# -*- coding: utf-8 -*-
"""
Created on Wed Jul 22 02:26:13 2026

@author: muhuri
"""

#===========================================================
# Program:
# pgm3_BDM_Incident_Category_BarCharts.py
#
# Purpose:
# Create publication-quality horizontal bar chart
# showing incident categories.
#===========================================================

import pandas as pd
import matplotlib.pyplot as plt

#-----------------------------------------------------------
# Input and output files
#-----------------------------------------------------------

input_file = (
    r"C:\Explore\BDMDataTables\CSVText"
    r"\BD_Minority_Victims_2025_incident.csv"
)

output_png = (
    r"C:\Explore\BDMDataTables\Charts"
    r"\Incident_Categories_BarChart.png"
)

output_pdf = (
    r"C:\Explore\BDMDataTables\Charts"
    r"\Incident_Categories_BarChart.pdf"
)

#-----------------------------------------------------------
# Read CSV file
#-----------------------------------------------------------

df = pd.read_csv(input_file)

#-----------------------------------------------------------
# Number of incidents with descriptions
#-----------------------------------------------------------

n_incidents = (
    df["incident_category"] != "Missing Description"
).sum()

#-----------------------------------------------------------
# Categories to display
#-----------------------------------------------------------

category_labels = {
    "violence_against_persons": "Violence Against Persons",
    "attacks_on_religious_sites": "Attacks on Religious Sites",
    "property_damage": "Property Damage",
    "land_grabbing": "Land Grabbing",
    "killings": "Killings",
    "missing_abducted": "Missing-Abducted",
    "blasphemy_related_attacks": "Blasphemy-Related Attacks",
    "rape": "Rape"
}

#-----------------------------------------------------------
# Semantic colors
#-----------------------------------------------------------

category_colors = {
    "Violence Against Persons": "#CD5C5C",      # Indian Red
    "Attacks on Religious Sites": "#B22222",    # Crimson
    "Property Damage": "#D2691E",
    "Land Grabbing": "#8B4513",
    "Killings": "#8B0000",
    "Missing-Abducted": "#4682B4",
    "Blasphemy-Related Attacks": "#2F4F4F",
    "Rape": "#800080"
}



#-----------------------------------------------------------
# Calculate counts and percentages
#-----------------------------------------------------------

results = []

for variable, label in category_labels.items():

    count = int(df[variable].sum())

    percent = 100 * count / n_incidents

    results.append([label, count, percent])

results = pd.DataFrame(
    results,
    columns=["Category", "Count", "Percent"]
)

results = results.sort_values(
    by="Percent",
    ascending=True
)

#-----------------------------------------------------------
# Create chart
#-----------------------------------------------------------

plt.figure(figsize=(11, 6.5))

bar_colors = [
    category_colors[c]
    for c in results["Category"]
]

bars = plt.barh(
    results["Category"],
    results["Percent"],
    color=bar_colors,
    edgecolor="black",
    linewidth=0.8
)

#-----------------------------------------------------------
# Add count and percentage labels
#-----------------------------------------------------------

for bar, count, pct in zip(
    bars,
    results["Count"],
    results["Percent"]
):

    plt.text(
        bar.get_width() + 0.6,
        bar.get_y() + bar.get_height()/2,
        f"{count} ({pct:.1f}%)",
        va="center",
        fontsize=11,
        fontweight="bold"
    )

#-----------------------------------------------------------
# Titles
#-----------------------------------------------------------

plt.title(
    "Incident Categories Among Bangladesh Minority Victims (2025)",
    fontsize=18,
    fontweight="bold",
    pad=18
)

plt.xlabel(
    "Percent of Incidents",
    fontsize=13
)

#-----------------------------------------------------------
# Grid
#-----------------------------------------------------------

plt.grid(
    axis="x",
    linestyle="--",
    alpha=0.35
)

#-----------------------------------------------------------
# Footnote
#-----------------------------------------------------------

plt.figtext(
    0.01,
    0.01,
    "Percentages are based on incidents with descriptions only (n = "
    f"{n_incidents:,}).\n"
    "Incidents may belong to multiple categories; therefore percentages "
    "sum to more than 100%.\n"
    "Data Owner and Source: Bangladesh Hindu Buddhist Christian Unity Council (BHBCUC)",
    ha="left",
    fontsize=10
)


#-----------------------------------------------------------
# Axis limits
#-----------------------------------------------------------

plt.xlim(
    0,
    results["Percent"].max() + 12
)

plt.tight_layout(rect=[0, 0.06, 1, 1])

#-----------------------------------------------------------
# Save charts
#-----------------------------------------------------------

plt.savefig(
    output_png,
    dpi=300,
    bbox_inches="tight"
)

plt.savefig(
    output_pdf,
    bbox_inches="tight"
)

plt.show()

#-----------------------------------------------------------
# Console output
#-----------------------------------------------------------

print("\nCharts written to:")

print(output_png)
print(output_pdf)