Merge pull request #598 from EttoreZ/issue239_extendForecast

Issue239 extend forecast

data/data_manager.py

@@ -333,20 +333,36 @@ def get_data(self, horizon=24*3600, interval=None, index=None,
             # the closest possible point under stop will be the end
             # point in order to keep interval unchanged among index.
             index = np.arange(start,stop+0.1,interval).astype(int)
+        else:
+            if not isinstance(index, np.ndarray):
+                index = np.asarray(index)
 
         # Reindex to the desired index
-        data_slice_reindexed = data_slice.reindex(index)
-
-        for key in data_slice_reindexed.keys():
-            # Use linear interpolation for continuous variables
-            if key in self.categories['weather']:
-                f = interpolate.interp1d(self.case.data.index,
-                    self.case.data[key], kind='linear')
-            # Use forward fill for discrete variables
-            else:
-                f = interpolate.interp1d(self.case.data.index,
-                    self.case.data[key], kind='zero')
-            data_slice_reindexed.loc[:,key] = f(index)
+        start = index[0]
+        # 1 year in (s)
+        year = 31536000 
+        # Starting year
+        year_start = int(np.floor(start/year))*year
+        # Normalizing index with respect to starting year
+        index_norm = index - year_start
+        stop_norm = index_norm[-1]
+        # If stop happens across the year divide df and interpolate separately
+        if stop_norm > data_slice.index[-1]:
+            idx_year = (np.abs(index_norm - year)).argmin() + 1
+            # Take previous index value if index at idx_year > year
+            if index_norm[idx_year - 1] - year > np.finfo(float).eps:
+                idx_year = idx_year -1
+            df_slice1 = data_slice.reindex(index_norm[:idx_year])
+            df_slice1 = self.interpolate_data(df_slice1,index_norm[:idx_year])
+            df_slice2 = data_slice.reindex(index_norm[idx_year:] - year)
+            df_slice2 = self.interpolate_data(df_slice2,index_norm[idx_year:] - year)
+            df_slice2.index = df_slice2.index + year
+            data_slice_reindexed = pd.concat([df_slice1,df_slice2])
+        else:
+            data_slice_reindexed = data_slice.reindex(index_norm)
+            data_slice_reindexed = self.interpolate_data(data_slice_reindexed,index_norm)
+        # Add starting year back to index desired by user 
+        data_slice_reindexed.index = data_slice_reindexed.index + year_start
 
         if plot:
             if category is None:
@@ -503,7 +519,34 @@ def get_data_metadata(self):
             data_metadata[key] = metadata
 
         return data_metadata
+
+    def interpolate_data(self,df,index):
+        '''Interpolate testcase data.
+
+        Parameters
+        ----------
+        df: pandas.DataFrame
+        Dataframe that needs to be interpolated
+        index: np.array()
+        Index to use to get interpolated data
+
+        Returns
+        -------
+        df: pandas.DataFrame
+        Interpolated dataframe
 
+        '''
+        for key in df.keys():
+            # Use linear interpolation for continuous variables
+            if key in self.categories['weather']:
+                f = interpolate.interp1d(self.case.data.index,
+                    self.case.data[key], kind='linear')
+            # Use forward fill for discrete variables
+            else:
+                f = interpolate.interp1d(self.case.data.index,
+                    self.case.data[key], kind='zero')
+            df.loc[:,key] = f(index) 
+        return df
 
 if __name__ == "__main__":
     import sys

testing/references/testcase2/advance_over_year.csv

@@ -0,0 +1,12 @@
+keys,value
+CO2RooAir_y,123.1382838102673
+PCoo_y,0.0
+PFan_y,0.8739606953892677
+PHea_y,1844.306909142125
+PPum_y,0.0
+TRooAir_y,295.1440582575692
+oveTSetRooCoo_activate,0.0
+oveTSetRooCoo_u,296.15
+oveTSetRooHea_activate,0.0
+oveTSetRooHea_u,295.15
+time,31537800.0

