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| author |  Roelof Groenewald <40245517+roelof-groenewald@users.noreply.github.com>
| 2022-01-20 12:34:14 -0800 |
|---|---|---|
| committer |  GitHub <noreply@github.com>
| 2022-01-20 12:34:14 -0800 |
| commit | 1087dd1225ee07a80f2abb20ad325604fc8516aa (patch) | |
| tree | 05caf6d6cdf49126bddf12baf46f10d32635d823 /Examples/Tests/restart/PICMI_inputs_runtime_component_analyze.py | |
| parent | cd3488e0b0547ac9b7fb4d68577ab1269c598fd2 (diff) | |
| download | WarpX-1087dd1225ee07a80f2abb20ad325604fc8516aa.tar.gz WarpX-1087dd1225ee07a80f2abb20ad325604fc8516aa.tar.zst WarpX-1087dd1225ee07a80f2abb20ad325604fc8516aa.zip | |
Fix unstable Python_particle_attr_access CI tests (#2766)
* explicitly set the numpy random seed in Python_particle_attr_access tests
* also shrink boundaries in which particles are injected for good measure
* also explicitly set the numpy random seed in Python_restart_runtime_components CI test
Diffstat (limited to 'Examples/Tests/restart/PICMI_inputs_runtime_component_analyze.py')
| -rwxr-xr-x | Examples/Tests/restart/PICMI_inputs_runtime_component_analyze.py | 7 |
1 files changed, 5 insertions, 2 deletions
diff --git a/Examples/Tests/restart/PICMI_inputs_runtime_component_analyze.py b/Examples/Tests/restart/PICMI_inputs_runtime_component_analyze.py index 1fa8862f0..e3bcab22f 100755 --- a/Examples/Tests/restart/PICMI_inputs_runtime_component_analyze.py +++ b/Examples/Tests/restart/PICMI_inputs_runtime_component_analyze.py @@ -111,15 +111,18 @@ sim.initialize_warpx() # python particle data access ########################## +# set numpy random seed so that the particle properties generated +# below will be reproducible from run to run +np.random.seed(30025025) sim.extension.add_real_comp('electrons', 'newPid') def add_particles(): nps = 10 - x = np.random.rand(nps) * 0.03 + x = np.linspace(0.005, 0.025, nps) y = np.zeros(nps) - z = np.random.random(nps) * 0.03 + z = np.linspace(0.005, 0.025, nps) ux = np.random.normal(loc=0, scale=1e3, size=nps) uy = np.random.normal(loc=0, scale=1e3, size=nps) uz = np.random.normal(loc=0, scale=1e3, size=nps) |