├── .eggs
    ├── README.txt
    ├── bleach-3.1.0-py3.6.egg
    │   ├── EGG-INFO
    │   │   ├── DESCRIPTION.rst
    │   │   ├── PKG-INFO
    │   │   ├── RECORD
    │   │   ├── WHEEL
    │   │   ├── metadata.json
    │   │   ├── requires.txt
    │   │   └── top_level.txt
    │   └── bleach
    │   │   ├── __init__.py
    │   │   ├── __pycache__
    │   │       ├── __init__.cpython-36.pyc
    │   │       ├── callbacks.cpython-36.pyc
    │   │       ├── html5lib_shim.cpython-36.pyc
    │   │       ├── linkifier.cpython-36.pyc
    │   │       ├── sanitizer.cpython-36.pyc
    │   │       └── utils.cpython-36.pyc
    │   │   ├── _vendor
    │   │       ├── README.rst
    │   │       ├── __init__.py
    │   │       ├── __pycache__
    │   │       │   └── __init__.cpython-36.pyc
    │   │       ├── html5lib-1.0.1.dist-info
    │   │       │   ├── DESCRIPTION.rst
    │   │       │   ├── INSTALLER
    │   │       │   ├── LICENSE.txt
    │   │       │   ├── METADATA
    │   │       │   ├── RECORD
    │   │       │   ├── WHEEL
    │   │       │   ├── metadata.json
    │   │       │   └── top_level.txt
    │   │       ├── html5lib
    │   │       │   ├── __init__.py
    │   │       │   ├── __pycache__
    │   │       │   │   ├── __init__.cpython-36.pyc
    │   │       │   │   ├── _inputstream.cpython-36.pyc
    │   │       │   │   ├── _tokenizer.cpython-36.pyc
    │   │       │   │   ├── _utils.cpython-36.pyc
    │   │       │   │   ├── constants.cpython-36.pyc
    │   │       │   │   ├── html5parser.cpython-36.pyc
    │   │       │   │   └── serializer.cpython-36.pyc
    │   │       │   ├── _ihatexml.py
    │   │       │   ├── _inputstream.py
    │   │       │   ├── _tokenizer.py
    │   │       │   ├── _trie
    │   │       │   │   ├── __init__.py
    │   │       │   │   ├── __pycache__
    │   │       │   │   │   ├── __init__.cpython-36.pyc
    │   │       │   │   │   ├── _base.cpython-36.pyc
    │   │       │   │   │   ├── datrie.cpython-36.pyc
    │   │       │   │   │   └── py.cpython-36.pyc
    │   │       │   │   ├── _base.py
    │   │       │   │   ├── datrie.py
    │   │       │   │   └── py.py
    │   │       │   ├── _utils.py
    │   │       │   ├── constants.py
    │   │       │   ├── filters
    │   │       │   │   ├── __init__.py
    │   │       │   │   ├── __pycache__
    │   │       │   │   │   ├── __init__.cpython-36.pyc
    │   │       │   │   │   ├── base.cpython-36.pyc
    │   │       │   │   │   └── sanitizer.cpython-36.pyc
    │   │       │   │   ├── alphabeticalattributes.py
    │   │       │   │   ├── base.py
    │   │       │   │   ├── inject_meta_charset.py
    │   │       │   │   ├── lint.py
    │   │       │   │   ├── optionaltags.py
    │   │       │   │   ├── sanitizer.py
    │   │       │   │   └── whitespace.py
    │   │       │   ├── html5parser.py
    │   │       │   ├── serializer.py
    │   │       │   ├── treeadapters
    │   │       │   │   ├── __init__.py
    │   │       │   │   ├── genshi.py
    │   │       │   │   └── sax.py
    │   │       │   ├── treebuilders
    │   │       │   │   ├── __init__.py
    │   │       │   │   ├── __pycache__
    │   │       │   │   │   ├── __init__.cpython-36.pyc
    │   │       │   │   │   └── base.cpython-36.pyc
    │   │       │   │   ├── base.py
    │   │       │   │   ├── dom.py
    │   │       │   │   ├── etree.py
    │   │       │   │   └── etree_lxml.py
    │   │       │   └── treewalkers
    │   │       │   │   ├── __init__.py
    │   │       │   │   ├── __pycache__
    │   │       │   │       └── __init__.cpython-36.pyc
    │   │       │   │   ├── base.py
    │   │       │   │   ├── dom.py
    │   │       │   │   ├── etree.py
    │   │       │   │   ├── etree_lxml.py
    │   │       │   │   └── genshi.py
    │   │       ├── pip_install_vendor.sh
    │   │       └── vendor.txt
    │   │   ├── callbacks.py
    │   │   ├── html5lib_shim.py
    │   │   ├── linkifier.py
    │   │   ├── sanitizer.py
    │   │   └── utils.py
    └── webencodings-0.5.1-py3.6.egg
    │   ├── EGG-INFO
    │       ├── DESCRIPTION.rst
    │       ├── PKG-INFO
    │       ├── RECORD
    │       ├── WHEEL
    │       ├── metadata.json
    │       └── top_level.txt
    │   └── webencodings
    │       ├── __init__.py
    │       ├── __pycache__
    │           ├── __init__.cpython-36.pyc
    │           └── labels.cpython-36.pyc
    │       ├── labels.py
    │       ├── mklabels.py
    │       ├── tests.py
    │       └── x_user_defined.py
├── .gitattributes
├── .gitmodules
├── LICENSE
├── PathFlowAI.pdf
├── README.md
├── bin
    ├── install_apex
    └── install_lightnet
├── deprecated_work
    ├── build
    │   ├── lib
    │   │   └── pathflowai
    │   │   │   ├── __init__.py
    │   │   │   ├── cli_preprocessing.py
    │   │   │   ├── cli_visualizations.py
    │   │   │   ├── datasets.py
    │   │   │   ├── deprecated.py
    │   │   │   ├── fast_scnn.py
    │   │   │   ├── losses.py
    │   │   │   ├── model_training.py
    │   │   │   ├── models.py
    │   │   │   ├── monitor_memory_usage.py
    │   │   │   ├── sampler.py
    │   │   │   ├── schedulers.py
    │   │   │   ├── unet.py
    │   │   │   ├── unet2.py
    │   │   │   ├── unet3.py
    │   │   │   ├── unet4.py
    │   │   │   ├── utils.py
    │   │   │   └── visualize.py
    │   └── scripts-3.6
    │   │   └── install_apex
    ├── deprecated.py
    ├── dist
    │   ├── pathflowai-0.1-py3-none-any.whl
    │   ├── pathflowai-0.1-py3.6.egg
    │   ├── pathflowai-0.1.1-py3-none-any.whl
    │   ├── pathflowai-0.1.1.tar.gz
    │   └── pathflowai-0.1.tar.gz
    ├── pathflowai.egg-info
    │   ├── PKG-INFO
    │   ├── SOURCES.txt
    │   ├── dependency_links.txt
    │   ├── entry_points.txt
    │   ├── requires.txt
    │   └── top_level.txt
    ├── unet2.py
    ├── unet3.py
    └── unet4.py
├── docker
    ├── Dockerfile
    ├── Singularity
    ├── requirements.txt
    └── spec-list.pathflow.txt
├── docs
    ├── .buildinfo
    ├── .nojekyll
    ├── _images
    │   ├── 1.jpeg
    │   ├── 2.jpeg
    │   ├── 3.jpeg
    │   └── 4.jpeg
    ├── _modules
    │   ├── index.html
    │   └── pathflowai
    │   │   ├── datasets.html
    │   │   ├── losses.html
    │   │   ├── sampler.html
    │   │   ├── schedulers.html
    │   │   ├── utils.html
    │   │   └── visualize.html
    ├── _sources
    │   └── index.rst.txt
    ├── _static
    │   ├── basic.css
    │   ├── css
    │   │   ├── badge_only.css
    │   │   └── theme.css
    │   ├── doctools.js
    │   ├── documentation_options.js
    │   ├── file.png
    │   ├── fonts
    │   │   ├── Inconsolata-Bold.ttf
    │   │   ├── Inconsolata-Regular.ttf
    │   │   ├── Inconsolata.ttf
    │   │   ├── Lato-Bold.ttf
    │   │   ├── Lato-Regular.ttf
    │   │   ├── Lato
    │   │   │   ├── lato-bold.eot
    │   │   │   ├── lato-bold.ttf
    │   │   │   ├── lato-bold.woff
    │   │   │   ├── lato-bold.woff2
    │   │   │   ├── lato-bolditalic.eot
    │   │   │   ├── lato-bolditalic.ttf
    │   │   │   ├── lato-bolditalic.woff
    │   │   │   ├── lato-bolditalic.woff2
    │   │   │   ├── lato-italic.eot
    │   │   │   ├── lato-italic.ttf
    │   │   │   ├── lato-italic.woff
    │   │   │   ├── lato-italic.woff2
    │   │   │   ├── lato-regular.eot
    │   │   │   ├── lato-regular.ttf
    │   │   │   ├── lato-regular.woff
    │   │   │   └── lato-regular.woff2
    │   │   ├── RobotoSlab-Bold.ttf
    │   │   ├── RobotoSlab-Regular.ttf
    │   │   ├── RobotoSlab
    │   │   │   ├── roboto-slab-v7-bold.eot
    │   │   │   ├── roboto-slab-v7-bold.ttf
    │   │   │   ├── roboto-slab-v7-bold.woff
    │   │   │   ├── roboto-slab-v7-bold.woff2
    │   │   │   ├── roboto-slab-v7-regular.eot
    │   │   │   ├── roboto-slab-v7-regular.ttf
    │   │   │   ├── roboto-slab-v7-regular.woff
    │   │   │   └── roboto-slab-v7-regular.woff2
    │   │   ├── fontawesome-webfont.eot
    │   │   ├── fontawesome-webfont.svg
    │   │   ├── fontawesome-webfont.ttf
    │   │   ├── fontawesome-webfont.woff
    │   │   └── fontawesome-webfont.woff2
    │   ├── jquery-3.2.1.js
    │   ├── jquery.js
    │   ├── js
    │   │   ├── modernizr.min.js
    │   │   └── theme.js
    │   ├── language_data.js
    │   ├── minus.png
    │   ├── plus.png
    │   ├── pygments.css
    │   ├── searchtools.js
    │   ├── underscore-1.3.1.js
    │   └── underscore.js
    ├── genindex.html
    ├── index.html
    ├── objects.inv
    ├── old_docs
    │   ├── Makefile
    │   ├── conf.py
    │   ├── index.rst
    │   ├── ybuild
    │   │   ├── doctrees
    │   │   │   ├── environment.pickle
    │   │   │   └── index.doctree
    │   │   └── latex
    │   │   │   ├── 1.jpeg
    │   │   │   ├── 2.jpeg
    │   │   │   ├── 3.jpeg
    │   │   │   ├── 4.jpeg
    │   │   │   ├── LICRcyr2utf8.xdy
    │   │   │   ├── LICRlatin2utf8.xdy
    │   │   │   ├── LatinRules.xdy
    │   │   │   ├── Makefile
    │   │   │   ├── PathFlowAI.aux
    │   │   │   ├── PathFlowAI.fdb_latexmk
    │   │   │   ├── PathFlowAI.fls
    │   │   │   ├── PathFlowAI.idx
    │   │   │   ├── PathFlowAI.ilg
    │   │   │   ├── PathFlowAI.ind
    │   │   │   ├── PathFlowAI.log
    │   │   │   ├── PathFlowAI.tex
    │   │   │   ├── PathFlowAI.toc
    │   │   │   ├── footnotehyper-sphinx.sty
    │   │   │   ├── latexmkjarc
    │   │   │   ├── latexmkrc
    │   │   │   ├── make.bat
    │   │   │   ├── python.ist
    │   │   │   ├── sphinx.sty
    │   │   │   ├── sphinx.xdy
    │   │   │   ├── sphinxcyrillic.sty
    │   │   │   ├── sphinxhighlight.sty
    │   │   │   ├── sphinxhowto.cls
    │   │   │   ├── sphinxmanual.cls
    │   │   │   ├── sphinxmessages.sty
    │   │   │   └── sphinxmulticell.sty
    │   └── yimages
    │   │   ├── 1.jpeg
    │   │   ├── 2.jpeg
    │   │   ├── 3.jpeg
    │   │   └── 4.jpeg
    ├── py-modindex.html
    ├── search.html
    └── searchindex.js
├── example_transform_yamls
    ├── albumentation_transforms.yaml
    └── torch_transforms.yaml
├── experimental
    ├── datasets.py
    ├── get_anchors.py
    ├── get_bounding_boxes_from_seg_point_masks.py
    ├── get_counts.py
    └── object_detection.py
├── pathflowai
    ├── __init__.py
    ├── __pycache__
    │   ├── __init__.cpython-36.pyc
    │   ├── cli_preprocessing.cpython-36.pyc
    │   ├── cli_visualizations.cpython-36.pyc
    │   ├── datasets.cpython-36.pyc
    │   ├── fast_scnn.cpython-36.pyc
    │   ├── losses.cpython-36.pyc
    │   ├── model_training.cpython-36.pyc
    │   ├── models.cpython-36.pyc
    │   ├── monitor_memory_usage.cpython-36.pyc
    │   ├── sampler.cpython-36.pyc
    │   ├── schedulers.cpython-36.pyc
    │   ├── unet.cpython-36.pyc
    │   ├── unet2.cpython-36.pyc
    │   ├── unet4.cpython-36.pyc
    │   ├── utils.cpython-36.pyc
    │   └── visualize.cpython-36.pyc
    ├── cli_preprocessing.py
    ├── cli_visualizations.py
    ├── datasets.py
    ├── fast_scnn.py
    ├── losses.py
    ├── model_training.py
    ├── models.py
    ├── monitor_memory_usage.py
    ├── sampler.py
    ├── schedulers.py
    ├── stain_norm.py
    ├── unet.py
    ├── utils.py
    └── visualize.py
└── setup.py


/.eggs/README.txt:
--------------------------------------------------------------------------------
1 | This directory contains eggs that were downloaded by setuptools to build, test, and run plug-ins.
2 | 
3 | This directory caches those eggs to prevent repeated downloads.
4 | 
5 | However, it is safe to delete this directory.
6 | 
7 | 


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/.eggs/bleach-3.1.0-py3.6.egg/EGG-INFO/WHEEL:
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1 | Wheel-Version: 1.0
2 | Generator: bdist_wheel (0.30.0)
3 | Root-Is-Purelib: true
4 | Tag: py2-none-any
5 | Tag: py3-none-any
6 | 
7 | 


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/.eggs/bleach-3.1.0-py3.6.egg/EGG-INFO/metadata.json:
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1 | {"classifiers": ["Development Status :: 5 - Production/Stable", "Environment :: Web Environment", "Intended Audience :: Developers", "License :: OSI Approved :: Apache Software License", "Operating System :: OS Independent", "Programming Language :: Python", "Programming Language :: Python :: 2", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.4", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Programming Language :: Python :: Implementation :: CPython", "Programming Language :: Python :: Implementation :: PyPy", "Topic :: Software Development :: Libraries :: Python Modules"], "description_content_type": "UNKNOWN", "extensions": {"python.details": {"contacts": [{"email": "willkg@mozilla.com", "name": "Will Kahn-Greene", "role": "author"}], "document_names": {"description": "DESCRIPTION.rst"}, "project_urls": {"Home": "https://github.com/mozilla/bleach"}}}, "extras": [], "generator": "bdist_wheel (0.30.0)", "license": "Apache Software License", "metadata_version": "2.0", "name": "bleach", "requires_python": ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*", "run_requires": [{"requires": ["six (>=1.9.0)", "webencodings"]}], "summary": "An easy safelist-based HTML-sanitizing tool.", "test_requires": [{"requires": ["pytest (>=3.0.0)"]}], "version": "3.1.0"}


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/.eggs/bleach-3.1.0-py3.6.egg/EGG-INFO/requires.txt:
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1 | six>=1.9.0
2 | webencodings
3 | 


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/.eggs/bleach-3.1.0-py3.6.egg/EGG-INFO/top_level.txt:
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1 | bleach
2 | 


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/.eggs/bleach-3.1.0-py3.6.egg/bleach/__init__.py:
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  1 | # -*- coding: utf-8 -*-
  2 | 
  3 | from __future__ import unicode_literals
  4 | 
  5 | from pkg_resources import parse_version
  6 | 
  7 | from bleach.linkifier import (
  8 |     DEFAULT_CALLBACKS,
  9 |     Linker,
 10 | )
 11 | from bleach.sanitizer import (
 12 |     ALLOWED_ATTRIBUTES,
 13 |     ALLOWED_PROTOCOLS,
 14 |     ALLOWED_STYLES,
 15 |     ALLOWED_TAGS,
 16 |     Cleaner,
 17 | )
 18 | 
 19 | 
 20 | # yyyymmdd
 21 | __releasedate__ = '20190109'
 22 | # x.y.z or x.y.z.dev0 -- semver
 23 | __version__ = '3.1.0'
 24 | VERSION = parse_version(__version__)
 25 | 
 26 | 
 27 | __all__ = ['clean', 'linkify']
 28 | 
 29 | 
 30 | def clean(text, tags=ALLOWED_TAGS, attributes=ALLOWED_ATTRIBUTES,
 31 |           styles=ALLOWED_STYLES, protocols=ALLOWED_PROTOCOLS, strip=False,
 32 |           strip_comments=True):
 33 |     """Clean an HTML fragment of malicious content and return it
 34 | 
 35 |     This function is a security-focused function whose sole purpose is to
 36 |     remove malicious content from a string such that it can be displayed as
 37 |     content in a web page.
 38 | 
 39 |     This function is not designed to use to transform content to be used in
 40 |     non-web-page contexts.
 41 | 
 42 |     Example::
 43 | 
 44 |         import bleach
 45 | 
 46 |         better_text = bleach.clean(yucky_text)
 47 | 
 48 | 
 49 |     .. Note::
 50 | 
 51 |        If you're cleaning a lot of text and passing the same argument values or
 52 |        you want more configurability, consider using a
 53 |        :py:class:`bleach.sanitizer.Cleaner` instance.
 54 | 
 55 |     :arg str text: the text to clean
 56 | 
 57 |     :arg list tags: allowed list of tags; defaults to
 58 |         ``bleach.sanitizer.ALLOWED_TAGS``
 59 | 
 60 |     :arg dict attributes: allowed attributes; can be a callable, list or dict;
 61 |         defaults to ``bleach.sanitizer.ALLOWED_ATTRIBUTES``
 62 | 
 63 |     :arg list styles: allowed list of css styles; defaults to
 64 |         ``bleach.sanitizer.ALLOWED_STYLES``
 65 | 
 66 |     :arg list protocols: allowed list of protocols for links; defaults
 67 |         to ``bleach.sanitizer.ALLOWED_PROTOCOLS``
 68 | 
 69 |     :arg bool strip: whether or not to strip disallowed elements
 70 | 
 71 |     :arg bool strip_comments: whether or not to strip HTML comments
 72 | 
 73 |     :returns: cleaned text as unicode
 74 | 
 75 |     """
 76 |     cleaner = Cleaner(
 77 |         tags=tags,
 78 |         attributes=attributes,
 79 |         styles=styles,
 80 |         protocols=protocols,
 81 |         strip=strip,
 82 |         strip_comments=strip_comments,
 83 |     )
 84 |     return cleaner.clean(text)
 85 | 
 86 | 
 87 | def linkify(text, callbacks=DEFAULT_CALLBACKS, skip_tags=None, parse_email=False):
 88 |     """Convert URL-like strings in an HTML fragment to links
 89 | 
 90 |     This function converts strings that look like URLs, domain names and email
 91 |     addresses in text that may be an HTML fragment to links, while preserving:
 92 | 
 93 |     1. links already in the string
 94 |     2. urls found in attributes
 95 |     3. email addresses
 96 | 
 97 |     linkify does a best-effort approach and tries to recover from bad
 98 |     situations due to crazy text.
 99 | 
100 |     .. Note::
101 | 
102 |        If you're linking a lot of text and passing the same argument values or
103 |        you want more configurability, consider using a
104 |        :py:class:`bleach.linkifier.Linker` instance.
105 | 
106 |     .. Note::
107 | 
108 |        If you have text that you want to clean and then linkify, consider using
109 |        the :py:class:`bleach.linkifier.LinkifyFilter` as a filter in the clean
110 |        pass. That way you're not parsing the HTML twice.
111 | 
112 |     :arg str text: the text to linkify
113 | 
114 |     :arg list callbacks: list of callbacks to run when adjusting tag attributes;
115 |         defaults to ``bleach.linkifier.DEFAULT_CALLBACKS``
116 | 
117 |     :arg list skip_tags: list of tags that you don't want to linkify the
118 |         contents of; for example, you could set this to ``['pre']`` to skip
119 |         linkifying contents of ``pre`` tags
120 | 
121 |     :arg bool parse_email: whether or not to linkify email addresses
122 | 
123 |     :returns: linkified text as unicode
124 | 
125 |     """
126 |     linker = Linker(
127 |         callbacks=callbacks,
128 |         skip_tags=skip_tags,
129 |         parse_email=parse_email
130 |     )
131 |     return linker.linkify(text)
132 | 


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/.eggs/bleach-3.1.0-py3.6.egg/bleach/_vendor/README.rst:
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 1 | =======================
 2 | Vendored library policy
 3 | =======================
 4 | 
 5 | To simplify Bleach development, we're now vendoring certain libraries that
 6 | we use.
 7 | 
 8 | Vendored libraries must follow these rules:
 9 | 
10 | 1. Vendored libraries must be pure Python--no compiling.
11 | 2. Source code for the libary is included in this directory.
12 | 3. License must be included in this repo and in the Bleach distribution.
13 | 4. Requirements of the library become requirements of Bleach.
14 | 5. No modifications to the library may be made.
15 | 
16 | 
17 | Adding/Updating a vendored library
18 | ==================================
19 | 
20 | Way to vendor a library or update a version:
21 | 
22 | 1. Update ``vendor.txt`` with the library, version, and hash. You can use 
23 |    `hashin <https://pypi.org/project/hashin/>`_.
24 | 2. Remove all old files and directories of the old version.
25 | 3. Run ``pip_install_vendor.sh`` and check everything it produced in including
26 |    the ``.dist-info`` directory and contents.
27 | 
28 | 
29 | Reviewing a change involving a vendored library
30 | ===============================================
31 | 
32 | Way to verify a vendored library addition/update:
33 | 
34 | 1. Pull down the branch.
35 | 2. Delete all the old files and directories of the old version.
36 | 3. Run ``pip_install_vendor.sh``.
37 | 4. Run ``git diff`` and verify there are no changes.
38 | 


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1 | pip
2 | 


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/.eggs/bleach-3.1.0-py3.6.egg/bleach/_vendor/html5lib-1.0.1.dist-info/LICENSE.txt:
--------------------------------------------------------------------------------
 1 | Copyright (c) 2006-2013 James Graham and other contributors
 2 | 
 3 | Permission is hereby granted, free of charge, to any person obtaining
 4 | a copy of this software and associated documentation files (the
 5 | "Software"), to deal in the Software without restriction, including
 6 | without limitation the rights to use, copy, modify, merge, publish,
 7 | distribute, sublicense, and/or sell copies of the Software, and to
 8 | permit persons to whom the Software is furnished to do so, subject to
 9 | the following conditions:
10 | 
11 | The above copyright notice and this permission notice shall be
12 | included in all copies or substantial portions of the Software.
13 | 
14 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
15 | EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
16 | MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND
17 | NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE
18 | LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION
19 | OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION
20 | WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
21 | 


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/.eggs/bleach-3.1.0-py3.6.egg/bleach/_vendor/html5lib-1.0.1.dist-info/RECORD:
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10 | html5lib/_trie/_base.py,sha256=uJHVhzif9S0MJXgy9F98iEev5evi_rgUk5BmEbUSp8c,930
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12 | html5lib/_trie/py.py,sha256=zg7RZSHxJ8mLmuI_7VEIV8AomISrgkvqCP477AgXaG0,1763
13 | html5lib/filters/__init__.py,sha256=47DEQpj8HBSa-_TImW-5JCeuQeRkm5NMpJWZG3hSuFU,0
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19 | html5lib/filters/sanitizer.py,sha256=V6_cpCq9EXgXkL1CblWEUxSgHy466Hy8k0453x8PSs8,26236
20 | html5lib/filters/whitespace.py,sha256=8eWqZxd4UC4zlFGW6iyY6f-2uuT8pOCSALc3IZt7_t4,1214
21 | html5lib/treeadapters/__init__.py,sha256=18hyI-at2aBsdKzpwRwa5lGF1ipgctaTYXoU9En2ZQg,650
22 | html5lib/treeadapters/genshi.py,sha256=CH27pAsDKmu4ZGkAUrwty7u0KauGLCZRLPMzaO3M5vo,1715
23 | html5lib/treeadapters/sax.py,sha256=BKS8woQTnKiqeffHsxChUqL4q2ZR_wb5fc9MJ3zQC8s,1776
24 | html5lib/treebuilders/__init__.py,sha256=AysSJyvPfikCMMsTVvaxwkgDieELD5dfR8FJIAuq7hY,3592
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28 | html5lib/treebuilders/etree_lxml.py,sha256=9V0dXxbJYYq-Skgb5-_OL2NkVYpjioEb4CHajo0e9yI,14122
29 | html5lib/treewalkers/__init__.py,sha256=yhXxHpjlSqfQyUag3v8-vWjMPriFBU8YRAPNpDgBTn8,5714
30 | html5lib/treewalkers/base.py,sha256=ouiOsuSzvI0KgzdWP8PlxIaSNs9falhbiinAEc_UIJY,7476
31 | html5lib/treewalkers/dom.py,sha256=EHyFR8D8lYNnyDU9lx_IKigVJRyecUGua0mOi7HBukc,1413
32 | html5lib/treewalkers/etree.py,sha256=gRzfuNnWg6r-fvtXRp4xPVTC1CHPowcn8Dc4-WcDoOg,4538
33 | html5lib/treewalkers/etree_lxml.py,sha256=AR07dDrdkDqrQT4yNK_5WeGiZMHfOrM3ZmmII6YrSgs,6297
34 | html5lib/treewalkers/genshi.py,sha256=4D2PECZ5n3ZN3qu3jMl9yY7B81jnQApBQSVlfaIuYbA,2309
35 | html5lib-1.0.1.dist-info/DESCRIPTION.rst,sha256=1QkiA38mSikkzyQO1kAQXkBUtQSTl-MR63Zd2TMe06s,13763
36 | html5lib-1.0.1.dist-info/LICENSE.txt,sha256=FqOZkWGekvGGgJMtoqkZn999ld8-yu3FLqBiGKq6_W8,1084
37 | html5lib-1.0.1.dist-info/METADATA,sha256=ViKKPHTrTam-_oHIB2cxmtg4gKgqdfl4ahDnIWBdyUE,15484
38 | html5lib-1.0.1.dist-info/RECORD,,
39 | html5lib-1.0.1.dist-info/WHEEL,sha256=5wvfB7GvgZAbKBSE9uX9Zbi6LCL-_KgezgHblXhCRnM,113
40 | html5lib-1.0.1.dist-info/metadata.json,sha256=bbLAvG6pYvgK2EdWNVi1mNa5pu9bI4qLwGm0IvuesFA,1731
41 | html5lib-1.0.1.dist-info/top_level.txt,sha256=XEX6CHpskSmvjJB4tP6m4Q5NYXhIf_0ceMc0PNbzJPQ,9
42 | html5lib-1.0.1.dist-info/INSTALLER,sha256=zuuue4knoyJ-UwPPXg8fezS7VCrXJQrAP7zeNuwvFQg,4
43 | 


