def _call(self):
utime = self.data['Sensors']['Lumbar']['Unix Time']
# get the first timepoint to know which day to start and end with
time_sdt = udatetime.utcfromtimestamp(utime[0])
time_edt = udatetime.utcfromtimestamp(utime[-1])
n_days = (time_edt.date() - time_sdt.date()).days
if time_edt.hour > self._hours[0]:
n_days += 1
# set the start and end hours for the first day
day_start = time_sdt.replace(hour=self._hours[0],
minute=0,
second=0,
microsecond=0)
day_end = time_sdt.replace(hour=self._hours[1],
minute=0,
second=0,
microsecond=0)
iend = 10 # set so can reference in the i=0 loop
for i in range(n_days):
istart = argmin(
abs(utime[iend - 10:] - day_start.timestamp())) + iend - 10
iend = argmin(
abs(utime[istart:] - day_end.timestamp())) + istart + 1
self.data = (PROC.format(day_n=i + 1,
value='Indices'), array([istart, iend]))
day_start += datetime.timedelta(days=1)
day_end += datetime.timedelta(days=1)
def test_utcfromtimestamp(self):
DAY = 86400
HOUR = 3600
for t in range(0, DAY, HOUR):
dt = datetime.utcfromtimestamp(t)
udt = udatetime.utcfromtimestamp(t)
self.assertIsInstance(udt, datetime)
self.assertEqual(udt.year, dt.year)
self.assertEqual(udt.month, dt.month)
self.assertEqual(udt.day, dt.day)
self.assertEqual(udt.hour, dt.hour)
self.assertEqual(udt.minute, dt.minute)
self.assertEqual(udt.second, dt.second)
self.assertEqual(udt.microsecond, dt.microsecond)
for t in range(0, DAY * -1, HOUR * -1):
dt = datetime.utcfromtimestamp(t)
udt = udatetime.utcfromtimestamp(t)
self.assertIsInstance(udt, datetime)
self.assertEqual(udt.year, dt.year)
self.assertEqual(udt.month, dt.month)
self.assertEqual(udt.day, dt.day)
self.assertEqual(udt.hour, dt.hour)
self.assertEqual(udt.minute, dt.minute)
self.assertEqual(udt.second, dt.second)
self.assertEqual(udt.microsecond, dt.microsecond)
def test_utcfromtimestamp(self):
DAY = 86400
HOUR = 3600
for t in range(0, DAY, HOUR):
dt = datetime.utcfromtimestamp(t)
udt = udatetime.utcfromtimestamp(t)
self.assertIsInstance(udt, datetime)
self.assertEqual(udt.year, dt.year)
self.assertEqual(udt.month, dt.month)
self.assertEqual(udt.day, dt.day)
self.assertEqual(udt.hour, dt.hour)
self.assertEqual(udt.minute, dt.minute)
self.assertEqual(udt.second, dt.second)
self.assertEqual(udt.microsecond, dt.microsecond)
self.assertEqual(udt.utcoffset(), timedelta(0))
self.assertEqual(udt.dst(), NO_DST)
for t in range(0, DAY * -1, HOUR * -1):
dt = datetime.utcfromtimestamp(t)
udt = udatetime.utcfromtimestamp(t)
self.assertIsInstance(udt, datetime)
self.assertEqual(udt.year, dt.year)
self.assertEqual(udt.month, dt.month)
self.assertEqual(udt.day, dt.day)
self.assertEqual(udt.hour, dt.hour)
self.assertEqual(udt.minute, dt.minute)
self.assertEqual(udt.second, dt.second)
self.assertEqual(udt.microsecond, dt.microsecond)
self.assertEqual(udt.utcoffset(), timedelta(0))
self.assertEqual(udt.dst(), NO_DST)
def reconstruct_interval(experiment_id):
"""
Reverse the construct_experiment_id operation
:param experiment_id: The experiment id
:return: time interval
"""
start, end = map(lambda x: udatetime.utcfromtimestamp(x / 1000.0),
map(float, experiment_id.split("-")))
from ..time_interval import TimeInterval
return TimeInterval(start, end)
def construct_experiment_id(time_interval):
"""
Construct an experiment id from a time interval
:return: The experiment id
:type time_interval: TimeInterval
:rtype: str
"""
# Construct id based on unix epoch timestamps
epoch = udatetime.utcfromtimestamp(0).replace(tzinfo=UTC)
start = int((time_interval.start - epoch).total_seconds() * 1000.0)
