def get_data(rawdatafile, start, duration=None, nbins=None, mask=None):
"""Return numpy data array.
Inputs:
rawdatafile: A data file as returned by 'open_data_files(...)'
start: The start of the waterfall plot (in s)
duration: The duration of the waterfall plot (in s)
nbins: The duration of the waterfall plot (in bins)
mask: An rfifind mask to use (Default: no masking)
Output:
data: A Spectra object.
"""
# Read data
start_bin = np.round(start/rawdatafile.tsamp).astype('int')
if nbins is None:
if duration is None:
raise ValueError("At least one of 'duration' and " \
"'nbins' must be provided!")
else:
nbins = np.round(duration/rawdatafile.tsamp).astype('int')
elif duration is not None:
warnings.warn("Both 'duration' and 'nbins' provided. Will use 'nbins'.")
data = rawdatafile.get_spectra(start_bin, nbins)
if mask is not None:
if isinstance(mask, rfifind.rfifind):
rfimask = mask
else:
rfimask = rfifind.rfifind(mask)
datamask = get_mask(rfimask, start_bin, nbins)
# Mask data
data = data.masked(datamask, maskval='median-mid80')
return data
def get_data(rawdatafile, start, duration=None, nbins=None, mask=None):
"""Return numpy data array.
Inputs:
rawdatafile: A data file as returned by 'open_data_files(...)'
start: The start of the waterfall plot (in s)
duration: The duration of the waterfall plot (in s)
nbins: The duration of the waterfall plot (in bins)
mask: An rfifind mask to use (Default: no masking)
Output:
data: A Spectra object.
"""
# Read data
start_bin = np.round(start / rawdatafile.tsamp).astype('int')
if nbins is None:
if duration is None:
raise ValueError("At least one of 'duration' and " \
"'nbins' must be provided!")
else:
nbins = np.round(duration / rawdatafile.tsamp).astype('int')
elif duration is not None:
warnings.warn(
"Both 'duration' and 'nbins' provided. Will use 'nbins'.")
data = rawdatafile.get_spectra(start_bin, nbins)
if mask is not None:
if isinstance(mask, rfifind.rfifind):
rfimask = mask
else:
rfimask = rfifind.rfifind(mask)
datamask = get_mask(rfimask, start_bin, nbins)
# Mask data
data = data.masked(datamask, maskval='median-mid80')
return data
def maskfile(maskfn,
data,
start_bin,
nbinsextra,
extra_begin_chan=None,
extra_end_chan=None):
rfimask = rfifind.rfifind(maskfn)
mask = get_mask(rfimask, start_bin, nbinsextra)[::-1]
num_chan = mask.shape[0]
if extra_begin_chan != None and extra_end_chan != None:
if len(extra_begin_chan) != len(extra_end_chan):
print(
"length of extra mask begin channels and extra mask end channels need to be the same"
)
sys.exit()
else:
for ms in range(len(extra_begin_chan)):
end = num_chan - extra_begin_chan[ms]
begin = num_chan - extra_end_chan[ms]
mask[begin:end + 1, :] = True
masked_chans = mask.all(axis=1)
# Mask data
data = data.masked(mask, maskval='median-mid80')
#datacopy = copy.deepcopy(data)
return data, masked_chans
def maskfile(data, start_bin, nbinsextra):
if options.maskfile is not None:
rfimask = rfifind.rfifind(options.maskfile)
mask = get_mask(rfimask, start_bin, nbinsextra)
# Mask data
data = data.masked(mask, maskval='median-mid80')
datacopy = copy.deepcopy(data)
return data
def maskfile(data, start_bin, nbinsextra):
if options.maskfile is not None:
rfimask = rfifind.rfifind(options.maskfile)
mask = get_mask(rfimask, start_bin, nbinsextra)
# Mask data
data = data.masked(mask, maskval='median-mid80')
datacopy = copy.deepcopy(data)
return data
def maskfile(maskfn, data, start_bin, nbinsextra):
rfimask = rfifind.rfifind(maskfn)
mask = get_mask(rfimask, start_bin, nbinsextra)[::-1]
masked_chans = mask.all(axis=1)
# Mask data
data = data.masked(mask, maskval='median-mid80')
#datacopy = copy.deepcopy(data)
return data, masked_chans
def get_diagnostic(self):
# find *rfifind.out file
rfimask_fn = glob.glob(os.path.join(self.directory, "*%s_rfifind.mask"%os.path.split(self.directory)[-1]))[0]
mask = rfifind.rfifind(rfimask_fn)
zaps = 0
for integ in mask.mask_zap_chans_per_int:
zaps += len(integ)
self.value = 100. * zaps/ float(mask.nchan * mask.nint)
def maskfile(maskfn, data, start_bin, nbinsextra):
rfimask = rfifind.rfifind(maskfn)
mask = get_mask(rfimask, start_bin, nbinsextra)[::-1]
masked_chans = mask.all(axis=1)
# Mask data
data = data.masked(mask, maskval='median-mid80')
#datacopy = copy.deepcopy(data)
return data, masked_chans
def pick_rawdatafile(dm, fitsfilenm, freq_lim_1, freq_lim_2, pulse_width,
prevDM, prevsubfile, init_flag):
center_freq = 350. #MHz
if init_flag == 1:
subband = 1 #If this is the first candidate, sub-band at its DM
else:
dm_diff = dm - prevDM #error in DM
#calculate dispersive smearing in each channel
t_smear = 8.3e3 * dm_diff * (center_freq**-3) * (
np.abs(freq_lim_1 - freq_lim_2) / 128.) #Number of channels = 128
if pulse_width > (10 * t_smear):
#pulse does not get smeared out by subbanding at DM of prev. cand
subband = 0
rawdatafile = prevsubfile
else:
#pulse gets smeared out by subbanding at DM of prev. cand
subband = 1
if subband: #Subband at DM of candidate
basenm = fitsfilenm[:-5] #basenm = *_0001, fitsfilenm = *_0001.fits
subband_file = "%s_subband_%.1f_0001.fits" % (basenm, dm)
#check if file subbanded at same DM exists, subband at the given DM if it doesn't
if isfile(subband_file):
print "Sub-banded file exists at DM of candidate"
else:
mask_subband = rfifind.rfifind("%s_rfifind.mask" % (basenm))
mask_subband.set_zap_chans(power=1000, plot=False)
mask_subband.set_weights_and_offsets()
mask_subband.write_weights(filename="%s_weights.txt" % (basenm))
chan, weights = np.loadtxt("%s_weights.txt" % (basenm),
unpack=True,
skiprows=1,
dtype=int)
rev_weights = weights[::-1]
rev_weights[819:1640] = 0
with open('%s_weights.txt' % (basenm), 'r') as f:
header = f.readline()
data = np.column_stack([chan, rev_weights])
with open('%s_rev_weights.txt' % (basenm), 'w') as f:
f.write(header)
np.savetxt(f, data, fmt="%d", delimiter="\t")
cmd = "psrfits_subband -dm %.1f -nsub 128 -o %s_subband_%.1f -weights %s_rev_weights.txt %s" % (
dm, basenm, dm, basenm, fitsfilenm)
print "executing %s" % cmd
subprocess.call(cmd, shell=True)
rawdatafile = subband_file
prevsubfile = subband_file
prevDM = dm
return rawdatafile, prevsubfile, prevDM
def maskdata(data, start_bin, nbinsextra, maskfile):
"""
Performs the masking on the raw data using the boolean array from get_mask.
Inputs:
data: raw data (psrfits object)
start_bin: the sample number where we want the waterfall plot window to start.
nbinsextra: number of bins in the waterfall plot
Output:
data: 2D array after masking.
"""
if maskfile is not None:
print 'masking'
rfimask = rfifind.rfifind(maskfile)
mask = waterfaller.get_mask(rfimask, start_bin, nbinsextra)
# Mask data
data = data.masked(mask, maskval='median-mid80')
return data
def maskdata(data, start_bin, nbinsextra, maskfile):
"""
Performs the masking on the raw data using the boolean array from get_mask.
