✨ Feat: Add scripts, test, and example for violin plot (#39)

* ✨ Feat(vuecore/schemas/basic/violin.py): Create Pydantic schema of the violin plot

* ✨ Feat(vuecore/engines/plotly/violin.py): Create script with build function for violin plot

* ✨ Feat(vuecore/engines/plotly/theming.py): Add apply_violin_theme function to the script

* ✨ Feat(vuecore/engines/plotly/__init__.py): Register violin plot builder and add it to the PlotType StrEnum

* ✨ Feat(vuecore/plots/violin.py): Create script with the user-facing function for the violin plot, and update box plot api examples names docstring

* 🐛 Fix(vuecore/utils/docs_utils.py): add code to handle annotations with more than one type (e.g., str | bool)

* 🐛 Fix(vuecore/engines/plotly/theming.py): add code to handle box parameter from violin config as expected by go.Figure in the apply_violin_theme function

* ✨ Feat(docs/api_examples/box_violin_plot.ipynb): Update notebook api example for box plot to also include the violin plot, and sync it with a python script

* 📝 Docs: update index.md to add box and violin plot example

* ✅ Feat(tests/test_violinplot.py): Create violin plot test based on the api example code

* 📝 Doc: update notebook name on the docstring of the test_boxplot.py file and rename test_scatterplot.py file to follow the naming standard of the other scripts

* 🐛 Fix(docs/api_examples/box_violin_plot.py): apply black formatting

Author: sayalaruano

docs/api_examples/box_plot.ipynb

@@ -13,7 +13,7 @@
 # ---
 
 # %% [markdown]
-# # Box Plot
+# # Box and Violin Plots
 #
 # ![VueCore logo][vuecore_logo]
 #
@@ -22,15 +22,17 @@
 # [VueCore][vuecore_repo] is a Python package for creating interactive and static visualizations of multi-omics data.
 # It is part of a broader ecosystem of tools—including [ACore][acore_repo] for data processing and [VueGen][vuegen_repo] for automated reporting—that together enable end-to-end workflows for omics analysis.
 #
-# This notebook demonstrates how to generate box plots using plotting functions from VueCore. We showcase basic and advanced plot configurations, highlighting key customization options such as grouping, color mapping, text annotations, and export to multiple file formats.
+# This notebook demonstrates how to generate box and violin plots using plotting functions from VueCore. We showcase basic and advanced plot configurations, highlighting key customization options such as grouping, color mapping, text annotations, and export to multiple file formats.
 #
 # ## Notebook structure
 #
 # First, we will set up the work environment by installing the necessary packages and importing the required libraries. Next, we will create basic and advanced box plots.
 #
 # 0. [Work environment setup](#0-work-environment-setup)
 # 1. [Basic box plot](#1-basic-box-plot)
-# 2. [Advanced box plot](#2-advanced-box-plot)
+# 2. [Basic violin plot](#2-basic-violin-plot)
+# 3. [Advanced box plot](#3-advanced-box-plot)
+# 4. [Advanced violin plot](#3-advanced-violin-plot)
 #
 # ## Credits and Contributors
 # - This notebook was created by Sebastián Ayala-Ruano under the supervision of Henry Webel and Alberto Santos, head of the [Multiomics Network Analytics Group (MoNA)][Mona] at the [Novo Nordisk Foundation Center for Biosustainability (DTU Biosustain)][Biosustain].
@@ -75,8 +77,13 @@
 import pandas as pd
 import numpy as np
 from pathlib import Path
+import plotly.io as pio
 
 from vuecore.plots.basic.box import create_box_plot
+from vuecore.plots.basic.violin import create_violin_plot
+
+# Set the Plotly renderer based on the environment
+pio.renderers.default = "notebook"
 
 # %% [markdown]
 # ### 0.3. Create sample data
@@ -127,27 +134,46 @@
 # A basic box plot can be created by simply providing the `x` and `y` columns from the DataFrame, along with style options like `title`.
 
