Skip to main content

Analysis tool for the search of narrow band drifting signals in filterbank data

Project description

Build Status Documentation Status codecov

TURBO_SETI

turboSETI is an analysis tool for the search of narrow band drifting signals in filterbank data (frequency vs. time). The main purpose of the code is to hopefully one day find signals of extraterrestrial origin!! It can search the data for hundreds of drift rates (in Hz/sec). It can handle either .fil or .h5 file formats.

NOTE: This code is stable, but new features are currently under development. 'Git pull' for the latest version.

Some details for the expert eye:

  • Python based, with taylor tree in Numba for improved performance.
  • Pre-calculated drift index arrays.
  • Output plain text file with information on each hit.
  • Including output reader into a pandas DataFrame.

It was originally based on dedoppler dedoppler; which is based on rawdopplersearch.c gbt_seti/src/rawdopplersearch.c)

 


Dependencies

 


Installation

If you have not yet installed blimpy, do so in this manner:

python3 -m pip install -U git+https://github.com/UCBerkeleySETI/blimpy

Then, install turbo_seti directly from this repository:

python3 -m pip install -U git+https://github.com/UCBerkeleySETI/turbo_seti

NVIDIA GPU Users

Already included is NUMBA Just-in-Time (JIT) CPU performance enhancements. However, if you have NVIDIA GPU hardware on the computer where turbo_seti is going to execute, you can get significant additional performance improvement. Enable GPU enhanced processing with these steps:

  1. Install pypi package "cupy": python3 -m pip install cupy
  2. Run the executable this way: turboSETI <FULL_PATH_TO_INPUT_HDF5_FILE> -g y [OTHER OPTIONS]

Usage

Expected Input File Format

At the moment, the turboSETI command line and the FindDoppler object expect an HDF5 file (.h5) or a Filterbank file (.fil) such as produced by one of the blimpy utilities.

Usage as a Command Line

Run with data: turboSETI <FULL_PATH_TO_INPUT_HDF5_FILE> [OPTIONS]

For an explanation of the program options: turboSETI -h

Usage as a Python Package

from turbo_seti.find_doppler.find_doppler import FindDoppler
fdop = FindDoppler(datafile=my_HDF5_file, ...)
fdop.search(...)

Example Usage as a Python Package

import time
from blimpy import Waterfall
from turbo_seti.find_doppler.find_doppler import FindDoppler

H5DIR = "/seti_data/voyager/"
H5PATH = H5DIR + "Voyager1.single_coarse.fine_res.h5"
OUT_DIR_BASE = H5DIR

print("\nUsing HDF5 file: {}\nHeader and data shape:".format(H5PATH))
# -- Get a report of header and data shape
wf = Waterfall(H5PATH)
wf.info()
# -- Instantiate FindDoppler.
print("\nInstantiating the FindDoppler object.")
fdop = FindDoppler(datafile=H5PATH, max_drift=4, snr=25, out_dir=H5DIR)
# -- Search for hits and report elapsed time.
print("\nBegin doppler search.  Please wait ...")
t1 = time.time()
fdop.search()
elapsed_time = time.time() - t1
print("\nFindDoppler.search() elapsed time = {} seconds".format(elapsed_time))

Sample DAT File Output

# -------------------------- o --------------------------
# File ID: Voyager1.single_coarse.fine_res.h5 
# -------------------------- o --------------------------
# Source:Voyager1
# MJD: 57650.782094907408	RA: 17h10m03.984s	DEC: 12d10m58.8s
# DELTAT:  18.253611	DELTAF(Hz):  -2.793968
# --------------------------
# Top_Hit_# 	Drift_Rate 	SNR 	Uncorrected_Frequency 	Corrected_Frequency 	Index 	freq_start 	freq_end 	SEFD 	SEFD_freq 	Coarse_Channel_Number 	Full_number_of_hits 	
# --------------------------
001	 -0.392226	 30.612128	   8419.319368	   8419.319368	739933	   8419.321003	   8419.317740	0.0	      0.000000	0	858	
002	 -0.373093	245.707984	   8419.297028	   8419.297028	747929	   8419.298662	   8419.295399	0.0	      0.000000	0	858	
003	 -0.392226	 31.220652	   8419.274374	   8419.274374	756037	   8419.276009	   8419.272745	0.0	      0.000000	0	858	

Sample Console Logging (level=INFO) Output

Note that the coarse channel number appears as a suffix of the logger name. For example, "find_doppler.8" depicts logging for find_doppler.py in coarse channel number 8 (relative to 0).