Inputs:
data: raw data (psrfits object)
start_bin: the sample number where we want the waterfall plot window to start.
nbinsextra: number of bins in the waterfall plot
Output:
data: 2D array after masking.
"""
if maskfile is not None:
print 'masking'
rfimask = rfifind.rfifind(maskfile)
mask = waterfaller.get_mask(rfimask, start_bin, nbinsextra)
# Mask data
data = data.masked(mask, maskval='median-mid80')
return data
def pick_rawdatafile(dm, fitsfilenm, freq_lim_1, freq_lim_2, pulse_width, prevDM, prevsubfile, init_flag):
center_freq = 350. #MHz
if init_flag == 1:
subband = 1 #If this is the first candidate, sub-band at its DM
else:
dm_diff = dm - prevDM #error in DM
#calculate dispersive smearing in each channel
t_smear = 8.3e3 * dm_diff * ( center_freq**-3 ) * ( np.abs(freq_lim_1 - freq_lim_2) / 128.) #Number of channels = 128
if pulse_width > (10 * t_smear):
#pulse does not get smeared out by subbanding at DM of prev. cand
subband = 0
rawdatafile = prevsubfile
else:
#pulse gets smeared out by subbanding at DM of prev. cand
subband = 1
if subband: #Subband at DM of candidate
basenm = fitsfilenm[:-5] #basenm = *_0001, fitsfilenm = *_0001.fits
subband_file = "%s_subband_%.1f_0001.fits" %(basenm,dm)
#check if file subbanded at same DM exists, subband at the given DM if it doesn't
if isfile(subband_file):
print "Sub-banded file exists at DM of candidate"
else:
mask_subband = rfifind.rfifind("%s_rfifind.mask"%(basenm))
mask_subband.set_zap_chans(power=1000,plot=False)
mask_subband.set_weights_and_offsets()
mask_subband.write_weights(filename="%s_weights.txt"%(basenm))
chan, weights = np.loadtxt("%s_weights.txt"%(basenm), unpack = True, skiprows=1, dtype=int)
rev_weights = weights[::-1]
rev_weights[819:1640] = 0
with open('%s_weights.txt'%(basenm), 'r') as f:
header = f.readline()
data = np.column_stack([chan,rev_weights])
with open('%s_rev_weights.txt'%(basenm), 'w') as f:
f.write(header)
np.savetxt(f, data, fmt="%d", delimiter="\t")
cmd = "psrfits_subband -dm %.1f -nsub 128 -o %s_subband_%.1f -weights %s_rev_weights.txt %s"%(dm,basenm,dm,basenm,fitsfilenm)
print "executing %s" %cmd
subprocess.call(cmd, shell=True)
rawdatafile = subband_file
prevsubfile = subband_file
prevDM = dm
return rawdatafile, prevsubfile, prevDM
def main():
fitsfilenm = sys.argv[1]
basenm = fitsfilenm.split('.')[0]
dm = 0
mask_subband = rfifind.rfifind("%s_rfifind.mask"%(basenm))
mask_subband.set_zap_chans(power=1000,plot=False)
mask_subband.set_weights_and_offsets()
mask_subband.write_weights(filename="%s_weights.txt"%(basenm))
chan, weights = np.loadtxt("%s_weights.txt"%(basenm), unpack = True, skiprows=1, dtype=int)
rev_weights = weights[::-1]
with open('%s_weights.txt'%(basenm), 'r') as f:
header = f.readline()
data = np.column_stack([chan,rev_weights])
with open('%s_rev_weights.txt'%(basenm), 'w') as f:
f.write(header)
np.savetxt(f, data, fmt="%d", delimiter="\t")
cmd = "psrfits_subband -dm %.1f -nsub 128 -o %s_subband_%.1f -weights %s_rev_weights.txt %s"%(dm,basenm,dm,basenm,fitsfilenm)
subprocess.call(cmd, shell=True)
print('end')
def waterfall(rawdatafile, start, duration, dm=None, nbins=None, nsub=None,\
subdm=None, zerodm=False, downsamp=1, scaleindep=False,\
width_bins=1, mask=False, maskfn=None, bandpass_corr=False, \
ref_freq=None):
"""
Create a waterfall plot (i.e. dynamic specrum) from a raw data file.
Inputs:
rawdatafile - a PsrfitsData instance.
start - start time of the data to be read in for waterfalling.
duration - duration of data to be waterfalled.
Optional Inputs:
dm - DM to use when dedispersing data.
Default: Don't de-disperse
nbins - Number of time bins to plot. This option overrides
the duration argument.
Default: determine nbins from duration.
nsub - Number of subbands to use. Must be a factor of number of channels.
Default: Number of channels.
subdm - DM to use when subbanding. Default: same as dm argument.
zerodm - subtract mean of each time-sample from data before
de-dispersing.
downsamp - Factor to downsample in time by. Default: Don't downsample.
scaleindep - Scale each channel independently.
Default: Scale using global maximum.
width_bins - Smooth each channel/subband with a boxcar width_bins wide.
Default: Don't smooth.
maskfn - Filename of RFIFIND mask to use for masking data.
Default: Don't mask data.
bandpass_corr - Correct for the bandpass. Requires an rfifind
mask provided by maskfn keyword argument.
Default: Do not remove bandpass.
ref_freq - Reference frequency to de-disperse to.
If subbanding and de-dispersing the start time
will be corrected to account for change in
reference frequency.
Default: Frequency of top channel.
Outputs:
data - Spectra instance of waterfalled data cube.
nbinsextra - number of time bins read in from raw data.
nbins - number of bins in duration.
start - corrected start time.
"""
if subdm is None:
subdm = dm
# Read data
if ref_freq is None:
ref_freq = rawdatafile.freqs.max()
if nsub and dm:
nchan_per_sub = rawdatafile.nchan/nsub
top_ctrfreq = rawdatafile.freqs.max() - \
0.5*nchan_per_sub*rawdatafile.specinfo.df # center of top subband
start += 4.15e3 * np.abs(1./ref_freq**2 - 1./top_ctrfreq**2) * dm
start_bin = np.round(start/rawdatafile.tsamp).astype('int')
dmfac = 4.15e3 * np.abs(1./rawdatafile.frequencies[0]**2 - 1./rawdatafile.frequencies[-1]**2)
if nbins is None:
nbins = np.round(duration/rawdatafile.tsamp).astype('int')
if dm:
nbinsextra = np.round((duration + dmfac * dm)/rawdatafile.tsamp).astype('int')
else:
nbinsextra = nbins
# If at end of observation
if (start_bin + nbinsextra) > rawdatafile.specinfo.N-1:
nbinsextra = rawdatafile.specinfo.N-1-start_bin
data = rawdatafile.get_spectra(start_bin, nbinsextra)
# Masking
if mask and maskfn:
data, masked_chans = maskfile(maskfn, data, start_bin, nbinsextra)
else:
masked_chans = np.zeros(rawdatafile.nchan,dtype=bool)
# Bandpass correction
if maskfn and bandpass_corr:
bandpass = rfifind.rfifind(maskfn).bandpass_avg[::-1]
#bandpass[bandpass == 0] = np.min(bandpass[np.nonzero(bandpass)])
masked_chans[bandpass == 0] = True
# ignore top and bottom 1% of band
ignore_chans = np.ceil(0.01*rawdatafile.nchan)
masked_chans[:ignore_chans] = True
masked_chans[-ignore_chans:] = True
data_masked = np.ma.masked_array(data.data)
data_masked[masked_chans] = np.ma.masked
data.data = data_masked
if bandpass_corr:
data.data /= bandpass[:, None]
# Zerodm filtering
if (zerodm == True):
data.data -= data.data.mean(axis=0)
# Subband data
if (nsub is not None) and (subdm is not None):
data.subband(nsub, subdm, padval='mean')
# Dedisperse
if dm:
data.dedisperse(dm, padval='mean')
# Downsample
data.downsample(downsamp)
# scale data
data = data.scaled(scaleindep)
# Smooth
if width_bins > 1:
data.smooth(width_bins, padval='mean')
return data, nbinsextra, nbins, start
#!/usr/bin/env python
import rfifind, sys
if __name__=="__main__":
a = rfifind.rfifind(sys.argv[1])
sys.stderr.write("\nWARNING!: If raw data have channels in decreasing freq\n")
sys.stderr.write(" order, the channel ordering as given will be\n")
sys.stderr.write(" inverted! Use 'invertband=True' in \n")
sys.stderr.write(" write_weights() in that case!\n")
if (a.idata.telescope=='GBT' and a.idata.lofreq < 1000.0):
sys.stderr.write("Data is from GBT Prime Focus, auto-flipping the weights/offsets...\n\n")
invert = True
else:
invert = False
a.set_zap_chans(power=200.0,
edges=0.01,
asigma=2.0,
ssigma=2.0,
usemask=True,
plot=True,
chans=[])
a.write_zap_chans()
a.set_weights_and_offsets()
a.write_weights(invertband=invert)
a.write_bandpass(invertband=invert)
#a.write_weights_and_offsets(invertband=invert)
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 plot(spdfile, singlepulsefiles=None, maskfn = None, spec_width=1.5, loc_pulse=0.5, xwin=False, outfile="spdplot", just_waterfall=True, \
integrate_spec=True, integrate_ts=True, disp_pulse=True, bandpass_corr= None, tar=None):
"""
Generates spd plots which include the following subplots:
De-dispersed Zero-DM filtered Waterfall plot
De-dispersed Waterfall plot
optional subplots:
Dispersed Zero-DM filtered Waterfall plot (Inset of the corresponding dedispersed plot).
