Support for Multiple Zone Systems and Transit Virtual Path Building

.travis.yml

@@ -18,6 +18,9 @@ install:
 - pip install .
 - pip freeze
 script:
+# build 2 and 3 zone test data
+- python activitysim/examples/example_multiple_zone/two_zone_example_data.py
+- python activitysim/examples/example_multiple_zone/three_zone_example_data.py
 - pycodestyle activitysim
 - py.test --cov activitysim --cov-report term-missing
 after_success:

activitysim/abm/misc.py

@@ -20,7 +20,7 @@ def households_sample_size(settings, override_hh_ids):
     if override_hh_ids is None:
         return settings.get('households_sample_size', 0)
     else:
-        return len(override_hh_ids)
+        return 0 if override_hh_ids is None else len(override_hh_ids)
 
 
 @inject.injectable(cache=True)
@@ -79,7 +79,7 @@ def trace_od(settings):
 
 @inject.injectable(cache=True)
 def chunk_size(settings):
-    return int(settings.get('chunk_size', 0))
+    return int(settings.get('chunk_size', 0) or 0)
 
 
 @inject.injectable(cache=True)

activitysim/abm/models/__init__.py

@@ -9,6 +9,8 @@
 from . import cdap
 from . import free_parking
 from . import initialize
+from . import initialize_tours
+from . import initialize_los
 from . import joint_tour_composition
 from . import joint_tour_destination
 from . import joint_tour_frequency
@@ -28,3 +30,4 @@
 from . import trip_purpose_and_destination
 from . import trip_scheduling
 from . import trip_matrices
+from . import summarize

activitysim/abm/models/accessibility.py

@@ -10,95 +10,99 @@
 from activitysim.core import config
 from activitysim.core import inject
 from activitysim.core import pipeline
+from activitysim.core import mem
 
+from activitysim.core import los
+from activitysim.core.pathbuilder import TransitVirtualPathBuilder
 
 logger = logging.getLogger(__name__)
 
 
-class AccessibilitySkims(object):
-    """
-    Wrapper for skim arrays to facilitate use of skims by accessibility model
-
-    Parameters
-    ----------
-    skims : 2D array
-    omx: open omx file object
-        this is only used to load skims on demand that were not preloaded
-    length: int
-        number of zones in skim to return in skim matrix
-        in case the skims contain additional external zones that should be trimmed out so skim
-        array is correct shape to match (flattened) O-D tiled columns in the od dataframe
-    transpose: bool
-        whether to transpose the matrix before flattening. (i.e. act as a D-O instead of O-D skim)
-    """
-
-    def __init__(self, skim_dict, orig_zones, dest_zones, transpose=False):
-
-        omx_shape = skim_dict.skim_info['omx_shape']
-        logger.info("init AccessibilitySkims with %d dest zones %d orig zones omx_shape %s" %
-                    (len(dest_zones), len(orig_zones), omx_shape, ))
-
-        assert len(orig_zones) <= len(dest_zones)
-        assert np.isin(orig_zones, dest_zones).all()
-        assert len(np.unique(orig_zones)) == len(orig_zones)
-        assert len(np.unique(dest_zones)) == len(dest_zones)
-
-        self.skim_dict = skim_dict
-        self.transpose = transpose
-
-        if omx_shape[0] == len(orig_zones) and skim_dict.offset_mapper.offset_series is None:
-            # no slicing required because whatever the offset_int, the skim data aligns with zone list
-            self.map_data = False
-        else:
-
-            if omx_shape[0] == len(orig_zones):
-                logger.debug("AccessibilitySkims - applying offset_mapper")
-
-            skim_index = list(range(omx_shape[0]))
-            orig_map = skim_dict.offset_mapper.map(orig_zones)
-            dest_map = skim_dict.offset_mapper.map(dest_zones)
-
-            # (we might be sliced multiprocessing)
-            # assert np.isin(skim_index, orig_map).all()
-
-            if np.isin(skim_index, dest_map).all():
-                # not using the whole skim matrix
-                logger.info("%s skim zones not in dest_map: %s" %
-                            ((~dest_map).sum(), np.ix_(~dest_map)))
-
-            self.map_data = True
-            self.orig_map = orig_map
-            self.dest_map = dest_map
-
-    def __getitem__(self, key):
-        """
-        accessor to return flattened skim array with specified key
-        flattened array will have length length*length and will match tiled OD df used by assign
-
-        this allows the skim array to be accessed from expressions as
-        skim['DISTANCE'] or skim[('SOVTOLL_TIME', 'MD')]
-        """
-
-        data = self.skim_dict.get(key).data
-
-        if self.transpose:
-            data = data.transpose()
-
-        if self.map_data:
-
-            # slice skim to include only orig rows and dest columns
-            # 2-d boolean slicing in numpy is a bit tricky
-            # data = data[orig_map, dest_map]          # <- WRONG!
-            # data = data[orig_map, :][:, dest_map]    # <- RIGHT
-            # data = data[np.ix_(orig_map, dest_map)]  # <- ALSO RIGHT
-
-            data = data[self.orig_map, :][:, self.dest_map]
-
-        return data.flatten()
+# class AccessibilitySkims(object):
+#     """
+#     Wrapper for skim arrays to facilitate use of skims by accessibility model
+#
+#     Parameters
+#     ----------
+#     skims : 2D array
+#     omx: open omx file object
+#         this is only used to load skims on demand that were not preloaded
+#     length: int
+#         number of zones in skim to return in skim matrix
+#         in case the skims contain additional external zones that should be trimmed out so skim
+#         array is correct shape to match (flattened) O-D tiled columns in the od dataframe
+#     transpose: bool
+#         whether to transpose the matrix before flattening. (i.e. act as a D-O instead of O-D skim)
+#     """
+#
+#     def __init__(self, skim_dict, orig_zones, dest_zones, transpose=False):
+#
+#         logger.info(f"init AccessibilitySkims with {len(dest_zones)} dest zones {len(orig_zones)} orig zones")
+#
+#         assert len(orig_zones) <= len(dest_zones)
+#         assert np.isin(orig_zones, dest_zones).all()
+#         assert len(np.unique(orig_zones)) == len(orig_zones)
+#         assert len(np.unique(dest_zones)) == len(dest_zones)
+#
+#         self.skim_dict = skim_dict
+#         self.transpose = transpose
+#
+#         num_skim_zones = skim_dict.get_skim_info('omx_shape')[0]
+#         if num_skim_zones == len(orig_zones) and skim_dict.offset_mapper.offset_series is None:
+#             # no slicing required because whatever the offset_int, the skim data aligns with zone list
+#             self.map_data = False
+#         else:
+#
+#             logger.debug("AccessibilitySkims - applying offset_mapper")
+#
+#             skim_index = list(range(num_skim_zones))
+#             orig_map = skim_dict.offset_mapper.map(orig_zones)
+#             dest_map = skim_dict.offset_mapper.map(dest_zones)
+#
+#             # (we might be sliced multiprocessing)
+#             # assert np.isin(skim_index, orig_map).all()
+#
+#             out_of_bounds = ~np.isin(skim_index, dest_map)
+#             # if out_of_bounds.any():
+#             #    print(f"{(out_of_bounds).sum()} skim zones not in dest_map")
+#             #    print(f"dest_zones {dest_zones}")
+#             #    print(f"dest_map {dest_map}")
+#             #    print(f"skim_index {skim_index}")
+#             assert not out_of_bounds.any(), \
+#                 f"AccessibilitySkims {(out_of_bounds).sum()} skim zones not in dest_map: {np.ix_(out_of_bounds)[0]}"
+#
+#             self.map_data = True
+#             self.orig_map = orig_map
+#             self.dest_map = dest_map
+#
+#     def __getitem__(self, key):
+#         """
+#         accessor to return flattened skim array with specified key
+#         flattened array will have length length*length and will match tiled OD df used by assign
+#
+#         this allows the skim array to be accessed from expressions as
+#         skim['DISTANCE'] or skim[('SOVTOLL_TIME', 'MD')]
+#         """
+#
+#         data = self.skim_dict.get(key).data
+#
+#         if self.transpose:
+#             data = data.transpose()
+#
+#         if self.map_data:
+#             # slice skim to include only orig rows and dest columns
+#             # 2-d boolean slicing in numpy is a bit tricky
+#             # data = data[orig_map, dest_map]          # <- WRONG!
+#             # data = data[orig_map, :][:, dest_map]    # <- RIGHT
+#             # data = data[np.ix_(orig_map, dest_map)]  # <- ALSO RIGHT
+#
+#             data = data[self.orig_map, :][:, self.dest_map]
+#
+#         return data.flatten()
 
