thereadme/world-ecoregion
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

fix(map_generator): distance_to_water distribution sync

Browse Source
This commit is contained in:
Mahdi Dibaiee 2019-05-22 11:28:16 +04:30
parent c9c7f26910
commit 8ffeff179f
2 changed files with 40 additions and 17 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

52
biomes/map_generator.py
View File

@ -17,11 +17,11 @@ from utils import *
parameters = { parameters = {
'width': { 'width': {
'default': 700, 'default': 360,
'type': 'int', 'type': 'int',
}, },
'height': { 'height': {
'default': 450, 'default': 180,
'type': 'int', 'type': 'int',
}, },
'mountain_ratio': { 'mountain_ratio': {
@ -32,7 +32,7 @@ parameters = {
'step': 0.01 'step': 0.01
}, },
'sharpness': { 'sharpness': {
'default': 0.7, 'default': 0.9,
'type': 'float', 'type': 'float',
'min': 0, 'min': 0,
'max': 1, 'max': 1,
@ -57,7 +57,7 @@ parameters = {
'max': 1e5, 'max': 1e5,
}, },
'water_proportion': { 'water_proportion': {
'default': 0.8, 'default': 0.3,
'type': 'float', 'type': 'float',
'min': 0, 'min': 0,
'max': 0.99, 'max': 0.99,
@ -280,13 +280,15 @@ def generate_map(biomes=False, **kwargs):
p.update(kwargs) p.update(kwargs)
np.random.seed(p['seed'] or None) rs = np.random.randint(0, 999)
np.random.seed(p['seed'] or rs)
print('seed', rs)
width, height = p['width'], p['height'] width, height = p['width'], p['height']
continents = p['continents'] continents = p['continents']
ground = np.zeros((width, height)) ground = np.zeros((width, height))
ground_size = width * height * (1 - p['water_proportion']) ground_size = width * height * (1 - p['water_proportion'])**3
print(ground_size / ground.size) print(ground_size / ground.size)
# position = (int(width / 2), int(height / 2)) # position = (int(width / 2), int(height / 2))
@ -313,11 +315,11 @@ def generate_map(biomes=False, **kwargs):
greys = cm.get_cmap('Greys') greys = cm.get_cmap('Greys')
greys.set_under(color=SEA_COLOR) greys.set_under(color=SEA_COLOR)
ground = ndimage.gaussian_filter(ground, sigma=4) # ground = ndimage.gaussian_filter(ground, sigma=4)
ground = ndimage.generic_filter(ground, constant_filter, size=1) ground = ndimage.generic_filter(ground, constant_filter, size=1)
print(np.min(ground), np.max(ground), p['max_elevation']) print(np.min(ground), np.max(ground), p['max_elevation'])
print('water proportion', np.count_nonzero(ground) / ground.size) print('water proportion', 1 - (np.count_nonzero(ground) / ground.size))
plt.gca().invert_yaxis() plt.gca().invert_yaxis()
plt.imshow(ground.T, cmap=greys, norm=norm) plt.imshow(ground.T, cmap=greys, norm=norm)
@ -355,19 +357,33 @@ def generate_biomes(ground):
data['latitude'].append(height_to_latitude(y)) data['latitude'].append(height_to_latitude(y))
data['elevation'].append(v) data['elevation'].append(v)
print(len(points))
print('buffering points')
points = MultiPoint(points) points = MultiPoint(points)
boundary = points.buffer(1).boundary boundary = points.convex_hull.boundary
# fig = plt.figure()
# ax = fig.add_subplot(111)
# minx, miny, maxx, maxy = boundary.bounds
# w, h = maxx - minx, maxy - miny
# ax.set_xlim(minx - 0.2 * w, maxx + 0.2 * w)
# ax.set_ylim(miny - 0.2 * h, maxy + 0.2 * h)
# ax.set_aspect(1)
# ax.add_collection(PatchCollection([PolygonPatch(boundary_buf, fc='red', ec='black', zorder=1)], match_original=True))
# plt.show()
for x, y in np.ndindex(ground.shape): for x, y in np.ndindex(ground.shape):
if ground[x,y] > p['water_level']: if ground[x, y] > p['water_level']:
data['distance_to_water'].append(Point(x, y).distance(boundary)) d = Point(x, y).distance(boundary)
d = (d - root_df['distance_to_water'].mean()) / root_df['distance_to_water'].std()
d = max(0, d)
data['distance_to_water'].append(d)
df = pd.DataFrame(data) df = pd.DataFrame(data)
print(df['elevation'].min(), df['elevation'].max()) print(df['elevation'].min(), df['elevation'].max())
print(df['distance_to_water'].min(), df['distance_to_water'].max()) print('distance_to_water', df['distance_to_water'].min(), df['distance_to_water'].max())
print('dtw', df['distance_to_water'].mean(), df['distance_to_water'].std())
print(df['latitude'].min(), df['latitude'].max()) print(df['latitude'].min(), df['latitude'].max())
print('running prediction models') print('running prediction models')
@ -387,8 +403,10 @@ def generate_biomes(ground):
# plt.show() # plt.show()
df = pd.read_pickle('data.p') df = pd.read_pickle('data.p')
root_df = df
print(df['elevation'].min(), df['elevation'].max()) print(df['elevation'].min(), df['elevation'].max())
print(df['distance_to_water'].min(), df['distance_to_water'].max()) print('distance_to_water', df['distance_to_water'].min(), df['distance_to_water'].max())
print('dtw', df['distance_to_water'].mean(), df['distance_to_water'].std())
print(df['latitude'].min(), df['latitude'].max()) print(df['latitude'].min(), df['latitude'].max())
def predict_end_to_end(input_df, checkpoint_temp='checkpoints/temp.h5', checkpoint_precip='checkpoints/precip.h5', checkpoint_biomes='checkpoints/b.h5', year=2000): def predict_end_to_end(input_df, checkpoint_temp='checkpoints/temp.h5', checkpoint_precip='checkpoints/precip.h5', checkpoint_biomes='checkpoints/b.h5', year=2000):
@ -489,7 +507,7 @@ def predict_end_to_end(input_df, checkpoint_temp='checkpoints/temp.h5', checkpoi
draw(new_data, earth=False, only_draw=True, longitude_max=p['width'], latitude_max=p['height'], alpha=0.7) draw(new_data, earth=False, only_draw=True, longitude_max=p['width'], latitude_max=p['height'], alpha=0.7)
if __name__ == "__main__": if __name__ == "__main__":
# p['width'] = 50 # p['width'] = 300
# p['height'] = 50 # p['height'] = 250
generate_map(True) generate_map(True)
plt.show() plt.show()

5
biomes/random_map_generator.py
View File

@ -2,6 +2,11 @@ from map_generator import generate_map
from pathlib import Path from pathlib import Path
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
grid = {
'water_proportion': (0.3, 0.7),
'mountain_ratio': ()
}
seed = 0 seed = 0
while True: while True:
generate_map(biomes=True, seed=seed) generate_map(biomes=True, seed=seed)