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/.eggs/bleach-3.1.0-py3.6.egg/bleach/_vendor/html5lib-1.0.1.dist-info/WHEEL:
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1 | Wheel-Version: 1.0
2 | Generator: bdist_wheel (0.30.0.a0)
3 | Root-Is-Purelib: true
4 | Tag: py2-none-any
5 | Tag: py3-none-any
6 | 
7 | 


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/.eggs/bleach-3.1.0-py3.6.egg/bleach/_vendor/html5lib-1.0.1.dist-info/metadata.json:
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1 | {"classifiers": ["Development Status :: 5 - Production/Stable", "Intended Audience :: Developers", "License :: OSI Approved :: MIT License", "Operating System :: OS Independent", "Programming Language :: Python", "Programming Language :: Python :: 2", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.3", "Programming Language :: Python :: 3.4", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Topic :: Software Development :: Libraries :: Python Modules", "Topic :: Text Processing :: Markup :: HTML"], "description_content_type": "UNKNOWN", "extensions": {"python.details": {"contacts": [{"email": "james@hoppipolla.co.uk", "name": "James Graham", "role": "author"}], "document_names": {"description": "DESCRIPTION.rst", "license": "LICENSE.txt"}, "project_urls": {"Home": "https://github.com/html5lib/html5lib-python"}}}, "extras": ["all", "chardet", "datrie", "genshi", "lxml"], "generator": "bdist_wheel (0.30.0.a0)", "license": "MIT License", "metadata_version": "2.0", "name": "html5lib", "run_requires": [{"extra": "all", "requires": ["chardet (>=2.2)", "genshi"]}, {"extra": "chardet", "requires": ["chardet (>=2.2)"]}, {"extra": "genshi", "requires": ["genshi"]}, {"requires": ["six (>=1.9)", "webencodings"]}, {"environment": "platform_python_implementation == 'CPython'", "extra": "all", "requires": ["datrie", "lxml"]}, {"environment": "platform_python_implementation == 'CPython'", "extra": "datrie", "requires": ["datrie"]}, {"environment": "platform_python_implementation == 'CPython'", "extra": "lxml", "requires": ["lxml"]}], "summary": "HTML parser based on the WHATWG HTML specification", "version": "1.0.1"}


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/.eggs/bleach-3.1.0-py3.6.egg/bleach/_vendor/html5lib-1.0.1.dist-info/top_level.txt:
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1 | html5lib
2 | 


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/.eggs/bleach-3.1.0-py3.6.egg/bleach/_vendor/html5lib/__init__.py:
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 1 | """
 2 | HTML parsing library based on the `WHATWG HTML specification
 3 | <https://whatwg.org/html>`_. The parser is designed to be compatible with
 4 | existing HTML found in the wild and implements well-defined error recovery that
 5 | is largely compatible with modern desktop web browsers.
 6 | 
 7 | Example usage::
 8 | 
 9 |     import html5lib
10 |     with open("my_document.html", "rb") as f:
11 |         tree = html5lib.parse(f)
12 | 
13 | For convenience, this module re-exports the following names:
14 | 
15 | * :func:`~.html5parser.parse`
16 | * :func:`~.html5parser.parseFragment`
17 | * :class:`~.html5parser.HTMLParser`
18 | * :func:`~.treebuilders.getTreeBuilder`
19 | * :func:`~.treewalkers.getTreeWalker`
20 | * :func:`~.serializer.serialize`
21 | """
22 | 
23 | from __future__ import absolute_import, division, unicode_literals
24 | 
25 | from .html5parser import HTMLParser, parse, parseFragment
26 | from .treebuilders import getTreeBuilder
27 | from .treewalkers import getTreeWalker
28 | from .serializer import serialize
29 | 
30 | __all__ = ["HTMLParser", "parse", "parseFragment", "getTreeBuilder",
31 |            "getTreeWalker", "serialize"]
32 | 
33 | # this has to be at the top level, see how setup.py parses this
34 | #: Distribution version number.
35 | __version__ = "1.0.1"
36 | 


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/.eggs/bleach-3.1.0-py3.6.egg/bleach/_vendor/html5lib/_trie/__init__.py:
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 1 | from __future__ import absolute_import, division, unicode_literals
 2 | 
 3 | from .py import Trie as PyTrie
 4 | 
 5 | Trie = PyTrie
 6 | 
 7 | # pylint:disable=wrong-import-position
 8 | try:
 9 |     from .datrie import Trie as DATrie
10 | except ImportError:
11 |     pass
12 | else:
13 |     Trie = DATrie
14 | # pylint:enable=wrong-import-position
15 | 


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/.eggs/bleach-3.1.0-py3.6.egg/bleach/_vendor/html5lib/_trie/_base.py:
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 1 | from __future__ import absolute_import, division, unicode_literals
 2 | 
 3 | from collections import Mapping
 4 | 
 5 | 
 6 | class Trie(Mapping):
 7 |     """Abstract base class for tries"""
 8 | 
 9 |     def keys(self, prefix=None):
10 |         # pylint:disable=arguments-differ
11 |         keys = super(Trie, self).keys()
12 | 
13 |         if prefix is None:
14 |             return set(keys)
15 | 
16 |         return {x for x in keys if x.startswith(prefix)}
17 | 
18 |     def has_keys_with_prefix(self, prefix):
19 |         for key in self.keys():
20 |             if key.startswith(prefix):
21 |                 return True
22 | 
23 |         return False
24 | 
25 |     def longest_prefix(self, prefix):
26 |         if prefix in self:
27 |             return prefix
28 | 
29 |         for i in range(1, len(prefix) + 1):
30 |             if prefix[:-i] in self:
31 |                 return prefix[:-i]
32 | 
33 |         raise KeyError(prefix)
34 | 
35 |     def longest_prefix_item(self, prefix):
36 |         lprefix = self.longest_prefix(prefix)
37 |         return (lprefix, self[lprefix])
38 | 


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/.eggs/bleach-3.1.0-py3.6.egg/bleach/_vendor/html5lib/_trie/datrie.py:
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 1 | from __future__ import absolute_import, division, unicode_literals
 2 | 
 3 | from datrie import Trie as DATrie
 4 | from six import text_type
 5 | 
 6 | from ._base import Trie as ABCTrie
 7 | 
 8 | 
 9 | class Trie(ABCTrie):
10 |     def __init__(self, data):
11 |         chars = set()
12 |         for key in data.keys():
13 |             if not isinstance(key, text_type):
14 |                 raise TypeError("All keys must be strings")
15 |             for char in key:
16 |                 chars.add(char)
17 | 
18 |         self._data = DATrie("".join(chars))
19 |         for key, value in data.items():
20 |             self._data[key] = value
21 | 
22 |     def __contains__(self, key):
23 |         return key in self._data
24 | 
25 |     def __len__(self):
26 |         return len(self._data)
27 | 
28 |     def __iter__(self):
29 |         raise NotImplementedError()
30 | 
31 |     def __getitem__(self, key):
32 |         return self._data[key]
33 | 
34 |     def keys(self, prefix=None):
35 |         return self._data.keys(prefix)
36 | 
37 |     def has_keys_with_prefix(self, prefix):
38 |         return self._data.has_keys_with_prefix(prefix)
39 | 
40 |     def longest_prefix(self, prefix):
41 |         return self._data.longest_prefix(prefix)
42 | 
43 |     def longest_prefix_item(self, prefix):
44 |         return self._data.longest_prefix_item(prefix)
45 | 


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/.eggs/bleach-3.1.0-py3.6.egg/bleach/_vendor/html5lib/_trie/py.py:
--------------------------------------------------------------------------------
 1 | from __future__ import absolute_import, division, unicode_literals
 2 | from six import text_type
 3 | 
 4 | from bisect import bisect_left
 5 | 
 6 | from ._base import Trie as ABCTrie
 7 | 
 8 | 
 9 | class Trie(ABCTrie):
10 |     def __init__(self, data):
11 |         if not all(isinstance(x, text_type) for x in data.keys()):
12 |             raise TypeError("All keys must be strings")
13 | 
14 |         self._data = data
15 |         self._keys = sorted(data.keys())
16 |         self._cachestr = ""
17 |         self._cachepoints = (0, len(data))
18 | 
19 |     def __contains__(self, key):
20 |         return key in self._data
21 | 
22 |     def __len__(self):
23 |         return len(self._data)
24 | 
25 |     def __iter__(self):
26 |         return iter(self._data)
27 | 
28 |     def __getitem__(self, key):
29 |         return self._data[key]
30 | 
31 |     def keys(self, prefix=None):
32 |         if prefix is None or prefix == "" or not self._keys:
33 |             return set(self._keys)
34 | 
35 |         if prefix.startswith(self._cachestr):
36 |             lo, hi = self._cachepoints
37 |             start = i = bisect_left(self._keys, prefix, lo, hi)
38 |         else:
39 |             start = i = bisect_left(self._keys, prefix)
40 | 
41 |         keys = set()
42 |         if start == len(self._keys):
43 |             return keys
44 | 
45 |         while self._keys[i].startswith(prefix):
46 |             keys.add(self._keys[i])
47 |             i += 1
48 | 
49 |         self._cachestr = prefix
50 |         self._cachepoints = (start, i)
51 | 
52 |         return keys
53 | 
54 |     def has_keys_with_prefix(self, prefix):
55 |         if prefix in self._data:
56 |             return True
57 | 
58 |         if prefix.startswith(self._cachestr):
59 |             lo, hi = self._cachepoints
60 |             i = bisect_left(self._keys, prefix, lo, hi)
61 |         else:
62 |             i = bisect_left(self._keys, prefix)
63 | 
64 |         if i == len(self._keys):
65 |             return False
66 | 
67 |         return self._keys[i].startswith(prefix)
68 | 


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/.eggs/bleach-3.1.0-py3.6.egg/bleach/_vendor/html5lib/_utils.py:
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  1 | from __future__ import absolute_import, division, unicode_literals
  2 | 
  3 | from types import ModuleType
  4 | 
  5 | from six import text_type
  6 | 
  7 | try:
  8 |     import xml.etree.cElementTree as default_etree
  9 | except ImportError:
 10 |     import xml.etree.ElementTree as default_etree
 11 | 
 12 | 
 13 | __all__ = ["default_etree", "MethodDispatcher", "isSurrogatePair",
 14 |            "surrogatePairToCodepoint", "moduleFactoryFactory",
 15 |            "supports_lone_surrogates"]
 16 | 
 17 | 
 18 | # Platforms not supporting lone surrogates (\uD800-\uDFFF) should be
 19 | # caught by the below test. In general this would be any platform
 20 | # using UTF-16 as its encoding of unicode strings, such as
 21 | # Jython. This is because UTF-16 itself is based on the use of such
 22 | # surrogates, and there is no mechanism to further escape such
 23 | # escapes.
 24 | try:
 25 |     _x = eval('"\\uD800"')  # pylint:disable=eval-used
 26 |     if not isinstance(_x, text_type):
 27 |         # We need this with u"" because of http://bugs.jython.org/issue2039
 28 |         _x = eval('u"\\uD800"')  # pylint:disable=eval-used
 29 |         assert isinstance(_x, text_type)
 30 | except:  # pylint:disable=bare-except
 31 |     supports_lone_surrogates = False
 32 | else:
 33 |     supports_lone_surrogates = True
 34 | 
 35 | 
 36 | class MethodDispatcher(dict):
 37 |     """Dict with 2 special properties:
 38 | 
 39 |     On initiation, keys that are lists, sets or tuples are converted to
 40 |     multiple keys so accessing any one of the items in the original
 41 |     list-like object returns the matching value
 42 | 
 43 |     md = MethodDispatcher({("foo", "bar"):"baz"})
 44 |     md["foo"] == "baz"
 45 | 
 46 |     A default value which can be set through the default attribute.
 47 |     """
 48 | 
 49 |     def __init__(self, items=()):
 50 |         # Using _dictEntries instead of directly assigning to self is about
 51 |         # twice as fast. Please do careful performance testing before changing
 52 |         # anything here.
 53 |         _dictEntries = []
 54 |         for name, value in items:
 55 |             if isinstance(name, (list, tuple, frozenset, set)):
 56 |                 for item in name:
 57 |                     _dictEntries.append((item, value))
 58 |             else:
 59 |                 _dictEntries.append((name, value))
 60 |         dict.__init__(self, _dictEntries)
 61 |         assert len(self) == len(_dictEntries)
 62 |         self.default = None
 63 | 
 64 |     def __getitem__(self, key):
 65 |         return dict.get(self, key, self.default)
 66 | 
 67 | 
 68 | # Some utility functions to deal with weirdness around UCS2 vs UCS4
 69 | # python builds
 70 | 
 71 | def isSurrogatePair(data):
 72 |     return (len(data) == 2 and
 73 |             ord(data[0]) >= 0xD800 and ord(data[0]) <= 0xDBFF and
 74 |             ord(data[1]) >= 0xDC00 and ord(data[1]) <= 0xDFFF)
 75 | 
 76 | 
 77 | def surrogatePairToCodepoint(data):
 78 |     char_val = (0x10000 + (ord(data[0]) - 0xD800) * 0x400 +
 79 |                 (ord(data[1]) - 0xDC00))
 80 |     return char_val
 81 | 
 82 | # Module Factory Factory (no, this isn't Java, I know)
 83 | # Here to stop this being duplicated all over the place.
 84 | 
 85 | 
 86 | def moduleFactoryFactory(factory):
 87 |     moduleCache = {}
 88 | 
 89 |     def moduleFactory(baseModule, *args, **kwargs):
 90 |         if isinstance(ModuleType.__name__, type("")):
 91 |             name = "_%s_factory" % baseModule.__name__
 92 |         else:
 93 |             name = b"_%s_factory" % baseModule.__name__
 94 | 
 95 |         kwargs_tuple = tuple(kwargs.items())
 96 | 
 97 |         try:
 98 |             return moduleCache[name][args][kwargs_tuple]
 99 |         except KeyError:
100 |             mod = ModuleType(name)
101 |             objs = factory(baseModule, *args, **kwargs)
102 |             mod.__dict__.update(objs)
103 |             if "name" not in moduleCache:
104 |                 moduleCache[name] = {}
105 |             if "args" not in moduleCache[name]:
106 |                 moduleCache[name][args] = {}
107 |             if "kwargs" not in moduleCache[name][args]:
108 |                 moduleCache[name][args][kwargs_tuple] = {}
109 |             moduleCache[name][args][kwargs_tuple] = mod
110 |             return mod
111 | 
112 |     return moduleFactory
113 | 
114 | 
115 | def memoize(func):
116 |     cache = {}
117 | 
118 |     def wrapped(*args, **kwargs):
119 |         key = (tuple(args), tuple(kwargs.items()))
120 |         if key not in cache:
121 |             cache[key] = func(*args, **kwargs)
122 |         return cache[key]
123 | 
124 |     return wrapped
125 | 


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/.eggs/bleach-3.1.0-py3.6.egg/bleach/_vendor/html5lib/filters/alphabeticalattributes.py:
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 1 | from __future__ import absolute_import, division, unicode_literals
 2 | 
 3 | from . import base
 4 | 
 5 | from collections import OrderedDict
 6 | 
 7 | 
 8 | def _attr_key(attr):
 9 |     """Return an appropriate key for an attribute for sorting
10 | 
11 |     Attributes have a namespace that can be either ``None`` or a string. We
12 |     can't compare the two because they're different types, so we convert
13 |     ``None`` to an empty string first.
14 | 
15 |     """
16 |     return (attr[0][0] or ''), attr[0][1]
17 | 
18 | 
19 | class Filter(base.Filter):
20 |     """Alphabetizes attributes for elements"""
21 |     def __iter__(self):
22 |         for token in base.Filter.__iter__(self):
23 |             if token["type"] in ("StartTag", "EmptyTag"):
24 |                 attrs = OrderedDict()
25 |                 for name, value in sorted(token["data"].items(),
26 |                                           key=_attr_key):
27 |                     attrs[name] = value
28 |                 token["data"] = attrs
29 |             yield token
30 | 


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/.eggs/bleach-3.1.0-py3.6.egg/bleach/_vendor/html5lib/filters/base.py:
--------------------------------------------------------------------------------
 1 | from __future__ import absolute_import, division, unicode_literals
 2 | 
 3 | 
 4 | class Filter(object):
 5 |     def __init__(self, source):
 6 |         self.source = source
 7 | 
 8 |     def __iter__(self):
 9 |         return iter(self.source)
10 | 
11 |     def __getattr__(self, name):
12 |         return getattr(self.source, name)
13 | 


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/.eggs/bleach-3.1.0-py3.6.egg/bleach/_vendor/html5lib/filters/inject_meta_charset.py:
--------------------------------------------------------------------------------
 1 | from __future__ import absolute_import, division, unicode_literals
 2 | 
 3 | from . import base
 4 | 
 5 | 
 6 | class Filter(base.Filter):
 7 |     """Injects ``<meta charset=ENCODING>`` tag into head of document"""
 8 |     def __init__(self, source, encoding):
 9 |         """Creates a Filter
10 | 
11 |         :arg source: the source token stream
12 | 
13 |         :arg encoding: the encoding to set
14 | 
15 |         """
16 |         base.Filter.__init__(self, source)
17 |         self.encoding = encoding
18 | 
19 |     def __iter__(self):
20 |         state = "pre_head"
21 |         meta_found = (self.encoding is None)
22 |         pending = []
23 | 
24 |         for token in base.Filter.__iter__(self):
25 |             type = token["type"]
26 |             if type == "StartTag":
27 |                 if token["name"].lower() == "head":
28 |                     state = "in_head"
29 | 
30 |             elif type == "EmptyTag":
31 |                 if token["name"].lower() == "meta":
32 |                     # replace charset with actual encoding
33 |                     has_http_equiv_content_type = False
34 |                     for (namespace, name), value in token["data"].items():
35 |                         if namespace is not None:
36 |                             continue
37 |                         elif name.lower() == 'charset':
38 |                             token["data"][(namespace, name)] = self.encoding
39 |                             meta_found = True
40 |                             break
41 |                         elif name == 'http-equiv' and value.lower() == 'content-type':
42 |                             has_http_equiv_content_type = True
43 |                     else:
44 |                         if has_http_equiv_content_type and (None, "content") in token["data"]:
45 |                             token["data"][(None, "content")] = 'text/html; charset=%s' % self.encoding
46 |                             meta_found = True
47 | 
48 |                 elif token["name"].lower() == "head" and not meta_found:
49 |                     # insert meta into empty head
50 |                     yield {"type": "StartTag", "name": "head",
51 |                            "data": token["data"]}
52 |                     yield {"type": "EmptyTag", "name": "meta",
53 |                            "data": {(None, "charset"): self.encoding}}
54 |                     yield {"type": "EndTag", "name": "head"}
55 |                     meta_found = True
56 |                     continue
57 | 
58 |             elif type == "EndTag":
59 |                 if token["name"].lower() == "head" and pending:
60 |                     # insert meta into head (if necessary) and flush pending queue
61 |                     yield pending.pop(0)
62 |                     if not meta_found:
63 |                         yield {"type": "EmptyTag", "name": "meta",
64 |                                "data": {(None, "charset"): self.encoding}}
65 |                     while pending:
66 |                         yield pending.pop(0)
67 |                     meta_found = True
68 |                     state = "post_head"
69 | 
70 |             if state == "in_head":
71 |                 pending.append(token)
72 |             else:
73 |                 yield token
74 | 


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/.eggs/bleach-3.1.0-py3.6.egg/bleach/_vendor/html5lib/filters/lint.py:
--------------------------------------------------------------------------------
 1 | from __future__ import absolute_import, division, unicode_literals
 2 | 
 3 | from six import text_type
 4 | 
 5 | from . import base
 6 | from ..constants import namespaces, voidElements
 7 | 
 8 | from ..constants import spaceCharacters
 9 | spaceCharacters = "".join(spaceCharacters)
10 | 
11 | 
12 | class Filter(base.Filter):
13 |     """Lints the token stream for errors
14 | 
15 |     If it finds any errors, it'll raise an ``AssertionError``.
16 | 
17 |     """
18 |     def __init__(self, source, require_matching_tags=True):
19 |         """Creates a Filter
20 | 
21 |         :arg source: the source token stream
22 | 
23 |         :arg require_matching_tags: whether or not to require matching tags
24 | 
25 |         """
26 |         super(Filter, self).__init__(source)
27 |         self.require_matching_tags = require_matching_tags
28 | 
29 |     def __iter__(self):
30 |         open_elements = []
31 |         for token in base.Filter.__iter__(self):
32 |             type = token["type"]
33 |             if type in ("StartTag", "EmptyTag"):
34 |                 namespace = token["namespace"]
35 |                 name = token["name"]
36 |                 assert namespace is None or isinstance(namespace, text_type)
37 |                 assert namespace != ""
38 |                 assert isinstance(name, text_type)
39 |                 assert name != ""
40 |                 assert isinstance(token["data"], dict)
41 |                 if (not namespace or namespace == namespaces["html"]) and name in voidElements:
42 |                     assert type == "EmptyTag"
43 |                 else:
44 |                     assert type == "StartTag"
45 |                 if type == "StartTag" and self.require_matching_tags:
46 |                     open_elements.append((namespace, name))
47 |                 for (namespace, name), value in token["data"].items():
48 |                     assert namespace is None or isinstance(namespace, text_type)
49 |                     assert namespace != ""
50 |                     assert isinstance(name, text_type)
51 |                     assert name != ""
52 |                     assert isinstance(value, text_type)
53 | 
54 |             elif type == "EndTag":
55 |                 namespace = token["namespace"]
56 |                 name = token["name"]
57 |                 assert namespace is None or isinstance(namespace, text_type)
58 |                 assert namespace != ""
59 |                 assert isinstance(name, text_type)
60 |                 assert name != ""
61 |                 if (not namespace or namespace == namespaces["html"]) and name in voidElements:
62 |                     assert False, "Void element reported as EndTag token: %(tag)s" % {"tag": name}
63 |                 elif self.require_matching_tags:
64 |                     start = open_elements.pop()
65 |                     assert start == (namespace, name)
66 | 
67 |             elif type == "Comment":
68 |                 data = token["data"]
69 |                 assert isinstance(data, text_type)
70 | 
71 |             elif type in ("Characters", "SpaceCharacters"):
72 |                 data = token["data"]
73 |                 assert isinstance(data, text_type)
74 |                 assert data != ""
75 |                 if type == "SpaceCharacters":
76 |                     assert data.strip(spaceCharacters) == ""
77 | 
78 |             elif type == "Doctype":
79 |                 name = token["name"]
80 |                 assert name is None or isinstance(name, text_type)
81 |                 assert token["publicId"] is None or isinstance(name, text_type)
82 |                 assert token["systemId"] is None or isinstance(name, text_type)
83 | 
84 |             elif type == "Entity":
85 |                 assert isinstance(token["name"], text_type)
86 | 
87 |             elif type == "SerializerError":
88 |                 assert isinstance(token["data"], text_type)
89 | 
90 |             else:
91 |                 assert False, "Unknown token type: %(type)s" % {"type": type}
92 | 
93 |             yield token
94 | 


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/.eggs/bleach-3.1.0-py3.6.egg/bleach/_vendor/html5lib/filters/whitespace.py:
--------------------------------------------------------------------------------
 1 | from __future__ import absolute_import, division, unicode_literals
 2 | 
 3 | import re
 4 | 
 5 | from . import base
 6 | from ..constants import rcdataElements, spaceCharacters
 7 | spaceCharacters = "".join(spaceCharacters)
 8 | 
 9 | SPACES_REGEX = re.compile("[%s]+" % spaceCharacters)
10 | 
11 | 
12 | class Filter(base.Filter):
13 |     """Collapses whitespace except in pre, textarea, and script elements"""
14 |     spacePreserveElements = frozenset(["pre", "textarea"] + list(rcdataElements))
15 | 
16 |     def __iter__(self):
17 |         preserve = 0
18 |         for token in base.Filter.__iter__(self):
19 |             type = token["type"]
20 |             if type == "StartTag" \
21 |                     and (preserve or token["name"] in self.spacePreserveElements):
22 |                 preserve += 1
23 | 
24 |             elif type == "EndTag" and preserve:
25 |                 preserve -= 1
26 | 
27 |             elif not preserve and type == "SpaceCharacters" and token["data"]:
28 |                 # Test on token["data"] above to not introduce spaces where there were not
29 |                 token["data"] = " "
30 | 
31 |             elif not preserve and type == "Characters":
32 |                 token["data"] = collapse_spaces(token["data"])
33 | 
34 |             yield token
35 | 
36 | 
37 | def collapse_spaces(text):
38 |     return SPACES_REGEX.sub(' ', text)
39 | 


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/.eggs/bleach-3.1.0-py3.6.egg/bleach/_vendor/html5lib/treeadapters/__init__.py:
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 1 | """Tree adapters let you convert from one tree structure to another
 2 | 
 3 | Example:
 4 | 
 5 | .. code-block:: python
 6 | 
 7 |    import html5lib
 8 |    from html5lib.treeadapters import genshi
 9 | 
10 |    doc = '<html><body>Hi!</body></html>'
11 |    treebuilder = html5lib.getTreeBuilder('etree')
12 |    parser = html5lib.HTMLParser(tree=treebuilder)
13 |    tree = parser.parse(doc)
14 |    TreeWalker = html5lib.getTreeWalker('etree')
15 | 
16 |    genshi_tree = genshi.to_genshi(TreeWalker(tree))
17 | 
18 | """
19 | from __future__ import absolute_import, division, unicode_literals
20 | 
21 | from . import sax
22 | 
23 | __all__ = ["sax"]
24 | 
25 | try:
26 |     from . import genshi  # noqa
27 | except ImportError:
28 |     pass
29 | else:
30 |     __all__.append("genshi")
31 | 


