def testAirOverrideHack(self):
self.testPredictedProb()
self.pipeline.toPredictSections[1]["data"]["sensed_mode"] = ecwma.MotionTypes.AIR_OR_HSR.value
self.pipeline.toPredictSections[3]["data"]["sensed_mode"] = ecwma.MotionTypes.AIR_OR_HSR.value
self.pipeline.savePredictionsStep()
for i, section in enumerate(self.pipeline.toPredictSections):
predicted_mode = esds.get_inferred_mode_entry(self.testUUID, section.get_id())
self.assertIsNotNone(predicted_mode)
ise = esds.cleaned2inferred_section(self.testUUID, section.get_id())
self.assertIsNotNone(ise)
if i == 0:
self.assertEqual(predicted_mode.data["predicted_mode_map"],
{'WALKING': 0.7, 'BUS': 0.3})
self.assertEqual(ise.data.sensed_mode, ecwm.PredictedModeTypes.WALKING)
if i == 1:
self.assertEqual(predicted_mode.data["predicted_mode_map"],
{'AIR_OR_HSR': 1.0})
self.assertEqual(ise.data.sensed_mode, ecwm.PredictedModeTypes.AIR_OR_HSR)
if i == 2:
self.assertEqual(predicted_mode.data["predicted_mode_map"],
{'WALKING': 0.2, 'BUS': 0.8})
self.assertEqual(ise.data.sensed_mode, ecwm.PredictedModeTypes.BUS)
if i == 3:
self.assertEqual(predicted_mode.data["predicted_mode_map"],
{'AIR_OR_HSR': 1.0})
self.assertEqual(ise.data.sensed_mode, ecwm.PredictedModeTypes.AIR_OR_HSR)
def testAirOverrideHack(self):
self.testPredictedProb()
self.pipeline.toPredictSections[1]["data"][
"sensed_mode"] = ecwma.MotionTypes.AIR_OR_HSR.value
self.pipeline.toPredictSections[3]["data"][
"sensed_mode"] = ecwma.MotionTypes.AIR_OR_HSR.value
self.pipeline.savePredictionsStep()
for i, section in enumerate(self.pipeline.toPredictSections):
predicted_mode = esds.get_inferred_mode_entry(
self.testUUID, section.get_id())
self.assertIsNotNone(predicted_mode)
ise = esds.cleaned2inferred_section(self.testUUID,
section.get_id())
self.assertIsNotNone(ise)
if i == 0:
self.assertEqual(predicted_mode.data["predicted_mode_map"], {
'WALKING': 0.7,
'BUS': 0.3
})
self.assertEqual(ise.data.sensed_mode,
ecwm.PredictedModeTypes.WALKING)
if i == 1:
self.assertEqual(predicted_mode.data["predicted_mode_map"],
{'AIR_OR_HSR': 1.0})
self.assertEqual(ise.data.sensed_mode,
ecwm.PredictedModeTypes.AIR_OR_HSR)
if i == 2:
self.assertEqual(predicted_mode.data["predicted_mode_map"], {
'WALKING': 0.2,
'BUS': 0.8
})
self.assertEqual(ise.data.sensed_mode,
ecwm.PredictedModeTypes.BUS)
if i == 3:
self.assertEqual(predicted_mode.data["predicted_mode_map"],
{'AIR_OR_HSR': 1.0})
self.assertEqual(ise.data.sensed_mode,
ecwm.PredictedModeTypes.AIR_OR_HSR)
def section_to_geojson(section, tl):
"""
This is the trickiest part of the visualization.
The section is basically a collection of points with a line through them.
So the representation is a feature in which one feature which is the line, and one feature collection which is the set of point features.
:param section: the section to be converted
:return: a feature collection which is the geojson version of the section
"""
ts = esta.TimeSeries.get_time_series(section.user_id)
entry_it = ts.find_entries(["analysis/recreated_location"],
esda.get_time_query_for_trip_like(
"analysis/cleaned_section",
section.get_id()))
