Issue 209: Add layers accessors

tests/draw_test.py

@@ -113,13 +113,13 @@ def test_draw_river_map(self):
 
     def test_draw_grayscale_heightmap(self):
         w = World.open_protobuf("%s/seed_28070.world" % self.tests_data_dir)
-        target = PNGWriter.grayscale_from_array(w.layers['elevation'].data, scale_to_range=True)
+        target = PNGWriter.grayscale_from_array(w.elevation.data, scale_to_range=True)
         self._assert_img_equal("grayscale_heightmap_28070", target)
 
     def test_draw_ocean(self):
         w = World.open_protobuf("%s/seed_28070.world" % self.tests_data_dir)
         target = PNGWriter.rgba_from_dimensions(w.width, w.height)
-        draw_ocean(w.layers['ocean'].data, target)
+        draw_ocean(w.ocean.data, target)
         self._assert_img_equal("ocean_28070", target)
 
     def test_draw_precipitation(self):

tests/serialization_test.py

@@ -38,7 +38,7 @@ def test_hdf5_serialize_unserialize(self):
             serialized = save_world_to_hdf5(w, filename)
             unserialized = load_world_to_hdf5(filename)
             self.assertEqual(Set(w.layers.keys()), Set(unserialized.layers.keys()))
-            self.assertEqual(w.layers['humidity'].quantiles, unserialized.layers['humidity'].quantiles)
+            self.assertEqual(w.humidity.quantiles, unserialized.humidity.quantiles)
             for l in w.layers.keys():
                 self.assertEqual(w.layers[l], unserialized.layers[l], "Comparing %s" % l)
             self.assertTrue(_equal(w.ocean_level,       unserialized.ocean_level))

worldengine/cli/main.py

@@ -54,7 +54,7 @@ def generate_world(world_name, width, height, seed, num_plates, output_dir,
 
     # Generate images
     filename = '%s/%s_ocean.png' % (output_dir, world_name)
-    draw_ocean_on_file(w.layers['ocean'].data, filename)
+    draw_ocean_on_file(w.ocean.data, filename)
     print("* ocean image generated in '%s'" % filename)
 
     if step.include_precipitations:

worldengine/draw.py

@@ -235,8 +235,8 @@ def get_normalized_elevation_array(world):
     ''' Convert raw elevation into normalized values between 0 and 255,
         and return a numpy array of these values '''
 
-    e = world.layers['elevation'].data
-    ocean = world.layers['ocean'].data
+    e = world.elevation.data
+    ocean = world.ocean.data
 
     mask = numpy.ma.array(e, mask=ocean)  # only land
     min_elev_land = mask.min()
@@ -324,24 +324,24 @@ def draw_simple_elevation(world, sea_level, target):
     """ This function can be used on a generic canvas (either an image to save
         on disk or a canvas part of a GUI)
     """
-    e = world.layers['elevation'].data
+    e = world.elevation.data
     c = numpy.empty(e.shape, dtype=numpy.float)
 
-    has_ocean = not (sea_level is None or world.layers['ocean'].data is None or not world.layers['ocean'].data.any())  # or 'not any ocean'
-    mask_land = numpy.ma.array(e, mask=world.layers['ocean'].data if has_ocean else False)  # only land
+    has_ocean = not (sea_level is None or world.ocean.data is None or not world.ocean.data.any())  # or 'not any ocean'
+    mask_land = numpy.ma.array(e, mask=world.ocean.data if has_ocean else False)  # only land
 
     min_elev_land = mask_land.min()
     max_elev_land = mask_land.max()
     elev_delta_land = (max_elev_land - min_elev_land) / 11.0
 
     if has_ocean:
-        land = numpy.logical_not(world.layers['ocean'].data)
+        land = numpy.logical_not(world.ocean.data)
         mask_ocean = numpy.ma.array(e, mask=land)  # only ocean
         min_elev_sea = mask_ocean.min()
         max_elev_sea = mask_ocean.max()
         elev_delta_sea = max_elev_sea - min_elev_sea
 
