ascl_id stringclasses 752
values | repo_url stringclasses 737
values | file_path stringlengths 4 194 | content large_stringlengths 1 1M | language stringclasses 29
values | license_type stringclasses 24
values | size int64 1 1M |
|---|---|---|---|---|---|---|
[ascl:2306.019] | https://github.com/realfastvla/realfast | .travis.yml | language: python
python:
- 2.7
- 3.6
branches:
only:
- main
- development
install:
- sudo apt-get update -y
- sudo apt-get install -y libfftw3-dev
# set up conda
- wget https://repo.continuum.io/miniconda/Miniconda2-latest-Linux-x86_64.sh -O miniconda.sh
- bash miniconda.sh -b -p $HOME/miniconda
- export PATH="$HOME/miniconda/bin:$PATH"
- hash -r
- conda config --set always_yes yes --set changeps1 no
- conda update -q conda
- conda config --add channels conda-forge
- conda config --append channels pkgw-forge
# install with conda and pip
- conda create -n conda pip "pycosat>=0.6.3"
- source activate conda
- conda install -q python=$TRAVIS_PYTHON_VERSION numpy scipy jupyter bokeh cython matplotlib numba astropy pytest setuptools coverage pyfftw
- conda install -q python=$TRAVIS_PYTHON_VERSION pwkit casa-tools casa-python casa-data
- python setup.py install
script:
- pytest
notifications:
email: false
slack: realfastvla:ID1qIFG70HGYf3G6Y3oWoFFk
| YAML | BSD | 1,017 |
[ascl:2306.019] | https://github.com/realfastvla/realfast | README.md | # realfast
Realfast is the name of a project and software package related to radio interferometric data analysis at the Very Large Array. For more information, see [realfast.io](http://realfast.io) or visit the [VLA](https://public.nrao.edu/telescopes/vla/) web site.
This repo includes a Python 3 application that integrates a transient search pipeline with the real-time environment at the VLA. This requires running asynchronous processes in a cluster environment and using VLA services for distributing data, metadata, and archiving. This application:
* monitors multicast messages with [evla_mcast](https://github.com/demorest/evla_mcast),
* catches visibility data via the [vys](https://github.com/mpokorny/vysmaw) protocol,
* defines a fast transient search pipeline with [rfpipe](http://github.com/realfastvla/rfpipe),
* submits search pipeline to a compute cluster with [distributed](https://github.com/dask/distributed),
* indexes candidate transients and other metadata for the [search interface](https://github.com/realfastvla/realfast.io-search), and
* writes and archives new visibility files for candidate transients.
These are the core functions required for the realfast project. Other features include:
* Support for GPU algorithms,
* Management of distributed futures,
* Blind injection and management of mock transients, and
* Reading of sdm data via [sdmpy](https://github.com/demorest/sdmpy).
Requirements:
---------
The realfast application is build upon the following libraries:
* Python 3.5 and the scientific python stack (numpy, etc.)
* [evla_mcast](https://github.com/demorest/evla_mcast)
* [vys](https://github.com/mpokorny/vysmaw) and [vysmaw_apps](https://github.com/realfastvla/vysmaw_apps)
* [distributed](https://github.com/dask/distributed)
* [rfpipe](http://github.com/realfastvla/rfpipe)
* [rfgpu](http://github.com/realfastvla/rfgpu)
* [sdmpy](https://github.com/demorest/sdmpy) (to read/write sdm/bdf data)
* [elasticsearch](https://github.com/elastic/elasticsearch-py) (version<7.0)
* [supervisor](http://supervisord.org) (to manage/daemonize processes)
* [click](http://click.pocoo.org)
* [fetch](https://github.com/devanshkv/fetch) (incl cuda, tensorflow, etc.)
Install
------
This application is designed to integrate specific protocols and services only availble on the correlator cluster at the VLA. If you'd like to build it yourself, you will need to use the [anaconda](http://anaconda.com) installer and follow instructions at [rfpipe](http://github.com/realfastvla/rfpipe). Then install the dependencies above (vys and pycuda being the trickiest) and:
python setup.py install
Contributors:
--------
The custom realfast software includes contributions from:
* Sarah Burke-Spolaor -- WVU
* Paul Demorest -- NRAO
* Andrew Halle -- UC Berkeley
* Casey Law -- UC Berkeley
* Joe Lazio -- JPL
* Martin Pokorny -- NRAO
| Markdown | BSD | 2,874 |
[ascl:2306.019] | https://github.com/realfastvla/realfast | setup.py | from setuptools import setup, find_packages
import glob
setup(
name='realfast',
description='Real-time data analysis at the VLA',
author='Casey Law and the realfast team',
author_email='caseyjlaw@gmail.com',
version='3.6.12',
url='http://realfast.io',
include_package_data=True,
packages=find_packages(),
package_data={'realfast': ['xsd/*.xsd']},
data_files=[('conf', glob.glob('conf/*'))],
# scripts=['scripts/sdm_chop-n-serve.pl'], # add non-python scripts
install_requires=['rfpipe', 'evla_mcast', 'sdmpy', 'click', 'dask', 'requests',
'elasticsearch>=6.0.0,<7.0.0', 'distributed', 'future'],
# 'rfgpu', 'vysmaw_reader'],
entry_points='''
[console_scripts]
realfast=realfast.cli:cli
realfast_portal=realfast.cli:cli2
'''
)
| Python | BSD | 848 |
[ascl:2306.019] | https://github.com/realfastvla/realfast | scripts/rf_get_cand.py | #! /usr/bin/env python
import sys
import os
import json
import astropy.coordinates
import astropy.units as u
import sdmpy
from elasticsearch import Elasticsearch
# This script takes a realfast portal candidate ID, and
# assembles the SDM, BDF and PNG files into the current
# directory. It then modifies the SDM Annotation table
# to contain correct metadata about the candidate, as
# retrieved from the portal.
try:
candid = sys.argv[1]
except IndexError:
print("Usage: rf_get_cand.py candId")
sys.exit(1)
es = Elasticsearch('http://realfast.nrao.edu:9200/')
rfidx = 'new'
# Get the cand from portal
q = {'query':{
'match':{'candId':candid},
}}
r = es.search(index=rfidx+'cands',body=q,size=1)
try:
cand = r['hits']['hits'][0]['_source']
except IndexError:
raise(RuntimeError,"No matching candId")
noiseid= '%s.%d.*' % (cand['scanId'], cand['segment'])
#print(cand)
# Get the noise values from portal
q = {'query':{
'wildcard':{'_id':noiseid}
}}
rn = es.search(index=rfidx+'noises',body=q,size=1)
try:
noise = rn['hits']['hits'][0]['_source']
except IndexError:
raise(RuntimeError,"No noise entry found")
#print(noise)
print('SDM: %s' % (cand['sdmname']))
print('PNG: %s' % (cand['png_url']))
# Get the SDM from MCAF dir
sdmname = cand['sdmname']
mcafdir = '/home/mctest/evla/mcaf/workspace'
if not os.path.exists(sdmname):
os.system('rsync -aqP %s/%s .' % (mcafdir,sdmname))
# Get the BDF from CBE lustre
bdfdir = '/lustre/evla/wcbe/data/realfast'
sdm = sdmpy.SDM(sdmname,bdfdir=bdfdir,use_xsd=False)
scan = sdm.scan(1)
bdfpath = os.path.join(sdmname,'ASDMBinary')
try:
os.mkdir(bdfpath)
except OSError:
pass
rsync_cmd = "rsync -aq cbe-master:%s %s" % (scan.bdf_fname, bdfpath)
print(rsync_cmd)
os.system(rsync_cmd)
# Get the PNG from web
os.system("wget -q -N %s" % (cand['png_url']))
# Fix up Annotation values in SDM
ann = json.loads(str(sdm['Annotation'][0].sValue))
if cand['frbprob']>0.9:
ann['rf_QA_label'] = 'Good'
elif cand['frbprob']>0.5:
ann['rf_QA_label'] = 'Marginal'
elif cand['frbprob']>0.0:
ann['rf_QA_label'] = 'Questionable'
else:
ann['rf_QA_label'] = 'None'
ann['rf_QA_zero_fraction'] = noise['zerofrac']
ann['rf_QA_visibility_noise'] = noise['noiseperbl']
ann['rf_QA_image_noise'] = noise['imstd']
# Fix busted RA and dec
if ann['transient_RA']=='[':
c = astropy.coordinates.SkyCoord(cand['ra']*u.deg, cand['dec']*u.deg)
ann['transient_RA'] = c.ra.to_string(unit=u.hourangle,sep=':',pad=True)
ann['transient_Dec'] = c.dec.to_string(unit=u.deg,sep=':',pad=True,alwayssign=True)
