j40-cejst-2/data/data-pipeline/poetry.lock

2874 lines
117 KiB
TOML
Raw Normal View History

[[package]]
name = "ansiwrap"
version = "0.8.4"
description = "textwrap, but savvy to ANSI colors and styles"
category = "dev"
optional = false
python-versions = "*"
[package.dependencies]
textwrap3 = ">=0.9.2"
Add FUDS ETL (#1817) * Add spatial join method (#1871) Since we'll need to figure out the tracts for a large number of points in future tickets, add a utility to handle grabbing the tract geometries and adding tract data to a point dataset. * Add FUDS, also jupyter lab (#1871) * Add YAML configs for FUDS (#1871) * Allow input geoid to be optional (#1871) * Add FUDS ETL, tests, test-datae noteobook (#1871) This adds the ETL class for Formerly Used Defense Sites (FUDS). This is different from most other ETLs since these FUDS are not provided by tract, but instead by geographic point, so we need to assign FUDS to tracts and then do calculations from there. * Floats -> Ints, as I intended (#1871) * Floats -> Ints, as I intended (#1871) * Formatting fixes (#1871) * Add test false positive GEOIDs (#1871) * Add gdal binaries (#1871) * Refactor pandas code to be more idiomatic (#1871) Per Emma, the more pandas-y way of doing my counts is using np.where to add the values i need, then groupby and size. It is definitely more compact, and also I think more correct! * Update configs per Emma suggestions (#1871) * Type fixed! (#1871) * Remove spurious import from vscode (#1871) * Snapshot update after changing col name (#1871) * Move up GDAL (#1871) * Adjust geojson strategy (#1871) * Try running census separately first (#1871) * Fix import order (#1871) * Cleanup cache strategy (#1871) * Download census data from S3 instead of re-calculating (#1871) * Clarify pandas code per Emma (#1871)
2022-08-16 13:28:39 -04:00
[[package]]
name = "anyio"
version = "3.6.1"
description = "High level compatibility layer for multiple asynchronous event loop implementations"
category = "dev"
optional = false
python-versions = ">=3.6.2"
[package.dependencies]
idna = ">=2.8"
sniffio = ">=1.1"
[package.extras]
doc = ["packaging", "sphinx-rtd-theme", "sphinx-autodoc-typehints (>=1.2.0)"]
test = ["coverage[toml] (>=4.5)", "hypothesis (>=4.0)", "pytest (>=7.0)", "pytest-mock (>=3.6.1)", "trustme", "contextlib2", "uvloop (<0.15)", "mock (>=4)", "uvloop (>=0.15)"]
trio = ["trio (>=0.16)"]
[[package]]
name = "appnope"
2022-04-18 18:12:18 -04:00
version = "0.1.3"
description = "Disable App Nap on macOS >= 10.9"
category = "main"
optional = false
python-versions = "*"
[[package]]
name = "argon2-cffi"
version = "21.3.0"
description = "The secure Argon2 password hashing algorithm."
category = "main"
optional = false
python-versions = ">=3.6"
[package.dependencies]
argon2-cffi-bindings = "*"
[package.extras]
dev = ["pre-commit", "cogapp", "tomli", "coverage[toml] (>=5.0.2)", "hypothesis", "pytest", "sphinx", "sphinx-notfound-page", "furo"]
docs = ["sphinx", "sphinx-notfound-page", "furo"]
tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pytest"]
[[package]]
name = "argon2-cffi-bindings"
version = "21.2.0"
description = "Low-level CFFI bindings for Argon2"
category = "main"
optional = false
python-versions = ">=3.6"
[package.dependencies]
cffi = ">=1.0.1"
[package.extras]
dev = ["pytest", "cogapp", "pre-commit", "wheel"]
tests = ["pytest"]
[[package]]
name = "astroid"
version = "2.11.7"
description = "An abstract syntax tree for Python with inference support."
category = "main"
optional = false
python-versions = ">=3.6.2"
[package.dependencies]
lazy-object-proxy = ">=1.4.0"
typing-extensions = {version = ">=3.10", markers = "python_version < \"3.10\""}
wrapt = ">=1.11,<2"
[[package]]
name = "atomicwrites"
version = "1.4.1"
description = "Atomic file writes."
category = "dev"
optional = false
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
[[package]]
name = "attrs"
version = "22.1.0"
description = "Classes Without Boilerplate"
category = "main"
optional = false
python-versions = ">=3.5"
[package.extras]
tests_no_zope = ["cloudpickle", "pytest-mypy-plugins", "mypy (>=0.900,!=0.940)", "pytest (>=4.3.0)", "pympler", "hypothesis", "coverage[toml] (>=5.0.2)"]
tests = ["cloudpickle", "zope.interface", "pytest-mypy-plugins", "mypy (>=0.900,!=0.940)", "pytest (>=4.3.0)", "pympler", "hypothesis", "coverage[toml] (>=5.0.2)"]
docs = ["sphinx-notfound-page", "zope.interface", "sphinx", "furo"]
dev = ["cloudpickle", "pre-commit", "sphinx-notfound-page", "sphinx", "furo", "zope.interface", "pytest-mypy-plugins", "mypy (>=0.900,!=0.940)", "pytest (>=4.3.0)", "pympler", "hypothesis", "coverage[toml] (>=5.0.2)"]
Add FUDS ETL (#1817) * Add spatial join method (#1871) Since we'll need to figure out the tracts for a large number of points in future tickets, add a utility to handle grabbing the tract geometries and adding tract data to a point dataset. * Add FUDS, also jupyter lab (#1871) * Add YAML configs for FUDS (#1871) * Allow input geoid to be optional (#1871) * Add FUDS ETL, tests, test-datae noteobook (#1871) This adds the ETL class for Formerly Used Defense Sites (FUDS). This is different from most other ETLs since these FUDS are not provided by tract, but instead by geographic point, so we need to assign FUDS to tracts and then do calculations from there. * Floats -> Ints, as I intended (#1871) * Floats -> Ints, as I intended (#1871) * Formatting fixes (#1871) * Add test false positive GEOIDs (#1871) * Add gdal binaries (#1871) * Refactor pandas code to be more idiomatic (#1871) Per Emma, the more pandas-y way of doing my counts is using np.where to add the values i need, then groupby and size. It is definitely more compact, and also I think more correct! * Update configs per Emma suggestions (#1871) * Type fixed! (#1871) * Remove spurious import from vscode (#1871) * Snapshot update after changing col name (#1871) * Move up GDAL (#1871) * Adjust geojson strategy (#1871) * Try running census separately first (#1871) * Fix import order (#1871) * Cleanup cache strategy (#1871) * Download census data from S3 instead of re-calculating (#1871) * Clarify pandas code per Emma (#1871)
2022-08-16 13:28:39 -04:00
[[package]]
name = "babel"
version = "2.10.3"
description = "Internationalization utilities"
category = "dev"
optional = false
python-versions = ">=3.6"
[package.dependencies]
pytz = ">=2015.7"
[[package]]
name = "backcall"
version = "0.2.0"
description = "Specifications for callback functions passed in to an API"
category = "main"
optional = false
python-versions = "*"
[[package]]
name = "beautifulsoup4"
2022-04-18 18:12:18 -04:00
version = "4.11.1"
description = "Screen-scraping library"
category = "main"
optional = false
2022-04-18 18:12:18 -04:00
python-versions = ">=3.6.0"
[package.dependencies]
soupsieve = ">1.2"
[package.extras]
html5lib = ["html5lib"]
lxml = ["lxml"]
[[package]]
name = "black"
version = "21.12b0"
description = "The uncompromising code formatter."
category = "dev"
optional = false
python-versions = ">=3.6.2"
[package.dependencies]
click = ">=7.1.2"
mypy-extensions = ">=0.4.3"
pathspec = ">=0.9.0,<1"
platformdirs = ">=2"
tomli = ">=0.2.6,<2.0.0"
typing-extensions = [
{version = ">=3.10.0.0", markers = "python_version < \"3.10\""},
{version = "!=3.10.0.1", markers = "python_version >= \"3.10\""},
]
[package.extras]
colorama = ["colorama (>=0.4.3)"]
d = ["aiohttp (>=3.7.4)"]
jupyter = ["ipython (>=7.8.0)", "tokenize-rt (>=3.2.0)"]
python2 = ["typed-ast (>=1.4.3)"]
uvloop = ["uvloop (>=0.15.2)"]
[[package]]
name = "bleach"
version = "5.0.1"
description = "An easy safelist-based HTML-sanitizing tool."
category = "main"
optional = false
2022-04-18 18:12:18 -04:00
python-versions = ">=3.7"
[package.dependencies]
six = ">=1.9.0"
webencodings = "*"
2022-04-18 18:12:18 -04:00
[package.extras]
css = ["tinycss2 (>=1.1.0,<1.2)"]
dev = ["build (==0.8.0)", "flake8 (==4.0.1)", "hashin (==0.17.0)", "pip-tools (==6.6.2)", "pytest (==7.1.2)", "Sphinx (==4.3.2)", "tox (==3.25.0)", "twine (==4.0.1)", "wheel (==0.37.1)", "black (==22.3.0)", "mypy (==0.961)"]
2022-04-18 18:12:18 -04:00
[[package]]
name = "censusdata"
version = "1.15.post1"
description = "Download data from U.S. Census API"
category = "main"
optional = false
python-versions = ">=2.7"
[package.dependencies]
pandas = "*"
requests = "*"
[[package]]
name = "certifi"
version = "2022.6.15"
description = "Python package for providing Mozilla's CA Bundle."
category = "main"
optional = false
python-versions = ">=3.6"
[[package]]
name = "cffi"
version = "1.15.1"
description = "Foreign Function Interface for Python calling C code."
category = "main"
optional = false
python-versions = "*"
[package.dependencies]
pycparser = "*"
[[package]]
name = "charset-normalizer"
version = "2.1.0"
description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
category = "main"
optional = false
python-versions = ">=3.6.0"
[package.extras]
unicode_backport = ["unicodedata2"]
[[package]]
name = "click"
version = "8.0.4"
description = "Composable command line interface toolkit"
category = "main"
optional = false
python-versions = ">=3.6"
[package.dependencies]
colorama = {version = "*", markers = "platform_system == \"Windows\""}
[[package]]
name = "click-plugins"
version = "1.1.1"
description = "An extension module for click to enable registering CLI commands via setuptools entry-points."
category = "main"
optional = false
python-versions = "*"
[package.dependencies]
click = ">=4.0"
[package.extras]
dev = ["pytest (>=3.6)", "pytest-cov", "wheel", "coveralls"]
[[package]]
name = "cligj"
version = "0.7.2"
description = "Click params for commmand line interfaces to GeoJSON"
category = "main"
optional = false
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, <4"
[package.dependencies]
click = ">=4.0"
[package.extras]
test = ["pytest-cov"]
[[package]]
name = "colorama"
version = "0.4.5"
description = "Cross-platform colored terminal text."
category = "main"
optional = false
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
[[package]]
name = "configparser"
version = "5.2.0"
description = "Updated configparser from Python 3.8 for Python 2.6+."
category = "dev"
optional = false
python-versions = ">=3.6"
[package.extras]
docs = ["sphinx", "jaraco.packaging (>=8.2)", "rst.linker (>=1.9)", "jaraco.tidelift (>=1.4)"]
testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "pytest-cov", "pytest-enabler (>=1.0.1)", "types-backports", "pytest-black (>=0.3.7)", "pytest-mypy"]
[[package]]
name = "cycler"
version = "0.11.0"
description = "Composable style cycles"
category = "main"
optional = false
python-versions = ">=3.6"
[[package]]
name = "debugpy"
version = "1.6.3"
description = "An implementation of the Debug Adapter Protocol for Python"
category = "main"
optional = false
python-versions = ">=3.7"
[[package]]
name = "decorator"
version = "5.1.1"
description = "Decorators for Humans"
category = "main"
optional = false
python-versions = ">=3.5"
[[package]]
name = "defusedxml"
version = "0.7.1"
description = "XML bomb protection for Python stdlib modules"
category = "main"
optional = false
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
[[package]]
name = "dill"
version = "0.3.5.1"
description = "serialize all of python"
category = "main"
optional = false
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, !=3.6.*"
[package.extras]
graph = ["objgraph (>=1.7.2)"]
[[package]]
name = "distlib"
version = "0.3.5"
description = "Distribution utilities"
category = "dev"
optional = false
python-versions = "*"
[[package]]
name = "dparse"
version = "0.5.2"
description = "A parser for Python dependency files"
category = "dev"
optional = false
python-versions = ">=3.5"
[package.dependencies]
packaging = "*"
toml = "*"
[package.extras]
pipenv = ["pipenv"]
conda = ["pyyaml"]
[[package]]
name = "dynaconf"
version = "3.1.9"
description = "The dynamic configurator for your Python Project"
category = "main"
optional = false
python-versions = ">=3.7"
[package.extras]
Add FUDS ETL (#1817) * Add spatial join method (#1871) Since we'll need to figure out the tracts for a large number of points in future tickets, add a utility to handle grabbing the tract geometries and adding tract data to a point dataset. * Add FUDS, also jupyter lab (#1871) * Add YAML configs for FUDS (#1871) * Allow input geoid to be optional (#1871) * Add FUDS ETL, tests, test-datae noteobook (#1871) This adds the ETL class for Formerly Used Defense Sites (FUDS). This is different from most other ETLs since these FUDS are not provided by tract, but instead by geographic point, so we need to assign FUDS to tracts and then do calculations from there. * Floats -> Ints, as I intended (#1871) * Floats -> Ints, as I intended (#1871) * Formatting fixes (#1871) * Add test false positive GEOIDs (#1871) * Add gdal binaries (#1871) * Refactor pandas code to be more idiomatic (#1871) Per Emma, the more pandas-y way of doing my counts is using np.where to add the values i need, then groupby and size. It is definitely more compact, and also I think more correct! * Update configs per Emma suggestions (#1871) * Type fixed! (#1871) * Remove spurious import from vscode (#1871) * Snapshot update after changing col name (#1871) * Move up GDAL (#1871) * Adjust geojson strategy (#1871) * Try running census separately first (#1871) * Fix import order (#1871) * Cleanup cache strategy (#1871) * Download census data from S3 instead of re-calculating (#1871) * Clarify pandas code per Emma (#1871)
2022-08-16 13:28:39 -04:00
yaml = ["ruamel.yaml"]
vault = ["hvac"]
toml = ["toml"]
test = ["configobj", "hvac", "redis", "codecov", "toml", "python-dotenv", "django", "flask (>=0.12)", "radon", "flake8-todo", "flake8-print", "flake8-debugger", "pep8-naming", "flake8", "pytest-mock", "pytest-xdist", "pytest-cov", "pytest"]
redis = ["redis"]
ini = ["configobj"]
configobj = ["configobj"]
all = ["hvac", "configobj", "ruamel.yaml", "redis"]
[[package]]
name = "entrypoints"
version = "0.4"
description = "Discover and load entry points from installed packages."
category = "main"
optional = false
python-versions = ">=3.6"
[[package]]
name = "et-xmlfile"
version = "1.1.0"
description = "An implementation of lxml.xmlfile for the standard library"
category = "dev"
optional = false
python-versions = ">=3.6"
2022-04-18 18:12:18 -04:00
[[package]]
name = "fastjsonschema"
version = "2.16.1"
2022-04-18 18:12:18 -04:00
description = "Fastest Python implementation of JSON schema"
category = "main"
optional = false
python-versions = "*"
[package.extras]
devel = ["colorama", "jsonschema", "json-spec", "pylint", "pytest", "pytest-benchmark", "pytest-cache", "validictory"]
[[package]]
name = "filelock"
version = "3.8.0"
description = "A platform independent file lock."
category = "dev"
optional = false
python-versions = ">=3.7"
[package.extras]
docs = ["furo (>=2022.6.21)", "sphinx (>=5.1.1)", "sphinx-autodoc-typehints (>=1.19.1)"]
testing = ["covdefaults (>=2.2)", "coverage (>=6.4.2)", "pytest (>=7.1.2)", "pytest-cov (>=3)", "pytest-timeout (>=2.1)"]
[[package]]
name = "fiona"
version = "1.8.21"
description = "Fiona reads and writes spatial data files"
category = "main"
optional = false
python-versions = "*"
[package.dependencies]
attrs = ">=17"
certifi = "*"
click = ">=4.0"
click-plugins = ">=1.0"
cligj = ">=0.5"
munch = "*"
six = ">=1.7"
[package.extras]
all = ["boto3 (>=1.2.4)", "pytest-cov", "shapely", "pytest (>=3)", "mock"]
calc = ["shapely"]
s3 = ["boto3 (>=1.2.4)"]
test = ["pytest (>=3)", "pytest-cov", "boto3 (>=1.2.4)", "mock"]
[[package]]
name = "flake8"
version = "3.9.2"
description = "the modular source code checker: pep8 pyflakes and co"
category = "dev"
optional = false
python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.7"
[package.dependencies]
mccabe = ">=0.6.0,<0.7.0"
pycodestyle = ">=2.7.0,<2.8.0"
pyflakes = ">=2.3.0,<2.4.0"
[[package]]
name = "fonttools"
version = "4.35.0"
description = "Tools to manipulate font files"
category = "main"
optional = false
python-versions = ">=3.7"
[package.extras]
all = ["fs (>=2.2.0,<3)", "lxml (>=4.0,<5)", "zopfli (>=0.1.4)", "lz4 (>=1.7.4.2)", "matplotlib", "sympy", "skia-pathops (>=0.5.0)", "uharfbuzz (>=0.23.0)", "brotlicffi (>=0.8.0)", "scipy", "brotli (>=1.0.1)", "munkres", "unicodedata2 (>=14.0.0)", "xattr"]
graphite = ["lz4 (>=1.7.4.2)"]
interpolatable = ["scipy", "munkres"]
lxml = ["lxml (>=4.0,<5)"]
pathops = ["skia-pathops (>=0.5.0)"]
plot = ["matplotlib"]
repacker = ["uharfbuzz (>=0.23.0)"]
symfont = ["sympy"]
type1 = ["xattr"]
ufo = ["fs (>=2.2.0,<3)"]
unicode = ["unicodedata2 (>=14.0.0)"]
woff = ["zopfli (>=0.1.4)", "brotlicffi (>=0.8.0)", "brotli (>=1.0.1)"]
[[package]]
name = "geopandas"
Add FUDS ETL (#1817) * Add spatial join method (#1871) Since we'll need to figure out the tracts for a large number of points in future tickets, add a utility to handle grabbing the tract geometries and adding tract data to a point dataset. * Add FUDS, also jupyter lab (#1871) * Add YAML configs for FUDS (#1871) * Allow input geoid to be optional (#1871) * Add FUDS ETL, tests, test-datae noteobook (#1871) This adds the ETL class for Formerly Used Defense Sites (FUDS). This is different from most other ETLs since these FUDS are not provided by tract, but instead by geographic point, so we need to assign FUDS to tracts and then do calculations from there. * Floats -> Ints, as I intended (#1871) * Floats -> Ints, as I intended (#1871) * Formatting fixes (#1871) * Add test false positive GEOIDs (#1871) * Add gdal binaries (#1871) * Refactor pandas code to be more idiomatic (#1871) Per Emma, the more pandas-y way of doing my counts is using np.where to add the values i need, then groupby and size. It is definitely more compact, and also I think more correct! * Update configs per Emma suggestions (#1871) * Type fixed! (#1871) * Remove spurious import from vscode (#1871) * Snapshot update after changing col name (#1871) * Move up GDAL (#1871) * Adjust geojson strategy (#1871) * Try running census separately first (#1871) * Fix import order (#1871) * Cleanup cache strategy (#1871) * Download census data from S3 instead of re-calculating (#1871) * Clarify pandas code per Emma (#1871)
2022-08-16 13:28:39 -04:00
version = "0.11.1"
description = "Geographic pandas extensions"
category = "main"
optional = false
Add FUDS ETL (#1817) * Add spatial join method (#1871) Since we'll need to figure out the tracts for a large number of points in future tickets, add a utility to handle grabbing the tract geometries and adding tract data to a point dataset. * Add FUDS, also jupyter lab (#1871) * Add YAML configs for FUDS (#1871) * Allow input geoid to be optional (#1871) * Add FUDS ETL, tests, test-datae noteobook (#1871) This adds the ETL class for Formerly Used Defense Sites (FUDS). This is different from most other ETLs since these FUDS are not provided by tract, but instead by geographic point, so we need to assign FUDS to tracts and then do calculations from there. * Floats -> Ints, as I intended (#1871) * Floats -> Ints, as I intended (#1871) * Formatting fixes (#1871) * Add test false positive GEOIDs (#1871) * Add gdal binaries (#1871) * Refactor pandas code to be more idiomatic (#1871) Per Emma, the more pandas-y way of doing my counts is using np.where to add the values i need, then groupby and size. It is definitely more compact, and also I think more correct! * Update configs per Emma suggestions (#1871) * Type fixed! (#1871) * Remove spurious import from vscode (#1871) * Snapshot update after changing col name (#1871) * Move up GDAL (#1871) * Adjust geojson strategy (#1871) * Try running census separately first (#1871) * Fix import order (#1871) * Cleanup cache strategy (#1871) * Download census data from S3 instead of re-calculating (#1871) * Clarify pandas code per Emma (#1871)
2022-08-16 13:28:39 -04:00
python-versions = ">=3.8"
[package.dependencies]
fiona = ">=1.8"
Add FUDS ETL (#1817) * Add spatial join method (#1871) Since we'll need to figure out the tracts for a large number of points in future tickets, add a utility to handle grabbing the tract geometries and adding tract data to a point dataset. * Add FUDS, also jupyter lab (#1871) * Add YAML configs for FUDS (#1871) * Allow input geoid to be optional (#1871) * Add FUDS ETL, tests, test-datae noteobook (#1871) This adds the ETL class for Formerly Used Defense Sites (FUDS). This is different from most other ETLs since these FUDS are not provided by tract, but instead by geographic point, so we need to assign FUDS to tracts and then do calculations from there. * Floats -> Ints, as I intended (#1871) * Floats -> Ints, as I intended (#1871) * Formatting fixes (#1871) * Add test false positive GEOIDs (#1871) * Add gdal binaries (#1871) * Refactor pandas code to be more idiomatic (#1871) Per Emma, the more pandas-y way of doing my counts is using np.where to add the values i need, then groupby and size. It is definitely more compact, and also I think more correct! * Update configs per Emma suggestions (#1871) * Type fixed! (#1871) * Remove spurious import from vscode (#1871) * Snapshot update after changing col name (#1871) * Move up GDAL (#1871) * Adjust geojson strategy (#1871) * Try running census separately first (#1871) * Fix import order (#1871) * Cleanup cache strategy (#1871) * Download census data from S3 instead of re-calculating (#1871) * Clarify pandas code per Emma (#1871)
2022-08-16 13:28:39 -04:00
packaging = "*"
pandas = ">=1.0.0"
pyproj = ">=2.6.1.post1"
shapely = ">=1.7,<2"
[[package]]
name = "idna"
version = "3.3"
description = "Internationalized Domain Names in Applications (IDNA)"
category = "main"
optional = false
python-versions = ">=3.5"
