--- file_format: mystnb kernelspec: name: python3 --- # Key Functionality ```{code-cell} ipython3 import xarray as xr import sdf_xarray as sdfxr import matplotlib.pyplot as plt ``` ## Loading SDF files ### Loading single files ```{code-cell} ipython3 sdfxr.open_dataset("tutorial_dataset_1d/0010.sdf") ``` You can also load the data in as a , which organises the data hierarchically into `groups` (for example grouping related quantities such as the individual components of the electric and magnetic fields) while keeping each item as a . ```{code-cell} ipython3 sdfxr.open_datatree("tutorial_dataset_1d/0010.sdf") ``` (loading-raw-files)= ### Loading raw files If you wish to load data directly from the `SDF.C` library and ignore the interface layer. ```{code-cell} ipython3 raw_ds = sdfxr.SDFFile("tutorial_dataset_1d/0010.sdf") raw_ds.variables.keys() ``` ### Loading multiple files Multiple files can be loaded using one of two methods. The first of which is by using the . ```{tip} If your simulation includes multiple `output` blocks that specify different variables for output at various time steps, variables not present at a specific step will default to a nan value. To remove these nan values we suggest using the function or following our implmentation in [](#loading-sparse-data). ``` ```{code-cell} ipython3 sdfxr.open_mfdataset("tutorial_dataset_1d/*.sdf") ``` Alternatively, files can be loaded using however when loading in all the files we have do some processing of the data so that we can correctly align it along the time dimension; This is done via the `preprocess` parameter utilising the function. ```{code-cell} ipython3 xr.open_mfdataset( "tutorial_dataset_1d/*.sdf", join="outer", compat="no_conflicts", preprocess=sdfxr.SDFPreprocess() ) ``` You can also load the data in as a , which organises the data hierarchically into `groups` (for example grouping related quantities such as the individual components of the electric and magnetic fields) while keeping each item as a . ```{code-cell} ipython3 sdfxr.open_mfdatatree("tutorial_dataset_1d/*.sdf") ``` ### Loading sparse data When dealing with sparse data (where different variables are saved at different, non-overlapping time steps) you can optimize memory usage by loading the data with using the parameter `separate_times=True`. This approach creates a distinct time dimension for each output block, avoiding the need for a single, large time dimension that would be filled with nan values. This significantly reduces memory consumption, though it requires more deliberate handling if you need to compare variables that exist on these different time coordinates. ```{code-cell} ipython3 sdfxr.open_mfdataset("tutorial_dataset_1d/*.sdf", separate_times=True) ``` ### Loading particle data ```{warning} It is **not recommended** to use or to load particle data from multiple SDF outputs. The number of particles often varies between outputs, which can lead to inconsistent array shapes that these functions cannot handle. Instead, consider loading each file individually and then concatenating them manually. ``` ```{note} When loading multiple probes from a single SDF file, you **must** use the `probe_names` parameter to assign a unique name to each. For example, use `probe_names=["Front_Electron_Probe", "Back_Electron_Probe"]`. Failing to do so will result in dimension name conflicts. ``` By default, particle data isn't kept as it takes up a lot of space. Pass `keep_particles=True` as a keyword argument to (for single files) or (for multiple files). ```{code-cell} ipython3 sdfxr.open_dataset("tutorial_dataset_1d/0010.sdf", keep_particles=True) ``` ### Loading specific variables When loading datasets containing several (`>10`) coordinates/dimensions using , may struggle to locate the necessary RAM to concatenate all of the data (as seen in [Issue #57](https://github.com/epochpic/sdf-xarray/issues/57)). In this instance, you can optimize memory usage by loading only the data you need using the keyword argument `data_vars` and passing one or more variables. This creates a dataset consisting only of the given variable(s) and the relevant coordinates/dimensions, significantly reducing memory consumption. ```{code-cell} ipython3 sdfxr.open_mfdataset("tutorial_dataset_1d/*.sdf", data_vars=["Electric_Field_Ex"]) ``` (loading-input-deck)= ### Loading the input.deck When loading SDF files, will attempt to automatically load the `input.deck` file used to initialise the simulation from the same directory as the SDF file. If the file is not found, it will silently fail and continue loading the SDF file as normal. This file contains the initial simulation setup information which is not present in SDF outputs. By loading this file, you can access these parameters as part of your dataset's metadata. To do this, use the `deck_path` parameter when loading an SDF file with , , , , or . There are a few ways you can load an input deck: - **Default behaviour**: The input deck is loaded from the same directory as the SDF file if it exists. If it does not exist, it will silently fail. - **Relative path**: (e.g. `"template.deck"`) Searches for that specific filename within the same directory as the SDF file. - **Absolute path**: (e.g. `"/path/to/input.deck"`) Uses the full, specified path to locate the file. An example of loading a deck can be seen below ```{code-cell} ipython3 :tags: [hide-output] import json from IPython.display import Code ds = xr.open_dataset("tutorial_dataset_1d/0010.sdf") # The results are accessible by calling deck = ds.attrs["deck"] # Some prettification to make it looks nice in jupyter notebooks json_str = json.dumps(deck, indent=4) Code(json_str, language='json') ``` ## Data interaction examples When loading in either a single dataset or a group of datasets you can access the following methods to explore the dataset: - `ds.variables` to list variables. (e.g. Electric Field, Magnetic Field, Particle Count) - `ds.coords` for accessing coordinates/dimensions. (e.g. x-axis, y-axis, time) - `ds.attrs` for metadata attached to the dataset. (e.g. filename, step, time) It is important to note here that lazily loads the data meaning that it only explicitly loads the results your currently looking at when you call `.values` ```{code-cell} ipython3 ds = sdfxr.open_mfdataset("tutorial_dataset_1d/*.sdf") ds["Electric_Field_Ex"] ``` On top of accessing variables, you can plot these datasets using [`xarray.DataArray.epoch.plot`](project:#sdf_xarray.dataarray_accessor.EpochAccessor.plot). This is a custom plotting routine that builds on top of , so you keep the familiar plotting behaviour while using conveniences (see [here](project:#sdf_xarray.dataarray_accessor.EpochAccessor.plot) for details). Under the hood, plotting is still handled by , which means you can use the full API to customise your figure. ```{code-cell} ipython3 # This is discretized in both space and time ds["Electric_Field_Ex"].epoch.plot() plt.title("Electric field along the x-axis") plt.show() ``` When loading a multi-file dataset using , a time dimension is automatically added to the resulting . This dimension represents all the recorded simulation steps and allows for easy indexing. To quickly determine the number of time steps available, you can check the size of the time dimension. ```{code-cell} ipython3 # This corresponds to the number of individual SDF files loaded print(f"There are a total of {ds['time'].size} time steps") # You can look up the actual simulation time for any given index sim_time = ds['time'].values[20] print(f"The time at the 20th simulation step is {sim_time:.2e} s") ``` You can select and extract a single simulation snapshot using the integer index of the time step with the function. This can be done by passsing the index to the `time` parameter (e.g., `time=0` for the first snapshot). ```{code-cell} ipython3 ds["Electric_Field_Ex"].isel(time=20) ``` We can also use the function if you wish to pass a value intead of an index. ```{tip} If you know roughly what time you wish to select but not the exact value you can use the parameter `method="nearest"`. ``` ```{code-cell} ipython3 ds["Electric_Field_Ex"].sel(time=sim_time) ``` ## Visualisation on HPCs In many cases you will be running EPOCH simulations via a HPC cluster and your subsequent SDF files will probably be rather large and cumbersome to interact with via traditional Jupyter notebooks. In some cases your HPC may outright block the use of Jupyter notebooks entirely. To circumvent this issue you can use a Terminal User Interface (TUI) which renders the contents of SDF files directly in a Terminal and allows for you to do some simple data analysis and visualisation. To do this we shall leverage the [xr-tui](https://github.com/samueljackson92/xr-tui) package which can be installed to either a venv or globally using: ```bash pipx install xr-tui sdf-xarray ``` or if you are using `uv` ```bash uv tool install xr-tui --with sdf-xarray ``` Once installed you can visualise SDF files by simply writing in the command line ```bash xr path/to/simulation/0000.sdf # OR xr path/to/simulation/*.sdf ``` Below is an example gif of how this interfacing looks as seen on [xr-tui](https://github.com/samueljackson92/xr-tui) `README.md`: ![xr-tui interfacing gif](https://raw.githubusercontent.com/samueljackson92/xr-tui/main/demo.gif) ## Manipulating data These datasets can also be easily manipulated the same way as you would with `numpy` arrays. ```{code-cell} ipython3 ds["Laser_Absorption_Fraction_in_Simulation"] = ( (ds["Total_Particle_Energy_in_Simulation"] - ds["Total_Particle_Energy_in_Simulation"][0]) / ds["Absorption_Total_Laser_Energy_Injected"] ) * 100 # We can also manipulate the units and other attributes ds["Laser_Absorption_Fraction_in_Simulation"].attrs["units"] = "%" ds["Laser_Absorption_Fraction_in_Simulation"].attrs["long_name"] = "Laser Absorption Fraction" ds["Laser_Absorption_Fraction_in_Simulation"].epoch.plot() plt.title("Laser absorption fraction in simulation") plt.show() ``` You can also call the [`xarray.DataArray.epoch.plot`](project:#sdf_xarray.dataarray_accessor.EpochAccessor.plot) function on several variables with labels by delaying the call to `plt.show()`. ```{code-cell} ipython3 ds["Total_Particle_Energy_Electron"].epoch.plot(label="Electron") ds["Total_Particle_Energy_Ion"].epoch.plot(label="Ion") plt.title("Particle Energy in Simulation per Species") plt.legend() plt.show() ``` ```{code-cell} ipython3 print(f"Total laser energy injected: {ds["Absorption_Total_Laser_Energy_Injected"][-1].values:.1e} J") print(f"Total particle energy absorbed: {ds["Total_Particle_Energy_in_Simulation"][-1].values:.1e} J") print(f"The laser absorption fraction: {ds["Laser_Absorption_Fraction_in_Simulation"][-1].values:.1f} %") ```