diff options
| author |  NeilZaim <49716072+NeilZaim@users.noreply.github.com>
| 2021-03-02 02:06:35 +0100 |
|---|---|---|
| committer |  GitHub <noreply@github.com>
| 2021-03-01 17:06:35 -0800 |
| commit | 0531485b04eac9cbe82e7ee79f78fb268ba253f8 (patch) | |
| tree | c89265015214722b889ba9cffcd3c1260673e284 /Examples/Modules/resampling/analysis_leveling_thinning.py | |
| parent | fcf78519af9d02991d1b2d99179466ef2bd5383e (diff) | |
| download | WarpX-0531485b04eac9cbe82e7ee79f78fb268ba253f8.tar.gz WarpX-0531485b04eac9cbe82e7ee79f78fb268ba253f8.tar.zst WarpX-0531485b04eac9cbe82e7ee79f78fb268ba253f8.zip | |
Add CI coverage for the plotfile particle filters (#1645)
* Add CI coverage for the plotfile particle filter function
* Minor fix in analysis script
* Test all 3 particle filters in 2D/3D/RZ
* Dirty trick to deal with same ID from multiple MPI ranks + enforce 100 characters per line rule
* Fix CI when running on a single MPI rank
* Add new module to avoid duplicate code
Diffstat (limited to 'Examples/Modules/resampling/analysis_leveling_thinning.py')
| -rwxr-xr-x | Examples/Modules/resampling/analysis_leveling_thinning.py | 8 |
1 files changed, 4 insertions, 4 deletions
diff --git a/Examples/Modules/resampling/analysis_leveling_thinning.py b/Examples/Modules/resampling/analysis_leveling_thinning.py index dc96b0138..a9e8f380d 100755 --- a/Examples/Modules/resampling/analysis_leveling_thinning.py +++ b/Examples/Modules/resampling/analysis_leveling_thinning.py @@ -55,7 +55,7 @@ numparts_final = w.shape[0] expected_numparts_final = numparts_init/t_r**2 error = np.abs(numparts_final - expected_numparts_final) std_numparts_final = np.sqrt(numparts_init/t_r**2*(1.-1./t_r**2)) -# 5 sigma test that has an intrisic probability to fail of 1 over ~2 millions +# 5 sigma test that has an intrinsic probability to fail of 1 over ~2 millions print("difference between expected and actual final number of particles (1st species): " + str(error)) print("tolerance: " + str(5*std_numparts_final)) assert(error<5*std_numparts_final) @@ -89,7 +89,7 @@ expected_mean_initial_weight = 2.*np.sqrt(2.) error = np.abs(mean_initial_weight - expected_mean_initial_weight) expected_std_initial_weight = 1./np.sqrt(2.) std_mean_initial_weight = expected_std_initial_weight/np.sqrt(numparts_init) -# 5 sigma test that has an intrisic probability to fail of 1 over ~2 millions +# 5 sigma test that has an intrinsic probability to fail of 1 over ~2 millions print("difference between expected and actual mean initial weight (2nd species): " + str(error)) print("tolerance: " + str(5*std_mean_initial_weight)) assert(error<5*std_mean_initial_weight) @@ -99,7 +99,7 @@ variance_initial_weight = np.var(w0) expected_variance_initial_weight = 0.5 error = np.abs(variance_initial_weight - expected_variance_initial_weight) std_variance_initial_weight = expected_variance_initial_weight*np.sqrt(2./numparts_init) -# 5 sigma test that has an intrisic probability to fail of 1 over ~2 millions +# 5 sigma test that has an intrinsic probability to fail of 1 over ~2 millions print("difference between expected and actual variance of initial weight (2nd species): " + str(error)) print("tolerance: " + str(5*std_variance_initial_weight)) @@ -122,7 +122,7 @@ std_numparts_leveled = np.sqrt(expected_numparts_leveled - numparts_init/np.sqrt (2.*expected_mean_initial_weight**2+1.)*(1.-erf(expected_mean_initial_weight* \ (t_r-1.)))-0.5*np.exp(-(expected_mean_initial_weight*(t_r-1.))**2* \ (expected_mean_initial_weight*(t_r+1.))))) -# 5 sigma test that has an intrisic probability to fail of 1 over ~2 millions +# 5 sigma test that has an intrinsic probability to fail of 1 over ~2 millions print("difference between expected and actual number of leveled particles (2nd species): " + str(error)) print("tolerance: " + str(5*std_numparts_leveled)) |