mtcontrib/mapgen_rivers
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Rewritten map viewer

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Now displays map statistics even if there is no matplotlib
This commit is contained in:
Gael-de-Sailly 2020-11-13 11:04:27 +01:00
parent 30136bf60a
commit 3fda369fb5
1 changed files with 65 additions and 19 deletions
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84
view_map.py
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@ -2,30 +2,76 @@
import numpy as np
import zlib
import matplotlib.colors as mcol
import matplotlib.colors as mcl
import matplotlib.pyplot as plt
def view_map(dem, lakes, rivers, scale):
plt.subplot(1,3,1)
plt.pcolormesh(np.arange(dem.shape[0]+1)*scale, np.arange(dem.shape[1]+1)*scale, dem, cmap='viridis')
plt.gca().set_aspect('equal', 'box')
plt.colorbar(orientation='horizontal')
plt.title('Raw elevation')
has_matplotlib = True
try:
import matplotlib.colors as mcl
import matplotlib.pyplot as plt
try:
import colorcet as cc
cmap1 = cc.cm.CET_L11
cmap2 = cc.cm.CET_L12
except ImportError: # No module colorcet
import matplotlib.cm as cm
cmap1 = cm.summer
cmap2 = cm.Blues
except ImportError: # No module matplotlib
has_matplotlib = False
plt.subplot(1,3,2)
plt.pcolormesh(np.arange(lakes.shape[0]+1)*scale, np.arange(lakes.shape[1]+1)*scale, lakes, cmap='viridis')
plt.gca().set_aspect('equal', 'box')
plt.colorbar(orientation='horizontal')
plt.title('Lake surface elevation')
plt.subplot(1,3,3)
plt.pcolormesh(np.arange(rivers.shape[0]+1)*scale, np.arange(rivers.shape[1]+1)*scale, rivers, cmap='Blues', norm=mcol.LogNorm())
plt.gca().set_aspect('equal', 'box')
plt.colorbar(orientation='horizontal')
plt.title('Rivers flux')
def view_map(dem, lakes, scale):
if not has_matplotlib:
return
lakes_sea = np.maximum(lakes, 0)
water = np.maximum(lakes_sea - dem, 0)
max_elev = lakes_sea.max()
max_depth = water.max()
ls = mcl.LightSource(azdeg=315, altdeg=45)
rgb = ls.shade(lakes_sea, cmap=cmap1, vert_exag=1/scale, blend_mode='soft', vmin=0, vmax=max_elev)
(X, Y) = dem.shape
extent = (0, Y*scale, 0, X*scale)
plt.imshow(np.flipud(rgb), extent=extent, interpolation='antialiased')
alpha = (water > 0).astype('u1')
plt.imshow(np.flipud(water), alpha=np.flipud(alpha), cmap=cmap2, extent=extent, vmin=0, vmax=max_depth, interpolation='antialiased')
sm1 = plt.cm.ScalarMappable(cmap=cmap1, norm=plt.Normalize(vmin=0, vmax=max_elev))
plt.colorbar(sm1).set_label('Altitude')
sm2 = plt.cm.ScalarMappable(cmap=cmap2, norm=plt.Normalize(vmin=0, vmax=max_depth))
plt.colorbar(sm2).set_label('Profondeur d\'eau')
plt.show()
def map_stats(dem, lake_dem, scale):
surface = dem.size
continent = lake_dem >= 0
continent_surface = continent.sum()
lake = continent & (lake_dem>dem)
lake_surface = lake.sum()
print('--- General ---')
print('Grid size: {:5d}x{:5d}'.format(dem.shape[0], dem.shape[1]))
print('Map size: {:5d}x{:5d}'.format(int(dem.shape[0]*scale), int(dem.shape[1]*scale)))
print()
print('--- Surfaces ---')
print('Continents: {:6.2%}'.format(continent_surface/surface))
print('-> Ground: {:6.2%}'.format((continent_surface-lake_surface)/surface))
print('-> Lakes: {:6.2%}'.format(lake_surface/surface))
print('Oceans: {:6.2%}'.format(1-continent_surface/surface))
print()
print('--- Elevations ---')
print('Mean elevation: {:4.0f}'.format(dem.mean()))
print('Mean ocean depth: {:4.0f}'.format((dem*~continent).sum()/(surface-continent_surface)))
print('Mean continent elev: {:4.0f}'.format((dem*continent).sum()/continent_surface))
print('Lowest elevation: {:4.0f}'.format(dem.min()))
print('Highest elevation: {:4.0f}'.format(dem.max()))
if __name__ == "__main__":
import sys
import os
@ -47,6 +93,6 @@ if __name__ == "__main__":
shape = np.loadtxt('size', dtype='u4')
dem = load_map('dem', '>i2', shape)
lakes = load_map('lakes', '>i2', shape)
rivers = load_map('rivers', '>u4', shape)
view_map(dem, lakes, rivers, scale)
map_stats(dem, lakes, scale)
view_map(dem, lakes, scale)