Generalize sub-bound handling (#469)

Scripts/datahandling/zonedata.py

@@ -13,29 +13,30 @@ class ZoneData:
     def __init__(self, data_dir, zone_numbers):
         self._values = {}
         self.share = ShareChecker(self)
-        zone_numbers = numpy.array(zone_numbers)
+        all_zone_numbers = numpy.array(zone_numbers)
+        self.all_zone_numbers = all_zone_numbers
         surrounding = param.areas["surrounding"]
         peripheral = param.areas["peripheral"]
         external = param.areas["external"]
-        first_extra = numpy.searchsorted(zone_numbers, peripheral[1], "right")
-        self.zone_numbers = zone_numbers[:first_extra]
+        self.zone_numbers = all_zone_numbers[:all_zone_numbers.searchsorted(
+            peripheral[1], "right")]
         Zone.counter = 0
         self.zones = {number: Zone(number) for number in self.zone_numbers}
-        self.first_not_helsinki_zone = numpy.searchsorted(
-            self.zone_numbers, param.municipalities["Espoo"][0])
-        self.first_surrounding_zone = numpy.searchsorted(
-            self.zone_numbers, surrounding[0])
-        first_peripheral = numpy.searchsorted(self.zone_numbers, peripheral[0])
+        self.first_not_helsinki_zone = self.zone_numbers.searchsorted(
+            param.municipalities["Espoo"][0])
+        self.first_surrounding_zone = self.zone_numbers.searchsorted(
+            surrounding[0])
+        first_peripheral = self.zone_numbers.searchsorted(peripheral[0])
         self.first_peripheral_zone = first_peripheral
-        first_external = numpy.searchsorted(zone_numbers, external[0])
-        self.first_external_zone = first_external
-        external_zones = zone_numbers[first_external:]
         popdata = read_csv_file(data_dir, ".pop", self.zone_numbers, float)
         workdata = read_csv_file(data_dir, ".wrk", self.zone_numbers, float)
         schooldata = read_csv_file(data_dir, ".edu", self.zone_numbers, float)
         landdata = read_csv_file(data_dir, ".lnd", self.zone_numbers, float)
         parkdata = read_csv_file(data_dir, ".prk", self.zone_numbers, float)
-        self.externalgrowth = read_csv_file(data_dir, ".ext", external_zones, float)
+        self.externalgrowth = read_csv_file(
+            data_dir, ".ext",
+            all_zone_numbers[all_zone_numbers.searchsorted(external[0]):],
+            float)
         transit = read_csv_file(data_dir, ".tco")
         try:
             transit["fare"] = transit["fare"].astype(
@@ -69,8 +70,8 @@ def __init__(self, data_dir, zone_numbers):
                                         + self["share_age_65-99"])
         self.share["share_age_18-99"] = (self["share_age_7-99"]
                                          -self["share_age_7-17"])
-        self.share["share_female"] = pandas.Series(0.5, zone_numbers)
-        self.share["share_male"] = pandas.Series(0.5, zone_numbers)
+        self.share["share_female"] = pandas.Series(0.5, self.zone_numbers)
+        self.share["share_male"] = pandas.Series(0.5, self.zone_numbers)
         self.nr_zones = len(self.zone_numbers)
         self["population_density"] = pop / landdata["builtar"]
         wp = workdata["total"]

Scripts/datatypes/purpose.py

@@ -36,13 +36,14 @@ def __init__(self, specification, zone_data, resultdata=None):
         self.dest = specification["dest"]
         self.area = specification["area"]
         self.sources = []
-        zone_numbers = zone_data.zone_numbers
+        zone_numbers = zone_data.all_zone_numbers
         zone_intervals = param.purpose_areas[self.area]
         self.bounds = slice(*zone_numbers.searchsorted(
             [zone_intervals[0], zone_intervals[-1]]))
         sub_intervals = zone_numbers[self.bounds].searchsorted(zone_intervals)
         self.sub_bounds = [slice(sub_intervals[i-1], sub_intervals[i])
             for i in range(1, len(sub_intervals))]
+        self.sub_intervals = sub_intervals[1:]
         self.zone_data = zone_data
         self.resultdata = resultdata
         self.model = None

Scripts/datatypes/tour.py

@@ -227,10 +227,9 @@ def _get_cost(self, impedance, mtx_type):
             pass
         # scale transit costs from month to day
         if self.mode == "transit" and mtx_type == "cost":
-            idx = numpy.searchsorted(
-                [bounds.stop for bounds in self.purpose.sub_bounds],
+            i = self.purpose.sub_intervals.searchsorted(
                 self.position[0], side="right")
-            cost /= transit_trips_per_month[self.purpose.area][demand_type][idx]
+            cost /= transit_trips_per_month[self.purpose.area][demand_type][i]
         return cost
 
     def __str__(self):

Scripts/models/logit.py

@@ -379,10 +379,10 @@ def calc_individual_mode_prob(self, is_car_user, zone):
                 try:
                     mode_exps[mode] *= math.exp(b["car_users"])
                 except TypeError:
-                    if zone < self.zone_data.first_surrounding_zone:
-                        mode_exps[mode] *= math.exp(b["car_users"][0])
-                    else:
-                        mode_exps[mode] *= math.exp(b["car_users"][1])
+                    # Separate sub-region parameters
+                    i = self.purpose.sub_intervals.searchsorted(
+                        zone, side="right")
+                    mode_exps[mode] *= math.exp(b["car_users"][i])
             mode_expsum += mode_exps[mode]
         probs = numpy.empty(len(modes))
         for i, mode in enumerate(modes):
@@ -395,8 +395,8 @@ def calc_individual_mode_prob(self, is_car_user, zone):
             money_utility = 1 / b
         except TypeError:
             # Separate sub-region parameters
-            money_utility = (1 / b[0] if self.sub_bounds[0].stop < zone
-                else 1 / b[1])
+            i = self.purpose.sub_intervals.searchsorted(zone, side="right")
+            money_utility = 1 / b[i]
         money_utility /= self.mode_choice_param["car"]["log"]["logsum"]
         accessibility = -money_utility * logsum
         return probs, accessibility