from opensimplex import OpenSimplex
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from matplotlib.patches import Patch
import random

size = 100
levels = 5
colors = ["#0000FF", "#008000", "#A52A2A", "#808080", "#FFFFFF"]
labels = ["Water", "Plains", "Hills", "Mountains", "High Mountains"]
min_adjacent = [5, 7, 7, 7, 4]

# Generate OpenSimplex noise
scale = 0.1  # Increase this to make the noise more "smooth"
seed = 0  # Change this to any integer to get a different noise pattern
gen = OpenSimplex(seed)

world = np.zeros((size, size))
for i in range(size):
    for j in range(size):
        world[i][j] = gen.noise2(i*scale, j*scale)

#  Normalize to [0, levels - 1.5]
world = np.interp(world, (world.min(), world.max()), (0, levels - 1.5))
world = world.astype(int)

# Enforce constraints
# This is a simple implementation and may not work perfectly for all inputs
for level in range(levels):
    while (world == level).sum() < min_adjacent[level]:
        world[np.where(world == level - 1)[0][0], np.where(world == level - 1)[1][0]] = level

# Post-processing: Add level 4 in the middle of every level 3 cluster
mountain_level = 3
high_mountain_level = 4
for i in range(1, size - 1):
    for j in range(1, size - 1):
        if world[i, j] == mountain_level:
            # Check if the cell is surrounded by mountains
            if all(world[i + di, j + dj] == mountain_level for di in [-1, 0, 1] for dj in [-1, 0, 1]):
                # Check if there are less than 3 high mountains in the neighborhood
                if sum(world[i + di, j + dj] == high_mountain_level for di in [-1, 0, 1] for dj in [-1, 0, 1]) < 3:
                    world[i, j] = high_mountain_level

# Convert the world array to a list of x, y, z tuples
xyz = [(i, j, world[i, j]) for i in range(size) for j in range(size)]

# Save to a CSV file
filename = f"map_{seed}.csv"
np.savetxt(filename, xyz, delimiter=",", fmt="%d")
print(f"Saved to {filename}")

# Visualize
fig, ax = plt.subplots(figsize=(11, 7))  # Adjust as needed
cmap = ListedColormap(colors)
plt.imshow(world, cmap=cmap)

# Create a custom legend
legend_elements = [Patch(facecolor=colors[i], edgecolor=colors[i], label=f"{labels[i]}: {(world == i).sum()}") for i in range(levels)]
plt.legend(handles=legend_elements, bbox_to_anchor=(1.05, 1), loc='upper left')

# Save the figure to a .jpg file
fig_filename = filename.replace(".csv", ".jpg")
plt.savefig(fig_filename)
print(f"Saved figure to {fig_filename}")

plt.show()