Fixing pytest failures, adding new forestplot, tutorial and inmage tests

Author: Lucas1213WZY

dabest/forest_plot.py

@@ -17,12 +17,17 @@ def load_plot_data(
     """
     Loads plot data based on specified effect size and contrast type.
 
-    Parameters:
-    contrasts (List): List of contrast objects.
-    effect_size (str): Type of effect size ('mean_diff', 'median_diff', etc.).
-    contrast_type (str): Type of contrast ('delta2', 'mini_meta').
-
-    Returns:
+    Parameters
+    ----------
+    contrasts : List
+        List of contrast objects.
+    effect_size: str
+        Type of effect size ('mean_diff', 'median_diff', etc.).
+    contrast_type: str
+        Type of contrast ('delta2', 'mini_meta').
+
+    Returns
+    -------
     List: Contrast plot data based on specified parameters.
     """
     effect_attr_map = {
@@ -31,12 +36,13 @@ def load_plot_data(
         "cliffs_delta": "cliffs_delta",
         "cohens_d": "cohens_d",
         "hedges_g": "hedges_g",
+        "delta_g": "delta_g"
     }
 
-    contrast_attr_map = {"delta2": "delta_delta", "mini_meta": "mini_meta"}
+    contrast_attr_map = {"delta2": "delta_delta", "mini_meta": "mini_meta_delta"}
 
     effect_attr = effect_attr_map.get(effect_size)
-    contrast_attr = contrast_attr_map.get(contrast_type, "delta_delta")
+    contrast_attr = contrast_attr_map.get(contrast_type)
 
     if not effect_attr:
         raise ValueError(f"Invalid effect_size: {effect_size}")
@@ -46,9 +52,9 @@ def load_plot_data(
     ]
 
 
-def extract_plot_data(contrast_plot_data, contrast_labels):
+def extract_plot_data(contrast_plot_data, contrast_type):
     """Extracts bootstrap, difference, and confidence intervals based on contrast labels."""
-    if contrast_labels == "mini_meta":
+    if contrast_type == "mini_meta":
         attribute_suffix = "weighted_delta"
     else:
         attribute_suffix = "delta_delta"
@@ -57,26 +63,25 @@ def extract_plot_data(contrast_plot_data, contrast_labels):
         getattr(result, f"bootstraps_{attribute_suffix}")
         for result in contrast_plot_data
     ]
+    
     differences = [result.difference for result in contrast_plot_data]
     bcalows = [result.bca_low for result in contrast_plot_data]
     bcahighs = [result.bca_high for result in contrast_plot_data]
-
+    
     return bootstraps, differences, bcalows, bcahighs
 
