"""
Violin Plot Examples
====================

This example demonstrates violin plot functionality in PubliPlots,
including simple violin plots, grouped violin plots, split violins, and combined violin+swarm plots.

Examples
--------
"""

import publiplots as pp
import pandas as pd
import numpy as np

# %%
# Simple Violin Plot
# ~~~~~~~~~~~~~~~~~~
# Basic violin plot showing distribution by category.

# Create sample data
np.random.seed(42)
n = 200
violin_data = pd.DataFrame({
    'category': np.repeat(['A', 'B', 'C', 'D'], n // 4),
    'value': np.concatenate([
        np.random.normal(10, 2, n // 4),
        np.random.normal(15, 3, n // 4),
        np.random.normal(12, 2.5, n // 4),
        np.random.normal(18, 4, n // 4)
    ])
})

# Create simple violin plot
ax = pp.violinplot(
    data=violin_data,
    x='category',
    y='value',
    title='Simple Violin Plot',
    xlabel='Category',
    ylabel='Value',
)
pp.show()

# %%
# Violin Plot with Hue Grouping
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Use the hue parameter to create grouped violin plots.

# Add group variable
violin_data['group'] = np.tile(['Group 1', 'Group 2'], n // 2)

# Create grouped violin plot
ax = pp.violinplot(
    data=violin_data,
    x='category',
    y='value',
    hue='group',
    gap=0.1,
    title='Grouped Violin Plot',
    xlabel='Category',
    ylabel='Value',
    palette={'Group 1': '#8E8EC1', 'Group 2': '#75B375'},
)
pp.show()

# %%
# Split Violin Plot
# ~~~~~~~~~~~~~~~~~
# Split violins to compare two groups side by side.

ax = pp.violinplot(
    data=violin_data,
    x='category',
    y='value',
    hue='group',
    split=True,
    inner='quart',
    gap=0.1,
    title='Split Violin Plot',
    xlabel='Category',
    ylabel='Value',
)
pp.show()

# %%
# Horizontal Violin Plot
# ~~~~~~~~~~~~~~~~~~~~~~
# Create horizontal violin plots by swapping x and y.

ax = pp.violinplot(
    data=violin_data[violin_data['group'] == 'Group 1'],
    x='value',
    y='category',
    title='Horizontal Violin Plot',
    xlabel='Value',
    ylabel='Category',
)
pp.show()

# %%
# Univariate (1D) Violin Plot
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Pass only ``x=`` or only ``y=`` to summarize a single distribution.
# A constant categorical axis is synthesized internally so all 2D
# features (``hue=``, ``side=``, ``inner=``, ``alpha``) remain
# available. The synthetic axis ticks and spine are hidden so the
# result reads cleanly as a single-distribution summary. This is the
# form used by :func:`pp.JointGrid.plot_marginals` to put violins on
# the marginal panels of a joint plot.

fig, axes = pp.subplots(1, 2, axes_size=(40, 50))
pp.violinplot(data=violin_data, y='value', ax=axes[0], title='Vertical (y= only)')
pp.violinplot(data=violin_data, x='value', ax=axes[1], title='Horizontal (x= only)')
pp.show()

# %%
# Combined Violin and Swarm Plot
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Overlay swarm plot on violin plot to show distribution shape and individual data points.

fig, ax = pp.subplots(axes_size=(80, 65))

# First, create the violin plot
pp.violinplot(
    data=violin_data[violin_data['group'] == 'Group 1'],
    x='category',
    y='value',
    ax=ax,
    inner=None,
)

# Then overlay the swarm plot
pp.swarmplot(
    data=violin_data[violin_data['group'] == 'Group 1'],
    x='category',
    y='value',
    ax=ax,
    alpha=1,
    legend=False,
    size=3,
)

ax.set_title('Combined Violin and Swarm Plot')
ax.set_xlabel('Category')
ax.set_ylabel('Value')
pp.show()

# %%
# Customization
# -------------
#
# Violin Plot with Custom Alpha
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Adjust transparency of violin fill.

ax = pp.violinplot(
    data=violin_data,
    x='category',
    y='value',
    hue='group',
    gap=0.1,
    title='Violin Plot with Custom Alpha',
    xlabel='Category',
    ylabel='Value',
    alpha=0.3,
)
pp.show()

# %%
# Violin Plot with Different Inner Representations
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Compare different inner representations: box, quart, stick, point.

fig, axes = pp.subplots(2, 2, axes_size=(70, 55))

inner_types = ['box', 'quart', 'stick', 'point']
for ax, inner in zip(axes.flat, inner_types):
    pp.violinplot(
        data=violin_data[violin_data['group'] == 'Group 1'],
        x='category',
        y='value',
        inner=inner,
        ax=ax,
        title=f'Inner: {inner}',
        xlabel='Category',
        ylabel='Value',
    )

pp.show()

# %%
# One-Sided Violin Plots
# ~~~~~~~~~~~~~~~~~~~~~~
# Create one-sided (half) violin plots using the side parameter.

fig, axes = pp.subplots(1, 2, axes_size=(70, 60))

# Left-sided violin
pp.violinplot(
    data=violin_data,
    x='category',
    y='value',
    hue='group',
    side='left',
    gap=0.1,
    ax=axes[0],
    title='Left-Sided Violin Plot',
    xlabel='Category',
    ylabel='Value',
    palette={'Group 1': '#8E8EC1', 'Group 2': '#75B375'},
)

# Right-sided violin
pp.violinplot(
    data=violin_data,
    x='category',
    y='value',
    hue='group',
    side='right',
    gap=0.1,
    ax=axes[1],
    title='Right-Sided Violin Plot',
    xlabel='Category',
    ylabel='Value',
    palette={'Group 1': '#8E8EC1', 'Group 2': '#75B375'},
)

pp.show()

# %%
# Annotated violin stats
# ~~~~~~~~~~~~~~~~~~~~~~
# Violinplots share the ``box_stats`` annotation strategy with boxplots:
# ``annotate=True`` labels the median by default; pass
# ``stats=[...]`` to label multiple statistics per violin. See the
# dedicated :doc:`annotations gallery <plot_23_annotate>` for more.

ax = pp.violinplot(
    data=violin_data,
    x='category', y='value',
    annotate={"stats": ["median", "q1", "q3"], "fmt": ".1f"},
    title="annotate={'stats': ['median', 'q1', 'q3']}",
)
pp.show()