def main():
parser = optparse.OptionParser(prog="sp_pipeline..py", \
version=" Chitrang Patel (May. 12, 2015)", \
usage="%prog INFILE(PsrFits FILE, SINGLEPULSE FILES)", \
description="Create single pulse plots to show the " \
"frequency sweeps of a single pulse, " \
"DM vs time, and SNR vs DM,"\
"in psrFits data.")
parser.add_option('--infile', dest='infile', type='string', \
help="Give a .inf file to read the appropriate header information.")
parser.add_option('--groupsfile', dest='txtfile', type='string', \
help="Give the groups.txt file to read in the groups information.")
parser.add_option('--mask', dest='maskfile', type='string', \
help="Mask file produced by rfifind. (Default: No Mask).", \
default=None)
options, args = parser.parse_args()
if not hasattr(options, 'infile'):
raise ValueError("A .inf file must be given on the command line! ")
if not hasattr(options, 'txtfile'):
raise ValueError(
"The groups.txt file must be given on the command line! ")
files = get_textfile(options.txtfile)
print_debug("Begining waterfaller... " + strftime("%Y-%m-%d %H:%M:%S"))
Detrendlen = 50
if not args[0].endswith("fits"):
raise ValueError("The first file must be a psrFits file! ")
basename = args[0][:-5]
filetype = "psrfits"
inffile = options.infile
topo, bary = bary_and_topo.bary_to_topo(inffile)
time_shift = bary - topo
inf = infodata.infodata(inffile)
RA = inf.RA
dec = inf.DEC
MJD = inf.epoch
mjd = Popen(["mjd2cal", "%f" % MJD], stdout=PIPE, stderr=PIPE)
date, err = mjd.communicate()
date = date.split()[2:5]
telescope = inf.telescope
N = inf.N
Total_observed_time = inf.dt * N
print_debug('getting file..')
rawdatafile = psrfits.PsrfitsFile(args[0])
print "rawdatafile", memory.resident() / (1024.0**3)
bin_shift = np.round(time_shift / rawdatafile.tsamp).astype('int')
for group in [6, 5, 4, 3, 2]:
rank = group + 1
if files[group] != "Number of rank %i groups: 0 " % rank:
print_debug(files[group])
values = split_parameters(rank, options.txtfile)
lis = np.where(files == '\tRank: %i.000000' % rank)[0]
for ii in range(len(values)):
#### Array for Plotting DM vs SNR
print "DM, S/N", memory.resident() / (1024.0**3)
print_debug("Making arrays for DM vs Signal to Noise...")
temp_list = files[lis[ii] - 6].split()
npulses = int(temp_list[2])
temp_lines = files[(lis[ii] + 3):(lis[ii] + npulses + 1)]
arr = np.split(temp_lines, len(temp_lines))
dm_list = []
time_list = []
for i in range(len(arr)):
dm_val = float(arr[i][0].split()[0])
time_val = float(arr[i][0].split()[2])
dm_list.append(dm_val)
time_list.append(time_val)
arr_2 = np.array([arr[i][0].split() for i in range(len(arr))],
dtype=np.float32)
dm_arr = np.array([arr_2[i][0] for i in range(len(arr))],
dtype=np.float32)
sigma_arr = np.array([arr_2[i][1] for i in range(len(arr))],
dtype=np.float32)
print "After DM, S/N", memory.resident() / (1024.0**3)
#### Array for Plotting DM vs Time is in show_spplots.plot(...)
#### Setting variables up for the waterfall arrays.
j = ii + 1
subdm = dm = sweep_dm = values[ii][0]
integrate_dm = None
sigma = values[ii][1]
sweep_posn = 0.0
topo_start_time = values[ii][2] - topo_timeshift(
values[ii][2], time_shift, topo)[0]
sample_number = values[ii][3]
width_bins = values[ii][4]
binratio = 50
scaleindep = False
zerodm = None
downsamp = np.round((values[ii][2] / sample_number /
6.54761904761905e-05)).astype('int')
duration = binratio * width_bins * rawdatafile.tsamp * downsamp
start = topo_start_time - (0.25 * duration)
if (start < 0.0):
start = 0.0
pulse_width = width_bins * downsamp * 6.54761904761905e-05
if sigma <= 10:
nsub = 32
elif sigma >= 10 and sigma < 15:
nsub = 64
else:
nsub = 96
nbins = np.round(duration / rawdatafile.tsamp).astype('int')
start_bin = np.round(start / rawdatafile.tsamp).astype('int')
dmfac = 4.15e3 * np.abs(1. / rawdatafile.frequencies[0]**2 -
1. / rawdatafile.frequencies[-1]**2)
nbinsextra = np.round(
(duration + dmfac * dm) / rawdatafile.tsamp).astype('int')
if (start_bin + nbinsextra) > N - 1:
nbinsextra = N - 1 - start_bin
data = rawdatafile.get_spectra(start_bin, nbinsextra)
print "After rawdata", memory.resident() / (1024.0**3)
data = maskdata(data, start_bin, nbinsextra, options.maskfile)
#make an array to store header information for the .npz files
temp_filename = basename + "_DM%.1f_%.1fs_rank_%i" % (
subdm, topo_start_time, rank)
# Array for Plotting Dedispersed waterfall plot - zerodm - OFF
print_debug("Running waterfaller with Zero-DM OFF...")
data, Data_dedisp_nozerodm = waterfall_array(
start_bin, dmfac, duration, nbins, zerodm, nsub, subdm, dm,
integrate_dm, downsamp, scaleindep, width_bins,
rawdatafile, binratio, data)
print "waterfall", memory.resident() / (1024.0**3)
# Add additional information to the header information array
text_array = np.array([
args[0], 'Arecibo', RA, dec, MJD, rank, nsub, nbins, subdm,
sigma, sample_number, duration, width_bins, pulse_width,
rawdatafile.tsamp, Total_observed_time, 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...")
