# Copyright (C) 2019 Alex Nitz
#
# This program is free software; you can redistribute it and/or modify it
# under the terms of the GNU General Public License as published by the
# Free Software Foundation; either version 3 of the License, or (at your
# option) any later version.
#
# This program is distributed in the hope that it will be useful, but
# WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU Generals
# Public License for more details.
#
# You should have received a copy of the GNU General Public License along
# with this program; if not, write to the Free Software Foundation, Inc.,
# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.
"""
This modules contains functions for reading in data from hdf stores
"""
import logging
import h5py
import numpy
from pycbc.types import TimeSeries
logger = logging.getLogger('pycbc.frame.store')
[docs]def read_store(fname, channel, start_time, end_time):
""" Read time series data from hdf store
Parameters
----------
fname: str
Name of hdf store file
channel: str
Channel name to read
start_time: int
GPS time to start reading from
end_time: int
GPS time to end time series
Returns
-------
ts: pycbc.types.TimeSeries
Time series containing the requested data
"""
fhandle = h5py.File(fname, 'r')
if channel not in fhandle:
raise ValueError('Could not find channel name {}'.format(channel))
# Determine which segment data lies in (can only read contiguous data now)
starts = fhandle[channel]['segments']['start'][:]
ends = fhandle[channel]['segments']['end'][:]
diff = start_time - starts
loc = numpy.where(diff >= 0)[0]
sidx = loc[diff[loc].argmin()]
stime = starts[sidx]
etime = ends[sidx]
if stime > start_time:
raise ValueError("Cannot read data segment before {}".format(stime))
if etime < end_time:
raise ValueError("Cannot read data segment past {}".format(etime))
data = fhandle[channel][str(sidx)]
sample_rate = len(data) / (etime - stime)
start = int((start_time - stime) * sample_rate)
end = int((end_time - stime) * sample_rate)
return TimeSeries(data[start:end], delta_t=1.0/sample_rate,
epoch=start_time)