def main():
fn = args[0]
if fn.endswith(".fil"):
# Filterbank file
filetype = "filterbank"
print_debug("Reading filterbank file..")
rawdatafile = filterbank.filterbank(fn)
basename = fn[:-4]
if fn.endswith(".fits"):
# PSRFITS file
filetype = "psrfits"
print_debug("Reading PSRFITS file..")
rawdatafile = psrfits.PsrfitsFile(fn)
basename = fn[:-5]
else:
raise ValueError("Cannot recognize data file type from "
"extension. (Only '.fits' and '.fil' "
"are supported.)")
if options.outbasenm:
basename = options.outbasenm
spdcand = spcand.params()
if not options.man_params:
print_debug('Maximum number of candidates to plot: %i' %
options.maxnumcands)
make_spd_from_file(spdcand, rawdatafile, \
options.txtfile, options.maskfile, \
options.min_rank, options.group_rank, \
options.plot, options.just_waterfall, \
options.integrate_ts, options.integrate_spec, options.disp_pulse, \
options.loc_pulse, options.nsub, \
options.maxnumcands, \
basename, \
mask=options.mask, barytime=options.barytime, \
bandpass_corr=options.bandpass_corr)
else:
print_debug("Making spd files based on mannual parameters. I suggest" \
"reading in parameters from the groups.txt file.")
make_spd_from_man_params(spdcand, rawdatafile, \
options.txtfile, options.maskfile, \
options.plot, options.just_waterfall, \
options.subdm, options.dm, options.sweep_dms, \
options.sigma, \
options.start, options.duration, \
options.width_bins, options.nbins, options.downsamp, \
options.nsub, \
options.scaleindep, \
options.spec_width, options.loc_pulse, \
options.integrate_ts, options.integrate_spec, options.disp_pulse, \
basename, \
options.mask, options.bandpass_corr, options.barytime, \
options.man_params)
def main():
fn = args[0]
if fn.endswith(".fil"):
# Filterbank file
filetype = "filterbank"
print_debug("Reading filterbank file..")
rawdatafile = filterbank.filterbank(fn)
basename = fn[:-4]
if fn.endswith(".fits"):
# PSRFITS file
filetype = "psrfits"
print_debug("Reading PSRFITS file..")
rawdatafile = psrfits.PsrfitsFile(fn)
basename = fn[:-5]
else:
raise ValueError("Cannot recognize data file type from "
"extension. (Only '.fits' and '.fil' "
"are supported.)")
if options.outbasenm:
basename=options.outbasenm
spdcand = spcand.params()
if not options.man_params:
print_debug('Maximum number of candidates to plot: %i'%options.maxnumcands)
make_spd_from_file(spdcand, rawdatafile, \
options.txtfile, options.maskfile, \
options.min_rank, options.group_rank, \
options.plot, options.just_waterfall, \
options.integrate_ts, options.integrate_spec, options.disp_pulse, \
options.loc_pulse, options.nsub, \
options.maxnumcands, \
basename, \
mask=options.mask, barytime=options.barytime, \
bandpass_corr=options.bandpass_corr)
else:
print_debug("Making spd files based on mannual parameters. I suggest" \
"reading in parameters from the groups.txt file.")
make_spd_from_man_params(spdcand, rawdatafile, \
options.txtfile, options.maskfile, \
options.plot, options.just_waterfall, \
options.subdm, options.dm, options.sweep_dms, \
options.sigma, \
options.start, options.duration, \
options.width_bins, options.nbins, options.downsamp, \
options.nsub, \
options.scaleindep, \
options.spec_width, options.loc_pulse, \
options.integrate_ts, options.integrate_spec, options.disp_pulse, \
basename, \
options.mask, options.bandpass_corr, options.barytime, \
options.man_params)
def GBNCC_wrapper(txtfile, maskfile, fitsfilenm, path_sp_files):
"""
The pipeline should pass job.fits_filenm as argument.