data = rawdatafile.get_spectra(start_bin, nbinsextra)
data = maskdata(data, start_bin, nbinsextra, options.maskfile)
zerodm = True
data, Data_dedisp_zerodm = waterfall_array(
start_bin, dmfac, duration, nbins, zerodm, nsub, subdm, dm,
integrate_dm, downsamp, scaleindep, width_bins,
rawdatafile, binratio, data)
print "waterfall", memory.resident() / (1024.0**3)
####Sweeped without zerodm
print_debug("Running waterfaller for sweeped arrays.")
start = start + (0.25 * duration)
start_bin = np.round(start / rawdatafile.tsamp).astype('int')
sweep_duration = 4.15e3 * np.abs(
1. / rawdatafile.frequencies[0]**2 -
1. / rawdatafile.frequencies[-1]**2) * sweep_dm
nbins = np.round(sweep_duration /
(rawdatafile.tsamp)).astype('int')
if ((nbins + start_bin) > (N - 1)):
nbins = N - 1 - start_bin
data = rawdatafile.get_spectra(start_bin, nbins)
data = maskdata(data, start_bin, nbins, options.maskfile)
zerodm = None
dm = None
data, Data_nozerodm = waterfall_array(
start_bin, dmfac, duration, nbins, zerodm, nsub, subdm, dm,
integrate_dm, downsamp, scaleindep, width_bins,
rawdatafile, binratio, data)
print "waterfall", memory.resident() / (1024.0**3)
text_array = np.append(text_array, sweep_duration)
text_array = np.append(text_array, data.starttime)
# Array to Construct the sweep
if sweep_dm is not None:
ddm = 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(start_bin, dmfac, duration,
nbins, zerodm, nsub, subdm,
dm, integrate_dm,
downsamp_temp, scaleindep,
width_bins, rawdatafile,
binratio, data)
print "waterfall", memory.resident() / (1024.0**3)
# 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=map(np.float16, dm_arr),
sigma_arr=map(np.float16, sigma_arr),
dm_list=map(np.float16, dm_list),
time_list=map(np.float16, time_list),
text_array=text_array)
print_debug("Now plotting...")
print "Before plot..", memory.resident() / (1024.0**3)
show_spplots.plot(temp_filename + ".spd",
args[1:],
xwin=False,
outfile=basename,
tar=None)
print "After plot..", memory.resident() / (1024.0**3)
print_debug("Finished plot %i " % j +
strftime("%Y-%m-%d %H:%M:%S"))
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 main():
parser = optparse.OptionParser(prog="sp_pipeline..py", \
version=" Chitrang Patel (May. 12, 2015)", \
usage="%prog INFILE(PsrFits FILE, SINGLEPULSE FILES)", \
description="Create single pulse plots to show the " \
"frequency sweeps of a single pulse, " \
"DM vs time, and SNR vs DM,"\
"in psrFits data.")
parser.add_option('--infile', dest='infile', type='string', \
help="Give a .inf file to read the appropriate header information.")
parser.add_option('--groupsfile', dest='txtfile', type='string', \
help="Give the groups.txt file to read in the groups information.")
parser.add_option('--mask', dest='maskfile', type='string', \
help="Mask file produced by rfifind. (Default: No Mask).", \
default=None)
parser.add_option('-n', dest='maxnumcands', type='int', \
help="Maximum number of candidates to plot. (Default: 100).", \
default=100)
options, args = parser.parse_args()
if not hasattr(options, 'infile'):
raise ValueError("A .inf file must be given on the command line! ")
if not hasattr(options, 'txtfile'):
raise ValueError("The groups.txt file must be given on the command line! ")
files = sp_utils.spio.get_textfile(options.txtfile)
print_debug("Begining waterfaller... "+strftime("%Y-%m-%d %H:%M:%S"))
if not args[0].endswith("fits"):
raise ValueError("The first file must be a psrFits file! ")
print_debug('Maximum number of candidates to plot: %i'%options.maxnumcands)
basename = args[0][:-5]
filetype = "psrfits"
inffile = options.infile
topo, bary = bary_and_topo.bary_to_topo(inffile)
time_shift = bary-topo
inf = infodata.infodata(inffile)
RA = inf.RA
dec = inf.DEC
MJD = inf.epoch
mjd = Popen(["mjd2cal", "%f"%MJD], stdout=PIPE, stderr=PIPE)
date, err = mjd.communicate()
date = date.split()[2:5]
telescope = inf.telescope
N = inf.N
Total_observed_time = inf.dt *N
print_debug('getting file..')
rawdatafile = psrfits.PsrfitsFile(args[0])
bin_shift = np.round(time_shift/rawdatafile.tsamp).astype('int')
numcands = 0 # candidate counter. Use this to decide the maximum bumber of candidates to plot.
loop_must_break = False # dont break the loop unless num of cands >100.
for group in [6, 5, 4, 3, 2]:
rank = group+1
if files[group] != "Number of rank %i groups: 0 "%rank:
print_debug(files[group])
values = sp_utils.spio.split_parameters(rank, options.txtfile)
lis = np.where(files == '\tRank: %i.000000'%rank)[0]
for ii in range(len(values)):
#### Array for Plotting DM vs SNR
print_debug("Making arrays for DM vs Signal to Noise...")
temp_list = files[lis[ii]-6].split()
npulses = int(temp_list[2])
temp_lines = files[(lis[ii]+3):(lis[ii]+npulses+1)]
arr = np.split(temp_lines, len(temp_lines))
dm_list = []
time_list = []
for i in range(len(arr)):
dm_val= float(arr[i][0].split()[0])
time_val = float(arr[i][0].split()[2])
dm_list.append(dm_val)
time_list.append(time_val)
arr_2 = np.array([arr[i][0].split() for i in range(len(arr))], dtype = np.float32)
dm_arr = np.array([arr_2[i][0] for i in range(len(arr))], dtype = np.float32)
sigma_arr = np.array([arr_2[i][1] for i in range(len(arr))], dtype = np.float32)
