School location

activitysim/defaults/misc.py

@@ -25,7 +25,7 @@ def data_dir():
     return '.'
 
 
-@sim.injectable(cache=True)
+@sim.injectable()
 def settings(configs_dir):
     with open(os.path.join(configs_dir, "configs", "settings.yaml")) as f:
         return yaml.load(f)

activitysim/defaults/models/__init__.py

@@ -8,5 +8,6 @@
 import non_mandatory_tour_frequency
 import mandatory_scheduling
 import non_mandatory_scheduling
+import school_location
 import workplace_location
 import mode

activitysim/defaults/models/auto_ownership.py

@@ -7,9 +7,6 @@
 with given characteristics owns
 """
 
-# this is the max number of cars allowable in the auto ownership model
-MAX_NUM_CARS = 5
-
 
 @sim.injectable()
 def auto_ownership_spec(configs_dir):

activitysim/defaults/models/school_location.py

@@ -0,0 +1,53 @@
+import os
+import pandas as pd
+import urbansim.sim.simulation as sim
+from activitysim import activitysim as asim
+
+
+"""
+The school location model predicts the zones in which various people will
+go to school.
+"""
+
+
+@sim.table()
+def school_location_spec(configs_dir):
+    f = os.path.join(configs_dir, 'configs', "school_location.csv")
+    return asim.read_model_spec(f).fillna(0)
+
+
+@sim.model()
+def school_location_simulate(set_random_seed,
+                             persons_merged,
+                             school_location_spec,
+                             skims,
+                             destination_size_terms):
+
+    choosers = persons_merged.to_frame()
+    alternatives = destination_size_terms.to_frame()
+    spec = school_location_spec.to_frame()
+
+    # set the keys for this lookup - in this case there is a TAZ in the choosers
+    # and a TAZ in the alternatives which get merged during interaction
+    skims.set_keys("TAZ", "TAZ_r")
+    # the skims will be available under the name "skims" for any @ expressions
+    locals_d = {"skims": skims}
+
+    choices_list = []
+    for school_type in ['university', 'highschool', 'gradeschool']:
+
+        locals_d['segment'] = school_type
+
+        choices, _ = asim.interaction_simulate(choosers[choosers["is_" +
+                                                                 school_type]],
+                                               alternatives,
+                                               spec[[school_type]],
+                                               skims=skims,
+                                               locals_d=locals_d)
+        choices_list.append(choices)
+
+    choices = pd.concat(choices_list)
+    choices = choices.reindex(persons_merged.index).fillna(-1).astype('int')
+
+    print "Describe of choices:\n", choices.describe()
+    sim.add_column("persons", "school_taz", choices)

activitysim/defaults/models/workplace_location.py

@@ -15,8 +15,6 @@ def workplace_location_spec(configs_dir):
     return asim.read_model_spec(f).fillna(0)
 
 
-# FIXME there are three school models that go along with this one which have
-# FIXME not been implemented yet
 @sim.model()
 def workplace_location_simulate(set_random_seed,
                                 persons_merged,
@@ -25,6 +23,7 @@ def workplace_location_simulate(set_random_seed,
                                 destination_size_terms):
 
     choosers = persons_merged.to_frame()
+    choosers = choosers[choosers.employed_cat.isin(["full", "part"])]
     alternatives = destination_size_terms.to_frame()
 
     # set the keys for this lookup - in this case there is a TAZ in the choosers
@@ -39,5 +38,7 @@ def workplace_location_simulate(set_random_seed,
                                            skims=skims,
                                            locals_d=locals_d)
 
+    choices = choices.reindex(persons_merged.index).fillna(-1).astype('int')
+
     print "Describe of choices:\n", choices.describe()
     sim.add_column("persons", "workplace_taz", choices)

activitysim/defaults/tables/persons.py

@@ -95,9 +95,6 @@ def female(persons):
 # count the number of mandatory tours for each person
 @sim.column("persons")
 def num_mand(persons):
-    # FIXME this is really because we ask for ALL columns in the persons data
-    # FIXME frame - urbansim actually only asks for the columns that are used by
-    # FIXME the model specs in play at that time
     if "mandatory_tour_frequency" not in persons.columns:
         return pd.Series(0, index=persons.index)
 
@@ -146,7 +143,7 @@ def student_is_employed(persons):
 @sim.column("persons")
 def nonstudent_to_school(persons):
     return (persons.ptype_cat.isin(['full', 'part', 'nonwork', 'retired']) &
-            persons.student_cat.isin(['high', 'college']))
+            persons.student_cat.isin(['grade_or_high', 'college']))
 
 
 @sim.column("persons")
@@ -162,14 +159,28 @@ def is_worker(persons):
 
 @sim.column("persons")
 def is_student(persons):
-    return persons.student_cat.isin(['high', 'college'])
+    return persons.student_cat.isin(['grade_or_high', 'college'])
+
+
+@sim.column("persons")
+def is_gradeschool(persons, settings):
+    return (persons.student_cat == "grade_or_high") & \
+           (persons.age <= settings['grade_school_max_age'])
+
+
+@sim.column("persons")
+def is_highschool(persons, settings):
+    return (persons.student_cat == "grade_or_high") & \
+           (persons.age > settings['grade_school_max_age'])
+
+
+@sim.column("persons")
+def is_university(persons):
+    return persons.student_cat == "university"
 
 
 @sim.column("persons")
 def workplace_taz(persons):
-    # FIXME this is really because we ask for ALL columns in the persons data
-    # FIXME frame - urbansim actually only asks for the columns that are used by
-    # FIXME the model specs in play at that time
     return pd.Series(1, persons.index)
 
