def get_telem(msids, start=None, stop=None, sampling='full', unit_system='eng',
interpolate_dt=None, remove_events=None, select_events=None,
time_format=None, outfile=None, quiet=False,
max_fetch_Mb=None, max_output_Mb=None):
"""
High-level routine to get telemetry for one or more MSIDs and perform
common post-processing functions.
This is a non-public version that really does the work. The public interface
is fetch.get_telem(), which is a thin wrapper for this. (Trying to factor code
out to separate modules and keep import times down). See get_telem() for param
docs.
"""
# Set up output logging
from pyyaks.logger import get_logger
logger = get_logger(name='Ska.engarchive.get_telem', level=(100 if quiet else -100))
# Set defaults and translate to fetch keywords
stop = DateTime(stop)
start = stop - 30 if start is None else DateTime(start)
stat = None if sampling == 'full' else sampling
filter_bad = interpolate_dt is None
if isinstance(msids, basestring):
msids = [msids]
logger.info('Fetching {}-resolution data for MSIDS={}\n from {} to {}'
.format(sampling, msids, start.date, stop.date))
fetch.set_units(unit_system)
# Make sure that the dataset being fetched is reasonable (if checking requested)
if max_fetch_Mb is not None or max_output_Mb is not None:
fetch_Mb, output_Mb = utils.get_fetch_size(msids, start, stop, stat=stat,
interpolate_dt=interpolate_dt, fast=True)
if max_fetch_Mb is not None and fetch_Mb > max_fetch_Mb:
raise MemoryError('Requested fetch requires {:.2f} Mb vs. limit of {:.2f} Mb'
.format(fetch_Mb, max_fetch_Mb))
# If outputting to a file then check output size
if outfile and max_output_Mb is not None and output_Mb > max_output_Mb:
raise MemoryError('Requested fetch (interpolated) requires {:.2f} Mb '
'vs. limit of {:.2f} Mb'
.format(output_Mb, max_output_Mb))
dat = fetch.MSIDset(msids, start, stop, stat=stat, filter_bad=filter_bad)
if interpolate_dt is not None:
logger.info('Interpolating at {} second intervals'.format(interpolate_dt))
msidset_resample(dat, interpolate_dt)
if remove_events is not None:
logger.info('Removing events: {}'.format(remove_events))
queryset = get_queryset(remove_events)
for msid in dat:
dat[msid].remove_intervals(queryset)
if select_events is not None:
logger.info('Selecting events: {}'.format(select_events))
queryset = get_queryset(select_events)
for msid in dat:
dat[msid].select_intervals(queryset)
if time_format not in (None, 'secs'):
for dat_msid in dat.values():
dat_msid.times = getattr(DateTime(dat_msid.times, format='secs'), time_format)
if outfile:
logger.info('Writing data to {}'.format(outfile))
dat.write_zip(outfile)
return dat
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from astropy import table
from astropy.table import Table
from pyyaks.logger import get_logger
from kadi import events
import Ska.DBI
from Ska.Matplotlib import plot_cxctime
from Ska.engarchive import fetch_eng as fetch
from Chandra.Time import DateTime
logger = get_logger()
def get_opt(args):
parser = argparse.ArgumentParser(description='Commanded vs. telemetry fid positions')
parser.add_argument('--start',
type=str,
help='Start date (default=NOW - 90 days)')
parser.add_argument('--stop',
type=str,
help='Stop date (default=NOW)')
parser.add_argument('--out',
type=str,
help='Output plot file')
opt = parser.parse_args(args)
def get_telem(msids,
start=None,
stop=None,
sampling='full',
unit_system='eng',
interpolate_dt=None,
remove_events=None,
select_events=None,
time_format=None,
outfile=None,
quiet=False,
max_fetch_Mb=None,
max_output_Mb=None):
"""
High-level routine to get telemetry for one or more MSIDs and perform
common post-processing functions.
This is a non-public version that really does the work. The public interface
is fetch.get_telem(), which is a thin wrapper for this. (Trying to factor code
out to separate modules and keep import times down). See get_telem() for param
docs.
"""
# Set up output logging
from pyyaks.logger import get_logger
logger = get_logger(name='Ska.engarchive.get_telem',
level=(100 if quiet else -100))
# Set defaults and translate to fetch keywords
stop = DateTime(stop)
start = stop - 30 if start is None else DateTime(start)
stat = None if sampling == 'full' else sampling
filter_bad = interpolate_dt is None
if isinstance(msids, six.string_types):
msids = [msids]
logger.info(
'Fetching {}-resolution data for MSIDS={}\n from {} to {}'.format(
sampling, msids, start.date, stop.date))
fetch.set_units(unit_system)
# Make sure that the dataset being fetched is reasonable (if checking requested)
if max_fetch_Mb is not None or max_output_Mb is not None:
fetch_Mb, output_Mb = utils.get_fetch_size(
msids,
start,
stop,
stat=stat,
interpolate_dt=interpolate_dt,
fast=True)
if max_fetch_Mb is not None and fetch_Mb > max_fetch_Mb:
raise MemoryError(
'Requested fetch requires {:.2f} Mb vs. limit of {:.2f} Mb'.
format(fetch_Mb, max_fetch_Mb))
# If outputting to a file then check output size
if outfile and max_output_Mb is not None and output_Mb > max_output_Mb:
raise MemoryError(
'Requested fetch (interpolated) requires {:.2f} Mb '
'vs. limit of {:.2f} Mb'.format(output_Mb, max_output_Mb))
dat = fetch.MSIDset(msids, start, stop, stat=stat, filter_bad=filter_bad)
if interpolate_dt is not None:
logger.info(
'Interpolating at {} second intervals'.format(interpolate_dt))
msidset_resample(dat, interpolate_dt)
if remove_events is not None:
logger.info('Removing events: {}'.format(remove_events))
queryset = get_queryset(remove_events)
for msid in dat:
dat[msid].remove_intervals(queryset)
if select_events is not None:
logger.info('Selecting events: {}'.format(select_events))
queryset = get_queryset(select_events)
for msid in dat:
dat[msid].select_intervals(queryset)
if time_format not in (None, 'secs'):
for dat_msid in dat.values():
dat_msid.times = getattr(DateTime(dat_msid.times, format='secs'),
time_format)
if outfile:
logger.info('Writing data to {}'.format(outfile))
dat.write_zip(outfile)
return dat
