diff --git a/activitysim/activitysim.py b/activitysim/activitysim.py
index 6148e20ab3..846508dc13 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 50f0bd245f..5d4d80067d 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 c50a58bba7..03b8af8b2f 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 0000000000..e6d2898717
--- /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 0000000000..cc1dae874a
--- /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 0fe176c110..0000000000
--- 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 0000000000..9f239eafdb
--- /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 0000000000..2a33dac443
--- /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 0000000000..6ef694785a
--- /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 0000000000..854505462b
--- /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 0000000000..49499f514c
--- /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 0000000000..1c0052f963
--- /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 05cf2cdb08..c80b37fa89 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_alternatives.csv b/example/configs/tour_departure_and_duration_alternatives.csv
new file mode 100644
index 0000000000..05f02b7964
--- /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/tour_departure_and_duration_mandatory.csv b/example/configs/tour_departure_and_duration_mandatory.csv
new file mode 100644
index 0000000000..6a2afd916a
--- /dev/null
+++ b/example/configs/tour_departure_and_duration_mandatory.csv
@@ -0,0 +1,64 @@
+Description,Expression,Coefficient
+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_nonmandatory.csv b/example/configs/tour_departure_and_duration_nonmandatory.csv
new file mode 100644
index 0000000000..b940c997ac
--- /dev/null
+++ b/example/configs/tour_departure_and_duration_nonmandatory.csv
@@ -0,0 +1,91 @@
+Description,,Expression,Coefficient
+subsequet tour must start after previous tour ends,subsequentTour,start < end_of_previous_tour,999
+Free-flow round trip auto time shift effects - duration,,roundtrip_auto_time_to_work * duration,0.00474
+Shopping tour - departure shift effects,tourPurposeIsShopping,"(tour_type == ""shopping"") * start",0.06015
+Shopping tour - duration shift effects,tourPurposeIsShopping,"(tour_type == ""shopping"") * duration",0.1208
+Maintenance tour - departure shift effects,tourPurposeIsMaint,"(tour_type == ""oth_maint"") * start",0.1489
+Maintenance tour - departure shift effects,tourPurposeIsMaint,"(tour_type == ""oth_maint"") * duration",0.08372
+Visit tour - departure shift effects,tourPurposeIsVisit,"(tour_type == ""social"") * start",0.09688
+Visit tour - departure shift effects,tourPurposeIsVisit,"(tour_type == ""social"") * duration",0.1638
+Eat Out tour - departure shift effects,tourPurposeIsEatOut,"(tour_type == ""eatout"") * start",0.07549
+School child age 16+ - departure shift effects,studentDrivingAge,(ptype == 6) * start,0.07266
+School child age 16+ - duration shift effects,studentDrivingAge,(ptype == 6) * duration,0.2095
+School child age under 16 - departure shift effects,studentNonDrivingAge,(ptype == 7) * start,0.04657
+School child age under 16 - duration shift effects,studentNonDrivingAge,(ptype == 7) * duration,0.3272
+Destination in CBD - duration shift effects,destinationInCBD,duration,0.1067
+Number of mandatory tours - departure shift effects,,personMandatoryTotal * start,0.04673
+Number of joint tours - departure shift effects,,personJointTotal * start,0.05208
+Number of escort tours - departure shift effects,,personEscortTotal * start,0.02013
+Number of idividual non-mandatory tours (excluding escort) - departure shift effects,,personNonMandatoryTotalNoEscort * start,0.03896
+First of 2+ tours for same purpose - departure shift effect,firstTour,start,0.2364
+subsequent of 2+ tours for same purpose - duration shift effect,subsequentTour,duration,0.1731
+Maintenance tour - depart before 7,tourPurposeIsMaint,"(tour_type == ""oth_maint"") & (start < 7)",0.8826
+Shopping tour - depart before 8,tourPurposeIsShopping,"(tour_type == ""shopping"" & (start < 8)",1.037
+Shopping tour - arrive after 22,,"(tour_type == ""shopping"" & (end > 22)",0.6027
+School child under 16 - arrive after 22,studentNonDrivingAge,(ptype == 7) & (end > 22),1.18
+University student - arrive after 22,universityStudent,(ptype == 3) & (end > 22),0.5466
+Shopping tour - duration < 2 hours,tourPurposeIsShopping,"(tour_type == ""shopping"") & (duration < 2)",0.5168
+Discretionary tour - duration < 2 hours,tourPurposeIsDiscr,"(tour_type == ""othdisc"") & (duration < 2)",0.6974
+Adult with children in HH - arrive 19 - 21,personIsAdult * ( numChildrenInHH > 0 ),( end > 18 ) * ( end < 22 ),0.336
+Mode Choice Logsum,0,mode_choice_logsum,0
+Some previously-scheduled tour ends in this departure hour,,prev_tour_end == start,0.4562
+Some previously-scheduled tour begins in this arrival hour,,prev_tour_begin == end,0.3992
+Adjacent window exists before this departure hour - first tour interaction,firstTour,@@adjWindowBeforeThisHourAlt,0.00844
+Adjacent window exists afetr this arrival hour - first tour interaction,firstTour,@@adjWindowAfterThisHourAlt,0.0257
+Adjacent window exists before this departure hour - second+ tour interaction,subsequentTour,@@adjWindowBeforeThisHourAlt,0.0593
+Adjacent window exists after this arrival hour - second+ tour interaction,subsequentTour,@@adjWindowAfterThisHourAlt,0.02734
+Remaining individual non-mandatory tours to be scheduled / number of unscheduled hours,,@@remainingInmToursToAvailableHoursRatioAlt,13.63
+Departure Constants -- Early (up to 5),escortTour==0,"(tour_type != ""escort"") & (start < 6)",1.74014
+Departure Constants -- AM peak 1 (6),escortTour==0,"(tour_type != ""escort"") & (start == 6)",0.65416
+Departure Constants -- AM peak 2 (7),escortTour==0,"(tour_type != ""escort"") & (start == 7)",0.55428
+Departure Constants -- AM peak 3 (8),escortTour==0,"(tour_type != ""escort"") & (start == 8)",1.05056
+Departure Constants -- AM peak 4 (9),escortTour==0,"(tour_type != ""escort"") & (start == 9)",0.97157
+Departure Constants -- Midday 1 (10 to 12),escortTour==0,"(tour_type != ""escort"") & (start > 9) & (start < 13)",0.88199
+Departure Constants -- Midday 2 (13 to 15),escortTour==0,"(tour_type != ""escort"") & (start > 12) & (start < 16)",0.4111
+Departure Constants -- PM peak (16 to 18),escortTour==0,"(tour_type != ""escort"") & (start > 15) & (start < 19)",0
+Departure Constants -- Evening (19 to 21),escortTour==0,"(tour_type != ""escort"") & (start > 18) & (start < 22)",1.85648
+Departure Constants -- Late (22 and later),escortTour==0,"(tour_type != ""escort"") & (start > 21)",8.22888
+Arrival Constants -- Early (up to 6),escortTour==0,"(tour_type != ""escort"") & (end < 7)",0.05199
+Arrival Constants -- AM peak (7 to 9),escortTour==0,"(tour_type != ""escort"") & (end > 6) & (end < 10)",1.81482
+Arrival Constants -- Midday 1 (10 to 12),escortTour==0,"(tour_type != ""escort"") & (end > 9) & (end < 13)",0.00037
+Arrival Constants -- Midday 2 (13 to 14),escortTour==0,"(tour_type != ""escort"") & (end > 12) & (end < 15)",0.53212
+Arrival Constants -- PM peak 1 (15),escortTour==0,"(tour_type != ""escort"") & (end == 15)",0.62848
+Arrival Constants -- PM peak 2 (16),escortTour==0,"(tour_type != ""escort"") & (end == 16)",0.65052
+Arrival Constants -- PM peak 3 (17),escortTour==0,"(tour_type != ""escort"") & (end == 17)",0.40289
+Arrival Constants -- PM peak 4 (18),escortTour==0,"(tour_type != ""escort"") & (end == 18)",0.15421
+Arrival Constants -- Evening (19 to 21),escortTour==0,"(tour_type != ""escort"") & (end > 18) & (end < 22)",0
+Arrival Constants -- Late (22 and later),escortTour==0,"(tour_type != ""escort"") & (end > 21)",0.86667
+Duration Constants -- 0 to 1 hours,escortTour==0,"(tour_type != ""escort"") & (duration < 2)",0
+Duration Constants -- 2 to 3 hours,escortTour==0,"(tour_type != ""escort"") & (duration > 1) & (duration < 4)",0.05139
+Duration Constants -- 4 to 5 hours,escortTour==0,"(tour_type != ""escort"") & (duration > 3) & (duration < 6)",0.59395
+Duration Constants -- 6 to 7 hours,escortTour==0,"(tour_type != ""escort"") & (duration > 5) & (duration < 8)",0.95116
+Duration Constants -- 8 to 10 hours,escortTour==0,"(tour_type != ""escort"") & (duration > 7) & (duration < 11)",0.82811
+Duration Constants -- 11 to 13 hours,escortTour==0,"(tour_type != ""escort"") & (duration > 10) & (duration < 14)",0.95564
+Duration Constants -- 14 to 18 hours,escortTour==0,"(tour_type != ""escort"") & (duration > 13) & (duration < 19)",1.04258
+Escort Tour Departure Constants -- Early (up to 5),escortTour==1,"(tour_type == ""escort"") & (start < 6)",1.74014
+Escort Tour Departure Constants -- AM peak 1 (6),escortTour==1,"(tour_type == ""escort"") & (start == 6)",1.11236
+Escort Tour Departure Constants -- AM peak 2 (7),escortTour==1,"(tour_type == ""escort"") & (start == 7)",0.69879
+Escort Tour Departure Constants -- AM peak 3 (8),escortTour==1,"(tour_type == ""escort"") & (start == 8)",1.19627
+Escort Tour Departure Constants -- AM peak 4 (9),escortTour==1,"(tour_type == ""escort"") & (start == 9)",0.22526
+Escort Tour Departure Constants -- Midday 1 (10 to 12),escortTour==1,"(tour_type == ""escort"") & (start > 9) & (start < 13)",0.02866
+Escort Tour Departure Constants -- Midday 2 (13 to 15),escortTour==1,"(tour_type == ""escort"") & (start > 12) & (start < 16)",0
+Escort Tour Departure Constants -- PM peak (16 to 18),escortTour==1,"(tour_type == ""escort"") & (start > 15) & (start < 19)",1.18014
+Escort Tour Departure Constants -- Evening (19 to 21),escortTour==1,"(tour_type == ""escort"") & (start > 18) & (start < 22)",3.94873
+Escort Tour Departure Constants -- Late (22 and later),escortTour==1,"(tour_type == ""escort"") & (start > 21)",8.22888
+Escort Tour Arrival Constants -- Early (up to 6),escortTour==1,"(tour_type == ""escort"") & (end < 7)",0
+Escort Tour Arrival Constants -- AM peak (7 to 9),escortTour==1,"(tour_type == ""escort"") & (end > 6) & (end < 10)",0
+Escort Tour Arrival Constants -- Midday 1 (10 to 12),escortTour==1,"(tour_type == ""escort"") & (end > 9) & (end < 13)",0
+Escort Tour Arrival Constants -- Midday 2 (13 to 14),escortTour==1,"(tour_type == ""escort"") & (end > 12) & (end < 15)",0
+Escort Tour Arrival Constants -- PM peak 1 (15),escortTour==1,"(tour_type == ""escort"") & (end == 15)",0
+Escort Tour Arrival Constants -- PM peak 2 (16),escortTour==1,"(tour_type == ""escort"") & (end == 16)",0
+Escort Tour Arrival Constants -- PM peak 3 (17),escortTour==1,"(tour_type == ""escort"") & (end == 17)",0
+Escort Tour Arrival Constants -- PM peak 4 (18),escortTour==1,"(tour_type == ""escort"") & (end == 18)",0
+Escort Tour Arrival Constants -- Evening (19 to 21),escortTour==1,"(tour_type == ""escort"") & (end > 18) & (end < 22)",0.53692
+Escort Tour Arrival Constants -- Late (22 and later),escortTour==1,"(tour_type == ""escort"") & (end > 21)",1.00829
+Escort Tour Duration Constants -- 0 to 1 hours,escortTour==1,"(tour_type == ""escort"") & (duration < 2)",0
+Escort Tour Duration Constants -- 2 to 3 hours,escortTour==1,"(tour_type == ""escort"") & (duration > 1) & (duration < 4)",2.04201
+Escort Tour Duration Constants -- 4 to 5 hours,escortTour==1,"(tour_type == ""escort"") & (duration > 3) & (duration < 6)",2.88029
+Escort Tour Duration Constants -- 6 to 7 hours,escortTour==1,"(tour_type == ""escort"") & (duration > 5) & (duration < 8)",2.97353
+Escort Tour Duration Constants -- 8 to 10 hours,escortTour==1,"(tour_type == ""escort"") & (duration > 7) & (duration < 11)",3.02021
+Escort Tour Duration Constants -- 11 to 13 hours,escortTour==1,"(tour_type == ""escort"") & (duration > 10) & (duration < 14)",2.97436
+Escort Tour Duration Constants -- 14 to 18 hours,escortTour==1,"(tour_type == ""escort"") & (duration > 13) & (duration < 19)",2.50745
\ No newline at end of file
diff --git a/example/configs/workplace_location.csv b/example/configs/workplace_location.csv
index af7b3ce918..0adf1b611d 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 f9b7da433a..0000000000
--- 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 941d9973c9..1450e90033 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 +8,167 @@
 import pandas as pd
 
 
+# this is the max number of cars allowable in the auto ownership model
+MAX_NUM_CARS = 5
+
+
+"""
+Definition of terms:
+
+CDAP = coordinated daily activity pattern
+TDD = tour departure and duration
+"""
+
+
+"""
+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 tdd_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("tdd_alts")
+def duration(tdd_alts):
+    return tdd_alts.end - tdd_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_matrix(nonmotskm_omx):
+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 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 tdd_mandatory_spec():
+    f = os.path.join('configs', 'tour_departure_and_duration_mandatory.csv')
+    return asim.read_model_spec(f, stack=False)
+
+
+@sim.table()
+def tdd_non_mandatory_spec():
+    f = os.path.join('configs', 'tour_departure_and_duration_nonmandatory.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 +177,7 @@ 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 +191,12 @@ 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 +213,38 @@ 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 +256,392 @@ 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", "tour_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 tour_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", "tour_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('tour_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(segment,
+                                 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 for
+mandatory tours
+"""
+
+
+@sim.model()
+def tour_departure_and_duration_mandatory(mandatory_tours,
+                                          persons,
+                                          households,
+                                          land_use,
+                                          tdd_alts,
+                                          tdd_mandatory_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 = tdd_mandatory_spec.to_frame().head(27)
+    alts = tdd_alts.to_frame()
+
+    print choosers.mandatory_tour_frequency.value_counts()
+    print spec.tail()
+
+    # FIXME the "windowing" variables are not currently implemented
+
+    # FIXME this version hard-codes the 2 passes of the mandatory models -
+    # FIXME the non-mandatory version below does not - might should use that
+    # FIXME kind of structure here instead?
