feat(histogram): add faceted plots with adaptive layout #428

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zhan4329 wants to merge 57 commits into FNLCR-DMAP:dev from ramyap06:feat/histogram-facet

.gitignore

-Original file line number
+Diff line change
@@ Expand Up / @@ -3,4 +3,4 @@ @@
     .vscode/launch.json
     test/__pychache__
     build/
-    *.egg-info/
+    *.egg-info/

src/spac/templates/histogram_template.py

            
                      Original file line number
                      Diff line number
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    @@ -96,23 +96,27 @@ def run_from_json(
  
        feature = text_to_value(params.get("Feature", "None"))

        annotation = text_to_value(params.get("Annotation", "None"))

        layer = params.get("Table_", "Original")

        group_by = params.get("Group_by", "None")

        group_by = text_to_value(params.get("Group_by", "None"))

        max_groups = params.get("Max_Groups", 20)

        together = params.get("Together", True)

        fig_width = params.get("Figure_Width", 8)

        fig_height = params.get("Figure_Height", 6)

        fig_width = params.get("Figure_Width", "auto")

        fig_height = params.get("Figure_Height", "auto")

        font_size = params.get("Font_Size", 12)

        fig_dpi = params.get("Figure_DPI", 300)

        legend_location = params.get("Legend_Location", "best")

        legend_in_figure = params.get("Legend_in_Figure", False)

        take_X_log = params.get("Take_X_Log", False)

        take_Y_log = params.get("Take_Y_log", False)

        multiple = params.get("Multiple", "dodge")

        element = params.get("Element", "bars")

        shrink = params.get("Shrink_Number", 1)

        bins = params.get("Bins", "auto")

        alpha = params.get("Bin_Transparency", 0.75)

        stat = params.get("Stat", "count")

        x_rotate = params.get("X_Axis_Label_Rotation", 0)

        histplot_by = params.get("Plot_By", "Annotation")

        facet = params.get("Facet", False)

        facet_ncol = params.get("Facet_Ncol", "auto")

        # Close all existing figures to prevent extra plots

        plt.close('all')

    @@ -151,7 +155,9 @@ def run_from_json(
  
                        'No features available in adata.var_names to plot.'

                    )

        # Validate and set bins

        # Bins use a strict template contract in feature mode:

        # "auto" or a positive integer. Loose null-like values are intentionally

        # not treated as aliases here.

        if feature is not None:

            bins = text_to_value(

                bins,

    @@ -180,42 +186,138 @@ def run_from_json(
  
                    "Setting bin number calculation to auto."

                )

        if group_by and together:

            multiple = str(multiple).strip().lower()

        element = str(element).strip().lower()

        stat = str(stat).strip().lower()

        # Figure_Width and Figure_Height use "auto" for template defaults.

        # In facet mode, it is forwarded as None to derive layout geometry.

        # In non-facet mode, it falls back to 8x6 inches.

        fig_width = text_to_value(

            fig_width,

            default_none_text="auto",

            value_to_convert_to=None if facet else 8,

            to_float=True,

            param_name="Figure_Width"

        )

        fig_height = text_to_value(

            fig_height,

            default_none_text="auto",

            value_to_convert_to=None if facet else 6,

            to_float=True,

            param_name="Figure_Height"

        )

        if fig_width is not None and fig_height is not None:

            if fig_width <= 0 or fig_height <= 0:

                raise ValueError(

                    f'Figure_Width/Height should be a positive number.'

                    f'Received "{fig_width}"/"{fig_height}".'

                )

        if fig_dpi <= 0:

            raise ValueError(

                f'Figure_DPI should be a positive number. Received "{fig_dpi}".'

            )

        # Validate x-axis label rotation

        if (x_rotate < 0) or (x_rotate > 360):

            raise ValueError(

                f'The X label rotation should fall within 0 to 360 degree. '

                f'Received "{x_rotate}".'

            )

        # Max_Groups applies only when Group_by is set.

        # It accepts a positive integer or "unlimited".

        # Missing values default to 20.

        if group_by:

            parsed_max_groups = max_groups

            if parsed_max_groups != "unlimited":

                parsed_max_groups = text_to_value(

                    parsed_max_groups,

                    value_to_convert_to=20,

                    to_int=True,

                    param_name="Max_Groups",

                )

                if parsed_max_groups <= 0:

                    raise ValueError(

                        f'Max_Groups should be a positive integer or "unlimited". '

                        f'Received "{parsed_max_groups}".'

                    )

        # Facet requires Group_by and forbids Together=True.

        # Facet_Ncol accepts "auto" or a positive integer.

        if facet:

            if group_by is None:

                raise ValueError(

                    'Facet is True but Group_by is not specified. '

                    'Please specify Group_by when using Facet.'

                )

            if together:

                raise ValueError(

                    'Together and Facet cannot both be True. Please set one to False.'

                )

            facet_ncol = text_to_value(

                facet_ncol,

                default_none_text="auto",

                to_int=True,

                param_name="Facet_Ncol"

            )

            if facet_ncol is not None:

                if facet_ncol <= 0:

                    raise ValueError(

                        f'Facet_Ncol must be a positive integer or "auto". '

                        f'Received "{facet_ncol}".'