end = int((time_interval.end - epoch).total_seconds() * 1000.0)
return "{}-{}".format(start, end)
def _udatetime():
return udatetime.utcfromtimestamp(TIME)
def udatetime_utcfromtimestamp():
udatetime.utcfromtimestamp(TIME)
def rfc3339strFromTimestamp(ts):
return udatetime.to_string(udatetime.utcfromtimestamp(ts))
def udatetimeFromTimestamp(ts):
"""Convert a unix timestamp to a udatetime"""
return udatetime.utcfromtimestamp(ts)
from datetime import datetime, timedelta import udatetime import pytz import six import apache_beam as beam from apache_beam.transforms.window import TimestampedValue from apache_beam import typehints from pipe_tools.coders import JSONDict EPOCH = udatetime.utcfromtimestamp(0) SECONDS_IN_DAY = 60 * 60 * 24 # This is the format the dataflow uses for TIMESTAMP fields returned from BigQuery BEAM_BQ_TIMESTAMP_FORMAT = "%Y-%m-%d %H:%M:%S.%f UTC" # this is the format bigquery uses for I/O # This is a much better string format for datetimes that is supported by udatetime # Note that %z is not supported in pythng 2.7 for udatetime.strptime() RFC3339_TIMESTAMP_FORMAT = "%Y-%m-%dT%H:%M:%S.%f%z" DATE_FORMAT = "%Y-%m-%d" """ Timestamp tools Convert between datetime, unix timestamp and various string represetnations Dataflow BigQuery Timestamp Issues ----------------------------------
def udatetime_utcfromtimestamp():
udatetime.utcfromtimestamp(TIME)
def process_timestamps(times,
accel,
time_units=None,
conv_kw=None,
window=False,
hours=('08:00', '20:00')):
"""
Convert timestamps into pandas datetime64 objects, and window as appropriate.
Parameters
----------
times : array_like
N-length array of timestamps to convert.
accel : {numpy.ndarray, pd.Series}
(N, 3) array of acceleration values. They will be windowed the same way as the timestamps if `window` is set
to True.
time_units : {None, str}, optional
Time units. Useful if conversion is from unix timestamps in seconds (s), milliseconds (ms), microseconds (us),
or nanoseconds (ns). If not None, will override the value in conv_kw, though one or the other must be provided.
Default is None.
conv_kw : {None, dict}, optional
Additional key-word arguments for the pandas.to_datetime function. If time_units is not None, that value
will be used and overwrite the value in conv_kw. If the timestamps are in unix time, it is unlikely this
argument will be necessary. Default is None.
window : bool, optional
Window the timestamps into the selected hours per day.
hours : array_like, optional
Length two array_like of hours (24-hour format) as strings, defining the start (inclusive) and end (exclusive)
times to include in the processing. Default is ('08:00', '20:00').
Returns
-------
timestamps : {pandas.DatetimeIndex, pandas.Series, dict}
Array_like of timestamps. DatetimeIndex if times was a numpy.ndarray, or list. pandas.Series with a dtype of
'datetime64' if times was a pandas.Series. If `window` is set to True, then a dictionary of timestamps for
each day is returned.
dt : float
Sampling time in seconds.
accel : {numpy.ndarray, pd.Series, dict}, optional
Acceleration windowed the same way as the timestamps (dictionary of acceleration for each day), if `window` is
True. If `window` is False, then the acceleration is not returned.
"""
if conv_kw is not None:
if time_units is not None:
conv_kw['unit'] = time_units
else:
if time_units is not None:
conv_kw = {'unit': time_units}
else:
raise ValueError(
'Either (time_units) must be defined, or "unit" must be a key of (conv_kw).'
