def do_etendue_via_imager(conj_type,
imager,
etendue_inputs,
etendue_grid,
n_0=1,
n_k=1):
imager_inputs = {}
li = dict2d.num_items_by_type(etendue_inputs, fld_ape_set, obj_img_set)
if li['field'] == 1:
row = dict2d.row(etendue_inputs, 'field')
obj_img_key = 'object' if len(row['object']) else 'image'
do_field_via_imager(conj_type,
imager,
etendue_inputs,
obj_img_key,
etendue_grid,
n_0=n_0,
n_k=n_k)
if li['aperture'] == 1:
row = dict2d.row(etendue_inputs, 'aperture')
obj_img_key = 'object' if len(row['object']) else 'image'
do_aperture_via_imager(conj_type, imager, etendue_inputs, obj_img_key,
etendue_grid)
if li['field'] == 2 or li['aperture'] == 2:
fld_ape_key = 'field' if li['field'] == 2 else 'aperture'
# solve for imager
imager_inputs = do_etendue_to_imager(fld_ape_key,
etendue_inputs,
etendue_grid,
n_0=n_0,
n_k=n_k)
return imager_inputs
def generate_from_inputs(self, imgr_inputs, etendue_inputs):
""" compute imager and etendue values given input dicts """
max_partition, max_num_inputs = self.partition_defined()
num_imager_inputs = self.partitions['imager']
num_field_inputs = self.partitions['field']
num_aperture_inputs = self.partitions['aperture']
conj_type = self.conjugate_type
imager_inputs = {}
if max_num_inputs <= 1:
# fill in imager_inputs with any previous calculations for m or f
if conj_type == 'finite':
if num_imager_inputs < 2 and self.imager.m is not None:
imager_inputs['m'] = self.imager.m
else:
if num_imager_inputs < 2 and self.imager.f is not None:
imager_inputs['f'] = self.imager.f
# update imager_inputs with user entries
imager_inputs.update(imgr_inputs)
imager_inputs = {
k: v
for (k, v) in imager_inputs.items() if v is not None
}
# calculate an ideal imager for imager_inputs
imager = ideal_imager_setup(**imager_inputs)
if conj_type == 'finite':
imager_defined = True if imager.m is not None else False
else:
imager_defined = True if imager.f is not None else False
etendue_values = self.etendue_values
for fa_key, fa_value in etendue_inputs.items():
for oi_key, oi_value in fa_value.items():
etendue.fill_in_etendue_data(imager, fa_key,
etendue_inputs[fa_key][oi_key],
etendue_values[fa_key][oi_key])
if imager_defined:
if num_field_inputs >= 1 and num_aperture_inputs >= 1:
# we have enough data to calculate all of the etendue grid
ii = etendue.do_etendue_via_imager(conj_type, imager,
etendue_inputs,
etendue_values)
if ii:
imager_inputs[ii[0]] = ii[1]
imager = ideal_imager_setup(**imager_inputs)
etendue.do_etendue_via_imager(conj_type, imager,
etendue_inputs,
etendue_values)
elif num_field_inputs == 1:
# we have enough data to calculate all of the etendue grid
row = dict2d.row(etendue_inputs, 'field')
obj_img_key = 'object' if len(row['object']) else 'image'
etendue.do_field_via_imager(conj_type, imager, etendue_inputs,
obj_img_key, etendue_values)
elif num_aperture_inputs == 1:
# we have enough data to calculate all of the etendue grid
row = dict2d.row(etendue_inputs, 'aperture')
obj_img_key = 'object' if len(row['object']) else 'image'
etendue.do_aperture_via_imager(conj_type, imager,
etendue_inputs, obj_img_key,
etendue_values)
else: # imager not specified
if num_field_inputs == 2 or num_aperture_inputs == 2:
fld_ape_key = 'field' if num_field_inputs == 2 else 'aperture'
# solve for imager
ii = etendue.do_etendue_to_imager(fld_ape_key, etendue_inputs,
etendue_values)
imager_inputs[ii[0]] = ii[1]
imager = ideal_imager_setup(**imager_inputs)
# update etendue grid
etendue.do_etendue_via_imager(conj_type, imager,
etendue_inputs, etendue_values)
self.imager = imager
self.etendue_values = etendue_values
return imager, etendue_values