Add FUDS ETL (#1817) * Add spatial join method (#1871) Since we'll need to figure out the tracts for a large number of points in future tickets, add a utility to handle grabbing the tract geometries and adding tract data to a point dataset. * Add FUDS, also jupyter lab (#1871) * Add YAML configs for FUDS (#1871) * Allow input geoid to be optional (#1871) * Add FUDS ETL, tests, test-datae noteobook (#1871) This adds the ETL class for Formerly Used Defense Sites (FUDS). This is different from most other ETLs since these FUDS are not provided by tract, but instead by geographic point, so we need to assign FUDS to tracts and then do calculations from there. * Floats -> Ints, as I intended (#1871) * Floats -> Ints, as I intended (#1871) * Formatting fixes (#1871) * Add test false positive GEOIDs (#1871) * Add gdal binaries (#1871) * Refactor pandas code to be more idiomatic (#1871) Per Emma, the more pandas-y way of doing my counts is using np.where to add the values i need, then groupby and size. It is definitely more compact, and also I think more correct! * Update configs per Emma suggestions (#1871) * Type fixed! (#1871) * Remove spurious import from vscode (#1871) * Snapshot update after changing col name (#1871) * Move up GDAL (#1871) * Adjust geojson strategy (#1871) * Try running census separately first (#1871) * Fix import order (#1871) * Cleanup cache strategy (#1871) * Download census data from S3 instead of re-calculating (#1871) * Clarify pandas code per Emma (#1871)
2022-08-16 13:28:39 -04:00
[[package]]
name = "importlib-metadata"
version = "4.12.0"
description = "Read metadata from Python packages"
category = "dev"
optional = false
python-versions = ">=3.7"
[package.dependencies]
zipp = ">=0.5"
[package.extras]
docs = ["sphinx", "jaraco.packaging (>=9)", "rst.linker (>=1.9)"]
perf = ["ipython"]
testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "pytest-cov", "pytest-enabler (>=1.3)", "packaging", "pyfakefs", "flufl.flake8", "pytest-perf (>=0.9.2)", "pytest-black (>=0.3.7)", "pytest-mypy (>=0.9.1)", "importlib-resources (>=1.3)"]
[[package]]
name = "importlib-resources"
version = "5.9.0"
description = "Read resources from Python packages"
category = "main"
optional = false
python-versions = ">=3.7"
[package.dependencies]
zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""}
[package.extras]
docs = ["sphinx", "jaraco.packaging (>=9)", "rst.linker (>=1.9)", "jaraco.tidelift (>=1.4)"]
testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-black (>=0.3.7)", "pytest-mypy (>=0.9.1)"]
[[package]]
name = "iniconfig"
version = "1.1.1"
description = "iniconfig: brain-dead simple config-ini parsing"
category = "dev"
optional = false
python-versions = "*"
[[package]]
name = "ipdb"
version = "0.13.9"
description = "IPython-enabled pdb"
category = "main"
optional = false
python-versions = ">=2.7"
[package.dependencies]
decorator = {version = "*", markers = "python_version > \"3.6\""}
ipython = {version = ">=7.17.0", markers = "python_version > \"3.6\""}
toml = {version = ">=0.10.2", markers = "python_version > \"3.6\""}
[[package]]
name = "ipykernel"
version = "6.15.1"
description = "IPython Kernel for Jupyter"
category = "main"
optional = false
python-versions = ">=3.7"
[package.dependencies]
appnope = {version = "*", markers = "platform_system == \"Darwin\""}
2022-04-18 18:12:18 -04:00
debugpy = ">=1.0"
ipython = ">=7.23.1"
2022-04-18 18:12:18 -04:00
jupyter-client = ">=6.1.12"
matplotlib-inline = ">=0.1"
nest-asyncio = "*"
2022-04-18 18:12:18 -04:00
packaging = "*"
psutil = "*"
pyzmq = ">=17"
2022-04-18 18:12:18 -04:00
tornado = ">=6.1"
traitlets = ">=5.1.0"
[package.extras]
test = ["pytest (>=6.0)", "pytest-timeout", "pytest-cov", "pre-commit", "ipyparallel", "flaky"]
[[package]]
name = "ipython"
version = "7.34.0"
description = "IPython: Productive Interactive Computing"
category = "main"
optional = false
python-versions = ">=3.7"
[package.dependencies]
appnope = {version = "*", markers = "sys_platform == \"darwin\""}
backcall = "*"
colorama = {version = "*", markers = "sys_platform == \"win32\""}
decorator = "*"
jedi = ">=0.16"
matplotlib-inline = "*"
pexpect = {version = ">4.3", markers = "sys_platform != \"win32\""}
pickleshare = "*"
prompt-toolkit = ">=2.0.0,<3.0.0 || >3.0.0,<3.0.1 || >3.0.1,<3.1.0"
pygments = "*"
traitlets = ">=4.2"
[package.extras]
test = ["numpy (>=1.17)", "ipykernel", "nbformat", "pygments", "testpath", "requests", "nose (>=0.10.1)"]
Add FUDS ETL (#1817) * Add spatial join method (#1871) Since we'll need to figure out the tracts for a large number of points in future tickets, add a utility to handle grabbing the tract geometries and adding tract data to a point dataset. * Add FUDS, also jupyter lab (#1871) * Add YAML configs for FUDS (#1871) * Allow input geoid to be optional (#1871) * Add FUDS ETL, tests, test-datae noteobook (#1871) This adds the ETL class for Formerly Used Defense Sites (FUDS). This is different from most other ETLs since these FUDS are not provided by tract, but instead by geographic point, so we need to assign FUDS to tracts and then do calculations from there. * Floats -> Ints, as I intended (#1871) * Floats -> Ints, as I intended (#1871) * Formatting fixes (#1871) * Add test false positive GEOIDs (#1871) * Add gdal binaries (#1871) * Refactor pandas code to be more idiomatic (#1871) Per Emma, the more pandas-y way of doing my counts is using np.where to add the values i need, then groupby and size. It is definitely more compact, and also I think more correct! * Update configs per Emma suggestions (#1871) * Type fixed! (#1871) * Remove spurious import from vscode (#1871) * Snapshot update after changing col name (#1871) * Move up GDAL (#1871) * Adjust geojson strategy (#1871) * Try running census separately first (#1871) * Fix import order (#1871) * Cleanup cache strategy (#1871) * Download census data from S3 instead of re-calculating (#1871) * Clarify pandas code per Emma (#1871)
2022-08-16 13:28:39 -04:00
qtconsole = ["qtconsole"]
parallel = ["ipyparallel"]
notebook = ["ipywidgets", "notebook"]
nbformat = ["nbformat"]
nbconvert = ["nbconvert"]
kernel = ["ipykernel"]
doc = ["Sphinx (>=1.3)"]
all = ["testpath", "requests", "qtconsole", "pygments", "numpy (>=1.17)", "notebook", "nose (>=0.10.1)", "nbformat", "nbconvert", "ipywidgets", "ipyparallel", "ipykernel", "Sphinx (>=1.3)"]
[[package]]
name = "ipython-genutils"
version = "0.2.0"
description = "Vestigial utilities from IPython"
category = "main"
optional = false
python-versions = "*"
[[package]]
name = "ipywidgets"
version = "7.7.1"
description = "IPython HTML widgets for Jupyter"
category = "main"
optional = false
python-versions = "*"
[package.dependencies]
ipykernel = ">=4.5.1"
ipython = {version = ">=4.0.0", markers = "python_version >= \"3.3\""}
ipython-genutils = ">=0.2.0,<0.3.0"
jupyterlab-widgets = {version = ">=1.0.0", markers = "python_version >= \"3.6\""}
traitlets = ">=4.3.1"
widgetsnbextension = ">=3.6.0,<3.7.0"
[package.extras]
test = ["mock", "pytest-cov", "pytest (>=3.6.0)"]
[[package]]
name = "isort"
version = "5.10.1"
description = "A Python utility / library to sort Python imports."
category = "main"
optional = false
python-versions = ">=3.6.1,<4.0"
[package.extras]
pipfile_deprecated_finder = ["pipreqs", "requirementslib"]
requirements_deprecated_finder = ["pipreqs", "pip-api"]
colors = ["colorama (>=0.4.3,<0.5.0)"]
plugins = ["setuptools"]
[[package]]
name = "jedi"
version = "0.18.1"
description = "An autocompletion tool for Python that can be used for text editors."
category = "main"
optional = false
python-versions = ">=3.6"
[package.dependencies]
parso = ">=0.8.0,<0.9.0"
[package.extras]
qa = ["flake8 (==3.8.3)", "mypy (==0.782)"]
testing = ["Django (<3.1)", "colorama", "docopt", "pytest (<7.0.0)"]
[[package]]
name = "jellyfish"
version = "0.6.1"
description = "a library for doing approximate and phonetic matching of strings."
category = "main"
optional = false
python-versions = "*"
[[package]]
name = "jinja2"
version = "3.1.2"
description = "A very fast and expressive template engine."
category = "main"
optional = false
python-versions = ">=3.7"
[package.dependencies]
MarkupSafe = ">=2.0"
[package.extras]
i18n = ["Babel (>=2.7)"]
Add FUDS ETL (#1817) * Add spatial join method (#1871) Since we'll need to figure out the tracts for a large number of points in future tickets, add a utility to handle grabbing the tract geometries and adding tract data to a point dataset. * Add FUDS, also jupyter lab (#1871) * Add YAML configs for FUDS (#1871) * Allow input geoid to be optional (#1871) * Add FUDS ETL, tests, test-datae noteobook (#1871) This adds the ETL class for Formerly Used Defense Sites (FUDS). This is different from most other ETLs since these FUDS are not provided by tract, but instead by geographic point, so we need to assign FUDS to tracts and then do calculations from there. * Floats -> Ints, as I intended (#1871) * Floats -> Ints, as I intended (#1871) * Formatting fixes (#1871) * Add test false positive GEOIDs (#1871) * Add gdal binaries (#1871) * Refactor pandas code to be more idiomatic (#1871) Per Emma, the more pandas-y way of doing my counts is using np.where to add the values i need, then groupby and size. It is definitely more compact, and also I think more correct! * Update configs per Emma suggestions (#1871) * Type fixed! (#1871) * Remove spurious import from vscode (#1871) * Snapshot update after changing col name (#1871) * Move up GDAL (#1871) * Adjust geojson strategy (#1871) * Try running census separately first (#1871) * Fix import order (#1871) * Cleanup cache strategy (#1871) * Download census data from S3 instead of re-calculating (#1871) * Clarify pandas code per Emma (#1871)
2022-08-16 13:28:39 -04:00
[[package]]
name = "json5"
version = "0.9.9"
description = "A Python implementation of the JSON5 data format."
category = "dev"
optional = false
python-versions = "*"
[package.extras]
dev = ["hypothesis"]
[[package]]
name = "jsonschema"
version = "4.10.0"
description = "An implementation of JSON Schema validation for Python"
category = "main"
optional = false
python-versions = ">=3.7"
[package.dependencies]
attrs = ">=17.4.0"
importlib-resources = {version = ">=1.4.0", markers = "python_version < \"3.9\""}
pkgutil-resolve-name = {version = ">=1.3.10", markers = "python_version < \"3.9\""}
pyrsistent = ">=0.14.0,<0.17.0 || >0.17.0,<0.17.1 || >0.17.1,<0.17.2 || >0.17.2"
[package.extras]
Add FUDS ETL (#1817) * Add spatial join method (#1871) Since we'll need to figure out the tracts for a large number of points in future tickets, add a utility to handle grabbing the tract geometries and adding tract data to a point dataset. * Add FUDS, also jupyter lab (#1871) * Add YAML configs for FUDS (#1871) * Allow input geoid to be optional (#1871) * Add FUDS ETL, tests, test-datae noteobook (#1871) This adds the ETL class for Formerly Used Defense Sites (FUDS). This is different from most other ETLs since these FUDS are not provided by tract, but instead by geographic point, so we need to assign FUDS to tracts and then do calculations from there. * Floats -> Ints, as I intended (#1871) * Floats -> Ints, as I intended (#1871) * Formatting fixes (#1871) * Add test false positive GEOIDs (#1871) * Add gdal binaries (#1871) * Refactor pandas code to be more idiomatic (#1871) Per Emma, the more pandas-y way of doing my counts is using np.where to add the values i need, then groupby and size. It is definitely more compact, and also I think more correct! * Update configs per Emma suggestions (#1871) * Type fixed! (#1871) * Remove spurious import from vscode (#1871) * Snapshot update after changing col name (#1871) * Move up GDAL (#1871) * Adjust geojson strategy (#1871) * Try running census separately first (#1871) * Fix import order (#1871) * Cleanup cache strategy (#1871) * Download census data from S3 instead of re-calculating (#1871) * Clarify pandas code per Emma (#1871)
2022-08-16 13:28:39 -04:00
format = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3987", "uri-template", "webcolors (>=1.11)"]
format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3986-validator (>0.1.0)", "uri-template", "webcolors (>=1.11)"]
[[package]]
name = "jupyter"
version = "1.0.0"
description = "Jupyter metapackage. Install all the Jupyter components in one go."
category = "main"
optional = false
python-versions = "*"
[package.dependencies]
ipykernel = "*"
ipywidgets = "*"
jupyter-console = "*"
nbconvert = "*"
notebook = "*"
qtconsole = "*"
[[package]]
name = "jupyter-client"
version = "7.3.4"
description = "Jupyter protocol implementation and client libraries"
category = "main"
optional = false
python-versions = ">=3.7"
[package.dependencies]
entrypoints = "*"
jupyter-core = ">=4.9.2"
nest-asyncio = ">=1.5.4"
python-dateutil = ">=2.8.2"
pyzmq = ">=23.0"
tornado = ">=6.0"
traitlets = "*"
[package.extras]
test = ["pytest-timeout", "pytest-cov", "pytest-asyncio (>=0.18)", "pytest", "pre-commit", "mypy", "ipython", "ipykernel (>=6.5)", "coverage", "codecov"]
doc = ["sphinxcontrib-github-alt", "sphinx (>=1.3.6)", "sphinx-rtd-theme", "myst-parser", "ipykernel"]
[[package]]
name = "jupyter-console"
version = "6.4.4"
description = "Jupyter terminal console"
category = "main"
optional = false
python-versions = ">=3.7"
[package.dependencies]
ipykernel = "*"
ipython = "*"
jupyter-client = ">=7.0.0"
prompt-toolkit = ">=2.0.0,<3.0.0 || >3.0.0,<3.0.1 || >3.0.1,<3.1.0"
pygments = "*"
[package.extras]
test = ["pexpect"]
[[package]]
name = "jupyter-contrib-core"
version = "0.4.0"
description = "Common utilities for jupyter-contrib projects."
category = "main"
optional = false
python-versions = "*"
[package.dependencies]
jupyter-core = "*"
notebook = ">=4.0"
tornado = "*"
traitlets = "*"
[package.extras]
testing_utils = ["mock", "nose"]
[[package]]
name = "jupyter-contrib-nbextensions"
version = "0.5.1"
description = "A collection of Jupyter nbextensions."
category = "main"
optional = false
python-versions = "*"
[package.dependencies]
ipython-genutils = "*"
jupyter-contrib-core = ">=0.3.3"
jupyter-core = "*"
jupyter-highlight-selected-word = ">=0.1.1"
jupyter-latex-envs = ">=1.3.8"
jupyter-nbextensions-configurator = ">=0.4.0"
lxml = "*"
nbconvert = ">=4.2"
notebook = ">=4.0"
pyyaml = "*"
tornado = "*"
traitlets = ">=4.1"
[package.extras]
test = ["nbformat", "nose", "pip", "requests", "mock"]
[[package]]
name = "jupyter-core"
version = "4.11.1"
description = "Jupyter core package. A base package on which Jupyter projects rely."
category = "main"
optional = false
2022-04-18 18:12:18 -04:00
python-versions = ">=3.7"
[package.dependencies]
pywin32 = {version = ">=1.0", markers = "sys_platform == \"win32\" and platform_python_implementation != \"PyPy\""}
traitlets = "*"
2022-04-18 18:12:18 -04:00
[package.extras]
test = ["pytest-timeout", "pytest-cov", "pytest", "pre-commit", "ipykernel"]
2022-04-18 18:12:18 -04:00
[[package]]
name = "jupyter-highlight-selected-word"
version = "0.2.0"
description = "Jupyter notebook extension that enables highlighting every instance of the current word in the notebook."
category = "main"
optional = false
python-versions = "*"
[[package]]
name = "jupyter-latex-envs"
version = "1.4.6"
description = "Jupyter notebook extension which supports (some) LaTeX environments within markdown cells. Also provides support for labels and crossreferences, document wide numbering, bibliography, and more..."
category = "main"
optional = false
python-versions = "*"
[package.dependencies]
ipython = "*"
jupyter_core = "*"
nbconvert = "*"
notebook = ">=4.0"
traitlets = ">=4.1"
[[package]]
name = "jupyter-nbextensions-configurator"
version = "0.5.0"
description = "jupyter serverextension providing configuration interfaces for nbextensions."
category = "main"
optional = false
python-versions = "*"
[package.dependencies]
jupyter-contrib-core = ">=0.3.3"
jupyter-core = "*"
notebook = ">=6.0"
pyyaml = "*"
tornado = "*"
traitlets = "*"
[package.extras]
test = ["mock", "selenium", "requests", "nose", "jupyter-contrib-core"]
Add FUDS ETL (#1817) * Add spatial join method (#1871) Since we'll need to figure out the tracts for a large number of points in future tickets, add a utility to handle grabbing the tract geometries and adding tract data to a point dataset. * Add FUDS, also jupyter lab (#1871) * Add YAML configs for FUDS (#1871) * Allow input geoid to be optional (#1871) * Add FUDS ETL, tests, test-datae noteobook (#1871) This adds the ETL class for Formerly Used Defense Sites (FUDS). This is different from most other ETLs since these FUDS are not provided by tract, but instead by geographic point, so we need to assign FUDS to tracts and then do calculations from there. * Floats -> Ints, as I intended (#1871) * Floats -> Ints, as I intended (#1871) * Formatting fixes (#1871) * Add test false positive GEOIDs (#1871) * Add gdal binaries (#1871) * Refactor pandas code to be more idiomatic (#1871) Per Emma, the more pandas-y way of doing my counts is using np.where to add the values i need, then groupby and size. It is definitely more compact, and also I think more correct! * Update configs per Emma suggestions (#1871) * Type fixed! (#1871) * Remove spurious import from vscode (#1871) * Snapshot update after changing col name (#1871) * Move up GDAL (#1871) * Adjust geojson strategy (#1871) * Try running census separately first (#1871) * Fix import order (#1871) * Cleanup cache strategy (#1871) * Download census data from S3 instead of re-calculating (#1871) * Clarify pandas code per Emma (#1871)
2022-08-16 13:28:39 -04:00
[[package]]
name = "jupyter-server"
version = "1.18.1"
description = "The backend—i.e. core services, APIs, and REST endpoints—to Jupyter web applications."
category = "dev"
optional = false
python-versions = ">=3.7"
[package.dependencies]
anyio = ">=3.1.0,<4"
argon2-cffi = "*"
jinja2 = "*"
jupyter-client = ">=6.1.12"
jupyter-core = ">=4.7.0"
nbconvert = ">=6.4.4"
nbformat = ">=5.2.0"
packaging = "*"
prometheus-client = "*"
pywinpty = {version = "*", markers = "os_name == \"nt\""}
pyzmq = ">=17"
Send2Trash = "*"
terminado = ">=0.8.3"
tornado = ">=6.1.0"
traitlets = ">=5.1"
websocket-client = "*"
[package.extras]
test = ["coverage", "ipykernel", "pre-commit", "pytest-console-scripts", "pytest-cov", "pytest-mock", "pytest-timeout", "pytest-tornasync", "pytest (>=6.0)", "requests"]
[[package]]
name = "jupyterlab"
version = "3.4.5"
description = "JupyterLab computational environment"
category = "dev"
optional = false
python-versions = ">=3.7"
[package.dependencies]
ipython = "*"
jinja2 = ">=2.1"
jupyter-core = "*"
jupyter-server = ">=1.16,<2.0"
jupyterlab-server = ">=2.10,<3.0"
nbclassic = "*"
notebook = "<7"
packaging = "*"
tornado = ">=6.1.0"
[package.extras]
test = ["check-manifest", "coverage", "jupyterlab-server", "pre-commit", "pytest (>=6.0)", "pytest-cov", "pytest-console-scripts", "pytest-check-links (>=0.5)", "requests", "requests-cache", "virtualenv"]
ui-tests = ["build"]
[[package]]
name = "jupyterlab-pygments"
2022-04-18 18:12:18 -04:00
version = "0.2.2"
description = "Pygments theme using JupyterLab CSS variables"
category = "main"
optional = false
2022-04-18 18:12:18 -04:00
python-versions = ">=3.7"
Add FUDS ETL (#1817) * Add spatial join method (#1871) Since we'll need to figure out the tracts for a large number of points in future tickets, add a utility to handle grabbing the tract geometries and adding tract data to a point dataset. * Add FUDS, also jupyter lab (#1871) * Add YAML configs for FUDS (#1871) * Allow input geoid to be optional (#1871) * Add FUDS ETL, tests, test-datae noteobook (#1871) This adds the ETL class for Formerly Used Defense Sites (FUDS). This is different from most other ETLs since these FUDS are not provided by tract, but instead by geographic point, so we need to assign FUDS to tracts and then do calculations from there. * Floats -> Ints, as I intended (#1871) * Floats -> Ints, as I intended (#1871) * Formatting fixes (#1871) * Add test false positive GEOIDs (#1871) * Add gdal binaries (#1871) * Refactor pandas code to be more idiomatic (#1871) Per Emma, the more pandas-y way of doing my counts is using np.where to add the values i need, then groupby and size. It is definitely more compact, and also I think more correct! * Update configs per Emma suggestions (#1871) * Type fixed! (#1871) * Remove spurious import from vscode (#1871) * Snapshot update after changing col name (#1871) * Move up GDAL (#1871) * Adjust geojson strategy (#1871) * Try running census separately first (#1871) * Fix import order (#1871) * Cleanup cache strategy (#1871) * Download census data from S3 instead of re-calculating (#1871) * Clarify pandas code per Emma (#1871)
2022-08-16 13:28:39 -04:00
[[package]]
name = "jupyterlab-server"
version = "2.15.0"
description = "A set of server components for JupyterLab and JupyterLab like applications."
category = "dev"
optional = false
python-versions = ">=3.7"
[package.dependencies]
babel = "*"
importlib-metadata = {version = ">=3.6", markers = "python_version < \"3.10\""}
jinja2 = ">=3.0.3"
json5 = "*"
jsonschema = ">=3.0.1"
jupyter-server = ">=1.8,<2"
packaging = "*"
requests = "*"
[package.extras]
openapi = ["openapi-core (>=0.14.2)", "ruamel-yaml"]
test = ["codecov", "ipykernel", "jupyter-server", "openapi-core (>=0.14.2)", "openapi-spec-validator (<0.5)", "pytest-console-scripts", "pytest-cov", "pytest (>=5.3.2)", "ruamel-yaml", "strict-rfc3339"]
[[package]]
name = "jupyterlab-widgets"
version = "1.1.1"
description = "A JupyterLab extension."
category = "main"
optional = false
python-versions = ">=3.6"
[[package]]
name = "kiwisolver"
version = "1.4.4"
description = "A fast implementation of the Cassowary constraint solver"
category = "main"
optional = false
python-versions = ">=3.7"
[[package]]
name = "lazy-object-proxy"
version = "1.7.1"
description = "A fast and thorough lazy object proxy."
category = "main"
optional = false
python-versions = ">=3.6"
[[package]]
name = "liccheck"
version = "0.6.5"
description = "Check python packages from requirement.txt and report issues"
category = "dev"
optional = false
python-versions = ">=2.7"
[package.dependencies]
configparser = {version = "*", markers = "python_version >= \"3.4\""}
semantic-version = ">=2.7.0"
toml = "*"
[[package]]
name = "lxml"
version = "4.9.1"
description = "Powerful and Pythonic XML processing library combining libxml2/libxslt with the ElementTree API."
