mirror of
https://github.com/DOI-DO/j40-cejst-2.git
synced 2025-02-22 09:41:26 -08:00
Score F, testing methodology (#510)
* fixing dependency issue * fixing more dependencies * including fraction of state AMI * wip * nitpick whitespace * etl working now * wip on scoring * fix rename error * reducing metrics * fixing score f * fixing readme * adding dependency * passing tests; * linting/black * removing unnecessary sample * fixing error * adding verify flag on etl/base Co-authored-by: Jorge Escobar <jorge.e.escobar@omb.eop.gov>
This commit is contained in:
Lucas Merrill Brown
GitHub
parent
043ed983ea
commit
65ceb7900f
23 changed files with 557 additions and 153 deletions
8
data/data-pipeline/.vscode/settings.json
vendored
8
data/data-pipeline/.vscode/settings.json
vendored
|
|
@ -1,10 +1,16 @@
|
|||
{
|
||||
"python.formatting.provider": "black",
|
||||
"python.formatting.blackArgs": [
|
||||
"--line-length=80"
|
||||
],
|
||||
"python.linting.enabled": true,
|
||||
"python.linting.flake8Enabled": true,
|
||||
"python.linting.pylintEnabled": true,
|
||||
"python.testing.pytestEnabled": true,
|
||||
"python.testing.pytestArgs": ["-s", "."],
|
||||
"python.testing.pytestArgs": [
|
||||
"-s",
|
||||
"."
|
||||
],
|
||||
"python.testing.unittestEnabled": false,
|
||||
"python.testing.nosetestsEnabled": false
|
||||
}
|
||||
|
|
|
|||
14
data/data-pipeline/.vscode/tasks.json
vendored
14
data/data-pipeline/.vscode/tasks.json
vendored
|
|
@ -4,7 +4,7 @@
|
|||
"version": "2.0.0",
|
||||
"tasks": [
|
||||
{
|
||||
"label": "test with tox",
|
||||
"label": "Test with tox",
|
||||
"type": "shell",
|
||||
"command": "tox",
|
||||
"group": {
|
||||
|
|
@ -16,19 +16,25 @@
|
|||
"label": "Run Black Formatter",
|
||||
"type": "shell",
|
||||
"command": "black",
|
||||
"args": ["data_pipeline"]
|
||||
"args": [
|
||||
"data_pipeline"
|
||||
]
|
||||
},
|
||||
{
|
||||
"label": "Run Flake8 Style Enforcer",
|
||||
"type": "shell",
|
||||
"command": "black",
|
||||
"args": ["data_pipeline"]
|
||||
"args": [
|
||||
"data_pipeline"
|
||||
]
|
||||
},
|
||||
{
|
||||
"label": "Run Pylint",
|
||||
"type": "shell",
|
||||
"command": "pylint",
|
||||
"args": ["data_pipeline"]
|
||||
"args": [
|
||||
"data_pipeline"
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
|
|
|
|||
|
|
@ -120,17 +120,6 @@ To run this comparison tool:
|
|||
1. Make sure you've gone through the above steps to run the data ETL and score generation.
|
||||
1. From the package directory (`data/data-pipeline/data_pipeline/`), navigate to the `ipython` directory: `cd ipython`.
|
||||
1. Ensure you have `pandoc` installed on your computer. If you're on a Mac, run `brew install pandoc`; for other OSes, see pandoc's [installation guide](https://pandoc.org/installing.html).
|
||||
1. Install the extra dependencies:
|
||||
|
||||
```python
|
||||
pip install pypandoc
|
||||
pip install requests
|
||||
pip install us
|
||||
pip install tqdm
|
||||
pip install dynaconf
|
||||
pip instal xlsxwriter
|
||||
```
|
||||
|
||||
1. Start the notebooks: `jupyter notebook`
|
||||
1. In your browser, navigate to one of the URLs returned by the above command.
|
||||
1. Select `scoring_comparison.ipynb` from the options in your browser.
|
||||
|
|
|
|||
|
|
@ -1,4 +1,5 @@
|
|||
from pathlib import Path
|
||||
from typing import Optional
|
||||
|
||||
from data_pipeline.config import settings
|
||||
from data_pipeline.utils import unzip_file_from_url, remove_all_from_dir
|
||||
|
|
@ -33,14 +34,21 @@ class ExtractTransformLoad:
|
|||
|
||||
pass
|
||||
|
||||
def extract(self, source_url: str = None, extract_path: Path = None) -> None:
|
||||
def extract(
|
||||
self,
|
||||
source_url: str = None,
|
||||
extract_path: Path = None,
|
||||
verify: Optional[bool] = True,
|
||||
) -> None:
|
||||
"""Extract the data from
|
||||
a remote source. By default it provides code to get the file from a source url,
|
||||
unzips it and stores it on an extract_path."""
|
||||
|
||||
# this can be accessed via super().extract()
|
||||
if source_url and extract_path:
|
||||
unzip_file_from_url(source_url, self.TMP_PATH, extract_path)
|
||||
unzip_file_from_url(
|
||||
source_url, self.TMP_PATH, extract_path, verify=verify
|
||||
)
|
||||
|
||||
def transform(self) -> None:
|
||||
"""Transform the data extracted into a format that can be consumed by the
|
||||
|
|
|
|||
|
|
@ -34,6 +34,11 @@ DATASET_LIST = [
|
|||
"module_dir": "hud_recap",
|
||||
"class_name": "HudRecapETL",
|
||||
},
|
||||
{
|
||||
"name": "cdc_places",
|
||||
"module_dir": "cdc_places",
|
||||
"class_name": "CDCPlacesETL",
|
||||
},
|
||||
]
|
||||
CENSUS_INFO = {
|
||||
"name": "census",
|
||||
|
|
|
|||
|
|
@ -50,6 +50,7 @@ class ScoreETL(ExtractTransformLoad):
|
|||
self.census_df: pd.DataFrame
|
||||
self.housing_and_transportation_df: pd.DataFrame
|
||||
self.hud_housing_df: pd.DataFrame
|
||||
self.cdc_places_df: pd.DataFrame
|
||||
|
||||
def data_sets(self) -> list:
|
||||
# Define a named tuple that will be used for each data set input.
|
||||
|
|
@ -81,6 +82,36 @@ class ScoreETL(ExtractTransformLoad):
|
|||
renamed_field=self.MEDIAN_INCOME_AS_PERCENT_OF_STATE_FIELD_NAME,
|
||||
bucket=None,
|
||||
),
|
||||
DataSet(
|
||||
input_field="Current asthma among adults aged >=18 years",
|
||||
renamed_field="Current asthma among adults aged >=18 years",
|
||||
bucket=None,
|
||||
),
|
||||
DataSet(
|
||||
input_field="Coronary heart disease among adults aged >=18 years",
|
||||
renamed_field="Coronary heart disease among adults aged >=18 years",
|
||||
bucket=None,
|
||||
),
|
||||
DataSet(
|
||||
input_field="Cancer (excluding skin cancer) among adults aged >=18 years",
|
||||
renamed_field="Cancer (excluding skin cancer) among adults aged >=18 years",
|
||||
bucket=None,
|
||||
),
|
||||
DataSet(
|
||||
input_field="Current lack of health insurance among adults aged 18-64 years",
|
||||
renamed_field="Current lack of health insurance among adults aged 18-64 years",
|
||||
bucket=None,
|
||||
),
|
||||
DataSet(
|
||||
input_field="Diagnosed diabetes among adults aged >=18 years",
|
||||
renamed_field="Diagnosed diabetes among adults aged >=18 years",
|
||||
bucket=None,
|
||||
),
|
||||
DataSet(
|
||||
input_field="Physical health not good for >=14 days among adults aged >=18 years",
|
||||
renamed_field="Physical health not good for >=14 days among adults aged >=18 years",
|
||||
bucket=None,
|
||||
),
|
||||
# The following data sets have buckets, because they're used in Score C
|
||||
DataSet(
|
||||
input_field="CANCER",
|
||||
|
|
@ -218,6 +249,14 @@ class ScoreETL(ExtractTransformLoad):
|
|||
low_memory=False,
|
||||
)
|
||||
|
||||
# Load CDC Places data
|
||||
cdc_places_csv = self.DATA_PATH / "dataset" / "cdc_places" / "usa.csv"
|
||||
self.cdc_places_df = pd.read_csv(
|
||||
cdc_places_csv,
|
||||
dtype={self.GEOID_TRACT_FIELD_NAME: "string"},
|
||||
low_memory=False,
|
||||
)
|
||||
|
||||
def transform(self) -> None:
|
||||
## IMPORTANT: THIS METHOD IS CLOSE TO THE LIMIT OF STATEMENTS
|
||||
|
||||
|
|
@ -247,8 +286,28 @@ class ScoreETL(ExtractTransformLoad):
|
|||
)
|
||||
|
||||
# Join all the data sources that use census tracts
|
||||
# TODO: when there's more than one data source using census tract, reduce/merge them here.
|
||||
census_tract_df = self.hud_housing_df
|
||||
census_tract_dfs = [
|
||||
self.hud_housing_df,
|
||||
self.cdc_places_df,
|
||||
]
|
||||
census_tract_df = functools.reduce(
|
||||
lambda left, right: pd.merge(
|
||||
left=left,
|
||||
right=right,
|
||||
on=self.GEOID_TRACT_FIELD_NAME,
|
||||
how="outer",
|
||||
),
|
||||
census_tract_dfs,
|
||||
)
|
||||
|
||||
# Sanity check the join.
|
||||
if (
|
||||
len(census_tract_df[self.GEOID_TRACT_FIELD_NAME].str.len().unique())
|
||||
!= 1
|
||||
):
|
||||
raise ValueError(
|
||||
f"One of the input CSVs uses {self.GEOID_TRACT_FIELD_NAME} with a different length."