+
+    # 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("mandatory_tours", "mandatory_tdd", choices)
+
+
+"""
+This model predicts the departure time and duration of each activity for
+non-mandatory tours
+"""
+
+
+@sim.model()
+def tour_departure_and_duration_non_mandatory(non_mandatory_tours,
+                                              persons,
+                                              households,
+                                              land_use,
+                                              tdd_alts,
+                                              tdd_non_mandatory_spec):
+
+    tours = sim.merge_tables(non_mandatory_tours.name,
+                             tables=[non_mandatory_tours,
+                                     persons,
+                                     households,
+                                     land_use])
+
+    print "Running %d non-mandatory tour scheduling choices" % len(tours)
+
+    spec = tdd_non_mandatory_spec.Coefficient.head(4)
+    print spec
+    alts = tdd_alts.to_frame()
+
+    max_num_trips = tours.groupby('person_id').size().max()
+
+    # because this is Python, we have to vectorize everything by doing the
+    # "nth" trip for each person in a for loop (in other words, because each
+    # trip is dependent on the time windows left by the previous decision) -
+    # hopefully this will work out ok!
+
+    choices = []
+
+    for i in range(max_num_trips):
+
+        nth_tours = tours.groupby('person_id').nth(i)
+
+        print "Running %d non-mandatory #%d tour choices" % \
+              (len(nth_tours), i+1)
+
+        nth_tours["end_of_previous_tour"] = -1
+
+        nth_choices, _ = \
+            asim.simple_simulate(nth_tours, alts, spec, mult_by_alt_col=False)
+
+        choices.append(nth_choices)
+
+    choices = pd.concat(choices)
+
+    print "Choices:\n", choices.describe()
+
+    sim.add_column("non_mandatory_tours", "non_mandatory_tdd", 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 +659,357 @@ 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 1c065abf11..e6329cec65 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 1643e621dd..0a97dd600f 100644
--- a/notebooks/simulation.ipynb
+++ b/notebooks/simulation.ipynb
@@ -1,7 +1,7 @@
 {
  "metadata": {
   "name": "",
-  "signature": "sha256:68dd59fb87c331bcb79d97a97557ee2ee411309bd3a85e58d6bee4b169b221df"
+  "signature": "sha256:9a607455266c43290b762ebb7f1984d7fe8afb27633c63fe6568f0607f6e8de4"
  },
  "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    2549.000000\n",
+        "mean      722.169086\n",
+        "std       425.570891\n",
+        "min         1.000000\n",
+        "25%       343.000000\n",
+        "50%       723.000000\n",
+        "75%      1087.000000\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': 8.85s\n",
+        "Total time to execute: 8.85s\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    2549.000000\n",
+        "mean       39.473127\n",
+        "std        24.095286\n",
+        "min         0.430000\n",
+        "25%        21.450000\n",
+        "50%        37.150000\n",
+        "75%        53.480000\n",
+        "max       132.160000\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    489\n",
+        "3    365\n",
+        "1     78\n",
+        "4     61\n",
+        "0      7\n",
         "dtype: int64\n",
-        "Time to execute model 'auto_ownership_simulate': 4.62s"
+        "Time to execute model 'auto_ownership_simulate': 0.72s\n",
+        "Total time to execute: 0.72s\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",
+        "856 persons run for mandatory tour model"
        ]
       },
       {
@@ -104,615 +145,1462 @@
        "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  \\\n",
+        "((ptype == 1) & (auto_ownership == 0)) * work2                                       4280   \n",
+        "((ptype == 1) & (auto_ownership == 0)) * work_and_school                             4280   \n",
+        "((ptype == 2) & (auto_ownership == 0)) * work2                                       4280   \n",
+        "((ptype == 2) & (auto_ownership == 0)) * work_and_school                             4280   \n",
+        "((ptype == 2) & nonstudent_to_school) * school1                                      4280   \n",
+        "((ptype == 2) & nonstudent_to_school) * work_and_school                              4280   \n",
+        "((ptype == 4) & nonstudent_to_school) * school1                                      4280   \n",
+        "((ptype == 5) & (auto_ownership == 0)) * work2                                       4280   \n",
+        "((ptype == 5) & nonstudent_to_school) * school1                                      4280   \n",
+        "((ptype == 6) & (auto_ownership == 0)) * school2                                     4280   \n",
+        "((ptype == 6) & (auto_ownership == 0)) * work_and_school                             4280   \n",
+        "((ptype == 6) & (distance_to_school < 3)) * school2                                  4280   \n",
+        "((ptype == 6) & (distance_to_work < 3 | distance_to_school < 3)) * work_and_school   4280   \n",
+        "((ptype == 6) & non_family) * school1                                                4280   \n",
+        "((ptype == 6) & non_family) * work1                                                  4280   \n",
+        "((ptype == 6) & non_family) * work_and_school                                        4280   \n",
+        "((ptype == 6) * (num_young_children)) * school1                                      4280   \n",
+        "((ptype == 6) * (num_young_children)) * school2                                      4280   \n",
+        "((ptype == 6) * (num_young_children)) * work1                                        4280   \n",
+        "((ptype == 6) * (num_young_children)) * work_and_school                              4280   \n",
+        "(~(school_taz > -1)) * school1                                                       4280   \n",
+        "(~(school_taz > -1)) * school2                                                       4280   \n",
+        "(~(school_taz > -1)) * work_and_school                                               4280   \n",
+        "(~(workplace_taz > -1)) * work1                                                      4280   \n",
+        "(~(workplace_taz > -1)) * work2                                                      4280   \n",
+        "(~(workplace_taz > -1)) * work_and_school                                            4280   \n",
+        "\n",
+        "                                                                                    mean  \\\n",
+        "((ptype == 1) & (auto_ownership == 0)) * work2                                         0   \n",
+        "((ptype == 1) & (auto_ownership == 0)) * work_and_school                               0   \n",
+        "((ptype == 2) & (auto_ownership == 0)) * work2                                         0   \n",
+        "((ptype == 2) & (auto_ownership == 0)) * work_and_school                               0   \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) & (auto_ownership == 0)) * work2                                         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) & (distance_to_school < 3)) * school2                                    0   \n",
+        "((ptype == 6) & (distance_to_work < 3 | distance_to_school < 3)) * 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 == 6) * (num_young_children)) * school1                                        0   \n",
+        "((ptype == 6) * (num_young_children)) * school2                                        0   \n",
+        "((ptype == 6) * (num_young_children)) * work1                                          0   \n",
+        "((ptype == 6) * (num_young_children)) * work_and_school                                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",
+        "\n",
+        "                                                                                    std  \\\n",
+        "((ptype == 1) & (auto_ownership == 0)) * work2                                        0   \n",
+        "((ptype == 1) & (auto_ownership == 0)) * work_and_school                              0   \n",
+        "((ptype == 2) & (auto_ownership == 0)) * work2                                        0   \n",
+        "((ptype == 2) & (auto_ownership == 0)) * work_and_school                              0   \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) & (auto_ownership == 0)) * work2                                        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) & (distance_to_school < 3)) * school2                                   0   \n",
+        "((ptype == 6) & (distance_to_work < 3 | distance_to_school < 3)) * 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 == 6) * (num_young_children)) * school1                                       0   \n",
+        "((ptype == 6) * (num_young_children)) * school2                                       0   \n",
+        "((ptype == 6) * (num_young_children)) * work1                                         0   \n",
+        "((ptype == 6) * (num_young_children)) * work_and_school                               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",
+        "\n",
+        "                                                                                    min  \\\n",
+        "((ptype == 1) & (auto_ownership == 0)) * work2                                        0   \n",
+        "((ptype == 1) & (auto_ownership == 0)) * work_and_school                              0   \n",
+        "((ptype == 2) & (auto_ownership == 0)) * work2                                        0   \n",
+        "((ptype == 2) & (auto_ownership == 0)) * work_and_school                              0   \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) & (auto_ownership == 0)) * work2                                        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) & (distance_to_school < 3)) * school2                                   0   \n",
+        "((ptype == 6) & (distance_to_work < 3 | distance_to_school < 3)) * 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 == 6) * (num_young_children)) * school1                                       0   \n",
+        "((ptype == 6) * (num_young_children)) * school2                                       0   \n",
+        "((ptype == 6) * (num_young_children)) * work1                                         0   \n",
+        "((ptype == 6) * (num_young_children)) * work_and_school                               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",
+        "\n",
+        "                                                                                    25%  \\\n",
+        "((ptype == 1) & (auto_ownership == 0)) * work2                                        0   \n",
+        "((ptype == 1) & (auto_ownership == 0)) * work_and_school                              0   \n",
+        "((ptype == 2) & (auto_ownership == 0)) * work2                                        0   \n",
+        "((ptype == 2) & (auto_ownership == 0)) * work_and_school                              0   \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) & (auto_ownership == 0)) * work2                                        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) & (distance_to_school < 3)) * school2                                   0   \n",
+        "((ptype == 6) & (distance_to_work < 3 | distance_to_school < 3)) * 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 == 6) * (num_young_children)) * school1                                       0   \n",
+        "((ptype == 6) * (num_young_children)) * school2                                       0   \n",
+        "((ptype == 6) * (num_young_children)) * work1                                         0   \n",
+        "((ptype == 6) * (num_young_children)) * work_and_school                               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",
+        "\n",
+        "                                                                                    50%  \\\n",
+        "((ptype == 1) & (auto_ownership == 0)) * work2                                        0   \n",
+        "((ptype == 1) & (auto_ownership == 0)) * work_and_school                              0   \n",
+        "((ptype == 2) & (auto_ownership == 0)) * work2                                        0   \n",
+        "((ptype == 2) & (auto_ownership == 0)) * work_and_school                              0   \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) & (auto_ownership == 0)) * work2                                        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) & (distance_to_school < 3)) * school2                                   0   \n",
+        "((ptype == 6) & (distance_to_work < 3 | distance_to_school < 3)) * 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 == 6) * (num_young_children)) * school1                                       0   \n",
+        "((ptype == 6) * (num_young_children)) * school2                                       0   \n",
+        "((ptype == 6) * (num_young_children)) * work1                                         0   \n",
+        "((ptype == 6) * (num_young_children)) * work_and_school                               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",
+        "\n",
+        "                                                                                    75%  \\\n",
+        "((ptype == 1) & (auto_ownership == 0)) * work2                                        0   \n",
+        "((ptype == 1) & (auto_ownership == 0)) * work_and_school                              0   \n",
+        "((ptype == 2) & (auto_ownership == 0)) * work2                                        0   \n",
+        "((ptype == 2) & (auto_ownership == 0)) * work_and_school                              0   \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) & (auto_ownership == 0)) * work2                                        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) & (distance_to_school < 3)) * school2                                   0   \n",
+        "((ptype == 6) & (distance_to_work < 3 | distance_to_school < 3)) * 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 == 6) * (num_young_children)) * school1                                       0   \n",
+        "((ptype == 6) * (num_young_children)) * school2                                       0   \n",
+        "((ptype == 6) * (num_young_children)) * work1                                         0   \n",
+        "((ptype == 6) * (num_young_children)) * work_and_school                               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",
+        "\n",
+        "                                                                                    max  \n",
+        "((ptype == 1) & (auto_ownership == 0)) * work2                                        0  \n",
+        "((ptype == 1) & (auto_ownership == 0)) * work_and_school                              0  \n",
+        "((ptype == 2) & (auto_ownership == 0)) * work2                                        0  \n",
+        "((ptype == 2) & (auto_ownership == 0)) * work_and_school                              0  \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) & (auto_ownership == 0)) * work2                                        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) & (distance_to_school < 3)) * school2                                   0  \n",
+        "((ptype == 6) & (distance_to_work < 3 | distance_to_school < 3)) * 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 == 6) * (num_young_children)) * school1                                       0  \n",
+        "((ptype == 6) * (num_young_children)) * school2                                       0  \n",
+        "((ptype == 6) * (num_young_children)) * work1                                         0  \n",
+        "((ptype == 6) * (num_young_children)) * work_and_school                               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    322\n",
+        "work2              221\n",
+        "work1              179\n",
+        "school1             67\n",
+        "school2             67\n",
+        "dtype: int64\n",