category = "main"
optional = false
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, != 3.4.*"
[package.extras]
Add FUDS ETL (#1817) * Add spatial join method (#1871) Since we'll need to figure out the tracts for a large number of points in future tickets, add a utility to handle grabbing the tract geometries and adding tract data to a point dataset. * Add FUDS, also jupyter lab (#1871) * Add YAML configs for FUDS (#1871) * Allow input geoid to be optional (#1871) * Add FUDS ETL, tests, test-datae noteobook (#1871) This adds the ETL class for Formerly Used Defense Sites (FUDS). This is different from most other ETLs since these FUDS are not provided by tract, but instead by geographic point, so we need to assign FUDS to tracts and then do calculations from there. * Floats -> Ints, as I intended (#1871) * Floats -> Ints, as I intended (#1871) * Formatting fixes (#1871) * Add test false positive GEOIDs (#1871) * Add gdal binaries (#1871) * Refactor pandas code to be more idiomatic (#1871) Per Emma, the more pandas-y way of doing my counts is using np.where to add the values i need, then groupby and size. It is definitely more compact, and also I think more correct! * Update configs per Emma suggestions (#1871) * Type fixed! (#1871) * Remove spurious import from vscode (#1871) * Snapshot update after changing col name (#1871) * Move up GDAL (#1871) * Adjust geojson strategy (#1871) * Try running census separately first (#1871) * Fix import order (#1871) * Cleanup cache strategy (#1871) * Download census data from S3 instead of re-calculating (#1871) * Clarify pandas code per Emma (#1871)
2022-08-16 13:28:39 -04:00
source = ["Cython (>=0.29.7)"]
htmlsoup = ["beautifulsoup4"]
html5 = ["html5lib"]
cssselect = ["cssselect (>=0.7)"]
[[package]]
name = "markupsafe"
version = "2.1.1"
description = "Safely add untrusted strings to HTML/XML markup."
category = "main"
optional = false
python-versions = ">=3.7"
[[package]]
name = "marshmallow"
version = "3.17.0"
description = "A lightweight library for converting complex datatypes to and from native Python datatypes."
category = "main"
optional = false
python-versions = ">=3.7"
[package.dependencies]
packaging = ">=17.0"
[package.extras]
dev = ["pytest", "pytz", "simplejson", "mypy (==0.961)", "flake8 (==4.0.1)", "flake8-bugbear (==22.6.22)", "pre-commit (>=2.4,<3.0)", "tox"]
docs = ["sphinx (==4.5.0)", "sphinx-issues (==3.0.1)", "alabaster (==0.7.12)", "sphinx-version-warning (==1.1.2)", "autodocsumm (==0.2.8)"]
lint = ["mypy (==0.961)", "flake8 (==4.0.1)", "flake8-bugbear (==22.6.22)", "pre-commit (>=2.4,<3.0)"]
tests = ["pytest", "pytz", "simplejson"]
[[package]]
name = "marshmallow-dataclass"
version = "8.5.8"
description = "Python library to convert dataclasses into marshmallow schemas."
category = "main"
optional = false
python-versions = ">=3.6"
[package.dependencies]
marshmallow = ">=3.13.0,<4.0"
typing-inspect = ">=0.7.1"
[package.extras]
Add FUDS ETL (#1817) * Add spatial join method (#1871) Since we'll need to figure out the tracts for a large number of points in future tickets, add a utility to handle grabbing the tract geometries and adding tract data to a point dataset. * Add FUDS, also jupyter lab (#1871) * Add YAML configs for FUDS (#1871) * Allow input geoid to be optional (#1871) * Add FUDS ETL, tests, test-datae noteobook (#1871) This adds the ETL class for Formerly Used Defense Sites (FUDS). This is different from most other ETLs since these FUDS are not provided by tract, but instead by geographic point, so we need to assign FUDS to tracts and then do calculations from there. * Floats -> Ints, as I intended (#1871) * Floats -> Ints, as I intended (#1871) * Formatting fixes (#1871) * Add test false positive GEOIDs (#1871) * Add gdal binaries (#1871) * Refactor pandas code to be more idiomatic (#1871) Per Emma, the more pandas-y way of doing my counts is using np.where to add the values i need, then groupby and size. It is definitely more compact, and also I think more correct! * Update configs per Emma suggestions (#1871) * Type fixed! (#1871) * Remove spurious import from vscode (#1871) * Snapshot update after changing col name (#1871) * Move up GDAL (#1871) * Adjust geojson strategy (#1871) * Try running census separately first (#1871) * Fix import order (#1871) * Cleanup cache strategy (#1871) * Download census data from S3 instead of re-calculating (#1871) * Clarify pandas code per Emma (#1871)
2022-08-16 13:28:39 -04:00
union = ["typeguard"]
tests = ["typing-extensions (>=3.7.2)", "pytest-mypy-plugins (>=1.2.0)", "pytest (>=5.4)"]
lint = ["pre-commit (>=2.17,<3.0)"]
enum = ["marshmallow-enum"]
docs = ["sphinx"]
dev = ["typing-extensions (>=3.7.2)", "pytest-mypy-plugins (>=1.2.0)", "pytest (>=5.4)", "sphinx", "pre-commit (>=2.17,<3.0)", "typeguard", "marshmallow-enum"]
[[package]]
name = "marshmallow-enum"
version = "1.5.1"
description = "Enum field for Marshmallow"
category = "main"
optional = false
python-versions = "*"
[package.dependencies]
marshmallow = ">=2.0.0"
[[package]]
name = "matplotlib"
version = "3.5.3"
description = "Python plotting package"
category = "main"
optional = false
python-versions = ">=3.7"
[package.dependencies]
cycler = ">=0.10"
fonttools = ">=4.22.0"
kiwisolver = ">=1.0.1"
numpy = ">=1.17"
packaging = ">=20.0"
pillow = ">=6.2.0"
pyparsing = ">=2.2.1"
python-dateutil = ">=2.7"
setuptools_scm = ">=4,<7"
[[package]]
name = "matplotlib-inline"
version = "0.1.3"
description = "Inline Matplotlib backend for Jupyter"
category = "main"
optional = false
python-versions = ">=3.5"
[package.dependencies]
traitlets = "*"
[[package]]
name = "mccabe"
version = "0.6.1"
description = "McCabe checker, plugin for flake8"
category = "main"
optional = false
python-versions = "*"
[[package]]
name = "mistune"
version = "0.8.4"
description = "The fastest markdown parser in pure Python"
category = "main"
optional = false
python-versions = "*"
[[package]]
name = "munch"
version = "2.5.0"
description = "A dot-accessible dictionary (a la JavaScript objects)"
category = "main"
optional = false
python-versions = "*"
[package.dependencies]
six = "*"
[package.extras]
testing = ["pytest", "coverage", "astroid (>=1.5.3,<1.6.0)", "pylint (>=1.7.2,<1.8.0)", "astroid (>=2.0)", "pylint (>=2.3.1,<2.4.0)"]
yaml = ["PyYAML (>=5.1.0)"]
[[package]]
name = "mypy"
version = "0.910"
description = "Optional static typing for Python"
category = "dev"
optional = false
python-versions = ">=3.5"
[package.dependencies]
mypy-extensions = ">=0.4.3,<0.5.0"
toml = "*"
typing-extensions = ">=3.7.4"
[package.extras]
dmypy = ["psutil (>=4.0)"]
python2 = ["typed-ast (>=1.4.0,<1.5.0)"]
[[package]]
name = "mypy-extensions"
version = "0.4.3"
description = "Experimental type system extensions for programs checked with the mypy typechecker."
category = "main"
optional = false
python-versions = "*"
[[package]]
name = "nb-black"
version = "1.0.7"
description = "A simple extension for Jupyter Notebook and Jupyter Lab to beautify Python code automatically using Black."
category = "dev"
optional = false
python-versions = "*"
[package.dependencies]
ipython = "*"
Add FUDS ETL (#1817) * Add spatial join method (#1871) Since we'll need to figure out the tracts for a large number of points in future tickets, add a utility to handle grabbing the tract geometries and adding tract data to a point dataset. * Add FUDS, also jupyter lab (#1871) * Add YAML configs for FUDS (#1871) * Allow input geoid to be optional (#1871) * Add FUDS ETL, tests, test-datae noteobook (#1871) This adds the ETL class for Formerly Used Defense Sites (FUDS). This is different from most other ETLs since these FUDS are not provided by tract, but instead by geographic point, so we need to assign FUDS to tracts and then do calculations from there. * Floats -> Ints, as I intended (#1871) * Floats -> Ints, as I intended (#1871) * Formatting fixes (#1871) * Add test false positive GEOIDs (#1871) * Add gdal binaries (#1871) * Refactor pandas code to be more idiomatic (#1871) Per Emma, the more pandas-y way of doing my counts is using np.where to add the values i need, then groupby and size. It is definitely more compact, and also I think more correct! * Update configs per Emma suggestions (#1871) * Type fixed! (#1871) * Remove spurious import from vscode (#1871) * Snapshot update after changing col name (#1871) * Move up GDAL (#1871) * Adjust geojson strategy (#1871) * Try running census separately first (#1871) * Fix import order (#1871) * Cleanup cache strategy (#1871) * Download census data from S3 instead of re-calculating (#1871) * Clarify pandas code per Emma (#1871)
2022-08-16 13:28:39 -04:00
[[package]]
name = "nbclassic"
version = "0.4.3"
description = "A web-based notebook environment for interactive computing"
category = "dev"
optional = false
python-versions = ">=3.7"
[package.dependencies]
argon2-cffi = "*"
ipykernel = "*"
ipython-genutils = "*"
jinja2 = "*"
jupyter-client = ">=6.1.1"
jupyter-core = ">=4.6.1"
jupyter-server = ">=1.8"
nbconvert = ">=5"
nbformat = "*"
nest-asyncio = ">=1.5"
notebook-shim = ">=0.1.0"
prometheus-client = "*"
pyzmq = ">=17"
Send2Trash = ">=1.8.0"
terminado = ">=0.8.3"
tornado = ">=6.1"
traitlets = ">=4.2.1"
[package.extras]
test = ["requests-unixsocket", "pytest-tornasync", "pytest-cov", "selenium (==4.1.5)", "nbval", "testpath", "requests", "coverage", "pytest"]
Add FUDS ETL (#1817) * Add spatial join method (#1871) Since we'll need to figure out the tracts for a large number of points in future tickets, add a utility to handle grabbing the tract geometries and adding tract data to a point dataset. * Add FUDS, also jupyter lab (#1871) * Add YAML configs for FUDS (#1871) * Allow input geoid to be optional (#1871) * Add FUDS ETL, tests, test-datae noteobook (#1871) This adds the ETL class for Formerly Used Defense Sites (FUDS). This is different from most other ETLs since these FUDS are not provided by tract, but instead by geographic point, so we need to assign FUDS to tracts and then do calculations from there. * Floats -> Ints, as I intended (#1871) * Floats -> Ints, as I intended (#1871) * Formatting fixes (#1871) * Add test false positive GEOIDs (#1871) * Add gdal binaries (#1871) * Refactor pandas code to be more idiomatic (#1871) Per Emma, the more pandas-y way of doing my counts is using np.where to add the values i need, then groupby and size. It is definitely more compact, and also I think more correct! * Update configs per Emma suggestions (#1871) * Type fixed! (#1871) * Remove spurious import from vscode (#1871) * Snapshot update after changing col name (#1871) * Move up GDAL (#1871) * Adjust geojson strategy (#1871) * Try running census separately first (#1871) * Fix import order (#1871) * Cleanup cache strategy (#1871) * Download census data from S3 instead of re-calculating (#1871) * Clarify pandas code per Emma (#1871)
2022-08-16 13:28:39 -04:00
json-logging = ["json-logging"]
docs = ["myst-parser", "sphinx-rtd-theme", "sphinxcontrib-github-alt", "nbsphinx", "sphinx"]
Add FUDS ETL (#1817) * Add spatial join method (#1871) Since we'll need to figure out the tracts for a large number of points in future tickets, add a utility to handle grabbing the tract geometries and adding tract data to a point dataset. * Add FUDS, also jupyter lab (#1871) * Add YAML configs for FUDS (#1871) * Allow input geoid to be optional (#1871) * Add FUDS ETL, tests, test-datae noteobook (#1871) This adds the ETL class for Formerly Used Defense Sites (FUDS). This is different from most other ETLs since these FUDS are not provided by tract, but instead by geographic point, so we need to assign FUDS to tracts and then do calculations from there. * Floats -> Ints, as I intended (#1871) * Floats -> Ints, as I intended (#1871) * Formatting fixes (#1871) * Add test false positive GEOIDs (#1871) * Add gdal binaries (#1871) * Refactor pandas code to be more idiomatic (#1871) Per Emma, the more pandas-y way of doing my counts is using np.where to add the values i need, then groupby and size. It is definitely more compact, and also I think more correct! * Update configs per Emma suggestions (#1871) * Type fixed! (#1871) * Remove spurious import from vscode (#1871) * Snapshot update after changing col name (#1871) * Move up GDAL (#1871) * Adjust geojson strategy (#1871) * Try running census separately first (#1871) * Fix import order (#1871) * Cleanup cache strategy (#1871) * Download census data from S3 instead of re-calculating (#1871) * Clarify pandas code per Emma (#1871)
2022-08-16 13:28:39 -04:00
[[package]]
name = "nbclient"
version = "0.6.6"
description = "A client library for executing notebooks. Formerly nbconvert's ExecutePreprocessor."
category = "main"
optional = false
python-versions = ">=3.7.0"
[package.dependencies]
jupyter-client = ">=6.1.5"
nbformat = ">=5.0"
nest-asyncio = "*"
traitlets = ">=5.2.2"
[package.extras]
sphinx = ["autodoc-traits", "mock", "moto", "myst-parser", "Sphinx (>=1.7)", "sphinx-book-theme"]
2022-04-18 18:12:18 -04:00
test = ["black", "check-manifest", "flake8", "ipykernel", "ipython (<8.0.0)", "ipywidgets (<8.0.0)", "mypy", "pip (>=18.1)", "pre-commit", "pytest (>=4.1)", "pytest-asyncio", "pytest-cov (>=2.6.1)", "setuptools (>=60.0)", "testpath", "twine (>=1.11.0)", "xmltodict"]
[[package]]
name = "nbconvert"
version = "6.5.3"
description = "Converting Jupyter Notebooks"
category = "main"
optional = false
python-versions = ">=3.7"
[package.dependencies]
beautifulsoup4 = "*"
bleach = "*"
defusedxml = "*"
entrypoints = ">=0.2.2"
2022-04-18 18:12:18 -04:00
jinja2 = ">=3.0"
jupyter-core = ">=4.7"
jupyterlab-pygments = "*"
lxml = "*"
MarkupSafe = ">=2.0"
mistune = ">=0.8.1,<2"
2022-04-18 18:12:18 -04:00
nbclient = ">=0.5.0"
nbformat = ">=5.1"
packaging = "*"
pandocfilters = ">=1.4.1"
pygments = ">=2.4.1"
2022-04-18 18:12:18 -04:00
tinycss2 = "*"
traitlets = ">=5.0"
[package.extras]
2022-04-18 18:12:18 -04:00
all = ["pytest", "pytest-cov", "pytest-dependency", "ipykernel", "ipywidgets (>=7)", "pre-commit", "pyppeteer (>=1,<1.1)", "tornado (>=6.1)", "sphinx (>=1.5.1)", "sphinx-rtd-theme", "nbsphinx (>=0.2.12)", "ipython"]
docs = ["sphinx (>=1.5.1)", "sphinx-rtd-theme", "nbsphinx (>=0.2.12)", "ipython"]
2022-04-18 18:12:18 -04:00
serve = ["tornado (>=6.1)"]
test = ["pytest", "pytest-cov", "pytest-dependency", "ipykernel", "ipywidgets (>=7)", "pre-commit", "pyppeteer (>=1,<1.1)"]
webpdf = ["pyppeteer (>=1,<1.1)"]
[[package]]
name = "nbformat"
version = "5.4.0"
description = "The Jupyter Notebook format"
category = "main"
optional = false
python-versions = ">=3.7"
[package.dependencies]
2022-04-18 18:12:18 -04:00
fastjsonschema = "*"
jsonschema = ">=2.6"
jupyter-core = "*"
traitlets = ">=5.1"
[package.extras]
2022-04-18 18:12:18 -04:00
test = ["check-manifest", "testpath", "pytest", "pre-commit"]
[[package]]
name = "nest-asyncio"
2022-04-18 18:12:18 -04:00
version = "1.5.5"
description = "Patch asyncio to allow nested event loops"
category = "main"
optional = false
python-versions = ">=3.5"
[[package]]
name = "notebook"
version = "6.4.12"
description = "A web-based notebook environment for interactive computing"
category = "main"
optional = false
python-versions = ">=3.7"
[package.dependencies]
argon2-cffi = "*"
ipykernel = "*"
ipython-genutils = "*"
jinja2 = "*"
jupyter-client = ">=5.3.4"
jupyter-core = ">=4.6.1"
nbconvert = ">=5"
nbformat = "*"
nest-asyncio = ">=1.5"
prometheus-client = "*"
pyzmq = ">=17"
Send2Trash = ">=1.8.0"
terminado = ">=0.8.3"
tornado = ">=6.1"
traitlets = ">=4.2.1"
[package.extras]
test = ["requests-unixsocket", "pytest-cov", "selenium", "nbval", "testpath", "requests", "coverage", "pytest"]
json-logging = ["json-logging"]
docs = ["myst-parser", "sphinx-rtd-theme", "sphinxcontrib-github-alt", "nbsphinx", "sphinx"]
Add FUDS ETL (#1817) * Add spatial join method (#1871) Since we'll need to figure out the tracts for a large number of points in future tickets, add a utility to handle grabbing the tract geometries and adding tract data to a point dataset. * Add FUDS, also jupyter lab (#1871) * Add YAML configs for FUDS (#1871) * Allow input geoid to be optional (#1871) * Add FUDS ETL, tests, test-datae noteobook (#1871) This adds the ETL class for Formerly Used Defense Sites (FUDS). This is different from most other ETLs since these FUDS are not provided by tract, but instead by geographic point, so we need to assign FUDS to tracts and then do calculations from there. * Floats -> Ints, as I intended (#1871) * Floats -> Ints, as I intended (#1871) * Formatting fixes (#1871) * Add test false positive GEOIDs (#1871) * Add gdal binaries (#1871) * Refactor pandas code to be more idiomatic (#1871) Per Emma, the more pandas-y way of doing my counts is using np.where to add the values i need, then groupby and size. It is definitely more compact, and also I think more correct! * Update configs per Emma suggestions (#1871) * Type fixed! (#1871) * Remove spurious import from vscode (#1871) * Snapshot update after changing col name (#1871) * Move up GDAL (#1871) * Adjust geojson strategy (#1871) * Try running census separately first (#1871) * Fix import order (#1871) * Cleanup cache strategy (#1871) * Download census data from S3 instead of re-calculating (#1871) * Clarify pandas code per Emma (#1871)
2022-08-16 13:28:39 -04:00
[[package]]
name = "notebook-shim"
version = "0.1.0"
description = "A shim layer for notebook traits and config"
category = "dev"
optional = false
python-versions = ">=3.7"
[package.dependencies]
jupyter-server = ">=1.8,<2.0"
[package.extras]
test = ["pytest-console-scripts", "pytest-tornasync", "pytest"]
[[package]]
name = "numpy"
version = "1.23.2"
description = "NumPy is the fundamental package for array computing with Python."
category = "main"
optional = false
python-versions = ">=3.8"
[[package]]
name = "openpyxl"
version = "3.0.10"
description = "A Python library to read/write Excel 2010 xlsx/xlsm files"
category = "dev"
optional = false
python-versions = ">=3.6"
[package.dependencies]
et-xmlfile = "*"
[[package]]
name = "packaging"
version = "21.3"
description = "Core utilities for Python packages"
category = "main"
optional = false
python-versions = ">=3.6"
[package.dependencies]
pyparsing = ">=2.0.2,<3.0.5 || >3.0.5"
[[package]]
name = "pandas"
version = "1.4.3"
description = "Powerful data structures for data analysis, time series, and statistics"
category = "main"
optional = false
python-versions = ">=3.8"
[package.dependencies]
numpy = [
Add FUDS ETL (#1817) * Add spatial join method (#1871) Since we'll need to figure out the tracts for a large number of points in future tickets, add a utility to handle grabbing the tract geometries and adding tract data to a point dataset. * Add FUDS, also jupyter lab (#1871) * Add YAML configs for FUDS (#1871) * Allow input geoid to be optional (#1871) * Add FUDS ETL, tests, test-datae noteobook (#1871) This adds the ETL class for Formerly Used Defense Sites (FUDS). This is different from most other ETLs since these FUDS are not provided by tract, but instead by geographic point, so we need to assign FUDS to tracts and then do calculations from there. * Floats -> Ints, as I intended (#1871) * Floats -> Ints, as I intended (#1871) * Formatting fixes (#1871) * Add test false positive GEOIDs (#1871) * Add gdal binaries (#1871) * Refactor pandas code to be more idiomatic (#1871) Per Emma, the more pandas-y way of doing my counts is using np.where to add the values i need, then groupby and size. It is definitely more compact, and also I think more correct! * Update configs per Emma suggestions (#1871) * Type fixed! (#1871) * Remove spurious import from vscode (#1871) * Snapshot update after changing col name (#1871) * Move up GDAL (#1871) * Adjust geojson strategy (#1871) * Try running census separately first (#1871) * Fix import order (#1871) * Cleanup cache strategy (#1871) * Download census data from S3 instead of re-calculating (#1871) * Clarify pandas code per Emma (#1871)
2022-08-16 13:28:39 -04:00
{version = ">=1.21.0", markers = "python_version >= \"3.10\""},
{version = ">=1.20.0", markers = "platform_machine == \"arm64\" and python_version < \"3.10\""},
{version = ">=1.19.2", markers = "platform_machine == \"aarch64\" and python_version < \"3.10\""},
{version = ">=1.18.5", markers = "platform_machine != \"aarch64\" and platform_machine != \"arm64\" and python_version < \"3.10\""},
]
python-dateutil = ">=2.8.1"
pytz = ">=2020.1"
[package.extras]
test = ["pytest-xdist (>=1.31)", "pytest (>=6.0)", "hypothesis (>=5.5.3)"]
[[package]]
name = "pandas-vet"
version = "0.2.3"
description = "A flake8 plugin to lint pandas in an opinionated way"
category = "dev"
optional = false
python-versions = "*"
[package.dependencies]
attrs = "*"
flake8 = ">3.0.0"
[[package]]
name = "pandocfilters"
version = "1.5.0"
description = "Utilities for writing pandoc filters in python"
category = "main"
optional = false
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
[[package]]
name = "papermill"
version = "2.4.0"
description = "Parametrize and run Jupyter and nteract Notebooks"
category = "dev"
optional = false
python-versions = ">=3.7"
[package.dependencies]
ansiwrap = "*"
click = "*"
entrypoints = "*"
nbclient = ">=0.2.0"
nbformat = ">=5.1.2"
pyyaml = "*"
requests = "*"
tenacity = "*"
tqdm = ">=4.32.2"
[package.extras]
all = ["boto3", "azure-datalake-store (>=0.0.30)", "azure-storage-blob (>=12.1.0)", "requests (>=2.21.0)", "gcsfs (>=0.2.0)", "pyarrow (>=2.0)", "black (>=19.3b0)"]
azure = ["azure-datalake-store (>=0.0.30)", "azure-storage-blob (>=12.1.0)", "requests (>=2.21.0)"]
black = ["black (>=19.3b0)"]
dev = ["boto3", "botocore", "codecov", "coverage", "google-compute-engine", "ipython (>=5.0)", "ipywidgets", "notebook", "moto", "pytest (>=4.1)", "pytest-cov (>=2.6.1)", "pytest-mock (>=1.10)", "pytest-env (>=0.6.2)", "requests (>=2.21.0)", "check-manifest", "attrs (>=17.4.0)", "pre-commit", "flake8", "tox", "bumpversion", "recommonmark", "pip (>=18.1)", "wheel (>=0.31.0)", "setuptools (>=38.6.0)", "twine (>=1.11.0)", "azure-datalake-store (>=0.0.30)", "azure-storage-blob (>=12.1.0)", "gcsfs (>=0.2.0)", "pyarrow (>=2.0)", "black (>=19.3b0)"]
gcs = ["gcsfs (>=0.2.0)"]
github = ["PyGithub (>=1.55)"]
hdfs = ["pyarrow (>=2.0)"]
s3 = ["boto3"]
test = ["boto3", "botocore", "codecov", "coverage", "google-compute-engine", "ipython (>=5.0)", "ipywidgets", "notebook", "moto", "pytest (>=4.1)", "pytest-cov (>=2.6.1)", "pytest-mock (>=1.10)", "pytest-env (>=0.6.2)", "requests (>=2.21.0)", "check-manifest", "attrs (>=17.4.0)", "pre-commit", "flake8", "tox", "bumpversion", "recommonmark", "pip (>=18.1)", "wheel (>=0.31.0)", "setuptools (>=38.6.0)", "twine (>=1.11.0)", "azure-datalake-store (>=0.0.30)", "azure-storage-blob (>=12.1.0)", "gcsfs (>=0.2.0)", "pyarrow (>=2.0)", "black (>=19.3b0)"]
[[package]]
name = "parso"
version = "0.8.3"
description = "A Python Parser"
category = "main"
optional = false
python-versions = ">=3.6"
[package.extras]
qa = ["flake8 (==3.8.3)", "mypy (==0.782)"]
testing = ["docopt", "pytest (<6.0.0)"]
[[package]]
name = "pathspec"
version = "0.9.0"
description = "Utility library for gitignore style pattern matching of file paths."