|
||||
)
|
||||
|
||||
# Calculate the tract for the CBG data.
|
||||
census_block_group_df[
|
||||
|
|
@ -437,12 +496,56 @@ class ScoreETL(ExtractTransformLoad):
|
|||
)
|
||||
|
||||
self.df[meets_burden_field_name] = (
|
||||
self.df["Particulate matter (PM2.5)"] > 10
|
||||
) | (self.df["Respiratory hazard " "index"] > 0.75)
|
||||
(self.df["Particulate matter (PM2.5) (percentile)"] > 0.9)
|
||||
| (self.df["Respiratory hazard index (percentile)"] > 0.9)
|
||||
| (self.df["Traffic proximity and volume (percentile)"] > 0.9)
|
||||
| (
|
||||
self.df[
|
||||
"Percent pre-1960s housing (lead paint indicator) (percentile)"
|
||||
]
|
||||
> 0.9
|
||||
)
|
||||
| (self.df["Proximity to RMP sites (percentile)"] > 0.9)
|
||||
| (
|
||||
self.df[
|
||||
"Current asthma among adults aged >=18 years (percentile)"
|
||||
]
|
||||
> 0.9
|
||||
)
|
||||
| (
|
||||
self.df[
|
||||
"Coronary heart disease among adults aged >=18 years (percentile)"
|
||||
]
|
||||
> 0.9
|
||||
)
|
||||
| (
|
||||
self.df[
|
||||
"Cancer (excluding skin cancer) among adults aged >=18 years (percentile)"
|
||||
]
|
||||
> 0.9
|
||||
)
|
||||
# | (
|
||||
# self.df[
|
||||
# "Current lack of health insurance among adults aged 18-64 years (percentile)"
|
||||
# ]
|
||||
# > 0.9
|
||||
# )
|
||||
| (
|
||||
self.df[
|
||||
"Diagnosed diabetes among adults aged >=18 years (percentile)"
|
||||
]
|
||||
> 0.9
|
||||
)
|
||||
# | (
|
||||
# self.df[
|
||||
# "Physical health not good for >=14 days among adults aged >=18 years (percentile)"
|
||||
# ]
|
||||
# > 0.9
|
||||
# )
|
||||
)
|
||||
|
||||
self.df["Score F (communities)"] = (
|
||||
self.df[ami_and_high_school_field_name]
|
||||
& self.df[meets_burden_field_name]
|
||||
self.df[meets_socio_field_name] & self.df[meets_burden_field_name]
|
||||
)
|
||||
|
||||
def load(self) -> None:
|
||||
|
|
@ -450,10 +553,4 @@ class ScoreETL(ExtractTransformLoad):
|
|||
|
||||
# write nationwide csv
|
||||
self.SCORE_CSV_PATH.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
# TODO: drop
|
||||
self.df[0:10000].to_csv(
|
||||
self.SCORE_CSV_PATH / "usa-10000.csv", index=False
|
||||
)
|
||||
|
||||
self.df.to_csv(self.SCORE_CSV_PATH / "usa.csv", index=False)
|
||||
|
|
|
|||
|
|
@ -10,14 +10,19 @@ logger = get_module_logger(__name__)
|
|||
class CalEnviroScreenETL(ExtractTransformLoad):
|
||||
def __init__(self):
|
||||
self.CALENVIROSCREEN_FTP_URL = (
|
||||
settings.AWS_JUSTICE40_DATASOURCES_URL + "/CalEnviroScreen_4.0_2021.zip"
|
||||
settings.AWS_JUSTICE40_DATASOURCES_URL
|
||||
+ "/CalEnviroScreen_4.0_2021.zip"
|
||||
)
|
||||
self.CALENVIROSCREEN_CSV = (
|
||||
self.TMP_PATH / "CalEnviroScreen_4.0_2021.csv"
|
||||
)
|
||||
self.CALENVIROSCREEN_CSV = self.TMP_PATH / "CalEnviroScreen_4.0_2021.csv"
|
||||
self.CSV_PATH = self.DATA_PATH / "dataset" / "calenviroscreen4"
|
||||
|
||||
# Definining some variable names
|
||||
self.CALENVIROSCREEN_SCORE_FIELD_NAME = "calenviroscreen_score"
|
||||
self.CALENVIROSCREEN_PERCENTILE_FIELD_NAME = "calenviroscreen_percentile"
|
||||
self.CALENVIROSCREEN_PERCENTILE_FIELD_NAME = (
|
||||
"calenviroscreen_percentile"
|
||||
)
|
||||
self.CALENVIROSCREEN_PRIORITY_COMMUNITY_FIELD_NAME = (
|
||||
"calenviroscreen_priority_community"
|
||||
)
|
||||
|
|
|
|||
|
|
@ -0,0 +1,66 @@
|
|||
import pandas as pd
|
||||
|
||||
from data_pipeline.etl.base import ExtractTransformLoad
|
||||
from data_pipeline.utils import get_module_logger, download_file_from_url
|
||||
|
||||
logger = get_module_logger(__name__)
|
||||
|
||||
|
||||
class CDCPlacesETL(ExtractTransformLoad):
|
||||
def __init__(self):
|
||||
self.OUTPUT_PATH = self.DATA_PATH / "dataset" / "cdc_places"
|
||||
|
||||
self.CDC_PLACES_URL = "https://chronicdata.cdc.gov/api/views/cwsq-ngmh/rows.csv?accessType=DOWNLOAD"
|
||||
self.CDC_GEOID_FIELD_NAME = "LocationID"
|
||||
self.CDC_VALUE_FIELD_NAME = "Data_Value"
|
||||
self.CDC_MEASURE_FIELD_NAME = "Measure"
|
||||
|
||||
self.df: pd.DataFrame
|
||||
|
||||
def extract(self) -> None:
|
||||
logger.info("Starting to download 520MB CDC Places file.")
|
||||
file_path = download_file_from_url(
|
||||
file_url=self.CDC_PLACES_URL,
|
||||
download_file_name=self.TMP_PATH
|
||||
/ "cdc_places"
|
||||
/ "census_tract.csv",
|
||||
)
|
||||
|
||||
self.df = pd.read_csv(
|
||||
filepath_or_buffer=file_path,
|
||||
dtype={self.CDC_GEOID_FIELD_NAME: "string"},
|
||||
low_memory=False,
|
||||
)
|
||||
|
||||
def transform(self) -> None:
|
||||
logger.info("Starting CDC Places transform")
|
||||
|
||||
# Rename GEOID field
|
||||
self.df.rename(
|
||||
columns={self.CDC_GEOID_FIELD_NAME: self.GEOID_TRACT_FIELD_NAME},
|
||||
inplace=True,
|
||||
errors="raise",
|
||||
)
|
||||
|
||||
# Note: Puerto Rico not included.
|
||||
self.df = self.df.pivot(
|
||||
index=self.GEOID_TRACT_FIELD_NAME,
|
||||
columns=self.CDC_MEASURE_FIELD_NAME,
|
||||
values=self.CDC_VALUE_FIELD_NAME,
|
||||
)
|
||||
|
||||
# Make the index (the census tract ID) a column, not the index.
|
||||
self.df.reset_index(inplace=True)
|
||||
|
||||
def load(self) -> None:
|
||||
logger.info("Saving CDC Places Data")
|
||||
|
||||
# mkdir census
|
||||
self.OUTPUT_PATH.mkdir(parents=True, exist_ok=True)
|
||||
|
||||
self.df.to_csv(path_or_buf=self.OUTPUT_PATH / "usa.csv", index=False)
|
||||
|
||||
def validate(self) -> None:
|
||||
logger.info("Validating Census ACS Data")
|
||||
|
||||
pass
|
||||
|
|
@ -33,7 +33,9 @@ class CensusETL(ExtractTransformLoad):
|
|||
self.NATIONAL_CBG_CSV_PATH = self.CSV_BASE_PATH / "us.csv"
|
||||
self.NATIONAL_CBG_JSON_PATH = self.GEOJSON_BASE_PATH / "us.json"
|
||||
|
||||
def _path_for_fips_file(self, fips_code: str, file_type: GeoFileType) -> Path:
|
||||
def _path_for_fips_file(
|
||||
self, fips_code: str, file_type: GeoFileType
|
||||
) -> Path:
|
||||
"""Get paths for associated geospatial files for the provided FIPS code
|
||||
|
||||
Args:
|
||||
|
|
@ -93,7 +95,9 @@ class CensusETL(ExtractTransformLoad):
|
|||
None
|
||||
"""
|
||||
shp_file_path = self._path_for_fips_file(fips_code, GeoFileType.SHP)
|
||||
geojson_file_path = self._path_for_fips_file(fips_code, GeoFileType.GEOJSON)
|
||||
geojson_file_path = self._path_for_fips_file(
|
||||
fips_code, GeoFileType.GEOJSON
|
||||
)
|
||||
logger.info(f"Checking if {fips_code} geoJSON file exists ")
|
||||
if not geojson_file_path.is_file():
|
||||
logger.info(
|
||||
|
|
@ -176,7 +180,9 @@ class CensusETL(ExtractTransformLoad):
|
|||
|
||||
if not self.NATIONAL_CBG_CSV_PATH.is_file():
|
||||
logger.info(f"Creating {self.NATIONAL_CBG_CSV_PATH}")
|
||||
with open(self.NATIONAL_CBG_CSV_PATH, mode="w", newline="") as cbg_csv_file:
|
||||
with open(
|
||||
self.NATIONAL_CBG_CSV_PATH, mode="w", newline=""
|
||||
) as cbg_csv_file:
|
||||
cbg_csv_file_writer = csv.writer(
|
||||
cbg_csv_file,
|
||||
delimiter=",",
|
||||
|
|
@ -205,7 +211,9 @@ class CensusETL(ExtractTransformLoad):
|
|||
state_gdf = gpd.read_file(file_name)
|
||||
usa_df = usa_df.append(state_gdf)
|
||||
|
||||
usa_df = usa_df.to_crs("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs")
|
||||
usa_df = usa_df.to_crs(
|
||||
"+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs"
|
||||
)
|
||||
logger.info("Writing national geojson file")
|
||||
usa_df.to_file(self.NATIONAL_CBG_JSON_PATH, driver="GeoJSON")
|
||||
|
||||
|
|
|
|||
|
|
@ -41,10 +41,10 @@ class CensusACSETL(ExtractTransformLoad):
|
|||
|
||||
self.STATE_MEDIAN_INCOME_FTP_URL = (
|
||||
settings.AWS_JUSTICE40_DATASOURCES_URL
|
||||
+ "/2014_to_2019_state_median_income.zip"
|
||||
+ "/2015_to_2019_state_median_income.zip"
|
||||
)
|
||||
self.STATE_MEDIAN_INCOME_FILE_PATH = (
|
||||
self.TMP_PATH / "2014_to_2019_state_median_income.csv"
|
||||
self.TMP_PATH / "2015_to_2019_state_median_income.csv"
|
||||
)
|
||||
|
||||
def _fips_from_censusdata_censusgeo(
|
||||
|
|
|
|||
|
|
@ -8,9 +8,7 @@ logger = get_module_logger(__name__)
|
|||
|
||||
class EJScreenETL(ExtractTransformLoad):
|
||||
def __init__(self):
|
||||
self.EJSCREEN_FTP_URL = (
|
||||
"https://gaftp.epa.gov/EJSCREEN/2019/EJSCREEN_2019_StatePctile.csv.zip"
|
||||
)
|
||||
self.EJSCREEN_FTP_URL = "https://gaftp.epa.gov/EJSCREEN/2019/EJSCREEN_2019_StatePctile.csv.zip"
|
||||
self.EJSCREEN_CSV = self.TMP_PATH / "EJSCREEN_2019_StatePctiles.csv"
|
||||
self.CSV_PATH = self.DATA_PATH / "dataset" / "ejscreen_2019"
|
||||
self.df: pd.DataFrame
|
||||
|
|
@ -20,6 +18,7 @@ class EJScreenETL(ExtractTransformLoad):
|
|||
super().extract(
|
||||
self.EJSCREEN_FTP_URL,
|
||||
self.TMP_PATH,
|
||||
verify=False, # EPA EJScreen end point has certificate issues often
|
||||
)
|
||||
|
||||
def transform(self) -> None:
|
||||
|
|
|
|||
|
|
@ -35,7 +35,9 @@ class HousingTransportationETL(ExtractTransformLoad):
|
|||
)
|
||||
|
||||
# New file name:
|
||||
tmp_csv_file_path = zip_file_dir / f"htaindex_data_blkgrps_{fips}.csv"
|
||||
tmp_csv_file_path = (
|
||||
zip_file_dir / f"htaindex_data_blkgrps_{fips}.csv"
|
||||
)
|
||||
tmp_df = pd.read_csv(filepath_or_buffer=tmp_csv_file_path)
|
||||
|
||||
dfs.append(tmp_df)
|
||||
|
|
@ -47,9 +49,9 @@ class HousingTransportationETL(ExtractTransformLoad):
|
|||
|
||||
# Rename and reformat block group ID
|
||||
self.df.rename(columns={"blkgrp": self.GEOID_FIELD_NAME}, inplace=True)
|
||||
self.df[self.GEOID_FIELD_NAME] = self.df[self.GEOID_FIELD_NAME].str.replace(
|
||||
'"', ""
|
||||
)
|
||||
self.df[self.GEOID_FIELD_NAME] = self.df[
|
||||
self.GEOID_FIELD_NAME
|
||||
].str.replace('"', "")
|
||||
|
||||
def load(self) -> None:
|
||||
logger.info("Saving Housing and Transportation Data")
|
||||
|
|
|
|||
|
|
@ -9,16 +9,16 @@ class HudHousingETL(ExtractTransformLoad):
|
|||
def __init__(self):
|
||||
self.OUTPUT_PATH = self.DATA_PATH / "dataset" / "hud_housing"
|
||||
self.GEOID_TRACT_FIELD_NAME = "GEOID10_TRACT"
|
||||
self.HOUSING_FTP_URL = (
|
||||
"https://www.huduser.gov/portal/datasets/cp/2012thru2016-140-csv.zip"
|
||||
)
|
||||
self.HOUSING_FTP_URL = "https://www.huduser.gov/portal/datasets/cp/2012thru2016-140-csv.zip"
|
||||
self.HOUSING_ZIP_FILE_DIR = self.TMP_PATH / "hud_housing"
