Update scatter plot to align with the Plotly API

docs/api_examples/scatter_plot.py

@@ -84,10 +84,10 @@
 pio.renderers.default = "notebook"
 
 # %% [markdown]
-# ## 1. Basic Scatter Plot
+# ### 0.3. Create sample data
+# We create a synthetic dataset that contains simulated gene expression values, p-values, regulation status, and significance scores for 8 genes across two cell types.
 
 # %%
-# Created sample data
 sample_df = pd.DataFrame(
     {
         "gene_expression": [1.2, 2.5, 3.1, 4.5, 5.2, 6.8, 3.9, 2.1],
@@ -110,6 +110,10 @@
 
 sample_df
 
+# %% [markdown]
+# ## 1. Basic Scatter Plot
+# A basic scatter plot can be created by providing the `x` and `y` columns from the DataFrame, along with style options like `title`.
+
 # %%
 # Define output path
 file_path_png = os.path.join(output_dir, "scatter_basic.png")
@@ -127,6 +131,7 @@
 
 # %% [markdown]
 # ## 2. Advanced Scatter Plot
+# An example of an advanced scatter plot that includes grouping by a categorical variable, color mapping, text annotations, and adding several styling options.
 
 # %%
 # Define output path
@@ -137,14 +142,20 @@
     data=sample_df,
     x="gene_expression",
     y="log_p_value",
-    group="regulation",
+    color="regulation",
     size="significance_score",
     text="gene_name",
     title="Advanced Gene Expression Scatter Plot",
-    x_title="Log2 Fold Change",
-    y_title="-Log10(P-value)",
-    colors={"Up": "#FF5733", "Down": "#3380FF", "None": "#33FF57"},
-    marker_opacity=0.8,
+    subtitle="Visualizing Gene Expression with Regulation and Significance",
+    labels={
+        "gene_expression": "Log2 Fold Change",
+        "log_p_value": "Log P-value",
+        "regulation": "Regulation Status",
+        "significance_score": "Significance Score",
+        "gene_name": "Gene Name",
+    },
+    color_discrete_map={"Up": "#508AA8", "Down": "#A8505E", "None": "#838383"},
+    opacity=0.8,
     marker_line_width=1,
     marker_line_color="darkgray",
     width=900,

src/vuecore/engines/plotly/scatter.py

@@ -10,46 +10,73 @@
 
 def build(data: pd.DataFrame, config: ScatterConfig) -> go.Figure:
     """
-    Creates a Plotly scatter plot figure from a DataFrame and configuration.
+    Creates a Plotly scatter plot 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 configuration
-    into the arguments for `plotly.express.scatter`.
+    Plotly Express implementation. It translates the validated `ScattereConfig`
+    into the arguments for `plotly.express.scatter` 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.scatter.html).
 
     Parameters
     ----------
     data : pd.DataFrame
         The DataFrame containing the plot data.
     config : ScatterConfig
-        The validated Pydantic model object containing all plot configurations.
+        The validated Pydantic model object with all plot configurations.
 
     Returns
     -------
     go.Figure
         A `plotly.graph_objects.Figure` object representing the scatter plot.
     """
+    # Get all parameters from the config model, including extras
+    all_config_params = config.model_dump()
+
+    # Define parameters handled by the theme script
+    theming_params = [
+        "opacity",
+        "log_x",
+        "log_y",
+        "range_x",
+        "range_y",
+        "title",
+        "subtitle",
+        "x_title",
+        "y_title",
+        "template",
+        "width",
+        "height",
+        "marker_line_width",
+        "marker_line_color",
+        "color_by_density",
+    ]
+
+    # Create the dictionary of arguments for px.scatter
     plot_args = {
-        "x": config.x,
-        "y": config.y,
-        "size": config.size,
-        "symbol": config.symbol,
-        "text": config.text,
-        "hover_data": config.hover_cols,
-        "trendline": config.trendline,
+        k: v
+        for k, v in all_config_params.items()
+        if k not in theming_params and v is not None
     }
 
+    # Handle density coloring separately
     if config.color_by_density:
         # Calculate density and pass it to the 'color' argument
         density_values = get_density(data[config.x].values, data[config.y].values)
         plot_args["color"] = density_values
+
+        # Remove discrete color mapping for density plots
+        if "color_discrete_map" in plot_args:
+            del plot_args["color_discrete_map"]
     else:
         # Use standard group-based coloring
-        plot_args["color"] = config.group
-        plot_args["color_discrete_map"] = config.colors
+        plot_args["color"] = config.color
 
+    # Create the base figure using only the arguments for px.scatter
     fig = px.scatter(data, **plot_args)
 
-    # Apply theme
+    # Apply theme and additional styling
     fig = apply_scatter_theme(fig, config)
 
     return fig

src/vuecore/engines/plotly/theming.py

@@ -5,40 +5,55 @@
 
 def apply_scatter_theme(fig: go.Figure, config: ScatterConfig) -> go.Figure:
     """
-    Applies a consistent layout and theme to a Plotly figure.
+    Applies a consistent layout and theme to a Plotly scatter plot.
 