category = "dev"
optional = false
python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.7"
[[package]]
name = "pexpect"
version = "4.8.0"
description = "Pexpect allows easy control of interactive console applications."
category = "main"
optional = false
python-versions = "*"
[package.dependencies]
ptyprocess = ">=0.5"
[[package]]
name = "pickleshare"
version = "0.7.5"
description = "Tiny 'shelve'-like database with concurrency support"
category = "main"
optional = false
python-versions = "*"
[[package]]
name = "pillow"
version = "9.0.1"
description = "Python Imaging Library (Fork)"
category = "main"
optional = false
python-versions = ">=3.7"
[[package]]
name = "pkgutil-resolve-name"
version = "1.3.10"
description = "Resolve a name to an object."
category = "main"
optional = false
python-versions = ">=3.6"
[[package]]
name = "platformdirs"
2022-04-18 18:12:18 -04:00
version = "2.5.2"
description = "A small Python module for determining appropriate platform-specific dirs, e.g. a \"user data dir\"."
category = "main"
optional = false
python-versions = ">=3.7"
[package.extras]
2022-04-18 18:12:18 -04:00
docs = ["furo (>=2021.7.5b38)", "proselint (>=0.10.2)", "sphinx-autodoc-typehints (>=1.12)", "sphinx (>=4)"]
test = ["appdirs (==1.4.4)", "pytest-cov (>=2.7)", "pytest-mock (>=3.6)", "pytest (>=6)"]
[[package]]
name = "pluggy"
version = "1.0.0"
description = "plugin and hook calling mechanisms for python"
category = "dev"
optional = false
python-versions = ">=3.6"
[package.extras]
dev = ["pre-commit", "tox"]
testing = ["pytest", "pytest-benchmark"]
[[package]]
name = "prometheus-client"
2022-04-18 18:12:18 -04:00
version = "0.14.1"
description = "Python client for the Prometheus monitoring system."
category = "main"
optional = false
python-versions = ">=3.6"
[package.extras]
twisted = ["twisted"]
[[package]]
name = "prompt-toolkit"
version = "3.0.30"
description = "Library for building powerful interactive command lines in Python"
category = "main"
optional = false
python-versions = ">=3.6.2"
[package.dependencies]
wcwidth = "*"
[[package]]
name = "psutil"
version = "5.9.1"
description = "Cross-platform lib for process and system monitoring in Python."
category = "main"
optional = false
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
[package.extras]
test = ["wmi", "pywin32", "enum34", "mock", "ipaddress"]
[[package]]
name = "ptyprocess"
version = "0.7.0"
description = "Run a subprocess in a pseudo terminal"
category = "main"
optional = false
python-versions = "*"
[[package]]
name = "py"
version = "1.11.0"
description = "library with cross-python path, ini-parsing, io, code, log facilities"
category = "main"
optional = false
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
[[package]]
name = "pycodestyle"
version = "2.7.0"
description = "Python style guide checker"
category = "dev"
optional = false
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
[[package]]
name = "pycparser"
version = "2.21"
description = "C parser in Python"
category = "main"
optional = false
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
[[package]]
name = "pydantic"
version = "1.9.2"
description = "Data validation and settings management using python type hints"
category = "main"
optional = false
python-versions = ">=3.6.1"
[package.dependencies]
typing-extensions = ">=3.7.4.3"
[package.extras]
dotenv = ["python-dotenv (>=0.10.4)"]
email = ["email-validator (>=1.0.3)"]
[[package]]
name = "pyflakes"
version = "2.3.1"
description = "passive checker of Python programs"
category = "dev"
optional = false
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
[[package]]
name = "pygments"
version = "2.13.0"
description = "Pygments is a syntax highlighting package written in Python."
category = "main"
optional = false
python-versions = ">=3.6"
[package.extras]
plugins = ["importlib-metadata"]
[[package]]
name = "pylint"
version = "2.14.5"
description = "python code static checker"
category = "main"
optional = false
python-versions = ">=3.7.2"
[package.dependencies]
astroid = ">=2.11.6,<=2.12.0-dev0"
colorama = {version = ">=0.4.5", markers = "sys_platform == \"win32\""}
dill = ">=0.2"
isort = ">=4.2.5,<6"
mccabe = ">=0.6,<0.8"
platformdirs = ">=2.2.0"
tomli = {version = ">=1.1.0", markers = "python_version < \"3.11\""}
tomlkit = ">=0.10.1"
typing-extensions = {version = ">=3.10.0", markers = "python_version < \"3.10\""}
[package.extras]
spelling = ["pyenchant (>=3.2,<4.0)"]
testutils = ["gitpython (>3)"]
[[package]]
name = "pypandoc"
version = "1.8.1"
description = "Thin wrapper for pandoc."
category = "main"
optional = false
python-versions = ">=3.6"
[[package]]
name = "pyparsing"
version = "3.0.9"
2022-04-18 18:12:18 -04:00
description = "pyparsing module - Classes and methods to define and execute parsing grammars"
category = "main"
optional = false
2022-04-18 18:12:18 -04:00
python-versions = ">=3.6.8"
[package.extras]
diagrams = ["jinja2", "railroad-diagrams"]
[[package]]
name = "pyproj"
version = "3.3.1"
description = "Python interface to PROJ (cartographic projections and coordinate transformations library)"
category = "main"
optional = false
python-versions = ">=3.8"
[package.dependencies]
certifi = "*"
[[package]]
name = "pyrsistent"
version = "0.18.1"
description = "Persistent/Functional/Immutable data structures"
category = "main"
optional = false
python-versions = ">=3.7"
[[package]]
name = "pytest"
version = "6.2.5"
description = "pytest: simple powerful testing with Python"
category = "dev"
optional = false
python-versions = ">=3.6"
[package.dependencies]
atomicwrites = {version = ">=1.0", markers = "sys_platform == \"win32\""}
attrs = ">=19.2.0"
colorama = {version = "*", markers = "sys_platform == \"win32\""}
iniconfig = "*"
packaging = "*"
pluggy = ">=0.12,<2.0"
py = ">=1.8.2"
toml = "*"
[package.extras]
testing = ["argcomplete", "hypothesis (>=3.56)", "mock", "nose", "requests", "xmlschema"]
[[package]]
name = "pytest-mock"
version = "3.8.2"
description = "Thin-wrapper around the mock package for easier use with pytest"
category = "dev"
optional = false
python-versions = ">=3.7"
[package.dependencies]
pytest = ">=5.0"
[package.extras]
dev = ["pytest-asyncio", "tox", "pre-commit"]
[[package]]
name = "pytest-snapshot"
version = "0.8.1"
description = "A plugin for snapshot testing with pytest."
category = "dev"
optional = false
python-versions = ">=3.5"
[package.dependencies]
pytest = ">=3.0.0"
[[package]]
name = "python-dateutil"
version = "2.8.2"
description = "Extensions to the standard Python datetime module"
category = "main"
optional = false
python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7"
[package.dependencies]
six = ">=1.5"
[[package]]
name = "pytz"
version = "2022.2.1"
description = "World timezone definitions, modern and historical"
category = "main"
optional = false
python-versions = "*"
[[package]]
name = "pywin32"
version = "304"
description = "Python for Window Extensions"
category = "main"
optional = false
python-versions = "*"
[[package]]
name = "pywinpty"
version = "2.0.7"
description = "Pseudo terminal support for Windows from Python."
category = "main"
optional = false
python-versions = ">=3.7"
[[package]]
name = "pyyaml"
version = "6.0"
description = "YAML parser and emitter for Python"
category = "main"
optional = false
python-versions = ">=3.6"
[[package]]
name = "pyzmq"
version = "23.2.1"
description = "Python bindings for 0MQ"
category = "main"
optional = false
python-versions = ">=3.6"
[package.dependencies]
cffi = {version = "*", markers = "implementation_name == \"pypy\""}
py = {version = "*", markers = "implementation_name == \"pypy\""}
[[package]]
name = "qtconsole"
version = "5.3.1"
description = "Jupyter Qt console"
category = "main"
optional = false
python-versions = ">= 3.7"
[package.dependencies]
ipykernel = ">=4.1"
ipython-genutils = "*"
jupyter-client = ">=4.1"
jupyter-core = "*"
pygments = "*"
pyzmq = ">=17.1"
qtpy = ">=2.0.1"
traitlets = "<5.2.1 || >5.2.1,<5.2.2 || >5.2.2"
[package.extras]
test = ["pytest-qt", "pytest", "flaky"]
doc = ["Sphinx (>=1.3)"]
[[package]]
name = "qtpy"
version = "2.2.0"
description = "Provides an abstraction layer on top of the various Qt bindings (PyQt5/6 and PySide2/6)."
category = "main"
optional = false
python-versions = ">=3.7"
[package.dependencies]
packaging = "*"
[package.extras]
test = ["pytest-qt", "pytest-cov (>=3.0.0)", "pytest (>=6,!=7.0.0,!=7.0.1)"]
[[package]]
name = "requests"
version = "2.28.1"
description = "Python HTTP for Humans."
category = "main"
optional = false
python-versions = ">=3.7, <4"
[package.dependencies]
certifi = ">=2017.4.17"
charset-normalizer = ">=2,<3"
idna = ">=2.5,<4"
urllib3 = ">=1.21.1,<1.27"
[package.extras]
Add FUDS ETL (#1817) * Add spatial join method (#1871) Since we'll need to figure out the tracts for a large number of points in future tickets, add a utility to handle grabbing the tract geometries and adding tract data to a point dataset. * Add FUDS, also jupyter lab (#1871) * Add YAML configs for FUDS (#1871) * Allow input geoid to be optional (#1871) * Add FUDS ETL, tests, test-datae noteobook (#1871) This adds the ETL class for Formerly Used Defense Sites (FUDS). This is different from most other ETLs since these FUDS are not provided by tract, but instead by geographic point, so we need to assign FUDS to tracts and then do calculations from there. * Floats -> Ints, as I intended (#1871) * Floats -> Ints, as I intended (#1871) * Formatting fixes (#1871) * Add test false positive GEOIDs (#1871) * Add gdal binaries (#1871) * Refactor pandas code to be more idiomatic (#1871) Per Emma, the more pandas-y way of doing my counts is using np.where to add the values i need, then groupby and size. It is definitely more compact, and also I think more correct! * Update configs per Emma suggestions (#1871) * Type fixed! (#1871) * Remove spurious import from vscode (#1871) * Snapshot update after changing col name (#1871) * Move up GDAL (#1871) * Adjust geojson strategy (#1871) * Try running census separately first (#1871) * Fix import order (#1871) * Cleanup cache strategy (#1871) * Download census data from S3 instead of re-calculating (#1871) * Clarify pandas code per Emma (#1871)
2022-08-16 13:28:39 -04:00
use_chardet_on_py3 = ["chardet (>=3.0.2,<6)"]
socks = ["PySocks (>=1.5.6,!=1.5.7)"]
Add FUDS ETL (#1817) * Add spatial join method (#1871) Since we'll need to figure out the tracts for a large number of points in future tickets, add a utility to handle grabbing the tract geometries and adding tract data to a point dataset. * Add FUDS, also jupyter lab (#1871) * Add YAML configs for FUDS (#1871) * Allow input geoid to be optional (#1871) * Add FUDS ETL, tests, test-datae noteobook (#1871) This adds the ETL class for Formerly Used Defense Sites (FUDS). This is different from most other ETLs since these FUDS are not provided by tract, but instead by geographic point, so we need to assign FUDS to tracts and then do calculations from there. * Floats -> Ints, as I intended (#1871) * Floats -> Ints, as I intended (#1871) * Formatting fixes (#1871) * Add test false positive GEOIDs (#1871) * Add gdal binaries (#1871) * Refactor pandas code to be more idiomatic (#1871) Per Emma, the more pandas-y way of doing my counts is using np.where to add the values i need, then groupby and size. It is definitely more compact, and also I think more correct! * Update configs per Emma suggestions (#1871) * Type fixed! (#1871) * Remove spurious import from vscode (#1871) * Snapshot update after changing col name (#1871) * Move up GDAL (#1871) * Adjust geojson strategy (#1871) * Try running census separately first (#1871) * Fix import order (#1871) * Cleanup cache strategy (#1871) * Download census data from S3 instead of re-calculating (#1871) * Clarify pandas code per Emma (#1871)
2022-08-16 13:28:39 -04:00
[[package]]
name = "rtree"
version = "1.0.0"
description = "R-Tree spatial index for Python GIS"
category = "main"
optional = false
python-versions = ">=3.7"
[[package]]
name = "safety"
version = "1.10.3"
description = "Checks installed dependencies for known vulnerabilities."
category = "dev"
optional = false
python-versions = ">=3.5"
[package.dependencies]
Click = ">=6.0"
dparse = ">=0.5.1"
packaging = "*"
requests = "*"
[[package]]
name = "scipy"
version = "1.6.1"
description = "SciPy: Scientific Library for Python"
category = "dev"
optional = false
python-versions = ">=3.7"
[package.dependencies]
numpy = ">=1.16.5"
[[package]]
name = "seaborn"
version = "0.11.2"
description = "seaborn: statistical data visualization"
category = "dev"
optional = false
python-versions = ">=3.6"
[package.dependencies]
matplotlib = ">=2.2"
numpy = ">=1.15"
pandas = ">=0.23"
scipy = ">=1.0"
[[package]]
name = "semantic-version"
version = "2.10.0"
description = "A library implementing the 'SemVer' scheme."
category = "dev"
optional = false
python-versions = ">=2.7"
[package.extras]
doc = ["sphinx-rtd-theme", "sphinx"]
dev = ["colorama (<=0.4.1)", "readme-renderer (<25.0)", "zest.releaser", "wheel", "flake8", "coverage", "check-manifest", "tox", "nose2", "Django (>=1.11)"]
[[package]]
name = "send2trash"
version = "1.8.0"
description = "Send file to trash natively under Mac OS X, Windows and Linux."
category = "main"
optional = false
python-versions = "*"
[package.extras]
nativelib = ["pyobjc-framework-cocoa", "pywin32"]
objc = ["pyobjc-framework-cocoa"]
win32 = ["pywin32"]
[[package]]
name = "setuptools-scm"
version = "6.4.2"
description = "the blessed package to manage your versions by scm tags"
category = "main"
optional = false
python-versions = ">=3.6"
[package.dependencies]
packaging = ">=20.0"
tomli = ">=1.0.0"
[package.extras]
test = ["pytest (>=6.2)", "virtualenv (>20)"]
toml = ["setuptools (>=42)"]
[[package]]
name = "shapely"
version = "1.8.2"
description = "Geometric objects, predicates, and operations"
category = "main"
optional = false
python-versions = ">=3.6"
[package.extras]
all = ["pytest", "pytest-cov", "numpy"]
test = ["pytest", "pytest-cov"]
vectorized = ["numpy"]
[[package]]
name = "six"
version = "1.16.0"
description = "Python 2 and 3 compatibility utilities"
category = "main"
optional = false
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*"
Add FUDS ETL (#1817) * Add spatial join method (#1871) Since we'll need to figure out the tracts for a large number of points in future tickets, add a utility to handle grabbing the tract geometries and adding tract data to a point dataset. * Add FUDS, also jupyter lab (#1871) * Add YAML configs for FUDS (#1871) * Allow input geoid to be optional (#1871) * Add FUDS ETL, tests, test-datae noteobook (#1871) This adds the ETL class for Formerly Used Defense Sites (FUDS). This is different from most other ETLs since these FUDS are not provided by tract, but instead by geographic point, so we need to assign FUDS to tracts and then do calculations from there. * Floats -> Ints, as I intended (#1871) * Floats -> Ints, as I intended (#1871) * Formatting fixes (#1871) * Add test false positive GEOIDs (#1871) * Add gdal binaries (#1871) * Refactor pandas code to be more idiomatic (#1871) Per Emma, the more pandas-y way of doing my counts is using np.where to add the values i need, then groupby and size. It is definitely more compact, and also I think more correct! * Update configs per Emma suggestions (#1871) * Type fixed! (#1871) * Remove spurious import from vscode (#1871) * Snapshot update after changing col name (#1871) * Move up GDAL (#1871) * Adjust geojson strategy (#1871) * Try running census separately first (#1871) * Fix import order (#1871) * Cleanup cache strategy (#1871) * Download census data from S3 instead of re-calculating (#1871) * Clarify pandas code per Emma (#1871)
2022-08-16 13:28:39 -04:00
[[package]]
name = "sniffio"
version = "1.2.0"
description = "Sniff out which async library your code is running under"
category = "dev"
optional = false
python-versions = ">=3.5"
[[package]]
name = "soupsieve"
2022-04-18 18:12:18 -04:00
version = "2.3.2.post1"
description = "A modern CSS selector implementation for Beautiful Soup."
category = "main"
optional = false
python-versions = ">=3.6"
[[package]]
name = "tenacity"
version = "8.0.1"
description = "Retry code until it succeeds"
category = "dev"
optional = false
python-versions = ">=3.6"
[package.extras]
doc = ["reno", "sphinx", "tornado (>=4.5)"]
[[package]]
name = "terminado"
version = "0.15.0"
description = "Tornado websocket backend for the Xterm.js Javascript terminal emulator library."
category = "main"
optional = false
python-versions = ">=3.7"
[package.dependencies]
ptyprocess = {version = "*", markers = "os_name != \"nt\""}
pywinpty = {version = ">=1.1.0", markers = "os_name == \"nt\""}
tornado = ">=6.1.0"
[package.extras]
test = ["pytest (>=6.0)", "pytest-timeout", "pre-commit"]
[[package]]
name = "textwrap3"
version = "0.9.2"
description = "textwrap from Python 3.6 backport (plus a few tweaks)"
category = "dev"
optional = false
python-versions = "*"
2022-04-18 18:12:18 -04:00
[[package]]
name = "tinycss2"
version = "1.1.1"
description = "A tiny CSS parser"
category = "main"
optional = false
python-versions = ">=3.6"
[package.dependencies]
webencodings = ">=0.4"
[package.extras]
doc = ["sphinx", "sphinx-rtd-theme"]
test = ["pytest", "pytest-cov", "pytest-flake8", "pytest-isort", "coverage"]
[[package]]
name = "toml"
version = "0.10.2"
description = "Python Library for Tom's Obvious, Minimal Language"
category = "main"
optional = false
python-versions = ">=2.6, !=3.0.*, !=3.1.*, !=3.2.*"
[[package]]
name = "tomli"
version = "1.2.3"
description = "A lil' TOML parser"
category = "main"
optional = false
python-versions = ">=3.6"
[[package]]
name = "tomlkit"
version = "0.11.4"
description = "Style preserving TOML library"
category = "main"
optional = false
python-versions = ">=3.6,<4.0"
[[package]]
name = "tornado"
version = "6.2"
description = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed."
category = "main"
optional = false
python-versions = ">= 3.7"
[[package]]
name = "tox"
version = "3.25.1"
description = "tox is a generic virtualenv management and test command line tool"
category = "dev"
optional = false
python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.7"
[package.dependencies]
colorama = {version = ">=0.4.1", markers = "platform_system == \"Windows\""}
filelock = ">=3.0.0"
packaging = ">=14"
pluggy = ">=0.12.0"
py = ">=1.4.17"
six = ">=1.14.0"
toml = ">=0.9.4"
virtualenv = ">=16.0.0,<20.0.0 || >20.0.0,<20.0.1 || >20.0.1,<20.0.2 || >20.0.2,<20.0.3 || >20.0.3,<20.0.4 || >20.0.4,<20.0.5 || >20.0.5,<20.0.6 || >20.0.6,<20.0.7 || >20.0.7"
[package.extras]
testing = ["pathlib2 (>=2.3.3)", "psutil (>=5.6.1)", "pytest-randomly (>=1.0.0)", "pytest-mock (>=1.10.0)", "pytest-cov (>=2.5.1)", "pytest (>=4.0.0)", "freezegun (>=0.3.11)", "flaky (>=3.4.0)"]
docs = ["towncrier (>=18.5.0)", "sphinxcontrib-autoprogram (>=0.1.5)", "sphinx (>=2.0.0)", "pygments-github-lexers (>=0.0.5)"]
[[package]]
name = "tox-poetry"
version = "0.4.1"
description = "Tox poetry plugin"
category = "dev"
optional = false
python-versions = "*"
[package.dependencies]
pluggy = "*"
toml = "*"
tox = {version = ">=3.7.0", markers = "python_version >= \"3\""}
[package.extras]
test = ["coverage", "pytest", "pycodestyle", "pylint"]
[[package]]
name = "tqdm"
version = "4.62.0"
description = "Fast, Extensible Progress Meter"
category = "main"
optional = false
python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,>=2.7"
[package.dependencies]
colorama = {version = "*", markers = "platform_system == \"Windows\""}
[package.extras]
dev = ["py-make (>=0.1.0)", "twine", "wheel"]
notebook = ["ipywidgets (>=6)"]
telegram = ["requests"]
[[package]]
name = "traitlets"
version = "5.3.0"
description = ""
category = "main"
optional = false
python-versions = ">=3.7"
[package.extras]
test = ["pytest", "pre-commit"]
[[package]]
name = "types-requests"
version = "2.28.8"
description = "Typing stubs for requests"
category = "main"
optional = false
python-versions = "*"
[package.dependencies]
types-urllib3 = "<1.27"
[[package]]
name = "types-urllib3"
version = "1.26.22"
description = "Typing stubs for urllib3"
category = "main"
optional = false
python-versions = "*"
[[package]]
name = "typing-extensions"
version = "4.3.0"
2022-04-18 18:12:18 -04:00
description = "Backported and Experimental Type Hints for Python 3.7+"
category = "main"
optional = false
2022-04-18 18:12:18 -04:00
python-versions = ">=3.7"
[[package]]
name = "typing-inspect"
version = "0.7.1"
description = "Runtime inspection utilities for typing module."
