Violing Chart demonstraing various mpl-visual-context features

/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/categorical.py:641: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  grouped_vals = vals.groupby(grouper)
/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/categorical.py:641: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  grouped_vals = vals.groupby(grouper)
/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/categorical.py:641: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  grouped_vals = vals.groupby(grouper)
/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/categorical.py:641: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.
  grouped_vals = vals.groupby(grouper)

# import numpy as np
from mpl_visual_context.patheffects import AlphaGradient
import matplotlib.pyplot as plt
import mpl_visual_context.patheffects as pe
import seaborn

seaborn.set()

tips = seaborn.load_dataset("tips")

# We start from a simple seaborn violin plot
fig, axs = plt.subplots(2, 2, num=1, clear=True, figsize=(8, 6), layout="constrained")
for ax in axs.flat:
    seaborn.violinplot(x='day', y='tip', data=tips, ax=ax)

ax = axs[0, 0]
ax.annotate(
    "(a) Original violin plot",
    (0, 1),
    xytext=(5, -5),
    xycoords="axes fraction",
    va="top",
    ha="left",
    textcoords="offset points",  # size=20,
)

# (b) w/ Brighter fill color
ax = axs[0, 1]
# We select violin patches.
colls = ax.collections[::2]


ax.annotate(
    "(b) Make fill color lighter,\nand stroke with the (original) fill color",
    (0, 1),
    xytext=(5, -5),
    xycoords="axes fraction",
    va="top",
    ha="left",
    textcoords="offset points",  # size=20,
)

pe_list = [
    pe.HLSModify(l=0.8) | pe.FillOnly(),
    pe.StrokeColorFromFillColor() | pe.StrokeOnly(),
]

for x, coll in enumerate(colls):
    coll.set_path_effects(pe_list)

# (c) AlphaGradient
ax = axs[1, 0]
colls = ax.collections[::2]

ax.annotate(
    "(c) Fill w/ alpha gradient",
    (0, 1),
    xytext=(5, -5),
    xycoords="axes fraction",
    va="top",
    ha="left",
    textcoords="offset points",  # size=20,
)

pe_list = [
    pe.AlphaGradient("0.8 > 0.2 > 0.8"),
    (pe.StrokeColorFromFillColor() | pe.StrokeOnly()),
]

for x, coll in enumerate(colls):
    coll.set_path_effects(pe_list)

# (4) w/ Light effect
ax = axs[1, 1]
colls = ax.collections[::2]

ax.annotate(
    "(d) Light effect and shadow",
    (0, 1),
    xytext=(5, -5),
    xycoords="axes fraction",
    va="top",
    ha="left",
    textcoords="offset points",  # size=20,
)

import mpl_visual_context.image_effect as ie

pe_list = [
    # shadow
    pe.FillOnly()
    | pe.ImageEffect(
        ie.AlphaAxb((0.5, 0))
        | ie.Pad(10)
        | ie.Fill("k")
        | ie.Dilation(3)
        | ie.Gaussian(4)
        | ie.Offset(3, -3)
    ),
    # light effect
    pe.HLSModify(l=0.7)
    | pe.FillOnly()
    | pe.ImageEffect(ie.LightSource(erosion_size=5, gaussian_size=5)),
]

for x, coll in enumerate(colls):
    coll.set_path_effects(pe_list)

plt.show()