 # %%
-# Define output file path for the PNG plot
-file_path_basic_png = Path(output_dir) / "box_plot_basic.png"
+# Define output file path for the PNG basic box plot
+file_path_basic_box_png = Path(output_dir) / "box_plot_basic.png"
 
 # Generate the basic box plot
 box_plot_basic = create_box_plot(
     data=gene_exp_df,
     x="Treatment",
     y="Expression",
     title="Gene Expression Levels by Treatment",
-    file_path=file_path_basic_png,
+    file_path=file_path_basic_box_png,
 )
 
 box_plot_basic.show()
 
 # %% [markdown]
-# ## 2. Advanced Box Plot
+# ## 2. Basic Violin Plot
+# A basic violin plot can be created by simply providing the `x` and `y` columns from the DataFrame, along with style options like `title`.
+
+# %%
+# Define output file path for the PNG basic violin plot
+file_path_basic_violin_png = Path(output_dir) / "violin_plot_basic.png"
+
+# Generate the basic violin plot
+violin_plot_basic = create_violin_plot(
+    data=gene_exp_df,
+    x="Treatment",
+    y="Expression",
+    title="Gene Expression Levels by Treatment",
+    file_path=file_path_basic_violin_png,
+)
+
+violin_plot_basic.show()
+
+# %% [markdown]
+# ## 3. Advanced Box Plot
 # Here is an example of an advanced box plot with more descriptive parameters, including `color and box grouping`, `text annotations`, `hover tooltips`, and export to `HTML`.
 
 # %%
-# Define the output file path for the advanced HTML plot
-file_path_adv_html = Path(output_dir) / "box_plot_advanced.html"
+# Define the output file path for the advanced HTML box plot
+file_path_adv_box_html = Path(output_dir) / "box_plot_advanced.html"
 
 # Generate the advanced box plot
 box_plot_adv = create_box_plot(
@@ -172,7 +198,43 @@
     },
     category_orders={"Sample_ID": ["Patient A", "Patient B", "Patient C", "Patient D"]},
     hover_data=["Gene_ID"],
-    file_path=file_path_adv_html,
+    file_path=file_path_adv_box_html,
 )
 
 box_plot_adv.show()
+
+# %% [markdown]
+# ## 4. Advanced Violin Plot
+# Here is an example of an advanced violin plot with more descriptive parameters, including `color and box grouping`, `text annotations`, `hover tooltips`, and export to `HTML`.
+
+# %%
+# Define the output file path for the advanced HTML violin plot
+file_path_adv_violin_html = Path(output_dir) / "violin_plot_advanced.html"
+
+# Generate the advanced box plot
+violin_plot_adv = create_violin_plot(
+    data=gene_exp_df,
+    x="Treatment",
+    y="Expression",
+    color="Sample_ID",
+    violinmode="group",
+    points="outliers",
+    title="Gene Expression Levels with Control and Treatment Condition",
+    subtitle="Distribution of gene expression across different treatments and patient samples",
+    labels={
+        "Treatment": "Treatment",
+        "Expression": "Gene Expression",
+        "Sample_ID": "Patient Sample ID",
+    },
+    color_discrete_map={
+        "Patient A": "#508AA8",
+        "Patient B": "#A8505E",
+        "Patient C": "#86BF84",
+        "Patient D": "#A776AF",
+    },
+    category_orders={"Sample_ID": ["Patient A", "Patient B", "Patient C", "Patient D"]},
+    hover_data=["Gene_ID"],
+    file_path=file_path_adv_violin_html,
+)
+
+violin_plot_adv.show()

docs/api_examples/box_violin_plot.ipynb

@@ -23,7 +23,7 @@ sections_readme/license
 api_examples/scatter_plot
 api_examples/line_plot
 api_examples/bar_plot
-api_examples/box_plot
+api_examples/box_violin_plot
 ```
 
 ```{toctree}

docs/api_examples/box_plot.py docs/api_examples/box_violin_plot.pydocs/api_examples/box_plot.py renamed to docs/api_examples/box_violin_plot.py

