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iple-axes, polar charts, and bubble charts.") (license license:expat))) (define-public python-plotly-2.4.1 (package (inherit python-plotly) (version "2.4.1") (source (origin (method url-fetch) (uri (pypi-uri "plotly" version)) (sha256 (base32 "0s9gk2fl53x8wwncs3fwii1vzfngr0sskv15v3mpshqmrqfrk27m")))) (native-inputs '()) (propagated-inputs (list python-decorator python-nbformat python-pandas python-pytz python-requests python-six)) (arguments (list #:tests? #false ;The tests are not distributed in the release #:phases '(modify-phases %standard-phases (add-after 'unpack 'python-compatibility (lambda _ (substitute* "plotly/grid_objs/grid_objs.py" (("from collections import MutableSequence") "from collections.abc import MutableSequence"))))))))) (define-public python-louvain (package (name "python-louvain") (version "0.16") (source (origin (method url-fetch) (uri (pypi-uri "python-louvain" version)) (patches (search-patches "python-louvain-fix-test.patch")) (sha256 (base32 "0sx53l555rwq0z7if8agirjgw4ddp8r9b949wwz8vlig03sjvfmp")))) (build-system python-build-system) (native-inputs (list python-setuptools-57)) ;for use_2to3 support (propagated-inputs (list python-networkx python-numpy)) (home-page "https://github.com/taynaud/python-louvain") (synopsis "Louvain algorithm for community detection") (description "This package provides a pure Python implementation of the Louvain algorithm for community detection in large networks.") (license license:bsd-3))) (define-public python-vtraag-louvain (package (name "python-vtraag-louvain") (version "0.8.0") (source (origin (method url-fetch) (uri (pypi-uri "louvain" version)) (sha256 (base32 "16l2zi4jwc3vpvpnz32jv7xy0g5087dp9y57wxplj1xa9r312x0i")))) (build-system pyproject-build-system) (arguments (list #:phases '(modify-phases %standard-phases (add-after 'unpack 'do-not-use-bundled-igraph (lambda _ (substitute* "setup.py" (("self.external = False") "self.external = True") (("self.use_pkgconfig = False") "self.use_pkgconfig = True"))))))) (inputs (list igraph)) (propagated-inputs (list python-igraph)) (native-inputs (list pkg-config python-ddt python-setuptools-scm)) (home-page "https://github.com/vtraag/louvain") (synopsis "Community detection in large networks") (description "Louvain is a general algorithm for methods of community detection in large networks.") (license license:gpl3+))) (define-public python-graphtools (package (name "python-graphtools") (version "1.5.3") (source (origin (method git-fetch) (uri (git-reference (url "https://github.com/KrishnaswamyLab/graphtools") (commit (string-append "v" version)))) (file-name (git-file-name name version)) (sha256 (base32 "1aaxhij4y5z2vvc34qnb5py6nw3ciz35a3z4lfr223f9kvfpqgak")))) (build-system pyproject-build-system) (arguments (list #:phases '(modify-phases %standard-phases (add-after 'unpack 'patch-tests (lambda _ ;; The warning message has changed in numpy. (substitute* "test/test_data.py" (("\"A sparse matrix was passed, but.*array.\",") "\"Sparse data was passed, but dense data is required. Use '.toarray()' to convert to a dense numpy array.\",") ;; anndata prints a warning that causes the test to fail. (("import warnings" m) (string-append m "\nwarnings.filterwarnings(\"ignore\")"))))) (replace 'check (lambda* (#:key tests? #:allow-other-keys) (when tests? ;; Incompatibility with sklearn. ;; 'kNNLandmarkGraph' object has no attribute '_landmark_op' (delete-file "test/test_landmark.py") (setenv "LOKY_MAX_CPU_COUNT" "1") (invoke "nose2" "-v"))))))) (propagated-inputs (list python-deprecated python-future python-numpy python-pygsp python-scikit-learn python-scipy python-tasklogger)) (native-inputs (list util-linux ;for lscpu python-anndata python-black python-coverage python-coveralls python-nose python-nose2 python-pandas python-parameterized python-igraph)) (home-page "https://github.com/KrishnaswamyLab/graphtools") (synopsis "Tools for building and manipulating graphs in Python") (description "This package provides