# Fix units on RA error
ann['transient_RA_error'] = ann['transient_Dec_error'] / 15.0
sdm['Annotation'][0].sValue = json.dumps(ann)
# Write out updated SDM
sdm.write(sdmname)
| Python | BSD | 2,779 |
[ascl:2306.019] | https://github.com/realfastvla/realfast | scripts/rfarchive.sh | #!/usr/bin/env bash
MODE=$1
SDMNAME=$2
if [ $MODE == "create" ]; then
ssh rfr "conda activate development; realfast buildsdm --indexprefix final --copybdf --sdmname $SDMNAME; rsync -rL --remove-source-files ${SDMNAME} claw@nmpost-master:~/fasttransients/realfast/tmp ; find ${SDMNAME} -type d -empty -delete"
elif [ $MODE == "ingest" ]; then
ssh vlapipe@nmngas "activate_profile dsoc-test; realfastIngest -s /lustre/aoc/projects/fasttransients/realfast/tmp -p /lustre/aoc/projects/fasttransients/realfast/plots/final ${SDMNAME}"
else
echo "Oops"
fi
| Shell | BSD | 562 |
[ascl:2306.019] | https://github.com/realfastvla/realfast | scripts/rfarchive.py | import os.path
from subprocess import Popen, PIPE
import shutil
import pickle
from realfast import elastic
from time import sleep
from astropy.time import Time
# get all Ids
Ids = elastic.get_ids('finalcands', caseyjlaw_tags="astrophysical,archive")
# any project code filters can be added here
# Ids = [Id for Id in Ids if <code string> in Id]
# filter out most recent
mjds = [int(Id.split('.eb')[1].split('.')[1]) if '.eb' in Id else 0 for Id in Ids]
now = Time.now().mjd
Ids = [Id for i, Id in enumerate(Ids) if mjds[i] < now-365*4.9] # archived after 14 days
try:
with open('finished_Ids.pkl', 'rb') as fp: # sdmnames already done
finished = pickle.load(fp)
except FileNotFoundError:
print("No finished pkl found. Starting anew")
finished = []
raningest = False
for i, Id in enumerate(Ids):
source = elastic.get_doc('finalcands', Id)['_source']
if 'sdmname' in source:
sdmname = source['sdmname']
if sdmname in finished:
print(f"Skipping ingested file: {sdmname}")
continue
print(f"Working on Id {Id} with sdmname {sdmname}")
# p = Popen(["./rfarchive.sh", "create", sdmname], stdout=PIPE)
p = Popen(["./archive.sh", sdmname, "dsoc-prod"], stdout=PIPE, stderr=PIPE)
p.wait()
# parse p.stdout for "BDF not found" and "No SDM found", then ingest
stdout = p.stdout.read().decode("utf8")
stderr = p.stderr.read().decode("utf8")
# if "BDF not found" not in stderr and "No SDM found" not in stderr and "No bdf found" not in stderr:
if "No SDM found" not in stderr:
# p = Popen(["./rfarchive.sh", "ingest", sdmname], stdout=PIPE)
# p.wait()
raningest = True
else:
raningest = False
print("Not archiving if no SDM or BDF")
# line = p.stdout.read().decode("utf8")
# print(line)
# if os.path.exists(f"/users/claw/fasttransients/realfast/tmp/{sdmname}"):
# print("Cleaning up...")
# shutil.rmtree(f'/users/claw/fasttransients/realfast/tmp/{sdmname}')
# else:
# print("No SDM to clean up")
finished.append(sdmname)
# occasionally...
if not i % 5:
print("Updating finished_Ids.pkl")
with open('finished_Ids.pkl', 'wb') as fp:
pickle.dump(finished, fp)
if raningest:
print('Ran ingest. Sleep for 10 seconds...')
sleep(10)
print()
print()
| Python | BSD | 2,516 |
[ascl:2306.019] | https://github.com/realfastvla/realfast | scripts/archive.sh | #!/usr/bin/env bash
SDMNAME=$1
PROFILE=$2 # can be dsoc-test or dsoc-prod
ssh rfr "conda activate deployment; cd lustre_workdir; realfast buildsdm --indexprefix final --sdmname "${SDMNAME}"; ~/soft/sdmpy/scripts/realfast_sdm_fix.py "${SDMNAME}"; rsync -aL --bwlimit=20m --remove-source-files "${SDMNAME}".fix claw@nmpost-master:~/fasttransients/realfast/sdm_archive/"${SDMNAME}
ssh nmngas "activate_profile "${PROFILE}"; realfastIngest -s /lustre/aoc/projects/fasttransients/realfast/sdm_archive -p /lustre/aoc/projects/fasttransients/realfast/plots/final "${SDMNAME}
| Shell | BSD | 572 |
[ascl:2306.019] | https://github.com/realfastvla/realfast | conf/realfast.yml | rfpipe:
default:
nthread: 2 # not taking all in case multiple workers going
dtarr: [1] # integer to integrate in time for independent searches
maxdm: 100
flagantsol: True
timesub: 'mean'
searchtype: 'image'
# sigma_image1: 6.4
# sigma_kalman: 0.
npix_max: 2048
badspwpol: 2.
