˜0Ï»ˆ$^®Î¢¨zÅ.ïòÍßú_xl…^,‹ÜA(B³coÝUŽç ½dWƒ†×“ý×AÑ€“îœgR.É?÷Ö!аPS÷Ø‹n²¿»4¥JÃ1«ÏÿDû‰n An¤b=Ù Nà”ÇÓ¥Ô@Z»©©3œ¬[裠«x™‘¦ZÉÆ*¸I·Y§W¬{ p×Íí¹+Ÿ­$fwPžHÿqå¹ÞbðØ=õm’bKLgüV[ØœÔnïÏ»ÜC5ð¸oM¤ˆµ±œÓÞxï‚Ê#o’6míDù¥ÿ$ŒÕD:Ï‘Õ9h5•“·pÐUE*X‡z«,%N¥Zµù"€ø·,ƒ+g;˜q”¹ÃÆ\v$@Èb[l{÷ƒ=üt’=’ŒžàVzb:7•þœ °°8y5Ù»e/з5&ybÌÈãM½Í îXuž5”•CcPÜÆGx¸ÒâÊØý1³Ö¹e!ÔÞi¬°"e„:t”fÒT˜Š56ô©î™ßðE€ôéVñðºu*/ G€*Žýù,œñ‹sÀгP_ =“œ¸¶AN9²_±ÚnrM»ÎW]\†VÄ+ÿùÚ×óo"š}‡ÖjÔ©ksdŸþ›|Ò\¡"±¹|r’4tòæÎþ‘òðßvXè?ãÆiÏÞ0-&Òa]µ¤ƒY°0Uq²Hî;´Í0ÛA|pè¾±â¬Ö™ma“É–êf˜Å¥ö\\Õó6L˜üy6¸µAŠaœ¢Š‚óB4—Q}šÅÕöÚJ 6Çñ ,uT°ö1¨bÃÌÉN¹”ÂÞÖL7‰ t]Ÿå ’¹r Ï‹ö ¶Sð­L(³i–ž)¦Ñw䨧!‹ÛkÐÐ;¾!†¶¶˜dK5‰i=¹€ j_)TóÃHÐQ)} ë ŽýJÿÌh‡ ‚cëÝryÏ)È2ù;*úB/X8‚Š›2)Ý Óqž[ˆE¡eÎYj„UÄ›…ÙÙ0ÚÍH«‡-õK‰÷µygPO¼í´nƒìOF-o\§äƒð¾nYÁ‘œ¹ RÀ  |Mp‹À‡î¡,ÿT°J—*Àš5B­ÙïT_Tåf9Ѩl@üCuœ“8€µ"š`9»Ê×}L7â§¼;‘»‡† \à¨:Sáõ¶Ò‹¥iÑéTü‘uýúY}¬ñ…T¶s †Œÿ[µ(à C~é¶DDZ¿¸À´ÇGõ¨æû£5£–üVÊðíí†ÞË'ajø³­%:Æñ$ì—þyf ù‚Ò±Yn¥âå„_ÖHÃJå5Þ+eƒý ¶éP5&Ó~ˆß™Ÿ-á4üº_ÖƒÆË/gk“wô˜yCÆþ®Ùcsó~»È‘½JíεÝ‹o_zbT¢Jë];Æ_“ŠÙ”w«ówR¬¶‚U$Wo#ÀÉKôÂÌúŒ^_øš²²| —]ì@ê¦òHlþ3Ÿ [^p˰²ÌBþ-»ütÉÍøâjÃÙ_?}Ò©HEH\é2÷$J¹ÂÀ Z@ƒœé¬òå?ßIÔRc•“ Öƒºy“œ$wE3û¶%è[ |Œ|—núLä´«;änü¹ãÄÚ'+ÆÝ>:2MáªC–¬ýˆPXžSµÝX8NÛá8¯´Ð±³J‡Ç«Q'hÌb¬ìS* ÝGWe ¨µ¶u²Žæ…Ë[Æ‚º¯2·!‡[2{ §ý½z"7zvhfw¯¨ß°5êO±-ESÕ 7TÓ/Án½` ø±îöëû¤úx•ºŽÎÑ?(qóLf°«>èS…„„œøŠŠËµ£ñ©+…r¯Í‹‡Æ.VªÜî«åÏ&qšÞÁÕ†Ñt[Ÿ0/lwÈš§àqK#¹8L86²0üu±ªõWchuWϲ{ZɦÏc/Žü>_\^ç£Ïªu|jËÞN¢‘ª¿£×4 ¼ °ý²í=#²Pa,gõ ³Å¢¦q„‡Ðw­§îüÉO½Möè«õ]a+Z¦ÚÛ®½¾Y×#]­™5çØ£- µ5(Øâ} %áŽÇ·õÅÏ "~ÆËëPZy2z³€p6 Žü%ñ»6N®ÂÁÞ‹s@5þ§x~ì“DGE+$ƒ:XçR|W2ž¡¥+ß’ÝE¹Û§aH?í 2²îÈËg5Grstì½›Rí ’+ünû‡‹ ŸÁ0*IÔÔ)SmòWtâÓi¼%¾@ΗãÂv 8¤=»#Ä2Zžå­Ç¤0,‹ÅrL¦õޱ·¾­ÆM’Äû^tXO#Š ð5ØŠìÝ žhÆw´ÎÖn”^8 ˆ³ `€|,LWR]1¤£tw5Lö,l³PŽfÙé}úÔ+x9êŠT$›`ëÛ[·ç*vÉÅxd¥ì Ú º x•¹k–ƒN=ç§ C¸ ÙqTð0•AÓ@·oŒË’ÔØ{–_kNYu³™|mÍC†gf†õ¢Ü¤à ÌàáýÛ'B/fX³ÓzÓøëŠQu…ÜòIPP`é¿Áq•Ämh[ÊË ÙßΪŒíi!Ós¶EßÒ¦æÔ_Ýã>~* nßÕRmë# R¾9I‘‹q£ìÅÈ ûš 쵕û YZœƈh ït/ÓOè=ô±s=Ú÷äD.@«V>ÆVÄSHêÕh< »HÃ}4ü,MÁ#ÁfÒzäA´tø>Ýõ—7:;¶9Ìx2òPTŸÒ,Ö<Â…^‰©±B3š%:,>:çN*RŸ¶S àBsÓuWÉP„¢jV5½§ñÈÂð‹×s æf¢ã¡,P•3´ùŽÊÎ(}!9¢Í÷ñüžFe”«Þ2ÐàêçF©±¿c n–€ËŸ¹u8i¸_j¹ ™i’ফ F7«,ÅK–å+›¦Ô½ç3D¨»N4ñ~O÷„÷ §GwúÆ *6“jV]. £\#rí 㬚^õ6À! "sèëÎM{qBé<ð’Ì+œÑU÷q:(ŸA¡1²éìuåäøõYøKU)[ïöMŒïÖ¬™´TPé€Ô˜ð”žœ××›áq¤^{šàͱ¤–Õûü‹ítöj?nø,Òy¡«×9Nï7Öli³rU—gšÌ\QÓ\+ûAUwYÀ&î$sqûˆ–²“2'Be²ST{)k3¤Od?q~ÀÔ0TÊ‘Öú2tPÏèž| ¾“ÄÜ#ðü›Øîcáús7Yv;ù "xÝ'hN$ËœåW€àñºç.Háñ94é“Sc-ù…±3¸p70±Ò€¦x‡÷‡X±ÿò³wuóüq.~1Î{FJ&"õ%7QÿJv)dT×Q¤ïz¯À–ãàeðFçîW~_Šå@/… ,0 Š'f6”U­lM]ôé…ão‡ÝÎg¾‰Ë,õu*oèé€Z ÉHWD> ˜Š3 Ø,Þ`Tžx広+ÜÿÒ³ï?ˆk«W—h½Dö fv¿÷y¤¸ÍzáÔ-Êמø¼‘Z¡x…|C2Ä£3À(û“Pk|ãÃöQWÎô-•DÈ]™Ì088 žKK‘ñß#ujb^o®b¤‡wS†4©ÕS ˆÿÖ†WèC£ÁÓlRHëØÜ BÑ]HšjýtN®Ãù'•¹ 2ûÑA†­Ðè~½†8æë¸5rð oL ‚&+åÐdÜÍP/“Ö/'¥)Yá0âÛ·ÜïY“=WÚó‚DÁŒýI6^š] ‰]¯#¢k€ ’}ëüâ¡l „ú¬ÒÙ«¶XXÀ>Ó<Ôýp­ÌìOÌÝãÛ@^ùPMF<ѧºxJÉRùùêOvÞ‰æ˜|üXÐê8¾/ùÊ»¾XcwgOg0·öãiJÔ ¹ò—ÄOASi<ö`Ah _Ä7BI20©ò=hø,µ¢þˆ±p!x*”Ás\#5µ"4ò 强c§ZÇÄ®аßõ±6 Ç*rá=(ófÐFðÉŸ‘cZÆúq.à9?ZUþùlù^Ƹ\“r Á'؛ǻÉݵ'"ÊüÙóóÃ!¨¨.úšhÿôF›ÉÉ&'òShrUL»Õ2ÇBC®aeQ Ù¿­}®°þ>oG R(Ž…b[±‘õ qI?­Ðíùóø'ßÜHgà3öðŸ UA¬]T{6ô ` ð‹çbN+em‘4¶>¦‰ŒãÇÿZ &:]@‡ÕðÌúw##Ÿf¨t ÑHc*É Ö-Vm¯×GmÎȱ÷y¥]„ƒ×û®×Ø £‹^K’i1“„,Mñª@³#ö~£M¿…÷xœ³SphäyIš>A‡gyßO?0 d¿x(Eð>‘tŠ^à§/ӈ̭ÛçmzâÅu~W 51DŠl:¾*êwÑAÑša™Ý‰ÍR_nÌ.†8îÊ ÊËÞ&F¤¯ÌnnË+ø°㱊}点ÙbÛÀdc±˜Z{˜âoˆûh…{&jp9LMn%˜Ÿ*–Aúñ¹¼°±¦ÙÝ[Ì…­ÿÍ[¢Ôò­Q»ne>^Pgƃ`òdòô‹ºvdR¸"íj×ñÓeòMCUfíÐGßfO}B2°õ¦ÈØÔ„î÷Êž°Šwñu¾€ÖRÒ8±½¬`—cCåöCë-,µ–AZfÇ ’nF.Ôc"’µƱxëÕäÌF´Wö…qe˜­~Œ ×ìÀ~^ë%Ðk½}Ϧ;óN´Ð‹‹àп*J5r7„Ú®*UˆÕǤÍSéÅØ Z·ù1H‹·xQÒ•ØÄ/£ŒG'­È½>Bëë +}¹ä:¤*Ù#àq(²*mÇèH{îæl­EÉûc‹(Ÿ`§Š–‹H;ƒz¸˜›1ÚRœìí$’¥Ömt膠ÿñµo6_¯…ôyÜE£Ó† ëAjE\à_õuš•we2RÎ ˜ÒÑskíâõ¯Ô«.îÄëþ+Å—\½qõˆ”?~¹±4Þ¾6q‡K>GJ—‡”üàNìÜ ?p*8b^£jÄÃK(÷x€aÎ@i‹,“ 7‡yr:n<°õ]NQ¦®õ´ñ3ÀÌB]p-[§&åe;C†0A|Åp‰NÕBh6àöbRfìp‰vÓ¢ÆhDF8W €n ‰ûˆJRiÈ*ûŒ£ôNÉ>‹n˜ K“ùpÇÇ&9+JªD¨Z»à©È—4 n°úõ úåT4þ5گ̨fZr‰¿{@ŽââWÊŒ1@Ù^Ÿƒwemü‚›e¢ô<- ©¶îûоt,”H޽^”½G¥Ã¥U*žg5Ùÿ[ƒ°˜½ºê1‡BY*OŽ5Ía.ÑBàº5ºLrg†%k‰ÄëÄH5f5­¼» >r½›åÂåIÊBšX3‘*=ÜjÇ=jCÛðlñ\´œhŽÝç<ÆhKXBptÁÖnìÜ<§´·ÿNôŸä»@‰Ó ¥]‰öà°øý&Ìù×»ïÀDvg½´ãßþó"^†pZÈeòxÍ~lØ·Œ—Ö.\¡ÅÆkUJ7µ)Ýœ}Ñôàã YÌ-wf‹E–ÝÿëHÊY€ÜÜ€ÙV.d#þ{»h™´’ÌwÙøª½~<'Ú„;5¸v[ÝM›fµîè&&àúÇ*¨îz_;ãöçGÉ“¡¶¤¦·3¨~îjbê’ <ð!c ÔJ¨®¯,.ô:x@p£®l»ˆ$ïZƒþm° n‡‚z¬Yñºqˆ›@n:uIÌtHŸ{6oôŒIe¥…µ·Ûãœìúš©ùk@í±ô½P)XF[¬„ç#‰i…ÐëS‘­¶Çô§¤Š®¾x ‰Ó)óÙlÎüsˆr$ŸKu6è³# ò×F[ᨱ}ÑCoWo©X¨ˆ—w/Ê©õ‹gÀ+êVƒ)šÉ…BFUÛ-*Q͉m¯¨¶ðzŪS‚͈ íÊí½Âëñ™‰O$9B0Ë­RN}ÐÒ–oÉ$¬ïÔÔ½OP¯“2ލò¬˜2ËvoÒl_$’Jé"ä÷‹æ°˜ëÃݲÆÿŽÑW¤¼}Jž„[‚°ß÷[¬ºÒi×óØ*}Ó “°[wG%EÓR>á„r燼w&LJ×óùN'|uè²¾\êm]"k üÚ¤Bt­\ÆuÅÕ!ki}q'O”뽃òj§îkŠî­åc%Šb1hTý{ ÓnNÜ´ ?Ž’¸-À4™¯#œ „^)%KqÏ\²WÝ煮ê éιçŬ¸R,¹*a–’Î4s. H¾K/KD~6·p¢€ ¢BîI<'V×¢#í°ôéãå}¬R~îôÞáõI£¥™«Å{‘ ü«}{ P­ÄuùSyŠ_5j‡&(¶]ÌZ… µþ±mÑ4ºÚœ7¿z˜¿TE³+µ î§6N ÓXõe2/“Ä|—”þÍlDèÛ–aÕmæ¢M݉k ¼s‘§~gŘb_†du¯GE訳 æ[ ïy &šY˜8 ©´Dü–òvÝV»ˆ¢S¨·[]fª2nÐH#Ì„†³L-§Ë•3Ï;oû§Ñ NœÅ›¾{bÒu;ehnHPéÊš©4åc0Ê]õE&à,ŽÜ Ƭ@/â>– SlJ³áté5ý…n Õ·“3zåcš v<‘ú~²âèV1y«TÈ@GÇ[KðŠšrn’Êè»c=k_/Ôuæòq,î­ºÐÍAviµ7¨ÿ›È„ã Ý‘on}Ô?"¦Pêgƒ’cÑà`y@u#ÚPÆ);*ˆ´ùèņ†š.]MÛ%Å•.›Áƒ^u8ÌÙîãà ߕ;1L%1ù1¾ÝœkV«2cUºç­«×í§GüæeF¿Àœ%`¯ïóúÊ]’ˆe›^¤c}[8[q|Ê}0:«!£ümédùýl»…Åõ¹Å`5v “zâã™FSJŽÊv8³ 8¤ýÀÇ!-!G^(}r±ˆRÏ?úW~<Œ\ŒŽóA‹‰¢÷A³$ø:¡È ð¦"ÿµ²Yö_Ê Ð|]±qêõWô™éróEîÉUÀhåÂáÄû—ñÞ¿üTýþ—Œ› Ýr Á6†ÕÓ‰}s˜nÖYy­Ó (é.?»Öy•Ö6O>x©Ú/cUŒ{~ڼͬ(H¿¾¸¶»«Á*9â;•†:§ n`‚ýùÝHjâfúúµÙuÎá4Ú8`KÖÝ“©¾(•Rnîþ™Tõ¡õC‰se¨j.‡{Ô»Eÿrš‡ƒÄ¼ ©52?`3€OÎ0`p4ʵÊm¨wh‰8pÆ&\!þµ”Šn”™÷N`~˜ßxpø•ˆ=éN‡@RXó:÷MÉH´;mZ2½à{OJÀû»‚xÛktHèÒ·†I“¡ÁÁZ¥ 7$·­äˆ»‘Ù/<L¢NÀÏž!t{.nJkšx„à/—nlHbÔ˜yI^ØÈZ‡n]ÖÍU9|RIJ9íVåÝó ŠmLÖ $¨õS5t¢v^l#†Içî&„´¡=@8G/äcz 7<á]©€gK˜0º=þ7_ ‰Iq £È•züu¤Š¬UÌ_EÀÁ®D×yX—á—¦5ú†ÔóvêóK1bF*É/±‚ð(ò}n +O7ÖMòÖ¢Ú€’¦uÏV̦1­]öìWl«ÛWú¯›LÏ]õª7ãašî´^’FCq¾ºcåHö`Q/³"NCƒû¼¡ø #B'ÝK_Qª‡d0pÁ»`Å-ÿ/kúø³àŒÉ 1†Q„ò“ mãòñ‹fD8t³@¢*"ÊëÏÌ\>:¾à‹²áú[`V›ÄÓ(»™Ùyöé’ñþW‡{ða—Žd8¢Ÿá®»÷ ²NP£oE'pÌ?\zÝ1jU”˜ºÍFWÖPîT³ö6žiU¢»,z6l¬k>h EÒ½«/\Qìð¹&G à˜mSÒÏOæ­x¦ã7SUjÁÓMPÞ‹[±×‡ç`z{¾èwƒ¶=ž¯“Z÷Kÿ ë¢âÝ|q=€4¼%ÅÆQ“z±NFe_ÙfÚQÔqY}V–’¨XæÎWW1,fæ [_²â€Ðͦ–˜«fb®rCKêgI†NXÑwôlÛËDxdx›íZ‡£¥Ôó K~ÌA÷“ÞVûè×ÚQ¼¤Ä#!E x¸s~G‡%oBòá~ߎUÁ|½`/f'¾\%XúŠ¥ÍLZ)¸ßŒÊ=Ûæl§.u§£,á5™sW0-f‘˾¦Kñû‹çL•n~>ì)dä¨p‰#Ž€ÌIÜ΋Ìb”G¡ÃËWã3–ø¶yï2€€ë7®”5›{îW“ål¸±T W Ç!¿) ]óH<9•þú; Å$Y;¢$€ÿ>Ü#g›?}÷ÑÝÇÚl(5n‚j,,t›CÊì¦ì"5 ÿ"gö‚"‰>B5G)CÊ‘â sg’‰¯–¤{Ñx‚g‹æaÆêúhKÄËé\ÉCH;ᎾVRãO.ö¬Í0+˜”+ˆJGNGØÛÞ²Š ÜΫÿV®¨ñÜÛMÚgѶ306³1íÊþz‹÷%¬Ãx‘W—@Àëg¹Þ>: äígø@ Õ”ö†øT0ÑY`$3ªôÚôb÷˜F G¶ 51} mén7MþHªÓô8]e¹€~&BÃŒþÔ0•*43È ¨ô?Ó‘4ÃGß3¬Œ¢Žzˆœ.³òfMºë *MØ!îÁwÏW G^(¼:@„•Ó½‹…9Ñ¡­ùvqÖ2M†©ÙÊiuÈÃ-F“¥aÃàt¼E–ð5éæÛ]M,äùhFñ§Ûœa v4§î+¥KkÌ<ýu5[Òª)oõkÎ’>Ù* uš=çVÓ¨ú_‡f9•Ó;w‚l/Àè}4[íóÎÑ´äž,ËGÃ<íéWq(ê /QÓ{Ð5^ *»{ÐÂ/Á-ŒDlŒÿÏ£„ñUaeš·½ X*ÁZòÁç€NÕïDðU/ýÙDR<(˜”b,ŸÛÀmH·‰4Ù0Ù»!@®G¥Zo?·EÇH1N:oÓ—ª<©uçpæÇlª?Ø·vSD¿ñüƒD#å4ö„LHÏôDrº‹o$’Âô¶+'(bŒÀò;GíÃÎ8Y–4ÍœÌ ÆQ&šáƒ/–—Úæsapá‰Ës$ðŠâ æ°)E“HËs´£¹„(Àž’ãÇ7!³þÄû)“›Ì›ÿÄŒÚW2?‘ñïÐÁG¡«!Íȹkäÿ¼øÐq(ö/§˽ÙT ÑÍÍĈ—×^µY§åŒ©ŒÞâ˜i~š¢ïÓÃý4—ÆŠ &¬wÑTÃÀϵ*ò‹SÈUrôó¿#Ù¾¦P‘LVNxô¢cž&V|ø1q{¶ÙXK›Õ ᾇQ•hÇ^ø‚1ãT뎋¸ײÙÜÑ;Ë&PÊ$SÜ„1©z4*z™!˜ƒ£-UÔ^ÿÚ;ÃüDoþ9X¯õ«¾oŸá|WŠŒ™É_$j1uöËÍ´—Pù²¡&1÷lˆ;‚!¬^©ÃÖ‹ñí4éÔˆëÇ!a'Fôv”“w¶!Z¾ÕO»ÂWÈüÜXmÓ̦ÿÌ‘ œ@xæÜ“çs!SHWO>Lÿ9}à›ÑžâFÿ@%èÆ³­ÊÆè•¸8§/›áâá?jRÈ?Ê1™ìèùTÿ yÒj.¬¸@ Œ]uÔ$x[Ë¥‘Ù± w\ :-<$XŸÞ8—Û¯³µÃ„HR™ó›öÁ Ȫ¦0ï¿!%Éìù-g=7ÇÞScý‚´­ÇÿCOc âoÿwܾ f°<Ô©&ÿ² ðk ,„C?HW¢ñi¨«ä±Hd6Åæ¤Zô•rGj%]ÜdìÆN¡ÂiŒyºÉèzLJ†ÉI¾ýÜ‹™»—VJd6ç]åyH"%9ž~2dž •1 ew/LøêiF±Æ#¯*1ÃgPL!o÷`ˆmU°Î·ÚÿÖ¸G"SMªˆéŒP\ške Øø£†Tì & …B쟶¾m—±Š|—GÊ”Žë¶5#{†—Pæ0¸•NŠÅ@ªx¨;-}Z }®ßva#72+šø2…VÈÝ$›æxó^5J/áûMwÀ`Ny¸ô¼¼×¼m¹ÐuªÒ8–Ç®+J!%ò« o¤À‚µ477S– ëÜï3½zS¹îøÝ®j^Mû¸v#å˜Æ¹z|ºïC«¯e±5§69²'漆ü‰W[ã ®}ƒ9&U &Ƶé×Фã_ŠÌÞè¥YøÎu˜ ÝÄrî‰1™ìÎ\SèñIå9€”›¸ü|‡w)Hi#q¬SGê OÓÊÔ@­Ó¢TCE‹™àm§ÑK~  Ïs´-zªƒ+¿-O/¿ÿw1 êšFWÚZíÃ})ÞIœÓñ4ûÖi +#a‘NE¸mˆDÀ´ ˜¾ÖK™`;:5Æ7 vqîDSu]ls¥ÌÒá¿]“qå0`Ô9žvðöŠ!™úlÔó]³‡uàoàˆÎ™¶“({¬8ñä=Ò˜+…¢Ë’ø.·e%P§’Ç9*_Zü9ã…KéP;­¼•øzäŸ4_éÖžë¥Zÿ®2Ö>hk²:tu[´LºÎÊ©’æ<_Ë(3")NWP‹+ª„(jÆy…Iò‰®)Ùã$’Ú{Ú°”vŸæ«àî%ùóž?mtá!Ò%êÑYÀ‰aIB -<5MÅj9‘[#ín}·ç{¤«yj6$‡ú}bMÝQuÜnëiWõ oÍu³³Ei Y&ÏæÚ’š~† õd%îPÔïûÆí7逌+ÛúŒüµŠ*t2¶<4ôÉMümBV̾"WƒÀL¶ ˆeaÆ3Qó|ÖùH›©ì)¤™ÎÇpê~›IsT¤M×Äwaä9ó\?k¼ç‚UE©IÔ¥·0VaؽWl42Ë6Nh9òh°”€Ü‰Vß´%3KmýFHüú cMÛn£XÉ…ü¾ «|ú nÕê>”Å܃°,(àíWKNBS ¼¶=¾B¼F†`ß—ïÃø‚ú„J³;xœë…Û±!ÂxÂè¨ð4§zz¤lûû÷ÖN”š²2¯tÊóG¥ ^ÿêK‚iwyKÇüÀ¹€ÝhcGöO;‚%™—ýÅ.éöÞöÖÖP Ú(ÃâÔœëù7½ñøúžlýf•”»OyÙHœˆŒCÔúþdtUƒÕ´¦ö)#6_wó‡³ÌÔœ@{Ò»éH-'=Él‹³ñ†*)ÓC4;‚×%ÐÕ† *†#¦Ôã{»¼TÉ¢2ÊÖAAIÔÙ•N`Ý“ŒF〠ØÝ᥄F£³.lÔÎ7¯É»i‘wÚ3Íofl"‡9ÑZð¿÷w/Þ™üâDÂÙnµ ‘Ðȵðåç¡]‡vœVÍk€Šôó€JI& ÆâˆâS ­Àìj↰˜me¼eæI#G Q ‘Ò+YÃŒb-à§_Aí òoX†4¯â§œdtœáLÊ ¸Æ‰‡—yáîQ[„ò,â:é•Ô.žyîq)’-±ÝƬÌöYAèMb:Þ`íÀCq†¦„nN`°8‡â0¾ ˜óYŠäÍÏæ¬Š%Áá{+qx |†a¢»EÐÆy (ZòŠÕü†ºùYËi[âŠ\,HGdéRŸpŒ¤ˆu›k>lmÃqf1'ÀØòˆo-ð<ëLu*eášj½bDk½Æ[¤\²ijM÷Yü´†ÜlÙ‹óe«ÉÀMdÃÄÀ¬JÎÈÖ•­sm™7U%ˆK<öÛ׺˜~I *Ó¯>¤äŒ *lö(âWß·«¤.‘UÙñR>”î2+3‘®ÿ9˜ÕâtÎ]G''r7UŒ—8Gw<+(Åç\K5R3” mã[Îï¯h^¸e86D*¹pYÂwƒt 0ä¢äˆÕÈúxûVº2T Ê«OÝ ³r¯/ŒË«Ã¢O\g À²X[¯ÖÙŠs¾íQ‡°qõï–R‡ÔœÁ‹a¹Vþ¶¶ÝI—y|@ÚýzIoV›Û¬õ¿f]ç"ƒ"¹í“м] cð͈Ïa²äp:kl×\©ô÷T¢™Ûenm˜?‘¶];ŽAÑùUÏœpG´ÄÉâ¸MË­ùx§”;>N5ê„Ú' 1Á á†BSôäøýÂRg¾`ðÏŠé¼Ù]ö´ÿÞŽG øäÐ75– ´˜£§5@k+”‰Y{€s–€ÒÛc¹!zƒ@aŒ¡Àྰ¿t§–Æ€[Pjãº"Ø­gÁl7{YùAŸÔ´3ãE‡À7­)g!dà#kO‰q¬ž+:gŠ—G ¤(3G»ÍeðýH¯×XLêr°ÿÒ{ „íG­ðöQ9 ØK+c:î.³ÛÈ{à_à\u l°_KôHæïŸ= SäPü*ÁN½èÈ¢:V%§1"Îñá3AvÐy«p¡áZùŠ`¢T{Lóüôo¨mÉGQ U‘áÑ+MêHP”).ÃW$ì¼ÍØbòèÌñ†w𩉷ˆ¥Ó·4T­/ñû>’KßÇì´VÃæÖº,¹–”6P YWýKï$èÑM3ßîˆæDb5Öù ˜¦lyÎå>ä®?VŠ!áºüfJƒ‰Ž#ã÷…%`ÖiæêÅ7Hj]õ%IÆM¸3Ø$¡¦x3Ф£c¾±Õ'ßóÖ ¯I(¥¦åDPž´Ðý΃W:agÄuή"‚7ŒžI‡+.ÆL—(B>7|¨îÅ…ŽpP&•Í„hÆtë³€ÜÍQ³ªÚ 6%Ûòëôî ¶\èló„ë×iVŒ[Âõ”Êòà…AdŸÅ¢çÙ;½ €nã‚X‘ŸÊ§¦Ñ/›€~x&HtQ×LĨ‚ÙÑZ%Q›„Q{þ{Í>1âàÔ ÌÁEw8ò:]ÆNúº‰=h#Í“ÿkž ãkòøÏ™Ç¦W}(¶MöÃÔÀóUå±F™v¹Tåÿ«½],"ÞÆŽE‘5ÂkL æ“[µï­v›´º‹wÇöÄ0nA+ÉA˜G‘Ä?R‰´™óÅs¨Ÿ…k¶?Χv+ d˜Í‘q¨-p2 ¨Õ6†¥Z%ò”–RHÈHøaW?ŸVŒ B>tU€ÐY¶îé,ƒ`8|°öya Ë  DNMo—u£D5ŠÅ‹/o`U·¬ÀÏN±,º‹Eöý0d½ÐNk£qÇã*V|Mư™d僄ëß\À“ó­;‡Ujø†¹+WZR3?”kþ&|)s_ÕÏ•ù6ÃûÛ¨$´ØiW/¹Hh\ét™8ŠETóú†›ÔênTÆ#™BK]+|#Îdœ’*]äaFh¡ÏqË^î€ ÀgQzßÞí¼ ¥ÿÐrBX‡· ºàÁ÷1De« «èfá§õ¶§«ÑMÖU›ù²\}{aYú’«(¥‹v’p "Bµ~ŽÃ¿Fï1ˆ¼ãéU˜‰¹ï|5ôˆñÿD»ør«×´ô„uN9Äç-¤KIZ<-FÄÁZœwܾá>–]ý×[0ÁѦfø·w¾¼l¢¸º˜/œb3׉ÞóÞ4ñ`ý½.š´{à г@E")Ÿ¾/¶ðYL_dxž]šQêî-×eÜhŸ ŒiS\縉ÏhnSóŒ³Ì~æk²dì Íry~„u®£Ød†¦[šÉeªmU2KòÇ.)‰C<ØÕ,þÙ2ôgÌ„r © YN‘Ò–IlTw>Q¯)úg¹î1üD”âuËw~¿ËË× Ä™¯4ê0ùJ¾ÔÄg=ÅÞjµ“ðÿ¿%º9d?xIO²ýê[¤O,-…ð9⎣}¤gbø|÷È¢ò…û’8ŒO;²6°¢Í•8J\ä§“ Ò¨Át\-{üKbKÌÅÚý­ “<)nÓ×—ÈE$<%CÏu‚®Ž6ìf˜ù“êÓDÊHa奓ç.êçŠ9jÞKÅéjÿcLA-Üv³RH):¤Üøp¬ým'õ—–V2¦õ"Qàõf ô¿¶'¯N¥š@UvI  /(ÛþCP¢¨¥v§uw¢´<$Ý2éΦßà@É+u¬KBЀõ×…ê|ÕÀP”'»÷ìëS‰)©DÀ~˜¢„‘{ž¦¤Ü†ÔÚ¿¸q“©?‹¬ÂöÎFSöã‡FòV|ÊAîmG¯ÝÆÖV¼JÐYÍÒü’˜ð¯Ø7 ë8±ÊÂbÁ… Í@H8ŽM˸ tx/Jóú}t…—‰Ûƒ-`ÜF¹¦ tÎ>_…†Ó8;ð×Ç$- <1“t.ä'˜.ú"© Ï=«ôËuÉ\Š|ÿ¡ÎÜCÏÑ·˜_d­GË*À¯#øjï|Ó·zqÓÈêˆÁ©™¦ýŸýóêÓì÷¼cÝñÃog&ºíN< $BÀ¨ïÑöð3å©iÌøùE©èÄ Yu]2|¾/8›Z"ßt¥+qì6’e+ nõo =ˆ‡¦º¤бeœjÕ'l|ò`Ñ……±‚<ÝBÚÆ!"©êzâ}ÈzYÈLÁÆ7Ž‚Ó;•ßÍWÀIå´@c‚T²?¡¢»ñå›éáôãƒæØõÏ2¶9ÜŒ§ ¾5ª×%Ú&0ä­â‰[ÀòІÒJoY3âÒ×Ãé ‹*6p]`Ç”An½3_üzC‡Æ­’RK[ê,§S×H¿'ຠ˜®™~ý‚él÷!HÎ$$H½ýyòK¶:òE‡ÅÑ<¦%Ô¹ú,B¸fed'õ`|í\Î/Î3ü¯Ë #¹ÜO…­ó€ÑºÈÆîmßFý ¡+2Œüªró¡@IãK¾dÕ"L³„l Çeƒ|é{éäL-Ÿ:F¤ÓÜ5r‰©ßùÆÓ¸ª\Ù‡}ÀlF¤¶*iX’‚á`ñ2|¸Kê*tt|UÔ5½‰ÏæBI]ŸµˆÉ$^Eøm-w@'âÌ Üp ë`_f?‹t—eEâƒâ€ãÙ÷&7¼;@‹m½6“däÒÚƒÏØ¡"ÿ³µØ%¼t‰‘~êêMíXþåí?‘_Õ%¹´ƒÑ´Þ3xÀ²Ú½T_²ð¤j´ÁŠ¢Zé¼ÓånpŽ+V«KüÇ Šf0ÑõÜl7·eKJ;[óž:àfŒ +¯E ²ßLø/°ññaQ…ô§£1EÚΛÅ\FBMá%ü«‹³{KÞ‘}Aѧ;I—ÐÛÝÈÙ]Æ ¸.Dˆ6%EwF_cÜÆ:Hi@i~ÒÉõ±ß8ºkXÖ’aeƒ)ÎdÙ†¬XS :ŒÐØÔ¨3\Óy³6k\b¢ïÙÞ,v˜øUÜA>¸A4“öÙéCæcfäÿ¿¡º Žßo¶ÁtÊ ¯»!J¤µÍ*kTC‘Vkoh¼‹¬2èx/¿2#Õ²ATE¸“\Á…,²éQšBÎ:©î†%?˜é/å%éï(Ü~G=E˜@\Õÿ´äÓŸõ»ø¸›ÍèÉ,T3ÔN✥mõž¬ºâÛòˆýí> stream xÚlzS0¼–mÛ¶mókÛ¶mÛ¶mÛîþÚ¶mÛ¶uÿ9gæLMÕ­¼l¬$k¯ì—TBJ(jgë¬ìaoÂHËHÇÀI $ª,ÁÈÀÀ@À@Ç EJ*ähbàlag+làlÂI fbL dbOÀÈDÀÄÀÀEJ dgïáhafîL@aDù¯ ªµ±……#ªµ«…‘9·««+¿«“ £ /Ý?“”LLœÍML-¬M„ää5$dÅ(ÄdUÄLlM ¬ ä] ­-Œ¤-ŒLlL( Lí ¬ÿíÙÙ[ü''º°u5qtþ‡˜©£ Œˆ²€¨œ¬2ˆ½²­1´ø?Óm8ÿA›9[üWu4Îÿ±¬ þcþÇúOÖÑýß##±…‘3¡‰™…-ýé&akjGðçßacûÿIýCÈé~ÿhHI`lbúÚÅÚZÖÀÆ„€BÈÎÆÞÅÙÄ‘@ÆÎØÄÑ–@ÂÙàŸÊþf`caíñÿþ7âßp[³¤£ed¡c`ùwÜÂIÔÂÝÄXÞÂùÁM ¬ÿê_q5“ŸŽŒ‰±…‹Í¿1ùG•ÎÕØÎÖÚã¿÷ý§˜íJ¯,  ,+NýŸ6øWZÄÖÈÎØÂÖŒ@ÉùE ÿøWZÞÀâß=DÀð¿ËýËgü__ÆÀÙÑÂ@‹î¿š„á¿ÆÿX:ÿ‹´s÷¢å`% efb$`dad%à`fòù?l\Mlÿ¥ú?¥üÿ¯n21q71‚Z_±3â ¶Lk ­ð)š«¥ž›ÑW¡^a\î¹ÕB´27Ö;ºÆzPÆÊwƒÁ 0äÙ»Ú•xh&2ˆ39™œŠ& ×YÁz+!®4ó­ÝþP(xo×½QÅ´1’¹¸wÎØX”§šbÉʽ¨•t(ãAcŒÖ/ïèE؉_[|²£Ô!GÊÔü19 ®A˜3)¼1ä{Û_ ËÚƒ¹%ÅÔ3/¬°? ӏר„¹taîl;1£Ù¾‰ä²ÊbâkÊôÜ9 ]2X“Ü—!=s9x=>B9ä‚[©†•ß1~ºóœ"}å[™ Ñ@I "äÜNÃ1LXPºQûñð®_õ‹´6{Ù#d¥âûª—ܾ5Páì% Îçw¡Ã¥ í-ç5U›^‡‡ù8ôoˆO C¼ß(ÑO­ì&­¦¦Ö+EA…á¨T’l¯#œÍT×Õý ri ƒ:j¸K ½‰÷jn¢õ#x2Y/}tGJˆ9¶¼Æ$ŒÝcæÐ„TÝ#¢¢õ6Ef#@º˜ÛsE¸ ˜DÀoYÊ2^í¹ÑC†™¥¹&7@Çõ Í¢‡¡ì7/\2Ì 'Ãí­?G³$—o^  dµé,DÅÏfǧ>ás17cd'L\Ò‰Bj0Ö±Ìpèb ìï+ìµ]•ekpDBÞ aH©T–¿œÕˆZýžIqè.©¤F72S|ÿÆäiÉ>ûÉrsO|Ž_jh{§”¢èw¥]Ú¯ÐÏ×µd}wðÁ†zS¬;$ïì×ÇÙþ0—äG½&Të¦`)%–O$}Ò2&äö&zŠå1nàOÒ£$#iy°¹­"Å ŽŸèŽ‘_ã„Ø«Cä$ªÛ`W’ïú!±Ÿ:³ÝÝõ/ö4:Âå‡Ä§öâ §ÊûéÞ“(‰óno¤{:ô&$ë`áÇ@ËBïÖ«“ã£á£¢•EyùâdÛºD9g m.sÄÆjÍò51 GÑò|a3AÅŠüq=«HîÃ'Ø*mè¡§xëµl –×UjîÈÂÖQ»ûªxs<”ˆ…”sƒY¾9Åano¤rú‰C¶†’ÚÏ?.ÓPשÄä‰ê·qÖ±?oî^¢ÙÀ8¿BºÒêú£ÜF!žŒ>cbT]Žè܇¾…y7n-;жgÁl4E¯9AIllÊíÚtŒ‰Î ˆé¦sZ”¥W¢õ6w˜ABõ±‰9bðÌâ{qʘöp(Ò_Æ"ÙÓ7Ïqˆ.\|÷ÛÉï›èš¡˜¨;[¯ÕÖ'N'´’M¶ Íaš-·µÀæ1ON°ydð34°ïR8K §°pžó7¢#2»F ®IÍıP*E¾ð,|¬÷ÇmÏ>”’áo GdðX›>Ý\‡Í>¥¤RIÈØWÄ}õ¹´%éM¦~£ýn;¯óeǘ7¡´•CšÇ4ëqüIašm“ Ú*Aët h-n)­µ‰ †¡$N7{Up«R4™¯è;à²ùá^¨Æ€_TÃLl|SæÅh®õ vŸ÷{…ÃSu&5gĸj=¥.}` zuHSÝw¡Ú‚ÉÉ·ÆÙ3L“>ÿïÊÁô¶«}…÷ÌÆ©IöãôéŒ'þ8g ë`QÒ½Õå#(”X~lŒ/þœ\X¦-|ƒ`€8 ú¡Úy%.;Q¢Õ›K¹íXA>Q*i¡‹?£S"=b6l)äÖ³ïÓpùÉ—’Èg.O…R<þËú’WÊÕεb8ØÓ jžÏAõ¹æÈÒšŠÒW ] •j5±ÔÊ3›‰ Z¡þ&©¥8Ìb²Aͺ±ˆQ¾ß³¼'Ëà A‰÷¼;%‹—˜_‚á`÷¦²£†}nûr2“-]G$Í3¦P÷<$Ó—g©–]‡ÿ™‚'JYv@sI4ꞈ•–ò¼åÖŽP.­üVGvR%èyÚúxiâÄG•MD¯ç\@,3K5ûš"~ø¡Ìi)„“S­¯ª–ЩúâñÏPWÆó×Oø%ÇÞêç†öI Ô£ ª£n@/d™Ñë“X€Ðª§Û×M6Æœ>)+·|âžuc¢ÆxÓø%lwo‚"üYÏËn»ÎKûtñ_ êME%íƒTù¢=ImG.ÿúŘk’#–õ'f=4 ï\ K?bÝËÆä0KÍIq¢3xŒ§6(!mÝÖQäWp¿ &æ 0îBÑÚÁŠ0ˆÛlF¼•ƒ¬ÏÞ¿sdA[4ó+&yaE¨ñþeg%æ!Ø;bªéhšƒ™ÔÁlv¢ùõPcyUâ`Q#Ÿ"õOBЬå’{•Ä‹MRÃÛ ñvŒ£6ˆö·ôOº{%bu¶¸fÉ– [2´:½µò6µ@%+<>âT…ñ”w¶8sޝ–³‹}±ÌGŠ|â`åÌ èÉf¦8xæ=$D÷HìIUÍM)’QíÐK),ÔÝÀI=ÿ!.XÆWòÄ´<Œ8×»Ê4£È¿ælôÁ°€ª6jƒïêCa2Èêw$·U¬i…Úfvò;–09g8+hWžU-V„÷µ ©áÚ·Þè˰É{¼ï tg}‹˜`Ù±¨i“ë‘åF³W4—-ÂaºR,ïbƒ­­¯Ü¥ º§iÀP|•º¡E=½<ËoÑá¤"ň”Í·ÅÏÃi{¨ê¿~f”üÄ €It…K­õ㼯Î@,ðŒ‘¥L'áèy_©4îAné‰/;²öÖ!MÅë=ß0º³œ5É7ýâŠâ¶áfûBÝ iÛˆÛµ{`̰ZköÑ‹f †ªx“í,Àõ%%Rô\qíc8íRáüž^EfÔݹ¤† ¼ÈÅ¡×ÚÔ£þ:루³,Z×[‚ãÓf„åƒ2 ɰw1¨[ÿ½h:khw?+FËøI¡—ƒK\Ñöÿœ3¢ï2¿ƒú½I™i6žÇßcUν/é%E&é(®×¾’îG5¶­-¬¹÷°\¶ÚÍdMÎÀMf%Ç2Ϻ¥òjy; ^k²½²ãˆòÔþ¹³+PäÑUä;íoº?RÆPµ»a)‘ÒÉß·‘°_b?;ÛIÝ·«fãA;æÔ3š'ÿä¤eÀû¼íÍÚ— A‚x8ê—Ü¢ìR@Ëöypõ´zÉ(ŠÈðž2šŠ {uÀlâþÚˆœRuI Ø“oŸŒÒi1ŠïNÇ\k¸¡bZ”çQéôÛÐãr‚N÷ï°ElÊæU¾ƒ0Á­ÈË)ÀWe;@pÜ +¿-b-íŠ|O~VmM#*àõBÔ$® +Iòa<èÆ¤_&57 ;µ:GUîÿs¡¸ îò˜}OÞŽT(]úÙ`‚¯okóð«fu<”¼ØäT”ÅÍÔýV¤<>ÓOŸêÄ´R³ '$–‰"ºDÿBóùSk¡øîA˜Eâ9/ý {ºù'Áy´òm¸ñ*Lä¹ä×<+.½Èi*ò耾ü³èÁ×>Û näTR¯B ws§tJª4¥¬]3éñ ÄC·3”Ýòû|÷wñM“¸Ù&GͬÎÿuZ»‡–¶1’_̦j€b.üÊûÍÍŽ&׊}ÑþBWÔÐ*Ñã\›<<pä–‘tí2 »WõÞx$í\‘Ë%øzBZ…uþãcÏ*%W±×ôßK[©ÇCxtgJ lÙ‚„‚Lˆx)Ô6jNJ؇§3Z9¤áÞ42ÉØ}2ÙÀþÐ8Ånzê@X‡ñó(Ÿ<ž×bÚ‰ »…º¢ƒ¤?Š3;a•óŠõîq—,dù~¥H^ÊÛ=PODç*8ÝÃAu†Ëˆ§ué“\”Ÿåò›¤Ò|C¹<¥x{ ð»;óÂðrËèîã2ãâê¥cðDãCº H¥\•ÎÞÍñJ¤djüŸ¢kwC¸¥,ï(ve¼Hª Þ;Rµ!®QR Yúó$§'&÷]>¤¹Q²'܇%Ÿ Ï´…ê ¸Ž.íS¯àßh‰™Ü Ëøé^Ê­€÷­†µŽ5 ‹®‡ñF˜÷bGÃÿ"¿•7Ñ<=ÕmY„. e¤¯År¹ß?U!K笶Ü#¢ª0ËËÏ0ƒÃ¦oXÄh‰Ì@L®-´–0×­Ä—\Ðüùâ^?øè¬µºçL•¯>›(ÿE姃{/Ž&7´®Rû]¯^[ý̸§PüÌðÔßÓ˜·#h¦½ÊåŒäÀWC˜~¬êjE2PN ‰ù¹^Ð>rdÇò}÷”“¤.sû l*ã"#±b@½“³{ltÝcÉ]×3mÙU#'y‡ù^ðË0ÀÐ÷‹Àëá¼EF¼9œð‹þB¯jrz/Ñ]¤FFz«Ã_ÐÌôžœ%÷àá½ìÚ (y‡Mjð¥ÝЊ7˜;ŸãÀLìÛÛÌ–Q¯X±5Z¯»¥ !|2pï{«Ãæº{>vxÈ:Z`ö ‰J¿Õݵ]ô¸n$à}?:MŽŒ9¢·Á¡Wª÷Kv©VåaxFªòØc«5Ò Ó'S#)©‰^v%¿„Ætw&𮘤“gïÄ kÊ˺F{†XEd|XZ§hÖæ®ß`!¦È˜¨ÈSÅÖ’F9CuJµž=IÇikŠBÍà oŽ_ÇÊd–öR$çe3OºìçBya+RZ®U¦r£ˆhduüöì΃TÒuEÑÁ“A×p»Gs|Þ…ê82›¢Oê©‚Þvs&g/ÓöxÅuͳ‚oÅN8„ß¾ñ[µPA‹Ô‡”ŠYÁÀ]æ˜úË$^£QeËk×1YV|ívƒ²ynï‰Óà%ëL>§ò™ÍÑ“ŽÊ颽Ԧ=·Q§b8Az¾÷‰÷XOÖ%4ñÀcL¹‹BÑ™†n>ªË¡àžðÙÚläá:ÔŠ–_è"bC'6|CÌîÈr/#r¦C)Õ1…FÞ«¢zÁ*è¨Ú/Ñ„ë4î"ü~Ä_dÄjdÜ4ÏÁñÙrnnÏ9;§ç°~´ÐêŒ?O!m›Ë-ôIqó©ˆë#Å7PÅBÚG|9ªÙƒ!KÍÛª‡ÑÀ{îýúîDH‹¸ g…xnƒ¿õšm²<©9M0ØßÌ®u:ûâ”Q èÑ™^s^‡f ²Fþ†UˆúâbÖí)¼Ýoév—C6…´v’Âç:-ð¼B¢^ÖoänWþq»‚‰aÌÈ3eô[¯[:ã‹ø+ðñ³!<¯…æâº‘Š%M¢PiT-©_¯òÉŒDUÌ~WGˆ­ ß ÐüÌÿ,j`°²óÐA›ÑYÔž_Pd]á!ÛöÂ%é)k­¬Q¶F´ä‘9IÕ–0 ƪ¬yBë„TRwÆy5ÊOÝN©©Ôî€=?Œ Ž–vé)úYRæ¹ÃÙZåô—Ù¾¾6m"Œ-}X£íMû>«XΙçÕ†…´† Á"FENŒ¥H9!¡BœH⺅xØá‹äº¹|’¨úòä•ôb_=ˆjŠ3U»¸~ýͬeƉžö(?KÎxŽ}ã‹fÑ%©Ôɺ(°§Ä–÷Æ™‰3Am*m#.‰©¯!Ý\/³/þtÿ’ѯ,ý*FIæ¼ /î2šv—é«þúÒ~éõÌøëF-Õ‰xïzó)6Z¯==~ixÞ½>ð¯f=ªtãø«GÀ#¾éîÉòQËÆ÷§v6h#{}'¾9Êâû·„bö…R$úÇÓŽ›J½1Km…{gJöÒÊ\ןóCnZnÖ¤Ìe¡–‚-+Ø?«›xƵžr€­JYÆO4­óZG_ô·„'®ÓÍ»rÝPðâæ×_Ç8m‘üq•0-pK½ÌUqÆ)–¸•wr{‰p =™$‹¦ø´]Úòã¾P4J9;«Mõ Z‰E¤Œ…u¤Cdö Å7"S!Cm5±`ªjæÊ^$ ]øˆY¥Mà®ßÒÆÏåÆå¡å«‡òÎÊ JÈ¡½~ä?Y` f6ˈL™×È0! M>kÕ· Ûþp)¶õ«Ãð,/œ!b Í íÀÿÀ¾%…àÿúõWöûé¿)Ýß~.9«\«VøhoZ€p(Èf=K8µ~¿qvÔù ¥ÔÐðeål>Ž?”ç”L ÊŒQ‰Ëk1Ì]ÏÌ E )-l‮M²S¾>¸|³êæ×Oû^ú*˜ ˆ(KµUð6¼Qçfx1¼,E„'·Á…@¿ û àSñŒF&ë¢e³UÆúïð_nÙÀÜ$!¾Xåäðaèg [ÎrCcž¢h¦æ"ÕW“ò~I»*æ•„TªF~6'Q=%¯ˆP»wé\·‘´ˆ5.e¤PÛS×ÌïµÌdl¢ˆ¨·…ø … ŸžgU¸Õh!Dâ)5’0¢Øê›<§èújº¥çúy„zYÃ8<LZt¹ýPK……÷€ ç‡Ó"“5Óíào hìâ<϶WA£ã;0ïÀžÄk]Éë9ÿ‹ré ‰HÜÒ™Éhæì]¸Ó úÀ¼„…#èŒò‰ŒÚ³åÈ–ß’3íîäj[§¥Ò‰¤Ð%ªZ_«¨ïG)†_ƒúÔÉŠsÐôtˆuGXÚ/ŸDóÎ|Ìž?Àâø©|û­¦„Ä|×`)4yͼ«õ¥ýˆ×7cçÒ<Ùt â5;<õ‚3ùga_»‡×H«ƒó¡7®hêoëâŠZö«Nï‹$GÔÖ±Æ" m²Þâ×.+>Üê<þ{ŠÝâCšïå±§2æý€R͘FóÇzü|•æ–%êlPíI¹Y⯜ª‹~ «dÔ‹ÆÁ1îÖw.gý6 x€òà fÄ7›oª½pº¨ÓH1SžÀŸœJ÷À¬Èüö`8'^›6Y³šI0Ñ›ziª&`— p-b»Ï A<6æÛõê‡zg¼/Ž€]OE ,êÃÐŒ3Vwˆ›‰ÝWÿ4Yã)áø%¹lÄ¿ìÐR´h¶Î_Åi‡é˜«ôŒàf~é7êye6YD-G•í0‘ëÚ(ÌžnÉ1 1QƵr#açØao­M±:#IëäÑè¹$⃸‰ìP ‰ãÄí?¾ÔÌ—`Ú6g.ïêÊr²+¾ZÃ=dêò šw` *r?{€ßúæN1‹1ŽÊ©êªå–Ú³Ùaë׆B®G3Ò±èj—ìîvˆ^f½dqÎì©óâII†ºR ü­þ&ˆ¨ørX-êñ_­:ͪC:ᯩÚ%Zß×'˜îæÝÒf0îöò²1@žf‡ó j,³$Ó ïݵ*Ý=X®ãÑöõ–ƒ0¾ñËl§GCüqó®ÝÌ­ŒîUXú°`få^-€T¬üДаg5C´@–ò-û£Å½ïèCÉK¿ÉBËxgƒH`nb°°™ü´á‚Û!( 0"­ú"pîb1¡ÀV{ˆ+ÁSör/àƒ•Üz㘶¸kr&‰ 9Hynš®)+ä¡•)ÏX%p®3z †-¿n—¬L÷ªXÜqÆGT,ÅnkÂÖ$©Ú;àRŽD¢Ä?o;‚˜™áF$ïžüKZz1Ã…šá$Qï@$H‡çôñ±±@z£œý}k ŽNT20ãUžGi“žfÿg{°@ þ¹“Úf[fœ›?Ú)8µvÙi¢q$en­tùæ>§êñ®6Мš÷Ù`ïœIß×m´¬Ûÿ"d›ò|厨åȧ`ðù]¦\ ŒÝ…#©Ö³é†_\Ü!dÕtcÂBYËu@úÜr úçKík"²0Y; ,9ª²àÂ1DÓÍ©øé±KùªçмtÂ'Ó”&1±­)%š¨vŒ#Æ>½QŸz…?râTêô5Ò¤˜I¾ë©Ê¡»ó„Ó[»a2Ñ=-óa¬ŠôO±fN#• ñd¡ôò‚rpcƒNk¤ºg}w&×Vd%„3ë­cW·èÐÜߣDgý37*0ۻ݉ÆHæv?ž§çV/MÀ·ŸµH°~uüÀ9‰RÚéãU¬¦Ê²è£UžÈÅ_n+â86åÈSg¥zª-Í\o"ùëvómsÚê›ü£ Þ©+ø¶&]«›¯Û':âx/QKK›ÝQ©1}ßdG†œØŽ¸ žuÄdôiÿ„«¥ŠοÝÊK5Èòìöj_øÇº­†c°6¥Ù¼ï8²gÚüJG 7Æ­ý9…;B(Î>­M>ài9Í#íáBâ:àÛu[f-.Þdz”zÆÌ u7.yÉ]EþºžfÃ>°PÛ§¡›¶hmº>ê½~¢oç/ûQá\ù‰Ð Ù‚‡iˆDÔ#ÍEÉ!Ë6®+ý<¾É8Ÿô(°Æã*ãºúò&šÚñž—NXõ/iœ~æ×[ñ‘½7Ö}dܪµ÷|‚²&ÅéLq `NÓ›\KΔ£ˆæ¤!Åý ùã„ôåíÌ#µó¾ž¶ís©¿m>%XÐE>¬òYj¾O‘V@Õ^¾..Hšેú“Yéì½øAUE]ä+;,OZ:¾N‰ð ÂûÆÌpT*7°Ÿd•²5Kš?çºf­²«reï«@4WóUd°¥térîÁ´†O;_8’'$õƒ-²læNR„¨ðüù–O–ç!.?x2(º˜'û0QÌíÿè¤úùSˆ:÷aŸqª¿)·}^³Ç‘ý.‘ïY¸EÁêá%{‚1¯#§dãsB …WžzBÈW#zË«Ÿ yÛƒ_¹¦,ŠLãCÛòÒˆ¢ÇW¹¼®ÿ€Î£{é ¤³EÚ›õÝ}08¾ÕÉóSÄ%°ŸØ€µuß è#”5À*(¼È3Ëqÿ v)eß äÑk~·ÇÌ’¡¬°ö“5‘’\A!йúª£S_,£4ÜMÏT‘Ééè6õäåSJ¹Î̉á>fŒGU„á§éZÖ2i'U=.^’̼ßkp'r¸x'Î(ÛU²D÷°?ädQ ˜nÔå ð#Ø<ºs4·ÜQ))–4™†¨o®¬g¹ˆ†ô‡°ëB— ‘µXò¤ó\ï}¥Úö À×㦠ÚË}9wƒ¶ÿL[ÕÒaçØ”ÓçÄÈ…È”kpT—‚bz›RoÎpÛO”™âmgØÉð=­økaÒ%Û½lò‚^á” 90?÷,Ž•ÑIÞÊÝb?´í¢z}Q(÷#½ït·³ªŠ½_’IGŸñÊ5÷—dûzeu—Ñü+¡À&`_é3£õ5}G„e-ƒ0´Î¶%Œ³~ÁÝô°ò¤ÇQƒ¯LXŸœ«Y­º€u8^¤‰üMe]'Ba| ùìiïΈ|e‡ñ)c´ %Ý(ŠM¿öÉcó3´aêli‡dbÓ`dôå‰'÷¥m/’ºž‰„­äáÑH"³Q‚ƒüAÛ==¥#ÉmŒ#7‚öšl‘E…É¢’gÚ‹‹ >€á÷jCº–ÊÀ(’¤’,eR½ÍwRÄ~*¾ÚF‰C¶šUIHè0Ðãø ‚¾ÛL »õg±3Žh²6g7O2·™ÔˆÁ±Ea$\ùЍúqÐË”F¡‰z}r|ÛÀÁ!RwÙDí¨th%V¬Õ‚‰½p¦çcöé1ê&æ±€â /Ô&uroÁšœ5W7âUˆjYÊ0]íøa"“Ìʨš)*™ü´¸@ Dfp†³ùâ"Þ'oifÃú.,‚¨˜ ¡qZë&#ŒÄ¶Õ̳W 7[d2¾íÅçG¼×F'L°õ FÙ[¢(Â^CÀuh~„wš=ñÄ·ƒMRR¼º¨Ž÷;íió•,(ÞA¯SW¹ Gé%þ|ÔÍ£ãî³à ôeZclc‹¿xaÒ¾‰±¯“*ÛëûW8Ÿ·N{`GœžDÚ¹øU¶n˜Ð³éu•‹1±'è8'‚û¾Ð×ì—‡ êÇâ;òî.A>ÜØJ*ÎH !uRU*¾0Š[ŸgAþbLG·EX¯CrlÑ+Ê·ï·BXŸ½z/“ÿ˜J quñ^'9H)k. …ë%åÀˆˆd‰^=} ã¹µèÇyàéßÎÈ—iÇZ̆hû G4 äi¬öû)ƒå¬Î=G¸§’zÄ?ÐaOH¬¼$fKž—žWBÆ3E¿:z-ö®8­&=Ø­{ùâµEùt £©GÀQ9U‚‡4YÃÉH]äÌf(Gx$³|Æ[œu繄¼ÂبȮÃ#÷õh%‹ØgÛÀÙ²ªÀmޤ)cŠŽ|÷HœJ¢•RGI’K”ûï€HÙ°îHNšÀ#4ƒ,^\?j+¹2½@æÒZª**Ó¦Clìo‚ ª¨‹(uHÏrr<³Èá˜0©´[§·>= ¨Bê„4͇TªÅ2¤DM ŽR=xÜ{jâÔEž´0Ä> ¢Â+刹w"¶u"cÛÌa;Š€Laõ¦Äç4ÝØØÉÜXIöRHeC»NàMæÏ¾˜¶ † Úæè,_¾¬9aí5`’ÃÓU8‘¾ƒÓÙåÛ0 þÊE:3hä“Z<èºá5ó×»·ÙG,&K²€¨µ¨„‚§vrƺZaLh}ó±à‡jQe0t.e„IÐÆ<沂œ-7v(?¬–FÒÿÔÄ&{>Â/lú*^Ï>QÙĪãk€´h(œ1gL«Dg°8KÁÝ•Çy4+Ò€$Õ^ÑGˆ¸s²g6ç ¾Å)a•ß]:®øl­Â9¿$×gzFÚwñ²ŽÐë凼³7vJ#ˆR8ï«j±Ã|ñ¹ê,—çayokAº§–óDáñ ”®æwdCÃ0–D–¨÷˜ß¿‰š4]‰˜~謢ááZÙ4@Ê É‹ÅÊlKžcKÓ%å]-ä÷baÈ@t`(‡Z2hG“1\j\æ8r“”aÏoß½’jP½`JIà ª®ëlîh{¸Àâipˆòúˆ(ºqÅx’TѸ#%xT<¨› ÂG%l˜€óƒ`€Õîµ’ñ‡˜M¥‚mnA 9ü%vz{Ó†ï­}LB©}›æ>lKw)(7¢n.Ò3‡Æ–c“ÍÛ©‚­ÒwdCÙŽ*TÉÓ@ÄF'©Ëf¢Xd‰ÑŸ%l¶Íj1#9­|a‚c2[_’½H®îÕ r+â»&B»=$*3ž:|8(•ï‰ï Å> à ‘äÈQôAÖUŸµ4w ¡E ã§Z €t…R«åÑOftõ#ÑJŽ›¼„$n÷¥n\ýÞžø>êï%ž¹ -²ù&Ä>ÝÒ„˜#oúäáÊHòçãh¨O9gx*Å©· ·#Šéò ÁŸÓ+)C$•·51ÿA_é 3@›c~$À¥’!·ØÅQ,C²™õpìD<IÄ ù"Ü»E Ñ…j³û”¸h6²èdÓÆ²Ü–ëÒRHîÜhæ šv“òot°… 2¦7sÓoöì]ïã¯gU„ÒF+Ft‚MB†Ç6;‚ \â®(ØDh„üÔváÛ,uð¿kØìÓŒfãØð“B€±mzzæÇ¶Á†áqŽ—†ÚÝlò"ûq˜4õb 7÷ˆ#ú&˜9°xïücØÔÃñ|9+9æ¨Ä$Öt†¼ÝÉÝÈ$ oÆ;/g·P]ÚÆÀµè…וaòÜ¡Èé9¶ç¥À¿t§{'ÝÕ!vâÔÕ¦@µ» ã) á(—fcž9ŸjãW`•ÃÔÝÍ£„PtÝïsz<0Œõ;nMMÿÀ+*{¡Ø1ìfŠÆ®UZ<Ë·Ö¡h˜ØÀ÷„nÙµe¬ï€á°Æ’Òªœú‚[ÑÑg‘=aÿ.ä×G;˜ùN]Të9ÞrÇ:¬Eþ|&,iI‚©í-FW©ÝqL¡sõ(öÈ.ð;ëå;rÅó (2º-‘kOnZh—A›Q"Z1ÂÙæeZÌn,;mÉäùUº¸¼þÂ\LYþl§ 6¬ϯu_Ùp½]>~ÏIÿ7–‰ø=ð§ú©`ÞP~^_êÎðJO»wß.ubêóØæ+§nN9ßôe kUdAöCþÉL%Í^ô\R;b=6¼I3ÌqŸÊBD 3ˆßá¢_%“ECî‚Cüë?b²R¬õà[½82ÓàF„`ÏXé°,pJÊÉNÿ°®p“Gôc©%ÁW6›xSJ9ú{[°'…|Ïòp͈¸~ÎàN°å¨îIK0Íòq¨Þ¾Œ\¿ŽÏ§:ržCõ¶‰€P&ÚDXX()L$»Ã ;_'ô*‡»Z±àeréŢƫ7>Ü,y4%Ðæ¯sùõ0du«jnbh<=9³ ÎuÎUu©l^Q„ÒTþNÀ¦~Oݹ Tƒ-ƈÅÉuÚGÇÃ÷@­éD¤.êI§ËöÜê« cWn(­U¸!½­ð¢ÊÒžxM“R‚ib¹±T¾3§VdDN)}ÙÐJ|,Q³pho¿ç5+—¸¡{êÄ\é%çÒ'ÝÆÝõl5µHìè0þ¡gL¤üù¦&Ui=>Òþ𠬥NóSh5ðõ5Ø–çó‚h1æÌCÍYݤº‰î8Uûù,$âÉ SZ/ƒeµaw){SÀ¥i'@ëí©nVRŒñ>A`|Þ ke§'ïæ©©§ì4Ø4:áÓí: YÙÛ0½…Ú ]üõoÏYéÎ;—4+Aâ½k¬DjFg%ÓVI`”Ôï¬%|ÜËWEiF(약úÈË&»ÑûªÐKðƒ8_ön6 ”/=¶9d³n‘qñÇP¹˜Àe¸b«uLB³J$ôJþaÏÌü*ÿµåH?Ãk>ç…¤êP|ÃíTõp$þÕ¬bu0°‡"þm ó‘œÀL p'I"ßñòß7<uÿ¨h§¿še,Œuâk™Š?sH%³+\‰gºUò .ëîvÍt²ú±Oh ÕUÒd .ÍÇ™º)”Eã5@ª,ý4ÊêµkJû ž÷ÐJ¬>_’ôdÀks÷á¶ŠÇžá¬bˆ­}·(Ñ£À^ôìÏ‘ƒxÔ´Çsäã ²Õãí.í*T@4“`EKަ‰LÄ!$Á(Ñg±ùÌÄÓÙC‘xø± @=)“*¹ê­ÕQNä;ÕÞ½ûÁçÑp9ðôÇ¡-Þ"â,|=jºÅÌ(dÍìm=ûžßñyœëúËÎ0lë÷î:È{ž°mQâ¥Ú…&Ï´·c³žÌ‰T®(‘×…€S Ÿ‹{.4c¾{Å8áÖ<Ý7¤ ]0¬l–(×µgF¯é7c‚, 5  ÍØ'ÕÍŽõ“¼Ù±«z ±î†>Üžœƒ@DÞ‰ÿú_t£{»ýe@§ºÛ"ÄÚ;ûenHü„†ñpî¤?ÉñE™YmQç§a(ØûŒw}¼˜¢PdKš2TŸå¹ÊNGD{`xéD]??gñZ¡d-Af•PY@ ÐÓ²útZÈì~2´ëß‘þÕÙU™|\RÒÝt–µæ¤É£Ù±­ùК쩒ë_PÛºåk€Ïc¹ê³{«1)¢žDñ÷‡}óÅ™+b°N°+&en™ Êél%5I‘–]íø¨ÞØ4Žƒù §~›Ã$cY1×í!Ú²^<‡ª¿Wñ³•ñ>UdaÄ>ç)ß·ö„!‘­²®o4£1ØÊÅ%Œ§\-¯{ OÀ®û•­D‘LcŽD$ôœ+‹uþªÒ›À ª{ [=ø&jÅ€¹Mz¬±ÊÔä2D‹î÷Ë,R Ôø€Õ™¨¤ŠÛŒBý•“«ù$ ÇUð8u´˜¹!ªÍ`::C‹ŒšðÀx^uxa„o«`®nrÎñ4õN¢ï¯†Æ†¿¼Ÿ”r+šxMûpwÜW”Ò›0F=‹Räá?òR6cÀ<šÕ†Â`üÇB,ÀBEH¶6ÞßÐêÐÑãÇ© xÓ Iî}(oãéÁHXœ¥¹q@œÝ€Þ¡1˜´.›³âº¿`ôtåÿ>ì4̃3h£%¸`逋\€šÅi‡òGáÄÛB3ã’Ú½›pÍ4ªeþò®:€úMZ6Lå ìë,ûL¢_zîò8lùS˜¾ïE›PR/I>l ¼û^ã6ž/Œ)ð»™ópÏ7œÂÄrd[s[N N÷ù™e[Éß¼e1;Y«‘]J'uÑ$Wyáb²DÈÜõ]?ËóЕvZ¢` /¦kv¸D$ÓŒD‹ÜìéoX…÷x;UG§=Ìí¢ö×rˆŠp+f6ìÉ µø¦¦t¨ésFoÍ @‚3!l&® Åì¯2²¹¶îú°#yB›’sjï‰M·”ÂFzïÐátz¦ÙäŸ.Å=" 9_ÀÜÓµ.½¼ÂN¥D{\Ž4Oá›Ê“䆠pì7< ´¥$;£•Ež³:JÂÖÏ…\ º Õ}ÔlwÃ9è<{|5VÊb{…&Ç\T'³ÇNJÎ-^x3{!*Êqó&)UH‡aƒK±ú¿ ˜„q'ò”ó£ €ª3Z/£>”ÂßÑü >½wްÅ×Ê•0]¿gý®;~ˆ‡|Ea¢w“gáÖ9P0Qi¦’¥’s¥àY2uѦÓèÙGŸÎɸ ÚÊ‚oÀT«@§,g$Dƒób°ÿrnA?ëíÿÍ×ËÄ <àà¦Aï-ࡌb@q¼?êÚP× s[¬Y¤Üȹ|²zZþç©r‰@—Û[Aư¢Úñ˜¤º@¤ëï8Ò’Ä7ªkSöé3®xÈ‚YÕÑmá¯'Œ¡[âM³´Õc¡=sÔÆ—G[”}Ä{D¯$ËQÉAæ§á‹yûș󣀄 †·U–ß³÷-‹ÆLKÚûÏý¬°ííÅeí)A§SVE‹.õX3÷·e½Ñ›×.‰ƒNŠ¿§Ã)Êùîõv 0b…FÃŽ©´‘z¾2\†ç¦zÚå»5ÝÜv²Ç¯õ‡©…ðK º‘¥…ýÁLzÄ ¾ÃXðE‹NnቂNG¤à]Ó~sÍsªà>½²F›ß©¬­ ±šÉ›&B;|\a2UVÚ¦ Ù€FÃV¾dËw•u¬¬#Ö&"ižŒkÌWWê,cÿ¹vn¤û÷:Èg¨€+4ôuZkF (fÊ‘a­ÑˆGØk G*kôÃ1– í#fª¼"L”E$ÃfRê2Æ «ÏöÓÒžÝFÚnU”0©)<Óñ2ÔÚ”ª&-~òZe†:[òñÔ¯2‚ú•·x´”U#bÜ#œÞ``"¾QNo»Ô[C }lÖn¿ …éUwý´æ*rAÂQXkheÉaJ¶h¾Bb„h™;r©oæ£cB_u¶Ä׿Ï\Yeàøép‚O8¸p°_˜N J!¡Sëìå{¼MÃÙ¯Ä<VÂfvbÚÁ¯Ø% ¾“-ýU·¸Ùþðžµ¢.¢¡.¨ÇŠÄk‘ cé¨aï—A“™œÝŒ–!1-«{W±W«Ë,1¸Ïo’7à-„b0_AøÃ],1jY†oÑ…‡%`(Ö%õ`v¶ÐKoOÔݺ d ðí×9we’7W”lê¿e=ÝÒ¼øÇBÇú”┯àÔM u—r5·ˆ¨·öÈÉR·¯V¤³>qÛþROH~¦F䚪V? Û4àë·»’i…«_bmIö‹LZŸ{ÌÓ‹ 9c÷щuåþ‚DI÷n€ ±)XC¥‡õ$–tƒµ”4ó¸#=wÒ,@»ðÖò몘x ôEö ÝÛÍbrÜ춯”íZãJ÷9a.nŒG¯¿©³ê4¯ºãíøSiN'ì÷MÒ$œK@á<ºŽ×Ë/ ãð:ÏP wÀ\L¶’C¯|euÖxv×2½+F–M¦Fyõë”Ìvy&%X&hž®ISÓŒù•. æÅU–°ÉÃ?ì¡ÁâÈH/ž>äÔõãkšó±‚¯:òo–ú…7"ª@*{þ ZÆ.* žBQ1šüh¹×}ÛÅ·àÃhœàâØ©AV´Åƒ¬ÍÀ49Ó„x:BËòƒ†À+û¯ –ÀöcÏŸÅõÁ«r6 äJEWàrèº_Nþ2œ_ –·ÒÚ•èĦoRÑ+æËQ[8yñ¨q5&¡QÓ¾EµÁžHÐ|QÑÞe´·š¬3$vFî )÷+€ÈÂÆöT×sÓb¥ãföîbgò¹½Àf{‚<ÊHuG–ïRîY6‘áDåvÔ#¹BT$#ÀdòH Ö‰)¾(1ñE’ôw©OA©_Zlf)0p²1I7´ ÆÖ?NŠY,þͶu™Ð´—·"Ë@xnùYbF„³ÅŒ‘·êb‰R§F:€ Ó"4þ¤åy‰:ƒtÅÓÅé•·XQò8‡9¼mìFRòé-hËÕ%/ˆFvòS´J¬I. 4¨p÷ܘì)‰•ñ#7jÍØlÜ.ì%S’Ùi“´àǹ1½s$* yGkGDMC[ù·<9µ. ÷“ì¨4´Ø 4âMÑ øïi¯š6Ôžh‘—iXµãsÓÄ ¦ÄŽ'ÕhÁ ƒ¬b8ììhÈË]Ô v#cþZðêÁ€s‘݇㮻ìâJøCY0‚Ì{ñGV<€ ºüiR@ÓKõMôb6–DM£wñáHEãÜ vgÆÃïïÏø,Ýs®ëìâEÔR=aô‰Í ;I?ðøüGC!yRÓ߈7×›ÄyòÓÀ£DŠÔ%?|†Då ž·Y#J¥Sȹâ[² àí^#.]m…@ŒV§ü›aßq‡ù|ðéë`›-ôÅÆ5”ãšUK…3gUn~;K–JW¿ —ØeÐ#©š)†Ìõ¤ÄM’д²m(œQ‘þÖëÄ—”#eÀ ÆÞ9V4 ÔöO0ä{K#¶á×Ä$zßX›š2Ö¢oÊxàÖ±*(F²2ŸÍhZþrªÖ ÓÁrW†­°t‘êELÜotVUù[Ðþ¼òÃå¦;à´¢{mžÆ#…EäÒ bAw ýã­ø¿©ð3›ëy|Žë&YЇÎ9nùFà¡Ýì ÍÔÑGNëøERg}߇ü‚–ËÄÙU­ŸI)ÍuÃÜ>…ãÉÁ8ÔQ+b»r>¾·7<,U‚¼”(áïúëŒ~EYlL£™´{Ï^_ùt#JDQIq·mn8/º›:ÎePÁþy¿ s‘he ;‡jr0ñ!äø§–³Â\P:¢Á­¡0º¢¾¿Žñ€!-·ËS7Ï“8í,ù¿nö<ÂÚ=CuIÖ=JkÈ®Ùä …ÙŠbêJÈ0Ѻ¥üÊËqÅòK¤‘øò“zãßþY[#à 8alГI·žÀ}k 2O]FN©0\OôÆÖ„‚Í<í”3@ƒ4-R1ï¡0—}\ 3L*¨9LÊæ| Eb]›ïG.,:™Ó“_’wSè,JÏÎPc/Ôz*âšKR„ Qe6åH™1`#âÉwŽa²³š¥1¯!7㌫;„¹[û ©$ËñáqÚamh€êîæuôdlÿ™U¸À7­©âÀÅ×}B• ï€v7Ô“iÊ­à#¢¼käíbV@κhÀï2I*ÙúØÄHšS´ÆŒ¶Ëæ%HÁÇRñµx¢ÇxãÅ“w–*J-RòÍ23ZlÄGW#YšDgÂòudY ˆ.°ˆ{73aBìƒìÜC¢›S·Ïl½õbœªxš£=³ß«8ß韄Aµ¶þ‘§=]33é*õ« P­¾2îâ˜vªE˾ŠçáÝ–oyQ¡(Y߸¬*° òû +U0EUô­T¼ÌAv”x5Î*¥ìÍíí~E¨Is5"[àµLŸ*^ìÀv‰4†¯A.ÄPEÁvì¼ü…A:„Ì}_—sí¥…|w>áv•0ÐÐGJhülßÉ# ðµâ,x¸Ål‘=9~Û ¿µèXü|>w†A(ï$›™$M꺕ïÙZ—ïßІO=ñçEƒ½Pm«[8‘Š(íüöêÊx$‹ïÜ/¬ÓWÙMŠ€»ÉQ`8áÊ .õŽV¥þÅiÍ? wªExv”W*Ía2Ÿ¸Øju?2ný"‡XaÌÜÚ` Føø,ì¹Ì!^XOéžä$÷¬hÃ<Ìœd"±õYGO ä@ÇâD¨ôuLäŸÑçN‹ÖÕjž+Ò0Ýœ›à.·êó²ÍœëÝ¥Qö¨w™•ÍÆ-üóþ E–CKÉÝqŸ}SçÄO%`¸Âؾ€a?IìdÛTÙÏR:4›­ áòÏ—A§½³n(2ˆãí[ÿ×\OëŸR„?àÒ*zÛ©ùm–9ð4õë·I`ò#µ´;ç†T°0µ«ú³£c¥’»$ ¶„¾6ÍiÑt¥)X‹‡ ëÆ£’›ªñS!B˜~_ã,éûjrñ²½Àçìöû„Û8Ԍ㘰P W¶WjÄÍ~žޏpФ$vdê‚××lBÜú«´TéG褆…fûÿÇ@§@Ì•p‹·å†u̯@„áËã¤iöÁƒ-Òæ‹ÚÈí¢»Òzaé=aCt:_j– ‚“‰¼¸E/i¬ñ¿ß¹Š$¦Ç ¸iFÄ^drlàXN¬”Œ{Ør’%I×'ûÄüƒ ½T@&¡âBϰâؘj—ã»Õ¯Æ¡†¦;©ªÜ°b;h¸ÖÅGø,¡¼­ ü™O ÿÐÌïÞÌ[¢Ñ4bAß$òM O½HḤ t}¾Ÿ¤`¾°ÈŸ™ñY]µZÊ#8;0H!$A ÿþ;Q<8…!0Ys‘¥Ê¥ Åî$ ‚¸9¼¹ÍªÌq‡°)ˆ¨t¸Ñ.° ëueæ/‚}…ÖZÆyRÂζ tÝ}о¡=/Ÿë ?½¼>‰ûØzºc¾’l%4–8Uö*tðÍF:!ü¯3ñ¿‹Åkò(©ÿÿê wÀõ#>ÎSÉÐDaÝ=LâÚÎbE6ˆ@óbsÆYšû ×ÛQO9ý`Ù á‚@@|OÕ³m=ÖÖÍutôi_¹Weú–&|T-öÏ‹+«@N‡ÿ$µLn'^ äh!‰w†Û’þ ÷ὄƒì7IþF&D×$\D ±Gh~›=Q„1vì)·ØZ¾y«:s—DH.xÓùÜíÅä`1;"HMÂõäÒÖˆ(Åé<&„ôbéAz“¥øØöëä¼bø¶pé–²Ø,0Ý)f·ž¿/ÜYcÊ#½–oåëQ®v˜;ÕŠÚŽU¨aÛ&…ºðŠý6ØŒ.οƻо ¦ÏZsž&®B õ/f'¹–iN ÉEªf¹>ôK5uek“³ÛN¦ÿ—³ýypY«~Ñâeñó(KôÞ=À1{Љ£°BàˆUpH—ü:v4A¤ØPS$.ºÑYReÊü{îE«@€€•DûÀPN/¬“TšÝ’™ÎxR©¼­¢þþÓ¡ýÎØQ#½_ÜÔ]ѵ}–Úç¼á}ê®™´—#ø·:gNÌí8 =_w(“CzC´Fôr72G– s%д©LThàH2} S>8~Cñ$—´þçí/Š·çƒ2qcä¹,ñ‹ö‡sð[Ko£”çQì¿ÌÙü’{(Àh…- ø´k4w¾W:§é}‡Ÿ$r+B »üÙ-EUŠØüÞ* Pq©<3 £^„©” °ôÚ5¦tp¶VWÂû.çÔ%ц·×£ï†c{< ¯6}]/a¸mùÚëwRs#z3CŸµÈÃçäS,f@/•¯_c’ˆ;›hÀ‚ƒW9WpUjŸß„ö5ñ'ãj¤–ÿp6Ó¬¼R 5½4]¼‰¥Ù3îns¿ÏNæ?…54ìÀmr-x³î /4D®­òÁ 3‰LZTìŸÒÒCN”$|ÞÛŒš*¹<ºãÓá0²“ÜÚ{Ör¦OŸP·ð¹C¾K™¶Ñ\¢ÎìYùƒ¯Ûâ™;»‹žd˜Æwl(›pV‡iK׎·/3.-“@Ù͈ÙÎO!…ÞB›ÈR!9ÐÝî~ºèoŽ0âò*Ruh{QàÒÍ‹g?É‚þ‚%ÏŠ´…9²¤š=ž/#ï_´/à+ÖŽ™:ÆÌ’eïï(jHJG¬]bÃ-ŸU¶«?çfæ!—]¯þÍŒÇ'àü|$‰l©þ¾–ìþ&•$nmÚsþü¯Ê¢×Ä)Nޤãi\†;‚Ýñ"(±Ì¶.ƒ(ŸÏì&x…i=”ýT«.•sÍc€¥Œ¶Î>_°)±WÁ'å#Ù$H¸Ï˜«vîÃfµÌKÅ£Žye~ÇLà9Þ“J-¹²Pzž†L«Ë¸þ_bêçCÑkâÆeô-¡à;²€^oÑ\É‚Ð~IÞ_Ê6?î>Øí ×<® Ï}ðÂ{¤|RˆîAŒ¨&À7‚Ϭ£Ìe(0ŒÅâîíR¼îæuæï´­ý°¥Œ 8®£Ž°{eS`@Æ!pÞÙßÜŒcæ°qbb¼N¿ô*Á[Áô»µÜxuú©ˆÀå›§àtGÆr™k€ôá<Í*‡¾Àk Çõ g‡+º:Y  ìÃÂ#ޤoGmO SNP0̾õ‘ìÌ*iMQ—«—ŸCp]ë_°+A€Œ¢ú}:ÔÍÐG+A°ÿáy©§(›|ÊåVÜ£!~(žxI/yÀ©ÝOcn*i§éd÷L‹©\>έ…x¨Íp]§ð访ÈÙ¯ˆa'V¼3ÅzÈ<ÂL]‰çëè|:¦(xï°"e•é0ço­0ÙFŠw—cjùÎ^ÊF`Òrµñ}É'F?ø²ö)!(±èyvo_àM½ý-FkÙDD„©Þw.™-ÀešgÒÇ4àYÒ-QûÞØÎb»Q'©ê2&¹ ‹§gñÞfµÊIsº%óP/°]Ìή4¥ 2ÃØ­ŒQ+ß<4×¶«Ó¦ ÀF³A†¶MWÜJo¹‰Ù@Ø{3tý6¸&5o°æaÛÀ2Ø © ”/çùU|Û]âp´÷-¶ßg>›NäKš¼qà+”kM·«ÑåO¥ážv”‚Þ3ï gUàøYdeÒ`¡…T 0Ù'ª½üL_šjdêÚÊ úGPô?;FIšffÅ ¢ ÃfP¡2YŠ·d€ýàý'_>ȹo&vpþ4þ1£³LýH+””Í,æ´©}}ÿ'ž þ ?Õ¾1žËÞàoz`¸ÇÀïó”ðÊéâÓ¥‰5š¢[c`ÿÁøþ~ýþmÁL(“€7˜˜?0lõÝ;áÙá|ý—.½—3÷›fàÎrPØáH¸Keˆ²@•û)\êÌÚoÜœŠ“^¨Åø%¢üµ$¬Ûýk#œ|`8ˆa‰tTˆä œ¼”/hYîÝ.+³temyú¾Œä«#`¦üÖÆöÀO,©9a v²1–åȧŸœi!ûÉZ»[áL®ÝöŒí¥‰9Õ*Ä…õín¦F/ýa4›aT~AQØžq-%ëm‘`:’§Ó9G‚˜ "_d—éÑÒµñ®k[³ú~*-¸-Á£/>k$Ðs…9,<«O?uÌu‹Ïòø%án¨¡«íë­‹:Òጃê?÷´641qGåðâÜe>Ú}NFÊ`]âï0†!‡rå~ãݧ*µjÚºÒØ± ,ÌäÚ%„f‰žCöà:â³9–mßþwã2Àf¬1{g¤‰õäŠr€Q'äSdå\w~Ýlæ!×I¯žÞͼ|ƒiIÓ–ÉŠŠ—áÐRoà=±ÐXksÞT—„‘ÃIvˆä£ú:½Cûµ,ÄZÄú6~‹{Ø´9iqlëÀŒš–¿X-9¹Œc•òkZµÀ[Té ßG‹uõtL‚°cÊCtd?ç{J-Æò 6Ý-i‡ïQÉÈ )ä·J¦±šäj×CiÈóÜ/B{Ì¿™ä$ †ÿìYêf:Õ¬áäP™‡¨¢N}zM×­àÆçšºF†µÛ@'š`,³6ãþÀT÷~!IÙ›a ƒMÓœEìÜ…?4jÀñÙ¼GÅ0³½UöUÙEîÒÝ/ÃG¨ÂBÊcw]§îÚí®­êLjÍh>ßÖ¦Jùøîl’yú»ò ÐŒê¦%)à)"œ¤ºš÷"8ê»%}‘‘)4죦*ØÁp¾) ³ÖÕs)¹A ×cŒC¤Jô,JÜïÓ†í[¯™v$Û\w©Ýª8dxdxLHÇìæÕ±#öÝ.,åèãµÇÀ$EL¿É¼r-gD“êªx™3@Øž!­i«4@Ã{ËŽ "¶¼“)îE± <ûBÑ#©éiC0óød¹@¢l`êùSƒfz}ïW˜íÞýø&•)Ê÷âtû®Q¾pshAŽVá¤7ƹ×ü­Zsë*Þ@ ¿ÝDškï‘×ÓÕ]q’‰ÃíñCÝ•,³ç8KÞ™Ïô5Ó‚N–æ\n‹& B5j<«éÂ<;ÔýH+-ŠÏ=á–¶QôH03-u$Jñ¿t¼`¤°—¯›Í–ßóÕ“NÑ,ËseN[4ÇS¹¯Ãr€5y*DòÇ= S_õ÷íQíåå_|äç‡@W¤l³ˆžeoÙ XT/èwy/úÎp?%šöh{$¼£½°–ôK ¯„ð°§'Gžz=5CšútCj‰…þç_šï«µæþÖ[ÿo–e¯†§sO¶ÖI}S<Š…YMïÞ€§þO—r=N ÊDá Ún všHˆ¼Ø>Q\t äYLzgyn¼Ðä~4 n/óöQyü%C—Ç‹.d= }þ¥ÙžEë”`Kà¢ÅgÕ(|ÎI±œHe°’ÞŒ ù1—PûmKŒ3X2vvAd$Èj‡O¢ŒÍšð&IÅÏnÕ¦9±Ë–$}Äã ÷[Ü< ñ¿_}4'Yµ€ÈâÛ¤¬Ò5ç¾ÎƒÔ}ëÍò‹Ü3ì”0¸6pèO]D]ñǃ¥9-®´”DŽ1 r#ÐØDø‡‰}Îk·ºƒv~ø)ïèàj™x…€A¯«´4?[L!iŸö£ 7í¢Ü¥¶·í$B º•©½Ñë /—C„ü$~«ð8€ÃlÁªÖÄÃÞ(îãÚ`/žûw\¶˜;3@7-eGX¾øâ[Nà¼é Bö²:„èB¼¬´¿T- io|ÇðSPn¥7à³ömɶ½¨²áæ,Å!wf¢–°,uiÊ,ÏnzÔÕ³NJl©¹œ¨4uÊÙ›Àt2•Ð9ü5ói.W=`›e][äÂi’Låj~êi¢¦êlÉ6üU £]ù2º%–ƒÞÍ^IwÃÚMÌxà÷º‚+S~-‡¦ÁÞ­1É?D'y5’á+!¤Sù‚'ügOî¸Õ„Wön„g¡¥KÞx$‰Úø®·ã¶Í {^`îMj¶Á'%¨ƒ¸"YÐÃÊP€¬‘|d¥“5±4?Í>ÁsIüüÙ§?#Tl%œÌÎõ1ˆþ" °¾ÃÎö&•DsòðAæ~Ò€±Ï})FAS)ñý&Ù(¾ªN-è ¾.ož¥¨Ò^/œú´ð˜ç°æþÐF"«)ÝE—t5z­ñ–]‚¤¨®ÓŒÝ?IÝ¥‚ ,JÏûB8O<ÔD°4)ˆÚŸòc¢1ÍöRLtÆ¿óÚ«6``ÒFýêú&LÉ—ò ®*y—iH<¢aÙÝ/šjÉ\á‹å=‡5SõbLd[6"ämÞÞÔ¯+íÂÄaãn[Ž_Z ?0ödeºÔL‚ñÕÍ©ÑÕ‘ÀW…¿<~«´hr¿YÄJGëÍé‡iï)~S›„7ŸÙÁ2?<¶î[ǹJå¡G°àúœÚZȪ±£Ãॴ:¿îݰ\Y“¾ùÆQ¡ ÔyCÉÿ “5†ÅqÌ eW/¤K_;¯ïá/vžs¤§$u©-QžœÇÌPÑìM‘Ë`7óŸ³4rì W¿:aî@¬‚’¢Å}}-“ø„½ânSóŒ¤Í¸øÊÑ«J>(ÈH§XŒv…8êÒ΃.»y—"šœ/~YG·#‚ê¦лöÛôzMˆàkÓ,)¯Qj…³ŽÄl ÅéAYhÔÃHfvŒ B«ÏÞ{} åFO6Fù6wóJ=Gnò¤ÏŽ½Ë‹rPNx&g­,C ëÍááà˜Ï`…‘¤½%Œ…èþÑž[åk“ÃâXŸ1…„rÀdnÁÖ+¤ä8Ó_’¹ç°/ë2Ò¨,¢B?ÚxS ;à‘®Ø 0‘ Eéƒá1¾Å»ÍÌYƒ‚Ã}½Çq_ÅfË C¿ð%”¤ðã@ËÐBC”÷‚Ÿ •ˆýùZdãøÑ ¥ârÆ'«N­ >Ã8Ʀ%"õï\fë žb/è´äüìh’0á”òúü¦z/ GèàƒÄñ0Ìá IiŠÎîÍšk%+´å Æ-¼%,¥”e÷ÍpµYCà¦ÔÈDðcòÖvHBÁ´+â\Å 9JZ‘ù û/yU-Öj686h'à&YL‹ËPöÑS3òæ ö‡)d«vxvß’7úZáÁÇu•µøF>bÿ ýÒ§A–¿{Ð Ó-ﮨÓûC~§üh•wãïÝîÑñò¯ÿãÀ¡w€˜ôܱ`ö]Ìœ$ðhÜ£Çàw}+ÑëÝë}æ$ Vu9¤¾Q±nŒ ùm÷\¡`OT»uÙìe"’ý,ÒNR{vÍ!›RÝæ·~vGœj直ÕM%‰Ÿ-îAÓÍbæÕ`ìfë]a°yRïf[ooÒƒʘ¸ÀÂÓ2û5®º`Ò×LêëoDàèüvižÎëχ»õ50Mï„ý×âë÷†r‹âœß ëË”ÀÒ6‘6 >i?> (&snze®‹0µúb©‚ž©©9˜ý‡¨¥z<‚éØòÖÌÚ 4£nµ i/½Xª\$x,Õx´Õ>†0¦¼1ÁÑ^*¤& xÜdã(KÙøJWíO2¤zs xxOxhÝšZzÚÙ†¨¯G"Õ…Ã #hî~ëkª–§/èp“¦\¦Æ×û¶GÔ‚$*Ã6ÊòS'ýQãŒKs÷òð“û=€´«¼®çw ‰n¦í‚Æ‚7Ì)»ˆ ¦[r%w_½3¢‚F IJr*ü·Ë"–GácྌZ`80*’0©ø`Aç a‡r·®€µBzö,ós6à@ߤÞBÃ2Ðö僤´0¦wÊõã’Vgª½8ÊsÙR‘'çs¯Îkù?Ûº„—BÙPR£n°XõÃéÏB¥¡Hpnìj¾¢ÁF~N–£’iª‡ò¶†×2¢þËh"ÕãLÉÏ ìJã ÜSŽÙ ;3®}°”ÿ˧fNq—¶±z‡Eµ:,%»+9XcÇé4lƒ”¬§Kerˆ0€ÄÀ ;qʱ¼ûƒ{}GÐ(¡Üéꀥ¼ÙR@ܰƽ)ÞWN7›þÚÄ+ Rü§û@A«ƒŒvdïHÉ® "!Vî­ósàÙžÏ$¾ð_ àQ€ñÝ ((ŸX.>·>ñb"ñtœA)ÛÏLµQkgóÒ¶‹d?™K³ÇÃHá#xo!áXµ>ŽSv»ÈÏù˜M1_æv0žN=Ã6V€¤a·=, 'ì¼¥šC.é )Ü¥É逛éòG¢©D3/æd£Žc¦/…¢(+öäë ÑàÁ[ÿ½¤{kÇ šØ±q•aHpàï‡!lp–€`mRíîŒXíY\"ökW@Žü\–a„ ‹¸]c[ÂÙcháj\1¬-ÆK—Gl5USƒí~Ø ƒb¥:ÜxK{“¶n¯›Ý;ôVò¸jžq‰¥Žàªì‘™&ÛUváèù-hµ£EîÓ „p?sÎ%+\ie›=âz1>Ò•ÅzÕï†ɉ5ì5uX¬Ã®†ZÎAÙg«–¶”>í Ì ¨ï>JÔíd‹%>À›¦§³uÒºå°Èu÷ÑÅñ²›ïOåæÊ7:M‘z°²‡W+‚Å‚¬Q·)`Ú#\Æ{>–ä­2ãpB¸´s¡C$Š(u3EÔoêùÄËqäçSF!¥‡ RÔS­xjÃ6»*®Ø"B㓜’^]30+§œ¨KŠœq©š±šÒœ×·‡¹Ó/fó™Ýë«×dÊ—(~±;(fc…Ô`DŸêŠ1ˆZß\m¯Žûcˆ\¼ÛýWêèQŠÀ]¾nXªÉa—îîüعÂ.]ÆboQ4ç“ÎוËaóØDÜÆmî?*ߡ죞ëÔ`¡ªÓ³×'àPâ4¯#·ªç×6OBÓTóžž^Vw’Û¨ÆTdiíîT¥E¹Æ© $°³ ª}±rþª“<ä»tL-¹“ˆS§dx²|ª¨8ú¶BÔG¶þðç]PUâ*UuÅ%«µsóúËžEð >àmB{w¦eŒ ¿ñø÷'Ü‹ú3¶Ü98þJS²Õ˜Ý¹¡p(ìwû¸£t‰ùÑ"ynZdéXÕSÂ[`‹ ÇÎÒX¢`“ü9{HÝï!‚Ñõ½À3³pãZÈ  üÈÌ–.ˆ'2–ÜiN·•јÃÈ€XØSo=Ùž{içyÖ…ˆ’ €•J?ÖyÍ:Ë  U%“ÔÇS)ȧàb¦I§ïŒ'rx¨ ã¿Ï"Šß}içðYâmÒG\'òM–“kûæ6Ó»A¥ßðÖ(}Éá—ÞZöMÉņMm•'ôã‹^T³éÃ3Ú¥%‹Zæ‹þ1!@©¢öM'4ÇÅ R³â\g‰™}sbÇѸw^SDéŽlþD…btHâ³áqXý…ÒA0Ïüm`’ÛT^[û&[¾»@ ºGobõ† + 9ð‡5> fvÎv<.šVÍhfF±IYHT %ƒ÷Ñìë›Töï!£ƒ“>óÖó,N+ªÕûÌbÀ„éô#;é\c®ÒáÎüÀ‹”¥Ó©j£|“ߣƈXøçwå§PËWÛºô>j1érävKNQÜæ©s´DbÙäI×=俦FªF¸ƒóþIiîcŸëL¾…_Ðì.=K)Áx.´n=Ÿ 6¶ÀïõD°a\Á{”›¸_JlFAèJ-B†M÷¾MÌQéïƒú×5)_na'v\¾¤¼E’´§ƒåH€¶´Qµ)·)‰ÖFŠ•XZÓß”ßbºÀK‹#^Цç£×|[ÀÛÉ$Ø?++Ÿ~‰¡¶i_V›UߺHG_›óÅ–D˜œ¢Ê̾÷™úw¿øÀh¯ÉBûl(˜“ÇÓÛ@ò£¯I»²ÿ6Þ‹¶v;`»Áˆaö‚»˜D›¦uÉíyä À˜_¶×gÙÖöä¾M·¼H9Þo=½ü›µjmld—Óüî<ûãÎÞ–ÇÆ¤ÌÀˆ.1#¥¼¤GS¦Ãéïur9ÁtCÝähÉ“ü쬚éõϱQ¢„úÍ2’…¶¹ws¹Ñ  <¸F-Ï6·J±Õ™A‹Ôö)Óbµæ<ŽbëlÑ9}ž#u˜÷pæLÒ¬]v«ÿO´ì²†?\\Û kLÁ¸ø ½MMÍW$”Vp¹€ÁLÃê¡Á„Eº©;¯E yý]¥/5[ÂH¥û}4örÆ’{ ‹«çç1(—ÙmX}ºm&4I(;¯S¶¦ãSWq­éHÃ@éÁq”ù¡.½CI~ÃØÌo!Ñ‘¾œH73ŽgåEyF´ýÐ ójIé×á!:OËêë‘s0õ©ºÉ€jëª"ÏJ_žLâ€y¹?F³µÛïʹn\´a$Ñw¸MC¬O“¹ŽìãcýúXðy¦Óê ëP*µôg˜•BŠI÷›øþ8… oLçÏŽE‚ÉhºÌ±@’%« »›‘…µgÚ³IÔ‹M‚b?ãtIn»äý‡ƒ‘î À¾L¸ÆÚhQr>Ç+,™+0xè i­<|3Ìò|%ÓoñcÅŒ¨Þ´‚h Õ †€êIÀu¼+4KD¼¥(M2žñˆCžñµô$îÎU:„sŸ4b!48R),­ü~Έœa$™ !÷]¿ÒY¢©d¶5ã ~ žÚMòd]”’lßm> ,ƒ´b¿šÊ<ZÇêY†“³¡‘±ðìå_•ZK¹o4¯ôÎê«W÷F’ɇ‡ &l Ñ‚et œSåE¿ºŸ1ÿÁ}õæh;jwPµ+AˆÚ¬ :˜q¤Æwó÷ãŒÁ\ÚylÂâBè×Êäœa«:v?:ž*  Bîã뉵.&¦ 埇/K[¹¼Õ5Ü[Ó–s*€B쳚¤ÂRÌæh#CJå1Ô[ E´éE_n(š÷˜‰…ÖQÀI%?¹&å(·°ÿéóYúb󨌓- Œ?PÔ¹d:”F–$¿´Ë˜ ÛÝP¾¿-Ô6ôÑ/ûrAÜé{H{*Ä[*d¥X1Ü¿ùçCªhÌ !T.§€Å½IÆ àL߉Ó!÷?´ã2øó’ï'ÃTÊŲ9±ŸÏí½nÝCÁ–þ³ìÐ3|6rFúgÏWì½›¢übòCHÐv ÂFeB-é6(®“XzFbmè÷Be ãÍa„O|dÿè?ŒWáD÷G°$yY²—F9m‡uÏ1D`\a+‚©ÑL"åxì³éÈŠ”[æ«Í§1¾Oµ¯Or{µð‡9‡DÑS½9JÓŠ4’½rîràÌìVæ#]'7ðÞ…ä`žß,e)‡…[ë¶#Þûõ…ìTyé‹§ aïï¾åQ”Øtèq-ÛKf¡Æ‚Òžv=°Íw Ñ»ñ4Hj«b®)“ëoPo^•¼~FF z©G?ÓÛy·»ó—òuÛ‡S#Wf®BÚ2s6©h´û£/(¡½²ž¦2öxÍEbžC“Ó[¡Î€‹’¢U?n JO½_îÍæq8€é^%#ѶØ•ùèĽiz§ÍY×zec¼lL Tchï{U=l#WØIAÜO¥È‹ ižy½ØÊW-¨tS\9‰ÌñØ“|aÄÕ½víxMxK?mÃ둃i8ˆV’Þùá'I y=‡0€~9Bй›Îùü?Óш.¿ØÃ°Šß ãbÂâû¤Ç5÷¿½çólA¦oV&Ä´@ú“•¦H¨)q ‚ô÷5œƒÀׄ­ÿ&”ý ©¥ÆÓ®Íi€mÏI‘$6È\;LÞâ0]è˜nä§*ü¤ï?èÈ:3!­ñÁ¯ÚRå 4{‘?e­ÒQ¢¿3Køÿ V«ë“ËZue:u’ ž±‹Æ~ªz£äah`ÛBéJv­x—}ÒTÐhq?c.X³Xy©zˆÂ ¬D¤ÓåÀä !,°C&Ìh F£ñ‡‚|%<ö’TÌ JÔžŠh¬¿Ð”Gˆ‹dm|O쀣—aㄯùvjøÎ¦ã³@ôÿC`Bx…i¶³aAzUebZõýC€,òøqä!ˆ£™!åÙ_ˆ ÔÌÃ|C©ZDceµúvý±íÑDJNqÆ1IÇ/_* d4‚U+wŠv}/nã)ÑM&VÕÛáÎ lŽïõƾqA²Dä`U”£3døª¸åÍ)%»ÚûÐ/F}2JZÛÁ64$[ºÞ× ÿÂQ7o _ˆBàeÓN ŒjéÃI5«{öÁ©ýU-Óxw¤ê!  (!ú·Ò©Ç'&©Ñà¾êÝŸ§@ÕunmÒ=ÑœÎÑù·:ïe£n¢ï{~ zaÙã¡,•ô¸_ʱì„5õ1.NöívÁyn€ ¨¡Ã<3‹ õó²•×øŽ2ЮÅOQ-͡ɻÅ~òé 4~”6,f)~ÁmH ˆ3ËׯkÝ_>SǺ|Å}ë7Tâì¡Ð½‚î̆?Þ–ÐøQ=•êúÎ<_Î`ØUlÛgŠ~ Øâ `ü·PÒKÀÃ42Yë¯><ó¶:uB@=ž5§ÆU…’ßð.ìóW¿æ +ft•"ëÏÃJ·OÆù¦^P°ñuªH°Úô¯E§‚ǨĀ0@²·®ZGâ(íQA§tƒ¯žå)Ú,ˆz:6Ay¹ºöC*0òG­~¹ž±¥¥g&¤ÈÎŒ‡îZ‘°AæF­’ï¡v7 Ìâ{ ß1Ägìf‘ß,3˜,B\öL6uEÓ GâNTÉz#Öš+|žÝ áณ)ºZ¶î‹·ÇQINþ>Õìn¤šU:ãaÌ@«\ßøqꤙ¤SX‘æÀdª˜&W_õ½e,{ßžIšmÜØ…­õC zÃë/‘":VµÊèµ/e޾«Hf+*|§¶,û¶±©[àqudã!çÍ«éVçGR/Xá c²ª,ï¸nÂ{fà ¢b?ѬgyXÜ™øüà–‡í Er.Ý:~'“=y^¹¾¥zƒ7´¼SwÔÎþVeEC­Î]_õ+ÚÂÛÍÚ@á÷eߦµ-~ìƒ3Âñ"q¢³S;çðûõÞꥷ~ªlÑÌ)ÿ[¦%——lçCÁyäÛý2zˆKa“ÑP*–ähD j•YžaZÞÝd=ªÕÑüîw—êŽã÷¨é,Í@ÝzWkûæV ÏÉ¿Ã'n K>MÎØÖI¸^ÂØ®h]MÈ|›ÈN,§ŠÞY¹±>>ÁS¦Øs©ˆcÀHº<Ùò³R!o-óPÔ¾ÎLEͼð›ÞŸÖ$EÈ HÓ·h ¶ž¾EGd‰¦h¬õ¬Ö„S‹\y§8Sy'a’·x@z˜ˆif°ñ—¹}…Ñé¥Ú("ZØðÂÐ^-6“‚Lìæ+p³›ÖÜhd}ÞW8_l¬Ž©=F4&: X÷ [IüD~Á+$’LÙxí#°ÉQIŒ.tD©¸QÚŽmÆu5w™”ï(ƒ²ãQã~2é¼ïÊ—ì”’œlß±ìb¤õßà#ç[õöñAz¦N]Õ” }oXá "ƒj9ØuݻƧ>¸õå(“îÂXžgÿ÷! ±5^ÊO&£ú ±Nž7—JN-h=ŒÆ…Æ*tMPa£«ÀîÙc\¿ü_ØÏh]ÚˆuÏ`΢v½¦;g“%Ô¼ÕXÀ®Ò²IZêÎ#vjæÖÙRÁ ±°ïè) Ƀÿòß.V[NäH_Ú[³§{x•KUWCÙ°ù9Ã_ÕNŤ¦òùFLÄs}Î÷7a‘VÞE‘g¡”TýQ ?@cÀÎÆ6ꡃ¢@Þ•9¹c—œ]èËPœ‡n¬b&¾‚µƒ£öH‚§Óv.‹mgÅIôp²âQ»ÙO3Ë,cŽV¬Í©ª™A5%@zeP«¹wUÛE.nõ„€´J¢‰Šà#9ÒÍË¿û¬q”2:¼8b›&40yD¨kÏZÉâe~‚÷ˆ%´fOÙ¹&‚:wÅ$ZÝGìc95Êýñ´åý@s¶NuRabNÄíõ endstream endobj 838 0 obj << /Length1 725 /Length2 37060 /Length3 0 /Length 37531 /Filter /FlateDecode >> stream xÚlºc.ݲ-ܶí~Ú¶mÛÝ«mÛöjÛ\mÛ¶mÛÆjû{÷ÞwŸçÆõ'g¢räÈŒªŠš“ŒHÜÞÎEÕÓÁ”‰Ž‰ž‘ "®ªÊÄÈÈ`¤g!#q25t±´·5t1åh˜šTLLÌfFF&2€ˆ½ƒ§“¥¹… €Ò˜êßJ€º¡‰¥­¥@ÝÞÆÞÍÒØÀëææ&èæìJïäÊOÿOŠ©)ÀÅÂ`fic QPÔ’’—PJÈ«$LíL mŠ®F6–ÆYKcS;gS*€™½Àæ? €±½‰å¿09Óÿ€›©“Ë?ÀÌœìmrbªBâ òª1U€¡ @VòŸp;gî¼M]\þU-Àå$Ãÿ‘ŒþGú«“Ç$z&&€‰¥± ÀÈÔÜÒ†á_¼IÙ™ÙØÿ£6quø¯é@ÎÿàPþÃ!ÀÄÔìoWyC[S¥ˆ½­ƒ«‹©@ÎÞÄÔÉð/îÝ,ÿÑü×ÕÐÖÒÆóÿ×ùÿxH¹þC†ù?ô1þGeé,néaj¢héòß.N®¦ÿQk˜þ§7r¦&–®¶ÿ 7ý‡“ºjbogãù2þSÊ¿ó1H É(jÈÒüÏüÛ,fglobigPqù‡OC'“ÿQüÛ¬hhùŸ ú/–ÿN€éÿ®å ]œ,=ÚŒôÿÆ]ÿ•tÿ¯—°°½‡73#'€Ž…ÀÄÂÁ àdáôý_p]œLí\þMú?µüwýïa25õ05†Y_±7æ ±Jkù]î'V8WN37c F³Â¼:Ýs«l9da¢tóW'Ï6ЈoïjWêo3±aœ)ÜÉäT4Q¸î Îk1I…¹_Íö»Rþ[}¸Þ ˜:¶­±<ØmĽKÆÆz"õkVîE´c)²S´AYG?8ÒNüÚ⣕.Jæ¯o8Óšj¤9‹Ð‚#×ýu°¼à=¸[2l}‹‚ò@‡£°_ùõ×V"ÚZŠóL]’˜´±±E·“ï¸U|Åm ý½fÙ‡HßrZ9ÒyÊcY€ˆ,¿åUØÚ ÃW¸ã†Ì^ú”©äU‹ŒÛ~âc­èô8Ú›u{ñ›ÑÐСpŒ p™¡ÐÝ‘^#† b„=òµÝçÎÝYv‘uÜôžàÚ_Ó@¡Æ/·<™KÐß ®Œj‹LZ¸q:/xwÆæ³Ù%VN‡pÙ4‘¿d/µÛ4ùhw '+röz쬭M:®œåè‘apÌn3³/¡«ûÞö9ü\ùÔ¶ËèHkÍ‹ªÏCë&¿…¯à$„EŠ„„mó\Ò9¼ªß¸$åfúËü0†ä¼lŒÍ„~JÜ®ÏL×Ü»“ªã¾²É²œj{zž l¢–hûùzÞz?4BÏ8,WõÆ|ײ©VÁ‹#ß+ö¶Ô:¼ÚÜûSYyÍ}+Z4ŠŠKvf+#à äuqÙAI²u›¬ ÕKK¶è°m`Iì)tþ5&1_údSQnü‹àõåm›q– ½ äxBØóÍ3Ù,È›9Öæ öV›E7ȳšïW…»"FšØ9,]Z¶üOJ½ÔYuXÐôÛíºa ’š¤÷¡|îö§šU Ùî—úøáœ€yÃí [ Œñ„Rõe|¹îñø«°5 ÍbG9a}•Sœã„/„m­â0K¸l#iÒÕÂÄ£‡šåMÕ¹2q>㹟­`J¡‡/¤Áv‰Y‰NÞÊ .Kçû'?Ÿ0YȇµÀ%H÷ñ‘š’Ö\äüíõbpõíÉÀ—'\Át˜/S(—®×²\ÔÈ#(ùè£Çv{‡rÇÌd9X§ Þ%ÄiÜ'¦ÎÇåí†yã"9gØi¤Ë× Ññ¸”gkËo«à™B&ʵ]Fg]“˜LΧ¿«m,ï(0G`t5ÀQ\1ZE(.~1{ïh‹æš!úÎë'Ì_ ©¯V•Aé#íÔszÐ0Ä,ò8 ¥]C¹êª| \òèd8ýå©”¥¬ä´—Ed>NÇYoù–‘ËÍ3L9:I>÷Ÿ£Ul,^é´Ð!*Žáí’’™GB¨‚ÅÝײļÄûE\w¹Xš0³RºèVƒúÓ•ª_SÒbYuJ1Ø3_4á€RD7ÕqÎåO­Ä¼y¯÷dò³ÔñL]:0ø˜û0w/¯ÀWH×­ÿúèé»ê.〃GtK}ÐmŸÞ[AÃû¿¨C 1l©16HõðÀåþ¶§É/y½{V Ö6qè´FSFH”Ù>Ѭ˜”ø­&—Iåí¼vÍ:#º‹:–¸á,?B¹fËåo+°÷ä÷-öØU×DµTÝizn+Oûè×›Jm쟇4ZÙ,‘&¨Ý€‰³¹)ÆLòŒÝ9u—~ ÇT,nOFÔ1è¹O¬ê.H»•Y6=hÇG$…¯]_ÜîXÚ”Ü(Ä7nD~ÜTžéK¯¦Oâ°´1÷&sýþ,¸h}³ˆñ¥+à²?g¡ÖóFH¶‚Õ=Õ= õ‰(àŒO#Ž@iŽ5ÙšÕ¾µ¸ÖÖ1Ó¯RôºÞ#Øʶ@‘)hådsH ƒîð†ßgD:JN„þ&-ºö‡¹àj>+åBZÐÒ¸_ jIn`ž3,à7óçt^Q7©‹«¡¸@Àbƺ7&3Ög¶ÜÓ"Ç;ƒ‹Ž”]Ú]y±Ê4¸êžp~/¦'wü×ù®ùÏx›ËÒY¥}!¹Bìö.NdQŒ¡}Øêœl»¼C êH^o×#©àª½m¤Ö\A·%%·Ë|ˆ%£V…o˜¸eëtM½G¿È‹Ø’I N0øL½ÝÎÓ¶[`L‹—÷0Qr˜ñDc¯ðUj¢Ú d¬=`{§V²»ÌLu”¬71CÌ0P˜Üv U˜EHžE'_Ù±’£Þl÷A­Ùl÷æ4/%zÑÀdÃscÿÌÏ'ˆsU3ïñÛ¹áÀ,PÕË<PtJàÚQl«ÛˆTŽO®µ.šNR±iz¢Y ˆg}ðse3`À0 Œê­®,^Æcè5T÷äÎ6$ø‹§uw/ÃÍxýFXÜDÈ3y™ðÄþ;ré!î.’b 3ý+‚ºø.¬£ëݘ2,8ðçl¿\‰ˆÁëIeÓ/÷¸Ãk²=0²=Ìüi™éî ‚}5ƒ!v?: Dëž‘Šä±cÈ6jöòIÖÒìj€âxû8a’ô¾n³pøu ­UÏã#Üm÷±r[¡ƒ…ûÃ5¨¢¸8Qa¹$zŒ…„„#E…±Ø3W –zÒ3 ļòãp”GG‰ÍXñ|/ùuˆû>pCf0Íí'R‰,#©ÖŠZˆÿÞìŸSŠ­sãÒ¢Á£Ñ²-ÌÆð¸}th6ï¹êäXÓîd°¥ßtÔïc×Ýd ªyaƒBK£YÕÆ]f‰kü€N ¡’¤ù1·è»>*·qõu±T)nèÃôàw¡V´³³¿OÅX/™còêñ s^öiu J`5‡¿ÍÑÙD_?;ïö›ÖrÞòEUÄûÀŠnèûße¯´ÍsyîöΦ¨ÅCÖN. ¿½þ y‰mh9–,yTLÎàÖ§‰Í`" «ÞÛ}š³?0ÞÙYî¨å¼fQç¼è6B_Ñ|Bz/ÍðVXw"mt< |™„M{¯¬.ß­)ªé BqZrj@á¤þ€¹zЋ‰c/¶¼ø'‹ e¨¾Nt±Ùz_xè…Çppàjµ|÷³¶°©4«f¢¨â•ÊA³¼Õ(òÎO[¿±NàV™º\#Ýï³9’À•[÷Så ©§ô¹)l=ÝV”ÙÝ››Õú¼HÞŒLÜ ëg–û€]ë‚1l •)D¿P†ÈˆVYÅ£ÂU‹Ìµš÷|šv,>©ŒœŠˆØ¾D£t«Ë¡ ™û1i0Ç%Ôßœi7[E5¤ÿé-s•’in}doR£ƒ$ Ç»Ž¿Zò‰^oa@C@£¨©“ì# {ô`ñ$Ä$*‡–èÍcy}ÜàZ-åõV•ž8çyL{#k*>²Ñ¿ÓC-ÃxT“‹8¼Mµ¾4d'KVvî-ê¾ '380“z©C‚ÖDÿ™q4E^7/°ùCM*þ®Ó2cc¨,mÚàzw¿ ¸£†ÐjÊV\»‰“U*¿ë¹‰¬2ÍGÛ ì¯q…ìõœt|[…1+¢4ŸX\sZW9íKB™ÿD†¹uVV Í0h œmlsé›:)RÄàD÷zÿ‡06Ã7-¶ÔüŒl…žràÌd'” „ÈöųYoèW®36 oìÏÂ_ìWÆŸœ| ½dÜßy§YKeÄ[PÞüÔTè{b²4ÑåÏÌø±ßPÌ1:ÒʼÀ“TŽÒD‰]ë±òòòïïˆÒ]1Y“É2”)‚Ö£yyfî^yzÊO$+³ä/–¿Ýw'(A)å,&vLÇ&¤¯$6üi>ã@TL6ËŒ%L3ÇÄ3 Š ´EÇ,r´Í·ö²Y›–U*óž´¨¿ñM¢ òî'Õ¡;[Áž&Ð~´öCálXB©îöRÇŠt}±P¯!Êr‡¤©DgdÒˆÍÜ+÷‰å³T%›7Œ1g0PŸ-ˆ]¨埰´¬K;‚\ŽŸÿ6ÜA«|Ùh"bè~ðYð°÷,ë âGHpêO-¥†À´œ«B½õôKÂØ4[×Dš°+¹¥£$Œuï!8ec·øóÁDv¯§få€&éÞÔ4*aw·¦ˆ•ˆ°œŠÁ¾+$ƒà1]r<(B€EGÀBzNÃd0Ýcq­ž”\G«Wþ,;Z¾Å“qþ¨ ›}LåÌIw¦!ql†â{àGƤ’Ñk°1'íôøAZóéfIº+ÆM£u/í·Æ¨GkÎ)1óñì.høfWCo€bûÄ÷ Aø¼0Zÿ¬éMÌ(>ËøRZ ösºñÊø5ÚõÌÒ•cŲù¡-z…M]#gœ%B«¾íR,´H+Z)ýTðWÛkí†P´6pàQ펻ÖÝZs:¸é"\g}$¢“—²É‡¬âh¦dðM}; ~þkÈi‹U…)s× žÀ%e‰Í¤>Ò[šSº}–È×o¨É«þJB—烆HÒ‹jªo“· `yÁ¯eë-™èG"äÑÚ È`22KÛ;†;Û{Am…’fàD;_´dt·ÙmUþu A¸R#¡-=q2¿D·äbÏõ(ÛƒeS˜R*û2½›õãàÃt?Ñï<Û‡¦G~,eøU{¸kC1¥”ŽKoíçÉð´-嗌ɒáÞÏ6&ôtÞ¯áߢåÞ4¤H*L»Ð4¾,ã[bm[Éfš„Г“Œ`OG|Á¢ë]5“lðlȾÌXÓ¡éU‡€mYðkaw)•Nóø—ký¦|—¾¦’}¼¬–yß„¢ o^ñ‰–/ð[3æ,™¡µ°ÉG·ï¦ðœò-†/¸\ªkõ˜'·o3t®ã¼¢qn±MÈ»ø(Ûñia`x7ûq¿3”Y˜tý·WþÛp¥ƒ¼”ôþî®/Bõ_¤OGÑσj1„€£•O¶ƒ–µ*r³šÍ&E÷I¶Ñªæ„¤‰ïY—òTtÂ1üÑþÉü%h•lÔº'øËvàbÅtØ—ŸåTMÒÖ¯‹81È̱]Ò¦‡KƒÇ™Š,zÕ‚ö¼²‘C—±H›É^©Žß Î3—ê̵“¼¬Â•—[;žÇõ Ûê<^xiò Fbo'Xw'b-½f~|tj)íEˆ¹£ ]¶ãÁ:I‹ëÊjÊvÞ©åA¨ÔŒ·+űŸ·ÝʇõMf5¦Eçܵk^¡¾ zX‚«©ÛeÎŽ^æ#ß bƒÑH®'v€ß‚\Dé–4!"-~ínŽ(Ui좂B€¿ô,Ê<¸«Ê FK¥|[²î^IÏ·w²Ú[¨2x:»²Q¦- Ÿiåc0»íh¥»O€Z¥Ís¼8ê(Þ}^jºêtöyH8)]×@âžÜµ–EY4£«¯S‹XÀƒ`ȼÅ(ø¤;¢Æ/cM³ø`è‡R30»ÅïUPÝÜpø½š’s$[“¹E.Ó,6Ê/È¿qBäè}w=RÊ)Y’Þd­C @޳§n2æ@„”“A Œ×áfû©®N"W‹ÙY÷f__ôi,…àƒ¢þZ^#sãuRò#ì 0ž‡D¸`DÚÌP š5E²Â"޽¢ª!"2—´J‚È臂éšA1ËÏÏx/çKAŠÜ×v!„Îëµiå÷„‘œ¸¯éÛgË!êú» €™°*‰Ào7ߺ•Éf©‰Nië§Ó«'íÙôµÂ÷&ÊA8t ¶áÀ%Œ4áÞ´p!‰¶¤‡â€ò3³i7å÷O#ž+ü9æ—¾¤5Ô˜Ëh$àBȇ_ I#¸…xô2D• ¡úíõó­I쎿 ­¡fxÈ4¶Gf*å¤gÖÌp8f@‘»ç¼!ŠYon)uYÜÖ®p¡Tg IñòIm¢Ê@üJ"NÅ-¹ônRœSzǦþ¡à–Os;ˆÈVEW“{½Þ ¥·žËðM²Š ž¤‚ Ň"~Ôø ÆBÌ.Ó€[ƒmIç¾úÄiÀ|W;Çk½ÄH»ãéY3ä%7«ìÆÍ&Ws7tßÉä#l^¥a¥rÐáG% ̽ªæÔÐ^܌3%³ÓÑVôy‘ãN…–=¨Cê ™ñ|ezïÒ²"wíÿê²qÕ¶“-»$ïöd~iy· ÿÚâ‘ÔÅóˆU¡ *‚í”vïI/ñ€²U½¿Õ°Û¿BòtU¥ùm[iûªý–r6É`Èù•íQEÍÆ9C‰&Ö›:m=ô-¢ŸÑHq–/ÉB~Žøü;Šª#§ÒÞ*q™½bå½Ûßùæ„§¾ë¶.äc©ÇÈ%â—ÀÇÈ$V@¾¤^|F é-öáȈ’Š»'6:òÓ] Ò›cúú ñÖ(Jç’B=°Ãz|.7JTÀ.Œ„ÌÌÅ__Ù³óæ—ýФ^FZߨTÞ€?Jœ1ð×Pæ_ì`Äóòž<=ÞzVÈÍ}¬“„}²BÏ¿Àõ=ÌßÑ(e¾2ç3äVÞáÂŽ¥WªÒÆ—?× ”kC|g d! ö@˜‘ØNèGGS§zL…ž§0~Ûntældló wB—o™x}>=ŠŒ™¿yvÈŽܲ›­Dô‡N[1¨¼‡)°‡€/â¿{ö„jÞ"øKmB3ôëÎ⼂jEã–¸¹ìT ±Ó›ú0Õm«æ¾H݉í;¶56´@ªà™XÿXˉHÜëoÃ<½ø&{ûM}Òó[-•ŃG¾#0Š_)ÆÌóй?­D|ޤ#6©C@'kÅ"¡&êa„É Ðqóy„½½ÈW¼’¦E†'¢šŽn.Älršô^äK¤§Ã.FêdÃ&±¯£ˆÎÑL¾¡Ùʰ˜<7ÊAÔ#™K•ðl–»ÆMQ†ô.BH’t¥q×Ú5ÊjÓ~Û3!: ¨aÿå½ óŸ$V¦p¥óõ¬Àx„oÑ”ÎТÒïî!gUöuºU_í TRý‰»ÒýÑJäUýê‡À_ë»yÿÑË·²ÑªÄLóÂñ•þäÉmšo#QÜx2r¥zïúcé:œÝ( D‘”ðHÞï%:rd‘FÁ©Û‹¦ ôñpupdÛ7xp~·foYSY,ŒOueXé'õd'œnxAñå q¨eˆ3)}Äç×cK¼WU·Y‘T(IF¨òñˆ´xÅ… ŸÃ°[4L#gÈ(Æ«Êã<²X׫³¸¢¨ô"íÀ·þ¤LÎÊ$¾‹…¥(ý¢X`¿ÛU×§¨wìS¹¶i_-P[ps3XêÀͪ…OBzÀ'u¡Ÿø:?{)ÙÓN›½ó Íí=Ò Â”ë®„Ña_Ò…ù¬*óy©];¢Ì“ÿ*Ï5ÿbeÏr¹§O¸p+u°y~©D¸¨+èܹAÖ˜Éi㡲’cê_eø”þx˜;   ¿wwGP?. .Éaâ6µIæ2P°»¶(LöÞ¢Õäµ—L­ì‹Q œ¯1x€®ùÃäû•‹^±‚(÷hÞ%ýšá“ŒGÆ|hZùVÊR2ÓaZŸÕÜFºRĨJz°÷ó&IT¤t ÅæÒA" ^SƒÍËIné½¶ñH¡yEÄx†®smwža9¿ƒü(à.—EÁÿì1á9¥ÑƒI_Û—'»'W¿+Þ®ž|CP¯ Pî[{½]Mogø64Åt>Q*Y?ÙÿÓí {åEAš´¦iDûð·õ/1²dÚ¥íÕu¥‘'Gä‚û¼ q¾Õ»~qÎ*<øŒp.˜éÁ+zk‘%¶ß >dP©tã[ü ¼­#mÖ­|d†âדùaõFÚáR?¤ì?£Rœ—C…hº|ÍOqa6vרÅéþÅ2‚óï‹Äˆ+1ú(st@ƒÃ„š¯|(ã$*©ù½§±pg9öC³8U¾¤æ`§1 )‡€@ì5Y yºÓY©ÕšuÉ6vXùyWZÍ}šÓ}vŒ*…(ïúu!ÚQ^›…s³ J73ô¨µ/P U Î$j\–h”}aº•à«“ÔHcùIaðxÅ—GªG›}×ø³¦<ó%¯¿E²Pû¦HNÝCXq§O²ÃK“‰DL>1IŠ ÑΞ‹…”µV(J*ZqiÙ‘Ššîï/ ,‰›|ÐÙ1*Õ/£¹P¶&8ðÄ$„pÕ4ª¸ƒ”TnÈÀªÊ7L¹±NH¨Š Ö^-?p†&\¶(ÿ}S½ØSþˆŽ·­Øþé´µ€OK½Æ†I†jí žòd¬àT¢Äl­"ìÌNö®Tß·h\Ì®M럴‡—‚¢¬e7‚êªa¾jZ®y_Â1õW™÷¾ OÀêøºÏYÂ$ãbyì1/À0™H•5?(!$ýÙFc߇ýDTÑ%òr1å%tqC^ý1J„ëÜ/ßBØÕ=’=û˜I­%~mÙï8’ÝáKpCÓü ú\éä$üE@cÔOiÿhÌ-…"}ú ðÖ¨úÞ¯¿ÕY0Ì#3¸ò§JŠŸeIri|°û¤Üo,2ÆÚ”JŸ86e¡:¶ݵíf€GMFæ¿°][w qµRrY¯ŽŽ²Æ²w¡‰)Zi) «‡ÛSZ°‚Óš&*E÷6"rf¤"ÿ`ÕÎs¿³Eü]íºÝý½¨ ¢¡'?ã_ šðC¼±—Qœõ=/á¦ñ2¦r–jAØuþUk`Ž~Ãü³îu\L!ÔÜ×ÛÀºTBûIJDûµ(H›‰ãßÓØÒúÌòðóûVœ©YòX¾Ø_Œž3µ«¤åÛ3-uf™šWèû2І‰êñ¼îY¢jáóFK^HrÈd”™¢3¨„£]Ò…¡:Y×ú–#Tlæki}PÿnçöˆÕVn­÷ÆóÛÔG1 Èm%ÍŒï Œ…¬Y¾™³¸ÀØe±.ðó½ñÙª¢/-`)¹m}O¦N_A"ÃÛw†¤ ¶¾Cœ_;š ñwŒ2bŽfÜß íØ«…phפõ'×…ü¶ÎÏ%Íy]¨œzØË9c®Û°lù©÷³lb¥›á¶„̼¥fÌDÄù›–|Çœl’¶“ɶ í‘æ{îíÀgÌë<"Ѽ55y2¯þÚnšûä©ÃÄ!™‹g¥s|Ms£ë]÷XH<æÉqºÚxD¥ñß0~ßÚ“ZBŒ¼ÈÄOdF—ü¡uHÍÍgôple$ˆôIwÃ+oA½³I• [çU%ð¿F÷µfêp›"¨†óÎI0sAÉ 2Zvþ!©‘ ýTR)Nq2ÄêŸjÚh}{:xV³CMÜs17EpΙ?Ñâ«”ÍLnPBK/…tØòƨ{;)ˆþÛïoJ‰ö.«Šým’]ëÆ«ú.ýBkÜ<Ú\þ„=Iƒ‡\/JˆÜaåóQ±sh<Ù(zÄÏmPYpúÞ*â¥ñ÷2ˆÒj»”ïÆöŸz2M±å@E}(»±6RH>f—WGˆÔJø/Óåò<(Ûåå±á`†ÜÒ_ζðl6önŸ}bâ(O¥Qפ<ËZÔ£{†¡Ø»™Q!ÎQ;%<âëŽy$ï¢ê;Ò­ LçãA|¡ƒ|!h•Ãd¨Iã+–~*EÜcÎ÷enãY0/Ûý6‹µT ©­“1‹·µFGLËûû¢ ¥¯ AûŸ ‘>ïæÄW«¾ i¹i¸§~µ’²$ÑcÂþJÖ‘iT]?Ë?~y3ô¡ÚÄÇÛ‹}DuAÖÐ^?ÄŸ†Ê´At=E³™×ò¨7ÕÄêÙ§²k~çÒ”ÿ9 1@n ¬ÄÓ‡“88’’ìMzä}r>Àt 8Z͘…ÞZÑi€œ`¬ögz|EëšòUȬ¹ÿõ#„ÀrÑó÷ÚoÞ^Å@¹Vuõç¹>J¤ù “”`?m¤)ØOþ.x¯ðým왜¢ê®NùCôY›œŽXr¸]=ßS·B¼0Œä)½dF•@¢| ãÉ£xƒh'jRíåiÖàjœVñãÜÒSi뎄ÍMë¤ûï#Bð¦FÌY×?¢8Äçk?do€ \¶ò5"ªo³ŠY&ïÔäoö°_¦XüõÎiwR¤gã4gÄùª [“Ã?v­òÇ ÔÒ„‹¿•2‡Ç©}(÷+HõXꈄ!-æÑŸI)T•])¥Áˆs4KÜgØf58òVTS4D¢@ÀÓ ‡jnžm%I‡i‰n•Ì(¤¾~¢kàܾdMžËLÊ~—­*:TMÌÇsÍLÛ3n½ÆieÛ‰hr à¦H†;=2h|…n~".—”H{Z¶ïÏÄžB{ü¸ogo9i”1­g]犈dYn³ s1:{õ'l8X<º`Yˆ•–-ùÐb©×±±¢,3„P[%å<áì†[ŽÐ)ãþ…qKó”élXŸ@Ø0êá ËÆT4¥GåÝ„ï}s3Øü‚X>{tŒèŸ¶·™ej¼úåFj²¥ÓÃG¥÷8áj cýó¼*u5T¶º ý!GÇ­‚Qß2NOšƒÊ\±EU&Àe¸Bj_Ê%3³æR^r¢ŽþŸ]†›Õ2ÿs}Ü&w×ãiÉÁ±É!ÃFF©ÛD èËùéçØ{öø¼§Éä"Bû¾ˆ#8Ý+Qü=¸ë=íÌ´1òƒ¡§ØHmä}­2óhÇÄ-ÃFÉÃîÅcÔ àh‘o½« lh•€ m°+§)0@° ÞÈ~J¨»¨ùI8 €‹‹žíD§ ®Ä/„}=”üöÃÓñ¼ØˆŒ!Ģ▋ÿ5¿°².àù£%¯ !ªí~é–†áX\{LXkÏ-rÝÀícvû¹Ù‚ÿ¬"ÆÖ «¢—ûw2S:±,µP×PŽåÛëê%¹õH oo.¤ò‘c¹ÚÓD†W ²Çy\®ç(?g«&!«8°‘ÏÈ£A@p·zæÃHâ)‡¶)•Í) Íbªû²;éäÃŽ*û:o¨ZãFÞxQéô¼?é­®=3…{Î’)?N¸ÜÓ ¬áݲ bçI5MáÎ#QÜ0^Ø8ö×X½#Í&Mȯ'¢âqµ=4, XB,ï!•ÕtŒöö”p²Ç"€³(}˜OŠºùÓbùZ ª•pZl`„Eès<íh‰/av÷w¤yÁK—»Â›Þu;ì× X‹ õð—~ãÈ4Yp7ë6˜ªr¢.sÍBPÒÙò"L{Yñq(ÚwŒ¢ªŸ+ït°ý$ßZƒÑixüä¡1¨Ž«·] ÎÛ;ï}å­¹°’QžßL±}6¦zgø“ÂÇ ïyäÏô'(B°ìµ}þsúìÝHqT 2XOCÖTëØtJØ€”~8Ûm hÑ¡?xÛö¥Û†Æñ‚刷ì ň/ã±6ÃÈzý/àºÙËs›Å3,5½s ²,‘Øá”Þß×ÒòÎÒñ^»ù÷1Q=gÒ à>Áôê"‰nÚ·ýnP«>s©Ì#;›%tç%6l?Ï á$Öj›`‹l€›;pÂþ•ÒÏ­‹ŠÑ½¬}ž¸ÓÀ ô=´Y² ðÁeË™pǦÌZ¶Òôα…8…€:ä\‹¯XA¡T’8ý‹â=ûØ,.·vÝr†Ÿ<‰ôKeƒ„¦Hñl'%œ\ œî…$Ðã&ì-™™â˜ ¼ñÆzr¯2(WÌÕ XŽË7ÂHšßRí'g“3ð¶,Tc>D²ÖŽiž;°Ìfgykrw禣<§”"Ý7ÚoìãU@çÒm˜ïæKÏ´"ùÇ·rŸï÷«ƒÜŽŠ‹É“PuÁeÂçºfî!®“no–fGJ½…öÌe¿×eªgzžåéí|do›Óp~¾îÂ>¡Ô’w†—•%zjœuUƒJšKÛéQ-øÒI”°’âf4ý©Í¤>Ê·Ä']¹Š+ÙýchÚ‹âr]Ð#”¯3wYsoÔ°El·VbÑi¶åt>Ø+‘×ÿÏ /ï°ç&°(ïòmÃÿ³¾ªßv;4ý»·Ü£$Æ(}䛦>{Šw™RÕ{3QLD¾oX!z€'u˜*>ªïm·&cɶF}#££BÍ–‰'¾Úm³à+¯ 1 ¦ôÖªŒÆ?a¸W°²2ÆœŒZÑ®à ꈞ Ê Tò×S©ÌÊ“ÐÀ'ø€é+’9w/£ÁÍÑÎíPJ0åeØòRµ—?