#### Array for Plotting DM vs Time is in show_spplots.plot(...)
#### Setting variables up for the waterfall arrays.
j = ii+1
subdm = dm = sweep_dm= values[ii][0]
integrate_dm = None
sigma = values[ii][1]
sweep_posn = 0.0
bary_start_time = values[ii][2]
topo_start_time = bary_start_time - topo_timeshift(bary_start_time, time_shift, topo)[0]
sample_number = values[ii][3]
width_bins = values[ii][4]
binratio = 50
scaleindep = False
zerodm = None
downsamp = np.round((values[ii][2]/sample_number/rawdatafile.tsamp)).astype('int')
duration = binratio * width_bins * rawdatafile.tsamp * downsamp
start = topo_start_time - (0.25 * duration)
if (start<0.0):
start = 0.0
pulse_width = width_bins*downsamp*rawdatafile.tsamp
if sigma < 10:
nsub = 32
elif sigma >= 10 and sigma < 15:
nsub = 64
else:
nsub = 96
if nsub > inf.numchan:
nsub = inf.numchan
nbins = np.round(duration/rawdatafile.tsamp).astype('int')
start_bin = np.round(start/rawdatafile.tsamp).astype('int')
dmfac = 4.15e3 * np.abs(1./rawdatafile.frequencies[0]**2 - 1./rawdatafile.frequencies[-1]**2)
nbinsextra = np.round((duration + dmfac * dm)/rawdatafile.tsamp).astype('int')
if (start_bin+nbinsextra) > N-1:
nbinsextra = N-1-start_bin
data = rawdatafile.get_spectra(start_bin, nbinsextra)
data = maskdata(data, start_bin, nbinsextra, options.maskfile)
#make an array to store header information for the .npz files
temp_filename = basename+"_DM%.1f_%.1fs_rank_%i"%(subdm, topo_start_time, rank)
# Array for Plotting Dedispersed waterfall plot - zerodm - OFF
print_debug("Running waterfaller with Zero-DM OFF...")
data, Data_dedisp_nozerodm = waterfall_array(start_bin, dmfac, duration, nbins, zerodm, nsub, subdm, dm, integrate_dm, downsamp, scaleindep, width_bins, rawdatafile, binratio, data)
# Add additional information to the header information array
text_array = np.array([args[0], 'Arecibo', RA, dec, MJD, rank, nsub, nbins, subdm, sigma, sample_number, duration, width_bins, pulse_width, rawdatafile.tsamp, Total_observed_time, 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...")
data = rawdatafile.get_spectra(start_bin, nbinsextra)
data = maskdata(data, start_bin, nbinsextra, options.maskfile)
zerodm = True
data, Data_dedisp_zerodm = waterfall_array(start_bin, dmfac, duration, nbins, zerodm, nsub, subdm, dm, integrate_dm, downsamp, scaleindep, width_bins, rawdatafile, binratio, data)
####Sweeped without zerodm
start = start + (0.25*duration)
start_bin = np.round(start/rawdatafile.tsamp).astype('int')
sweep_duration = 4.15e3 * np.abs(1./rawdatafile.frequencies[0]**2-1./rawdatafile.frequencies[-1]**2)*sweep_dm
nbins = np.round(sweep_duration/(rawdatafile.tsamp)).astype('int')
if ((nbins+start_bin)> (N-1)):
nbins = N-1-start_bin
data = rawdatafile.get_spectra(start_bin, nbins)
data = maskdata(data, start_bin, nbins, options.maskfile)
zerodm = None
dm = None
data, Data_nozerodm = waterfall_array(start_bin, dmfac, duration, nbins, zerodm, nsub, subdm, dm, integrate_dm, downsamp, scaleindep, width_bins, rawdatafile, binratio, data)
text_array = np.append(text_array, sweep_duration)
text_array = np.append(text_array, data.starttime)
text_array = np.append(text_array, bary_start_time)
# Array to Construct the sweep
if sweep_dm is not None:
ddm = 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(start_bin, dmfac, duration, nbins, zerodm, nsub, subdm, dm, integrate_dm, downsamp_temp, scaleindep, width_bins, rawdatafile, binratio, data)
# 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= map(np.float16, dm_arr), sigma_arr = map(np.float16, sigma_arr), dm_list= map(np.float16, dm_list), time_list = map(np.float16, time_list), text_array = text_array)
print_debug("Now plotting...")
show_spplots.plot(temp_filename+".spd", args[1:], xwin=False, outfile = basename, tar = None)
print_debug("Finished plot %i " %j+strftime("%Y-%m-%d %H:%M:%S"))
numcands+= 1
print_debug('Finished sp_candidate : %i'%numcands)
if numcands >= options.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 main():
parser = optparse.OptionParser(prog="sp_pipeline..py", \
version=" Chitrang Patel (May. 12, 2015)", \
usage="%prog INFILE(PsrFits FILE, SINGLEPULSE FILES)", \
description="Create single pulse plots to show the " \
"frequency sweeps of a single pulse, " \
"DM vs time, and SNR vs DM,"\
"in psrFits data.")
parser.add_option('--infile', dest='infile', type='string', \
help="Give a .inf file to read the appropriate header information.")
parser.add_option('--groupsfile', dest='txtfile', type='string', \
help="Give the groups.txt file to read in the groups information.")
parser.add_option('--mask', dest='maskfile', type='string', \
help="Mask file produced by rfifind. (Default: No Mask).", \
default=None)
parser.add_option('-n', dest='maxnumcands', type='int', \
help="Maximum number of candidates to plot. (Default: 100).", \
default=100)
options, args = parser.parse_args()
if not hasattr(options, 'infile'):
raise ValueError("A .inf file must be given on the command line! ")
if not hasattr(options, 'txtfile'):
raise ValueError("The groups.txt file must be given on the command line! ")
files = get_textfile(options.txtfile)
print_debug("Begining waterfaller... "+strftime("%Y-%m-%d %H:%M:%S"))
if not args[0].endswith("fits"):
raise ValueError("The first file must be a psrFits file! ")
print_debug('Maximum number of candidates to plot: %i'%options.maxnumcands)
basename = args[0][:-5]
filetype = "psrfits"
inffile = options.infile
topo, bary = bary_and_topo.bary_to_topo(inffile)
time_shift = bary-topo
inf = infodata.infodata(inffile)
RA = inf.RA
dec = inf.DEC
MJD = inf.epoch
mjd = Popen(["mjd2cal", "%f"%MJD], stdout=PIPE, stderr=PIPE)
date, err = mjd.communicate()
date = date.split()[2:5]
telescope = inf.telescope
N = inf.N
numcands=0
Total_observed_time = inf.dt *N
print_debug('getting file..')