@@ -14,6 +14,7 @@ class PlotType(StrEnum):
     LINE = auto()
     BAR = auto()
     BOX = auto()
+    VIOLIN = auto()
 
 
 class EngineType(StrEnum):

docs/index.md

@@ -5,6 +5,7 @@
 from .line import build as build_line
 from .bar import build as build_bar
 from .box import build as build_box
+from .violin import build as build_violin
 from .saver import save
 
 # Import build_utils to ensure it's available
@@ -17,5 +18,6 @@
 register_builder(plot_type=PlotType.LINE, engine=EngineType.PLOTLY, func=build_line)
 register_builder(plot_type=PlotType.BAR, engine=EngineType.PLOTLY, func=build_bar)
 register_builder(plot_type=PlotType.BOX, engine=EngineType.PLOTLY, func=build_box)
+register_builder(plot_type=PlotType.VIOLIN, engine=EngineType.PLOTLY, func=build_violin)
 
 register_saver(engine=EngineType.PLOTLY, func=save)

src/vuecore/constants.py

@@ -4,6 +4,7 @@
 from vuecore.schemas.basic.line import LineConfig
 from vuecore.schemas.basic.bar import BarConfig
 from vuecore.schemas.basic.box import BoxConfig
+from vuecore.schemas.basic.violin import ViolinConfig
 
 
 def _get_axis_title(config, axis: str) -> str:
@@ -213,3 +214,35 @@ def apply_box_theme(fig: go.Figure, config: BoxConfig) -> go.Figure:
     fig = _apply_common_layout(fig, config)
 
     return fig
+
+
+def apply_violin_theme(fig: go.Figure, config: ViolinConfig) -> go.Figure:
+    """
+    Applies a consistent layout and theme to a Plotly violin plot.
+
+    This function handles all styling and layout adjustments, such as titles,
+    dimensions, templates, and trace properties, separating these concerns
+    from the initial data mapping.
+
+    Parameters
+    ----------
+    fig : go.Figure
+        The Plotly figure object to be styled.
+    config : ViolinConfig
+        The configuration object containing all styling and layout info.
+
+    Returns
+    -------
+    go.Figure
+        The styled Plotly figure object.
+    """
+    # Convert the box boolean parameter from the config to the go.Figure expected format
+    box_dict = {"visible": config.box}
+
+    # Apply trace-specific updates for violin plots
+    fig.update_traces(points=config.points, box=box_dict, selector=dict(type="violin"))
+
+    # Apply common layout
+    fig = _apply_common_layout(fig, config)
+
+    return fig

src/vuecore/engines/plotly/__init__.py

@@ -0,0 +1,57 @@
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+
+from vuecore.schemas.basic.violin import ViolinConfig
+from .theming import apply_violin_theme
+from .plot_builder import build_plot
+
+# Define parameters handled by the theme script
+THEMING_PARAMS = [
+    "violinmode",
+    "log_x",
+    "log_y",
+    "range_x",
+    "range_y",
+    "points",
+    "box",
+    "title",
+    "x_title",
+    "y_title",
+    "subtitle",
+    "template",
+    "width",
+    "height",
+]
+
+
+def build(data: pd.DataFrame, config: ViolinConfig) -> go.Figure:
+    """
+    Creates a Plotly violin plot figure from a DataFrame and a Pydantic configuration.
+
+    This function acts as a bridge between the abstract plot definition and the
+    Plotly Express implementation. It translates the validated `ViolinConfig`
+    into the arguments for `plotly.express.violin` and also forwards any
+    additional, unvalidated keyword arguments from Plotly. The resulting figure
+    is then customized with layout and theme settings using `plotly.graph_objects`.
+    (https://plotly.com/python-api-reference/generated/plotly.express.violin.html).
+
+    Parameters
+    ----------
+    data : pd.DataFrame
+        The DataFrame containing the plot data.
+    config : ViolinConfig
+        The validated Pydantic model with all plot configurations.
+
+    Returns
+    -------
+    go.Figure
+        A `plotly.graph_objects.Figure` object representing the violin plot.
+    """
+    return build_plot(
+        data=data,
+        config=config,
+        px_function=px.violin,
+        theming_function=apply_violin_theme,
+        theming_params=THEMING_PARAMS,
+    )

src/vuecore/engines/plotly/theming.py

@@ -58,10 +58,10 @@ def create_box_plot(
     --------
     For detailed examples and usage, please refer to the documentation:
 