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/.eggs/bleach-3.1.0-py3.6.egg/bleach/_vendor/html5lib/treeadapters/genshi.py:
--------------------------------------------------------------------------------
 1 | from __future__ import absolute_import, division, unicode_literals
 2 | 
 3 | from genshi.core import QName, Attrs
 4 | from genshi.core import START, END, TEXT, COMMENT, DOCTYPE
 5 | 
 6 | 
 7 | def to_genshi(walker):
 8 |     """Convert a tree to a genshi tree
 9 | 
10 |     :arg walker: the treewalker to use to walk the tree to convert it
11 | 
12 |     :returns: generator of genshi nodes
13 | 
14 |     """
15 |     text = []
16 |     for token in walker:
17 |         type = token["type"]
18 |         if type in ("Characters", "SpaceCharacters"):
19 |             text.append(token["data"])
20 |         elif text:
21 |             yield TEXT, "".join(text), (None, -1, -1)
22 |             text = []
23 | 
24 |         if type in ("StartTag", "EmptyTag"):
25 |             if token["namespace"]:
26 |                 name = "{%s}%s" % (token["namespace"], token["name"])
27 |             else:
28 |                 name = token["name"]
29 |             attrs = Attrs([(QName("{%s}%s" % attr if attr[0] is not None else attr[1]), value)
30 |                            for attr, value in token["data"].items()])
31 |             yield (START, (QName(name), attrs), (None, -1, -1))
32 |             if type == "EmptyTag":
33 |                 type = "EndTag"
34 | 
35 |         if type == "EndTag":
36 |             if token["namespace"]:
37 |                 name = "{%s}%s" % (token["namespace"], token["name"])
38 |             else:
39 |                 name = token["name"]
40 | 
41 |             yield END, QName(name), (None, -1, -1)
42 | 
43 |         elif type == "Comment":
44 |             yield COMMENT, token["data"], (None, -1, -1)
45 | 
46 |         elif type == "Doctype":
47 |             yield DOCTYPE, (token["name"], token["publicId"],
48 |                             token["systemId"]), (None, -1, -1)
49 | 
50 |         else:
51 |             pass  # FIXME: What to do?
52 | 
53 |     if text:
54 |         yield TEXT, "".join(text), (None, -1, -1)
55 | 


--------------------------------------------------------------------------------
/.eggs/bleach-3.1.0-py3.6.egg/bleach/_vendor/html5lib/treeadapters/sax.py:
--------------------------------------------------------------------------------
 1 | from __future__ import absolute_import, division, unicode_literals
 2 | 
 3 | from xml.sax.xmlreader import AttributesNSImpl
 4 | 
 5 | from ..constants import adjustForeignAttributes, unadjustForeignAttributes
 6 | 
 7 | prefix_mapping = {}
 8 | for prefix, localName, namespace in adjustForeignAttributes.values():
 9 |     if prefix is not None:
10 |         prefix_mapping[prefix] = namespace
11 | 
12 | 
13 | def to_sax(walker, handler):
14 |     """Call SAX-like content handler based on treewalker walker
15 | 
16 |     :arg walker: the treewalker to use to walk the tree to convert it
17 | 
18 |     :arg handler: SAX handler to use
19 | 
20 |     """
21 |     handler.startDocument()
22 |     for prefix, namespace in prefix_mapping.items():
23 |         handler.startPrefixMapping(prefix, namespace)
24 | 
25 |     for token in walker:
26 |         type = token["type"]
27 |         if type == "Doctype":
28 |             continue
29 |         elif type in ("StartTag", "EmptyTag"):
30 |             attrs = AttributesNSImpl(token["data"],
31 |                                      unadjustForeignAttributes)
32 |             handler.startElementNS((token["namespace"], token["name"]),
33 |                                    token["name"],
34 |                                    attrs)
35 |             if type == "EmptyTag":
36 |                 handler.endElementNS((token["namespace"], token["name"]),
37 |                                      token["name"])
38 |         elif type == "EndTag":
39 |             handler.endElementNS((token["namespace"], token["name"]),
40 |                                  token["name"])
41 |         elif type in ("Characters", "SpaceCharacters"):
42 |             handler.characters(token["data"])
43 |         elif type == "Comment":
44 |             pass
45 |         else:
46 |             assert False, "Unknown token type"
47 | 
48 |     for prefix, namespace in prefix_mapping.items():
49 |         handler.endPrefixMapping(prefix)
50 |     handler.endDocument()
51 | 


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/.eggs/bleach-3.1.0-py3.6.egg/bleach/_vendor/html5lib/treebuilders/__init__.py:
--------------------------------------------------------------------------------
 1 | """A collection of modules for building different kinds of trees from HTML
 2 | documents.
 3 | 
 4 | To create a treebuilder for a new type of tree, you need to do
 5 | implement several things:
 6 | 
 7 | 1. A set of classes for various types of elements: Document, Doctype, Comment,
 8 |    Element. These must implement the interface of ``base.treebuilders.Node``
 9 |    (although comment nodes have a different signature for their constructor,
10 |    see ``treebuilders.etree.Comment``) Textual content may also be implemented
11 |    as another node type, or not, as your tree implementation requires.
12 | 
13 | 2. A treebuilder object (called ``TreeBuilder`` by convention) that inherits
14 |    from ``treebuilders.base.TreeBuilder``. This has 4 required attributes:
15 | 
16 |    * ``documentClass`` - the class to use for the bottommost node of a document
17 |    * ``elementClass`` - the class to use for HTML Elements
18 |    * ``commentClass`` - the class to use for comments
19 |    * ``doctypeClass`` - the class to use for doctypes
20 | 
21 |    It also has one required method:
22 | 
23 |    * ``getDocument`` - Returns the root node of the complete document tree
24 | 
25 | 3. If you wish to run the unit tests, you must also create a ``testSerializer``
26 |    method on your treebuilder which accepts a node and returns a string
27 |    containing Node and its children serialized according to the format used in
28 |    the unittests
29 | 
30 | """
31 | 
32 | from __future__ import absolute_import, division, unicode_literals
33 | 
34 | from .._utils import default_etree
35 | 
36 | treeBuilderCache = {}
37 | 
38 | 
39 | def getTreeBuilder(treeType, implementation=None, **kwargs):
40 |     """Get a TreeBuilder class for various types of trees with built-in support
41 | 
42 |     :arg treeType: the name of the tree type required (case-insensitive). Supported
43 |         values are:
44 | 
45 |         * "dom" - A generic builder for DOM implementations, defaulting to a
46 |           xml.dom.minidom based implementation.
47 |         * "etree" - A generic builder for tree implementations exposing an
48 |           ElementTree-like interface, defaulting to xml.etree.cElementTree if
49 |           available and xml.etree.ElementTree if not.
50 |         * "lxml" - A etree-based builder for lxml.etree, handling limitations
51 |           of lxml's implementation.
52 | 
53 |     :arg implementation: (Currently applies to the "etree" and "dom" tree
54 |         types). A module implementing the tree type e.g. xml.etree.ElementTree
55 |         or xml.etree.cElementTree.
56 | 
57 |     :arg kwargs: Any additional options to pass to the TreeBuilder when
58 |         creating it.
59 | 
60 |     Example:
61 | 
62 |     >>> from html5lib.treebuilders import getTreeBuilder
63 |     >>> builder = getTreeBuilder('etree')
64 | 
65 |     """
66 | 
67 |     treeType = treeType.lower()
68 |     if treeType not in treeBuilderCache:
69 |         if treeType == "dom":
70 |             from . import dom
71 |             # Come up with a sane default (pref. from the stdlib)
72 |             if implementation is None:
73 |                 from xml.dom import minidom
74 |                 implementation = minidom
75 |             # NEVER cache here, caching is done in the dom submodule
76 |             return dom.getDomModule(implementation, **kwargs).TreeBuilder
77 |         elif treeType == "lxml":
78 |             from . import etree_lxml
79 |             treeBuilderCache[treeType] = etree_lxml.TreeBuilder
80 |         elif treeType == "etree":
81 |             from . import etree
82 |             if implementation is None:
83 |                 implementation = default_etree
84 |             # NEVER cache here, caching is done in the etree submodule
85 |             return etree.getETreeModule(implementation, **kwargs).TreeBuilder
86 |         else:
87 |             raise ValueError("""Unrecognised treebuilder "%s" """ % treeType)
88 |     return treeBuilderCache.get(treeType)
89 | 


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/.eggs/bleach-3.1.0-py3.6.egg/bleach/_vendor/html5lib/treewalkers/dom.py:
--------------------------------------------------------------------------------
 1 | from __future__ import absolute_import, division, unicode_literals
 2 | 
 3 | from xml.dom import Node
 4 | 
 5 | from . import base
 6 | 
 7 | 
 8 | class TreeWalker(base.NonRecursiveTreeWalker):
 9 |     def getNodeDetails(self, node):
10 |         if node.nodeType == Node.DOCUMENT_TYPE_NODE:
11 |             return base.DOCTYPE, node.name, node.publicId, node.systemId
12 | 
13 |         elif node.nodeType in (Node.TEXT_NODE, Node.CDATA_SECTION_NODE):
14 |             return base.TEXT, node.nodeValue
15 | 
16 |         elif node.nodeType == Node.ELEMENT_NODE:
17 |             attrs = {}
18 |             for attr in list(node.attributes.keys()):
19 |                 attr = node.getAttributeNode(attr)
20 |                 if attr.namespaceURI:
21 |                     attrs[(attr.namespaceURI, attr.localName)] = attr.value
22 |                 else:
23 |                     attrs[(None, attr.name)] = attr.value
24 |             return (base.ELEMENT, node.namespaceURI, node.nodeName,
25 |                     attrs, node.hasChildNodes())
26 | 
27 |         elif node.nodeType == Node.COMMENT_NODE:
28 |             return base.COMMENT, node.nodeValue
29 | 
30 |         elif node.nodeType in (Node.DOCUMENT_NODE, Node.DOCUMENT_FRAGMENT_NODE):
31 |             return (base.DOCUMENT,)
32 | 
33 |         else:
34 |             return base.UNKNOWN, node.nodeType
35 | 
36 |     def getFirstChild(self, node):
37 |         return node.firstChild
38 | 
39 |     def getNextSibling(self, node):
40 |         return node.nextSibling
41 | 
42 |     def getParentNode(self, node):
43 |         return node.parentNode
44 | 


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/.eggs/bleach-3.1.0-py3.6.egg/bleach/_vendor/html5lib/treewalkers/etree.py:
--------------------------------------------------------------------------------
  1 | from __future__ import absolute_import, division, unicode_literals
  2 | 
  3 | from collections import OrderedDict
  4 | import re
  5 | 
  6 | from six import string_types
  7 | 
  8 | from . import base
  9 | from .._utils import moduleFactoryFactory
 10 | 
 11 | tag_regexp = re.compile("{([^}]*)}(.*)")
 12 | 
 13 | 
 14 | def getETreeBuilder(ElementTreeImplementation):
 15 |     ElementTree = ElementTreeImplementation
 16 |     ElementTreeCommentType = ElementTree.Comment("asd").tag
 17 | 
 18 |     class TreeWalker(base.NonRecursiveTreeWalker):  # pylint:disable=unused-variable
 19 |         """Given the particular ElementTree representation, this implementation,
 20 |         to avoid using recursion, returns "nodes" as tuples with the following
 21 |         content:
 22 | 
 23 |         1. The current element
 24 | 
 25 |         2. The index of the element relative to its parent
 26 | 
 27 |         3. A stack of ancestor elements
 28 | 
 29 |         4. A flag "text", "tail" or None to indicate if the current node is a
 30 |            text node; either the text or tail of the current element (1)
 31 |         """
 32 |         def getNodeDetails(self, node):
 33 |             if isinstance(node, tuple):  # It might be the root Element
 34 |                 elt, _, _, flag = node
 35 |                 if flag in ("text", "tail"):
 36 |                     return base.TEXT, getattr(elt, flag)
 37 |                 else:
 38 |                     node = elt
 39 | 
 40 |             if not(hasattr(node, "tag")):
 41 |                 node = node.getroot()
 42 | 
 43 |             if node.tag in ("DOCUMENT_ROOT", "DOCUMENT_FRAGMENT"):
 44 |                 return (base.DOCUMENT,)
 45 | 
 46 |             elif node.tag == "<!DOCTYPE>":
 47 |                 return (base.DOCTYPE, node.text,
 48 |                         node.get("publicId"), node.get("systemId"))
 49 | 
 50 |             elif node.tag == ElementTreeCommentType:
 51 |                 return base.COMMENT, node.text
 52 | 
 53 |             else:
 54 |                 assert isinstance(node.tag, string_types), type(node.tag)
 55 |                 # This is assumed to be an ordinary element
 56 |                 match = tag_regexp.match(node.tag)
 57 |                 if match:
 58 |                     namespace, tag = match.groups()
 59 |                 else:
 60 |                     namespace = None
 61 |                     tag = node.tag
 62 |                 attrs = OrderedDict()
 63 |                 for name, value in list(node.attrib.items()):
 64 |                     match = tag_regexp.match(name)
 65 |                     if match:
 66 |                         attrs[(match.group(1), match.group(2))] = value
 67 |                     else:
 68 |                         attrs[(None, name)] = value
 69 |                 return (base.ELEMENT, namespace, tag,
 70 |                         attrs, len(node) or node.text)
 71 | 
 72 |         def getFirstChild(self, node):
 73 |             if isinstance(node, tuple):
 74 |                 element, key, parents, flag = node
 75 |             else:
 76 |                 element, key, parents, flag = node, None, [], None
 77 | 
 78 |             if flag in ("text", "tail"):
 79 |                 return None
 80 |             else:
 81 |                 if element.text:
 82 |                     return element, key, parents, "text"
 83 |                 elif len(element):
 84 |                     parents.append(element)
 85 |                     return element[0], 0, parents, None
 86 |                 else:
 87 |                     return None
 88 | 
 89 |         def getNextSibling(self, node):
 90 |             if isinstance(node, tuple):
 91 |                 element, key, parents, flag = node
 92 |             else:
 93 |                 return None
 94 | 
 95 |             if flag == "text":
 96 |                 if len(element):
 97 |                     parents.append(element)
 98 |                     return element[0], 0, parents, None
 99 |                 else:
100 |                     return None
101 |             else:
102 |                 if element.tail and flag != "tail":
103 |                     return element, key, parents, "tail"
104 |                 elif key < len(parents[-1]) - 1:
105 |                     return parents[-1][key + 1], key + 1, parents, None
106 |                 else:
107 |                     return None
108 | 
109 |         def getParentNode(self, node):
110 |             if isinstance(node, tuple):
111 |                 element, key, parents, flag = node
112 |             else:
113 |                 return None
114 | 
115 |             if flag == "text":
116 |                 if not parents:
117 |                     return element
118 |                 else:
119 |                     return element, key, parents, None
120 |             else:
121 |                 parent = parents.pop()
122 |                 if not parents:
123 |                     return parent
124 |                 else:
125 |                     assert list(parents[-1]).count(parent) == 1
126 |                     return parent, list(parents[-1]).index(parent), parents, None
127 | 
128 |     return locals()
129 | 
130 | getETreeModule = moduleFactoryFactory(getETreeBuilder)
131 | 


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/.eggs/bleach-3.1.0-py3.6.egg/bleach/_vendor/html5lib/treewalkers/genshi.py:
--------------------------------------------------------------------------------
 1 | from __future__ import absolute_import, division, unicode_literals
 2 | 
 3 | from genshi.core import QName
 4 | from genshi.core import START, END, XML_NAMESPACE, DOCTYPE, TEXT
 5 | from genshi.core import START_NS, END_NS, START_CDATA, END_CDATA, PI, COMMENT
 6 | 
 7 | from . import base
 8 | 
 9 | from ..constants import voidElements, namespaces
10 | 
11 | 
12 | class TreeWalker(base.TreeWalker):
13 |     def __iter__(self):
14 |         # Buffer the events so we can pass in the following one
15 |         previous = None
16 |         for event in self.tree:
17 |             if previous is not None:
18 |                 for token in self.tokens(previous, event):
19 |                     yield token
20 |             previous = event
21 | 
22 |         # Don't forget the final event!
23 |         if previous is not None:
24 |             for token in self.tokens(previous, None):
25 |                 yield token
26 | 
27 |     def tokens(self, event, next):
28 |         kind, data, _ = event
29 |         if kind == START:
30 |             tag, attribs = data
31 |             name = tag.localname
32 |             namespace = tag.namespace
33 |             converted_attribs = {}
34 |             for k, v in attribs:
35 |                 if isinstance(k, QName):
36 |                     converted_attribs[(k.namespace, k.localname)] = v
37 |                 else:
38 |                     converted_attribs[(None, k)] = v
39 | 
40 |             if namespace == namespaces["html"] and name in voidElements:
41 |                 for token in self.emptyTag(namespace, name, converted_attribs,
42 |                                            not next or next[0] != END or
43 |                                            next[1] != tag):
44 |                     yield token
45 |             else:
46 |                 yield self.startTag(namespace, name, converted_attribs)
47 | 
48 |         elif kind == END:
49 |             name = data.localname
50 |             namespace = data.namespace
51 |             if namespace != namespaces["html"] or name not in voidElements:
52 |                 yield self.endTag(namespace, name)
53 | 
54 |         elif kind == COMMENT:
55 |             yield self.comment(data)
56 | 
57 |         elif kind == TEXT:
58 |             for token in self.text(data):
59 |                 yield token
60 | 
61 |         elif kind == DOCTYPE:
62 |             yield self.doctype(*data)
63 | 
64 |         elif kind in (XML_NAMESPACE, DOCTYPE, START_NS, END_NS,
65 |                       START_CDATA, END_CDATA, PI):
66 |             pass
67 | 
68 |         else:
69 |             yield self.unknown(kind)
70 | 


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/.eggs/bleach-3.1.0-py3.6.egg/bleach/_vendor/pip_install_vendor.sh:
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1 | #!/bin/bash
2 | 
3 | 
4 | pip install --no-binary all --no-compile --no-deps -r vendor.txt --target .
5 | 


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/.eggs/bleach-3.1.0-py3.6.egg/bleach/_vendor/vendor.txt:
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1 | html5lib==1.0.1 \
2 |     --hash=sha256:20b159aa3badc9d5ee8f5c647e5efd02ed2a66ab8d354930bd9ff139fc1dc0a3 \
3 |     --hash=sha256:66cb0dcfdbbc4f9c3ba1a63fdb511ffdbd4f513b2b6d81b80cd26ce6b3fb3736
4 | 


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/.eggs/bleach-3.1.0-py3.6.egg/bleach/callbacks.py:
--------------------------------------------------------------------------------
 1 | """A set of basic callbacks for bleach.linkify."""
 2 | from __future__ import unicode_literals
 3 | 
 4 | 
 5 | def nofollow(attrs, new=False):
 6 |     href_key = (None, u'href')
 7 | 
 8 |     if href_key not in attrs:
 9 |         return attrs
10 | 
11 |     if attrs[href_key].startswith(u'mailto:'):
12 |         return attrs
13 | 
14 |     rel_key = (None, u'rel')
15 |     rel_values = [val for val in attrs.get(rel_key, u'').split(u' ') if val]
16 |     if u'nofollow' not in [rel_val.lower() for rel_val in rel_values]:
17 |         rel_values.append(u'nofollow')
18 |     attrs[rel_key] = u' '.join(rel_values)
19 | 
20 |     return attrs
21 | 
22 | 
23 | def target_blank(attrs, new=False):
24 |     href_key = (None, u'href')
25 | 
26 |     if href_key not in attrs:
27 |         return attrs
28 | 
29 |     if attrs[href_key].startswith(u'mailto:'):
30 |         return attrs
31 | 
32 |     attrs[(None, u'target')] = u'_blank'
33 |     return attrs
34 | 


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/.eggs/bleach-3.1.0-py3.6.egg/bleach/utils.py:
--------------------------------------------------------------------------------
 1 | from collections import OrderedDict
 2 | 
 3 | import six
 4 | 
 5 | 
 6 | def _attr_key(attr):
 7 |     """Returns appropriate key for sorting attribute names
 8 | 
 9 |     Attribute names are a tuple of ``(namespace, name)`` where namespace can be
10 |     ``None`` or a string. These can't be compared in Python 3, so we conver the
11 |     ``None`` to an empty string.
12 | 
13 |     """
14 |     key = (attr[0][0] or ''), attr[0][1]
15 |     return key
16 | 
17 | 
18 | def alphabetize_attributes(attrs):
19 |     """Takes a dict of attributes (or None) and returns them alphabetized"""
20 |     if not attrs:
21 |         return attrs
22 | 
23 |     return OrderedDict(
24 |         [(k, v) for k, v in sorted(attrs.items(), key=_attr_key)]
25 |     )
26 | 
27 | 
28 | def force_unicode(text):
29 |     """Takes a text (Python 2: str/unicode; Python 3: unicode) and converts to unicode
30 | 
31 |     :arg str/unicode text: the text in question
32 | 
33 |     :returns: text as unicode
34 | 
35 |     :raises UnicodeDecodeError: if the text was a Python 2 str and isn't in
36 |         utf-8
37 | 
38 |     """
39 |     # If it's already unicode, then return it
40 |     if isinstance(text, six.text_type):
41 |         return text
42 | 
43 |     # If not, convert it
44 |     return six.text_type(text, 'utf-8', 'strict')
45 | 


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/.eggs/webencodings-0.5.1-py3.6.egg/EGG-INFO/DESCRIPTION.rst:
--------------------------------------------------------------------------------
 1 | python-webencodings
 2 | ===================
 3 | 
 4 | This is a Python implementation of the `WHATWG Encoding standard
 5 | <http://encoding.spec.whatwg.org/>`_.
 6 | 
 7 | * Latest documentation: http://packages.python.org/webencodings/
 8 | * Source code and issue tracker:
 9 |   https://github.com/gsnedders/python-webencodings
10 | * PyPI releases: http://pypi.python.org/pypi/webencodings
11 | * License: BSD
12 | * Python 2.6+ and 3.3+
13 | 
14 | In order to be compatible with legacy web content
15 | when interpreting something like ``Content-Type: text/html; charset=latin1``,
16 | tools need to use a particular set of aliases for encoding labels
17 | as well as some overriding rules.
18 | For example, ``US-ASCII`` and ``iso-8859-1`` on the web are actually
19 | aliases for ``windows-1252``, and an UTF-8 or UTF-16 BOM takes precedence
20 | over any other encoding declaration.
21 | The Encoding standard defines all such details so that implementations do
22 | not have to reverse-engineer each other.
23 | 
24 | This module has encoding labels and BOM detection,
25 | but the actual implementation for encoders and decoders is Python’s.
26 | 
27 | 
28 | 


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/.eggs/webencodings-0.5.1-py3.6.egg/EGG-INFO/PKG-INFO:
--------------------------------------------------------------------------------
 1 | Metadata-Version: 2.0
 2 | Name: webencodings
 3 | Version: 0.5.1
 4 | Summary: Character encoding aliases for legacy web content
 5 | Home-page: https://github.com/SimonSapin/python-webencodings
 6 | Author: Geoffrey Sneddon
 7 | Author-email: me@gsnedders.com
 8 | License: BSD
 9 | Platform: UNKNOWN
10 | Classifier: Development Status :: 4 - Beta
11 | Classifier: Intended Audience :: Developers
12 | Classifier: License :: OSI Approved :: BSD License
13 | Classifier: Programming Language :: Python
14 | Classifier: Programming Language :: Python :: 2
15 | Classifier: Programming Language :: Python :: 2.6
16 | Classifier: Programming Language :: Python :: 2.7
17 | Classifier: Programming Language :: Python :: 3
18 | Classifier: Programming Language :: Python :: 3.3
19 | Classifier: Programming Language :: Python :: 3.4
20 | Classifier: Programming Language :: Python :: 3.5
21 | Classifier: Programming Language :: Python :: 3.6
22 | Classifier: Programming Language :: Python :: Implementation :: CPython
23 | Classifier: Programming Language :: Python :: Implementation :: PyPy
24 | Classifier: Topic :: Internet :: WWW/HTTP
25 | 
26 | python-webencodings
27 | ===================
28 | 
29 | This is a Python implementation of the `WHATWG Encoding standard
30 | <http://encoding.spec.whatwg.org/>`_.
31 | 
32 | * Latest documentation: http://packages.python.org/webencodings/
33 | * Source code and issue tracker:
34 |   https://github.com/gsnedders/python-webencodings
35 | * PyPI releases: http://pypi.python.org/pypi/webencodings
36 | * License: BSD
37 | * Python 2.6+ and 3.3+
38 | 
39 | In order to be compatible with legacy web content
40 | when interpreting something like ``Content-Type: text/html; charset=latin1``,
41 | tools need to use a particular set of aliases for encoding labels
42 | as well as some overriding rules.
43 | For example, ``US-ASCII`` and ``iso-8859-1`` on the web are actually
44 | aliases for ``windows-1252``, and an UTF-8 or UTF-16 BOM takes precedence
45 | over any other encoding declaration.
46 | The Encoding standard defines all such details so that implementations do
47 | not have to reverse-engineer each other.
48 | 
49 | This module has encoding labels and BOM detection,
50 | but the actual implementation for encoders and decoders is Python’s.
51 | 
52 | 
53 | 


--------------------------------------------------------------------------------
/.eggs/webencodings-0.5.1-py3.6.egg/EGG-INFO/RECORD:
--------------------------------------------------------------------------------
 1 | webencodings/__init__.py,sha256=qOBJIuPy_4ByYH6W_bNgJF-qYQ2DoU-dKsDu5yRWCXg,10579
 2 | webencodings/labels.py,sha256=4AO_KxTddqGtrL9ns7kAPjb0CcN6xsCIxbK37HY9r3E,8979
 3 | webencodings/mklabels.py,sha256=GYIeywnpaLnP0GSic8LFWgd0UVvO_l1Nc6YoF-87R_4,1305
 4 | webencodings/tests.py,sha256=OtGLyjhNY1fvkW1GvLJ_FV9ZoqC9Anyjr7q3kxTbzNs,6563
 5 | webencodings/x_user_defined.py,sha256=yOqWSdmpytGfUgh_Z6JYgDNhoc-BAHyyeeT15Fr42tM,4307
 6 | webencodings-0.5.1.dist-info/DESCRIPTION.rst,sha256=_NzU86VndzvBejXtyshDbUUz_lzzOrh54_OY9T0OuhQ,1039
 7 | webencodings-0.5.1.dist-info/METADATA,sha256=KQ7Fg8Lv4_nbUAbzAS29QmiXzG7HnWLIXpF2oN4P2u4,2063
 8 | webencodings-0.5.1.dist-info/RECORD,,
 9 | webencodings-0.5.1.dist-info/WHEEL,sha256=o2k-Qa-RMNIJmUdIc7KU6VWR_ErNRbWNlxDIpl7lm34,110
10 | webencodings-0.5.1.dist-info/metadata.json,sha256=AYbksnhE5Frl5Ao68tMDgGeytnRxYvvWlvNgxKMiQFY,1092
11 | webencodings-0.5.1.dist-info/top_level.txt,sha256=bZs_aZHSf_PNlfIHD4-BETJmRi99BJdKLrOW7rQngeo,13
12 | 


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/.eggs/webencodings-0.5.1-py3.6.egg/EGG-INFO/WHEEL:
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1 | Wheel-Version: 1.0
2 | Generator: bdist_wheel (0.29.0)
3 | Root-Is-Purelib: true
4 | Tag: py2-none-any
5 | Tag: py3-none-any
6 | 
7 | 


