def waterfall_array(rawdatafile, start, duration, dm, nbins, nsub, subdm, zerodm, \
downsamp, scaleindep, width_bins, mask, maskfn, bandpass_corr):
"""
Runs the waterfaller. If dedispersing, there will be extra bins added to the 2D plot.
Inputs:
Inputs required for the waterfaller. dm, nbins, etc.
Outputs:
data: 2D array as an "object"
array: 2D array ready to be plotted by sp_pgplot.plot_waterfall(array).
"""
data, bins, nbins, start = waterfaller.waterfall(rawdatafile, start, duration, dm=dm, nbins=nbins, \
nsub=nsub, subdm=subdm, zerodm=zerodm, \
downsamp=downsamp, scaleindep=scaleindep, \
width_bins=width_bins, mask=mask, \
maskfn=maskfn, bandpass_corr=bandpass_corr)
array = np.array(data.data)
if dm is not None: # If dedispersing the data, extra bins will be added. We need to cut off the extra bins to get back the appropriate window size.
ragfac = float(nbins) / bins
dmrange, trange = array.shape
nbinlim = np.int(trange * ragfac)
else:
nbinlim = nbins
array = array[..., :nbinlim]
array = (array[::-1]).astype(np.float16)
return data, array
def waterfall_array(rawdatafile, start, duration, dm, nbins, nsub, subdm, zerodm, \
downsamp, scaleindep, width_bins, mask, maskfn, bandpass_corr):
"""
Runs the waterfaller. If dedispersing, there will be extra bins added to the 2D plot.
Inputs:
Inputs required for the waterfaller. dm, nbins, etc.
Outputs:
data: 2D array as an "object"
array: 2D array ready to be plotted by sp_pgplot.plot_waterfall(array).
"""
data, bins, nbins, start = waterfaller.waterfall(rawdatafile, start, duration, dm=dm, nbins=nbins, \
nsub=nsub, subdm=subdm, zerodm=zerodm, \
downsamp=downsamp, scaleindep=scaleindep, \
width_bins=width_bins, mask=mask, \
maskfn=maskfn, bandpass_corr=bandpass_corr)
array = np.array(data.data)
if dm is not None: # If dedispersing the data, extra bins will be added. We need to cut off the extra bins to get back the appropriate window size.
ragfac = float(nbins)/bins
dmrange, trange = array.shape
nbinlim = np.int(trange * ragfac)
else:
nbinlim = nbins
array = array[..., :nbinlim]
array = (array[::-1]).astype(np.float16)
return data, array
def waterfall_array(duration, nbins, zerodm, nsub, subdm, dm, downsamp, scaleindep, width_bins, rawdatafile, start, mask , maskfn, bandpass_corr):
"""
Runs the waterfaller. If dedispersing, there will be extra bins added to the 2D plot.
Inputs:
Inputs required for the waterfaller. dm, nbins, etc.
Outputs:
data: 2D array as an "object"
array: 2D array ready to be plotted by sp_pgplot.plot_waterfall(array).
"""
data, bins, nbins, new_start = waterfaller.waterfall(rawdatafile, start, \
duration, dm=dm, nbins=nbins, nsub=nsub, subdm=subdm, zerodm=zerodm, \
downsamp=downsamp, scaleindep=scaleindep, width_bins=width_bins, \
mask=mask, maskfn=maskfn, bandpass_corr=bandpass_corr)
array = np.array(data.data)
#if dm is not None: # If dedispersing the data, extra bins will be added. We need to cut off the extra bins to get back the appropriate window size.
# ragfac = float(nbins)/bins
# dmrange, trange = array.shape
# nbinlim = np.int(trange * ragfac)
#else:
# nbinlim = nbins
if dm is None:
nbinlim = nbins # this can be handled by sweep_duration (since already replicated)
else:
nbinlim = np.int(duration/data.dt) # should be equivalent of above, may be able to stop returning bins and nbins
array = array[..., :nbinlim]
array = (array[::-1]).astype(np.float16)
return data, array, new_start
def fil2spec(fname,
num_channels,
num_time,
spectra_array,
total_samples,
samples_per_file=50):
"""
Given a filename, takes time samples from filterbank file
and converts them into Spectra objects, which will then be
randomly dedispersed and used to inject FRBs into.
Returns:
spectra_array : list
A list of Spectra objects.
freq : numpy.ndarray
Frequency range of filterbank file.
