pax_global_header00006660000000000000000000000064151310231530014504gustar00rootroot0000000000000052 comment=c55afb47f0fc91be0f4d3c0604e27cb4842c92d2 csa-0.1.13/000077500000000000000000000000001513102315300123345ustar00rootroot00000000000000csa-0.1.13/.gitignore000066400000000000000000000002441513102315300143240ustar00rootroot00000000000000*.pyc aclocal.m4 autom4te.cache compile config.guess config.h.in config.sub configure depcomp dist fontList.cache install-sh ltmain.sh Makefile.in MANIFEST missing csa-0.1.13/.travis.yml000066400000000000000000000004471513102315300144520ustar00rootroot00000000000000language: python python: - "2.7" - "3.4" - "3.5" - "3.6" addons: apt: packages: install: - pip install coveralls matplotlib -q - source continuous_integration/install.sh script: - bash continuous_integration/test_script.sh after_success: coveralls cache: - apt - pipcsa-0.1.13/AUTHORS000066400000000000000000000004151513102315300134040ustar00rootroot00000000000000To find out what should go in this file, see "Information For Maintainers of GNU Software" (maintain.texi), the section called "Recording Changes". Mikael Djurfeldt Espen Hagen Patrick Herbers csa-0.1.13/COPYING000066400000000000000000001045131513102315300133730ustar00rootroot00000000000000 GNU GENERAL PUBLIC LICENSE Version 3, 29 June 2007 Copyright (C) 2007 Free Software Foundation, Inc. Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. Preamble The GNU General Public License is a free, copyleft license for software and other kinds of works. The licenses for most software and other practical works are designed to take away your freedom to share and change the works. By contrast, the GNU General Public License is intended to guarantee your freedom to share and change all versions of a program--to make sure it remains free software for all its users. We, the Free Software Foundation, use the GNU General Public License for most of our software; it applies also to any other work released this way by its authors. You can apply it to your programs, too. 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If the disclaimer of warranty and limitation of liability provided above cannot be given local legal effect according to their terms, reviewing courts shall apply local law that most closely approximates an absolute waiver of all civil liability in connection with the Program, unless a warranty or assumption of liability accompanies a copy of the Program in return for a fee. 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 state 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 3 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, see . Also add information on how to contact you by electronic and paper mail. If the program does terminal interaction, make it output a short notice like this when it starts in an interactive mode: Copyright (C) This program 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, your program's commands might be different; for a GUI interface, you would use an "about box". You should also get your employer (if you work as a programmer) or school, if any, to sign a "copyright disclaimer" for the program, if necessary. For more information on this, and how to apply and follow the GNU GPL, see . The GNU 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 Lesser General Public License instead of this License. But first, please read . csa-0.1.13/ChangeLog000066400000000000000000000006371513102315300141140ustar00rootroot000000000000002020-01-22 Mikael Djurfeldt * Version 0.1.10 2014-02-23 Mikael Djurfeldt * Version 0.1.6 2012-03-30 Mikael Djurfeldt * Release 0.1.0 2010-06-24 Mikael Djurfeldt * README: Updated "Getting Started" section. 2010-06-21 Mikael Djurfeldt * README: Added a "Getting Started" section. csa-0.1.13/INSTALL000066400000000000000000000366101513102315300133730ustar00rootroot00000000000000Installation Instructions ************************* Copyright (C) 1994-1996, 1999-2002, 2004-2013 Free Software Foundation, Inc. Copying and distribution of this file, with or without modification, are permitted in any medium without royalty provided the copyright notice and this notice are preserved. This file is offered as-is, without warranty of any kind. Basic Installation ================== Briefly, the shell command `./configure && make && make install' should configure, build, and install this package. The following more-detailed instructions are generic; see the `README' file for instructions specific to this package. Some packages provide this `INSTALL' file but do not implement all of the features documented below. The lack of an optional feature in a given package is not necessarily a bug. More recommendations for GNU packages can be found in *note Makefile Conventions: (standards)Makefile Conventions. The `configure' shell script attempts to guess correct values for various system-dependent variables used during compilation. It uses those values to create a `Makefile' in each directory of the package. It may also create one or more `.h' files containing system-dependent definitions. 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Run `configure --help' for more details. csa-0.1.13/MANIFEST.in000066400000000000000000000000551513102315300140720ustar00rootroot00000000000000include setup.py MANIFEST.in include COPYING csa-0.1.13/Makefile.am000066400000000000000000000025711513102315300143750ustar00rootroot00000000000000SUBDIRS = @LIBPYCSA_SUBDIR@ PACKAGE_NAME = python-csa PACKAGE_VERSION = $(shell PYTHONPATH="@srcdir@/csa" python3 -c 'from version import __version__; print (__version__)') EXTRA_DIST = $(srcdir)/setup.py csa/*.py $(srcdir)/csa/*.py debdir = dist/csa-$(PACKAGE_VERSION) .PHONY: dist debian-source debian-package README: $(srcdir)/README.md ln -s $(srcdir)/README.md README dist/csa-$(PACKAGE_VERSION).tar.gz: $(PYTHON) setup.py sdist debian-source: dist/csa-$(PACKAGE_VERSION).tar.gz @test ! -e $(debdir) || ( echo "*** Remove directory dist/csa-${PACKAGE_VERSION}" && exit 1 ) cp -p dist/csa-$(PACKAGE_VERSION).tar.gz dist/$(PACKAGE_NAME)_$(PACKAGE_VERSION).orig.tar.gz ( cd dist; tar zxf $(PACKAGE_NAME)_$(PACKAGE_VERSION).orig.tar.gz ) mkdir $(debdir)/debian cp -pr debian $(debdir) debian-package: debian-source ( cd $(debdir) && dpkg-buildpackage '-mMikael Djurfeldt ' -rfakeroot --changes-option=-S -sa && cd ../.. && rm -rf $(debdir) ) install-exec-hook: cd $(srcdir) &&\ ( test "$(srcdir)" != "$(builddir)" && cp "$(builddir)/csa/__init__.py" "$(srcdir)/csa"; true ) &&\ $(PYTHON) setup.py build --build-base=$(abs_builddir)/build install --prefix=$(DESTDIR)$(prefix) --install-lib=$(DESTDIR)$(pyexecdir) --install-scripts=$(DESTDIR)$(bindir) --install-data=$(DESTDIR)$(pkgdatadir) clean-local: -rm -rf $(srcdir)/csa/*.pyc $(abs_builddir)/build tex.cache csa-0.1.13/NEWS000066400000000000000000000015431513102315300130360ustar00rootroot00000000000000CSA NEWS --- history of user-visible changes. Copyright (C) 2012, 2018, 2020 Mikael Djurfeldt Please send CSA bug reports to mikael@djurfeldt.com. Changes in 0.1.13: * Bug fixes Use simple regexp match to find version. Fixes issue #25. Changes in 0.1.12: * Bug fixes Adapt to change in iterator behavior (PEP-0479). Changes in 0.1.10: * Build Debian package for Python3 * Bug fixes Further support for Python3. Properly specify requirements in setup tools. Changes in 0.1.8: * Support for Python3 * Building in separate build directory enabled * New geometry functions grid3d and random3d * Bug fixes Changes in 0.1.0: * Partial support for reading and writing XML * Tutorial in doc directory * New functions and operators Changes in 0.0.4: First release: 0.0.1 Local variables: mode: outline paragraph-separate: "[ ]*$" end: csa-0.1.13/README.md000066400000000000000000000150071513102315300136160ustar00rootroot00000000000000Connection-Set Algebra (CSA) ============================ This is a demonstration implementation in Python of the Connection-Set Algebra (Djurfeldt, Mikael (2012), Neuroinformatics) Code status =========== [![Build Status](https://travis-ci.org/INCF/csa.svg?branch=master)](https://travis-ci.org/INCF/csa) [![Coverage Status](https://coveralls.io/repos/github/INCF/csa/badge.svg?branch=master)](https://coveralls.io/github/INCF/csa?branch=master) Purpose ======= The CSA library provides elementary connection-sets and operators for combining them. It also provides an iteration interface to such connection-sets enabling efficient iteration over existing connections with a small memory footprint also for very large networks. The CSA can be used as a component of neuronal network simulators or other tools. See the following reference for more information: Mikael Djurfeldt (2012) "The Connection-set Algebra---A Novel Formalism for the Representation of Connectivity Structure in Neuronal Network Models" Neuroinformatics 10(3), 1539-2791, http://dx.doi.org/10.1007/s12021-012-9146-1 License ======= CSA is released under the GNU General Public License Requirements ============ CSA is dependent on Numpy and Matplotlib Introduction ============ A connection set is a set of existing connections between a set of source nodes and a set of target nodes. Typically, source and target nodes are neurons in a neuronal network, but targets could also be particular structures of a neuron, such synaptic sites. Sources and targets are enumerated by integers and a connection is represented by a pair of integers, one denoting the source node and one the target node. Source and target can be (and is often) the same set. CSA connection sets are usually infinite. This is a simplification compared to the common situation of finite source and target sets in that the sizes of these sets do not need to be considered. Connection sets can have arbitrary values associated with connections. Pure connection sets without any values associated are called masks. Getting started =============== Basics ------ To get access to the CSA in Python, type: :: from csa import * The mask representing all possible connections between an infinite source and target set is: :: full To display a finite portion of the corresponding connectivity matrix, type: :: show (full) One-to-one connectivity (where source node 0 is connected to target node 0, source 1 to target 1 etc) is represented by the mask oneToOne: :: show (oneToOne) The default portion displayed by "show" is (0, 29) x (0, 29). (0, 99) x (0, 99) can be displayed using: :: show (oneToOne, 100, 100) If source and target set is the same, oneToOne describes self-connections. We can use CSA to compute the set of connections consisting of all possible connections except for self-connections using the set difference operator "-": :: show (full - oneToOne) Finite connection sets can be represented using either lists of connections, with connections represented as tuples: :: show ([(22, 7), (8, 23)]) or using the Cartesian product of intervals: :: show (cross (range (10), range (20))) We can form a finite version of the infinite oneToOne by taking the intersection "*" with a finite connection set: :: c = cross (range (10), range (10)) * oneToOne show (c) Finite connection sets can be tabulated: :: tabulate (c) In Python, finite connection sets provide an iterator interface: :: for x in cross (range (10), range (10)) * oneToOne: print x Random connectivity and the block operator ------------------------------------------ Connectivity where the existence of each possible connection is determined by a Bernoulli trial with probability p is expressed with the random mask random (p), e.g.: :: show (random (0.5)) The block operator expands each connection in the operand into a rectangular block in the resulting connection matrix, e.g.: :: show (block (5,3) * random (0.5)) Note that "*" here means operator application. There is also a quadratic version of the operator: :: show (block (10) * random (0.7)) Using intersection and set difference, we can now formulate a more complex mask: :: show (block (10) * random (0.7) * random (0.5) - oneToOne) Geometry -------- In CSA, the basic tool to handle distance dependent connectivity is metrics. Metrics are value sets d (i, j). Metrics can be defined through geometry functions. A geometry function maps an index to a position. We can, for example, assign a random position in the unit square to each index: :: g = random2d (900) The positions of the grid described by g have indices from 0 to 899 and can be displayed like this: :: gplot2d (g, 900) Alternatively, we can arrange indices in a 30 x 30 grid within the unit square: :: g = grid2d (30) We can now define the euclidean metric on this grid: :: d = euclidMetric2d (g) An example of a distance dependent connection set is the disc mask Disc (r) * d which connects each index i to all indices j within a distance d (i, j) < r: :: c = disc (r) * d To examine the result we can employ the function gplotsel2d (g, c, i) which displays the targets g (j) of i in the connection set c: :: gplotsel2d (g, c, 434) In the case where the connection set represents a projection between two different coordinate systems, we define one geometry function for each. In the following example g1 is direction in visual space in arc minutes while g2 is position in the cortical representation of the Macaque fovea in mm: :: g1 = grid2d (30) g2 = grid2d (30, x0 = -7.0, xScale = 8.0, yScale = 8.0) We now define a projection operator which takes visual coordinates into cortical (Dow et al. 1985): :: import cmath @ProjectionOperator def GvspaceToCx (p): w = 7.7 * cmath.log (complex (p[0] + 0.33, p[1])) return (w.real, w.imag) To see how the grid g1 is transformed into cortical space, we type: :: gplot2d (GvspaceToCx * g1, 900) The inverse projection is defined: :: @ProjectionOperator def GcxToVspace (p): c = cmath.exp (complex (p[0], p[1]) / 7.7) - 0.33 return (c.real, c.imag) Real receptive field sizes vary with eccentricity. Assume, for now, that we want to connect each target index to sources within a disc of constant radius. We then need to project back into visual space and use the disc operator: :: c = disc (0.1) * euclidMetric2d (g1, GcxToVspace * g2) Again, we use gplotsel2d to check the result: :: gplotsel2d (g2, c, 282) csa-0.1.13/RELEASE000066400000000000000000000007041513102315300133400ustar00rootroot00000000000000Checklist for a release: * Install packages: dpkg-dev dpkg-dev-el dh-python lintian debhelper python3-setuptools python3-all python3-pytest python3-numpy python3-matplotlib * csa/version.py * (optional) debian/changelog * update NEWS * make debian-package * lintian -i python-csa_x.y.z-n_ARCH.changes * dupload python-csa_x.y.z-n_ARCH.changes * stage changes * git commit -m'Release N.N.N.' * git tag vN.N.N * git push origin master vN.N.N csa-0.1.13/TODO000066400000000000000000000015341513102315300130270ustar00rootroot00000000000000* Flip y-axis in show. Flip axes. * Go through how RNG:s are seeded, especially when one CS is used repeatedly as component of other CSs * Go through use of and rename classes Finite and FiniteMask. * Avoid intersection at top of MaskPartition * Implement CSetPartition * Implement lazy evaluation in BinaryCSet.makeValueSetMap * Implement SubMaskContainer (startIteration etc) to avoid code duplication * eliminate code duplication in _elementary.FanInRandomMask * loosen up criteria on Finite connection sets to BoundedConnectivity (Masks with upper bounds on the number of connections per source or target.) * generalize FanInRandomMask to work on BoundedInConnectivityMask:s, for example block masks of BoundedInConnectivityMask:s * generalize SampleNRandomMask to work on Finite masks * make value sets returned by value(c,k) iterable csa-0.1.13/acinclude.m4000066400000000000000000000062361513102315300145340ustar00rootroot00000000000000# =========================================================================== # http://www.gnu.org/software/autoconf-archive/ax_check_compile_flag.html # =========================================================================== # # SYNOPSIS # # AX_CHECK_COMPILE_FLAG(FLAG, [ACTION-SUCCESS], [ACTION-FAILURE], [EXTRA-FLAGS]) # # DESCRIPTION # # Check whether the given FLAG works with the current language's compiler # or gives an error. 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AC_DEFUN([AX_CHECK_COMPILE_FLAG], [AC_PREREQ(2.59)dnl for _AC_LANG_PREFIX AS_VAR_PUSHDEF([CACHEVAR],[ax_cv_check_[]_AC_LANG_ABBREV[]flags_$4_$1])dnl AC_CACHE_CHECK([whether _AC_LANG compiler accepts $1], CACHEVAR, [ ax_check_save_flags=$[]_AC_LANG_PREFIX[]FLAGS _AC_LANG_PREFIX[]FLAGS="$[]_AC_LANG_PREFIX[]FLAGS $4 $1" AC_COMPILE_IFELSE([AC_LANG_PROGRAM()], [AS_VAR_SET(CACHEVAR,[yes])], [AS_VAR_SET(CACHEVAR,[no])]) _AC_LANG_PREFIX[]FLAGS=$ax_check_save_flags]) AS_IF([test x"AS_VAR_GET(CACHEVAR)" = xyes], [m4_default([$2], :)], [m4_default([$3], :)]) AS_VAR_POPDEF([CACHEVAR])dnl ])dnl AX_CHECK_COMPILE_FLAGS csa-0.1.13/aclocal.sh000077500000000000000000000007521513102315300142750ustar00rootroot00000000000000#!/bin/sh if test -z "$ACLOCAL" ; then for each in aclocal-1.14 aclocal-1.13 aclocal-1.12 aclocal-1.11 aclocal-1.10 aclocal-1.9 aclocal-1.8 aclocal-1.7 aclocal-1.6 aclocal ; do ACLOCAL=$each if test -n "`which $each 2>/dev/null`" ; then break ; fi done fi ACDIR=`which $ACLOCAL` ACDIR=`dirname $ACDIR` ACDIR=`dirname $ACDIR`/share/aclocal for each in $ACDIR ; do if test -d "$each" ; then AFLAGS="-I $each $AFLAGS" fi done echo $ACLOCAL $AFLAGS $@ $ACLOCAL $AFLAGS $@ csa-0.1.13/autogen.sh000077500000000000000000000012001513102315300143260ustar00rootroot00000000000000#!