"""
files = spio.get_textfile(txtfile)
min_rank = 3
groups = [i for i in range(7) if (i >= min_rank)][::-1]
numcands = 0 # counter for max number of candidates
loop_must_break = False # dont break the loop unless num of cands >100.
values = []
lis = []
ranks = []
for group in groups:
rank = group + 1
if files[group] != "Number of rank %i groups: 0 " % rank:
add_values = spio.split_parameters(rank, txtfile)
values.extend(add_values)
lis = np.append(
lis,
np.where(files == '\tRank: %i.000000' % rank)[0])
add_ranks = np.ones(len(add_values)) * rank
ranks = np.append(ranks, add_ranks)
if len(values) > 100:
values = values[0:100]
lis = lis[0:100]
#Sort candidates based on DM
zip_list = zip(values, lis, ranks)
zip_list = sorted(zip_list, key=itemgetter(0, 0))
values = [x[0] for x in zip_list]
lis = [x[1] for x in zip_list]
ranks = [x[2] for x in zip_list]
basename = fitsfilenm[:-5]
#generate subbanded file at a DM of 0 to extract observation parameters
cmd = "psrfits_subband -dm 0.0 -nsub 128 -o %s_subband_0.0 %s" % (
basename, fitsfilenm)
print "executing %s" % cmd
subprocess.call(cmd, shell=True)
subfilenm = basename + "_subband_0.0_0001.fits"
subfile = psrfits.PsrfitsFile(subfilenm)
for ii in range(len(values)):
dm_list, time_list, dm_arr, sigma_arr, width_arr = spio.read_RRATrap_info(
txtfile, lis[ii], int(ranks[ii]))
wrapper_cand = spcand.params()
wrapper_cand.read_from_file(values[ii], subfile.tsamp, subfile.specinfo.N, \
get_obs_info(fitsfilenm)['hifreq'], get_obs_info(fitsfilenm)['lofreq'], \
subfile, loc_pulse=0.5, dedisp = True,\
scaleindep = None, zerodm = None, mask = None,\
barytime=True, nsub = None, bandpass_corr = False)
temp_filename = basename + "_rank_%i" % int(ranks[ii])
if ii == 0: #check if index is 0
correct_rawdatafile, prevsubfile, prevDM = pick_rawdatafile(wrapper_cand.subdm, fitsfilenm, \
get_obs_info(fitsfilenm)['hifreq'], get_obs_info(fitsfilenm)['lofreq'], \
wrapper_cand.pulse_width, prevDM = 0, prevsubfile = '', \
init_flag = 1)
else:
correct_rawdatafile, prevsubfile, prevDM = pick_rawdatafile(wrapper_cand.subdm, fitsfilenm, \
get_obs_info(fitsfilenm)['hifreq'], get_obs_info(fitsfilenm)['lofreq'], \
wrapper_cand.pulse_width, prevDM = prevDM, \
prevsubfile=prevsubfile, init_flag = 0)
path = os.path.dirname(os.path.abspath(__file__))
cmd = "python " + path + "/make_spd.py --use_manual_params --subdm %f --nsub %d" \
" --dm %f -T %f -t %f --width-bins %d --downsamp %d --show-spec"\
" --noplot --notopo -o %s %s " %(wrapper_cand.subdm, \
wrapper_cand.nsub, wrapper_cand.dm, \
wrapper_cand.topo_start_time, wrapper_cand.duration, \
wrapper_cand.width_bins, wrapper_cand.downsamp,\
temp_filename, correct_rawdatafile)
print "executing %s" % cmd
subprocess.call(cmd, shell=True)
# Add additional information to the header information array
text_array = np.array([correct_rawdatafile, subfile.specinfo.telescope, \
subfile.specinfo.ra_str, subfile.specinfo.dec_str, \
subfile.specinfo.start_MJD[0], int(ranks[ii]), \
wrapper_cand.nsub, wrapper_cand.nbins, \
wrapper_cand.subdm, wrapper_cand.sigma, wrapper_cand.sample_number, \
wrapper_cand.duration, wrapper_cand.width_bins, wrapper_cand.pulse_width, \
subfile.tsamp, subfile.specinfo.T, wrapper_cand.topo_start_time])
