def make_spd_from_file(spdcand, rawdatafile, \
txtfile, maskfile, \
min_rank, group_rank, \
plot, just_waterfall, \
integrate_ts, integrate_spec, disp_pulse, \
loc_pulse, nsub, \
maxnumcands, \
basename, \
mask=False, bandpass_corr=True, barytime=True, \
man_params=None):
"""
Makes spd files from output files of rratrap.
Inputs:
spdcand: spcand parameters instance (read in spcand.params)
rawdatafile: psrfits file instance
txtfile: rratrap output file (groups.txt file)
maskfile: rfifind mask file. need this file if you want to remove the bandpass
or use rfifind mask information.
min_rank: plot all groups with rank more than this. min 1, max 6
group_rank: plot groups ranked whatever you specify
plot: do you want to produce the plots as well?
just_waterfall: Do you just want to make the waterfall plots.
integrate_ts: Do you want to display the dedispersed time series in the plot?
integrate_spec: Do you want to display the pulse spectrum in the plot?
disp_pulse: Do you want to see the inset dispersed pulse in the plot?
loc_pulse: Fraction of the window length where the pulse is located.(eg. 0.25 = 1/4th of the way in.
0.5 = middle of the plot)
maxnumcands: What is the maximum number of candidates you would like to generate?
basename: output basename of the file. Appended with _DM_TIME(s)_RANK.spd
Optional arguments:
mask: Do you want to mask out rfi contaminated channels?
bandpass_corr: Do you want to remove the bandpass?
barytime: Is the given time(s) barycentric?
man_params: Do you want to specify the parameters for waterfalling
manually? If yes, I suggest using the function make_spd_from_man_params().
(I suggest giving it the rratrap output file)
Outputs:
Binary npz file containing the necessary arrays and header information to generate the spd plots.
"""
numcands = 0 # counter for max number of candidates
loop_must_break = False # dont break the loop unless num of cands >100.
files = spio.get_textfile(options.txtfile)
if group_rank:
groups = [group_rank - 1]
else:
groups = [i for i in range(6) if (i >= min_rank)][::-1]
for group in groups:
rank = group + 1
if files[group] != "Number of rank %i groups: 0 " % rank:
values = spio.split_parameters(rank, txtfile)
lis = np.where(files == '\tRank: %i.000000' % rank)[0]
for ii in range(len(values)):
#### Arrays for Plotting DM vs SNR
dm_list, time_list, dm_arr, sigma_arr, width_arr = spio.read_RRATrap_info(
txtfile, lis[ii], rank)
# Array for Plotting Dedispersed waterfall plot - zerodm - OFF
spdcand.read_from_file(values[ii], rawdatafile.tsamp, rawdatafile.specinfo.N, \
rawdatafile.frequencies[0], rawdatafile.frequencies[-1], \
rawdatafile, loc_pulse=loc_pulse, dedisp = True, \
scaleindep = None, zerodm = None, mask = mask, \
barytime=barytime, \
nsub = nsub, bandpass_corr = bandpass_corr)
#make an array to store header information for the spd files
temp_filename = basename+"_DM%.1f_%.1fs_rank_%i"%(spdcand.subdm, \
spdcand.topo_start_time, rank)
print_debug("Running waterfaller with Zero-DM OFF...")
# Add additional information to the header information array
data, Data_dedisp_nozerodm = waterfall_array(rawdatafile, spdcand.start, \
spdcand.duration, spdcand.dm, spdcand.nbins, spdcand.nsub, \
spdcand.subdm, spdcand.zerodm, spdcand.downsamp, \
spdcand.scaleindep, spdcand.width_bins, \
spdcand.mask, maskfile, spdcand.bandpass_corr)
text_array = np.array([args[0], rawdatafile.specinfo.telescope, \
rawdatafile.specinfo.ra_str, rawdatafile.specinfo.dec_str, \
rawdatafile.specinfo.start_MJD[0], \
rank, spdcand.nsub, spdcand.nbins, spdcand.subdm, \
spdcand.sigma, spdcand.sample_number, spdcand.duration, \
spdcand.width_bins, spdcand.pulse_width, rawdatafile.tsamp,\
rawdatafile.specinfo.T, spdcand.topo_start_time, data.starttime, \
data.dt,data.numspectra, data.freqs.min(), data.freqs.max()])
#### Array for plotting Dedispersed waterfall plot zerodm - ON
print_debug("Running Waterfaller with Zero-DM ON...")