ÆÊøQ@nÙa•\ UsÏϺÔ1ÊKÂþ‚=x¤XÒ1Šß†uð*N%ZT9:¶Z¤R,4>q$Y>+ð›+{Âc¨xâI—9±dT¾¿Daï’„é}ÑÀë-A£!QÒ­Ó|â[$Ô<ãxÔœÏóÖ£U#Ý8¡T¿ÄѬž(+ã6Uq ¶þ{T €ò¨9°³ âÃ'9=O9¯>ߎ\¯yÒÉlUö‚Ý„œþNó/Ö‰€Å NûŽBŒ™·€›¬ óÍÎÔmT´1Uz¬ÕÀ4ó„d:RËò_-¡:ÊyÏÉ-p»}‹iÔw ¿¢ê¿Ï¶5Kø ŸÌ·'{x}=7Œ ¹'ªsBÜ0þ±§Ó™—0c¯Šcî‘”Õ l¬l7j者5½0V³6XC- Qÿ˜F2eˆ‚vƒ?¥yäï^ªÒðäÚ KS ãá¸ÉqÖRù¾¯1Á ¼Ìò§Ñ©65wùgòÊ77Ñ@‘¤Nv9çæx«îc1ßø/Âì_K "_c¬xãÇôu™OhS"ƒª²DYŽOÃtò¾Ú0ZçãÕƒ>¹:|Öƒ† ~²Á©Ü–‚%ºm«à­–šªÐŸìê–ï;"k:(}âûßzÊùËÏTúëZ]-â2 |Pìd?³1Ý#o@íx —е@@\88aYÚ,ÈL5ˆk7=ä­Æ®lVº}“¦LÕçcA¹G‰¥{yîï69¿[tÎ8"QÄäêŸJÇa;­Â$£â¯¶¸·úvõ¼V 1ô®Açh3i_ð•?äp\í˜ëPgÙÓ®(PûÜü‚io£ö ž:?Â_-°ª›¨]®±Þ<{LS òŠü±Ë¤Ì3±%K†Î-ÎR¤åÛ"2æˆkß ’ˆ±\Rd†Zó%ÊY.opBò§ÐßouêVx¯·÷YµÉîÂ<‘V×ÞgßLm…àxµw³D'Khîð¼^øX”&ü± „SgPÔ`®W‡ƒi3*³ËU†7ÃØ~‚s…ÓäkŽû‘[qß÷z{‘3Nöâs‡tî$=„òä-tzÍß•×FÁtV”1ó—¤VYë]ÍkUZ•í ©œÊÖØGW(³‚¶“Ž%ÐP cJ©ò"éQ‹¦r°TrLÕ„k¦8ú(zXXLÖ8¿>³,°ý¸¢uwmwøÆ°þµg³]ÕTDZ²Ä–¯uJtñ^3êiߟ ]RL©ÝÖ:cyðS;΄䊫-—a§Å–5ÅN5þs19î§Iѱ‚‚ö·ëÚžzÙÿv +; î]^¸˜ún¼Œ½î§K3ö CïÉ|„×>G²zÙí#»pÆÀyÝcÃc8ô&žsòú+Ž¢(±ÞM"cÓ‡s2øa€!5øò“sºÜX4væ‰ãÁà s¶ô7¨ÁØ®"¶{D’ˆY£Û’ä ]Lý¬ÝÑšuϘL!hÛî\»ŒI®ÅÑ)¶jšŒ~¥²Ÿy–B§áÕÖq»ÉF¾ƒ­5ÿÙedú[à Q knK”e$se?:3kKFüÖ+íðßà†åÒVFc`/¤{ÙÎêß9œá]RÞnnhÖ3ŸõÏc<•Är”ÉŒðç/’Ñào}ÃÿBÑiÀàž¥Î·ý,DXǦU`^Dûõ¤Û)å<ò\ù$Z&Éy“ cËM"9ÚÅŰœœD:® äSŒQ{€5HÞÚ<‘ls>60~àEuÍɲÚtP¯bçu^ïÌÜLBæ¸ys­^ÜêîMÿÁÑÕQ¹©¿-÷}…²'M—©ì~éò{îk7x3T̉O3e³'å&¹øfc\µ%¿ó¢oçìp¼É8¼Q<ú¨¹Ï£u¹r×|ƒò×s% Ú(+z“ÛÓìl¶ÈmÒ`Ú´*í'é`¹nd†ë…dO`èaH€†ÕÒb³DÕ ,T­d¥¾PúU6%ßù‚'~/8dkc ÷&yuâ4cC+6ÔÎã,‹WXê »À·$•~^k¦é‹È‚W>ýñ|#®iq³2=~§5»íÅ1ÍtZh*\ž SóǨtžÉ:(œ¡óE!Ì€²€Ðüëé|ª@J pà·­Î~oÛgâ8NÔ‰QÜ÷‘Lªúžh5v”bÞ¢«°l—pð‹ ‡d¶1@#C©Ìcá#¨ŸMû^Iõõ£K3õÕë`tˆ† sa”ͯaè:=AT˜¹ÈÕ÷ÆöÖqÇNô+][ÙÛ/nˆDÇoÍ=Ù-d|"xå­zÚO‰õ‹(8 Ôª†¢/¸ñu’È]_—r{?Ù¡ê$:õXHöé[nÄùDÝfW*߈Jó2’±ºÊQçΡÈG´3:qƒ`1bÞô£¬ºÝæh ÔïKi©dõ+¦ô ר©k†ÿý«à£•ت«oÒ%aV:°?‘ŒMìIÎ…•ãSB@7ëáÄb»ÐZXëŠ×ÿk–‘%ˆÃ ë+H)ªß*âÝ ðTl+í¾˜òôsH¶çB"H5;6 ‘dÑÃô™¨2'%G½ëcCÙë®/0D"óÉãàíÐ| ŠßÉÇöW(s.qÐM‡Œ/îÊæ‡Âÿ ,=Xoio8HàôV‡ì³¹qþm…¼ì)ĵTÔüÏ™žŒyáµ%“qY»V|ätŸ}êQKS>Q$Ôáì Ñ9©X²¦¢ÊàTCiùú÷vÞ4ž²›"•¬“z¢SÌmn¯äÙÖe‡(“DÿÛ­goî®àTQ68)ì,‡²ÕEÝÈžøúïì9¶fB›d°“G$]ñIuŸóKFû݉‹Ð¥® îð‚¿gQ©Xðí§YVòFV?ÁhGá²xÀ¾ O®Íý&É¢(÷uªËeò>íô¸š9?¶‚Ûð©a²ߦ:9É;Ì÷¢ÿßAC 7ID±ïS—´°-x.“%MUáçñ3wÓŠ5‰z±o]á§J…&-p|J8%Ôq˜GÒRŒ¿²!™úªtÉBÙ¿—ú­3ÏK•9ÏäHÉ‚qݲ6ä] ¥4W³Ï%æH7zŒãŸ+BIrâ?ÀW¡ƒÑÏÄ+‰H–Aÿ-yëò„:}ýF+ PÑ”0áË‘mLW'\‰/¬×˜è7…€—ž<+ÔÉ4rÐrPg J}9v1!ûz×ÊTЃ†K¹´ ¦P6ræ?qp&æˆåx“õžNÐÎó`Êž:Ç’‹É«Eçé×FRA ©äÌ0‰¾E~–SDŸL¸h•d³m \Èõ>üfÁº¹6Z½düªsÎXÖôPÙT2M Üjæ:¦>?™ähé‡ÉYüyêdãqY|·ñ&>DG¨Å“E«¡QÛˆuØ~ªFå4ºyâ¦Ñ_.‚Š#æi@6—«ðI÷ÂJ}õZ¾ëL±Ô?Îà9å-Új2¾ï™Œž‚û1…žÎ¿Dùó•ýÆ-g9÷õëÄô„ /ž}ÒTAìp{¯Né÷´dœ 8pM‹mvE ­Y¯ñ° ´ûMËs ËÒ¹š$€Î#Hbg ÞEøàx½u05¹3RÍ«1·Ðî I:m§àþ¸}$ÃÒUk §Ú ÀµÕå'òCvîÙ¶UÓ<4u~ñÄI))ëµrÙ«OfÝYÙ~øUá ñú”—~ ë ƒí½3£qN ÿóD>ýÀö-)v8vûËGF8:ÉV@yìNÂÊ鈨Ó{s†€V2Áó¼ÖC"µ¼»Š,!2ú'æ#ulþ ©x dGM3ŸKÐG•iE…WÞ/O•&Ë·Y]ÐN‚Çl/ GÙî–Zñ$Î)¼öwý¬ÄÄév°Q­`jú ¼& <ÎÃ#2ÆD)Ö£ªƒ&퀰J[SR#!^ÛÀbùP~Ü\J«ê¤&vRÐ’Y”ÑŠ®›|¢xŒý¸¸)ŠÎ.‰œÓ¸}¼èàòæ¾ãšNÓ«ËGIWëŽSDdòö`àeYdT… æP¦=gëPÔÒyhÍïãÕ½ýãJ«FHÀý ê»hGAf† d—övòÉ–Î ô»—ŠÞô“vnp]Âæ¹ÖšUå5j¿éðí©Â_üZ‚±dÂ[HJ_µiåšmJ0à\Ú¹j3X/D»8Fš°Ð‰ÈpŒtÛÈ<¥3˜6À†ÒØ»ÁJO†Ø{¢k˜EñèßkÝ3+^áÄc_‹ä\ZÙ@½n§™,”ªæKîf.ï×Q]"Ae~CbÊp™_T¶ÑFqœ'7äZìènŸžá~™îæ¨%n³§:ɲ¾Õ»×Ožß×U„¿Îw9‚ÓùâØå0÷©rÙD‚Cñ°”(έT߀¤i95Ó/E¹ËÐíÙ\„çÕä’Ñü"ü”©UýzÊ9ž£!2˜‹dî(Ãz[÷„*‹h`Ëm#BÒ¥º4{Øg–o¿ª§óЧS5ø¨ìðŽéBä¨áé¨j;©½ÍŠ¡*+*¤Î4:#¾_nOeù—7WlÌzo¬vðWÔªOꎬÃÄÒ£ôMè9Þzå¡"Už€ˆ>gpË…t¨ØNˆ€QÑDÉöíerO¤ê;7šLM‰„Ü#…¥,¾ö½–Ðæñ€C:r\º€0M& oXúú¯Ž¸ÛEæ'FÛÅ.« Êä;ô΃7 PS»DhÛ½ŸŒµî+Èø|‚{È,«™u¯ñŒý#ø VYÂtlÐ ”<¦(Ì&_H[xzº=Íœ3 M÷/R’–ÀE…‘¬ÐxHN™ŒV*áä|w@ Bñ£„+/øÆö²qx˜¸”ûâÆØ¿'ׯðžT ;3ó:ÆT WÆÿf,`h?“¹9¿0J¢ 8S©ˆ=ÊûÈ;ëõÓuž‰òP¼:~ah¤¢N’SBÇ:¿Ï^þ°Xê+íq;Á;¡ù ^¦Fñ“¹®.ntb«ƒKûÖ"Î!íÞ%5Íu³K]?‹ÀlºŽ‹ëÀ¿ðD¡[G ¯Ðò#MÄŠÏ  Œƒ9ò q’êÛ=w2žÕs { ºÍLrè ýòp1AK7¦Ø¿[º¶UÁ}L Uðŵ2žDí!ÞEw^S3ýç ¦š& “íÐ:„*®ÎV‹f´Â8Ú2'ÊêIÃGq¶ØÖÚÉÎÛP[‰šX~¦gœù€Ótä;%æœaKì¹1½92Fm }«B˜BÇÙ.qe)œC·ž×bƸ×J<_®¸r„Î!ðwN®ÓB-$9A˜US·‘ÅÎ5¢Ch^Íß)f1ؾ†=ÞIè¾ÔXglùˆQ‹— Lr‹ñ{l™â)3 h¥U ÝŸ—ZhB!ä_Àæ'x@²ïº#Ò÷*åžšõ°¤îÙÊ¡|‹É=7ú0?IyB‡vøcNhj¶®N"31P‡¸]HïoÎä|0œ‰‹ÿ¥ƒjéV¼Kšfíª$£™n€{CÌÄ^RÊGM™Ø^žÁßr³G—<Ë·›ª&PÌv›2ýi畚 ¸ü¼]r¢}´z½ÃXâ+A‘Á5xîžuµÉÉœ<ÏÿY v?U€ :e!ÛZƒª4Ña}Ä¢×üK³±x¨*gšêà«Rƒhýb—ö*¹æêü¼^47>áß…¦ö¾ÉQu]¯8ü¶*ž»ˆUÀ9‚úêSåÌ}ÄÌý¹ßDÚz„ø)mÂmyí/zOeP.àç™È¡"íÈ÷tvÄ'Ý e™_„@on@ìƒ]6³y¼ß8Ñ$d aG[¥J²}C4°ýªà–ºåë>ˆãñf*mÈÅŸñÍ,ƒpÍì!ožòöja©T}m‚xôÉ6ÔI¹!†{¨ð5òç­¬?['y²É· +q$M)V[¥?Ò¤Ÿ0aØÊTe¹[?K«A!¬U›§T^LnÚù£ïz&ïÚçÔi6K©©ÿAZÊ_c®û2¦3 ÆQ°7Ãôþì êCmËzP±~ h©Ý—>µ!k àk¡»u|›²õ â7{žEàÎJ™=^‘¹Kç,ÉS*Ze&áÌ+Ix/ÃMªë<°$¶~Z˜ZvŽ2ˆT>"ª"<×e„]é?•C0»µ ($¡RŽg¯IŽ54\^ᜦ ¡jŸ\¥VøÜÚÿ™x;ç?`†ó•ü‚Ï=Db"ŒZ=‘÷C=0®3ÙÏsé}ÓÕØ f§SÃDm e¥²ä£3þm`Zÿoí[O!)Z¦¡ |û- Ü¸Âó1f#k÷UîÔÍ_¥C„þ>™ÒIy†5hb_+–šÞ2š)Ž9z)ÀÌ"¹}Ùµ¡ÎãIÊÀ®ãbÁÓéx×¶cxq>dÐêÀè+×pãŒ!þ }·ùÊvlÎ ÞÆ]Æ:7“ŒHæ–—Õµ¯…w›Mk\ª#0 عPNô'…1æ¢Gˆü=hðîòÏáU7qµKp}Ò¤ŒzæÏ)n[IXPà”„cÚìÿ¯ÛNoþ+|fw)NúAw~I†ÿKýc3çd†?Ø<ÛÂÒsu¡ FA]õßÁñßû¹Æ¯ÓÁrÒ‡ÜTîÞßAÊT…ãË‹»û2uÂX´þXÀÒ¬èüå»Õ’s‹Ø‚5¥Õ‚ ̼¢Š!Òû‚·»pµ,Ϩ14— J”7YTpÜ»ááLã ”{ùíó·¥0ø‰W Ñ3wÇÉÙ ÿ¡J«løØ®ù[’GŸÐè½,•F¼ÉBj,Û¾+Èä[vÆ3o ­tØL\4ŠöìÎV&š %lUAQ…E|]9þË¿Ågæ"R–ô1ÜôûÂ}«¢ L0Ÿ ß^¢hæv+¬u wÝû#Þ™ ÿ¡šç× Ù2‡è£|x¾ÏÅD(~2еÉý¸58ˆ“_æR×]oœ†ÝØ:Ñ-è©Iz´‘FÝCÉEö…Éî<Û•>&q›#$ÊfÀŒ2ûËbžÅÂH¸ÿ k~sò$)ùR5ò “•ÿÝšS¥¿¦;Alu¬\ÇôÜJ:Ь•Cˆ?èwxù4É)€ç“¢ µޝ'Ï]+>}ItiïÌã÷"kµç’áoú‚V¸.Ñ76üŽ¹ÆºðÿL8ù—ä5Dnð¸˜SPLLÆçÏ&^©_ ð©š5袶±ý ™Ú‡ö{(_@à$ÉCSH‰Õ”Ä–”ôIyvj`ô¢¹lœ¿²i ±¼dVdŽøxkú¾LìY97„¤ëMÜÊŒ˜çIà AáV§ˆ™Ï”Öo'dñœbB¡§­Y·B¬¿ûø`Á§Rî>)Eg¼eý41ïì`ízȶCDë¡× ×ÿA|r÷••®ø=Jú'°-ºï¡s“c'RŸfß–¡z}wJ濱l×aœ —3™Yðþ¯¢Gô¯Ö¢§e‘^z°8}=Ïxèü«8ŠH „œMº0/ª7U–8YT®¯Eõ‘Âm²H;ÇbýSh²ÿHl|ÄI `Mms²Ê&ÒÅ%Õq¯ÜHs´ärFíÄAù ³j-_spy¡sØ<} »­'•ËŸ¼Óéâþ1„84y-Ö{pD Ç÷&™·“¯‡èjœª½¬'y´¤›¬¥¤™ÇÝžËp`¥Ìh“+kÉ35ÏÖ®ÓÌÇ2îa;kíÚ”N÷ .#tD [EÛ^)„\¬‚zgÿlg…Ñq©„~”®ÞZ.¶—½² ÊUšGÇž7ô•qé ú‰1d“ÍïLÊMÞ{¼õ]8ôÞ¸´‡<U· Ñ"ì¿É „wäñÞ€ûäXÅgǵè;/%°Ÿ’±ldÛbQ:h #±Sf0÷Óxº$NJõðUkœöºzeªÒÅÐBä÷jéu5–¥IÆtZÁ.¥±Pwƒ¦ôµú‹ØdÖe~zcî̬‘ÏÒeƒ)<#Ý6•'ƒÙu»MuŒÕ4ªÎl4c¬C:f.ÕѾ ƒ¦òÅ4ã“^¶˜ÔjÞõü<êÚØ¹žY¦âän¿ÂN ?æ.8˜=ËÉX9òg<·½„ns"äœ蜰cyX+­h÷´ž^¸2”àÉ_TfˆµX —¶„y!à ˜Ù^…* (_‰ÐÝ‘©…1²ëuŠêW¬Nóþ…¸Â& ÆV\sºMûtOqU½xã2KôŽÔ!#çi¬@ 2Á¬ÑôK¬ž=Æn¿²òjš¾Þ‘Ä9âÐf*ÁÒ+w›'ãžÖ”¨Ò늒 îÑD[®Ë䪓³YX|±X½TÑÁårRûåùÈ0y]ÑÆ™¿J«gâ‚ú¢ƒ=ð8Ð…ñæó¯¼/%>ù+¿õ‰^Y¶¥ßÎç1ëíÓC-3¿«28ÉðSщ¶/)V¤³;œ±¬C/)Ã[á ºÏDÔ›¸J÷¹–$]ý Ä"¤‡×BL[®(ÀÒ3š>¹cÖ ‡tFG²êØCÈCÈkëÃÿû’òK'æÖKè¤Jüo^â‡2ú…Å!î< ÚÑ‚óÍÙM™B ˜€“k\J8N2ä7ÊG³³s,Ì× ¬-’T&ºJŸÕ.ìnrÒðy¦ÉÌS#Ú…¶dm‘HC“‹Î½è¿›Þý°~æê> è½;ÝD–Eô{@5É$¿W¼Ä÷É·„›ÃDÔµ%w¡“Z¹^g*·,ìâîìõŽ„þ´‹É'Ÿ)½ÂÓ“hþc’Ò"*Ыeã þÜ»¹s1¤bO㌮ÃQ¼Ñ)GBǯäbÍæ[Z R;MƒÁGE²0=.¦Tdhà9²¹€ñ´v¸hþ/ 3¸kDt·÷VÀ_7¸¥%ŸªUhÞX[¹DòqÈIÜ#B¶së­Ðþ(öÓ×wÕŠ1v„S\Ð45¢ï¨c§pðn¥Ø¬5EŽ Ýë厒‡¸wãþùÝv1Â8÷&+7ê¡nãE~pb¿ òr@oØNÁiÛBSó¨a‡z戜dŸ Rúo-oçøH¾{ òÒ—È5â;ˆ5ºKÄŽhž¿ˆ37|?›?€‘~X äZÉþ Fá©{î^–‰1ÿ–l;ü€(ìçy÷ï\n+Ã3ô©Ó¿yƒ^yW÷:ÂŒìñ}k¾ZkJˆÄ,zˆ4¦´¶ÂŒv¦5h¶Ÿ·ãDûa£×v“V¶Q­Ñì ÷3RüÝtï.–¬} õöÝ(SØEÆ¿"¶"—Çmƽ8S1Sì´åúEMo~) ÃÛ#™ š`Z½¿Š<&ʹŠk‘2VJaÞ˜`6øÒ‡ð_ÃeiT1JYssãH;•Xöô/\ª«þ v襡;Hð’7ŸBƒ*K2¡JS:y8ð€à—¯%7ücdáÃ}ïÝ<ôؘêy¶EêäðÜ«K'Ôàc'Ô>, ©#š6ìŒñy˜ÇH5ìNoÛLËMÊþ3EGðŽÕáÜU¾h»õç¶s\—}·¼Å‰3X%á‹´`€P1X-KÀš'Ѿj;€…bÏFÒHxc™~Ðiˆ ŸáÔk„d<+öÜU„„ü´„o¸ k—"ã5Ç+aß¾-¯çzI,!Öëuƒó ÷•Ô÷ÌÓÃÑUTÎBNDržD{"†­ŽÖ†o¬faJ5]ËVš8ÊbÀظ®‰—(•ÄÇã:à€Ë±‘‰YC f ¶û°t,¥$â Hð´×À‘ÍP½B¸¶&ôM˜I,)Enë–°o4Æ5ñÜÜ)vø½à…Œ2±;!Ùqôôk9VëTj8µŒ¥ç#À^b”8 ê½øø%9SÓÁëÇiq&ž ³Ž/šŽ3(U0âÄŒ0hk# á!›´ÐO(}*Ô_aî*oç ´q^Žx˜£Ê!´`è{Q?™ €+-¨©3AéaG½ëAÕÊ€)4Ðo¦pRΘ#OÄ¿«2NòɯÖ/\/Î/†ÉYºž#Åaí¼¾]=¬M*ª²úVÿæž÷ª¨ŒÑd@£bÀãÛze(nùJ8ÆáâGñB =4Y5ó£¼H«LÝY7Ãø§áQò¦õZ„W?>Y—dÀ V¥8…‘8àöð~躯ÈÛã %ÆdÑðÀ5RzRÒ8Üy/²²x3¬o%É£zå|¦<¡u•­žê‹¤öPÔ¤¼Oxˆ’²Óïº_’)´ß`[&¥ž9ÞçÈ™w–I=(çE©Ô"hpFMÐIà¬×¬ÓWŠwG´ú;aÂ𥣱&6i¹¨ y a™„º‡²aœ:á‹y`¯c»ÕUGÿ$.2ÉŒÛ&c$mTï?Ò”2C‚APÖ|T;# ü¼ÐÀoÁ,n¼:[úE úÿ|mÞÉÑ\ M ZÔÎr>åIýJÈm]ò¡ÎfbÒøÀ•T'·éb.U µ6FˆhF}ÞuKÐÅǽ±ô§B ÍÐö(r|q°c’JUà ŽÝs\º†‡[»óš8-ÚÜÓó<ˆLʼn¼KÂ7ˆ"m8Fź![–Ø9…¯ê¢—|r¡_X=Ó—óÒc{`l÷h}ô½};'%JfÇ#[ñUŸMˆösθ—þ õöÒá:4ª¡Â6°ÈAî6\ƒ‰Ñgùªw”³UȹÁ„³ú<æ_<ð¼Ê¹¡ÙÃ"ç¡ó‡è<¬R æ£ÃLЖOŸ}MÆüË¹Åø6¨Y‹G«+¢ôQà\þ3òüã{9e –˜*òõ›4 -ïGã[Ú["ÓÛþ‘)*?k!PV=ý>”6 tBF!¿Ol¿GÀýÀÅ%.”i´™ídWûBw}i±DƒdÛ,Ëh†š â‚¼ÚÎÇ)T•qÀT&P©ðA\¤œŸSÔ“íÉ EÕ †Ó/Ôôµöx·m×Ù”r¸¬ˆÉo•I´.âAtø?(C 4ÝIáٹIJ[!j:Ë{kI’èFßx´H˜LGúö=ƒgÐÄ&Iëþç»ü¢ºµ.VšÈ¹@HtJÀ[[¶ê1ŒcsÓ¿»sî;É,4L9»ö¿µ<Úœ"q\ùkâÜaCyõS˃«šÙñƒš/Êï½ó]¨Ãˆ¨~EÁq²§W75* 1=î\w‹-ÐϦkÛÁzÉëÑWŠå0ׇ^£<5ƒ¦3Ym}š¯23’ä Þè èû¬!‰DæÈÂ']+)ŽÞ…'¼¿ŽÔXz7P„Ã}[?$ Loƒ$óîZ‚1ˆÑrÕyžÌ¿žŸ«»ÿºdDÔåù¹ó¶cÐLÁƒ+Tèƒu3ªºó¥KÃ{=c8WCzÈ[/ Žósed{hæo’úŸ"6ö9ñXì0Pý^•¥ö$ê*Ù Ûù‡!éÍÚ„EbË_¬úË0–µç¿•ŸWã8¹ìqvÏV‰ ¡žÙlgk…‚w¤©ðOzxâ;}Rï t”Cuê23Íz¡6žD>ã«@ÃÌÎÝÞƒ”œÅäN[µ6ô”ör…=û’öãü—„¥ÕŠ#³u$¹ýv€gtí±–;]½Þ‹ªj›S°5ºÏT˜÷é?ýegX)Éž{}cRPhâ' ñN+Q¤O¢CNñ{)ÉbÍØË®€¢„[ }yÿA ·ª(ȇRc¹¼ÑZ +ó0ŸÀÅûÊ{,ý>›Ø(ßGx£—|(HûÍéߢ®kƒ÷>û% ÿz/%é«eR»b±¾]òXZî†u¹§± 4þÑg––HË»ÄÜóO³¹9z êÄ ¡€U‘ú¡u>Þ’Õ“ðŽÛS¬Ïô€š…zSR³É‘rÿSÔÕ+^ýN½,‚oúÚ’ÿkǧ“^¿Fe·x[8m¦ëü‘ÔÝãÉ¢×l2T*Ë›wñø†ù·àçIðÀÜPÑúNõÁR)xþ.¡ÆIOô£JÛÌrà4Š-½Ÿ†YÚ^¶— ’‹ÀéøçisõØ!_Å1ÊDŒ ;Gca°/UÛþôgêFp ÑD°€Ø7²rQ)ˆ¬~j½ ËÊ—‹e=ƒëd„¹þÄ´>ÔÉ‘}¡a$«)­}¸Ëw%Ño,uL,äV½È<î­èû.+ûŒ±í[缃TæMˆrø©¢ ,›QUÔ_Š¿²,ÒG-šÕAá›UÅç(Y[’´‚›-¸…µDÓÆ¤©f÷{‘¢Më0üKÖ¹-Êè’·"~²ÏgÏÆ§UôG”êßôx6’Ï„F`ƒëB˜óîÕ¿²iÈmKÄúÇ*·Òk±‡r -X–¤`¯÷DåÚжM¯í=,zZÞùsÊʪ±9¤oüþtYýʯÆo%ÅÊÝÑ•a¯Š³qG++?É;›è•úC_+÷ÒÂÏ ,E†{²%a öY.<©V¡ÈZ˜r¾àZ(Ü _&Ó Û…Ä¼ÁR\ á^ƒ™.ë•Ñ…°•D^J¹ÜŸI¾þ–ñ3'\lï0«Ð™–­´€=r k– Iõ\¯PL3ýª–evfÿʪ]¿HB×Èk)ÚLò1=øžé5Ù/±±DØð[»_/<8PÙCº‚=X¤ÀÍ<§ÍàõcãµÑŸúBT ¤ÇD¾ûû•_ažÓ}é!É^:ÍY®z–M¯ÅðšÛq)uŸÚ ΘղÂ.PnòTs×ܳ@COg™2UÖgRRx’²l:E[îfØqj%&PE5a¸ï›ëK!é!J0"_½úÜOvq?/µ¼Cq‰°Æ ]â– na¯U½žîù¡ƒPMV†l—³Œm–Ÿ#k$ZE%b^³ˆ-©{Ä5òÛHÙ¼E_¼ìyXIÿаO^8P_ŒW¡á©ƒ§nÞ¡ ¶ƒþÐ=äH5Â1íÝ|¦æüeÝÿtÍ—4-4åõºžÞ¨ž;mó©Ø‹«Òqm°‡r`gá‡õØÇ`fÉy—ôlèÙ»á°ÊzhBîÏ=1%¸%*Äj¯6i÷o«ù3¶­±·LMöã÷õ}œ' Bû¦ÊŸ3ݺ³íZuA'Ãq8L˜”8 ËõŸþB§£ý]á— èðŽr•òÓH=]bŸé¥{tä'³œîØû¼þõ¡2r¢TÀ@Iû©ÛP!>v¼#·.…ÍZh"³±÷³˜ð¶î5´ŽŽûV4·áDêRœŒTâ'ŽÀN‘¢ Ô¤G/Ù}8ƒ’îõ¸XXEï¥Üš®î>¹ÎDI·ëÀ±g)g Š¯Žo Bk¼ôL|šrlÙus;®pE8wä~Š÷ÜÂK†¹~ÍAr˺Øg"Ö'ðœlðiþ*\ÙHµZV&^—ÄWø@þ~RÃα9šŒ©ë«Y-EAPʼnÀ¯™9à—•Zò¹@3jÁ õ#§#ì5Yj[MàÎm…ÜŸÐ9Ú2(îØ+¾PN5½mÔÙl«8Aê?~ûNO}qf‰O?>UfTÎ"¢ZAÏßÁ5÷,Ì®ëéÆËƒ©Ê]¤!•¼ ›ïuþ)ÔJà—FÚM½O6g’}²Î/"«Á2æâYº{ çYŠKWîúlË!ž,9/ŒÊZàÌÈv¥O.´ãß«TBés—ÆUP5yƼš% uw†tú¤ãFÝzêTxÝ©¥¿Rà ä®h¯ÿnŒ\)O>–MióF’«óßò0‰ëé¸hƒ]ü;ÿäg¨³ÒzÞ1•ÈJ£Ó‚Ä<¥M,B‘º8Þ-‚ªÇÜ¿ ôs«òp­¨°EÂzÊ—ÓD¦‹– 3–Ô¸mp xÿxÄœx¤|ä\¶½ îÚ ž&3IA–/ߣɩ·á³6©jh .¨JWL‡-}¾÷¶RÁÉ»Iœ¥ÆuQîFøgM°þîB^H>¢ 8©kÃý5ÁüM²ß öaP7HŸóùùWžüxgÎËe¸{ã窒!»;nðàS¦öaR‰7ÂŽÑð–ÙÚ±Úg[öbg.rË™KpœT4l¯ã-‰ÑþktÉÂò—FG"ýYø¯VU½Äçâ„Í2ú´¼a5ÐFê–=ø}[bfðÖÖšc_ŸÿpåÁËåæÁ ıôS÷áÁ‡*e9‡˜¯ð2A@WÜTðУ5×ÏQ#Xÿ6ÙÛCYY€ÝT¸EH¬ŠÙï’J®èY—äñV)Š=u4¯g‹8(—Âà=šwh8-U1Jî_!*«äA£Ö‘œ ü: R l†¶Éb¶œZ!ÿu›Ùˆ™ "öâ'ñ"eט´=K×ã×Â…4§}óp N·òë侩ã™pòBþëÅäoS&JN›+ˆ1úERë±z×”ÀÆc½œÞwmÚ›ë¹"¼Ìáàq„’T¶4ÿjƒâà^®ÛBà9õ(Ìòê#t¨í·¯èÓòy«£} xèÎVޝ‰+ÞDMIà®×ÍT6¿f*€l÷šwPòáþp¯é~«¾ìÙЈÛ(fª e§ŠÕöö›y“UP«yÔá§Jñ=ÙñNeÎ;æ~«‘õ‰ºlìr¬Þkdv‰"¯ƒoZ„¿Ý AQB,.«4-Fú’+Ð^d˜D§µ!VÚµ;q¦æ5 ÿОB­¸ ª œH6øxm¾LôŒƒ0 èÅ»ßý)¼ êÍQè7óÌ©ÞdÉ¿?ÕªÁ:]ÉÎZç<km‰_K GÔF²¼ ÐØÑí"Ã>AI¼¥c¼t§ýugK}5Þ+t¿@.àsß>¨,ËTjà‹Añul‹D>#09fêvÆì–Ô ™zÖmÐOàœåqd†Àji#¬_s£#B‹nîÃoKiXŸÕ˜¦Ÿ«Ÿ{j“9„OÐ,€ÉÝålk3»ë‚]xÖ„U©ìgç¨~eE½ÎðiÁ.¡g¶HRútDAÛ$á/è#’§íSîó¿ÀV=–x)M‰`ƒ^m|Daî^t_” U5ÁLþ·Ú7jú¼*ÒDt†.‡µõòÐí¾­-³:À¡%b§“ê"j±›2ãó¥ ™ñ\ÇêµïØ0ÀË¢ÒtÂÇG-Ûk«/vE^¨1p‰;V_/•¯0žBê ^u—wúݸÎ<èlÙµ°DÓúòç`;N5\Ý™ro@zcê-óšq£b#³>nse—ÕÒƒö‰Ž—=BÖ5wÕ¤&¯­ÐÏÜ6p–‰ð++3¬Ð¼ñ§’k15:{NõÀôC KlÆ9>L¢)EÁÆL¼_ý0îÏ $™À¸¨è|X#Ï©?BŒ iÕ&‡“.¥f¤<2×V•ûwöæhïqÔ7Á=/]¸Û7´›bßÏ™¹’M(» ›;´dœÈQó.Ÿy€å4¨šhØ›)éÍ‘,Ü90¹Ížõ· Ë Y©KvR³}‚‡5·øÜ7`™Ëì$ ½Aù™¶ `@M Ð<àåÁ|B¤ãk5;(r#œ SÁ5$œO5 ªêBÿ¼Lv,‰Ó ²Ï|½_ Z½zÀ§÷Ñ1¨q‘°ÏoGú’û•¨³ô`åz¤AþQ­¯p?{¦þæºW¸±×²Õ@@^q.ŠN¦^Îé®Ë]‹L­¸÷§y”‚÷t{ᯋ vßèÐN6]&.G#Ô°@ñíFŠsÞ ^¼å‹ñewsìé|L"@ eŒ$Ír!è_Vrд½¶›÷7Ûfd‘ ¸ž’¦<‘_PMdÛÑí>îÔ ¡yè˜mÁùBÙymx½…å×RÑ ›•êSÖ‹+¼‚оԱÖ¥ö9Üeœ\>8]æBWÌó+µ"ŽPô5t^¥!ÉsŒMßû.}l2Õ4yvöýÀö#=î _Ìñ4á,|S4%EVwê,WŒ"‘}ŒA* ŠmUe)tØÌx֊ȯFÚ’‹~å#ÁðS`Ú*{{bHK•EBD•+TZU€^Æçâ¹³èP9®w8Ĭù¸iˆ¦:Lgc5ŸPâ{*‡)ê}{íý©cœ£¦Ó!Øç~¡–:t<¢¸qÞ¡9ª2ðçü»•t5;qfR•èÑÌRë¢WíïôyuTÍ"§vßÉúòö </)Vß ^ph]œQ:ây(óx–Rû¡Á»›mûñU¨e­¶DfHq\ÌÜé¼3$ß-˜‚EÓÍc—“ÄQÉ™"œQèÐýs¸lí“V΂ӢþLÌyX„  ƒžŸö£ÿö拏-™©X3¹6Í`ùî…ê1”A‹1eiä¼ Ô6P“¤˜?ˆëÂ\µ®‹ B KÜ8cl׃YÇüíþà aäÚ~Ĺœg‘[SÜÃÆ<Ñé’¸Ù¢¡ :reæړ˲h ‡+&ß&̬  þ GæƒVʶ5'(R÷žƒ~×n¤0O¾T³ïV…#DÑüünY®}2¦c@öæ4i‹­ù¼{º丈ëÌèÙÛ"ÞS{U¶î®?‡gÚý‘8תÎu ïSÓ]ܸµíîkƒ#„ì¹Úîs†ÁÁfœj‚¿YïúœÈuQ+'ÆTxéG1xË\¤}vnÁB{OGrjí!Òé20øL •jÿïÓGç“Õu`ç,}–a¯Š‹tkŒe#ìw*p© bù©&÷€1nrE_/ÿVØCbHh%%™'2¦S³©âþÌdJ&!_žHå?Öc¯HQvôÎkÉ‘yŸ—ê~† ²Eê£Ñ&)¬ä©uºv”l^¨ù†èe"AøpëïgGÛ5•Ç5ÈJOÚÜÿ¼ì[Š×È)ÀC3ÉâçûN‘š´ðõòî; δ~U;3 Ä|Žˆ±ÆÚ9IIDµ, ÈºEArSæ\SùîYºštäÖ€\y9¤àœùÂSA°`Ï~_­V.ÿ=4¤ÀçõFgíþ0ĤÍ #/ÊÒ=ïMS•í&Š“Óü%£d´cªV¼P?«x采Í,£§ü)èWíJ-c©ÉÈê]} Ç–ç_°ùþ`¢¿¿¢$c]bÝ—¾E=Ú‡?¬»ƒå§Í ÈÝ™ë]S~;d¨]–]{^…:åå+#¸#õ2Ôî†eú¸edž¢ R îkIÏéè/»OõÇ>ôï¿ýX’Êñù³aɹznkÕ P\‡–·®¸<^D©šYVk–KqôÃçG>oè;ÙªõÕj3#ýÌô‘ Mü¡ ”…8lÒ>­µRº^¯øMÈ|í (§ Šk»îÂÃ2C¿„èe!Tú½ECÛíóa˱É"0ñ–ƒ%>²!ÿ.j7Z¯jn$m >]Mæe ”FqÝ*ËrZyOW_H™\„,÷ù)vF¼‚øÇ`’Š'wÖ[£s¢akØ@÷­.ÙlŽ^d˃ÆJ>ÿ„îÓ÷ýKM¶~¸IŽÑõTÓöžËÔÈËr†Šíó´‡ëêz˜w[HÔÎg[Vþå¾ÿÖb³'îÚ5œå9O'åk{}Ïxm)çÇ'¤¿û1¶P½ qºº8oÂåCáf²ÐÕGŠäëe…5事ƒ±÷n[µy]Æ6:¾Bž>:•«c‰»ÿP™C¢1&•æ~C3Á¡ž‹q¬$X’ÄÎç5Kêtº¿a¢ºÙ÷Ip’0ñq½±”µs±œ×ñ8âWèƒó^~Ƹ¥ŽŠÊ¾!c‰–§&ˆÜõ°ÄB´RWžÊ¿ó)‘ZIòËí¯Ñ#dJ,«ÿØ}Ã×Ú™49ï¯vþcÄχ“Çu³.›9õ8h8 K¢Gÿì‚É0Ùº[ór·G|¤’y,‚öc!íS¨Ó§1Qii¡y‚Ü|p.ô ™ŠÏËT•¶å¨Œv¤ètï[nd´¾F:V{þÄÄN”LÖ½& Ä×ãÝ}z_.ùàÊaqn±ý0jL59Û›×gï¨y¨VÞ:d k¥°Ö™•P ªÅ©Ñ 2šs›ÑœyÍÜ!s@$3ØŒ¸ ML÷>qgÇ/†¾eÑ"ýýBé‹5j=áŒíR­"¤»Ž½ìÖêðíÿ ÷ŸöJDÍ$)‹dºÌQ.¸›ÄE4Ù÷gjR|ð—í¹.¦år?'(xèpÏI½¯ŒÍèL±þª^óîMpòZS8±©—žFO@1Ù#úЬµÕÂÙv ­è†u’Ye~‚èM!á³â*ùXêîeŽÝW줽^§¾¼ÌÎ ÀÜ•;ÚM*ÆÜZ‹éòC-Ls#ehŸoMÿ·f®làÔb®ˆ@8~èô|üçcu—>'¶^Òõ’¦ØpF.Ž‚™ñoÙ»‰U&#05‚HúމRØþåÔU_ÅJF¡Ïé!0¼(: C²ê;vPZâlc%:38»#QÿVnÈ nÒ@ûq̲OŽ£à¾b(0/à*Ò‘qœ$û‡¢GÆáW£ ßzLt¿ú¼µ„„}T§Ä‘ÅO7yAý>#é§3ruv1¬¶Ê¤¼J ëçæNîû(ãœãi¨a[Ù?¦UŠ.н±%6Û仺u­| þ7óD{‹O 8oå,Úðt!¨¬ ÁÖ#pVÜk‘t‡Ï ­Pƒ´ò[§Æl³é噀¢õI|:r<:±Õ¨P³då·öbfÌõêqKÛZd«äuøN%ö'ÆzWŠfc¤NVx¤;%PbZ¯#UQ?dNÍæ‹ôa™H6hDãA¾ý™Â^JLw2ƒôËéZ‡ÛÍEìˆGÄÝ Ǫ ·f’\„ßò¤õ îÑH¢Ì›'÷FNËUç ›ö+úoÌDBKn¥I!™ôPm¬ ± qIêÑkE}ÛPEÞ\5'ŠY™Ä˜£ W,~:%é¨ÝðÕSDÜÖú,&[ŒÚµÓ 6:»c'æí‚QÐ×G§ë$·©Œ.p"¥n«tqþ¡K·¢caÃjâCñ |ß}Gf³! q›þÆT~[Ôà2ï̼nÈ3žuW>’dÇÃ{¹„‹B¥+1ßÉ4dU#•ý&´ MIÁÊÀ±9v0rý£¥t#{A¬ä©‚^æðüã”[ïÔ†‚=•bµëpõ§‰Hýçשaɽtzàj÷ýÖ²W·vô¡1{Ÿ@¨ÐJ?Ò&,à”Ñ®5Q\ºÛÜ1ŽU‘ì’•½ÝÅrñÃùÈÄ¿öã—ý§èˆƒ0å+º q(ÇÐù…µŸ‘@²õ@ž ‡rùÓ™˜K©ÒЕ‘ÚÙ)&@ž?gsзW­ á¢ÆÏ=ûž¾qŒGFÜç>_ÿѧ¤>|: )žfSLVzàö™;5£Åk™mõxÅk]Ê.¹knÒºiCg‰½þkuKXTÞVbW2ƒò:*´ æ Ù>óøs›nÅØQ³ŽžR±l1qÔt_ÚZ¦ÏO'ôÊÝÊëÇ*2•áÚ/—û•ýŸ¡—0"]î*—ôzÅŠ qÕ¹NÒÝÝ‚¾½ÿbE DŒðü0ãøáÏyá‘Ftëø æã‚éò›S VÿûÀsækÄa{Vô[ý¢ÜK éò7`ʇз.iØU!ÃÖniß_OjÍf£CÇ/²ÌƒØƒÛKÕ0¥ 8¤ŒÜj:‰ü»’³|sß)þ,žøŸj9+¾í3êJª,<ߦ®ž‰±a5ð!B)5žU8EJàímõIA¹Î{6½ Í`érÜ‘pÒ"å73æëÖ¯‰-©UCÉøÕí^ÐsÂíR<K5¯Zn•$SÍ-cò? ©?“ÐÒ™q Î%˜l;ãÆÃ"*î2gl´æKíbKAgÔDF;–êQ1üær, !9~XÉiÌwu~–cëªt3cRZ\-ÏåƒuîÅBô0é[êGúd&‚æhÿ{b§è¦äc"ZíÒÓ'*»wÁ'ÛR]ŸTT.“ÄàÜÚÏ)Ög°ždY> /ž~ã'§Šrì42WBýS·!pû}ëiZÜõìÚ¡L †f¢¾ÜÎMï’Oìr¡…þ4h!‘D¯7d7s®˜4~ÒUe±l¶É_UaÇQœ)q ÿnÔ¼ ñYúŒ›ÌÇ=Ž÷ ¥þn¯Ì`2&«»|‘£Ì!ÙͼÁM¼ãvöFú>“žð$J £;@Fˆ-á)?[ÁB èJî&¶ø½Á¶¹=»aÔ¶œ’òÓ²áoª¢çß+$ ]óˆ\sÇàvÿõ&÷“¦èkWÊ%UiËW^ìƒ<ý#™)ªUH :MàääÍ‚__”Xù¤Kg@¶J iŠúf^žŠýŸà÷ÄZK: ¡&û§nî‰çƒ¥Iâ{™Í*m*yom:3åÈ.×1ú£u¿é"]0i!AɸZN@u¥bKäšH¡Š­†mV×Þ°y0Cùãä”4ÐH{n®DØ‹ßߨf¢")‘uL¦éÀŽÈFÏý‹)£Ògwû@À*V•`x…—Z£|ElÕS5úRNß¼Y8YIœ µ¶,ºFäö ¿ÿæL)›csr'®—ÀµlÇ%WŸÜ?W>ÎiëDÝaºV>N§½Ì) ´V)÷uãæïW¢$š7°ÖéÅó>NEx=0Œ×Øþ –É¢Q2e`8ˆú\È1v¢–ÈàÆÜ>pDoaæ±ÓÓ°õǬì^4iÍek±é¥bjäXm¥vyU3öÄán”:àDbЇ3FB rD‡2Íz{|KšopÝR“¢A(²w:Ÿ{bÍs+[-Ýä¡€:\­[jÆ”ìõUÈ.>1BäkÇKÎS`§8åU[Ì}7<´HT¿ J$PÁšÈdå A·9BÌ-4ÂNÊÐff Ÿp° 4Á£«(„5v²Ÿ¦[‚„áþ%ƒ‘ôÁËäé]ú„íSc ˜“çóSèá伈RôRå_L”áüUÅOoa5ÔŽ‡÷Bn£ÑE;Ä%í¸ÁeÅ7™9‹ýõ î[áî=< ì"¢¾s6q. ¼ýú?Ö+> 3„åÜí(´¡`©¨u¾ìEXÐyŒqê£$˜¢8|h@„¥ £•óõ««¢…µ/À&˜3‡}@FxÖPó'6ªÊ,IAî{lzrܤÜG v 5 \ÉÎ`ø ¡þËf,™h¤²ƒk# ô”Ršx]1X-öˆŠT8â]²ŠZ˜ÈŠ7¢$•0KôöB¬`˜r 5ØV߯ÙÖ¡:Y·Éâï%’½#90thV—9‰ÔöBöa Y•žhþëåçjßvìáŠÙ&|UBÓ<;Ènó&X2Ó ŒåÄ®›¢‰C ná§–þ6Në ¨ogv­„Ù*x×ThÖ ¸T¡)¸ ¬K\Ô;Hµ«£Ì£÷¥ñ¥w¶^~¶tåØHPǸ+KmFxœuj¬A3]ïJc‘oÜÁ^Ï—RPŠÌ§Ð(M¤´*,ÅHœg¡yˆ×Ý<Þ{® jŠÅÌôÚÐÐ̬@¹mÚñuïºë0ÔlÍß è°n:)­¶VIæ¡öñ>X€ ‡}ŽœrÇ,g­¿~(î¸Å#çJ@ØJ  ¿êš2³&5É7‘¬¾:! ;õžObñÏ2sºiƒˆVHžrù^½ k!cÏ´ÇÒ‚­~RöŠ‘RâÇUÈÕ‚m†…øç’Œ\ÖKNÖsÙL ºh™,Ðnsy™²"¸eÐÐDšâ)P=j†ç©ÝÉ—Â]Å@JÏLÝ‘²mß—ß·“ûo8xQ7>ác¤€ÌªÁ3Áy\=/:òÕð^¥ô/‘:ÿÀäQ¦×Ž¡«Ì‰ŒP~¢+Ø ­Ùðk¾«±}Ò¨réAõ?ŽƒSz¾ZXÑãMˆ~³hkÆý¼õý!ª!u°RKk9yÒâ»íd¤]å‹MêIŠœÑ •Póc³ór8#Û,á I”&c€LHàlWï‘ D¿—á”ô½6ùgLªÖzó•Pÿ°îíØ HŸb Éçs<ÞP^÷ ‡åÎÙ™7bjÀ¢îläqËñ ÷’º6¾+ÂXe>MÓÒöW4yÜèûxrë¶’(äv‹ dÓåÅÉâÖGΘTtFí·ÁÔTø5©ô%ɧðέ-yÇD…AÌ#kðd"æçQ.R¡È)³+2dÆ·»áUëa8všA§gˆ™’ƒ[~|çI†ÚùRä@ZLè}‡ñÅi’¬oL š¾;CÚJIyëä0þÓ¥Õ=ø­'c— Unշᓇš<8¯B6 5Õ‚–2¦¢ƒr€ì$èì‹¥£ø4É[μ/ pˆÃpä÷ŠîªÍmÂG EÉ>!Gró„ßoªïÃ6¥±°£R\;'úmF“‹ÒÇéÝÜ‚²“Éú¦†/bîáŶ¿†¨ç»Š}6Dà´Çõ2„ÍãÁÍ’8×-ì‚ú(€ófåKCÊŒþ“aá~ÚÒ¦– |&¿SqM±ÈÅ3Ù®/á',ývYÝo"RCó<-2©%~œHÉ„ÂåÅÔ,<“PcE¼¢˜¥pÓFEÏ-T“/Ž7=AÆG¼ û cêÔË&§q)™jK†Ü—"‰5Á~‰E‘ _Ð%g(i® –õ\ûÄp+|4pšk,`¶Š9§ùý˜AáKÃ~ÈФwÖø 3ÒÁŠ\wËRRch˜}þCD¾•œçÓÄ™¦„!ˆÀÀ;Dw·‹†aÛ;Ua¢5# !õŸzîh`°óR«%þqÏAŸÝ¯Ús”zÙeNÞ·']¡$û™|µ‚™û¹m}âoÎïÇÁÙK3HLK<À¬Ûàb¡aB)Ò c"tDWaìžk5Ó˶´ Í–E¨ ¬žx¨9ÒÈŒ³Ä7pÕ>^º[Uö1±ÏŠÖœbQz%º±Ð Í´Tl¿@W’àƒ3™ú@EVèšyí6Sc”ÉÝYñàÉ1ÌÀœ±l ²)¨zŸkè×­¡cÂ3vÚI$.êÜ®ä·èè>º qPèz©îò‡õÄ5èéj 1¶v(AÝ:™nž cÞ Ïlk¾bé"•Ó±¾Â=YN‘ðIh5‚X[KîÙô / ÿSéáë•4ÿeCB> stream xÚ}UMkã0½ûWh…öF²ã¯²CÛ–¦,{Ml¥hìà$°ý÷«73nw—ÒƒÍÓèÍèͳ,]}{\OlÛoÝ$ºÕêÉúËиIù}s ®®ª¾¹\w¾w®uí8{ºSC߬ÝY]—«jÕíÏ7ž¼êš×KëFÖç¤Â½ì» ÖQ×ÏîçÄ5ÛßFk=i“Óåè†ÉÙL4Rž÷çWOý‚¥ü”úlJQn8íûîN™[?ïË®-û;S§¦£ÜݾkQ¨¶Ð˜Pµûæ,#z7ï’×o§³;¬º]Ìçjúä'OçáôÞÓ‡¡uþ{Qן ô„õåx|u£t°X¨Öí|]ïËýæàÔô‹®ß™ÏoG§BÖØô­;76Ý‹ æZ/Ô¼®ëÚÿæLÄ)ÛÝÈ]z®®ý+ÔQ¾æÉ&¤€)ˆH8ù@h€ÐÚcÈ<Žkd>€‘Re€‘ƒ‘瘌«T\Ãã`^QqJÆ’šÐX‚Q£hÍ)5ŠÖH©K ¥®à£ý±Ï|6öÝüÚ b‘‡ÂÚ.fÀh\‡e®`‚žÇbs¼N[à”sSàŒãÄÏ9·¶‡Óºàu‰Sr¼®ØkÔ4ç"nXCA8%ަľFðÄpý ×O<—cúÒÕǘê,ÿâœúßx!¼ g!íÖaÀM4cô“Ên€Gym¼µÆð: C[1FýlÆx L^ò"K£~–2&NÆýCC&^к䅙Á—¬` ÍYÉecªOý›{"«CgÎúc¬›³þüœõÓÊY =9ëO(—õ'è=gý)ñYB|Ö™BsÎ:S|ãœuF”Ë:#Êe†~,öÙ¢+>£G+>ÇÀâ3qÄg¬eÅgìK+>c]+>ÃO+>G|FïV|†~+>C§ŸÑ»Ÿá›Ÿ©¾ø ýV|†ÎB|ƺ…ø ~!>ƒ_ˆÏÐSˆÏ”+>£÷B|&¾øLüŒOÂr¡—BüG/…ø}XˆÿT“ÿK5ù?)¨ŽøNÅkÅð¡âxáÁÑ$s„y®ªå„¢ G5.™÷» ¹ ƒ¿&è&¢s'þ¾sï—Õ±?"‹ºåÆ‹£‡:øÜZç endstream endobj 841 0 obj << /Length 867 /Filter /FlateDecode >> stream xÚ}UMkã0½ûWh…öF²ã¯²CÛ–¦,{Ml¥hìà$°ý÷«73nw—ÒƒÍÓèÍèͳ,]}{\OlÛoÝ$ºÕêÉúËиIù}s ®®ª¾¹\w¾w®uí8{ºSC߬ÝY]—«jÕíÏ7ž¼êš×KëFÖç¤Â½ì» ÖQ×ÏîçÄ5Ûß&ÔzÒ&§ËÑ “³™h¤<ïϯžúKù)õÙ”¢?ÜpÚ÷Ý2·ZkXvmÙÐØ)˜Š85åîö];ˆBµ…ÞÀ„ªÝ7gÑ»9x‡¼~;ÝaÕíú`>WÓ'?y:o¤÷&˜> ­öÝ‹ºþL '¬/Ç㫃¥ƒÅBµnçëz_î7§¦_týÎ|~;:ÒØ°Æ¦oÝé¸iܰé^\0×z¡æu½\×þ7g"NÙîFîÒsuí_¡ŽòE07H6!L‰@Œ@ÂÈBŒ€Öû@æq\s óŒ”*댌ÆTgùäÔÿÆÀ á]8 i/°n¢£ŸTv<ÊÈkã­õØ0†×YÈÚ²ˆ1êg3ÆK`òÚYÂõ³”1q2î2ñ‚Ö%/Ì ¾dchÎJÆð(«S}êßÄØYÍ:sÖcÝœõÇà笟öPÎúcèÉYB¹¬?Aï9ëO‰Ïúâ³ÎšsÖ™âç¬3¢\ÖQ.ë4ôc±Ï½Xñ=Zñ9Ÿ‰#>c-+>c_ZñëZñ~Zñ™8â3z·â3ô[ñ:­øŒÞ­ø ߬øLõÅgè·â3tâ3Ö-Ägð ñüB|†žB|¦\ñ½â3ñÅgâg|2–³½â?z)ÄìÃBü§šü¯XªÉÿIAuÄp*^+†ÇãŽ&™#ÌsU-'H8ªqɼßÍeü5A7û8ñ÷{¿¬ŽýYôÐ-7^´=ÔÁ!Uç endstream endobj 842 0 obj << /Length 868 /Filter /FlateDecode >> stream xÚ}UMkã0½ûWh…öF²ã¯²CÛ–¦,{Ml¥hìà$°ý÷«73nw—ÒƒÍÓèÍèͳ,]}{\OlÛoÝ$ºÕêÉúËиIù}s ®®ª¾¹\w¾w®uí8{ºSC߬ÝY]—«jÕíÏ7ž¼êš×KëFÖç¤Â½ì» ÖQ×ÏîçÄ5Ûßf6Ó“æ09]Žn˜œÍD#åy~õÔ/XÊO©Ï¦øá†Ó¾ïÕZûÀ²kËþ€ÆNÁTÄ©é(w·ïÚAª-ô&Tí¾9ˈÞÍÁ;„äõÛéì«n×ó¹š>ùÉÓyx#½7Áôahݰï^Ôõg=a}9_Ä(,ªu;_×ûr¿985ý¢ëwæóÛѩƆ56}ëNÇMã†M÷â‚¹Ö 5¯ëEàºö¿9qÊv7r—ž«kÿ u”/‚¹A² )`JbD>`´öØ2ãš™$`¤TY'`ä`ä9&£Ä*×ð8˜W`TœR±¤&4–`Ô(ZsJ¢5Rê’H©+¸†Çhì3Ÿ}7¿6ƒXäÅ¡°6„‹0×a™G„+˜ gıXFǯ€Æ8åÜ8ã8ñsέ-Çá´.x]â”/€+ö5MĹˆÖPN‰£é±¯<1\?‚Ãõ“Æåǘ¾tõ1¦:Ë¿ø#§þ7^ïÂYH{upÍý¤²àQF^o­Ç†1¼ÎBÆÐ–EŒQ?›1^“׆¼ÈƨŸ¥Œ‰“qÿЉ´.yafð%+CsV2†GYŘêSÿ&ÆžÈjÆÐ™³þëæ¬??gý´‡rÖCOÎúÊeý zÏYJ|ÖŸŸu¦Ðœ³Îß8gå²ÎˆrY§¡‹}¶èÅŠÏèÑŠÏ1°øLñkYñûÒŠÏX׊ÏðÓŠÏÄŸÑ»Ÿ¡ßŠÏÐiÅgônÅgøfÅgª/>C¿Ÿ¡³Ÿ±n!>ƒ_ˆÏàâ3ôâ3åŠÏ轟‰/>?ã“°œEè¥ÿÑK!þcâ?ÕäÅRMþO ª#þƒSñZ1|¨8Gxp4Éaž«j9¡èDÂQKæý.h.Ã௠º‰èÜlj¿ïÜûeuìÈ¢‡n¹ñ¢Åè¡þðç( endstream endobj 843 0 obj << /Length 868 /Filter /FlateDecode >> stream xÚ}UÁn£0½óÞC¥öƆ¦Š"RÛVmµÚkN7RI¤í߯ß̤Ý]U=€žÇoÆoƾøöð4qݰö“äZ«GNcë'Õ÷Õ>º¸¨‡ö´óýñÎûÎwçÙÃz‡öÉÕeu[ßöÛãU ßöíë©ógÖç¤Ò¿lû ÖQ—ÏþçÄ·ëß&í¤ÝM§½'G3ÑHyÞ_õ – Sê³)E~øñ°úe®µÖ!°ì»jØ¡±C4qjz–»ÙöÝ( Õz#«nÛeDïvBòÓÛáèw·ýfˆæs5} “‡ãøFz¯¢éýØùqÛ¿¨ËÏÂÓi¿õ£t´X¨ÎoBÝàËÝjçÕô‹®ß™Ïo{¯bÖØ?ìW­Wý‹æZ/Ô¼i‘ï»ÿæLÂ)ëÍ™» \Ý„W¬“bÍ ’MLS!"q Ø# uÀ!`NØÈÀÈ©²ÎÀ(À( LF…Uj®p4¯Á¨9¥cIMh ,ÁhP´á”E¤4ÒÔp€Ñþ¹Ïbvî»ýµÅ¢ …µ!\Î€Ñ¸Ž« 8!\Ã=#ŽÃ2:åx œ1vÀ9çæÀ–ãÄ/8·v‡Óºäu‰Sq¼®ÙkÔ4 ç"nXCI8'ަľ&ðÄpý ×Ï2<WcúÒõǘê,ÿâŸ9Í¿1ðbxÏbÚ ¬Ã€›iÆè'—Ý,ym‚µÆðÚÆŒ¡Í&ŒQßÎ/ÉkC^ØŒ1êÛœ1q,÷ V¼ uÉ 3ƒ/¶d Ͷb l͘êSÿ&Åž° cè,XŠu ÖŸ‚_°~ÚCëO¡§`ýå²þ ½¬?'>ëψÏ:sh.XgŽo\°Î„rYgB¹¬ÓÐÅ>;ôâÄgôèÄçX|&ŽøŒµœøŒ}éÄg¬ëÄgøéÄgâˆÏè݉ÏÐïÄgètâ3zwâ3|sâ3ÕŸ¡ß‰ÏÐYŠÏX·ŸÁ/ÅgðKñzJñ™rÅgô^ŠÏÄŸ‰oùd ,gz)ÅôRŠÿ؇¥øO5ù_qT“ÿ“’êˆÿàÔ¼V jާ M2G˜çêFN(:‘pTã’y¿ ÚÓ8†k‚n":÷qâo{ÿ~Yí‡=²è¡[î|ÑbtßD&çU endstream endobj 844 0 obj << /Length 866 /Filter /FlateDecode >> stream xÚ}UMo£0½ó+¼‡Jí! ᫊"R»­šjµ×œ.RIý÷ë73´»«ªÐóøÍøÍÃØWß¶3ÛÏnÝjõèNÃelܬü¾;WWÕÐ\®?ÿp®uí4{ºSãÐlÝY]—›jÓwçOÞôÍë¥uësRá^ºþƒ‚uÔõ“û5sÍx0ZëYs˜.G7ÎÎf¦‘òÔ_=õ –òSê³)E~ºñÔ ý2·~ÞÖ}[4v æ"NÍ'¹û®oGQ¨ž¡70¡j»æ,#z7ï’·o§³;lúý,—jþè'OçñôÞóû±uc׿¨ëÏzÂör<¾:ˆQ:X­Tëö¾®÷åÇîàÔü‹®ß™OoG§BÖØ ­;wwý‹ –Z¯Ô²®WëÛÿæLÄ)Ïû‰»ö\]ûW¨£|, ’MHS"#p òÐ# µÇ>y×È| #¥Ê:##Ï0)%V©¸†ÇÁ²£â” Œ55¡)°£FÑšSj­‘R—@J]!À5£w+>Ã7+>S}ñú­ø …øŒu ñüB|¿Ÿ¡§Ÿ)W|Fï…øL|ñ™øŸ „å,B/…ø^ ñû°ÿ©&ÿ+–jòRPñœŠ×ŠáCÅñ8ƒ£Iæó\UË E'Žj\2ïwAsGMÐMDç>Nü®wï—Õq8"‹ºå¦‹£û:ø‰9ç endstream endobj 845 0 obj << /Length 866 /Filter /FlateDecode >> stream xÚ}UMo£0½ó+¼‡Jí! ᫊"R»­šjµ×œ.RIý÷ë73´»«ªÐóøÍøÍÃØWß¶3ÛÏnÝjõèNÃelܬü¾;WWÕÐ\®?ÿp®uí4{ºSãÐlÝY]—›jÓwçOÞôÍë¥uësRá^ºþƒ‚uÔõ“û5sÍx0¡Ö³æ0;]ŽnœÍL#å©;¿zê,å§ÔgSŠ ütã©ú;enµÖ>°îÛr8 ±S0qj>ÉÝw};ŠBõ ½ UÛ5gÑ»9x‡¼};ÝaÓï‡`¹TóG?y:o¤÷&˜ß­»þE]&ж—ãñÕAŒÒÁj¥Z·÷u½/?v§æ_týÎ|z;:ÒØ°ÆfhÝé¸kܸë_\°Ôz¥–u½ \ßþ7g"NyÞOܵçêÚ¿Bå«`ilB ˜„‘„­=öÌã¸æ@æ )UÖ 9yŽ€IÁ(±JÅ5<–§T`¬© M55ŠÖœR£h”ºäRê ®á1ÚŸúÌSßÍïÝ(yq(¬ ábŒÆuX&Àá &èq,–Ñ1Ç+à„±N97Î8Nüœsk`Ëq8­ ^—8%Ç àŠ½FMq.â†5„Sâhú@ìkO × Ápý$Áƒqù1¦/]}Œ©Îú/þÄ©ÿ»pÒ^`ÜD3F?©ìx”‘ׯ[ë±a ¯³1´ecÔÏŒ×Àäµ!/²„1êg)câdÜ?4dâ­K^˜|É ÆÐœ•ŒáQV1¦úÔ¿‰±'²š1tæ¬?ƺ9ëÁÏY?í¡œõÇГ³þ„rY‚ÞsÖŸŸõ'Äg)4ç¬3Å7ÎYgD¹¬3¢\ÖièÇbŸ-z±â3z´âs ,>G|ÆZV|ƾ´â3Öµâ3ü´â3qÄgônÅgè·â3tZñ½[ñ¾Yñ™ê‹ÏÐoÅgè,Äg¬[ˆÏàâ3ø…ø =…øL¹â3z/Ägâ‹ÏÄÏød ,gz)ÄôRˆÿ؇…øO5ù_±T“ÿ“‚êˆÿàT¼V *ŽÇM2G˜çªZN(:‘pTã’y¿ šË8úk‚n":÷qâw½{¿¬ŽÃYôÐ-7]´Ý×ÁÎ%ç# endstream endobj 846 0 obj << /Length 866 /Filter /FlateDecode >> stream xÚ}UMo£0½ó+¼‡Jí! ᫊"R»­šjµ×œn¤" ‡þûõ›™´»«ªÐóøÍøÍÃØWß¶3ÛöÏnÝjõèÆþ<4nV~ß‚««ªoÎG×M?œk]{™ïÔÃÐ7[7©ërSmºÃtãÉ›®y=·îÂúœT¸—C÷AÁ:êúÉýš¹fœŒÖzÖgãùä†Ùdf)O‡éÕS¿`)?¥>›RTà§ÆCßÝ)sëç}`ݵeDcc0qj~‘»?tí Õ3ô&Tí¡™dDïæèBòömœÜqÓíû`¹TóG?9NÃé½ æ÷Cë†C÷¢®?è Ûóéôê Fé`µR­ÛûºÞ—»£Só/º~g>½œ ilXcÓ·n<í7ìº,µ^©e]¯×µÿÍ™ˆSž÷îÚsuí_¡ŽòU°4H6!L‰@Œ@ÂÈBŒ€Öû@æq\s óŒ”*댌C¿Ÿ¡³Ÿ±n!>ƒ_ˆÏàâ3ôâ3åŠÏ轟‰/>?ã“°œEè¥ÿÑK!þcâ?ÕäÅRMþO ª#þƒSñZ1|¨8Gxp4Éaž«j9¡èDÂQKæý.hÎÃ௠º‰èÜljèÜûeuêOÈ¢‡n¹ËE‹Ñ}üMçA endstream endobj 847 0 obj << /Length 866 /Filter /FlateDecode >> stream xÚ}UMo£0½ó+¼‡Jí! ᫊"R»­šjµ×œ.R!‡þûõ›™´»«ªÐóøÍøÍÃØWß¶3ÛÏnÝjõèNÃylܬü¾;WWÕМ®Ÿ~8׺ö2{ºSãÐlݤ®ËMµé»éÆ“7}óznÝ…õ9©p/]ÿAÁ:êúÉýš¹fêŒÖzÖf§óѳÉÌ4RžºéÕS¿`)?¥>›RTà§OÝÐß)sëç}`Ý·åp@c§`.âÔü"wßõí( Õ3ô&Tm×L2¢wsð!yûvšÜaÓï‡`¹TóG?yšÆ7Ò{ÌïÇÖ]ÿ¢®?è Ûóñøê Fé`µR­ÛûºÞ—»ƒSó/º~g>½ ilXc3´îtÜ5nÜõ/.Xj½R˺^®oÿ›3§<ï/ܵçêÚ¿Bå«`ilB ˜„‘„­=öÌã¸æ@æ )UÖ 9yŽ€IÁ(±JÅ5<–§T`¬© M55ŠÖœR£h”ºäRê ®á1Ú¿ô™/.}7¿w£XäÅ¡°6„‹0×a™G„+˜ ıXFǯ€Æ8åÜ8ã8ñsέ-Çá´.x]â”/€+ö5MĹˆÖPN‰£é±¯<1\?‚Ãõ“Æåǘ¾tõ1¦:ë¿øNýo ¼Þ…‹öë0à&š1úIe7À£Œ¼6ÞZ cx…Œ¡-‹£~¶`¼&¯ y‘%ŒQ?K'ãþ¡!/h]òÂ,àKV0†æ¬d ²Š1Õ§þMŒ=‘ÕŒ¡3gý1ÖÍY ~Îúiå¬?†žœõ'”Ëúôž³þ”ø¬?!>ëL¡9g)¾qÎ:#Êeå²NC?ûlÑ‹ŸÑ£Ÿc`ñ™8â3Ö²â3ö¥Ÿ±®Ÿá§Ÿ‰#>£w+>C¿Ÿ¡ÓŠÏèÝŠÏðÍŠÏT_|†~+>Cg!>cÝB|¿ŸÁ/Ägè)ÄgÊŸÑ{!>_|&~Æ'a9‹ÐK!þ£—BüÇ>,ĪÉÿŠ¥šüŸTGü§âµbøPq<Žðàh’9Â> stream xÚ}UMo£0½ó+¼‡Jí! ᫊"R»­šjµ×œn¤"Bý÷ë73´»«ªÐóøÍøÍÃØWß¶3ÛöÏnÝjõèÎýehܬü¾;WWUß\Ž®8׺vš=ß©‡¡o¶nT×å¦Út‡ñÆ“7]óziÝÄúœT¸—C÷AÁ:êúÉýš¹fÖzÖgçËÉ ³ÑÌ4Ržã«§~ÁR~J}6¥¨ÀO7œ}w§Ì­Ÿ÷u×–ýƒ¹ˆSóIîþеƒ(TÏИPµ‡f”½›£wÉÛ·ó莛nßË¥š?úÉó8¼‘Þ›`~?´n8t/êú3ž°½œN¯b”V+Õº½¯ë}ù±;:5ÿ¢ëwæÓÛɩƆ56}ëΧ]ã†]÷â‚¥Ö+µ¬ëUàºö¿9qÊó~â®=W×þê(_KƒdRÀ”Ä$ˆ| 4Àhí±dÇ52HÀH©²NÀÈÁÈsL F‰U*®áq°¬À¨8¥cMMh ¬Á¨Q´æ”Ek¤Ô%RWp ÑþÔg¾˜ún~ï±È‹Cam `4®Ã2ŽW0A/ˆc±ŒŽ9^'Œ-pʹ)pÆqâçœ[[ŽÃi]ðºÄ)9^Wì5jšˆs7¬¡ œGÓb_#xb¸~ †ë' ŒË1}éêcLuÖñ'Nýo ¼Þ…‹öë0à&š1úIe7À£Œ¼6ÞZ cx…Œ¡-‹£~¶`¼&¯ y‘%ŒQ?K'ãþ¡!/h]òÂ,àKV0†æ¬d ²Š1Õ§þMŒ=‘ÕŒ¡3gý1ÖÍY ~Îúiå¬?†žœõ'”Ëúôž³þ”ø¬?!>ëL¡9g)¾qÎ:#Êeå²NC?ûlÑ‹ŸÑ£Ÿc`ñ™8â3Ö²â3ö¥Ÿ±®Ÿá§Ÿ‰#>£w+>C¿Ÿ¡ÓŠÏèÝŠÏðÍŠÏT_|†~+>Cg!>cÝB|¿ŸÁ/Ägè)ÄgÊŸÑ{!>_|&~Æ'a9‹ÐK!þ£—BüÇ>,ĪÉÿŠ¥šüŸTGü§âµbøPq<Žðàh’9Â> stream xÚ}TMo£0½ó+¼‡JíÆ6„˜*ŠÄ§”öUS­öš‚ÓEj9ôß¯ß IV«ªÐcüfüæÙÌÍçŸÔÝ›õƒ{)^ìØ†ÊúÙÏ}ïÝÜä]u:Úvz´¶¶õyu|ÏCWíì$n³m¾m›éΑ·mõqªí™õ5)µïM{¥`qûjûS5•”Ò¯ŽþxêíàO£ò%r^›éÃq¿£ ·&¾\Tâ—Ʀk„ºw(Ú:ëŽèmô³>±8+>4m=Ì"Å${J‹º©¦ù‹ÞÕÑ™„äÝç8Ùã¶=tÞz-/nqœ†OR|ç-ž†ÚMû.n¿Tè»SßX¨ÒÛlDm®°óæq´bñ]ãêëgo…¦oÅ*«®¶c¿¯ì°oß­·–r#Öe¹ñl[ÿ·¶âŒ·ÃL @ b÷’ÒaomB‡ÍÒ½´Ô$`$Šá@B)»@ápºâ@á)ê¥ÌpØ2 d¤ë¬ ŠÎŠª?ûaÖ.e„­¥âŒ8äúò¤Îs`ÚWg+`î#)SÎ%~Æü8§NTâ°ÒŒ¡YEð,Ê ¯(îÒ6À*†3FÓª¼r44ëP#®Y§†fÍžhhÓ°CkE8ã8å²+ûj®"7Gö9ç¢~PpúCŠ+ªRî2CËÇ#`øp¿Ëœ1q îE—Œ‡£üˆEÈ5’=„ŸF]=7úz&`ÿq•p&b¡Ã°§|Ç _/ôcØS‰½MrõÎ¤× fÈ/öÝðù%Ðm ÆðȰw öŠù\Ô‰CÞúbÖ“ ÿxö5cÖ fÌw(¥:¤§ÐKœŸz‰gßqf1iXgù·á3NpÆ óéìÓy/hHS>Wø›ÎúÁÏ#ÊÍÁ/—«ù¯¡¿ÿ5fÒerT§apC… Œ‡¦µ—ÙÖw=²è¡¡xÍøz*½¿î¡Œ² endstream endobj 850 0 obj << /Length 719 /Filter /FlateDecode >> stream xÚ}TMo£0½ó+¼‡JíÆ6„˜*ŠÄ§”öUS­öš‚ÓEj9ôß¯ß IV«ªÐcüfüæÙÌÍçŸÔÝ›õƒ{)^ìØ†ÊúÙÏ}ïÝÜä]u:Úvz´¶¶õyu|ÏCWíì$n³m¾m›éΑ·mõqªí™õ5)µïM{¥`qûjûS5NJJéWG<õvð§Qù9¯Íôá¸ßÑ„[_® *ñËcÓµBÝ;‚ muGô6z‹YŸXœš¶f‘â ’=¥EÝTÓüEïêèLBòîsœìqÛ:o½‹·8NÃ')¾óOCm‡¦}·_*tŒÝ©ï?,Ôém6¢¶WØyó¸?Z±ø®ñ õõ³·BÓ·b•UWÛ±ßWvØ·ïÖ[K¹ë²Üx¶­ÿ[[qÆÛa¦ ±{Ié°·6¡Ãfé^Zj0Ep‰€F ¡‡] p8]q põRf8ì2ÒuVEgEÕŸý0k—2ÂÖRqFr} yRç90í«³0÷‘”À)ç?c~ œS'*qXiÆÐ¬"xå„Wwi`CŠ£iU^9šu¨׬SC³fO4´iØ¡µ"œqœrÙ}5× ‘€£ û‚œsQ?(8ý!ÅÕ)w™¡ÇåŠã0|¸ßeΘ8÷¢€KÆÃÑ?þGĉ"äÉÂO£®ž}= °ÿ¸J8±¿ÐaØS¾c†¯ú1ì©ÄÞ&¹zgÒë3äûnøüè6cxdØ»{Å|® êÄ!ï }1ëIÐ<{„š1ë P3æ;”RÒSè%N‰O½Ä³ï8³˜4, гüÛð'8ã„ùtöé¼4¤)Ÿ+üMgýàçåæà—ŒËÕü×Ð_‚ÿ3é29ªÓ0¸¡Bƒ‹†ÆCÓÚËlë»YôÐP> stream xÚ}TMo£0½ó+¼‡JíÆ6„˜*ŠÄ§”öUS­öš‚ÓEj9ôß¯ß IV«ªÐcüfüæÙÌÍçŸÔÝ›õƒ{)^ìØ†ÊúÙÏ}ïÝÜä]u:Úvz´¶¶õyu|ÏCWíì$n³m¾m›éΑ·mõqªí™õ5)µïM{¥`qûjûS5MJJéWG<õvð§Qù9¯Íôá¸ßÑ„[_® *ñËcÓµBÝ;‚ muGô6z‹YŸXœš¶f‘â ’=¥EÝTÓüEïêèLBòîsœìqÛ:o½‹·8NÃ')¾óOCm‡¦}·_*tŒÝ©ï?,Ôém6¢¶WØyó¸?Z±ø®ñ õõ³·BÓ·b•UWÛ±ßWvØ·ïÖ[K¹ë²Üx¶­ÿ[[qÆÛa¦ ±{Ié°·6¡Ãfé^Zj0Ep‰€F ¡‡] p8]q põRf8ì2ÒuVEgEÕŸý0k—2ÂÖRqFr} yRç90í«³0÷‘”À)ç?c~ œS'*qXiÆÐ¬"xå„Wwi`CŠ£iU^9šu¨׬SC³fO4´iØ¡µ"œqœrÙ}5× ‘€£ û‚œsQ?(8ý!ÅÕ)w™¡ÇåŠã0|¸ßeΘ8÷¢€KÆÃÑ?þGĉ"äÉÂO£®ž}= °ÿ¸J8±¿ÐaØS¾c†¯ú1ì©ÄÞ&¹zgÒë3äûnøüè6cxdØ»{Å|® êÄ!ï }1ëIÐ<{„š1ë P3æ;”RÒSè%N‰O½Ä³ï8³˜4, гüÛð'8ã„ùtöé¼4¤)Ÿ+üMgýàçåæà—ŒËÕü×Ð_‚ÿ3é29ªÓ0¸¡Bƒ‹†ÆCÓÚËlë»YôÐP> stream xÚ½[[sÛ¶~ׯàce¸Ba¸BâSŠ+”iŠ+48–¸B[·?i>Á8[¡‚h AbqM‰Ô¡oj€}Âi !Qf ãþ CL #‰Â¶!QcÌŽ Ü+µð·ÊJ>Ã] *Ü(c÷ɘÂX…D儹=#(, †pLBØ ,úEu‰ŽB›@–™Ÿ R>Ì!d€Qt(¢åB@|+Ç7h4ȇh+0(D¬Å퉸6€ˆ Æq-Ø€(¥0BOJ´| %£,zWhŒ7Yk&!ü¦¶Ø÷ÊK9•á ñ6ÉUR;åÍ /§·†N5D¯ÀyÁ¦ÜE§ˆª›•fÁ‘hCé©#®¡`¤ZË%Ò06TƒÈrf ²ïÌRÊ%N¸þª4ƒ…y¼?ï1ÇOQ­0‘ÀHb8@`tÏ8gÊv­¥ ÆnE+éŸ ¡¬àwËôTGÏ¥ÆÍž‹í«÷XòCWkû‰6tõ^Æ>‰ÛÛ!Ãð6 ñ5®qI’g܆¯qbô¯X¿ ­ t®’ã 8³a:»§\â¼û§ÿæêõø«·_eÙY7T8(—6n†…äÅÔÒÚ5¥vµÝb{&|ØK„vµŠq—!(ç?W•ëKnl±J¸‚—e麖qOÑ8kDú«’(sáÇ~>f-ìÙ):,d\=™ÀµŸWÝŒñ…ÏÉÄò^£< ü÷iòÃrú÷MU'ËÅæQµ>[Õ7›åjâ>¿š]Óï_>?~ù¿“'>@-/áÑ|v¹.„—yð`ù­øxŸA¸Ïñè¤áøl¹ý4!Çë³j±–kÎnžVõåUøˆ–ðÙ}jAϳÍl^Ÿ/.çUO6Õõ/àÝ ùÖR¯f«“jSÜ#Çä!yDžgä%yCNÈ)ù•ÌÈgrFÎIEÜrQ­ÈÅòvE®HMæäš,È’,¹!+²&ëêkµ ëúÙÍÕªªÈæ¯%¹%·çuµªÖõš|#“È?Õjyä·ú¤|†1xÿy7ùñÇÈ{ôöáã_yl˜<ȃW8*Ëÿ–¼޾Ç@àOä©#ñgòм*ß92ß“_€ÐߥgËùrQ{I.WÕlS!ÉWß\£5ùØžWëµ£|Qã ï7Õª^ž“/·ÕzSƒÒ/·ËMuþy>ê¯ä/ïŠÔ b/üôòñ³7½àµmÀ ðî¼€/PT~¿ö¼o…ðÁA |¥l©}Ø:yÿúÃëÏ–ffˆ­6fá½ÜRó_²õÚ¥9²SAÌÕä– B+G²I·möÙö‡ßßößý<\ç¨Á:‡ßz|ÝæºÛv²éhËå.ûååxŽ>'/òôù-äêçÕììÏj3¯.6ÍØ3óùv>¯6m.Ÿ-¯¯giœá¿š¸qó‘ôþc,Ág«já컑·Y<´ÙŸÉÿž»°^Ïgë«Á Ý–†í*ÍË}\ÿîÉ“ßß¼ð®gî—èz|݆‹,Šxc“ˆÇûF|?ÐûOÁãïBƒëêqã½lapY°ÅTž£½:ÙóÓ§?ÿô8:9IUGîkü6ƒ›½Óã¾Ý3?òÌçŌ̮!ìÖ³Å9™­1ÄÖ’ÙsÒÃ×çÙÊeKÕæMˆÄÁtêçÑùr>%M:U_ngó»Ûd?n¯?Òúr±W6ÝÌo×.¥âLZ×HeH¥ëÚ£½#©ç`ýl¹ªF³j¯®{züöôÕSˆ˜ÓgÃceSO©„zzpRݧb§Àéʪ š·mØä<Õõ92\º‹7û'žÞQgø“á|¯ÞýìøÅwÞ<ÉRü4d©eé¡M¬KÔ·QûÍ·¯.%wͼ±”ëHêU‡d×Ay•9¨<^œ-ÏÁø ¾¸€:¾8«ÖÅGÆB8«¾ëÛµk×uÁE4ª&¡:5;ë bÔt³\´Ó±à)”z¼·p7ŽSܱ߻ss†«7Žï)炆²†<—6Þ‡Mjµg¨Öw†ÞfÎCIõËí¯°2òNautþ¾pñÓdµOèß\ãt:GÓG¼v&Y×?íäZnãÿ„“Û§íì‡ü»ý¼qq’NȃٺÂ'¹ö¤\¸¿Õa¸‘'õj½Á„-œò_΀>!¿Ö盫5~!ïeÌîsnætù~<¯ðÏaù5†rû͸RöQr¡¤,FIwAI÷G¹ýæØG©·¸´ J¡4» dû£Ü~c룴[\&(©êP²¸äû£Ü~Áê¡ä´’éÁ¸T» û£Ü~è£ÜÎ1„’•» ”û£Ü>÷QÞ•=2â’ï‚RírûØG¹•=˜Õ9.vA©÷G¹}fꣴ{T"¹ Jó]²:H “GINYf©·aÚï˜q) ÈÒìR–ßÇムí. 3­çÍì²ZÃyy‹/;ø¼Â“6þÄ-QŸÃ¡€jÇWÁ|Ù»öwßÍà>ûeŸ±Á¤ò:TЩ‚NmýÝ:ñ‚—ü`Ü'†ûK¡»‹ S†yå1p­·a¼nQ–á.ÂÝø»/t…ðßdCø÷1¨ùA§ ¶´çN˜æ.·a½Nüq‘»ûÒWH$*dÀ ý¹kW<¶!EÐ%½¯¥òÜIl›`Ãêƒm(Ÿ … ¼+Ï{¡xs÷6•à‡Û~PÁ*øA™æ.ÃÝnÃüÖÛÒ>àîñkd*´/ÁÙÐÞ—…ö¡PhlhîÁ¶¹3Ïù àSòYßí[IÑ ¡8ÈÐá3d‰°!üuÜ]Fø)B„ÐàÝÀþÊí@–ìÝ™`¥!Ûvz‹^ßnæð’ºÅ¿x°öã´ü'¯Ò´+ë ¾°éÀö1 ƒ`ØV»ðͪúê~Ä·¿ZÙvµR£«¤WÕ·ûe`F—ìt±Q]e¬+‹¶º¤Ó%U¢KdtIÝ飺X¢«ÌéêØ–£l‡ÓVÐ%UF—踣܋„{™ã^tÜ‹îýj‘c[tló”m#eŒ$|/¢´æã4EˆÓûý@å¸3ÂRÀ97ðÎ ¼ç–PÇrÛeñL®Nˆç9âYG;y}•CDSïpBF_ng‹ÍõòçU4¿ºÆ?‹­qžGó›uµª+7Þ;ÉËúºÞô€¤ÂŸúÝÓ–KTã_•ï]Ô—·«jJð'ËéTyÙÚ›9ÞZÛ9väþ´dŽÃœÎig?ÜBÙ< #|3ÑÎI˜“=L æÂ[z;§a.¼±·sælOÎA™•}œ‚Hr(쟙T/î¤Ï¡n¸“ž“nÖJÑ›îdø¥$R³œì(„ ÜI“òÉ€;UölwªçOÜ)ÖÛp§x:Й¢t‚C09P§z.æ@깘îUÏÅÀ/SD`“Ó”`Ø3ç|$šï‡@E\¤C\¦†€ ¾Î»w3»ôYFiÁ·qÙ£N*‹c-b‰Q¬, =\a]K`hœIV=R&ÛÁj¬iZm¥òÀ@‚‰˜/UØÄ(Ö`#HQŽ51™XeW Kˆ!XkgN«.Of°æŠ8V\‡¥Àz›„œÄ Û<,yÔ åaI¬³I¼J¬²:&PbMBP"9IÄI$'Éb‰¶yºÐj+å€ýªo a endstream endobj 892 0 obj << /Producer (pdfTeX-1.40.25) /Author()/Title()/Subject()/Creator(LaTeX with hyperref)/Keywords() /CreationDate (D:20251212124531Z) /ModDate (D:20251212124531Z) /Trapped /False /PTEX.Fullbanner (This is pdfTeX, Version 3.141592653-2.6-1.40.25 (TeX Live 2023/Debian) kpathsea version 6.3.5) >> endobj 872 0 obj << /Type /ObjStm /N 20 /First 171 /Length 831 /Filter /FlateDecode >> stream xÚ•VËnÛ0¼û+x¬ 4ßK P$·>Ñ´§"×V‰ÄN‹þ}g%Ú¥¨•žV.gg4IQ«FQ´J{ë”m<¬WÎ;Ø ‚µ°QQ`I¥ÈþIià@Ò6`•4b3#2JGì#g"‘SF'vöÊJøÊX8Ee\äRÆGöIÊí©Q&:8'0_^Ηí^}õ´¼o/|˜«Ø4ÐÿEe$2âK„€h]" ˆ±ƒd  ‹í\îf‹÷›ÇÍaôäu7»ºë Û) a $”Jƒ‚ºˆBe~3&' ã¨'¯ a\Tº äò¤0.OJ¹<å&ì1¡,`b ɪâüä4µoW‡ÍnûúÒÎÅørÒ…/›~BQ_C¦QbÛ„ D•­5µÖRIé;-½±®‡öXªµ¡ÎÄ ìÆ²Ì¥§4YTëç¥ã”X®Î0~Wš¡x.Œ¯bóüU2Ժ îÙ‘¼ðœÔHLr$~ùqÚÍ8žßÞÓÈ”8RÓò;žúŽ$ûîHâi>ògP®óŒÕÚ®IÀ‘…³Ž,\pî’Ä,ü¸•²z˹TKciŽ%‡(àü¦œÿׯòÌOVÀÁOñL*®?¤Ã-çRñ,-i!¤%Iª™’€óô7’f>Mx¬ûñȦWï9ŸMbαìÐaÆCwVLø°óJ•0˜ÔQ¹x6¡4í9—u*¼¤Âñ‚ ÈÏÍýË3?Hjú`òv›­ËÖg›Ã‡Qg2åé¶iLžµ†¬5f­ù4óã°·òÃà 7|fÎçžò³†Ÿ‘½ÍóíBý9}!Oœõ…²Îü ²ÿsÞ¤nR./eý”õSÖß¿¡¦º8E~ÓîáKý ¿þyjÕâzyX>ìîg‹Ï($|ú·âlñéåð°Ùvˆï‘þñë¸ûÉþvëvñmß·¿Ûå¯öz·]íž×ËíªÅ„‡LøÔnßvÚ€¹£¿µ%3 endstream endobj 893 0 obj << /Type /XRef /Index [0 894] /Size 894 /W [1 3 1] /Root 891 0 R /Info 892 0 R /ID [ ] /Length 2262 /Filter /FlateDecode >> stream xÚ%˜il׆绫¯¯W¼bãƒm¼`lƒmŒ¹àk l/€1ŠrÕ6Q•*?"PQh“(RššnjÑ´¡…(‰I@u¡š(jÚ¤?ú#9UuQ+**µTmªvž—?ÏûÎ93wfÞïÌ9ö<Ïû_Äó"žyéÃ?žëˆxѾÃx"xƒx‡QÃÃ;ˆŒƒÞ!¼dä…§ÍFð¦‘)×€w™xmxû‘… oo Y JðÆñö!KÁ*¼)¼½È2PŽwo²T†^ÛDVj¼B¼ÝÈÕ ¯oY ÖàÕãe‘u o=Þ8²4âõàíB6f¼­x;‘kA ÞN¼ rX·o²´á陎!ÛÁ¼cxÛ‘ 3âE2zΣÈ.ÐW„· ¹ôàUá 7^¼5xÃÈ>Ðׂ7„Ü ¶àuámE€A¼^¼A¤ á齩‹N:‚—ÁÛ‚ÔÅÓýêôº™1¼ÃxýH=ˆ žÞ¥~šbøt#¾î·©ÅKàé¶ôòvãUãõ õâ÷à5âé‘(4ûðÚñº‘ Ü~<Ý›§Â:7‚׉TÐâ©ô*T$‡ñvám@³`Î<¯˜*‹ó`,‚%p 'À28 VÀ)ªLeJq:'%éT’¢S!R~.Pt.Pj®P`®PV®TªÂUƒrócºUæVq3¥€se´J€¦£5fmËA¹@õ¸5€šqõ€Jq€úpÍ€ªp”«0¯ÌÓYZ@Xo^Å×åQŽgïÚCïž¼ \\K;Þªë2¯é€Žvs œQq=æu,é)qDÏõ…ÞOäõÓY¿™(»-æu¶èÀªÌÛòMIªÖ ™×[/¹£z’$ÖyG9‚îFÍË|N¯3Ï«33—ÛiÞÔ¢$™tÌRŽ$:æ Gž3#Ån·yç*œŸÔÒä¦Ì{æ¢$3°cºvÌŽœ:f[7cÞ•¿‡Ãn\P?"åfÍ{óŒ$sÎ87Ȥ#“nÁ¼ÿ.yféwø2d®fÉ?KˆrÓzµDÔQGDÝ)ކc{>ñ,z¶4lÖk˜fô¸Ùðç$Ó@3!H€$ÐW#òÍ Opî‚F0]g‹Íξ+Y˜Ö³U@‡R OB(7»ø@x‚«¯kDXmvù®$Sx–9;ˤŸ­LõÙ:³Ÿ=û`^ý˜r³Ífï—J¶‚v°0™g×õfw’á°ÿœU¿>Ðaö¯ó’=`è  tƒ©¾íYdý´:€~‹¬}Tr+æ‰×ÑÚ ¶€0h‘Óo¨ßv°Í"OŽHŽ‚  ½åíY°Ó"OÅäí²Èù—Õ:&,òÌ«’ûÀ~°L‚=`¯En4„?7¸¤~GÁ´En]–<fÁ˜Á!pØ¢%‡Ù¨û¥:Ÿó­MKËܪ^ÏXKà˜E'ñ‡? s Ð¬XtæG’QÇ#„<³Ä¥µ"}â‹áØ ß’,I‹>÷oI’Ñ€üä/`­¤-úé™pØ7ÕOk†‹þ-!Iò½€èD/(·XÝÕ…\«-Öö°¤ `MèýF^Å6¶«¥O~£Å:S’ä*håih´¢ zA‹ÅžÁ³ØKWÔ™\í»Ø&¹¸@k†@êRt[<þÏâe‹ê< ú,^üOÉA0ú \0`ñÓ¿ ‡=]«~ã`ÄâO>(IÖ‚]` ™ ÆÀ‹ÿàõ#MAÖâ¯}&9ÈZ0iñ×—åí±øõo¨Eš‚)‹_ûª$A ´HÐr‹tÄ6˜3–è®ò,1üž:Ÿ³–|I’ $, ˜± æ1 -qô¡pìw<1¼eK¬Ü“4 „‘Ø`œÂ G\~̳TÕ¨ú%AÜ×¢’Z’¬¶ÄÛ’% Ï×û$ A1H|–øtWxæ;ûÔo5(µÄí_IV‚j° ”rPaÉÊ'Ô¥ÔX²©X²–ŸÑdÉuW%׃zK6_”\ ÖК@³%¯>íYòÆÃê·´Zò­2I­;µÐlí`è°äoï_WKÉK~òÉM  €Í–üÃý~Z^ƒÁÐû’¼­–üË[j±àËŒXòO¿–Ü´~n¦µ h=¹ŒY^ùŒgyM÷/9Æ-¯þcIÖ|VŒ™ À:13iy7†ÃÞ½­~³`¿åýÂIGÀÀJ03Z*µI]æÁœ¥ÊnJK`ÁRå òÁ28z_‘w(H'-Uù‘¼î—âGA®:ñÃOòøµˆ™³TÃ-É8#ª-Õ¢°úÄÌÏ e«d f>1ó‰™OÌüPh©ö÷Ãkl^Pg²æ—ZªN² 3Ÿ˜ùÄ̯•–º|M]ˆŠ_c©W®K’!¿¬$ѯ·ÔÝ—u”€øÍ–ºó‚¤¶}-´t¿j‘DŸˆú­–î¦gùÏN«3Éñ;,ÿkã’¤É'„~ ‰>õ{,¸ëYzÕ—Õ€øý–.}L’˜ù›©óI¢ÏVÆß †,Ýtÿ3ÔÒ IÂ僌¥»ž–·h›‘µtï„<"åkK1iéþ:yÚuh·£{ÓFbÊÒ¯é¨öäÊ_3–Ðm.ˆž¯-ùó ¦?H˜?ÏI™F|Âå/!õVÂ埤Î? VÀ)úyVôaöˆXúÀ_%£–þ~·Z1KösµâV04­V jT+i^Q+Ï ‹?¯VÊ ¿]£V¾ÜS+mE/æ‡W»ôNˆ>âÇ­!^u!Þx.Äõp-zû!üWBüt1Ä­Ó!‚ïêÚhë;¨¼h¯vÌ‚9pèi,€E §¡Íòq m¤Ö'ªŒ‘ /ô1_âhŽ‘ck™c›ã9j+Ç<žK‚<ù ÿIȱ!ÊöG9öL9öG¹2+úè{á…~ÿ¸÷Gz endstream endobj startxref 974721 %%EOF timeSeries/inst/unitTests/0000755000176200001440000000000015116766331015353 5ustar liggesuserstimeSeries/inst/unitTests/runit.daily.R0000644000176200001440000000162715104730074017736 0ustar liggesusers # Rmetrics is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # Rmetrics is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ test.daily <- function() { NA } ################################################################################ timeSeries/inst/unitTests/runit.timeSeries.R0000644000176200001440000000327615104730074020747 0ustar liggesusers # Rmetrics is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # Rmetrics is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ test.signalSeries.Internal <- function() { # RUnit Test: x = rnorm(12) y = rnorm(12) timeSeries:::.signalSeries(as.matrix(x), 1:12) timeSeries:::.signalSeries(as.matrix(cbind(x,y)), 1:12) } # ------------------------------------------------------------------------------ test.timeSeries.Internal <- function() { # this is to test the problem when a ts object is passed to # timeSeries. It seems that as.matrix does not convert the object # to a matrix !!! z <- ts(matrix(rnorm(300), 100, 3), start=c(1961, 1), frequency=12) # class(as.matrix(z)) #<< mts ts and not matrix in R 2.9.0 # Note that is is possible that a ts object is considered as a # matrix when timeSeries method as dispatched. Hence this check t <- timeSeries(z) checkTrue(identical(as(z, "matrix"), as(t, "matrix"))) } ################################################################################ timeSeries/inst/unitTests/runit.colCum.R0000644000176200001440000000370315104730074020053 0ustar liggesusers # Rmetrics is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # Rmetrics is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ test.colCum <- function() { # RUnit Test: # Signal Series ts <- dummyMonthlySeries(format = "counts") colCumsums(ts) colCummaxs(ts) colCummins(ts) colCumprods(ts) colCumreturns(ts) # Time Series: ts <- dummyMonthlySeries() colCumsums(ts) colCummaxs(ts) colCummins(ts) colCumprods(ts) colCumreturns(ts) # check that timeSeries with one row still works ... t <- ts[1,] checkTrue(is(colCumsums(t), "timeSeries")) checkTrue(is(colCummaxs(t), "timeSeries")) checkTrue(is(colCummins(t), "timeSeries")) checkTrue(is(colCumprods(t), "timeSeries")) checkTrue(is(colCumreturns(t), "timeSeries")) checkEquals(nrow(colCumsums(t)), 1) checkEquals(nrow(colCummaxs(t)), 1) checkEquals(nrow(colCummins(t)), 1) checkEquals(nrow(colCumprods(t)), 1) checkEquals(nrow(colCumreturns(t)), 1) ## 2022-07-27 GB: check fix for #2121 x=dummyMonthlySeries() x[1,2]=NA colCumsums(x, na.rm = TRUE) colCummaxs(x, na.rm = TRUE) colCummins(x, na.rm = TRUE) colCumprods(x, na.rm = TRUE) } ################################################################################ timeSeries/inst/unitTests/runit.NA.R0000644000176200001440000000732415116766331017142 0ustar liggesusers # Rmetrics is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # Rmetrics is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ ## test.removeNA = ## function() ## { ## ## # Create matrix object: ## set.seed(1985) ## M = 5 ## N = 20 ## x = matrix(round(rnorm(M*N), 3), ncol = M) ## colnames(x) = 1:M ## rownames(x) = 1:N ## nNA = 10 ## nCol = trunc(runif(nNA, 1, M+1)) ## nRow = trunc(runif(nNA, 1, N+1)) ## for (i in 1:nNA) x[nRow[i], nCol[i]] = NA ## print(x) ## ans = removeNA(x) ## print(ans) ## ## # Create data.frame object: ## x.df = as.data.frame(x) ## class(x.df) ## ans = removeNA(x.df) ## print(ans) ## class(ans) ## ## # Create timeSeries object: ## tD = timeCalendar(m = 1, d = 1:N) ## x.tS = timeSeries(x, tD) ## print(x.tS) ## ans = removeNA(x.tS) ## print(ans) ## class(ans) ## ## # Return Value: ## return() ## } ## ## ## # ------------------------------------------------------------------------------ ## ## ## test.substituteNA = ## function() ## { ## # Create matrix object: ## set.seed(1985) ## M = 5 ## N = 20 ## x = matrix(round(rnorm(M*N), 3), ncol = M) ## colnames(x) = 1:M ## rownames(x) = 1:N ## nNA = 10 ## nCol = trunc(runif(nNA, 1, M+1)) ## nRow = trunc(runif(nNA, 1, N+1)) ## for (i in 1:nNA) x[nRow[i], nCol[i]] = NA ## print(x) ## ## # Substitute: ## ans = substituteNA(x) ## print(ans) ## ans = substituteNA(x, "mean") ## print(ans) ## ans = substituteNA(x, "median") ## print(ans) ## ## # Create data.frame object: ## x.df = as.data.frame(x) ## print(x.df) ## class(x.df) ## ## # Substitute: ## ans = substituteNA(x.df) ## print(ans) ## ans = substituteNA(x.df, "mean") ## print(ans) ## ans = substituteNA(x.df, "median") ## print(ans) ## ## # Create timeSeries object: ## tD = timeCalendar(m = 1, d = 1:N) ## x.tS = timeSeries(x, tD) ## print(x.tS) ## class(x.tS) ## ## # Substitute: ## ans = substituteNA(x.tS) ## print(ans) ## ans = substituteNA(x.tS, "mean") ## print(ans) ## ans = substituteNA(x.tS, "median") ## print(ans) ## ## # Return Value: ## return() ## } # ------------------------------------------------------------------------------ ## test.interpNA = ## function() ## { ## # Interpolate Column-by-Column ## ## # Create matrix object: ## set.seed(1985) ## M = 5 ## N = 20 ## x = matrix(round(rnorm(M*N), 3), ncol = M) ## colnames(x) = 1:M ## rownames(x) = 1:N ## nNA = 10 ## nCol = trunc(runif(nNA, 1, M+1)) ## nRow = trunc(runif(nNA, 1, N+1)) ## for (i in 1:nNA) x[nRow[i], nCol[i]] = NA ## print(x) ## ## # Interpolate: ## ans = interpNA(x, "linear") ## print(ans) ## ans = interpNA(x, "before") ## print(ans) ## ans = interpNA(x, "after") ## print(ans) ## ## # Return Value: ## return() ## } ################################################################################ timeSeries/inst/unitTests/runit.Omit.R0000644000176200001440000000346715104730074017550 0ustar liggesusers # Rmetrics is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # Rmetrics is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ test.naOmitMatrix = function() { x = as.timeSeries(data(LPP2005REC))[1:20, 1:4] colnames(x) = abbreviate(colnames(x), 6) x[1, 1] = NA x[3:4, 2] = NA x[18:20, 4] = NA show(x) timeSeries:::.naOmitMatrix(as.matrix(x)) timeSeries:::.naOmitMatrix(as.matrix(x), "s") timeSeries:::.naOmitMatrix(as.matrix(x), "z") timeSeries:::.naOmitMatrix(as.matrix(x), "ir") timeSeries:::.naOmitMatrix(as.matrix(x), "iz") timeSeries:::.naOmitMatrix(as.matrix(x), "ie") # Return Value: return() } # ------------------------------------------------------------------------------ test.na.omit = function() { x = as.timeSeries(data(LPP2005REC))[1:20, 1:4] colnames(x) = abbreviate(colnames(x), 6) x[1, 1] = NA x[3:4, 2] = NA x[18:20, 4] = NA show(x) na.omit(x) na.omit(x, "s") na.omit(x, "z") na.omit(x, "ir") na.omit(x, "iz") na.omit(x, "ie") # Return Value: return() } ################################################################################ timeSeries/inst/unitTests/runit.cor.R0000644000176200001440000000213515104730074017412 0ustar liggesusers # Rmetrics is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # Rmetrics is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ test.cor.timeSeries = function() { # RUnit Test: # Signal Series tS = dummyMonthlySeries(format = "counts") tS cor(tS) cov(tS) # timeDate Series: tS = dummyMonthlySeries() tS cor(tS) cov(tS) } ################################################################################ timeSeries/inst/unitTests/runit.as.R0000644000176200001440000000253115104730074017232 0ustar liggesusers # Rmetrics is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # Rmetrics is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ test.as <- function() { # RUnit Test: # Note, you can also use ... is(timeSeries(), "timeSeries") # Series ts = dummyMonthlySeries() x = timeSeries:::.signalSeries(as.matrix(ts)) y = timeSeries:::.timeSeries(as.matrix(ts), as.numeric(time(ts), "sec")) # A vector to a timeSeries as.vector(x) as.vector(x[,1]) as.vector(y) as.vector(y[,1]) # as.numeric: as.numeric(x) as.numeric(x[,1]) as.numeric(y) as.numeric(y[,1]) } ################################################################################ timeSeries/inst/unitTests/runit.apply.R0000644000176200001440000000243415104730074017756 0ustar liggesusers # Rmetrics is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # Rmetrics is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ test.apply <- function() { x = timeSeries(rnorm(90), timeSequence(length.out = 90)) fapply applySeries <- function(x, from = NULL, to = NULL, by = c("monthly", "quarterly"), FUN = colMeans, units = NULL, format = x@format, zone = x@FinCenter, FinCenter = x@FinCenter, recordIDs = data.frame(), title = x@title, documentation = x@documentation, ...) applySeries(x, from = start(x), to = end(x)) } ################################################################################ timeSeries/inst/unitTests/runit.TimeSeriesData.R0000644000176200001440000002416515104730074021501 0ustar liggesusers # Rmetrics is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # Rmetrics is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ test.diffTimeSeries = function() { # diff.timeSeries - Differences a 'timeSeries' object # Univariate Series: # Multivariate Data Set: set.seed(4711) data = cbind(RNORM = round(rnorm(6), 2)) charvec = timeCalendar()[1:6] recordIDs = data.frame(IDs = LETTERS[1:6]) uTS = timeSeries(data, charvec, recordIDs = recordIDs) uTS uTS@recordIDs # Differencing over 1 lag X = diff(x = uTS, lag = 1, diff = 1, trim = FALSE, pad = NA) X X@recordIDs # X = diff(x = uTS, lag = 1, diff = 1, trim = TRUE, pad = NA) # X # X@recordIDs X = diff(x = uTS, lag = 1, diff = 1, trim = FALSE, pad = 0) X X@recordIDs # Differencing over 2 lags X = diff(x = uTS, lag = 2, diff = 1, trim = FALSE, pad = NA) X X@recordIDs # X = diff(x = uTS, lag = 2, diff = 1, trim = TRUE, pad = NA) # X # X@recordIDs X = diff(x = uTS, lag = 2, diff = 1, trim = FALSE, pad = 0) X X@recordIDs # Differencing twice: # X = diff(x = uTS, lag = 1, diff = 2, trim = FALSE, pad = NA) #ERROR # X # X@recordIDs # X = diff(x = uTS, lag = 2, diff = 2, trim = FALSE, pad = NA) # ERROR # X # X@recordIDs # X = diff(x = uTS, lag = 1, diff = 2, trim = TRUE, pad = NA) # X # X@recordIDs # X = diff(x = uTS, lag = 2, diff = 2, trim = TRUE, pad = NA) # X # X@recordIDs # Return Value: return() } # ------------------------------------------------------------------------------ test.lagTimeSeries = function() { # lag.timeSeries - Lags a 'timeSeries' object # Univariate Series: set.seed(4711) data = cbind(RNORM = round(rnorm(6), 2)) charvec = timeCalendar()[1:6] recordIDs = data.frame(IDs = LETTERS[1:6]) uTS = timeSeries(data, charvec, recordIDs = recordIDs) # Multivariate Data Set: set.seed(4711) data = cbind(round(rnorm(12), 