diff --git a/activitysim/activitysim.py b/activitysim/activitysim.py
index 6148e20ab..846508dc1 100644
--- a/activitysim/activitysim.py
+++ b/activitysim/activitysim.py
@@ -14,13 +14,16 @@ def random_rows(df, n):
def read_model_spec(fname,
description_name="Description",
- expression_name="Expression"):
+ expression_name="Expression",
+ stack=True):
"""
Read in the excel file and reformat for machines
"""
cfg = pd.read_csv(fname)
# don't need description and set the expression to the index
- cfg = cfg.drop(description_name, axis=1).set_index(expression_name).stack()
+ cfg = cfg.drop(description_name, axis=1).set_index(expression_name)
+ if stack:
+ cfg = cfg.stack()
return cfg
@@ -93,7 +96,11 @@ def simple_simulate(choosers, alternatives, spec,
if mult_by_alt_col:
expr = "({}) * df.{}".format(expr[0][1:], expr[1])
else:
- expr = expr[0][1:]
+ if isinstance(expr, tuple):
+ expr = expr[0][1:]
+ else:
+ # it's already a string, but need to remove the "@"
+ expr = expr[1:]
try:
s = eval(expr)
except Exception as e:
@@ -103,7 +110,11 @@ def simple_simulate(choosers, alternatives, spec,
if mult_by_alt_col:
expr = "({}) * {}".format(*expr)
else:
- expr = expr[0]
+ if isinstance(expr, tuple):
+ expr = expr[0]
+ else:
+ # it's already a string, which is fine
+ pass
try:
s = df.eval(expr)
except Exception as e:
diff --git a/activitysim/defaults/datasources.py b/activitysim/defaults/datasources.py
index 50f0bd245..5d4d80067 100644
--- a/activitysim/defaults/datasources.py
+++ b/activitysim/defaults/datasources.py
@@ -17,7 +17,7 @@
pd.options.mode.chained_assignment = None
-@sim.injectable('settings', cache=True)
+@sim.injectable(cache=True)
def settings():
with open(os.path.join(misc.configs_dir(), "settings.yaml")) as f:
settings = yaml.load(f)
@@ -27,24 +27,24 @@ def settings():
return settings
-@sim.injectable('run_number')
+@sim.injectable()
def run_number():
return misc.get_run_number()
-@sim.injectable('uuid', cache=True)
-def uuid_hex():
+@sim.injectable(cache=True)
+def uuid():
return uuid.uuid4().hex
-@sim.injectable('store', cache=True)
-def hdfstore(settings):
+@sim.injectable(cache=True)
+def store(settings):
return pd.HDFStore(
os.path.join(misc.data_dir(), settings["store"]),
mode='r')
-@sim.injectable("scenario")
+@sim.injectable()
def scenario(settings):
return settings["scenario"]
diff --git a/activitysim/defaults/variables.py b/activitysim/defaults/variables.py
index c50a58bba..03b8af8b2 100644
--- a/activitysim/defaults/variables.py
+++ b/activitysim/defaults/variables.py
@@ -20,6 +20,11 @@ def income_segment(households):
labels=[1, 2, 3, 4])
+@sim.column("households")
+def non_workers(households, persons):
+ return persons.household_id.value_counts() - households.workers
+
+
@sim.column("households")
def drivers(households, persons):
# we assume that everyone 16 and older is a potential driver
diff --git a/example/README.md b/example/README.md
new file mode 100644
index 000000000..e6d289871
--- /dev/null
+++ b/example/README.md
@@ -0,0 +1,77 @@
+This is a list of items to double check before using in practice:
+
+* Make sure the units in things like distance_to_work match the walk thresholds
+ in the mandatory tour frequency spec. The original divided by 100. This is
+ true also of round trip auto to work and round trip auto to school.
+
+* There might be a few variables left off of some of the models. Look for
+`head` in reading of the spec files as this is meant to eliminate some of the
+ rows. Also can look for `#` to comment out variables in the spec.
+
+* Go back to the 3 school location choices, and run the models for the
+appropriate persons.
+
+* Probably needs code review of the variable definitions. How much of the
+variable definitions are shared between regions and how much unique? Age
+categories are shared? Income categories are unique?
+
+* This will be pretty easy to catch, but need to make sure the
+non_mandatory_tour model runs with Matt's changes to simple simulate that are
+ coming.
+
+
+
+A few overarching principles
+
+* A little discussion of "NOT so object oriented" - this is more like a
+database - data is in standard tables, NOT in objects... although the
+simulation framework is sort of like adding methods to objects
+
+* The implications of this are that most of the core code is pandas and thus
+the quality is controlled by the larger community. We are thankful that its
+quality is very high. Specifically, there's not so much code in activitysim
+"proper"
+
+* What it takes to add a new model
+ * define a new model
+ * define any new data sources necessary
+ * add any new assumptions in settings.yaml
+ * co-create the spec and any variables that are too complicated (or
+ reusable) for the spec
+ * run in notebook
+
+* Literally everything is really Python functions that compute something.
+Case study of `num_under16_not_at_school` to show the inter-dependencies.
+
+
+
+
+A few questions about "best practices"
+
+* What to put into the default data sources and variable specs and what to
+put in the example / client-specific stuff?
+
+* Want to split up injectables from variables from tables or all one big file
+ so it's easier to search?
+
+* How much variable computation to put in excel versus Python
+
+* There were some hard coded limits in the original csv - (area_type < 4 and
+distance_to_work < 3) - these are now just left in the csv spec. Why would
+this be different than (income_in_thousands > 50)? I've made an effort to
+not have such "magic numbers" in Python code. EDIT: I've now added an
+`isurban` variable which reads the area_type from the settings.yaml. So my
+convention so far is to leave hard-coded numbers out of the Python,
+but putting them in the CSV is ok. (Elizabeth: MAX_NUM_AUTOS exists now)
+
+* Want to name or number the person types in the spec files?
+
+* We're verging on the need to use YAML to configure the model runs - give
+the non_mandatory_tour model as an example. Is this too much code for a
+modeler to manage or is this just right as it makes the model execution
+transparent to the modeler?
+
+* Big issue: testing for client-specific code? It's harder because outputs are "data
+dependent." It's easier to take a small dataset and make sure it always runs.
+
+* Should I go back and put the Q&A I've had with Dave as issues on github to save for posterity?
diff --git a/example/configs/auto_ownership.csv b/example/configs/auto_ownership.csv
new file mode 100644
index 000000000..cc1dae874
--- /dev/null
+++ b/example/configs/auto_ownership.csv
@@ -0,0 +1,30 @@
+Description,Expression,cars0,cars1,cars2,cars3,cars4
+2 Adults (age 16+),drivers==2,,0,3.0773,3.1962,2.6616
+3 Adults (age 16+),drivers==3,,0,3.5401,5.5131,5.208
+4+ Adults (age 16+),drivers>3,,2.0107,6.3662,8.5148,9.5807
+Persons age 16-17,num_adolescents,,0,-0.881,-1.7313,-1.7313
+Persons age 18-24,num_college_age,,-0.4087,-1.0095,-1.0107,-1.0107
+Persons age 35-34,num_young_adults,,0,-0.4849,-0.8596,-0.8596
+Presence of children age 0-4,num_young_children>0,,0.3669,0.7627,0.7627,0.7627
+Presence of children age 5-17,(num_children+num_adolescents)>0,,0.0158,0.2936,0.4769,0.4769
+"Number of workers, capped at 3",@df.workers.clip(upper=3),,0,0.2936,0.6389,0.8797
+"Piecewise Linear household income, $0-30k","@df.income_in_thousands.clip(0, 30)",,0.0383,0.054,0.0559,0.0619
+"Piecewise Linear household income, $30-75k","@(df.income_in_thousands-30).clip(0, 45)",,0,0.0083,0.011,0.0147
+"Piecewise Linear household income, $75k+, capped at $125k","@(df.income_in_thousands-75).clip(0, 50)",,0,0.0083,0.011,0.0147
+"Density index up to 10, if 0 workers","@(df.workers==0)*df.density_index.clip(0, 10)",,0,-0.2028,-0.3654,-0.3654
+"Density index in excess of 10, if 0 workers",@(df.workers==0)*(df.density_index-10).clip(0),,-0.0152,-0.1106,-0.1766,-0.1766
+"Density index up to 10, if 1+ workers","@(df.workers>0)*df.density_index.clip(0, 10)",,0,-0.2028,-0.3654,-0.3654
+"Density index in excess of 10, if 1+ workers",@(df.workers>0)*(df.density_index-10).clip(0),,-0.0152,-0.1106,-0.1766,-0.1766
+Constants,@1,,1.1865,-1.0846,-3.2502,-5.313
+San Francisco county,county_name == 'San Francisco',,0.4259,0.4683,0.1458,0.1458
+Solano county,county_name == 'Solano',,-0.566,-0.4429,-0.2372,-0.2372
+Napa county,county_name == 'Napa',,-0.566,-0.4429,-0.2372,-0.2372
+Sonoma county,county_name == 'Sonoma',,-0.566,-0.4429,-0.2372,-0.2372
+Marin county,county_name == 'Marin',,-0.2434,0,0,0
+"Retail accessibility (0.66*PK + 0.34*OP) by auto, if 0 workers",(workers==0)*(0.66*AUTOPEAKRETAIL+0.34*AUTOOFFPEAKRETAIL),,0.0626,0.0626,0.0626,0.0626
+"Retail accessibility (0.66*PK + 0.34*OP) by auto, if 1+ workers",(workers>0)*(0.66*AUTOPEAKRETAIL+0.34*AUTOOFFPEAKRETAIL),,0.1646,0.1646,0.1646,0.1646
+"Retail accessibility (0.66*PK + 0.34*OP) by transit, if 0 workers",(workers==0)*(0.66*TRANSITPEAKRETAIL+0.34*TRANSITOFFPEAKRETAIL),,-0.3053,-0.3053,-0.3053,-0.3053
+"Retail accessibility (0.66*PK + 0.34*OP) by transit, if 1+ workers",(workers>0)*(0.66*TRANSITPEAKRETAIL+0.34*TRANSITOFFPEAKRETAIL),,-0.5117,-0.5117,-0.5117,-0.5117
+"Retail accessibility by non-motorized, if 0 workers",(workers==0)*NONMOTORIZEDRETAIL,,-0.03,-0.03,-0.03,-0.03
+"Retail accessibility by non-motorized, if 1+ workers",(workers>0)*NONMOTORIZEDRETAIL,,-0.03,-0.03,-0.03,-0.03
+"Auto time savings per worker (over walk or transit, max 120) to work",workTourAutoTimeSavings/workers,,0.4707,0.6142,0.5705,0.7693
diff --git a/example/configs/auto_ownership_coeffs.csv b/example/configs/auto_ownership_coeffs.csv
deleted file mode 100644
index 0fe176c11..000000000
--- a/example/configs/auto_ownership_coeffs.csv
+++ /dev/null
@@ -1 +0,0 @@
-Description,Expression,cars0,cars1,cars2,cars3,cars4
2 Adults (age 16+),drivers==2,,0,3.0773,3.1962,2.6616
3 Adults (age 16+),drivers==3,,0,3.5401,5.5131,5.208
4+ Adults (age 16+),drivers>3,,2.0107,6.3662,8.5148,9.5807
Persons age 16-17,num_adolescents,,0,-0.881,-1.7313,-1.7313
Persons age 18-24,num_college_age,,-0.4087,-1.0095,-1.0107,-1.0107
Persons age 35-34,num_young_adults,,0,-0.4849,-0.8596,-0.8596
Presence of children age 0-4,num_young_children>0,,0.3669,0.7627,0.7627,0.7627
Presence of children age 5-17,(num_children+num_adolescents)>0,,0.0158,0.2936,0.4769,0.4769
"Number of workers, capped at 3",@df.workers.clip(upper=3),,0,0.2936,0.6389,0.8797
"Piecewise Linear household income, $0-30k","@df.income_in_thousands.clip(0, 30)",,0.0383,0.054,0.0559,0.0619
"Piecewise Linear household income, $30-75k","@(df.income_in_thousands-30).clip(0, 45)",,0,0.0083,0.011,0.0147
"Piecewise Linear household income, $75k+, capped at $125k","@(df.income_in_thousands-75).clip(0, 50)",,0,0.0083,0.011,0.0147
"Density index up to 10, if 0 workers","@(df.workers==0)*df.density_index.clip(0, 10)",,0,-0.2028,-0.3654,-0.3654
"Density index in excess of 10, if 0 workers",@(df.workers==0)*(df.density_index-10).clip(0),,-0.0152,-0.1106,-0.1766,-0.1766
"Density index up to 10, if 1+ workers","@(df.workers>0)*df.density_index.clip(0, 10)",,0,-0.2028,-0.3654,-0.3654
"Density index in excess of 10, if 1+ workers",@(df.workers>0)*(df.density_index-10).clip(0),,-0.0152,-0.1106,-0.1766,-0.1766
Constants,@1,,1.1865,-1.0846,-3.2502,-5.313
San Francisco county,county_name == 'San Francisco',,0.4259,0.4683,0.1458,0.1458
Solano county,county_name == 'Solano',,-0.566,-0.4429,-0.2372,-0.2372
Napa county,county_name == 'Napa',,-0.566,-0.4429,-0.2372,-0.2372
Sonoma county,county_name == 'Sonoma',,-0.566,-0.4429,-0.2372,-0.2372
Marin county,county_name == 'Marin',,-0.2434,0,0,0
"Retail accessibility (0.66*PK + 0.34*OP) by auto, if 0 workers",(workers==0)*(0.66*AUTOPEAKRETAIL+0.34*AUTOOFFPEAKRETAIL),,0.0626,0.0626,0.0626,0.0626
"Retail accessibility (0.66*PK + 0.34*OP) by auto, if 1+ workers",(workers>0)*(0.66*AUTOPEAKRETAIL+0.34*AUTOOFFPEAKRETAIL),,0.1646,0.1646,0.1646,0.1646
"Retail accessibility (0.66*PK + 0.34*OP) by transit, if 0 workers",(workers==0)*(0.66*TRANSITPEAKRETAIL+0.34*TRANSITOFFPEAKRETAIL),,-0.3053,-0.3053,-0.3053,-0.3053
"Retail accessibility (0.66*PK + 0.34*OP) by transit, if 1+ workers",(workers>0)*(0.66*TRANSITPEAKRETAIL+0.34*TRANSITOFFPEAKRETAIL),,-0.5117,-0.5117,-0.5117,-0.5117
"Retail accessibility by non-motorized, if 0 workers",(workers==0)*NONMOTORIZEDRETAIL,,-0.03,-0.03,-0.03,-0.03
"Retail accessibility by non-motorized, if 1+ workers",(workers>0)*NONMOTORIZEDRETAIL,,-0.03,-0.03,-0.03,-0.03
