✨ Feat: Add scripts, test, and example for histogram plot

docs/api_examples/histogram_plot.py

@@ -0,0 +1,162 @@
+# ---
+# jupyter:
+#   jupytext:
+#     text_representation:
+#       extension: .py
+#       format_name: percent
+#       format_version: '1.3'
+#       jupytext_version: 1.17.2
+#   kernelspec:
+#     display_name: vuecore-dev
+#     language: python
+#     name: python3
+# ---
+
+# %% [markdown]
+# # Histogram Plot
+#
+# ![VueCore logo][vuecore_logo]
+#
+# [![Open In Colab][colab_badge]][colab_link]
+#
+# [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 histogram 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 histogram plots.
+#
+# 0. [Work environment setup](#0-work-environment-setup)
+# 1. [Basic histogram plot](#1-basic-histogram-plot)
+# 2. [Advanced histogram plot](#2-advanced-histogram-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].
+# - You can find more details about the project in this [GitHub repository][vuecore_repo].
+#
+# [colab_badge]: https://colab.research.google.com/assets/colab-badge.svg
+# [colab_link]: https://colab.research.google.com/github/Multiomics-Analytics-Group/vuecore/blob/main/docs/api_examples/bar_plot.ipynb
+# [vuecore_logo]: https://raw.githubusercontent.com/Multiomics-Analytics-Group/vuecore/main/docs/images/logo/vuecore_logo.svg
+# [Mona]: https://multiomics-analytics-group.github.io/
+# [Biosustain]: https://www.biosustain.dtu.dk/
+# [vuecore_repo]: https://github.com/Multiomics-Analytics-Group/vuecore
+# [vuegen_repo]: https://github.com/Multiomics-Analytics-Group/vuegen
+# [acore_repo]: https://github.com/Multiomics-Analytics-Group/acore
+
+# %% [markdown]
+# ## 0. Work environment setup
+
+# %% [markdown]
+# ### 0.1. Installing libraries and creating global variables for platform and working directory
+#
+# To run this notebook locally, you should create a virtual environment with the required libraries. If you are running this notebook on Google Colab, everything should be set.
+
+# %% tags=["hide-output"]
+# VueCore library
+# %pip install vuecore
+
+# %% tags=["hide-cell"]
+import os
+
+IN_COLAB = "COLAB_GPU" in os.environ
+
+# %% tags=["hide-cell"]
+# Create a directory for outputs
+output_dir = "./outputs"
+os.makedirs(output_dir, exist_ok=True)
+
+# %% [markdown]
+# ### 0.2. Importing libraries
+
+# %%
+# Imports
+import pandas as pd
+import numpy as np
+from pathlib import Path
+import plotly.io as pio
+
+from vuecore.plots.basic.histogram import create_histogram_plot
+
+# Set the Plotly renderer based on the environment
+pio.renderers.default = "notebook"
+
+# %% [markdown]
+# ### 0.3. Create sample data
+# We create a synthetic dataset simulating gene expression data across two experimental conditions to demonstrate how histograms can visualize data distribution.
+
+# %%
+# Set a random seed for reproducibility of the synthetic data
+np.random.seed(42)
+
+# Define parameters for synthetic gene expression data
+num_genes = 1000
+conditions = ["Control", "Treated"]
+gene_names = [f"Gene_{i}" for i in range(num_genes)]
+
+# Simulate expression data with a slight shift in the "Treated" group
+expression_values = np.concatenate(
+    [
+        np.random.normal(loc=10, scale=2, size=num_genes // 2),
+        np.random.normal(loc=12, scale=2, size=num_genes // 2),
+    ]
+)
+condition_values = np.concatenate(
+    [["Control"] * (num_genes // 2), ["Treated"] * (num_genes // 2)]
+)
+
+# Create the DataFrame
+gene_exp_df = pd.DataFrame(
+    {
+        "Gene_ID": gene_names,
+        "Expression": expression_values,
+        "Condition": condition_values,
+    }
+)
+
+gene_exp_df.head()
+
+# %% [markdown]
+# ## 1. Basic Histogram Plot
+# A basic histogram 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 histogram
+file_path_basic_hist_png = Path(output_dir) / "histogram_plot_basic.png"
+
+# Generate the basic histogram plot
+histogram_plot_basic = create_histogram_plot(
+    data=gene_exp_df,
+    x="Expression",
+    title="Distribution of Gene Expression Levels",
+    file_path=file_path_basic_hist_png,
+)
+
+histogram_plot_basic.show()
+
+# %% [markdown]
+# ## 2. Advanced Histogram Plot
+# Here is an example of an advanced histogram plot with more descriptive parameters, including `color grouping`, `overlay barmode`, `probability density normalization`, `hover tooltips`, and export to `HTML`.
+
+# %%
+# Define the output file path for the advanced HTML histogram
+file_path_adv_hist_html = Path(output_dir) / "histogram_plot_advanced.html"
+
+# Generate the advanced histogram plot
+histogram_plot_adv = create_histogram_plot(
+    data=gene_exp_df,
+    x="Expression",
+    color="Condition",
+    barmode="overlay",
+    histnorm="probability density",
+    title="Gene Expression Distribution by Treatment Condition",
+    subtitle="Histogram with probability density normalized",
+    labels={"Expression": "Gene Expression", "Condition": "Treatment Condition"},
+    hover_data=["Gene_ID"],
+    opacity=0.75,
+    file_path=file_path_adv_hist_html,
+)
+
+histogram_plot_adv.show()

docs/index.md

@@ -24,6 +24,7 @@ api_examples/scatter_plot
 api_examples/line_plot
 api_examples/bar_plot
 api_examples/box_violin_plot
+api_examples/histogram_plot
 ```
 
