From f3f9b53868cf530cdef015b69a4a224d6215bc40 Mon Sep 17 00:00:00 2001 From: Yannick Stoll Date: Thu, 4 Aug 2022 16:41:42 +0200 Subject: [PATCH 01/17] up gitignore --- .gitignore | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index 683938b5b..2d58b1a80 100644 --- a/.gitignore +++ b/.gitignore @@ -86,7 +86,7 @@ celerybeat-schedule .venv venv/ ENV/ - +.easyocr_venv # Spyder project settings .spyderproject .spyproject From 39c624b6f4186c97e50264502deb6c6f5fa7690c Mon Sep 17 00:00:00 2001 From: Yannick Stoll Date: Fri, 19 Aug 2022 15:33:47 +0200 Subject: [PATCH 02/17] up gitignore --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index 2d58b1a80..a62ed18d9 100644 --- a/.gitignore +++ b/.gitignore @@ -107,3 +107,4 @@ ENV/ .vscode/ .vs/ .idea/ +easy_ocr_dev.ipynb \ No newline at end of file From 592e0d97a4c5f7b7b5aafbaf99d054db3babbb25 Mon Sep 17 00:00:00 2001 From: Yannick Stoll Date: Fri, 26 Aug 2022 15:51:09 +0200 Subject: [PATCH 03/17] up gitignore --- .gitignore | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/.gitignore b/.gitignore index a62ed18d9..21e0450c2 100644 --- a/.gitignore +++ b/.gitignore @@ -107,4 +107,8 @@ ENV/ .vscode/ .vs/ .idea/ -easy_ocr_dev.ipynb \ No newline at end of file + +# Personnal files +easy_ocr_dev.ipynb +easy_ocr_dev.py +notes_persos.txt From 99c58e227cfd97ffdc9b7057bc0bca068ad20f50 Mon Sep 17 00:00:00 2001 From: Yannick Stoll Date: Fri, 26 Aug 2022 16:02:32 +0200 Subject: [PATCH 04/17] commit initial on branch github_master sync from master branch on Github official Easyocr repo --- .easyocr_venv/bin/Activate.ps1 | 241 + .easyocr_venv/bin/activate | 76 + .easyocr_venv/bin/activate.csh | 37 + .easyocr_venv/bin/activate.fish | 75 + .easyocr_venv/bin/black | 8 + .easyocr_venv/bin/blackd | 8 + .easyocr_venv/bin/convert-caffe2-to-onnx | 8 + .easyocr_venv/bin/convert-onnx-to-caffe2 | 8 + .easyocr_venv/bin/easy_install | 8 + .easyocr_venv/bin/easy_install-3.8 | 8 + .easyocr_venv/bin/easyocr | 12 + .easyocr_venv/bin/epylint | 8 + .easyocr_venv/bin/f2py | 8 + .easyocr_venv/bin/f2py3 | 8 + .easyocr_venv/bin/f2py3.8 | 8 + .easyocr_venv/bin/get_objgraph | 54 + .easyocr_venv/bin/imageio_download_bin | 8 + .easyocr_venv/bin/imageio_remove_bin | 8 + .easyocr_venv/bin/ipython | 8 + .easyocr_venv/bin/ipython3 | 8 + .easyocr_venv/bin/isort | 8 + .easyocr_venv/bin/isort-identify-imports | 8 + .easyocr_venv/bin/jupyter | 8 + .easyocr_venv/bin/jupyter-kernel | 8 + .easyocr_venv/bin/jupyter-kernelspec | 8 + .easyocr_venv/bin/jupyter-migrate | 8 + .easyocr_venv/bin/jupyter-run | 8 + .easyocr_venv/bin/jupyter-troubleshoot | 8 + .easyocr_venv/bin/lsm2bin | 8 + .easyocr_venv/bin/nltk | 8 + .easyocr_venv/bin/pip | 8 + .easyocr_venv/bin/pip3 | 8 + .easyocr_venv/bin/pip3.8 | 8 + .easyocr_venv/bin/pybidi | 8 + .easyocr_venv/bin/pygmentize | 8 + .easyocr_venv/bin/pylint | 8 + .easyocr_venv/bin/pylint-config | 8 + .easyocr_venv/bin/pyreverse | 8 + .easyocr_venv/bin/python | 1 + .easyocr_venv/bin/python3 | 1 + .easyocr_venv/bin/python3.8 | 1 + .easyocr_venv/bin/skivi | 8 + .easyocr_venv/bin/symilar | 8 + .easyocr_venv/bin/tiff2fsspec | 8 + .easyocr_venv/bin/tiffcomment | 8 + .easyocr_venv/bin/tifffile | 8 + .easyocr_venv/bin/tqdm | 8 + .easyocr_venv/bin/undill | 22 + .easyocr_venv/lib64 | 1 + .easyocr_venv/pyvenv.cfg | 3 + .../share/doc/networkx-2.8.5/LICENSE.txt | 37 + .../examples/3d_drawing/README.txt | 2 + .../examples/3d_drawing/mayavi2_spring.py | 43 + .../examples/3d_drawing/plot_basic.py | 51 + .../doc/networkx-2.8.5/examples/README.txt | 8 + .../examples/algorithms/README.txt | 2 + .../algorithms/WormNet.v3.benchmark.txt | 78736 ++++++++++++++++ .../algorithms/hartford_drug.edgelist | 338 + .../examples/algorithms/plot_beam_search.py | 112 + .../algorithms/plot_betweenness_centrality.py | 83 + .../examples/algorithms/plot_blockmodel.py | 79 + .../examples/algorithms/plot_circuits.py | 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.../examples/drawing/plot_ego_graph.py | 35 + .../examples/drawing/plot_eigenvalues.py | 22 + .../examples/drawing/plot_four_grids.py | 52 + .../drawing/plot_house_with_colors.py | 26 + .../examples/drawing/plot_knuth_miles.py | 142 + .../drawing/plot_labels_and_colors.py | 54 + .../drawing/plot_multipartite_graph.py | 41 + .../examples/drawing/plot_node_colormap.py | 15 + .../examples/drawing/plot_rainbow_coloring.py | 68 + .../drawing/plot_random_geometric_graph.py | 44 + .../examples/drawing/plot_sampson.py | 47 + .../examples/drawing/plot_selfloops.py | 29 + .../examples/drawing/plot_simple_path.py | 14 + .../examples/drawing/plot_spectral_grid.py | 58 + .../examples/drawing/plot_tsp.py | 52 + .../examples/drawing/plot_unix_email.py | 62 + .../examples/drawing/plot_weighted_graph.py | 44 + .../examples/drawing/unix_email.mbox | 84 + .../networkx-2.8.5/examples/graph/README.txt | 2 + .../examples/graph/plot_degree_sequence.py | 34 + .../examples/graph/plot_erdos_renyi.py | 36 + .../graph/plot_expected_degree_sequence.py | 20 + .../examples/graph/plot_football.py | 44 + .../examples/graph/plot_karate_club.py | 25 + .../examples/graph/plot_morse_trie.py | 96 + .../graph/plot_napoleon_russian_campaign.py | 133 + .../examples/graph/plot_roget.py | 80 + .../examples/graph/plot_words.py | 88 + .../examples/graph/roget_dat.txt.gz | Bin 0 -> 15758 bytes .../examples/graph/words_dat.txt.gz | Bin 0 -> 33695 bytes .../examples/subclass/README.txt | 2 + .../examples/subclass/plot_antigraph.py | 192 + .../examples/subclass/plot_printgraph.py | 88 + .../share/jupyter/kernels/python3/kernel.json | 14 + .../jupyter/kernels/python3/logo-32x32.png | Bin 0 -> 1084 bytes .../jupyter/kernels/python3/logo-64x64.png | Bin 0 -> 2180 bytes .easyocr_venv/share/man/man1/ipython.1 | 60 + .../CacheControl-0.12.6-py2.py3-none-any.whl | Bin 0 -> 28023 bytes .../appdirs-1.4.3-py2.py3-none-any.whl | Bin 0 -> 18776 bytes .../certifi-2019.11.28-py2.py3-none-any.whl | Bin 0 -> 164552 bytes 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.easyocr_venv/share/python-wheels/pep517-0.8.2-py2.py3-none-any.whl create mode 100644 .easyocr_venv/share/python-wheels/pip-20.0.2-py2.py3-none-any.whl create mode 100644 .easyocr_venv/share/python-wheels/pkg_resources-0.0.0-py2.py3-none-any.whl create mode 100644 .easyocr_venv/share/python-wheels/progress-1.5-py2.py3-none-any.whl create mode 100644 .easyocr_venv/share/python-wheels/pyparsing-2.4.6-py2.py3-none-any.whl create mode 100644 .easyocr_venv/share/python-wheels/requests-2.22.0-py2.py3-none-any.whl create mode 100644 .easyocr_venv/share/python-wheels/retrying-1.3.3-py2.py3-none-any.whl create mode 100644 .easyocr_venv/share/python-wheels/setuptools-44.0.0-py2.py3-none-any.whl create mode 100644 .easyocr_venv/share/python-wheels/six-1.14.0-py2.py3-none-any.whl create mode 100644 .easyocr_venv/share/python-wheels/toml-0.10.0-py2.py3-none-any.whl create mode 100644 .easyocr_venv/share/python-wheels/urllib3-1.25.8-py2.py3-none-any.whl create mode 100644 .easyocr_venv/share/python-wheels/webencodings-0.5.1-py2.py3-none-any.whl create mode 100644 .easyocr_venv/share/python-wheels/wheel-0.34.2-py2.py3-none-any.whl create mode 100644 easy_ocr_dev.ipynb create mode 100755 easy_ocr_dev.py create mode 100644 notes_persos.txt diff --git a/.easyocr_venv/bin/Activate.ps1 b/.easyocr_venv/bin/Activate.ps1 new file mode 100644 index 000000000..2fb3852c3 --- /dev/null +++ b/.easyocr_venv/bin/Activate.ps1 @@ -0,0 +1,241 @@ +<# +.Synopsis +Activate a Python virtual environment for the current PowerShell session. + +.Description +Pushes the python executable for a virtual environment to the front of the +$Env:PATH environment variable and sets the prompt to signify that you are +in a Python virtual environment. Makes use of the command line switches as +well as the `pyvenv.cfg` file values present in the virtual environment. + +.Parameter VenvDir +Path to the directory that contains the virtual environment to activate. The +default value for this is the parent of the directory that the Activate.ps1 +script is located within. + +.Parameter Prompt +The prompt prefix to display when this virtual environment is activated. By +default, this prompt is the name of the virtual environment folder (VenvDir) +surrounded by parentheses and followed by a single space (ie. '(.venv) '). + +.Example +Activate.ps1 +Activates the Python virtual environment that contains the Activate.ps1 script. + +.Example +Activate.ps1 -Verbose +Activates the Python virtual environment that contains the Activate.ps1 script, +and shows extra information about the activation as it executes. + +.Example +Activate.ps1 -VenvDir C:\Users\MyUser\Common\.venv +Activates the Python virtual environment located in the specified location. + +.Example +Activate.ps1 -Prompt "MyPython" +Activates the Python virtual environment that contains the Activate.ps1 script, +and prefixes the current prompt with the specified string (surrounded in +parentheses) while the virtual environment is active. + +.Notes +On Windows, it may be required to enable this Activate.ps1 script by setting the +execution policy for the user. You can do this by issuing the following PowerShell +command: + +PS C:\> Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser + +For more information on Execution Policies: +https://go.microsoft.com/fwlink/?LinkID=135170 + +#> +Param( + [Parameter(Mandatory = $false)] + [String] + $VenvDir, + [Parameter(Mandatory = $false)] + [String] + $Prompt +) + +<# Function declarations --------------------------------------------------- #> + +<# +.Synopsis +Remove all shell session elements added by the Activate script, including the +addition of the virtual environment's Python executable from the beginning of +the PATH variable. + +.Parameter NonDestructive +If present, do not remove this function from the global namespace for the +session. + +#> +function global:deactivate ([switch]$NonDestructive) { + # Revert to original values + + # The prior prompt: + if (Test-Path -Path Function:_OLD_VIRTUAL_PROMPT) { + Copy-Item -Path Function:_OLD_VIRTUAL_PROMPT -Destination Function:prompt + Remove-Item -Path Function:_OLD_VIRTUAL_PROMPT + } + + # The prior PYTHONHOME: + if (Test-Path -Path Env:_OLD_VIRTUAL_PYTHONHOME) { + Copy-Item -Path Env:_OLD_VIRTUAL_PYTHONHOME -Destination Env:PYTHONHOME + Remove-Item -Path Env:_OLD_VIRTUAL_PYTHONHOME + } + + # The prior PATH: + if (Test-Path -Path Env:_OLD_VIRTUAL_PATH) { + Copy-Item -Path Env:_OLD_VIRTUAL_PATH -Destination Env:PATH + Remove-Item -Path Env:_OLD_VIRTUAL_PATH + } + + # Just remove the VIRTUAL_ENV altogether: + if (Test-Path -Path Env:VIRTUAL_ENV) { + Remove-Item -Path env:VIRTUAL_ENV + } + + # Just remove the _PYTHON_VENV_PROMPT_PREFIX altogether: + if (Get-Variable -Name "_PYTHON_VENV_PROMPT_PREFIX" -ErrorAction SilentlyContinue) { + Remove-Variable -Name _PYTHON_VENV_PROMPT_PREFIX -Scope Global -Force + } + + # Leave deactivate function in the global namespace if requested: + if (-not $NonDestructive) { + Remove-Item -Path function:deactivate + } +} + +<# +.Description +Get-PyVenvConfig parses the values from the pyvenv.cfg file located in the +given folder, and returns them in a map. + +For each line in the pyvenv.cfg file, if that line can be parsed into exactly +two strings separated by `=` (with any amount of whitespace surrounding the =) +then it is considered a `key = value` line. The left hand string is the key, +the right hand is the value. + +If the value starts with a `'` or a `"` then the first and last character is +stripped from the value before being captured. + +.Parameter ConfigDir +Path to the directory that contains the `pyvenv.cfg` file. +#> +function Get-PyVenvConfig( + [String] + $ConfigDir +) { + Write-Verbose "Given ConfigDir=$ConfigDir, obtain values in pyvenv.cfg" + + # Ensure the file exists, and issue a warning if it doesn't (but still allow the function to continue). + $pyvenvConfigPath = Join-Path -Resolve -Path $ConfigDir -ChildPath 'pyvenv.cfg' -ErrorAction Continue + + # An empty map will be returned if no config file is found. + $pyvenvConfig = @{ } + + if ($pyvenvConfigPath) { + + Write-Verbose "File exists, parse `key = value` lines" + $pyvenvConfigContent = Get-Content -Path $pyvenvConfigPath + + $pyvenvConfigContent | ForEach-Object { + $keyval = $PSItem -split "\s*=\s*", 2 + if ($keyval[0] -and $keyval[1]) { + $val = $keyval[1] + + # Remove extraneous quotations around a string value. + if ("'""".Contains($val.Substring(0, 1))) { + $val = $val.Substring(1, $val.Length - 2) + } + + $pyvenvConfig[$keyval[0]] = $val + Write-Verbose "Adding Key: '$($keyval[0])'='$val'" + } + } + } + return $pyvenvConfig +} + + +<# Begin Activate script --------------------------------------------------- #> + +# Determine the containing directory of this script +$VenvExecPath = Split-Path -Parent $MyInvocation.MyCommand.Definition +$VenvExecDir = Get-Item -Path $VenvExecPath + +Write-Verbose "Activation script is located in path: '$VenvExecPath'" +Write-Verbose "VenvExecDir Fullname: '$($VenvExecDir.FullName)" +Write-Verbose "VenvExecDir Name: '$($VenvExecDir.Name)" + +# Set values required in priority: CmdLine, ConfigFile, Default +# First, get the location of the virtual environment, it might not be +# VenvExecDir if specified on the command line. +if ($VenvDir) { + Write-Verbose "VenvDir given as parameter, using '$VenvDir' to determine values" +} +else { + Write-Verbose "VenvDir not given as a parameter, using parent directory name as VenvDir." + $VenvDir = $VenvExecDir.Parent.FullName.TrimEnd("\\/") + Write-Verbose "VenvDir=$VenvDir" +} + +# Next, read the `pyvenv.cfg` file to determine any required value such +# as `prompt`. +$pyvenvCfg = Get-PyVenvConfig -ConfigDir $VenvDir + +# Next, set the prompt from the command line, or the config file, or +# just use the name of the virtual environment folder. +if ($Prompt) { + Write-Verbose "Prompt specified as argument, using '$Prompt'" +} +else { + Write-Verbose "Prompt not specified as argument to script, checking pyvenv.cfg value" + if ($pyvenvCfg -and $pyvenvCfg['prompt']) { + Write-Verbose " Setting based on value in pyvenv.cfg='$($pyvenvCfg['prompt'])'" + $Prompt = $pyvenvCfg['prompt']; + } + else { + Write-Verbose " Setting prompt based on parent's directory's name. (Is the directory name passed to venv module when creating the virutal environment)" + Write-Verbose " Got leaf-name of $VenvDir='$(Split-Path -Path $venvDir -Leaf)'" + $Prompt = Split-Path -Path $venvDir -Leaf + } +} + +Write-Verbose "Prompt = '$Prompt'" +Write-Verbose "VenvDir='$VenvDir'" + +# Deactivate any currently active virtual environment, but leave the +# deactivate function in place. +deactivate -nondestructive + +# Now set the environment variable VIRTUAL_ENV, used by many tools to determine +# that there is an activated venv. +$env:VIRTUAL_ENV = $VenvDir + +if (-not $Env:VIRTUAL_ENV_DISABLE_PROMPT) { + + Write-Verbose "Setting prompt to '$Prompt'" + + # Set the prompt to include the env name + # Make sure _OLD_VIRTUAL_PROMPT is global + function global:_OLD_VIRTUAL_PROMPT { "" } + Copy-Item -Path function:prompt -Destination function:_OLD_VIRTUAL_PROMPT + New-Variable -Name _PYTHON_VENV_PROMPT_PREFIX -Description "Python virtual environment prompt prefix" -Scope Global -Option ReadOnly -Visibility Public -Value $Prompt + + function global:prompt { + Write-Host -NoNewline -ForegroundColor Green "($_PYTHON_VENV_PROMPT_PREFIX) " + _OLD_VIRTUAL_PROMPT + } +} + +# Clear PYTHONHOME +if (Test-Path -Path Env:PYTHONHOME) { + Copy-Item -Path Env:PYTHONHOME -Destination Env:_OLD_VIRTUAL_PYTHONHOME + Remove-Item -Path Env:PYTHONHOME +} + +# Add the venv to the PATH +Copy-Item -Path Env:PATH -Destination Env:_OLD_VIRTUAL_PATH +$Env:PATH = "$VenvExecDir$([System.IO.Path]::PathSeparator)$Env:PATH" diff --git a/.easyocr_venv/bin/activate b/.easyocr_venv/bin/activate new file mode 100644 index 000000000..c4fb0190d --- /dev/null +++ b/.easyocr_venv/bin/activate @@ -0,0 +1,76 @@ +# This file must be used with "source bin/activate" *from bash* +# you cannot run it directly + +deactivate () { + # reset old environment variables + if [ -n "${_OLD_VIRTUAL_PATH:-}" ] ; then + PATH="${_OLD_VIRTUAL_PATH:-}" + export PATH + unset _OLD_VIRTUAL_PATH + fi + if [ -n "${_OLD_VIRTUAL_PYTHONHOME:-}" ] ; then + PYTHONHOME="${_OLD_VIRTUAL_PYTHONHOME:-}" + export PYTHONHOME + unset _OLD_VIRTUAL_PYTHONHOME + fi + + # This should detect bash and zsh, which have a hash command that must + # be called to get it to forget past commands. Without forgetting + # past commands the $PATH changes we made may not be respected + if [ -n "${BASH:-}" -o -n "${ZSH_VERSION:-}" ] ; then + hash -r + fi + + if [ -n "${_OLD_VIRTUAL_PS1:-}" ] ; then + PS1="${_OLD_VIRTUAL_PS1:-}" + export PS1 + unset _OLD_VIRTUAL_PS1 + fi + + unset VIRTUAL_ENV + if [ ! "${1:-}" = "nondestructive" ] ; then + # Self destruct! + unset -f deactivate + fi +} + +# unset irrelevant variables +deactivate nondestructive + +VIRTUAL_ENV="/home/ystoll/PixelForest/easyocr/.easyocr_venv" +export VIRTUAL_ENV + +_OLD_VIRTUAL_PATH="$PATH" +PATH="$VIRTUAL_ENV/bin:$PATH" +export PATH + +# unset PYTHONHOME if set +# this will fail if PYTHONHOME is set to the empty string (which is bad anyway) +# could use `if (set -u; : $PYTHONHOME) ;` in bash +if [ -n "${PYTHONHOME:-}" ] ; then + _OLD_VIRTUAL_PYTHONHOME="${PYTHONHOME:-}" + unset PYTHONHOME +fi + +if [ -z "${VIRTUAL_ENV_DISABLE_PROMPT:-}" ] ; then + _OLD_VIRTUAL_PS1="${PS1:-}" + if [ "x(.easyocr_venv) " != x ] ; then + PS1="(.easyocr_venv) ${PS1:-}" + else + if [ "`basename \"$VIRTUAL_ENV\"`" = "__" ] ; then + # special case for Aspen magic directories + # see https://aspen.io/ + PS1="[`basename \`dirname \"$VIRTUAL_ENV\"\``] $PS1" + else + PS1="(`basename \"$VIRTUAL_ENV\"`)$PS1" + fi + fi + export PS1 +fi + +# This should detect bash and zsh, which have a hash command that must +# be called to get it to forget past commands. Without forgetting +# past commands the $PATH changes we made may not be respected +if [ -n "${BASH:-}" -o -n "${ZSH_VERSION:-}" ] ; then + hash -r +fi diff --git a/.easyocr_venv/bin/activate.csh b/.easyocr_venv/bin/activate.csh new file mode 100644 index 000000000..bff1445c7 --- /dev/null +++ b/.easyocr_venv/bin/activate.csh @@ -0,0 +1,37 @@ +# This file must be used with "source bin/activate.csh" *from csh*. +# You cannot run it directly. +# Created by Davide Di Blasi . +# Ported to Python 3.3 venv by Andrew Svetlov + +alias deactivate 'test $?_OLD_VIRTUAL_PATH != 0 && setenv PATH "$_OLD_VIRTUAL_PATH" && unset _OLD_VIRTUAL_PATH; rehash; test $?_OLD_VIRTUAL_PROMPT != 0 && set prompt="$_OLD_VIRTUAL_PROMPT" && unset _OLD_VIRTUAL_PROMPT; unsetenv VIRTUAL_ENV; test "\!:*" != "nondestructive" && unalias deactivate' + +# Unset irrelevant variables. +deactivate nondestructive + +setenv VIRTUAL_ENV "/home/ystoll/PixelForest/easyocr/.easyocr_venv" + +set _OLD_VIRTUAL_PATH="$PATH" +setenv PATH "$VIRTUAL_ENV/bin:$PATH" + + +set _OLD_VIRTUAL_PROMPT="$prompt" + +if (! "$?VIRTUAL_ENV_DISABLE_PROMPT") then + if (".easyocr_venv" != "") then + set env_name = ".easyocr_venv" + else + if (`basename "VIRTUAL_ENV"` == "__") then + # special case for Aspen magic directories + # see https://aspen.io/ + set env_name = `basename \`dirname "$VIRTUAL_ENV"\`` + else + set env_name = `basename "$VIRTUAL_ENV"` + endif + endif + set prompt = "[$env_name] $prompt" + unset env_name +endif + +alias pydoc python -m pydoc + +rehash diff --git a/.easyocr_venv/bin/activate.fish b/.easyocr_venv/bin/activate.fish new file mode 100644 index 000000000..02beb4006 --- /dev/null +++ b/.easyocr_venv/bin/activate.fish @@ -0,0 +1,75 @@ +# This file must be used with ". bin/activate.fish" *from fish* (http://fishshell.org) +# you cannot run it directly + +function deactivate -d "Exit virtualenv and return to normal shell environment" + # reset old environment variables + if test -n "$_OLD_VIRTUAL_PATH" + set -gx PATH $_OLD_VIRTUAL_PATH + set -e _OLD_VIRTUAL_PATH + end + if test -n "$_OLD_VIRTUAL_PYTHONHOME" + set -gx PYTHONHOME $_OLD_VIRTUAL_PYTHONHOME + set -e _OLD_VIRTUAL_PYTHONHOME + end + + if test -n "$_OLD_FISH_PROMPT_OVERRIDE" + functions -e fish_prompt + set -e _OLD_FISH_PROMPT_OVERRIDE + functions -c _old_fish_prompt fish_prompt + functions -e _old_fish_prompt + end + + set -e VIRTUAL_ENV + if test "$argv[1]" != "nondestructive" + # Self destruct! + functions -e deactivate + end +end + +# unset irrelevant variables +deactivate nondestructive + +set -gx VIRTUAL_ENV "/home/ystoll/PixelForest/easyocr/.easyocr_venv" + +set -gx _OLD_VIRTUAL_PATH $PATH +set -gx PATH "$VIRTUAL_ENV/bin" $PATH + +# unset PYTHONHOME if set +if set -q PYTHONHOME + set -gx _OLD_VIRTUAL_PYTHONHOME $PYTHONHOME + set -e PYTHONHOME +end + +if test -z "$VIRTUAL_ENV_DISABLE_PROMPT" + # fish uses a function instead of an env var to generate the prompt. + + # save the current fish_prompt function as the function _old_fish_prompt + functions -c fish_prompt _old_fish_prompt + + # with the original prompt function renamed, we can override with our own. + function fish_prompt + # Save the return status of the last command + set -l old_status $status + + # Prompt override? + if test -n "(.easyocr_venv) " + printf "%s%s" "(.easyocr_venv) " (set_color normal) + else + # ...Otherwise, prepend env + set -l _checkbase (basename "$VIRTUAL_ENV") + if test $_checkbase = "__" + # special case for Aspen magic directories + # see https://aspen.io/ + printf "%s[%s]%s " (set_color -b blue white) (basename (dirname "$VIRTUAL_ENV")) (set_color normal) + else + printf "%s(%s)%s" (set_color -b blue white) (basename "$VIRTUAL_ENV") (set_color normal) + end + end + + # Restore the return status of the previous command. + echo "exit $old_status" | . + _old_fish_prompt + end + + set -gx _OLD_FISH_PROMPT_OVERRIDE "$VIRTUAL_ENV" +end diff --git a/.easyocr_venv/bin/black b/.easyocr_venv/bin/black new file mode 100755 index 000000000..f4e6fe29a --- /dev/null +++ b/.easyocr_venv/bin/black @@ -0,0 +1,8 @@ +#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 +# -*- coding: utf-8 -*- +import re +import sys +from black import patched_main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(patched_main()) diff --git a/.easyocr_venv/bin/blackd b/.easyocr_venv/bin/blackd new file mode 100755 index 000000000..68d81ca97 --- /dev/null +++ b/.easyocr_venv/bin/blackd @@ -0,0 +1,8 @@ +#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 +# -*- coding: utf-8 -*- +import re +import sys +from blackd import patched_main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(patched_main()) diff --git a/.easyocr_venv/bin/convert-caffe2-to-onnx b/.easyocr_venv/bin/convert-caffe2-to-onnx new file mode 100755 index 000000000..5f3b0ac3b --- /dev/null +++ b/.easyocr_venv/bin/convert-caffe2-to-onnx @@ -0,0 +1,8 @@ +#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 +# -*- coding: utf-8 -*- +import re +import sys +from caffe2.python.onnx.bin.conversion import caffe2_to_onnx +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(caffe2_to_onnx()) diff --git a/.easyocr_venv/bin/convert-onnx-to-caffe2 b/.easyocr_venv/bin/convert-onnx-to-caffe2 new file mode 100755 index 000000000..3f32e7d59 --- /dev/null +++ b/.easyocr_venv/bin/convert-onnx-to-caffe2 @@ -0,0 +1,8 @@ +#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 +# -*- coding: utf-8 -*- +import re +import sys +from caffe2.python.onnx.bin.conversion import onnx_to_caffe2 +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(onnx_to_caffe2()) diff --git a/.easyocr_venv/bin/easy_install b/.easyocr_venv/bin/easy_install new file mode 100755 index 000000000..a8f8acdb9 --- /dev/null +++ b/.easyocr_venv/bin/easy_install @@ -0,0 +1,8 @@ +#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 +# -*- coding: utf-8 -*- +import re +import sys +from setuptools.command.easy_install import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/.easyocr_venv/bin/easy_install-3.8 b/.easyocr_venv/bin/easy_install-3.8 new file mode 100755 index 000000000..a8f8acdb9 --- /dev/null +++ b/.easyocr_venv/bin/easy_install-3.8 @@ -0,0 +1,8 @@ +#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 +# -*- coding: utf-8 -*- +import re +import sys +from setuptools.command.easy_install import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/.easyocr_venv/bin/easyocr b/.easyocr_venv/bin/easyocr new file mode 100755 index 000000000..1c20adad7 --- /dev/null +++ b/.easyocr_venv/bin/easyocr @@ -0,0 +1,12 @@ +#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 +# EASY-INSTALL-ENTRY-SCRIPT: 'easyocr','console_scripts','easyocr' +__requires__ = 'easyocr' +import re +import sys +from pkg_resources import load_entry_point + +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0]) + sys.exit( + load_entry_point('easyocr', 'console_scripts', 'easyocr')() + ) diff --git a/.easyocr_venv/bin/epylint b/.easyocr_venv/bin/epylint new file mode 100755 index 000000000..774a5dbc3 --- /dev/null +++ b/.easyocr_venv/bin/epylint @@ -0,0 +1,8 @@ +#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 +# -*- coding: utf-8 -*- +import re +import sys +from pylint import run_epylint +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(run_epylint()) diff --git a/.easyocr_venv/bin/f2py b/.easyocr_venv/bin/f2py new file mode 100755 index 000000000..b78eed5d9 --- /dev/null +++ b/.easyocr_venv/bin/f2py @@ -0,0 +1,8 @@ +#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 +# -*- coding: utf-8 -*- +import re +import sys +from numpy.f2py.f2py2e import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/.easyocr_venv/bin/f2py3 b/.easyocr_venv/bin/f2py3 new file mode 100755 index 000000000..b78eed5d9 --- /dev/null +++ b/.easyocr_venv/bin/f2py3 @@ -0,0 +1,8 @@ +#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 +# -*- coding: utf-8 -*- +import re +import sys +from numpy.f2py.f2py2e import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/.easyocr_venv/bin/f2py3.8 b/.easyocr_venv/bin/f2py3.8 new file mode 100755 index 000000000..b78eed5d9 --- /dev/null +++ b/.easyocr_venv/bin/f2py3.8 @@ -0,0 +1,8 @@ +#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 +# -*- coding: utf-8 -*- +import re +import sys +from numpy.f2py.f2py2e import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/.easyocr_venv/bin/get_objgraph b/.easyocr_venv/bin/get_objgraph new file mode 100755 index 000000000..9709008eb --- /dev/null +++ b/.easyocr_venv/bin/get_objgraph @@ -0,0 +1,54 @@ +#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 +# +# Author: Mike McKerns (mmckerns @caltech and @uqfoundation) +# Copyright (c) 2008-2016 California Institute of Technology. +# Copyright (c) 2016-2022 The Uncertainty Quantification Foundation. +# License: 3-clause BSD. The full license text is available at: +# - https://github.com/uqfoundation/dill/blob/master/LICENSE +""" +display the reference paths for objects in ``dill.types`` or a .pkl file + +Notes: + the generated image is useful in showing the pointer references in + objects that are or can be pickled. Any object in ``dill.objects`` + listed in ``dill.load_types(picklable=True, unpicklable=True)`` works. + +Examples:: + + $ get_objgraph FrameType + Image generated as FrameType.png +""" + +import dill as pickle +#pickle.debug.trace(True) +#import pickle + +# get all objects for testing +from dill import load_types +load_types(pickleable=True,unpickleable=True) +from dill import objects + +if __name__ == "__main__": + import sys + if len(sys.argv) != 2: + print ("Please provide exactly one file or type name (e.g. 'IntType')") + msg = "\n" + for objtype in list(objects.keys())[:40]: + msg += objtype + ', ' + print (msg + "...") + else: + objtype = str(sys.argv[-1]) + try: + obj = objects[objtype] + except KeyError: + obj = pickle.load(open(objtype,'rb')) + import os + objtype = os.path.splitext(objtype)[0] + try: + import objgraph + objgraph.show_refs(obj, filename=objtype+'.png') + except ImportError: + print ("Please install 'objgraph' to view object graphs") + + +# EOF diff --git a/.easyocr_venv/bin/imageio_download_bin b/.easyocr_venv/bin/imageio_download_bin new file mode 100755 index 000000000..2989fd08b --- /dev/null +++ b/.easyocr_venv/bin/imageio_download_bin @@ -0,0 +1,8 @@ +#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 +# -*- coding: utf-8 -*- +import re +import sys +from imageio.__main__ import download_bin_main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(download_bin_main()) diff --git a/.easyocr_venv/bin/imageio_remove_bin b/.easyocr_venv/bin/imageio_remove_bin new file mode 100755 index 000000000..01f02548c --- /dev/null +++ b/.easyocr_venv/bin/imageio_remove_bin @@ -0,0 +1,8 @@ +#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 +# -*- coding: utf-8 -*- +import re +import sys +from imageio.__main__ import remove_bin_main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(remove_bin_main()) diff --git a/.easyocr_venv/bin/ipython b/.easyocr_venv/bin/ipython new file mode 100755 index 000000000..fdbbcda3b --- /dev/null +++ b/.easyocr_venv/bin/ipython @@ -0,0 +1,8 @@ +#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 +# -*- coding: utf-8 -*- +import re +import sys +from IPython import start_ipython +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(start_ipython()) diff --git a/.easyocr_venv/bin/ipython3 b/.easyocr_venv/bin/ipython3 new file mode 100755 index 000000000..fdbbcda3b --- /dev/null +++ b/.easyocr_venv/bin/ipython3 @@ -0,0 +1,8 @@ +#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 +# -*- coding: utf-8 -*- +import re +import sys +from IPython import start_ipython +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(start_ipython()) diff --git a/.easyocr_venv/bin/isort b/.easyocr_venv/bin/isort new file mode 100755 index 000000000..c0901e9f1 --- /dev/null +++ b/.easyocr_venv/bin/isort @@ -0,0 +1,8 @@ +#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 +# -*- coding: utf-8 -*- +import re +import sys +from isort.main import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/.easyocr_venv/bin/isort-identify-imports b/.easyocr_venv/bin/isort-identify-imports new file mode 100755 index 000000000..88da8aff8 --- /dev/null +++ b/.easyocr_venv/bin/isort-identify-imports @@ -0,0 +1,8 @@ +#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 +# -*- coding: utf-8 -*- +import re +import sys +from isort.main import identify_imports_main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(identify_imports_main()) diff --git a/.easyocr_venv/bin/jupyter b/.easyocr_venv/bin/jupyter new file mode 100755 index 000000000..6a733a8ad --- /dev/null +++ b/.easyocr_venv/bin/jupyter @@ -0,0 +1,8 @@ +#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 +# -*- coding: utf-8 -*- +import re +import sys +from jupyter_core.command import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/.easyocr_venv/bin/jupyter-kernel b/.easyocr_venv/bin/jupyter-kernel new file mode 100755 index 000000000..df5b851cc --- /dev/null +++ b/.easyocr_venv/bin/jupyter-kernel @@ -0,0 +1,8 @@ +#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 +# -*- coding: utf-8 -*- +import re +import sys +from jupyter_client.kernelapp import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/.easyocr_venv/bin/jupyter-kernelspec b/.easyocr_venv/bin/jupyter-kernelspec new file mode 100755 index 000000000..7beedd7d1 --- /dev/null +++ b/.easyocr_venv/bin/jupyter-kernelspec @@ -0,0 +1,8 @@ +#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 +# -*- coding: utf-8 -*- +import re +import sys +from jupyter_client.kernelspecapp import KernelSpecApp +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(KernelSpecApp.launch_instance()) diff --git a/.easyocr_venv/bin/jupyter-migrate b/.easyocr_venv/bin/jupyter-migrate new file mode 100755 index 000000000..11fc42eb7 --- /dev/null +++ b/.easyocr_venv/bin/jupyter-migrate @@ -0,0 +1,8 @@ +#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 +# -*- coding: utf-8 -*- +import re +import sys +from jupyter_core.migrate import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/.easyocr_venv/bin/jupyter-run b/.easyocr_venv/bin/jupyter-run new file mode 100755 index 000000000..97bfff1df --- /dev/null +++ b/.easyocr_venv/bin/jupyter-run @@ -0,0 +1,8 @@ +#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 +# -*- coding: utf-8 -*- +import re +import sys +from jupyter_client.runapp import RunApp +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(RunApp.launch_instance()) diff --git a/.easyocr_venv/bin/jupyter-troubleshoot b/.easyocr_venv/bin/jupyter-troubleshoot new file mode 100755 index 000000000..e28c30518 --- /dev/null +++ b/.easyocr_venv/bin/jupyter-troubleshoot @@ -0,0 +1,8 @@ +#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 +# -*- coding: utf-8 -*- +import re +import sys +from jupyter_core.troubleshoot import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/.easyocr_venv/bin/lsm2bin b/.easyocr_venv/bin/lsm2bin new file mode 100755 index 000000000..586f4bd3d --- /dev/null +++ b/.easyocr_venv/bin/lsm2bin @@ -0,0 +1,8 @@ +#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 +# -*- coding: utf-8 -*- +import re +import sys +from tifffile.lsm2bin import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/.easyocr_venv/bin/nltk b/.easyocr_venv/bin/nltk new file mode 100755 index 000000000..4db12ea15 --- /dev/null +++ b/.easyocr_venv/bin/nltk @@ -0,0 +1,8 @@ +#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 +# -*- coding: utf-8 -*- +import re +import sys +from nltk.cli import cli +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(cli()) diff --git a/.easyocr_venv/bin/pip b/.easyocr_venv/bin/pip new file mode 100755 index 000000000..2ac1b3814 --- /dev/null +++ b/.easyocr_venv/bin/pip @@ -0,0 +1,8 @@ +#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 +# -*- coding: utf-8 -*- +import re +import sys +from pip._internal.cli.main import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/.easyocr_venv/bin/pip3 b/.easyocr_venv/bin/pip3 new file mode 100755 index 000000000..2ac1b3814 --- /dev/null +++ b/.easyocr_venv/bin/pip3 @@ -0,0 +1,8 @@ +#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 +# -*- coding: utf-8 -*- +import re +import sys +from pip._internal.cli.main import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/.easyocr_venv/bin/pip3.8 b/.easyocr_venv/bin/pip3.8 new file mode 100755 index 000000000..2ac1b3814 --- /dev/null +++ b/.easyocr_venv/bin/pip3.8 @@ -0,0 +1,8 @@ +#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 +# -*- coding: utf-8 -*- +import re +import sys +from pip._internal.cli.main import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/.easyocr_venv/bin/pybidi b/.easyocr_venv/bin/pybidi new file mode 100755 index 000000000..c07a4c9a7 --- /dev/null +++ b/.easyocr_venv/bin/pybidi @@ -0,0 +1,8 @@ +#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 +# -*- coding: utf-8 -*- +import re +import sys +from bidi import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/.easyocr_venv/bin/pygmentize b/.easyocr_venv/bin/pygmentize new file mode 100755 index 000000000..8c6593682 --- /dev/null +++ b/.easyocr_venv/bin/pygmentize @@ -0,0 +1,8 @@ +#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 +# -*- coding: utf-8 -*- +import re +import sys +from pygments.cmdline import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/.easyocr_venv/bin/pylint b/.easyocr_venv/bin/pylint new file mode 100755 index 000000000..d6cea3a01 --- /dev/null +++ b/.easyocr_venv/bin/pylint @@ -0,0 +1,8 @@ +#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 +# -*- coding: utf-8 -*- +import re +import sys +from pylint import run_pylint +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(run_pylint()) diff --git a/.easyocr_venv/bin/pylint-config b/.easyocr_venv/bin/pylint-config new file mode 100755 index 000000000..2443627f1 --- /dev/null +++ b/.easyocr_venv/bin/pylint-config @@ -0,0 +1,8 @@ +#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 +# -*- coding: utf-8 -*- +import re +import sys +from pylint import _run_pylint_config +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(_run_pylint_config()) diff --git a/.easyocr_venv/bin/pyreverse b/.easyocr_venv/bin/pyreverse new file mode 100755 index 000000000..656e75ac1 --- /dev/null +++ b/.easyocr_venv/bin/pyreverse @@ -0,0 +1,8 @@ +#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 +# -*- coding: utf-8 -*- +import re +import sys +from pylint import run_pyreverse +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(run_pyreverse()) diff --git a/.easyocr_venv/bin/python b/.easyocr_venv/bin/python new file mode 120000 index 000000000..4e58b606c --- /dev/null +++ b/.easyocr_venv/bin/python @@ -0,0 +1 @@ +python3.8 \ No newline at end of file diff --git a/.easyocr_venv/bin/python3 b/.easyocr_venv/bin/python3 new file mode 120000 index 000000000..4e58b606c --- /dev/null +++ b/.easyocr_venv/bin/python3 @@ -0,0 +1 @@ +python3.8 \ No newline at end of file diff --git a/.easyocr_venv/bin/python3.8 b/.easyocr_venv/bin/python3.8 new file mode 120000 index 000000000..02a138965 --- /dev/null +++ b/.easyocr_venv/bin/python3.8 @@ -0,0 +1 @@ +/usr/bin/python3.8 \ No newline at end of file diff --git a/.easyocr_venv/bin/skivi b/.easyocr_venv/bin/skivi new file mode 100755 index 000000000..0a7948e36 --- /dev/null +++ b/.easyocr_venv/bin/skivi @@ -0,0 +1,8 @@ +#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 +# -*- coding: utf-8 -*- +import re +import sys +from skimage.scripts.skivi import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/.easyocr_venv/bin/symilar b/.easyocr_venv/bin/symilar new file mode 100755 index 000000000..9e4637838 --- /dev/null +++ b/.easyocr_venv/bin/symilar @@ -0,0 +1,8 @@ +#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 +# -*- coding: utf-8 -*- +import re +import sys +from pylint import run_symilar +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(run_symilar()) diff --git a/.easyocr_venv/bin/tiff2fsspec b/.easyocr_venv/bin/tiff2fsspec new file mode 100755 index 000000000..93d5cc922 --- /dev/null +++ b/.easyocr_venv/bin/tiff2fsspec @@ -0,0 +1,8 @@ +#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 +# -*- coding: utf-8 -*- +import re +import sys +from tifffile.tiff2fsspec import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/.easyocr_venv/bin/tiffcomment b/.easyocr_venv/bin/tiffcomment new file mode 100755 index 000000000..dede233c7 --- /dev/null +++ b/.easyocr_venv/bin/tiffcomment @@ -0,0 +1,8 @@ +#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 +# -*- coding: utf-8 -*- +import re +import sys +from tifffile.tiffcomment import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/.easyocr_venv/bin/tifffile b/.easyocr_venv/bin/tifffile new file mode 100755 index 000000000..1ad2f3216 --- /dev/null +++ b/.easyocr_venv/bin/tifffile @@ -0,0 +1,8 @@ +#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 +# -*- coding: utf-8 -*- +import re +import sys +from tifffile import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/.easyocr_venv/bin/tqdm b/.easyocr_venv/bin/tqdm new file mode 100755 index 000000000..a82b20429 --- /dev/null +++ b/.easyocr_venv/bin/tqdm @@ -0,0 +1,8 @@ +#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 +# -*- coding: utf-8 -*- +import re +import sys +from tqdm.cli import main +if __name__ == '__main__': + sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) + sys.exit(main()) diff --git a/.easyocr_venv/bin/undill b/.easyocr_venv/bin/undill new file mode 100755 index 000000000..1749bc11e --- /dev/null +++ b/.easyocr_venv/bin/undill @@ -0,0 +1,22 @@ +#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 +# +# Author: Mike McKerns (mmckerns @caltech and @uqfoundation) +# Copyright (c) 2008-2016 California Institute of Technology. +# Copyright (c) 2016-2022 The Uncertainty Quantification Foundation. +# License: 3-clause BSD. The full license text is available at: +# - https://github.com/uqfoundation/dill/blob/master/LICENSE +""" +unpickle the contents of a pickled object file + +Examples:: + + $ undill hello.pkl + ['hello', 'world'] +""" + +if __name__ == '__main__': + import sys + import dill + for file in sys.argv[1:]: + print (dill.load(open(file,'rb'))) + diff --git a/.easyocr_venv/lib64 b/.easyocr_venv/lib64 new file mode 120000 index 000000000..7951405f8 --- /dev/null +++ b/.easyocr_venv/lib64 @@ -0,0 +1 @@ +lib \ No newline at end of file diff --git a/.easyocr_venv/pyvenv.cfg b/.easyocr_venv/pyvenv.cfg new file mode 100644 index 000000000..853404e23 --- /dev/null +++ b/.easyocr_venv/pyvenv.cfg @@ -0,0 +1,3 @@ +home = /usr/bin +include-system-site-packages = false +version = 3.8.10 diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/LICENSE.txt b/.easyocr_venv/share/doc/networkx-2.8.5/LICENSE.txt new file mode 100644 index 000000000..a274a66aa --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/LICENSE.txt @@ -0,0 +1,37 @@ +NetworkX is distributed with the 3-clause BSD license. + +:: + + Copyright (C) 2004-2022, NetworkX Developers + Aric Hagberg + Dan Schult + Pieter Swart + All rights reserved. + + Redistribution and use in source and binary forms, with or without + modification, are permitted provided that the following conditions are + met: + + * Redistributions of source code must retain the above copyright + notice, this list of conditions and the following disclaimer. + + * Redistributions in binary form must reproduce the above + copyright notice, this list of conditions and the following + disclaimer in the documentation and/or other materials provided + with the distribution. + + * Neither the name of the NetworkX Developers nor the names of its + contributors may be used to endorse or promote products derived + from this software without specific prior written permission. + + THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS + "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT + LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR + A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT + OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, + SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT + LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, + DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY + THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT + (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/3d_drawing/README.txt b/.easyocr_venv/share/doc/networkx-2.8.5/examples/3d_drawing/README.txt new file mode 100644 index 000000000..1a7668282 --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/3d_drawing/README.txt @@ -0,0 +1,2 @@ +3D Drawing +---------- diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/3d_drawing/mayavi2_spring.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/3d_drawing/mayavi2_spring.py new file mode 100644 index 000000000..bec096948 --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/3d_drawing/mayavi2_spring.py @@ -0,0 +1,43 @@ +""" +======= +Mayavi2 +======= + +""" + +import networkx as nx +import numpy as np +from mayavi import mlab + +# some graphs to try +# H=nx.krackhardt_kite_graph() +# H=nx.Graph();H.add_edge('a','b');H.add_edge('a','c');H.add_edge('a','d') +# H=nx.grid_2d_graph(4,5) +H = nx.cycle_graph(20) + +# reorder nodes from 0,len(G)-1 +G = nx.convert_node_labels_to_integers(H) +# 3d spring layout +pos = nx.spring_layout(G, dim=3, seed=1001) +# numpy array of x,y,z positions in sorted node order +xyz = np.array([pos[v] for v in sorted(G)]) +# scalar colors +scalars = np.array(list(G.nodes())) + 5 + +mlab.figure() + +pts = mlab.points3d( + xyz[:, 0], + xyz[:, 1], + xyz[:, 2], + scalars, + scale_factor=0.1, + scale_mode="none", + colormap="Blues", + resolution=20, +) + +pts.mlab_source.dataset.lines = np.array(list(G.edges())) +tube = mlab.pipeline.tube(pts, tube_radius=0.01) +mlab.pipeline.surface(tube, color=(0.8, 0.8, 0.8)) +mlab.orientation_axes() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/3d_drawing/plot_basic.