2), round(rt(12, df = 4), 2) ) charvec = format(timeCalendar(2006)) mTS = timeSeries(data, charvec, units = c("RNORM", "RT")) mTS # Time Series Lags: X = lag(x = uTS, k = 1, trim = FALSE, units = NULL) X X@recordIDs X = lag(x = uTS, k = c(2,4), trim = FALSE, units = NULL) X X@recordIDs X = lag(x = uTS, k = c(2,4), trim = TRUE, units = NULL) X X@recordIDs X = lag(x = uTS, k = -1:1, trim = FALSE, units = LETTERS[1:3]) X X@recordIDs # Multivariaye Series: diff(mTS, 1, 1) lag(mTS, 1) # Return Value: return() } # ------------------------------------------------------------------------------ test.mergeTimeSeries = function() { # merge.timeSeries - Merges two 'timeSeries' objects # scale.timeSeries - Centers and/or scales a 'timeSeries' object # summary.timeSeries - Summarizes a 'timeDate' object # var.timeSeries - Returns variance for a 'timeSeries' object # Univariate Series: set.seed(4711) data = cbind(RNORM = round(rnorm(6), 2)) charvec = timeCalendar()[1:6] recordIDs = data.frame(IDs = LETTERS[1:6]) uTS = timeSeries(data, charvec, recordIDs = recordIDs) # Merge: X = uTS Y = log(abs(uTS)) merge(x = X, y = Y, units = "One column") colnames(Y) <- "log" merge(x = X, y = Y, units = c("RN", "logAbsRN")) merge(x = X[-6,], y = Y[-3,], units = c("RN", "logAbsRN")) merge(x = X[2:5,], y = Y[4:6,], units = c("RN", "logAbsRN")) # Return Value: return() } # ------------------------------------------------------------------------------ test.scaleTimeSeries = function() { # scale.timeSeries - Centers and/or scales a 'timeSeries' object # Univariate Series: set.seed(4711) data = cbind(RNORM = round(rnorm(6), 2)) charvec = timeCalendar()[1:6] recordIDs = data.frame(IDs = LETTERS[1:6]) uTS = timeSeries(data, charvec, recordIDs = recordIDs) # Multivariate Data Set: set.seed(4711) data = cbind(round(rnorm(12), 2), round(rt(12, df = 4), 2) ) charvec = format(timeCalendar(2006)) mTS = timeSeries(data, charvec, units = c("RNORM", "RT")) # Scale: scale(uTS) scale(mTS) # Return Value: return() } # ------------------------------------------------------------------------------ test.summaryTimeSeries = function() { # summary.timeSeries - Summarizes a 'timeDate' object # Univariate Series: set.seed(4711) data = cbind(RNORM = round(rnorm(6), 2)) charvec = timeCalendar()[1:6] recordIDs = data.frame(IDs = LETTERS[1:6]) uTS = timeSeries(data, charvec, recordIDs = recordIDs) # Multivariate Data Set: set.seed(4711) data = cbind(round(rnorm(12), 2), round(rt(12, df = 4), 2) ) charvec = format(timeCalendar(2006)) mTS = timeSeries(data, charvec, units = c("RNORM", "RT")) # Summary: summary(uTS) summary(mTS) # Return Value: return() } # ------------------------------------------------------------------------------ test.varTimeSeries = function() { # var.timeSeries - Returns variance for a 'timeSeries' object # Univariate Series: set.seed(4711) data = cbind(RNORM = round(rnorm(6), 2)) charvec = timeCalendar()[1:6] recordIDs = data.frame(IDs = LETTERS[1:6]) uTS = timeSeries(data, charvec, recordIDs = recordIDs) # Multivariate Data Set: set.seed(4711) data = cbind(round(rnorm(12), 2), round(rt(12, df = 4), 2) ) charvec = format(timeCalendar(2006)) mTS = timeSeries(data, charvec, units = c("RNORM", "RT")) # Covariance Matrix: var(x = uTS, y = NULL, na.rm = FALSE) var(x = mTS, y = NULL, na.rm = FALSE) # Note, using function cov() fails, since cov() requires an atomic # object as input. # Return Value: return() } # ------------------------------------------------------------------------------ test.mathOpsTimeSeries = function() { # Ops.timeSeries - Arith method for a 'timeSeries' object # abs.timeSeries - Returns abolute values of a 'timeSeries' object # sqrt.timeSeries - Returns sqrt values of a 'timeSeries' object # exp.timeSeries - Returns exponentials of a 'timeSeries' object # log.timeSeries - Returns logarithms of a 'timeSeries' object # quantile.timeSeries - produces sample quantiles of a 'timeSeries' object # Univariate Series: setRmetricsOptions(myFinCenter = "GMT") data = matrix(round(rnorm(12), 2)) charvec = format(timeCalendar(2006)) uTS = timeSeries(data, charvec, units = "RNORM") uTS # Multivariate Series: data = cbind(round(rnorm(12), 2), round(rt(12, df = 4), 2) ) charvec = format(timeCalendar(2006)) mTS = timeSeries(data, charvec, units = c("RNORM", "RT")) mTS # Univariate Ops: uTS < 0 uTS == abs(uTS) # Math Operations: uTS + 5 uTS - 5 100 * uTS uTS / 100 uTS^2 # mathematical Functions: log(abs(uTS)) sqrt(exp(uTS)) # Quantiles: quantile(uTS) quantile(uTS, probs = c(0.9, 0.95)) quantile(uTS, probs = c(0.9, 0.95), type = 5) # Logical Operations: mTS < 0 # Return Value: return() } # ------------------------------------------------------------------------------ test.subsetTimeSeries = function() { # [.timeSeries - subsets of a 'timeSeries' object # cut.timeSeries - cuts a block from a 'timeSeries' object # head.timeSeries - returns the head of a 'timeSeries' object # tail.timeSeries - returns the tail of a 'timeSeries' object # outlier.timeSeries - Removes outliers from a 'timeSeries' object # Univariate Series: setRmetricsOptions(myFinCenter = "GMT") data = matrix(round(rnorm(12), 2)) charvec = format(timeCalendar(2006)) uTS = timeSeries(data, charvec, units = "RNORM") uTS # Multivariate Series: data = cbind(round(rnorm(12), 2), round(rt(12, df = 4), 2) ) charvec = format(timeCalendar(2006)) mTS = timeSeries(data, charvec, units = c("RNORM", "RT")) mTS # Subsets: X = uTS[4:6, ] X X@recordIDs # Head and Tail: head(uTS) tail(uTS) head(mTS) tail(mTS) # Data Subsetting: mTS[, 1] # First Series mTS[4:6, 1] # Second Quarter # Return Value: return() } # ------------------------------------------------------------------------------ test.dimOpsTimeSeries = function() { # dim - Returns the dimension of a 'timeSeries' object # dimnames - Returns the dimension names of a 'timeSeries' object # colnames<-.timeS* - Assigns column names to a 'timeSeries' object # rownames<-.timeS* - Assigns row names to a 'timeSeries' object # is.array.timeSeries - Allows that NCOL and NROW work properly # Univariate Series: setRmetricsOptions(myFinCenter = "GMT") data = matrix(round(rnorm(12), 2)) charvec = format(timeCalendar(2006)) uTS = timeSeries(data, charvec, units = "RNORM") uTS # Multivariate Series: data = cbind(round(rnorm(12), 2), round(rt(12, df = 4), 2) ) charvec = format(timeCalendar(2006)) mTS = timeSeries(data, charvec, units = c("RNORM", "RT")) mTS # Dimension: dim(uTS) == c(12, 1) dimnames(uTS) # Column and Rownames: # X = uTS # colnames(X) = "X" # rownames(X) = as.character(timeCalendar()+24*3600) # X # series(X) # Array: is.array(uTS) # Number of Columns/Rows: NCOL(uTS) NROW(uTS) ncol(uTS) nrow(uTS) # Return Value: return() } ################################################################################ timeSeries/inst/unitTests/runit.methods-print.R0000644000176200001440000000162715104730074021431 0ustar liggesusers # Rmetrics is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # Rmetrics is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ test.print <- function() { NA } ################################################################################ timeSeries/inst/unitTests/runit.merge.R0000644000176200001440000000317615104730074017734 0ustar liggesusers # Rmetrics is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # Rmetrics is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ test.merge.timeSeries = function() { # RUnit Test: # Time Stamps: x = timeSeries()[,1] x y = timeSeries() y merge(x, y) # Signal Counts: x = timeSeries(format = "counts")[,1] x y = timeSeries(format = "counts") y merge(x, y) x <- dummyMonthlySeries()[,1] x y <- dummyMonthlySeries() y merge(x, y) # check that merge method can deal with timeSeries that have # colnames that are invalid data.frame colnames. For example # "S[-1]". data <- matrix(runif(18), ncol = 3) charvec <- rev(paste("2009-0", 1:6, "-01", sep = "")) S <- timeSeries(data, charvec) colnames(S) <- paste("S", 1:3, sep = ".") ts <- merge(S[,2], lag(S[,1], -1:1)) checkIdentical(dim(ts), c(6L,4L)) } ################################################################################ timeSeries/inst/unitTests/runit.attach.R0000644000176200001440000000200215104730074020064 0ustar liggesusers # Rmetrics is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # Rmetrics is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ test.attach.timeSeries = function() { # RUnit Test: # Attach Signal Series tS = timeSeries() attach(tS) SS.1 detach(tS) } ################################################################################ timeSeries/inst/unitTests/runit.durations.R0000644000176200001440000000235515104730074020643 0ustar liggesusers # Rmetrics is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # Rmetrics is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ test.durations <- function() { # RUnit Test: # Signal Series: tS = sort(sample(dummyMonthlySeries())[1:6, ]) tS durations(tS) durations(tS, trim = TRUE) durations(tS, trim = TRUE)/(24*3600) # Time Series: tS = sort(sample(dummyMonthlySeries(format = "counts"))[1:6, ]) tS # BUG !!! # durations(tS) # durations(tS, trim = TRUE) } ################################################################################ timeSeries/inst/unitTests/runit.subset.R0000644000176200001440000001244015104730074020134 0ustar liggesusers # Rmetrics is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # Rmetrics is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ test.subset <- function() { ts <- dummyMonthlySeries() mat <- as.matrix(ts) # we want the same subset-ting rules as for a matrix # but we always print result in vertical style ! # -------------------------------------------------------------------------- # index checkIdentical( ts[], ts) checkTrue(suppressWarnings(is.na(ts[""]))) checkTrue(is.na(mat[""])) checkIdentical( as.matrix(ts[seq(4),2]), mat[seq(4),2,drop=FALSE]) checkIdentical( as.matrix(ts[rep(FALSE, 3), 1]), mat[rep(FALSE, 3), 1,drop=FALSE]) checkIdentical( as.matrix(ts[FALSE, 1]), mat[FALSE, 1, drop = FALSE]) checkIdentical( as.matrix(ts[rep(TRUE), 2]), mat[rep(TRUE), 2, drop=FALSE]) charvec <- as.character(timeCalendar()[1:3]) checkIdentical( as.matrix(ts[charvec, 1]), mat[charvec, 1, drop = FALSE]) checkIdentical( as.matrix(ts[seq(4),]), mat[seq(4),,drop=FALSE]) checkIdentical( as.matrix(ts[rep(FALSE, 3), ]), mat[rep(FALSE, 3), ,drop=FALSE]) checkIdentical( as.matrix(ts[FALSE, ]), mat[FALSE, ,drop=FALSE]) checkIdentical( as.matrix(ts[rep(TRUE), ]), mat[rep(TRUE), ,drop=FALSE ]) dd <- as.character(time(ts)[1]) checkIdentical( as.matrix(ts[dd, ]), mat[dd, ,drop=FALSE]) checkIdentical( as.matrix(ts[,2]), mat[,2,drop=FALSE]) checkIdentical( as.matrix(ts[2,FALSE]), mat[2,FALSE, drop=FALSE]) # prefer to have an empty timeSeries instead of empty data with row names checkIdentical( as.matrix(ts[,FALSE]), mat[,FALSE, drop = FALSE]) checkIdentical( as.matrix(ts[,TRUE ]), mat[,TRUE ,drop=FALSE]) checkIdentical( as.matrix(ts[, "TS.1"]), mat[, "TS.1", drop = FALSE]) # -------------------------------------------------------------------------- # timeDate checkIdentical( ts[timeCalendar()[1:5], 2], ts[1:5,2]) checkIdentical( ts[timeCalendar()[1:5], ], ts[1:5,]) # -------------------------------------------------------------------------- # logical matrix and timeSeries i <- ts < 0.4 checkException(ts[series(i), ], silent = TRUE) checkException(ts[i, ], silent = TRUE) checkException(mat[series(i), ], silent = TRUE) # it fails as expected checkIdentical( as.matrix(ts[series(i)[,1], ]), mat[series(i)[,1], , drop=FALSE]) checkIdentical( as.matrix(ts[i[,1], ]), mat[series(i)[,1], , drop=FALSE]) checkIdentical( as.matrix(ts[series(i)[,1],1]), mat[series(i)[,1],1,drop=FALSE]) checkIdentical( as.matrix(ts[i[,1],1]), mat[series(i)[,1],1,drop=FALSE]) # this should fail checkException(ts[series(i), 2], silent = TRUE) checkException(ts[i, 2], silent = TRUE) checkException(ts[series(i), 1], silent = TRUE) checkException(ts[series(i),1], silent = TRUE) checkException(ts[i,1], silent = TRUE) checkException(mat[series(i),1], silent = TRUE) checkException(ts[series(i),], silent = TRUE) checkException(mat[series(i),], silent = TRUE) checkIdentical( ts[series(i)], mat[series(i)]) checkIdentical( ts[i], mat[series(i)]) # -------------------------------------------------------------------------- # $,timeSeries method df <- as.data.frame(ts) checkIdentical( ts$TS., df$TS.) checkIdentical( ts$TS.1, df$TS.1) checkIdentical( ts$a, df$a) colnames(ts) <- c("aa", "bb") colnames(df) <- c("aa", "bb") checkIdentical( ts$a, df$a) checkIdentical( ts$b, df$b) } ################################################################################ timeSeries/inst/unitTests/runit.time.R0000644000176200001440000000162615104730074017571 0ustar liggesusers # Rmetrics is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # Rmetrics is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ test.time <- function() { NA } ################################################################################ timeSeries/inst/unitTests/Makefile0000644000176200001440000000042415117007154017003 0ustar liggesusersPKG=timeSeries TOP=../.. SUITE=doRUnit.R R=R all: inst test inst: # Install package -- but where ?? -- will that be in R_LIBS ? cd ${TOP}/..;\ ${R} CMD INSTALL ${PKG} test: # Run unit tests export RCMDCHECK=FALSE;\ cd ${TOP}/tests;\ ${R} --vanilla --slave < ${SUITE} timeSeries/inst/unitTests/runit.aggregate.R0000644000176200001440000000447415104730074020565 0ustar liggesusers # Rmetrics is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # Rmetrics is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ test.aggregate.timeSeries <- function() { # RUnit Test: # aggregate.timeSeries(x, by = c("monthly", "quarterly"), FUN = colMeans, # units = NULL, ...) # A daily Series: charvec <- timeSequence(length.out = 365) x <- timeSeries(rnorm(365), charvec) # Aggregate Returns Monthly: by <- unique(timeFirstDayInMonth(charvec)) aggregate(x, by, sum, units = "MonthReturns") # Count Monthly Records: aggregate(sign(abs(x)), end(charvec), sum, units = "NoOfRecords") # Aggregate Returns Quarterly: by <- unique(timeLastDayInQuarter(charvec)) aggregate(x, by, sum, units = "QrtReturns") # Another example x <- as.timeSeries(data(LPP2005REC))[,1:4] by <- timeSequence(from = "2006-01-01", to = "2008-01-01", by = "quarter") aggregate(x, by, mean) x <- timeSeries(seq(12), timeCalendar()) # relative from and to to make test work for future years ... from <- timeCalendar(y=getRmetricsOptions("currentYear") - 1, m=1, d=1) to <- timeCalendar(y=getRmetricsOptions("currentYear") + 1, m=1, d=1) by <- timeSequence( from = from, to = to, by = "quarter") x a <- aggregate(x, by, sum) a ### DW here are mismatches - corrected above ... checkEquals(sum(x[1]), a[1]) checkEquals(sum(x[2:4]), a[2]) checkEquals(sum(x[5:7]), a[3]) checkEquals(sum(x[8:10]), a[4]) checkEquals(sum(x[11:12]), a[5]) } ################################################################################ timeSeries/inst/unitTests/runit.na.contiguous.R0000644000176200001440000000245215104730074021425 0ustar liggesusers # Rmetrics is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # Rmetrics is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ test.na.contiguous = function() { ## Dummy timeSeries with NAs entries data1 <- matrix(c(NA, 1), ncol = 2) data2 <- matrix(rep(2, 4), ncol = 2) data3 <- matrix(c(NA, 3), ncol = 2) data4 <- matrix(rep(4, 4), ncol = 2) data <- rbind(data1, data2, data3, data4) ts <- timeSeries(data, timeCalendar()[1:6]) ## Find the longest consecutive non-missing values ans <- na.contiguous(ts) check <- getDataPart(ans) dimnames(check) <- NULL checkIdentical(data2, getDataPart(check)) } timeSeries/inst/unitTests/runit.returns.R0000644000176200001440000000163115104730074020331 0ustar liggesusers # Rmetrics is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # Rmetrics is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ test.returns <- function() { NA } ################################################################################ timeSeries/inst/unitTests/runit.dim.R0000644000176200001440000000344515104730074017405 0ustar liggesusers # Rmetrics is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # Rmetrics is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ test.dim.timeSeries = function() { # RUnit Test: # Univariate Case: tS = timeSeries(format = "counts")[, 1] dim(tS) NCOL(tS) NROW(tS) ncol(tS) nrow(tS) dimnames(tS) colnames(tS) rownames(tS) # colnames<- # rownames<- is.array(tS) # Multivariate Case: tS = timeSeries(format = "counts") dim(tS) NCOL(tS) NROW(tS) ncol(tS) nrow(tS) dimnames(tS) colnames(tS) rownames(tS) # colnames<- # rownames<- is.array(tS) # Univariate Case: tS = timeSeries()[, 1] dim(tS) NCOL(tS) NROW(tS) ncol(tS) nrow(tS) dimnames(tS) colnames(tS) rownames(tS) # colnames<- # rownames<- is.array(tS) # Multivariate Case: tS = timeSeries() dim(tS) NCOL(tS) NROW(tS) ncol(tS) nrow(tS) dimnames(tS) colnames(tS) rownames(tS) # colnames<- # rownames<- is.array(tS) } ################################################################################ timeSeries/inst/unitTests/runit.align.R0000644000176200001440000000254715104730074017730 0ustar liggesusers # Rmetrics is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # Rmetrics is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ test.align.timeSeries <- function() { # RUnit Test: # .align.timeSeries(x, method = c("before", "after", "interp"), # startOn = "hours", by = "30 m") set.seed(1953) tD = timeCalendar( y = rep(2008, times = 6), m = rep(4, times = 6), d = rep(10:11, each = 3), h = sample(1:23)[1:6], min = sample(1:59)[1:6], s = sample(1:59)[1:6]) tS = timeSeries(rnorm(6), tD) align(tS) align(tS, method="interp") # Note, we should als add an argument to trim NAs } ################################################################################ timeSeries/inst/unitTests/runit.methods-plot.R0000644000176200001440000000162615104730074021252 0ustar liggesusers # Rmetrics is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # Rmetrics is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ test.plot <- function() { NA } ################################################################################ timeSeries/inst/unitTests/runTests.R0000644000176200001440000000453315104730074017322 0ustar liggesuserspkg <- "timeSeries" if(require("RUnit", quietly = TRUE)) { library(package=pkg, character.only = TRUE) if(!(exists("path") && file.exists(path))) path <- system.file("unitTests", package = pkg) ## --- Testing --- ## Define tests testSuite <- defineTestSuite(name = paste(pkg, "unit testing"), dirs = path) if(interactive()) { cat("Now have RUnit Test Suite 'testSuite' for package '", pkg, "' :\n", sep='') str(testSuite) cat('', "Consider doing", "\t tests <- runTestSuite(testSuite)", "\nand later", "\t printTextProtocol(tests)", '', sep = "\n") } else { ## run from shell / Rscript / R CMD Batch / ... ## Run tests <- runTestSuite(testSuite) if(file.access(path, 02) != 0) { ## cannot write to path -> use writable one tdir <- tempfile(paste(pkg, "unitTests", sep="_")) dir.create(tdir) pathReport <- file.path(tdir, "report") cat("RUnit reports are written into ", tdir, "/report.(txt|html)", sep = "") } else { pathReport <- file.path(path, "report") } ## Print Results: printTextProtocol(tests, showDetails = FALSE) printTextProtocol(tests, showDetails = FALSE, fileName = paste(pathReport, "Summary.txt", sep = "")) printTextProtocol(tests, showDetails = TRUE, fileName = paste(pathReport, ".txt", sep = "")) ## Print HTML Version to a File: ## printHTMLProtocol has problems on Mac OS X if (Sys.info()["sysname"] != "Darwin") printHTMLProtocol(tests, fileName = paste(pathReport, ".html", sep = "")) ## stop() if there are any failures i.e. FALSE to unit test. ## This will cause R CMD check to return error and stop tmp <- getErrors(tests) if(tmp$nFail > 0 | tmp$nErr > 0) { stop(paste("\n\nunit testing failed (#test failures: ", tmp$nFail, ", R errors: ", tmp$nErr, ")\n\n", sep="")) } } } else { cat("R package 'RUnit' cannot be loaded -- no unit tests run\n", "for package", pkg,"\n") } ################################################################################ timeSeries/inst/unitTests/runit.methods-summary.R0000644000176200001440000000163115104730074021765 0ustar liggesusers # Rmetrics is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # Rmetrics is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ test.summary <- function() { NA } ################################################################################ timeSeries/inst/unitTests/runit.cumulated.R0000644000176200001440000000213315104730074020610 0ustar liggesusers # Rmetrics is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # Rmetrics is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ test.cumulated = function() { # RUnit Test: # Signal Series: tS = dummyMonthlySeries(format = "counts") # problem with Fincenter cumulated(tS) # timeDate Series: tS = dummyMonthlySeries() cumulated(tS) } ################################################################################ timeSeries/inst/unitTests/runit.lag.R0000644000176200001440000000305215104730074017371 0ustar liggesusers # Rmetrics is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # Rmetrics is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ test.lag <- function() { # RUnit Test: tS = round(dummyMonthlySeries(flormat = "counts"), 3)[, 1] tS lag(tS) lag(tS, k = -2:2) lag(tS, k = -2:2, trim = TRUE) tS = round(dummyMonthlySeries(), 3)[, 1] tS lag(tS) lag(tS, k = -2:2) lag(tS, k = -2:2, trim = TRUE) # check colnames when using multiple lag indexes. data <- matrix(runif(12), ncol = 2) charvec <- rev(paste("2009-0", 1:6, "-01", sep = "")) S <- timeSeries(data, charvec) colnames(S) <- paste("S", 1:2, sep = ".") ts <- lag(S, -1:1) checkIdentical(colnames(ts), c("S.1[-1]", "S.1[0]", "S.1[1]", "S.2[-1]", "S.2[0]", "S.2[1]")) } ################################################################################ timeSeries/inst/unitTests/runit.monthly.R0000644000176200001440000000163115104730074020321 0ustar liggesusers # Rmetrics is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # Rmetrics is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ test.monthly <- function() { NA } ################################################################################ timeSeries/inst/unitTests/runit.colStats.R0000644000176200001440000000266115104730074020427 0ustar liggesusers # Rmetrics is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # Rmetrics is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ test.colStats = function() { # RUnit Test: # Signal Series: tS = dummyMonthlySeries(format = "counts") tS colStats(tS, mean) colSums(tS) colMeans(tS) colSds(tS) colVars(tS) colSkewness(tS) colKurtosis(tS) colMaxs(tS) colMins(tS) colProds(tS) colQuantiles(tS) # timDate Series: tS = dummyMonthlySeries() tS colStats(tS, mean) colSums(tS) colMeans(tS) colSds(tS) colVars(tS) colSkewness(tS) colKurtosis(tS) colMaxs(tS) colMins(tS) colProds(tS) colQuantiles(tS) } ################################################################################ timeSeries/inst/unitTests/runit.signalCounts.R0000644000176200001440000000255715104730074021310 0ustar liggesusers # Rmetrics is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # Rmetrics is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ test.signalCounts <- function() { # RUnit Test: int = c(1, 10, 100, 21, 135) print(timeSeries:::.signalCounts(sample(int))) nc = timeSeries:::.signalCounts(int) nc ns = sample(nc) ns sorted = sort(ns) sorted as.integer(sorted) ns ordered = order(ns) ordered ns[ordered] as.integer(ns[ordered]) timeSeries:::.signalCounts(1:12) timeSeries:::.signalCounts(sample(1:12)) timeSeries:::.signalCounts(timeSeries:::.signalCounts(1:12)) } ################################################################################ timeSeries/inst/unitTests/runit.drawdowns.R0000644000176200001440000000234615104730074020643 0ustar liggesusers # Rmetrics is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # Rmetrics is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ test.drawdowns <- function() { # RUnit Test: tS = timeSeries( data = matrix(rnorm(200, sd = 1e-3), 100), charvec = format(timeSequence(length.out = 100)) ) tS drawdowns(tS) tS = timeSeries( data = matrix(rnorm(200, sd = 1e-3), 100), charvec = 1:100, format = "counts" ) tS drawdowns(tS) } ################################################################################ timeSeries/inst/unitTests/runit.TimeSeriesClass.R0000644000176200001440000002602015104730074021665 0ustar liggesusers # Rmetrics is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # Rmetrics is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ test.timeSeries = function() { # timeSeries - Creates a 'timeSeries' object from scratch # Settings: setRmetricsOptions(myFinCenter = "GMT") set.seed(4711) data = matrix(round(rnorm(12), 3)) data class(data) charvec = format(timeCalendar(2006)) charvec class(charvec) # Compose Univariate daily random sequence setRmetricsOptions(myFinCenter = "GMT") uTS = timeSeries(data, charvec, units = "uTS") series(uTS) print(uTS) # FinCenter Functionality: timeSeries(data, charvec, units = "uTS", zone = "GMT", FinCenter = "GMT") timeSeries(data, charvec, units = "uTS", zone = "Zurich", FinCenter = "Zurich") timeSeries(data, charvec, units = "uTS", zone = "GMT", FinCenter = "Zurich") timeSeries(data, charvec, units = "uTS", zone = "Zurich", FinCenter = "GMT") # Return Value: return() } # ------------------------------------------------------------------------------ test.readSeries = function() { # readSeries - Reads from a spreadsheet and creates a 'timeSeries' # Load Microsoft Data: data(MSFT) MSFT.df = as.data.frame(MSFT) # Read Data Frame: write.table(MSFT.df, file = "msft.dat.csv", sep = ";") read.table("msft.dat.csv", sep = ";") # Read Time Series: # X = readSeries("msft.dat.csv") # X = X[1:12, ] # class(X) # Show Part of Series: # head(X)[, 1:5] # head(X[, 1:5]) # head(X[, 1:5], 2) # Return Value: return() } # ------------------------------------------------------------------------------ test.returns = function() { # returns - Computes returns from a 'timeSeries' object # Load Time Series: X = MSFT head(X) # returns : OPEN = X[, 1] print(OPEN) MSFT.RET = returns(OPEN) print(MSFT.RET) # Return Value: return() } # ------------------------------------------------------------------------------ test.applySeries = function() { # applySeries - Applies a function to blocks of a 'timeSeries' NA # Return Value: return() } # ------------------------------------------------------------------------------ test.orderStatistics = function() { # orderStatistics - Compute order statistic of a 'timeSeries' # Load Data: X = MSFT head(X) # returns: OPEN = X[, 1] print(OPEN) # ORDER STATISTICS: orderStatistics(OPEN) orderStatistics(X[, -5]) orderStatistics(X[, -5])$Open # Return Value: return() } # ------------------------------------------------------------------------------ test.series = function() { # series - Extracts data slot from 'timeSeries' object # Load Microsoft Data: X = MSFT X = X[1:12, ] class(X) # Return Series: OPEN = X[, 1] OPEN returns(OPEN) # Volatility Series: abs(returns(OPEN)) # Data Matrix: series(OPEN) Y = series(X) Y class(Y) # Position Vector: PO = time(OPEN) PO PX = time(X) PX class(PX) checkEquals( target = sum(as.integer(PO - PX)), current = 0) # Return Value: return() } # ------------------------------------------------------------------------------ test.isUnivariate = function() { # isUnivariate Tests if an object of class 'timeSeries' is univariate # Load Microsoft Data: X = MSFT OPEN = X[, 1] # Is Univariate? checkTrue(!isUnivariate(X)) checkTrue(isUnivariate(OPEN)) checkTrue(isMultivariate(X)) checkTrue(!isMultivariate(OPEN)) # Return Value: return() } # ------------------------------------------------------------------------------ test.isMultivariate = function() { # isMultivariate - Tests if an object of class 'timeSeries' is multivariate # Load Microsoft Data: X = MSFT OPEN = X[, 1] # Is Multivariate? checkTrue(isMultivariate(X)) checkTrue(!isMultivariate(OPEN)) # Return Value: return() } # ------------------------------------------------------------------------------ test.displayMethods = function() { # print.timeSeries Print method for a 'timeSeries' object # plot.timeSeries Plot method for a 'timeSeries' object # lines.timeSeries Lines method for a 'timeSeries' object # points.timeSeries Points method for a 'timeSeries' object ## FIXME(MM) - if we store this -- make it a package data set! ## Microsoft Data: ## MSFT.df = data.frame(matrix(c( ## 20010326, 57.1250, 57.5000, 55.5625, 56.0625, 31559300, ## 20010327, 56.0625, 58.5625, 55.8750, 58.2500, 47567800, ## 20010328, 57.3750, 57.9375, 55.3750, 55.5625, 39340800, ## 20010329, 55.3750, 57.1875, 54.5625, 55.3750, 43492500, ## 20010330, 55.7500, 56.1875, 53.8750, 54.6875, 45600800, ## 20010402, 54.8125, 56.9375, 54.6250, 55.8125, 37962000, ## 20010403, 55.3125, 55.3125, 52.7500, 53.3750, 47093800, ## 20010404, 53.3750, 55.0000, 51.0625, 51.9375, 52023300, ## 20010405, 53.7500, 57.3750, 53.5000, 56.7500, 56682000, ## 20010406, 56.3750, 57.1875, 55.0625, 56.1875, 46311000, ## 20010409, 56.5700, 57.4200, 55.6600, 57.1500, 28147800, ## 20010410, 57.9500, 60.0900, 57.7800, 59.6800, 54599700, ## 20010411, 60.6500, 61.5000, 59.7000, 60.0400, 54939800, ## 20010412, 59.5600, 62.3100, 59.3500, 62.1800, 43760000, ## 20010416, 61.4000, 61.5800, 60.1200, 60.7900, 32928700, ## 20010417, 60.5200, 62.1100, 60.0400, 61.4800, 42574600, ## 20010418, 63.3900, 66.3100, 63.0000, 65.4300, 78348200, ## 20010419, 65.8100, 69.0000, 65.7500, 68.0400, 79687800, ## 20010420, 70.3000, 71.1000, 68.5000, 69.0000, 96459800, ## 20010423, 68.1100, 68.4700, 66.9000, 68.2500, 46085600, ## 20010424, 68.2000, 69.9300, 67.1400, 67.5500, 44588300, ## 20010425, 67.5700, 69.7900, 67.2500, 69.6900, 38372000, ## 20010426, 70.0700, 71.0000, 68.2500, 69.1300, 59368800, ## 20010427, 69.5300, 69.6800, 66.2100, 67.1200, 60786200, ## 20010430, 68.5300, 69.0600, 67.6800, 67.7500, 37184100, ## 20010501, 67.6600, 70.3000, 67.6000, 70.1700, 41851400, ## 20010502, 71.0000, 71.1500, 69.3500, 69.7600, 46432200, ## 20010503, 69.2500, 70.1800, 68.1400, 68.5300, 33136700, ## 20010504, 68.0000, 71.0500, 67.9600, 70.7500, 59769200, ## 20010507, 70.8300, 72.1500, 70.7000, 71.3800, 54678100), ## byrow = TRUE, ncol = 6)) ## colnames(MSFT.df) = c("YYMMDD", "Open", "High", "Low", "Close", "Volume") # Data: X = MSFT X = X[1:12, ] OPEN = X[, 1] # Print: print(X) print(OPEN) # Plot: par(mfrow = c(1, 1)) plot(OPEN, type = "l") # GMT - Plot: tC = timeCalendar(2006, 1, 1, 0:23, 0, 0, zone = "GMT", FinCenter = "GMT") tS = timeSeries(data = matrix(rnorm(24), ncol = 1), charvec = tC) plot(tS) # Zurich - Plot: tC = timeCalendar(2006, 1, 1, 0:23, 0, 0, zone = "GMT", FinCenter = "Zurich") tS = timeSeries(data = matrix(rnorm(24), ncol = 1), charvec = tC, zone = "GMT", FinCenter = "Zurich") plot(tS) # New York - Plot: tC = timeCalendar(2006, 1, 1, 0:23, 0, 0, zone = "GMT", FinCenter = "NewYork") tS = timeSeries(data = matrix(rnorm(24), ncol = 1), charvec = tC, zone = "GMT", FinCenter = "NewYork") plot(tS, type = "h") lines (tS, col = "red", lty = 3) points(tS, col = "blue", pch = 19) abline(h=0, col = "grey") # Return Value: return() } # ------------------------------------------------------------------------------ test.dummyDailySeries = function() { # dummyDailySeries - Creates a dummy daily 'timeSeries' object # Create Dummy Time Series: setRmetricsOptions(myFinCenter = "GMT") tS = dummyDailySeries(matrix(rnorm(12))) print(tS) # Return Value: return() } # ------------------------------------------------------------------------------ test.alignDailySeries = function() { # alignDailySeries - Aligns a 'timeSeries' object to new positions # Time Series: setRmetricsOptions(myFinCenter = "GMT") tS = MSFT[1:25, ] print(tS) dim(tS) # Align Daily Series: alignDailySeries(tS, method = "interp") # Align Daily Series: alignDailySeries(tS, method = "fillNA") # Align Daily Series: alignDailySeries(tS, method = "fillNA", include.weekends = TRUE) # Return Value: return() } # ------------------------------------------------------------------------------ ## DW > ## test.ohlcDailyPlot = ## function() ## { ## # ohlcDailyPlot - Plots open–high–low–close bar chart ## ## # Price or Incdex Series: ## setRmetricsOptions(myFinCenter = "GMT") ## tS = MSFT[1:25, ] ## print(tS) ## dim(tS) ## colnames(tS) ## ## # Graph Frame: ## par(mfrow = c(2, 1), cex = 0.7) ## ohlcDailyPlot(tS) ## ## # Return Value: ## return() ## } # ------------------------------------------------------------------------------ test.modelSeries = function() { if (FALSE) { # Move to fArma ... # Undocumented Material: Matrix = cbind(X = rnorm(10), Y = rnorm(10)) Matrix = cbind(Matrix, Z = Matrix[, "Y"] - Matrix[, "X"]) TS = dummyDailySeries(Matrix, units = c("X", "Y", "Z") ) head(TS) .modelSeries(Y ~ ar(2), data = TS, lhs = TRUE) .modelSeries(log(abs(Z)) ~ lm(X + sin(Y)), data = TS, fake = TRUE) .modelSeries(log(abs(Z)) ~ lm(X + sin(Y)), data = TS, lhs = TRUE) .modelSeries(Y ~ ar(2), data = as.data.frame(TS), lhs = TRUE) .modelSeries(log(abs(Z)) ~ lm(X + sin(Y)), data = TS, fake = TRUE) .modelSeries(log(abs(Z)) ~ lm(X + sin(Y)), data = TS, lhs = TRUE) require(timeSeries) .modelSeries(Y ~ ar(2), data = rnorm(10)) .modelSeries(Y ~ ar(2), data = as.ts(rnorm(10))) .modelSeries(x ~ arima(2, 0, 1), data = armaSim(n=10)) .modelSeries(~ ar(2), rnorm(10)) # attach(TS) # CHECK # .modelSeries(Y ~ ar(2), lhs = TRUE) .modelSeries(Y ~ ar(2) + garch(1,1), data = rnorm(10)) .modelSeries(Y ~ ar(2) + garch(1,1), data = rnorm(10), lhs = TRUE) .modelSeries(Y ~ ar(2) + garch(1,1), data = TS, lhs = TRUE) } else { NA } # Return Value: return() } ################################################################################ timeSeries/inst/unitTests/runit.model.frame.R0000644000176200001440000000163515104730074021024 0ustar liggesusers # Rmetrics is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # Rmetrics is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ test.model.frame <- function() { NA } ################################################################################ timeSeries/inst/unitTests/runit.mathOps.R0000644000176200001440000000215315104730074020242 0ustar liggesusers # Rmetrics is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # Rmetrics is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ test.mathOps <- function() { # RUnit Test: tS = dummyMonthlySeries(format = "counts") tS tS - 2 log(abs(tS)) diff(tS) scale(tS) tS = dummyMonthlySeries() tS tS - 2 log(abs(tS)) diff(tS) scale(tS) } ################################################################################ timeSeries/inst/unitTests/runit.TimeSeriesPositions.R0000644000176200001440000000513215104730074022610 0ustar liggesusers # Rmetrics is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # Rmetrics is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ test.time = function() { # Generate nivariate daily random sequence set.seed(4711) data = round(rnorm(12), 2) charvec = timeCalendar(2006) uTS = timeSeries(data, charvec, units = "uTS") uTS # Get Positions: POS = time(uTS) POS checkIdentical(charvec, POS) # Return Value: return() } # ------------------------------------------------------------------------------ "test.time<-" = function() { # Generate nivariate daily random sequence set.seed(4711) data = round(rnorm(12), 2) charvec = timeCalendar(2006) uTS = timeSeries(data, charvec, units = "uTS") uTS # Add one Day to Positions: POS = time(uTS) time(uTS) <- POS + 24*3600 uTS # Return Value: return() } # ------------------------------------------------------------------------------ test.timeSeriesOrdering = function() { # sample.timeSeries - Resamples a 'timeSeries' object in time # sort.timeSeries - Sorts reverts a 'timeSeries' object in time # rev.timeSeries - Reverts a 'timeSeries' object in time # start.timeSeries - Extracts start date of a 'timeSeries' object # end.timeSeries - Extracts end date of a 'timeSeries' object # Generate univariate monthly random sequence: set.seed(4711) data = cbind(1:12, round(rnorm(12), 2)) positions = timeCalendar(2006) uTS = timeSeries(data, positions) uTS # Sample/Sort: target = uTS target # current = sort(sample(uTS)) # current # checkIdentical(target, current) # Revert: target = uTS target current = rev(rev(uTS)) current checkTrue(!sum(target - current)) # Start/End date of Series: start(uTS) end(uTS) # Return Value: return() } ################################################################################ timeSeries/inst/unitTests/runit.TimeSeriesCoercion.R0000644000176200001440000001554115104730074022367 0ustar liggesusers # Rmetrics is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # Rmetrics is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ test.asTimeSeries = function() { # as.timeSeries.default - Returns the input # as.timeSeries.numeric - Transforms a numeric vector into a 'timeSeries' # as.timeSeries.data.frame - Transformas a 'data.frame' into a 'timeSeries' # as.timeSeries.matrix - Trasformas a 'matrix' into a 'timeSeries' # as.timeSeries.ts - Tranf orms a 'ts' object into a 'timeSeries' # as.timeSeries.character - Loads and transformas from a demo file # as.timeSeries.zoo - Transforms a 'zoo' object into a 'timeSeries' # Create timeSeries Object: RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion") set.seed(4711, kind = "Marsaglia-Multicarry") data = round(rnorm(12), 3) charvec = timeCalendar(2006) uTS = timeSeries(data, charvec, units = "uTS") uTS checkTrue(inherits(uTS, "timeSeries")) checkTrue(is.timeSeries(uTS)) # Check Positions: positions = timeCalendar() class(positions) whichFormat(format(positions)) whichFormat(as.character(positions)) # Data Input is a Vector - Returns a timeSeries with dummy positions: RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion") set.seed(4711, kind = "Marsaglia-Multicarry") x = rnorm(12) # as.numeric - add dummy dates: data = as.numeric(x) tS = as.timeSeries(data) head(tS) # as. numeric [as.vector] - add dummy dates: data = as.vector(x) tS = as.timeSeries(data) head(tS) # Data Inpiut is a data.frame: data(MSFT) x.df = as.data.frame(MSFT) head(x.df) # First Column holds Positions: tS = MSFT head(tS) # Missing Positions - return signal series # x.df = msft.dat[, -1] # head(x.df) # tS = as.timeSeries(x.df) # head(tS) # Data Input is a Matrix: data(MSFT) x.mat = as.matrix(MSFT) # tS = as.timeSeries(x.mat) # head(tS) # CHECK # Data Input is an Univariate/Muiltivariate timeSeries: x = MSFT class(x) tS = as.timeSeries(x) head(tS) # Note, data is a demo file ... tS = MSFT head(tS) # Return Value: return() } # ------------------------------------------------------------------------------ test.asTimeSeriesDJ1 = function() { # Load Data: # use instead dummy data set just for testing ... Data = matrix(exp(cumsum(rnorm(30*100, sd = 0.1))), ncol = 30) Positions = format(timeSequence("2006-01-01", length.out = 100)) DowJones30 = data.frame(Positions, Data) # Taking Dates from First Column: DJ = DowJones30[21:30, c(1, 11:15)] DJ class(DJ) as.timeSeries(DJ) # Adding Dates through Rownames Assignment: DJ = DowJones30[21:30, c(11:15)] rownames(DJ)<-DowJones30[21:30, 1] DJ as.timeSeries(DJ) # Missing Dates - Using Dummy Dates: DJ = DowJones30[21:30, c(11:15)] DJ class(DJ) as.timeSeries(DJ) # With recordIDs: if (FALSE) { DJ = DowJones30[21:30, c(1,11:15)] DJ = cbind(DJ, LETTERS[1:10]) class(DJ) tsDJ = as.timeSeries(DJ) tsDJ tsDJ@recordIDs } DJ = DowJones30[21:30, c(11:15)] rownames(DJ) = DowJones30[21:30, 1] DJ = cbind(DJ, LETTERS[1:10]) tsDJ = as.timeSeries(DJ) tsDJ tsDJ@recordIDs DJ = DowJones30[21:30, c(11:15)] DJ =cbind(DJ, LETTERS[1:10]) tsDJ = as.timeSeries(DJ) tsDJ tsDJ@recordIDs # Return Value: return() } # ------------------------------------------------------------------------------ test.fromTimeSeriesUV = function() { if (FALSE) { # DW has to be fixed ... # as.vector.timeSeries - Converts a univariate 'timeSeries' to a vector # as.matrix.timeSeries - Converts a 'timeSeries' to a 'matrix' # as.data.frame.timeSeries - Converts a 'timeSeries' to a 'data.frame' # as.ts.timeSeries - Converts a 'timeSeries' to a 'ts' # Univariate Case: RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion") set.seed(4711, kind = "Marsaglia-Multicarry") data = round(rnorm(12), 3) charvec = timeCalendar(2006) uTS = timeSeries(data, charvec, units = "uTS") uTS # Vector: VEC = as.vector(uTS) head(VEC) class(VEC) checkIdentical(class(VEC), "numeric") # Numeric: # VEC = as.numeric(uTS) # Not implemented ! # head(VEC) # class(VEC) # checkIdentical(class(VEC), "numeric") # Matrix: MAT = as.matrix(uTS) head(MAT) class(MAT) checkIdentical(class(MAT), "matrix") # Data Frame: DF = as.data.frame(uTS) head(DF) checkIdentical(class(DF), "data.frame") # Time Series: TS = as.ts(uTS) head(TS) class(TS) checkIdentical(class(TS), "ts") } # Return Value: return() } # ------------------------------------------------------------------------------ test.fromTimeSeriesMV = function() { if (FALSE) { # DW has to be fixed ... # as.vector.timeSeries - Converts a univariate 'timeSeries' to a vector # as.matrix.timeSeries - Converts a 'timeSeries' to a 'matrix' # as.data.frame.timeSeries - Converts a 'timeSeries' to a 'data.frame' # as.ts.timeSeries - Converts a 'timeSeries' to a 'ts' # Multivariate Case: RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion") set.seed(4711, kind = "Marsaglia-Multicarry") data = matrix(round(rnorm(24), 3), ncol = 2) charvec = timeCalendar(2006) mTS = timeSeries(data, charvec) mTS # Matrix: MAT = as.matrix(mTS) head(MAT) class(MAT) checkIdentical( target = class(MAT), current = "matrix") checkIdentical( target = as.vector(MAT[, 1]), current = as.numeric(MAT)[1:12]) # Data Frame: DF = as.data.frame(mTS) head(DF) class(DF) checkIdentical( target = class(DF), current = "data.frame") # Time Series: TS = as.ts(mTS) head(TS) class(TS) checkIdentical( target = class(TS), current = c("mts", "ts")) } # Return Value: return() } ################################################################################ timeSeries/inst/unitTests/runit.spreads.R0000644000176200001440000000200515104730074020264 0ustar liggesusers # Rmetrics is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # Rmetrics is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ test.spreads <- function() { # RUnit Test: tS = dummyMonthlySeries(units = c("Bid", "Ask")) head(tS) midquotes(tS) spreads(tS) } ################################################################################ timeSeries/inst/unitTests/runit.periodical.R0000644000176200001440000000163415104730074020745 0ustar liggesusers # Rmetrics is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # Rmetrics is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ test.periodical <- function() { NA } ################################################################################ timeSeries/inst/unitTests/runit.rank.R0000644000176200001440000000162615104730074017566 0ustar liggesusers # Rmetrics is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # Rmetrics is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ test.rank <- function() { NA } ################################################################################ timeSeries/inst/unitTests/runit.bind.R0000644000176200001440000000512315104730074017543 0ustar liggesusers # Rmetrics is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # Rmetrics is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ test.bind <- function() { Documentation <- as.character(date()) Title <- "Dummy Series" ts <- dummyMonthlySeries() ts@documentation <- Documentation ts@title <- Title # -------------------------------------------------------------------------- # if NULL are in args, result identical except @documentation !!! cts <- cbind(ts, NULL) rts <- rbind(ts, NULL) ##> checkTrue(!identical(slot(cts, "documentation")[[1]], Documentation)) ##> checkTrue(!identical(slot(rts, "documentation")[[1]], Documentation)) # ... DW [[1]] removes attributes, check this # ... also take care of the title! # check that the rest is identical cts@documentation <- Documentation rts@documentation <- Documentation cts@title <- Title rts@title <- Title checkIdentical(cts, ts) checkIdentical(rts, ts) # -------------------------------------------------------------------------- ts1 <- ts[seq(1, nrow(ts), by = 2),] ts0 <- ts[seq(2, nrow(ts), by = 2),] # test rbind checkTrue(all(time(rbind(ts1, ts0)) == c(time(ts1),time(ts0)))) # test cbind checkIdentical(as.vector(is.na(cbind(ts1, ts0))), c(rep(c(FALSE, TRUE), 12), rep(c(TRUE, FALSE), 12))) checkTrue(all(time(cbind(ts1, ts0)) == time(ts))) # -------------------------------------------------------------------------- # issues with single number element a <- timeSeries(1, as.Date(0, origin="2010-01-01") ) b <- timeSeries( 2:3, as.Date(1:2, origin="2010-01-01") ) d <- timeSeries( 2:10, as.Date(1:9, origin="2010-01-01") ) cbind(a, b) cbind(b, a) cbind(b, d) cbind(d, b) cbind(a, 1) cbind(b, 1) cbind(a, matrix(1)) cbind(b, matrix(1)) } ################################################################################ timeSeries/inst/unitTests/runit.order.R0000644000176200001440000000266215104730074017747 0ustar liggesusers # Rmetrics is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # Rmetrics is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ test.order <- function() { # RUnit Test: tS = timeSeries(matrix(rnorm(500), 100), units = sample(LETTERS[1:5])) head(tS) colnames(tS) sortColnames(tS) sampleColnames(tS) orderColnames(tS) statsColnames(tS, FUN = colMeans) pcaColnames(tS) hclustColnames(tS) tS = timeSeries(matrix(rnorm(500), 100), units = sample(LETTERS[1:5]), format = "counts") head(tS) colnames(tS) sortColnames(tS) sampleColnames(tS) orderColnames(tS) statsColnames(tS, FUN = colMeans) pcaColnames(tS) hclustColnames(tS) } ################################################################################ timeSeries/inst/unitTests/runit.rowCum.R0000644000176200001440000000163015104730074020102 0ustar liggesusers # Rmetrics is free software; you can redistribute it and/or # modify it under the terms of the GNU Library General Public # License as published by the Free Software Foundation; either # version 2 of the License, or (at your option) any later version. # # Rmetrics is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Library General Public License for more details. # # You should have received a copy of the GNU Library General # Public License along with this library; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ test.rowCum <- function() { NA } ################################################################################ timeSeries/inst/COPYING0000644000176200001440000004310715104730074014401 0ustar liggesusers GNU GENERAL PUBLIC LICENSE Version 2, June 1991 Copyright (C) 1989, 1991 Free Software Foundation, Inc. 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. Preamble The licenses for most software are designed to take away your freedom to share and change it. By contrast, the GNU General Public License is intended to guarantee your freedom to share and change free software--to make sure the software is free for all its users. This General Public License applies to most of the Free Software Foundation's software and to any other program whose authors commit to using it. (Some other Free Software Foundation software is covered by the GNU Library General Public License instead.) You can apply it to your programs, too. When we speak of free software, we are referring to freedom, not price. Our General Public Licenses are designed to make sure that you have the freedom to distribute copies of free software (and charge for this service if you wish), that you receive source code or can get it if you want it, that you can change the software or use pieces of it in new free programs; and that you know you can do these things. To protect your rights, we need to make restrictions that forbid anyone to deny you these rights or to ask you to surrender the rights. These restrictions translate to certain responsibilities for you if you distribute copies of the software, or if you modify it. For example, if you distribute copies of such a program, whether gratis or for a fee, you must give the recipients all the rights that you have. You must make sure that they, too, receive or can get the source code. And you must show them these terms so they know their rights. We protect your rights with two steps: (1) copyright the software, and (2) offer you this license which gives you legal permission to copy, distribute and/or modify the software. Also, for each author's protection and ours, we want to make certain that everyone understands that there is no warranty for this free software. If the software is modified by someone else and passed on, we want its recipients to know that what they have is not the original, so that any problems introduced by others will not reflect on the original authors' reputations. Finally, any free program is threatened constantly by software patents. We wish to avoid the danger that redistributors of a free program will individually obtain patent licenses, in effect making the program proprietary. To prevent this, we have made it clear that any patent must be licensed for everyone's free use or not licensed at all. The precise terms and conditions for copying, distribution and modification follow. GNU GENERAL PUBLIC LICENSE TERMS AND CONDITIONS FOR COPYING, DISTRIBUTION AND MODIFICATION 0. This License applies to any program or other work which contains a notice placed by the copyright holder saying it may be distributed under the terms of this General Public License. The "Program", below, refers to any such program or work, and a "work based on the Program" means either the Program or any derivative work under copyright law: that is to say, a work containing the Program or a portion of it, either verbatim or with modifications and/or translated into another language. (Hereinafter, translation is included without limitation in the term "modification".) Each licensee is addressed as "you". Activities other than copying, distribution and modification are not covered by this License; they are outside its scope. The act of running the Program is not restricted, and the output from the Program is covered only if its contents constitute a work based on the Program (independent of having been made by running the Program). Whether that is true depends on what the Program does. 1. You may copy and distribute verbatim copies of the Program's source code as you receive it, in any medium, provided that you conspicuously and appropriately publish on each copy an appropriate copyright notice and disclaimer of warranty; keep intact all the notices that refer to this License and to the absence of any warranty; and give any other recipients of the Program a copy of this License along with the Program. You may charge a fee for the physical act of transferring a copy, and you may at your option offer warranty protection in exchange for a fee. 2. You may modify your copy or copies of the Program or any portion of it, thus forming a work based on the Program, and copy and distribute such modifications or work under the terms of Section 1 above, provided that you also meet all of these conditions: a) You must cause the modified files to carry prominent notices stating that you changed the files and the date of any change. b) You must cause any work that you distribute or publish, that in whole or in part contains or is derived from the Program or any part thereof, to be licensed as a whole at no charge to all third parties under the terms of this License. c) If the modified program normally reads commands interactively when run, you must cause it, when started running for such interactive use in the most ordinary way, to print or display an announcement including an appropriate copyright notice and a notice that there is no warranty (or else, saying that you provide a warranty) and that users may redistribute the program under these conditions, and telling the user how to view a copy of this License. (Exception: if the Program itself is interactive but does not normally print such an announcement, your work based on the Program is not required to print an announcement.) These requirements apply to the modified work as a whole. If identifiable sections of that work are not derived from the Program, and can be reasonably considered independent and separate works in themselves, then this License, and its terms, do not apply to those sections when you distribute them as separate works. But when you distribute the same sections as part of a whole which is a work based on the Program, the distribution of the whole must be on the terms of this License, whose permissions for other licensees extend to the entire whole, and thus to each and every part regardless of who wrote it. Thus, it is not the intent of this section to claim rights or contest your rights to work written entirely by you; rather, the intent is to exercise the right to control the distribution of derivative or collective works based on the Program. In addition, mere aggregation of another work not based on the Program with the Program (or with a work based on the Program) on a volume of a storage or distribution medium does not bring the other work under the scope of this License. 3. You may copy and distribute the Program (or a work based on it, under Section 2) in object code or executable form under the terms of Sections 1 and 2 above provided that you also do one of the following: a) Accompany it with the complete corresponding machine-readable source code, which must be distributed under the terms of Sections 1 and 2 above on a medium customarily used for software interchange; or, b) Accompany it with a written offer, valid for at least three years, to give any third party, for a charge no more than your cost of physically performing source distribution, a complete machine-readable copy of the corresponding source code, to be distributed under the terms of Sections 1 and 2 above on a medium customarily used for software interchange; or, c) Accompany it with the information you received as to the offer to distribute corresponding source code. (This alternative is allowed only for noncommercial distribution and only if you received the program in object code or executable form with such an offer, in accord with Subsection b above.) The source code for a work means the preferred form of the work for making modifications to it. For an executable work, complete source code means all the source code for all modules it contains, plus any associated interface definition files, plus the scripts used to control compilation and installation of the executable. However, as a special exception, the source code distributed need not include anything that is normally distributed (in either source or binary form) with the major components (compiler, kernel, and so on) of the operating system on which the executable runs, unless that component itself accompanies the executable. If distribution of executable or object code is made by offering access to copy from a designated place, then offering equivalent access to copy the source code from the same place counts as distribution of the source code, even though third parties are not compelled to copy the source along with the object code. 4. You may not copy, modify, sublicense, or distribute the Program except as expressly provided under this License. Any attempt otherwise to copy, modify, sublicense or distribute the Program is void, and will automatically terminate your rights under this License. However, parties who have received copies, or rights, from you under this License will not have their licenses terminated so long as such parties remain in full compliance. 5. You are not required to accept this License, since you have not signed it. However, nothing else grants you permission to modify or distribute the Program or its derivative works. These actions are prohibited by law if you do not accept this License. Therefore, by modifying or distributing the Program (or any work based on the Program), you indicate your acceptance of this License to do so, and all its terms and conditions for copying, distributing or modifying the Program or works based on it. 6. Each time you redistribute the Program (or any work based on the Program), the recipient automatically receives a license from the original licensor to copy, distribute or modify the Program subject to these terms and conditions. You may not impose any further restrictions on the recipients' exercise of the rights granted herein. You are not responsible for enforcing compliance by third parties to this License. 7. If, as a consequence of a court judgment or allegation of patent infringement or for any other reason (not limited to patent issues), conditions are imposed on you (whether by court order, agreement or otherwise) that contradict the conditions of this License, they do not excuse you from the conditions of this License. If you cannot distribute so as to satisfy simultaneously your obligations under this License and any other pertinent obligations, then as a consequence you may not distribute the Program at all. For example, if a patent license would not permit royalty-free redistribution of the Program by all those who receive copies directly or indirectly through you, then the only way you could satisfy both it and this License would be to refrain entirely from distribution of the Program. If any portion of this section is held invalid or unenforceable under any particular circumstance, the balance of the section is intended to apply and the section as a whole is intended to apply in other circumstances. It is not the purpose of this section to induce you to infringe any patents or other property right claims or to contest validity of any such claims; this section has the sole purpose of protecting the integrity of the free software distribution system, which is implemented by public license practices. Many people have made generous contributions to the wide range of software distributed through that system in reliance on consistent application of that system; it is up to the author/donor to decide if he or she is willing to distribute software through any other system and a licensee cannot impose that choice. This section is intended to make thoroughly clear what is believed to be a consequence of the rest of this License. 8. If the distribution and/or use of the Program is restricted in certain countries either by patents or by copyrighted interfaces, the original copyright holder who places the Program under this License may add an explicit geographical distribution limitation excluding those countries, so that distribution is permitted only in or among countries not thus excluded. In such case, this License incorporates the limitation as if written in the body of this License. 9. The Free Software Foundation may publish revised and/or new versions of the General Public License from time to time. Such new versions will be similar in spirit to the present version, but may differ in detail to address new problems or concerns. Each version is given a distinguishing version number. If the Program specifies a version number of this License which applies to it and "any later version", you have the option of following the terms and conditions either of that version or of any later version published by the Free Software Foundation. If the Program does not specify a version number of this License, you may choose any version ever published by the Free Software Foundation. 10. If you wish to incorporate parts of the Program into other free programs whose distribution conditions are different, write to the author to ask for permission. For software which is copyrighted by the Free Software Foundation, write to the Free Software Foundation; we sometimes make exceptions for this. Our decision will be guided by the two goals of preserving the free status of all derivatives of our free software and of promoting the sharing and reuse of software generally. NO WARRANTY 11. BECAUSE THE PROGRAM IS LICENSED FREE OF CHARGE, THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 12. IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MAY MODIFY AND/OR REDISTRIBUTE THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. END OF TERMS AND CONDITIONS How to Apply These Terms to Your New Programs If you develop a new program, and you want it to be of the greatest possible use to the public, the best way to achieve this is to make it free software which everyone can redistribute and change under these terms. To do so, attach the following notices to the program. It is safest to attach them to the start of each source file to most effectively convey the exclusion of warranty; and each file should have at least the "copyright" line and a pointer to where the full notice is found. Copyright (C) This program is free software; you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation; either version 2 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA Also add information on how to contact you by electronic and paper mail. If the program is interactive, make it output a short notice like this when it starts in an interactive mode: Gnomovision version 69, Copyright (C) year name of author Gnomovision comes with ABSOLUTELY NO WARRANTY; for details type `show w'. This is free software, and you are welcome to redistribute it under certain conditions; type `show c' for details. The hypothetical commands `show w' and `show c' should show the appropriate parts of the General Public License. Of course, the commands you use may be called something other than `show w' and `show c'; they could even be mouse-clicks or menu items--whatever suits your program. You should also get your employer (if you work as a programmer) or your school, if any, to sign a "copyright disclaimer" for the program, if necessary. Here is a sample; alter the names: Yoyodyne, Inc., hereby disclaims all copyright interest in the program `Gnomovision' (which makes passes at compilers) written by James Hacker. , 1 April 1989 Ty Coon, President of Vice This General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Library General Public License instead of this License.timeSeries/inst/extdata/0000755000176200001440000000000015117007154014773 5ustar liggesuserstimeSeries/inst/extdata/msft.csv0000644000176200001440000002571415104730074016472 0ustar liggesusers"%Y-%m-%d";Open;High;Low;Close;Volume 2000-09-27;63.4375;63.5625;59.8125;60.625;53077800 2000-09-28;60.8125;61.875;60.625;61.3125;26180200 2000-09-29;61;61.3125;58.625;60.3125;37026800 2000-10-02;60.5;60.8125;58.25;59.125;29281200 2000-10-03;59.5625;59.8125;56.5;56.5625;42687000 2000-10-04;56.375;56.5625;54.5;55.4375;68226700 2000-10-05;55.5;57.25;55.25;55.375;40549700 2000-10-06;55.8125;56.75;54.75;55.5625;30897000 2000-10-09;55.625;55.75;53;54.1875;29161800 2000-10-10;53.9375;55.5625;53.8125;54.5625;31033100 2000-10-11;54;56.9375;54;55.75;50602900 2000-10-12;56.3125;56.875;53.8125;54.375;45109800 2000-10-13;53.875;54.875;52.125;53.75;52260600 2000-10-16;53.5;53.8125;49.5625;50.375;59879500 2000-10-17;51.875;52.4375;50.25;50.4375;40638300 2000-10-18;49.625;53.25;48.4375;51.75;55268200 2000-10-19;58.4375;62.1875;58;61.875;128496600 2000-10-20;61.3125;66.125;61.125;65.1875;80189300 2000-10-23;64.625;66.25;60.6875;62.125;92585200 2000-10-24;62.625;62.9375;60.1875;61.5;47213700 2000-10-25;61.9375;63.4375;60.4375;61.25;83801900 2000-10-26;61;65.0625;60.8125;64.4375;57413300 2000-10-27;64.6875;69.1875;64.625;67.6875;62146200 2000-10-30;67.5;70.125;67.375;69.0625;55028800 2000-10-31;69;69.5;68;68.875;52237000 2000-11-01;68.5;70.0625;68.4375;69.625;40654700 2000-11-02;70.375;70.8438;69.625;70.3125;38992600 2000-11-03;69.25;69.625;68.0625;68.25;34355500 2000-11-06;68.6875;70.125;68.25;69.5;37425700 2000-11-07;69.75;71.875;69.5;70.5;52165600 2000-11-08;71.125;72.375;68;69.4375;103074700 2000-11-09;68.5;71.3125;68.4375;70.875;45529300 2000-11-10;69.9375;70.3125;66.8125;67.375;46872200 2000-11-13;66.6875;68.125;64.4062;66.4375;41682400 2000-11-14;68;69.8125;67.3125;68.8125;42109300 2000-11-15;69.0625;70.875;68.6875;70.0625;30211100 2000-11-16;69.4375;71.5;68.9375;68.9375;46064300 2000-11-17;69.4375;70;67.7969;69.0625;53262800 2000-11-20;68.125;68.5;65.5625;67.1875;40078600 2000-11-21;67.375;69.25;67.375;67.75;29743800 2000-11-22;66.0625;69.5;66;68.25;38171600 2000-11-24;69;70.4375;68.5;69.9375;17219600 2000-11-27;71.4375;72.25;70.625;70.6875;42653800 2000-11-28;69.375;69.75;66.8125;67;63723100 2000-11-29;66.8125;67.125;63.25;65.0625;49140200 2000-11-30;62;62.0625;57;57.375;98600400 2000-12-01;58.0625;60.625;56.0625;56.625;54904900 2000-12-04;57.25;59;55.1875;56.4375;40203600 2000-12-05;59.1875;60.5;58.25;59.875;50867200 2000-12-06;60;60.0625;56.0625;56.6875;45280400 2000-12-07;53.4375;54;52.25;53.125;72654200 2000-12-08;54.625;55.875;53.4375;54.4375;60469900 2000-12-11;55.5;58.75;55;58.0625;47788100 2000-12-12;57.8125;60;56.75;58.375;31553000 2000-12-13;60.5;60.5;56.8125;57.25;49180200 2000-12-14;57.9375;58.7344;55.375;55.5;35600700 2000-12-15;51.0469;52;47.75;49.1875;58449900 2000-12-18;49;50;47;47.8125;53593700 2000-12-19;47.4375;48;44.5;44.8125;60135900 2000-12-20;42.8125;44;41.375;41.5;74518900 2000-12-21;40.75;45.125;40.3125;43.4375;81586500 2000-12-22;44.75;47.125;44.75;46.4375;54775900 2000-12-26;46.875;48.5625;45.875;46.875;33470800 2000-12-27;46.125;46.8125;45;46.4375;34501900 2000-12-28;45.125;46.25;43.875;44.5625;38809600 2000-12-29;43.9375;45.8125;43;43.375;49988800 2001-01-02;44.125;45;42.875;43.375;41206600 2001-01-03;43.1875;48.875;43.125;47.9375;67981100 2001-01-04;47.8125;50.5;46.875;48.4375;56198500 2001-01-05;48.5;49.875;47.5625;49.125;46707300 2001-01-08;48.9375;49.75;46.6875;48.9375;39908800 2001-01-09;50;52.625;49.75;51.8125;57482700 2001-01-10;51;53.8125;50.75;52.875;45115100 2001-01-11;53;55.75;52.3125;55;50927400 2001-01-12;54.875;55;52.5;53.5;36856000 2001-01-16;53.375;53.5;51.125;52.5625;34231200 2001-01-17;53.625;54.875;52.5625;52.9375;36422100 2001-01-18;53.6875;56.1875;52.625;55.5;54894400 2001-01-19;60;61.4375;58.875;61;104674400 2001-01-22;60.75;61;59;60.125;38336500 2001-01-23;59.75;60.9375;58.9375;60.5625;35147600 2001-01-24;61;63.4375;60.75;62.9375;55227500 2001-01-25;62.75;64;61.5625;61.8125;42828700 2001-01-26;61;64.3125;61;64;46540000 2001-01-29;63.5625;64.625;63.5;64.5;42491900 2001-01-30;64.5;64.75;62.875;63.375;28638400 2001-01-31;63;63.75;61;61.0625;40949400 2001-02-01;60.8125;62.625;60.375;62.375;35896400 2001-02-02;62.5;63.375;60.75;60.8125;35550000 2001-02-05;60.75;62.0625;60.25;61.9375;25699600 2001-02-06;62.0625;63.8125;61.6875;62.5625;48221000 2001-02-07;62;65.0625;61.8125;64.6875;63030900 2001-02-08;63.75;64.5;62;62.25;44020600 2001-02-09;61.3125;61.5625;58.5;59.125;50287600 2001-02-12;58.8125;59.4375;57.1875;58.75;35644700 2001-02-13;59.625;61.0625;58.125;58.1875;38035300 2001-02-14;57.625;59;56.375;58.375;30864200 2001-02-15;59;60.1875;57.875;58.8125;32813900 2001-02-16;57;58.25;56.125;57.3125;33479200 2001-02-20;57.375;58.25;55.375;55.875;30365400 2001-02-21;55.25;58.0625;55.1875;56.25;31973600 2001-02-22;56.3125;56.8125;53.875;55.1875;50408200 2001-02-23;54.4375;57.5;54.3125;56.75;46310300 2001-02-26;57.625;59.9375;57.375;59.5625;43968400 2001-02-27;59.375;61.1875;58.6719;59.375;49574300 2001-02-28;59.5625;60.0781;58.1875;59;42304200 2001-03-01;58.5625;59.5;56.25;59.3594;40890800 2001-03-02;57.5;58.125;56.4375;56.6875;39900400 2001-03-05;57.25;58.625;56.5625;57.4375;24691800 2001-03-06;58.625;60;58.375;59.4375;33390900 2001-03-07;59.875;61.125;59.3125;60.6875;29871800 2001-03-08;60.3125;60.5938;58.4375;59.25;27313000 2001-03-09;57.9375;58.1875;54.875;56.6875;51897200 2001-03-12;54.6875;55;51.625;51.9375;57188000 2001-03-13;52.1875;54.75;52;54.1875;45517800 2001-03-14;52.5;55.25;52.1875;54;45343100 2001-03-15;55.3125;56.0781;53.5;53.6875;35819200 2001-03-16;52.5;55.125;52.4844;54.5625;56424400 2001-03-19;54.5;55.5;53.125;54.3125;30518200 2001-03-20;54.5625;56.125;52.625;52.6875;45911400 2001-03-21;52.25;53.25;49.75;50.0625;62494300 2001-03-22;50.5625;54.0625;50.5;54;63181600 2001-03-23;54.9375;57;54.375;56.5625;49759800 2001-03-26;57.125;57.5;55.5625;56.0625;31559300 2001-03-27;56.0625;58.5625;55.875;58.25;47567800 2001-03-28;57.375;57.9375;55.375;55.5625;39340800 2001-03-29;55.375;57.1875;54.5625;55.375;43492500 2001-03-30;55.75;56.1875;53.875;54.6875;45600800 2001-04-02;54.8125;56.9375;54.625;55.8125;37962000 2001-04-03;55.3125;55.3125;52.75;53.375;47093800 2001-04-04;53.375;55;51.0625;51.9375;52023300 2001-04-05;53.75;57.375;53.5;56.75;56682000 2001-04-06;56.375;57.1875;55.0625;56.1875;46311000 2001-04-09;56.57;57.42;55.66;57.15;28147800 2001-04-10;57.95;60.09;57.78;59.68;54599700 2001-04-11;60.65;61.5;59.7;60.04;54939800 2001-04-12;59.56;62.31;59.35;62.18;43760000 2001-04-16;61.4;61.58;60.12;60.79;32928700 2001-04-17;60.52;62.11;60.04;61.48;42574600 2001-04-18;63.39;66.31;63;65.43;78348200 2001-04-19;65.81;69;65.75;68.04;79687800 2001-04-20;70.3;71.1;68.5;69;96459800 2001-04-23;68.11;68.47;66.9;68.25;46085600 2001-04-24;68.2;69.93;67.14;67.55;44588300 2001-04-25;67.57;69.79;67.25;69.69;38372000 2001-04-26;70.07;71;68.25;69.13;59368800 2001-04-27;69.53;69.68;66.21;67.12;60786200 2001-04-30;68.53;69.06;67.68;67.75;37184100 2001-05-01;67.66;70.3;67.6;70.17;41851400 2001-05-02;71;71.15;69.35;69.76;46432200 2001-05-03;69.25;70.18;68.14;68.53;33136700 2001-05-04;68;71.05;67.96;70.75;59769200 2001-05-07;70.83;72.15;70.7;71.38;54678100 2001-05-08;71.75;72.1;70.75;72.06;37542000 2001-05-09;71.24;71.3;69.86;70.4;38338300 2001-05-10;71.13;71.24;69.96;70;32167300 2001-05-11;69.96;70;68.65;69.4;25564400 2001-05-14;69.13;69.2;68.3;68.72;22484000 2001-05-15;68.74;69.3;68;68.27;30692800 2001-05-16;67.7;69.88;67.33;69.16;45946900 2001-05-17;69.1;70.14;67.55;68.17;53492400 2001-05-18;67.69;69.2;67.25;68.09;45302700 2001-05-21;68.05;69.99;67.75;68.79;51745800 2001-05-22;69.45;70.35;69.18;70.31;41727800 2001-05-23;70.39;71.6;69.51;69.7;46818700 2001-05-24;69.94;71.78;69.27;71.72;40390800 2001-05-25;71.66;71.9;70.36;70.91;26373800 2001-05-29;70.8;71.75;70.05;70.34;35605400 2001-05-30;69.56;70.58;68.65;69.19;43250900 2001-05-31;69.49;70.38;68.4;69.18;35341300 2001-06-01;69.6;70.7;68.7;70.34;28793800 2001-06-04;70.55;71.02;69.8;70.78;21868300 2001-06-05;70.76;73.08;70.5;72.6;44727100 2001-06-06;72.89;73.48;71.55;72.36;40011400 2001-06-07;72.12;73.73;72.08;73.68;33480000 2001-06-08;73.7;73.75;72.05;73.19;25933500 2001-06-11;72.85;72.85;71.51;72.12;23672800 2001-06-12;71.02;72.41;70.81;72.08;33357300 2001-06-13;72.05;72.3;70.64;70.69;27651200 2001-06-14;70.22;70.55;68.4;68.9;35986200 2001-06-15;67.51;68.3;66.4;68.02;54177200 2001-06-18;67.95;67.96;66.01;66.88;28423400 2001-06-19;68.21;68.85;66.85;67.32;31728700 2001-06-20;67.14;69.59;67.1;69.41;32054200 2001-06-21;69.15;70.55;68.92;69.84;34801900 2001-06-22;70;70.61;68.58;68.83;25546000 2001-06-25;69.1;69.81;67.77;68.85;24607800 2001-06-26;67.82;70.21;67.7;70.14;31538500 2001-06-27;69.86;71.53;69.36;71.14;34599900 2001-06-28;71.55;76.15;70.53;72.74;64487800 2001-06-29;72.6;73.41;71.4;73;47141900 2001-07-02;72.05;73.15;70.15;70.6;36405100 2001-07-03;70.3;70.8;69.93;70.47;14018700 2001-07-05;70.22;70.72;68.44;68.51;24621300 2001-07-06;68.3;68.4;65.67;66.06;33733900 2001-07-09;66.2;66.91;65.04;65.69;33238300 2001-07-10;65.9;66.25;64.35;64.48;33281300 2001-07-11;64.21;66.75;64.2;66.5;36911300 2001-07-12;70.7;72.05;70.33;71.6;64039000 2001-07-13;71.4;72;70.94;71.34;29467300 2001-07-16;71.45;72.16;70.15;71.18;27995400 2001-07-17;70.66;72.01;70.14;71.82;31620500 2001-07-18;70.6;71.5;69.87;70.57;28795400 2001-07-19;71.22;73;71.22;72.57;38274700 2001-07-20;68.03;69.4;67.94;69.18;62101800 2001-07-23;69.24;69.24;66.35;67.09;39999700 2001-07-24;67;67.99;65.7;66.32;33765100 2001-07-25;66.26;67.52;65.61;67.48;37032700 2001-07-26;67.12;67.32;65.5;66.59;38987000 2001-07-27;66.05;66.25;65.05;65.47;32698000 2001-07-30;65.65;66.88;65.54;65.8;21098200 2001-07-31;66.01;67.39;65.85;66.19;29515800 2001-08-01;66.8;66.81;65.76;66.47;27839500 2001-08-02;67.21;67.54;66.26;67.45;27099200 2001-08-03;67.3;67.36;66;66.89;21630200 2001-08-06;66.53;67.12;65.68;66.13;13915800 2001-08-07;66.04;67.05;65.99;66.35;15673900 2001-08-08;66.51;67.24;64.49;64.86;27498200 2001-08-09;64.98;65.55;64.3;65.01;22768100 2001-08-10;64.77;65.86;62.9;65.52;25878200 2001-08-13;65.24;65.99;64.75;65.83;16337700 2001-08-14;65.75;66.09;64.45;64.69;18240600 2001-08-15;64.71;65.05;63.2;63.2;19751500 2001-08-16;62.84;64.71;62.7;64.62;21952800 2001-08-17;63.78;64.13;61.5;61.88;26117100 2001-08-20;61.66;62.75;61.1;62.7;24185600 2001-08-21;62.7;63.2;60.71;60.78;23555900 2001-08-22;61.13;61.15;59.08;60.66;39053600 2001-08-23;60.67;61.53;59;59.12;25906600 2001-08-24;59.6;62.28;59.23;62.05;31699500 2001-08-27;61.9;63.36;61.57;62.31;22281400 2001-08-28;62.34;62.95;60.58;60.74;23711400 2001-08-29;61.05;61.3;59.54;60.25;24085000 2001-08-30;59.04;59.66;56.52;56.94;48816000 2001-08-31;56.85;58.06;56.3;57.05;28950400 2001-09-04;57.19;59.08;56.07;56.1;33594600 2001-09-05;56.18;58.39;55.39;57.74;44735300 2001-09-06;56.56;58.39;55.9;56.02;56178400 2001-09-07;56.11;57.36;55.31;55.4;44931900 2001-09-10;54.92;57.95;54.7;57.58;42235900 2001-09-17;54.02;55.1;52.8;52.91;63751000 2001-09-18;53.41;55;53.17;54.32;41591300 2001-09-19;54.46;54.7;50.6;53.87;63475100 2001-09-20;52.35;52.61;50.67;50.76;58991600 2001-09-21;47.92;50.6;47.5;49.71;92488300 2001-09-24;50.65;52.45;49.87;52.01;42790100 2001-09-25;52.27;53;50.16;51.3;42470300 2001-09-26;51.51;51.8;49.55;50.27;29262200 2001-09-27;50.1;50.68;48;49.96;40595600timeSeries/inst/COPYRIGHTS0000644000176200001440000000770515104730074014770 0ustar liggesusers________________________________________________________________________________ Copyrights (C) for R: see R's copyright and license file Version R 2.0.0 claims: - The stub packages from 1.9.x have been removed. - All the datasets formerly in packages 'base' and 'stats' have been moved to a new package 'datasets'. - Package 'graphics' has been split into 'grDevices' (the graphics devices shared between base and grid graphics) and 'graphics' (base graphics). - Packages must have been re-installed for this version, and library() will enforce this. - Package names must now be given exactly in library() and require(), regardless of whether the underlying file system is case-sensitive or not. ________________________________________________________________________________ for Rmetrics: (C) 1999-2005, Diethelm Wuertz, GPL Diethelm Wuertz www.rmetrics.org info@rmetrics.org ________________________________________________________________________________ for non default loaded basic packages part of R's basic distribution MASS: Main Package of Venables and Ripley's MASS. We assume that MASS is available. Package 'lqs' has been returned to 'MASS'. S original by Venables & Ripley. R port by Brian Ripley . Earlier work by Kurt Hornik and Albrecht Gebhardt. methods: Formally defined methods and classes for R objects, plus other programming tools, as described in the reference "Programming with Data" (1998), John M. Chambers, Springer NY. R Development Core Team. mgcv: Routines for GAMs and other generalized ridge regression with multiple smoothing parameter selection by GCV or UBRE. Also GAMMs by REML or PQL. Includes a gam() function. Simon Wood nnet: Feed-forward Neural Networks and Multinomial Log-Linear Models Original by Venables & Ripley. R port by Brian Ripley . Earlier work by Kurt Hornik and Albrecht Gebhardt. ________________________________________________________________________________ for the code partly included as builtin functions from other R ports: fSeries:bdstest.c C Program to compute the BDS Test. Blake LeBaron fSeries:fracdiff R functions, help pages and the Fortran Code for the 'fracdiff' function are included. S original by Chris Fraley R-port by Fritz Leisch since 2003-12: Martin Maechler fSeries:lmtest R functions and help pages for the linear modelling tests are included . Compiled by Torsten Hothorn , Achim Zeileis , and David Mitchell fSeries:mda R functions, help pages and the Fortran Code for the 'mars' function are implemeted. S original by Trevor Hastie & Robert Tibshirani, R port by Friedrich Leisch, Kurt Hornik and Brian D. Ripley fSeries:modreg Brian Ripley and the R Core Team fSeries:polspline R functions, help pages and the C/Fortran Code for the 'polymars' function are implemented Charles Kooperberg fSeries:systemfit Simultaneous Equation Estimation Package. R port by Jeff D. Hamann and Arne Henningsen fSeries:tseries Functions for time series analysis and computational finance. Compiled by Adrian Trapletti fSeries:UnitrootDistribution: The program uses the Fortran routine and the tables from J.G. McKinnon. fSeries:urca Unit root and cointegration tests for time series data. R port by Bernhard Pfaff . timeSeries/README.md0000644000176200001440000000331715104730074013647 0ustar liggesusers [![CRANStatusBadge](http://www.r-pkg.org/badges/version/timeSeries)](https://cran.r-project.org/package=timeSeries) [![CRAN RStudio mirror downloads](https://cranlogs.r-pkg.org/badges/timeSeries)](https://www.r-pkg.org/pkg/timeSeries) [![CRAN RStudio mirror downloads](https://cranlogs.r-pkg.org/badges/grand-total/timeSeries?color=blue)](https://r-pkg.org/pkg/timeSeries) The R package 'timeSeries' provides a time series class and tools for creation, import, manipulation, statistical and financial computations on time series. Package `timeSeries` is part of the Rmetrics suite of R packages and is developed on R-forge at [timeSeries](https://r-forge.r-project.org/scm/viewvc.php/pkg/timeSeries/?root=rmetrics). The root of Rmetrics is at [R-forge](https://r-forge.r-project.org/projects/rmetrics). # Installing timeSeries Install the [latest stable version](https://cran.r-project.org/package=timeSeries) of `timeSeries` from CRAN: install.packages("timeSeries") You can install the [development version](https://r-forge.r-project.org/scm/viewvc.php/pkg/timeSeries/?root=rmetrics) of `timeSeries` from R-forge: install.packages("timeSeries", repos = "http://R-Forge.R-project.org") To report bugs visit [Rmetrics](https://r-forge.r-project.org/projects/rmetrics/). # Documentation You can view the documentation of `timeSeries` at [timeSeriesDoc](https://geobosh.github.io/timeSeriesDoc/) (an web site produce with pkgdown) or download the [reference manual](https://cran.r-project.org/package=timeSeries/timeSeries.pdf) of the latest release from CRAN. There is also a large [vignette on plots](https://cran.r-project.org/package=timeSeries/vignettes/timeSeriesPlot.pdf). timeSeries/build/0000755000176200001440000000000015117007154013463 5ustar liggesuserstimeSeries/build/vignette.rds0000644000176200001440000000033015117007154016016 0ustar liggesusers‹‹àb```b`aeb`b2™… 1# '*ÉÌM N-ÊL-ÈÉ/Ñ Ê+GS! /ÉÌKWPG¨UWðOÊJM.)