Loading peakintegration/peakfitter.py +7 −2 Original line number Diff line number Diff line Loading @@ -1238,13 +1238,18 @@ class PeaksIntegrator(object): wavelen = np.maximum(wavelen,wave_min) wavelen = wavelen.reshape((-1,1)) if base_predictor is not None: params = base_predictor(wavelen)[0] if params.ndim==1: params = params[np.newaxis,:] # angles angles_val = np.zeros((wavelen.size,len(models_angles))) angles_confint = np.zeros((wavelen.size,len(models_angles))) angles_predint = np.zeros((wavelen.size,len(models_angles))) if base_predictor is not None: for i,model in enumerate(models_axes): angles_val[:,i], angles_confint[:,i], angles_predint[:,i] = p_angles[i][0] * base_predictor(wavelen)[0][:,i] + p_angles[i][1], np.zeros_like(wavelen).ravel(), np.ones_like(wavelen).ravel() * np.std(angreg[:,i]) angles_val[:,i], angles_confint[:,i], angles_predint[:,i] = p_angles[i][0] * params[:,i] + p_angles[i][1], np.zeros_like(wavelen).ravel(), np.ones_like(wavelen).ravel() * np.std(angreg[:,i]) else: for i,model in enumerate(models_angles): angles_val[:,i], angles_confint[:,i], angles_predint[:,i] = model(wavelen) Loading @@ -1255,7 +1260,7 @@ class PeaksIntegrator(object): axes_predint = np.zeros((wavelen.size,len(models_axes))) if base_predictor is not None: for i,model in enumerate(models_axes): axes_val[:,i], axes_confint[:,i], axes_predint[:,i] = p_axes[i][0] * (base_predictor(wavelen)[0][:,3+i]-y3[3+i]) + p_axes[i][1], np.zeros_like(wavelen).ravel(), np.ones_like(wavelen).ravel() * np.std(axereg[:,i]) axes_val[:,i], axes_confint[:,i], axes_predint[:,i] = p_axes[i][0] * (params[:,3+i]-y3[3+i]) + p_axes[i][1], np.zeros_like(wavelen).ravel(), np.ones_like(wavelen).ravel() * np.std(axereg[:,i]) else: for i,model in enumerate(models_axes): axes_val[:,i], axes_confint[:,i], axes_predint[:,i] = model(wavelen) Loading Loading
peakintegration/peakfitter.py +7 −2 Original line number Diff line number Diff line Loading @@ -1238,13 +1238,18 @@ class PeaksIntegrator(object): wavelen = np.maximum(wavelen,wave_min) wavelen = wavelen.reshape((-1,1)) if base_predictor is not None: params = base_predictor(wavelen)[0] if params.ndim==1: params = params[np.newaxis,:] # angles angles_val = np.zeros((wavelen.size,len(models_angles))) angles_confint = np.zeros((wavelen.size,len(models_angles))) angles_predint = np.zeros((wavelen.size,len(models_angles))) if base_predictor is not None: for i,model in enumerate(models_axes): angles_val[:,i], angles_confint[:,i], angles_predint[:,i] = p_angles[i][0] * base_predictor(wavelen)[0][:,i] + p_angles[i][1], np.zeros_like(wavelen).ravel(), np.ones_like(wavelen).ravel() * np.std(angreg[:,i]) angles_val[:,i], angles_confint[:,i], angles_predint[:,i] = p_angles[i][0] * params[:,i] + p_angles[i][1], np.zeros_like(wavelen).ravel(), np.ones_like(wavelen).ravel() * np.std(angreg[:,i]) else: for i,model in enumerate(models_angles): angles_val[:,i], angles_confint[:,i], angles_predint[:,i] = model(wavelen) Loading @@ -1255,7 +1260,7 @@ class PeaksIntegrator(object): axes_predint = np.zeros((wavelen.size,len(models_axes))) if base_predictor is not None: for i,model in enumerate(models_axes): axes_val[:,i], axes_confint[:,i], axes_predint[:,i] = p_axes[i][0] * (base_predictor(wavelen)[0][:,3+i]-y3[3+i]) + p_axes[i][1], np.zeros_like(wavelen).ravel(), np.ones_like(wavelen).ravel() * np.std(axereg[:,i]) axes_val[:,i], axes_confint[:,i], axes_predint[:,i] = p_axes[i][0] * (params[:,3+i]-y3[3+i]) + p_axes[i][1], np.zeros_like(wavelen).ravel(), np.ones_like(wavelen).ravel() * np.std(axereg[:,i]) else: for i,model in enumerate(models_axes): axes_val[:,i], axes_confint[:,i], axes_predint[:,i] = model(wavelen) Loading