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/.eggs/webencodings-0.5.1-py3.6.egg/EGG-INFO/metadata.json:
--------------------------------------------------------------------------------
1 | {"classifiers": ["Development Status :: 4 - Beta", "Intended Audience :: Developers", "License :: OSI Approved :: BSD License", "Programming Language :: Python", "Programming Language :: Python :: 2", "Programming Language :: Python :: 2.6", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3", "Programming Language :: Python :: 3.3", "Programming Language :: Python :: 3.4", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: Implementation :: CPython", "Programming Language :: Python :: Implementation :: PyPy", "Topic :: Internet :: WWW/HTTP"], "extensions": {"python.details": {"contacts": [{"email": "me@gsnedders.com", "name": "Geoffrey Sneddon", "role": "author"}], "document_names": {"description": "DESCRIPTION.rst"}, "project_urls": {"Home": "https://github.com/SimonSapin/python-webencodings"}}}, "generator": "bdist_wheel (0.29.0)", "license": "BSD", "metadata_version": "2.0", "name": "webencodings", "summary": "Character encoding aliases for legacy web content", "version": "0.5.1"}


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/.eggs/webencodings-0.5.1-py3.6.egg/EGG-INFO/top_level.txt:
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1 | webencodings
2 | 


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https://raw.githubusercontent.com/jlevy44/PathFlowAI/5bd66ec800efbe413e16f5f0b8fd30278147c0ac/.eggs/webencodings-0.5.1-py3.6.egg/webencodings/__pycache__/labels.cpython-36.pyc


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/.eggs/webencodings-0.5.1-py3.6.egg/webencodings/mklabels.py:
--------------------------------------------------------------------------------
 1 | """
 2 | 
 3 |     webencodings.mklabels
 4 |     ~~~~~~~~~~~~~~~~~~~~~
 5 | 
 6 |     Regenarate the webencodings.labels module.
 7 | 
 8 |     :copyright: Copyright 2012 by Simon Sapin
 9 |     :license: BSD, see LICENSE for details.
10 | 
11 | """
12 | 
13 | import json
14 | try:
15 |     from urllib import urlopen
16 | except ImportError:
17 |     from urllib.request import urlopen
18 | 
19 | 
20 | def assert_lower(string):
21 |     assert string == string.lower()
22 |     return string
23 | 
24 | 
25 | def generate(url):
26 |     parts = ['''\
27 | """
28 | 
29 |     webencodings.labels
30 |     ~~~~~~~~~~~~~~~~~~~
31 | 
32 |     Map encoding labels to their name.
33 | 
34 |     :copyright: Copyright 2012 by Simon Sapin
35 |     :license: BSD, see LICENSE for details.
36 | 
37 | """
38 | 
39 | # XXX Do not edit!
40 | # This file is automatically generated by mklabels.py
41 | 
42 | LABELS = {
43 | ''']
44 |     labels = [
45 |         (repr(assert_lower(label)).lstrip('u'),
46 |          repr(encoding['name']).lstrip('u'))
47 |         for category in json.loads(urlopen(url).read().decode('ascii'))
48 |         for encoding in category['encodings']
49 |         for label in encoding['labels']]
50 |     max_len = max(len(label) for label, name in labels)
51 |     parts.extend(
52 |         '    %s:%s %s,\n' % (label, ' ' * (max_len - len(label)), name)
53 |         for label, name in labels)
54 |     parts.append('}')
55 |     return ''.join(parts)
56 | 
57 | 
58 | if __name__ == '__main__':
59 |     print(generate('http://encoding.spec.whatwg.org/encodings.json'))
60 | 


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/.gitattributes:
--------------------------------------------------------------------------------
1 | # Auto detect text files and perform LF normalization
2 | * text=auto
3 | 


--------------------------------------------------------------------------------
/.gitmodules:
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1 | [submodule "PathPretrain"]
2 | 	path = PathPretrain
3 | 	url = https://github.com/jlevy44/PathPretrain
4 | 


--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
 1 | MIT License
 2 | 
 3 | Copyright (c) 2019 Joshua Levy
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 | 


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/PathFlowAI.pdf:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/jlevy44/PathFlowAI/5bd66ec800efbe413e16f5f0b8fd30278147c0ac/PathFlowAI.pdf


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/README.md:
--------------------------------------------------------------------------------
 1 | <h1 align="center">Welcome to PathFlowAI </h1>
 2 | <p>
 3 |   <img alt="Version" src="https://img.shields.io/badge/version-0.1-blue.svg?cacheSeconds=2592000" />
 4 |   <a href="https://jlevy44.github.io/PathFlowAI/">
 5 |     <img alt="Documentation" src="https://img.shields.io/badge/documentation-yes-brightgreen.svg" target="_blank" />
 6 |   </a>
 7 | </p>
 8 | 
 9 | > A Convenient High-Throughput Workflow for Preprocessing, Deep Learning Analytics and Interpretation in Digital Pathology
10 | 
11 | ### 🏠 [Homepage](https://github.com/jlevy44/PathFlowAI)
12 | 
13 | Published in the Proceedings of the Pacific Symposium for Biocomputing 2020, Manuscript: https://psb.stanford.edu/psb-online/proceedings/psb20/Levy.pdf
14 | 
15 | ## Install
16 | 
17 | First, install [openslide](https://openslide.org/download/). Note: may need to install libiconv and shapely using conda. Will update with more installation information, please submit issues as well.
18 | 
19 | ```sh
20 | pip install pathflowai
21 | install_apex
22 | ```
23 | 
24 | ## Usage
25 | 
26 | ```sh
27 | pathflowai-preprocess -h
28 | pathflowai-train_model -h
29 | pathflowai-monitor -h
30 | pathflowai-visualize -h
31 | ```
32 | 
33 | See [Wiki](https://github.com/jlevy44/PathFlowAI/wiki) for more information on setting up and running the workflow. Please submit feedback as issues and let me know if there is any trouble with installation and I am more than happy to provide advice and fixes.
34 | 
35 | ## Author
36 | 
37 | 👤 **Joshua Levy**
38 | 
39 | * Github: [@jlevy44](https://github.com/jlevy44)
40 | 
41 | ## 🤝 Contributing
42 | 
43 | Contributions, issues and feature requests are welcome!<br />Feel free to check [issues page](https://github.com/jlevy44/PathFlowAI/issues).
44 | 
45 | **Figures from the Paper**
46 | 
47 | ![1](https://user-images.githubusercontent.com/19698023/62230963-0199d780-b391-11e9-96eb-ac9b86686723.jpeg)
48 | 
49 | Fig. 1. PathFlowAI Framework: a) Annotations and whole slide images are preprocessed in parallel using
50 | Dask; b) Deep learning prediction model is trained on the model; c) Results are visualized; d) UMAP
51 | embeddings provide diagnostics; e) SHAP framework is used to find important regions for the prediction
52 | 
53 | ![2](https://user-images.githubusercontent.com/19698023/62231545-41ad8a00-b392-11e9-8d47-f9f83f4b764a.jpeg)
54 | 
55 | Fig. 2. Comparison of PathFlowAI to Preprocessing WSI in Series for: a) Preprocessing time, b) Storage
56 | Space, c) Impact on the filesystem. The PathFlowAI method of parallel processing followed by
57 | centralized storage saves both time and storage space
58 | 
59 | ![3](https://user-images.githubusercontent.com/19698023/62231546-41ad8a00-b392-11e9-9b16-ea3b2b92bf3f.jpeg)
60 | 
61 | Fig. 3. Segmentation: Original (a) Annotations Compared to Predicted (b) Annotations; (c) Pathologist
62 | annotations guided by the classification model
63 | 
64 | ![4](https://user-images.githubusercontent.com/19698023/62230966-02326e00-b391-11e9-989c-155ff0a9be67.jpeg)
65 | 
66 | Fig. 4. Portal Classification Results: a) Darker tiles indicate a higher assigned probability of portal classification, b)
67 | AUC-ROC curves for the test images that estimate overall accuracy given different sensitivity cutoffs, c) H&E patch
68 | (left) with corresponding SHAP interpretations (right) for four patches; the probability value of portal classification
69 | is shown, and on the SHAP value scale, red indicates regions that the model attributes to portal prediction, d) Model trained UMAP embeddings of patches colored by original portal coverage (area of patch covered by portal) as judged
70 | by pathologist and visualization of individual patches
71 | 


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/bin/install_apex:
--------------------------------------------------------------------------------
 1 | #!/bin/bash
 2 | 
 3 | export TMPDIR=$HOME/tmp
 4 | mkdir -p $TMPDIR
 5 | rm -rf apex
 6 | git clone https://github.com/NVIDIA/apex
 7 | cd apex
 8 | pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./ || pip install -v --no-cache-dir ./
 9 | cd ..
10 | rm -rf apex
11 | 


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/bin/install_lightnet:
--------------------------------------------------------------------------------
 1 | #!/bin/bash
 2 | #python -m lightnet download tiny-yolo
 3 | #python -m lightnet download yolo
 4 | rm -rf lightnet
 5 | git clone https://gitlab.com/EAVISE/lightnet.git
 6 | cd lightnet
 7 | pip install .
 8 | cd ..
 9 | rm -rf lightnet
10 | #wget https://pjreddie.com/media/files/yolo.weights
11 | #wget https://pjreddie.com/media/files/tiny-yolo.weights
12 | 


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/deprecated_work/build/lib/pathflowai/__init__.py:
--------------------------------------------------------------------------------
1 | from . import *
2 | __version__='0.1.1'
3 | 


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/deprecated_work/build/lib/pathflowai/monitor_memory_usage.py:
--------------------------------------------------------------------------------
 1 | import GPUtil
 2 | import psutil
 3 | import time
 4 | from threading import Thread
 5 | import pandas as pd
 6 | import argparse
 7 | import click
 8 | 
 9 | CONTEXT_SETTINGS = dict(help_option_names=['-h','--help'], max_content_width=90)
10 | 
11 | @click.group(context_settings= CONTEXT_SETTINGS)
12 | @click.version_option(version='0.1')
13 | def monitor():
14 | 	pass
15 | 
16 | class Monitor(Thread):
17 | 	def __init__(self, start_time, delay, end_time):
18 | 		super(Monitor, self).__init__()
19 | 		self.stopped = False
20 | 		self.start_time = start_time
21 | 		self.end_time = end_time
22 | 		self.delay = delay # Time between calls to GPUtil
23 | 		self.records = []
24 | 		self.start()
25 | 
26 | 	def run(self):
27 | 		time_from_start = 0.
28 | 		while time_from_start <= self.end_time:
29 | 			memory = psutil.virtual_memory()
30 | 			stats = {"gpu.{}.memory.used".format(gpu.id):gpu.memoryUsed for gpu in GPUtil.getGPUs()}
31 | 			stats['cpu.utilization'] = psutil.cpu_percent()
32 | 			current_time = time.time()
33 | 			stats['current.time'] = current_time
34 | 			time_from_start = current_time - self.start_time
35 | 			stats['system.memory.used'] = memory.used
36 | 			stats['system.memory.used.percent'] = memory.percent
37 | 			stats['elapsed.time'] = time_from_start
38 | 			self.records.append(stats)
39 | 			time.sleep(self.delay)
40 | 		self.stop()
41 | 
42 | 	def stop(self):
43 | 		self.stopped = True
44 | 
45 | 	def return_records(self):
46 | 		return pd.DataFrame(self.records)
47 | 
48 | def get_usage(total_time, delay_time, records_output_csv):
49 | 
50 | 	start_time = time.time()
51 | 
52 | 	monitor = Monitor(start_time, delay_time, total_time)
53 | 
54 | 	monitor.run()
55 | 
56 | 	while not monitor.stopped:
57 | 		time.sleep(delay_time)
58 | 
59 | 	records = monitor.return_records()
60 | 
61 | 	records.to_csv(records_output_csv)
62 | 
63 | @monitor.command()
64 | @click.option('-csv', '--records_output_csv', default='records.csv', help='Where to store records.', type=click.Path(exists=False), show_default=True)
65 | @click.option('-tt', '--total_time', default=1., help='Total time to monitor for in minutes.', show_default=True)
66 | @click.option('-dt', '--delay_time', default=1., help='Time between samples, in seconds.', show_default=True)
67 | def monitor_usage(records_output_csv,total_time,delay_time):
68 | 	"""Monitor Usage over Time Interval."""
69 | 	total_time*= 60. # convert to seconds
70 | 	get_usage(total_time, delay_time, records_output_csv)
71 | 
72 | 
73 | if __name__ == '__main__':
74 | 	monitor()
75 | 


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/deprecated_work/build/lib/pathflowai/sampler.py:
--------------------------------------------------------------------------------
 1 | """
 2 | sampler.py
 3 | =======================
 4 | Balanced sampling based on one of the columns of the patch information.
 5 | """
 6 | 
 7 | import torch
 8 | import torch.utils.data
 9 | import torchvision
10 | import numpy as np
11 | 
12 | 
13 | class ImbalancedDatasetSampler(torch.utils.data.sampler.Sampler):
14 |     """Samples elements randomly from a given list of indices for imbalanced dataset
15 |     https://raw.githubusercontent.com/ufoym/imbalanced-dataset-sampler/master/sampler.py
16 |     Arguments:
17 |         indices (list, optional): a list of indices
18 |         num_samples (int, optional): number of samples to draw
19 |     """
20 | 
21 |     def __init__(self, dataset, indices=None, num_samples=None):
22 | 
23 |         # if indices is not provided,
24 |         # all elements in the dataset will be considered
25 |         self.indices = list(range(len(dataset))) \
26 |             if indices is None else indices
27 | 
28 |         self.n_targets=len(dataset.targets)
29 | 
30 |         # if num_samples is not provided,
31 |         # draw `len(indices)` samples in each iteration
32 |         self.num_samples = len(self.indices) \
33 |             if num_samples is None else num_samples
34 | 
35 |         # distribution of classes in the dataset
36 |         label_to_count = {}
37 |         for idx in self.indices:
38 |             label = self._get_label(dataset, idx)
39 |             if label in label_to_count:
40 |                 label_to_count[label] += 1
41 |             else:
42 |                 label_to_count[label] = 1
43 | 
44 |         # weight for each sample
45 |         weights = [1.0 / label_to_count[self._get_label(dataset, idx)]
46 |                    for idx in self.indices]
47 |         self.weights = torch.DoubleTensor(weights)
48 | 
49 |     def _get_label(self, dataset, idx):
50 |         dataset_type = type(dataset)
51 |         if dataset_type is torchvision.datasets.MNIST:
52 |             return dataset.train_labels[idx].item()
53 |         elif dataset_type is torchvision.datasets.ImageFolder:
54 |             return dataset.imgs[idx][1]
55 |         else:
56 |             y=dataset.patch_info.iloc[idx][dataset.targets].values
57 |             if self.n_targets>1:
58 |                 y=np.argmax(y)
59 |             elif isinstance(y,(list,np.ndarray)):
60 |                 y=y[0]
61 |             #print(y)
62 |             return int(y)
63 | 
64 |     def __iter__(self):
65 |         return (self.indices[i] for i in torch.multinomial(
66 |             self.weights, self.num_samples, replacement=True))
67 | 
68 |     def __len__(self):
69 |         return self.num_samples
70 | 
71 | 
72 | """MIT License
73 | 
74 | Copyright (c) 2018 Ming
75 | 
76 | Permission is hereby granted, free of charge, to any person obtaining a copy
77 | of this software and associated documentation files (the "Software"), to deal
78 | in the Software without restriction, including without limitation the rights
79 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
80 | copies of the Software, and to permit persons to whom the Software is
81 | furnished to do so, subject to the following conditions:
82 | 
83 | The above copyright notice and this permission notice shall be included in all
84 | copies or substantial portions of the Software.
85 | 
86 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
87 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
88 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
89 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
90 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
91 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
92 | SOFTWARE."""
93 | 


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/deprecated_work/build/lib/pathflowai/schedulers.py:
--------------------------------------------------------------------------------
  1 | """
  2 | schedulers.py
  3 | =======================
  4 | Modulates the learning rate during the training process.
  5 | """
  6 | import torch
  7 | import math
  8 | from torch.optim.lr_scheduler import ExponentialLR
  9 | 
 10 | class CosineAnnealingWithRestartsLR(torch.optim.lr_scheduler._LRScheduler):
 11 | 	r"""Set the learning rate of each parameter group using a cosine annealing
 12 | 	schedule, where :math:`\eta_{max}` is set to the initial lr and
 13 | 	:math:`T_{cur}` is the number of epochs since the last restart in SGDR:
 14 | 	 .. math::
 15 | 		 \eta_t = \eta_{min} + \frac{1}{2}(\eta_{max} - \eta_{min})(1 +
 16 | 		\cos(\frac{T_{cur}}{T_{max}}\pi))
 17 | 	 When last_epoch=-1, sets initial lr as lr.
 18 | 	 It has been proposed in
 19 | 	`SGDR: Stochastic Gradient Descent with Warm Restarts`_. This implements
 20 | 	the cosine annealing part of SGDR, the restarts and number of iterations multiplier.
 21 | 	 Args:
 22 | 		optimizer (Optimizer): Wrapped optimizer.
 23 | 		T_max (int): Maximum number of iterations.
 24 | 		T_mult (float): Multiply T_max by this number after each restart. Default: 1.
 25 | 		eta_min (float): Minimum learning rate. Default: 0.
 26 | 		last_epoch (int): The index of last epoch. Default: -1.
 27 | 	 .. _SGDR\: Stochastic Gradient Descent with Warm Restarts:
 28 | 		https://arxiv.org/abs/1608.03983
 29 | 	"""
 30 | 	def __init__(self, optimizer, T_max, eta_min=0, last_epoch=-1, T_mult=1., alpha_decay=1.0):
 31 | 		self.T_max = T_max
 32 | 		self.T_mult = T_mult
 33 | 		self.restart_every = T_max
 34 | 		self.eta_min = eta_min
 35 | 		self.restarts = 0
 36 | 		self.restarted_at = 0
 37 | 		self.alpha = alpha_decay
 38 | 		super().__init__(optimizer, last_epoch)
 39 | 
 40 | 	def restart(self):
 41 | 		self.restarts += 1
 42 | 		self.restart_every = int(round(self.restart_every * self.T_mult))
 43 | 		self.restarted_at = self.last_epoch
 44 | 
 45 | 	def cosine(self, base_lr):
 46 | 		return self.eta_min + self.alpha**self.restarts * (base_lr - self.eta_min) * (1 + math.cos(math.pi * self.step_n / self.restart_every)) / 2
 47 | 
 48 | 	@property
 49 | 	def step_n(self):
 50 | 		return self.last_epoch - self.restarted_at
 51 | 
 52 | 	def get_lr(self):
 53 | 		if self.step_n >= self.restart_every:
 54 | 			self.restart()
 55 | 		return [self.cosine(base_lr) for base_lr in self.base_lrs]
 56 | 
 57 | class Scheduler:
 58 | 	"""Scheduler class that modulates learning rate of torch optimizers over epochs.
 59 | 
 60 | 	Parameters
 61 | 	----------
 62 | 	optimizer : type
 63 | 		torch.Optimizer object
 64 | 	opts : type
 65 | 		Options of setting the learning rate scheduler, see default.
 66 | 
 67 | 	Attributes
 68 | 	----------
 69 | 	schedulers : type
 70 | 		Different types of schedulers to choose from.
 71 | 	scheduler_step_fn : type
 72 | 		How scheduler updates learning rate.
 73 | 	initial_lr : type
 74 | 		Initial set learning rate.
 75 | 	scheduler_choice : type
 76 | 		What scheduler type was chosen.
 77 | 	scheduler : type
 78 | 		Scheduler object chosen that will more directly update optimizer LR.
 79 | 
 80 | 	"""
 81 | 	def __init__(self, optimizer=None, opts=dict(scheduler='null',lr_scheduler_decay=0.5,T_max=10,eta_min=5e-8,T_mult=2)):
 82 | 		self.schedulers = {'exp':(lambda optimizer: ExponentialLR(optimizer, opts["lr_scheduler_decay"])),
 83 | 							'null':(lambda optimizer: None),
 84 | 							'warm_restarts':(lambda optimizer: CosineAnnealingWithRestartsLR(optimizer, T_max=opts['T_max'], eta_min=opts['eta_min'], last_epoch=-1, T_mult=opts['T_mult']))}
 85 | 		self.scheduler_step_fn = {'exp':(lambda scheduler: scheduler.step()),
 86 | 								  'warm_restarts':(lambda scheduler: scheduler.step()),
 87 | 								  'null':(lambda scheduler: None)}
 88 | 		self.initial_lr = optimizer.param_groups[0]['lr']
 89 | 		self.scheduler_choice = opts['scheduler']
 90 | 		self.scheduler = self.schedulers[self.scheduler_choice](optimizer) if optimizer is not None else None
 91 | 
 92 | 	def step(self):
 93 | 		"""Update optimizer learning rate"""
 94 | 		self.scheduler_step_fn[self.scheduler_choice](self.scheduler)
 95 | 
 96 | 	def get_lr(self):
 97 | 		"""Return current learning rate.
 98 | 
 99 |         Returns
100 |         -------
101 |         float
102 |             Current learning rate.
103 | 
104 |         """
105 | 		lr = (self.initial_lr if self.scheduler_choice == 'null' else self.scheduler.optimizer.param_groups[0]['lr'])
106 | 		return lr
107 | 


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/deprecated_work/build/lib/pathflowai/unet.py:
--------------------------------------------------------------------------------
  1 | # From https://raw.githubusercontent.com/milesial/Pytorch-UNet/master/unet/unet_model.py
  2 | 
  3 | import torch
  4 | import torch.nn as nn
  5 | import torch.nn.functional as F
  6 | 
  7 | 
  8 | class double_conv(nn.Module):
  9 |     '''(conv => BN => ReLU) * 2'''
 10 |     def __init__(self, in_ch, out_ch):
 11 |         super(double_conv, self).__init__()
 12 |         self.conv = nn.Sequential(
 13 |             nn.Conv2d(in_ch, out_ch, 3, padding=1),
 14 |             nn.BatchNorm2d(out_ch),
 15 |             nn.ReLU(inplace=True),
 16 |             nn.Conv2d(out_ch, out_ch, 3, padding=1),
 17 |             nn.BatchNorm2d(out_ch),
 18 |             nn.ReLU(inplace=True)
 19 |         )
 20 | 
 21 |     def forward(self, x):
 22 |         x = self.conv(x)
 23 |         return x
 24 | 
 25 | 
 26 | class inconv(nn.Module):
 27 |     def __init__(self, in_ch, out_ch):
 28 |         super(inconv, self).__init__()
 29 |         self.conv = double_conv(in_ch, out_ch)
 30 | 
 31 |     def forward(self, x):
 32 |         x = self.conv(x)
 33 |         return x
 34 | 
 35 | 
 36 | class down(nn.Module):
 37 |     def __init__(self, in_ch, out_ch):
 38 |         super(down, self).__init__()
 39 |         self.mpconv = nn.Sequential(
 40 |             nn.MaxPool2d(2),
 41 |             double_conv(in_ch, out_ch)
 42 |         )
 43 | 
 44 |     def forward(self, x):
 45 |         x = self.mpconv(x)
 46 |         return x
 47 | 
 48 | 
 49 | class up(nn.Module):
 50 |     def __init__(self, in_ch, out_ch, bilinear=True):
 51 |         super(up, self).__init__()
 52 | 
 53 |         #  would be a nice idea if the upsampling could be learned too,
 54 |         #  but my machine do not have enough memory to handle all those weights
 55 |         if bilinear:
 56 |             self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
 57 |         else:
 58 |             self.up = nn.ConvTranspose2d(in_ch//2, in_ch//2, 2, stride=2)
 59 | 
 60 |         self.conv = double_conv(in_ch, out_ch)
 61 | 
 62 |     def forward(self, x1, x2):
 63 |         x1 = self.up(x1)
 64 | 
 65 |         # input is CHW
 66 |         diffY = x2.size()[2] - x1.size()[2]
 67 |         diffX = x2.size()[3] - x1.size()[3]
 68 | 
 69 |         x1 = F.pad(x1, (diffX // 2, diffX - diffX//2,
 70 |                         diffY // 2, diffY - diffY//2))
 71 | 
 72 |         # for padding issues, see
 73 |         # https://github.com/HaiyongJiang/U-Net-Pytorch-Unstructured-Buggy/commit/0e854509c2cea854e247a9c615f175f76fbb2e3a
 74 |         # https://github.com/xiaopeng-liao/Pytorch-UNet/commit/8ebac70e633bac59fc22bb5195e513d5832fb3bd
 75 | 
 76 |         x = torch.cat([x2, x1], dim=1)
 77 |         x = self.conv(x)
 78 |         return x
 79 | 
 80 | 
 81 | class outconv(nn.Module):
 82 |     def __init__(self, in_ch, out_ch):
 83 |         super(outconv, self).__init__()
 84 |         self.conv = nn.Conv2d(in_ch, out_ch, 1)
 85 | 
 86 |     def forward(self, x):
 87 |         x = self.conv(x)
 88 |         return x
 89 | 
 90 | class UNet(nn.Module):
 91 |     def __init__(self, n_channels, n_classes, use_sigmoid=False, use_softmax=False):
 92 |         super(UNet, self).__init__()
 93 |         self.inc = inconv(n_channels, 64)
 94 |         self.down1 = down(64, 128)
 95 |         self.down2 = down(128, 256)
 96 |         self.down3 = down(256, 512)
 97 |         self.down4 = down(512, 512)
 98 |         self.up1 = up(1024, 256)
 99 |         self.up2 = up(512, 128)
100 |         self.up3 = up(256, 64)
101 |         self.up4 = up(128, 64)
102 |         self.outc = outconv(64, n_classes)
103 |         self.sigmoid = nn.Sequential(nn.Sigmoid() if use_sigmoid else nn.Dropout(p=0.),nn.LogSoftmax(dim=1) if use_softmax else nn.Dropout(p=0.))
104 | 
105 |     def forward(self, x):
106 |         x1 = self.inc(x)
107 |         x2 = self.down1(x1)
108 |         x3 = self.down2(x2)
109 |         x4 = self.down3(x3)
110 |         x5 = self.down4(x4)
111 |         x = self.up1(x5, x4)
112 |         x = self.up2(x, x3)
113 |         x = self.up3(x, x2)
114 |         x = self.up4(x, x1)
115 |         x = self.outc(x)
116 |         return self.sigmoid(x)
117 | 