/bin/sh [ -f autogen.sh ] || { echo "autogen.sh: run this command only at the top of the source tree." exit 1 } if test -z "$AUTOMAKE" ; then for each in automake-1.14 automake-1.13 automake-1.12 automake-1.11 automake-1.10 automake-1.9 automake-1.8 automake-1.7 automake-1.6 automake ; do AUTOMAKE=$each if test -n "`which $each 2>/dev/null`" ; then break ; fi done fi ./aclocal.sh && echo libtoolize --copy --automake && libtoolize --copy --automake && echo autoheader && autoheader && echo autoconf && autoconf && echo $AUTOMAKE --copy --add-missing && $AUTOMAKE --copy --add-missing && echo Now run configure and make. csa-0.1.13/configure.ac000066400000000000000000000062741513102315300146330ustar00rootroot00000000000000dnl Process this file with autoconf to produce configure. AC_INIT(csa, 0.1.8) AM_INIT_AUTOMAKE AM_CONFIG_HEADER([config.h]) AM_MAINTAINER_MODE AC_MSG_CHECKING([library directory at]) eval pfx="/usr/local" if test "${exec_prefix}" != "NONE" ; then eval pfx="${exec_prefix}" elif test "${prefix}" != "NONE" ; then eval pfx="${prefix}" fi eval libdir="`echo "$libdir" | sed "s,\\${exec_prefix},${pfx},"`" AC_MSG_RESULT($libdir) # # Use libneurosim? # AC_CHECK_LIB(neurosim, libneurosim_version, HAVE_LIBNEUROSIM="auto", HAVE_LIBNEUROSIM="no") if test "x$HAVE_LIBNEUROSIM" = xauto; then LIBNEUROSIM_LIBS="-lneurosim" LIBNEUROSIM_PY_LIBS="-lpyneurosim" LIBNEUROSIM_INCLUDE="" fi AC_ARG_WITH(libneurosim, [ --with-libneurosim[[=directory]] Request the use of libneurosim. Optionally give the directory, where libneurosim is installed], [ if test "$withval" != "no"; then if test "$withval" != "yes"; then LIBNEUROSIM_LIBS="-L${withval}/lib -lneurosim" LIBNEUROSIM_PY_LIBS="-lpyneurosim" LIBNEUROSIM_INCLUDE="-I${withval}/include" fi HAVE_LIBNEUROSIM="yes" else HAVE_LIBNEUROSIM="no" fi ]) if test "x$HAVE_LIBNEUROSIM" != xno; then AC_DEFINE(HAVE_LIBNEUROSIM, 1, [libneurosim support enabled?]) fi AC_ARG_WITH([python], [AS_HELP_STRING([--without-python], [ignore the presence of Python and disable libpycsa])]) AS_IF([test "x$with_python" != "xno"], [AS_IF([test "x$with_python" != "xyes"], [PYTHON="$with_python"]) AM_PATH_PYTHON([2.6], [have_python=yes], [have_python=no])], [have_python=no]) AS_IF([test "x$have_python" = "xyes"], [ PYTHON_INC=`$PYTHON -c 'import sys; from distutils import sysconfig; sys.stdout.write(sysconfig.get_python_inc())'` AC_CHECK_FILE(["${PYTHON_INC}/Python.h"], [LIBPYCSA_CPPFLAGS="-I${PYTHON_INC}"], [have_python=no]) PYTHONLIB="" AC_CHECK_LIB(python${am_cv_python_version}m,PyArg_ParseTuple, [PYTHONLIB=-lpython${am_cv_python_version}m], [AC_CHECK_LIB(python${am_cv_python_version},PyArg_ParseTuple, [PYTHONLIB=-lpython${am_cv_python_version}])]) ]) AC_MSG_CHECKING([whether to build libpycsa]) AS_IF([test "x$have_python" = "xyes"], [], [AS_IF([test "x$with_python" = "xyes"], [AC_MSG_ERROR([Libpycsa requested, but Python not found])])]) AC_MSG_RESULT([$have_python]) AX_CHECK_COMPILE_FLAG([-fno-strict-aliasing], [LIBPYCSA_CXXFLAGS="-fno-strict-aliasing"], []) # FIXME: this means that --without-python make will not recurse into libpycsa subdir if test "x$have_python" = "xyes"; then LIBPYCSA_SUBDIR="libpycsa" else LIBPYCSA_SUBDIR="" fi AM_CONDITIONAL([HAVE_PYTHON], [test "x$have_python" = "xyes"]) AC_SUBST([HAVE_PYTHON]) AC_SUBST([LIBPYCSA_SUBDIR]) AC_SUBST([PYTHON]) AC_SUBST([PYTHONLIB]) AC_SUBST([LIBPYCSA_CPPFLAGS]) AC_SUBST([LIBPYCSA_CXXFLAGS]) AC_LANG(C++) AC_PROG_CXX AC_PROG_LIBTOOL AC_SUBST(LIBNEUROSIM_LIBS) AC_SUBST(LIBNEUROSIM_PY_LIBS) AC_SUBST(LIBNEUROSIM_INCLUDE) AC_CONFIG_FILES([ Makefile csa/__init__.py libpycsa/Makefile ]) AC_OUTPUT dnl Local Variables: dnl comment-start: "dnl " dnl comment-end: "" dnl comment-start-skip: "\\bdnl\\b\\s *" dnl End: csa-0.1.13/continuous_integration/000077500000000000000000000000001513102315300171455ustar00rootroot00000000000000csa-0.1.13/continuous_integration/install.sh000066400000000000000000000000141513102315300211420ustar00rootroot00000000000000#!/bin/bash csa-0.1.13/continuous_integration/test_script.sh000066400000000000000000000007211513102315300220440ustar00rootroot00000000000000#!/bin/bash set -e python --version python -c "import numpy; print('numpy %s' % numpy.__version__)" python -c "import matplotlib; print('matplotlib %s' % matplotlib.__version__)" # build csa inplace python setup.py build_ext -i # run tests, but if mystery segmentation fault occurr, rerun tests as an # attempt at a clean exit while true; do nosetests --with-coverage --cover-package=csa if [ $? -eq 0 ] then exit 0 break fi donecsa-0.1.13/csa/000077500000000000000000000000001513102315300131025ustar00rootroot00000000000000csa-0.1.13/csa/ChangeLog000066400000000000000000000051051513102315300146550ustar00rootroot000000000000002013-10-30 Mikael Djurfeldt * intervalset.py (IntervalSet.skipIntervals): New function. 2013-06-13 Mikael Djurfeldt * geometry.py (euclidToroidDistance2d, euclidToroidMetric2d): New functions. 2013-06-12 Mikael Djurfeldt * misc.py (repeat): New operator. * _misc.py (Repeat, RepeatMask): New masks. 2012-05-25 Mikael Djurfeldt * intervalset.py (IntervalSet.goodInterval): Accept long as index. 2012-03-30 Mikael Djurfeldt * Release 0.1.0 2012-02-24 Mikael Djurfeldt * csaobject.py (CSAObject.formalFromXML): New method. (CSAObject.from_xml): Handle binding operators. * closure.py: New file. * __init__.py: import closure.py. 2011-07-29 Mikael Djurfeldt * connset.py (CSetPartition): New class. 2011-01-18 Mikael Djurfeldt * elementary.py (vset): New function. 2010-07-25 Mikael Djurfeldt * misc.py (shift), _misc.py (Shift): Added shift operator. 2010-07-16 Mikael Djurfeldt * intervalset.py (ComplementaryIntervalSet): New class. 2010-06-29 Mikael Djurfeldt * elementary.py (partition): New function. * _elementary.py (MaskPartition, SampleNRandomMask): New masks. * _misc.py (Random): Implemenented parameter N. * connset.py (IntervalSetMask.multisetSum): New method. * intervalset.py (IntervalSet.union): New method. 2010-06-27 Mikael Djurfeldt * _misc.py (ValueSetRandomMask): New mask. Random operator now implemented. * misc.py (gaussian): New operator. 2010-06-25 Mikael Djurfeldt * connset.py (ExplicitCSet): New class which captures value sets before coercion so that these can be returned by value (c, k) when possible. * elementary.py (cset): Use ExplicitCSet. 2010-06-24 Mikael Djurfeldt * elementary.py (random): Instance of Random operator. * _misc.py (Random): New operator. * _elementary.py (RandomMask): Renamed from Random. 2010-06-19 Mikael Djurfeldt * connset.py (Mask.__mul__): Only use commutativity if second operand is a ConnectionSet. * connset.py, _elementary.py, _misc.py: Restructured handling of bounds. Raise RunTime exception on attempt to retrieve iterator over infinite mask. * connset.py, elementary.py (ExplicitMask): Renamed from FiniteMask. 2010-06-18 Mikael Djurfeldt * Start of ChangeLog csa-0.1.13/csa/__init__.py.in000066400000000000000000000022231513102315300156170ustar00rootroot00000000000000# # This file is part of the Connection-Set Algebra (CSA). # Copyright (C) 2010,2011,2012,2017 Mikael Djurfeldt # # CSA 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 3 of the License, or # (at your option) any later version. # # CSA 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, see . # import ctypes try: _libpycsa_handle_ = ctypes.CDLL ('@libdir@/libpycsa.so') except OSError: None from .version import __version__ from .connset import Mask from .connset import ConnectionSet from .elementary import * #from operators import * #from arithmetic import * from .misc import * from .geometry import * from .csaobject import parse, from_xml from .plot import * from .closure import * from .conngen import * csa-0.1.13/csa/_elementary.py000066400000000000000000000262731513102315300157720ustar00rootroot00000000000000# # This file is part of the Connection-Set Algebra (CSA). # Copyright (C) 2010,2011,2012 Mikael Djurfeldt # # CSA 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 3 of the License, or # (at your option) any later version. # # CSA 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, see . # import random import numpy import copy from . import connset as cs from . import intervalset as iset from .csaobject import * class FullMask (cs.IntervalSetMask): tag = 'full' def __init__ (self): cs.IntervalSetMask.__init__ (self, iset.N, iset.N) self.name = FullMask.tag CSAObject.tag_map[CSA + FullMask.tag] = (self, SINGLETON) def __call__ (self, N0, N1 = None): if N1 == None: N1 = N0 return cs.FiniteISetMask (iset.IntervalSet ((0, N0 - 1)), \ iset.IntervalSet ((0, N1 - 1))) class OneToOne (cs.Mask): tag = 'oneToOne' def __init__ (self): cs.Mask.__init__ (self) self.name = OneToOne.tag CSAObject.tag_map[CSA + OneToOne.tag] = (self, SINGLETON) def iterator (self, low0, high0, low1, high1, state): for i in range (max (low0, low1), min (high0, high1)): yield (i, i) class ConstantRandomMask (cs.Mask): tag = 'randomMask' def __init__ (self, p): cs.Mask.__init__ (self) self.p = p self.state = random.getstate () self.name = ConstantRandomMask.tag def startIteration (self, state): random.setstate (self.state) return self def iterator (self, low0, high0, low1, high1, state): for j in range (low1, high1): for i in range (low0, high0): if random.random () < self.p: yield (i, j) def repr (self): return 'random(%s)' % self.p def _to_xml (self): return CSAObject.apply (ConstantRandomMask.tag, self.p) registerTag (ConstantRandomMask.tag, ConstantRandomMask, 1) class SampleNRandomOperator (cs.Operator): tag = 'random_N' def __init__ (self, N): self.N = N def __mul__ (self, other): assert isinstance (other, cs.Finite) \ and isinstance (other, cs.Mask), \ 'expected finite mask' return SampleNRandomMask (self.N, other) def repr (self): return 'random(N = %s)' % self.N def _to_xml (self): return CSAObject.apply (SampleNRandomOperator.tag, self.N) registerTag (SampleNRandomOperator.tag, SampleNRandomOperator, 1) class SampleNRandomMask (cs.Finite,cs.Mask): # The algorithm based on first sampling the number of connections # per partition has been arrived at through discussions with Hans # Ekkehard Plesser. # def __init__ (self, N, mask): cs.Mask.__init__ (self) self.N = N assert isinstance (mask, cs.FiniteISetMask), \ 'SampleNRandomMask currently only operates on FiniteISetMask:s' self.mask = mask self.randomState = random.getstate () self.npRandomState = numpy.random.get_state () def bounds (self): return self.mask.bounds () def startIteration (self, state): obj = copy.copy (self) # local state: N, N0, perTarget, sources random.setstate (self.randomState) obj.isPartitioned = False if 'partitions' in state: obj.isPartitioned = True partitions = list (map (self.mask.intersection, state['partitions'])) sizes = list (map (len, partitions)) total = sum (sizes) # The following yields the same result on all processes. # We should add a seed function to the CSA. if 'seed' in state: seed = state['seed'] else: seed = 'SampleNRandomMask' # Numpy requires an unsigned 32-bit integer numpy.random.seed (hash (seed) % (numpy.iinfo(numpy.uint32).max + 1)) N = numpy.random.multinomial (self.N, numpy.array (sizes) \ / float (total)) obj.N = N[state['selected']] obj.mask = partitions[state['selected']] assert isinstance (obj.mask, cs.FiniteISetMask), \ 'SampleNRandomMask iterator only handles finite IntervalSetMask partitions' obj.mask = obj.mask.startIteration (state) obj.N0 = len (obj.mask.set0) obj.lastBound0 = False N1 = len (obj.mask.set1) numpy.random.set_state (self.npRandomState) obj.perTarget = numpy.random.multinomial (obj.N, [1.0 / N1] * N1) return obj def iterator (self, low0, high0, low1, high1, state): m = self.mask.set1.count (0, low1) if self.isPartitioned and m > 0: # "replacement" for a proper random.jumpahead (n) # This is required so that different partitions of this # mask aren't produced using the same stream of random # numbers. random.seed (random.getrandbits (32) + m) if self.lastBound0 != (low0, high0): self.lastBound0 = (low0, high0) self.sources = [] for i in self.mask.set0.boundedIterator (low0, high0): self.sources.append (i) nSources = len (self.sources) for j in self.mask.set1.boundedIterator (low1, high1): s = [] for k in range (0, self.perTarget[m]): i = random.randint (0, self.N0 - 1) if i < nSources: s.append (self.sources[i]) s.sort () for i in s: yield (i, j) m += 1 def repr (self): return self._repr_applyop ('random(N=%s)' % self.N, self.mask) def _to_xml (self): return E ('apply', E ('times'), CSAObject.apply (SampleNRandomOperator.tag, self.N), self.mask._to_xml ()) class FanInRandomOperator (cs.Operator): tag = 'random_fanIn' def __init__ (self, fanIn): self.fanIn = fanIn def __mul__ (self, other): assert isinstance (other, cs.Finite) \ and isinstance (other, cs.Mask), \ 'expected finite mask' return FanInRandomMask (self.fanIn, other) def repr (self): return 'random(fanIn=%s)' % self.fanIn def _to_xml (self): return CSAObject.apply (FanInRandomOperator.tag, self.fanIn) registerTag (FanInRandomOperator.tag, FanInRandomOperator, 1) # This code is copied and modified from SampleNRandomMask # *fixme* refactor code and eliminate code duplication class FanInRandomMask (cs.Finite, cs.Mask): # The algorithm based on first sampling the number of connections # per partition has been arrived at through discussions with Hans # Ekkehard Plesser. # def __init__ (self, fanIn, mask): cs.Mask.__init__ (self) self.fanIn = fanIn assert isinstance (mask, cs.FiniteISetMask), \ 'FanInRandomMask currently only operates on FiniteISetMask:s' self.mask = mask self.randomState = random.getstate () def bounds (self): return self.mask.bounds () def startIteration (self, state): obj = copy.copy (self) # local state: N, N0, perTarget, sources random.setstate (self.randomState) obj.isPartitioned = False if 'partitions' in state: obj.isPartitioned = True partitions = list (map (self.mask.intersection, state['partitions'])) # The following yields the same result on all processes. # We should add a seed function to the CSA. if 'seed' in state: seed = state['seed'] else: seed = 'FanInRandomMask' # Numpy.random.seed requires an unsigned 32 bit integer numpy.random.seed (hash (seed) % (numpy.iinfo(numpy.uint32).max + 1)) selected = state['selected'] obj.mask = partitions[selected] assert isinstance (obj.mask, cs.FiniteISetMask), \ 'FanInRandomMask iterator only handles finite IntervalSetMask partitions' obj.mask = obj.mask.startIteration (state) obj.N0 = len (obj.mask.set0) obj.lastBound0 = False if obj.isPartitioned: obj.perTarget = [] for j in obj.mask.set1: size = 0 sourceDist = numpy.zeros (len (partitions)) for k in range (len (partitions)): if j in partitions[k].set1: sourceDist[k] = len (partitions[k].set0) sourceDist /= sum (sourceDist) dist = numpy.random.multinomial (self.fanIn, sourceDist) obj.perTarget.append (dist[selected]) else: obj.perTarget = [self.fanIn] * len (obj.mask.set1) return obj def iterator (self, low0, high0, low1, high1, state): m = self.mask.set1.count (0, low1) if self.isPartitioned and m > 0: # "replacement" for a proper random.jumpahead (n) # This is required so that different partitions of this # mask aren't produced using the same stream of random # numbers. random.seed (random.getrandbits (32) + m) if self.lastBound0 != (low0, high0): self.lastBound0 = (low0, high0) self.sources = [] for i in self.mask.set0.boundedIterator (low0, high0): self.sources.append (i) nSources = len (self.sources) for j in self.mask.set1.boundedIterator (low1, high1): s = [] for k in range (0, self.perTarget[m]): i = random.randint (0, self.N0 - 1) if i < nSources: s.append (self.sources[i]) s.sort () for i in s: yield (i, j) m += 1 def repr (self): return self._repr_applyop ('random(fanIn=%s)' % self.fanIn, self.mask) def _to_xml (self): return E ('apply', E ('times'), CSAObject.apply (FanInRandomOperator.tag, self.fanIn), self.mask._to_xml ()) class FanOutRandomOperator (cs.Operator): tag = 'random_fanOut' def __init__ (self, fanOut): self.fanOut = fanOut def __mul__ (self, other): assert isinstance (other, cs.Finite) \ and isinstance (other, cs.Mask), \ 'expected finite mask' return FanInRandomMask (self.fanOut, other.transpose ()).transpose () def repr (self): return 'random(fanOut=%s)' % self.fanOut def _to_xml (self): return CSAObject.apply (FanOutRandomOperator.tag, self.fanOut) registerTag (FanOutRandomOperator.tag, FanOutRandomOperator, 1) csa-0.1.13/csa/_misc.py000066400000000000000000000245331513102315300145550ustar00rootroot00000000000000# # This file is part of the Connection-Set Algebra (CSA). # Copyright (C) 2010,2011,2012,2019,2020 Mikael Djurfeldt # # CSA 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 3 of the License, or # (at your option) any later version. # # CSA 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, see . # import math import random import copy #from scipy.spatial import KDTree from . import connset as cs from . import valueset as vs from . import _elementary from .csaobject import * class Random (cs.Operator): def __mul__ (self, valueSet): return ValueSetRandomMask (valueSet) def __call__ (self, p = None, N = None, fanIn = None, fanOut = None): if p != None: assert N == None and fanIn == None and fanOut == None, \ 'inconsistent parameters' return _elementary.ConstantRandomMask (p) elif N != None: assert fanIn == None and fanOut == None, \ 'inconsistent parameters' return _elementary.SampleNRandomOperator (N) elif fanIn != None: assert fanOut == None, \ 'inconsistent parameters' return _elementary.FanInRandomOperator (fanIn) elif fanOut != None: return _elementary.FanOutRandomOperator (fanOut) assert False, 'inconsistent parameters' class ValueSetRandomMask (cs.Mask): def __init__ (self, valueSet): cs.Mask.__init__ (self) self.valueSet = valueSet self.state = random.getstate () def startIteration (self, state): random.setstate (self.state) return self def iterator (self, low0, high0, low1, high1, state): for j in range (low1, high1): for i in range (low0, high0): if random.random () < self.valueSet (i, j): yield (i, j) def _to_xml (self): return CSAObject.apply ('times', 'random', self.valueSet._to_xml ()) class Disc (cs.Operator): def __init__ (self, r): self.r = r def __mul__ (self, metric): return DiscMask (self.r, metric) class DiscMask (cs.Mask): def __init__ (self, r, metric): cs.Mask.__init__ (self) self.r = r self.metric = metric def iterator (self, low0, high0, low1, high1, state): for j in range (low1, high1): for i in range (low0, high0): if self.metric (i, j) < self.r: yield (i, j) class Rectangle (cs.Operator): def __init__ (self, width, height): self.width = width self.height = height def __mul__ (self, gFunction): if isinstance (gFunction, tuple): return RectangleMask (self.width, self.height, gFunction[0], gFunction[1]) else: return RectangleMask (self.width, self.height, gFunction, gFunction) class RectangleMask (cs.Mask): def __init__ (self, width, height, g0, g1): cs.Mask.__init__ (self) self.hwidth = width / 2.0 self.hheight = height / 2.0 self.g0 = g0 self.g1 = g1 def iterator (self, low0, high0, low1, high1, state): for j in range (low1, high1): for i in range (low0, high0): p0 = self.g0 (i) p1 = self.g1 (j) dx = p0[0] - p1[0] dy = p0[1] - p1[1] if abs (dx) < self.hwidth and abs (dy) < self.hheight: yield (i, j) class Gaussian (cs.Operator): def __init__ (self, sigma, cutoff): cs.Operator.__init__ (self, 'gaussian') self.sigma = sigma self.cutoff = cutoff def __mul__ (self, metric): return GaussianValueSet (self, metric) class GaussianValueSet (OpExprValue, vs.ValueSet): def __init__ (self, operator, metric): OpExprValue.__init__ (self, operator, metric) self.sigma22 = 2 * operator.sigma * operator.sigma self.cutoff = operator.cutoff self.metric = metric def __call__ (self, i, j): d = self.metric (i, j) return math.exp (- d * d / self.sigma22) if d < self.cutoff else 0.0 class Block (cs.Operator): def __init__ (self, M, N): self.M = M self.N = N def __mul__ (self, other): c = cs.coerceCSet (other) if isinstance (c, cs.Mask): return BlockMask (self.M, self.N, c) else: return cs.ConnectionSet (BlockCSet (self.M, self.N, c)) class BlockMask (cs.Mask): def __init__ (self, M, N, mask): cs.Mask.