&`\AJš t nËCÔ ’zÖ¼ÄÜTtkØ]R RóR@Âÿ°ëgü¦…Ã;µ²<¿¦E T ‹[fN*ÌÞÌ8‡9ÀÅ Êd Bw†ù(îç,Ê/׃ù# @â? {49'±Ý£\)‰%‰ziE@ý wºýqÓtimeSeries/man/0000755000176200001440000000000015117007154013137 5ustar liggesuserstimeSeries/man/base-cbind.Rd0000644000176200001440000000476415104730074015430 0ustar liggesusers\name{cbind} \alias{cbind} \alias{rbind} \alias{cbind2} \alias{rbind2} % \alias{cbind,timeSeries-method} % \alias{rbind,timeSeries-method} \alias{cbind.timeSeries} \alias{rbind.timeSeries} % \alias{c.timeSeries} \alias{cbind2,ANY,timeSeries-method} \alias{cbind2,timeSeries,ANY-method} \alias{cbind2,timeSeries,missing-method} \alias{cbind2,timeSeries,timeSeries-method} \alias{rbind2,ANY,timeSeries-method} \alias{rbind2,timeSeries,ANY-method} \alias{rbind2,timeSeries,missing-method} \alias{rbind2,timeSeries,timeSeries-method} \title{Bind 'timeSeries' objects by column or row} \description{ Binds \code{"timeSeries"} objects either by column or by row. } % \S4method{merge}{timeSeries,timeSeries}(x, y, ...) \usage{ \method{cbind}{timeSeries}(\dots, deparse.level = 1) \method{rbind}{timeSeries}(\dots, deparse.level = 1) \S4method{cbind2}{timeSeries,ANY}(x, y) ## other methods for 'cbind2' with the same arguments, see Details \S4method{rbind2}{timeSeries,ANY}(x, y) ## other methods for 'rbind2' with the same arguments, see Details } \arguments{ \item{x, y}{ objects, at least one of whom is of class \code{"timeSeries"}. } \item{\dots}{ further arguments to bind. } \item{deparse.level}{ see the documentation of \code{base::cbind}. } } \details{ These functions bind the objects by row \code{rXXX} or column (\code{cXXX}. \code{cbind} and \code{rbind} are S3 generics, so the \code{"timeSeries"} methods describe here are called only when the first argument is \code{"timeSeries"}. \code{cbind2} and \code{rbind2} are S4 generics which dispatch on the first two arguments. The \code{"timeSeries"} methods for these are invoked whenever at least one of the first two arguments is of class \code{"timeSeries"}. All functions can be called with more than two arguments. After the first two are merged, the result is merged with the third, and so on. } \value{ an object of class \code{"timeSeries"} } \seealso{ \code{\link{merge}} for another way to merge \code{"timeSeries"} object column-wise. \code{\link[base]{rbind}} and \code{\link[base]{cbind}} from base R, \code{\link[methods]{rbind2}} and \code{\link[methods]{cbind2}} from package \code{"methods"}, } \examples{ ## Load Microsoft Data Set - x <- MSFT[1:12, ] x ## Bind Columnwise - X <- cbind(x[, "Open"], returns(x[, "Open"])) colnames(X) <- c("Open", "Return") X ## Bind Rowwise - Y <- rbind(x[1:3, "Open"], x[10:12, "Open"]) Y } \keyword{chron} timeSeries/man/statistics-colSums.Rd0000644000176200001440000000442715104730074017252 0ustar liggesusers\name{colStats} \alias{colStats} \alias{colSums,timeSeries-method} \alias{colMeans,timeSeries-method} \alias{colSds} \alias{colVars} \alias{colSkewness} \alias{colKurtosis} \alias{colMaxs} \alias{colMins} \alias{colProds} \alias{colQuantiles} % \alias{colAvgs} \alias{colStdevs} % removed, but leave the alias for now % \alias{mean.timeSeries} % \alias{var.timeSeries} \title{Column statistics} \description{ A collection of functions to compute column statistical properties of financial and economic time series data. } \usage{ colStats(x, FUN, \dots) colSds(x, \dots) colVars(x, \dots) colSkewness(x, \dots) colKurtosis(x, \dots) colMaxs(x, \dots) colMins(x, \dots) colProds(x, \dots) colQuantiles(x, prob = 0.05, \dots) } % colAvgs(x, \dots) % \method{mean}{timeSeries}(x, \dots) % \method{var}{timeSeries}(x, \dots) \arguments{ \item{x}{ a rectangular object which can be transformed into a matrix by the function \code{as.matrix}. } \item{FUN}{ a function name, the statistical function to be applied. } \item{prob}{ a numeric value in [0,1]. } \item{\dots}{ arguments to be passed. } } \details{ \tabular{ll}{ \code{colStats}\tab calculates column statistics, \cr \code{colSums} \tab calculates column sums, \cr \code{colMeans} \tab calculates column means, \cr \code{colSds} \tab calculates column standard deviations, \cr \code{colVars} \tab calculates column variances, \cr \code{colSkewness} \tab calculates column skewness, \cr \code{colKurtosis} \tab calculates column kurtosis, \cr \code{colMaxs} \tab calculates maximum values in each column, \cr \code{colMins} \tab calculates minimum values in each column, \cr \code{colProds} \tab computes product of all values in each column, \cr \code{colQuantiles} \tab computes quantiles of each column. } } \value{ each function returns a numeric vector of the statistics, one for each column } \seealso{ \code{\link{rollStats}} } \examples{ ## Simulated Return Data in Matrix Form - x = matrix(rnorm(252), ncol = 2) ## Mean Columnwise Statistics - colStats(x, FUN = mean) ## Quantiles Column by Column - colQuantiles(x, prob = 0.10, type = 1) } \keyword{univar} timeSeries/man/timeSeries-deprecated.Rd0000644000176200001440000000203115116777355017651 0ustar liggesusers\name{timeSeries-deprecated} \alias{removeNA} \title{Deprecated functions in 'timeSeries' package} \usage{ removeNA(x, \dots) } \arguments{ \item{x}{ a numeric matrix, or any other object which can be transformed into a matrix through \code{x = as.matrix(x, ...)}. If \code{x} is a vector, it will be transformed into a one-dimensional matrix. } \item{\dots}{ arguments to be passed to the function \code{as.matrix}. } } \description{ \code{seriesData} (removed) extracts data slot from a 'timeSeries'. use \code{\link{as.matrix}} instead. % \item{object}{ % [is][seriesData][seriesPositions][show][summary] - % an object of class \code{timeSeries}. % } % \code{.time.timeSeries} \tab Extracts the time(s) from a % 'timeSeries', use \code{\link{time}} instead. \cr % \code{seriesPositions} \tab Extracts positions slot from a 'timeSeries', \cr % \code{newPositions<-} \tab Modifies positions of a 'timeSeries' object, \cr } \keyword{internal} timeSeries/man/fin-monthly.Rd0000644000176200001440000000574015104730074015700 0ustar liggesusers\name{monthly} \alias{monthly} \alias{countMonthlyRecords} \alias{rollMonthlyWindows} \alias{rollMonthlySeries} \title{Special monthly series} \description{ Functions and methods dealing with special monthly \code{"timeSeries"} objects. } \usage{ rollMonthlyWindows(x, period = "12m", by = "1m") rollMonthlySeries(x, period = "12m", by = "1m", FUN, \dots) countMonthlyRecords(x) } \arguments{ \item{x}{ a \code{"timeSeries"} object. } \item{period,by}{ character strings specifying the rollling period composed by the length of the period and its unit. Examples: \code{"3m"}, \code{"6m"}, \code{"12m"}, and \code{"24m"} represent quarterly, semi-annual, annual and bi-annual shifts, respectively. It is the responsibility of the user to determine proper start of the series. } \item{FUN}{ the function for the statistic to be applied. For example, \code{colMean} in the case of aggregation. } \item{\dots}{ arguments passed to the function \code{FUN}. } } \details{ \code{rollMonthlySeries} computes the statistics defined by the function \code{FUN} over rolling windows, internally computed by the function \code{rollMonthlyWindows}. Note, the periods may be overlapping, may be dense, or even may have gaps. \code{countMonthlyRecords} computes a \code{"timeSeries"} that holds the number of records for each month, see examples. The dates are set to the end of the month. \code{rollMonthlyWindows} computes start and end dates for rolling time windows. Argument \code{period} specifies the length of the periods over which \code{FUN} is applied, while \code{by} gives the amount by which the window is shifted. Non-overlapping windows correspond to \code{by >= period}. } \value{ for \code{countMonthlyRecords} and \code{rollMonthlySeries}, a \code{"timeSeries"} object. for \code{rollMonthlyWindows}, a list with attribute \code{"control"} keeping the \code{start} and \code{end} dates of the series. The components of the list are: \item{from}{an object from class \code{"timeDate"}.} \item{to}{an object from class \code{"timeDate"}.} } \seealso{ \code{\link{isMonthly}}, \code{\link{isRegular}} } \examples{ ## load Microsoft daily dataset x <- MSFT ## count monthly records head(x) # 3 obs. for Sep 2000 counts <- countMonthlyRecords(x) counts ## diy computation of the counts diy <- rollMonthlySeries(x[ , 1], period = "1m", by = "1m", FUN = NROW) ## difference is only in some attributes (e.g. column names) all.equal(diy, counts) ## quaterly non-overlapping time periods windows <- rollMonthlyWindows(counts[-1, ], period = "3m", by = "3m") windows ## nicely print results as a data.frame, each row is a time window data.frame(cbind(FROM = format(windows$from), TO = format(windows$to))) ## compute the average number of monthly trading days per quarter rollMonthlySeries(counts[-1, ], period = "3m", by = "3m", FUN = mean) } \keyword{chron} timeSeries/man/stats-window.Rd0000644000176200001440000000171415104730074016074 0ustar liggesusers\name{window} \alias{window} \alias{window,timeSeries-method} \alias{window.timeSeries} \title{Methods for 'window' in package 'timeSeries'} \description{ Extract a part from a \code{"timeSeries"} object. } \usage{ \method{window}{timeSeries}(x, start, end, \dots) } \arguments{ \item{x}{ an object of class \code{"timeSeries"}. } \item{start, end}{ starting date and end date, \code{end} must be after \code{start}. } \item{\dots}{ arguments passed to other methods. } } \details{ \code{window} extracts the subset of the \code{"timeSeries"} object \code{x} observed between the times \code{start} and \code{end}. } \seealso{ \code{\link[=head.timeSeries]{head}}, \code{\link{outlier}} } \examples{ ## load LPP benchmark returns x <- LPP2005REC[, 7:9] range(time(x)) ## extract data for January 2006 window(x, "2006-01-01", "2006-01-31") } \keyword{chron} timeSeries/man/methods-is.Rd0000644000176200001440000000131515104730074015502 0ustar liggesusers\name{is.timeSeries} \alias{is.timeSeries} \alias{is.signalSeries} \title{Check if an object is from class 'timeSeries'} \description{ \code{is.timeSeries} tests if its argument is a \code{timeSeries}. \code{is.signalSeries} tests if series has no timestamps. } \usage{ is.timeSeries(x) is.signalSeries(x) } \arguments{ \item{x}{ an object. } } \value{ a logical value, \code{TRUE} or \code{FALSE}. } \examples{ ## Create an artificial 'timeSeries' object - setRmetricsOptions(myFinCenter = "GMT") charvec <- timeCalendar() data <- matrix(rnorm(12)) TS <- timeSeries(data, charvec, units = "RAND") TS ## Test for 'timeSeries' - is.timeSeries(TS) } \keyword{chron} timeSeries/man/00timeSeries-package.Rd0000644000176200001440000002707215117007055017300 0ustar liggesusers\name{timeSeries-package} \docType{package} \alias{timeSeries-package} \title{Utilities and tools package} \description{ Package \pkg{timeSeries} is part of the Rmetrics suit of R packages. It provides a class, \code{timeSeries}, particularly aimed at analysis of financial data, along with many methods, functions, and utilities for statistical and financial computations on time series. } \author{ Diethelm Wuertz [aut] (original code), Tobias Setz [aut], Yohan Chalabi [aut], Martin Maechler [ctb] (), Georgi N. Boshnakov [cre, aut] Maintainer: Georgi N. Boshnakov } \details{ The following sections have not been updated for some time. } \section{timeSeries - S4 'timeSeries' Class}{ % \code{getDataPart, series} \tab ... \cr \tabular{ll}{ \code{\link{timeSeries}} \tab Creates a \code{"timeSeries"} from scratch\cr \code{\link{series}}, coredata \tab Extracts the data \cr \code{\link{getUnits}} \tab Extracts the time serie units \cr \code{\link{time}} \tab Extracts the positions of timestamps \cr \code{x@format} \tab Extracts the format of the timestamp \cr \code{\link{finCenter}} \tab Extracts the financial center \cr \code{x@recordIDs} \tab Extracts the record IDs \cr \code{x@title} \tab Extracts the title \cr \code{x@documentation} \tab Extracts the documentation } } \section{Base Time Series Functions}{ % \code{comment} \tab ? ... \cr \tabular{ll}{ \code{\link[=apply,timeSeries-method]{apply}} \tab Applies a function to blocks of a \code{"timeSeries"} \cr \code{\link{cbind}} \tab Combines columns of two \code{"timeSeries"} objects \cr \code{\link{rbind}} \tab Combines rows of two \code{"timeSeries"} objects \cr \code{\link{diff}} \tab Returns differences of a \code{"timeSeries"} object \cr \code{\link[=dim,timeSeries-method]{dim}} \tab returns dimensions of a \code{"timeSeries"} object \cr \code{\link{merge}} \tab Merges two \code{"timeSeries"} objects \cr \code{\link[=rank,timeSeries-method]{rank}} \tab Returns sample ranks of a \code{"timeSeries"} object \cr \code{\link[=apply,timeSeries-method]{rev}} \tab Reverts a \code{"timeSeries"} object \cr \code{\link{sample}} \tab Resamples a \code{"timeSeries"} object \cr \code{\link{scale}} \tab Scales a \code{"timeSeries"} object \cr \code{\link[=sort.timeSeries]{sort}} \tab Sorts a \code{"timeSeries"} object \cr \code{\link[=start.timeSeries]{start}} \tab Returns start date/time of a \code{"timeSeries"} \cr \code{\link[=end.timeSeries]{end}} \tab Returns end date/time of a \code{"timeSeries"} \cr \code{\link[=apply,timeSeries-method]{end}} \tab Returns end date/time of a \code{"timeSeries"} \cr \code{\link[=t,timeSeries-method]{t}} \tab Returns the transpose of a \code{"timeSeries"} object \cr \code{\link[=attach,timeSeries-method]{attach}} \tab Attaches a \code{"timeSeries"} to the search path } } \section{Subsetting 'timeSeries' Objects}{ % \code{.subset_} \tab Subsets \code{"timeSeries"} objects \cr % \code{.findIndex} \tab Index search in a \code{"timeSeries"} object \cr \tabular{ll}{ \code{[} \tab Subsets a \code{"timeSeries"} object \cr \code{[<-} \tab Assigns values to a subset \cr \code{$} \tab Subsets a \code{"timeSeries"} by column names \cr \code{$<-} \tab Replaces subset by column names \cr \code{\link[=head.timeSeries]{head}} \tab Returns the head of a \code{"timeSeries"} \cr \code{\link[=tail.timeSeries]{tail}} \tab Returns the tail of a time Series \cr \code{\link{na.omit}} \tab Handles NAs in a \code{"timeSeries"} object \cr \code{removeNA} \tab removes NAs from a matrix object \cr \code{substituteNA} \tab substitutes NAs by zero, column mean or median \cr \code{interpNA} \tab interpolates NAs using R's "approx" function } } \section{Mathematical Operation}{ \tabular{ll}{ \code{\link[=Ops,timeSeries,timeSeries-method]{Ops}} \tab S4: Arith method for a \code{"timeSeries"} object \cr \code{\link[=Math,timeSeries-method]{Math}} \tab S4: Math method for a \code{"timeSeries"} object \cr \code{\link[=Math2,timeSeries-method]{Math2}} \tab S4: Maths method for a \code{"timeSeries"} object \cr \code{abs} \tab Returns absolute values of a \code{"timeSeries"} object \cr \code{sqrt} \tab Returns square root of a \code{"timeSeries"} object \cr \code{exp} \tab Returns the exponential values of a \code{"timeSeries"} object \cr \code{log} \tab Returns the logarithm of a \code{"timeSeries"} object \cr \code{sign} \tab Returns the signs of a \code{"timeSeries"} object \cr \code{\link{diff}} \tab Differences a \code{"timeSeries"} object \cr \code{\link{scale}} \tab Centers and/or scales a \code{"timeSeries"} object \cr \code{\link[=quantile.timeSeries]{quantile}} \tab Returns quantiles of an univariate \code{"timeSeries"}} } \section{Methods}{ \tabular{ll}{ \code{\link{as.timeSeries}} \tab Defines method for a \code{"timeSeries"} \cr \code{as.*.default} \tab Returns the input \cr \code{as.*.ts} \tab Transforma a 'ts' object into a \code{"timeSeries"} \cr \code{as.*.data.frame} \tab Transforms a 'data.frame' intp a 'timeSeries \cr \code{as.*.character} \tab Loads and transforms from a demo file \cr \code{as.*.zoo} \tab Transforms a 'zoo' object into a \code{"timeSeries"} \cr \code{as.vector.*} \tab Converts univariate \code{"timeSeries"} to vector \cr \code{as.matrix.*} \tab Converts \code{"timeSeries"} to matrix \cr \code{as.numeric.*} \tab Converts \code{"timeSeries"} to numeric \cr \code{as.data.frame.*} \tab Converts \code{"timeSeries"} to data.frame \cr \code{as.ts.*} \tab Converts \code{"timeSeries"} to ts \cr \code{as.logical.*} \tab Converts \code{"timeSeries"} to logical \cr \code{\link{is.timeSeries}} \tab Tests for a \code{"timeSeries"} object \cr \code{\link[=plot,timeSeries-method]{plot}} \tab Displays a X-Y \code{"timeSeries"} Plot \cr \code{\link[=lines,timeSeries-method]{lines}} \tab Adds connected line segments to a plot \cr \code{\link[=points,timeSeries-method]{points}} \tab Adds Points to a plot \cr \code{\link[=show,timeSeries-method]{show}} \tab Prints a 'timeSeries oobject} } \section{Financial time series functions}{ \tabular{ll}{ \code{\link{align}} \tab Aligns a \code{"timeSeries"} to time stamps \cr \code{\link{cumulated}} \tab Computes cumulated series from a returns \cr \code{\link{alignDailySeries}} \tab Aligns a \code{"timeSeries"} to calendarical dates \cr \code{\link{rollDailySeries}} \tab Rolls a 'timeSeries daily\cr \code{\link{drawdowns}} \tab Computes series of drawdowns from financial returns \cr \code{\link{drawdownsStats}} \tab Computes drawdowns statistics \cr \code{\link{durations}} \tab Computes durations from a financial time series \cr \code{\link{countMonthlyRecords}} \tab Counts monthly records in a \code{"timeSeries"} \cr \code{\link{rollMonthlyWindows}} \tab Rolls Monthly windows \cr \code{\link{rollMonthlySeries}} \tab Rolls a \code{"timeSeries"} monthly \cr \code{\link{endOfPeriodSeries}} \tab Returns end of periodical series \cr \code{\link{endOfPeriodStats}} \tab Returns end of period statistics \cr \code{\link{endOfPeriodBenchmarks}} \tab Returns period benchmarks \cr \code{\link{returns}} \tab Computes returns from prices or indexes \cr \code{\link{returns0}} \tab Computes untrimmed returns from prices or indexes \cr \code{\link{runlengths}} \tab Computes run lenghts of a \code{"timeSeries"} \cr \code{\link{smoothLowess}} \tab Smoothes a \code{"timeSeries"} \cr \code{\link{smoothSpline}} \tab Smoothes a \code{"timeSeries"} \cr \code{\link{smoothSupsmu}} \tab Smoothes a \code{"timeSeries"} \cr \code{\link{splits}} \tab Detects \code{"timeSeries"} splits by outlier detection \cr \code{\link{spreads}} \tab Computes spreads from a price/index stream \cr \code{\link{turns}} \tab Computes turning points in a \code{"timeSeries"} object \cr \code{\link{turnsStats}} \tab Computes turning points statistics } } \section{Statistics Time Series functions}{ \tabular{ll}{ \code{\link{colCumsums}} \tab Computes cumulated column sums of a \code{"timeSeries"} \cr \code{\link{colCummaxs}} \tab Computes cumulated maximum of a \code{"timeSeries"} \cr \code{\link{colCummins}} \tab Computes cumulated minimum of a \code{"timeSeries"} \cr \code{\link{colCumprods}} \tab Computes cumulated pruduct values by column \cr \code{\link{colCumreturns}} \tab Computes cumulated returns by column \cr \code{\link[=colSums,timeSeries-method]{colSums}} \tab Computes sums of all values in each column \cr \code{\link[=colMeans,timeSeries-method]{colMeans}} \tab Computes means of all values in each column \cr \code{\link{colSds}} \tab Computes standard deviations of all values in each column \cr \code{\link{colVars}} \tab Computes variances of all values in each column \cr \code{\link{colSkewness}} \tab Computes skewness of all values in each column \cr \code{\link{colKurtosis}} \tab Computes kurtosis of all values in each column \cr \code{\link{colMaxs}} \tab Computes maxima of all values in each column \cr \code{\link{colMins}} \tab Computes minima of all values in each column \cr \code{\link{colProds}} \tab Computes products of all values in each column \cr \code{\link{colStats}} \tab Computes statistics of all values in each column \cr \code{\link{orderColnames}} \tab Returns ordered column names of a \code{"timeSeries"} \cr \code{\link{sortColnames}} \tab Returns alphabetically sorted column names \cr \code{\link{sampleColnames}} \tab Returns sampled column names of a \code{"timeSeries"} \cr \code{\link{pcaColnames}} \tab Returns PCA correlation ordered column names \cr \code{\link{hclustColnames}} \tab Returns hierarchically clustered columnames \cr \code{\link{statsColnames}} \tab Returns statisticall rearrange columnames \cr \code{\link{orderStatistics}} \tab Computes order statistics of a \code{"timeSeries"} object \cr \code{\link{rollMean}} \tab Computes rolling means of a \code{"timeSeries"} object \cr \code{\link{rollMin}} \tab Computes rolling minima of a \code{"timeSeries"} object \cr \code{\link{rollMax}} \tab Computes rolling maxima of a \code{"timeSeries"} object \cr \code{\link{rollMedian}} \tab Computes rolling medians of a \code{"timeSeries"} object \cr \code{\link{rollStats}} \tab Computes rolling statistics of a \code{"timeSeries"} objectcr \cr \code{\link{rowCumsums}} \tab Computes cumulated column sums of a \code{"timeSeries"} \cr \code{\link{smoothLowess}} \tab Smoothes a series with lowess function \cr \code{\link{smoothSupsmu}} \tab Smoothes a series with supsmu function \cr \code{\link{smoothSpline}} \tab Smoothes a series with smooth.spline function } } \section{Misc Functions}{ \tabular{ll}{ \code{\link{dummyDailySeries}} \tab Creates a dummy daily \code{"timeSeries"} object \cr \code{\link[=isMonthly,timeSeries-method]{isMonthly}} \tab Decides if the series consists of monthly records \cr \code{\link[=isDaily,timeSeries-method]{isDaily}} \tab Decides if the series consists of daily records \cr \code{\link[=isQuarterly,timeSeries-method]{isQuarterly}} \tab Decides if the series consists of Quarterly records \cr \code{\link{description}} \tab Creates default description string % \code{\link{getArgs}} \tab Extracts arguments from a S4 method } } \keyword{package} \keyword{ts} timeSeries/man/base-attach.Rd0000644000176200001440000000350215104730074015602 0ustar liggesusers\name{attach} \alias{attach} \alias{attach,timeSeries-method} \title{Attach a 'timeSeries' to the search path} \description{ Attaches a \code{"timeSeries"} object to the search path. } \usage{ \S4method{attach}{timeSeries}(what, pos = 2, name = deparse(substitute(what)), warn.conflicts = TRUE) } \note{ The function \code{detach} from the \code{base} package can be used to detach the attached objects. } \arguments{ \item{name}{ alternative way to specify the database to be attached. See for details \code{help(attach, package = base)}. } \item{pos}{ an integer specifying position in \code{search()} where to attach the database. See for details \code{help(attach, package = base)}. } \item{warn.conflicts}{ a logical value. If \code{TRUE}, warnings are printed about conflicts from attaching the database, unless that database contains an object \code{.conflicts.OK}. A conflict is a function masking a function, or a non-function masking a non-function. See for details \code{help(attach, package = base)}. } \item{what}{ database to be attached. This may currently be a \code{"timeSeries"} object, a data.frame, a list, an R data file created with \code{save}, \code{NULL}, or an environment. See for details \code{help(attach, package = base)}. } } \value{ the environment, invisibly, with a \code{name} attribute } \examples{ ## Load Microsoft Data Set - x <- MSFT[1:10, ] colnames(x) ## Attach the Series and Compute the Range - attach(x) range <- High - Low range ## Convert Vector to a \code{"timeSeries"} Object - timeSeries(data=range, charvec=time(x), units="Range") ## Detach the series from the search path - detach("x") ans <- try(High, silent=TRUE) cat(ans[1]) } \keyword{chron} timeSeries/man/fin-runlengths.Rd0000644000176200001440000000164615104730074016400 0ustar liggesusers\name{runlengths} \alias{runlengths} \title{Runlengths of a time series} \description{ Computes runlengths of an univariate \code{"timeSeries"} object. } \usage{ runlengths(x, \dots) } \arguments{ \item{x}{ an univariate time series of class \code{"timeSeries"}. } \item{\dots}{ arguments passed to the function \code{na.omit}. } } \details{ Runlengths are defined here as contiguous sequences of values having the same sign. Zeroes are treated as \code{NA}s. } \value{ an object of class \code{"timeSeries"} } \examples{ ## random time series - set.seed(4711) x <- rnorm(12) tS <- timeSeries(data = x, charvec = timeCalendar(), units = "x") tS ## return runlengths - runlengths(tS) ## replace the middle value of the negative stretch of 3 values tS[5] <- NA ## the two negative values separated by NA are still one run runlengths(tS) } \keyword{chron} timeSeries/man/statistics-orderColnames.Rd0000644000176200001440000001023115104730074020410 0ustar liggesusers\name{orderColnames} \alias{orderColnames} \alias{sortColnames} \alias{sampleColnames} \alias{statsColnames} \alias{pcaColnames} \alias{hclustColnames} \title{Reorder column names of a time series} \description{ Functions and methods dealing with the rearrangement of column names of 'timeSeries' objects. \cr \tabular{ll}{ \code{orderColnames} \tab Returns ordered column names of a time Series, \cr \code{sortColnames} \tab Returns sorted column names of a time Series, \cr \code{sampleColnames} \tab Returns sampled column names of a time Series, \cr \code{statsColnames} \tab Returns statistically rearranged column names, \cr \code{pcaColnames} \tab Returns PCA correlation ordered column names, \cr \code{hclustColnames} \tab Returns hierarchical clustered column names. } } \usage{ orderColnames(x, \dots) sortColnames(x, \dots) sampleColnames(x, \dots) statsColnames(x, FUN = colMeans, \dots) pcaColnames(x, robust = FALSE, \dots) hclustColnames(x, method = c("euclidean", "complete"), \dots) } \arguments{ \item{x}{ an object of class \code{timesSeries} or any other rectangular object which can be transformed by the function \code{as.matrix} into a numeric matrix. } \item{FUN}{ a character string indicating which statistical function should be applied. By default statistical ordering operates on the column means of the time series. } \item{method}{ a character string with two elements. The first determines the choice of the distance measure, see \code{\link[stats]{dist}}, and the second determines the choice of the agglomeration method, see \code{\link[stats]{hclust}}. } \item{robust}{ a logical flag which indicates if robust correlations should be used. } \item{\dots}{ further arguments to be passed to the underlying functions doing the main work, see section \sQuote{Details}. } } \details{ These functions reorder the column names of a \code{"timeSeries"} object according to some statistical measure. \bold{Statistically Motivated Rearrangement} The function \code{statsColnames} rearranges the column names according to a statical measure. These measure must operate on the columns of the time series and return a vector of values which can be sorted. Typical functions ar those listed in help page \code{colStats} but custom functions can be used that compute for example risk or any other statistical measure. The \code{\dots} argument allows to pass additional arguments to the underlying function \code{FUN}. \bold{PCA Ordering of the Correlation Matrix} The function \code{pcaColnames} rearranges the column names according to the PCA ordered correlation matrix. The argument \code{robust} allsows to select between the use of the standard \code{cor} and computation of robust correlations using the function \code{covMcd} from contributed R package \code{robustbase}. The \code{\dots} argument allows to pass additional arguments to the two underlying functions \code{cor} or \code{covMcd}. E.g., adding \code{method="kendall"} to the argument list calculates Kendall's rank correlations instead the default which calculates Person's correlations.\cr \bold{Ordering by Hierarchical Clustering} The function \code{pcaColnames} uses the hierarchical clustering approach \code{hclust} to rearrange the column names of the time series. } \value{ for \code{orderColnames}, an integer vector representing the permutaion that will sort the column names, for the other functions, a character vector giving the rearranged column names } \examples{ ## Load Swiss Pension Fund Benchmark Data - data <- LPP2005REC[,1:6] ## Abbreviate Column Names - colnames(data) ## Sort Alphabetically - sortColnames(data) ## Sort by Column Names by Hierarchical Clustering - hclustColnames(data) head(data[, hclustColnames(data)]) } \keyword{chron} timeSeries/man/stats-na.contiguous.Rd0000644000176200001440000000264615104730074017366 0ustar liggesusers\name{na.contiguous} \alias{na.contiguous} \alias{na.contiguous.timeSeries} \alias{is.na} \alias{is.na,timeSeries-method} \title{Find longest contiguous stretch of non-NAs or check for NAs} \description{ Find the longest consecutive stretch of non-missing values in a \code{"timeSeries"} object. In the event of a tie, the first such stretch. Also, \code{"timeSeries"} method for \code{is.na}. } \usage{ \method{na.contiguous}{timeSeries}(object, ...) \S4method{is.na}{timeSeries}(x) } \arguments{ \item{object,x}{ a \code{"timeSeries"} object. } \item{\dots}{ further arguments passed to other methods. } } \value{ for the \code{na.contiguous} method, a \code{"timeSeries"} object without missing values, for the \code{is.na} method, a \code{"timeSeries"} object whose data part is a logical matrix of the same dimension as in \code{x} indicating if the corresponding values are \code{NA} or not. } \examples{ ## Dummy 'timeSeries' containing NAs \dontshow{set.seed(2023)} data <- matrix(sample(c(1:20, rep(NA,4))), ncol = 2) s <- timeSeries(data, timeCalendar(2023)) is.na(s) ## Find the longest consecutive non-missing values na.contiguous(s) ## tied longest stretches: 1:3, 6:9 and 10:12 x <- c(1:3, NA, NA, 6:8, NA, 10:12) ## should return the 1st one na.contiguous(x) # correct for R > 4.3.0 na.contiguous(timeSeries(x)) # correct for timeSeries version > 4030.106 } timeSeries/man/statistics-rowCumsums.Rd0000644000176200001440000000161115104730074020001 0ustar liggesusers\name{rowCum} \alias{rowCum} \alias{rowCumsums} \alias{rowCumsums,ANY-method} \alias{rowCumsums,timeSeries-method} \title{Cumulative row statistics} \description{ Compute cumulative row statistics. } \usage{ \S4method{rowCumsums}{ANY}(x, na.rm = FALSE, \dots) \S4method{rowCumsums}{timeSeries}(x, na.rm = FALSE, \dots) } \arguments{ \item{x}{ a time series, may be an object of class \code{"matrix"} or \code{"timeSeries"}. } \item{na.rm}{ a logical. Should missing values be removed? } \item{\dots}{ arguments to be passed. } } \value{ for the default method, a matrix, for the \code{"timeSeries"} method, an S4 object of class \code{"timeSeries"}. } \seealso{ \code{\link[=colCum]{colCumXXX}} } \examples{ ## Simulated Monthly Return Data - X = matrix(rnorm(24), ncol = 2) ## Compute cumulated Sums - rowCumsums(X) } \keyword{univar} timeSeries/man/fin-dummy.Rd0000644000176200001440000000260215104730074015333 0ustar liggesusers\name{dummyTimeSeries} \alias{dummyTimeSeries} \alias{dummyDailySeries} \alias{dummyMonthlySeries} \alias{dummySeries} % deprecated; now removed \title{Create dummy time series} \description{ Create dummy daily and monthly time series for examples and exploration. } \usage{ dummyDailySeries(x = rnorm(365), units = NULL, zone = "", FinCenter = "") dummyMonthlySeries(\dots) } \arguments{ \item{x}{ an object of class \code{timeSeries}. } \item{units}{ an optional character string, which allows to overwrite the current column names of a \code{timeSeries} object. By default \code{NULL} which means that the column names are selected automatically. } \item{FinCenter}{ a character with the the location of the financial center named as \code{"continent/city"}. } \item{zone}{ the time zone or financial center where the data were recorded. } \item{...}{ optional arguments passed to \code{timeSeries}. } } \details{ \code{dummyDailySeries} creates a \code{timeSeries} object with dummy daily dates from a numeric matrix with daily records of unknown dates. \code{dummyMonthlySeries} creates a dummy monthly \code{"timeSeries"} object. } \value{ a \code{"timeSeries"} object } \examples{ dd <- dummyDailySeries() head(dd) tail(dd) dummyMonthlySeries(y = 2022) } \keyword{chron} \keyword{ts} timeSeries/man/statistics-colCumsums.Rd0000644000176200001440000000433415104730074017754 0ustar liggesusers\name{colCum} \alias{colCum} \alias{colCummaxs} \alias{colCummins} \alias{colCumprods} \alias{colCumreturns} \alias{colCumsums} \alias{colCummaxs,matrix-method} \alias{colCummaxs,timeSeries-method} \alias{colCummins,matrix-method} \alias{colCummins,timeSeries-method} \alias{colCumprods,matrix-method} \alias{colCumprods,timeSeries-method} \alias{colCumreturns,matrix-method} \alias{colCumreturns,timeSeries-method} \alias{colCumsums,matrix-method} \alias{colCumsums,timeSeries-method} \title{Cumulated column statistics} \description{ Functions to compute cumulative column statistics. } \usage{ \S4method{colCumsums}{timeSeries}(x, na.rm = FALSE, \dots) \S4method{colCummaxs}{timeSeries}(x, na.rm = FALSE, \dots) \S4method{colCummins}{timeSeries}(x, na.rm = FALSE, \dots) \S4method{colCumprods}{timeSeries}(x, na.rm = FALSE, \dots) \S4method{colCumreturns}{timeSeries}(x, method = c("geometric", "simple"), na.rm = FALSE, \dots) } \arguments{ \item{x}{ a time series, may be an object of class \code{"matrix"}, or \code{"timeSeries"}. } \item{na.rm}{ a logical. Should missing values be removed? } \item{method}{ a character string to indicate if geometric (\code{TRUE}) or simple (\code{FALSE}) returns should be computed. } \item{\dots}{ arguments to be passed. } } \details{ These functions compute the requested cumulative quantities columnwise to obtain a matrix of the same dimension as the data. The \code{"timeSeries"} methods replace the data part of the original object with the resulting matrix. The \code{"timeSeries"} methods for the \code{Math} group functions \code{cummin}, \code{cummax}, \code{cumsum}, and \code{cumprod}, work similarly but don't have the \code{na.rm} argument. } \value{ \code{"matrix"} for the default methods of all functions, \code{"timeSeries"} for the \code{"timeSeries"} methods } \seealso{ \code{\link{Math,timeSeries-method}}, \code{\link{rowCumsums}} } \examples{ ## simulate return data x <- matrix(rnorm(24), ncol = 2) X <- as.timeSeries(x) ## cumulative sums by column - class(colCumsums(x)) # "matrix" class(colCumsums(X)) # "timeSeries" colCumsums(X) } \keyword{univar} timeSeries/man/base-sample.Rd0000644000176200001440000000301215104730074015613 0ustar liggesusers\name{sample} \alias{sample} \alias{sample,timeSeries-method} \title{Resample 'timeSeries' objects} \description{ Takes a sample of the specified size from the elements of a \code{"timeSeries"}. } \usage{ \S4method{sample}{timeSeries}(x, size, replace = FALSE, prob = NULL) } \arguments{ \item{x}{ an object from class \code{"timeSeries"}. } \item{size}{ a non-negative integer giving the number of items to choose. } \item{replace}{ sample with replacement if \code{TRUE}, otherwise without replacement. } \item{prob}{ a vector of probability weights for obtaining the elements of the vector being sampled. } } \details{ The function takes a sample of size \code{size} from the elements of the time series with or without replacement depending on argument \code{replace}. The result is returned as a \code{"timeSeries"} object. For details about the arguments see the documentation of \code{base:sample}. } \value{ an object from class \code{"timeSeries"} } \seealso{ \code{\link[base]{sample}} (\code{sample} in base \R), \code{\link[timeDate]{sample}} (the \code{"timeDate"} method) } \examples{ ## Monthly Calendar Series - x <- daily2monthly(LPP2005REC[, 1:2])[3:14, ] \dontshow{set.seed(1234)} ## Resample the Series with respect to the time stamps - resampled <- sample(x) resampled is.unsorted(resampled) } \keyword{chron} timeSeries/man/statistics-orderStatistics.Rd0000644000176200001440000000164615104730074021013 0ustar liggesusers\name{orderStatistics} \alias{orderStatistics} \title{Order statistics} \description{ Computes the order statistics of a \code{"timeSeries"} object. } \usage{ orderStatistics(x) } \arguments{ \item{x}{ a \code{"timeSeries"} object. } } \details{ \code{orderStatistics} computes the order statistics for each column of a \code{"timeSeries"} object. The output is a named list with the order statistics for each column in a separate component. } \value{ a named list, in which each component is an univariate \code{"timeSeries"} containing the order statistics of the corresponding column of the input time series. } \examples{ ## Load Swiss Pension Fund Benchmark Data - setRmetricsOptions(myFinCenter = "GMT") X <- LPP2005REC[, "SPI"] colnames(X) ## Compute 1\% Order Statistics - N <- round(0.01*nrow(X)) N OS <- orderStatistics(X)[[1]] OS[1:N, ] } \keyword{chron} timeSeries/man/base-apply.Rd0000644000176200001440000001430215104730074015463 0ustar liggesusers\name{apply} \alias{apply} \alias{apply,timeSeries-method} \alias{fapply} \alias{applySeries} \alias{rollDailySeries} \title{Apply functions over time windows} \description{ Applies a function to a \code{"timeSeries"} object over regular or irregular time windows, possibly overlapping. } \usage{ \S4method{apply}{timeSeries}(X, MARGIN, FUN, \dots, simplify = TRUE) fapply(x, from, to, FUN, \dots) applySeries(x, from = NULL, to = NULL, by = c("monthly", "quarterly"), FUN = colMeans, units = NULL, format = x@format, zone = x@FinCenter, FinCenter = x@FinCenter, recordIDs = data.frame(), title = x@title, documentation = x@documentation, \dots) rollDailySeries(x, period = "7d", FUN, \dots) } \arguments{ \item{x,X}{ an object of class \code{timeSeries}. } \item{MARGIN}{ a vector giving the subscripts which the function will be applied over, see base R's \code{\link[base]{apply}}. } \item{FUN}{ the function to be applied. For the function \code{applySeries} the default setting is \code{FUN = colMeans}. } \item{simplify}{ simplify the result? } \item{from, to}{ starting date and end date as \code{"timeDate"} objects. Note, \code{to} must be time ordered after \code{from}. If \code{from} and \code{to} are missing in function \code{fapply} they are set by default to \code{from=start(x)}, and \code{to=end(x)}. } \item{by}{ a character value either \code{"monthly"} or \code{"quarterly"} used in the function \code{applySeries}. The default value is \code{"monthly"}. Only operative when both arguments \code{from} and \code{to} have their default values \code{NULL}. In this case the function \code{FUN} will be applied to monthly or quarterly periods. } \item{units}{ an optional character string, which allows to overwrite the current column names of a \code{timeSeries} object. By default \code{NULL} which means that the column names are selected automatically. } \item{format}{ the format specification of the input character vector in POSIX notation. } \item{zone}{ the time zone or financial center where the data were recorded. } \item{FinCenter}{ a character value with the the location of the financial center named as "continent/city", or "city". } \item{recordIDs}{ a data frame which can be used for record identification information. Note, this is not yet handled by the apply functions, an empty data.frame will be returned. } \item{title}{ an optional title string, if not specified the input's data name is deparsed. } \item{documentation}{ optional documentation string, or a vector of character strings. } \item{period}{ a character string specifying the rollling period composed by the length of the period and its unit, e.g. \code{"7d"} represents one week. } \item{\dots}{ arguments passed to other methods. } } \details{ The \code{"timeSeries"} method for \code{apply} extracts the core data (a matrix) from \code{X} and calls \code{apply}, passing on all the remaining arguments. If the result is suitable, it converts it to \code{"timeSeries"}, otherwise returns it as is. \sQuote{Suitable} here means that it is a matrix or a vector (which is converted to a matrix) and the number of observations is the same as \code{X}. Like \code{apply} applies a function to the margins of an array, the function \code{fapply} applies a function to the time stamps or signal counts of a financial (therefore the \dQuote{f} in front of the function name) time series of class \code{"timeSeries"}. \code{applySeries} takes a \code{"timeSeries"} object as input and applies \code{FUN} to windows of \code{x}. The windows are specified by \code{from} and \code{to}, which need to have the same length. Then \code{from[i], to[i]} specifies the \code{i}-th window. If \code{time(x)} is a \code{"timeDate"} object, then \code{from} and \code{to} are converted to \code{"timeDate"} (if they are not already such objects), otherwise they are converted to integers. An alternative way to specify the window(s) on which \code{applySeries} operates is with argument \code{by}. It is used only if \code{from} and \code{to} are missing or \code{NULL}. \code{by = "monthly"} or \code{by = "quarterly"} applies \code{FUN} to the data for each year-month or year-quarter, respectively. By year-month we mean that there are separate windows for the months in different years. The resulting time stamps are the time stamps of the \code{to} vector. The periods can be regular or irregular, and they can even overlap. If \code{from = start(x)} and \code{to = end(x)}, then the function behaves like \code{apply} on the column margin. \code{fapply} is the same as \code{applySeries} (in fact, the former calls the latter), except that the defaults for \code{from} and \code{to} are \code{start(x)} and \code{end(x)}, respectively. (GNB: in addition, \code{fapply} throws error if \code{x} is a \sQuote{signal series}.) \code{rollDailySeries} rolls a daily 'timeSeries' on a given period. } \value{ for \code{rollDailySeries}, an object of class \code{"timeSeries"} with rolling values, computed from the function \code{FUN}. } \examples{ ## Percentual Returns of Swiss Bond Index and Performance Index - LPP <- 100 * LPP2005REC[, c("SBI", "SPI")] head(LPP, 20) ## Aggregate Quarterly Returns - applySeries(LPP, by = "quarterly", FUN = colSums) ## Aggregate Quarterly every last Friday in Quarter - oneDay <- 24*3600 from <- unique(timeFirstDayInQuarter(time(LPP))) - oneDay from <- timeLastNdayInMonth(from, nday = 5) to <- unique(timeLastDayInQuarter(time(LPP))) to <- timeLastNdayInMonth(to, nday = 5) data.frame(from = as.character(from), to = as.character(to)) applySeries(LPP, from, to, FUN = colSums) ## Alternative Use - fapply(LPP, from, to, FUN = colSums) ## Count Trading Days per Month - colCounts <- function(x) rep(NROW(x), times = NCOL(x)) applySeries(LPP, FUN = colCounts, by = "monthly") ## TODO: examples for rollDailySeries() } \keyword{chron} \keyword{ts} timeSeries/man/timeSeries-isUnivariate.Rd0000644000176200001440000000166215104730074020205 0ustar liggesusers\name{isUnivariate} \alias{isUnivariate} \alias{isMultivariate} \title{Checks if a time series is univariate} \description{ Checks if a time series object or any other rectangular object is univariate or multivariate. } \usage{ isUnivariate(x) isMultivariate(x) } \arguments{ \item{x}{ an object of class \code{"timeSeries"} or any other rectangular object. } } \details{ A rectangular object \code{x} is considered to be univariate if the function \code{NCOL(x)} returns one, and is considered to be multivariate if \code{NCOL(x)} returns a value bigger than one. } \value{ a logical value } \examples{ ## Load Microsoft Data - setRmetricsOptions(myFinCenter = "GMT") data(MSFT) Open = MSFT[, "Open"] ## Is the 'timeSeries' Univariate - isUnivariate(MSFT) isUnivariate(Open) ## Is the 'timeSeries' Multivariate - isMultivariate(MSFT) isMultivariate(Open) } \keyword{chron} timeSeries/man/stats-filter.Rd0000644000176200001440000000266515104730074016060 0ustar liggesusers\name{filter} \alias{filter} \alias{filter,timeSeries-method} \title{Linear filtering on a time series} \description{ Applies linear filtering to a univariate \code{"timeSeries"}. } \usage{ \S4method{filter}{timeSeries}(x, filter, method = c("convolution", "recursive"), sides = 2, circular = FALSE, init = NULL) } \arguments{ \item{x}{ an object from class \code{"timeSeries"}. } \item{filter}{ coefficients of the filter. } \item{method}{ \code{"convolution"} or \code{"recursive"}. } \item{sides,circular}{ for convolution filters only. Onesided if \code{sides = 1}, centred around lag 0 if \code{sides = 2}. Circular if \code{circular = TRUE.