==> Building on q ==> Checking for remote environment... ==> Syncing package to remote host... sending incremental file list created directory packages/python-networkx ./ 0001-Fix-StopIteration-handling-which-breaks-in-python-3..patch 4,953 100% 0.00kB/s 0:00:00 4,953 100% 0.00kB/s 0:00:00 (xfr#1, to-chk=2/4) PKGBUILD 1,443 100% 1.38MB/s 0:00:00 1,443 100% 1.38MB/s 0:00:00 (xfr#2, to-chk=1/4) python-networkx-2.7.1-1.log 464 100% 453.12kB/s 0:00:00 464 100% 453.12kB/s 0:00:00 (xfr#3, to-chk=0/4) sent 3,043 bytes received 123 bytes 1,266.40 bytes/sec total size is 6,584 speedup is 2.08 ==> Running extra-riscv64-build -- -d /home/felix/packages/riscv64-pkg-cache:/var/cache/pacman/pkg -l felix29 on remote host... [?25l:: Synchronizing package databases... core downloading... extra downloading... community downloading... :: Starting full system upgrade... there is nothing to do [?25h==> Building in chroot for [extra] (riscv64)... ==> Synchronizing chroot copy [/var/lib/archbuild/extra-riscv64/root] -> [felix29]...done ==> Making package: python-networkx 2.7.1-1 (Sun Mar 27 17:11:11 2022) ==> Retrieving sources...  -> Downloading networkx-2.7.1.tar.gz... % Total % Received % Xferd Average Speed Time Time Time Current Dload Upload Total Spent Left Speed 0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0 0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0 100 143 100 143 0 0 583 0 --:--:-- --:--:-- --:--:-- 581 100 1983k 0 1983k 0 0 2391k 0 --:--:-- --:--:-- --:--:-- 2391k ==> Validating source files with sha512sums... networkx-2.7.1.tar.gz ... Passed ==> Making package: python-networkx 2.7.1-1 (Sun 27 Mar 2022 05:11:24 PM CEST) ==> Checking runtime dependencies... ==> Installing missing dependencies... [?25lresolving dependencies... looking for conflicting packages... warning: dependency cycle detected: warning: harfbuzz will be installed before its freetype2 dependency Package (37) New Version Net Change extra/blas 3.10.0-1 0.20 MiB extra/cblas 3.10.0-1 0.15 MiB extra/freetype2 2.11.1-1 1.44 MiB extra/fribidi 1.0.11-1 0.20 MiB extra/graphite 1:1.3.14-1 0.16 MiB extra/harfbuzz 4.1.0-1 5.42 MiB extra/lapack 3.10.0-1 4.17 MiB extra/lcms2 2.13.1-1 0.58 MiB community/libimagequant 2.17.0-3 0.09 MiB extra/libjpeg-turbo 2.1.3-1 1.37 MiB core/libnsl 2.0.0-2 0.06 MiB extra/libpng 1.6.37-3 0.46 MiB extra/libraqm 0.9.0-1 0.14 MiB extra/libtiff 4.3.0-1 2.54 MiB extra/libxau 1.0.9-3 0.02 MiB extra/libxcb 1.14-1 36.12 MiB extra/libxdmcp 1.1.3-3 0.29 MiB extra/openjpeg2 2.4.0-1 13.78 MiB core/python 3.10.1-2 79.11 MiB extra/python-appdirs 1.4.4-6 0.07 MiB community/python-cycler 0.11.0-1 0.04 MiB community/python-dateutil 2.8.2-4 0.82 MiB community/python-kiwisolver 1.3.2-3 0.09 MiB community/python-more-itertools 8.12.0-1 0.48 MiB extra/python-ordered-set 4.0.2-6 0.06 MiB extra/python-packaging 21.0-1 0.26 MiB community/python-pillow 9.0.1-1 2.84 MiB extra/python-pyparsing 3.0.7-1 0.96 MiB community/python-pytz 2022.1-1 0.14 MiB extra/python-setuptools 1:59.1.0-1 2.95 MiB extra/python-six 1.16.0-5 0.09 MiB extra/qhull 2020.2-4 8.11 MiB extra/xcb-proto 1.14.1-5 0.82 MiB community/python-matplotlib 3.4.3-1 21.76 MiB extra/python-numpy 1.22.3-1 28.66 MiB community/python-pandas 1.4.1-1 57.59 MiB community/python-scipy 1.8.0-1 59.80 MiB Total Installed Size: 331.84 MiB :: Proceed with installation? [Y/n] checking keyring... checking package integrity... loading package files... checking for file conflicts... :: Processing package changes... installing blas... installing cblas... installing lapack... installing libnsl... installing python... Optional dependencies for python python-setuptools [pending] python-pip sqlite [installed] mpdecimal: for decimal xz: for lzma [installed] tk: for tkinter installing python-numpy... Optional dependencies for python-numpy openblas: faster linear algebra installing python-scipy... Optional dependencies for python-scipy python-pillow: for image saving module [pending] installing libpng... installing graphite... installing harfbuzz... Optional dependencies for harfbuzz cairo: hb-view program chafa: hb-view program installing freetype2... installing python-six... installing python-cycler... installing python-dateutil... installing python-kiwisolver... installing libjpeg-turbo... Optional dependencies for libjpeg-turbo java-runtime>11: for TurboJPEG Java wrapper installing libtiff... Optional dependencies for libtiff freeglut: for using tiffgt installing lcms2... installing fribidi... installing libraqm... installing openjpeg2... installing libimagequant... installing xcb-proto... installing libxdmcp... installing libxau... installing libxcb... installing python-pillow... Optional dependencies for python-pillow libwebp: for webp images tk: for the ImageTK module python-olefile: OLE2 file support python-pyqt5: for the ImageQt module installing python-pyparsing... Optional dependencies for python-pyparsing python-railroad-diagrams: for generating Railroad Diagrams python-jinja: for generating Railroad Diagrams installing qhull... installing python-matplotlib... Optional dependencies for python-matplotlib tk: Tk{Agg,Cairo} backends pyside2: alternative for Qt5{Agg,Cairo} backends python-pyqt5: Qt5{Agg,Cairo} backends python-gobject: for GTK3{Agg,Cairo} backend python-wxpython: WX{,Agg,Cairo} backend python-cairo: {GTK3,Qt5,Tk,WX}Cairo backends python-cairocffi: alternative for Cairo backends python-tornado: WebAgg backend ffmpeg: for saving movies imagemagick: for saving animated gifs ghostscript: usetex dependencies texlive-bin: usetex dependencies texlive-latexextra: usetex usage with pdflatex python-certifi: https support installing python-pytz... installing python-appdirs... installing python-more-itertools... installing python-ordered-set... installing python-packaging... installing python-setuptools... installing python-pandas... Optional dependencies for python-pandas python-pandas-datareader: pandas.io.data replacement (recommended) python-numexpr: needed for accelerating certain numerical operations (recommended) python-bottleneck: needed for accelerating certain types of nan evaluations (recommended) python-beautifulsoup4: needed for read_html function python-jinja: needed for conditional HTML formatting python-pyqt5: needed for read_clipboard function (only one needed) python-pytables: needed for HDF5-based storage python-sqlalchemy: needed for SQL database support python-scipy: needed for miscellaneous statistical functions [installed] python-xlsxwriter: alternative Excel XLSX output python-blosc: for msgpack compression using blosc python-html5lib: needed for read_hmlt function (and/or python-lxml) python-lxml: needed for read_html function (and/or python-html5lib) python-matplotlib: needed for plotting [installed] python-openpyxl: needed for Excel XLSX input/output python-psycopg2: needed for PostgreSQL engine for sqlalchemy python-pymysql: needed for MySQL engine for sqlalchemy python-qtpy: needed for read_clipboard function (only one needed) python-tabulate: needed for printing in Markdown-friendly format python-fsspec: needed for handling files aside from local and HTTP xclip: needed for read_clipboard function (only one needed) python-xlrd: needed for Excel XLS input python-xlwt: needed for Excel XLS output xsel: needed for read_clipboard function (only one needed) zlib: needed for compression for msgpack [installed] [?25h==> Checking buildtime dependencies... ==> Installing missing dependencies... [?25lresolving dependencies... looking for conflicting packages... Package (61) New Version Net Change Download Size extra/aom 3.3.0-1 4.02 MiB extra/avahi 0.8+22+gfd482a7-3 1.70 MiB extra/cairo 1.17.6-1 3.10 MiB extra/dav1d 0.9.2-1 0.53 MiB core/dbus 1.12.20-1 0.72 MiB extra/fontconfig 2:2.13.96-1 0.97 MiB extra/gd 2.3.3-3 0.55 MiB extra/gdk-pixbuf2 2.42.6-2 2.91 MiB extra/ghostscript 9.55.0-4 43.08 MiB extra/giflib 5.2.1-2 0.22 MiB extra/graphviz 3.0.0-1 8.24 MiB extra/gsfonts 20200910-2 3.11 MiB extra/gts 0.7.6.121130-2 0.50 MiB extra/ijs 0.35-3 0.11 MiB extra/jbig2dec 0.19-1 0.12 MiB community/libavif 0.9.3-1 0.27 MiB extra/libcups 1:2.4.1-1 0.74 MiB extra/libdaemon 0.14-5 0.05 MiB extra/libdatrie 0.2.13-1 0.05 MiB extra/libde265 1.0.8-2 0.79 MiB extra/libheif 1.12.0-3 0.63 MiB extra/libice 1.0.10-3 0.78 MiB extra/libidn 1.38-1 0.73 MiB extra/libpaper 1.1.28-1 0.08 MiB extra/librsvg 2:2.54.0-1 12.55 MiB extra/libsm 1.2.3-2 0.66 MiB extra/libthai 0.1.29-1 0.64 MiB core/libusb 1.0.25-3 0.18 MiB extra/libwebp 1.2.2-1 0.72 MiB extra/libx11 1.7.3.1-1 10.00 MiB extra/libxaw 1.0.14-1 1.55 MiB extra/libxext 1.3.4-3 0.58 MiB extra/libxft 2.3.4-1 0.09 MiB extra/libxmu 1.1.3-2 0.58 MiB extra/libxpm 3.5.13-2 0.11 MiB extra/libxrender 0.9.10-4 0.06 MiB extra/libxslt 1.1.35-1 2.73 MiB extra/libxt 1.2.1-1 1.91 MiB extra/libyaml 0.2.5-1 0.14 MiB community/libyuv r2266+eb6e7bb6-1 0.92 MiB core/lzo 2.10-3 0.34 MiB extra/netpbm 10.73.37-1 5.85 MiB extra/pango 1:1.50.6-1 2.17 MiB extra/pixman 0.40.0-1 0.36 MiB community/python-apipkg 2.1.0-1 0.03 MiB extra/python-attrs 21.4.0-1 0.45 MiB community/python-iniconfig 1.1.1-5 0.02 MiB community/python-pluggy 1.0.0-1 0.10 MiB community/python-py 1.11.0-1 0.71 MiB community/python-pytest 7.1.1-1 2.62 MiB extra/python-tomli 2.0.1-1 0.08 MiB extra/rav1e 0.4.1-2 3.94 MiB core/run-parts 5.5-1 0.04 MiB extra/shared-mime-info 2.0+115+gd74a913-1 4.39 MiB extra/svt-av1 0.9.0-2 3.27 MiB extra/x265 3.5-3 3.62 MiB extra/xorgproto 2021.5-1 1.43 MiB extra/python-lxml 4.8.0-1 3.46 MiB community/python-pydot 1.4.2-3 0.19 MiB 0.04 MiB community/python-pytest-runner 5.3.1-3 0.03 MiB community/python-yaml 6.0-1 0.68 MiB Total Download Size: 0.04 MiB Total Installed Size: 141.16 MiB :: Proceed with installation? [Y/n] :: Retrieving packages... python-pydot-1.4.2-3-any downloading... checking keyring... checking package integrity... loading package files... checking for file conflicts... :: Processing package changes... installing python-attrs... installing python-iniconfig... installing python-pluggy... installing python-apipkg... installing python-py... installing python-tomli... installing python-pytest... installing python-pytest-runner... installing libxslt... Optional dependencies for libxslt python: Python bindings [installed] installing python-lxml... Optional dependencies for python-lxml python-beautifulsoup4: support for beautifulsoup parser to parse not well formed HTML python-cssselect: support for cssselect python-html5lib: support for html5lib parser python-lxml-docs: offline docs installing fontconfig... Creating fontconfig configuration... Rebuilding fontconfig cache... installing libice... installing libsm... installing xorgproto... installing libx11... installing libxt... installing libxext... installing libxpm... installing giflib... installing libwebp... Optional dependencies for libwebp freeglut: vwebp viewer installing aom... installing dav1d... Optional dependencies for dav1d dav1d-doc: HTML documentation installing rav1e... installing svt-av1... installing libyuv... installing libavif... installing libde265... Optional dependencies for libde265 ffmpeg: for sherlock265 qt5-base: for sherlock265 sdl: dec265 YUV overlay output installing x265... installing libheif... Optional dependencies for libheif libjpeg: for heif-convert and heif-enc [installed] libpng: for heif-convert and heif-enc [installed] installing gd... Optional dependencies for gd perl: bdftogd script [installed] installing lzo... installing libxrender... installing pixman... installing cairo... installing shared-mime-info... installing gdk-pixbuf2... Optional dependencies for gdk-pixbuf2 libwmf: Load .wmf and .apm libopenraw: Load .dng, .cr2, .crw, .nef, .orf, .pef, .arw, .erf, .mrw, and .raf libavif: Load .avif [installed] libheif: Load .heif, .heic, and .avif [installed] librsvg: Load .svg, .svgz, and .svg.gz [pending] webp-pixbuf-loader: Load .webp installing libdatrie... installing libthai... installing libxft... installing pango... installing librsvg... installing libxmu... installing libxaw... installing libdaemon... installing dbus... installing avahi... Optional dependencies for avahi gtk3: avahi-discover, avahi-discover-standalone, bshell, bssh, bvnc qt5-base: qt5 bindings libevent: libevent bindings nss-mdns: NSS support for mDNS python-twisted: avahi-bookmarks python-gobject: avahi-bookmarks, avahi-discover python-dbus: avahi-bookmarks, avahi-discover installing libusb... installing libcups... installing jbig2dec... installing run-parts... installing libpaper... installing ijs... installing libidn... installing ghostscript... Optional dependencies for ghostscript texlive-core: needed for dvipdf gtk3: needed for gsx installing netpbm... installing gts... installing gsfonts... installing graphviz... Warning: Could not load "/usr/lib/graphviz/libgvplugin_gdk.so.6" - It was found, so perhaps one of its dependents was not. Try ldd. Warning: Could not load "/usr/lib/graphviz/libgvplugin_gtk.so.6" - It was found, so perhaps one of its dependents was not. Try ldd. Warning: Could not load "/usr/lib/graphviz/libgvplugin_gdk.so.6" - It was found, so perhaps one of its dependents was not. Try ldd. Warning: Could not load "/usr/lib/graphviz/libgvplugin_gtk.so.6" - It was found, so perhaps one of its dependents was not. Try ldd. Optional dependencies for graphviz mono: sharp bindings guile: guile bindings [installed] lua: lua bindings ocaml: ocaml bindings perl: perl bindings [installed] python: python bindings [installed] r: r bindings tcl: tcl bindings qt5-base: gvedit gtk2: gtk output plugin xterm: vimdot installing python-pydot... installing libyaml... installing python-yaml... :: Running post-transaction hooks... (1/7) Updating the MIME type database... (2/7) Updating fontconfig configuration... (3/7) Reloading system bus configuration... call to execv failed (No such file or directory) error: command failed to execute correctly (4/7) Warn about old perl modules (5/7) Updating fontconfig cache... (6/7) Probing GDK-Pixbuf loader modules... (7/7) Updating the info directory file... [?25h==> Retrieving sources...  -> Found networkx-2.7.1.tar.gz ==> WARNING: Skipping all source file integrity checks. ==> Extracting sources...  -> Extracting networkx-2.7.1.tar.gz with bsdtar ==> Starting build()... running build running build_py creating build creating build/lib creating build/lib/networkx copying networkx/__init__.py -> build/lib/networkx copying networkx/conftest.py -> build/lib/networkx copying networkx/convert.py -> build/lib/networkx copying networkx/convert_matrix.py -> build/lib/networkx copying networkx/exception.py -> build/lib/networkx copying networkx/lazy_imports.py -> build/lib/networkx copying networkx/relabel.py -> build/lib/networkx creating build/lib/networkx/algorithms copying networkx/algorithms/__init__.py -> build/lib/networkx/algorithms copying networkx/algorithms/asteroidal.py -> build/lib/networkx/algorithms copying networkx/algorithms/boundary.py -> build/lib/networkx/algorithms copying networkx/algorithms/bridges.py -> build/lib/networkx/algorithms copying networkx/algorithms/chains.py -> build/lib/networkx/algorithms copying networkx/algorithms/chordal.py -> build/lib/networkx/algorithms copying networkx/algorithms/clique.py -> build/lib/networkx/algorithms copying networkx/algorithms/cluster.py -> build/lib/networkx/algorithms copying networkx/algorithms/communicability_alg.py -> build/lib/networkx/algorithms copying networkx/algorithms/core.py -> build/lib/networkx/algorithms copying networkx/algorithms/covering.py -> build/lib/networkx/algorithms copying networkx/algorithms/cuts.py -> build/lib/networkx/algorithms copying networkx/algorithms/cycles.py -> build/lib/networkx/algorithms copying networkx/algorithms/d_separation.py -> build/lib/networkx/algorithms copying networkx/algorithms/dag.py -> build/lib/networkx/algorithms copying networkx/algorithms/distance_measures.py -> build/lib/networkx/algorithms copying networkx/algorithms/distance_regular.py -> build/lib/networkx/algorithms copying networkx/algorithms/dominance.py -> build/lib/networkx/algorithms copying networkx/algorithms/dominating.py -> build/lib/networkx/algorithms copying networkx/algorithms/efficiency_measures.py -> build/lib/networkx/algorithms copying networkx/algorithms/euler.py -> build/lib/networkx/algorithms copying networkx/algorithms/graph_hashing.py -> build/lib/networkx/algorithms copying networkx/algorithms/graphical.py -> build/lib/networkx/algorithms copying networkx/algorithms/hierarchy.py -> build/lib/networkx/algorithms copying networkx/algorithms/hybrid.py -> build/lib/networkx/algorithms copying networkx/algorithms/isolate.py -> build/lib/networkx/algorithms copying networkx/algorithms/link_prediction.py -> build/lib/networkx/algorithms copying networkx/algorithms/lowest_common_ancestors.py -> build/lib/networkx/algorithms copying networkx/algorithms/matching.py -> build/lib/networkx/algorithms copying networkx/algorithms/mis.py -> build/lib/networkx/algorithms copying networkx/algorithms/moral.py -> build/lib/networkx/algorithms copying networkx/algorithms/non_randomness.py -> build/lib/networkx/algorithms copying networkx/algorithms/planar_drawing.py -> build/lib/networkx/algorithms copying networkx/algorithms/planarity.py -> build/lib/networkx/algorithms copying networkx/algorithms/reciprocity.py -> build/lib/networkx/algorithms copying networkx/algorithms/regular.py -> build/lib/networkx/algorithms copying networkx/algorithms/richclub.py -> build/lib/networkx/algorithms copying networkx/algorithms/similarity.py -> build/lib/networkx/algorithms copying networkx/algorithms/simple_paths.py -> build/lib/networkx/algorithms copying networkx/algorithms/smallworld.py -> build/lib/networkx/algorithms copying networkx/algorithms/smetric.py -> build/lib/networkx/algorithms copying networkx/algorithms/sparsifiers.py -> build/lib/networkx/algorithms copying networkx/algorithms/structuralholes.py -> build/lib/networkx/algorithms copying networkx/algorithms/summarization.py -> build/lib/networkx/algorithms copying networkx/algorithms/swap.py -> build/lib/networkx/algorithms copying networkx/algorithms/threshold.py -> build/lib/networkx/algorithms copying networkx/algorithms/tournament.py -> build/lib/networkx/algorithms copying networkx/algorithms/triads.py -> build/lib/networkx/algorithms copying networkx/algorithms/vitality.py -> build/lib/networkx/algorithms copying networkx/algorithms/voronoi.py -> build/lib/networkx/algorithms copying networkx/algorithms/wiener.py -> build/lib/networkx/algorithms creating build/lib/networkx/algorithms/assortativity copying networkx/algorithms/assortativity/__init__.py -> build/lib/networkx/algorithms/assortativity copying networkx/algorithms/assortativity/connectivity.py -> build/lib/networkx/algorithms/assortativity copying networkx/algorithms/assortativity/correlation.py -> build/lib/networkx/algorithms/assortativity copying networkx/algorithms/assortativity/mixing.py -> build/lib/networkx/algorithms/assortativity copying networkx/algorithms/assortativity/neighbor_degree.py -> build/lib/networkx/algorithms/assortativity copying networkx/algorithms/assortativity/pairs.py -> build/lib/networkx/algorithms/assortativity creating build/lib/networkx/algorithms/bipartite copying networkx/algorithms/bipartite/__init__.py -> build/lib/networkx/algorithms/bipartite copying networkx/algorithms/bipartite/basic.py -> build/lib/networkx/algorithms/bipartite copying networkx/algorithms/bipartite/centrality.py -> build/lib/networkx/algorithms/bipartite copying networkx/algorithms/bipartite/cluster.py -> build/lib/networkx/algorithms/bipartite copying networkx/algorithms/bipartite/covering.py -> build/lib/networkx/algorithms/bipartite copying networkx/algorithms/bipartite/edgelist.py -> build/lib/networkx/algorithms/bipartite copying networkx/algorithms/bipartite/generators.py -> build/lib/networkx/algorithms/bipartite copying networkx/algorithms/bipartite/matching.py -> build/lib/networkx/algorithms/bipartite copying networkx/algorithms/bipartite/matrix.py -> build/lib/networkx/algorithms/bipartite copying networkx/algorithms/bipartite/projection.py -> build/lib/networkx/algorithms/bipartite copying networkx/algorithms/bipartite/redundancy.py -> build/lib/networkx/algorithms/bipartite copying networkx/algorithms/bipartite/spectral.py -> build/lib/networkx/algorithms/bipartite creating build/lib/networkx/algorithms/node_classification copying networkx/algorithms/node_classification/__init__.py -> build/lib/networkx/algorithms/node_classification copying networkx/algorithms/node_classification/hmn.py -> build/lib/networkx/algorithms/node_classification copying networkx/algorithms/node_classification/lgc.py -> build/lib/networkx/algorithms/node_classification copying networkx/algorithms/node_classification/utils.py -> build/lib/networkx/algorithms/node_classification creating build/lib/networkx/algorithms/centrality copying networkx/algorithms/centrality/__init__.py -> build/lib/networkx/algorithms/centrality copying networkx/algorithms/centrality/betweenness.py -> build/lib/networkx/algorithms/centrality copying networkx/algorithms/centrality/betweenness_subset.py -> build/lib/networkx/algorithms/centrality copying networkx/algorithms/centrality/closeness.py -> build/lib/networkx/algorithms/centrality copying networkx/algorithms/centrality/current_flow_betweenness.py -> build/lib/networkx/algorithms/centrality copying networkx/algorithms/centrality/current_flow_betweenness_subset.py -> build/lib/networkx/algorithms/centrality copying networkx/algorithms/centrality/current_flow_closeness.py -> build/lib/networkx/algorithms/centrality copying networkx/algorithms/centrality/degree_alg.py -> build/lib/networkx/algorithms/centrality copying networkx/algorithms/centrality/dispersion.py -> build/lib/networkx/algorithms/centrality copying networkx/algorithms/centrality/eigenvector.py -> build/lib/networkx/algorithms/centrality copying networkx/algorithms/centrality/flow_matrix.py -> build/lib/networkx/algorithms/centrality copying networkx/algorithms/centrality/group.py -> build/lib/networkx/algorithms/centrality copying networkx/algorithms/centrality/harmonic.py -> build/lib/networkx/algorithms/centrality copying networkx/algorithms/centrality/katz.py -> build/lib/networkx/algorithms/centrality copying networkx/algorithms/centrality/load.py -> build/lib/networkx/algorithms/centrality copying networkx/algorithms/centrality/percolation.py -> build/lib/networkx/algorithms/centrality copying networkx/algorithms/centrality/reaching.py -> build/lib/networkx/algorithms/centrality copying networkx/algorithms/centrality/second_order.py -> build/lib/networkx/algorithms/centrality copying networkx/algorithms/centrality/subgraph_alg.py -> build/lib/networkx/algorithms/centrality copying networkx/algorithms/centrality/trophic.py -> build/lib/networkx/algorithms/centrality copying networkx/algorithms/centrality/voterank_alg.py -> build/lib/networkx/algorithms/centrality creating build/lib/networkx/algorithms/community copying networkx/algorithms/community/__init__.py -> build/lib/networkx/algorithms/community copying networkx/algorithms/community/asyn_fluid.py -> build/lib/networkx/algorithms/community copying networkx/algorithms/community/centrality.py -> build/lib/networkx/algorithms/community copying networkx/algorithms/community/community_utils.py -> build/lib/networkx/algorithms/community copying networkx/algorithms/community/kclique.py -> build/lib/networkx/algorithms/community copying networkx/algorithms/community/kernighan_lin.py -> build/lib/networkx/algorithms/community copying networkx/algorithms/community/label_propagation.py -> build/lib/networkx/algorithms/community copying networkx/algorithms/community/louvain.py -> build/lib/networkx/algorithms/community copying networkx/algorithms/community/lukes.py -> build/lib/networkx/algorithms/community copying networkx/algorithms/community/modularity_max.py -> build/lib/networkx/algorithms/community copying networkx/algorithms/community/quality.py -> build/lib/networkx/algorithms/community creating build/lib/networkx/algorithms/components copying networkx/algorithms/components/__init__.py -> build/lib/networkx/algorithms/components copying networkx/algorithms/components/attracting.py -> build/lib/networkx/algorithms/components copying networkx/algorithms/components/biconnected.py -> build/lib/networkx/algorithms/components copying networkx/algorithms/components/connected.py -> build/lib/networkx/algorithms/components copying networkx/algorithms/components/semiconnected.py -> build/lib/networkx/algorithms/components copying networkx/algorithms/components/strongly_connected.py -> build/lib/networkx/algorithms/components copying networkx/algorithms/components/weakly_connected.py -> build/lib/networkx/algorithms/components creating build/lib/networkx/algorithms/connectivity copying networkx/algorithms/connectivity/__init__.py -> build/lib/networkx/algorithms/connectivity copying networkx/algorithms/connectivity/connectivity.py -> build/lib/networkx/algorithms/connectivity copying networkx/algorithms/connectivity/cuts.py -> build/lib/networkx/algorithms/connectivity copying networkx/algorithms/connectivity/disjoint_paths.py -> build/lib/networkx/algorithms/connectivity copying networkx/algorithms/connectivity/edge_augmentation.py -> build/lib/networkx/algorithms/connectivity copying networkx/algorithms/connectivity/edge_kcomponents.py -> build/lib/networkx/algorithms/connectivity copying networkx/algorithms/connectivity/kcomponents.py -> build/lib/networkx/algorithms/connectivity copying networkx/algorithms/connectivity/kcutsets.py -> build/lib/networkx/algorithms/connectivity copying networkx/algorithms/connectivity/stoerwagner.py -> build/lib/networkx/algorithms/connectivity copying networkx/algorithms/connectivity/utils.py -> build/lib/networkx/algorithms/connectivity creating build/lib/networkx/algorithms/coloring copying networkx/algorithms/coloring/__init__.py -> build/lib/networkx/algorithms/coloring copying networkx/algorithms/coloring/equitable_coloring.py -> build/lib/networkx/algorithms/coloring copying networkx/algorithms/coloring/greedy_coloring.py -> build/lib/networkx/algorithms/coloring creating build/lib/networkx/algorithms/flow copying networkx/algorithms/flow/__init__.py -> build/lib/networkx/algorithms/flow copying networkx/algorithms/flow/boykovkolmogorov.py -> build/lib/networkx/algorithms/flow copying networkx/algorithms/flow/capacityscaling.py -> build/lib/networkx/algorithms/flow copying networkx/algorithms/flow/dinitz_alg.py -> build/lib/networkx/algorithms/flow copying networkx/algorithms/flow/edmondskarp.py -> build/lib/networkx/algorithms/flow copying networkx/algorithms/flow/gomory_hu.py -> build/lib/networkx/algorithms/flow copying networkx/algorithms/flow/maxflow.py -> build/lib/networkx/algorithms/flow copying networkx/algorithms/flow/mincost.py -> build/lib/networkx/algorithms/flow copying networkx/algorithms/flow/networksimplex.py -> build/lib/networkx/algorithms/flow copying networkx/algorithms/flow/preflowpush.py -> build/lib/networkx/algorithms/flow copying networkx/algorithms/flow/shortestaugmentingpath.py -> build/lib/networkx/algorithms/flow copying networkx/algorithms/flow/utils.py -> build/lib/networkx/algorithms/flow creating build/lib/networkx/algorithms/minors copying networkx/algorithms/minors/__init__.py -> build/lib/networkx/algorithms/minors copying networkx/algorithms/minors/contraction.py -> build/lib/networkx/algorithms/minors creating build/lib/networkx/algorithms/traversal copying networkx/algorithms/traversal/__init__.py -> build/lib/networkx/algorithms/traversal copying networkx/algorithms/traversal/beamsearch.py -> build/lib/networkx/algorithms/traversal copying networkx/algorithms/traversal/breadth_first_search.py -> build/lib/networkx/algorithms/traversal copying networkx/algorithms/traversal/depth_first_search.py -> build/lib/networkx/algorithms/traversal copying networkx/algorithms/traversal/edgebfs.py -> build/lib/networkx/algorithms/traversal copying networkx/algorithms/traversal/edgedfs.py -> build/lib/networkx/algorithms/traversal creating build/lib/networkx/algorithms/isomorphism copying networkx/algorithms/isomorphism/__init__.py -> build/lib/networkx/algorithms/isomorphism copying networkx/algorithms/isomorphism/ismags.py -> build/lib/networkx/algorithms/isomorphism copying networkx/algorithms/isomorphism/isomorph.py -> build/lib/networkx/algorithms/isomorphism copying networkx/algorithms/isomorphism/isomorphvf2.py -> build/lib/networkx/algorithms/isomorphism copying networkx/algorithms/isomorphism/matchhelpers.py -> build/lib/networkx/algorithms/isomorphism copying networkx/algorithms/isomorphism/temporalisomorphvf2.py -> build/lib/networkx/algorithms/isomorphism copying networkx/algorithms/isomorphism/tree_isomorphism.py -> build/lib/networkx/algorithms/isomorphism copying networkx/algorithms/isomorphism/vf2userfunc.py -> build/lib/networkx/algorithms/isomorphism creating build/lib/networkx/algorithms/shortest_paths copying networkx/algorithms/shortest_paths/__init__.py -> build/lib/networkx/algorithms/shortest_paths copying networkx/algorithms/shortest_paths/astar.py -> build/lib/networkx/algorithms/shortest_paths copying networkx/algorithms/shortest_paths/dense.py -> build/lib/networkx/algorithms/shortest_paths copying networkx/algorithms/shortest_paths/generic.py -> build/lib/networkx/algorithms/shortest_paths copying networkx/algorithms/shortest_paths/unweighted.py -> build/lib/networkx/algorithms/shortest_paths copying networkx/algorithms/shortest_paths/weighted.py -> build/lib/networkx/algorithms/shortest_paths creating build/lib/networkx/algorithms/link_analysis copying networkx/algorithms/link_analysis/__init__.py -> build/lib/networkx/algorithms/link_analysis copying networkx/algorithms/link_analysis/hits_alg.py -> build/lib/networkx/algorithms/link_analysis copying networkx/algorithms/link_analysis/pagerank_alg.py -> build/lib/networkx/algorithms/link_analysis creating build/lib/networkx/algorithms/operators copying networkx/algorithms/operators/__init__.py -> build/lib/networkx/algorithms/operators copying networkx/algorithms/operators/all.py -> build/lib/networkx/algorithms/operators copying networkx/algorithms/operators/binary.py -> build/lib/networkx/algorithms/operators copying networkx/algorithms/operators/product.py -> build/lib/networkx/algorithms/operators copying networkx/algorithms/operators/unary.py -> build/lib/networkx/algorithms/operators creating build/lib/networkx/algorithms/approximation copying networkx/algorithms/approximation/__init__.py -> build/lib/networkx/algorithms/approximation copying networkx/algorithms/approximation/clique.py -> build/lib/networkx/algorithms/approximation copying networkx/algorithms/approximation/clustering_coefficient.py -> build/lib/networkx/algorithms/approximation copying networkx/algorithms/approximation/connectivity.py -> build/lib/networkx/algorithms/approximation copying networkx/algorithms/approximation/distance_measures.py -> build/lib/networkx/algorithms/approximation copying networkx/algorithms/approximation/dominating_set.py -> build/lib/networkx/algorithms/approximation copying networkx/algorithms/approximation/kcomponents.py -> build/lib/networkx/algorithms/approximation copying networkx/algorithms/approximation/matching.py -> build/lib/networkx/algorithms/approximation copying networkx/algorithms/approximation/maxcut.py -> build/lib/networkx/algorithms/approximation copying networkx/algorithms/approximation/ramsey.py -> build/lib/networkx/algorithms/approximation copying networkx/algorithms/approximation/steinertree.py -> build/lib/networkx/algorithms/approximation copying networkx/algorithms/approximation/traveling_salesman.py -> build/lib/networkx/algorithms/approximation copying networkx/algorithms/approximation/treewidth.py -> build/lib/networkx/algorithms/approximation copying networkx/algorithms/approximation/vertex_cover.py -> build/lib/networkx/algorithms/approximation creating build/lib/networkx/algorithms/tree copying networkx/algorithms/tree/__init__.py -> build/lib/networkx/algorithms/tree copying networkx/algorithms/tree/branchings.py -> build/lib/networkx/algorithms/tree copying networkx/algorithms/tree/coding.py -> build/lib/networkx/algorithms/tree copying networkx/algorithms/tree/decomposition.py -> build/lib/networkx/algorithms/tree copying networkx/algorithms/tree/mst.py -> build/lib/networkx/algorithms/tree copying networkx/algorithms/tree/operations.py -> build/lib/networkx/algorithms/tree copying networkx/algorithms/tree/recognition.py -> build/lib/networkx/algorithms/tree creating build/lib/networkx/classes copying networkx/classes/__init__.py -> build/lib/networkx/classes copying networkx/classes/coreviews.py -> build/lib/networkx/classes copying networkx/classes/digraph.py -> build/lib/networkx/classes copying networkx/classes/filters.py -> build/lib/networkx/classes copying networkx/classes/function.py -> build/lib/networkx/classes copying networkx/classes/graph.py -> build/lib/networkx/classes copying networkx/classes/graphviews.py -> build/lib/networkx/classes copying networkx/classes/multidigraph.py -> build/lib/networkx/classes copying networkx/classes/multigraph.py -> build/lib/networkx/classes copying networkx/classes/ordered.py -> build/lib/networkx/classes copying networkx/classes/reportviews.py -> build/lib/networkx/classes creating build/lib/networkx/generators copying networkx/generators/__init__.py -> build/lib/networkx/generators copying networkx/generators/atlas.py -> build/lib/networkx/generators copying networkx/generators/classic.py -> build/lib/networkx/generators copying networkx/generators/cographs.py -> build/lib/networkx/generators copying networkx/generators/community.py -> build/lib/networkx/generators copying networkx/generators/degree_seq.py -> build/lib/networkx/generators copying networkx/generators/directed.py -> build/lib/networkx/generators copying networkx/generators/duplication.py -> build/lib/networkx/generators copying networkx/generators/ego.py -> build/lib/networkx/generators copying networkx/generators/expanders.py -> build/lib/networkx/generators copying networkx/generators/geometric.py -> build/lib/networkx/generators copying networkx/generators/harary_graph.py -> build/lib/networkx/generators copying networkx/generators/internet_as_graphs.py -> build/lib/networkx/generators copying networkx/generators/intersection.py -> build/lib/networkx/generators copying networkx/generators/interval_graph.py -> build/lib/networkx/generators copying networkx/generators/joint_degree_seq.py -> build/lib/networkx/generators copying networkx/generators/lattice.py -> build/lib/networkx/generators copying networkx/generators/line.py -> build/lib/networkx/generators copying networkx/generators/mycielski.py -> build/lib/networkx/generators copying networkx/generators/nonisomorphic_trees.py -> build/lib/networkx/generators copying networkx/generators/random_clustered.py -> build/lib/networkx/generators copying networkx/generators/random_graphs.py -> build/lib/networkx/generators copying networkx/generators/small.py -> build/lib/networkx/generators copying networkx/generators/social.py -> build/lib/networkx/generators copying networkx/generators/spectral_graph_forge.py -> build/lib/networkx/generators copying networkx/generators/stochastic.py -> build/lib/networkx/generators copying networkx/generators/sudoku.py -> build/lib/networkx/generators copying networkx/generators/trees.py -> build/lib/networkx/generators copying networkx/generators/triads.py -> build/lib/networkx/generators creating build/lib/networkx/drawing copying networkx/drawing/__init__.py -> build/lib/networkx/drawing copying networkx/drawing/layout.py -> build/lib/networkx/drawing copying networkx/drawing/nx_agraph.py -> build/lib/networkx/drawing copying networkx/drawing/nx_pydot.py -> build/lib/networkx/drawing copying networkx/drawing/nx_pylab.py -> build/lib/networkx/drawing creating build/lib/networkx/linalg copying networkx/linalg/__init__.py -> build/lib/networkx/linalg copying networkx/linalg/algebraicconnectivity.py -> build/lib/networkx/linalg copying networkx/linalg/attrmatrix.py -> build/lib/networkx/linalg copying networkx/linalg/bethehessianmatrix.py -> build/lib/networkx/linalg copying networkx/linalg/graphmatrix.py -> build/lib/networkx/linalg copying networkx/linalg/laplacianmatrix.py -> build/lib/networkx/linalg copying networkx/linalg/modularitymatrix.py -> build/lib/networkx/linalg copying networkx/linalg/spectrum.py -> build/lib/networkx/linalg creating build/lib/networkx/readwrite copying networkx/readwrite/__init__.py -> build/lib/networkx/readwrite copying networkx/readwrite/adjlist.py -> build/lib/networkx/readwrite copying networkx/readwrite/edgelist.py -> build/lib/networkx/readwrite copying networkx/readwrite/gexf.py -> build/lib/networkx/readwrite copying networkx/readwrite/gml.py -> build/lib/networkx/readwrite copying networkx/readwrite/gpickle.py -> build/lib/networkx/readwrite copying networkx/readwrite/graph6.py -> build/lib/networkx/readwrite copying networkx/readwrite/graphml.py -> build/lib/networkx/readwrite copying networkx/readwrite/leda.py -> build/lib/networkx/readwrite copying networkx/readwrite/multiline_adjlist.py -> build/lib/networkx/readwrite copying networkx/readwrite/nx_shp.py -> build/lib/networkx/readwrite copying networkx/readwrite/nx_yaml.py -> build/lib/networkx/readwrite copying networkx/readwrite/p2g.py -> build/lib/networkx/readwrite copying networkx/readwrite/pajek.py -> build/lib/networkx/readwrite copying networkx/readwrite/sparse6.py -> build/lib/networkx/readwrite copying networkx/readwrite/text.py -> build/lib/networkx/readwrite creating build/lib/networkx/readwrite/json_graph copying networkx/readwrite/json_graph/__init__.py -> build/lib/networkx/readwrite/json_graph copying networkx/readwrite/json_graph/adjacency.py -> build/lib/networkx/readwrite/json_graph copying networkx/readwrite/json_graph/cytoscape.py -> build/lib/networkx/readwrite/json_graph copying networkx/readwrite/json_graph/jit.py -> build/lib/networkx/readwrite/json_graph copying networkx/readwrite/json_graph/node_link.py -> build/lib/networkx/readwrite/json_graph copying networkx/readwrite/json_graph/tree.py -> build/lib/networkx/readwrite/json_graph creating build/lib/networkx/tests copying networkx/tests/__init__.py -> build/lib/networkx/tests copying networkx/tests/test_all_random_functions.py -> build/lib/networkx/tests copying networkx/tests/test_convert.py -> build/lib/networkx/tests copying networkx/tests/test_convert_numpy.py -> build/lib/networkx/tests copying networkx/tests/test_convert_pandas.py -> build/lib/networkx/tests copying networkx/tests/test_convert_scipy.py -> build/lib/networkx/tests copying networkx/tests/test_exceptions.py -> build/lib/networkx/tests copying networkx/tests/test_import.py -> build/lib/networkx/tests copying networkx/tests/test_lazy_imports.py -> build/lib/networkx/tests copying networkx/tests/test_relabel.py -> build/lib/networkx/tests creating build/lib/networkx/testing copying networkx/testing/__init__.py -> build/lib/networkx/testing copying networkx/testing/test.py -> build/lib/networkx/testing copying networkx/testing/utils.py -> build/lib/networkx/testing creating