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