diff --git a/tutorials/base_tutorial.ipynb b/tutorials/base_tutorial.ipynb new file mode 100644 index 0000000..b11ef92 --- /dev/null +++ b/tutorials/base_tutorial.ipynb @@ -0,0 +1,3452 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Key Functionalities of sdf-xarray\n", + "\n", + "sdf-xarray provides a backend for [xarray](https://xarray.dev/) to read SDF files as created by the [EPOCH](https://epochpic.github.io/) plasma PIC code." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import xarray as xr\n", + "from sdf_xarray import SDFPreprocess, SDFFile\n", + "import matplotlib.pyplot as plt\n", + "from IPython.display import display, HTML\n", + "\n", + "\n", + "# Folders\n", + "simulation_path_1d = \"base_tutorial_dataset_1d\"" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Loading SDF Files" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Loading a Single SDF File" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.Dataset> Size: 246kB\n",
+       "Dimensions:                                       (X_Grid_mid: 1536,\n",
+       "                                                   X_Grid: 1537)\n",
+       "Coordinates:\n",
+       "  * X_Grid                                        (X_Grid) float64 12kB -1e-0...\n",
+       "  * X_Grid_mid                                    (X_Grid_mid) float64 12kB -...\n",
+       "Data variables: (12/27)\n",
+       "    Wall_time                                     float64 8B ...\n",
+       "    Electric_Field_Ex                             (X_Grid_mid) float64 12kB ...\n",
+       "    Electric_Field_Ey                             (X_Grid_mid) float64 12kB ...\n",
+       "    Electric_Field_Ez                             (X_Grid_mid) float64 12kB ...\n",
+       "    Magnetic_Field_Bx                             (X_Grid_mid) float64 12kB ...\n",
+       "    Magnetic_Field_By                             (X_Grid_mid) float64 12kB ...\n",
+       "    ...                                            ...\n",
+       "    Derived_Number_Density_Electron               (X_Grid_mid) float64 12kB ...\n",
+       "    Derived_Number_Density_Ion                    (X_Grid_mid) float64 12kB ...\n",
+       "    Derived_Number_Density_Photon                 (X_Grid_mid) float64 12kB ...\n",
+       "    Derived_Number_Density_Positron               (X_Grid_mid) float64 12kB ...\n",
+       "    Absorption_Total_Laser_Energy_Injected        float64 8B ...\n",
+       "    Absorption_Fraction_of_Laser_Energy_Absorbed  float64 8B ...\n",
+       "Attributes: (12/21)\n",
+       "    filename:         example_dataset_1d/0000.sdf\n",
+       "    file_version:     1\n",
+       "    file_revision:    4\n",
+       "    code_name:        Epoch1d\n",
+       "    step:             0\n",
+       "    time:             2.6059694937355635e-17\n",
+       "    ...               ...\n",
+       "    compile_machine:  login1.viking2.yor.alces.network\n",
+       "    compile_flags:    unknown\n",
+       "    defines:          50364608\n",
+       "    compile_date:     Fri Oct 11 16:12:01 2024\n",
+       "    run_date:         Fri Oct 25 12:34:55 2024\n",
+       "    io_date:          Fri Oct 25 12:34:57 2024
" + ], + "text/plain": [ + " Size: 246kB\n", + "Dimensions: (X_Grid_mid: 1536,\n", + " X_Grid: 1537)\n", + "Coordinates:\n", + " * X_Grid (X_Grid) float64 12kB -1e-0...\n", + " * X_Grid_mid (X_Grid_mid) float64 12kB -...\n", + "Data variables: (12/27)\n", + " Wall_time float64 8B ...\n", + " Electric_Field_Ex (X_Grid_mid) float64 12kB ...\n", + " Electric_Field_Ey (X_Grid_mid) float64 12kB ...\n", + " Electric_Field_Ez (X_Grid_mid) float64 12kB ...\n", + " Magnetic_Field_Bx (X_Grid_mid) float64 12kB ...\n", + " Magnetic_Field_By (X_Grid_mid) float64 12kB ...\n", + " ... ...\n", + " Derived_Number_Density_Electron (X_Grid_mid) float64 12kB ...\n", + " Derived_Number_Density_Ion (X_Grid_mid) float64 12kB ...\n", + " Derived_Number_Density_Photon (X_Grid_mid) float64 12kB ...\n", + " Derived_Number_Density_Positron (X_Grid_mid) float64 12kB ...\n", + " Absorption_Total_Laser_Energy_Injected float64 8B ...\n", + " Absorption_Fraction_of_Laser_Energy_Absorbed float64 8B ...\n", + "Attributes: (12/21)\n", + " filename: example_dataset_1d/0000.sdf\n", + " file_version: 1\n", + " file_revision: 4\n", + " code_name: Epoch1d\n", + " step: 0\n", + " time: 2.6059694937355635e-17\n", + " ... ...\n", + " compile_machine: login1.viking2.yor.alces.network\n", + " compile_flags: unknown\n", + " defines: 50364608\n", + " compile_date: Fri Oct 11 16:12:01 2024\n", + " run_date: Fri Oct 25 12:34:55 2024\n", + " io_date: Fri Oct 25 12:34:57 2024" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "da = xr.open_dataset(f\"{simulation_path_1d}/0000.sdf\")\n", + "da" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Loading a Single Raw SDF File" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "Wall-time: Constant(_id='elapsed_time', name='Wall-time', data=0.4286527819931507, units=None)