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/3d_drawing/plot_basic.py new file mode 100644 index 000000000..75a9c79a2 --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/3d_drawing/plot_basic.py @@ -0,0 +1,51 @@ +""" +================ +Basic matplotlib +================ + +A basic example of 3D Graph visualization using `mpl_toolkits.mplot_3d`. + +""" + +import networkx as nx +import numpy as np +import matplotlib.pyplot as plt +from mpl_toolkits.mplot3d import Axes3D + +# The graph to visualize +G = nx.cycle_graph(20) + +# 3d spring layout +pos = nx.spring_layout(G, dim=3, seed=779) +# Extract node and edge positions from the layout +node_xyz = np.array([pos[v] for v in sorted(G)]) +edge_xyz = np.array([(pos[u], pos[v]) for u, v in G.edges()]) + +# Create the 3D figure +fig = plt.figure() +ax = fig.add_subplot(111, projection="3d") + +# Plot the nodes - alpha is scaled by "depth" automatically +ax.scatter(*node_xyz.T, s=100, ec="w") + +# Plot the edges +for vizedge in edge_xyz: + ax.plot(*vizedge.T, color="tab:gray") + + +def _format_axes(ax): + """Visualization options for the 3D axes.""" + # Turn gridlines off + ax.grid(False) + # Suppress tick labels + for dim in (ax.xaxis, ax.yaxis, ax.zaxis): + dim.set_ticks([]) + # Set axes labels + ax.set_xlabel("x") + ax.set_ylabel("y") + ax.set_zlabel("z") + + +_format_axes(ax) +fig.tight_layout() +plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/README.txt b/.easyocr_venv/share/doc/networkx-2.8.5/examples/README.txt new file mode 100644 index 000000000..d0049bdba --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/README.txt @@ -0,0 +1,8 @@ +.. _examples_gallery: + +Gallery +======= + +General-purpose and introductory examples for NetworkX. +The `tutorial <../tutorial.html>`_ introduces conventions and basic graph +manipulations. diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/README.txt b/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/README.txt new file mode 100644 index 000000000..a78a4d7d0 --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/README.txt @@ -0,0 +1,2 @@ +Algorithms +---------- diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/WormNet.v3.benchmark.txt b/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/WormNet.v3.benchmark.txt new file mode 100644 index 000000000..824cfa1cd --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/WormNet.v3.benchmark.txt @@ -0,0 +1,78736 @@ +C41D11.8 AH9.2 +CD4.2 AH9.2 +K12H4.8 AH9.2 +T07A9.5 AH9.2 +Y113G7A.9 AH9.2 +Y47G6A.8 AH9.2 +Y48B6A.3 AH9.2 +Y56A3A.32 AH9.2 +ZC513.5 B0024.13 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b/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_beam_search.py new file mode 100644 index 000000000..c465bbb40 --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_beam_search.py @@ -0,0 +1,112 @@ +""" +=========== +Beam Search +=========== + +Beam search with dynamic beam width. + +The progressive widening beam search repeatedly executes a beam search +with increasing beam width until the target node is found. +""" +import math + +import matplotlib.pyplot as plt +import networkx as nx + + +def progressive_widening_search(G, source, value, condition, initial_width=1): + """Progressive widening beam search to find a node. + + The progressive widening beam search involves a repeated beam + search, starting with a small beam width then extending to + progressively larger beam widths if the target node is not + found. This implementation simply returns the first node found that + matches the termination condition. + + `G` is a NetworkX graph. + + `source` is a node in the graph. The search for the node of interest + begins here and extends only to those nodes in the (weakly) + connected component of this node. + + `value` is a function that returns a real number indicating how good + a potential neighbor node is when deciding which neighbor nodes to + enqueue in the breadth-first search. Only the best nodes within the + current beam width will be enqueued at each step. + + `condition` is the termination condition for the search. This is a + function that takes a node as input and return a Boolean indicating + whether the node is the target. If no node matches the termination + condition, this function raises :exc:`NodeNotFound`. + + `initial_width` is the starting beam width for the beam search (the + default is one). If no node matching the `condition` is found with + this beam width, the beam search is restarted from the `source` node + with a beam width that is twice as large (so the beam width + increases exponentially). The search terminates after the beam width + exceeds the number of nodes in the graph. + + """ + # Check for the special case in which the source node satisfies the + # termination condition. + if condition(source): + return source + # The largest possible value of `i` in this range yields a width at + # least the number of nodes in the graph, so the final invocation of + # `bfs_beam_edges` is equivalent to a plain old breadth-first + # search. Therefore, all nodes will eventually be visited. + log_m = math.ceil(math.log2(len(G))) + for i in range(log_m): + width = initial_width * pow(2, i) + # Since we are always starting from the same source node, this + # search may visit the same nodes many times (depending on the + # implementation of the `value` function). + for u, v in nx.bfs_beam_edges(G, source, value, width): + if condition(v): + return v + # At this point, since all nodes have been visited, we know that + # none of the nodes satisfied the termination condition. + raise nx.NodeNotFound("no node satisfied the termination condition") + + +############################################################################### +# Search for a node with high centrality. +# --------------------------------------- +# +# We generate a random graph, compute the centrality of each node, then perform +# the progressive widening search in order to find a node of high centrality. + +# Set a seed for random number generation so the example is reproducible +seed = 89 + +G = nx.gnp_random_graph(100, 0.5, seed=seed) +centrality = nx.eigenvector_centrality(G) +avg_centrality = sum(centrality.values()) / len(G) + + +def has_high_centrality(v): + return centrality[v] >= avg_centrality + + +source = 0 +value = centrality.get +condition = has_high_centrality + +found_node = progressive_widening_search(G, source, value, condition) +c = centrality[found_node] +print(f"found node {found_node} with centrality {c}") + + +# Draw graph +pos = nx.spring_layout(G, seed=seed) +options = { + "node_color": "blue", + "node_size": 20, + "edge_color": "grey", + "linewidths": 0, + "width": 0.1, +} +nx.draw(G, pos, **options) +# Draw node with high centrality as large and red +nx.draw_networkx_nodes(G, pos, nodelist=[found_node], node_size=100, node_color="r") +plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_betweenness_centrality.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_betweenness_centrality.py new file mode 100644 index 000000000..e8354c662 --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_betweenness_centrality.py @@ -0,0 +1,83 @@ +""" +===================== +Betweeness Centrality +===================== + +Betweenness centrality measures of positive gene functional associations +using WormNet v.3-GS. + +Data from: https://www.inetbio.org/wormnet/downloadnetwork.php +""" + +from random import sample +import networkx as nx +import matplotlib.pyplot as plt + +# Gold standard data of positive gene functional associations +# from https://www.inetbio.org/wormnet/downloadnetwork.php +G = nx.read_edgelist("WormNet.v3.benchmark.txt") + +# remove randomly selected nodes (to make example fast) +num_to_remove = int(len(G) / 1.5) +nodes = sample(list(G.nodes), num_to_remove) +G.remove_nodes_from(nodes) + +# remove low-degree nodes +low_degree = [n for n, d in G.degree() if d < 10] +G.remove_nodes_from(low_degree) + +# largest connected component +components = nx.connected_components(G) +largest_component = max(components, key=len) +H = G.subgraph(largest_component) + +# compute centrality +centrality = nx.betweenness_centrality(H, k=10, endpoints=True) + +# compute community structure +lpc = nx.community.label_propagation_communities(H) +community_index = {n: i for i, com in enumerate(lpc) for n in com} + +#### draw graph #### +fig, ax = plt.subplots(figsize=(20, 15)) +pos = nx.spring_layout(H, k=0.15, seed=4572321) +node_color = [community_index[n] for n in H] +node_size = [v * 20000 for v in centrality.values()] +nx.draw_networkx( + H, + pos=pos, + with_labels=False, + node_color=node_color, + node_size=node_size, + edge_color="gainsboro", + alpha=0.4, +) + +# Title/legend +font = {"color": "k", "fontweight": "bold", "fontsize": 20} +ax.set_title("Gene functional association network (C. elegans)", font) +# Change font color for legend +font["color"] = "r" + +ax.text( + 0.80, + 0.10, + "node color = community structure", + horizontalalignment="center", + transform=ax.transAxes, + fontdict=font, +) +ax.text( + 0.80, + 0.06, + "node size = betweeness centrality", + horizontalalignment="center", + transform=ax.transAxes, + fontdict=font, +) + +# Resize figure for label readibility +ax.margins(0.1, 0.05) +fig.tight_layout() +plt.axis("off") +plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_blockmodel.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_blockmodel.py new file mode 100644 index 000000000..e3fec60bf --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_blockmodel.py @@ -0,0 +1,79 @@ +""" +========== +Blockmodel +========== + +Example of creating a block model using the quotient_graph function in NX. Data +used is the Hartford, CT drug users network:: + + @article{weeks2002social, + title={Social networks of drug users in high-risk sites: Finding the connections}, + url = {https://doi.org/10.1023/A:1015457400897}, + doi = {10.1023/A:1015457400897}, + author={Weeks, Margaret R and Clair, Scott and Borgatti, Stephen P and Radda, Kim and Schensul, Jean J}, + journal={{AIDS and Behavior}}, + volume={6}, + number={2}, + pages={193--206}, + year={2002}, + publisher={Springer} + } + +""" + +from collections import defaultdict + +import matplotlib.pyplot as plt +import networkx as nx +import numpy as np +from scipy.cluster import hierarchy +from scipy.spatial import distance + + +def create_hc(G): + """Creates hierarchical cluster of graph G from distance matrix""" + path_length = nx.all_pairs_shortest_path_length(G) + distances = np.zeros((len(G), len(G))) + for u, p in path_length: + for v, d in p.items(): + distances[u][v] = d + # Create hierarchical cluster + Y = distance.squareform(distances) + Z = hierarchy.complete(Y) # Creates HC using farthest point linkage + # This partition selection is arbitrary, for illustrive purposes + membership = list(hierarchy.fcluster(Z, t=1.15)) + # Create collection of lists for blockmodel + partition = defaultdict(list) + for n, p in zip(list(range(len(G))), membership): + partition[p].append(n) + return list(partition.values()) + + +G = nx.read_edgelist("hartford_drug.edgelist") + +# Extract largest connected component into graph H +H = G.subgraph(next(nx.connected_components(G))) +# Makes life easier to have consecutively labeled integer nodes +H = nx.convert_node_labels_to_integers(H) +# Create parititions with hierarchical clustering +partitions = create_hc(H) +# Build blockmodel graph +BM = nx.quotient_graph(H, partitions, relabel=True) + +# Draw original graph +pos = nx.spring_layout(H, iterations=100, seed=83) # Seed for reproducibility +plt.subplot(211) +nx.draw(H, pos, with_labels=False, node_size=10) + +# Draw block model with weighted edges and nodes sized by number of internal nodes +node_size = [BM.nodes[x]["nnodes"] * 10 for x in BM.nodes()] +edge_width = [(2 * d["weight"]) for (u, v, d) in BM.edges(data=True)] +# Set positions to mean of positions of internal nodes from original graph +posBM = {} +for n in BM: + xy = np.array([pos[u] for u in BM.nodes[n]["graph"]]) + posBM[n] = xy.mean(axis=0) +plt.subplot(212) +nx.draw(BM, posBM, node_size=node_size, width=edge_width, with_labels=False) +plt.axis("off") +plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_circuits.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_circuits.py new file mode 100644 index 000000000..5481aedb5 --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_circuits.py @@ -0,0 +1,103 @@ +""" +======== +Circuits +======== + +Convert a Boolean circuit to an equivalent Boolean formula. + +A Boolean circuit can be exponentially more expressive than an +equivalent formula in the worst case, since the circuit can reuse +subcircuits multiple times, whereas a formula cannot reuse subformulas +more than once. Thus creating a Boolean formula from a Boolean circuit +in this way may be infeasible if the circuit is large. + +""" +import matplotlib.pyplot as plt +import networkx as nx + + +def circuit_to_formula(circuit): + # Convert the circuit to an equivalent formula. + formula = nx.dag_to_branching(circuit) + # Transfer the operator or variable labels for each node from the + # circuit to the formula. + for v in formula: + source = formula.nodes[v]["source"] + formula.nodes[v]["label"] = circuit.nodes[source]["label"] + return formula + + +def formula_to_string(formula): + def _to_string(formula, root): + # If there are no children, this is a variable node. + label = formula.nodes[root]["label"] + if not formula[root]: + return label + # Otherwise, this is an operator. + children = formula[root] + # If one child, the label must be a NOT operator. + if len(children) == 1: + child = nx.utils.arbitrary_element(children) + return f"{label}({_to_string(formula, child)})" + # NB "left" and "right" here are a little misleading: there is + # no order on the children of a node. That's okay because the + # Boolean AND and OR operators are symmetric. It just means that + # the order of the operands cannot be predicted and hence the + # function does not necessarily behave the same way on every + # invocation. + left, right = formula[root] + left_subformula = _to_string(formula, left) + right_subformula = _to_string(formula, right) + return f"({left_subformula} {label} {right_subformula})" + + root = next(v for v, d in formula.in_degree() if d == 0) + return _to_string(formula, root) + + +############################################################################### +# Create an example Boolean circuit. +# ---------------------------------- +# +# This circuit has a ∧ at the output and two ∨s at the next layer. +# The third layer has a variable x that appears in the left ∨, a +# variable y that appears in both the left and right ∨s, and a +# negation for the variable z that appears as the sole node in the +# fourth layer. +circuit = nx.DiGraph() +# Layer 0 +circuit.add_node(0, label="∧", layer=0) +# Layer 1 +circuit.add_node(1, label="∨", layer=1) +circuit.add_node(2, label="∨", layer=1) +circuit.add_edge(0, 1) +circuit.add_edge(0, 2) +# Layer 2 +circuit.add_node(3, label="x", layer=2) +circuit.add_node(4, label="y", layer=2) +circuit.add_node(5, label="¬", layer=2) +circuit.add_edge(1, 3) +circuit.add_edge(1, 4) +circuit.add_edge(2, 4) +circuit.add_edge(2, 5) +# Layer 3 +circuit.add_node(6, label="z", layer=3) +circuit.add_edge(5, 6) +# Convert the circuit to an equivalent formula. +formula = circuit_to_formula(circuit) +print(formula_to_string(formula)) + + +labels = nx.get_node_attributes(circuit, "label") +options = { + "node_size": 600, + "alpha": 0.5, + "node_color": "blue", + "labels": labels, + "font_size": 22, +} +plt.figure(figsize=(8, 8)) +pos = nx.multipartite_layout(circuit, subset_key="layer") +nx.draw_networkx(circuit, pos, **options) +plt.title(formula_to_string(formula)) +plt.axis("equal") +plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_davis_club.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_davis_club.py new file mode 100644 index 000000000..517d52386 --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_davis_club.py @@ -0,0 +1,43 @@ +""" +========== +Davis Club +========== + +Davis Southern Club Women + +Shows how to make unipartite projections of the graph and compute the +properties of those graphs. + +These data were collected by Davis et al. in the 1930s. +They represent observed attendance at 14 social events by 18 Southern women. +The graph is bipartite (clubs, women). +""" +import matplotlib.pyplot as plt +import networkx as nx +import networkx.algorithms.bipartite as bipartite + +G = nx.davis_southern_women_graph() +women = G.graph["top"] +clubs = G.graph["bottom"] + +print("Biadjacency matrix") +print(bipartite.biadjacency_matrix(G, women, clubs)) + +# project bipartite graph onto women nodes +W = bipartite.projected_graph(G, women) +print() +print("#Friends, Member") +for w in women: + print(f"{W.degree(w)} {w}") + +# project bipartite graph onto women nodes keeping number of co-occurence +# the degree computed is weighted and counts the total number of shared contacts +W = bipartite.weighted_projected_graph(G, women) +print() +print("#Friend meetings, Member") +for w in women: + print(f"{W.degree(w, weight='weight')} {w}") + +pos = nx.spring_layout(G, seed=648) # Seed layout for reproducible node positions +nx.draw(G, pos) +plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_dedensification.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_dedensification.py new file mode 100644 index 000000000..9104d5468 --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_dedensification.py @@ -0,0 +1,92 @@ +""" +=============== +Dedensification +=============== + +Examples of dedensification of a graph. Dedensification retains the structural +pattern of the original graph and will only add compressor nodes when doing so +would result in fewer edges in the compressed graph. +""" +import matplotlib.pyplot as plt +import networkx as nx + +plt.suptitle("Dedensification") + +original_graph = nx.DiGraph() +white_nodes = ["1", "2", "3", "4", "5", "6"] +red_nodes = ["A", "B", "C"] +node_sizes = [250 for node in white_nodes + red_nodes] +node_colors = ["white" for n in white_nodes] + ["red" for n in red_nodes] + +original_graph.add_nodes_from(white_nodes + red_nodes) +original_graph.add_edges_from( + [ + ("1", "C"), + ("1", "B"), + ("2", "C"), + ("2", "B"), + ("2", "A"), + ("3", "B"), + ("3", "A"), + ("3", "6"), + ("4", "C"), + ("4", "B"), + ("4", "A"), + ("5", "B"), + ("5", "A"), + ("6", "5"), + ("A", "6"), + ] +) +base_options = dict(with_labels=True, edgecolors="black") +pos = { + "3": (0, 1), + "2": (0, 2), + "1": (0, 3), + "6": (1, 0), + "A": (1, 1), + "B": (1, 2), + "C": (1, 3), + "4": (2, 3), + "5": (2, 1), +} +ax1 = plt.subplot(1, 2, 1) +plt.title("Original (%s edges)" % original_graph.number_of_edges()) +nx.draw_networkx(original_graph, pos=pos, node_color=node_colors, **base_options) + +nonexp_graph, compression_nodes = nx.summarization.dedensify( + original_graph, threshold=2, copy=False +) +nonexp_node_colors = list(node_colors) +nonexp_node_sizes = list(node_sizes) +for node in compression_nodes: + nonexp_node_colors.append("yellow") + nonexp_node_sizes.append(600) +plt.subplot(1, 2, 2) + +plt.title("Dedensified (%s edges)" % nonexp_graph.number_of_edges()) +nonexp_pos = { + "5": (0, 0), + "B": (0, 2), + "1": (0, 3), + "6": (1, 0.75), + "3": (1.5, 1.5), + "A": (2, 0), + "C": (2, 3), + "4": (3, 1.5), + "2": (3, 2.5), +} +c_nodes = list(compression_nodes) +c_nodes.sort() +for spot, node in enumerate(c_nodes): + nonexp_pos[node] = (2, spot + 2) +nx.draw_networkx( + nonexp_graph, + pos=nonexp_pos, + node_color=nonexp_node_colors, + node_size=nonexp_node_sizes, + **base_options +) + +plt.tight_layout() +plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_iterated_dynamical_systems.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_iterated_dynamical_systems.py new file mode 100644 index 000000000..a19baeb5a --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_iterated_dynamical_systems.py @@ -0,0 +1,210 @@ +""" +========================== +Iterated Dynamical Systems +========================== + +Digraphs from Integer-valued Iterated Functions + +Sums of cubes on 3N +------------------- + +The number 153 has a curious property. + +Let 3N={3,6,9,12,...} be the set of positive multiples of 3. Define an +iterative process f:3N->3N as follows: for a given n, take each digit +of n (in base 10), cube it and then sum the cubes to obtain f(n). + +When this process is repeated, the resulting series n, f(n), f(f(n)),... +terminate in 153 after a finite number of iterations (the process ends +because 153 = 1**3 + 5**3 + 3**3). + +In the language of discrete dynamical systems, 153 is the global +attractor for the iterated map f restricted to the set 3N. + +For example: take the number 108 + +f(108) = 1**3 + 0**3 + 8**3 = 513 + +and + +f(513) = 5**3 + 1**3 + 3**3 = 153 + +So, starting at 108 we reach 153 in two iterations, +represented as: + +108->513->153 + +Computing all orbits of 3N up to 10**5 reveals that the attractor +153 is reached in a maximum of 14 iterations. In this code we +show that 13 cycles is the maximum required for all integers (in 3N) +less than 10,000. + +The smallest number that requires 13 iterations to reach 153, is 177, i.e., + +177->687->1071->345->216->225->141->66->432->99->1458->702->351->153 + +The resulting large digraphs are useful for testing network software. + +The general problem +------------------- + +Given numbers n, a power p and base b, define F(n; p, b) as the sum of +the digits of n (in base b) raised to the power p. The above example +corresponds to f(n)=F(n; 3,10), and below F(n; p, b) is implemented as +the function powersum(n,p,b). The iterative dynamical system defined by +the mapping n:->f(n) above (over 3N) converges to a single fixed point; +153. Applying the map to all positive integers N, leads to a discrete +dynamical process with 5 fixed points: 1, 153, 370, 371, 407. Modulo 3 +those numbers are 1, 0, 1, 2, 2. The function f above has the added +property that it maps a multiple of 3 to another multiple of 3; i.e. it +is invariant on the subset 3N. + + +The squaring of digits (in base 10) result in cycles and the +single fixed point 1. I.e., from a certain point on, the process +starts repeating itself. + +keywords: "Recurring Digital Invariant", "Narcissistic Number", +"Happy Number" + +The 3n+1 problem +---------------- + +There is a rich history of mathematical recreations +associated with discrete dynamical systems. The most famous +is the Collatz 3n+1 problem. See the function +collatz_problem_digraph below. The Collatz conjecture +--- that every orbit returns to the fixed point 1 in finite time +--- is still unproven. Even the great Paul Erdos said "Mathematics +is not yet ready for such problems", and offered $500 +for its solution. + +keywords: "3n+1", "3x+1", "Collatz problem", "Thwaite's conjecture" +""" + +import networkx as nx + +nmax = 10000 +p = 3 + + +def digitsrep(n, b=10): + """Return list of digits comprising n represented in base b. + n must be a nonnegative integer""" + + if n <= 0: + return [0] + + dlist = [] + while n > 0: + # Prepend next least-significant digit + dlist = [n % b] + dlist + # Floor-division + n = n // b + return dlist + + +def powersum(n, p, b=10): + """Return sum of digits of n (in base b) raised to the power p.""" + dlist = digitsrep(n, b) + sum = 0 + for k in dlist: + sum += k**p + return sum + + +def attractor153_graph(n, p, multiple=3, b=10): + """Return digraph of iterations of powersum(n,3,10).""" + G = nx.DiGraph() + for k in range(1, n + 1): + if k % multiple == 0 and k not in G: + k1 = k + knext = powersum(k1, p, b) + while k1 != knext: + G.add_edge(k1, knext) + k1 = knext + knext = powersum(k1, p, b) + return G + + +def squaring_cycle_graph_old(n, b=10): + """Return digraph of iterations of powersum(n,2,10).""" + G = nx.DiGraph() + for k in range(1, n + 1): + k1 = k + G.add_node(k1) # case k1==knext, at least add node + knext = powersum(k1, 2, b) + G.add_edge(k1, knext) + while k1 != knext: # stop if fixed point + k1 = knext + knext = powersum(k1, 2, b) + G.add_edge(k1, knext) + if G.out_degree(knext) >= 1: + # knext has already been iterated in and out + break + return G + + +def sum_of_digits_graph(nmax, b=10): + def f(n): + return powersum(n, 1, b) + + return discrete_dynamics_digraph(nmax, f) + + +def squaring_cycle_digraph(nmax, b=10): + def f(n): + return powersum(n, 2, b) + + return discrete_dynamics_digraph(nmax, f) + + +def cubing_153_digraph(nmax): + def f(n): + return powersum(n, 3, 10) + + return discrete_dynamics_digraph(nmax, f) + + +def discrete_dynamics_digraph(nmax, f, itermax=50000): + G = nx.DiGraph() + for k in range(1, nmax + 1): + kold = k + G.add_node(kold) + knew = f(kold) + G.add_edge(kold, knew) + while kold != knew and kold << itermax: + # iterate until fixed point reached or itermax is exceeded + kold = knew + knew = f(kold) + G.add_edge(kold, knew) + if G.out_degree(knew) >= 1: + # knew has already been iterated in and out + break + return G + + +def collatz_problem_digraph(nmax): + def f(n): + if n % 2 == 0: + return n // 2 + else: + return 3 * n + 1 + + return discrete_dynamics_digraph(nmax, f) + + +def fixed_points(G): + """Return a list of fixed points for the discrete dynamical + system represented by the digraph G. + """ + return [n for n in G if G.out_degree(n) == 0] + + +nmax = 10000 +print(f"Building cubing_153_digraph({nmax})") +G = cubing_153_digraph(nmax) +print("Resulting digraph has", len(G), "nodes and", G.size(), " edges") +print("Shortest path from 177 to 153 is:") +print(nx.shortest_path(G, 177, 153)) +print(f"fixed points are {fixed_points(G)}") diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_krackhardt_centrality.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_krackhardt_centrality.py new file mode 100644 index 000000000..9d3e2a268 --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_krackhardt_centrality.py @@ -0,0 +1,31 @@ +""" +===================== +Krackhardt Centrality +===================== + +Centrality measures of Krackhardt social network. +""" + +import matplotlib.pyplot as plt +import networkx as nx + +G = nx.krackhardt_kite_graph() + +print("Betweenness") +b = nx.betweenness_centrality(G) +for v in G.nodes(): + print(f"{v:2} {b[v]:.3f}") + +print("Degree centrality") +d = nx.degree_centrality(G) +for v in G.nodes(): + print(f"{v:2} {d[v]:.3f}") + +print("Closeness centrality") +c = nx.closeness_centrality(G) +for v in G.nodes(): + print(f"{v:2} {c[v]:.3f}") + +pos = nx.spring_layout(G, seed=367) # Seed layout for reproducibility +nx.draw(G, pos) +plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_parallel_betweenness.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_parallel_betweenness.py new file mode 100644 index 000000000..d9333f458 --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_parallel_betweenness.py @@ -0,0 +1,82 @@ +""" +==================== +Parallel Betweenness +==================== + +Example of parallel implementation of betweenness centrality using the +multiprocessing module from Python Standard Library. + +The function betweenness centrality accepts a bunch of nodes and computes +the contribution of those nodes to the betweenness centrality of the whole +network. Here we divide the network in chunks of nodes and we compute their +contribution to the betweenness centrality of the whole network. + +Note: The example output below shows that the non-parallel implementation is +faster. This is a limitation of our CI/CD pipeline running on a single core. + +Depending on your setup, you will likely observe a speedup. +""" +from multiprocessing import Pool +import time +import itertools + +import matplotlib.pyplot as plt +import networkx as nx + + +def chunks(l, n): + """Divide a list of nodes `l` in `n` chunks""" + l_c = iter(l) + while 1: + x = tuple(itertools.islice(l_c, n)) + if not x: + return + yield x + + +def betweenness_centrality_parallel(G, processes=None): + """Parallel betweenness centrality function""" + p = Pool(processes=processes) + node_divisor = len(p._pool) * 4 + node_chunks = list(chunks(G.nodes(), G.order() // node_divisor)) + num_chunks = len(node_chunks) + bt_sc = p.starmap( + nx.betweenness_centrality_subset, + zip( + [G] * num_chunks, + node_chunks, + [list(G)] * num_chunks, + [True] * num_chunks, + [None] * num_chunks, + ), + ) + + # Reduce the partial solutions + bt_c = bt_sc[0] + for bt in bt_sc[1:]: + for n in bt: + bt_c[n] += bt[n] + return bt_c + + +G_ba = nx.barabasi_albert_graph(1000, 3) +G_er = nx.gnp_random_graph(1000, 0.01) +G_ws = nx.connected_watts_strogatz_graph(1000, 4, 0.1) +for G in [G_ba, G_er, G_ws]: + print("") + print("Computing betweenness centrality for:") + print(nx.info(G)) + print("\tParallel version") + start = time.time() + bt = betweenness_centrality_parallel(G) + print(f"\t\tTime: {(time.time() - start):.4F} seconds") + print(f"\t\tBetweenness centrality for node 0: {bt[0]:.5f}") + print("\tNon-Parallel version") + start = time.time() + bt = nx.betweenness_centrality(G) + print(f"\t\tTime: {(time.time() - start):.4F} seconds") + print(f"\t\tBetweenness centrality for node 0: {bt[0]:.5f}") +print("") + +nx.draw(G_ba, node_size=100) +plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_rcm.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_rcm.py new file mode 100644 index 000000000..fc08739bd --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_rcm.py @@ -0,0 +1,40 @@ +""" +====================== +Reverse Cuthill--McKee +====================== + +Cuthill-McKee ordering of matrices + +The reverse Cuthill--McKee algorithm gives a sparse matrix ordering that +reduces the matrix bandwidth. +""" + +import numpy as np +import matplotlib.pyplot as plt +import seaborn as sns +import networkx as nx + + +# build low-bandwidth matrix +G = nx.grid_2d_graph(3, 3) +rcm = list(nx.utils.reverse_cuthill_mckee_ordering(G)) +print("ordering", rcm) + +print("unordered Laplacian matrix") +A = nx.laplacian_matrix(G) +x, y = np.nonzero(A) +# print(f"lower bandwidth: {(y - x).max()}") +# print(f"upper bandwidth: {(x - y).max()}") +print(f"bandwidth: {(y - x).max() + (x - y).max() + 1}") +print(A) + +B = nx.laplacian_matrix(G, nodelist=rcm) +print("low-bandwidth Laplacian matrix") +x, y = np.nonzero(B) +# print(f"lower bandwidth: {(y - x).max()}") +# print(f"upper bandwidth: {(x - y).max()}") +print(f"bandwidth: {(y - x).max() + (x - y).max() + 1}") +print(B) + +sns.heatmap(B.todense(), cbar=False, square=True, linewidths=0.5, annot=True) +plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_snap.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_snap.py new file mode 100644 index 000000000..85ea71de5 --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_snap.py @@ -0,0 +1,108 @@ +""" +================== +SNAP Graph Summary +================== +An example of summarizing a graph based on node attributes and edge attributes +using the Summarization by Grouping Nodes on Attributes and Pairwise +edges (SNAP) algorithm (not to be confused with the Stanford Network +Analysis Project). The algorithm groups nodes by their unique +combinations of node attribute values and edge types with other groups +of nodes to produce a summary graph. The summary graph can then be used to +infer how nodes with different attributes values relate to other nodes in the +graph. +""" +import networkx as nx +import matplotlib.pyplot as plt + + +nodes = { + "A": dict(color="Red"), + "B": dict(color="Red"), + "C": dict(color="Red"), + "D": dict(color="Red"), + "E": dict(color="Blue"), + "F": dict(color="Blue"), + "G": dict(color="Blue"), + "H": dict(color="Blue"), + "I": dict(color="Yellow"), + "J": dict(color="Yellow"), + "K": dict(color="Yellow"), + "L": dict(color="Yellow"), +} +edges = [ + ("A", "B", "Strong"), + ("A", "C", "Weak"), + ("A", "E", "Strong"), + ("A", "I", "Weak"), + ("B", "D", "Weak"), + ("B", "J", "Weak"), + ("B", "F", "Strong"), + ("C", "G", "Weak"), + ("D", "H", "Weak"), + ("I", "J", "Strong"), + ("J", "K", "Strong"), + ("I", "L", "Strong"), +] +original_graph = nx.Graph() +original_graph.add_nodes_from(n for n in nodes.items()) +original_graph.add_edges_from((u, v, {"type": label}) for u, v, label in edges) + + +plt.suptitle("SNAP Summarization") + +base_options = dict(with_labels=True, edgecolors="black", node_size=500) + +ax1 = plt.subplot(1, 2, 1) +plt.title( + "Original (%s nodes, %s edges)" + % (original_graph.number_of_nodes(), original_graph.number_of_edges()) +) +pos = nx.spring_layout(original_graph, seed=7482934) +node_colors = [d["color"] for _, d in original_graph.nodes(data=True)] + +edge_type_visual_weight_lookup = {"Weak": 1.0, "Strong": 3.0} +edge_weights = [ + edge_type_visual_weight_lookup[d["type"]] + for _, _, d in original_graph.edges(data=True) +] + +nx.draw_networkx( + original_graph, pos=pos, node_color=node_colors, width=edge_weights, **base_options +) + +node_attributes = ("color",) +edge_attributes = ("type",) +summary_graph = nx.snap_aggregation( + original_graph, node_attributes, edge_attributes, prefix="S-" +) + +plt.subplot(1, 2, 2) + +plt.title( + "SNAP Aggregation (%s nodes, %s edges)" + % (summary_graph.number_of_nodes(), summary_graph.number_of_edges()) +) +summary_pos = nx.spring_layout(summary_graph, seed=8375428) +node_colors = [] +for node in summary_graph: + color = summary_graph.nodes[node]["color"] + node_colors.append(color) + +edge_weights = [] +for edge in summary_graph.edges(): + edge_types = summary_graph.get_edge_data(*edge)["types"] + edge_weight = 0.0 + for edge_type in edge_types: + edge_weight += edge_type_visual_weight_lookup[edge_type["type"]] + edge_weights.append(edge_weight) + +nx.draw_networkx( + summary_graph, + pos=summary_pos, + node_color=node_colors, + width=edge_weights, + **base_options +) + +plt.tight_layout() +plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_subgraphs.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_subgraphs.py new file mode 100644 index 000000000..081270287 --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_subgraphs.py @@ -0,0 +1,170 @@ +""" +========= +Subgraphs +========= +Example of partitioning a directed graph with nodes labeled as +supported and unsupported nodes into a list of subgraphs +that contain only entirely supported or entirely unsupported nodes. +Adopted from +https://github.com/lobpcg/python_examples/blob/master/networkx_example.py +""" + +import networkx as nx +import matplotlib.pyplot as plt + + +def graph_partitioning(G, plotting=True): + """Partition a directed graph into a list of subgraphs that contain + only entirely supported or entirely unsupported nodes. + """ + # Categorize nodes by their node_type attribute + supported_nodes = {n for n, d in G.nodes(data="node_type") if d == "supported"} + unsupported_nodes = {n for n, d in G.nodes(data="node_type") if d == "unsupported"} + + # Make a copy of the graph. + H = G.copy() + # Remove all edges connecting supported and unsupported nodes. + H.remove_edges_from( + (n, nbr, d) + for n, nbrs in G.adj.items() + if n in supported_nodes + for nbr, d in nbrs.items() + if nbr in unsupported_nodes + ) + H.remove_edges_from( + (n, nbr, d) + for n, nbrs in G.adj.items() + if n in unsupported_nodes + for nbr, d in nbrs.items() + if nbr in supported_nodes + ) + + # Collect all removed edges for reconstruction. + G_minus_H = nx.DiGraph() + G_minus_H.add_edges_from(set(G.edges) - set(H.edges)) + + if plotting: + # Plot the stripped graph with the edges removed. + _node_colors = [c for _, c in H.nodes(data="node_color")] + _pos = nx.spring_layout(H) + plt.figure(figsize=(8, 8)) + nx.draw_networkx_edges(H, _pos, alpha=0.3, edge_color="k") + nx.draw_networkx_nodes(H, _pos, node_color=_node_colors) + nx.draw_networkx_labels(H, _pos, font_size=14) + plt.axis("off") + plt.title("The stripped graph with the edges removed.") + plt.show() + # Plot the the edges removed. + _pos = nx.spring_layout(G_minus_H) + plt.figure(figsize=(8, 8)) + ncl = [G.nodes[n]["node_color"] for n in G_minus_H.nodes] + nx.draw_networkx_edges(G_minus_H, _pos, alpha=0.3, edge_color="k") + nx.draw_networkx_nodes(G_minus_H, _pos, node_color=ncl) + nx.draw_networkx_labels(G_minus_H, _pos, font_size=14) + plt.axis("off") + plt.title("The removed edges.") + plt.show() + + # Find the connected components in the stripped undirected graph. + # And use the sets, specifying the components, to partition + # the original directed graph into a list of directed subgraphs + # that contain only entirely supported or entirely unsupported nodes. + subgraphs = [ + H.subgraph(c).copy() for c in nx.connected_components(H.to_undirected()) + ] + + return subgraphs, G_minus_H + + +############################################################################### +# Create an example directed graph. +# --------------------------------- +# +# This directed graph has one input node labeled `in` and plotted in blue color +# and one output node labeled `out` and plotted in magenta color. +# The other six nodes are classified as four `supported` plotted in green color +# and two `unsupported` plotted in red color. The goal is computing a list +# of subgraphs that contain only entirely `supported` or `unsupported` nodes. +G_ex = nx.DiGraph() +G_ex.add_nodes_from(["In"], node_type="input", node_color="b") +G_ex.add_nodes_from(["A", "C", "E", "F"], node_type="supported", node_color="g") +G_ex.add_nodes_from(["B", "D"], node_type="unsupported", node_color="r") +G_ex.add_nodes_from(["Out"], node_type="output", node_color="m") +G_ex.add_edges_from( + [ + ("In", "A"), + ("A", "B"), + ("B", "C"), + ("B", "D"), + ("D", "E"), + ("C", "F"), + ("E", "F"), + ("F", "Out"), + ] +) + +############################################################################### +# Plot the original graph. +# ------------------------ +# +node_color_list = [nc for _, nc in G_ex.nodes(data="node_color")] +pos = nx.spectral_layout(G_ex) +plt.figure(figsize=(8, 8)) +nx.draw_networkx_edges(G_ex, pos, alpha=0.3, edge_color="k") +nx.draw_networkx_nodes(G_ex, pos, alpha=0.8, node_color=node_color_list) +nx.draw_networkx_labels(G_ex, pos, font_size=14) +plt.axis("off") +plt.title("The original graph.") +plt.show() + +############################################################################### +# Calculate the subgraphs with plotting all results of intemediate steps. +# ----------------------------------------------------------------------- +# +subgraphs_of_G_ex, removed_edges = graph_partitioning(G_ex, plotting=True) + +############################################################################### +# Plot the results: every subgraph in the list. +# --------------------------------------------- +# +for subgraph in subgraphs_of_G_ex: + _pos = nx.spring_layout(subgraph) + plt.figure(figsize=(8, 8)) + nx.draw_networkx_edges(subgraph, _pos, alpha=0.3, edge_color="k") + node_color_list_c = [nc for _, nc in subgraph.nodes(data="node_color")] + nx.draw_networkx_nodes(subgraph, _pos, node_color=node_color_list_c) + nx.draw_networkx_labels(subgraph, _pos, font_size=14) + plt.axis("off") + plt.title("One of the subgraphs.") + plt.show() + +############################################################################### +# Put the graph back from the list of subgraphs +# --------------------------------------------- +# +G_ex_r = nx.DiGraph() +# Composing all subgraphs. +for subgraph in subgraphs_of_G_ex: + G_ex_r = nx.compose(G_ex_r, subgraph) +# Adding the previously stored edges. +G_ex_r.add_edges_from(removed_edges.edges()) + +############################################################################### +# Check that the original graph and the reconstructed graphs are isomorphic. +# -------------------------------------------------------------------------- +# +assert nx.is_isomorphic(G_ex, G_ex_r) + +############################################################################### +# Plot the reconstructed graph. +# ----------------------------- +# +node_color_list = [nc for _, nc in G_ex_r.nodes(data="node_color")] +pos = nx.spectral_layout(G_ex_r) +plt.figure(figsize=(8, 8)) +nx.draw_networkx_edges(G_ex_r, pos, alpha=0.3, edge_color="k") +nx.draw_networkx_nodes(G_ex_r, pos, alpha=0.8, node_color=node_color_list) +nx.draw_networkx_labels(G_ex_r, pos, font_size=14) +plt.axis("off") +plt.title("The reconstructed graph.") +plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/basic/README.txt b/.easyocr_venv/share/doc/networkx-2.8.5/examples/basic/README.txt new file mode 100644 index 000000000..c1cc18b1f --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/basic/README.txt @@ -0,0 +1,2 @@ +Basic +----- diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/basic/plot_properties.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/basic/plot_properties.py new file mode 100644 index 000000000..7eb25f334 --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/basic/plot_properties.py @@ -0,0 +1,49 @@ +""" +========== +Properties +========== + +Compute some network properties for the lollipop graph. +""" + +import matplotlib.pyplot as plt +import networkx as nx + +G = nx.lollipop_graph(4, 6) + +pathlengths = [] + +print("source vertex {target:length, }") +for v in G.nodes(): + spl = dict(nx.single_source_shortest_path_length(G, v)) + print(f"{v} {spl} ") + for p in spl: + pathlengths.append(spl[p]) + +print() +print(f"average shortest path length {sum(pathlengths) / len(pathlengths)}") + +# histogram of path lengths +dist = {} +for p in pathlengths: + if p in dist: + dist[p] += 1 + else: + dist[p] = 1 + +print() +print("length #paths") +verts = dist.keys() +for d in sorted(verts): + print(f"{d} {dist[d]}") + +print(f"radius: {nx.radius(G)}") +print(f"diameter: {nx.diameter(G)}") +print(f"eccentricity: {nx.eccentricity(G)}") +print(f"center: {nx.center(G)}") +print(f"periphery: {nx.periphery(G)}") +print(f"density: {nx.density(G)}") + +pos = nx.spring_layout(G, seed=3068) # Seed layout for reproducibility +nx.draw(G, pos=pos, with_labels=True) +plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/basic/plot_read_write.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/basic/plot_read_write.py new file mode 100644 index 000000000..c9fa89ebc --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/basic/plot_read_write.py @@ -0,0 +1,24 @@ +""" +====================== +Read and write graphs. +====================== + +Read and write graphs. +""" + +import matplotlib.pyplot as plt +import networkx as nx + +G = nx.grid_2d_graph(5, 5) # 5x5 grid + +# print the adjacency list +for line in nx.generate_adjlist(G): + print(line) +# write edgelist to grid.edgelist +nx.write_edgelist(G, path="grid.edgelist", delimiter=":") +# read edgelist from grid.edgelist +H = nx.read_edgelist(path="grid.edgelist", delimiter=":") + +pos = nx.spring_layout(H, seed=200) +nx.draw(H, pos) +plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/basic/plot_simple_graph.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/basic/plot_simple_graph.py new file mode 100644 index 000000000..fcd7caf6b --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/basic/plot_simple_graph.py @@ -0,0 +1,60 @@ +""" +============ +Simple graph +============ + +Draw simple graph with manual layout. +""" + +import networkx as nx +import matplotlib.pyplot as plt + +G = nx.Graph() +G.add_edge(1, 2) +G.add_edge(1, 3) +G.add_edge(1, 5) +G.add_edge(2, 3) +G.add_edge(3, 4) +G.add_edge(4, 5) + +# explicitly set positions +pos = {1: (0, 0), 2: (-1, 0.3), 3: (2, 0.17), 4: (4, 0.255), 5: (5, 0.03)} + +options = { + "font_size": 36, + "node_size": 3000, + "node_color": "white", + "edgecolors": "black", + "linewidths": 5, + "width": 5, +} +nx.draw_networkx(G, pos, **options) + +# Set margins for the axes so that nodes aren't clipped +ax = plt.gca() +ax.margins(0.20) +plt.axis("off") +plt.show() + +# %% +# A directed graph + +G = nx.DiGraph([(0, 3), (1, 3), (2, 4), (3, 5), (3, 6), (4, 6), (5, 6)]) + +# group nodes by column +left_nodes = [0, 1, 2] +middle_nodes = [3, 4] +right_nodes = [5, 6] + +# set the position according to column (x-coord) +pos = {n: (0, i) for i, n in enumerate(left_nodes)} +pos.update({n: (1, i + 0.5) for i, n in enumerate(middle_nodes)}) +pos.update({n: (2, i + 0.5) for i, n in enumerate(right_nodes)}) + +nx.draw_networkx(G, pos, **options) + +# Set margins for the axes so that nodes aren't clipped +ax = plt.gca() +ax.margins(0.20) +plt.axis("off") +plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/README.txt b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/README.txt new file mode 100644 index 000000000..c25993dec --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/README.txt @@ -0,0 +1,2 @@ +Drawing +------- diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/chess_masters_WCC.pgn.bz2 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Here the (compressed) +default file:: + + chess_masters_WCC.pgn.bz2 + +contains all 685 World Chess Championship matches from 1886--1985. +(data from http://chessproblem.my-free-games.com/chess/games/Download-PGN.php) + +The `chess_pgn_graph()` function returns a `MultiDiGraph` with multiple edges. +Each node is the last name of a chess master. Each edge is directed from white +to black and contains selected game info. + +The key statement in `chess_pgn_graph` below is:: + + G.add_edge(white, black, game_info) + +where `game_info` is a `dict` describing each game. +""" + +import matplotlib.pyplot as plt +import networkx as nx + +# tag names specifying what game info should be +# stored in the dict on each digraph edge +game_details = ["Event", "Date", "Result", "ECO", "Site"] + + +def chess_pgn_graph(pgn_file="chess_masters_WCC.pgn.bz2"): + """Read chess games in pgn format in pgn_file. + + Filenames ending in .bz2 will be uncompressed. + + Return the MultiDiGraph of players connected by a chess game. + Edges contain game data in a dict. + + """ + import bz2 + + G = nx.MultiDiGraph() + game = {} + with bz2.BZ2File(pgn_file) as datafile: + lines = [line.decode().rstrip("\r\n") for line in datafile] + for line in lines: + if line.startswith("["): + tag, value = line[1:-1].split(" ", 1) + game[str(tag)] = value.strip('"') + else: + # empty line after tag set indicates + # we finished reading game info + if game: + white = game.pop("White") + black = game.pop("Black") + G.add_edge(white, black, **game) + game = {} + return G + + +G = chess_pgn_graph() + +print( + f"Loaded {G.number_of_edges()} chess games between {G.number_of_nodes()} players\n" +) + +# identify connected components of the undirected version +H = G.to_undirected() +Gcc = [H.subgraph(c) for c in nx.connected_components(H)] +if len(Gcc) > 1: + print(f"Note the disconnected component consisting of:\n{Gcc[1].nodes()}") + +# find all games with B97 opening (as described in ECO) +openings = {game_info["ECO"] for (white, black, game_info) in G.edges(data=True)} +print(f"\nFrom a total of {len(openings)} different openings,") +print("the following games used the Sicilian opening") +print('with the Najdorff 7...Qb6 "Poisoned Pawn" variation.\n') + +for (white, black, game_info) in G.edges(data=True): + if game_info["ECO"] == "B97": + summary = f"{white} vs {black}\n" + for k, v in game_info.items(): + summary += f" {k}: {v}\n" + summary += "\n" + print(summary) + +# make new undirected graph H without multi-edges +H = nx.Graph(G) + +# edge width is proportional number of games played +edgewidth = [len(G.get_edge_data(u, v)) for u, v in H.edges()] + +# node size is proportional to number of games won +wins = dict.fromkeys(G.nodes(), 0.0) +for (u, v, d) in G.edges(data=True): + r = d["Result"].split("-") + if r[0] == "1": + wins[u] += 1.0 + elif r[0] == "1/2": + wins[u] += 0.5 + wins[v] += 0.5 + else: + wins[v] += 1.0 +nodesize = [wins[v] * 50 for v in H] + +# Generate layout for visualization +pos = nx.kamada_kawai_layout(H) +# Manual tweaking to limit node label overlap in the visualization +pos["Reshevsky, Samuel H"] += (0.05, -0.10) +pos["Botvinnik, Mikhail M"] += (0.03, -0.06) +pos["Smyslov, Vassily V"] += (0.05, -0.03) + +fig, ax = plt.subplots(figsize=(12, 12)) +# Visualize graph components +nx.draw_networkx_edges(H, pos, alpha=0.3, width=edgewidth, edge_color="m") +nx.draw_networkx_nodes(H, pos, node_size=nodesize, node_color="#210070", alpha=0.9) +label_options = {"ec": "k", "fc": "white", "alpha": 0.7} +nx.draw_networkx_labels(H, pos, font_size=14, bbox=label_options) + +# Title/legend +font = {"fontname": "Helvetica", "color": "k", "fontweight": "bold", "fontsize": 14} +ax.set_title("World Chess Championship Games: 1886 - 1985", font) +# Change font color for legend +font["color"] = "r" + +ax.text( + 0.80, + 0.10, + "edge width = # games played", + horizontalalignment="center", + transform=ax.transAxes, + fontdict=font, +) +ax.text( + 0.80, + 0.06, + "node size = # games won", + horizontalalignment="center", + transform=ax.transAxes, + fontdict=font, +) + +# Resize figure for label readibility +ax.margins(0.1, 0.05) +fig.tight_layout() +plt.axis("off") +plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_custom_node_icons.