+        "Time to execute model 'mandatory_tour_frequency': 3.83s\n",
+        "Total time to execute: 3.83s\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": [
-        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-        "<table border=\"1\" class=\"dataframe\">\n",
-        "  <thead>\n",
-        "    <tr style=\"text-align: right;\">\n",
-        "      <th></th>\n",
-        "      <th>DISTRICT</th>\n",
-        "      <th>SD</th>\n",
-        "      <th>COUNTY</th>\n",
-        "      <th>TOTHH</th>\n",
-        "      <th>HHPOP</th>\n",
-        "      <th>TOTPOP</th>\n",
-        "      <th>EMPRES</th>\n",
-        "      <th>SFDU</th>\n",
-        "      <th>MFDU</th>\n",
-        "      <th>HHINCQ1</th>\n",
-        "      <th>...</th>\n",
-        "      <th>hhlds</th>\n",
-        "      <th>sftaz</th>\n",
-        "      <th>gqpop</th>\n",
-        "      <th>employment_density</th>\n",
-        "      <th>total_acres</th>\n",
-        "      <th>county_id</th>\n",
-        "      <th>density_index</th>\n",
-        "      <th>household_density</th>\n",
-        "      <th>total_households</th>\n",
-        "      <th>total_employment</th>\n",
-        "    </tr>\n",
-        "  </thead>\n",
-        "  <tbody>\n",
-        "    <tr>\n",
-        "      <th>count</th>\n",
-        "      <td> 1454.000000</td>\n",
-        "      <td> 1454.000000</td>\n",
-        "      <td> 1454.000000</td>\n",
-        "      <td>  1454.000000</td>\n",
-        "      <td>  1454.000000</td>\n",
-        "      <td>  1454.000000</td>\n",
-        "      <td>  1454.000000</td>\n",
-        "      <td>  1454.000000</td>\n",
-        "      <td> 1454.000000</td>\n",
-        "      <td> 1454.000000</td>\n",
-        "      <td>...</td>\n",
-        "      <td>  1454.000000</td>\n",
-        "      <td> 1454.000000</td>\n",
-        "      <td> 1454.000000</td>\n",
-        "      <td> 1454.000000</td>\n",
-        "      <td>   1454.000000</td>\n",
-        "      <td> 1454.000000</td>\n",
-        "      <td> 1453.000000</td>\n",
-        "      <td> 1454.000000</td>\n",
-        "      <td>  1454.000000</td>\n",
-        "      <td>  1454.000000</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>mean</th>\n",
-        "      <td>   14.908528</td>\n",
-        "      <td>   14.908528</td>\n",
-        "      <td>    3.835626</td>\n",
-        "      <td>  1793.688446</td>\n",
-        "      <td>  4816.408528</td>\n",
-        "      <td>  4917.978680</td>\n",
-        "      <td>  2168.684319</td>\n",
-        "      <td>  1122.798487</td>\n",
-        "      <td>  670.889959</td>\n",
-        "      <td>  508.134801</td>\n",
-        "      <td>...</td>\n",
-        "      <td>  1793.688446</td>\n",
-        "      <td>  727.500000</td>\n",
-        "      <td>  101.570151</td>\n",
-        "      <td>    9.596395</td>\n",
-        "      <td>   3146.071457</td>\n",
-        "      <td>    3.835626</td>\n",
-        "      <td>    2.279554</td>\n",
-        "      <td>    6.008186</td>\n",
-        "      <td>  1793.688446</td>\n",
-        "      <td>  2247.736589</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>std</th>\n",
-        "      <td>    8.701078</td>\n",
-        "      <td>    8.701078</td>\n",
-        "      <td>    2.040153</td>\n",
-        "      <td>   961.021405</td>\n",
-        "      <td>  2686.029808</td>\n",
-        "      <td>  2690.352928</td>\n",
-        "      <td>  1211.109335</td>\n",
-        "      <td>   854.895353</td>\n",
-        "      <td>  717.261660</td>\n",
-        "      <td>  378.753528</td>\n",
-        "      <td>...</td>\n",
-        "      <td>   961.021405</td>\n",
-        "      <td>  419.877958</td>\n",
-        "      <td>  393.886676</td>\n",
-        "      <td>   45.067313</td>\n",
-        "      <td>  16945.908840</td>\n",
-        "      <td>    2.040153</td>\n",
-        "      <td>    3.945717</td>\n",
-        "      <td>    8.565908</td>\n",
-        "      <td>   961.021405</td>\n",
-        "      <td>  3538.356220</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>min</th>\n",
-        "      <td>    1.000000</td>\n",
-        "      <td>    1.000000</td>\n",
-        "      <td>    1.000000</td>\n",
-        "      <td>     0.000000</td>\n",
-        "      <td>     0.000000</td>\n",
-        "      <td>     0.000000</td>\n",
-        "      <td>     0.000000</td>\n",
-        "      <td>     0.000000</td>\n",
-        "      <td>    0.000000</td>\n",
-        "      <td>    0.000000</td>\n",
-        "      <td>...</td>\n",
-        "      <td>     0.000000</td>\n",
-        "      <td>    1.000000</td>\n",
-        "      <td>   -1.000000</td>\n",
-        "      <td>    0.000000</td>\n",
-        "      <td>     13.000000</td>\n",
-        "      <td>    1.000000</td>\n",
-        "      <td>    0.000000</td>\n",
-        "      <td>    0.000000</td>\n",
-        "      <td>     0.000000</td>\n",
-        "      <td>     0.000000</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>25%</th>\n",
-        "      <td>    8.000000</td>\n",
-        "      <td>    8.000000</td>\n",
-        "      <td>    3.000000</td>\n",
-        "      <td>  1200.250000</td>\n",
-        "      <td>  3288.250000</td>\n",
-        "      <td>  3384.500000</td>\n",
-        "      <td>  1460.500000</td>\n",
-        "      <td>   602.000000</td>\n",
-        "      <td>  144.500000</td>\n",
-        "      <td>  257.000000</td>\n",
-        "      <td>...</td>\n",
-        "      <td>  1200.250000</td>\n",
-        "      <td>  364.250000</td>\n",
-        "      <td>    5.000000</td>\n",
-        "      <td>    0.877829</td>\n",
-        "      <td>    230.000000</td>\n",
-        "      <td>    3.000000</td>\n",
-        "      <td>    0.550232</td>\n",
-        "      <td>    1.910701</td>\n",
-        "      <td>  1200.250000</td>\n",
-        "      <td>   482.000000</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>50%</th>\n",
-        "      <td>   15.000000</td>\n",
-        "      <td>   15.000000</td>\n",
-        "      <td>    4.000000</td>\n",
-        "      <td>  1681.500000</td>\n",
-        "      <td>  4504.500000</td>\n",
-        "      <td>  4577.000000</td>\n",
-        "      <td>  2016.000000</td>\n",
-        "      <td>  1034.000000</td>\n",
-        "      <td>  460.000000</td>\n",
-        "      <td>  434.000000</td>\n",
-        "      <td>...</td>\n",
-        "      <td>  1681.500000</td>\n",
-        "      <td>  727.500000</td>\n",
-        "      <td>   18.000000</td>\n",
-        "      <td>    2.158701</td>\n",
-        "      <td>    397.000000</td>\n",
-        "      <td>    4.000000</td>\n",
-        "      <td>    1.289224</td>\n",
-        "      <td>    3.939122</td>\n",
-        "      <td>  1681.500000</td>\n",
-        "      <td>  1005.500000</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>75%</th>\n",
-        "      <td>   20.750000</td>\n",
-        "      <td>   20.750000</td>\n",
-        "      <td>    5.000000</td>\n",
-        "      <td>  2259.750000</td>\n",
-        "      <td>  6033.750000</td>\n",
-        "      <td>  6098.500000</td>\n",
-        "      <td>  2735.500000</td>\n",
-        "      <td>  1496.000000</td>\n",
-        "      <td>  907.750000</td>\n",
-        "      <td>  674.750000</td>\n",
-        "      <td>...</td>\n",
-        "      <td>  2259.750000</td>\n",
-        "      <td> 1090.750000</td>\n",
-        "      <td>   71.000000</td>\n",
-        "      <td>    5.492696</td>\n",
-        "      <td>    883.500000</td>\n",
-        "      <td>    5.000000</td>\n",
-        "      <td>    2.337577</td>\n",
-        "      <td>    6.693238</td>\n",
-        "      <td>  2259.750000</td>\n",
-        "      <td>  2215.750000</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>max</th>\n",
-        "      <td>   34.000000</td>\n",
-        "      <td>   34.000000</td>\n",
-        "      <td>    9.000000</td>\n",
-        "      <td> 12542.000000</td>\n",
-        "      <td> 39671.000000</td>\n",
-        "      <td> 40020.000000</td>\n",
-        "      <td> 16799.000000</td>\n",
-        "      <td> 12413.000000</td>\n",
-        "      <td> 4920.000000</td>\n",
-        "      <td> 3754.000000</td>\n",
-        "      <td>...</td>\n",
-        "      <td> 12542.000000</td>\n",
-        "      <td> 1454.000000</td>\n",
-        "      <td> 7810.000000</td>\n",
-        "      <td>  877.564767</td>\n",
-        "      <td> 372520.000000</td>\n",
-        "      <td>    9.000000</td>\n",
-        "      <td>   46.360371</td>\n",
-        "      <td>   90.891304</td>\n",
-        "      <td> 12542.000000</td>\n",
-        "      <td> 37950.000000</td>\n",
-        "    </tr>\n",
-        "  </tbody>\n",
-        "</table>\n",
-        "<p>8 rows \u00d7 48 columns</p>\n",
-        "</div>"
-       ],
-       "metadata": {},
-       "output_type": "pyout",
-       "prompt_number": 4,
-       "text": [
-        "          DISTRICT           SD       COUNTY         TOTHH         HHPOP  \\\n",
-        "count  1454.000000  1454.000000  1454.000000   1454.000000   1454.000000   \n",
-        "mean     14.908528    14.908528     3.835626   1793.688446   4816.408528   \n",
-        "std       8.701078     8.701078     2.040153    961.021405   2686.029808   \n",
-        "min       1.000000     1.000000     1.000000      0.000000      0.000000   \n",
-        "25%       8.000000     8.000000     3.000000   1200.250000   3288.250000   \n",
-        "50%      15.000000    15.000000     4.000000   1681.500000   4504.500000   \n",
-        "75%      20.750000    20.750000     5.000000   2259.750000   6033.750000   \n",
-        "max      34.000000    34.000000     9.000000  12542.000000  39671.000000   \n",
-        "\n",
-        "             TOTPOP        EMPRES          SFDU         MFDU      HHINCQ1  \\\n",
-        "count   1454.000000   1454.000000   1454.000000  1454.000000  1454.000000   \n",
-        "mean    4917.978680   2168.684319   1122.798487   670.889959   508.134801   \n",
-        "std     2690.352928   1211.109335    854.895353   717.261660   378.753528   \n",
-        "min        0.000000      0.000000      0.000000     0.000000     0.000000   \n",
-        "25%     3384.500000   1460.500000    602.000000   144.500000   257.000000   \n",
-        "50%     4577.000000   2016.000000   1034.000000   460.000000   434.000000   \n",
-        "75%     6098.500000   2735.500000   1496.000000   907.750000   674.750000   \n",
-        "max    40020.000000  16799.000000  12413.000000  4920.000000  3754.000000   \n",
-        "\n",
-        "             ...                hhlds        sftaz        gqpop  \\\n",
-        "count        ...          1454.000000  1454.000000  1454.000000   \n",
-        "mean         ...          1793.688446   727.500000   101.570151   \n",
-        "std          ...           961.021405   419.877958   393.886676   \n",
-        "min          ...             0.000000     1.000000    -1.000000   \n",
-        "25%          ...          1200.250000   364.250000     5.000000   \n",
-        "50%          ...          1681.500000   727.500000    18.000000   \n",
-        "75%          ...          2259.750000  1090.750000    71.000000   \n",
-        "max          ...         12542.000000  1454.000000  7810.000000   \n",
-        "\n",
-        "       employment_density    total_acres    county_id  density_index  \\\n",
-        "count         1454.000000    1454.000000  1454.000000    1453.000000   \n",
-        "mean             9.596395    3146.071457     3.835626       2.279554   \n",
-        "std             45.067313   16945.908840     2.040153       3.945717   \n",
-        "min              0.000000      13.000000     1.000000       0.000000   \n",
-        "25%              0.877829     230.000000     3.000000       0.550232   \n",
-        "50%              2.158701     397.000000     4.000000       1.289224   \n",
-        "75%              5.492696     883.500000     5.000000       2.337577   \n",
-        "max            877.564767  372520.000000     9.000000      46.360371   \n",
-        "\n",
-        "       household_density  total_households  total_employment  \n",
-        "count        1454.000000       1454.000000       1454.000000  \n",
-        "mean            6.008186       1793.688446       2247.736589  \n",
-        "std             8.565908        961.021405       3538.356220  \n",
-        "min             0.000000          0.000000          0.000000  \n",
-        "25%             1.910701       1200.250000        482.000000  \n",
-        "50%             3.939122       1681.500000       1005.500000  \n",
-        "75%             6.693238       2259.750000       2215.750000  \n",
-        "max            90.891304      12542.000000      37950.000000  \n",
-        "\n",
-        "[8 rows x 48 columns]"
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Running model 'non_mandatory_tour_frequency'\n",
+        "1683 persons run for non-mandatory tour model"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "Running segment 'driving' of size 45\n",
+        "WARNING: Describe of columns with no variability:\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "                                                             count  mean  std  \\\n",
+        "(tot_tours == 0) & (num_mand == 0) & (num_joint_tours == 0)   4320     0    0   \n",
+        "escort * no_cars                                              4320     0    0   \n",
+        "max_window*(tot_tours == 0)                                   4320     0    0   \n",
+        "max_window*(tot_tours == 1)                                   4320     0    0   \n",
+        "max_window*(tot_tours == 2)                                   4320     0    0   \n",
+        "max_window*(tot_tours == 3)                                   4320     0    0   \n",
+        "max_window*(tot_tours == 4)                                   4320     0    0   \n",
+        "max_window*(tot_tours >  4)                                   4320     0    0   \n",
+        "no_cars & (tot_tours == 1)                                    4320     0    0   \n",
+        "no_cars & (tot_tours == 2)                                    4320     0    0   \n",
+        "no_cars & (tot_tours == 3)                                    4320     0    0   \n",
+        "no_cars & (tot_tours == 4)                                    4320     0    0   \n",
+        "no_cars & (tot_tours >  4)                                    4320     0    0   \n",
+        "num_disc_j                                                    4320     0    0   \n",
+        "num_eat_j                                                     4320     0    0   \n",
+        "num_joint_tours*(tot_tours == 0)                              4320     0    0   \n",
+        "num_joint_tours*(tot_tours == 1)                              4320     0    0   \n",
+        "num_joint_tours*(tot_tours == 2)                              4320     0    0   \n",
+        "num_joint_tours*(tot_tours == 3)                              4320     0    0   \n",
+        "num_joint_tours*(tot_tours == 4)                              4320     0    0   \n",
+        "num_joint_tours*(tot_tours >  4)                              4320     0    0   \n",
+        "num_main_j                                                    4320     0    0   \n",
+        "num_shop_j                                                    4320     0    0   \n",
+        "num_visi_j                                                    4320     0    0   \n",
+        "\n",
+        "                                                             min  25%  50%  \\\n",
+        "(tot_tours == 0) & (num_mand == 0) & (num_joint_tours == 0)    0    0    0   \n",
+        "escort * no_cars                                               0    0    0   \n",
+        "max_window*(tot_tours == 0)                                    0    0    0   \n",
+        "max_window*(tot_tours == 1)                                    0    0    0   \n",
+        "max_window*(tot_tours == 2)                                    0    0    0   \n",
+        "max_window*(tot_tours == 3)                                    0    0    0   \n",