"Auto time savings per worker (over walk or transit, max 120) to work",workTourAutoTimeSavings/workers,,0.4707,0.6142,0.5705,0.7693
\ No newline at end of file
diff --git a/example/configs/destination_choice.csv b/example/configs/destination_choice.csv
new file mode 100644
index 000000000..9f239eafd
--- /dev/null
+++ b/example/configs/destination_choice.csv
@@ -0,0 +1,10 @@
+Description,Expression,university,highschool,gradeschool,escortkids,escortnokids,shopping,eatout,othmaint,social,othdiscr,workbased
+Sample of alternatives correction factor,"min(dcSoaCorrections,60)",1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000
+"Distance, piecewise linear from 0 to 1 miles","if(distance<1.0,distance,1.0)",-3.2451,-0.9523,-1.6419,-0.1499,-0.1499,0.0000,-0.5609,0.0000,-0.7841,-0.1677,-0.7926
+"Distance, piecewise linear from 1 to 2 miles","if(distance<2.0,distance-1.0,1.0)",-2.7011,-0.5700,-0.5700,-0.1499,-0.1499,0.0000,-0.5609,0.0000,-0.7841,-0.1677,-0.7926
+"Distance, piecewise linear from 2 to 5 miles","if(distance<5.0,distance-2.0,3.0)",-0.5707,-0.5700,-0.5700,-0.8671,-0.8671,-0.5655,-0.3192,-0.6055,-0.3485,-0.4955,-0.5197
+"Distance, piecewise linear from 5 to 15 miles","if(distance<15.0,distance-5.0,10.0)",-0.5002,-0.1930,-0.2031,-0.2137,-0.2137,-0.1832,-0.1238,-0.1093,-0.1306,-0.1193,-0.2045
+"Distance, piecewise linear for 15+ miles",distance-15.0,-0.0730,-0.1882,-0.0460,-0.2137,-0.2137,-0.1832,-0.1238,-0.1093,-0.1306,-0.1193,-0.2045
+Mode choice logsum,mcLogsum,0.5358,0.5358,0.5358,0.6755,0.6755,0.6755,0.6755,0.6755,0.6755,0.6755,0.5136
+Size variable,sizeTerm,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000,1.0000
+No attractions,"if(sizeTerm==0,1,0)",-999.0000,-999.0000,-999.0000,-999.0000,-999.0000,-999.0000,-999.0000,-999.0000,-999.0000,-999.0000,-999.0000
\ No newline at end of file
diff --git a/example/configs/destination_choice_alternatives_sample.csv b/example/configs/destination_choice_alternatives_sample.csv
new file mode 100644
index 000000000..2a33dac44
--- /dev/null
+++ b/example/configs/destination_choice_alternatives_sample.csv
@@ -0,0 +1,9 @@
+Description,Expression,worklow,workmed,workhigh,workveryhigh,university,highschool,gradeschool,escortkids,escortnokids,shopping,eatout,othmaint,social,othdiscr,workbased
+"Distance, piecewise linear from 0 to 1 miles",@df.distance.clip(1),-0.8428,-0.8428,-0.8428,-0.8428,-3.2451,-0.9523,-1.6419,-0.1499,-0.1499,0,-0.5609,0,-0.7841,-0.1677,-0.7926
+"Distance, piecewise linear from 1 to 2 miles","@(df.distance-1).clip(0,1)",-0.3104,-0.3104,-0.3104,-0.3104,-2.7011,-0.57,-0.57,-0.1499,-0.1499,0,-0.5609,0,-0.7841,-0.1677,-0.7926
+"Distance, piecewise linear from 2 to 5 miles","@(df.distance-2).clip(0,3)",-0.3783,-0.3783,-0.3783,-0.3783,-0.5707,-0.57,-0.57,-0.8671,-0.8671,-0.5655,-0.3192,-0.6055,-0.3485,-0.4955,-0.5197
+"Distance, piecewise linear from 5 to 15 miles","@(df.distance-5).clip(0,10)",-0.1285,-0.1285,-0.1285,-0.1285,-0.5002,-0.193,-0.2031,-0.2137,-0.2137,-0.1832,-0.1238,-0.1093,-0.1306,-0.1193,-0.2045
+"Distance, piecewise linear for 15+ miles",@(df.distance-15.0).clip(0),-0.0917,-0.0917,-0.0917,-0.0917,-0.073,-0.1882,-0.046,-0.2137,-0.2137,-0.1832,-0.1238,-0.1093,-0.1306,-0.1193,-0.2045
+Size variable,@(df.income_segment==1)*df.size_low,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1
+No attractions,@(df.income_segment==1)*df.size_low == 0,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999,-999
+Destination taz is home taz,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0
\ No newline at end of file
diff --git a/example/configs/destination_choice_size_terms.csv b/example/configs/destination_choice_size_terms.csv
new file mode 100644
index 000000000..6ef694785
--- /dev/null
+++ b/example/configs/destination_choice_size_terms.csv
@@ -0,0 +1,16 @@
+purpose,segment,TOTHH,RETEMPN,FPSEMPN,HEREMPN,OTHEMPN,AGREMPN,MWTEMPN,AGE0519,HSENROLL,COLLFTE,COLLPTE
+work,low,0,0.129,0.193,0.383,0.12,0.01,0.164,0,0,0,0
+work,med,0,0.12,0.197,0.325,0.139,0.008,0.21,0,0,0,0
+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,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
+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,shopping,0,1,0,0,0,0,0,0,0,0,0
+eatOut,eatOut,0,0.742,0,0.258,0,0,0,0,0,0,0
+othMaint,othMaint,0,0.482,0,0.518,0,0,0,0,0,0,0
+social,social,0,0.522,0,0.478,0,0,0,0,0,0,0
+othDiscr,othDiscr,0.252,0.212,0,0.272,0.165,0,0,0,0.098,0,0
+atwork,atwork,0,0.742,0,0.258,0,0,0,0,0,0,0
diff --git a/example/configs/mandatory_tour_frequency.csv b/example/configs/mandatory_tour_frequency.csv
new file mode 100644
index 000000000..854505462
--- /dev/null
+++ b/example/configs/mandatory_tour_frequency.csv
@@ -0,0 +1,100 @@
+Description,Expression,work1,work2,school1,school2,work_and_school
+Full-time worker alternative-specific constants,ptype == 1,0,-3.3781,,,
+Part-time worker alternative-specific constants,ptype == 2,0,-3.0476,,,
+University student alternative-specific constants,ptype == 3,2.166,-1.3965,0,-3.7429,0.1073
+Non-working adult alternative-specific constants,ptype == 4,,,,,
+Retired alternative-specific constants,ptype == 5,,,,,
+Driving-age child alternative-specific constants,ptype == 6,,,0,-3.136,-4.4362
+Pre-driving age child who is in school alternative-specific constants,ptype == 7,,,0,-3.9703,
+Female - Full-time worker interaction,(ptype == 1) & female,0,-0.2255,0.1592,,-0.3442
+Female - Part-time worker interaction,(ptype == 2) & female,0,-0.2255,0.1592,,-0.3442
+Female - University student interaction,(ptype == 3) & female,0.1737,-0.2255,0.1592,0.114,-0.3442
+Female - Non-working adult interaction,(ptype == 4) & female,0,-0.2255,0.1592,,
+Female - Retired interaction,(ptype == 5) & female,0,-0.2255,0.1592,,
+Female - Driving-age child interaction,(ptype == 6) & female,0.1737,,0,0.114,-0.3442
+Female - Pre-driving age child who is in school interaction,(ptype == 7) & female,0.1737,,0,0.114,
+Under 35 - Full-time worker interaction,(ptype == 1) & (age <= 35),0,-0.1375,0.7218,,0.9761
+Under 35 - Part-time worker interaction,(ptype == 2) & (age <= 35),0,-0.1375,0.7218,,0.9761
+Under 35 - University student interaction,(ptype == 3) & (age <= 35),-0.4629,-0.1375,0,1.275,0.9761
+Under 35 - Non-working adult interaction,(ptype == 4) & (age <= 35),0,-0.1375,0.7218,,
+Can walk to work - Full-time worker interaction,(ptype == 1) & (distance_to_work < 3),,0.5268,,,
+Can walk to work - Part-time worker interaction,(ptype == 2) & (distance_to_work < 3),,0.5268,,,
+Can walk to work - University student interaction,(ptype == 3) & (distance_to_work < 3),,0.5268,,,
+Can walk to work - Non-working adult interaction,(ptype == 4) & (distance_to_work < 3),,0.5268,,,
+Can walk to work - Retired interaction,(ptype == 5) & (distance_to_work < 3),,0.5268,,,
+Can walk to school - University student interaction,(ptype == 3) & (distance_to_school < 3),,,,0.7114,
+Can walk to school - Driving-age child interaction,(ptype == 6) & (distance_to_school < 3),,,,0.7114,
+Can walk to school - Pre-driving age child who is in school interaction,(ptype == 7) & (distance_to_school < 3),,,,0.7114,
+Can walk to work or school - Full-time worker interaction,(ptype == 1) & (distance_to_work < 3 | distance_to_school < 3),,,,,0.1391
+Can walk to work or school - Part-time worker interaction,(ptype == 2) & (distance_to_work < 3 | distance_to_school < 3),,,,,0.1391
+Can walk to work or school - University student interaction,(ptype == 3) & (distance_to_work < 3 | distance_to_school < 3),,,,,0.1391
+Can walk to work or school - Driving-age child interaction,(ptype == 6) & (distance_to_work < 3 | distance_to_school < 3),,,,,0.1391
+Round trip auto time to work - Full-time worker interaction,(ptype == 1) * roundtrip_auto_time_to_work,,-0.0035,,,-0.0031
+Round trip auto time to work - Part-time worker interaction,(ptype == 2) * roundtrip_auto_time_to_work,,-0.0035,,,-0.0031
+Round trip auto time to work - University student interaction,(ptype == 3) * roundtrip_auto_time_to_work,,-0.0035,,,-0.0031
+Round trip auto time to work - Non-working adult interaction,(ptype == 4) * roundtrip_auto_time_to_work,,-0.0035,,,
+Round trip auto time to work - Retired,(ptype == 5) * roundtrip_auto_time_to_work,,-0.0035,,,
+Round trip auto time to school - University student interaction,(ptype == 3) * roundtrip_auto_time_to_school,,,,-0.0034,-0.0031
+Round trip auto time to school - Driving-age child interaction,(ptype == 6) * roundtrip_auto_time_to_school,,,,-0.0034,-0.0031
+Round trip auto time to school - Pre-driving age child who is in school interaction,(ptype == 7) * roundtrip_auto_time_to_school,,,,-0.0034,
+Student is employed - University student interaction,(ptype == 3) & student_is_employed,3.014,3.014,,,3.014
+Student is employed - Driving-age child interaction,(ptype == 6) & student_is_employed,3.014,3.014,,,3.014
+Non-student goes to school - Full-time worker interaction,(ptype == 1) & nonstudent_to_school,,,3.883,,3.883
+Non-student goes to school - Part-time worker interaction,(ptype == 2) & nonstudent_to_school,,,3.883,,3.883
+Non-student goes to school - Non-working adult interaction,(ptype == 4) & nonstudent_to_school,,,3.883,,
+Non-student goes to school - Retired interaction,(ptype == 5) & nonstudent_to_school,,,3.883,,
+No cars in household - Full-time worker interaction,(ptype == 1) & (auto_ownership == 0),,-1.306,,,-1.302
+No cars in household - Part-time worker interaction,(ptype == 2) & (auto_ownership == 0),,-1.306,,,-1.302
+No cars in household - University student interaction,(ptype == 3) & (auto_ownership == 0),,-1.306,,-1.413,-1.302
+No cars in household - Non-working adult interaction,(ptype == 4) & (auto_ownership == 0),,-1.306,,,
+No cars in household - Retired interaction,(ptype == 5) & (auto_ownership == 0),,-1.306,,,
+No cars in household - Driving-age student interaction,(ptype == 6) & (auto_ownership == 0),,,,-1.413,-1.302
+No cars in household - Pre-driving age child who is in school interaction,(ptype == 7) & (auto_ownership == 0),,,,-1.413,
+Fewer cars than drivers in household - University student interaction,(ptype == 3) & (auto_ownership < drivers),,,,-0.5759,
+Fewer cars than drivers in household - Driving-age student interaction,(ptype == 6) & (auto_ownership < drivers),,,,-0.5759,
+Fewer cars than drivers in household - Pre-driving age child who is in school interaction,(ptype == 7) & (auto_ownership < drivers),,,,-0.5759,
+Number of preschool children in household - Full-time worker interaction,(ptype == 1) * (num_young_children),0,-0.1478,-0.1335,,-0.1251
+Number of preschool children in household - Part-time worker interaction,(ptype == 2) * (num_young_children),0,-0.1478,-0.1335,,-0.1251
+Number of preschool children in household - University student interaction,(ptype == 3) * (num_young_children),0.2191,-0.1478,0,-0.5577,-0.1251
+Number of preschool children in household - Non-working adult interaction,(ptype == 4) * (num_young_children),0,-0.1478,-0.1335,,
+Number of preschool children in household - Retired interaction,(ptype == 5) * (num_young_children),0,-0.1478,-0.1335,,
+Number of preschool children in household - Driving-age student interaction,(ptype == 6) * (num_young_children),0.2191,,0,-0.5577,-0.1251
+Number of preschool children in household - Pre-driving age child who is in school interaction,(ptype == 7) * (num_young_children),0.2191,,0,-0.5577,
+Number of non-workers in the household - Full-time worker interaction,(ptype == 1) * non_workers,,,0.2574,,
+Number of non-workers in the household - Part-time worker interaction,(ptype == 2) * non_workers,,,0.2574,,
+Household income higher than $50k - Full-time worker interaction,(ptype == 1) & (income_in_thousands > 50),0,,0.0347,,0.0347
+Household income higher than $50k - Part-time worker interaction,(ptype == 2) & (income_in_thousands > 50),0,,0.0347,,0.0347
+Household income higher than $50k - University student interaction,(ptype == 3) & (income_in_thousands > 50),-0.0528,-0.0528,0,,-0.0528
+Household income higher than $50k - Non-working adult interaction,(ptype == 4) & (income_in_thousands > 50),0,,0.0347,,
+Household income higher than $50k - Retired interaction,(ptype == 5) & (income_in_thousands > 50),0,,0.0347,,
+Household income higher than $50k - Driving-age student interaction,(ptype == 6) & (income_in_thousands > 50),-0.0528,,0,,-0.0528
