Feat/plot filtered data

bibmon/_bibmon_tools.py

@@ -692,4 +692,76 @@ def comparative_table (models, X_train, X_validation, X_test,
 
         return_tables.append(times_df)
 
-    return return_tables
+    return return_tables
+
+##############################################################################
+
+def filter_and_plot_data(data, color, title, not_wanted_tags = [], remove_NaN_columns = True, remove_zero_columns = True):
+    """
+    Filters and plots data from a dictionary, concatenating the values into a single DataFrame.
+    Optionally removes columns that contain only NaN values or only zeros. The remaining data is
+    plotted with each column displayed in individual vertically stacked subplots, sharing the same X-axis.
+
+    Parameters
+    ----------
+    data: dict
+        A dictionary containing the data. The keys represent categories or filenames, and 
+        the values are pandas DataFrames.
+    color: string
+        The color to be used for the plot lines.
+    title: string
+        The main title for the plot.
+    not_wanted_tags: list, optional
+        A list of column names (tags) to be excluded from the plots. Default is an empty list.
+    remove_NaN_columns: bool, optional
+        If True, columns with only NaN values will be removed from the data before plotting. Default is True.
+    remove_zero_columns: bool, optional
+        If True, columns that contain only zeros will be removed from the data before plotting. Default is True.
+
+    Returns
+    ----------
+    filteredByData: pandas.DataFrame
+        The processed and filtered data that was used in the visualization.
+    tags: list
+        A list of column names (tags) that were plotted.
+    """    
+    from matplotlib.ticker import MaxNLocator
+
+    filtered_by_data = data.apply(pd.to_numeric, errors='coerce')
+
+    if remove_NaN_columns:
+        filtered_by_data = filtered_by_data.dropna(axis=1, how='all')
+
+    if remove_zero_columns:
+        filtered_by_data = filtered_by_data.loc[:, (filtered_by_data != 0).any(axis=0)]
+
+    tags = list(filtered_by_data.keys())
+    tags = [key for key in tags if key not in not_wanted_tags]
+
+    fig, ax = plt.subplots(len(tags), 1, figsize=(18, 10), sharex=True)
+    fig.suptitle(f"{title}", fontsize=16)
+
+    timestamp_label = filtered_by_data.index
+
+    for i, tag in enumerate(tags):
+        tagData = filtered_by_data[tag].values
+        ax[i].plot(tagData, c=color, linewidth=0.8)
+        ax[i].set_ylabel(tag, rotation=0, fontsize=14,labelpad=100)
+        ax[i].set_yticks([])
+
+        ax[i].spines["top"].set_visible(False)
+        ax[i].spines["right"].set_visible(False)
+        ax[i].spines["left"].set_visible(False)
+
+        ax[i].yaxis.set_major_locator(MaxNLocator(nbins=3))
+
+        if i < len(tags) - 1:
+            ax[i].set_xticks([])
+            ax[i].spines["bottom"].set_visible(False)
+            ax[i].xaxis.set_ticks_position('none')
+        else:
+            ax[i].set_xlabel('Time', fontsize=14)
+            ax[i].xaxis.set_major_locator(MaxNLocator(nbins=5))
+            ax[i].set_xticklabels(timestamp_label.strftime('%Y-%m-%d %H:%M:%S'), rotation=0, ha='right')
+
+    return filtered_by_data, tags