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report_generation.py
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report_generation.py
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# report_generation.py
from config import HTML_REPORT
import pandas as pd
import matplotlib.pyplot as plt
# Reporting and visualization
def generate_report(totals, file_path):
months = list(totals.keys())
amounts = list(totals.values())
plt.figure(figsize=(10, 5))
plt.plot(months, amounts, marker='o')
plt.xlabel('Month')
plt.ylabel('Total Valor en Dólares')
plt.title('Reporte Financiero Mensual')
plt.xticks(rotation=45)
plt.tight_layout()
plt.savefig(file_path)
plt.close()
# Table generation
def generate_table(totals):
df = pd.DataFrame(list(totals.items()), columns=['Month', 'Total Valor en Dólares'])
df.sort_values(by='Month', inplace=True)
return df
# Comprehensive table of all financial records
def generate_comprehensive_table(records):
df = pd.DataFrame([{
'TARJETA': record.tarjeta,
'FECHA': record.fecha,
'NUMERO_REFERENCIA': record.numero_referencia,
'DESCRIPCION': record.descripcion,
'FORMA_PAGO': record.forma_pago,
'VALOR_DOLARES': record.valor_dolares,
'SALDO_DIFERIDO': record.saldo_diferido,
'MONTH': record.month
} for record in records])
df.sort_values(by='DESCRIPCION', inplace=True) # Sort by DESCRIPTION alphabetically
return df
# Function to sort the comprehensive table by any column
def sort_table(df, column, ascending=True):
sorted_df = df.sort_values(by=column, ascending=ascending)
return sorted_df
# Function to filter the comprehensive table based on specific criteria
def filter_table(df, column, value):
filtered_df = df[df[column] == value]
return filtered_df
# Function to filter the comprehensive table based on partial match criteria
def filter_table_contains(df, column, value):
filtered_df = df[df[column].str.contains(value, case=False, na=False)]
return filtered_df
# Function to group the data and aggregate values
def group_table(df, by_column, agg_column, agg_func='sum'):
grouped_df = df.groupby(by_column).agg({agg_column: agg_func}).reset_index()
return grouped_df
# Function to group the data based on partial match criteria and aggregate values
def group_table_contains(df, by_column, value, agg_column, agg_func='sum'):
df_filtered = df[df[by_column].str.contains(value, case=False, na=False)]
grouped_df = df_filtered.groupby(by_column).agg({agg_column: agg_func}).reset_index()
return grouped_df
# Function to sum all positive values in VALOR_DOLARES
def sum_positive_values(df):
positive_sum = df[df['VALOR_DOLARES'] > 0]['VALOR_DOLARES'].sum()
return positive_sum
# Function to sum all negative values in VALOR_DOLARES
def sum_negative_values(df):
negative_sum = df[df['VALOR_DOLARES'] < 0]['VALOR_DOLARES'].sum()
return negative_sum
# Function to generate HTML report
# Function to generate HTML report
def generate_html_report(totals_by_month, comprehensive_df, sorted_df, filtered_df, filtered_df_contains, grouped_df, grouped_df_contains, sum_positive, sum_negative, plot_path):
html = f"""
<html>
<head>
<title>Financial Report</title>
<style>
table, th, td {{ border: 1px solid black; border-collapse: collapse; padding: 5px; }}
th {{ background-color: #f2f2f2; }}
th, td {{ text-align: center; }}
</style>
</head>
<body>
<h1>Financial Report</h1>
<h2>Monthly Totals</h2>
{totals_by_month.to_html(index=False)}
<h2>Comprehensive Financial Records</h2>
{comprehensive_df.to_html(index=False)}
<h2>Sorted by Valor Dolares (Descending)</h2>
{sorted_df.to_html(index=False)}
<h2>Filtered by Forma Pago = 'CORRIENTE'</h2>
{filtered_df.to_html(index=False)}
<h2>Filtered by Descripcion containing 'pizza'</h2>
{filtered_df_contains.to_html(index=False)}
<h2>Grouped by Month (Sum of Valor Dolares)</h2>
{grouped_df.to_html(index=False)}
<h2>Grouped by Descripcion containing 'pizza' (Sum of Valor Dolares)</h2>
{grouped_df_contains.to_html(index=False)}
<h2>Sum of Positive Valor Dolares</h2>
<p>{sum_positive}</p>
<h2>Sum of Negative Valor Dolares</h2>
<p>{sum_negative}</p>
<h2>Monthly Financial Report Plot</h2>
<img src="{plot_path}" alt="Monthly Financial Report Plot">
</body>
</html>
"""
with open(HTML_REPORT, "w") as file:
file.write(html)