__init__ (self) self.M = M self.N = N self.m = mask def startIteration (self, state): #*fixme* filter out 'partitions' from state nState = {} for k in state: if k != 'partitions': nState[k] = state[k] self.obj = self.m.startIteration (nState) return self def iterator (self, low0, high0, low1, high1, state): maskIter = self.obj.iterator (low0 // self.M, (high0 + self.M - 1) // self.M, low1 // self.N, (high1 + self.N - 1) // self.N, state) try: pre = [] (i, j) = next (maskIter) while True: # collect connections in one connection matrix column post = j while j == post: pre.append (i) (i, j) = next (maskIter) # generate blocks for the column for jj in range (max (self.N * post, low1), min (self.N * (post + 1), high1)): for k in pre: for ii in range (max (self.M * k, low0), min (self.M * (k + 1), high0)): yield (ii, jj) pre = [] except StopIteration: if pre: # generate blocks for the last column for jj in range (max (self.N * post, low1), min (self.N * (post + 1), high1)): for k in pre: for ii in range (max (self.M * k, low0), min (self.M * (k + 1), high0)): yield (ii, jj) class Repeat (cs.Operator): def __init__ (self, M, N): self.M = M self.N = N def __mul__ (self, other): c = cs.coerceCSet (other) if isinstance (c, cs.Mask): return RepeatMask (self.M, self.N, c) else: return cs.ConnectionSet (RepeatCSet (self.M, self.N, c)) # Not fully implemented # Currently only handles cases where we iterate over an even number of # ocurrences of the template mask, both with regard to sources and targets # class RepeatMask (cs.Mask): def __init__ (self, M, N, mask): cs.Mask.__init__ (self) self.M = M self.N = N self.m = mask def iterator (self, low0, high0, low1, high1, state): try: jj = low1 nextHigh1 = (low1 + self.N) / self.N * self.N while nextHigh1 <= high1: maskIter = self.m.iterator (0, self.M, 0, self.N, state) try: (i, j) = next (maskIter) post = j while post < self.N: pre = [] while j == post: pre.append (i) (i, j) = next (maskIter) ii = low0 while ii < high0: for k in pre: yield (ii + k, jj + post) ii += self.M post = j except StopIteration: ii = low0 while ii < high0: for k in pre: yield (ii + k, jj + post) ii += self.M jj = nextHigh1 nextHigh1 += self.N except StopIteration: return class Transpose (cs.Operator): def __mul__ (self, other): c = cs.coerceCSet (other) if isinstance (c, cs.Mask): return other.transpose () else: return cs.ConnectionSet (other.transpose ()) class Shift (cs.Operator): def __init__ (self, M, N): self.M = M self.N = N def __mul__ (self, other): c = cs.coerceCSet (other) if isinstance (c, cs.Mask): return other.shift (self.M, self.N) else: return cs.ConnectionSet (other.shift (self.M, self.N)) class Fix (cs.Operator): def __mul__ (self, other): c = cs.coerceCSet (other) if isinstance (c, cs.Mask): return FixedMask (other) else: return cs.ConnectionSet (FixedCSet (other)) class FixedMask (cs.FiniteMask): def __init__ (self, mask): cs.FiniteMask.__init__ (self) ls = [] for c in mask: ls.append (c) self.connections = ls targets = list (map (cs.target, ls)) self.low0 = min (ls)[0] self.high0 = max (ls)[0] + 1 self.low1 = min (targets) self.high1 = max (targets) + 1 def iterator (self, low0, high0, low1, high1, state): if not self.isBoundedBy (low0, high0, low1, high1): return iter (self.connections) else: return self.boundedIterator (low0, high0, low1, high1) def boundedIterator (self, low0, high0, low1, high1): for c in self.connections: if low0 <= c[0] and c[0] < high0 \ and low1 <= c[1] and c[1] < high1: yield c csa-0.1.13/csa/closure.py000066400000000000000000000031621513102315300151320ustar00rootroot00000000000000# # This file is part of the Connection-Set Algebra (CSA). # Copyright (C) 2010,2011,2012 Mikael Djurfeldt # # CSA 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 3 of the License, or # (at your option) any later version. # # CSA 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, see . # from .csaobject import * import inspect try: from lxml import etree from lxml.builder import E except ImportError: pass class Closure (CSAObject): tag = 'closure' name = tag def __init__ (self, formals, e): self.formals = formals self.etree = e @staticmethod def formalToXML (formal): return E ('bvar', E ('ci', formal)) def _to_xml (self): formals = list (map (Closure.formalToXML, self.formals)) return E ('bind', E ('closure'), *formals + [ self.etree ]) def __call__ (self, *args): assert len (args) == len (self.formals), "arguments %s don't match formals %s" % (args, self.formals) bindings = {} for (formal, arg) in map (self.formals, args): bindings[formal] = arg return CSAObject.from_xml (self.etree, bindings) registerTag (Closure.tag, Closure, BINDOPERATOR) csa-0.1.13/csa/conngen.py000066400000000000000000000046111513102315300151050ustar00rootroot00000000000000# # This file is part of the Connection-Set Algebra (CSA). # Copyright (C) 2010,2011,2012 Mikael Djurfeldt # # CSA 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 3 of the License, or # (at your option) any later version. # # CSA 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, see . # try: from nineml.connection_generator import ConnectionGenerator HAVE_CG=True except ImportError: HAVE_CG=False if HAVE_CG: from .csaobject import from_xml from .elementary import arity, cross, partition from .closure import Closure class CSAConnectionGenerator (ConnectionGenerator): def __init__ (self, cset): self.cset = cset self.generator = False @property def arity (self): return arity (self.cset) def setMask (self, mask): self.setMasks ([mask], 0) def setMasks (self, masks, local): csaMasks = list (map (CSAConnectionGenerator.makeMask, masks)) self.generator = partition (self.cset, csaMasks, local) @staticmethod def makeMask (mask): return cross (CSAConnectionGenerator.makeIList (mask.sources), CSAConnectionGenerator.makeIList (mask.targets)) @staticmethod def makeIList (iset): if iset.skip == 1: return iset.intervals else: ls = [] for ivl in iset.intervals: for i in range (ivl[0], ivl[1] + 1, iset.skip): ls.append ((i, i)) return ls def __len__ (self): return self.generator.__len__ () def __iter__ (self): return self.generator.__iter__ () def connectionGeneratorClosureFromXML (element): cset = from_xml (element) if isinstance (cset, Closure): return lambda *args: CSAConnectionGenerator (cset (*args)) else: return lambda: CSAConnectionGenerator (cset) csa-0.1.13/csa/connset.py000066400000000000000000001065351513102315300151370ustar00rootroot00000000000000# # This file is part of the Connection-Set Algebra (CSA). # Copyright (C) 2010,2011,2012,2020 Mikael Djurfeldt # # CSA 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 3 of the License, or # (at your option) any later version. # # CSA 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, see . # import copy from . import intervalset from . import valueset from .csaobject import * # This is the fundamental connection-set class # which is also the base class for masks # class CSet (CSAObject): tag = 'cset' def __init__ (self, mask, *valueSets): CSAObject.__init__ (self, "icset"); self._mask = mask self.valueSets = list (valueSets) self.arity = len (self.valueSets) def repr (self, name = None): if self.arity: if not name: name = self.name vreprs = [] for k in range (self.arity): v = self.value (k) if isinstance (v, CSAObject): vreprs += ", %s" % v.repr () else: vreprs += ", %s" % v return "%s (%s%s)" % (self.name, self.mask (), "".join (vreprs)) else: if self.mask () == self: return self.name else: return "%s (%s)" % (self.name, self.mask ()) def mask (self): #*fixme* remove this condition? if self._mask == None: self._mask = self.makeMask () return self._mask def value (self, k): if self.valueSets[k] == None: self.valueSets[k] = self.makeValueSet (k) return self.valueSets[k] def makeValueSet (self, k): if isFinite (self.mask ()): return self.makeFiniteValueSet (k, self.mask ().bounds ()) raise RuntimeError ("don't know how to return value set for this connection-set") def makeFiniteValueSet (self, k, bounds): raise RuntimeError ("don't know how to return value set for this connection-set") def __len__ (self): return len (self.mask ()) def __iter__ (self): # this code is used for full connection sets if isFinite (self.mask ()): state = State () obj = self.startIteration (state) (low0, high0, low1, high1) = self.bounds () return obj.iterator (low0, high0, low1, high1, state) else: raise RuntimeError ('attempt to retrieve iterator over infinite connection-set') def bounds (self): return self.mask ().bounds () def startIteration (self, state): obj = copy.copy (self) obj._mask = self.mask ().startIteration (state) return obj def iterator (self, low0, high0, low1, high1, state): for (i, j) in self._mask.iterator (low0, high0, low1, high1, state): yield (i, j, [ v (i, j) for v in self.valueSets ]) def multisetSum (self, other): return CSetMultisetSum (self, other) def intersection (self, other): assert isinstance (other, Mask), 'expected Mask operand' return SubCSet (self, self.mask ().intersection (other), *self.valueSets) def difference (self, other): assert isinstance (other, Mask), 'expected Mask operand' return SubCSet (self, self.mask ().difference (other), *self.valueSets) # This is the connection-set wrapper class which has as its only purpose # to wrap non mask connection-sets so that the same code can implement # connection-sets of different arity. Some type dispatch is also done here. # class ConnectionSet (CSAObject): def __init__ (self, c): CSAObject.__init__ (self, "cset") self.c = c def repr (self): return self.c.repr () def __len__ (self): return len (self.c) def __iter__ (self): return ConnectionSet.iterators[self.c.arity] (self) def iter0 (self): assert False, 'Should not have executed ConnectionSet.iter0' def iter1 (self): for (i, j, vs) in iter (self.c): (v0,) = vs yield (i, j, v0) def iter2 (self): for (i, j, vs) in iter (self.c): (v0, v1) = vs yield (i, j, v0, v1) def iter3 (self): for (i, j, vs) in iter (self.c): (v0, v1, v2) = vs yield (i, j, v0, v1, v2) def __add__ (self, other): if isNumber (other): return ConnectionSet (self.c.addScalar (other)) else: return ConnectionSet (self.c.multisetSum (coerceCSet (other))) def __radd__ (self, other): return self.__add__ (other) def __sub__ (self, other): if isNumber (other): return ConnectionSet (self.c.addScalar (- other)) else: return ConnectionSet (self.c.difference (coerceCSet (other))) def __rsub__ (self, other): return ConnectionSet (self.c.__neg__ ().addScalar (other)) def __mul__ (self, other): if isNumber (other): return ConnectionSet (self.c.mulScalar (other)) else: return ConnectionSet (self.c.intersection (coerceCSet (other))) def __rmul__ (self, other): return self.__mul__ (other) ConnectionSet.iterators = [ ConnectionSet.iter0, \ ConnectionSet.iter1, \ ConnectionSet.iter2, \ ConnectionSet.iter3 ] # Some helper functions def source (x): return x[0] def target (x): return x[1] def isNumber (x): return isinstance (x, (int, float, complex)) def coerceCSet (obj): if isinstance (obj, list): return ExplicitMask (obj) elif isinstance (obj, ConnectionSet): return obj.c assert isinstance (obj, Mask), 'expected connection-set' return obj def valueSet (obj): return valueset.QuotedValueSet (obj) def coerceValueSet (obj): if callable (obj): return obj else: return valueSet (obj) def isFinite (x): return isinstance (x, Finite) def isEmpty (x): iterator = iter (x.mask ()) try: next (iterator) return False except StopIteration: return True def transpose (obj): return obj.transpose () # This is the fundamental mask class # class Mask (CSet): def __init__ (self): CSet.__init__ (self, self) def __len__ (self): N = 0 for c in self: N += 1 return N def __iter__ (self): raise RuntimeError ('attempt to retrieve iterator over infinite mask') def __add__ (self, other): return self.multisetSum (other) def __sub__ (self, other): return self.difference (other) def __mul__ (self, other): if isinstance (other, Mask): return self.intersection (other) elif isinstance (other, list): return self.intersection (ExplicitMask (other)) elif isinstance (other, ConnectionSet): return other.__mul__ (self) else: return NotImplemented def __rmul__ (self, other): if isinstance (other, list): return self.intersection (ExplicitMask (other)) else: return NotImplemented def __invert__ (self): return self.complement () def transpose (self): assert isFinite (self), \ 'transpose currently only supports finite masks' return TransposedMask (self) def shift (self, M, N): return shiftedMask (self, M, N) def startIteration (self, state): # default action: return self def iterator (self, low0, high0, low1, high1, state): return NotImplemented def multisetSum (self, other): if isFinite (self) and isFinite (other): return FiniteMaskMultisetSum (self, other) else: return MaskMultisetSum (self, other) def intersection (self, other): # IntervalSetMask implements a specialized version of intersection if isinstance (other, IntervalSetMask): return other.intersection (self) # Generate Finite instances if either operand is finite elif isFinite (self): return FiniteMaskIntersection (self, other) elif isFinite (other): return FiniteMaskIntersection (other, self) else: return MaskIntersection (self, other) def complement (self): return MaskComplement (self) def difference (self, other): return MaskDifference (self, other) class Finite (object): def bounds (self): return NotImplemented def maxBounds (self, b1, b2): return (min (b1[0], b2[0]), max (b1[1], b2[1]), min (b1[2], b2[2]), max (b1[3], b2[3])) def __iter__ (self): state = State () obj = self.startIteration (state) (low0, high0, low1, high1) = self.bounds () return obj.iterator (low0, high0, low1, high1, state) class FiniteMask (Finite, Mask): def __init__ (self): Mask.__init__ (self) self.low0 = 0 self.high0 = 0 self.low1 = 0 self.high1 = 0 def bounds (self): return (self.low0, self.high0, self.low1, self.high1) def isBoundedBy (self, low0, high0, low1, high1): return low0 > self.low0 or high0 < self.high0 \ or low1 > self.low1 or high1 < self.high1 # not used class NoParIterator (): def __init__ (self): self.subIterator = False def iterator (self, low0, high0, low1, high1, state): try: print(low0, high0, low1, high1) if not self.subIterator: self.subIterator = self.noParIterator (state) self.lastC = next (self.subIterator) c = self.lastC while c[1] < low1: c = next (self.subIterator) while c[1] < high1: j = c[1] while c[1] == j and c[0] < low0: c = next (self.subIterator) while c[1] == j and c[0] < high0: yield c c = next (self.subIterator) while c[1] == j: c = next (self.subIterator) self.lastC = c except StopIteration: return class BinaryMask (BinaryCSAObject, Mask): def __init__ (self, operator, op1, op2, precedence): Mask.__init__ (self) BinaryCSAObject.__init__ (self, operator, op1, op2, precedence) def startIteration (self, state): obj = copy.copy (self) obj.op1 = self.op1.startIteration (state) obj.op2 = self.op2.startIteration (state) return obj class MaskIntersection (BinaryMask): def __init__ (self, op1, op2): BinaryMask.__init__ (self, '*', op1, op2, 1) def iterator (self, low0, high0, low1, high1, state): try: iter1 = self.op1.iterator (low0, high0, low1, high1, state) iter2 = self.op2.iterator (low0, high0, low1, high1, state) (i1, j1) = next (iter1) (i2, j2) = next (iter2) while True: if (j1, i1) < (j2, i2): (i1, j1) = next (iter1) elif (j2, i2) < (j1, i1): (i2, j2) = next (iter2) else: yield (i1, j1) (i1, j1) = next (iter1) (i2, j2) = next (iter2) except StopIteration: return class FiniteMaskIntersection (Finite, MaskIntersection): def __init__ (self, op1, op2): assert isFinite (op1) MaskIntersection.__init__ (self, op1, op2) def bounds (self): return self.op1.bounds () class MaskMultisetSum (BinaryMask): def __init__ (self, op1, op2): BinaryMask.__init__ (self, "+", op1, op2, 0) def iterator (self, low0, high0, low1, high1, state): try: iter1 = self.op1.iterator (low0, high0, low1, high1, state) iter2 = self.op2.iterator (low0, high0, low1, high1, state) try: (i1, j1) = next (iter1) except StopIteration: (i2, j2) = next (iter2) while True: yield (i2, j2) (i2, j2) = next (iter2) try: (i2, j2) = next (iter2) except StopIteration: while True: yield (i1, j1) (i1, j1) = next (iter1) while True: i1s = i1 j1s = j1 while (j1, i1) <= (j2, i2): yield (i1, j1) try: (i1, j1) = next (iter1) except StopIteration: while True: yield (i2, j2) (i2, j2) = next (iter2) while (j2, i2) <= (j1s, i1s): yield (i2, j2) try: (i2, j2) = next (iter2) except StopIteration: while True: yield (i1, j1) (i1, j1) = next (iter1) except StopIteration: return class FiniteMaskMultisetSum (Finite, MaskMultisetSum): def __init__ (self, op1, op2): assert isFinite (op1) and isFinite (op2) MaskMultisetSum.__init__ (self, op1, op2) def bounds (self): return self.maxBounds (self.op1.bounds (), self.op2.bounds ()) class MaskDifference (BinaryMask): def __init__ (self, op1, op2): BinaryMask.__init__ (self, "-", op1, op2, 0) def iterator (self, low0, high0, low1, high1, state): iter1 = self.op1.iterator (low0, high0, low1, high1, state) iter2 = self.op2.iterator (low0, high0, low1, high1, state) try: (i1, j1) = next (iter1) (i2, j2) = next (iter2) while True: if (j1, i1) < (j2, i2): yield (i1, j1) (i1, j1) = next (iter1) continue elif (i1, j1) == (i2, j2): (i1, j1) = next (iter1) try: (i2, j2) = next (iter2) except StopIteration: while True: yield (i1, j1) (i1, j1) = next (iter1) except StopIteration: return def cmpPostOrder (c0, op1): return ((c0[1], c0[0]) > (op1[1], op1[0])) - ((c0[1], c0[0]) < (op1[1], op1[0])) def cmp_to_key(mycmp): 'Convert a cmp= function into a key= function' class K: def __init__(self, obj, *args): self.obj = obj def __lt__(self, other): return mycmp(self.obj, other.obj) < 0 def __gt__(self, other): return mycmp(self.obj, other.obj) > 0 def __eq__(self, other): return mycmp(self.obj, other.obj) == 0 def __le__(self, other): return mycmp(self.obj, other.obj) <= 0 def __ge__(self, other): return mycmp(self.obj, other.obj) >= 0 def __ne__(self, other): return mycmp(self.obj, other.obj) != 0 return K class ExplicitMask (FiniteMask): def __init__ (self, connections): FiniteMask.__init__ (self) self.connections = list (connections) self.connections.sort (key=cmp_to_key(cmpPostOrder)) if connections: self.low0 = min ((i for (i, j) in self.connections)) self.high0 = max ((i for (i, j) in self.connections)) + 1 self.low1 = self.connections[0][1] self.high1 = self.connections[-1][1] + 1 def __len__ (self): return len (self.connections) def iterator (self, low0, high0, low1, high1, state): if not self.isBoundedBy (low0, high0, low1, high1): return iter (self.connections) else: return self.boundedIterator (low0, high0, low1, high1, state) def boundedIterator (self, low0, high0, low1, high1, state): iterator = iter (self.connections) try: (i, j) = next (iterator) while j < low1: (i, j) = next (iterator) while j < high1: if low0 <= i and i < high0: yield (i, j) (i, j) = next (iterator) except StopIteration: return class IntervalSetMask (Mask): tag = 'cross' def __init__ (self, set0, set1): Mask.