def testPointFilteringShanghaiJump(self):
classicJumpTrip1 = self.trips[0]
self.loadPointsForTrip(classicJumpTrip1.get_id())
classicJumpSections1 = [s for s in self.sections if s.trip_id == classicJumpTrip1.get_id()]
outlier_algo = eaics.BoxplotOutlier()
jump_algo = eaicj.SmoothZigzag()
for i, section in enumerate(classicJumpSections1):
logging.debug("-" * 20 + "Considering section %s" % i + "-" * 20)
section_df = self.ts.get_data_df("background/filtered_location", esds.get_time_query_for_section(section.get_id()))
with_speeds_df = eaicl.add_dist_heading_speed(section_df)
maxSpeed = outlier_algo.get_threshold(with_speeds_df)
logging.debug("Max speed for section %s = %s" % (i, maxSpeed))
jump_algo.filter(with_speeds_df)
logging.debug("Retaining points %s" % np.nonzero(jump_algo.inlier_mask_))
to_delete_mask = np.logical_not(jump_algo.inlier_mask_)
logging.debug("Deleting points %s" % np.nonzero(to_delete_mask))
delete_ids = list(with_speeds_df[to_delete_mask]._id)
logging.debug("Deleting ids %s" % delete_ids)
# Automated checks. Might be able to remove logging statements later
if i != 2:
# Not the bad section. Should not be filtered
self.assertEqual(np.count_nonzero(to_delete_mask), 0)
self.assertEqual(len(delete_ids), 0)
else:
# The bad section, should have the third point filtered
self.assertEqual(np.count_nonzero(to_delete_mask), 1)
self.assertEqual([str(id) for id in delete_ids], ["55d8c4837d65cb39ee983cb4"])
def testPointFilteringRichmondJump(self):
classicJumpTrip1 = self.trip_entries[6]
self.loadPointsForTrip(classicJumpTrip1.get_id())
classicJumpSections1 = [s for s in self.section_entries
if s.data.trip_id == classicJumpTrip1.get_id()]
outlier_algo = eaics.BoxplotOutlier()
jump_algo = eaicj.SmoothZigzag(False, 100)
for i, section_entry in enumerate(classicJumpSections1):
logging.debug("-" * 20 + "Considering section %s" % i + "-" * 20)
section_df = self.ts.get_data_df("background/filtered_location",
esda.get_time_query_for_trip_like(esda.RAW_SECTION_KEY,
section_entry.get_id()))
with_speeds_df = eaicl.add_dist_heading_speed(section_df)
maxSpeed = outlier_algo.get_threshold(with_speeds_df)
logging.debug("Max speed for section %s = %s" % (i, maxSpeed))
jump_algo.filter(with_speeds_df)
logging.debug("Retaining points %s" % np.nonzero(jump_algo.inlier_mask_))
to_delete_mask = np.logical_not(jump_algo.inlier_mask_)
logging.debug("Deleting points %s" % np.nonzero(to_delete_mask))
delete_ids = list(with_speeds_df[to_delete_mask]._id)
logging.debug("Deleting ids %s" % delete_ids)
# There is only one section
self.assertEqual(i, 0)
# The bad section, should have the third point filtered
self.assertEqual(np.count_nonzero(to_delete_mask), 1)
self.assertEqual([str(id) for id in delete_ids], ["55e86dbb7d65cb39ee987e09"])
def get_median_speed(self, mcs, mce):
loc_df = self.seg_method.filter_points_for_range(
self.seg_method.location_points, mcs, mce)
if len(loc_df) > 0:
loc_df.reset_index(inplace=True)
with_speed_df = eaicl.add_dist_heading_speed(loc_df)
return with_speed_df.speed.median()
else:
return None
def testPointFilteringZigzag(self):
classicJumpTrip1 = self.trip_entries[8]
self.loadPointsForTrip(classicJumpTrip1.get_id())
classicJumpSections1 = [
s for s in self.section_entries