} } \item{init}{ for recursive filters only. Values before the start of the time series. } } \details{ \code{filter} is a generic function with default method \code{stats::filter}. The method for \code{"timeSeries"} is a wrapper for the latter. See \code{?stats::filter} for details about the arguments. } \value{ a \code{"timeSeries"} object } \seealso{ base R function \code{\link[stats]{filter}} } \examples{ ## Creata a dummy signal 'timeSeries' - data <- matrix(rnorm(100), ncol = 2) s <- timeSeries(data, units=c("A", "B")) head(s) ## Filter the series - f <- filter(s, rep(1, 3)) head(f) ## Plot and compare the first series - plot(cbind(s[, 1], f[, 1]), plot.type="s") } timeSeries/man/fin-cumulated.Rd0000644000176200001440000000324015104730074016162 0ustar liggesusers\name{cumulated} \alias{cumulated} \alias{cumulated.default} \title{Cumulated time series from returns} \description{ Computes a cumulated financial \code{"timeSeries"}, e.g. prices or indexes, from financial returns. } \usage{ cumulated(x, \dots) \method{cumulated}{default}(x, method = c("continuous", "discrete", "compound", "simple"), percentage = FALSE, \dots) } \arguments{ \item{x}{ an object of class \code{timeSeries}. } \item{method}{ a character string, the method for computation of returns. } \item{percentage}{ a logical value. By default \code{FALSE}, if \code{TRUE} the series will be expressed in percentage changes. } \item{\dots}{ ignored by the default method. } } \details{ Note, the function \code{cumulated} assumes as input discrete returns from a price or index series. Only then the cumulated series agrees with the original price or index series. The first values of the cumulated series cannot be computed, it is assumed that the series is indexed to 1. } \value{ a \code{"timeSeries"} object } \seealso{ \code{\link{returns}}, %\code{\link{cumulated}}, \code{\link{drawdowns}}, \code{\link{splits}}, %\code{\link{spreads}}, \code{\link{midquotes}}, \code{\link{index2wealth}} } \examples{ ## Use the Microsofts' Close Prices Indexed to 1 - MSFT.CL <- MSFT[, "Close"] MSFT.CL <- MSFT.CL/MSFT[[1, "Close"]] head(MSFT.CL) ## Compute Discrete Return - MSFT.RET <- returns(MSFT.CL, method = "discrete") ## Cumulated Series and Compare - MSFT.CUM <- cumulated(MSFT.RET, method = "discrete") head(cbind(MSFT.CL, MSFT.CUM)) } \keyword{chron} timeSeries/man/fin-returns.Rd0000644000176200001440000000460115104730074015703 0ustar liggesusers\name{returns} \alias{returns} \alias{returns,ANY-method} \alias{returns,timeSeries-method} \alias{returns0} \alias{returnSeries} \alias{getReturns} \title{Financial returns} \description{ Compute financial returns from prices or indexes. } \usage{ returns(x, \dots) returns0(x, \dots) \S4method{returns}{ANY}(x, method = c("continuous", "discrete", "compound", "simple"), percentage = FALSE, \dots) \S4method{returns}{timeSeries}(x, method = c("continuous", "discrete", "compound", "simple"), percentage = FALSE, na.rm = TRUE, trim = TRUE, \dots) } \arguments{ \item{x}{ an object of class \code{timeSeries}. } \item{method}{ a character string. Which method should be used to compute the returns, one of "continuous", "discrete", or "compound", "simple". The second pair of methods is a synonym for the first two methods. } \item{percentage}{ a logical value. By default \code{FALSE}, if \code{TRUE} the series will be expressed in percentage changes. } \item{na.rm}{ a logical value. Should NAs be removed? By default \code{TRUE}. } \item{trim}{ a logical value. Should the time series be trimmed? By Default \code{TRUE}. } \item{\dots}{ arguments to be passed. } } \value{ an object of class \code{timeSeries}. \code{returns0} returns an untrimmed series with the first row of returns set to zero(s). } \note{ The functions \code{returnSeries} and \code{getReturns} will be removed in the near future. They are synonyms for the function \code{returns} and their use was discouraged for many years. Just use \code{returns}. The function \code{returnSeries} is no longer exported. \code{getReturns} is exported only because we are waiting for a package on CRAN to be updated. } \seealso{ %\code{\link{returns}}, \code{\link{cumulated}}, \code{\link{drawdowns}}, \code{\link{splits}}, \code{\link{spreads}}, \code{\link{midquotes}}, \code{\link{index2wealth}} } \examples{ ## Load Microsoft Data - setRmetricsOptions(myFinCenter = "GMT") data(MSFT) X = MSFT[1:10, 1:4] X ## Continuous Returns - returns(X) returns0(X) ## Discrete Returns: returns(X, method = "discrete") ## Don't trim: returns(X, trim = FALSE) ## Use Percentage Values: returns(X, percentage = TRUE, trim = FALSE) } \keyword{chron} timeSeries/man/fin-wealth.Rd0000644000176200001440000000142615104730074015467 0ustar liggesusers\name{wealth} \alias{index2wealth} \title{Conversion of an index to wealth} \description{ Converts an index series to a wealth series normalizing the starting value to one. } \usage{ index2wealth(x) } \arguments{ \item{x}{ an object of class 'timeSeries'. } } \value{ returns a time series object of the same class as the input argument \code{x} normalizing the starting value to one. } \seealso{ \code{\link{returns}}, \code{\link{cumulated}}, \code{\link{drawdowns}}, \code{\link{splits}}, \code{\link{spreads}}, \code{\link{midquotes}}, %\code{\link{index2wealth}} } \examples{ ## Load MSFT Open Prices - INDEX <- MSFT[1:20, 1] INDEX ## Compute Wealth Normalized to 100 - 100 * index2wealth(INDEX) } \keyword{chron} timeSeries/man/timeSeries-class.Rd0000644000176200001440000004421515104730074016650 0ustar liggesusers\name{timeSeries-class} \Rdversion{1.1} \docType{class} \alias{timeSeries-class} % \alias{[,timeSeries,ANY,index_timeSeries-method} % \alias{[,timeSeries,character,character-method} % \alias{[,timeSeries,character,index_timeSeries-method} % \alias{[,timeSeries,character,missing-method} % \alias{[,timeSeries,index_timeSeries,character-method} % \alias{[,timeSeries,index_timeSeries,index_timeSeries-method} % \alias{[,timeSeries,index_timeSeries,missing-method} % \alias{[,timeSeries,matrix,missing-method} % \alias{[,timeSeries,missing,character-method} % \alias{[,timeSeries,missing,index_timeSeries-method} % \alias{[,timeSeries,missing,missing-method} % \alias{[,timeSeries,time_timeSeries,ANY-method} % \alias{[,timeSeries,time_timeSeries,character-method} % \alias{[,timeSeries,time_timeSeries,index_timeSeries-method} % \alias{[,timeSeries,time_timeSeries,missing-method} % \alias{[,timeSeries,timeDate,character-method} % \alias{[,timeSeries,timeDate,index_timeSeries-method} % \alias{[,timeSeries,timeDate,missing-method} % \alias{[,timeSeries,timeSeries,index_timeSeries-method} % \alias{[,timeSeries,timeSeries,missing-method} % \alias{[<-,timeSeries,character,ANY-method} % \alias{[<-,timeSeries,character,missing-method} % \alias{[<-,timeSeries,timeDate,ANY-method} % \alias{[<-,timeSeries,timeDate,missing-method} % \alias{$,timeSeries-method} % \alias{$<-,timeSeries,ANY-method} % \alias{$<-,timeSeries,factor-method} % \alias{$<-,timeSeries,numeric-method} % \alias{aggregate,timeSeries-method} % \alias{align,timeSeries-method} % \alias{apply,timeSeries-method} % \alias{as.data.frame,timeSeries-method} % \alias{as.list,timeSeries-method} % \alias{as.matrix,timeSeries-method} % \alias{as.ts,timeSeries-method} % \alias{attach,timeSeries-method} % \alias{cbind2,ANY,timeSeries-method} % \alias{cbind2,timeSeries,ANY-method} % \alias{cbind2,timeSeries,missing-method} % \alias{cbind2,timeSeries,timeSeries-method} % \alias{coerce,ANY,timeSeries-method} % \alias{coerce,character,timeSeries-method} % \alias{coerce,data.frame,timeSeries-method} % \alias{coerce,timeSeries,data.frame-method} % \alias{coerce,timeSeries,list-method} % \alias{coerce,timeSeries,matrix-method} % \alias{coerce,timeSeries,ts-method} % \alias{coerce,ts,timeSeries-method} % \alias{colCummaxs,timeSeries-method} % \alias{colCummins,timeSeries-method} % \alias{colCumprods,timeSeries-method} % \alias{colCumreturns,timeSeries-method} % \alias{colCumsums,timeSeries-method} % \alias{colMeans,timeSeries-method} % \alias{colnames,timeSeries-method} % \alias{colnames<-,timeSeries-method} % \alias{colSums,timeSeries-method} % \alias{comment,timeSeries-method} % \alias{comment<-,timeSeries-method} % \alias{coredata,timeSeries-method} % \alias{coredata<-,timeSeries,ANY-method} % \alias{coredata<-,timeSeries,matrix-method} % \alias{cummax,timeSeries-method} % \alias{cummin,timeSeries-method} % \alias{cumprod,timeSeries-method} % \alias{cumsum,timeSeries-method} % \alias{diff,timeSeries-method} % \alias{dim,timeSeries-method} % \alias{dim<-,timeSeries-method} % \alias{dimnames,timeSeries-method} % \alias{dimnames<-,timeSeries,list-method} % \alias{end,timeSeries-method} % \alias{filter,timeSeries-method} % \alias{finCenter,timeSeries-method} % \alias{finCenter<-,timeSeries-method} % \alias{frequency,timeSeries-method} % \alias{getDataPart,timeSeries-method} % \alias{head,timeSeries-method} \alias{initialize,timeSeries-method} % \alias{is.na,timeSeries-method} % \alias{is.unsorted,timeSeries-method} % \alias{isDaily,timeSeries-method} % \alias{isMonthly,timeSeries-method} % \alias{isQuarterly,timeSeries-method} % \alias{isRegular,timeSeries-method} % \alias{lag,timeSeries-method} % \alias{lines,timeSeries-method} % \alias{median,timeSeries-method} % \alias{merge,ANY,timeSeries-method} % \alias{merge,matrix,timeSeries-method} % \alias{merge,numeric,timeSeries-method} % \alias{merge,timeSeries,ANY-method} % \alias{merge,timeSeries,matrix-method} % \alias{merge,timeSeries,missing-method} % \alias{merge,timeSeries,numeric-method} % \alias{merge,timeSeries,timeSeries-method} % \alias{na.contiguous,timeSeries-method} % \alias{na.omit,timeSeries-method} % \alias{names,timeSeries-method} % \alias{names<-,timeSeries-method} % \alias{Ops,array,timeSeries-method} % \alias{Ops,timeSeries,array-method} % \alias{Ops,timeSeries,timeSeries-method} % \alias{Ops,timeSeries,ts-method} % \alias{Ops,timeSeries,vector-method} % \alias{Ops,ts,timeSeries-method} % \alias{Ops,vector,timeSeries-method} % \alias{outlier,timeSeries-method} % \alias{plot,timeSeries-method} % \alias{points,timeSeries-method} % \alias{print,timeSeries-method} % \alias{quantile,timeSeries-method} % \alias{rank,timeSeries-method} % \alias{rbind2,ANY,timeSeries-method} % \alias{rbind2,timeSeries,ANY-method} % \alias{rbind2,timeSeries,missing-method} % \alias{rbind2,timeSeries,timeSeries-method} % \alias{returns,timeSeries-method} % \alias{rev,timeSeries-method} % \alias{rowCumsums,timeSeries-method} % \alias{rownames,timeSeries-method} % \alias{rownames<-,timeSeries,ANY-method} % \alias{rownames<-,timeSeries,timeDate-method} % \alias{sample,timeSeries-method} % \alias{scale,timeSeries-method} % \alias{series,timeSeries-method} % \alias{series<-,timeSeries,ANY-method} % \alias{series<-,timeSeries,matrix-method} % \alias{setDataPart,timeSeries-method} % \alias{show,timeSeries-method} % \alias{sort,timeSeries-method} % \alias{start,timeSeries-method} % \alias{str,timeSeries-method} % \alias{t,timeSeries-method} % \alias{tail,timeSeries-method} % \alias{time,timeSeries-method} % \alias{window,timeSeries-method} \title{Class 'timeSeries' in package timeSeries} \description{ Class \code{"timeSeries"} in package timeSeries. } \section{Objects from the Class}{ The main functions for creating objects from class \code{"timeSeries"} \code{\link{timeSeries}} and \code{\link{as.timeSeries}}. Objects can also be created by calls of the form \code{new("timeSeries", .Data, units, positions, format, FinCenter, recordIDs, title, documentation)} but this is not recommended for routine work. } \section{Slots}{ The structure of the \code{"timeSeries"} objects should, in general, be considered internal. The accessor functions to get and set the components should be used to get and set values of the slots. \describe{ \item{\code{.Data}:}{ Object of class \code{"matrix"} containing the data, one column for each variable. } \item{\code{units}:}{ Object of class \code{"character"}, the unit (or variable, or column) names of the time series object. } \item{\code{positions}:}{ Object of class \code{"numeric"}, the datetime stamps. If the time series doesn't have datetime stamps, then \code{positions} is of length zero. } \item{\code{format}:}{ Object of class \code{"character"}, a datetime format (such as \code{"\%Y-\%m-\%d"}). if there are no time stamps \code{"format"} is equal to \code{"counts"}. } \item{\code{FinCenter}:}{ Object of class \code{"character"}, the financial center. } \item{\code{recordIDs}:}{ Object of class \code{"data.frame"} ~~ } \item{\code{title}:}{ Object of class \code{"character"}, a title for printing. } \item{\code{documentation}:}{ Object of class \code{"character"}, by default it is set to the current date. } } } \section{Extends}{ Class \code{"\linkS4class{structure}"}, from data part. Class \code{"\linkS4class{vector}"}, by class "structure", distance 2, with explicit coerce. } \section{Methods}{ Below is a list of the methods that have \code{"timeSeries"} in their signature. It can be useful for technical purposes, for example in reporting bugs but most methods that need explanation are documented with the corresponding functions and looking at their help pages is recommended. There are short explanations for methods for functions that are not supposed to be called directly. \describe{ \item{[}{\code{signature(x = "timeSeries", i = "ANY", j = "index_timeSeries")}: ... } \item{[}{\code{signature(x = "timeSeries", i = "character", j = "character")}: ... } \item{[}{\code{signature(x = "timeSeries", i = "character", j = "index_timeSeries")}: ... } \item{[}{\code{signature(x = "timeSeries", i = "character", j = "missing")}: ... } \item{[}{\code{signature(x = "timeSeries", i = "index_timeSeries", j = "character")}: ... } \item{[}{\code{signature(x = "timeSeries", i = "index_timeSeries", j = "index_timeSeries")}: ... } \item{[}{\code{signature(x = "timeSeries", i = "index_timeSeries", j = "missing")}: ... } \item{[}{\code{signature(x = "timeSeries", i = "matrix", j = "missing")}: ... } \item{[}{\code{signature(x = "timeSeries", i = "missing", j = "character")}: ... } \item{[}{\code{signature(x = "timeSeries", i = "missing", j = "index_timeSeries")}: ... } \item{[}{\code{signature(x = "timeSeries", i = "missing", j = "missing")}: ... } \item{[}{\code{signature(x = "timeSeries", i = "time_timeSeries", j = "ANY")}: ... } \item{[}{\code{signature(x = "timeSeries", i = "time_timeSeries", j = "character")}: ... } \item{[}{\code{signature(x = "timeSeries", i = "time_timeSeries", j = "index_timeSeries")}: ... } \item{[}{\code{signature(x = "timeSeries", i = "time_timeSeries", j = "missing")}: ... } \item{[}{\code{signature(x = "timeSeries", i = "timeDate", j = "character")}: ... } \item{[}{\code{signature(x = "timeSeries", i = "timeDate", j = "index_timeSeries")}: ... } \item{[}{\code{signature(x = "timeSeries", i = "timeDate", j = "missing")}: ... } \item{[}{\code{signature(x = "timeSeries", i = "timeSeries", j = "index_timeSeries")}: ... } \item{[}{\code{signature(x = "timeSeries", i = "timeSeries", j = "missing")}: ... } \item{[<-}{\code{signature(x = "timeSeries", i = "character", j = "ANY")}: ... } \item{[<-}{\code{signature(x = "timeSeries", i = "character", j = "missing")}: ... } \item{[<-}{\code{signature(x = "timeSeries", i = "timeDate", j = "ANY")}: ... } \item{[<-}{\code{signature(x = "timeSeries", i = "timeDate", j = "missing")}: ... } \item{$}{\code{signature(x = "timeSeries")}: ... } \item{$<-}{\code{signature(x = "timeSeries", value = "ANY")}: ... } \item{$<-}{\code{signature(x = "timeSeries", value = "factor")}: ... } \item{$<-}{\code{signature(x = "timeSeries", value = "numeric")}: ... } \item{aggregate}{\code{signature(x = "timeSeries")}: ... } \item{align}{\code{signature(x = "timeSeries")}: ... } \item{apply}{\code{signature(X = "timeSeries")}: ... } \item{as.data.frame}{\code{signature(x = "timeSeries")}: ... } \item{as.list}{\code{signature(x = "timeSeries")}: ... } \item{as.matrix}{\code{signature(x = "timeSeries")}: ... } \item{as.ts}{\code{signature(x = "timeSeries")}: ... } \item{attach}{\code{signature(what = "timeSeries")}: ... } \item{cbind2}{\code{signature(x = "ANY", y = "timeSeries")}: ... } \item{cbind2}{\code{signature(x = "timeSeries", y = "ANY")}: ... } \item{cbind2}{\code{signature(x = "timeSeries", y = "missing")}: ... } \item{cbind2}{\code{signature(x = "timeSeries", y = "timeSeries")}: ... } \item{coerce}{\code{signature(from = "ANY", to = "timeSeries")} } \item{coerce}{\code{signature(from = "character", to = "timeSeries")} } \item{coerce}{\code{signature(from = "data.frame", to = "timeSeries")} } \item{coerce}{\code{signature(from = "timeSeries", to = "data.frame")} } \item{coerce}{\code{signature(from = "timeSeries", to = "list")}: } \item{coerce}{\code{signature(from = "timeSeries", to = "matrix")} } \item{coerce}{\code{signature(from = "timeSeries", to = "ts")}: } \item{coerce}{\code{signature(from = "ts", to = "timeSeries")}: \code{coerce} should not be called directly. Use \code{as(object, "target_class")} instead. } \item{colCummaxs}{\code{signature(x = "timeSeries")}: ... } \item{colCummins}{\code{signature(x = "timeSeries")}: ... } \item{colCumprods}{\code{signature(x = "timeSeries")}: ... } \item{colCumreturns}{\code{signature(x = "timeSeries")}: ... } \item{colCumsums}{\code{signature(x = "timeSeries")}: ... } \item{colMeans}{\code{signature(x = "timeSeries")}: ... } \item{colnames}{\code{signature(x = "timeSeries")}: ... } \item{colnames<-}{\code{signature(x = "timeSeries")}: ... } \item{colSums}{\code{signature(x = "timeSeries")}: ... } \item{comment}{\code{signature(x = "timeSeries")}: ... } \item{comment<-}{\code{signature(x = "timeSeries")}: ... } \item{coredata}{\code{signature(x = "timeSeries")}: ... } \item{coredata<-}{\code{signature(x = "timeSeries", value = "ANY")}: ... } \item{coredata<-}{\code{signature(x = "timeSeries", value = "matrix")}: ... } \item{cummax}{\code{signature(x = "timeSeries")}: ... } \item{cummin}{\code{signature(x = "timeSeries")}: ... } \item{cumprod}{\code{signature(x = "timeSeries")}: ... } \item{cumsum}{\code{signature(x = "timeSeries")}: ... } \item{diff}{\code{signature(x = "timeSeries")}: ... } \item{dim}{\code{signature(x = "timeSeries")}: ... } \item{dim<-}{\code{signature(x = "timeSeries")}: ... } \item{dimnames}{\code{signature(x = "timeSeries")}: ... } \item{dimnames<-}{\code{signature(x = "timeSeries", value = "list")}: ... } \item{end}{\code{signature(x = "timeSeries")}: ... } \item{filter}{\code{signature(x = "timeSeries")}: ... } \item{finCenter}{\code{signature(x = "timeSeries")}: ... } \item{finCenter<-}{\code{signature(x = "timeSeries")}: ... } \item{frequency}{\code{signature(x = "timeSeries")}: ... } \item{getDataPart}{\code{signature(object = "timeSeries")}: ... } \item{head}{\code{signature(x = "timeSeries")}: ... } \item{initialize}{\code{signature(.Object = "timeSeries")}: don't call \code{"initialize"}, call \code{new("timeSeries", ...)} instead. Even better, call \code{timeSeries}. } \item{is.na}{\code{signature(x = "timeSeries")}: ... } \item{is.unsorted}{\code{signature(x = "timeSeries")}: ... } \item{isDaily}{\code{signature(x = "timeSeries")}: ... } \item{isMonthly}{\code{signature(x = "timeSeries")}: ... } \item{isQuarterly}{\code{signature(x = "timeSeries")}: ... } \item{isRegular}{\code{signature(x = "timeSeries")}: ... } \item{lag}{\code{signature(x = "timeSeries")}: ... } \item{lines}{\code{signature(x = "timeSeries")}: ... } \item{median}{\code{signature(x = "timeSeries")}: ... } \item{merge}{\code{signature(x = "ANY", y = "timeSeries")}: ... } \item{merge}{\code{signature(x = "matrix", y = "timeSeries")}: ... } \item{merge}{\code{signature(x = "numeric", y = "timeSeries")}: ... } \item{merge}{\code{signature(x = "timeSeries", y = "ANY")}: ... } \item{merge}{\code{signature(x = "timeSeries", y = "matrix")}: ... } \item{merge}{\code{signature(x = "timeSeries", y = "missing")}: ... } \item{merge}{\code{signature(x = "timeSeries", y = "numeric")}: ... } \item{merge}{\code{signature(x = "timeSeries", y = "timeSeries")}: ... } \item{na.contiguous}{\code{signature(object = "timeSeries")}: ... } \item{na.omit}{\code{signature(object = "timeSeries")}: ... } \item{names}{\code{signature(x = "timeSeries")}: ... } \item{names<-}{\code{signature(x = "timeSeries")}: ... } \item{Ops}{\code{signature(e1 = "array", e2 = "timeSeries")}: ... } \item{Ops}{\code{signature(e1 = "timeSeries", e2 = "array")}: ... } \item{Ops}{\code{signature(e1 = "timeSeries", e2 = "timeSeries")}: ... } \item{Ops}{\code{signature(e1 = "timeSeries", e2 = "ts")}: ... } \item{Ops}{\code{signature(e1 = "timeSeries", e2 = "vector")}: ... } \item{Ops}{\code{signature(e1 = "ts", e2 = "timeSeries")}: ... } \item{Ops}{\code{signature(e1 = "vector", e2 = "timeSeries")}: ... } \item{outlier}{\code{signature(x = "timeSeries")}: ... } \item{plot}{\code{signature(x = "timeSeries")}: ... } \item{points}{\code{signature(x = "timeSeries")}: ... } \item{print}{\code{signature(x = "timeSeries")}: ... } \item{quantile}{\code{signature(x = "timeSeries")}: ... } \item{rank}{\code{signature(x = "timeSeries")}: ... } \item{rbind2}{\code{signature(x = "ANY", y = "timeSeries")}: ... } \item{rbind2}{\code{signature(x = "timeSeries", y = "ANY")}: ... } \item{rbind2}{\code{signature(x = "timeSeries", y = "missing")}: ... } \item{rbind2}{\code{signature(x = "timeSeries", y = "timeSeries")}: ... } \item{returns}{\code{signature(x = "timeSeries")}: ... } \item{rev}{\code{signature(x = "timeSeries")}: ... } \item{rowCumsums}{\code{signature(x = "timeSeries")}: ... } \item{rownames}{\code{signature(x = "timeSeries")}: ... } \item{rownames<-}{\code{signature(x = "timeSeries", value = "ANY")}: ... } \item{rownames<-}{\code{signature(x = "timeSeries", value = "timeDate")}: ... } \item{sample}{\code{signature(x = "timeSeries")}: ... } \item{scale}{\code{signature(x = "timeSeries")}: ... } \item{series}{\code{signature(x = "timeSeries")}: ... } \item{series<-}{\code{signature(x = "timeSeries", value = "ANY")}: ... } \item{series<-}{\code{signature(x = "timeSeries", value = "matrix")}: ... } \item{setDataPart}{\code{signature(object = "timeSeries")}: ... } \item{show}{\code{signature(object = "timeSeries")}: ... } \item{sort}{\code{signature(x = "timeSeries")}: ... } \item{start}{\code{signature(x = "timeSeries")}: ... } \item{str}{\code{signature(object = "timeSeries")}: ... } \item{t}{\code{signature(x = "timeSeries")}: ... } \item{tail}{\code{signature(x = "timeSeries")}: ... } \item{time}{\code{signature(x = "timeSeries")}: ... } \item{window}{\code{signature(x = "timeSeries")}: ... } } } \seealso{ \code{\link{timeSeries}} and \code{\link{as.timeSeries}} for creating and converting to \code{"timeSeries"}, \code{\link{readSeries}} for importing from a text file, \code{\link{dummyDailySeries}} for creation of dummy daily and monthly time series, \code{\link{as.matrix}}, \code{\link{time}}, \code{\link{finCenter}}, \code{\link{getUnits}}, \code{\link{dim}}, \code{\link{start}}, etc., for accessing properties of the time series. } \examples{ ## see the help page for timeSeries() showClass("timeSeries") } \keyword{classes} timeSeries/man/data-examples.Rd0000644000176200001440000000240215104730074016151 0ustar liggesusers\name{TimeSeriesData} \alias{TimeSeriesData} \alias{LPP2005REC} \alias{MSFT} \alias{USDCHF} \title{Time series data sets} \description{ Three data sets used in example files. } \details{ The following datasets are available: \describe{ \item{MSFT}{ Daily Microsoft OHLC (Open-high-low-close) prices and volume from 2000-09-27 to 2001-09-27. } \item{USDCHF}{ USD/CHF intraday foreign exchange rates. } \item{LPP2005REC}{ Swiss pension fund assets returns benchmark from 2005-11-01 to 2007-04-11. } } The datasets are objects from class \code{"timeSeries"}. } \note{ No further information about the \code{LPP2005REC} is available. The meaning of the columns? } \seealso{ \code{\link{readSeries}}, \code{\link{timeSeries}} } \examples{ ## LPP2005 example data set data(LPP2005REC) plot(LPP2005REC, type = "l") class(LPP2005REC) dim(LPP2005REC) head(LPP2005REC) LPP2005REC[1:5, 2:4] range(time(LPP2005REC)) summary(LPP2005REC) ## MSFT example data set data(MSFT) plot(MSFT[, 1:4], type = "l") plot(MSFT[, 5], type = "h") class(MSFT) range(time(MSFT)) head(MSFT) ## Plot USDCHF example data set data(USDCHF) plot(USDCHF) range(time(USDCHF)) head(USDCHF) } \keyword{datasets} timeSeries/man/methods-mathOps.Rd0000644000176200001440000000632515104730074016510 0ustar liggesusers\name{math} \alias{math} \alias{Math,timeSeries-method} \alias{Math2,timeSeries-method} \alias{Summary,timeSeries-method} \alias{Ops,vector,timeSeries-method} \alias{Ops,array,timeSeries-method} \alias{Ops,ts,timeSeries-method} \alias{Ops,timeSeries,vector-method} \alias{Ops,timeSeries,array-method} \alias{Ops,timeSeries,ts-method} \alias{Ops,timeSeries,timeSeries-method} %\alias{-,timeSeries,missing-method} %\alias{+,timeSeries,missing-method} \alias{cummax,timeSeries-method} \alias{cummin,timeSeries-method} \alias{cumprod,timeSeries-method} \alias{cumsum,timeSeries-method} \alias{trunc,timeSeries-method} \alias{log,timeSeries-method} %\alias{\%*\%,timeSeries,vector-method} %\alias{\%*\%,timeSeries,ANY-method} %\alias{\%*\%,ANY,timeSeries-method} \alias{quantile} \alias{quantile.timeSeries} \alias{median} \alias{median.timeSeries} \title{Mathematical operations on 'timeSeries'} \description{ Functions and methods for mathematical operations on \code{"timeSeries"}. } \usage{ \S4method{Ops}{timeSeries,timeSeries}(e1, e2) \S4method{Math}{timeSeries}(x) \S4method{Math2}{timeSeries}(x, digits) \method{quantile}{timeSeries}(x, \dots) \method{median}{timeSeries}(x, na.rm = FALSE, \dots) } \arguments{ \item{x}{ an object of class \code{timeSeries}. } \item{digits}{ number of digits to be used in 'round' or 'signif'. } \item{e1, e2}{ at least one of the two objects is from class \code{"timeSeries"} (for the methods described on this page). } \item{na.rm}{ a logical value: should missing values be removed? } \item{\dots}{ arguments to be passed. } } \details{ The methods for the \code{Math} and \code{Math2} groups of mathematical functions return 'timeSeries' objects. Most of them work element-wise on the data part of the time series with the exception of \code{cummin}, \code{cummax}, \code{cumsum}, and \code{cumprod} which work columnwise. The \code{Ops} group includes mathematical operators. For the binary operators methods are defined for pairs of at least one 'timeSeries' object. These work as expected on the data parts of the arguments. If the operation gives a value of the same dimension as the data part of the 'timeSeries' object, it replaces the original data in the object. There are also methods for \code{quantile} and \code{median}. } \value{ the value from a mathematical or logical operation operating on objects of class \code{"timeSeries"} or the value computed by a mathematical function. } \seealso{ \code{\link[=colCum]{colCumXXX}} } \examples{ ## create an artificial 'timeSeries' object setRmetricsOptions(myFinCenter = "GMT") charvec = timeCalendar() set.seed(4711) data = matrix(exp(cumsum(rnorm(12, sd = 0.1)))) TS = timeSeries(data, charvec, units = "TS") TS ## mathematical operations: | +/- * ^ ... TS^2 TS[2:4] OR = returns(TS) OR OR > 0 ## median, quantile median(TS) quantile(TS) TS[3] <- NA # to demonstrate 'na.rm' median(TS) # NA #quantile(TS) # error median(TS, na.rm = TRUE) quantile(TS, na.rm = TRUE) } \keyword{chron} \keyword{methods} timeSeries/man/fin-drawdowns.Rd0000644000176200001440000000421115104730074016206 0ustar liggesusers\name{drawdowns} \alias{drawdowns} \alias{drawdownsStats} \title{Calculations of drawdowns} \description{ Compute series of drawdowns from financial returns and calculate drawdown statisitcs. } \usage{ drawdowns(x, \dots) drawdownsStats(x, \dots) } \arguments{ \item{x}{ a \code{"timeSeries"} object of financial returns. Note, drawdowns can be calculated from an uni- or multivariate time deries object, statistics can only be computed from an univariate time series object. } \item{\dots}{ optional arguments passed to \code{na.omit}. } } \value{ for \code{drawdowns}, an object of class \code{timeSeries}. for \code{drawdownsStats} an object of class \code{"data.frame"} with the following components: \item{drawdown}{the depth of the drawdown, } \item{from}{the start date, } \item{trough}{the trough period, } \item{to}{the end date, } \item{length}{the length in number of records, } \item{peaktrough}{the peak trough, and } \item{recovery}{the recovery length in number of records.} } \details{ The code in the core of the function \code{drawdownsStats} was borrowed from the package \code{PerformanceAnalytics} authored by Peter Carl and Sankalp Upadhyay. } \author{ Peter Carl and Sankalp Upadhyay for code from the contributed R package \code{PerformanceAnalytics} used in the function \code{drawdownsStats}. } \seealso{ \code{\link{returns}}, \code{\link{cumulated}}, %\code{\link{drawdowns}}, \code{\link{splits}}, %\code{\link{spreads}}, \code{\link{midquotes}}, \code{\link{index2wealth}} } \examples{ ## Use Swiss Pension Fund Data Set of Returns - head(LPP2005REC) SPI <- LPP2005REC[, "SPI"] head(SPI) ## Plot Drawdowns - dd = drawdowns(LPP2005REC[, "SPI"], main = "Drawdowns") plot(dd) dd = drawdowns(LPP2005REC[, 1:6], main = "Drawdowns") plot(dd) ## Compute Drawdowns Statistics - ddStats <- drawdownsStats(SPI) class(ddStats) ddStats ## Note, Only Univariate Series are allowd - ddStats <- try(drawdownsStats(LPP2005REC)) class(ddStats) } \keyword{chron} timeSeries/man/timeSeries-slotFinCenter.Rd0000644000176200001440000000205315104730074020314 0ustar liggesusers\name{finCenter} \alias{finCenter} \alias{finCenter<-} \alias{finCenter,timeSeries-method} \alias{finCenter<-,timeSeries-method} \alias{getFinCenter} \alias{setFinCenter<-} \title{Get and set Financial center of a 'timeSeries'} \description{ Get or assign a financial center to a \code{"timeSeries"} object. } \usage{ \S4method{finCenter}{timeSeries}(x) \S4method{finCenter}{timeSeries}(x) <- value getFinCenter(x) setFinCenter(x) <- value } \arguments{ \item{x}{ a \code{"timeSeries"} object. } \item{value}{ a character with the the location of the financial center named as \code{"continent/city"}. } } \seealso{ \code{\link[timeDate]{listFinCenter}} and \code{\link[timeDate]{finCenter}} in package \code{"timeDate"} } \examples{ ## An artificial 'timeSeries' Object - tS <- dummyMonthlySeries() tS ## Print Financial Center - finCenter(tS) getFinCenter(tS) ## Assign New Financial Center - finCenter(tS) <- "Zurich" tS setFinCenter(tS) <- "New_York" tS } \keyword{programming} timeSeries/man/base-start.Rd0000644000176200001440000000150315104730074015472 0ustar liggesusers\name{start} \alias{start} \alias{start.timeSeries} \alias{end} \alias{end.timeSeries} %\alias{start,timeSeries-method} %\alias{end,timeSeries-method} \title{Start and end of a 'timeSeries'} \description{ Returns start or end time stamp of a \code{"timeSeries"} object. } \usage{ \method{start}{timeSeries}(x, \dots) \method{end}{timeSeries}(x, \dots) } \arguments{ \item{x}{ an uni- or multivariate \code{"timeSeries"} object. } \item{\dots}{ optional arguments passed to other methods. } } \value{ a \code{"timeSeries"} object } \examples{ \dontshow{set.seed(1234)} ## Create a dummy \code{"timeSeries"} tS <- dummyMonthlySeries()[, 1] tS ## Return start and end time stamp c(start(tS), end(tS)) range(time(tS)) } \keyword{chron} timeSeries/man/stats-lag.Rd0000644000176200001440000000246215104730074015331 0ustar liggesusers\name{lag} \alias{lag} \alias{lag.timeSeries} \title{Lag a 'timeSeries' object} \description{ Compute a lagged version of a \code{"timeSeries"} object. } \usage{ \method{lag}{timeSeries}(x, k = 1, trim = FALSE, units = NULL, \dots) } \arguments{ \item{x}{ an object of class \code{timeSeries}. } \item{k}{ an integer number, the number of lags (in units of observations). By default 1. Can also be a vector, in which case the result is a multivariate \code{"timeSeries"} in which column \code{i} contains the series lagged by \code{k[i]}, see the examples. } \item{trim}{ a logical value. By default \code{TRUE}, the first missing observation in the return series will be removed. } \item{units}{ an optional character string, which allows to overwrite the current column names of a \code{"timeSeries"} object. By default \code{NULL} which means that the column names are selected automatically. } \item{\dots}{ arguments passed to other methods. } } \value{ an object of class \code{"timeSeries"} } \seealso{ \code{\link[stats]{lag}} for \verb{stats::lag}, \code{\link{diff}} } \examples{ ## Load Micsrosoft Data Set x <- MSFT[1:20, "Open"] ## Lag the 'timeSeries' Object lag(x, k = -1:1) } \keyword{chron} timeSeries/man/base-subsetting.Rd0000644000176200001440000001060415104730074016526 0ustar liggesusers\name{TimeSeriesSubsettings} \alias{TimeSeriesSubsettings} \alias{$,timeSeries-method} \alias{[,timeSeries,ANY,index_timeSeries-method} \alias{[,timeSeries,character,character-method} \alias{[,timeSeries,character,index_timeSeries-method} \alias{[,timeSeries,character,missing-method} \alias{[,timeSeries,index_timeSeries,character-method} \alias{[,timeSeries,index_timeSeries,index_timeSeries-method} \alias{[,timeSeries,index_timeSeries,missing-method} \alias{[,timeSeries,matrix,missing-method} \alias{[,timeSeries,missing,character-method} \alias{[,timeSeries,missing,index_timeSeries-method} \alias{[,timeSeries,missing,missing-method} \alias{[,timeSeries,time_timeSeries,ANY-method} \alias{[,timeSeries,time_timeSeries,character-method} \alias{[,timeSeries,time_timeSeries,index_timeSeries-method} \alias{[,timeSeries,time_timeSeries,missing-method} \alias{[,timeSeries,timeDate,character-method} \alias{[,timeSeries,timeDate,index_timeSeries-method} \alias{[,timeSeries,timeDate,missing-method} \alias{[,timeSeries,timeSeries,index_timeSeries-method} \alias{[,timeSeries,timeSeries,missing-method} \alias{$<-,timeSeries,ANY-method} \alias{$<-,timeSeries,factor-method} \alias{$<-,timeSeries,numeric-method} \alias{[<-,timeSeries,character,ANY-method} \alias{[<-,timeSeries,character,missing-method} \alias{[<-,timeSeries,timeDate,ANY-method} \alias{[<-,timeSeries,timeDate,missing-method} \alias{head} %\alias{head,timeSeries-method} \alias{head.timeSeries} \alias{tail} %\alias{tail,timeSeries-method} \alias{tail.timeSeries} \alias{outlier} \alias{outlier,timeSeries-method} \alias{outlier,ANY-method} \title{Subsetting time series} \description{ Objects from class \code{"timeSeries"} can be subsetted in different ways. Methods are defined for the subsetting operators \code{"$"}, \code{"["} and their assignment versions, as well as for some related functions from base \R. A function to drop or extract outliers is also described here. } % \method{[}{timeSeries}(x, i, j, drop) % \method{[}{timeSeries}(x, i, j) <- value \usage{ \method{head}{timeSeries}(x, n = 6, recordIDs = FALSE, \dots) \method{tail}{timeSeries}(x, n = 6, recordIDs = FALSE, \dots) outlier(x, sd = 5, complement = TRUE, ...) } \arguments{ \item{x}{ an object of class \code{timeSeries}. } % \item{from, to}{ % starting date and end date, \code{to} must be after \code{from}. % } % \item{start, end}{ % starting date and end date, \code{end} must be after \code{start}. % } % \item{i, j}{ % ["["] - \cr % index arguments used for subsettings. % } \item{n}{ an integer specifying the number of lines to be returned. By