diff --git a/packages/calitp-data-analysis/calitp_data_analysis/calitp_color_palette.py b/packages/calitp-data-analysis/calitp_data_analysis/calitp_color_palette.py new file mode 100644 index 0000000000..0e0c2f825e --- /dev/null +++ b/packages/calitp-data-analysis/calitp_data_analysis/calitp_color_palette.py @@ -0,0 +1,40 @@ +# --------------------------------------------------------------# +# Cal-ITP style guide +# Google Drive > Cal-ITP Team > Project Resources > +# Branded Resources and External Comms Guidelines > Branded Resources > Style Guide +# --------------------------------------------------------------# +CALITP_CATEGORY_BRIGHT_COLORS = [ + "#2EA8CE", # darker blue + "#EB9F3C", # orange + "#F4D837", # yellow + "#51BF9D", # green + "#8CBCCB", # lighter blue + "#9487C0", # purple +] + +CALITP_CATEGORY_BOLD_COLORS = [ + "#136C97", # darker blue + "#E16B26", # orange + "#F6BF16", # yellow + "#00896B", # green + "#7790A3", # lighter blue + "#5B559C", # purple +] + +CALITP_DIVERGING_COLORS = [ + "#E16B26", + "#EB9F3C", # oranges + "#f6e7e1", # linen + "#8CBCCB", + "#2EA8CE", + "#136C97", # blues +] + + +CALITP_SEQUENTIAL_COLORS = [ + "#B9D6DF", # light blue (lightest) + "#8CBCCB", # lighter blue bright + "#2EA8CE", # darker blue bright + "#136C97", # darker blue bold + "#0B405B", # indigo dye (darkest) +] diff --git a/packages/calitp-data-analysis/calitp_data_analysis/geography_utils.py b/packages/calitp-data-analysis/calitp_data_analysis/geography_utils.py new file mode 100644 index 0000000000..b8523346a0 --- /dev/null +++ b/packages/calitp-data-analysis/calitp_data_analysis/geography_utils.py @@ -0,0 +1,210 @@ +""" +Utility functions for geospatial data. +Some functions for dealing with census tract or other geographic unit dfs. +""" +from typing import Literal, Union, cast + +import dask.dataframe as dd +import geopandas as gpd # type: ignore +import pandas as pd +import shapely # type: ignore + +WGS84 = "EPSG:4326" +CA_StatePlane = "EPSG:2229" # units are in feet +CA_NAD83Albers = "EPSG:3310" # units are in meters + +SQ_MI_PER_SQ_M = 3.86 * 10**-7 +FEET_PER_MI = 5_280 +SQ_FT_PER_SQ_MI = 2.788 * 10**7 + + +# Laurie's example: https://github.com/cal-itp/data-analyses/blob/752eb5639771cb2cd5f072f70a06effd232f5f22/gtfs_shapes_geo_examples/example_shapes_geo_handling.ipynb +# have to convert to linestring +def make_linestring(x: str) -> shapely.geometry.LineString: + # shapely errors if the array contains only one point + if len(x) > 1: + # each point in the array is wkt + # so convert them to shapely points via list comprehension + as_wkt = [shapely.wkt.loads(i) for i in x] + return shapely.geometry.LineString(as_wkt) + + +def make_routes_gdf( + df: pd.DataFrame, + crs: str = "EPSG:4326", +) -> gpd.GeoDataFrame: + """ + Parameters: + + crs: str, a projected coordinate reference system. + Defaults to EPSG:4326 (WGS84) + """ + # Use apply to use map_partitions + # https://stackoverflow.com/questions/70829491/dask-map-partitions-meta-when-using-lambda-function-to-add-column + ddf = dd.from_pandas(df, npartitions=1) + + ddf["geometry"] = ddf.pt_array.apply(make_linestring, meta=("geometry", "geometry")) + shapes = ddf.compute() + + # convert to geopandas; re-project if needed + gdf = gpd.GeoDataFrame( + shapes.drop(columns="pt_array"), geometry="geometry", crs=WGS84 + ).to_crs(crs) + + return gdf + + +def create_point_geometry( + df: pd.DataFrame, + longitude_col: str = "stop_lon", + latitude_col: str = "stop_lat", + crs: str = WGS84, +) -> gpd.GeoDataFrame: + """ + Parameters: + df: pandas.DataFrame to turn into geopandas.GeoDataFrame, + default dataframe in mind is gtfs_schedule.stops + + longitude_col: str, column name corresponding to longitude + in gtfs_schedule.stops, this column is "stop_lon" + + latitude_col: str, column name corresponding to latitude + in gtfs_schedule.stops, this