spread example

/home/docs/checkouts/readthedocs.org/user_builds/mpl-visual-context/envs/0.9.2/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if pd.api.types.is_categorical_dtype(vector):
/home/docs/checkouts/readthedocs.org/user_builds/mpl-visual-context/envs/0.9.2/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if pd.api.types.is_categorical_dtype(vector):
/home/docs/checkouts/readthedocs.org/user_builds/mpl-visual-context/envs/0.9.2/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if pd.api.types.is_categorical_dtype(vector):
/home/docs/checkouts/readthedocs.org/user_builds/mpl-visual-context/envs/0.9.2/lib/python3.10/site-packages/seaborn/_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead
  if pd.api.types.is_categorical_dtype(vector):
/home/docs/checkouts/readthedocs.org/user_builds/mpl-visual-context/envs/0.9.2/lib/python3.10/site-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.
  with pd.option_context('mode.use_inf_as_na', True):

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
import seaborn as sns

# Create the data : copied from examples/kde_ridgeplot.py
rs = np.random.RandomState(1979)
x = rs.randn(500)
g = np.tile(list("ABCDEFGHIJ"), 50)
df = pd.DataFrame(dict(x=x, g=g))
m = df.g.map(ord)
df["x"] += m


fig, ax = plt.subplots(1, 1, num=1, clear=True)

sns.kdeplot(
    df,
    x="x",
    hue="g",
    ax=ax,
    fill=True,
    linewidth=2,
    clip_on=False,
    ec="w",
    alpha=1,
)

from mpl_visual_context.spreader import spready

polys = ax.collections

dy = 0.5 * max([p.get_datalim(p.axes.transData).height for p in polys])

yindices = np.arange(len(polys))
gg = df["g"].unique()
yoffsets = spready(polys, yindices, dy=dy)

ax.set_ylim(min(yoffsets), max(yoffsets) + dy)
ax.set_yticks(yoffsets, labels=gg[::-1])

ax.legend_.remove()

plt.show()