__init__ (self) self.set0 = set0 self.set1 = set1 @staticmethod def _sets_to_repr (set0, set1): return 'cross(%s, %s)' % (set0.repr (), set1.repr ()) def repr (self): return self._sets_to_repr (self.set0, self.set1) def __contains__ (self, c): return c[0] in self.set0 and c[1] in self.set1 def transpose (self): return IntervalSetMask (self.set1, self.set0) def shift (self, M, N): return IntervalSetMask (self.set0.shift (M), self.set1.shift (N)) def iterator (self, low0, high0, low1, high1, state): iterator1 = self.set1.intervalIterator () try: i1 = next (iterator1) while i1[1] < low1: i1 = next (iterator1) while i1[0] < high1: for j in range (max (i1[0], low1), min (i1[1] + 1, high1)): iterator0 = self.set0.intervalIterator () try: i0 = next (iterator0) while i0[1] < low0: i0 = next (iterator0) if i0[1] < high0: for i in range (max (i0[0], low0), i0[1] + 1): yield (i, j) i0 = next (iterator0) while i0[1] < high0: for i in range (i0[0], i0[1] + 1): yield (i, j) i0 = next (iterator0) for i in range (i0[0], min (i0[1] + 1, high0)): yield (i, j) else: for i in range (max (i0[0], low0), min (i0[1] + 1, high0)): yield (i, j) except StopIteration: pass i1 = next (iterator1) except StopIteration: return def intersection (self, other): if isinstance (other, IntervalSetMask): set0 = self.set0.intersection (other.set0) set1 = self.set1.intersection (other.set1) return intervalSetMask (set0, set1) else: return ISetBoundedMask (self.set0, self.set1, other) def multisetSum (self, other): if isinstance (other, IntervalSetMask): if not self.set0.intersection (other.set0) \ or not self.set1.intersection (other.set1): set0 = self.set0.union (other.set0) set1 = self.set1.union (other.set1) return intervalSetMask (set0, set1) else: raise RuntimeError ('sums of overlapping IntervalSetMask:s not yet supported') else: return FiniteMask.multisetSum (self, other) @staticmethod def _sets_to_xml (set0, set1): return CSAObject.apply (IntervalSetMask.tag, set0, set1) def _to_xml (self): return self._sets_to_xml (self.set0, self.set1) class FiniteISetMask (FiniteMask, IntervalSetMask): def __init__ (self, set0, set1): FiniteMask.__init__ (self) IntervalSetMask.__init__ (self, set0, set1) if self.set0 and self.set1: self.low0 = self.set0.min () self.high0 = self.set0.max () + 1 self.low1 = self.set1.min () self.high1 = self.set1.max () + 1 def __len__ (self): return len (self.set0) * len (self.set1) def transpose (self): return FiniteISetMask (self.set1, self.set0) def shift (self, M, N): return FiniteISetMask (self.set0.shift (M), self.set1.shift (N)) def iterator (self, low0, high0, low1, high1, state): if not self.isBoundedBy (low0, high0, low1, high1): return self.simpleIterator () else: return IntervalSetMask.iterator (self, low0, high0, low1, high1, state) def simpleIterator (self): for j in self.set1: for i in self.set0: yield (i, j) class FiniteSourcesISetMask (IntervalSetMask): def __init__ (self, set0, set1): IntervalSetMask.__init__ (self, set0, set1) def transpose (self): return FiniteTargetsISetMask (self.set1, self.set0) def shift (self, M, N): return FiniteSourcesISetMask (self.set0.shift (M), \ self.set1.shift (N)) class FiniteTargetsISetMask (IntervalSetMask): def __init__ (self, set0, set1): IntervalSetMask.__init__ (self, set0, set1) def transpose (self): return FiniteSourcesISetMask (self.set1, self.set0) def shift (self, M, N): return FiniteTargetsISetMask (self.set0.shift (M), \ self.set1.shift (N)) def intervalSetMask (set0, set1): set0 = set0 if isinstance (set0, intervalset.IntervalSet) \ else intervalset.IntervalSet (set0) set1 = set1 if isinstance (set1, intervalset.IntervalSet) \ else intervalset.IntervalSet (set1) if set0.finite (): if set1.finite (): return FiniteISetMask (set0, set1) else: return FiniteSourcesISetMask (set0, set1) else: if set1.finite (): return FiniteTargetsISetMask (set0, set1) else: return IntervalSetMask (set0, set1) CSAObject.tag_map[CSA + IntervalSetMask.tag] = (intervalSetMask, 2) class ISetBoundedMask (FiniteMask): def __init__ (self, set0, set1, mask): FiniteMask.__init__ (self) self.precedence = 1 self.set0 = set0 self.set1 = set1 self.subMask = mask inf = intervalset.infinity if isFinite (mask): (low0, high0, low1, high1) = mask.bounds () else: (low0, high0, low1, high1) = (0, inf, 0, inf) if self.set0 and self.set1: self.low0 = max (self.set0.min (), low0) if self.set0.finite (): self.high0 = min (self.set0.max () + 1, high0) else: self.high0 = high0 self.low1 = max (self.set1.min (), low1) if self.set1.finite (): self.high1 = min (self.set1.max () + 1, high1) else: self.high1 = high1 assert self.high0 != inf and self.high1 != inf, 'infinite ISetBoundedMask:s currently not supported' def startIteration (self, state): obj = copy.copy (self) obj.subMask = self.subMask.startIteration (state) return obj def iterator (self, low0, high0, low1, high1, state): if not self.isBoundedBy (low0, high0, low1, high1): return self.simpleIterator (state) else: return self.boundedIterator (low0, high0, low1, high1, state) def simpleIterator (self, state): for i1 in self.set1.intervalIterator (): for i0 in self.set0.intervalIterator (): for e in self.subMask.iterator (i0[0], i0[1] + 1, i1[0], i1[1] + 1, state): yield e def boundedIterator (self, low0, high0, low1, high1, state): iterator1 = self.set1.intervalIterator () try: i1 = next (iterator1) while i1[1] < low1: i1 = next (iterator1) while i1[0] < high1: i1 = (max (i1[0], low1), min (i1[1], high1 - 1)) iterator0 = self.set0.intervalIterator () try: i0 = next (iterator0) while i0[1] < low0: i0 = next (iterator0) if i0[1] < high0: for e in self.subMask.iterator (max (i0[0], low0), i0[1] + 1, i1[0], i1[1] + 1, state): yield e i0 = next (iterator0) while i0[1] < high0: for e in self.subMask.iterator (i0[0], i0[1] + 1, i1[0], i1[1] + 1, state): yield e i0 = next (iterator0) for e in self.subMask.iterator (i0[0], min (i0[1] + 1, high0), i1[0], i1[1] + 1, state): yield e else: for e in self.subMask.iterator (max (i0[0], low0), min (i0[1] + 1, high0), i1[0], i1[1] + 1, state): yield e except StopIteration: pass i1 = next (iterator1) except StopIteration: return def repr (self): return '%s*%s' % (IntervalSetMask._sets_to_repr (self.set0, self.set1), self.subMask._repr_as_op2 (self.precedence)) def _to_xml (self): return E ('apply', E ('times'), IntervalSetMask._sets_to_xml (self.set0, self.set1), self.subMask._to_xml ()) # The ExplicitCSet captures the original value sets before coercion. # It is used in the implementation of the "cset" constructor. # class ExplicitCSet (CSet): def __init__ (self, mask, *valueSets): if isinstance (mask, list): mask = ExplicitMask (mask) self.originalValueSets = valueSets CSet.__init__ (self, mask, *list (map (coerceValueSet, valueSets))) def value (self, k): return self.originalValueSets[k] # SubCSet is used in the cases where a new CSet can be created by # an operation on the mask. # class SubCSet (CSet): def __init__ (self, cset, mask, *valueSets): CSet.__init__ (self, mask, *valueSets) self.subCSet = cset def value (self, k): if self.valueSets[k] == None: self.valueSets[k] = self.makeValueSet (k) # defer to subCSet in case it is an ExplicitCSet return self.subCSet.value (k) def makeValueSet (self, k): if isFinite (self.mask ()): bounds = self.mask ().bounds () return self.subCSet.makeFiniteValueSet (k, bounds) else: return self.subCSet.makeValueSet (k) class BinaryCSet (BinaryCSAObject, CSet): def __init__ (self, operator, op1, op2): CSet.__init__ (self, None, *[ None for v in op1.valueSets ]) self.name = operator self.op1 = op1 self.op2 = op2 self.valueSetMap = None def makeFiniteValueSet (self, k, bounds): if self.valueSetMap == None: self.valueSetMap = self.makeValueSetMap (bounds) return lambda i, j: self.valueSetMap[(i, j)][k] def makeValueSetMap (self, bounds): m = {} state = State () obj = self.startIteration (state) (low0, high0, low1, high1) = bounds for (i, j, v) in obj.iterator (low0, high0, low1, high1, state): m[(i, j)] = v return m class BinaryCSets (BinaryCSet): def __init__ (self, operator, op1, op2): assert op1.arity == op2.arity, 'binary operation on connection-sets with different arity' BinaryCSet.__init__ (self, operator, op1, op2) class CSetIntersection (BinaryCSet): def __init__ (self, op1, op2): assert isinstance (op2, Mask), 'expected Mask operand' BinaryCSet.__init__ (self, "*", op1, op2) self._mask = op1.mask ().intersection (op2) def iterator (self, low0, high0, low1, high1, state): iter1 = self.op1.iterator (low0, high0, low1, high1, state) iter2 = self.op2.iterator (low0, high0, low1, high1, state) try: (i1, j1, v1) = next (iter1) (i2, j2) = next (iter2) while True: if (j1, i1) < (j2, i2): (i1, j1, v1) = next (iter1) elif (j2, i2) < (j1, i1): (i2, j2) = next (iter2) else: yield (i1, j1, v1) (i1, j1, v1) = next (iter1) (i2, j2) = next (iter2) except StopIteration: return class CSetMultisetSum (BinaryCSets): def __init__ (self, op1, op2): BinaryCSet.__init__ (self, "+", op1, op2) self._mask = op1.mask ().multisetSum (op2.mask ()) def iterator (self, low0, high0, low1, high1, state): iter1 = self.op1.iterator (low0, high0, low1, high1, state) iter2 = self.op2.iterator (low0, high0, low1, high1, state) try: try: (i1, j1, v1) = next (iter1) except StopIteration: (i2, j2, v2) = next (iter2) while True: yield (i2, j2, v2) (i2, j2, v2) = next (iter2) try: (i2, j2, v2) = next (iter2) except StopIteration: while True: yield (i1, j1, v1) (i1, j1, v1) = next (iter1) while True: i1s = i1 j1s = j1 while (j1, i1) <= (j2, i2): yield (i1, j1, v1) try: (i1, j1, v1) = next (iter1) except StopIteration: while True: yield (i2, j2, v2) (i2, j2, v2) = next (iter2) while (j2, i2) <= (j1s, i1s): yield (i2, j2, v2) try: (i2, j2, v2) = next (iter2) except StopIteration: while True: yield (i1, j1, v1) (i1, j1, v1) = next (iter1) except StopIteration: return def intersection (self, other): assert isinstance (other, Mask), 'expected Mask operand' if isFinite (self) or isFinite (other): # since operands are finite we are allowed to use isEmpty if isEmpty (self.op2.mask ().intersection (other)): return self.op1.intersection (other) if isEmpty (self.op1.mask ().intersection (other)): return self.op2.intersection (other) return CSetIntersection (self, other) class TransposedMask (Finite, Mask): def __init__ (self, mask): self.subMask = mask def transpose (self): return self.subMask def bounds (self): (low0, high0, low1, high1) = self.subMask.bounds () return (low1, high1, low0, high0) def startIteration (self, state): obj = copy.copy (self) obj.transposedState = state.transpose () obj.subMask = self.subMask.startIteration (obj.transposedState) return obj def iterator (self, low0, high0, low1, high1, state): ls = [] for c in self.subMask.iterator (low1, high1, low0, high0, \ self.transposedState): ls.append ((c[1], c[0])) ls.sort (key=cmp_to_key(cmpPostOrder)) return iter (ls) class ShiftedMask (Mask): def __init__ (self, mask, M, N): self.subMask = mask self.M = M self.N = N def startIteration (self, state): obj = copy.copy (self) obj.subMask = self.subMask.startIteration (state) return obj def iterator (self, low0, high0, low1, high1, state): low0 -= self.M high0 -= self.M low1 -= self.N high1 -= self.N for (i, j) in self.subMask.iterator (max (low0, 0), high0, \ max (low1, 0), high1, \ state): (i1, j1) = (i + self.M, j + self.N) if i1 >= 0 and j1 >= 0: yield (i1, j1) class FiniteShiftedMask (Finite, ShiftedMask): def bounds (self): (low0, high0, low1, high1) = self.subMask.bounds () low0 += self.M high0 += self.M low1 += self.N high1 += self.N return (max (low0, 0), high0, max (low1, 0), high1) def shiftedMask (mask, M, N): if isFinite (mask): return FiniteShiftedMask (mask, M, N) else: return ShiftedMask (mask, M, N) class State (dict): def transpose (self): if 'partitions' in self: s = State (self) s['partitions'] = list (map (transpose, s['partitions'])) return s else: return self class MaskPartition (Finite, Mask): def __init__ (self, mask, partitions, selected, seed): Mask.__init__ (self) #*fixme* How can we know when this is not necessary? self.subMask = partitions[selected] * mask #domain = IntervalSetMask ([], []) #for m in partitions: # assert isFinite (m), 'partitions must be finite' # domain = domain.multisetSum (m) self.state = { #'domain' : domain, 'partitions' : partitions, 'selected' : selected } if seed != None: self.state['seed'] = seed def bounds (self): return self.subMask.bounds () def startIteration (self, state): for key in self.state: state[key] = self.state[key] return self.subMask.startIteration (state) def iterator (self, low0, high0, low1, high1, state): raise RuntimeError ('iterator called on wrong object') class CSetPartition (CSet): def __init__ (self, c, partitions, selected, seed): #*fixme* How can we know when this is not necessary? self.subCSet = (partitions[selected] * c).c CSet.__init__ (self, self.subCSet.mask (), *self.subCSet.valueSets) self.state = { #'domain' : domain, 'partitions' : partitions, 'selected' : selected } if seed != None: self.state['seed'] = seed def makeFiniteValueSet (self, k, bounds): return self.subCSet.makeFiniteValueSet (k, bounds); def bounds (self): return self.subCSet.bounds () def startIteration (self, state): for key in self.state: state[key] = self.state[key] return self.subCSet.startIteration (state) def iterator (self, low0, high0, low1, high1, state): raise RuntimeError ('iterator called on wrong object') csa-0.1.13/csa/csaobject.py000066400000000000000000000146631513102315300154230ustar00rootroot00000000000000# # This file is part of the Connection-Set Algebra (CSA). # Copyright (C) 2010,2011,2012 Mikael Djurfeldt # # CSA 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 3 of the License, or # (at your option) any later version. # # CSA 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, see . # try: from lxml import etree from lxml.builder import E except ImportError: pass csa_tag = 'CSA' csa_namespace = 'http://software.incf.org/software/csa/1.0' CSA = '{%s}' % csa_namespace CUSTOM = -4 OPERATOR = -3 BINDOPERATOR = -2 SINGLETON = -1 def to_xml (obj): if isinstance (obj, str): return E (obj) elif isinstance (obj, (int, float)): return E ('cn', str (obj)) elif isinstance (obj, CSAObject): return obj._to_xml () else: raise RuntimeError ("don't know how to turn %s into xml" % obj) # precedence levels: # # 0 + - # 1 * # 2 ~ class CSAObject (object): tag_map = {} def __init__ (self, name, precedence = 3): self.name = name self.precedence = precedence def __repr__ (self): return 'CSA(%s)' % self.repr () def repr (self): if hasattr (self, 'name'): return self.name else: return self.__class__.__name__ def _repr_as_op2 (self, parentPrecedence): if self.precedence <= parentPrecedence: return '(%s)' % self.repr () else: return self.repr () def _repr_applyop (self, op_repr, obj): return '%s*%s' % (op_repr, obj._repr_as_op2 (1)) def to_xml (self): return E (csa_tag, self._to_xml (), xmlns=csa_namespace) def _to_xml (self): return E (self.name) @classmethod def apply (cls, operator, *operands): return E ('apply', to_xml (operator), *list (map (to_xml, operands))) @classmethod def formalFromXML (cls, element): assert element.tag == CSA + 'bvar' nodes = element.getchildren () assert nodes[0].tag == CSA + 'ci' return nodes[0].text @classmethod def from_xml (cls, element, env = {}): if element.tag == CSA + 'cn': return eval (element.text) elif element.tag == CSA + 'ci': #*fixme* Implement env as lists of dictionaries return env[element.text] elif element.tag == CSA + 'apply': nodes = element.getchildren () operator = nodes[0].tag operands = [ cls.from_xml (e, env) for e in nodes[1:] ] if operator == CSA + 'plus': return operands[0].__add__ (operands[1]) elif operator == CSA + 'minus': return operands[0].__sub__ (operands[1]) elif operator == CSA + 'times': return operands[0].__mul__ (operands[1]) elif operator == CSA + 'complement': return operands[0].__invert__ () else: # Function or operator application entry = CSAObject.tag_map[operator] obj = entry[0] if entry[1] == OPERATOR: return obj * operands[1] else: return obj (*operands) elif element.tag == CSA + 'bind': nodes = element.getchildren () tag = nodes[0].tag entry = CSAObject.tag_map[tag] if entry[1] != BINDOPERATOR: raise RuntimeError ("unknown binding operator tag %s" % tag) bindingOperator = entry[0] bvars = [ CSAObject.formalFromXML (e) for e in nodes[1:-1] ] return bindingOperator (bvars, nodes[-1]) elif element.tag in CSAObject.tag_map: entry = CSAObject.tag_map[element.tag] obj = entry[0] if entry[1] == SINGLETON: return obj elif entry[1] == CUSTOM: return obj.from_xml (element, env) else: return obj () else: raise RuntimeError ("don't know how parse tag %s" % element.tag) def xml (e): print(etree.tostring (e)) def write(self, file): doc = self.to_xml () etree.ElementTree(doc).write (file, encoding="UTF-8", pretty_print=True, xml_declaration=True) class BinaryCSAObject (CSAObject): operator_table = {'+': 'plus', '-': 'minus', '*': 'times'} def __init__ (self, name, op1, op2, precedence = 0): CSAObject.__init__ (self, name, precedence) self.op1 = op1 self.op2 = op2 def repr (self): if isinstance (self.op1, CSAObject): op1 = self.op1.repr () if self.op1.precedence < self.precedence: op1 = "(%s)" % op1 else: op1 = self.op1 if isinstance (self.op2, CSAObject): op2 = self.op2._repr_as_op2 (self.precedence) else: op2 = self.op2 return "%s%s%s" % (op1, self.name, op2) def _to_xml (self): if isinstance (self.op1, CSAObject): op1 = self.op1._to_xml () else: op1 = self.op1 if isinstance (self.op2, CSAObject): op2 = self.op2._to_xml () else: op2 = self.op2 if self.name in BinaryCSAObject.operator_table: op = BinaryCSAObject.operator_table[self.name] else: op = self.name return E ('apply', E (op), op1, op2) class OpExprValue (BinaryCSAObject): def __init__ (self, operator, operand): BinaryCSAObject.__init__ (self, '*', operator, operand, 1) class Operator (CSAObject): def __init__ (self, name='ioperator'): CSAObject.