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_custom_node_icons.py new file mode 100644 index 000000000..c13458f88 --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_custom_node_icons.py @@ -0,0 +1,75 @@ +""" +================= +Custom node icons +================= + +Example of using custom icons to represent nodes with matplotlib. + +Images for node icons courtesy of www.materialui.co +""" + +import matplotlib.pyplot as plt +import networkx as nx +import PIL + +# Image URLs for graph nodes +icons = { + "router": "icons/router_black_144x144.png", + "switch": "icons/switch_black_144x144.png", + "PC": "icons/computer_black_144x144.png", +} + +# Load images +images = {k: PIL.Image.open(fname) for k, fname in icons.items()} + +# Generate the computer network graph +G = nx.Graph() + +G.add_node("router", image=images["router"]) +for i in range(1, 4): + G.add_node(f"switch_{i}", image=images["switch"]) + for j in range(1, 4): + G.add_node("PC_" + str(i) + "_" + str(j), image=images["PC"]) + +G.add_edge("router", "switch_1") +G.add_edge("router", "switch_2") +G.add_edge("router", "switch_3") +for u in range(1, 4): + for v in range(1, 4): + G.add_edge("switch_" + str(u), "PC_" + str(u) + "_" + str(v)) + +# Get a reproducible layout and create figure +pos = nx.spring_layout(G, seed=1734289230) +fig, ax = plt.subplots() + +# Note: the min_source/target_margin kwargs only work with FancyArrowPatch objects. +# Force the use of FancyArrowPatch for edge drawing by setting `arrows=True`, +# but suppress arrowheads with `arrowstyle="-"` +nx.draw_networkx_edges( + G, + pos=pos, + ax=ax, + arrows=True, + arrowstyle="-", + min_source_margin=15, + min_target_margin=15, +) + +# Transform from data coordinates (scaled between xlim and ylim) to display coordinates +tr_figure = ax.transData.transform +# Transform from display to figure coordinates +tr_axes = fig.transFigure.inverted().transform + +# Select the size of the image (relative to the X axis) +icon_size = (ax.get_xlim()[1] - ax.get_xlim()[0]) * 0.025 +icon_center = icon_size / 2.0 + +# Add the respective image to each node +for n in G.nodes: + xf, yf = tr_figure(pos[n]) + xa, ya = tr_axes((xf, yf)) + # get overlapped axes and plot icon + a = plt.axes([xa - icon_center, ya - icon_center, icon_size, icon_size]) + a.imshow(G.nodes[n]["image"]) + a.axis("off") +plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_degree.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_degree.py new file mode 100644 index 000000000..9d7abe4ae --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_degree.py @@ -0,0 +1,50 @@ +""" +=============== +Degree Analysis +=============== + +This example shows several ways to visualize the distribution of the degree of +nodes with two common techniques: a *degree-rank plot* and a +*degree histogram*. + +In this example, a random Graph is generated with 100 nodes. The degree of +each node is determined, and a figure is generated showing three things: +1. The subgraph of connected components +2. The degree-rank plot for the Graph, and +3. The degree histogram +""" +import networkx as nx +import numpy as np +import matplotlib.pyplot as plt + +G = nx.gnp_random_graph(100, 0.02, seed=10374196) + +degree_sequence = sorted((d for n, d in G.degree()), reverse=True) +dmax = max(degree_sequence) + +fig = plt.figure("Degree of a random graph", figsize=(8, 8)) +# Create a gridspec for adding subplots of different sizes +axgrid = fig.add_gridspec(5, 4) + +ax0 = fig.add_subplot(axgrid[0:3, :]) +Gcc = G.subgraph(sorted(nx.connected_components(G), key=len, reverse=True)[0]) +pos = nx.spring_layout(Gcc, seed=10396953) +nx.draw_networkx_nodes(Gcc, pos, ax=ax0, node_size=20) +nx.draw_networkx_edges(Gcc, pos, ax=ax0, alpha=0.4) +ax0.set_title("Connected components of G") +ax0.set_axis_off() + +ax1 = fig.add_subplot(axgrid[3:, :2]) +ax1.plot(degree_sequence, "b-", marker="o") +ax1.set_title("Degree Rank Plot") +ax1.set_ylabel("Degree") +ax1.set_xlabel("Rank") + +ax2 = fig.add_subplot(axgrid[3:, 2:]) +ax2.bar(*np.unique(degree_sequence, return_counts=True)) +ax2.set_title("Degree histogram") +ax2.set_xlabel("Degree") +ax2.set_ylabel("# of Nodes") + +fig.tight_layout() +plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_directed.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_directed.py new file mode 100644 index 000000000..8af58d3f4 --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_directed.py @@ -0,0 +1,46 @@ +""" +============== +Directed Graph +============== + +Draw a graph with directed edges using a colormap and different node sizes. + +Edges have different colors and alphas (opacity). Drawn using matplotlib. +""" + +import matplotlib as mpl +import matplotlib.pyplot as plt +import networkx as nx + +seed = 13648 # Seed random number generators for reproducibility +G = nx.random_k_out_graph(10, 3, 0.5, seed=seed) +pos = nx.spring_layout(G, seed=seed) + +node_sizes = [3 + 10 * i for i in range(len(G))] +M = G.number_of_edges() +edge_colors = range(2, M + 2) +edge_alphas = [(5 + i) / (M + 4) for i in range(M)] +cmap = plt.cm.plasma + +nodes = nx.draw_networkx_nodes(G, pos, node_size=node_sizes, node_color="indigo") +edges = nx.draw_networkx_edges( + G, + pos, + node_size=node_sizes, + arrowstyle="->", + arrowsize=10, + edge_color=edge_colors, + edge_cmap=cmap, + width=2, +) +# set alpha value for each edge +for i in range(M): + edges[i].set_alpha(edge_alphas[i]) + +pc = mpl.collections.PatchCollection(edges, cmap=cmap) +pc.set_array(edge_colors) +plt.colorbar(pc) + +ax = plt.gca() +ax.set_axis_off() +plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_edge_colormap.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_edge_colormap.py new file mode 100644 index 000000000..e0d569b4a --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_edge_colormap.py @@ -0,0 +1,23 @@ +""" +============= +Edge Colormap +============= + +Draw a graph with matplotlib, color edges. +""" + +import matplotlib.pyplot as plt +import networkx as nx + +G = nx.star_graph(20) +pos = nx.spring_layout(G, seed=63) # Seed layout for reproducibility +colors = range(20) +options = { + "node_color": "#A0CBE2", + "edge_color": colors, + "width": 4, + "edge_cmap": plt.cm.Blues, + "with_labels": False, +} +nx.draw(G, pos, **options) +plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_ego_graph.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_ego_graph.py new file mode 100644 index 000000000..fde57406c --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_ego_graph.py @@ -0,0 +1,35 @@ +""" +========= +Ego Graph +========= + +Example using the NetworkX ego_graph() function to return the main egonet of +the largest hub in a Barabási-Albert network. +""" + +from operator import itemgetter + +import matplotlib.pyplot as plt +import networkx as nx + +# Create a BA model graph - use seed for reproducibility +n = 1000 +m = 2 +seed = 20532 +G = nx.barabasi_albert_graph(n, m, seed=seed) + +# find node with largest degree +node_and_degree = G.degree() +(largest_hub, degree) = sorted(node_and_degree, key=itemgetter(1))[-1] + +# Create ego graph of main hub +hub_ego = nx.ego_graph(G, largest_hub) + +# Draw graph +pos = nx.spring_layout(hub_ego, seed=seed) # Seed layout for reproducibility +nx.draw(hub_ego, pos, node_color="b", node_size=50, with_labels=False) + +# Draw ego as large and red +options = {"node_size": 300, "node_color": "r"} +nx.draw_networkx_nodes(hub_ego, pos, nodelist=[largest_hub], **options) +plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_eigenvalues.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_eigenvalues.py new file mode 100644 index 000000000..b0df67ae9 --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_eigenvalues.py @@ -0,0 +1,22 @@ +""" +=========== +Eigenvalues +=========== + +Create an G{n,m} random graph and compute the eigenvalues. +""" +import matplotlib.pyplot as plt +import networkx as nx +import numpy.linalg + +n = 1000 # 1000 nodes +m = 5000 # 5000 edges +G = nx.gnm_random_graph(n, m, seed=5040) # Seed for reproducibility + +L = nx.normalized_laplacian_matrix(G) +e = numpy.linalg.eigvals(L.A) +print("Largest eigenvalue:", max(e)) +print("Smallest eigenvalue:", min(e)) +plt.hist(e, bins=100) # histogram with 100 bins +plt.xlim(0, 2) # eigenvalues between 0 and 2 +plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_four_grids.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_four_grids.py new file mode 100644 index 000000000..17af4e544 --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_four_grids.py @@ -0,0 +1,52 @@ +""" +========== +Four Grids +========== + +Draw a 4x4 graph with matplotlib. + +This example illustrates the use of keyword arguments to `networkx.draw` to +customize the visualization of a simple Graph comprising a 4x4 grid. +""" + +import matplotlib.pyplot as plt +import networkx as nx + +G = nx.grid_2d_graph(4, 4) # 4x4 grid + +pos = nx.spring_layout(G, iterations=100, seed=39775) + +# Create a 2x2 subplot +fig, all_axes = plt.subplots(2, 2) +ax = all_axes.flat + +nx.draw(G, pos, ax=ax[0], font_size=8) +nx.draw(G, pos, ax=ax[1], node_size=0, with_labels=False) +nx.draw( + G, + pos, + ax=ax[2], + node_color="tab:green", + edgecolors="tab:gray", # Node surface color + edge_color="tab:gray", # Color of graph edges + node_size=250, + with_labels=False, + width=6, +) +H = G.to_directed() +nx.draw( + H, + pos, + ax=ax[3], + node_color="tab:orange", + node_size=20, + with_labels=False, + arrowsize=10, + width=2, +) + +# Set margins for the axes so that nodes aren't clipped +for a in ax: + a.margins(0.10) +fig.tight_layout() +plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_house_with_colors.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_house_with_colors.py new file mode 100644 index 000000000..c3a2809dd --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_house_with_colors.py @@ -0,0 +1,26 @@ +""" +================= +House With Colors +================= + +Draw a graph with matplotlib. +""" +import matplotlib.pyplot as plt +import networkx as nx + +G = nx.house_graph() +# explicitly set positions +pos = {0: (0, 0), 1: (1, 0), 2: (0, 1), 3: (1, 1), 4: (0.5, 2.0)} + +# Plot nodes with different properties for the "wall" and "roof" nodes +nx.draw_networkx_nodes( + G, pos, node_size=3000, nodelist=[0, 1, 2, 3], node_color="tab:blue" +) +nx.draw_networkx_nodes(G, pos, node_size=2000, nodelist=[4], node_color="tab:orange") +nx.draw_networkx_edges(G, pos, alpha=0.5, width=6) +# Customize axes +ax = plt.gca() +ax.margins(0.11) +plt.tight_layout() +plt.axis("off") +plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_knuth_miles.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_knuth_miles.py new file mode 100644 index 000000000..e0ebea874 --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_knuth_miles.py @@ -0,0 +1,142 @@ +""" +=========== +Knuth Miles +=========== + +`miles_graph()` returns an undirected graph over 128 US cities. The +cities each have location and population data. The edges are labeled with the +distance between the two cities. + +This example is described in Section 1.1 of + + Donald E. Knuth, "The Stanford GraphBase: A Platform for Combinatorial + Computing", ACM Press, New York, 1993. + http://www-cs-faculty.stanford.edu/~knuth/sgb.html + +The data file can be found at: + +- https://github.com/networkx/networkx/blob/main/examples/drawing/knuth_miles.txt.gz +""" + +import gzip +import re + +# Ignore any warnings related to downloading shpfiles with cartopy +import warnings + +warnings.simplefilter("ignore") + +import numpy as np +import matplotlib.pyplot as plt +import networkx as nx + + +def miles_graph(): + """Return the cites example graph in miles_dat.txt + from the Stanford GraphBase. + """ + # open file miles_dat.txt.gz (or miles_dat.txt) + + fh = gzip.open("knuth_miles.txt.gz", "r") + + G = nx.Graph() + G.position = {} + G.population = {} + + cities = [] + for line in fh.readlines(): + line = line.decode() + if line.startswith("*"): # skip comments + continue + + numfind = re.compile(r"^\d+") + + if numfind.match(line): # this line is distances + dist = line.split() + for d in dist: + G.add_edge(city, cities[i], weight=int(d)) + i = i + 1 + else: # this line is a city, position, population + i = 1 + (city, coordpop) = line.split("[") + cities.insert(0, city) + (coord, pop) = coordpop.split("]") + (y, x) = coord.split(",") + + G.add_node(city) + # assign position - Convert string to lat/long + G.position[city] = (-float(x) / 100, float(y) / 100) + G.population[city] = float(pop) / 1000 + return G + + +G = miles_graph() + +print("Loaded miles_dat.txt containing 128 cities.") +print(G) + +# make new graph of cites, edge if less then 300 miles between them +H = nx.Graph() +for v in G: + H.add_node(v) +for (u, v, d) in G.edges(data=True): + if d["weight"] < 300: + H.add_edge(u, v) + +# draw with matplotlib/pylab +fig = plt.figure(figsize=(8, 6)) + +# nodes colored by degree sized by population +node_color = [float(H.degree(v)) for v in H] + +# Use cartopy to provide a backdrop for the visualization +try: + import cartopy.crs as ccrs + import cartopy.io.shapereader as shpreader + + ax = fig.add_axes([0, 0, 1, 1], projection=ccrs.LambertConformal(), frameon=False) + ax.set_extent([-125, -66.5, 20, 50], ccrs.Geodetic()) + # Add map of countries & US states as a backdrop + for shapename in ("admin_1_states_provinces_lakes_shp", "admin_0_countries"): + shp = shpreader.natural_earth( + resolution="110m", category="cultural", name=shapename + ) + ax.add_geometries( + shpreader.Reader(shp).geometries(), + ccrs.PlateCarree(), + facecolor="none", + edgecolor="k", + ) + # NOTE: When using cartopy, use matplotlib directly rather than nx.draw + # to take advantage of the cartopy transforms + ax.scatter( + *np.array([v for v in G.position.values()]).T, + s=[G.population[v] for v in H], + c=node_color, + transform=ccrs.PlateCarree(), + zorder=100 # Ensure nodes lie on top of edges/state lines + ) + # Plot edges between the cities + for edge in H.edges(): + edge_coords = np.array([G.position[v] for v in edge]) + ax.plot( + edge_coords[:, 0], + edge_coords[:, 1], + transform=ccrs.PlateCarree(), + linewidth=0.75, + color="k", + ) + +except ImportError: + # If cartopy is unavailable, the backdrop for the plot will be blank; + # though you should still be able to discern the general shape of the US + # from graph nodes and edges! + nx.draw( + H, + G.position, + node_size=[G.population[v] for v in H], + node_color=node_color, + with_labels=False, + ) + +plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_labels_and_colors.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_labels_and_colors.py new file mode 100644 index 000000000..b7eddfd23 --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_labels_and_colors.py @@ -0,0 +1,54 @@ +""" +================= +Labels And Colors +================= + +Use `nodelist` and `edgelist` to apply custom coloring and labels to various +components of a graph. +""" +import matplotlib.pyplot as plt +import networkx as nx + +G = nx.cubical_graph() +pos = nx.spring_layout(G, seed=3113794652) # positions for all nodes + +# nodes +options = {"edgecolors": "tab:gray", "node_size": 800, "alpha": 0.9} +nx.draw_networkx_nodes(G, pos, nodelist=[0, 1, 2, 3], node_color="tab:red", **options) +nx.draw_networkx_nodes(G, pos, nodelist=[4, 5, 6, 7], node_color="tab:blue", **options) + +# edges +nx.draw_networkx_edges(G, pos, width=1.0, alpha=0.5) +nx.draw_networkx_edges( + G, + pos, + edgelist=[(0, 1), (1, 2), (2, 3), (3, 0)], + width=8, + alpha=0.5, + edge_color="tab:red", +) +nx.draw_networkx_edges( + G, + pos, + edgelist=[(4, 5), (5, 6), (6, 7), (7, 4)], + width=8, + alpha=0.5, + edge_color="tab:blue", +) + + +# some math labels +labels = {} +labels[0] = r"$a$" +labels[1] = r"$b$" +labels[2] = r"$c$" +labels[3] = r"$d$" +labels[4] = r"$\alpha$" +labels[5] = r"$\beta$" +labels[6] = r"$\gamma$" +labels[7] = r"$\delta$" +nx.draw_networkx_labels(G, pos, labels, font_size=22, font_color="whitesmoke") + +plt.tight_layout() +plt.axis("off") +plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_multipartite_graph.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_multipartite_graph.py new file mode 100644 index 000000000..15c4d82cf --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_multipartite_graph.py @@ -0,0 +1,41 @@ +""" +=================== +Multipartite Layout +=================== +""" + +import itertools +import matplotlib.pyplot as plt +import networkx as nx + +subset_sizes = [5, 5, 4, 3, 2, 4, 4, 3] +subset_color = [ + "gold", + "violet", + "violet", + "violet", + "violet", + "limegreen", + "limegreen", + "darkorange", +] + + +def multilayered_graph(*subset_sizes): + extents = nx.utils.pairwise(itertools.accumulate((0,) + subset_sizes)) + layers = [range(start, end) for start, end in extents] + G = nx.Graph() + for (i, layer) in enumerate(layers): + G.add_nodes_from(layer, layer=i) + for layer1, layer2 in nx.utils.pairwise(layers): + G.add_edges_from(itertools.product(layer1, layer2)) + return G + + +G = multilayered_graph(*subset_sizes) +color = [subset_color[data["layer"]] for v, data in G.nodes(data=True)] +pos = nx.multipartite_layout(G, subset_key="layer") +plt.figure(figsize=(8, 8)) +nx.draw(G, pos, node_color=color, with_labels=False) +plt.axis("equal") +plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_node_colormap.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_node_colormap.py new file mode 100644 index 000000000..ea31a4631 --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_node_colormap.py @@ -0,0 +1,15 @@ +""" +============= +Node Colormap +============= + +Draw a graph with matplotlib, color by degree. +""" + +import matplotlib.pyplot as plt +import networkx as nx + +G = nx.cycle_graph(24) +pos = nx.circular_layout(G) +nx.draw(G, pos, node_color=range(24), node_size=800, cmap=plt.cm.Blues) +plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_rainbow_coloring.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_rainbow_coloring.py new file mode 100644 index 000000000..1d9e6075c --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_rainbow_coloring.py @@ -0,0 +1,68 @@ +""" +================ +Rainbow Coloring +================ + +Generate a complete graph with 13 nodes in a circular layout with the +edges colored by node distance. The node distance is given by the minimum +number of nodes traversed along an arc between any two nodes on the circle. + +Such graphs are the subject of Ringel's conjecture, which states: any complete +graph with ``2n + 1`` nodes can be tiled by any tree with ``n + 1`` nodes +(i.e. copies of the tree can be placed over the complete graph such that each +edge in the complete graph is covered exactly once). The edge coloring is +helpful in determining how to place the tree copies. + +References +---------- +https://www.quantamagazine.org/mathematicians-prove-ringels-graph-theory-conjecture-20200219/ +""" +import matplotlib.pyplot as plt +import networkx as nx + +# A rainbow color mapping using matplotlib's tableau colors +node_dist_to_color = { + 1: "tab:red", + 2: "tab:orange", + 3: "tab:olive", + 4: "tab:green", + 5: "tab:blue", + 6: "tab:purple", +} + +# Create a complete graph with an odd number of nodes +nnodes = 13 +G = nx.complete_graph(nnodes) + +# A graph with (2n + 1) nodes requires n colors for the edges +n = (nnodes - 1) // 2 +ndist_iter = list(range(1, n + 1)) + +# Take advantage of circular symmetry in determining node distances +ndist_iter += ndist_iter[::-1] + + +def cycle(nlist, n): + return nlist[-n:] + nlist[:-n] + + +# Rotate nodes around the circle and assign colors for each edge based on +# node distance +nodes = list(G.nodes()) +for i, nd in enumerate(ndist_iter): + for u, v in zip(nodes, cycle(nodes, i + 1)): + G[u][v]["color"] = node_dist_to_color[nd] + +pos = nx.circular_layout(G) +# Create a figure with 1:1 aspect ratio to preserve the circle. +fig, ax = plt.subplots(figsize=(8, 8)) +node_opts = {"node_size": 500, "node_color": "w", "edgecolors": "k", "linewidths": 2.0} +nx.draw_networkx_nodes(G, pos, **node_opts) +nx.draw_networkx_labels(G, pos, font_size=14) +# Extract color from edge data +edge_colors = [edgedata["color"] for _, _, edgedata in G.edges(data=True)] +nx.draw_networkx_edges(G, pos, width=2.0, edge_color=edge_colors) + +ax.set_axis_off() +fig.tight_layout() +plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_random_geometric_graph.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_random_geometric_graph.py new file mode 100644 index 000000000..6b7c95d35 --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_random_geometric_graph.py @@ -0,0 +1,44 @@ +""" +====================== +Random Geometric Graph +====================== + +Example +""" + +import matplotlib.pyplot as plt +import networkx as nx + +# Use seed when creating the graph for reproducibility +G = nx.random_geometric_graph(200, 0.125, seed=896803) +# position is stored as node attribute data for random_geometric_graph +pos = nx.get_node_attributes(G, "pos") + +# find node near center (0.5,0.5) +dmin = 1 +ncenter = 0 +for n in pos: + x, y = pos[n] + d = (x - 0.5) ** 2 + (y - 0.5) ** 2 + if d < dmin: + ncenter = n + dmin = d + +# color by path length from node near center +p = dict(nx.single_source_shortest_path_length(G, ncenter)) + +plt.figure(figsize=(8, 8)) +nx.draw_networkx_edges(G, pos, alpha=0.4) +nx.draw_networkx_nodes( + G, + pos, + nodelist=list(p.keys()), + node_size=80, + node_color=list(p.values()), + cmap=plt.cm.Reds_r, +) + +plt.xlim(-0.05, 1.05) +plt.ylim(-0.05, 1.05) +plt.axis("off") +plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_sampson.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_sampson.py new file mode 100644 index 000000000..d6eff642d --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_sampson.py @@ -0,0 +1,47 @@ +""" +======= +Sampson +======= + +Sampson's monastery data. + +Shows how to read data from a zip file and plot multiple frames. + +The data file can be found at: + +- https://github.com/networkx/networkx/blob/main/examples/drawing/sampson_data.zip +""" + +import zipfile +from io import BytesIO as StringIO + +import matplotlib.pyplot as plt +import networkx as nx + +with zipfile.ZipFile("sampson_data.zip") as zf: + e1 = StringIO(zf.read("samplike1.txt")) + e2 = StringIO(zf.read("samplike2.txt")) + e3 = StringIO(zf.read("samplike3.txt")) + +G1 = nx.read_edgelist(e1, delimiter="\t") +G2 = nx.read_edgelist(e2, delimiter="\t") +G3 = nx.read_edgelist(e3, delimiter="\t") +pos = nx.spring_layout(G3, iterations=100, seed=173) +plt.clf() + +plt.subplot(221) +plt.title("samplike1") +nx.draw(G1, pos, node_size=50, with_labels=False) +plt.subplot(222) +plt.title("samplike2") +nx.draw(G2, pos, node_size=50, with_labels=False) +plt.subplot(223) +plt.title("samplike3") +nx.draw(G3, pos, node_size=50, with_labels=False) +plt.subplot(224) +plt.title("samplike1,2,3") +nx.draw(G3, pos, edgelist=list(G3.edges()), node_size=50, with_labels=False) +nx.draw_networkx_edges(G1, pos, alpha=0.25) +nx.draw_networkx_edges(G2, pos, alpha=0.25) +plt.tight_layout() +plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_selfloops.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_selfloops.py new file mode 100644 index 000000000..11bcf50fb --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_selfloops.py @@ -0,0 +1,29 @@ +""" +========== +Self-loops +========== + +A self-loop is an edge that originates from and terminates the same node. +This example shows how to draw self-loops with `nx_pylab`. + +""" +import networkx as nx +import matplotlib.pyplot as plt + +# Create a graph and add a self-loop to node 0 +G = nx.complete_graph(3, create_using=nx.DiGraph) +G.add_edge(0, 0) +pos = nx.circular_layout(G) + +# As of version 2.6, self-loops are drawn by default with the same styling as +# other edges +nx.draw(G, pos, with_labels=True) + +# Add self-loops to the remaining nodes +edgelist = [(1, 1), (2, 2)] +G.add_edges_from(edgelist) + +# Draw the newly added self-loops with different formatting +nx.draw_networkx_edges(G, pos, edgelist=edgelist, arrowstyle="<|-", style="dashed") + +plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_simple_path.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_simple_path.py new file mode 100644 index 000000000..ec5218570 --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_simple_path.py @@ -0,0 +1,14 @@ +""" +=========== +Simple Path +=========== + +Draw a graph with matplotlib. +""" +import matplotlib.pyplot as plt +import networkx as nx + +G = nx.path_graph(8) +pos = nx.spring_layout(G, seed=47) # Seed layout for reproducibility +nx.draw(G, pos=pos) +plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_spectral_grid.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_spectral_grid.py new file mode 100644 index 000000000..80ef6f1fc --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_spectral_grid.py @@ -0,0 +1,58 @@ +""" +================== +Spectral Embedding +================== + +The spectral layout positions the nodes of the graph based on the +eigenvectors of the graph Laplacian $L = D - A$, where $A$ is the +adjacency matrix and $D$ is the degree matrix of the graph. +By default, the spectral layout will embed the graph in two +dimensions (you can embed your graph in other dimensions using the +``dim`` argument to either :func:`~drawing.nx_pylab.draw_spectral` or +:func:`~drawing.layout.spectral_layout`). + +When the edges of the graph represent similarity between the incident +nodes, the spectral embedding will place highly similar nodes closer +to one another than nodes which are less similar. + +This is particularly striking when you spectrally embed a grid +graph. In the full grid graph, the nodes in the center of the +graph are pulled apart more than nodes on the periphery. +As you remove internal nodes, this effect increases. +""" + +import matplotlib.pyplot as plt +import networkx as nx + + +options = {"node_color": "C0", "node_size": 100} + +G = nx.grid_2d_graph(6, 6) +plt.subplot(332) +nx.draw_spectral(G, **options) + +G.remove_edge((2, 2), (2, 3)) +plt.subplot(334) +nx.draw_spectral(G, **options) + +G.remove_edge((3, 2), (3, 3)) +plt.subplot(335) +nx.draw_spectral(G, **options) + +G.remove_edge((2, 2), (3, 2)) +plt.subplot(336) +nx.draw_spectral(G, **options) + +G.remove_edge((2, 3), (3, 3)) +plt.subplot(337) +nx.draw_spectral(G, **options) + +G.remove_edge((1, 2), (1, 3)) +plt.subplot(338) +nx.draw_spectral(G, **options) + +G.remove_edge((4, 2), (4, 3)) +plt.subplot(339) +nx.draw_spectral(G, **options) + +plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_tsp.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_tsp.py new file mode 100644 index 000000000..78c731b8d --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_tsp.py @@ -0,0 +1,52 @@ +""" +========================== +Traveling Salesman Problem +========================== + +This is an example of a drawing solution of the traveling salesman problem + +The function is used to produce the solution is christofides, +where given a set of nodes, it calculates the route of the nodes +that the traveler has to follow in order to minimize the total cost. +""" + +import matplotlib.pyplot as plt +import networkx as nx +import networkx.algorithms.approximation as nx_app +import math + +G = nx.random_geometric_graph(20, radius=0.4, seed=3) +pos = nx.get_node_attributes(G, "pos") + +# Depot should be at (0,0) +pos[0] = (0.5, 0.5) + +H = G.copy() + + +# Calculating the distances between the nodes as edge's weight. +for i in range(len(pos)): + for j in range(i + 1, len(pos)): + dist = math.hypot(pos[i][0] - pos[j][0], pos[i][1] - pos[j][1]) + dist = dist + G.add_edge(i, j, weight=dist) + +cycle = nx_app.christofides(G, weight="weight") +edge_list = list(nx.utils.pairwise(cycle)) + +# Draw closest edges on each node only +nx.draw_networkx_edges(H, pos, edge_color="blue", width=0.5) + +# Draw the route +nx.draw_networkx( + G, + pos, + with_labels=True, + edgelist=edge_list, + edge_color="red", + node_size=200, + width=3, +) + +print("The route of the traveller is:", cycle) +plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_unix_email.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_unix_email.py new file mode 100644 index 000000000..25fce7ae6 --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_unix_email.py @@ -0,0 +1,62 @@ +""" +========== +Unix Email +========== + +Create a directed graph, allowing multiple edges and self loops, from a unix +mailbox. The nodes are email addresses with links that point from the sender +to the receivers. The edge data is a Python email.Message object which +contains all of the email message data. + +This example shows the power of `DiGraph` to hold edge data of arbitrary Python +objects (in this case a list of email messages). + + +The sample unix email mailbox called "unix_email.mbox" may be found here: + +- https://github.com/networkx/networkx/blob/main/examples/drawing/unix_email.mbox +""" + +from email.utils import getaddresses, parseaddr +import mailbox + +import matplotlib.pyplot as plt +import networkx as nx + +# unix mailbox recipe +# see https://docs.python.org/3/library/mailbox.html + + +def mbox_graph(): + mbox = mailbox.mbox("unix_email.mbox") # parse unix mailbox + + G = nx.MultiDiGraph() # create empty graph + + # parse each messages and build graph + for msg in mbox: # msg is python email.Message.Message object + (source_name, source_addr) = parseaddr(msg["From"]) # sender + # get all recipients + # see https://docs.python.org/3/library/email.html + tos = msg.get_all("to", []) + ccs = msg.get_all("cc", []) + resent_tos = msg.get_all("resent-to", []) + resent_ccs = msg.get_all("resent-cc", []) + all_recipients = getaddresses(tos + ccs + resent_tos + resent_ccs) + # now add the edges for this mail message + for (target_name, target_addr) in all_recipients: + G.add_edge(source_addr, target_addr, message=msg) + + return G + + +G = mbox_graph() + +# print edges with message subject +for (u, v, d) in G.edges(data=True): + print(f"From: {u} To: {v} Subject: {d['message']['Subject']}") + +pos = nx.spring_layout(G, iterations=10, seed=227) +nx.draw(G, pos, node_size=0, alpha=0.4, edge_color="r", font_size=16, with_labels=True) +ax = plt.gca() +ax.margins(0.08) +plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_weighted_graph.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_weighted_graph.py new file mode 100644 index 000000000..ba7721c40 --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_weighted_graph.py @@ -0,0 +1,44 @@ +""" +============== +Weighted Graph +============== + +An example using Graph as a weighted network. +""" +import matplotlib.pyplot as plt +import networkx as nx + +G = nx.Graph() + +G.add_edge("a", "b", weight=0.6) +G.add_edge("a", "c", weight=0.2) +G.add_edge("c", "d", weight=0.1) +G.add_edge("c", "e", weight=0.7) +G.add_edge("c", "f", weight=0.9) +G.add_edge("a", "d", weight=0.3) + +elarge = [(u, v) for (u, v, d) in G.edges(data=True) if d["weight"] > 0.5] +esmall = [(u, v) for (u, v, d) in G.edges(data=True) if d["weight"] <= 0.5] + +pos = nx.spring_layout(G, seed=7) # positions for all nodes - seed for reproducibility + +# nodes +nx.draw_networkx_nodes(G, pos, node_size=700) + +# edges +nx.draw_networkx_edges(G, pos, edgelist=elarge, width=6) +nx.draw_networkx_edges( + G, pos, edgelist=esmall, width=6, alpha=0.5, edge_color="b", style="dashed" +) + +# node labels +nx.draw_networkx_labels(G, pos, font_size=20, font_family="sans-serif") +# edge weight labels +edge_labels = nx.get_edge_attributes(G, "weight") +nx.draw_networkx_edge_labels(G, pos, edge_labels) + +ax = plt.gca() +ax.margins(0.08) +plt.axis("off") +plt.tight_layout() +plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/unix_email.mbox b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/unix_email.mbox new file mode 100644 index 000000000..a3a7cf8dd --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/unix_email.mbox @@ -0,0 +1,84 @@ +From alice@edu Thu Jun 16 16:12:12 2005 +From: Alice +Subject: NetworkX +Date: Thu, 16 Jun 2005 16:12:13 -0700 +To: Bob +Status: RO +Content-Length: 86 +Lines: 5 + +Bob, check out the new networkx release - you and +Carol might really like it. + +Alice + + +From bob@gov Thu Jun 16 18:13:12 2005 +Return-Path: +Subject: Re: NetworkX +From: Bob +To: Alice +Content-Type: text/plain +Date: Thu, 16 Jun 2005 18:13:12 -0700 +Status: RO +Content-Length: 26 +Lines: 4 + +Thanks for the tip. + +Bob + + +From ted@com Thu Jul 28 09:53:31 2005 +Return-Path: +Subject: Graph package in Python? +From: Ted +To: Bob +Content-Type: text/plain +Date: Thu, 28 Jul 2005 09:47:03 -0700 +Status: RO +Content-Length: 90 +Lines: 3 + +Hey Ted - I'm looking for a Python package for +graphs and networks. Do you know of any? + + +From bob@gov Thu Jul 28 09:59:31 2005 +Return-Path: +Subject: Re: Graph package in Python? +From: Bob +To: Ted +Content-Type: text/plain +Date: Thu, 28 Jul 2005 09:59:03 -0700 +Status: RO +Content-Length: 180 +Lines: 9 + + +Check out the NetworkX package - Alice sent me the tip! + +Bob + +>> bob@gov scrawled: +>> Hey Ted - I'm looking for a Python package for +>> graphs and networks. Do you know of any? + + +From ted@com Thu Jul 28 15:53:31 2005 +Return-Path: +Subject: get together for lunch to discuss Networks? +From: Ted +To: Bob , Carol , Alice +Content-Type: text/plain +Date: Thu, 28 Jul 2005 15:47:03 -0700 +Status: RO +Content-Length: 139 +Lines: 5 + +Hey everyrone! Want to meet at that restaurant on the +island in Konigsburg tonight? Bring your laptops +and we can install NetworkX. + +Ted + diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/README.txt b/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/README.txt new file mode 100644 index 000000000..9b0e3b212 --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/README.txt @@ -0,0 +1,2 @@ +Graph +----- diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/plot_degree_sequence.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/plot_degree_sequence.py new file mode 100644 index 000000000..0d32091c9 --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/plot_degree_sequence.py @@ -0,0 +1,34 @@ +""" +=============== +Degree Sequence +=============== + +Random graph from given degree sequence. +""" +import matplotlib.pyplot as plt +import networkx as nx + +# Specify seed for reproducibility +seed = 668273 + +z = [5, 3, 3, 3, 3, 2, 2, 2, 1, 1, 1] +print(nx.is_graphical(z)) + +print("Configuration model") +G = nx.configuration_model(z, seed=seed) # configuration model, seed for reproduciblity +degree_sequence = [d for n, d in G.degree()] # degree sequence +print(f"Degree sequence {degree_sequence}") +print("Degree histogram") +hist = {} +for d in degree_sequence: + if d in hist: + hist[d] += 1 + else: + hist[d] = 1 +print("degree #nodes") +for d in hist: + print(f"{d:4} {hist[d]:6}") + +pos = nx.spring_layout(G, seed=seed) # Seed layout for reproducibility +nx.draw(G, pos=pos) +plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/plot_erdos_renyi.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/plot_erdos_renyi.py new file mode 100644 index 000000000..e13267608 --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/plot_erdos_renyi.py @@ -0,0 +1,36 @@ +""" +=========== +Erdos Renyi +=========== + +Create an G{n,m} random graph with n nodes and m edges +and report some properties. + +This graph is sometimes called the Erdős-Rényi graph +but is different from G{n,p} or binomial_graph which is also +sometimes called the Erdős-Rényi graph. +""" + +import matplotlib.pyplot as plt +import networkx as nx + +n = 10 # 10 nodes +m = 20 # 20 edges +seed = 20160 # seed random number generators for reproducibility + +# Use seed for reproducibility +G = nx.gnm_random_graph(n, m, seed=seed) + +# some properties +print("node degree clustering") +for v in nx.nodes(G): + print(f"{v} {nx.degree(G, v)} {nx.clustering(G, v)}") + +print() +print("the adjacency list") +for line in nx.generate_adjlist(G): + print(line) + +pos = nx.spring_layout(G, seed=seed) # Seed for reproducible layout +nx.draw(G, pos=pos) +plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/plot_expected_degree_sequence.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/plot_expected_degree_sequence.py new file mode 100644 index 000000000..1fc67990e --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/plot_expected_degree_sequence.py @@ -0,0 +1,20 @@ +""" +======================== +Expected Degree Sequence +======================== + +Random graph from given degree sequence. +""" + +import networkx as nx + +# make a random graph of 500 nodes with expected degrees of 50 +n = 500 # n nodes +p = 0.1 +w = [p * n for i in range(n)] # w = p*n for all nodes +G = nx.expected_degree_graph(w) # configuration model +print("Degree histogram") +print("degree (#nodes) ****") +dh = nx.degree_histogram(G) +for i, d in enumerate(dh): + print(f"{i:2} ({d:2}) {'*'*d}") diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/plot_football.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/plot_football.py new file mode 100644 index 000000000..f6397f0e4 --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/plot_football.py @@ -0,0 +1,44 @@ +""" +======== +Football +======== + +Load football network in GML format and compute some network statistcs. + +Shows how to download GML graph in a zipped file, unpack it, and load +into a NetworkX graph. + +Requires Internet connection to download the URL +http://www-personal.umich.edu/~mejn/netdata/football.zip +""" + +import urllib.request +import io +import zipfile + +import matplotlib.pyplot as plt +import networkx as nx + +url = "http://www-personal.umich.edu/~mejn/netdata/football.zip" + +sock = urllib.request.urlopen(url) # open URL +s = io.BytesIO(sock.read()) # read into BytesIO "file" +sock.close() + +zf = zipfile.ZipFile(s) # zipfile object +txt = zf.read("football.txt").decode() # read info file +gml = zf.read("football.gml").decode() # read gml data +# throw away bogus first line with # from mejn files +gml = gml.split("\n")[1:] +G = nx.parse_gml(gml) # parse gml data + +print(txt) +# print degree for each team - number of games +for n, d in G.degree(): + print(f"{n:20} {d:2}") + +options = {"node_color": "black", "node_size": 50, "linewidths": 0, "width": 0.1} + +pos = nx.spring_layout(G, seed=1969) # Seed for reproducible layout +nx.draw(G, pos, **options) +plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/plot_karate_club.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/plot_karate_club.py new file mode 100644 index 000000000..c4fe5bc0d --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/plot_karate_club.py @@ -0,0 +1,25 @@ +""" +=========== +Karate Club +=========== + +Zachary's Karate Club graph + +Data file from: +http://vlado.fmf.uni-lj.si/pub/networks/data/Ucinet/UciData.htm + +Zachary W. (1977). +An information flow model for conflict and fission in small groups. +Journal of Anthropological Research, 33, 452-473. +""" + +import matplotlib.pyplot as plt +import networkx as nx + +G = nx.karate_club_graph() +print("Node Degree") +for v in G: + print(f"{v:4} {G.degree(v):6}") + +nx.draw_circular(G, with_labels=True) +plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/plot_morse_trie.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/plot_morse_trie.py new file mode 100644 index 000000000..88b424ce5 --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/plot_morse_trie.py @@ -0,0 +1,96 @@ +""" +========== +Morse Trie +========== + +A prefix tree (aka a "trie") representing the Morse encoding of the alphabet. +A letter can be encoded by tracing the path from the corresponding node in the +tree to the root node, reversing the order of the symbols encountered along +the path. +""" +import networkx as nx + +# Unicode characters to represent the dots/dashes (or dits/dahs) of Morse code +dot = "•" +dash = "—" + +# Start with the direct mapping of letter -> code +morse_direct_mapping = { + "a": dot + dash, + "b": dash + dot * 3, + "c": dash + dot + dash + dot, + "d": dash + dot * 2, + "e": dot, + "f": dot * 2 + dash + dot, + "g": dash * 2 + dot, + "h": dot * 4, + "i": dot * 2, + "j": dot + dash * 3, + "k": dash + dot + dash, + "l": dot + dash + dot * 2, + "m": dash * 2, + "n": dash + dot, + "o": dash * 3, + "p": dot + dash * 2 + dot, + "q": dash * 2 + dot + dash, + "r": dot + dash + dot, + "s": dot * 3, + "t": dash, + "u": dot * 2 + dash, + "v": dot * 3 + dash, + "w": dot + dash * 2, + "x": dash + dot * 2 + dash, + "y": dash + dot + dash * 2, + "z": dash * 2 + dot * 2, +} + +### Manually construct the prefix tree from this mapping + +# Some preprocessing: sort the original mapping by code length and character +# value +morse_mapping_sorted = dict( + sorted(morse_direct_mapping.items(), key=lambda item: (len(item[1]), item[1])) +) + +# More preprocessing: create the reverse mapping to simplify lookup +reverse_mapping = {v: k for k, v in morse_direct_mapping.items()} +reverse_mapping[""] = "" # Represent the "root" node with an empty string + +# Construct the prefix tree from the sorted mapping +G = nx.DiGraph() +for node, char in morse_mapping_sorted.items(): + pred = char[:-1] + # Store the dot/dash relating the two letters as an edge attribute "char" + G.add_edge(reverse_mapping[pred], node, char=char[-1]) + +# For visualization purposes, layout the nodes in topological order +for i, layer in enumerate(nx.topological_generations(G)): + for n in layer: + G.nodes[n]["layer"] = i +pos = nx.multipartite_layout(G, subset_key="layer", align="horizontal") +# Flip the layout so the root node is on top +for k in pos: + pos[k][-1] *= -1 + +# Visualize the trie +nx.draw(G, pos=pos, with_labels=True) +elabels = {(u, v): l for u, v, l in G.edges(data="char")} +nx.draw_networkx_edge_labels(G, pos, edge_labels=elabels) + +# A letter can be encoded by following the path from the given letter (node) to +# the root node +def morse_encode(letter): + pred = next(G.predecessors(letter)) # Each letter has only 1 predecessor + symbol = G[pred][letter]["char"] + if pred != "": + return morse_encode(pred) + symbol # Traversing the trie in reverse + return symbol + + +# Verify that the trie encoding is correct +import string + +for letter in string.ascii_lowercase: + assert morse_encode(letter) == morse_direct_mapping[letter] + +print(" ".join([morse_encode(ltr) for ltr in "ilovenetworkx"])) diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/plot_napoleon_russian_campaign.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/plot_napoleon_russian_campaign.py new file mode 100644 index 000000000..46ef64d66 --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/plot_napoleon_russian_campaign.py @@ -0,0 +1,133 @@ +""" +========================= +Napoleon Russian Campaign +========================= + +Minard's data from Napoleon's 1812-1813 Russian Campaign. +https://web.archive.org/web/20080112042656/http://www.math.yorku.ca/SCS/Gallery/minard/minard.txt +""" + +import matplotlib.pyplot as plt +import networkx as nx + + +def minard_graph(): + data1 = """\ +24.0,54.9,340000,A,1 +24.5,55.0,340000,A,1 +25.5,54.5,340000,A,1 +26.0,54.7,320000,A,1 +27.0,54.8,300000,A,1 +28.0,54.9,280000,A,1 +28.5,55.0,240000,A,1 +29.0,55.1,210000,A,1 +30.0,55.2,180000,A,1 +30.3,55.3,175000,A,1 +32.0,54.8,145000,A,1 +33.2,54.9,140000,A,1 +34.4,55.5,127100,A,1 +35.5,55.4,100000,A,1 +36.0,55.5,100000,A,1 +37.6,55.8,100000,A,1 +37.7,55.7,100000,R,1 +37.5,55.7,98000,R,1 +37.0,55.0,97000,R,1 +36.8,55.0,96000,R,1 +35.4,55.3,87000,R,1 +34.3,55.2,55000,R,1 +33.3,54.8,37000,R,1 +32.0,54.6,24000,R,1 +30.4,54.4,20000,R,1 +29.2,54.3,20000,R,1 +28.5,54.2,20000,R,1 +28.3,54.3,20000,R,1 +27.5,54.5,20000,R,1 +26.8,54.3,12000,R,1 +26.4,54.4,14000,R,1 +25.0,54.4,8000,R,1 +24.4,54.4,4000,R,1 +24.2,54.4,4000,R,1 +24.1,54.4,4000,R,1""" + data2 = """\ +24.0,55.1,60000,A,2 +24.5,55.2,60000,A,2 +25.5,54.7,60000,A,2 +26.6,55.7,40000,A,2 +27.4,55.6,33000,A,2 +28.7,55.5,33000,R,2 +29.2,54.2,30000,R,2 +28.5,54.1,30000,R,2 +28.3,54.2,28000,R,2""" + data3 = """\ +24.0,55.2,22000,A,3 +24.5,55.3,22000,A,3 +24.6,55.8,6000,A,3 +24.6,55.8,6000,R,3 +24.2,54.4,6000,R,3 +24.1,54.4,6000,R,3""" + cities = """\ +24.0,55.0,Kowno +25.3,54.7,Wilna +26.4,54.4,Smorgoni +26.8,54.3,Moiodexno +27.7,55.2,Gloubokoe +27.6,53.9,Minsk +28.5,54.3,Studienska +28.7,55.5,Polotzk +29.2,54.4,Bobr +30.2,55.3,Witebsk +30.4,54.5,Orscha +30.4,53.9,Mohilow +32.0,54.8,Smolensk +33.2,54.9,Dorogobouge +34.3,55.2,Wixma +34.4,55.5,Chjat +36.0,55.5,Mojaisk +37.6,55.8,Moscou +36.6,55.3,Tarantino +36.5,55.0,Malo-Jarosewii""" + + c = {} + for line in cities.split("\n"): + x, y, name = line.split(",") + c[name] = (float(x), float(y)) + + g = [] + + for data in [data1, data2, data3]: + G = nx.Graph() + i = 0 + G.pos = {} # location + G.pop = {} # size + last = None + for line in data.split("\n"): + x, y, p, r, n = line.split(",") + G.pos[i] = (float(x), float(y)) + G.pop[i] = int(p) + if last is None: + last = i + else: + G.add_edge(i, last, **{r: int(n)}) + last = i + i = i + 1 + g.append(G) + + return g, c + + +(g, city) = minard_graph() + +plt.figure(1, figsize=(11, 5)) +plt.clf() +colors = ["b", "g", "r"] +for G in g: + c = colors.pop(0) + node_size = [G.pop[n] // 300 for n in G] + nx.draw_networkx_edges(G, G.pos, edge_color=c, width=4, alpha=0.5) + nx.draw_networkx_nodes(G, G.pos, node_size=node_size, node_color=c, alpha=0.5) + nx.draw_networkx_nodes(G, G.pos, node_size=5, node_color="k") + +for c in city: + x, y = city[c] + plt.text(x, y + 0.1, c) +plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/plot_roget.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/plot_roget.py new file mode 100644 index 000000000..777c0174f --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/plot_roget.py @@ -0,0 +1,80 @@ +""" +===== +Roget +===== + +Build a directed graph of 1022 categories and 5075 cross-references as defined +in the 1879 version of Roget's Thesaurus. This example is described in Section +1.2 of + + Donald E. Knuth, "The Stanford GraphBase: A Platform for Combinatorial + Computing", ACM Press, New York, 1993. + http://www-cs-faculty.stanford.edu/~knuth/sgb.html + +Note that one of the 5075 cross references is a self loop yet it is included in +the graph built here because the standard networkx `DiGraph` class allows self +loops. (cf. 400pungency:400 401 403 405). + +The data file can be found at: + +- https://github.com/networkx/networkx/blob/main/examples/graph/roget_dat.txt.gz +""" + +import gzip +import re +import sys + +import matplotlib.pyplot as plt +import networkx as nx + + +def roget_graph(): + """Return the thesaurus graph from the roget.dat example in + the Stanford Graph Base. + """ + # open file roget_dat.txt.gz + fh = gzip.open("roget_dat.txt.gz", "r") + + G = nx.DiGraph() + + for line in fh.readlines(): + line = line.decode() + if line.startswith("*"): # skip comments + continue + if line.startswith(" "): # this is a continuation line, append + line = oldline + line + if line.endswith("\\\n"): # continuation line, buffer, goto next + oldline = line.strip("\\\n") + continue + + (headname, tails) = line.split(":") + + # head + numfind = re.compile(r"^\d+") # re to find the number of this word + head = numfind.findall(headname)[0] # get the number + + G.add_node(head) + + for tail in tails.split(): + if head == tail: + print("skipping self loop", head, tail, file=sys.stderr) + G.add_edge(head, tail) + + return G + + +G = roget_graph() +print("Loaded roget_dat.txt containing 1022 categories.") +print(G) +UG = G.to_undirected() +print(nx.number_connected_components(UG), "connected components") + +options = { + "node_color": "black", + "node_size": 1, + "edge_color": "gray", + "linewidths": 0, + "width": 0.1, +} +nx.draw_circular(UG, **options) +plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/plot_words.