uvoversample: 1.5
savenoise: True
savecandcollection: True
savecanddata: True
returncanddata: True
saveplots: True
savesols: False
selectpol: 'auto'
fftmode: 'cuda'
clustercands: !!python/tuple [2, 5]
cluster_downsampling: 1
memory_limit: 1 # in GB
NRAOdefaultX: # X-band 8-10 GHz, 16 spw, 2 MHz channels, 10 ms, 4 pols
nthread: 2
dtarr: [1,2,4,8] # integer to integrate in time for independent searches
maxdm: 3000
flagantsol: True
timesub: 'mean'
searchtype: 'image'
# sigma_image1: 6.8
# sigma_kalman: 2.8
npix_max: 2048
badspwpol: 2.
uvoversample: 1.5
savenoise: False
savecandcollection: True
savecanddata: False
returncanddata: True
saveplots: True
savesols: False
selectpol: 'auto'
fftmode: 'cuda'
clustercands: !!python/tuple [2, 5]
cluster_downsampling: 1
memory_limit: 5.0 # in GB
NRAOdefaultC: # C-band 4-8 GHz, 16 spw, 2 MHz channels, 10 ms, 4 pols
nthread: 2
dtarr: [1,2,4,8] # integer to integrate in time for independent searches
maxdm: 3000
flagantsol: True
timesub: 'mean'
searchtype: 'image'
# sigma_image1: 7.0
# sigma_kalman: 0.0 # snr1=7.5 and snrk=2.8 gives snrtot=8
npix_max: 2048
badspwpol: 2.
uvoversample: 1.5
savenoise: False
savecandcollection: True
savecanddata: False
returncanddata: True
saveplots: True
savesols: False
selectpol: 'auto'
fftmode: 'cuda'
clustercands: !!python/tuple [2, 5]
cluster_downsampling: 1
memory_limit: 5.0 # in GB
NRAOdefaultS: # S-band 2-4 GHz, 16 spw, 2 MHz channels, 10 ms, 4 pols
nthread: 2
dtarr: [1,2,4,8] # integer to integrate in time for independent searches
maxdm: 2000
flagantsol: True
timesub: 'mean'
searchtype: 'image'
# sigma_image1: 7.0
# sigma_kalman: 0.0 # snr1=7.5 and snrk=2.8 gives snrtot=8
# sigma_image1: 7.5
# sigma_kalman: 2.8 # snr1=7.5 and snrk=2.8 gives snrtot=8
npix_max: 2048
# spw: [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]
spw: [4, 5, 6, 7, 8, 9, 10, 11, 12, 13] # avoid low-level RFI
badspwpol: 2.
uvoversample: 1.5
savenoise: False
savecandcollection: True
savecanddata: False
returncanddata: True
saveplots: True
savesols: False
selectpol: 'auto'
fftmode: 'cuda'
clustercands: !!python/tuple [2, 5]
cluster_downsampling: 1
memory_limit: 5.0 # in GB
VLASS: # S-band 2-4 GHz, 16 spw, 2 MHz channels, 18 ms, 4 pols
nthread: 2
dtarr: [1,2] # integer to integrate in time for independent searches
maxdm: 2000
flagantsol: True
#timesub: 'cs'
timesub: 'splfit2'
searchtype: 'image'
sigma_image1: 8.0
# sigma_kalman: 0.0 # snr1=7.5 and snrk=2.8 gives snrtot=8
# sigma_image1: 7.5
# sigma_kalman: 2.8 # snr1=7.5 and snrk=2.8 gives snrtot=8
npix_max: 2048
# spw: [4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]
spw: [4, 5, 6, 7, 8, 9, 10, 11, 12, 13] # avoid low-level RFI
excludeants: !!python/tuple ['ea17',]
badspwpol: 2.