Electric Field/Ex: Variable(_id='ex', name='Electric Field/Ex', dtype=dtype('float64'), shape=(1536,), is_point_data=False, sdffile=, units='V/m', mult=1.0, grid='grid', grid_mid='grid_mid')
Electric Field/Ey: Variable(_id='ey', name='Electric Field/Ey', dtype=dtype('float64'), shape=(1536,), is_point_data=False, sdffile=, units='V/m', mult=1.0, grid='grid', grid_mid='grid_mid')
Electric Field/Ez: Variable(_id='ez', name='Electric Field/Ez', dtype=dtype('float64'), shape=(1536,), is_point_data=False, sdffile=, units='V/m', mult=1.0, grid='grid', grid_mid='grid_mid')
Magnetic Field/Bx: Variable(_id='bx', name='Magnetic Field/Bx', dtype=dtype('float64'), shape=(1536,), is_point_data=False, sdffile=, units='T', mult=1.0, grid='grid', grid_mid='grid_mid')
Magnetic Field/By: Variable(_id='by', name='Magnetic Field/By', dtype=dtype('float64'), shape=(1536,), is_point_data=False, sdffile=, units='T', mult=1.0, grid='grid', grid_mid='grid_mid')
Magnetic Field/Bz: Variable(_id='bz', name='Magnetic Field/Bz', dtype=dtype('float64'), shape=(1536,), is_point_data=False, sdffile=, units='T', mult=1.0, grid='grid', grid_mid='grid_mid')
Current/Jx: Variable(_id='jx', name='Current/Jx', dtype=dtype('float64'), shape=(1536,), is_point_data=False, sdffile=, units='A/m^2', mult=1.0, grid='grid', grid_mid='grid_mid')
Current/Jy: Variable(_id='jy', name='Current/Jy', dtype=dtype('float64'), shape=(1536,), is_point_data=False, sdffile=, units='A/m^2', mult=1.0, grid='grid', grid_mid='grid_mid')
Total Particle Energy/Electron: Constant(_id='total_particle_energy/Electron', name='Total Particle Energy/Electron', data=0.0, units='J')
Total Particle Energy/Ion: Constant(_id='total_particle_energy/Ion', name='Total Particle Energy/Ion', data=0.0, units='J')
Total Particle Energy/Photon: Constant(_id='total_particle_energy/Photon', name='Total Particle Energy/Photon', data=0.0, units='J')
Total Particle Energy/Positron: Constant(_id='total_particle_energy/Positron', name='Total Particle Energy/Positron', data=0.0, units='J')
Total Particle Energy in Simulation: Constant(_id='total_particle_energy', name='Total Particle Energy in Simulation', data=0.0, units='J')
Total Field Energy in Simulation: Constant(_id='total_field_energy', name='Total Field Energy in Simulation', data=0.0, units='J')
Derived/Average_Particle_Energy: Variable(_id='ekbar', name='Derived/Average_Particle_Energy', dtype=dtype('float64'), shape=(1536,), is_point_data=False, sdffile=, units='J', mult=1.0, grid='grid', grid_mid='grid_mid')
Derived/Average_Particle_Energy/Electron: Variable(_id='ekbar/Electron', name='Derived/Average_Particle_Energy/Electron', dtype=dtype('float64'), shape=(1536,), is_point_data=False, sdffile=, units='J', mult=1.0, grid='grid', grid_mid='grid_mid')
Derived/Average_Particle_Energy/Ion: Variable(_id='ekbar/Ion', name='Derived/Average_Particle_Energy/Ion', dtype=dtype('float64'), shape=(1536,), is_point_data=False, sdffile=, units='J', mult=1.0, grid='grid', grid_mid='grid_mid')
Derived/Average_Particle_Energy/Photon: Variable(_id='ekbar/Photon', name='Derived/Average_Particle_Energy/Photon', dtype=dtype('float64'), shape=(1536,), is_point_data=False, sdffile=, units='J', mult=1.0, grid='grid', grid_mid='grid_mid')
Derived/Average_Particle_Energy/Positron: Variable(_id='ekbar/Positron', name='Derived/Average_Particle_Energy/Positron', dtype=dtype('float64'), shape=(1536,), is_point_data=False, sdffile=, units='J', mult=1.0, grid='grid', grid_mid='grid_mid')
Derived/Number_Density: Variable(_id='number_density', name='Derived/Number_Density', dtype=dtype('float64'), shape=(1536,), is_point_data=False, sdffile=, units='1/m^3', mult=1.0, grid='grid', grid_mid='grid_mid')
Derived/Number_Density/Electron: Variable(_id='number_density/Electron', name='Derived/Number_Density/Electron', dtype=dtype('float64'), shape=(1536,), is_point_data=False, sdffile=, units='1/m^3', mult=1.0, grid='grid', grid_mid='grid_mid')
Derived/Number_Density/Ion: Variable(_id='number_density/Ion', name='Derived/Number_Density/Ion', dtype=dtype('float64'), shape=(1536,), is_point_data=False, sdffile=, units='1/m^3', mult=1.0, grid='grid', grid_mid='grid_mid')
Derived/Number_Density/Photon: Variable(_id='number_density/Photon', name='Derived/Number_Density/Photon', dtype=dtype('float64'), shape=(1536,), is_point_data=False, sdffile=, units='1/m^3', mult=1.0, grid='grid', grid_mid='grid_mid')
Derived/Number_Density/Positron: Variable(_id='number_density/Positron', name='Derived/Number_Density/Positron', dtype=dtype('float64'), shape=(1536,), is_point_data=False, sdffile=, units='1/m^3', mult=1.0, grid='grid', grid_mid='grid_mid')
Absorption/Total Laser Energy Injected: Constant(_id='laser_enTotal', name='Absorption/Total Laser Energy Injected', data=90808028.35251899, units='J')
Absorption/Fraction of Laser Energy Absorbed: Constant(_id='abs_frac', name='Absorption/Fraction of Laser Energy Absorbed', data=0.0, units='%')
CPUs/Original rank: Variable(_id='cpu_rank', name='CPUs/Original rank', dtype=dtype('int32'), shape=(96,), is_point_data=False, sdffile=, units='CPU', mult=None, grid='grid_cpu_rank', grid_mid='grid_cpu_rank_mid')