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/plot_words.py new file mode 100644 index 000000000..e5211f594 --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/plot_words.py @@ -0,0 +1,88 @@ +""" +================== +Words/Ladder Graph +================== + +Generate an undirected graph over the 5757 5-letter words in the datafile +`words_dat.txt.gz`. Two words are connected by an edge if they differ in one +letter, resulting in 14,135 edges. This example is described in Section 1.1 of + + Donald E. Knuth, "The Stanford GraphBase: A Platform for Combinatorial + Computing", ACM Press, New York, 1993. + http://www-cs-faculty.stanford.edu/~knuth/sgb.html + +The data file can be found at: + +- https://github.com/networkx/networkx/blob/main/examples/graph/words_dat.txt.gz +""" + +import gzip +from string import ascii_lowercase as lowercase + +import matplotlib.pyplot as plt +import networkx as nx + + +def generate_graph(words): + G = nx.Graph(name="words") + lookup = {c: lowercase.index(c) for c in lowercase} + + def edit_distance_one(word): + for i in range(len(word)): + left, c, right = word[0:i], word[i], word[i + 1 :] + j = lookup[c] # lowercase.index(c) + for cc in lowercase[j + 1 :]: + yield left + cc + right + + candgen = ( + (word, cand) + for word in sorted(words) + for cand in edit_distance_one(word) + if cand in words + ) + G.add_nodes_from(words) + for word, cand in candgen: + G.add_edge(word, cand) + return G + + +def words_graph(): + """Return the words example graph from the Stanford GraphBase""" + fh = gzip.open("words_dat.txt.gz", "r") + words = set() + for line in fh.readlines(): + line = line.decode() + if line.startswith("*"): + continue + w = str(line[0:5]) + words.add(w) + return generate_graph(words) + + +G = words_graph() +print("Loaded words_dat.txt containing 5757 five-letter English words.") +print("Two words are connected if they differ in one letter.") +print(G) +print(f"{nx.number_connected_components(G)} connected components") + +for (source, target) in [("chaos", "order"), ("nodes", "graph"), ("pound", "marks")]: + print(f"Shortest path between {source} and {target} is") + try: + shortest_path = nx.shortest_path(G, source, target) + for n in shortest_path: + print(n) + except nx.NetworkXNoPath: + print("None") + + +# draw a subset of the graph +boundary = list(nx.node_boundary(G, shortest_path)) +G.add_nodes_from(shortest_path, color="red") +G.add_nodes_from(boundary, color="blue") +H = G.subgraph(shortest_path + boundary) +colors = 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edges that *do not exist* in the dense +graph. However, when applying algorithms to this complement graph data +structure, it behaves as if it were the dense version. So it can be used +directly in several NetworkX algorithms. + +This subclass has only been tested for k-core, connected_components, +and biconnected_components algorithms but might also work for other +algorithms. + +""" +import matplotlib.pyplot as plt +import networkx as nx +from networkx import Graph + + +class AntiGraph(Graph): + """ + Class for complement graphs. + + The main goal is to be able to work with big and dense graphs with + a low memory footprint. + + In this class you add the edges that *do not exist* in the dense graph, + the report methods of the class return the neighbors, the edges and + the degree as if it was the dense graph. Thus it's possible to use + an instance of this class with some of NetworkX functions. + """ + + all_edge_dict = {"weight": 1} + + def single_edge_dict(self): + return self.all_edge_dict + + edge_attr_dict_factory = single_edge_dict + + def __getitem__(self, n): + """Return a dict of neighbors of node n in the dense graph. + + Parameters + ---------- + n : node + A node in the graph. + + Returns + ------- + adj_dict : dictionary + The adjacency dictionary for nodes connected to n. + + """ + return { + node: self.all_edge_dict for node in set(self.adj) - set(self.adj[n]) - {n} + } + + def neighbors(self, n): + """Return an iterator over all neighbors of node n in the + dense graph. + + """ + try: + return iter(set(self.adj) - set(self.adj[n]) - {n}) + except KeyError as err: + raise nx.NetworkXError(f"The node {n} is not in the graph.") from err + + def degree(self, nbunch=None, weight=None): + """Return an iterator for (node, degree) in the dense graph. + + The node degree is the number of edges adjacent to the node. + + Parameters + ---------- + nbunch : iterable container, optional (default=all nodes) + A container of nodes. The container will be iterated + through once. + + weight : string or None, optional (default=None) + The edge attribute that holds the numerical value used + as a weight. If None, then each edge has weight 1. + The degree is the sum of the edge weights adjacent to the node. + + Returns + ------- + nd_iter : iterator + The iterator returns two-tuples of (node, degree). + + See Also + -------- + degree + + Examples + -------- + >>> G = nx.path_graph(4) # or DiGraph, MultiGraph, MultiDiGraph, etc + >>> G.degree(0) # node 0 with degree 1 + 1 + >>> list(G.degree([0, 1])) + [(0, 1), (1, 2)] + + """ + if nbunch is None: + nodes_nbrs = ( + ( + n, + { + v: self.all_edge_dict + for v in set(self.adj) - set(self.adj[n]) - {n} + }, + ) + for n in self.nodes() + ) + elif nbunch in self: + nbrs = set(self.nodes()) - set(self.adj[nbunch]) - {nbunch} + return len(nbrs) + else: + nodes_nbrs = ( + ( + n, + { + v: self.all_edge_dict + for v in set(self.nodes()) - set(self.adj[n]) - {n} + }, + ) + for n in self.nbunch_iter(nbunch) + ) + + if weight is None: + return ((n, len(nbrs)) for n, nbrs in nodes_nbrs) + else: + # AntiGraph is a ThinGraph so all edges have weight 1 + return ( + (n, sum((nbrs[nbr].get(weight, 1)) for nbr in nbrs)) + for n, nbrs in nodes_nbrs + ) + + def adjacency(self): + """Return an iterator of (node, adjacency set) tuples for all nodes + in the dense graph. + + This is the fastest way to look at every edge. + For directed graphs, only outgoing adjacencies are included. + + Returns + ------- + adj_iter : iterator + An iterator of (node, adjacency set) for all nodes in + the graph. + """ + nodes = set(self.adj) + for n, nbrs in self.adj.items(): + yield (n, nodes - set(nbrs) - {n}) + + +# Build several pairs of graphs, a regular graph +# and the AntiGraph of it's complement, which behaves +# as if it were the original graph. +Gnp = nx.gnp_random_graph(20, 0.8, seed=42) +Anp = AntiGraph(nx.complement(Gnp)) +Gd = nx.davis_southern_women_graph() +Ad = AntiGraph(nx.complement(Gd)) +Gk = nx.karate_club_graph() +Ak = AntiGraph(nx.complement(Gk)) +pairs = [(Gnp, Anp), (Gd, Ad), (Gk, Ak)] +# test connected components +for G, A in pairs: + gc = [set(c) for c in nx.connected_components(G)] + ac = [set(c) for c in nx.connected_components(A)] + for comp in ac: + assert comp in gc +# test biconnected components +for G, A in pairs: + gc = [set(c) for c in nx.biconnected_components(G)] + ac = [set(c) for c in nx.biconnected_components(A)] + for comp in ac: + assert comp in gc +# test degree +for G, A in pairs: + node = list(G.nodes())[0] + nodes = list(G.nodes())[1:4] + assert G.degree(node) == A.degree(node) + assert sum(d for n, d in G.degree()) == sum(d for n, d in A.degree()) + # AntiGraph is a ThinGraph, so all the weights are 1 + assert sum(d for n, d in A.degree()) == sum(d for n, d in A.degree(weight="weight")) + assert sum(d for n, d in G.degree(nodes)) == sum(d for n, d in A.degree(nodes)) + +pos = nx.spring_layout(G, seed=268) # Seed for reproducible layout +nx.draw(Gnp, pos=pos) +plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/subclass/plot_printgraph.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/subclass/plot_printgraph.py new file mode 100644 index 000000000..08efcf1c6 --- /dev/null +++ b/.easyocr_venv/share/doc/networkx-2.8.5/examples/subclass/plot_printgraph.py @@ -0,0 +1,88 @@ +""" +=========== +Print Graph +=========== + +Example subclass of the Graph class. +""" + +import matplotlib.pyplot as plt +import networkx as nx +from networkx import Graph + + +class PrintGraph(Graph): + """ + Example subclass of the Graph class. + + Prints activity log to file or standard output. + """ + + def __init__(self, data=None, name="", file=None, **attr): + super().__init__(data=data, name=name, **attr) + if file is None: + import sys + + self.fh = sys.stdout + else: + self.fh = open(file, "w") + + def add_node(self, n, attr_dict=None, **attr): + super().add_node(n, attr_dict=attr_dict, **attr) + self.fh.write(f"Add node: {n}\n") + + def add_nodes_from(self, nodes, **attr): + for n in nodes: + self.add_node(n, **attr) + + def remove_node(self, n): + super().remove_node(n) + self.fh.write(f"Remove node: {n}\n") + + def remove_nodes_from(self, nodes): + for n in nodes: + self.remove_node(n) + + def add_edge(self, u, v, attr_dict=None, **attr): + super().add_edge(u, v, attr_dict=attr_dict, **attr) + self.fh.write(f"Add edge: {u}-{v}\n") + + def add_edges_from(self, ebunch, attr_dict=None, **attr): + for e in ebunch: + u, v = e[0:2] + self.add_edge(u, v, attr_dict=attr_dict, **attr) + + def remove_edge(self, u, v): + super().remove_edge(u, v) + self.fh.write(f"Remove edge: {u}-{v}\n") + + def remove_edges_from(self, ebunch): + for e in ebunch: + u, v = e[0:2] + self.remove_edge(u, v) + + def clear(self): + super().clear() + self.fh.write("Clear graph\n") + + +G = PrintGraph() +G.add_node("foo") +G.add_nodes_from("bar", weight=8) +G.remove_node("b") +G.remove_nodes_from("ar") +print("Nodes in G: ", G.nodes(data=True)) +G.add_edge(0, 1, weight=10) +print("Edges in G: ", G.edges(data=True)) +G.remove_edge(0, 1) +G.add_edges_from(zip(range(0, 3), range(1, 4)), weight=10) +print("Edges in G: ", G.edges(data=True)) +G.remove_edges_from(zip(range(0, 3), range(1, 4))) +print("Edges in G: ", G.edges(data=True)) + +G = PrintGraph() +nx.add_path(G, range(10)) +nx.add_star(G, range(9, 13)) +pos = nx.spring_layout(G, seed=225) # 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zR!zRocBS9?(9O2+%Z1JhJwD5ST#ml)f6z3?eOkRLfB#3>CnuXT3->RXzqx*by88EO z&mDy>_7i{oFNxgn@W1Z54+3p%$?m7uKA8CjIJ?crB*F|j8X&&s%eRRXnPw7S&wpa_NVURPs(Tzes-xp!j7T}s- z9R38K_KR*3`hmO%lYI4YnuLBlFS?=V=jtL1y^Y&Ylv8%mO-w~QUl(DbssYrQ$fskV zYezp;7oj~DNjv>Xjiq$rpH7>nT14MAT~gD|8f4Q>cxeGR&H^qEqG_Q${@ts!B6GH;4*0QxWw z!hrM&xB>7%A9UU5qY4P!W>qlV&@l#dt>}GYgx1y7Fs)$sLwm{r-mJidN1zjEL>X4I KGB8B7f_MO;o(lH> literal 0 HcmV?d00001 diff --git a/easy_ocr_dev.ipynb b/easy_ocr_dev.ipynb new file mode 100644 index 000000000..e5e6c2e04 --- /dev/null +++ b/easy_ocr_dev.ipynb @@ -0,0 +1,223 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# import torch\n", + "# print(torch.cuda.is_available())\n", + "# cuda_device = torch.device(\"cuda:0\" if torch.cuda.is_available else \"cpu\")\n", + "# print(cuda_device)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Torch version: 1.9.1+cu111\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/ystoll/PixelForest/easyocr/.easyocr_venv/lib/python3.8/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", + " from .autonotebook import tqdm as notebook_tqdm\n" + ] + } + ], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import easyocr\n", + "#from easyocr.easyocr import Reader\n", + "import torch\n", + "print(f\"Torch version: {torch.__version__}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Mairie du /\"', '[Palais du LOUVRE', 'LES ARTS DÉCORATIFS', '[Musée du LOUVRe', 'Théâtre', 'du PALAIS-RoYAl^']\n" + ] + } + ], + "source": [ + "reader = easyocr.Reader(['fr'])\n", + "# reader = easyocr.Reader(['en'])\n", + "result = reader.readtext('examples/french.jpg', detail=0, decoder=\"wordbeamsearch\")\n", + "print(result)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[{'boxes': [[0, 0], [692, 0], [692, 1024], [0, 1024]], 'text': '5', 'confident': 0.6201150760928478}]\n" + ] + } + ], + "source": [ + "reader = easyocr.Reader(['en', 'fr'])\n", + "result = reader.recognize('examples/french.jpg',\n", + " detail=1,\n", + " output_format=\"dict\")\n", + "print(result)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Yannick\n", + "'Prediction: torch.Size([1, 63, 352])'\n", + "Yannick\n", + "'Prediction: torch.Size([1, 63, 352])'\n", + "Yannick\n", + "'Prediction: torch.Size([1, 79, 352])'\n", + "Yannick\n", + "'Prediction: torch.Size([1, 79, 352])'\n", + "Yannick\n", + "'Prediction: torch.Size([1, 47, 352])'\n", + "Yannick\n", + "'Prediction: torch.Size([1, 79, 352])'\n", + "Yannick\n", + "'Prediction: torch.Size([1, 79, 352])'\n", + "['Mairie du /\"', '[Palais du LOUVRE', 'LES ARTS DÉCORATIFS', '[Musée du LOUVRe', 'Théâtre', 'du PALAIS-RoYAl^']\n" + ] + } + ], + "source": [ + "reader = easyocr.Reader(['en', 'fr'])\n", + "result = reader.readtext('examples/french.jpg', detail=0)\n", + "print(result)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "('M', 'a', 'i', 'r', 'i', 'e', ' ', 'd', 'u', ' ', '/', '\"', 'en')\n", + "('[', 'P', 'a', 'l', 'a', 'i', 's', ' ', 'd', 'u', ' ', 'L', 'O', 'U', 'V', 'R', 'E', 'en')\n", + "('L', 'E', 'S', ' ', 'A', 'R', 'T', 'S', ' ', 'D', 'É', 'C', 'O', 'R', 'A', 'T', 'I', 'F', 'S', 'en')\n", + "('[', 'M', 'u', 's', 'é', 'e', ' ', 'd', 'u', ' ', 'L', 'O', 'U', 'V', 'R', 'e', 'en')\n", + "('T', 'h', 'é', 'â', 't', 'r', 'e', 'en')\n", + "('d', 'u', ' ', 'P', 'A', 'L', 'A', 'I', 'S', '-', 'R', 'o', 'Y', 'A', 'l', '^', 'en')\n", + "('M', 'a', 'i', 'r', 'i', 'e', ' ', 'd', 'u', ' ', '/', '\"', 'ch')\n", + "('T', 'h', 'é', 'â', 't', 'r', 'e', 'ch')\n", + "('d', 'u', ' ', 'P', 'A', 'L', 'A', 'I', 'S', '-', 'R', 'o', 'Y', 'A', 'l', '^', 'ch')\n", + "None\n" + ] + } + ], + "source": [ + "reader = easyocr.Reader(['fr'])\n", + "result = reader.readtextlang('examples/french.jpg', detail=0)\n", + "print(result)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[['Reduce your risk of coronavirus infection;', 'Clean hands with soap and water', \"'alcohol hased handruh\", 'Cover nose and mouth when coughing and', 'sneezing with tissue or flexed elhow', 'Avoid close contact with anyone with', 'coldor flu-Hike symptoms', 'Thoroughly cook meat andeggs', 'No unprotected contact with Jive wild', 'or farm animals', 'World Heakth', \"'Organization\"], ['Mairie du \"@', '[Palais du LOUVRE', 'LES ARTS DÉCORATIFS', 'Musée du LOUVRE', 'du PALAIS-ROYAL', '4', 'Théâtre']]\n" + ] + } + ], + "source": [ + "reader = easyocr.Reader(['fr', 'en'])\n", + "result = reader.readtext_batched([ 'examples/english.png' , 'examples/french.jpg'], detail=0, n_width=600, n_height=600)\n", + "print(result)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.8.10 ('.easyocr_venv': venv)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.10" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "4e7ed28815ff5fa6604e7428584610e00d12043870e25697ecfca17a117ee241" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/easy_ocr_dev.py b/easy_ocr_dev.py new file mode 100755 index 000000000..a93969120 --- /dev/null +++ b/easy_ocr_dev.py @@ -0,0 +1,11 @@ +import easyocr + +reader = easyocr.Reader(['fr']) +# reader = easyocr.Reader(['en']) +results = reader.readtext('examples/french.jpg', + detail=1, + decoder="beamsearch" + ) + +for result in results: + print(result) diff --git a/notes_persos.txt b/notes_persos.txt new file mode 100644 index 000000000..cb7cbad85 --- /dev/null +++ b/notes_persos.txt @@ -0,0 +1,5 @@ +dict_list = {'en': '/home/ystoll/PixelForest/easyocr/easyocr/dict/en.txt', + 'fr': '/home/ystoll/PixelForest/easyocr/easyocr/dict/fr.txt'} + + +utils.BeamState.wordsearch() uses dict_list to decode NN outputted matrix to a specific word. \ No newline at end of file From 489429a456e8a819101d735aba5fa78ec7446fb4 Mon Sep 17 00:00:00 2001 From: Yannick Stoll Date: Fri, 26 Aug 2022 16:30:57 +0200 Subject: [PATCH 05/17] add to gitignore venv --- .gitignore | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.gitignore b/.gitignore index 683938b5b..a39efcd47 100644 --- a/.gitignore +++ b/.gitignore @@ -107,3 +107,6 @@ ENV/ .vscode/ .vs/ .idea/ + +# virtual environment: +.easyocr_venv \ No newline at end of file From 604ad7ec24910fd969a9187d1d518b471da01cb9 Mon Sep 17 00:00:00 2001 From: Yannick Stoll Date: Thu, 1 Sep 2022 17:34:57 +0200 Subject: [PATCH 06/17] removing .easyocr_venv/* from master branch --- .easyocr_venv/bin/Activate.ps1 | 241 - .easyocr_venv/bin/activate | 76 - .easyocr_venv/bin/activate.csh | 37 - .easyocr_venv/bin/activate.fish | 75 - .easyocr_venv/bin/black | 8 - .easyocr_venv/bin/blackd | 8 - .easyocr_venv/bin/convert-caffe2-to-onnx | 8 - .easyocr_venv/bin/convert-onnx-to-caffe2 | 8 - .easyocr_venv/bin/easy_install | 8 - .easyocr_venv/bin/easy_install-3.8 | 8 - .easyocr_venv/bin/easyocr | 12 - 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.easyocr_venv/share/python-wheels/wheel-0.34.2-py2.py3-none-any.whl diff --git a/.easyocr_venv/bin/Activate.ps1 b/.easyocr_venv/bin/Activate.ps1 deleted file mode 100644 index 2fb3852c3..000000000 --- a/.easyocr_venv/bin/Activate.ps1 +++ /dev/null @@ -1,241 +0,0 @@ -<# -.Synopsis -Activate a Python virtual environment for the current PowerShell session. - -.Description -Pushes the python executable for a virtual environment to the front of the -$Env:PATH environment variable and sets the prompt to signify that you are -in a Python virtual environment. Makes use of the command line switches as -well as the `pyvenv.cfg` file values present in the virtual environment. - -.Parameter VenvDir -Path to the directory that contains the virtual environment to activate. The -default value for this is the parent of the directory that the Activate.ps1 -script is located within. - -.Parameter Prompt -The prompt prefix to display when this virtual environment is activated. By -default, this prompt is the name of the virtual environment folder (VenvDir) -surrounded by parentheses and followed by a single space (ie. '(.venv) '). - -.Example -Activate.ps1 -Activates the Python virtual environment that contains the Activate.ps1 script. - -.Example -Activate.ps1 -Verbose -Activates the Python virtual environment that contains the Activate.ps1 script, -and shows extra information about the activation as it executes. - -.Example -Activate.ps1 -VenvDir C:\Users\MyUser\Common\.venv -Activates the Python virtual environment located in the specified location. - -.Example -Activate.ps1 -Prompt "MyPython" -Activates the Python virtual environment that contains the Activate.ps1 script, -and prefixes the current prompt with the specified string (surrounded in -parentheses) while the virtual environment is active. - -.Notes -On Windows, it may be required to enable this Activate.ps1 script by setting the -execution policy for the user. You can do this by issuing the following PowerShell -command: - -PS C:\> Set-ExecutionPolicy -ExecutionPolicy RemoteSigned -Scope CurrentUser - -For more information on Execution Policies: -https://go.microsoft.com/fwlink/?LinkID=135170 - -#> -Param( - [Parameter(Mandatory = $false)] - [String] - $VenvDir, - [Parameter(Mandatory = $false)] - [String] - $Prompt -) - -<# Function declarations --------------------------------------------------- #> - -<# -.Synopsis -Remove all shell session elements added by the Activate script, including the -addition of the virtual environment's Python executable from the beginning of -the PATH variable. - -.Parameter NonDestructive -If present, do not remove this function from the global namespace for the -session. - -#> -function global:deactivate ([switch]$NonDestructive) { - # Revert to original values - - # The prior prompt: - if (Test-Path -Path Function:_OLD_VIRTUAL_PROMPT) { - Copy-Item -Path Function:_OLD_VIRTUAL_PROMPT -Destination Function:prompt - Remove-Item -Path Function:_OLD_VIRTUAL_PROMPT - } - - # The prior PYTHONHOME: - if (Test-Path -Path Env:_OLD_VIRTUAL_PYTHONHOME) { - Copy-Item -Path Env:_OLD_VIRTUAL_PYTHONHOME -Destination Env:PYTHONHOME - Remove-Item -Path Env:_OLD_VIRTUAL_PYTHONHOME - } - - # The prior PATH: - if (Test-Path -Path Env:_OLD_VIRTUAL_PATH) { - Copy-Item -Path Env:_OLD_VIRTUAL_PATH -Destination Env:PATH - Remove-Item -Path Env:_OLD_VIRTUAL_PATH - } - - # Just remove the VIRTUAL_ENV altogether: - if (Test-Path -Path Env:VIRTUAL_ENV) { - Remove-Item -Path env:VIRTUAL_ENV - } - - # Just remove the _PYTHON_VENV_PROMPT_PREFIX altogether: - if (Get-Variable -Name "_PYTHON_VENV_PROMPT_PREFIX" -ErrorAction SilentlyContinue) { - Remove-Variable -Name _PYTHON_VENV_PROMPT_PREFIX -Scope Global -Force - } - - # Leave deactivate function in the global namespace if requested: - if (-not $NonDestructive) { - Remove-Item -Path function:deactivate - } -} - -<# -.Description -Get-PyVenvConfig parses the values from the pyvenv.cfg file located in the -given folder, and returns them in a map. - -For each line in the pyvenv.cfg file, if that line can be parsed into exactly -two strings separated by `=` (with any amount of whitespace surrounding the =) -then it is considered a `key = value` line. The left hand string is the key, -the right hand is the value. - -If the value starts with a `'` or a `"` then the first and last character is -stripped from the value before being captured. - -.Parameter ConfigDir -Path to the directory that contains the `pyvenv.cfg` file. -#> -function Get-PyVenvConfig( - [String] - $ConfigDir -) { - Write-Verbose "Given ConfigDir=$ConfigDir, obtain values in pyvenv.cfg" - - # Ensure the file exists, and issue a warning if it doesn't (but still allow the function to continue). - $pyvenvConfigPath = Join-Path -Resolve -Path $ConfigDir -ChildPath 'pyvenv.cfg' -ErrorAction Continue - - # An empty map will be returned if no config file is found. - $pyvenvConfig = @{ } - - if ($pyvenvConfigPath) { - - Write-Verbose "File exists, parse `key = value` lines" - $pyvenvConfigContent = Get-Content -Path $pyvenvConfigPath - - $pyvenvConfigContent | ForEach-Object { - $keyval = $PSItem -split "\s*=\s*", 2 - if ($keyval[0] -and $keyval[1]) { - $val = $keyval[1] - - # Remove extraneous quotations around a string value. - if ("'""".Contains($val.Substring(0, 1))) { - $val = $val.Substring(1, $val.Length - 2) - } - - $pyvenvConfig[$keyval[0]] = $val - Write-Verbose "Adding Key: '$($keyval[0])'='$val'" - } - } - } - return $pyvenvConfig -} - - -<# Begin Activate script --------------------------------------------------- #> - -# Determine the containing directory of this script -$VenvExecPath = Split-Path -Parent $MyInvocation.MyCommand.Definition -$VenvExecDir = Get-Item -Path $VenvExecPath - -Write-Verbose "Activation script is located in path: '$VenvExecPath'" -Write-Verbose "VenvExecDir Fullname: '$($VenvExecDir.FullName)" -Write-Verbose "VenvExecDir Name: '$($VenvExecDir.Name)" - -# Set values required in priority: CmdLine, ConfigFile, Default -# First, get the location of the virtual environment, it might not be -# VenvExecDir if specified on the command line. -if ($VenvDir) { - Write-Verbose "VenvDir given as parameter, using '$VenvDir' to determine values" -} -else { - Write-Verbose "VenvDir not given as a parameter, using parent directory name as VenvDir." - $VenvDir = $VenvExecDir.Parent.FullName.TrimEnd("\\/") - Write-Verbose "VenvDir=$VenvDir" -} - -# Next, read the `pyvenv.cfg` file to determine any required value such -# as `prompt`. -$pyvenvCfg = Get-PyVenvConfig -ConfigDir $VenvDir - -# Next, set the prompt from the command line, or the config file, or -# just use the name of the virtual environment folder. -if ($Prompt) { - Write-Verbose "Prompt specified as argument, using '$Prompt'" -} -else { - Write-Verbose "Prompt not specified as argument to script, checking pyvenv.cfg value" - if ($pyvenvCfg -and $pyvenvCfg['prompt']) { - Write-Verbose " Setting based on value in pyvenv.cfg='$($pyvenvCfg['prompt'])'" - $Prompt = $pyvenvCfg['prompt']; - } - else { - Write-Verbose " Setting prompt based on parent's directory's name. (Is the directory name passed to venv module when creating the virutal environment)" - Write-Verbose " Got leaf-name of $VenvDir='$(Split-Path -Path $venvDir -Leaf)'" - $Prompt = Split-Path -Path $venvDir -Leaf - } -} - -Write-Verbose "Prompt = '$Prompt'" -Write-Verbose "VenvDir='$VenvDir'" - -# Deactivate any currently active virtual environment, but leave the -# deactivate function in place. -deactivate -nondestructive - -# Now set the environment variable VIRTUAL_ENV, used by many tools to determine -# that there is an activated venv. -$env:VIRTUAL_ENV = $VenvDir - -if (-not $Env:VIRTUAL_ENV_DISABLE_PROMPT) { - - Write-Verbose "Setting prompt to '$Prompt'" - - # Set the prompt to include the env name - # Make sure _OLD_VIRTUAL_PROMPT is global - function global:_OLD_VIRTUAL_PROMPT { "" } - Copy-Item -Path function:prompt -Destination function:_OLD_VIRTUAL_PROMPT - New-Variable -Name _PYTHON_VENV_PROMPT_PREFIX -Description "Python virtual environment prompt prefix" -Scope Global -Option ReadOnly -Visibility Public -Value $Prompt - - function global:prompt { - Write-Host -NoNewline -ForegroundColor Green "($_PYTHON_VENV_PROMPT_PREFIX) " - _OLD_VIRTUAL_PROMPT - } -} - -# Clear PYTHONHOME -if (Test-Path -Path Env:PYTHONHOME) { - Copy-Item -Path Env:PYTHONHOME -Destination Env:_OLD_VIRTUAL_PYTHONHOME - Remove-Item -Path Env:PYTHONHOME -} - -# Add the venv to the PATH -Copy-Item -Path Env:PATH -Destination Env:_OLD_VIRTUAL_PATH -$Env:PATH = "$VenvExecDir$([System.IO.Path]::PathSeparator)$Env:PATH" diff --git a/.easyocr_venv/bin/activate b/.easyocr_venv/bin/activate deleted file mode 100644 index c4fb0190d..000000000 --- a/.easyocr_venv/bin/activate +++ /dev/null @@ -1,76 +0,0 @@ -# This file must be used with "source bin/activate" *from bash* -# you cannot run it directly - -deactivate () { - # reset old environment variables - if [ -n "${_OLD_VIRTUAL_PATH:-}" ] ; then - PATH="${_OLD_VIRTUAL_PATH:-}" - export PATH - unset _OLD_VIRTUAL_PATH - fi - if [ -n "${_OLD_VIRTUAL_PYTHONHOME:-}" ] ; then - PYTHONHOME="${_OLD_VIRTUAL_PYTHONHOME:-}" - export PYTHONHOME - unset _OLD_VIRTUAL_PYTHONHOME - fi - - # This should detect bash and zsh, which have a hash command that must - # be called to get it to forget past commands. Without forgetting - # past commands the $PATH changes we made may not be respected - if [ -n "${BASH:-}" -o -n "${ZSH_VERSION:-}" ] ; then - hash -r - fi - - if [ -n "${_OLD_VIRTUAL_PS1:-}" ] ; then - PS1="${_OLD_VIRTUAL_PS1:-}" - export PS1 - unset _OLD_VIRTUAL_PS1 - fi - - unset VIRTUAL_ENV - if [ ! "${1:-}" = "nondestructive" ] ; then - # Self destruct! - unset -f deactivate - fi -} - -# unset irrelevant variables -deactivate nondestructive - -VIRTUAL_ENV="/home/ystoll/PixelForest/easyocr/.easyocr_venv" -export VIRTUAL_ENV - -_OLD_VIRTUAL_PATH="$PATH" -PATH="$VIRTUAL_ENV/bin:$PATH" -export PATH - -# unset PYTHONHOME if set -# this will fail if PYTHONHOME is set to the empty string (which is bad anyway) -# could use `if (set -u; : $PYTHONHOME) ;` in bash -if [ -n "${PYTHONHOME:-}" ] ; then - _OLD_VIRTUAL_PYTHONHOME="${PYTHONHOME:-}" - unset PYTHONHOME -fi - -if [ -z "${VIRTUAL_ENV_DISABLE_PROMPT:-}" ] ; then - _OLD_VIRTUAL_PS1="${PS1:-}" - if [ "x(.easyocr_venv) " != x ] ; then - PS1="(.easyocr_venv) ${PS1:-}" - else - if [ "`basename \"$VIRTUAL_ENV\"`" = "__" ] ; then - # special case for Aspen magic directories - # see https://aspen.io/ - PS1="[`basename \`dirname \"$VIRTUAL_ENV\"\``] $PS1" - else - PS1="(`basename \"$VIRTUAL_ENV\"`)$PS1" - fi - fi - export PS1 -fi - -# This should detect bash and zsh, which have a hash command that must -# be called to get it to forget past commands. Without forgetting -# past commands the $PATH changes we made may not be respected -if [ -n "${BASH:-}" -o -n "${ZSH_VERSION:-}" ] ; then - hash -r -fi diff --git a/.easyocr_venv/bin/activate.csh b/.easyocr_venv/bin/activate.csh deleted file mode 100644 index bff1445c7..000000000 --- a/.easyocr_venv/bin/activate.csh +++ /dev/null @@ -1,37 +0,0 @@ -# This file must be used with "source bin/activate.csh" *from csh*. -# You cannot run it directly. -# Created by Davide Di Blasi . -# Ported to Python 3.3 venv by Andrew Svetlov - -alias deactivate 'test $?_OLD_VIRTUAL_PATH != 0 && setenv PATH "$_OLD_VIRTUAL_PATH" && unset _OLD_VIRTUAL_PATH; rehash; test $?_OLD_VIRTUAL_PROMPT != 0 && set prompt="$_OLD_VIRTUAL_PROMPT" && unset _OLD_VIRTUAL_PROMPT; unsetenv VIRTUAL_ENV; test "\!:*" != "nondestructive" && unalias deactivate' - -# Unset irrelevant variables. -deactivate nondestructive - -setenv VIRTUAL_ENV "/home/ystoll/PixelForest/easyocr/.easyocr_venv" - -set _OLD_VIRTUAL_PATH="$PATH" -setenv PATH "$VIRTUAL_ENV/bin:$PATH" - - -set _OLD_VIRTUAL_PROMPT="$prompt" - -if (! "$?VIRTUAL_ENV_DISABLE_PROMPT") then - if (".easyocr_venv" != "") then - set env_name = ".easyocr_venv" - else - if (`basename "VIRTUAL_ENV"` == "__") then - # special case for Aspen magic directories - # see https://aspen.io/ - set env_name = `basename \`dirname "$VIRTUAL_ENV"\`` - else - set env_name = `basename "$VIRTUAL_ENV"` - endif - endif - set prompt = "[$env_name] $prompt" - unset env_name -endif - -alias pydoc python -m pydoc - -rehash diff --git a/.easyocr_venv/bin/activate.fish b/.easyocr_venv/bin/activate.fish deleted file mode 100644 index 02beb4006..000000000 --- a/.easyocr_venv/bin/activate.fish +++ /dev/null @@ -1,75 +0,0 @@ -# This file must be used with ". bin/activate.fish" *from fish* (http://fishshell.org) -# you cannot run it directly - -function deactivate -d "Exit virtualenv and return to normal shell environment" - # reset old environment variables - if test -n "$_OLD_VIRTUAL_PATH" - set -gx PATH $_OLD_VIRTUAL_PATH - set -e _OLD_VIRTUAL_PATH - end - if test -n "$_OLD_VIRTUAL_PYTHONHOME" - set -gx PYTHONHOME $_OLD_VIRTUAL_PYTHONHOME - set -e _OLD_VIRTUAL_PYTHONHOME - end - - if test -n "$_OLD_FISH_PROMPT_OVERRIDE" - functions -e fish_prompt - set -e _OLD_FISH_PROMPT_OVERRIDE - functions -c _old_fish_prompt fish_prompt - functions -e _old_fish_prompt - end - - set -e VIRTUAL_ENV - if test "$argv[1]" != "nondestructive" - # Self destruct! - functions -e deactivate - end -end - -# unset irrelevant variables -deactivate nondestructive - -set -gx VIRTUAL_ENV "/home/ystoll/PixelForest/easyocr/.easyocr_venv" - -set -gx _OLD_VIRTUAL_PATH $PATH -set -gx PATH "$VIRTUAL_ENV/bin" $PATH - -# unset PYTHONHOME if set -if set -q PYTHONHOME - set -gx _OLD_VIRTUAL_PYTHONHOME $PYTHONHOME - set -e PYTHONHOME -end - -if test -z "$VIRTUAL_ENV_DISABLE_PROMPT" - # fish uses a function instead of an env var to generate the prompt. - - # save the current fish_prompt function as the function _old_fish_prompt - functions -c fish_prompt _old_fish_prompt - - # with the original prompt function renamed, we can override with our own. - function fish_prompt - # Save the return status of the last command - set -l old_status $status - - # Prompt override? - if test -n "(.easyocr_venv) " - printf "%s%s" "(.easyocr_venv) " (set_color normal) - else - # ...Otherwise, prepend env - set -l _checkbase (basename "$VIRTUAL_ENV") - if test $_checkbase = "__" - # special case for Aspen magic directories - # see https://aspen.io/ - printf "%s[%s]%s " (set_color -b blue white) (basename (dirname "$VIRTUAL_ENV")) (set_color normal) - else - printf "%s(%s)%s" (set_color -b blue white) (basename "$VIRTUAL_ENV") (set_color normal) - end - end - - # Restore the return status of the previous command. - echo "exit $old_status" | . - _old_fish_prompt - end - - set -gx _OLD_FISH_PROMPT_OVERRIDE "$VIRTUAL_ENV" -end diff --git a/.easyocr_venv/bin/black b/.easyocr_venv/bin/black deleted file mode 100755 index f4e6fe29a..000000000 --- a/.easyocr_venv/bin/black +++ /dev/null @@ -1,8 +0,0 @@ -#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 -# -*- coding: utf-8 -*- -import re -import sys -from black import patched_main -if __name__ == '__main__': - sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) - sys.exit(patched_main()) diff --git a/.easyocr_venv/bin/blackd b/.easyocr_venv/bin/blackd deleted file mode 100755 index 68d81ca97..000000000 --- a/.easyocr_venv/bin/blackd +++ /dev/null @@ -1,8 +0,0 @@ -#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 -# -*- coding: utf-8 -*- -import re -import sys -from blackd import patched_main -if __name__ == '__main__': - sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) - sys.exit(patched_main()) diff --git a/.easyocr_venv/bin/convert-caffe2-to-onnx b/.easyocr_venv/bin/convert-caffe2-to-onnx deleted file mode 100755 index 5f3b0ac3b..000000000 --- a/.easyocr_venv/bin/convert-caffe2-to-onnx +++ /dev/null @@ -1,8 +0,0 @@ -#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 -# -*- coding: utf-8 -*- -import re -import sys -from caffe2.python.onnx.bin.conversion import caffe2_to_onnx -if __name__ == '__main__': - sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) - sys.exit(caffe2_to_onnx()) diff --git a/.easyocr_venv/bin/convert-onnx-to-caffe2 b/.easyocr_venv/bin/convert-onnx-to-caffe2 deleted file mode 100755 index 3f32e7d59..000000000 --- a/.easyocr_venv/bin/convert-onnx-to-caffe2 +++ /dev/null @@ -1,8 +0,0 @@ -#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 -# -*- coding: utf-8 -*- -import re -import sys -from caffe2.python.onnx.bin.conversion import onnx_to_caffe2 -if __name__ == '__main__': - sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) - sys.exit(onnx_to_caffe2()) diff --git a/.easyocr_venv/bin/easy_install b/.easyocr_venv/bin/easy_install deleted file mode 100755 index a8f8acdb9..000000000 --- a/.easyocr_venv/bin/easy_install +++ /dev/null @@ -1,8 +0,0 @@ -#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 -# -*- coding: utf-8 -*- -import re -import sys -from setuptools.command.easy_install import main -if __name__ == '__main__': - sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) - sys.exit(main()) diff --git a/.easyocr_venv/bin/easy_install-3.8 b/.easyocr_venv/bin/easy_install-3.8 deleted file mode 100755 index a8f8acdb9..000000000 --- a/.easyocr_venv/bin/easy_install-3.8 +++ /dev/null @@ -1,8 +0,0 @@ -#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 -# -*- coding: utf-8 -*- -import re -import sys -from setuptools.command.easy_install import main -if __name__ == '__main__': - sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) - sys.exit(main()) diff --git a/.easyocr_venv/bin/easyocr b/.easyocr_venv/bin/easyocr deleted file mode 100755 index 1c20adad7..000000000 --- a/.easyocr_venv/bin/easyocr +++ /dev/null @@ -1,12 +0,0 @@ -#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 -# EASY-INSTALL-ENTRY-SCRIPT: 'easyocr','console_scripts','easyocr' -__requires__ = 'easyocr' -import re -import sys -from pkg_resources import load_entry_point - -if __name__ == '__main__': - sys.argv[0] = re.sub(r'(-script\.pyw?|\.exe)?$', '', sys.argv[0]) - sys.exit( - load_entry_point('easyocr', 'console_scripts', 'easyocr')() - ) diff --git a/.easyocr_venv/bin/epylint b/.easyocr_venv/bin/epylint deleted file mode 100755 index 774a5dbc3..000000000 --- a/.easyocr_venv/bin/epylint +++ /dev/null @@ -1,8 +0,0 @@ -#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 -# -*- coding: utf-8 -*- -import re -import sys -from pylint import run_epylint -if __name__ == '__main__': - sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) - sys.exit(run_epylint()) diff --git a/.easyocr_venv/bin/f2py b/.easyocr_venv/bin/f2py deleted file mode 100755 index b78eed5d9..000000000 --- a/.easyocr_venv/bin/f2py +++ /dev/null @@ -1,8 +0,0 @@ -#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 -# -*- coding: utf-8 -*- -import re -import sys -from numpy.f2py.f2py2e import main -if __name__ == '__main__': - sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) - sys.exit(main()) diff --git a/.easyocr_venv/bin/f2py3 b/.easyocr_venv/bin/f2py3 deleted file mode 100755 index b78eed5d9..000000000 --- a/.easyocr_venv/bin/f2py3 +++ /dev/null @@ -1,8 +0,0 @@ -#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 -# -*- coding: utf-8 -*- -import re -import sys -from numpy.f2py.f2py2e import main -if __name__ == '__main__': - sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) - sys.exit(main()) diff --git a/.easyocr_venv/bin/f2py3.8 b/.easyocr_venv/bin/f2py3.8 deleted file mode 100755 index b78eed5d9..000000000 --- a/.easyocr_venv/bin/f2py3.8 +++ /dev/null @@ -1,8 +0,0 @@ -#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 -# -*- coding: utf-8 -*- -import re -import sys -from numpy.f2py.f2py2e import main -if __name__ == '__main__': - sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) - sys.exit(main()) diff --git a/.easyocr_venv/bin/get_objgraph b/.easyocr_venv/bin/get_objgraph deleted file mode 100755 index 9709008eb..000000000 --- a/.easyocr_venv/bin/get_objgraph +++ /dev/null @@ -1,54 +0,0 @@ -#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 -# -# Author: Mike McKerns (mmckerns @caltech and @uqfoundation) -# Copyright (c) 2008-2016 California Institute of Technology. -# Copyright (c) 2016-2022 The Uncertainty Quantification Foundation. -# License: 3-clause BSD. The full license text is available at: -# - https://github.com/uqfoundation/dill/blob/master/LICENSE -""" -display the reference paths for objects in ``dill.types`` or a .pkl file - -Notes: - the generated image is useful in showing the pointer references in - objects that are or can be pickled. Any object in ``dill.objects`` - listed in ``dill.load_types(picklable=True, unpicklable=True)`` works. - -Examples:: - - $ get_objgraph FrameType - Image generated as FrameType.png -""" - -import dill as pickle -#pickle.debug.trace(True) -#import pickle - -# get all objects for testing -from dill import load_types -load_types(pickleable=True,unpickleable=True) -from dill import objects - -if __name__ == "__main__": - import sys - if len(sys.argv) != 2: - print ("Please provide exactly one file or type name (e.g. 'IntType')") - msg = "\n" - for objtype in list(objects.keys())[:40]: - msg += objtype + ', ' - print (msg + "...") - else: - objtype = str(sys.argv[-1]) - try: - obj = objects[objtype] - except KeyError: - obj = pickle.load(open(objtype,'rb')) - import os - objtype = os.path.splitext(objtype)[0] - try: - import objgraph - objgraph.show_refs(obj, filename=objtype+'.png') - except ImportError: - print ("Please install 'objgraph' to view object graphs") - - -# EOF diff --git a/.easyocr_venv/bin/imageio_download_bin b/.easyocr_venv/bin/imageio_download_bin deleted file mode 100755 index 2989fd08b..000000000 --- a/.easyocr_venv/bin/imageio_download_bin +++ /dev/null @@ -1,8 +0,0 @@ -#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 -# -*- coding: utf-8 -*- -import re -import sys -from imageio.__main__ import download_bin_main -if __name__ == '__main__': - sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) - sys.exit(download_bin_main()) diff --git a/.easyocr_venv/bin/imageio_remove_bin b/.easyocr_venv/bin/imageio_remove_bin deleted file mode 100755 index 01f02548c..000000000 --- a/.easyocr_venv/bin/imageio_remove_bin +++ /dev/null @@ -1,8 +0,0 @@ -#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 -# -*- coding: utf-8 -*- -import re -import sys -from imageio.__main__ import remove_bin_main -if __name__ == '__main__': - sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) - sys.exit(remove_bin_main()) diff --git a/.easyocr_venv/bin/ipython b/.easyocr_venv/bin/ipython deleted file mode 100755 index fdbbcda3b..000000000 --- a/.easyocr_venv/bin/ipython +++ /dev/null @@ -1,8 +0,0 @@ -#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 -# -*- coding: utf-8 -*- -import re -import sys -from IPython import start_ipython -if __name__ == '__main__': - sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) - sys.exit(start_ipython()) diff --git a/.easyocr_venv/bin/ipython3 b/.easyocr_venv/bin/ipython3 deleted file mode 100755 index fdbbcda3b..000000000 --- a/.easyocr_venv/bin/ipython3 +++ /dev/null @@ -1,8 +0,0 @@ -#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 -# -*- coding: utf-8 -*- -import re -import sys -from IPython import start_ipython -if __name__ == '__main__': - sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) - sys.exit(start_ipython()) diff --git a/.easyocr_venv/bin/isort b/.easyocr_venv/bin/isort deleted file mode 100755 index c0901e9f1..000000000 --- a/.easyocr_venv/bin/isort +++ /dev/null @@ -1,8 +0,0 @@ -#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 -# -*- coding: utf-8 -*- -import re -import sys -from isort.main import main -if __name__ == '__main__': - sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) - sys.exit(main()) diff --git a/.easyocr_venv/bin/isort-identify-imports b/.easyocr_venv/bin/isort-identify-imports deleted file mode 100755 index 88da8aff8..000000000 --- a/.easyocr_venv/bin/isort-identify-imports +++ /dev/null @@ -1,8 +0,0 @@ -#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 -# -*- coding: utf-8 -*- -import re -import sys -from isort.main import identify_imports_main -if __name__ == '__main__': - sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) - sys.exit(identify_imports_main()) diff --git a/.easyocr_venv/bin/jupyter b/.easyocr_venv/bin/jupyter deleted file mode 100755 index 6a733a8ad..000000000 --- a/.easyocr_venv/bin/jupyter +++ /dev/null @@ -1,8 +0,0 @@ -#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 -# -*- coding: utf-8 -*- -import re -import sys -from jupyter_core.command import main -if __name__ == '__main__': - sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) - sys.exit(main()) diff --git a/.easyocr_venv/bin/jupyter-kernel b/.easyocr_venv/bin/jupyter-kernel deleted file mode 100755 index df5b851cc..000000000 --- a/.easyocr_venv/bin/jupyter-kernel +++ /dev/null @@ -1,8 +0,0 @@ -#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 -# -*- coding: utf-8 -*- -import re -import sys -from jupyter_client.kernelapp import main -if __name__ == '__main__': - sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) - sys.exit(main()) diff --git a/.easyocr_venv/bin/jupyter-kernelspec b/.easyocr_venv/bin/jupyter-kernelspec deleted file mode 100755 index 7beedd7d1..000000000 --- a/.easyocr_venv/bin/jupyter-kernelspec +++ /dev/null @@ -1,8 +0,0 @@ -#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 -# -*- coding: utf-8 -*- -import re -import sys -from jupyter_client.kernelspecapp import KernelSpecApp -if __name__ == '__main__': - sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) - sys.exit(KernelSpecApp.launch_instance()) diff --git a/.easyocr_venv/bin/jupyter-migrate b/.easyocr_venv/bin/jupyter-migrate deleted file mode 100755 index 11fc42eb7..000000000 --- a/.easyocr_venv/bin/jupyter-migrate +++ /dev/null @@ -1,8 +0,0 @@ -#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 -# -*- coding: utf-8 -*- -import re -import sys -from jupyter_core.migrate import main -if __name__ == '__main__': - sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) - sys.exit(main()) diff --git a/.easyocr_venv/bin/jupyter-run b/.easyocr_venv/bin/jupyter-run deleted file mode 100755 index 97bfff1df..000000000 --- a/.easyocr_venv/bin/jupyter-run +++ /dev/null @@ -1,8 +0,0 @@ -#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 -# -*- coding: utf-8 -*- -import re -import sys -from jupyter_client.runapp import RunApp -if __name__ == '__main__': - sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) - sys.exit(RunApp.launch_instance()) diff --git a/.easyocr_venv/bin/jupyter-troubleshoot b/.easyocr_venv/bin/jupyter-troubleshoot deleted file mode 100755 index e28c30518..000000000 --- a/.easyocr_venv/bin/jupyter-troubleshoot +++ /dev/null @@ -1,8 +0,0 @@ -#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 -# -*- coding: utf-8 -*- -import re -import sys -from jupyter_core.troubleshoot import main -if __name__ == '__main__': - sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) - sys.exit(main()) diff --git a/.easyocr_venv/bin/lsm2bin b/.easyocr_venv/bin/lsm2bin deleted file mode 100755 index 586f4bd3d..000000000 --- a/.easyocr_venv/bin/lsm2bin +++ /dev/null @@ -1,8 +0,0 @@ -#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 -# -*- coding: utf-8 -*- -import re -import sys -from tifffile.lsm2bin import main -if __name__ == '__main__': - sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) - sys.exit(main()) diff --git a/.easyocr_venv/bin/nltk b/.easyocr_venv/bin/nltk deleted file mode 100755 index 4db12ea15..000000000 --- a/.easyocr_venv/bin/nltk +++ /dev/null @@ -1,8 +0,0 @@ -#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 -# -*- coding: utf-8 -*- -import re -import sys -from nltk.cli import cli -if __name__ == '__main__': - sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) - sys.exit(cli()) diff --git a/.easyocr_venv/bin/pip b/.easyocr_venv/bin/pip deleted file mode 100755 index 2ac1b3814..000000000 --- a/.easyocr_venv/bin/pip +++ /dev/null @@ -1,8 +0,0 @@ -#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 -# -*- coding: utf-8 -*- -import re -import sys -from pip._internal.cli.main import main -if __name__ == '__main__': - sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) - sys.exit(main()) diff --git a/.easyocr_venv/bin/pip3 b/.easyocr_venv/bin/pip3 deleted file mode 100755 index 2ac1b3814..000000000 --- a/.easyocr_venv/bin/pip3 +++ /dev/null @@ -1,8 +0,0 @@ -#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 -# -*- coding: utf-8 -*- -import re -import sys -from pip._internal.cli.main import main -if __name__ == '__main__': - sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) - sys.exit(main()) diff --git a/.easyocr_venv/bin/pip3.8 b/.easyocr_venv/bin/pip3.8 deleted file mode 100755 index 2ac1b3814..000000000 --- a/.easyocr_venv/bin/pip3.8 +++ /dev/null @@ -1,8 +0,0 @@ -#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 -# -*- coding: utf-8 -*- -import re -import sys -from pip._internal.cli.main import main -if __name__ == '__main__': - sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) - sys.exit(main()) diff --git a/.easyocr_venv/bin/pybidi b/.easyocr_venv/bin/pybidi deleted file mode 100755 index c07a4c9a7..000000000 --- a/.easyocr_venv/bin/pybidi +++ /dev/null @@ -1,8 +0,0 @@ -#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 -# -*- coding: utf-8 -*- -import re -import sys -from bidi import main -if __name__ == '__main__': - sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) - sys.exit(main()) diff --git a/.easyocr_venv/bin/pygmentize b/.easyocr_venv/bin/pygmentize deleted file mode 100755 index 8c6593682..000000000 --- a/.easyocr_venv/bin/pygmentize +++ /dev/null @@ -1,8 +0,0 @@ -#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 -# -*- coding: utf-8 -*- -import re -import sys -from pygments.cmdline import main -if __name__ == '__main__': - sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) - sys.exit(main()) diff --git a/.easyocr_venv/bin/pylint b/.easyocr_venv/bin/pylint deleted file mode 100755 index d6cea3a01..000000000 --- a/.easyocr_venv/bin/pylint +++ /dev/null @@ -1,8 +0,0 @@ -#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 -# -*- coding: utf-8 -*- -import re -import sys -from pylint import run_pylint -if __name__ == '__main__': - sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) - sys.exit(run_pylint()) diff --git a/.easyocr_venv/bin/pylint-config b/.easyocr_venv/bin/pylint-config deleted file mode 100755 index 2443627f1..000000000 --- a/.easyocr_venv/bin/pylint-config +++ /dev/null @@ -1,8 +0,0 @@ -#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 -# -*- coding: utf-8 -*- -import re -import sys -from pylint import _run_pylint_config -if __name__ == '__main__': - sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) - sys.exit(_run_pylint_config()) diff --git a/.easyocr_venv/bin/pyreverse b/.easyocr_venv/bin/pyreverse deleted file mode 100755 index 656e75ac1..000000000 --- a/.easyocr_venv/bin/pyreverse +++ /dev/null @@ -1,8 +0,0 @@ -#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 -# -*- coding: utf-8 -*- -import re -import sys -from pylint import run_pyreverse -if __name__ == '__main__': - sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) - sys.exit(run_pyreverse()) diff --git a/.easyocr_venv/bin/python b/.easyocr_venv/bin/python deleted file mode 120000 index 4e58b606c..000000000 --- a/.easyocr_venv/bin/python +++ /dev/null @@ -1 +0,0 @@ -python3.8 \ No newline at end of file diff --git a/.easyocr_venv/bin/python3 b/.easyocr_venv/bin/python3 deleted file mode 120000 index 4e58b606c..000000000 --- a/.easyocr_venv/bin/python3 +++ /dev/null @@ -1 +0,0 @@ -python3.8 \ No newline at end of file diff --git a/.easyocr_venv/bin/python3.8 b/.easyocr_venv/bin/python3.8 deleted file mode 120000 index 02a138965..000000000 --- a/.easyocr_venv/bin/python3.8 +++ /dev/null @@ -1 +0,0 @@ -/usr/bin/python3.8 \ No newline at end of file diff --git a/.easyocr_venv/bin/skivi b/.easyocr_venv/bin/skivi deleted file mode 100755 index 0a7948e36..000000000 --- a/.easyocr_venv/bin/skivi +++ /dev/null @@ -1,8 +0,0 @@ -#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 -# -*- coding: utf-8 -*- -import re -import sys -from skimage.scripts.skivi import main -if __name__ == '__main__': - sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) - sys.exit(main()) diff --git a/.easyocr_venv/bin/symilar b/.easyocr_venv/bin/symilar deleted file mode 100755 index 9e4637838..000000000 --- a/.easyocr_venv/bin/symilar +++ /dev/null @@ -1,8 +0,0 @@ -#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 -# -*- coding: utf-8 -*- -import re -import sys -from pylint import run_symilar -if __name__ == '__main__': - sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) - sys.exit(run_symilar()) diff --git a/.easyocr_venv/bin/tiff2fsspec b/.easyocr_venv/bin/tiff2fsspec deleted file mode 100755 index 93d5cc922..000000000 --- a/.easyocr_venv/bin/tiff2fsspec +++ /dev/null @@ -1,8 +0,0 @@ -#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 -# -*- coding: utf-8 -*- -import re -import sys -from tifffile.tiff2fsspec import main -if __name__ == '__main__': - sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) - sys.exit(main()) diff --git a/.easyocr_venv/bin/tiffcomment b/.easyocr_venv/bin/tiffcomment deleted file mode 100755 index dede233c7..000000000 --- a/.easyocr_venv/bin/tiffcomment +++ /dev/null @@ -1,8 +0,0 @@ -#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 -# -*- coding: utf-8 -*- -import re -import sys -from tifffile.tiffcomment import main -if __name__ == '__main__': - sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) - sys.exit(main()) diff --git a/.easyocr_venv/bin/tifffile b/.easyocr_venv/bin/tifffile deleted file mode 100755 index 1ad2f3216..000000000 --- a/.easyocr_venv/bin/tifffile +++ /dev/null @@ -1,8 +0,0 @@ -#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 -# -*- coding: utf-8 -*- -import re -import sys -from tifffile import main -if __name__ == '__main__': - sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) - sys.exit(main()) diff --git a/.easyocr_venv/bin/tqdm b/.easyocr_venv/bin/tqdm deleted file mode 100755 index a82b20429..000000000 --- a/.easyocr_venv/bin/tqdm +++ /dev/null @@ -1,8 +0,0 @@ -#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 -# -*- coding: utf-8 -*- -import re -import sys -from tqdm.cli import main -if __name__ == '__main__': - sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys.argv[0]) - sys.exit(main()) diff --git a/.easyocr_venv/bin/undill b/.easyocr_venv/bin/undill deleted file mode 100755 index 1749bc11e..000000000 --- a/.easyocr_venv/bin/undill +++ /dev/null @@ -1,22 +0,0 @@ -#!