--------------------------------------------------------------------------------
/deprecated_work/build/lib/pathflowai/unet3.py:
--------------------------------------------------------------------------------
  1 | # Adopted from https://raw.githubusercontent.com/jvanvugt/pytorch-unet/master/unet.py
  2 | # Adapted from https://discuss.pytorch.org/t/unet-implementation/426
  3 | 
  4 | import torch
  5 | from torch import nn
  6 | import torch.nn.functional as F
  7 | 
  8 | 
  9 | class UNet(nn.Module):
 10 |     def __init__(
 11 |         self,
 12 |         in_channels=1,
 13 |         n_classes=2,
 14 |         depth=5,
 15 |         wf=6,
 16 |         padding=True,
 17 |         batch_norm=False,
 18 |         up_mode='upconv',
 19 |     ):
 20 |         """
 21 |         Implementation of
 22 |         U-Net: Convolutional Networks for Biomedical Image Segmentation
 23 |         (Ronneberger et al., 2015)
 24 |         https://arxiv.org/abs/1505.04597
 25 | 
 26 |         Using the default arguments will yield the exact version used
 27 |         in the original paper
 28 | 
 29 |         Args:
 30 |             in_channels (int): number of input channels
 31 |             n_classes (int): number of output channels
 32 |             depth (int): depth of the network
 33 |             wf (int): number of filters in the first layer is 2**wf
 34 |             padding (bool): if True, apply padding such that the input shape
 35 |                             is the same as the output.
 36 |                             This may introduce artifacts
 37 |             batch_norm (bool): Use BatchNorm after layers with an
 38 |                                activation function
 39 |             up_mode (str): one of 'upconv' or 'upsample'.
 40 |                            'upconv' will use transposed convolutions for
 41 |                            learned upsampling.
 42 |                            'upsample' will use bilinear upsampling.
 43 |         """
 44 |         super(UNet, self).__init__()
 45 |         assert up_mode in ('upconv', 'upsample')
 46 |         self.padding = padding
 47 |         self.depth = depth
 48 |         prev_channels = in_channels
 49 |         self.down_path = nn.ModuleList()
 50 |         for i in range(depth):
 51 |             self.down_path.append(
 52 |                 UNetConvBlock(prev_channels, 2 ** (wf + i), padding, batch_norm)
 53 |             )
 54 |             prev_channels = 2 ** (wf + i)
 55 | 
 56 |         self.up_path = nn.ModuleList()
 57 |         for i in reversed(range(depth - 1)):
 58 |             self.up_path.append(
 59 |                 UNetUpBlock(prev_channels, 2 ** (wf + i), up_mode, padding, batch_norm)
 60 |             )
 61 |             prev_channels = 2 ** (wf + i)
 62 | 
 63 |         self.last = nn.Conv2d(prev_channels, n_classes, kernel_size=1)
 64 | 
 65 |     def forward(self, x):
 66 |         blocks = []
 67 |         for i, down in enumerate(self.down_path):
 68 |             x = down(x)
 69 |             if i != len(self.down_path) - 1:
 70 |                 blocks.append(x)
 71 |                 x = F.max_pool2d(x, 2)
 72 | 
 73 |         for i, up in enumerate(self.up_path):
 74 |             x = up(x, blocks[-i - 1])
 75 | 
 76 |         return self.last(x)
 77 | 
 78 | 
 79 | class UNetConvBlock(nn.Module):
 80 |     def __init__(self, in_size, out_size, padding, batch_norm):
 81 |         super(UNetConvBlock, self).__init__()
 82 |         block = []
 83 | 
 84 |         block.append(nn.Conv2d(in_size, out_size, kernel_size=3, padding=int(padding)))
 85 |         block.append(nn.ReLU())
 86 |         if batch_norm:
 87 |             block.append(nn.BatchNorm2d(out_size))
 88 | 
 89 |         block.append(nn.Conv2d(out_size, out_size, kernel_size=3, padding=int(padding)))
 90 |         block.append(nn.ReLU())
 91 |         if batch_norm:
 92 |             block.append(nn.BatchNorm2d(out_size))
 93 | 
 94 |         self.block = nn.Sequential(*block)
 95 | 
 96 |     def forward(self, x):
 97 |         out = self.block(x)
 98 |         return out
 99 | 
100 | 
101 | class UNetUpBlock(nn.Module):
102 |     def __init__(self, in_size, out_size, up_mode, padding, batch_norm):
103 |         super(UNetUpBlock, self).__init__()
104 |         if up_mode == 'upconv':
105 |             self.up = nn.ConvTranspose2d(in_size, out_size, kernel_size=2, stride=2)
106 |         elif up_mode == 'upsample':
107 |             self.up = nn.Sequential(
108 |                 nn.Upsample(mode='bilinear', scale_factor=2),
109 |                 nn.Conv2d(in_size, out_size, kernel_size=1),
110 |             )
111 | 
112 |         self.conv_block = UNetConvBlock(in_size, out_size, padding, batch_norm)
113 | 
114 |     def center_crop(self, layer, target_size):
115 |         _, _, layer_height, layer_width = layer.size()
116 |         diff_y = (layer_height - target_size[0]) // 2
117 |         diff_x = (layer_width - target_size[1]) // 2
118 |         return layer[
119 |             :, :, diff_y : (diff_y + target_size[0]), diff_x : (diff_x + target_size[1])
120 |         ]
121 | 
122 |     def forward(self, x, bridge):
123 |         up = self.up(x)
124 |         crop1 = self.center_crop(bridge, up.shape[2:])
125 |         out = torch.cat([up, crop1], 1)
126 |         out = self.conv_block(out)
127 | 
128 |         return out
129 | 


--------------------------------------------------------------------------------
/deprecated_work/build/scripts-3.6/install_apex:
--------------------------------------------------------------------------------
1 | #!/bin/bash
2 | 
3 | rm -rf apex
4 | git clone https://github.com/NVIDIA/apex
5 | cd apex
6 | pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./ || pip install -v --no-cache-dir ./
7 | cd ..
8 | rm -rf apex
9 | 


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/deprecated_work/dist/pathflowai-0.1-py3-none-any.whl:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/jlevy44/PathFlowAI/5bd66ec800efbe413e16f5f0b8fd30278147c0ac/deprecated_work/dist/pathflowai-0.1-py3-none-any.whl


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/deprecated_work/dist/pathflowai-0.1-py3.6.egg:
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https://raw.githubusercontent.com/jlevy44/PathFlowAI/5bd66ec800efbe413e16f5f0b8fd30278147c0ac/deprecated_work/dist/pathflowai-0.1-py3.6.egg


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/deprecated_work/dist/pathflowai-0.1.1-py3-none-any.whl:
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https://raw.githubusercontent.com/jlevy44/PathFlowAI/5bd66ec800efbe413e16f5f0b8fd30278147c0ac/deprecated_work/dist/pathflowai-0.1.1-py3-none-any.whl


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/deprecated_work/dist/pathflowai-0.1.1.tar.gz:
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https://raw.githubusercontent.com/jlevy44/PathFlowAI/5bd66ec800efbe413e16f5f0b8fd30278147c0ac/deprecated_work/dist/pathflowai-0.1.1.tar.gz


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/deprecated_work/dist/pathflowai-0.1.tar.gz:
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https://raw.githubusercontent.com/jlevy44/PathFlowAI/5bd66ec800efbe413e16f5f0b8fd30278147c0ac/deprecated_work/dist/pathflowai-0.1.tar.gz


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/deprecated_work/pathflowai.egg-info/PKG-INFO:
--------------------------------------------------------------------------------
 1 | Metadata-Version: 2.1
 2 | Name: pathflowai
 3 | Version: 0.1.1
 4 | Summary: A modular approach for preprocessing and deep learning on histopathology images.
 5 | Home-page: https://github.com/jlevy44/PathFlowAI
 6 | Author: Joshua Levy
 7 | Author-email: joshualevy44@berkeley.edu
 8 | License: MIT
 9 | Description: <h1 align="center">Welcome to PathFlowAI 👋</h1>
10 |         <p>
11 |           <img alt="Version" src="https://img.shields.io/badge/version-0.1-blue.svg?cacheSeconds=2592000" />
12 |           <a href="https://jlevy44.github.io/PathFlowAI/">
13 |             <img alt="Documentation" src="https://img.shields.io/badge/documentation-yes-brightgreen.svg" target="_blank" />
14 |           </a>
15 |         </p>
16 |         
17 |         > A Convenient High-Throughput Workflow for Preprocessing, Deep Learning Analytics and Interpretation in Digital Pathology
18 |         
19 |         ### 🏠 [Homepage](https://github.com/jlevy44/PathFlowAI)
20 |         
21 |         NOTE: MedRxiv manuscript and set-up wiki will be posted on this repository shortly! Stay tuned!
22 |         
23 |         ## Install
24 |         
25 |         First, install [openslide](https://openslide.org/download/).
26 |         
27 |         ```sh
28 |         pip install pathflowai
29 |         ```
30 |         
31 |         ## Usage
32 |         
33 |         ```sh
34 |         pathflowai-preprocess -h
35 |         pathflowai-train_model -h
36 |         pathflowai-monitor -h
37 |         pathflowai-visualize -h
38 |         ```
39 |         
40 |         See [Wiki](https://github.com/jlevy44/PathFlowAI/wiki) for more information on setting up and running the workflow.
41 |         
42 |         ## Author
43 |         
44 |         👤 **Joshua Levy**
45 |         
46 |         * Github: [@jlevy44](https://github.com/jlevy44)
47 |         
48 |         ## 🤝 Contributing
49 |         
50 |         Contributions, issues and feature requests are welcome!<br />Feel free to check [issues page](https://github.com/jlevy44/PathFlowAI/issues).
51 |         
52 |         ![1](https://user-images.githubusercontent.com/19698023/62230963-0199d780-b391-11e9-96eb-ac9b86686723.jpeg)
53 |         ![2](https://user-images.githubusercontent.com/19698023/62231545-41ad8a00-b392-11e9-8d47-f9f83f4b764a.jpeg)
54 |         ![3](https://user-images.githubusercontent.com/19698023/62231546-41ad8a00-b392-11e9-9b16-ea3b2b92bf3f.jpeg)
55 |         ![4](https://user-images.githubusercontent.com/19698023/62230966-02326e00-b391-11e9-989c-155ff0a9be67.jpeg)
56 |         
57 | Platform: UNKNOWN
58 | Description-Content-Type: text/markdown
59 | 


--------------------------------------------------------------------------------
/deprecated_work/pathflowai.egg-info/SOURCES.txt:
--------------------------------------------------------------------------------
 1 | README.md
 2 | setup.py
 3 | bin/install_apex
 4 | pathflowai/__init__.py
 5 | pathflowai/cli_preprocessing.py
 6 | pathflowai/cli_visualizations.py
 7 | pathflowai/datasets.py
 8 | pathflowai/fast_scnn.py
 9 | pathflowai/losses.py
10 | pathflowai/model_training.py
11 | pathflowai/models.py
12 | pathflowai/monitor_memory_usage.py
13 | pathflowai/sampler.py
14 | pathflowai/schedulers.py
15 | pathflowai/unet.py
16 | pathflowai/unet2.py
17 | pathflowai/unet3.py
18 | pathflowai/unet4.py
19 | pathflowai/utils.py
20 | pathflowai/visualize.py
21 | pathflowai.egg-info/PKG-INFO
22 | pathflowai.egg-info/SOURCES.txt
23 | pathflowai.egg-info/dependency_links.txt
24 | pathflowai.egg-info/entry_points.txt
25 | pathflowai.egg-info/requires.txt
26 | pathflowai.egg-info/top_level.txt


--------------------------------------------------------------------------------
/deprecated_work/pathflowai.egg-info/dependency_links.txt:
--------------------------------------------------------------------------------
1 | 
2 | 


--------------------------------------------------------------------------------
/deprecated_work/pathflowai.egg-info/entry_points.txt:
--------------------------------------------------------------------------------
1 | [console_scripts]
2 | pathflowai-monitor = pathflowai.monitor_memory_usage:monitor
3 | pathflowai-preprocess = pathflowai.cli_preprocessing:preprocessing
4 | pathflowai-train_model = pathflowai.model_training:train
5 | pathflowai-visualize = pathflowai.cli_visualizations:visualize
6 | 
7 | 


--------------------------------------------------------------------------------
/deprecated_work/pathflowai.egg-info/requires.txt:
--------------------------------------------------------------------------------
 1 | pandas==0.25.0
 2 | numpy
 3 | dask[dataframe]
 4 | distributed
 5 | nonechucks
 6 | dask-image
 7 | opencv-python
 8 | scikit-learn
 9 | scipy
10 | umap-learn
11 | pysnooper
12 | tifffile
13 | seaborn
14 | scikit-image
15 | openslide-python
16 | Shapely
17 | click==6.7
18 | torch
19 | torchvision
20 | albumentations
21 | GPUtil
22 | beautifulsoup4
23 | plotly
24 | xarray
25 | matplotlib
26 | networkx
27 | shap
28 | 


--------------------------------------------------------------------------------
/deprecated_work/pathflowai.egg-info/top_level.txt:
--------------------------------------------------------------------------------
1 | pathflowai
2 | 


--------------------------------------------------------------------------------
/deprecated_work/unet3.py:
--------------------------------------------------------------------------------
  1 | # Adopted from https://raw.githubusercontent.com/jvanvugt/pytorch-unet/master/unet.py
  2 | # Adapted from https://discuss.pytorch.org/t/unet-implementation/426
  3 | 
  4 | import torch
  5 | from torch import nn
  6 | import torch.nn.functional as F
  7 | 
  8 | 
  9 | class UNet(nn.Module):
 10 |     def __init__(
 11 |         self,
 12 |         in_channels=1,
 13 |         n_classes=2,
 14 |         depth=5,
 15 |         wf=6,
 16 |         padding=True,
 17 |         batch_norm=False,
 18 |         up_mode='upconv',
 19 |     ):
 20 |         """
 21 |         Implementation of
 22 |         U-Net: Convolutional Networks for Biomedical Image Segmentation
 23 |         (Ronneberger et al., 2015)
 24 |         https://arxiv.org/abs/1505.04597
 25 | 
 26 |         Using the default arguments will yield the exact version used
 27 |         in the original paper
 28 | 
 29 |         Args:
 30 |             in_channels (int): number of input channels
 31 |             n_classes (int): number of output channels
 32 |             depth (int): depth of the network
 33 |             wf (int): number of filters in the first layer is 2**wf
 34 |             padding (bool): if True, apply padding such that the input shape
 35 |                             is the same as the output.
 36 |                             This may introduce artifacts
 37 |             batch_norm (bool): Use BatchNorm after layers with an
 38 |                                activation function
 39 |             up_mode (str): one of 'upconv' or 'upsample'.
 40 |                            'upconv' will use transposed convolutions for
 41 |                            learned upsampling.
 42 |                            'upsample' will use bilinear upsampling.
 43 |         """
 44 |         super(UNet, self).__init__()
 45 |         assert up_mode in ('upconv', 'upsample')
 46 |         self.padding = padding
 47 |         self.depth = depth
 48 |         prev_channels = in_channels
 49 |         self.down_path = nn.ModuleList()
 50 |         for i in range(depth):
 51 |             self.down_path.append(
 52 |                 UNetConvBlock(prev_channels, 2 ** (wf + i), padding, batch_norm)
 53 |             )
 54 |             prev_channels = 2 ** (wf + i)
 55 | 
 56 |         self.up_path = nn.ModuleList()
 57 |         for i in reversed(range(depth - 1)):
 58 |             self.up_path.append(
 59 |                 UNetUpBlock(prev_channels, 2 ** (wf + i), up_mode, padding, batch_norm)
 60 |             )
 61 |             prev_channels = 2 ** (wf + i)
 62 | 
 63 |         self.last = nn.Conv2d(prev_channels, n_classes, kernel_size=1)
 64 | 
 65 |     def forward(self, x):
 66 |         blocks = []
 67 |         for i, down in enumerate(self.down_path):
 68 |             x = down(x)
 69 |             if i != len(self.down_path) - 1:
 70 |                 blocks.append(x)
 71 |                 x = F.max_pool2d(x, 2)
 72 | 
 73 |         for i, up in enumerate(self.up_path):
 74 |             x = up(x, blocks[-i - 1])
 75 | 
 76 |         return self.last(x)
 77 | 
 78 | 
 79 | class UNetConvBlock(nn.Module):
 80 |     def __init__(self, in_size, out_size, padding, batch_norm):
 81 |         super(UNetConvBlock, self).__init__()
 82 |         block = []
 83 | 
 84 |         block.append(nn.Conv2d(in_size, out_size, kernel_size=3, padding=int(padding)))
 85 |         block.append(nn.ReLU())
 86 |         if batch_norm:
 87 |             block.append(nn.BatchNorm2d(out_size))
 88 | 
 89 |         block.append(nn.Conv2d(out_size, out_size, kernel_size=3, padding=int(padding)))
 90 |         block.append(nn.ReLU())
 91 |         if batch_norm:
 92 |             block.append(nn.BatchNorm2d(out_size))
 93 | 
 94 |         self.block = nn.Sequential(*block)
 95 | 
 96 |     def forward(self, x):
 97 |         out = self.block(x)
 98 |         return out
 99 | 
100 | 
101 | class UNetUpBlock(nn.Module):
102 |     def __init__(self, in_size, out_size, up_mode, padding, batch_norm):
103 |         super(UNetUpBlock, self).__init__()
104 |         if up_mode == 'upconv':
105 |             self.up = nn.ConvTranspose2d(in_size, out_size, kernel_size=2, stride=2)
106 |         elif up_mode == 'upsample':
107 |             self.up = nn.Sequential(
108 |                 nn.Upsample(mode='bilinear', scale_factor=2),
109 |                 nn.Conv2d(in_size, out_size, kernel_size=1),
110 |             )
111 | 
112 |         self.conv_block = UNetConvBlock(in_size, out_size, padding, batch_norm)
113 | 
114 |     def center_crop(self, layer, target_size):
115 |         _, _, layer_height, layer_width = layer.size()
116 |         diff_y = (layer_height - target_size[0]) // 2
117 |         diff_x = (layer_width - target_size[1]) // 2
118 |         return layer[
119 |             :, :, diff_y : (diff_y + target_size[0]), diff_x : (diff_x + target_size[1])
120 |         ]
121 | 
122 |     def forward(self, x, bridge):
123 |         up = self.up(x)
124 |         crop1 = self.center_crop(bridge, up.shape[2:])
125 |         out = torch.cat([up, crop1], 1)
126 |         out = self.conv_block(out)
127 | 
128 |         return out
129 | 


--------------------------------------------------------------------------------
/docker/Dockerfile:
--------------------------------------------------------------------------------
 1 | FROM continuumio/anaconda3
 2 | 
 3 | USER root
 4 | 
 5 | RUN apt-get -y update && apt install -y build-essential
 6 | 
 7 | COPY spec-list.pathflow.txt .
 8 | 
 9 | RUN conda create --name default --file spec-list.pathflow.txt && \
10 |   rm spec-list.pathflow.txt
11 | 
12 | RUN apt-get install -y openslide-tools
13 | 
14 | COPY requirements.txt .
15 | 
16 | RUN pip install -r requirements.txt && \
17 |     rm requirements.txt
18 | 
19 | RUN pip install git+https://github.com/jlevy44/PathFlowAI.git
20 | 
21 | RUN install_apex
22 | 
23 | #ENTRYPOINT [ "/usr/bin/tini", "--" ]
24 | CMD [ "/bin/bash" ]
25 | 


--------------------------------------------------------------------------------
/docker/Singularity:
--------------------------------------------------------------------------------
 1 | Bootstrap: docker
 2 | From: nvidia/cuda:10.1-devel-ubuntu18.04
 3 | 
 4 | %post
 5 |    apt-get update -y
 6 |    apt install -y build-essential
 7 |    DEBIAN_FRONTEND=noninteractive apt-get install -y --no-install-recommends \
 8 |          wget \
 9 |          python3 \
10 |          python3-tk \
11 |          python3-pip \
12 |          cmake \
13 |          git \
14 |          openslide-tools \
15 |          libsm6 \
16 |          libxext6 \
17 |          libxrender-dev
18 | 
19 |    rm -rf /var/lib/apt/lists/*
20 | 
21 |    wget https://repo.anaconda.com/archive/Anaconda3-2020.02-Linux-x86_64.sh -O ~/anaconda3.sh
22 |    bash ~/anaconda3.sh -b -p /home/anaconda3
23 |    rm ~/anaconda3.sh
24 |    echo "export PATH=/home/anaconda3/bin:$PATH" >> ~/.bashrc
25 |    /bin/bash -c "source /root/.bashrc"
26 | 
27 |    echo "source /home/anaconda3/etc/profile.d/conda.sh" >> /.singularity_bash
28 | 
29 |    export PATH=/home/anaconda3/bin:$PATH
30 | 
31 |    wget https://raw.githubusercontent.com/jlevy44/PathFlowAI/master/docker/spec-list.pathflow.txt
32 | 
33 |    conda create --name default --file spec-list.pathflow.txt && \
34 |       rm spec-list.pathflow.txt
35 | 
36 |    conda install -y numpy ninja pyyaml mkl mkl-include setuptools cmake cffi
37 | 
38 |    export TORCH_CUDA_ARCH_LIST="5.0 5.2 5.3 6.0 6.2 7.0 7.2 7.5"
39 | 
40 |    export TORCH=4
41 | 
42 |    export CUDA=cu101
43 | 
44 |    pip install torch==1.4.0 torchvision==0.5.0 -f https://download.pytorch.org/whl/cu101/torch-1.4.0-cp37-cp37m-linux_x86_64.whl --upgrade
45 | 
46 |    mkdir -p $SINGULARITY_ROOTFS/tmp/sing_build_cuda
47 |    cd $SINGULARITY_ROOTFS/tmp/sing_build_cuda
48 | 
49 |    wget https://raw.githubusercontent.com/jlevy44/PathFlowAI/master/docker/requirements.txt
50 | 
51 |    pip install -r requirements.txt && \
52 |        rm requirements.txt
53 | 
54 |    pip install git+https://github.com/jlevy44/PathFlowAI.git
55 | 
56 |    install_apex
57 | 
58 |    pip install opencv-python-headless histomicstk girder-large-image girder-slicer-cli-web --find-links https://girder.github.io/large_image_wheels
59 | 
60 |    cd $CURDIR
61 |    rm -rf $SINGULARITY_ROOTFS/tmp/sing_build_cuda
62 | 
63 | 
64 | 
65 | %runScript
66 |    LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda/lib64
67 |    export PATH="/home/anaconda3/bin:/usr/local/bin:/usr/bin:/bin:"
68 |    unset CONDA_DEFAULT_ENV
69 |    export ANACONDA_HOME=/home/anaconda3
70 | 
71 | 
72 | %environment
73 |       CPATH="/usr/local/cuda/include:$CPATH"
74 |       PATH="/usr/local/cuda/bin:$PATH"
75 |       LD_LIBRARY_PATH="/usr/local/cuda/lib64:$LD_LIBRARY_PATH"
76 |       CUDA_HOME="/usr/local/cuda"
77 |       CONDA_INSTALL_PATH="/home/anaconda3"
78 |       CONDA_BIN_PATH="/home/anaconda3/bin"
79 |       export PATH="$CONDA_BIN_PATH:$PATH"
80 |       export CPATH PATH LD_LIBRARY_PATH CUDA_HOME
81 | 


--------------------------------------------------------------------------------
/docker/requirements.txt:
--------------------------------------------------------------------------------
 1 | albumentations==0.4.1
 2 | asciitree==0.3.3
 3 | beautifulsoup4==4.8.1
 4 | blosc==1.8.1
 5 | brambox==2.1.0
 6 | certifi==2019.9.11
 7 | chardet==3.0.4
 8 | click==6.7
 9 | cloudpickle==1.2.2
10 | cycler==0.10.0
11 | dask==2.6.0
12 | dask-image==0.2.0
13 | decorator==4.4.0
14 | dill==0.3.1.1
15 | distributed==2.6.0
16 | efficientnet-pytorch==0.5.1
17 | fasteners==0.15
18 | fire==0.2.1
19 | fsspec==0.5.2
20 | future==0.18.1
21 | googledrivedownloader==0.4
22 | GPUtil==1.4.0
23 | h5py==2.10.0
24 | HeapDict==1.0.1
25 | idna==2.8
26 | imageio==2.6.1
27 | imgaug==0.2.6
28 | isodate==0.6.0
29 | joblib==0.14.0
30 | kiwisolver==1.1.0
31 | locket==0.2.0
32 | matplotlib==3.1.1
33 | monotonic==1.5
34 | msgpack==0.6.2
35 | multiprocess==0.70.9
36 | networkx==2.4
37 | nonechucks==0.4.0
38 | nose==1.3.7
39 | numba==0.46.0
40 | numcodecs==0.6.3
41 | numpy==1.17.3
42 | opencv-python==4.1.1.26
43 | opencv-python-headless==4.1.1.26
44 | openslide-python==1.1.1
45 | pandas==0.25.0
46 | partd==1.0.0
47 | pathos==0.2.5
48 | Pillow==6.2.1
49 | PIMS==0.4.1
50 | plotly==4.2.1
51 | plyfile==0.7.1
52 | pox==0.2.7
53 | ppft==1.6.6.1
54 | progressbar2==3.47.0
55 | psutil==5.6.6
56 | pyparsing==2.4.2
57 | PySnooper==0.2.8
58 | python-dateutil==2.7.5
59 | python-utils==2.3.0
60 | pytz==2018.7
61 | PyWavelets==1.1.1
62 | rdflib==4.2.2
63 | requests==2.22.0
64 | retrying==1.3.3
65 | scikit-image==0.16.2
66 | scikit-learn==0.21.3
67 | scipy==1.3.1
68 | seaborn==0.9.0
69 | shap==0.31.0
70 | Shapely==1.6.4.post2
71 | six==1.12.0
72 | slicerator==1.0.0
73 | sortedcontainers==2.1.0
74 | soupsieve==1.9.4
75 | tblib==1.5.0
76 | termcolor==1.1.0
77 | tifffile==2019.7.26
78 | toolz==0.10.0
79 | torch==1.4.0
80 | torch-encoding==1.0.1
81 | torchvision==0.5.0
82 | tornado==6.0.3
83 | tqdm==4.36.1
84 | umap-learn==0.3.10
85 | urllib3==1.25.6
86 | xarray==0.14.0
87 | zarr==2.3.2
88 | zict==1.0.0
89 | 