|
||||
|
||||
# We measure households earning less than 80% of HUD Area Median Family Income by county
|
||||
# and paying greater than 30% of their income to housing costs.
|
||||
self.HOUSING_BURDEN_FIELD_NAME = "Housing burden (percent)"
|
||||
self.HOUSING_BURDEN_NUMERATOR_FIELD_NAME = "HOUSING_BURDEN_NUMERATOR"
|
||||
self.HOUSING_BURDEN_DENOMINATOR_FIELD_NAME = "HOUSING_BURDEN_DENOMINATOR"
|
||||
self.HOUSING_BURDEN_DENOMINATOR_FIELD_NAME = (
|
||||
"HOUSING_BURDEN_DENOMINATOR"
|
||||
)
|
||||
|
||||
# Note: some variable definitions.
|
||||
# HUD-adjusted median family income (HAMFI).
|
||||
|
|
@ -55,7 +55,9 @@ class HudHousingETL(ExtractTransformLoad):
|
|||
)
|
||||
|
||||
# Rename and reformat block group ID
|
||||
self.df.rename(columns={"geoid": self.GEOID_TRACT_FIELD_NAME}, inplace=True)
|
||||
self.df.rename(
|
||||
columns={"geoid": self.GEOID_TRACT_FIELD_NAME}, inplace=True
|
||||
)
|
||||
|
||||
# The CHAS data has census tract ids such as `14000US01001020100`
|
||||
# Whereas the rest of our data uses, for the same tract, `01001020100`.
|
||||
|
|
@ -273,7 +275,9 @@ class HudHousingETL(ExtractTransformLoad):
|
|||
# TODO: add small sample size checks
|
||||
self.df[self.HOUSING_BURDEN_FIELD_NAME] = self.df[
|
||||
self.HOUSING_BURDEN_NUMERATOR_FIELD_NAME
|
||||
].astype(float) / self.df[self.HOUSING_BURDEN_DENOMINATOR_FIELD_NAME].astype(
|
||||
].astype(float) / self.df[
|
||||
self.HOUSING_BURDEN_DENOMINATOR_FIELD_NAME
|
||||
].astype(
|
||||
float
|
||||
)
|
||||
|
||||
|
|
|
|||
|
|
@ -18,7 +18,9 @@ class HudRecapETL(ExtractTransformLoad):
|
|||
self.CSV_PATH = self.DATA_PATH / "dataset" / "hud_recap"
|
||||
|
||||
# Definining some variable names
|
||||
self.HUD_RECAP_PRIORITY_COMMUNITY_FIELD_NAME = "hud_recap_priority_community"
|
||||
self.HUD_RECAP_PRIORITY_COMMUNITY_FIELD_NAME = (
|
||||
"hud_recap_priority_community"
|
||||
)
|
||||
|
||||
self.df: pd.DataFrame
|
||||
|
||||
|
|
|
|||
|
|
@ -8,9 +8,7 @@ logger = get_module_logger(__name__)
|
|||
|
||||
class TreeEquityScoreETL(ExtractTransformLoad):
|
||||
def __init__(self):
|
||||
self.TES_URL = (
|
||||
"https://national-tes-data-share.s3.amazonaws.com/national_tes_share/"
|
||||
)
|
||||
self.TES_URL = "https://national-tes-data-share.s3.amazonaws.com/national_tes_share/"
|
||||
self.TES_CSV = self.TMP_PATH / "tes_2021_data.csv"
|
||||
self.CSV_PATH = self.DATA_PATH / "dataset" / "tree_equity_score"
|
||||
self.df: gpd.GeoDataFrame
|
||||
|
|
@ -78,8 +76,12 @@ class TreeEquityScoreETL(ExtractTransformLoad):
|
|||
logger.info("Transforming Tree Equity Score Data")
|
||||
tes_state_dfs = []
|
||||
for state in self.states:
|
||||
tes_state_dfs.append(gpd.read_file(f"{self.TMP_PATH}/{state}/{state}.shp"))
|
||||
self.df = gpd.GeoDataFrame(pd.concat(tes_state_dfs), crs=tes_state_dfs[0].crs)
|
||||
tes_state_dfs.append(
|
||||
gpd.read_file(f"{self.TMP_PATH}/{state}/{state}.shp")
|
||||
)
|
||||
self.df = gpd.GeoDataFrame(
|
||||
pd.concat(tes_state_dfs), crs=tes_state_dfs[0].crs
|
||||
)
|
||||
|
||||
def load(self) -> None:
|
||||
logger.info("Saving Tree Equity Score GeoJSON")
|
||||
|
|
|
|||
|
|
@ -3,9 +3,6 @@
|
|||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "0491828b",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"import censusdata\n",
|
||||
|
|
@ -32,30 +29,26 @@
|
|||
"# Some display settings to make pandas outputs more readable.\n",
|
||||
"pd.set_option(\"display.expand_frame_repr\", False)\n",
|
||||
"pd.set_option(\"display.precision\", 2)"
|
||||
]
|
||||
],
|
||||
"outputs": [],
|
||||
"metadata": {}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "654f25a1",
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Following the tutorial at https://jtleider.github.io/censusdata/example1.html.\n",
|
||||
"# Full list of fields is at https://www2.census.gov/programs-surveys/acs/summary_file/2019/documentation/user_tools/ACS2019_Table_Shells.xlsx\n",
|
||||
"censusdata.printtable(censusdata.censustable(src=\"acs5\", year=ACS_YEAR, table=\"B19013\"))"
|
||||
]
|
||||
],
|
||||
"outputs": [],
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "8999cea4",
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def fips_from_censusdata_censusgeo(censusgeo: censusdata.censusgeo) -> str:\n",
|
||||
" \"\"\"Create a FIPS code from the proprietary censusgeo index.\"\"\"\n",
|
||||
|
|
@ -85,31 +78,33 @@
|
|||
"df[GEOID_FIELD_NAME] = df.index.to_series().apply(func=fips_from_censusdata_censusgeo)\n",
|
||||
"\n",
|
||||
"df.head()"
|
||||
]
|
||||
],
|
||||
"outputs": [],
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "2a269bb1",
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"columns_to_include = [\"GEOID2\", \"Median household income (State)\"]\n",
|
||||
"\n",
|
||||
"df.rename(columns={\"GEOID10\": \"GEOID2\", \"B19013_001E\": \"Median household income (State)\"}, inplace=True)\n",
|
||||
"\n",
|
||||
"df[columns_to_include].to_csv(path_or_buf= \"/Users/lucas/Documents/usds/repos/justice40-tool/data/data-pipeline/data_pipeline/data/needs_to_be_moved_to_s3/2014_to_2019_state_median_income.csv\", index=False)"
|
||||
]
|
||||
"# df[columns_to_include].to_csv(path_or_buf= \"/Users/lucas/Documents/usds/repos/justice40-tool/data/data-pipeline/data_pipeline/data/needs_to_be_moved_to_s3/2014_to_2019_state_median_income.csv\", index=False)"
|
||||
],
|
||||
"outputs": [],
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "91932af5",
|
||||
"metadata": {},
|
||||
"source": [],
|
||||
"outputs": [],
|
||||
"source": []
|
||||
"metadata": {}
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
|
|
@ -133,4 +128,4 @@
|
|||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
||||
}
|
||||
|
|
@ -28,7 +28,7 @@
|
|||
"from datetime import datetime\n",
|
||||
"from tqdm.notebook import tqdm_notebook\n",
|
||||
"\n",
|
||||
"module_path = os.path.abspath(os.path.join(\"..\"))\n",
|
||||
"module_path = os.path.abspath(os.path.join(\"../..\"))\n",
|
||||
"if module_path not in sys.path:\n",
|
||||
" sys.path.append(module_path)\n",
|
||||
"\n",
|
||||
|
|
@ -215,7 +215,7 @@
|
|||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "8b795fb4",
|
||||
"id": "274f6bc6",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -234,6 +234,21 @@
|
|||
"# (`census_tract_indices`).\n",
|
||||
"census_block_group_indices = [\n",
|
||||
" Index(\n",
|
||||
" method_name=\"Score F\",\n",
|
||||
" priority_communities_field=\"Score F (communities)\",\n",
|
||||
" other_census_tract_fields_to_keep=[],\n",
|
||||
" ),\n",
|
||||
" Index(\n",
|
||||
" method_name=\"Score F (socioeconomic only)\",\n",
|
||||
" priority_communities_field=\"Meets socioeconomic criteria\",\n",
|
||||
" other_census_tract_fields_to_keep=[],\n",
|
||||
" ),\n",
|
||||
" Index(\n",
|
||||
" method_name=\"Score F (burden only)\",\n",
|
||||
" priority_communities_field=\"Meets burden criteria\",\n",
|
||||
" other_census_tract_fields_to_keep=[],\n",
|
||||
" ),\n",
|
||||
" Index(\n",
|
||||
" method_name=\"Score A\",\n",
|
||||
" priority_communities_field=\"Score A (top 25th percentile)\",\n",
|
||||
" other_census_tract_fields_to_keep=[],\n",
|
||||
|
|
@ -253,21 +268,21 @@
|
|||
" priority_communities_field=\"Score D (top 25th percentile)\",\n",
|
||||
" other_census_tract_fields_to_keep=[],\n",
|
||||
" ),\n",
|
||||
" Index(\n",
|
||||
" method_name=\"Score D (30th percentile)\",\n",
|
||||
" priority_communities_field=\"Score D (top 30th percentile)\",\n",
|
||||
" other_census_tract_fields_to_keep=[],\n",
|
||||
" ),\n",
|
||||
" Index(\n",
|
||||
" method_name=\"Score D (35th percentile)\",\n",
|
||||
" priority_communities_field=\"Score D (top 35th percentile)\",\n",
|
||||
" other_census_tract_fields_to_keep=[],\n",
|
||||
" ),\n",
|
||||
" Index(\n",
|
||||
" method_name=\"Score D (40th percentile)\",\n",
|
||||
" priority_communities_field=\"Score D (top 40th percentile)\",\n",
|
||||
" other_census_tract_fields_to_keep=[],\n",
|
||||
" ),\n",
|
||||
"# Index(\n",
|
||||
"# method_name=\"Score D (30th percentile)\",\n",
|
||||
"# priority_communities_field=\"Score D (top 30th percentile)\",\n",
|
||||
"# other_census_tract_fields_to_keep=[],\n",
|
||||
"# ),\n",
|
||||
"# Index(\n",
|
||||
"# method_name=\"Score D (35th percentile)\",\n",
|
||||
"# priority_communities_field=\"Score D (top 35th percentile)\",\n",
|
||||
"# other_census_tract_fields_to_keep=[],\n",
|
||||
"# ),\n",
|
||||
"# Index(\n",
|
||||
"# method_name=\"Score D (40th percentile)\",\n",
|
||||
"# priority_communities_field=\"Score D (top 40th percentile)\",\n",
|
||||
"# other_census_tract_fields_to_keep=[],\n",
|
||||