values = split_parameters(options.txtfile)
if len(values)> options.maxnumcands:
values=sorted(values, key=itemgetter(5,1)) #sorting candidates based on ranks and snr
values=values[-options.maxnumcands:]
print "More than", options.maxnumcands, "candidates, making plots for", options.maxnumcands, "candidates"
values = sorted(values, key=itemgetter(0))
for ii in range(len(values)):
#### Array for Plotting DM vs SNR
print_debug("Making arrays for DM vs Signal to Noise...")
temp_list = files[values[ii][6]-6].split()
npulses = int(temp_list[2])
temp_lines = files[(values[ii][6]+3):(values[ii][6]+npulses+1)]
arr = np.split(temp_lines, len(temp_lines))
dm_list = []
time_list = []
for i in range(len(arr)):
dm_val= float(arr[i][0].split()[0])
time_val = float(arr[i][0].split()[2])
dm_list.append(dm_val)
time_list.append(time_val)
arr_2 = np.array([arr[i][0].split() for i in range(len(arr))], dtype = np.float32)
dm_arr = np.array([arr_2[i][0] for i in range(len(arr))], dtype = np.float32)
sigma_arr = np.array([arr_2[i][1] for i in range(len(arr))], dtype = np.float32)
#### Array for Plotting DM vs Time is in show_spplots.plot(...)
#### Setting variables up for the waterfall arrays.
j = ii+1
subdm = dm = sweep_dm= values[ii][0]
sample_number = values[ii][3]
rank=values[ii][5]
width_bins = values[ii][4]
#print "dm", dm
#print "width_bins", width_bins
downsamp = np.round((values[ii][2]/sample_number/inf.dt)).astype('int')
#print "downsamp", downsamp
pulse_width = width_bins*downsamp*inf.dt
#print "pulse_width", pulse_width
if ii == 0:
mask_subband=rfifind.rfifind("%s_rfifind.mask"%(basename))
mask_subband.set_zap_chans(power=1000,plot=False)
mask_subband.set_weights_and_offsets()
mask_subband.write_weights(filename="%s_weights.txt"%(basename))
cmd="psrfits_subband -dm %.2f -nsub 128 -o %s_subband_%.2f -weights %s_weights.txt %s"%(dm,basename,dm,basename,args[0])
call(cmd, shell=True)
#subband args[0] at dm and then generate a file that will be set equal to rawdatafile
subband_file="%s_subband_%.2f_0001.fits" %(basename,dm)
dm_prev=dm
subband_prev= subband_file
else:
dm_diff=dm-dm_prev
t_smear=8.3e3*dm_diff*(350**-3)*(np.abs(rawdatafile.frequencies[0]-rawdatafile.frequencies[-1])/128.)
if (5*t_smear) > pulse_width:
cmd="psrfits_subband -dm %.2f -nsub 128 -o %s_subband_%.2f -weights %s_weights.txt %s"%(dm,basename,dm,basename,args[0])
call(cmd, shell=True)
#subband args[0] at dm and then generate a file that will be set equal to rawdatafile
subband_file="%s_subband_%.2f_0001.fits" %(basename,dm)
dm_prev=dm
subband_prev=subband_file
rawdatafile = psrfits.PsrfitsFile(subband_file)
bin_shift = np.round(time_shift/rawdatafile.tsamp).astype('int')
integrate_dm = None
sigma = values[ii][1]
sweep_posn = 0.0
bary_start_time = values[ii][2]
topo_start_time = bary_start_time - topo_timeshift(bary_start_time, time_shift, topo)[0]
binratio = 50
scaleindep = False
zerodm = None
duration = binratio * width_bins * rawdatafile.tsamp * downsamp
start = topo_start_time - (0.25 * duration)
if (start<0.0):
start = 0.0
if sigma <= 7:
nsub = 32
elif sigma >= 7 and sigma < 10:
nsub = 64
else:
nsub = 128
nbins = np.round(duration/rawdatafile.tsamp).astype('int')
start_bin = np.round(start/rawdatafile.tsamp).astype('int')
dmfac = 4.15e3 * np.abs(1./rawdatafile.frequencies[0]**2 - 1./rawdatafile.frequencies[-1]**2)
nbinsextra = np.round((duration + dmfac * dm)/rawdatafile.tsamp).astype('int')
if (start_bin+nbinsextra) > N-1:
nbinsextra = N-1-start_bin
data = rawdatafile.get_spectra(start_bin, nbinsextra)
data = maskdata(data, start_bin, nbinsextra, options.maskfile)
#make an array to store header information for the .npz files
temp_filename = basename+"_DM%.1f_%.1fs_rank_%i"%(subdm, topo_start_time, rank)
# Array for Plotting Dedispersed waterfall plot - zerodm - OFF
print_debug("Running waterfaller with Zero-DM OFF...")
data, Data_dedisp_nozerodm = waterfall_array(start_bin, dmfac, duration, nbins, zerodm, nsub, subdm, dm, integrate_dm, downsamp, scaleindep, width_bins, rawdatafile, binratio, data)
#Add additional information to the header information array
text_array = np.array([subband_file, 'GBT', RA, dec, MJD, rank, nsub, nbins, subdm, sigma, sample_number, duration, width_bins, pulse_width, rawdatafile.tsamp, Total_observed_time, 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...")
