Finalisation of standby analysis (#19)

opengrid/library/analysis.py

@@ -9,6 +9,7 @@
 import datetime as dt
 import pandas as pd
 import numpy as np
+import numbers
 from opengrid.library.exceptions import EmptyDataFrame
 
 
@@ -68,22 +69,83 @@ def do_analysis(self, agg, starttime=dt.time.min, endtime=dt.time.max):
             self.result = pd.DataFrame()
 
 
-def standby(df, resolution='d'):
+def standby(df, resolution='24h', time_window=None):
     """
     Compute standby power
 
     Parameters
     ----------
-    df : Pandas DataFrame
+    df : pandas.DataFrame or pandas.Series
         Electricity Power
-    resolution : str
+    resolution : str, default='d'
+        Resolution of the computation.  Data will be resampled to this resolution (as mean) before computation
+        of the minimum.
+        String that can be parsed by the pandas resample function, example ='h', '15min', '6h'
+    time_window : tuple with start-hour and end-hour, default=None
+        Specify the start-time and end-time for the analysis.
+        Only data within this time window will be considered.
+        Both times have to be specified as string ('01:00', '06:30') or as datetime.time() objects
+
+    Returns
+    -------
+    df : pandas.Series with DateTimeIndex in the given resolution
     """
 
     if df.empty:
         raise EmptyDataFrame()
+
+    df = pd.DataFrame(df)  # if df was a pd.Series, convert to DataFrame
+    def parse_time(t):
+        if isinstance(t, numbers.Number):
+            return pd.Timestamp.utcfromtimestamp(t).time()
+        else:
+            return pd.Timestamp(t).time()
+
+
+    # first filter based on the time-window
+    if time_window is not None:
+        t_start = parse_time(time_window[0])
+        t_end = parse_time(time_window[1])
+        if t_start > t_end:
+            # start before midnight
+            df = df[(df.index.time >= t_start) | (df.index.time < t_end)]
+        else:
+            df = df[(df.index.time >= t_start) & (df.index.time < t_end)]
+
     return df.resample(resolution).min()
 
 
+def share_of_standby(df, resolution='24h', time_window=None):
+    """
+    Compute the share of the standby power in the total consumption.
+
+    Parameters
+    ----------
+    df : pandas.DataFrame or pandas.Series
+        Power (typically electricity, can be anything)
+    resolution : str, default='d'
+        Resolution of the computation.  Data will be resampled to this resolution (as mean) before computation
+        of the minimum.
+        String that can be parsed by the pandas resample function, example ='h', '15min', '6h'
+    time_window : tuple with start-hour and end-hour, default=None
+        Specify the start-time and end-time for the analysis.
+        Only data within this time window will be considered.
+        Both times have to be specified as string ('01:00', '06:30') or as datetime.time() objects
+
+    Returns
+    -------
+    fraction : float between 0-1 with the share of the standby consumption
+    """
+
+    p_sb = standby(df, resolution, time_window)
+    df = df.resample(resolution).mean()
+    p_tot = df.sum()
+    p_standby = p_sb.sum()
+    share_standby = p_standby / p_tot
+    res = share_standby.iloc[0]
+    return res
+
+
 def count_peaks(ts):
     """
     Toggle counter for gas boilers

opengrid/tests/test_analyses.py

@@ -7,6 +7,7 @@
 
 import unittest
 import pandas as pd
+import numpy as np
 
 import opengrid as og
 from opengrid import datasets
@@ -22,6 +23,30 @@ def test_standby(self):
 
         self.assertRaises(EmptyDataFrame, og.analysis.standby, pd.DataFrame)
 
+    def test_standby_with_time_window(self):
+        df = datasets.get('elec_power_min_1sensor')
+        res = og.analysis.standby(df, 'D', time_window=('01:00', '06:00'))
+        self.assertEqual(res.index.tz.zone, 'Europe/Brussels')
+        self.assertEqual(res.squeeze().to_json(), '{"1507327200000":61.739999936,"1507413600000":214.9799999222,"1507500000000":53.0399997951,"1507586400000":55.7399999164,"1507672800000":59.94000006,"1507759200000":69.4800002407,"1507845600000":56.8200000236,"1507932000000":54.1799997864,"1508018400000":54.779999801,"1508104800000":54.7199997772,"1508191200000":98.5199999576,"1508277600000":55.6799999066,"1508364000000":53.9399997052,"1508450400000":109.5599999931,"1508536800000":144.3600001093,"1508623200000":52.7999997279}')
+
+        res = og.analysis.standby(df, 'D', time_window=('22:00', '06:00'))
+        self.assertEqual(res.index.tz.zone, 'Europe/Brussels')
+        self.assertEqual(res.squeeze().to_json(), '{"1507327200000":61.739999936,"1507413600000":119.2800000636,"1507500000000":53.0399997951,"1507586400000":55.7399999164,"1507672800000":59.94000006,"1507759200000":69.4800002407,"1507845600000":56.8200000236,"1507932000000":54.1799997864,"1508018400000":54.779999801,"1508104800000":54.7199997772,"1508191200000":98.5199999576,"1508277600000":55.6799999066,"1508364000000":53.9399997052,"1508450400000":96.3000000408,"1508536800000":133.9200000744,"1508623200000":52.7999997279}')
+
+    def test_share_of_standby_1(self):
+        df = pd.DataFrame(data={'conso':np.ones(48)},
+                          index=pd.DatetimeIndex(start=pd.Timestamp('20180304'), periods=48, freq='h'))
+        share_of_standby = og.analysis.share_of_standby(df, resolution='24h')
+        self.assertEqual(share_of_standby, 1.0)
+
+    def test_share_of_standby_2(self):
+        df = pd.DataFrame(data={'conso':np.ones(48)},
+                          index=pd.DatetimeIndex(start=pd.Timestamp('20180304'), periods=48, freq='h'))
+        df.iloc[0,0] = 0
+        share_of_standby = og.analysis.share_of_standby(df, resolution='24h')
+        self.assertAlmostEqual(share_of_standby, 0.5106382978723404)
+
+
     def test_count_peaks(self):
         df = datasets.get('gas_dec2016_min')
         ts = df['313b'].head(100)