-    This function separates styling from plot creation, allowing for a consistent
-    look and feel across different plot types. It updates traces and layout
-    properties based on the provided configuration.
+    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 : ScatterConfig
-        The configuration object containing styling info like titles and dimensions.
+        The configuration object containing all styling and layout info.
 
     Returns
     -------
     go.Figure
         The styled Plotly figure object.
     """
+    # Apply trace-specific updates
     fig.update_traces(
         marker=dict(
-            opacity=config.marker_opacity,
+            opacity=config.opacity,
             line=dict(width=config.marker_line_width, color=config.marker_line_color),
         ),
         selector=dict(mode="markers"),
     )
 
+    # Use the labels dictionary to set axis titles, falling back to defaults
+    x_title = config.x_title or (
+        config.labels.get(config.x) if config.labels else None or config.x.title()
+    )
+    y_title = config.y_title or (
+        config.labels.get(config.y) if config.labels else None or config.y.title()
+    )
+
+    # Apply layout updates
     fig.update_layout(
         title_text=config.title,
-        xaxis_title=config.x_title or config.x.title(),
-        yaxis_title=config.y_title or config.y.title(),
+        title_subtitle_text=config.subtitle,
+        xaxis_title=x_title,
+        yaxis_title=y_title,
         height=config.height,
         width=config.width,
-        template="plotly_white",
-        legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1),
+        template=config.template,
+        xaxis_type="log" if config.log_x else "linear",
+        yaxis_type="log" if config.log_y else "linear",
+        xaxis_range=config.range_x,
+        yaxis_range=config.range_y,
+        # legend=dict(orientation="h", yanchor="bottom", y=1.02, xanchor="right", x=1),
         hovermode="closest",
     )
     return fig

src/vuecore/plots/basic/scatter.py

@@ -41,38 +41,19 @@ def create_scatter_plot(
     Raises
     ------
     pydantic.ValidationError
-        If the provided kwargs do not match the `ScatterConfig` schema.
+        If the provided keyword arguments do not conform to the `ScatterConfig` schema,
+        e.g., a required parameter is missing or a value has an incorrect type.
     ValueError
-        If columns specified in the configuration do not exist in the DataFrame.
+        Raised by the plotting engine if a column specified in the configuration is not
+        found in the provided DataFrame.
 
     Examples
     --------
-    >>> import pandas as pd
-    >>> sample_df = pd.DataFrame({
-    ...     'gene_expression': [1.2, 2.5, 3.1, 4.5, 5.2, 6.8],
-    ...     'log_p_value': [0.5, 1.5, 2.0, 3.5, 4.0, 5.5],
-    ...     'regulation': ['Up', 'Up', 'None', 'Down', 'Down', 'Down'],
-    ...     'significance_score': [10, 20, 5, 40, 55, 80],
-    ...     'gene_name': ['GENE_A', 'GENE_B', 'GENE_C', 'GENE_D', 'GENE_E', 'GENE_F']
-    ... })
-    >>>
-    >>> # Create a simple scatter plot and save it to HTML
-    >>> fig = create_scatter_plot(
-    ...     data=sample_df,
-    ...     x='gene_expression',
-    ...     y='log_p_value',
-    ...     group='regulation',
-    ...     size='significance_score',
-    ...     text='gene_name',
-    ...     title="Gene Expression vs. Significance",
-    ...     x_title="Log2 Fold Change",
-    ...     y_title="-Log10(P-value)",
-    ...     colors={'Up': '#d62728', 'Down': '#1f77b4', 'None': '#7f7f7f'},
-    ...     file_path="my_scatter_plot.html"
-    ... )
-    >>>
-    >>> # The returned `fig` object can be displayed in a notebook or further modified
-    >>> # fig.show()
+    For detailed examples and usage, please refer to the documentation:
+
+    * **Jupyter Notebook:** `docs/api_examples/scatter_plot.ipynb` -
+    https://vuecore.readthedocs.io/en/latest/api_examples/scatter_plot.html
+    * **Python Script:** `docs/api_examples/scatter_plot.py`
     """
     # 1. Validate configuration using Pydantic
     config = ScatterConfig(**kwargs)