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| author | tlatorre <tlatorre@uchicago.edu> | 2020-05-12 14:02:58 -0500 |
|---|---|---|
| committer | tlatorre <tlatorre@uchicago.edu> | 2020-05-12 14:02:58 -0500 |
| commit | 40d962936761cb356d021fa1d4413974c8a65c7a (patch) | |
| tree | 4b05212f7492267b63a1490bac90964c420a960b /utils/dc | |
| parent | 28777814e8c2341d3f1349c7df988fb9316134c7 (diff) | |
| download | sddm-40d962936761cb356d021fa1d4413974c8a65c7a.tar.gz sddm-40d962936761cb356d021fa1d4413974c8a65c7a.tar.bz2 sddm-40d962936761cb356d021fa1d4413974c8a65c7a.zip | |
speed up the contamination analysis script
Diffstat (limited to 'utils/dc')
| -rwxr-xr-x | utils/dc | 12 |
1 files changed, 6 insertions, 6 deletions
@@ -185,7 +185,7 @@ def make_nll(data, sacrifice, constraints): [p_r_psi_z_udotr_muon_hihihilo, p_r_psi_z_udotr_muon_hihihihi]]]]) expected_muon = p_muon*contamination_muon*mu_muon + sacrifice['muon']*mu_signal - nll -= poisson.logpmf(data['muon'],expected_muon).sum() + nll -= fast_poisson_logpmf(data['muon'],expected_muon).sum() # Noise events p_r_noise = np.array([p_r_noise_lo,1-p_r_noise_lo]) @@ -196,7 +196,7 @@ def make_nll(data, sacrifice, constraints): p_noise = p_r_noise[:,np.newaxis,np.newaxis,np.newaxis]*p_psi_noise[:,np.newaxis,np.newaxis]*p_z_udotr_noise expected_noise = p_noise*contamination_noise*mu_noise + sacrifice['noise']*mu_signal - nll -= poisson.logpmf(data['noise'],expected_noise).sum() + nll -= fast_poisson_logpmf(data['noise'],expected_noise).sum() # Neck events # FIXME: for now assume parameterized same as muon @@ -211,7 +211,7 @@ def make_nll(data, sacrifice, constraints): # FIXME: pdf should be different for muon given neck expected_neck += p_muon*p_neck_given_muon*mu_muon - nll -= poisson.logpmf(data['neck'],expected_neck).sum() + nll -= fast_poisson_logpmf(data['neck'],expected_neck).sum() # Flasher events p_r_udotr_flasher = np.array([\ @@ -222,7 +222,7 @@ def make_nll(data, sacrifice, constraints): p_flasher = p_r_udotr_flasher[:,np.newaxis,np.newaxis,:]*p_psi_flasher[:,np.newaxis,np.newaxis]*p_z_flasher[:,np.newaxis] expected_flasher = p_flasher*contamination_flasher*mu_flasher + sacrifice['flasher']*mu_signal - nll -= poisson.logpmf(data['flasher'],expected_flasher).sum() + nll -= fast_poisson_logpmf(data['flasher'],expected_flasher).sum() # Breakdown events p_r_udotr_breakdown = np.array([\ @@ -233,7 +233,7 @@ def make_nll(data, sacrifice, constraints): p_breakdown = p_r_udotr_breakdown[:,np.newaxis,np.newaxis,:]*p_psi_breakdown[:,np.newaxis,np.newaxis]*p_z_breakdown[:,np.newaxis] expected_breakdown = p_breakdown*contamination_breakdown*mu_breakdown + sacrifice['breakdown']*mu_signal - nll -= poisson.logpmf(data['breakdown'],expected_breakdown).sum() + nll -= fast_poisson_logpmf(data['breakdown'],expected_breakdown).sum() # Signal like events expected_signal = np.zeros_like(expected_muon) @@ -244,7 +244,7 @@ def make_nll(data, sacrifice, constraints): expected_signal += p_flasher*(1-contamination_flasher)*mu_flasher expected_signal += p_breakdown*(1-contamination_breakdown)*mu_breakdown - nll -= poisson.logpmf(data['signal'],expected_signal).sum() + nll -= fast_poisson_logpmf(data['signal'],expected_signal).sum() if not np.isfinite(nll): print("x = ", x) |