 
 def forest_plot(
     contrasts: List,
     selected_indices: Optional[List] = None,
-    analysis_type: str = "delta2",
+    contrast_type: str = "delta2",
     xticklabels: Optional[List] = None,
     effect_size: str = "mean_diff",
-    contrast_labels: str = "delta_delta",
-    ylabel: str = "ΔΔ Volume (nL)",
+    contrast_labels: List[str] = None,
+    ylabel: str = "value",
     plot_elements_to_extract: Optional[List] = None,
     title: str = "ΔΔ Forest",
-    custom_palette: Optional[
-        Union[dict, list, str]
-    ] = None,  # Custom color palette parameter
+    custom_palette: Optional[Union[dict, list, str]] = None,
     fontsize: int = 20,
     violin_kwargs: Optional[dict] = None,
     marker_size: int = 20,
@@ -87,73 +92,107 @@ def forest_plot(
     additional_plotting_kwargs: Optional[dict] = None,
     rotation_for_xlabels: int = 45,
     alpha_violin_plot: float = 0.4,
-) -> plt.Figure:
-    """
-    Generates a customized forest plot using contrast objects from DABEST-python package or similar.
-
-    Parameters:
-    contrasts (List): List of contrast objects.
-    selected_indices (Optional[List]): Indices of contrasts to be plotted, if not all.
-    analysis_type (str): Type of analysis ('delta2', 'minimeta').
-    xticklabels (Optional[List]): Custom labels for x-axis ticks.
-    effect_size (str): Type of effect size ('mean_diff', 'median_diff', etc.).
-    contrast_labels (str): Labels for each contrast.
-    ylabel (str): Label for the y-axis.
-    plot_elements_to_extract (Optional[List]): Plot elements to be extracted for custom plotting.
-    title (str): Title of the plot.
-    ylim (Tuple[float, float]): y-axis limits.
-    custom_palette (Optional[Union[dict, list, str]]): Custom palette for violin plots.
-    fontsize (int): Font size for labels.
-    violin_kwargs (Optional[dict]): Additional kwargs for violin plots.
-    marker_size (int): Size of the markers for mean differences.
-    ci_line_width (float): Line width for confidence intervals.
-    zero_line_width (int): Width of the zero line.
-    remove_spines (bool): Whether to remove the plot spines.
-    ax (Optional[plt.Axes]): Axes object to plot on, if provided.
-    additional_plotting_kwargs (Optional[dict]): Additional plotting parameters.
-    rotation_for_xlabels (int): Rotation angle for x-axis labels.
-    alpha_violin_plot (float): Transparency level for violin plots.
-
-    Returns:
-    plt.Figure: The matplotlib figure object with the plot.
+    horizontal: bool = False  # New argument for horizontal orientation
+)-> plt.Figure:
+    """  
+    Custom function that generates a forest plot from given contrast objects, suitable for a range of data analysis types, including those from packages like DABEST-python.
+
+    Parameters
+    ----------
+    contrasts : List
+        List of contrast objects.
+    selected_indices : Optional[List], default=None
+        Indices of specific contrasts to plot, if not plotting all.
+    analysis_type : str
+         the type of analysis (e.g., 'delta2', 'minimeta').
+    xticklabels : Optional[List], default=None
+        Custom labels for the x-axis ticks.
+    effect_size : str
+        Type of effect size to plot (e.g., 'mean_diff', 'median_diff').
+    contrast_labels : List[str]
+        Labels for each contrast.
+    ylabel : str
+        Label for the y-axis, describing the plotted data or effect size.
+    plot_elements_to_extract : Optional[List], default=None
+        Elements to extract for detailed plot customization.
+    title : str
+        Plot title, summarizing the visualized data.
+    ylim : Tuple[float, float]
+        Limits for the y-axis.
+    custom_palette : Optional[Union[dict, list, str]], default=None
+        Custom color palette for the plot.
+    fontsize : int
+        Font size for text elements in the plot.
+    violin_kwargs : Optional[dict], default=None
+        Additional arguments for violin plot customization.
+    marker_size : int
+        Marker size for plotting mean differences or effect sizes.
+    ci_line_width : float
+        Width of confidence interval lines.
+    zero_line_width : int
+        Width of the line indicating zero effect size.
+    remove_spines : bool, default=False
+        If True, removes top and right plot spines.
+    ax : Optional[plt.Axes], default=None
+        Matplotlib Axes object for the plot; creates new if None.
+        additional_plotting_kwargs : Optional[dict], default=None
+        Further customization arguments for the plot.
+    rotation_for_xlabels : int, default=0
+        Rotation angle for x-axis labels, improving readability.
+    alpha_violin_plot : float, default=1.0
+        Transparency level for violin plots.
+
+    Returns
+    -------
+    plt.Figure
+        The matplotlib figure object with the generated forest plot.
     """
     from .plot_tools import halfviolin
 
+    # Validate inputs
+    if not contrasts:
+        raise ValueError("The `contrasts` list cannot be empty.")
+    
+    if contrast_labels is not None and len(contrast_labels) != len(contrasts):
+        raise ValueError("`contrast_labels` must match the number of `contrasts` if provided.")
+    
     # Load plot data
-    contrast_plot_data = load_plot_data(contrasts, effect_size, analysis_type)
+    contrast_plot_data = load_plot_data(contrasts, effect_size, contrast_type)
 
     # Extract data for plotting
     bootstraps, differences, bcalows, bcahighs = extract_plot_data(
-        contrast_plot_data, contrast_labels
+        contrast_plot_data, contrast_type
     )
-
-    # Infer the figsize based on the number of contrasts
+    # Adjust figure size based on orientation
     all_groups_count = len(contrasts)
-    each_group_width_inches = 2.5  # Adjust as needed for width
-    base_height_inches = 4  # Base height, adjust as needed
-    height_inches = base_height_inches
-    width_inches = each_group_width_inches * all_groups_count
-    fig_size = (width_inches, height_inches)
+    if horizontal:
+        fig_size = (4, 1.5 * all_groups_count)
+    else:
+        fig_size = (1.5 * all_groups_count, 4)
 