"""
# get filterbank file as input and output a Spectra object
raw_filterbank_file = filterbank.FilterbankFile(fname)
# grab total observation time to split up samples
t_obs = float(
subprocess.check_output(
['/usr/local/sigproc/bin/header', fname, '-tobs']))
# generate samples_per_file random timesteps to sample from in filterbank file
random_timesteps = np.random.choice(np.arange(int(t_obs)),
size=samples_per_file)
# grab time samples beginning at random start times + ending at num_time bins
for timestep in tqdm(random_timesteps):
# get spectra object at some timestep, incrementing timestep if successful
spectra_obj = waterfall(raw_filterbank_file,
start=timestep,
duration=1,
dm=0,
nbins=num_time,
nsub=num_channels)[0]
spectra_array.append(spectra_obj)
freq = raw_filterbank_file.frequencies
return spectra_array, freq
def main(fits, database, time, DM, IMJD, SMJD, sigma, duration=0.01, pulse_id=4279, top_freq=0., directory='.',\
FRB_name='FRB121102', downsamp=1., beam=0, group=0, plot_standard=True, plot_zoom=True, plot_wide=False):
num_elements = time.size
if isinstance(DM, float) or isinstance(DM, int):
DM = np.zeros(num_elements) + DM
if isinstance(sigma, float) or isinstance(sigma, int):
sigma = np.zeros(num_elements) + sigma
if isinstance(duration, float) or isinstance(duration, int):
duration = np.zeros(num_elements) + duration
if isinstance(pulse_id, float) or isinstance(pulse_id, int):
pulse_id = np.zeros(num_elements) + pulse_id
if isinstance(downsamp, float) or isinstance(downsamp, int):
downsamp = np.zeros(num_elements) + downsamp
if FRB_name == 'FRB121102':
rawdata = psrfits.PsrfitsFile(fits)
observation = os.path.basename(fits)
observation = observation[:observation.find('_subs_')]
if FRB_name == 'FRB180814':
rawdata = filterbank.FilterbankFile(fits)
observation = os.path.basename(fits)
observation = observation[:observation.find('.0001.')]
events = pd.read_hdf(database, 'events')
#Fractional day
SMJD = SMJD / 86400.
for i, t in enumerate(time):
if FRB_name == 'FRB130628':
fits = glob("%s/*b%ds%d*.fits" % (fits, beam[i], group[i]))[0]
rawdata = psrfits.PsrfitsFile(fits)
observation = os.path.basename(fits)
observation = os.path.splitext(observation)[0]
pulse_events = events[events.Pulse == pulse_id[i]]
#zero-DM filering version
start_time = t - 0.05
plot_duration = 0.1
if plot_standard:
zero_dm_data, nbinsextra, nbins, zero_dm_start = waterfall(rawdata, start_time, plot_duration, DM[i],\
nbins=None, nsub=None, subdm = DM, zerodm=True, downsamp=1,\
scaleindep=False, width_bins=1, mask=False, maskfn=None,\
bandpass_corr=False, ref_freq=None)
#non-zero-DM filtering version
data, nbinsextra, nbins, start = waterfall(rawdata, start_time, plot_duration, DM[i],\
nbins=None, nsub=None, subdm = DM, zerodm=False, downsamp=1,\
scaleindep=False, width_bins=1, mask=False, maskfn=None,\
bandpass_corr=False, ref_freq=None)
plotter(data, start, plot_duration, t, DM[i], IMJD[i], SMJD[i], duration[i], top_freq,\
sigma[i], directory, FRB_name, observation, zero_dm_data, zero_dm_start, pulse_events=pulse_events, zoom=False, idx=i, pulse_id=pulse_id[i], downsamp=False)
#Zoomed version
start_time = t - 0.01
plot_duration = 0.03
if plot_zoom:
zero_dm_data, nbinsextra, nbins, zero_dm_start = waterfall(rawdata, start_time, plot_duration, DM[i],\
nbins=None, nsub=None, subdm = DM, zerodm=True, downsamp=1,\
scaleindep=False, width_bins=1, mask=False, maskfn=None,\
bandpass_corr=False, ref_freq=None)
data, nbinsextra, nbins, start = waterfall(rawdata, start_time, plot_duration, DM[i],\
nbins=None, nsub=None, subdm = DM, zerodm=False, downsamp=1,\
scaleindep=False, width_bins=1, mask=False, maskfn=None,\
bandpass_corr=False, ref_freq=None)
plotter(data, start, plot_duration, t, DM[i], IMJD[i], SMJD[i], duration[i], top_freq,\
sigma[i], directory, FRB_name, observation, zero_dm_data, zero_dm_start, pulse_events=pulse_events, zoom=True, idx=i, pulse_id=pulse_id[i], downsamp=False)
#Wide version
start_time = t - 0.5
plot_duration = 1.
if plot_wide:
zero_dm_data, nbinsextra, nbins, zero_dm_start = waterfall(rawdata, start_time, plot_duration, DM[i],\
nbins=None, nsub=None, subdm = DM, zerodm=True, downsamp=1,\
scaleindep=False, width_bins=1, mask=False, maskfn=None,\
bandpass_corr=False, ref_freq=None)
#non-zero-DM filtering version
data, nbinsextra, nbins, start = waterfall(rawdata, start_time, plot_duration, DM[i],\
nbins=None, nsub=None, subdm = DM, zerodm=False, downsamp=1,\
scaleindep=False, width_bins=1, mask=False, maskfn=None,\
bandpass_corr=False, ref_freq=None)
plotter(data, start, plot_duration, t, DM[i], IMJD[i], SMJD[i], duration[i], top_freq,\
sigma[i], directory, FRB_name, observation, zero_dm_data, zero_dm_start, pulse_events=pulse_events, zoom=False, idx=i, pulse_id=pulse_id[i], downsamp=False)