Dispersed Waterfall plot ((Inset of the corresponding dedispersed plot).).
Dedispersed zero-DM filtered time series for the corresponding waterfall plot.
Dedispersed time series for the corresponding waterfall plot.
Spectra of the de-dispersed pulse for each of the above waterfalled plots.
SNR vs DM
DM vs. Time
Inputs:
spdfile: A .spd file.
Optional Inputs:
spec_width: Twice this number times the pulse_width around the pulse to consider for the spectrum
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)
singlepulsefiles: list of .singlepulse files
xwin: plot in an xwin window?
outfile: name of the output file you want.
just_waterfall: Do you only want to display the waterfall plots?
integrate_spec: Do you want to show the pulse spectrum?
integrate_ts: Do you want to show the time series?
disp_pulse: Do you want to show the inset dispersed pulse?
tar: Supply the tarball of the singlepulse files instead of individual files.
"""
if not spdfile.endswith(".spd"):
raise ValueError("The first file must be a .spd file")
#npzfile = np.load(spdfile)
spdobj = read_spd.spd(spdfile)
##### Read in the header information and other required variables for the plots. ######
#text_array = npzfile['text_array']
man_params = spdobj.man_params
fn = spdobj.filename
telescope = spdobj.telescope
RA = spdobj.ra
dec = spdobj.dec
MJD = spdobj.mjd
mjd = Popen(["mjd2cal", "%f"%MJD], stdout=PIPE, stderr=PIPE)
date, err = mjd.communicate()
date = date.split()[2:5]
rank = spdobj.rank
nsub = spdobj.waterfall_nsubs
nbins = spdobj.nsamp
subdm = dm = sweep_dm = spdobj.best_dm
sigma = spdobj.sigma
sample_number = spdobj.pulse_peak_sample
duration = spdobj.waterfall_duration
width_bins = spdobj.pulsewidth_bins
pulse_width = spdobj.pulsewidth_seconds
tsamp = spdobj.tsamp
Total_observed_time = spdobj.total_obs_time
topo_start = spdobj.pulse_peak_time
datastart = spdobj.waterfall_start_time
datasamp = spdobj.waterfall_tsamp
#datanumspectra = spdobj.waterfall_prededisp_nbins
datanumspectra = spdobj.waterfall_nbins
start = topo_start - loc_pulse*duration
min_freq = spdobj.min_freq
max_freq = spdobj.max_freq
sweep_duration = spdobj.sweep_duration
sweeped_start = spdobj.sweep_start_time
bary_start = spdobj.bary_pulse_peak_time
downsamp = datasamp/tsamp
if xwin:
pgplot_device = "/XWIN"
else:
pgplot_device = ""
if pgplot_device:
sp_pgplot.ppgplot.pgopen(pgplot_device)
else:
if (outfile == "spdplot"): # default filename
if rank:
sp_pgplot.ppgplot.pgopen(fn[:-5]+'_DM%.1f_%.1fs_rank_%i.spd.ps/VPS'%(subdm, (start+loc_pulse*duration), rank))
else:
sp_pgplot.ppgplot.pgopen(fn[:-5]+'_DM%.1f_%.1fs.spd.ps/VPS'%(subdm, (start+loc_pulse*duration)))
else:
if rank:
sp_pgplot.ppgplot.pgopen(outfile+'_DM%.1f_%.1fs_rank_%i.spd.ps/VPS'%(subdm, (start+loc_pulse*duration), rank))
else:
sp_pgplot.ppgplot.pgopen(outfile+'_DM%.1f_%.1fs.spd.ps/VPS'%(subdm, (start+loc_pulse*duration)))
if (just_waterfall == False):
sp_pgplot.ppgplot.pgpap(10.25, 8.5/11.0)
# Dedispersed waterfall plot - zerodm - OFF
array = spdobj.data_nozerodm_dedisp.astype(np.float64)
sp_pgplot.ppgplot.pgsvp(0.07, 0.40, 0.50, 0.80)
sp_pgplot.ppgplot.pgswin(datastart-start, datastart-start+datanumspectra*datasamp, min_freq, max_freq)
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgbox("BCST", 0, 0, "BCNST", 0, 0)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgmtxt('L', 1.8, 0.5, 0.5, "Observing Frequency (MHz)")
if not integrate_spec:
sp_pgplot.ppgplot.pgmtxt('R', 1.8, 0.5, 0.5, "Zero-dm filtering - Off")
sp_pgplot.plot_waterfall(array,rangex = [datastart-start, datastart-start+datanumspectra*datasamp], rangey = [min_freq, max_freq], image = 'apjgrey')
#### Plot Dedispersed Time series - Zerodm filter - Off
Dedisp_ts = array[::-1].sum(axis = 0)
times = np.arange(int((datastart-start)/datasamp),int((datastart-start)/datasamp)+datanumspectra)*datasamp
if integrate_ts:
sp_pgplot.ppgplot.pgsvp(0.07, 0.40, 0.80, 0.90)
sp_pgplot.ppgplot.pgswin(datastart-start, datastart-start+datanumspectra*datasamp, np.min(Dedisp_ts), 1.05*np.max(Dedisp_ts))
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgbox("BC", 0, 0, "BC", 0, 0)
sp_pgplot.ppgplot.pgsci(1)
sp_pgplot.ppgplot.pgline(times,Dedisp_ts)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgsci(1)
errx1 = np.array([0.60 * (datastart-start+duration)])
erry1 = np.array([0.60 * np.max(Dedisp_ts)])
erry2 = np.array([np.std(Dedisp_ts)])
errx2 = np.array([pulse_width])
sp_pgplot.ppgplot.pgerrb(5, errx1, erry1, errx2, 1.0)
sp_pgplot.ppgplot.pgpt(errx1, erry1, -1)
#### Plot Spectrum - Zerodm filter - Off
if integrate_spec:
spectrum_window = spec_width*pulse_width
window_width = int(spectrum_window/datasamp)
burst_bin = int(nbins*loc_pulse/downsamp)
on_spec = array[..., burst_bin-window_width:burst_bin+window_width]
Dedisp_spec = on_spec.sum(axis=1)
if bandpass_corr:
bandpass = rfifind.rfifind(maskfn).bandpass_avg
bandpass = bandpass.reshape(-1, len(bandpass)/len(Dedisp_spec)).mean(axis=1)
Dedisp_spec /= bandpass
del_percent = 0.10 #remove total 10% of band
chans_del = np.ceil(len(Dedisp_spec)*del_percent/2.)