uvoversample: 1.5
savenoise: False
savecandcollection: True
savecanddata: False
returncanddata: True
saveplots: True
savesols: False
selectpol: 'auto'
fftmode: 'cuda'
clustercands: !!python/tuple [2, 5]
cluster_downsampling: 1
memory_limit: 5.0 # in GB
NRAOdefaultL: # L-band 1-2 GHz, 16 spw, 1 MHz channels, 10 ms, 4 pols
nthread: 2
dtarr: [1,2,4,8] # integer to integrate in time for independent searches
maxdm: 1500
# maxdm: 1000
flagantsol: True
timesub: 'mean'
searchtype: 'image'
# sigma_image1: 7.0
# sigma_kalman: 0.0 # snr1=7.5 and snrk=2.8 gives snrtot=8
npix_max: 2048
spw: [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]
badspwpol: 2.
uvoversample: 1.5
savenoise: False
savecandcollection: True
savecanddata: False
returncanddata: True
saveplots: True
savesols: False
selectpol: 'auto'
fftmode: 'cuda'
clustercands: !!python/tuple [2, 5]
cluster_downsampling: 1
memory_limit: 5.0 # in GB
20A-346: # X-proposal, L-band 1-2 GHz, 8 spw, 20 ms
nthread: 2
dtarr: [1,2,4,8] # integer to integrate in time for independent searches
maxdm: 1500
# maxdm: 1000
flagantsol: True
timesub: 'mean'
searchtype: 'image'
# sigma_image1: 7.0
# sigma_kalman: 0.0 # snr1=7.5 and snrk=2.8 gives snrtot=8
npix_max: 2048
# spw: [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]
badspwpol: 2.
uvoversample: 1.5
savenoise: False
savecandcollection: True
savecanddata: False
returncanddata: True
saveplots: True
savesols: False
selectpol: 'auto'
fftmode: 'cuda'
clustercands: !!python/tuple [2, 5]
cluster_downsampling: 1
memory_limit: 5.0 # in GB
NRAOdefaultP:
nthread: 2
dtarr: [1,2,4,8] # integer to integrate in time for independent searches
maxdm: 500
flagantsol: True
timesub: 'mean'
searchtype: 'image'
# sigma_image1: 7.0
# sigma_kalman: 0.0 # snr1=7.5 and snrk=2.8 gives snrtot=8
npix_max: 2048
# spw: [5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]
badspwpol: 2.
uvoversample: 1.5
savenoise: False
savecandcollection: True
savecanddata: False
returncanddata: True
saveplots: True
savesols: False
selectpol: 'auto'
fftmode: 'cuda'
clustercands: !!python/tuple [2, 5]
cluster_downsampling: 1
memory_limit: 5.0 # in GB
realfast:
vys_timeout: 2 # fixed timeout factor relative to realtime reading
# vys_sec_per_spec: !!float 3.0e-5 # timeout factor from time per spectrum
searchintents: ['OBSERVE_TARGET']
ignoreintents: ['CALIBRATE_FLUX', 'CALIBRATE_PHASE', 'CALIBRATE_AMPLI', 'CALIBRATE_POINTING']
read_overhead: 3 # scale reader memory higher than nominal vismem requirement
read_totfrac: 0.5 # require total of readers memories usage
throttle: 0.7 # scale submission relative to realtime and conditional on cluster status
requirecalibration: True # must have calibration to submit
mockprob: 0.0 # chance of injecting mock transient per scan
indexprefix: 'new' # save to production indices
indexresults: True # push info to elasticsearch indices
createproducts: True # save minisdm
classify: True # use fetch to classify and index results
index_with_fetch: False # use fetch workers to run indexcands_and_plots
index_with_reader: True # use reader to run indexcands_and_plots
# testing trigger
# voevent: 0 # False, 'FRB', or DM value to select cands for voevent xml generation
# voevent_snrtot: 6.6 # filter candidates based on snrtot
# voevent_frbprobt: 0.00 # filter candidates based on frbprob
# voevent_dt: 999 # filter on max width in seconds
# swift trigger
voevent_destination: '3.13.26.235' # ip address or comment out to set to None
