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Refine the stacked bar graph

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This commit is contained in:
Brian Read 2024-07-13 11:12:18 +01:00
parent e014d91060
commit a9be56deae
1 changed files with 84 additions and 50 deletions
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134
root/usr/bin/mailstats.py
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@ -82,6 +82,7 @@ import mysql.connector
import numpy as np
import plotly.graph_objects as go
import plotly.express as px
import colorsys
Mailstats_version = '1.2'
build_date_time = "2024-06-18 12:03:40OURCE"
@ -125,15 +126,21 @@ ColPercent = 25
import mysql.connector
import json
def sanitize_data(data2d):
def sanitize_and_filter_data_for_stacked_bar(data2d, xLabels, yLabels, exclude_columns_labels, exclude_rows_labels):
"""
Convert data to numeric values, stripping out non-numeric characters.
Sanitize data by removing unwanted columns and rows, and converting to numeric values.
Parameters:
- data2d (list of lists): A 2D list containing the data.
- xLabels (list): Current labels for the x-axis.
- yLabels (list): Current labels for the y-axis.
- exclude_columns_labels (list): Labels of columns to exclude from the data and x-axis.
- exclude_rows_labels (list): Labels of rows to exclude from the y-axis.
Returns:
- numpy.ndarray: Sanitized 2D numpy array with numeric data.
- list: Filtered x-axis labels.
- list: Filtered y-axis labels.
"""
def to_numeric(value):
try:
@ -145,67 +152,92 @@ def sanitize_data(data2d):
except ValueError:
return 0.0 # Default to 0 if conversion fails
sanitized_data = []
for row in data2d:
sanitized_row = [to_numeric(value) for value in row]
sanitized_data.append(sanitized_row)
# Filter out columns based on their labels
exclude_columns_indices = [xLabels.index(label) for label in exclude_columns_labels if label in xLabels]
return np.array(sanitized_data)
filtered_data2d = [
[to_numeric(value) for idx, value in enumerate(row) if idx not in exclude_columns_indices]
for row in data2d
]
filtered_xLabels = [label for idx, label in enumerate(xLabels) if idx not in exclude_columns_indices]
# Filter out rows based on their labels
filtered_data2d = [row for label, row in zip(yLabels, filtered_data2d) if label not in exclude_rows_labels]
filtered_yLabels = [label for label in yLabels if label not in exclude_rows_labels]
# Convert filtered data to numpy array
return np.array(filtered_data2d), filtered_xLabels, filtered_yLabels
def generate_distinct_colors(num_colors):
"""Generate distinct colors using HSV color space."""
colors = []
for i in range(num_colors):
hue = i / num_colors
saturation = 0.7
value = 0.9
r, g, b = colorsys.hsv_to_rgb(hue, saturation, value)
colors.append(f'rgb({int(r * 255)},{int(g * 255)},{int(b * 255)})')
return colors
def create_stacked_bar_graph(data2d, xLabels, save_path='stacked_bar_graph.html'):
def create_stacked_bar_graph(data2d, xLabels, yLabels, save_path='stacked_bar_graph.html'):
"""
Creates and saves a stacked bar graph from given 2D numpy array data using Plotly.
Parameters:
- data2d (list of lists or numpy.ndarray): A 2D list or numpy array containing the data.
- xLabels (list): A list of category labels for the x-axis.
- yLabels (list): A list of labels for the y-axis (e.g., hours).
- save_path (str): The path where the plot image will be saved.
