pywicta 0.1.dev6

PyWI CTA wrapper - a signal processing library for Imaging Atmospheric Cherenkov Telescopes

Copyright (c) 2016,2017,2018 Jeremie DECOCK (www.jdhp.org) and Tino Michael

• Online documentation: https://jdhp-sap.github.io/ctapipe-wavelet-filter/

• Source code: https://github.com/jdhp-sap/ctapipe-wavelet-filter

• Issue tracker: https://github.com/jdhp-sap/ctapipe-wavelet-filter/issues

• CTAPipe wavelet filter on PyPI: https://pypi.python.org/pypi/ctapipe-wavelet-filter

Description

Signal processing for gamma-ray science.

Note:

This project is in beta stage.

Dependencies

• Python >= 3.0

Installation

Gnu/Linux

You can install, upgrade, uninstall SAp CTA data pipeline with these commands (in a terminal):

pip install --pre ctapipe-wavelet-filter
pip install --upgrade ctapipe-wavelet-filter
pip uninstall ctapipe-wavelet-filter

Or, if you have downloaded the SAp CTA data pipeline source code:

python3 setup.py install

Windows

You can install, upgrade, uninstall SAp CTA data pipeline with these commands (in a command prompt):

py -m pip install --pre ctapipe-wavelet-filter
py -m pip install --upgrade ctapipe-wavelet-filter
py -m pip uninstall ctapipe-wavelet-filter

Or, if you have downloaded the SAp CTA data pipeline source code:

py setup.py install

MacOSX

You can install, upgrade, uninstall SAp CTA data pipeline with these commands (in a terminal):

pip install --pre ctapipe-wavelet-filter
pip install --upgrade ctapipe-wavelet-filter
pip uninstall ctapipe-wavelet-filter

Or, if you have downloaded the SAp CTA data pipeline source code:

python3 setup.py install

Image cleaning guidelines

Here is the basic guidelines to clean images (and assess cleaning algorithms).

Step 1

Extract images from Simtel files, crop them, convert them to “regular” 2D images and write them into fits files (one fits file per image with the ADC signal in HDU0 and the photoelectron signal in HDU1):

1. clone http://github.com/jdhp-sap/snippets

2. check snippets/ctapipe/extract_and_crop_simtel_images.py on lines 64 and 66, these lines may need to be fixed

3. in snippets/ctapipe run ./extract_crop_and_plot_all_astri_images.sh ASTRI_SIMTEL_FILE

Step 1.4 generate a lot of fits files in your current directory ; its execution may be long (up to several hours) as the script is not optimized at all and many instructions are redundant (but this is not a big deal because you only need to run it once to generate your input files).

Step 2

Install mr_transform (the cosmostat wavelet transform tool):

1. download http://www.cosmostat.org/wp-content/uploads/2014/12/ISAP_V3.1.tgz (see http://www.cosmostat.org/software/isap/)

2. unzip this archive, go to the “sparse2d” directory and compile the sparse2d library. It should generate an executable named “mr_transform”:

   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

Step 3

Clean images generated in step 1:

1. clone and install http://github.com/jdhp-sap/data-pipeline-standalone-scripts (see https://github.com/jdhp-sap/data-pipeline-standalone-scripts#installation)

2. to clean one fits file (see for instance run_experiments.sh):

   • with Tailcut : in data-pipeline-standalone-scripts, run ./pywicta/denoising/tailcut.py -T 0.75 -t 0.5 FITS_FILE (-T = max threshold, -t = min threshold, use the -h option to see command usage)

   • with FFT : in data-pipeline-standalone-scripts, run ./pywicta/denoising/fft.py -s -t 0.02 FITS_FILE (-t = threshold in the Fourier space, use the -h option to see command usage)

   • with Wavelets : in data-pipeline-standalone-scripts, run ./pywicta/denoising/wavelets_mrtrransform.py FITS_FILE (use the -h option to see command usage)

3. instead of the step 3.2, the “benchmark mode” can be set to clean images and assess cleaning algorithms (it’s still a bit experimental) : use the same instructions than for step 3.2 with the additional option “-b 1” in each command (and put several fits files in input e.g. “*.fits”)

Step 4

Optionally, plot some stats about scores: in data-pipeline-standalone-scripts/utils, use the plot_score_*.py scripts on the JSON files generated in step 3.3 (use the -h option to see command usage)

Bug reports

To search for bugs or report them, please use the SAp Data Pipeline Standalone Scripts Bug Tracker at:

https://github.com/jdhp-sap/sap-cta-data-pipeline/issues