temp_filename += "_DM%.1f_%.1fs" % (wrapper_cand.subdm,
wrapper_cand.topo_start_time)
spd = read_spd.spd(temp_filename + '.spd')
spd.man_params = None
text_array = np.append(text_array, spd.waterfall_start_time)
text_array = np.append(text_array, spd.waterfall_tsamp)
text_array = np.append(text_array, spd.waterfall_prededisp_nbins)
text_array = np.append(text_array, spd.min_freq)
text_array = np.append(text_array, spd.max_freq)
text_array = np.append(text_array, spd.sweep_duration)
text_array = np.append(text_array, spd.sweep_start_time)
text_array = np.append(text_array, spd.bary_pulse_peak_time)
text_array = np.append(text_array, spd.man_params)
with open(temp_filename + ".spd", 'wb') as f:
np.savez_compressed(f, \
Data_dedisp_nozerodm = spd.data_nozerodm_dedisp,\
Data_dedisp_zerodm = spd.data_zerodm_dedisp,\
Data_nozerodm = spd.data_nozerodm,\
delays_nozerodm = spd.dmsweep_delays, \
freqs_nozerodm = spd.dmsweep_freqs,\
Data_zerodm = spd.data_zerodm, \
dm_arr= map(np.float16, dm_arr),\
sigma_arr = map(np.float16, sigma_arr), \
width_arr =map(np.uint8, width_arr),\
dm_list= map(np.float16, dm_list), \
time_list = map(np.float16, time_list), \
text_array = text_array)
plot_spd.plot(temp_filename+".spd", glob.glob(path_sp_files+'/*.singlepulse'), maskfile, \
outfile=basename, just_waterfall=False, \
integrate_spec=True, integrate_ts=True, \
disp_pulse=False, bandpass_corr = True, tar = None)
numcands += 1
print 'Finished sp_candidate : %i' % numcands
if numcands >= 100: # Max number of candidates to plot 100.
loop_must_break = True
break
def GBNCC_wrapper(txtfile, maskfile, fitsfilenm, path_sp_files):
"""
The pipeline should pass job.fits_filenm as argument.
"""
files = spio.get_textfile(txtfile)
min_rank = 3
groups = [i for i in range(7) if(i>=min_rank)][::-1]
numcands=0 # counter for max number of candidates
loop_must_break = False # dont break the loop unless num of cands >100.
values = []
lis = []
ranks = []
for group in groups:
rank = group+1
if files[group] != "Number of rank %i groups: 0 "%rank:
add_values = spio.split_parameters(rank, txtfile)
values.extend(add_values)
lis = np.append(lis, np.where(files == '\tRank: %i.000000'%rank)[0])
add_ranks = np.ones(len(add_values)) * rank
ranks = np.append(ranks, add_ranks)
if len(values) > 100:
values = values[0:100]
lis = lis[0:100]
#Sort candidates based on DM
zip_list = zip(values, lis, ranks)
zip_list = sorted(zip_list, key=itemgetter(0,0))
values = [x[0] for x in zip_list]
lis = [x[1] for x in zip_list]
ranks = [x[2] for x in zip_list]
basename = fitsfilenm[:-5]
#generate subbanded file at a DM of 0 to extract observation parameters
cmd = "psrfits_subband -dm 0.0 -nsub 128 -o %s_subband_0.0 %s"%(basename,fitsfilenm)
print "executing %s" %cmd
subprocess.call(cmd, shell=True)
subfilenm = basename + "_subband_0.0_0001.fits"
subfile = psrfits.PsrfitsFile(subfilenm)
for ii in range(len(values)):
dm_list, time_list, dm_arr, sigma_arr, width_arr = spio.read_RRATrap_info(txtfile, lis[ii], int(ranks[ii]))
wrapper_cand = spcand.params()
wrapper_cand.read_from_file(values[ii], subfile.tsamp, subfile.specinfo.N, \
get_obs_info(fitsfilenm)['hifreq'], get_obs_info(fitsfilenm)['lofreq'], \
subfile, loc_pulse=0.5, dedisp = True,\