"# ),\n",
|
||||
" Index(\n",
|
||||
" method_name=\"Poverty\",\n",
|
||||
" priority_communities_field=\"Poverty (Less than 200% of federal poverty line) (top 25th percentile)\",\n",
|
||||
|
|
@ -534,7 +549,7 @@
|
|||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "d7acf80d",
|
||||
"id": "eeb9699d",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -682,7 +697,7 @@
|
|||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "777a4623",
|
||||
"id": "4f44426c",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
|
|
@ -1140,14 +1155,6 @@
|
|||
"\n",
|
||||
"print(file_paths)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "e679502a",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
|
|
|
|||
|
|
@ -98,11 +98,50 @@ def remove_all_dirs_from_dir(dir_path: Path) -> None:
|
|||
logging.info(f"Removing directory {file_path}")
|
||||
|
||||
|
||||
def download_file_from_url(
|
||||
file_url: str,
|
||||
download_file_name: Path,
|
||||
verify: bool = True,
|
||||
) -> str:
|
||||
"""Downloads a file from a remote URL location and returns the file location.
|
||||
|
||||
Args:
|
||||
file_url (str): URL where the zip file is located
|
||||
download_file_name (pathlib.Path): file path where the file will be downloaded (called downloaded.zip by default)
|
||||
verify (bool): A flag to check if the certificate is valid. If truthy, an invalid certificate will throw an error (optional, default to False)
|
||||
|
||||
Returns:
|
||||
None
|
||||
|
||||
"""
|
||||
# disable https warning
|
||||
urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
|
||||
|
||||
if not os.path.isdir(download_file_name.parent):
|
||||
os.mkdir(download_file_name.parent)
|
||||
|
||||
logger.info(f"Downloading {file_url}")
|
||||
response = requests.get(file_url, verify=verify)
|
||||
if response.status_code == 200:
|
||||
file_contents = response.content
|
||||
else:
|
||||
sys.exit(
|
||||
f"HTTP response {response.status_code} from url {file_url}. Info: {response.content}"
|
||||
)
|
||||
|
||||
# Write the contents to disk.
|
||||
file = open(download_file_name, "wb")
|
||||
file.write(file_contents)
|
||||
file.close()
|
||||
|
||||
return download_file_name
|
||||
|
||||
|
||||
def unzip_file_from_url(
|
||||
file_url: str,
|
||||
download_path: Path,
|
||||
unzipped_file_path: Path,
|
||||
verify: bool = False,
|
||||
verify: bool = True,
|
||||
) -> None:
|
||||
"""Downloads a zip file from a remote URL location and unzips it in a specific directory, removing the temporary file after
|
||||
|
||||
|
|
@ -116,23 +155,11 @@ def unzip_file_from_url(
|
|||
None
|
||||
|
||||
"""
|
||||
|
||||
# disable https warning
|
||||
urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)
|
||||
|
||||
logger.info(f"Downloading {file_url}")
|
||||
response = requests.get(file_url, verify=verify)
|
||||
if response.status_code == 200:
|
||||
file_contents = response.content
|
||||
else:
|
||||
sys.exit(
|
||||
f"HTTP response {response.status_code} from url {file_url}. Info: {response.content}"
|
||||
)
|
||||
|
||||
zip_file_path = download_path / "downloaded.zip"
|
||||
zip_file = open(zip_file_path, "wb")
|
||||
zip_file.write(file_contents)
|
||||
zip_file.close()
|
||||
zip_file_path = download_file_from_url(
|
||||
file_url=file_url,
|
||||
download_file_name=download_path / "downloaded.zip",
|
||||
verify=verify,
|
||||
)
|
||||
|
||||
logger.info(f"Extracting {zip_file_path}")
|
||||
with zipfile.ZipFile(zip_file_path, "r") as zip_ref:
|
||||
|
|
|
|||
201
data/data-pipeline/poetry.lock
generated
201
data/data-pipeline/poetry.lock
generated
|
|
@ -14,6 +14,20 @@ category = "main"
|
|||
optional = false
|
||||
python-versions = "*"
|
||||
|
||||
[[package]]
|
||||
name = "argcomplete"
|
||||
version = "1.12.3"
|
||||
description = "Bash tab completion for argparse"
|
||||
category = "main"
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
|
||||
[package.dependencies]
|
||||
importlib-metadata = {version = ">=0.23,<5", markers = "python_version == \"3.7\""}
|
||||
|
||||
[package.extras]
|
||||
test = ["coverage", "flake8", "pexpect", "wheel"]
|
||||
|
||||
[[package]]
|
||||
name = "argon2-cffi"
|
||||
version = "20.1.0"
|
||||
|
|
@ -237,6 +251,17 @@ python-versions = ">=3.6"
|
|||
docs = ["sphinx", "jaraco.packaging (>=8.2)", "rst.linker (>=1.9)"]
|
||||
testing = ["pytest (>=4.6)", "pytest-checkdocs (>=1.2.3)", "pytest-flake8", "pytest-cov", "pytest-enabler", "pytest-black (>=0.3.7)", "pytest-mypy"]
|
||||
|
||||
[[package]]
|
||||
name = "cycler"
|
||||
version = "0.10.0"
|
||||
description = "Composable style cycles"
|
||||
category = "main"
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
|
||||
[package.dependencies]
|
||||
six = "*"
|
||||
|
||||
[[package]]
|
||||
name = "debugpy"
|
||||
version = "1.4.1"
|
||||
|
|
@ -387,7 +412,7 @@ python-versions = ">=3.5"
|
|||
|
||||
[[package]]
|
||||
name = "importlib-metadata"
|
||||
version = "3.10.1"
|
||||
version = "4.6.3"
|
||||
description = "Read metadata from Python packages"
|
||||
category = "main"
|
||||
optional = false
|
||||
|
|
@ -399,7 +424,8 @@ zipp = ">=0.5"
|
|||
|
||||
[package.extras]
|
||||
docs = ["sphinx", "jaraco.packaging (>=8.2)", "rst.linker (>=1.9)"]
|
||||
testing = ["pytest (>=4.6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "pytest-cov", "pytest-enabler (>=1.0.1)", "packaging", "pep517", "pyfakefs", "flufl.flake8", "pytest-black (>=0.3.7)", "pytest-mypy", "importlib-resources (>=1.3)"]
|
||||
perf = ["ipython"]
|
||||
testing = ["pytest (>=4.6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "pytest-cov", "pytest-enabler (>=1.0.1)", "packaging", "pep517", "pyfakefs", "flufl.flake8", "pytest-perf (>=0.9.2)", "pytest-black (>=0.3.7)", "pytest-mypy", "importlib-resources (>=1.3)"]
|
||||
|
||||
[[package]]
|
||||
name = "iniconfig"
|
||||
|
|
@ -411,7 +437,7 @@ python-versions = "*"
|
|||
|
||||
[[package]]
|
||||
name = "ipykernel"
|
||||
version = "6.0.3"
|
||||
version = "6.1.0"
|
||||
description = "IPython Kernel for Jupyter"
|
||||
category = "main"
|
||||
optional = false
|
||||
|
|
@ -419,8 +445,9 @@ python-versions = ">=3.7"
|
|||
|
||||
[package.dependencies]
|
||||
appnope = {version = "*", markers = "platform_system == \"Darwin\""}
|
||||
argcomplete = {version = ">=1.12.3", markers = "python_version < \"3.8.0\""}
|
||||
debugpy = ">=1.0.0,<2.0"
|
||||
importlib-metadata = {version = "<4", markers = "python_version < \"3.8.0\""}
|
||||
importlib-metadata = {version = "<5", markers = "python_version < \"3.8.0\""}
|
||||
ipython = ">=7.23.1,<8.0"
|
||||
jupyter-client = "<7.0"
|
||||
matplotlib-inline = ">=0.1.0,<0.2.0"
|
||||
|
|
@ -727,6 +754,14 @@ category = "main"
|
|||
optional = false
|
||||
python-versions = ">=3.6"
|
||||
|
||||
[[package]]
|
||||
name = "kiwisolver"
|
||||
version = "1.3.1"
|
||||
description = "A fast implementation of the Cassowary constraint solver"
|
||||
category = "main"
|
||||
optional = false
|
||||
python-versions = ">=3.6"
|
||||
|
||||
[[package]]
|
||||
name = "lazy-object-proxy"
|
||||
version = "1.6.0"
|
||||
|
|
@ -770,6 +805,22 @@ category = "main"
|
|||
optional = false
|
||||
python-versions = ">=3.6"
|
||||
|
||||
[[package]]
|
||||
name = "matplotlib"
|
||||
version = "3.4.3"
|
||||
description = "Python plotting package"
|
||||
category = "main"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
|
||||
[package.dependencies]
|
||||
cycler = ">=0.10"
|
||||
kiwisolver = ">=1.0.1"
|
||||
numpy = ">=1.16"
|
||||
pillow = ">=6.2.0"
|
||||
pyparsing = ">=2.2.1"
|
||||
python-dateutil = ">=2.7"
|
||||
|
||||
[[package]]
|
||||
name = "matplotlib-inline"
|
||||
version = "0.1.2"
|
||||
|
|
@ -916,7 +967,7 @@ python-versions = ">=3.5"
|
|||
|
||||
[[package]]
|
||||
name = "notebook"
|
||||
version = "6.4.2"
|
||||
version = "6.4.3"
|
||||
description = "A web-based notebook environment for interactive computing"
|
||||
category = "main"
|
||||
optional = false
|
||||
|
|
@ -1036,6 +1087,14 @@ category = "main"
|
|||
optional = false
|
||||
python-versions = "*"
|
||||
|
||||
[[package]]
|
||||
name = "pillow"
|
||||
version = "8.3.1"
|
||||
description = "Python Imaging Library (Fork)"
|
||||
category = "main"
|
||||
optional = false
|
||||
python-versions = ">=3.6"
|
||||
|
||||
[[package]]
|
||||
name = "platformdirs"
|
||||
version = "2.2.0"
|
||||
|
|
@ -1373,7 +1432,7 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*"
|
|||
|
||||
[[package]]
|
||||
name = "terminado"
|
||||
version = "0.10.1"
|
||||
version = "0.11.0"
|
||||
description = "Tornado websocket backend for the Xterm.js Javascript terminal emulator library."