CPUs/Current rank: Variable(_id='cpus_current', name='CPUs/Current rank', dtype=dtype('int32'), shape=(0,), is_point_data=False, sdffile=, units='CPU', mult=None, grid='grid_cpus_current', grid_mid='grid_cpus_current_mid')" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sdf_file = SDFFile(f\"{simulation_path_1d}/0000.sdf\")\n", + "\n", + "# You can access the variables of the SDF file as a dictionary\n", + "# sdf_file.variables\n", + "\n", + "# With a little bit of HTML magic, we can display the variables in a nice way\n", + "display(HTML(\"
\".join([f\"{key}: {value}\" for key, value in sdf_file.variables.items()])))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Loading all SDF Files for a Simulation\n", + "\n", + "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." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.Dataset> Size: 10MB\n",
+       "Dimensions:                                       (X_Grid: 1537,\n",
+       "                                                   X_Grid_mid: 1536, time: 41,\n",
+       "                                                   dim_laser_x_min_phase_0: 1,\n",
+       "                                                   dim_Random States_0: 384)\n",
+       "Coordinates:\n",
+       "  * X_Grid                                        (X_Grid) float64 12kB -1e-0...\n",
+       "  * X_Grid_mid                                    (X_Grid_mid) float64 12kB -...\n",
+       "  * time                                          (time) float64 328B 2.606e-...\n",
+       "Dimensions without coordinates: dim_laser_x_min_phase_0, dim_Random States_0\n",
+       "Data variables: (12/40)\n",
+       "    Wall_time                                     (time) float64 328B 0.4287 ...\n",
+       "    Time_increment                                (time) float64 328B nan ......\n",
+       "    Plasma_frequency_timestep_restriction         (time) float64 328B nan ......\n",
+       "    Minimum_grid_position                         (time) float64 328B nan ......\n",
+       "    laser_x_min_phase                             (time, dim_laser_x_min_phase_0) float64 328B dask.array<chunksize=(1, 1), meta=np.ndarray>\n",
+       "    time_prev_normal                              (time) float64 328B nan ......\n",
+       "    ...                                            ...\n",
+       "    Derived_Number_Density_Electron               (time, X_Grid_mid) float64 504kB dask.array<chunksize=(1, 1536), meta=np.ndarray>\n",
+       "    Derived_Number_Density_Ion                    (time, X_Grid_mid) float64 504kB dask.array<chunksize=(1, 1536), meta=np.ndarray>\n",
+       "    Derived_Number_Density_Photon                 (time, X_Grid_mid) float64 504kB dask.array<chunksize=(1, 1536), meta=np.ndarray>\n",
+       "    Derived_Number_Density_Positron               (time, X_Grid_mid) float64 504kB dask.array<chunksize=(1, 1536), meta=np.ndarray>\n",
+       "    Absorption_Total_Laser_Energy_Injected        (time) float64 328B 9.081e+...\n",
+       "    Absorption_Fraction_of_Laser_Energy_Absorbed  (time) float64 328B 0.0 ......\n",
+       "Attributes: (12/21)\n",
+       "    filename:         /Users/joel/Source/sdf-xarray/examples/example_dataset_...\n",
+       "    file_version:     1\n",
+       "    file_revision:    4\n",
+       "    code_name:        Epoch1d\n",
+       "    step:             3838\n",
+       "    time:             2.0003421833910338e-13\n",
+       "    ...               ...\n",
+       "    compile_machine:  login1.viking2.yor.alces.network\n",
+       "    compile_flags:    unknown\n",
+       "    defines:          50364608\n",
+       "    compile_date:     Fri Oct 11 16:12:01 2024\n",
+       "    run_date:         Fri Oct 25 12:34:55 2024\n",
+       "    io_date:          Fri Oct 25 12:40:14 2024
" + ], + "text/plain": [ + " Size: 10MB\n", + "Dimensions: (X_Grid: 1537,\n", + " X_Grid_mid: 1536, time: 41,\n", + " dim_laser_x_min_phase_0: 1,\n", + " dim_Random States_0: 384)\n", + "Coordinates:\n", + " * X_Grid (X_Grid) float64 12kB -1e-0...\n", + " * X_Grid_mid (X_Grid_mid) float64 12kB -...\n", + " * time (time) float64 328B 2.606e-...\n", + "Dimensions without coordinates: dim_laser_x_min_phase_0, dim_Random States_0\n", + "Data variables: (12/40)\n", + " Wall_time (time) float64 328B 0.4287 ...\n", + " Time_increment (time) float64 328B nan ......\n", + " Plasma_frequency_timestep_restriction (time) float64 328B nan ......\n", + " Minimum_grid_position (time) float64 328B nan ......\n", + " laser_x_min_phase (time, dim_laser_x_min_phase_0) float64 328B dask.array\n", + " time_prev_normal (time) float64 328B nan ......\n", + " ... ...\n", + " Derived_Number_Density_Electron (time, X_Grid_mid) float64 504kB dask.array\n", + " Derived_Number_Density_Ion (time, X_Grid_mid) float64 504kB dask.array\n", + " Derived_Number_Density_Photon (time, X_Grid_mid) float64 504kB dask.array\n", + " Derived_Number_Density_Positron (time, X_Grid_mid) float64 504kB dask.array\n", + " Absorption_Total_Laser_Energy_Injected (time) float64 328B 9.081e+...