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Description:

pywicta 0.4.dev3

PyWI CTA - A CTA wrapper for PyWI
Copyright (c) 2016-2018 Jeremie DECOCK (www.jdhp.org)

Web site: http://cta.pywi.org/
Online documentation: http://cta.pywi.org/docs/
Source code: https://github.com/jeremiedecock/pywi-cta
Issue tracker: https://github.com/jeremiedecock/pywi-cta/issues
PyWI-CTA on PyPI: https://pypi.org/project/pywicta/
PyWI-CTA on Anaconda Cloud: https://anaconda.org/jdhp/pywicta

Description
PyWI-CTA is a ctapipe wrapper for PyWI.

Warning
This project is in beta stage.

Features
The PyWI and PyWI-CTA libraries contain:

wavelet transform and wavelet filtering functions for image multiresolution
analysis and filtering;
additional filter to remove some image components (non-significant pixels
clusters);
a set of generic filtering performance estimators (MSE, angular precision,
energy loss, contamination rate, …), some relying on the scikit-image
Python library (supplementary estimators can be easily added to meet
particular needs);
a graphical user interface to visualize the filtering process in the wavelet
transformed space;
an Evolution Strategies (ES) algorithm known in the mathematical optimization
community for its good convergence rate on generic derivative-free continuous
global optimization problems (Beyer, H. G. (2013) “The theory of evolution
strategies”, Springer Science & Business Media);
additional tools to manage and monitor the parameter optimization.

Dependencies
PyWI-CTA has the following strict requirements:

Python 3.5 or 3.6
Numpy
ctapipe 0.6.1
pyhessio 2.0.1

PyWI-CTA also depends on other packages for optional features:

Scipy
Scikit-image
Pillow (a.k.a. PIL) to read and write many image formats (PNG, JPEG, TIFF, …)
Astropy to provide Fits file format
Pandas
Matplotlib 1.5 or later to provide plotting functionality
PyWI
Cosmostat iSAP Sparce2D

However, note that these only need to be installed if those particular features
are needed. pywicta will import even if these dependencies are not installed.

Installation
PyWI-CTA and its dependencies may be installed using the Anaconda or
Miniconda package system. We recommend creating a conda virtual environment
first, to isolate the installed version and dependencies from your master
environment (this is optional).
The following command will set up a conda virtual environment, add the
necessary package channels, and download PyWI-CTA and its dependencies. The
file environment.yml can be found in this repository.
Note this is beta stage software and is not yet stable enough for end-users
(expect large API changes until the first stable 1.0 release).
conda env create -n pywi-cta -f environment.yml
source activate pywi-cta
pip install pywicta --no-deps
If you have already installed ctapipe following the
official installation procedure,
you can add PyWI-CTA to the cta virtual environment like this:
source activate cta
pip install pywicta --no-deps
Developers should follow the development install instructions found in the
documentation.

Note
As ctapipe is not tested to work on Microsoft Windows systems, PyWI-CTA
does not officially support these systems neither.

Note
The --no-deps flag is optional, but highly recommended otherwise pip
will sometimes try to “help” you by upgrading PyWI-CTA dependencies like
Numpy, which may not always be desired.

Cosmostat iSAP Sparce2D installation (optional)

Download http://www.cosmostat.org/wp-content/uploads/2014/12/ISAP_V3.1.tgz (see http://www.cosmostat.org/software/isap/)
Unzip this archive, go to the “sparse2d” directory and compile the sparse2d
library. It should generate two executables named mr_transform and mr_filter:
tar -xzvf ISAP_V3.1.tgz
cd ISAP_V3.1/cxx
tar -xzvf sparse2d_V1.1.tgz
cd sparse2d
compile the content of this directory

An automated compilation and installation script for Linux is available
here
(author: Tino Michael).

Getting started: tutorial Notebooks
PyWI-CTA provides some Jupyter notebooks that can be used as examples or tutorials: https://github.com/jeremiedecock/pywi-cta-notebooks.
New users should check them in the following order:

Tutorial #0: Check PyWI-CTA install
Tutorial #1a: Load and plot a FITS image using PyWI-CTA
Tutorial #1b: Load and plot a Simtel event using PyHESSIO
Tutorial #1c: Load and plot a Simtel image using PyWI-CTA
Tutorial #1d: Plot Hillas parameters using PyWI-CTA
Tutorial #2a: Tailcut cleaning with PyWI-CTA
Tutorial #2b: Plot Starlet planes with PyWI-CTA
Tutorial #2c: Starlet cleaning with PyWI-CTA
Tutorial #3a: Tailcut interactive notebook
Tutorial #3b: Starlet cleaning interactive notebook
Tutorial #3c: Wavelet Sparce2D MrTransform interactive notebook
Tutoriel #4a: Optimize Tailcut thresholds with exhaustive search
Tutoriel #4b: Optimize Starlet thresholds with exhaustive search
Tutoriel #4c: Optimize Tailcut thresholds with Differential Evolution
Tutoriel #4d: Optimize Starlet thresholds with Differential Evolution

Recommended JupyterLab extensions for these tutorials (type the following commands in a terminal within the right conda environment):
jupyter labextension install @jupyter-widgets/jupyterlab-manager
jupyter labextension install @jupyterlab/toc
jupyter labextension install @ijmbarr/jupyterlab_spellchecker

Console usage example
PyWI-CTA can also be used through console commands. The following is an usage example:

Get a simtel file (e.g. from there)
In your system terminal, from the directory that contains the sample image,
type the following commands (where SIMTEL_FILE is the path to your simtel
file):
pywicta-starlet -f common_hard_filtering -t 13.,1.5 -L mask --camid LSTCam --max-images 1 --plot SIMTEL_FILE
pywicta-mrtransform -f common_hard_filtering -t 13.,1.5 -L mask --camid LSTCam --max-images 1 --plot SIMTEL_FILE
pywicta-mrfilter -K -k -C1 -m3 -n4 -s2,4.5,3.5,3 --kill-isolated-pixels --camid LSTCam --max-images 1 --plot SIMTEL_FILE

Type pywicta-starlet -h, pywicta-mrtransform -h or pywicta-mrfilter -h to display the list of
available options and their documentation.

Analysis results
Notebooks containing the analysis results can be found there: https://gitlab.com/jdhp/pywi-cta-analysis

LST analysis (essential plots)
LST analysis (very detailed)

Bug reports
To search for bugs or report them, please use the PyWI Bug Tracker at:

https://github.com/jeremiedecock/pywi-cta/issues