+Household income higher than $50k - Pre-driving age child who is in school interaction,(ptype == 7) & (income_in_thousands > 50),-0.0528,,0,,
+Non-family household - Full-time worker interaction,(ptype == 1) & non_family,0,,-0.25,,-0.25
+Non-family household - Part-time worker interaction,(ptype == 2) & non_family,0,,-0.25,,-0.25
+Non-family household - University student interaction,(ptype == 3) & non_family,-0.1792,-0.1792,0,,-0.1792
+Non-family household - Non-working adult interaction,(ptype == 4) & non_family,0,,-0.25,,
+Non-family household - Retired interaction,(ptype == 5) & non_family,0,,-0.25,,
+Non-family household - Driving-age student interaction,(ptype == 6) & non_family,-0.1792,,0,,-0.1792
+Non-family household - Pre-driving age child who is in school interaction,(ptype == 7) & non_family,-0.1792,,0,,
+Number of children under 16 not at school - Full-time worker interaction,(ptype == 1) * num_under16_not_at_school,,0.1804,,,-0.1955
+Number of children under 16 not at school - Part-time worker interaction,(ptype == 2) * num_under16_not_at_school,,0.1804,,,-0.1955
+Number of children under 16 not at school - University student interaction,(ptype == 3) * num_under16_not_at_school,,0.1804,,0.0866,-0.1955
+Number of children under 16 not at school - Non-working adult interaction,(ptype == 4) * num_under16_not_at_school,,0.1804,,,
+Number of children under 16 not at school - Retired,(ptype == 5) * num_under16_not_at_school,,0.1804,,,
+Number of children under 16 not at school - Driving-age student interaction,(ptype == 6) * num_under16_not_at_school,,,,0.0866,-0.1955
+Number of children under 16 not at school - Pre-driving age child who is in school interaction,(ptype == 7) * num_under16_not_at_school,,,,0.0866,
+Home is in urban area - Full-time worker interaction,(ptype == 1) & home_is_urban,0,0.2308,-0.1361,,-0.3509
+Home is in urban area - Part-time worker interaction,(ptype == 2) & home_is_urban,0,0.2308,-0.1361,,-0.3509
+Home is in urban area - University student interaction,(ptype == 3) & home_is_urban,-0.2831,0.2308,0,0.317,-0.3509
+Home is in urban area - Non-working adult interaction,(ptype == 4) & home_is_urban,0,0.238,-0.1361,,
+Home is in urban area - Retired interaction,(ptype == 5) & home_is_urban,0,0.2308,-0.1361,,
+Home is in urban area - Driving-age student interaction,(ptype == 6) & home_is_urban,-0.2831,,0,0.317,-0.3509
+Home is in urban area - Pre-driving age child who is in school interaction,(ptype == 7) & home_is_urban,-0.2831,,0,0.317,
+Unavailable: Full-time worker,ptype == 1,,,,-999,
+Unavailable: Part-time worker,ptype == 2,,,,-999,
+Unavailable: Non-working adult,ptype == 4,,,,-999,-999
+Unavailable: Retired,ptype == 5,,,,-999,-999
+Unavailable: Driving-age child,ptype == 6,-999,-999,,,
+Unavailable: Pre-driving age child who is in school,ptype == 7,,-999,,,-999
+Unavailable: Work tours for those with no usual work location,~(workplace_taz > -1),-999,-999,,,-999
+Unavailalbe: School tours for those with no usual school location,~(school_taz > -1),,,-999,-999,-999
diff --git a/example/configs/non_mandatory_tour_frequency.csv b/example/configs/non_mandatory_tour_frequency.csv
new file mode 100644
index 000000000..49499f514
--- /dev/null
+++ b/example/configs/non_mandatory_tour_frequency.csv
@@ -0,0 +1,205 @@
+Description,Expression,full,part,university,nonwork,retired,driving,school,preschool
+Total Number of Tours = 0 (No Prior Tours),(tot_tours == 0) & (num_mand == 0) & (num_joint_tours == 0),-999,-999,-999,-999,-999,-999,-999,-999
+Total Number of Tours = 0 (1 or more Prior Tours),(tot_tours == 0) & ((num_mand > 0) | (num_joint_tours > 0)),0,0,0,0,0,0,0,0
+Total Number of Tours = 1,tot_tours == 1,-7.3572,-7.6391,-6.2138,-8.9791,-8.5684,-7.1506,-7.1506,-5.759
+Total Number of Tours = 2,tot_tours == 2,-10.647,-10.4557,-8.908,-12.0248,-12.7416,-11.1214,-11.1214,-11.517
+Total Number of Tours = 3,tot_tours == 3,-13.5005,-14.0176,-12.3261,-14.8516,-15.0978,-13.175,-13.175,-17.276
+Total Number of Tours = 4,tot_tours == 4,-16.3965,-16.9717,-15.8114,-17.7037,-19.5439,-999,-999,-23.035
+Total Number of Tours = 5,tot_tours == 5,-19.6843,-999,-999,-999,-20.7897,-999,-999,-999
+Total Number of Tours = 6+,tot_tours > 5,-999,-999,-999,-999,-999,-999,-999,-999
+Number of Mandatory tours & tour frequency =0,num_mand*(tot_tours == 0),0,0,0,0,0,0,0,2.491
+Number of Mandatory tours & tour frequency =1,num_mand*(tot_tours == 1),0,-0.239,-0.1852,-0.6766,0,-0.234,-0.234,0.903
+Number of Mandatory tours & tour frequency =2,num_mand*(tot_tours == 2),-0.8887,-1.8208,-0.8753,-1.0518,-5.0196,-0.9231,-0.9231,0
+Number of Mandatory tours & tour frequency =3,num_mand*(tot_tours == 3),-2.3343,-2.5923,-1.6158,-1.0518,-5.0196,-6.5835,-6.5835,1.022
+Number of Mandatory tours & tour frequency =4,num_mand*(tot_tours == 4),-2.3343,-2.5923,-999,-999,-999,-999,-999,0.769
+Number of Mandatory tours & tour frequency = 5+,num_mand*(tot_tours > 4),-2.3343,-2.5923,-999,-999,-999,-999,-999,0
+Number of Joint tours & tour frequency =0,num_joint_tours*(tot_tours == 0),0,0,0,0,0,0,0,0
+Number of Joint tours & tour frequency =1,num_joint_tours*(tot_tours == 1),0,0,0,-0.1699,0,-0.2162,-0.2162,0
+Number of Joint tours & tour frequency =2,num_joint_tours*(tot_tours == 2),0,-1.1986,-0.3153,-0.4285,-0.95,-0.3587,-0.3587,0
+Number of Joint tours & tour frequency =3,num_joint_tours*(tot_tours == 3),0,-1.1986,-0.7351,-0.6551,-7.143,-4.2701,-4.2701,0
+Number of Joint tours & tour frequency =4,num_joint_tours*(tot_tours == 4),0,-1.1986,-999,-1.0411,-999,-999,-999,0
+Number of Joint tours & tour frequency = 5+,num_joint_tours*(tot_tours > 4),0,-999,-999,-1.0411,-999,-999,-999,0
+Number of Joint Shopping tours,num_shop_j,0,0,-0.713,-0.2391,-0.8072,0,0,0
+Number of Joint Maintenance tours,num_main_j,0,0,0,0,0,0,0,0
+Number of Joint Eating Out tours,num_eat_j,-0.5866,0,0,-0.7727,0,0,0,0
+Number of Joint Visit tours,num_visi_j,0,0,0,0,0,0,0,0
+Number of Joint Discretionary tours,num_disc_j,0,0,0.6713,0,0,0,0,0
+"Logged Maximum Residual Window, tour frequency =0",max_window*(tot_tours == 0),0,0,1.1858,0,0,0,0,0
+"Logged Maximum Residual Window, tour frequency =1",max_window*(tot_tours == 1),1.2562,1.5748,1.4842,1.7637,1.8357,1.3298,1.3298,0
+"Logged Maximum Residual Window, tour frequency =2",max_window*(tot_tours == 2),1.2868,2.0026,1.4842,1.7928,2.2707,1.3759,1.3759,0
+"Logged Maximum Residual Window, tour frequency =3",max_window*(tot_tours == 3),1.3993,2.0026,1.4842,1.7928,4.4023,3.2808,3.2808,0
+"Logged Maximum Residual Window, tour frequency =4",max_window*(tot_tours == 4),1.3993,2.0026,1.4842,1.7928,4.4023,3.2808,3.2808,0
+"Logged Maximum Residual Window, tour frequency =5+",max_window*(tot_tours > 4),1.3993,2.0026,1.4842,1.7928,4.4023,3.2808,3.2808,0
+Dummy for Mediumlow Income group (20K-50K) & tour frequency=1,(20 < income_in_thousands <= 50) & (tot_tours == 1),0.4981,0.5981,0,0.5709,0,0,0,0
+Dummy for Mediumlow Income group (20K-50K) & tour frequency=2,(20 < income_in_thousands <= 50) & (tot_tours == 2),0.8345,0.9178,0,0.8315,0,0,0,0
+Dummy for Mediumlow Income group (20K-50K) & tour frequency=3,(20 < income_in_thousands <= 50) & (tot_tours == 3),1.0213,1.7539,0,0.8315,0,0,0,0
+Dummy for Mediumlow Income group (20K-50K) & tour frequency=4,(20 < income_in_thousands <= 50) & (tot_tours == 4),1.0213,1.7539,0,0.8315,0,0,0,0
+Dummy for Mediumlow Income group (20K-50K) & tour frequency=5+,(20 < income_in_thousands <= 50) & (tot_tours > 4),1.0213,1.7539,0,0.8315,0,0,0,0
+Dummy for MediumHigh Income group (50K-100K) & tour frequency=1,(50 < income_in_thousands <= 100) & (tot_tours == 1),0.4981,0.8682,0.1109,0.7426,0,0,0,0
+Dummy for MediumHigh Income group (50K-100K) & tour frequency=2,(50 < income_in_thousands <= 100) & (tot_tours == 2),0.8345,1.5362,0.3914,0.8546,0,0,0,0
+Dummy for MediumHigh Income group (50K-100K) & tour frequency=3,(50 < income_in_thousands <= 100) & (tot_tours == 3),1.0213,1.9331,0.6137,1.0792,0,0,0,0
+Dummy for MediumHigh Income group (50K-100K) & tour frequency=4,(50 < income_in_thousands <= 100) & (tot_tours == 4),1.0213,1.9331,0.6137,1.0792,0,0,0,0
+Dummy for MediumHigh Income group (50K-100K) & tour frequency=5+,(50 < income_in_thousands <= 100) & (tot_tours > 4),1.0213,1.9331,0.6137,1.0792,0,0,0,0
+Dummy for High Income group (>100K) & tour frequency=1,(100 < income_in_thousands) & (tot_tours == 1),0.5189,0.8682,0.3986,1.0633,0,0,0,0
+Dummy for High Income group (>100K) & tour frequency=2,(100 < income_in_thousands) & (tot_tours == 2),1.1336,1.5362,0.8009,1.0633,0,0,0,0
+Dummy for High Income group (>100K) & tour frequency=3,(100 < income_in_thousands) & (tot_tours == 3),1.3899,1.9331,0.8254,1.7742,0,0,0,0
+Dummy for High Income group (>100K) & tour frequency=4,(100 < income_in_thousands) & (tot_tours == 4),1.3899,1.9331,0.8254,2.3941,0,0,0,0
+Dummy for High Income group (>100K) & tour frequency=5+,(100 < income_in_thousands) & (tot_tours > 4),1.3899,1.9331,0.8254,2.3941,0,0,0,0
+Dummy for Mediumlow Income group (20K-50K) & shopping tour,(20 < income_in_thousands <= 50) * shopping,0,0.4421,0.5693,0.7734,1.0949,0,0,0
+Dummy for Mediumhigh Income group (50K-100K) & shopping tour,(50 < income_in_thousands <= 100) * shopping,0,0.4421,0.5693,0.8906,1.0949,0.2443,0.2443,0
+Dummy for High Income group (>100K) & shopping tour,(100 < income_in_thousands) * shopping,0,0.7066,0.5693,0.9776,1.0949,0.2443,0.2443,0
+Dummy for Mediumlow Income group (20K-50K) & maintenance tour,(20 < income_in_thousands <= 50) * othmaint,0,0.6763,0,0,0.7648,0,0,0
+Dummy for Mediumhigh Income group (50K-100K) & maintenance tour,(50 < income_in_thousands <= 100) * othmaint,0,0.6763,0,0,0.7648,0.3982,0.3982,0
+Dummy for High Income group (>100K) & maintenance tour,(100 < income_in_thousands) * othmaint,0,0.6763,0,0,1.3795,0.3982,0.3982,0
+Dummy for Mediumlow Income group (20K-50K) & Eating out tour,(20 < income_in_thousands <= 50) * eatout,0,0,0,0.2766,0.9769,0,0,0
+Dummy for Mediumhigh Income group (50K-100K) & Eating out tour,(50 < income_in_thousands <= 100) * eatout,0.5581,0,-0.7207,0.4631,1.181,0.4916,0.4916,0
+Dummy for High Income group (>100K) & Eating out tour,(100 < income_in_thousands) * eatout,0.5581,0,-0.7207,0.7086,1.4842,0.4916,0.4916,0
+Dummy for Mediumlow Income group (20K-50K) & Discretionary tour,(20 < income_in_thousands <= 50) * othdiscr,0,0.296,0,0.1707,1.0095,0.9169,0.9169,0
+Dummy for Mediumhigh Income group (50K-100K) & Discretionary tour,(50 < income_in_thousands <= 100) * othdiscr,0.2565,0.296,0,0.5009,1.0095,1.405,1.405,0
+Dummy for High Income group (>100K) & Discretionary tour,(100 < income_in_thousands) * othdiscr,0.2565,0.296,0,0.8846,1.0095,2.327,2.327,0
+Dummy for Mediumlow Income group (20K-50K) & Visiting tour,(20 < income_in_thousands <= 50) * social,0,-0.6868,0,-0.267,0,0,0,0
+Dummy for Mediumhigh Income group (50K-100K) & Visiting tour,(50 < income_in_thousands <= 100) * social,-0.2423,-0.6868,-0.3694,-0.267,-0.4368,0.2858,0.2858,0
+Dummy for High Income group (>100K) & Visiting tour,(100 < income_in_thousands) * social,-0.2423,-0.6868,-0.3694,-0.9449,-0.5137,0.2858,0.2858,0
+Dummy for Female & tour frequency =1,female & (tot_tours == 1),-0.0766,0,0.0973,0.3902,-0.9348,0,0,0
+Dummy for Female & tour frequency =2,female & (tot_tours == 2),-0.1062,0,0.2361,0.5323,-1.3028,0,0,0
+Dummy for Female & tour frequency =3,female & (tot_tours == 3),-0.3274,0,1.9002,0.7452,-2.266,0,0,0
+Dummy for Female & tour frequency =4,female & (tot_tours == 4),-0.3274,0,1.9002,1.1294,-2.266,0,0,0
+Dummy for Female & tour frequency =5,female & (tot_tours == 5),-0.3274,0,1.9002,1.1294,-2.266,0,0,0
+Dummy for Female & Escorting Tour,female * escort,0.1824,0,0,0,0,0,0,0
+Dummy for Female & Shopping Tour,female * shopping,0,0.4524,0,0,0.9688,0,0,0
+Dummy for Female & Maintenance Tour,female * othmaint,0,0,0,-0.2464,0.7424,0,0,0
+Dummy for Female & EatingOut Tour,female * eatout,0,0,-0.6568,0,0,0,0,0
+Dummy for Female & Discretionary Tour,female * othdiscr,0,0.3072,-0.3266,0,0.4954,0,0,0
+Dummy for zero car ownership & tour frequency =1,no_cars & (tot_tours == 1),-0.3486,-0.5498,-0.581,-0.3623,0,-0.6369,-0.6369,0
+Dummy for zero car ownership & tour frequency =2,no_cars & (tot_tours == 2),-0.3486,-0.5498,-0.581,-1.272,0,-0.6369,-0.6369,0
+Dummy for zero car ownership & tour frequency =3,no_cars & (tot_tours == 3),-0.3486,-0.5498,-0.581,-1.9307,0,-0.6369,-0.6369,0
+Dummy for zero car ownership & tour frequency =4,no_cars & (tot_tours == 4),-0.3486,-0.5498,-0.581,-1.9307,0,-0.6369,-0.6369,0
