Metadata-Version: 1.1
Name: pyplis
Version: 0.9.1
Summary: A Python library for the analysis UV SO2 camera data
Home-page: https://github.com/jgliss/pyplis
Author: Jonas Gliss
Author-email: jg@nilu.no
License: GPLv3+
Description: pyplis is a Python toolbox for the analysis of UV SO2 camera data. It includes a large collection of routines for the analysis of such data, for instance:

        

          1. Several routines for plume background estimation

          #. Automatic cell calibration 

          #. 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

          #. Detailed analysis of the measurement geometry including automized retrieval of distances to the emission plume and/or to topographic features in the camera images (on a pixel basis)

          #. Routine for image based light dilution correction

          

        .. note::

        

          The software was renamed from **piscope** to **pyplis** on 17.02.2017 

        

        Requirements

        ------------

        

        Requirements are listed in the order of likelyhood to run into problems when using pip for installing them (on Windows machines you may use the pre-compiled binary wheels on Christoph Gohlke's `webpage <http://www.lfd.uci.edu/~gohlke/pythonlibs/>`_)

        

          - 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

            

          - pandas >= 0.16.2

          - matplotlib >= 1.4.3

        

        **Optional dependencies (to use extra features)**

        

          - pydoas >= 1.0.0

          - scikit-image (skimage) >= 0.11.3 (for blob detection in optical flow analysis)

          

        

        We recommend using `Anaconda <https://www.continuum.io/downloads>`_ 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 <http://stackoverflow.com/questions/23119413/how-to-install-python-opencv-through-conda>`__) or the scipy stack.

        

        Installation

        ------------

        pyplis can be installed from `PyPi <https://pypi.python.org/pypi/pyplis>`_ 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 is hosted on `Read the Docs <http://pyplis.readthedocs.io/en/latest/>`__

        

        Getting started

        ---------------

        

        After installation try running and understanding the `example scripts <https://github.com/jgliss/pyplis/tree/master/scripts>`_. The scripts require the example data (see also following section for details).

        

        Example and test data

        ---------------------

        

        The pyplis example data (required to run example scripts) is not part of the installation. It can be downloaded `here <https://folk.nilu.no/~gliss/pyplis_testdata/pyplis_etna_testdata.zip>`__ or automatically within a Python console (after installation) using::

        

          import pyplis

          pyplis.inout.download_test_data(*local_path*)

          

        which downloads the data to the installation *data* directory if *local_path* is unspecified. Else, (and if *local_path* is a valid location) it will be downloaded into the specific directory which will then be added to the supplementary file *./data/_paths.txt* such that it can be found by the test data search method::

        

          pyplis.inout.find_test_data()

          

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

        

        .. note::

        

          If you download the data manually (e.g. using the link provided above), please unzip it to a suitable location *my_testdata_is_here* and let pyplis know about it using::

          

            import pyplis

            pyplis.inout.set_test_data_path(*my_testdata_is_here*)

            

          The path is then added to *./data/_paths.txt* such that it can be found by::

          

            pyplis.inout.find_test_data()
Keywords: sample setuptools development
Platform: UNKNOWN
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Education
Classifier: License :: OSI Approved :: GNU General Public License v3 or later (GPLv3+)
Classifier: Programming Language :: Python :: 2.7
