Altered transport profile weighting

GQF/Calcs3.py

@@ -117,6 +117,19 @@ def QF(areaCodes, timeStepEnd, timeStepDuration, annualEnergyUse, diurnalProfile
     # Factor of 86400 to convert from J/m2/day to J/m2/s (W/m2)
     dslSc = prop['diesel']
     petSc = prop['petrol']
+    # print('Fuel cons density (kg/m2)', trans.getCar(areaCodes, 'petrol', timeStepEnd))
+    # print('HoC (J km -1)', petHoc)
+    # print('J m-2 day-1', petHoc * trans.getCar(areaCodes, 'petrol', timeStepEnd))
+    # print('J m-2 s-1', (petHoc * trans.getCar(areaCodes, 'petrol', timeStepEnd) /86400))
+    # issue with mean or sum division of transport profile could be coming out here
+    # comes as HoC * profile time * fuel proportion
+    # cardiur = diurnalTrans.getCar(timeStepEnd, timeStepDuration)[0]
+    # print('printing getCar Diurnal', type(cardiur))
+    # print('getCar Diurnal:', cardiur)
+    # print('HoC:', petHoc, dslHoc)
+    # print(areaCodes)
+    # print('FC:', trans.getCar(areaCodes, 'petrol', timeStepEnd))
+    # print('Sc:', petSc, dslSc)
     WattHour.loc[areaCodes,columns[9]] = (petHoc * trans.getMotorcycle(areaCodes, 'petrol', timeStepEnd) * petSc +
                                        dslHoc * trans.getMotorcycle(areaCodes, 'diesel', timeStepEnd) * dslSc) * \
         diurnalTrans.getMotorcycle(timeStepEnd, timeStepDuration)[0]/86400.0  # motorcycles

GQF/DataManagement/spatialHelpers.py

@@ -264,6 +264,7 @@ def calculate_fuel_use(inputLayer, inputIdField,
                     QgsVectorLayerUtils.getDoubleValues(
                     inputLayer, vAADTFields[key], selectedOnly=True)[0])  # Populate and translate names at the same time
                 # aadtData[key] = np.array(QgsVectorLayerUtils.getDoubleValues(inputLayer,vAADTFields[key], selectedOnly=True)[0])  # Populate and translate names at the same time
+            # aadtData.to_csv('')
 
             # Make use of the extra detail available to us
             newValues[fieldMap['diesel']['car']].loc[ids] = aadtData['diesel_car'] * \
@@ -312,6 +313,9 @@ def calculate_fuel_use(inputLayer, inputIdField,
             newValues['_FC_Dbus'].loc[ids] = 0
             alreadyCalculated = ['car', 'lgv', 'bus']
 
+        # aadtData.to_pickle(f'/Users/suegrimmond/Documents/IQF/GQF-PYQGIS3/logfiles/small_london/AADT_data_{roadType}.pkl')
+        # lkm.to_pickle(f'/Users/suegrimmond/Documents/IQF/GQF-PYQGIS3/logfiles/small_london/lkm_{roadType}.pkl')
+        # print('AADT and lkm pickled!')
         # The remaining vehicle types all get calculated in the same way so loop over fieldMap to save on code...
         for fuelType in list(fieldMap.keys()):
             for vehType in list(fieldMap[fuelType].keys()):

GQF/Disaggregate.py

@@ -75,8 +75,9 @@ def disaggregate(qfDataSources, qfParams, outputFolder):
                                      inputFieldId=ie['featureIds'],
                                      weight_by=workpopattrib,
                                      epsgCode=ie['epsgCode'])
-        (ds, attrib) = bldgEnergy.getIndustrialElecLayer(makeUTC(ie['startDate']))
+        # different for others, next two lines are in other order
         outFile = os.path.join(outputFolder, 'IndElec_starting' + ie['startDate'].strftime('%Y-%m-%d') + '.shp')
+        (ds, attrib) = bldgEnergy.getIndustrialElecLayer(makeUTC(ie['startDate']))
         saveLayerToFile(ds, outFile, bldgEnergy.getOutputLayer().crs(), 'ind elec gas downscaled')
         returnDict['indElec'].append({'file':outFile, 'EPSG':ie['epsgCode'], 'startDate':ie['startDate'], 'attribute':attrib, 'featureIds':outFeatIds})
 