column is "stop_lat" + + crs: str, coordinate reference system for point geometry + """ + # Default CRS for stop_lon, stop_lat is WGS84 + df = df.assign( + geometry=gpd.points_from_xy(df[longitude_col], df[latitude_col], crs=WGS84) + ) + + # ALlow projection to different CRS + gdf = gpd.GeoDataFrame(df).to_crs(crs) + + return gdf + + +def create_segments( + geometry: Union[ + shapely.geometry.linestring.LineString, + shapely.geometry.multilinestring.MultiLineString, + ], + segment_distance: int, +) -> gpd.GeoSeries: + """ + Splits a Shapely LineString into smaller LineStrings. + If a MultiLineString passed, splits each LineString in that collection. + + Input a geometry column, such as gdf.geometry. + + Double check: segment_distance must be given in the same units as the CRS! + """ + lines = [] + + if hasattr(geometry, "geoms"): # check if MultiLineString + linestrings = geometry.geoms + else: + linestrings = [geometry] + + for linestring in linestrings: + for i in range(0, int(linestring.length), segment_distance): + lines.append(shapely.ops.substring(linestring, i, i + segment_distance)) + + return lines + + +def cut_segments( + gdf: gpd.GeoDataFrame, + group_cols: list = ["calitp_itp_id", "calitp_url_number", "route_id"], + segment_distance: int = 1_000, +) -> gpd.GeoDataFrame: + """ + Cut segments from linestrings at defined segment lengths. + Make sure segment distance is defined in the same CRS as the gdf. + + group_cols: list of columns. + The set of columns that represents how segments should be cut. + Ex: for transit route, it's calitp_itp_id-calitp_url_number-route_id + Ex: for highways, it's Route-RouteType-County-District. + + Returns a gpd.GeoDataFrame where each linestring row is now multiple + rows (each at the pre-defined segment_distance). A new column called + `segment_sequence` is also created, which differentiates each + new row created (since they share the same group_cols). + """ + EPSG_CODE = gdf.crs.to_epsg() + + segmented = gpd.GeoDataFrame() + + gdf = gdf[group_cols + ["geometry"]].drop_duplicates().reset_index(drop=True) + + for row in gdf.itertuples(): + row_geom = getattr(row, "geometry") + segment = create_segments(row_geom, int(segment_distance)) + + to_append = pd.DataFrame() + to_append["geometry"] = segment + for c in group_cols: + to_append[c] = getattr(row, c) + + segmented = pd.concat([segmented, to_append], axis=0, ignore_index=True) + + segmented = segmented.assign( + temp_index=segmented.sort_values(group_cols).reset_index(drop=True).index + ) + + # Why would there be NaNs? + # could this be coming from group_cols...one of the cols has a NaN in some rows? + segmented = segmented[segmented.temp_index.notna()] + + segmented = ( + segmented.assign( + segment_sequence=( + segmented.groupby(group_cols)["temp_index"].transform("rank") - 1 + ).astype("int16") + ) + .sort_values(group_cols) + .reset_index(drop=True) + .drop(columns="temp_index") + ) + + segmented2 = gpd.GeoDataFrame(segmented, crs=f"EPSG:{EPSG_CODE}") + + return segmented2 + + +return_options = Literal[ + "point", "line", "polygon", "missing", "linearring", "geometry_collection" +] + + +def find_geometry_type( + geometry, +) -> return_options: + """ + Find the broad geometry type of a geometry value. + + shapely.get_type_id returns integers and differentiates between + point/multipoint, linestring/multilinestring, etc. + Sometimes, we just want a broader category. + + https://shapely.readthedocs.io/en/stable/reference/shapely.get_type_id.html + """ + point_type_ids = [0, 4] + line_type_ids = [1, 5] + polygon_type_ids = [3, 6] + + if shapely.get_type_id(geometry) in point_type_ids: + geo_type = "point" + elif shapely.get_type_id(geometry) in line_type_ids: + geo_type = "line" + elif shapely.get_type_id(geometry) in polygon_type_ids: + geo_type = "polygon" + elif shapely.get_type_id(geometry) == -1: + geo_type = "missing" + elif shapely.get_type_id(geometry) == 2: + geo_type = "linearring" + elif shapely.get_type_id(geometry) == 7: + geo_type = "geometry_collection" + + return cast(return_options, geo_type) diff --git a/packages/calitp-data-analysis/calitp_data_analysis/styleguide.py b/packages/calitp-data-analysis/calitp_data_analysis/styleguide.py new file mode 100644 index 0000000000..df3ffedc04 --- /dev/null +++ b/packages/calitp-data-analysis/calitp_data_analysis/styleguide.py @@ -0,0 +1,251 @@ +""" +Set Cal-ITP altair style template, +top-level configuration (for pngs), +and color palettes. + +References: + +Setting custom Altair theme as .py (Urban): +https://towardsdatascience.com/consistently-beautiful-visualizations-with-altair-themes-c7f9f889602 + +GH code: +https://github.com/chekos/altair_themes_blog/tree/master/notebooks + +Download Google fonts: +https://gist.github.com/ravgeetdhillon/0063aaee240c0cddb12738c232bd8a49 + +Altair GH issue setting custom theme: +https://github.com/altair-viz/altair/issues/1333 +https://discourse.holoviz.org/t/altair-theming/1421/2 + +https://stackoverflow.com/questions/33061785/can-i-load-google-fonts-with-matplotlib-and-jupyter + +matplotlib: +https://github.com/CityOfLosAngeles/los-angeles-citywide-data-style + +""" +import altair as alt # type: ignore +from calitp_data_analysis import calitp_color_palette as cp + +# --------------------------------------------------------------# +# Chart parameters +# --------------------------------------------------------------# +font_size = 18 +chart_width = 400 +chart_height = 250 + +markColor = "#8CBCCB" +axisColor = "#cbcbcb" +guideLabelColor = "#474747" +guideTitleColor = "#333" +blackTitle = "#333" +font = "Raleway" +labelFont = "Nunito Sans" +backgroundColor = "white" + +PALETTE = { + "category_bright": cp.CALITP_CATEGORY_BRIGHT_COLORS, + "category_bold": cp.CALITP_CATEGORY_BOLD_COLORS, + "diverging": cp.CALITP_DIVERGING_COLORS, + "sequential": cp.CALITP_SEQUENTIAL_COLORS, +} + + +""" +# Run this in notebook +%%html + +""" + + +def calitp_theme( + font: str = font, + labelFont: str = labelFont, + font_size: int = font_size, + chart_width: int = chart_width, + chart_height: int = chart_height, + markColor: str = markColor, + axisColor: str = axisColor, + guideLabelColor: str = guideLabelColor, + guideTitleColor: str = guideTitleColor, + blackTitle: str = blackTitle, + backgroundColor: str = backgroundColor, + PALETTE: dict = PALETTE, +): + # Typography + # At Urban it's the same font for all text but it's good to keep them separate in case you want to change one later. + labelFont = labelFont + sourceFont = labelFont + + return { + # width and height are configured outside the config dict because they are Chart configurations/properties not chart-elements' configurations/properties. + "width": chart_width, # from the guide + "height": chart_height, # not in the guide + "background": backgroundColor, + "config": { + "title": { + "fontSize": font_size, + "font": font, + "anchor": "middle", + "fontColor": blackTitle, + "fontWeight": "bold", # 300 was default. can also use lighter, bold, normal, bolder + "offset": 20, + }, + "header": { + "labelFont": labelFont, + "titleFont": font, + }, + "axis": { + "domain": True, + "domainColor": axisColor, + "grid": True, + "gridColor": axisColor, + "gridWidth": 1, + "labelColor": guideLabelColor, + "labelFontSize": 10, + "titleColor": guideTitleColor, + "tickColor": axisColor, + "tickSize": 10, + "titleFontSize": 12, + "titlePadding": 10, + "labelPadding": 4, + }, + "axisBand": { + "grid": False, + }, + "range": { + "category_bright": PALETTE["category_bright"], + "category_bold": PALETTE["category_bold"], + "diverging": PALETTE["diverging"], + "sequential": PALETTE["sequential"], + }, + "legend": { + "labelFont": labelFont, + "labelFontSize": 