/home/ystoll/PixelForest/easyocr/.easyocr_venv/bin/python3.8 -# -# Author: Mike McKerns (mmckerns @caltech and @uqfoundation) -# Copyright (c) 2008-2016 California Institute of Technology. -# Copyright (c) 2016-2022 The Uncertainty Quantification Foundation. -# License: 3-clause BSD. The full license text is available at: -# - https://github.com/uqfoundation/dill/blob/master/LICENSE -""" -unpickle the contents of a pickled object file - -Examples:: - - $ undill hello.pkl - ['hello', 'world'] -""" - -if __name__ == '__main__': - import sys - import dill - for file in sys.argv[1:]: - print (dill.load(open(file,'rb'))) - diff --git a/.easyocr_venv/lib64 b/.easyocr_venv/lib64 deleted file mode 120000 index 7951405f8..000000000 --- a/.easyocr_venv/lib64 +++ /dev/null @@ -1 +0,0 @@ -lib \ No newline at end of file diff --git a/.easyocr_venv/pyvenv.cfg b/.easyocr_venv/pyvenv.cfg deleted file mode 100644 index 853404e23..000000000 --- a/.easyocr_venv/pyvenv.cfg +++ /dev/null @@ -1,3 +0,0 @@ -home = /usr/bin -include-system-site-packages = false -version = 3.8.10 diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/LICENSE.txt b/.easyocr_venv/share/doc/networkx-2.8.5/LICENSE.txt deleted file mode 100644 index a274a66aa..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/LICENSE.txt +++ /dev/null @@ -1,37 +0,0 @@ -NetworkX is distributed with the 3-clause BSD license. - -:: - - Copyright (C) 2004-2022, NetworkX Developers - Aric Hagberg - Dan Schult - Pieter Swart - All rights reserved. - - Redistribution and use in source and binary forms, with or without - modification, are permitted provided that the following conditions are - met: - - * Redistributions of source code must retain the above copyright - notice, this list of conditions and the following disclaimer. - - * Redistributions in binary form must reproduce the above - copyright notice, this list of conditions and the following - disclaimer in the documentation and/or other materials provided - with the distribution. - - * Neither the name of the NetworkX Developers nor the names of its - contributors may be used to endorse or promote products derived - from this software without specific prior written permission. - - THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS - "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT - LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR - A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT - OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, - SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT - LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, - DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY - THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT - (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE - OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/3d_drawing/README.txt b/.easyocr_venv/share/doc/networkx-2.8.5/examples/3d_drawing/README.txt deleted file mode 100644 index 1a7668282..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/3d_drawing/README.txt +++ /dev/null @@ -1,2 +0,0 @@ -3D Drawing ----------- diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/3d_drawing/mayavi2_spring.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/3d_drawing/mayavi2_spring.py deleted file mode 100644 index bec096948..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/3d_drawing/mayavi2_spring.py +++ /dev/null @@ -1,43 +0,0 @@ -""" -======= -Mayavi2 -======= - -""" - -import networkx as nx -import numpy as np -from mayavi import mlab - -# some graphs to try -# H=nx.krackhardt_kite_graph() -# H=nx.Graph();H.add_edge('a','b');H.add_edge('a','c');H.add_edge('a','d') -# H=nx.grid_2d_graph(4,5) -H = nx.cycle_graph(20) - -# reorder nodes from 0,len(G)-1 -G = nx.convert_node_labels_to_integers(H) -# 3d spring layout -pos = nx.spring_layout(G, dim=3, seed=1001) -# numpy array of x,y,z positions in sorted node order -xyz = np.array([pos[v] for v in sorted(G)]) -# scalar colors -scalars = np.array(list(G.nodes())) + 5 - -mlab.figure() - -pts = mlab.points3d( - xyz[:, 0], - xyz[:, 1], - xyz[:, 2], - scalars, - scale_factor=0.1, - scale_mode="none", - colormap="Blues", - resolution=20, -) - -pts.mlab_source.dataset.lines = np.array(list(G.edges())) -tube = mlab.pipeline.tube(pts, tube_radius=0.01) -mlab.pipeline.surface(tube, color=(0.8, 0.8, 0.8)) -mlab.orientation_axes() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/3d_drawing/plot_basic.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/3d_drawing/plot_basic.py deleted file mode 100644 index 75a9c79a2..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/3d_drawing/plot_basic.py +++ /dev/null @@ -1,51 +0,0 @@ -""" -================ -Basic matplotlib -================ - -A basic example of 3D Graph visualization using `mpl_toolkits.mplot_3d`. - -""" - -import networkx as nx -import numpy as np -import matplotlib.pyplot as plt -from mpl_toolkits.mplot3d import Axes3D - -# The graph to visualize -G = nx.cycle_graph(20) - -# 3d spring layout -pos = nx.spring_layout(G, dim=3, seed=779) -# Extract node and edge positions from the layout -node_xyz = np.array([pos[v] for v in sorted(G)]) -edge_xyz = np.array([(pos[u], pos[v]) for u, v in G.edges()]) - -# Create the 3D figure -fig = plt.figure() -ax = fig.add_subplot(111, projection="3d") - -# Plot the nodes - alpha is scaled by "depth" automatically -ax.scatter(*node_xyz.T, s=100, ec="w") - -# Plot the edges -for vizedge in edge_xyz: - ax.plot(*vizedge.T, color="tab:gray") - - -def _format_axes(ax): - """Visualization options for the 3D axes.""" - # Turn gridlines off - ax.grid(False) - # Suppress tick labels - for dim in (ax.xaxis, ax.yaxis, ax.zaxis): - dim.set_ticks([]) - # Set axes labels - ax.set_xlabel("x") - ax.set_ylabel("y") - ax.set_zlabel("z") - - -_format_axes(ax) -fig.tight_layout() -plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/README.txt b/.easyocr_venv/share/doc/networkx-2.8.5/examples/README.txt deleted file mode 100644 index d0049bdba..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/README.txt +++ /dev/null @@ -1,8 +0,0 @@ -.. _examples_gallery: - -Gallery -======= - -General-purpose and introductory examples for NetworkX. -The `tutorial <../tutorial.html>`_ introduces conventions and basic graph -manipulations. diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/README.txt b/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/README.txt deleted file mode 100644 index a78a4d7d0..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/README.txt +++ /dev/null @@ -1,2 +0,0 @@ -Algorithms ----------- diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/WormNet.v3.benchmark.txt b/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/WormNet.v3.benchmark.txt deleted file mode 100644 index 824cfa1cd..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/WormNet.v3.benchmark.txt +++ /dev/null @@ -1,78736 +0,0 @@ -C41D11.8 AH9.2 -CD4.2 AH9.2 -K12H4.8 AH9.2 -T07A9.5 AH9.2 -Y113G7A.9 AH9.2 -Y47G6A.8 AH9.2 -Y48B6A.3 AH9.2 -Y56A3A.32 AH9.2 -ZC513.5 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b/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_beam_search.py deleted file mode 100644 index c465bbb40..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_beam_search.py +++ /dev/null @@ -1,112 +0,0 @@ -""" -=========== -Beam Search -=========== - -Beam search with dynamic beam width. - -The progressive widening beam search repeatedly executes a beam search -with increasing beam width until the target node is found. -""" -import math - -import matplotlib.pyplot as plt -import networkx as nx - - -def progressive_widening_search(G, source, value, condition, initial_width=1): - """Progressive widening beam search to find a node. - - The progressive widening beam search involves a repeated beam - search, starting with a small beam width then extending to - progressively larger beam widths if the target node is not - found. This implementation simply returns the first node found that - matches the termination condition. - - `G` is a NetworkX graph. - - `source` is a node in the graph. The search for the node of interest - begins here and extends only to those nodes in the (weakly) - connected component of this node. - - `value` is a function that returns a real number indicating how good - a potential neighbor node is when deciding which neighbor nodes to - enqueue in the breadth-first search. Only the best nodes within the - current beam width will be enqueued at each step. - - `condition` is the termination condition for the search. This is a - function that takes a node as input and return a Boolean indicating - whether the node is the target. If no node matches the termination - condition, this function raises :exc:`NodeNotFound`. - - `initial_width` is the starting beam width for the beam search (the - default is one). If no node matching the `condition` is found with - this beam width, the beam search is restarted from the `source` node - with a beam width that is twice as large (so the beam width - increases exponentially). The search terminates after the beam width - exceeds the number of nodes in the graph. - - """ - # Check for the special case in which the source node satisfies the - # termination condition. - if condition(source): - return source - # The largest possible value of `i` in this range yields a width at - # least the number of nodes in the graph, so the final invocation of - # `bfs_beam_edges` is equivalent to a plain old breadth-first - # search. Therefore, all nodes will eventually be visited. - log_m = math.ceil(math.log2(len(G))) - for i in range(log_m): - width = initial_width * pow(2, i) - # Since we are always starting from the same source node, this - # search may visit the same nodes many times (depending on the - # implementation of the `value` function). - for u, v in nx.bfs_beam_edges(G, source, value, width): - if condition(v): - return v - # At this point, since all nodes have been visited, we know that - # none of the nodes satisfied the termination condition. - raise nx.NodeNotFound("no node satisfied the termination condition") - - -############################################################################### -# Search for a node with high centrality. -# --------------------------------------- -# -# We generate a random graph, compute the centrality of each node, then perform -# the progressive widening search in order to find a node of high centrality. - -# Set a seed for random number generation so the example is reproducible -seed = 89 - -G = nx.gnp_random_graph(100, 0.5, seed=seed) -centrality = nx.eigenvector_centrality(G) -avg_centrality = sum(centrality.values()) / len(G) - - -def has_high_centrality(v): - return centrality[v] >= avg_centrality - - -source = 0 -value = centrality.get -condition = has_high_centrality - -found_node = progressive_widening_search(G, source, value, condition) -c = centrality[found_node] -print(f"found node {found_node} with centrality {c}") - - -# Draw graph -pos = nx.spring_layout(G, seed=seed) -options = { - "node_color": "blue", - "node_size": 20, - "edge_color": "grey", - "linewidths": 0, - "width": 0.1, -} -nx.draw(G, pos, **options) -# Draw node with high centrality as large and red -nx.draw_networkx_nodes(G, pos, nodelist=[found_node], node_size=100, node_color="r") -plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_betweenness_centrality.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_betweenness_centrality.py deleted file mode 100644 index e8354c662..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_betweenness_centrality.py +++ /dev/null @@ -1,83 +0,0 @@ -""" -===================== -Betweeness Centrality -===================== - -Betweenness centrality measures of positive gene functional associations -using WormNet v.3-GS. - -Data from: https://www.inetbio.org/wormnet/downloadnetwork.php -""" - -from random import sample -import networkx as nx -import matplotlib.pyplot as plt - -# Gold standard data of positive gene functional associations -# from https://www.inetbio.org/wormnet/downloadnetwork.php -G = nx.read_edgelist("WormNet.v3.benchmark.txt") - -# remove randomly selected nodes (to make example fast) -num_to_remove = int(len(G) / 1.5) -nodes = sample(list(G.nodes), num_to_remove) -G.remove_nodes_from(nodes) - -# remove low-degree nodes -low_degree = [n for n, d in G.degree() if d < 10] -G.remove_nodes_from(low_degree) - -# largest connected component -components = nx.connected_components(G) -largest_component = max(components, key=len) -H = G.subgraph(largest_component) - -# compute centrality -centrality = nx.betweenness_centrality(H, k=10, endpoints=True) - -# compute community structure -lpc = nx.community.label_propagation_communities(H) -community_index = {n: i for i, com in enumerate(lpc) for n in com} - -#### draw graph #### -fig, ax = plt.subplots(figsize=(20, 15)) -pos = nx.spring_layout(H, k=0.15, seed=4572321) -node_color = [community_index[n] for n in H] -node_size = [v * 20000 for v in centrality.values()] -nx.draw_networkx( - H, - pos=pos, - with_labels=False, - node_color=node_color, - node_size=node_size, - edge_color="gainsboro", - alpha=0.4, -) - -# Title/legend -font = {"color": "k", "fontweight": "bold", "fontsize": 20} -ax.set_title("Gene functional association network (C. elegans)", font) -# Change font color for legend -font["color"] = "r" - -ax.text( - 0.80, - 0.10, - "node color = community structure", - horizontalalignment="center", - transform=ax.transAxes, - fontdict=font, -) -ax.text( - 0.80, - 0.06, - "node size = betweeness centrality", - horizontalalignment="center", - transform=ax.transAxes, - fontdict=font, -) - -# Resize figure for label readibility -ax.margins(0.1, 0.05) -fig.tight_layout() -plt.axis("off") -plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_blockmodel.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_blockmodel.py deleted file mode 100644 index e3fec60bf..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_blockmodel.py +++ /dev/null @@ -1,79 +0,0 @@ -""" -========== -Blockmodel -========== - -Example of creating a block model using the quotient_graph function in NX. Data -used is the Hartford, CT drug users network:: - - @article{weeks2002social, - title={Social networks of drug users in high-risk sites: Finding the connections}, - url = {https://doi.org/10.1023/A:1015457400897}, - doi = {10.1023/A:1015457400897}, - author={Weeks, Margaret R and Clair, Scott and Borgatti, Stephen P and Radda, Kim and Schensul, Jean J}, - journal={{AIDS and Behavior}}, - volume={6}, - number={2}, - pages={193--206}, - year={2002}, - publisher={Springer} - } - -""" - -from collections import defaultdict - -import matplotlib.pyplot as plt -import networkx as nx -import numpy as np -from scipy.cluster import hierarchy -from scipy.spatial import distance - - -def create_hc(G): - """Creates hierarchical cluster of graph G from distance matrix""" - path_length = nx.all_pairs_shortest_path_length(G) - distances = np.zeros((len(G), len(G))) - for u, p in path_length: - for v, d in p.items(): - distances[u][v] = d - # Create hierarchical cluster - Y = distance.squareform(distances) - Z = hierarchy.complete(Y) # Creates HC using farthest point linkage - # This partition selection is arbitrary, for illustrive purposes - membership = list(hierarchy.fcluster(Z, t=1.15)) - # Create collection of lists for blockmodel - partition = defaultdict(list) - for n, p in zip(list(range(len(G))), membership): - partition[p].append(n) - return list(partition.values()) - - -G = nx.read_edgelist("hartford_drug.edgelist") - -# Extract largest connected component into graph H -H = G.subgraph(next(nx.connected_components(G))) -# Makes life easier to have consecutively labeled integer nodes -H = nx.convert_node_labels_to_integers(H) -# Create parititions with hierarchical clustering -partitions = create_hc(H) -# Build blockmodel graph -BM = nx.quotient_graph(H, partitions, relabel=True) - -# Draw original graph -pos = nx.spring_layout(H, iterations=100, seed=83) # Seed for reproducibility -plt.subplot(211) -nx.draw(H, pos, with_labels=False, node_size=10) - -# Draw block model with weighted edges and nodes sized by number of internal nodes -node_size = [BM.nodes[x]["nnodes"] * 10 for x in BM.nodes()] -edge_width = [(2 * d["weight"]) for (u, v, d) in BM.edges(data=True)] -# Set positions to mean of positions of internal nodes from original graph -posBM = {} -for n in BM: - xy = np.array([pos[u] for u in BM.nodes[n]["graph"]]) - posBM[n] = xy.mean(axis=0) -plt.subplot(212) -nx.draw(BM, posBM, node_size=node_size, width=edge_width, with_labels=False) -plt.axis("off") -plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_circuits.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_circuits.py deleted file mode 100644 index 5481aedb5..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_circuits.py +++ /dev/null @@ -1,103 +0,0 @@ -""" -======== -Circuits -======== - -Convert a Boolean circuit to an equivalent Boolean formula. - -A Boolean circuit can be exponentially more expressive than an -equivalent formula in the worst case, since the circuit can reuse -subcircuits multiple times, whereas a formula cannot reuse subformulas -more than once. Thus creating a Boolean formula from a Boolean circuit -in this way may be infeasible if the circuit is large. - -""" -import matplotlib.pyplot as plt -import networkx as nx - - -def circuit_to_formula(circuit): - # Convert the circuit to an equivalent formula. - formula = nx.dag_to_branching(circuit) - # Transfer the operator or variable labels for each node from the - # circuit to the formula. - for v in formula: - source = formula.nodes[v]["source"] - formula.nodes[v]["label"] = circuit.nodes[source]["label"] - return formula - - -def formula_to_string(formula): - def _to_string(formula, root): - # If there are no children, this is a variable node. - label = formula.nodes[root]["label"] - if not formula[root]: - return label - # Otherwise, this is an operator. - children = formula[root] - # If one child, the label must be a NOT operator. - if len(children) == 1: - child = nx.utils.arbitrary_element(children) - return f"{label}({_to_string(formula, child)})" - # NB "left" and "right" here are a little misleading: there is - # no order on the children of a node. That's okay because the - # Boolean AND and OR operators are symmetric. It just means that - # the order of the operands cannot be predicted and hence the - # function does not necessarily behave the same way on every - # invocation. - left, right = formula[root] - left_subformula = _to_string(formula, left) - right_subformula = _to_string(formula, right) - return f"({left_subformula} {label} {right_subformula})" - - root = next(v for v, d in formula.in_degree() if d == 0) - return _to_string(formula, root) - - -############################################################################### -# Create an example Boolean circuit. -# ---------------------------------- -# -# This circuit has a ∧ at the output and two ∨s at the next layer. -# The third layer has a variable x that appears in the left ∨, a -# variable y that appears in both the left and right ∨s, and a -# negation for the variable z that appears as the sole node in the -# fourth layer. -circuit = nx.DiGraph() -# Layer 0 -circuit.add_node(0, label="∧", layer=0) -# Layer 1 -circuit.add_node(1, label="∨", layer=1) -circuit.add_node(2, label="∨", layer=1) -circuit.add_edge(0, 1) -circuit.add_edge(0, 2) -# Layer 2 -circuit.add_node(3, label="x", layer=2) -circuit.add_node(4, label="y", layer=2) -circuit.add_node(5, label="¬", layer=2) -circuit.add_edge(1, 3) -circuit.add_edge(1, 4) -circuit.add_edge(2, 4) -circuit.add_edge(2, 5) -# Layer 3 -circuit.add_node(6, label="z", layer=3) -circuit.add_edge(5, 6) -# Convert the circuit to an equivalent formula. -formula = circuit_to_formula(circuit) -print(formula_to_string(formula)) - - -labels = nx.get_node_attributes(circuit, "label") -options = { - "node_size": 600, - "alpha": 0.5, - "node_color": "blue", - "labels": labels, - "font_size": 22, -} -plt.figure(figsize=(8, 8)) -pos = nx.multipartite_layout(circuit, subset_key="layer") -nx.draw_networkx(circuit, pos, **options) -plt.title(formula_to_string(formula)) -plt.axis("equal") -plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_davis_club.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_davis_club.py deleted file mode 100644 index 517d52386..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_davis_club.py +++ /dev/null @@ -1,43 +0,0 @@ -""" -========== -Davis Club -========== - -Davis Southern Club Women - -Shows how to make unipartite projections of the graph and compute the -properties of those graphs. - -These data were collected by Davis et al. in the 1930s. -They represent observed attendance at 14 social events by 18 Southern women. -The graph is bipartite (clubs, women). -""" -import matplotlib.pyplot as plt -import networkx as nx -import networkx.algorithms.bipartite as bipartite - -G = nx.davis_southern_women_graph() -women = G.graph["top"] -clubs = G.graph["bottom"] - -print("Biadjacency matrix") -print(bipartite.biadjacency_matrix(G, women, clubs)) - -# project bipartite graph onto women nodes -W = bipartite.projected_graph(G, women) -print() -print("#Friends, Member") -for w in women: - print(f"{W.degree(w)} {w}") - -# project bipartite graph onto women nodes keeping number of co-occurence -# the degree computed is weighted and counts the total number of shared contacts -W = bipartite.weighted_projected_graph(G, women) -print() -print("#Friend meetings, Member") -for w in women: - print(f"{W.degree(w, weight='weight')} {w}") - -pos = nx.spring_layout(G, seed=648) # Seed layout for reproducible node positions -nx.draw(G, pos) -plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_dedensification.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_dedensification.py deleted file mode 100644 index 9104d5468..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_dedensification.py +++ /dev/null @@ -1,92 +0,0 @@ -""" -=============== -Dedensification -=============== - -Examples of dedensification of a graph. Dedensification retains the structural -pattern of the original graph and will only add compressor nodes when doing so -would result in fewer edges in the compressed graph. -""" -import matplotlib.pyplot as plt -import networkx as nx - -plt.suptitle("Dedensification") - -original_graph = nx.DiGraph() -white_nodes = ["1", "2", "3", "4", "5", "6"] -red_nodes = ["A", "B", "C"] -node_sizes = [250 for node in white_nodes + red_nodes] -node_colors = ["white" for n in white_nodes] + ["red" for n in red_nodes] - -original_graph.add_nodes_from(white_nodes + red_nodes) -original_graph.add_edges_from( - [ - ("1", "C"), - ("1", "B"), - ("2", "C"), - ("2", "B"), - ("2", "A"), - ("3", "B"), - ("3", "A"), - ("3", "6"), - ("4", "C"), - ("4", "B"), - ("4", "A"), - ("5", "B"), - ("5", "A"), - ("6", "5"), - ("A", "6"), - ] -) -base_options = dict(with_labels=True, edgecolors="black") -pos = { - "3": (0, 1), - "2": (0, 2), - "1": (0, 3), - "6": (1, 0), - "A": (1, 1), - "B": (1, 2), - "C": (1, 3), - "4": (2, 3), - "5": (2, 1), -} -ax1 = plt.subplot(1, 2, 1) -plt.title("Original (%s edges)" % original_graph.number_of_edges()) -nx.draw_networkx(original_graph, pos=pos, node_color=node_colors, **base_options) - -nonexp_graph, compression_nodes = nx.summarization.dedensify( - original_graph, threshold=2, copy=False -) -nonexp_node_colors = list(node_colors) -nonexp_node_sizes = list(node_sizes) -for node in compression_nodes: - nonexp_node_colors.append("yellow") - nonexp_node_sizes.append(600) -plt.subplot(1, 2, 2) - -plt.title("Dedensified (%s edges)" % nonexp_graph.number_of_edges()) -nonexp_pos = { - "5": (0, 0), - "B": (0, 2), - "1": (0, 3), - "6": (1, 0.75), - "3": (1.5, 1.5), - "A": (2, 0), - "C": (2, 3), - "4": (3, 1.5), - "2": (3, 2.5), -} -c_nodes = list(compression_nodes) -c_nodes.sort() -for spot, node in enumerate(c_nodes): - nonexp_pos[node] = (2, spot + 2) -nx.draw_networkx( - nonexp_graph, - pos=nonexp_pos, - node_color=nonexp_node_colors, - node_size=nonexp_node_sizes, - **base_options -) - -plt.tight_layout() -plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_iterated_dynamical_systems.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_iterated_dynamical_systems.py deleted file mode 100644 index a19baeb5a..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_iterated_dynamical_systems.py +++ /dev/null @@ -1,210 +0,0 @@ -""" -========================== -Iterated Dynamical Systems -========================== - -Digraphs from Integer-valued Iterated Functions - -Sums of cubes on 3N -------------------- - -The number 153 has a curious property. - -Let 3N={3,6,9,12,...} be the set of positive multiples of 3. Define an -iterative process f:3N->3N as follows: for a given n, take each digit -of n (in base 10), cube it and then sum the cubes to obtain f(n). - -When this process is repeated, the resulting series n, f(n), f(f(n)),... -terminate in 153 after a finite number of iterations (the process ends -because 153 = 1**3 + 5**3 + 3**3). - -In the language of discrete dynamical systems, 153 is the global -attractor for the iterated map f restricted to the set 3N. - -For example: take the number 108 - -f(108) = 1**3 + 0**3 + 8**3 = 513 - -and - -f(513) = 5**3 + 1**3 + 3**3 = 153 - -So, starting at 108 we reach 153 in two iterations, -represented as: - -108->513->153 - -Computing all orbits of 3N up to 10**5 reveals that the attractor -153 is reached in a maximum of 14 iterations. In this code we -show that 13 cycles is the maximum required for all integers (in 3N) -less than 10,000. - -The smallest number that requires 13 iterations to reach 153, is 177, i.e., - -177->687->1071->345->216->225->141->66->432->99->1458->702->351->153 - -The resulting large digraphs are useful for testing network software. - -The general problem -------------------- - -Given numbers n, a power p and base b, define F(n; p, b) as the sum of -the digits of n (in base b) raised to the power p. The above example -corresponds to f(n)=F(n; 3,10), and below F(n; p, b) is implemented as -the function powersum(n,p,b). The iterative dynamical system defined by -the mapping n:->f(n) above (over 3N) converges to a single fixed point; -153. Applying the map to all positive integers N, leads to a discrete -dynamical process with 5 fixed points: 1, 153, 370, 371, 407. Modulo 3 -those numbers are 1, 0, 1, 2, 2. The function f above has the added -property that it maps a multiple of 3 to another multiple of 3; i.e. it -is invariant on the subset 3N. - - -The squaring of digits (in base 10) result in cycles and the -single fixed point 1. I.e., from a certain point on, the process -starts repeating itself. - -keywords: "Recurring Digital Invariant", "Narcissistic Number", -"Happy Number" - -The 3n+1 problem ----------------- - -There is a rich history of mathematical recreations -associated with discrete dynamical systems. The most famous -is the Collatz 3n+1 problem. See the function -collatz_problem_digraph below. The Collatz conjecture ---- that every orbit returns to the fixed point 1 in finite time ---- is still unproven. Even the great Paul Erdos said "Mathematics -is not yet ready for such problems", and offered $500 -for its solution. - -keywords: "3n+1", "3x+1", "Collatz problem", "Thwaite's conjecture" -""" - -import networkx as nx - -nmax = 10000 -p = 3 - - -def digitsrep(n, b=10): - """Return list of digits comprising n represented in base b. - n must be a nonnegative integer""" - - if n <= 0: - return [0] - - dlist = [] - while n > 0: - # Prepend next least-significant digit - dlist = [n % b] + dlist - # Floor-division - n = n // b - return dlist - - -def powersum(n, p, b=10): - """Return sum of digits of n (in base b) raised to the power p.""" - dlist = digitsrep(n, b) - sum = 0 - for k in dlist: - sum += k**p - return sum - - -def attractor153_graph(n, p, multiple=3, b=10): - """Return digraph of iterations of powersum(n,3,10).""" - G = nx.DiGraph() - for k in range(1, n + 1): - if k % multiple == 0 and k not in G: - k1 = k - knext = powersum(k1, p, b) - while k1 != knext: - G.add_edge(k1, knext) - k1 = knext - knext = powersum(k1, p, b) - return G - - -def squaring_cycle_graph_old(n, b=10): - """Return digraph of iterations of powersum(n,2,10).""" - G = nx.DiGraph() - for k in range(1, n + 1): - k1 = k - G.add_node(k1) # case k1==knext, at least add node - knext = powersum(k1, 2, b) - G.add_edge(k1, knext) - while k1 != knext: # stop if fixed point - k1 = knext - knext = powersum(k1, 2, b) - G.add_edge(k1, knext) - if G.out_degree(knext) >= 1: - # knext has already been iterated in and out - break - return G - - -def sum_of_digits_graph(nmax, b=10): - def f(n): - return powersum(n, 1, b) - - return discrete_dynamics_digraph(nmax, f) - - -def squaring_cycle_digraph(nmax, b=10): - def f(n): - return powersum(n, 2, b) - - return discrete_dynamics_digraph(nmax, f) - - -def cubing_153_digraph(nmax): - def f(n): - return powersum(n, 3, 10) - - return discrete_dynamics_digraph(nmax, f) - - -def discrete_dynamics_digraph(nmax, f, itermax=50000): - G = nx.DiGraph() - for k in range(1, nmax + 1): - kold = k - G.add_node(kold) - knew = f(kold) - G.add_edge(kold, knew) - while kold != knew and kold << itermax: - # iterate until fixed point reached or itermax is exceeded - kold = knew - knew = f(kold) - G.add_edge(kold, knew) - if G.out_degree(knew) >= 1: - # knew has already been iterated in and out - break - return G - - -def collatz_problem_digraph(nmax): - def f(n): - if n % 2 == 0: - return n // 2 - else: - return 3 * n + 1 - - return discrete_dynamics_digraph(nmax, f) - - -def fixed_points(G): - """Return a list of fixed points for the discrete dynamical - system represented by the digraph G. - """ - return [n for n in G if G.out_degree(n) == 0] - - -nmax = 10000 -print(f"Building cubing_153_digraph({nmax})") -G = cubing_153_digraph(nmax) -print("Resulting digraph has", len(G), "nodes and", G.size(), " edges") -print("Shortest path from 177 to 153 is:") -print(nx.shortest_path(G, 177, 153)) -print(f"fixed points are {fixed_points(G)}") diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_krackhardt_centrality.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_krackhardt_centrality.py deleted file mode 100644 index 9d3e2a268..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_krackhardt_centrality.py +++ /dev/null @@ -1,31 +0,0 @@ -""" -===================== -Krackhardt Centrality -===================== - -Centrality measures of Krackhardt social network. -""" - -import matplotlib.pyplot as plt -import networkx as nx - -G = nx.krackhardt_kite_graph() - -print("Betweenness") -b = nx.betweenness_centrality(G) -for v in G.nodes(): - print(f"{v:2} {b[v]:.3f}") - -print("Degree centrality") -d = nx.degree_centrality(G) -for v in G.nodes(): - print(f"{v:2} {d[v]:.3f}") - -print("Closeness centrality") -c = nx.closeness_centrality(G) -for v in G.nodes(): - print(f"{v:2} {c[v]:.3f}") - -pos = nx.spring_layout(G, seed=367) # Seed layout for reproducibility -nx.draw(G, pos) -plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_parallel_betweenness.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_parallel_betweenness.py deleted file mode 100644 index d9333f458..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_parallel_betweenness.py +++ /dev/null @@ -1,82 +0,0 @@ -""" -==================== -Parallel Betweenness -==================== - -Example of parallel implementation of betweenness centrality using the -multiprocessing module from Python Standard Library. - -The function betweenness centrality accepts a bunch of nodes and computes -the contribution of those nodes to the betweenness centrality of the whole -network. Here we divide the network in chunks of nodes and we compute their -contribution to the betweenness centrality of the whole network. - -Note: The example output below shows that the non-parallel implementation is -faster. This is a limitation of our CI/CD pipeline running on a single core. - -Depending on your setup, you will likely observe a speedup. -""" -from multiprocessing import Pool -import time -import itertools - -import matplotlib.pyplot as plt -import networkx as nx - - -def chunks(l, n): - """Divide a list of nodes `l` in `n` chunks""" - l_c = iter(l) - while 1: - x = tuple(itertools.islice(l_c, n)) - if not x: - return - yield x - - -def betweenness_centrality_parallel(G, processes=None): - """Parallel betweenness centrality function""" - p = Pool(processes=processes) - node_divisor = len(p._pool) * 4 - node_chunks = list(chunks(G.nodes(), G.order() // node_divisor)) - num_chunks = len(node_chunks) - bt_sc = p.starmap( - nx.betweenness_centrality_subset, - zip( - [G] * num_chunks, - node_chunks, - [list(G)] * num_chunks, - [True] * num_chunks, - [None] * num_chunks, - ), - ) - - # Reduce the partial solutions - bt_c = bt_sc[0] - for bt in bt_sc[1:]: - for n in bt: - bt_c[n] += bt[n] - return bt_c - - -G_ba = nx.barabasi_albert_graph(1000, 3) -G_er = nx.gnp_random_graph(1000, 0.01) -G_ws = nx.connected_watts_strogatz_graph(1000, 4, 0.1) -for G in [G_ba, G_er, G_ws]: - print("") - print("Computing betweenness centrality for:") - print(nx.info(G)) - print("\tParallel version") - start = time.time() - bt = betweenness_centrality_parallel(G) - print(f"\t\tTime: {(time.time() - start):.4F} seconds") - print(f"\t\tBetweenness centrality for node 0: {bt[0]:.5f}") - print("\tNon-Parallel version") - start = time.time() - bt = nx.betweenness_centrality(G) - print(f"\t\tTime: {(time.time() - start):.4F} seconds") - print(f"\t\tBetweenness centrality for node 0: {bt[0]:.5f}") -print("") - -nx.draw(G_ba, node_size=100) -plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_rcm.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_rcm.py deleted file mode 100644 index fc08739bd..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_rcm.py +++ /dev/null @@ -1,40 +0,0 @@ -""" -====================== -Reverse Cuthill--McKee -====================== - -Cuthill-McKee ordering of matrices - -The reverse Cuthill--McKee algorithm gives a sparse matrix ordering that -reduces the matrix bandwidth. -""" - -import numpy as np -import matplotlib.pyplot as plt -import seaborn as sns -import networkx as nx - - -# build low-bandwidth matrix -G = nx.grid_2d_graph(3, 3) -rcm = list(nx.utils.reverse_cuthill_mckee_ordering(G)) -print("ordering", rcm) - -print("unordered Laplacian matrix") -A = nx.laplacian_matrix(G) -x, y = np.nonzero(A) -# print(f"lower bandwidth: {(y - x).max()}") -# print(f"upper bandwidth: {(x - y).max()}") -print(f"bandwidth: {(y - x).max() + (x - y).max() + 1}") -print(A) - -B = nx.laplacian_matrix(G, nodelist=rcm) -print("low-bandwidth Laplacian matrix") -x, y = np.nonzero(B) -# print(f"lower bandwidth: {(y - x).max()}") -# print(f"upper bandwidth: {(x - y).max()}") -print(f"bandwidth: {(y - x).max() + (x - y).max() + 1}") -print(B) - -sns.heatmap(B.todense(), cbar=False, square=True, linewidths=0.5, annot=True) -plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_snap.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_snap.py deleted file mode 100644 index 85ea71de5..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_snap.py +++ /dev/null @@ -1,108 +0,0 @@ -""" -================== -SNAP Graph Summary -================== -An example of summarizing a graph based on node attributes and edge attributes -using the Summarization by Grouping Nodes on Attributes and Pairwise -edges (SNAP) algorithm (not to be confused with the Stanford Network -Analysis Project). The algorithm groups nodes by their unique -combinations of node attribute values and edge types with other groups -of nodes to produce a summary graph. The summary graph can then be used to -infer how nodes with different attributes values relate to other nodes in the -graph. -""" -import networkx as nx -import matplotlib.pyplot as plt - - -nodes = { - "A": dict(color="Red"), - "B": dict(color="Red"), - "C": dict(color="Red"), - "D": dict(color="Red"), - "E": dict(color="Blue"), - "F": dict(color="Blue"), - "G": dict(color="Blue"), - "H": dict(color="Blue"), - "I": dict(color="Yellow"), - "J": dict(color="Yellow"), - "K": dict(color="Yellow"), - "L": dict(color="Yellow"), -} -edges = [ - ("A", "B", "Strong"), - ("A", "C", "Weak"), - ("A", "E", "Strong"), - ("A", "I", "Weak"), - ("B", "D", "Weak"), - ("B", "J", "Weak"), - ("B", "F", "Strong"), - ("C", "G", "Weak"), - ("D", "H", "Weak"), - ("I", "J", "Strong"), - ("J", "K", "Strong"), - ("I", "L", "Strong"), -] -original_graph = nx.Graph() -original_graph.add_nodes_from(n for n in nodes.items()) -original_graph.add_edges_from((u, v, {"type": label}) for u, v, label in edges) - - -plt.suptitle("SNAP Summarization") - -base_options = dict(with_labels=True, edgecolors="black", node_size=500) - -ax1 = plt.subplot(1, 2, 1) -plt.title( - "Original (%s nodes, %s edges)" - % (original_graph.number_of_nodes(), original_graph.number_of_edges()) -) -pos = nx.spring_layout(original_graph, seed=7482934) -node_colors = [d["color"] for _, d in original_graph.nodes(data=True)] - -edge_type_visual_weight_lookup = {"Weak": 1.0, "Strong": 3.0} -edge_weights = [ - edge_type_visual_weight_lookup[d["type"]] - for _, _, d in original_graph.edges(data=True) -] - -nx.draw_networkx( - original_graph, pos=pos, node_color=node_colors, width=edge_weights, **base_options -) - -node_attributes = ("color",) -edge_attributes = ("type",) -summary_graph = nx.snap_aggregation( - original_graph, node_attributes, edge_attributes, prefix="S-" -) - -plt.subplot(1, 2, 2) - -plt.title( - "SNAP Aggregation (%s nodes, %s edges)" - % (summary_graph.number_of_nodes(), summary_graph.number_of_edges()) -) -summary_pos = nx.spring_layout(summary_graph, seed=8375428) -node_colors = [] -for node in summary_graph: - color = summary_graph.nodes[node]["color"] - node_colors.append(color) - -edge_weights = [] -for edge in summary_graph.edges(): - edge_types = summary_graph.get_edge_data(*edge)["types"] - edge_weight = 0.0 - for edge_type in edge_types: - edge_weight += edge_type_visual_weight_lookup[edge_type["type"]] - edge_weights.append(edge_weight) - -nx.draw_networkx( - summary_graph, - pos=summary_pos, - node_color=node_colors, - width=edge_weights, - **base_options -) - -plt.tight_layout() -plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_subgraphs.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_subgraphs.py deleted file mode 100644 index 081270287..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/algorithms/plot_subgraphs.py +++ /dev/null @@ -1,170 +0,0 @@ -""" -========= -Subgraphs -========= -Example of partitioning a directed graph with nodes labeled as -supported and unsupported nodes into a list of subgraphs -that contain only entirely supported or entirely unsupported nodes. -Adopted from -https://github.com/lobpcg/python_examples/blob/master/networkx_example.py -""" - -import networkx as nx -import matplotlib.pyplot as plt - - -def graph_partitioning(G, plotting=True): - """Partition a directed graph into a list of subgraphs that contain - only entirely supported or entirely unsupported nodes. - """ - # Categorize nodes by their node_type attribute - supported_nodes = {n for n, d in G.nodes(data="node_type") if d == "supported"} - unsupported_nodes = {n for n, d in G.nodes(data="node_type") if d == "unsupported"} - - # Make a copy of the graph. - H = G.copy() - # Remove all edges connecting supported and unsupported nodes. - H.remove_edges_from( - (n, nbr, d) - for n, nbrs in G.adj.items() - if n in supported_nodes - for nbr, d in nbrs.items() - if nbr in unsupported_nodes - ) - H.remove_edges_from( - (n, nbr, d) - for n, nbrs in G.adj.items() - if n in unsupported_nodes - for nbr, d in nbrs.items() - if nbr in supported_nodes - ) - - # Collect all removed edges for reconstruction. - G_minus_H = nx.DiGraph() - G_minus_H.add_edges_from(set(G.edges) - set(H.edges)) - - if plotting: - # Plot the stripped graph with the edges removed. - _node_colors = [c for _, c in H.nodes(data="node_color")] - _pos = nx.spring_layout(H) - plt.figure(figsize=(8, 8)) - nx.draw_networkx_edges(H, _pos, alpha=0.3, edge_color="k") - nx.draw_networkx_nodes(H, _pos, node_color=_node_colors) - nx.draw_networkx_labels(H, _pos, font_size=14) - plt.axis("off") - plt.title("The stripped graph with the edges removed.") - plt.show() - # Plot the the edges removed. - _pos = nx.spring_layout(G_minus_H) - plt.figure(figsize=(8, 8)) - ncl = [G.nodes[n]["node_color"] for n in G_minus_H.nodes] - nx.draw_networkx_edges(G_minus_H, _pos, alpha=0.3, edge_color="k") - nx.draw_networkx_nodes(G_minus_H, _pos, node_color=ncl) - nx.draw_networkx_labels(G_minus_H, _pos, font_size=14) - plt.axis("off") - plt.title("The removed edges.") - plt.show() - - # Find the connected components in the stripped undirected graph. - # And use the sets, specifying the components, to partition - # the original directed graph into a list of directed subgraphs - # that contain only entirely supported or entirely unsupported nodes. - subgraphs = [ - H.subgraph(c).copy() for c in nx.connected_components(H.to_undirected()) - ] - - return subgraphs, G_minus_H - - -############################################################################### -# Create an example directed graph. -# --------------------------------- -# -# This directed graph has one input node labeled `in` and plotted in blue color -# and one output node labeled `out` and plotted in magenta color. -# The other six nodes are classified as four `supported` plotted in green color -# and two `unsupported` plotted in red color. The goal is computing a list -# of subgraphs that contain only entirely `supported` or `unsupported` nodes. -G_ex = nx.DiGraph() -G_ex.add_nodes_from(["In"], node_type="input", node_color="b") -G_ex.add_nodes_from(["A", "C", "E", "F"], node_type="supported", node_color="g") -G_ex.add_nodes_from(["B", "D"], node_type="unsupported", node_color="r") -G_ex.add_nodes_from(["Out"], node_type="output", node_color="m") -G_ex.add_edges_from( - [ - ("In", "A"), - ("A", "B"), - ("B", "C"), - ("B", "D"), - ("D", "E"), - ("C", "F"), - ("E", "F"), - ("F", "Out"), - ] -) - -############################################################################### -# Plot the original graph. -# ------------------------ -# -node_color_list = [nc for _, nc in G_ex.nodes(data="node_color")] -pos = nx.spectral_layout(G_ex) -plt.figure(figsize=(8, 8)) -nx.draw_networkx_edges(G_ex, pos, alpha=0.3, edge_color="k") -nx.draw_networkx_nodes(G_ex, pos, alpha=0.8, node_color=node_color_list) -nx.draw_networkx_labels(G_ex, pos, font_size=14) -plt.axis("off") -plt.title("The original graph.") -plt.show() - -############################################################################### -# Calculate the subgraphs with plotting all results of intemediate steps. -# ----------------------------------------------------------------------- -# -subgraphs_of_G_ex, removed_edges = graph_partitioning(G_ex, plotting=True) - -############################################################################### -# Plot the results: every subgraph in the list. -# --------------------------------------------- -# -for subgraph in subgraphs_of_G_ex: - _pos = nx.spring_layout(subgraph) - plt.figure(figsize=(8, 8)) - nx.draw_networkx_edges(subgraph, _pos, alpha=0.3, edge_color="k") - node_color_list_c = [nc for _, nc in subgraph.nodes(data="node_color")] - nx.draw_networkx_nodes(subgraph, _pos, node_color=node_color_list_c) - nx.draw_networkx_labels(subgraph, _pos, font_size=14) - plt.axis("off") - plt.title("One of the subgraphs.") - plt.show() - -############################################################################### -# Put the graph back from the list of subgraphs -# --------------------------------------------- -# -G_ex_r = nx.DiGraph() -# Composing all subgraphs. -for subgraph in subgraphs_of_G_ex: - G_ex_r = nx.compose(G_ex_r, subgraph) -# Adding the previously stored edges. -G_ex_r.add_edges_from(removed_edges.edges()) - -############################################################################### -# Check that the original graph and the reconstructed graphs are isomorphic. -# -------------------------------------------------------------------------- -# -assert nx.is_isomorphic(G_ex, G_ex_r) - -############################################################################### -# Plot the reconstructed graph. -# ----------------------------- -# -node_color_list = [nc for _, nc in G_ex_r.nodes(data="node_color")] -pos = nx.spectral_layout(G_ex_r) -plt.figure(figsize=(8, 8)) -nx.draw_networkx_edges(G_ex_r, pos, alpha=0.3, edge_color="k") -nx.draw_networkx_nodes(G_ex_r, pos, alpha=0.8, node_color=node_color_list) -nx.draw_networkx_labels(G_ex_r, pos, font_size=14) -plt.axis("off") -plt.title("The reconstructed graph.") -plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/basic/README.txt b/.easyocr_venv/share/doc/networkx-2.8.5/examples/basic/README.txt deleted file mode 100644 index c1cc18b1f..