In order to run \(\omega radlib\), you need to have a Python interpreter installed on your local computer, as well as a number of Python packages (Dependencies). We recommend installing Anaconda as it includes Python, numerous required packages, and other useful tools (e.g. Spyder).
Using Anaconda the installation process is harmonised across platforms. Download and install the latest Anaconda distribution from https://www.anaconda.com/download/ for your specific OS. You might also consider the minimal Miniconda if you do not want to install a full scientific python stack.
We are constantly performing tests with these distributions (for python versions 2.7, 3.5, 3.6 and 3.7 respectively).
If your Anaconda Python installation is working, the following command (in a console) should work:
$ python --version Python 3.5.1 :: Continuum Analytics, Inc.
Now you can use the
conda package and environment manager (conda documentation) to setup your \(\omega radlib\) installation.
Add the conda-forge channel, where \(\omega radlib\) and its dependencies are located. Read more about the community effort conda-forge:
$ conda config --add channels conda-forge
Create a new environment from scratch:
$ conda create --name wradlib python=3.6
Activate the \(\omega radlib\) environment
$ source activate wradlib
> activate wradlib
Install \(\omega radlib\) and its dependencies:
(wradlib) $ conda install wradlib
Make sure the GDAL_DATA environment variable (needed for georeferencing) is set within your environment.
(wradlib) $ echo $GDAL_DATA
[wradlib] > echo %GDAL_DATA%
If not, you can set it like this:
(wradlib) $ export GDAL_DATA=/path/to/anaconda/envs/wradlib/share/gdal
[wradlib] > setx GDAL_DATA C:\path\to\anaconda\envs\wradlib\Library\share\gdal
Now you have a
conda environment with a working \(\omega radlib\) installation.
Test the integrity of your \(\omega radlib\) installation by opening a console window and typing calling the python interpreter:
$ python Python 3.6.2 | packaged by conda-forge | (default, Jul 23 2017, 22:59:30) [GCC 4.8.2 20140120 (Red Hat 4.8.2-15)] on linux Type "help", "copyright", "credits" or "license" for more information.
The Python prompt should appear. Then type:
>>> import wradlib >>> wradlib.__version__ '1.0.0'
If everything is ok, this will show the running \(\omega radlib\) version. If the \(\omega radlib\) package is not found by the interpreter, you will get:
>>> import wradlib ImportError: No module named wradlib
Alternatively, you can install the Bleeding edge code, but you have to keep track of \(\omega radlib's\) dependencies yourself.
Bleeding edge code¶
The \(\omega radlib\) version on PyPI might lag behind the actual developments. You can use the bleeding edge code from the \(\omega radlib\) repository. Note, however, that you need to make sure yourself that all Dependencies are met (see below).
Download the source, unzip, and run:
$ python setup.py install
Alternatively, you can add the \(\omega radlib\) directory to your environment variable
Installing via pip¶
Although we recommend using the Anaconda Python Environment you can install \(\omega radlib\) from PyPi via
Open a terminal and run:
$ pip install wradlib
Depending on your system you might need to be root (or sudo the above command) for this to work.
pip will then fetch the source distribution from the Python Package Index and run the installation.
Afterwards it will check for any dependencies not met, yet.
Be aware that using
pip we can only look for python-module dependencies.
For example the numpy module itself depends on some other libraries, which need to be present in order for the module to compile properly after being downloaded by
pip. We have no control over these dependencies and it is rather hard to give a complete overview.
Therefore we recommend trying to satisfy the dependencies using your favorite package management system.
\(\omega radlib\) was not designed to be a self-contained library. Besides extensive use of Numpy and Scipy, \(\omega radlib\) uses additional libraries, which you will need to install before you can use \(\omega radlib\).
|numpy||>= 1.6.1||>= 1.14.0|
|matplotlib||>= 1.5.1||>= 2.1.0|
|scipy||>= 0.9||>= 1.0.0|
|h5py||>= 2.0.1||>= 2.7.0|
|netCDF4||>= 1.0||>= 1.3.0|
|gdal||>= 1.9||>= 2.2.0|
You can check whether the required Dependencies are available on your computer by opening a Python console and enter:
>>> import <package_name> ImportError: No module named <package_name>
This will be the response in case the package is not available.
In case the import is successful, you should also check the version number:
>>> package_name.__version__ some version number
The version number should be consistent with the above Dependencies.
Apart from the obligatory Dependencies, some dependencies in \(\omega radlib\) are optional. This is because the installation of these dependencies can be somewhat tedious while many \(\omega radlib\) users will not need them anyway. In case users use a \(\omega radlib\) function that requires an optional dependency, and this dependency is not satisfied in the local environment, \(\omega radlib\) will raise an exception.
As for now, the following dependencies are defined as optional:
The speedup module
The speedup module is intended as a collection of Fortran code in order to speed up specific \(\omega radlib\) function that are critical for performance.
In order to build the speedup module as a shared library, you need to use f2py (https://sysbio.ioc.ee/projects/f2py2e/). f2py usually ships with numpy and should be available via the command line. To test whether f2py is available on your system, execute
f2py on the system console. Or, alternatively,
f2py.py. If it is available, you should get a bunch of help instructions. Now change to the \(\omega radlib\) module directory and execute on the system console:
$ f2py.py -c -m speedup speedup.f
Now the speedup module should be available.
We use xmltodict to convert the Rainbow Data Files (which have a metadata XML header) to an ordered dict. It is easily installed with
$ pip install xmltodict
Depending on your OS and installation method you may encounter different problems. Here are some guidelines for attacking them.
We strongly recommend using the Anaconda conda package and environment manager (see Installation). Using conda-forge we will maintain the wradlib-feedstock for constant availability of recent \(\omega radlib\) versions.
If you can’t use Anaconda/Miniconda, it is generally a good idea to use your systems package manager to install dependencies. This will also take account for other needed bindings, libs etc.