#print "before get_spectra",memory.resident()/(1024.0**3)
data = rawdatafile.get_spectra(start_bin, nbinsextra)
#print "after get_spectra",memory.resident()/(1024.0**3)
data = maskdata(data, start_bin, nbinsextra, options.maskfile)
zerodm = True
data, Data_dedisp_zerodm = waterfall_array(start_bin, dmfac, duration, nbins, zerodm, nsub, subdm, dm, integrate_dm, downsamp, scaleindep, width_bins, rawdatafile, binratio, data)
#print "waterfall",memory.resident()/(1024.0**3)
####Sweeped without zerodm
start = start + (0.25*duration)
start_bin = np.round(start/rawdatafile.tsamp).astype('int')
sweep_duration = 4.15e3 * np.abs(1./rawdatafile.frequencies[0]**2-1./rawdatafile.frequencies[-1]**2)*sweep_dm
nbins = np.round(sweep_duration/(rawdatafile.tsamp)).astype('int')
if ((nbins+start_bin)> (N-1)):
nbins = N-1-start_bin
#print "before get_spectra",memory.resident()/(1024.0**3)
data = rawdatafile.get_spectra(start_bin, nbins)
#print "after get_spectra",memory.resident()/(1024.0**3)
data = maskdata(data, start_bin, nbins, options.maskfile)
zerodm = None
dm = None
data, Data_nozerodm = waterfall_array(start_bin, dmfac, duration, nbins, zerodm, nsub, subdm, dm, integrate_dm, downsamp, scaleindep, width_bins, rawdatafile, binratio, data)
#print "waterfall",memory.resident()/(1024.0**3)
text_array = np.append(text_array, sweep_duration)
text_array = np.append(text_array, data.starttime)
text_array = np.append(text_array, bary_start_time)
# Array to Construct the sweep
if sweep_dm is not None:
ddm = 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(start_bin, dmfac, duration, nbins, zerodm, nsub, subdm, dm, integrate_dm, downsamp_temp, scaleindep, width_bins, rawdatafile, binratio, data)
#print "waterfall",memory.resident()/(1024.0**3)
# 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= map(np.float16, dm_arr), sigma_arr = map(np.float16, sigma_arr), dm_list= map(np.float16, dm_list), time_list = map(np.float16, time_list), text_array = text_array)
print_debug("Now plotting...")
#print "Before plot..",memory.resident()/(1024.0**3)
show_spplots.plot(temp_filename+".spd", args[1:], xwin=False, outfile = basename, tar = None)
print_debug("Finished plot %i " %j+strftime("%Y-%m-%d %H:%M:%S"))
#print "After plot..",memory.resident()/(1024.0**3)
numcands+=1
print_debug('Finished sp_candidate : %i'%numcands)
print_debug("Finished running waterfaller... "+strftime("%Y-%m-%d %H:%M:%S"))
def main():
parser = optparse.OptionParser(prog="sp_pipeline..py", \
version=" Chitrang Patel (May. 12, 2015)", \
usage="%prog INFILE(PsrFits FILE, SINGLEPULSE FILES)", \
description="Create single pulse plots to show the " \
"frequency sweeps of a single pulse, " \
"DM vs time, and SNR vs DM,"\
"in psrFits data.")
parser.add_option('--infile', dest='infile', type='string', \
help="Give a .inf file to read the appropriate header information.")
parser.add_option('--groupsfile', dest='txtfile', type='string', \
help="Give the groups.txt file to read in the groups information.")
parser.add_option('--mask', dest='maskfile', type='string', \
help="Mask file produced by rfifind. (Default: No Mask).", \
default=None)
parser.add_option('-n', dest='maxnumcands', type='int', \
help="Maximum number of candidates to plot. (Default: 100).", \
default=100)
options, args = parser.parse_args()
if not hasattr(options, 'infile'):
raise ValueError("A .inf file must be given on the command line! ")
if not hasattr(options, 'txtfile'):
raise ValueError(
"The groups.txt file must be given on the command line! ")
files = get_textfile(options.txtfile)
print_debug("Begining waterfaller... " + strftime("%Y-%m-%d %H:%M:%S"))
if not args[0].endswith("fits"):
raise ValueError("The first file must be a psrFits file! ")
print_debug('Maximum number of candidates to plot: %i' %
options.maxnumcands)
basename = args[0][:-5]
filetype = "psrfits"
inffile = options.infile
topo, bary = bary_and_topo.bary_to_topo(inffile)
time_shift = bary - topo
inf = infodata.infodata(inffile)
RA = inf.RA
dec = inf.DEC
MJD = inf.epoch
mjd = Popen(["mjd2cal", "%f" % MJD], stdout=PIPE, stderr=PIPE)
date, err = mjd.communicate()
date = date.split()[2:5]
telescope = inf.telescope
N = inf.N
numcands = 0
Total_observed_time = inf.dt * N
print_debug('getting file..')
values = split_parameters(options.txtfile)
if len(values) > options.maxnumcands:
values = sorted(values, key=itemgetter(
5, 1)) #sorting candidates based on ranks and snr
values = values[-options.maxnumcands:]
print "More than", options.maxnumcands, "candidates, making plots for", options.maxnumcands, "candidates"
values = sorted(values, key=itemgetter(0))
for ii in range(len(values)):
#### Array for Plotting DM vs SNR
print_debug("Making arrays for DM vs Signal to Noise...")
temp_list = files[values[ii][6] - 6].split()
npulses = int(temp_list[2])
temp_lines = files[(values[ii][6] + 3):(values[ii][6] + npulses + 1)]
arr = np.split(temp_lines, len(temp_lines))
dm_list = []
time_list = []
for i in range(len(arr)):
dm_val = float(arr[i][0].split()[0])
time_val = float(arr[i][0].split()[2])
dm_list.append(dm_val)
time_list.append(time_val)
arr_2 = np.array([arr[i][0].split() for i in range(len(arr))],
dtype=np.float32)
dm_arr = np.array([arr_2[i][0] for i in range(len(arr))],
dtype=np.float32)
sigma_arr = np.array([arr_2[i][1] for i in range(len(arr))],
dtype=np.float32)