-        c[world.layers['ocean'].data] = ((e[world.layers['ocean'].data] - min_elev_sea) / elev_delta_sea)
+        c[world.ocean.data] = ((e[world.ocean.data] - min_elev_sea) / elev_delta_sea)
         c[land] = ((e[land] - min_elev_land) / elev_delta_land) + 1
     else:
         c = ((e - min_elev_land) / elev_delta_land) + 1
@@ -377,7 +377,7 @@ def draw_satellite(world, target):
 
     # Get an elevation mask where heights are normalized between 0 and 255
     elevation_mask = get_normalized_elevation_array(world)
-    smooth_mask = numpy.invert(world.layers['ocean'].data)  # all land shall be smoothed (other tiles can be included by setting them to True)
+    smooth_mask = numpy.invert(world.ocean.data)  # all land shall be smoothed (other tiles can be included by setting them to True)
 
     rng = numpy.random.RandomState(world.seed)  # create our own random generator; necessary for now to make the tests reproducible, even though it is a bit ugly
 
@@ -399,7 +399,7 @@ def draw_satellite(world, target):
     ice_color_variation = int(30)  # 0 means perfectly white ice; must be in [0, 255]; only affects R- and G-channel
     for y in range(world.height):
         for x in range(world.width):
-            if world.layers['icecap'].data[y, x] > 0.0:
+            if world.icecap.data[y, x] > 0.0:
                 smooth_mask[y, x] = True  # smooth the frozen areas, too
                 variation = rng.randint(0, ice_color_variation)
                 target.set_pixel(x, y, (255 - ice_color_variation + variation, 255 - ice_color_variation + variation, 255, 255))
@@ -438,14 +438,14 @@ def draw_satellite(world, target):
     for y in range(world.height):
         for x in range(world.width):
             ## Color rivers
-            if world.is_land((x, y)) and (world.layers['river_map'].data[y, x] > 0.0):
+            if world.is_land((x, y)) and (world.river_map.data[y, x] > 0.0):
                 base_color = target[y, x]
 
                 r, g, b = add_colors(base_color, RIVER_COLOR_CHANGE)
                 target.set_pixel(x, y, (r, g, b, 255))
 
             ## Color lakes
-            if world.is_land((x, y)) and (world.layers['lake_map'].data[y, x] != 0):
+            if world.is_land((x, y)) and (world.lake_map.data[y, x] != 0):
                 base_color = target[y, x]
 
                 r, g, b = add_colors(base_color, LAKE_COLOR_CHANGE)
@@ -459,13 +459,13 @@ def draw_satellite(world, target):
 
                 # Build up list of elevations in the previous n tiles, where n is the shadow size.
                 # This goes northwest to southeast
-                prev_elevs = [ world.layers['elevation'].data[y-n, x-n] for n in range(1, SAT_SHADOW_SIZE+1) ]
+                prev_elevs = [ world.elevation.data[y-n, x-n] for n in range(1, SAT_SHADOW_SIZE+1) ]
 
                 # Take the average of the height of the previous n tiles
                 avg_prev_elev = int( sum(prev_elevs) / len(prev_elevs) )
 
                 # Find the difference between this tile's elevation, and the average of the previous elevations
-                difference = int(world.layers['elevation'].data[y, x] - avg_prev_elev)
+                difference = int(world.elevation.data[y, x] - avg_prev_elev)
 
                 # Amplify the difference
                 adjusted_difference = difference * SAT_SHADOW_DISTANCE_MULTIPLIER
@@ -485,8 +485,8 @@ def draw_elevation(world, shadow, target):
     width = world.width
     height = world.height
 
-    data = world.layers['elevation'].data
-    ocean = world.layers['ocean'].data
+    data = world.elevation.data
+    ocean = world.ocean.data
 
     mask = numpy.ma.array(data, mask=ocean)
 
