initial benefit cost check in

scripts/summarize/benefit_cost.py

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+import numpy as np
+import pandas as pd
+import time
+import h5py
+import math
+from summary_functions import *
+
+
+# where are your files?
+# this should be a run or config parameter
+model_dir = 'C:/soundcast615/'
+
+h5_base_file = 'outputs/daysim_outputs3.h5'
+h5_base_name = 'Base'
+h5_scen_file = 'outputs/daysim_outputs1.h5'
+h5_scen_name = 'Scenario'
+report_output_location = 'outputs/benefits_zone.csv'
+guidefile = 'scripts/summarize/CatVarDict.xlsx'
+zone_dim = 4050 #hard code should be in the config
+#output
+
+##################################
+
+# Construct file location names
+h5_base_file = model_dir + h5_base_file
+h5_scen_file =  model_dir + h5_scen_file
+guidefile = model_dir + guidefile
+report_output_location = model_dir + report_output_location
+
+
+#read in the data
+my_store_base = h5py.File(h5_base_file, "r+")
+my_store_scen = h5py.File(h5_scen_file, "r+")
+
+trips_base = my_store_base['Trip']
+trips_scen = my_store_scen['Trip']
+
+otaz_base = np.asarray(trips_base["otaz"])
+otaz_base = otaz_base.astype('int')
+
+dtaz_base = np.asarray(trips_base["dtaz"])
+dtaz_base = dtaz_base.astype('int')
+
+vot_base = np.asarray(trips_base["vot"])
+time_base = np.asarray(trips_base["travtime"])
+cost_base =vot_base*time_base
+
+otaz_scen = np.asarray(trips_scen["otaz"])
+otaz_scen = otaz_scen.astype('int')
+
+dtaz_scen = np.asarray(trips_scen["dtaz"])
+dtaz_scen = dtaz_base.astype('int')
+
+vot_scen = np.asarray(trips_scen["vot"])
+time_scen = np.asarray(trips_scen["travtime"])
+cost_scen =vot_scen*time_scen
+
+
+trip_base_matrix = np.zeros((zone_dim,zone_dim), np.float64)
+cost_base_matrix= np.zeros((zone_dim,zone_dim), np.float64)
+trip_scen_matrix = np.zeros((zone_dim,zone_dim), np.float64)
+cost_scen_matrix= np.zeros((zone_dim,zone_dim), np.float64)
+
+# Fill up the base matrices from the arrays of data because they will be faster to work with
+for trip in range (0, len(otaz_base)):
+    this_otaz = otaz_base[trip]
+    this_dtaz = dtaz_base[trip]
+    this_cost = cost_base[trip]
+    trip_base_matrix [this_otaz, this_dtaz] = trip_base_matrix[this_otaz, this_dtaz] + 1
+    cost_base_matrix [this_otaz, this_dtaz] = cost_base_matrix[this_otaz, this_dtaz] + this_cost
+
+# Fill up the scenario matrices
+for trip in range (0, len(otaz_scen)):
+    this_otaz = otaz_scen[trip]
+    this_dtaz = dtaz_scen[trip]
+    this_cost = cost_scen[trip]
+    trip_scen_matrix [this_otaz, this_dtaz] = trip_scen_matrix[this_otaz, this_dtaz] + 1
+    cost_scen_matrix [this_otaz, this_dtaz] = cost_scen_matrix[this_otaz, this_dtaz] + this_cost
+
+cost_diff = (cost_scen_matrix - cost_base_matrix)
+trips_total = (trip_scen_matrix + trip_base_matrix)/2
+
+benefit_matrix = cost_diff*trips_total
+
+taz_labels = np.array(range(1, zone_dim + 1))
+
+#benefits by origin:
+bens_by_origin = np.nansum(benefit_matrix , axis = 1, dtype=np.float64)
+
+#benefits by destination:
+bens_by_destination = np.nansum(benefit_matrix, axis = 0, dtype=np.float64)
+
+#combine arrays:
+all_colls = zip(taz_labels,bens_by_origin, bens_by_destination)
+
+#convert to dataframe
+results = pd.DataFrame(data=all_colls, columns=['taz','origin_benefit', 'destination_benefit'])
+
+results.to_csv(report_output_location)
+
+
+