+        "max_window*(tot_tours == 4)                                    0    0    0   \n",
+        "max_window*(tot_tours >  4)                                    0    0    0   \n",
+        "no_cars & (tot_tours == 1)                                     0    0    0   \n",
+        "no_cars & (tot_tours == 2)                                     0    0    0   \n",
+        "no_cars & (tot_tours == 3)                                     0    0    0   \n",
+        "no_cars & (tot_tours == 4)                                     0    0    0   \n",
+        "no_cars & (tot_tours >  4)                                     0    0    0   \n",
+        "num_disc_j                                                     0    0    0   \n",
+        "num_eat_j                                                      0    0    0   \n",
+        "num_joint_tours*(tot_tours == 0)                               0    0    0   \n",
+        "num_joint_tours*(tot_tours == 1)                               0    0    0   \n",
+        "num_joint_tours*(tot_tours == 2)                               0    0    0   \n",
+        "num_joint_tours*(tot_tours == 3)                               0    0    0   \n",
+        "num_joint_tours*(tot_tours == 4)                               0    0    0   \n",
+        "num_joint_tours*(tot_tours >  4)                               0    0    0   \n",
+        "num_main_j                                                     0    0    0   \n",
+        "num_shop_j                                                     0    0    0   \n",
+        "num_visi_j                                                     0    0    0   \n",
+        "\n",
+        "                                                             75%  max  \n",
+        "(tot_tours == 0) & (num_mand == 0) & (num_joint_tours == 0)    0    0  \n",
+        "escort * no_cars                                               0    0  \n",
+        "max_window*(tot_tours == 0)                                    0    0  \n",
+        "max_window*(tot_tours == 1)                                    0    0  \n",
+        "max_window*(tot_tours == 2)                                    0    0  \n",
+        "max_window*(tot_tours == 3)                                    0    0  \n",
+        "max_window*(tot_tours == 4)                                    0    0  \n",
+        "max_window*(tot_tours >  4)                                    0    0  \n",
+        "no_cars & (tot_tours == 1)                                     0    0  \n",
+        "no_cars & (tot_tours == 2)                                     0    0  \n",
+        "no_cars & (tot_tours == 3)                                     0    0  \n",
+        "no_cars & (tot_tours == 4)                                     0    0  \n",
+        "no_cars & (tot_tours >  4)                                     0    0  \n",
+        "num_disc_j                                                     0    0  \n",
+        "num_eat_j                                                      0    0  \n",
+        "num_joint_tours*(tot_tours == 0)                               0    0  \n",
+        "num_joint_tours*(tot_tours == 1)                               0    0  \n",
+        "num_joint_tours*(tot_tours == 2)                               0    0  \n",
+        "num_joint_tours*(tot_tours == 3)                               0    0  \n",
+        "num_joint_tours*(tot_tours == 4)                               0    0  \n",
+        "num_joint_tours*(tot_tours >  4)                               0    0  \n",
+        "num_main_j                                                     0    0  \n",
+        "num_shop_j                                                     0    0  \n",
+        "num_visi_j                                                     0    0  \n",
+        "Running segment 'full' of size 584\n",
+        "WARNING: Describe of columns with no variability:\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "                                                             count  mean  std  \\\n",
+        "(tot_tours == 0) & (num_mand == 0) & (num_joint_tours == 0)  56064     0    0   \n",
+        "max_window*(tot_tours == 0)                                  56064     0    0   \n",
+        "max_window*(tot_tours == 1)                                  56064     0    0   \n",
+        "max_window*(tot_tours == 2)                                  56064     0    0   \n",
+        "max_window*(tot_tours == 3)                                  56064     0    0   \n",
+        "max_window*(tot_tours == 4)                                  56064     0    0   \n",
+        "max_window*(tot_tours >  4)                                  56064     0    0   \n",
+        "num_disc_j                                                   56064     0    0   \n",
+        "num_eat_j                                                    56064     0    0   \n",
+        "num_joint_tours*(tot_tours == 0)                             56064     0    0   \n",
+        "num_joint_tours*(tot_tours == 1)                             56064     0    0   \n",
+        "num_joint_tours*(tot_tours == 2)                             56064     0    0   \n",
+        "num_joint_tours*(tot_tours == 3)                             56064     0    0   \n",
+        "num_joint_tours*(tot_tours == 4)                             56064     0    0   \n",
+        "num_joint_tours*(tot_tours >  4)                             56064     0    0   \n",
+        "num_main_j                                                   56064     0    0   \n",
+        "num_shop_j                                                   56064     0    0   \n",
+        "num_visi_j                                                   56064     0    0   \n",
+        "\n",
+        "                                                             min  25%  50%  \\\n",
+        "(tot_tours == 0) & (num_mand == 0) & (num_joint_tours == 0)    0    0    0   \n",
+        "max_window*(tot_tours == 0)                                    0    0    0   \n",
+        "max_window*(tot_tours == 1)                                    0    0    0   \n",
+        "max_window*(tot_tours == 2)                                    0    0    0   \n",
+        "max_window*(tot_tours == 3)                                    0    0    0   \n",
+        "max_window*(tot_tours == 4)                                    0    0    0   \n",
+        "max_window*(tot_tours >  4)                                    0    0    0   \n",
+        "num_disc_j                                                     0    0    0   \n",
+        "num_eat_j                                                      0    0    0   \n",
+        "num_joint_tours*(tot_tours == 0)                               0    0    0   \n",
+        "num_joint_tours*(tot_tours == 1)                               0    0    0   \n",
+        "num_joint_tours*(tot_tours == 2)                               0    0    0   \n",
+        "num_joint_tours*(tot_tours == 3)                               0    0    0   \n",
+        "num_joint_tours*(tot_tours == 4)                               0    0    0   \n",
+        "num_joint_tours*(tot_tours >  4)                               0    0    0   \n",
+        "num_main_j                                                     0    0    0   \n",
+        "num_shop_j                                                     0    0    0   \n",
+        "num_visi_j                                                     0    0    0   \n",
+        "\n",
+        "                                                             75%  max  \n",
+        "(tot_tours == 0) & (num_mand == 0) & (num_joint_tours == 0)    0    0  \n",
+        "max_window*(tot_tours == 0)                                    0    0  \n",
+        "max_window*(tot_tours == 1)                                    0    0  \n",
+        "max_window*(tot_tours == 2)                                    0    0  \n",
+        "max_window*(tot_tours == 3)                                    0    0  \n",
+        "max_window*(tot_tours == 4)                                    0    0  \n",
+        "max_window*(tot_tours >  4)                                    0    0  \n",
+        "num_disc_j                                                     0    0  \n",
+        "num_eat_j                                                      0    0  \n",
+        "num_joint_tours*(tot_tours == 0)                               0    0  \n",
+        "num_joint_tours*(tot_tours == 1)                               0    0  \n",
+        "num_joint_tours*(tot_tours == 2)                               0    0  \n",
+        "num_joint_tours*(tot_tours == 3)                               0    0  \n",
+        "num_joint_tours*(tot_tours == 4)                               0    0  \n",
+        "num_joint_tours*(tot_tours >  4)                               0    0  \n",
+        "num_main_j                                                     0    0  \n",
+        "num_shop_j                                                     0    0  \n",
+        "num_visi_j                                                     0    0  \n",
+        "Running segment 'nonwork' of size 289\n",
+        "WARNING: Describe of columns with no variability:\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "                                                             count  mean  std  \\\n",
+        "(tot_tours == 0) & (num_mand == 0) & (num_joint_tours == 0)  27744     0    0   \n",
+        "max_window*(tot_tours == 0)                                  27744     0    0   \n",
+        "max_window*(tot_tours == 1)                                  27744     0    0   \n",
+        "max_window*(tot_tours == 2)                                  27744     0    0   \n",
+        "max_window*(tot_tours == 3)                                  27744     0    0   \n",
+        "max_window*(tot_tours == 4)                                  27744     0    0   \n",
+        "max_window*(tot_tours >  4)                                  27744     0    0   \n",
+        "num_disc_j                                                   27744     0    0   \n",
+        "num_eat_j                                                    27744     0    0   \n",
+        "num_joint_tours*(tot_tours == 0)                             27744     0    0   \n",
+        "num_joint_tours*(tot_tours == 1)                             27744     0    0   \n",
+        "num_joint_tours*(tot_tours == 2)                             27744     0    0   \n",
+        "num_joint_tours*(tot_tours == 3)                             27744     0    0   \n",
+        "num_joint_tours*(tot_tours == 4)                             27744     0    0   \n",
+        "num_joint_tours*(tot_tours >  4)                             27744     0    0   \n",
+        "num_main_j                                                   27744     0    0   \n",
+        "num_shop_j                                                   27744     0    0   \n",
+        "num_visi_j                                                   27744     0    0   \n",
+        "\n",
+        "                                                             min  25%  50%  \\\n",
+        "(tot_tours == 0) & (num_mand == 0) & (num_joint_tours == 0)    0    0    0   \n",
+        "max_window*(tot_tours == 0)                                    0    0    0   \n",
+        "max_window*(tot_tours == 1)                                    0    0    0   \n",
+        "max_window*(tot_tours == 2)                                    0    0    0   \n",
+        "max_window*(tot_tours == 3)                                    0    0    0   \n",
+        "max_window*(tot_tours == 4)                                    0    0    0   \n",
+        "max_window*(tot_tours >  4)                                    0    0    0   \n",
+        "num_disc_j                                                     0    0    0   \n",
+        "num_eat_j                                                      0    0    0   \n",
+        "num_joint_tours*(tot_tours == 0)                               0    0    0   \n",
+        "num_joint_tours*(tot_tours == 1)                               0    0    0   \n",
+        "num_joint_tours*(tot_tours == 2)                               0    0    0   \n",
+        "num_joint_tours*(tot_tours == 3)                               0    0    0   \n",
+        "num_joint_tours*(tot_tours == 4)                               0    0    0   \n",
+        "num_joint_tours*(tot_tours >  4)                               0    0    0   \n",
+        "num_main_j                                                     0    0    0   \n",
+        "num_shop_j                                                     0    0    0   \n",
+        "num_visi_j                                                     0    0    0   \n",
+        "\n",
+        "                                                             75%  max  \n",
+        "(tot_tours == 0) & (num_mand == 0) & (num_joint_tours == 0)    0    0  \n",
+        "max_window*(tot_tours == 0)                                    0    0  \n",
+        "max_window*(tot_tours == 1)                                    0    0  \n",
+        "max_window*(tot_tours == 2)                                    0    0  \n",
+        "max_window*(tot_tours == 3)                                    0    0  \n",
+        "max_window*(tot_tours == 4)                                    0    0  \n",
+        "max_window*(tot_tours >  4)                                    0    0  \n",
+        "num_disc_j                                                     0    0  \n",
+        "num_eat_j                                                      0    0  \n",
+        "num_joint_tours*(tot_tours == 0)                               0    0  \n",
+        "num_joint_tours*(tot_tours == 1)                               0    0  \n",
+        "num_joint_tours*(tot_tours == 2)                               0    0  \n",
+        "num_joint_tours*(tot_tours == 3)                               0    0  \n",
+        "num_joint_tours*(tot_tours == 4)                               0    0  \n",
+        "num_joint_tours*(tot_tours >  4)                               0    0  \n",
+        "num_main_j                                                     0    0  \n",
+        "num_shop_j                                                     0    0  \n",
+        "num_visi_j                                                     0    0  \n",
+        "Running segment 'part' of size 135\n",
+        "WARNING: Describe of columns with no variability:\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "                                                             count  mean  std  \\\n",
+        "(tot_tours == 0) & (num_mand == 0) & (num_joint_tours == 0)  12960     0    0   \n",
+        "max_window*(tot_tours == 0)                                  12960     0    0   \n",
+        "max_window*(tot_tours == 1)                                  12960     0    0   \n",
+        "max_window*(tot_tours == 2)                                  12960     0    0   \n",
+        "max_window*(tot_tours == 3)                                  12960     0    0   \n",
+        "max_window*(tot_tours == 4)                                  12960     0    0   \n",
+        "max_window*(tot_tours >  4)                                  12960     0    0   \n",
+        "num_disc_j                                                   12960     0    0   \n",
+        "num_eat_j                                                    12960     0    0   \n",
+        "num_joint_tours*(tot_tours == 0)                             12960     0    0   \n",
+        "num_joint_tours*(tot_tours == 1)                             12960     0    0   \n",
+        "num_joint_tours*(tot_tours == 2)                             12960     0    0   \n",
+        "num_joint_tours*(tot_tours == 3)                             12960     0    0   \n",
+        "num_joint_tours*(tot_tours == 4)                             12960     0    0   \n",
+        "num_joint_tours*(tot_tours >  4)                             12960     0    0   \n",
+        "num_main_j                                                   12960     0    0   \n",
+        "num_shop_j                                                   12960     0    0   \n",
+        "num_visi_j                                                   12960     0    0   \n",
+        "\n",
+        "                                                             min  25%  50%  \\\n",
+        "(tot_tours == 0) & (num_mand == 0) & (num_joint_tours == 0)    0    0    0   \n",
+        "max_window*(tot_tours == 0)                                    0    0    0   \n",
+        "max_window*(tot_tours == 1)                                    0    0    0   \n",
+        "max_window*(tot_tours == 2)                                    0    0    0   \n",
+        "max_window*(tot_tours == 3)                                    0    0    0   \n",
+        "max_window*(tot_tours == 4)                                    0    0    0   \n",
+        "max_window*(tot_tours >  4)                                    0    0    0   \n",
+        "num_disc_j                                                     0    0    0   \n",
+        "num_eat_j                                                      0    0    0   \n",
+        "num_joint_tours*(tot_tours == 0)                               0    0    0   \n",
+        "num_joint_tours*(tot_tours == 1)                               0    0    0   \n",
+        "num_joint_tours*(tot_tours == 2)                               0    0    0   \n",
+        "num_joint_tours*(tot_tours == 3)                               0    0    0   \n",
+        "num_joint_tours*(tot_tours == 4)                               0    0    0   \n",
+        "num_joint_tours*(tot_tours >  4)                               0    0    0   \n",
+        "num_main_j                                                     0    0    0   \n",
+        "num_shop_j                                                     0    0    0   \n",
+        "num_visi_j                                                     0    0    0   \n",
+        "\n",
+        "                                                             75%  max  \n",
+        "(tot_tours == 0) & (num_mand == 0) & (num_joint_tours == 0)    0    0  \n",
+        "max_window*(tot_tours == 0)                                    0    0  \n",
+        "max_window*(tot_tours == 1)                                    0    0  \n",
+        "max_window*(tot_tours == 2)                                    0    0  \n",
+        "max_window*(tot_tours == 3)                                    0    0  \n",
+        "max_window*(tot_tours == 4)                                    0    0  \n",
+        "max_window*(tot_tours >  4)                                    0    0  \n",
+        "num_disc_j                                                     0    0  \n",
+        "num_eat_j                                                      0    0  \n",
+        "num_joint_tours*(tot_tours == 0)                               0    0  \n",
+        "num_joint_tours*(tot_tours == 1)                               0    0  \n",
+        "num_joint_tours*(tot_tours == 2)                               0    0  \n",
+        "num_joint_tours*(tot_tours == 3)                               0    0  \n",
+        "num_joint_tours*(tot_tours == 4)                               0    0  \n",
+        "num_joint_tours*(tot_tours >  4)                               0    0  \n",
+        "num_main_j                                                     0    0  \n",
+        "num_shop_j                                                     0    0  \n",
+        "num_visi_j                                                     0    0  \n",
+        "Running segment 'preschool' of size 127\n",
+        "WARNING: Describe of columns with no variability:\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "                                                             count  mean  std  \\\n",
+        "(tot_tours == 0) & (num_mand == 0) & (num_joint_tours == 0)  12192     0    0   \n",
+        "max_window*(tot_tours == 0)                                  12192     0    0   \n",
+        "max_window*(tot_tours == 1)                                  12192     0    0   \n",
+        "max_window*(tot_tours == 2)                                  12192     0    0   \n",
+        "max_window*(tot_tours == 3)                                  12192     0    0   \n",
+        "max_window*(tot_tours == 4)                                  12192     0    0   \n",
+        "max_window*(tot_tours >  4)                                  12192     0    0   \n",
+        "num_disc_j                                                   12192     0    0   \n",
+        "num_eat_j                                                    12192     0    0   \n",
+        "num_joint_tours*(tot_tours == 0)                             12192     0    0   \n",
+        "num_joint_tours*(tot_tours == 1)                             12192     0    0   \n",
+        "num_joint_tours*(tot_tours == 2)                             12192     0    0   \n",
+        "num_joint_tours*(tot_tours == 3)                             12192     0    0   \n",
+        "num_joint_tours*(tot_tours == 4)                             12192     0    0   \n",
+        "num_joint_tours*(tot_tours >  4)                             12192     0    0   \n",
+        "num_main_j                                                   12192     0    0   \n",
+        "num_shop_j                                                   12192     0    0   \n",
+        "num_visi_j                                                   12192     0    0   \n",
+        "\n",
+        "                                                             min  25%  50%  \\\n",
+        "(tot_tours == 0) & (num_mand == 0) & (num_joint_tours == 0)    0    0    0   \n",
+        "max_window*(tot_tours == 0)                                    0    0    0   \n",
+        "max_window*(tot_tours == 1)                                    0    0    0   \n",
+        "max_window*(tot_tours == 2)                                    0    0    0   \n",
+        "max_window*(tot_tours == 3)                                    0    0    0   \n",
+        "max_window*(tot_tours == 4)                                    0    0    0   \n",
+        "max_window*(tot_tours >  4)                                    0    0    0   \n",
+        "num_disc_j                                                     0    0    0   \n",
+        "num_eat_j                                                      0    0    0   \n",
+        "num_joint_tours*(tot_tours == 0)                               0    0    0   \n",
+        "num_joint_tours*(tot_tours == 1)                               0    0    0   \n",
+        "num_joint_tours*(tot_tours == 2)                               0    0    0   \n",
+        "num_joint_tours*(tot_tours == 3)                               0    0    0   \n",
+        "num_joint_tours*(tot_tours == 4)                               0    0    0   \n",
+        "num_joint_tours*(tot_tours >  4)                               0    0    0   \n",
+        "num_main_j                                                     0    0    0   \n",
+        "num_shop_j                                                     0    0    0   \n",
+        "num_visi_j                                                     0    0    0   \n",
+        "\n",
+        "                                                             75%  max  \n",
+        "(tot_tours == 0) & (num_mand == 0) & (num_joint_tours == 0)    0    0  \n",
+        "max_window*(tot_tours == 0)                                    0    0  \n",
+        "max_window*(tot_tours == 1)                                    0    0  \n",
+        "max_window*(tot_tours == 2)                                    0    0  \n",
+        "max_window*(tot_tours == 3)                                    0    0  \n",
+        "max_window*(tot_tours == 4)                                    0    0  \n",
+        "max_window*(tot_tours >  4)                                    0    0  \n",
+        "num_disc_j                                                     0    0  \n",
+        "num_eat_j                                                      0    0  \n",
+        "num_joint_tours*(tot_tours == 0)                               0    0  \n",
+        "num_joint_tours*(tot_tours == 1)                               0    0  \n",
+        "num_joint_tours*(tot_tours == 2)                               0    0  \n",
+        "num_joint_tours*(tot_tours == 3)                               0    0  \n",
+        "num_joint_tours*(tot_tours == 4)                               0    0  \n",
+        "num_joint_tours*(tot_tours >  4)                               0    0  \n",
+        "num_main_j                                                     0    0  \n",
+        "num_shop_j                                                     0    0  \n",
+        "num_visi_j                                                     0    0  \n",
+        "Running segment 'retired' of size 196\n",
+        "WARNING: Describe of columns with no variability:\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "                                                             count  mean  std  \\\n",
+        "(tot_tours == 0) & (num_mand == 0) & (num_joint_tours == 0)  18816     0    0   \n",
+        "escort * no_cars                                             18816     0    0   \n",
+        "has_preschool_kid_at_home * eatout                           18816     0    0   \n",
+        "has_preschool_kid_at_home * escort                           18816     0    0   \n",
+        "has_preschool_kid_at_home * othdiscr                         18816     0    0   \n",
+        "has_preschool_kid_at_home * othmaint                         18816     0    0   \n",
+        "has_preschool_kid_at_home * shopping                         18816     0    0   \n",
+        "max_window*(tot_tours == 0)                                  18816     0    0   \n",
+        "max_window*(tot_tours == 1)                                  18816     0    0   \n",
+        "max_window*(tot_tours == 2)                                  18816     0    0   \n",
+        "max_window*(tot_tours == 3)                                  18816     0    0   \n",
+        "max_window*(tot_tours == 4)                                  18816     0    0   \n",
+        "max_window*(tot_tours >  4)                                  18816     0    0   \n",
+        "no_cars & (tot_tours == 1)                                   18816     0    0   \n",
+        "no_cars & (tot_tours == 2)                                   18816     0    0   \n",
+        "no_cars & (tot_tours == 3)                                   18816     0    0   \n",
+        "no_cars & (tot_tours == 4)                                   18816     0    0   \n",
+        "no_cars & (tot_tours >  4)                                   18816     0    0   \n",
+        "num_disc_j                                                   18816     0    0   \n",
+        "num_eat_j                                                    18816     0    0   \n",
+        "num_joint_tours*(tot_tours == 0)                             18816     0    0   \n",
+        "num_joint_tours*(tot_tours == 1)                             18816     0    0   \n",
+        "num_joint_tours*(tot_tours == 2)                             18816     0    0   \n",
+        "num_joint_tours*(tot_tours == 3)                             18816     0    0   \n",
+        "num_joint_tours*(tot_tours == 4)                             18816     0    0   \n",
+        "num_joint_tours*(tot_tours >  4)                             18816     0    0   \n",
+        "num_main_j                                                   18816     0    0   \n",
+        "num_shop_j                                                   18816     0    0   \n",
+        "num_visi_j                                                   18816     0    0   \n",
+        "\n",
+        "                                                             min  25%  50%  \\\n",
+        "(tot_tours == 0) & (num_mand == 0) & (num_joint_tours == 0)    0    0    0   \n",
+        "escort * no_cars                                               0    0    0   \n",
+        "has_preschool_kid_at_home * eatout                             0    0    0   \n",
+        "has_preschool_kid_at_home * escort                             0    0    0   \n",
+        "has_preschool_kid_at_home * othdiscr                           0    0    0   \n",
+        "has_preschool_kid_at_home * othmaint                           0    0    0   \n",
+        "has_preschool_kid_at_home * shopping                           0    0    0   \n",
+        "max_window*(tot_tours == 0)                                    0    0    0   \n",
+        "max_window*(tot_tours == 1)                                    0    0    0   \n",
+        "max_window*(tot_tours == 2)                                    0    0    0   \n",
+        "max_window*(tot_tours == 3)                                    0    0    0   \n",
+        "max_window*(tot_tours == 4)                                    0    0    0   \n",
+        "max_window*(tot_tours >  4)                                    0    0    0   \n",
+        "no_cars & (tot_tours == 1)                                     0    0    0   \n",
+        "no_cars & (tot_tours == 2)                                     0    0    0   \n",
+        "no_cars & (tot_tours == 3)                                     0    0    0   \n",
+        "no_cars & (tot_tours == 4)                                     0    0    0   \n",
+        "no_cars & (tot_tours >  4)                                     0    0    0   \n",
+        "num_disc_j                                                     0    0    0   \n",
+        "num_eat_j                                                      0    0    0   \n",
+        "num_joint_tours*(tot_tours == 0)                               0    0    0   \n",
+        "num_joint_tours*(tot_tours == 1)                               0    0    0   \n",
+        "num_joint_tours*(tot_tours == 2)                               0    0    0   \n",
+        "num_joint_tours*(tot_tours == 3)                               0    0    0   \n",
+        "num_joint_tours*(tot_tours == 4)                               0    0    0   \n",
+        "num_joint_tours*(tot_tours >  4)                               0    0    0   \n",
+        "num_main_j                                                     0    0    0   \n",
+        "num_shop_j                                                     0    0    0   \n",
+        "num_visi_j                                                     0    0    0   \n",
+        "\n",
+        "                                                             75%  max  \n",
+        "(tot_tours == 0) & (num_mand == 0) & (num_joint_tours == 0)    0    0  \n",
+        "escort * no_cars                                               0    0  \n",
+        "has_preschool_kid_at_home * eatout                             0    0  \n",
+        "has_preschool_kid_at_home * escort                             0    0  \n",
+        "has_preschool_kid_at_home * othdiscr                           0    0  \n",
+        "has_preschool_kid_at_home * othmaint                           0    0  \n",
+        "has_preschool_kid_at_home * shopping                           0    0  \n",
+        "max_window*(tot_tours == 0)                                    0    0  \n",
+        "max_window*(tot_tours == 1)                                    0    0  \n",
+        "max_window*(tot_tours == 2)                                    0    0  \n",
+        "max_window*(tot_tours == 3)                                    0    0  \n",
+        "max_window*(tot_tours == 4)                                    0    0  \n",
+        "max_window*(tot_tours >  4)                                    0    0  \n",
+        "no_cars & (tot_tours == 1)                                     0    0  \n",
+        "no_cars & (tot_tours == 2)                                     0    0  \n",
+        "no_cars & (tot_tours == 3)                                     0    0  \n",
+        "no_cars & (tot_tours == 4)                                     0    0  \n",
+        "no_cars & (tot_tours >  4)                                     0    0  \n",
+        "num_disc_j                                                     0    0  \n",
+        "num_eat_j                                                      0    0  \n",
+        "num_joint_tours*(tot_tours == 0)                               0    0  \n",
+        "num_joint_tours*(tot_tours == 1)                               0    0  \n",
+        "num_joint_tours*(tot_tours == 2)                               0    0  \n",
+        "num_joint_tours*(tot_tours == 3)                               0    0  \n",
+        "num_joint_tours*(tot_tours == 4)                               0    0  \n",
+        "num_joint_tours*(tot_tours >  4)                               0    0  \n",
+        "num_main_j                                                     0    0  \n",
+        "num_shop_j                                                     0    0  \n",
+        "num_visi_j                                                     0    0  \n",
+        "Running segment 'school' of size 209\n",
+        "WARNING: Describe of columns with no variability:\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "                                                             count  mean  std  \\\n",
+        "(tot_tours == 0) & (num_mand == 0) & (num_joint_tours == 0)  20064     0    0   \n",
+        "max_window*(tot_tours == 0)                                  20064     0    0   \n",
+        "max_window*(tot_tours == 1)                                  20064     0    0   \n",
+        "max_window*(tot_tours == 2)                                  20064     0    0   \n",
+        "max_window*(tot_tours == 3)                                  20064     0    0   \n",
+        "max_window*(tot_tours == 4)                                  20064     0    0   \n",
+        "max_window*(tot_tours >  4)                                  20064     0    0   \n",
+        "num_disc_j                                                   20064     0    0   \n",
+        "num_eat_j                                                    20064     0    0   \n",
+        "num_joint_tours*(tot_tours == 0)                             20064     0    0   \n",
+        "num_joint_tours*(tot_tours == 1)                             20064     0    0   \n",
+        "num_joint_tours*(tot_tours == 2)                             20064     0    0   \n",
+        "num_joint_tours*(tot_tours == 3)                             20064     0    0   \n",
+        "num_joint_tours*(tot_tours == 4)                             20064     0    0   \n",
+        "num_joint_tours*(tot_tours >  4)                             20064     0    0   \n",
+        "num_main_j                                                   20064     0    0   \n",
+        "num_shop_j                                                   20064     0    0   \n",
+        "num_visi_j                                                   20064     0    0   \n",
+        "\n",
+        "                                                             min  25%  50%  \\\n",
+        "(tot_tours == 0) & (num_mand == 0) & (num_joint_tours == 0)    0    0    0   \n",
+        "max_window*(tot_tours == 0)                                    0    0    0   \n",
+        "max_window*(tot_tours == 1)                                    0    0    0   \n",
+        "max_window*(tot_tours == 2)                                    0    0    0   \n",
+        "max_window*(tot_tours == 3)                                    0    0    0   \n",
+        "max_window*(tot_tours == 4)                                    0    0    0   \n",
+        "max_window*(tot_tours >  4)                                    0    0    0   \n",
+        "num_disc_j                                                     0    0    0   \n",
+        "num_eat_j                                                      0    0    0   \n",
+        "num_joint_tours*(tot_tours == 0)                               0    0    0   \n",
+        "num_joint_tours*(tot_tours == 1)                               0    0    0   \n",
+        "num_joint_tours*(tot_tours == 2)                               0    0    0   \n",
+        "num_joint_tours*(tot_tours == 3)                               0    0    0   \n",
+        "num_joint_tours*(tot_tours == 4)                               0    0    0   \n",
+        "num_joint_tours*(tot_tours >  4)                               0    0    0   \n",
+        "num_main_j                                                     0    0    0   \n",
+        "num_shop_j                                                     0    0    0   \n",
+        "num_visi_j                                                     0    0    0   \n",
+        "\n",
+        "                                                             75%  max  \n",
+        "(tot_tours == 0) & (num_mand == 0) & (num_joint_tours == 0)    0    0  \n",
+        "max_window*(tot_tours == 0)                                    0    0  \n",
+        "max_window*(tot_tours == 1)                                    0    0  \n",
+        "max_window*(tot_tours == 2)                                    0    0  \n",
+        "max_window*(tot_tours == 3)                                    0    0  \n",
+        "max_window*(tot_tours == 4)                                    0    0  \n",
+        "max_window*(tot_tours >  4)                                    0    0  \n",
+        "num_disc_j                                                     0    0  \n",
+        "num_eat_j                                                      0    0  \n",
+        "num_joint_tours*(tot_tours == 0)                               0    0  \n",
+        "num_joint_tours*(tot_tours == 1)                               0    0  \n",
+        "num_joint_tours*(tot_tours == 2)                               0    0  \n",
+        "num_joint_tours*(tot_tours == 3)                               0    0  \n",
+        "num_joint_tours*(tot_tours == 4)                               0    0  \n",
+        "num_joint_tours*(tot_tours >  4)                               0    0  \n",
+        "num_main_j                                                     0    0  \n",
+        "num_shop_j                                                     0    0  \n",
+        "num_visi_j                                                     0    0  \n",
+        "Running segment 'university' of size 98\n",
+        "WARNING: Describe of columns with no variability:\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "                                                             count  mean  std  \\\n",
+        "(tot_tours == 0) & (num_mand == 0) & (num_joint_tours == 0)   9408     0    0   \n",
+        "max_window*(tot_tours == 0)                                   9408     0    0   \n",
+        "max_window*(tot_tours == 1)                                   9408     0    0   \n",
+        "max_window*(tot_tours == 2)                                   9408     0    0   \n",
+        "max_window*(tot_tours == 3)                                   9408     0    0   \n",
+        "max_window*(tot_tours == 4)                                   9408     0    0   \n",
+        "max_window*(tot_tours >  4)                                   9408     0    0   \n",
+        "num_disc_j                                                    9408     0    0   \n",
+        "num_eat_j                                                     9408     0    0   \n",
+        "num_joint_tours*(tot_tours == 0)                              9408     0    0   \n",
+        "num_joint_tours*(tot_tours == 1)                              9408     0    0   \n",
+        "num_joint_tours*(tot_tours == 2)                              9408     0    0   \n",
+        "num_joint_tours*(tot_tours == 3)                              9408     0    0   \n",
+        "num_joint_tours*(tot_tours == 4)                              9408     0    0   \n",
+        "num_joint_tours*(tot_tours >  4)                              9408     0    0   \n",
+        "num_main_j                                                    9408     0    0   \n",
+        "num_shop_j                                                    9408     0    0   \n",
+        "num_visi_j                                                    9408     0    0   \n",
+        "\n",
+        "                                                             min  25%  50%  \\\n",
+        "(tot_tours == 0) & (num_mand == 0) & (num_joint_tours == 0)    0    0    0   \n",
+        "max_window*(tot_tours == 0)                                    0    0    0   \n",
+        "max_window*(tot_tours == 1)                                    0    0    0   \n",
+        "max_window*(tot_tours == 2)                                    0    0    0   \n",
+        "max_window*(tot_tours == 3)                                    0    0    0   \n",
+        "max_window*(tot_tours == 4)                                    0    0    0   \n",
+        "max_window*(tot_tours >  4)                                    0    0    0   \n",
+        "num_disc_j                                                     0    0    0   \n",
+        "num_eat_j                                                      0    0    0   \n",
+        "num_joint_tours*(tot_tours == 0)                               0    0    0   \n",
+        "num_joint_tours*(tot_tours == 1)                               0    0    0   \n",
+        "num_joint_tours*(tot_tours == 2)                               0    0    0   \n",
+        "num_joint_tours*(tot_tours == 3)                               0    0    0   \n",
+        "num_joint_tours*(tot_tours == 4)                               0    0    0   \n",
+        "num_joint_tours*(tot_tours >  4)                               0    0    0   \n",
+        "num_main_j                                                     0    0    0   \n",
+        "num_shop_j                                                     0    0    0   \n",
+        "num_visi_j                                                     0    0    0   \n",
+        "\n",
+        "                                                             75%  max  \n",
+        "(tot_tours == 0) & (num_mand == 0) & (num_joint_tours == 0)    0    0  \n",
+        "max_window*(tot_tours == 0)                                    0    0  \n",
+        "max_window*(tot_tours == 1)                                    0    0  \n",
+        "max_window*(tot_tours == 2)                                    0    0  \n",
+        "max_window*(tot_tours == 3)                                    0    0  \n",
+        "max_window*(tot_tours == 4)                                    0    0  \n",
+        "max_window*(tot_tours >  4)                                    0    0  \n",
+        "num_disc_j                                                     0    0  \n",
+        "num_eat_j                                                      0    0  \n",
+        "num_joint_tours*(tot_tours == 0)                               0    0  \n",
+        "num_joint_tours*(tot_tours == 1)                               0    0  \n",
+        "num_joint_tours*(tot_tours == 2)                               0    0  \n",
+        "num_joint_tours*(tot_tours == 3)                               0    0  \n",
+        "num_joint_tours*(tot_tours == 4)                               0    0  \n",
+        "num_joint_tours*(tot_tours >  4)                               0    0  \n",
+        "num_main_j                                                     0    0  \n",
+        "num_shop_j                                                     0    0  \n",
+        "num_visi_j                                                     0    0  \n",
+        "Choices:\n",
+        "0     1117\n",
+        "18      62\n",
+        "64      55\n",
+        "34      47\n",
+        "77      39\n",
+        "95      36\n",
+        "68      26\n",
+        "70      20\n",
+        "6       19\n",
+        "48      18\n",
+        "36      17\n",
+        "86      15\n",
+        "31      15\n",
+        "80      11\n",
+        "14      11\n",
+        "...\n",
+        "58    2\n",
+        "32    1\n",
+        "59    1\n",
+        "2     1\n",
+        "79    1\n",
+        "88    1\n",
+        "73    1\n",
+        "56    1\n",
+        "33    1\n",
+        "50    1\n",
+        "94    1\n",
+        "20    1\n",
+        "44    1\n",
+        "92    1\n",
+        "49    1\n",
+        "Length: 71, dtype: int64\n",
+        "Time to execute model 'non_mandatory_tour_frequency': 12.80s\n",
+        "Total time to execute: 12.80s\n"
        ]
       }
      ],
-     "prompt_number": 4
+     "prompt_number": 6
     },
     {
      "cell_type": "code",
      "collapsed": false,
      "input": [
-      "sim.get_table(\"households\").to_frame().describe()"
+      "sim.get_table(\"non_mandatory_tours\").tour_type.value_counts()"
      ],
      "language": "python",
      "metadata": {},
      "outputs": [
       {
-       "html": [
-        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
-        "<table border=\"1\" class=\"dataframe\">\n",
-        "  <thead>\n",
-        "    <tr style=\"text-align: right;\">\n",
-        "      <th></th>\n",
-        "      <th>TAZ</th>\n",
-        "      <th>SERIALNO</th>\n",
-        "      <th>PUMA5</th>\n",
-        "      <th>income</th>\n",
-        "      <th>PERSONS</th>\n",
-        "      <th>HHT</th>\n",
-        "      <th>UNITTYPE</th>\n",
-        "      <th>NOC</th>\n",
-        "      <th>BLDGSZ</th>\n",
-        "      <th>TENURE</th>\n",
-        "      <th>...</th>\n",
-        "      <th>bucketBin</th>\n",
-        "      <th>originalPUMA</th>\n",
-        "      <th>hmultiunit</th>\n",
-        "      <th>num_young_adults</th>\n",
-        "      <th>drivers</th>\n",
-        "      <th>num_children</th>\n",
-        "      <th>num_adolescents</th>\n",
-        "      <th>income_in_thousands</th>\n",
-        "      <th>num_young_children</th>\n",
-        "      <th>num_college_age</th>\n",
-        "    </tr>\n",
-        "  </thead>\n",
-        "  <tbody>\n",
-        "    <tr>\n",
-        "      <th>count</th>\n",
-        "      <td> 100000.000000</td>\n",
-        "      <td>  100000.000000</td>\n",
-        "      <td> 100000.000000</td>\n",
-        "      <td>  100000.000000</td>\n",
-        "      <td> 100000.000000</td>\n",
-        "      <td> 100000.000000</td>\n",
-        "      <td> 100000.000000</td>\n",
-        "      <td> 100000.000000</td>\n",
-        "      <td> 100000.000000</td>\n",
-        "      <td> 100000.000000</td>\n",
-        "      <td>...</td>\n",
-        "      <td> 100000.000000</td>\n",
-        "      <td> 100000.000000</td>\n",
-        "      <td> 100000.000000</td>\n",
-        "      <td> 100000.000000</td>\n",
-        "      <td> 100000.000000</td>\n",
-        "      <td> 100000.000000</td>\n",
-        "      <td> 100000.000000</td>\n",
-        "      <td> 100000.000000</td>\n",
-        "      <td> 100000.000000</td>\n",
-        "      <td> 100000.000000</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>mean</th>\n",
-        "      <td>    752.439040</td>\n",
-        "      <td> 4924260.422350</td>\n",
-        "      <td>   2168.287950</td>\n",
-        "      <td>   77684.096720</td>\n",
-        "      <td>      2.580780</td>\n",
-        "      <td>      2.642540</td>\n",
-        "      <td>      0.077640</td>\n",
-        "      <td>      0.468280</td>\n",
-        "      <td>      3.529460</td>\n",
-        "      <td>      1.892780</td>\n",
-        "      <td>...</td>\n",
-        "      <td>      4.484880</td>\n",
-        "      <td>   2168.287950</td>\n",
-        "      <td>      0.401800</td>\n",
-        "      <td>      0.394180</td>\n",
-        "      <td>      2.063810</td>\n",
-        "      <td>      0.357290</td>\n",
-        "      <td>      0.060680</td>\n",
-        "      <td>     77.684097</td>\n",
-        "      <td>      0.159680</td>\n",
-        "      <td>      0.226650</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>std</th>\n",
-        "      <td>    430.258155</td>\n",
-        "      <td> 2863687.886756</td>\n",
-        "      <td>    516.271349</td>\n",
-        "      <td>   81341.474187</td>\n",
-        "      <td>      1.606362</td>\n",
-        "      <td>      2.066835</td>\n",
-        "      <td>      0.367387</td>\n",
-        "      <td>      0.913982</td>\n",
-        "      <td>      2.517375</td>\n",
-        "      <td>      1.010353</td>\n",
-        "      <td>...</td>\n",
-        "      <td>      2.871932</td>\n",
-        "      <td>    516.271349</td>\n",
-        "      <td>      0.490264</td>\n",
-        "      <td>      0.728922</td>\n",
-        "      <td>      1.122662</td>\n",
-        "      <td>      0.760368</td>\n",
-        "      <td>      0.260074</td>\n",
-        "      <td>     81.341474</td>\n",
-        "      <td>      0.462498</td>\n",
-        "      <td>      0.589748</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>min</th>\n",
-        "      <td>      1.000000</td>\n",
-        "      <td>     496.000000</td>\n",
-        "      <td>   1000.000000</td>\n",
-        "      <td>  -20000.000000</td>\n",
-        "      <td>      1.000000</td>\n",
-        "      <td>      0.000000</td>\n",
-        "      <td>      0.000000</td>\n",
-        "      <td>      0.000000</td>\n",
-        "      <td>      0.000000</td>\n",
-        "      <td>      0.000000</td>\n",
-        "      <td>...</td>\n",
-        "      <td>      0.000000</td>\n",
-        "      <td>   1000.000000</td>\n",
-        "      <td>      0.000000</td>\n",
-        "      <td>      0.000000</td>\n",
-        "      <td>      0.000000</td>\n",
-        "      <td>      0.000000</td>\n",
-        "      <td>      0.000000</td>\n",
-        "      <td>    -20.000000</td>\n",
-        "      <td>      0.000000</td>\n",
-        "      <td>      0.000000</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>25%</th>\n",
-        "      <td>    374.000000</td>\n",
-        "      <td> 2456274.500000</td>\n",
-        "      <td>   2104.000000</td>\n",
-        "      <td>   26500.000000</td>\n",
-        "      <td>      1.000000</td>\n",
-        "      <td>      1.000000</td>\n",
-        "      <td>      0.000000</td>\n",
-        "      <td>      0.000000</td>\n",
-        "      <td>      2.000000</td>\n",
-        "      <td>      1.000000</td>\n",
-        "      <td>...</td>\n",
-        "      <td>      2.000000</td>\n",
-        "      <td>   2104.000000</td>\n",
-        "      <td>      0.000000</td>\n",
-        "      <td>      0.000000</td>\n",
-        "      <td>      1.000000</td>\n",
-        "      <td>      0.000000</td>\n",
-        "      <td>      0.000000</td>\n",
-        "      <td>     26.500000</td>\n",
-        "      <td>      0.000000</td>\n",
-        "      <td>      0.000000</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>50%</th>\n",
-        "      <td>    764.000000</td>\n",
-        "      <td> 4895910.500000</td>\n",
-        "      <td>   2303.000000</td>\n",
-        "      <td>   58000.000000</td>\n",
-        "      <td>      2.000000</td>\n",
-        "      <td>      1.000000</td>\n",
-        "      <td>      0.000000</td>\n",
-        "      <td>      0.000000</td>\n",
-        "      <td>      2.000000</td>\n",
-        "      <td>      2.000000</td>\n",
-        "      <td>...</td>\n",
-        "      <td>      4.000000</td>\n",
-        "      <td>   2303.000000</td>\n",
-        "      <td>      0.000000</td>\n",
-        "      <td>      0.000000</td>\n",
-        "      <td>      2.000000</td>\n",
-        "      <td>      0.000000</td>\n",
-        "      <td>      0.000000</td>\n",
-        "      <td>     58.000000</td>\n",
-        "      <td>      0.000000</td>\n",
-        "      <td>      0.000000</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>75%</th>\n",
-        "      <td>   1144.000000</td>\n",
-        "      <td> 7357629.750000</td>\n",
-        "      <td>   2410.000000</td>\n",
-        "      <td>  100000.000000</td>\n",
-        "      <td>      4.000000</td>\n",
-        "      <td>      4.000000</td>\n",
-        "      <td>      0.000000</td>\n",
-        "      <td>      1.000000</td>\n",
-        "      <td>      5.000000</td>\n",
-        "      <td>      3.000000</td>\n",
-        "      <td>...</td>\n",
-        "      <td>      7.000000</td>\n",
-        "      <td>   2410.000000</td>\n",
-        "      <td>      1.000000</td>\n",
-        "      <td>      1.000000</td>\n",
-        "      <td>      2.000000</td>\n",
-        "      <td>      0.000000</td>\n",
-        "      <td>      0.000000</td>\n",
-        "      <td>    100.000000</td>\n",
-        "      <td>      0.000000</td>\n",
-        "      <td>      0.000000</td>\n",
-        "    </tr>\n",
-        "    <tr>\n",
-        "      <th>max</th>\n",
-        "      <td>   1454.000000</td>\n",
-        "      <td> 9999811.000000</td>\n",
-        "      <td>   2714.000000</td>\n",
-        "      <td> 1237000.000000</td>\n",
-        "      <td>     25.000000</td>\n",
-        "      <td>      7.000000</td>\n",
-        "      <td>      2.000000</td>\n",
-        "      <td>     10.000000</td>\n",
-        "      <td>     10.000000</td>\n",
-        "      <td>      4.000000</td>\n",
-        "      <td>...</td>\n",
-        "      <td>      9.000000</td>\n",
-        "      <td>   2714.000000</td>\n",
-        "      <td>      1.000000</td>\n",
-        "      <td>      9.000000</td>\n",
-        "      <td>     25.000000</td>\n",
-        "      <td>      8.000000</td>\n",
-        "      <td>      5.000000</td>\n",
-        "      <td>   1237.000000</td>\n",
-        "      <td>      8.000000</td>\n",
-        "      <td>     24.000000</td>\n",
-        "    </tr>\n",
-        "  </tbody>\n",
-        "</table>\n",
-        "<p>8 rows \u00d7 53 columns</p>\n",
-        "</div>"
-       ],
        "metadata": {},
        "output_type": "pyout",
-       "prompt_number": 5,
-       "text": [
-        "                 TAZ        SERIALNO          PUMA5          income  \\\n",
-        "count  100000.000000   100000.000000  100000.000000   100000.000000   \n",
-        "mean      752.439040  4924260.422350    2168.287950    77684.096720   \n",
-        "std       430.258155  2863687.886756     516.271349    81341.474187   \n",
-        "min         1.000000      496.000000    1000.000000   -20000.000000   \n",
-        "25%       374.000000  2456274.500000    2104.000000    26500.000000   \n",
-        "50%       764.000000  4895910.500000    2303.000000    58000.000000   \n",
-        "75%      1144.000000  7357629.750000    2410.000000   100000.000000   \n",
-        "max      1454.000000  9999811.000000    2714.000000  1237000.000000   \n",
-        "\n",
-        "             PERSONS            HHT       UNITTYPE            NOC  \\\n",
-        "count  100000.000000  100000.000000  100000.000000  100000.000000   \n",
-        "mean        2.580780       2.642540       0.077640       0.468280   \n",
-        "std         1.606362       2.066835       0.367387       0.913982   \n",
-        "min         1.000000       0.000000       0.000000       0.000000   \n",
-        "25%         1.000000       1.000000       0.000000       0.000000   \n",
-        "50%         2.000000       1.000000       0.000000       0.000000   \n",
-        "75%         4.000000       4.000000       0.000000       1.000000   \n",
-        "max        25.000000       7.000000       2.000000      10.000000   \n",
-        "\n",
-        "              BLDGSZ         TENURE       ...             bucketBin  \\\n",
-        "count  100000.000000  100000.000000       ...         100000.000000   \n",
-        "mean        3.529460       1.892780       ...              4.484880   \n",
-        "std         2.517375       1.010353       ...              2.871932   \n",
-        "min         0.000000       0.000000       ...              0.000000   \n",
-        "25%         2.000000       1.000000       ...              2.000000   \n",
-        "50%         2.000000       2.000000       ...              4.000000   \n",
-        "75%         5.000000       3.000000       ...              7.000000   \n",
-        "max        10.000000       4.000000       ...              9.000000   \n",
-        "\n",
-        "        originalPUMA     hmultiunit  num_young_adults        drivers  \\\n",
-        "count  100000.000000  100000.000000     100000.000000  100000.000000   \n",
-        "mean     2168.287950       0.401800          0.394180       2.063810   \n",
-        "std       516.271349       0.490264          0.728922       1.122662   \n",
-        "min      1000.000000       0.000000          0.000000       0.000000   \n",
-        "25%      2104.000000       0.000000          0.000000       1.000000   \n",
-        "50%      2303.000000       0.000000          0.000000       2.000000   \n",
-        "75%      2410.000000       1.000000          1.000000       2.000000   \n",
-        "max      2714.000000       1.000000          9.000000      25.000000   \n",
-        "\n",
-        "        num_children  num_adolescents  income_in_thousands  \\\n",
-        "count  100000.000000    100000.000000        100000.000000   \n",
-        "mean        0.357290         0.060680            77.684097   \n",
-        "std         0.760368         0.260074            81.341474   \n",
-        "min         0.000000         0.000000           -20.000000   \n",
-        "25%         0.000000         0.000000            26.500000   \n",
-        "50%         0.000000         0.000000            58.000000   \n",
-        "75%         0.000000         0.000000           100.000000   \n",
-        "max         8.000000         5.000000          1237.000000   \n",
-        "\n",
-        "       num_young_children  num_college_age  \n",
-        "count       100000.000000    100000.000000  \n",
-        "mean             0.159680         0.226650  \n",
-        "std              0.462498         0.589748  \n",
-        "min              0.000000         0.000000  \n",
-        "25%              0.000000         0.000000  \n",
-        "50%              0.000000         0.000000  \n",
-        "75%              0.000000         0.000000  \n",
-        "max              8.000000        24.000000  \n",
-        "\n",
-        "[8 rows x 53 columns]"
+       "prompt_number": 7,
+       "text": [
+        "escort      662\n",
+        "social      332\n",
+        "eatout      295\n",
+        "shopping    258\n",
+        "othdiscr    188\n",
+        "othmaint    182\n",
+        "dtype: int64"
        ]
       }
      ],
-     "prompt_number": 5
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "sim.run([\"destination_choice\"])"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Running model 'destination_choice'\n",
+        "1917"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "Running segment 'eatout' of size 295\n",
+        "Running segment 'othdiscr' of size 188"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "Running segment 'othmaint' of size 182"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "Running segment 'shopping' of size 258\n",
+        "Running segment 'social' of size 332"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "Choices:\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "count    1255.000000\n",
+        "mean      733.726693\n",
+        "std       426.865923\n",
+        "min         2.000000\n",
+        "25%       377.000000\n",
+        "50%       722.000000\n",
+        "75%      1131.000000\n",
+        "max      1451.000000\n",
+        "Name: TAZ, dtype: float64\n",
+        "Time to execute model 'destination_choice': 2.65s\n",
+        "Total time to execute: 2.65s\n"
+       ]
+      }
+     ],
+     "prompt_number": 8
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "sim.run([\"tour_departure_and_duration_mandatory\"])"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Running model 'tour_departure_and_duration_mandatory'\n",
+        "Running 1466 mandatory tour scheduling choices"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "work_and_school    644\n",
+        "work2              442\n",
+        "work1              179\n",
+        "school2            134\n",
+        "school1             67\n",
+        "dtype: int64\n",
+        "                                                                               Coefficient\n",
+        "Expression                                                                                \n",
+        "(tour_num == 1) & (duration < 8)                                                    1.9800\n",
+        "(tour_num == 2) & (duration < 8)                                                    2.5820\n",
+        "(mandatory_tour_frequency == 'work_and_school') & is_worker & (duration < 8)        0.9126\n",
+        "(mandatory_tour_frequency == 'work_and_school') & is_student & (duration < 8)       2.5820\n",
+        "mode_choice_logsum                                                                  1.0270\n",
+        "Running 856 mandatory first tour choices\n",
+        "WARNING: Describe of columns with no variability:\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "                                                   count  mean  std  min  25%  \\\n",
+        "(start < end_of_previous_tour) & (tour_num == 2)  100000     0    0    0    0   \n",
+        "(tour_num == 2) & (duration < 8)                  100000     0    0    0    0   \n",
+        "(tour_num == 2) * duration                        100000     0    0    0    0   \n",
+        "(tour_num == 2) * start                           100000     0    0    0    0   \n",
+        "home_is_rural & (end > 22)                        100000     0    0    0    0   \n",
+        "home_is_rural & (start < 6)                       100000     0    0    0    0   \n",
+        "mode_choice_logsum                                100000     0    0    0    0   \n",
+        "\n",
+        "                                                  50%  75%  max  \n",
+        "(start < end_of_previous_tour) & (tour_num == 2)    0    0    0  \n",
+        "(tour_num == 2) & (duration < 8)                    0    0    0  \n",
+        "(tour_num == 2) * duration                          0    0    0  \n",
+        "(tour_num == 2) * start                             0    0    0  \n",
+        "home_is_rural & (end > 22)                          0    0    0  \n",
+        "home_is_rural & (start < 6)                         0    0    0  \n",
+        "mode_choice_logsum                                  0    0    0  \n",
+        "Running 610 mandatory second tour choices"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "WARNING: Describe of columns with no variability:\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "                                                   count  mean  std  min  25%  \\\n",
+        "(ptype == 3) * start                              100000     0    0    0    0   \n",
+        "(start < end_of_previous_tour) & (tour_num == 2)  100000     0    0    0    0   \n",
+        "(tour_num == 1) & (duration < 8)                  100000     0    0    0    0   \n",
+        "(tour_num == 1) * duration                        100000     0    0    0    0   \n",
+        "(tour_num == 1) * start                           100000     0    0    0    0   \n",
+        "home_is_rural & (end > 22)                        100000     0    0    0    0   \n",
+        "home_is_rural & (start < 6)                       100000     0    0    0    0   \n",
+        "mode_choice_logsum                                100000     0    0    0    0   \n",
+        "\n",
+        "                                                  50%  75%  max  \n",
+        "(ptype == 3) * start                                0    0    0  \n",
+        "(start < end_of_previous_tour) & (tour_num == 2)    0    0    0  \n",
+        "(tour_num == 1) & (duration < 8)                    0    0    0  \n",
+        "(tour_num == 1) * duration                          0    0    0  \n",
+        "(tour_num == 1) * start                             0    0    0  \n",
+        "home_is_rural & (end > 22)                          0    0    0  \n",
+        "home_is_rural & (start < 6)                         0    0    0  \n",
+        "mode_choice_logsum                                  0    0    0  \n",
+        "Choices:\n",
+        "count    1466.000000\n",
+        "mean       88.510232\n",
+        "std        57.398650\n",
+        "min         0.000000\n",
+        "25%        38.000000\n",
+        "50%        85.000000\n",
+        "75%       139.000000\n",
+        "max       189.000000\n",
+        "dtype: float64\n",
+        "Time to execute model 'tour_departure_and_duration_mandatory': 3.47s\n",
+        "Total time to execute: 3.47s\n"
+       ]
+      }
+     ],
+     "prompt_number": 9
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "sim.run([\"tour_departure_and_duration_non_mandatory\"])"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "Running model 'tour_departure_and_duration_non_mandatory'\n",
+        "Running 1917 non-mandatory tour scheduling choices"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "\n",
+        "Expression\n",
+        "start < end_of_previous_tour              999.00000\n",
+        "roundtrip_auto_time_to_work * duration      0.00474\n",
+        "(tour_type == \"shopping\") * start           0.06015\n",
+        "(tour_type == \"shopping\") * duration        0.12080\n",
+        "Name: Coefficient, dtype: float64\n",
+        "Running 566 non-mandatory #1 tour choices\n",
+        "WARNING: Describe of columns with no variability:\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "                               count  mean  std  min  25%  50%  75%  max\n",
+        "start < end_of_previous_tour  100000     0    0    0    0    0    0    0\n",
+        "Running 561 non-mandatory #2 tour choices\n",
+        "WARNING: Describe of columns with no variability:\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "                               count  mean  std  min  25%  50%  75%  max\n",
+        "start < end_of_previous_tour  100000     0    0    0    0    0    0    0\n",
+        "Running 327 non-mandatory #3 tour choices\n",
+        "WARNING: Describe of columns with no variability:\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "                              count  mean  std  min  25%  50%  75%  max\n",
+        "start < end_of_previous_tour  62130     0    0    0    0    0    0    0\n",
+        "Running 230 non-mandatory #4 tour choices\n",
+        "WARNING: Describe of columns with no variability:\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "                                      count  mean  std  min  25%  50%  75%  \\\n",
+        "(tour_type == \"shopping\") * duration  43700     0    0    0    0    0    0   \n",
+        "(tour_type == \"shopping\") * start     43700     0    0    0    0    0    0   \n",
+        "start < end_of_previous_tour          43700     0    0    0    0    0    0   \n",
+        "\n",
+        "                                      max  \n",
+        "(tour_type == \"shopping\") * duration    0  \n",
+        "(tour_type == \"shopping\") * start       0  \n",
+        "start < end_of_previous_tour            0  \n",
+        "Running 146 non-mandatory #5 tour choices\n",
+        "WARNING: Describe of columns with no variability:\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "                                      count  mean  std  min  25%  50%  75%  \\\n",
+        "(tour_type == \"shopping\") * duration  27740     0    0    0    0    0    0   \n",
+        "(tour_type == \"shopping\") * start     27740     0    0    0    0    0    0   \n",
+        "start < end_of_previous_tour          27740     0    0    0    0    0    0   \n",
+        "\n",
+        "                                      max  \n",
+        "(tour_type == \"shopping\") * duration    0  \n",
+        "(tour_type == \"shopping\") * start       0  \n",
+        "start < end_of_previous_tour            0  \n",
+        "Running 51 non-mandatory #6 tour choices\n",
+        "WARNING: Describe of columns with no variability:\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "                                      count  mean  std  min  25%  50%  75%  \\\n",
+        "(tour_type == \"shopping\") * duration   9690     0    0    0    0    0    0   \n",
+        "(tour_type == \"shopping\") * start      9690     0    0    0    0    0    0   \n",
+        "start < end_of_previous_tour           9690     0    0    0    0    0    0   \n",
+        "\n",
+        "                                      max  \n",
+        "(tour_type == \"shopping\") * duration    0  \n",
+        "(tour_type == \"shopping\") * start       0  \n",
+        "start < end_of_previous_tour            0  \n",
+        "Running 36 non-mandatory #7 tour choices\n",
+        "WARNING: Describe of columns with no variability:\n"
+       ]
+      },
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "                                      count  mean  std  min  25%  50%  75%  \\\n",
+        "(tour_type == \"shopping\") * duration   6840     0    0    0    0    0    0   \n",
+        "(tour_type == \"shopping\") * start      6840     0    0    0    0    0    0   \n",
+        "start < end_of_previous_tour           6840     0    0    0    0    0    0   \n",
+        "\n",
+        "                                      max  \n",
+        "(tour_type == \"shopping\") * duration    0  \n",
+        "(tour_type == \"shopping\") * start       0  \n",
+        "start < end_of_previous_tour            0  \n",
+        "Choices:\n",
+        "count    1917.000000\n",
+        "mean       31.080334\n",
+        "std        33.082042\n",
+        "min         1.000000\n",
+        "25%        18.000000\n",
+        "50%        18.000000\n",
+        "75%        18.000000\n",
+        "max       188.000000\n",
+        "dtype: float64\n",
+        "Time to execute model 'tour_departure_and_duration_non_mandatory': 3.31s\n",
+        "Total time to execute: 3.31s\n"
+       ]
+      }
+     ],
+     "prompt_number": 22
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "sim.get_table(\"land_use\").to_frame().info()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "<class 'pandas.core.frame.DataFrame'>\n",
+        "Int64Index: 1454 entries, 1 to 1454\n",
+        "Data columns (total 49 columns):\n",
+        "DISTRICT              1454 non-null int64\n",
+        "SD                    1454 non-null int64\n",
+        "COUNTY                1454 non-null int64\n",
+        "TOTHH                 1454 non-null int64\n",
+        "HHPOP                 1454 non-null int64\n",
+        "TOTPOP                1454 non-null int64\n",
+        "EMPRES                1454 non-null int64\n",
+        "SFDU                  1454 non-null int64\n",
+        "MFDU                  1454 non-null int64\n",
+        "HHINCQ1               1454 non-null int64\n",
+        "HHINCQ2               1454 non-null int64\n",
+        "HHINCQ3               1454 non-null int64\n",
+        "HHINCQ4               1454 non-null int64\n",
+        "TOTACRE               1454 non-null float64\n",
+        "RESACRE               1454 non-null int64\n",
+        "CIACRE                1454 non-null int64\n",
+        "SHPOP62P              1454 non-null float64\n",
+        "TOTEMP                1454 non-null int64\n",
+        "AGE0004               1454 non-null int64\n",
+        "AGE0519               1454 non-null int64\n",
+        "AGE2044               1454 non-null int64\n",
+        "AGE4564               1454 non-null int64\n",
+        "AGE65P                1454 non-null int64\n",
+        "RETEMPN               1454 non-null int64\n",
+        "FPSEMPN               1454 non-null int64\n",
+        "HEREMPN               1454 non-null int64\n",
+        "OTHEMPN               1454 non-null int64\n",
+        "AGREMPN               1454 non-null int64\n",
+        "MWTEMPN               1454 non-null int64\n",
+        "PRKCST                1454 non-null float64\n",
+        "OPRKCST               1454 non-null float64\n",
+        "area_type             1454 non-null int64\n",
+        "HSENROLL              1454 non-null float64\n",
+        "COLLFTE               1454 non-null float64\n",
+        "COLLPTE               1454 non-null float64\n",
+        "TOPOLOGY              1454 non-null int64\n",
+        "TERMINAL              1454 non-null float64\n",
+        "ZERO                  1454 non-null int64\n",
+        "hhlds                 1454 non-null int64\n",
+        "sftaz                 1454 non-null int64\n",
+        "gqpop                 1454 non-null int64\n",
+        "employment_density    1454 non-null float64\n",
+        "total_acres           1454 non-null float64\n",
+        "county_id             1454 non-null int64\n",
+        "density_index         1453 non-null float64\n",
+        "county_name           1454 non-null object\n",
+        "household_density     1454 non-null float64\n",
+        "total_households      1454 non-null int64\n",
+        "total_employment      1454 non-null int64\n",
+        "dtypes: float64(12), int64(36), object(1)\n",
+        "memory usage: 568.0 KB\n"
+       ]
+      }
+     ],
+     "prompt_number": 16
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "sim.get_table(\"households\").to_frame().info()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "<class 'pandas.core.frame.DataFrame'>\n",
+        "Int64Index: 10000 entries, 688607 to 2424588\n",
+        "Data columns (total 61 columns):\n",
+        "TAZ                          10000 non-null int64\n",
+        "SERIALNO                     10000 non-null int64\n",
+        "PUMA5                        10000 non-null int64\n",
+        "income                       10000 non-null int64\n",
+        "PERSONS                      10000 non-null int64\n",
+        "HHT                          10000 non-null int64\n",
+        "UNITTYPE                     10000 non-null int64\n",
+        "NOC                          10000 non-null int64\n",
+        "BLDGSZ                       10000 non-null int64\n",
+        "TENURE                       10000 non-null int64\n",
+        "VEHICL                       10000 non-null int64\n",
+        "hinccat1                     10000 non-null int64\n",
+        "hinccat2                     10000 non-null int64\n",
+        "hhagecat                     10000 non-null int64\n",
+        "hsizecat                     10000 non-null int64\n",
+        "hfamily                      10000 non-null int64\n",
+        "hunittype                    10000 non-null int64\n",
+        "hNOCcat                      10000 non-null int64\n",
+        "hwrkrcat                     10000 non-null int64\n",
+        "h0004                        10000 non-null int64\n",
+        "h0511                        10000 non-null int64\n",
+        "h1215                        10000 non-null int64\n",
+        "h1617                        10000 non-null int64\n",
+        "h1824                        10000 non-null int64\n",
+        "h2534                        10000 non-null int64\n",
+        "h3549                        10000 non-null int64\n",
+        "h5064                        10000 non-null int64\n",
+        "h6579                        10000 non-null int64\n",
+        "h80up                        10000 non-null int64\n",
+        "workers                      10000 non-null int64\n",
+        "hwork_f                      10000 non-null int64\n",
+        "hwork_p                      10000 non-null int64\n",
+        "huniv                        10000 non-null int64\n",
+        "hnwork                       10000 non-null int64\n",
+        "hretire                      10000 non-null int64\n",
+        "hpresch                      10000 non-null int64\n",
+        "hschpred                     10000 non-null int64\n",
+        "hschdriv                     10000 non-null int64\n",
+        "htypdwel                     10000 non-null int64\n",
+        "hownrent                     10000 non-null int64\n",
+        "hadnwst                      10000 non-null int64\n",
+        "hadwpst                      10000 non-null int64\n",
+        "hadkids                      10000 non-null int64\n",
+        "bucketBin                    10000 non-null int64\n",
+        "originalPUMA                 10000 non-null int64\n",
+        "hmultiunit                   10000 non-null int64\n",
+        "income_segment               10000 non-null category\n",
+        "non_workers                  10000 non-null int64\n",
+        "family                       10000 non-null bool\n",
+        "num_young_adults             10000 non-null float64\n",
+        "household_type               10000 non-null object\n",
+        "auto_ownership               10000 non-null int64\n",
+        "drivers                      10000 non-null float64\n",
+        "num_under16_not_at_school    10000 non-null int64\n",
+        "home_taz                     10000 non-null int64\n",
+        "num_children                 10000 non-null float64\n",
+        "non_family                   10000 non-null bool\n",
+        "num_adolescents              10000 non-null float64\n",
+        "income_in_thousands          10000 non-null float64\n",
+        "num_young_children           10000 non-null float64\n",
+        "num_college_age              10000 non-null float64\n",
+        "dtypes: bool(2), category(1), float64(7), int64(50), object(1)\n",
+        "memory usage: 4.5 MB\n"
+       ]
+      }
+     ],
+     "prompt_number": 7
+    },
+    {
+     "cell_type": "code",
+     "collapsed": false,
+     "input": [
+      "sim.get_table(\"persons\").to_frame().info()"
+     ],
+     "language": "python",
+     "metadata": {},
+     "outputs": [
+      {
+       "output_type": "stream",
+       "stream": "stdout",
+       "text": [
+        "<class 'pandas.core.frame.DataFrame'>\n",
+        "Int64Index: 25495 entries, 29 to 7053105\n",
+        "Data columns (total 34 columns):\n",
+        "household_id                     25495 non-null int64\n",
+        "age                              25495 non-null int64\n",
+        "RELATE                           25495 non-null int64\n",
+        "ESR                              25495 non-null int64\n",
+        "GRADE                            25495 non-null int64\n",
+        "PNUM                             25495 non-null int64\n",
+        "PAUG                             25495 non-null int64\n",
+        "DDP                              25495 non-null int64\n",
+        "sex                              25495 non-null int64\n",
+        "WEEKS                            25495 non-null int64\n",
+        "HOURS                            25495 non-null int64\n",
+        "MSP                              25495 non-null int64\n",
+        "POVERTY                          25495 non-null int64\n",
+        "EARNS                            25495 non-null int64\n",
+        "pagecat                          25495 non-null int64\n",
+        "pemploy                          25495 non-null int64\n",
+        "pstudent                         25495 non-null int64\n",
+        "ptype                            25495 non-null int64\n",
+        "padkid                           25495 non-null int64\n",
+        "school_taz                       25495 non-null int64\n",
+        "mandatory_tour_frequency         25495 non-null object\n",
+        "employed_cat                     25495 non-null object\n",
+        "student_is_employed              25495 non-null bool\n",
+        "roundtrip_auto_time_to_school    25495 non-null float64\n",
+        "cdap_activity                    25495 non-null object\n",
+        "distance_to_school               25495 non-null float64\n",
+        "workplace_taz                    25495 non-null int64\n",
+        "roundtrip_auto_time_to_work      25495 non-null float64\n",
+        "distance_to_work                 25495 non-null float64\n",
+        "student_cat                      25495 non-null object\n",
+        "ptype_cat                        25495 non-null object\n",
+        "under16_not_at_school            25495 non-null bool\n",
+        "home_taz                         25495 non-null int64\n",
+        "nonstudent_to_school             25495 non-null bool\n",
+        "dtypes: bool(3), float64(4), int64(22), object(5)\n",
+        "memory usage: 6.3 MB\n"
+       ]
+      }
+     ],
+     "prompt_number": 8
     },
     {
      "cell_type": "code",
@@ -720,8 +1608,7 @@
      "input": [],
      "language": "python",
      "metadata": {},
-     "outputs": [],
-     "prompt_number": 5
+     "outputs": []
     }
    ],
    "metadata": {}