build/lib/networkx/utils copying networkx/utils/__init__.py -> build/lib/networkx/utils copying networkx/utils/contextmanagers.py -> build/lib/networkx/utils copying networkx/utils/decorators.py -> build/lib/networkx/utils copying networkx/utils/heaps.py -> build/lib/networkx/utils copying networkx/utils/mapped_queue.py -> build/lib/networkx/utils copying networkx/utils/misc.py -> build/lib/networkx/utils copying networkx/utils/random_sequence.py -> build/lib/networkx/utils copying networkx/utils/rcm.py -> build/lib/networkx/utils copying networkx/utils/union_find.py -> build/lib/networkx/utils creating build/lib/networkx/algorithms/tests copying networkx/algorithms/tests/__init__.py -> build/lib/networkx/algorithms/tests copying networkx/algorithms/tests/test_asteroidal.py -> build/lib/networkx/algorithms/tests copying networkx/algorithms/tests/test_boundary.py -> build/lib/networkx/algorithms/tests copying networkx/algorithms/tests/test_bridges.py -> build/lib/networkx/algorithms/tests copying networkx/algorithms/tests/test_chains.py -> build/lib/networkx/algorithms/tests copying networkx/algorithms/tests/test_chordal.py -> build/lib/networkx/algorithms/tests copying networkx/algorithms/tests/test_clique.py -> build/lib/networkx/algorithms/tests copying networkx/algorithms/tests/test_cluster.py -> build/lib/networkx/algorithms/tests copying networkx/algorithms/tests/test_communicability.py -> build/lib/networkx/algorithms/tests copying networkx/algorithms/tests/test_core.py -> build/lib/networkx/algorithms/tests copying networkx/algorithms/tests/test_covering.py -> build/lib/networkx/algorithms/tests copying networkx/algorithms/tests/test_cuts.py -> build/lib/networkx/algorithms/tests copying networkx/algorithms/tests/test_cycles.py -> build/lib/networkx/algorithms/tests copying networkx/algorithms/tests/test_d_separation.py -> build/lib/networkx/algorithms/tests copying networkx/algorithms/tests/test_dag.py -> build/lib/networkx/algorithms/tests copying networkx/algorithms/tests/test_distance_measures.py -> build/lib/networkx/algorithms/tests copying networkx/algorithms/tests/test_distance_regular.py -> build/lib/networkx/algorithms/tests copying networkx/algorithms/tests/test_dominance.py -> build/lib/networkx/algorithms/tests copying networkx/algorithms/tests/test_dominating.py -> build/lib/networkx/algorithms/tests copying networkx/algorithms/tests/test_efficiency.py -> build/lib/networkx/algorithms/tests copying networkx/algorithms/tests/test_euler.py -> build/lib/networkx/algorithms/tests copying networkx/algorithms/tests/test_graph_hashing.py -> build/lib/networkx/algorithms/tests copying networkx/algorithms/tests/test_graphical.py -> build/lib/networkx/algorithms/tests copying networkx/algorithms/tests/test_hierarchy.py -> build/lib/networkx/algorithms/tests copying networkx/algorithms/tests/test_hybrid.py -> build/lib/networkx/algorithms/tests copying networkx/algorithms/tests/test_isolate.py -> build/lib/networkx/algorithms/tests copying networkx/algorithms/tests/test_link_prediction.py -> build/lib/networkx/algorithms/tests copying networkx/algorithms/tests/test_lowest_common_ancestors.py -> build/lib/networkx/algorithms/tests copying networkx/algorithms/tests/test_matching.py -> build/lib/networkx/algorithms/tests copying networkx/algorithms/tests/test_max_weight_clique.py -> build/lib/networkx/algorithms/tests copying networkx/algorithms/tests/test_mis.py -> build/lib/networkx/algorithms/tests copying networkx/algorithms/tests/test_moral.py -> build/lib/networkx/algorithms/tests copying networkx/algorithms/tests/test_node_classification.py -> build/lib/networkx/algorithms/tests copying networkx/algorithms/tests/test_node_classification_deprecations.py -> build/lib/networkx/algorithms/tests copying networkx/algorithms/tests/test_non_randomness.py -> build/lib/networkx/algorithms/tests copying networkx/algorithms/tests/test_planar_drawing.py -> build/lib/networkx/algorithms/tests copying networkx/algorithms/tests/test_planarity.py -> build/lib/networkx/algorithms/tests copying networkx/algorithms/tests/test_reciprocity.py -> build/lib/networkx/algorithms/tests copying networkx/algorithms/tests/test_regular.py -> build/lib/networkx/algorithms/tests copying networkx/algorithms/tests/test_richclub.py -> build/lib/networkx/algorithms/tests copying networkx/algorithms/tests/test_similarity.py -> build/lib/networkx/algorithms/tests copying networkx/algorithms/tests/test_simple_paths.py -> build/lib/networkx/algorithms/tests copying networkx/algorithms/tests/test_smallworld.py -> build/lib/networkx/algorithms/tests copying networkx/algorithms/tests/test_smetric.py -> build/lib/networkx/algorithms/tests copying networkx/algorithms/tests/test_sparsifiers.py -> build/lib/networkx/algorithms/tests copying networkx/algorithms/tests/test_structuralholes.py -> build/lib/networkx/algorithms/tests copying networkx/algorithms/tests/test_summarization.py -> build/lib/networkx/algorithms/tests copying networkx/algorithms/tests/test_swap.py -> build/lib/networkx/algorithms/tests copying networkx/algorithms/tests/test_threshold.py -> build/lib/networkx/algorithms/tests copying networkx/algorithms/tests/test_tournament.py -> build/lib/networkx/algorithms/tests copying networkx/algorithms/tests/test_triads.py -> build/lib/networkx/algorithms/tests copying networkx/algorithms/tests/test_vitality.py -> build/lib/networkx/algorithms/tests copying networkx/algorithms/tests/test_voronoi.py -> build/lib/networkx/algorithms/tests copying networkx/algorithms/tests/test_wiener.py -> build/lib/networkx/algorithms/tests creating build/lib/networkx/algorithms/assortativity/tests copying networkx/algorithms/assortativity/tests/__init__.py -> build/lib/networkx/algorithms/assortativity/tests copying networkx/algorithms/assortativity/tests/base_test.py -> build/lib/networkx/algorithms/assortativity/tests copying networkx/algorithms/assortativity/tests/test_connectivity.py -> build/lib/networkx/algorithms/assortativity/tests copying networkx/algorithms/assortativity/tests/test_correlation.py -> build/lib/networkx/algorithms/assortativity/tests copying networkx/algorithms/assortativity/tests/test_mixing.py -> build/lib/networkx/algorithms/assortativity/tests copying networkx/algorithms/assortativity/tests/test_neighbor_degree.py -> build/lib/networkx/algorithms/assortativity/tests copying networkx/algorithms/assortativity/tests/test_pairs.py -> build/lib/networkx/algorithms/assortativity/tests creating build/lib/networkx/algorithms/bipartite/tests copying networkx/algorithms/bipartite/tests/__init__.py -> build/lib/networkx/algorithms/bipartite/tests copying networkx/algorithms/bipartite/tests/test_basic.py -> build/lib/networkx/algorithms/bipartite/tests copying networkx/algorithms/bipartite/tests/test_centrality.py -> build/lib/networkx/algorithms/bipartite/tests copying networkx/algorithms/bipartite/tests/test_cluster.py -> build/lib/networkx/algorithms/bipartite/tests copying networkx/algorithms/bipartite/tests/test_covering.py -> build/lib/networkx/algorithms/bipartite/tests copying networkx/algorithms/bipartite/tests/test_edgelist.py -> build/lib/networkx/algorithms/bipartite/tests copying networkx/algorithms/bipartite/tests/test_generators.py -> build/lib/networkx/algorithms/bipartite/tests copying networkx/algorithms/bipartite/tests/test_matching.py -> build/lib/networkx/algorithms/bipartite/tests copying networkx/algorithms/bipartite/tests/test_matrix.py -> build/lib/networkx/algorithms/bipartite/tests copying networkx/algorithms/bipartite/tests/test_project.py -> build/lib/networkx/algorithms/bipartite/tests copying networkx/algorithms/bipartite/tests/test_redundancy.py -> build/lib/networkx/algorithms/bipartite/tests copying networkx/algorithms/bipartite/tests/test_spectral_bipartivity.py -> build/lib/networkx/algorithms/bipartite/tests creating build/lib/networkx/algorithms/centrality/tests copying networkx/algorithms/centrality/tests/__init__.py -> build/lib/networkx/algorithms/centrality/tests copying networkx/algorithms/centrality/tests/test_betweenness_centrality.py -> build/lib/networkx/algorithms/centrality/tests copying networkx/algorithms/centrality/tests/test_betweenness_centrality_subset.py -> build/lib/networkx/algorithms/centrality/tests copying networkx/algorithms/centrality/tests/test_closeness_centrality.py -> build/lib/networkx/algorithms/centrality/tests copying networkx/algorithms/centrality/tests/test_current_flow_betweenness_centrality.py -> build/lib/networkx/algorithms/centrality/tests copying networkx/algorithms/centrality/tests/test_current_flow_betweenness_centrality_subset.py -> build/lib/networkx/algorithms/centrality/tests copying networkx/algorithms/centrality/tests/test_current_flow_closeness.py -> build/lib/networkx/algorithms/centrality/tests copying networkx/algorithms/centrality/tests/test_degree_centrality.py -> build/lib/networkx/algorithms/centrality/tests copying networkx/algorithms/centrality/tests/test_dispersion.py -> build/lib/networkx/algorithms/centrality/tests copying networkx/algorithms/centrality/tests/test_eigenvector_centrality.py -> build/lib/networkx/algorithms/centrality/tests copying networkx/algorithms/centrality/tests/test_group.py -> build/lib/networkx/algorithms/centrality/tests copying networkx/algorithms/centrality/tests/test_harmonic_centrality.py -> build/lib/networkx/algorithms/centrality/tests copying networkx/algorithms/centrality/tests/test_katz_centrality.py -> build/lib/networkx/algorithms/centrality/tests copying networkx/algorithms/centrality/tests/test_load_centrality.py -> build/lib/networkx/algorithms/centrality/tests copying networkx/algorithms/centrality/tests/test_percolation_centrality.py -> build/lib/networkx/algorithms/centrality/tests copying networkx/algorithms/centrality/tests/test_reaching.py -> build/lib/networkx/algorithms/centrality/tests copying networkx/algorithms/centrality/tests/test_second_order_centrality.py -> build/lib/networkx/algorithms/centrality/tests copying networkx/algorithms/centrality/tests/test_subgraph.py -> build/lib/networkx/algorithms/centrality/tests copying networkx/algorithms/centrality/tests/test_trophic.py -> build/lib/networkx/algorithms/centrality/tests copying networkx/algorithms/centrality/tests/test_voterank.py -> build/lib/networkx/algorithms/centrality/tests creating build/lib/networkx/algorithms/community/tests copying networkx/algorithms/community/tests/__init__.py -> build/lib/networkx/algorithms/community/tests copying networkx/algorithms/community/tests/test_asyn_fluid.py -> build/lib/networkx/algorithms/community/tests copying networkx/algorithms/community/tests/test_centrality.py -> build/lib/networkx/algorithms/community/tests copying networkx/algorithms/community/tests/test_kclique.py -> build/lib/networkx/algorithms/community/tests copying networkx/algorithms/community/tests/test_kernighan_lin.py -> build/lib/networkx/algorithms/community/tests copying networkx/algorithms/community/tests/test_label_propagation.py -> build/lib/networkx/algorithms/community/tests copying networkx/algorithms/community/tests/test_louvain.py -> build/lib/networkx/algorithms/community/tests copying networkx/algorithms/community/tests/test_lukes.py -> build/lib/networkx/algorithms/community/tests copying networkx/algorithms/community/tests/test_modularity_max.py -> build/lib/networkx/algorithms/community/tests copying networkx/algorithms/community/tests/test_quality.py -> build/lib/networkx/algorithms/community/tests copying networkx/algorithms/community/tests/test_utils.py -> build/lib/networkx/algorithms/community/tests creating build/lib/networkx/algorithms/components/tests copying networkx/algorithms/components/tests/__init__.py -> build/lib/networkx/algorithms/components/tests copying networkx/algorithms/components/tests/test_attracting.py -> build/lib/networkx/algorithms/components/tests copying networkx/algorithms/components/tests/test_biconnected.py -> build/lib/networkx/algorithms/components/tests copying networkx/algorithms/components/tests/test_connected.py -> build/lib/networkx/algorithms/components/tests copying networkx/algorithms/components/tests/test_semiconnected.py -> build/lib/networkx/algorithms/components/tests copying networkx/algorithms/components/tests/test_strongly_connected.py -> build/lib/networkx/algorithms/components/tests copying networkx/algorithms/components/tests/test_weakly_connected.py -> build/lib/networkx/algorithms/components/tests creating build/lib/networkx/algorithms/connectivity/tests copying networkx/algorithms/connectivity/tests/__init__.py -> build/lib/networkx/algorithms/connectivity/tests copying networkx/algorithms/connectivity/tests/test_connectivity.py -> build/lib/networkx/algorithms/connectivity/tests copying networkx/algorithms/connectivity/tests/test_cuts.py -> build/lib/networkx/algorithms/connectivity/tests copying networkx/algorithms/connectivity/tests/test_disjoint_paths.py -> build/lib/networkx/algorithms/connectivity/tests copying networkx/algorithms/connectivity/tests/test_edge_augmentation.py -> build/lib/networkx/algorithms/connectivity/tests copying networkx/algorithms/connectivity/tests/test_edge_kcomponents.py -> build/lib/networkx/algorithms/connectivity/tests copying networkx/algorithms/connectivity/tests/test_kcomponents.py -> build/lib/networkx/algorithms/connectivity/tests copying networkx/algorithms/connectivity/tests/test_kcutsets.py -> build/lib/networkx/algorithms/connectivity/tests copying networkx/algorithms/connectivity/tests/test_stoer_wagner.py -> build/lib/networkx/algorithms/connectivity/tests creating build/lib/networkx/algorithms/coloring/tests copying networkx/algorithms/coloring/tests/__init__.py -> build/lib/networkx/algorithms/coloring/tests copying networkx/algorithms/coloring/tests/test_coloring.py -> build/lib/networkx/algorithms/coloring/tests creating build/lib/networkx/algorithms/flow/tests copying networkx/algorithms/flow/tests/__init__.py -> build/lib/networkx/algorithms/flow/tests copying networkx/algorithms/flow/tests/test_gomory_hu.py -> build/lib/networkx/algorithms/flow/tests copying networkx/algorithms/flow/tests/test_maxflow.py -> build/lib/networkx/algorithms/flow/tests copying networkx/algorithms/flow/tests/test_maxflow_large_graph.py -> build/lib/networkx/algorithms/flow/tests copying networkx/algorithms/flow/tests/test_mincost.py -> build/lib/networkx/algorithms/flow/tests copying networkx/algorithms/flow/tests/test_networksimplex.py -> build/lib/networkx/algorithms/flow/tests copying networkx/algorithms/flow/tests/gl1.gpickle.bz2 -> build/lib/networkx/algorithms/flow/tests copying networkx/algorithms/flow/tests/gw1.gpickle.bz2 -> build/lib/networkx/algorithms/flow/tests copying networkx/algorithms/flow/tests/netgen-2.gpickle.bz2 -> build/lib/networkx/algorithms/flow/tests copying networkx/algorithms/flow/tests/wlm3.gpickle.bz2 -> build/lib/networkx/algorithms/flow/tests creating build/lib/networkx/algorithms/minors/tests copying networkx/algorithms/minors/tests/test_contraction.py -> build/lib/networkx/algorithms/minors/tests creating build/lib/networkx/algorithms/traversal/tests copying networkx/algorithms/traversal/tests/__init__.py -> build/lib/networkx/algorithms/traversal/tests copying networkx/algorithms/traversal/tests/test_beamsearch.py -> build/lib/networkx/algorithms/traversal/tests copying networkx/algorithms/traversal/tests/test_bfs.py -> build/lib/networkx/algorithms/traversal/tests copying networkx/algorithms/traversal/tests/test_dfs.py -> build/lib/networkx/algorithms/traversal/tests copying networkx/algorithms/traversal/tests/test_edgebfs.py -> build/lib/networkx/algorithms/traversal/tests copying networkx/algorithms/traversal/tests/test_edgedfs.py -> build/lib/networkx/algorithms/traversal/tests creating build/lib/networkx/algorithms/isomorphism/tests copying networkx/algorithms/isomorphism/tests/__init__.py -> build/lib/networkx/algorithms/isomorphism/tests copying networkx/algorithms/isomorphism/tests/test_ismags.py -> build/lib/networkx/algorithms/isomorphism/tests copying networkx/algorithms/isomorphism/tests/test_isomorphism.py -> build/lib/networkx/algorithms/isomorphism/tests copying networkx/algorithms/isomorphism/tests/test_isomorphvf2.py -> build/lib/networkx/algorithms/isomorphism/tests copying networkx/algorithms/isomorphism/tests/test_match_helpers.py -> build/lib/networkx/algorithms/isomorphism/tests copying networkx/algorithms/isomorphism/tests/test_temporalisomorphvf2.py -> build/lib/networkx/algorithms/isomorphism/tests copying networkx/algorithms/isomorphism/tests/test_tree_isomorphism.py -> build/lib/networkx/algorithms/isomorphism/tests copying networkx/algorithms/isomorphism/tests/test_vf2userfunc.py -> build/lib/networkx/algorithms/isomorphism/tests copying networkx/algorithms/isomorphism/tests/iso_r01_s80.A99 -> build/lib/networkx/algorithms/isomorphism/tests copying networkx/algorithms/isomorphism/tests/iso_r01_s80.B99 -> build/lib/networkx/algorithms/isomorphism/tests copying networkx/algorithms/isomorphism/tests/si2_b06_m200.A99 -> build/lib/networkx/algorithms/isomorphism/tests copying networkx/algorithms/isomorphism/tests/si2_b06_m200.B99 -> build/lib/networkx/algorithms/isomorphism/tests creating build/lib/networkx/algorithms/shortest_paths/tests copying networkx/algorithms/shortest_paths/tests/__init__.py -> build/lib/networkx/algorithms/shortest_paths/tests copying networkx/algorithms/shortest_paths/tests/test_astar.py -> build/lib/networkx/algorithms/shortest_paths/tests copying networkx/algorithms/shortest_paths/tests/test_dense.py -> build/lib/networkx/algorithms/shortest_paths/tests copying networkx/algorithms/shortest_paths/tests/test_dense_numpy.py -> build/lib/networkx/algorithms/shortest_paths/tests copying networkx/algorithms/shortest_paths/tests/test_generic.py -> build/lib/networkx/algorithms/shortest_paths/tests copying networkx/algorithms/shortest_paths/tests/test_unweighted.py -> build/lib/networkx/algorithms/shortest_paths/tests copying networkx/algorithms/shortest_paths/tests/test_weighted.py -> build/lib/networkx/algorithms/shortest_paths/tests creating build/lib/networkx/algorithms/link_analysis/tests copying networkx/algorithms/link_analysis/tests/__init__.py -> build/lib/networkx/algorithms/link_analysis/tests copying networkx/algorithms/link_analysis/tests/test_hits.py -> build/lib/networkx/algorithms/link_analysis/tests copying networkx/algorithms/link_analysis/tests/test_pagerank.py -> build/lib/networkx/algorithms/link_analysis/tests creating build/lib/networkx/algorithms/operators/tests copying networkx/algorithms/operators/tests/__init__.py -> build/lib/networkx/algorithms/operators/tests copying networkx/algorithms/operators/tests/test_all.py -> build/lib/networkx/algorithms/operators/tests copying networkx/algorithms/operators/tests/test_binary.py -> build/lib/networkx/algorithms/operators/tests copying networkx/algorithms/operators/tests/test_product.py -> build/lib/networkx/algorithms/operators/tests copying networkx/algorithms/operators/tests/test_unary.py -> build/lib/networkx/algorithms/operators/tests creating build/lib/networkx/algorithms/approximation/tests copying networkx/algorithms/approximation/tests/__init__.py -> build/lib/networkx/algorithms/approximation/tests copying networkx/algorithms/approximation/tests/test_approx_clust_coeff.py -> build/lib/networkx/algorithms/approximation/tests copying networkx/algorithms/approximation/tests/test_clique.py -> build/lib/networkx/algorithms/approximation/tests copying networkx/algorithms/approximation/tests/test_connectivity.py -> build/lib/networkx/algorithms/approximation/tests copying networkx/algorithms/approximation/tests/test_distance_measures.py -> build/lib/networkx/algorithms/approximation/tests copying networkx/algorithms/approximation/tests/test_dominating_set.py -> build/lib/networkx/algorithms/approximation/tests copying networkx/algorithms/approximation/tests/test_kcomponents.py -> build/lib/networkx/algorithms/approximation/tests copying networkx/algorithms/approximation/tests/test_matching.py -> build/lib/networkx/algorithms/approximation/tests copying networkx/algorithms/approximation/tests/test_maxcut.py -> build/lib/networkx/algorithms/approximation/tests copying networkx/algorithms/approximation/tests/test_ramsey.py -> build/lib/networkx/algorithms/approximation/tests copying networkx/algorithms/approximation/tests/test_steinertree.py -> build/lib/networkx/algorithms/approximation/tests copying networkx/algorithms/approximation/tests/test_traveling_salesman.py -> build/lib/networkx/algorithms/approximation/tests copying networkx/algorithms/approximation/tests/test_treewidth.py -> build/lib/networkx/algorithms/approximation/tests copying networkx/algorithms/approximation/tests/test_vertex_cover.py -> build/lib/networkx/algorithms/approximation/tests creating build/lib/networkx/algorithms/tree/tests copying networkx/algorithms/tree/tests/__init__.py -> build/lib/networkx/algorithms/tree/tests copying networkx/algorithms/tree/tests/test_branchings.py -> build/lib/networkx/algorithms/tree/tests copying networkx/algorithms/tree/tests/test_coding.py -> build/lib/networkx/algorithms/tree/tests copying networkx/algorithms/tree/tests/test_decomposition.py -> build/lib/networkx/algorithms/tree/tests copying networkx/algorithms/tree/tests/test_mst.py -> build/lib/networkx/algorithms/tree/tests copying networkx/algorithms/tree/tests/test_operations.py -> build/lib/networkx/algorithms/tree/tests copying networkx/algorithms/tree/tests/test_recognition.py -> build/lib/networkx/algorithms/tree/tests creating build/lib/networkx/classes/tests copying networkx/classes/tests/__init__.py -> build/lib/networkx/classes/tests copying networkx/classes/tests/historical_tests.py -> build/lib/networkx/classes/tests copying networkx/classes/tests/test_coreviews.py -> build/lib/networkx/classes/tests copying networkx/classes/tests/test_digraph.py -> build/lib/networkx/classes/tests copying networkx/classes/tests/test_digraph_historical.py -> build/lib/networkx/classes/tests copying networkx/classes/tests/test_filters.py -> build/lib/networkx/classes/tests copying networkx/classes/tests/test_function.py -> build/lib/networkx/classes/tests copying networkx/classes/tests/test_graph.py -> build/lib/networkx/classes/tests copying networkx/classes/tests/test_graph_historical.py -> build/lib/networkx/classes/tests copying networkx/classes/tests/test_graphviews.py -> build/lib/networkx/classes/tests copying networkx/classes/tests/test_multidigraph.py -> build/lib/networkx/classes/tests copying networkx/classes/tests/test_multigraph.py -> build/lib/networkx/classes/tests copying networkx/classes/tests/test_ordered.py -> build/lib/networkx/classes/tests copying networkx/classes/tests/test_reportviews.py -> build/lib/networkx/classes/tests copying networkx/classes/tests/test_special.py -> build/lib/networkx/classes/tests copying networkx/classes/tests/test_subgraphviews.py -> build/lib/networkx/classes/tests creating build/lib/networkx/generators/tests copying networkx/generators/tests/__init__.py -> build/lib/networkx/generators/tests copying networkx/generators/tests/test_atlas.py -> build/lib/networkx/generators/tests copying networkx/generators/tests/test_classic.py -> build/lib/networkx/generators/tests copying networkx/generators/tests/test_cographs.py -> build/lib/networkx/generators/tests copying networkx/generators/tests/test_community.py -> build/lib/networkx/generators/tests copying networkx/generators/tests/test_degree_seq.py -> build/lib/networkx/generators/tests copying networkx/generators/tests/test_directed.py -> build/lib/networkx/generators/tests copying networkx/generators/tests/test_duplication.py -> build/lib/networkx/generators/tests copying networkx/generators/tests/test_ego.py -> build/lib/networkx/generators/tests copying networkx/generators/tests/test_expanders.py -> build/lib/networkx/generators/tests copying networkx/generators/tests/test_geometric.py -> build/lib/networkx/generators/tests copying networkx/generators/tests/test_harary_graph.py -> build/lib/networkx/generators/tests copying networkx/generators/tests/test_internet_as_graphs.py -> build/lib/networkx/generators/tests copying networkx/generators/tests/test_intersection.py -> build/lib/networkx/generators/tests copying networkx/generators/tests/test_interval_graph.py -> build/lib/networkx/generators/tests copying networkx/generators/tests/test_joint_degree_seq.py -> build/lib/networkx/generators/tests copying networkx/generators/tests/test_lattice.py -> build/lib/networkx/generators/tests copying networkx/generators/tests/test_line.py -> build/lib/networkx/generators/tests copying networkx/generators/tests/test_mycielski.py -> build/lib/networkx/generators/tests copying networkx/generators/tests/test_nonisomorphic_trees.py -> build/lib/networkx/generators/tests copying networkx/generators/tests/test_random_clustered.py -> build/lib/networkx/generators/tests copying networkx/generators/tests/test_random_graphs.py -> build/lib/networkx/generators/tests copying networkx/generators/tests/test_small.py -> build/lib/networkx/generators/tests copying networkx/generators/tests/test_spectral_graph_forge.py -> build/lib/networkx/generators/tests copying networkx/generators/tests/test_stochastic.py -> build/lib/networkx/generators/tests copying networkx/generators/tests/test_sudoku.py -> build/lib/networkx/generators/tests copying networkx/generators/tests/test_trees.py -> build/lib/networkx/generators/tests copying networkx/generators/tests/test_triads.py -> build/lib/networkx/generators/tests copying networkx/generators/atlas.dat.gz -> build/lib/networkx/generators creating build/lib/networkx/drawing/tests copying networkx/drawing/tests/__init__.py -> build/lib/networkx/drawing/tests copying networkx/drawing/tests/test_agraph.py -> build/lib/networkx/drawing/tests copying networkx/drawing/tests/test_layout.py -> build/lib/networkx/drawing/tests copying networkx/drawing/tests/test_pydot.py -> build/lib/networkx/drawing/tests copying networkx/drawing/tests/test_pylab.py -> build/lib/networkx/drawing/tests creating build/lib/networkx/drawing/tests/baseline copying networkx/drawing/tests/baseline/test_house_with_colors.png -> build/lib/networkx/drawing/tests/baseline creating build/lib/networkx/linalg/tests copying networkx/linalg/tests/__init__.py -> build/lib/networkx/linalg/tests copying networkx/linalg/tests/test_algebraic_connectivity.py -> build/lib/networkx/linalg/tests copying networkx/linalg/tests/test_attrmatrix.py -> build/lib/networkx/linalg/tests copying networkx/linalg/tests/test_bethehessian.py -> build/lib/networkx/linalg/tests copying networkx/linalg/tests/test_graphmatrix.py -> build/lib/networkx/linalg/tests copying networkx/linalg/tests/test_laplacian.py -> build/lib/networkx/linalg/tests copying networkx/linalg/tests/test_modularity.py -> build/lib/networkx/linalg/tests copying networkx/linalg/tests/test_spectrum.py -> build/lib/networkx/linalg/tests creating build/lib/networkx/readwrite/tests copying networkx/readwrite/tests/__init__.py -> build/lib/networkx/readwrite/tests copying networkx/readwrite/tests/test_adjlist.py -> build/lib/networkx/readwrite/tests copying networkx/readwrite/tests/test_edgelist.py -> build/lib/networkx/readwrite/tests copying networkx/readwrite/tests/test_getattr_nxyaml_removal.py -> build/lib/networkx/readwrite/tests copying networkx/readwrite/tests/test_gexf.py -> build/lib/networkx/readwrite/tests copying networkx/readwrite/tests/test_gml.py -> build/lib/networkx/readwrite/tests copying networkx/readwrite/tests/test_gpickle.py -> build/lib/networkx/readwrite/tests copying networkx/readwrite/tests/test_graph6.py -> build/lib/networkx/readwrite/tests copying networkx/readwrite/tests/test_graphml.py -> build/lib/networkx/readwrite/tests copying networkx/readwrite/tests/test_leda.py -> build/lib/networkx/readwrite/tests copying networkx/readwrite/tests/test_p2g.py -> build/lib/networkx/readwrite/tests copying networkx/readwrite/tests/test_pajek.py -> build/lib/networkx/readwrite/tests copying networkx/readwrite/tests/test_shp.py -> build/lib/networkx/readwrite/tests copying networkx/readwrite/tests/test_sparse6.py -> build/lib/networkx/readwrite/tests copying networkx/readwrite/tests/test_text.py -> build/lib/networkx/readwrite/tests creating build/lib/networkx/readwrite/json_graph/tests copying networkx/readwrite/json_graph/tests/__init__.py -> build/lib/networkx/readwrite/json_graph/tests copying networkx/readwrite/json_graph/tests/test_adjacency.py -> build/lib/networkx/readwrite/json_graph/tests copying networkx/readwrite/json_graph/tests/test_cytoscape.py -> build/lib/networkx/readwrite/json_graph/tests copying networkx/readwrite/json_graph/tests/test_jit.py -> build/lib/networkx/readwrite/json_graph/tests copying networkx/readwrite/json_graph/tests/test_node_link.py -> build/lib/networkx/readwrite/json_graph/tests copying networkx/readwrite/json_graph/tests/test_tree.py -> build/lib/networkx/readwrite/json_graph/tests creating build/lib/networkx/testing/tests copying networkx/testing/tests/__init__.py -> build/lib/networkx/testing/tests copying networkx/testing/tests/test_utils.py -> build/lib/networkx/testing/tests creating build/lib/networkx/utils/tests copying networkx/utils/tests/__init__.py -> build/lib/networkx/utils/tests copying networkx/utils/tests/test__init.py -> build/lib/networkx/utils/tests copying networkx/utils/tests/test_contextmanager.py -> build/lib/networkx/utils/tests copying networkx/utils/tests/test_decorators.py -> build/lib/networkx/utils/tests copying networkx/utils/tests/test_heaps.py -> build/lib/networkx/utils/tests copying networkx/utils/tests/test_mapped_queue.py -> build/lib/networkx/utils/tests copying networkx/utils/tests/test_misc.py -> build/lib/networkx/utils/tests copying networkx/utils/tests/test_random_sequence.py -> build/lib/networkx/utils/tests copying networkx/utils/tests/test_rcm.py -> build/lib/networkx/utils/tests copying networkx/utils/tests/test_unionfind.py -> build/lib/networkx/utils/tests ==> Starting check()... running pytest running egg_info creating networkx.egg-info writing networkx.egg-info/PKG-INFO writing dependency_links to networkx.egg-info/dependency_links.txt writing requirements to networkx.egg-info/requires.txt writing top-level names to networkx.egg-info/top_level.txt writing manifest file 'networkx.egg-info/SOURCES.txt' reading manifest file 'networkx.egg-info/SOURCES.txt' reading manifest template 'MANIFEST.in' warning: no files found matching '*.txt' under directory 'doc' warning: no files found matching '*.inc' under directory 'doc' warning: no previously-included files matching '*~' found anywhere in distribution warning: no previously-included files matching '*.pyc' found anywhere in distribution warning: no previously-included files matching '.svn' found anywhere in distribution no previously-included directories found matching 'doc/build' no previously-included directories found matching 'doc/auto_examples' no previously-included directories found matching 'doc/modules' no previously-included directories found matching 'doc/reference/generated' no previously-included directories found matching 'doc/reference/algorithms/generated' no previously-included directories found matching 'doc/reference/classes/generated' no previously-included directories found matching 'doc/reference/readwrite/generated' adding license file 'LICENSE.txt' writing manifest file 'networkx.egg-info/SOURCES.txt' running build_ext ============================= test session starts ============================== platform linux -- Python 3.10.1, pytest-7.1.1, pluggy-1.0.0 rootdir: /build/python-networkx/src/networkx-networkx-2.7.1 collected 4786 items / 3 skipped networkx/algorithms/approximation/tests/test_approx_clust_coeff.py ..... [ 0%] . [ 0%] networkx/algorithms/approximation/tests/test_clique.py ........ [ 0%] networkx/algorithms/approximation/tests/test_connectivity.py ........... [ 0%] ....... [ 0%] networkx/algorithms/approximation/tests/test_distance_measures.py ...... [ 0%] .. [ 0%] networkx/algorithms/approximation/tests/test_dominating_set.py ... [ 0%] networkx/algorithms/approximation/tests/test_kcomponents.py ............ [ 1%] .... [ 1%] networkx/algorithms/approximation/tests/test_matching.py . [ 1%] networkx/algorithms/approximation/tests/test_maxcut.py ..... [ 1%] networkx/algorithms/approximation/tests/test_ramsey.py . [ 1%] networkx/algorithms/approximation/tests/test_steinertree.py .... [ 1%] networkx/algorithms/approximation/tests/test_traveling_salesman.py ..... [ 1%] .......................FFFFFF..sFFF...s. [ 2%] networkx/algorithms/approximation/tests/test_treewidth.py ............ [ 2%] networkx/algorithms/approximation/tests/test_vertex_cover.py .... [ 2%] networkx/algorithms/assortativity/tests/test_connectivity.py .......... [ 2%] networkx/algorithms/assortativity/tests/test_correlation.py ....FFFF.... [ 3%] ....... [ 3%] networkx/algorithms/assortativity/tests/test_mixing.py ................. [ 3%] .. [ 3%] networkx/algorithms/assortativity/tests/test_neighbor_degree.py ...... [ 3%] networkx/algorithms/assortativity/tests/test_pairs.py ........... [ 4%] networkx/algorithms/bipartite/tests/test_basic.py ............... [ 4%] networkx/algorithms/bipartite/tests/test_centrality.py ...... [ 4%] networkx/algorithms/bipartite/tests/test_cluster.py ......... [ 4%] networkx/algorithms/bipartite/tests/test_covering.py .... [ 4%] networkx/algorithms/bipartite/tests/test_edgelist.py .............. [ 5%] networkx/algorithms/bipartite/tests/test_generators.py .......... [ 5%] networkx/algorithms/bipartite/tests/test_matching.py ............FFFFFFF [ 5%] F [ 5%] networkx/algorithms/bipartite/tests/test_matrix.py ........... [ 5%] networkx/algorithms/bipartite/tests/test_project.py ................. [ 6%] networkx/algorithms/bipartite/tests/test_redundancy.py ... [ 6%] networkx/algorithms/bipartite/tests/test_spectral_bipartivity.py ... [ 6%] networkx/algorithms/centrality/tests/test_betweenness_centrality.py .... [ 6%] ..................................... [ 7%] networkx/algorithms/centrality/tests/test_betweenness_centrality_subset.py . [ 7%] ................ [ 7%] networkx/algorithms/centrality/tests/test_closeness_centrality.py ...... [ 7%] ....... [ 7%] networkx/algorithms/centrality/tests/test_current_flow_betweenness_centrality.py . [ 7%] ............... [ 8%] networkx/algorithms/centrality/tests/test_current_flow_betweenness_centrality_subset.py . [ 8%] ........ [ 8%] networkx/algorithms/centrality/tests/test_current_flow_closeness.py ... [ 8%] networkx/algorithms/centrality/tests/test_degree_centrality.py ....... [ 8%] networkx/algorithms/centrality/tests/test_dispersion.py ... 