Reduce number of windows by one in WindowedForecast

This is a partial revert of c08a256
that added one to the number of windows to ensure that for low durations
the number of windows is the same as the number of data points. While this
fixed errors for durations close to 48h, it introduced #119 that caused
errors for low durations (5 min). This is caused by the interpolation
trying to read past the last data frame.

Fixes: #119

Author: abhidg

cats/forecast.py

@@ -67,6 +67,7 @@ def bisect_left(data, t):
             for i, d in enumerate(data):
                 if d.datetime + self.data_stepsize >= t:
                     return i + 1
+            raise ValueError("No index found for closest data point past job end time")
 
         self.ndata = bisect_left(self.data, self.end)  # window size
 
@@ -147,4 +148,4 @@ def __iter__(self):
             yield self.__getitem__(index)
 
     def __len__(self):
-        return len(self.data) - self.ndata + 1
+        return len(self.data) - self.ndata

tests/test_windowed_forecast.py

@@ -53,7 +53,7 @@ def test_has_right_length():
 
     # Expecting (200 - 160 + 1) (41) data points in the time
     # integrated timeseries.
-    assert len(wf) == NDATA - window_size + 1
+    assert len(wf) == NDATA - window_size
 
 
 def test_values():
@@ -67,7 +67,7 @@ def test_values():
     wf = WindowedForecast(DATA, window_size, start=DATA[0].datetime)
     expected = [
         math.cos((i + window_size) * step) - math.cos(i * step)
-        for i in range(len(DATA) - window_size + 1)
+        for i in range(len(DATA) - window_size)
     ]
     # average
     expected = [e / (window_size * step) for e in expected]