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Python library for the analysis UV SO2 camera data

Project description

Pyplis is a Python toolbox for the analysis of UV SO2 camera data. The software includes a comprehensive collection of algorithms for the analysis of such data.

Contact: Jonas Gliss (jonasgliss@gmail.com)

Pyplis paper

The software Pyplis and implementation details was published in December 2017 within a special issue on Volcanic plumes of the Journal Geosciences (MDPI). The paper can be downloaded here.

Citation

If you find Pyplis useful for your data analysis, we would highly appreciate if you acknowledge our work by citing the paper. Citing details can be found here.

Note

The software was renamed from piscope to Pyplis on 17.02.2017

Main features

  • Detailed analysis of the measurement geometry including automised 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.

  • 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.

Requirements

Requirements are listed ordered in decreasing likelyhood to run into problems when using pip for installation (on Windows machines you may use the pre-compiled binary wheels on Christoph Gohlke’s webpage)

  • numpy >= 1.11.0

  • scipy >= 0.17.0

  • opencv (cv2) >= 2.4.11

  • Pillow (PIL fork) >= 3.3.0 (installs scipy.misc.pilutil)

  • astropy >= 1.0.3

  • geonum >= 1.0.0

    • latlon >= 1.0.2

    • srtm.py >= 0.3.2

    • pyproj >= 1.9.5.1

    • basemap >= 1.0.7

  • pandas == 0.16.2

  • matplotlib >= 1.4.3

Optional dependencies (to use extra features)

  • pydoas >= 1.0.0

We recommend using Anaconda as package manager since it includes most of the required dependencies and is updated on a regular basis. Moreover, it is probably the most comfortable way to postinstall and upgrade dependencies such as OpenCV (see here) or the scipy stack.

Please, if you have problems installing Pyplis, contact us or better, raise an Issue.

Installation

pyplis can be installed from PyPi using:

pip install pyplis

or from source by downloading and extracting the latest release. After navigating to the source folder (where the setup.py file is located), call:

python setup.py install

On Linux:

sudo python setup.py install

In case the installation fails make sure that all dependencies (see above) are installed correctly. pyplis is currently only supported for Python v2.7.

Code documentation

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

Getting started

The Pyplis example scripts 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).

Example and test data

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

import pyplis
pyplis.inout.download_test_data(LOCAL_DIR)

which downloads the data to the installation data directory if LOCAL_DIR is unspecified. Else, (and if LOCAL_DIR is a valid location) it will be downloaded into LOCAL_DIR 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.

TODO’s

  1. Automatic velocity cross correlation analysis from image list objects

  2. Automatic and continuous DOAS / cell calibration data

Future developments / ideas

  1. Re-implementation of GUI framework

  2. Include DOAS analysis for camera calibration by combining pydoas with flexDOAS.

  3. Include online access to meteorological databases (e.g. to estimate wind direction, velocity)

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