+Dummy for zero car ownership & tour frequency =5+,no_cars & (tot_tours > 4),-0.3486,-0.5498,-0.581,-1.9307,0,-0.6369,-0.6369,0
+Dummy for Car Shortage vs Workers & tour frequency =1,(car_sufficiency < 0) & (tot_tours == 1),0,-0.5498,-0.581,-0.3623,0,-0.6369,-0.6369,0
+Dummy for Car Shortage vs Workers & tour frequency =2,(car_sufficiency < 0) & (tot_tours == 2),0,-0.5498,-0.581,-1.272,0,-0.6369,-0.6369,0
+Dummy for Car Shortage vs Workers & tour frequency =3,(car_sufficiency < 0) & (tot_tours == 3),0,-0.5498,-0.581,-1.9307,0,-0.6369,-0.6369,0
+Dummy for Car Shortage vs Workers & tour frequency =4,(car_sufficiency < 0) & (tot_tours == 4),0,-0.5498,-0.581,-1.9307,0,-0.6369,-0.6369,0
+Dummy for Car Shortage vs Workers & tour frequency =5+,(car_sufficiency < 0) & (tot_tours > 4),0,-0.5498,-0.581,-1.9307,0,-0.6369,-0.6369,0
+Dummy for Car Surplus vs Workers & tour frequency =1,(car_sufficiency > 0) & (tot_tours == 1),0.1304,0,0,0.7738,0.7965,0.2902,0.2902,0
+Dummy for Car Surplus vs Workers & tour frequency =2,(car_sufficiency > 0) & (tot_tours == 2),0.1304,0,0,0.7738,2.1302,2.0352,2.0352,0
+Dummy for Car Surplus vs Workers & tour frequency =3,(car_sufficiency > 0) & (tot_tours == 3),0.1304,0,0,0.7738,2.1302,2.0352,2.0352,0
+Dummy for Car Surplus vs Workers & tour frequency =4,(car_sufficiency > 0) & (tot_tours == 4),0.1304,0,0,0.7738,2.1302,2.0352,2.0352,0
+Dummy for Car Surplus vs Workers & tour frequency =5+,(car_sufficiency > 0) & (tot_tours > 4),0.1304,0,0,0.7738,2.1302,2.0352,2.0352,0
+Dummy for Presence of Non-Worker(other than modeled person) & tour frequency =1,has_non_worker & (tot_tours == 1),0,0,-0.8506,-0.3763,0.224,0,0,0
+Dummy for Presence of Non-Worker(other than modeled person) & tour frequency =2,has_non_worker & (tot_tours == 2),0,0,-1.1804,-0.719,0.2436,-0.6571,-0.6571,0
+Dummy for Presence of Non-Worker(other than modeled person) & tour frequency =3,has_non_worker & (tot_tours == 3),0,0,-1.1804,-1.0229,0.62,-1.4044,-1.4044,0
+Dummy for Presence of Non-Worker(other than modeled person) & tour frequency =4,has_non_worker & (tot_tours == 4),0,0,-1.1804,-1.0229,3.3742,-1.4044,-1.4044,0
+Dummy for Presence of Non-Worker(other than modeled person) & tour frequency =5,has_non_worker & (tot_tours == 5),0,0,-1.1804,-1.0229,3.3742,-1.4044,-1.4044,0
+Dummy for Presence of Retiree(other than modeled person) & tour frequency =1,has_retiree & (tot_tours == 1),0,0,0,-0.464,-0.4458,0,0,0
+Dummy for Presence of Retiree(other than modeled person) & tour frequency =2,has_retiree & (tot_tours == 2),0,0,0,-0.4795,-0.5315,0,0,0
+Dummy for Presence of Retiree(other than modeled person) & tour frequency =3,has_retiree & (tot_tours == 3),0,0,0,-0.4795,-0.5315,0,0,0
+Dummy for Presence of Retiree(other than modeled person) & tour frequency =4,has_retiree & (tot_tours == 4),0,0,0,-0.4795,-0.5315,0,0,0
+Dummy for Presence of Retiree(other than modeled person) & tour frequency =5,has_retiree & (tot_tours == 5),0,0,0,-0.4795,-0.5315,0,0,0
+Dummy for Presence of PreSchool Kid (other than modeled person) in Household & tour frequency =1,has_preschool_kid & (tot_tours == 1),0,-0.1559,-0.9961,-0.7161,0,0,0,0
+Dummy for Presence of PreSchool Kid (other than modeled person) in Household & tour frequency =2,has_preschool_kid & (tot_tours == 2),0,-0.5681,-1.9096,-0.7161,0,0,0,0
+Dummy for Presence of PreSchool Kid (other than modeled person) in Household & tour frequency =3,has_preschool_kid & (tot_tours == 3),0,-0.5681,-2.8469,-0.7161,0,0,0,0
+Dummy for Presence of PreSchool Kid (other than modeled person) in Household & tour frequency =4,has_preschool_kid & (tot_tours == 4),0,-0.5681,-2.8469,-0.7161,0,0,0,0
+Dummy for Presence of PreSchool Kid (other than modeled person) in Household & tour frequency =5,has_preschool_kid & (tot_tours == 5),0,-0.5681,-2.8469,-0.7161,0,0,0,0
+Dummy for Presence of Predriving School Kid (other than modeled person) in Household & tour frequency =1,has_school_kid & (tot_tours == 1),0,0,0,0.1486,0,-0.3219,-0.3219,0
+Dummy for Presence of Predriving School Kid (other than modeled person) in Household & tour frequency =2,has_school_kid & (tot_tours == 2),0,0,0,0.484,0,-1.0874,-1.0874,0
+Dummy for Presence of Predriving School Kid (other than modeled person) in Household & tour frequency =3,has_school_kid & (tot_tours == 3),0,0,0,0.484,0,-1.0874,-1.0874,0
+Dummy for Presence of Predriving School Kid (other than modeled person) in Household & tour frequency =4,has_school_kid & (tot_tours == 4),0,0,0,0.484,0,-1.0874,-1.0874,0
+Dummy for Presence of Predriving School Kid (other than modeled person) in Household & tour frequency =5,has_school_kid & (tot_tours == 5),0,0,0,0.484,0,-1.0874,-1.0874,0
+Dummy for Presence of Full time Worker (other than modeled person) & Escorting tour ,has_full_time * escort,0,0,0,0.3947,0,0,0,-0.893
+Dummy for Presence of Part time Worker (other than modeled person) & Escorting tour ,has_part_time * escort,0,0,-1.8213,-0.5861,0,0,0,0
+Dummy for Presence of Non-Worker (other than modeled person) & Escorting tour ,has_non_worker * escort,-0.4815,-0.5263,0,0,0,0,0,0.89
+Dummy for Presence of Retiree (other than modeled person) & Escorting tour ,has_retiree * escort,-0.808,-0.7516,0,0,0,0,0,0
+Dummy for Presence of University Student (other than modeled person) & Escorting tour ,has_university * escort,0,0,0,0,0,0,0,0
+Dummy for Presence of Driving School Kid (other than modeled person) & Escorting tour ,has_driving_kid * escort,0.3601,0.4164,0,0,0,0,0,0
+Dummy for Presence of Pre-Driving School Kid (other than modeled person) & Escorting tour ,has_school_kid * escort,1.3974,1.5795,0.9489,1.3773,1.4903,0,0,0
+Dummy for Presence of Pre-School Kid (other than modeled person) & Escorting tour ,has_preschool_kid * escort,0.6842,0.5414,2.1465,0.7194,0.5027,0,0,0
+Dummy for At home Pre-Driving School Kid & Escorting tour ,has_school_kid_at_home * escort,-0.2746,0,0,-1.148,0,0,0,0
+Dummy for At homef Pre-School Kid & Escorting tour ,has_preschool_kid_at_home * escort,-1.5675,0,0,-0.1373,0,0,0,0
+Dummy for Presence of Full time Worker (other than modeled person) & Shopping tour ,has_full_time * shopping,-0.3059,0,-0.7728,0,-0.3609,0,0,0
+Dummy for Presence of Part time Worker (other than modeled person) & Shopping tour ,has_part_time * shopping,-0.1541,0,-0.5199,0,0,0,0,1.155
+Dummy for Presence of Non-Worker (other than modeled person) & Shopping tour ,has_non_worker * shopping,-0.416,0,0,0,0,0,0,0.808
+Dummy for Presence of Retiree (other than modeled person) & Shopping tour ,has_retiree * shopping,0,0,0,0,0,0,0,0
+Dummy for Presence of University Student (other than modeled person) & Shopping tour ,has_university * shopping,0,0,0,0,0,0,0,0
+Dummy for Presence of Driving School Kid (other than modeled person) & Shopping tour ,has_driving_kid * shopping,0,0,0,0,0,0,0,0
+Dummy for Presence of Pre-Driving School Kid (other than modeled person) & Shopping tour ,has_school_kid * shopping,0,0,0,0,0,0,0,0
+Dummy for Presence of Pre-School Kid (other than modeled person) & Shopping tour ,has_preschool_kid * shopping,-0.208,0,1.3135,0,0,0,0,0
+Dummy for At home Pre-Driving School Kid & Shopping tour ,has_school_kid_at_home * shopping,0,0,0,0,0,0,0,0
+Dummy for At homef Pre-School Kid & Shopping tour ,has_preschool_kid_at_home * shopping,0,0,0,0,0,0,0,0
+Dummy for Presence of Full time Worker (other than modeled person) & Maintenance tour ,has_full_time * othmaint,-0.1685,-0.3131,0,0,0,0,0,0
+Dummy for Presence of Part time Worker (other than modeled person) & Maintenance tour ,has_part_time * othmaint,-0.1584,-0.5621,0,0,0,0,0,0
+Dummy for Presence of Non-Worker(other than modeled person) & Maintenance tour ,has_non_worker * othmaint,-0.3237,0,0,0,0,0,0,0
+Dummy for Presence of Retiree (other than modeled person) & Maintenance tour ,has_retiree * othmaint,0,0,0,0,0,0,0,0
+Dummy for Presence of University Student (other than modeled person) & Maintenance tour ,has_university * othmaint,0,0,0,0,0,0,0,0
+Dummy for Presence of Driving School Kid (other than modeled person) & Maintenance tour ,has_driving_kid * othmaint,0,0,0,0,0,0,0,0
+Dummy for Presence of Pre-Driving School Kid (other than modeled person) & Maintenance tour ,has_school_kid * othmaint,0,0,0.3863,0,0,0,0,0
+Dummy for Presence of Pre-School Kid (other than modeled person) & Maintenance tour ,has_preschool_kid * othmaint,0,0,0.9694,0,0,0,0,0
+Dummy for At home Pre-Driving School Kid & Maintenance tour ,has_school_kid_at_home * othmaint,0,0,0,0,0,0,0,0
+Dummy for At homef Pre-School Kid & Maintenance tour ,has_preschool_kid_at_home * othmaint,0,0,0,0,0,0,0,0
+Dummy for Presence of Full time Worker (other than modeled person) & Eating Out tour ,has_full_time * eatout,-0.3571,0,-0.5251,-0.4667,-0.788,0,0,0
+Dummy for Presence of Part time Worker (other than modeled person) & Eating Out tour ,has_part_time * eatout,0,0,-1.9795,0,-0.788,0,0,1.037
+Dummy for Presence of Non-Worker (other than modeled person) & Eating Out tour ,has_non_worker * eatout,-0.2014,-0.6545,0,-0.4976,-0.788,0,0,1.157
+Dummy for Presence of Retiree (other than modeled person) & Eating Out tour ,has_retiree * eatout,-0.5708,-1.389,0,-0.6911,-0.9282,0,0,0
+Dummy for Presence of University Student (other than modeled person) & Eating Out tour ,has_university * eatout,0,-1.4318,-0.6529,0,0,0,0,0
+Dummy for Presence of Driving School Kid (other than modeled person) & Eating Out tour ,has_driving_kid * eatout,0,0,0,0,0,-0.6377,-0.6377,0
+Dummy for Presence of Pre-Driving School Kid (other than modeled person) & Eating Out tour ,has_school_kid * eatout,0,0,0,0,0,-1.5698,-1.5698,0
+Dummy for Presence of Pre-School Kid (other than modeled person) & Eating Out tour ,has_preschool_kid * eatout,-0.4225,0,0,0,0,-0.2987,-0.2987,0
+Dummy for At home Pre-Driving School Kid & Eating Out tour ,has_school_kid_at_home * eatout,0,0,0,-0.3926,0,0,0,0
+Dummy for At homef Pre-School Kid & Eating Out tour ,has_preschool_kid_at_home * eatout,0,0,0,-0.3926,0,0,0,0
+Dummy for Presence of Full time Worker (other than modeled person) & Discretionary tour ,has_full_time * othdiscr,-0.667,0,-0.4833,-0.3545,-0.4835,0,0,0
+Dummy for Presence of Part time Worker (other than modeled person) & Discretionary tour ,has_part_time * othdiscr,-0.2102,0,0,-0.3545,0,0,0,0
+Dummy for Presence of Non-Worker (other than modeled person) & Discretionary tour ,has_non_worker * othdiscr,-0.4281,-1.0371,0.9781,0,-0.5603,0,0,0.791
+Dummy for Presence of Retiree (other than modeled person) & Discretionary tour ,has_retiree * othdiscr,-0.9104,0,0,0,0,0,0,0
+Dummy for Presence of University Student (other than modeled person) & Discretionary tour ,has_university * othdiscr,-0.8551,0,-0.6542,0,0,-1.2834,-1.2834,0
+Dummy for Presence of Driving School Kid (other than modeled person) & Discretionary tour ,has_driving_kid * othdiscr,-0.3963,0,0,0,0,-0.9202,-0.9202,0
+Dummy for Presence of Pre-Driving School Kid (other than modeled person) & Discretionary tour ,has_school_kid * othdiscr,-0.3959,0,0,0,0,0,0,0
+Dummy for Presence of Pre-School Kid (other than modeled person) & Discretionary tour ,has_preschool_kid * othdiscr,-0.5081,0,0,0,0,0,0,0
+Dummy for At home Pre-Driving School Kid & Discretionary tour ,has_school_kid_at_home * othdiscr,-0.4703,0,0,0,0,0,0,0
+Dummy for At homef Pre-School Kid & Discretionary tour ,has_preschool_kid_at_home * othdiscr,-0.4703,0,0,0,0,0,0,0
+Walk Access to Retail & Tour Frequency =1,NONMOTORIZEDRETAIL * (tot_tours == 1),0,0.0899,0,0.0713,0.0616,0,0,0
+Walk Access to Retail & Tour Frequency =2,NONMOTORIZEDRETAIL * (tot_tours == 2),0,0.1447,0,0.1256,0.0616,0,0,0
+Walk Access to Retail & Tour Frequency =3,NONMOTORIZEDRETAIL * (tot_tours == 3),0,0.3479,0,0.1508,0.0616,0,0,0
+Walk Access to Retail & Tour Frequency =4,NONMOTORIZEDRETAIL * (tot_tours == 4),0,0.3479,0,0.1508,0.0616,0,0,0
+Walk Access to Retail & Tour Frequency =5+,NONMOTORIZEDRETAIL * (tot_tours > 4),0,0.3479,0,0.1508,0.0616,0,0,0
+Transit Access to Retail & Tour Frequency =1,TRANSITOFFPEAKRETAIL * (tot_tours == 1),0.0226,0,0.0664,0,0,0,0,0
+Transit Access to Retail & Tour Frequency =2,TRANSITOFFPEAKRETAIL * (tot_tours == 2),0.0226,0,0.0664,0,0,0,0,0
+Transit Access to Retail & Tour Frequency =3,TRANSITOFFPEAKRETAIL * (tot_tours == 3),0.0226,0,0.0664,0,0,0,0,0
+Transit Access to Retail & Tour Frequency =4,TRANSITOFFPEAKRETAIL * (tot_tours == 4),0.0226,0,0.0664,0,0,0,0,0