\n", + " Absorption_Fraction_of_Laser_Energy_Absorbed (time) float64 328B 0.0 ......\n", + "Attributes: (12/21)\n", + " filename: /Users/joel/Source/sdf-xarray/examples/example_dataset_...\n", + " file_version: 1\n", + " file_revision: 4\n", + " code_name: Epoch1d\n", + " step: 3838\n", + " time: 2.0003421833910338e-13\n", + " ... ...\n", + " compile_machine: login1.viking2.yor.alces.network\n", + " compile_flags: unknown\n", + " defines: 50364608\n", + " compile_date: Fri Oct 11 16:12:01 2024\n", + " run_date: Fri Oct 25 12:34:55 2024\n", + " io_date: Fri Oct 25 12:40:14 2024" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds = xr.open_mfdataset(f\"{simulation_path_1d}/*.sdf\", preprocess=SDFPreprocess())\n", + "ds" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Interaction examples\n", + "\n", + "When loading in either a single dataset or a group of datasets you can access the following methods to explore the dataset:\n", + "- `ds.variables` to list variables. (e.g. Electric Field, Magnetic Field, Particle Count)\n", + "- `ds.coords` for accessing coordinates/dimensions. (e.g. x-axis, y-axis, time)\n", + "- `ds.attrs` for metadata attached to the dataset. (e.g. filename, step, time)\n", + "\n", + "It is important to note here that `xarray` lazily loads the data meaning that it only explicitly loads the results your currently looking at when you call `.values`" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.DataArray 'Electric_Field_Ex' (time: 41, X_Grid_mid: 1536)> Size: 504kB\n",
+       "dask.array<where, shape=(41, 1536), dtype=float64, chunksize=(1, 1536), chunktype=numpy.ndarray>\n",
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+       "Attributes:\n",
+       "    units:       V/m\n",
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+       "    full_name:   Electric Field/Ex
" + ], + "text/plain": [ + " Size: 504kB\n", + "dask.array\n", + "Coordinates:\n", + " * X_Grid_mid (X_Grid_mid) float64 12kB -9.99e-06 -9.971e-06 ... 1.999e-05\n", + " * time (time) float64 328B 2.606e-17 5.003e-15 ... 1.95e-13 2e-13\n", + "Attributes:\n", + " units: V/m\n", + " point_data: False\n", + " full_name: Electric Field/Ex" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds = xr.open_mfdataset(f\"{simulation_path_1d}/*.sdf\", preprocess=SDFPreprocess())\n", + "\n", + "ds[\"Electric_Field_Ex\"]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "On top of accessing variables you can plot these `xarray.Datasets` using the built-in `plot()` function (see https://docs.xarray.dev/en/stable/user-guide/plotting.html) which is a simple call to `matplotlib`. This also means that you can access all the methods from `matplotlib` to manipulate your plot." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# This is discretized in both space and time\n", + "ds[\"Electric_Field_Ex\"].plot()\n", + "plt.title(\"Electric Field along the x-axis\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After having loaded in a series of datasets we can select a simulation file by calling the `.isel()` function where we pass in the parameter of `time=0` where `0` can be a number between `0` and the total number of simulation files. \n", + "\n", + "We can also use the `.sel()` function if we know the exact simulation time we want to select. There must be a corresponding dataset with this time for it work correctly." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are a total of 41 time steps. (This is the same as the number of SDF files in the folder)\n", + "The time steps are: \n", + "[2.60596949e-17 5.00346143e-15 1.00069229e-14 1.50103843e-14\n", + " 2.00138457e-14 2.50173071e-14 3.00207686e-14 3.50242300e-14\n", + " 4.00276914e-14 4.50311529e-14 5.00346143e-14 5.50380757e-14\n", + " 6.00415371e-14 6.50449986e-14 7.00484600e-14 7.50519214e-14\n", + " 8.00032635e-14 8.50067249e-14 9.00101863e-14 9.50136477e-14\n", + " 1.00017109e-13 1.05020571e-13 1.10024032e-13 1.15027493e-13\n", + " 1.20030955e-13 1.25034416e-13 1.30037878e-13 1.35041339e-13\n", + " 1.40044801e-13 1.45048262e-13 1.50051723e-13 1.55003065e-13\n", + " 1.60006527e-13 1.65009988e-13 1.70013450e-13 1.75016911e-13\n", + " 1.80020373e-13 1.85023834e-13 1.90027295e-13 1.95030757e-13\n", + " 2.00034218e-13]\n", + "The time at the 20th simulation step is 1.00e-13 s\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(f\"There are a total of {ds[\"time\"].size} time steps. (This is the same as the number of SDF files in the folder)\")\n", + "print(\"The time steps are: \")\n", + "print(ds[\"time\"].values)\n", + "\n", + "# The time at the 20th simulation step\n", + "sim_time = ds['time'].isel(time=20).values\n", + "print(f\"The time at the 20th simulation step is {sim_time:.2e} s\")\n", + "\n", + "# We can plot the time using either the isel or sel method passing in either the index or the value of the