-    * **Jupyter Notebook:** `docs/api_examples/box_plot.ipynb` -
-    https://vuecore.readthedocs.io/en/latest/api_examples/box_plot.html
-    * **Python Script:** `docs/api_examples/box_plot.py` -
-    https://github.com/Multiomics-Analytics-Group/vuecore/blob/main/docs/api_examples/box_plot.py
+    * **Jupyter Notebook:** `docs/api_examples/box_violin_plot.ipynb` -
+    https://vuecore.readthedocs.io/en/latest/api_examples/box_violin_plot.html
+    * **Python Script:** `docs/api_examples/box_violin_plot.py` -
+    https://github.com/Multiomics-Analytics-Group/vuecore/blob/main/docs/api_examples/box_violin_plot.py
     """
     return create_plot(
         data=data,

src/vuecore/engines/plotly/violin.py

@@ -0,0 +1,73 @@
+from typing import Any
+
+import pandas as pd
+
+from vuecore import EngineType, PlotType
+from vuecore.schemas.basic.violin import ViolinConfig
+from vuecore.plots.plot_factory import create_plot
+from vuecore.utils.docs_utils import document_pydant_params
+
+
+@document_pydant_params(ViolinConfig)
+def create_violin_plot(
+    data: pd.DataFrame,
+    engine: EngineType = EngineType.PLOTLY,
+    file_path: str = None,
+    **kwargs,
+) -> Any:
+    """
+    Creates, styles, and optionally saves a violin plot using the specified engine.
+
+    This function serves as the main entry point for users to generate violin plots.
+    It validates the provided configuration against the `ViolinConfig` schema,
+    retrieves the appropriate plotting builder and saver functions based on the
+    selected engine, builds the plot, and optionally saves it to a file.
+
+    Parameters
+    ----------
+    data : pd.DataFrame
+        The DataFrame containing the data to be plotted. Each row represents
+        an observation, and columns correspond to variables.
+    engine : EngineType, optional
+        The plotting engine to use for rendering the plot.
+        Defaults to `EngineType.PLOTLY`.
+    file_path : str, optional
+        If provided, the path where the final plot will be saved.
+        The file format is automatically inferred from the file extension
+        (e.g., '.html', '.png', '.jpeg', '.svg'). Defaults to None, meaning
+        the plot will not be saved.
+
+    Returns
+    -------
+    Any
+        The final plot object returned by the selected engine.
+        For Plotly, this will typically be a `plotly.graph_objects.Figure`.
+        The exact type depends on the chosen engine.
+
+    Raises
+    ------
+    pydantic.ValidationError
+        If the provided keyword arguments do not conform to the `ViolinConfig` schema.
+        e.g., a required parameter is missing or a value has an incorrect type.
+    ValueError
+        Raised by the plotting engine (e.g., Plotly Express) if a
+        column specified in the configuration (e.g., 'x', 'y', 'color') is
+        not found in the provided DataFrame.
+
+    Examples
+    --------
+    For detailed examples and usage, please refer to the documentation:
+
+    * **Jupyter Notebook:** `docs/api_examples/box_violin_plot.ipynb` -
+    https://vuecore.readthedocs.io/en/latest/api_examples/box_violin_plot.html
+    * **Python Script:** `docs/api_examples/box_violin_plot.py` -
+    https://github.com/Multiomics-Analytics-Group/vuecore/blob/main/docs/api_examples/box_violin_plot.py
+    """
+    return create_plot(
+        data=data,
+        config=ViolinConfig,
+        plot_type=PlotType.VIOLIN,
+        engine=engine,
+        file_path=file_path,
+        **kwargs,
+    )