Merge pull request #211 from knaaptime/classify

WIP classify to rgba

mapclassify/__init__.py

@@ -1,6 +1,7 @@
 import contextlib
 from importlib.metadata import PackageNotFoundError, version
 
+from . import util
 from ._classify_API import classify
 from .classifiers import (
  CLASSIFIERS,

mapclassify/tests/test_rgba.py

@@ -0,0 +1,17 @@
+import geopandas
+import numpy as np
+from mapclassify.util import get_rgba
+
+world = geopandas.read_file(
+ "https://naciscdn.org/naturalearth/110m/cultural/ne_110m_admin_0_countries.zip"
+)
+
+
+def test_rgba():
+ colors = get_rgba(world.area, cmap="viridis")[0]
+ assert colors == [
+ np.float64(68.08602),
+ np.float64(1.24287),
+ np.float64(84.000825),
+ np.float64(255.0),
+ ]

mapclassify/util.py

@@ -0,0 +1,73 @@
+from ._classify_API import classify as _classify
+
+
+def get_rgba(
+ values,
+ classifier="quantiles",
+ cmap="viridis",
+ alpha=1,
+ nan_color=[255, 255, 255, 255],
+ **kwargs,
+):
+ """Convert array of values into RGBA colors using a colormap and classifier.
+
+ Parameters
+ ----------
+ values : list-like
+ array of input values
+ classifier : str, optional
+ string description of a mapclassify classifier, by default "quantiles"
+ cmap : str, optional
+ name of matplotlib colormap to use, by default "viridis"
+ alpha : float
+ alpha parameter that defines transparency. Should be in the range [0,1]
+ nan_color : list, optional
+ RGBA color to fill NaN values, by default [255, 255, 255, 255]
+ kwargs : dict
+ additional keyword arguments are passed to `mapclassify.classify`
+
+ Returns
+ -------
+ numpy.array
+ array of lists with each list containing four values that define a color using
+ RGBA specification.
+ """
+ try:
+ import pandas as pd
+ from matplotlib import cm
+ from matplotlib.colors import Normalize
+ except ImportError as e:
+ raise ImportError("This function requires pandas and matplotlib") from e
+ if not (alpha <= 1) and (alpha >= 0):
+ raise ValueError("alpha must be in the range [0,1]")
+ if not pd.api.types.is_list_like(nan_color) and not len(nan_color) == 4:
+ raise ValueError("`nan_color` must be list-like of 4 values: (R,G,B,A)")
+
+ # only operate on non-NaN values
+ v = pd.Series(values, dtype=object)
+ legit_indices = v[~v.isna()].index.values
+
+ # transform (non-NaN) values into class bins
+ bins = _classify(v.dropna().values, scheme=classifier, **kwargs).yb
+
+ # create a normalizer using the data's range (not strictly 1-k...)
+ norm = Normalize(min(bins), max(bins))
+
+ # map values to colors
+ n_cmap = cm.ScalarMappable(norm=norm, cmap=cmap)
+
+ # create array of RGB values (lists of 4) of length n
+ vals = [n_cmap.to_rgba(i, alpha=alpha) for i in bins]
+
+ # convert decimals to whole numbers
+ rgbas = []
+ for val in vals:
+ # convert each value in the array of lists
+ rgbas.append([i * 255 for i in val])
+
+ # replace non-nan values with colors
+ colors = pd.Series(rgbas, index=legit_indices)
+ v.update(colors)
+ v = v.fillna(f"{nan_color}").apply(list)
+
+ return v.values