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.. _dataanalysis-openpmd-viewer:
openPMD-viewer
==============
`openPMD-viewer <https://github.com/openPMD/openPMD-viewer>`__ is an open-source Python package to access openPMD data.
It allows to:
* Quickly browse through the data, with a GUI-type interface in the Jupyter notebook
* Have access to the data numpy array, for more detailed analysis
Installation
------------
openPMD-viewer can be installed via ``conda`` or ``pip``:
.. code-block:: bash
conda install -c conda-forge openpmd-viewer openpmd-api
.. code-block:: bash
pip install openPMD-viewer openPMD-api
Usage
-----
openPMD-viewer can be used either in simple Python scripts or in `Jupyter <https://jupyter.org>`__.
For interactive plots in Jupyter notebook, add this `"cell magic" <https://ipython.readthedocs.io/en/stable/interactive/magics.html>`__ to the first line of your notebook:
.. code-block:: python
%matplotlib notebook
and for Jupyter Lab use this instead:
.. code-block:: python
%matplotlib widget
If none of those work, e.g. because `ipympl <https://github.com/matplotlib/ipympl#installation>`__ is not properly installed, you can as a last resort always try ``%matplotlib inline`` for non-interactive plots.
In both interactive and scripted usage, you can import openPMD-viewer, and load the data with the following commands:
.. code-block:: python
from openpmd_viewer import OpenPMDTimeSeries
ts = OpenPMDTimeSeries('./diags/diag1/')
.. note::
If you are using the Jupyter notebook, then you can start a pre-filled
notebook, which already contains the above lines, by typing in a terminal:
::
openPMD_notebook
When using the Jupyter notebook, you can quickly browse through the data
by using the command:
::
ts.slider()
You can also access the particle and field data as numpy arrays with the methods ``ts.get_field`` and ``ts.get_particle``.
See the openPMD-viewer tutorials `here <https://github.com/openPMD/openPMD-viewer/tree/master/tutorials>`_ for more info.
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