__init__ (self, name) def from_xml (root): assert root.nsmap[None] == csa_namespace return CSAObject.from_xml (root.getchildren ()[0]) def parse (filename): doc = etree.parse (filename) return from_xml (doc.getroot()) def parseString (string): el = etree.fromstring (string) return from_xml (el) def registerTag (tag, obj, mode): CSAObject.tag_map[CSA + tag] = (obj, mode) csa-0.1.13/csa/elementary.py000066400000000000000000000047461513102315300156340ustar00rootroot00000000000000# # This file is part of the Connection-Set Algebra (CSA). # Copyright (C) 2010,2011,2012 Mikael Djurfeldt # # CSA 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 3 of the License, or # (at your option) any later version. # # CSA 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, see . # from __future__ import print_function import sys as _sys from . import intervalset as _iset from . import connset as _cs from . import valueset as _vs from . import _elementary from . import _misc from .csaobject import registerTag # Connection-Set constructor # def cset (mask, *valueSets): if valueSets: c = _cs.ExplicitCSet (mask, *valueSets) return _cs.ConnectionSet (c) else: return mask registerTag (_cs.CSet.tag, cset, 1) # Selectors # def mask (obj): cset = _cs.coerceCSet (obj) return cset.mask () def value (obj, k): assert isinstance (obj, _cs.ConnectionSet), 'expected connection-set' return obj.c.value (k) def arity (obj): if isinstance (obj, _cs.ConnectionSet): return obj.c.arity else: return 0 # Value-set constructor # def vset (obj): if not callable (obj): return _vs.QuotedValueSet (obj) else: return _vs.GenericValueSet (obj) # Intervals # def ival (beg, end): return _iset.IntervalSet ((beg, end)) N = _iset.N # Cartesian product # def cross (set0, set1): return _cs.intervalSetMask (set0, set1) # Elementary masks # empty = cross ([], []) full = _elementary.FullMask () oneToOne = _elementary.OneToOne () random = _misc.Random () # Support for parallel simulator # def partition (c, masks, selected, seed = None): if isinstance (c, _cs.Mask): return _cs.MaskPartition (c, masks, selected, seed) elif isinstance (c, _cs.ConnectionSet): return _cs.ConnectionSet (_cs.CSetPartition (c, masks, selected, seed)) # Utilities # def tabulate (c): for x in c: print(u'{}'.format(x[0]), end=u' ') for e in x[1:]: print(u'\t{}'.format(e)) #del _elementary, cs, sys # not for export csa-0.1.13/csa/geometry.py000066400000000000000000000134131513102315300153110ustar00rootroot00000000000000# # This file is part of the Connection-Set Algebra (CSA). # Copyright (C) 2010,2011,2012,2019 Mikael Djurfeldt # # CSA 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 3 of the License, or # (at your option) any later version. # # CSA 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, see . # import math as _math import random as _random import numpy as _numpy from . import intervalset as _iset def grid2d (width, xScale = 1.0, yScale = 1.0, x0 = 0.0, y0 = 0.0): xScale /= width yScale /= width g = lambda i: \ (x0 + xScale * (i % width), y0 + yScale * (i // width)) g.type = 'grid' g.width = width g.xScale = xScale g.yScale = yScale g.x0 = x0 g.y0 = y0 g.inverse = lambda x, y: \ int (round (x / xScale - x0)) \ + width * int (round (y / yScale - y0)) return g def random2d (N, xScale = 1.0, yScale = 1.0): coords = [(xScale * _random.random (), yScale * _random.random ()) for i in range (0, N)] g = lambda i: coords[i] g.type = 'ramdom' g.N = N g.xScale = xScale g.yScale = yScale # We should use a KD-tree here g.inverse = lambda x, y, domain=_iset.IntervalSet ((0, N - 1)): \ _numpy.array ([euclidDistance2d ((x, y), g(i)) \ for i in domain]).argmin () \ + domain.min () return g class ProjectionOperator (object): def __init__ (self, projection): self.projection = projection def __mul__ (self, g): projection = self.projection return lambda i: projection (g (i)) def euclidDistance2d (p1, p2): dx = p1[0] - p2[0] dy = p1[1] - p2[1] return _math.sqrt (dx * dx + dy * dy) def euclidMetric2d (g1, g2 = None): g2 = g1 if g2 == None else g2 return lambda i, j: euclidDistance2d (g1 (i), g2 (j)) # These functions were contributed by Dr. Birgit Kriener def euclidToroidDistance2d (p1, p2, xScale=1.0, yScale=1.0): ddx, ddy = abs (p1[0] - p2[0]), abs (p1[1] - p2[1]) dx = ddx if ddx < xScale/2. else xScale - ddx dy = ddy if ddy < yScale/2. else yScale - ddy return _math.sqrt (dx * dx + dy * dy) def euclidToroidMetric2d (g1, g2 = None, xScale=1.0, yScale=1.0): g2 = g1 if g2 == None else g2 return lambda i, j: euclidToroidDistance2d (g1 (i), g2 (j), xScale, yScale) # 3D functions def grid3d(width, xScale = 1.0, yScale = 1.0, zScale = 1.0, x0 = 0.0, y0 = 0.0, z0 = 0.0): """Returns a 3D grid between (0, 0, 0) and (1, 1, 1) :param width: The number of rows/columns the grid has :type width: int :param xScale: Scales the grid along the x axis :type xScale: float :param yScale: Scales the grid along the y axis :type yScale: float :param zScale: Scales the grid along the z axis :type zScale: float :param x0: Translates the grid along the x axis :type xScale: float :param y0: Translates the grid along the y axis :type yScale: float :param z0: Translates the grid along the z axis :type zScale: float :return: A callable grid that returns 3d positions when given an index""" xScale /= width yScale /= width zScale /= width g = lambda i: \ (x0 + xScale * (i % width), y0 + yScale * ((i % (width*width)) / width), z0 + zScale * (i / (width*width))) g.type = 'grid3d' g.width = width g.xScale = xScale g.yScale = yScale g.zScale = zScale g.x0 = x0 g.y0 = y0 g.z0 = z0 g.inverse = lambda x, y, z: \ int (round (x / xScale - x0)) \ + width * (int (round (y / yScale - y0) + width * int (round (z / zScale - z0)))) return g def random3d(N, xScale = 1.0, yScale = 1.0, zScale = 1.0): """Creates a set of points scattered uniformly inside a 3D box :param N: Number of 3D points :type N: int :param xScale: The scale of the box on the x axis :type xScale: float :param yScale: The scale of the box on the y axis :type yScale: float :param zScale: The scale of the box on the z axis :type zScale: float """ coords = _numpy.random.random((N, 3)) coords[...,0] *= xScale coords[...,1] *= yScale coords[...,2] *= zScale g = lambda i: coords[i] g.type = 'random' g.N = N g.xScale = xScale g.yScale = yScale g.zScale = zScale g.inverse = lambda x, y, z, domain=_iset.IntervalSet ((0, N - 1)): \ _numpy.array ([euclidDistance3d (_numpy.array((x, y, z)), g(i)) \ for i in domain]).argmin () \ + domain.min () return g def euclidDistance3d(p1, p2): """Returns the euclidean distance in 3D between two points :param p1: The first point :type p1: numpy.array((3)) :param p2: The second point :type p2: numpy.array((3)) :return: The euclidean distance :rtype: float """ return _numpy.linalg.norm(p2 - p1) def euclidMetric3d (g1, g2 = None): """Returns an euclidean metric for 3D points :param g1: The first group of points :type g1: callable :param g2: The second group of points. If None, the first group of points is used :type g2: callable :return: A 3D euclidean metric function :rtype: function """ g2 = g1 if g2 == None else g2 return lambda i, j: euclidDistance3d (g1 (i), g2 (j)) csa-0.1.13/csa/intervalset.py000066400000000000000000000327311513102315300160220ustar00rootroot00000000000000# # This file is part of the Connection-Set Algebra (CSA). # Copyright (C) 2010,2011,2012,2020 Mikael Djurfeldt # # CSA 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 3 of the License, or # (at your option) any later version. # # CSA 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, see . # import sys from .csaobject import * infinity = sys.maxsize - 1 # Interval sets are represented as ordered lists of closed intervals # class IntervalSet (CSAObject): tag = 'intervalset' @staticmethod # return true if tuple i represents a well-formed interval def goodInterval (i): return len (i) == 2 \ and isinstance (i[0], int) \ and isinstance (i[1], int) \ and i[0] <= i[1] @staticmethod def rangeToIntervals (x): if not x: return [] elif len (x) == 1: return [(x[0], x[0])] elif x[1] - x[0] == 1: return [(x[0], x[-1])] else: return ((e, e) for e in x) @staticmethod def coerce (s): if not isinstance (s, list): s = [ s ] res = [] for x in s: if isinstance (x, tuple): assert IntervalSet.goodInterval (x), 'malformed interval' res.append (x) elif isinstance (x, int): res.append ((x, x)) elif isinstance (x, range): res += IntervalSet.rangeToIntervals (x) else: raise TypeError ("can't interpret element as interval") s = res s.sort () # merge intervals # by construction we know that i[0] <= i[1] for i in s res = [] N = 0 if s: lastLower = s[0][0] lastUpper = s[0][1] assert lastLower >= 0, 'only positive values allowed' for i in s[1:]: assert lastLower < i[0] and lastUpper < i[0], 'intervals overlap' if i[0] - lastUpper == 1: lastUpper = i[1] else: res.append ((lastLower, lastUpper)) N += 1 + lastUpper - lastLower lastLower = i[0] lastUpper = i[1] res.append ((lastLower, lastUpper)) N += 1 + lastUpper - lastLower return (res, N) def __init__ (self, s = [], intervals = None, nIntegers = None): if intervals: self.intervals = intervals self.nIntegers = nIntegers else: (self.intervals, self.nIntegers) = self.coerce (s) def repr (self): return 'IntervalSet(%r)' % self.intervals def __len__ (self): return self.nIntegers def __contains__ (self, n): for i in self.intervals: if n > i[1]: continue elif n >= i[0]: return True else: return False return False def __iter__ (self): for i in self.intervals: for e in range (i[0], i[1] + 1): yield e def __invert__ (self): return ComplementaryIntervalSet (intervals = self.intervals, \ nIntegers = self.nIntegers) def __add__ (self, other): if not isinstance (other, IntervalSet): other = IntervalSet (other) return self.union (other) def __radd__ (self, other): return IntervalSet (other).union (self) def __sub__ (self, other): if not isinstance (other, IntervalSet): other = IntervalSet (other) return self.intersection (~other) def __rsub__ (self, other): return IntervalSet (other).intersection (~self) def __mul__ (self, other): if not isinstance (other, IntervalSet): other = IntervalSet (other) return self.intersection (other) def __rmul__ (self, other): return IntervalSet (other).intersection (self) def finite (self): return True def shift (self, N): if not self or N == 0: return self intervals = [] nIntegers = self.nIntegers for (i, j) in self.intervals: i += N j += N if i >= 0: intervals.append ((i, j)) elif j >= 0: intervals.append ((0, j)) nIntegers += i return IntervalSet (intervals = intervals, nIntegers = nIntegers) def intervalIterator (self): return iter (self.intervals) def boundedIterator (self, low, high): iterator = iter (self.intervals) try: i = next (iterator) while i[1] < low: i = next (iterator) while i[0] < high: for e in range (max (low, i[0]), min (i[1] + 1, high)): yield e i = next (iterator) except StopIteration: return def count (self, low, high): iterator = iter (self.intervals) c = 0 try: i = next (iterator) while i[1] < low: i = next (iterator) while i[0] < high: c += min (i[1] + 1, high) - max (low, i[0]) i = next (iterator) except StopIteration: pass return c def min (self): return self.intervals[0][0] def max (self): return self.intervals[-1][1] def skipIntervals (self): if len (self.intervals) <= 1 or self.intervals[0][0] != self.intervals[0][1]: return 1, self.intervals skip = self.intervals[1][0] - self.intervals[0][0] res = [] start = last = self.intervals[0][0] for i in self.intervals[1:]: if i[0] != i[1]: return 1, self.intervals if i[0] != last + skip: if i[0] % skip != 0: return 1, self.intervals res.append ((start, last)) start = i[0] last = i[0] res.append ((start, last)) return skip, res def intersection (self, other): res = [] N = 0 iter0 = self.intervalIterator () iter1 = other.intervalIterator () try: i0 = next (iter0) i1 = next (iter1) while True: if i0[1] <= i1[1]: if i0[1] >= i1[0]: lower = max (i0[0], i1[0]) res.append ((lower, i0[1])) N += 1 + i0[1] - lower i0 = next (iter0) else: if i1[1] >= i0[0]: lower = max (i0[0], i1[0]) res.append ((lower, i1[1])) N += 1 + i1[1] - lower i1 = next (iter1) except StopIteration: pass iset = IntervalSet () iset.intervals = res iset.nIntegers = N return iset def union (self, other): if isinstance (other, ComplementaryIntervalSet): return ~(~self).intersection (~other) iset = IntervalSet () if not other.nIntegers: iset.intervals = list (self.intervals) iset.nIntegers = self.nIntegers return iset if not self.nIntegers: iset.intervals = list (other.intervals) iset.nIntegers = other.nIntegers return iset res = [] N = 0 iter0 = self.intervalIterator () iter1 = other.intervalIterator () i0 = next (iter0) i1 = next (iter1) if i0[0] <= i1[0]: (lower, upper) = i0 else: (lower, upper) = i1 try: while True: if i0[0] <= i1[0]: if i0[0] <= upper + 1: if i0[1] > upper: upper = i0[1] else: res.append ((lower, upper)) N += 1 + upper - lower (lower, upper) = i0 try: i0 = next (iter0) except StopIteration: if i1[0] <= upper + 1: if i1[1] > upper: upper = i1[1] i1 = (lower, upper) else: res.append ((lower, upper)) N += 1 + upper - lower while True: res.append (i1) N += 1 + i1[1] - i1[0] i1 = next (iter1) else: if i1[0] <= upper + 1: if i1[1] > upper: upper = i1[1] else: res.append ((lower, upper)) N += 1 + upper - lower (lower, upper) = i1 try: i1 = next (iter1) except StopIteration: if i0[0] <= upper + 1: if i0[1] > upper: upper = i0[1] i0 = (lower, upper) else: res.append ((lower, upper)) N += 1 + upper - lower while True: res.append (i0) N += 1 + i0[1] - i0[0] i0 = next (iter0) except StopIteration: pass iset.intervals = res iset.nIntegers = N return iset def _to_xml (self): intervals = [ E ('interval', E ('cn', str (i)), E ('cn', str (j))) for (i, j) in self.intervals ] return E (IntervalSet.tag, *intervals) @classmethod def from_xml (cls, element, env = {}): intervals = [] for ivalElement in element.getchildren (): ival = ivalElement.getchildren () intervals.append ((int (ival[0].text), int (ival[1].text))) return IntervalSet (intervals) CSAObject.tag_map[CSA + IntervalSet.tag] = (IntervalSet, CUSTOM) class ComplementaryIntervalSet (IntervalSet): def __init__ (self, s = [], intervals = None, nIntegers = None): IntervalSet.__init__ (self, s, intervals, nIntegers) def repr (self): if not self.intervals: return 'N' else: return '~IntervalSet(%r)' % self.intervals def __bool__ (self): return True # def __len__ (self): # raise RuntimeError ('ComplementaryIntervalSet has infinite length') def __contains__ (self, n): for i in self.intervals: if n < i[0]: continue elif n <= i[1]: return False else: return True return True def __iter__ (self): raise RuntimeError ("can't interate over ComplementaryIntervalSet") def __invert__ (self): return IntervalSet (intervals = self.intervals, \ nIntegers = self.nIntegers) def finite (self): return False def shift (self, N): iset = IntervalSet (intervals = self.intervals, \ nIntegers = self.nIntegers).shift (N) return ComplementaryIntervalSet (intervals = iset.intervals, \ nIntegers = iset.nIntegers) def intervalIterator (self): start = 0 for i in self.intervals: if i[0] > 0: yield (start, i[0] - 1) start = i[1] + 1 yield (start, infinity) def boundedIterator (self, low, high): raise RuntimeError ("can't interate over ComplementaryIntervalSet") def count (self, low, high): iterator = iter (self.intervals) c = 0 prev = low try: i = next (iterator) while i[1] < low: i = next (iterator) while i[0] < high: c += i[0] - prev prev = i[1] + 1 i = next (iterator) except StopIteration: pass if prev < high: c += high - prev return c def min (self): if not self.intervals or self.intervals[0][0] > 0: return 0 else: return self.intervals[0][1] + 1 def max (self): raise RuntimeError ('the maximum of a ComplementaryIntervalSet is infinity') def intersection (self, other): if isinstance (other, ComplementaryIntervalSet): return ~(~self).union (~other) else: return IntervalSet.intersection (self, other) def union (self, other): return ~(~self).intersection (~other) def _to_xml (self): if not self.intervals: return E ('N') else: return E ('apply', E ('complement'), IntervalSet._to_xml (self)) N = ComplementaryIntervalSet ([]) CSAObject.tag_map[CSA + 'N'] = (N, SINGLETON) csa-0.1.13/csa/misc.py000066400000000000000000000024341513102315300144120ustar00rootroot00000000000000# # This file is part of the Connection-Set Algebra (CSA). # Copyright (C) 2010,2011,2012 Mikael Djurfeldt # # CSA 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 3 of the License, or # (at your option) any later version. # # CSA 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, see . # from . import _misc def disc (r): return _misc.Disc (r) def rectangle (width, height): return _misc.Rectangle (width, height) def gaussian (sigma, cutoff): return _misc.Gaussian (sigma, cutoff) def block (M, N = None): return _misc.Block (M, M if N == None else N) def repeat (M, N = None): return _misc.Repeat (M, M if N == None else N) transpose = _misc.Transpose () def shift (M, N): return _misc.Shift (M, N) fix = _misc.Fix () def block1 (N): return _misc.Block (N) #del _misc # not for export csa-0.1.13/csa/plot.py000066400000000000000000000061371513102315300144410ustar00rootroot00000000000000# # This file is part of the Connection-Set Algebra (CSA). # Copyright (C) 2010,2011,2012,2022 Mikael Djurfeldt # # CSA 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 3 of the License, or # (at your option) any later version. # # CSA 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, see . # import numpy as _numpy import matplotlib import matplotlib.pyplot as _plt from . import elementary # This function was autogenerated by boilerplate.py. Do not edit as # changes will be lost def inverseGray(): ''' set the default colormap to gray and apply to current image if any. See help(colormaps) for more information ''' _plt.rc('image', cmap='gray_r') im = _plt.gci() if im is not None: im.set_cmap(_plt.cm.gray_r) _plt.draw_if_interactive() def show (cset, N0 = 30, N1 = None): N1 = N0 if N1 == None else N1 _plt.clf () _plt.axis ('equal') a = _numpy.zeros ((N0, N1)) for (i, j) in elementary.cross (range (N0), range (N1)) * cset: a[i,j] += 1.0 _plt.imshow (a, interpolation='nearest', vmin = 0.0, vmax = 1.0) _plt.show () def gplotsel2d (g, cset, source = elementary.N, target = elementary.N, N0 = 900, N1 = None, value = None, range=[], lines = True): N1 = N0 if N1 == None else N1 _plt.clf () _plt.axis ('equal') gplot2d (g, N1, color = 'grey', show = False) cset = elementary.cross (source, target) * cset N = len (cset) if lines: marker = 'ro-' else: marker = 'ro' if elementary.arity (cset): if value != None: if range: normalize = matplotlib.colors.Normalize (*range) else: normalize = matplotlib.colors.Normalize () normalize.autoscale ([v[value] for (i, j, v) in cset.c]) cmap = matplotlib.cm.get_cmap () for (i, j, v) in cset.c: color = cmap (normalize (v[value])) _plt.plot ([g (i)[0], g (j)[0]], [g (i)[1], g (j)[1]], \ marker, color = color, mfc = color) else: for (i, j, v) in cset.c: _plt.plot ([g (i)[0], g (j)[0]], [g (i)[1], g (j)[1]], marker) else: for (i, j) in cset: _plt.plot ([g (i)[0], g (j)[0]], [g (i)[1], g (j)[1]], marker) _plt.show () def gplot2d (g, N, color = None, show = True): if show: _plt.clf () _plt.axis ('equal') x = [] y = [] for i in range (0, N): pos = g (i) x.append (pos[0]) y.append (pos[1]) if color != None: _plt.plot (x, y, 'o', color = color) else: _plt.plot (x, y, 'bo') if show: _plt.show () #del numpy, plt csa-0.1.13/csa/valueset.py000066400000000000000000000150021513102315300153020ustar00rootroot00000000000000# # This file is part of the Connection-Set Algebra (CSA). # Copyright (C) 2010,2011,2012 Mikael Djurfeldt # # CSA 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 3 of the License, or # (at your option) any later version. # # CSA 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, see . # from .csaobject import * class ValueSet (CSAObject): def __init__ (self): CSAObject.__init__ (self, "valueset") def __neg__ (self): return GenericValueSet (lambda i, j: - self (i, j)) def __add__ (self, other): if not callable (other): return maybeAffine (other, 1.0, self) elif isinstance (other, (QuotedValueSet, AffineValueSet)): return other.__add__ (self) elif isinstance (other, GenericValueSet): return GenericValueSet (lambda i, j: self (i, j) + other.function (i, j)) else: return GenericValueSet (lambda i, j: self (i, j) + other (i, j)) def __radd__ (self, other): return self.__add__ (other) def __sub__ (self, other): return self.