Utility renames and hdf_param_study simplification

pyzebra/ccl_process.py

@@ -1,7 +1,7 @@
 import os
 
 import numpy as np
-from lmfit.models import Gaussian2dModel, GaussianModel, LinearModel, PseudoVoigtModel, VoigtModel
+from lmfit.models import GaussianModel, LinearModel, PseudoVoigtModel, VoigtModel
 from scipy.integrate import simpson, trapezoid
 
 from .ccl_io import CCL_ANGLES
@@ -259,31 +259,3 @@ def get_area(scan, area_method, lorentz):
         area_s = np.abs(area_s * corr_factor)
 
     scan["area"] = (area_v, area_s)
-
-
-def fit_event(scan, fr_from, fr_to, y_from, y_to, x_from, x_to):
-    data_roi = scan["data"][fr_from:fr_to, y_from:y_to, x_from:x_to]
-
-    model = GaussianModel()
-    fr = np.arange(fr_from, fr_to)
-    counts_per_fr = np.sum(data_roi, axis=(1, 2))
-    params = model.guess(counts_per_fr, fr)
-    result = model.fit(counts_per_fr, x=fr, params=params)
-    frC = result.params["center"].value
-    intensity = result.params["height"].value
-
-    counts_std = counts_per_fr.std()
-    counts_mean = counts_per_fr.mean()
-    snr = 0 if counts_std == 0 else counts_mean / counts_std
-
-    model = Gaussian2dModel()
-    xs, ys = np.meshgrid(np.arange(x_from, x_to), np.arange(y_from, y_to))
-    xs = xs.flatten()
-    ys = ys.flatten()
-    counts = np.sum(data_roi, axis=0).flatten()
-    params = model.guess(counts, xs, ys)
-    result = model.fit(counts, x=xs, y=ys, params=params)
-    xC = result.params["centerx"].value
-    yC = result.params["centery"].value
-
-    scan["fit"] = {"frame": frC, "x_pos": xC, "y_pos": yC, "intensity": intensity, "snr": snr}