 
@@ -181,8 +192,7 @@ def home_taz(households, persons):
 
 @sim.column("persons")
 def school_taz(persons):
-    # FIXME need to fix this after getting school lcm working
-    return persons.workplace_taz
+    return pd.Series(1, persons.index)
 
 
 # this use the distance skims to compute the raw distance to work from home

activitysim/defaults/test/configs/settings.yaml

@@ -7,6 +7,8 @@ rural_threshold: 6
 
 households_sample_size: 1000
 
+grade_school_max_age: 14
+
 county_map:
     San Francisco: 1
     San Mateo: 2

example/configs/destination_choice_size_terms.csv

@@ -4,8 +4,8 @@ segment,TOTHH,RETEMPN,FPSEMPN,HEREMPN,OTHEMPN,AGREMPN,MWTEMPN,AGE0519,HSENROLL,C
 "work, high",0,0.11,0.207,0.284,0.154,0.006,0.239,0,0,0,0
 "work, veryhigh",0,0.093,0.27,0.241,0.146,0.004,0.246,0,0,0,0
 university,0,0,0,0,0,0,0,0,0,0.592,0.408
-"school, grade",0,0,0,0,0,0,0,1,0,0,0
-"school, high",0,0,0,0,0,0,0,0,1,0,0
+gradeschool,0,0,0,0,0,0,0,1,0,0,0
+highschool,0,0,0,0,0,0,0,0,1,0,0
 "escort, kids",0,0.225,0,0.144,0,0,0,0.465,0.166,0,0
 "escort, no kids",0,0.225,0,0.144,0,0,0,0.465,0.166,0,0
 shopping,0,1,0,0,0,0,0,0,0,0,0

example/configs/school_location.csv

@@ -0,0 +1,9 @@
+Description,Expression,university,highschool,gradeschool
+"Distance, piecewise linear from 0 to 1 miles",@skims['DISTANCE'].clip(1),-3.2451,-0.9523,-1.6419
+"Distance, piecewise linear from 1 to 2 miles","@(skims['DISTANCE']-1).clip(0,1)",-2.7011,-0.5700,-0.5700
+"Distance, piecewise linear from 2 to 5 miles","@(skims['DISTANCE']-2).clip(0,3)",-0.5707,-0.5700,-0.5700
+"Distance, piecewise linear from 5 to 15 miles","@(skims['DISTANCE']-5).clip(0,10)",-0.5002,-0.1930,-0.2031
+"Distance, piecewise linear for 15+ miles",@(skims['DISTANCE']-15.0).clip(0),-0.0730,-0.1882,-0.0460
+Mode choice logsum,mode_choice_logsums,0.5358,0.5358,0.5358
+Size variable,@df[segment].apply(np.log1p),1.0000,1.0000,1.0000
+No attractions,@df[segment]==0,-999.0000,-999.0000,-999.0000

example/configs/settings.yaml

@@ -7,6 +7,8 @@ rural_threshold: 6
 
 households_sample_size: 1000
 
+grade_school_max_age: 14
+
 county_map:
     San Francisco: 1
     San Mateo: 2
@@ -25,8 +27,8 @@ employment_map:
     4: child
 
 student_map:
-    1: high
-    2: college
+    1: grade_or_high
+    2: university
     3: not
 
 person_type_map:

example/simulation.ipynb

@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:880950d73f4f9e461598c60051b1f421518aa82880440b1f17bdf1ae89f0bc48"
+  "signature": "sha256:8dd6ed7b0367358850955b8044ce5cd4396e31fd6b83bd444987c950b7c3696a"
  },
  "nbformat": 3,
  "nbformat_minor": 0,
@@ -22,6 +22,67 @@
      "outputs": [],
      "prompt_number": 1
     },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "sim.run([\"school_location_simulate\"])"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Running model 'school_location_simulate'\n",
+        "WARNING: Some columns have no variability:\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "['mode_choice_logsums']\n",
+        "WARNING: Some columns have no variability:\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "['mode_choice_logsums']\n",
+        "WARNING: Some columns have no variability:\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "['mode_choice_logsums']\n",
+        "Describe of choices:\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "count    2538.000000\n",
+        "mean      190.435776\n",
+        "std       389.915126\n",
+        "min        -1.000000\n",
+        "25%        -1.000000\n",
+        "50%        -1.000000\n",
+        "75%        69.000000\n",
+        "max      1450.000000\n",
+        "Name: TAZ, dtype: float64\n",
+        "Time to execute model 'school_location_simulate': 18.74s\n",
+        "Total time to execute: 18.74s\n"
+       ]
+      }
+     ],
+     "prompt_number": 2
+    },
     {
      "cell_type": "code",
      "collapsed": false,
@@ -51,17 +112,17 @@
        "output_type": "stream",
        "stream": "stdout",
        "text": [
-        "count    2546.000000\n",
-        "mean      775.958759\n",
-        "std       425.999370\n",
-        "min         1.000000\n",
-        "25%       418.000000\n",
-        "50%       776.500000\n",
-        "75%      1168.000000\n",
+        "count    2538.000000\n",
+        "mean      332.508668\n",
+        "std       473.007494\n",
+        "min        -1.000000\n",
+        "25%        -1.000000\n",
+        "50%        -1.000000\n",
+        "75%       651.000000\n",
         "max      1452.000000\n",
         "Name: TAZ, dtype: float64\n",
-        "Time to execute model 'workplace_location_simulate': 20.54s\n",
-        "Total time to execute: 20.54s\n"
+        "Time to execute model 'workplace_location_simulate': 10.24s\n",
+        "Total time to execute: 10.24s\n"
        ]
       }
      ],