 ```{toctree}

src/vuecore/constants.py

@@ -15,6 +15,7 @@ class PlotType(StrEnum):
     BAR = auto()
     BOX = auto()
     VIOLIN = auto()
+    HISTOGRAM = auto()
 
 
 class EngineType(StrEnum):

src/vuecore/engines/plotly/__init__.py

@@ -6,6 +6,7 @@
 from .bar import build as build_bar
 from .box import build as build_box
 from .violin import build as build_violin
+from .histogram import build as build_histogram
 from .saver import save
 
 # Import build_utils to ensure it's available
@@ -19,5 +20,8 @@
 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_builder(
+    plot_type=PlotType.HISTOGRAM, engine=EngineType.PLOTLY, func=build_histogram
+)
 
 register_saver(engine=EngineType.PLOTLY, func=save)

src/vuecore/engines/plotly/histogram.py

@@ -0,0 +1,58 @@
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+
+from vuecore.schemas.basic.histogram import HistogramConfig
+from .theming import apply_histogram_theme
+from .plot_builder import build_plot
+
+# Define parameters handled by the theme script
+THEMING_PARAMS = [
+    "opacity",
+    "barmode",
+    "barnorm",
+    "histnorm",
+    "log_x",
+    "log_y",
+    "range_x",
+    "range_y",
+    "title",
+    "x_title",
+    "y_title",
+    "subtitle",
+    "template",
+    "width",
+    "height",
+]
+
+
+def build(data: pd.DataFrame, config: HistogramConfig) -> go.Figure:
+    """
+    Creates a Plotly histogram 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 `HistogramConfig`
+    into the arguments for `plotly.express.histogram` 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.histogram.html).
+
+    Parameters
+    ----------
+    data : pd.DataFrame
+        The DataFrame containing the plot data.
+    config : HistogramConfig
+        The validated Pydantic model with all plot configurations.
+
+    Returns
+    -------
+    go.Figure
+        A `plotly.graph_objects.Figure` object representing the histogram.
+    """
+    return build_plot(
+        data=data,
+        config=config,
+        px_function=px.histogram,
+        theming_function=apply_histogram_theme,
+        theming_params=THEMING_PARAMS,
+    )

src/vuecore/engines/plotly/theming.py

@@ -5,6 +5,7 @@
 from vuecore.schemas.basic.bar import BarConfig
 from vuecore.schemas.basic.box import BoxConfig
 from vuecore.schemas.basic.violin import ViolinConfig
+from vuecore.schemas.basic.histogram import HistogramConfig
 
 
 def _get_axis_title(config, axis: str) -> str:
@@ -246,3 +247,36 @@ def apply_violin_theme(fig: go.Figure, config: ViolinConfig) -> go.Figure:
     fig = _apply_common_layout(fig, config)
 
     return fig
+
+
+def apply_histogram_theme(fig: go.Figure, config: HistogramConfig) -> go.Figure:
+    """
+    Applies a consistent layout and theme to a Plotly histogram 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 : HistogramConfig
+        The configuration object containing all styling and layout info.
+
+    Returns
+    -------
+    go.Figure
+        The styled Plotly figure object.
+    """
+    # Apply trace-specific updates for histogram
+    fig.update_traces(
+        opacity=config.opacity,
+        orientation=config.orientation,
+        selector=dict(type="histogram"),
+    )
+
+    # Apply common layout
+    fig = _apply_common_layout(fig, config)
+
+    return fig

src/vuecore/plots/basic/__init__.py

@@ -1,12 +1,16 @@
 # vuecore/plots/basic/__init__.py
 from .bar import create_bar_plot
 from .box import create_box_plot
+from .histogram import create_histogram_plot
 from .line import create_line_plot
 from .scatter import create_scatter_plot
+from .violin import create_violin_plot
 
 __all__ = [
     "create_bar_plot",
     "create_box_plot",
     "create_line_plot",
     "create_scatter_plot",
+    "create_histogram_plot",
+    "create_violin_plot",
 ]

src/vuecore/plots/basic/histogram.py

@@ -0,0 +1,73 @@
+from typing import Any
+
+import pandas as pd
+
+from vuecore import EngineType, PlotType
+from vuecore.schemas.basic.histogram import HistogramConfig
+from vuecore.plots.plot_factory import create_plot
+from vuecore.utils.docs_utils import document_pydant_params
+
+
+@document_pydant_params(HistogramConfig)
+def create_histogram_plot(
+    data: pd.DataFrame,
+    engine: EngineType = EngineType.PLOTLY,
+    file_path: str = None,
+    **kwargs,
+) -> Any:
+    """
+    Creates, styles, and optionally saves a histogram using the specified engine.
+
+    This function serves as the main entry point for users to generate histograms.
+    It validates the provided configuration against the `HistogramConfig` 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 `HistogramConfig` 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/histogram_plot.ipynb` -
+    https://vuecore.readthedocs.io/en/latest/api_examples/histogram_plot.html
+    * **Python Script:** `docs/api_examples/histogram_plot.py` -
+    https://github.com/Multiomics-Analytics-Group/vuecore/blob/main/docs/api_examples/histogram_plot.py
+    """
+    return create_plot(
+        data=data,
+        config=HistogramConfig,
+        plot_type=PlotType.HISTOGRAM,
+        engine=engine,
+        file_path=file_path,
+        **kwargs,
+    )