def prepare_file(self, file):
"""
Prepares the PSRFITS file in the correct format for the program.
"""
try:
hdul = fits.open(file)
except OSError:
return -1
name = hdul[0].header['SRC_NAME']
fe = hdul[0].header['FRONTEND']
if hdul[0].header[
'OBS_MODE'] != "PSR" or name != self.psr_name or fe != self.frontend:
hdul.close()
return -1
hdul.close()
ar = Archive(file, verbose=self.verbose)
ar.tscrunch(nsubint=1)
ar.fscrunch(nchan=1)
n = 1
nbin = ar.getNbin()
data = ar.getData()
return np.copy(data), n, nbin
def prepare_file(self, file):
"""
Prepares the PSRFITS file in the correct format for the program.
"""
try:
hdul = fits.open(file)
except:
return -1
name = hdul[0].header['SRC_NAME']
fe = hdul[0].header['FRONTEND']
mjd = hdul[0].header['STT_IMJD']
if hdul[0].header['OBS_MODE'] != "PSR" or name != self.psr_name:
hdul.close()
return -1
hdul.close()
tmp_fn = "{0}_{1}_nchan{2}_template.npy".format(
self.psr_name, fe, self.subbands)
try:
template = self.load_template(self.temp_dir, tmp_fn)
except TemplateLoadError:
print("Template not found")
reply = str(input("Would you like to make a suitable one? (y / n)")
).lower().strip()
if reply[0] == 'y':
temp = FD_Template(self.psr_name,
fe,
self.subbands,
template_dir="templates",
verbose=self.verbose,
*self.dirs)
template = temp.make_template()
else:
raise TemplateLoadError(
"You can make a suitable template via the following command: python template_builder.py psr_name -b [frontend] -d [dirs]"
)
ar = Archive(file, verbose=self.verbose)
ar.tscrunch(nsubint=self.epochs)
ar.fscrunch(nchan=self.subbands)
return ar, template, fe, mjd
#for i in np.arange( ar.getNsubint() ):
# data_lin = np.append( data_lin, data[ i, 324, : ] )
#data = np.reshape( data, ( num_profs * ar.getNbin() ) )
# D_FAC = 32
# for i in range(D_FAC):
# st, ed = i*(chan // D_FAC), (i + 1)*(chan // D_FAC)
# fig = plt.figure( figsize = (7, 7) )
# ax = fig.add_subplot(111)
# cmap = plt.cm.Blues
# ax.imshow( rms.T[st:ed, :], cmap = cmap, interpolation = 'nearest', extent = [ 0, ch, ed, st ], aspect = 'auto', norm = clr.Normalize( vmin = 0, vmax = np.amax(rms) ) )
# fig.colorbar( plt.cm.ScalarMappable( norm = clr.Normalize( vmin = 0, vmax = np.amax(rms) ), cmap = cmap ), ax = ax )
# cid = fig.canvas.mpl_connect('key_press_event', on_key)
#
# #fig.canvas.mpl_disconnect(cid)
# #ax.plot( np.linspace( 1, num_profs, num = sub_pol * ar.getNbin() ), data_lin, linewidth = 0.1, color = 'k' )
# #ax.set_ylim( -630, 630 )
# #for i in np.arange( 1, num_profs + 1 ):
# # if (i % ar.getNsubint()) == 0:
# # ax.axvline( i, linewidth = 0.2, color = 'r' )
# #ax.set_xlim( 2400, 2600 )
#
# plt.show()
# fig.canvas.mpl_disconnect(cid)
ar.tscrunch()
ar.fscrunch()
ar.plot()
exit()
ar.imshow(origin='upper')