-    # Create figure and axes if not provided
     if ax is None:
         fig, ax = plt.subplots(figsize=fig_size)
     else:
         fig = ax.figure
 
-    # Zero line
-    ax.plot([0, len(contrasts) + 1], [0, 0], "k", linewidth=zero_line_width)
-
-    # Violin plots with customizable colors
+    # Adjust violin plot orientation based on the 'horizontal' argument
     violin_kwargs = violin_kwargs or {
         "widths": 0.5,
-        "vert": True,
         "showextrema": False,
         "showmedians": False,
     }
+    violin_kwargs["vert"] = not horizontal
     v = ax.violinplot(bootstraps, **violin_kwargs)
-    halfviolin(v, alpha=alpha_violin_plot)  # Apply halfviolin from dabest
 
+    # Adjust the halfviolin function call based on 'horizontal'
+    if horizontal:
+        half = "top"
+    else:
+        half = "right"  # Assuming "right" is the default or another appropriate value
+
+    # Assuming halfviolin has been updated to accept a 'half' parameter
+    halfviolin(v, alpha=alpha_violin_plot, half=half)
+    
     # Handle the custom color palette
     if custom_palette:
         if isinstance(custom_palette, dict):
@@ -176,30 +215,32 @@ def forest_plot(
         patch.set_facecolor(color)
         patch.set_alpha(alpha_violin_plot)
 
-    # Effect size dot and confidence interval
+    # Flipping the axes for plotting based on 'horizontal'
     for k in range(1, len(contrasts) + 1):
-        ax.plot(k, differences[k - 1], "k.", markersize=marker_size)
-        ax.plot([k, k], [bcalows[k - 1], bcahighs[k - 1]], "k", linewidth=ci_line_width)
-
-    # Custom settings
-    ax.set_xticks(range(1, len(contrasts) + 1))
-    ax.set_xticklabels(
-        xticklabels or range(1, len(contrasts) + 1),
-        rotation=rotation_for_xlabels,
-        fontsize=fontsize,
-    )
-    ax.set_xlim([0, len(contrasts) + 1])
-    ax.set_ylabel(ylabel, fontsize=fontsize)
-    ax.set_title(title, fontsize=fontsize)
-    ylim = (min(bcalows) - 0.25, max(bcahighs) + 0.25)
-    ax.set_ylim(ylim)
+        if horizontal:
+            ax.plot(differences[k - 1], k, "k.", markersize=marker_size)  # Flipped axes
+            ax.plot([bcalows[k - 1], bcahighs[k - 1]], [k, k], "k", linewidth=ci_line_width)  # Flipped axes
+        else:
+            ax.plot(k, differences[k - 1], "k.", markersize=marker_size)
+            ax.plot([k, k], [bcalows[k - 1], bcahighs[k - 1]], "k", linewidth=ci_line_width)
+
+    # Adjusting labels, ticks, and limits based on 'horizontal'
+    if horizontal:
+        ax.set_yticks(range(1, len(contrasts) + 1))
+        ax.set_yticklabels(contrast_labels,  rotation=rotation_for_xlabels, fontsize=fontsize)
+        ax.set_xlabel(ylabel, fontsize=fontsize)
+    else:
+        ax.set_xticks(range(1, len(contrasts) + 1))
+        ax.set_xticklabels(contrast_labels, rotation=rotation_for_xlabels, fontsize=fontsize)
+        ax.set_ylabel(ylabel, fontsize=fontsize)
 
-    # Remove spines if requested
+    # Setting the title and adjusting spines as before
+    ax.set_title(title, fontsize=fontsize)
     if remove_spines:
         for spine in ax.spines.values():
             spine.set_visible(False)
 
-    # Additional customization
+    # Apply additional customizations if provided
     if additional_plotting_kwargs:
         ax.set(**additional_plotting_kwargs)