Return cumulative multiplier, not last multiplier

supply_and_demand was returning the multipliers only from the last
iteration of the price updates. This fixes it to accumulate the
multipliers and return that.

urbansim/models/supplydemand.py

@@ -124,16 +124,21 @@ def supply_and_demand(
         bm = base_multiplier.loc[alt_segmenter]
         bm.index = alt_segmenter.index
         alternatives[price_col] = alternatives[price_col] * bm
+        base_multiplier = base_multiplier.copy()
 
     for _ in range(iterations):
         alts_muliplier, submarkets_multiplier = _calculate_adjustment(
             lcm, choosers, alternatives, alt_segmenter,
             clip_change_low, clip_change_high)
         alternatives[price_col] = alternatives[price_col] * alts_muliplier
 
-    # if we started with a base ratio, return a cumulative ratio
-    if base_multiplier is not None:
-        submarkets_multiplier *= base_multiplier
+        # might need to initialize this for holding cumulative multiplier
+        if base_multiplier is None:
+            base_multiplier = pd.Series(
+                np.ones(len(submarkets_multiplier)),
+                index=submarkets_multiplier.index)
+
+        base_multiplier *= submarkets_multiplier
 
     logger.debug('finish: calculating supply and demand price adjustment')
-    return alternatives[price_col], submarkets_multiplier
+    return alternatives[price_col], base_multiplier

urbansim/models/tests/test_supplydemand.py

@@ -118,7 +118,7 @@ def test_supply_and_demand(
             index=filtered_alts.index) ** 5)
     pdt.assert_series_equal(
         submarkets_multiplier,
-        pd.Series([w, x, y, z], index=['w', 'x', 'y', 'z']))
+        pd.Series([w, x, y, z], index=['w', 'x', 'y', 'z']) ** 5)
 
 
 def test_supply_and_demand_base_ratio(
@@ -144,4 +144,4 @@ def test_supply_and_demand_base_ratio(
             index=filtered_alts.index) ** 6)
     pdt.assert_series_equal(
         submarkets_multiplier,
-        pd.Series([w, x, y, z], index=['w', 'x', 'y', 'z']) ** 2)
+        pd.Series([w, x, y, z], index=['w', 'x', 'y', 'z']) ** 6)