Dedisp_spec[-int(chans_del):] = 0
Dedisp_spec[:int(chans_del)] = 0
freqs = np.linspace(min_freq, max_freq, len(Dedisp_spec))
sp_pgplot.ppgplot.pgsvp(0.4, 0.47, 0.5, 0.8)
sp_pgplot.ppgplot.pgswin(np.min(Dedisp_spec), 1.05*np.max(Dedisp_spec), min_freq, max_freq)
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgbox("BC", 0, 0, "BC", 0, 0)
sp_pgplot.ppgplot.pgsci(1)
sp_pgplot.ppgplot.pgline(Dedisp_spec,freqs)
sp_pgplot.ppgplot.pgmtxt('R', 1.8, 0.5, 0.5, "Zero-dm filtering - Off")
sp_pgplot.ppgplot.pgsch(0.7)
sp_pgplot.ppgplot.pgmtxt('T', 1.8, 0.5, 0.5, "Spectrum")
sp_pgplot.ppgplot.pgsch(0.8)
#Dedispersed waterfall plot - Zerodm ON
sp_pgplot.ppgplot.pgsvp(0.07, 0.40, 0.1, 0.40)
sp_pgplot.ppgplot.pgswin(datastart-start , datastart-start+datanumspectra*datasamp, min_freq, max_freq)
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgbox("BCNST", 0, 0, "BCNST", 0, 0)
sp_pgplot.ppgplot.pgmtxt('B', 2.5, 0.5, 0.5, "Time - %.2f s"%datastart)
sp_pgplot.ppgplot.pgmtxt('L', 1.8, 0.5, 0.5, "Observing Frequency (MHz)")
if not integrate_spec:
sp_pgplot.ppgplot.pgmtxt('R', 1.8, 0.5, 0.5, "Zero-dm filtering - On")
array = spdobj.data_zerodm_dedisp.astype(np.float64)
sp_pgplot.plot_waterfall(array,rangex = [datastart-start, datastart-start+datanumspectra*datasamp],rangey = [min_freq, max_freq],image = 'apjgrey')
#### Plot Dedispersed Time series - Zerodm filter - On
dedisp_ts = array[::-1].sum(axis = 0)
times = np.arange(int((datastart-start)/datasamp),int((datastart-start)/datasamp)+datanumspectra)*datasamp
#times = np.arange(datanumspectra)*datasamp
if integrate_ts:
sp_pgplot.ppgplot.pgsvp(0.07, 0.40, 0.40, 0.50)
sp_pgplot.ppgplot.pgswin(datastart - start, datastart-start+datanumspectra*datasamp, np.min(dedisp_ts), 1.05*np.max(dedisp_ts))
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgbox("BC", 0, 0, "BC", 0, 0)
sp_pgplot.ppgplot.pgsci(1)
sp_pgplot.ppgplot.pgline(times,dedisp_ts)
errx1 = np.array([0.60 * (datastart-start+duration)])
erry1 = np.array([0.60 * np.max(dedisp_ts)])
erry2 = np.array([np.std(dedisp_ts)])
errx2 = np.array([pulse_width])
sp_pgplot.ppgplot.pgerrb(5, errx1, erry1, errx2, 1.0)
sp_pgplot.ppgplot.pgpt(errx1, erry1, -1)
#### Plot Spectrum - Zerodm filter - On
if integrate_spec:
spectrum_window = spec_width*pulse_width
window_width = int(spectrum_window/datasamp)
burst_bin = int(nbins*loc_pulse/downsamp)
on_spec = array[..., burst_bin-window_width:burst_bin+window_width]
Dedisp_spec = on_spec.sum(axis=1)
if bandpass_corr:
bandpass = rfifind.rfifind(maskfn).bandpass_avg
bandpass = bandpass.reshape(-1, len(bandpass)/len(Dedisp_spec)).mean(axis=1)
Dedisp_spec /= bandpass
del_percent = 0.10 # total
chans_del = np.ceil(len(Dedisp_spec)*del_percent/2.)
Dedisp_spec[-int(chans_del):] = 0
Dedisp_spec[:int(chans_del)] = 0
freqs = np.linspace(min_freq, max_freq, len(Dedisp_spec))
sp_pgplot.ppgplot.pgsvp(0.4, 0.47, 0.1, 0.4)
sp_pgplot.ppgplot.pgswin(np.min(Dedisp_spec), 1.05*np.max(Dedisp_spec), min_freq, max_freq)
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgbox("BC", 0, 0, "BC", 0, 0)
sp_pgplot.ppgplot.pgsci(1)
sp_pgplot.ppgplot.pgline(Dedisp_spec,freqs)
sp_pgplot.ppgplot.pgmtxt('R', 1.8, 0.5, 0.5, "Zero-dm filtering - On")
sp_pgplot.ppgplot.pgsch(0.7)
sp_pgplot.ppgplot.pgmtxt('T', 1.8, 0.5, 0.5, "Spectrum")
sp_pgplot.ppgplot.pgsch(0.8)
if disp_pulse:
# Sweeped waterfall plot Zerodm - OFF
array = spdobj.data_nozerodm.astype(np.float64)
sp_pgplot.ppgplot.pgsvp(0.20, 0.40, 0.50, 0.70)
sp_pgplot.ppgplot.pgswin(sweeped_start, sweeped_start+sweep_duration, min_freq, max_freq)
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(4)
sp_pgplot.ppgplot.pgbox("BCST", 0, 0, "BCST", 0, 0)
sp_pgplot.ppgplot.pgsch(3)
sp_pgplot.plot_waterfall(array,rangex = [sweeped_start, sweeped_start+sweep_duration],rangey = [min_freq, max_freq],image = 'apjgrey')
delays = spdobj.dmsweep_delays
freqs = spdobj.dmsweep_freqs
sp_pgplot.ppgplot.pgslw(5)
sweepstart = sweeped_start- 0.2*sweep_duration
sp_pgplot.ppgplot.pgsci(0)
sp_pgplot.ppgplot.pgline(delays+sweepstart, freqs)
sp_pgplot.ppgplot.pgsci(1)
sp_pgplot.ppgplot.pgslw(3)
# Sweeped waterfall plot Zerodm - ON
array = spdobj.data_zerodm.astype(np.float64)
sp_pgplot.ppgplot.pgsvp(0.20, 0.40, 0.1, 0.3)
sp_pgplot.ppgplot.pgswin(sweeped_start, sweeped_start+sweep_duration, min_freq, max_freq)
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(4)
sp_pgplot.ppgplot.pgbox("BCST", 0, 0, "BCST", 0, 0)
sp_pgplot.ppgplot.pgsch(3)
sp_pgplot.plot_waterfall(array,rangex = [sweeped_start, sweeped_start+sweep_duration],rangey = [min_freq, max_freq],image = 'apjgrey')
sp_pgplot.ppgplot.pgslw(5)
sweepstart = sweeped_start- 0.2*sweep_duration
sp_pgplot.ppgplot.pgsci(0)
sp_pgplot.ppgplot.pgline(delays+sweepstart, freqs)
sp_pgplot.ppgplot.pgsci(1)
#### Figure texts
if integrate_spec:
sp_pgplot.ppgplot.pgsvp(0.81, 0.97, 0.64, 0.909)
sp_pgplot.ppgplot.pgsch(0.62)
else:
sp_pgplot.ppgplot.pgsvp(0.745, 0.97, 0.64, 0.909)
sp_pgplot.ppgplot.pgsch(0.7)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgmtxt('T', -1.1, 0.01, 0.0, "RA: %s" %RA)
sp_pgplot.ppgplot.pgmtxt('T', -2.6, 0.01, 0.0, "DEC: %s" %dec)
sp_pgplot.ppgplot.pgmtxt('T', -4.1, 0.01, 0.0, "MJD: %f" %MJD)
