remove commented out lines

Author: chiarabellenghi

skyllh/analyses/i3/publicdata_ps/utils.py

@@ -133,8 +133,6 @@ class from scipy.
         self.y_max = self.y_binedges[-1]
 
         x = get_bincenters_from_binedges(self.x_binedges)
-        # y = get_bincenters_from_binedges(self.y_binedges)
-
         # Hack to avoid awkward behaviors due to splining in sin(dec)
         y = np.repeat(self.y_binedges, repeats=2)[1:-1]
         y[1::2] -= 1e-10
@@ -271,7 +269,7 @@ def __call__(self, x, y, oor_value=0, grid=False, renorm=True):
         if renorm:
             f2d = self._renorm_per_y_grid(f2d, y)
 
-        # OOR mask on tensor grid in user order
+        # OOR mask on tensor grid
         mx2d, my2d = np.meshgrid(m_x_oor, m_y_oor, indexing='ij')
         f2d[mx2d | my2d] = oor_value
         return f2d
@@ -363,45 +361,6 @@ def psi_to_dec_and_ra(
 
     return (dec, ra)
 
-
-def create_energy_cut_spline_old(
-        ds,
-        exp_data,
-        spl_smooth):
-    """Create the spline for the declination-dependent energy cut
-    that the signal generator needs for injection in the southern sky
-    Some special conditions are needed for IC79 and IC86_I, because
-    their experimental dataset shows events that should probably have
-    been cut by the IceCube selection.
-    """
-    data_exp = exp_data.copy(keep_fields=['sin_dec', 'log_energy'])
-    if ds.name == 'IC79':
-        m = np.invert(np.logical_and(
-            data_exp['sin_dec'] < -0.75,
-            data_exp['log_energy'] < 4.2))
-        data_exp = data_exp[m]
-    if ds.name == 'IC86_I':
-        m = np.invert(np.logical_and(
-            data_exp['sin_dec'] < -0.2,
-            data_exp['log_energy'] < 2.5))
-        data_exp = data_exp[m]
-
-    sin_dec_binning = ds.get_binning_definition('sin_dec')
-    sindec_edges = sin_dec_binning.binedges
-    min_log_e = np.zeros(len(sindec_edges)-1, dtype=float)
-    for i in range(len(sindec_edges)-1):
-        mask = np.logical_and(
-            data_exp['sin_dec'] >= sindec_edges[i],
-            data_exp['sin_dec'] < sindec_edges[i+1])
-        min_log_e[i] = np.min(data_exp['log_energy'][mask])
-    del data_exp
-    sindec_centers = 0.5 * (sindec_edges[1:]+sindec_edges[:-1])
-
-    spline = interpolate.UnivariateSpline(
-        sindec_centers, min_log_e, k=2, s=spl_smooth)
-
-    return spline
-
 def create_energy_cut_spline(
         ds,
         exp_data,