#### Array for Plotting DM vs Time is in show_spplots.plot(...)
#### Setting variables up for the waterfall arrays.
j = ii + 1
subdm = dm = sweep_dm = values[ii][0]
sample_number = values[ii][3]
rank = values[ii][5]
width_bins = values[ii][4]
#print "dm", dm
#print "width_bins", width_bins
downsamp = np.round(
(values[ii][2] / sample_number / inf.dt)).astype('int')
#print "downsamp", downsamp
pulse_width = width_bins * downsamp * inf.dt
#print "pulse_width", pulse_width
if ii == 0:
mask_subband = rfifind.rfifind("%s_rfifind.mask" % (basename))
mask_subband.set_zap_chans(power=1000, plot=False)
mask_subband.set_weights_and_offsets()
mask_subband.write_weights(filename="%s_weights.txt" % (basename))
cmd = "psrfits_subband -dm %.2f -nsub 128 -o %s_subband_%.2f -weights %s_weights.txt %s" % (
dm, basename, dm, basename, args[0])
call(cmd, shell=True)
#subband args[0] at dm and then generate a file that will be set equal to rawdatafile
subband_file = "%s_subband_%.2f_0001.fits" % (basename, dm)
dm_prev = dm
subband_prev = subband_file
else:
dm_diff = dm - dm_prev
t_smear = 8.3e3 * dm_diff * (350**-3) * (
np.abs(rawdatafile.frequencies[0] -
rawdatafile.frequencies[-1]) / 128.)
if (5 * t_smear) > pulse_width:
cmd = "psrfits_subband -dm %.2f -nsub 128 -o %s_subband_%.2f -weights %s_weights.txt %s" % (
dm, basename, dm, basename, args[0])
call(cmd, shell=True)
#subband args[0] at dm and then generate a file that will be set equal to rawdatafile
subband_file = "%s_subband_%.2f_0001.fits" % (basename, dm)
dm_prev = dm
subband_prev = subband_file
rawdatafile = psrfits.PsrfitsFile(subband_file)
bin_shift = np.round(time_shift / rawdatafile.tsamp).astype('int')
integrate_dm = None
sigma = values[ii][1]
sweep_posn = 0.0
bary_start_time = values[ii][2]
topo_start_time = bary_start_time - topo_timeshift(
bary_start_time, time_shift, topo)[0]
binratio = 50
scaleindep = False
zerodm = None
duration = binratio * width_bins * rawdatafile.tsamp * downsamp
start = topo_start_time - (0.25 * duration)
if (start < 0.0):
start = 0.0
if sigma <= 7:
nsub = 32
elif sigma >= 7 and sigma < 10:
nsub = 64
else:
nsub = 128
nbins = np.round(duration / rawdatafile.tsamp).astype('int')
start_bin = np.round(start / rawdatafile.tsamp).astype('int')
dmfac = 4.15e3 * np.abs(1. / rawdatafile.frequencies[0]**2 -
1. / rawdatafile.frequencies[-1]**2)
nbinsextra = np.round(
(duration + dmfac * dm) / rawdatafile.tsamp).astype('int')
if (start_bin + nbinsextra) > N - 1:
nbinsextra = N - 1 - start_bin
data = rawdatafile.get_spectra(start_bin, nbinsextra)
data = maskdata(data, start_bin, nbinsextra, options.maskfile)
#make an array to store header information for the .npz files
temp_filename = basename + "_DM%.1f_%.1fs_rank_%i" % (
subdm, topo_start_time, rank)
# Array for Plotting Dedispersed waterfall plot - zerodm - OFF
print_debug("Running waterfaller with Zero-DM OFF...")
data, Data_dedisp_nozerodm = waterfall_array(
start_bin, dmfac, duration, nbins, zerodm, nsub, subdm, dm,
integrate_dm, downsamp, scaleindep, width_bins, rawdatafile,
binratio, data)
#Add additional information to the header information array
text_array = np.array([
subband_file, 'GBT', RA, dec, MJD, rank, nsub, nbins, subdm, sigma,
sample_number, duration, width_bins, pulse_width,
rawdatafile.tsamp, Total_observed_time, 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...")
#print "before get_spectra",memory.resident()/(1024.0**3)
data = rawdatafile.get_spectra(start_bin, nbinsextra)
#print "after get_spectra",memory.resident()/(1024.0**3)
data = maskdata(data, start_bin, nbinsextra, options.maskfile)
zerodm = True
data, Data_dedisp_zerodm = waterfall_array(start_bin, dmfac, duration,
nbins, zerodm, nsub, subdm,
dm, integrate_dm, downsamp,
scaleindep, width_bins,
rawdatafile, binratio, data)
#print "waterfall",memory.resident()/(1024.0**3)
####Sweeped without zerodm
start = start + (0.25 * duration)
start_bin = np.round(start / rawdatafile.tsamp).astype('int')
sweep_duration = 4.15e3 * np.abs(
1. / rawdatafile.frequencies[0]**2 -
1. / rawdatafile.frequencies[-1]**2) * sweep_dm
nbins = np.round(sweep_duration / (rawdatafile.tsamp)).astype('int')
if ((nbins + start_bin) > (N - 1)):
nbins = N - 1 - start_bin
#print "before get_spectra",memory.resident()/(1024.0**3)
data = rawdatafile.get_spectra(start_bin, nbins)
#print "after get_spectra",memory.resident()/(1024.0**3)
data = maskdata(data, start_bin, nbins, options.maskfile)
zerodm = None
dm = None
data, Data_nozerodm = waterfall_array(start_bin, dmfac, duration,
nbins, zerodm, nsub, subdm, dm,
integrate_dm, downsamp,
scaleindep, width_bins,
rawdatafile, binratio, data)
#print "waterfall",memory.resident()/(1024.0**3)
text_array = np.append(text_array, sweep_duration)
text_array = np.append(text_array, data.starttime)
text_array = np.append(text_array, bary_start_time)
# Array to Construct the sweep
if sweep_dm is not None:
ddm = 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(start_bin, dmfac, duration, nbins,
zerodm, nsub, subdm, dm,
integrate_dm, downsamp_temp,
scaleindep, width_bins,
rawdatafile, binratio, data)
#print "waterfall",memory.resident()/(1024.0**3)
# 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=map(np.float16, dm_arr),
sigma_arr=map(np.float16, sigma_arr),
dm_list=map(np.float16, dm_list),
time_list=map(np.float16, time_list),
text_array=text_array)
print_debug("Now plotting...")