time\n", + "ds[\"Electric_Field_Ex\"].isel(time=20).plot()\n", + "# ds[\"Electric_Field_Ex\"].sel(time=sim_time).plot()\n", + "plt.title(f\"Electric Field along the x-axis at {sim_time:.2e} s\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These datasets can also be easily manipulated the same way as you would with `numpy` arrays" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "ds[\"Laser_Absorption_Fraction_in_Simulation\"] = (ds[\"Total_Particle_Energy_in_Simulation\"] / ds[\"Absorption_Total_Laser_Energy_Injected\"]) * 100\n", + "# We can also manipulate the units and other attributes\n", + "ds[\"Laser_Absorption_Fraction_in_Simulation\"].attrs[\"units\"] = \"%\"\n", + "\n", + "ds[\"Laser_Absorption_Fraction_in_Simulation\"].plot()\n", + "plt.title(\"Laser Absorption Fraction in Simulation\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "You can also call the `plot()` function on several variables with labels by delaying the call to `plt.show()`" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The total laser energy injected into the simulation is 4.5e+12 J\n", + "The total particle energy absorbed by the simulation is 3.0e+12 J\n", + "The laser absorption fraction in the simulation is 66.0 %\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "print(f\"The total laser energy injected into the simulation is {ds[\"Absorption_Total_Laser_Energy_Injected\"].max().values:.1e} J\")\n", + "print(f\"The total particle energy absorbed by the simulation is {ds[\"Total_Particle_Energy_in_Simulation\"].max().values:.1e} J\")\n", + "print(f\"The laser absorption fraction in the simulation is {ds[\"Laser_Absorption_Fraction_in_Simulation\"].max().values:.1f} %\")\n", + "ds[\"Total_Particle_Energy_Electron\"].plot(label=\"Electron\")\n", + "ds[\"Total_Particle_Energy_Photon\"].plot(label=\"Photon\")\n", + "ds[\"Total_Particle_Energy_Ion\"].plot(label=\"Ion\")\n", + "ds[\"Total_Particle_Energy_Positron\"].plot(label=\"Positron\")\n", + "plt.legend()\n", + "plt.title(\"Particle Energy in Simulation per Species\")\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tutorials/base_tutorial_dataset_1d/0000.sdf b/tutorials/base_tutorial_dataset_1d/0000.sdf new file mode 100644 index 0000000..9541271 Binary files /dev/null and b/tutorials/base_tutorial_dataset_1d/0000.sdf differ diff --git a/tutorials/base_tutorial_dataset_1d/0001.sdf b/tutorials/base_tutorial_dataset_1d/0001.sdf new file mode 100644 index 0000000..2ed4701 Binary files /dev/null and b/tutorials/base_tutorial_dataset_1d/0001.sdf differ diff --git a/tutorials/base_tutorial_dataset_1d/0002.sdf b/tutorials/base_tutorial_dataset_1d/0002.sdf new file mode 100644 index 0000000..d466176 Binary files /dev/null and b/tutorials/base_tutorial_dataset_1d/0002.sdf differ diff --git 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b/tutorials/base_tutorial_dataset_1d/0037.sdf differ diff --git a/tutorials/base_tutorial_dataset_1d/0038.sdf b/tutorials/base_tutorial_dataset_1d/0038.sdf new file mode 100644 index 0000000..f5aa9fb Binary files /dev/null and b/tutorials/base_tutorial_dataset_1d/0038.sdf differ diff --git a/tutorials/base_tutorial_dataset_1d/0039.sdf b/tutorials/base_tutorial_dataset_1d/0039.sdf new file mode 100644 index 0000000..80f96e0 Binary files /dev/null and b/tutorials/base_tutorial_dataset_1d/0039.sdf differ diff --git a/tutorials/base_tutorial_dataset_1d/0040.sdf b/tutorials/base_tutorial_dataset_1d/0040.sdf new file mode 100644 index 0000000..4adc5d6 Binary files /dev/null and b/tutorials/base_tutorial_dataset_1d/0040.sdf differ diff --git a/tutorials/base_tutorial_dataset_1d/deck.status b/tutorials/base_tutorial_dataset_1d/deck.status new file mode 100644 index 0000000..c18465c --- /dev/null +++ b/tutorials/base_tutorial_dataset_1d/deck.status @@ -0,0 +1,343 @@ + EPOCH1D v4.19.3 v4.19.3-24-gaafed395-dirty 1729856095.720 + +Deck state: 1 + + Beginning "constant" block + + Element nel=1.750208573569848e+22 handled OK + Element intens=1.2142321215274375e+22 handled OK + Element omega=2.0 * pi * c / (1.0e-6) handled OK + Element den_crit=critical(omega) handled OK + Element scale=3.5e-06 handled OK + Element den_max=5.0 * den_crit handled OK + Element den_maxpoint=4e-05 handled OK + Element den_contrast=1.0 handled OK + Element amax=1.0 handled OK + + Ending "constant" block + + Beginning "control" block + + Element nx=1024*1.5 handled OK + Element nparticles=1024*2 * 64 handled OK + Element nsteps=-1 handled OK + Element t_end=2e-13 handled OK + Element x_min=-1e-05 handled OK + Element x_max=2e-05 handled OK + Element dt_multiplier=0.8 handled OK + + Ending "control" block + + Beginning "qed" block + + Element use_qed=F handled OK + Element qed_start_time=0 handled OK + Element produce_photons=F handled OK + Element photon_energy_min=50 * kev handled OK + Element produce_pairs=F handled OK + Element photon_dynamics=F handled OK + + Ending "qed" block + + Beginning "collisions" block + + Element use_collisions=T handled OK + Element coulomb_log=auto handled OK + Element collide=all handled