if s.data.trip_id == classicJumpTrip1.get_id()
]
outlier_algo = eaics.BoxplotOutlier()
jump_algo = eaicj.SmoothZigzag(False, 100)
for i, section_entry in enumerate(classicJumpSections1):
logging.debug("-" * 20 + "Considering section %s" % i + "-" * 20)
section_df = self.ts.get_data_df(
"background/filtered_location",
esda.get_time_query_for_trip_like(esda.RAW_SECTION_KEY,
section_entry.get_id()))
with_speeds_df = eaicl.add_dist_heading_speed(section_df)
maxSpeed = outlier_algo.get_threshold(with_speeds_df)
logging.debug("Max speed for section %s = %s" % (i, maxSpeed))
jump_algo.filter(with_speeds_df)
logging.debug("Retaining points %s" %
np.nonzero(jump_algo.inlier_mask_.to_numpy()))
to_delete_mask = np.logical_not(jump_algo.inlier_mask_)
logging.debug("Deleting points %s" %
np.nonzero(to_delete_mask.to_numpy()))
delete_ids = list(with_speeds_df[to_delete_mask]._id)
logging.debug("Deleting ids %s" % delete_ids)
if i == 0:
# this is the zigzag section
self.assertEqual(
np.nonzero(to_delete_mask.to_numpy())[0].tolist(),
[25, 64, 114, 115, 116, 117, 118, 119, 120, 123, 126])
self.assertEqual(delete_ids, [
boi.ObjectId('55edafe77d65cb39ee9882ff'),
boi.ObjectId('55edcc157d65cb39ee98836e'),
boi.ObjectId('55edcc1f7d65cb39ee988400'),
boi.ObjectId('55edcc1f7d65cb39ee988403'),
boi.ObjectId('55edcc1f7d65cb39ee988406'),
boi.ObjectId('55edcc1f7d65cb39ee988409'),
boi.ObjectId('55edcc1f7d65cb39ee98840c'),
boi.ObjectId('55edcc207d65cb39ee988410'),
boi.ObjectId('55edcc207d65cb39ee988412'),
boi.ObjectId('55edcc217d65cb39ee98841f'),
boi.ObjectId('55edcc217d65cb39ee988429')
])
else:
self.assertEqual(len(np.nonzero(to_delete_mask.to_numpy())[0]),
0)
self.assertEqual(len(delete_ids), 0)
def get_filtered_points(section, filtered_section_data):
logging.debug("Getting filtered points for section %s" % section)
ts = esta.TimeSeries.get_time_series(section.user_id)
loc_entry_it = ts.find_entries(["background/filtered_location"],
esda.get_time_query_for_trip_like(
esda.RAW_SECTION_KEY, section.get_id()))
loc_entry_list = [ecwe.Entry(e) for e in loc_entry_it]
# We know that the assertion fails in the geojson conversion code and we
# handle it there, so we are just going to comment this out for now.
# assert (loc_entry_list[-1].data.loc == section.data.end_loc,
# "section_location_array[-1].loc != section.end_loc even after df.ts fix",
# (loc_entry_list[-1].data.loc, section.data.end_loc))
# Find the list of points to filter
filtered_points_entry_doc = ts.get_entry_at_ts("analysis/smoothing",
"data.section",
section.get_id())
if filtered_points_entry_doc is None:
logging.debug(
"No filtered_points_entry, filtered_points_list is empty")
filtered_point_id_list = []
else:
# TODO: Figure out how to make collections work for the wrappers and then change this to an Entry
filtered_points_entry = ad.AttrDict(filtered_points_entry_doc)
filtered_point_id_list = list(
filtered_points_entry.data.deleted_points)
logging.debug("deleting %s points from section points" %
len(filtered_point_id_list))
filtered_loc_list = remove_outliers(loc_entry_list, filtered_point_id_list)
# filtered_loc_list has removed the outliers. Now, we resample the data at
# 30 sec intervals
resampled_loc_df = resample(filtered_loc_list, interval=30)