zerodm = True
data, Data_dedisp_zerodm = waterfall_array(rawdatafile, spdcand.start, \
spdcand.duration, spdcand.dm, spdcand.nbins, spdcand.nsub, \
spdcand.subdm, zerodm, spdcand.downsamp, \
spdcand.scaleindep, spdcand.width_bins, \
spdcand.mask, maskfile, spdcand.bandpass_corr)
####Sweeped without zerodm
spdcand.read_from_file(values[ii], rawdatafile.tsamp, rawdatafile.specinfo.N, \
rawdatafile.frequencies[0], rawdatafile.frequencies[-1], \
rawdatafile, loc_pulse=loc_pulse, dedisp = None, \
scaleindep = None, zerodm = None, mask = mask, \
barytime=barytime, \
nsub = nsub, bandpass_corr = bandpass_corr)
data, Data_nozerodm = waterfall_array(rawdatafile, spdcand.start, \
spdcand.duration, spdcand.dm, spdcand.nbins, spdcand.nsub, \
spdcand.subdm, spdcand.zerodm, spdcand.downsamp, \
spdcand.scaleindep, spdcand.width_bins, \
spdcand.mask, maskfile, spdcand.bandpass_corr)
text_array = np.append(text_array, spdcand.sweep_duration)
text_array = np.append(text_array, data.starttime)
text_array = np.append(text_array, spdcand.bary_start_time)
text_array = np.append(text_array, man_params)
# Array to Construct the sweep
if spdcand.sweep_dm is not None:
ddm = spdcand.sweep_dm - data.dm
delays = psr_utils.delay_from_DM(ddm, data.freqs)
delays -= delays.min()
delays_nozerodm = delays
freqs_nozerodm = data.freqs
# Sweeped with zerodm-on
zerodm = True
#downsamp_temp = 1
data, Data_zerodm = waterfall_array(rawdatafile, spdcand.start, \
spdcand.duration, spdcand.dm, spdcand.nbins, spdcand.nsub, \
spdcand.subdm, zerodm, spdcand.downsamp, \
spdcand.scaleindep, spdcand.width_bins, \
spdcand.mask, maskfile, spdcand.bandpass_corr)
# Saving the arrays into the .spd file.
with open(temp_filename + ".spd", 'wb') as f:
np.savez_compressed(f, \
Data_dedisp_nozerodm = Data_dedisp_nozerodm.astype(np.float16),\
Data_dedisp_zerodm = Data_dedisp_zerodm.astype(np.float16),\
Data_nozerodm = Data_nozerodm.astype(np.float16),\
delays_nozerodm = delays_nozerodm, \
freqs_nozerodm = freqs_nozerodm,\
Data_zerodm = Data_zerodm.astype(np.float16), \
dm_arr= list(map(np.float16, dm_arr)),\
sigma_arr = list(map(np.float16, sigma_arr)), \
width_arr =list(map(np.uint8, width_arr)),\
dm_list= list(map(np.float16, dm_list)), \
time_list = list(map(np.float16, time_list)), \
text_array = text_array)
#### Arrays for Plotting DM vs Time is in plot_spd.plot(...)
if plot:
print_debug("Now plotting...")
plot_spd.plot(temp_filename+".spd", args[1:], \
spec_width=1.5, loc_pulse=loc_pulse, \
xwin=False, outfile=basename, \
just_waterfall=just_waterfall, \
integrate_spec=integrate_spec, \
integrate_ts=integrate_ts, \
disp_pulse=disp_pulse, tar = None)
print_debug("Finished plot %i " % ii +
strftime("%Y-%m-%d %H:%M:%S"))
numcands += 1
print_debug('Finished sp_candidate : %i' % numcands)
if numcands >= maxnumcands: # Max number of candidates to plot 100.
loop_must_break = True
break
if loop_must_break:
break
print_debug("Finished group %i... " % rank +
strftime("%Y-%m-%d %H:%M:%S"))
print_debug("Finished running waterfaller... " +
strftime("%Y-%m-%d %H:%M:%S"))
def make_spd_from_file(spdcand, rawdatafile, \
txtfile, maskfile, \
min_rank, group_rank, \
plot, just_waterfall, \
integrate_ts, integrate_spec, disp_pulse, \
loc_pulse, nsub, \
maxnumcands, \
basename, \
mask=False, bandpass_corr=True, barytime=True, \
man_params=None):
"""
Makes spd files from output files of rratrap.
