def datetimerange_inversion():
value = DateTimeRange(
TEST_END_DATETIME, TEST_START_DATETIME)
value.start_time_format = ISO_TIME_FORMAT
value.end_time_format = ISO_TIME_FORMAT
return value
def datetimerange_null_start():
value = DateTimeRange(None, TEST_END_DATETIME)
value.time_format = None
value.end_time_format = ISO_TIME_FORMAT
return value
def datetimerange_null():
value = DateTimeRange(None, None)
value.time_format = None
value.end_time_format = None
return value
def datetimerange_inversion():
value = DateTimeRange(TEST_END_DATETIME, TEST_START_DATETIME)
value.start_time_format = ISO_TIME_FORMAT
value.end_time_format = ISO_TIME_FORMAT
return value
def datetimerange_null_start():
value = DateTimeRange(None, TEST_END_DATETIME)
value.time_format = None
value.end_time_format = ISO_TIME_FORMAT
return value
def datetimerange_null():
value = DateTimeRange(None, None)
value.time_format = None
value.end_time_format = None
return value
def intersect_arcs(df_rs: pd.DataFrame,
logger: logging.Logger) -> Tuple[int, pd.DataFrame]:
"""
intersect_arcs determines which observation intervals belong to which TLE interval
"""
cFuncName = colored(os.path.basename(__file__),
'yellow') + ' - ' + colored(
sys._getframe().f_code.co_name, 'green')
# number of arcs according to observations
nr_arcs_obs = []
for prn in df_rs.index:
# find maximum number of arcs in observations
nr_arcs_obs.append(longest(df_rs.loc[prn]['obs_arc_count']))
logger.info(
'{func:s}: number of observed arcs per prn: {arcs!s}'.format(
arcs=nr_arcs_obs, func=cFuncName))
# find number of ars per PRN from TLE
nr_arcs_tle = []
for prn in df_rs.index:
# find maximum number of arcs in observations
nr_arcs_tle.append(longest(df_rs.loc[prn]['tle_arc_count']))
nr_arcs = max(nr_arcs_tle)
logger.info(
'{func:s}: number of predicted arcs per prn: {arcs!s}'.format(
arcs=nr_arcs_tle, func=cFuncName))
# make the arcs fit together by comparing the start / end dates between observed and TLEs
df_rs['intersect'] = pd.Series(dtype=object)
J2000 = datetime(2000, 1, 1)
for i_prn, prn in enumerate(df_rs.index):
logger.info('{func:s}: PRN {prn:s}'.format(prn=prn, func=cFuncName))
prn_intersect = [np.nan] * nr_arcs_obs[i_prn]
for i_tle, (tle_rise, tle_set) in enumerate(
zip(df_rs.loc[prn]['tle_rise'], df_rs.loc[prn]['tle_set'])):
tle_range = DateTimeRange(datetime.combine(J2000, tle_rise),
datetime.combine(J2000, tle_set))
tle_range.start_time_format = '%H:%M:%S'
tle_range.end_time_format = '%H:%M:%S'
logger.info('{func:s}: tle_range = {tler!s}'.format(
tler=tle_range, func=cFuncName))
for i_obs, (obs_start, obs_end) in enumerate(
zip(df_rs.loc[prn]['obs_rise'],
df_rs.loc[prn]['obs_set'])):
obs_range = DateTimeRange(datetime.combine(J2000, obs_start),
datetime.combine(J2000, obs_end))
obs_range.start_time_format = '%H:%M:%S'
obs_range.end_time_format = '%H:%M:%S'
logger.info(
'{func:s}: obs_range = {obsr!s} intersect = {int!s}'
.format(obsr=obs_range,
int=tle_range.is_intersection(obs_range),
func=cFuncName))
if tle_range.is_intersection(obs_range):
prn_intersect[i_obs] = i_tle
# store in the itersection column
df_rs.loc[prn, 'intersect'] = prn_intersect
return nr_arcs, df_rs