voevent: 'FRB' # False, 'FRB', or DM value to select cands for voevent xml generation
voevent_snrtot: 9.0 # filter candidates based on snrtot
voevent_frbprobt: 0.2 # filter candidates based on frbprob
voevent_dt: 0.039 # filter on max width in seconds
| YAML | BSD | 7,498 |
[ascl:2306.019] | https://github.com/realfastvla/realfast | conf/realfastdev.sh | #!/bin/bash
. ~/anaconda/etc/profile.d/conda.sh
conda activate development36
cd /lustre/evla/test/realfast
exec realfast run --mode development
| Shell | BSD | 144 |
[ascl:2306.019] | https://github.com/realfastvla/realfast | conf/env_realfast.yml | name: development36
channels:
- anaconda
- conda-forge
- pkgw-forge
- defaults
dependencies:
- _tflow_select=2.1.0=gpu
- absl-py=0.7.1=py36_0
- astor=0.7.1=py36_0
- atk=2.25.90=hf2eb9ee_1001
- blas=2.10=openblas
- boost=1.68.0=py36h8619c78_1001
- boost-cpp=1.68.0=h11c811c_1000
- bzip2=1.0.6=h14c3975_1002
- c-ares=1.15.0=h7b6447c_1
- ca-certificates=2019.3.9=hecc5488_0
- cairo=1.14.12=h8948797_3
- casa-data=r20190419=0
- casa-python=5.4.1=py36hde29b37_2
- casa-tools=5.4.1=h738cfa5_2
- casacore=2.4.2a0=py36hde8ec18_1005
- certifi=2019.3.9=py36_0
- cfitsio=3.430=hc04bd9f_1001
- cloudpickle=1.0.0=py_0
- cudatoolkit=9.2=0
- cudnn=7.3.1=cuda9.2_0
- cupti=9.2.148=0
- curl=7.64.0=h646f8bb_0
- cython=0.29.7=py36he1b5a44_0
- cytoolz=0.9.0.1=py36h14c3975_1
- dask-core=1.2.2=py_0
- dbus=1.13.2=h714fa37_1
- dbus-cxx=0.9.2=h9ea1f2e_1000
- decorator=4.4.0=py36_1
- expat=2.2.5=hf484d3e_1002
- fftw=3.3.8=nompi_h7f3a6c3_1105
- fontconfig=2.13.1=he4413a7_1000
- freeglut=3.0.0=hf484d3e_1005
- freetype=2.10.0=he983fc9_0
- gast=0.2.2=py36_0
- gdk-pixbuf=2.36.12=h4f1c04b_1001
- gettext=0.19.8.1=hc5be6a0_1002
- glib=2.56.2=hd408876_0
- gobject-introspection=1.56.1=py36hbc4ca2d_2
- graphite2=1.3.13=hf484d3e_1000
- grpcio=1.14.1=py36h9ba97e2_0
- gsl=2.4=h294904e_1006
- gst-plugins-base=1.14.0=hbbd80ab_1
- gstreamer=1.14.0=hb453b48_1
- gtk2=2.24.31=h5baeb44_1000
- h5py=2.9.0=py36h7918eee_0
- harfbuzz=1.9.0=he243708_1001
- hdf5=1.10.4=nompi_h3c11f04_1106
- icu=58.2=hf484d3e_1000
- imageio=2.5.0=py36_0
- joblib=0.13.2=py36_0
- jpeg=9c=h14c3975_1001
- keras=2.2.4=0
- keras-applications=1.0.7=py_0
- keras-base=2.2.4=py36_0
- keras-preprocessing=1.0.9=py_0
- kiwisolver=1.1.0=py36hc9558a2_0
- krb5=1.16.3=hc83ff2d_1000
- libblas=3.8.0=10_openblas
- libcblas=3.8.0=10_openblas
- libcurl=7.64.0=h01ee5af_0
- libedit=3.1.20170329=hf8c457e_1001
- libffi=3.2.1=he1b5a44_1006
- libgcc-ng=8.2.0=hdf63c60_1
- libgfortran-ng=7.3.0=hdf63c60_0
- libiconv=1.15=h516909a_1005
- liblapack=3.8.0=10_openblas
- liblapacke=3.8.0=10_openblas
- libopenblas=0.3.3=h5a2b251_3
- libpng=1.6.37=hed695b0_0
- libprotobuf=3.7.1=hd408876_0
- libsakura=3.0.0=hf484d3e_1000
- libssh2=1.8.0=h1ad7b7a_1003
- libstdcxx-ng=8.2.0=hdf63c60_1
- libtiff=4.0.10=h648cc4a_1001
- libuuid=2.32.1=h14c3975_1000
- libxcb=1.13=h14c3975_1002
- libxml2=2.9.9=h13577e0_0
- libxslt=1.1.32=h4785a14_1002
- llvmlite=0.28.0=py36hdbcaa40_0
- markdown=3.1=py36_0
- matplotlib=3.0.3=py36h5429711_0
- matplotlib-base=3.1.0=py36h5f35d83_0
- mock=2.0.0=py36_0
- ncurses=6.1=hf484d3e_1002
- networkx=2.3=py_0
- numba=0.43.1=py36hf2d7682_0
- numpy=1.16.3=py36h99e49ec_0
- numpy-base=1.16.3=py36h2f8d375_0
- olefile=0.46=py36_0
- openblas=0.3.6=h6e990d7_2
- openssl=1.0.2r=h14c3975_0
- pandas=0.24.2=py36he6710b0_0