+Transit Access to Retail & Tour Frequency =5+,TRANSITOFFPEAKRETAIL * (tot_tours > 4),0.0226,0,0.0664,0,0,0,0,0
+Auto Access to Retail & Tour Frequency =1,AUTOOFFPEAKRETAIL * (tot_tours == 1),0,0,0,0,0,0.1004,0.1004,0
+Auto Access to Retail & Tour Frequency =2,AUTOOFFPEAKRETAIL * (tot_tours == 2),0,0,0,0,0,0.1004,0.1004,0
+Auto Access to Retail & Tour Frequency =3,AUTOOFFPEAKRETAIL * (tot_tours == 3),0,0,0,0,0,0.1004,0.1004,0
+Auto Access to Retail & Tour Frequency =4,AUTOOFFPEAKRETAIL * (tot_tours == 4),0,0,0,0,0,0.1004,0.1004,0
+Auto Access to Retail & Tour Frequency =5+,AUTOOFFPEAKRETAIL * (tot_tours > 4),0,0,0,0,0,0.1004,0.1004,0
+Walk Access to Retail & Escorting ,NONMOTORIZEDRETAIL * escort,0.0451,0,0,0,0,0,0,0
+Transit Access to Retail & Escorting ,TRANSITOFFPEAKRETAIL * escort,0,0,0,0,0,0,0,0
+Auto Access to Retail & Escorting ,AUTOOFFPEAKRETAIL * escort,0,0,0,0,0,0,0,0
+Walk Access to Retail & Shopping ,NONMOTORIZEDRETAIL * shopping,0.033,0,0.0972,0.0598,0,0,0,0
+Transit Access to Retail & Shopping ,TRANSITOFFPEAKRETAIL * shopping,0,0,0,0,0,0,0,0
+Auto Access to Retail & Shopping ,AUTOOFFPEAKRETAIL * shopping,0.1067,0,0,0,0,0,0,0
+Walk Access to Retail & Maintenance ,NONMOTORIZEDRETAIL * othmaint,0,0,0,0,0,0,0,0
+Transit Access to Retail & Maintenance ,TRANSITOFFPEAKRETAIL * othmaint,0,0,0.0314,0,0,0,0,0
+Auto Access to Retail & Maintenance ,AUTOOFFPEAKRETAIL * othmaint,0.0749,0,0,0.0956,0,0,0,0
+Walk Access to Retail & Eating Out ,NONMOTORIZEDRETAIL * eatout,0.145,0,0,0,0,0,0,0
+Transit Access to Retail & Eating Out ,TRANSITOFFPEAKRETAIL * eatout,0,0,0,0,0,0,0,0
+Auto Access to Retail & Eating Out ,AUTOOFFPEAKRETAIL * eatout,0,0,0.1018,0,0,0,0,0
+Walk Access to Retail & Discretionary ,NONMOTORIZEDRETAIL * othdiscr,0.0567,0,0,0.0772,0,0,0,0
+Transit Access to Retail & Discretionary ,TRANSITOFFPEAKRETAIL * othdiscr,0,0,0,0,0,0,0,0
+Auto Access to Retail & Discretionary ,AUTOOFFPEAKRETAIL * othdiscr,0.0844,0,0.094,0,0,0,0,0
+Urban Areatype & Tour Frequency =1,home_is_urban & (tot_tours == 1),0,0,-1.1648,0,0,0,0,0
+Urban Areatype & Tour Frequency =2,home_is_urban & (tot_tours == 2),0,0,-2.3177,0,0,0,0,0
+Urban Areatype & Tour Frequency =3,home_is_urban & (tot_tours == 3),0,0,-2.5027,0,0,0,0,0
+Urban Areatype & Tour Frequency =4,home_is_urban & (tot_tours == 4),0,0,-2.5027,0,0,0,0,0
+Urban Areatype & Tour Frequency =5+,home_is_urban & (tot_tours > 4),0,0,-2.5027,0,0,0,0,0
+Urban Areatype & Escorting tour,home_is_urban * escort,-0.4316,-0.3929,0.8516,0,0,0,0,0
+Urban Areatype &Shopping tour,home_is_urban * shopping,0,0,0.533,0,0,0,0,0
+Urban Areatype & Maintenance tour,home_is_urban * othmaint,0,0,1.0316,0,0,1.0394,1.0394,0
+Urban Areatype & EatingOut tour,home_is_urban * eatout,0,0,0.68,0,0,0,0,0
+Urban Areatype & Discretionary tour,home_is_urban * othdiscr,0,0,0.9563,0,0,0,0,0
+1 Escort Tour Constant,escort == 1,0.0298,0.5272,1.7028,-0.0629,-0.3992,-0.4934,-0.4934,0.3622
+2+ Escort Tours Constant,escort >= 2,0.7402,1.5987,2.8379,0.9273,0.5175,1.4155,1.4155,2.2219
+1+ Shopping Tours Constant,shopping >= 1,0.4774,0.7569,1.8403,0.4683,0.5947,0.532,0.532,1.6919
+1+ Maintenance Tours Constant,othmaint >= 1,0.1202,0.5533,0.3348,-0.0653,0.1046,-0.4344,-0.4344,0.6788
+1+ Eating Out Tours Constant,eatout >= 1,0.0097,0.6914,2.0723,-0.1429,0.0245,-0.0242,-0.0242,0.9612
+1+ Visting Tours Constant,social >= 1,0.0522,0.1405,1.2172,-0.1272,0.2789,0.2367,0.2367,0.4424
+1+ Other Discretionary Tours Constant,othdiscr >= 1,0.7412,0.7989,1.3389,0.3334,0.4282,-0.2602,-0.2602,1.4935
+Dummy for 0-auto household & Escorting Tour,escort * no_cars,-2,-2,-2,-2,-2,-2,-2,-2
\ No newline at end of file
diff --git a/example/configs/non_mandatory_tour_frequency_alternatives.csv b/example/configs/non_mandatory_tour_frequency_alternatives.csv
new file mode 100644
index 000000000..1c0052f96
--- /dev/null
+++ b/example/configs/non_mandatory_tour_frequency_alternatives.csv
@@ -0,0 +1,97 @@
+escort,shopping,othmaint,othdiscr,eatout,social
+0,0,0,0,0,0
+0,0,0,1,0,0
+0,0,0,0,0,1
+0,0,0,1,0,1
+0,0,0,0,1,0
+0,0,0,1,1,0
+0,0,0,0,1,1
+0,0,0,1,1,1
+0,0,1,0,0,0
+0,0,1,1,0,0
+0,0,1,0,0,1
+0,0,1,1,0,1
+0,0,1,0,1,0
+0,0,1,1,1,0
+0,0,1,0,1,1
+0,0,1,1,1,1
+0,1,0,0,0,0
+0,1,0,1,0,0
+0,1,0,0,0,1
+0,1,0,1,0,1
+0,1,0,0,1,0
+0,1,0,1,1,0
+0,1,0,0,1,1
+0,1,0,1,1,1
+0,1,1,0,0,0
+0,1,1,1,0,0
+0,1,1,0,0,1
+0,1,1,1,0,1
+0,1,1,0,1,0
+0,1,1,1,1,0
+0,1,1,0,1,1
+0,1,1,1,1,1
+1,0,0,0,0,0
+1,0,0,1,0,0
+1,0,0,0,0,1
+1,0,0,1,0,1
+1,0,0,0,1,0
+1,0,0,1,1,0
+1,0,0,0,1,1
+1,0,0,1,1,1
+1,0,1,0,0,0
+1,0,1,1,0,0
+1,0,1,0,0,1
+1,0,1,1,0,1
+1,0,1,0,1,0
+1,0,1,1,1,0
+1,0,1,0,1,1
+1,0,1,1,1,1
+1,1,0,0,0,0
+1,1,0,1,0,0
+1,1,0,0,0,1
+1,1,0,1,0,1
+1,1,0,0,1,0
+1,1,0,1,1,0
+1,1,0,0,1,1
+1,1,0,1,1,1
+1,1,1,0,0,0
+1,1,1,1,0,0
+1,1,1,0,0,1
+1,1,1,1,0,1
+1,1,1,0,1,0
+1,1,1,1,1,0
+1,1,1,0,1,1
+1,1,1,1,1,1
+2,0,0,0,0,0
+2,0,0,1,0,0
+2,0,0,0,0,1
+2,0,0,1,0,1
+2,0,0,0,1,0
+2,0,0,1,1,0
+2,0,0,0,1,1
+2,0,0,1,1,1
+2,0,1,0,0,0
+2,0,1,1,0,0
+2,0,1,0,0,1
+2,0,1,1,0,1
+2,0,1,0,1,0
+2,0,1,1,1,0
+2,0,1,0,1,1
+2,0,1,1,1,1
+2,1,0,0,0,0
+2,1,0,1,0,0
+2,1,0,0,0,1
+2,1,0,1,0,1
+2,1,0,0,1,0
+2,1,0,1,1,0
+2,1,0,0,1,1
+2,1,0,1,1,1
+2,1,1,0,0,0
+2,1,1,1,0,0
+2,1,1,0,0,1
+2,1,1,1,0,1
+2,1,1,0,1,0
+2,1,1,1,1,0
+2,1,1,0,1,1
+2,1,1,1,1,1
diff --git a/example/configs/settings.yaml b/example/configs/settings.yaml
index 05cf2cdb0..c80b37fa8 100644
--- a/example/configs/settings.yaml
+++ b/example/configs/settings.yaml
@@ -1,6 +1,11 @@
store: mtc_asim.h5
-households_sample_size: 100000
+# area_types less than this are considered urban
+urban_threshold: 4
+cbd_threshold: 2
+rural_threshold: 6
+
+households_sample_size: 1000
county_map:
San Francisco: 1
@@ -12,3 +17,34 @@ county_map:
Napa: 7
Sonoma: 8
Marin: 9
+
+employment_map:
+ 1: "full"
+ 2: "part"
+ 3: "not"
+ 4: "child"
+
+student_map:
+ 1: "high"
+ 2: "college"
+ 3: "not"
+
+person_type_map:
+ 1: "full"
+ 2: "part"
+ 3: "university"
+ 4: "nonwork"
+ 5: "retired"
+ 6: "driving"
+ 7: "school"
+ 8: "preschool"
+
+household_type_map:
+ 0: "null"
+ 1: "family_married"
+ 2: "family_male"
+ 3: "family_female"
+ 4: "nonfamily_male_alone"
+ 5: "nonfamily_male_notalone"
+ 6: "nonfamily_female_alone"
+ 7: "nonfamily_female_notalone"
diff --git a/example/configs/tour_departure_and_duration.csv b/example/configs/tour_departure_and_duration.csv
new file mode 100644
index 000000000..e501d097b
--- /dev/null
+++ b/example/configs/tour_departure_and_duration.csv
@@ -0,0 +1 @@
+Description,Expression,work
Free-flow round trip auto time shift effects - departure,roundtrip_auto_time_to_work * start,0.00114
Free-flow round trip auto time shift effects - duration,roundtrip_auto_time_to_work * duration,0.00221
Part-time worker departure shift effects,(ptype == 2) * start,0.06736
Non-working adult duration shift effects,(ptype == 4) * duration,0.1207
University student departure shift effects,(ptype == 3) * start,0.05747
Household income departure shift effects,income_in_thousands * start,0.00021
Destination in CBD departure shift effects,workplace_in_cbd * start,0.04717
Destination in CBD duration shift effects,workplace_in_cbd * duration,0.08679
subsequent tour must start after previous tour ends,(start < end_of_previous_tour) & (tour_num == 2),-999
First of 2+ work tours departure shift effects,(tour_num == 1) * start,0.3033
First of 2+ work tours duration shift effects,(tour_num == 1) * duration,0.1861
Subsequent 2+ work departure tours shift effects,(tour_num == 2) * start,0.5381
Subsequent 2+ work duration tours shift effects,(tour_num == 2) * duration,0.3174
Household income -- Early departure interaction,(income_in_thousands > 100) & (start < 6),0.4854
Household income -- Late arrival interaction,(income_in_thousands > 100) & (end > 22),0.3839
Destination in CBD -- Early departure interaction,workplace_in_cbd & (start < 6),0.4566
Destination in CBD -- Late arrival interaction,workplace_in_cbd & (end > 22),0.2334
Rural household -- Early departure interaction,home_is_rural & (start < 6),0.4039
Rural household -- Late arrival interaction,home_is_rural & (end > 22),0.3451
Full-time worker -- duration < 9 hours interaction,(ptype == 1) & (duration < 9),1.257
Full-time worker -- 10 to 12 departure interaction,(ptype == 1) & (start > 9) & (start < 13),0.5182
Part-time worker -- 13 to 15 arrival interaction,(ptype == 2) & (end > 12) & (end < 16),0.5433
First of 2+ work tours- duration<8 hrs,(tour_num == 1) & (duration < 8),1.98
Subsequent of 2+ work tours- duration<8 hrs,(tour_num == 2) & (duration < 8),2.582
Work+school tours by worker- duration<8 hrs,(mandatory_tour_frequency == 'work_and_school') & is_worker & (duration < 8),0.9126
School+work tours by student- duration<8 hrs,(mandatory_tour_frequency == 'work_and_school') & is_student & (duration < 8),2.582
Mode Choice Logsum,mode_choice_logsum,1.027
Previously-scheduled tour ends in this departure hour,prev_tour_end == start,0.8935
Previously-scheduled tour begins in this arrival hour,prev_tour_begin == end,1.334
Adjacent window exists before this departure hour - first tour interaction,@@adjWindowBeforeThisHourAlt,0.1771
Adjacent window exists afetr this arrival hour - first tour interaction,@@adjWindowAfterThisHourAlt,0.3627
Adjacent window exists before this departure hour - second+ tour interaction,@@adjWindowBeforeThisHourAlt,0.2123
Adjacent window exists after this arrival hour - second+ tour interaction,@@adjWindowAfterThisHourAlt,0.1012
Remaining work/school tours to be scheduled / number of unscheduled hours,1 / @@remainingHoursAvailableAlt,18.68
Departure Constants -- Early (up to 5),start < 6,0.95273
Departure Constants -- AM peak 1 (6),start == 6,0.61618
Departure Constants -- AM peak 2 (7),start == 7,0
Departure Constants -- AM peak 3 (8),start == 8,0.25471
Departure Constants -- AM peak 4 (9),start == 9,1.25135
Departure Constants -- Midday 1 (10 to 12),(start > 9) & (start < 13),1.70587
Departure Constants -- Midday 2 (13 to 15),(start > 12) & (start < 16),1.69357
Departure Constants -- PM peak (16 to 18),(start > 15) & (start < 19),1.43999
Departure Constants -- Evening (19 to 21),(start > 18) & (start < 22),1.61051
Departure Constants -- Late (22 and later),start > 21,2.88342
Arrival Constants -- Early (up to 6),end < 7,0
Arrival Constants -- AM peak (7 to 9),(end > 6) & (end < 10),1.85452
Arrival Constants -- Midday 1 (10 to 12),(end > 9) & (end < 13),0.49597
Arrival Constants -- Midday 2 (13 to 14),(end > 12) & (end < 15),0.37855
Arrival Constants -- PM peak 1 (15),end == 15,0
Arrival Constants -- PM peak 2 (16),end == 16,0.27608
Arrival Constants -- PM peak 3 (17),end == 17,0.69959
Arrival Constants -- PM peak 4 (18),end == 18,0.79929
Arrival Constants -- Evening (19 to 21),(end > 18) & (end < 22),0.10357
Arrival Constants -- Late (22 and later),end > 21,0.96596
Duration Constants -- 0 to 2 hours,duration < 3,2.52827
Duration Constants -- 3 to 4 hours,(duration > 2) & (duration < 5),0.91897
Duration Constants -- 5 to 6 hours,(duration > 4) & (duration < 7),0.71855
Duration Constants -- 7 to 8 hours,(duration > 6) & (duration < 9),0.13962
Duration Constants -- 9 hours,duration == 9,0.05571
Duration Constants -- 10 hours,duration == 10,0
Duration Constants -- 11 hours,duration == 11,0.3478
Duration Constants -- 12 to 13 hours,(duration > 11) & (duration < 14),1.00822
Duration Constants -- 14 to 18 hours,(duration > 13) & (duration < 19),1.70186
\ No newline at end of file
diff --git a/example/configs/tour_departure_and_duration_alternatives.csv b/example/configs/tour_departure_and_duration_alternatives.csv
new file mode 100644
index 000000000..05f02b796
--- /dev/null
+++ b/example/configs/tour_departure_and_duration_alternatives.csv
@@ -0,0 +1,191 @@
+start,end
+5,5
+5,6
+5,7
+5,8
+5,9
+5,10
+5,11
+5,12
+5,13
+5,14
+5,15
+5,16
+5,17
+5,18
+5,19
+5,20
+5,21
+5,22
+5,23
+6,6
+6,7
+6,8
+6,9
+6,10
+6,11
+6,12
+6,13
+6,14
+6,15
+6,16
+6,17
+6,18
+6,19
+6,20
+6,21
+6,22
+6,23
+7,7
+7,8
+7,9
+7,10
+7,11
+7,12
+7,13
+7,14
+7,15
+7,16
+7,17
+7,18
+7,19
+7,20
+7,21
+7,22
+7,23
+8,8
+8,9
+8,10
+8,11
+8,12
+8,13
+8,14
+8,15
+8,16
+8,17
+8,18
+8,19
+8,20
+8,21
+8,22
+8,23
+9,9
+9,10
+9,11
+9,12
+9,13
+9,14
+9,15
+9,16
+9,17
+9,18
+9,19
+9,20
+9,21
+9,22
+9,23
+10,10
+10,11
+10,12
+10,13
+10,14
+10,15
+10,16
+10,17
+10,18
+10,19
+10,20
+10,21
+10,22
+10,23
+11,11
+11,12
+11,13
+11,14
+11,15
+11,16
+11,17
+11,18
+11,19
+11,20
