def _check_pb_parms(img_dataset, pb_parms):
import numbers
parms_passed = True
arc_sec_to_rad = np.pi / (3600 * 180)
#if not(_check_parms(pb_parms, 'pb_name', [str], default='PB')): parms_passed = False
if not (_check_parms(pb_parms, 'function', [str], default='casa_airy')):
parms_passed = False
if not (_check_parms(pb_parms,
'list_dish_diameters', [list],
list_acceptable_data_types=[numbers.Number],
list_len=-1)):
parms_passed = False
if not (_check_parms(pb_parms,
'list_blockage_diameters', [list],
list_acceptable_data_types=[numbers.Number],
list_len=-1)):
parms_passed = False
if len(pb_parms['list_dish_diameters']) != len(
pb_parms['list_blockage_diameters']):
print(
'######### ERROR:Parameter ',
'list_dish_diameters and list_blockage_diameters must be the same length.'
)
parms_passed = False
return parms_passed
def _check_mosaic_pb_parms(pb_mosaic_parms):
parms_passed = True
if not(_check_parms(pb_mosaic_parms, 'pb_name', [str], default='PB')): parms_passed = False
if not(_check_parms(pb_mosaic_parms, 'weight_name', [str], default='WEIGHT_PB')): parms_passed = False
return parms_passed
def _check_rotation_parms(rotation_parms):
import numbers
parms_passed = True
if not(_check_parms(rotation_parms, 'new_phase_center', [list], list_acceptable_data_types=[numbers.Number], list_len=2)): parms_passed = False
if not(_check_parms(rotation_parms, 'common_tangent_reprojection', [bool], default=True)): parms_passed = False
if not(_check_parms(rotation_parms, 'single_precision', [bool], default=True)): parms_passed = False
return parms_passed
def _check_norm_parms(norm_parms):
import numbers
parms_passed = True
if not(_check_parms(norm_parms, 'norm_type', [str], default='flat_sky', acceptable_data=['flat_noise','flat_sky','none'])): parms_passed = False
if not(_check_parms(norm_parms, 'single_precision', [bool], default=True)): parms_passed = False
if not(_check_parms(norm_parms, 'pb_limit', [numbers.Number], default=0.2)): parms_passed = False
return parms_passed
def _check_self_cal(solve_parms):
import numbers
parms_passed = True
if not (_check_parms(solve_parms,
'gaintype', [str],
acceptable_data=['G', 'T'],
default='T')):
parms_passed = False
if not (_check_parms(solve_parms,
'solint', [str],
acceptable_data=['int', 'all'],
default='int')):
parms_passed = False
if not (_check_parms(solve_parms, 'refant_id', [int], default=-1)):
parms_passed = False
if not (_check_parms(solve_parms, 'phase_only', [bool], default=False)):
parms_passed = False
if not (_check_parms(solve_parms, 'minsnr', [numbers.Number],
default=0.0)):
parms_passed = False
if not (_check_parms(solve_parms, 'minblperant', [int], default=4)):
parms_passed = False
if not (_check_parms(solve_parms, 'ginfo', [bool], default=False)):
parms_passed = False
if solve_parms['refant_id'] == -1:
solve_parms['refant_id'] = None
return parms_passed
def _check_grid_parms(grid_parms):
import numbers
parms_passed = True
arc_sec_to_rad = np.pi / (3600 * 180)
if not(_check_parms(grid_parms, 'image_size', [list], list_acceptable_data_types=[np.int], list_len=2)): parms_passed = False
if not(_check_parms(grid_parms, 'image_center', [list], list_acceptable_data_types=[np.int], list_len=2, default = np.array(grid_parms['image_size'])//2)): parms_passed = False
if not(_check_parms(grid_parms, 'cell_size', [list], list_acceptable_data_types=[numbers.Number], list_len=2)): parms_passed = False
if not(_check_parms(grid_parms, 'fft_padding', [numbers.Number], default=1.2,acceptable_range=[1,10])): parms_passed = False
if not(_check_parms(grid_parms, 'chan_mode', [str], acceptable_data=['cube','continuum'], default='cube')): parms_passed = False
if parms_passed == True:
grid_parms['image_size'] = np.array(grid_parms['image_size']).astype(int)
grid_parms['image_size_padded'] = (grid_parms['fft_padding']* grid_parms['image_size']).astype(int)
grid_parms['image_center'] = np.array(grid_parms['image_center'])
grid_parms['cell_size'] = arc_sec_to_rad * np.array(grid_parms['cell_size'])
grid_parms['cell_size'][0] = -grid_parms['cell_size'][0]
return parms_passed
def _check_imaging_weights_parms(imaging_weights_parms):
import numbers