default \code{n=6}. } \item{recordIDs}{ a logical value. Should the \code{recordIDs} be returned together with the data matrix and time series positions? } \item{sd}{ a numeric value of standard deviations, e.g. 10 means that values larger or smaller than ten times the standard deviation will be removed from the series. } \item{complement}{ a logical flag. If \code{TRUE}, the default, return the series free of outliers. If \code{FALSE}, return the outliers series. } % \item{value}{ % a numeric value to use as a replacement. It will be repeated a % whole number of times if necessary. % } \item{\dots}{ arguments passed to other methods. } } \details{ The \code{"timeSeries"} methods for the subsetting operators \code{"$"}, \code{"["} and their assignment versions, as well as for the functions \code{head} and \code{tail} are meant to do what the user expects. \strong{TODO:} Further details are needed here, despite the above paragraph. \code{outlier} drops the outliers if \code{complement = TRUE} and returns only them if \code{complement = FALSE}. All functions described here return \code{"timeSeries"} objects. See also \code{\link{window}} which extracts the sub-series between two datetimes. } \value{ All functions return an object of class \code{"timeSeries"}. } \seealso{ \code{\link{window}} } \examples{ ## Create an Artificial 'timeSeries' Object setRmetricsOptions(myFinCenter = "GMT") charvec <- timeCalendar() set.seed(4711) data <- matrix(exp(cumsum(rnorm(12, sd = 0.1)))) tS <- timeSeries(data, charvec, units = "tS") tS ## Subset Series by Counts "[" tS[1:3, ] ## Subset the Head of the Series head(tS, 6) } \keyword{chron} timeSeries/man/base-sort.Rd0000644000176200001440000000641115104730074015327 0ustar liggesusers\name{sort} \alias{sort} \alias{sort.timeSeries} \alias{is.unsorted} \alias{is.unsorted.timeSeries} \alias{is.unsorted,timeSeries-method} \title{Sort a 'timeSeries' by time stamps} \description{ Sort a \code{"timeSeries"} object with respect to its time stamps. } \usage{ \method{sort}{timeSeries}(x, decreasing = FALSE, \dots) \S4method{is.unsorted}{timeSeries}(x, na.rm = FALSE, strictly = FALSE) } \arguments{ \item{x}{ a \code{"timeSeries"} object. } \item{decreasing}{ a logical flag. Should we sort in increasing or decreasing order? By default \code{FALSE}. } \item{na.rm}{ a logical value, should missing values be removed? } \item{strictly}{ logical indicating if the check should be for strictly increasing values. } \item{\dots}{ optional arguments passed to other methods. } } \details{ The method for \code{sort} sorts \code{x} either in increasing or decreasing time stamp order. The method for \code{is.unsorted} returns \code{TRUE} if the time stamps of \code{x} are not sorted in increasing order (including the case when they are sorted in decreasing order) and \code{FALSE} otherwise. \code{is.unsorted} may also return \code{NA} when there are \code{NA}s among the time stamps of \code{x}. All this is in line with the documented functionality of \code{base::is.unsorted}. } \value{ for \code{sort}, a \code{"timeSeries"} object, for the \code{is.unsorted} method, \code{TRUE}, \code{FALSE}, or \code{NA}, as described in section \sQuote{Details}. } \note{ If \code{is.unsorted} returns \code{NA} when there are \code{NA}s in the data but not in the time stamps use \code{library{timeSeries}} or call the function as \code{timeSeries::is.unsorted}. If you need more details, read the rest of this note. \code{base::is.unsorted} 'sees' the method for \code{"timeSeries"} objects when package timeSeries is loaded (whether or not it is attached). However, due to the way \code{base::is.unsorted} is implemented, it may give wrong answers when there are \code{NA}'s among the values of the time series. Developers of packages applying \code{is.unsorted} on timeSeries objects should import if from package timeSeries. The above feature is not a shortcoming of \code{base::is.unsorted} but a consequence of the fact that the timeSeries method is not consistent with its semantics. For example, it works on the time stamps, while \code{is.na} works on the data values. } \seealso{ \code{\link[base]{is.unsorted}} for further details on the \code{NA} case } \examples{ ## a monthly calendar series x <- daily2monthly(LPP2005REC[, 1:2])[3:14, ] \dontshow{set.seed(1234)} ## resample the series with respect to the time stamps, resampled <- sample(x) ## the time stamps are unordered resampled is.unsorted(resampled) # TRUE (i.e., not sorted) ## Now sort the series in decreasing time order backward_in_time <- sort(resampled, , decreasing = TRUE) ## time stamps ordered in decreasing order ## but is.unordered requires increasing order: backward_in_time is.unsorted(backward_in_time) # still TRUE ## Is the reverted series ordered? forward_in_time <- rev(backward_in_time) forward_in_time is.unsorted(forward_in_time) # FALSE (i.e., sorted) } \keyword{chron} timeSeries/man/stats-aggregate.Rd0000644000176200001440000000337015104730074016513 0ustar liggesusers\name{aggregate-methods} \alias{aggregate-methods} \docType{methods} \alias{aggregate} \alias{aggregate.timeSeries} \alias{aggregate,timeSeries-method} \title{Aggregate time series} \description{ Aggregate a \code{"timeSeries"} object over general periods. } \usage{ \S4method{aggregate}{timeSeries}(x, by, FUN, \dots) } \arguments{ \item{x}{ an object of class \code{"timeSeries"}. } \item{by}{ a sequence of \code{"timeDate"} objects denoting the aggregation periods, see section \sQuote{Details}. } \item{FUN}{ the function to be applied. } \item{\dots}{ arguments passed to other methods. } } \details{ \code{aggregate} aggregates \code{x} by applying \code{FUN} on the values of the time series in each of the aggregation periods, specified by argument \code{by}. Argument \code{by} should be of the same class as \code{time(x)}. \code{by} is sorted and duplicated values are removed from it. Each pair of consecutive values in \code{by} then determines a period over which to apply the aggregation function \code{FUN}, see \code{\link[base]{findInterval}}. } \value{ an object of class \code{"timeSeries"} } \seealso{ \code{\link{apply}}, \code{\link{align}} } \examples{ ## Load Microsoft Data Set - x <- MSFT ## Aggregate by Weeks - by <- timeSequence(from = start(x), to = end(x), by = "week") aggregate(x, by, mean) ## Aggregate to Last Friday of Month - by <- unique(timeLastNdayInMonth(time(x), 5)) X <- aggregate(x, by, mean) X dayOfWeek(time(X)) isMonthly(X) ## Aggregate to Last Day of Quarter - by <- unique(timeLastDayInQuarter(time(x))) X <- aggregate(x, by, mean) X isQuarterly(X) } \keyword{methods} \keyword{chron} timeSeries/man/stats-na.omit.Rd0000644000176200001440000001535315117006731016136 0ustar liggesusers\name{na} \alias{na} \alias{na.omit} \alias{na.omit.timeSeries} \title{Handle missing values in 'timeSeries' objects} \description{ Functions for handling missing values in \code{"timeSeries"} objects. } \usage{ \method{na.omit}{timeSeries}(object, method = c("r", "s", "z", "ir", "iz", "ie"), interp = c("before", "linear", "after"), FUN, \dots) } \arguments{ \item{object}{ an object of class \code{"timeSeries"}. } \item{method}{ the method of handling NAs, see section \sQuote{Details}. } \item{interp}{ Three alternative methods are provided to remove NAs from the data: \code{type="zeros"} replaces the missing values with zeros, \code{type="mean"} replaces the missing values with the column mean, \code{type="median"} replaces the missing values with the column median. } \item{FUN}{ a function or a name of a function, such as \code{"mean"} or \code{median}. \code{FUN} is applied to the non-NA values in each column to determine the replacement value. The call looks like \code{FUN(coli, na.rm = TRUE)}, so \code{FUN} should have argument \code{na.rm}. All arguments except \code{object} are ignored if \code{FUN} is specified. } \item{\dots}{ arguments to be passed to the function \code{as.matrix}. } } \details{ Functions for handling missing values in \code{"timeSeries"} objects and in objects which can be transformed into a vector or a two dimensional matrix. For \code{na.omit} argument \code{method} specifies how to handle \code{NA}s. Can be one of the following strings: \describe{ \item{method = "s"}{\code{na.rm = FALSE}, skip, i.e. do nothing, } \item{method = "r"}{remove NAs,} \item{method = "z"}{substitute NAs by zeros,} \item{method = "ir"}{interpolate NAs and remove NAs at the beginning and end of the series,} \item{method = "iz"}{interpolate NAs and substitute NAs at the beginning and end of the series,} \item{method = "ie"}{interpolate NAs and extrapolate NAs at the beginning and end of theseries.} } % For \code{interpNA} argument \code{method} specifies how to % interpolate the matrix column by column. One of the following % character strings: \code{"linear"}, \code{"before"}, \code{"after"}. % For interpolation the function \code{approx} is used. % % The functions are listed by topic. \cr % % \tabular{ll}{ % \code{removeNA} \tab removes NAs from a matrix object, \cr % \code{substituteNA} \tab substitute NAs by zero, the column mean or median, \cr % \code{interpNA} \tab interpolates NAs using R's "approx" function. } % % % \bold{Missing Values in Price and Index Series:} % % Applied to \code{timeSeries} objects the function \code{removeNA} % just removes rows with NAs from the series. For an interpolation % of time series points one can use the function \code{interpNA}. % Three different methods of interpolation are offered: \code{"linear"} % does a linear interpolation, \code{"before"} uses the previous value, % and \code{"after"} uses the following value. Note, that the % interpolation is done on the index scale and not on the time scale. % % \bold{Missing Values in Return Series:} % % For return series the function \code{substituteNA} may be useful. The % function allows to fill missing values either by \code{method="zeros"}, % the \code{method="mean"} or the \code{method="median"} value of the % appropriate columns. } \note{ When dealing with daily data sets, there exists another function \code{alignDailySeries} which can handle missing data in un-aligned calendrical \code{"timeSeries"} objects. % The functions \code{removeNA}, \code{substituteNA} and \code{interpNA} % are older implementations. Please use in all cases if possible the % new function \code{na.omit}. \strong{Additional remarks by GNB:} \code{removeNA(x)} is equivalent to \code{na.omit(x)} or \code{na.omit(x), methods = "r"}. \code{interpNA} can be replaced by a call to \code{na.omit} with argument method equal to \code{ir}, \code{iz}, or \code{ie}, and argument \code{"interp"} equal to the \code{"method"} argument for \code{interpNA} (note that the defaults are not the same). \code{substituteNA(x, type = "zeros")} is equivalent to \code{na.omit(x, method = "z")}. For other values of \code{type} one can use argument \code{FUN}, as in \code{na.omit(x, FUN = "mean")}. A final remark: the three deprecated functions are non-generic. \code{removeNA(x)} is completely redundant as it simply calls \code{na.omit}. The other two however may be useful for matrix-like objects. Please inform the maintainer of the package if you use them on objects other than from class \code{"timeSeries"} and wish them kept in the future. } \references{ Troyanskaya O., Cantor M., Sherlock G., Brown P., Hastie T., Tibshirani R., Botstein D., Altman R.B., (2001); \emph{Missing Value Estimation Methods for DNA microarrays} Bioinformatics 17, 520--525. } \seealso{ \code{\link{alignDailySeries}} } \examples{ X <- matrix(rnorm(100), ncol = 5) # Create a Matrix X X[3, 5] <- NA # Replace a Single NA Inside X[17, 2:4] <- c(NA, NA, NA) # Replace Three in a Row Inside X[13:15, 4] <- c(NA, NA, NA) # Replace Three in a Column Inside X[11:12, 5] <- c(NA, NA) # Replace Two at the Right Border X[20, 1] <- NA # Replace One in the Lower Left Corner X Xts <- timeSeries(X) # convert X to timeSeries Xts ## remove rows with NAs na.omit(Xts) ## Subsitute NA's with zeros or column means (formerly substituteNA()) na.omit(Xts, method = "z") na.omit(Xts, FUN = "mean") na.omit(Xts, FUN = "median") ## Subsitute NA's with a trimmed mean na.omit(Xts, FUN = function(x, na.rm) mean(x, trim = 0.10, na.rm = na.rm)) ## interpolate NA's linearily (formerly interpNA()) na.omit(X, method = "ir", interp = "linear") na.omit(X, method = "iz", interp = "linear") na.omit(X, method = "ie", interp = "linear") ## take previous values in a column na.omit(X, method = "ir", interp = "before") na.omit(X, method = "iz", interp = "before") na.omit(X, method = "ie", interp = "before") ## examples with X (which is a matrix, not "timeSeries") ## (these examples are not run automatically as these functions are ## deprecated.) if(FALSE){ ## Remove Rows with NAs removeNA(X) ## ## subsitute NA's by zeros or column means ## substituteNA(X, type = "zeros") ## substituteNA(X, type = "mean") ## ## ## interpolate NA's linearily ## interpNA(X, method = "linear") ## # Note the corner missing value cannot be interpolated! ## take previous values in a column interpNA(X, method = "before") # Also here, the corner value is excluded } } \keyword{math} timeSeries/man/base-scale.Rd0000644000176200001440000000353115104730074015427 0ustar liggesusers\name{scale} \alias{scale} \alias{scale,timeSeries-method} \title{Center and scale 'timeSeries' objects} \description{ Center and scale a \code{"timeSeries"} object. } \usage{ \S4method{scale}{timeSeries}(x, center = TRUE, scale = TRUE) } \arguments{ \item{x}{ an object from class \code{"timeSeries"}. } \item{center, scale}{ a numeric vector or a logical value, see \sQuote{Details}. } } \details{ \code{scale} centers and/or scales the columns of a \code{"timeSeries"} object. The value of \code{center} determines how column centering is performed. If \code{center} is a numeric vector with length equal to the number of columns of \code{x}, then each column of \code{x} has the corresponding value from \code{center} subtracted from it. If \code{center} is TRUE then centering is done by subtracting the column means (omitting NAs) of \code{x} from their corresponding columns, and if \code{center} is FALSE, no centering is done. The value of \code{scale} determines how column scaling is performed (after centering). If \code{scale} is a numeric vector with length equal to the number of columns of \code{x}, then each column of \code{x} is divided by the corresponding value from \code{scale}. If \code{scale} is TRUE then scaling is done by dividing the (centered) columns of \code{x} by their standard deviations if \code{center} is TRUE, and the root mean square otherwise. If \code{scale} is FALSE, no scaling is done. } \value{ a centered and/or scaled \code{"timeSeries"} object } \examples{ ## Load Series: x <- 100* LPP2005REC[, c("SBI", "SPI")] ## Scale and Center - X <- scale(x) hist(X[, 1], prob=TRUE) s <- seq(-3, 3, length=201) lines(s, dnorm(s), col="red") } \keyword{chron} \keyword{ts} timeSeries/man/fin-periodical.Rd0000644000176200001440000000422115104730074016312 0ustar liggesusers\name{periodical} \alias{endOfPeriod} \alias{endOfPeriodSeries} \alias{endOfPeriodStats} \alias{endOfPeriodBenchmarks} \title{End-of-Period series, stats, and benchmarks} \description{ Computes periodical statistics back to a given period. } \usage{ endOfPeriodSeries(x, nYearsBack = c("1y", "2y", "3y", "5y", "10y", "YTD")) endOfPeriodStats(x, nYearsBack = c("1y", "2y", "3y", "5y", "10y", "YTD")) endOfPeriodBenchmarks(x, benchmark = ncol(x), nYearsBack = c("1y", "2y", "3y", "5y", "10y", "YTD")) } \arguments{ \item{x}{ an end-of-month recorded multivariate \code{"timeSeries"} object. One of the columns holds the benchmark series specified by argument \code{benchmark}, } \item{nYearsBack}{ a period string. How long back should the series be treated? Options include values from 1 year to 10 years, and year-to-date: "1y", "2y", "3y", "5y", "10y", "YTD". } \item{benchmark}{ an integer giving the position of the benchmark series in \code{x}. By default this is the last column of \code{x}. } } \details{ \code{endOfPeriodSeries} extract the data for the last few years, as specified by argument \code{nYearsBack}. \code{endOfPeriodStats} computes basic exploratory statistics for the last few years in the data. \code{endOfPeriodBenchmarks} returns benchmarks back to a given period. \code{x} must be end of month data. Such series can be created using functions like \code{align}, \code{alignDailySeries}, \code{daily2monthly}. } \value{ for \code{endOfPeriodSeries}, a \code{"timeSeries"}, for \code{endOfPeriodStats}, a data frame, for \code{endOfPeriodBenchmarks} - currently \code{NULL} (invisibly), the function is unfinished. } \examples{ ## load series: column 1:3 Swiss market, column 8 (4) benchmark x <- 100 * LPP2005REC[, c(1:3, 8)] colnames(x) x <- daily2monthly(x) x ## Get the Monthly Series - endOfPeriodSeries(x, nYearsBack="1y") ## Compute the Monthly Statistics endOfPeriodStats(x, nYearsBack="1y") ## Compute the Benchmark endOfPeriodBenchmarks(x, benchmark=4) } \keyword{chron} timeSeries/man/fin-durations.Rd0000644000176200001440000000251415104730074016212 0ustar liggesusers\name{durations} \alias{durations} \alias{durationSeries} % removed \title{Durations from a 'timeSeries'} \description{ Computes durations from an object of class \code{"timeSeries"}. } \usage{ durations(x, trim = FALSE, units = c("secs", "mins", "hours", "days")) } \arguments{ \item{x}{ an object of class \code{"timeSeries"}. } \item{trim}{ a logical value. By default \code{TRUE}, the first missing observation in the return series will be removed. } \item{units}{ a character value or vector which allows to set the units in which the durations are measured. By default durations are measured in seconds. } } \details{ Durations measure how long it takes until we get the next record in a \code{timesSeries} object. We return a time series in which for each time stamp we get the length of the period from when we got the last record. This period is measured in length specified by the argument \code{units}, for daily data use \code{units="days"}. } \value{ an object of class \code{"timeSeries"} } \examples{ ## Compute Durations in days for the MSFT Sereries - head(durations(MSFT, units = "days")) head(durations(MSFT, trim = TRUE, units = "days")) ## The same in hours - head(durations(MSFT, trim = TRUE, units = "hours")) } \keyword{chron} timeSeries/man/timeSeries-readSeries.Rd0000644000176200001440000000540615104730074017630 0ustar liggesusers\name{readSeries} \alias{readSeries} \title{Read a 'timeSeries' from a text file} \description{ Reads a file in table format and creates a \code{"timeSeries"} object from it. The first column of the table must hold the timestamps. } \usage{ readSeries(file, header = TRUE, sep = ";", zone = "", FinCenter = "", format, \dots) } \arguments{ \item{file}{ the filename of a spreadsheet dataset from which to import the data records. } \item{header}{ a logical value indicating whether the file contains the names of the variables as its first line. If missing, the value is determined from the file format: 'header' is set to 'TRUE' if and only if the first row contains one fewer fields than the number of columns. } \item{sep}{ the field seperator used in the spreadsheet file to separate columns, by default \code{";"}. If \code{sep = ";"} and reading the series fails, then the reading is automatically repeated with \code{sep=","}. } \item{zone}{ the time zone or financial center where the data were recorded. By default \code{zone = ""} which is short for GMT. } \item{FinCenter}{ a character with the the location of the financial center named as "continent/city". } \item{format}{ a character string with the format in POSIX notation specifying the timestamp format. The format has not to be specified if the first column in the file has the timestamp format specifier, e.g. "\%Y-\%m-\%d" for the short ISO 8601 format. } \item{\dots}{ Additional arguments passed to \code{read.table()} which is used to read the file. } } \details{ The file is imported with \code{\link{read.table}}. Note the different default for argument \code{"sep"}. The first column of the table must hold the timestamps. Format of the timestamps can be either specified in the header of the first column or by the \code{format} argument. } \value{ an object of class \code{"timeSeries"} } \seealso{ \code{\link{as.timeSeries}}, \code{\link{timeSeries}}, \code{\link{dummyMonthlySeries}}, \code{\link{dummyDailySeries}} } \examples{ ## full path to an example file fn <- system.file("extdata/msft.csv", package = "timeSeries") ## first few lines of the file readLines(fn, n = 5) ## import the file msft <- readSeries(fn) head(msft) ## is msft the same as the data object MSFT? all.equal(msft, MSFT) ## ... almost, except for slot 'documentation' c(msft@documentation, MSFT@documentation) ## actually, all.equal() says 'attribute', not slot. this is ok too: c(attr(MSFT, "documentation"), attr(msft, "documentation")) ## make 'documentation' equal, here "", and compare again: msft@documentation <- "" all.equal(msft, MSFT) # TRUE } timeSeries/man/base-rev.Rd0000644000176200001440000000105515104730074015133 0ustar liggesusers\name{rev} \alias{rev.timeSeries} \title{Reverse a 'timeSeries'} \description{ Reverses an uni- or multivariate \code{"timeSeries"} object. } \usage{ \method{rev}{timeSeries}(x) } \arguments{ \item{x}{ an uni- or multivariate \code{"timeSeries"} object. } } \value{ a \code{"timeSeries"} object } \examples{ \dontshow{set.seed(1234)} ## Create Dummy "timeSeries" tS <- dummyMonthlySeries() ## reverse series rev(tS) } \keyword{chron} timeSeries/man/methods-as.Rd0000644000176200001440000000677215104730074015506 0ustar liggesusers\name{as} \alias{as} \alias{as.timeSeries} \alias{as.timeSeries.default} \alias{as.timeSeries.data.frame} \alias{as.timeSeries.character} \alias{as.timeSeries.ts} \alias{as.timeSeries.zoo} \alias{as.matrix,timeSeries-method} \alias{as.data.frame,timeSeries-method} \alias{as.list,timeSeries-method} %\alias{as.ts,timeSeries-method} % S3 versions of the above \alias{as.matrix.timeSeries} \alias{as.data.frame.timeSeries} \alias{as.list.timeSeries} \alias{as.ts} \alias{as.ts.timeSeries} % for as(x, "class") where x is the 1st element of the signature \alias{coerce,ANY,timeSeries-method} \alias{coerce,data.frame,timeSeries-method} \alias{coerce,character,timeSeries-method} \alias{coerce,ts,timeSeries-method} % for as(x, "class") where x is timeSeries \alias{coerce,timeSeries,matrix-method} \alias{coerce,timeSeries,data.frame-method} \alias{coerce,timeSeries,list-method} \alias{coerce,timeSeries,ts-method} \alias{coerce,timeSeries,tse-method} \title{Convert objects to/from class 'timeSeries'} \description{ Functions and methods dealing with the coercion between \code{"timeSeries"} and other classes. } \usage{ ## convert to 'timeSeries' as.timeSeries(x, \dots) ## convert from 'timeSeries' to other classes \method{as.ts}{timeSeries}(x, \dots) \S4method{as.matrix}{timeSeries}(x, \dots) \S4method{as.data.frame}{timeSeries}(x, row.names = NULL, optional = FALSE, \dots) \S4method{as.list}{timeSeries}(x, \dots) } \arguments{ \item{x}{ the object to be converted, see Section \sQuote{Details} for the special case when \code{class(x)} is \code{"character"}. } \item{row.names}{ \code{NULL} or a character vector giving the row names for the data frame. Missing values are not allowed. } \item{optional}{ a logical value. If \code{TRUE}, setting row names and converting column names (to syntactic names) is optional. } \item{\dots}{ arguments passed to other methods. } } \details{ Functions to create \code{"timeSeries"} objects from other objects and to convert \code{"timeSeries"} objects to other classes. \code{as.timeSeries} is a generic function to convert an object to \code{"timeSeries"}. There are specialised methods for the following classes: \code{"ts"}, \code{"data.frame"}, \code{"character"}, and \code{"zoo"}. The default method is equivalent to calling \code{"timeSeries()"}, so \code{x} can be of any type that \code{"timeSeries()"} accepts. The \code{character} method of \code{as.timeSeries} is special, in that its contents are parsed and evaluated, then \code{as.timeSeries} is called on the returned value (passing also the \code{"..."} arguments. Care is needed to avoid infinite recursion here since currently the code doesn't guard against it. } \value{ for \code{as.timeSeries}, an object of class \code{"timeSeries"}. \cr for \code{as.numeric}, \code{as.data.frame}, \code{as.matrix}, \code{as.ts}, \code{as.list} - a numeric vector, a data frame, a matrix, an object of class \code{ts}, or a \code{"list"}, respectively. } \seealso{ \code{\link{timeSeries}}, class \code{\linkS4class{timeSeries}} } \examples{ ## Create an Artificial 'timeSeries' Object setRmetricsOptions(myFinCenter = "GMT") charvec <- timeCalendar() data <- matrix(rnorm(12)) TS <- timeSeries(data, charvec, units = "RAND") TS ## Coerce to Vector as.vector(TS) ## Coerce to Matrix as.matrix(TS) ## Coerce to Data Frame as.data.frame(TS) } \keyword{chron} \keyword{ts} timeSeries/man/timeSeries-slotUnits.Rd0000644000176200001440000000137115104730074017543 0ustar liggesusers\name{units} \alias{getUnits} \alias{getUnits.default} \alias{setUnits<-} \title{Get and set unit names of a 'timeSeries'} \description{ Gets and sets the column names of a \code{"timeSeries"} object. The column names are also called units or unit names. } \usage{ getUnits(x) setUnits(x) <- value } \arguments{ \item{x}{ a \code{"timeSeries"} object. } \item{value}{ a character vector of unit names. } } \seealso{ \code{\link{timeSeries}} } \examples{ ## A Dummy 'timeSeries' Object tS <- dummyMonthlySeries() tS ## Get the Units - getUnits(tS) ## Assign New Units to the Series - setUnits(tS) <- c("A", "B") head(tS) } \keyword{programming} timeSeries/man/base-dim.Rd0000644000176200001440000000611715104730074015114 0ustar liggesusers\name{dimnames} \alias{dim,timeSeries-method} \alias{dim<-,timeSeries-method} \alias{dimnames,timeSeries-method} \alias{dimnames<-,timeSeries,list-method} \alias{colnames<-,timeSeries-method} \alias{colnames,timeSeries-method} \alias{rownames,timeSeries-method} \alias{rownames<-,timeSeries,timeDate-method} \alias{rownames<-,timeSeries,ANY-method} \alias{names,timeSeries-method} \alias{names<-,timeSeries-method} \title{Dimension and their names for 'timeSeries' objects} \description{ Get and assign names, row names, column names, and dim names of \code{"timeSeries"} objects. } % \usage{ % %\S4method{dim}{timeSeries}(x) % %\S4method{dimnames}{timeSeries}(x) % %\S4method{dimnames}{timeSeries}(x) <- value % dim(x) % dimnames(x) % dimnames(x) <- value % colnames(x) % colnames(x) <- value % rownames(x) % rownames(x) <- value % \method{is.array}{timeSeries}(x) % } %\arguments{ % % \item{value}{ % a valid value for names component of \code{dimnames(x)}. % For a \code{"timeSeries"} object this is either \code{NULL} or a % character vector of length the column dimension. Not, row names % cannot be assigne for a \code{"timeSeries"} object, the function % \code{rownames()} will stop and return an error message. % } % \item{x}{ % an object of class \code{timeSeries}. % } % %} \details{ \code{"timeSeries"} methods are available for base R functions working on dimension names, including \code{dim}, \code{dimnames}, \code{colnames}, \code{rownames}, \code{names} and their assignment variants. \code{dim} is the dimension of the underlying data matrix. \code{rownames} gives the datetime stamps as a character vector. \code{rownames<-} sets them. \code{colnames} gives the values of \code{x@units}. These are conceptually the column names of the data matrix. \code{colnames<-} sets slot \code{units} of \code{x}. \code{dimnames} gives \code{list(rownames(x), colnames(x)}. \code{dimnames<-} calls \code{rownames} and \code{colnames} on \code{value[[1]]} and \code{value[[2]]}, respectively. } \note{ (GNB; todo) The \code{"dim<-"}, currently converts \code{x} to a vector if \code{value} is \code{NULL}, otherwise it ignores \code{value}, does nothing and returns \code{x} unchanged. This behaviour should not be relied upon and may be changed in the future, e.g. by issuing warning when \code{value} is not \code{NULL}. Or throwing error altogether if assignment with \code{"dim<-"} is attempted. } \examples{ ## Load Swiss Pension Fund Benchmark Data - X <- LPP2005REC[1:10, 1:3] ## Get Dimension - dim(X) ## Get Column and Row Names - dimnames(X) ## Get Column / Row Names - colnames(X) rownames(X) ## Try your own DIM - DIM <- function(x) {c(NROW(x), NCOL(x))} DIM(X) DIM(X[, 1]) ## Try length / LENGTH - length(X) length(X[, 1]) LENGTH <- function(X) NROW(X) LENGTH(X) ## Columns / Rows - ncol(X); NCOL(X) nrow(X); NROW(X) ## See also - isUnivariate(X) isMultivariate(X) } \keyword{chron} timeSeries/man/utils-structure.Rd0000644000176200001440000000140215104730074016621 0ustar liggesusers\name{str-methods} \alias{str-methods} \alias{str} \alias{str.timeSeries} \title{Display the structure of 'timeSeries' objects} \description{ Compactly display the structure of a \code{"timeSeries"} object. } \usage{ \method{str}{timeSeries}(object, \dots) } \arguments{ \item{object}{ an object of class \code{timeSeries}. } \item{\dots}{ arguments passed to other methods. } } \value{ \code{NULL}, invisibly. The function is called for its side effect of printing a compact representation of the structure of the \code{"timeSeries"} object. } \examples{ ## Load Microsoft Data Set data(MSFT) X <- MSFT[1:12, 1:4] colnames(X) <- abbreviate(colnames(X), 4) ## Display Structure str(X) } \keyword{chron} timeSeries/man/base-diff.Rd0000644000176200001440000000227015104730074015247 0ustar liggesusers\name{diff} \alias{diff} \alias{diff.timeSeries} \title{Difference a 'timeSeries' object} \description{ Difference a \code{"timeSeries"} object. } \usage{ \method{diff}{timeSeries}(x, lag = 1, diff = 1, trim = FALSE, pad = NA, \dots) } \arguments{ \item{x}{ an object of class \code{"timeSeries"}. } \item{lag}{ an integer indicating which lag to use. } \item{diff}{ an integer indicating the order of the difference. } \item{trim}{ a logical flag. Should \code{NA}s at the beginning of the series be removed? } \item{pad}{ a numeric value with which \code{NA}s should be replaced at the beginning of the series. } \item{\dots}{ currently not used. } } %\details{ %} \value{ the differenced \code{"timeSeries"} object } \seealso{ \code{\link[base]{diff}} for \verb{base::diff}, \code{\link{lag}} } \examples{ ## load Microsoft dataset x <- MSFT[1:12, ] x ## compute differences diff(x) ## trimmed differences diff(x, trim = TRUE) ## padded differences diff(x, trim = FALSE, pad = 0) } \keyword{chron} \keyword{ts} timeSeries/man/utils-description.Rd0000644000176200001440000000047515104730074017115 0ustar liggesusers\name{description} \alias{description} \title{Creates date and user information} \description{ Creates and returns a string containing the user, the current datetime and the user name. } \usage{ description() } \examples{ ## Show Default Description String - description() } \keyword{programming} timeSeries/man/statistics-rollMean.Rd0000644000176200001440000000424015104730074017367 0ustar liggesusers\name{rollMean} \alias{rollMean} \alias{rollStats} \alias{rollMin} \alias{rollMax} \alias{rollMedian} \title{Rolling statistics} \description{ Computes rolling mean, min, max and median for a \code{"timeSeries"} object. } \usage{ rollStats(x, k, FUN = mean, na.pad = FALSE, align=c("center", "left", "right"), \dots) rollMean(x, k, na.pad = FALSE, align = c("center", "left", "right"), \dots) rollMin(x, k, na.pad = FALSE, align = c("center", "left", "right"), \dots) rollMax(x, k, na.pad = FALSE, align = c("center", "left", "right"), \dots) rollMedian(x, k, na.pad = FALSE, align = c("center", "left", "right"), \dots) } \arguments{ \item{x}{ an uni- or multivariate \code{"timeSeries"} object. } \item{k}{ an integer width of the rolling window. Must be odd for \code{rollMedian}. } \item{FUN}{ the function to be rolled. } \item{na.pad}{ a logical flag. Should NA padding be added at beginning? By default \code{FALSE}. } \item{align}{ a character string specifying whether the index of the result should be left- or right-aligned or centered compared to the rolling window of observations. The default choice is set to \code{align="center"}. } \item{\dots}{ optional arguments to be passed. } } \value{ an object of class \code{"timeSeries"} } \details{ The code in the core of the functions \code{rollMean}, \code{rollMin}, \code{rollMax}, and \code{rollMedian} was borrowed from the package \code{zoo} authored by Achim Zeileis, Gabor Grothendieck and Felix Andrews. } \author{ Achim Zeileis, Gabor Grothendieck and Felix Andrews for code from the contributed R package \code{zoo} used in the functions \code{rollMean}, \code{rollMin}, \code{rollMax}, and \code{rollMedian}. } \examples{ ## Use Swiss Pension Fund Data Set of Returns - head(LPP2005REC) SPI <- LPP2005REC[, "SPI"] head(SPI) ## Plot Drawdowns - rmean <- rollMean(SPI, k = 10) plot(rmean) } \keyword{chron} timeSeries/man/timeSeries.Rd0000644000176200001440000001266515104730074015551 0ustar liggesusers\name{TimeSeriesClass} \alias{TimeSeriesClass} \alias{timeSeries} \alias{timeSeries,ANY,ANY-method} \alias{timeSeries,ANY,missing-method} \alias{timeSeries,ANY,timeDate-method} \alias{timeSeries,matrix,ANY-method} \alias{timeSeries,matrix,missing-method} \alias{timeSeries,matrix,timeDate-method} \alias{timeSeries,missing,ANY-method} \alias{timeSeries,missing,missing-method} \alias{timeSeries,missing,timeDate-method} \alias{timeSeries,matrix,numeric-method} \alias{index_timeSeries} \alias{time_timeSeries} \alias{index_timeSeries-class} \alias{time_timeSeries-class} \alias{summary.timeseries} \title{Create objects from class 'timeSeries'} \description{ \code{timeSeries} creates a \code{"timeSeries"} object from scratch. } \usage{ timeSeries(data, charvec, units = NULL, format = NULL, zone = "", FinCenter = "", recordIDs = data.frame(), title = NULL, documentation = NULL, \dots) } \arguments{ \item{data}{ a \code{matrix} object or any objects which can be coerced to a matrix. } \item{charvec}{ a character vector of dates and times or any objects which can be coerced to a \code{"timeDate"} object. } \item{units}{ an optional character string, which allows to overwrite the current column names of a \code{"timeSeries"} object. By default \code{NULL} which means that the column names are selected automatically. } \item{format}{ the format specification of the input character vector, a character string with the format in POSIX notation. } \item{zone}{ the time zone or financial center where the data were recorded. } \item{FinCenter}{ a character with the the location of the financial center named as "continent/city". } \item{recordIDs}{ for \code{timeSeries}, a data frame which can be used for record identification. } \item{title}{ an optional title string, if not specified the input's data name is deparsed. } \item{documentation}{ optional documentation string, or a vector of character strings. } \item{\dots}{ arguments passed to other methods. } } \value{ an S4 object of class \code{"timeSeries"} } \details{ \bold{Generation of Time Series Objects:} \cr We have defined a \code{"timeSeries"} class which is in many aspects similar to the S-Plus class with the same name, but has also some important differences. The class has seven Slots, the 'Data' slot which holds the time series data in matrix form, the 'position' slot which holds the time/date as a character vector, the 'format' and 'FinCenter' slots which are the same as for the 'timeDate' object, the 'units' slot which holds the column names of the data matrix, and a 'title' and a 'documentation' slot which hold descriptive character strings. Date and time is managed in the same way as for \code{timeDate} objects. \code{\link{as.timeSeries}} also creates \code{"timeSeries"} objects. \code{as.timeSeries(x)} is mostly equivalent to \code{timeSeries(x)} but the two functions have different methods. Beside that, the main difference between the two functions is that \code{as.timeSeries} doesn't accept additional arguments. The one argument call is naturally interpreted as \sQuote{convert to}, so \code{\link{as.timeSeries}} is more expressive and is recommended in that case. \code{"timeSeries"} methods are provided for many base R functions, including arithmetic operations, mathematical functions, \code{print}, \code{summary}, and time series functions. Not all are explicitly documented, since they can just be used. } % \note{ % % These functions were written for Rmetrics users using R and Rmetrics % under Microsoft's Windows operating system where time zones, % daylight saving times and holiday calendars are insuffeciently % supported. % % } \seealso{ \code{\link{as.timeSeries}}, class \code{\linkS4class{timeSeries}}, } \examples{ ## Load Microsoft data - # Microsoft Data: setRmetricsOptions(myFinCenter = "GMT") data(MSFT) head(MSFT) ## Create a 'timeSeries' object, the direct Way ... Close <- MSFT[, 5] head(Close) ## Create a 'timeSeries' object from scratch - data <- as.matrix(MSFT[, 4]) charvec <- rownames(MSFT) Close <- timeSeries(data, charvec, units = "Close") head(Close) c(start(Close), end(Close)) ## Cut out April data from 2001 - tsApril01 <- window(Close, "2001-04-01", "2001-04-30") tsApril01 ## Compute Continuous Returns - returns(tsApril01) ## Compute Discrete Returns - returns(tsApril01, type = "discrete") ## Compute Discrete Returns, Don't trim - returns(tsApril01, trim = FALSE) ## Compute Discrete Returns, Use Percentage Values - tsRet <- returns(tsApril01, percentage = TRUE, trim = FALSE) tsRet ## Aggregate Weekly - GoodFriday(2001) to <- timeSequence(from = "2001-04-11", length.out = 3, by = "week") from <- to - 6*24*3600 from to applySeries(tsRet, from, to, FUN = sum) ## Create large 'timeSeries' objects with different 'charvec' object classes - # charvec is a 'timeDate' object head(timeSeries(1:1e6L, timeSequence(length.out = 1e6L, by = "sec"))) head(timeSeries(1:1e6L, seq(Sys.timeDate(), length.out = 1e6L, by = "sec"))) # 'charvec' is a 'POSIXt' object head(timeSeries(1:1e6L, seq(Sys.time(), length.out = 1e6L, by = "sec"))) # 'charvec' is a 'numeric' object head(timeSeries(1:1e6L, 1:1e6L)) } \keyword{chron} timeSeries/man/fin-turnpoints.Rd0000644000176200001440000000644615104730074016437 0ustar liggesusers\name{turns} \alias{turns} \alias{turnsStats} \title{Turning points of a time series} \description{ Extracts and analyzes turning points of an univariate \code{"timeSeries"} object. } \usage{ turns(x, \dots) turnsStats(x, doplot = TRUE) } \arguments{ \item{x}{ an univariate \code{"timeSeries"} object of financial indices or prices. } \item{\dots}{ optional arguments passed to the function \code{na.omit}. } \item{doplot}{ a logical flag, should the results be plotted? By default TRUE. } } \details{ The function \code{turns} determines the number and the positions of extrema (turning points, either peaks or pits) in a regular time series. The function \code{turnsStats} calculates the quantity of information associated with the observations in this series, according to Kendall's information theory. The functions are borrowed from the contributed R package \code{pastecs} and made ready for working together with univariate \code{timeSeries} objects. You need not to load the R package \code{pastecs}, the code parts we need here are builtin in the \code{timeSeries} package. We have renamed the function \code{turnpoints} to \code{turns} to distinguish between the original function in the contributed R package \code{pastecs} and our Rmetrics function wrapper. For further details please consult the help page from the contributed R package \code{pastecs}. } \value{ for \code{turns}, an object of class \code{timeSeries}.