Loading peakintegration/peakfitter.py +7 −1 Original line number Diff line number Diff line Loading @@ -1231,6 +1231,7 @@ class PeaksIntegrator(object): wave_min -= 0.1*wave_spr wave_max += 0.1*wave_spr def ellipsoid_predictor(wavelen): wavelen = np.atleast_1d(wavelen) # restrict to the range of wavelengths used for training wavelen = np.minimum(wavelen,wave_max) wavelen = np.maximum(wavelen,wave_min) Loading Loading @@ -1258,7 +1259,12 @@ class PeaksIntegrator(object): for i,model in enumerate(models_axes): axes_val[:,i], axes_confint[:,i], axes_predint[:,i] = model(wavelen) return np.hstack([angles_val, axes_val]), np.hstack([angles_confint, axes_confint]), np.hstack([angles_predint, axes_predint]) params, confint, predint = np.hstack([angles_val, axes_val]), np.hstack([angles_confint, axes_confint]), np.hstack([angles_predint, axes_predint]) if wavelen.size==1: return params[0], confint[0], predint[0] else: return params, confint, predint ####################################################################### Loading Loading
peakintegration/peakfitter.py +7 −1 Original line number Diff line number Diff line Loading @@ -1231,6 +1231,7 @@ class PeaksIntegrator(object): wave_min -= 0.1*wave_spr wave_max += 0.1*wave_spr def ellipsoid_predictor(wavelen): wavelen = np.atleast_1d(wavelen) # restrict to the range of wavelengths used for training wavelen = np.minimum(wavelen,wave_max) wavelen = np.maximum(wavelen,wave_min) Loading Loading @@ -1258,7 +1259,12 @@ class PeaksIntegrator(object): for i,model in enumerate(models_axes): axes_val[:,i], axes_confint[:,i], axes_predint[:,i] = model(wavelen) return np.hstack([angles_val, axes_val]), np.hstack([angles_confint, axes_confint]), np.hstack([angles_predint, axes_predint]) params, confint, predint = np.hstack([angles_val, axes_val]), np.hstack([angles_confint, axes_confint]), np.hstack([angles_predint, axes_predint]) if wavelen.size==1: return params[0], confint[0], predint[0] else: return params, confint, predint ####################################################################### Loading