#print "Before plot..",memory.resident()/(1024.0**3)
show_spplots.plot(temp_filename + ".spd",
args[1:],
xwin=False,
outfile=basename,
tar=None)
print_debug("Finished plot %i " % j + strftime("%Y-%m-%d %H:%M:%S"))
#print "After plot..",memory.resident()/(1024.0**3)
numcands += 1
print_debug('Finished sp_candidate : %i' % numcands)
print_debug("Finished running waterfaller... " +
strftime("%Y-%m-%d %H:%M:%S"))
def read_from_file(self, params, tsamp, N, lofreq, hifreq, rawdatafile, loc_pulse = 0.5, dedisp = False, \
scaleindep = None, zerodm = None, mask = None, barytime = True, \
nsub = None, bandpass_corr = False):
"""
Set up parameters based on input from the groups.txt file.
Input: params: list of parameters (DM, S/N, Time, Sample number, downfactor)
tsamp: sampling time (downsampled: 65.5 us for PALFA)
N : The total number of time samples in the observation
lofreq: lowest observation frequency
hifreq: highest observation frequency
rawdatafile: supply a PSRFITS file instance
optional arguments:
dedisp: Do you want to dedisperse?(Type: Boolean).
scaleindep:Do you want to scale each subband independently?(Type: Boolean)
zerodm:Do you want to use zero-DM filtering?(Type: Boolean)
mask: Do you want to use a rfifind mask? (Type: Boolean)
barytime: Is the given time(s) barycentric?
bandpass_corr:Would you like to remove the bandpass? (Type: Boolean)
"""
self.subdm = params[0]
self.sigma = params[1]
topo, bary = bary_and_topo.bary_to_topo(rawdatafile.filename, rawdatafile=rawdatafile)
time_shift = bary-topo
if barytime:
#### if the times in the groups.txt file are barycentric then get the corresponding
#### topocentric times.
self.bary_start_time = params[2]
self.topo_start_time = self.bary_start_time - topo_timeshift(self.bary_start_time, \
time_shift, topo)[0]
else:
#### Vice versa.
self.topo_start_time = params[2]
self.bary_start_time = self.topo_start_time + topo_timeshift(self.bary_start_time, \
time_shift, topo)[0]
self.sample_number = params[3]
self.width_bins = params[4]
sweep_posn = 0.0
self.scaleindep = scaleindep
self.downsamp = np.round((params[2]/self.sample_number/tsamp)).astype('int')
self.duration = self.binratio * self.width_bins * tsamp * self.downsamp
self.start = self.topo_start_time - (loc_pulse * self.duration)
if (self.start<0.0):
self.start = 0.0
self.start_bin = np.round(self.start/tsamp).astype('int')
self.pulse_width = self.width_bins*self.downsamp*tsamp
if nsub is not None:
self.nsub = nsub
else:
self.nsub = numsub(rawdatafile.nchan, self.sigma)
self.zerodm = zerodm
if dedisp:
self.dm = self.subdm
self.sweep_dm = None
self.sweep_duration = 0.0
self.dmfac = 4.15e3 * np.abs(1./lofreq**2 - 1./hifreq**2)
self.nbins = np.round(self.duration/tsamp).astype('int')
self.nbinsextra = np.round((self.duration + self.dmfac * self.dm)/tsamp).astype('int')
if (self.start_bin+self.nbinsextra) > N-1:
self.nbinsextra = N-1-self.start_bin
self.bandpass_corr = bandpass_corr
else:
self.dm = None
self.sweep_dm = self.subdm
self.sweep_duration = 4.15e3 * np.abs(1./lofreq**2-1./hifreq**2)*self.sweep_dm
self.start = self.start + (loc_pulse*self.duration)
self.start_bin = np.round(self.start/tsamp).astype('int')
self.nbins = np.round(self.sweep_duration/tsamp).astype('int')
self.nbinsextra = self.nbins
if (self.start_bin+self.nbinsextra) > N-1:
self.nbinsextra = N-1-self.start_bin
self.bandpass_corr = False
def manual_params(self, subdm, dm, sweep_dm, sigma, start_time, width_bins, downsamp, \
duration, nbins, nsub, tsamp, N, lofreq, hifreq, rawdatafile, loc_pulse=0.5, dedisp = False, \
scaleindep = None, zerodm = None, mask = False, barytime = True, \
bandpass_corr = False):
"""
Set up parameters based on input from the groups.txt file.
Input:
subdm: DM to use when subbanding.
dm: DM to use when dedispersing data for plot.
sweep_dm: Show the frequency sweep using this DM.
sigma: signal-to-noise of the pulse
start_time: start time of the data to be read in for waterfalling.
width_bins: Smooth each channel/subband with a boxcar width_bins wide.
downsamp: Factor to downsample in time by. Default: Don't downsample.
duration: duration of data to be waterfalled.
nbins: Number of time bins to plot. This option overrides
the duration argument.
nsub: Number of subbands to use. Must be a factor of number of channels.
tsamp: sampling time (downsampled: 65.5 us for PALFA)
N: total number of samples in an observations
lofreq: lowest observation frequency
hifreq: highest observation frequency
rawdatafile: supply a psrfits file instance
optional arguments:
dedisp: Do you want to dedisperse?(Type: Boolean).
scaleindep:Do you want to scale each subband independently?(Type: Boolean)
zerodm:Do you want to use zero-DM filtering?(Type: Boolean)
mask: Do you want to use a rfifind mask? (Type: Boolean)
barytime: Is the given time(s) barycentric?