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[100%] =================================== FAILURES =================================== ____________________________ test_held_karp_ascent _____________________________ def test_held_karp_ascent(): """ Test the Held-Karp relaxation with the ascent method """ import networkx.algorithms.approximation.traveling_salesman as tsp np = pytest.importorskip("numpy") pytest.importorskip("scipy") # Adjacency matrix from page 1153 of the 1970 Held and Karp paper # which have been edited to be directional, but also symmetric G_array = np.array( [ [0, 97, 60, 73, 17, 52], [97, 0, 41, 52, 90, 30], [60, 41, 0, 21, 35, 41], [73, 52, 21, 0, 95, 46], [17, 90, 35, 95, 0, 81], [52, 30, 41, 46, 81, 0], ] ) solution_edges = [(1, 3), (2, 4), (3, 2), (4, 0), (5, 1), (0, 5)] G = nx.from_numpy_array(G_array, create_using=nx.DiGraph) > opt_hk, z_star = tsp.held_karp_ascent(G) networkx/algorithms/approximation/tests/test_traveling_salesman.py:410: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ networkx/algorithms/approximation/traveling_salesman.py:533: in held_karp_ascent import scipy.optimize as optimize /usr/lib/python3.10/site-packages/scipy/optimize/__init__.py:420: in from ._shgo import shgo /usr/lib/python3.10/site-packages/scipy/optimize/_shgo.py:9: in from scipy import spatial /usr/lib/python3.10/site-packages/scipy/spatial/__init__.py:108: in from ._geometric_slerp import geometric_slerp /usr/lib/python3.10/site-packages/scipy/spatial/_geometric_slerp.py:9: in from scipy.spatial.distance import euclidean _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ """ Distance computations (:mod:`scipy.spatial.distance`) ===================================================== .. sectionauthor:: Damian Eads Function reference ------------------ Distance matrix computation from a collection of raw observation vectors stored in a rectangular array. .. autosummary:: :toctree: generated/ pdist -- pairwise distances between observation vectors. cdist -- distances between two collections of observation vectors squareform -- convert distance matrix to a condensed one and vice versa directed_hausdorff -- directed Hausdorff distance between arrays Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix. .. autosummary:: :toctree: generated/ is_valid_dm -- checks for a valid distance matrix is_valid_y -- checks for a valid condensed distance matrix num_obs_dm -- # of observations in a distance matrix num_obs_y -- # of observations in a condensed distance matrix Distance functions between two numeric vectors ``u`` and ``v``. Computing distances over a large collection of vectors is inefficient for these functions. Use ``pdist`` for this purpose. .. autosummary:: :toctree: generated/ braycurtis -- the Bray-Curtis distance. canberra -- the Canberra distance. chebyshev -- the Chebyshev distance. cityblock -- the Manhattan distance. correlation -- the Correlation distance. cosine -- the Cosine distance. euclidean -- the Euclidean distance. jensenshannon -- the Jensen-Shannon distance. mahalanobis -- the Mahalanobis distance. minkowski -- the Minkowski distance. seuclidean -- the normalized Euclidean distance. sqeuclidean -- the squared Euclidean distance. Distance functions between two boolean vectors (representing sets) ``u`` and ``v``. As in the case of numerical vectors, ``pdist`` is more efficient for computing the distances between all pairs. .. autosummary:: :toctree: generated/ dice -- the Dice dissimilarity. hamming -- the Hamming distance. jaccard -- the Jaccard distance. kulsinski -- the Kulsinski distance. kulczynski1 -- the Kulczynski 1 distance. rogerstanimoto -- the Rogers-Tanimoto dissimilarity. russellrao -- the Russell-Rao dissimilarity. sokalmichener -- the Sokal-Michener dissimilarity. sokalsneath -- the Sokal-Sneath dissimilarity. yule -- the Yule dissimilarity. :func:`hamming` also operates over discrete numerical vectors. """ # Copyright (C) Damian Eads, 2007-2008. New BSD License. __all__ = [ 'braycurtis', 'canberra', 'cdist', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'directed_hausdorff', 'euclidean', 'hamming', 'is_valid_dm', 'is_valid_y', 'jaccard', 'jensenshannon', 'kulsinski', 'kulczynski1', 'mahalanobis', 'matching', 'minkowski', 'num_obs_dm', 'num_obs_y', 'pdist', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'squareform', 'yule' ] import warnings import numpy as np import dataclasses from typing import List, Optional, Set, Callable from functools import partial from scipy._lib._util import _asarray_validated > from . import _distance_wrap E ImportError: /usr/lib/python3.10/site-packages/scipy/spatial/_distance_wrap.cpython-310-riscv64-linux-gnu.so: undefined symbol: npy_copysign /usr/lib/python3.10/site-packages/scipy/spatial/distance.py:120: ImportError _______________________ test_ascent_fractional_solution ________________________ def test_ascent_fractional_solution(): """ Test the ascent method using a modified version of Figure 2 on page 1140 in 'The Traveling Salesman Problem and Minimum Spanning Trees' by Held and Karp """ import networkx.algorithms.approximation.traveling_salesman as tsp np = pytest.importorskip("numpy") pytest.importorskip("scipy") # This version of Figure 2 has all of the edge weights multiplied by 100 # and is a complete directed graph with infinite edge weights for the # edges not listed in the original graph G_array = np.array( [ [0, 100, 100, 100000, 100000, 1], [100, 0, 100, 100000, 1, 100000], [100, 100, 0, 1, 100000, 100000], [100000, 100000, 1, 0, 100, 100], [100000, 1, 100000, 100, 0, 100], [1, 100000, 100000, 100, 100, 0], ] ) solution_z_star = { (0, 1): 5 / 12, (0, 2): 5 / 12, (0, 5): 5 / 6, (1, 0): 5 / 12, (1, 2): 1 / 3, (1, 4): 5 / 6, (2, 0): 5 / 12, (2, 1): 1 / 3, (2, 3): 5 / 6, (3, 2): 5 / 6, (3, 4): 1 / 3, (3, 5): 1 / 2, (4, 1): 5 / 6, (4, 3): 1 / 3, (4, 5): 1 / 2, (5, 0): 5 / 6, (5, 3): 1 / 2, (5, 4): 1 / 2, } G = nx.from_numpy_array(G_array, create_using=nx.DiGraph) > opt_hk, z_star = tsp.held_karp_ascent(G) networkx/algorithms/approximation/tests/test_traveling_salesman.py:467: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ networkx/algorithms/approximation/traveling_salesman.py:533: in held_karp_ascent import scipy.optimize as optimize /usr/lib/python3.10/site-packages/scipy/optimize/__init__.py:420: in from ._shgo import shgo /usr/lib/python3.10/site-packages/scipy/optimize/_shgo.py:9: in from scipy import spatial /usr/lib/python3.10/site-packages/scipy/spatial/__init__.py:108: in from ._geometric_slerp import geometric_slerp /usr/lib/python3.10/site-packages/scipy/spatial/_geometric_slerp.py:9: in from scipy.spatial.distance import euclidean _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ """ Distance computations (:mod:`scipy.spatial.distance`) ===================================================== .. sectionauthor:: Damian Eads Function reference ------------------ Distance matrix computation from a collection of raw observation vectors stored in a rectangular array. .. autosummary:: :toctree: generated/ pdist -- pairwise distances between observation vectors. cdist -- distances between two collections of observation vectors squareform -- convert distance matrix to a condensed one and vice versa directed_hausdorff -- directed Hausdorff distance between arrays Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix. .. autosummary:: :toctree: generated/ is_valid_dm -- checks for a valid distance matrix is_valid_y -- checks for a valid condensed distance matrix num_obs_dm -- # of observations in a distance matrix num_obs_y -- # of observations in a condensed distance matrix Distance functions between two numeric vectors ``u`` and ``v``. Computing distances over a large collection of vectors is inefficient for these functions. Use ``pdist`` for this purpose. .. autosummary:: :toctree: generated/ braycurtis -- the Bray-Curtis distance. canberra -- the Canberra distance. chebyshev -- the Chebyshev distance. cityblock -- the Manhattan distance. correlation -- the Correlation distance. cosine -- the Cosine distance. euclidean -- the Euclidean distance. jensenshannon -- the Jensen-Shannon distance. mahalanobis -- the Mahalanobis distance. minkowski -- the Minkowski distance. seuclidean -- the normalized Euclidean distance. sqeuclidean -- the squared Euclidean distance. Distance functions between two boolean vectors (representing sets) ``u`` and ``v``. As in the case of numerical vectors, ``pdist`` is more efficient for computing the distances between all pairs. .. autosummary:: :toctree: generated/ dice -- the Dice dissimilarity. hamming -- the Hamming distance. jaccard -- the Jaccard distance. kulsinski -- the Kulsinski distance. kulczynski1 -- the Kulczynski 1 distance. rogerstanimoto -- the Rogers-Tanimoto dissimilarity. russellrao -- the Russell-Rao dissimilarity. sokalmichener -- the Sokal-Michener dissimilarity. sokalsneath -- the Sokal-Sneath dissimilarity. yule -- the Yule dissimilarity. :func:`hamming` also operates over discrete numerical vectors. """ # Copyright (C) Damian Eads, 2007-2008. New BSD License. __all__ = [ 'braycurtis', 'canberra', 'cdist', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'directed_hausdorff', 'euclidean', 'hamming', 'is_valid_dm', 'is_valid_y', 'jaccard', 'jensenshannon', 'kulsinski', 'kulczynski1', 'mahalanobis', 'matching', 'minkowski', 'num_obs_dm', 'num_obs_y', 'pdist', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'squareform', 'yule' ] import warnings import numpy as np import dataclasses from typing import List, Optional, Set, Callable from functools import partial from scipy._lib._util import _asarray_validated > from . import _distance_wrap E ImportError: /usr/lib/python3.10/site-packages/scipy/spatial/_distance_wrap.cpython-310-riscv64-linux-gnu.so: undefined symbol: npy_copysign /usr/lib/python3.10/site-packages/scipy/spatial/distance.py:120: ImportError ________________________ test_ascent_method_asymmetric _________________________ def test_ascent_method_asymmetric(): """ Tests the ascent method using a truly asymmetric graph for which the solution has been brute forced """ import networkx.algorithms.approximation.traveling_salesman as tsp np = pytest.importorskip("numpy") pytest.importorskip("scipy") G_array = np.array( [ [0, 26, 63, 59, 69, 31, 41], [62, 0, 91, 53, 75, 87, 47], [47, 82, 0, 90, 15, 9, 18], [68, 19, 5, 0, 58, 34, 93], [11, 58, 53, 55, 0, 61, 79], [88, 75, 13, 76, 98, 0, 40], [41, 61, 55, 88, 46, 45, 0], ] ) solution_edges = [(0, 1), (1, 3), (3, 2), (2, 5), (5, 6), (4, 0), (6, 4)] G = nx.from_numpy_array(G_array, create_using=nx.DiGraph) > opt_hk, z_star = tsp.held_karp_ascent(G) networkx/algorithms/approximation/tests/test_traveling_salesman.py:502: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ networkx/algorithms/approximation/traveling_salesman.py:533: in held_karp_ascent import scipy.optimize as optimize /usr/lib/python3.10/site-packages/scipy/optimize/__init__.py:420: in from ._shgo import shgo /usr/lib/python3.10/site-packages/scipy/optimize/_shgo.py:9: in from scipy import spatial /usr/lib/python3.10/site-packages/scipy/spatial/__init__.py:108: in from ._geometric_slerp import geometric_slerp /usr/lib/python3.10/site-packages/scipy/spatial/_geometric_slerp.py:9: in from scipy.spatial.distance import euclidean _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ """ Distance computations (:mod:`scipy.spatial.distance`) ===================================================== .. sectionauthor:: Damian Eads Function reference ------------------ Distance matrix computation from a collection of raw observation vectors stored in a rectangular array. .. autosummary:: :toctree: generated/ pdist -- pairwise distances between observation vectors. cdist -- distances between two collections of observation vectors squareform -- convert distance matrix to a condensed one and vice versa directed_hausdorff -- directed Hausdorff distance between arrays Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix. .. autosummary:: :toctree: generated/ is_valid_dm -- checks for a valid distance matrix is_valid_y -- checks for a valid condensed distance matrix num_obs_dm -- # of observations in a distance matrix num_obs_y -- # of observations in a condensed distance matrix Distance functions between two numeric vectors ``u`` and ``v``. Computing distances over a large collection of vectors is inefficient for these functions. Use ``pdist`` for this purpose. .. autosummary:: :toctree: generated/ braycurtis -- the Bray-Curtis distance. canberra -- the Canberra distance. chebyshev -- the Chebyshev distance. cityblock -- the Manhattan distance. correlation -- the Correlation distance. cosine -- the Cosine distance. euclidean -- the Euclidean distance. jensenshannon -- the Jensen-Shannon distance. mahalanobis -- the Mahalanobis distance. minkowski -- the Minkowski distance. seuclidean -- the normalized Euclidean distance. sqeuclidean -- the squared Euclidean distance. Distance functions between two boolean vectors (representing sets) ``u`` and ``v``. As in the case of numerical vectors, ``pdist`` is more efficient for computing the distances between all pairs. .. autosummary:: :toctree: generated/ dice -- the Dice dissimilarity. hamming -- the Hamming distance. jaccard -- the Jaccard distance. kulsinski -- the Kulsinski distance. kulczynski1 -- the Kulczynski 1 distance. rogerstanimoto -- the Rogers-Tanimoto dissimilarity. russellrao -- the Russell-Rao dissimilarity. sokalmichener -- the Sokal-Michener dissimilarity. sokalsneath -- the Sokal-Sneath dissimilarity. yule -- the Yule dissimilarity. :func:`hamming` also operates over discrete numerical vectors. """ # Copyright (C) Damian Eads, 2007-2008. New BSD License. __all__ = [ 'braycurtis', 'canberra', 'cdist', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'directed_hausdorff', 'euclidean', 'hamming', 'is_valid_dm', 'is_valid_y', 'jaccard', 'jensenshannon', 'kulsinski', 'kulczynski1', 'mahalanobis', 'matching', 'minkowski', 'num_obs_dm', 'num_obs_y', 'pdist', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'squareform', 'yule' ] import warnings import numpy as np import dataclasses from typing import List, Optional, Set, Callable from functools import partial from scipy._lib._util import _asarray_validated > from . import _distance_wrap E ImportError: /usr/lib/python3.10/site-packages/scipy/spatial/_distance_wrap.cpython-310-riscv64-linux-gnu.so: undefined symbol: npy_copysign /usr/lib/python3.10/site-packages/scipy/spatial/distance.py:120: ImportError _______________________ test_ascent_method_asymmetric_2 ________________________ def test_ascent_method_asymmetric_2(): """ Tests the ascent method using a truly asymmetric graph for which the solution has been brute forced """ import networkx.algorithms.approximation.traveling_salesman as tsp np = pytest.importorskip("numpy") pytest.importorskip("scipy") G_array = np.array( [ [0, 45, 39, 92, 29, 31], [72, 0, 4, 12, 21, 60], [81, 6, 0, 98, 70, 53], [49, 71, 59, 0, 98, 94], [74, 95, 24, 43, 0, 47], [56, 43, 3, 65, 22, 0], ] ) solution_edges = [(0, 5), (5, 4), (1, 3), (3, 0), (2, 1), (4, 2)] G = nx.from_numpy_array(G_array, create_using=nx.DiGraph) > opt_hk, z_star = tsp.held_karp_ascent(G) networkx/algorithms/approximation/tests/test_traveling_salesman.py:536: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ networkx/algorithms/approximation/traveling_salesman.py:533: in held_karp_ascent import scipy.optimize as optimize /usr/lib/python3.10/site-packages/scipy/optimize/__init__.py:420: in from ._shgo import shgo /usr/lib/python3.10/site-packages/scipy/optimize/_shgo.py:9: in from scipy import spatial /usr/lib/python3.10/site-packages/scipy/spatial/__init__.py:108: in from ._geometric_slerp import geometric_slerp /usr/lib/python3.10/site-packages/scipy/spatial/_geometric_slerp.py:9: in from scipy.spatial.distance import euclidean _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ """ Distance computations (:mod:`scipy.spatial.distance`) ===================================================== .. sectionauthor:: Damian Eads Function reference ------------------ Distance matrix computation from a collection of raw observation vectors stored in a rectangular array. .. autosummary:: :toctree: generated/ pdist -- pairwise distances between observation vectors. cdist -- distances between two collections of observation vectors squareform -- convert distance matrix to a condensed one and vice versa directed_hausdorff -- directed Hausdorff distance between arrays Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix. .. autosummary:: :toctree: generated/ is_valid_dm -- checks for a valid distance matrix is_valid_y -- checks for a valid condensed distance matrix num_obs_dm -- # of observations in a distance matrix num_obs_y -- # of observations in a condensed distance matrix Distance functions between two numeric vectors ``u`` and ``v``. Computing distances over a large collection of vectors is inefficient for these functions. Use ``pdist`` for this purpose. .. autosummary:: :toctree: generated/ braycurtis -- the Bray-Curtis distance. canberra -- the Canberra distance. chebyshev -- the Chebyshev distance. cityblock -- the Manhattan distance. correlation -- the Correlation distance. cosine -- the Cosine distance. euclidean -- the Euclidean distance. jensenshannon -- the Jensen-Shannon distance. mahalanobis -- the Mahalanobis distance. minkowski -- the Minkowski distance. seuclidean -- the normalized Euclidean distance. sqeuclidean -- the squared Euclidean distance. Distance functions between two boolean vectors (representing sets) ``u`` and ``v``. As in the case of numerical vectors, ``pdist`` is more efficient for computing the distances between all pairs. .. autosummary:: :toctree: generated/ dice -- the Dice dissimilarity. hamming -- the Hamming distance. jaccard -- the Jaccard distance. kulsinski -- the Kulsinski distance. kulczynski1 -- the Kulczynski 1 distance. rogerstanimoto -- the Rogers-Tanimoto dissimilarity. russellrao -- the Russell-Rao dissimilarity. sokalmichener -- the Sokal-Michener dissimilarity. sokalsneath -- the Sokal-Sneath dissimilarity. yule -- the Yule dissimilarity. :func:`hamming` also operates over discrete numerical vectors. """ # Copyright (C) Damian Eads, 2007-2008. New BSD License. __all__ = [ 'braycurtis', 'canberra', 'cdist', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'directed_hausdorff', 'euclidean', 'hamming', 'is_valid_dm', 'is_valid_y', 'jaccard', 'jensenshannon', 'kulsinski', 'kulczynski1', 'mahalanobis', 'matching', 'minkowski', 'num_obs_dm', 'num_obs_y', 'pdist', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'squareform', 'yule' ] import warnings import numpy as np import dataclasses from typing import List, Optional, Set, Callable from functools import partial from scipy._lib._util import _asarray_validated > from . import _distance_wrap E ImportError: /usr/lib/python3.10/site-packages/scipy/spatial/_distance_wrap.cpython-310-riscv64-linux-gnu.so: undefined symbol: npy_copysign /usr/lib/python3.10/site-packages/scipy/spatial/distance.py:120: ImportError ______________________ test_held_karp_ascent_asymmetric_3 ______________________ def test_held_karp_ascent_asymmetric_3(): """ Tests the ascent method using a truly asymmetric graph with a fractional solution for which the solution has been brute forced. In this graph their are two different optimal, integral solutions (which are also the overall atsp solutions) to the Held Karp relaxation. However, this particular graph has two different tours of optimal value and the possible solutions in the held_karp_ascent function are not stored in an ordered data structure. """ import networkx.algorithms.approximation.traveling_salesman as tsp np = pytest.importorskip("numpy") pytest.importorskip("scipy") G_array = np.array( [ [0, 1, 5, 2, 7, 4], [7, 0, 7, 7, 1, 4], [4, 7, 0, 9, 2, 1], [7, 2, 7, 0, 4, 4], [5, 5, 4, 4, 0, 3], [3, 9, 1, 3, 4, 0], ] ) solution1_edges = [(0, 3), (1, 4), (2, 5), (3, 1), (4, 2), (5, 0)] solution2_edges = [(0, 3), (3, 1), (1, 4), (4, 5), (2, 0), (5, 2)] G = nx.from_numpy_array(G_array, create_using=nx.DiGraph) > opt_hk, z_star = tsp.held_karp_ascent(G) networkx/algorithms/approximation/tests/test_traveling_salesman.py:578: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ networkx/algorithms/approximation/traveling_salesman.py:533: in held_karp_ascent import scipy.optimize as optimize /usr/lib/python3.10/site-packages/scipy/optimize/__init__.py:420: in from ._shgo import shgo /usr/lib/python3.10/site-packages/scipy/optimize/_shgo.py:9: in from scipy import spatial /usr/lib/python3.10/site-packages/scipy/spatial/__init__.py:108: in from ._geometric_slerp import geometric_slerp /usr/lib/python3.10/site-packages/scipy/spatial/_geometric_slerp.py:9: in from scipy.spatial.distance import euclidean _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ """ Distance computations (:mod:`scipy.spatial.distance`) ===================================================== .. sectionauthor:: Damian Eads Function reference ------------------ Distance matrix computation from a collection of raw observation vectors stored in a rectangular array. .. autosummary:: :toctree: generated/ pdist -- pairwise distances between observation vectors. cdist -- distances between two collections of observation vectors squareform -- convert distance matrix to a condensed one and vice versa directed_hausdorff -- directed Hausdorff distance between arrays Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix. .. autosummary:: :toctree: generated/ is_valid_dm -- checks for a valid distance matrix is_valid_y -- checks for a valid condensed distance matrix num_obs_dm -- # of observations in a distance matrix num_obs_y -- # of observations in a condensed distance matrix Distance functions between two numeric vectors ``u`` and ``v``. Computing distances over a large collection of vectors is inefficient for these functions. Use ``pdist`` for this purpose. .. autosummary:: :toctree: generated/ braycurtis -- the Bray-Curtis distance. canberra -- the Canberra distance. chebyshev -- the Chebyshev distance. cityblock -- the Manhattan distance. correlation -- the Correlation distance. cosine -- the Cosine distance. euclidean -- the Euclidean distance. jensenshannon -- the Jensen-Shannon distance. mahalanobis -- the Mahalanobis distance. minkowski -- the Minkowski distance. seuclidean -- the normalized Euclidean distance. sqeuclidean -- the squared Euclidean distance. Distance functions between two boolean vectors (representing sets) ``u`` and ``v``. As in the case of numerical vectors, ``pdist`` is more efficient for computing the distances between all pairs. .. autosummary:: :toctree: generated/ dice -- the Dice dissimilarity. hamming -- the Hamming distance. jaccard -- the Jaccard distance. kulsinski -- the Kulsinski distance. kulczynski1 -- the Kulczynski 1 distance. rogerstanimoto -- the Rogers-Tanimoto dissimilarity. russellrao -- the Russell-Rao dissimilarity. sokalmichener -- the Sokal-Michener dissimilarity. sokalsneath -- the Sokal-Sneath dissimilarity. yule -- the Yule dissimilarity. :func:`hamming` also operates over discrete numerical vectors. """ # Copyright (C) Damian Eads, 2007-2008. New BSD License. __all__ = [ 'braycurtis', 'canberra', 'cdist', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'directed_hausdorff', 'euclidean', 'hamming', 'is_valid_dm', 'is_valid_y', 'jaccard', 'jensenshannon', 'kulsinski', 'kulczynski1', 'mahalanobis', 'matching', 'minkowski', 'num_obs_dm', 'num_obs_y', 'pdist', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'squareform', 'yule' ] import warnings import numpy as np import dataclasses from typing import List, Optional, Set, Callable from functools import partial from scipy._lib._util import _asarray_validated > from . import _distance_wrap E ImportError: /usr/lib/python3.10/site-packages/scipy/spatial/_distance_wrap.cpython-310-riscv64-linux-gnu.so: undefined symbol: npy_copysign /usr/lib/python3.10/site-packages/scipy/spatial/distance.py:120: ImportError _________________ test_held_karp_ascent_fractional_asymmetric __________________ def test_held_karp_ascent_fractional_asymmetric(): """ Tests the ascent method using a truly asymmetric graph with a fractional solution for which the solution has been brute forced """ import networkx.algorithms.approximation.traveling_salesman as tsp np = pytest.importorskip("numpy") pytest.importorskip("scipy") G_array = np.array( [ [0, 100, 150, 100000, 100000, 1], [150, 0, 100, 100000, 1, 100000], [100, 150, 0, 1, 100000, 100000], [100000, 100000, 1, 0, 150, 100], [100000, 2, 100000, 100, 0, 150], [2, 100000, 100000, 150, 100, 0], ] ) solution_z_star = { (0, 1): 5 / 12, (0, 2): 5 / 12, (0, 5): 5 / 6, (1, 0): 5 / 12, (1, 2): 5 / 12, (1, 4): 5 / 6, (2, 0): 5 / 12, (2, 1): 5 / 12, (2, 3): 5 / 6, (3, 2): 5 / 6, (3, 4): 5 / 12, (3, 5): 5 / 12, (4, 1): 5 / 6, (4, 3): 5 / 12, (4, 5): 5 / 12, (5, 0): 5 / 6, (5, 3): 5 / 12, (5, 4): 5 / 12, } G = nx.from_numpy_array(G_array, create_using=nx.DiGraph) > opt_hk, z_star = tsp.held_karp_ascent(G) networkx/algorithms/approximation/tests/test_traveling_salesman.py:634: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ networkx/algorithms/approximation/traveling_salesman.py:533: in held_karp_ascent import scipy.optimize as optimize /usr/lib/python3.10/site-packages/scipy/optimize/__init__.py:420: in from ._shgo import shgo /usr/lib/python3.10/site-packages/scipy/optimize/_shgo.py:9: in from scipy import spatial /usr/lib/python3.10/site-packages/scipy/spatial/__init__.py:108: in from ._geometric_slerp import geometric_slerp /usr/lib/python3.10/site-packages/scipy/spatial/_geometric_slerp.py:9: in from scipy.spatial.distance import euclidean _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ """ Distance computations (:mod:`scipy.spatial.distance`) ===================================================== .. sectionauthor:: Damian Eads Function reference ------------------ Distance matrix computation from a collection of raw observation vectors stored in a rectangular array. .. autosummary:: :toctree: generated/ pdist -- pairwise distances between observation vectors. cdist -- distances between two collections of observation vectors squareform -- convert distance matrix to a condensed one and vice versa directed_hausdorff -- directed Hausdorff distance between arrays Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix. .. autosummary:: :toctree: generated/ is_valid_dm -- checks for a valid distance matrix is_valid_y -- checks for a valid condensed distance matrix num_obs_dm -- # of observations in a distance matrix num_obs_y -- # of observations in a condensed distance matrix Distance functions between two numeric vectors ``u`` and ``v``. Computing distances over a large collection of vectors is inefficient for these functions. Use ``pdist`` for this purpose. .. autosummary:: :toctree: generated/ braycurtis -- the Bray-Curtis distance. canberra -- the Canberra distance. chebyshev -- the Chebyshev distance. cityblock -- the Manhattan distance. correlation -- the Correlation distance. cosine -- the Cosine distance. euclidean -- the Euclidean distance. jensenshannon -- the Jensen-Shannon distance. mahalanobis -- the Mahalanobis distance. minkowski -- the Minkowski distance. seuclidean -- the normalized Euclidean distance. sqeuclidean -- the squared Euclidean distance. Distance functions between two boolean vectors (representing sets) ``u`` and ``v``. As in the case of numerical vectors, ``pdist`` is more efficient for computing the distances between all pairs. .. autosummary:: :toctree: generated/ dice -- the Dice dissimilarity. hamming -- the Hamming distance. jaccard -- the Jaccard distance. kulsinski -- the Kulsinski distance. kulczynski1 -- the Kulczynski 1 distance. rogerstanimoto -- the Rogers-Tanimoto dissimilarity. russellrao -- the Russell-Rao dissimilarity. sokalmichener -- the Sokal-Michener dissimilarity. sokalsneath -- the Sokal-Sneath dissimilarity. yule -- the Yule dissimilarity. :func:`hamming` also operates over discrete numerical vectors. """ # Copyright (C) Damian Eads, 2007-2008. New BSD License. __all__ = [ 'braycurtis', 'canberra', 'cdist', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'directed_hausdorff', 'euclidean', 'hamming', 'is_valid_dm', 'is_valid_y', 'jaccard', 'jensenshannon', 'kulsinski', 'kulczynski1', 'mahalanobis', 'matching', 'minkowski', 'num_obs_dm', 'num_obs_y', 'pdist', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'squareform', 'yule' ] import warnings import numpy as np import dataclasses from typing import List, Optional, Set, Callable from functools import partial from scipy._lib._util import _asarray_validated > from . import _distance_wrap E ImportError: /usr/lib/python3.10/site-packages/scipy/spatial/_distance_wrap.cpython-310-riscv64-linux-gnu.so: undefined symbol: npy_copysign /usr/lib/python3.10/site-packages/scipy/spatial/distance.py:120: ImportError ______________________________ test_asadpour_tsp _______________________________ def test_asadpour_tsp(): """ Test the complete asadpour tsp algorithm with the fractional, symmetric Held Karp solution. This test also uses an incomplete graph as input. """ # This version of Figure 2 has all of the edge weights multiplied by 100 # and the 0 weight edges have a weight of 1. pytest.importorskip("numpy") pytest.importorskip("scipy") edge_list = [ (0, 1, 100), (0, 2, 100), (0, 5, 1), (1, 2, 100), (1, 4, 1), (2, 3, 1), (3, 4, 100), (3, 5, 100), (4, 5, 100), (1, 0, 100), (2, 0, 100), (5, 0, 1), (2, 1, 100), (4, 1, 1), (3, 2, 1), (4, 3, 100), (5, 3, 100), (5, 4, 100), ] G = nx.DiGraph() G.add_weighted_edges_from(edge_list) def fixed_asadpour(G, weight): return nx_app.asadpour_atsp(G, weight, 19) > tour = nx_app.traveling_salesman_problem(G, weight="weight", method=fixed_asadpour) networkx/algorithms/approximation/tests/test_traveling_salesman.py:870: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ networkx/algorithms/approximation/traveling_salesman.py:319: in traveling_salesman_problem best_GG = method(GG, weight) networkx/algorithms/approximation/tests/test_traveling_salesman.py:868: in fixed_asadpour return nx_app.asadpour_atsp(G, weight, 19) networkx/utils/decorators.py:816: in func return argmap._lazy_compile(__wrapper)(*args, **kwargs) compilation 171:4: in argmap_asadpour_atsp_168 ??? networkx/algorithms/approximation/traveling_salesman.py:423: in asadpour_atsp opt_hk, z_star = held_karp_ascent(G, weight) networkx/algorithms/approximation/traveling_salesman.py:533: in held_karp_ascent import scipy.optimize as optimize /usr/lib/python3.10/site-packages/scipy/optimize/__init__.py:420: in from ._shgo import shgo /usr/lib/python3.10/site-packages/scipy/optimize/_shgo.py:9: in from scipy import spatial /usr/lib/python3.10/site-packages/scipy/spatial/__init__.py:108: in from ._geometric_slerp import geometric_slerp /usr/lib/python3.10/site-packages/scipy/spatial/_geometric_slerp.py:9: in from scipy.spatial.distance import euclidean _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ """ Distance computations (:mod:`scipy.spatial.distance`) ===================================================== .. sectionauthor:: Damian Eads Function reference ------------------ Distance matrix computation from a collection of raw observation vectors stored in a rectangular array. .. autosummary:: :toctree: generated/ pdist -- pairwise distances between observation vectors. cdist -- distances between two collections of observation vectors squareform -- convert distance matrix to a condensed one and vice versa directed_hausdorff -- directed Hausdorff distance between arrays Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix. .. autosummary:: :toctree: generated/ is_valid_dm -- checks for a valid distance matrix is_valid_y -- checks for a valid condensed distance matrix num_obs_dm -- # of observations in a distance matrix num_obs_y -- # of observations in a condensed distance matrix Distance functions between two numeric vectors ``u`` and ``v``. Computing distances over a large collection of vectors is inefficient for these functions. Use ``pdist`` for this purpose. .. autosummary:: :toctree: generated/ braycurtis -- the Bray-Curtis distance. canberra -- the Canberra distance. chebyshev -- the Chebyshev distance. cityblock -- the Manhattan distance. correlation -- the Correlation distance. cosine -- the Cosine distance. euclidean -- the Euclidean distance. jensenshannon -- the Jensen-Shannon distance. mahalanobis -- the Mahalanobis distance. minkowski -- the Minkowski distance. seuclidean -- the normalized Euclidean distance. sqeuclidean -- the squared Euclidean distance. Distance functions between two boolean vectors (representing sets) ``u`` and ``v``. As in the case of numerical vectors, ``pdist`` is more efficient for computing the distances between all pairs. .. autosummary:: :toctree: generated/ dice -- the Dice dissimilarity. hamming -- the Hamming distance. jaccard -- the Jaccard distance. kulsinski -- the Kulsinski distance. kulczynski1 -- the Kulczynski 1 distance. rogerstanimoto -- the Rogers-Tanimoto dissimilarity. russellrao -- the Russell-Rao dissimilarity. sokalmichener -- the Sokal-Michener dissimilarity. sokalsneath -- the Sokal-Sneath dissimilarity. yule -- the Yule dissimilarity. :func:`hamming` also operates over discrete numerical vectors. """ # Copyright (C) Damian Eads, 2007-2008. New BSD License. __all__ = [ 'braycurtis', 'canberra', 'cdist', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'directed_hausdorff', 'euclidean', 'hamming', 'is_valid_dm', 'is_valid_y', 'jaccard', 'jensenshannon', 'kulsinski', 'kulczynski1', 'mahalanobis', 'matching', 'minkowski', 'num_obs_dm', 'num_obs_y', 'pdist', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'squareform', 'yule' ] import warnings import numpy as np import dataclasses from typing import List, Optional, Set, Callable from functools import partial from scipy._lib._util import _asarray_validated > from . import _distance_wrap E ImportError: /usr/lib/python3.10/site-packages/scipy/spatial/_distance_wrap.cpython-310-riscv64-linux-gnu.so: undefined symbol: npy_copysign /usr/lib/python3.10/site-packages/scipy/spatial/distance.py:120: ImportError ___________________________ test_asadpour_real_world ___________________________ def test_asadpour_real_world(): """ This test uses airline prices between the six largest cities in the US. * New York City -> JFK * Los Angeles -> LAX * Chicago -> ORD * Houston -> IAH * Phoenix -> PHX * Philadelphia -> PHL Flight prices from August 2021 using Delta or American airlines to get nonstop flight. The brute force solution found the optimal tour to cost $872 This test also uses the `source` keyword argument to ensure that the tour always starts at city 0. """ np = pytest.importorskip("numpy") pytest.importorskip("scipy") G_array = np.array( [ # JFK LAX ORD IAH PHX PHL [0, 243, 199, 208, 169, 183], # JFK [277, 0, 217, 123, 127, 252], # LAX [297, 197, 0, 197, 123, 177], # ORD [303, 169, 197, 0, 117, 117], # IAH [257, 127, 160, 117, 0, 319], # PHX [183, 332, 217, 117, 319, 0], # PHL ] ) node_map = {0: "JFK", 1: "LAX", 2: "ORD", 3: "IAH", 4: "PHX", 5: "PHL"} expected_tours = [ ["JFK", "LAX", "PHX", "ORD", "IAH", "PHL", "JFK"], ["JFK", "ORD", "PHX", "LAX", "IAH", "PHL", "JFK"], ] G = nx.from_numpy_array(G_array, create_using=nx.DiGraph) nx.relabel_nodes(G, node_map, copy=False) def fixed_asadpour(G, weight): return nx_app.asadpour_atsp(G, weight, 37, source="JFK") > tour = nx_app.traveling_salesman_problem(G, weight="weight", method=fixed_asadpour) networkx/algorithms/approximation/tests/test_traveling_salesman.py:937: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ networkx/algorithms/approximation/traveling_salesman.py:319: in traveling_salesman_problem best_GG = method(GG, weight) networkx/algorithms/approximation/tests/test_traveling_salesman.py:935: in fixed_asadpour return nx_app.asadpour_atsp(G, weight, 37, source="JFK") compilation 171:4: in argmap_asadpour_atsp_168 ??? networkx/algorithms/approximation/traveling_salesman.py:423: in asadpour_atsp opt_hk, z_star = held_karp_ascent(G, weight) networkx/algorithms/approximation/traveling_salesman.py:533: in held_karp_ascent import scipy.optimize as optimize /usr/lib/python3.10/site-packages/scipy/optimize/__init__.py:420: in from ._shgo import shgo /usr/lib/python3.10/site-packages/scipy/optimize/_shgo.py:9: in from scipy import spatial /usr/lib/python3.10/site-packages/scipy/spatial/__init__.py:108: in from ._geometric_slerp import geometric_slerp /usr/lib/python3.10/site-packages/scipy/spatial/_geometric_slerp.py:9: in from scipy.spatial.distance import euclidean _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ """ Distance computations (:mod:`scipy.spatial.distance`) ===================================================== .. sectionauthor:: Damian Eads Function reference ------------------ Distance matrix computation from a collection of raw observation vectors stored in a rectangular array. .. autosummary:: :toctree: generated/ pdist -- pairwise distances between observation vectors. cdist -- distances between two collections of observation vectors squareform -- convert distance matrix to a condensed one and vice versa directed_hausdorff -- directed Hausdorff distance between arrays Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix. .. autosummary:: :toctree: generated/ is_valid_dm -- checks for a valid distance matrix is_valid_y -- checks for a valid condensed distance matrix num_obs_dm -- # of observations in a distance matrix num_obs_y -- # of observations in a condensed distance matrix Distance functions between two numeric vectors ``u`` and ``v``. Computing distances over a large collection of vectors is inefficient for these functions. Use ``pdist`` for this purpose. .. autosummary:: :toctree: generated/ braycurtis -- the Bray-Curtis distance. canberra -- the Canberra distance. chebyshev -- the Chebyshev distance. cityblock -- the Manhattan distance. correlation -- the Correlation distance. cosine -- the Cosine distance. euclidean -- the Euclidean distance. jensenshannon -- the Jensen-Shannon distance. mahalanobis -- the Mahalanobis distance. minkowski -- the Minkowski distance. seuclidean -- the normalized Euclidean distance. sqeuclidean -- the squared Euclidean distance. Distance functions between two boolean vectors (representing sets) ``u`` and ``v``. As in the case of numerical vectors, ``pdist`` is more efficient for computing the distances between all pairs. .. autosummary:: :toctree: generated/ dice -- the Dice dissimilarity. hamming -- the Hamming distance. jaccard -- the Jaccard distance. kulsinski -- the Kulsinski distance. kulczynski1 -- the Kulczynski 1 distance. rogerstanimoto -- the Rogers-Tanimoto dissimilarity. russellrao -- the Russell-Rao dissimilarity. sokalmichener -- the Sokal-Michener dissimilarity. sokalsneath -- the Sokal-Sneath dissimilarity. yule -- the Yule dissimilarity. :func:`hamming` also operates over discrete numerical vectors. """ # Copyright (C) Damian Eads, 2007-2008. New BSD License. __all__ = [ 'braycurtis', 'canberra', 'cdist', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'directed_hausdorff', 'euclidean', 'hamming', 'is_valid_dm', 'is_valid_y', 'jaccard', 'jensenshannon', 'kulsinski', 'kulczynski1', 'mahalanobis', 'matching', 'minkowski', 'num_obs_dm', 'num_obs_y', 'pdist', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'squareform', 'yule' ] import warnings import numpy as np import dataclasses from typing import List, Optional, Set, Callable from functools import partial from scipy._lib._util import _asarray_validated > from . import _distance_wrap E ImportError: /usr/lib/python3.10/site-packages/scipy/spatial/_distance_wrap.cpython-310-riscv64-linux-gnu.so: undefined symbol: npy_copysign /usr/lib/python3.10/site-packages/scipy/spatial/distance.py:120: ImportError ________________________ test_asadpour_real_world_path _________________________ def test_asadpour_real_world_path(): """ This test uses airline prices between the six largest cities in the US. This time using a path, not a cycle. * New York City -> JFK * Los Angeles -> LAX * Chicago -> ORD * Houston -> IAH * Phoenix -> PHX * Philadelphia -> PHL Flight prices from August 2021 using Delta or American airlines to get nonstop flight. The brute force solution found the optimal tour to cost $872 """ np = pytest.importorskip("numpy") pytest.importorskip("scipy") G_array = np.array( [ # JFK LAX ORD IAH PHX PHL [0, 243, 199, 208, 169, 183], # JFK [277, 0, 217, 123, 127, 252], # LAX [297, 197, 0, 197, 123, 177], # ORD [303, 169, 197, 0, 117, 117], # IAH [257, 127, 160, 117, 0, 319], # PHX [183, 332, 217, 117, 319, 0], # PHL ] ) node_map = {0: "JFK", 1: "LAX", 2: "ORD", 3: "IAH", 4: "PHX", 5: "PHL"} expected_paths = [ ["ORD", "PHX", "LAX", "IAH", "PHL", "JFK"], ["JFK", "PHL", "IAH", "ORD", "PHX", "LAX"], ] G = nx.from_numpy_array(G_array, create_using=nx.DiGraph) nx.relabel_nodes(G, node_map, copy=False) def fixed_asadpour(G, weight): return nx_app.asadpour_atsp(G, weight, 56) > path = nx_app.traveling_salesman_problem( G, weight="weight", cycle=False, method=fixed_asadpour ) networkx/algorithms/approximation/tests/test_traveling_salesman.py:985: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ networkx/algorithms/approximation/traveling_salesman.py:319: in traveling_salesman_problem best_GG = method(GG, weight) networkx/algorithms/approximation/tests/test_traveling_salesman.py:983: in fixed_asadpour return nx_app.asadpour_atsp(G, weight, 56) compilation 171:4: in argmap_asadpour_atsp_168 ??? networkx/algorithms/approximation/traveling_salesman.py:423: in asadpour_atsp opt_hk, z_star = held_karp_ascent(G, weight) networkx/algorithms/approximation/traveling_salesman.py:533: in held_karp_ascent import scipy.optimize as optimize /usr/lib/python3.10/site-packages/scipy/optimize/__init__.py:420: in from ._shgo import shgo /usr/lib/python3.10/site-packages/scipy/optimize/_shgo.py:9: in from scipy import spatial /usr/lib/python3.10/site-packages/scipy/spatial/__init__.py:108: in from ._geometric_slerp import geometric_slerp /usr/lib/python3.10/site-packages/scipy/spatial/_geometric_slerp.py:9: in from scipy.spatial.distance import euclidean _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ """ Distance computations (:mod:`scipy.spatial.distance`) ===================================================== .. sectionauthor:: Damian Eads Function reference ------------------ Distance matrix computation from a collection of raw observation vectors stored in a rectangular array. .. autosummary:: :toctree: generated/ pdist -- pairwise distances between observation vectors. cdist -- distances between two collections of observation vectors squareform -- convert distance matrix to a condensed one and vice versa directed_hausdorff -- directed Hausdorff distance between arrays Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix. .. autosummary:: :toctree: generated/ is_valid_dm -- checks for a valid distance matrix is_valid_y -- checks for a valid condensed distance matrix num_obs_dm -- # of observations in a distance matrix num_obs_y -- # of observations in a condensed distance matrix Distance functions between two numeric vectors ``u`` and ``v``. Computing distances over a large collection of vectors is inefficient for these functions. Use ``pdist`` for this purpose. .. autosummary:: :toctree: generated/ braycurtis -- the Bray-Curtis distance. canberra -- the Canberra distance. chebyshev -- the Chebyshev distance. cityblock -- the Manhattan distance. correlation -- the Correlation distance. cosine -- the Cosine distance. euclidean -- the Euclidean distance. jensenshannon -- the Jensen-Shannon distance. mahalanobis -- the Mahalanobis distance. minkowski -- the Minkowski distance. seuclidean -- the normalized Euclidean distance. sqeuclidean -- the squared Euclidean distance. Distance functions between two boolean vectors (representing sets) ``u`` and ``v``. As in the case of numerical vectors, ``pdist`` is more efficient for computing the distances between all pairs. .. autosummary:: :toctree: generated/ dice -- the Dice dissimilarity. hamming -- the Hamming distance. jaccard -- the Jaccard distance. kulsinski -- the Kulsinski distance. kulczynski1 -- the Kulczynski 1 distance. rogerstanimoto -- the Rogers-Tanimoto dissimilarity. russellrao -- the Russell-Rao dissimilarity. sokalmichener -- the Sokal-Michener dissimilarity. sokalsneath -- the Sokal-Sneath dissimilarity. yule -- the Yule dissimilarity. :func:`hamming` also operates over discrete numerical vectors. """ # Copyright (C) Damian Eads, 2007-2008. New BSD License. __all__ = [ 'braycurtis', 'canberra', 'cdist', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'directed_hausdorff', 'euclidean', 'hamming', 'is_valid_dm', 'is_valid_y', 'jaccard', 'jensenshannon', 'kulsinski', 'kulczynski1', 'mahalanobis', 'matching', 'minkowski', 'num_obs_dm', 'num_obs_y', 'pdist', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'squareform', 'yule' ] import warnings import numpy as np import dataclasses from typing import List, Optional, Set, Callable from functools import partial from scipy._lib._util import _asarray_validated > from . import _distance_wrap E ImportError: /usr/lib/python3.10/site-packages/scipy/spatial/_distance_wrap.cpython-310-riscv64-linux-gnu.so: undefined symbol: npy_copysign /usr/lib/python3.10/site-packages/scipy/spatial/distance.py:120: ImportError ___ TestDegreeMixingCorrelation.test_degree_pearson_assortativity_undirected ___ self = def test_degree_pearson_assortativity_undirected(self): > r = nx.degree_pearson_correlation_coefficient(self.P4) networkx/algorithms/assortativity/tests/test_correlation.py:37: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ networkx/algorithms/assortativity/correlation.py:153: in degree_pearson_correlation_coefficient import scipy.stats # call as sp.stats /usr/lib/python3.10/site-packages/scipy/stats/__init__.py:453: in from ._stats_py import * /usr/lib/python3.10/site-packages/scipy/stats/_stats_py.py:38: in from scipy.spatial.distance import cdist /usr/lib/python3.10/site-packages/scipy/spatial/__init__.py:108: in from ._geometric_slerp import geometric_slerp /usr/lib/python3.10/site-packages/scipy/spatial/_geometric_slerp.py:9: in from scipy.spatial.distance import euclidean _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ """ Distance computations (:mod:`scipy.spatial.distance`) ===================================================== .. sectionauthor:: Damian Eads Function reference ------------------ Distance matrix computation from a collection of raw observation vectors stored in a rectangular array. .. autosummary:: :toctree: generated/ pdist -- pairwise distances between observation vectors. cdist -- distances between two collections of observation vectors squareform -- convert distance matrix to a condensed one and vice versa directed_hausdorff -- directed Hausdorff distance between arrays Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix. .. autosummary:: :toctree: generated/ is_valid_dm -- checks for a valid distance matrix is_valid_y -- checks for a valid condensed distance matrix num_obs_dm -- # of observations in a distance matrix num_obs_y -- # of observations in a condensed distance matrix Distance functions between two numeric vectors ``u`` and ``v``. Computing distances over a large collection of vectors is inefficient for these functions. Use ``pdist`` for this purpose. .. autosummary:: :toctree: generated/ braycurtis -- the Bray-Curtis distance. canberra -- the Canberra distance. chebyshev -- the Chebyshev distance. cityblock -- the Manhattan distance. correlation -- the Correlation distance. cosine -- the Cosine distance. euclidean -- the Euclidean distance. jensenshannon -- the Jensen-Shannon distance. mahalanobis -- the Mahalanobis distance. minkowski -- the Minkowski distance. seuclidean -- the normalized Euclidean distance. sqeuclidean -- the squared Euclidean distance. Distance functions between two boolean vectors (representing sets) ``u`` and ``v``. As in the case of numerical vectors, ``pdist`` is more efficient for computing the distances between all pairs. .. autosummary:: :toctree: generated/ dice -- the Dice dissimilarity. hamming -- the Hamming distance. jaccard -- the Jaccard distance. kulsinski -- the Kulsinski distance. kulczynski1 -- the Kulczynski 1 distance. rogerstanimoto -- the Rogers-Tanimoto dissimilarity. russellrao -- the Russell-Rao dissimilarity. sokalmichener -- the Sokal-Michener dissimilarity. sokalsneath -- the Sokal-Sneath dissimilarity. yule -- the Yule dissimilarity. :func:`hamming` also operates over discrete numerical vectors. """ # Copyright (C) Damian Eads, 2007-2008. New BSD License. __all__ = [ 'braycurtis', 'canberra', 'cdist', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'directed_hausdorff', 'euclidean', 'hamming', 'is_valid_dm', 'is_valid_y', 'jaccard', 'jensenshannon', 'kulsinski', 'kulczynski1', 'mahalanobis', 'matching', 'minkowski', 'num_obs_dm', 'num_obs_y', 'pdist', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'squareform', 'yule' ] import warnings import numpy as np import dataclasses from typing import List, Optional, Set, Callable from functools import partial from scipy._lib._util import _asarray_validated > from . import _distance_wrap E ImportError: /usr/lib/python3.10/site-packages/scipy/spatial/_distance_wrap.cpython-310-riscv64-linux-gnu.so: undefined symbol: npy_copysign /usr/lib/python3.10/site-packages/scipy/spatial/distance.py:120: ImportError ____ TestDegreeMixingCorrelation.test_degree_pearson_assortativity_directed ____ self = def test_degree_pearson_assortativity_directed(self): > r = nx.degree_pearson_correlation_coefficient(self.D) networkx/algorithms/assortativity/tests/test_correlation.py:41: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ networkx/algorithms/assortativity/correlation.py:153: in degree_pearson_correlation_coefficient import scipy.stats # call as sp.stats /usr/lib/python3.10/site-packages/scipy/stats/__init__.py:453: in from ._stats_py import * /usr/lib/python3.10/site-packages/scipy/stats/_stats_py.py:38: in from scipy.spatial.distance import cdist /usr/lib/python3.10/site-packages/scipy/spatial/__init__.py:108: in from ._geometric_slerp import geometric_slerp /usr/lib/python3.10/site-packages/scipy/spatial/_geometric_slerp.py:9: in from scipy.spatial.distance import euclidean _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ """ Distance computations (:mod:`scipy.spatial.distance`) ===================================================== .. sectionauthor:: Damian Eads Function reference ------------------ Distance matrix computation from a collection of raw observation vectors stored in a rectangular array. .. autosummary:: :toctree: generated/ pdist -- pairwise distances between observation vectors. cdist -- distances between two collections of observation vectors squareform -- convert distance matrix to a condensed one and vice versa directed_hausdorff -- directed Hausdorff distance between arrays Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix. .. autosummary:: :toctree: generated/ is_valid_dm -- checks for a valid distance matrix is_valid_y -- checks for a valid condensed distance matrix num_obs_dm -- # of observations in a distance matrix num_obs_y -- # of observations in a condensed distance matrix Distance functions between two numeric vectors ``u`` and ``v``. Computing distances over a large collection of vectors is inefficient for these functions. Use ``pdist`` for this purpose. .. autosummary:: :toctree: generated/ braycurtis -- the Bray-Curtis distance. canberra -- the Canberra distance. chebyshev -- the Chebyshev distance. cityblock -- the Manhattan distance. correlation -- the Correlation distance. cosine -- the Cosine distance. euclidean -- the Euclidean distance. jensenshannon -- the Jensen-Shannon distance. mahalanobis -- the Mahalanobis distance. minkowski -- the Minkowski distance. seuclidean -- the normalized Euclidean distance. sqeuclidean -- the squared Euclidean distance. Distance functions between two boolean vectors (representing sets) ``u`` and ``v``. As in the case of numerical vectors, ``pdist`` is more efficient for computing the distances between all pairs. .. autosummary:: :toctree: generated/ dice -- the Dice dissimilarity. hamming -- the Hamming distance. jaccard -- the Jaccard distance. kulsinski -- the Kulsinski distance. kulczynski1 -- the Kulczynski 1 distance. rogerstanimoto -- the Rogers-Tanimoto dissimilarity. russellrao -- the Russell-Rao dissimilarity. sokalmichener -- the Sokal-Michener dissimilarity. sokalsneath -- the Sokal-Sneath dissimilarity. yule -- the Yule dissimilarity. :func:`hamming` also operates over discrete numerical vectors. """ # Copyright (C) Damian Eads, 2007-2008. New BSD License. __all__ = [ 'braycurtis', 'canberra', 'cdist', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'directed_hausdorff', 'euclidean', 'hamming', 'is_valid_dm', 'is_valid_y', 'jaccard', 'jensenshannon', 'kulsinski', 'kulczynski1', 'mahalanobis', 'matching', 'minkowski', 'num_obs_dm', 'num_obs_y', 'pdist', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'squareform', 'yule' ] import warnings import numpy as np import dataclasses from typing import List, Optional, Set, Callable from functools import partial from scipy._lib._util import _asarray_validated > from . import _distance_wrap E ImportError: /usr/lib/python3.10/site-packages/scipy/spatial/_distance_wrap.cpython-310-riscv64-linux-gnu.so: undefined symbol: npy_copysign /usr/lib/python3.10/site-packages/scipy/spatial/distance.py:120: ImportError ___ TestDegreeMixingCorrelation.test_degree_pearson_assortativity_directed2 ____ self = def test_degree_pearson_assortativity_directed2(self): """Test degree assortativity with Pearson for a directed graph where the set of in/out degree does not equal the total degree.""" > r = nx.degree_pearson_correlation_coefficient(self.D2) networkx/algorithms/assortativity/tests/test_correlation.py:47: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ networkx/algorithms/assortativity/correlation.py:153: in degree_pearson_correlation_coefficient import scipy.stats # call as sp.stats /usr/lib/python3.10/site-packages/scipy/stats/__init__.py:453: in from ._stats_py import * /usr/lib/python3.10/site-packages/scipy/stats/_stats_py.py:38: in from scipy.spatial.distance import cdist /usr/lib/python3.10/site-packages/scipy/spatial/__init__.py:108: in from ._geometric_slerp import geometric_slerp /usr/lib/python3.10/site-packages/scipy/spatial/_geometric_slerp.py:9: in from scipy.spatial.distance import euclidean _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ """ Distance computations (:mod:`scipy.spatial.distance`) ===================================================== .. sectionauthor:: Damian Eads Function reference ------------------ Distance matrix computation from a collection of raw observation vectors stored in a rectangular array. .. autosummary:: :toctree: generated/ pdist -- pairwise distances between observation vectors. cdist -- distances between two collections of observation vectors squareform -- convert distance matrix to a condensed one and vice versa directed_hausdorff -- directed Hausdorff distance between arrays Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix. .. autosummary:: :toctree: generated/ is_valid_dm -- checks for a valid distance matrix is_valid_y -- checks for a valid condensed distance matrix num_obs_dm -- # of observations in a distance matrix num_obs_y -- # of observations in a condensed distance matrix Distance functions between two numeric vectors ``u`` and ``v``. Computing distances over a large collection of vectors is inefficient for these functions. Use ``pdist`` for this purpose. .. autosummary:: :toctree: generated/ braycurtis -- the Bray-Curtis distance. canberra -- the Canberra distance. chebyshev -- the Chebyshev distance. cityblock -- the Manhattan distance. correlation -- the Correlation distance. cosine -- the Cosine distance. euclidean -- the Euclidean distance. jensenshannon -- the Jensen-Shannon distance. mahalanobis -- the Mahalanobis distance. minkowski -- the Minkowski distance. seuclidean -- the normalized Euclidean distance. sqeuclidean -- the squared Euclidean distance. Distance functions between two boolean vectors (representing sets) ``u`` and ``v``. As in the case of numerical vectors, ``pdist`` is more efficient for computing the distances between all pairs. .. autosummary:: :toctree: generated/ dice -- the Dice dissimilarity. hamming -- the Hamming distance. jaccard -- the Jaccard distance. kulsinski -- the Kulsinski distance. kulczynski1 -- the Kulczynski 1 distance. rogerstanimoto -- the Rogers-Tanimoto dissimilarity. russellrao -- the Russell-Rao dissimilarity. sokalmichener -- the Sokal-Michener dissimilarity. sokalsneath -- the Sokal-Sneath dissimilarity. yule -- the Yule dissimilarity. :func:`hamming` also operates over discrete numerical vectors. """ # Copyright (C) Damian Eads, 2007-2008. New BSD License. __all__ = [ 'braycurtis', 'canberra', 'cdist', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'directed_hausdorff', 'euclidean', 'hamming', 'is_valid_dm', 'is_valid_y', 'jaccard', 'jensenshannon', 'kulsinski', 'kulczynski1', 'mahalanobis', 'matching', 'minkowski', 'num_obs_dm', 'num_obs_y', 'pdist', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'squareform', 'yule' ] import warnings import numpy as np import dataclasses from typing import List, Optional, Set, Callable from functools import partial from scipy._lib._util import _asarray_validated > from . import _distance_wrap E ImportError: /usr/lib/python3.10/site-packages/scipy/spatial/_distance_wrap.cpython-310-riscv64-linux-gnu.so: undefined symbol: npy_copysign /usr/lib/python3.10/site-packages/scipy/spatial/distance.py:120: ImportError ___ TestDegreeMixingCorrelation.test_degree_pearson_assortativity_multigraph ___ self = def test_degree_pearson_assortativity_multigraph(self): > r = nx.degree_pearson_correlation_coefficient(self.M) networkx/algorithms/assortativity/tests/test_correlation.py:51: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ networkx/algorithms/assortativity/correlation.py:153: in degree_pearson_correlation_coefficient import scipy.stats # call as sp.stats /usr/lib/python3.10/site-packages/scipy/stats/__init__.py:453: in from ._stats_py import * /usr/lib/python3.10/site-packages/scipy/stats/_stats_py.py:38: in from scipy.spatial.distance import cdist /usr/lib/python3.10/site-packages/scipy/spatial/__init__.py:108: in from ._geometric_slerp import geometric_slerp /usr/lib/python3.10/site-packages/scipy/spatial/_geometric_slerp.py:9: in from scipy.spatial.distance import euclidean _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ """ Distance computations (:mod:`scipy.spatial.distance`) ===================================================== .. sectionauthor:: Damian Eads Function reference ------------------ Distance matrix computation from a collection of raw observation vectors stored in a rectangular array. .. autosummary:: :toctree: generated/ pdist -- pairwise distances between observation vectors. cdist -- distances between two collections of observation vectors squareform -- convert distance matrix to a condensed one and vice versa directed_hausdorff -- directed Hausdorff distance between arrays Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix. .. autosummary:: :toctree: generated/ is_valid_dm -- checks for a valid distance matrix is_valid_y -- checks for a valid condensed distance matrix num_obs_dm -- # of observations in a distance matrix num_obs_y -- # of observations in a condensed distance matrix Distance functions between two numeric vectors ``u`` and ``v``. Computing distances over a large collection of vectors is inefficient for these functions. Use ``pdist`` for this purpose. .. autosummary:: :toctree: generated/ braycurtis -- the Bray-Curtis distance. canberra -- the Canberra distance. chebyshev -- the Chebyshev distance. cityblock -- the Manhattan distance. correlation -- the Correlation distance. cosine -- the Cosine distance. euclidean -- the Euclidean distance. jensenshannon -- the Jensen-Shannon distance. mahalanobis -- the Mahalanobis distance. minkowski -- the Minkowski distance. seuclidean -- the normalized Euclidean distance. sqeuclidean -- the squared Euclidean distance. Distance functions between two boolean vectors (representing sets) ``u`` and ``v``. As in the case of numerical vectors, ``pdist`` is more efficient for computing the distances between all pairs. .. autosummary:: :toctree: generated/ dice -- the Dice dissimilarity. hamming -- the Hamming distance. jaccard -- the Jaccard distance. kulsinski -- the Kulsinski distance. kulczynski1 -- the Kulczynski 1 distance. rogerstanimoto -- the Rogers-Tanimoto dissimilarity. russellrao -- the Russell-Rao dissimilarity. sokalmichener -- the Sokal-Michener dissimilarity. sokalsneath -- the Sokal-Sneath dissimilarity. yule -- the Yule dissimilarity. :func:`hamming` also operates over discrete numerical vectors. """ # Copyright (C) Damian Eads, 2007-2008. New BSD License. __all__ = [ 'braycurtis', 'canberra', 'cdist', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'directed_hausdorff', 'euclidean', 'hamming', 'is_valid_dm', 'is_valid_y', 'jaccard', 'jensenshannon', 'kulsinski', 'kulczynski1', 'mahalanobis', 'matching', 'minkowski', 'num_obs_dm', 'num_obs_y', 'pdist', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'squareform', 'yule' ] import warnings import numpy as np import dataclasses from typing import List, Optional, Set, Callable from functools import partial from scipy._lib._util import _asarray_validated > from . import _distance_wrap E ImportError: /usr/lib/python3.10/site-packages/scipy/spatial/_distance_wrap.cpython-310-riscv64-linux-gnu.so: undefined symbol: npy_copysign /usr/lib/python3.10/site-packages/scipy/spatial/distance.py:120: ImportError _ TestMinimumWeightFullMatching.test_minimum_weight_full_matching_incomplete_graph _ self = def test_minimum_weight_full_matching_incomplete_graph(self): B = nx.Graph() B.add_nodes_from([1, 2], bipartite=0) B.add_nodes_from([3, 4], bipartite=1) B.add_edge(1, 4, weight=100) B.add_edge(2, 3, weight=100) B.add_edge(2, 4, weight=50) > matching = minimum_weight_full_matching(B) networkx/algorithms/bipartite/tests/test_matching.py:229: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ networkx/algorithms/bipartite/matching.py:561: in minimum_weight_full_matching import scipy.optimize # call as sp.optimize /usr/lib/python3.10/site-packages/scipy/optimize/__init__.py:420: in from ._shgo import shgo /usr/lib/python3.10/site-packages/scipy/optimize/_shgo.py:9: in from scipy import spatial /usr/lib/python3.10/site-packages/scipy/spatial/__init__.py:108: in from ._geometric_slerp import geometric_slerp /usr/lib/python3.10/site-packages/scipy/spatial/_geometric_slerp.py:9: in from scipy.spatial.distance import euclidean _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ """ Distance computations (:mod:`scipy.spatial.distance`) ===================================================== .. sectionauthor:: Damian Eads Function reference ------------------ Distance matrix computation from a collection of raw observation vectors stored in a rectangular array. .. autosummary:: :toctree: generated/ pdist -- pairwise distances between observation vectors. cdist -- distances between two collections of observation vectors squareform -- convert distance matrix to a condensed one and vice versa directed_hausdorff -- directed Hausdorff distance between arrays Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix. .. autosummary:: :toctree: generated/ is_valid_dm -- checks for a valid distance matrix is_valid_y -- checks for a valid condensed distance matrix num_obs_dm -- # of observations in a distance matrix num_obs_y -- # of observations in a condensed distance matrix Distance functions between two numeric vectors ``u`` and ``v``. Computing distances over a large collection of vectors is inefficient for these functions. Use ``pdist`` for this purpose. .. autosummary:: :toctree: generated/ braycurtis -- the Bray-Curtis distance. canberra -- the Canberra distance. chebyshev -- the Chebyshev distance. cityblock -- the Manhattan distance. correlation -- the Correlation distance. cosine -- the Cosine distance. euclidean -- the Euclidean distance. jensenshannon -- the Jensen-Shannon distance. mahalanobis -- the Mahalanobis distance. minkowski -- the Minkowski distance. seuclidean -- the normalized Euclidean distance. sqeuclidean -- the squared Euclidean distance. Distance functions between two boolean vectors (representing sets) ``u`` and ``v``. As in the case of numerical vectors, ``pdist`` is more efficient for computing the distances between all pairs. .. autosummary:: :toctree: generated/ dice -- the Dice dissimilarity. hamming -- the Hamming distance. jaccard -- the Jaccard distance. kulsinski -- the Kulsinski distance. kulczynski1 -- the Kulczynski 1 distance. rogerstanimoto -- the Rogers-Tanimoto dissimilarity. russellrao -- the Russell-Rao dissimilarity. sokalmichener -- the Sokal-Michener dissimilarity. sokalsneath -- the Sokal-Sneath dissimilarity. yule -- the Yule dissimilarity. :func:`hamming` also operates over discrete numerical vectors. """ # Copyright (C) Damian Eads, 2007-2008. New BSD License. __all__ = [ 'braycurtis', 'canberra', 'cdist', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'directed_hausdorff', 'euclidean', 'hamming', 'is_valid_dm', 'is_valid_y', 'jaccard', 'jensenshannon', 'kulsinski', 'kulczynski1', 'mahalanobis', 'matching', 'minkowski', 'num_obs_dm', 'num_obs_y', 'pdist', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'squareform', 'yule' ] import warnings import numpy as np import dataclasses from typing import List, Optional, Set, Callable from functools import partial from scipy._lib._util import _asarray_validated > from . import _distance_wrap E ImportError: /usr/lib/python3.10/site-packages/scipy/spatial/_distance_wrap.cpython-310-riscv64-linux-gnu.so: undefined symbol: npy_copysign /usr/lib/python3.10/site-packages/scipy/spatial/distance.py:120: ImportError _ TestMinimumWeightFullMatching.test_minimum_weight_full_matching_with_no_full_matching _ self = def test_minimum_weight_full_matching_with_no_full_matching(self): B = nx.Graph() B.add_nodes_from([1, 2, 3], bipartite=0) B.add_nodes_from([4, 5, 6], bipartite=1) B.add_edge(1, 4, weight=100) B.add_edge(2, 4, weight=100) B.add_edge(3, 4, weight=50) B.add_edge(3, 5, weight=50) B.add_edge(3, 6, weight=50) with pytest.raises(ValueError): > minimum_weight_full_matching(B) networkx/algorithms/bipartite/tests/test_matching.py:242: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ networkx/algorithms/bipartite/matching.py:561: in minimum_weight_full_matching import scipy.optimize # call as sp.optimize /usr/lib/python3.10/site-packages/scipy/optimize/__init__.py:420: in from ._shgo import shgo /usr/lib/python3.10/site-packages/scipy/optimize/_shgo.py:9: in from scipy import spatial /usr/lib/python3.10/site-packages/scipy/spatial/__init__.py:108: in from ._geometric_slerp import geometric_slerp /usr/lib/python3.10/site-packages/scipy/spatial/_geometric_slerp.py:9: in from scipy.spatial.distance import euclidean _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ """ Distance computations (:mod:`scipy.spatial.distance`) ===================================================== .. sectionauthor:: Damian Eads Function reference ------------------ Distance matrix computation from a collection of raw observation vectors stored in a rectangular array. .. autosummary:: :toctree: generated/ pdist -- pairwise distances between observation vectors. cdist -- distances between two collections of observation vectors squareform -- convert distance matrix to a condensed one and vice versa directed_hausdorff -- directed Hausdorff distance between arrays Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix. .. autosummary:: :toctree: generated/ is_valid_dm -- checks for a valid distance matrix is_valid_y -- checks for a valid condensed distance matrix num_obs_dm -- # of observations in a distance matrix num_obs_y -- # of observations in a condensed distance matrix Distance functions between two numeric vectors ``u`` and ``v``. Computing distances over a large collection of vectors is inefficient for these functions. Use ``pdist`` for this purpose. .. autosummary:: :toctree: generated/ braycurtis -- the Bray-Curtis distance. canberra -- the Canberra distance. chebyshev -- the Chebyshev distance. cityblock -- the Manhattan distance. correlation -- the Correlation distance. cosine -- the Cosine distance. euclidean -- the Euclidean distance. jensenshannon -- the Jensen-Shannon distance. mahalanobis -- the Mahalanobis distance. minkowski -- the Minkowski distance. seuclidean -- the normalized Euclidean distance. sqeuclidean -- the squared Euclidean distance. Distance functions between two boolean vectors (representing sets) ``u`` and ``v``. As in the case of numerical vectors, ``pdist`` is more efficient for computing the distances between all pairs. .. autosummary:: :toctree: generated/ dice -- the Dice dissimilarity. hamming -- the Hamming distance. jaccard -- the Jaccard distance. kulsinski -- the Kulsinski distance. kulczynski1 -- the Kulczynski 1 distance. rogerstanimoto -- the Rogers-Tanimoto dissimilarity. russellrao -- the Russell-Rao dissimilarity. sokalmichener -- the Sokal-Michener dissimilarity. sokalsneath -- the Sokal-Sneath dissimilarity. yule -- the Yule dissimilarity. :func:`hamming` also operates over discrete numerical vectors. """ # Copyright (C) Damian Eads, 2007-2008. New BSD License. __all__ = [ 'braycurtis', 'canberra', 'cdist', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'directed_hausdorff', 'euclidean', 'hamming', 'is_valid_dm', 'is_valid_y', 'jaccard', 'jensenshannon', 'kulsinski', 'kulczynski1', 'mahalanobis', 'matching', 'minkowski', 'num_obs_dm', 'num_obs_y', 'pdist', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'squareform', 'yule' ] import warnings import numpy as np import dataclasses from typing import List, Optional, Set, Callable from functools import partial from scipy._lib._util import _asarray_validated > from . import _distance_wrap E ImportError: /usr/lib/python3.10/site-packages/scipy/spatial/_distance_wrap.cpython-310-riscv64-linux-gnu.so: undefined symbol: npy_copysign /usr/lib/python3.10/site-packages/scipy/spatial/distance.py:120: ImportError ____ TestMinimumWeightFullMatching.test_minimum_weight_full_matching_square ____ self = def test_minimum_weight_full_matching_square(self): G = nx.complete_bipartite_graph(3, 3) G.add_edge(0, 3, weight=400) G.add_edge(0, 4, weight=150) G.add_edge(0, 5, weight=400) G.add_edge(1, 3, weight=400) G.add_edge(1, 4, weight=450) G.add_edge(1, 5, weight=600) G.add_edge(2, 3, weight=300) G.add_edge(2, 4, weight=225) G.add_edge(2, 5, weight=300) > matching = minimum_weight_full_matching(G) networkx/algorithms/bipartite/tests/test_matching.py:255: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ networkx/algorithms/bipartite/matching.py:561: in minimum_weight_full_matching import scipy.optimize # call as sp.optimize /usr/lib/python3.10/site-packages/scipy/optimize/__init__.py:420: in from ._shgo import shgo /usr/lib/python3.10/site-packages/scipy/optimize/_shgo.py:9: in from scipy import spatial /usr/lib/python3.10/site-packages/scipy/spatial/__init__.py:108: in from ._geometric_slerp import geometric_slerp /usr/lib/python3.10/site-packages/scipy/spatial/_geometric_slerp.py:9: in from scipy.spatial.distance import euclidean _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ """ Distance computations (:mod:`scipy.spatial.distance`) ===================================================== .. sectionauthor:: Damian Eads Function reference ------------------ Distance matrix computation from a collection of raw observation vectors stored in a rectangular array. .. autosummary:: :toctree: generated/ pdist -- pairwise distances between observation vectors. cdist -- distances between two collections of observation vectors squareform -- convert distance matrix to a condensed one and vice versa directed_hausdorff -- directed Hausdorff distance between arrays Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix. .. autosummary:: :toctree: generated/ is_valid_dm -- checks for a valid distance matrix is_valid_y -- checks for a valid condensed distance matrix num_obs_dm -- # of observations in a distance matrix num_obs_y -- # of observations in a condensed distance matrix Distance functions between two numeric vectors ``u`` and ``v``. Computing distances over a large collection of vectors is inefficient for these functions. Use ``pdist`` for this purpose. .. autosummary:: :toctree: generated/ braycurtis -- the Bray-Curtis distance. canberra -- the Canberra distance. chebyshev -- the Chebyshev distance. cityblock -- the Manhattan distance. correlation -- the Correlation distance. cosine -- the Cosine distance. euclidean -- the Euclidean distance. jensenshannon -- the Jensen-Shannon distance. mahalanobis -- the Mahalanobis distance. minkowski -- the Minkowski distance. seuclidean -- the normalized Euclidean distance. sqeuclidean -- the squared Euclidean distance. Distance functions between two boolean vectors (representing sets) ``u`` and ``v``. As in the case of numerical vectors, ``pdist`` is more efficient for computing the distances between all pairs. .. autosummary:: :toctree: generated/ dice -- the Dice dissimilarity. hamming -- the Hamming distance. jaccard -- the Jaccard distance. kulsinski -- the Kulsinski distance. kulczynski1 -- the Kulczynski 1 distance. rogerstanimoto -- the Rogers-Tanimoto dissimilarity. russellrao -- the Russell-Rao dissimilarity. sokalmichener -- the Sokal-Michener dissimilarity. sokalsneath -- the Sokal-Sneath dissimilarity. yule -- the Yule dissimilarity. :func:`hamming` also operates over discrete numerical vectors. """ # Copyright (C) Damian Eads, 2007-2008. New BSD License. __all__ = [ 'braycurtis', 'canberra', 'cdist', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'directed_hausdorff', 'euclidean', 'hamming', 'is_valid_dm', 'is_valid_y', 'jaccard', 'jensenshannon', 'kulsinski', 'kulczynski1', 'mahalanobis', 'matching', 'minkowski', 'num_obs_dm', 'num_obs_y', 'pdist', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'squareform', 'yule' ] import warnings import numpy as np import dataclasses from typing import List, Optional, Set, Callable from functools import partial from scipy._lib._util import _asarray_validated > from . import _distance_wrap E ImportError: /usr/lib/python3.10/site-packages/scipy/spatial/_distance_wrap.cpython-310-riscv64-linux-gnu.so: undefined symbol: npy_copysign /usr/lib/python3.10/site-packages/scipy/spatial/distance.py:120: ImportError _ TestMinimumWeightFullMatching.test_minimum_weight_full_matching_smaller_left _ self = def test_minimum_weight_full_matching_smaller_left(self): G = nx.complete_bipartite_graph(3, 4) G.add_edge(0, 3, weight=400) G.add_edge(0, 4, weight=150) G.add_edge(0, 5, weight=400) G.add_edge(0, 6, weight=1) G.add_edge(1, 3, weight=400) G.add_edge(1, 4, weight=450) G.add_edge(1, 5, weight=600) G.add_edge(1, 6, weight=2) G.add_edge(2, 3, weight=300) G.add_edge(2, 4, weight=225) G.add_edge(2, 5, weight=290) G.add_edge(2, 6, weight=3) > matching = minimum_weight_full_matching(G) networkx/algorithms/bipartite/tests/test_matching.py:272: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ networkx/algorithms/bipartite/matching.py:561: in minimum_weight_full_matching import scipy.optimize # call as sp.optimize /usr/lib/python3.10/site-packages/scipy/optimize/__init__.py:420: in from ._shgo import shgo /usr/lib/python3.10/site-packages/scipy/optimize/_shgo.py:9: in from scipy import spatial /usr/lib/python3.10/site-packages/scipy/spatial/__init__.py:108: in from ._geometric_slerp import geometric_slerp /usr/lib/python3.10/site-packages/scipy/spatial/_geometric_slerp.py:9: in from scipy.spatial.distance import euclidean _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ """ Distance computations (:mod:`scipy.spatial.distance`) ===================================================== .. sectionauthor:: Damian Eads Function reference ------------------ Distance matrix computation from a collection of raw observation vectors stored in a rectangular array. .. autosummary:: :toctree: generated/ pdist -- pairwise distances between observation vectors. cdist -- distances between two collections of observation vectors squareform -- convert distance matrix to a condensed one and vice versa directed_hausdorff -- directed Hausdorff distance between arrays Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix. .. autosummary:: :toctree: generated/ is_valid_dm -- checks for a valid distance matrix is_valid_y -- checks for a valid condensed distance matrix num_obs_dm -- # of observations in a distance matrix num_obs_y -- # of observations in a condensed distance matrix Distance functions between two numeric vectors ``u`` and ``v``. Computing distances over a large collection of vectors is inefficient for these functions. Use ``pdist`` for this purpose. .. autosummary:: :toctree: generated/ braycurtis -- the Bray-Curtis distance. canberra -- the Canberra distance. chebyshev -- the Chebyshev distance. cityblock -- the Manhattan distance. correlation -- the Correlation distance. cosine -- the Cosine distance. euclidean -- the Euclidean distance. jensenshannon -- the Jensen-Shannon distance. mahalanobis -- the Mahalanobis distance. minkowski -- the Minkowski distance. seuclidean -- the normalized Euclidean distance. sqeuclidean -- the squared Euclidean distance. Distance functions between two boolean vectors (representing sets) ``u`` and ``v``. As in the case of numerical vectors, ``pdist`` is more efficient for computing the distances between all pairs. .. autosummary:: :toctree: generated/ dice -- the Dice dissimilarity. hamming -- the Hamming distance. jaccard -- the Jaccard distance. kulsinski -- the Kulsinski distance. kulczynski1 -- the Kulczynski 1 distance. rogerstanimoto -- the Rogers-Tanimoto dissimilarity. russellrao -- the Russell-Rao dissimilarity. sokalmichener -- the Sokal-Michener dissimilarity. sokalsneath -- the Sokal-Sneath dissimilarity. yule -- the Yule dissimilarity. :func:`hamming` also operates over discrete numerical vectors. """ # Copyright (C) Damian Eads, 2007-2008. New BSD License. __all__ = [ 'braycurtis', 'canberra', 'cdist', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'directed_hausdorff', 'euclidean', 'hamming', 'is_valid_dm', 'is_valid_y', 'jaccard', 'jensenshannon', 'kulsinski', 'kulczynski1', 'mahalanobis', 'matching', 'minkowski', 'num_obs_dm', 'num_obs_y', 'pdist', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'squareform', 'yule' ] import warnings import numpy as np import dataclasses from typing import List, Optional, Set, Callable from functools import partial from scipy._lib._util import _asarray_validated > from . import _distance_wrap E ImportError: /usr/lib/python3.10/site-packages/scipy/spatial/_distance_wrap.cpython-310-riscv64-linux-gnu.so: undefined symbol: npy_copysign /usr/lib/python3.10/site-packages/scipy/spatial/distance.py:120: ImportError _ TestMinimumWeightFullMatching.test_minimum_weight_full_matching_smaller_top_nodes_right _ self = def test_minimum_weight_full_matching_smaller_top_nodes_right(self): G = nx.complete_bipartite_graph(3, 4) G.add_edge(0, 3, weight=400) G.add_edge(0, 4, weight=150) G.add_edge(0, 5, weight=400) G.add_edge(0, 6, weight=1) G.add_edge(1, 3, weight=400) G.add_edge(1, 4, weight=450) G.add_edge(1, 5, weight=600) G.add_edge(1, 6, weight=2) G.add_edge(2, 3, weight=300) G.add_edge(2, 4, weight=225) G.add_edge(2, 5, weight=290) G.add_edge(2, 6, weight=3) > matching = minimum_weight_full_matching(G, top_nodes=[3, 4, 5, 6]) networkx/algorithms/bipartite/tests/test_matching.py:289: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ networkx/algorithms/bipartite/matching.py:561: in minimum_weight_full_matching import scipy.optimize # call as sp.optimize /usr/lib/python3.10/site-packages/scipy/optimize/__init__.py:420: in from ._shgo import shgo /usr/lib/python3.10/site-packages/scipy/optimize/_shgo.py:9: in from scipy import spatial /usr/lib/python3.10/site-packages/scipy/spatial/__init__.py:108: in from ._geometric_slerp import geometric_slerp /usr/lib/python3.10/site-packages/scipy/spatial/_geometric_slerp.py:9: in from scipy.spatial.distance import euclidean _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ """ Distance computations (:mod:`scipy.spatial.distance`) ===================================================== .. sectionauthor:: Damian Eads Function reference ------------------ Distance matrix computation from a collection of raw observation vectors stored in a rectangular array. .. autosummary:: :toctree: generated/ pdist -- pairwise distances between observation vectors. cdist -- distances between two collections of observation vectors squareform -- convert distance matrix to a condensed one and vice versa directed_hausdorff -- directed Hausdorff distance between arrays Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix. .. autosummary:: :toctree: generated/ is_valid_dm -- checks for a valid distance matrix is_valid_y -- checks for a valid condensed distance matrix num_obs_dm -- # of observations in a distance matrix num_obs_y -- # of observations in a condensed distance matrix Distance functions between two numeric vectors ``u`` and ``v``. Computing distances over a large collection of vectors is inefficient for these functions. Use ``pdist`` for this purpose. .. autosummary:: :toctree: generated/ braycurtis -- the Bray-Curtis distance. canberra -- the Canberra distance. chebyshev -- the Chebyshev distance. cityblock -- the Manhattan distance. correlation -- the Correlation distance. cosine -- the Cosine distance. euclidean -- the Euclidean distance. jensenshannon -- the Jensen-Shannon distance. mahalanobis -- the Mahalanobis distance. minkowski -- the Minkowski distance. seuclidean -- the normalized Euclidean distance. sqeuclidean -- the squared Euclidean distance. Distance functions between two boolean vectors (representing sets) ``u`` and ``v``. As in the case of numerical vectors, ``pdist`` is more efficient for computing the distances between all pairs. .. autosummary:: :toctree: generated/ dice -- the Dice dissimilarity. hamming -- the Hamming distance. jaccard -- the Jaccard distance. kulsinski -- the Kulsinski distance. kulczynski1 -- the Kulczynski 1 distance. rogerstanimoto -- the Rogers-Tanimoto dissimilarity. russellrao -- the Russell-Rao dissimilarity. sokalmichener -- the Sokal-Michener dissimilarity. sokalsneath -- the Sokal-Sneath dissimilarity. yule -- the Yule dissimilarity. :func:`hamming` also operates over discrete numerical vectors. """ # Copyright (C) Damian Eads, 2007-2008. New BSD License. __all__ = [ 'braycurtis', 'canberra', 'cdist', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'directed_hausdorff', 