scaleindep = None, zerodm = None, mask = None,\
barytime=True, nsub = None, bandpass_corr = False)
temp_filename = basename+"_rank_%i"%int(ranks[ii])
if ii == 0: #check if index is 0
correct_rawdatafile, prevsubfile, prevDM = pick_rawdatafile(wrapper_cand.subdm, fitsfilenm, \
get_obs_info(fitsfilenm)['hifreq'], get_obs_info(fitsfilenm)['lofreq'], \
wrapper_cand.pulse_width, prevDM = 0, prevsubfile = '', \
init_flag = 1)
else:
correct_rawdatafile, prevsubfile, prevDM = pick_rawdatafile(wrapper_cand.subdm, fitsfilenm, \
get_obs_info(fitsfilenm)['hifreq'], get_obs_info(fitsfilenm)['lofreq'], \
wrapper_cand.pulse_width, prevDM = prevDM, \
prevsubfile=prevsubfile, init_flag = 0)
path = os.path.dirname(os.path.abspath(__file__))
cmd = "python " + path + "/make_spd.py --use_manual_params --subdm %f --nsub %d" \
" --dm %f -T %f -t %f --width-bins %d --downsamp %d --show-spec"\
" --noplot --notopo -o %s %s " %(wrapper_cand.subdm, \
wrapper_cand.nsub, wrapper_cand.dm, \
wrapper_cand.topo_start_time, wrapper_cand.duration, \
wrapper_cand.width_bins, wrapper_cand.downsamp,\
temp_filename, correct_rawdatafile)
print "executing %s" %cmd
subprocess.call(cmd, shell=True)
# Add additional information to the header information array
text_array = np.array([correct_rawdatafile, subfile.specinfo.telescope, \
subfile.specinfo.ra_str, subfile.specinfo.dec_str, \
subfile.specinfo.start_MJD[0], int(ranks[ii]), \
wrapper_cand.nsub, wrapper_cand.nbins, \
wrapper_cand.subdm, wrapper_cand.sigma, wrapper_cand.sample_number, \
wrapper_cand.duration, wrapper_cand.width_bins, wrapper_cand.pulse_width, \
subfile.tsamp, subfile.specinfo.T, wrapper_cand.topo_start_time])
temp_filename +="_DM%.1f_%.1fs"%(wrapper_cand.subdm, wrapper_cand.topo_start_time)
spd = read_spd.spd(temp_filename+'.spd')
spd.man_params = None
text_array = np.append(text_array, spd.waterfall_start_time)
text_array = np.append(text_array, spd.waterfall_tsamp)
text_array = np.append(text_array, spd.waterfall_prededisp_nbins)
text_array = np.append(text_array, spd.min_freq)
text_array = np.append(text_array, spd.max_freq)
text_array = np.append(text_array, spd.sweep_duration)
text_array = np.append(text_array, spd.sweep_start_time)
text_array = np.append(text_array, spd.bary_pulse_peak_time)
text_array = np.append(text_array, spd.man_params)
with open(temp_filename+".spd", 'wb') as f:
np.savez_compressed(f, \
Data_dedisp_nozerodm = spd.data_nozerodm_dedisp,\
Data_dedisp_zerodm = spd.data_zerodm_dedisp,\
Data_nozerodm = spd.data_nozerodm,\
delays_nozerodm = spd.dmsweep_delays, \
freqs_nozerodm = spd.dmsweep_freqs,\
Data_zerodm = spd.data_zerodm, \
dm_arr= map(np.float16, dm_arr),\
sigma_arr = map(np.float16, sigma_arr), \
width_arr =map(np.uint8, width_arr),\
dm_list= map(np.float16, dm_list), \
time_list = map(np.float16, time_list), \
text_array = text_array)
plot_spd.plot(temp_filename+".spd", glob.glob(path_sp_files+'/*.singlepulse'), maskfile, \
outfile=basename, just_waterfall=False, \
integrate_spec=True, integrate_ts=True, \
disp_pulse=False, bandpass_corr = True, tar = None)
numcands+= 1
print 'Finished sp_candidate : %i'%numcands
if numcands >= 100: # Max number of candidates to plot 100.
loop_must_break = True
break