__add__ (- other) def __rsub__ (self, other): return self.__neg__ ().__add__ (other) def __mul__ (self, other): if not callable (other): return maybeAffine (0.0, other, self) elif isinstance (other, (QuotedValueSet, AffineValueSet)): return other.__mul__ (self) elif isinstance (other, GenericValueSet): return GenericValueSet (lambda i, j: self (i, j) * other.function (i, j)) else: return GenericValueSet (lambda i, j: self (i, j) * other (i, j)) def __rmul__ (self, other): return self.__mul__ (other) class QuotedValueSet (ValueSet): def __init__ (self, expression): ValueSet.__init__ (self) self.expression = expression def __call__ (self, i, j): return self.expression def __neg__ (self): return QuotedValueSet (- self.expression) def __add__ (self, other): if not callable (other): return QuotedValueSet (self.expression + other) elif isinstance (other, QuotedValueSet): return QuotedValueSet (self.expression + other.expression) elif isinstance (other, AffineValueSet): return other.__add__ (self) else: return maybeAffine (self.expression, 1.0, other) def __mul__ (self, other): if not callable (other): return QuotedValueSet (self.expression * other) elif isinstance (other, QuotedValueSet): return QuotedValueSet (self.expression * other.expression) elif isinstance (other, AffineValueSet): return other.__mul__ (self) else: return maybeAffine (0.0, self.expression, other) class GenericValueSet (ValueSet): def __init__ (self, function): ValueSet.__init__ (self) self.function = function def __call__ (self, i, j): return self.function (i, j) def __neg__ (self): return GenericValueSet (lambda i, j: - self.function (i, j)) def __add__ (self, other): if not callable (other): return maybeAffine (other, 1.0, self.function) elif isinstance (other, (QuotedValueSet, AffineValueSet)): return other.__add__ (self) elif isinstance (other, GenericValueSet): return GenericValueSet (lambda i, j: self.function (i, j) + other.function (i, j)) else: return GenericValueSet (lambda i, j: self.function (i, j) + other (i, j)) def __mul__ (self, other): if not callable (other): return maybeAffine (0.0, other, self.function) elif isinstance (other, (QuotedValueSet, AffineValueSet)): return other.__mul__ (self) elif isinstance (other, GenericValueSet): return GenericValueSet (lambda i, j: self.function (i, j) * other.function (i, j)) else: return GenericValueSet (lambda i, j: self.function (i, j) * other (i, j)) class AffineValueSet (ValueSet): def __init__ (self, constant, coefficient, function): ValueSet.__init__ (self) self.const = constant self.coeff = coefficient self.func = function def __call__ (self, i, j): return self.const + self.coeff * self.func (i, j) def __neg__ (self): return maybeAffine (- self.const, - self.coeff, self.func) def __add__ (self, other): if not callable (other): return maybeAffine (self.const + other, self.coeff, self.func) elif isinstance (other, QuotedValueSet): return maybeAffine (self.const + other.expression, self.coeff, self.func) elif isinstance (other, AffineValueSet): f = lambda i, j: \ self.const * self.func (i, j) \ + other.const * other.func (i, j) return maybeAffine (self.const + other.const, 1.0, f) def __mul__ (self, other): if not callable (other): return maybeAffine (self.const * other, self.coeff * other, self.func) elif isinstance (other, QuotedValueSet): return maybeAffine (self.const * other.expression, self.coeff * other.expression, self.func) elif isinstance (other, AffineValueSet): f = lambda i, j: \ other.const * self.coeff * self.func (i, j) \ + self.const * other.coeff * other.func (i, j) \ + self.coeff * other.coeff \ * self.func (i, j) * other.func (i, j) return maybeAffine (self.const * other.const, 1.0, f) def maybeAffine (const, coeff, func): if coeff == 0.0: return QuotedValueSet (const) elif const == 0.0 and coeff == 1.0: return GenericValueSet (func) else: return AffineValueSet (const, coeff, func) csa-0.1.13/csa/version.py000066400000000000000000000014051513102315300151410ustar00rootroot00000000000000# # This file is part of the Connection-Set Algebra (CSA). # Copyright (C) 2010,2011,2012,2018,2020 Mikael Djurfeldt # # CSA 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 3 of the License, or # (at your option) any later version. # # CSA 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, see . # __version__ = "0.1.13" csa-0.1.13/debian/000077500000000000000000000000001513102315300135565ustar00rootroot00000000000000csa-0.1.13/debian/changelog000066400000000000000000000041451513102315300154340ustar00rootroot00000000000000python-csa (0.1.13-1) unstable; urgency=high * New upstream version (Closes: #1122462) -- Mikael Djurfeldt Sun, 11 Jan 2026 21:43:54 +0200 python-csa (0.1.12-1.3) unstable; urgency=medium * Non-maintainer upload. * Temporary disable missing tests. -- Alexandre Detiste Thu, 12 Sep 2024 17:07:46 +0200 python-csa (0.1.12-1.2) unstable; urgency=medium * Non-maintainer upload. * Add a watch file that follows GitHub tarball that contains tests. * Switch to DebHelper 13 * Replace Nose with Pytest (Closes: #1018475) -- Alexandre Detiste Thu, 12 Sep 2024 16:19:55 +0200 python-csa (0.1.12-1.1) unstable; urgency=medium * Non-maintainer upload. * Source-only upload. (Closes: #952499) -- Carlos Henrique Lima Melara Mon, 08 Jun 2020 23:31:08 -0300 python-csa (0.1.12-1) unstable; urgency=medium * New upstream version -- Mikael Djurfeldt Tue, 07 Apr 2020 11:58:54 +0200 python-csa (0.1.10-1) unstable; urgency=medium * New upstream version -- Mikael Djurfeldt Wed, 22 Jan 2020 21:23:37 +0100 python-csa (0.1.0-1) unstable; urgency=low * New upstream version (Closes: #597299) -- Mikael Djurfeldt Fri, 30 Mar 2012 15:32:17 +0200 python-csa (0.0.4-1) unstable; urgency=high * New upstream version (Closes: #590343) -- Mikael Djurfeldt Wed, 28 Jul 2010 01:19:48 +0200 python-csa (0.0.3-2) unstable; urgency=high * Added missing depends (Closes: #590343) -- Mikael Djurfeldt Tue, 27 Jul 2010 16:54:20 +0200 python-csa (0.0.3-1) unstable; urgency=high * Added shift operator (+ make sure previous bug fixes goes in quickly to testing). -- Mikael Djurfeldt Tue, 27 Jul 2010 07:58:54 +0200 python-csa (0.0.2-1) unstable; urgency=low * Bug fixes and extensions. -- Mikael Djurfeldt Sat, 17 Jul 2010 11:30:38 +0200 python-csa (0.0.1-1) unstable; urgency=low * Initial release (Closes: #588360) -- Mikael Djurfeldt Wed, 07 Jul 2010 17:45:19 +0200 csa-0.1.13/debian/control000066400000000000000000000015241513102315300151630ustar00rootroot00000000000000Source: python-csa Section: python Priority: optional Maintainer: Mikael Djurfeldt Build-Depends: debhelper-compat (= 13), dh-sequence-python3, python3-setuptools, python3-all, python3-pytest, python3-numpy, python3-matplotlib, Standards-Version: 4.4.1 Homepage: https://github.com/INCF/csa Package: python3-csa Architecture: any Depends: ${python3:Depends}, ${misc:Depends}, python3-numpy, python3-matplotlib Description: Connection-Set Algebra (CSA) implemented in Python The CSA library provides elementary connection-sets and operators for combining them. It also provides an iteration interface to such connection-sets enabling efficient iteration over existing connections with a small memory footprint also for very large networks. The CSA can be used as a component of neuronal network simulators or other tools. csa-0.1.13/debian/copyright000066400000000000000000000022041513102315300155070ustar00rootroot00000000000000This work was packaged for Debian by: Mikael Djurfeldt on Wed, 7 Jul 2010 16:55:01 +0100 Upstream Author: Mikael Djurfeldt Copyright: Copyright © 2010 Mikael Djurfeldt License: GPL-3 This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see . On Debian systems, the complete text of the GNU General Public License version 3 can be found in "/usr/share/common-licenses/GPL-3". The Debian packaging is: Copyright (C) 2010 Mikael Djurfeldt and is licensed under the GPL version 3, see above. csa-0.1.13/debian/python-csa.docs000066400000000000000000000000071513102315300165120ustar00rootroot00000000000000README csa-0.1.13/debian/rules000077500000000000000000000005041513102315300146350ustar00rootroot00000000000000#!/usr/bin/make -f #override_dh_auto_test: #ifeq ($(filter nocheck,$(DEB_BUILD_OPTIONS)),) # set -e; \ # for python in $(shell pyversions -r); do \ # $$python /usr/bin/nosetests ../../tests/test_csa.py; \ # done #endif %: dh $@ --buildsystem=pybuild override_dh_auto_test: echo "Tests are missing from latest tarball" csa-0.1.13/debian/source/000077500000000000000000000000001513102315300150565ustar00rootroot00000000000000csa-0.1.13/debian/source/format000066400000000000000000000000141513102315300162640ustar00rootroot000000000000003.0 (quilt) csa-0.1.13/doc/000077500000000000000000000000001513102315300131015ustar00rootroot00000000000000csa-0.1.13/doc/ChangeLog000066400000000000000000000002751513102315300146570ustar00rootroot000000000000002012-03-30 Mikael Djurfeldt * Release 0.1.0 * tutorial.tex: Version 0.2. 2011-01-18 Mikael Djurfeldt * tutorial.tex: New file: Version 0.1. csa-0.1.13/doc/figures/000077500000000000000000000000001513102315300145455ustar00rootroot00000000000000csa-0.1.13/doc/figures/blockRandom.pdf000066400000000000000000000165451513102315300175060ustar00rootroot00000000000000%PDF-1.4 %¬Ü «º 1 0 obj << /Type /Catalog /Pages 3 0 R >> endobj 2 0 obj << /CreationDate (D:20110118173842+02'00') /Producer (matplotlib pdf backend) /Creator (matplotlib 0.99.3, http://matplotlib.sf.net) >> endobj 8 0 obj << /Pattern 6 0 R /XObject 7 0 R /Font 4 0 R /ExtGState 5 0 R /ProcSet [ /PDF /Text /ImageB /ImageC /ImageI ] >> endobj 10 0 obj << /Contents 9 0 R /Type /Page /Resources 8 0 R /Parent 3 0 R /MediaBox [ 0 0 585 441 ] 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0000006394 00000 n 0000006426 00000 n 0000006447 00000 n 0000006468 00000 n 0000000216 00000 n 0000000451 00000 n 0000000344 00000 n 0000001817 00000 n 0000006525 00000 n 0000005202 00000 n 0000005002 00000 n 0000004652 00000 n 0000006255 00000 n 0000001838 00000 n 0000001978 00000 n 0000002368 00000 n 0000002779 00000 n 0000003100 00000 n 0000003262 00000 n 0000003545 00000 n 0000003865 00000 n 0000004330 00000 n 0000004482 00000 n trailer << /Info 2 0 R /Root 1 0 R /Size 27 >> startxref 6859 %%EOF csa-0.1.13/doc/manual/000077500000000000000000000000001513102315300143565ustar00rootroot00000000000000csa-0.1.13/doc/manual/basicfunc.rst000066400000000000000000000010141513102315300170410ustar00rootroot00000000000000Basic functions =============== Connection-set constructor -------------------------- .. function:: cset (mask, valueSet, ...) Selectors --------- .. function:: mask (object) .. function:: value (object, k) .. function:: arity (object) Cartesian product ----------------- .. function:: cross (set1, set2) Elementary masks ---------------- Support for parallel simulators ------------------------------- .. function:: partition (cset, masks, selected[, seed]) Utilities --------- .. function:: tabulate (cset) csa-0.1.13/doc/manual/datatypes.rst000066400000000000000000000030521513102315300171060ustar00rootroot00000000000000Datatypes in the Python CSA implementation ========================================== Connection Set Objects ---------------------- Connection-set objects (:class:`Mask`, :class:`ConnectionSet`) +--------------------+---------------------------------+--------+ | Operation | Result | Notes | +====================+=================================+========+ | ``x * y`` | intersection of *x* and | | | | *y* | | +--------------------+---------------------------------+--------+ | ``x - y`` | set difference of *x* and *y* | | | | | | +--------------------+---------------------------------+--------+ Test: +-------------+---------------------------------+-------+ | Operation | Result | Notes | +=============+=================================+=======+ | ``x or y`` | if *x* is false, then *y*, else | \(1) | | | *x* | | +-------------+---------------------------------+-------+ | ``x and y`` | if *x* is false, then *x*, else | \(2) | | | *y* | | +-------------+---------------------------------+-------+ | ``not x`` | if *x* is false, then ``True``, | \(3) | | | else ``False`` | | +-------------+---------------------------------+-------+ Value Set Objects ----------------- Interval Set Objects -------------------- csa-0.1.13/doc/manual/geometry.rst000066400000000000000000000000221513102315300167350ustar00rootroot00000000000000Geometry ======== csa-0.1.13/doc/manual/index.rst000066400000000000000000000004141513102315300162160ustar00rootroot00000000000000The Python CSA Reference Manual =============================== Contents: .. toctree:: :maxdepth: 2 intro datatypes basicfunc random geometry tutorial Indices and tables ================== * :ref:`genindex` * :ref:`modindex` * :ref:`search` csa-0.1.13/doc/manual/intro.rst000066400000000000000000000001321513102315300162370ustar00rootroot00000000000000Introduction ============ Getting started --------------- Installing CSA -------------- csa-0.1.13/doc/manual/random.rst000066400000000000000000000000501513102315300163630ustar00rootroot00000000000000Random connectivity =================== csa-0.1.13/doc/manual/tutorial.rst000066400000000000000000000000361513102315300167520ustar00rootroot00000000000000A CSA tutorial ============== csa-0.1.13/doc/tutorial.tex000066400000000000000000000674001513102315300154750ustar00rootroot00000000000000\documentclass[a4paper,twoside]{report} \usepackage{listings} \usepackage{color} \usepackage{graphicx} \usepackage{makeidx} % Use the head environment around method heads \lstnewenvironment{head}[1]% {\lstset{frame=topline,emph={#1},emphstyle=\color{blue}\textbf}}% {} % Use the parameters environment after heads \newenvironment{parameters}% {\begin{tabular}{@{\hspace{2em}}lp{0.6\textwidth}}}% {\end{tabular}\par\vspace{1mm}\par\hrule\par\vspace{5mm}} % Use the code environment around method code examples \lstnewenvironment{code}[1]% {\lstset{frame=single,caption={#1}}}% {} \renewcommand{\lstlistingname}{Example} %\lstset{language=python,basicstyle=\ttfamily\small} \lstset{language=python,identifierstyle=\ttfamily} \newcommand{\cls}[1]{\lstinline|#1|} \newcommand{\fa}[1]{\lstinline|#1|} \newcommand{\expr}[1]{\lstinline|#1|} \newcommand{\ret}{\emph{return value}} \newcommand{\self}{\emph{self}} \title{Python-csa tutorial v0.2} \author{Mikael Djurfeldt} \date{2011-01-17} \makeindex \begin{document} \maketitle \tableofcontents \chapter{Purpose of this document} This is a preliminary documentation and tutorial for the python-csa demonstration implementation in Python of the Connection-Set Algebra (Djurfeldt, 2011, submitted) The CSA library provides elementary connection-sets and operators for combining them. It also provides an iteration interface to such connection-sets enabling efficient iteration over existing connections with a small memory footprint also for very large networks. The CSA can be used as a component of neuronal network simulators or other tools. Section \ref{sec:introduction} introduces some basic concepts while section \ref{sec:tutorial} provides some \emph{hands-on} material for getting started. Section \ref{sec:reference} contain a preliminary reference documentation. \chapter{Introduction}\label{sec:introduction} When building a neuronal network model, we often want to connect one set of neurons---the \emph{source} set---with another set---the \emph{target} set. When applying the Connection-Set Algebra (hereafter denoted \emph{CSA}), we start by \emph{enumerating} the source and target sets, i.e. we assign arbitrary integer indices to the neurons of each set. This allows us to represent a connection between source neuron number 3 and target neuron number 17 as a pair of integers (3, 17). More generally, the source and target sets do not need to be neurons. For example, the target set might be a set of synaptic sites. Also, source and target sets can be (and is often) the same set. This is the case when using CSA to describe connectivity within a neuronal population. \begin{itemize} \item A \emph{mask} contains information about which connections exist. It is a set of (source, target) pairs, one pair for each existing connection. It can also be regarded as a function mapping a pair of arbitrary non-negative integers to a boolean value---\emph{true} for each existing connection. \item A \emph{value-set} is a function mapping each existing connection to a value, such as a synaptic weight. \item A \emph{connection-set} is a tuple of a mask and zero or more value sets. \end{itemize} CSA connection sets are usually infinite. This is a simplification compared to the common situation of finite source and target sets in that the sizes of these sets do not need to be considered. Connection sets can have arbitrary values associated with connections. Pure connection sets without any values associated are called masks. \chapter{Tutorial}\label{sec:tutorial} \section{Basic concepts} To get access to the CSA in Python, type: \begin{code}{} from csa import * \end{code} The mask representing all possible connections between an infinite source and target set is: \begin{code}{} full \end{code} To display a finite portion of the corresponding connectivity matrix, type: \begin{code}{} show (full) \end{code} One-to-one connectivity (where source node 0 is connected to target node 0, source 1 to target 1 etc) is represented by the mask oneToOne (Figure \ref{fig:oneToOne}): \begin{code}{} show (oneToOne) \end{code} \begin{figure} \begin{center} \includegraphics[width=0.5\textwidth]{figures/oneToOne} \caption[oneToOne mask]{\label{fig:oneToOne} \expr{oneToOne} } \end{center} \end{figure} \pagebreak The default portion displayed by "show" is (0, 29) x (0, 29). (0, 99) x (0, 99) can be displayed using: \begin{code}{} show (oneToOne, 100, 100) \end{code} If source and target set is the same, oneToOne describes self-connections. We can use CSA to compute the set of connections consisting of all possible connections except for self-connections using the set difference operator "-" (Figure \ref{fig:setDifference}): \begin{code}{} show (full - oneToOne) \end{code} \begin{figure} \begin{center} \includegraphics[width=0.5\textwidth]{figures/setDifference} \caption[Set difference]{\label{fig:setDifference} \expr{full - oneToOne} } \end{center} \end{figure} Finite connection sets can be represented using either lists of connections, with connections represented as tuples (Figure \ref{fig:twoPoints}): \begin{code}{} show ([(22, 7), (8, 23)]) \end{code} \begin{figure} \begin{center} \includegraphics[width=0.5\textwidth]{figures/twoPoints} \caption[Mask with two connections]{\label{fig:twoPoints} \expr{[(22, 7), (8, 23)]} } \end{center} \end{figure} or using the Cartesian product of intervals (Figure \ref{fig:cartesian}): \begin{code}{} show (cross (xrange (10), xrange (20))) \end{code} \begin{figure} \begin{center} \includegraphics[width=0.5\textwidth]{figures/cartesian} \caption[Cartesian mask]{\label{fig:cartesian} \expr{xrange (10), xrange (20)} } \end{center} \end{figure} \pagebreak We can form a finite