--------------------------------------------------------------------------------
/docker/spec-list.pathflow.txt:
--------------------------------------------------------------------------------
 1 | # This file may be used to create an environment using:
 2 | # $ conda create --name <env> --file <this file>
 3 | # platform: linux-64
 4 | @EXPLICIT
 5 | https://repo.anaconda.com/pkgs/main/linux-64/_libgcc_mutex-0.1-main.conda
 6 | https://conda.anaconda.org/conda-forge/linux-64/binutils_impl_linux-64-2.31.1-h7fc9f1b_2.tar.bz2
 7 | https://conda.anaconda.org/conda-forge/linux-64/ca-certificates-2019.9.11-hecc5488_0.tar.bz2
 8 | https://repo.anaconda.com/pkgs/main/linux-64/libstdcxx-ng-9.1.0-hdf63c60_0.conda
 9 | https://conda.anaconda.org/conda-forge/linux-64/binutils_linux-64-2.31.1-h6176602_12.tar.bz2
10 | https://repo.anaconda.com/pkgs/main/linux-64/libgcc-ng-9.1.0-hdf63c60_0.conda
11 | https://conda.anaconda.org/conda-forge/linux-64/bzip2-1.0.8-h516909a_1.tar.bz2
12 | https://conda.anaconda.org/conda-forge/linux-64/icu-64.2-he1b5a44_1.tar.bz2
13 | https://conda.anaconda.org/conda-forge/linux-64/jpeg-9c-h14c3975_1001.tar.bz2
14 | https://conda.anaconda.org/conda-forge/linux-64/libffi-3.2.1-he1b5a44_1006.tar.bz2
15 | https://conda.anaconda.org/conda-forge/linux-64/libgcc-7.2.0-h69d50b8_2.tar.bz2
16 | https://conda.anaconda.org/conda-forge/linux-64/libiconv-1.15-h516909a_1005.tar.bz2
17 | https://conda.anaconda.org/conda-forge/linux-64/libuuid-2.32.1-h14c3975_1000.tar.bz2
18 | https://conda.anaconda.org/conda-forge/linux-64/lz4-c-1.8.3-he1b5a44_1001.tar.bz2
19 | https://conda.anaconda.org/conda-forge/linux-64/ncurses-6.1-hf484d3e_1002.tar.bz2
20 | https://conda.anaconda.org/conda-forge/linux-64/openssl-1.1.1c-h516909a_0.tar.bz2
21 | https://conda.anaconda.org/conda-forge/linux-64/pcre-8.43-he1b5a44_0.tar.bz2
22 | https://conda.anaconda.org/conda-forge/linux-64/pixman-0.38.0-h516909a_1003.tar.bz2
23 | https://conda.anaconda.org/conda-forge/linux-64/pthread-stubs-0.4-h14c3975_1001.tar.bz2
24 | https://conda.anaconda.org/conda-forge/linux-64/xorg-kbproto-1.0.7-h14c3975_1002.tar.bz2
25 | https://conda.anaconda.org/conda-forge/linux-64/xorg-libice-1.0.10-h516909a_0.tar.bz2
26 | https://conda.anaconda.org/conda-forge/linux-64/xorg-libxau-1.0.9-h14c3975_0.tar.bz2
27 | https://conda.anaconda.org/conda-forge/linux-64/xorg-libxdmcp-1.1.3-h516909a_0.tar.bz2
28 | https://conda.anaconda.org/conda-forge/linux-64/xorg-renderproto-0.11.1-h14c3975_1002.tar.bz2
29 | https://conda.anaconda.org/conda-forge/linux-64/xorg-xextproto-7.3.0-h14c3975_1002.tar.bz2
30 | https://conda.anaconda.org/conda-forge/linux-64/xorg-xproto-7.0.31-h14c3975_1007.tar.bz2
31 | https://conda.anaconda.org/conda-forge/linux-64/xz-5.2.4-h14c3975_1001.tar.bz2
32 | https://conda.anaconda.org/conda-forge/linux-64/zlib-1.2.11-h516909a_1006.tar.bz2
33 | https://conda.anaconda.org/conda-forge/linux-64/gcc_linux-64-7.3.0-h553295d_12.tar.bz2
34 | https://conda.anaconda.org/conda-forge/linux-64/gettext-0.19.8.1-hc5be6a0_1002.tar.bz2
35 | https://conda.anaconda.org/conda-forge/linux-64/libedit-3.1.20170329-hf8c457e_1001.tar.bz2
36 | https://conda.anaconda.org/conda-forge/linux-64/libpng-1.6.37-hed695b0_0.tar.bz2
37 | https://conda.anaconda.org/conda-forge/linux-64/libxcb-1.13-h14c3975_1002.tar.bz2
38 | https://conda.anaconda.org/conda-forge/linux-64/libxml2-2.9.9-hee79883_5.tar.bz2
39 | https://conda.anaconda.org/conda-forge/linux-64/readline-8.0-hf8c457e_0.tar.bz2
40 | https://conda.anaconda.org/conda-forge/linux-64/tk-8.6.9-hed695b0_1003.tar.bz2
41 | https://conda.anaconda.org/conda-forge/linux-64/xorg-libsm-1.2.3-h84519dc_1000.tar.bz2
42 | https://conda.anaconda.org/conda-forge/linux-64/zstd-1.4.3-h3b9ef0a_0.tar.bz2
43 | https://conda.anaconda.org/conda-forge/linux-64/freetype-2.10.0-he983fc9_1.tar.bz2
44 | https://conda.anaconda.org/conda-forge/linux-64/glib-2.58.3-h6f030ca_1002.tar.bz2
45 | https://conda.anaconda.org/conda-forge/linux-64/gxx_linux-64-7.3.0-h553295d_12.tar.bz2
46 | https://conda.anaconda.org/conda-forge/linux-64/libtiff-4.0.10-hfc65ed5_1004.tar.bz2
47 | https://conda.anaconda.org/conda-forge/linux-64/sqlite-3.30.1-hcee41ef_0.tar.bz2
48 | https://conda.anaconda.org/conda-forge/linux-64/xorg-libx11-1.6.9-h516909a_0.tar.bz2
49 | https://conda.anaconda.org/conda-forge/linux-64/fontconfig-2.13.1-h86ecdb6_1001.tar.bz2
50 | https://conda.anaconda.org/conda-forge/linux-64/openjpeg-2.3.1-h21c5421_1.tar.bz2
51 | https://conda.anaconda.org/conda-forge/linux-64/python-3.7.3-h33d41f4_1.tar.bz2
52 | https://conda.anaconda.org/conda-forge/linux-64/xorg-libxext-1.3.4-h516909a_0.tar.bz2
53 | https://conda.anaconda.org/conda-forge/linux-64/xorg-libxrender-0.9.10-h516909a_1002.tar.bz2
54 | https://conda.anaconda.org/conda-forge/linux-64/cairo-1.16.0-hfb77d84_1002.tar.bz2
55 | https://conda.anaconda.org/conda-forge/linux-64/certifi-2019.9.11-py37_0.tar.bz2
56 | https://conda.anaconda.org/conda-forge/linux-64/gobject-introspection-1.58.2-py37h5503ade_1002.tar.bz2
57 | https://conda.anaconda.org/conda-forge/linux-64/setuptools-41.4.0-py37_0.tar.bz2
58 | https://conda.anaconda.org/conda-forge/linux-64/gdk-pixbuf-2.36.12-h7a26e22_1003.tar.bz2
59 | https://conda.anaconda.org/conda-forge/linux-64/wheel-0.33.6-py37_0.tar.bz2
60 | https://conda.anaconda.org/conda-forge/linux-64/openslide-3.4.1-h8137273_0.tar.bz2
61 | https://conda.anaconda.org/conda-forge/linux-64/pip-19.3.1-py37_0.tar.bz2
62 | 


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3 | config: 8209897d39b44ed1a2b95c515b96d06e
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/docs/_sources/index.rst.txt:
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 1 | Welcome to PathFlowAI's documentation!
 2 | ======================================
 3 | 
 4 | .. toctree::
 5 |    :maxdepth: 2
 6 |    :caption: Contents:
 7 | 
 8 | .. image:: yimages/1.jpeg
 9 |   :width: 800px
10 |   :height: 600px
11 |   :scale: 60%
12 |   :alt: Download
13 |   :align: center
14 | 
15 | .. image:: yimages/2.jpeg
16 |   :width: 800px
17 |   :height: 600px
18 |   :scale: 60%
19 |   :alt: Download
20 |   :align: center
21 | 
22 | .. image:: yimages/3.jpeg
23 |   :width: 800px
24 |   :height: 600px
25 |   :scale: 60%
26 |   :alt: Download
27 |   :align: center
28 | 
29 | .. image:: yimages/4.jpeg
30 |   :width: 800px
31 |   :height: 600px
32 |   :scale: 60%
33 |   :alt: Download
34 |   :align: center
35 | 
36 | .. click:: pathflowai.cli_preprocessing:preprocessing
37 |  :prog: pathflowai-preprocess
38 |  :show-nested:
39 | 
40 | .. click:: pathflowai.cli_visualizations:visualize
41 |   :prog: pathflowai-visualize
42 |   :show-nested:
43 | 
44 | .. click:: pathflowai.model_training:train
45 |   :prog: pathflowai-train_model
46 |   :show-nested:
47 | 
48 | .. click:: pathflowai.monitor_memory_usage:monitor
49 |   :prog: pathflowai-monitor
50 |   :show-nested:
51 | 
52 | .. automodule:: pathflowai.datasets
53 |  :members:
54 | 
55 | .. automodule:: pathflowai.losses
56 |   :members:
57 | 
58 | .. automodule:: pathflowai.models
59 |  :members:
60 | 
61 | .. automodule:: pathflowai.sampler
62 |   :members:
63 | 
64 | .. automodule:: pathflowai.schedulers
65 |  :members:
66 | 
67 | .. automodule:: pathflowai.visualize
68 |  :members:
69 | 
70 | .. automodule:: pathflowai.utils
71 |  :members:
72 | 
73 | Indices and tables
74 | ==================
75 | 
76 | * :ref:`genindex`
77 | * :ref:`modindex`
78 | * :ref:`search`
79 | 


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 1 | # Minimal makefile for Sphinx documentation
 2 | #
 3 | 
 4 | # You can set these variables from the command line.
 5 | SPHINXOPTS    =
 6 | SPHINXBUILD   = sphinx-build
 7 | SOURCEDIR     = .
 8 | BUILDDIR      = ybuild
 9 | 
10 | # Put it first so that "make" without argument is like "make help".
11 | help:
12 | 	@$(SPHINXBUILD) -M help "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)
13 | 
14 | .PHONY: help Makefile
15 | 
16 | # Catch-all target: route all unknown targets to Sphinx using the new
17 | # "make mode" option.  $(O) is meant as a shortcut for $(SPHINXOPTS).
18 | %: Makefile
19 | 	@$(SPHINXBUILD) -M $@ "$(SOURCEDIR)" "$(BUILDDIR)" $(SPHINXOPTS) $(O)


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 1 | Welcome to PathFlowAI's documentation!
 2 | ======================================
 3 | 
 4 | .. toctree::
 5 |    :maxdepth: 2
 6 |    :caption: Contents:
 7 | 
 8 | .. image:: yimages/1.jpeg
 9 |   :width: 800px
10 |   :height: 600px
11 |   :scale: 60%
12 |   :alt: Download
13 |   :align: center
14 | 
15 | .. image:: yimages/2.jpeg
16 |   :width: 800px
17 |   :height: 600px
18 |   :scale: 60%
19 |   :alt: Download
20 |   :align: center
21 | 
22 | .. image:: yimages/3.jpeg
23 |   :width: 800px
24 |   :height: 600px
25 |   :scale: 60%
26 |   :alt: Download
27 |   :align: center
28 | 
29 | .. image:: yimages/4.jpeg
30 |   :width: 800px
31 |   :height: 600px
32 |   :scale: 60%
33 |   :alt: Download
34 |   :align: center
35 | 
36 | .. click:: pathflowai.cli_preprocessing:preprocessing
37 |  :prog: pathflowai-preprocess
38 |  :show-nested:
39 | 
40 | .. click:: pathflowai.cli_visualizations:visualize
41 |   :prog: pathflowai-visualize
42 |   :show-nested:
43 | 
44 | .. click:: pathflowai.model_training:train
45 |   :prog: pathflowai-train_model
46 |   :show-nested:
47 | 
48 | .. click:: pathflowai.monitor_memory_usage:monitor
49 |   :prog: pathflowai-monitor
50 |   :show-nested:
51 | 
52 | .. automodule:: pathflowai.datasets
53 |  :members:
54 | 
55 | .. automodule:: pathflowai.losses
56 |   :members:
57 | 
58 | .. automodule:: pathflowai.models
59 |  :members:
60 | 
61 | .. automodule:: pathflowai.sampler
62 |   :members:
63 | 
64 | .. automodule:: pathflowai.schedulers
65 |  :members:
66 | 
67 | .. automodule:: pathflowai.visualize
68 |  :members:
69 | 
70 | .. automodule:: pathflowai.utils
71 |  :members:
72 | 
73 | Indices and tables
74 | ==================
75 | 
76 | * :ref:`genindex`
77 | * :ref:`modindex`
78 | * :ref:`search`
79 | 


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/docs/old_docs/ybuild/latex/3.jpeg:
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/docs/old_docs/ybuild/latex/4.jpeg:
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/docs/old_docs/ybuild/latex/LICRcyr2utf8.xdy:
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  1 | ;; -*- coding: utf-8; mode: Lisp; -*-
  2 | ;; style file for xindy
  3 | ;; filename: LICRcyr2utf8.xdy
  4 | ;; description: style file for xindy which maps back LaTeX Internal
  5 | ;;   Character Representation of Cyrillic to utf-8
  6 | ;; usage: for use with pdflatex produced .idx files.
  7 | ;; Contributed by the Sphinx team, July 2018.
  8 | (merge-rule "\IeC {\'\CYRG }" "Ѓ" :string)
  9 | (merge-rule "\IeC {\'\CYRK }" "Ќ" :string)
 10 | (merge-rule "\IeC {\'\cyrg }" "ѓ" :string)
 11 | (merge-rule "\IeC {\'\cyrk }" "ќ" :string)
 12 | (merge-rule "\IeC {\CYRA }" "А" :string)
 13 | (merge-rule "\IeC {\CYRB }" "Б" :string)
 14 | (merge-rule "\IeC {\CYRC }" "Ц" :string)
 15 | (merge-rule "\IeC {\CYRCH }" "Ч" :string)
 16 | (merge-rule "\IeC {\CYRD }" "Д" :string)
 17 | (merge-rule "\IeC {\CYRDJE }" "Ђ" :string)
 18 | (merge-rule "\IeC {\CYRDZE }" "Ѕ" :string)
 19 | (merge-rule "\IeC {\CYRDZHE }" "Џ" :string)
 20 | (merge-rule "\IeC {\CYRE }" "Е" :string)
 21 | (merge-rule "\IeC {\CYREREV }" "Э" :string)
 22 | (merge-rule "\IeC {\CYRERY }" "Ы" :string)
 23 | (merge-rule "\IeC {\CYRF }" "Ф" :string)
 24 | (merge-rule "\IeC {\CYRG }" "Г" :string)
 25 | (merge-rule "\IeC {\CYRGUP }" "Ґ" :string)
 26 | (merge-rule "\IeC {\CYRH }" "Х" :string)
 27 | (merge-rule "\IeC {\CYRHRDSN }" "Ъ" :string)
 28 | (merge-rule "\IeC {\CYRI }" "И" :string)
 29 | (merge-rule "\IeC {\CYRIE }" "Є" :string)
 30 | (merge-rule "\IeC {\CYRII }" "І" :string)
 31 | (merge-rule "\IeC {\CYRISHRT }" "Й" :string)
 32 | (merge-rule "\IeC {\CYRJE }" "Ј" :string)
 33 | (merge-rule "\IeC {\CYRK }" "К" :string)
 34 | (merge-rule "\IeC {\CYRL }" "Л" :string)
 35 | (merge-rule "\IeC {\CYRLJE }" "Љ" :string)
 36 | (merge-rule "\IeC {\CYRM }" "М" :string)
 37 | (merge-rule "\IeC {\CYRN }" "Н" :string)
 38 | (merge-rule "\IeC {\CYRNJE }" "Њ" :string)
 39 | (merge-rule "\IeC {\CYRO }" "О" :string)
 40 | (merge-rule "\IeC {\CYRP }" "П" :string)
 41 | (merge-rule "\IeC {\CYRR }" "Р" :string)
 42 | (merge-rule "\IeC {\CYRS }" "С" :string)
 43 | (merge-rule "\IeC {\CYRSFTSN }" "Ь" :string)
 44 | (merge-rule "\IeC {\CYRSH }" "Ш" :string)
 45 | (merge-rule "\IeC {\CYRSHCH }" "Щ" :string)
 46 | (merge-rule "\IeC {\CYRT }" "Т" :string)
 47 | (merge-rule "\IeC {\CYRTSHE }" "Ћ" :string)
 48 | (merge-rule "\IeC {\CYRU }" "У" :string)
 49 | (merge-rule "\IeC {\CYRUSHRT }" "Ў" :string)
 50 | (merge-rule "\IeC {\CYRV }" "В" :string)
 51 | (merge-rule "\IeC {\CYRYA }" "Я" :string)
 52 | (merge-rule "\IeC {\CYRYI }" "Ї" :string)
 53 | (merge-rule "\IeC {\CYRYO }" "Ё" :string)
 54 | (merge-rule "\IeC {\CYRYU }" "Ю" :string)
 55 | (merge-rule "\IeC {\CYRZ }" "З" :string)
 56 | (merge-rule "\IeC {\CYRZH }" "Ж" :string)
 57 | (merge-rule "\IeC {\cyra }" "а" :string)
 58 | (merge-rule "\IeC {\cyrb }" "б" :string)
 59 | (merge-rule "\IeC {\cyrc }" "ц" :string)
 60 | (merge-rule "\IeC {\cyrch }" "ч" :string)
 61 | (merge-rule "\IeC {\cyrd }" "д" :string)
 62 | (merge-rule "\IeC {\cyrdje }" "ђ" :string)
 63 | (merge-rule "\IeC {\cyrdze }" "ѕ" :string)
 64 | (merge-rule "\IeC {\cyrdzhe }" "џ" :string)
 65 | (merge-rule "\IeC {\cyre }" "е" :string)
 66 | (merge-rule "\IeC {\cyrerev }" "э" :string)
 67 | (merge-rule "\IeC {\cyrery }" "ы" :string)
 68 | (merge-rule "\IeC {\cyrf }" "ф" :string)
 69 | (merge-rule "\IeC {\cyrg }" "г" :string)
 70 | (merge-rule "\IeC {\cyrgup }" "ґ" :string)
 71 | (merge-rule "\IeC {\cyrh }" "х" :string)
 72 | (merge-rule "\IeC {\cyrhrdsn }" "ъ" :string)
 73 | (merge-rule "\IeC {\cyri }" "и" :string)
 74 | (merge-rule "\IeC {\cyrie }" "є" :string)
 75 | (merge-rule "\IeC {\cyrii }" "і" :string)
 76 | (merge-rule "\IeC {\cyrishrt }" "й" :string)
 77 | (merge-rule "\IeC {\cyrje }" "ј" :string)
 78 | (merge-rule "\IeC {\cyrk }" "к" :string)
 79 | (merge-rule "\IeC {\cyrl }" "л" :string)
 80 | (merge-rule "\IeC {\cyrlje }" "љ" :string)
 81 | (merge-rule "\IeC {\cyrm }" "м" :string)
 82 | (merge-rule "\IeC {\cyrn }" "н" :string)
 83 | (merge-rule "\IeC {\cyrnje }" "њ" :string)
 84 | (merge-rule "\IeC {\cyro }" "о" :string)
 85 | (merge-rule "\IeC {\cyrp }" "п" :string)
 86 | (merge-rule "\IeC {\cyrr }" "р" :string)
 87 | (merge-rule "\IeC {\cyrs }" "с" :string)
 88 | (merge-rule "\IeC {\cyrsftsn }" "ь" :string)
 89 | (merge-rule "\IeC {\cyrsh }" "ш" :string)
 90 | (merge-rule "\IeC {\cyrshch }" "щ" :string)
 91 | (merge-rule "\IeC {\cyrt }" "т" :string)
 92 | (merge-rule "\IeC {\cyrtshe }" "ћ" :string)
 93 | (merge-rule "\IeC {\cyru }" "у" :string)
 94 | (merge-rule "\IeC {\cyrushrt }" "ў" :string)
 95 | (merge-rule "\IeC {\cyrv }" "в" :string)
 96 | (merge-rule "\IeC {\cyrya }" "я" :string)
 97 | (merge-rule "\IeC {\cyryi }" "ї" :string)
 98 | (merge-rule "\IeC {\cyryo }" "ё" :string)
 99 | (merge-rule "\IeC {\cyryu }" "ю" :string)
100 | (merge-rule "\IeC {\cyrz }" "з" :string)
101 | (merge-rule "\IeC {\cyrzh }" "ж" :string)
102 | 


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/docs/old_docs/ybuild/latex/LatinRules.xdy:
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https://raw.githubusercontent.com/jlevy44/PathFlowAI/5bd66ec800efbe413e16f5f0b8fd30278147c0ac/docs/old_docs/ybuild/latex/LatinRules.xdy


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/docs/old_docs/ybuild/latex/Makefile:
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 1 | # Makefile for Sphinx LaTeX output
 2 | 
 3 | ALLDOCS = $(basename $(wildcard *.tex))
 4 | ALLPDF = $(addsuffix .pdf,$(ALLDOCS))
 5 | ALLDVI = $(addsuffix .dvi,$(ALLDOCS))
 6 | ALLXDV =
 7 | ALLPS  = $(addsuffix .ps,$(ALLDOCS))
 8 | ALLIMGS = $(wildcard *.png *.gif *.jpg *.jpeg)
 9 | 
10 | # Prefix for archive names
11 | ARCHIVEPREFIX =
12 | # Additional LaTeX options (passed via variables in latexmkrc/latexmkjarc file)
13 | export LATEXOPTS =
14 | # Additional latexmk options
15 | LATEXMKOPTS =
16 | # format: pdf or dvi (used only by archive targets)
17 | FMT = pdf
18 | 
19 | LATEX = latexmk -dvi
20 | PDFLATEX = latexmk -pdf -dvi- -ps-
21 | 
22 | 
23 | %.png %.gif %.jpg %.jpeg: FORCE_MAKE
24 | 	extractbb '$@'
25 | 
26 | %.dvi: %.tex FORCE_MAKE
27 | 	$(LATEX) $(LATEXMKOPTS) '$<'
28 | 
29 | %.ps: %.dvi
30 | 	dvips '$<'
31 | 
32 | %.pdf: %.tex FORCE_MAKE
33 | 	$(PDFLATEX) $(LATEXMKOPTS) '$<'
34 | 
35 | all: $(ALLPDF)
36 | 
37 | all-dvi: $(ALLDVI)
38 | 
39 | all-ps: $(ALLPS)
40 | 
41 | all-pdf: $(ALLPDF)
42 | 
43 | zip: all-$(FMT)
44 | 	mkdir $(ARCHIVEPREFIX)docs-$(FMT)
45 | 	cp $(ALLPDF) $(ARCHIVEPREFIX)docs-$(FMT)
46 | 	zip -q -r -9 $(ARCHIVEPREFIX)docs-$(FMT).zip $(ARCHIVEPREFIX)docs-$(FMT)
47 | 	rm -r $(ARCHIVEPREFIX)docs-$(FMT)
48 | 
49 | tar: all-$(FMT)
50 | 	mkdir $(ARCHIVEPREFIX)docs-$(FMT)
51 | 	cp $(ALLPDF) $(ARCHIVEPREFIX)docs-$(FMT)
52 | 	tar cf $(ARCHIVEPREFIX)docs-$(FMT).tar $(ARCHIVEPREFIX)docs-$(FMT)
53 | 	rm -r $(ARCHIVEPREFIX)docs-$(FMT)
54 | 
55 | gz: tar
56 | 	gzip -9 < $(ARCHIVEPREFIX)docs-$(FMT).tar > $(ARCHIVEPREFIX)docs-$(FMT).tar.gz
57 | 
58 | bz2: tar
59 | 	bzip2 -9 -k $(ARCHIVEPREFIX)docs-$(FMT).tar
60 | 
61 | xz: tar
62 | 	xz -9 -k $(ARCHIVEPREFIX)docs-$(FMT).tar
63 | 
64 | clean:
65 | 	rm -f *.log *.ind *.aux *.toc *.syn *.idx *.out *.ilg *.pla *.ps *.tar *.tar.gz *.tar.bz2 *.tar.xz $(ALLPDF) $(ALLDVI) $(ALLXDV) *.fls *.fdb_latexmk
66 | 
67 | .PHONY: all all-pdf all-dvi all-ps clean zip tar gz bz2 xz
68 | .PHONY: FORCE_MAKE


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/docs/old_docs/ybuild/latex/PathFlowAI.ilg:
--------------------------------------------------------------------------------
1 | This is makeindex, version 2.15 [TeX Live 2018] (kpathsea + Thai support).
2 | Scanning style file ./python.ist.......done (7 attributes redefined, 0 ignored).
3 | Scanning input file PathFlowAI.idx....done (277 entries accepted, 0 rejected).
4 | Sorting entries.....done (2411 comparisons).
5 | Generating output file PathFlowAI.ind....done (505 lines written, 0 warnings).
6 | Output written in PathFlowAI.ind.
7 | Transcript written in PathFlowAI.ilg.
8 | 


--------------------------------------------------------------------------------
/docs/old_docs/ybuild/latex/PathFlowAI.toc:
--------------------------------------------------------------------------------
 1 | \babel@toc {english}{}
 2 | \contentsline {chapter}{\numberline {1}pathflowai-preprocess}{3}{chapter.1}
 3 | \contentsline {section}{\numberline {1.1}alter\_masks}{3}{section.1.1}
 4 | \contentsline {section}{\numberline {1.2}collapse\_annotations}{3}{section.1.2}
 5 | \contentsline {section}{\numberline {1.3}preprocess\_pipeline}{4}{section.1.3}
 6 | \contentsline {section}{\numberline {1.4}remove\_basename\_from\_db}{5}{section.1.4}
 7 | \contentsline {chapter}{\numberline {2}pathflowai-visualize}{7}{chapter.2}
 8 | \contentsline {section}{\numberline {2.1}extract\_patch}{7}{section.2.1}
 9 | \contentsline {section}{\numberline {2.2}overlay\_new\_annotations}{8}{section.2.2}
10 | \contentsline {section}{\numberline {2.3}plot\_embeddings}{8}{section.2.3}
11 | \contentsline {section}{\numberline {2.4}plot\_image}{9}{section.2.4}
12 | \contentsline {section}{\numberline {2.5}plot\_image\_umap\_embeddings}{9}{section.2.5}
13 | \contentsline {section}{\numberline {2.6}plot\_predictions}{10}{section.2.6}
14 | \contentsline {section}{\numberline {2.7}shapley\_plot}{11}{section.2.7}
15 | \contentsline {chapter}{\numberline {3}pathflowai-monitor}{13}{chapter.3}
16 | \contentsline {section}{\numberline {3.1}monitor\_usage}{13}{section.3.1}
17 | \contentsline {chapter}{\numberline {4}datasets.py}{15}{chapter.4}
18 | \contentsline {chapter}{\numberline {5}losses.py}{19}{chapter.5}
19 | \contentsline {chapter}{\numberline {6}sampler.py}{25}{chapter.6}
20 | \contentsline {chapter}{\numberline {7}schedulers.py}{27}{chapter.7}
21 | \contentsline {chapter}{\numberline {8}visualize.py}{29}{chapter.8}
22 | \contentsline {chapter}{\numberline {9}utils.py}{35}{chapter.9}
23 | \contentsline {chapter}{\numberline {10}Indices and tables}{43}{chapter.10}
24 | \contentsline {chapter}{Python Module Index}{45}{section*.186}
25 | \contentsline {chapter}{Index}{47}{section*.187}
26 | 


