Merge pull request UDST#99 from urbansim/data-types

Bug fixes to support NumPy 1.16

baus/models.py

@@ -132,7 +132,7 @@ def _proportional_jobs_model(
     # city but not locations to put them in.  we need to drop this demand
     drop = need_more_jobs.index.difference(locations_series.unique())
     print "We don't have any locations for these locations:\n", drop
-    need_more_jobs = need_more_jobs.drop(drop)
+    need_more_jobs = need_more_jobs.drop(drop).astype('int')
 
     # choose random locations within jurises to match need_more_jobs totals
     choices = groupby_random_choice(locations_series, need_more_jobs,
@@ -570,7 +570,7 @@ def residential_developer(feasibility, households, buildings, parcels, year,
             year=year,
             form_to_btype_callback=form_to_btype_func,
             add_more_columns_callback=add_extra_columns_func,
-            num_units_to_build=target,
+            num_units_to_build=int(target),
             profit_to_prob_func=subsidies.profit_to_prob_func,
             **kwargs)
 
@@ -778,7 +778,7 @@ def office_developer(feasibility, jobs, buildings, parcels, year,
                 form_to_btype_callback=form_to_btype_func,
                 add_more_columns_callback=add_extra_columns_func,
                 residential=False,
-                num_units_to_build=target,
+                num_units_to_build=int(target),
                 profit_to_prob_func=subsidies.profit_to_prob_func,
                 **dev_settings['kwargs'])
 

baus/utils.py

@@ -207,8 +207,14 @@ def constrained_normalization(marginals, constraint, total):
 def simple_ipf(seed_matrix, col_marginals, row_marginals, tolerance=1, cnt=0):
     assert np.absolute(row_marginals.sum() - col_marginals.sum()) < 5.0
 
+    # most numpy/pandas combinations will perform this conversion
+    # automatically, but explicit is safer - see PR #99
+    if isinstance(col_marginals, pd.Series):
+        col_marginals = col_marginals.values
+
     # first normalize on columns
     ratios = col_marginals / seed_matrix.sum(axis=0)
+
     seed_matrix *= ratios
     closeness = np.absolute(row_marginals - seed_matrix.sum(axis=1)).sum()
     assert np.absolute(col_marginals - seed_matrix.sum(axis=0)).sum() < .01