- pango=1.40.14=hf0c64fd_1003
- pathlib=1.0.1=py_1
- pbr=5.1.3=py_0
- pcre=8.43=he6710b0_0
- pgplot=5.2.2=h3433452_1003
- pillow=6.0.0=py36h34e0f95_0
- pip=19.1=py36_0
- pixman=0.34.0=h14c3975_1003
- protobuf=3.7.1=py36he6710b0_0
- pthread-stubs=0.4=h14c3975_1001
- pwkit=0.8.20=pyh24bf2e0_0
- pyfftw=0.11.1=py36h3010b51_1001
- pyparsing=2.4.0=py_0
- pyqt=5.9.2=py36h22d08a2_1
- python=3.6.7=hd21baee_1002
- python-dateutil=2.8.0=py_0
- pytz=2019.1=py_0
- pywavelets=1.0.3=py36hdd07704_1
- pyyaml=3.13=py36h14c3975_0
- qt=5.9.6=h8703b6f_2
- qt4=4.8.7=hde88ee2_5
- qwt5=5.2.3=h6bb024c_6
- readline=7.0=hf8c457e_1001
- rpfits=2.24=h9ac9557_1002
- scikit-image=0.15.0=py36he6710b0_0
- scipy=1.2.1=py36he2b7bc3_0
- setuptools=41.0.1=py36_0
- sip=4.19.13=py36he6710b0_0
- six=1.12.0=py36_1000
- sqlite=3.28.0=h8b20d00_0
- tensorboard=1.13.1=py36hf484d3e_0
- tensorflow=1.13.1=gpu_py36h9b25d83_0
- tensorflow-base=1.13.1=gpu_py36h611c6d2_0
- tensorflow-estimator=1.13.0=py_0
- tensorflow-gpu=1.13.1=h0d30ee6_0
- termcolor=1.1.0=py36_1
- tk=8.6.9=h84994c4_1001
- toolz=0.9.0=py36_0
- tornado=6.0.2=py36h516909a_0
- tqdm=4.31.1=py36_1
- wcslib=5.20=h9ac9557_1000
- werkzeug=0.15.2=py_0
- wheel=0.33.4=py36_0
- xerces-c28=2.8.0=h449daf6_1002
- xorg-fixesproto=5.0=h14c3975_1002
- xorg-inputproto=2.3.2=h14c3975_1002
- xorg-kbproto=1.0.7=h14c3975_1002
- xorg-libice=1.0.9=h516909a_1004
- xorg-libsm=1.2.3=h84519dc_1000
- xorg-libx11=1.6.7=h14c3975_1000
- xorg-libxau=1.0.9=h14c3975_0
- xorg-libxdmcp=1.1.3=h516909a_0
- xorg-libxext=1.3.4=h516909a_0
- xorg-libxfixes=5.0.3=h516909a_1004
- xorg-libxi=1.7.9=h516909a_1002
- xorg-libxrender=0.9.10=h516909a_1002
- xorg-libxt=1.1.5=h14c3975_1002
- xorg-renderproto=0.11.1=h14c3975_1002
- xorg-xextproto=7.3.0=h14c3975_1002
- xorg-xproto=7.0.31=h14c3975_1007
- xz=5.2.4=h14c3975_1001
- yaml=0.1.7=h96e3832_1
- zlib=1.2.11=h14c3975_1004
- pip:
- ansiwrap==0.8.4
- astropy==3.2rc1
- atomicwrites==1.3.0
- attrs==19.1.0
- backcall==0.1.0
- bleach==3.1.0
- bokeh==1.1.0
- chardet==3.0.4
- click==7.0
- cycler==0.10.0
- defusedxml==0.6.0
- distributed==1.28.1
- elasticsearch==6.3.1
- entrypoints==0.3
- evla-mcast==0.2.1
- fetch==0.1.8
- future==0.17.1
- fuzzywuzzy==0.17.0
- hdbscan==0.8.18
- idna==2.8
- ipykernel==5.1.1
- ipython==7.5.0
- ipython-genutils==0.2.0
- jedi==0.13.3
- jinja2==2.10.1
- jsonschema==3.0.1
- jupyter-client==5.2.4
- jupyter-core==4.4.0
- kalman-detector==0.0.4
- lxml==4.3.3
- markupsafe==1.1.1
- meld3==1.0.2
- mistune==0.8.4
- more-itertools==7.0.0
- msgpack==0.6.1
- nbconvert==5.5.0
- nbformat==4.4.0
- packaging==19.0
- pandocfilters==1.4.2
- papermill==1.0.0
- parso==0.4.0
- pexpect==4.7.0
- pickleshare==0.7.5
- pluggy==0.11.0
- prompt-toolkit==2.0.9
- psutil==5.6.2
- ptyprocess==0.6.0
- py==1.8.0
- pygments==2.4.0
- pyrsistent==0.15.2
- pytest==4.5.0
- pyzmq==18.0.1
- realfast==3.4.1
- requests==2.22.0
- rfpipe==1.3.2
- scikit-learn==0.20.3
- sdmpy==1.70.3
- sortedcontainers==2.1.0
- supervisor==4.0.3
- supervisor-wildcards==0.1.3
- tblib==1.4.0
- tenacity==5.0.4
- testpath==0.4.2
- textwrap3==0.9.2
- traitlets==4.3.2
- urllib3==1.25.3
- wcwidth==0.1.7
- webencodings==0.5.1
- zict==0.1.4
prefix: /home/cbe-master/realfast/anaconda/envs/development36
| YAML | BSD | 6,574 |
[ascl:2306.019] | https://github.com/realfastvla/realfast | conf/realfast.sh | #!/bin/bash
. ~/anaconda/etc/profile.d/conda.sh
conda activate deployment3
cd /lustre/evla/test/realfast