Using HDF5 file: /seti_data/voyager/Voyager1.single_coarse.fine_res.h5
Header and data shape:

--- File Info ---
DIMENSION_LABELS :   ['frequency' 'feed_id' 'time']
        az_start :                              0.0
       data_type :                                1
            fch1 :            8421.386717353016 MHz
            foff :      -2.7939677238464355e-06 MHz
           ibeam :                                1
      machine_id :                               20
          nbeams :                                1
           nbits :                               32
          nchans :                          1048576
            nifs :                                1
     rawdatafile : guppi_57650_67573_Voyager1_0002.0000.raw
     source_name :                         Voyager1
         src_dej :                       12:10:58.8
         src_raj :                     17:10:03.984
    telescope_id :                                6
           tsamp :                     18.253611008
   tstart (ISOT) :          2016-09-19T18:46:13.000
    tstart (MJD) :                57650.78209490741
        za_start :                              0.0

Num ints in file :                               16
      File shape :                 (16, 1, 1048576)
--- Selection Info ---
Data selection shape :                 (16, 1, 1048576)
Minimum freq (MHz) :                8418.457032646984
Maximum freq (MHz) :                8421.386717353016

Instantiating the FindDoppler object.
find_doppler.0  INFO     {'DIMENSION_LABELS': array(['frequency', 'feed_id', 'time'], dtype=object), 'az_start': 0.0, 'data_type': 1, 'fch1': 8421.386717353016, 'foff': -2.7939677238464355e-06, 'ibeam': 1, 'machine_id': 20, 'nbeams': 1, 'nbits': 32, 'nchans': 1048576, 'nifs': 1, 'rawdatafile': 'guppi_57650_67573_Voyager1_0002.0000.raw', 'source_name': 'Voyager1', 'src_dej': <Angle 12.183 deg>, 'src_raj': <Angle 17.16777333 hourangle>, 'telescope_id': 6, 'tsamp': 18.253611008, 'tstart': 57650.78209490741, 'za_start': 0.0}

Begin doppler search.  Please wait ...
find_doppler.0  INFO     File: /seti_data/voyager/Voyager1.single_coarse.fine_res.h5
 drift rates (min, max): (0.000000, 4.000000)
 SNR: 25.000000

Starting ET search using /seti_data/voyager/Voyager1.single_coarse.fine_res.h5
find_doppler.0  INFO     Parameters: datafile=/seti_data/voyager/Voyager1.single_coarse.fine_res.h5, max_drift=4, min_drift=0.0, snr=25, out_dir=/seti_data/voyager/, coarse_chans=None, flagging=False, n_coarse_chan=None, kernels=None, gpu_backend=False, precision=2, append_output=False, log_level_int=20, obs_info={'pulsar': 0, 'pulsar_found': 0, 'pulsar_dm': 0.0, 'pulsar_snr': 0.0, 'pulsar_stats': array([0., 0., 0., 0., 0., 0.]), 'RFI_level': 0.0, 'Mean_SEFD': 0.0, 'psrflux_Sens': 0.0, 'SEFDs_val': [0.0], 'SEFDs_freq': [0.0], 'SEFDs_freq_up': [0.0]}
find_doppler.0  INFO     Top hit found! SNR 30.612128, Drift Rate -0.392226, index 739933
find_doppler.0  INFO     Top hit found! SNR 245.707984, Drift Rate -0.373093, index 747929
find_doppler.0  INFO     Top hit found! SNR 31.220652, Drift Rate -0.392226, index 756037

FindDoppler.search() elapsed time = 9.972093105316162 seconds

 

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

turbo_seti-2.0.23.tar.gz (167.8 kB view details)

Uploaded Source

Built Distribution

turbo_seti-2.0.23-py2.py3-none-any.whl (162.7 kB view details)

Uploaded Python 2 Python 3

File details

Details for the file turbo_seti-2.0.23.tar.gz.

File metadata

  • Download URL: turbo_seti-2.0.23.tar.gz
  • Upload date:
  • Size: 167.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.6.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.1 CPython/3.9.5

File hashes

Hashes for turbo_seti-2.0.23.tar.gz
Algorithm Hash digest
SHA256 9e9213742de7ab3d9452c57cc79d5221f0b178c759a5887091df9dbb8abb27a5
MD5 293a99d050b7cdc3643087f25c4627ae
BLAKE2b-256 ba1c8b215daaca40e7e9af8d6921fce1f66dbc6962e723533807f35d6b69a4f8

See more details on using hashes here.

Provenance

File details

Details for the file turbo_seti-2.0.23-py2.py3-none-any.whl.

File metadata

  • Download URL: turbo_seti-2.0.23-py2.py3-none-any.whl
  • Upload date:
  • Size: 162.7 kB
  • Tags: Python 2, Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/3.4.1 importlib_metadata/4.6.0 pkginfo/1.7.0 requests/2.25.1 requests-toolbelt/0.9.1 tqdm/4.61.1 CPython/3.9.5

File hashes

Hashes for turbo_seti-2.0.23-py2.py3-none-any.whl
Algorithm Hash digest
SHA256 a45086670f1a12c7ef654083010b001473bac294a2c9c7d3dd1b42fc627792a8
MD5 11f013a06de9dcff4d7bd0889b94bf1b
BLAKE2b-256 c28686808b8798c93bb24af50440588bce87044481e8d040c0a3e4e101f630a6

See more details on using hashes here.

Provenance

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page