OK + + Ending "collisions" block + + Beginning "boundaries" block + + Element bc_x_min=simple_laser handled OK + Element bc_x_max=simple_laser handled OK + Element bc_x_max_field=simple_outflow handled OK + Element bc_x_max_particle=reflect handled OK + + Ending "boundaries" block + + Beginning "species" block + + Element name=Electron handled OK + Element fraction=0.5 handled OK + Element dump=T handled OK + Element temperature=0 handled OK + Element number_density=if (x gt 0e-6, nel * 1.0e6, 0) handled OK + Element number_density_min=1 handled OK + Element identify=electron handled OK + + Ending "species" block + + Beginning "species" block + + Element name=Ion handled OK + Element fraction=0.5 handled OK + Element dump=T handled OK + Element number_density=number_density(Electron) handled OK + Element temperature=temperature_x(Electron) handled OK + Element number_density_min=1 handled OK + Element identify=proton handled OK + + Ending "species" block + + Beginning "species" block + + Element name=Photon handled OK + Element nparticles=0 handled OK + Element dump=T handled OK + Element identify=photon handled OK + + Ending "species" block + + Beginning "species" block + + Element name=Positron handled OK + Element nparticles=0 handled OK + Element dump=T handled OK + Element identify=positron handled OK + + Ending "species" block + + Beginning "output_global" block + + Element force_final_to_be_restartable=T handled OK + + Ending "output_global" block + + Beginning "output" block + + Element name=normal handled OK + Element use_offset_grid=F handled OK + Element dt_snapshot=5e-15 handled OK + Element particles=never handled OK + Element px=never handled OK + Element py=never handled OK + Element pz=never handled OK + Element vx=never handled OK + Element vy=never handled OK + Element vz=never handled OK + Element charge=never handled OK + Element mass=never handled OK + Element particle_weight=never handled OK + Element species_id=never handled OK + Element grid=always handled OK + Element ex=always handled OK + Element ey=always handled OK + Element ez=always handled OK + Element bx=always handled OK + Element by=always handled OK + Element bz=always handled OK + Element jx=always handled OK + Element jy=always handled OK + Element jz=never handled OK + Element average_particle_energy=always + species handled OK + Element mass_density=never + species handled OK + Element charge_density=never handled OK + Element number_density=always + species handled OK + Element temperature=never + species handled OK + Element distribution_functions=always handled OK + Element particle_probes=never handled OK + Element absorption=always handled OK + Element total_energy_sum=always + species handled OK + + Ending "output" block + + Beginning "laser" block + + Element boundary=x_min handled OK + Element intensity=intens * 1.0e4 handled OK + Element omega=omega handled OK + Element polarisation=0.0 handled OK + Element phase=0.0 handled OK + Element t_profile=gauss(time, 40.0e-15, 30.0e-15) handled OK + Element t_start=0.0 handled OK + Element t_end=end handled OK + + Ending "laser" block + + Beginning "dist_fn" block + + Element name=px_py handled OK + Element ndims=2 handled OK + Element dumpmask=always handled OK + Element direction1=dir_px handled OK + Element direction2=dir_py handled OK + Element range1=(-1.5e-21, 1.5e-21) handled OK + Element range2=(-1.5e-21, 1.5e-21) handled OK + Element resolution1=200 handled OK + Element resolution2=200 handled OK + Element include_species=Photon handled OK + + Ending "dist_fn" block + +Deck state: 2 + + Beginning "constant" block + + Element nel=1.750208573569848e+22 handled OK + Element intens=1.2142321215274375e+22 handled OK + Element omega=2.0 * pi * c / (1.0e-6) handled OK + Element den_crit=critical(omega) handled OK + Element scale=3.5e-06 handled OK + Element den_max=5.0 * den_crit handled OK + Element den_maxpoint=4e-05 handled OK + Element den_contrast=1.0 handled OK + Element amax=1.0 handled OK + + Ending "constant" block + + Beginning "control" block + + Element nx=1024*1.5 handled OK + Element nparticles=1024*2 * 64 handled OK + Element nsteps=-1 handled OK + Element t_end=2e-13 handled OK + Element x_min=-1e-05 handled OK + Element x_max=2e-05 handled OK + Element dt_multiplier=0.8 handled OK + + Ending "control" block + + Beginning "qed" block + + Element use_qed=F handled OK + Element qed_start_time=0 handled OK + Element produce_photons=F handled OK + Element photon_energy_min=50 * kev handled OK + Element produce_pairs=F handled OK + Element photon_dynamics=F handled OK + + Ending "qed" block + + Beginning "collisions" block + + Element use_collisions=T handled OK + Element coulomb_log=auto handled OK + Element collide=all handled OK + + Ending "collisions" block + + Beginning "boundaries" block + + Element bc_x_min=simple_laser handled OK + Element bc_x_max=simple_laser handled OK + Element bc_x_max_field=simple_outflow handled OK + Element bc_x_max_particle=reflect handled OK + + Ending "boundaries" block + + Beginning "species" block + + Element name=Electron handled OK + Element fraction=0.5 handled OK + Element dump=T handled OK + Element temperature=0 handled OK + Element number_density=if (x gt 0e-6, nel * 1.0e6, 0) handled OK + Element number_density_min=1 handled OK + Element identify=electron handled OK + + Ending "species" block + + Beginning "species" block + + Element name=Ion handled OK + Element fraction=0.5 handled OK + Element dump=T handled OK + Element number_density=number_density(Electron) handled OK + Element temperature=temperature_x(Electron) handled OK + Element number_density_min=1 handled OK + Element identify=proton handled OK + + Ending "species" block + + Beginning "species" block + + Element name=Photon handled OK + Element nparticles=0 handled OK + Element dump=T handled OK + Element identify=photon handled OK + + Ending "species" block + + Beginning "species" block + + Element name=Positron handled OK + Element nparticles=0 handled OK + Element dump=T handled OK + Element identify=positron handled OK + + Ending "species" block + + Beginning "output_global" block + + Element force_final_to_be_restartable=T handled OK + + Ending "output_global" block + + Beginning "output" block + + Element name=normal handled OK + Element use_offset_grid=F handled OK + Element dt_snapshot=5e-15 handled OK + Element particles=never handled OK + Element px=never handled OK + Element py=never handled OK + Element pz=never handled OK + Element vx=never handled OK + Element vy=never handled OK + Element vz=never handled OK + Element charge=never handled OK + Element mass=never handled OK + Element particle_weight=never handled OK + Element species_id=never handled OK + Element grid=always handled OK + Element ex=always handled OK + Element ey=always handled OK + Element ez=always handled OK + Element bx=always handled OK + Element by=always handled OK + Element bz=always handled OK + Element jx=always handled OK + Element jy=always handled OK + Element jz=never handled OK + Element average_particle_energy=always + species handled OK + Element mass_density=never + species handled OK + Element charge_density=never handled OK + Element number_density=always + species handled OK + Element temperature=never + species handled OK + Element distribution_functions=always handled OK + Element particle_probes=never handled OK + Element absorption=always handled OK + Element total_energy_sum=always + species handled OK + + Ending "output" block + + Beginning "laser" block + + Element boundary=x_min handled OK + Element intensity=intens * 1.0e4 handled OK + Element omega=omega handled OK + Element polarisation=0.0 handled OK + Element phase=0.0 handled OK + Element t_profile=gauss(time, 40.0e-15, 30.0e-15) handled OK + Element t_start=0.0 handled OK + Element t_end=end handled OK + + Ending "laser" block + + Beginning "dist_fn" block + + Element name=px_py handled OK + Element ndims=2 handled OK + Element dumpmask=always handled OK + Element direction1=dir_px handled OK + Element direction2=dir_py handled OK + Element range1=(-1.5e-21, 1.5e-21) handled OK + Element range2=(-1.5e-21, 1.5e-21) handled OK + Element resolution1=200 handled OK + Element resolution2=200 handled OK + Element include_species=Photon handled OK + + Ending "dist_fn" block + + Initial conditions complete and valid. diff --git a/tutorials/base_tutorial_dataset_1d/epoch1d.dat b/tutorials/base_tutorial_dataset_1d/epoch1d.dat new file mode 100644 index 0000000..f42e253 --- /dev/null +++ b/tutorials/base_tutorial_dataset_1d/epoch1d.dat @@ -0,0 +1,45 @@ + EPOCH1D v4.19.3 v4.19.3-24-gaafed395-dirty 1729856095.720 + + Loaded 65536 particles of species "Electron" + Loaded 65536 particles of species "Ion" +Wrote normal , 0000.sdf at time 0.2606E-16 and iteration 0 +Wrote normal , 0001.sdf at time 0.5003E-14 and iteration 96 +Wrote normal , 0002.sdf at time 0.1001E-13 and iteration 192 +Wrote normal , 0003.sdf at time 0.1501E-13 and iteration 288 +Wrote normal , 0004.sdf at time 0.2001E-13 and iteration 384 +Wrote normal , 0005.sdf at time 0.2502E-13 and iteration 480 +Wrote normal , 0006.sdf at time 0.3002E-13 and iteration 576 +Wrote normal , 0007.sdf at time 0.3502E-13 and iteration 672 +Wrote normal , 0008.sdf at time 0.4003E-13 and iteration 768 +Wrote normal , 0009.sdf at time 0.4503E-13 and iteration 864 +Wrote normal , 0010.sdf at time 0.5003E-13 and iteration 960 +Wrote normal , 0011.sdf at time 0.5504E-13 and iteration 1056 +Wrote normal , 0012.sdf at time 0.6004E-13 and iteration 1152 +Wrote normal , 0013.sdf at time 0.6504E-13 and iteration 1248 +Wrote normal , 0014.sdf at time 0.7005E-13 and iteration 1344 +Wrote normal , 0015.sdf at time 0.7505E-13 and iteration 1440 +Wrote normal , 0016.sdf at time 0.8000E-13 and iteration 1535 +Wrote normal , 0017.sdf at time 0.8501E-13 and iteration 1631 +Wrote normal , 0018.sdf at time 0.9001E-13 and iteration 1727 +Wrote normal , 0019.sdf at time 0.9501E-13 and iteration 1823 +Wrote normal , 0020.sdf at time 