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/basic/README.txt +++ /dev/null @@ -1,2 +0,0 @@ -Basic ------ diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/basic/plot_properties.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/basic/plot_properties.py deleted file mode 100644 index 7eb25f334..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/basic/plot_properties.py +++ /dev/null @@ -1,49 +0,0 @@ -""" -========== -Properties -========== - -Compute some network properties for the lollipop graph. -""" - -import matplotlib.pyplot as plt -import networkx as nx - -G = nx.lollipop_graph(4, 6) - -pathlengths = [] - -print("source vertex {target:length, }") -for v in G.nodes(): - spl = dict(nx.single_source_shortest_path_length(G, v)) - print(f"{v} {spl} ") - for p in spl: - pathlengths.append(spl[p]) - -print() -print(f"average shortest path length {sum(pathlengths) / len(pathlengths)}") - -# histogram of path lengths -dist = {} -for p in pathlengths: - if p in dist: - dist[p] += 1 - else: - dist[p] = 1 - -print() -print("length #paths") -verts = dist.keys() -for d in sorted(verts): - print(f"{d} {dist[d]}") - -print(f"radius: {nx.radius(G)}") -print(f"diameter: {nx.diameter(G)}") -print(f"eccentricity: {nx.eccentricity(G)}") -print(f"center: {nx.center(G)}") -print(f"periphery: {nx.periphery(G)}") -print(f"density: {nx.density(G)}") - -pos = nx.spring_layout(G, seed=3068) # Seed layout for reproducibility -nx.draw(G, pos=pos, with_labels=True) -plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/basic/plot_read_write.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/basic/plot_read_write.py deleted file mode 100644 index c9fa89ebc..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/basic/plot_read_write.py +++ /dev/null @@ -1,24 +0,0 @@ -""" -====================== -Read and write graphs. -====================== - -Read and write graphs. -""" - -import matplotlib.pyplot as plt -import networkx as nx - -G = nx.grid_2d_graph(5, 5) # 5x5 grid - -# print the adjacency list -for line in nx.generate_adjlist(G): - print(line) -# write edgelist to grid.edgelist -nx.write_edgelist(G, path="grid.edgelist", delimiter=":") -# read edgelist from grid.edgelist -H = nx.read_edgelist(path="grid.edgelist", delimiter=":") - -pos = nx.spring_layout(H, seed=200) -nx.draw(H, pos) -plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/basic/plot_simple_graph.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/basic/plot_simple_graph.py deleted file mode 100644 index fcd7caf6b..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/basic/plot_simple_graph.py +++ /dev/null @@ -1,60 +0,0 @@ -""" -============ -Simple graph -============ - -Draw simple graph with manual layout. -""" - -import networkx as nx -import matplotlib.pyplot as plt - -G = nx.Graph() -G.add_edge(1, 2) -G.add_edge(1, 3) -G.add_edge(1, 5) -G.add_edge(2, 3) -G.add_edge(3, 4) -G.add_edge(4, 5) - -# explicitly set positions -pos = {1: (0, 0), 2: (-1, 0.3), 3: (2, 0.17), 4: (4, 0.255), 5: (5, 0.03)} - -options = { - "font_size": 36, - "node_size": 3000, - "node_color": "white", - "edgecolors": "black", - "linewidths": 5, - "width": 5, -} -nx.draw_networkx(G, pos, **options) - -# Set margins for the axes so that nodes aren't clipped -ax = plt.gca() -ax.margins(0.20) -plt.axis("off") -plt.show() - -# %% -# A directed graph - -G = nx.DiGraph([(0, 3), (1, 3), (2, 4), (3, 5), (3, 6), (4, 6), (5, 6)]) - -# group nodes by column -left_nodes = [0, 1, 2] -middle_nodes = [3, 4] -right_nodes = [5, 6] - -# set the position according to column (x-coord) -pos = {n: (0, i) for i, n in enumerate(left_nodes)} -pos.update({n: (1, i + 0.5) for i, n in enumerate(middle_nodes)}) -pos.update({n: (2, i + 0.5) for i, n in enumerate(right_nodes)}) - -nx.draw_networkx(G, pos, **options) - -# Set margins for the axes so that nodes aren't clipped -ax = plt.gca() -ax.margins(0.20) -plt.axis("off") -plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/README.txt b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/README.txt deleted file mode 100644 index c25993dec..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/README.txt +++ /dev/null @@ -1,2 +0,0 @@ -Drawing -------- diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/chess_masters_WCC.pgn.bz2 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Here the (compressed) -default file:: - - chess_masters_WCC.pgn.bz2 - -contains all 685 World Chess Championship matches from 1886--1985. -(data from http://chessproblem.my-free-games.com/chess/games/Download-PGN.php) - -The `chess_pgn_graph()` function returns a `MultiDiGraph` with multiple edges. -Each node is the last name of a chess master. Each edge is directed from white -to black and contains selected game info. - -The key statement in `chess_pgn_graph` below is:: - - G.add_edge(white, black, game_info) - -where `game_info` is a `dict` describing each game. -""" - -import matplotlib.pyplot as plt -import networkx as nx - -# tag names specifying what game info should be -# stored in the dict on each digraph edge -game_details = ["Event", "Date", "Result", "ECO", "Site"] - - -def chess_pgn_graph(pgn_file="chess_masters_WCC.pgn.bz2"): - """Read chess games in pgn format in pgn_file. - - Filenames ending in .bz2 will be uncompressed. - - Return the MultiDiGraph of players connected by a chess game. - Edges contain game data in a dict. - - """ - import bz2 - - G = nx.MultiDiGraph() - game = {} - with bz2.BZ2File(pgn_file) as datafile: - lines = [line.decode().rstrip("\r\n") for line in datafile] - for line in lines: - if line.startswith("["): - tag, value = line[1:-1].split(" ", 1) - game[str(tag)] = value.strip('"') - else: - # empty line after tag set indicates - # we finished reading game info - if game: - white = game.pop("White") - black = game.pop("Black") - G.add_edge(white, black, **game) - game = {} - return G - - -G = chess_pgn_graph() - -print( - f"Loaded {G.number_of_edges()} chess games between {G.number_of_nodes()} players\n" -) - -# identify connected components of the undirected version -H = G.to_undirected() -Gcc = [H.subgraph(c) for c in nx.connected_components(H)] -if len(Gcc) > 1: - print(f"Note the disconnected component consisting of:\n{Gcc[1].nodes()}") - -# find all games with B97 opening (as described in ECO) -openings = {game_info["ECO"] for (white, black, game_info) in G.edges(data=True)} -print(f"\nFrom a total of {len(openings)} different openings,") -print("the following games used the Sicilian opening") -print('with the Najdorff 7...Qb6 "Poisoned Pawn" variation.\n') - -for (white, black, game_info) in G.edges(data=True): - if game_info["ECO"] == "B97": - summary = f"{white} vs {black}\n" - for k, v in game_info.items(): - summary += f" {k}: {v}\n" - summary += "\n" - print(summary) - -# make new undirected graph H without multi-edges -H = nx.Graph(G) - -# edge width is proportional number of games played -edgewidth = [len(G.get_edge_data(u, v)) for u, v in H.edges()] - -# node size is proportional to number of games won -wins = dict.fromkeys(G.nodes(), 0.0) -for (u, v, d) in G.edges(data=True): - r = d["Result"].split("-") - if r[0] == "1": - wins[u] += 1.0 - elif r[0] == "1/2": - wins[u] += 0.5 - wins[v] += 0.5 - else: - wins[v] += 1.0 -nodesize = [wins[v] * 50 for v in H] - -# Generate layout for visualization -pos = nx.kamada_kawai_layout(H) -# Manual tweaking to limit node label overlap in the visualization -pos["Reshevsky, Samuel H"] += (0.05, -0.10) -pos["Botvinnik, Mikhail M"] += (0.03, -0.06) -pos["Smyslov, Vassily V"] += (0.05, -0.03) - -fig, ax = plt.subplots(figsize=(12, 12)) -# Visualize graph components -nx.draw_networkx_edges(H, pos, alpha=0.3, width=edgewidth, edge_color="m") -nx.draw_networkx_nodes(H, pos, node_size=nodesize, node_color="#210070", alpha=0.9) -label_options = {"ec": "k", "fc": "white", "alpha": 0.7} -nx.draw_networkx_labels(H, pos, font_size=14, bbox=label_options) - -# Title/legend -font = {"fontname": "Helvetica", "color": "k", "fontweight": "bold", "fontsize": 14} -ax.set_title("World Chess Championship Games: 1886 - 1985", font) -# Change font color for legend -font["color"] = "r" - -ax.text( - 0.80, - 0.10, - "edge width = # games played", - horizontalalignment="center", - transform=ax.transAxes, - fontdict=font, -) -ax.text( - 0.80, - 0.06, - "node size = # games won", - horizontalalignment="center", - transform=ax.transAxes, - fontdict=font, -) - -# Resize figure for label readibility -ax.margins(0.1, 0.05) -fig.tight_layout() -plt.axis("off") -plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_custom_node_icons.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_custom_node_icons.py deleted file mode 100644 index c13458f88..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_custom_node_icons.py +++ /dev/null @@ -1,75 +0,0 @@ -""" -================= -Custom node icons -================= - -Example of using custom icons to represent nodes with matplotlib. - -Images for node icons courtesy of www.materialui.co -""" - -import matplotlib.pyplot as plt -import networkx as nx -import PIL - -# Image URLs for graph nodes -icons = { - "router": "icons/router_black_144x144.png", - "switch": "icons/switch_black_144x144.png", - "PC": "icons/computer_black_144x144.png", -} - -# Load images -images = {k: PIL.Image.open(fname) for k, fname in icons.items()} - -# Generate the computer network graph -G = nx.Graph() - -G.add_node("router", image=images["router"]) -for i in range(1, 4): - G.add_node(f"switch_{i}", image=images["switch"]) - for j in range(1, 4): - G.add_node("PC_" + str(i) + "_" + str(j), image=images["PC"]) - -G.add_edge("router", "switch_1") -G.add_edge("router", "switch_2") -G.add_edge("router", "switch_3") -for u in range(1, 4): - for v in range(1, 4): - G.add_edge("switch_" + str(u), "PC_" + str(u) + "_" + str(v)) - -# Get a reproducible layout and create figure -pos = nx.spring_layout(G, seed=1734289230) -fig, ax = plt.subplots() - -# Note: the min_source/target_margin kwargs only work with FancyArrowPatch objects. -# Force the use of FancyArrowPatch for edge drawing by setting `arrows=True`, -# but suppress arrowheads with `arrowstyle="-"` -nx.draw_networkx_edges( - G, - pos=pos, - ax=ax, - arrows=True, - arrowstyle="-", - min_source_margin=15, - min_target_margin=15, -) - -# Transform from data coordinates (scaled between xlim and ylim) to display coordinates -tr_figure = ax.transData.transform -# Transform from display to figure coordinates -tr_axes = fig.transFigure.inverted().transform - -# Select the size of the image (relative to the X axis) -icon_size = (ax.get_xlim()[1] - ax.get_xlim()[0]) * 0.025 -icon_center = icon_size / 2.0 - -# Add the respective image to each node -for n in G.nodes: - xf, yf = tr_figure(pos[n]) - xa, ya = tr_axes((xf, yf)) - # get overlapped axes and plot icon - a = plt.axes([xa - icon_center, ya - icon_center, icon_size, icon_size]) - a.imshow(G.nodes[n]["image"]) - a.axis("off") -plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_degree.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_degree.py deleted file mode 100644 index 9d7abe4ae..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_degree.py +++ /dev/null @@ -1,50 +0,0 @@ -""" -=============== -Degree Analysis -=============== - -This example shows several ways to visualize the distribution of the degree of -nodes with two common techniques: a *degree-rank plot* and a -*degree histogram*. - -In this example, a random Graph is generated with 100 nodes. The degree of -each node is determined, and a figure is generated showing three things: -1. The subgraph of connected components -2. The degree-rank plot for the Graph, and -3. The degree histogram -""" -import networkx as nx -import numpy as np -import matplotlib.pyplot as plt - -G = nx.gnp_random_graph(100, 0.02, seed=10374196) - -degree_sequence = sorted((d for n, d in G.degree()), reverse=True) -dmax = max(degree_sequence) - -fig = plt.figure("Degree of a random graph", figsize=(8, 8)) -# Create a gridspec for adding subplots of different sizes -axgrid = fig.add_gridspec(5, 4) - -ax0 = fig.add_subplot(axgrid[0:3, :]) -Gcc = G.subgraph(sorted(nx.connected_components(G), key=len, reverse=True)[0]) -pos = nx.spring_layout(Gcc, seed=10396953) -nx.draw_networkx_nodes(Gcc, pos, ax=ax0, node_size=20) -nx.draw_networkx_edges(Gcc, pos, ax=ax0, alpha=0.4) -ax0.set_title("Connected components of G") -ax0.set_axis_off() - -ax1 = fig.add_subplot(axgrid[3:, :2]) -ax1.plot(degree_sequence, "b-", marker="o") -ax1.set_title("Degree Rank Plot") -ax1.set_ylabel("Degree") -ax1.set_xlabel("Rank") - -ax2 = fig.add_subplot(axgrid[3:, 2:]) -ax2.bar(*np.unique(degree_sequence, return_counts=True)) -ax2.set_title("Degree histogram") -ax2.set_xlabel("Degree") -ax2.set_ylabel("# of Nodes") - -fig.tight_layout() -plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_directed.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_directed.py deleted file mode 100644 index 8af58d3f4..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_directed.py +++ /dev/null @@ -1,46 +0,0 @@ -""" -============== -Directed Graph -============== - -Draw a graph with directed edges using a colormap and different node sizes. - -Edges have different colors and alphas (opacity). Drawn using matplotlib. -""" - -import matplotlib as mpl -import matplotlib.pyplot as plt -import networkx as nx - -seed = 13648 # Seed random number generators for reproducibility -G = nx.random_k_out_graph(10, 3, 0.5, seed=seed) -pos = nx.spring_layout(G, seed=seed) - -node_sizes = [3 + 10 * i for i in range(len(G))] -M = G.number_of_edges() -edge_colors = range(2, M + 2) -edge_alphas = [(5 + i) / (M + 4) for i in range(M)] -cmap = plt.cm.plasma - -nodes = nx.draw_networkx_nodes(G, pos, node_size=node_sizes, node_color="indigo") -edges = nx.draw_networkx_edges( - G, - pos, - node_size=node_sizes, - arrowstyle="->", - arrowsize=10, - edge_color=edge_colors, - edge_cmap=cmap, - width=2, -) -# set alpha value for each edge -for i in range(M): - edges[i].set_alpha(edge_alphas[i]) - -pc = mpl.collections.PatchCollection(edges, cmap=cmap) -pc.set_array(edge_colors) -plt.colorbar(pc) - -ax = plt.gca() -ax.set_axis_off() -plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_edge_colormap.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_edge_colormap.py deleted file mode 100644 index e0d569b4a..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_edge_colormap.py +++ /dev/null @@ -1,23 +0,0 @@ -""" -============= -Edge Colormap -============= - -Draw a graph with matplotlib, color edges. -""" - -import matplotlib.pyplot as plt -import networkx as nx - -G = nx.star_graph(20) -pos = nx.spring_layout(G, seed=63) # Seed layout for reproducibility -colors = range(20) -options = { - "node_color": "#A0CBE2", - "edge_color": colors, - "width": 4, - "edge_cmap": plt.cm.Blues, - "with_labels": False, -} -nx.draw(G, pos, **options) -plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_ego_graph.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_ego_graph.py deleted file mode 100644 index fde57406c..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_ego_graph.py +++ /dev/null @@ -1,35 +0,0 @@ -""" -========= -Ego Graph -========= - -Example using the NetworkX ego_graph() function to return the main egonet of -the largest hub in a Barabási-Albert network. -""" - -from operator import itemgetter - -import matplotlib.pyplot as plt -import networkx as nx - -# Create a BA model graph - use seed for reproducibility -n = 1000 -m = 2 -seed = 20532 -G = nx.barabasi_albert_graph(n, m, seed=seed) - -# find node with largest degree -node_and_degree = G.degree() -(largest_hub, degree) = sorted(node_and_degree, key=itemgetter(1))[-1] - -# Create ego graph of main hub -hub_ego = nx.ego_graph(G, largest_hub) - -# Draw graph -pos = nx.spring_layout(hub_ego, seed=seed) # Seed layout for reproducibility -nx.draw(hub_ego, pos, node_color="b", node_size=50, with_labels=False) - -# Draw ego as large and red -options = {"node_size": 300, "node_color": "r"} -nx.draw_networkx_nodes(hub_ego, pos, nodelist=[largest_hub], **options) -plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_eigenvalues.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_eigenvalues.py deleted file mode 100644 index b0df67ae9..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_eigenvalues.py +++ /dev/null @@ -1,22 +0,0 @@ -""" -=========== -Eigenvalues -=========== - -Create an G{n,m} random graph and compute the eigenvalues. -""" -import matplotlib.pyplot as plt -import networkx as nx -import numpy.linalg - -n = 1000 # 1000 nodes -m = 5000 # 5000 edges -G = nx.gnm_random_graph(n, m, seed=5040) # Seed for reproducibility - -L = nx.normalized_laplacian_matrix(G) -e = numpy.linalg.eigvals(L.A) -print("Largest eigenvalue:", max(e)) -print("Smallest eigenvalue:", min(e)) -plt.hist(e, bins=100) # histogram with 100 bins -plt.xlim(0, 2) # eigenvalues between 0 and 2 -plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_four_grids.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_four_grids.py deleted file mode 100644 index 17af4e544..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_four_grids.py +++ /dev/null @@ -1,52 +0,0 @@ -""" -========== -Four Grids -========== - -Draw a 4x4 graph with matplotlib. - -This example illustrates the use of keyword arguments to `networkx.draw` to -customize the visualization of a simple Graph comprising a 4x4 grid. -""" - -import matplotlib.pyplot as plt -import networkx as nx - -G = nx.grid_2d_graph(4, 4) # 4x4 grid - -pos = nx.spring_layout(G, iterations=100, seed=39775) - -# Create a 2x2 subplot -fig, all_axes = plt.subplots(2, 2) -ax = all_axes.flat - -nx.draw(G, pos, ax=ax[0], font_size=8) -nx.draw(G, pos, ax=ax[1], node_size=0, with_labels=False) -nx.draw( - G, - pos, - ax=ax[2], - node_color="tab:green", - edgecolors="tab:gray", # Node surface color - edge_color="tab:gray", # Color of graph edges - node_size=250, - with_labels=False, - width=6, -) -H = G.to_directed() -nx.draw( - H, - pos, - ax=ax[3], - node_color="tab:orange", - node_size=20, - with_labels=False, - arrowsize=10, - width=2, -) - -# Set margins for the axes so that nodes aren't clipped -for a in ax: - a.margins(0.10) -fig.tight_layout() -plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_house_with_colors.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_house_with_colors.py deleted file mode 100644 index c3a2809dd..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_house_with_colors.py +++ /dev/null @@ -1,26 +0,0 @@ -""" -================= -House With Colors -================= - -Draw a graph with matplotlib. -""" -import matplotlib.pyplot as plt -import networkx as nx - -G = nx.house_graph() -# explicitly set positions -pos = {0: (0, 0), 1: (1, 0), 2: (0, 1), 3: (1, 1), 4: (0.5, 2.0)} - -# Plot nodes with different properties for the "wall" and "roof" nodes -nx.draw_networkx_nodes( - G, pos, node_size=3000, nodelist=[0, 1, 2, 3], node_color="tab:blue" -) -nx.draw_networkx_nodes(G, pos, node_size=2000, nodelist=[4], node_color="tab:orange") -nx.draw_networkx_edges(G, pos, alpha=0.5, width=6) -# Customize axes -ax = plt.gca() -ax.margins(0.11) -plt.tight_layout() -plt.axis("off") -plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_knuth_miles.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_knuth_miles.py deleted file mode 100644 index e0ebea874..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_knuth_miles.py +++ /dev/null @@ -1,142 +0,0 @@ -""" -=========== -Knuth Miles -=========== - -`miles_graph()` returns an undirected graph over 128 US cities. The -cities each have location and population data. The edges are labeled with the -distance between the two cities. - -This example is described in Section 1.1 of - - Donald E. Knuth, "The Stanford GraphBase: A Platform for Combinatorial - Computing", ACM Press, New York, 1993. - http://www-cs-faculty.stanford.edu/~knuth/sgb.html - -The data file can be found at: - -- https://github.com/networkx/networkx/blob/main/examples/drawing/knuth_miles.txt.gz -""" - -import gzip -import re - -# Ignore any warnings related to downloading shpfiles with cartopy -import warnings - -warnings.simplefilter("ignore") - -import numpy as np -import matplotlib.pyplot as plt -import networkx as nx - - -def miles_graph(): - """Return the cites example graph in miles_dat.txt - from the Stanford GraphBase. - """ - # open file miles_dat.txt.gz (or miles_dat.txt) - - fh = gzip.open("knuth_miles.txt.gz", "r") - - G = nx.Graph() - G.position = {} - G.population = {} - - cities = [] - for line in fh.readlines(): - line = line.decode() - if line.startswith("*"): # skip comments - continue - - numfind = re.compile(r"^\d+") - - if numfind.match(line): # this line is distances - dist = line.split() - for d in dist: - G.add_edge(city, cities[i], weight=int(d)) - i = i + 1 - else: # this line is a city, position, population - i = 1 - (city, coordpop) = line.split("[") - cities.insert(0, city) - (coord, pop) = coordpop.split("]") - (y, x) = coord.split(",") - - G.add_node(city) - # assign position - Convert string to lat/long - G.position[city] = (-float(x) / 100, float(y) / 100) - G.population[city] = float(pop) / 1000 - return G - - -G = miles_graph() - -print("Loaded miles_dat.txt containing 128 cities.") -print(G) - -# make new graph of cites, edge if less then 300 miles between them -H = nx.Graph() -for v in G: - H.add_node(v) -for (u, v, d) in G.edges(data=True): - if d["weight"] < 300: - H.add_edge(u, v) - -# draw with matplotlib/pylab -fig = plt.figure(figsize=(8, 6)) - -# nodes colored by degree sized by population -node_color = [float(H.degree(v)) for v in H] - -# Use cartopy to provide a backdrop for the visualization -try: - import cartopy.crs as ccrs - import cartopy.io.shapereader as shpreader - - ax = fig.add_axes([0, 0, 1, 1], projection=ccrs.LambertConformal(), frameon=False) - ax.set_extent([-125, -66.5, 20, 50], ccrs.Geodetic()) - # Add map of countries & US states as a backdrop - for shapename in ("admin_1_states_provinces_lakes_shp", "admin_0_countries"): - shp = shpreader.natural_earth( - resolution="110m", category="cultural", name=shapename - ) - ax.add_geometries( - shpreader.Reader(shp).geometries(), - ccrs.PlateCarree(), - facecolor="none", - edgecolor="k", - ) - # NOTE: When using cartopy, use matplotlib directly rather than nx.draw - # to take advantage of the cartopy transforms - ax.scatter( - *np.array([v for v in G.position.values()]).T, - s=[G.population[v] for v in H], - c=node_color, - transform=ccrs.PlateCarree(), - zorder=100 # Ensure nodes lie on top of edges/state lines - ) - # Plot edges between the cities - for edge in H.edges(): - edge_coords = np.array([G.position[v] for v in edge]) - ax.plot( - edge_coords[:, 0], - edge_coords[:, 1], - transform=ccrs.PlateCarree(), - linewidth=0.75, - color="k", - ) - -except ImportError: - # If cartopy is unavailable, the backdrop for the plot will be blank; - # though you should still be able to discern the general shape of the US - # from graph nodes and edges! - nx.draw( - H, - G.position, - node_size=[G.population[v] for v in H], - node_color=node_color, - with_labels=False, - ) - -plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_labels_and_colors.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_labels_and_colors.py deleted file mode 100644 index b7eddfd23..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_labels_and_colors.py +++ /dev/null @@ -1,54 +0,0 @@ -""" -================= -Labels And Colors -================= - -Use `nodelist` and `edgelist` to apply custom coloring and labels to various -components of a graph. -""" -import matplotlib.pyplot as plt -import networkx as nx - -G = nx.cubical_graph() -pos = nx.spring_layout(G, seed=3113794652) # positions for all nodes - -# nodes -options = {"edgecolors": "tab:gray", "node_size": 800, "alpha": 0.9} -nx.draw_networkx_nodes(G, pos, nodelist=[0, 1, 2, 3], node_color="tab:red", **options) -nx.draw_networkx_nodes(G, pos, nodelist=[4, 5, 6, 7], node_color="tab:blue", **options) - -# edges -nx.draw_networkx_edges(G, pos, width=1.0, alpha=0.5) -nx.draw_networkx_edges( - G, - pos, - edgelist=[(0, 1), (1, 2), (2, 3), (3, 0)], - width=8, - alpha=0.5, - edge_color="tab:red", -) -nx.draw_networkx_edges( - G, - pos, - edgelist=[(4, 5), (5, 6), (6, 7), (7, 4)], - width=8, - alpha=0.5, - edge_color="tab:blue", -) - - -# some math labels -labels = {} -labels[0] = r"$a$" -labels[1] = r"$b$" -labels[2] = r"$c$" -labels[3] = r"$d$" -labels[4] = r"$\alpha$" -labels[5] = r"$\beta$" -labels[6] = r"$\gamma$" -labels[7] = r"$\delta$" -nx.draw_networkx_labels(G, pos, labels, font_size=22, font_color="whitesmoke") - -plt.tight_layout() -plt.axis("off") -plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_multipartite_graph.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_multipartite_graph.py deleted file mode 100644 index 15c4d82cf..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_multipartite_graph.py +++ /dev/null @@ -1,41 +0,0 @@ -""" -=================== -Multipartite Layout -=================== -""" - -import itertools -import matplotlib.pyplot as plt -import networkx as nx - -subset_sizes = [5, 5, 4, 3, 2, 4, 4, 3] -subset_color = [ - "gold", - "violet", - "violet", - "violet", - "violet", - "limegreen", - "limegreen", - "darkorange", -] - - -def multilayered_graph(*subset_sizes): - extents = nx.utils.pairwise(itertools.accumulate((0,) + subset_sizes)) - layers = [range(start, end) for start, end in extents] - G = nx.Graph() - for (i, layer) in enumerate(layers): - G.add_nodes_from(layer, layer=i) - for layer1, layer2 in nx.utils.pairwise(layers): - G.add_edges_from(itertools.product(layer1, layer2)) - return G - - -G = multilayered_graph(*subset_sizes) -color = [subset_color[data["layer"]] for v, data in G.nodes(data=True)] -pos = nx.multipartite_layout(G, subset_key="layer") -plt.figure(figsize=(8, 8)) -nx.draw(G, pos, node_color=color, with_labels=False) -plt.axis("equal") -plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_node_colormap.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_node_colormap.py deleted file mode 100644 index ea31a4631..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_node_colormap.py +++ /dev/null @@ -1,15 +0,0 @@ -""" -============= -Node Colormap -============= - -Draw a graph with matplotlib, color by degree. -""" - -import matplotlib.pyplot as plt -import networkx as nx - -G = nx.cycle_graph(24) -pos = nx.circular_layout(G) -nx.draw(G, pos, node_color=range(24), node_size=800, cmap=plt.cm.Blues) -plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_rainbow_coloring.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_rainbow_coloring.py deleted file mode 100644 index 1d9e6075c..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_rainbow_coloring.py +++ /dev/null @@ -1,68 +0,0 @@ -""" -================ -Rainbow Coloring -================ - -Generate a complete graph with 13 nodes in a circular layout with the -edges colored by node distance. The node distance is given by the minimum -number of nodes traversed along an arc between any two nodes on the circle. - -Such graphs are the subject of Ringel's conjecture, which states: any complete -graph with ``2n + 1`` nodes can be tiled by any tree with ``n + 1`` nodes -(i.e. copies of the tree can be placed over the complete graph such that each -edge in the complete graph is covered exactly once). The edge coloring is -helpful in determining how to place the tree copies. - -References ----------- -https://www.quantamagazine.org/mathematicians-prove-ringels-graph-theory-conjecture-20200219/ -""" -import matplotlib.pyplot as plt -import networkx as nx - -# A rainbow color mapping using matplotlib's tableau colors -node_dist_to_color = { - 1: "tab:red", - 2: "tab:orange", - 3: "tab:olive", - 4: "tab:green", - 5: "tab:blue", - 6: "tab:purple", -} - -# Create a complete graph with an odd number of nodes -nnodes = 13 -G = nx.complete_graph(nnodes) - -# A graph with (2n + 1) nodes requires n colors for the edges -n = (nnodes - 1) // 2 -ndist_iter = list(range(1, n + 1)) - -# Take advantage of circular symmetry in determining node distances -ndist_iter += ndist_iter[::-1] - - -def cycle(nlist, n): - return nlist[-n:] + nlist[:-n] - - -# Rotate nodes around the circle and assign colors for each edge based on -# node distance -nodes = list(G.nodes()) -for i, nd in enumerate(ndist_iter): - for u, v in zip(nodes, cycle(nodes, i + 1)): - G[u][v]["color"] = node_dist_to_color[nd] - -pos = nx.circular_layout(G) -# Create a figure with 1:1 aspect ratio to preserve the circle. -fig, ax = plt.subplots(figsize=(8, 8)) -node_opts = {"node_size": 500, "node_color": "w", "edgecolors": "k", "linewidths": 2.0} -nx.draw_networkx_nodes(G, pos, **node_opts) -nx.draw_networkx_labels(G, pos, font_size=14) -# Extract color from edge data -edge_colors = [edgedata["color"] for _, _, edgedata in G.edges(data=True)] -nx.draw_networkx_edges(G, pos, width=2.0, edge_color=edge_colors) - -ax.set_axis_off() -fig.tight_layout() -plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_random_geometric_graph.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_random_geometric_graph.py deleted file mode 100644 index 6b7c95d35..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_random_geometric_graph.py +++ /dev/null @@ -1,44 +0,0 @@ -""" -====================== -Random Geometric Graph -====================== - -Example -""" - -import matplotlib.pyplot as plt -import networkx as nx - -# Use seed when creating the graph for reproducibility -G = nx.random_geometric_graph(200, 0.125, seed=896803) -# position is stored as node attribute data for random_geometric_graph -pos = nx.get_node_attributes(G, "pos") - -# find node near center (0.5,0.5) -dmin = 1 -ncenter = 0 -for n in pos: - x, y = pos[n] - d = (x - 0.5) ** 2 + (y - 0.5) ** 2 - if d < dmin: - ncenter = n - dmin = d - -# color by path length from node near center -p = dict(nx.single_source_shortest_path_length(G, ncenter)) - -plt.figure(figsize=(8, 8)) -nx.draw_networkx_edges(G, pos, alpha=0.4) -nx.draw_networkx_nodes( - G, - pos, - nodelist=list(p.keys()), - node_size=80, - node_color=list(p.values()), - cmap=plt.cm.Reds_r, -) - -plt.xlim(-0.05, 1.05) -plt.ylim(-0.05, 1.05) -plt.axis("off") -plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_sampson.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_sampson.py deleted file mode 100644 index d6eff642d..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_sampson.py +++ /dev/null @@ -1,47 +0,0 @@ -""" -======= -Sampson -======= - -Sampson's monastery data. - -Shows how to read data from a zip file and plot multiple frames. - -The data file can be found at: - -- https://github.com/networkx/networkx/blob/main/examples/drawing/sampson_data.zip -""" - -import zipfile -from io import BytesIO as StringIO - -import matplotlib.pyplot as plt -import networkx as nx - -with zipfile.ZipFile("sampson_data.zip") as zf: - e1 = StringIO(zf.read("samplike1.txt")) - e2 = StringIO(zf.read("samplike2.txt")) - e3 = StringIO(zf.read("samplike3.txt")) - -G1 = nx.read_edgelist(e1, delimiter="\t") -G2 = nx.read_edgelist(e2, delimiter="\t") -G3 = nx.read_edgelist(e3, delimiter="\t") -pos = nx.spring_layout(G3, iterations=100, seed=173) -plt.clf() - -plt.subplot(221) -plt.title("samplike1") -nx.draw(G1, pos, node_size=50, with_labels=False) -plt.subplot(222) -plt.title("samplike2") -nx.draw(G2, pos, node_size=50, with_labels=False) -plt.subplot(223) -plt.title("samplike3") -nx.draw(G3, pos, node_size=50, with_labels=False) -plt.subplot(224) -plt.title("samplike1,2,3") -nx.draw(G3, pos, edgelist=list(G3.edges()), node_size=50, with_labels=False) -nx.draw_networkx_edges(G1, pos, alpha=0.25) -nx.draw_networkx_edges(G2, pos, alpha=0.25) -plt.tight_layout() -plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_selfloops.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_selfloops.py deleted file mode 100644 index 11bcf50fb..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_selfloops.py +++ /dev/null @@ -1,29 +0,0 @@ -""" -========== -Self-loops -========== - -A self-loop is an edge that originates from and terminates the same node. -This example shows how to draw self-loops with `nx_pylab`. - -""" -import networkx as nx -import matplotlib.pyplot as plt - -# Create a graph and add a self-loop to node 0 -G = nx.complete_graph(3, create_using=nx.DiGraph) -G.add_edge(0, 0) -pos = nx.circular_layout(G) - -# As of version 2.6, self-loops are drawn by default with the same styling as -# other edges -nx.draw(G, pos, with_labels=True) - -# Add self-loops to the remaining nodes -edgelist = [(1, 1), (2, 2)] -G.add_edges_from(edgelist) - -# Draw the newly added self-loops with different formatting -nx.draw_networkx_edges(G, pos, edgelist=edgelist, arrowstyle="<|-", style="dashed") - -plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_simple_path.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_simple_path.py deleted file mode 100644 index ec5218570..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_simple_path.py +++ /dev/null @@ -1,14 +0,0 @@ -""" -=========== -Simple Path -=========== - -Draw a graph with matplotlib. -""" -import matplotlib.pyplot as plt -import networkx as nx - -G = nx.path_graph(8) -pos = nx.spring_layout(G, seed=47) # Seed layout for reproducibility -nx.draw(G, pos=pos) -plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_spectral_grid.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_spectral_grid.py deleted file mode 100644 index 80ef6f1fc..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_spectral_grid.py +++ /dev/null @@ -1,58 +0,0 @@ -""" -================== -Spectral Embedding -================== - -The spectral layout positions the nodes of the graph based on the -eigenvectors of the graph Laplacian $L = D - A$, where $A$ is the -adjacency matrix and $D$ is the degree matrix of the graph. -By default, the spectral layout will embed the graph in two -dimensions (you can embed your graph in other dimensions using the -``dim`` argument to either :func:`~drawing.nx_pylab.draw_spectral` or -:func:`~drawing.layout.spectral_layout`). - -When the edges of the graph represent similarity between the incident -nodes, the spectral embedding will place highly similar nodes closer -to one another than nodes which are less similar. - -This is particularly striking when you spectrally embed a grid -graph. In the full grid graph, the nodes in the center of the -graph are pulled apart more than nodes on the periphery. -As you remove internal nodes, this effect increases. -""" - -import matplotlib.pyplot as plt -import networkx as nx - - -options = {"node_color": "C0", "node_size": 100} - -G = nx.grid_2d_graph(6, 6) -plt.subplot(332) -nx.draw_spectral(G, **options) - -G.remove_edge((2, 2), (2, 3)) -plt.subplot(334) -nx.draw_spectral(G, **options) - -G.remove_edge((3, 2), (3, 3)) -plt.subplot(335) -nx.draw_spectral(G, **options) - -G.remove_edge((2, 2), (3, 2)) -plt.subplot(336) -nx.draw_spectral(G, **options) - -G.remove_edge((2, 3), (3, 3)) -plt.subplot(337) -nx.draw_spectral(G, **options) - -G.remove_edge((1, 2), (1, 3)) -plt.subplot(338) -nx.draw_spectral(G, **options) - -G.remove_edge((4, 2), (4, 3)) -plt.subplot(339) -nx.draw_spectral(G, **options) - -plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_tsp.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_tsp.py deleted file mode 100644 index 78c731b8d..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_tsp.py +++ /dev/null @@ -1,52 +0,0 @@ -""" -========================== -Traveling Salesman Problem -========================== - -This is an example of a drawing solution of the traveling salesman problem - -The function is used to produce the solution is christofides, -where given a set of nodes, it calculates the route of the nodes -that the traveler has to follow in order to minimize the total cost. -""" - -import matplotlib.pyplot as plt -import networkx as nx -import networkx.algorithms.approximation as nx_app -import math - -G = nx.random_geometric_graph(20, radius=0.4, seed=3) -pos = nx.get_node_attributes(G, "pos") - -# Depot should be at (0,0) -pos[0] = (0.5, 0.5) - -H = G.copy() - - -# Calculating the distances between the nodes as edge's weight. -for i in range(len(pos)): - for j in range(i + 1, len(pos)): - dist = math.hypot(pos[i][0] - pos[j][0], pos[i][1] - pos[j][1]) - dist = dist - G.add_edge(i, j, weight=dist) - -cycle = nx_app.christofides(G, weight="weight") -edge_list = list(nx.utils.pairwise(cycle)) - -# Draw closest edges on each node only -nx.draw_networkx_edges(H, pos, edge_color="blue", width=0.5) - -# Draw the route -nx.draw_networkx( - G, - pos, - with_labels=True, - edgelist=edge_list, - edge_color="red", - node_size=200, - width=3, -) - -print("The route of the traveller is:", cycle) -plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_unix_email.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_unix_email.py deleted file mode 100644 index 25fce7ae6..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_unix_email.py +++ /dev/null @@ -1,62 +0,0 @@ -""" -========== -Unix Email -========== - -Create a directed graph, allowing multiple edges and self loops, from a unix -mailbox. The nodes are email addresses with links that point from the sender -to the receivers. The edge data is a Python email.Message object which -contains all of the email message data. - -This example shows the power of `DiGraph` to hold edge data of arbitrary Python -objects (in this case a list of email messages). - - -The sample unix email mailbox called "unix_email.mbox" may be found here: - -- https://github.com/networkx/networkx/blob/main/examples/drawing/unix_email.mbox -""" - -from email.utils import getaddresses, parseaddr -import mailbox - -import matplotlib.pyplot as plt -import networkx as nx - -# unix mailbox recipe -# see https://docs.python.org/3/library/mailbox.html - - -def mbox_graph(): - mbox = mailbox.mbox("unix_email.mbox") # parse unix mailbox - - G = nx.MultiDiGraph() # create empty graph - - # parse each messages and build graph - for msg in mbox: # msg is python email.Message.Message object - (source_name, source_addr) = parseaddr(msg["From"]) # sender - # get all recipients - # see https://docs.python.org/3/library/email.html - tos = msg.get_all("to", []) - ccs = msg.get_all("cc", []) - resent_tos = msg.get_all("resent-to", []) - resent_ccs = msg.get_all("resent-cc", []) - all_recipients = getaddresses(tos + ccs + resent_tos + resent_ccs) - # now add the edges for this mail message - for (target_name, target_addr) in all_recipients: - G.add_edge(source_addr, target_addr, message=msg) - - return G - - -G = mbox_graph() - -# print edges with message subject -for (u, v, d) in G.edges(data=True): - print(f"From: {u} To: {v} Subject: {d['message']['Subject']}") - -pos = nx.spring_layout(G, iterations=10, seed=227) -nx.draw(G, pos, node_size=0, alpha=0.4, edge_color="r", font_size=16, with_labels=True) -ax = plt.gca() -ax.margins(0.08) -plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_weighted_graph.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_weighted_graph.py deleted file mode 100644 index ba7721c40..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/plot_weighted_graph.py +++ /dev/null @@ -1,44 +0,0 @@ -""" -============== -Weighted Graph -============== - -An example using Graph as a weighted network. -""" -import matplotlib.pyplot as plt -import networkx as nx - -G = nx.Graph() - -G.add_edge("a", "b", weight=0.6) -G.add_edge("a", "c", weight=0.2) -G.add_edge("c", "d", weight=0.1) -G.add_edge("c", "e", weight=0.7) -G.add_edge("c", "f", weight=0.9) -G.add_edge("a", "d", weight=0.3) - -elarge = [(u, v) for (u, v, d) in G.edges(data=True) if d["weight"] > 0.5] -esmall = [(u, v) for (u, v, d) in G.edges(data=True) if d["weight"] <= 0.5] - -pos = nx.spring_layout(G, seed=7) # positions for all nodes - seed for reproducibility - -# nodes -nx.draw_networkx_nodes(G, pos, node_size=700) - -# edges -nx.draw_networkx_edges(G, pos, edgelist=elarge, width=6) -nx.draw_networkx_edges( - G, pos, edgelist=esmall, width=6, alpha=0.5, edge_color="b", style="dashed" -) - -# node labels -nx.draw_networkx_labels(G, pos, font_size=20, font_family="sans-serif") -# edge weight labels -edge_labels = nx.get_edge_attributes(G, "weight") -nx.draw_networkx_edge_labels(G, pos, edge_labels) - -ax = plt.gca() -ax.margins(0.08) -plt.axis("off") -plt.tight_layout() -plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/unix_email.mbox b/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/unix_email.mbox deleted file mode 100644 index a3a7cf8dd..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/drawing/unix_email.mbox +++ /dev/null @@ -1,84 +0,0 @@ -From alice@edu Thu Jun 16 16:12:12 2005 -From: Alice -Subject: NetworkX -Date: Thu, 16 Jun 2005 16:12:13 -0700 -To: Bob -Status: RO -Content-Length: 86 -Lines: 5 - -Bob, check out the new networkx release - you and -Carol might really like it. - -Alice - - -From bob@gov Thu Jun 16 18:13:12 2005 -Return-Path: -Subject: Re: NetworkX -From: Bob -To: Alice -Content-Type: text/plain -Date: Thu, 16 Jun 2005 18:13:12 -0700 -Status: RO -Content-Length: 26 -Lines: 4 - -Thanks for the tip. - -Bob - - -From ted@com Thu Jul 28 09:53:31 2005 -Return-Path: -Subject: Graph package in Python? -From: Ted -To: Bob -Content-Type: text/plain -Date: Thu, 28 Jul 2005 09:47:03 -0700 -Status: RO -Content-Length: 90 -Lines: 3 - -Hey Ted - I'm looking for a Python package for -graphs and networks. Do you know of any? - - -From bob@gov Thu Jul 28 09:59:31 2005 -Return-Path: -Subject: Re: Graph package in Python? -From: Bob -To: Ted -Content-Type: text/plain -Date: Thu, 28 Jul 2005 09:59:03 -0700 -Status: RO -Content-Length: 180 -Lines: 9 - - -Check out the NetworkX package - Alice sent me the tip! - -Bob - ->> bob@gov scrawled: ->> Hey Ted - I'm looking for a Python package for ->> graphs and networks. Do you know of any? - - -From ted@com Thu Jul 28 15:53:31 2005 -Return-Path: -Subject: get together for lunch to discuss Networks? -From: Ted -To: Bob , Carol , Alice -Content-Type: text/plain -Date: Thu, 28 Jul 2005 15:47:03 -0700 -Status: RO -Content-Length: 139 -Lines: 5 - -Hey everyrone! Want to meet at that restaurant on the -island in Konigsburg tonight? Bring your laptops -and we can install NetworkX. - -Ted - diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/README.txt b/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/README.txt deleted file mode 100644 index 9b0e3b212..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/README.txt +++ /dev/null @@ -1,2 +0,0 @@ -Graph ------ diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/plot_degree_sequence.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/plot_degree_sequence.py deleted file mode 100644 index 0d32091c9..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/plot_degree_sequence.py +++ /dev/null @@ -1,34 +0,0 @@ -""" -=============== -Degree Sequence -=============== - -Random graph from given degree sequence. -""" -import matplotlib.pyplot as plt -import networkx as nx - -# Specify seed for reproducibility -seed = 668273 - -z = [5, 3, 3, 3, 3, 2, 2, 2, 1, 1, 1] -print(nx.is_graphical(z)) - -print("Configuration model") -G = nx.configuration_model(z, seed=seed) # configuration model, seed for reproduciblity -degree_sequence = [d for n, d in G.degree()] # degree sequence -print(f"Degree sequence {degree_sequence}") -print("Degree histogram") -hist = {} -for d in degree_sequence: - if d in hist: - hist[d] += 1 - else: - hist[d] = 1 -print("degree #nodes") -for d in hist: - print(f"{d:4} {hist[d]:6}") - -pos = nx.spring_layout(G, seed=seed) # Seed layout for reproducibility -nx.draw(G, pos=pos) -plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/plot_erdos_renyi.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/plot_erdos_renyi.py deleted file mode 100644 index e13267608..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/plot_erdos_renyi.py +++ /dev/null @@ -1,36 +0,0 @@ -""" -=========== -Erdos Renyi -=========== - -Create an G{n,m} random graph with n nodes and m edges -and report some properties. - -This graph is sometimes called the Erdős-Rényi graph -but is different from G{n,p} or binomial_graph which is also -sometimes called the Erdős-Rényi graph. -""" - -import matplotlib.pyplot as plt -import networkx as nx - -n = 10 # 10 nodes -m = 20 # 20 edges -seed = 20160 # seed random number generators for reproducibility - -# Use seed for reproducibility -G = nx.gnm_random_graph(n, m, seed=seed) - -# some properties -print("node degree clustering") -for v in nx.nodes(G): - print(f"{v} {nx.degree(G, v)} {nx.clustering(G, v)}") - -print() -print("the adjacency list") -for line in nx.generate_adjlist(G): - print(line) - -pos = nx.spring_layout(G, seed=seed) # Seed for reproducible layout -nx.draw(G, pos=pos) -plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/plot_expected_degree_sequence.