pyplis 1.4.4

News / Notifications

• Pyplis 1.4.3 is released

  • supports now both Python 2 and 3

  • can be easily installed (including all requirements) via conda install -c conda-forge pyplis

• NOTE (Python 2.7 retires soon)

  If you are still using Python 2.7 (or any other Python 2 version), please consider updating your installation to Python 3, since Python 2 is reaching its end-of-life soon.

Code documentation and more

The code documentation of pyplis and more information is hosted on Read the Docs.

Main features

• Detailed analysis of the measurement geometry including automatic retrieval of distances to the emission plume and/or to topographic features in the camera images (at pixel-level).

• Several routines for the retrieval of plume background intensities (either from plume images directly or using an additional sky reference image).

• Automatic analysis of cell calibration data.

• Correction for cross-detector variations in the SO2 sensitivity arising from wavelength shifts in the filter transmission windows.

• DOAS calibration routine including two methods to identify the field of view of a DOAS instrument within the camera images.

• Plume velocity retrieval either using an optical flow analysis or using signal cross correlation.

• Histogram based post analysis of optical flow field for gas velocity analysis in low contrast image regions, where the optical flow fails to detect motion (cf. Gliss et al., 2018, AMT).

• Routine for image based correction of the signal dilution effect based on contrast variations of dark terrain features located at different distances in the images.

• Support of standard image formats including FITS format.

• Easy and flexible setup for data import and camera specifications.

A detailed description of pyplis and its features (including analysis examples) can be found in Gliss et al., 2017, MDPI Geosciences.

Installation instructions and Requirements

We recommend using the Anaconda Python distribution (or Miniconda, if you want to save disk space) and to use the conda package manager. Why? See, e.g. here for some good reasons.

Below it is assumed that you made yourself familiar with the conda package manager and that it is installed on your system. It is recommended to have a look at the guidelines related to conda virtual environments.

conda create -n my_awesome_conda_environment

conda install -c conda-forge pyplis

conda env create -n pyplis_env_test -f pyplis_env.yml

conda env update -f=pyplis_env.yml

conda create --name pyplis # creates new conda environment with name pyplis (optional)
conda activate pyplis # activates new environment (optional)
conda install -c conda-forge scipy pandas astropy basemap opencv geonum pydoas

conda install -c conda-forge pyplis

pip install pyplis

python setup.py install

Getting started

The Pyplis example scripts (see previous point) are a good starting point to get familiar with the features of Pyplis and for writing customised analysis scripts. The scripts require downloading the Etna example dataset (see following section for instructions). If you require more thorough testing, refer to this wiki entry

Example and test data

The pyplis example data (required to run example scripts) is not part of the installation. It can be downloaded from here or automatically downloaded in a Python shell (after installation) using:

import pyplis
pyplis.inout.download_test_data(<desired_location>)

which downloads the data into the my_pyplis directory if <desired_location> is unspecified. Else, (and if <desired_location> is a valid location) it will be downloaded into <desired_location> which will then be added to the supplementary file _paths.txt located in the installation data directory. It can then be found by the test data search method:

pyplis.inout.find_test_data()

The latter searches all paths provided in the file _paths.txt whenever access to the test data is required. It raises an Exception, if the data cannot be found.

import pyplis
pyplis.inout.set_test_data_path(<desired_location>)

Scientific background

The article:

Pyplis - A Python Software Toolbox for the Analysis of SO2 Camera Images for Emission Rate Retrievals from Point Sources, Gliß, J., Stebel, K., Kylling, A., Dinger, A. S., Sihler, H., and Sudbø, A., Geosciences, 2017

introduces Pyplis and implementation details. Furthermore, the article provides a comprehensive review of the technique of SO2 cameras with a focus on the required image analysis. The paper was published in December 2017 as part of a special issue on Volcanic plumes of the Journal Geosciences (MDPI). Download paper.

Copyright

Copyright (C) 2017 Jonas Gliss (jonasgliss@gmail.com)

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License a 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 here.