Candidate
Candidate
your.candidate.Candidate(
fp=None,
dm=None,
tcand=0,
width=0,
label=-1,
snr=0,
min_samp=256,
device=0,
kill_mask=array([False]),
spectral_kurtosis_sigma=4,
savgol_frequency_window=15,
savgol_sigma=4,
flag_rfi=False,
)
Candidate Class
Args:
fp Union[str, list]: String or a list of files. It can either filterbank or psrfits files.
dm (float): Dispersion Measure of the candidate
tcand (float): start time of the candidate in seconds at the highest frequency channel
width (int): pulse width of the candidate in samples
label (int): 1 for pulsars/FRBs, 0 for RFI
snr (float): Signal to Noise Ratio
min_samp (int): Minimum number of time samples
device (int): GPU ID if using GPUs
kill_mask (numpy.ndarray): Boolean mask of channels to kill
spectral_kurtosis_sigma (float): Sigma for spectral kurtosis filter
savgol_frequency_window (float): Filter window for savgol filter
savgol_sigma (float): Sigma for savgol filter
flag_rfi (bool): To turn on RFI flagging
save_h5
Candidate.save_h5(out_dir=None, fnout=None)
Save the candidate to a hdf5 file
Args:
out_dir (str): path to the output directory
fnout (str): output name of the file
Returns:
str: output name of the file
dispersion_delay
Candidate.dispersion_delay(dms=None)
Calculate the dispersion delay for the candidate DM or at given dispersion DM
Args:
dms (Union[float,np.ndarray]): DM or a list of DMs
Returns:
Union[float, np.ndarray]: dispersion delay in seconds
get_chunk
Candidate.get_chunk(tstart=None, tstop=None, for_preprocessing=True)
Get a chunk of data. The data is saved in self.data.
Args:
tstart (float): start time of the chunk in seconds
tstop (float): stop time of the chunk in seconds
for_preprocessing (bool): if the data is to be preprocessed later. This will modify the number of samples
read based on the width of the candidate
dedisperse
Candidate.dedisperse(dms=None, target="CPU")
Dedisperse a chunk of data. Saves the dedispersed chunk in self.dedispersed.
Note
Our method rolls the data around while dedispersing it.
Args:
dms (float): The DM to dedisperse the data at.
target (str): 'CPU' to run the code on the CPU or 'GPU' to run it on a GPU.
dedispersets
Candidate.dedispersets(dms=None)
Create a dedispersed time series
Note
Our method rolls the data around while dedispersing it.
Args:
dms (float): The DM to dedisperse the data at.
Returns:
numpy.ndarray: Dedispersed time series.
dmtime
Candidate.dmtime(dmsteps=256, target="CPU")
Generates DM-time array of the candidate by dedispersing at adjacent DM values. Saves the data in self.dmt.
Note
Our method rolls the data around while dedispersing it.
Args:
dmsteps (int): Number of DMs to dedisperse at.
target (str): 'CPU' to run the code on the CPU or 'GPU' to run it on a GPU.
get_snr
Candidate.get_snr(time_series=None)
Calculates the SNR of the candidate
Args:
time_series (np.ndarray): time series array to calculate the SNR of
Returns:
float: SNR
optimize_dm
Candidate.optimize_dm()
Calculate more precise value of the DM by interpolating between DM values to maximise the SNR
Note
This function has not been fully tested.
Returns:
Optimized DM, optimised SNR
decimate
Candidate.decimate(key, decimate_factor, axis, pad=False, **kwargs)
Decimate FT or DMT data.
Todo: * Update candidate parameters as per decimation factor
Args:
key (str): Keywords to chose which data to decimate ('dmt' or 'ft')
decimate_factor (int): Number of samples to average
axis (int): Axis to decimate along
pad (bool): Optional argument if padding is to be done
**kwargs: kwargs for numpy.pad
resize
Candidate.resize(key, size, axis, **kwargs)
Resize FT or DMT data
Todo: * Update candidate parameters as per final size
Args:
key (str): Keywords to chose which data to resize ('dmt' or 'ft')
size: Final size of the data array required
axis (int): Axis to resize alone
**kwargs: Arguments for skimage.transform resize function