|
||||
category = "main"
|
||||
optional = false
|
||||
|
|
@ -1525,7 +1584,7 @@ jellyfish = "0.6.1"
|
|||
|
||||
[[package]]
|
||||
name = "virtualenv"
|
||||
version = "20.7.1"
|
||||
version = "20.7.2"
|
||||
description = "Virtual Python Environment builder"
|
||||
category = "dev"
|
||||
optional = false
|
||||
|
|
@ -1601,7 +1660,7 @@ testing = ["pytest (>=4.6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "pytes
|
|||
[metadata]
|
||||
lock-version = "1.1"
|
||||
python-versions = "^3.7.1"
|
||||
content-hash = "14f5225f41212af2785865f984c8ce402712df0398b59c460cb849a0792b3f17"
|
||||
content-hash = "7fd9dc6d4902d1010da038192ba1a074d9c50264cb4b5d5fabfc45869f9f718a"
|
||||
|
||||
[metadata.files]
|
||||
appdirs = [
|
||||
|
|
@ -1612,6 +1671,10 @@ appnope = [
|
|||
{file = "appnope-0.1.2-py2.py3-none-any.whl", hash = "sha256:93aa393e9d6c54c5cd570ccadd8edad61ea0c4b9ea7a01409020c9aa019eb442"},
|
||||
{file = "appnope-0.1.2.tar.gz", hash = "sha256:dd83cd4b5b460958838f6eb3000c660b1f9caf2a5b1de4264e941512f603258a"},
|
||||
]
|
||||
argcomplete = [
|
||||
{file = "argcomplete-1.12.3-py2.py3-none-any.whl", hash = "sha256:291f0beca7fd49ce285d2f10e4c1c77e9460cf823eef2de54df0c0fec88b0d81"},
|
||||
{file = "argcomplete-1.12.3.tar.gz", hash = "sha256:2c7dbffd8c045ea534921e63b0be6fe65e88599990d8dc408ac8c542b72a5445"},
|
||||
]
|
||||
argon2-cffi = [
|
||||
{file = "argon2-cffi-20.1.0.tar.gz", hash = "sha256:d8029b2d3e4b4cea770e9e5a0104dd8fa185c1724a0f01528ae4826a6d25f97d"},
|
||||
{file = "argon2_cffi-20.1.0-cp27-cp27m-macosx_10_6_intel.whl", hash = "sha256:6ea92c980586931a816d61e4faf6c192b4abce89aa767ff6581e6ddc985ed003"},
|
||||
|
|
@ -1741,6 +1804,10 @@ configparser = [
|
|||
{file = "configparser-5.0.2-py3-none-any.whl", hash = "sha256:af59f2cdd7efbdd5d111c1976ecd0b82db9066653362f0962d7bf1d3ab89a1fa"},
|
||||
{file = "configparser-5.0.2.tar.gz", hash = "sha256:85d5de102cfe6d14a5172676f09d19c465ce63d6019cf0a4ef13385fc535e828"},
|
||||
]
|
||||
cycler = [
|
||||
{file = "cycler-0.10.0-py2.py3-none-any.whl", hash = "sha256:1d8a5ae1ff6c5cf9b93e8811e581232ad8920aeec647c37316ceac982b08cb2d"},
|
||||
{file = "cycler-0.10.0.tar.gz", hash = "sha256:cd7b2d1018258d7247a71425e9f26463dfb444d411c39569972f4ce586b0c9d8"},
|
||||
]
|
||||
debugpy = [
|
||||
{file = "debugpy-1.4.1-cp27-cp27m-macosx_10_14_x86_64.whl", hash = "sha256:a2c5a1c49239707ed5bc8e97d8f9252fb392d9e13c79c7b477593d7dde4ae24a"},
|
||||
{file = "debugpy-1.4.1-cp27-cp27m-manylinux1_i686.whl", hash = "sha256:ebc241351791595796864a960892e1cd58627064feda939d0377edd0730bbff2"},
|
||||
|
|
@ -1855,16 +1922,16 @@ idna = [
|
|||
{file = "idna-3.2.tar.gz", hash = "sha256:467fbad99067910785144ce333826c71fb0e63a425657295239737f7ecd125f3"},
|
||||
]
|
||||
importlib-metadata = [
|
||||
{file = "importlib_metadata-3.10.1-py3-none-any.whl", hash = "sha256:2ec0faae539743ae6aaa84b49a169670a465f7f5d64e6add98388cc29fd1f2f6"},
|
||||
{file = "importlib_metadata-3.10.1.tar.gz", hash = "sha256:c9356b657de65c53744046fa8f7358afe0714a1af7d570c00c3835c2d724a7c1"},
|
||||
{file = "importlib_metadata-4.6.3-py3-none-any.whl", hash = "sha256:51c6635429c77cf1ae634c997ff9e53ca3438b495f10a55ba28594dd69764a8b"},
|
||||
{file = "importlib_metadata-4.6.3.tar.gz", hash = "sha256:0645585859e9a6689c523927a5032f2ba5919f1f7d0e84bd4533312320de1ff9"},
|
||||
]
|
||||
iniconfig = [
|
||||
{file = "iniconfig-1.1.1-py2.py3-none-any.whl", hash = "sha256:011e24c64b7f47f6ebd835bb12a743f2fbe9a26d4cecaa7f53bc4f35ee9da8b3"},
|
||||
{file = "iniconfig-1.1.1.tar.gz", hash = "sha256:bc3af051d7d14b2ee5ef9969666def0cd1a000e121eaea580d4a313df4b37f32"},
|
||||
]
|
||||
ipykernel = [
|
||||
{file = "ipykernel-6.0.3-py3-none-any.whl", hash = "sha256:9f9f41a14caf2fde2b7802446adf83885afcbf50585a46d6c687292599a3c3af"},
|
||||
{file = "ipykernel-6.0.3.tar.gz", hash = "sha256:0df34a78c7e1422800d6078cde65ccdcdb859597046c338c759db4dbc535c58f"},
|
||||
{file = "ipykernel-6.1.0-py3-none-any.whl", hash = "sha256:804202fb4a621dce163bf88ce2687c98450d7ed728ef1d17d6f5ed20744c6e02"},
|
||||
{file = "ipykernel-6.1.0.tar.gz", hash = "sha256:e21a718c696ded7d4d5e25b13d2bdd88e099e782fd3be66f9d2e66397543d283"},
|
||||
]
|
||||
ipython = [
|
||||
{file = "ipython-7.26.0-py3-none-any.whl", hash = "sha256:892743b65c21ed72b806a3a602cca408520b3200b89d1924f4b3d2cdb3692362"},
|
||||
|
|
@ -1940,6 +2007,40 @@ jupyterlab-widgets = [
|
|||
{file = "jupyterlab_widgets-1.0.0-py3-none-any.whl", hash = "sha256:caeaf3e6103180e654e7d8d2b81b7d645e59e432487c1d35a41d6d3ee56b3fef"},
|
||||
{file = "jupyterlab_widgets-1.0.0.tar.gz", hash = "sha256:5c1a29a84d3069208cb506b10609175b249b6486d6b1cbae8fcde2a11584fb78"},
|
||||
]
|
||||
kiwisolver = [
|
||||
{file = "kiwisolver-1.3.1-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:fd34fbbfbc40628200730bc1febe30631347103fc8d3d4fa012c21ab9c11eca9"},
|
||||
{file = "kiwisolver-1.3.1-cp36-cp36m-manylinux1_i686.whl", hash = "sha256:d3155d828dec1d43283bd24d3d3e0d9c7c350cdfcc0bd06c0ad1209c1bbc36d0"},
|
||||
{file = "kiwisolver-1.3.1-cp36-cp36m-manylinux1_x86_64.whl", hash = "sha256:5a7a7dbff17e66fac9142ae2ecafb719393aaee6a3768c9de2fd425c63b53e21"},
|
||||
{file = "kiwisolver-1.3.1-cp36-cp36m-manylinux2014_aarch64.whl", hash = "sha256:f8d6f8db88049a699817fd9178782867bf22283e3813064302ac59f61d95be05"},
|
||||
{file = "kiwisolver-1.3.1-cp36-cp36m-manylinux2014_ppc64le.whl", hash = "sha256:5f6ccd3dd0b9739edcf407514016108e2280769c73a85b9e59aa390046dbf08b"},
|
||||
{file = "kiwisolver-1.3.1-cp36-cp36m-win32.whl", hash = "sha256:225e2e18f271e0ed8157d7f4518ffbf99b9450fca398d561eb5c4a87d0986dd9"},
|
||||
{file = "kiwisolver-1.3.1-cp36-cp36m-win_amd64.whl", hash = "sha256:cf8b574c7b9aa060c62116d4181f3a1a4e821b2ec5cbfe3775809474113748d4"},
|
||||
{file = "kiwisolver-1.3.1-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:232c9e11fd7ac3a470d65cd67e4359eee155ec57e822e5220322d7b2ac84fbf0"},
|
||||
{file = "kiwisolver-1.3.1-cp37-cp37m-manylinux1_i686.whl", hash = "sha256:b38694dcdac990a743aa654037ff1188c7a9801ac3ccc548d3341014bc5ca278"},
|
||||
{file = "kiwisolver-1.3.1-cp37-cp37m-manylinux1_x86_64.whl", hash = "sha256:ca3820eb7f7faf7f0aa88de0e54681bddcb46e485beb844fcecbcd1c8bd01689"},
|
||||
{file = "kiwisolver-1.3.1-cp37-cp37m-manylinux2014_aarch64.whl", hash = "sha256:c8fd0f1ae9d92b42854b2979024d7597685ce4ada367172ed7c09edf2cef9cb8"},
|
||||
{file = "kiwisolver-1.3.1-cp37-cp37m-manylinux2014_ppc64le.whl", hash = "sha256:1e1bc12fb773a7b2ffdeb8380609f4f8064777877b2225dec3da711b421fda31"},
|
||||
{file = "kiwisolver-1.3.1-cp37-cp37m-win32.whl", hash = "sha256:72c99e39d005b793fb7d3d4e660aed6b6281b502e8c1eaf8ee8346023c8e03bc"},
|
||||
{file = "kiwisolver-1.3.1-cp37-cp37m-win_amd64.whl", hash = "sha256:8be8d84b7d4f2ba4ffff3665bcd0211318aa632395a1a41553250484a871d454"},
|
||||
{file = "kiwisolver-1.3.1-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:31dfd2ac56edc0ff9ac295193eeaea1c0c923c0355bf948fbd99ed6018010b72"},
|
||||
{file = "kiwisolver-1.3.1-cp38-cp38-manylinux1_i686.whl", hash = "sha256:563c649cfdef27d081c84e72a03b48ea9408c16657500c312575ae9d9f7bc1c3"},