11, + "symbolType": "square", + "symbolSize": 30, # default + "titleFont": font, + "titleFontSize": 14, + "titlePadding": 10, + "padding": 1, + "orient": "right", + # "offset": 0, # literally right next to the y-axis. + "labelLimit": 0, # legend can fully display text instead of truncating it + }, + "view": { + "stroke": "transparent", # altair uses gridlines to box the area where the data is visualized. This takes that off. + }, + "group": { + "fill": backgroundColor, + }, + # MARKS CONFIGURATIONS # + "arc": { + "fill": markColor, + }, + "area": { + "fill": markColor, + }, + "line": { + # "color": markColor, + "stroke": markColor, + "strokeWidth": 2, + }, + "trail": { + "color": markColor, + "stroke": markColor, + "strokeWidth": 0, + "size": 1, + }, + "path": { + "stroke": markColor, + "strokeWidth": 0.5, + }, + "rect": { + "fill": markColor, + }, + "point": { + "filled": True, + "shape": "circle", + }, + "shape": {"stroke": markColor}, + "text": { + "font": sourceFont, + "color": markColor, + "fontSize": 11, + "align": "center", + "fontWeight": 400, + "size": 11, + }, + "bar": { + # "size": 40, + "binSpacing": 2, + # "continuousBandSize": 30, + # "discreteBandSize": 30, + "fill": markColor, + "stroke": False, + }, + }, + } + + +# Let's add in more top-level chart configuratinos +# Need to add more since altair_saver will lose a lot of the theme applied + + +# Apply top-level chart config but do not set properties (before hconcat, vconcat, etc) +def apply_chart_config(chart: alt.Chart) -> alt.Chart: + chart = ( + chart.configure(background=backgroundColor, font=font) + .configure_axis( + domainColor=axisColor, + grid=True, + gridColor=axisColor, + gridWidth=1, + labelColor=guideLabelColor, + labelFont=labelFont, + labelFontSize=10, + titleColor=guideTitleColor, + titleFont=font, + tickColor=axisColor, + tickSize=10, + titleFontSize=12, + titlePadding=10, + labelPadding=4, + ) + .configure_axisBand(grid=False) + .configure_title( + font=font, + fontSize=font_size, + anchor="middle", + fontWeight=300, + offset=20, + ) + .configure_header(labelFont=labelFont, titleFont=font) + .configure_legend( + labelColor=blackTitle, + labelFont=labelFont, + labelFontSize=11, + padding=1, + symbolSize=30, + symbolType="square", + titleColor=blackTitle, + titleFont=font, + titleFontSize=14, + titlePadding=10, + labelLimit=0, + ) + ) + return chart + + +# Single-chart top-level chart config +# Cannot use this if hconcat or vconcat is used +def preset_chart_config(chart: alt.Chart) -> alt.Chart: + chart = apply_chart_config(chart).properties( + width=chart_width, + height=chart_height, + ) + + return chart diff --git a/packages/calitp-data-analysis/calitp_data_analysis/utils.py b/packages/calitp-data-analysis/calitp_data_analysis/utils.py new file mode 100644 index 0000000000..b68fc383a9 --- /dev/null +++ b/packages/calitp-data-analysis/calitp_data_analysis/utils.py @@ -0,0 +1,273 @@ +""" +General utility functions. +""" +import base64 +import os +import shutil +from pathlib import Path +from typing import Literal, Optional, Union + +import dask_geopandas as dg # type: ignore +import fsspec # type: ignore +import geopandas as gpd # type: ignore +import requests # type: ignore +from calitp_data_analysis import get_fs + +fs = get_fs() + + +def sanitize_file_path(file_name: str) -> str: + """ + Remove the .parquet or .geojson in a filepath. + """ + return str(Path(file_name).stem) + + +def geoparquet_gcs_export( + gdf: Union[gpd.GeoDataFrame, dg.GeoDataFrame], + gcs_file_path: str, + file_name: str, + **kwargs, +): + """ + Save geodataframe as parquet locally, + then move to GCS bucket and delete local file. + + gdf: geopandas.GeoDataFrame + gcs_file_path: str + Ex: gs://calitp-analytics-data/data-analyses/my-folder/ + file_name: str + Filename, with or without .parquet. + """ + file_name_sanitized = sanitize_file_path(file_name) + + if