 
 @inject.step()
-def compute_accessibility(accessibility, skim_dict, land_use, trace_od):
+def compute_accessibility(accessibility, network_los, land_use, trace_od):
 
     """
     Compute accessibility for each zone in land use file using expressions from accessibility_spec
@@ -143,8 +147,8 @@ def compute_accessibility(accessibility, skim_dict, land_use, trace_od):
     # create OD dataframe
     od_df = pd.DataFrame(
         data={
-            'orig': np.repeat(np.asanyarray(accessibility_df.index), dest_zone_count),
-            'dest': np.tile(np.asanyarray(land_use_df.index), orig_zone_count)
+            'orig': np.repeat(orig_zones, dest_zone_count),
+            'dest': np.tile(dest_zones, orig_zone_count)
         }
     )
 
@@ -160,9 +164,16 @@ def compute_accessibility(accessibility, skim_dict, land_use, trace_od):
     locals_d = {
         'log': np.log,
         'exp': np.exp,
-        'skim_od': AccessibilitySkims(skim_dict, orig_zones, dest_zones),
-        'skim_do': AccessibilitySkims(skim_dict, orig_zones, dest_zones, transpose=True)
+        'network_los': network_los,
     }
+
+    skim_dict = network_los.get_default_skim_dict()
+    locals_d['skim_od'] = skim_dict.wrap('orig', 'dest').set_df(od_df)
+    locals_d['skim_do'] = skim_dict.wrap('dest', 'orig').set_df(od_df)
+
+    if network_los.zone_system == los.THREE_ZONE:
+        locals_d['tvpb'] = TransitVirtualPathBuilder(network_los)
+
     if constants is not None:
         locals_d.update(constants)
 
@@ -174,13 +185,15 @@ def compute_accessibility(accessibility, skim_dict, land_use, trace_od):
         data.shape = (orig_zone_count, dest_zone_count)  # (o,d)
         accessibility_df[column] = np.log(np.sum(data, axis=1) + 1)
 
+    logger.info("{trace_label} added {len(results.columns} columns")
+
     # - write table to pipeline
     pipeline.replace_table("accessibility", accessibility_df)
 
     if trace_od:
 
         if not trace_od_rows.any():
-            logger.warning("trace_od not found origin = %s, dest = %s" % (trace_orig, trace_dest))
+            logger.warning(f"trace_od not found origin = {trace_orig}, dest = {trace_dest}")
         else:
 
             # add OD columns to trace results