#!/usr/bin/env python3
"""Plot the inverse distribution of profit for the three WDWR scenarios.

One curve per scenario: the sorted profits of its 100 sampled realisations
against their empirical cumulative probability, which is how the report shows
which scenario stochastically dominates the others.
"""

import logging

import matplotlib.pyplot as plt
import numpy as np

logger = logging.getLogger(__name__)
logging.basicConfig(format="%(message)s", level=logging.INFO)

# Sized for a report figure rather than for the screen.
LABEL_FONT_SIZE = 16

# Data for the three scenarios
scenario1_profits = [
    8283.101223531,
    8360.092861464,
    8023.436774628,
    7996.435353438,
    8108.734620296,
    8709.702668322,
    9236.429507678,
    8492.980846575,
    8912.962073143,
    8201.079841867,
    7947.08574225,
    8426.386242154,
    8633.510288097,
    7925.419317516,
    8216.653093455,
    8162.239336848,
    10606.276724793,
    7766.753696884,
    8902.966530729,
    8329.954853003,
    8615.534725088,
    9307.905933844,
    8711.836200338,
    7853.713101162,
    8160.850707138,
    8505.74747647,
    7807.599624626,
    8650.006339759,
    7571.333855925,
    9103.892851116,
]

scenario2_profits = [
    8779.880622914,
    8905.098118171,
    8457.038873118,
    8476.337700492,
    8611.662470696,
    9284.604268643,
    9787.525726881,
    9033.316521658,
    9469.444921654,
    8726.555858861,
    8437.173060618,
    8935.043125766,
    9172.334141304,
    8425.564554151,
    8725.52174345,
    8672.538442695,
    11321.501722433,
    8233.9854963,
    9465.167737138,
    8841.407418574,
    9162.816556878,
    9946.670992861,
    9281.411111594,
    8349.695093959,
    8671.731748701,
    9074.152941559,
    8296.712958538,
    9208.592940681,
    8041.287735389,
    9707.228511359,
]

scenario3_profits = [
    9268.899026395,
    9430.210649623,
    8919.631184021,
    8944.062758608,
    9095.775246456,
    9886.312580673,
    10387.393647889,
    9637.953603446,
    10098.221535588,
    9238.913391815,
    8980.977156104,
    9414.393022001,
    9722.356857713,
    8917.726783311,
    9178.474614142,
    9167.292074295,
    12011.512542804,
    8693.048121325,
    10039.47692674,
    9375.735645214,
    9730.36014247,
    10531.376234688,
    9750.101324443,
    8881.341453044,
    9186.289888284,
    9675.754350507,
    8773.329747496,
    9722.523537949,
    8489.277170975,
    10325.425491734,
]

# Create figure with single plot
plt.figure(figsize=(12, 8))

# Sort the data for each scenario (descending order)
scenario1_sorted = sorted(scenario1_profits, reverse=True)
scenario2_sorted = sorted(scenario2_profits, reverse=True)
scenario3_sorted = sorted(scenario3_profits, reverse=True)

# Create y-axis values (cumulative distribution from 0 to 1)
num_points = len(scenario1_profits)
y_values = [i / num_points for i in range(1, num_points + 1)]

# Plot all three scenarios on the same graph
plt.plot(
    scenario1_sorted, y_values, "b-o", label="Scenariusz 1", linewidth=2, markersize=4
)
plt.plot(
    scenario2_sorted, y_values, "g-s", label="Scenariusz 2", linewidth=2, markersize=4
)
plt.plot(
    scenario3_sorted, y_values, "r-^", label="Scenariusz 3", linewidth=2, markersize=4
)
# Add labels and title
plt.xlabel("Przeciętny Zysk", fontsize=LABEL_FONT_SIZE)
plt.ylabel("Odwrotna dystrybuanta", fontsize=LABEL_FONT_SIZE)
plt.xticks(fontsize=LABEL_FONT_SIZE)
plt.yticks(fontsize=LABEL_FONT_SIZE)
plt.ylim(0, 1)  # Set y-axis limits to cut off values outside 0-1 range
plt.grid(visible=True, alpha=0.3)
plt.legend(fontsize=LABEL_FONT_SIZE)

# Format the plot
plt.tight_layout()

# Show the plot
plt.show()

# Print statistics for each scenario
logger.info("Scenario Statistics:")
for number, profits in enumerate(
    (scenario1_profits, scenario2_profits, scenario3_profits), start=1
):
    logger.info(
        "Scenario %d - Mean: %.2f, Std: %.2f",
        number,
        np.mean(profits),
        np.std(profits),
    )