|
||||
{file = "kiwisolver-1.3.1-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:78751b33595f7f9511952e7e60ce858c6d64db2e062afb325985ddbd34b5c131"},
|
||||
{file = "kiwisolver-1.3.1-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:a357fd4f15ee49b4a98b44ec23a34a95f1e00292a139d6015c11f55774ef10de"},
|
||||
{file = "kiwisolver-1.3.1-cp38-cp38-manylinux2014_ppc64le.whl", hash = "sha256:5989db3b3b34b76c09253deeaf7fbc2707616f130e166996606c284395da3f18"},
|
||||
{file = "kiwisolver-1.3.1-cp38-cp38-win32.whl", hash = "sha256:c08e95114951dc2090c4a630c2385bef681cacf12636fb0241accdc6b303fd81"},
|
||||
{file = "kiwisolver-1.3.1-cp38-cp38-win_amd64.whl", hash = "sha256:44a62e24d9b01ba94ae7a4a6c3fb215dc4af1dde817e7498d901e229aaf50e4e"},
|
||||
{file = "kiwisolver-1.3.1-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:50af681a36b2a1dee1d3c169ade9fdc59207d3c31e522519181e12f1b3ba7000"},
|
||||
{file = "kiwisolver-1.3.1-cp39-cp39-manylinux1_i686.whl", hash = "sha256:a53d27d0c2a0ebd07e395e56a1fbdf75ffedc4a05943daf472af163413ce9598"},
|
||||
{file = "kiwisolver-1.3.1-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:834ee27348c4aefc20b479335fd422a2c69db55f7d9ab61721ac8cd83eb78882"},
|
||||
{file = "kiwisolver-1.3.1-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:5c3e6455341008a054cccee8c5d24481bcfe1acdbc9add30aa95798e95c65621"},
|
||||
{file = "kiwisolver-1.3.1-cp39-cp39-manylinux2014_ppc64le.whl", hash = "sha256:acef3d59d47dd85ecf909c359d0fd2c81ed33bdff70216d3956b463e12c38a54"},
|
||||
{file = "kiwisolver-1.3.1-cp39-cp39-win32.whl", hash = "sha256:c5518d51a0735b1e6cee1fdce66359f8d2b59c3ca85dc2b0813a8aa86818a030"},
|
||||
{file = "kiwisolver-1.3.1-cp39-cp39-win_amd64.whl", hash = "sha256:b9edd0110a77fc321ab090aaa1cfcaba1d8499850a12848b81be2222eab648f6"},
|
||||
{file = "kiwisolver-1.3.1-pp36-pypy36_pp73-macosx_10_9_x86_64.whl", hash = "sha256:0cd53f403202159b44528498de18f9285b04482bab2a6fc3f5dd8dbb9352e30d"},
|
||||
{file = "kiwisolver-1.3.1-pp36-pypy36_pp73-manylinux2010_x86_64.whl", hash = "sha256:33449715e0101e4d34f64990352bce4095c8bf13bed1b390773fc0a7295967b3"},
|
||||
{file = "kiwisolver-1.3.1-pp36-pypy36_pp73-win32.whl", hash = "sha256:401a2e9afa8588589775fe34fc22d918ae839aaaf0c0e96441c0fdbce6d8ebe6"},
|
||||
{file = "kiwisolver-1.3.1.tar.gz", hash = "sha256:950a199911a8d94683a6b10321f9345d5a3a8433ec58b217ace979e18f16e248"},
|
||||
]
|
||||
lazy-object-proxy = [
|
||||
{file = "lazy-object-proxy-1.6.0.tar.gz", hash = "sha256:489000d368377571c6f982fba6497f2aa13c6d1facc40660963da62f5c379726"},
|
||||
{file = "lazy_object_proxy-1.6.0-cp27-cp27m-macosx_10_14_x86_64.whl", hash = "sha256:c6938967f8528b3668622a9ed3b31d145fab161a32f5891ea7b84f6b790be05b"},
|
||||
|
|
@ -2052,6 +2153,29 @@ markupsafe = [
|
|||
{file = "MarkupSafe-2.0.1-cp39-cp39-win_amd64.whl", hash = "sha256:693ce3f9e70a6cf7d2fb9e6c9d8b204b6b39897a2c4a1aa65728d5ac97dcc1d8"},
|
||||
{file = "MarkupSafe-2.0.1.tar.gz", hash = "sha256:594c67807fb16238b30c44bdf74f36c02cdf22d1c8cda91ef8a0ed8dabf5620a"},
|
||||
]
|
||||
matplotlib = [
|
||||
{file = "matplotlib-3.4.3-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:5c988bb43414c7c2b0a31bd5187b4d27fd625c080371b463a6d422047df78913"},
|
||||
{file = "matplotlib-3.4.3-cp37-cp37m-manylinux1_i686.whl", hash = "sha256:f1c5efc278d996af8a251b2ce0b07bbeccb821f25c8c9846bdcb00ffc7f158aa"},
|
||||
{file = "matplotlib-3.4.3-cp37-cp37m-manylinux1_x86_64.whl", hash = "sha256:eeb1859efe7754b1460e1d4991bbd4a60a56f366bc422ef3a9c5ae05f0bc70b5"},
|
||||
{file = "matplotlib-3.4.3-cp37-cp37m-manylinux2014_aarch64.whl", hash = "sha256:844a7b0233e4ff7fba57e90b8799edaa40b9e31e300b8d5efc350937fa8b1bea"},
|
||||
{file = "matplotlib-3.4.3-cp37-cp37m-win32.whl", hash = "sha256:85f0c9cf724715e75243a7b3087cf4a3de056b55e05d4d76cc58d610d62894f3"},
|
||||
{file = "matplotlib-3.4.3-cp37-cp37m-win_amd64.whl", hash = "sha256:c70b6311dda3e27672f1bf48851a0de816d1ca6aaf3d49365fbdd8e959b33d2b"},
|
||||
{file = "matplotlib-3.4.3-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:b884715a59fec9ad3b6048ecf3860f3b2ce965e676ef52593d6fa29abcf7d330"},
|
||||
{file = "matplotlib-3.4.3-cp38-cp38-manylinux1_i686.whl", hash = "sha256:a78a3b51f29448c7f4d4575e561f6b0dbb8d01c13c2046ab6c5220eb25c06506"},
|
||||
{file = "matplotlib-3.4.3-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:6a724e3a48a54b8b6e7c4ae38cd3d07084508fa47c410c8757e9db9791421838"},
|
||||
{file = "matplotlib-3.4.3-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:48e1e0859b54d5f2e29bb78ca179fd59b971c6ceb29977fb52735bfd280eb0f5"},
|
||||
{file = "matplotlib-3.4.3-cp38-cp38-win32.whl", hash = "sha256:01c9de93a2ca0d128c9064f23709362e7fefb34910c7c9e0b8ab0de8258d5eda"},
|
||||
{file = "matplotlib-3.4.3-cp38-cp38-win_amd64.whl", hash = "sha256:ebfb01a65c3f5d53a8c2a8133fec2b5221281c053d944ae81ff5822a68266617"},
|
||||
{file = "matplotlib-3.4.3-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:b8b53f336a4688cfce615887505d7e41fd79b3594bf21dd300531a4f5b4f746a"},
|
||||
{file = "matplotlib-3.4.3-cp39-cp39-manylinux1_i686.whl", hash = "sha256:fcd6f1954943c0c192bfbebbac263f839d7055409f1173f80d8b11a224d236da"},
|
||||
{file = "matplotlib-3.4.3-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:6be8df61b1626e1a142c57e065405e869e9429b4a6dab4a324757d0dc4d42235"},
|
||||
{file = "matplotlib-3.4.3-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:41b6e307458988891fcdea2d8ecf84a8c92d53f84190aa32da65f9505546e684"},
|
||||
{file = "matplotlib-3.4.3-cp39-cp39-win32.whl", hash = "sha256:f72657f1596199dc1e4e7a10f52a4784ead8a711f4e5b59bea95bdb97cf0e4fd"},
|
||||
{file = "matplotlib-3.4.3-cp39-cp39-win_amd64.whl", hash = "sha256:f15edcb0629a0801738925fe27070480f446fcaa15de65946ff946ad99a59a40"},
|
||||
{file = "matplotlib-3.4.3-pp37-pypy37_pp73-macosx_10_9_x86_64.whl", hash = "sha256:556965514b259204637c360d213de28d43a1f4aed1eca15596ce83f768c5a56f"},
|
||||
{file = "matplotlib-3.4.3-pp37-pypy37_pp73-manylinux2010_x86_64.whl", hash = "sha256:54a026055d5f8614f184e588f6e29064019a0aa8448450214c0b60926d62d919"},
|
||||
{file = "matplotlib-3.4.3.tar.gz", hash = "sha256:fc4f526dfdb31c9bd6b8ca06bf9fab663ca12f3ec9cdf4496fb44bc680140318"},
|
||||
]
|
||||
matplotlib-inline = [
|
||||
{file = "matplotlib-inline-0.1.2.tar.gz", hash = "sha256:f41d5ff73c9f5385775d5c0bc13b424535c8402fe70ea8210f93e11f3683993e"},
|
||||
{file = "matplotlib_inline-0.1.2-py3-none-any.whl", hash = "sha256:5cf1176f554abb4fa98cb362aa2b55c500147e4bdbb07e3fda359143e1da0811"},
|
||||
|
|
@ -2114,8 +2238,8 @@ nest-asyncio = [
|
|||
{file = "nest_asyncio-1.5.1.tar.gz", hash = "sha256:afc5a1c515210a23c461932765691ad39e8eba6551c055ac8d5546e69250d0aa"},
|
||||
]
|
||||
notebook = [
|
||||
{file = "notebook-6.4.2-py3-none-any.whl", hash = "sha256:5ae23d7f831a5788e8bd51a0ba65c486db3bfd43e9db97a62330b6273e3175e3"},
|
||||
{file = "notebook-6.4.2.tar.gz", hash = "sha256:ba9db5e5a9bd2d272b67e3de9143cca2be5125578f1c4f2902d7178ce2f0b4ff"},
|
||||
{file = "notebook-6.4.3-py3-none-any.whl", hash = "sha256:b50eafa8208d5db966efd1caa4076b4dfc51815e02a805b32ecd717e9e6cc071"},
