Don't do error computation if not requested

jwst/outlier_detection/utils.py

@@ -112,7 +112,10 @@ def median_without_resampling(input_models,
 
  drizzled_model = input_models.borrow(i)
  drizzled_data = drizzled_model.data.copy()
- drizzled_err = drizzled_model.err.copy()
+ if return_error:
+ drizzled_err = drizzled_model.err.copy()
+ else:
+ drizzled_err = None
  weight = build_driz_weight(drizzled_model,
  weight_type=weight_type,
  good_bits=good_bits)
@@ -121,19 +124,26 @@ def median_without_resampling(input_models,
  input_shape = (ngroups,) + drizzled_data.shape
  dtype = drizzled_data.dtype
  computer = MedianComputer(input_shape, in_memory, buffer_size, dtype)
- err_computer = MedianComputer(input_shape, in_memory, buffer_size, dtype)
+ if return_error:
+ err_computer = MedianComputer(input_shape, in_memory, buffer_size, dtype)
+ else:
+ err_computer = None
 
  weight_threshold = compute_weight_threshold(weight, maskpt)
  drizzled_data[weight < weight_threshold] = np.nan
- drizzled_err[weight < weight_threshold] = np.nan
  computer.append(drizzled_data, i)
- err_computer.append(drizzled_err, i)
+ if return_error:
+ drizzled_err[weight < weight_threshold] = np.nan
+ err_computer.append(drizzled_err, i)
 
  input_models.shelve(drizzled_model, i, modify=False)
 
  # Perform median combination on set of drizzled mosaics
  median_data = computer.evaluate()
- median_err = err_computer.evaluate()
+ if return_error:
+ median_err = err_computer.evaluate()
+ else:
+ median_err = None
 
  if save_intermediate_results:
  # Save median model to fits
@@ -224,17 +234,24 @@ def median_with_resampling(input_models,
  input_shape = (ngroups,)+drizzled_model.data.shape
  dtype = drizzled_model.data.dtype
  computer = MedianComputer(input_shape, in_memory, buffer_size, dtype)
- err_computer = MedianComputer(input_shape, in_memory, buffer_size, dtype)
+ if return_error:
+ err_computer = MedianComputer(input_shape, in_memory, buffer_size, dtype)
+ else:
+ err_computer = None
 
  weight_threshold = compute_weight_threshold(drizzled_model.wht, maskpt)
  drizzled_model.data[drizzled_model.wht < weight_threshold] = np.nan
- drizzled_model.err[drizzled_model.wht < weight_threshold] = np.nan
  computer.append(drizzled_model.data, i)
- err_computer.append(drizzled_model.err, i)
+ if return_error:
+ drizzled_model.err[drizzled_model.wht < weight_threshold] = np.nan
+ err_computer.append(drizzled_model.err, i)
 
  # Perform median combination on set of drizzled mosaics
  median_data = computer.evaluate()
- median_err = err_computer.evaluate()
+ if return_error:
+ median_err = err_computer.evaluate()
+ else:
+ median_err = None
 
  if save_intermediate_results:
  # Save median model to fits