exec realfast run --mode deployment
| Shell | BSD | 141 |
End of preview. Expand in Data Studio
ASCL Astronomy Source Code
The Astrophysics Source Code Library (ASCL) is a curated registry of source code used in astronomy and astrophysics research. This dataset contains source files extracted from ASCL-listed repositories, paired with catalog metadata.
Dataset Structure
Manifest (manifest.parquet)
One row per ASCL catalog entry with the following fields:
| Field | Description |
|---|---|
ascl_id |
ASCL identifier (e.g., [ascl:2306.019]) |
title |
Software title |
authors |
Author list |
description |
Abstract / description from ASCL |
detail_url |
ASCL detail page URL |
repo_url |
GitHub/GitLab/Bitbucket URL (if found) |
code_site |
Project homepage URL |
ads_url |
ADS bibcode URL |
license_type |
Detected license (e.g., MIT, GPL-3.0) |
license_file |
Path to license file in repo |
Source Code (code/*.parquet)
Stack-style source files extracted from cloned repositories (one row per file):
| Field | Description |
|---|---|
ascl_id |
ASCL identifier |
repo_url |
Source repository URL |
file_path |
Relative path within repo |
content |
File text content |
language |
Detected programming language (from file extension) |
license_type |
License detected from the repository |
size |
File size in bytes |
Data Collection Methodology
Phase 1: Catalog Scrape
The ASCL catalog is scraped to extract metadata for each entry: title, authors, description, repository URLs, and ADS bibcode links. Only entries with a repository URL on GitHub, GitLab, or Bitbucket proceed to Phase 2.
Phase 2: Code Extraction
Each repository is shallow-cloned (--depth 1), its license file is detected and classified
via regex pattern matching, and all recognised source files are extracted into Parquet batches.
Language detection uses file extension mapping (Python, C, C++, Fortran, Julia, R,
MATLAB/Octave, IDL, Java, Rust, Go, JavaScript, Shell, and others).
Limitations
- Repository coverage: only repos hosted on GitHub, GitLab, or Bitbucket are included; code distributed via tarballs, personal websites, or other non-git hosting is skipped.
- Shallow clones only: only the latest commit is captured — no version history.
- Language detection is extension-based: file extensions are mapped to languages; there is no content-based language classification.
- License detection is regex-based: licenses are identified by pattern matching against
common license file names and text; unusual or custom licenses may be misclassified or
reported as
Unknown. - No deduplication: if multiple ASCL entries point to the same repository, its files may appear more than once.
Licensing
This is a multi-license dataset. Each row carries a license_type field indicating the
license detected for that repository. Individual source files retain their original licenses
as set by their authors. Catalog metadata originates from ASCL.
Usage
from datasets import load_dataset
# Load catalog metadata
ds_manifest = load_dataset("Smith42/ascl-code", data_files="manifest.parquet")
# Load source code files
ds_code = load_dataset("Smith42/ascl-code", data_files="code/*.parquet")
# Filter to a specific license
mit_code = ds_code["train"].filter(lambda x: x["license_type"] == "MIT")
# Filter to Python files
python_code = ds_code["train"].filter(lambda x: x["language"] == "Python")
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