category = "main"
optional = false
python-versions = "*"
[package.dependencies]
mypy-extensions = ">=0.3.0"
typing-extensions = ">=3.7.4"
[[package]]
name = "urllib3"
version = "1.26.11"
description = "HTTP library with thread-safe connection pooling, file post, and more."
category = "main"
optional = false
python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*, !=3.5.*, <4"
[package.extras]
socks = ["PySocks (>=1.5.6,!=1.5.7,<2.0)"]
secure = ["ipaddress", "certifi", "idna (>=2.0.0)", "cryptography (>=1.3.4)", "pyOpenSSL (>=0.14)"]
brotli = ["brotlipy (>=0.6.0)", "brotli (>=1.0.9)", "brotlicffi (>=0.8.0)"]
[[package]]
name = "us"
version = "2.0.2"
description = "US state meta information and other fun stuff"
category = "main"
optional = false
python-versions = "*"
[package.dependencies]
jellyfish = "0.6.1"
[[package]]
name = "virtualenv"
version = "20.16.3"
description = "Virtual Python Environment builder"
category = "dev"
optional = false
python-versions = ">=3.6"
[package.dependencies]
distlib = ">=0.3.5,<1"
filelock = ">=3.4.1,<4"
platformdirs = ">=2.4,<3"
[package.extras]
docs = ["proselint (>=0.13)", "sphinx (>=5.1.1)", "sphinx-argparse (>=0.3.1)", "sphinx-rtd-theme (>=1)", "towncrier (>=21.9)"]
testing = ["coverage (>=6.2)", "coverage-enable-subprocess (>=1)", "flaky (>=3.7)", "packaging (>=21.3)", "pytest (>=7.0.1)", "pytest-env (>=0.6.2)", "pytest-freezegun (>=0.4.2)", "pytest-mock (>=3.6.1)", "pytest-randomly (>=3.10.3)", "pytest-timeout (>=2.1)"]
[[package]]
name = "wcwidth"
version = "0.2.5"
description = "Measures the displayed width of unicode strings in a terminal"
category = "main"
optional = false
python-versions = "*"
[[package]]
name = "webencodings"
version = "0.5.1"
description = "Character encoding aliases for legacy web content"
category = "main"
optional = false
python-versions = "*"
Add FUDS ETL (#1817) * Add spatial join method (#1871) Since we'll need to figure out the tracts for a large number of points in future tickets, add a utility to handle grabbing the tract geometries and adding tract data to a point dataset. * Add FUDS, also jupyter lab (#1871) * Add YAML configs for FUDS (#1871) * Allow input geoid to be optional (#1871) * Add FUDS ETL, tests, test-datae noteobook (#1871) This adds the ETL class for Formerly Used Defense Sites (FUDS). This is different from most other ETLs since these FUDS are not provided by tract, but instead by geographic point, so we need to assign FUDS to tracts and then do calculations from there. * Floats -> Ints, as I intended (#1871) * Floats -> Ints, as I intended (#1871) * Formatting fixes (#1871) * Add test false positive GEOIDs (#1871) * Add gdal binaries (#1871) * Refactor pandas code to be more idiomatic (#1871) Per Emma, the more pandas-y way of doing my counts is using np.where to add the values i need, then groupby and size. It is definitely more compact, and also I think more correct! * Update configs per Emma suggestions (#1871) * Type fixed! (#1871) * Remove spurious import from vscode (#1871) * Snapshot update after changing col name (#1871) * Move up GDAL (#1871) * Adjust geojson strategy (#1871) * Try running census separately first (#1871) * Fix import order (#1871) * Cleanup cache strategy (#1871) * Download census data from S3 instead of re-calculating (#1871) * Clarify pandas code per Emma (#1871)
2022-08-16 13:28:39 -04:00
[[package]]
name = "websocket-client"
version = "1.3.3"
description = "WebSocket client for Python with low level API options"
category = "dev"
optional = false
python-versions = ">=3.7"
[package.extras]
docs = ["Sphinx (>=3.4)", "sphinx-rtd-theme (>=0.5)"]
optional = ["python-socks", "wsaccel"]
test = ["websockets"]
[[package]]
name = "widgetsnbextension"
version = "3.6.1"
description = "IPython HTML widgets for Jupyter"
category = "main"
optional = false
python-versions = "*"
[package.dependencies]
notebook = ">=4.4.1"
[[package]]
name = "wrapt"
version = "1.14.1"
description = "Module for decorators, wrappers and monkey patching."
category = "main"
optional = false
python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,>=2.7"
[[package]]
name = "xlsxwriter"
version = "2.0.0"
description = "A Python module for creating Excel XLSX files."
category = "main"
optional = false
python-versions = "*"
[[package]]
name = "zipp"
version = "3.8.1"
description = "Backport of pathlib-compatible object wrapper for zip files"
category = "main"
optional = false
python-versions = ">=3.7"
[package.extras]
docs = ["sphinx", "jaraco.packaging (>=9)", "rst.linker (>=1.9)", "jaraco.tidelift (>=1.4)"]
testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "pytest-cov", "pytest-enabler (>=1.3)", "jaraco.itertools", "func-timeout", "pytest-black (>=0.3.7)", "pytest-mypy (>=0.9.1)"]
[metadata]
lock-version = "1.1"
python-versions = "^3.8"
Add FUDS ETL (#1817) * Add spatial join method (#1871) Since we'll need to figure out the tracts for a large number of points in future tickets, add a utility to handle grabbing the tract geometries and adding tract data to a point dataset. * Add FUDS, also jupyter lab (#1871) * Add YAML configs for FUDS (#1871) * Allow input geoid to be optional (#1871) * Add FUDS ETL, tests, test-datae noteobook (#1871) This adds the ETL class for Formerly Used Defense Sites (FUDS). This is different from most other ETLs since these FUDS are not provided by tract, but instead by geographic point, so we need to assign FUDS to tracts and then do calculations from there. * Floats -> Ints, as I intended (#1871) * Floats -> Ints, as I intended (#1871) * Formatting fixes (#1871) * Add test false positive GEOIDs (#1871) * Add gdal binaries (#1871) * Refactor pandas code to be more idiomatic (#1871) Per Emma, the more pandas-y way of doing my counts is using np.where to add the values i need, then groupby and size. It is definitely more compact, and also I think more correct! * Update configs per Emma suggestions (#1871) * Type fixed! (#1871) * Remove spurious import from vscode (#1871) * Snapshot update after changing col name (#1871) * Move up GDAL (#1871) * Adjust geojson strategy (#1871) * Try running census separately first (#1871) * Fix import order (#1871) * Cleanup cache strategy (#1871) * Download census data from S3 instead of re-calculating (#1871) * Clarify pandas code per Emma (#1871)
2022-08-16 13:28:39 -04:00
content-hash = "f3d61a8c4ca54c580ba8d459aa76a9d956e046c2b8339602497ce53393ba9983"
[metadata.files]
ansiwrap = [
{file = "ansiwrap-0.8.4-py2.py3-none-any.whl", hash = "sha256:7b053567c88e1ad9eed030d3ac41b722125e4c1271c8a99ade797faff1f49fb1"},
{file = "ansiwrap-0.8.4.zip", hash = "sha256:ca0c740734cde59bf919f8ff2c386f74f9a369818cdc60efe94893d01ea8d9b7"},
]
anyio = []
appnope = [
2022-04-18 18:12:18 -04:00
{file = "appnope-0.1.3-py2.py3-none-any.whl", hash = "sha256:265a455292d0bd8a72453494fa24df5a11eb18373a60c7c0430889f22548605e"},
{file = "appnope-0.1.3.tar.gz", hash = "sha256:02bd91c4de869fbb1e1c50aafc4098827a7a54ab2f39d9dcba6c9547ed920e24"},
]
argon2-cffi = [
{file = "argon2-cffi-21.3.0.tar.gz", hash = "sha256:d384164d944190a7dd7ef22c6aa3ff197da12962bd04b17f64d4e93d934dba5b"},
{file = "argon2_cffi-21.3.0-py3-none-any.whl", hash = "sha256:8c976986f2c5c0e5000919e6de187906cfd81fb1c72bf9d88c01177e77da7f80"},
]
argon2-cffi-bindings = [
{file = "argon2-cffi-bindings-21.2.0.tar.gz", hash = "sha256:bb89ceffa6c791807d1305ceb77dbfacc5aa499891d2c55661c6459651fc39e3"},
{file = "argon2_cffi_bindings-21.2.0-cp36-abi3-macosx_10_9_x86_64.whl", hash = "sha256:ccb949252cb2ab3a08c02024acb77cfb179492d5701c7cbdbfd776124d4d2367"},
{file = "argon2_cffi_bindings-21.2.0-cp36-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9524464572e12979364b7d600abf96181d3541da11e23ddf565a32e70bd4dc0d"},
{file = "argon2_cffi_bindings-21.2.0-cp36-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b746dba803a79238e925d9046a63aa26bf86ab2a2fe74ce6b009a1c3f5c8f2ae"},
{file = "argon2_cffi_bindings-21.2.0-cp36-abi3-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:58ed19212051f49a523abb1dbe954337dc82d947fb6e5a0da60f7c8471a8476c"},
{file = "argon2_cffi_bindings-21.2.0-cp36-abi3-musllinux_1_1_aarch64.whl", hash = "sha256:bd46088725ef7f58b5a1ef7ca06647ebaf0eb4baff7d1d0d177c6cc8744abd86"},
{file = "argon2_cffi_bindings-21.2.0-cp36-abi3-musllinux_1_1_i686.whl", hash = "sha256:8cd69c07dd875537a824deec19f978e0f2078fdda07fd5c42ac29668dda5f40f"},
{file = "argon2_cffi_bindings-21.2.0-cp36-abi3-musllinux_1_1_x86_64.whl", hash = "sha256:f1152ac548bd5b8bcecfb0b0371f082037e47128653df2e8ba6e914d384f3c3e"},
{file = "argon2_cffi_bindings-21.2.0-cp36-abi3-win32.whl", hash = "sha256:603ca0aba86b1349b147cab91ae970c63118a0f30444d4bc80355937c950c082"},
{file = "argon2_cffi_bindings-21.2.0-cp36-abi3-win_amd64.whl", hash = "sha256:b2ef1c30440dbbcba7a5dc3e319408b59676e2e039e2ae11a8775ecf482b192f"},
{file = "argon2_cffi_bindings-21.2.0-cp38-abi3-macosx_10_9_universal2.whl", hash = "sha256:e415e3f62c8d124ee16018e491a009937f8cf7ebf5eb430ffc5de21b900dad93"},
{file = "argon2_cffi_bindings-21.2.0-pp37-pypy37_pp73-macosx_10_9_x86_64.whl", hash = "sha256:3e385d1c39c520c08b53d63300c3ecc28622f076f4c2b0e6d7e796e9f6502194"},
{file = "argon2_cffi_bindings-21.2.0-pp37-pypy37_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2c3e3cc67fdb7d82c4718f19b4e7a87123caf8a93fde7e23cf66ac0337d3cb3f"},
{file = "argon2_cffi_bindings-21.2.0-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6a22ad9800121b71099d0fb0a65323810a15f2e292f2ba450810a7316e128ee5"},
{file = "argon2_cffi_bindings-21.2.0-pp37-pypy37_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f9f8b450ed0547e3d473fdc8612083fd08dd2120d6ac8f73828df9b7d45bb351"},
{file = "argon2_cffi_bindings-21.2.0-pp37-pypy37_pp73-win_amd64.whl", hash = "sha256:93f9bf70084f97245ba10ee36575f0c3f1e7d7724d67d8e5b08e61787c320ed7"},
{file = "argon2_cffi_bindings-21.2.0-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:3b9ef65804859d335dc6b31582cad2c5166f0c3e7975f324d9ffaa34ee7e6583"},
{file = "argon2_cffi_bindings-21.2.0-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d4966ef5848d820776f5f562a7d45fdd70c2f330c961d0d745b784034bd9f48d"},
{file = "argon2_cffi_bindings-21.2.0-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:20ef543a89dee4db46a1a6e206cd015360e5a75822f76df533845c3cbaf72670"},
{file = "argon2_cffi_bindings-21.2.0-pp38-pypy38_pp73-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ed2937d286e2ad0cc79a7087d3c272832865f779430e0cc2b4f3718d3159b0cb"},
{file = "argon2_cffi_bindings-21.2.0-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:5e00316dabdaea0b2dd82d141cc66889ced0cdcbfa599e8b471cf22c620c329a"},
]
astroid = []
atomicwrites = []
attrs = []
babel = []
backcall = [
{file = "backcall-0.2.0-py2.py3-none-any.whl", hash = "sha256:fbbce6a29f263178a1f7915c1940bde0ec2b2a967566fe1c65c1dfb7422bd255"},
{file = "backcall-0.2.0.tar.gz", hash = "sha256:5cbdbf27be5e7cfadb448baf0aa95508f91f2bbc6c6437cd9cd06e2a4c215e1e"},
]
beautifulsoup4 = [
2022-04-18 18:12:18 -04:00
{file = "beautifulsoup4-4.11.1-py3-none-any.whl", hash = "sha256:58d5c3d29f5a36ffeb94f02f0d786cd53014cf9b3b3951d42e0080d8a9498d30"},
{file = "beautifulsoup4-4.11.1.tar.gz", hash = "sha256:ad9aa55b65ef2808eb405f46cf74df7fcb7044d5cbc26487f96eb2ef2e436693"},
]
black = [
{file = "black-21.12b0-py3-none-any.whl", hash = "sha256:a615e69ae185e08fdd73e4715e260e2479c861b5740057fde6e8b4e3b7dd589f"},
{file = "black-21.12b0.tar.gz", hash = "sha256:77b80f693a569e2e527958459634f18df9b0ba2625ba4e0c2d5da5be42e6f2b3"},
]
bleach = []
censusdata = [
{file = "CensusData-1.15.post1.tar.gz", hash = "sha256:408410b2942e0d2885a18a5b1cff85c283564fe0ae6c8bd65ddccee7e234d4fb"},
]
certifi = []
cffi = []
charset-normalizer = []
click = [
{file = "click-8.0.4-py3-none-any.whl", hash = "sha256:6a7a62563bbfabfda3a38f3023a1db4a35978c0abd76f6c9605ecd6554d6d9b1"},
{file = "click-8.0.4.tar.gz", hash = "sha256:8458d7b1287c5fb128c90e23381cf99dcde74beaf6c7ff6384ce84d6fe090adb"},
]
click-plugins = [
{file = "click-plugins-1.1.1.tar.gz", hash = "sha256:46ab999744a9d831159c3411bb0c79346d94a444df9a3a3742e9ed63645f264b"},
{file = "click_plugins-1.1.1-py2.py3-none-any.whl", hash = "sha256:5d262006d3222f5057fd81e1623d4443e41dcda5dc815c06b442aa3c02889fc8"},
]
cligj = [
{file = "cligj-0.7.2-py3-none-any.whl", hash = "sha256:c1ca117dbce1fe20a5809dc96f01e1c2840f6dcc939b3ddbb1111bf330ba82df"},
{file = "cligj-0.7.2.tar.gz", hash = "sha256:a4bc13d623356b373c2c27c53dbd9c68cae5d526270bfa71f6c6fa69669c6b27"},
]
colorama = []
configparser = [
{file = "configparser-5.2.0-py3-none-any.whl", hash = "sha256:e8b39238fb6f0153a069aa253d349467c3c4737934f253ef6abac5fe0eca1e5d"},
{file = "configparser-5.2.0.tar.gz", hash = "sha256:1b35798fdf1713f1c3139016cfcbc461f09edbf099d1fb658d4b7479fcaa3daa"},
]
cycler = [
{file = "cycler-0.11.0-py3-none-any.whl", hash = "sha256:3a27e95f763a428a739d2add979fa7494c912a32c17c4c38c4d5f082cad165a3"},
{file = "cycler-0.11.0.tar.gz", hash = "sha256:9c87405839a19696e837b3b818fed3f5f69f16f1eec1a1ad77e043dcea9c772f"},
]
debugpy = []
decorator = [
{file = "decorator-5.1.1-py3-none-any.whl", hash = "sha256:b8c3f85900b9dc423225913c5aace94729fe1fa9763b38939a95226f02d37186"},
{file = "decorator-5.1.1.tar.gz", hash = "sha256:637996211036b6385ef91435e4fae22989472f9d571faba8927ba8253acbc330"},
]
defusedxml = [
{file = "defusedxml-0.7.1-py2.py3-none-any.whl", hash = "sha256:a352e7e428770286cc899e2542b6cdaedb2b4953ff269a210103ec58f6198a61"},
{file = "defusedxml-0.7.1.tar.gz", hash = "sha256:1bb3032db185915b62d7c6209c5a8792be6a32ab2fedacc84e01b52c51aa3e69"},
]
dill = []
distlib = []
dparse = []
dynaconf = []
entrypoints = [
{file = "entrypoints-0.4-py3-none-any.whl", hash = "sha256:f174b5ff827504fd3cd97cc3f8649f3693f51538c7e4bdf3ef002c8429d42f9f"},
{file = "entrypoints-0.4.tar.gz", hash = "sha256:b706eddaa9218a19ebcd67b56818f05bb27589b1ca9e8d797b74affad4ccacd4"},
]
et-xmlfile = [
{file = "et_xmlfile-1.1.0-py3-none-any.whl", hash = "sha256:a2ba85d1d6a74ef63837eed693bcb89c3f752169b0e3e7ae5b16ca5e1b3deada"},
{file = "et_xmlfile-1.1.0.tar.gz", hash = "sha256:8eb9e2bc2f8c97e37a2dc85a09ecdcdec9d8a396530a6d5a33b30b9a92da0c5c"},
]
fastjsonschema = []
filelock = []
fiona = [
{file = "Fiona-1.8.21-cp310-cp310-macosx_10_10_x86_64.whl", hash = "sha256:39c656421e25b4d0d73d0b6acdcbf9848e71f3d9b74f44c27d2d516d463409ae"},
{file = "Fiona-1.8.21-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:43b1d2e45506e56cf3a9f59ba5d6f7981f3f75f4725d1e6cb9a33ba856371ebd"},
{file = "Fiona-1.8.21-cp36-cp36m-macosx_10_10_x86_64.whl", hash = "sha256:315e186cb880a8128e110312eb92f5956bbc54d7152af999d3483b463758d6f9"},
{file = "Fiona-1.8.21-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9fb2407623c4f44732a33b3f056f8c58c54152b51f0324bf8f10945e711eb549"},
{file = "Fiona-1.8.21-cp37-cp37m-macosx_10_10_x86_64.whl", hash = "sha256:b69054ed810eb7339d7effa88589afca48003206d7627d0b0b149715fc3fde41"},
{file = "Fiona-1.8.21-cp37-cp37m-manylinux2014_x86_64.whl", hash = "sha256:11532ccfda1073d3f5f558e4bb78d45b268e8680fd6e14993a394c564ddbd069"},
{file = "Fiona-1.8.21-cp38-cp38-macosx_10_10_x86_64.whl", hash = "sha256:3789523c811809a6e2e170cf9c437631f959f4c7a868f024081612d30afab468"},
{file = "Fiona-1.8.21-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:085f18d943097ac3396f3f9664ac1ae04ad0ff272f54829f03442187f01b6116"},
{file = "Fiona-1.8.21-cp39-cp39-macosx_10_10_x86_64.whl", hash = "sha256:388acc9fa07ba7858d508dfe826d4b04d813818bced16c4049de19cc7ca322ef"},
{file = "Fiona-1.8.21-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:40b4eaf5b88407421d6c9e707520abd2ff16d7cd43efb59cd398aa41d2de332c"},
{file = "Fiona-1.8.21.tar.gz", hash = "sha256:3a0edca2a7a070db405d71187214a43d2333a57b4097544a3fcc282066a58bfc"},
]
flake8 = [
{file = "flake8-3.9.2-py2.py3-none-any.whl", hash = "sha256:bf8fd333346d844f616e8d47905ef3a3384edae6b4e9beb0c5101e25e3110907"},
{file = "flake8-3.9.2.tar.gz", hash = "sha256:07528381786f2a6237b061f6e96610a4167b226cb926e2aa2b6b1d78057c576b"},
]
fonttools = []
Add FUDS ETL (#1817) * Add spatial join method (#1871) Since we'll need to figure out the tracts for a large number of points in future tickets, add a utility to handle grabbing the tract geometries and adding tract data to a point dataset. * Add FUDS, also jupyter lab (#1871) * Add YAML configs for FUDS (#1871) * Allow input geoid to be optional (#1871) * Add FUDS ETL, tests, test-datae noteobook (#1871) This adds the ETL class for Formerly Used Defense Sites (FUDS). This is different from most other ETLs since these FUDS are not provided by tract, but instead by geographic point, so we need to assign FUDS to tracts and then do calculations from there. * Floats -> Ints, as I intended (#1871) * Floats -> Ints, as I intended (#1871) * Formatting fixes (#1871) * Add test false positive GEOIDs (#1871) * Add gdal binaries (#1871) * Refactor pandas code to be more idiomatic (#1871) Per Emma, the more pandas-y way of doing my counts is using np.where to add the values i need, then groupby and size. It is definitely more compact, and also I think more correct! * Update configs per Emma suggestions (#1871) * Type fixed! (#1871) * Remove spurious import from vscode (#1871) * Snapshot update after changing col name (#1871) * Move up GDAL (#1871) * Adjust geojson strategy (#1871) * Try running census separately first (#1871) * Fix import order (#1871) * Cleanup cache strategy (#1871) * Download census data from S3 instead of re-calculating (#1871) * Clarify pandas code per Emma (#1871)
2022-08-16 13:28:39 -04:00
geopandas = []
idna = [
{file = "idna-3.3-py3-none-any.whl", hash = "sha256:84d9dd047ffa80596e0f246e2eab0b391788b0503584e8945f2368256d2735ff"},
{file = "idna-3.3.tar.gz", hash = "sha256:9d643ff0a55b762d5cdb124b8eaa99c66322e2157b69160bc32796e824360e6d"},
]
Add FUDS ETL (#1817) * Add spatial join method (#1871) Since we'll need to figure out the tracts for a large number of points in future tickets, add a utility to handle grabbing the tract geometries and adding tract data to a point dataset. * Add FUDS, also jupyter lab (#1871) * Add YAML configs for FUDS (#1871) * Allow input geoid to be optional (#1871) * Add FUDS ETL, tests, test-datae noteobook (#1871) This adds the ETL class for Formerly Used Defense Sites (FUDS). This is different from most other ETLs since these FUDS are not provided by tract, but instead by geographic point, so we need to assign FUDS to tracts and then do calculations from there. * Floats -> Ints, as I intended (#1871) * Floats -> Ints, as I intended (#1871) * Formatting fixes (#1871) * Add test false positive GEOIDs (#1871) * Add gdal binaries (#1871) * Refactor pandas code to be more idiomatic (#1871) Per Emma, the more pandas-y way of doing my counts is using np.where to add the values i need, then groupby and size. It is definitely more compact, and also I think more correct! * Update configs per Emma suggestions (#1871) * Type fixed! (#1871) * Remove spurious import from vscode (#1871) * Snapshot update after changing col name (#1871) * Move up GDAL (#1871) * Adjust geojson strategy (#1871) * Try running census separately first (#1871) * Fix import order (#1871) * Cleanup cache strategy (#1871) * Download census data from S3 instead of re-calculating (#1871) * Clarify pandas code per Emma (#1871)