@@ -574,7 +574,7 @@ def draw_world(world, target):
                 biome = world.biome_at((x, y))
                 target.set_pixel(x, y, _biome_colors[biome.name()])
             else:
-                c = int(world.layers['sea_depth'].data[y, x] * 200 + 50)
+                c = int(world.sea_depth.data[y, x] * 200 + 50)
                 target.set_pixel(x, y, (0, 0, 255 - c, 255))
 
 
@@ -617,7 +617,7 @@ def draw_biome(world, target):
     width = world.width
     height = world.height
 
-    biome = world.layers['biome'].data
+    biome = world.biome.data
 
     for y in range(height):
         for x in range(width):
@@ -632,8 +632,8 @@ def draw_scatter_plot(world, size, target):
 
     #Find min and max values of humidity and temperature on land so we can
     #normalize temperature and humidity to the chart
-    humid = numpy.ma.masked_array(world.layers['humidity'].data, mask=world.layers['ocean'].data)
-    temp = numpy.ma.masked_array(world.layers['temperature'].data, mask=world.layers['ocean'].data)
+    humid = numpy.ma.masked_array(world.humidity.data, mask=world.ocean.data)
+    temp = numpy.ma.masked_array(world.temperature.data, mask=world.ocean.data)
     min_humidity = humid.min()
     max_humidity = humid.max()
     min_temperature = temp.min()
@@ -650,12 +650,12 @@ def draw_scatter_plot(world, size, target):
     h_values = ['62', '50', '37', '25', '12']
     t_values = [   0,    1,    2,   3,    5 ]
     for loop in range(0, 5):
-        h_min = (size - 1) * ((world.layers['humidity'].quantiles[h_values[loop]] - min_humidity) / humidity_delta)
+        h_min = (size - 1) * ((world.humidity.quantiles[h_values[loop]] - min_humidity) / humidity_delta)
         if loop != 4:
-            h_max = (size - 1) * ((world.layers['humidity'].quantiles[h_values[loop + 1]] - min_humidity) / humidity_delta)
+            h_max = (size - 1) * ((world.humidity.quantiles[h_values[loop + 1]] - min_humidity) / humidity_delta)
         else:
             h_max = size
-        v_max = (size - 1) * ((world.layers['temperature'].thresholds[t_values[loop]][1] - min_temperature) / temperature_delta)
+        v_max = (size - 1) * ((world.temperature.thresholds[t_values[loop]][1] - min_temperature) / temperature_delta)
         if h_min < 0:
             h_min = 0
         if h_max > size:
@@ -672,13 +672,13 @@ def draw_scatter_plot(world, size, target):
 
     #draw lines based on thresholds
     for t in range(0, 6):
-        v = (size - 1) * ((world.layers['temperature'].thresholds[t][1] - min_temperature) / temperature_delta)
+        v = (size - 1) * ((world.temperature.thresholds[t][1] - min_temperature) / temperature_delta)
         if 0 < v < size:
             for y in range(0, size):
                 target.set_pixel(int(v), (size - 1) - y, (0, 0, 0, 255))
     ranges = ['87', '75', '62', '50', '37', '25', '12']
     for p in ranges:
-        h = (size - 1) * ((world.layers['humidity'].quantiles[p] - min_humidity) / humidity_delta)
+        h = (size - 1) * ((world.humidity.quantiles[p] - min_humidity) / humidity_delta)
         if 0 < h < size:
             for x in range(0, size):
                 target.set_pixel(x, (size - 1) - int(h), (0, 0, 0, 255))
@@ -756,7 +756,7 @@ def draw_riversmap_on_file(world, filename):
 
 
 def draw_grayscale_heightmap_on_file(world, filename):
-    img = PNGWriter.grayscale_from_array(world.layers['elevation'].data, filename, scale_to_range=True)
+    img = PNGWriter.grayscale_from_array(world.elevation.data, filename, scale_to_range=True)
     img.complete()
 
