Package: lfd
Linear Feature Detector|docs|Linear Feature Detector (LFD) library is a collection of packages that enable
user to detect and analyze linear features on astronomical images. The code was
designed to be run interactively, or at scale on a cluster and specifically
targets SDSS survey data and meteor trails therein.LFD is a more complete version of LFDS that contains all of the never-published
features of LFDS. Except for the linear feature detection code in the detecttrails
module, most of the LFDS code was recoded from scratch and made compatible with
Python 3 and OpenCv 3.0. You can find the old LFDS code  here_... _here:https://github.com/DinoBektesevic/LFDAInstallationInstall from pip by running.. code-block:: bashpip install lfdor clone it locally and userequirements.txtto create an environment from
which you can run lfd.. code-block:: bashgit clonehttps://github.com/DinoBektesevic/lfd.gitImport lfd and be on your merry way. One issue can occur when using conda or
miniconda virtual environments wherenumpycan not be found. In that case it
is neccessary to runconda install numpyand repeat the pip install for the
lfd to install properly.RequirementsMajor requirements are as followsPython 3+OpenCV 3+NumPy 1.11+SciPy 0.19+Fitsio 0.9.7+SQLAlchemy 1.2.11+parts of Erin Sheldon's esutil_ and sdsspy_ utilities are bundled with the
provided code. Some of the code might have been altered.. _esutil:https://github.com/esheldon/sdsspy/.. _sdsspy:https://github.com/esheldon/esutilRunning the codeRead the docs! They contain many examples.By default lfd is setup to work with SDSS files and directory structure. This
can be altered significantly, although complete departure from SDSS file and
directory structures are not supported out of the box.Although slightly out of data much of the processing steps are still adequatly
described in::Bektesevic & Vinkovic, 2017, MNRAS, 1612.04748, Linear Feature Detection Algorithm for Astronomical Surveys - I. Algorithm descriptionTo start processing use any of the following:.. code-block:: pythonimport lfd
 lfd.setup_detecttrails("~/boss")


 foo = lfd.detecttrails.DetectTrails(run=2888)
 foo = lfd.detecttrails.DetectTrails(run=2888, camcol=1)
 foo = lfd.detecttrails.DetectTrails(run=2888, camcol=1, filter='i')
 foo = lfd.detecttrails.DetectTrails(run=2888, camcol=1, filter='i', field=139)
 foo.process()It is possible to change detection parameters of any step in the processing by.. code-block:: pythonfoo.params_dim
 foo.params_bright["debug"] = True
 foo.params_removestars["filter_caps"]["i"] = 20Results are outputted to a file provided by the filepathresults, by default
set toresults.txt. Results file is a CSV file in which the detected
parameters. Results module provides functionality to parse these CSV files into
a database for which an SQLAlchemy ORM is provided... code-block:: pythonfrom lfd.results import Event, Frame, Point
 from lfd import results

 # create or connect to a database
 results.connect2db("foo.db")

 # populate it with data either from output of detecttrails
 results.from_file("results.txt")

 # or create mock data to play with
 results.utils.create_test_sample()

 # query on Event or Frame parameters fo a single or a collection of items
 with results.session_scope() as s:
     # returns all Events found on run 2888, but pick only one
     e = s.query(Event).filter(Event.run=2888).first()
     results.utils.deep_expunge(e)

     # get a collection of frames 
     fquery = query.filter(Frame.t.iso > '2009-09-27 10:06:10.430')
     f = fquery.all()
     lfd.results.deep_expunge_all(f, s)

 # create table like output
 results.utils.pprint(f)

 # manipulate them as OO objects and commit the changes back, f.e. move one
 # of the points of the line somewhere else
 e.p1 = Point(10, 10, camcol=5, filter='r')

 # or just move one of P1(x1, y1), P2(x2, y2) line coordinates
 e.y2 = 10

 # see and work with the coordinates values in reference to the origin of
 # the entire CCD array and not just individual CCDs within
 e.p1.x
 e.p1.switchCoordSys()
 e.p1.x

 # equivalent to
 e.cx1 = 100

 # find the points where the line corsses the individual CCD edges again and go there
 e.snap2ccd()

 # persist the changes to the DB
 with results.session_scope() as s:
     s.add(e)
     s.commit()LFD was designed to be able to handle processing large ammounts of data, in fact
it was used to process the entire SDSS database of images by using the Fermi
cluster at Astronomical Observatory Belgrade in Serbia. To make the creation of
scripts that ran LFD on the cluster easier createjobs module was written. By
default it is oriented towards running on that particular cluster, but it should
be easily adaptable to any Sun Grid cluster out there... code-block:: pythonjobs = cj.Jobs(500)
 jobs.create()
 There are no runs to create jobs from.
   Creating jobs for all runs in runlist.par file.

 Creating:
   765 jobs with 1 runs per job
   Queue:     standard
 Wallclock: 24:00:00
 Cputime:   48:00:00
 Ppn:       3
 Path:      /home/user/Desktop/.../jobswhich is of course very flexible.. code-block:: pythonruns = [125, 99, 2888, 1447]
cmd = """python3 -c "import detecttrails as dt;
x = dt.DetectTrails($);
x.params_bright['debug']=True;
x.process()"""
jobs = cj.Jobs(2, runs=runs, camcol=1, filter='i', command=cmd)
jobs.create()User will be notified about all important parameters that were set. LFD also
comes with Graphical User Interfaces through which users can create these jobs
via mouseclicks but also visually inspect their results by using the provided
specially designed image browser.An analysis module is provided as well through which theoretical meteor profiles
can be generated as described in::Bektesevic & Vinkovic et. al. 2017 (arxiv: 1707.07223)... code-block:: pythonfrom lfd.analysis import profiles

 point = profiles.PointSource(100)
 seeing = profiles.GausKolmogorov(profiles.SDSSSEEING)
 defocus = profiles.FluxPerAngle(100, *profiles.SDSS)

 a = profiles.convolve(point, seeing, defocus)

 import matplotlib.pyplot as plt
 fig, ax = plt.subplots(1, 1)
 profiles.plot_profiles(ax, (point, seeing, defocus, a))
 plt.legend()
 plt.show()All of this is, of course, just a quick overview of all functionalities. There
are many more details describing this and other useful utilities, including
Graphical User Interfaces to common functionality, provided by LFD availible in
the documentation.LicenseGNU GPLv3 Copyright (C) 2018  Dino BektesevicThis program is free software: you can redistribute it and/or modify it under the
terms of the GNU General Public License as published by the Free Software Foundation,
either version 3 of the License, or (at your option) any later version.This program is distributed in the hope that it will be useful, but WITHOUT ANY
WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A
PARTICULAR PURPOSE.  See the GNU General Public License for more details.You should have received a copy of the GNU General Public License along with this
program.  If not, see gnu.org/licenses__.. __licenses:https://www.gnu.org/licenses/gpl-3.0.en.html.. |docs| image::https://readthedocs.org/projects/linear-feature-detector/badge/?version=latest:alt: Documentation Status
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