                    )

        # Initialize the x-variable before the loop

        if histplot_by == "Annotation":

            x_var = annotation

        else:

            x_var = feature

        # Assemble validated histogram kwargs right before the plotting call.

        hist_kwargs = dict(

            element=element,

            shrink=shrink,

            bins=bins,

            alpha=alpha,

            stat=stat,

        )

        if group_by and together:

            hist_kwargs["multiple"] = multiple

        if group_by:

            hist_kwargs["max_groups"] = parsed_max_groups

        if facet:

            hist_kwargs["facet_ncol"] = facet_ncol

            hist_kwargs["facet_fig_width"] = fig_width

            hist_kwargs["facet_fig_height"] = fig_height

            hist_kwargs["facet_tick_rotation"] = x_rotate

        result = histogram(

            adata=adata,

            feature=feature,

            annotation=annotation,

            layer=text_to_value(layer, "Original"),

            group_by=text_to_value(group_by),

            group_by=group_by,

            together=together,

            ax=None,

            x_log_scale=take_X_log,

            y_log_scale=take_Y_log,

            multiple=multiple,

            shrink=shrink,

            bins=bins,

            alpha=alpha,

            stat=stat

            facet=facet,

            **hist_kwargs,

        )

        fig = result["fig"]

        axs = result["axs"]

        df_counts = result["df"]

        # Set figure size and dpi

        fig.set_size_inches(fig_width, fig_height)

        if fig_width is not None and fig_height is not None:

            fig.set_size_inches(fig_width, fig_height)

            logger.info(f"Set figure size to {fig_width}x{fig_height} inches.")

        fig.set_dpi(fig_dpi)

        logger.info(f"Set figure DPI to {fig_dpi}.")

        # Ensure axes is a list

        if isinstance(axs, list):

    @@ -249,8 +351,12 @@ def run_from_json(
  
            # Rotate x labels

            ax.tick_params(axis='x', rotation=x_rotate)

            if x_rotate:

                for label in ax.get_xticklabels():

                    label.set_rotation_mode('anchor')

                    label.set_horizontalalignment('right')

        # Set titles based on group_by

        # Set titles based on group_by and facet

        if text_to_value(group_by):

            if together:

                for ax in axes:

    @@ -267,15 +373,37 @@ def run_from_json(
  
                        "Number of axes does not match number of "

                        "groups. Titles may not correspond correctly."

                    )

                if facet:

                    fig.suptitle(

                        f'Histogram of "{x_var}" faceted by "{group_by}"'

                    )

                    ax_title_prefix = f'Group'

                else:

                    ax_title_prefix = f'Histogram of "{x_var}" for group'

                for ax, grp in zip(axes, unique_groups):

                    ax.set_title(

                        f'Histogram of "{x_var}" for group: "{grp}"'

                        f'{ax_title_prefix}: "{grp}"'

                    )

        else:

            for ax in axes:

                ax.set_title(f'Count plot of "{x_var}"')

        plt.tight_layout()

        # Adjust layout to prevent title overlap

        if facet:

            rows = len({round(ax.get_position().y0, 3) for ax in axes})

            fig.tight_layout(

                rect=[

                    min(0.030, 0.02 + 0.0025 * rows),

                    max(0.022, 0.036 - 0.003 * rows),

                    min(0.992, 0.98 + 0.0025 * rows),

                    max(0.969, 0.975 - 0.001 * rows),

                ],

                pad=max(0.35, 0.6 - 0.05 * rows),

                h_pad=max(0.2, 0.43 - 0.04 * rows) * 6,

                w_pad=max(0.2, 0.43 - 0.04 * rows) * 6,

            )

        else:

            fig.tight_layout()

        logger.info("Displaying top 10 rows of histogram dataframe:")

        print(df_counts.head(10))

src/spac/utils.py

            
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                      Diff line number
                      Diff line change
                  
    @@ -1196,20 +1196,20 @@ def compute_metrics(data):
  
    # compute summary statistics for the specified columns

    def compute_summary_qc_stats(

            df: pd.DataFrame, 

            df: pd.DataFrame,

            n_mad: int = 5,

            upper_quantile: float = 0.95,

            lower_quantile: float = 0.05,

            stat_columns_list: List[str] = ['nFeature', 'nCount', 'percent.mt']

            ) -> pd.DataFrame:

        """

        Compute summary quality control statistics for specified columns in a dataset.

        For each column in stat_columns_list, this function calculates:

            - Mean

            - Median

            - Upper and lower thresholds based on median ± n_mad * MAD 

            - Upper and lower thresholds based on median ± n_mad * MAD

            (median absolute deviation)

            - Upper and lower quantiles

    @@ -1230,7 +1230,7 @@ def compute_summary_qc_stats(
  
        -------

        pd.DataFrame

            DataFrame with summary statistics for each specified column.

            Columns: ["metric_name", "mean", "median", "upper_mad", "lower_mad", 

            Columns: ["metric_name", "mean", "median", "upper_mad", "lower_mad",

            "upper_quantile", "lower_quantile"]

        Raises

    @@ -1269,8 +1269,8 @@ def compute_summary_qc_stats(
  
        return pd.DataFrame(

            stat_vals,

            columns=[

                "metric_name", "mean", "median", 

                "upper_mad", "lower_mad", 

                "metric_name", "mean", "median",

                "upper_mad", "lower_mad",

                "upper_quantile", "lower_quantile"

            ]

        )
      
        )

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