+11,21
+11,22
+11,23
+12,12
+12,13
+12,14
+12,15
+12,16
+12,17
+12,18
+12,19
+12,20
+12,21
+12,22
+12,23
+13,13
+13,14
+13,15
+13,16
+13,17
+13,18
+13,19
+13,20
+13,21
+13,22
+13,23
+14,14
+14,15
+14,16
+14,17
+14,18
+14,19
+14,20
+14,21
+14,22
+14,23
+15,15
+15,16
+15,17
+15,18
+15,19
+15,20
+15,21
+15,22
+15,23
+16,16
+16,17
+16,18
+16,19
+16,20
+16,21
+16,22
+16,23
+17,17
+17,18
+17,19
+17,20
+17,21
+17,22
+17,23
+18,18
+18,19
+18,20
+18,21
+18,22
+18,23
+19,19
+19,20
+19,21
+19,22
+19,23
+20,20
+20,21
+20,22
+20,23
+21,21
+21,22
+21,23
+22,22
+22,23
+23,23
\ No newline at end of file
diff --git a/example/configs/workplace_location.csv b/example/configs/workplace_location.csv
index af7b3ce91..0adf1b611 100644
--- a/example/configs/workplace_location.csv
+++ b/example/configs/workplace_location.csv
@@ -1 +1,17 @@
-Description,Expression,Alt
"Distance, piecewise linear from 0 to 1 miles",@df.distance.clip(1),-0.8428
"Distance, piecewise linear from 1 to 2 miles","@(df.distance-1).clip(0,1)",-0.3104
"Distance, piecewise linear from 2 to 5 miles","@(df.distance-2).clip(0,3)",-0.3783
"Distance, piecewise linear from 5 to 15 miles","@(df.distance-5).clip(0,10)",-0.1285
"Distance, piecewise linear for 15+ miles",@(df.distance-15.0).clip(0),-0.0917
"Distance 0 to 5 mi, high and very high income",@(df.income_segment>=3)*df.distance.clip(upper=5),0.15
"Distance 5+ mi, high and very high income",@(df.income_segment>=3)*(df.distance-5).clip(0),0.02
"Size variable full-time worker, low income",@(df.income_segment==1)*df.size_low,1
"Size variable full-time worker, medium income",@(df.income_segment==2)*df.size_med,1
"Size variable full-time worker, high income",@(df.income_segment==3)*df.size_high,1
"Size variable full-time worker, very high income",@(df.income_segment==4)*df.size_veryhigh,1
"No attractions full-time worker, low income",@(df.income_segment==1)&(df.size_low==0),-999
"No attractions full-time worker, medium income",@(df.income_segment==2)&(df.size_med==0),-999
"No attractions full-time worker, high income",@(df.income_segment==3)&(df.size_high==0),-999
"No attractions full-time worker, very high income",@(df.income_segment==4)&(df.size_veryhigh==0),-999
Mode choice logsum,mcLogsum,0.3
\ No newline at end of file
+Description,Expression,Alt
+"Distance, piecewise linear from 0 to 1 miles",@df.distance.clip(1),-0.8428
+"Distance, piecewise linear from 1 to 2 miles","@(df.distance-1).clip(0,1)",-0.3104
+"Distance, piecewise linear from 2 to 5 miles","@(df.distance-2).clip(0,3)",-0.3783
+"Distance, piecewise linear from 5 to 15 miles","@(df.distance-5).clip(0,10)",-0.1285
+"Distance, piecewise linear for 15+ miles",@(df.distance-15.0).clip(0),-0.0917
+"Distance 0 to 5 mi, high and very high income",@(df.income_segment>=3)*df.distance.clip(upper=5),0.15
+"Distance 5+ mi, high and very high income",@(df.income_segment>=3)*(df.distance-5).clip(0),0.02
+"Size variable full-time worker, low income",@(df.income_segment==1)*df.size_low,1
+"Size variable full-time worker, medium income",@(df.income_segment==2)*df.size_med,1
+"Size variable full-time worker, high income",@(df.income_segment==3)*df.size_high,1
+"Size variable full-time worker, very high income",@(df.income_segment==4)*df.size_veryhigh,1
+"No attractions full-time worker, low income",@(df.income_segment==1)&(df.size_low==0),-999
+"No attractions full-time worker, medium income",@(df.income_segment==2)&(df.size_med==0),-999
+"No attractions full-time worker, high income",@(df.income_segment==3)&(df.size_high==0),-999
+"No attractions full-time worker, very high income",@(df.income_segment==4)&(df.size_veryhigh==0),-999
+Mode choice logsum,#mcLogsum,0.3
diff --git a/example/configs/workplace_location_size_terms.csv b/example/configs/workplace_location_size_terms.csv
deleted file mode 100644
index f9b7da433..000000000
--- a/example/configs/workplace_location_size_terms.csv
+++ /dev/null
@@ -1 +0,0 @@
-purpose,segment,TOTHH,RETEMPN,FPSEMPN,HEREMPN,OTHEMPN,AGREMPN,MWTEMPN,AGE0519,HSENROLL,COLLFTE,COLLPTE
work,low,0,0.129,0.193,0.383,0.12,0.01,0.164,0,0,0,0
work,med,0,0.12,0.197,0.325,0.139,0.008,0.21,0,0,0,0
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,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
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,shopping,0,1,0,0,0,0,0,0,0,0,0
eatOut,eatOut,0,0.742,0,0.258,0,0,0,0,0,0,0
othMaint,othMaint,0,0.482,0,0.518,0,0,0,0,0,0,0
social,social,0,0.522,0,0.478,0,0,0,0,0,0,0
othDiscr,othDiscr,0.252,0.212,0,0.272,0.165,0,0,0,0.098,0,0
atwork,atwork,0,0.742,0,0.258,0,0,0,0,0,0,0
\ No newline at end of file
diff --git a/example/models.py b/example/models.py
index 941d9973c..840212d2b 100644
--- a/example/models.py
+++ b/example/models.py
@@ -1,4 +1,5 @@
import urbansim.sim.simulation as sim
+import urbansim.utils.misc as usim_misc
import os
from activitysim import activitysim as asim
import openmatrix as omx
@@ -7,47 +8,154 @@
import pandas as pd
+# this is the max number of cars allowable in the auto ownership model
+MAX_NUM_CARS = 5
+
+
+"""
+This part of this file is currently creating small tables to serve as
+alternatives in the various models
+"""
+
+
@sim.table()
def auto_alts():
- return asim.identity_matrix(["cars%d" % i for i in range(5)])
+ return asim.identity_matrix(["cars%d" % i for i in range(MAX_NUM_CARS)])
+@sim.table()
+def mandatory_tour_frequency_alts():
+ return asim.identity_matrix(["work1", "work2", "school1", "school2",
+ "work_and_school"])
+
+
+# these are the alternatives for the workplace choice
@sim.table()
def zones():
# I grant this is a weird idiom but it helps to name the index
return pd.DataFrame({"TAZ": np.arange(1454)+1}).set_index("TAZ")
+@sim.table()
+def non_mandatory_tour_frequency_alts():
+ f = os.path.join("configs",
+ "non_mandatory_tour_frequency_alternatives.csv")
+ return pd.read_csv(f)
+
+
+@sim.column("non_mandatory_tour_frequency_alts")
+def tot_tours(non_mandatory_tour_frequency_alts):
+ # this assumes that the alt dataframe is only counts of trip types
+ return non_mandatory_tour_frequency_alts.local.sum(axis=1)
+
+
+@sim.table()
+def tour_departure_and_duration_alts():
+ # right now this file just contains the start and end hour
+ f = os.path.join("configs",
+ "tour_departure_and_duration_alternatives.csv")
+ return pd.read_csv(f)
+
+
+# used to have duration in the actual alternative csv file,
+# but this is probably better as a computed column
+@sim.column("tour_departure_and_duration_alts")
+def duration(tour_departure_and_duration_alts):
+ return tour_departure_and_duration_alts.end - \
+ tour_departure_and_duration_alts.start
+
+
+"""
+Read in the omx files and create the skim objects
+"""
+
+
@sim.injectable()
def nonmotskm_omx():
- return omx.openFile('data/nonmotskm.omx')
+ return omx.openFile(os.path.join('data', "nonmotskm.omx"))
+
+
+@sim.injectable()
+def distance_skim(nonmotskm_omx):
+ return skim.Skim(nonmotskm_omx['DIST'], offset=-1)
+
+
+@sim.injectable()
+def sovam_skim(nonmotskm_omx):
+ # FIXME use the right omx file
+ return skim.Skim(nonmotskm_omx['DIST'], offset=-1)
+
+
+@sim.injectable()
+def sovmd_skim(nonmotskm_omx):
+ # FIXME use the right omx file
+ return skim.Skim(nonmotskm_omx['DIST'], offset=-1)
@sim.injectable()
-def distance_matrix(nonmotskm_omx):
+def sovpm_skim(nonmotskm_omx):
+ # FIXME use the right omx file
return skim.Skim(nonmotskm_omx['DIST'], offset=-1)
+"""
+Read in the spec files and reformat as necessary
+"""
+
+
@sim.injectable()
def auto_ownership_spec():
- f = os.path.join('configs', "auto_ownership_coeffs.csv")
+ f = os.path.join('configs', "auto_ownership.csv")
+ # FIXME should read in all variables and comment out ones not used
return asim.read_model_spec(f).head(4*26)
@sim.injectable()
def workplace_location_spec():
f = os.path.join('configs', "workplace_location.csv")
+ # FIXME should read in all variables and comment out ones not used
return asim.read_model_spec(f).head(15)
+@sim.injectable()
+def mandatory_tour_frequency_spec():
+ f = os.path.join('configs', "mandatory_tour_frequency.csv")
+ return asim.read_model_spec(f)
+
+
+@sim.injectable()
+def non_mandatory_tour_frequency_spec():
+ f = os.path.join('configs', "non_mandatory_tour_frequency.csv")
+ # this is a spec in already stacked format
+ # it also has multiple segments in different columns in the spec
+ return asim.read_model_spec(f, stack=False)
+
+
@sim.table()
-def workplace_size_spec():
- f = os.path.join('configs', 'workplace_location_size_terms.csv')
+def destination_choice_size_terms():
+ f = os.path.join('configs', 'destination_choice_size_terms.csv')
return pd.read_csv(f)
@sim.table()
-def workplace_size_terms(land_use, workplace_size_spec):
+def destination_choice_spec():
+ f = os.path.join('configs', 'destination_choice_alternatives_sample.csv')
+ return asim.read_model_spec(f, stack=False).head(5)
+
+
+@sim.table()
+def tour_departure_and_duration_spec():
+ f = os.path.join('configs', 'tour_departure_and_duration.csv')
+ return asim.read_model_spec(f, stack=False)
+
+
+"""
+This is a special submodel for the workplace location choice
+"""
+
+
+@sim.table()
+def workplace_size_terms(land_use, destination_choice_size_terms):
"""
This method takes the land use data and multiplies various columns of the
land use data by coefficients from the workplace_size_spec table in order
@@ -56,7 +164,7 @@ def workplace_size_terms(land_use, workplace_size_spec):
income)
"""
land_use = land_use.to_frame()
- df = workplace_size_spec.to_frame().query("purpose == 'work'")
+ df = destination_choice_size_terms.to_frame().query("purpose == 'work'")
df = df.drop("purpose", axis=1).set_index("segment")
new_df = {}
for index, row in df.iterrows():
@@ -70,6 +178,12 @@ def workplace_size_terms(land_use, workplace_size_spec):
return new_df
+"""
+Auto ownership is a standard model which predicts how many cars a household
+with given characteristics owns
+"""
+
+
@sim.model()
def auto_ownership_simulate(households,
auto_alts,
@@ -86,25 +200,38 @@ def auto_ownership_simulate(households,
asim.simple_simulate(choosers, alternatives, auto_ownership_spec,
mult_by_alt_col=True)
+ # map these back to integers
+ choices = choices.map(dict([("cars%d" % i, i)
+ for i in range(MAX_NUM_CARS)]))
+
print "Choices:\n", choices.value_counts()
sim.add_column("households", "auto_ownership", choices)
return model_design
+"""
+The workplace location model predicts the zones in which various people will
+work. Interestingly there's not really any supply side to this model - we
+assume there are workplaces for the people to work.
+"""
+
+
+# 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(persons,
households,
zones,
workplace_location_spec,
- distance_matrix,
+ distance_skim,
workplace_size_terms):
choosers = sim.merge_tables(persons.name, tables=[persons, households])
alternatives = zones.to_frame().join(workplace_size_terms.to_frame())
skims = {
- "distance": distance_matrix
+ "distance": distance_skim
}
choices, model_design = \
@@ -116,12 +243,329 @@ def workplace_location_simulate(persons,
mult_by_alt_col=False,
sample_size=50)
- print "Describe of hoices:\n", choices.describe()
+ print "Describe of choices:\n", choices.describe()
sim.add_column("persons", "workplace_taz", choices)
return model_design
+"""
+This model predicts the frequency of making mandatory trips (see the
+alternatives above) - these trips include work and school in some combination.
+"""
+
+
+@sim.model()
+def mandatory_tour_frequency(persons,
+ households,
+ land_use,
+ mandatory_tour_frequency_alts,
+ mandatory_tour_frequency_spec):
+
+ choosers = sim.merge_tables(persons.name, tables=[persons,
+ households,
+ land_use])
+
+ # filter based on results of CDAP
+ choosers = choosers[choosers.cdap_activity == 'M']
+ print "%d persons run for mandatory tour model" % len(choosers)
+
+ choices, model_design = \
+ asim.simple_simulate(choosers,
+ mandatory_tour_frequency_alts.to_frame(),
+ mandatory_tour_frequency_spec,
+ mult_by_alt_col=True)
+
+ print "Choices:\n", choices.value_counts()
+ sim.add_column("persons", "mandatory_tour_frequency", choices)
+
+ return model_design
+
+
+"""
+This model predicts the frequency of making non-mandatory trips (
+alternatives for this model come from a seaparate csv file which is
+configured by the user) - these trips include escort, shopping, othmaint,
+othdiscr, eatout, and social trips in various combination.