sp_pgplot.ppgplot.pgmtxt('T', -5.6, 0.01, 0.0, "Obs date: %s %s %s" %(date[0], date[1], date[2]))
sp_pgplot.ppgplot.pgmtxt('T', -7.1, 0.01, 0.0, "Telescope: %s" %telescope)
sp_pgplot.ppgplot.pgmtxt('T', -8.6, 0.01, 0.0, "DM: %.2f pc cm\u-3\d" %dm)
if sigma:
sp_pgplot.ppgplot.pgmtxt('T', -10.1, 0.01, 0.0, "S/N\dMAX\u: %.2f" %sigma)
else:
sp_pgplot.ppgplot.pgmtxt('T', -10.1, 0.01, 0.0, "S/N\dMAX\u: N/A")
sp_pgplot.ppgplot.pgmtxt('T', -11.6, 0.01, 0.0, "Number of samples: %i" %nbins)
sp_pgplot.ppgplot.pgmtxt('T', -13.1, 0.01, 0.0, "Number of subbands: %i" %nsub)
sp_pgplot.ppgplot.pgmtxt('T', -14.6, 0.01, 0.0, "Pulse width: %.2f ms" %(pulse_width*1e3))
sp_pgplot.ppgplot.pgmtxt('T', -16.1, 0.01, 0.0, "Sampling time: %.3f \gms" %(tsamp*1e6))
sp_pgplot.ppgplot.pgmtxt('T', -17.6, 0.0, 0.0, "Bary pulse peak time: %.2f s" %(bary_start))
sp_pgplot.ppgplot.pgsvp(0.07, 0.7, 0.01, 0.05)
sp_pgplot.ppgplot.pgmtxt('T', -2.1, 0.01, 0.0, "%s" %fn)
#DM vs SNR
if not man_params:
dm_arr = np.float32(spdobj.dmVt_this_dms)
sigma_arr = np.float32 (spdobj.dmVt_this_sigmas)
time_arr = np.float32 (spdobj.dmVt_this_times)
if integrate_spec:
sp_pgplot.ppgplot.pgsvp(0.55, 0.80, 0.65, 0.90)
else:
sp_pgplot.ppgplot.pgsvp(0.48, 0.73, 0.65, 0.90)
sp_pgplot.ppgplot.pgswin(np.min(dm_arr), np.max(dm_arr), 0.95*np.min(sigma_arr), 1.05*np.max(sigma_arr))
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgbox("BCNST", 0, 0, "BCNST", 0, 0)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgmtxt('B', 2.5, 0.5, 0.5, "DM (pc cm\u-3\d)")
sp_pgplot.ppgplot.pgmtxt('L', 1.8, 0.5, 0.5, "Signal-to-noise")
sp_pgplot.ppgplot.pgpt(dm_arr, sigma_arr, 20)
else:
dm_arr = np.array([])
sigma_arr = np.array([])
time_arr = np.array([])
if integrate_spec:
sp_pgplot.ppgplot.pgsvp(0.55, 0.80, 0.65, 0.90)
else:
sp_pgplot.ppgplot.pgsvp(0.48, 0.73, 0.65, 0.90)
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgbox("BCNST", 0, 0, "BCNST", 0, 0)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgmtxt('B', 2.5, 0.5, 0.5, "DM (pc cm\u-3\d)")
sp_pgplot.ppgplot.pgmtxt('L', 1.8, 0.5, 0.5, "Signal-to-noise")
# DM vs Time
print "Making arrays for DM vs time plot"
spfiles = singlepulsefiles
threshold = 5.0
if len(spfiles) > 2:
dm_list = map(np.float32, list(dm_arr))
time_list = map(np.float32, list(time_arr))
if integrate_spec:
sp_pgplot.ppgplot.pgsvp(0.55, 0.97, 0.1, 0.54)
else:
sp_pgplot.ppgplot.pgsvp(0.48, 0.97, 0.1, 0.54)
dms, times, sigmas, widths, filelist = spio.gen_arrays(dm_arr, spfiles, tar, threshold)
sp_pgplot.dm_time_plot(dms, times, sigmas, dm_list, sigma_arr, time_list, Total_observed_time, xwin)
else:
print "You need a .singlepulse.tgz file to plot DM vs Time plot."
if integrate_spec:
sp_pgplot.ppgplot.pgsvp(0.55, 0.97, 0.1, 0.54)
else:
sp_pgplot.ppgplot.pgsvp(0.48, 0.97, 0.1, 0.54)
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgbox("BCNST", 0, 0, "BCNST", 0, 0)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgmtxt('B', 2.5, 0.5, 0.5, "Time (s)")
sp_pgplot.ppgplot.pgmtxt('L', 1.8, 0.5, 0.5, "DM (pc cm\u-3\d)")
else:
#sp_pgplot.ppgplot.pgpap(10.25, 10.0/5.0)
sp_pgplot.ppgplot.pgpap(8.0, 1.5)
# Dedispersed waterfall plot - zerodm - OFF
array = spdobj.data_nozerodm_dedisp.astype(np.float64)
sp_pgplot.ppgplot.pgsvp(0.1, 0.70, 0.44, 0.75)
sp_pgplot.ppgplot.pgswin(datastart - start, datastart -start+datanumspectra*datasamp, min_freq, max_freq)
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgbox("BCST", 0, 0, "BCNST", 0, 0)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgmtxt('L', 1.8, 0.5, 0.5, "Observing Frequency (MHz)")
sp_pgplot.plot_waterfall(array,rangex = [datastart-start, datastart-start+datanumspectra*datasamp], rangey = [min_freq, max_freq], image = 'apjgrey')
#### Plot Dedispersed Time series - Zerodm filter - Off
Dedisp_ts = array[::-1].sum(axis = 0)
times = np.arange(int((datastart-start)/datasamp),int((datastart-start)/datasamp)+datanumspectra)*datasamp
#times = np.arange(datanumspectra)*datasamp
if integrate_ts:
sp_pgplot.ppgplot.pgsvp(0.1, 0.70, 0.75, 0.83)
sp_pgplot.ppgplot.pgswin(datastart - start, datastart-start+datanumspectra*datasamp, np.min(Dedisp_ts), 1.05*np.max(Dedisp_ts))
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgbox("BC", 0, 0, "BC", 0, 0)
sp_pgplot.ppgplot.pgsci(1)
sp_pgplot.ppgplot.pgline(times,Dedisp_ts)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgsci(1)
errx1 = np.array([0.60 * (datastart-start+duration)])
erry1 = np.array([0.60 * np.max(Dedisp_ts)])
erry2 = np.array([np.std(Dedisp_ts)])
errx2 = np.array([pulse_width])
sp_pgplot.ppgplot.pgerrb(5, errx1, erry1, errx2, 1.0)
sp_pgplot.ppgplot.pgpt(errx1, erry1, -1)
#### Plot Spectrum - Zerodm filter - Off
if integrate_spec:
spectrum_window = spec_width*pulse_width
window_width = int(spectrum_window/datasamp)
burst_bin = int(nbins*loc_pulse/downsamp)
on_spec = array[..., burst_bin-window_width:burst_bin+window_width]
Dedisp_spec = on_spec.sum(axis=1)
if bandpass_corr:
bandpass = rfifind.rfifind(maskfn).bandpass_avg
bandpass = bandpass.reshape(-1, len(bandpass)/len(Dedisp_spec)).mean(axis=1)
Dedisp_spec /= bandpass
del_percent = 0.10 # total
chans_del = np.ceil(len(Dedisp_spec)*del_percent/2.)