parms_passed = True
arc_sec_to_rad = np.pi / (3600 * 180)
'''
if not(_check_parms(imaging_weights_parms, 'data_name', [str], default='DATA')): parms_passed = False
if not(_check_dataset(vis_dataset,imaging_weights_parms['data_name'])): parms_passed = False
if not(_check_parms(imaging_weights_parms, 'uvw_name', [str], default='UVW')): parms_passed = False
if not(_check_dataset(vis_dataset,imaging_weights_parms['uvw_name'])): parms_passed = False
if not(_check_parms(imaging_weights_parms, 'imaging_weight_name', [str], default='IMAGING_WEIGHT')): parms_passed = False
'''
if not (_check_parms(
imaging_weights_parms,
'weighting', [str],
acceptable_data=['natural', 'uniform', 'briggs', 'briggs_abs'],
default='natural')):
parms_passed = False
if imaging_weights_parms['weighting'] == 'briggs_abs':
if not (_check_parms(imaging_weights_parms,
'briggs_abs_noise', [numbers.Number],
default=1.0)):
parms_passed = False
if (imaging_weights_parms['weighting']
== 'briggs') or (imaging_weights_parms['weighting']
== 'briggs_abs'):
if not (_check_parms(imaging_weights_parms,
'robust', [numbers.Number],
default=0.5,
acceptable_range=[-2, 2])):
parms_passed = False
return parms_passed
def _check_gcf_parms(gcf_parms):
import numbers
parms_passed = True
if not(_check_parms(gcf_parms, 'function', [str], acceptable_data=['alma_airy','airy'], default='alma_airy')): parms_passed = False
if not(_check_parms(gcf_parms, 'freq_chan', [list,np.array],list_acceptable_data_types=[numbers.Number],list_len=-1)): parms_passed = False
if not(_check_parms(gcf_parms, 'list_dish_diameters', [list,np.array],list_acceptable_data_types=[numbers.Number],list_len=-1)): parms_passed = False
if not(_check_parms(gcf_parms, 'list_blockage_diameters', [list,np.array],list_acceptable_data_types=[numbers.Number],list_len=-1)): parms_passed = False
if not(_check_parms(gcf_parms, 'unique_ant_indx', [list,np.array],list_acceptable_data_types=[numbers.Number],list_len=-1)): parms_passed = False
if not(_check_parms(gcf_parms, 'pol', [list,np.array],list_acceptable_data_types=[numbers.Number],list_len=-1)): parms_passed = False
if not(_check_parms(gcf_parms, 'chan_tolerance_factor', [numbers.Number], default=0.005)): parms_passed = False
if not(_check_parms(gcf_parms, 'oversampling', [list,np.array], list_acceptable_data_types=[np.int], list_len=2, default=[10,10])): parms_passed = False
if not(_check_parms(gcf_parms, 'max_support', [list,np.array], list_acceptable_data_types=[np.int], list_len=2, default=[15,15])): parms_passed = False
#if not(_check_parms(gcf_parms, 'image_phase_center', [list,np.array], list_acceptable_data_types=[numbers.Number], list_len=2)): parms_passed = False
if not(_check_parms(gcf_parms, 'support_cut_level', [numbers.Number], default=2.5*10**-2)): parms_passed = False
if not(_check_parms(gcf_parms, 'a_chan_num_chunk', [np.int], default=3)): parms_passed = False
if gcf_parms['function'] == 'airy' or gcf_parms['function'] == 'alma_airy':
if not(_check_parms(gcf_parms, 'list_dish_diameters', [list,np.array],list_acceptable_data_types=[numbers.Number],list_len=-1)): parms_passed = False
if not(_check_parms(gcf_parms, 'list_blockage_diameters', [list,np.array],list_acceptable_data_types=[numbers.Number],list_len=-1)): parms_passed = False
if len(gcf_parms['list_dish_diameters']) != len(gcf_parms['list_blockage_diameters']):
print('######### ERROR:Parameter ', 'list_dish_diameters and list_blockage_diameters must be the same length.')
parms_passed = False
if parms_passed == True:
gcf_parms['oversampling'] = np.array(gcf_parms['oversampling']).astype(int)
gcf_parms['max_support'] = np.array(gcf_parms['max_support']).astype(int)
#gcf_parms['image_phase_center'] = np.array(gcf_parms['image_phase_center'])
gcf_parms['freq_chan'] = np.array(gcf_parms['freq_chan'])
gcf_parms['list_dish_diameters'] = np.array(gcf_parms['list_dish_diameters'])
gcf_parms['list_blockage_diameters'] = np.array(gcf_parms['list_blockage_diameters'])
gcf_parms['unique_ant_indx'] = np.array(gcf_parms['unique_ant_indx'])
#gcf_parms['basline_ant'] = np.array(gcf_parms['basline_ant'])
gcf_parms['pol'] = np.array(gcf_parms['pol'])
return parms_passed