)
# convert using pandas
if 'unit' in conv_kw:
if conv_kw['unit'] == 'ms':
timestamps = to_datetime([
utcfromtimestamp(t).replace(tzinfo=None) for t in times / 1e3
])
elif conv_kw['unit'] == 'us':
timestamps = to_datetime([
utcfromtimestamp(t).replace(tzinfo=None) for t in times / 1e6
])
elif conv_kw['unit'] == 'ns':
timestamps = to_datetime([
utcfromtimestamp(t).replace(tzinfo=None) for t in times / 1e9
])
elif conv_kw['unit'] == 's':
timestamps = to_datetime(
[utcfromtimestamp(t).replace(tzinfo=None) for t in times])
else:
timestamps = to_datetime(times, **conv_kw)
# find the sampling time
dt = mean(diff(timestamps[:100])) / timedelta64(1,
's') # convert to seconds
# windowing
if window:
hour_inds = timestamps.indexer_between_time(hours[0], hours[1])
day_splits = argwhere(diff(hour_inds) > 1) + 1
day_splits = append(insert(day_splits, 0, 0), hour_inds.size)
timestamps_ = {}
accel_ = {}
for i in range(len(day_splits) - 1):
timestamps_[f'Day {i + 1}'] = timestamps[hour_inds][
day_splits[i]:day_splits[i + 1]]
accel_[f'Day {i + 1}'] = accel[hour_inds][
day_splits[i]:day_splits[i + 1]]
return timestamps_, dt, accel_
else:
return timestamps, dt
def tabulate_results(results, csv_path, method='stillness'):
"""
Tabulate the results as calculated by the sequential pipeline.
Parameters
----------
results : {dict, str}
Either a dictionary of the results, or the path to the h5 file where the results were stored.
csv_path : str
Path to save the tabular data at
method : {'stillness', 'displacement'}, optional
Which method to tabulate results for. Default is 'stillness'.
"""
# get the results
days, times, duration, vdisp, mxa, mna, sparc = [], [], [], [], [], [], []
mtd = f'{method.capitalize()} Method'
if isinstance(results, dict):
day_list = [i for i in results['Processed']['Sit2Stand'] if 'Day' in i]
for day in day_list:
days.extend([int(day[4:])] * results['Processed']['Sit2Stand'][day]
[mtd]['STS Times'].shape[0])
times.extend(
results['Processed']['Sit2Stand'][day][mtd]['STS Times'])
duration.extend(
results['Processed']['Sit2Stand'][day][mtd]['Duration'])
vdisp.extend(results['Processed']['Sit2Stand'][day][mtd]
['Vertical Displacement'])
mxa.extend(
results['Processed']['Sit2Stand'][day][mtd]['Max. Accel.'])
mna.extend(
results['Processed']['Sit2Stand'][day][mtd]['Min. Accel.'])
sparc.extend(results['Processed']['Sit2Stand'][day][mtd]['SPARC'])
else:
with h5py.File(results, 'r') as f:
day_list = [i for i in f['Processed/Sit2Stand'] if 'Day' in i]
for day in day_list:
days.extend(
[int(day[4:])] *
f[f'Processed/Sit2Stand/{day}/{mtd}/STS Times'].shape[0])
times.extend(f[f'Processed/Sit2Stand/{day}/{mtd}/STS Times'])
duration.extend(f[f'Processed/Sit2Stand/{day}/{mtd}/Duration'])
vdisp.extend(
f[f'Processed/Sit2Stand/{day}/{mtd}/Vertical Displacement']
)
mxa.extend(f[f'Processed/Sit2Stand/{day}/{mtd}/Max. Accel.'])
mna.extend(f[f'Processed/Sit2Stand/{day}/{mtd}/Min. Accel.'])
sparc.extend(f[f'Processed/Sit2Stand/{day}/{mtd}/SPARC'])
table = zeros((len(days), 12), dtype='object')
table[:, 0] = days
table[:, 1:3] = array(times)
table[:, 7] = duration
# table[:, 8] = vdisp
table[:, 9] = mxa
table[:, 10] = mna
table[:, 11] = sparc
for i, ts in enumerate(table[:, 1]):
dt = udt.utcfromtimestamp(ts)
table[i, 3] = dt.strftime('%Y-%m-%d %H:%M:%S.%f')
table[i, 4] = dt.hour
table[i, 5] = dt.minute
table[
i,
6] = dt.weekday() >= 5 # is the day a weekend. 0=Monday, 6=Sunday
hdr = 'Day,Start Unix Time,End Unix Time,Start Time,Hour,Minute,Weekend,Duration,Vertical Displacement,' \
'Max. Accel.,Min. Accel., SPARC'
fmt = '%d, %f, %f, %s, %i, %i, %s, %f, %f, %f, %f, %f'
savetxt(csv_path, table, header=hdr, fmt=fmt)
def process_result_value(self, value: Optional[int],
dialect) -> Optional[datetime]:
if value is None:
return None
return udatetime.utcfromtimestamp(value / 1000)