Dedisp_spec[-int(chans_del):] = 0
Dedisp_spec[:int(chans_del)] = 0
freqs = np.linspace(min_freq, max_freq, len(Dedisp_spec))
sp_pgplot.ppgplot.pgsvp(0.7, 0.9, 0.44, 0.75)
sp_pgplot.ppgplot.pgswin(np.min(Dedisp_spec), 1.05*np.max(Dedisp_spec), min_freq, max_freq)
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgbox("BC", 0, 0, "BC", 0, 0)
sp_pgplot.ppgplot.pgsci(1)
sp_pgplot.ppgplot.pgline(Dedisp_spec,freqs)
sp_pgplot.ppgplot.pgmtxt('R', 1.8, 0.5, 0.5, "Zero-dm filtering - Off")
sp_pgplot.ppgplot.pgsch(0.7)
sp_pgplot.ppgplot.pgmtxt('T', 1.8, 0.5, 0.5, "Spectrum")
sp_pgplot.ppgplot.pgsch(0.8)
#Dedispersed waterfall plot - Zerodm ON
array = spdobj.data_zerodm_dedisp.astype(np.float64)
sp_pgplot.ppgplot.pgsvp(0.1, 0.70, 0.05, 0.36)
sp_pgplot.ppgplot.pgswin(datastart-start , datastart-start+datanumspectra*datasamp, min_freq, max_freq)
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgbox("BCNST", 0, 0, "BCNST", 0, 0)
sp_pgplot.ppgplot.pgmtxt('B', 2.5, 0.5, 0.5, "Time - %.2f s"%datastart)
sp_pgplot.ppgplot.pgmtxt('L', 1.8, 0.5, 0.5, "Observing Frequency (MHz)")
sp_pgplot.plot_waterfall(array,rangex = [datastart-start, datastart-start+datanumspectra*datasamp],rangey = [min_freq, max_freq],image = 'apjgrey')
#### Plot Dedispersed Time series - Zerodm filter - On
dedisp_ts = array[::-1].sum(axis = 0)
#times = np.arange(datanumspectra)*datasamp
times = np.arange(int((datastart-start)/datasamp),int((datastart-start)/datasamp)+datanumspectra)*datasamp
if integrate_ts:
sp_pgplot.ppgplot.pgsvp(0.1, 0.7, 0.36, 0.44)
sp_pgplot.ppgplot.pgswin(datastart - start, datastart-start+datanumspectra*datasamp, np.min(dedisp_ts), 1.05*np.max(dedisp_ts))
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgbox("BC", 0, 0, "BC", 0, 0)
sp_pgplot.ppgplot.pgsci(1)
sp_pgplot.ppgplot.pgline(times,dedisp_ts)
errx1 = np.array([0.60 * (datastart-start+duration)])
erry1 = np.array([0.60 * np.max(dedisp_ts)])
erry2 = np.array([np.std(dedisp_ts)])
errx2 = np.array([pulse_width])
sp_pgplot.ppgplot.pgerrb(5, errx1, erry1, errx2, 1.0)
sp_pgplot.ppgplot.pgpt(errx1, erry1, -1)
#### Plot Spectrum - Zerodm filter - On
if integrate_spec:
spectrum_window = spec_width*pulse_width
window_width = int(spectrum_window/datasamp)
burst_bin = int(nbins*loc_pulse/downsamp)
on_spec = array[..., burst_bin-window_width:burst_bin+window_width]
Dedisp_spec = on_spec.sum(axis=1)
if bandpass_corr:
bandpass = rfifind.rfifind(maskfn).bandpass_avg
bandpass = bandpass.reshape(-1, len(bandpass)/len(Dedisp_spec)).mean(axis=1)
Dedisp_spec /= bandpass
del_percent = 0.10 # total
chans_del = np.ceil(len(Dedisp_spec)*del_percent/2.)
Dedisp_spec[-int(chans_del):] = 0
Dedisp_spec[:int(chans_del)] = 0
freqs = np.linspace(min_freq, max_freq, len(Dedisp_spec))
sp_pgplot.ppgplot.pgsvp(0.70, 0.90, 0.05, 0.36)
sp_pgplot.ppgplot.pgswin(np.min(Dedisp_spec), 1.05*np.max(Dedisp_spec), min_freq, max_freq)
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgbox("BC", 0, 0, "BC", 0, 0)
sp_pgplot.ppgplot.pgsci(1)
sp_pgplot.ppgplot.pgline(Dedisp_spec,freqs)
sp_pgplot.ppgplot.pgmtxt('R', 1.8, 0.5, 0.5, "Zero-dm filtering - On")
sp_pgplot.ppgplot.pgsch(0.7)
sp_pgplot.ppgplot.pgmtxt('T', 1.8, 0.5, 0.5, "Spectrum")
sp_pgplot.ppgplot.pgsch(0.8)
if disp_pulse:
# Sweeped waterfall plot Zerodm - OFF
array = spdobj.data_nozerodm.astype(np.float64)
sp_pgplot.ppgplot.pgsvp(0.3, 0.70, 0.44, 0.65)
sp_pgplot.ppgplot.pgswin(sweeped_start, sweeped_start+sweep_duration, min_freq, max_freq)
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(4)
sp_pgplot.ppgplot.pgbox("BCST", 0, 0, "BCST", 0, 0)
sp_pgplot.ppgplot.pgsch(3)
sp_pgplot.plot_waterfall(array,rangex = [sweeped_start, sweeped_start+sweep_duration],rangey = [min_freq, max_freq],image = 'apjgrey')
delays = spdobj.dmsweep_delays
freqs = spdobj.dmsweep_freqs
sp_pgplot.ppgplot.pgslw(5)
sweepstart = sweeped_start- 0.2*sweep_duration
sp_pgplot.ppgplot.pgsci(0)
sp_pgplot.ppgplot.pgline(delays+sweepstart, freqs)
sp_pgplot.ppgplot.pgsci(1)
sp_pgplot.ppgplot.pgslw(3)
# Sweeped waterfall plot Zerodm - ON
array = spdobj.data_zerodm.astype(np.float64)
sp_pgplot.ppgplot.pgsvp(0.3, 0.70, 0.05, 0.25)
sp_pgplot.ppgplot.pgswin(sweeped_start, sweeped_start+sweep_duration, min_freq, max_freq)
sp_pgplot.ppgplot.pgsch(0.8)
sp_pgplot.ppgplot.pgslw(4)
sp_pgplot.ppgplot.pgbox("BCST", 0, 0, "BCST", 0, 0)
sp_pgplot.ppgplot.pgsch(3)
sp_pgplot.plot_waterfall(array,rangex = [sweeped_start, sweeped_start+sweep_duration],rangey = [min_freq, max_freq],image = 'apjgrey')
sp_pgplot.ppgplot.pgslw(5)
sweepstart = sweeped_start- 0.2*sweep_duration
sp_pgplot.ppgplot.pgsci(0)
sp_pgplot.ppgplot.pgline(delays+sweepstart, freqs)
sp_pgplot.ppgplot.pgsci(1)
#### Figure texts
sp_pgplot.ppgplot.pgsvp(0.05, 0.95, 0.8, 0.9)
sp_pgplot.ppgplot.pgsch(0.65)
sp_pgplot.ppgplot.pgslw(3)
sp_pgplot.ppgplot.pgmtxt('T', -1.1, 0.01, 0.0, "RA: %s" %RA)
sp_pgplot.ppgplot.pgmtxt('T', -2.5, 0.01, 0.0, "DEC: %s" %dec)
sp_pgplot.ppgplot.pgmtxt('T', -3.9, 0.01, 0.0, "MJD: %f" %MJD)
sp_pgplot.ppgplot.pgmtxt('T', -5.3, 0.01, 0.0, "Obs date: %s %s %s" %(date[0], date[1], date[2]))
sp_pgplot.ppgplot.pgmtxt('T', -1.1, 0.35, 0.0, "Telescope: %s" %telescope)
sp_pgplot.ppgplot.pgmtxt('T', -2.5, 0.35, 0.0, "DM: %.2f pc cm\u-3\d" %dm)
if sigma:
sp_pgplot.ppgplot.pgmtxt('T', -3.9, 0.35, 0.0, "S/N\dMAX\u: %.2f" %sigma)
else:
sp_pgplot.ppgplot.pgmtxt('T', -3.9, 0.35, 0.0, "S/N\dMAX\u: N/A")
sp_pgplot.ppgplot.pgmtxt('T', -5.3, 0.35, 0.0, "Number of samples: %i" %nbins)
sp_pgplot.ppgplot.pgmtxt('T', -1.1, 0.65, 0.0, "Number of subbands: %i" %nsub)
sp_pgplot.ppgplot.pgmtxt('T', -2.5, 0.65, 0.0, "Pulse width: %.2f ms" %(pulse_width*1e3))
sp_pgplot.ppgplot.pgmtxt('T', -3.9, 0.65, 0.0, "Sampling time: %.3f \gms" %(tsamp*1e6))
sp_pgplot.ppgplot.pgmtxt('T', -5.3, 0.65, 0.0, "Bary pulse peak time: %.2f s" %(bary_start))
sp_pgplot.ppgplot.pgiden()
sp_pgplot.ppgplot.pgclos()
def main():
filfns = args # addtional argument is fileterbank files
if options.debug:
print "Input filterbank files:", filfns
obs = fbobs.fbobs(filfns)
# filfile.print_header()
obslen = obs.obslen
# Determine start and end of interval (in seconds and samples)
if options.start < 0:
