Visualization with yt (for plotfiles)¶
yt is a Python package that can help in analyzing and visualizing WarpX data (among other data formats). It is convenient to use yt within a Jupyter notebook.
Installation¶
From the terminal:
pip install yt jupyter
or with the Anaconda distribution of python (recommended):
conda install -c conda-forge yt
The latest version of yt can be required for advanced options (e.g., rigid injection for particles). To built yt directly from source, you can use
pip install git+https://github.com/yt-project/yt.git
Visualizing the data¶
Once data (“plotfiles”) has been created by the simulation, open a Jupyter notebook from the terminal:
jupyter notebook
Then use the following commands in the first cell of the notebook to import yt and load the first plot file:
import yt
ds = yt.load('./diags/plotfiles/plt00000/')
The list of field data and particle data stored can be seen with:
ds.field_list
For a quick start-up, the most useful commands for post-processing can be found
in our Jupyter notebook
Visualization.ipynb
Field data¶
Field data can be visualized using yt.SlicePlot (see the docstring of
this function here)
For instance, in order to plot the field Ex in a slice orthogonal to y (1):
yt.SlicePlot( ds, 1, 'Ex', origin='native' )
Note
yt.SlicePlot creates a 2D plot with the same aspect ratio as the physical size of the simulation box. Sometimes this can lead to very elongated plots that are difficult to read. You can modify the aspect ratio with the aspect argument ; for instance:
yt.SlicePlot( ds, 1, 'Ex', aspect=1./10 )
Alternatively, the data can be obtained as a numpy array.
For instance, in order to obtain the field jz (on level 0) as a numpy array:
ad0 = ds.covering_grid(level=0, left_edge=ds.domain_left_edge, dims=ds.domain_dimensions)
jz_array = ad0['jz'].to_ndarray()
Particle data¶
Particle data can be visualized using yt.ParticlePhasePlot (see the docstring
here).
For instance, in order to plot the particles’ x and y positions:
yt.ParticlePhasePlot( ds.all_data(), 'particle_position_x', 'particle_position_y', 'particle_weight')
Alternatively, the data can be obtained as a numpy array.
For instance, in order to obtain the array of position x as a numpy array:
ad = ds.all_data()
x = ad['particle_position_x'].to_ndarray()
Read back-transformed diagnotics¶
When running a simulation in a boosted frame, WarpX has the capability to
back-transform the simulation results to the laboratory frame of reference, which
is often useful to study the physics. A set of function can be found in the
python file read_raw_data.py.
Alternatively, the main commands can be found in our example jupyter
notebook for postprocessing
Visualization.ipynb.