Refine the stacked bar graph
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@ -82,6 +82,7 @@ import mysql.connector
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import numpy as np
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import plotly.graph_objects as go
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import plotly.express as px
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import colorsys
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Mailstats_version = '1.2'
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build_date_time = "2024-06-18 12:03:40OURCE"
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@ -125,15 +126,21 @@ ColPercent = 25
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import mysql.connector
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import json
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def sanitize_data(data2d):
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def sanitize_and_filter_data_for_stacked_bar(data2d, xLabels, yLabels, exclude_columns_labels, exclude_rows_labels):
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"""
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Convert data to numeric values, stripping out non-numeric characters.
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Sanitize data by removing unwanted columns and rows, and converting to numeric values.
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Parameters:
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- data2d (list of lists): A 2D list containing the data.
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- xLabels (list): Current labels for the x-axis.
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- yLabels (list): Current labels for the y-axis.
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- exclude_columns_labels (list): Labels of columns to exclude from the data and x-axis.
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- exclude_rows_labels (list): Labels of rows to exclude from the y-axis.
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Returns:
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- numpy.ndarray: Sanitized 2D numpy array with numeric data.
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- list: Filtered x-axis labels.
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- list: Filtered y-axis labels.
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"""
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def to_numeric(value):
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try:
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@ -145,67 +152,92 @@ def sanitize_data(data2d):
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except ValueError:
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return 0.0 # Default to 0 if conversion fails
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sanitized_data = []
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for row in data2d:
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sanitized_row = [to_numeric(value) for value in row]
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sanitized_data.append(sanitized_row)
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# Filter out columns based on their labels
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exclude_columns_indices = [xLabels.index(label) for label in exclude_columns_labels if label in xLabels]
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return np.array(sanitized_data)
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filtered_data2d = [
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[to_numeric(value) for idx, value in enumerate(row) if idx not in exclude_columns_indices]
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for row in data2d
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]
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def create_stacked_bar_graph(data2d, xLabels, save_path='stacked_bar_graph.html'):
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filtered_xLabels = [label for idx, label in enumerate(xLabels) if idx not in exclude_columns_indices]
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# Filter out rows based on their labels
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filtered_data2d = [row for label, row in zip(yLabels, filtered_data2d) if label not in exclude_rows_labels]
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filtered_yLabels = [label for label in yLabels if label not in exclude_rows_labels]
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# Convert filtered data to numpy array
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return np.array(filtered_data2d), filtered_xLabels, filtered_yLabels
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def generate_distinct_colors(num_colors):
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"""Generate distinct colors using HSV color space."""
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colors = []
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for i in range(num_colors):
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hue = i / num_colors
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saturation = 0.7
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value = 0.9
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r, g, b = colorsys.hsv_to_rgb(hue, saturation, value)
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colors.append(f'rgb({int(r * 255)},{int(g * 255)},{int(b * 255)})')
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return colors
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def create_stacked_bar_graph(data2d, xLabels, yLabels, save_path='stacked_bar_graph.html'):
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"""
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Creates and saves a stacked bar graph from given 2D numpy array data using Plotly.
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Parameters:
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- data2d (list of lists or numpy.ndarray): A 2D list or numpy array containing the data.
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- xLabels (list): A list of category labels for the x-axis.
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- yLabels (list): A list of labels for the y-axis (e.g., hours).
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- save_path (str): The path where the plot image will be saved.
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"""
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# Identify columns to be removed based on their headers
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excluded_columns = ["Count", "PERCENT"]
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# Identify columns to be removed based on their headers (label names) and indices (hours 24 and 25)
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exclude_columns_labels = ["Count", "PERCENT","TOTALS"]
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exclude_rows_labels = ["24:00", "25:00"]
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# Create a boolean array for columns to keep (not in excluded_columns)
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columns_to_keep = [label not in excluded_columns for label in xLabels]
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# Ensure input yLabels correspond to the data
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if len(yLabels) != len(data2d):
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raise ValueError(f"The length of yLabels {len(yLabels)} must match the number of rows in the data {len(data2d)}.")