# If this is the first section, we need to find the start place of the parent trip
# and actually start from there. That will fix the distances but not the duration
# because we haven't yet figured out how to get the correct start time.
# TODO: Fix this!!
# For now, we will fudge this in the geojson converter, as always
with_speeds_df = eaicl.add_dist_heading_speed(resampled_loc_df)
with_speeds_df["idx"] = np.arange(0, len(with_speeds_df))
with_speeds_df_nona = with_speeds_df.dropna()
logging.info("removed %d entries containing n/a" %
(len(with_speeds_df_nona) - len(with_speeds_df)))
return with_speeds_df_nona
def get_filtered_points(section, filtered_section_data):
logging.debug("Getting filtered points for section %s" % section)
ts = esta.TimeSeries.get_time_series(section.user_id)
loc_entry_it = ts.find_entries(["background/filtered_location"],
esda.get_time_query_for_trip_like(
esda.RAW_SECTION_KEY, section.get_id()))
loc_entry_list = [ecwe.Entry(e) for e in loc_entry_it]
# We know that the assertion fails in the geojson conversion code and we
# handle it there, so we are just going to comment this out for now.
# assert (loc_entry_list[-1].data.loc == section.data.end_loc,
# "section_location_array[-1].loc != section.end_loc even after df.ts fix",
# (loc_entry_list[-1].data.loc, section.data.end_loc))
# Find the list of points to filter
filtered_points_entry_doc = ts.get_entry_at_ts("analysis/smoothing",
"data.section",
section.get_id())
if filtered_points_entry_doc is None:
logging.debug("No filtered_points_entry, filtered_points_list is empty")
filtered_point_id_list = []
else:
# TODO: Figure out how to make collections work for the wrappers and then change this to an Entry
filtered_points_entry = ad.AttrDict(filtered_points_entry_doc)
filtered_point_id_list = list(filtered_points_entry.data.deleted_points)
logging.debug("deleting %s points from section points" % len(
filtered_point_id_list))
filtered_loc_list = remove_outliers(loc_entry_list, filtered_point_id_list)
# filtered_loc_list has removed the outliers. Now, we resample the data at
# 30 sec intervals
resampled_loc_df = resample(filtered_loc_list, interval=30)
# If this is the first section, we need to find the start place of the parent trip
# and actually start from there. That will fix the distances but not the duration
# because we haven't yet figured out how to get the correct start time.
# TODO: Fix this!!
# For now, we will fudge this in the geojson converter, as always
with_speeds_df = eaicl.add_dist_heading_speed(resampled_loc_df)
with_speeds_df["idx"] = np.arange(0, len(with_speeds_df))
with_speeds_df_nona = with_speeds_df.dropna()
logging.info("removed %d entries containing n/a" %
(len(with_speeds_df_nona) - len(with_speeds_df)))
return with_speeds_df_nona
def testPointFilteringZigzag(self):
classicJumpTrip1 = self.trip_entries[8]
self.loadPointsForTrip(classicJumpTrip1.get_id())
classicJumpSections1 = [s for s in self.section_entries
if s.data.trip_id == classicJumpTrip1.get_id()]
outlier_algo = eaics.BoxplotOutlier()
jump_algo = eaicj.SmoothZigzag(False, 100)
for i, section_entry in enumerate(classicJumpSections1):
logging.debug("-" * 20 + "Considering section %s" % i + "-" * 20)
section_df = self.ts.get_data_df("background/filtered_location",
esda.get_time_query_for_trip_like(esda.RAW_SECTION_KEY,
section_entry.get_id()))
with_speeds_df = eaicl.add_dist_heading_speed(section_df)
maxSpeed = outlier_algo.get_threshold(with_speeds_df)
logging.debug("Max speed for section %s = %s" % (i, maxSpeed))
jump_algo.filter(with_speeds_df)
logging.debug("Retaining points %s" % np.nonzero(jump_algo.inlier_mask_))
to_delete_mask = np.logical_not(jump_algo.inlier_mask_)
logging.debug("Deleting points %s" % np.nonzero(to_delete_mask))
delete_ids = list(with_speeds_df[to_delete_mask]._id)
logging.debug("Deleting ids %s" % delete_ids)
if i == 0:
# this is the zigzag section
self.assertEqual(np.nonzero(to_delete_mask)[0].tolist(),
[25, 64, 114, 115, 116, 117, 118, 119, 120, 123, 126])
self.assertEqual(delete_ids,
[boi.ObjectId('55edafe77d65cb39ee9882ff'),
boi.ObjectId('55edcc157d65cb39ee98836e'),
boi.ObjectId('55edcc1f7d65cb39ee988400'),