bandpass_corr:Would you like to remove the bandpass? (Type: Boolean)
"""
self.subdm = subdm
self.mask = mask
self.sigma = sigma
topo, bary = bary_and_topo.bary_to_topo(rawdatafile.filename, rawdatafile=rawdatafile)
time_shift = bary-topo
if barytime:
#### if the time is barycentric then get the corresponding topocentric time.
self.bary_start_time = start_time
self.topo_start_time = self.bary_start_time - topo_timeshift(self.bary_start_time, \
time_shift, topo)[0]
else:
#### Vice versa.
self.topo_start_time = start_time
self.bary_start_time = self.topo_start_time + topo_timeshift(self.bary_start_time, \
time_shift, topo)[0]
self.sample_number = np.round(self.bary_start_time/tsamp).astype('int')
self.width_bins = width_bins
sweep_posn = 0.0
if scaleindep:
self.scaleindep = True
self.downsamp = downsamp
if nbins:
self.nbins = nbins
self.duration = self.nbins*tsamp
if duration:
self.duration = duration
self.nbins = np.round(self.duration/tsamp).astype('int')
self.start = self.topo_start_time - (loc_pulse * self.duration)
if (self.start<0.0):
self.start = 0.0
self.start_bin = np.round(self.start/tsamp).astype('int')
self.pulse_width = self.width_bins*self.downsamp*tsamp
self.nsub = nsub
self.zerodm = zerodm
if dedisp:
if dm:
self.dm = dm
else:
self.dm = self.subdm
self.sweep_dm = None
self.sweep_duration = 0.0
self.dmfac = 4.15e3 * np.abs(1./lofreq**2 - 1./hifreq**2)
self.nbinsextra = self.nbins + np.round((self.dmfac * self.dm)/tsamp).astype('int')
if (self.start_bin+self.nbinsextra) > N-1:
self.nbinsextra = N-1-self.start_bin
self.bandpass_corr = bandpass_corr
else:
self.dm = None
if not sweep_dm:
self.sweep_dm = self.subdm
else:
self.sweep_dm = sweep_dm
self.sweep_duration = 4.15e3 * np.abs(1./lofreq**2-1./hifreq**2)*self.sweep_dm
self.start = self.start + (loc_pulse*self.duration)
self.start_bin = np.round(self.start/tsamp).astype('int')
self.nbinsextra = self.nbins
if (self.start_bin+self.nbinsextra) > N-1:
self.nbinsextra = N-1-self.start_bin
self.bandpass_corr = False
def manual_params(self, subdm, dm, sweep_dm, sigma, start_time, width_bins, downsamp, \
duration, nbins, nsub, tsamp, N, lofreq, hifreq, rawdatafile, loc_pulse=0.5, dedisp = False, \
scaleindep = None, zerodm = None, mask = False, barytime = True, \
bandpass_corr = False):
"""
Set up parameters based on input from the groups.txt file.
Input:
subdm: DM to use when subbanding.
dm: DM to use when dedispersing data for plot.
sweep_dm: Show the frequency sweep using this DM.
sigma: signal-to-noise of the pulse
start_time: start time of the data to be read in for waterfalling.
width_bins: Smooth each channel/subband with a boxcar width_bins wide.
downsamp: Factor to downsample in time by. Default: Don't downsample.
duration: duration of data to be waterfalled.
nbins: Number of time bins to plot. This option overrides
the duration argument.
nsub: Number of subbands to use. Must be a factor of number of channels.
tsamp: sampling time (downsampled: 65.5 us for PALFA)
N: total number of samples in an observations
lofreq: lowest observation frequency
hifreq: highest observation frequency
rawdatafile: supply a psrfits file instance
optional arguments:
dedisp: Do you want to dedisperse?(Type: Boolean).
scaleindep:Do you want to scale each subband independently?(Type: Boolean)
zerodm:Do you want to use zero-DM filtering?(Type: Boolean)
mask: Do you want to use a rfifind mask? (Type: Boolean)
barytime: Is the given time(s) barycentric?
bandpass_corr:Would you like to remove the bandpass? (Type: Boolean)
"""
self.subdm = subdm
self.mask = mask
self.sigma = sigma
topo, bary = bary_and_topo.bary_to_topo(rawdatafile.filename, rawdatafile=rawdatafile)
time_shift = bary-topo
if barytime:
#### if the time is barycentric then get the corresponding topocentric time.
self.bary_start_time = start_time
self.topo_start_time = self.bary_start_time - topo_timeshift(self.bary_start_time, \
time_shift, topo)[0]
else:
#### Vice versa.
self.topo_start_time = start_time
self.bary_start_time = self.topo_start_time + topo_timeshift(self.bary_start_time, \
time_shift, topo)[0]
self.sample_number = np.round(self.bary_start_time/tsamp).astype('int')
self.width_bins = width_bins
sweep_posn = 0.0
if scaleindep:
self.scaleindep = True
self.downsamp = downsamp
if nbins:
self.nbins = nbins
self.duration = self.nbins*tsamp
if duration:
self.duration = duration
self.nbins = np.round(self.duration/tsamp).astype('int')
self.start = self.topo_start_time - (loc_pulse * self.duration)
if (self.start<0.0):
self.start = 0.0
self.start_bin = np.round(self.start/tsamp).astype('int')
self.pulse_width = self.width_bins*self.downsamp*tsamp
self.nsub = nsub
self.zerodm = zerodm
if dedisp:
if dm:
self.dm = dm
else:
self.dm = self.subdm
self.sweep_dm = None
self.sweep_duration = 0.0
self.dmfac = 4.15e3 * np.abs(1./lofreq**2 - 1./hifreq**2)
self.nbinsextra = self.nbins + np.round((self.dmfac * self.dm)/tsamp).astype('int')
if (self.start_bin+self.nbinsextra) > N-1:
self.nbinsextra = N-1-self.start_bin
self.bandpass_corr = bandpass_corr
else:
self.dm = None
if not sweep_dm:
self.sweep_dm = self.subdm
else:
self.sweep_dm = sweep_dm
self.sweep_duration = 4.15e3 * np.abs(1./lofreq**2-1./hifreq**2)*self.sweep_dm
self.start = self.start + (loc_pulse*self.duration)
self.start_bin = np.round(self.start/tsamp).astype('int')
self.nbinsextra = self.nbins
if (self.start_bin+self.nbinsextra) > N-1:
self.nbinsextra = N-1-self.start_bin
self.bandpass_corr = False
