refactor: change variable names, documentation, or move hardcoded parameters into func arguments

src/cal_tools/jungfrau/jungfrau_ff.py

@@ -168,7 +168,7 @@ def set_histo_range(bin_centers, histogram, h_range):
         bin_centers (array, float): the bin centers array
         histogram (array, integers): the histogram with shape
             (bins, cells, columns, row)
-        h_range (tuple, float): the (liminf, limsup) of the desired range
+        h_range (list, float): the (liminf, limsup) of the desired range
 
     Returns: the new bin centers array and the new histogram
 
@@ -232,7 +232,8 @@ def fit_histogram(
     rebin,
     ratio,
     noise,
-    histo,
+    initial_sigma,
+    histo,  # Added at the end to parallelize with multiprocessing.
 ):
     """
     Wrap around function for fitting of histogram
@@ -260,7 +261,6 @@ def fit_histogram(
         CHARGE_SHARING_2=fit_double_charge_sharing,
         GAUSS=fit_gauss,
     )
-    initial_sigma = 15.
     fit_func = _funcs[fit_func]
     n_cells, n_rows, n_cols = histo.shape[1:]
 
@@ -579,13 +579,13 @@ def fit_double_charge_sharing(x, y, yerr, initial_sigma, n_sigma, ratio):
     return q, sigma, chi2ndf, alpha
 
 
-def fit_gauss(x, y, yerr, initial_sigma, n_sigma, ratio):
+def fit_gauss(bin_centers, histogram, yerr, initial_sigma, n_sigma, ratio):
     """
     Fits histogram with a gaussian function
 
     Args:
-        x (array, float): x values
-        y (array, float): y values
+        bin_centers (array, float): bin_centers values
+        histogram (array, float): histogram values
         yerr (array, float): errors of the y values
         initial_sigma (float): rough estimate of peak variance
         n_sigma (int): to calculate threshold of the peak finder as
@@ -597,16 +597,16 @@ def fit_gauss(x, y, yerr, initial_sigma, n_sigma, ratio):
         (last one alway == 0)
         all of them are kept for compatibility with the wrap around function
     """
-    norm = np.sum(y) * (x[1] - x[0])/np.sqrt(2.*np.pi*initial_sigma**2)
+    norm = np.sum(histogram) * (bin_centers[1] - bin_centers[0])/np.sqrt(2.*np.pi*initial_sigma**2)
 
-    _peaks, _ = _peak_position(x, y, thr=n_sigma*initial_sigma, ratio=ratio)
+    _peaks, _ = _peak_position(bin_centers, histogram, thr=n_sigma*initial_sigma, ratio=ratio)
     if len(_peaks) > 0:
-        q0 = np.min(x[_peaks])
+        q0 = np.min(bin_centers[_peaks])
     else:
         return -1, -1, -1, -1
 
-    x_fit, i1, i2 = set_fit_range(x, q0 - initial_sigma, q0 + 2.*initial_sigma)
-    y_fit = y[i1:i2]
+    x_fit, i1, i2 = set_fit_range(bin_centers, q0 - initial_sigma, q0 + 2.*initial_sigma)
+    y_fit = histogram[i1:i2]
     yerr_fit = yerr[i1:i2]
 
     def cost_function(amp, mean, sigma):