boi.ObjectId('55edcc1f7d65cb39ee988403'),
boi.ObjectId('55edcc1f7d65cb39ee988406'),
boi.ObjectId('55edcc1f7d65cb39ee988409'),
boi.ObjectId('55edcc1f7d65cb39ee98840c'),
boi.ObjectId('55edcc207d65cb39ee988410'),
boi.ObjectId('55edcc207d65cb39ee988412'),
boi.ObjectId('55edcc217d65cb39ee98841f'),
boi.ObjectId('55edcc217d65cb39ee988429')])
else:
self.assertEqual(len(np.nonzero(to_delete_mask)[0]), 0)
self.assertEqual(len(delete_ids), 0)
def testPointFilteringShanghaiJump(self):
classicJumpTrip1 = self.trip_entries[0]
self.loadPointsForTrip(classicJumpTrip1.get_id())
classicJumpSections1 = [
s for s in self.section_entries
if s.data.trip_id == classicJumpTrip1.get_id()
]
outlier_algo = eaics.BoxplotOutlier()
jump_algo = eaicj.SmoothZigzag(False, 100)
for i, section_entry in enumerate(classicJumpSections1):
logging.debug("-" * 20 + "Considering section %s" % i + "-" * 20)
section_df = self.ts.get_data_df(
"background/filtered_location",
esda.get_time_query_for_trip_like(esda.RAW_SECTION_KEY,
section_entry.get_id()))
with_speeds_df = eaicl.add_dist_heading_speed(section_df)
maxSpeed = outlier_algo.get_threshold(with_speeds_df)
logging.debug("Max speed for section %s = %s" % (i, maxSpeed))
jump_algo.filter(with_speeds_df)
logging.debug("Retaining points %s" %
np.nonzero(jump_algo.inlier_mask_.to_numpy()))
to_delete_mask = np.logical_not(jump_algo.inlier_mask_)
logging.debug("Deleting points %s" %
np.nonzero(to_delete_mask.to_numpy()))
delete_ids = list(with_speeds_df[to_delete_mask]._id)
logging.debug("Deleting ids %s" % delete_ids)
# Automated checks. Might be able to remove logging statements later
if i != 2:
# Not the bad section. Should not be filtered
self.assertEqual(np.count_nonzero(to_delete_mask), 0)
self.assertEqual(len(delete_ids), 0)
else:
# The bad section, should have the third point filtered
self.assertEqual(np.count_nonzero(to_delete_mask), 1)
self.assertEqual([str(id) for id in delete_ids],
["55d8c4837d65cb39ee983cb4"])
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(["background/filtered_location"], esds.get_time_query_for_section(section.get_id()))
# points_df = ts.get_data_df("background/filtered_location", esds.get_time_query_for_section(section.get_id()))
# points_df = points_df.drop("elapsedRealTimeNanos", axis=1)
# logging.debug("points_df.columns = %s" % points_df.columns)
# 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_array = [ecwl.Location(ts._to_df_entry(entry)) for entry in entry_it]
logging.debug("first element in section_location_array = %s" % section_location_array[0])
# Fudge the end point so that we don't have a gap because of the ts != write_ts mismatch
# TODO: Fix this once we are able to query by the data timestamp instead of the metadata ts
if section_location_array[-1].loc != section.end_loc:
last_loc_doc = ts.get_entry_at_ts("background/filtered_location", "data.ts", section.end_ts)
last_loc_data = ecwe.Entry(last_loc_doc).data
last_loc_data["_id"] = last_loc_doc["_id"]
section_location_array.append(last_loc_data)
logging.debug("Adding new entry %s to fill the end point gap between %s and %s"
% (last_loc_data.loc, section_location_array[-2].loc, section.end_loc))
# Find the list of points to filter
filtered_points_entry_doc = ts.get_entry_at_ts("analysis/smoothing", "data.section",
section.get_id())
if filtered_points_entry_doc is None:
logging.debug("No filtered_points_entry, returning unchanged array")
filtered_section_location_array = section_location_array
else:
# TODO: Figure out how to make collections work for the wrappers and then change this to an Entry
filtered_points_entry = ad.AttrDict(filtered_points_entry_doc)
filtered_point_list = list(filtered_points_entry.data.deleted_points)
logging.debug("deleting %s points from section points" % len(filtered_point_list))
filtered_section_location_array = [l for l in section_location_array if l.get_id() not in filtered_point_list]
with_speeds = eaicl.add_dist_heading_speed(pd.DataFrame(filtered_section_location_array))
speeds = list(with_speeds.speed)
distances = list(with_speeds.distance)
for idx, row in with_speeds.iterrows():
# TODO: Remove instance of setting value without going through wrapper class
filtered_section_location_array[idx]["speed"] = row["speed"]