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# Filter out the columns both from the data and xLabels
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filtered_data2d = []
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for row in data2d:
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filtered_row = [value for keep, value in zip(columns_to_keep, row) if keep]
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filtered_data2d.append(filtered_row)
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# Sanitize and filter the data
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sanitized_data, filtered_xLabels, filtered_yLabels = sanitize_and_filter_data_for_stacked_bar(data2d, xLabels, yLabels, exclude_columns_labels, exclude_rows_labels)
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filtered_xLabels = [label for label, keep in zip(xLabels, columns_to_keep) if keep]
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# Ensure that the length of yLabels matches the number of rows (0 to n should be n+1 rows)
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if len(filtered_yLabels) != sanitized_data.shape[0]:
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raise ValueError(f"The length of filtered_yLabels {len(filtered_yLabels)} must match the number of rows in the data {sanitized_data.shape[0]}.")
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# Sanitize data and convert it to a numpy array
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data = sanitize_data(filtered_data2d)
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# Find columns that are not fully zero
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non_zero_columns = np.any(data != 0, axis=0)
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# Filter out fully zero columns from both the data and x_labels
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filtered_data = data[:, non_zero_columns]
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filtered_x_labels = np.array(filtered_xLabels)[non_zero_columns]
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# Transpose the data so that hours are on the x-axis and categories are stacked in the y-axis
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transposed_data = sanitized_data.T
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fig = go.Figure()
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for i in range(filtered_data.shape[0]):
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if i <= 23: # Ensure to annotate rows with proper names (e.g., Hours)
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# Get unique colors for each category
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extended_colors = generate_distinct_colors(len(filtered_xLabels))
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#print(len(filtered_xLabels))
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#print(extended_colors)
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#quit()
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for i, category in enumerate(filtered_xLabels):
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fig.add_trace(go.Bar(
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name=f'Hour {i}',
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x=filtered_x_labels,
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y=filtered_data[i]
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name=category,
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x=filtered_yLabels,
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y=transposed_data[i],
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marker_color=extended_colors[i % len(extended_colors)] # Cycle through the colors if there are more categories than colors
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))
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fig.update_layout(
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barmode='stack',
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title='Stacked Bar Graph Example',
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xaxis=dict(title='Category'),
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title='Stacked Bar Graph by Hour',
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xaxis=dict(title='Hour'),
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yaxis=dict(title='Values'),
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legend_title_text='Rows'
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legend_title_text='Categories'
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)
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# Save the graph to an HTML file
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fig.write_html(save_path)
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def sanitize_and_filter_data(data2d, exclude_labels, xLabels):
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"""
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Sanitize data by removing unwanted columns and converting to numeric values.
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@ -1419,11 +1451,13 @@ if __name__ == "__main__":
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else:
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text_file_path = ""
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# Create graph of data
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create_stacked_bar_graph(columnCounts_2d,columnHeaders)
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yLabels = [f'Hour {i}' for i in range(26)]
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create_heatmap(columnCounts_2d,columnHeaders,yLabels)
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create_line_chart(columnCounts_2d,columnHeaders,yLabels)
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# Create graphs of data
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#yLabels = [f'Hour {i}' for i in range(len(columnCounts_2d))]
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yLabels = [f'{i:02d}:00' for i in range(len(columnCounts_2d))]
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create_stacked_bar_graph(columnCounts_2d,columnHeaders,yLabels,html_page_dir+'stacked_bar_'+analysis_date+'.html')
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#yLabels = [f'Hour {i}' for i in range(26)]
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create_heatmap(columnCounts_2d,columnHeaders,yLabels,html_page_dir+'heatmap_'+analysis_date+'.html')
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create_line_chart(columnCounts_2d,columnHeaders,yLabels,html_page_dir+'line_graph_'+analysis_date+'.html')
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html_content = None
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text_content = None
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