import json
import pkgutil
from typing import Any, Dict, List, Optional, Tuple, Union
import numpy as np
import pandas as pd
import plotly.express as px
from scipy import spatial
from .base import BaseEncoder
[docs]class GeoEncoder(BaseEncoder):
r"""Plotly Sample Datasets."""
ECD_PATH: Optional[bytes] = pkgutil.get_data(
"rsdiv.encoding", "geojson-counties-fips.json"
)
if ECD_PATH:
encode_source: Dict[str, Any] = json.loads(ECD_PATH)
def __init__(self) -> None:
super().__init__()
self.encoder, self.geo_county_dict = self.read_source()
self.coord: List[np.ndarray] = self.encoder.coord.to_list()
self.index: pd.Index = pd.Index(self.encoder["index"])
[docs] def read_source(self) -> Tuple[pd.DataFrame, Dict]:
"""Parse the location information from `geojson-counties-fips.json`.
Returns:
Tuple[pd.DataFrame, Dict]: dataframe and county-wise dictionary.
"""
geo_county_dict: Dict[str, List] = {}
for item in self.encode_source["features"]:
coordinates = item["geometry"]["coordinates"]
parts = []
for part in coordinates:
parts.append(np.asarray(part).squeeze().mean(axis=0).squeeze())
coord = np.asarray(parts).mean(axis=0)[::-1] # reverse lat/lng
name = item["properties"]["NAME"]
lsad = item["properties"]["LSAD"]
id = item["id"]
geo_county_dict[id] = [coord, name, lsad]
dataframe = pd.DataFrame.from_dict(
geo_county_dict, orient="index", columns=["coord", "name", "lstd"]
).reset_index()
return (dataframe, geo_county_dict)
[docs] def encoding_single(self, org: Union[List, str]) -> Union[int, str]:
"""Encoding for a single location.
Args:
org (Union[List, str]): location to be encoded.
Returns:
Union[int, str]: the coding corresponds to the given target.
"""
tree = spatial.KDTree(self.coord)
return str(self.index[int(tree.query(org)[1])])
[docs] def encoding_series(self, series: pd.Series) -> pd.Series:
"""Encoding for the series of locations.
Args:
series (pd.Series): ths series of locations.
Returns:
pd.Series: the corresponding series of locations.
"""
encodings = pd.Series(series.apply(lambda x: self.encoding_single(x)))
return encodings
[docs] def draw_geo_graph(
self,
dataframe: pd.DataFrame,
source_name: str,
hover_name: Optional[str] = None,
) -> None:
"""Method to indicate the values in a geography view.
Args:
dataframe (pd.DataFrame): data source.
source_name (str): the name of column for target data.
hover_name (Optional[str], optional): the name of column for hovering labels. Defaults to None.
"""
max_value: float = np.ceil(dataframe[source_name].max())
min_value: float = np.floor(dataframe[source_name].min())
fig = px.choropleth(
dataframe,
geojson=self.encode_source,
locations="index",
color=source_name,
hover_name=hover_name,
color_continuous_scale="OrRd",
range_color=(min_value, max_value),
scope="usa",
)
fig.update_layout(margin={"r": 0, "t": 0, "l": 0, "b": 0})
fig.show()