filtered_section_location_array[idx]["distance"] = row["distance"]
points_feature_array = [location_to_geojson(l) for l in filtered_section_location_array]
points_line_feature = point_array_to_line(filtered_section_location_array)
# If this is the first section, we already start from the trip start. But we actually need to start from the
# prior place. Fudge this too. Note also that we may want to figure out how to handle this properly in the model
# without needing fudging. TODO: Unclear how exactly to do this
if section.start_stop is None:
# This is the first section. So we need to find the start place of the parent trip
parent_trip = tl.get_object(section.trip_id)
start_place_of_parent_trip = tl.get_object(parent_trip.start_place)
points_line_feature.geometry.coordinates.insert(0, start_place_of_parent_trip.location.coordinates)
for i, point_feature in enumerate(points_feature_array):
point_feature.properties["idx"] = i
points_line_feature.id = str(section.get_id())
points_line_feature.properties = copy.copy(section)
points_line_feature.properties["feature_type"] = "section"
points_line_feature.properties["sensed_mode"] = str(points_line_feature.properties.sensed_mode)
points_line_feature.properties["distance"] = sum(distances)
points_line_feature.properties["speeds"] = speeds
points_line_feature.properties["distances"] = distances
_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(["background/filtered_location"],
esds.get_time_query_for_section(
section.get_id()))
# points_df = ts.get_data_df("background/filtered_location", esds.get_time_query_for_section(section.get_id()))
# points_df = points_df.drop("elapsedRealTimeNanos", axis=1)
# logging.debug("points_df.columns = %s" % points_df.columns)
# 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_array = [
ecwl.Location(ts._to_df_entry(entry)) for entry in entry_it
]
if len(section_location_array) != 0:
logging.debug("first element in section_location_array = %s" %
section_location_array[0])
# Fudge the end point so that we don't have a gap because of the ts != write_ts mismatch
# TODO: Fix this once we are able to query by the data timestamp instead of the metadata ts
if section_location_array[-1].loc != section.end_loc:
last_loc_doc = ts.get_entry_at_ts("background/filtered_location",
"data.ts", section.end_ts)
last_loc_data = ecwe.Entry(last_loc_doc).data
last_loc_data["_id"] = last_loc_doc["_id"]
section_location_array.append(last_loc_data)
logging.debug(
"Adding new entry %s to fill the end point gap between %s and %s"
% (last_loc_data.loc, section_location_array[-2].loc,
section.end_loc))
# Find the list of points to filter
filtered_points_entry_doc = ts.get_entry_at_ts("analysis/smoothing",
"data.section",
section.get_id())
if filtered_points_entry_doc is None:
logging.debug("No filtered_points_entry, returning unchanged array")
filtered_section_location_array = section_location_array
else:
# TODO: Figure out how to make collections work for the wrappers and then change this to an Entry
filtered_points_entry = ad.AttrDict(filtered_points_entry_doc)
filtered_point_list = list(filtered_points_entry.data.deleted_points)
logging.debug("deleting %s points from section points" %
len(filtered_point_list))
filtered_section_location_array = [
l for l in section_location_array
if l.get_id() not in filtered_point_list
]
with_speeds = eaicl.add_dist_heading_speed(
pd.DataFrame(filtered_section_location_array))
speeds = list(with_speeds.speed)
distances = list(with_speeds.distance)
if len(filtered_section_location_array) != 0:
for idx, row in with_speeds.iterrows():
# TODO: Remove instance of setting value without going through wrapper class
filtered_section_location_array[idx]["speed"] = row["speed"]
filtered_section_location_array[idx]["distance"] = row["distance"]
points_feature_array = [
location_to_geojson(l) for l in filtered_section_location_array
]
points_line_feature = point_array_to_line(filtered_section_location_array)
# If this is the first section, we already start from the trip start. But we actually need to start from the
# prior place. Fudge this too. Note also that we may want to figure out how to handle this properly in the model
# without needing fudging. TODO: Unclear how exactly to do this
if section.start_stop is None:
# This is the first section. So we need to find the start place of the parent trip
parent_trip = tl.get_object(section.trip_id)
start_place_of_parent_trip = tl.get_object(parent_trip.start_place)
points_line_feature.geometry.coordinates.insert(
0, start_place_of_parent_trip.location.coordinates)
for i, point_feature in enumerate(points_feature_array):
point_feature.properties["idx"] = i
points_line_feature.id = str(section.get_id())
points_line_feature.properties = copy.copy(section)
points_line_feature.properties["feature_type"] = "section"
points_line_feature.properties["sensed_mode"] = str(
points_line_feature.properties.sensed_mode)
points_line_feature.properties["distance"] = sum(distances)
points_line_feature.properties["speeds"] = speeds
points_line_feature.properties["distances"] = distances
_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 get_merge_direction(self, streak_start, streak_end):
"""
Checks to decide merge direction
- if either direction is WALKING and speed is greater than 1.4 + slosh then
must be the other direction
- pick direction that is closer to the median speed
"""
start_change = self.motion_changes[streak_start]
end_change = self.motion_changes[streak_end]
ssm, sem = start_change
esm, eem = end_change
if streak_start == 0:
# There is no before section - only choices are to merge backward
# or make a new section
logging.debug(
"get_merge_direction: at beginning of changes, can only merge backward"
)
return MergeResult(Direction.BACKWARD, FinalMode.UNMERGED)
before_motion = self.motion_changes[streak_start - 1]
bsm, bem = before_motion
if streak_end + 1 == len(self.motion_changes):
# There is no after section - only one way to merge!
logging.debug(
"get_merge_direction: at end of changes, can only merge forward"
)
return MergeResult(Direction.FORWARD, FinalMode.UNMERGED)
after_motion = self.motion_changes[streak_end + 1]
asm, aem = after_motion
if bsm.type == asm.type:
logging.debug(
"before type = %s, after type = %s, merge direction is don't care, returning forward"
% (bsm.type, asm.type))
return MergeResult(Direction.FORWARD, FinalMode.UNMERGED)
loc_points = self.seg_method.filter_points_for_range(
self.seg_method.location_points, ssm, eem)
loc_points.reset_index(inplace=True)
with_speed_loc_points = eaicl.add_dist_heading_speed(loc_points)
points_before = self.seg_method.filter_points_for_range(
self.seg_method.location_points, bsm, bem)
points_before.reset_index(inplace=True)
with_speed_points_before = eaicl.add_dist_heading_speed(points_before)
points_after = self.seg_method.filter_points_for_range(
self.seg_method.location_points, asm, aem)
points_after.reset_index(inplace=True)
with_speed_points_after = eaicl.add_dist_heading_speed(points_after)
curr_median_speed = self.get_section_speed(loc_points,
with_speed_loc_points,
points_before, points_after)
# check for walking speed, which is the one constant is a cruel,
# shifting world where there is no truth
if (eaid.is_walking_type(asm.type)
and (not eaid.is_walking_speed(curr_median_speed))):
logging.debug(
"after is walking, but speed is %d, merge forward, returning 1"
% curr_median_speed)
return MergeResult(Direction.FORWARD, FinalMode.UNMERGED)
elif (eaid.is_walking_type(bsm.type)
and (not eaid.is_walking_speed(curr_median_speed))):
logging.debug(
"before is walking, but speed is %d, merge backward, returning -1"
)
return MergeResult(Direction.BACKWARD, FinalMode.UNMERGED)
logging.debug(
"while merging, comparing curr speed %s with before %s and after %s"
% (curr_median_speed, with_speed_points_before.speed.median(),
with_speed_points_after.speed.median()))
if (abs(curr_median_speed - with_speed_points_before.speed.median()) <
abs(curr_median_speed -
with_speed_points_after.speed.median())):
# speed is closer to before than after, merge with before, merge forward
logging.debug("before is closer, merge forward, returning 1")
return MergeResult(Direction.FORWARD, FinalMode.UNMERGED)
else:
logging.debug("after is closer, merge backward, returning -1")
return MergeResult(Direction.BACKWARD, FinalMode.UNMERGED)