isinstance(gdf, dg.GeoDataFrame): + gdf.to_parquet( + f"{gcs_file_path}{file_name_sanitized}", overwrite=True, **kwargs + ) + + else: + gdf.to_parquet(f"./{file_name_sanitized}.parquet", **kwargs) + fs.put( + f"./{file_name_sanitized}.parquet", + f"{gcs_file_path}{file_name_sanitized}.parquet", + ) + os.remove(f"./{file_name_sanitized}.parquet", **kwargs) + + +def geojson_gcs_export( + gdf: gpd.GeoDataFrame, + gcs_file_path: str, + file_name: str, + geojson_type: str = "geojson", +): + """ + Save geodataframe as geojson locally, + then move to GCS bucket and delete local file. + + gcs_file_path: str + Ex: gs://calitp-analytics-data/data-analyses/my-folder/ + file_name: str + name of file (with .geojson or .geojsonl). + """ + + if geojson_type == "geojson": + DRIVER = "GeoJSON" + elif geojson_type == "geojsonl": + DRIVER = "GeoJSONSeq" + else: + raise ValueError("Not a valid geojson type! Use `geojson` or `geojsonl`") + + file_name_sanitized = sanitize_file_path(file_name) + + gdf.to_file(f"./{file_name_sanitized}.{geojson_type}", driver=DRIVER) + + fs.put( + f"./{file_name_sanitized}.{geojson_type}", + f"{gcs_file_path}{file_name_sanitized}.{geojson_type}", + ) + os.remove(f"./{file_name_sanitized}.{geojson_type}") + + +def read_geojson( + gcs_file_path: str, + file_name: str, + geojson_type: Literal["geojson", "geojsonl"] = "geojson", + save_locally: bool = False, +) -> gpd.GeoDataFrame: + """ + Parameters: + gcs_file_path: str + Ex: gs://calitp-analytics-data/data-analyses/my-folder/ + file_name: str + name of file (with or without the .geojson). + geojson_type: str. + valid values are geojson or geojsonl. + save_locally: bool + defaults to False. if True, will save geojson locally. + """ + file_name_sanitized = sanitize_file_path(file_name) + + object_path = fs.open(f"{gcs_file_path}{file_name_sanitized}.{geojson_type}") + gdf = gpd.read_file(object_path) + + if geojson_type == "geojson": + DRIVER = "GeoJSON" + elif geojson_type == "geojsonl": + DRIVER = "GeoJSONSeq" + + if save_locally: + gdf.to_file(f"./{file_name}.{geojson_type}", driver=DRIVER) + + return gdf + + +# Make zipped shapefile +# https://github.com/CityOfLosAngeles/planning-entitlements/blob/master/notebooks/utils.py +def make_shapefile(gdf: gpd.GeoDataFrame, path: Union[Path, str]) -> tuple[Path, Path]: + """ + Make a zipped shapefile and save locally + Parameters + ========== + gdf: gpd.GeoDataFrame to be saved as zipped shapefile + path: str, local path to where the zipped shapefile is saved. + Ex: "folder_name/census_tracts" + "folder_name/census_tracts.zip" + + Remember: ESRI only takes 10 character column names!! + + Returns a folder name (dirname) where the shapefile is stored and + a filename. Both are strings. + """ + if isinstance(path, str): + path = Path(path) + + # Use pathlib instead of os + # https://towardsdatascience.com/goodbye-os-path-15-pathlib-tricks-to-quickly-master-the-file-system-in-python-881213ca7c21 + # Grab first element of path (can input filename.zip or filename) + dirname = path.parent.joinpath(path.stem) + print(f"Path name: {path}") + print(f"Dirname (1st element of path): {dirname}") + + # Make sure there's no folder with the same name + shutil.rmtree(dirname, ignore_errors=True) + + # Make folder + Path.mkdir(dirname, parents=True) + shapefile_name = Path(path.stem).with_suffix(".shp") + print(f"Shapefile name: {shapefile_name}") + + # Export shapefile into its own folder with the same name + gdf.to_file(driver="ESRI Shapefile", filename=dirname.joinpath(shapefile_name)) + print(f"Shapefile component parts folder: {dirname}/{shapefile_name}") + + return dirname, shapefile_name + + +def make_zipped_shapefile( + gdf: gpd.GeoDataFrame, + local_path: Union[str, Path], + gcs_folder: Optional[str] = None, +): + """ + Make a zipped shapefile and save locally + Parameters + ========== + gdf: gpd.GeoDataFrame to be saved as zipped shapefile + local_path: str, local path to where the zipped shapefile is saved. + Ex: "folder_name/census_tracts" + "folder_name/census_tracts.zip" + gcs_folder: str, the gcs folder to save the local_path file name into. + Ex: if local_path is "folder_name/census_tracts.zip" abd + gcs_folder is "gs://my_bucket/new_folder", + the object in GCS would be "gs//my_bucket/new_folder/census_tracts.zip" + + Remember: ESRI only takes 10 character column names!! + """ + dirname, shapefile_name = make_shapefile(gdf, local_path) + + # Zip it up + shutil.make_archive(dirname.name, "zip", dirname) + # Remove the unzipped folder + shutil.rmtree(dirname.name, ignore_errors=True) + + if gcs_folder: + if gcs_folder[-1] != "/": + gcs_folder = f"{gcs_folder}/" + + fs.put( + f"./{dirname.parent}.zip", + f"{gcs_folder}{dirname.parent}.zip", + ) + + +# Function to overwrite file in GitHub +# Based on https://github.com/CityOfLosAngeles/aqueduct/tree/master/civis-aqueduct-utils/civis_aqueduct_utils + +DEFAULT_COMMITTER = { + "name": "Service User", + "email": "my-email@email.com", +} + + +def upload_file_to_github( + token: str, + repo: str, + branch: str, + path: str, + local_file_path: str, + commit_message: str, + committer: dict = DEFAULT_COMMITTER, +): + """ + Parameters + ---------- + token: str + GitHub personal access token and corresponds to GITHUB_TOKEN + in Civis credentials. + repo: str + Repo name, such as 'CityofLosAngeles/covid19-indicators` + branch: str + Branch name, such as 'master' + path: str + Path to the file within the repo. + local_file_path: str + Path to the local file to be uploaded to the repo, which can differ + from the path within the GitHub repo. + commit_message: str + Commit message used when making the git commit. + commiter: dict + name and email associated with the committer. + """ + + BASE = "https://api.github.com" + + # Get the sha of the previous version. + # Operate on the dirname rather than the path itself so we + # don't run into file size limitations. + r = requests.get( + f"{BASE}/repos/{repo}/contents/{os.path.dirname(path)}", + params={"ref": branch}, + headers={"Authorization": f"token {token}"}, + ) + r.raise_for_status() + item = next(i for i in r.json() if i["path"] == path) + sha = item["sha"] + + # Upload the new version + with fsspec.open(local_file_path, "rb") as f: + contents = f.read() + + r = requests.put( + f"{BASE}/repos/{repo}/contents/{path}", + headers={"Authorization": f"token {token}"}, + json={ + "message": commit_message, + "committer": committer, + "branch": branch, + "sha": sha, + "content": base64.b64encode(contents).decode("utf-8"), + }, + ) + r.raise_for_status() + return diff --git a/packages/calitp-data-analysis/poetry.lock b/packages/calitp-data-analysis/poetry.lock index b55285bc25..8dd6698478 100644 --- a/packages/calitp-data-analysis/poetry.lock +++ b/packages/calitp-data-analysis/poetry.lock @@ -122,6 +122,30 @@ files = [ [package.dependencies] frozenlist = ">=1.1.0" +[[package]] +name = "altair" +version = "5.1.1" +description = "Vega-Altair: A declarative statistical visualization library for Python." +optional = false +python-versions = ">=3.8" +files = [ + {file = "altair-5.1.1-py3-none-any.whl", hash = "sha256:bb421459b53c80ad45f2bd009c87da2a81165b8f7d5a90658e0fc1ffc741bf34"}, + {file = "altair-5.1.1.tar.gz", hash = "sha256:ad6cd6983c8db69a34dd68e42653f6172b7fc3775b7190005107f1b4fc60d64d"}, +] + +[package.dependencies] +jinja2 = "*" +jsonschema = ">=3.0" +numpy = "*" +packaging = "*" +pandas = ">=0.25" +toolz = "*" +typing-extensions = {version = ">=4.0.1", markers = "python_version < \"3.11\""} + +[package.extras] +dev = ["anywidget", "black (<24)", "hatch", "ipython", "m2r", "mypy", "pandas-stubs", "pyarrow (>=11)", "pytest", "pytest-cov", 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