# TODO: Decide whether we want to use Rewrite to use dataframes throughout instead of python arrays.
# dataframes insert nans. We could use fillna to fill with default values, but if we are not actually
# using dataframe features here, it is unclear how much that would help.
feature_array = []
section_location_entries = [ecwe.Entry(entry) for entry in entry_it]
if len(section_location_entries) != 0:
logging.debug("first element in section_location_array = %s" % section_location_entries[0])
if not ecc.compare_rounded_arrays(section.data.end_loc.coordinates,
section_location_entries[-1].data.loc.coordinates,
digits=4):
logging.info("section_location_array[-1].data.loc %s != section.data.end_loc %s even after df.ts fix, filling gap" % \
(section_location_entries[-1].data.loc, section.data.end_loc))
assert(False)
last_loc_doc = ts.get_entry_at_ts("background/filtered_location", "data.ts", section.data.end_ts)
if last_loc_doc is None:
logging.warning("can't find entry to patch gap, leaving gap")
else:
last_loc_entry = ecwe.Entry(last_loc_doc)
logging.debug("Adding new entry %s to fill the end point gap between %s and %s"
% (last_loc_entry.data.loc, section_location_entries[-1].data.loc,
section.data.end_loc))
section_location_entries.append(last_loc_entry)
points_line_feature = point_array_to_line(section_location_entries)
points_line_feature.id = str(section.get_id())
points_line_feature.properties.update(copy.copy(section.data))
# Update works on dicts, convert back to a section object to make the modes
# work properly
points_line_feature.properties = ecwcs.Cleanedsection(points_line_feature.properties)
points_line_feature.properties["feature_type"] = "section"
if eac.get_section_key_for_analysis_results() == esda.INFERRED_SECTION_KEY:
ise = esds.cleaned2inferred_section(section.user_id, section.get_id())
if ise is not None:
logging.debug("mapped cleaned section %s -> inferred section %s" %
(section.get_id(), ise.get_id()))
logging.debug("changing mode from %s -> %s" %
(points_line_feature.properties.sensed_mode, ise.data.sensed_mode))
points_line_feature.properties["sensed_mode"] = str(ise.data.sensed_mode)
else:
points_line_feature.properties["sensed_mode"] = str(points_line_feature.properties.sensed_mode)
else:
points_line_feature.properties["sensed_mode"] = str(points_line_feature.properties.sensed_mode)
_del_non_derializable(points_line_feature.properties, ["start_loc", "end_loc"])
# feature_array.append(gj.FeatureCollection(points_feature_array))
feature_array.append(points_line_feature)
return gj.FeatureCollection(feature_array)
def section_to_geojson(section, tl):
"""
This is the trickiest part of the visualization.
The section is basically a collection of points with a line through them.
So the representation is a feature in which one feature which is the line, and one feature collection which is the set of point features.
:param section: the section to be converted
:return: a feature collection which is the geojson version of the section
"""
ts = esta.TimeSeries.get_time_series(section.user_id)
entry_it = ts.find_entries(["analysis/recreated_location"],
esda.get_time_query_for_trip_like(
"analysis/cleaned_section",
section.get_id()))
# TODO: Decide whether we want to use Rewrite to use dataframes throughout instead of python arrays.
# dataframes insert nans. We could use fillna to fill with default values, but if we are not actually
# using dataframe features here, it is unclear how much that would help.
feature_array = []
section_location_entries = [ecwe.Entry(entry) for entry in entry_it]
if len(section_location_entries) != 0:
logging.debug("first element in section_location_array = %s" %
section_location_entries[0])
if not ecc.compare_rounded_arrays(
section.data.end_loc.coordinates,
section_location_entries[-1].data.loc.coordinates,
digits=4):
logging.info("section_location_array[-1].data.loc %s != section.data.end_loc %s even after df.ts fix, filling gap" % \
(section_location_entries[-1].data.loc, section.data.end_loc))
assert (False)
last_loc_doc = ts.get_entry_at_ts("background/filtered_location",
"data.ts", section.data.end_ts)
if last_loc_doc is None:
logging.warning("can't find entry to patch gap, leaving gap")
else:
last_loc_entry = ecwe.Entry(last_loc_doc)
logging.debug(
"Adding new entry %s to fill the end point gap between %s and %s"
% (last_loc_entry.data.loc,
section_location_entries[-1].data.loc,
section.data.end_loc))
section_location_entries.append(last_loc_entry)
points_line_feature = point_array_to_line(section_location_entries)
points_line_feature.id = str(section.get_id())
points_line_feature.properties.update(copy.copy(section.data))
# Update works on dicts, convert back to a section object to make the modes
# work properly
points_line_feature.properties = ecwcs.Cleanedsection(
points_line_feature.properties)
points_line_feature.properties["feature_type"] = "section"
if eac.get_section_key_for_analysis_results() == esda.INFERRED_SECTION_KEY:
ise = esds.cleaned2inferred_section(section.user_id, section.get_id())
if ise is not None:
logging.debug("mapped cleaned section %s -> inferred section %s" %
(section.get_id(), ise.get_id()))
logging.debug("changing mode from %s -> %s" %
(points_line_feature.properties.sensed_mode,
ise.data.sensed_mode))
points_line_feature.properties["sensed_mode"] = str(
ise.data.sensed_mode)
else:
points_line_feature.properties["sensed_mode"] = str(
points_line_feature.properties.sensed_mode)
else:
points_line_feature.properties["sensed_mode"] = str(
points_line_feature.properties.sensed_mode)
_del_non_derializable(points_line_feature.properties,
["start_loc", "end_loc"])
# feature_array.append(gj.FeatureCollection(points_feature_array))
feature_array.append(points_line_feature)
return gj.FeatureCollection(feature_array)