|
||||
{file = "notebook-6.4.3.tar.gz", hash = "sha256:e6b6dfed36b00cf950f63c0d42e947c101d4258aec21624de62b9e0c11ed5c0d"},
|
||||
]
|
||||
numpy = [
|
||||
{file = "numpy-1.21.1-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:38e8648f9449a549a7dfe8d8755a5979b45b3538520d1e735637ef28e8c2dc50"},
|
||||
|
|
@ -2195,6 +2319,47 @@ 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-8.3.1-1-cp36-cp36m-win_amd64.whl", hash = "sha256:fd7eef578f5b2200d066db1b50c4aa66410786201669fb76d5238b007918fb24"},
|
||||
{file = "Pillow-8.3.1-1-cp37-cp37m-win_amd64.whl", hash = "sha256:75e09042a3b39e0ea61ce37e941221313d51a9c26b8e54e12b3ececccb71718a"},
|
||||
{file = "Pillow-8.3.1-1-cp38-cp38-win_amd64.whl", hash = "sha256:c0e0550a404c69aab1e04ae89cca3e2a042b56ab043f7f729d984bf73ed2a093"},
|
||||
{file = "Pillow-8.3.1-1-cp39-cp39-win_amd64.whl", hash = "sha256:479ab11cbd69612acefa8286481f65c5dece2002ffaa4f9db62682379ca3bb77"},
|
||||
{file = "Pillow-8.3.1-1-pp37-pypy37_pp73-win_amd64.whl", hash = "sha256:f156d6ecfc747ee111c167f8faf5f4953761b5e66e91a4e6767e548d0f80129c"},
|
||||
{file = "Pillow-8.3.1-cp36-cp36m-macosx_10_10_x86_64.whl", hash = "sha256:196560dba4da7a72c5e7085fccc5938ab4075fd37fe8b5468869724109812edd"},
|
||||
{file = "Pillow-8.3.1-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:29c9569049d04aaacd690573a0398dbd8e0bf0255684fee512b413c2142ab723"},
|
||||
{file = "Pillow-8.3.1-cp36-cp36m-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:c088a000dfdd88c184cc7271bfac8c5b82d9efa8637cd2b68183771e3cf56f04"},
|
||||
{file = "Pillow-8.3.1-cp36-cp36m-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:fc214a6b75d2e0ea7745488da7da3c381f41790812988c7a92345978414fad37"},
|
||||
{file = "Pillow-8.3.1-cp36-cp36m-win32.whl", hash = "sha256:a17ca41f45cf78c2216ebfab03add7cc350c305c38ff34ef4eef66b7d76c5229"},
|
||||
{file = "Pillow-8.3.1-cp36-cp36m-win_amd64.whl", hash = "sha256:67b3666b544b953a2777cb3f5a922e991be73ab32635666ee72e05876b8a92de"},
|
||||
{file = "Pillow-8.3.1-cp37-cp37m-macosx_10_10_x86_64.whl", hash = "sha256:ff04c373477723430dce2e9d024c708a047d44cf17166bf16e604b379bf0ca14"},
|
||||
{file = "Pillow-8.3.1-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9364c81b252d8348e9cc0cb63e856b8f7c1b340caba6ee7a7a65c968312f7dab"},
|
||||
{file = "Pillow-8.3.1-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:a2f381932dca2cf775811a008aa3027671ace723b7a38838045b1aee8669fdcf"},
|
||||
{file = "Pillow-8.3.1-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:d0da39795049a9afcaadec532e7b669b5ebbb2a9134576ebcc15dd5bdae33cc0"},
|
||||
{file = "Pillow-8.3.1-cp37-cp37m-win32.whl", hash = "sha256:2b6dfa068a8b6137da34a4936f5a816aba0ecc967af2feeb32c4393ddd671cba"},
|
||||
{file = "Pillow-8.3.1-cp37-cp37m-win_amd64.whl", hash = "sha256:a4eef1ff2d62676deabf076f963eda4da34b51bc0517c70239fafed1d5b51500"},
|
||||
{file = "Pillow-8.3.1-cp38-cp38-macosx_10_10_x86_64.whl", hash = "sha256:660a87085925c61a0dcc80efb967512ac34dbb256ff7dd2b9b4ee8dbdab58cf4"},
|
||||
{file = "Pillow-8.3.1-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:15a2808e269a1cf2131930183dcc0419bc77bb73eb54285dde2706ac9939fa8e"},
|
||||
{file = "Pillow-8.3.1-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:969cc558cca859cadf24f890fc009e1bce7d7d0386ba7c0478641a60199adf79"},
|
||||
{file = "Pillow-8.3.1-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:2ee77c14a0299d0541d26f3d8500bb57e081233e3fa915fa35abd02c51fa7fae"},
|
||||
{file = "Pillow-8.3.1-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:c11003197f908878164f0e6da15fce22373ac3fc320cda8c9d16e6bba105b844"},
|
||||
{file = "Pillow-8.3.1-cp38-cp38-win32.whl", hash = "sha256:3f08bd8d785204149b5b33e3b5f0ebbfe2190ea58d1a051c578e29e39bfd2367"},
|
||||
{file = "Pillow-8.3.1-cp38-cp38-win_amd64.whl", hash = "sha256:70af7d222df0ff81a2da601fab42decb009dc721545ed78549cb96e3a1c5f0c8"},
|
||||
{file = "Pillow-8.3.1-cp39-cp39-macosx_10_10_x86_64.whl", hash = "sha256:37730f6e68bdc6a3f02d2079c34c532330d206429f3cee651aab6b66839a9f0e"},
|
||||
{file = "Pillow-8.3.1-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:4bc3c7ef940eeb200ca65bd83005eb3aae8083d47e8fcbf5f0943baa50726856"},
|
||||
{file = "Pillow-8.3.1-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:c35d09db702f4185ba22bb33ef1751ad49c266534339a5cebeb5159d364f6f82"},
|
||||
{file = "Pillow-8.3.1-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:0b2efa07f69dc395d95bb9ef3299f4ca29bcb2157dc615bae0b42c3c20668ffc"},
|
||||
{file = "Pillow-8.3.1-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.whl", hash = "sha256:cc866706d56bd3a7dbf8bac8660c6f6462f2f2b8a49add2ba617bc0c54473d83"},
|
||||
{file = "Pillow-8.3.1-cp39-cp39-win32.whl", hash = "sha256:9a211b663cf2314edbdb4cf897beeb5c9ee3810d1d53f0e423f06d6ebbf9cd5d"},
|
||||
{file = "Pillow-8.3.1-cp39-cp39-win_amd64.whl", hash = "sha256:c2a5ff58751670292b406b9f06e07ed1446a4b13ffced6b6cab75b857485cbc8"},
|
||||
{file = "Pillow-8.3.1-pp36-pypy36_pp73-macosx_10_10_x86_64.whl", hash = "sha256:c379425c2707078dfb6bfad2430728831d399dc95a7deeb92015eb4c92345eaf"},
|
||||
{file = "Pillow-8.3.1-pp36-pypy36_pp73-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:114f816e4f73f9ec06997b2fde81a92cbf0777c9e8f462005550eed6bae57e63"},
|
||||
{file = "Pillow-8.3.1-pp36-pypy36_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:8960a8a9f4598974e4c2aeb1bff9bdd5db03ee65fd1fce8adf3223721aa2a636"},
|
||||
{file = "Pillow-8.3.1-pp37-pypy37_pp73-macosx_10_10_x86_64.whl", hash = "sha256:147bd9e71fb9dcf08357b4d530b5167941e222a6fd21f869c7911bac40b9994d"},
|
||||
{file = "Pillow-8.3.1-pp37-pypy37_pp73-manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:1fd5066cd343b5db88c048d971994e56b296868766e461b82fa4e22498f34d77"},
|
||||
{file = "Pillow-8.3.1-pp37-pypy37_pp73-manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:f4ebde71785f8bceb39dcd1e7f06bcc5d5c3cf48b9f69ab52636309387b097c8"},
|
||||
{file = "Pillow-8.3.1-pp37-pypy37_pp73-win_amd64.whl", hash = "sha256:1c03e24be975e2afe70dfc5da6f187eea0b49a68bb2b69db0f30a61b7031cee4"},
|
||||
{file = "Pillow-8.3.1.tar.gz", hash = "sha256:2cac53839bfc5cece8fdbe7f084d5e3ee61e1303cccc86511d351adcb9e2c792"},
|
||||
]
|
||||
platformdirs = [
|
||||
{file = "platformdirs-2.2.0-py3-none-any.whl", hash = "sha256:4666d822218db6a262bdfdc9c39d21f23b4cfdb08af331a81e92751daf6c866c"},
|
||||
{file = "platformdirs-2.2.0.tar.gz", hash = "sha256:632daad3ab546bd8e6af0537d09805cec458dce201bccfe23012df73332e181e"},
|
||||
|
|
@ -2481,8 +2646,8 @@ six = [
|
|||
{file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
|
||||
]
|
||||
terminado = [
|
||||
{file = "terminado-0.10.1-py3-none-any.whl", hash = "sha256:c89ace5bffd0e7268bdcf22526830eb787fd146ff9d78691a0528386f92b9ae3"},
|
||||
{file = "terminado-0.10.1.tar.gz", hash = "sha256:89d5dac2f4e2b39758a0ff9a3b643707c95a020a6df36e70583b88297cd59cbe"},
|
||||
{file = "terminado-0.11.0-py3-none-any.whl", hash = "sha256:221eef83e6a504894842f7dccfa971ca2e98ec22a8a9118577e5257527674b42"},
|
||||
