fix(map_generator): fix new changes on web version

biomes/details.py

@@ -0,0 +1,11 @@
+import pandas as pd
+from utils import normalize_ndarray
+
+df = pd.read_pickle('data.p')
+
+print('Columns', df.columns);
+print(df[df.columns[0:8]])
+
+columns = ['longitude', 'latitude', 'biome_num', 'elevation', 'distance_to_water', 'temp_winter_1900', 'precip_winter_1900', 'temp_spring_1900', 'precip_spring_1900']
+normalized = normalize_ndarray(df[columns].to_numpy())
+print(pd.DataFrame(columns=columns, data=normalized))

biomes/map_generator.py

@@ -39,10 +39,10 @@ parameters = {
         'step': 0.01
     },
     'max_elevation': {
-        'default': 1e4,
+        'default': 10000,
         'type': 'int',
         'min': 0,
-        'max': 1e4,
+        'max': 10000,
     },
     'min_elevation': {
         'default': -400,
@@ -51,10 +51,10 @@ parameters = {
         'max': 0
     },
     'ground_noise': {
-        'default': 6e3,
+        'default': 6000,
         'type': 'int',
         'min': 0,
-        'max': 1e5,
+        'max': 10000,
     },
     'water_proportion': {
         'default': 0.3,
@@ -315,7 +315,7 @@ def generate_map(biomes=False, **kwargs):
     greys = cm.get_cmap('Greys')
     greys.set_under(color=SEA_COLOR)
 
-    # ground = ndimage.gaussian_filter(ground, sigma=4)
+    ground = ndimage.gaussian_filter(ground, sigma=1)
     ground = ndimage.generic_filter(ground, constant_filter, size=1)
     print(np.min(ground), np.max(ground), p['max_elevation'])
 
@@ -325,13 +325,13 @@ def generate_map(biomes=False, **kwargs):
     plt.imshow(ground.T, cmap=greys, norm=norm)
     plt.gca().invert_yaxis()
 
+    if biomes:
+        generate_biomes(ground)
+
     figfile = BytesIO()
     plt.savefig(figfile, format='png')
     figfile.seek(0)
 
-    if biomes:
-        generate_biomes(ground)
-
     return figfile
 
 

biomes/predict.py

@@ -138,7 +138,7 @@ def predicted_precips_cmd(checkpoint='checkpoints/precip.h5', year=2000):
     Precip.restore(checkpoint)
     predicted_precips(Precip, year=year)
 
-def predict_end_to_end(Temp, Precip, Biomes, df=pd.read_pickle('data.p'), year=2000):
+def predict_end_to_end(Temp, Precip, Biomes, df=pd.read_pickle('data.p'), year=2000, path=None):
     columns = INPUTS
 
     inputs = df[INPUTS]
@@ -207,9 +207,9 @@ def predict_end_to_end(Temp, Precip, Biomes, df=pd.read_pickle('data.p'), year=2
         new_data = new_data.append(f)
 
     print(new_data)
-    draw(new_data)
+    draw(new_data, path=path)
 
-def predict_end_to_end_cmd(checkpoint_temp='checkpoints/temp.h5', checkpoint_precip='checkpoints/precip.h5', checkpoint_biomes='checkpoints/b.h5', year=2000, **kwargs):
+def predict_end_to_end_cmd(checkpoint_temp='checkpoints/temp.h5', checkpoint_precip='checkpoints/precip.h5', checkpoint_biomes='checkpoints/b.h5', year=2000, path=None, **kwargs):
     batch_size = A_params['batch_size']['grid_search'][0]
     layers = A_params['layers']['grid_search'][0]
     optimizer = A_params['optimizer']['grid_search'][0](A_params['lr']['grid_search'][0])
@@ -242,7 +242,7 @@ def predict_end_to_end_cmd(checkpoint_temp='checkpoints/temp.h5', checkpoint_pre
     Biomes.prepare_for_use()
     Biomes.restore(checkpoint_biomes)
 
-    predict_end_to_end(Temp=Temp, Precip=Precip, Biomes=Biomes, year=year, **kwargs)
+    predict_end_to_end(Temp=Temp, Precip=Precip, Biomes=Biomes, year=year, path=path, **kwargs)
 
 
 if __name__ == "__main__":

biomes/static/script.js

@@ -12,7 +12,7 @@ function generate() {
   const queryString = new URLSearchParams(formData).toString()
   map.src = '/map?' + queryString;
   map.classList.add('d-none');
-  map.width = formData.get('width');
+  //map.width = formData.get('width');
 }
 
 mapSettings.addEventListener('submit', (e) => {

biomes/templates/index.html

@@ -11,7 +11,7 @@
   <div class='row'>
     <main class='col d-flex justify-content-center align-items-center'>
     <!-- <canvas id='board'></canvas> -->
-      <img src='' id='map'>
+      <img src='' id='map' width='80%'>
 
       <div class='spinner-border text-primary' role='status' id='spinner'>
         <span class='sr-only'>Loading...</span>

biomes/train.py

@@ -10,7 +10,7 @@ from model import Model
 B_params = {
     'batch_size': tune.grid_search([256]),
     'layers': tune.grid_search([[512, 512]]),
-    'lr': tune.grid_search([3e-4]),
+    'lr': tune.grid_search([1e-4]),
     'optimizer': tune.grid_search([tf.keras.optimizers.Adam]),
 }