2022-08-16 13:28:39 -04:00
importlib-metadata = []
importlib-resources = []
iniconfig = [
{file = "iniconfig-1.1.1-py2.py3-none-any.whl", hash = "sha256:011e24c64b7f47f6ebd835bb12a743f2fbe9a26d4cecaa7f53bc4f35ee9da8b3"},
{file = "iniconfig-1.1.1.tar.gz", hash = "sha256:bc3af051d7d14b2ee5ef9969666def0cd1a000e121eaea580d4a313df4b37f32"},
]
ipdb = [
{file = "ipdb-0.13.9.tar.gz", hash = "sha256:951bd9a64731c444fd907a5ce268543020086a697f6be08f7cc2c9a752a278c5"},
]
ipykernel = []
ipython = []
ipython-genutils = [
{file = "ipython_genutils-0.2.0-py2.py3-none-any.whl", hash = "sha256:72dd37233799e619666c9f639a9da83c34013a73e8bbc79a7a6348d93c61fab8"},
{file = "ipython_genutils-0.2.0.tar.gz", hash = "sha256:eb2e116e75ecef9d4d228fdc66af54269afa26ab4463042e33785b887c628ba8"},
]
ipywidgets = []
isort = [
{file = "isort-5.10.1-py3-none-any.whl", hash = "sha256:6f62d78e2f89b4500b080fe3a81690850cd254227f27f75c3a0c491a1f351ba7"},
{file = "isort-5.10.1.tar.gz", hash = "sha256:e8443a5e7a020e9d7f97f1d7d9cd17c88bcb3bc7e218bf9cf5095fe550be2951"},
]
jedi = [
{file = "jedi-0.18.1-py2.py3-none-any.whl", hash = "sha256:637c9635fcf47945ceb91cd7f320234a7be540ded6f3e99a50cb6febdfd1ba8d"},
{file = "jedi-0.18.1.tar.gz", hash = "sha256:74137626a64a99c8eb6ae5832d99b3bdd7d29a3850fe2aa80a4126b2a7d949ab"},
]
jellyfish = [
{file = "jellyfish-0.6.1.tar.gz", hash = "sha256:5104e45a2b804b48a46a92a5e6d6e86830fe60ae83b1da32c867402c8f4c2094"},
]
jinja2 = []
Add FUDS ETL (#1817) * Add spatial join method (#1871) Since we'll need to figure out the tracts for a large number of points in future tickets, add a utility to handle grabbing the tract geometries and adding tract data to a point dataset. * Add FUDS, also jupyter lab (#1871) * Add YAML configs for FUDS (#1871) * Allow input geoid to be optional (#1871) * Add FUDS ETL, tests, test-datae noteobook (#1871) This adds the ETL class for Formerly Used Defense Sites (FUDS). This is different from most other ETLs since these FUDS are not provided by tract, but instead by geographic point, so we need to assign FUDS to tracts and then do calculations from there. * Floats -> Ints, as I intended (#1871) * Floats -> Ints, as I intended (#1871) * Formatting fixes (#1871) * Add test false positive GEOIDs (#1871) * Add gdal binaries (#1871) * Refactor pandas code to be more idiomatic (#1871) Per Emma, the more pandas-y way of doing my counts is using np.where to add the values i need, then groupby and size. It is definitely more compact, and also I think more correct! * Update configs per Emma suggestions (#1871) * Type fixed! (#1871) * Remove spurious import from vscode (#1871) * Snapshot update after changing col name (#1871) * Move up GDAL (#1871) * Adjust geojson strategy (#1871) * Try running census separately first (#1871) * Fix import order (#1871) * Cleanup cache strategy (#1871) * Download census data from S3 instead of re-calculating (#1871) * Clarify pandas code per Emma (#1871)
2022-08-16 13:28:39 -04:00
json5 = []
jsonschema = []
jupyter = [
{file = "jupyter-1.0.0-py2.py3-none-any.whl", hash = "sha256:5b290f93b98ffbc21c0c7e749f054b3267782166d72fa5e3ed1ed4eaf34a2b78"},
{file = "jupyter-1.0.0.tar.gz", hash = "sha256:d9dc4b3318f310e34c82951ea5d6683f67bed7def4b259fafbfe4f1beb1d8e5f"},
{file = "jupyter-1.0.0.zip", hash = "sha256:3e1f86076bbb7c8c207829390305a2b1fe836d471ed54be66a3b8c41e7f46cc7"},
]
jupyter-client = []
jupyter-console = []
jupyter-contrib-core = []
jupyter-contrib-nbextensions = [
{file = "jupyter_contrib_nbextensions-0.5.1-py2.py3-none-any.whl", hash = "sha256:2c071f0aa208c569666f656bdc0f66906ca493cf9f06f46db6350db11030ff40"},
{file = "jupyter_contrib_nbextensions-0.5.1.tar.gz", hash = "sha256:eecd28ecc2fc410226c0a3d4932ed2fac4860ccf8d9e9b1b29548835a35b22ab"},
]
jupyter-core = []
jupyter-highlight-selected-word = [
{file = "jupyter_highlight_selected_word-0.2.0-py2.py3-none-any.whl", hash = "sha256:9545dfa9cb057eebe3a5795604dcd3a5294ea18637e553f61a0b67c1b5903c58"},
{file = "jupyter_highlight_selected_word-0.2.0.tar.gz", hash = "sha256:9fa740424859a807950ca08d2bfd28a35154cd32dd6d50ac4e0950022adc0e7b"},
]
jupyter-latex-envs = [
{file = "jupyter_latex_envs-1.4.6.tar.gz", hash = "sha256:070a31eb2dc488bba983915879a7c2939247bf5c3b669b398bdb36a9b5343872"},
]
jupyter-nbextensions-configurator = []
Add FUDS ETL (#1817) * Add spatial join method (#1871) Since we'll need to figure out the tracts for a large number of points in future tickets, add a utility to handle grabbing the tract geometries and adding tract data to a point dataset. * Add FUDS, also jupyter lab (#1871) * Add YAML configs for FUDS (#1871) * Allow input geoid to be optional (#1871) * Add FUDS ETL, tests, test-datae noteobook (#1871) This adds the ETL class for Formerly Used Defense Sites (FUDS). This is different from most other ETLs since these FUDS are not provided by tract, but instead by geographic point, so we need to assign FUDS to tracts and then do calculations from there. * Floats -> Ints, as I intended (#1871) * Floats -> Ints, as I intended (#1871) * Formatting fixes (#1871) * Add test false positive GEOIDs (#1871) * Add gdal binaries (#1871) * Refactor pandas code to be more idiomatic (#1871) Per Emma, the more pandas-y way of doing my counts is using np.where to add the values i need, then groupby and size. It is definitely more compact, and also I think more correct! * Update configs per Emma suggestions (#1871) * Type fixed! (#1871) * Remove spurious import from vscode (#1871) * Snapshot update after changing col name (#1871) * Move up GDAL (#1871) * Adjust geojson strategy (#1871) * Try running census separately first (#1871) * Fix import order (#1871) * Cleanup cache strategy (#1871) * Download census data from S3 instead of re-calculating (#1871) * Clarify pandas code per Emma (#1871)
2022-08-16 13:28:39 -04:00
jupyter-server = []
jupyterlab = []
jupyterlab-pygments = [
2022-04-18 18:12:18 -04:00
{file = "jupyterlab_pygments-0.2.2-py2.py3-none-any.whl", hash = "sha256:2405800db07c9f770863bcf8049a529c3dd4d3e28536638bd7c1c01d2748309f"},
{file = "jupyterlab_pygments-0.2.2.tar.gz", hash = "sha256:7405d7fde60819d905a9fa8ce89e4cd830e318cdad22a0030f7a901da705585d"},
]
Add FUDS ETL (#1817) * Add spatial join method (#1871) Since we'll need to figure out the tracts for a large number of points in future tickets, add a utility to handle grabbing the tract geometries and adding tract data to a point dataset. * Add FUDS, also jupyter lab (#1871) * Add YAML configs for FUDS (#1871) * Allow input geoid to be optional (#1871) * Add FUDS ETL, tests, test-datae noteobook (#1871) This adds the ETL class for Formerly Used Defense Sites (FUDS). This is different from most other ETLs since these FUDS are not provided by tract, but instead by geographic point, so we need to assign FUDS to tracts and then do calculations from there. * Floats -> Ints, as I intended (#1871) * Floats -> Ints, as I intended (#1871) * Formatting fixes (#1871) * Add test false positive GEOIDs (#1871) * Add gdal binaries (#1871) * Refactor pandas code to be more idiomatic (#1871) Per Emma, the more pandas-y way of doing my counts is using np.where to add the values i need, then groupby and size. It is definitely more compact, and also I think more correct! * Update configs per Emma suggestions (#1871) * Type fixed! (#1871) * Remove spurious import from vscode (#1871) * Snapshot update after changing col name (#1871) * Move up GDAL (#1871) * Adjust geojson strategy (#1871) * Try running census separately first (#1871) * Fix import order (#1871) * Cleanup cache strategy (#1871) * Download census data from S3 instead of re-calculating (#1871) * Clarify pandas code per Emma (#1871)
2022-08-16 13:28:39 -04:00
jupyterlab-server = []
jupyterlab-widgets = []
kiwisolver = []
lazy-object-proxy = [
{file = "lazy-object-proxy-1.7.1.tar.gz", hash = "sha256:d609c75b986def706743cdebe5e47553f4a5a1da9c5ff66d76013ef396b5a8a4"},
{file = "lazy_object_proxy-1.7.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:bb8c5fd1684d60a9902c60ebe276da1f2281a318ca16c1d0a96db28f62e9166b"},
{file = "lazy_object_proxy-1.7.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a57d51ed2997e97f3b8e3500c984db50a554bb5db56c50b5dab1b41339b37e36"},
{file = "lazy_object_proxy-1.7.1-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:fd45683c3caddf83abbb1249b653a266e7069a09f486daa8863fb0e7496a9fdb"},
{file = "lazy_object_proxy-1.7.1-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:8561da8b3dd22d696244d6d0d5330618c993a215070f473b699e00cf1f3f6443"},
{file = "lazy_object_proxy-1.7.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:fccdf7c2c5821a8cbd0a9440a456f5050492f2270bd54e94360cac663398739b"},
{file = "lazy_object_proxy-1.7.1-cp310-cp310-win32.whl", hash = "sha256:898322f8d078f2654d275124a8dd19b079080ae977033b713f677afcfc88e2b9"},
{file = "lazy_object_proxy-1.7.1-cp310-cp310-win_amd64.whl", hash = "sha256:85b232e791f2229a4f55840ed54706110c80c0a210d076eee093f2b2e33e1bfd"},
{file = "lazy_object_proxy-1.7.1-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:46ff647e76f106bb444b4533bb4153c7370cdf52efc62ccfc1a28bdb3cc95442"},
{file = "lazy_object_proxy-1.7.1-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:12f3bb77efe1367b2515f8cb4790a11cffae889148ad33adad07b9b55e0ab22c"},
{file = "lazy_object_proxy-1.7.1-cp36-cp36m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:c19814163728941bb871240d45c4c30d33b8a2e85972c44d4e63dd7107faba44"},
{file = "lazy_object_proxy-1.7.1-cp36-cp36m-musllinux_1_1_aarch64.whl", hash = "sha256:e40f2013d96d30217a51eeb1db28c9ac41e9d0ee915ef9d00da639c5b63f01a1"},
{file = "lazy_object_proxy-1.7.1-cp36-cp36m-musllinux_1_1_x86_64.whl", hash = "sha256:2052837718516a94940867e16b1bb10edb069ab475c3ad84fd1e1a6dd2c0fcfc"},
{file = "lazy_object_proxy-1.7.1-cp36-cp36m-win32.whl", hash = "sha256:6a24357267aa976abab660b1d47a34aaf07259a0c3859a34e536f1ee6e76b5bb"},
{file = "lazy_object_proxy-1.7.1-cp36-cp36m-win_amd64.whl", hash = "sha256:6aff3fe5de0831867092e017cf67e2750c6a1c7d88d84d2481bd84a2e019ec35"},
{file = "lazy_object_proxy-1.7.1-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:6a6e94c7b02641d1311228a102607ecd576f70734dc3d5e22610111aeacba8a0"},
{file = "lazy_object_proxy-1.7.1-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c4ce15276a1a14549d7e81c243b887293904ad2d94ad767f42df91e75fd7b5b6"},
{file = "lazy_object_proxy-1.7.1-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e368b7f7eac182a59ff1f81d5f3802161932a41dc1b1cc45c1f757dc876b5d2c"},
{file = "lazy_object_proxy-1.7.1-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:6ecbb350991d6434e1388bee761ece3260e5228952b1f0c46ffc800eb313ff42"},
{file = "lazy_object_proxy-1.7.1-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:553b0f0d8dbf21890dd66edd771f9b1b5f51bd912fa5f26de4449bfc5af5e029"},
{file = "lazy_object_proxy-1.7.1-cp37-cp37m-win32.whl", hash = "sha256:c7a683c37a8a24f6428c28c561c80d5f4fd316ddcf0c7cab999b15ab3f5c5c69"},
{file = "lazy_object_proxy-1.7.1-cp37-cp37m-win_amd64.whl", hash = "sha256:df2631f9d67259dc9620d831384ed7732a198eb434eadf69aea95ad18c587a28"},
{file = "lazy_object_proxy-1.7.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:07fa44286cda977bd4803b656ffc1c9b7e3bc7dff7d34263446aec8f8c96f88a"},
{file = "lazy_object_proxy-1.7.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:4dca6244e4121c74cc20542c2ca39e5c4a5027c81d112bfb893cf0790f96f57e"},
{file = "lazy_object_proxy-1.7.1-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:91ba172fc5b03978764d1df5144b4ba4ab13290d7bab7a50f12d8117f8630c38"},
{file = "lazy_object_proxy-1.7.1-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:043651b6cb706eee4f91854da4a089816a6606c1428fd391573ef8cb642ae4f7"},
{file = "lazy_object_proxy-1.7.1-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:b9e89b87c707dd769c4ea91f7a31538888aad05c116a59820f28d59b3ebfe25a"},
{file = "lazy_object_proxy-1.7.1-cp38-cp38-win32.whl", hash = "sha256:9d166602b525bf54ac994cf833c385bfcc341b364e3ee71e3bf5a1336e677b55"},
{file = "lazy_object_proxy-1.7.1-cp38-cp38-win_amd64.whl", hash = "sha256:8f3953eb575b45480db6568306893f0bd9d8dfeeebd46812aa09ca9579595148"},
{file = "lazy_object_proxy-1.7.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:dd7ed7429dbb6c494aa9bc4e09d94b778a3579be699f9d67da7e6804c422d3de"},
{file = "lazy_object_proxy-1.7.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:70ed0c2b380eb6248abdef3cd425fc52f0abd92d2b07ce26359fcbc399f636ad"},
{file = "lazy_object_proxy-1.7.1-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7096a5e0c1115ec82641afbdd70451a144558ea5cf564a896294e346eb611be1"},
{file = "lazy_object_proxy-1.7.1-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:f769457a639403073968d118bc70110e7dce294688009f5c24ab78800ae56dc8"},
{file = "lazy_object_proxy-1.7.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:39b0e26725c5023757fc1ab2a89ef9d7ab23b84f9251e28f9cc114d5b59c1b09"},
{file = "lazy_object_proxy-1.7.1-cp39-cp39-win32.whl", hash = "sha256:2130db8ed69a48a3440103d4a520b89d8a9405f1b06e2cc81640509e8bf6548f"},
{file = "lazy_object_proxy-1.7.1-cp39-cp39-win_amd64.whl", hash = "sha256:677ea950bef409b47e51e733283544ac3d660b709cfce7b187f5ace137960d61"},
{file = "lazy_object_proxy-1.7.1-pp37.pp38-none-any.whl", hash = "sha256:d66906d5785da8e0be7360912e99c9188b70f52c422f9fc18223347235691a84"},
]
liccheck = [
{file = "liccheck-0.6.5-py2.py3-none-any.whl", hash = "sha256:10846e587127d08609a973570eb3b8ee8cfe32a4689c8fd76d6dc74c29013c7a"},
{file = "liccheck-0.6.5.tar.gz", hash = "sha256:d4009f1876eb7e4228ecf495e36573ef5b8a226d4cd91235138e417f990a67e8"},
]
lxml = []
markupsafe = [
{file = "MarkupSafe-2.1.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:86b1f75c4e7c2ac2ccdaec2b9022845dbb81880ca318bb7a0a01fbf7813e3812"},
{file = "MarkupSafe-2.1.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:f121a1420d4e173a5d96e47e9a0c0dcff965afdf1626d28de1460815f7c4ee7a"},
{file = "MarkupSafe-2.1.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a49907dd8420c5685cfa064a1335b6754b74541bbb3706c259c02ed65b644b3e"},
{file = "MarkupSafe-2.1.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:10c1bfff05d95783da83491be968e8fe789263689c02724e0c691933c52994f5"},
{file = "MarkupSafe-2.1.1-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:b7bd98b796e2b6553da7225aeb61f447f80a1ca64f41d83612e6139ca5213aa4"},
{file = "MarkupSafe-2.1.1-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:b09bf97215625a311f669476f44b8b318b075847b49316d3e28c08e41a7a573f"},
{file = "MarkupSafe-2.1.1-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:694deca8d702d5db21ec83983ce0bb4b26a578e71fbdbd4fdcd387daa90e4d5e"},
{file = "MarkupSafe-2.1.1-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:efc1913fd2ca4f334418481c7e595c00aad186563bbc1ec76067848c7ca0a933"},
{file = "MarkupSafe-2.1.1-cp310-cp310-win32.whl", hash = "sha256:4a33dea2b688b3190ee12bd7cfa29d39c9ed176bda40bfa11099a3ce5d3a7ac6"},
{file = "MarkupSafe-2.1.1-cp310-cp310-win_amd64.whl", hash = "sha256:dda30ba7e87fbbb7eab1ec9f58678558fd9a6b8b853530e176eabd064da81417"},
{file = "MarkupSafe-2.1.1-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:671cd1187ed5e62818414afe79ed29da836dde67166a9fac6d435873c44fdd02"},
{file = "MarkupSafe-2.1.1-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3799351e2336dc91ea70b034983ee71cf2f9533cdff7c14c90ea126bfd95d65a"},
{file = "MarkupSafe-2.1.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e72591e9ecd94d7feb70c1cbd7be7b3ebea3f548870aa91e2732960fa4d57a37"},
{file = "MarkupSafe-2.1.1-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:6fbf47b5d3728c6aea2abb0589b5d30459e369baa772e0f37a0320185e87c980"},
{file = "MarkupSafe-2.1.1-cp37-cp37m-musllinux_1_1_aarch64.whl", hash = "sha256:d5ee4f386140395a2c818d149221149c54849dfcfcb9f1debfe07a8b8bd63f9a"},
{file = "MarkupSafe-2.1.1-cp37-cp37m-musllinux_1_1_i686.whl", hash = "sha256:bcb3ed405ed3222f9904899563d6fc492ff75cce56cba05e32eff40e6acbeaa3"},
{file = "MarkupSafe-2.1.1-cp37-cp37m-musllinux_1_1_x86_64.whl", hash = "sha256:e1c0b87e09fa55a220f058d1d49d3fb8df88fbfab58558f1198e08c1e1de842a"},
{file = "MarkupSafe-2.1.1-cp37-cp37m-win32.whl", hash = "sha256:8dc1c72a69aa7e082593c4a203dcf94ddb74bb5c8a731e4e1eb68d031e8498ff"},
{file = "MarkupSafe-2.1.1-cp37-cp37m-win_amd64.whl", hash = "sha256:97a68e6ada378df82bc9f16b800ab77cbf4b2fada0081794318520138c088e4a"},
{file = "MarkupSafe-2.1.1-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:e8c843bbcda3a2f1e3c2ab25913c80a3c5376cd00c6e8c4a86a89a28c8dc5452"},
{file = "MarkupSafe-2.1.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:0212a68688482dc52b2d45013df70d169f542b7394fc744c02a57374a4207003"},
{file = "MarkupSafe-2.1.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8e576a51ad59e4bfaac456023a78f6b5e6e7651dcd383bcc3e18d06f9b55d6d1"},
{file = "MarkupSafe-2.1.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4b9fe39a2ccc108a4accc2676e77da025ce383c108593d65cc909add5c3bd601"},
{file = "MarkupSafe-2.1.1-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:96e37a3dc86e80bf81758c152fe66dbf60ed5eca3d26305edf01892257049925"},
{file = "MarkupSafe-2.1.1-cp38-cp38-musllinux_1_1_aarch64.whl", hash = "sha256:6d0072fea50feec76a4c418096652f2c3238eaa014b2f94aeb1d56a66b41403f"},
{file = "MarkupSafe-2.1.1-cp38-cp38-musllinux_1_1_i686.whl", hash = "sha256:089cf3dbf0cd6c100f02945abeb18484bd1ee57a079aefd52cffd17fba910b88"},
{file = "MarkupSafe-2.1.1-cp38-cp38-musllinux_1_1_x86_64.whl", hash = "sha256:6a074d34ee7a5ce3effbc526b7083ec9731bb3cbf921bbe1d3005d4d2bdb3a63"},
{file = "MarkupSafe-2.1.1-cp38-cp38-win32.whl", hash = "sha256:421be9fbf0ffe9ffd7a378aafebbf6f4602d564d34be190fc19a193232fd12b1"},
{file = "MarkupSafe-2.1.1-cp38-cp38-win_amd64.whl", hash = "sha256:fc7b548b17d238737688817ab67deebb30e8073c95749d55538ed473130ec0c7"},
{file = "MarkupSafe-2.1.1-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:e04e26803c9c3851c931eac40c695602c6295b8d432cbe78609649ad9bd2da8a"},
{file = "MarkupSafe-2.1.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:b87db4360013327109564f0e591bd2a3b318547bcef31b468a92ee504d07ae4f"},
{file = "MarkupSafe-2.1.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:99a2a507ed3ac881b975a2976d59f38c19386d128e7a9a18b7df6fff1fd4c1d6"},
{file = "MarkupSafe-2.1.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:56442863ed2b06d19c37f94d999035e15ee982988920e12a5b4ba29b62ad1f77"},
