Add readme

README.md

@@ -0,0 +1,27 @@
+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

@@ -21,9 +21,26 @@ feature_col = [tf.feature_column.numeric_column(str(idx)) for idx in range(336)]
 n = pywatts.neural.Net(feature_cols=feature_col)
 
 predictions = []
-for query in queries:
+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()
+
     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%")