Inputs:
spdcand: spcand parameters instance (read in spcand.params)
rawdatafile: psrfits file instance
txtfile: rratrap output file (groups.txt file)
maskfile: rfifind mask file. need this file if you want to remove the bandpass
or use rfifind mask information.
min_rank: plot all groups with rank more than this. min 1, max 6
group_rank: plot groups ranked whatever you specify
plot: do you want to produce the plots as well?
just_waterfall: Do you just want to make the waterfall plots.
integrate_ts: Do you want to display the dedispersed time series in the plot?
integrate_spec: Do you want to display the pulse spectrum in the plot?
disp_pulse: Do you want to see the inset dispersed pulse in the plot?
loc_pulse: Fraction of the window length where the pulse is located.(eg. 0.25 = 1/4th of the way in.
0.5 = middle of the plot)
maxnumcands: What is the maximum number of candidates you would like to generate?
basename: output basename of the file. Appended with _DM_TIME(s)_RANK.spd
Optional arguments:
mask: Do you want to mask out rfi contaminated channels?
bandpass_corr: Do you want to remove the bandpass?
barytime: Is the given time(s) barycentric?
man_params: Do you want to specify the parameters for waterfalling
manually? If yes, I suggest using the function make_spd_from_man_params().
(I suggest giving it the rratrap output file)
Outputs:
Binary npz file containing the necessary arrays and header information to generate the spd plots.
"""
numcands=0 # counter for max number of candidates
loop_must_break = False # dont break the loop unless num of cands >100.
files = spio.get_textfile(options.txtfile)
if group_rank:
groups=[group_rank-1]
else:
groups = [i for i in range(6) if(i>=min_rank)][::-1]
for group in groups:
rank = group+1
if files[group] != "Number of rank %i groups: 0 "%rank:
values = spio.split_parameters(rank, txtfile)
lis = np.where(files == '\tRank: %i.000000'%rank)[0]
for ii in range(len(values)):
#### Arrays for Plotting DM vs SNR
dm_list, time_list, dm_arr, sigma_arr, width_arr = spio.read_RRATrap_info(txtfile, lis[ii], rank)
# Array for Plotting Dedispersed waterfall plot - zerodm - OFF
spdcand.read_from_file(values[ii], rawdatafile.tsamp, rawdatafile.specinfo.N, \
rawdatafile.frequencies[0], rawdatafile.frequencies[-1], \
rawdatafile, loc_pulse=loc_pulse, dedisp = True, \
scaleindep = None, zerodm = None, mask = mask, \
barytime=barytime, \
nsub = nsub, bandpass_corr = bandpass_corr)
#make an array to store header information for the spd files
temp_filename = basename+"_DM%.1f_%.1fs_rank_%i"%(spdcand.subdm, \
spdcand.topo_start_time, rank)
print_debug("Running waterfaller with Zero-DM OFF...")
# Add additional information to the header information array
data, Data_dedisp_nozerodm = waterfall_array(rawdatafile, spdcand.start, \
spdcand.duration, spdcand.dm, spdcand.nbins, spdcand.nsub, \
spdcand.subdm, spdcand.zerodm, spdcand.downsamp, \
spdcand.scaleindep, spdcand.width_bins, \
spdcand.mask, maskfile, spdcand.bandpass_corr)
text_array = np.array([args[0], rawdatafile.specinfo.telescope, \
rawdatafile.specinfo.ra_str, rawdatafile.specinfo.dec_str, \
rawdatafile.specinfo.start_MJD[0], \
rank, spdcand.nsub, spdcand.nbins, spdcand.subdm, \
spdcand.sigma, spdcand.sample_number, spdcand.duration, \
spdcand.width_bins, spdcand.pulse_width, rawdatafile.tsamp,\
rawdatafile.specinfo.T, spdcand.topo_start_time, data.starttime, \
data.dt,data.numspectra, data.freqs.min(), data.freqs.max()])
#### Array for plotting Dedispersed waterfall plot zerodm - ON
print_debug("Running Waterfaller with Zero-DM ON...")