GQF/FuelConsumption.py

@@ -4,6 +4,7 @@
     pass
 import os
 from .string_func import   lower
+import pickle
 class FuelConsumption():
     def __init__(self, filename):
         ''' Class to read in fuel consumption file with prescribed format in g/km,
@@ -14,6 +15,10 @@ def __init__(self, filename):
         self.fuelTypes = ['diesel', 'petrol']
         self.roadTypes = ['motorway', 'rural_single', 'rural_dual', 'urban']
         self.dealWithFile(filename)
+        fuel_vehs = ['dieselcar', 'petrolcar', 'dieseltaxi', 'petroltaxi', 'dieselbus', 'petrolbus',
+                     'diesellgv', 'petrollgv', 'dieselartic', 'petrolartic', 'dieselrigid', 'petrolrigid',
+                     'dieselmotorcycle', 'petrolmotorcycle']
+        self.FC_record = {m: [] for m in fuel_vehs}
 
     def dealWithFile(self, filename):
         '''
@@ -82,5 +87,12 @@ def getFuelConsumption(self, date, vehicle, road, fuel):
         timePick = self.data.index.levels[0].asof(date)
 
         tidx = self.data.index.levels[0].asof(timePick)
+
+        fc_val = self.data.loc[tidx].loc[self.fuelMatch[fuel]].loc[self.vehicleMatch[vehicle]][self.roadMatch[road]]/1000.0
+        self.FC_record[fuel+vehicle].append(fc_val)
+        # pickle.dump(self.FC_record, open('/Users/suegrimmond/Documents/IQF/GQF-PYQGIS3/logfiles/FC_GQF.pkl', 'wb'))
+        # f_c_pkl.to_pickle(f'/Users/suegrimmond/Documents/IQF/GQF-PYQGIS3/logfiles/FC_{road}_{fuel}_{vehicle}.pkl')
+        # print('Saved FC pkl')
+        # print(self.data.loc[tidx].loc[self.fuelMatch[fuel]].loc[self.vehicleMatch[vehicle]][self.roadMatch[road]]/1000.0)
         return self.data.loc[tidx].loc[self.fuelMatch[fuel]].loc[self.vehicleMatch[vehicle]][self.roadMatch[road]]/1000.0 # Convert to kg/km
 

GQF/GreaterQF.py

@@ -269,7 +269,7 @@ def mostcalcs(self):
         [diurnalE.addIndElec(ie['profileFile']) for ie in self.ds.diurnIndElec]
         [diurnalE.addIndGas(ig['profileFile']) for ig in self.ds.diurnIndGas]
 
-        # Diurnal traffic patterns
+        # Diurnal traffic patterns %%% for whole run as one var?
         diurnalT = TransportProfiles(
             self.parameters.city, use_uk_holidays=self.parameters.useUKholidays, customHolidays=self.parameters.customHolidays)
         [diurnalT.addProfiles(tr['profileFile'])

GQF/TransportProfiles.py

@@ -9,6 +9,7 @@
 except:
     pass
 import pytz
+from copy import copy
 from .DataManagement.LookupLogger import LookupLogger
 from .DataManagement.TemporalProfileSampler import TemporalProfileSampler
 
@@ -115,7 +116,7 @@ def dealWithInputFile(self, file):
         if len(matches) != len(list(dl.keys()))-1:
             raise ValueError('Top row of transport diurnal profiles must contain each of: ' + str(expectedHeadings))
 
-        firstDataRow = 3
+        fDR = 3
         # Expect certain keywords
         if 'transporttype' not in dl.columns.tolist():
             raise ValueError('First column of row 1 must be \'TransportType\' in ' + file)
@@ -149,10 +150,16 @@ def dealWithInputFile(self, file):
 
