Candidate

your.candidate.Candidate(
    fp=None, dm=None, tcand=0, width=0, label=-1, snr=0, min_samp=256, device=0, kill_mask=None
)

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

[source]

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

[source]

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

[source]

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

[source]

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.

[source]

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.

[source]

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.

[source]

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

[source]

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

[source]

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

[source]

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