 

worldengine/drawing_functions.py

@@ -40,11 +40,11 @@ def draw_rivers_on_image(world, target, factor=1):
 
     for y in range(world.height):
         for x in range(world.width):
-            if world.is_land((x, y)) and (world.layers['river_map'].data[y, x] > 0.0):
+            if world.is_land((x, y)) and (world.river_map.data[y, x] > 0.0):
                 for dx in range(factor):
                     for dy in range(factor):
                         target.set_pixel(x * factor + dx, y * factor + dy, (0, 0, 128, 255))
-            if world.is_land((x, y)) and (world.layers['lake_map'].data[y, x] != 0):
+            if world.is_land((x, y)) and (world.lake_map.data[y, x] != 0):
                 for dx in range(factor):
                     for dy in range(factor):
                         target.set_pixel(x * factor + dx, y * factor + dy, (0, 100, 128, 255))

worldengine/generation.py

@@ -24,19 +24,19 @@ def center_land(world):
     """Translate the map horizontally and vertically to put as much ocean as
        possible at the borders. It operates on elevation and plates map"""
 
-    y_sums = world.layers['elevation'].data.sum(1)  # 1 == sum along x-axis
+    y_sums = world.elevation.data.sum(1)  # 1 == sum along x-axis
     y_with_min_sum = y_sums.argmin()
     if get_verbose():
         print("geo.center_land: height complete")
 
-    x_sums = world.layers['elevation'].data.sum(0)  # 0 == sum along y-axis
+    x_sums = world.elevation.data.sum(0)  # 0 == sum along y-axis
     x_with_min_sum = x_sums.argmin()
     if get_verbose():
         print("geo.center_land: width complete")
 
     latshift = 0
-    world.layers['elevation'].data = numpy.roll(numpy.roll(world.layers['elevation'].data, -y_with_min_sum + latshift, axis=0), - x_with_min_sum, axis=1)
-    world.layers['plates'].data = numpy.roll(numpy.roll(world.layers['plates'].data, -y_with_min_sum + latshift, axis=0), - x_with_min_sum, axis=1)
+    world.elevation.data = numpy.roll(numpy.roll(world.elevation.data, -y_with_min_sum + latshift, axis=0), - x_with_min_sum, axis=1)
+    world.plates.data = numpy.roll(numpy.roll(world.plates.data, -y_with_min_sum + latshift, axis=0), - x_with_min_sum, axis=1)
     if get_verbose():
         print("geo.center_land: width complete")
 
@@ -49,8 +49,8 @@ def place_oceans_at_map_borders(world):
     ocean_border = int(min(30, max(world.width / 5, world.height / 5)))
 
     def place_ocean(x, y, i):
-        world.layers['elevation'].data[y, x] = \
-            (world.layers['elevation'].data[y, x] * i) / ocean_border
+        world.elevation.data[y, x] = \
+            (world.elevation.data[y, x] * i) / ocean_border
 
     for x in range(world.width):
         for i in range(ocean_border):
@@ -69,7 +69,7 @@ def add_noise_to_elevation(world, seed):
     for y in range(world.height):
         for x in range(world.width):
             n = snoise2(x / freq * 2, y / freq * 2, octaves, base=seed)
-            world.layers['elevation'].data[y, x] += n
+            world.elevation.data[y, x] += n
 
 
 def fill_ocean(elevation, sea_level):#TODO: Make more use of numpy?
@@ -105,7 +105,7 @@ def initialize_ocean_and_thresholds(world, ocean_level=1.0):
     :param ocean_level: the elevation representing the ocean level
     :return: nothing, the world will be changed
     """
-    e = world.layers['elevation'].data
+    e = world.elevation.data
     ocean = fill_ocean(e, ocean_level)
     hl = find_threshold_f(e, 0.10)  # the highest 10% of all (!) land are declared hills
     ml = find_threshold_f(e, 0.03)  # the highest 3% are declared mountains
@@ -141,7 +141,7 @@ def harmonize_ocean(ocean, elevation, ocean_level):
 # ----
 
 def sea_depth(world, sea_level):
-    sea_depth = sea_level - world.layers['elevation'].data
+    sea_depth = sea_level - world.elevation.data
     for y in range(world.height):
         for x in range(world.width):
             if world.tiles_around((x, y), radius=1, predicate=world.is_land):