def format(entry):
formatted_entry = ad.AttrDict()
formatted_entry["_id"] = entry["_id"]
formatted_entry.user_id = entry.user_id
metadata = entry.metadata
if "time_zone" not in metadata:
metadata.time_zone = "America/Los_Angeles"
fc.expand_metadata_times(metadata)
formatted_entry.metadata = metadata
data = ad.AttrDict()
if 'agb' in entry.data:
data.type = ecwa.MotionTypes(entry.data.agb).value
elif 'zzaEg' in entry.data:
data.type = ecwa.MotionTypes(entry.data.zzaEg).value
else:
data.type = ecwa.MotionTypes(entry.data.zzaKM).value
if 'agc' in entry.data:
data.confidence = entry.data.agc
elif 'zzaEh' in entry.data:
data.confidence = entry.data.zzaEh
else:
data.confidence = entry.data.zzaKN
data.ts = formatted_entry.metadata.write_ts
data.local_dt = formatted_entry.metadata.write_local_dt
data.fmt_time = formatted_entry.metadata.write_fmt_time
formatted_entry.data = data
return formatted_entry
def grouped_to_summary(time_grouped_df, key_to_fill_fn, summary_fn):
ret_list = []
# When we group by a time range, the key is the end of the range
for key, section_group_df in time_grouped_df:
curr_msts = ecwms.ModeStatTimeSummary()
key_to_fill_fn(key, section_group_df, curr_msts)
curr_msts.nUsers = len(section_group_df.user_id.unique())
mode_grouped_df = section_group_df.groupby('sensed_mode')
mode_results = summary_fn(mode_grouped_df)
for mode, result in mode_results.items():
curr_msts[ecwm.MotionTypes(mode).name] = result
ret_list.append(curr_msts)
return ret_list
def format(entry):
formatted_entry = ad.AttrDict()
formatted_entry["_id"] = entry["_id"]
formatted_entry.user_id = entry.user_id
metadata = entry.metadata
if "time_zone" not in metadata:
metadata.time_zone = "America/Los_Angeles"
fc.expand_metadata_times(metadata)
formatted_entry.metadata = metadata
#logging.info('*** Motion Data write_ts: %d' % metadata.write_ts)
if 'ts' not in entry.data:
# old style entries
data = ad.AttrDict()
else:
data = entry.data
if 'agb' in entry.data:
data.type = ecwa.MotionTypes(entry.data.agb).value
elif 'zzaEg' in entry.data:
data.type = ecwa.MotionTypes(entry.data.zzaEg).value
elif 'zzbjA' in entry.data:
data.type = ecwa.MotionTypes(entry.data.zzbjA).value
elif 'ajO' in entry.data:
data.type = ecwa.MotionTypes(entry.data.ajO).value
elif 'zzaKM' in entry.data:
data.type = ecwa.MotionTypes(entry.data.zzaKM).value
elif 'zzbhB' in entry.data:
data.type = ecwa.MotionTypes(entry.data.zzbhB).value
if 'agc' in entry.data:
data.confidence = entry.data.agc
elif 'zzaEh' in entry.data:
data.confidence = entry.data.zzaEh
elif 'zzbjB' in entry.data:
data.confidence = entry.data.zzbjB
elif 'ajP' in entry.data:
data.confidence = entry.data.ajP
elif 'zzaKN' in entry.data:
data.confidence = entry.data.zzaKN
elif 'zzbhC' in entry.data:
data.confidence = entry.data.zzbhC
if 'ts' not in entry.data:
data.ts = formatted_entry.metadata.write_ts
data.local_dt = formatted_entry.metadata.write_local_dt
data.fmt_time = formatted_entry.metadata.write_fmt_time
formatted_entry.data = data
return formatted_entry
def is_huge_invalid_ts_offset(filterMethod, lastPoint, currPoint, timeseries,
motionInRange):
intermediate_transitions = filterMethod.transition_df[
(filterMethod.transition_df.ts >= lastPoint.ts)
& (filterMethod.transition_df.ts <= currPoint.ts)]
ignore_modes_list = [
ecwm.MotionTypes.TILTING.value, ecwm.MotionTypes.UNKNOWN.value,
ecwm.MotionTypes.STILL.value, ecwm.MotionTypes.NONE.value,
ecwm.MotionTypes.STOPPED_WHILE_IN_VEHICLE.value
]
non_still_motions = [
ma for ma in motionInRange
if ma["data"]["type"] not in ignore_modes_list
and ma["data"]["confidence"] == 100
]
logging.debug("non_still_motions = %s" % [(ecwm.MotionTypes(
ma["data"]["type"]), ma["data"]["confidence"], ma["data"]["fmt_time"])
for ma in non_still_motions])
non_still_motions_rate = len(non_still_motions) / (currPoint.ts -
lastPoint.ts)
logging.debug(
"in is_huge_invalid_ts_offset: len(intermediate_transitions) = %d, non_still_motions = %d, time_diff = %s mins, non_still_motions_rate = %s"
% (len(intermediate_transitions), len(non_still_motions),
(currPoint.ts - lastPoint.ts) / 60, non_still_motions_rate))
# If we have no transitions and at least one high confidence motion
# activity every 5 minutes, we claim that we were actually moving during the
# interim and the currPoint is invalid
if len(intermediate_transitions
) == 0 and non_still_motions_rate > 1 / (5 * 60):
logging.debug("in is_huge_invalid_ts_offset: returning True")
return True
else:
logging.debug("in is_huge_invalid_ts_offset: returning False")
return False
def grouped_to_summary(time_grouped_df, key_to_fill_fn, summary_fn):
ret_list = []
# When we group by a time range, the key is the end of the range
for key, section_group_df in time_grouped_df:
curr_msts = ecwms.ModeStatTimeSummary()
key = fix_int64_key_if_needed(key)
key_to_fill_fn(key, section_group_df, curr_msts)
curr_msts.nUsers = len(section_group_df.user_id.unique())
mode_grouped_df = section_group_df.groupby('sensed_mode')
mode_results = summary_fn(mode_grouped_df)
for mode, result in mode_results.items():
if eac.get_section_key_for_analysis_results() == "analysis/inferred_section":
curr_msts[ecwmp.PredictedModeTypes(mode).name] = result
else:
curr_msts[ecwm.MotionTypes(mode).name] = result
ret_list.append(curr_msts)
# import bson.json_util as bju
# logging.debug("After appending %s, ret_list = %s" % (curr_msts, ret_list))
# for k in curr_msts.keys():
# print("Serializing key = %s" % k)
# logging.debug("Serializing key %s = %s" %
# (k, bju.dumps(curr_msts[k])))
return ret_list