testing/references/testcase2/put_forecast_over_year.csv

@@ -0,0 +1,6 @@
+time,EmissionsBiomassPower,EmissionsDistrictHeatingPower,EmissionsElectricPower,EmissionsGasPower,EmissionsSolarThermalPower,HDifHor,HDirNor,HGloHor,HHorIR,InternalGainsCon[1],InternalGainsLat[1],InternalGainsRad[1],LowerSetp[1],Occupancy[1],PriceBiomassPower,PriceDistrictHeatingPower,PriceElectricPowerConstant,PriceElectricPowerDynamic,PriceElectricPowerHighlyDynamic,PriceGasPower,PriceSolarThermalPower,TBlaSky,TDewPoi,TDryBul,TWetBul,UpperCO2[1],UpperSetp[1],ceiHei,cloTim,lat,lon,nOpa,nTot,pAtm,relHum,solAlt,solDec,solHouAng,solTim,solZen,winDir,winSpe
+31534200,0.0,0.1,0.5,0.2,0.0,0.0,0.0,0.0,245.708333333,19.2,9.6,28.8,295.15,0.0,0.2,0.1,0.2,0.1,-0.0776457135308,0.07,0.0,256.636351915,259.379166667,272.625,268.72819641,894.0,296.15,31452.875,31534200.0,0.693942910593,-1.83015225364,0.3875,0.775,101325.0,0.317916666667,-1.25485882436,-0.4032265228,2290.07494135,31533990.938,2.82565515116,1.82168740677,1.7675
+31536000,0.0,0.1,0.5,0.2,0.0,0.0,0.0,0.0,245.0,19.2,9.6,28.8,295.15,0.0,0.2,0.1,0.2,0.1,2.6470940280699998e-14,0.07,0.0,256.473613407,259.15,272.45,268.540745643,894.0,296.15,36600.0,31536000.0,0.693942910593,-1.83015225364,0.4,0.8,101325.0,0.31,-1.27976763694,-0.403200665926,2290.20580064,31535790.3823,2.85056396373,1.91986217719,2.1
+31537800,0.0,0.1,0.5,0.2,0.0,0.0,0.0,0.0,251.5,19.2,9.6,28.8,295.15,0.0,0.2,0.1,0.2,0.1,0.0,0.07,0.0,257.868968045,266.025,273.15,270.496356538,894.0,296.15,77777.0,1800.0,0.693942910593,-1.83015225364,0.225,0.525,101325.0,0.54,-1.26316008346,-0.402859568716,-3.02402826328,1616.62641254,2.83395641025,3.44266194956,2.525
+31539600,0.0,0.1,0.5,0.2,0.0,0.0,0.0,0.0,253.0,19.2,9.6,28.8,295.15,0.0,0.2,0.1,0.2,0.1,0.0776457135308,0.07,0.0,258.454386802,266.55,273.15,270.675045605,894.0,296.15,77777.0,3600.0,0.693942910593,-1.83015225364,0.3,0.7,101325.0,0.57,-1.21170311874,-0.402832949277,-2.89316937192,3416.06533739,2.78249944554,3.54301838155,2.8
+31541400,0.0,0.1,0.5,0.2,0.0,0.0,0.0,0.0,254.375,19.2,9.6,28.8,295.15,0.0,0.2,0.1,0.2,0.1,0.0776457135308,0.07,0.0,258.910390202,266.957720588,273.15,270.815940601,894.0,296.15,77777.0,5400.0,0.693942910593,-1.83015225364,0.36875,0.84125,101325.0,0.59375,-1.1376691186,-0.402806275407,-2.76231047229,5215.50437585,2.70846544539,3.59319659754,3.06875

testing/test_data.py

@@ -290,6 +290,22 @@ def test_get_data_index(self):
         # Check the data retrieved with the manager
         df_man = pd.DataFrame(data_dict).set_index('time')
         self.compare_ref_timeseries_df(df_man, self.ref_data_index)
+
+    def test_get_data_over_year(self):
+        '''Check that the data manager can retrieve the test case data
+        when an arbitrary time index across the year is provided.
+
+        '''
+
+        # Define index
+        index = np.arange(362*24*3600, (362+7)*24*3600, 321)
+
+        # Get the data
+        data_dict = self.man.get_data(index=index)
+
+        # Check the data retrieved with the manager
+        df_man = pd.DataFrame(data_dict).set_index('time')
+        self.compare_ref_timeseries_df(df_man, self.ref_data_over_year)
 
 class DataManagerSingleZoneTest(unittest.TestCase, utilities.partialChecks,
                       PartialDataManagerTest):
@@ -321,6 +337,8 @@ def setUp(self):
             'references', 'data', 'testcase2', 'tc2_data_retrieved_default.csv')
         self.ref_data_index = os.path.join(testing_root_dir,
             'references', 'data', 'testcase2', 'tc2_data_retrieved_index.csv')
+        self.ref_data_over_year = os.path.join(testing_root_dir,
+            'references', 'data', 'testcase2', 'tc2_data_retrieved_over_year.csv')
 
 class DataManagerMultiZoneTest(unittest.TestCase, utilities.partialChecks,
                                PartialDataManagerTest):
@@ -352,6 +370,8 @@ def setUp(self):
             'references', 'data', 'testcase3', 'tc3_data_retrieved_default.csv')
         self.ref_data_index = os.path.join(testing_root_dir,
             'references', 'data', 'testcase3', 'tc3_data_retrieved_index.csv')
+        self.ref_data_over_year = os.path.join(testing_root_dir,
+            'references', 'data', 'testcase3', 'tc3_data_retrieved_over_year.csv')
 
