README.md

@@ -1,27 +0,0 @@
-PyWatts - Predict Output of Solar Panels
-
-# Dependencies
-
-PyWatts is based on python3.6 and uses the following dependencies:
-
-* requests (2.19.1)
-* pypvwatts (2.1.0)
-* numpy (1.15.0)
-* peewee (3.5.4)
-* scikit-learn (0.19.2)
-* pandas (0.23.4)
-* tensorflow (1.9.0)
-* matplotlib (2.2.3)
-* scipy (1.1.0)
-
-We suggest using a python virtualenv.
-
-# Execute
-
-The script can be executed by issuing the follwing command:
-
-```bash
-$ python photovoltaic_gruppe4.py data.json
-```
-
-The output can be found in the same directory in `test_data_gruppe4.json`

photovoltaic_gruppe4.py

@@ -21,26 +21,9 @@ feature_col = [tf.feature_column.numeric_column(str(idx)) for idx in range(336)]
 n = pywatts.neural.Net(feature_cols=feature_col)
 
 predictions = []
-total = len(queries)
-for idx, query in enumerate(queries):
-
-    percent = idx / total
-    sys.stdout.write("\r")
-    progress = ""
-    for i in range(20):
-        if i < int(20 * percent):
-            progress += "="
-        else:
-            progress += " "
-    sys.stdout.write("[ %s ] %.2f%%" % (progress, percent * 100))
-    sys.stdout.flush()
-
+for query in queries:
     if oneH:
         predictions.extend(predict(n, query).astype('Float64').tolist())
     else:
         predictions.append(predict24h(n, query))
-
 print(predictions, file=open("test_data_gruppe4.json", "w"))
-
-sys.stdout.write("\r")
-print("[ ==================== ] 100.00%")