'euclidean', 'hamming', 'is_valid_dm', 'is_valid_y', 'jaccard', 'jensenshannon', 'kulsinski', 'kulczynski1', 'mahalanobis', 'matching', 'minkowski', 'num_obs_dm', 'num_obs_y', 'pdist', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'squareform', 'yule' ] import warnings import numpy as np import dataclasses from typing import List, Optional, Set, Callable from functools import partial from scipy._lib._util import _asarray_validated > from . import _distance_wrap E ImportError: /usr/lib/python3.10/site-packages/scipy/spatial/_distance_wrap.cpython-310-riscv64-linux-gnu.so: undefined symbol: npy_copysign /usr/lib/python3.10/site-packages/scipy/spatial/distance.py:120: ImportError _ TestMinimumWeightFullMatching.test_minimum_weight_full_matching_smaller_right _ self = def test_minimum_weight_full_matching_smaller_right(self): G = nx.complete_bipartite_graph(4, 3) G.add_edge(0, 4, weight=400) G.add_edge(0, 5, weight=400) G.add_edge(0, 6, weight=300) G.add_edge(1, 4, weight=150) G.add_edge(1, 5, weight=450) G.add_edge(1, 6, weight=225) G.add_edge(2, 4, weight=400) G.add_edge(2, 5, weight=600) G.add_edge(2, 6, weight=290) G.add_edge(3, 4, weight=1) G.add_edge(3, 5, weight=2) G.add_edge(3, 6, weight=3) > matching = minimum_weight_full_matching(G) networkx/algorithms/bipartite/tests/test_matching.py:306: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ networkx/algorithms/bipartite/matching.py:561: in minimum_weight_full_matching import scipy.optimize # call as sp.optimize /usr/lib/python3.10/site-packages/scipy/optimize/__init__.py:420: in from ._shgo import shgo /usr/lib/python3.10/site-packages/scipy/optimize/_shgo.py:9: in from scipy import spatial /usr/lib/python3.10/site-packages/scipy/spatial/__init__.py:108: in from ._geometric_slerp import geometric_slerp /usr/lib/python3.10/site-packages/scipy/spatial/_geometric_slerp.py:9: in from scipy.spatial.distance import euclidean _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ """ Distance computations (:mod:`scipy.spatial.distance`) ===================================================== .. sectionauthor:: Damian Eads Function reference ------------------ Distance matrix computation from a collection of raw observation vectors stored in a rectangular array. .. autosummary:: :toctree: generated/ pdist -- pairwise distances between observation vectors. cdist -- distances between two collections of observation vectors squareform -- convert distance matrix to a condensed one and vice versa directed_hausdorff -- directed Hausdorff distance between arrays Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix. .. autosummary:: :toctree: generated/ is_valid_dm -- checks for a valid distance matrix is_valid_y -- checks for a valid condensed distance matrix num_obs_dm -- # of observations in a distance matrix num_obs_y -- # of observations in a condensed distance matrix Distance functions between two numeric vectors ``u`` and ``v``. Computing distances over a large collection of vectors is inefficient for these functions. Use ``pdist`` for this purpose. .. autosummary:: :toctree: generated/ braycurtis -- the Bray-Curtis distance. canberra -- the Canberra distance. chebyshev -- the Chebyshev distance. cityblock -- the Manhattan distance. correlation -- the Correlation distance. cosine -- the Cosine distance. euclidean -- the Euclidean distance. jensenshannon -- the Jensen-Shannon distance. mahalanobis -- the Mahalanobis distance. minkowski -- the Minkowski distance. seuclidean -- the normalized Euclidean distance. sqeuclidean -- the squared Euclidean distance. Distance functions between two boolean vectors (representing sets) ``u`` and ``v``. As in the case of numerical vectors, ``pdist`` is more efficient for computing the distances between all pairs. .. autosummary:: :toctree: generated/ dice -- the Dice dissimilarity. hamming -- the Hamming distance. jaccard -- the Jaccard distance. kulsinski -- the Kulsinski distance. kulczynski1 -- the Kulczynski 1 distance. rogerstanimoto -- the Rogers-Tanimoto dissimilarity. russellrao -- the Russell-Rao dissimilarity. sokalmichener -- the Sokal-Michener dissimilarity. sokalsneath -- the Sokal-Sneath dissimilarity. yule -- the Yule dissimilarity. :func:`hamming` also operates over discrete numerical vectors. """ # Copyright (C) Damian Eads, 2007-2008. New BSD License. __all__ = [ 'braycurtis', 'canberra', 'cdist', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'directed_hausdorff', 'euclidean', 'hamming', 'is_valid_dm', 'is_valid_y', 'jaccard', 'jensenshannon', 'kulsinski', 'kulczynski1', 'mahalanobis', 'matching', 'minkowski', 'num_obs_dm', 'num_obs_y', 'pdist', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'squareform', 'yule' ] import warnings import numpy as np import dataclasses from typing import List, Optional, Set, Callable from functools import partial from scipy._lib._util import _asarray_validated > from . import _distance_wrap E ImportError: /usr/lib/python3.10/site-packages/scipy/spatial/_distance_wrap.cpython-310-riscv64-linux-gnu.so: undefined symbol: npy_copysign /usr/lib/python3.10/site-packages/scipy/spatial/distance.py:120: ImportError _ TestMinimumWeightFullMatching.test_minimum_weight_full_matching_negative_weights _ self = def test_minimum_weight_full_matching_negative_weights(self): G = nx.complete_bipartite_graph(2, 2) G.add_edge(0, 2, weight=-2) G.add_edge(0, 3, weight=0.2) G.add_edge(1, 2, weight=-2) G.add_edge(1, 3, weight=0.3) > matching = minimum_weight_full_matching(G) networkx/algorithms/bipartite/tests/test_matching.py:315: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ networkx/algorithms/bipartite/matching.py:561: in minimum_weight_full_matching import scipy.optimize # call as sp.optimize /usr/lib/python3.10/site-packages/scipy/optimize/__init__.py:420: in from ._shgo import shgo /usr/lib/python3.10/site-packages/scipy/optimize/_shgo.py:9: in from scipy import spatial /usr/lib/python3.10/site-packages/scipy/spatial/__init__.py:108: in from ._geometric_slerp import geometric_slerp /usr/lib/python3.10/site-packages/scipy/spatial/_geometric_slerp.py:9: in from scipy.spatial.distance import euclidean _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ """ Distance computations (:mod:`scipy.spatial.distance`) ===================================================== .. sectionauthor:: Damian Eads Function reference ------------------ Distance matrix computation from a collection of raw observation vectors stored in a rectangular array. .. autosummary:: :toctree: generated/ pdist -- pairwise distances between observation vectors. cdist -- distances between two collections of observation vectors squareform -- convert distance matrix to a condensed one and vice versa directed_hausdorff -- directed Hausdorff distance between arrays Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix. .. autosummary:: :toctree: generated/ is_valid_dm -- checks for a valid distance matrix is_valid_y -- checks for a valid condensed distance matrix num_obs_dm -- # of observations in a distance matrix num_obs_y -- # of observations in a condensed distance matrix Distance functions between two numeric vectors ``u`` and ``v``. Computing distances over a large collection of vectors is inefficient for these functions. Use ``pdist`` for this purpose. .. autosummary:: :toctree: generated/ braycurtis -- the Bray-Curtis distance. canberra -- the Canberra distance. chebyshev -- the Chebyshev distance. cityblock -- the Manhattan distance. correlation -- the Correlation distance. cosine -- the Cosine distance. euclidean -- the Euclidean distance. jensenshannon -- the Jensen-Shannon distance. mahalanobis -- the Mahalanobis distance. minkowski -- the Minkowski distance. seuclidean -- the normalized Euclidean distance. sqeuclidean -- the squared Euclidean distance. Distance functions between two boolean vectors (representing sets) ``u`` and ``v``. As in the case of numerical vectors, ``pdist`` is more efficient for computing the distances between all pairs. .. autosummary:: :toctree: generated/ dice -- the Dice dissimilarity. hamming -- the Hamming distance. jaccard -- the Jaccard distance. kulsinski -- the Kulsinski distance. kulczynski1 -- the Kulczynski 1 distance. rogerstanimoto -- the Rogers-Tanimoto dissimilarity. russellrao -- the Russell-Rao dissimilarity. sokalmichener -- the Sokal-Michener dissimilarity. sokalsneath -- the Sokal-Sneath dissimilarity. yule -- the Yule dissimilarity. :func:`hamming` also operates over discrete numerical vectors. """ # Copyright (C) Damian Eads, 2007-2008. New BSD License. __all__ = [ 'braycurtis', 'canberra', 'cdist', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'directed_hausdorff', 'euclidean', 'hamming', 'is_valid_dm', 'is_valid_y', 'jaccard', 'jensenshannon', 'kulsinski', 'kulczynski1', 'mahalanobis', 'matching', 'minkowski', 'num_obs_dm', 'num_obs_y', 'pdist', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'squareform', 'yule' ] import warnings import numpy as np import dataclasses from typing import List, Optional, Set, Callable from functools import partial from scipy._lib._util import _asarray_validated > from . import _distance_wrap E ImportError: /usr/lib/python3.10/site-packages/scipy/spatial/_distance_wrap.cpython-310-riscv64-linux-gnu.so: undefined symbol: npy_copysign /usr/lib/python3.10/site-packages/scipy/spatial/distance.py:120: ImportError _ TestMinimumWeightFullMatching.test_minimum_weight_full_matching_different_weight_key _ self = def test_minimum_weight_full_matching_different_weight_key(self): G = nx.complete_bipartite_graph(2, 2) G.add_edge(0, 2, mass=2) G.add_edge(0, 3, mass=0.2) G.add_edge(1, 2, mass=1) G.add_edge(1, 3, mass=2) > matching = minimum_weight_full_matching(G, weight="mass") networkx/algorithms/bipartite/tests/test_matching.py:324: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ networkx/algorithms/bipartite/matching.py:561: in minimum_weight_full_matching import scipy.optimize # call as sp.optimize /usr/lib/python3.10/site-packages/scipy/optimize/__init__.py:420: in from ._shgo import shgo /usr/lib/python3.10/site-packages/scipy/optimize/_shgo.py:9: in from scipy import spatial /usr/lib/python3.10/site-packages/scipy/spatial/__init__.py:108: in from ._geometric_slerp import geometric_slerp /usr/lib/python3.10/site-packages/scipy/spatial/_geometric_slerp.py:9: in from scipy.spatial.distance import euclidean _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ """ Distance computations (:mod:`scipy.spatial.distance`) ===================================================== .. sectionauthor:: Damian Eads Function reference ------------------ Distance matrix computation from a collection of raw observation vectors stored in a rectangular array. .. autosummary:: :toctree: generated/ pdist -- pairwise distances between observation vectors. cdist -- distances between two collections of observation vectors squareform -- convert distance matrix to a condensed one and vice versa directed_hausdorff -- directed Hausdorff distance between arrays Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix. .. autosummary:: :toctree: generated/ is_valid_dm -- checks for a valid distance matrix is_valid_y -- checks for a valid condensed distance matrix num_obs_dm -- # of observations in a distance matrix num_obs_y -- # of observations in a condensed distance matrix Distance functions between two numeric vectors ``u`` and ``v``. Computing distances over a large collection of vectors is inefficient for these functions. Use ``pdist`` for this purpose. .. autosummary:: :toctree: generated/ braycurtis -- the Bray-Curtis distance. canberra -- the Canberra distance. chebyshev -- the Chebyshev distance. cityblock -- the Manhattan distance. correlation -- the Correlation distance. cosine -- the Cosine distance. euclidean -- the Euclidean distance. jensenshannon -- the Jensen-Shannon distance. mahalanobis -- the Mahalanobis distance. minkowski -- the Minkowski distance. seuclidean -- the normalized Euclidean distance. sqeuclidean -- the squared Euclidean distance. Distance functions between two boolean vectors (representing sets) ``u`` and ``v``. As in the case of numerical vectors, ``pdist`` is more efficient for computing the distances between all pairs. .. autosummary:: :toctree: generated/ dice -- the Dice dissimilarity. hamming -- the Hamming distance. jaccard -- the Jaccard distance. kulsinski -- the Kulsinski distance. kulczynski1 -- the Kulczynski 1 distance. rogerstanimoto -- the Rogers-Tanimoto dissimilarity. russellrao -- the Russell-Rao dissimilarity. sokalmichener -- the Sokal-Michener dissimilarity. sokalsneath -- the Sokal-Sneath dissimilarity. yule -- the Yule dissimilarity. :func:`hamming` also operates over discrete numerical vectors. """ # Copyright (C) Damian Eads, 2007-2008. New BSD License. __all__ = [ 'braycurtis', 'canberra', 'cdist', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'directed_hausdorff', 'euclidean', 'hamming', 'is_valid_dm', 'is_valid_y', 'jaccard', 'jensenshannon', 'kulsinski', 'kulczynski1', 'mahalanobis', 'matching', 'minkowski', 'num_obs_dm', 'num_obs_y', 'pdist', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'squareform', 'yule' ] import warnings import numpy as np import dataclasses from typing import List, Optional, Set, Callable from functools import partial from scipy._lib._util import _asarray_validated > from . import _distance_wrap E ImportError: /usr/lib/python3.10/site-packages/scipy/spatial/_distance_wrap.cpython-310-riscv64-linux-gnu.so: undefined symbol: npy_copysign /usr/lib/python3.10/site-packages/scipy/spatial/distance.py:120: ImportError __________ TestSimilarity.test_graph_edit_distance_roots_and_timeout ___________ self = def test_graph_edit_distance_roots_and_timeout(self): G0 = nx.star_graph(5) G1 = G0.copy() > pytest.raises(ValueError, graph_edit_distance, G0, G1, roots=[2]) networkx/algorithms/tests/test_similarity.py:47: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ networkx/algorithms/similarity.py:190: in graph_edit_distance for vertex_path, edge_path, cost in optimize_edit_paths( networkx/algorithms/similarity.py:673: in optimize_edit_paths import scipy.optimize # call as sp.optimize /usr/lib/python3.10/site-packages/scipy/optimize/__init__.py:420: in from ._shgo import shgo /usr/lib/python3.10/site-packages/scipy/optimize/_shgo.py:9: in from scipy import spatial /usr/lib/python3.10/site-packages/scipy/spatial/__init__.py:108: in from ._geometric_slerp import geometric_slerp /usr/lib/python3.10/site-packages/scipy/spatial/_geometric_slerp.py:9: in from scipy.spatial.distance import euclidean _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ """ Distance computations (:mod:`scipy.spatial.distance`) ===================================================== .. sectionauthor:: Damian Eads Function reference ------------------ Distance matrix computation from a collection of raw observation vectors stored in a rectangular array. .. autosummary:: :toctree: generated/ pdist -- pairwise distances between observation vectors. cdist -- distances between two collections of observation vectors squareform -- convert distance matrix to a condensed one and vice versa directed_hausdorff -- directed Hausdorff distance between arrays Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix. .. autosummary:: :toctree: generated/ is_valid_dm -- checks for a valid distance matrix is_valid_y -- checks for a valid condensed distance matrix num_obs_dm -- # of observations in a distance matrix num_obs_y -- # of observations in a condensed distance matrix Distance functions between two numeric vectors ``u`` and ``v``. Computing distances over a large collection of vectors is inefficient for these functions. Use ``pdist`` for this purpose. .. autosummary:: :toctree: generated/ braycurtis -- the Bray-Curtis distance. canberra -- the Canberra distance. chebyshev -- the Chebyshev distance. cityblock -- the Manhattan distance. correlation -- the Correlation distance. cosine -- the Cosine distance. euclidean -- the Euclidean distance. jensenshannon -- the Jensen-Shannon distance. mahalanobis -- the Mahalanobis distance. minkowski -- the Minkowski distance. seuclidean -- the normalized Euclidean distance. sqeuclidean -- the squared Euclidean distance. Distance functions between two boolean vectors (representing sets) ``u`` and ``v``. As in the case of numerical vectors, ``pdist`` is more efficient for computing the distances between all pairs. .. autosummary:: :toctree: generated/ dice -- the Dice dissimilarity. hamming -- the Hamming distance. jaccard -- the Jaccard distance. kulsinski -- the Kulsinski distance. kulczynski1 -- the Kulczynski 1 distance. rogerstanimoto -- the Rogers-Tanimoto dissimilarity. russellrao -- the Russell-Rao dissimilarity. sokalmichener -- the Sokal-Michener dissimilarity. sokalsneath -- the Sokal-Sneath dissimilarity. yule -- the Yule dissimilarity. :func:`hamming` also operates over discrete numerical vectors. """ # Copyright (C) Damian Eads, 2007-2008. New BSD License. __all__ = [ 'braycurtis', 'canberra', 'cdist', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'directed_hausdorff', 'euclidean', 'hamming', 'is_valid_dm', 'is_valid_y', 'jaccard', 'jensenshannon', 'kulsinski', 'kulczynski1', 'mahalanobis', 'matching', 'minkowski', 'num_obs_dm', 'num_obs_y', 'pdist', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'squareform', 'yule' ] import warnings import numpy as np import dataclasses from typing import List, Optional, Set, Callable from functools import partial from scipy._lib._util import _asarray_validated > from . import _distance_wrap E ImportError: /usr/lib/python3.10/site-packages/scipy/spatial/_distance_wrap.cpython-310-riscv64-linux-gnu.so: undefined symbol: npy_copysign /usr/lib/python3.10/site-packages/scipy/spatial/distance.py:120: ImportError ___________________ TestSimilarity.test_graph_edit_distance ____________________ self = def test_graph_edit_distance(self): G0 = nx.Graph() G1 = path_graph(6) G2 = cycle_graph(6) G3 = wheel_graph(7) > assert graph_edit_distance(G0, G0) == 0 networkx/algorithms/tests/test_similarity.py:66: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ networkx/algorithms/similarity.py:190: in graph_edit_distance for vertex_path, edge_path, cost in optimize_edit_paths( networkx/algorithms/similarity.py:673: in optimize_edit_paths import scipy.optimize # call as sp.optimize /usr/lib/python3.10/site-packages/scipy/optimize/__init__.py:420: in from ._shgo import shgo /usr/lib/python3.10/site-packages/scipy/optimize/_shgo.py:9: in from scipy import spatial /usr/lib/python3.10/site-packages/scipy/spatial/__init__.py:108: in from ._geometric_slerp import geometric_slerp /usr/lib/python3.10/site-packages/scipy/spatial/_geometric_slerp.py:9: in from scipy.spatial.distance import euclidean _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ """ Distance computations (:mod:`scipy.spatial.distance`) ===================================================== .. sectionauthor:: Damian Eads Function reference ------------------ Distance matrix computation from a collection of raw observation vectors stored in a rectangular array. .. autosummary:: :toctree: generated/ pdist -- pairwise distances between observation vectors. cdist -- distances between two collections of observation vectors squareform -- convert distance matrix to a condensed one and vice versa directed_hausdorff -- directed Hausdorff distance between arrays Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix. .. autosummary:: :toctree: generated/ is_valid_dm -- checks for a valid distance matrix is_valid_y -- checks for a valid condensed distance matrix num_obs_dm -- # of observations in a distance matrix num_obs_y -- # of observations in a condensed distance matrix Distance functions between two numeric vectors ``u`` and ``v``. Computing distances over a large collection of vectors is inefficient for these functions. Use ``pdist`` for this purpose. .. autosummary:: :toctree: generated/ braycurtis -- the Bray-Curtis distance. canberra -- the Canberra distance. chebyshev -- the Chebyshev distance. cityblock -- the Manhattan distance. correlation -- the Correlation distance. cosine -- the Cosine distance. euclidean -- the Euclidean distance. jensenshannon -- the Jensen-Shannon distance. mahalanobis -- the Mahalanobis distance. minkowski -- the Minkowski distance. seuclidean -- the normalized Euclidean distance. sqeuclidean -- the squared Euclidean distance. Distance functions between two boolean vectors (representing sets) ``u`` and ``v``. As in the case of numerical vectors, ``pdist`` is more efficient for computing the distances between all pairs. .. autosummary:: :toctree: generated/ dice -- the Dice dissimilarity. hamming -- the Hamming distance. jaccard -- the Jaccard distance. kulsinski -- the Kulsinski distance. kulczynski1 -- the Kulczynski 1 distance. rogerstanimoto -- the Rogers-Tanimoto dissimilarity. russellrao -- the Russell-Rao dissimilarity. sokalmichener -- the Sokal-Michener dissimilarity. sokalsneath -- the Sokal-Sneath dissimilarity. yule -- the Yule dissimilarity. :func:`hamming` also operates over discrete numerical vectors. """ # Copyright (C) Damian Eads, 2007-2008. New BSD License. __all__ = [ 'braycurtis', 'canberra', 'cdist', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'directed_hausdorff', 'euclidean', 'hamming', 'is_valid_dm', 'is_valid_y', 'jaccard', 'jensenshannon', 'kulsinski', 'kulczynski1', 'mahalanobis', 'matching', 'minkowski', 'num_obs_dm', 'num_obs_y', 'pdist', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'squareform', 'yule' ] import warnings import numpy as np import dataclasses from typing import List, Optional, Set, Callable from functools import partial from scipy._lib._util import _asarray_validated > from . import _distance_wrap E ImportError: /usr/lib/python3.10/site-packages/scipy/spatial/_distance_wrap.cpython-310-riscv64-linux-gnu.so: undefined symbol: npy_copysign /usr/lib/python3.10/site-packages/scipy/spatial/distance.py:120: ImportError ______________ TestSimilarity.test_graph_edit_distance_node_match ______________ self = def test_graph_edit_distance_node_match(self): G1 = cycle_graph(5) G2 = cycle_graph(5) for n, attr in G1.nodes.items(): attr["color"] = "red" if n % 2 == 0 else "blue" for n, attr in G2.nodes.items(): attr["color"] = "red" if n % 2 == 1 else "blue" > assert graph_edit_distance(G1, G2) == 0 networkx/algorithms/tests/test_similarity.py:93: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ networkx/algorithms/similarity.py:190: in graph_edit_distance for vertex_path, edge_path, cost in optimize_edit_paths( networkx/algorithms/similarity.py:673: in optimize_edit_paths import scipy.optimize # call as sp.optimize /usr/lib/python3.10/site-packages/scipy/optimize/__init__.py:420: in from ._shgo import shgo /usr/lib/python3.10/site-packages/scipy/optimize/_shgo.py:9: in from scipy import spatial /usr/lib/python3.10/site-packages/scipy/spatial/__init__.py:108: in from ._geometric_slerp import geometric_slerp /usr/lib/python3.10/site-packages/scipy/spatial/_geometric_slerp.py:9: in from scipy.spatial.distance import euclidean _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ """ Distance computations (:mod:`scipy.spatial.distance`) ===================================================== .. sectionauthor:: Damian Eads Function reference ------------------ Distance matrix computation from a collection of raw observation vectors stored in a rectangular array. .. autosummary:: :toctree: generated/ pdist -- pairwise distances between observation vectors. cdist -- distances between two collections of observation vectors squareform -- convert distance matrix to a condensed one and vice versa directed_hausdorff -- directed Hausdorff distance between arrays Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix. .. autosummary:: :toctree: generated/ is_valid_dm -- checks for a valid distance matrix is_valid_y -- checks for a valid condensed distance matrix num_obs_dm -- # of observations in a distance matrix num_obs_y -- # of observations in a condensed distance matrix Distance functions between two numeric vectors ``u`` and ``v``. Computing distances over a large collection of vectors is inefficient for these functions. Use ``pdist`` for this purpose. .. autosummary:: :toctree: generated/ braycurtis -- the Bray-Curtis distance. canberra -- the Canberra distance. chebyshev -- the Chebyshev distance. cityblock -- the Manhattan distance. correlation -- the Correlation distance. cosine -- the Cosine distance. euclidean -- the Euclidean distance. jensenshannon -- the Jensen-Shannon distance. mahalanobis -- the Mahalanobis distance. minkowski -- the Minkowski distance. seuclidean -- the normalized Euclidean distance. sqeuclidean -- the squared Euclidean distance. Distance functions between two boolean vectors (representing sets) ``u`` and ``v``. As in the case of numerical vectors, ``pdist`` is more efficient for computing the distances between all pairs. .. autosummary:: :toctree: generated/ dice -- the Dice dissimilarity. hamming -- the Hamming distance. jaccard -- the Jaccard distance. kulsinski -- the Kulsinski distance. kulczynski1 -- the Kulczynski 1 distance. rogerstanimoto -- the Rogers-Tanimoto dissimilarity. russellrao -- the Russell-Rao dissimilarity. sokalmichener -- the Sokal-Michener dissimilarity. sokalsneath -- the Sokal-Sneath dissimilarity. yule -- the Yule dissimilarity. :func:`hamming` also operates over discrete numerical vectors. """ # Copyright (C) Damian Eads, 2007-2008. New BSD License. __all__ = [ 'braycurtis', 'canberra', 'cdist', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'directed_hausdorff', 'euclidean', 'hamming', 'is_valid_dm', 'is_valid_y', 'jaccard', 'jensenshannon', 'kulsinski', 'kulczynski1', 'mahalanobis', 'matching', 'minkowski', 'num_obs_dm', 'num_obs_y', 'pdist', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'squareform', 'yule' ] import warnings import numpy as np import dataclasses from typing import List, Optional, Set, Callable from functools import partial from scipy._lib._util import _asarray_validated > from . import _distance_wrap E ImportError: /usr/lib/python3.10/site-packages/scipy/spatial/_distance_wrap.cpython-310-riscv64-linux-gnu.so: undefined symbol: npy_copysign /usr/lib/python3.10/site-packages/scipy/spatial/distance.py:120: ImportError ______________ TestSimilarity.test_graph_edit_distance_edge_match ______________ self = def test_graph_edit_distance_edge_match(self): G1 = path_graph(6) G2 = path_graph(6) for e, attr in G1.edges.items(): attr["color"] = "red" if min(e) % 2 == 0 else "blue" for e, attr in G2.edges.items(): attr["color"] = "red" if min(e) // 3 == 0 else "blue" > assert graph_edit_distance(G1, G2) == 0 networkx/algorithms/tests/test_similarity.py:108: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ networkx/algorithms/similarity.py:190: in graph_edit_distance for vertex_path, edge_path, cost in optimize_edit_paths( networkx/algorithms/similarity.py:673: in optimize_edit_paths import scipy.optimize # call as sp.optimize /usr/lib/python3.10/site-packages/scipy/optimize/__init__.py:420: in from ._shgo import shgo /usr/lib/python3.10/site-packages/scipy/optimize/_shgo.py:9: in from scipy import spatial /usr/lib/python3.10/site-packages/scipy/spatial/__init__.py:108: in from ._geometric_slerp import geometric_slerp /usr/lib/python3.10/site-packages/scipy/spatial/_geometric_slerp.py:9: in from scipy.spatial.distance import euclidean _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ """ Distance computations (:mod:`scipy.spatial.distance`) ===================================================== .. sectionauthor:: Damian Eads Function reference ------------------ Distance matrix computation from a collection of raw observation vectors stored in a rectangular array. .. autosummary:: :toctree: generated/ pdist -- pairwise distances between observation vectors. cdist -- distances between two collections of observation vectors squareform -- convert distance matrix to a condensed one and vice versa directed_hausdorff -- directed Hausdorff distance between arrays Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix. .. autosummary:: :toctree: generated/ is_valid_dm -- checks for a valid distance matrix is_valid_y -- checks for a valid condensed distance matrix num_obs_dm -- # of observations in a distance matrix num_obs_y -- # of observations in a condensed distance matrix Distance functions between two numeric vectors ``u`` and ``v``. Computing distances over a large collection of vectors is inefficient for these functions. Use ``pdist`` for this purpose. .. autosummary:: :toctree: generated/ braycurtis -- the Bray-Curtis distance. canberra -- the Canberra distance. chebyshev -- the Chebyshev distance. cityblock -- the Manhattan distance. correlation -- the Correlation distance. cosine -- the Cosine distance. euclidean -- the Euclidean distance. jensenshannon -- the Jensen-Shannon distance. mahalanobis -- the Mahalanobis distance. minkowski -- the Minkowski distance. seuclidean -- the normalized Euclidean distance. sqeuclidean -- the squared Euclidean distance. Distance functions between two boolean vectors (representing sets) ``u`` and ``v``. As in the case of numerical vectors, ``pdist`` is more efficient for computing the distances between all pairs. .. autosummary:: :toctree: generated/ dice -- the Dice dissimilarity. hamming -- the Hamming distance. jaccard -- the Jaccard distance. kulsinski -- the Kulsinski distance. kulczynski1 -- the Kulczynski 1 distance. rogerstanimoto -- the Rogers-Tanimoto dissimilarity. russellrao -- the Russell-Rao dissimilarity. sokalmichener -- the Sokal-Michener dissimilarity. sokalsneath -- the Sokal-Sneath dissimilarity. yule -- the Yule dissimilarity. :func:`hamming` also operates over discrete numerical vectors. """ # Copyright (C) Damian Eads, 2007-2008. New BSD License. __all__ = [ 'braycurtis', 'canberra', 'cdist', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'directed_hausdorff', 'euclidean', 'hamming', 'is_valid_dm', 'is_valid_y', 'jaccard', 'jensenshannon', 'kulsinski', 'kulczynski1', 'mahalanobis', 'matching', 'minkowski', 'num_obs_dm', 'num_obs_y', 'pdist', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'squareform', 'yule' ] import warnings import numpy as np import dataclasses from typing import List, Optional, Set, Callable from functools import partial from scipy._lib._util import _asarray_validated > from . import _distance_wrap E ImportError: /usr/lib/python3.10/site-packages/scipy/spatial/_distance_wrap.cpython-310-riscv64-linux-gnu.so: undefined symbol: npy_copysign /usr/lib/python3.10/site-packages/scipy/spatial/distance.py:120: ImportError ______________ TestSimilarity.test_graph_edit_distance_node_cost _______________ self = def test_graph_edit_distance_node_cost(self): G1 = path_graph(6) G2 = path_graph(6) for n, attr in G1.nodes.items(): attr["color"] = "red" if n % 2 == 0 else "blue" for n, attr in G2.nodes.items(): attr["color"] = "red" if n % 2 == 1 else "blue" def node_subst_cost(uattr, vattr): if uattr["color"] == vattr["color"]: return 1 else: return 10 def node_del_cost(attr): if attr["color"] == "blue": return 20 else: return 50 def node_ins_cost(attr): if attr["color"] == "blue": return 40 else: return 100 > assert ( graph_edit_distance( G1, G2, node_subst_cost=node_subst_cost, node_del_cost=node_del_cost, node_ins_cost=node_ins_cost, ) == 6 ) networkx/algorithms/tests/test_similarity.py:142: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ networkx/algorithms/similarity.py:190: in graph_edit_distance for vertex_path, edge_path, cost in optimize_edit_paths( networkx/algorithms/similarity.py:673: in optimize_edit_paths import scipy.optimize # call as sp.optimize /usr/lib/python3.10/site-packages/scipy/optimize/__init__.py:420: in from ._shgo import shgo /usr/lib/python3.10/site-packages/scipy/optimize/_shgo.py:9: in from scipy import spatial /usr/lib/python3.10/site-packages/scipy/spatial/__init__.py:108: in from ._geometric_slerp import geometric_slerp /usr/lib/python3.10/site-packages/scipy/spatial/_geometric_slerp.py:9: in from scipy.spatial.distance import euclidean _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ """ Distance computations (:mod:`scipy.spatial.distance`) ===================================================== .. sectionauthor:: Damian Eads Function reference ------------------ Distance matrix computation from a collection of raw observation vectors stored in a rectangular array. .. autosummary:: :toctree: generated/ pdist -- pairwise distances between observation vectors. cdist -- distances between two collections of observation vectors squareform -- convert distance matrix to a condensed one and vice versa directed_hausdorff -- directed Hausdorff distance between arrays Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix. .. autosummary:: :toctree: generated/ is_valid_dm -- checks for a valid distance matrix is_valid_y -- checks for a valid condensed distance matrix num_obs_dm -- # of observations in a distance matrix num_obs_y -- # of observations in a condensed distance matrix Distance functions between two numeric vectors ``u`` and ``v``. Computing distances over a large collection of vectors is inefficient for these functions. Use ``pdist`` for this purpose. .. autosummary:: :toctree: generated/ braycurtis -- the Bray-Curtis distance. canberra -- the Canberra distance. chebyshev -- the Chebyshev distance. cityblock -- the Manhattan distance. correlation -- the Correlation distance. cosine -- the Cosine distance. euclidean -- the Euclidean distance. jensenshannon -- the Jensen-Shannon distance. mahalanobis -- the Mahalanobis distance. minkowski -- the Minkowski distance. seuclidean -- the normalized Euclidean distance. sqeuclidean -- the squared Euclidean distance. Distance functions between two boolean vectors (representing sets) ``u`` and ``v``. As in the case of numerical vectors, ``pdist`` is more efficient for computing the distances between all pairs. .. autosummary:: :toctree: generated/ dice -- the Dice dissimilarity. hamming -- the Hamming distance. jaccard -- the Jaccard distance. kulsinski -- the Kulsinski distance. kulczynski1 -- the Kulczynski 1 distance. rogerstanimoto -- the Rogers-Tanimoto dissimilarity. russellrao -- the Russell-Rao dissimilarity. sokalmichener -- the Sokal-Michener dissimilarity. sokalsneath -- the Sokal-Sneath dissimilarity. yule -- the Yule dissimilarity. :func:`hamming` also operates over discrete numerical vectors. """ # Copyright (C) Damian Eads, 2007-2008. New BSD License. __all__ = [ 'braycurtis', 'canberra', 'cdist', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'directed_hausdorff', 'euclidean', 'hamming', 'is_valid_dm', 'is_valid_y', 'jaccard', 'jensenshannon', 'kulsinski', 'kulczynski1', 'mahalanobis', 'matching', 'minkowski', 'num_obs_dm', 'num_obs_y', 'pdist', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'squareform', 'yule' ] import warnings import numpy as np import dataclasses from typing import List, Optional, Set, Callable from functools import partial from scipy._lib._util import _asarray_validated > from . import _distance_wrap E ImportError: /usr/lib/python3.10/site-packages/scipy/spatial/_distance_wrap.cpython-310-riscv64-linux-gnu.so: undefined symbol: npy_copysign /usr/lib/python3.10/site-packages/scipy/spatial/distance.py:120: ImportError ______________ TestSimilarity.test_graph_edit_distance_edge_cost _______________ self = def test_graph_edit_distance_edge_cost(self): G1 = path_graph(6) G2 = path_graph(6) for e, attr in G1.edges.items(): attr["color"] = "red" if min(e) % 2 == 0 else "blue" for e, attr in G2.edges.items(): attr["color"] = "red" if min(e) // 3 == 0 else "blue" def edge_subst_cost(gattr, hattr): if gattr["color"] == hattr["color"]: return 0.01 else: return 0.1 def edge_del_cost(attr): if attr["color"] == "blue": return 0.2 else: return 0.5 def edge_ins_cost(attr): if attr["color"] == "blue": return 0.4 else: return 1.0 > assert ( graph_edit_distance( G1, G2, edge_subst_cost=edge_subst_cost, edge_del_cost=edge_del_cost, edge_ins_cost=edge_ins_cost, ) == 0.23 ) networkx/algorithms/tests/test_similarity.py:179: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ networkx/algorithms/similarity.py:190: in graph_edit_distance for vertex_path, edge_path, cost in optimize_edit_paths( networkx/algorithms/similarity.py:673: in optimize_edit_paths import scipy.optimize # call as sp.optimize /usr/lib/python3.10/site-packages/scipy/optimize/__init__.py:420: in from ._shgo import shgo /usr/lib/python3.10/site-packages/scipy/optimize/_shgo.py:9: in from scipy import spatial /usr/lib/python3.10/site-packages/scipy/spatial/__init__.py:108: in from ._geometric_slerp import geometric_slerp /usr/lib/python3.10/site-packages/scipy/spatial/_geometric_slerp.py:9: in from scipy.spatial.distance import euclidean _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ """ Distance computations (:mod:`scipy.spatial.distance`) ===================================================== .. sectionauthor:: Damian Eads Function reference ------------------ Distance matrix computation from a collection of raw observation vectors stored in a rectangular array. .. autosummary:: :toctree: generated/ pdist -- pairwise distances between observation vectors. cdist -- distances between two collections of observation vectors squareform -- convert distance matrix to a condensed one and vice versa directed_hausdorff -- directed Hausdorff distance between arrays Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix. .. autosummary:: :toctree: generated/ is_valid_dm -- checks for a valid distance matrix is_valid_y -- checks for a valid condensed distance matrix num_obs_dm -- # of observations in a distance matrix num_obs_y -- # of observations in a condensed distance matrix Distance functions between two numeric vectors ``u`` and ``v``. Computing distances over a large collection of vectors is inefficient for these functions. Use ``pdist`` for this purpose. .. autosummary:: :toctree: generated/ braycurtis -- the Bray-Curtis distance. canberra -- the Canberra distance. chebyshev -- the Chebyshev distance. cityblock -- the Manhattan distance. correlation -- the Correlation distance. cosine -- the Cosine distance. euclidean -- the Euclidean distance. jensenshannon -- the Jensen-Shannon distance. mahalanobis -- the Mahalanobis distance. minkowski -- the Minkowski distance. seuclidean -- the normalized Euclidean distance. sqeuclidean -- the squared Euclidean distance. Distance functions between two boolean vectors (representing sets) ``u`` and ``v``. As in the case of numerical vectors, ``pdist`` is more efficient for computing the distances between all pairs. .. autosummary:: :toctree: generated/ dice -- the Dice dissimilarity. hamming -- the Hamming distance. jaccard -- the Jaccard distance. kulsinski -- the Kulsinski distance. kulczynski1 -- the Kulczynski 1 distance. rogerstanimoto -- the Rogers-Tanimoto dissimilarity. russellrao -- the Russell-Rao dissimilarity. sokalmichener -- the Sokal-Michener dissimilarity. sokalsneath -- the Sokal-Sneath dissimilarity. yule -- the Yule dissimilarity. :func:`hamming` also operates over discrete numerical vectors. """ # Copyright (C) Damian Eads, 2007-2008. New BSD