tools for building and manipulating graphs in Python.") (license license:gpl3))) (define-public python-louvain-igraph (package (name "python-louvain-igraph") (version "0.8.1") (source (origin (method git-fetch) (uri (git-reference (url "https://github.com/vtraag/louvain-igraph") (commit version))) (file-name (git-file-name name version)) (sha256 (base32 "1j2ybihvvzggwjb9zvm829aqb5b94q10h8bw6v0h42xd9w75z9sv")))) (build-system pyproject-build-system) (arguments (list #:phases #~(modify-phases %standard-phases (add-after 'unpack 'find-igraph (lambda _ (setenv "SETUPTOOLS_SCM_PRETEND_VERSION" #$version) (substitute* "setup.py" (("/usr/include/igraph") (string-append #$(this-package-input "igraph") "/include/igraph")))))))) (propagated-inputs (list python-igraph)) (inputs (list igraph)) (native-inputs (list python-ddt python-setuptools-scm pkg-config)) (home-page "https://github.com/vtraag/louvain-igraph") (synopsis "Implementation of the Louvain algorithm") (description "This package implements the Louvain algorithm for community detection in C++ and exposes it to Python. Besides the relative flexibility of the implementation, it also scales well, and can be run on graphs of millions of nodes (as long as they can fit in memory). The core function is @code{find_partition} which finds the optimal partition using the louvain algorithm for a number of different methods.") (license license:gpl3+))) (define-public python-pygsp (package (name "python-pygsp") (version "0.5.1") (source (origin (method url-fetch) (uri (pypi-uri "PyGSP" version)) (sha256 (base32 "002q4z3p3ka81rzhgi66qqmz1ccrg9hwch4bax7jsqixg64asx28")))) (build-system pyproject-build-system) (arguments (list #:test-flags ;; These all fail due to a type error in scipy. '(list "-k" (string-append "not test_bunny" " and not test_lowstretchtree" " and not test_nngraph" " and not test_plot_graphs" " and not test_randomregular")) #:phases '(modify-phases %standard-phases (add-after 'unpack 'disable-doctests (lambda _ (substitute* "pygsp/tests/test_all.py" (("def test_docstrings.*") "def _disabled_test_docstrings():\n") (("return doctest.DocFileSuite.*") "return False\n") (("suites.append\\(test_docstrings.*") ""))))))) (propagated-inputs (list python-numpy python-scikit-image python-scipy)) (native-inputs (list python-coverage python-coveralls python-flake8 python-pytest)) (home-page "https://github.com/epfl-lts2/pygsp") (synopsis "Graph Signal Processing in Python") (description "The PyGSP is a Python package to ease signal processing on graphs.") (license license:bsd-3))) (define-public faiss (package (name "faiss") (version "1.5.0") (source (origin (method git-fetch) (uri (git-reference (url "https://github.com/facebookresearch/faiss") (commit (string-append "v" version)))) (file-name (git-file-name name version)) (sha256 (base32 "0pk15jfa775cy2pqmzq62nhd6zfjxmpvz5h731197c28aq3zw39w")) (modules '((guix build utils))) (snippet '(begin (substitute* "utils.cpp" (("#include <immintrin.h>") "#ifdef __SSE__\n#include <immintrin.h>\n#endif")) #t)))) (build-system cmake-build-system) (arguments `(#:configure-flags (list "-DBUILD_WITH_GPU=OFF" ; thanks, but no thanks, CUDA. "-DBUILD_TUTORIAL=OFF") ; we don't need those #:phases (modify-phases %standard-phases (add-after 'unpack 'prepare-build (lambda _ (let ((features (list ,@(let ((system (or (%current-target-system) (%current-system)))) (cond ((string-prefix? "x86_64" system) '("-mavx" "-msse2" "-mpopcnt")) ((string-prefix? "i686" system) '("-msse2" "-mpopcnt")) (else '())))))) (substitute* "CMakeLists.txt" (("-m64") "") (("-mpopcnt") "") ; only some architectures (("-msse4") (string-append (string-join features) " -I" (getcwd))) ;; Build also the shared library (("ARCHIVE DESTINATION lib") "LIBRARY DESTINATION lib") (("add_library.