Update app.py

app.py

@@ -17,25 +17,26 @@ def load_data():
 
   from datasets import load_dataset
 
+  S = 5
+  N = 1000
+  D = 3
+  F = 64
+  G = 32
 
   data = load_dataset("cmudrc/wave-energy", data_files=["data.zip"], split='train')
-  curvey_curves = [numpy.array(x)/1000000 for x in data['curves']]
+  # curvey_curves = [numpy.array(x)/1000000 for x in data['curves']]
   # round_geometry = [numpy.array(x) for x in data['geometry']]
   # round_geometry = numpy.array(data['geometry'])
+  curvey_curves = None
   round_geometry = None
-  geometry = numpy.reshape(data['geometry'], (5000, 32*32*32))
-  curves = numpy.reshape(data['curves'], (5000, 3*64))/1000000
+  geometry = numpy.reshape(data['geometry'], (S*N, G*G*G))
+  curves = numpy.reshape(data['curves'], (S*N, D*F))/1000000
   # curves = load_dataset("cmudrc/wave-energy", data_files="curves.zip", split='train').to_pandas().drop(labels="index", axis=1)
   # geometry = load_dataset("cmudrc/wave-energy", data_files="geometry.zip", split='train').to_pandas().drop(labels="index", axis=1)
 
   # Open all the files we downloaded at the beginning and take out hte good bits
   # curves = data.iloc[:, [i for i in range(1, 3*64+1)]]
   # geometry = data.iloc[:, [i for i in range(1 + 3*64, 1 + 3*64 + 32**3)]]
-  S = 5
-  N = 1000
-  D = 3
-  F = 64
-  G = 32
 
   # flattened_curves = curves.values / 1000000
   # curvey_curves = [c.reshape([D, F])/1000000 for c in curves.values]