Removing unneeded comments.

src/stcal/ramp_fitting/ols_fit.py

@@ -677,10 +677,10 @@ def ols_ramp_fit_single(
 
         if ramp_data.drop_frames1 is None:
             ramp_data.drop_frames1 = 0
-        dbg_print("    ---------------- Entering C Code ----------------")
+        print("    ---------------- Entering C Code ----------------")
         image_info, integ_info, opt_info = ols_slope_fitter(
                 ramp_data, gain_2d, readnoise_2d, weighting, save_opt)
-        dbg_print("    ----------------  Return C Code  ----------------")
+        print("    ----------------  Return C Code  ----------------")
 
         c_end = time.time()
         print("=" * 80)
@@ -691,10 +691,10 @@ def ols_ramp_fit_single(
     p_start = time.time()
 
     print("\n");
-    dbg_print("    ---------------- Entering Python Code ----------------")
+    print("    ---------------- Entering Python Code ----------------")
     image_info, integ_info, opt_info = ols_ramp_fit_single_python(
         ramp_data, buffsize, save_opt, readnoise_2d, gain_2d, weighting)
-    dbg_print("    ----------------  Return Python Code  ----------------")
+    print("    ----------------  Return Python Code  ----------------")
 
     # XXX end python time
     p_end = time.time()
@@ -810,9 +810,9 @@ def c_python_time_comparison(c_start, c_end, p_start, p_end):
     c_diff = c_end - c_start
     p_diff = p_end - p_start
     c_div_p = c_diff / p_diff * 100.
-    dbg_print(f"{c_diff = }")
-    dbg_print(f"{p_diff = }")
-    dbg_print(f"{c_div_p = :.4f}%")
+    print(f"{c_diff = }")
+    print(f"{p_diff = }")
+    print(f"{c_div_p = :.4f}%")
 
 
 def discard_miri_groups(ramp_data):
@@ -1250,13 +1250,6 @@ def ramp_fit_compute_variances(ramp_data, gain_2d, readnoise_2d, fit_slopes_ans)
         var_p4[num_int, :, med_rates <= 0.] = 0.
         var_both4[num_int, :, :, :] = var_r4[num_int, :, :, :] + var_p4[num_int, :, :, :]
 
-        '''
-        if num_int == 0:
-            print("=" * 80)
-            dbg_print(f"var_both4 = integration 0\n{var_both4[0, :, 0, 0]}")
-            print("=" * 80)
-        '''
-
         inv_var_both4[num_int, :, :, :] = 1. / var_both4[num_int, :, :, :]
 
         # Want to retain values in the 4D arrays only for the segments that each
@@ -1315,12 +1308,6 @@ def ramp_fit_compute_variances(ramp_data, gain_2d, readnoise_2d, fit_slopes_ans)
     ramp_data.groupdq = groupdq
     ramp_data.pixeldq = inpixeldq
 
-    '''
-    print("=" * 80)
-    dbg_print(f"var_both4 = integration 0\n{var_both4[0, :, 0, 0]}")
-    print("=" * 80)
-    '''
-
     return var_p3, var_r3, var_p4, var_r4, var_both4, var_both3, inv_var_both4, \
         s_inv_var_p3, s_inv_var_r3, s_inv_var_both3
 
@@ -2695,14 +2682,6 @@ def fit_next_segment_long_end_of_ramp(
     if len(g_pix) > 0:
         inv_var[g_pix] += 1.0 / variance[g_pix]
 
-        '''
-        print("=" * 80)
-        dbg_print(f"{ramp_data.current_integ = }")
-        dbg_print(f"{variance = }")
-        dbg_print(f"{inv_var = }")
-        print("=" * 80)
-        '''
-
         # Append results to arrays
         opt_res.append_arr(num_seg, g_pix, intercept, slope, sig_intercept,
                            sig_slope, inv_var, save_opt)
@@ -3166,15 +3145,6 @@ def fit_double_read(mask_2d, wh_pix_2r, data_masked, slope_s, intercept_s,
         sig_slope_s[pixel_ff] = np.sqrt(2) * rn
         sig_intercept_s[pixel_ff] = np.sqrt(2) * rn
 
-    '''
-    if len(wh_pix_2r[0]) > 0:
-        print("=" * 80)
-        dbg_print(f"{mask_2d[:, 0] = }")
-        dbg_print(f"{rn = }")
-        dbg_print(f"{variance_s = }")
-        print("=" * 80)
-    '''
-
     return slope_s, intercept_s, variance_s, sig_slope_s, sig_intercept_s