import numpy as np import matplotlib.pyplot as plt from matplotlib import colors, cm import scipy.interpolate as interpolate from scipy import ndimage import math from io import BytesIO import base64 parameters = { 'width': { 'default': 900, 'type': 'int', }, 'height': { 'default': 450, 'type': 'int', }, 'mountain_ratio': { 'default': 0.3, 'type': 'float', 'min': 0, 'max': 1, 'step': 0.01 }, 'sharpness': { 'default': 0.7, 'type': 'float', 'min': 0, 'max': 1, 'step': 0.01 }, 'max_elevation': { 'default': 30, 'type': 'int', 'min': 0, 'max': 50, }, 'ground_noise': { 'default': 15, 'type': 'int', 'min': 0, 'max': 50, }, 'water_proportion': { 'default': 0.6, 'type': 'float', 'min': 0, 'max': 0.99, 'step': 0.01 }, 'mountain_jaggedness': { 'default': 1, 'type': 'int', 'min': 0, 'max': 5, }, 'mountain_sea_distance': { 'default': 50, 'type': 'int', 'min': 0, 'max': 200, }, 'mountain_sea_threshold': { 'default': 2, 'type': 'int', 'min': 0, 'max': 5, }, 'water_level': { 'default': 0, 'type': 'int', }, 'mountain_area_elevation': { 'default': 0.4, 'type': 'float', 'min': 0, 'max': 1, 'step': 0.01 }, 'mountain_area_elevation_n': { 'default': 5, 'type': 'int', 'min': 0, 'max': 15, }, 'mountain_area_elevation_area': { 'default': 10, 'type': 'int', 'min': 0, 'max': 25, }, 'continents': { 'default': 5, 'type': 'int', }, 'seed': { 'default': '', 'type': 'int', 'description': 'Leave empty for a random seed generated from the current timestamp.' }, } p = { k: parameters[k]['default'] for k in parameters } CONTINENT_MAX_TRIALS = 1e4 SEA_COLOR = np.array((53, 179, 220, 255)) / 255 DIRECTIONS = [(-1, -1), (-1, 0), (-1, 1), (1, 1), (1, 0), (1, -1), (0, -1), (0, 1)] def s(x): return -2 * x**3 + 3 * x**2 def is_ground(value): return value > p['water_level'] def in_range(p, m, size): x, y = p mx, my = m return ((x - mx)**2 + (y - my)**2) < size def max_recursion(fn, max_recursion=0): def f(*args, recursion=0, **kwargs): if recursion > max_recursion: return return fn(*args, **kwargs) def bound_check(ground, point): x, y = point w, h = ground.shape x = max(min(x, w - 1), 0) y = max(min(y, h - 1), 0) return (x, y) def continent_agent(ground, position, size): if size <= 0: return x, y = position w, h = ground.shape trials = 0 while True: if size <= 0 or trials > CONTINENT_MAX_TRIALS: break dx = np.random.randint(2) or -1 dy = np.random.randint(2) or -1 r = np.random.randint(3) new_point = bound_check(ground, (x + dx, y + dy)) if r == 0: x = new_point[0] elif r == 1: y = new_point[1] else: x, y = new_point if not is_ground(ground[x, y]) and in_range((x, y), position, size): trials = 0 size -= 1 ground[x, y] = np.random.randint(1, p['ground_noise']) else: trials += 1 def neighbours(ground, position, radius): x, y = position return ground[x-radius:x+radius+1, y-radius:y+radius+1] def away_from_sea(ground, position, radius=p['mountain_sea_distance']): ns = neighbours(ground, position, radius).flatten() sea = len([1 for x in ns if not is_ground(x)]) return sea < p['mountain_sea_threshold'] def random_elevate_agent(ground, position, height, size=p['mountain_area_elevation_n']): position = position + np.random.random_integers(-p['mountain_area_elevation_area'], p['mountain_area_elevation_area'], size=2) for i in range(size): d = DIRECTIONS[np.random.randint(len(DIRECTIONS))] change = height * p['mountain_area_elevation'] + np.random.randint(p['mountain_jaggedness'] + 1) new_index = bound_check(ground, position + np.array(d)) if is_ground(ground[new_index]): ground[new_index] += change def mountain_agent(ground, position): if not away_from_sea(ground, position): return x, y = position height = np.random.randint(p['max_elevation']) ground[x, y] = height last_height = height for i in range(1, height): for d in DIRECTIONS: change = np.random.randint(p['mountain_jaggedness'] + 1) distance = np.array(d)*i new_index = bound_check(ground, position + distance) if is_ground(ground[new_index]): ground[new_index] = last_height - change last_height = last_height - p['mountain_jaggedness'] random_elevate_agent(ground, position, height) # takes an initial position and a list of (direction, probability) tuples to walk on # def split_agent(ground, position, directions): def constant_filter(a): if a[0] > (1 - p['sharpness']): return max(1, a[0]) return 0 def generate_map(**kwargs): plt.clf() p.update(kwargs) np.random.seed(p['seed'] or None) width, height = p['width'], p['height'] continents = p['continents'] ground = np.zeros((width, height)) ground_size = width * height * (1 - p['water_proportion']) # position = (int(width / 2), int(height / 2)) # ground_size = width * height * GROUND_PROPORTION # continent_agent(ground, position, size=ground_size) for continent in range(continents): position = (np.random.randint(0, width), np.random.randint(0, height)) print(position) continent_agent(ground, position, size=ground_size) ground = ndimage.gaussian_filter(ground, sigma=(1 - p['sharpness']) * 20) for i in range(int(ground_size * p['mountain_ratio'] / p['max_elevation']**2)): position = (np.random.randint(0, width), np.random.randint(0, height)) mountain_agent(ground, position) norm = colors.Normalize(vmin=1) greys = cm.get_cmap('Greys') greys.set_under(color=SEA_COLOR) ground = ndimage.gaussian_filter(ground, sigma=4) ground = ndimage.generic_filter(ground, constant_filter, size=1) print(np.min(ground), np.max(ground), p['max_elevation']) print(np.unique(ground)) plt.imshow(ground.T, cmap=greys, norm=norm) figfile = BytesIO() plt.savefig(figfile, format='png') figfile.seek(0) return figfile if __name__ == "__main__": generate_map() plt.show()