zerodm=True
data, Data_dedisp_zerodm = waterfall_array(rawdatafile, spdcand.start, \
spdcand.duration, spdcand.dm, spdcand.nbins, spdcand.nsub, \
spdcand.subdm, zerodm, spdcand.downsamp, \
spdcand.scaleindep, spdcand.width_bins, \
spdcand.mask, maskfile, spdcand.bandpass_corr)
####Sweeped without zerodm
spdcand.read_from_file(values[ii], rawdatafile.tsamp, rawdatafile.specinfo.N, \
rawdatafile.frequencies[0], rawdatafile.frequencies[-1], \
rawdatafile, loc_pulse=loc_pulse, dedisp = None, \
scaleindep = None, zerodm = None, mask = mask, \
barytime=barytime, \
nsub = nsub, bandpass_corr = bandpass_corr)
data, Data_nozerodm = waterfall_array(rawdatafile, spdcand.start, \
spdcand.duration, spdcand.dm, spdcand.nbins, spdcand.nsub, \
spdcand.subdm, spdcand.zerodm, spdcand.downsamp, \
spdcand.scaleindep, spdcand.width_bins, \
spdcand.mask, maskfile, spdcand.bandpass_corr)
text_array = np.append(text_array, spdcand.sweep_duration)
text_array = np.append(text_array, data.starttime)
text_array = np.append(text_array, spdcand.bary_start_time)
text_array = np.append(text_array, man_params)
# Array to Construct the sweep
if spdcand.sweep_dm is not None:
ddm = spdcand.sweep_dm-data.dm
delays = psr_utils.delay_from_DM(ddm, data.freqs)
delays -= delays.min()
delays_nozerodm = delays
freqs_nozerodm = data.freqs
# Sweeped with zerodm-on
zerodm = True
#downsamp_temp = 1
data, Data_zerodm = waterfall_array(rawdatafile, spdcand.start, \
spdcand.duration, spdcand.dm, spdcand.nbins, spdcand.nsub, \
spdcand.subdm, zerodm, spdcand.downsamp, \
spdcand.scaleindep, spdcand.width_bins, \
spdcand.mask, maskfile, spdcand.bandpass_corr)
# Saving the arrays into the .spd file.
with open(temp_filename+".spd", 'wb') as f:
np.savez_compressed(f, \
Data_dedisp_nozerodm = Data_dedisp_nozerodm.astype(np.float16),\
Data_dedisp_zerodm = Data_dedisp_zerodm.astype(np.float16),\
Data_nozerodm = Data_nozerodm.astype(np.float16),\
delays_nozerodm = delays_nozerodm, \
freqs_nozerodm = freqs_nozerodm,\
Data_zerodm = Data_zerodm.astype(np.float16), \
dm_arr= list(map(np.float16, dm_arr)),\
sigma_arr = list(map(np.float16, sigma_arr)), \
width_arr =list(map(np.uint8, width_arr)),\
dm_list= list(map(np.float16, dm_list)), \
time_list = list(map(np.float16, time_list)), \
text_array = text_array)
#### Arrays for Plotting DM vs Time is in plot_spd.plot(...)
if plot:
print_debug("Now plotting...")
plot_spd.plot(temp_filename+".spd", args[1:], \
spec_width=1.5, loc_pulse=loc_pulse, \
xwin=False, outfile=basename, \
just_waterfall=just_waterfall, \
integrate_spec=integrate_spec, \
integrate_ts=integrate_ts, \
disp_pulse=disp_pulse, tar = None)
print_debug("Finished plot %i " %ii+strftime("%Y-%m-%d %H:%M:%S"))
numcands+= 1
print_debug('Finished sp_candidate : %i'%numcands)
if numcands >= maxnumcands: # Max number of candidates to plot 100.
loop_must_break = True
break
if loop_must_break:
break
print_debug("Finished group %i... "%rank+strftime("%Y-%m-%d %H:%M:%S"))
print_debug("Finished running waterfaller... "+strftime("%Y-%m-%d %H:%M:%S"))
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