options.start = 0
if (options.end is None) or (options.end > obslen):
# set to end of filterbank file
options.end = obslen
reqstartsamp = int(options.start / obs.tsamp) # requested
# Round down to a multiple of downsamp bins
reqstartsamp = reqstartsamp - (reqstartsamp % options.downsamp)
# Get extra bins for smoothing
startsamp = reqstartsamp - options.width*options.downsamp
reqendsamp = int(options.end / obs.tsamp) # requested
# Round up to a multiple of downsamp bins
reqendsamp = reqendsamp - (reqendsamp % options.downsamp) + options.downsamp
# Get extra bins for smoothing
endsamp = reqendsamp + options.width*options.downsamp
if options.dm:
# Get extra bins for dedispersion
# Compute DM delays
delay_seconds = psr_utils.delay_from_DM(options.dm, obs.frequencies)
delay_seconds -= np.min(delay_seconds)
delay_samples = delay_seconds/(options.downsamp*obs.tsamp)
maxsamps = np.max(delay_samples*options.downsamp)
maxsamps = int(np.round(float(maxsamps)/options.downsamp))*options.downsamp
endsamp += maxsamps
reqnumsaps = reqendsamp - reqstartsamp
numsamps = endsamp - startsamp
if options.debug:
print "Requested start time: %s s (%d samples)" % \
(options.start, reqstartsamp)
print "Actual start time: %s s (%d samples)" % \
(startsamp*obs.tsamp, startsamp)
print "Requested end time: %s s (%d samples)" % \
(options.end, reqendsamp)
print "Actual end time: %s s (%d samples)" % \
(endsamp*obs.tsamp, endsamp)
# read data
data = obs.get_sample_interval(startsamp, endsamp).astype('float32')
obs.close_all()
data.shape = (numsamps, obs.nchans)
if options.mask is not None:
if options.debug:
print "Masking channels using %s" % options.mask
# Mask channels
mask = rfifind.rfifind(options.mask)
maskchans = mask.mask_zap_chans
maskchans = obs.nchans - 1 - np.array(list(maskchans))
data = mask_channels(data, maskchans)
# Modify data
if options.downsamp > 1:
if options.debug:
print "Downsampling by %d bins" % options.downsamp
data = downsample(data, factor=options.downsamp)
if options.width > 1:
if options.debug:
print "Smoothing with boxcar %d bins wide" % options.width
data = smooth(data, factor=options.width)[options.width:-options.width,:]
startsamp += options.width*options.downsamp
endsamp -= options.width*options.downsamp
# plot data as an image
fig = plt.figure()
fig.canvas.set_window_title("Frequency vs. Time")
ax = plt.axes((0.15, 0.15, 0.8, 0.7))
data_scaled = scale(data, indep=options.scaleindep)
data_scaled = data_scaled[:-maxsamps/options.downsamp]
endsamp -= maxsamps
plt.imshow(data_scaled.transpose(), \
aspect='auto', cmap=matplotlib.cm.binary, interpolation='nearest', \
extent=(startsamp/options.downsamp, endsamp/options.downsamp, \
obs.frequencies[-1], obs.frequencies[0]))
plt.xlabel("Sample")
plt.ylabel("Observing frequency (MHz)")
plt.suptitle("Frequency vs. Time")
fig.text(0.05, 0.02, \
r"Start time: $\sim$ %s s, End time: $\sim$ %s s; " \
"Downsampled: %d bins, Smoothed: %d bins; " \
"DM trace: %g $cm^{-3}pc$" % \
(options.start, options.end, options.downsamp, \
options.width, options.dm), \
ha="left", va="center", size="x-small")
if options.dm is not None:
xlim = plt.xlim()
ylim = plt.ylim()
# Plot dispersion delay trace
plt.plot(startsamp/options.downsamp+delay_samples, obs.frequencies, \
'r-', lw=5, alpha=0.25)
plt.xlim(xlim)
plt.ylim(ylim)
profax = plt.axes((0.15, 0.85, 0.8, 0.1), sharex=ax)
dedisp_prof = dedisperse(data, delay_samples)[:-maxsamps/options.downsamp]
plt.plot(np.linspace(xlim[0],xlim[1],dedisp_prof.size), dedisp_prof, 'k-')
plt.setp(profax.xaxis.get_ticklabels(), visible=False)
plt.setp(profax.yaxis.get_ticklabels(), visible=False)
plt.xlim(xlim)
fig.canvas.mpl_connect('key_press_event', keypress)
plt.show()
def foldfile(filename, simdataPath, foldpath, dm, period):
os.chdir(foldpath)
cutfilename = filename[:-5] + '_cut' + filename[-5:]
#cut file
#----------------------------------------------
if dm < 500:
cutfile = str(
'python ~/psrsoft/OPTIMUS/python/fitsio_cutfreq.py %s/%s %s %s %s/%s'
%
(simdataPath, filename, 270, 270 + 256 * 2, foldpath, cutfilename))
print cutfile
output = getoutput(cutfile)
elif dm < 1000:
cutfile = str(
'python ~/psrsoft/OPTIMUS/python/fitsio_cutfreq.py %s/%s %s %s %s/%s'
% (simdataPath, filename, 416, 416 + 256, foldpath, cutfilename))
print cutfile
output = getoutput(cutfile)
else:
cutfile = str(
'python ~/psrsoft/OPTIMUS/python/fitsio_cutfreq.py %s/%s %s %s %s/%s'
% (simdataPath, filename, 500, 500 + 256, foldpath, cutfilename))
print cutfile
output = getoutput(cutfile)
#rfifind
maskfilename = filename[:-5]
rfifind = str('rfifind %s/%s -o %s -time 1' %
(foldpath, cutfilename, maskfilename))
print rfifind
output = getoutput(rfifind)
#get number of bad intervals
maskFile = prestoRFIfind.rfifind(
glob.glob('%s/%s*.mask' % (foldpath, maskfilename))[0])
Intervals = maskFile.nchan * maskFile.nint * 1.
badIntervals = 0.
for i in maskFile.mask_zap_chans_per_int:
badIntervals = len(i) + badIntervals
#fold file
#if badIntervals more than 50% of Intervals: fold without maskFile
#if badIntervals less than 50% of Intervals: fold with maskFile
#foldfile = str('prepfold -noxwin -nosearch -p %s -dm %s -mask %s/%s %s/%s' %(period, dm, foldpath, maskfilename+'_rfifind.mask', foldpath, cutfilename))
if ((badIntervals / Intervals) > 0.5):
foldfile = str('prepfold -noxwin -nosearch -p %s -dm %s -o %s %s/%s' %
(period, dm, cutfilename[:-5], foldpath, cutfilename))
else:
foldfile = str(
'prepfold -noxwin -nosearch -p %s -dm %s -o %s -mask %s/%s %s/%s' %
(period, dm, cutfilename[:-5], foldpath,
maskfilename + '_rfifind.mask', foldpath, cutfilename))
print foldfile
output = getoutput(foldfile)
#find pfdfile
#pfdfilename = glob.glob('%s/%s*.pfd' %(foldpath, maskfilename))
pfdfilename = glob.glob('%s/%s*.pfd' % (foldpath, cutfilename[:-5]))
return pfdfilename
def waterfall(rawdatafile, start, duration, dm=None, nbins=None, nsub=None,\
subdm=None, zerodm=False, downsamp=1, scaleindep=False,\
width_bins=1, mask=False, maskfn=None, bandpass_corr=False, \
ref_freq=None):
"""
Create a waterfall plot (i.e. dynamic specrum) from a raw data file.