0.1000E-12 and iteration 1919 +Wrote normal , 0021.sdf at time 0.1050E-12 and iteration 2015 +Wrote normal , 0022.sdf at time 0.1100E-12 and iteration 2111 +Wrote normal , 0023.sdf at time 0.1150E-12 and iteration 2207 +Wrote normal , 0024.sdf at time 0.1200E-12 and iteration 2303 +Wrote normal , 0025.sdf at time 0.1250E-12 and iteration 2399 +Wrote normal , 0026.sdf at time 0.1300E-12 and iteration 2495 +Wrote normal , 0027.sdf at time 0.1350E-12 and iteration 2591 +Wrote normal , 0028.sdf at time 0.1400E-12 and iteration 2687 +Wrote normal , 0029.sdf at time 0.1450E-12 and iteration 2783 +Wrote normal , 0030.sdf at time 0.1501E-12 and iteration 2879 +Wrote normal , 0031.sdf at time 0.1550E-12 and iteration 2974 +Wrote normal , 0032.sdf at time 0.1600E-12 and iteration 3070 +Wrote normal , 0033.sdf at time 0.1650E-12 and iteration 3166 +Wrote normal , 0034.sdf at time 0.1700E-12 and iteration 3262 +Wrote normal , 0035.sdf at time 0.1750E-12 and iteration 3358 +Wrote normal , 0036.sdf at time 0.1800E-12 and iteration 3454 +Wrote normal , 0037.sdf at time 0.1850E-12 and iteration 3550 +Wrote normal , 0038.sdf at time 0.1900E-12 and iteration 3646 +Wrote normal , 0039.sdf at time 0.1950E-12 and iteration 3742 +Wrote restart, 0040.sdf at time 0.2000E-12 and iteration 3838 diff --git a/tutorials/base_tutorial_dataset_1d/input.deck b/tutorials/base_tutorial_dataset_1d/input.deck new file mode 100644 index 0000000..3189cd5 --- /dev/null +++ b/tutorials/base_tutorial_dataset_1d/input.deck @@ -0,0 +1,142 @@ +begin:constant + nel = 1.750208573569848e+22 + intens = 1.2142321215274375e+22 + omega = 2.0 * pi * c / (1.0e-6) + den_crit = critical(omega) + scale = 3.5e-06 + den_max = 5.0 * den_crit + den_maxpoint = 4e-05 + den_contrast = 1.0 + amax = 1.0 +end:constant + +begin:control + nx = 1024*1.5 + nparticles = 1024*2 * 64 + nsteps = -1 + t_end = 2e-13 + x_min = -1e-05 + x_max = 2e-05 + dt_multiplier = 0.8 +end:control + +begin:qed + use_qed = F + qed_start_time = 0 + produce_photons = F + photon_energy_min = 50 * kev + produce_pairs = F + photon_dynamics = F +end:qed + +begin:collisions + use_collisions = T + coulomb_log = auto + collide = all +end:collisions + +begin:boundaries + bc_x_min = simple_laser + bc_x_max = simple_laser + bc_x_max_field = simple_outflow + bc_x_max_particle = reflect +end:boundaries + +begin:species + name = Electron + fraction = 0.5 + dump = T + temperature = 0 + number_density = if (x gt 0e-6, nel * 1.0e6, 0) + number_density_min = 1 + identify:electron +end:species + +begin:species + name = Ion + fraction = 0.5 + dump = T + number_density = number_density(Electron) + temperature = temperature_x(Electron) + number_density_min = 1 + identify:proton +end:species + +begin:species + name = Photon + nparticles = 0 + dump = T + identify:photon +end:species + +begin:species + name = Positron + nparticles = 0 + dump = T + identify:positron +end:species + +begin:output_global + force_final_to_be_restartable = T +end:output_global + +begin:output + name = normal + use_offset_grid = F + dt_snapshot = 5e-15 + particles = never + px = never + py = never + pz = never + vx = never + vy = never + vz = never + charge = never + mass = never + particle_weight = never + species_id = never + grid = always + ex = always + ey = always + ez = always + bx = always + by = always + bz = always + jx = always + jy = always + jz = never + average_particle_energy = always + species + mass_density = never + species + charge_density = never + number_density = always + species + temperature = never + species + distribution_functions = always + particle_probes = never + absorption = always + total_energy_sum = always + species +end:output + +begin:laser + boundary = x_min + intensity = intens * 1.0e4 + omega = omega + polarisation = 0.0 + phase = 0.0 + t_profile = gauss(time, 40.0e-15, 30.0e-15) + t_start = 0.0 + t_end = end +end:laser + +begin:dist_fn + name = px_py + ndims = 2 + dumpmask = always + direction1 = dir_px + direction2 = dir_py + range1 = (-1.5e-21, 1.5e-21) + range2 = (-1.5e-21, 1.5e-21) + resolution1 = 200 + resolution2 = 200 + include_species:Photon +end:dist_fn + diff --git a/tutorials/base_tutorial_dataset_1d/normal.visit b/tutorials/base_tutorial_dataset_1d/normal.visit new file mode 100644 index 0000000..967fdb5 --- /dev/null +++ b/tutorials/base_tutorial_dataset_1d/normal.visit @@ -0,0 +1,41 @@ +0000.sdf +0001.sdf +0002.sdf +0003.sdf +0004.sdf +0005.sdf +0006.sdf +0007.sdf +0008.sdf +0009.sdf +0010.sdf +0011.sdf +0012.sdf +0013.sdf +0014.sdf +0015.sdf +0016.sdf +0017.sdf +0018.sdf +0019.sdf +0020.sdf +0021.sdf +0022.sdf +0023.sdf +0024.sdf +0025.sdf +0026.sdf +0027.sdf +0028.sdf +0029.sdf +0030.sdf +0031.sdf +0032.sdf +0033.sdf +0034.sdf +0035.sdf +0036.sdf +0037.sdf +0038.sdf +0039.sdf +0040.sdf diff --git a/tutorials/base_tutorial_dataset_1d/restart.visit b/tutorials/base_tutorial_dataset_1d/restart.visit new file mode 100644 index 0000000..b4b55de --- /dev/null +++ b/tutorials/base_tutorial_dataset_1d/restart.visit @@ -0,0 +1 @@ +0040.sdf