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='alma_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_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
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_rotation_parms(rotation_parms):
import numbers
parms_passed = True
if not (_check_parms(rotation_parms,
'image_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_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='airy')):
parms_passed = False
if pb_parms['function'] == 'airy':
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
if not (_check_parms(pb_parms,
'imsize', [list],
list_acceptable_data_types=[int, np.int64],
list_len=2)):
parms_passed = False
if not (_check_parms(pb_parms,
'cell', [list],
list_acceptable_data_types=[numbers.Number],
list_len=2)):
parms_passed = False
if not (_check_parms(pb_parms,
'pb_limit', [numbers.Number],
acceptable_range=[0, 1],
default=0.0)):
parms_passed = False
if parms_passed == True:
pb_parms['imsize'] = np.array(pb_parms['imsize']).astype(int)
pb_parms['cell'] = arc_sec_to_rad * np.array(pb_parms['cell'])
pb_parms['cell'][0] = -pb_parms['cell'][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
def _check_grid_params(vis_dataset,
grid_parms,
default_image_name='DIRTY_IMAGE',
default_sum_weight_name='SUM_WEIGHT'):
import numbers
parms_passed = True
arc_sec_to_rad = np.pi / (3600 * 180)
n_chunks_in_each_dim = vis_dataset.DATA.data.numblocks
if n_chunks_in_each_dim[3] != 1:
print('######### ERROR chunking along polarization is not supported')
return False
if not (_check_parms(grid_parms, 'data_name', [str], default='DATA')):
parms_passed = False
if not (_check_dataset(vis_dataset, grid_parms['data_name'])):
parms_passed = False
if not (_check_parms(grid_parms, 'uvw_name', [str], default='UVW')):
parms_passed = False
if not (_check_dataset(vis_dataset, grid_parms['uvw_name'])):
parms_passed = False
if not (_check_parms(grid_parms,
'imaging_weight_name', [str],
default='IMAGING_WEIGHT')):
parms_passed = False
if not (_check_dataset(vis_dataset, grid_parms['imaging_weight_name'])):
parms_passed = False
if not (_check_parms(
grid_parms, 'image_name', [str], default=default_image_name)):
parms_passed = False
if not (_check_parms(grid_parms,
'sum_weight_name', [str],
default=default_sum_weight_name)):
parms_passed = False
if not (_check_parms(grid_parms,
'chan_mode', [str],
acceptable_data=['cube', 'continuum'],
default='cube')):
parms_passed = False
if not (_check_parms(grid_parms,
'imsize', [list],
list_acceptable_data_types=[np.int],
list_len=2)):
parms_passed = False
if not (_check_parms(grid_parms,
'cell', [list],
list_acceptable_data_types=[numbers.Number],
list_len=2)):
parms_passed = False
if not (_check_parms(grid_parms, 'oversampling', [np.int], default=100)):
parms_passed = False
if not (_check_parms(grid_parms, 'support', [np.int], default=7)):
parms_passed = False
if not (_check_parms(grid_parms,
'fft_padding', [numbers.Number],
default=1.2,
acceptable_range=[1, 100])):
parms_passed = False
if parms_passed == True:
grid_parms['imsize'] = np.array(grid_parms['imsize']).astype(int)
grid_parms['imsize_padded'] = (grid_parms['fft_padding'] *
grid_parms['imsize']).astype(int)
grid_parms['cell'] = arc_sec_to_rad * np.array(grid_parms['cell'])
grid_parms['cell'][0] = -grid_parms['cell'][0]
grid_parms['complex_grid'] = True
return parms_passed
def _check_imaging_weights_parms(vis_dataset, 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 not (_check_parms(imaging_weights_parms,
'robust', [numbers.Number],
default=0.5,
acceptable_range=[-2, 2])):
parms_passed = False
if imaging_weights_parms['weighting'] != 'natural':
if not (_check_parms(imaging_weights_parms,
'chan_mode', [str],
acceptable_data=['cube', 'continuum'],
default='cube')):
parms_passed = False
if not (_check_parms(imaging_weights_parms,
'imsize', [list],
list_acceptable_data_types=[int, np.int64],
list_len=2)):
parms_passed = False
if not (_check_parms(imaging_weights_parms,
'cell', [list],
list_acceptable_data_types=[numbers.Number],
list_len=2)):
parms_passed = False
if parms_passed == True:
imaging_weights_parms['imsize'] = np.array(
imaging_weights_parms['imsize']).astype(int)
imaging_weights_parms['cell'] = arc_sec_to_rad * np.array(
imaging_weights_parms['cell'])
imaging_weights_parms['cell'][0] = -imaging_weights_parms['cell'][0]
return parms_passed