Using custom color maps

This example will show how custom color maps can be used for generating colors.

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from matplotlib import pyplot as plt
from matplotlib.colors import ListedColormap
import pandas as pd
from sklearn.datasets import load_wine
from sklearn.preprocessing import scale
from sklearn.decomposition import PCA
from psynlig import (
    pca_explained_variance_pie,
    pca_1d_loadings,
    pca_2d_scores,
)
plt.style.use('seaborn-talk')


data_set = load_wine()
data = pd.DataFrame(data_set['data'], columns=data_set['feature_names'])
data = scale(data)
class_data = data_set['target']
class_names = dict(enumerate(data_set['target_names']))

pca = PCA(n_components=5)
scores = pca.fit_transform(data)

# Create some color maps:
colorbrewer = ListedColormap(
    [
        '#762a83',
        '#af8dc3',
        '#e7d4e8',
        '#d9f0d3',
        '#7fbf7b',
        '#1b7837',
    ],
    name='Colorbrewer'
)

bold = ListedColormap(
    [
        '#7F3C8D',
        '#11A579',
        '#3969AC',
        '#F2B701',
        '#E73F74',
        '#80BA5A',
        '#E68310',
        '#008695',
        '#CF1C90',
        '#f97b72',
        '#4b4b8f',
        '#A5AA99'
    ],
    name='bold',
)

dompap = ListedColormap(
    [
        '#BB4E37',
        '#7791BB',
        '#7C635B',
    ],
    name='dompap',
)

figures, axes = pca_2d_scores(
    pca,
    scores,
    class_data=class_data,
    class_names=class_names,
    select_components={(1, 2)},
    s=200,
    alpha=.8,
    cmap_class=dompap,
)
axes[0].set_title('Using the "dompap" color map:')
figures[0].tight_layout()

_, axi = pca_explained_variance_pie(pca, cmap=colorbrewer)
axi.set_title('Using a colorbrewer color map:')

_, axes = pca_1d_loadings(
    pca,
    data_set['feature_names'],
    select_components={1},
    cmap=bold,
    text_settings={'weight': 'bold', 'fontsize': 'x-large'}
)
axes[0].set_title('Loadings with the "bold" color map:')

plt.show()