{file = "MarkupSafe-2.1.1-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:3ce11ee3f23f79dbd06fb3d63e2f6af7b12db1d46932fe7bd8afa259a5996603"},
{file = "MarkupSafe-2.1.1-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:33b74d289bd2f5e527beadcaa3f401e0df0a89927c1559c8566c066fa4248ab7"},
{file = "MarkupSafe-2.1.1-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:43093fb83d8343aac0b1baa75516da6092f58f41200907ef92448ecab8825135"},
{file = "MarkupSafe-2.1.1-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:8e3dcf21f367459434c18e71b2a9532d96547aef8a871872a5bd69a715c15f96"},
{file = "MarkupSafe-2.1.1-cp39-cp39-win32.whl", hash = "sha256:d4306c36ca495956b6d568d276ac11fdd9c30a36f1b6eb928070dc5360b22e1c"},
{file = "MarkupSafe-2.1.1-cp39-cp39-win_amd64.whl", hash = "sha256:46d00d6cfecdde84d40e572d63735ef81423ad31184100411e6e3388d405e247"},
{file = "MarkupSafe-2.1.1.tar.gz", hash = "sha256:7f91197cc9e48f989d12e4e6fbc46495c446636dfc81b9ccf50bb0ec74b91d4b"},
]
marshmallow = []
marshmallow-dataclass = []
marshmallow-enum = [
{file = "marshmallow-enum-1.5.1.tar.gz", hash = "sha256:38e697e11f45a8e64b4a1e664000897c659b60aa57bfa18d44e226a9920b6e58"},
{file = "marshmallow_enum-1.5.1-py2.py3-none-any.whl", hash = "sha256:57161ab3dbfde4f57adeb12090f39592e992b9c86d206d02f6bd03ebec60f072"},
]
matplotlib = []
matplotlib-inline = [
{file = "matplotlib-inline-0.1.3.tar.gz", hash = "sha256:a04bfba22e0d1395479f866853ec1ee28eea1485c1d69a6faf00dc3e24ff34ee"},
{file = "matplotlib_inline-0.1.3-py3-none-any.whl", hash = "sha256:aed605ba3b72462d64d475a21a9296f400a19c4f74a31b59103d2a99ffd5aa5c"},
]
mccabe = [
{file = "mccabe-0.6.1-py2.py3-none-any.whl", hash = "sha256:ab8a6258860da4b6677da4bd2fe5dc2c659cff31b3ee4f7f5d64e79735b80d42"},
{file = "mccabe-0.6.1.tar.gz", hash = "sha256:dd8d182285a0fe56bace7f45b5e7d1a6ebcbf524e8f3bd87eb0f125271b8831f"},
]
mistune = [
{file = "mistune-0.8.4-py2.py3-none-any.whl", hash = "sha256:88a1051873018da288eee8538d476dffe1262495144b33ecb586c4ab266bb8d4"},
{file = "mistune-0.8.4.tar.gz", hash = "sha256:59a3429db53c50b5c6bcc8a07f8848cb00d7dc8bdb431a4ab41920d201d4756e"},
]
munch = [
{file = "munch-2.5.0-py2.py3-none-any.whl", hash = "sha256:6f44af89a2ce4ed04ff8de41f70b226b984db10a91dcc7b9ac2efc1c77022fdd"},
{file = "munch-2.5.0.tar.gz", hash = "sha256:2d735f6f24d4dba3417fa448cae40c6e896ec1fdab6cdb5e6510999758a4dbd2"},
]
mypy = [
{file = "mypy-0.910-cp35-cp35m-macosx_10_9_x86_64.whl", hash = "sha256:a155d80ea6cee511a3694b108c4494a39f42de11ee4e61e72bc424c490e46457"},
{file = "mypy-0.910-cp35-cp35m-manylinux1_x86_64.whl", hash = "sha256:b94e4b785e304a04ea0828759172a15add27088520dc7e49ceade7834275bedb"},
{file = "mypy-0.910-cp35-cp35m-manylinux2010_x86_64.whl", hash = "sha256:088cd9c7904b4ad80bec811053272986611b84221835e079be5bcad029e79dd9"},
{file = "mypy-0.910-cp35-cp35m-win_amd64.whl", hash = "sha256:adaeee09bfde366d2c13fe6093a7df5df83c9a2ba98638c7d76b010694db760e"},
{file = "mypy-0.910-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:ecd2c3fe726758037234c93df7e98deb257fd15c24c9180dacf1ef829da5f921"},
{file = "mypy-0.910-cp36-cp36m-manylinux1_x86_64.whl", hash = "sha256:d9dd839eb0dc1bbe866a288ba3c1afc33a202015d2ad83b31e875b5905a079b6"},
{file = "mypy-0.910-cp36-cp36m-manylinux2010_x86_64.whl", hash = "sha256:3e382b29f8e0ccf19a2df2b29a167591245df90c0b5a2542249873b5c1d78212"},
{file = "mypy-0.910-cp36-cp36m-win_amd64.whl", hash = "sha256:53fd2eb27a8ee2892614370896956af2ff61254c275aaee4c230ae771cadd885"},
{file = "mypy-0.910-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:b6fb13123aeef4a3abbcfd7e71773ff3ff1526a7d3dc538f3929a49b42be03f0"},
{file = "mypy-0.910-cp37-cp37m-manylinux1_x86_64.whl", hash = "sha256:e4dab234478e3bd3ce83bac4193b2ecd9cf94e720ddd95ce69840273bf44f6de"},
{file = "mypy-0.910-cp37-cp37m-manylinux2010_x86_64.whl", hash = "sha256:7df1ead20c81371ccd6091fa3e2878559b5c4d4caadaf1a484cf88d93ca06703"},
{file = "mypy-0.910-cp37-cp37m-win_amd64.whl", hash = "sha256:0aadfb2d3935988ec3815952e44058a3100499f5be5b28c34ac9d79f002a4a9a"},
{file = "mypy-0.910-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:ec4e0cd079db280b6bdabdc807047ff3e199f334050db5cbb91ba3e959a67504"},
{file = "mypy-0.910-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:119bed3832d961f3a880787bf621634ba042cb8dc850a7429f643508eeac97b9"},
{file = "mypy-0.910-cp38-cp38-manylinux2010_x86_64.whl", hash = "sha256:866c41f28cee548475f146aa4d39a51cf3b6a84246969f3759cb3e9c742fc072"},
{file = "mypy-0.910-cp38-cp38-win_amd64.whl", hash = "sha256:ceb6e0a6e27fb364fb3853389607cf7eb3a126ad335790fa1e14ed02fba50811"},
{file = "mypy-0.910-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:1a85e280d4d217150ce8cb1a6dddffd14e753a4e0c3cf90baabb32cefa41b59e"},
{file = "mypy-0.910-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:42c266ced41b65ed40a282c575705325fa7991af370036d3f134518336636f5b"},
{file = "mypy-0.910-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:3c4b8ca36877fc75339253721f69603a9c7fdb5d4d5a95a1a1b899d8b86a4de2"},
{file = "mypy-0.910-cp39-cp39-manylinux2010_x86_64.whl", hash = "sha256:c0df2d30ed496a08de5daed2a9ea807d07c21ae0ab23acf541ab88c24b26ab97"},
{file = "mypy-0.910-cp39-cp39-win_amd64.whl", hash = "sha256:c6c2602dffb74867498f86e6129fd52a2770c48b7cd3ece77ada4fa38f94eba8"},
{file = "mypy-0.910-py3-none-any.whl", hash = "sha256:ef565033fa5a958e62796867b1df10c40263ea9ded87164d67572834e57a174d"},
{file = "mypy-0.910.tar.gz", hash = "sha256:704098302473cb31a218f1775a873b376b30b4c18229421e9e9dc8916fd16150"},
]
mypy-extensions = [
{file = "mypy_extensions-0.4.3-py2.py3-none-any.whl", hash = "sha256:090fedd75945a69ae91ce1303b5824f428daf5a028d2f6ab8a299250a846f15d"},
{file = "mypy_extensions-0.4.3.tar.gz", hash = "sha256:2d82818f5bb3e369420cb3c4060a7970edba416647068eb4c5343488a6c604a8"},
]
nb-black = [
{file = "nb_black-1.0.7.tar.gz", hash = "sha256:1ca52e3a46675f6a0a6d79ac73a1f8f951bef60f919eced56173e76ab1b6d62b"},
]
Add FUDS ETL (#1817) * Add spatial join method (#1871) Since we'll need to figure out the tracts for a large number of points in future tickets, add a utility to handle grabbing the tract geometries and adding tract data to a point dataset. * Add FUDS, also jupyter lab (#1871) * Add YAML configs for FUDS (#1871) * Allow input geoid to be optional (#1871) * Add FUDS ETL, tests, test-datae noteobook (#1871) This adds the ETL class for Formerly Used Defense Sites (FUDS). This is different from most other ETLs since these FUDS are not provided by tract, but instead by geographic point, so we need to assign FUDS to tracts and then do calculations from there. * Floats -> Ints, as I intended (#1871) * Floats -> Ints, as I intended (#1871) * Formatting fixes (#1871) * Add test false positive GEOIDs (#1871) * Add gdal binaries (#1871) * Refactor pandas code to be more idiomatic (#1871) Per Emma, the more pandas-y way of doing my counts is using np.where to add the values i need, then groupby and size. It is definitely more compact, and also I think more correct! * Update configs per Emma suggestions (#1871) * Type fixed! (#1871) * Remove spurious import from vscode (#1871) * Snapshot update after changing col name (#1871) * Move up GDAL (#1871) * Adjust geojson strategy (#1871) * Try running census separately first (#1871) * Fix import order (#1871) * Cleanup cache strategy (#1871) * Download census data from S3 instead of re-calculating (#1871) * Clarify pandas code per Emma (#1871)
2022-08-16 13:28:39 -04:00
nbclassic = []
nbclient = []
nbconvert = []
nbformat = []
nest-asyncio = [
2022-04-18 18:12:18 -04:00
{file = "nest_asyncio-1.5.5-py3-none-any.whl", hash = "sha256:b98e3ec1b246135e4642eceffa5a6c23a3ab12c82ff816a92c612d68205813b2"},
{file = "nest_asyncio-1.5.5.tar.gz", hash = "sha256:e442291cd942698be619823a17a86a5759eabe1f8613084790de189fe9e16d65"},
]
notebook = []
notebook-shim = []
numpy = []
openpyxl = []
packaging = [
{file = "packaging-21.3-py3-none-any.whl", hash = "sha256:ef103e05f519cdc783ae24ea4e2e0f508a9c99b2d4969652eed6a2e1ea5bd522"},
{file = "packaging-21.3.tar.gz", hash = "sha256:dd47c42927d89ab911e606518907cc2d3a1f38bbd026385970643f9c5b8ecfeb"},
]
pandas = []
pandas-vet = [
{file = "pandas-vet-0.2.3.tar.gz", hash = "sha256:58b64027a4c192b4b62272c1d8fdecc1733352452401282b697c1a32abe4656a"},
{file = "pandas_vet-0.2.3-py3-none-any.whl", hash = "sha256:349e4240399ead316f64f9afc8e94a5bd5cfff45d7f448c5c22989e86c4ac782"},
]
pandocfilters = [
{file = "pandocfilters-1.5.0-py2.py3-none-any.whl", hash = "sha256:33aae3f25fd1a026079f5d27bdd52496f0e0803b3469282162bafdcbdf6ef14f"},
{file = "pandocfilters-1.5.0.tar.gz", hash = "sha256:0b679503337d233b4339a817bfc8c50064e2eff681314376a47cb582305a7a38"},
]
Add FUDS ETL (#1817) * Add spatial join method (#1871) Since we'll need to figure out the tracts for a large number of points in future tickets, add a utility to handle grabbing the tract geometries and adding tract data to a point dataset. * Add FUDS, also jupyter lab (#1871) * Add YAML configs for FUDS (#1871) * Allow input geoid to be optional (#1871) * Add FUDS ETL, tests, test-datae noteobook (#1871) This adds the ETL class for Formerly Used Defense Sites (FUDS). This is different from most other ETLs since these FUDS are not provided by tract, but instead by geographic point, so we need to assign FUDS to tracts and then do calculations from there. * Floats -> Ints, as I intended (#1871) * Floats -> Ints, as I intended (#1871) * Formatting fixes (#1871) * Add test false positive GEOIDs (#1871) * Add gdal binaries (#1871) * Refactor pandas code to be more idiomatic (#1871) Per Emma, the more pandas-y way of doing my counts is using np.where to add the values i need, then groupby and size. It is definitely more compact, and also I think more correct! * Update configs per Emma suggestions (#1871) * Type fixed! (#1871) * Remove spurious import from vscode (#1871) * Snapshot update after changing col name (#1871) * Move up GDAL (#1871) * Adjust geojson strategy (#1871) * Try running census separately first (#1871) * Fix import order (#1871) * Cleanup cache strategy (#1871) * Download census data from S3 instead of re-calculating (#1871) * Clarify pandas code per Emma (#1871)
2022-08-16 13:28:39 -04:00
papermill = []
parso = [
{file = "parso-0.8.3-py2.py3-none-any.whl", hash = "sha256:c001d4636cd3aecdaf33cbb40aebb59b094be2a74c556778ef5576c175e19e75"},
{file = "parso-0.8.3.tar.gz", hash = "sha256:8c07be290bb59f03588915921e29e8a50002acaf2cdc5fa0e0114f91709fafa0"},
]
pathspec = [
{file = "pathspec-0.9.0-py2.py3-none-any.whl", hash = "sha256:7d15c4ddb0b5c802d161efc417ec1a2558ea2653c2e8ad9c19098201dc1c993a"},
{file = "pathspec-0.9.0.tar.gz", hash = "sha256:e564499435a2673d586f6b2130bb5b95f04a3ba06f81b8f895b651a3c76aabb1"},
]
pexpect = [
{file = "pexpect-4.8.0-py2.py3-none-any.whl", hash = "sha256:0b48a55dcb3c05f3329815901ea4fc1537514d6ba867a152b581d69ae3710937"},
{file = "pexpect-4.8.0.tar.gz", hash = "sha256:fc65a43959d153d0114afe13997d439c22823a27cefceb5ff35c2178c6784c0c"},
]
pickleshare = [
{file = "pickleshare-0.7.5-py2.py3-none-any.whl", hash = "sha256:9649af414d74d4df115d5d718f82acb59c9d418196b7b4290ed47a12ce62df56"},
{file = "pickleshare-0.7.5.tar.gz", hash = "sha256:87683d47965c1da65cdacaf31c8441d12b8044cdec9aca500cd78fc2c683afca"},
]
pillow = [
{file = "Pillow-9.0.1-1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:a5d24e1d674dd9d72c66ad3ea9131322819ff86250b30dc5821cbafcfa0b96b4"},
{file = "Pillow-9.0.1-1-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:2632d0f846b7c7600edf53c48f8f9f1e13e62f66a6dbc15191029d950bfed976"},
{file = "Pillow-9.0.1-1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:b9618823bd237c0d2575283f2939655f54d51b4527ec3972907a927acbcc5bfc"},
{file = "Pillow-9.0.1-cp310-cp310-macosx_10_10_universal2.whl", hash = "sha256:9bfdb82cdfeccec50aad441afc332faf8606dfa5e8efd18a6692b5d6e79f00fd"},
{file = "Pillow-9.0.1-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:5100b45a4638e3c00e4d2320d3193bdabb2d75e79793af7c3eb139e4f569f16f"},
{file = "Pillow-9.0.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:528a2a692c65dd5cafc130de286030af251d2ee0483a5bf50c9348aefe834e8a"},
{file = "Pillow-9.0.1-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:0f29d831e2151e0b7b39981756d201f7108d3d215896212ffe2e992d06bfe049"},
{file = "Pillow-9.0.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:855c583f268edde09474b081e3ddcd5cf3b20c12f26e0d434e1386cc5d318e7a"},
{file = "Pillow-9.0.1-cp310-cp310-win32.whl", hash = "sha256:d9d7942b624b04b895cb95af03a23407f17646815495ce4547f0e60e0b06f58e"},
{file = "Pillow-9.0.1-cp310-cp310-win_amd64.whl", hash = "sha256:81c4b81611e3a3cb30e59b0cf05b888c675f97e3adb2c8672c3154047980726b"},
{file = "Pillow-9.0.1-cp37-cp37m-macosx_10_10_x86_64.whl", hash = "sha256:413ce0bbf9fc6278b2d63309dfeefe452835e1c78398efb431bab0672fe9274e"},
{file = "Pillow-9.0.1-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:80fe64a6deb6fcfdf7b8386f2cf216d329be6f2781f7d90304351811fb591360"},
{file = "Pillow-9.0.1-cp37-cp37m-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:cef9c85ccbe9bee00909758936ea841ef12035296c748aaceee535969e27d31b"},
{file = "Pillow-9.0.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1d19397351f73a88904ad1aee421e800fe4bbcd1aeee6435fb62d0a05ccd1030"},
{file = "Pillow-9.0.1-cp37-cp37m-win32.whl", hash = "sha256:d21237d0cd37acded35154e29aec853e945950321dd2ffd1a7d86fe686814669"},
{file = "Pillow-9.0.1-cp37-cp37m-win_amd64.whl", hash = "sha256:ede5af4a2702444a832a800b8eb7f0a7a1c0eed55b644642e049c98d589e5092"},
{file = "Pillow-9.0.1-cp38-cp38-macosx_10_10_x86_64.whl", hash = "sha256:b5b3f092fe345c03bca1e0b687dfbb39364b21ebb8ba90e3fa707374b7915204"},
{file = "Pillow-9.0.1-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:335ace1a22325395c4ea88e00ba3dc89ca029bd66bd5a3c382d53e44f0ccd77e"},
{file = "Pillow-9.0.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:db6d9fac65bd08cea7f3540b899977c6dee9edad959fa4eaf305940d9cbd861c"},
{file = "Pillow-9.0.1-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:f154d173286a5d1863637a7dcd8c3437bb557520b01bddb0be0258dcb72696b5"},
{file = "Pillow-9.0.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:14d4b1341ac07ae07eb2cc682f459bec932a380c3b122f5540432d8977e64eae"},
{file = "Pillow-9.0.1-cp38-cp38-win32.whl", hash = "sha256:effb7749713d5317478bb3acb3f81d9d7c7f86726d41c1facca068a04cf5bb4c"},
{file = "Pillow-9.0.1-cp38-cp38-win_amd64.whl", hash = "sha256:7f7609a718b177bf171ac93cea9fd2ddc0e03e84d8fa4e887bdfc39671d46b00"},
{file = "Pillow-9.0.1-cp39-cp39-macosx_10_10_x86_64.whl", hash = "sha256:80ca33961ced9c63358056bd08403ff866512038883e74f3a4bf88ad3eb66838"},
{file = "Pillow-9.0.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:1c3c33ac69cf059bbb9d1a71eeaba76781b450bc307e2291f8a4764d779a6b28"},
{file = "Pillow-9.0.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:12875d118f21cf35604176872447cdb57b07126750a33748bac15e77f90f1f9c"},
{file = "Pillow-9.0.1-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:514ceac913076feefbeaf89771fd6febde78b0c4c1b23aaeab082c41c694e81b"},
{file = "Pillow-9.0.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d3c5c79ab7dfce6d88f1ba639b77e77a17ea33a01b07b99840d6ed08031cb2a7"},
{file = "Pillow-9.0.1-cp39-cp39-win32.whl", hash = "sha256:718856856ba31f14f13ba885ff13874be7fefc53984d2832458f12c38205f7f7"},
{file = "Pillow-9.0.1-cp39-cp39-win_amd64.whl", hash = "sha256:f25ed6e28ddf50de7e7ea99d7a976d6a9c415f03adcaac9c41ff6ff41b6d86ac"},
{file = "Pillow-9.0.1-pp37-pypy37_pp73-macosx_10_10_x86_64.whl", hash = "sha256:011233e0c42a4a7836498e98c1acf5e744c96a67dd5032a6f666cc1fb97eab97"},
{file = "Pillow-9.0.1-pp37-pypy37_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:253e8a302a96df6927310a9d44e6103055e8fb96a6822f8b7f514bb7ef77de56"},
{file = "Pillow-9.0.1-pp37-pypy37_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6295f6763749b89c994fcb6d8a7f7ce03c3992e695f89f00b741b4580b199b7e"},
{file = "Pillow-9.0.1-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:a9f44cd7e162ac6191491d7249cceb02b8116b0f7e847ee33f739d7cb1ea1f70"},
{file = "Pillow-9.0.1.tar.gz", hash = "sha256:6c8bc8238a7dfdaf7a75f5ec5a663f4173f8c367e5a39f87e720495e1eed75fa"},
]
pkgutil-resolve-name = []
platformdirs = [
{file = "platformdirs-2.5.2-py3-none-any.whl", hash = "sha256:027d8e83a2d7de06bbac4e5ef7e023c02b863d7ea5d079477e722bb41ab25788"},
{file = "platformdirs-2.5.2.tar.gz", hash = "sha256:58c8abb07dcb441e6ee4b11d8df0ac856038f944ab98b7be6b27b2a3c7feef19"},
]
pluggy = [
{file = "pluggy-1.0.0-py2.py3-none-any.whl", hash = "sha256:74134bbf457f031a36d68416e1509f34bd5ccc019f0bcc952c7b909d06b37bd3"},
{file = "pluggy-1.0.0.tar.gz", hash = "sha256:4224373bacce55f955a878bf9cfa763c1e360858e330072059e10bad68531159"},
]
prometheus-client = [
2022-04-18 18:12:18 -04:00
{file = "prometheus_client-0.14.1-py3-none-any.whl", hash = "sha256:522fded625282822a89e2773452f42df14b5a8e84a86433e3f8a189c1d54dc01"},
{file = "prometheus_client-0.14.1.tar.gz", hash = "sha256:5459c427624961076277fdc6dc50540e2bacb98eebde99886e59ec55ed92093a"},
]
prompt-toolkit = []
psutil = []
ptyprocess = [
{file = "ptyprocess-0.7.0-py2.py3-none-any.whl", hash = "sha256:4b41f3967fce3af57cc7e94b888626c18bf37a083e3651ca8feeb66d492fef35"},
{file = "ptyprocess-0.7.0.tar.gz", hash = "sha256:5c5d0a3b48ceee0b48485e0c26037c0acd7d29765ca3fbb5cb3831d347423220"},
]
py = [
{file = "py-1.11.0-py2.py3-none-any.whl", hash = "sha256:607c53218732647dff4acdfcd50cb62615cedf612e72d1724fb1a0cc6405b378"},
{file = "py-1.11.0.tar.gz", hash = "sha256:51c75c4126074b472f746a24399ad32f6053d1b34b68d2fa41e558e6f4a98719"},
]
pycodestyle = [
{file = "pycodestyle-2.7.0-py2.py3-none-any.whl", hash = "sha256:514f76d918fcc0b55c6680472f0a37970994e07bbb80725808c17089be302068"},
{file = "pycodestyle-2.7.0.tar.gz", hash = "sha256:c389c1d06bf7904078ca03399a4816f974a1d590090fecea0c63ec26ebaf1cef"},
]
pycparser = [
{file = "pycparser-2.21-py2.py3-none-any.whl", hash = "sha256:8ee45429555515e1f6b185e78100aea234072576aa43ab53aefcae078162fca9"},
{file = "pycparser-2.21.tar.gz", hash = "sha256:e644fdec12f7872f86c58ff790da456218b10f863970249516d60a5eaca77206"},
]
pydantic = []
pyflakes = [
{file = "pyflakes-2.3.1-py2.py3-none-any.whl", hash = "sha256:7893783d01b8a89811dd72d7dfd4d84ff098e5eed95cfa8905b22bbffe52efc3"},
{file = "pyflakes-2.3.1.tar.gz", hash = "sha256:f5bc8ecabc05bb9d291eb5203d6810b49040f6ff446a756326104746cc00c1db"},
]
pygments = []
pylint = []
pypandoc = []
pyparsing = []
pyproj = [
{file = "pyproj-3.3.1-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:473961faef7a9fd723c5d432f65220ea6ab3854e606bf84b4d409a75a4261c78"},
{file = "pyproj-3.3.1-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:2fef9c1e339f25c57f6ae0558b5ab1bbdf7994529a30d8d7504fc6302ea51c03"},
