astro-trigger-filter 0.1.8

A package for filtering triggers from radio astronomical matched template pulse searches.

astro-trigger-filter

A Python package for filtering radio astronomical pulse matched template searches.

Installation

pip install astro-trigger-filter

Basic Usage

Generate a set of random triggers, sorten on time:

import random

N = 1000

start_times = [random.uniform(0.0, 1.0) for _ in range(N)]
widths = [random.uniform(0.00, 0.01) for _ in range(N)]
DMs = [random.uniform(0.0, 100.0) for _ in range(N)]
SNRs = [random.uniform(5.0, 10.0) for _ in range(N)]

triggers = [t for t in zip(start_times, widths,DMs, SNRs)]

Make sure the list is sorted on start time. Aditional reverse sorting on DM will improve the effiency.

triggers.sort(key=lambda tup: (tup[0], -tup[2]))

Now filter the triggers:

from astrotf.radio_pulse import RadioPulseFilterGen

gen = RadioPulseFilterGen(freq_lo_mhz=1249.8, freq_hi_mhz=1549.8)

for filtered_trigger in gen(triggers):
    print(filtered_trigger)

Print filtering statistics

print('Reduced the number of triggers from  {} to {}'.format(
	gen.num_in, 
	gen.num_out
))

Processing Pandas Dataframes

Generate a pandas dataframe with random triggers:

import pandas as pd
import random

triggers = pd.DataFrame(
	[
		[
			random.uniform(0.0, 1.0),
			random.uniform(0.0, 0.01),
			random.uniform(0.0, 100.0),
			random.uniform(5.0, 10.0)
		]
		for _ in range(N)
	],
	columns=['t', 'w', 'DM', 'SNR']
)

..or read a csv file from disk:

triggers = pd.read_csv('triggers.txt', delim_whitespace=True)

Sort time increasing, then DM descending, then pulse width descending

triggers.sort_values(
	by=['t', 'DM', 'w'], 
	ascending=[True, False, False], 
	inplace=True
)

finally filter and print the triggers:

gen = RadioPulseFilterGen(freq_lo_mhz, freq_hi_mhz)

for filtered_trigger in gen(
		(e.t, e.w, e.DM, e.SNR) 
		for e in triggers.itertuples()
	):
    print(filtered_trigger)

Storing extra data for each trigger

You can add extra data for each trigger, as lomng as the first 4 elements remain [time, width, dispersion measure, signal-to-noise ration]. In the example below we have added a beam_id and sample_id.

for filtered_trigger in gen(
        (
            e.time, 
            e.w, 
            e.DM, 
            e.SNR, 
            e.beam_id, 
            e.sample_id
        ) 
        for e in triggers.itertuples()
    ):
    print(filtered_trigger)

More settings

buffersize=0 : Limit the buffer size. The default setting is 0 which means an unlimited buffer size. A buffer size of 25 speeds up filtering while only generating margiuamly more false triggers.

autoflush=True : Automatically flush buffer when finished processing the input trigger. Set this to false if you want to process triggers in chunks and not flush the buffers between each chunk.