Spaces:
Sleeping
Sleeping
File size: 8,151 Bytes
7f6e787 7a2556d 7f6e787 7a2556d 7f6e787 7a2556d 7f6e787 7a2556d 7f6e787 7a2556d 7f6e787 7a2556d 7f6e787 7a2556d 7f6e787 7a2556d 7f6e787 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 |
import pandas as pd
import streamlit as st
import matplotlib.pyplot as plt
import numpy as np
from pre import preprocess_uploaded_file
# Define the function to perform analysis
def perform_analysis(uploaded_dataframes):
# Concatenate all dataframes into a single dataframe
combined_data = pd.concat(uploaded_dataframes, ignore_index=True)
# Display scenarios with status "failed" grouped by functional area
failed_scenarios = combined_data[combined_data['Status'] == 'FAILED']
passed_scenarios = combined_data[combined_data['Status'] == 'PASSED']
# Display total count of failures
fail_count = len(failed_scenarios)
st.markdown(f"Failing scenarios Count: {fail_count}")
# Display total count of Passing
pass_count = len(passed_scenarios)
st.markdown(f"Passing scenarios Count: {pass_count}")
# Use radio buttons for selecting status
selected_status = st.radio("Select a status", ['Failed', 'Passed'])
# Determine which scenarios to display based on selected status
if selected_status == 'Failed':
unique_areas = np.append(failed_scenarios['Functional area'].unique(), "All")
selected_scenarios = failed_scenarios
elif selected_status == 'Passed':
unique_areas = np.append(passed_scenarios['Functional area'].unique(), "All")
selected_scenarios = passed_scenarios
else:
selected_scenarios = None
if selected_scenarios is not None:
# st.write(f"Scenarios with status '{selected_status}' grouped by functional area:")
st.markdown(f"### Scenarios with status '{selected_status}' grouped by functional area:")
# Select a range of functional areas to filter scenarios
selected_functional_areas = st.multiselect("Select functional areas", unique_areas, ["All"])
if "All" in selected_functional_areas:
filtered_scenarios = selected_scenarios
else:
filtered_scenarios = selected_scenarios[selected_scenarios['Functional area'].isin(selected_functional_areas)]
if not selected_functional_areas: # Check if the list is empty
st.error("Please select at least one functional area.")
else:
# Calculate the average time spent for each functional area
average_time_spent_seconds = filtered_scenarios.groupby('Functional area')['Time spent'].mean().reset_index()
# Convert average time spent from seconds to minutes and seconds format
average_time_spent_seconds['Time spent'] = pd.to_datetime(average_time_spent_seconds['Time spent'], unit='s').dt.strftime('%M:%S')
# Group by functional area and get the start datetime for sorting
start_datetime_group = filtered_scenarios.groupby('Functional area')['Start datetime'].min().reset_index()
end_datetime_group = filtered_scenarios.groupby('Functional area')['End datetime'].max().reset_index()
# Calculate the total time spent for each functional area (difference between end and start datetime)
total_time_spent_seconds = (end_datetime_group['End datetime'] - start_datetime_group['Start datetime']).dt.total_seconds()
# Convert total time spent from seconds to minutes and seconds format
total_time_spent_seconds = pd.to_datetime(total_time_spent_seconds, unit='s').dt.strftime('%M:%S')
# Merge the average_time_spent_seconds with start_datetime_group and end_datetime_group
average_time_spent_seconds = average_time_spent_seconds.merge(start_datetime_group, on='Functional area')
average_time_spent_seconds = average_time_spent_seconds.merge(end_datetime_group, on='Functional area')
average_time_spent_seconds['Total Time Spent'] = total_time_spent_seconds
# Filter scenarios based on selected functional area
if selected_status == 'Failed':
grouped_filtered_scenarios = filtered_scenarios.groupby('Environment')[['Functional area', 'Scenario name', 'Error message','Time spent(m:s)','Start datetime']].apply(lambda x: x.reset_index(drop=True))
elif selected_status == 'Passed':
grouped_filtered_scenarios = filtered_scenarios.groupby('Functional area')[['Scenario name', 'Time spent(m:s)']].apply(lambda x: x.reset_index(drop=True))
else:
grouped_filtered_scenarios = None
grouped_filtered_scenarios.reset_index(inplace=True)
grouped_filtered_scenarios.drop(columns=['level_1'], inplace=True)
grouped_filtered_scenarios.index = grouped_filtered_scenarios.index + 1
st.dataframe(grouped_filtered_scenarios)
# Sort the average time spent table by start datetime
average_time_spent_seconds = average_time_spent_seconds.sort_values(by='Start datetime')
# Display average time spent on each functional area in a table
st.markdown("### Total and Average Time Spent on Each Functional Area")
average_time_spent_seconds.index = average_time_spent_seconds.index + 1
# Rename the columns for clarity
average_time_spent_seconds.rename(columns={'Start datetime': 'Start Datetime', 'End datetime': 'End Datetime', 'Time spent':'Average Time Spent'}, inplace=True)
# Rearrange the columns
average_time_spent_seconds = average_time_spent_seconds[['Functional area', 'Total Time Spent', 'Start Datetime', 'End Datetime', 'Average Time Spent']]
st.dataframe(average_time_spent_seconds)
# Check if selected_status is 'Failed' and grouped_filtered_scenarifos length is less than or equal to 400
if selected_status != 'Passed':
# Create and display bar graph of errors by functional area
st.write(f"### Bar graph showing number of '{selected_status}' scenarios in each functional area:")
error_counts = grouped_filtered_scenarios['Functional area'].value_counts()
plt.figure(figsize=(12, 10))
bars = plt.bar(error_counts.index, error_counts.values)
plt.xlabel('Functional Area')
plt.ylabel('Number of Failures')
plt.title(f"Number of '{selected_status}' scenarios by Functional Area")
plt.xticks(rotation=45, ha='right', fontsize=10)
# Set y-axis limits and ticks for consistent interval of 1
y_max = max(error_counts.values) + 1
plt.ylim(0, y_max)
plt.yticks(range(0, y_max, 1), fontsize=10)
# Display individual numbers on y-axis
for bar in bars:
height = bar.get_height()
plt.text(bar.get_x() + bar.get_width() / 2, height, str(int(height)),
ha='center', va='bottom') # Reduce font size of individual numbers
plt.tight_layout() # Add this line to adjust layout
st.pyplot(plt)
pass
def multiple_main():
# Get the number of environments from the user
num_environments = st.number_input("Enter the number of environments", min_value=1, value=1, step=1)
# Initialize list to store uploaded dataframes
uploaded_dataframes = []
# Loop through the number of environments and create file uploaders
for i in range(num_environments):
uploaded_files = st.file_uploader(f"Upload CSV files for Environment {i + 1}", type="csv", accept_multiple_files=True)
for uploaded_file in uploaded_files:
# Preprocess the uploaded CSV file
data = preprocess_uploaded_file(uploaded_file)
# Append the dataframe to the list
uploaded_dataframes.append(data)
# Check if any files were uploaded
if uploaded_dataframes:
# Perform analysis for uploaded data
perform_analysis(uploaded_dataframes)
else:
st.write("Please upload at least one CSV file.")
pass
|