--------------------------------------------------------------------------------
/docs/old_docs/ybuild/latex/latexmkjarc:
--------------------------------------------------------------------------------
 1 | $latex = 'platex ' . $ENV{'LATEXOPTS'} . ' -kanji=utf8 %O %S';
 2 | $dvipdf = 'dvipdfmx %O -o %D %S';
 3 | $makeindex = 'internal mendex %S %B %D';
 4 | sub mendex {
 5 |   my ($source, $basename, $destination) = @_;
 6 |   my $dictfile = $basename . ".dic";
 7 |   unlink($destination);
 8 |   system("mendex", "-U", "-f", "-d", $dictfile, "-s", "python.ist", $source);
 9 |   if ($? > 0) {
10 |     print("mendex exited with error code $? (ignored)\n");
11 |   }
12 |   if (!-e $destination) {
13 |     # create an empty .ind file if nothing
14 |     open(FH, ">" . $destination);
15 |     close(FH);
16 |   }
17 |   return 0;
18 | }
19 | add_cus_dep( "glo", "gls", 0, "makeglo" );
20 | sub makeglo {
21 |  return system( "mendex -J -f -s gglo.ist -o '$_[0].gls' '$_[0].glo'" );
22 | }
23 | 


--------------------------------------------------------------------------------
/docs/old_docs/ybuild/latex/latexmkrc:
--------------------------------------------------------------------------------
1 | $latex = 'latex ' . $ENV{'LATEXOPTS'} . ' %O %S';
2 | $pdflatex = 'pdflatex ' . $ENV{'LATEXOPTS'} . ' %O %S';
3 | $lualatex = 'lualatex ' . $ENV{'LATEXOPTS'} . ' %O %S';
4 | $xelatex = 'xelatex --no-pdf ' . $ENV{'LATEXOPTS'} . ' %O %S';
5 | $makeindex = 'makeindex -s python.ist %O -o %D %S';
6 | add_cus_dep( "glo", "gls", 0, "makeglo" );
7 | sub makeglo {
8 |  return system( "makeindex -s gglo.ist -o '$_[0].gls' '$_[0].glo'" );
9 | }


--------------------------------------------------------------------------------
/docs/old_docs/ybuild/latex/make.bat:
--------------------------------------------------------------------------------
 1 | @ECHO OFF
 2 | 
 3 | REM Command file for Sphinx documentation
 4 | 
 5 | pushd %~dp0
 6 | 
 7 | set PDFLATEX=latexmk -pdf -dvi- -ps-
 8 | 
 9 | set "LATEXOPTS= "
10 | 
11 | if "%1" == "" goto all-pdf
12 | 
13 | if "%1" == "all-pdf" (
14 | 	:all-pdf
15 | 	for %%i in (*.tex) do (
16 | 		%PDFLATEX% %LATEXMKOPTS% %%i
17 | 	)
18 | 	goto end
19 | )
20 | 
21 | if "%1" == "all-pdf-ja" (
22 | 	goto all-pdf
23 | )
24 | 
25 | if "%1" == "clean" (
26 | 	del /q /s *.dvi *.log *.ind *.aux *.toc *.syn *.idx *.out *.ilg *.pla *.ps *.tar *.tar.gz *.tar.bz2 *.tar.xz *.fls *.fdb_latexmk
27 | 	goto end
28 | )
29 | 
30 | :end
31 | popd


--------------------------------------------------------------------------------
/docs/old_docs/ybuild/latex/python.ist:
--------------------------------------------------------------------------------
 1 | line_max 100
 2 | headings_flag 1
 3 | heading_prefix "  \\bigletter "
 4 | 
 5 | preamble "\\begin{sphinxtheindex}
 6 | \\let\\bigletter\\sphinxstyleindexlettergroup
 7 | \\let\\spxpagem \\sphinxstyleindexpagemain
 8 | \\let\\spxentry \\sphinxstyleindexentry
 9 | \\let\\spxextra \\sphinxstyleindexextra
10 | 
11 | "
12 | 
13 | postamble "\n\n\\end{sphinxtheindex}\n"
14 | 
15 | symhead_positive "{\\sphinxsymbolsname}"
16 | numhead_positive "{\\sphinxnumbersname}"
17 | 


--------------------------------------------------------------------------------
/docs/old_docs/ybuild/latex/sphinxcyrillic.sty:
--------------------------------------------------------------------------------
 1 | %% CYRILLIC IN NON-CYRILLIC DOCUMENTS (pdflatex only)
 2 | %
 3 | % refs: https://tex.stackexchange.com/q/460271/
 4 | \ProvidesPackage{sphinxcyrillic}%
 5 |   [2018/11/21 v2.0 support for Cyrillic in non-Cyrillic documents]
 6 | \RequirePackage{kvoptions}
 7 | \SetupKeyvalOptions{prefix=spx@cyropt@} % use \spx@cyropt@ prefix
 8 | \DeclareBoolOption[false]{Xtwo}
 9 | \DeclareBoolOption[false]{TtwoA}
10 | \DeclareDefaultOption{\@unknownoptionerror}
11 | \ProcessLocalKeyvalOptions* % ignore class options
12 | 
13 | \ifspx@cyropt@Xtwo
14 | % original code by tex.sx user egreg:
15 | %   https://tex.stackexchange.com/a/460325/
16 | % 159 Cyrillic glyphs as available in X2 TeX 8bit font encoding
17 | % This assumes inputenc loaded with utf8 option, or LaTeX release
18 | % as recent as 2018/04/01 which does it automatically. 
19 |   \@tfor\next:=%
20 |     {Ё}{Ђ}{Є}{Ѕ}{І}{Ј}{Љ}{Њ}{Ћ}{Ў}{Џ}{А}{Б}{В}{Г}{Д}{Е}{Ж}{З}{И}{Й}%
21 |     {К}{Л}{М}{Н}{О}{П}{Р}{С}{Т}{У}{Ф}{Х}{Ц}{Ч}{Ш}{Щ}{Ъ}{Ы}{Ь}{Э}{Ю}%
22 |     {Я}{а}{б}{в}{г}{д}{е}{ж}{з}{и}{й}{к}{л}{м}{н}{о}{п}{р}{с}{т}{у}%
23 |     {ф}{х}{ц}{ч}{ш}{щ}{ъ}{ы}{ь}{э}{ю}{я}{ё}{ђ}{є}{ѕ}{і}{ј}{љ}{њ}{ћ}%
24 |     {ў}{џ}{Ѣ}{ѣ}{Ѫ}{ѫ}{Ѵ}{ѵ}{Ґ}{ґ}{Ғ}{ғ}{Ҕ}{ҕ}{Җ}{җ}{Ҙ}{ҙ}{Қ}{қ}{Ҝ}{ҝ}%
25 |     {Ҟ}{ҟ}{Ҡ}{ҡ}{Ң}{ң}{Ҥ}{ҥ}{Ҧ}{ҧ}{Ҩ}{ҩ}{Ҫ}{ҫ}{Ҭ}{ҭ}{Ү}{ү}{Ұ}{ұ}{Ҳ}{ҳ}%
26 |     {Ҵ}{ҵ}{Ҷ}{ҷ}{Ҹ}{ҹ}{Һ}{һ}{Ҽ}{ҽ}{Ҿ}{ҿ}{Ӏ}{Ӄ}{ӄ}{Ӆ}{ӆ}{Ӈ}{ӈ}{Ӌ}{ӌ}%
27 |     {Ӎ}{ӎ}{Ӕ}{ӕ}{Ә}{ә}{Ӡ}{ӡ}{Ө}{ө}\do
28 |   {%
29 |     \begingroup\def\IeC{\protect\DeclareTextSymbolDefault}%
30 |     \protected@edef\@temp{\endgroup\next{X2}}\@temp
31 |   }%
32 | \else
33 | \ifspx@cyropt@TtwoA
34 | % original code by tex.sx user jfbu:
35 | %   https://tex.stackexchange.com/a/460305/
36 | % 63*2+1=127 Cyrillic glyphs as found in T2A 8bit TeX font-encoding
37 |   \@tfor\@tempa:=%
38 |     {ae}{a}{b}{chrdsc}{chvcrs}{ch}{c}{dje}{dze}{dzhe}{d}{erev}{ery}{e}%
39 |     {f}{ghcrs}{gup}{g}{hdsc}{hrdsn}{h}{ie}{ii}{ishrt}{i}{je}%
40 |     {kbeak}{kdsc}{kvcrs}{k}{lje}{l}{m}{ndsc}{ng}{nje}{n}{otld}{o}{p}{r}%
41 |     {schwa}{sdsc}{sftsn}{shch}{shha}{sh}{s}{tshe}{t}{ushrt}{u}{v}%
42 |     {ya}{yhcrs}{yi}{yo}{yu}{y}{zdsc}{zhdsc}{zh}{z}\do
43 |   {%
44 |     \expandafter\DeclareTextSymbolDefault\expandafter
45 |                {\csname cyr\@tempa\endcsname}{T2A}%
46 |     \expandafter\uppercase\expandafter{\expandafter
47 |                  \def\expandafter\@tempa\expandafter{\@tempa}}%
48 |     \expandafter\DeclareTextSymbolDefault\expandafter
49 |       {\csname CYR\@tempa\endcsname}{T2A}%
50 |   }%
51 |   \DeclareTextSymbolDefault{\CYRpalochka}{T2A}%
52 | \fi\fi
53 | \endinput
54 | 


--------------------------------------------------------------------------------
/docs/old_docs/ybuild/latex/sphinxhowto.cls:
--------------------------------------------------------------------------------
 1 | %
 2 | % sphinxhowto.cls for Sphinx (http://sphinx-doc.org/)
 3 | %
 4 | 
 5 | \NeedsTeXFormat{LaTeX2e}[1995/12/01]
 6 | \ProvidesClass{sphinxhowto}[2018/12/23 v2.0 Document class (Sphinx howto)]
 7 | 
 8 | % 'oneside' option overriding the 'twoside' default
 9 | \newif\if@oneside
10 | \DeclareOption{oneside}{\@onesidetrue}
11 | % Pass remaining document options to the parent class.
12 | \DeclareOption*{\PassOptionsToClass{\CurrentOption}{\sphinxdocclass}}
13 | \ProcessOptions\relax
14 | 
15 | % Default to two-side document
16 | \if@oneside
17 | % nothing to do (oneside is the default)
18 | \else
19 | \PassOptionsToClass{twoside}{\sphinxdocclass}
20 | \fi
21 | 
22 | \LoadClass{\sphinxdocclass}
23 | 
24 | % Set some sane defaults for section numbering depth and TOC depth.  You can
25 | % reset these counters in your preamble.
26 | %
27 | \setcounter{secnumdepth}{2}
28 | \setcounter{tocdepth}{2}% i.e. section and subsection
29 | 
30 | % Change the title page to look a bit better, and fit in with the fncychap
31 | % ``Bjarne'' style a bit better.
32 | %
33 | \newcommand{\sphinxmaketitle}{%
34 |   \noindent\rule{\textwidth}{1pt}\par
35 |     \begingroup % for PDF information dictionary
36 |        \def\endgraf{ }\def\and{\& }%
37 |        \pdfstringdefDisableCommands{\def\\{, }}% overwrite hyperref setup
38 |        \hypersetup{pdfauthor={\@author}, pdftitle={\@title}}%
39 |     \endgroup
40 |   \begin{flushright}
41 |     \sphinxlogo
42 |     \py@HeaderFamily
43 |     {\Huge \@title }\par
44 |     {\itshape\large \py@release \releaseinfo}\par
45 |     \vspace{25pt}
46 |     {\Large
47 |       \begin{tabular}[t]{c}
48 |         \@author
49 |       \end{tabular}\kern-\tabcolsep}\par
50 |     \vspace{25pt}
51 |     \@date \par
52 |     \py@authoraddress \par
53 |   \end{flushright}
54 |   \@thanks
55 |   \setcounter{footnote}{0}
56 |   \let\thanks\relax\let\maketitle\relax
57 |   %\gdef\@thanks{}\gdef\@author{}\gdef\@title{}
58 | }
59 | 
60 | \newcommand{\sphinxtableofcontents}{%
61 |   \begingroup
62 |     \parskip \z@skip
63 |     \sphinxtableofcontentshook
64 |     \tableofcontents
65 |   \endgroup
66 |   \noindent\rule{\textwidth}{1pt}\par
67 |   \vspace{12pt}%
68 | }  
69 | \newcommand\sphinxtableofcontentshook{}
70 | \pagenumbering{arabic}
71 | 
72 | % Fix the bibliography environment to add an entry to the Table of
73 | % Contents.
74 | % For an article document class this environment is a section,
75 | % so no page break before it.
76 | %
77 | \newenvironment{sphinxthebibliography}[1]{%
78 |   % \phantomsection % not needed here since TeXLive 2010's hyperref
79 |   \begin{thebibliography}{#1}%
80 |   \addcontentsline{toc}{section}{\ifdefined\refname\refname\else\ifdefined\bibname\bibname\fi\fi}}{\end{thebibliography}}
81 | 
82 | 
83 | % Same for the indices.
84 | % The memoir class already does this, so we don't duplicate it in that case.
85 | %
86 | \@ifclassloaded{memoir}
87 |  {\newenvironment{sphinxtheindex}{\begin{theindex}}{\end{theindex}}}
88 |  {\newenvironment{sphinxtheindex}{%
89 |     \phantomsection % needed because no chapter, section, ... is created by theindex
90 |     \begin{theindex}%
91 |     \addcontentsline{toc}{section}{\indexname}}{\end{theindex}}}
92 | 


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/docs/old_docs/ybuild/latex/sphinxmanual.cls:
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  1 | %
  2 | % sphinxmanual.cls for Sphinx (http://sphinx-doc.org/)
  3 | %
  4 | 
  5 | \NeedsTeXFormat{LaTeX2e}[1995/12/01]
  6 | \ProvidesClass{sphinxmanual}[2018/12/23 v2.0 Document class (Sphinx manual)]
  7 | 
  8 | % chapters starting at odd pages (overridden by 'openany' document option)
  9 | \PassOptionsToClass{openright}{\sphinxdocclass}
 10 | 
 11 | % 'oneside' option overriding the 'twoside' default
 12 | \newif\if@oneside
 13 | \DeclareOption{oneside}{\@onesidetrue}
 14 | % Pass remaining document options to the parent class.
 15 | \DeclareOption*{\PassOptionsToClass{\CurrentOption}{\sphinxdocclass}}
 16 | \ProcessOptions\relax
 17 | 
 18 | % Defaults two-side document
 19 | \if@oneside
 20 | % nothing to do (oneside is the default)
 21 | \else
 22 | \PassOptionsToClass{twoside}{\sphinxdocclass}
 23 | \fi
 24 | 
 25 | \LoadClass{\sphinxdocclass}
 26 | 
 27 | % Set some sane defaults for section numbering depth and TOC depth.  You can
 28 | % reset these counters in your preamble.
 29 | %
 30 | \setcounter{secnumdepth}{2}
 31 | \setcounter{tocdepth}{1}
 32 | 
 33 | % Change the title page to look a bit better, and fit in with the fncychap
 34 | % ``Bjarne'' style a bit better.
 35 | %
 36 | \newcommand{\sphinxmaketitle}{%
 37 |   \let\sphinxrestorepageanchorsetting\relax
 38 |   \ifHy@pageanchor\def\sphinxrestorepageanchorsetting{\Hy@pageanchortrue}\fi
 39 |   \hypersetup{pageanchor=false}% avoid duplicate destination warnings
 40 |   \begin{titlepage}%
 41 |     \let\footnotesize\small
 42 |     \let\footnoterule\relax
 43 |     \noindent\rule{\textwidth}{1pt}\par
 44 |       \begingroup % for PDF information dictionary
 45 |        \def\endgraf{ }\def\and{\& }%
 46 |        \pdfstringdefDisableCommands{\def\\{, }}% overwrite hyperref setup
 47 |        \hypersetup{pdfauthor={\@author}, pdftitle={\@title}}%
 48 |       \endgroup
 49 |     \begin{flushright}%
 50 |       \sphinxlogo
 51 |       \py@HeaderFamily
 52 |       {\Huge \@title \par}
 53 |       {\itshape\LARGE \py@release\releaseinfo \par}
 54 |       \vfill
 55 |       {\LARGE
 56 |         \begin{tabular}[t]{c}
 57 |           \@author
 58 |         \end{tabular}\kern-\tabcolsep
 59 |         \par}
 60 |       \vfill\vfill
 61 |       {\large
 62 |        \@date \par
 63 |        \vfill
 64 |        \py@authoraddress \par
 65 |       }%
 66 |     \end{flushright}%\par
 67 |     \@thanks
 68 |   \end{titlepage}%
 69 |   \setcounter{footnote}{0}%
 70 |   \let\thanks\relax\let\maketitle\relax
 71 |   %\gdef\@thanks{}\gdef\@author{}\gdef\@title{}
 72 |   \clearpage
 73 |   \ifdefined\sphinxbackoftitlepage\sphinxbackoftitlepage\fi
 74 |   \if@openright\cleardoublepage\else\clearpage\fi
 75 |   \sphinxrestorepageanchorsetting
 76 | }
 77 | 
 78 | \newcommand{\sphinxtableofcontents}{%
 79 |   \pagenumbering{roman}%
 80 |   \begingroup
 81 |     \parskip \z@skip
 82 |     \sphinxtableofcontentshook
 83 |     \tableofcontents
 84 |   \endgroup
 85 |   % before resetting page counter, let's do the right thing.
 86 |   \if@openright\cleardoublepage\else\clearpage\fi
 87 |   \pagenumbering{arabic}%
 88 | }
 89 | 
 90 | % This is needed to get the width of the section # area wide enough in the
 91 | % library reference.  Doing it here keeps it the same for all the manuals.
 92 | %
 93 | \newcommand{\sphinxtableofcontentshook}{%
 94 |   \renewcommand*\l@section{\@dottedtocline{1}{1.5em}{2.6em}}%
 95 |   \renewcommand*\l@subsection{\@dottedtocline{2}{4.1em}{3.5em}}%
 96 | }
 97 | 
 98 | % Fix the bibliography environment to add an entry to the Table of
 99 | % Contents.
100 | % For a report document class this environment is a chapter.
101 | %
102 | \newenvironment{sphinxthebibliography}[1]{%
103 |   \if@openright\cleardoublepage\else\clearpage\fi
104 |   % \phantomsection % not needed here since TeXLive 2010's hyperref
105 |   \begin{thebibliography}{#1}%
106 |   \addcontentsline{toc}{chapter}{\bibname}}{\end{thebibliography}}
107 | 
108 | % Same for the indices.
109 | % The memoir class already does this, so we don't duplicate it in that case.
110 | %
111 | \@ifclassloaded{memoir}
112 |  {\newenvironment{sphinxtheindex}{\begin{theindex}}{\end{theindex}}}
113 |  {\newenvironment{sphinxtheindex}{%
114 |     \if@openright\cleardoublepage\else\clearpage\fi
115 |     \phantomsection % needed as no chapter, section, ... created
116 |     \begin{theindex}%
117 |     \addcontentsline{toc}{chapter}{\indexname}}{\end{theindex}}}
118 | 


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/docs/old_docs/ybuild/latex/sphinxmessages.sty:
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 1 | %
 2 | % sphinxmessages.sty
 3 | %
 4 | % message resources for Sphinx
 5 | %
 6 | \ProvidesPackage{sphinxmessages}[2019/01/04 v2.0 Localized LaTeX macros (Sphinx team)]
 7 | 
 8 | \renewcommand{\literalblockcontinuedname}{continued from previous page}
 9 | \renewcommand{\literalblockcontinuesname}{continues on next page}
10 | \renewcommand{\sphinxnonalphabeticalgroupname}{Non-alphabetical}
11 | \renewcommand{\sphinxsymbolsname}{Symbols}
12 | \renewcommand{\sphinxnumbersname}{Numbers}
13 | \def\pageautorefname{page}
14 | 
15 | \addto\captionsenglish{\renewcommand{\figurename}{Fig.\@{} }}
16 | \def\fnum@figure{\figurename\thefigure{}}
17 | 
18 | \addto\captionsenglish{\renewcommand{\tablename}{Table }}
19 | \def\fnum@table{\tablename\thetable{}}
20 | 
21 | \addto\captionsenglish{\renewcommand{\literalblockname}{Listing}}


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/example_transform_yamls/albumentation_transforms.yaml:
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1 | huesaturation: {hue_shift_limit: 20, sat_shift_limit: 30, val_shift_limit: 20, p: 0.5}
2 | r90: {p: 0.5}
3 | transpose: {p: 0.5}
4 | affine: {p: 0.5}
5 | elastic: {p: 0.5}
6 | dilationjitter: {0: {mean: 5, std: 3}}
7 | 


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/example_transform_yamls/torch_transforms.yaml:
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1 | colorjitter: {brightness: 0.8, contrast: 0.8, saturation: 0.8, hue: 0.5}
2 | hflip: {}
3 | vflip: {}
4 | r90: {}
5 | 


--------------------------------------------------------------------------------
/experimental/datasets.py:
--------------------------------------------------------------------------------
  1 | #
  2 | #   Lightnet dataset that works with brambox annotations
  3 | #   Copyright EAVISE
  4 | #
  5 | # https://eavise.gitlab.io/lightnet/_modules/lightnet/models/_dataset_brambox.html#BramboxDataset
  6 | # https://eavise.gitlab.io/brambox/notes/02-getting_started.html#Loading-data
  7 | 
  8 | import os
  9 | import copy
 10 | import logging
 11 | from PIL import Image
 12 | import numpy as np
 13 | import lightnet.data as lnd
 14 | from pathflowai.utils import load_sql_df
 15 | import dask.array as da
 16 | from os.path import join
 17 | 
 18 | try:
 19 |     import brambox as bb
 20 | except ImportError:
 21 |     bb = None
 22 | 
 23 | __all__ = ['BramboxDataset']
 24 | log = logging.getLogger(__name__)
 25 | 
 26 | # ADD IMAGE ANNOTATION TRANSFORM
 27 | # ADD TRAIN VAL TEST INFO
 28 | 
 29 | class BramboxPathFlowDataset(lnd.Dataset):
 30 |     """ Dataset for any brambox annotations.
 31 | 
 32 |     Args:
 33 |         annotations (dataframe): Dataframe containing brambox annotations
 34 |         input_dimension (tuple): (width,height) tuple with default dimensions of the network
 35 |         class_label_map (list): List of class_labels
 36 |         identify (function, optional): Lambda/function to get image based of annotation filename or image id; Default **replace/add .png extension to filename/id**
 37 |         img_transform (torchvision.transforms.Compose): Transforms to perform on the images
 38 |         anno_transform (torchvision.transforms.Compose): Transforms to perform on the annotations
 39 | 
 40 |     Note:
 41 |         This dataset opens images with the Pillow library
 42 |     """
 43 |     def __init__(self, input_dir, patch_info_file, patch_size, annotations, input_dimension, class_label_map=None, identify=None, img_transform=None, anno_transform=None):
 44 |         if bb is None:
 45 |             raise ImportError('Brambox needs to be installed to use this dataset')
 46 |         super().__init__(input_dimension)
 47 | 
 48 |         self.annos = annotations
 49 |         self.annos['ignore']=0
 50 |         self.annos['class_label']=self.annos['class_label'].astype(int)#-1
 51 |         print(self.annos['class_label'].unique())
 52 |         #print(self.annos.shape)
 53 |         self.keys = self.annos.image.cat.categories # stores unique patches
 54 |         #print(self.keys)
 55 |         self.img_tf = img_transform
 56 |         self.anno_tf = anno_transform
 57 |         self.patch_info=load_sql_df(patch_info_file, patch_size)
 58 |         IDs=self.patch_info['ID'].unique()
 59 |         self.slides = {slide:da.from_zarr(join(input_dir,'{}.zarr'.format(slide))) for slide in IDs}
 60 |         self.id = lambda k: k.split('/')
 61 |         # experiment
 62 |         #self.annos['x_top_left'], self.annos['y_top_left']=self.annos['y_top_left'], self.annos['x_top_left']
 63 |         self.annos['width'], self.annos['height']=self.annos['height'], self.annos['width']
 64 |         # Add class_ids
 65 |         if class_label_map is None:
 66 |             log.warning(f'No class_label_map given, generating it by sorting unique class labels from data alphabetically, which is not always deterministic behaviour')
 67 |             class_label_map = list(np.sort(self.annos.class_label.unique()))
 68 |         self.annos['class_id'] = self.annos.class_label.map(dict((l, i) for i, l in enumerate(class_label_map)))
 69 | 
 70 |     def __len__(self):
 71 |         return len(self.keys)
 72 | 
 73 |     @lnd.Dataset.resize_getitem
 74 |     def __getitem__(self, index):
 75 |         """ Get transformed image and annotations based of the index of ``self.keys``
 76 | 
 77 |         Args:
 78 |             index (int): index of the ``self.keys`` list containing all the image identifiers of the dataset.
 79 | 
 80 |         Returns:
 81 |             tuple: (transformed image, list of transformed brambox boxes)
 82 |         """
 83 |         if index >= len(self):
 84 |             raise IndexError(f'list index out of range [{index}/{len(self)-1}]')
 85 | 
 86 |         # Load
 87 |         #print(self.keys[index])
 88 |         ID,x,y,patch_size=self.id(self.keys[index])
 89 |         x,y,patch_size=int(x),int(y),int(patch_size)
 90 |         img = self.slides[ID][x:x+patch_size,y:y+patch_size].compute()#Image.open(self.id(self.keys[index]))
 91 |         anno = bb.util.select_images(self.annos, [self.keys[index]])
 92 | 
 93 |         # Transform
 94 |         if self.img_tf is not None:
 95 |             img = self.img_tf(img)
 96 |         if self.anno_tf is not None:
 97 |             anno = self.anno_tf(anno)
 98 | 
 99 |         return img, anno
100 | 


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/experimental/get_anchors.py:
--------------------------------------------------------------------------------
 1 | from sklearn.cluster import KMeans
 2 | import numpy as np, pandas as pd, brambox as bb
 3 | import pickle, argparse
 4 | 
 5 | p=argparse.ArgumentParser()
 6 | p.add_argument('--patch_size',default=512,type=int)
 7 | p.add_argument('--n_anchors',default=20,type=int)
 8 | p.add_argument('--sample_p',default=1.,type=float)
 9 | 
10 | args=p.parse_args()
11 | np.random.seed(42)
12 | patch_size=args.patch_size
13 | n_anchors=args.n_anchors
14 | sample_p=args.sample_p
15 | annotation_file = 'annotations_bbox_{}.pkl'.format(patch_size)
16 | annotations=bb.io.load('pandas',annotation_file)
17 | if sample_p<1.:
18 |     annotations=annotations.sample(frac=sample_p)
19 | 
20 | X=annotations[['x_top_left','y_top_left']].astype(float).values+(annotations['width']/2.).astype(float).values.reshape(-1,1)
21 | km=KMeans(n_clusters=n_anchors,n_jobs=-1).fit(X)
22 | anchors=km.cluster_centers_
23 | pickle.dump(anchors,open('anchors.pkl','wb'))
24 | 