{file = "terminado-0.11.0.tar.gz", hash = "sha256:1e01183885f64c1bba3cf89a5a995ad4acfed4e5f00aebcce1bf7f089b0825a1"},
|
||||
]
|
||||
testpath = [
|
||||
{file = "testpath-0.5.0-py3-none-any.whl", hash = "sha256:8044f9a0bab6567fc644a3593164e872543bb44225b0e24846e2c89237937589"},
|
||||
|
|
@ -2600,8 +2765,8 @@ us = [
|
|||
{file = "us-2.0.2.tar.gz", hash = "sha256:cb11ad0d43deff3a1c3690c74f0c731cff5b862c73339df2edd91133e1496fbc"},
|
||||
]
|
||||
virtualenv = [
|
||||
{file = "virtualenv-20.7.1-py2.py3-none-any.whl", hash = "sha256:73863dc3be1efe6ee638e77495c0c195a6384ae7b15c561f3ceb2698ae7267c1"},
|
||||
{file = "virtualenv-20.7.1.tar.gz", hash = "sha256:57bcb59c5898818bd555b1e0cfcf668bd6204bc2b53ad0e70a52413bd790f9e4"},
|
||||
{file = "virtualenv-20.7.2-py2.py3-none-any.whl", hash = "sha256:e4670891b3a03eb071748c569a87cceaefbf643c5bac46d996c5a45c34aa0f06"},
|
||||
{file = "virtualenv-20.7.2.tar.gz", hash = "sha256:9ef4e8ee4710826e98ff3075c9a4739e2cb1040de6a2a8d35db0055840dc96a0"},
|
||||
]
|
||||
wcwidth = [
|
||||
{file = "wcwidth-0.2.5-py2.py3-none-any.whl", hash = "sha256:beb4802a9cebb9144e99086eff703a642a13d6a0052920003a230f3294bbe784"},
|
||||
|
|
|
|||
|
|
@ -12,6 +12,7 @@ geopandas = "^0.9.0"
|
|||
ipython = "^7.24.1"
|
||||
jupyter = "^1.0.0"
|
||||
jupyter-contrib-nbextensions = "^0.5.1"
|
||||
matplotlib = "^3.4.2"
|
||||
numpy = "^1.21.0"
|
||||
pandas = "^1.2.5"
|
||||
python = "^3.7.1"
|
||||
|
|
@ -61,6 +62,7 @@ disable = [
|
|||
"W0613", # Disables unused argument
|
||||
"C0116", # Disables missing function or method docstring
|
||||
"C0115", # Disables missing class docstring
|
||||
"R0915", # Disables too many statements (score generation transform)
|
||||
]
|
||||
|
||||
[tool.pylint.FORMAT]
|
||||
|
|
@ -73,6 +75,9 @@ ignore-docstrings = "yes"
|
|||
ignore-imports = "yes"
|
||||
min-similarity-lines = 4
|
||||
|
||||
[tool.black]
|
||||
line-length = 80
|
||||
|
||||
[tool.liccheck]
|
||||
# Authorized and unauthorized licenses in LOWER CASE
|
||||
authorized_licenses = [
|
||||
|
|
@ -102,6 +107,7 @@ authorized_licenses = [
|
|||
"python software foundation",
|
||||
"zpl 2.1",
|
||||
"gpl v3",
|
||||
"historical permission notice and disclaimer (hpnd)",
|
||||
]
|
||||
|
||||
[tool.poetry.scripts]
|
||||
|
|
|
|||
|
|
@ -1,4 +1,5 @@
|
|||
appnope==0.1.2; sys_platform == "darwin" and python_version >= "3.7" and platform_system == "Darwin"
|
||||
argcomplete==1.12.3; python_version < "3.8.0" and python_version >= "3.7"
|
||||
argon2-cffi==20.1.0; python_version >= "3.6"
|
||||
async-generator==1.10; python_full_version >= "3.6.1" and python_version >= "3.7"
|
||||
attrs==21.2.0; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.6"
|
||||
|
|
@ -12,6 +13,7 @@ click-plugins==1.1.1; python_version >= "3.6"
|
|||
click==8.0.1; python_version >= "3.6"
|
||||
cligj==0.7.2; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version < "4" and python_version >= "3.6"
|
||||
colorama==0.4.4; python_version >= "3.7" and python_full_version < "3.0.0" and platform_system == "Windows" and sys_platform == "win32" or platform_system == "Windows" and python_version >= "3.7" and python_full_version >= "3.5.0" and sys_platform == "win32"
|
||||
cycler==0.10.0; python_version >= "3.7"
|
||||
debugpy==1.4.1; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7"
|
||||
decorator==5.0.9; python_version >= "3.7"
|
||||
defusedxml==0.7.1; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0" and python_version >= "3.7"
|
||||
|
|
@ -20,8 +22,8 @@ entrypoints==0.3; python_version >= "3.7"
|
|||
fiona==1.8.20; python_version >= "3.6"
|
||||
geopandas==0.9.0; python_version >= "3.6"
|
||||
idna==3.2; python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.6.0" and python_version >= "3.5"
|
||||
importlib-metadata==3.10.1; python_version < "3.8" and python_version >= "3.7"
|
||||
ipykernel==6.0.3; python_version >= "3.7"
|
||||
importlib-metadata==4.6.3; python_version == "3.7"
|
||||
ipykernel==6.1.0; python_version >= "3.7"
|
||||
ipython-genutils==0.2.0; python_version >= "3.7"
|
||||
ipython==7.26.0; python_version >= "3.7"
|
||||
ipywidgets==7.6.3
|
||||
|
|
@ -40,16 +42,18 @@ jupyter-nbextensions-configurator==0.4.1
|
|||
jupyter==1.0.0
|
||||
jupyterlab-pygments==0.1.2; python_version >= "3.7"
|
||||
jupyterlab-widgets==1.0.0; python_version >= "3.6"
|
||||
kiwisolver==1.3.1; python_version >= "3.7"
|
||||
lxml==4.6.3; python_version >= "2.7" and python_full_version < "3.0.0" or python_full_version >= "3.5.0"
|
||||
markupsafe==2.0.1; python_version >= "3.7"
|
||||
matplotlib-inline==0.1.2; python_version >= "3.7"
|
||||
matplotlib==3.4.3; python_version >= "3.7"
|
||||
mistune==0.8.4; python_version >= "3.7"
|
||||
munch==2.5.0; python_version >= "3.6"
|
||||
nbclient==0.5.3; python_full_version >= "3.6.1" and python_version >= "3.7"
|
||||
nbconvert==6.1.0; python_version >= "3.7"
|
||||
nbformat==5.1.3; python_full_version >= "3.6.1" and python_version >= "3.7"
|
||||
nest-asyncio==1.5.1; python_full_version >= "3.6.1" and python_version >= "3.7"
|
||||
notebook==6.4.2; python_version >= "3.6"
|
||||
notebook==6.4.3; python_version >= "3.6"
|
||||
numpy==1.21.1; python_version >= "3.7"
|
||||
packaging==21.0; python_version >= "3.7"
|
||||
pandas==1.3.1; python_full_version >= "3.7.1"
|
||||
|
|
@ -57,6 +61,7 @@ pandocfilters==1.4.3; python_version >= "3.7" and python_full_version < "3.0.0"
|
|||
parso==0.8.2; python_version >= "3.7"
|
||||
pexpect==4.8.0; sys_platform != "win32" and python_version >= "3.7"
|
||||
pickleshare==0.7.5; python_version >= "3.7"
|
||||
pillow==8.3.1; python_version >= "3.7"
|
||||
prometheus-client==0.11.0; python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.4.0" and python_version >= "3.6"
|
||||
prompt-toolkit==3.0.19; python_full_version >= "3.6.1" and python_version >= "3.7"
|
||||
ptyprocess==0.7.0; sys_platform != "win32" and python_version >= "3.7" and os_name != "nt"
|
||||
|
|
@ -78,8 +83,8 @@ qtpy==1.9.0; python_version >= "3.6"
|
|||
requests==2.26.0; (python_version >= "2.7" and python_full_version < "3.0.0") or (python_full_version >= "3.6.0")
|
||||
send2trash==1.8.0; python_version >= "3.6"
|
||||
shapely==1.7.1; python_version >= "3.6"
|
||||
six==1.16.0; python_full_version >= "3.7.1" and python_version >= "3.6" and (python_version >= "3.6" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.6") and (python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.7") and (python_version >= "3.5" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.5")
|
||||
terminado==0.10.1; python_version >= "3.6"
|
||||
six==1.16.0; python_version >= "3.7" and python_full_version < "3.0.0" or python_full_version >= "3.3.0" and python_version >= "3.7"
|
||||
terminado==0.11.0; python_version >= "3.6"
|
||||
testpath==0.5.0; python_version >= "3.7"
|
||||
tornado==6.1; python_full_version >= "3.6.1" and python_version >= "3.7"
|
||||
tqdm==4.62.0; (python_version >= "2.7" and python_full_version < "3.0.0") or (python_full_version >= "3.4.0")
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue