diff --git a/.gitignore b/.gitignore
index a0ad585..520f23c 100644
--- a/.gitignore
+++ b/.gitignore
@@ -86,4 +86,7 @@ ENV/
 .spyderproject
 
 # Rope project settings
-.ropeproject
\ No newline at end of file
+.ropeproject
+
+# PyCharm
+.idea
\ No newline at end of file
diff --git a/.travis.yml b/.travis.yml
new file mode 100644
index 0000000..52e4207
--- /dev/null
+++ b/.travis.yml
@@ -0,0 +1,33 @@
+language: python
+sudo: false
+python:
+- '2.7'
+- '3.5'
+
+install:
+- if [[ "$TRAVIS_PYTHON_VERSION" == "2.7" ]]; then wget http://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh
+  -O miniconda.sh; else wget http://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
+  -O miniconda.sh; fi
+- bash miniconda.sh -b -p $HOME/miniconda
+- export PATH="$HOME/miniconda/bin:$PATH"
+- hash -r
+- conda config --set always_yes yes --set changeps1 no
+- conda update -q conda
+- conda info -a
+- |
+  conda create -q -n test-environment python=$TRAVIS_PYTHON_VERSION pip numpy pandas pytest matplotlib scipy statsmodels pyyaml pycodestyle
+- source activate test-environment
+- conda list
+# UPDATE THESE WHEN LATEST VERSIONS PUSHED TO PIP
+- pip install https://github.com/UDST/orca/archive/master.zip
+- pip install https://github.com/pksohn/urbansim/archive/python3.zip
+- pip install .
+# UPDATE BRANCH LATER
+- cd .. && git clone -b pep8 git@github.com:urbansim/zone_model.git
+- cd zone_model && pip install .
+- cd zone_model/data && curl -O https://storage.googleapis.com/urbansim/zone_model/model_data.h5
+- cd $TRAVIS_BUILD_DIR && pip install .
+
+script:
+- pycodestyle variable_generators
+- cd ../zone_model/zone_model/ && python simulate.py
\ No newline at end of file
diff --git a/variable_generators/__init__.py b/variable_generators/__init__.py
index a5876c1..538e75f 100644
--- a/variable_generators/__init__.py
+++ b/variable_generators/__init__.py
@@ -1 +1 @@
-version = __version__ = '0.1dev'
\ No newline at end of file
+version = __version__ = '0.1dev'
diff --git a/variable_generators/generators.py b/variable_generators/generators.py
index fd4763d..1e526f5 100644
--- a/variable_generators/generators.py
+++ b/variable_generators/generators.py
@@ -1,3 +1,5 @@
+from __future__ import print_function
+
 import numpy as np
 import pandas as pd
 
@@ -17,10 +19,12 @@ def make_agg_var(agent, geog, geog_id, var_to_aggregate, agg_function):
     Generator function for aggregation variables. Registers with orca.
     """
     var_name = agg_function + '_' + var_to_aggregate
+
     @orca.column(geog, var_name, cache=False)
     def func():
         agents = orca.get_table(agent)
-        print 'Calculating %s of %s for %s' % (var_name, agent, geog)
+        print('Calculating {} of {} for {}'
+              .format(var_name, agent, geog))
 
         groupby = agents[var_to_aggregate].groupby(agents[geog_id])
         if agg_function == 'mean':
@@ -35,7 +39,9 @@ def func():
         locations_index = orca.get_table(geog).index
         series = pd.Series(data=values, index=locations_index)
 
-        # Fillna.  For certain functions, must add other options, like puma value or neighboring value
+        # Fillna.
+        # For certain functions, must add other options,
+        # like puma value or neighboring value
         if agg_function == 'sum':
             series = series.fillna(0)
         else:
@@ -46,30 +52,37 @@ def func():
 
     return func
 
-def make_disagg_var(from_geog_name, to_geog_name, var_to_disaggregate, from_geog_id_name):
+
+def make_disagg_var(from_geog_name, to_geog_name, var_to_disaggregate,
+                    from_geog_id_name):
     """
     Generator function for disaggregating variables. Registers with orca.
     """
     var_name = from_geog_name + '_' + var_to_disaggregate
+
     @orca.column(to_geog_name, var_name, cache=False)
     def func():
-        print 'Disaggregating %s to %s from %s' % (var_to_disaggregate, to_geog_name, from_geog_name)
+        print('Disaggregating {} to {} from {}'
+              .format(var_to_disaggregate, to_geog_name, from_geog_name))
 
         from_geog = orca.get_table(from_geog_name)
         to_geog = orca.get_table(to_geog_name)
-        return misc.reindex(from_geog[var_to_disaggregate], to_geog[from_geog_id_name]).fillna(0)
+        return misc.reindex(from_geog[var_to_disaggregate],
+                            to_geog[from_geog_id_name]).fillna(0)
 
     return func
 
+
 def make_size_var(agent, geog, geog_id):
     """
     Generator function for size variables. Registers with orca.
     """
     var_name = 'total_' + agent
+
     @orca.column(geog, var_name, cache=False)
     def func():
         agents = orca.get_table(agent)
-        print 'Calculating number of %s for %s' % (agent, geog)
+        print('Calculating number of {} for {}'.format(agent, geog))
 
         size = agents[geog_id].value_counts()
 
@@ -81,29 +94,37 @@ def func():
 
     return func
 
+
 def make_proportion_var(agent, geog, geog_id, target_variable, target_value):
     """
     Generator function for proportion variables. Registers with orca.
     """
-    var_name = 'prop_%s_%s'%(target_variable, int(target_value))
+    var_name = 'prop_%s_%s' % (target_variable, int(target_value))
+
     @orca.column(geog, var_name, cache=False)
     def func():
-        agents = orca.get_table(agent).to_frame(columns=[target_variable, geog_id])
+        agents = orca.get_table(agent).to_frame(
+            columns=[target_variable, geog_id])
         locations = orca.get_table(geog)
-        print 'Calculating proportion %s %s for %s' % (target_variable, target_value, geog)
+        print('Calculating proportion {} {} for {}'
+              .format(target_variable, target_value, geog))
 
         agent_subset = agents[agents[target_variable] == target_value]
-        series = agent_subset.groupby(geog_id).size()*1.0/locations['total_' + agent]
+        series = (agent_subset.groupby(geog_id).size()
+                  * 1.0
+                  / locations['total_' + agent])
         series = series.fillna(0)
         return series
 
     return func
 
+
 def make_dummy_variable(agent, geog_var, geog_id):
     """
     Generator function for spatial dummy. Registers with orca.
     """
-    var_name = geog_var + '_is_' + str(int(geog_id))
+    var_name = '{}_is_{}'.format(geog_var, str(int(geog_id)))
+
     @orca.column(agent, var_name, cache=True)
     def func():
         agents = orca.get_table(agent)
@@ -111,35 +132,46 @@ def func():
 
     return func
 
+
 def make_ratio_var(agent1, agent2, geog):
     """
     Generator function for ratio variables. Registers with orca.
     """
-    var_name = 'ratio_%s_to_%s'%(agent1, agent2)
+    var_name = 'ratio_%s_to_%s' % (agent1, agent2)
+
     @orca.column(geog, var_name, cache=False)
     def func():
         locations = orca.get_table(geog)
-        print 'Calculating ratio of %s to %s for %s' % (agent1, agent2, geog)
+        print('Calculating ratio of {} to {} for {}'
+              .format(agent1, agent2, geog))
 
-        series = locations['total_' + agent1]*1.0/(locations['total_' + agent2] + 1.0)
+        series = (locations['total_' + agent1]
+                  * 1.0
+                  / (locations['total_' + agent2] + 1.0))
         series = series.fillna(0)
         return series
 
     return func
-    
+
+
 def make_access_var(name, agent, target_variable=False, target_value=False,
                     radius=1000, agg_function='sum', decay='flat', log=True,
                     filters=False):
     """
     Generator function for accessibility variables. Registers with orca.
     """
+
     @orca.column('nodes', name, cache=True, cache_scope='iteration')
     def func(net):
-        print 'Calculating %s' % name
+        print('Calculating {}'.format(name))
+
         nodes = pd.DataFrame(index=net.node_ids)
         flds = [target_variable] if target_variable else []
+
         if target_value:
-            flds += util.columns_in_filters(["%s == %s"%(target_variable,target_value)])
+            flds += util.columns_in_filters(
+                ["{} == {}".format(target_variable, target_value)])
+
         if filters:
             flds += util.columns_in_filters(filters)
         flds.append('node_id')
@@ -147,14 +179,17 @@ def func(net):
         df = orca.get_table(agent).to_frame(flds)
 
         if target_value:
-            df = util.apply_filter_query(df, ["%s == %s"%(target_variable,target_value)])
+            df = util.apply_filter_query(df, [
+                "{} == {}".format(target_variable, target_value)])
         if filters:
             df = util.apply_filter_query(df, filters)
 
-        net.set(df['node_id'], variable=df[target_variable] if target_variable else None)
+        net.set(df['node_id'],
+                variable=df[target_variable] if target_variable else None)
         nodes[name] = net.aggregate(radius, type=agg_function, decay=decay)
+
         if log:
             nodes[name] = nodes[name].apply(eval('np.log1p'))
         return nodes[name]
-    
+
     return func