def save(self, path, group=None, ifo='P1'):
"""
Save frequency series to a Numpy .npy, hdf, or text file. The first column
contains the sample frequencies, the second contains the values.
In the case of a complex frequency series saved as text, the imaginary
part is written as a third column. When using hdf format, the data is stored
as a single vector, along with relevant attributes.
Parameters
----------
path: string
Destination file path. Must end with either .hdf, .npy or .txt.
group: string
Additional name for internal storage use. Ex. hdf storage uses
this as the key value.
Raises
------
ValueError
If path does not end in .npy or .txt.
"""
ext = _os.path.splitext(path)[1]
if ext == '.npy':
output = _numpy.vstack(
(self.sample_frequencies.numpy(), self.numpy())).T
_numpy.save(path, output)
elif ext == '.txt':
if self.kind == 'real':
output = _numpy.vstack(
(self.sample_frequencies.numpy(), self.numpy())).T
elif self.kind == 'complex':
output = _numpy.vstack(
(self.sample_frequencies.numpy(), self.numpy().real,
self.numpy().imag)).T
_numpy.savetxt(path, output)
elif ext == '.xml' or path.endswith('.xml.gz'):
from pycbc.io.live import make_psd_xmldoc
from glue.ligolw import utils
if self.kind != 'real':
raise ValueError('XML only supports real frequency series')
output = self.lal()
# When writing in this format we must *not* have the 0 values at
# frequencies less than flow. To resolve this we set the first
# non-zero value < flow.
data_lal = output.data.data
first_idx = _numpy.argmax(data_lal > 0)
if not first_idx == 0:
data_lal[:first_idx] = data_lal[first_idx]
psddict = {ifo: output}
utils.write_filename(make_psd_xmldoc(psddict),
path,
gz=path.endswith(".gz"))
elif ext == '.hdf':
key = 'data' if group is None else group
with h5py.File(path, 'a') as f:
ds = f.create_dataset(key,
data=self.numpy(),
compression='gzip',
compression_opts=9,
shuffle=True)
ds.attrs['epoch'] = float(self.epoch)
ds.attrs['delta_f'] = float(self.delta_f)
else:
raise ValueError('Path must end with .npy, .txt, .xml, .xml.gz '
'or .hdf')
def save(self, path, group=None, ifo='P1'):
"""
Save frequency series to a Numpy .npy, hdf, or text file. The first column
contains the sample frequencies, the second contains the values.
In the case of a complex frequency series saved as text, the imaginary
part is written as a third column. When using hdf format, the data is stored
as a single vector, along with relevant attributes.
Parameters
----------
path: string
Destination file path. Must end with either .hdf, .npy or .txt.
group: string
Additional name for internal storage use. Ex. hdf storage uses
this as the key value.
Raises
------
ValueError
If path does not end in .npy or .txt.
"""
ext = _os.path.splitext(path)[1]
if ext == '.npy':
output = _numpy.vstack((self.sample_frequencies.numpy(),
self.numpy())).T
_numpy.save(path, output)
elif ext == '.txt':
if self.kind == 'real':
output = _numpy.vstack((self.sample_frequencies.numpy(),
self.numpy())).T
elif self.kind == 'complex':
output = _numpy.vstack((self.sample_frequencies.numpy(),
self.numpy().real,
self.numpy().imag)).T
_numpy.savetxt(path, output)
elif ext == '.xml' or path.endswith('.xml.gz'):
from pycbc.io.live import make_psd_xmldoc
from glue.ligolw import utils
if self.kind != 'real':
raise ValueError('XML only supports real frequency series')
output = self.lal()
# When writing in this format we must *not* have the 0 values at
# frequencies less than flow. To resolve this we set the first
# non-zero value < flow.
data_lal = output.data.data
first_idx = _numpy.argmax(data_lal>0)
if not first_idx == 0:
data_lal[:first_idx] = data_lal[first_idx]
psddict = {ifo: output}
utils.write_filename(make_psd_xmldoc(psddict), path,
gz=path.endswith(".gz"))
elif ext =='.hdf':
key = 'data' if group is None else group
f = h5py.File(path)
ds = f.create_dataset(key, data=self.numpy(), compression='gzip',
compression_opts=9, shuffle=True)
ds.attrs['epoch'] = float(self.epoch)
ds.attrs['delta_f'] = float(self.delta_f)
else:
raise ValueError('Path must end with .npy, .txt, .xml, .xml.gz '
'or .hdf')