+"""
+
+
+@sim.model()
+def non_mandatory_tour_frequency(persons,
+ households,
+ land_use,
+ accessibility,
+ non_mandatory_tour_frequency_alts,
+ non_mandatory_tour_frequency_spec):
+
+ choosers = sim.merge_tables(persons.name, tables=[persons,
+ households,
+ land_use,
+ accessibility])
+
+ # filter based on results of CDAP
+ choosers = choosers[choosers.cdap_activity.isin(['M', 'N'])]
+ print "%d persons run for non-mandatory tour model" % len(choosers)
+
+ choices_list = []
+ # segment by person type and pick the right spec for each person type
+ for name, segment in choosers.groupby('ptype_cat'):
+
+ print "Running segment '%s' of size %d" % (name, len(segment))
+
+ choices, _ = \
+ asim.simple_simulate(segment,
+ non_mandatory_tour_frequency_alts.to_frame(),
+ # notice that we pick the column for the
+ # segment for each segment we run
+ non_mandatory_tour_frequency_spec[name],
+ mult_by_alt_col=False)
+ choices_list.append(choices)
+
+ choices = pd.concat(choices_list)
+
+ print "Choices:\n", choices.value_counts()
+ # this is adding the INDEX of the alternative that is chosen - when
+ # we use the results of this choice we will need both these indexes AND
+ # the alternatives themselves
+ sim.add_column("persons", "non_mandatory_tour_frequency", choices)
+
+
+"""
+We have now generated mandatory and non-mandatory tours, but they are
+attributes of the person table - this function creates a "tours" table which
+has one row per tour that has been generated (and the person id it is
+associated with)
+"""
+
+
+@sim.table()
+def non_mandatory_tours(persons,
+ non_mandatory_tour_frequency_alts):
+
+ # get the actual alternatives for each person - have to go back to the
+ # non_mandatory_tour_frequency_alts dataframe to get this - the choice
+ # above just stored the index values for the chosen alts
+ tours = non_mandatory_tour_frequency_alts.local.\
+ loc[persons.non_mandatory_tour_frequency]
+
+ # assign person ids to the index
+ tours.index = persons.index[~persons.non_mandatory_tour_frequency.isnull()]
+
+ # reformat with the columns given below
+ tours = tours.stack().reset_index()
+ tours.columns = ["person_id", "trip_type", "num_tours"]
+
+ # now do a repeat and a take, so if you have two trips of given type you
+ # now have two rows, and zero trips yields zero rows
+ tours = tours.take(np.repeat(tours.index.values, tours.num_tours.values))
+
+ # make index unique and drop num_tours since we don't need it anymore
+ tours = tours.reset_index(drop=True).drop("num_tours", axis=1)
+
+ """
+ Pretty basic at this point - trip table looks like this so far
+ person_id trip_type
+ 0 4419 escort
+ 1 4419 escort
+ 2 4419 othmaint
+ 3 4419 eatout
+ 4 4419 social
+ 5 10001 escort
+ 6 10001 escort
+ """
+ return tours
+
+
+sim.broadcast('persons', 'non_mandatory_tours',
+ cast_index=True, onto_on='person_id')
+
+
+"""
+This does the same as the above but for mandatory tours. Ending format is
+the same as in the comment above except trip types are "work" and "school"
+"""
+
+@sim.table()
+def mandatory_tours(persons):
+
+ persons = persons.to_frame(columns=["mandatory_tour_frequency",
+ "is_worker"])
+ persons = persons[~persons.mandatory_tour_frequency.isnull()]
+
+ tours = []
+ # this is probably easier to do in non-vectorized fashion (at least for now)
+ for key, row in persons.iterrows():
+
+ mtour = row.mandatory_tour_frequency
+ is_worker = row.is_worker
+
+ # 1 work trip
+ if mtour == "work1":
+ tours += [(key, "work", 1)]
+ # 2 work trips
+ elif mtour == "work2":
+ tours += [(key, "work", 1), (key, "work", 2)]
+ # 1 school trip
+ elif mtour == "school1":
+ tours += [(key, "school", 1)]
+ # 2 school trips
+ elif mtour == "school2":
+ tours += [(key, "school", 1), (key, "school", 2)]
+ # 1 work and 1 school trip
+ elif mtour == "work_and_school":
+ if is_worker:
+ # is worker, work trip goes first
+ tours += [(key, "work", 1), (key, "school", 2)]
+ else:
+ # is student, work trip goes second
+ tours += [(key, "school", 1), (key, "work", 2)]
+ else:
+ assert 0
+
+ return pd.DataFrame(tours, columns=["person_id", "trip_type", "tour_num"])
+
+
+sim.broadcast('persons', 'mandatory_tours',
+ cast_index=True, onto_on='person_id')
+
+
+"""
+Given the tour generation from the above, each tour needs to have a
+destination, so in this case tours are the choosers (with the associated
+person that's making the tour)
+"""
+
+
+@sim.model()
+def destination_choice(non_mandatory_tours,
+ persons,
+ households,
+ land_use,
+ zones,
+ distance_skim,
+ destination_choice_spec):
+
+ tours = non_mandatory_tours
+
+ # FIXME these models don't have size terms at the moment
+
+ # FIXME these models don't use stratified sampling
+
+ # FIXME is the distance to the second trip based on the choice of the
+ # FIXME first trip - ouch!
+
+ # choosers are tours - in a sense tours are choosing their destination
+ choosers = sim.merge_tables(tours.name, tables=[tours,
+ persons,
+ households])
+
+ skims = {
+ "distance": distance_skim
+ }
+
+ choices_list = []
+ # segment by trip type and pick the right spec for each person type
+ for name, segment in choosers.groupby('trip_type'):
+
+ # FIXME - there are two options here escort with kids and without
+ if name == "escort":
+ continue
+
+ print "Running segment '%s' of size %d" % (name, len(segment))
+
+ choices, _ = \
+ asim.simple_simulate(choosers,
+ zones.to_frame(),
+ destination_choice_spec[name],
+ skims,
+ skim_join_name="TAZ",
+ mult_by_alt_col=False,
+ sample_size=50)
+
+ choices_list.append(choices)
+
+ choices = pd.concat(choices_list)
+
+ print "Choices:\n", choices.describe()
+ # every trip now has a destination which is the index from the
+ # alternatives table - in this case it's the destination taz
+ sim.add_column("non_mandatory_tours", "destination", choices)
+
+
+"""
+This model predicts the departure time and duration of each activity
+"""
+
+
+@sim.model()
+def mandatory_tour_departure_and_duration(mandatory_tours,
+ persons,
+ households,
+ land_use,
+ tour_departure_and_duration_alts,
+ tour_departure_and_duration_spec):
+
+ choosers = sim.merge_tables(mandatory_tours.name, tables=[mandatory_tours,
+ persons,
+ households,
+ land_use])
+
+ print "Running %d mandatory tour scheduling choices" % len(choosers)
+
+ # assert there's only a first or second mandatory tour - that's a basic
+ # assumption of this model formulation right now
+ assert choosers.tour_num.isin([1, 2]).value_counts()[True] == len(choosers)
+
+ first_tours = choosers[choosers.tour_num == 1]
+ second_tours = choosers[choosers.tour_num == 2]
+
+ spec = tour_departure_and_duration_spec.work.head(27)
+ alts = tour_departure_and_duration_alts.to_frame()
+
+ print choosers.mandatory_tour_frequency.value_counts()
+ print spec
+
+ # this is a bit odd to python - we can't run through in for loops for
+ # performance reasons - we first have to do a pass for the first tours and
+ # then for the second tours - this is mainly because the second tours are
+ # dependent on the first tours' scheduling
+
+ print "Running %d mandatory first tour choices" % len(first_tours)
+
+ alts["end_of_previous_tour"] = -1
+
+ # FIXME - a note to remember that this also needs the mode choice logsum
+ alts["mode_choice_logsum"] = 0
+
+ first_choices, _ = \
+ asim.simple_simulate(first_tours, alts, spec, mult_by_alt_col=False)
+
+ print "Running %d mandatory second tour choices" % len(second_tours)
+
+ # FIXME need to set end_of_previous_tour to the ends computed above
+ second_choices, _ = \
+ asim.simple_simulate(second_tours, alts, spec, mult_by_alt_col=False)
+
+ choices = pd.concat([first_choices, second_choices])
+
+ # as with non-mandatory tour generation, this stores the INDEX of
+ # the alternative in the tour_departure and_duration_alts dataframe -
+ # to actually use it we'll have ot go back and grab the start and end times
+ print "Choices:\n", choices.describe()
+
+ sim.add_column("persons", "mandatory_tour_departure_and_duration", choices)
+
+
+"""
+This section contains computed columns on each table.
+"""
+
+"""
+for the land use table
+"""
+
+
@sim.column("land_use")
def total_households(land_use):
return land_use.local.TOTHH
@@ -139,4 +583,357 @@ def total_acres(land_use):
@sim.column("land_use")
def county_id(land_use):
- return land_use.local.COUNTY
\ No newline at end of file
+ return land_use.local.COUNTY
+
+
+"""
+for households
+"""
+
+
+# just a rename / alias
+@sim.column("households")
+def home_taz(households):
+ return households.TAZ
+
+
+# map household type ids to strings
+@sim.column("households")
+def household_type(households, settings):
+ return households.HHT.map(settings["household_type_map"])
+
+
+@sim.column("households")
+def non_family(households):
+ return households.household_type.isin(["nonfamily_male_alone",
+ "nonfamily_male_notalone",
+ "nonfamily_female_alone",
+ "nonfamily_female_notalone"])
+
+
+# can't just invert these unfortunately because there's a null household type
+@sim.column("households")
+def family(households):
+ return households.household_type.isin(["family_married",
+ "family_male",
+ "family_female"])
+
+
+@sim.column("households")
+def num_under16_not_at_school(persons, households):
+ return persons.under16_not_at_school.groupby(persons.household_id).size().\
+ reindex(households.index).fillna(0)
+
+
+@sim.column("households")
+def auto_ownership(households):
+ # 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(0, households.index)
+
+
+@sim.column('households')
+def no_cars(households):
+ return (households.auto_ownership == 0)
+
+
+@sim.column('households')
+def home_is_urban(households, land_use, settings):
+ s = usim_misc.reindex(land_use.area_type, households.home_taz)
+ return s < settings['urban_threshold']
+
+
+@sim.column('households')
+def home_is_rural(households, land_use, settings):
+ s = usim_misc.reindex(land_use.area_type, households.home_taz)
+ return s > settings['rural_threshold']
+
+
+@sim.column('households')
+def car_sufficiency(households, persons):
+ return households.auto_ownership - persons.household_id.value_counts()
+
+
+# this is an idiom to grab the person of the specified type and check to see if
+# there is 1 or more of that kind of person in each household
+def presence_of(ptype, persons, households, at_home=False):
+ if at_home:
+ # if at_home, they need to be of given type AND at home
+ s = persons.household_id[(persons.ptype_cat == ptype) &
+ (persons.cdap_activity == "H")]
+ else:
+ s = persons.household_id[persons.ptype_cat == ptype]
+
+ return (s.value_counts() > 0).reindex(households.index).fillna(False)
+
+
+# FIXME this is in non-mandatory tour generation - and should really be from
+# FIXME the perspective of the current chooser - which it's not right now
+@sim.column('households')
+def has_non_worker(persons, households):
+ return presence_of("nonwork", persons, households)
+
+
+# FIXME this is in non-mandatory tour generation - and should really be from
+# FIXME the perspective of the current chooser - which it's not right now
+@sim.column('households')
+def has_retiree(persons, households):
+ return presence_of("retired", persons, households)
+
+
+# FIXME this is in non-mandatory tour generation - and should really be from
+# FIXME the perspective of the current chooser - which it's not right now
+@sim.column('households')
+def has_preschool_kid(persons, households):
+ return presence_of("preschool", persons, households)
+
+
+# FIXME this is in non-mandatory tour generation - and should really be from
+# FIXME the perspective of the current chooser - which it's not right now
+@sim.column('households')
+def has_preschool_kid_at_home(persons, households):
+ return presence_of("preschool", persons, households, at_home=True)
+
+
+# FIXME this is in non-mandatory tour generation - and should really be from
+# FIXME the perspective of the current chooser - which it's not right now
+@sim.column('households')
+def has_driving_kid(persons, households):
+ return presence_of("driving", persons, households)
+
+
+# FIXME this is in non-mandatory tour generation - and should really be from
+# FIXME the perspective of the current chooser - which it's not right now
+@sim.column('households')
+def has_school_kid(persons, households):
+ return presence_of("school", persons, households)
+
+
+# FIXME this is in non-mandatory tour generation - and should really be from
+# FIXME the perspective of the current chooser - which it's not right now
+@sim.column('households')
+def has_school_kid_at_home(persons, households):
+ return presence_of("school", persons, households, at_home=True)
+
+
+# FIXME this is in non-mandatory tour generation - and should really be from
+# FIXME the perspective of the current chooser - which it's not right now
+@sim.column('households')
+def has_full_time(persons, households):
+ return presence_of("full", persons, households)
+
+
+# FIXME this is in non-mandatory tour generation - and should really be from
+# FIXME the perspective of the current chooser - which it's not right now
+@sim.column('households')
+def has_part_time(persons, households):
+ return presence_of("part", persons, households)
+
+
+# FIXME this is in non-mandatory tour generation - and should really be from
+# FIXME the perspective of the current chooser - which it's not right now
+@sim.column('households')
+def has_university(persons, households):
+ return presence_of("university", persons, households)
+
+
+"""
+for the persons table
+"""
+
+
+# FIXME - this is my "placeholder" for the CDAP model ;)
+@sim.column("persons")
+def cdap_activity(persons):
+ return pd.Series(np.random.randint(3, size=len(persons)),
+ index=persons.index).map({0: 'M', 1: 'N', 2: 'H'})
+
+
+# FIXME - these are my "placeholder" for joint trip generation
+# number of joint shopping tours
+@sim.column("persons")
+def num_shop_j(persons):
+ return pd.Series(0, persons.index)
+
+
+# FIXME - these are my "placeholder" for joint trip generation
+# number of joint shopping tours
+@sim.column("persons")
+def num_main_j(persons):
+ return pd.Series(0, persons.index)
+
+
+# FIXME - these are my "placeholder" for joint trip generation
+# number of joint shopping tours
+@sim.column("persons")
+def num_eat_j(persons):
+ return pd.Series(0, persons.index)
+
+
+# FIXME - these are my "placeholder" for joint trip generation
+# number of joint shopping tours
+@sim.column("persons")
+def num_visi_j(persons):
+ return pd.Series(0, persons.index)
+
+
+# FIXME - these are my "placeholder" for joint trip generation
+# number of joint shopping tours
+@sim.column("persons")
+def num_disc_j(persons):
+ return pd.Series(0, persons.index)
+
+
+@sim.column("persons")
+def num_joint_tours(persons):
+ return persons.num_shop_j + persons.num_main_j + persons.num_eat_j +\
+ persons.num_visi_j + persons.num_disc_j
+
+
+@sim.column("persons")
+def male(persons):
+ return persons.sex == 1
+
+
+@sim.column("persons")
+def female(persons):
+ return persons.sex == 1
+
+
+# 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)
+
+ s = persons.mandatory_tour_frequency.map({
+ "work1": 1,
+ "work2": 2,
+ "school1": 1,
+ "school2": 2,
+ "work_and_school": 2
+ })
+ return s
+
+
+# FIXME now totally sure what this is but it's used in non mandatory tour
+# FIXME generation and probably has to do with remaining unscheduled time
+@sim.column('persons')
+def max_window(persons):
+ return pd.Series(0, persons.index)
+
+
+# convert employment categories to string descriptors
+@sim.column("persons")
+def employed_cat(persons, settings):
+ return persons.pemploy.map(settings["employment_map"])
+
+
+# convert student categories to string descriptors
+@sim.column("persons")
+def student_cat(persons, settings):
+ return persons.pstudent.map(settings["student_map"])
+
+
+# convert person type categories to string descriptors
+@sim.column("persons")
+def ptype_cat(persons, settings):
+ return persons.ptype.map(settings["person_type_map"])
+
+
+# borrowing these definitions from the original code
+@sim.column("persons")
+def student_is_employed(persons):
+ return (persons.ptype_cat.isin(['university', 'driving']) &
+ persons.employed_cat.isin(['full', 'part']))
+
+
+@sim.column("persons")
+def nonstudent_to_school(persons):
+ return (persons.ptype_cat.isin(['full', 'part', 'nonwork', 'retired']) &
+ persons.student_cat.isin(['high', 'college']))
+
+
+@sim.column("persons")
+def under16_not_at_school(persons):
+ return (persons.ptype_cat.isin(["school", "preschool"]) &
+ persons.cdap_activity.isin(["N", "H"]))
+
+
+@sim.column("persons")
+def is_worker(persons):
+ return persons.employed_cat.isin(['full', 'part'])
+
+
+@sim.column("persons")
+def is_student(persons):
+ return persons.student_cat.isin(['high', 'college'])
+
+
+@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)
+
+
+@sim.column("persons")
+def home_taz(households, persons):
+ return usim_misc.reindex(households.home_taz,
+ persons.household_id)
+
+
+@sim.column("persons")
+def school_taz(persons):
+ # FIXME need to fix this after getting school lcm working
+ return persons.workplace_taz
+
+
+# this use the distance skims to compute the raw distance to work from home
+@sim.column("persons")
+def distance_to_work(persons, distance_skim):
+ return pd.Series(distance_skim.get(persons.home_taz,
+ persons.workplace_taz),
+ index=persons.index)
+
+
+# same deal but to school
+@sim.column("persons")
+def distance_to_school(persons, distance_skim):
+ return pd.Series(distance_skim.get(persons.home_taz,
+ persons.school_taz),
+ index=persons.index)
+
+
+# similar but this adds the am peak travel time to the pm peak travel time in
+# the opposite direction (by car)
+@sim.column("persons")
+def roundtrip_auto_time_to_work(persons, sovam_skim, sovpm_skim):
+ return pd.Series(sovam_skim.get(persons.home_taz,
+ persons.workplace_taz) +
+ sovpm_skim.get(persons.workplace_taz,
+ persons.home_taz),
+ index=persons.index)
+
+
+# this adds the am peak travel time to the md peak travel time in
+# the opposite direction (by car), assuming students leave school earlier
+@sim.column("persons")
+def roundtrip_auto_time_to_school(persons, sovam_skim, sovmd_skim):
+ return pd.Series(sovam_skim.get(persons.home_taz,
+ persons.school_taz) +
+ sovmd_skim.get(persons.school_taz,
+ persons.home_taz),
+ index=persons.index)
+
+
+@sim.column('persons')
+def workplace_in_cbd(persons, land_use, settings):
+ s = usim_misc.reindex(land_use.area_type, persons.workplace_taz)
+ return s < settings['cbd_threshold']
+
diff --git a/notebooks/data_mover.ipynb b/notebooks/data_mover.ipynb
index 1c065abf1..e6329cec6 100644
--- a/notebooks/data_mover.ipynb
+++ b/notebooks/data_mover.ipynb
@@ -1,7 +1,7 @@
{
"metadata": {
"name": "",
- "signature": "sha256:afbc3e7040dd9e4a5b21433063f13a6a8abfcc04bcfc6574e7e43376c257cd33"
+ "signature": "sha256:07f23263339f1751ee4eb702d126692b9ed2785fe8640a8906eb0fb110d9e67a"
},
"nbformat": 3,
"nbformat_minor": 0,
@@ -43,8 +43,10 @@
"col_map = {\n",
" \"HHID\": \"household_id\",\n",
" \"AGE\": \"age\",\n",
+ " \"SEX\": \"sex\",\n",
" \"hworkers\": \"workers\",\n",
- " \"HINC\": \"income\"\n",
+ " \"HINC\": \"income\",\n",
+ " \"AREATYPE\": \"area_type\"\n",
"}"
],
"language": "python",
diff --git a/notebooks/simulation.ipynb b/notebooks/simulation.ipynb
index 1643e621d..d81303ad9 100644
--- a/notebooks/simulation.ipynb
+++ b/notebooks/simulation.ipynb
@@ -1,7 +1,7 @@
{
"metadata": {
"name": "",
- "signature": "sha256:68dd59fb87c331bcb79d97a97557ee2ee411309bd3a85e58d6bee4b169b221df"
+ "signature": "sha256:2ac723c10479d39a6126f1871723950f6eb803212ddda416f1652722e84434c1"
},
"nbformat": 3,
"nbformat_minor": 0,
@@ -39,35 +39,55 @@
"stream": "stdout",
"text": [
"Running model 'workplace_location_simulate'\n",
- "Describe of hoices:\n"
+ "Describe of choices:\n"
]
},
{
"output_type": "stream",
"stream": "stdout",
"text": [
- "count 258078.000000\n",
- "mean 718.510997\n",
- "std 422.975764\n",
- "min 1.000000\n",
- "25% 352.000000\n",
- "50% 719.000000\n",
- "75% 1083.000000\n",
- "max 1454.000000\n",
+ "count 2513.000000\n",
+ "mean 719.074413\n",
+ "std 426.054061\n",
+ "min 1.000000\n",
+ "25% 356.000000\n",
+ "50% 720.000000\n",
+ "75% 1090.000000\n",
+ "max 1454.000000\n",
"Name: TAZ, dtype: float64\n",
- "Time to execute model 'workplace_location_simulate': 35.33s"
+ "Time to execute model 'workplace_location_simulate': 7.61s\n",
+ "Total time to execute: 7.61s\n"
]
- },
+ }
+ ],
+ "prompt_number": 2
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "print sim.get_table(\"persons\").distance_to_work.describe()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
- "\n",
- "Total time to execute: 35.34s\n"
+ "count 2513.000000\n",
+ "mean 38.703832\n",
+ "std 24.329613\n",
+ "min 0.390000\n",
+ "25% 21.000000\n",
+ "50% 35.310000\n",
+ "75% 51.960000\n",
+ "max 137.480000\n",
+ "dtype: float64\n"
]
}
],
- "prompt_number": 2
+ "prompt_number": 3
},
{
"cell_type": "code",
@@ -90,13 +110,34 @@
"output_type": "stream",
"stream": "stdout",
"text": [
- "cars2 47959\n",
- "cars3 38186\n",
- "cars1 7528\n",
- "cars4 5123\n",
- "cars0 1204\n",
+ "2 477\n",
+ "3 379\n",
+ "1 94\n",
+ "4 38\n",
+ "0 12\n",
"dtype: int64\n",
- "Time to execute model 'auto_ownership_simulate': 4.62s"
+ "Time to execute model 'auto_ownership_simulate': 0.55s\n",
+ "Total time to execute: 0.55s\n"
+ ]
+ }
+ ],
+ "prompt_number": 4
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "sim.run(['mandatory_tour_frequency'])"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Running model 'mandatory_tour_frequency'\n",
+ "863 persons run for mandatory tour model"
]
},
{
@@ -104,615 +145,1154 @@
"stream": "stdout",
"text": [
"\n",
- "Total time to execute: 4.62s\n"
+ "WARNING: Describe of columns with no variability:\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ " count mean std \\\n",
+ "((ptype == 2) & nonstudent_to_school) * school1 4315 0 0 \n",
+ "((ptype == 2) & nonstudent_to_school) * work_and_school 4315 0 0 \n",
+ "((ptype == 4) & nonstudent_to_school) * school1 4315 0 0 \n",
+ "((ptype == 5) & nonstudent_to_school) * school1 4315 0 0 \n",
+ "((ptype == 6) & (auto_ownership == 0)) * school2 4315 0 0 \n",
+ "((ptype == 6) & (auto_ownership == 0)) * work_and_school 4315 0 0 \n",
+ "((ptype == 6) & non_family) * school1 4315 0 0 \n",
+ "((ptype == 6) & non_family) * work1 4315 0 0 \n",
+ "((ptype == 6) & non_family) * work_and_school 4315 0 0 \n",
+ "((ptype == 7) & non_family) * school1 4315 0 0 \n",
+ "((ptype == 7) & non_family) * work1 4315 0 0 \n",
+ "(~(school_taz > -1)) * school1 4315 0 0 \n",
+ "(~(school_taz > -1)) * school2 4315 0 0 \n",
+ "(~(school_taz > -1)) * work_and_school 4315 0 0 \n",
+ "(~(workplace_taz > -1)) * work1 4315 0 0 \n",
+ "(~(workplace_taz > -1)) * work2 4315 0 0 \n",
+ "(~(workplace_taz > -1)) * work_and_school 4315 0 0 \n",
+ "\n",
+ " min 25% 50% 75% \\\n",
+ "((ptype == 2) & nonstudent_to_school) * school1 0 0 0 0 \n",
+ "((ptype == 2) & nonstudent_to_school) * work_and_school 0 0 0 0 \n",
+ "((ptype == 4) & nonstudent_to_school) * school1 0 0 0 0 \n",
+ "((ptype == 5) & nonstudent_to_school) * school1 0 0 0 0 \n",
+ "((ptype == 6) & (auto_ownership == 0)) * school2 0 0 0 0 \n",
+ "((ptype == 6) & (auto_ownership == 0)) * work_and_school 0 0 0 0 \n",
+ "((ptype == 6) & non_family) * school1 0 0 0 0 \n",
+ "((ptype == 6) & non_family) * work1 0 0 0 0 \n",
+ "((ptype == 6) & non_family) * work_and_school 0 0 0 0 \n",
+ "((ptype == 7) & non_family) * school1 0 0 0 0 \n",
+ "((ptype == 7) & non_family) * work1 0 0 0 0 \n",
+ "(~(school_taz > -1)) * school1 0 0 0 0 \n",
+ "(~(school_taz > -1)) * school2 0 0 0 0 \n",
+ "(~(school_taz > -1)) * work_and_school 0 0 0 0 \n",
+ "(~(workplace_taz > -1)) * work1 0 0 0 0 \n",
+ "(~(workplace_taz > -1)) * work2 0 0 0 0 \n",
+ "(~(workplace_taz > -1)) * work_and_school 0 0 0 0 \n",
+ "\n",
+ " max \n",
+ "((ptype == 2) & nonstudent_to_school) * school1 0 \n",
+ "((ptype == 2) & nonstudent_to_school) * work_and_school 0 \n",
+ "((ptype == 4) & nonstudent_to_school) * school1 0 \n",
+ "((ptype == 5) & nonstudent_to_school) * school1 0 \n",
+ "((ptype == 6) & (auto_ownership == 0)) * school2 0 \n",
+ "((ptype == 6) & (auto_ownership == 0)) * work_and_school 0 \n",
+ "((ptype == 6) & non_family) * school1 0 \n",
+ "((ptype == 6) & non_family) * work1 0 \n",
+ "((ptype == 6) & non_family) * work_and_school 0 \n",
+ "((ptype == 7) & non_family) * school1 0 \n",
+ "((ptype == 7) & non_family) * work1 0 \n",
+ "(~(school_taz > -1)) * school1 0 \n",
+ "(~(school_taz > -1)) * school2 0 \n",
+ "(~(school_taz > -1)) * work_and_school 0 \n",
+ "(~(workplace_taz > -1)) * work1 0 \n",
+ "(~(workplace_taz > -1)) * work2 0 \n",
+ "(~(workplace_taz > -1)) * work_and_school 0 \n",
+ "Choices:\n",
+ "work_and_school 330\n",
+ "work2 262\n",
+ "work1 160\n",
+ "school2 56\n",
+ "school1 55\n",
+ "dtype: int64\n",
+ "Time to execute model 'mandatory_tour_frequency': 3.11s\n",
+ "Total time to execute: 3.11s\n"
]
}
],
- "prompt_number": 3
+ "prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
- "sim.get_table(\"land_use\").to_frame().describe()"
+ "sim.run(['non_mandatory_tour_frequency'])"
],
"language": "python",
"metadata": {},
"outputs": [
{
- "html": [
- "\n",
- "
\n",
- " \n",
- " \n",
- " | \n",
- " DISTRICT | \n",
- " SD | \n",
- " COUNTY | \n",
- " TOTHH | \n",
- " HHPOP | \n",
- " TOTPOP | \n",
- " EMPRES | \n",
- " SFDU | \n",
- " MFDU | \n",
- " HHINCQ1 | \n",
- " ... | \n",
- " hhlds | \n",
- " sftaz | \n",
- " gqpop | \n",
- " employment_density | \n",
- " total_acres | \n",
- " county_id | \n",
- " density_index | \n",
- " household_density | \n",
- " total_households | \n",
- " total_employment | \n",
- "
\n",
- " \n",
- " \n",
- " \n",
- " | count | \n",
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- " 1454.000000 | \n",
- " 1454.000000 | \n",
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- " 1454.000000 | \n",
- " 1454.000000 | \n",
- " 1454.000000 | \n",
- "
\n",
- " \n",
- " | mean | \n",
- " 14.908528 | \n",
- " 14.908528 | \n",
- " 3.835626 | \n",
- " 1793.688446 | \n",
- " 4816.408528 | \n",
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- " 2168.684319 | \n",
- " 1122.798487 | \n",
- " 670.889959 | \n",
- " 508.134801 | \n",
- " ... | \n",
- " 1793.688446 | \n",
- " 727.500000 | \n",
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- " 3146.071457 | \n",
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- " 6.008186 | \n",
- " 1793.688446 | \n",
- " 2247.736589 | \n",
- "
\n",
- " \n",
- " | std | \n",
- " 8.701078 | \n",
- " 8.701078 | \n",
- " 2.040153 | \n",
- " 961.021405 | \n",
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- " 16945.908840 | \n",
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- " 961.021405 | \n",
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- "
\n",
- " \n",
- " | min | \n",
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- "
\n",
- " \n",
- " | 25% | \n",
- " 8.000000 | \n",
- " 8.000000 | \n",
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- " 1200.250000 | \n",
- " 3288.250000 | \n",
- " 3384.500000 | \n",
- " 1460.500000 | \n",
- " 602.000000 | \n",
- " 144.500000 | \n",
- " 257.000000 | \n",
- " ... | \n",
- " 1200.250000 | \n",
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- " 5.000000 | \n",
- " 0.877829 | \n",
- " 230.000000 | \n",
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- " 1.910701 | \n",
- " 1200.250000 | \n",
- " 482.000000 | \n",
- "
\n",
- " \n",
- " | 50% | \n",
- " 15.000000 | \n",
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- " 4.000000 | \n",
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- " 434.000000 | \n",
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- " 18.000000 | \n",
- " 2.158701 | \n",
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- " 3.939122 | \n",
- " 1681.500000 | \n",
- " 1005.500000 | \n",
- "
\n",
- " \n",
- " | 75% | \n",
- " 20.750000 | \n",
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- " 6.693238 | \n",
- " 2259.750000 | \n",
- " 2215.750000 | \n",
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\n",
- " \n",
- "
\n",
- "
8 rows \u00d7 48 columns
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- "
\n",
- "
\n",
- " \n",
- " \n",
- " | \n",
- " TAZ | \n",
- " SERIALNO | \n",
- " PUMA5 | \n",
- " income | \n",
- " PERSONS | \n",
- " HHT | \n",
- " UNITTYPE | \n",
- " NOC | \n",
- " BLDGSZ | \n",
- " TENURE | \n",
- " ... | \n",
- " bucketBin | \n",
- " originalPUMA | \n",
- " hmultiunit | \n",
- " num_young_adults | \n",
- " drivers | \n",
- " num_children | \n",
- " num_adolescents | \n",
- " income_in_thousands | \n",
- " num_young_children | \n",
- " num_college_age | \n",
- "
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- " \n",
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- " 752.439040 | \n",
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- " 2168.287950 | \n",
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- " 2168.287950 | \n",
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- " 0.394180 | \n",
- " 2.063810 | \n",
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- " 0.226650 | \n",
- "
\n",
- " \n",
- " | std | \n",
- " 430.258155 | \n",
- " 2863687.886756 | \n",
- " 516.271349 | \n",
- " 81341.474187 | \n",
- " 1.606362 | \n",
- " 2.066835 | \n",
- " 0.367387 | \n",
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- " 516.271349 | \n",
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\n",
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- "
\n",
- " \n",
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- " 374.000000 | \n",
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- " 2104.000000 | \n",
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\n",
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- "
\n",
- " \n",
- " | 75% | \n",
- " 1144.000000 | \n",
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\n",
- " \n",
- " | max | \n",
- " 1454.000000 | \n",
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\n",
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\n",
- "
8 rows \u00d7 53 columns
\n",
- "