def specsheet_from_parax_data(opt_model, specsheet):
""" update specsheet to contents of opt_model, while preserving inputs """
if opt_model is None:
return None
seq_model = opt_model.seq_model
optical_spec = opt_model.optical_spec
fod = optical_spec.parax_data.fod
conj_type = 'finite'
if seq_model.gaps[0].thi > 10e8:
conj_type = 'infinite'
specsheet.conjugate_type = conj_type
# specsheet.imager_inputs contains values of independent variables of
# the optical system. Augment these as needed to get a defined imager.
imager_inputs = dict(specsheet.imager_inputs)
num_imager_inputs = len(imager_inputs)
if num_imager_inputs == 0:
# no user inputs, use model values
if conj_type == 'finite':
imager_inputs['m'] = fod.m
imager_inputs['f'] = fod.efl
specsheet.frozen_imager_inputs = [False] * 5
else: # conj_type == 'infinite'
imager_inputs['s'] = -math.inf
if fod.efl != 0:
imager_inputs['f'] = fod.efl
specsheet.frozen_imager_inputs = [True, True, True, True, False]
elif num_imager_inputs == 1:
# some/partial user input specification
if conj_type == 'finite':
# make sure that m is specified
if 'm' in imager_inputs:
imager_inputs['f'] = fod.efl
else:
imager_inputs['m'] = fod.m
specsheet.frozen_imager_inputs = [False] * 5
else: # conj_type == 'infinite'
imager_inputs['s'] = -math.inf
if fod.efl != 0:
imager_inputs['f'] = fod.efl
specsheet.frozen_imager_inputs = [True, True, True, True, False]
specsheet.imager = ideal_imager_setup(**imager_inputs)
ape_key, ape_value = optical_spec.pupil.get_input_for_specsheet()
fld_key, fld_value = optical_spec.field_of_view.get_input_for_specsheet()
etendue_inputs = specsheet.etendue_inputs
etendue_inputs[ape_key[0]][ape_key[1]][ape_key[2]] = ape_value
etendue_inputs[fld_key[0]][fld_key[1]][fld_key[2]] = fld_value
specsheet.generate_from_inputs(imager_inputs, etendue_inputs)
return specsheet
def create_new_optical_system(efl=10.0, fov=1.0):
imager_inputs = {'s': -math.inf, 'f': efl}
inf_conj_imgr = ideal_imager_setup(**imager_inputs)
ei = create_etendue_dict()
ei['field']['object']['angle'] = fov
ssi = SpecSheet('infinite',
imager=inf_conj_imgr,
imager_inputs=imager_inputs,
etendue_inputs=ei)
opt_model = create_new_optical_model_from_specsheet(ssi)
return opt_model
def test_ideal_imager_setup(**kwargs):
m1 = -0.5
s1 = -10.
m1s1 = ideal_imager_setup(m=m1, s=s1)
sp1 = m1s1.sp
tt1 = m1s1.tt
f1 = m1s1.f
assert ideal_imager_setup(m=m1, s=s1) == ideal_imager_setup(s=s1, m=m1)
assert ideal_imager_setup(m=m1, sp=sp1) == ideal_imager_setup(sp=sp1, m=m1)
assert ideal_imager_setup(m=m1, tt=tt1) == ideal_imager_setup(tt=tt1, m=m1)
assert ideal_imager_setup(m=m1, f=f1) == ideal_imager_setup(f=f1, m=m1)
tt2 = 4.0
f2 = 1.0
tt2f2 = ideal_imager_setup(tt=tt2, f=f2)
assert tt2f2.m == -1
assert tt2f2.s == -2
assert tt2f2.sp == 2
f3 = 2.0
with pytest.raises(ValueError):
ideal_imager_setup(tt=tt2, f=f3)
s_inf = -math.inf
efl = 25.0
s_inf_efl = ideal_imager_setup(s=s_inf, f=efl)
assert s_inf_efl.m == -0
assert s_inf_efl.s == -math.inf
assert s_inf_efl.sp == efl
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