def get_spectra(self, startsamp, N):
"""Return 2D array of data from PSRFITS file.
Inputs:
startsamp, Starting sample
N: number of samples to read
Output:
data: 2D numpy array
"""
# Calculate starting subint and ending subint
startsub = int(startsamp / self.nsamp_per_subint)
skip = startsamp - (startsub * self.nsamp_per_subint)
endsub = int((startsamp + N) / self.nsamp_per_subint)
trunc = ((endsub + 1) * self.nsamp_per_subint) - (startsamp + N)
# Read data
data = []
for isub in range(startsub, endsub + 1):
data.append(self.read_subint(isub))
if len(data) > 1:
data = np.concatenate(data)
else:
data = np.array(data).squeeze()
data = np.transpose(data)
# Truncate data to desired interval
if trunc > 0:
data = data[:, skip:-trunc]
elif trunc == 0:
data = data[:, skip:]
else:
raise ValueError("Number of bins to truncate is negative: %d" %
trunc)
if not self.specinfo.need_flipband:
# for psrfits module freqs go from low to high.
# spectra module expects high frequency first.
data = data[::-1, :]
freqs = self.freqs[::-1]
else:
freqs = self.freqs
return spectra.Spectra(freqs,
self.tsamp,
data,
starttime=self.tsamp * startsamp,
dm=0)
def get_spectra(self, start, nspec):
stop = min(start + nspec, self.nspec)
pos = self.header_size + start * self.bytes_per_spectrum
# Compute number of elements to read
nspec = int(stop) - int(start)
num_to_read = nspec * self.nchans
num_to_read = max(0, num_to_read)
self.filfile.seek(pos, os.SEEK_SET)
spectra_dat = np.fromfile(self.filfile,
dtype=self.dtype,
count=num_to_read)
spectra_dat.shape = nspec, self.nchans
spec = spectra.Spectra(self.freqs,
self.tsamp,
spectra_dat.T,
starttime=start * self.tsamp,
dm=0.0)
return spec