Source code for pycbc.frame.store

# 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 numpy

from pycbc.types import TimeSeries
from pycbc.io.hdf import HFile

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 = HFile(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)