License. __all__ = [ 'braycurtis', 'canberra', 'cdist', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'directed_hausdorff', 'euclidean', 'hamming', 'is_valid_dm', 'is_valid_y', 'jaccard', 'jensenshannon', 'kulsinski', 'kulczynski1', 'mahalanobis', 'matching', 'minkowski', 'num_obs_dm', 'num_obs_y', 'pdist', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'squareform', 'yule' ] import warnings import numpy as np import dataclasses from typing import List, Optional, Set, Callable from functools import partial from scipy._lib._util import _asarray_validated > from . import _distance_wrap E ImportError: /usr/lib/python3.10/site-packages/scipy/spatial/_distance_wrap.cpython-310-riscv64-linux-gnu.so: undefined symbol: npy_copysign /usr/lib/python3.10/site-packages/scipy/spatial/distance.py:120: ImportError _____________ TestSimilarity.test_graph_edit_distance_upper_bound ______________ self = def test_graph_edit_distance_upper_bound(self): G1 = circular_ladder_graph(2) G2 = circular_ladder_graph(6) > assert graph_edit_distance(G1, G2, upper_bound=5) is None networkx/algorithms/tests/test_similarity.py:193: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ networkx/algorithms/similarity.py:190: in graph_edit_distance for vertex_path, edge_path, cost in optimize_edit_paths( networkx/algorithms/similarity.py:673: in optimize_edit_paths import scipy.optimize # call as sp.optimize /usr/lib/python3.10/site-packages/scipy/optimize/__init__.py:420: in from ._shgo import shgo /usr/lib/python3.10/site-packages/scipy/optimize/_shgo.py:9: in from scipy import spatial /usr/lib/python3.10/site-packages/scipy/spatial/__init__.py:108: in from ._geometric_slerp import geometric_slerp /usr/lib/python3.10/site-packages/scipy/spatial/_geometric_slerp.py:9: in from scipy.spatial.distance import euclidean _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ """ Distance computations (:mod:`scipy.spatial.distance`) ===================================================== .. sectionauthor:: Damian Eads Function reference ------------------ Distance matrix computation from a collection of raw observation vectors stored in a rectangular array. .. autosummary:: :toctree: generated/ pdist -- pairwise distances between observation vectors. cdist -- distances between two collections of observation vectors squareform -- convert distance matrix to a condensed one and vice versa directed_hausdorff -- directed Hausdorff distance between arrays Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix. .. autosummary:: :toctree: generated/ is_valid_dm -- checks for a valid distance matrix is_valid_y -- checks for a valid condensed distance matrix num_obs_dm -- # of observations in a distance matrix num_obs_y -- # of observations in a condensed distance matrix Distance functions between two numeric vectors ``u`` and ``v``. Computing distances over a large collection of vectors is inefficient for these functions. Use ``pdist`` for this purpose. .. autosummary:: :toctree: generated/ braycurtis -- the Bray-Curtis distance. canberra -- the Canberra distance. chebyshev -- the Chebyshev distance. cityblock -- the Manhattan distance. correlation -- the Correlation distance. cosine -- the Cosine distance. euclidean -- the Euclidean distance. jensenshannon -- the Jensen-Shannon distance. mahalanobis -- the Mahalanobis distance. minkowski -- the Minkowski distance. seuclidean -- the normalized Euclidean distance. sqeuclidean -- the squared Euclidean distance. Distance functions between two boolean vectors (representing sets) ``u`` and ``v``. As in the case of numerical vectors, ``pdist`` is more efficient for computing the distances between all pairs. .. autosummary:: :toctree: generated/ dice -- the Dice dissimilarity. hamming -- the Hamming distance. jaccard -- the Jaccard distance. kulsinski -- the Kulsinski distance. kulczynski1 -- the Kulczynski 1 distance. rogerstanimoto -- the Rogers-Tanimoto dissimilarity. russellrao -- the Russell-Rao dissimilarity. sokalmichener -- the Sokal-Michener dissimilarity. sokalsneath -- the Sokal-Sneath dissimilarity. yule -- the Yule dissimilarity. :func:`hamming` also operates over discrete numerical vectors. """ # Copyright (C) Damian Eads, 2007-2008. New BSD License. __all__ = [ 'braycurtis', 'canberra', 'cdist', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'directed_hausdorff', 'euclidean', 'hamming', 'is_valid_dm', 'is_valid_y', 'jaccard', 'jensenshannon', 'kulsinski', 'kulczynski1', 'mahalanobis', 'matching', 'minkowski', 'num_obs_dm', 'num_obs_y', 'pdist', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'squareform', 'yule' ] import warnings import numpy as np import dataclasses from typing import List, Optional, Set, Callable from functools import partial from scipy._lib._util import _asarray_validated > from . import _distance_wrap E ImportError: /usr/lib/python3.10/site-packages/scipy/spatial/_distance_wrap.cpython-310-riscv64-linux-gnu.so: undefined symbol: npy_copysign /usr/lib/python3.10/site-packages/scipy/spatial/distance.py:120: ImportError ____________________ TestSimilarity.test_optimal_edit_paths ____________________ self = def test_optimal_edit_paths(self): G1 = path_graph(3) G2 = cycle_graph(3) > paths, cost = optimal_edit_paths(G1, G2) networkx/algorithms/tests/test_similarity.py:200: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ networkx/algorithms/similarity.py:351: in optimal_edit_paths for vertex_path, edge_path, cost in optimize_edit_paths( networkx/algorithms/similarity.py:673: in optimize_edit_paths import scipy.optimize # call as sp.optimize /usr/lib/python3.10/site-packages/scipy/optimize/__init__.py:420: in from ._shgo import shgo /usr/lib/python3.10/site-packages/scipy/optimize/_shgo.py:9: in from scipy import spatial /usr/lib/python3.10/site-packages/scipy/spatial/__init__.py:108: in from ._geometric_slerp import geometric_slerp /usr/lib/python3.10/site-packages/scipy/spatial/_geometric_slerp.py:9: in from scipy.spatial.distance import euclidean _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ """ Distance computations (:mod:`scipy.spatial.distance`) ===================================================== .. sectionauthor:: Damian Eads Function reference ------------------ Distance matrix computation from a collection of raw observation vectors stored in a rectangular array. .. autosummary:: :toctree: generated/ pdist -- pairwise distances between observation vectors. cdist -- distances between two collections of observation vectors squareform -- convert distance matrix to a condensed one and vice versa directed_hausdorff -- directed Hausdorff distance between arrays Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix. .. autosummary:: :toctree: generated/ is_valid_dm -- checks for a valid distance matrix is_valid_y -- checks for a valid condensed distance matrix num_obs_dm -- # of observations in a distance matrix num_obs_y -- # of observations in a condensed distance matrix Distance functions between two numeric vectors ``u`` and ``v``. Computing distances over a large collection of vectors is inefficient for these functions. Use ``pdist`` for this purpose. .. autosummary:: :toctree: generated/ braycurtis -- the Bray-Curtis distance. canberra -- the Canberra distance. chebyshev -- the Chebyshev distance. cityblock -- the Manhattan distance. correlation -- the Correlation distance. cosine -- the Cosine distance. euclidean -- the Euclidean distance. jensenshannon -- the Jensen-Shannon distance. mahalanobis -- the Mahalanobis distance. minkowski -- the Minkowski distance. seuclidean -- the normalized Euclidean distance. sqeuclidean -- the squared Euclidean distance. Distance functions between two boolean vectors (representing sets) ``u`` and ``v``. As in the case of numerical vectors, ``pdist`` is more efficient for computing the distances between all pairs. .. autosummary:: :toctree: generated/ dice -- the Dice dissimilarity. hamming -- the Hamming distance. jaccard -- the Jaccard distance. kulsinski -- the Kulsinski distance. kulczynski1 -- the Kulczynski 1 distance. rogerstanimoto -- the Rogers-Tanimoto dissimilarity. russellrao -- the Russell-Rao dissimilarity. sokalmichener -- the Sokal-Michener dissimilarity. sokalsneath -- the Sokal-Sneath dissimilarity. yule -- the Yule dissimilarity. :func:`hamming` also operates over discrete numerical vectors. """ # Copyright (C) Damian Eads, 2007-2008. New BSD License. __all__ = [ 'braycurtis', 'canberra', 'cdist', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'directed_hausdorff', 'euclidean', 'hamming', 'is_valid_dm', 'is_valid_y', 'jaccard', 'jensenshannon', 'kulsinski', 'kulczynski1', 'mahalanobis', 'matching', 'minkowski', 'num_obs_dm', 'num_obs_y', 'pdist', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'squareform', 'yule' ] import warnings import numpy as np import dataclasses from typing import List, Optional, Set, Callable from functools import partial from scipy._lib._util import _asarray_validated > from . import _distance_wrap E ImportError: /usr/lib/python3.10/site-packages/scipy/spatial/_distance_wrap.cpython-310-riscv64-linux-gnu.so: undefined symbol: npy_copysign /usr/lib/python3.10/site-packages/scipy/spatial/distance.py:120: ImportError _______________ TestSimilarity.test_optimize_graph_edit_distance _______________ self = def test_optimize_graph_edit_distance(self): G1 = circular_ladder_graph(2) G2 = circular_ladder_graph(6) bestcost = 1000 > for cost in optimize_graph_edit_distance(G1, G2): networkx/algorithms/tests/test_similarity.py:242: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ networkx/algorithms/similarity.py:506: in optimize_graph_edit_distance for vertex_path, edge_path, cost in optimize_edit_paths( networkx/algorithms/similarity.py:673: in optimize_edit_paths import scipy.optimize # call as sp.optimize /usr/lib/python3.10/site-packages/scipy/optimize/__init__.py:420: in from ._shgo import shgo /usr/lib/python3.10/site-packages/scipy/optimize/_shgo.py:9: in from scipy import spatial /usr/lib/python3.10/site-packages/scipy/spatial/__init__.py:108: in from ._geometric_slerp import geometric_slerp /usr/lib/python3.10/site-packages/scipy/spatial/_geometric_slerp.py:9: in from scipy.spatial.distance import euclidean _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ """ Distance computations (:mod:`scipy.spatial.distance`) ===================================================== .. sectionauthor:: Damian Eads Function reference ------------------ Distance matrix computation from a collection of raw observation vectors stored in a rectangular array. .. autosummary:: :toctree: generated/ pdist -- pairwise distances between observation vectors. cdist -- distances between two collections of observation vectors squareform -- convert distance matrix to a condensed one and vice versa directed_hausdorff -- directed Hausdorff distance between arrays Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix. .. autosummary:: :toctree: generated/ is_valid_dm -- checks for a valid distance matrix is_valid_y -- checks for a valid condensed distance matrix num_obs_dm -- # of observations in a distance matrix num_obs_y -- # of observations in a condensed distance matrix Distance functions between two numeric vectors ``u`` and ``v``. Computing distances over a large collection of vectors is inefficient for these functions. Use ``pdist`` for this purpose. .. autosummary:: :toctree: generated/ braycurtis -- the Bray-Curtis distance. canberra -- the Canberra distance. chebyshev -- the Chebyshev distance. cityblock -- the Manhattan distance. correlation -- the Correlation distance. cosine -- the Cosine distance. euclidean -- the Euclidean distance. jensenshannon -- the Jensen-Shannon distance. mahalanobis -- the Mahalanobis distance. minkowski -- the Minkowski distance. seuclidean -- the normalized Euclidean distance. sqeuclidean -- the squared Euclidean distance. Distance functions between two boolean vectors (representing sets) ``u`` and ``v``. As in the case of numerical vectors, ``pdist`` is more efficient for computing the distances between all pairs. .. autosummary:: :toctree: generated/ dice -- the Dice dissimilarity. hamming -- the Hamming distance. jaccard -- the Jaccard distance. kulsinski -- the Kulsinski distance. kulczynski1 -- the Kulczynski 1 distance. rogerstanimoto -- the Rogers-Tanimoto dissimilarity. russellrao -- the Russell-Rao dissimilarity. sokalmichener -- the Sokal-Michener dissimilarity. sokalsneath -- the Sokal-Sneath dissimilarity. yule -- the Yule dissimilarity. :func:`hamming` also operates over discrete numerical vectors. """ # Copyright (C) Damian Eads, 2007-2008. New BSD License. __all__ = [ 'braycurtis', 'canberra', 'cdist', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'directed_hausdorff', 'euclidean', 'hamming', 'is_valid_dm', 'is_valid_y', 'jaccard', 'jensenshannon', 'kulsinski', 'kulczynski1', 'mahalanobis', 'matching', 'minkowski', 'num_obs_dm', 'num_obs_y', 'pdist', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'squareform', 'yule' ] import warnings import numpy as np import dataclasses from typing import List, Optional, Set, Callable from functools import partial from scipy._lib._util import _asarray_validated > from . import _distance_wrap E ImportError: /usr/lib/python3.10/site-packages/scipy/spatial/_distance_wrap.cpython-310-riscv64-linux-gnu.so: undefined symbol: npy_copysign /usr/lib/python3.10/site-packages/scipy/spatial/distance.py:120: ImportError ________________________ TestSimilarity.test_selfloops _________________________ self = def test_selfloops(self): G0 = nx.Graph() G1 = nx.Graph() G1.add_edges_from((("A", "A"), ("A", "B"))) G2 = nx.Graph() G2.add_edges_from((("A", "B"), ("B", "B"))) G3 = nx.Graph() G3.add_edges_from((("A", "A"), ("A", "B"), ("B", "B"))) > assert graph_edit_distance(G0, G0) == 0 networkx/algorithms/tests/test_similarity.py:261: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ networkx/algorithms/similarity.py:190: in graph_edit_distance for vertex_path, edge_path, cost in optimize_edit_paths( networkx/algorithms/similarity.py:673: in optimize_edit_paths import scipy.optimize # call as sp.optimize /usr/lib/python3.10/site-packages/scipy/optimize/__init__.py:420: in from ._shgo import shgo /usr/lib/python3.10/site-packages/scipy/optimize/_shgo.py:9: in from scipy import spatial /usr/lib/python3.10/site-packages/scipy/spatial/__init__.py:108: in from ._geometric_slerp import geometric_slerp /usr/lib/python3.10/site-packages/scipy/spatial/_geometric_slerp.py:9: in from scipy.spatial.distance import euclidean _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ """ Distance computations (:mod:`scipy.spatial.distance`) ===================================================== .. sectionauthor:: Damian Eads Function reference ------------------ Distance matrix computation from a collection of raw observation vectors stored in a rectangular array. .. autosummary:: :toctree: generated/ pdist -- pairwise distances between observation vectors. cdist -- distances between two collections of observation vectors squareform -- convert distance matrix to a condensed one and vice versa directed_hausdorff -- directed Hausdorff distance between arrays Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix. .. autosummary:: :toctree: generated/ is_valid_dm -- checks for a valid distance matrix is_valid_y -- checks for a valid condensed distance matrix num_obs_dm -- # of observations in a distance matrix num_obs_y -- # of observations in a condensed distance matrix Distance functions between two numeric vectors ``u`` and ``v``. Computing distances over a large collection of vectors is inefficient for these functions. Use ``pdist`` for this purpose. .. autosummary:: :toctree: generated/ braycurtis -- the Bray-Curtis distance. canberra -- the Canberra distance. chebyshev -- the Chebyshev distance. cityblock -- the Manhattan distance. correlation -- the Correlation distance. cosine -- the Cosine distance. euclidean -- the Euclidean distance. jensenshannon -- the Jensen-Shannon distance. mahalanobis -- the Mahalanobis distance. minkowski -- the Minkowski distance. seuclidean -- the normalized Euclidean distance. sqeuclidean -- the squared Euclidean distance. Distance functions between two boolean vectors (representing sets) ``u`` and ``v``. As in the case of numerical vectors, ``pdist`` is more efficient for computing the distances between all pairs. .. autosummary:: :toctree: generated/ dice -- the Dice dissimilarity. hamming -- the Hamming distance. jaccard -- the Jaccard distance. kulsinski -- the Kulsinski distance. kulczynski1 -- the Kulczynski 1 distance. rogerstanimoto -- the Rogers-Tanimoto dissimilarity. russellrao -- the Russell-Rao dissimilarity. sokalmichener -- the Sokal-Michener dissimilarity. sokalsneath -- the Sokal-Sneath dissimilarity. yule -- the Yule dissimilarity. :func:`hamming` also operates over discrete numerical vectors. """ # Copyright (C) Damian Eads, 2007-2008. New BSD License. __all__ = [ 'braycurtis', 'canberra', 'cdist', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'directed_hausdorff', 'euclidean', 'hamming', 'is_valid_dm', 'is_valid_y', 'jaccard', 'jensenshannon', 'kulsinski', 'kulczynski1', 'mahalanobis', 'matching', 'minkowski', 'num_obs_dm', 'num_obs_y', 'pdist', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'squareform', 'yule' ] import warnings import numpy as np import dataclasses from typing import List, Optional, Set, Callable from functools import partial from scipy._lib._util import _asarray_validated > from . import _distance_wrap E ImportError: /usr/lib/python3.10/site-packages/scipy/spatial/_distance_wrap.cpython-310-riscv64-linux-gnu.so: undefined symbol: npy_copysign /usr/lib/python3.10/site-packages/scipy/spatial/distance.py:120: ImportError _________________________ TestSimilarity.test_digraph __________________________ self = def test_digraph(self): G0 = nx.DiGraph() G1 = nx.DiGraph() G1.add_edges_from((("A", "B"), ("B", "C"), ("C", "D"), ("D", "A"))) G2 = nx.DiGraph() G2.add_edges_from((("A", "B"), ("B", "C"), ("C", "D"), ("A", "D"))) G3 = nx.DiGraph() G3.add_edges_from((("A", "B"), ("A", "C"), ("B", "D"), ("C", "D"))) > assert graph_edit_distance(G0, G0) == 0 networkx/algorithms/tests/test_similarity.py:290: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ networkx/algorithms/similarity.py:190: in graph_edit_distance for vertex_path, edge_path, cost in optimize_edit_paths( networkx/algorithms/similarity.py:673: in optimize_edit_paths import scipy.optimize # call as sp.optimize /usr/lib/python3.10/site-packages/scipy/optimize/__init__.py:420: in from ._shgo import shgo /usr/lib/python3.10/site-packages/scipy/optimize/_shgo.py:9: in from scipy import spatial /usr/lib/python3.10/site-packages/scipy/spatial/__init__.py:108: in from ._geometric_slerp import geometric_slerp /usr/lib/python3.10/site-packages/scipy/spatial/_geometric_slerp.py:9: in from scipy.spatial.distance import euclidean _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ """ Distance computations (:mod:`scipy.spatial.distance`) ===================================================== .. sectionauthor:: Damian Eads Function reference ------------------ Distance matrix computation from a collection of raw observation vectors stored in a rectangular array. .. autosummary:: :toctree: generated/ pdist -- pairwise distances between observation vectors. cdist -- distances between two collections of observation vectors squareform -- convert distance matrix to a condensed one and vice versa directed_hausdorff -- directed Hausdorff distance between arrays Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix. .. autosummary:: :toctree: generated/ is_valid_dm -- checks for a valid distance matrix is_valid_y -- checks for a valid condensed distance matrix num_obs_dm -- # of observations in a distance matrix num_obs_y -- # of observations in a condensed distance matrix Distance functions between two numeric vectors ``u`` and ``v``. Computing distances over a large collection of vectors is inefficient for these functions. Use ``pdist`` for this purpose. .. autosummary:: :toctree: generated/ braycurtis -- the Bray-Curtis distance. canberra -- the Canberra distance. chebyshev -- the Chebyshev distance. cityblock -- the Manhattan distance. correlation -- the Correlation distance. cosine -- the Cosine distance. euclidean -- the Euclidean distance. jensenshannon -- the Jensen-Shannon distance. mahalanobis -- the Mahalanobis distance. minkowski -- the Minkowski distance. seuclidean -- the normalized Euclidean distance. sqeuclidean -- the squared Euclidean distance. Distance functions between two boolean vectors (representing sets) ``u`` and ``v``. As in the case of numerical vectors, ``pdist`` is more efficient for computing the distances between all pairs. .. autosummary:: :toctree: generated/ dice -- the Dice dissimilarity. hamming -- the Hamming distance. jaccard -- the Jaccard distance. kulsinski -- the Kulsinski distance. kulczynski1 -- the Kulczynski 1 distance. rogerstanimoto -- the Rogers-Tanimoto dissimilarity. russellrao -- the Russell-Rao dissimilarity. sokalmichener -- the Sokal-Michener dissimilarity. sokalsneath -- the Sokal-Sneath dissimilarity. yule -- the Yule dissimilarity. :func:`hamming` also operates over discrete numerical vectors. """ # Copyright (C) Damian Eads, 2007-2008. New BSD License. __all__ = [ 'braycurtis', 'canberra', 'cdist', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'directed_hausdorff', 'euclidean', 'hamming', 'is_valid_dm', 'is_valid_y', 'jaccard', 'jensenshannon', 'kulsinski', 'kulczynski1', 'mahalanobis', 'matching', 'minkowski', 'num_obs_dm', 'num_obs_y', 'pdist', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'squareform', 'yule' ] import warnings import numpy as np import dataclasses from typing import List, Optional, Set, Callable from functools import partial from scipy._lib._util import _asarray_validated > from . import _distance_wrap E ImportError: /usr/lib/python3.10/site-packages/scipy/spatial/_distance_wrap.cpython-310-riscv64-linux-gnu.so: undefined symbol: npy_copysign /usr/lib/python3.10/site-packages/scipy/spatial/distance.py:120: ImportError ________________________ TestSimilarity.test_multigraph ________________________ self = def test_multigraph(self): G0 = nx.MultiGraph() G1 = nx.MultiGraph() G1.add_edges_from((("A", "B"), ("B", "C"), ("A", "C"))) G2 = nx.MultiGraph() G2.add_edges_from((("A", "B"), ("B", "C"), ("B", "C"), ("A", "C"))) G3 = nx.MultiGraph() G3.add_edges_from((("A", "B"), ("B", "C"), ("A", "C"), ("A", "C"), ("A", "C"))) > assert graph_edit_distance(G0, G0) == 0 networkx/algorithms/tests/test_similarity.py:319: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ networkx/algorithms/similarity.py:190: in graph_edit_distance for vertex_path, edge_path, cost in optimize_edit_paths( networkx/algorithms/similarity.py:673: in optimize_edit_paths import scipy.optimize # call as sp.optimize /usr/lib/python3.10/site-packages/scipy/optimize/__init__.py:420: in from ._shgo import shgo /usr/lib/python3.10/site-packages/scipy/optimize/_shgo.py:9: in from scipy import spatial /usr/lib/python3.10/site-packages/scipy/spatial/__init__.py:108: in from ._geometric_slerp import geometric_slerp /usr/lib/python3.10/site-packages/scipy/spatial/_geometric_slerp.py:9: in from scipy.spatial.distance import euclidean _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ """ Distance computations (:mod:`scipy.spatial.distance`) ===================================================== .. sectionauthor:: Damian Eads Function reference ------------------ Distance matrix computation from a collection of raw observation vectors stored in a rectangular array. .. autosummary:: :toctree: generated/ pdist -- pairwise distances between observation vectors. cdist -- distances between two collections of observation vectors squareform -- convert distance matrix to a condensed one and vice versa directed_hausdorff -- directed Hausdorff distance between arrays Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix. .. autosummary:: :toctree: generated/ is_valid_dm -- checks for a valid distance matrix is_valid_y -- checks for a valid condensed distance matrix num_obs_dm -- # of observations in a distance matrix num_obs_y -- # of observations in a condensed distance matrix Distance functions between two numeric vectors ``u`` and ``v``. Computing distances over a large collection of vectors is inefficient for these functions. Use ``pdist`` for this purpose. .. autosummary:: :toctree: generated/ braycurtis -- the Bray-Curtis distance. canberra -- the Canberra distance. chebyshev -- the Chebyshev distance. cityblock -- the Manhattan distance. correlation -- the Correlation distance. cosine -- the Cosine distance. euclidean -- the Euclidean distance. jensenshannon -- the Jensen-Shannon distance. mahalanobis -- the Mahalanobis distance. minkowski -- the Minkowski distance. seuclidean -- the normalized Euclidean distance. sqeuclidean -- the squared Euclidean distance. Distance functions between two boolean vectors (representing sets) ``u`` and ``v``. As in the case of numerical vectors, ``pdist`` is more efficient for computing the distances between all pairs. .. autosummary:: :toctree: generated/ dice -- the Dice dissimilarity. hamming -- the Hamming distance. jaccard -- the Jaccard distance. kulsinski -- the Kulsinski distance. kulczynski1 -- the Kulczynski 1 distance. rogerstanimoto -- the Rogers-Tanimoto dissimilarity. russellrao -- the Russell-Rao dissimilarity. sokalmichener -- the Sokal-Michener dissimilarity. sokalsneath -- the Sokal-Sneath dissimilarity. yule -- the Yule dissimilarity. :func:`hamming` also operates over discrete numerical vectors. """ # Copyright (C) Damian Eads, 2007-2008. New BSD License. __all__ = [ 'braycurtis', 'canberra', 'cdist', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'directed_hausdorff', 'euclidean', 'hamming', 'is_valid_dm', 'is_valid_y', 'jaccard', 'jensenshannon', 'kulsinski', 'kulczynski1', 'mahalanobis', 'matching', 'minkowski', 'num_obs_dm', 'num_obs_y', 'pdist', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'squareform', 'yule' ] import warnings import numpy as np import dataclasses from typing import List, Optional, Set, Callable from functools import partial from scipy._lib._util import _asarray_validated > from . import _distance_wrap E ImportError: /usr/lib/python3.10/site-packages/scipy/spatial/_distance_wrap.cpython-310-riscv64-linux-gnu.so: undefined symbol: npy_copysign /usr/lib/python3.10/site-packages/scipy/spatial/distance.py:120: ImportError _______________________ TestSimilarity.test_multidigraph _______________________ self = def test_multidigraph(self): G1 = nx.MultiDiGraph() G1.add_edges_from( ( ("hardware", "kernel"), ("kernel", "hardware"), ("kernel", "userspace"), ("userspace", "kernel"), ) ) G2 = nx.MultiDiGraph() G2.add_edges_from( ( ("winter", "spring"), ("spring", "summer"), ("summer", "autumn"), ("autumn", "winter"), ) ) > assert graph_edit_distance(G1, G2) == 5 networkx/algorithms/tests/test_similarity.py:359: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ networkx/algorithms/similarity.py:190: in graph_edit_distance for vertex_path, edge_path, cost in optimize_edit_paths( networkx/algorithms/similarity.py:673: in optimize_edit_paths import scipy.optimize # call as sp.optimize /usr/lib/python3.10/site-packages/scipy/optimize/__init__.py:420: in from ._shgo import shgo /usr/lib/python3.10/site-packages/scipy/optimize/_shgo.py:9: in from scipy import spatial /usr/lib/python3.10/site-packages/scipy/spatial/__init__.py:108: in from ._geometric_slerp import geometric_slerp /usr/lib/python3.10/site-packages/scipy/spatial/_geometric_slerp.py:9: in from scipy.spatial.distance import euclidean _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ """ Distance computations (:mod:`scipy.spatial.distance`) ===================================================== .. sectionauthor:: Damian Eads Function reference ------------------ Distance matrix computation from a collection of raw observation vectors stored in a rectangular array. .. autosummary:: :toctree: generated/ pdist -- pairwise distances between observation vectors. cdist -- distances between two collections of observation vectors squareform -- convert distance matrix to a condensed one and vice versa directed_hausdorff -- directed Hausdorff distance between arrays Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix. .. autosummary:: :toctree: generated/ is_valid_dm -- checks for a valid distance matrix is_valid_y -- checks for a valid condensed distance matrix num_obs_dm -- # of observations in a distance matrix num_obs_y -- # of observations in a condensed distance matrix Distance functions between two numeric vectors ``u`` and ``v``. Computing distances over a large collection of vectors is inefficient for these functions. Use ``pdist`` for this purpose. .. autosummary:: :toctree: generated/ braycurtis -- the Bray-Curtis distance. canberra -- the Canberra distance. chebyshev -- the Chebyshev distance. cityblock -- the Manhattan distance. correlation -- the Correlation distance. cosine -- the Cosine distance. euclidean -- the Euclidean distance. jensenshannon -- the Jensen-Shannon distance. mahalanobis -- the Mahalanobis distance. minkowski -- the Minkowski distance. seuclidean -- the normalized Euclidean distance. sqeuclidean -- the squared Euclidean distance. Distance functions between two boolean vectors (representing sets) ``u`` and ``v``. As in the case of numerical vectors, ``pdist`` is more efficient for computing the distances between all pairs. .. autosummary:: :toctree: generated/ dice -- the Dice dissimilarity. hamming -- the Hamming distance. jaccard -- the Jaccard distance. kulsinski -- the Kulsinski distance. kulczynski1 -- the Kulczynski 1 distance. rogerstanimoto -- the Rogers-Tanimoto dissimilarity. russellrao -- the Russell-Rao dissimilarity. sokalmichener -- the Sokal-Michener dissimilarity. sokalsneath -- the Sokal-Sneath dissimilarity. yule -- the Yule dissimilarity. :func:`hamming` also operates over discrete numerical vectors. """ # Copyright (C) Damian Eads, 2007-2008. New BSD License. __all__ = [ 'braycurtis', 'canberra', 'cdist', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'directed_hausdorff', 'euclidean', 'hamming', 'is_valid_dm', 'is_valid_y', 'jaccard', 'jensenshannon', 'kulsinski', 'kulczynski1', 'mahalanobis', 'matching', 'minkowski', 'num_obs_dm', 'num_obs_y', 'pdist', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'squareform', 'yule' ] import warnings import numpy as np import dataclasses from typing import List, Optional, Set, Callable from functools import partial from scipy._lib._util import _asarray_validated > from . import _distance_wrap E ImportError: /usr/lib/python3.10/site-packages/scipy/spatial/_distance_wrap.cpython-310-riscv64-linux-gnu.so: undefined symbol: npy_copysign /usr/lib/python3.10/site-packages/scipy/spatial/distance.py:120: ImportError ___________________________ TestSimilarity.testCopy ____________________________ self = def testCopy(self): G = nx.Graph() G.add_node("A", label="A") G.add_node("B", label="B") G.add_edge("A", "B", label="a-b") > assert ( graph_edit_distance(G, G.copy(), node_match=nmatch, edge_match=ematch) == 0 ) networkx/algorithms/tests/test_similarity.py:368: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ networkx/algorithms/similarity.py:190: in graph_edit_distance for vertex_path, edge_path, cost in optimize_edit_paths( networkx/algorithms/similarity.py:673: in optimize_edit_paths import scipy.optimize # call as sp.optimize /usr/lib/python3.10/site-packages/scipy/optimize/__init__.py:420: in from ._shgo import shgo /usr/lib/python3.10/site-packages/scipy/optimize/_shgo.py:9: in from scipy import spatial /usr/lib/python3.10/site-packages/scipy/spatial/__init__.py:108: in from ._geometric_slerp import geometric_slerp /usr/lib/python3.10/site-packages/scipy/spatial/_geometric_slerp.py:9: in from scipy.spatial.distance import euclidean _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ """ Distance computations (:mod:`scipy.spatial.distance`) ===================================================== .. sectionauthor:: Damian Eads Function reference ------------------ Distance matrix computation from a collection of raw observation vectors stored in a rectangular array. .. autosummary:: :toctree: generated/ pdist -- pairwise distances between observation vectors. cdist -- distances between two collections of observation vectors squareform -- convert distance matrix to a condensed one and vice versa directed_hausdorff -- directed Hausdorff distance between arrays Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix. .. autosummary:: :toctree: generated/ is_valid_dm -- checks for a valid distance matrix is_valid_y -- checks for a valid condensed distance matrix num_obs_dm -- # of observations in a distance matrix num_obs_y -- # of observations in a condensed distance matrix Distance functions between two numeric vectors ``u`` and ``v``. Computing distances over a large collection of vectors is inefficient for these functions. Use ``pdist`` for this purpose. .. autosummary:: :toctree: generated/ braycurtis -- the Bray-Curtis distance. canberra -- the Canberra distance. chebyshev -- the Chebyshev distance. cityblock -- the Manhattan distance. correlation -- the Correlation distance. cosine -- the Cosine distance. euclidean -- the Euclidean distance. jensenshannon -- the Jensen-Shannon distance. mahalanobis -- the Mahalanobis distance. minkowski -- the Minkowski distance. seuclidean -- the normalized Euclidean distance. sqeuclidean -- the squared Euclidean distance. Distance functions between two boolean vectors (representing sets) ``u`` and ``v``. As in the case of numerical vectors, ``pdist`` is more efficient for computing the distances between all pairs. .. autosummary:: :toctree: generated/ dice -- the Dice dissimilarity. hamming -- the Hamming distance. jaccard -- the Jaccard distance. kulsinski -- the Kulsinski distance. kulczynski1 -- the Kulczynski 1 distance. rogerstanimoto -- the Rogers-Tanimoto dissimilarity. russellrao -- the Russell-Rao dissimilarity. sokalmichener -- the Sokal-Michener dissimilarity. sokalsneath -- the Sokal-Sneath dissimilarity. yule -- the Yule dissimilarity. :func:`hamming` also operates over discrete numerical vectors. """ # Copyright (C) Damian Eads, 2007-2008. New BSD License. __all__ = [ 'braycurtis', 'canberra', 'cdist', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'directed_hausdorff', 'euclidean', 'hamming', 'is_valid_dm', 'is_valid_y', 'jaccard', 'jensenshannon', 'kulsinski', 'kulczynski1', 'mahalanobis', 'matching', 'minkowski', 'num_obs_dm', 'num_obs_y', 'pdist', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'squareform', 'yule' ] import warnings import numpy as np import dataclasses from typing import List, Optional, Set, Callable from functools import partial from scipy._lib._util import _asarray_validated > from . import _distance_wrap E ImportError: /usr/lib/python3.10/site-packages/scipy/spatial/_distance_wrap.cpython-310-riscv64-linux-gnu.so: undefined symbol: npy_copysign /usr/lib/python3.10/site-packages/scipy/spatial/distance.py:120: ImportError ___________________________ TestSimilarity.testSame ____________________________ self = def testSame(self): G1 = nx.Graph() G1.add_node("A", label="A") G1.add_node("B", label="B") G1.add_edge("A", "B", label="a-b") G2 = nx.Graph() G2.add_node("A", label="A") G2.add_node("B", label="B") G2.add_edge("A", "B", label="a-b") > assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 0 networkx/algorithms/tests/test_similarity.py:381: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ networkx/algorithms/similarity.py:190: in graph_edit_distance for vertex_path, edge_path, cost in optimize_edit_paths( networkx/algorithms/similarity.py:673: in optimize_edit_paths import scipy.optimize # call as sp.optimize /usr/lib/python3.10/site-packages/scipy/optimize/__init__.py:420: in from ._shgo import shgo /usr/lib/python3.10/site-packages/scipy/optimize/_shgo.py:9: in from scipy import spatial /usr/lib/python3.10/site-packages/scipy/spatial/__init__.py:108: in from ._geometric_slerp import geometric_slerp /usr/lib/python3.10/site-packages/scipy/spatial/_geometric_slerp.py:9: in from scipy.spatial.distance import euclidean _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ """ Distance computations (:mod:`scipy.spatial.distance`) ===================================================== .. sectionauthor:: Damian Eads Function reference ------------------ Distance matrix computation from a collection of raw observation vectors stored in a rectangular array. .. autosummary:: :toctree: generated/ pdist -- pairwise distances between observation vectors. cdist -- distances between two collections of observation vectors squareform -- convert distance matrix to a condensed one and vice versa directed_hausdorff -- directed Hausdorff distance between arrays Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix. .. autosummary:: :toctree: generated/ is_valid_dm -- checks for a valid distance matrix is_valid_y -- checks for a valid condensed distance matrix num_obs_dm -- # of observations in a distance matrix num_obs_y -- # of observations in a condensed distance matrix Distance functions between two numeric vectors ``u`` and ``v``. Computing distances over a large collection of vectors is inefficient for these functions. Use ``pdist`` for this purpose. .. autosummary:: :toctree: generated/ braycurtis -- the Bray-Curtis distance. canberra -- the Canberra distance. chebyshev -- the Chebyshev distance. cityblock -- the Manhattan distance. correlation -- the Correlation distance. cosine -- the Cosine distance. euclidean -- the Euclidean distance. jensenshannon -- the Jensen-Shannon distance. mahalanobis -- the Mahalanobis distance. minkowski -- the Minkowski distance. seuclidean -- the normalized Euclidean distance. sqeuclidean -- the squared Euclidean distance. Distance functions between two boolean vectors (representing sets) ``u`` and ``v``. As in the case of numerical vectors, ``pdist`` is more efficient for computing the distances between all pairs. .. autosummary:: :toctree: generated/ dice -- the Dice dissimilarity. hamming -- the Hamming distance. jaccard -- the Jaccard distance. kulsinski -- the Kulsinski distance. kulczynski1 -- the Kulczynski 1 distance. rogerstanimoto -- the Rogers-Tanimoto dissimilarity. russellrao -- the Russell-Rao dissimilarity. sokalmichener -- the Sokal-Michener dissimilarity. sokalsneath -- the Sokal-Sneath dissimilarity. yule -- the Yule dissimilarity. :func:`hamming` also operates over discrete numerical vectors. """ # Copyright (C) Damian Eads, 2007-2008. New BSD License. __all__ = [ 'braycurtis', 'canberra', 'cdist', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'directed_hausdorff', 'euclidean', 'hamming', 'is_valid_dm', 'is_valid_y', 'jaccard', 'jensenshannon', 'kulsinski', 'kulczynski1', 'mahalanobis', 'matching', 'minkowski', 'num_obs_dm', 'num_obs_y', 'pdist', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'squareform', 'yule' ] import warnings import numpy as np import dataclasses from typing import List, Optional, Set, Callable from functools import partial from scipy._lib._util