diff --git a/biomes/draw.py b/biomes/draw.py
index 4c74baf..9581a61 100644
--- a/biomes/draw.py
+++ b/biomes/draw.py
@@ -8,7 +8,7 @@ from constants import BIOMES
 import pandas as pd
 import cartopy.crs as ccrs
 
-def draw(df, earth=True, width=23.22, height=13, only_draw=False, path=None):
+def draw(df, earth=True, width=23.22, height=13, only_draw=False, path=None, alpha=1):
     logger.debug('draw(df, %s)', path)
     biomes = {}
     biome_numbers = df['biome_num'].unique()
@@ -36,6 +36,7 @@ def draw(df, earth=True, width=23.22, height=13, only_draw=False, path=None):
 
         patches = [Circle(p, radius=0.4) for p in biomes[n]]
         collection = PatchCollection(patches, color=color)
+        collection.set_alpha(alpha)
 
         legend_handles.append(Patch(color=color, label=BIOMES[n]['name']))
         ax.add_collection(collection)
diff --git a/biomes/map_generator.py b/biomes/map_generator.py
index 9a99973..dcd5a77 100644
--- a/biomes/map_generator.py
+++ b/biomes/map_generator.py
@@ -51,7 +51,7 @@ parameters = {
         'max': 0
     },
     'ground_noise': {
-        'default': 1.1e4,
+        'default': 6e3,
         'type': 'int',
         'min': 0,
         'max': 1e5,
@@ -299,8 +299,6 @@ def generate_map(biomes=False, **kwargs):
         position = (position[0] + np.random.randint(p['continent_spacing'] * width * 0.8, p['continent_spacing'] * width * 1.2),
                     position[1] + ym * np.random.randint(p['continent_spacing'] * height * 0.8, p['continent_spacing'] * height * 1.2))
 
-        print(position)
-
         ym = ym * -1
 
         random_size = ground_size / continents
@@ -319,9 +317,8 @@ def generate_map(biomes=False, **kwargs):
     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))
 
-    print(np.count_nonzero(ground) / ground.size)
+    print('water proportion', np.count_nonzero(ground) / ground.size)
 
     plt.gca().invert_yaxis()
     plt.imshow(ground.T, cmap=greys, norm=norm)
@@ -436,7 +433,6 @@ def predict_end_to_end(input_df, boundary, checkpoint_temp='checkpoints/temp.h5'
 
     inputs = inputs.to_numpy()
     inputs = normalize_ndarray(inputs, inputs_copy)
-    print(inputs)
     out_columns = ['temp_{}_{}'.format(season, year) for season in SEASONS]
     out = Temp.predict(inputs)
     temp_output = pd.DataFrame(data=denormalize(out, df[out_columns].to_numpy()), columns=out_columns)
@@ -449,7 +445,6 @@ def predict_end_to_end(input_df, boundary, checkpoint_temp='checkpoints/temp.h5'
 
     inputs = inputs.to_numpy()
     inputs = normalize_ndarray(inputs, inputs_copy)
-    print(inputs)
     out_columns = ['precip_{}_{}'.format(season, year) for season in SEASONS]
     out = Precip.predict(inputs)
 
@@ -493,15 +488,13 @@ def predict_end_to_end(input_df, boundary, checkpoint_temp='checkpoints/temp.h5'
         new_data = new_data.append(f)
 
     #print(new_data)
-    draw(new_data, earth=False, only_draw=True, width=p['width'], height=p['height'])
+    draw(new_data, earth=False, only_draw=True, width=p['width'], height=p['height'], alpha=0.7)
 
 # TODO: reduce opacity of biome layer
 if __name__ == "__main__":
     # p['width'] = 50
     # p['height'] = 50
-    p['water_proportion'] = 0.9
-    p['continents'] = 3
-    p['seed'] = 1
+    p['seed'] = 3
     generate_map(True)
     # print(normalize_ndarray(np.array([[ 5.59359803,0.99879546,-90., 45.24], [  5.54976747, 0.99879546,-86.4, 45.24      ]])))
     plt.show()