Inputs:
rawdatafile - a PsrfitsData instance.
start - start time of the data to be read in for waterfalling.
duration - duration of data to be waterfalled.
Optional Inputs:
dm - DM to use when dedispersing data.
Default: Don't de-disperse
nbins - Number of time bins to plot. This option overrides
the duration argument.
Default: determine nbins from duration.
nsub - Number of subbands to use. Must be a factor of number of channels.
Default: Number of channels.
subdm - DM to use when subbanding. Default: same as dm argument.
zerodm - subtract mean of each time-sample from data before
de-dispersing.
downsamp - Factor to downsample in time by. Default: Don't downsample.
scaleindep - Scale each channel independently.
Default: Scale using global maximum.
width_bins - Smooth each channel/subband with a boxcar width_bins wide.
Default: Don't smooth.
maskfn - Filename of RFIFIND mask to use for masking data.
Default: Don't mask data.
bandpass_corr - Correct for the bandpass. Requires an rfifind
mask provided by maskfn keyword argument.
Default: Do not remove bandpass.
ref_freq - Reference frequency to de-disperse to.
If subbanding and de-dispersing the start time
will be corrected to account for change in
reference frequency.
Default: Frequency of top channel.
Outputs:
data - Spectra instance of waterfalled data cube.
nbinsextra - number of time bins read in from raw data.
nbins - number of bins in duration.
start - corrected start time.
"""
if subdm is None:
subdm = dm
# Read data
if ref_freq is None:
ref_freq = rawdatafile.freqs.max()
if nsub and dm:
df = rawdatafile.freqs[1] - rawdatafile.freqs[0]
nchan_per_sub = rawdatafile.nchan / nsub
top_ctrfreq = rawdatafile.freqs.max() - \
0.5*nchan_per_sub*df # center of top subband
start += 4.15e3 * np.abs(1. / ref_freq**2 - 1. / top_ctrfreq**2) * dm
try:
source_name = rawdatafile.header['source_name']
except:
source_name = "Unknown"
start_bin = np.round(start / rawdatafile.tsamp).astype('int')
dmfac = 4.15e3 * np.abs(1. / rawdatafile.frequencies[0]**2 -
1. / rawdatafile.frequencies[-1]**2)
if nbins is None:
nbins = np.round(duration / rawdatafile.tsamp).astype('int')
if dm:
nbinsextra = np.round(
(duration + dmfac * dm) / rawdatafile.tsamp).astype('int')
else:
nbinsextra = nbins
# If at end of observation
if (start_bin + nbinsextra) > rawdatafile.nspec - 1:
nbinsextra = rawdatafile.nspec - 1 - start_bin
data = rawdatafile.get_spectra(start_bin, nbinsextra)
# Masking
if mask and maskfn:
data, masked_chans = maskfile(maskfn, data, start_bin, nbinsextra)
else:
masked_chans = np.zeros(rawdatafile.nchan, dtype=bool)
# Bandpass correction
if maskfn and bandpass_corr:
bandpass = rfifind.rfifind(maskfn).bandpass_avg[::-1]
#bandpass[bandpass == 0] = np.min(bandpass[np.nonzero(bandpass)])
masked_chans[bandpass == 0] = True
# ignore top and bottom 1% of band
# VG: there is some bug here so "masking" these three lines
#ignore_chans = int(np.ceil(0.01*rawdatafile.nchan))
#masked_chans[:ignore_chans] = True
#masked_chans[-ignore_chans:] = True
data_masked = np.ma.masked_array(data.data)
data_masked[masked_chans] = np.ma.masked
data.data = data_masked
if bandpass_corr:
data.data /= bandpass[:, None]
# Zerodm filtering
if (zerodm == True):
data.data -= data.data.mean(axis=0)
# Subband data
if (nsub is not None) and (subdm is not None):
data.subband(nsub, subdm, padval='mean')
# Dedisperse
if dm:
data.dedisperse(dm, padval='rotate')
# Downsample
# Moving it after the DM-vs-time plot
#data.downsample(downsamp)
#Orignal scale has error if chan is flagged so writting this here
if not scaleindep:
std = data.data.std()
for ii in range(data.numchans):
chan = data.get_chan(ii)
median = np.median(chan)
if scaleindep:
std = chan.std()
if std:
chan[:] = (chan - median) / std
else:
chan[:] = chan
# scale data
#data = data.scaled(scaleindep)
#data = data.scaled(False)
# Smooth
if width_bins > 1:
for ii in range(data.numchans):
chan = data.get_chan(ii)
nbin = len(chan)
x = range(nbin)
if (nbin > 4000): deg = 10
if (nbin < 4000 and nbin > 2000): deg = 8
if (nbin < 2000 and nbin > 1000): deg = 6
if (nbin < 1000 and nbin > 150): deg = 5
if (nbin < 150): deg = 2
base = np.polyfit(x, chan, deg)
p = np.poly1d(base)
chan[:] = chan[:] - p(x)
return data, nbinsextra, nbins, start, source_name
#!/usr/bin/env python
import rfifind, sys
if __name__ == "__main__":
a = rfifind.rfifind(sys.argv[1])
sys.stderr.write(
"\nWARNING!: If raw data have channels in decreasing freq\n")
sys.stderr.write(
" order, the channel ordering as given will be\n")
sys.stderr.write(" inverted! Use 'invertband=True' in \n")
sys.stderr.write(" write_weights() in that case!\n")
if (a.idata.telescope == 'GBT' and a.idata.lofreq < 1000.0):
sys.stderr.write(
"Data is from GBT Prime Focus, auto-flipping the weights/offsets...\n\n"
)
invert = True
else:
invert = False
# Write the bandpass before we zap things
a.write_bandpass(invertband=invert)
# Now do the zapping and set the weights
a.set_zap_chans(power=200.0,
edges=0.01,
asigma=2.0,
ssigma=2.0,
usemask=True,
plot=True,
chans=[])
a.write_zap_chans()
a.set_weights_and_offsets()
a.write_weights(invertband=invert)
# Source
subprocess.check_output(['psredit', '-c', 'name=' + pulsar, '-m', pfd])
# Reciever
if telid == '19':
subprocess.check_output(['psredit', '-c', 'rcvr:name=AI', '-m', pfd])
else:
subprocess.check_output(['psredit', '-c', 'rcvr:name=AII', '-m', pfd])
# Backend
subprocess.check_output(['psredit', '-c', 'be:name=Ettus-B120', '-m', pfd])
# Proyect
subprocess.check_output(['psredit', '-c', 'obs:projid=PuMA', '-m', pfd])
# Save all relevant data from the pfd using rfifind and psrchive.
arch = psrchive.Archive_load(pfd)
maskrfi = rfifind.rfifind(maskname)
mjd = arch.start_time()
#Usable % of the observations due to RFI. We do not take into account pazi filters.
maskrfi.read_bytemask()
maskarr = maskrfi.bytemask
nbadint = float(np.count_nonzero(maskarr))
ntotalint = float(len(maskrfi.goodints)) * float(len(maskrfi.freqs))
# Parameters (we also have nobs and coords)
datemjd = [mjd.in_days()]
pulsar = sourceantenna[:len(sourceantenna) - 3]
antenna = [arch.get_receiver_name()]
bw = [arch.get_bandwidth()]