         for transportType in expectedHeadings:
             # Normalize each week series (over the whole week) so it's a weighting factor
-            # dl.loc[firstDataRow:,transportType] = dl.loc[firstDataRow:,transportType].astype('float')/dl.loc[firstDataRow:,transportType].astype('float').mean()
-            dl.loc[firstDataRow:,transportType] = dl.loc[firstDataRow:,transportType].astype('float')/dl.loc[firstDataRow:,transportType].astype('float').mean()
+            # Should it not be normalised by dividing by the sum instead of the mean? then sums to 1, instead of around 1
+            # dl.loc[fDR:,transportType] = dl.loc[fDR:,transportType].astype('float')/dl.loc[fDR:,transportType].astype('float').mean()
+            # NOrmalising the transpotr profile between 0 and 1 for each day by dividing by the sum
+            for i in range(int(dl.loc[fDR:, transportType].shape[0] / 48)):
+                dl.loc[fDR+(i*48):fDR+(i+1)*48, transportType] = \
+                    dl.loc[fDR+(i*48):fDR+(i+1)*48, transportType].astype('float') / \
+                    dl.loc[fDR+(i*48):fDR+(i+1)*48, transportType].astype('float').sum()
             # Assign to the relevant element of this object
             subObj = getattr(self, transportType.lower())
             if subObj is None:
                 raise Exception('Programming error: The wrong transport type has been referenced')
-            subObj.addPeriod(startDate=sd, endDate=ed, dataSeries=dl.loc[firstDataRow:,transportType])
+            subObj.addPeriod(startDate=sd, endDate=ed, dataSeries=dl.loc[fDR:,transportType])
+        # dl.to_pickle('logfiles/meanedtransportProfile.pkl')

GQF_Inputs/smalltestarea/DataSources.nml

@@ -145,16 +145,16 @@
 
 &fuelConsumption
     ! File containing fuel consumption performance data for each vehicle type as standards change over the years
-    profileFiles = './GQF_Inputs/smalltestarea/fuelConsumption.csv'
+    profileFiles = './GQF_Inputs_Izzy/fuelConsumption.csv'
 /
 
 ! Temporal transport data
 &diurnalTraffic
     ! Diurnal cycles of transport flow for different vehicle types
-    profileFiles = './GQF_Inputs/smalltestarea/Transport.csv'
+    profileFiles = './GQF_Inputs_Izzy/Transport.csv'
 /
 
 ! Temporal metabolism data
 &diurnalMetabolism
-    profileFiles = './GQF_Inputs/smalltestarea/Metabolism.csv'
+    profileFiles = './GQF_Inputs_Izzy/Metabolism.csv'
 /

QGIS3-Standalone-brew.py

@@ -33,7 +33,7 @@
 # print(QgsApplication.showSettings())
 
 # set path to run GQF
-path_GQF = Path('/Users/sunt05/Dropbox/6.Repos/GQF-PYQGIS3')
+path_GQF = Path('/Users/sunt05/Dropbox/6.Repos/GQF-PYQGIS3')  # /Users/suegrimmond/Documents/IQF/GQF-PYQGIS3
 sys.path.append(path_GQF.as_posix())
 os.chdir(path_GQF)
 print(os.getcwd())

RunGQF.py

@@ -2,7 +2,7 @@
 import sys
 from pathlib import Path
 # set path to run GQF
-path_GQF = Path('/Users/sunt05/Dropbox/6.Repos/GQF-PYQGIS3')
+path_GQF = Path('/Users/suegrimmond/Documents/IQF/GQF-PYQGIS3')
 sys.path.append(path_GQF.as_posix())
 
 
@@ -32,27 +32,27 @@ def random_generator(size=6, chars=string.ascii_uppercase + string.digits):
 b['sensible_qf'] = 1
 b['latent_qf'] = 1
 b['wastewater_qf'] = 0
-b['start_dates'] = '2015-01-01'
-b['end_dates'] = '2015-01-02'
+b['start_dates'] = '2015-06-18'
+b['end_dates'] = '2015-06-19'
 config.loadFromDictionary(b)
 # config.saveNamelist(f'./SampleRun/history-runs/{rand_file}.nml')
 
 model = Model()
 
 # model.setParameters('./GQF_Inputs/1/Parameters.nml')
-model.setParameters('./GQF_Inputs/centralLondon/Parameters.nml')
+model.setParameters('./GQF_Inputs_Izzy/Parameters.nml')
 
 # model.setDataSources('./GQF_Inputs/1/DataSources.nml')
-model.setDataSources('./GQF_Inputs/centralLondon/DataSources.nml')
+model.setDataSources('./GQF_Inputs_Izzy/DataSources.nml')
 
 model.setConfig(config)
 
 model.setOutputDir(f'./SampleRun/{rand_file}/')
 
 model.processInputData()
+# model.setPreProcessedInputFolder(f'./SampleRun/88WPL5/DownscaledData/')
 model.setPreProcessedInputFolder(f'./SampleRun/{rand_file}/DownscaledData/')
 
+print("I'M HERE!")
 model.run()
-
-
-
+print('FINISHED!')