import _asarray_validated > from . import _distance_wrap E ImportError: /usr/lib/python3.10/site-packages/scipy/spatial/_distance_wrap.cpython-310-riscv64-linux-gnu.so: undefined symbol: npy_copysign /usr/lib/python3.10/site-packages/scipy/spatial/distance.py:120: ImportError _____________________ TestSimilarity.testOneEdgeLabelDiff ______________________ self = def testOneEdgeLabelDiff(self): G1 = nx.Graph() G1.add_node("A", label="A") G1.add_node("B", label="B") G1.add_edge("A", "B", label="a-b") G2 = nx.Graph() G2.add_node("A", label="A") G2.add_node("B", label="B") G2.add_edge("A", "B", label="bad") > assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 1 networkx/algorithms/tests/test_similarity.py:392: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ networkx/algorithms/similarity.py:190: in graph_edit_distance for vertex_path, edge_path, cost in optimize_edit_paths( networkx/algorithms/similarity.py:673: in optimize_edit_paths import scipy.optimize # call as sp.optimize /usr/lib/python3.10/site-packages/scipy/optimize/__init__.py:420: in from ._shgo import shgo /usr/lib/python3.10/site-packages/scipy/optimize/_shgo.py:9: in from scipy import spatial /usr/lib/python3.10/site-packages/scipy/spatial/__init__.py:108: in from ._geometric_slerp import geometric_slerp /usr/lib/python3.10/site-packages/scipy/spatial/_geometric_slerp.py:9: in from scipy.spatial.distance import euclidean _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ """ Distance computations (:mod:`scipy.spatial.distance`) ===================================================== .. sectionauthor:: Damian Eads Function reference ------------------ Distance matrix computation from a collection of raw observation vectors stored in a rectangular array. .. autosummary:: :toctree: generated/ pdist -- pairwise distances between observation vectors. cdist -- distances between two collections of observation vectors squareform -- convert distance matrix to a condensed one and vice versa directed_hausdorff -- directed Hausdorff distance between arrays Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix. .. autosummary:: :toctree: generated/ is_valid_dm -- checks for a valid distance matrix is_valid_y -- checks for a valid condensed distance matrix num_obs_dm -- # of observations in a distance matrix num_obs_y -- # of observations in a condensed distance matrix Distance functions between two numeric vectors ``u`` and ``v``. Computing distances over a large collection of vectors is inefficient for these functions. Use ``pdist`` for this purpose. .. autosummary:: :toctree: generated/ braycurtis -- the Bray-Curtis distance. canberra -- the Canberra distance. chebyshev -- the Chebyshev distance. cityblock -- the Manhattan distance. correlation -- the Correlation distance. cosine -- the Cosine distance. euclidean -- the Euclidean distance. jensenshannon -- the Jensen-Shannon distance. mahalanobis -- the Mahalanobis distance. minkowski -- the Minkowski distance. seuclidean -- the normalized Euclidean distance. sqeuclidean -- the squared Euclidean distance. Distance functions between two boolean vectors (representing sets) ``u`` and ``v``. As in the case of numerical vectors, ``pdist`` is more efficient for computing the distances between all pairs. .. autosummary:: :toctree: generated/ dice -- the Dice dissimilarity. hamming -- the Hamming distance. jaccard -- the Jaccard distance. kulsinski -- the Kulsinski distance. kulczynski1 -- the Kulczynski 1 distance. rogerstanimoto -- the Rogers-Tanimoto dissimilarity. russellrao -- the Russell-Rao dissimilarity. sokalmichener -- the Sokal-Michener dissimilarity. sokalsneath -- the Sokal-Sneath dissimilarity. yule -- the Yule dissimilarity. :func:`hamming` also operates over discrete numerical vectors. """ # Copyright (C) Damian Eads, 2007-2008. New BSD License. __all__ = [ 'braycurtis', 'canberra', 'cdist', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'directed_hausdorff', 'euclidean', 'hamming', 'is_valid_dm', 'is_valid_y', 'jaccard', 'jensenshannon', 'kulsinski', 'kulczynski1', 'mahalanobis', 'matching', 'minkowski', 'num_obs_dm', 'num_obs_y', 'pdist', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'squareform', 'yule' ] import warnings import numpy as np import dataclasses from typing import List, Optional, Set, Callable from functools import partial from scipy._lib._util import _asarray_validated > from . import _distance_wrap E ImportError: /usr/lib/python3.10/site-packages/scipy/spatial/_distance_wrap.cpython-310-riscv64-linux-gnu.so: undefined symbol: npy_copysign /usr/lib/python3.10/site-packages/scipy/spatial/distance.py:120: ImportError _____________________ TestSimilarity.testOneNodeLabelDiff ______________________ self = def testOneNodeLabelDiff(self): G1 = nx.Graph() G1.add_node("A", label="A") G1.add_node("B", label="B") G1.add_edge("A", "B", label="a-b") G2 = nx.Graph() G2.add_node("A", label="Z") G2.add_node("B", label="B") G2.add_edge("A", "B", label="a-b") > assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 1 networkx/algorithms/tests/test_similarity.py:403: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ networkx/algorithms/similarity.py:190: in graph_edit_distance for vertex_path, edge_path, cost in optimize_edit_paths( networkx/algorithms/similarity.py:673: in optimize_edit_paths import scipy.optimize # call as sp.optimize /usr/lib/python3.10/site-packages/scipy/optimize/__init__.py:420: in from ._shgo import shgo /usr/lib/python3.10/site-packages/scipy/optimize/_shgo.py:9: in from scipy import spatial /usr/lib/python3.10/site-packages/scipy/spatial/__init__.py:108: in from ._geometric_slerp import geometric_slerp /usr/lib/python3.10/site-packages/scipy/spatial/_geometric_slerp.py:9: in from scipy.spatial.distance import euclidean _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ """ Distance computations (:mod:`scipy.spatial.distance`) ===================================================== .. sectionauthor:: Damian Eads Function reference ------------------ Distance matrix computation from a collection of raw observation vectors stored in a rectangular array. .. autosummary:: :toctree: generated/ pdist -- pairwise distances between observation vectors. cdist -- distances between two collections of observation vectors squareform -- convert distance matrix to a condensed one and vice versa directed_hausdorff -- directed Hausdorff distance between arrays Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix. .. autosummary:: :toctree: generated/ is_valid_dm -- checks for a valid distance matrix is_valid_y -- checks for a valid condensed distance matrix num_obs_dm -- # of observations in a distance matrix num_obs_y -- # of observations in a condensed distance matrix Distance functions between two numeric vectors ``u`` and ``v``. Computing distances over a large collection of vectors is inefficient for these functions. Use ``pdist`` for this purpose. .. autosummary:: :toctree: generated/ braycurtis -- the Bray-Curtis distance. canberra -- the Canberra distance. chebyshev -- the Chebyshev distance. cityblock -- the Manhattan distance. correlation -- the Correlation distance. cosine -- the Cosine distance. euclidean -- the Euclidean distance. jensenshannon -- the Jensen-Shannon distance. mahalanobis -- the Mahalanobis distance. minkowski -- the Minkowski distance. seuclidean -- the normalized Euclidean distance. sqeuclidean -- the squared Euclidean distance. Distance functions between two boolean vectors (representing sets) ``u`` and ``v``. As in the case of numerical vectors, ``pdist`` is more efficient for computing the distances between all pairs. .. autosummary:: :toctree: generated/ dice -- the Dice dissimilarity. hamming -- the Hamming distance. jaccard -- the Jaccard distance. kulsinski -- the Kulsinski distance. kulczynski1 -- the Kulczynski 1 distance. rogerstanimoto -- the Rogers-Tanimoto dissimilarity. russellrao -- the Russell-Rao dissimilarity. sokalmichener -- the Sokal-Michener dissimilarity. sokalsneath -- the Sokal-Sneath dissimilarity. yule -- the Yule dissimilarity. :func:`hamming` also operates over discrete numerical vectors. """ # Copyright (C) Damian Eads, 2007-2008. New BSD License. __all__ = [ 'braycurtis', 'canberra', 'cdist', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'directed_hausdorff', 'euclidean', 'hamming', 'is_valid_dm', 'is_valid_y', 'jaccard', 'jensenshannon', 'kulsinski', 'kulczynski1', 'mahalanobis', 'matching', 'minkowski', 'num_obs_dm', 'num_obs_y', 'pdist', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'squareform', 'yule' ] import warnings import numpy as np import dataclasses from typing import List, Optional, Set, Callable from functools import partial from scipy._lib._util import _asarray_validated > from . import _distance_wrap E ImportError: /usr/lib/python3.10/site-packages/scipy/spatial/_distance_wrap.cpython-310-riscv64-linux-gnu.so: undefined symbol: npy_copysign /usr/lib/python3.10/site-packages/scipy/spatial/distance.py:120: ImportError _______________________ TestSimilarity.testOneExtraNode ________________________ self = def testOneExtraNode(self): G1 = nx.Graph() G1.add_node("A", label="A") G1.add_node("B", label="B") G1.add_edge("A", "B", label="a-b") G2 = nx.Graph() G2.add_node("A", label="A") G2.add_node("B", label="B") G2.add_edge("A", "B", label="a-b") G2.add_node("C", label="C") > assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 1 networkx/algorithms/tests/test_similarity.py:415: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ networkx/algorithms/similarity.py:190: in graph_edit_distance for vertex_path, edge_path, cost in optimize_edit_paths( networkx/algorithms/similarity.py:673: in optimize_edit_paths import scipy.optimize # call as sp.optimize /usr/lib/python3.10/site-packages/scipy/optimize/__init__.py:420: in from ._shgo import shgo /usr/lib/python3.10/site-packages/scipy/optimize/_shgo.py:9: in from scipy import spatial /usr/lib/python3.10/site-packages/scipy/spatial/__init__.py:108: in from ._geometric_slerp import geometric_slerp /usr/lib/python3.10/site-packages/scipy/spatial/_geometric_slerp.py:9: in from scipy.spatial.distance import euclidean _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ """ Distance computations (:mod:`scipy.spatial.distance`) ===================================================== .. sectionauthor:: Damian Eads Function reference ------------------ Distance matrix computation from a collection of raw observation vectors stored in a rectangular array. .. autosummary:: :toctree: generated/ pdist -- pairwise distances between observation vectors. cdist -- distances between two collections of observation vectors squareform -- convert distance matrix to a condensed one and vice versa directed_hausdorff -- directed Hausdorff distance between arrays Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix. .. autosummary:: :toctree: generated/ is_valid_dm -- checks for a valid distance matrix is_valid_y -- checks for a valid condensed distance matrix num_obs_dm -- # of observations in a distance matrix num_obs_y -- # of observations in a condensed distance matrix Distance functions between two numeric vectors ``u`` and ``v``. Computing distances over a large collection of vectors is inefficient for these functions. Use ``pdist`` for this purpose. .. autosummary:: :toctree: generated/ braycurtis -- the Bray-Curtis distance. canberra -- the Canberra distance. chebyshev -- the Chebyshev distance. cityblock -- the Manhattan distance. correlation -- the Correlation distance. cosine -- the Cosine distance. euclidean -- the Euclidean distance. jensenshannon -- the Jensen-Shannon distance. mahalanobis -- the Mahalanobis distance. minkowski -- the Minkowski distance. seuclidean -- the normalized Euclidean distance. sqeuclidean -- the squared Euclidean distance. Distance functions between two boolean vectors (representing sets) ``u`` and ``v``. As in the case of numerical vectors, ``pdist`` is more efficient for computing the distances between all pairs. .. autosummary:: :toctree: generated/ dice -- the Dice dissimilarity. hamming -- the Hamming distance. jaccard -- the Jaccard distance. kulsinski -- the Kulsinski distance. kulczynski1 -- the Kulczynski 1 distance. rogerstanimoto -- the Rogers-Tanimoto dissimilarity. russellrao -- the Russell-Rao dissimilarity. sokalmichener -- the Sokal-Michener dissimilarity. sokalsneath -- the Sokal-Sneath dissimilarity. yule -- the Yule dissimilarity. :func:`hamming` also operates over discrete numerical vectors. """ # Copyright (C) Damian Eads, 2007-2008. New BSD License. __all__ = [ 'braycurtis', 'canberra', 'cdist', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'directed_hausdorff', 'euclidean', 'hamming', 'is_valid_dm', 'is_valid_y', 'jaccard', 'jensenshannon', 'kulsinski', 'kulczynski1', 'mahalanobis', 'matching', 'minkowski', 'num_obs_dm', 'num_obs_y', 'pdist', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'squareform', 'yule' ] import warnings import numpy as np import dataclasses from typing import List, Optional, Set, Callable from functools import partial from scipy._lib._util import _asarray_validated > from . import _distance_wrap E ImportError: /usr/lib/python3.10/site-packages/scipy/spatial/_distance_wrap.cpython-310-riscv64-linux-gnu.so: undefined symbol: npy_copysign /usr/lib/python3.10/site-packages/scipy/spatial/distance.py:120: ImportError _______________________ TestSimilarity.testOneExtraEdge ________________________ self = def testOneExtraEdge(self): G1 = nx.Graph() G1.add_node("A", label="A") G1.add_node("B", label="B") G1.add_node("C", label="C") G1.add_node("C", label="C") G1.add_edge("A", "B", label="a-b") G2 = nx.Graph() G2.add_node("A", label="A") G2.add_node("B", label="B") G2.add_node("C", label="C") G2.add_edge("A", "B", label="a-b") G2.add_edge("A", "C", label="a-c") > assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 1 networkx/algorithms/tests/test_similarity.py:430: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ networkx/algorithms/similarity.py:190: in graph_edit_distance for vertex_path, edge_path, cost in optimize_edit_paths( networkx/algorithms/similarity.py:673: in optimize_edit_paths import scipy.optimize # call as sp.optimize /usr/lib/python3.10/site-packages/scipy/optimize/__init__.py:420: in from ._shgo import shgo /usr/lib/python3.10/site-packages/scipy/optimize/_shgo.py:9: in from scipy import spatial /usr/lib/python3.10/site-packages/scipy/spatial/__init__.py:108: in from ._geometric_slerp import geometric_slerp /usr/lib/python3.10/site-packages/scipy/spatial/_geometric_slerp.py:9: in from scipy.spatial.distance import euclidean _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ """ Distance computations (:mod:`scipy.spatial.distance`) ===================================================== .. sectionauthor:: Damian Eads Function reference ------------------ Distance matrix computation from a collection of raw observation vectors stored in a rectangular array. .. autosummary:: :toctree: generated/ pdist -- pairwise distances between observation vectors. cdist -- distances between two collections of observation vectors squareform -- convert distance matrix to a condensed one and vice versa directed_hausdorff -- directed Hausdorff distance between arrays Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix. .. autosummary:: :toctree: generated/ is_valid_dm -- checks for a valid distance matrix is_valid_y -- checks for a valid condensed distance matrix num_obs_dm -- # of observations in a distance matrix num_obs_y -- # of observations in a condensed distance matrix Distance functions between two numeric vectors ``u`` and ``v``. Computing distances over a large collection of vectors is inefficient for these functions. Use ``pdist`` for this purpose. .. autosummary:: :toctree: generated/ braycurtis -- the Bray-Curtis distance. canberra -- the Canberra distance. chebyshev -- the Chebyshev distance. cityblock -- the Manhattan distance. correlation -- the Correlation distance. cosine -- the Cosine distance. euclidean -- the Euclidean distance. jensenshannon -- the Jensen-Shannon distance. mahalanobis -- the Mahalanobis distance. minkowski -- the Minkowski distance. seuclidean -- the normalized Euclidean distance. sqeuclidean -- the squared Euclidean distance. Distance functions between two boolean vectors (representing sets) ``u`` and ``v``. As in the case of numerical vectors, ``pdist`` is more efficient for computing the distances between all pairs. .. autosummary:: :toctree: generated/ dice -- the Dice dissimilarity. hamming -- the Hamming distance. jaccard -- the Jaccard distance. kulsinski -- the Kulsinski distance. kulczynski1 -- the Kulczynski 1 distance. rogerstanimoto -- the Rogers-Tanimoto dissimilarity. russellrao -- the Russell-Rao dissimilarity. sokalmichener -- the Sokal-Michener dissimilarity. sokalsneath -- the Sokal-Sneath dissimilarity. yule -- the Yule dissimilarity. :func:`hamming` also operates over discrete numerical vectors. """ # Copyright (C) Damian Eads, 2007-2008. New BSD License. __all__ = [ 'braycurtis', 'canberra', 'cdist', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'directed_hausdorff', 'euclidean', 'hamming', 'is_valid_dm', 'is_valid_y', 'jaccard', 'jensenshannon', 'kulsinski', 'kulczynski1', 'mahalanobis', 'matching', 'minkowski', 'num_obs_dm', 'num_obs_y', 'pdist', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'squareform', 'yule' ] import warnings import numpy as np import dataclasses from typing import List, Optional, Set, Callable from functools import partial from scipy._lib._util import _asarray_validated > from . import _distance_wrap E ImportError: /usr/lib/python3.10/site-packages/scipy/spatial/_distance_wrap.cpython-310-riscv64-linux-gnu.so: undefined symbol: npy_copysign /usr/lib/python3.10/site-packages/scipy/spatial/distance.py:120: ImportError ____________________ TestSimilarity.testOneExtraNodeAndEdge ____________________ self = def testOneExtraNodeAndEdge(self): G1 = nx.Graph() G1.add_node("A", label="A") G1.add_node("B", label="B") G1.add_edge("A", "B", label="a-b") G2 = nx.Graph() G2.add_node("A", label="A") G2.add_node("B", label="B") G2.add_node("C", label="C") G2.add_edge("A", "B", label="a-b") G2.add_edge("A", "C", label="a-c") > assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 2 networkx/algorithms/tests/test_similarity.py:443: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ networkx/algorithms/similarity.py:190: in graph_edit_distance for vertex_path, edge_path, cost in optimize_edit_paths( networkx/algorithms/similarity.py:673: in optimize_edit_paths import scipy.optimize # call as sp.optimize /usr/lib/python3.10/site-packages/scipy/optimize/__init__.py:420: in from ._shgo import shgo /usr/lib/python3.10/site-packages/scipy/optimize/_shgo.py:9: in from scipy import spatial /usr/lib/python3.10/site-packages/scipy/spatial/__init__.py:108: in from ._geometric_slerp import geometric_slerp /usr/lib/python3.10/site-packages/scipy/spatial/_geometric_slerp.py:9: in from scipy.spatial.distance import euclidean _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ """ Distance computations (:mod:`scipy.spatial.distance`) ===================================================== .. sectionauthor:: Damian Eads Function reference ------------------ Distance matrix computation from a collection of raw observation vectors stored in a rectangular array. .. autosummary:: :toctree: generated/ pdist -- pairwise distances between observation vectors. cdist -- distances between two collections of observation vectors squareform -- convert distance matrix to a condensed one and vice versa directed_hausdorff -- directed Hausdorff distance between arrays Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix. .. autosummary:: :toctree: generated/ is_valid_dm -- checks for a valid distance matrix is_valid_y -- checks for a valid condensed distance matrix num_obs_dm -- # of observations in a distance matrix num_obs_y -- # of observations in a condensed distance matrix Distance functions between two numeric vectors ``u`` and ``v``. Computing distances over a large collection of vectors is inefficient for these functions. Use ``pdist`` for this purpose. .. autosummary:: :toctree: generated/ braycurtis -- the Bray-Curtis distance. canberra -- the Canberra distance. chebyshev -- the Chebyshev distance. cityblock -- the Manhattan distance. correlation -- the Correlation distance. cosine -- the Cosine distance. euclidean -- the Euclidean distance. jensenshannon -- the Jensen-Shannon distance. mahalanobis -- the Mahalanobis distance. minkowski -- the Minkowski distance. seuclidean -- the normalized Euclidean distance. sqeuclidean -- the squared Euclidean distance. Distance functions between two boolean vectors (representing sets) ``u`` and ``v``. As in the case of numerical vectors, ``pdist`` is more efficient for computing the distances between all pairs. .. autosummary:: :toctree: generated/ dice -- the Dice dissimilarity. hamming -- the Hamming distance. jaccard -- the Jaccard distance. kulsinski -- the Kulsinski distance. kulczynski1 -- the Kulczynski 1 distance. rogerstanimoto -- the Rogers-Tanimoto dissimilarity. russellrao -- the Russell-Rao dissimilarity. sokalmichener -- the Sokal-Michener dissimilarity. sokalsneath -- the Sokal-Sneath dissimilarity. yule -- the Yule dissimilarity. :func:`hamming` also operates over discrete numerical vectors. """ # Copyright (C) Damian Eads, 2007-2008. New BSD License. __all__ = [ 'braycurtis', 'canberra', 'cdist', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'directed_hausdorff', 'euclidean', 'hamming', 'is_valid_dm', 'is_valid_y', 'jaccard', 'jensenshannon', 'kulsinski', 'kulczynski1', 'mahalanobis', 'matching', 'minkowski', 'num_obs_dm', 'num_obs_y', 'pdist', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'squareform', 'yule' ] import warnings import numpy as np import dataclasses from typing import List, Optional, Set, Callable from functools import partial from scipy._lib._util import _asarray_validated > from . import _distance_wrap E ImportError: /usr/lib/python3.10/site-packages/scipy/spatial/_distance_wrap.cpython-310-riscv64-linux-gnu.so: undefined symbol: npy_copysign /usr/lib/python3.10/site-packages/scipy/spatial/distance.py:120: ImportError __________________________ TestSimilarity.testGraph1 ___________________________ self = def testGraph1(self): G1 = getCanonical() G2 = nx.Graph() G2.add_node("A", label="A") G2.add_node("B", label="B") G2.add_node("D", label="D") G2.add_node("E", label="E") G2.add_edge("A", "B", label="a-b") G2.add_edge("B", "D", label="b-d") G2.add_edge("D", "E", label="d-e") > assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 3 networkx/algorithms/tests/test_similarity.py:455: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ networkx/algorithms/similarity.py:190: in graph_edit_distance for vertex_path, edge_path, cost in optimize_edit_paths( networkx/algorithms/similarity.py:673: in optimize_edit_paths import scipy.optimize # call as sp.optimize /usr/lib/python3.10/site-packages/scipy/optimize/__init__.py:420: in from ._shgo import shgo /usr/lib/python3.10/site-packages/scipy/optimize/_shgo.py:9: in from scipy import spatial /usr/lib/python3.10/site-packages/scipy/spatial/__init__.py:108: in from ._geometric_slerp import geometric_slerp /usr/lib/python3.10/site-packages/scipy/spatial/_geometric_slerp.py:9: in from scipy.spatial.distance import euclidean _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ """ Distance computations (:mod:`scipy.spatial.distance`) ===================================================== .. sectionauthor:: Damian Eads Function reference ------------------ Distance matrix computation from a collection of raw observation vectors stored in a rectangular array. .. autosummary:: :toctree: generated/ pdist -- pairwise distances between observation vectors. cdist -- distances between two collections of observation vectors squareform -- convert distance matrix to a condensed one and vice versa directed_hausdorff -- directed Hausdorff distance between arrays Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix. .. autosummary:: :toctree: generated/ is_valid_dm -- checks for a valid distance matrix is_valid_y -- checks for a valid condensed distance matrix num_obs_dm -- # of observations in a distance matrix num_obs_y -- # of observations in a condensed distance matrix Distance functions between two numeric vectors ``u`` and ``v``. Computing distances over a large collection of vectors is inefficient for these functions. Use ``pdist`` for this purpose. .. autosummary:: :toctree: generated/ braycurtis -- the Bray-Curtis distance. canberra -- the Canberra distance. chebyshev -- the Chebyshev distance. cityblock -- the Manhattan distance. correlation -- the Correlation distance. cosine -- the Cosine distance. euclidean -- the Euclidean distance. jensenshannon -- the Jensen-Shannon distance. mahalanobis -- the Mahalanobis distance. minkowski -- the Minkowski distance. seuclidean -- the normalized Euclidean distance. sqeuclidean -- the squared Euclidean distance. Distance functions between two boolean vectors (representing sets) ``u`` and ``v``. As in the case of numerical vectors, ``pdist`` is more efficient for computing the distances between all pairs. .. autosummary:: :toctree: generated/ dice -- the Dice dissimilarity. hamming -- the Hamming distance. jaccard -- the Jaccard distance. kulsinski -- the Kulsinski distance. kulczynski1 -- the Kulczynski 1 distance. rogerstanimoto -- the Rogers-Tanimoto dissimilarity. russellrao -- the Russell-Rao dissimilarity. sokalmichener -- the Sokal-Michener dissimilarity. sokalsneath -- the Sokal-Sneath dissimilarity. yule -- the Yule dissimilarity. :func:`hamming` also operates over discrete numerical vectors. """ # Copyright (C) Damian Eads, 2007-2008. New BSD License. __all__ = [ 'braycurtis', 'canberra', 'cdist', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'directed_hausdorff', 'euclidean', 'hamming', 'is_valid_dm', 'is_valid_y', 'jaccard', 'jensenshannon', 'kulsinski', 'kulczynski1', 'mahalanobis', 'matching', 'minkowski', 'num_obs_dm', 'num_obs_y', 'pdist', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'squareform', 'yule' ] import warnings import numpy as np import dataclasses from typing import List, Optional, Set, Callable from functools import partial from scipy._lib._util import _asarray_validated > from . import _distance_wrap E ImportError: /usr/lib/python3.10/site-packages/scipy/spatial/_distance_wrap.cpython-310-riscv64-linux-gnu.so: undefined symbol: npy_copysign /usr/lib/python3.10/site-packages/scipy/spatial/distance.py:120: ImportError __________________________ TestSimilarity.testGraph2 ___________________________ self = def testGraph2(self): G1 = getCanonical() G2 = nx.Graph() G2.add_node("A", label="A") G2.add_node("B", label="B") G2.add_node("C", label="C") G2.add_node("D", label="D") G2.add_node("E", label="E") G2.add_edge("A", "B", label="a-b") G2.add_edge("B", "C", label="b-c") G2.add_edge("C", "D", label="c-d") G2.add_edge("C", "E", label="c-e") > assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 4 networkx/algorithms/tests/test_similarity.py:469: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ networkx/algorithms/similarity.py:190: in graph_edit_distance for vertex_path, edge_path, cost in optimize_edit_paths( networkx/algorithms/similarity.py:673: in optimize_edit_paths import scipy.optimize # call as sp.optimize /usr/lib/python3.10/site-packages/scipy/optimize/__init__.py:420: in from ._shgo import shgo /usr/lib/python3.10/site-packages/scipy/optimize/_shgo.py:9: in from scipy import spatial /usr/lib/python3.10/site-packages/scipy/spatial/__init__.py:108: in from ._geometric_slerp import geometric_slerp /usr/lib/python3.10/site-packages/scipy/spatial/_geometric_slerp.py:9: in from scipy.spatial.distance import euclidean _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ """ Distance computations (:mod:`scipy.spatial.distance`) ===================================================== .. sectionauthor:: Damian Eads Function reference ------------------ Distance matrix computation from a collection of raw observation vectors stored in a rectangular array. .. autosummary:: :toctree: generated/ pdist -- pairwise distances between observation vectors. cdist -- distances between two collections of observation vectors squareform -- convert distance matrix to a condensed one and vice versa directed_hausdorff -- directed Hausdorff distance between arrays Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix. .. autosummary:: :toctree: generated/ is_valid_dm -- checks for a valid distance matrix is_valid_y -- checks for a valid condensed distance matrix num_obs_dm -- # of observations in a distance matrix num_obs_y -- # of observations in a condensed distance matrix Distance functions between two numeric vectors ``u`` and ``v``. Computing distances over a large collection of vectors is inefficient for these functions. Use ``pdist`` for this purpose. .. autosummary:: :toctree: generated/ braycurtis -- the Bray-Curtis distance. canberra -- the Canberra distance. chebyshev -- the Chebyshev distance. cityblock -- the Manhattan distance. correlation -- the Correlation distance. cosine -- the Cosine distance. euclidean -- the Euclidean distance. jensenshannon -- the Jensen-Shannon distance. mahalanobis -- the Mahalanobis distance. minkowski -- the Minkowski distance. seuclidean -- the normalized Euclidean distance. sqeuclidean -- the squared Euclidean distance. Distance functions between two boolean vectors (representing sets) ``u`` and ``v``. As in the case of numerical vectors, ``pdist`` is more efficient for computing the distances between all pairs. .. autosummary:: :toctree: generated/ dice -- the Dice dissimilarity. hamming -- the Hamming distance. jaccard -- the Jaccard distance. kulsinski -- the Kulsinski distance. kulczynski1 -- the Kulczynski 1 distance. rogerstanimoto -- the Rogers-Tanimoto dissimilarity. russellrao -- the Russell-Rao dissimilarity. sokalmichener -- the Sokal-Michener dissimilarity. sokalsneath -- the Sokal-Sneath dissimilarity. yule -- the Yule dissimilarity. :func:`hamming` also operates over discrete numerical vectors. """ # Copyright (C) Damian Eads, 2007-2008. New BSD License. __all__ = [ 'braycurtis', 'canberra', 'cdist', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'directed_hausdorff', 'euclidean', 'hamming', 'is_valid_dm', 'is_valid_y', 'jaccard', 'jensenshannon', 'kulsinski', 'kulczynski1', 'mahalanobis', 'matching', 'minkowski', 'num_obs_dm', 'num_obs_y', 'pdist', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'squareform', 'yule' ] import warnings import numpy as np import dataclasses from typing import List, Optional, Set, Callable from functools import partial from scipy._lib._util import _asarray_validated > from . import _distance_wrap E ImportError: /usr/lib/python3.10/site-packages/scipy/spatial/_distance_wrap.cpython-310-riscv64-linux-gnu.so: undefined symbol: npy_copysign /usr/lib/python3.10/site-packages/scipy/spatial/distance.py:120: ImportError __________________________ TestSimilarity.testGraph3 ___________________________ self = def testGraph3(self): G1 = getCanonical() G2 = nx.Graph() G2.add_node("A", label="A") G2.add_node("B", label="B") G2.add_node("C", label="C") G2.add_node("D", label="D") G2.add_node("E", label="E") G2.add_node("F", label="F") G2.add_node("G", label="G") G2.add_edge("A", "C", label="a-c") G2.add_edge("A", "D", label="a-d") G2.add_edge("D", "E", label="d-e") G2.add_edge("D", "F", label="d-f") G2.add_edge("D", "G", label="d-g") G2.add_edge("E", "B", label="e-b") > assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 12 networkx/algorithms/tests/test_similarity.py:487: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ networkx/algorithms/similarity.py:190: in graph_edit_distance for vertex_path, edge_path, cost in optimize_edit_paths( networkx/algorithms/similarity.py:673: in optimize_edit_paths import scipy.optimize # call as sp.optimize /usr/lib/python3.10/site-packages/scipy/optimize/__init__.py:420: in from ._shgo import shgo /usr/lib/python3.10/site-packages/scipy/optimize/_shgo.py:9: in from scipy import spatial /usr/lib/python3.10/site-packages/scipy/spatial/__init__.py:108: in from ._geometric_slerp import geometric_slerp /usr/lib/python3.10/site-packages/scipy/spatial/_geometric_slerp.py:9: in from scipy.spatial.distance import euclidean _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ """ Distance computations (:mod:`scipy.spatial.distance`) ===================================================== .. sectionauthor:: Damian Eads Function reference ------------------ Distance matrix computation from a collection of raw observation vectors stored in a rectangular array. .. autosummary:: :toctree: generated/ pdist -- pairwise distances between observation vectors. cdist -- distances between two collections of observation vectors squareform -- convert distance matrix to a condensed one and vice versa directed_hausdorff -- directed Hausdorff distance between arrays Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix. .. autosummary:: :toctree: generated/ is_valid_dm -- checks for a valid distance matrix is_valid_y -- checks for a valid condensed distance matrix num_obs_dm -- # of observations in a distance matrix num_obs_y -- # of observations in a condensed distance matrix Distance functions between two numeric vectors ``u`` and ``v``. Computing distances over a large collection of vectors is inefficient for these functions. Use ``pdist`` for this purpose. .. autosummary:: :toctree: generated/ braycurtis -- the Bray-Curtis distance. canberra -- the Canberra distance. chebyshev -- the Chebyshev distance. cityblock -- the Manhattan distance. correlation -- the Correlation distance. cosine -- the Cosine distance. euclidean -- the Euclidean distance. jensenshannon -- the Jensen-Shannon distance. mahalanobis -- the Mahalanobis distance. minkowski -- the Minkowski distance. seuclidean -- the normalized Euclidean distance. sqeuclidean -- the squared Euclidean distance. Distance functions between two boolean vectors (representing sets) ``u`` and ``v``. As in the case of numerical vectors, ``pdist`` is more efficient for computing the distances between all pairs. .. autosummary:: :toctree: generated/ dice -- the Dice dissimilarity. hamming -- the Hamming distance. jaccard -- the Jaccard distance. kulsinski -- the Kulsinski distance. kulczynski1 -- the Kulczynski 1 distance. rogerstanimoto -- the Rogers-Tanimoto dissimilarity. russellrao -- the Russell-Rao dissimilarity. sokalmichener -- the Sokal-Michener dissimilarity. sokalsneath -- the Sokal-Sneath dissimilarity. yule -- the Yule dissimilarity. :func:`hamming` also operates over discrete numerical vectors. """ # Copyright (C) Damian Eads, 2007-2008. New BSD License. __all__ = [ 'braycurtis', 'canberra', 'cdist', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'directed_hausdorff', 'euclidean', 'hamming', 'is_valid_dm', 'is_valid_y', 'jaccard', 'jensenshannon', 'kulsinski', 'kulczynski1', 'mahalanobis', 'matching', 'minkowski', 'num_obs_dm', 'num_obs_y', 'pdist', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'squareform', 'yule' ] import warnings import numpy as np import dataclasses from typing import List, Optional, Set, Callable from functools import partial from scipy._lib._util import _asarray_validated > from . import _distance_wrap E ImportError: /usr/lib/python3.10/site-packages/scipy/spatial/_distance_wrap.cpython-310-riscv64-linux-gnu.so: undefined symbol: npy_copysign /usr/lib/python3.10/site-packages/scipy/spatial/distance.py:120: ImportError __________________________ TestSimilarity.testGraph4 ___________________________ self = def testGraph4(self): G1 = getCanonical() G2 = nx.Graph() G2.add_node("A", label="A") G2.add_node("B", label="B") G2.add_node("C", label="C") G2.add_node("D", label="D") G2.add_edge("A", "B", label="a-b") G2.add_edge("B", "C", label="b-c") G2.add_edge("C", "D", label="c-d") > assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 2 networkx/algorithms/tests/test_similarity.py:499: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ networkx/algorithms/similarity.py:190: in graph_edit_distance for vertex_path, edge_path, cost in optimize_edit_paths( networkx/algorithms/similarity.py:673: in optimize_edit_paths import scipy.optimize # call as sp.optimize /usr/lib/python3.10/site-packages/scipy/optimize/__init__.py:420: in from ._shgo import shgo /usr/lib/python3.10/site-packages/scipy/optimize/_shgo.py:9: in from scipy import spatial /usr/lib/python3.10/site-packages/scipy/spatial/__init__.py:108: in from ._geometric_slerp import geometric_slerp /usr/lib/python3.10/site-packages/scipy/spatial/_geometric_slerp.py:9: in from scipy.spatial.distance import euclidean _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ """ Distance computations (:mod:`scipy.spatial.distance`) ===================================================== .. sectionauthor:: Damian Eads Function reference ------------------ Distance matrix computation from a collection of raw observation vectors stored in a rectangular array. .. autosummary:: :toctree: generated/ pdist -- pairwise distances between observation vectors. cdist -- distances between two collections of observation vectors squareform -- convert distance matrix to a condensed one and vice versa directed_hausdorff -- directed Hausdorff distance between arrays Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix. .. autosummary:: :toctree: generated/ is_valid_dm -- checks for a valid distance matrix is_valid_y -- checks for a valid condensed distance matrix num_obs_dm -- # of observations in a distance matrix num_obs_y -- # of observations in a condensed distance matrix Distance functions between two numeric vectors ``u`` and ``v``. Computing distances over a large collection of vectors is inefficient for these functions. Use ``pdist`` for this purpose. .. autosummary:: :toctree: generated/ braycurtis -- the Bray-Curtis distance. canberra -- the Canberra distance. chebyshev -- the Chebyshev distance. cityblock -- the Manhattan distance. correlation -- the Correlation distance. cosine -- the Cosine distance. euclidean -- the Euclidean distance. jensenshannon -- the Jensen-Shannon distance. mahalanobis -- the Mahalanobis distance. minkowski -- the Minkowski distance. seuclidean -- the normalized Euclidean distance. sqeuclidean -- the squared Euclidean distance. Distance functions between two boolean vectors (representing sets) ``u`` and ``v``. As in the case of numerical vectors, ``pdist`` is more efficient for computing the distances between all pairs. .. autosummary:: :toctree: generated/ dice -- the Dice dissimilarity. hamming -- the Hamming distance. jaccard -- the Jaccard distance. kulsinski -- the Kulsinski distance. kulczynski1 -- the Kulczynski 1 distance. rogerstanimoto -- the Rogers-Tanimoto dissimilarity. russellrao -- the Russell-Rao dissimilarity. sokalmichener -- the Sokal-Michener dissimilarity. sokalsneath -- the Sokal-Sneath dissimilarity. yule -- the Yule dissimilarity. :func:`hamming` also operates over discrete numerical vectors. """ # Copyright (C) Damian Eads, 2007-2008. New BSD License. __all__ = [ 'braycurtis', 