version of the infinite oneToOne by taking the intersection "*" with a finite connection set (Figure \ref{fig:intersection}): \begin{code}{} c = cross (xrange (10), xrange (10)) * oneToOne show (c) \end{code} \begin{figure} \begin{center} \includegraphics[width=0.5\textwidth]{figures/intersection} \caption[Finite part of infinite set]{\label{fig:intersection} \expr{cross (xrange (10), xrange (10)) * oneToOne} } \end{center} \end{figure} Finite connection sets can be tabulated: \begin{code}{} >>> tabulate(c) 0 0 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 \end{code} \pagebreak In Python, finite connection sets provide an iterator interface: \begin{code}{} >>> for x in cross (xrange (4), xrange (4)) * oneToOne: ... print x ... (0, 0) (1, 1) (2, 2) (3, 3) \end{code} \section{Random connectivity} Connectivity where the existence of each possible connection is determined by a Bernoulli trial with probability p is expressed with the random mask random (p), e.g. (Figure \ref{fig:random}): \begin{code}{} show (random (0.5)) \end{code} \begin{figure} \begin{center} \includegraphics[width=0.5\textwidth]{figures/random} \caption[Random mask]{\label{fig:random} \expr{random (0.5)} } \end{center} \end{figure} \section{The block operator} The block operator expands each connection in the operand into a rectangular block in the resulting connection matrix, e.g. (Figure \ref{fig:blockRandom}): \begin{code}{} show (block (5,3) * random (0.5)) \end{code} \begin{figure} \begin{center} \includegraphics[width=0.5\textwidth]{figures/blockRandom} \caption[Block expanded random mask]{\label{fig:blockRandom} \expr{block (5,3) * random (0.5)} } \end{center} \end{figure} Note that "*" here means operator application (see section \ref{sec:opap}). There is also a quadratic version of the operator: \begin{code}{} show (block (10) * random (0.7)) \end{code} Using intersection and set difference, we can now formulate a more complex mask: \begin{code}{} show (block (10) * random (0.7) * random (0.5) - oneToOne) \end{code} \begin{figure} \begin{center} \includegraphics[width=0.5\textwidth]{figures/brro} \caption[Random mask]{\label{fig:brro} \expr{block (10) * random (0.7) * random (0.5) - oneToOne} } \end{center} \end{figure} The block operator is especially useful when creating connectivity with hierarchical substructure, such as a set of cortical columns. \section{Geometry} In CSA, the basic tool to handle distance dependent connectivity is metrics. Metrics are value sets d (i, j). Metrics can be defined through geometry functions. A geometry function maps an index to a position. We can, for example, assign a random position in the unit square to each index: \begin{code}{} g = random2d (900) \end{code} The positions of the grid described by g have indices from 0 to 899 and can be displayed like this: \begin{code}{} gplot2d (g, 900) \end{code} \begin{figure} \begin{center} \includegraphics[width=0.5\textwidth]{figures/random2d} \caption[Random geometry]{\label{fig:random2d} \expr{gplot2d (random2d (900), 900)} } \end{center} \end{figure} Alternatively, we can arrange indices in a 30 x 30 grid within the unit square: \begin{code}{} g = grid2d (30) \end{code} \begin{figure} \begin{center} \includegraphics[width=0.5\textwidth]{figures/grid2d} \caption[Random geometry]{\label{fig:grid2d} \expr{gplot2d (grid2d (30), 900)} } \end{center} \end{figure} We can now define the euclidean metric on this grid: \begin{code}{} d = euclidMetric2d (g) \end{code} An example of a distance dependent connection set is the disc mask Disc (r) * d which connects each index i to all indices j within a distance d (i, j) < r: \begin{code}{} c = disc (r) * d \end{code} To examine the result we can employ the function gplotsel2d (g, c, i) which displays the targets g (j) of i in the connection set c (Figure \ref{fig:disc}): \begin{code}{} gplotsel2d (g, c, 434) \end{code} \noindent [A known bug in the current implementation makes the above expression crash. This only happens for infinite sets like \expr{c} and can be amended by intersecting it with a finite set: \expr{cross (xrange (900), xrange (900)) * c}.] \begin{figure} \begin{center} \includegraphics[width=0.5\textwidth]{figures/disc} \caption[Disc geometry]{\label{fig:disc} Projection from source neuron \#434 in \expr{disc (0.3) * d}. } \end{center} \end{figure} In the case where the connection set represents a projection between two different coordinate systems, we define one geometry function for each. In the following example \expr{g1} is direction in visual space in arc minutes while \expr{g2} is position in the cortical representation of the Macaque fovea in mm: \begin{code}{} g1 = grid2d (30) g2 = grid2d (30, x0 = -7.0, xScale = 8.0, yScale = 8.0) \end{code} We now define a projection operator which takes visual coordinates into cortical (Dow et al. 1985): \begin{code}{} import cmath @ProjectionOperator def GvspaceToCx (p): w = 7.7 * cmath.log (complex (p[0] + 0.33, p[1])) return (w.real, w.imag) \end{code} To see how the grid g1 is transformed into cortical space, we type: \begin{code}{} gplot2d (GvspaceToCx * g1, 900) \end{code} \begin{figure} \begin{center} \includegraphics[width=0.5\textwidth]{figures/projection} \caption[Logarithmic projection]{\label{fig:projection} \expr{gplot2d (GvspaceToCx * g1, 900)} } \end{center} \end{figure} The inverse projection is defined: \begin{code}{} @ProjectionOperator def GcxToVspace (p): c = cmath.exp (complex (p[0], p[1]) / 7.7) - 0.33 return (c.real, c.imag) \end{code} Real receptive field sizes vary with eccentricity. Assume, for now, that we want to connect each target index to sources within a disc of constant radius. We then need to project back into visual space and use the disc operator: \begin{code}{} c = disc (0.1) * euclidMetric2d (g1, GcxToVspace * g2) \end{code} Again, we use gplotsel2d to check the result (Figure \ref{fig:visualDisc}): \begin{code}{} gplotsel2d (g2, c, 282) \end{code} \begin{figure} \begin{center} \includegraphics[width=0.5\textwidth]{figures/visualDisc} \caption[Random geometry]{\label{fig:visualDisc} \expr{disc (0.1) * euclidMetric2d (g1, GcxToVspace * g2)} } \end{center} \end{figure} \clearpage \pagebreak \section{A network with gaussian connectivity} In the following example we represent the connectivity of a network with excitatory and inhibitory neurons and gaussian connectivity in a random geometry using a single connection-set (Figure \ref{fig:gaussnet}). \begin{code}{Network with gaussian geometry-dependent connectivity} from csa import * # Create index intervals for excitatory, inhibitory # and all cells e = ival (0, 599) i = ival (600, 899) a = e + i # Create geometry function g and metric d g = random2d (900) d = euclidMetric2d (g) # Excitatory and inhibitory conductances, computed as # gaussian value sets (provides the gaussian of the # distance for every index pair) g_e = gaussian (0.1, 0.3) * d g_i = gaussian (0.2, 0.3) * d # Create connection-sets with gaussian dependent random # masks, gaussian dependent conductance and distance # dependent delay: (mask, conductance, delay) c_e = cset (random * g_e, g_e, d) c_i = cset (random * g_i, -g_i, d) # Combine excitatory and inhibitory connectivity into one # network using intersection (*) and multiset sum (+) # operators c = cross (e, a) * c_e + cross (i, a) * c_i # We may also plot the outgoing connections from one # excitatory neuron around coordinate (0.33, 0.5) and one # inhibitory neuron around coordinate (0.67, 0.5) sources = [g.inverse(0.33,0.5,e), g.inverse(0.67,0.5,i)] gplotsel2d (g, c, sources, value=0, range=[-1,1]) \end{code} \begin{figure} \begin{center} \includegraphics[width=0.9\textwidth]{figures/gaussnet} \caption[Projections of an excitatory and an inhibitory neuron]{\label{fig:gaussnet} Projections of an excitatory (warm colors) and an inhibitory (cold colors). } \end{center} \end{figure} \chapter{Reference}\label{sec:reference} %\begin{head}{} %\end{head} %\begin{parameters} % \lstinline|| &% % \\ % \lstinline|| &% % \\ % \ret &% % \\ %\end{parameters} This section documents how to use existing python-csa classes. \section{Classes} This section briefly documents some important classes in the python-csa implementation and their public API. The examples use many elements which are defined in later sections. It is suggested to use the index on page \pageref{sec:index} to find the reference documentation for these elements. \subsection{ConnectionSet}\index{ConnectionSet} A connection-set can be regarded as a set of connections, represented by their source and target indices, with zero or more associated values. In the CSA, a connection-set with no associated values is a mask. Thus, in the python-csa implementation, in all cases where an instance of the class \cls{ConnectionSet} is expected, it is OK to pass an instance of \cls{Mask}. \begin{head}{__len__} __len__ (self) \end{head} \begin{parameters} \ret &% the number of connections in this connection-set\\ \end{parameters} This method returns the number of connections in this connection-set. An error is reported if this connection-set isn't finite. \begin{code}{Obtaining the number of connections in a connection-set} >>> len (cross ((0, 1), (0, 1))) 4 \end{code} \begin{head}{__iter__} __iter__ (self) \end{head} \begin{parameters} \ret &% iterator over the connections represented by this instance\\ \end{parameters} This method returns an iterator over the connections represented by this instance. Each item generated by the iterator is a tuple \[ (i, j, v_0, ..., v_{n-1}) \] \begin{code}{Iterating over a connection-set} >>> m = cross ((0, 1), (2, 3)) >>> v = vset (lambda i, j: i + j) >>> c = cset (m, v, v * v) >>> for x in c: ... print x ... (0, 2, 2, 4) (1, 2, 3, 9) (0, 3, 3, 9) (1, 3, 4, 16) \end{code} \subsection{Mask}\index{Mask} A mask gives information about which connections exist. It can be regarded as a set of connections, represented by their source and target indices. In the CSA, a connection-set with no associated values is a mask. In the python-csa implementation, an attempt to construct a connection-set with zero associated values, yields an instance of the class \cls{Mask}. In cases where a mask is expected, a python list of (source, target) tuples can also be passed. The class \cls{Mask} has the same public methods (\expr{__len__}, \expr{__iter__}) as the class \cls{ConnectionSet}. \subsection{ValueSet}\index{ValueSet} To be documented. \subsection{IntervalSet}\index{IntervalSet}\label{sec:intervalset} To be documented. \section{Constructor and selectors} \subsection{cset}\index{cset} \begin{head}{cset} cset (mask, valueSet, ...) \end{head} \begin{parameters} \lstinline|mask| &% a \lstinline|Mask| \\ \lstinline|valueSet| &% zero or more \lstinline|ValueSet|:s \\ \end{parameters} This function constructs and returns a connection-set from a \lstinline|Mask| and zero or more \lstinline|ValueSet|:s. [Note: In the current implementation, \lstinline|mask| is returned if no value-sets are given. This should probably change so that a new object is returned.] \subsection{mask}\index{mask} \begin{head}{mask} mask (cset) \end{head} \begin{parameters} \lstinline|cset| &% a \lstinline|ConnectionSet|\\ \emph{return value} &% the \lstinline|Mask| of \lstinline|cset|\\ \end{parameters} This function returns the \lstinline|Mask| of the \lstinline|ConnectionSet| \lstinline|cset|. \subsection{value}\index{value} \begin{head}{value} value (cset, k) \end{head} \begin{parameters} \lstinline|cset| &% a \lstinline|ConnectionSet|\\ \lstinline|k| &% index of the value-set to return\\ \emph{return value} &% the \lstinline|k|:th \lstinline|ValueSet| of \lstinline|cset|\\ \end{parameters} This function returns the \lstinline|k|:th \lstinline|ValueSet| of the \lstinline|ConnectionSet| \lstinline|cset|. \subsection{arity}\index{arity} \begin{head}{arity} arity (cset) \end{head} \begin{parameters} \lstinline|cset| &% a \lstinline|ConnectionSet|\\ \emph{return value} &% the \emph{arity} of \lstinline|cset|\\ \end{parameters} This function returns the \emph{arity} of the \lstinline|ConnectionSet| \lstinline|cset|. The arity of a connection-set is the number of value-sets of the connection-set. \subsection{vset}\index{vset} \begin{head}{vset} vset (x) vset (callable) \end{head} \begin{parameters} \lstinline|x| &% a value\\ \lstinline|callable| &% a callable taking two arguments\\ \end{parameters} This function constructs and returns a value-set. In the first form, the number \fa{x} is taken as the value of each of all existing connections. In the second form, the value of each existing connection is the one returned by applying \fa{callable} to the source and target indices of the connection. \section{Integer sets} In the current python-csa implementation, integer sets are usually represented using the class \cls{IntervalSet} (see section \ref{sec:intervalset}). Functions that take integer sets as arguments generally coerce \cls{tuple}:s of two non-negative integers into \cls{IntervalSet}:s: \begin{code}{} (1, 2) --> IntervalSet ([(1,2)]) \end{code} \subsection{ival}\index{ival} \begin{head}{ival} ival (beginning, end) \end{head} \begin{parameters} \lstinline|beginning| &% start of interval\\ \lstinline|end| &% end of interval (inclusive)\\ \emph{return value} &% the interval \lstinline|(beginning, end)|\\ \end{parameters} This function returns the interval \lstinline|(beginning, end)| represented as a set of non-negative integers. The underlying representation is space-efficient. \subsection{N}\index{N} \begin{head}{N} N \end{head} \begin{parameters} \end{parameters} This constant represents the set of all non-negative integers. \subsection{cross}\index{cross} \begin{head}{cross} cross (set0, set1) \end{head} \begin{parameters} \lstinline|set0| &% a set of non-negative integers\\ \lstinline|set1| &% a set of non-negative integers\\ \emph{return value} &% the Cartesian cross product of \fa{set0} and \fa{set1}\\ \end{parameters} This function returns the Cartesian cross product of \fa{set0} and \fa{set1} represented as a \cls{Mask}. \begin{code}{The Cartesian product of (1,2) and (3,4)} >>> tabulate (cross ((1,2), (3,4))) 1 3 2 3 1 4 2 4 \end{code} \section{Utilities} \begin{head}{tabulate} tabulate (cset) \end{head} \begin{parameters} \lstinline|cset| &% a \cls{ConnectionSet}\\ \end{parameters} This procedure tabulates the connection-set \fa{cset}. An iteration over the connections in \fa{cset} is performed. The source and target indices are tabulated in the first and second columns with value-sets tabulated in columns three and upwards. Tabulate can be used to print connection-sets during development. \section{Elementary masks} \subsection{empty}\index{empty} \begin{head}{empty} empty \end{head} \begin{parameters} \end{parameters} This constant \cls{Mask} represents the set of no connection. Iterating results in nothing, no matter how hard you try. \subsection{full}\index{full} \begin{head}{full} full \end{head} \begin{parameters} \end{parameters} This constant \cls{Mask} represents the (infinite) set of all connections. \begin{code}{Finite portion of the \expr{full} mask} >>> tabulate (cross ((0, 1), (0, 1)) * full) 0 0 1 0 0 1 1 1 \end{code} \subsection{oneToOne}\index{oneToOne} \begin{head}{oneToOne} oneToOne \end{head} \begin{parameters} \end{parameters} This constant \cls{Mask} represents the (infinite) set of one-to-one connections. It resembles Kronecker's delta or an infinite identity matrix. \begin{code}{Finite portion of the \expr{oneToOne} mask} >>> tabulate (cross ((0, 3), (0, 3)) * oneToOne) 0 0 1 1 2 2 3 3 \end{code} \subsection{random}\index{random} \begin{head}{random} random (p) \end{head} \begin{parameters} \lstinline|p| &% the probability for a potential connection to exist\\ \ret &% an infinite \cls{Mask} where the existence of each connection is determined by a Bernoulli trial with probability \fa{p}.\\ \end{parameters} This function returns a random mask where a connection between given source and target indices exists with probability \fa{p}. See also section \ref{sec:randomop} for the set of functions returning random \emph{operators}. These support sampling a given number of connections from a finite mask or random sampling with constraints on \fa{fanIn} or \fa{fanOut}. \section{Set operators} The following binary operators can be applied to integer sets, masks and connection-sets: \par\vspace{4mm}\hrule\par\vspace{1mm} \begin{tabular}{@{\hspace{2em}}lp{0.6\textwidth}} \expr{A + B} & the \emph{multiset sum} of A and B\\ \expr{A - B} & the \emph{set difference} between A and B\\ \expr{A * B} & the \emph{intersection} of A and B\\ \end{tabular}\par\vspace{1mm}\par\hrule\par\vspace{5mm} In addition, the following unary operator applies to integer sets and masks: \par\vspace{4mm}\hrule\par\vspace{1mm} \begin{tabular}{@{\hspace{2em}}lp{0.6\textwidth}} \expr{\~A} & the \emph{complement} of A\\ \end{tabular}\par\vspace{1mm}\par\hrule\par\vspace{5mm} \section{Arithmetic operators} The arithmetic operators on connection-sets which are defined in the connection-set algebra are not yet implemented in the python-csa demo implementation. \section{Operator application}\label{sec:opap} The operator application operator is used to apply unary connection-set algebra operators to their operand: \par\vspace{4mm}\hrule\par\vspace{1mm} \begin{tabular}{@{\hspace{2em}}lp{0.6\textwidth}} \expr{operator * operand} & apply \fa{operator} to \fa{operand}\\ \end{tabular}\par\vspace{1mm}\par\hrule\par\vspace{5mm} The operator application operator is overloaded with the arithmetic multiplication and set intersection operators. \section{Miscellaneous connection-set operators}\label{sec:miscop} \subsection{random}\index{random}\label{sec:randomop} \begin{head}{random} random (N = n) * cset \end{head} \begin{parameters} \lstinline|n| &% the number of connections to sample (keyword arg named \fa{N})\\ \fa{cset} &% any \emph{finite} connection-set\\ \ret &% a connection-set containing \fa{n} randomly sampled connections from \fa{cset}\\ \end{parameters} \begin{head}{random} random (fanIn = n) * cset \end{head} \begin{parameters} \lstinline|n| &% the number of sources sampled for each target (keyword arg named \fa{fanIn})\\ \fa{cset} &% any \emph{finite} connection-set\\ \ret &% a connection-set randomly sampled from \fa{cset} with fanIn \fa{n}\\ \end{parameters} \begin{head}{random} random (fanOut = n) * cset \end{head} \begin{parameters} \lstinline|n| &% the number of targets sampled for each source (keyword arg named \fa{fanOut})\\ \fa{cset} &% any \emph{finite} connection-set\\ \ret &% a connection-set randomly sampled from \fa{cset} with fanOut \fa{n}\\ \end{parameters} \subsection{disc}\index{disc} \begin{head}{disc} disc (r) * metric \end{head} \begin{parameters} \fa{r} & radius \\ \ret & a mask of all connections for which \expr{metric (source, target) < r} \\ \end{parameters} \subsection{gaussian}\index{gaussian} \begin{head}{gaussian} gaussian (sigma, cutoff) * metric \end{head} \begin{parameters} \lstinline|sigma| &% \\ \lstinline|cutoff| &% \\ \ret & a value set associating the result of applying the normalized gaussian function with standard deviation \fa{sigma} and cutoff \fa{cutoff} to \expr{metric (source, target)} to each connection\\ \end{parameters} \subsection{block}\index{block} \begin{head}{block} block (M, N) block (M) \end{head} \begin{parameters} \fa{M} & \\ \fa{N} & \\ \end{parameters} \subsection{block1}\index{block1} \begin{head}{block1} block1 (N) \end{head} \begin{parameters} \end{parameters} \subsection{transpose}\index{transpose} \begin{head}{} transpose \end{head} \begin{parameters} \end{parameters} \subsection{shift}\index{shift} \begin{head}{shift} shift (M, N) \end{head} \begin{parameters} \lstinline|M| &% \\ \lstinline|N| &% \\ \end{parameters} \subsection{fix}\index{fix} \begin{head}{fix} fix \end{head} \begin{parameters} \end{parameters} \section{Geometry} \subsection{grid2d}\index{grid2d} \begin{head}{grid2d} grid2d (width, xScale = 1.0, yScale = 1.0, x0 = 0.0, y0 = 0.0) \end{head} \begin{parameters} \lstinline|width| &% \\ \lstinline|xScale| &% \\ \emph{return value} &% \\ \end{parameters} \subsection{random2d}\index{random2d} \begin{head}{random2d} random2d (N, xScale = 1.0, yScale = 1.0) \end{head} \begin{parameters} \lstinline|| &% \\ \lstinline|| &% \\ \emph{return value} &% \\ \end{parameters} \subsection{euclidMetric2d}\index{euclidMetric2d} \begin{head}{} euclidMetric2d (g1, [g2]) \end{head} \begin{parameters} \lstinline|g1| &% \\ \lstinline|g2| optional &% \\ \emph{return value} &% \\ \end{parameters} \subsection{ProjectionOperator}\index{ProjectionOperator} \begin{head}{ProjectionOperator} @ProjectionOperator def fname (p): ... return q \end{head} \begin{parameters} \lstinline|fname| &% \\ \lstinline|p| &% \\ \end{parameters} \section{Plotting} \subsection{show}\index{show} \begin{head}{show} show (cset, N0 = 30, [N1]) \end{head} \begin{parameters} \lstinline|cset| &% \\ \lstinline|N0| &% \\ \end{parameters} \subsection{gplotsel2d}\index{gplotsel2d} \begin{head}{gplotsel2d} gplotsel2d (g, cset, source = N, target = N, N0 = 900, [N1], [value], range=[], lines = True) \end{head} \begin{parameters} \lstinline|| &% \\ \lstinline|| &% \\ \end{parameters} \begin{head}{gplot2d} gplot2d (g, N, [color], show = True) \end{head} \begin{parameters} \lstinline|| &% \\ \lstinline|| &% \\ \end{parameters} \label{sec:index} \printindex \end{document} %%% Local Variables: %%% mode: latex %%% TeX-master: t %%% eval: (flyspell-mode 1) %%% eval: (ispell-change-dictionary "american") %%% eval: (flyspell-buffer) %%% End: csa-0.1.13/examples/000077500000000000000000000000001513102315300141525ustar00rootroot00000000000000csa-0.1.13/examples/catLGNV1.py000066400000000000000000000007151513102315300160460ustar00rootroot00000000000000from csa import * # Dow et al. 1985 (Macaque fovea) @ProjectionOperator def GvspaceToCx (p): w = 7.7 * cmath.log (complex (p[0] + 0.33, p[1])) return (w.real, w.imag) @ProjectionOperator def GcxToVspace (p): c = cmath.exp (complex (p[0], p[1]) / 7.7) - 0.33 return (c.real, c.imag) # g1 = grid2d (30) # g2 = grid2d (30, x0 = -7.0, xScale = 8.0, yScale = 8.0) # c = disc (0.1) * euclidMetric2d (g1, GcxToVspace * g2) # gplotsel2d (g2, c, 282) csa-0.1.13/libpycsa/000077500000000000000000000000001513102315300141425ustar00rootroot00000000000000csa-0.1.13/libpycsa/Makefile.am000066400000000000000000000010341513102315300161740ustar00rootroot00000000000000## Process this file with Automake to create Makefile.in ACLOCAL = $(top_srcdir)/aclocal.sh lib_LTLIBRARIES = libpycsa.la libpycsa_la_SOURCES = pycsa.h pycsa.cpp libpycsa_la_HEADERS = pycsa.h libpycsa_la_CPPFLAGS = @LIBPYCSA_CPPFLAGS@ @LIBNEUROSIM_INCLUDE@ libpycsa_la_CXXFLAGS = @LIBPYCSA_CXXFLAGS@ libpycsa_la_LIBADD = @LIBNEUROSIM_PY_LIBS@ @LIBNEUROSIM_LIBS@ @PYTHONLIB@ libpycsa_la_LDFLAGS = \ -version-info 1:0:0 -export-dynamic -Wl,-z,defs libpycsa_ladir = $(includedir) MKDEP = gcc -M $(DEFS) $(INCLUDES) $(CPPFLAGS) $(CFLAGS) csa-0.1.13/libpycsa/pycsa.cpp000066400000000000000000000202701513102315300157660ustar00rootroot00000000000000/* * pycsa.cpp * * This file is part of libneurosim. * * Copyright (C) 2013 INCF and 2018, 2020 Mikael Djurfeldt * * libneurosim 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 3 of the License, or * (at your option) any later version. * * libneurosim 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, see . * */ #include "pycsa.h" #include #include #include #if PY_MAJOR_VERSION >= 3 #define PYINT_ASLONG PyLong_AsLong #define PYINT_FROMLONG PyLong_FromLong #else #define PYINT_ASLONG PyInt_AsLong #define PYINT_FROMLONG PyInt_FromLong #endif static PyObject* pMask = 0; static PyObject* pConnectionSet = 0; static PyObject* pCSAClasses = 0; static PyObject* pArity = 0; static PyObject* pCross = 0; static PyObject* pPartition = 0; static PyObject* pParse = 0; static PyObject* pParseString = 0; static void error (std::string errstring) { PYGILSTATE_ENSURE (gstate); PyErr_SetString (PyExc_RuntimeError, errstring.c_str ()); PYGILSTATE_RELEASE (gstate); } static bool CSAimported () { if (!Py_IsInitialized ()) Py_Initialize (); return PyMapping_HasKeyString (PyImport_GetModuleDict (), (char*)"csa"); } static bool loadCSA () { PYGILSTATE_ENSURE (gstate); PyObject* pModule = PyMapping_GetItemString (PyImport_GetModuleDict (), (char*)"csa"); pMask = PyObject_GetAttrString (pModule, "Mask"); if (pMask == NULL) { Py_DECREF (pModule); PYGILSTATE_RELEASE (gstate); error ("Couldn't find the Mask class in the CSA library"); return false; } pConnectionSet = PyObject_GetAttrString (pModule, "ConnectionSet"); if (pConnectionSet == NULL) { Py_DECREF (pModule); PYGILSTATE_RELEASE (gstate); error ("Couldn't find the ConnectionSet class in the CSA library"); return false; } pArity = PyObject_GetAttrString (pModule, "arity"); pCross = PyObject_GetAttrString (pModule, "cross"); pPartition = PyObject_GetAttrString (pModule, "partition"); pParse = PyObject_GetAttrString (pModule, "parse"); pParseString = PyObject_GetAttrString (pModule, "parseString"); Py_DECREF (pModule); if (pArity == NULL) { PYGILSTATE_RELEASE (gstate); error ("Couldn't find the arity function in the CSA library"); return false; } pCSAClasses = PyTuple_Pack (2, pMask, pConnectionSet); PYGILSTATE_RELEASE (gstate); return true; } static bool tryLoadCSA () { if (pCSAClasses == 0) { if (!CSAimported ()) PyRun_SimpleString ("import csa\n"); //*fixme* error handling // load CSA library bool status = loadCSA (); if (!status) return false; } } namespace PyCSA { PyCSAGenerator::PyCSAGenerator (PyObject* obj) : pCSAObject (obj), pPartitionedCSAObject (NULL), pIterator (NULL) { PYGILSTATE_ENSURE (gstate); Py_INCREF (pCSAObject); PyObject* a = PyObject_CallFunctionObjArgs (pArity, pCSAObject, NULL); arity_ = PYINT_ASLONG (a); Py_DECREF (a); PYGILSTATE_RELEASE (gstate); } PyCSAGenerator::~PyCSAGenerator () { PYGILSTATE_ENSURE (gstate); Py_XDECREF (pIterator); Py_XDECREF (pPartitionedCSAObject); Py_DECREF (pCSAObject); PYGILSTATE_RELEASE (gstate); } int PyCSAGenerator::arity () { return arity_; } PyObject* PyCSAGenerator::makeIntervals (IntervalSet& iset) { PyObject* ivals = PyList_New (0); if (iset.skip () == 1) { for (IntervalSet::iterator i = iset.begin (); i != iset.end (); ++i) PyList_Append (ivals, PyTuple_Pack (2, PYINT_FROMLONG (i->first), PYINT_FROMLONG (i->last))); } else { for (IntervalSet::iterator i = iset.begin (); i != iset.end (); ++i) { int last = i->last; for (int j = i->first; j < last; j += iset.skip ()) PyList_Append (ivals, PyTuple_Pack (2, PYINT_FROMLONG (j), PYINT_FROMLONG (j))); } } return ivals; } void PyCSAGenerator::setMask (std::vector& masks, int local) { PYGILSTATE_ENSURE (gstate); PyObject* pMasks = PyList_New (masks.size ()); for (size_t i = 0; i < masks.size (); ++i) { PyObject* pMask = PyObject_CallFunctionObjArgs (pCross, makeIntervals (masks[i].sources), makeIntervals (masks[i].targets), NULL); PyList_SetItem (pMasks, i, pMask); } Py_XDECREF (pPartitionedCSAObject); pPartitionedCSAObject = PyObject_CallFunctionObjArgs (pPartition, pCSAObject, pMasks, PYINT_FROMLONG (local), NULL); if (pPartitionedCSAObject == NULL) { PYGILSTATE_RELEASE (gstate); std::cerr << "Failed to create masked CSA object" << std::endl; return; } Py_INCREF (pPartitionedCSAObject); //*fixme* check if necessary! PYGILSTATE_RELEASE (gstate); } int PyCSAGenerator::size () { PYGILSTATE_ENSURE (gstate); int size = PySequence_Size (pCSAObject); PYGILSTATE_RELEASE (gstate); return size; } void PyCSAGenerator::start () { if (pPartitionedCSAObject == NULL) { error ("CSA connection generator not properly initialized"); return; } PYGILSTATE_ENSURE (gstate); Py_XDECREF (pIterator); pIterator = PyObject_GetIter (pPartitionedCSAObject); PYGILSTATE_RELEASE (gstate); } bool PyCSAGenerator::next (int& source, int& target, double* value) { if (pIterator == NULL) { error ("Must call start() before next()"); return false; } PYGILSTATE_ENSURE (gstate); PyObject* tuple = PyIter_Next (pIterator); PyObject* err = PyErr_Occurred (); if (err) { PYGILSTATE_RELEASE (gstate); return false; } if (tuple == NULL) { Py_DECREF (pIterator); pIterator = NULL; PYGILSTATE_RELEASE (gstate); return false; } source = PYINT_ASLONG (PyTuple_GET_ITEM (tuple, 0)); target = PYINT_ASLONG (PyTuple_GET_ITEM (tuple, 1)); for (int i = 0; i < arity_; ++i) { PyObject* v = PyTuple_GET_ITEM (tuple, i + 2); if (!PyFloat_Check (v)) { Py_DECREF (tuple); PYGILSTATE_RELEASE (gstate); error ("NEST cannot handle non-float CSA value sets"); return false; } value[i] = PyFloat_AsDouble (v); } Py_DECREF (tuple); PYGILSTATE_RELEASE (gstate); return true; } static bool isPyCSAGenerator (PyObject* obj) { if (pCSAClasses == 0) { if (!CSAimported ()) return false; // load CSA library bool status = loadCSA (); if (!status) return false; } return PyObject_IsInstance (obj, pCSAClasses); } static ConnectionGenerator* unpackPyCSAGenerator (PyObject* pObj) { if (isPyCSAGenerator (pObj)) return new PyCSAGenerator (pObj); else return 0; } static ConnectionGenerator* parseString (std::string xml) { if (!tryLoadCSA ()) return 0; PYGILSTATE_ENSURE (gstate); PyObject* pyXML = PyUnicode_FromString (xml.c_str ()); PyObject* cg = PyObject_CallFunctionObjArgs (pParseString, pyXML, NULL); Py_DECREF (pyXML); PYGILSTATE_RELEASE (gstate); return new PyCSAGenerator (cg); } static ConnectionGenerator* parseFile (std::string fname) { if (!tryLoadCSA ()) return 0; PYGILSTATE_ENSURE (gstate); PyObject* pyfname = PyUnicode_FromString (fname.c_str ()); PyObject* cg = PyObject_CallFunctionObjArgs (pParseString, pyfname, NULL); Py_DECREF (pyfname); PYGILSTATE_RELEASE (gstate); return new PyCSAGenerator (cg); } // Publicly visible in PyCSA namespace void init () { registerConnectionGeneratorLibrary ("libpycsa.so", parseString, parseFile, 0, 0); PNS::registerConnectionGeneratorType (isPyCSAGenerator, unpackPyCSAGenerator); } } namespace { struct initializer { initializer() { PyCSA::init (); } }; static initializer i; } csa-0.1.13/libpycsa/pycsa.h000066400000000000000000000031311513102315300154300ustar00rootroot00000000000000/* * pycsa.h * * This file is part of libneurosim. * * Copyright (C) 2013 INCF * * libneurosim 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 3 of the License, or * (at your option) any later version. * * libneurosim 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, see . * */ #ifndef PYCSA_H #define PYCSA_H extern "C" { #include } #ifdef WITH_THREAD /* CPython compiled with threads */ #define PYGILSTATE_ENSURE(VAR) PyGILState_STATE VAR = PyGILState_Ensure() #define PYGILSTATE_RELEASE(VAR) PyGILState_Release (VAR) #else #define PYGILSTATE_ENSURE(VAR) #define PYGILSTATE_RELEASE(VAR) #endif #include namespace PyCSA { class PyCSAGenerator : public ConnectionGenerator { PyObject* pCSAObject; PyObject* pPartitionedCSAObject; int arity_; PyObject* pIterator; private: PyObject* makeIntervals (IntervalSet& iset); public: PyCSAGenerator (PyObject* obj); ~PyCSAGenerator (); int arity (); void setMask (std::vector& masks, int local); int size (); void start (); bool next (int& source, int& target, double* value); }; } #endif csa-0.1.13/setup.py000066400000000000000000000041271513102315300140520ustar00rootroot00000000000000#!/usr/bin/env python from distutils.core import setup # ugly hack to prevent matplotlib from creating a configuration file # outside of the EasyInstall sandbox import os os.environ['MPLCONFIGDIR'] = "." # read version without actually importing the module import re from pathlib import Path def read_version(path: str) -> str: text = Path(path).read_text(encoding="utf-8") m = re.search(r"^__version__\s*=\s*\"(.+?)\"", text, flags=re.M) if not m: raise RuntimeError(f"Unable to find __version__ in {path}") return m.group(1) __version__ = read_version("csa/version.py") long_description = """The CSA library provides elementary connection-sets and operators for combining them. It also provides an iteration interface to such connection-sets enabling efficient iteration over existing connections with a small memory footprint also for very large networks. The CSA can be used as a component of neuronal network simulators or other tools.""" setup ( name = "csa", version = __version__, packages = ['csa',], author = "Mikael Djurfeldt", # add your name here if you contribute to the code author_email = "mikael@djurfeldt.com", description = "The Connection-Set Algebra implemented in Python", long_description = long_description, #... license = "GPLv3", keywords = "computational neuroscience modeling connectivity", url = "http://software.incf.org/software/csa/", classifiers = ['Development Status :: 3 - Alpha', 'Environment :: Console', 'Intended Audience :: Science/Research', 'License :: OSI Approved :: GNU General Public License (GPL)', 'Natural Language :: English', 'Operating System :: OS Independent', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3.4', 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', 'Topic :: Scientific/Engineering'], install_requires = ['numpy', 'matplotlib'], ) csa-0.1.13/tests/000077500000000000000000000000001513102315300134765ustar00rootroot00000000000000csa-0.1.13/tests/test_csa.py000066400000000000000000000153141513102315300156610ustar00rootroot00000000000000# # This file is part of the Connection-Set Algebra (CSA). # Copyright (C) 2010,2011,2012 Mikael Djurfeldt # # CSA 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 3 of the License, or # (at your option) any later version. # # CSA 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, see . # import sys import numpy from csa import * import unittest import sys if sys.version < '3.4': from contextlib import contextmanager @contextmanager def redirect_stdout(stream): old_stdout = sys.stdout sys.stdout = stream try: yield finally: sys.stdout = old_stdout else: from contextlib import redirect_stdout from io import StringIO class TestCSA(unittest.TestCase): def assertEqualCS (self, cs, ls, msg): self.assertEqual ([x for x in cs], ls, msg) def assertEqual4x4 (self, cs, ls, msg): self.assertEqualCS (cross ((0, 3), (0, 3)) * cs, ls, msg) def assertEqual30x30 (self, cs, ls, msg): self.assertEqualCS (cross ((0, 29), (0, 29)) * cs, ls, msg) def sampleN (self, func, dims, N): data = numpy.zeros ((N,) + dims) for k in range (N): data[k] = func () return numpy.mean (data, 0) class TestElementary (TestCSA): # Only use setUp() and tearDown() if necessary #def setUp(self): # ... code to execute in preparation for tests ... #def tearDown(self): # ... code to execute to clean up after tests ... def test_list (self): # Test list cs self.assertEqual30x30 ([(22,7),(8,23)], [(22,7),(8,23)], 'list cs') def test_range (self): # Test range self.assertEqual30x30 (cross (range (10), range (10, 20, 3)), [(i, j) for j in range (10, 20, 3) for i in range (10)], 'xrange specified interval set mask') def test_full (self): # Test full cs self.assertEqualCS (cross ((0, 7), (8, 15)) * full, [(i, j) for j in range (8, 16) for i in range (0, 8)], 'finite piece of full connection-set bogus') def test_oneToOne (self): # Test oneToOne cs self.assertEqualCS (cross ((0, 7), (1, 8)) * oneToOne, [(i, i) for i in range (1, 8)], 'finite piece of oneToOne connection-set bogus') def test_tabulate (self): # Test tabulate if sys.version < '2.6': #*fixme* return s = StringIO() with redirect_stdout(s): tabulate (cross ((0, 3), (0, 3)) * oneToOne) self.assertEqual (s.getvalue (), '0 \t0\n1 \t1\n2 \t2\n3 \t3\n', 'tabulate malfunctioning') def test_gaussnet (self): e = ival (0, 19) i = ival (20, 29) a = e + i g = random2d (900) d = euclidMetric2d (g) g_e = gaussian (0.1, 0.3) * d g_i = gaussian (0.2, 0.3) * d c_e = cset (random * g_e, g_e) c_i = cset (random * g_i, -g_i) c = cross (e, a) * c_e + cross (i, a) * c_i self.assertTrue (cross (N, 0) * c) self.assertFalse (cross (N, 100) * c) for (i, j, g) in cross (i, a) * c: self.assertTrue (g < 0.0) def partitionRandomN (self): K = self.K N = 3 * K R = (0, N - 1) R0 = (0, K - 1) R2 = (2 * K, 3 * K - 1) c = random (N = N) * cross (R, R) c0 = partition (c, [cross (R, R0), cross (R, R2)], 0) c1 = partition (c, [cross (R, R0), cross (R, R2)], 1) c2 = transpose * partition (c, [cross (R0, R), cross (R2, R)], 0) c3 = transpose * partition (c, [cross (R0, R), cross (R2, R)], 1) res = numpy.zeros ((4 * N, K)) row = 0 for (c, offset) in [(c0, 0), (c1, 2 * K), (c2, 0), (c3, 2 * K)]: a = numpy.zeros ((N, K)) for (i, j) in c: j -= offset if 0 <= i < N and 0 <= j < K: a[i, j] = 1 else: self.fail ('connection outside mask') res[row:row + N, :] = a row += N return 2.0 * K * res # normalization def test_partitionRandomN (self): self.K = 5 for _ in range(1000): res = self.partitionRandomN() self.assertEqual (res.min(), 0.) self.assertEqual (res.max(), self.K*2.) self.assertEqual (res.shape, (self.K*12, self.K)) self.assertTrue ((res.sum() <= self.K**2*12)) self.assertFalse (numpy.any((res != 0.) & (res != self.K*2.))) def intersectionRandomN (self): K = self.K N = 3 * K R = (0, N - 1) R0 = (0, K - 1) R2 = (2 * K, 3 * K - 1) c = random (N = N) * cross (R, R) c0 = cross (R, [R0, R2]) * c c1 = transpose * (cross ([R0, R2], R) * c) res = numpy.zeros ((2 * N, 2 * K)) row = 0 for c in [c0, c1]: a = numpy.zeros ((N, 2 * K)) for (i, j) in c: if 0 <= i < N and 0 <= j < K: a[i, j] = 1 elif 0 <= i < N and 2 * K <= j < 3 * K: a[i, j - K] = 1 else: self.fail ('connection outside mask') res[row:row + N, :] = a row += N return N * res # normalization def test_intersectionRandomN (self): self.K = 5 res = self.sampleN (self.intersectionRandomN, (6 * self.K, 2 * self.K), 1000) for x in res.flatten (): self.assertAlmostEqual (x, 1.0, 0, 'maybe wrong statistics %g != 1.' % x) class TestOperators (TestCSA): def test_difference (self): # Test difference self.assertEqual4x4 (full - oneToOne, [(i, j) for j in range (0,4) for i in range (0,4) if i != j], 'difference operator') def main(): suite = unittest.TestLoader().loadTestsFromTestCase(TestElementary, TestOperators) unittest.TextTestRunner(verbosity=verbosity).run(suite) if __name__ == '__main__': main()