"""
# Identify columns to be removed based on their headers
excluded_columns = ["Count", "PERCENT"]
# Create a boolean array for columns to keep (not in excluded_columns)
columns_to_keep = [label not in excluded_columns for label in xLabels]
# Filter out the columns both from the data and xLabels
filtered_data2d = []
for row in data2d:
filtered_row = [value for keep, value in zip(columns_to_keep, row) if keep]
filtered_data2d.append(filtered_row)
filtered_xLabels = [label for label, keep in zip(xLabels, columns_to_keep) if keep]
# Sanitize data and convert it to a numpy array
data = sanitize_data(filtered_data2d)
# Find columns that are not fully zero
non_zero_columns = np.any(data != 0, axis=0)
# Filter out fully zero columns from both the data and x_labels
filtered_data = data[:, non_zero_columns]
filtered_x_labels = np.array(filtered_xLabels)[non_zero_columns]
# Identify columns to be removed based on their headers (label names) and indices (hours 24 and 25)
exclude_columns_labels = ["Count", "PERCENT","TOTALS"]
exclude_rows_labels = ["24:00", "25:00"]
# Ensure input yLabels correspond to the data
if len(yLabels) != len(data2d):
raise ValueError(f"The length of yLabels {len(yLabels)} must match the number of rows in the data {len(data2d)}.")
# Sanitize and filter the data
sanitized_data, filtered_xLabels, filtered_yLabels = sanitize_and_filter_data_for_stacked_bar(data2d, xLabels, yLabels, exclude_columns_labels, exclude_rows_labels)
# Ensure that the length of yLabels matches the number of rows (0 to n should be n+1 rows)
if len(filtered_yLabels) != sanitized_data.shape[0]:
raise ValueError(f"The length of filtered_yLabels {len(filtered_yLabels)} must match the number of rows in the data {sanitized_data.shape[0]}.")
# Transpose the data so that hours are on the x-axis and categories are stacked in the y-axis
transposed_data = sanitized_data.T
fig = go.Figure()
# Get unique colors for each category
extended_colors = generate_distinct_colors(len(filtered_xLabels))
#print(len(filtered_xLabels))
#print(extended_colors)
#quit()
for i, category in enumerate(filtered_xLabels):
fig.add_trace(go.Bar(
name=category,
x=filtered_yLabels,
y=transposed_data[i],
marker_color=extended_colors[i % len(extended_colors)] # Cycle through the colors if there are more categories than colors
for i in range(filtered_data.shape[0]):
if i <= 23: # Ensure to annotate rows with proper names (e.g., Hours)
fig.add_trace(go.Bar(
name=f'Hour {i}',
x=filtered_x_labels,
y=filtered_data[i]
))
))
fig.update_layout(
barmode='stack',
title='Stacked Bar Graph Example',
xaxis=dict(title='Category'),
title='Stacked Bar Graph by Hour',
xaxis=dict(title='Hour'),
yaxis=dict(title='Values'),
legend_title_text='Rows'
legend_title_text='Categories'
)
# Save the graph to an HTML file
fig.write_html(save_path)
def sanitize_and_filter_data(data2d, exclude_labels, xLabels):
"""
Sanitize data by removing unwanted columns and converting to numeric values.
@ -1419,11 +1451,13 @@ if __name__ == "__main__":
else:
text_file_path = ""
# Create graph of data
create_stacked_bar_graph(columnCounts_2d,columnHeaders)
yLabels = [f'Hour {i}' for i in range(26)]
create_heatmap(columnCounts_2d,columnHeaders,yLabels)
create_line_chart(columnCounts_2d,columnHeaders,yLabels)
# Create graphs of data
#yLabels = [f'Hour {i}' for i in range(len(columnCounts_2d))]
yLabels = [f'{i:02d}:00' for i in range(len(columnCounts_2d))]
create_stacked_bar_graph(columnCounts_2d,columnHeaders,yLabels,html_page_dir+'stacked_bar_'+analysis_date+'.html')
#yLabels = [f'Hour {i}' for i in range(26)]
create_heatmap(columnCounts_2d,columnHeaders,yLabels,html_page_dir+'heatmap_'+analysis_date+'.html')
create_line_chart(columnCounts_2d,columnHeaders,yLabels,html_page_dir+'line_graph_'+analysis_date+'.html')
html_content = None
text_content = None