{file = "pyproj-3.3.1-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:140fa649fedd04f680a39f8ad339799a55cb1c49f6a84e1b32b97e49646647aa"},
{file = "pyproj-3.3.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:b59c08aea13ee428cf8a919212d55c036cc94784805ed77c8f31a4d1f541058c"},
{file = "pyproj-3.3.1-cp310-cp310-win32.whl", hash = "sha256:1adc9ccd1bf04998493b6a2e87e60656c75ab790653b36cfe351e9ef214828ed"},
{file = "pyproj-3.3.1-cp310-cp310-win_amd64.whl", hash = "sha256:42eea10afc750fccd1c5c4ba56de29ab791ab4d83c1f7db72705566282ac5396"},
{file = "pyproj-3.3.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:531ea36519fa7b581466d4b6ab32f66ae4dadd9499d726352f71ee5e19c3d1c5"},
{file = "pyproj-3.3.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:67025e37598a6bbed2c9c6c9e4c911f6dd39315d3e1148ead935a5c4d64309d5"},
{file = "pyproj-3.3.1-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:aed1a3c0cd4182425f91b48d5db39f459bc2fe0d88017ead6425a1bc85faee33"},
{file = "pyproj-3.3.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3cc4771403db54494e1e55bca8e6d33cde322f8cf0ed39f1557ff109c66d2cd1"},
{file = "pyproj-3.3.1-cp38-cp38-win32.whl", hash = "sha256:c99f7b5757a28040a2dd4a28c9805fdf13eef79a796f4a566ab5cb362d10630d"},
{file = "pyproj-3.3.1-cp38-cp38-win_amd64.whl", hash = "sha256:5dac03d4338a4c8bd0f69144c527474f517b4cbd7d2d8c532cd8937799723248"},
{file = "pyproj-3.3.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:56b0f9ee2c5b2520b18db30a393a7b86130cf527ddbb8c96e7f3c837474a9d79"},
{file = "pyproj-3.3.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5f92d8f6514516124abb714dce912b20867831162cfff9fae2678ef07b6fcf0f"},
{file = "pyproj-3.3.1-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1ef1bfbe2dcc558c7a98e2f1836abdcd630390f3160724a6f4f5c818b2be0ad5"},
{file = "pyproj-3.3.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5ca5f32b56210429b367ca4f9a57ffe67975c487af82e179a24370879a3daf68"},
{file = "pyproj-3.3.1-cp39-cp39-win32.whl", hash = "sha256:aba199704c824fb84ab64927e7bc9ef71e603e483130ec0f7e09e97259b8f61f"},
{file = "pyproj-3.3.1-cp39-cp39-win_amd64.whl", hash = "sha256:120d45ed73144c65e9677dc73ba8a531c495d179dd9f9f0471ac5acc02d7ac4b"},
{file = "pyproj-3.3.1-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:52efb681647dfac185cc655a709bc0caaf910031a0390f816f5fc8ce150cbedc"},
{file = "pyproj-3.3.1-pp38-pypy38_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:5ab0d6e38fda7c13726afacaf62e9f9dd858089d67910471758afd9cb24e0ecd"},
{file = "pyproj-3.3.1-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:45487942c19c5a8b09c91964ea3201f4e094518e34743cae373889a36e3d9260"},
{file = "pyproj-3.3.1-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:797ad5655d484feac14b0fbb4a4efeaac0cf780a223046e2465494c767fd1c3b"},
{file = "pyproj-3.3.1.tar.gz", hash = "sha256:b3d8e14d91cc95fb3dbc03a9d0588ac58326803eefa5bbb0978d109de3304fbe"},
]
pyrsistent = [
{file = "pyrsistent-0.18.1-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:df46c854f490f81210870e509818b729db4488e1f30f2a1ce1698b2295a878d1"},
{file = "pyrsistent-0.18.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:5d45866ececf4a5fff8742c25722da6d4c9e180daa7b405dc0a2a2790d668c26"},
{file = "pyrsistent-0.18.1-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:4ed6784ceac462a7d6fcb7e9b663e93b9a6fb373b7f43594f9ff68875788e01e"},
{file = "pyrsistent-0.18.1-cp310-cp310-win32.whl", hash = "sha256:e4f3149fd5eb9b285d6bfb54d2e5173f6a116fe19172686797c056672689daf6"},
{file = "pyrsistent-0.18.1-cp310-cp310-win_amd64.whl", hash = "sha256:636ce2dc235046ccd3d8c56a7ad54e99d5c1cd0ef07d9ae847306c91d11b5fec"},
{file = "pyrsistent-0.18.1-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:e92a52c166426efbe0d1ec1332ee9119b6d32fc1f0bbfd55d5c1088070e7fc1b"},
{file = "pyrsistent-0.18.1-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d7a096646eab884bf8bed965bad63ea327e0d0c38989fc83c5ea7b8a87037bfc"},
{file = "pyrsistent-0.18.1-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:cdfd2c361b8a8e5d9499b9082b501c452ade8bbf42aef97ea04854f4a3f43b22"},
{file = "pyrsistent-0.18.1-cp37-cp37m-win32.whl", hash = "sha256:7ec335fc998faa4febe75cc5268a9eac0478b3f681602c1f27befaf2a1abe1d8"},
{file = "pyrsistent-0.18.1-cp37-cp37m-win_amd64.whl", hash = "sha256:6455fc599df93d1f60e1c5c4fe471499f08d190d57eca040c0ea182301321286"},
{file = "pyrsistent-0.18.1-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:fd8da6d0124efa2f67d86fa70c851022f87c98e205f0594e1fae044e7119a5a6"},
{file = "pyrsistent-0.18.1-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7bfe2388663fd18bd8ce7db2c91c7400bf3e1a9e8bd7d63bf7e77d39051b85ec"},
{file = "pyrsistent-0.18.1-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:0e3e1fcc45199df76053026a51cc59ab2ea3fc7c094c6627e93b7b44cdae2c8c"},
{file = "pyrsistent-0.18.1-cp38-cp38-win32.whl", hash = "sha256:b568f35ad53a7b07ed9b1b2bae09eb15cdd671a5ba5d2c66caee40dbf91c68ca"},
{file = "pyrsistent-0.18.1-cp38-cp38-win_amd64.whl", hash = "sha256:d1b96547410f76078eaf66d282ddca2e4baae8964364abb4f4dcdde855cd123a"},
{file = "pyrsistent-0.18.1-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:f87cc2863ef33c709e237d4b5f4502a62a00fab450c9e020892e8e2ede5847f5"},
{file = "pyrsistent-0.18.1-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:6bc66318fb7ee012071b2792024564973ecc80e9522842eb4e17743604b5e045"},
{file = "pyrsistent-0.18.1-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:914474c9f1d93080338ace89cb2acee74f4f666fb0424896fcfb8d86058bf17c"},
{file = "pyrsistent-0.18.1-cp39-cp39-win32.whl", hash = "sha256:1b34eedd6812bf4d33814fca1b66005805d3640ce53140ab8bbb1e2651b0d9bc"},
{file = "pyrsistent-0.18.1-cp39-cp39-win_amd64.whl", hash = "sha256:e24a828f57e0c337c8d8bb9f6b12f09dfdf0273da25fda9e314f0b684b415a07"},
{file = "pyrsistent-0.18.1.tar.gz", hash = "sha256:d4d61f8b993a7255ba714df3aca52700f8125289f84f704cf80916517c46eb96"},
]
pytest = [
{file = "pytest-6.2.5-py3-none-any.whl", hash = "sha256:7310f8d27bc79ced999e760ca304d69f6ba6c6649c0b60fb0e04a4a77cacc134"},
{file = "pytest-6.2.5.tar.gz", hash = "sha256:131b36680866a76e6781d13f101efb86cf674ebb9762eb70d3082b6f29889e89"},
]
pytest-mock = []
pytest-snapshot = [
{file = "pytest-snapshot-0.8.1.tar.gz", hash = "sha256:0f8872d56bc3ceacb465967072b059a36714898a37c9eb1c75cd4054110106f2"},
{file = "pytest_snapshot-0.8.1-py3-none-any.whl", hash = "sha256:ccb72c8e40dd1ec96b40caf0d328a9e9124b91d6a06204ad47d67403d83a4fd2"},
]
python-dateutil = [
{file = "python-dateutil-2.8.2.tar.gz", hash = "sha256:0123cacc1627ae19ddf3c27a5de5bd67ee4586fbdd6440d9748f8abb483d3e86"},
{file = "python_dateutil-2.8.2-py2.py3-none-any.whl", hash = "sha256:961d03dc3453ebbc59dbdea9e4e11c5651520a876d0f4db161e8674aae935da9"},
]
pytz = []
pywin32 = []
pywinpty = []
pyyaml = [
{file = "PyYAML-6.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:d4db7c7aef085872ef65a8fd7d6d09a14ae91f691dec3e87ee5ee0539d516f53"},
{file = "PyYAML-6.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:9df7ed3b3d2e0ecfe09e14741b857df43adb5a3ddadc919a2d94fbdf78fea53c"},
{file = "PyYAML-6.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:77f396e6ef4c73fdc33a9157446466f1cff553d979bd00ecb64385760c6babdc"},
{file = "PyYAML-6.0-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:a80a78046a72361de73f8f395f1f1e49f956c6be882eed58505a15f3e430962b"},
{file = "PyYAML-6.0-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:f84fbc98b019fef2ee9a1cb3ce93e3187a6df0b2538a651bfb890254ba9f90b5"},
{file = "PyYAML-6.0-cp310-cp310-win32.whl", hash = "sha256:2cd5df3de48857ed0544b34e2d40e9fac445930039f3cfe4bcc592a1f836d513"},
{file = "PyYAML-6.0-cp310-cp310-win_amd64.whl", hash = "sha256:daf496c58a8c52083df09b80c860005194014c3698698d1a57cbcfa182142a3a"},
{file = "PyYAML-6.0-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:897b80890765f037df3403d22bab41627ca8811ae55e9a722fd0392850ec4d86"},
{file = "PyYAML-6.0-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:50602afada6d6cbfad699b0c7bb50d5ccffa7e46a3d738092afddc1f9758427f"},
{file = "PyYAML-6.0-cp36-cp36m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:48c346915c114f5fdb3ead70312bd042a953a8ce5c7106d5bfb1a5254e47da92"},
{file = "PyYAML-6.0-cp36-cp36m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:98c4d36e99714e55cfbaaee6dd5badbc9a1ec339ebfc3b1f52e293aee6bb71a4"},
{file = "PyYAML-6.0-cp36-cp36m-win32.whl", hash = "sha256:0283c35a6a9fbf047493e3a0ce8d79ef5030852c51e9d911a27badfde0605293"},
{file = "PyYAML-6.0-cp36-cp36m-win_amd64.whl", hash = "sha256:07751360502caac1c067a8132d150cf3d61339af5691fe9e87803040dbc5db57"},
{file = "PyYAML-6.0-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:819b3830a1543db06c4d4b865e70ded25be52a2e0631ccd2f6a47a2822f2fd7c"},
{file = "PyYAML-6.0-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:473f9edb243cb1935ab5a084eb238d842fb8f404ed2193a915d1784b5a6b5fc0"},
{file = "PyYAML-6.0-cp37-cp37m-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:0ce82d761c532fe4ec3f87fc45688bdd3a4c1dc5e0b4a19814b9009a29baefd4"},
{file = "PyYAML-6.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:231710d57adfd809ef5d34183b8ed1eeae3f76459c18fb4a0b373ad56bedcdd9"},
{file = "PyYAML-6.0-cp37-cp37m-win32.whl", hash = "sha256:c5687b8d43cf58545ade1fe3e055f70eac7a5a1a0bf42824308d868289a95737"},
{file = "PyYAML-6.0-cp37-cp37m-win_amd64.whl", hash = "sha256:d15a181d1ecd0d4270dc32edb46f7cb7733c7c508857278d3d378d14d606db2d"},
{file = "PyYAML-6.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:0b4624f379dab24d3725ffde76559cff63d9ec94e1736b556dacdfebe5ab6d4b"},
{file = "PyYAML-6.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:213c60cd50106436cc818accf5baa1aba61c0189ff610f64f4a3e8c6726218ba"},
{file = "PyYAML-6.0-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:9fa600030013c4de8165339db93d182b9431076eb98eb40ee068700c9c813e34"},
{file = "PyYAML-6.0-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:277a0ef2981ca40581a47093e9e2d13b3f1fbbeffae064c1d21bfceba2030287"},
{file = "PyYAML-6.0-cp38-cp38-win32.whl", hash = "sha256:d4eccecf9adf6fbcc6861a38015c2a64f38b9d94838ac1810a9023a0609e1b78"},
{file = "PyYAML-6.0-cp38-cp38-win_amd64.whl", hash = "sha256:1e4747bc279b4f613a09eb64bba2ba602d8a6664c6ce6396a4d0cd413a50ce07"},
{file = "PyYAML-6.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:055d937d65826939cb044fc8c9b08889e8c743fdc6a32b33e2390f66013e449b"},
{file = "PyYAML-6.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:e61ceaab6f49fb8bdfaa0f92c4b57bcfbea54c09277b1b4f7ac376bfb7a7c174"},
{file = "PyYAML-6.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d67d839ede4ed1b28a4e8909735fc992a923cdb84e618544973d7dfc71540803"},
{file = "PyYAML-6.0-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:cba8c411ef271aa037d7357a2bc8f9ee8b58b9965831d9e51baf703280dc73d3"},
{file = "PyYAML-6.0-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:40527857252b61eacd1d9af500c3337ba8deb8fc298940291486c465c8b46ec0"},
{file = "PyYAML-6.0-cp39-cp39-win32.whl", hash = "sha256:b5b9eccad747aabaaffbc6064800670f0c297e52c12754eb1d976c57e4f74dcb"},
{file = "PyYAML-6.0-cp39-cp39-win_amd64.whl", hash = "sha256:b3d267842bf12586ba6c734f89d1f5b871df0273157918b0ccefa29deb05c21c"},
{file = "PyYAML-6.0.tar.gz", hash = "sha256:68fb519c14306fec9720a2a5b45bc9f0c8d1b9c72adf45c37baedfcd949c35a2"},
]
pyzmq = []
qtconsole = []
qtpy = []
requests = []
rtree = []
safety = [
{file = "safety-1.10.3-py2.py3-none-any.whl", hash = "sha256:5f802ad5df5614f9622d8d71fedec2757099705c2356f862847c58c6dfe13e84"},
{file = "safety-1.10.3.tar.gz", hash = "sha256:30e394d02a20ac49b7f65292d19d38fa927a8f9582cdfd3ad1adbbc66c641ad5"},
]
scipy = [
{file = "scipy-1.6.1-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:a15a1f3fc0abff33e792d6049161b7795909b40b97c6cc2934ed54384017ab76"},
{file = "scipy-1.6.1-cp37-cp37m-manylinux1_i686.whl", hash = "sha256:e79570979ccdc3d165456dd62041d9556fb9733b86b4b6d818af7a0afc15f092"},
{file = "scipy-1.6.1-cp37-cp37m-manylinux1_x86_64.whl", hash = "sha256:a423533c55fec61456dedee7b6ee7dce0bb6bfa395424ea374d25afa262be261"},
{file = "scipy-1.6.1-cp37-cp37m-manylinux2014_aarch64.whl", hash = "sha256:33d6b7df40d197bdd3049d64e8e680227151673465e5d85723b3b8f6b15a6ced"},
{file = "scipy-1.6.1-cp37-cp37m-win32.whl", hash = "sha256:6725e3fbb47da428794f243864f2297462e9ee448297c93ed1dcbc44335feb78"},
{file = "scipy-1.6.1-cp37-cp37m-win_amd64.whl", hash = "sha256:5fa9c6530b1661f1370bcd332a1e62ca7881785cc0f80c0d559b636567fab63c"},
{file = "scipy-1.6.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:bd50daf727f7c195e26f27467c85ce653d41df4358a25b32434a50d8870fc519"},
{file = "scipy-1.6.1-cp38-cp38-manylinux1_i686.whl", hash = "sha256:f46dd15335e8a320b0fb4685f58b7471702234cba8bb3442b69a3e1dc329c345"},
{file = "scipy-1.6.1-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:0e5b0ccf63155d90da576edd2768b66fb276446c371b73841e3503be1d63fb5d"},
{file = "scipy-1.6.1-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:2481efbb3740977e3c831edfd0bd9867be26387cacf24eb5e366a6a374d3d00d"},
{file = "scipy-1.6.1-cp38-cp38-win32.whl", hash = "sha256:68cb4c424112cd4be886b4d979c5497fba190714085f46b8ae67a5e4416c32b4"},
{file = "scipy-1.6.1-cp38-cp38-win_amd64.whl", hash = "sha256:5f331eeed0297232d2e6eea51b54e8278ed8bb10b099f69c44e2558c090d06bf"},
{file = "scipy-1.6.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:0c8a51d33556bf70367452d4d601d1742c0e806cd0194785914daf19775f0e67"},
{file = "scipy-1.6.1-cp39-cp39-manylinux1_i686.whl", hash = "sha256:83bf7c16245c15bc58ee76c5418e46ea1811edcc2e2b03041b804e46084ab627"},
{file = "scipy-1.6.1-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:794e768cc5f779736593046c9714e0f3a5940bc6dcc1dba885ad64cbfb28e9f0"},
{file = "scipy-1.6.1-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:5da5471aed911fe7e52b86bf9ea32fb55ae93e2f0fac66c32e58897cfb02fa07"},
{file = "scipy-1.6.1-cp39-cp39-win32.whl", hash = "sha256:8e403a337749ed40af60e537cc4d4c03febddcc56cd26e774c9b1b600a70d3e4"},
{file = "scipy-1.6.1-cp39-cp39-win_amd64.whl", hash = "sha256:a5193a098ae9f29af283dcf0041f762601faf2e595c0db1da929875b7570353f"},
{file = "scipy-1.6.1.tar.gz", hash = "sha256:c4fceb864890b6168e79b0e714c585dbe2fd4222768ee90bc1aa0f8218691b11"},
]
seaborn = [
{file = "seaborn-0.11.2-py3-none-any.whl", hash = "sha256:85a6baa9b55f81a0623abddc4a26b334653ff4c6b18c418361de19dbba0ef283"},
{file = "seaborn-0.11.2.tar.gz", hash = "sha256:cf45e9286d40826864be0e3c066f98536982baf701a7caa386511792d61ff4f6"},
]
semantic-version = []
send2trash = [
{file = "Send2Trash-1.8.0-py3-none-any.whl", hash = "sha256:f20eaadfdb517eaca5ce077640cb261c7d2698385a6a0f072a4a5447fd49fa08"},
{file = "Send2Trash-1.8.0.tar.gz", hash = "sha256:d2c24762fd3759860a0aff155e45871447ea58d2be6bdd39b5c8f966a0c99c2d"},
]
setuptools-scm = [
{file = "setuptools_scm-6.4.2-py3-none-any.whl", hash = "sha256:acea13255093849de7ccb11af9e1fb8bde7067783450cee9ef7a93139bddf6d4"},
{file = "setuptools_scm-6.4.2.tar.gz", hash = "sha256:6833ac65c6ed9711a4d5d2266f8024cfa07c533a0e55f4c12f6eff280a5a9e30"},
]
shapely = []
six = [
{file = "six-1.16.0-py2.py3-none-any.whl", hash = "sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254"},
{file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
]
sniffio = []
soupsieve = [
2022-04-18 18:12:18 -04:00
{file = "soupsieve-2.3.2.post1-py3-none-any.whl", hash = "sha256:3b2503d3c7084a42b1ebd08116e5f81aadfaea95863628c80a3b774a11b7c759"},
{file = "soupsieve-2.3.2.post1.tar.gz", hash = "sha256:fc53893b3da2c33de295667a0e19f078c14bf86544af307354de5fcf12a3f30d"},
]
tenacity = [
{file = "tenacity-8.0.1-py3-none-any.whl", hash = "sha256:f78f4ea81b0fabc06728c11dc2a8c01277bfc5181b321a4770471902e3eb844a"},
{file = "tenacity-8.0.1.tar.gz", hash = "sha256:43242a20e3e73291a28bcbcacfd6e000b02d3857a9a9fff56b297a27afdc932f"},
]
terminado = []
textwrap3 = [
{file = "textwrap3-0.9.2-py2.py3-none-any.whl", hash = "sha256:bf5f4c40faf2a9ff00a9e0791fed5da7415481054cef45bb4a3cfb1f69044ae0"},
{file = "textwrap3-0.9.2.zip", hash = "sha256:5008eeebdb236f6303dcd68f18b856d355f6197511d952ba74bc75e40e0c3414"},
]
2022-04-18 18:12:18 -04:00
tinycss2 = [
{file = "tinycss2-1.1.1-py3-none-any.whl", hash = "sha256:fe794ceaadfe3cf3e686b22155d0da5780dd0e273471a51846d0a02bc204fec8"},
{file = "tinycss2-1.1.1.tar.gz", hash = "sha256:b2e44dd8883c360c35dd0d1b5aad0b610e5156c2cb3b33434634e539ead9d8bf"},
]
toml = [
{file = "toml-0.10.2-py2.py3-none-any.whl", hash = "sha256:806143ae5bfb6a3c6e736a764057db0e6a0e05e338b5630894a5f779cabb4f9b"},
{file = "toml-0.10.2.tar.gz", hash = "sha256:b3bda1d108d5dd99f4a20d24d9c348e91c4db7ab1b749200bded2f839ccbe68f"},
]
tomli = [
{file = "tomli-1.2.3-py3-none-any.whl", hash = "sha256:e3069e4be3ead9668e21cb9b074cd948f7b3113fd9c8bba083f48247aab8b11c"},
{file = "tomli-1.2.3.tar.gz", hash = "sha256:05b6166bff487dc068d322585c7ea4ef78deed501cc124060e0f238e89a9231f"},
]
tomlkit = []
tornado = []
tox = []
tox-poetry = [
{file = "tox-poetry-0.4.1.tar.gz", hash = "sha256:2395808e1ce487b5894c10f2202e14702bfa6d6909c0d1e525170d14809ac7ef"},
{file = "tox_poetry-0.4.1-py2.py3-none-any.whl", hash = "sha256:11d9cd4e51d4cd9484b3ba63f2650ab4cfb4096e5f0682ecf561ddfc3c8e8c92"},
]
tqdm = [
{file = "tqdm-4.62.0-py2.py3-none-any.whl", hash = "sha256:706dea48ee05ba16e936ee91cb3791cd2ea6da348a0e50b46863ff4363ff4340"},
{file = "tqdm-4.62.0.tar.gz", hash = "sha256:3642d483b558eec80d3c831e23953582c34d7e4540db86d9e5ed9dad238dabc6"},
]
traitlets = []
types-requests = []
types-urllib3 = []
typing-extensions = []
typing-inspect = [
{file = "typing_inspect-0.7.1-py2-none-any.whl", hash = "sha256:b1f56c0783ef0f25fb064a01be6e5407e54cf4a4bf4f3ba3fe51e0bd6dcea9e5"},
{file = "typing_inspect-0.7.1-py3-none-any.whl", hash = "sha256:3cd7d4563e997719a710a3bfe7ffb544c6b72069b6812a02e9b414a8fa3aaa6b"},
{file = "typing_inspect-0.7.1.tar.gz", hash = "sha256:047d4097d9b17f46531bf6f014356111a1b6fb821a24fe7ac909853ca2a782aa"},
]
urllib3 = []
us = [
{file = "us-2.0.2.tar.gz", hash = "sha256:cb11ad0d43deff3a1c3690c74f0c731cff5b862c73339df2edd91133e1496fbc"},
]
virtualenv = []
wcwidth = [
{file = "wcwidth-0.2.5-py2.py3-none-any.whl", hash = "sha256:beb4802a9cebb9144e99086eff703a642a13d6a0052920003a230f3294bbe784"},
{file = "wcwidth-0.2.5.tar.gz", hash = "sha256:c4d647b99872929fdb7bdcaa4fbe7f01413ed3d98077df798530e5b04f116c83"},
]
webencodings = [
{file = "webencodings-0.5.1-py2.py3-none-any.whl", hash = "sha256:a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78"},
{file = "webencodings-0.5.1.tar.gz", hash = "sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923"},
]
websocket-client = []
widgetsnbextension = []
wrapt = []
xlsxwriter = [
{file = "XlsxWriter-2.0.0-py2.py3-none-any.whl", hash = "sha256:51fbb1d727d8391ddf240ce665710d6b205944dc84842c7b8452ac40226eeb71"},
{file = "XlsxWriter-2.0.0.tar.gz", hash = "sha256:80ce4aadc638dea452f6e28f70b6223b9b5b5740ff9c57ef6387af115e129bbb"},
]
zipp = []