'canberra', 'cdist', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'directed_hausdorff', 'euclidean', 'hamming', 'is_valid_dm', 'is_valid_y', 'jaccard', 'jensenshannon', 'kulsinski', 'kulczynski1', 'mahalanobis', 'matching', 'minkowski', 'num_obs_dm', 'num_obs_y', 'pdist', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'squareform', 'yule' ] import warnings import numpy as np import dataclasses from typing import List, Optional, Set, Callable from functools import partial from scipy._lib._util import _asarray_validated > from . import _distance_wrap E ImportError: /usr/lib/python3.10/site-packages/scipy/spatial/_distance_wrap.cpython-310-riscv64-linux-gnu.so: undefined symbol: npy_copysign /usr/lib/python3.10/site-packages/scipy/spatial/distance.py:120: ImportError _________________________ TestSimilarity.testGraph4_a __________________________ self = def testGraph4_a(self): G1 = getCanonical() G2 = nx.Graph() G2.add_node("A", label="A") G2.add_node("B", label="B") G2.add_node("C", label="C") G2.add_node("D", label="D") G2.add_edge("A", "B", label="a-b") G2.add_edge("B", "C", label="b-c") G2.add_edge("A", "D", label="a-d") > assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 2 networkx/algorithms/tests/test_similarity.py:511: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ networkx/algorithms/similarity.py:190: in graph_edit_distance for vertex_path, edge_path, cost in optimize_edit_paths( networkx/algorithms/similarity.py:673: in optimize_edit_paths import scipy.optimize # call as sp.optimize /usr/lib/python3.10/site-packages/scipy/optimize/__init__.py:420: in from ._shgo import shgo /usr/lib/python3.10/site-packages/scipy/optimize/_shgo.py:9: in from scipy import spatial /usr/lib/python3.10/site-packages/scipy/spatial/__init__.py:108: in from ._geometric_slerp import geometric_slerp /usr/lib/python3.10/site-packages/scipy/spatial/_geometric_slerp.py:9: in from scipy.spatial.distance import euclidean _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ """ Distance computations (:mod:`scipy.spatial.distance`) ===================================================== .. sectionauthor:: Damian Eads Function reference ------------------ Distance matrix computation from a collection of raw observation vectors stored in a rectangular array. .. autosummary:: :toctree: generated/ pdist -- pairwise distances between observation vectors. cdist -- distances between two collections of observation vectors squareform -- convert distance matrix to a condensed one and vice versa directed_hausdorff -- directed Hausdorff distance between arrays Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix. .. autosummary:: :toctree: generated/ is_valid_dm -- checks for a valid distance matrix is_valid_y -- checks for a valid condensed distance matrix num_obs_dm -- # of observations in a distance matrix num_obs_y -- # of observations in a condensed distance matrix Distance functions between two numeric vectors ``u`` and ``v``. Computing distances over a large collection of vectors is inefficient for these functions. Use ``pdist`` for this purpose. .. autosummary:: :toctree: generated/ braycurtis -- the Bray-Curtis distance. canberra -- the Canberra distance. chebyshev -- the Chebyshev distance. cityblock -- the Manhattan distance. correlation -- the Correlation distance. cosine -- the Cosine distance. euclidean -- the Euclidean distance. jensenshannon -- the Jensen-Shannon distance. mahalanobis -- the Mahalanobis distance. minkowski -- the Minkowski distance. seuclidean -- the normalized Euclidean distance. sqeuclidean -- the squared Euclidean distance. Distance functions between two boolean vectors (representing sets) ``u`` and ``v``. As in the case of numerical vectors, ``pdist`` is more efficient for computing the distances between all pairs. .. autosummary:: :toctree: generated/ dice -- the Dice dissimilarity. hamming -- the Hamming distance. jaccard -- the Jaccard distance. kulsinski -- the Kulsinski distance. kulczynski1 -- the Kulczynski 1 distance. rogerstanimoto -- the Rogers-Tanimoto dissimilarity. russellrao -- the Russell-Rao dissimilarity. sokalmichener -- the Sokal-Michener dissimilarity. sokalsneath -- the Sokal-Sneath dissimilarity. yule -- the Yule dissimilarity. :func:`hamming` also operates over discrete numerical vectors. """ # Copyright (C) Damian Eads, 2007-2008. New BSD License. __all__ = [ 'braycurtis', 'canberra', 'cdist', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'directed_hausdorff', 'euclidean', 'hamming', 'is_valid_dm', 'is_valid_y', 'jaccard', 'jensenshannon', 'kulsinski', 'kulczynski1', 'mahalanobis', 'matching', 'minkowski', 'num_obs_dm', 'num_obs_y', 'pdist', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'squareform', 'yule' ] import warnings import numpy as np import dataclasses from typing import List, Optional, Set, Callable from functools import partial from scipy._lib._util import _asarray_validated > from . import _distance_wrap E ImportError: /usr/lib/python3.10/site-packages/scipy/spatial/_distance_wrap.cpython-310-riscv64-linux-gnu.so: undefined symbol: npy_copysign /usr/lib/python3.10/site-packages/scipy/spatial/distance.py:120: ImportError _________________________ TestSimilarity.testGraph4_b __________________________ self = def testGraph4_b(self): G1 = getCanonical() G2 = nx.Graph() G2.add_node("A", label="A") G2.add_node("B", label="B") G2.add_node("C", label="C") G2.add_node("D", label="D") G2.add_edge("A", "B", label="a-b") G2.add_edge("B", "C", label="b-c") G2.add_edge("B", "D", label="bad") > assert graph_edit_distance(G1, G2, node_match=nmatch, edge_match=ematch) == 1 networkx/algorithms/tests/test_similarity.py:523: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ networkx/algorithms/similarity.py:190: in graph_edit_distance for vertex_path, edge_path, cost in optimize_edit_paths( networkx/algorithms/similarity.py:673: in optimize_edit_paths import scipy.optimize # call as sp.optimize /usr/lib/python3.10/site-packages/scipy/optimize/__init__.py:420: in from ._shgo import shgo /usr/lib/python3.10/site-packages/scipy/optimize/_shgo.py:9: in from scipy import spatial /usr/lib/python3.10/site-packages/scipy/spatial/__init__.py:108: in from ._geometric_slerp import geometric_slerp /usr/lib/python3.10/site-packages/scipy/spatial/_geometric_slerp.py:9: in from scipy.spatial.distance import euclidean _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ """ Distance computations (:mod:`scipy.spatial.distance`) ===================================================== .. sectionauthor:: Damian Eads Function reference ------------------ Distance matrix computation from a collection of raw observation vectors stored in a rectangular array. .. autosummary:: :toctree: generated/ pdist -- pairwise distances between observation vectors. cdist -- distances between two collections of observation vectors squareform -- convert distance matrix to a condensed one and vice versa directed_hausdorff -- directed Hausdorff distance between arrays Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix. .. autosummary:: :toctree: generated/ is_valid_dm -- checks for a valid distance matrix is_valid_y -- checks for a valid condensed distance matrix num_obs_dm -- # of observations in a distance matrix num_obs_y -- # of observations in a condensed distance matrix Distance functions between two numeric vectors ``u`` and ``v``. Computing distances over a large collection of vectors is inefficient for these functions. Use ``pdist`` for this purpose. .. autosummary:: :toctree: generated/ braycurtis -- the Bray-Curtis distance. canberra -- the Canberra distance. chebyshev -- the Chebyshev distance. cityblock -- the Manhattan distance. correlation -- the Correlation distance. cosine -- the Cosine distance. euclidean -- the Euclidean distance. jensenshannon -- the Jensen-Shannon distance. mahalanobis -- the Mahalanobis distance. minkowski -- the Minkowski distance. seuclidean -- the normalized Euclidean distance. sqeuclidean -- the squared Euclidean distance. Distance functions between two boolean vectors (representing sets) ``u`` and ``v``. As in the case of numerical vectors, ``pdist`` is more efficient for computing the distances between all pairs. .. autosummary:: :toctree: generated/ dice -- the Dice dissimilarity. hamming -- the Hamming distance. jaccard -- the Jaccard distance. kulsinski -- the Kulsinski distance. kulczynski1 -- the Kulczynski 1 distance. rogerstanimoto -- the Rogers-Tanimoto dissimilarity. russellrao -- the Russell-Rao dissimilarity. sokalmichener -- the Sokal-Michener dissimilarity. sokalsneath -- the Sokal-Sneath dissimilarity. yule -- the Yule dissimilarity. :func:`hamming` also operates over discrete numerical vectors. """ # Copyright (C) Damian Eads, 2007-2008. New BSD License. __all__ = [ 'braycurtis', 'canberra', 'cdist', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'directed_hausdorff', 'euclidean', 'hamming', 'is_valid_dm', 'is_valid_y', 'jaccard', 'jensenshannon', 'kulsinski', 'kulczynski1', 'mahalanobis', 'matching', 'minkowski', 'num_obs_dm', 'num_obs_y', 'pdist', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'squareform', 'yule' ] import warnings import numpy as np import dataclasses from typing import List, Optional, Set, Callable from functools import partial from scipy._lib._util import _asarray_validated > from . import _distance_wrap E ImportError: /usr/lib/python3.10/site-packages/scipy/spatial/_distance_wrap.cpython-310-riscv64-linux-gnu.so: undefined symbol: npy_copysign /usr/lib/python3.10/site-packages/scipy/spatial/distance.py:120: ImportError __________________________ TestLayout.test_smoke_int ___________________________ self = def test_smoke_int(self): G = self.Gi nx.random_layout(G) nx.circular_layout(G) nx.planar_layout(G) nx.spring_layout(G) nx.fruchterman_reingold_layout(G) nx.fruchterman_reingold_layout(self.bigG) nx.spectral_layout(G) nx.spectral_layout(G.to_directed()) nx.spectral_layout(self.bigG) nx.spectral_layout(self.bigG.to_directed()) nx.shell_layout(G) nx.spiral_layout(G) > nx.kamada_kawai_layout(G) networkx/drawing/tests/test_layout.py:66: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ networkx/drawing/layout.py:709: in kamada_kawai_layout pos = _kamada_kawai_solve(dist_mtx, pos_arr, dim) networkx/drawing/layout.py:722: in _kamada_kawai_solve import scipy.optimize # call as sp.optimize /usr/lib/python3.10/site-packages/scipy/optimize/__init__.py:420: in from ._shgo import shgo /usr/lib/python3.10/site-packages/scipy/optimize/_shgo.py:9: in from scipy import spatial /usr/lib/python3.10/site-packages/scipy/spatial/__init__.py:108: in from ._geometric_slerp import geometric_slerp /usr/lib/python3.10/site-packages/scipy/spatial/_geometric_slerp.py:9: in from scipy.spatial.distance import euclidean _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ """ Distance computations (:mod:`scipy.spatial.distance`) ===================================================== .. sectionauthor:: Damian Eads Function reference ------------------ Distance matrix computation from a collection of raw observation vectors stored in a rectangular array. .. autosummary:: :toctree: generated/ pdist -- pairwise distances between observation vectors. cdist -- distances between two collections of observation vectors squareform -- convert distance matrix to a condensed one and vice versa directed_hausdorff -- directed Hausdorff distance between arrays Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix. .. autosummary:: :toctree: generated/ is_valid_dm -- checks for a valid distance matrix is_valid_y -- checks for a valid condensed distance matrix num_obs_dm -- # of observations in a distance matrix num_obs_y -- # of observations in a condensed distance matrix Distance functions between two numeric vectors ``u`` and ``v``. Computing distances over a large collection of vectors is inefficient for these functions. Use ``pdist`` for this purpose. .. autosummary:: :toctree: generated/ braycurtis -- the Bray-Curtis distance. canberra -- the Canberra distance. chebyshev -- the Chebyshev distance. cityblock -- the Manhattan distance. correlation -- the Correlation distance. cosine -- the Cosine distance. euclidean -- the Euclidean distance. jensenshannon -- the Jensen-Shannon distance. mahalanobis -- the Mahalanobis distance. minkowski -- the Minkowski distance. seuclidean -- the normalized Euclidean distance. sqeuclidean -- the squared Euclidean distance. Distance functions between two boolean vectors (representing sets) ``u`` and ``v``. As in the case of numerical vectors, ``pdist`` is more efficient for computing the distances between all pairs. .. autosummary:: :toctree: generated/ dice -- the Dice dissimilarity. hamming -- the Hamming distance. jaccard -- the Jaccard distance. kulsinski -- the Kulsinski distance. kulczynski1 -- the Kulczynski 1 distance. rogerstanimoto -- the Rogers-Tanimoto dissimilarity. russellrao -- the Russell-Rao dissimilarity. sokalmichener -- the Sokal-Michener dissimilarity. sokalsneath -- the Sokal-Sneath dissimilarity. yule -- the Yule dissimilarity. :func:`hamming` also operates over discrete numerical vectors. """ # Copyright (C) Damian Eads, 2007-2008. New BSD License. __all__ = [ 'braycurtis', 'canberra', 'cdist', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'directed_hausdorff', 'euclidean', 'hamming', 'is_valid_dm', 'is_valid_y', 'jaccard', 'jensenshannon', 'kulsinski', 'kulczynski1', 'mahalanobis', 'matching', 'minkowski', 'num_obs_dm', 'num_obs_y', 'pdist', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'squareform', 'yule' ] import warnings import numpy as np import dataclasses from typing import List, Optional, Set, Callable from functools import partial from scipy._lib._util import _asarray_validated > from . import _distance_wrap E ImportError: /usr/lib/python3.10/site-packages/scipy/spatial/_distance_wrap.cpython-310-riscv64-linux-gnu.so: undefined symbol: npy_copysign /usr/lib/python3.10/site-packages/scipy/spatial/distance.py:120: ImportError _________________________ TestLayout.test_smoke_string _________________________ self = def test_smoke_string(self): G = self.Gs nx.random_layout(G) nx.circular_layout(G) nx.planar_layout(G) nx.spring_layout(G) nx.fruchterman_reingold_layout(G) nx.spectral_layout(G) nx.shell_layout(G) nx.spiral_layout(G) > nx.kamada_kawai_layout(G) networkx/drawing/tests/test_layout.py:80: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ networkx/drawing/layout.py:709: in kamada_kawai_layout pos = _kamada_kawai_solve(dist_mtx, pos_arr, dim) networkx/drawing/layout.py:722: in _kamada_kawai_solve import scipy.optimize # call as sp.optimize /usr/lib/python3.10/site-packages/scipy/optimize/__init__.py:420: in from ._shgo import shgo /usr/lib/python3.10/site-packages/scipy/optimize/_shgo.py:9: in from scipy import spatial /usr/lib/python3.10/site-packages/scipy/spatial/__init__.py:108: in from ._geometric_slerp import geometric_slerp /usr/lib/python3.10/site-packages/scipy/spatial/_geometric_slerp.py:9: in from scipy.spatial.distance import euclidean _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ """ Distance computations (:mod:`scipy.spatial.distance`) ===================================================== .. sectionauthor:: Damian Eads Function reference ------------------ Distance matrix computation from a collection of raw observation vectors stored in a rectangular array. .. autosummary:: :toctree: generated/ pdist -- pairwise distances between observation vectors. cdist -- distances between two collections of observation vectors squareform -- convert distance matrix to a condensed one and vice versa directed_hausdorff -- directed Hausdorff distance between arrays Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix. .. autosummary:: :toctree: generated/ is_valid_dm -- checks for a valid distance matrix is_valid_y -- checks for a valid condensed distance matrix num_obs_dm -- # of observations in a distance matrix num_obs_y -- # of observations in a condensed distance matrix Distance functions between two numeric vectors ``u`` and ``v``. Computing distances over a large collection of vectors is inefficient for these functions. Use ``pdist`` for this purpose. .. autosummary:: :toctree: generated/ braycurtis -- the Bray-Curtis distance. canberra -- the Canberra distance. chebyshev -- the Chebyshev distance. cityblock -- the Manhattan distance. correlation -- the Correlation distance. cosine -- the Cosine distance. euclidean -- the Euclidean distance. jensenshannon -- the Jensen-Shannon distance. mahalanobis -- the Mahalanobis distance. minkowski -- the Minkowski distance. seuclidean -- the normalized Euclidean distance. sqeuclidean -- the squared Euclidean distance. Distance functions between two boolean vectors (representing sets) ``u`` and ``v``. As in the case of numerical vectors, ``pdist`` is more efficient for computing the distances between all pairs. .. autosummary:: :toctree: generated/ dice -- the Dice dissimilarity. hamming -- the Hamming distance. jaccard -- the Jaccard distance. kulsinski -- the Kulsinski distance. kulczynski1 -- the Kulczynski 1 distance. rogerstanimoto -- the Rogers-Tanimoto dissimilarity. russellrao -- the Russell-Rao dissimilarity. sokalmichener -- the Sokal-Michener dissimilarity. sokalsneath -- the Sokal-Sneath dissimilarity. yule -- the Yule dissimilarity. :func:`hamming` also operates over discrete numerical vectors. """ # Copyright (C) Damian Eads, 2007-2008. New BSD License. __all__ = [ 'braycurtis', 'canberra', 'cdist', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'directed_hausdorff', 'euclidean', 'hamming', 'is_valid_dm', 'is_valid_y', 'jaccard', 'jensenshannon', 'kulsinski', 'kulczynski1', 'mahalanobis', 'matching', 'minkowski', 'num_obs_dm', 'num_obs_y', 'pdist', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'squareform', 'yule' ] import warnings import numpy as np import dataclasses from typing import List, Optional, Set, Callable from functools import partial from scipy._lib._util import _asarray_validated > from . import _distance_wrap E ImportError: /usr/lib/python3.10/site-packages/scipy/spatial/_distance_wrap.cpython-310-riscv64-linux-gnu.so: undefined symbol: npy_copysign /usr/lib/python3.10/site-packages/scipy/spatial/distance.py:120: ImportError _____________________ TestLayout.test_scale_and_center_arg _____________________ self = def test_scale_and_center_arg(self): sc = self.check_scale_and_center c = (4, 5) G = nx.complete_graph(9) G.add_node(9) sc(nx.random_layout(G, center=c), scale=0.5, center=(4.5, 5.5)) # rest can have 2*scale length: [-scale, scale] sc(nx.spring_layout(G, scale=2, center=c), scale=2, center=c) sc(nx.spectral_layout(G, scale=2, center=c), scale=2, center=c) sc(nx.circular_layout(G, scale=2, center=c), scale=2, center=c) sc(nx.shell_layout(G, scale=2, center=c), scale=2, center=c) sc(nx.spiral_layout(G, scale=2, center=c), scale=2, center=c) > sc(nx.kamada_kawai_layout(G, scale=2, center=c), scale=2, center=c) networkx/drawing/tests/test_layout.py:106: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ networkx/drawing/layout.py:709: in kamada_kawai_layout pos = _kamada_kawai_solve(dist_mtx, pos_arr, dim) networkx/drawing/layout.py:722: in _kamada_kawai_solve import scipy.optimize # call as sp.optimize /usr/lib/python3.10/site-packages/scipy/optimize/__init__.py:420: in from ._shgo import shgo /usr/lib/python3.10/site-packages/scipy/optimize/_shgo.py:9: in from scipy import spatial /usr/lib/python3.10/site-packages/scipy/spatial/__init__.py:108: in from ._geometric_slerp import geometric_slerp /usr/lib/python3.10/site-packages/scipy/spatial/_geometric_slerp.py:9: in from scipy.spatial.distance import euclidean _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ """ Distance computations (:mod:`scipy.spatial.distance`) ===================================================== .. sectionauthor:: Damian Eads Function reference ------------------ Distance matrix computation from a collection of raw observation vectors stored in a rectangular array. .. autosummary:: :toctree: generated/ pdist -- pairwise distances between observation vectors. cdist -- distances between two collections of observation vectors squareform -- convert distance matrix to a condensed one and vice versa directed_hausdorff -- directed Hausdorff distance between arrays Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix. .. autosummary:: :toctree: generated/ is_valid_dm -- checks for a valid distance matrix is_valid_y -- checks for a valid condensed distance matrix num_obs_dm -- # of observations in a distance matrix num_obs_y -- # of observations in a condensed distance matrix Distance functions between two numeric vectors ``u`` and ``v``. Computing distances over a large collection of vectors is inefficient for these functions. Use ``pdist`` for this purpose. .. autosummary:: :toctree: generated/ braycurtis -- the Bray-Curtis distance. canberra -- the Canberra distance. chebyshev -- the Chebyshev distance. cityblock -- the Manhattan distance. correlation -- the Correlation distance. cosine -- the Cosine distance. euclidean -- the Euclidean distance. jensenshannon -- the Jensen-Shannon distance. mahalanobis -- the Mahalanobis distance. minkowski -- the Minkowski distance. seuclidean -- the normalized Euclidean distance. sqeuclidean -- the squared Euclidean distance. Distance functions between two boolean vectors (representing sets) ``u`` and ``v``. As in the case of numerical vectors, ``pdist`` is more efficient for computing the distances between all pairs. .. autosummary:: :toctree: generated/ dice -- the Dice dissimilarity. hamming -- the Hamming distance. jaccard -- the Jaccard distance. kulsinski -- the Kulsinski distance. kulczynski1 -- the Kulczynski 1 distance. rogerstanimoto -- the Rogers-Tanimoto dissimilarity. russellrao -- the Russell-Rao dissimilarity. sokalmichener -- the Sokal-Michener dissimilarity. sokalsneath -- the Sokal-Sneath dissimilarity. yule -- the Yule dissimilarity. :func:`hamming` also operates over discrete numerical vectors. """ # Copyright (C) Damian Eads, 2007-2008. New BSD License. __all__ = [ 'braycurtis', 'canberra', 'cdist', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'directed_hausdorff', 'euclidean', 'hamming', 'is_valid_dm', 'is_valid_y', 'jaccard', 'jensenshannon', 'kulsinski', 'kulczynski1', 'mahalanobis', 'matching', 'minkowski', 'num_obs_dm', 'num_obs_y', 'pdist', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'squareform', 'yule' ] import warnings import numpy as np import dataclasses from typing import List, Optional, Set, Callable from functools import partial from scipy._lib._util import _asarray_validated > from . import _distance_wrap E ImportError: /usr/lib/python3.10/site-packages/scipy/spatial/_distance_wrap.cpython-310-riscv64-linux-gnu.so: undefined symbol: npy_copysign /usr/lib/python3.10/site-packages/scipy/spatial/distance.py:120: ImportError ___________________ TestLayout.test_default_scale_and_center ___________________ self = def test_default_scale_and_center(self): sc = self.check_scale_and_center c = (0, 0) G = nx.complete_graph(9) G.add_node(9) sc(nx.random_layout(G), scale=0.5, center=(0.5, 0.5)) sc(nx.spring_layout(G), scale=1, center=c) sc(nx.spectral_layout(G), scale=1, center=c) sc(nx.circular_layout(G), scale=1, center=c) sc(nx.shell_layout(G), scale=1, center=c) sc(nx.spiral_layout(G), scale=1, center=c) > sc(nx.kamada_kawai_layout(G), scale=1, center=c) networkx/drawing/tests/test_layout.py:131: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ networkx/drawing/layout.py:709: in kamada_kawai_layout pos = _kamada_kawai_solve(dist_mtx, pos_arr, dim) networkx/drawing/layout.py:722: in _kamada_kawai_solve import scipy.optimize # call as sp.optimize /usr/lib/python3.10/site-packages/scipy/optimize/__init__.py:420: in from ._shgo import shgo /usr/lib/python3.10/site-packages/scipy/optimize/_shgo.py:9: in from scipy import spatial /usr/lib/python3.10/site-packages/scipy/spatial/__init__.py:108: in from ._geometric_slerp import geometric_slerp /usr/lib/python3.10/site-packages/scipy/spatial/_geometric_slerp.py:9: in from scipy.spatial.distance import euclidean _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ """ Distance computations (:mod:`scipy.spatial.distance`) ===================================================== .. sectionauthor:: Damian Eads Function reference ------------------ Distance matrix computation from a collection of raw observation vectors stored in a rectangular array. .. autosummary:: :toctree: generated/ pdist -- pairwise distances between observation vectors. cdist -- distances between two collections of observation vectors squareform -- convert distance matrix to a condensed one and vice versa directed_hausdorff -- directed Hausdorff distance between arrays Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix. .. autosummary:: :toctree: generated/ is_valid_dm -- checks for a valid distance matrix is_valid_y -- checks for a valid condensed distance matrix num_obs_dm -- # of observations in a distance matrix num_obs_y -- # of observations in a condensed distance matrix Distance functions between two numeric vectors ``u`` and ``v``. Computing distances over a large collection of vectors is inefficient for these functions. Use ``pdist`` for this purpose. .. autosummary:: :toctree: generated/ braycurtis -- the Bray-Curtis distance. canberra -- the Canberra distance. chebyshev -- the Chebyshev distance. cityblock -- the Manhattan distance. correlation -- the Correlation distance. cosine -- the Cosine distance. euclidean -- the Euclidean distance. jensenshannon -- the Jensen-Shannon distance. mahalanobis -- the Mahalanobis distance. minkowski -- the Minkowski distance. seuclidean -- the normalized Euclidean distance. sqeuclidean -- the squared Euclidean distance. Distance functions between two boolean vectors (representing sets) ``u`` and ``v``. As in the case of numerical vectors, ``pdist`` is more efficient for computing the distances between all pairs. .. autosummary:: :toctree: generated/ dice -- the Dice dissimilarity. hamming -- the Hamming distance. jaccard -- the Jaccard distance. kulsinski -- the Kulsinski distance. kulczynski1 -- the Kulczynski 1 distance. rogerstanimoto -- the Rogers-Tanimoto dissimilarity. russellrao -- the Russell-Rao dissimilarity. sokalmichener -- the Sokal-Michener dissimilarity. sokalsneath -- the Sokal-Sneath dissimilarity. yule -- the Yule dissimilarity. :func:`hamming` also operates over discrete numerical vectors. """ # Copyright (C) Damian Eads, 2007-2008. New BSD License. __all__ = [ 'braycurtis', 'canberra', 'cdist', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'directed_hausdorff', 'euclidean', 'hamming', 'is_valid_dm', 'is_valid_y', 'jaccard', 'jensenshannon', 'kulsinski', 'kulczynski1', 'mahalanobis', 'matching', 'minkowski', 'num_obs_dm', 'num_obs_y', 'pdist', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'squareform', 'yule' ] import warnings import numpy as np import dataclasses from typing import List, Optional, Set, Callable from functools import partial from scipy._lib._util import _asarray_validated > from . import _distance_wrap E ImportError: /usr/lib/python3.10/site-packages/scipy/spatial/_distance_wrap.cpython-310-riscv64-linux-gnu.so: undefined symbol: npy_copysign /usr/lib/python3.10/site-packages/scipy/spatial/distance.py:120: ImportError __________________________ test_spectral_graph_forge ___________________________ def test_spectral_graph_forge(): G = karate_club_graph() seed = 54321 # common cases, just checking node number preserving and difference # between identity and modularity cases > H = spectral_graph_forge(G, 0.1, transformation="identity", seed=seed) networkx/generators/tests/test_spectral_graph_forge.py:21: _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ networkx/utils/decorators.py:816: in func return argmap._lazy_compile(__wrapper)(*args, **kwargs) compilation 1455:4: in argmap_spectral_graph_forge_1452 ??? networkx/generators/spectral_graph_forge.py:145: in spectral_graph_forge import scipy.stats # call as sp.stats /usr/lib/python3.10/site-packages/scipy/stats/__init__.py:453: in from ._stats_py import * /usr/lib/python3.10/site-packages/scipy/stats/_stats_py.py:38: in from scipy.spatial.distance import cdist /usr/lib/python3.10/site-packages/scipy/spatial/__init__.py:108: in from ._geometric_slerp import geometric_slerp /usr/lib/python3.10/site-packages/scipy/spatial/_geometric_slerp.py:9: in from scipy.spatial.distance import euclidean _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ """ Distance computations (:mod:`scipy.spatial.distance`) ===================================================== .. sectionauthor:: Damian Eads Function reference ------------------ Distance matrix computation from a collection of raw observation vectors stored in a rectangular array. .. autosummary:: :toctree: generated/ pdist -- pairwise distances between observation vectors. cdist -- distances between two collections of observation vectors squareform -- convert distance matrix to a condensed one and vice versa directed_hausdorff -- directed Hausdorff distance between arrays Predicates for checking the validity of distance matrices, both condensed and redundant. Also contained in this module are functions for computing the number of observations in a distance matrix. .. autosummary:: :toctree: generated/ is_valid_dm -- checks for a valid distance matrix is_valid_y -- checks for a valid condensed distance matrix num_obs_dm -- # of observations in a distance matrix num_obs_y -- # of observations in a condensed distance matrix Distance functions between two numeric vectors ``u`` and ``v``. Computing distances over a large collection of vectors is inefficient for these functions. Use ``pdist`` for this purpose. .. autosummary:: :toctree: generated/ braycurtis -- the Bray-Curtis distance. canberra -- the Canberra distance. chebyshev -- the Chebyshev distance. cityblock -- the Manhattan distance. correlation -- the Correlation distance. cosine -- the Cosine distance. euclidean -- the Euclidean distance. jensenshannon -- the Jensen-Shannon distance. mahalanobis -- the Mahalanobis distance. minkowski -- the Minkowski distance. seuclidean -- the normalized Euclidean distance. sqeuclidean -- the squared Euclidean distance. Distance functions between two boolean vectors (representing sets) ``u`` and ``v``. As in the case of numerical vectors, ``pdist`` is more efficient for computing the distances between all pairs. .. autosummary:: :toctree: generated/ dice -- the Dice dissimilarity. hamming -- the Hamming distance. jaccard -- the Jaccard distance. kulsinski -- the Kulsinski distance. kulczynski1 -- the Kulczynski 1 distance. rogerstanimoto -- the Rogers-Tanimoto dissimilarity. russellrao -- the Russell-Rao dissimilarity. sokalmichener -- the Sokal-Michener dissimilarity. sokalsneath -- the Sokal-Sneath dissimilarity. yule -- the Yule dissimilarity. :func:`hamming` also operates over discrete numerical vectors. """ # Copyright (C) Damian Eads, 2007-2008. New BSD License. __all__ = [ 'braycurtis', 'canberra', 'cdist', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'directed_hausdorff', 'euclidean', 'hamming', 'is_valid_dm', 'is_valid_y', 'jaccard', 'jensenshannon', 'kulsinski', 'kulczynski1', 'mahalanobis', 'matching', 'minkowski', 'num_obs_dm', 'num_obs_y', 'pdist', 'rogerstanimoto', 'russellrao', 'seuclidean', 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'squareform', 'yule' ] import warnings import numpy as np import dataclasses from typing import List, Optional, Set, Callable from functools import partial from scipy._lib._util import _asarray_validated > from . import _distance_wrap E ImportError: /usr/lib/python3.10/site-packages/scipy/spatial/_distance_wrap.cpython-310-riscv64-linux-gnu.so: undefined symbol: npy_copysign /usr/lib/python3.10/site-packages/scipy/spatial/distance.py:120: ImportError =============================== warnings summary =============================== networkx/utils/decorators.py:292 /build/python-networkx/src/networkx-networkx-2.7.1/networkx/utils/decorators.py:292: DeprecationWarning: preserve_random_state is deprecated and will be removed in 3.0. warnings.warn(msg, DeprecationWarning) networkx/classes/tests/test_ordered.py::TestOrderedFeatures::test_subgraph_order /build/python-networkx/src/networkx-networkx-2.7.1/networkx/classes/tests/test_ordered.py:22: DeprecationWarning: OrderedDiGraph is deprecated and will be removed in version 3.0. Use `DiGraph` instead, which guarantees order is preserved for Python >= 3.7 cls.G = nx.OrderedDiGraph() networkx/linalg/tests/test_algebraic_connectivity.py::TestSpectralOrdering::test_cycle[lobpcg-False-expected_order0] /build/python-networkx/src/networkx-networkx-2.7.1/networkx/linalg/algebraicconnectivity.py:301: UserWarning: Exited at iteration 10 with accuracies [0.02743716] not reaching the requested tolerance 1e-08. sigma, X = sp.sparse.linalg.lobpcg( networkx/linalg/tests/test_algebraic_connectivity.py::TestSpectralOrdering::test_cycle[lobpcg-True-expected_order1] /build/python-networkx/src/networkx-networkx-2.7.1/networkx/linalg/algebraicconnectivity.py:301: UserWarning: Exited at iteration 10 with accuracies [0.00056623] not reaching the requested tolerance 1e-08. sigma, X = sp.sparse.linalg.lobpcg( -- Docs: https://docs.pytest.org/en/stable/how-to/capture-warnings.html =========================== short test summary info ============================ FAILED networkx/algorithms/approximation/tests/test_traveling_salesman.py::test_held_karp_ascent FAILED networkx/algorithms/approximation/tests/test_traveling_salesman.py::test_ascent_fractional_solution FAILED networkx/algorithms/approximation/tests/test_traveling_salesman.py::test_ascent_method_asymmetric FAILED networkx/algorithms/approximation/tests/test_traveling_salesman.py::test_ascent_method_asymmetric_2 FAILED networkx/algorithms/approximation/tests/test_traveling_salesman.py::test_held_karp_ascent_asymmetric_3 FAILED networkx/algorithms/approximation/tests/test_traveling_salesman.py::test_held_karp_ascent_fractional_asymmetric FAILED networkx/algorithms/approximation/tests/test_traveling_salesman.py::test_asadpour_tsp FAILED networkx/algorithms/approximation/tests/test_traveling_salesman.py::test_asadpour_real_world FAILED networkx/algorithms/approximation/tests/test_traveling_salesman.py::test_asadpour_real_world_path FAILED networkx/algorithms/assortativity/tests/test_correlation.py::TestDegreeMixingCorrelation::test_degree_pearson_assortativity_undirected FAILED networkx/algorithms/assortativity/tests/test_correlation.py::TestDegreeMixingCorrelation::test_degree_pearson_assortativity_directed FAILED networkx/algorithms/assortativity/tests/test_correlation.py::TestDegreeMixingCorrelation::test_degree_pearson_assortativity_directed2 FAILED networkx/algorithms/assortativity/tests/test_correlation.py::TestDegreeMixingCorrelation::test_degree_pearson_assortativity_multigraph FAILED networkx/algorithms/bipartite/tests/test_matching.py::TestMinimumWeightFullMatching::test_minimum_weight_full_matching_incomplete_graph FAILED networkx/algorithms/bipartite/tests/test_matching.py::TestMinimumWeightFullMatching::test_minimum_weight_full_matching_with_no_full_matching FAILED networkx/algorithms/bipartite/tests/test_matching.py::TestMinimumWeightFullMatching::test_minimum_weight_full_matching_square FAILED networkx/algorithms/bipartite/tests/test_matching.py::TestMinimumWeightFullMatching::test_minimum_weight_full_matching_smaller_left FAILED networkx/algorithms/bipartite/tests/test_matching.py::TestMinimumWeightFullMatching::test_minimum_weight_full_matching_smaller_top_nodes_right FAILED networkx/algorithms/bipartite/tests/test_matching.py::TestMinimumWeightFullMatching::test_minimum_weight_full_matching_smaller_right FAILED networkx/algorithms/bipartite/tests/test_matching.py::TestMinimumWeightFullMatching::test_minimum_weight_full_matching_negative_weights FAILED networkx/algorithms/bipartite/tests/test_matching.py::TestMinimumWeightFullMatching::test_minimum_weight_full_matching_different_weight_key FAILED networkx/algorithms/tests/test_similarity.py::TestSimilarity::test_graph_edit_distance_roots_and_timeout FAILED networkx/algorithms/tests/test_similarity.py::TestSimilarity::test_graph_edit_distance FAILED networkx/algorithms/tests/test_similarity.py::TestSimilarity::test_graph_edit_distance_node_match FAILED networkx/algorithms/tests/test_similarity.py::TestSimilarity::test_graph_edit_distance_edge_match FAILED networkx/algorithms/tests/test_similarity.py::TestSimilarity::test_graph_edit_distance_node_cost FAILED networkx/algorithms/tests/test_similarity.py::TestSimilarity::test_graph_edit_distance_edge_cost FAILED networkx/algorithms/tests/test_similarity.py::TestSimilarity::test_graph_edit_distance_upper_bound FAILED networkx/algorithms/tests/test_similarity.py::TestSimilarity::test_optimal_edit_paths FAILED networkx/algorithms/tests/test_similarity.py::TestSimilarity::test_optimize_graph_edit_distance FAILED networkx/algorithms/tests/test_similarity.py::TestSimilarity::test_selfloops FAILED networkx/algorithms/tests/test_similarity.py::TestSimilarity::test_digraph FAILED networkx/algorithms/tests/test_similarity.py::TestSimilarity::test_multigraph FAILED networkx/algorithms/tests/test_similarity.py::TestSimilarity::test_multidigraph FAILED networkx/algorithms/tests/test_similarity.py::TestSimilarity::testCopy FAILED networkx/algorithms/tests/test_similarity.py::TestSimilarity::testSame FAILED networkx/algorithms/tests/test_similarity.py::TestSimilarity::testOneEdgeLabelDiff FAILED networkx/algorithms/tests/test_similarity.py::TestSimilarity::testOneNodeLabelDiff FAILED networkx/algorithms/tests/test_similarity.py::TestSimilarity::testOneExtraNode FAILED networkx/algorithms/tests/test_similarity.py::TestSimilarity::testOneExtraEdge FAILED networkx/algorithms/tests/test_similarity.py::TestSimilarity::testOneExtraNodeAndEdge FAILED networkx/algorithms/tests/test_similarity.py::TestSimilarity::testGraph1 FAILED networkx/algorithms/tests/test_similarity.py::TestSimilarity::testGraph2 FAILED networkx/algorithms/tests/test_similarity.py::TestSimilarity::testGraph3 FAILED networkx/algorithms/tests/test_similarity.py::TestSimilarity::testGraph4 FAILED networkx/algorithms/tests/test_similarity.py::TestSimilarity::testGraph4_a FAILED networkx/algorithms/tests/test_similarity.py::TestSimilarity::testGraph4_b FAILED networkx/drawing/tests/test_layout.py::TestLayout::test_smoke_int - Im... FAILED networkx/drawing/tests/test_layout.py::TestLayout::test_smoke_string FAILED networkx/drawing/tests/test_layout.py::TestLayout::test_scale_and_center_arg FAILED networkx/drawing/tests/test_layout.py::TestLayout::test_default_scale_and_center FAILED networkx/generators/tests/test_spectral_graph_forge.py::test_spectral_graph_forge = 52 failed, 4721 passed, 13 skipped, 3 xfailed, 4 warnings in 593.46s (0:09:53) = ==> ERROR: A failure occurred in check().  Aborting... ==> ERROR: Build failed, check /var/lib/archbuild/extra-riscv64/felix29/build receiving incremental file list python-networkx-2.7.1-1-riscv64-build.log python-networkx-2.7.1-1-riscv64-check.log sent 62 bytes received 19,409 bytes 12,980.67 bytes/sec total size is 397,360 speedup is 20.41