*" m) "\ add_library(objlib OBJECT ${faiss_cpu_headers} ${faiss_cpu_cpp}) set_property(TARGET objlib PROPERTY POSITION_INDEPENDENT_CODE 1) add_library(${faiss_lib}_static STATIC $<TARGET_OBJECTS:objlib>) add_library(${faiss_lib} SHARED $<TARGET_OBJECTS:objlib>) install(TARGETS ${faiss_lib}_static ARCHIVE DESTINATION lib) \n"))) ;; See https://github.com/facebookresearch/faiss/issues/520 (substitute* "IndexScalarQuantizer.cpp" (("#define USE_AVX") "")) ;; Make header files available for compiling tests. (mkdir-p "faiss") (for-each (lambda (file) (mkdir-p (string-append "faiss/" (dirname file))) (copy-file file (string-append "faiss/" file))) (find-files "." "\\.h$")) #t)) (replace 'check (lambda _ (invoke "make" "-C" "tests" (format #f "-j~a" (parallel-job-count))))) (add-after 'install 'remove-tests (lambda* (#:key outputs #:allow-other-keys) (delete-file-recursively (string-append (assoc-ref outputs "out") "/test")) #t))))) (inputs (list openblas)) (native-inputs (list googletest)) (home-page "https://github.com/facebookresearch/faiss") (synopsis "Efficient similarity search and clustering of dense vectors") (description "Faiss is a library for efficient similarity search and clustering of dense vectors. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. It also contains supporting code for evaluation and parameter tuning.") (license license:bsd-3))) (define-public python-faiss (package (inherit faiss) (name "python-faiss") (build-system python-build-system) (arguments `(#:phases (modify-phases %standard-phases (add-after 'unpack 'chdir (lambda _ (chdir "python") #t)) (add-after 'chdir 'build-swig (lambda* (#:key inputs #:allow-other-keys) (with-output-to-file "../makefile.inc" (lambda () (let ((python-version ,(version-major+minor (package-version python)))) (format #t "\ PYTHONCFLAGS =-I~a/include/python~a/ -I~a/lib/python~a/site-packages/numpy/core/include LIBS = -lpython~a -lfaiss SHAREDFLAGS = -shared -fopenmp CXXFLAGS = -fpermissive -fopenmp -fPIC CPUFLAGS = ~{~a ~}~%" (assoc-ref inputs "python*") python-version (assoc-ref inputs "python-numpy") python-version python-version (list ,@(let ((system (or (%current-target-system) (%current-system)))) (cond ((string-prefix? "x86_64" system) '("-mavx" "-msse2" "-mpopcnt")) ((string-prefix? "i686" system) '("-msse2" "-mpopcnt")) (else '())))))))) (substitute* "Makefile" (("../libfaiss.a") "")) (invoke "make" "cpu")))))) (inputs `(("faiss" ,faiss) ("openblas" ,openblas) ("python*" ,python) ("swig" ,swig))) (propagated-inputs (list python-matplotlib python-numpy)) (description "Faiss is a library for efficient similarity search and clustering of dense vectors. This package provides Python bindings to the Faiss library."))) (define-public python-leidenalg (package (name "python-leidenalg") (version "0.9.1") (source (origin (method url-fetch) (uri (pypi-uri "leidenalg" version)) (sha256 (base32 "1wvmi6ca9kf8pbxg6b18n64h82wr9a6wcdazyn82pww0dwxzwp3y")))) (build-system python-build-system) (arguments '(#:tests? #f ;tests are not included #:phases (modify-phases %standard-phases (add-after 'unpack 'fix-requirements (lambda _ (substitute* "setup.py" (("self.external = False") "self.external = True") (("self.use_pkgconfig = False") "self.use_pkgconfig = True") (("python-igraph >=") "igraph >="))))))) (native-inputs (list pkg-config python-setuptools-scm)) (inputs (list igraph)) (propagated-inputs (list python-igraph)) (home-page "https://github.com/vtraag/leidenalg") (synopsis "Community detection in large networks") (description "Leiden is a general algorithm for methods of community detection in large networks. This package implements the Leiden algorithm in C++ and exposes it to Python. Besides the relative flexibility of the implementation, it also scales well, and can be run on graphs of millions of nodes (as long as they can fit in memory). The core function is @code{find_partition} which finds the optimal partition using the Leiden algorithm, which is an extension of the Louvain algorithm, for a number of different methods.") (license license:gpl3+))) (define-public edge-addition-planarity-suite (package (name "edge-addition-planarity-suite") (version "3.0.2.0") (source (origin (method git-fetch) (uri (git-reference (url (string-append "https://github.com/graph-algorithms/" name)) (commit (string-append "Version_" version)))) (file-name (git-file-name name version)) (sha256 (base32 "1c7bnxgiz28mqsq3a3msznmjq629w0qqjynm2rqnnjn2qpc22h3i")))) (build-system gnu-build-system) (native-inputs (list autoconf automake libtool)) (synopsis "Embedding of planar graphs") (description "The package provides a reference implementation of the linear time edge addition algorithm for embedding planar graphs and isolating planarity obstructions.") (license license:bsd-3) (home-page "https://github.com/graph-algorithms/edge-addition-planarity-suite"))) (define-public rw (package (name "rw") ;; There is a version 0.8, but the tarball is broken with symlinks ;; to /usr/share. (version "0.9") (source (origin (method url-fetch) (uri (string-append "mirror://sourceforge/rankwidth/" "rw-" version ".tar.gz")) (sha256 (base32 "0hdlxxmlccb6fp7g58zv0rdzpbyjn9bgqlf052sgrk95zq33bq61")) (patches (search-patches "rw-igraph-0.10.patch")))) (build-system gnu-build-system) (native-inputs (list pkg-config)) (inputs (list igraph)) (home-page "https://sourceforge.net/projects/rankwidth/") (synopsis "Rank-width and rank-decomposition of graphs") (description "rw computes rank-width and rank-decompositions of graphs.") (license license:gpl2+))) (define-public mscgen (package (name "mscgen") (version "0.20") (source (origin (method url-fetch) (uri (string-append "http://www.mcternan.me.uk/mscgen/software/mscgen-src-" version ".tar.gz")) (sha256 (base32 "08yw3maxhn5fl1lff81gmcrpa4j9aas4mmby1g9w5qcr0np82d1w")))) (build-system gnu-build-system) (native-inputs (list pkg-config)) (inputs (list gd)) (home-page "https://www.mcternan.me.uk/mscgen/") (synopsis "Message Sequence Chart Generator") (description "Mscgen is a small program that parses Message Sequence Chart descriptions and produces PNG, SVG, EPS or server side image maps (ismaps) as the output. Message Sequence Charts (MSCs) are a way of representing entities and interactions over some time period and are often used in combination with SDL. MSCs are popular in Telecoms to specify how protocols operate although MSCs need not be complicated to create or use. Mscgen aims to provide a simple text language that is clear to create, edit and understand, which can also be transformed into common image formats for display or printing.") (license license:gpl2+))) (define-public python-graph-tool (package (name "python-graph-tool") (version "2.59") (source (origin (method url-fetch) (uri (string-append "https://downloads.skewed.de/graph-tool/graph-tool-" version ".tar.bz2")) (sha256 (base32 "1bmck5fcihj9lr5kd8x624bdi9xhfc13pl4mwzv74jr5lz07kr6d")))) (build-system gnu-build-system) (arguments `(#:imported-modules (,@%default-gnu-imported-modules (guix build python-build-system)) #:modules (,@%default-gnu-modules ((guix build python-build-system) #:select (site-packages))) ;; The build process peaks around 4GB/RAM per core. #:parallel-build? #f #:configure-flags (list (string-append "--with-boost=" (assoc-ref %build-inputs "boost")) (string-append "--with-python-module-path=" (site-packages %build-inputs %outputs))))) (native-inputs (list ncurses pkg-config)) (inputs (list boost cairomm-1.14 cgal expat gmp gtk+ python-wrapper sparsehash)) (propagated-inputs (list python-matplotlib python-numpy python-pycairo python-scipy python-zstandard)) (synopsis "Manipulate and analyze graphs with Python efficiently") (description "Graph-tool is an efficient Python module for manipulation and statistical analysis of graphs (a.k.a. networks). Contrary to most other Python modules with similar functionality, the core data structures and algorithms are implemented in C++, making extensive use of template metaprogramming, based heavily on the Boost Graph Library. This confers it a level of performance that is comparable (both in memory usage and computation time) to that of a pure C/C++ library.") (home-page "https://graph-tool.skewed.de/") (license license:lgpl3+)))
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