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/experimental/get_counts.py:
--------------------------------------------------------------------------------
 1 | import brambox as bb
 2 | import os
 3 | from os.path import join, basename
 4 | from pathflowai.utils import load_sql_df, npy2da, df2sql
 5 | import skimage
 6 | import dask, dask.array as da, pandas as pd, numpy as np
 7 | import argparse
 8 | from scipy import ndimage
 9 | from scipy.ndimage.measurements import label
10 | import pickle
11 | from dask.distributed import Client
12 | from multiprocessing import Pool
13 | from functools import reduce
14 | 
15 | def count_cells(m, num_classes=3):
16 |     lbls,n_lbl=label(m)
17 |     obj_labels=np.zeros(num_classes)
18 |     for i in range(1,num_classes+1):
19 |         obj_labels[i-1]=len(np.unique(lbls[m==i].flatten()))
20 |     return obj_labels
21 | 
22 | if __name__=='__main__':
23 |     p=argparse.ArgumentParser()
24 |     p.add_argument('--num_classes',default=4,type=int)
25 |     p.add_argument('--patch_size',default=512,type=int)
26 |     p.add_argument('--n_workers',default=40,type=int)
27 |     p.add_argument('--p_sample',default=0.7,type=float)
28 |     p.add_argument('--input_dir',default='inputs',type=str)
29 |     p.add_argument('--patch_info_file',default='cell_info.db',type=str)
30 |     p.add_argument('--reference_mask',default='reference_mask.npy',type=str)
31 |     #c=Client()
32 |     # add mode to just use own extracted boudning boxes or from seg, maybe from histomicstk
33 | 
34 |     args=p.parse_args()
35 |     num_classes=args.num_classes
36 |     n_workers=args.n_workers
37 |     input_dir=args.input_dir
38 |     patch_info_file=args.patch_info_file
39 |     patch_size=args.patch_size
40 |     np.random.seed(42)
41 |     reference_mask=args.reference_mask
42 | 
43 |     patch_info=load_sql_df(patch_info_file, patch_size)
44 |     IDs=patch_info['ID'].unique()
45 |     #slides = {slide:da.from_zarr(join(input_dir,'{}.zarr'.format(slide))) for slide in IDs}
46 |     masks = {mask:npy2da(join(input_dir,'{}_mask.npy'.format(mask))) for mask in IDs}
47 | 
48 |     def process_chunk(patch_info_sub):
49 |         patch_info_sub=patch_info_sub.reset_index(drop=True)
50 |         counts=[]
51 |         for i in range(patch_info_sub.shape[0]):
52 |             #print(i)
53 |             patch=patch_info_sub.iloc[i]
54 |             ID,x,y,patch_size2=patch[['ID','x','y','patch_size']].tolist()
55 |             m=masks[ID][x:x+patch_size2,y:y+patch_size2]
56 |             counts.append(dask.delayed(count_cells)(m, num_classes=num_classes))
57 | 
58 |         return dask.compute(*counts,scheduler='threading')
59 | 
60 |     patch_info_subs=np.array_split(patch_info,n_workers)
61 | 
62 |     p=Pool(n_workers)
63 | 
64 |     counts=reduce(lambda x,y:x+y,p.map(process_chunk,patch_info_subs))
65 | 
66 |     #bbox_dfs=dask.compute(*bbox_dfs,scheduler='processes')
67 | 
68 |     counts=pd.DataFrame(np.vstack(counts))
69 | 
70 |     patch_info=pd.concat([patch_info[['ID','x','y','patch_size','annotation']].reset_index(drop=True),counts.reset_index(drop=True)],axis=1).reset_index()
71 |     print(patch_info)
72 | 
73 |     df2sql(patch_info, 'counts_test.db', patch_size, mode='replace')
74 | 


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/pathflowai/__init__.py:
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1 | from . import *
2 | __version__='0.1.1'
3 | 


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/pathflowai/monitor_memory_usage.py:
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 1 | import GPUtil
 2 | import psutil
 3 | import time
 4 | from threading import Thread
 5 | import pandas as pd
 6 | import argparse
 7 | import click
 8 | 
 9 | CONTEXT_SETTINGS = dict(help_option_names=['-h','--help'], max_content_width=90)
10 | 
11 | @click.group(context_settings= CONTEXT_SETTINGS)
12 | @click.version_option(version='0.1')
13 | def monitor():
14 | 	pass
15 | 
16 | class Monitor(Thread):
17 | 	def __init__(self, start_time, delay, end_time):
18 | 		super(Monitor, self).__init__()
19 | 		self.stopped = False
20 | 		self.start_time = start_time
21 | 		self.end_time = end_time
22 | 		self.delay = delay # Time between calls to GPUtil
23 | 		self.records = []
24 | 		self.start()
25 | 
26 | 	def run(self):
27 | 		time_from_start = 0.
28 | 		while time_from_start <= self.end_time:
29 | 			memory = psutil.virtual_memory()
30 | 			stats = {"gpu.{}.memory.used".format(gpu.id):gpu.memoryUsed for gpu in GPUtil.getGPUs()}
31 | 			stats['cpu.utilization'] = psutil.cpu_percent()
32 | 			current_time = time.time()
33 | 			stats['current.time'] = current_time
34 | 			time_from_start = current_time - self.start_time
35 | 			stats['system.memory.used'] = memory.used
36 | 			stats['system.memory.used.percent'] = memory.percent
37 | 			stats['elapsed.time'] = time_from_start
38 | 			self.records.append(stats)
39 | 			time.sleep(self.delay)
40 | 		self.stop()
41 | 
42 | 	def stop(self):
43 | 		self.stopped = True
44 | 
45 | 	def return_records(self):
46 | 		return pd.DataFrame(self.records)
47 | 
48 | def get_usage(total_time, delay_time, records_output_csv):
49 | 
50 | 	start_time = time.time()
51 | 
52 | 	monitor = Monitor(start_time, delay_time, total_time)
53 | 
54 | 	monitor.run()
55 | 
56 | 	while not monitor.stopped:
57 | 		time.sleep(delay_time)
58 | 
59 | 	records = monitor.return_records()
60 | 
61 | 	records.to_csv(records_output_csv)
62 | 
63 | @monitor.command()
64 | @click.option('-csv', '--records_output_csv', default='records.csv', help='Where to store records.', type=click.Path(exists=False), show_default=True)
65 | @click.option('-tt', '--total_time', default=1., help='Total time to monitor for in minutes.', show_default=True)
66 | @click.option('-dt', '--delay_time', default=1., help='Time between samples, in seconds.', show_default=True)
67 | def monitor_usage(records_output_csv,total_time,delay_time):
68 | 	"""Monitor Usage over Time Interval."""
69 | 	total_time*= 60. # convert to seconds
70 | 	get_usage(total_time, delay_time, records_output_csv)
71 | 
72 | 
73 | if __name__ == '__main__':
74 | 	monitor()
75 | 


--------------------------------------------------------------------------------
/pathflowai/sampler.py:
--------------------------------------------------------------------------------
 1 | """
 2 | sampler.py
 3 | =======================
 4 | Balanced sampling based on one of the columns of the patch information.
 5 | """
 6 | 
 7 | import torch
 8 | import torch.utils.data
 9 | import torchvision
10 | import numpy as np
11 | 
12 | 
13 | class ImbalancedDatasetSampler(torch.utils.data.sampler.Sampler):
14 |     """Samples elements randomly from a given list of indices for imbalanced dataset
15 |     https://raw.githubusercontent.com/ufoym/imbalanced-dataset-sampler/master/sampler.py
16 |     Arguments:
17 |         indices (list, optional): a list of indices
18 |         num_samples (int, optional): number of samples to draw
19 |     """
20 | 
21 |     def __init__(self, dataset, indices=None, num_samples=None):
22 | 
23 |         # if indices is not provided,
24 |         # all elements in the dataset will be considered
25 |         self.indices = list(range(len(dataset))) \
26 |             if indices is None else indices
27 | 
28 |         self.n_targets=len(dataset.targets)
29 | 
30 |         # if num_samples is not provided,
31 |         # draw `len(indices)` samples in each iteration
32 |         self.num_samples = len(self.indices) \
33 |             if num_samples is None else num_samples
34 | 
35 |         # distribution of classes in the dataset
36 |         label_to_count = {}
37 |         for idx in self.indices:
38 |             label = self._get_label(dataset, idx)
39 |             if label in label_to_count:
40 |                 label_to_count[label] += 1
41 |             else:
42 |                 label_to_count[label] = 1
43 | 
44 |         # weight for each sample
45 |         weights = [1.0 / label_to_count[self._get_label(dataset, idx)]
46 |                    for idx in self.indices]
47 |         self.weights = torch.DoubleTensor(weights)
48 | 
49 |     def _get_label(self, dataset, idx):
50 |         dataset_type = type(dataset)
51 |         if dataset_type is torchvision.datasets.MNIST:
52 |             return dataset.train_labels[idx].item()
53 |         elif dataset_type is torchvision.datasets.ImageFolder:
54 |             return dataset.imgs[idx][1]
55 |         else:
56 |             y=dataset.patch_info.iloc[idx][dataset.targets]
57 |             if not isinstance(y,np.float):
58 |                 y=y.values
59 |             if self.n_targets>1:
60 |                 y=np.argmax(y)
61 |             elif isinstance(y,(list,np.ndarray)):
62 |                 y=y[0]
63 |             #print(y)
64 |             return int(y)
65 | 
66 |     def __iter__(self):
67 |         return (self.indices[i] for i in torch.multinomial(
68 |             self.weights, self.num_samples, replacement=True))
69 | 
70 |     def __len__(self):
71 |         return self.num_samples
72 | 
73 | 
74 | """MIT License
75 | 
76 | Copyright (c) 2018 Ming
77 | 
78 | Permission is hereby granted, free of charge, to any person obtaining a copy
79 | of this software and associated documentation files (the "Software"), to deal
80 | in the Software without restriction, including without limitation the rights
81 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
82 | copies of the Software, and to permit persons to whom the Software is
83 | furnished to do so, subject to the following conditions:
84 | 
85 | The above copyright notice and this permission notice shall be included in all
86 | copies or substantial portions of the Software.
87 | 
88 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
89 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
90 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
91 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
92 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
93 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
94 | SOFTWARE."""
95 | 


--------------------------------------------------------------------------------
/pathflowai/schedulers.py:
--------------------------------------------------------------------------------
  1 | """
  2 | schedulers.py
  3 | =======================
  4 | Modulates the learning rate during the training process.
  5 | """
  6 | import torch
  7 | import math
  8 | from torch.optim.lr_scheduler import ExponentialLR
  9 | 
 10 | class CosineAnnealingWithRestartsLR(torch.optim.lr_scheduler._LRScheduler):
 11 | 	r"""Set the learning rate of each parameter group using a cosine annealing
 12 | 	schedule, where :math:`\eta_{max}` is set to the initial lr and
 13 | 	:math:`T_{cur}` is the number of epochs since the last restart in SGDR:
 14 | 	 .. math::
 15 | 		 \eta_t = \eta_{min} + \frac{1}{2}(\eta_{max} - \eta_{min})(1 +
 16 | 		\cos(\frac{T_{cur}}{T_{max}}\pi))
 17 | 	 When last_epoch=-1, sets initial lr as lr.
 18 | 	 It has been proposed in
 19 | 	`SGDR: Stochastic Gradient Descent with Warm Restarts`_. This implements
 20 | 	the cosine annealing part of SGDR, the restarts and number of iterations multiplier.
 21 | 	 Args:
 22 | 		optimizer (Optimizer): Wrapped optimizer.
 23 | 		T_max (int): Maximum number of iterations.
 24 | 		T_mult (float): Multiply T_max by this number after each restart. Default: 1.
 25 | 		eta_min (float): Minimum learning rate. Default: 0.
 26 | 		last_epoch (int): The index of last epoch. Default: -1.
 27 | 	 .. _SGDR\: Stochastic Gradient Descent with Warm Restarts:
 28 | 		https://arxiv.org/abs/1608.03983
 29 | 	"""
 30 | 	def __init__(self, optimizer, T_max, eta_min=0, last_epoch=-1, T_mult=1., alpha_decay=1.0):
 31 | 		self.T_max = T_max
 32 | 		self.T_mult = T_mult
 33 | 		self.restart_every = T_max
 34 | 		self.eta_min = eta_min
 35 | 		self.restarts = 0
 36 | 		self.restarted_at = 0
 37 | 		self.alpha = alpha_decay
 38 | 		super().__init__(optimizer, last_epoch)
 39 | 
 40 | 	def restart(self):
 41 | 		self.restarts += 1
 42 | 		self.restart_every = int(round(self.restart_every * self.T_mult))
 43 | 		self.restarted_at = self.last_epoch
 44 | 
 45 | 	def cosine(self, base_lr):
 46 | 		return self.eta_min + self.alpha**self.restarts * (base_lr - self.eta_min) * (1 + math.cos(math.pi * self.step_n / self.restart_every)) / 2
 47 | 
 48 | 	@property
 49 | 	def step_n(self):
 50 | 		return self.last_epoch - self.restarted_at
 51 | 
 52 | 	def get_lr(self):
 53 | 		if self.step_n >= self.restart_every:
 54 | 			self.restart()
 55 | 		return [self.cosine(base_lr) for base_lr in self.base_lrs]
 56 | 
 57 | class Scheduler:
 58 | 	"""Scheduler class that modulates learning rate of torch optimizers over epochs.
 59 | 
 60 | 	Parameters
 61 | 	----------
 62 | 	optimizer : type
 63 | 		torch.Optimizer object
 64 | 	opts : type
 65 | 		Options of setting the learning rate scheduler, see default.
 66 | 
 67 | 	Attributes
 68 | 	----------
 69 | 	schedulers : type
 70 | 		Different types of schedulers to choose from.
 71 | 	scheduler_step_fn : type
 72 | 		How scheduler updates learning rate.
 73 | 	initial_lr : type
 74 | 		Initial set learning rate.
 75 | 	scheduler_choice : type
 76 | 		What scheduler type was chosen.
 77 | 	scheduler : type
 78 | 		Scheduler object chosen that will more directly update optimizer LR.
 79 | 
 80 | 	"""
 81 | 	def __init__(self, optimizer=None, opts=dict(scheduler='null',lr_scheduler_decay=0.5,T_max=10,eta_min=5e-8,T_mult=2)):
 82 | 		self.schedulers = {'exp':(lambda optimizer: ExponentialLR(optimizer, opts["lr_scheduler_decay"])),
 83 | 							'null':(lambda optimizer: None),
 84 | 							'warm_restarts':(lambda optimizer: CosineAnnealingWithRestartsLR(optimizer, T_max=opts['T_max'], eta_min=opts['eta_min'], last_epoch=-1, T_mult=opts['T_mult']))}
 85 | 		self.scheduler_step_fn = {'exp':(lambda scheduler: scheduler.step()),
 86 | 								  'warm_restarts':(lambda scheduler: scheduler.step()),
 87 | 								  'null':(lambda scheduler: None)}
 88 | 		self.initial_lr = optimizer.param_groups[0]['lr']
 89 | 		self.scheduler_choice = opts['scheduler']
 90 | 		self.scheduler = self.schedulers[self.scheduler_choice](optimizer) if optimizer is not None else None
 91 | 
 92 | 	def step(self):
 93 | 		"""Update optimizer learning rate"""
 94 | 		self.scheduler_step_fn[self.scheduler_choice](self.scheduler)
 95 | 
 96 | 	def get_lr(self):
 97 | 		"""Return current learning rate.
 98 | 
 99 |         Returns
100 |         -------
101 |         float
102 |             Current learning rate.
103 | 
104 |         """
105 | 		lr = (self.initial_lr if self.scheduler_choice == 'null' else self.scheduler.optimizer.param_groups[0]['lr'])
106 | 		return lr
107 | 


--------------------------------------------------------------------------------
/pathflowai/stain_norm.py:
--------------------------------------------------------------------------------
 1 | import cv2
 2 | import sys
 3 | import fire
 4 | import histomicstk
 5 | import histomicstk as htk
 6 | import openslide
 7 | import dask
 8 | import tqdm
 9 | import numpy as np
10 | from dask.diagnostics import ProgressBar
11 | from pathflowai.utils import generate_tissue_mask
12 | from histomicstk.preprocessing.color_normalization.\
13 |     deconvolution_based_normalization import deconvolution_based_normalization
14 | 
15 | W_target = np.array([
16 |             [0.6185391,  0.1576997,  -0.01119131],
17 |             [0.7012888,  0.8638838,  0.45586256],
18 |             [0.3493163,  0.4657428, -0.85597752]
19 |         ])
20 | 
21 | def return_norm_image(img,mask,W_source=None,W_target=None):
22 |     img=deconvolution_based_normalization(
23 |         img, W_source=W_source, W_target=W_target, im_target=None,
24 |         stains=['hematoxylin', 'eosin'], mask_out=~mask,
25 |         stain_unmixing_routine_params={"I_0":215})
26 |     return img
27 | 
28 | def check_ext(image_file):
29 |     return any([image_file.endswith(ext) for ext in ['.svs','.png','.jpg','.jpeg','.tiff','.tif']])
30 | 
31 | def stain_norm(image_file,compression=10,patch_size=1024):
32 |     if check_ext(image_file):
33 |         img = openslide.open_slide(image_file)
34 |         image = np.array(img.read_region((0,0), 0, img.level_dimensions[0]))[...,:3]
35 |     elif image_file.endswith(".npy"):
36 |         image=np.load(image_file)
37 |     else: raise NotImplementedError
38 |     mask=generate_tissue_mask(image,compression=compression,keep_holes=False)
39 |     img_small=cv2.resize(image,None,fx=1/compression,fy=1/compression)
40 |     mask_small=cv2.resize(mask.astype(int),None,fx=1/compression,fy=1/compression,interpolation=cv2.INTER_NEAREST).astype(bool)
41 |     W_source = htk.preprocessing.color_deconvolution.rgb_separate_stains_macenko_pca(img_small, 215)
42 |     W_source = htk.preprocessing.color_deconvolution._reorder_stains(W_source)
43 |     res=[]
44 |     coords=[]
45 |     for i in np.arange(0,image.shape[0]-patch_size,patch_size):
46 |         for j in np.arange(0,image.shape[1]-patch_size,patch_size):
47 |             if mask[i:i+patch_size,j:j+patch_size].mean():
48 |                 coords.append((i,j))
49 |                 res.append(dask.delayed(return_norm_image)(image[i:i+patch_size,j:j+patch_size],mask[i:i+patch_size,j:j+patch_size],W_source,W_target))
50 |     with ProgressBar():
51 |         res_returned=dask.compute(*res,scheduler="processes")
52 |     img_new=np.ones(image.shape).astype(np.uint8)*255
53 |     for k in tqdm.trange(len(coords)):
54 |         i,j=coords[k]
55 |         img_new[i:i+patch_size,j:j+patch_size]=res_returned[k]
56 |     return img_new
57 | 
58 | def stain_norm_pipeline(image_file="stain_in.svs",
59 |                         npy_out='stain_out.npy',
60 |                         compression=10,
61 |                         patch_size=1024):
62 |     np.save(npy_out,stain_norm(image_file,compression,patch_size))
63 | 
64 | if __name__=="__main__":
65 |     fire.Fire(stain_norm_pipeline)
66 | 


--------------------------------------------------------------------------------
/pathflowai/unet.py:
--------------------------------------------------------------------------------
  1 | # From https://raw.githubusercontent.com/milesial/Pytorch-UNet/master/unet/unet_model.py
  2 | 
  3 | import torch
  4 | import torch.nn as nn
  5 | import torch.nn.functional as F
  6 | 
  7 | 
  8 | class double_conv(nn.Module):
  9 |     '''(conv => BN => ReLU) * 2'''
 10 |     def __init__(self, in_ch, out_ch):
 11 |         super(double_conv, self).__init__()
 12 |         self.conv = nn.Sequential(
 13 |             nn.Conv2d(in_ch, out_ch, 3, padding=1),
 14 |             nn.BatchNorm2d(out_ch),
 15 |             nn.ReLU(inplace=True),
 16 |             nn.Conv2d(out_ch, out_ch, 3, padding=1),
 17 |             nn.BatchNorm2d(out_ch),
 18 |             nn.ReLU(inplace=True)
 19 |         )
 20 | 
 21 |     def forward(self, x):
 22 |         x = self.conv(x)
 23 |         return x
 24 | 
 25 | 
 26 | class inconv(nn.Module):
 27 |     def __init__(self, in_ch, out_ch):
 28 |         super(inconv, self).__init__()
 29 |         self.conv = double_conv(in_ch, out_ch)
 30 | 
 31 |     def forward(self, x):
 32 |         x = self.conv(x)
 33 |         return x
 34 | 
 35 | 
 36 | class down(nn.Module):
 37 |     def __init__(self, in_ch, out_ch):
 38 |         super(down, self).__init__()
 39 |         self.mpconv = nn.Sequential(
 40 |             nn.MaxPool2d(2),
 41 |             double_conv(in_ch, out_ch)
 42 |         )
 43 | 
 44 |     def forward(self, x):
 45 |         x = self.mpconv(x)
 46 |         return x
 47 | 
 48 | 
 49 | class up(nn.Module):
 50 |     def __init__(self, in_ch, out_ch, bilinear=True):
 51 |         super(up, self).__init__()
 52 | 
 53 |         #  would be a nice idea if the upsampling could be learned too,
 54 |         #  but my machine do not have enough memory to handle all those weights
 55 |         if bilinear:
 56 |             self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
 57 |         else:
 58 |             self.up = nn.ConvTranspose2d(in_ch//2, in_ch//2, 2, stride=2)
 59 | 
 60 |         self.conv = double_conv(in_ch, out_ch)
 61 | 
 62 |     def forward(self, x1, x2):
 63 |         x1 = self.up(x1)
 64 | 
 65 |         # input is CHW
 66 |         diffY = x2.size()[2] - x1.size()[2]
 67 |         diffX = x2.size()[3] - x1.size()[3]
 68 | 
 69 |         x1 = F.pad(x1, (diffX // 2, diffX - diffX//2,
 70 |                         diffY // 2, diffY - diffY//2))
 71 | 
 72 |         # for padding issues, see
 73 |         # https://github.com/HaiyongJiang/U-Net-Pytorch-Unstructured-Buggy/commit/0e854509c2cea854e247a9c615f175f76fbb2e3a
 74 |         # https://github.com/xiaopeng-liao/Pytorch-UNet/commit/8ebac70e633bac59fc22bb5195e513d5832fb3bd
 75 | 
 76 |         x = torch.cat([x2, x1], dim=1)
 77 |         x = self.conv(x)
 78 |         return x
 79 | 
 80 | 
 81 | class outconv(nn.Module):
 82 |     def __init__(self, in_ch, out_ch):
 83 |         super(outconv, self).__init__()
 84 |         self.conv = nn.Conv2d(in_ch, out_ch, 1)
 85 | 
 86 |     def forward(self, x):
 87 |         x = self.conv(x)
 88 |         return x
 89 | 
 90 | class UNet(nn.Module):
 91 |     def __init__(self, n_channels, n_classes, use_sigmoid=False, use_softmax=False):
 92 |         super(UNet, self).__init__()
 93 |         self.inc = inconv(n_channels, 64)
 94 |         self.down1 = down(64, 128)
 95 |         self.down2 = down(128, 256)
 96 |         self.down3 = down(256, 512)
 97 |         self.down4 = down(512, 512)
 98 |         self.up1 = up(1024, 256)
 99 |         self.up2 = up(512, 128)
100 |         self.up3 = up(256, 64)
101 |         self.up4 = up(128, 64)
102 |         self.outc = outconv(64, n_classes)
103 |         self.sigmoid = nn.Sequential(nn.Sigmoid() if use_sigmoid else nn.Dropout(p=0.),nn.LogSoftmax(dim=1) if use_softmax else nn.Dropout(p=0.))
104 | 
105 |     def forward(self, x):
106 |         x1 = self.inc(x)
107 |         x2 = self.down1(x1)
108 |         x3 = self.down2(x2)
109 |         x4 = self.down3(x3)
110 |         x5 = self.down4(x4)
111 |         x = self.up1(x5, x4)
112 |         x = self.up2(x, x3)
113 |         x = self.up3(x, x2)
114 |         x = self.up4(x, x1)
115 |         x = self.outc(x)
116 |         return self.sigmoid(x)
117 | 


--------------------------------------------------------------------------------
/setup.py:
--------------------------------------------------------------------------------
 1 | from setuptools import setup
 2 | from setuptools.command.install import install
 3 | import subprocess
 4 | import os
 5 | PACKAGES=[  'pandas==0.25.0',
 6 |             'numpy',
 7 |             'dask[dataframe]',
 8 |             'distributed',
 9 |             'nonechucks',
10 |             'dask-image',
11 |             'opencv-python',
12 |             'scikit-learn',
13 |             'scipy',
14 |             'umap-learn',
15 |             'pysnooper',
16 |             'tifffile',
17 |             'seaborn',
18 |             'scikit-image',
19 |             'openslide-python',
20 |             'Shapely',
21 |             'click==6.7',
22 |             'torch',
23 |             'torchvision',
24 |             'albumentations',
25 |             'GPUtil',
26 |             'beautifulsoup4',
27 |             'plotly',
28 |             'xarray',
29 |             'matplotlib',
30 |             'networkx',
31 |             'shap',
32 |             'pyyaml',
33 |             'torch-encoding',
34 |             'xmltodict',
35 |             #'lightnet',
36 |             'brambox',
37 |             'blosc',
38 |             'numcodecs',
39 |             'zarr',
40 |             'pytorchcv',
41 |             'h5py',
42 |             'timm'
43 |             ]
44 | 
45 | with open('README.md','r', encoding='utf-8') as f:
46 |       long_description = f.read()
47 | 
48 | class CustomInstallCommand(install):
49 |     """Custom install setup to help run shell commands (outside shell) before installation"""
50 |     def run(self):
51 |         #for package in PACKAGES:
52 |         #os.system('pip install {}'.format(package))#install.do_egg_install(self)
53 |         self.do_egg_install()#install.run(self)
54 |         subprocess.call('rm -rf apex'.split())
55 |         os.system('git clone https://github.com/NVIDIA/apex')
56 |         #try:
57 |         #os.system('cd apex && pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./')
58 |         #except:
59 |         os.system('echo pwd && cd apex && (pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./ || pip install -v --no-cache-dir ./)')
60 |         subprocess.call('rm -rf apex'.split())
61 | 
62 | setup(name='pathflowai',
63 |       version='0.1.1',
64 |       description='A modular approach for preprocessing and deep learning on histopathology images.',
65 |       url='https://github.com/jlevy44/PathFlowAI',
66 |       author='Joshua Levy',
67 |       author_email='joshualevy44@berkeley.edu',
68 |       license='MIT',
69 |       scripts=['bin/install_apex',
70 |                'bin/install_lightnet'],
71 |       #cmdclass={'install': CustomInstallCommand},
72 |       entry_points={
73 |             'console_scripts':['pathflowai-preprocess=pathflowai.cli_preprocessing:preprocessing',
74 |                                'pathflowai-visualize=pathflowai.cli_visualizations:visualize',
75 |                                'pathflowai-monitor=pathflowai.monitor_memory_usage:monitor',
76 |                                'pathflowai-train_model=pathflowai.model_training:train']
77 |       },
78 |       long_description=long_description,
79 |       long_description_content_type='text/markdown',
80 |       packages=['pathflowai'],
81 |       install_requires=PACKAGES)
82 | 


--------------------------------------------------------------------------------