\cr for \code{turnsStats}, an object of class \code{turnpoints} with the following entries: \item{data}{The dataset to which the calculation is done.} \item{n}{The number of observations.} \item{points}{The value of the points in the series, after elimination of ex-aequos.} \item{pos}{The position of the points on the time scale in the series (including ex-aequos).} \item{exaequos}{Location of exaequos (1), or not (0).} \item{nturns}{Total number of turning points in the whole time series.} \item{firstispeak}{Is the first turning point a peak (TRUE), or not (FALSE).} \item{peaks}{Logical vector. Location of the peaks in the time series without ex-aequos.} \item{pits}{Logical vector. Location of the pits in the time series without ex-aequos.} \item{tppos}{Position of the turning points in the initial series (with ex-aequos).} \item{proba}{Probability to find a turning point at this location.} \item{info}{Quantity of information associated with this point.} } \author{ Frederic Ibanez and Philippe Grosjean for code from the contributed R package \code{pastecs} and Rmetrics for the function wrapper. } \references{ Ibanez, F., 1982, Sur une nouvelle application de la theorie de l'information a la description des series chronologiques planctoniques. J. Exp. Mar. Biol. Ecol., 4, 619--632 Kendall, M.G., 1976, Time Series, 2nd ed. Charles Griffin and Co, London. } \examples{ ## Load Swiss Equities Series - SPI.RET <- LPP2005REC[, "SPI"] head(SPI.RET) ## Cumulate and Smooth the Series - SPI <- smoothLowess(cumulated(SPI.RET), f=0.05) plot(SPI) ## Plot Turn Points Series - SPI.SMOOTH <- SPI[, 2] tP <- turns(SPI.SMOOTH) plot(tP) ## Compute Statistics - turnsStats(SPI.SMOOTH) } \keyword{chron} timeSeries/man/statistics-smoothLowess.Rd0000644000176200001440000000463315104730074020332 0ustar liggesusers\name{smooth} \alias{smoothLowess} \alias{smoothSpline} \alias{smoothSupsmu} \title{Smooths time series objects} \description{ Smooths a \code{"timeSeries"} object. } \usage{ smoothLowess(x, f = 0.5, \dots) smoothSpline(x, spar = NULL, \dots) smoothSupsmu(x, bass = 5, \dots) } \arguments{ \item{x}{ an univariate \code{"timeSeries"} object. } \item{f}{ the lowess smoother span. This gives the proportion of points in the plot which influence the smooth at each value. Larger values give more smoothness. } \item{spar}{ smoothing parameter, typically (but not necessarily) in (0,1]. By default \code{NULL}, i.e. the value will be automatically selected. } \item{bass}{ controls the smoothness of the fitted curve. Values of up to 10 indicate increasing smoothness. } \item{\dots}{ optional arguments to be passed to the underlying smoothers. } } \details{ The functions \code{smoothLowess}, \code{smoothSpline}, \code{smoothSupsmu} allow to smooth \code{timeSerie} object. The are interfaces to the function \code{lowess}, \code{supmsu}. and \code{smooth.spline} in R's \code{stats} package. The \code{\dots} arguments allow to pass optional arguments to the underlying \code{stats} functions and tailor the smoothing process. We refer to the manual pages of these functions for a proper setting of these options. } \value{ a bivariate \code{"timeSeries"} object, the first column holds the original time series data, the second the smoothed series. } \author{ The R core team for the underlying smoother functions. } \examples{ ## Use Close from MSFT's Price Series - head(MSFT) MSFT.CLOSE <- MSFT[, "Close"] head(MSFT.CLOSE) ## Plot Original and Smoothed Series by Lowess - MSFT.LOWESS <- smoothLowess(MSFT.CLOSE, f = 0.1) head(MSFT.LOWESS) plot(MSFT.LOWESS) title(main = "Close - Lowess Smoothed") ## Plot Original and Smoothed Series by Splines - MSFT.SPLINE <- smoothSpline(MSFT.CLOSE, spar = 0.4) head(MSFT.SPLINE) plot(MSFT.SPLINE) title(main = "Close - Spline Smoothed") ## Plot Original and Smoothed Series by Supsmu - MSFT.SUPSMU <- smoothSupsmu(MSFT.CLOSE) head(MSFT.SUPSMU) plot(MSFT.SUPSMU) title(main = "Close - Spline Smoothed") } \keyword{chron} timeSeries/man/base-t.Rd0000644000176200001440000000071615104730074014605 0ustar liggesusers\name{t} \alias{t,timeSeries-method} \title{Transpose 'timeSeries' objects} \description{ Returns the transpose of a \code{"timeSeries"} object. } \usage{ \S4method{t}{timeSeries}(x) } \arguments{ \item{x}{ a 'timeSeries' object. } } \value{ a matrix } \examples{ ## Dummy 'timeSeries' with NAs entries data <- matrix(1:24, ncol = 2) s <- timeSeries(data, timeCalendar()) s ## Transpose 'timeSeries' - t(s) } \keyword{chron} timeSeries/man/fin-spreads.Rd0000644000176200001440000000312515104730074015642 0ustar liggesusers\name{spreads} \alias{spreads} \alias{midquotes} \alias{spreadSeries} % removed \alias{midquoteSeries} % removed \title{Spreads and mid quotes} \description{ Compute spreads and midquotes from price streams. } \usage{ spreads(x, which = c("Bid", "Ask"), tickSize = NULL) midquotes(x, which = c("Bid", "Ask")) } \arguments{ \item{x}{ an object of class \code{timeSeries}. } \item{which}{ a vector with two character strings naming the column names of the time series from which to compute the mid quotes and spreads. By default these are the bid and ask prices with column names \code{c("Bid", "Ask")}. } \item{tickSize}{ the default is \code{NULL} to simply compute price changes in original price levels. If \code{ticksize} is supplied, the price changes will be divided by the value of \code{inTicksOfSize} to compute price changes in ticks. } } \value{ all functions return an object of class \code{timeSeries} } \seealso{ \code{\link{returns}}, \code{\link{cumulated}}, \code{\link{drawdowns}}, \code{\link{splits}}, %\code{\link{spreads}}, \code{\link{midquotes}}, \code{\link{index2wealth}} } \examples{ ## Load the Microsoft Data - setRmetricsOptions(myFinCenter = "GMT") data(MSFT) X = MSFT[1:10, ] head(X) ## Compute Open/Close Midquotes - X.MID <- midquotes(X, which = c("Close", "Open")) colnames(X.MID) <- "X.MID" X.MID ## Compute Open/Close Spreads - X.SPREAD <- spreads(X, which = c("Close", "Open")) colnames(X.SPREAD) <- "X.SPREAD" X.SPREAD } \keyword{chron} timeSeries/man/base-rank.Rd0000644000176200001440000000354715104730074015302 0ustar liggesusers\name{rank} \alias{rank} \alias{rank,timeSeries-method} \title{Sample ranks of a time series} \description{ Compute the sample ranks of the values of a 'timeSeries' object. } \usage{ \S4method{rank}{timeSeries}(x, na.last = TRUE, ties.method = ) } \arguments{ \item{x}{ an univariate object of class \code{timeSeries}. } \item{na.last}{ for controlling the treatment of \code{NA}s. If \code{TRUE}, missing values in the data are put last; if \code{FALSE}, they are put first; if \code{NA}, they are removed; if \code{"keep"} they are kept with rank \code{NA}. } \item{ties.method}{ a character string specifying how ties are treated; can be abbreviated. } } \details{ If all components are different (and no \code{NA}s), the ranks are well defined, with values in \code{seq_len(x)}. With some values equal (called \sQuote{ties}), argument \code{ties.method} determines the result at the corresponding indices. The \code{"first"} method results a permutation with increasing values at each index set of ties. The \code{"random"} method puts these in random order, whereas the default, \code{"average"}, replaces them by their mean, and \code{"max"} and \code{"min"} replace them with their maximum and minimum respectively, the latter being the typical sports ranking. \code{NA} values are never considered to be equal: for \code{na.last = TRUE} and \code{na.last = FALSE} they are given distinct ranks in the order in which they occur in \code{x}. } \value{ a \code{"timeSeries"} object } \examples{ ## Load Microsoft Data - X <- 100 * returns(MSFT) ## Compute the Ranks - head(rank(X[, "Open"]), 10) ## Only Interested in the Vector, then use - head(rank(series(X[, "Open"])), 10) } \keyword{chron} timeSeries/man/fin-align.Rd0000644000176200001440000001076415104730074015302 0ustar liggesusers\name{align-methods} \docType{methods} \alias{align} \alias{align-methods} \alias{align,timeSeries-method} \alias{alignDailySeries} \alias{daily2weekly} \alias{daily2monthly} \title{Align a 'timeSeries' object to equidistant time stamps} \description{ Aligns a \code{"timeSeries"} object to equidistant time stamps. There are also functions for the common cases of changing daily to weekly and daily to monthly. } \usage{ \S4method{align}{timeSeries}(x, by = "1d", offset = "0s", method = c("before", "after", "interp", "fillNA", "fmm", "periodic", "natural", "monoH.FC"), include.weekends = FALSE, ...) alignDailySeries(x, method = c("before", "after", "interp", "fillNA", "fmm", "periodic", "natural", "monoH.FC"), include.weekends = FALSE, units = NULL, zone = "", FinCenter = "", ...) daily2monthly(x, init = FALSE) daily2weekly(x, startOn = "Tue", init = FALSE) } \arguments{ \item{x}{ an object of class \code{"timeSeries"}. } \item{by}{ a character string denoting the period. } \item{offset}{ a character string denoting the offset. } \item{method}{ the method to be used for the alignment. A character string, one of \code{"before"}, use the data from the row whose position is just before the unmatched position, or \code{"after"}, use the data from the row whose position is just after the unmatched position, or \code{"linear"}, interpolate linearly between \code{"before"} and \code{"after"}. } \item{include.weekends}{ a logical value. Should weekend dates be included or removed from the series? } \item{units}{ an optional character string, which allows to overwrite the current column names of a \code{timeSeries} object. By default \code{NULL} which means that the column names are selected automatically. } \item{zone}{ the time zone or financial center where the data were recorded. } \item{FinCenter}{ a character with the the location of the financial center named as \code{"continent/city"}. } \item{startOn}{ a character string, specifying the day of week as a three letter abbreviation. Weekly aggregated data records are then fixed to the weekdays given by the argument \code{startOn}. } \item{init}{ a logical value, if set to \code{TRUE} then the time series will be indexed to 1 for its first value. By default \code{init} is set to \code{FALSE}. } \item{\ldots}{ further arguments to be passed to the interpolating function. } } \details{ TODO: complete. \code{alignDailySeries} aligns a daily 'timeSeries' to new positions, Effectively, it is a frontend to the \code{"timeSeries"} method for \code{align} with \code{by = "1d"}, and \code{offset = "0s"}. In addition, there are two tailored functions for common cases: \code{daily2monthly} and \code{daily2weekly} which aggregate \code{"timeSeries"} objects from daily to monthly or weekly levels, respectively. In the case of the function \code{daily2weekly} one can explicitly set the starting day of the week, the default value is Tuesday, \code{startOn = "Tue"}. } \seealso{ \code{\link{aggregate}}, \code{\link{apply}} } \value{ a \code{"timeSeries"} object, for \code{alignDailySeries}, a weekly aligned daily \code{"timeSeries"} object from a daily time series with missing holidays. } \examples{ ## Use Microsofts' OHLCV Price Series - head(MSFT) end(MSFT) ## Use MSFT and Compute Sample Size - dim(MSFT) ## Align the Series - MSFT.AL <- align(MSFT) ## Show the Size of the Aligned Series - dim(MSFT.AL) ## alignDailySeries ## Cut out April Data from 2001 - Close <- MSFT[, "Close"] tsApril01 <- window(Close, start="2001-04-01", end="2001-04-30") tsApril01 ## Align Daily Series with NA - tsRet <- returns(tsApril01, trim = TRUE) GoodFriday(2001) EasterMonday(2001) alignDailySeries(tsRet, method = "fillNA", include.weekends = FALSE) alignDailySeries(tsRet, method = "fillNA", include.weekends = TRUE) ## Align Daily Series by Interpolated Values - alignDailySeries(tsRet, method = "interp", include.weekend = FALSE) alignDailySeries(tsRet, method = "interp", include.weekend = TRUE) ## Load Microsoft Data Set - x <- MSFT ## Aggregate daily records to end of month records - X <- daily2monthly(x) X isMonthly(X) ## Aggregate daily records to end of week records - X <- daily2weekly(x, startOn="Fri") X dayOfWeek(time(X)) } \keyword{methods} \keyword{chron} timeSeries/man/fin-splits.Rd0000644000176200001440000000272615104730074015525 0ustar liggesusers\name{splits} \alias{splits} \title{splits} \description{ Searches for outlier splits in a \code{"timeSeries"} object. } \usage{ splits(x, sd = 3, complement = TRUE, ...) } \arguments{ \item{x}{ a \code{"timeSeries"} object. } \item{sd}{ \code{numeric(1)}; deviations of how many standard deviations to consider too big? Can be fractional. E.g., 5 means that values larger or smaller than five times the standard deviation of the series will be detected. } \item{complement}{ a logical flag, should the outlier series or its complements be returned? } \item{\dots}{ arguments to be passed. } } \details{ This function finds splits in financial price or index series. If a price or index is splitted we observe a big jump of several standard deviations in the returns, which is identified usually as an outlier. } \value{ a \code{"timeSeries"} object } \seealso{ \code{\link{returns}}, \code{\link{cumulated}}, \code{\link{drawdowns}}, %\code{\link{splits}}, \code{\link{spreads}}, \code{\link{midquotes}}, \code{\link{index2wealth}} } \examples{ ## Create a Return Series with a Split - data <- runif(12, -1, 1) data[6] <- 20 x <- timeSeries(data, timeCalendar(), units="RUNIF") x ## Search for the Split: splits(x, sd=3, complement=TRUE) splits(x, sd=3, complement=FALSE) } timeSeries/man/timeSeries-slotDocumentation.Rd0000644000176200001440000000414515104730074021254 0ustar liggesusers\name{attributes} \alias{attributes} \alias{getAttributes} \alias{setAttributes<-} \alias{documentation} \title{Get and set optional attributes of a 'timeSeries'} \description{ Extracts or assigns optional attributes from or to a \code{"timeSeries"} object. } \usage{ getAttributes(obj) setAttributes(obj) <- value } \arguments{ \item{obj}{ a \code{timeSeries} object whose optional attributes are to be accessed. } \item{value}{ an object, the new value of the attribute, or NULL to remove the attribute. } } \details{ Each \code{timeSeries} object is documented. By default a time series object holds in the documentation slot a string with creation time and the user who has defined it. But this is not all. Optionally the whole creation process and history can be recorded. For this the \code{@documentation} slot may have an optional \code{"Attributes"} element. This attribute is tracked over the whole life time of the object whenever the time series is changed. Whenever you like to be informed about the optional attributes, or you like to recover them you can dot it, and evenmore, whenever you like to add information as an addiitonal attribute you can also do it. The two functions \code{getAttributes} and \code{setAttributes} provide access to and allow to modify the optional attributes of a \code{timeSeries} object. %The replacement form causes the named attribute to take the value %specified (or create a new attribute with the value given). %Optional attributes are attached to the \code{@documentation} slot of the %S4 \code{timeSeries} object. These attributes are preserved during operations %on \code{timeSeries} objects using the internal function \code{.appendList}. } \examples{ \dontshow{set.seed(1234)} ## Create an artificial 'timeSeries' Object - tS <- dummyMonthlySeries() tS ## Get Optional Attributes - getAttributes(tS) tS@documentation ## Set a new Optional Attribute - setAttributes(tS) <- list(what="A dummy Series") tS getAttributes(tS) tS@documentation } \keyword{programming} timeSeries/man/methods-plot.Rd0000644000176200001440000002005115104730074016043 0ustar liggesusers\name{plot-methods} \alias{plot} \alias{lines} \alias{points} \alias{plot,timeSeries-method} \alias{lines,timeSeries-method} \alias{points,timeSeries-method} \alias{pretty.timeSeries} \title{Plot 'timeSeries' objects} \description{ \code{"timeSeries"} methods for \code{\link[base]{plot}}, \code{\link[graphics]{lines}} and \code{\link[graphics]{points}}. } \usage{ \S4method{plot}{timeSeries}(x, y, FinCenter = NULL, plot.type = c("multiple", "single"), format = "auto", at = pretty(x), widths = 1, heights = 1, xy.labels, xy.lines, panel = lines, nc, yax.flip = FALSE, mar.multi = c(0, 5.1, 0, if (yax.flip) 5.1 else 2.1), oma.multi = c(6, 0, 5, 0), axes = TRUE, \ldots) \S4method{lines}{timeSeries}(x, FinCenter = NULL, \dots) \S4method{points}{timeSeries}(x, FinCenter = NULL, \dots) \method{pretty}{timeSeries}(x, n=5, min.n=n\%/\%3, shrink.sml=0.75, high.u.bias=1.5, u5.bias=0.5+1.5*high.u.bias, eps.correct=0, \dots) } \details{ Our original method \code{plot} was build along R's plotting function \code{plot.ts} with an additional argument to tailor the position marks at user defined position specified by the argument \code{at}. We call this style or theme \code{"ts"}. With verson R 3.1 we have inroduced two new additional plotting themes called \code{"pretty"} and \code{"chick"}. They are becoming active when we set \code{at = "pretty"} or \code{at = "chic"}. Plot style or theme \code{"pretty"} is an extension of our original plotting method. Plot style or theme \code{"chic"} is an implementation along the contributed packages \code{xts} and \code{PerformanceAnalytics} from the Chicago finance group members (\code{"chic"} is an abbreviation of Chicago. For both themes, \code{"pretty"} and \code{"chic"}, additional arguments are passed through the \code{\dots} arguments. These are: \tabular{lll}{ \code{Argument} \tab Default \tab Description \cr \code{type} \tab "l" \tab types pf plot \cr \code{col} \tab 1 \tab colors for lines and points \cr \code{pch} \tab 20 \tab plot symbol \cr \code{cex} \tab 1 \tab character and symbol scales \cr \code{lty} \tab 1 \tab line types \cr \code{lwd} \tab 2 \tab line widths \cr \code{cex.axes} \tab 1 \tab scale of axes \cr \code{cex.lab} \tab 1 \tab scale of labels \cr \code{cex.pch} \tab 1 \tab scale of plot symbols \cr \code{} \tab \tab \cr \code{grid} \tab TRUE \tab should grid lines plotted? \cr \code{frame.plot} \tab TRUE \tab should b box around the plot? \cr \code{axes} \tab TRUE \tab should be axes drawn on the plot? \cr \code{ann} \tab TRUE \tab should default annotations appear? } Concerning the plot elements, the length of these vectors has to be the same as the number of columns in the time series to be plotted. If their length is only one, then they are repeated. There is an almost 70 pages vignette added to the package, with dozens of examples of tailored plots. Have a look in it. } \arguments{ \item{x, y }{ objects of class \code{timeSeries}. } \item{FinCenter}{ a character with the the location of the financial center named as \code{"continent/city"}. } \item{plot.type}{ for multivariate time series, should the series by plotted separately (with a common time axis) or on a single plot? } \item{format}{POSIX label format, e.g. "\%Y-\%m-\%d" or "\%F" for ISO-8601 standard date format. } \item{at}{ a \code{timeDate} object setting the plot label positions. If \code{at=pretty(x)}, the postitions are generated automatized calling the function \code{pretty}. Default option \code{at="auto"} selects 6 equal spaced time label positions. For the new plot themes set at="pretty" or at="chic". In this case additional arguments can be passed through the \code{\dots} arguments, see details. } \item{widths, heights}{ widths and heights for individual graphs, see \code{layout}. } \item{xy.labels}{ logical, indicating if \code{text()} labels should be used for an x-y plot, _or_ character, supplying a vector of labels to be used. The default is to label for up to 150 points, and not for more. } \item{xy.lines}{ logical, indicating if \code{lines} should be drawn for an x-y plot. Defaults to the value of \code{xy.labels} if that is logical, otherwise to \code{TRUE} } \item{panel}{ a \code{function(x, col, bg, pch, type, ...)} which gives the action to be carried out in each panel of the display for \code{plot.type="multiple"}. The default is \code{lines}. } \item{nc}{ the number of columns to use when \code{type="multiple"}. Defaults to 1 for up to 4 series, otherwise to 2. } \item{yax.flip}{ logical indicating if the y-axis (ticks and numbering) should flip from side 2 (left) to 4 (right) from series to series when \code{type="multiple"}. } \item{mar.multi, oma.multi}{ the (default) \code{par} settings for \code{plot.type="multiple"}. } \item{axes}{ logical indicating if x- and y- axes should be drawn. } \item{n}{ an integer giving the desired number of intervals. } \item{min.n}{ a nonnegative integer giving the minimal number of intervals. } \item{shrink.sml}{ a positive numeric by a which a default scale is shrunk in the case when range(x) is very small. } \item{high.u.bias}{ a non-negative numeric, typically > 1. Larger high.u.bias values favor larger units. } \item{u5.bias}{ a non-negative numeric multiplier favoring factor 5 over 2. } \item{eps.correct}{ an integer code, one of 0, 1, or 2. If non-0, a correction is made at the boundaries. } \item{\dots}{ additional graphical arguments, see \code{plot}, \code{plot.default} and \code{par}. } } \value{ \code{NULL} (invisibly), the functions are called for the side effect of producing plots } \seealso{ \code{vignette("timeSeriesPlot", package="timeSeries")}, which provides extensive plot examples. } \examples{ ## load Swiss pension fund benchmark data LPP <- LPP2005REC[1:12, 1:4] colnames(LPP) <- abbreviate(colnames(LPP), 2) finCenter(LPP) <- "GMT" ## Example Plot 1 plot(LPP[, 1], type = "o", col = "steelblue", main = "LPP", xlab = "2005", ylab = "Return") plot(LPP[, 1], at="auto", type = "o", col = "steelblue", main = "LPP", xlab = "2005", ylab = "Return") ## Example Plot 2 plot(LPP[, 1:2], type = "o", col = "steelblue", main = "LPP", xlab = "2005", ylab = "Return") ## Example Plot 3 plot(LPP[, 1], LPP[, 2], type = "p", col = "steelblue", main = "LPP", xlab = "Return 1", ylab = "Return 2") ## Example Plot 4a, the wrong way to do it! LPP <- as.timeSeries(data(LPP2005REC)) ZRH <- as.timeSeries(LPP[,"SPI"], zone = "Zurich", FinCenter = "Zurich") NYC <- as.timeSeries(LPP[,"LMI"], zone = "NewYork", FinCenter = "NewYork") finCenter(ZRH) finCenter(NYC) plot(ZRH, at="auto", type = "p", pch = 19, col = "blue") points(NYC, pch = 19, col = "red") ## Example Plot 4b, convert NYC to Zurich time finCenter(ZRH) <- "Zurich" finCenter(NYC) <- "Zurich" at <- unique(round(time(ZRH))) plot(ZRH, type = "p", pch = 19, col = "blue", format = "\%b \%d", at = at, xlab = paste(ZRH@FinCenter, "local Time"), main = ZRH@FinCenter) points(NYC, pch = 19, col = "red") ## Example 4c, force everything to GMT using "FinCenter" argument finCenter(ZRH) <- "Zurich" finCenter(NYC) <- "NewYork" at <- unique(round(time(ZRH))) plot(ZRH, type = "p", pch = 19, col = "blue", format = "\%b \%d", at = at, FinCenter = "GMT", xlab = "GMT", main = "ZRH - GMT") points(NYC, FinCenter = "GMT", pch = 19, col = "red") } \keyword{chron} timeSeries/man/methods-stats.Rd0000644000176200001440000000431015104730074016223 0ustar liggesusers\name{timeSeries-method-stats} \docType{methods} \alias{sd-methods} \alias{var-methods} \alias{cov-methods} \alias{cor-methods} %\alias{dcauchy-methods} %\alias{dnorm-methods} %\alias{dt-methods} %\alias{sd,timeSeries-method} %\alias{var,timeSeries-method} %\alias{cov,timeSeries-method} %\alias{cor,timeSeries-method} %\alias{dcauchy,timeSeries-method} %\alias{dnorm,timeSeries-method} %\alias{dt,timeSeries-method} \title{Base R functions applied to 'timeSeries' objects} \description{ Many base R statistical functions work on (the data part of) \code{timeSeries} objects without the need for special methods, e.g., \code{var}, \code{sd}, \code{cov}, \code{cor}, probability densities, and others. This page gives some examples with such functions. } %% 2023-05-29: these S4 methods don't exist! (and the functions are not generic!) %\usage{ %\S4method{cov}{timeSeries}(x, y = NULL, use = "all.obs", % method = c("pearson", "kendall", "spearman")) % %\S4method{cor}{timeSeries}(x, y = NULL, use = "all.obs", % method = c("pearson", "kendall", "spearman")) %} % %\arguments{ % \item{x}{ % an univariate object of class \code{timeSeries}. % } % \item{y}{ % \code{NULL} (default) or a \code{timeSeries} object with compatible % dimensions to \code{x}. The default is equivalent to \code{y = x} % (but more efficient). % } % \item{use}{ % an optional character string giving a method for computing % covariances in the presence of missing values. This must be (an % abbreviation of) one of the strings \code{"all.obs"}, % \code{"complete.obs"} or \code{"pairwise.complete.obs"}. % } % \item{method}{ % a character string indicating which correlation coefficient (or % covariance) is to be computed. One of \code{"pearson"} (default), % \code{"kendall"}, or \code{"spearman"}. Can be abbreviated. % } %} % %\value{ % covariance or correlation matrix %} \seealso{ \code{\link{colStats}}, \code{\link{colVars}}, and other \code{colXXX} functions } \examples{ ## Load Microsoft Data Set - data(MSFT) X = MSFT[, 1:4] X = 100 * returns(X) ## Compute Covariance Matrix - cov(X[, "Open"], X[, "Close"]) cov(X) cor(X) } \keyword{methods} \keyword{chron} timeSeries/man/timeSeries-isRegular.Rd0000644000176200001440000000466415104730074017504 0ustar liggesusers\name{isRegular} \alias{isRegular} \alias{isRegular,timeSeries-method} \alias{isRegular.timeSeries} \alias{isDaily} \alias{isDaily,timeSeries-method} \alias{isDaily.timeSeries} \alias{isMonthly} \alias{isMonthly,timeSeries-method} \alias{isMonthly.timeSeries} \alias{isQuarterly} \alias{isQuarterly,timeSeries-method} \alias{isQuarterly.timeSeries} \alias{frequency} \alias{frequency,timeSeries-method} \alias{frequency.timeSeries} \title{Checks if a time series is regular} \description{ Checks if a time series is regular. } \usage{ \S4method{isDaily}{timeSeries}(x) \S4method{isMonthly}{timeSeries}(x) \S4method{isQuarterly}{timeSeries}(x) \S4method{isRegular}{timeSeries}(x) \S4method{frequency}{timeSeries}(x, \dots) } \arguments{ \item{x}{ an R object of class 'timeSeries'. } \item{\dots}{ arguments to be passed. } } \details{ What is a regular time series? If a time series is daily, monthly, or weekly, then we speak of a regular series. This can be tested calling the functions \code{isDaily}, \code{isMonthly}, \code{isQuarterly}, or in general \code{isRegular}. If the series is regular then its frequency can be determined by calling \code{frequency}. Here are the definitions of daily, monthly, and quarterly time series: \describe{ \item{daily}{if the series has no more than one date/time stamp per day.} \item{monthly}{if the series has no more than one date/time stamp per month.} \item{quarterly}{if the series has no more than one date/time stamp per quarter.} } A regular series is either a monthly or a quarterly series. Note that with the above definitions a monthly series is also a daily series, a quarterly series is also a monthly series. On the other hand, a daily series is not regular! NOT yet implemented is the case of weekly series. } \value{ The \code{is*} functions return \code{TRUE} or \code{FALSE} depending on whether the series fulfills the condition or not.\cr \code{frequency} returns in general 1, for quarterly series 4, and for monthly series 12. } \seealso{ \code{\link[timeDate]{isRegular}} \code{\link[stats]{frequency}} } \examples{ data(MSFT) isRegular(MSFT) # FALSE frequency(MSFT) # 1 ## a monthly ts ap <- as.timeSeries(AirPassengers) isRegular(ap) # TRUE frequency(ap) # 12 ## a quarterly ts pres <- as.timeSeries(presidents) isRegular(pres) # TRUE frequency(pres) # 4 } \keyword{chron} timeSeries/man/timeSeries-slotSeries.Rd0000644000176200001440000000430615104730074017674 0ustar liggesusers\name{series-methods} \docType{methods} \alias{series} \alias{series<-} \alias{series,timeSeries-method} \alias{series<-,timeSeries,matrix-method} \alias{series<-,timeSeries,ANY-method} \alias{series<-,timeSeries,data.frame-method} \alias{series<-,timeSeries,vector-method} \alias{coredata} \alias{coredata<-} %\alias{coredata,timeSeries-method} %\alias{coredata<-,timeSeries,matrix-method} %\alias{coredata<-,timeSeries,ANY-method} %\alias{coredata<-,timeSeries,data.frame-method} %\alias{coredata<-,timeSeries,vector-method} \alias{coredata.timeSeries} \alias{coredata<-.timeSeries} \title{Get and set the data component of a 'timeSeries'} \description{ Get and set the data component of a 'timeSeries'. } \usage{ series(x) series(x) <- value } \arguments{ \item{x}{ a \code{timeSeries} object. } \item{value}{ a \code{vector}, a \code{data.frame} or a \code{matrix} object of numeric data. } } \details{ \code{series} returns the \code{@.Data} slot of a \code{timeSeries} object in \code{matrix} form. The assignment version of \code{series} replaces the values of the time series with \code{value}. The row and column names of \code{value} are used if not \code{NULL}, otherwise they are left as in \code{x}. The most natural use is when \code{value} has the same dimensions as \code{as.matrix(x)}, but if that is not the case the result is almost as if \code{value} was converted to \code{"timeSeries"} directly. Methods for \code{zoo::coredata} and its assignment counterpart are defined, as well. Users who wish to use them should ensure that \code{zoo::coredata} is visible (e.g., by calling \code{library('zoo')} or \code{library('xts')}) or employ the \code{zoo::} prefix in the calls. These methods are equivalent to \code{series} and \code{`series<-`}, respectively. } \seealso{ \code{\link{timeSeries}} } \examples{ ## A Dummy 'timeSeries' Object ts <- timeSeries() ts ## Get the Matrix Part - mat <- series(ts) class(mat) mat ## Assign a New Univariate Series - series(ts) <- rnorm(12) ts ## Assign a New Bivariate Series - series(ts) <- matrix(rnorm(12), ncol = 2) ts } \keyword{programming} timeSeries/man/base-merge.Rd0000644000176200001440000000452715104730074015445 0ustar liggesusers\name{merge} \docType{methods} \alias{merge} \alias{merge-methods} \alias{merge,ANY,ANY-method} \alias{merge,ANY,timeSeries-method} \alias{merge,matrix,timeSeries-method} \alias{merge,numeric,timeSeries-method} \alias{merge,timeSeries,ANY-method} \alias{merge,timeSeries,matrix-method} \alias{merge,timeSeries,missing-method} \alias{merge,timeSeries,numeric-method} \alias{merge,timeSeries,timeSeries-method} \title{Merge 'timeSeries' objects} \description{ Merges several object types with \code{"timeSeries"} objects. The number of rows must match. } \usage{ merge(x, y, \dots) } \arguments{ \item{x,y}{ objects to merge, at least one of class \code{"timeSeries"}. } \item{...}{further objects to merge.} } %\details{ %} \value{ a \code{"timeSeries"} object } \section{Methods}{ \describe{ \item{\code{signature(x = "timeSeries", y = "missing")}}{ %% ~~describe this method here~~ } \item{\code{signature(x = "timeSeries", y = "ANY")}}{ %% ~~describe this method here~~ } \item{\code{signature(x = "timeSeries", y = "matrix")}}{ %% ~~describe this method here~~ } \item{\code{signature(x = "timeSeries", y = "numeric")}}{ %% ~~describe this method here~~ } \item{\code{signature(x = "timeSeries", y = "timeSeries")}}{ %% ~~describe this method here~~ } \item{\code{signature(x = "ANY", y = "ANY")}}{ %% ~~describe this method here~~ } \item{\code{signature(x = "ANY", y = "timeSeries")}}{ %% ~~describe this method here~~ } \item{\code{signature(x = "matrix", y = "timeSeries")}}{ %% ~~describe this method here~~ } \item{\code{signature(x = "numeric", y = "timeSeries")}}{ %% ~~describe this method here~~ } } } \seealso{ \code{\link{cbind}} } \examples{ ## Load Series - x <- MSFT[1:12, ] ## Merge 'timeSeries' with missing Object - merge(x) \dontshow{set.seed(1234)} ## Merge 'timeSeries' with numeric Object - y <- rnorm(12) class(y) merge(x, y) ## Merge 'timeSeries' with matrix Object - y <- matrix(rnorm(24), ncol=2) class(y) merge(x, y) ## Merge 'timeSeries' with matrix Object - y <- timeSeries(data=rnorm(12), charvec=time(x)) class(y) merge(x, y) } \keyword{methods} \keyword{chron} \keyword{ts} timeSeries/man/internals.Rd0000644000176200001440000000120415104730074015422 0ustar liggesusers\name{internals} %\alias{.align.timeSeries} % \alias{.appendList} % \alias{.axTicksByTime2} \alias{.colorwheelPalette} % \alias{.endpoints2} % \alias{.ohlcDailyPlot} % \alias{.periodicity2} % \alias{.plotOHLC} % \alias{.xtsPlot} \title{Exported internal functions} \description{ Internal functions which are still exported only because some packages are using them. The intent is to stop exporting or removing them. } \usage{ .colorwheelPalette(n) } \arguments{ \item{n}{ } } \details{ If you think that any of these functions are useful and don't have exported analogs, please contact the maintainer. } \keyword{internal} timeSeries/man/methods-show.Rd0000644000176200001440000000422515104730074016052 0ustar liggesusers\name{print-methods} \alias{show,timeSeries-method} \alias{print.timeSeries} \title{Print 'timeSeries' objects} \description{ Print \code{"timeSeries"} objects. } \usage{ \S4method{show}{timeSeries}(object) \method{print}{timeSeries}(x, FinCenter = NULL, format = NULL, style = c("tS", "h", "ts"), by = c("month", "quarter"), ...) } \arguments{ \item{object,x}{ an object of class \code{"timeSeries"}. } \item{FinCenter}{ a character with the the location of the financial center named as "continent/city". } \item{format}{ the format specification of the input character vector, a character string with the format in POSIX notation. } \item{style}{ a character string, one of \code{"tS"}, \code{"h"}, or \code{"ts"}. } \item{by}{ a character string, one of \code{"month"}, \code{"quarter"}. } \item{\dots}{ arguments passed to the print method for the data part, which is a \code{"matrix"} or, in the case of \code{style = "ts"}, to the print method for class \code{"ts"}. } } % \item{recordIDs}{ % for the \code{print} method, a logical value - should the % \code{recordIDs} be printed together with the data matrix and time % series positions? \details{ \code{show} does not have additional arguments. The \code{print} method allows to modify the way the object is shown by explicitly calling \code{print}. The default for \code{style} is \code{tS}. For univariate time series \code{style = "h"} causes the object to be printed as a vector with the time stamps as labels. Finally, \code{style = "ts"} prints like objects from base R class \code{"ts"}, which is suitable for quarterly and monthly time series. } \value{ Prints an object of class \code{timeSeries}. } \examples{ ## Load Micsrosoft Data setRmetricsOptions(myFinCenter = "GMT") LPP <- MSFT[1:12, 1:4] ## Abbreviate Column Names colnames(LPP) <- abbreviate(colnames(LPP), 6) ## Print Data Set print(LPP) ## Alternative Use, Show Data Set LPP # equivalently, show(LPP) ## a short subseries to demo 'print' hC <- head(MSFT[ , "Close"]) class(hC) print(hC) print(hC, style = "h") } \keyword{chron} timeSeries/man/timeSeries-slotTime.Rd0000644000176200001440000000347315104730074017344 0ustar liggesusers\name{time} \title{Get and set time stamps of a 'timeSeries'} \alias{time} \alias{time<-} \alias{time,timeSeries-method} \alias{time.timeSeries} \alias{time<-.timeSeries} \alias{time<-.default} \alias{getTime} \alias{setTime<-} \description{ Functions and methods extracting and modifying positions of 'timeSeries' objects. } \usage{ \S4method{time}{timeSeries}(x, \dots) \method{time}{timeSeries}(x) <- value getTime(x) setTime(x) <- value } \arguments{ \item{value}{ a valid value for the time component of \code{x}. } \item{x}{ an object of class \code{timeSeries}. } \item{\dots}{ optional arguments passed to other methods. } } \details{ \code{time} and \code{time<-} are generic functions with methods for class \code{"timeSeries"}. They get and set the time component of the object. \code{getTime} and \code{setTime} are non-generic alternatives are non-generic wrappers of \code{time} and \code{time<-}, respectively. There is another generic function \code{time<-} defined in package \pkg{zoo}. When that package is loaded its \code{time<-} gets the \code{"timeSeries"} method. Also, if \code{"time<-"} is called with an object from class other than \code{"timeSeries"}, the call is dispatched to \code{"zoo:time<-"} to apply a suitable method. } \value{ for \code{time} and \code{getTime}, a \code{"timeDate"} object, for \code{time<-} and and \code{setTime}, the modified \code{"timeSeries"} object. } \examples{ ## Create Dummy 'timeSeries' - X <- timeSeries(matrix(rnorm(24), 12), timeCalendar()) ## Return Series Positions - getTime(X) time(X) ## Add / Subtract one Day from X setTime(X) <- time(X) - 24*3600 # sec X time(X) <- time(X) + 24*3600 # sec X } \keyword{chron} timeSeries/man/methods-comment.Rd0000644000176200001440000000146515104730074016537 0ustar liggesusers\name{comment} \alias{comment} \alias{comment<-} \alias{comment,timeSeries-method} \alias{comment<-,timeSeries-method} \title{Get and set comments for 'timeSeries' objects} \description{ Get or assign new comment to a \code{timeSeries} object. } \usage{ \S4method{comment}{timeSeries}(x) \S4method{comment}{timeSeries}(x) <- value } \arguments{ \item{x}{ a \code{timeSeries} object. } \item{value}{ a character vector, the comment. } } \details{ Objects from class \code{"timeSeries"} have a slot for documentation. These functions get and change its contents. } \examples{ ## Get description from a 'timeSeries' - comment(LPP2005REC) ## Add User to comment - comment(LPP2005REC) <- paste(comment(LPP2005REC), "by User Rmetrics") comment(LPP2005REC) } \keyword{chron} timeSeries/man/timeSeries-getDataPart.Rd0000644000176200001440000000060515104730074017736 0ustar liggesusers\name{DataPart,timeSeries-method} \alias{getDataPart,timeSeries-method} \alias{setDataPart,timeSeries-method} \title{DataPart,timeSeries-method} \description{ Utilities called to implement object@.Data of \code{timeSeries} objects. } \examples{ ## Load Microsoft Data - X <- MSFT[1:10, 1:4] ## Get Data Part - DATA <- getDataPart(X) class(DATA) } \keyword{chron} timeSeries/DESCRIPTION0000644000176200001440000000323015117036003014063 0ustar liggesusersPackage: timeSeries Title: Financial Time Series Objects (Rmetrics) Version: 4052.112 Authors@R: c(person("Diethelm", "Wuertz", role="aut", comment = "original code") , person("Tobias", "Setz", role = c("aut"), email = "tobias.setz@live.com") , person("Yohan", "Chalabi", role = "aut") , person("Martin","Maechler", role="ctb", email="maechler@stat.math.ethz.ch", comment = c(ORCID = "0000-0002-8685-9910")) , person(given = c("Georgi", "N."), family = "Boshnakov", role = c("cre", "aut"), email = "georgi.boshnakov@manchester.ac.uk", comment = c(ORCID = "0000-0003-2839-346X")) ) Description: 'S4' classes and various tools for financial time series: Basic functions such as scaling and sorting, subsetting, mathematical operations and statistical functions. Depends: R (>= 2.10), timeDate (>= 4041.110), methods Imports: graphics, grDevices, stats, utils Suggests: RUnit, robustbase, xts, zoo, PerformanceAnalytics, fTrading LazyData: yes License: GPL (>= 2) URL: https://geobosh.github.io/timeSeriesDoc/ (doc), https://CRAN.R-project.org/package=timeSeries, https://www.rmetrics.org BugReports: https://r-forge.r-project.org/tracker/?atid=633&group_id=156&func=browse NeedsCompilation: no Packaged: 2025-12-12 12:45:32 UTC; georgi Author: Diethelm Wuertz [aut] (original code), Tobias Setz [aut], Yohan Chalabi [aut], Martin Maechler [ctb] (ORCID: ), Georgi N. Boshnakov [cre, aut] (ORCID: ) Maintainer: Georgi N. Boshnakov Repository: CRAN Date/Publication: 2025-12-12 16:00:03 UTC