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/plot_expected_degree_sequence.py deleted file mode 100644 index 1fc67990e..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/plot_expected_degree_sequence.py +++ /dev/null @@ -1,20 +0,0 @@ -""" -======================== -Expected Degree Sequence -======================== - -Random graph from given degree sequence. -""" - -import networkx as nx - -# make a random graph of 500 nodes with expected degrees of 50 -n = 500 # n nodes -p = 0.1 -w = [p * n for i in range(n)] # w = p*n for all nodes -G = nx.expected_degree_graph(w) # configuration model -print("Degree histogram") -print("degree (#nodes) ****") -dh = nx.degree_histogram(G) -for i, d in enumerate(dh): - print(f"{i:2} ({d:2}) {'*'*d}") diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/plot_football.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/plot_football.py deleted file mode 100644 index f6397f0e4..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/plot_football.py +++ /dev/null @@ -1,44 +0,0 @@ -""" -======== -Football -======== - -Load football network in GML format and compute some network statistcs. - -Shows how to download GML graph in a zipped file, unpack it, and load -into a NetworkX graph. - -Requires Internet connection to download the URL -http://www-personal.umich.edu/~mejn/netdata/football.zip -""" - -import urllib.request -import io -import zipfile - -import matplotlib.pyplot as plt -import networkx as nx - -url = "http://www-personal.umich.edu/~mejn/netdata/football.zip" - -sock = urllib.request.urlopen(url) # open URL -s = io.BytesIO(sock.read()) # read into BytesIO "file" -sock.close() - -zf = zipfile.ZipFile(s) # zipfile object -txt = zf.read("football.txt").decode() # read info file -gml = zf.read("football.gml").decode() # read gml data -# throw away bogus first line with # from mejn files -gml = gml.split("\n")[1:] -G = nx.parse_gml(gml) # parse gml data - -print(txt) -# print degree for each team - number of games -for n, d in G.degree(): - print(f"{n:20} {d:2}") - -options = {"node_color": "black", "node_size": 50, "linewidths": 0, "width": 0.1} - -pos = nx.spring_layout(G, seed=1969) # Seed for reproducible layout -nx.draw(G, pos, **options) -plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/plot_karate_club.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/plot_karate_club.py deleted file mode 100644 index c4fe5bc0d..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/plot_karate_club.py +++ /dev/null @@ -1,25 +0,0 @@ -""" -=========== -Karate Club -=========== - -Zachary's Karate Club graph - -Data file from: -http://vlado.fmf.uni-lj.si/pub/networks/data/Ucinet/UciData.htm - -Zachary W. (1977). -An information flow model for conflict and fission in small groups. -Journal of Anthropological Research, 33, 452-473. -""" - -import matplotlib.pyplot as plt -import networkx as nx - -G = nx.karate_club_graph() -print("Node Degree") -for v in G: - print(f"{v:4} {G.degree(v):6}") - -nx.draw_circular(G, with_labels=True) -plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/plot_morse_trie.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/plot_morse_trie.py deleted file mode 100644 index 88b424ce5..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/plot_morse_trie.py +++ /dev/null @@ -1,96 +0,0 @@ -""" -========== -Morse Trie -========== - -A prefix tree (aka a "trie") representing the Morse encoding of the alphabet. -A letter can be encoded by tracing the path from the corresponding node in the -tree to the root node, reversing the order of the symbols encountered along -the path. -""" -import networkx as nx - -# Unicode characters to represent the dots/dashes (or dits/dahs) of Morse code -dot = "•" -dash = "—" - -# Start with the direct mapping of letter -> code -morse_direct_mapping = { - "a": dot + dash, - "b": dash + dot * 3, - "c": dash + dot + dash + dot, - "d": dash + dot * 2, - "e": dot, - "f": dot * 2 + dash + dot, - "g": dash * 2 + dot, - "h": dot * 4, - "i": dot * 2, - "j": dot + dash * 3, - "k": dash + dot + dash, - "l": dot + dash + dot * 2, - "m": dash * 2, - "n": dash + dot, - "o": dash * 3, - "p": dot + dash * 2 + dot, - "q": dash * 2 + dot + dash, - "r": dot + dash + dot, - "s": dot * 3, - "t": dash, - "u": dot * 2 + dash, - "v": dot * 3 + dash, - "w": dot + dash * 2, - "x": dash + dot * 2 + dash, - "y": dash + dot + dash * 2, - "z": dash * 2 + dot * 2, -} - -### Manually construct the prefix tree from this mapping - -# Some preprocessing: sort the original mapping by code length and character -# value -morse_mapping_sorted = dict( - sorted(morse_direct_mapping.items(), key=lambda item: (len(item[1]), item[1])) -) - -# More preprocessing: create the reverse mapping to simplify lookup -reverse_mapping = {v: k for k, v in morse_direct_mapping.items()} -reverse_mapping[""] = "" # Represent the "root" node with an empty string - -# Construct the prefix tree from the sorted mapping -G = nx.DiGraph() -for node, char in morse_mapping_sorted.items(): - pred = char[:-1] - # Store the dot/dash relating the two letters as an edge attribute "char" - G.add_edge(reverse_mapping[pred], node, char=char[-1]) - -# For visualization purposes, layout the nodes in topological order -for i, layer in enumerate(nx.topological_generations(G)): - for n in layer: - G.nodes[n]["layer"] = i -pos = nx.multipartite_layout(G, subset_key="layer", align="horizontal") -# Flip the layout so the root node is on top -for k in pos: - pos[k][-1] *= -1 - -# Visualize the trie -nx.draw(G, pos=pos, with_labels=True) -elabels = {(u, v): l for u, v, l in G.edges(data="char")} -nx.draw_networkx_edge_labels(G, pos, edge_labels=elabels) - -# A letter can be encoded by following the path from the given letter (node) to -# the root node -def morse_encode(letter): - pred = next(G.predecessors(letter)) # Each letter has only 1 predecessor - symbol = G[pred][letter]["char"] - if pred != "": - return morse_encode(pred) + symbol # Traversing the trie in reverse - return symbol - - -# Verify that the trie encoding is correct -import string - -for letter in string.ascii_lowercase: - assert morse_encode(letter) == morse_direct_mapping[letter] - -print(" ".join([morse_encode(ltr) for ltr in "ilovenetworkx"])) diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/plot_napoleon_russian_campaign.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/plot_napoleon_russian_campaign.py deleted file mode 100644 index 46ef64d66..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/plot_napoleon_russian_campaign.py +++ /dev/null @@ -1,133 +0,0 @@ -""" -========================= -Napoleon Russian Campaign -========================= - -Minard's data from Napoleon's 1812-1813 Russian Campaign. -https://web.archive.org/web/20080112042656/http://www.math.yorku.ca/SCS/Gallery/minard/minard.txt -""" - -import matplotlib.pyplot as plt -import networkx as nx - - -def minard_graph(): - data1 = """\ -24.0,54.9,340000,A,1 -24.5,55.0,340000,A,1 -25.5,54.5,340000,A,1 -26.0,54.7,320000,A,1 -27.0,54.8,300000,A,1 -28.0,54.9,280000,A,1 -28.5,55.0,240000,A,1 -29.0,55.1,210000,A,1 -30.0,55.2,180000,A,1 -30.3,55.3,175000,A,1 -32.0,54.8,145000,A,1 -33.2,54.9,140000,A,1 -34.4,55.5,127100,A,1 -35.5,55.4,100000,A,1 -36.0,55.5,100000,A,1 -37.6,55.8,100000,A,1 -37.7,55.7,100000,R,1 -37.5,55.7,98000,R,1 -37.0,55.0,97000,R,1 -36.8,55.0,96000,R,1 -35.4,55.3,87000,R,1 -34.3,55.2,55000,R,1 -33.3,54.8,37000,R,1 -32.0,54.6,24000,R,1 -30.4,54.4,20000,R,1 -29.2,54.3,20000,R,1 -28.5,54.2,20000,R,1 -28.3,54.3,20000,R,1 -27.5,54.5,20000,R,1 -26.8,54.3,12000,R,1 -26.4,54.4,14000,R,1 -25.0,54.4,8000,R,1 -24.4,54.4,4000,R,1 -24.2,54.4,4000,R,1 -24.1,54.4,4000,R,1""" - data2 = """\ -24.0,55.1,60000,A,2 -24.5,55.2,60000,A,2 -25.5,54.7,60000,A,2 -26.6,55.7,40000,A,2 -27.4,55.6,33000,A,2 -28.7,55.5,33000,R,2 -29.2,54.2,30000,R,2 -28.5,54.1,30000,R,2 -28.3,54.2,28000,R,2""" - data3 = """\ -24.0,55.2,22000,A,3 -24.5,55.3,22000,A,3 -24.6,55.8,6000,A,3 -24.6,55.8,6000,R,3 -24.2,54.4,6000,R,3 -24.1,54.4,6000,R,3""" - cities = """\ -24.0,55.0,Kowno -25.3,54.7,Wilna -26.4,54.4,Smorgoni -26.8,54.3,Moiodexno -27.7,55.2,Gloubokoe -27.6,53.9,Minsk -28.5,54.3,Studienska -28.7,55.5,Polotzk -29.2,54.4,Bobr -30.2,55.3,Witebsk -30.4,54.5,Orscha -30.4,53.9,Mohilow -32.0,54.8,Smolensk -33.2,54.9,Dorogobouge -34.3,55.2,Wixma -34.4,55.5,Chjat -36.0,55.5,Mojaisk -37.6,55.8,Moscou -36.6,55.3,Tarantino -36.5,55.0,Malo-Jarosewii""" - - c = {} - for line in cities.split("\n"): - x, y, name = line.split(",") - c[name] = (float(x), float(y)) - - g = [] - - for data in [data1, data2, data3]: - G = nx.Graph() - i = 0 - G.pos = {} # location - G.pop = {} # size - last = None - for line in data.split("\n"): - x, y, p, r, n = line.split(",") - G.pos[i] = (float(x), float(y)) - G.pop[i] = int(p) - if last is None: - last = i - else: - G.add_edge(i, last, **{r: int(n)}) - last = i - i = i + 1 - g.append(G) - - return g, c - - -(g, city) = minard_graph() - -plt.figure(1, figsize=(11, 5)) -plt.clf() -colors = ["b", "g", "r"] -for G in g: - c = colors.pop(0) - node_size = [G.pop[n] // 300 for n in G] - nx.draw_networkx_edges(G, G.pos, edge_color=c, width=4, alpha=0.5) - nx.draw_networkx_nodes(G, G.pos, node_size=node_size, node_color=c, alpha=0.5) - nx.draw_networkx_nodes(G, G.pos, node_size=5, node_color="k") - -for c in city: - x, y = city[c] - plt.text(x, y + 0.1, c) -plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/plot_roget.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/plot_roget.py deleted file mode 100644 index 777c0174f..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/plot_roget.py +++ /dev/null @@ -1,80 +0,0 @@ -""" -===== -Roget -===== - -Build a directed graph of 1022 categories and 5075 cross-references as defined -in the 1879 version of Roget's Thesaurus. This example is described in Section -1.2 of - - Donald E. Knuth, "The Stanford GraphBase: A Platform for Combinatorial - Computing", ACM Press, New York, 1993. - http://www-cs-faculty.stanford.edu/~knuth/sgb.html - -Note that one of the 5075 cross references is a self loop yet it is included in -the graph built here because the standard networkx `DiGraph` class allows self -loops. (cf. 400pungency:400 401 403 405). - -The data file can be found at: - -- https://github.com/networkx/networkx/blob/main/examples/graph/roget_dat.txt.gz -""" - -import gzip -import re -import sys - -import matplotlib.pyplot as plt -import networkx as nx - - -def roget_graph(): - """Return the thesaurus graph from the roget.dat example in - the Stanford Graph Base. - """ - # open file roget_dat.txt.gz - fh = gzip.open("roget_dat.txt.gz", "r") - - G = nx.DiGraph() - - for line in fh.readlines(): - line = line.decode() - if line.startswith("*"): # skip comments - continue - if line.startswith(" "): # this is a continuation line, append - line = oldline + line - if line.endswith("\\\n"): # continuation line, buffer, goto next - oldline = line.strip("\\\n") - continue - - (headname, tails) = line.split(":") - - # head - numfind = re.compile(r"^\d+") # re to find the number of this word - head = numfind.findall(headname)[0] # get the number - - G.add_node(head) - - for tail in tails.split(): - if head == tail: - print("skipping self loop", head, tail, file=sys.stderr) - G.add_edge(head, tail) - - return G - - -G = roget_graph() -print("Loaded roget_dat.txt containing 1022 categories.") -print(G) -UG = G.to_undirected() -print(nx.number_connected_components(UG), "connected components") - -options = { - "node_color": "black", - "node_size": 1, - "edge_color": "gray", - "linewidths": 0, - "width": 0.1, -} -nx.draw_circular(UG, **options) -plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/plot_words.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/plot_words.py deleted file mode 100644 index e5211f594..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/graph/plot_words.py +++ /dev/null @@ -1,88 +0,0 @@ -""" -================== -Words/Ladder Graph -================== - -Generate an undirected graph over the 5757 5-letter words in the datafile -`words_dat.txt.gz`. Two words are connected by an edge if they differ in one -letter, resulting in 14,135 edges. This example is described in Section 1.1 of - - Donald E. Knuth, "The Stanford GraphBase: A Platform for Combinatorial - Computing", ACM Press, New York, 1993. - http://www-cs-faculty.stanford.edu/~knuth/sgb.html - -The data file can be found at: - -- https://github.com/networkx/networkx/blob/main/examples/graph/words_dat.txt.gz -""" - -import gzip -from string import ascii_lowercase as lowercase - -import matplotlib.pyplot as plt -import networkx as nx - - -def generate_graph(words): - G = nx.Graph(name="words") - lookup = {c: lowercase.index(c) for c in lowercase} - - def edit_distance_one(word): - for i in range(len(word)): - left, c, right = word[0:i], word[i], word[i + 1 :] - j = lookup[c] # lowercase.index(c) - for cc in lowercase[j + 1 :]: - yield left + cc + right - - candgen = ( - (word, cand) - for word in sorted(words) - for cand in edit_distance_one(word) - if cand in words - ) - G.add_nodes_from(words) - for word, cand in candgen: - G.add_edge(word, cand) - return G - - -def words_graph(): - """Return the words example graph from the Stanford GraphBase""" - fh = gzip.open("words_dat.txt.gz", "r") - words = set() - for line in fh.readlines(): - line = line.decode() - if line.startswith("*"): - continue - w = str(line[0:5]) - words.add(w) - return generate_graph(words) - - -G = words_graph() -print("Loaded words_dat.txt containing 5757 five-letter English words.") -print("Two words are connected if they differ in one letter.") -print(G) -print(f"{nx.number_connected_components(G)} connected components") - -for (source, target) in [("chaos", "order"), ("nodes", "graph"), ("pound", "marks")]: - print(f"Shortest path between {source} and {target} is") - try: - shortest_path = nx.shortest_path(G, source, target) - for n in shortest_path: - print(n) - except nx.NetworkXNoPath: - print("None") - - -# draw a subset of the graph -boundary = list(nx.node_boundary(G, shortest_path)) -G.add_nodes_from(shortest_path, color="red") -G.add_nodes_from(boundary, color="blue") -H = G.subgraph(shortest_path + boundary) -colors = 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zN-71YX>Z&cC2{a0m-1>UQF_;2YK|8K0C+ShoDGx}Tp6(^TqTP{JcP`M151;O;hbNU z0kW9JsTS;stM5jmT;Ub3%k}CZGAI&UVqIR0K%Vj}lc2$JcH|Gv&MYF}K=by-Pp#yZ^nr?fU!fZb$O&-vm|v TaVzJ4`}qF=34&oLgB1Y)QF@!O diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/subclass/README.txt b/.easyocr_venv/share/doc/networkx-2.8.5/examples/subclass/README.txt deleted file mode 100644 index e3650b1ba..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/subclass/README.txt +++ /dev/null @@ -1,2 +0,0 @@ -Subclass --------- diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/subclass/plot_antigraph.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/subclass/plot_antigraph.py deleted file mode 100644 index 154a321b3..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/subclass/plot_antigraph.py +++ /dev/null @@ -1,192 +0,0 @@ -""" -========= -Antigraph -========= - -Complement graph class for small footprint when working on dense graphs. - -This class allows you to add the edges that *do not exist* in the dense -graph. However, when applying algorithms to this complement graph data -structure, it behaves as if it were the dense version. So it can be used -directly in several NetworkX algorithms. - -This subclass has only been tested for k-core, connected_components, -and biconnected_components algorithms but might also work for other -algorithms. - -""" -import matplotlib.pyplot as plt -import networkx as nx -from networkx import Graph - - -class AntiGraph(Graph): - """ - Class for complement graphs. - - The main goal is to be able to work with big and dense graphs with - a low memory footprint. - - In this class you add the edges that *do not exist* in the dense graph, - the report methods of the class return the neighbors, the edges and - the degree as if it was the dense graph. Thus it's possible to use - an instance of this class with some of NetworkX functions. - """ - - all_edge_dict = {"weight": 1} - - def single_edge_dict(self): - return self.all_edge_dict - - edge_attr_dict_factory = single_edge_dict - - def __getitem__(self, n): - """Return a dict of neighbors of node n in the dense graph. - - Parameters - ---------- - n : node - A node in the graph. - - Returns - ------- - adj_dict : dictionary - The adjacency dictionary for nodes connected to n. - - """ - return { - node: self.all_edge_dict for node in set(self.adj) - set(self.adj[n]) - {n} - } - - def neighbors(self, n): - """Return an iterator over all neighbors of node n in the - dense graph. - - """ - try: - return iter(set(self.adj) - set(self.adj[n]) - {n}) - except KeyError as err: - raise nx.NetworkXError(f"The node {n} is not in the graph.") from err - - def degree(self, nbunch=None, weight=None): - """Return an iterator for (node, degree) in the dense graph. - - The node degree is the number of edges adjacent to the node. - - Parameters - ---------- - nbunch : iterable container, optional (default=all nodes) - A container of nodes. The container will be iterated - through once. - - weight : string or None, optional (default=None) - The edge attribute that holds the numerical value used - as a weight. If None, then each edge has weight 1. - The degree is the sum of the edge weights adjacent to the node. - - Returns - ------- - nd_iter : iterator - The iterator returns two-tuples of (node, degree). - - See Also - -------- - degree - - Examples - -------- - >>> G = nx.path_graph(4) # or DiGraph, MultiGraph, MultiDiGraph, etc - >>> G.degree(0) # node 0 with degree 1 - 1 - >>> list(G.degree([0, 1])) - [(0, 1), (1, 2)] - - """ - if nbunch is None: - nodes_nbrs = ( - ( - n, - { - v: self.all_edge_dict - for v in set(self.adj) - set(self.adj[n]) - {n} - }, - ) - for n in self.nodes() - ) - elif nbunch in self: - nbrs = set(self.nodes()) - set(self.adj[nbunch]) - {nbunch} - return len(nbrs) - else: - nodes_nbrs = ( - ( - n, - { - v: self.all_edge_dict - for v in set(self.nodes()) - set(self.adj[n]) - {n} - }, - ) - for n in self.nbunch_iter(nbunch) - ) - - if weight is None: - return ((n, len(nbrs)) for n, nbrs in nodes_nbrs) - else: - # AntiGraph is a ThinGraph so all edges have weight 1 - return ( - (n, sum((nbrs[nbr].get(weight, 1)) for nbr in nbrs)) - for n, nbrs in nodes_nbrs - ) - - def adjacency(self): - """Return an iterator of (node, adjacency set) tuples for all nodes - in the dense graph. - - This is the fastest way to look at every edge. - For directed graphs, only outgoing adjacencies are included. - - Returns - ------- - adj_iter : iterator - An iterator of (node, adjacency set) for all nodes in - the graph. - """ - nodes = set(self.adj) - for n, nbrs in self.adj.items(): - yield (n, nodes - set(nbrs) - {n}) - - -# Build several pairs of graphs, a regular graph -# and the AntiGraph of it's complement, which behaves -# as if it were the original graph. -Gnp = nx.gnp_random_graph(20, 0.8, seed=42) -Anp = AntiGraph(nx.complement(Gnp)) -Gd = nx.davis_southern_women_graph() -Ad = AntiGraph(nx.complement(Gd)) -Gk = nx.karate_club_graph() -Ak = AntiGraph(nx.complement(Gk)) -pairs = [(Gnp, Anp), (Gd, Ad), (Gk, Ak)] -# test connected components -for G, A in pairs: - gc = [set(c) for c in nx.connected_components(G)] - ac = [set(c) for c in nx.connected_components(A)] - for comp in ac: - assert comp in gc -# test biconnected components -for G, A in pairs: - gc = [set(c) for c in nx.biconnected_components(G)] - ac = [set(c) for c in nx.biconnected_components(A)] - for comp in ac: - assert comp in gc -# test degree -for G, A in pairs: - node = list(G.nodes())[0] - nodes = list(G.nodes())[1:4] - assert G.degree(node) == A.degree(node) - assert sum(d for n, d in G.degree()) == sum(d for n, d in A.degree()) - # AntiGraph is a ThinGraph, so all the weights are 1 - assert sum(d for n, d in A.degree()) == sum(d for n, d in A.degree(weight="weight")) - assert sum(d for n, d in G.degree(nodes)) == sum(d for n, d in A.degree(nodes)) - -pos = nx.spring_layout(G, seed=268) # Seed for reproducible layout -nx.draw(Gnp, pos=pos) -plt.show() diff --git a/.easyocr_venv/share/doc/networkx-2.8.5/examples/subclass/plot_printgraph.py b/.easyocr_venv/share/doc/networkx-2.8.5/examples/subclass/plot_printgraph.py deleted file mode 100644 index 08efcf1c6..000000000 --- a/.easyocr_venv/share/doc/networkx-2.8.5/examples/subclass/plot_printgraph.py +++ /dev/null @@ -1,88 +0,0 @@ -""" -=========== -Print Graph -=========== - -Example subclass of the Graph class. -""" - -import matplotlib.pyplot as plt -import networkx as nx -from networkx import Graph - - -class PrintGraph(Graph): - """ - Example subclass of the Graph class. - - Prints activity log to file or standard output. - """ - - def __init__(self, data=None, name="", file=None, **attr): - super().__init__(data=data, name=name, **attr) - if file is None: - import sys - - self.fh = sys.stdout - else: - self.fh = open(file, "w") - - def add_node(self, n, attr_dict=None, **attr): - super().add_node(n, attr_dict=attr_dict, **attr) - self.fh.write(f"Add node: {n}\n") - - def add_nodes_from(self, nodes, **attr): - for n in nodes: - self.add_node(n, **attr) - - def remove_node(self, n): - super().remove_node(n) - self.fh.write(f"Remove node: {n}\n") - - def remove_nodes_from(self, nodes): - for n in nodes: - self.remove_node(n) - - def add_edge(self, u, v, attr_dict=None, **attr): - super().add_edge(u, v, attr_dict=attr_dict, **attr) - self.fh.write(f"Add edge: {u}-{v}\n") - - def add_edges_from(self, ebunch, attr_dict=None, **attr): - for e in ebunch: - u, v = e[0:2] - self.add_edge(u, v, attr_dict=attr_dict, **attr) - - def remove_edge(self, u, v): - super().remove_edge(u, v) - self.fh.write(f"Remove edge: {u}-{v}\n") - - def remove_edges_from(self, ebunch): - for e in ebunch: - u, v = e[0:2] - self.remove_edge(u, v) - - def clear(self): - super().clear() - self.fh.write("Clear graph\n") - - -G = PrintGraph() -G.add_node("foo") -G.add_nodes_from("bar", weight=8) -G.remove_node("b") -G.remove_nodes_from("ar") -print("Nodes in G: ", G.nodes(data=True)) -G.add_edge(0, 1, weight=10) -print("Edges in G: ", G.edges(data=True)) -G.remove_edge(0, 1) -G.add_edges_from(zip(range(0, 3), range(1, 4)), weight=10) -print("Edges in G: ", G.edges(data=True)) -G.remove_edges_from(zip(range(0, 3), range(1, 4))) -print("Edges in G: ", G.edges(data=True)) - -G = PrintGraph() -nx.add_path(G, range(10)) -nx.add_star(G, range(9, 13)) -pos = nx.spring_layout(G, seed=225) # Seed for reproducible layout -nx.draw(G, pos) -plt.show() diff --git a/.easyocr_venv/share/jupyter/kernels/python3/kernel.json b/.easyocr_venv/share/jupyter/kernels/python3/kernel.json deleted file mode 100644 index cca38a42a..000000000 --- a/.easyocr_venv/share/jupyter/kernels/python3/kernel.json +++ /dev/null @@ -1,14 +0,0 @@ -{ - 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-.B ipython subcommand -.RI [ options ] ... - -.SH DESCRIPTION -An interactive Python shell with automatic history (input and output), dynamic -object introspection, easier configuration, command completion, access to the -system shell, integration with numerical and scientific computing tools, -web notebook, Qt console, and more. - -For more information on how to use IPython, see 'ipython \-\-help', -or 'ipython \-\-help\-all' for all available command\(hyline options. - -.SH "ENVIRONMENT VARIABLES" -.sp -.PP -\fIIPYTHONDIR\fR -.RS 4 -This is the location where IPython stores all its configuration files. 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zR!zRocBS9?(9O2+%Z1JhJwD5ST#ml)f6z3?eOkRLfB#3>CnuXT3->RXzqx*by88EO z&mDy>_7i{oFNxgn@W1Z54+3p%$?m7uKA8CjIJ?crB*F|j8X&&s%eRRXnPw7S&wpa_NVURPs(Tzes-xp!j7T}s- z9R38K_KR*3`hmO%lYI4YnuLBlFS?=V=jtL1y^Y&Ylv8%mO-w~QUl(DbssYrQ$fskV zYezp;7oj~DNjv>Xjiq$rpH7>nT14MAT~gD|8f4Q>cxeGR&H^qEqG_Q${@ts!B6GH;4*0QxWw z!hrM&xB>7%A9UU5qY4P!W>qlV&@l#dt>}GYgx1y7Fs)$sLwm{r-mJidN1zjEL>X4I KGB8B7f_MO;o(lH> From 69a0e8d6351f1dbccbf5fbd8d96baddb1731fca8 Mon Sep 17 00:00:00 2001 From: Yannick Stoll Date: Mon, 5 Sep 2022 13:43:46 +0200 Subject: [PATCH 07/17] deleting unused files --- easy_ocr_dev.ipynb | 223 --------------------------------------------- easy_ocr_dev.py | 11 --- notes_persos.txt | 5 - 3 files changed, 239 deletions(-) delete mode 100644 easy_ocr_dev.ipynb delete mode 100755 easy_ocr_dev.py delete mode 100644 notes_persos.txt diff --git a/easy_ocr_dev.ipynb b/easy_ocr_dev.ipynb deleted file mode 100644 index e5e6c2e04..000000000 --- a/easy_ocr_dev.ipynb +++ /dev/null @@ -1,223 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# import torch\n", - "# print(torch.cuda.is_available())\n", - "# cuda_device = torch.device(\"cuda:0\" if torch.cuda.is_available else \"cpu\")\n", - "# print(cuda_device)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Torch version: 1.9.1+cu111\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/ystoll/PixelForest/easyocr/.easyocr_venv/lib/python3.8/site-packages/tqdm/auto.py:22: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - } - ], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "import easyocr\n", - "#from easyocr.easyocr import Reader\n", - "import torch\n", - "print(f\"Torch version: {torch.__version__}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['Mairie du /\"', '[Palais du LOUVRE', 'LES ARTS DÉCORATIFS', '[Musée du LOUVRe', 'Théâtre', 'du PALAIS-RoYAl^']\n" - ] - } - ], - "source": [ - "reader = easyocr.Reader(['fr'])\n", - "# reader = easyocr.Reader(['en'])\n", - "result = reader.readtext('examples/french.jpg', detail=0, decoder=\"wordbeamsearch\")\n", - "print(result)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[{'boxes': [[0, 0], [692, 0], [692, 1024], [0, 1024]], 'text': '5', 'confident': 0.6201150760928478}]\n" - ] - } - ], - "source": [ - "reader = easyocr.Reader(['en', 'fr'])\n", - "result = reader.recognize('examples/french.jpg',\n", - " detail=1,\n", - " output_format=\"dict\")\n", - "print(result)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Yannick\n", - "'Prediction: torch.Size([1, 63, 352])'\n", - "Yannick\n", - "'Prediction: torch.Size([1, 63, 352])'\n", - "Yannick\n", - "'Prediction: torch.Size([1, 79, 352])'\n", - "Yannick\n", - "'Prediction: torch.Size([1, 79, 352])'\n", - "Yannick\n", - "'Prediction: torch.Size([1, 47, 352])'\n", - "Yannick\n", - "'Prediction: torch.Size([1, 79, 352])'\n", - "Yannick\n", - "'Prediction: torch.Size([1, 79, 352])'\n", - "['Mairie du /\"', '[Palais du LOUVRE', 'LES ARTS DÉCORATIFS', '[Musée du LOUVRe', 'Théâtre', 'du PALAIS-RoYAl^']\n" - ] - } - ], - "source": [ - "reader = easyocr.Reader(['en', 'fr'])\n", - "result = reader.readtext('examples/french.jpg', detail=0)\n", - "print(result)" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "('M', 'a', 'i', 'r', 'i', 'e', ' ', 'd', 'u', ' ', '/', '\"', 'en')\n", - "('[', 'P', 'a', 'l', 'a', 'i', 's', ' ', 'd', 'u', ' ', 'L', 'O', 'U', 'V', 'R', 'E', 'en')\n", - "('L', 'E', 'S', ' ', 'A', 'R', 'T', 'S', ' ', 'D', 'É', 'C', 'O', 'R', 'A', 'T', 'I', 'F', 'S', 'en')\n", - "('[', 'M', 'u', 's', 'é', 'e', ' ', 'd', 'u', ' ', 'L', 'O', 'U', 'V', 'R', 'e', 'en')\n", - "('T', 'h', 'é', 'â', 't', 'r', 'e', 'en')\n", - "('d', 'u', ' ', 'P', 'A', 'L', 'A', 'I', 'S', '-', 'R', 'o', 'Y', 'A', 'l', '^', 'en')\n", - "('M', 'a', 'i', 'r', 'i', 'e', ' ', 'd', 'u', ' ', '/', '\"', 'ch')\n", - "('T', 'h', 'é', 'â', 't', 'r', 'e', 'ch')\n", - "('d', 'u', ' ', 'P', 'A', 'L', 'A', 'I', 'S', '-', 'R', 'o', 'Y', 'A', 'l', '^', 'ch')\n", - "None\n" - ] - } - ], - "source": [ - "reader = easyocr.Reader(['fr'])\n", - "result = reader.readtextlang('examples/french.jpg', detail=0)\n", - "print(result)" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[['Reduce your risk of coronavirus infection;', 'Clean hands with soap and water', \"'alcohol hased handruh\", 'Cover nose and mouth when coughing and', 'sneezing with tissue or flexed elhow', 'Avoid close contact with anyone with', 'coldor flu-Hike symptoms', 'Thoroughly cook meat andeggs', 'No unprotected contact with Jive wild', 'or farm animals', 'World Heakth', \"'Organization\"], ['Mairie du \"@', '[Palais du LOUVRE', 'LES ARTS DÉCORATIFS', 'Musée du LOUVRE', 'du PALAIS-ROYAL', '4', 'Théâtre']]\n" - ] - } - ], - "source": [ - "reader = easyocr.Reader(['fr', 'en'])\n", - "result = reader.readtext_batched([ 'examples/english.png' , 'examples/french.jpg'], detail=0, n_width=600, n_height=600)\n", - "print(result)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.8.10 ('.easyocr_venv': venv)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.10" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "4e7ed28815ff5fa6604e7428584610e00d12043870e25697ecfca17a117ee241" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/easy_ocr_dev.py b/easy_ocr_dev.py deleted file mode 100755 index a93969120..000000000 --- a/easy_ocr_dev.py +++ /dev/null @@ -1,11 +0,0 @@ -import easyocr - -reader = easyocr.Reader(['fr']) -# reader = easyocr.Reader(['en']) -results = reader.readtext('examples/french.jpg', - detail=1, - decoder="beamsearch" - ) - -for result in results: - print(result) diff --git a/notes_persos.txt b/notes_persos.txt deleted file mode 100644 index cb7cbad85..000000000 --- a/notes_persos.txt +++ /dev/null @@ -1,5 +0,0 @@ -dict_list = {'en': '/home/ystoll/PixelForest/easyocr/easyocr/dict/en.txt', - 'fr': '/home/ystoll/PixelForest/easyocr/easyocr/dict/fr.txt'} - - -utils.BeamState.wordsearch() uses dict_list to decode NN outputted matrix to a specific word. \ No newline at end of file From 44bb60090ce9cadd1aca5e4ad330c21f2dba806f Mon Sep 17 00:00:00 2001 From: JulianOrteil Date: Mon, 5 Sep 2022 23:47:40 -0500 Subject: [PATCH 08/17] Setup project metadata and tox --- .coveragerc | 27 +++++++ .pre-commit-config.yaml | 56 +++++++++++++ .readthedocs.yml | 23 ++++++ MANIFEST.in | 2 +- pyproject.toml | 9 +++ setup.cfg | 173 +++++++++++++++++++++++++++++++++++++++- setup.py | 58 +++++--------- tox.ini | 95 ++++++++++++++++++++++ 8 files changed, 403 insertions(+), 40 deletions(-) create mode 100644 .coveragerc create mode 100644 .pre-commit-config.yaml create mode 100644 .readthedocs.yml create mode 100644 pyproject.toml create mode 100644 tox.ini diff --git a/.coveragerc b/.coveragerc new file mode 100644 index 000000000..009a98f09 --- /dev/null +++ b/.coveragerc @@ -0,0 +1,27 @@ +# .coveragerc to control coverage.py +[run] +branch = True +source = easyocr +omit = + +[paths] +source = + */site-packages/ + +[report] +# Regexes for lines to exclude from consideration +exclude_lines = + # Have to re-enable the standard pragma + pragma: no cover + + # Don't complain about missing debug-only code: + def __repr__ + if self\.debug + + # Don't complain if tests don't hit defensive assertion code: + raise AssertionError + raise NotImplementedError + + # Don't complain if non-runnable code isn't run: + if 0: + if __name__ == .__main__.: diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml new file mode 100644 index 000000000..b26fd2b10 --- /dev/null +++ b/.pre-commit-config.yaml @@ -0,0 +1,56 @@ +exclude: '^docs/conf.py' + +repos: +- repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.3.0 + hooks: + - id: trailing-whitespace + - id: check-added-large-files + - id: check-ast + - id: check-json + - id: check-merge-conflict + - id: check-xml + - id: check-yaml + - id: debug-statements + - id: end-of-file-fixer + - id: requirements-txt-fixer + - id: mixed-line-ending + args: ['--fix=auto'] + exclude: tests + +- repo: https://github.com/myint/autoflake + rev: v1.4 + hooks: + - id: autoflake + args: [ + --in-place, + --remove-all-unused-imports, + --remove-unused-variables, + ] + exclude: tests + +- repo: https://github.com/pycqa/isort + rev: 5.10.1 + hooks: + - id: isort + exclude: tests + +- repo: https://github.com/psf/black + rev: 22.6.0 + hooks: + - id: black + language_version: python3 + exclude: tests + +- repo: https://github.com/PyCQA/flake8 + rev: 4.0.1 + hooks: + - id: flake8 + additional_dependencies: [wemake-python-styleguide] # Comment to disable wemake + exclude: tests + +- repo: https://github.com/pre-commit/mirrors-mypy + rev: v0.971 + hooks: + - id: mypy + exclude: tests diff --git a/.readthedocs.yml b/.readthedocs.yml new file mode 100644 index 000000000..21b08145f --- /dev/null +++ b/.readthedocs.yml @@ -0,0 +1,23 @@ +# Read the Docs configuration file +# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details + +# Required +version: 2 + +# Build documentation in the docs/ directory with Sphinx +sphinx: + configuration: docs/conf.py + +# Build documentation with MkDocs +#mkdocs: +# configuration: mkdocs.yml + +# Optionally build your docs in additional formats such as PDF +formats: + - pdf + +python: + version: 3.8 + install: + - requirements: docs/requirements.txt + - {path: ., method: pip} diff --git a/MANIFEST.in b/MANIFEST.in index 6d6f4de9b..145e71016 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1,4 +1,4 @@ -include LICENSE.txt +include LICENSE include README.md include easyocr/model/* diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 000000000..89a5bedf5 --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,9 @@ +[build-system] +# AVOID CHANGING REQUIRES: IT WILL BE UPDATED BY PYSCAFFOLD! +requires = ["setuptools>=46.1.0", "setuptools_scm[toml]>=5"] +build-backend = "setuptools.build_meta" + +[tool.setuptools_scm] +# For smarter version schemes and other configuration options, +# check out https://github.com/pypa/setuptools_scm +version_scheme = "no-guess-dev" diff --git a/setup.cfg b/setup.cfg index 224a77957..d56f71973 100644 --- a/setup.cfg +++ b/setup.cfg @@ -1,2 +1,173 @@ +# This file is used to configure your project. +# Read more about the various options under: +# https://setuptools.pypa.io/en/latest/userguide/declarative_config.html +# https://setuptools.pypa.io/en/latest/references/keywords.html + [metadata] -description-file = README.md \ No newline at end of file +name = easyocr +description = End-to-End Multi-Lingual Optical Character Recognition (OCR) Solution +author = Rakpong Kittinaradorn +author_email = r.kittinaradorn@gmail.com +license = Apache License 2.0 +license_files = LICENSE +long_description = file: README.md +long_description_content_type = text/markdown; charset=UTF-8 +url = https://github.com/jaidedai/easyocr +# Add here related links, for example: +project_urls = + # Documentation = + Source = https://github.com/JaidedAI/EasyOCR + Changelog = https://github.com/JaidedAI/EasyOCR/blob/master/releasenotes.md + Tracker = https://github.com/JaidedAI/EasyOCR/issues + Download = https://pypi.org/project/easyocr/#files +keywords = + easyocr + ocr + optical + character + recognition + deep + learning + neural + network + +# Change if running only on Windows, Mac or Linux (comma-separated) +platforms = any + +# Add here all kinds of additional classifiers as defined under +# https://pypi.org/classifiers/ +classifiers = + Development Status :: 5 - Production/Stable + Programming Language :: Python + + +[options] +zip_safe = False +packages = find_namespace: +include_package_data = True +package_dir = + =. + # TODO: =src +cmdclass = + install = .setup.CustomCommand_install + develop = .setup.CustomCommand_develop + +# Require a min/specific Python version (comma-separated conditions) +python_requires = >=3.7 + +# Add here dependencies of your project (line-separated), e.g. requests>=2.2,<3.0. +# Version specifiers like >=2.2,<3.0 avoid problems due to API changes in +# new major versions. This works if the required packages follow Semantic Versioning. +# For more information, check out https://semver.org/. +install_requires = + importlib-metadata; python_version<"3.8" + typing_extensions; python_version<"3.10" + torch + torchvision>=0.5 + opencv-python-headless<=4.5.4.60 + scipy + numpy + Pillow + scikit-image + python-bidi + PyYAML + Shapely + pyclipper + ninja + + +[options.packages.find] +# TODO: src +where = . +exclude = + tests + +[options.extras_require] +# Add here additional requirements for extra features, to install with: +# `pip install easyocr[PDF]` like: +# PDF = ReportLab; RXP + +# Add here test requirements (semicolon/line-separated) +testing = + setuptools + pytest + pytest-cov + typing_extensions + +[options.entry_points] +# Add here console scripts: +console_scripts = + easyocr = easyocr.cli:main + +[tool:pytest] +# Specify command line options as you would do when invoking pytest directly. +# e.g. --cov-report html (or xml) for html/xml output or --junitxml junit.xml +# in order to write a coverage file that can be read by Jenkins. +# CAUTION: --cov flags may prohibit setting breakpoints while debugging. +# Comment those flags to avoid this pytest issue. +addopts = + --cov easyocr --cov-report term-missing + --verbose +norecursedirs = + dist + build + .tox +testpaths = tests +# Use pytest markers to select/deselect specific tests +# markers = +# slow: mark tests as slow (deselect with '-m "not slow"') +# system: mark end-to-end system tests + +[devpi:upload] +# Options for the devpi: PyPI server and packaging tool +# VCS export must be deactivated since we are using setuptools-scm +no_vcs = 1 +formats = bdist_wheel + +[flake8] +# Some sane defaults for the code style checker flake8 +extend_ignore = E203, W503 +# ^ Black-compatible +# E203 and W503 have edge cases handled by black +exclude = + .tox + build + dist + .eggs + docs/conf.py +strictness = long +show-source = True +max-line-length = 100 +max-complexity = 6 +# TODO: numpy, google, sphinx? +docstring-style = sphinx +ignore = D100, D104, D401, DAR103, DAR203, E800, N802, Q000, RST303, RST304, W504, WPS326, WPS402, WPS433, WPS436 +per-file-ignores = + # WPS421: Found wrong function call: print + setup.py: WPS421 + # WPS412: Found `__init__.py` module with logic + # WPS420: Found wrong keywork: del + # WPS440: Found block variables overlap: PackageNotFoundError, version + src/command_based_framework/__init__.py: WPS412, WPS420, WPS440 + +[isort] +include_trailing_comma = True +use_parentheses = True +multi_line_output = 3 +line_length = 100 + +[mypy] +allow_redefinition = False +check_untyped_defs = True +ignore_errors = False +ignore_missing_imports = True +implicit_reexport = False +local_partial_types = True +strict_optional = True +strict_equality = True +no_implicit_optional = True +warn_unused_ignores = True +warn_redundant_casts = True +warn_unused_configs = True +warn_unreachable = True +warn_no_return = True diff --git a/setup.py b/setup.py index 31b2b8b45..45223576f 100644 --- a/setup.py +++ b/setup.py @@ -3,57 +3,39 @@ """ import os import subprocess -from io import open + from setuptools import setup -from setuptools.command.install import install from setuptools.command.develop import develop -from setuptools.command.egg_info import egg_info - -with open('requirements.txt', encoding="utf-8-sig") as f: - requirements = f.readlines() +from setuptools.command.install import install -def readme(): - with open('README.md', encoding="utf-8-sig") as f: - README = f.read() - return README def compile_dbnet_dcn(script_dir): - script_path = os.path.join(script_dir, 'easyocr', 'scripts', 'compile_dbnet_dcn.py') - subprocess.run( - "python {}".format(script_path), shell=True - ) + script_path = os.path.join(script_dir, "easyocr", "scripts", "compile_dbnet_dcn.py") + subprocess.run("python {}".format(script_path), shell=True) + class CustomCommand_install(install): def run(self): install.run(self) compile_dbnet_dcn(self.install_lib) + class CustomCommand_develop(develop): def run(self): develop.run(self) compile_dbnet_dcn(self.install_dir) -setup( - name='easyocr', - packages=['easyocr'], - include_package_data=True, - version='1.6.1', - install_requires=requirements, - entry_points={"console_scripts": ["easyocr= easyocr.cli:main"]}, - license='Apache License 2.0', - description='End-to-End Multi-Lingual Optical Character Recognition (OCR) Solution', - long_description=readme(), - long_description_content_type="text/markdown", - author='Rakpong Kittinaradorn', - author_email='r.kittinaradorn@gmail.com', - url='https://github.com/jaidedai/easyocr', - download_url='https://github.com/jaidedai/easyocr.git', - keywords=['ocr optical character recognition deep learning neural network'], - classifiers=[ - 'Development Status :: 5 - Production/Stable' - ], - cmdclass={ - 'install': CustomCommand_install, - 'develop': CustomCommand_develop, - } -) + +from setuptools import setup + +if __name__ == "__main__": + try: + setup(use_scm_version={"version_scheme": "no-guess-dev"}) + except Exception: + print( + "\n\nAn error occurred while building the project, " + + "please ensure you have the most updated version of setuptools, " + + "setuptools_scm and wheel with:\n" + + " pip install -U setuptools setuptools_scm wheel\n\n", + ) + raise diff --git a/tox.ini b/tox.ini new file mode 100644 index 000000000..8d2042c83 --- /dev/null +++ b/tox.ini @@ -0,0 +1,95 @@ +# Tox configuration file +# Read more under https://tox.wiki/ + +[tox] +minversion = 3.24 +envlist = py{37,38,39,310} +isolated_build = True + + +[testenv] +description = Invoke pytest to run automated tests +setenv = + TOXINIDIR = {toxinidir} +passenv = + HOME + SETUPTOOLS_* +extras = + testing +commands = + pytest {posargs} + + +# To run `tox -e lint` you need to make sure you have a +# `.pre-commit-config.yaml` file. See https://pre-commit.com +[testenv:lint] +description = + Perform static analysis and style checks +skip_install = True +deps = pre-commit +passenv = + HOMEPATH + PROGRAMDATA + SETUPTOOLS_* +commands = + pre-commit run --all-files + + +[testenv:{build,clean}] +description = + build: Build the package in isolation according to PEP517, see https://github.com/pypa/build + clean: Remove old distribution files and temporary build artifacts (./build and ./dist) +# https://setuptools.pypa.io/en/stable/build_meta.html#how-to-use-it +skip_install = True +changedir = {toxinidir} +deps = + build: build[virtualenv] +passenv = + SETUPTOOLS_* +commands = + clean: python -c 'import shutil; [shutil.rmtree(p, True) for p in ("build", "dist", "docs/_build")]' + clean: python -c 'import pathlib, shutil; [shutil.rmtree(p, True) for p in pathlib.Path("src").glob("*.egg-info")]' + build: python -m build {posargs} + + +[testenv:{docs,doctests,linkcheck,autodocs}] +description = + docs: Invoke sphinx-build to build the docs + doctests: Invoke sphinx-build to run doctests + linkcheck: Check for broken links in the documentation + autodocs: Launch a local webserver to hot reload doc changes +passenv = + SETUPTOOLS_* +setenv = + DOCSDIR = {toxinidir}/docs + BUILDDIR = {toxinidir}/docs/_build + docs: BUILD = html + doctests: BUILD = doctest + linkcheck: BUILD = linkcheck +deps = + -r {toxinidir}/docs/requirements.txt + autodocs: sphinx-autobuild + # ^ requirements.txt shared with Read The Docs +commands = + docs: sphinx-build --color -b {env:BUILD} -d "{env:BUILDDIR}/doctrees" "{env:DOCSDIR}" "{env:BUILDDIR}/{env:BUILD}" {posargs} + doctests: sphinx-build --color -b {env:BUILD} -d "{env:BUILDDIR}/doctrees" "{env:DOCSDIR}" "{env:BUILDDIR}/{env:BUILD}" {posargs} + linkcheck: sphinx-build --color -b {env:BUILD} -d "{env:BUILDDIR}/doctrees" "{env:DOCSDIR}" "{env:BUILDDIR}/{env:BUILD}" {posargs} + autodocs: sphinx-autobuild docs docs/_build/html + +[testenv:publish] +description = + Publish the package you have been developing to a package index server. + By default, it uses testpypi. If you really want to publish your package + to be publicly accessible in PyPI, use the `-- --repository pypi` option. +skip_install = True +changedir = {toxinidir} +passenv = + # See: https://twine.readthedocs.io/en/latest/ + TWINE_PASSWORD + TWINE_REPOSITORY +setenv = + TWINE_USERNAME = {env:TWINE_USERNAME:__token__} +deps = twine +commands = + python -m twine check dist/* + python -m twine upload {posargs:--repository {env:TWINE_REPOSITORY:testpypi}} dist/* From 90b7738989763513709fea6c40f6e7c8b5fa1cba Mon Sep 17 00:00:00 2001 From: JulianOrteil Date: Sun, 11 Sep 2022 11:28:21 -0500 Subject: [PATCH 09/17] Automate the build process --- tox.ini | 204 ++++++++++++++++++++++++++++++-------------------------- 1 file changed, 109 insertions(+), 95 deletions(-) diff --git a/tox.ini b/tox.ini index 8d2042c83..ab6a396b8 100644 --- a/tox.ini +++ b/tox.ini @@ -1,95 +1,109 @@ -# Tox configuration file -# Read more under https://tox.wiki/ - -[tox] -minversion = 3.24 -envlist = py{37,38,39,310} -isolated_build = True - - -[testenv] -description = Invoke pytest to run automated tests -setenv = - TOXINIDIR = {toxinidir} -passenv = - HOME - SETUPTOOLS_* -extras = - testing -commands = - pytest {posargs} - - -# To run `tox -e lint` you need to make sure you have a -# `.pre-commit-config.yaml` file. See https://pre-commit.com -[testenv:lint] -description = - Perform static analysis and style checks -skip_install = True -deps = pre-commit -passenv = - HOMEPATH - PROGRAMDATA - SETUPTOOLS_* -commands = - pre-commit run --all-files - - -[testenv:{build,clean}] -description = - build: Build the package in isolation according to PEP517, see https://github.com/pypa/build - clean: Remove old distribution files and temporary build artifacts (./build and ./dist) -# https://setuptools.pypa.io/en/stable/build_meta.html#how-to-use-it -skip_install = True -changedir = {toxinidir} -deps = - build: build[virtualenv] -passenv = - SETUPTOOLS_* -commands = - clean: python -c 'import shutil; [shutil.rmtree(p, True) for p in ("build", "dist", "docs/_build")]' - clean: python -c 'import pathlib, shutil; [shutil.rmtree(p, True) for p in pathlib.Path("src").glob("*.egg-info")]' - build: python -m build {posargs} - - -[testenv:{docs,doctests,linkcheck,autodocs}] -description = - docs: Invoke sphinx-build to build the docs - doctests: Invoke sphinx-build to run doctests - linkcheck: Check for broken links in the documentation - autodocs: Launch a local webserver to hot reload doc changes -passenv = - SETUPTOOLS_* -setenv = - DOCSDIR = {toxinidir}/docs - BUILDDIR = {toxinidir}/docs/_build - docs: BUILD = html - doctests: BUILD = doctest - linkcheck: BUILD = linkcheck -deps = - -r {toxinidir}/docs/requirements.txt - autodocs: sphinx-autobuild - # ^ requirements.txt shared with Read The Docs -commands = - docs: sphinx-build --color -b {env:BUILD} -d "{env:BUILDDIR}/doctrees" "{env:DOCSDIR}" "{env:BUILDDIR}/{env:BUILD}" {posargs} - doctests: sphinx-build --color -b {env:BUILD} -d "{env:BUILDDIR}/doctrees" "{env:DOCSDIR}" "{env:BUILDDIR}/{env:BUILD}" {posargs} - linkcheck: sphinx-build --color -b {env:BUILD} -d "{env:BUILDDIR}/doctrees" "{env:DOCSDIR}" "{env:BUILDDIR}/{env:BUILD}" {posargs} - autodocs: sphinx-autobuild docs docs/_build/html - -[testenv:publish] -description = - Publish the package you have been developing to a package index server. - By default, it uses testpypi. If you really want to publish your package - to be publicly accessible in PyPI, use the `-- --repository pypi` option. -skip_install = True -changedir = {toxinidir} -passenv = - # See: https://twine.readthedocs.io/en/latest/ - TWINE_PASSWORD - TWINE_REPOSITORY -setenv = - TWINE_USERNAME = {env:TWINE_USERNAME:__token__} -deps = twine -commands = - python -m twine check dist/* - python -m twine upload {posargs:--repository {env:TWINE_REPOSITORY:testpypi}} dist/* +# Tox configuration file +# Read more under https://tox.wiki/ + +[tox] +minversion = 3.24 +envlist = py{37,38,39,310} +isolated_build = True + + +[testenv] +description = Invoke pytest to run automated tests +setenv = + TOXINIDIR = {toxinidir} +passenv = + HOME + SETUPTOOLS_* +extras = + testing +commands = + pytest {posargs} + + +# To run `tox -e lint` you need to make sure you have a +# `.pre-commit-config.yaml` file. See https://pre-commit.com +[testenv:lint] +description = + Perform static analysis and style checks +skip_install = True +deps = pre-commit +passenv = + HOMEPATH + PROGRAMDATA + SETUPTOOLS_* +commands = + pre-commit run --all-files + + +[testenv:{build,clean}] +description = + build: Build the package in isolation according to PEP517, see https://github.com/pypa/build + clean: Remove old distribution files and temporary build artifacts (./build and ./dist) +# https://setuptools.pypa.io/en/stable/build_meta.html#how-to-use-it +skip_install = True +changedir = {toxinidir} +deps = + build: build[virtualenv] + build: setuptools_scm[toml] + build: -rrequirements.txt +conda_deps = + build: pytorch + build: torchvision + build: torchaudio + build: cudatoolkit=11.3 + build: cudatoolkit-dev + build: ninja + build: cxx-compiler=1.3.0 +conda_channels = + pytorch + conda-forge +passenv = + SETUPTOOLS_* +commands = + clean: python -c 'import shutil; [shutil.rmtree(p, True) for p in ("build", "dist", "docs/_build", "easyocr/DBNet/assets/ops/dcn/build")]' + clean: python -c 'import pathlib, shutil; [shutil.rmtree(p, True) for p in pathlib.Path("src").glob("*.egg-info")]' + clean: python -c 'import pathlib; pathlib.Path("easyocr/DBNet/dcn_compiling_success").unlink(missing_ok=True); pathlib.Path("easyocr/DBNet/log.txt").unlink(missing_ok=True)' + build: python easyocr/scripts/compile_dbnet_dcn.py + build: python -m build --no-isolation {posargs} + +[testenv:{docs,doctests,linkcheck,autodocs}] +description = + docs: Invoke sphinx-build to build the docs + doctests: Invoke sphinx-build to run doctests + linkcheck: Check for broken links in the documentation + autodocs: Launch a local webserver to hot reload doc changes +passenv = + SETUPTOOLS_* +setenv = + DOCSDIR = {toxinidir}/docs + BUILDDIR = {toxinidir}/docs/_build + docs: BUILD = html + doctests: BUILD = doctest + linkcheck: BUILD = linkcheck +deps = + -r {toxinidir}/docs/requirements.txt + autodocs: sphinx-autobuild + # ^ requirements.txt shared with Read The Docs +commands = + docs: sphinx-build --color -b {env:BUILD} -d "{env:BUILDDIR}/doctrees" "{env:DOCSDIR}" "{env:BUILDDIR}/{env:BUILD}" {posargs} + doctests: sphinx-build --color -b {env:BUILD} -d "{env:BUILDDIR}/doctrees" "{env:DOCSDIR}" "{env:BUILDDIR}/{env:BUILD}" {posargs} + linkcheck: sphinx-build --color -b {env:BUILD} -d "{env:BUILDDIR}/doctrees" "{env:DOCSDIR}" "{env:BUILDDIR}/{env:BUILD}" {posargs} + autodocs: sphinx-autobuild docs docs/_build/html + +[testenv:publish] +description = + Publish the package you have been developing to a package index server. + By default, it uses testpypi. If you really want to publish your package + to be publicly accessible in PyPI, use the `-- --repository pypi` option. +skip_install = True +changedir = {toxinidir} +passenv = + # See: https://twine.readthedocs.io/en/latest/ + TWINE_PASSWORD + TWINE_REPOSITORY +setenv = + TWINE_USERNAME = {env:TWINE_USERNAME:__token__} +deps = twine +commands = + python -m twine check dist/* + python -m twine upload {posargs:--repository {env:TWINE_REPOSITORY:testpypi}} dist/* From ec11af0d951a394ea935ffad748ac7558cba7b74 Mon Sep 17 00:00:00 2001 From: JulianOrteil Date: Sun, 11 Sep 2022 19:30:55 -0500 Subject: [PATCH 10/17] Add cleanall environment --- tox.ini | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/tox.ini b/tox.ini index ab6a396b8..22da23486 100644 --- a/tox.ini +++ b/tox.ini @@ -35,10 +35,11 @@ commands = pre-commit run --all-files -[testenv:{build,clean}] +[testenv:{build,clean,cleanall}] description = build: Build the package in isolation according to PEP517, see https://github.com/pypa/build clean: Remove old distribution files and temporary build artifacts (./build and ./dist) + cleanall: Remove old distribution files and temporary build artifacts including DBNet binaries # https://setuptools.pypa.io/en/stable/build_meta.html#how-to-use-it skip_install = True changedir = {toxinidir} @@ -60,9 +61,9 @@ conda_channels = passenv = SETUPTOOLS_* commands = - clean: python -c 'import shutil; [shutil.rmtree(p, True) for p in ("build", "dist", "docs/_build", "easyocr/DBNet/assets/ops/dcn/build")]' - clean: python -c 'import pathlib, shutil; [shutil.rmtree(p, True) for p in pathlib.Path("src").glob("*.egg-info")]' - clean: python -c 'import pathlib; pathlib.Path("easyocr/DBNet/dcn_compiling_success").unlink(missing_ok=True); pathlib.Path("easyocr/DBNet/log.txt").unlink(missing_ok=True)' + clean,cleanall: python -c 'import shutil; [shutil.rmtree(p, True) for p in ("build", "dist", "docs/_build", "easyocr/DBNet/assets/ops/dcn/build")]' + clean,cleanall: python -c 'import pathlib, shutil; [shutil.rmtree(p, True) for p in pathlib.Path(".").glob("*.egg-info")]' + cleanall: python -c 'import pathlib; pathlib.Path("easyocr/DBNet/dcn_compiling_success").unlink(missing_ok=True); pathlib.Path("easyocr/DBNet/log.txt").unlink(missing_ok=True)' build: python easyocr/scripts/compile_dbnet_dcn.py build: python -m build --no-isolation {posargs} From deae9902b66a95197b0742c22d879cb8a2e85c16 Mon Sep 17 00:00:00 2001 From: JulianOrteil Date: Sun, 11 Sep 2022 19:42:17 -0500 Subject: [PATCH 11/17] Remove `shell=True` security vulnerabilities --- easyocr/scripts/compile_dbnet_dcn.py | 121 +++++++++++++-------------- setup.py | 82 +++++++++--------- 2 files changed, 100 insertions(+), 103 deletions(-) diff --git a/easyocr/scripts/compile_dbnet_dcn.py b/easyocr/scripts/compile_dbnet_dcn.py index bea340ea1..72d8e10c7 100644 --- a/easyocr/scripts/compile_dbnet_dcn.py +++ b/easyocr/scripts/compile_dbnet_dcn.py @@ -1,62 +1,59 @@ -''' -Created by Jaided AI -Released Date: 19/08/2022 -Description: -A wrapper for DCN operator for DBNet. This script is called inside the setup.py -of EasyOCR. It can also be called as a standalone script to compile the operator -manually. -''' -import os -from datetime import datetime -import subprocess - -def print_error(errors, log_path): - if not isinstance(errors, list): - errors = [errors] - errors = [error if isinstance(error, bytes) else error.encode('utf-8') for error in errors] - url = "https://github.com/JaidedAI/EasyOCR/tree/master/easyocr/DBNet" - print("Failed to compile dcn operator for DBNet.") - with open(log_path, "wb") as fid: - fid.write((datetime.now().strftime("%H:%M:%S - %d %b %Y") + "\n").encode('utf-8')) - fid.write("Failed to compile dcn operator for DBNet with the following error.\n".encode('utf-8')) - fid.write(("#"*42 + '\n').encode('utf-8')) - [fid.write(error) for error in errors] - print("Error message can be found in {}.".format(os.path.abspath(log_path))) - print("#"*42) - print("EasyOCR can still be used with CRAFT text detector (default).") - print("To use DBNet text detector, please check {} for troubleshoot and compile dcn operator manually.".format(url)) - -def print_success(text, log_path): - with open(log_path, "wb") as fid: - fid.write((datetime.now().strftime("%H:%M:%S - %d %b %Y") + "\n").encode('utf-8')) - fid.write((text + "\n").encode('utf-8')) - print(text) - -def main(): - cwd = os.getcwd() - parent_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) - log_path = os.path.join(parent_dir,'DBNet', 'log.txt') - try: - print("Compiling DCN operator...") - os.chdir(os.path.join(parent_dir,'DBNet','assets','ops','dcn')) - result = subprocess.run( - "python setup.py build_ext --inplace", shell=True, capture_output = True - ) - if result.returncode == 0: - os.chdir(os.path.join(parent_dir,'DBNet')) - result = subprocess.run( - "touch dcn_compiling_success", shell=True, capture_output = True - ) - success_message = "DCN operator is compiled successfully at {}.".format(os.path.abspath(os.path.join(parent_dir,'DBNet'))) - print_success(success_message, log_path) - else: - print(result.__dict__) - print_error([result.stdout, result.stderr], log_path) - except Exception as error: - print_error("{}".format(error), log_path) - finally: - os.chdir(cwd) - -if __name__ == '__main__': - main() - +''' +Created by Jaided AI +Released Date: 19/08/2022 +Description: +A wrapper for DCN operator for DBNet. This script is called inside the setup.py +of EasyOCR. It can also be called as a standalone script to compile the operator +manually. +''' +import os +from datetime import datetime +import subprocess + +def print_error(errors, log_path): + if not isinstance(errors, list): + errors = [errors] + errors = [error if isinstance(error, bytes) else error.encode('utf-8') for error in errors] + url = "https://github.com/JaidedAI/EasyOCR/tree/master/easyocr/DBNet" + print("Failed to compile dcn operator for DBNet.") + with open(log_path, "wb") as fid: + fid.write((datetime.now().strftime("%H:%M:%S - %d %b %Y") + "\n").encode('utf-8')) + fid.write("Failed to compile dcn operator for DBNet with the following error.\n".encode('utf-8')) + fid.write(("#"*42 + '\n').encode('utf-8')) + [fid.write(error) for error in errors] + print("Error message can be found in {}.".format(os.path.abspath(log_path))) + print("#"*42) + print("EasyOCR can still be used with CRAFT text detector (default).") + print("To use DBNet text detector, please check {} for troubleshoot and compile dcn operator manually.".format(url)) + +def print_success(text, log_path): + with open(log_path, "wb") as fid: + fid.write((datetime.now().strftime("%H:%M:%S - %d %b %Y") + "\n").encode('utf-8')) + fid.write((text + "\n").encode('utf-8')) + print(text) + +def main(): + cwd = os.getcwd() + parent_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) + log_path = os.path.join(parent_dir,'DBNet', 'log.txt') + try: + print("Compiling DCN operator...") + os.chdir(os.path.join(parent_dir,'DBNet','assets','ops','dcn')) + result = subprocess.run(["python", "setup.py", "build_ext", "--inplace"], capture_output=True) + if result.returncode == 0: + os.chdir(os.path.join(parent_dir,'DBNet')) + with open("dcn_compiling_success", "w+") as fp: + fp.write("") + success_message = "DCN operator is compiled successfully at {}.".format(os.path.abspath(os.path.join(parent_dir,'DBNet'))) + print_success(success_message, log_path) + else: + print(result.__dict__) + print_error([result.stdout, result.stderr], log_path) + except Exception as error: + print_error("{}".format(error), log_path) + finally: + os.chdir(cwd) + +if __name__ == '__main__': + main() + diff --git a/setup.py b/setup.py index 45223576f..8e08a3699 100644 --- a/setup.py +++ b/setup.py @@ -1,41 +1,41 @@ -""" -End-to-End Multi-Lingual Optical Character Recognition (OCR) Solution -""" -import os -import subprocess - -from setuptools import setup -from setuptools.command.develop import develop -from setuptools.command.install import install - - -def compile_dbnet_dcn(script_dir): - script_path = os.path.join(script_dir, "easyocr", "scripts", "compile_dbnet_dcn.py") - subprocess.run("python {}".format(script_path), shell=True) - - -class CustomCommand_install(install): - def run(self): - install.run(self) - compile_dbnet_dcn(self.install_lib) - - -class CustomCommand_develop(develop): - def run(self): - develop.run(self) - compile_dbnet_dcn(self.install_dir) - - -from setuptools import setup - -if __name__ == "__main__": - try: - setup(use_scm_version={"version_scheme": "no-guess-dev"}) - except Exception: - print( - "\n\nAn error occurred while building the project, " - + "please ensure you have the most updated version of setuptools, " - + "setuptools_scm and wheel with:\n" - + " pip install -U setuptools setuptools_scm wheel\n\n", - ) - raise +""" +End-to-End Multi-Lingual Optical Character Recognition (OCR) Solution +""" +import os +import subprocess + +from setuptools import setup +from setuptools.command.develop import develop +from setuptools.command.install import install + + +def compile_dbnet_dcn(script_dir): + script_path = os.path.join(script_dir, "easyocr", "scripts", "compile_dbnet_dcn.py") + subprocess.run(["python", script_path]) + + +class CustomCommand_install(install): + def run(self): + install.run(self) + compile_dbnet_dcn(self.install_lib) + + +class CustomCommand_develop(develop): + def run(self): + develop.run(self) + compile_dbnet_dcn(self.install_dir) + + +from setuptools import setup + +if __name__ == "__main__": + try: + setup(use_scm_version={"version_scheme": "no-guess-dev"}) + except Exception: + print( + "\n\nAn error occurred while building the project, " + + "please ensure you have the most updated version of setuptools, " + + "setuptools_scm and wheel with:\n" + + " pip install -U setuptools setuptools_scm wheel\n\n", + ) + raise From 1bb55dc914c628e540abf1c051fd059ecb19da49 Mon Sep 17 00:00:00 2001 From: JulianOrteil Date: Sun, 11 Sep 2022 20:08:37 -0500 Subject: [PATCH 12/17] Remove unnecessary `cmdclasses` from setup --- setup.cfg | 343 +++++++++++++++++++++++++++--------------------------- setup.py | 28 ----- 2 files changed, 170 insertions(+), 201 deletions(-) diff --git a/setup.cfg b/setup.cfg index d56f71973..a13976db2 100644 --- a/setup.cfg +++ b/setup.cfg @@ -1,173 +1,170 @@ -# This file is used to configure your project. -# Read more about the various options under: -# https://setuptools.pypa.io/en/latest/userguide/declarative_config.html -# https://setuptools.pypa.io/en/latest/references/keywords.html - -[metadata] -name = easyocr -description = End-to-End Multi-Lingual Optical Character Recognition (OCR) Solution -author = Rakpong Kittinaradorn -author_email = r.kittinaradorn@gmail.com -license = Apache License 2.0 -license_files = LICENSE -long_description = file: README.md -long_description_content_type = text/markdown; charset=UTF-8 -url = https://github.com/jaidedai/easyocr -# Add here related links, for example: -project_urls = - # Documentation = - Source = https://github.com/JaidedAI/EasyOCR - Changelog = https://github.com/JaidedAI/EasyOCR/blob/master/releasenotes.md - Tracker = https://github.com/JaidedAI/EasyOCR/issues - Download = https://pypi.org/project/easyocr/#files -keywords = - easyocr - ocr - optical - character - recognition - deep - learning - neural - network - -# Change if running only on Windows, Mac or Linux (comma-separated) -platforms = any - -# Add here all kinds of additional classifiers as defined under -# https://pypi.org/classifiers/ -classifiers = - Development Status :: 5 - Production/Stable - Programming Language :: Python - - -[options] -zip_safe = False -packages = find_namespace: -include_package_data = True -package_dir = - =. - # TODO: =src -cmdclass = - install = .setup.CustomCommand_install - develop = .setup.CustomCommand_develop - -# Require a min/specific Python version (comma-separated conditions) -python_requires = >=3.7 - -# Add here dependencies of your project (line-separated), e.g. requests>=2.2,<3.0. -# Version specifiers like >=2.2,<3.0 avoid problems due to API changes in -# new major versions. This works if the required packages follow Semantic Versioning. -# For more information, check out https://semver.org/. -install_requires = - importlib-metadata; python_version<"3.8" - typing_extensions; python_version<"3.10" - torch - torchvision>=0.5 - opencv-python-headless<=4.5.4.60 - scipy - numpy - Pillow - scikit-image - python-bidi - PyYAML - Shapely - pyclipper - ninja - - -[options.packages.find] -# TODO: src -where = . -exclude = - tests - -[options.extras_require] -# Add here additional requirements for extra features, to install with: -# `pip install easyocr[PDF]` like: -# PDF = ReportLab; RXP - -# Add here test requirements (semicolon/line-separated) -testing = - setuptools - pytest - pytest-cov - typing_extensions - -[options.entry_points] -# Add here console scripts: -console_scripts = - easyocr = easyocr.cli:main - -[tool:pytest] -# Specify command line options as you would do when invoking pytest directly. -# e.g. --cov-report html (or xml) for html/xml output or --junitxml junit.xml -# in order to write a coverage file that can be read by Jenkins. -# CAUTION: --cov flags may prohibit setting breakpoints while debugging. -# Comment those flags to avoid this pytest issue. -addopts = - --cov easyocr --cov-report term-missing - --verbose -norecursedirs = - dist - build - .tox -testpaths = tests -# Use pytest markers to select/deselect specific tests -# markers = -# slow: mark tests as slow (deselect with '-m "not slow"') -# system: mark end-to-end system tests - -[devpi:upload] -# Options for the devpi: PyPI server and packaging tool -# VCS export must be deactivated since we are using setuptools-scm -no_vcs = 1 -formats = bdist_wheel - -[flake8] -# Some sane defaults for the code style checker flake8 -extend_ignore = E203, W503 -# ^ Black-compatible -# E203 and W503 have edge cases handled by black -exclude = - .tox - build - dist - .eggs - docs/conf.py -strictness = long -show-source = True -max-line-length = 100 -max-complexity = 6 -# TODO: numpy, google, sphinx? -docstring-style = sphinx -ignore = D100, D104, D401, DAR103, DAR203, E800, N802, Q000, RST303, RST304, W504, WPS326, WPS402, WPS433, WPS436 -per-file-ignores = - # WPS421: Found wrong function call: print - setup.py: WPS421 - # WPS412: Found `__init__.py` module with logic - # WPS420: Found wrong keywork: del - # WPS440: Found block variables overlap: PackageNotFoundError, version - src/command_based_framework/__init__.py: WPS412, WPS420, WPS440 - -[isort] -include_trailing_comma = True -use_parentheses = True -multi_line_output = 3 -line_length = 100 - -[mypy] -allow_redefinition = False -check_untyped_defs = True -ignore_errors = False -ignore_missing_imports = True -implicit_reexport = False -local_partial_types = True -strict_optional = True -strict_equality = True -no_implicit_optional = True -warn_unused_ignores = True -warn_redundant_casts = True -warn_unused_configs = True -warn_unreachable = True -warn_no_return = True +# This file is used to configure your project. +# Read more about the various options under: +# https://setuptools.pypa.io/en/latest/userguide/declarative_config.html +# https://setuptools.pypa.io/en/latest/references/keywords.html + +[metadata] +name = easyocr +description = End-to-End Multi-Lingual Optical Character Recognition (OCR) Solution +author = Rakpong Kittinaradorn +author_email = r.kittinaradorn@gmail.com +license = Apache License 2.0 +license_files = LICENSE +long_description = file: README.md +long_description_content_type = text/markdown; charset=UTF-8 +url = https://github.com/jaidedai/easyocr +# Add here related links, for example: +project_urls = + # Documentation = + Source = https://github.com/JaidedAI/EasyOCR + Changelog = https://github.com/JaidedAI/EasyOCR/blob/master/releasenotes.md + Tracker = https://github.com/JaidedAI/EasyOCR/issues + Download = https://pypi.org/project/easyocr/#files +keywords = + easyocr + ocr + optical + character + recognition + deep + learning + neural + network + +# Change if running only on Windows, Mac or Linux (comma-separated) +platforms = any + +# Add here all kinds of additional classifiers as defined under +# https://pypi.org/classifiers/ +classifiers = + Development Status :: 5 - Production/Stable + Programming Language :: Python + + +[options] +zip_safe = False +packages = find_namespace: +include_package_data = True +package_dir = + =. + # TODO: =src + +# Require a min/specific Python version (comma-separated conditions) +python_requires = >=3.7 + +# Add here dependencies of your project (line-separated), e.g. requests>=2.2,<3.0. +# Version specifiers like >=2.2,<3.0 avoid problems due to API changes in +# new major versions. This works if the required packages follow Semantic Versioning. +# For more information, check out https://semver.org/. +install_requires = + importlib-metadata; python_version<"3.8" + typing_extensions; python_version<"3.10" + torch + torchvision>=0.5 + opencv-python-headless<=4.5.4.60 + scipy + numpy + Pillow + scikit-image + python-bidi + PyYAML + Shapely + pyclipper + ninja + + +[options.packages.find] +# TODO: src +where = . +exclude = + tests + +[options.extras_require] +# Add here additional requirements for extra features, to install with: +# `pip install easyocr[PDF]` like: +# PDF = ReportLab; RXP + +# Add here test requirements (semicolon/line-separated) +testing = + setuptools + pytest + pytest-cov + typing_extensions + +[options.entry_points] +# Add here console scripts: +console_scripts = + easyocr = easyocr.cli:main + +[tool:pytest] +# Specify command line options as you would do when invoking pytest directly. +# e.g. --cov-report html (or xml) for html/xml output or --junitxml junit.xml +# in order to write a coverage file that can be read by Jenkins. +# CAUTION: --cov flags may prohibit setting breakpoints while debugging. +# Comment those flags to avoid this pytest issue. +addopts = + --cov easyocr --cov-report term-missing + --verbose +norecursedirs = + dist + build + .tox +testpaths = tests +# Use pytest markers to select/deselect specific tests +# markers = +# slow: mark tests as slow (deselect with '-m "not slow"') +# system: mark end-to-end system tests + +[devpi:upload] +# Options for the devpi: PyPI server and packaging tool +# VCS export must be deactivated since we are using setuptools-scm +no_vcs = 1 +formats = bdist_wheel + +[flake8] +# Some sane defaults for the code style checker flake8 +extend_ignore = E203, W503 +# ^ Black-compatible +# E203 and W503 have edge cases handled by black +exclude = + .tox + build + dist + .eggs + docs/conf.py +strictness = long +show-source = True +max-line-length = 100 +max-complexity = 6 +# TODO: numpy, google, sphinx? +docstring-style = sphinx +ignore = D100, D104, D401, DAR103, DAR203, E800, N802, Q000, RST303, RST304, W504, WPS326, WPS402, WPS433, WPS436 +per-file-ignores = + # WPS421: Found wrong function call: print + setup.py: WPS421 + # WPS412: Found `__init__.py` module with logic + # WPS420: Found wrong keywork: del + # WPS440: Found block variables overlap: PackageNotFoundError, version + src/command_based_framework/__init__.py: WPS412, WPS420, WPS440 + +[isort] +include_trailing_comma = True +use_parentheses = True +multi_line_output = 3 +line_length = 100 + +[mypy] +allow_redefinition = False +check_untyped_defs = True +ignore_errors = False +ignore_missing_imports = True +implicit_reexport = False +local_partial_types = True +strict_optional = True +strict_equality = True +no_implicit_optional = True +warn_unused_ignores = True +warn_redundant_casts = True +warn_unused_configs = True +warn_unreachable = True +warn_no_return = True diff --git a/setup.py b/setup.py index 8e08a3699..5cf6ddabf 100644 --- a/setup.py +++ b/setup.py @@ -1,31 +1,3 @@ -""" -End-to-End Multi-Lingual Optical Character Recognition (OCR) Solution -""" -import os -import subprocess - -from setuptools import setup -from setuptools.command.develop import develop -from setuptools.command.install import install - - -def compile_dbnet_dcn(script_dir): - script_path = os.path.join(script_dir, "easyocr", "scripts", "compile_dbnet_dcn.py") - subprocess.run(["python", script_path]) - - -class CustomCommand_install(install): - def run(self): - install.run(self) - compile_dbnet_dcn(self.install_lib) - - -class CustomCommand_develop(develop): - def run(self): - develop.run(self) - compile_dbnet_dcn(self.install_dir) - - from setuptools import setup if __name__ == "__main__": From a6a84927a29ff80f02e2023286f3c04bf55e05e7 Mon Sep 17 00:00:00 2001 From: JulianOrteil Date: Sun, 11 Sep 2022 20:22:58 -0500 Subject: [PATCH 13/17] Temporarily disable flake8 linter; pre-commit run on just staged files --- .pre-commit-config.yaml | 112 ++++++++++++++++++++-------------------- tox.ini | 2 +- 2 files changed, 57 insertions(+), 57 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index b26fd2b10..e767119ed 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,56 +1,56 @@ -exclude: '^docs/conf.py' - -repos: -- repo: https://github.com/pre-commit/pre-commit-hooks - rev: v4.3.0 - hooks: - - id: trailing-whitespace - - id: check-added-large-files - - id: check-ast - - id: check-json - - id: check-merge-conflict - - id: check-xml - - id: check-yaml - - id: debug-statements - - id: end-of-file-fixer - - id: requirements-txt-fixer - - id: mixed-line-ending - args: ['--fix=auto'] - exclude: tests - -- repo: https://github.com/myint/autoflake - rev: v1.4 - hooks: - - id: autoflake - args: [ - --in-place, - --remove-all-unused-imports, - --remove-unused-variables, - ] - exclude: tests - -- repo: https://github.com/pycqa/isort - rev: 5.10.1 - hooks: - - id: isort - exclude: tests - -- repo: https://github.com/psf/black - rev: 22.6.0 - hooks: - - id: black - language_version: python3 - exclude: tests - -- repo: https://github.com/PyCQA/flake8 - rev: 4.0.1 - hooks: - - id: flake8 - additional_dependencies: [wemake-python-styleguide] # Comment to disable wemake - exclude: tests - -- repo: https://github.com/pre-commit/mirrors-mypy - rev: v0.971 - hooks: - - id: mypy - exclude: tests +exclude: '^docs/conf.py' + +repos: +- repo: https://github.com/pre-commit/pre-commit-hooks + rev: v4.3.0 + hooks: + - id: trailing-whitespace + - id: check-added-large-files + - id: check-ast + - id: check-json + - id: check-merge-conflict + - id: check-xml + - id: check-yaml + - id: debug-statements + - id: end-of-file-fixer + - id: requirements-txt-fixer + - id: mixed-line-ending + args: ['--fix=auto'] + exclude: tests + +- repo: https://github.com/myint/autoflake + rev: v1.4 + hooks: + - id: autoflake + args: [ + --in-place, + --remove-all-unused-imports, + --remove-unused-variables, + ] + exclude: tests + +- repo: https://github.com/pycqa/isort + rev: 5.10.1 + hooks: + - id: isort + exclude: tests + +- repo: https://github.com/psf/black + rev: 22.6.0 + hooks: + - id: black + language_version: python3 + exclude: tests + +# - repo: https://github.com/PyCQA/flake8 +# rev: 4.0.1 +# hooks: +# - id: flake8 +# additional_dependencies: [wemake-python-styleguide] # Comment to disable wemake +# exclude: tests + +- repo: https://github.com/pre-commit/mirrors-mypy + rev: v0.971 + hooks: + - id: mypy + exclude: tests diff --git a/tox.ini b/tox.ini index 22da23486..b9f89bf26 100644 --- a/tox.ini +++ b/tox.ini @@ -32,7 +32,7 @@ passenv = PROGRAMDATA SETUPTOOLS_* commands = - pre-commit run --all-files + pre-commit run [testenv:{build,clean,cleanall}] From 727f7e85c8696b3f7788411c64ff7dbfeb0a2b30 Mon Sep 17 00:00:00 2001 From: JulianOrteil Date: Tue, 13 Sep 2022 20:53:56 -0500 Subject: [PATCH 14/17] Create environment and requirement files for both conda and pip --- environment.yml | 120 +++++++++++++++++++++++++++++++++++++++++++++++ requirements.txt | 64 ++++++++++++++++++++----- 2 files changed, 172 insertions(+), 12 deletions(-) create mode 100644 environment.yml diff --git a/environment.yml b/environment.yml new file mode 100644 index 000000000..c2cf2d67b --- /dev/null +++ b/environment.yml @@ -0,0 +1,120 @@ +channels: + - pytorch + - defaults +dependencies: + - _libgcc_mutex=0.1=main + - _openmp_mutex=5.1=1_gnu + - blas=1.0=mkl + - brotlipy=0.7.0=py39h27cfd23_1003 + - bzip2=1.0.8=h7b6447c_0 + - ca-certificates=2022.07.19=h06a4308_0 + - certifi=2022.6.15=py39h06a4308_0 + - cffi=1.15.1=py39h74dc2b5_0 + - charset-normalizer=2.0.4=pyhd3eb1b0_0 + - cryptography=37.0.1=py39h9ce1e76_0 + - cudatoolkit=11.3.1=h2bc3f7f_2 + - ffmpeg=4.3=hf484d3e_0 + - freetype=2.11.0=h70c0345_0 + - giflib=5.2.1=h7b6447c_0 + - gmp=6.2.1=h295c915_3 + - gnutls=3.6.15=he1e5248_0 + - idna=3.3=pyhd3eb1b0_0 + - intel-openmp=2021.4.0=h06a4308_3561 + - jpeg=9e=h7f8727e_0 + - lame=3.100=h7b6447c_0 + - lcms2=2.12=h3be6417_0 + - ld_impl_linux-64=2.38=h1181459_1 + - lerc=3.0=h295c915_0 + - libdeflate=1.8=h7f8727e_5 + - libffi=3.3=he6710b0_2 + - libgcc-ng=11.2.0=h1234567_1 + - libgomp=11.2.0=h1234567_1 + - libiconv=1.16=h7f8727e_2 + - libidn2=2.3.2=h7f8727e_0 + - libpng=1.6.37=hbc83047_0 + - libstdcxx-ng=11.2.0=h1234567_1 + - libtasn1=4.16.0=h27cfd23_0 + - libtiff=4.4.0=hecacb30_0 + - libunistring=0.9.10=h27cfd23_0 + - libwebp=1.2.2=h55f646e_0 + - libwebp-base=1.2.2=h7f8727e_0 + - lz4-c=1.9.3=h295c915_1 + - mkl=2021.4.0=h06a4308_640 + - mkl-service=2.4.0=py39h7f8727e_0 + - mkl_fft=1.3.1=py39hd3c417c_0 + - mkl_random=1.2.2=py39h51133e4_0 + - ncurses=6.3=h5eee18b_3 + - nettle=3.7.3=hbbd107a_1 + - numpy=1.23.1=py39h6c91a56_0 + - numpy-base=1.23.1=py39ha15fc14_0 + - openh264=2.1.1=h4ff587b_0 + - openssl=1.1.1q=h7f8727e_0 + - pillow=9.2.0=py39hace64e9_1 + - pip=22.1.2=py39h06a4308_0 + - pycparser=2.21=pyhd3eb1b0_0 + - pyopenssl=22.0.0=pyhd3eb1b0_0 + - pysocks=1.7.1=py39h06a4308_0 + - python=3.9.13=haa1d7c7_1 + - pytorch=1.12.1=py3.9_cuda11.3_cudnn8.3.2_0 + - pytorch-mutex=1.0=cuda + - readline=8.1.2=h7f8727e_1 + - requests=2.28.1=py39h06a4308_0 + - setuptools=63.4.1=py39h06a4308_0 + - six=1.16.0=pyhd3eb1b0_1 + - sqlite=3.39.2=h5082296_0 + - tk=8.6.12=h1ccaba5_0 + - torchaudio=0.12.1=py39_cu113 + - torchvision=0.13.1=py39_cu113 + - typing_extensions=4.3.0=py39h06a4308_0 + - tzdata=2022c=h04d1e81_0 + - urllib3=1.26.11=py39h06a4308_0 + - wheel=0.37.1=pyhd3eb1b0_0 + - xz=5.2.5=h7f8727e_1 + - zlib=1.2.12=h5eee18b_3 + - zstd=1.5.2=ha4553b6_0 + - pip: + - asttokens==2.0.8 + - backcall==0.2.0 + - cfgv==3.3.1 + - decorator==5.1.1 + - distlib==0.3.6 + - executing==1.0.0 + - filelock==3.8.0 + - identify==2.5.5 + - imageio==2.21.3 + - ipython==8.5.0 + - jedi==0.18.1 + - matplotlib-inline==0.1.6 + - networkx==2.8.6 + - ninja==1.10.2.3 + - nodeenv==1.7.0 + - opencv-python-headless==4.5.4.60 + - packaging==21.3 + - parso==0.8.3 + - pexpect==4.8.0 + - pickleshare==0.7.5 + - platformdirs==2.5.2 + - pluggy==1.0.0 + - pre-commit==2.20.0 + - prompt-toolkit==3.0.31 + - ptyprocess==0.7.0 + - pure-eval==0.2.2 + - py==1.11.0 + - pyclipper==1.3.0.post3 + - pygments==2.13.0 + - pyparsing==3.0.9 + - python-bidi==0.4.2 + - pywavelets==1.3.0 + - pyyaml==6.0 + - scikit-image==0.19.3 + - scipy==1.9.1 + - shapely==1.8.4 + - stack-data==0.5.0 + - tifffile==2022.8.12 + - toml==0.10.2 + - tomli==2.0.1 + - tox==3.26.0 + - tox-conda==0.9.2 + - traitlets==5.4.0 + - virtualenv==20.16.5 + - wcwidth==0.2.5 diff --git a/requirements.txt b/requirements.txt index 9a62026ed..4add72776 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,12 +1,52 @@ -torch -torchvision>=0.5 -opencv-python-headless<=4.5.4.60 -scipy -numpy -Pillow -scikit-image -python-bidi -PyYAML -Shapely -pyclipper -ninja +asttokens==2.0.8 +backcall==0.2.0 +brotlipy==0.7.0 +cfgv==3.3.1 +decorator==5.1.1 +distlib==0.3.6 +executing==1.0.0 +filelock==3.8.0 +identify==2.5.5 +imageio==2.21.3 +ipython==8.5.0 +jedi==0.18.1 +matplotlib-inline==0.1.6 +mkl-fft==1.3.1 +mkl-service==2.4.0 +networkx==2.8.6 +ninja==1.10.2.3 +nodeenv==1.7.0 +opencv-python-headless==4.5.4.60 +packaging==21.3 +parso==0.8.3 +pexpect==4.8.0 +pickleshare==0.7.5 +Pillow==9.2.0 +platformdirs==2.5.2 +pluggy==1.0.0 +pre-commit==2.20.0 +prompt-toolkit==3.0.31 +ptyprocess==0.7.0 +pure-eval==0.2.2 +py==1.11.0 +pyclipper==1.3.0.post3 +Pygments==2.13.0 +pyparsing==3.0.9 +python-bidi==0.4.2 +PyWavelets==1.3.0 +PyYAML==6.0 +scikit-image==0.19.3 +scipy==1.9.1 +Shapely==1.8.4 +stack-data==0.5.0 +tifffile==2022.8.12 +toml==0.10.2 +tomli==2.0.1 +torch==1.12.1 +torchaudio==0.12.1 +torchvision==0.13.1 +tox==3.26.0 +tox-conda==0.9.2 +traitlets==5.4.0 +virtualenv==20.16.5 +wcwidth==0.2.5 From da1d22e5e2043148727a58f280f8f269b415d38b Mon Sep 17 00:00:00 2001 From: JulianOrteil Date: Tue, 13 Sep 2022 20:58:47 -0500 Subject: [PATCH 15/17] Require 100% coverage for tests to pass --- .coveragerc | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.coveragerc b/.coveragerc index 009a98f09..fa1f9289b 100644 --- a/.coveragerc +++ b/.coveragerc @@ -9,6 +9,9 @@ source = */site-packages/ [report] +# Require 100% coverage for tests to succeed +fail_under = 100 + # Regexes for lines to exclude from consideration exclude_lines = # Have to re-enable the standard pragma From 4377b344632509f2c56b483433ca27496cdcb84d Mon Sep 17 00:00:00 2001 From: JulianOrteil Date: Tue, 13 Sep 2022 22:26:11 -0500 Subject: [PATCH 16/17] Automate getting version of library from git tags --- easyocr/__init__.py | 17 +++++++++++++++-- 1 file changed, 15 insertions(+), 2 deletions(-) diff --git a/easyocr/__init__.py b/easyocr/__init__.py index b9cb76c18..928d799fe 100644 --- a/easyocr/__init__.py +++ b/easyocr/__init__.py @@ -1,3 +1,16 @@ -from .easyocr import Reader +import sys + +if sys.version_info[:2] >= (3, 8): + from importlib.metadata import PackageNotFoundError, version +else: + from importlib_metadata import PackageNotFoundError, version -__version__ = '1.6.1' +dist_name = "easyocr" +try: + __version__ = version(dist_name) +except PackageNotFoundError: + __version__ = "unknown" +finally: + del version, PackageNotFoundError + +from .easyocr import Reader From e6c7695314ba0e5ea83c2ef26f7c4fc29a780a88 Mon Sep 17 00:00:00 2001 From: JulianOrteil Date: Sat, 1 Oct 2022 18:38:28 -0500 Subject: [PATCH 17/17] Automate build on Windows --- easyocr/__init__.py | 4 - easyocr/scripts/compile_dbnet_dcn.py | 179 ++++++++++++--------------- setup.cfg | 11 +- setup.py | 60 ++------- tox.ini | 4 + 5 files changed, 99 insertions(+), 159 deletions(-) diff --git a/easyocr/__init__.py b/easyocr/__init__.py index 3f741b3c0..928d799fe 100644 --- a/easyocr/__init__.py +++ b/easyocr/__init__.py @@ -5,7 +5,6 @@ else: from importlib_metadata import PackageNotFoundError, version -<<<<<<< HEAD dist_name = "easyocr" try: __version__ = version(dist_name) @@ -15,6 +14,3 @@ del version, PackageNotFoundError from .easyocr import Reader -======= -__version__ = '1.6.2' ->>>>>>> 06753992c0aa7b9c74f46ce558bba4ba5a28493b diff --git a/easyocr/scripts/compile_dbnet_dcn.py b/easyocr/scripts/compile_dbnet_dcn.py index f41c42ba4..23f6875ec 100644 --- a/easyocr/scripts/compile_dbnet_dcn.py +++ b/easyocr/scripts/compile_dbnet_dcn.py @@ -1,137 +1,115 @@ -<<<<<<< HEAD -''' -Created by Jaided AI -Released Date: 19/08/2022 -Description: -A wrapper for DCN operator for DBNet. This script is called inside the setup.py -of EasyOCR. It can also be called as a standalone script to compile the operator -manually. -''' -import os -from datetime import datetime -import subprocess - -def print_error(errors, log_path): - if not isinstance(errors, list): - errors = [errors] - errors = [error if isinstance(error, bytes) else error.encode('utf-8') for error in errors] - url = "https://github.com/JaidedAI/EasyOCR/tree/master/easyocr/DBNet" - print("Failed to compile dcn operator for DBNet.") - with open(log_path, "wb") as fid: - fid.write((datetime.now().strftime("%H:%M:%S - %d %b %Y") + "\n").encode('utf-8')) - fid.write("Failed to compile dcn operator for DBNet with the following error.\n".encode('utf-8')) - fid.write(("#"*42 + '\n').encode('utf-8')) - [fid.write(error) for error in errors] - print("Error message can be found in {}.".format(os.path.abspath(log_path))) - print("#"*42) - print("EasyOCR can still be used with CRAFT text detector (default).") - print("To use DBNet text detector, please check {} for troubleshoot and compile dcn operator manually.".format(url)) - -def print_success(text, log_path): - with open(log_path, "wb") as fid: - fid.write((datetime.now().strftime("%H:%M:%S - %d %b %Y") + "\n").encode('utf-8')) - fid.write((text + "\n").encode('utf-8')) - print(text) - -def main(): - cwd = os.getcwd() - parent_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) - log_path = os.path.join(parent_dir,'DBNet', 'log.txt') - try: - print("Compiling DCN operator...") - os.chdir(os.path.join(parent_dir,'DBNet','assets','ops','dcn')) - result = subprocess.run(["python", "setup.py", "build_ext", "--inplace"], capture_output=True) - if result.returncode == 0: - os.chdir(os.path.join(parent_dir,'DBNet')) - with open("dcn_compiling_success", "w+") as fp: - fp.write("") - success_message = "DCN operator is compiled successfully at {}.".format(os.path.abspath(os.path.join(parent_dir,'DBNet'))) - print_success(success_message, log_path) - else: - print(result.__dict__) - print_error([result.stdout, result.stderr], log_path) - except Exception as error: - print_error("{}".format(error), log_path) - finally: - os.chdir(cwd) - -if __name__ == '__main__': - main() - -======= -''' +""" Created by Jaided AI Released Date: 19/08/2022 Description: A wrapper for DCN operator for DBNet. This script is called inside the setup.py of EasyOCR. It can also be called as a standalone script to compile the operator manually. -''' -import os +""" import glob -from datetime import datetime +import os import subprocess +import sys +from datetime import datetime -def print_error(errors, log_path): + +def print_error(errors, log_path): if not isinstance(errors, list): errors = [errors] - errors = [error if isinstance(error, bytes) else error.encode('utf-8') for error in errors] + errors = [ + error if isinstance(error, bytes) else error.encode("utf-8") for error in errors + ] url = "https://github.com/JaidedAI/EasyOCR/tree/master/easyocr/DBNet" print("Failed to compile dcn operator for DBNet.") with open(log_path, "wb") as fid: - fid.write((datetime.now().strftime("%H:%M:%S - %d %b %Y") + "\n").encode('utf-8')) - fid.write("Failed to compile dcn operator for DBNet with the following error.\n".encode('utf-8')) - fid.write(("#"*42 + '\n').encode('utf-8')) + fid.write( + (datetime.now().strftime("%H:%M:%S - %d %b %Y") + "\n").encode("utf-8") + ) + fid.write( + "Failed to compile dcn operator for DBNet with the following error.\n".encode( + "utf-8" + ) + ) + fid.write(("#" * 42 + "\n").encode("utf-8")) [fid.write(error) for error in errors] print("Error message can be found in {}.".format(os.path.abspath(log_path))) - print("#"*42) + print("#" * 42) print("EasyOCR can still be used with CRAFT text detector (default).") - print("To use DBNet text detector, please check {} for troubleshoot and compile dcn operator manually.".format(url)) + print( + "To use DBNet text detector, please check {} for troubleshoot and compile dcn operator manually.".format( + url + ) + ) + def print_success(text, log_path): with open(log_path, "wb") as fid: - fid.write((datetime.now().strftime("%H:%M:%S - %d %b %Y") + "\n").encode('utf-8')) - fid.write((text + "\n").encode('utf-8')) + fid.write( + (datetime.now().strftime("%H:%M:%S - %d %b %Y") + "\n").encode("utf-8") + ) + fid.write((text + "\n").encode("utf-8")) print(text) + def validate_compilation(parent_dir, log_path, cpu_or_cuda): - dcn_dir = os.path.join(parent_dir, 'DBNet', 'assets', 'ops', 'dcn') - #Just to be safe, check explicitly. - if cpu_or_cuda == 'cpu': - conv_cpu_exist = glob.glob(os.path.join(dcn_dir, 'deform_conv_cpu.*.so')) - pool_cpu_exist = glob.glob(os.path.join(dcn_dir, 'deform_pool_cpu.*.so')) - success_message = "DCN CPU operator is compiled successfully at {}.".format(os.path.abspath(os.path.join(parent_dir,'DBNet'))) - print_success(success_message, log_path) - return conv_cpu_exist and pool_cpu_exist - elif cpu_or_cuda == 'cuda': - conv_cuda_exist = glob.glob(os.path.join(dcn_dir, 'deform_conv_cuda.*.so')) - pool_cuda_exist = glob.glob(os.path.join(dcn_dir, 'deform_pool_cuda.*.so')) - success_message = "DCN CUDA operator is compiled successfully at {}.".format(os.path.abspath(os.path.join(parent_dir,'DBNet'))) - print_success(success_message, log_path) + dcn_dir = os.path.join(parent_dir, "DBNet", "assets", "ops", "dcn") + # Just to be safe, check explicitly. + if cpu_or_cuda == "cpu": + conv_cpu_exist = glob.glob( + os.path.join(dcn_dir, "deform_conv_cpu.*.so") + ) or glob.glob(os.path.join(dcn_dir, "deform_conv_cpu.*.pyd")) + pool_cpu_exist = glob.glob( + os.path.join(dcn_dir, "deform_pool_cpu.*.so") + ) or glob.glob(os.path.join(dcn_dir, "deform_pool_cpu.*.pyd")) + return conv_cpu_exist and pool_cpu_exist + elif cpu_or_cuda == "cuda": + conv_cuda_exist = glob.glob( + os.path.join(dcn_dir, "deform_conv_cuda.*.so") + ) or glob.glob(os.path.join(dcn_dir, "deform_conv_cuda.*.pyd")) + pool_cuda_exist = glob.glob( + os.path.join(dcn_dir, "deform_pool_cuda.*.so") + ) or glob.glob(os.path.join(dcn_dir, "deform_pool_cuda.*.pyd")) return conv_cuda_exist and pool_cuda_exist else: raise ValueError("'cpu_or_cuda' must be either 'cpu' or 'cuda'") - + + def main(): cwd = os.getcwd() parent_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) - log_path = os.path.join(parent_dir,'DBNet', 'log.txt') + log_path = os.path.join(parent_dir, "DBNet", "log.txt") try: + if sys.platform == "win32": + os.environ["CXX"] = os.environ["CXX_"] print("Compiling DCN operators...") - os.chdir(os.path.join(parent_dir,'DBNet','assets','ops','dcn')) + os.chdir(os.path.join(parent_dir, "DBNet", "assets", "ops", "dcn")) result = subprocess.run( - "python setup.py build_ext --inplace", shell=True, capture_output = True + ["python", "setup.py", "build_ext", "--inplace"], capture_output=True ) if result.returncode == 0: - os.chdir(os.path.join(parent_dir, 'DBNet')) - if validate_compilation(parent_dir, log_path, 'cpu'): - result = subprocess.run( - "touch dcn_cpu_compiling_success", shell=True, capture_output = True + os.chdir(os.path.join(parent_dir, "DBNet")) + if validate_compilation(parent_dir, log_path, "cpu"): + success_message = ( + "DCN CPU operator is compiled successfully at {}.".format( + os.path.abspath(os.path.join(parent_dir, "DBNet")) + ) ) - if validate_compilation(parent_dir, log_path, 'cuda'): - result = subprocess.run( - "touch dcn_cuda_compiling_success", shell=True, capture_output = True + print_success(success_message, log_path) + with open("dcn_cpu_compiling_success", "w+") as fp: + fp.write("") + if validate_compilation(parent_dir, log_path, "cuda"): + success_message = ( + "DCN CUDA operator is compiled successfully at {}.".format( + os.path.abspath(os.path.join(parent_dir, "DBNet")) + ) ) + print_success(success_message, log_path) + with open("dcn_cuda_compiling_success", "w+") as fp: + fp.write("") + success_message = "DCN operator is compiled successfully at {}.".format( + os.path.abspath(os.path.join(parent_dir, "DBNet")) + ) + print_success(success_message, log_path) else: print(result.__dict__) print_error([result.stdout, result.stderr], log_path) @@ -140,7 +118,6 @@ def main(): finally: os.chdir(cwd) -if __name__ == '__main__': - main() ->>>>>>> 06753992c0aa7b9c74f46ce558bba4ba5a28493b +if __name__ == "__main__": + main() diff --git a/setup.cfg b/setup.cfg index cd2bd7e86..b81a902c7 100644 --- a/setup.cfg +++ b/setup.cfg @@ -1,4 +1,3 @@ -<<<<<<< HEAD # This file is used to configure your project. # Read more about the various options under: # https://setuptools.pypa.io/en/latest/userguide/declarative_config.html @@ -80,6 +79,12 @@ where = . exclude = tests +[options.package_data] +easyocr = model/*, character/*.txt, dict/*, scripts/compile_dbnet_dcn.py, DBNet/*, DBNet/configs/*, DBNet/assets/ops/dcn/*.pyd + +[options.exclude_package_data] +easyocr = DBNet/assets/ops/dcn/build/* + [options.extras_require] # Add here additional requirements for extra features, to install with: # `pip install easyocr[PDF]` like: @@ -169,7 +174,3 @@ warn_redundant_casts = True warn_unused_configs = True warn_unreachable = True warn_no_return = True -======= -[metadata] -description_file = README.md ->>>>>>> 06753992c0aa7b9c74f46ce558bba4ba5a28493b diff --git a/setup.py b/setup.py index de7b71fa8..77fa0a538 100644 --- a/setup.py +++ b/setup.py @@ -1,51 +1,13 @@ -<<<<<<< HEAD -from setuptools import setup - -if __name__ == "__main__": - try: - setup(use_scm_version={"version_scheme": "no-guess-dev"}) - except Exception: - print( - "\n\nAn error occurred while building the project, " - + "please ensure you have the most updated version of setuptools, " - + "setuptools_scm and wheel with:\n" - + " pip install -U setuptools setuptools_scm wheel\n\n", - ) - raise -======= -""" -End-to-End Multi-Lingual Optical Character Recognition (OCR) Solution -""" -from io import open from setuptools import setup -with open('requirements.txt', encoding="utf-8-sig") as f: - requirements = f.readlines() - -def readme(): - with open('README.md', encoding="utf-8-sig") as f: - README = f.read() - return README - -setup( - name='easyocr', - packages=['easyocr'], - include_package_data=True, - version='1.6.2', - install_requires=requirements, - entry_points={"console_scripts": ["easyocr= easyocr.cli:main"]}, - license='Apache License 2.0', - description='End-to-End Multi-Lingual Optical Character Recognition (OCR) Solution', - long_description=readme(), - long_description_content_type="text/markdown", - author='Rakpong Kittinaradorn', - author_email='r.kittinaradorn@gmail.com', - url='https://github.com/jaidedai/easyocr', - download_url='https://github.com/jaidedai/easyocr.git', - keywords=['ocr optical character recognition deep learning neural network'], - classifiers=[ - 'Development Status :: 5 - Production/Stable' - ], - -) ->>>>>>> 06753992c0aa7b9c74f46ce558bba4ba5a28493b +if __name__ == "__main__": + try: + setup(use_scm_version={"version_scheme": "no-guess-dev"}) + except Exception: + print( + "\n\nAn error occurred while building the project, " + + "please ensure you have the most updated version of setuptools, " + + "setuptools_scm and wheel with:\n" + + " pip install -U setuptools setuptools_scm wheel\n\n", + ) + raise diff --git a/tox.ini b/tox.ini index b9f89bf26..c012ae8de 100644 --- a/tox.ini +++ b/tox.ini @@ -60,9 +60,13 @@ conda_channels = conda-forge passenv = SETUPTOOLS_* + VSINSTALLDIR +setenv = + CXX_ = {env:CXX} commands = clean,cleanall: python -c 'import shutil; [shutil.rmtree(p, True) for p in ("build", "dist", "docs/_build", "easyocr/DBNet/assets/ops/dcn/build")]' clean,cleanall: python -c 'import pathlib, shutil; [shutil.rmtree(p, True) for p in pathlib.Path(".").glob("*.egg-info")]' + cleanall: python -c 'import pathlib; [p.unlink(missing_ok=True) for p in pathlib.Path("easyocr/DBNet/assets/ops/dcn").glob("*.pyd")]' cleanall: python -c 'import pathlib; pathlib.Path("easyocr/DBNet/dcn_compiling_success").unlink(missing_ok=True); pathlib.Path("easyocr/DBNet/log.txt").unlink(missing_ok=True)' build: python easyocr/scripts/compile_dbnet_dcn.py build: python -m build --no-isolation {posargs}