 class FindDaysTest(unittest.TestCase, utilities.partialChecks):
     '''Tests module to find peak and typical heating and cooling

testing/test_forecast.py

@@ -62,6 +62,24 @@ def test_get_forecast_interval(self):
         # Check the forecast
         df_forecaster = pd.DataFrame(forecast).set_index('time')
         self.compare_ref_timeseries_df(df_forecaster, ref_filepath)
+
+    def test_get_forecast_over_year(self):
+        '''Check that the forecaster is able to retrieve the data across
+        the year
+
+        '''
+        self.case.initialize(364*24*3600, 0)
+        # Load the data into the test case
+        forecast = self.forecaster.get_forecast(self.forecast_points,
+                                                horizon=2*24*3600,
+                                                interval=123)
+
+        # Set reference file path
+        ref_filepath = self.ref_forecast_over_year
+
+        # Check the forecast
+        df_forecaster = pd.DataFrame(forecast).set_index('time')
+        self.compare_ref_timeseries_df(df_forecaster, ref_filepath)
 
 class ForecasterSingleZoneTest(unittest.TestCase, utilities.partialChecks,
                                PartialForecasterTest):
@@ -91,6 +109,9 @@ def setUp(self):
 
         self.ref_forecast_interval = os.path.join(utilities.get_root_path(),
             'testing', 'references', 'forecast', 'testcase2', 'tc2_forecast_interval.csv')
+
+        self.ref_forecast_over_year = os.path.join(utilities.get_root_path(),
+            'testing', 'references', 'forecast', 'testcase2', 'tc2_forecast_over_year.csv')
 
 class ForecasterMultiZoneTest(unittest.TestCase, utilities.partialChecks,
                               PartialForecasterTest):
@@ -120,6 +141,9 @@ def setUp(self):
 
         self.ref_forecast_interval = os.path.join(utilities.get_root_path(),
             'testing', 'references', 'forecast', 'testcase3', 'tc3_forecast_interval.csv')
+
+        self.ref_forecast_over_year = os.path.join(utilities.get_root_path(),
+            'testing', 'references', 'forecast', 'testcase3', 'tc3_forecast_over_year.csv')
 
 if __name__ == '__main__':
     utilities.run_tests(os.path.basename(__file__))

testing/test_testcase2.py

@@ -174,5 +174,49 @@ def setUp(self):
         self.measurement = 'PFan_y'
         self.forecast_point = 'EmissionsBiomassPower'
 
+class SimOverYear(unittest.TestCase, utilities.partialChecks):
+    '''Test if testcase can simulate across the year and retrieve forecasts.
+
+    '''
+
+    def setUp(self):
+        '''Setup for each test.
+
+        '''
+
+        self.url = 'http://127.0.0.1:5000'
+
+    def test_advance_over_year(self):
+        '''Tests that simulation can advance over one year.
+
+        '''
+
+        # Run test
+        requests.put('{0}/initialize'.format(self.url), json={'start_time':365*24*3600 - 1800, 'warmup_period':0})
+        y = requests.post('{0}/advance'.format(self.url), json={}).json()['payload']
+        # Check trajectories
+        df = pd.DataFrame.from_dict(y, orient = 'index', columns=['value'])
+        df.index.name = 'keys'
+        ref_filepath = os.path.join(utilities.get_root_path(), 'testing', 'references', 'testcase2', 'advance_over_year.csv')
+        self.compare_ref_values_df(df, ref_filepath)
+
+    def test_put_forecast_over_year(self):
+        '''Tests that forecast across the year can be retrived.
+
+        '''
+
+        horizon = 7200
+        interval = 1800
+        # Initialize
+        requests.put('{0}/initialize'.format(self.url), json={'start_time':365*24*3600 - 1800, 'warmup_period':0})
+        # Test case forecast
+        forecast_points = list(requests.get('{0}/forecast_points'.format(self.url)).json()['payload'].keys())
+        forecast = requests.put('{0}/forecast'.format(self.url), json={'point_names':forecast_points, 'horizon':horizon, 'interval':interval}).json()['payload']
+        df_forecaster = pd.DataFrame(forecast).set_index('time')
+        # Set reference file path
+        ref_filepath = os.path.join(utilities.get_root_path(), 'testing', 'references', 'testcase2', 'put_forecast_over_year.csv')
+        # Check the forecast
+        self.compare_ref_timeseries_df(df_forecaster, ref_filepath)
+
 if __name__ == '__main__':
     utilities.run_tests(os.path.basename(__file__))