def test_get_shapelet_model_index(self):
"""
Test `get_power_law_model_index` method.
:return:
:rtype:
"""
config = deepcopy(self.config)
config.settings['model']['source_light'] = ['SERSIC', 'SHAPELETS']
assert Recipe(config)._get_shapelet_model_index() == 1
config.settings['model']['source_light'] = ['SERSIC']
assert Recipe(config)._get_shapelet_model_index() is None
def test_get_external_shear_model_index(self):
"""
Test `_get_external_shear_model_index` method.
:return:
:rtype:
"""
config = deepcopy(self.config)
config.settings['model']['lens'] = ['SHEAR_GAMMA_PSI', 'SPEMD']
assert Recipe(config)._get_external_shear_model_index() == 0
config.settings['model']['lens'] = ['SERSIC', 'SHEAR']
assert Recipe(config)._get_external_shear_model_index() == 1
config.settings['model']['lens'] = ['SERSIC']
assert Recipe(config)._get_external_shear_model_index() is None
def test_get_power_law_model_index(self):
"""
Test `get_power_law_model_index` method.
:return:
:rtype:
"""
config = deepcopy(self.config)
config.settings['model']['lens'] = ['SERSIC', 'SPEMD']
assert Recipe(config)._get_power_law_model_index() == 1
config.settings['model']['lens'] = ['SERSIC', 'SPEP']
assert Recipe(config)._get_power_law_model_index() == 1
config.settings['model']['lens'] = ['SERSIC']
assert Recipe(config)._get_power_law_model_index() is None
def test_get_galaxy_galaxy_recipe(self):
"""
Test `get_galaxy_galaxy_recipe` method.
:return:
:rtype:
"""
image = np.random.normal(size=(120, 120))
kwargs_data_joint = {
'multi_band_list': [[{
'image_data':
image,
'background_rms':
0.01,
'exposure_time':
np.ones_like(image),
'ra_at_xy_0':
0.,
'dec_at_xy_0':
0.,
'transform_pix2angle':
np.array([[-0.01, 0], [0, 0.01]])
}, {}, {}]],
'multi_band_type':
'multi-linear'
}
fitting_kwargs_list = self.recipe.get_galaxy_galaxy_recipe(
kwargs_data_joint)
assert isinstance(fitting_kwargs_list, list)
# test the recipe by running it fully
config = deepcopy(self.config)
config.settings['model']['source_light'] = ['SHAPELETS']
recipe = Recipe(config)
fitting_sequence = FittingSequence(
kwargs_data_joint,
config.get_kwargs_model(),
config.get_kwargs_constraints(),
config.get_kwargs_likelihood(),
config.get_kwargs_params(),
)
fitting_kwargs_list = recipe.get_recipe(
kwargs_data_joint=kwargs_data_joint, recipe_name='galaxy-galaxy')
fitting_sequence.fit_sequence(fitting_kwargs_list)
def test_get_sampling_sequence(self):
"""
Test `get_sampling_sequence` method.
:return:
:rtype:
"""
self.recipe.do_sampling = True
fitting_kwargs_list = self.recipe.get_sampling_sequence()
assert isinstance(fitting_kwargs_list, list)
config = deepcopy(self.config)
config.settings['fitting']['sampling'] = True
config.settings['fitting']['sampler'] = 'not-a-sampler'
recipe = Recipe(config)
with pytest.raises(ValueError):
recipe.get_sampling_sequence()
# test initiating from given `init_samples`
config = deepcopy(self.config)
config.settings['fitting']['mcmc_settings']['init_samples'] = \
np.ones(20)
recipe = Recipe(config)
sequence = recipe.get_sampling_sequence()
npt.assert_array_equal(sequence[0][1]['init_samples'], np.ones(20))
npt.assert_raises(AssertionError, npt.assert_array_equal,
sequence[0][1]['init_samples'], np.zeros(20))
def test_init(self):
"""
Test `__init__` method.
:return:
:rtype:
"""
config = deepcopy(self.config)
config.settings['fitting']['pso'] = None
config.settings['fitting']['psf_iteration'] = None
config.settings['fitting']['sampling'] = None
recipe = Recipe(config)
assert recipe.do_sampling is False
assert recipe.do_pso is False
assert recipe.reconstruct_psf is False
del config.settings['fitting']['pso']
del config.settings['fitting']['psf_iteration']
del config.settings['fitting']['sampling']
recipe = Recipe(config)
assert recipe.do_sampling is False
assert recipe.do_pso is False
assert recipe.reconstruct_psf is False
def test_get_recipe(self):
"""
Test `get_recipe` method.
:return:
:rtype:
"""
fitting_kwargs_list = self.recipe.get_recipe()
assert isinstance(fitting_kwargs_list, list)
config = deepcopy(self.config)
config.settings['fitting_kwargs_list'] = [{}, {}]
recipe = Recipe(config)
assert recipe.get_recipe()[:2] == [{}, {}]
config.settings['fitting_kwargs_list'] = None
recipe = Recipe(config)
assert isinstance(recipe.get_recipe(), list)
# check requirement to pass `kwargs_data_joint`
with pytest.raises(ValueError):
recipe.get_recipe(recipe_name='galaxy-galaxy')
with pytest.raises(ValueError):
recipe.get_recipe(recipe_name='tuna-salad')
# check that the first sequence is 'MCMC' when
# recipe 'skip' is used
assert recipe.get_recipe(recipe_name='skip')[0][0] == 'MCMC'
def setup_class(self):
self.test_setting_file = _ROOT_DIR / 'io_directory_example' \
/ 'settings' / 'lens_system1_config.yml'
self.config = ModelConfig(str(self.test_setting_file.resolve()))
self.recipe = Recipe(self.config)
class TestRecipe(object):
"""
Test the `Recipe` module.
"""
def setup_class(self):
self.test_setting_file = _ROOT_DIR / 'io_directory_example' \
/ 'settings' / 'lens_system1_config.yml'
self.config = ModelConfig(str(self.test_setting_file.resolve()))
self.recipe = Recipe(self.config)
@classmethod
def teardown_class(cls):
pass
def test_init(self):
"""
Test `__init__` method.
:return:
:rtype:
"""
config = deepcopy(self.config)
config.settings['fitting']['pso'] = None
config.settings['fitting']['psf_iteration'] = None
config.settings['fitting']['sampling'] = None
recipe = Recipe(config)
assert recipe.do_sampling is False
assert recipe.do_pso is False
assert recipe.reconstruct_psf is False
del config.settings['fitting']['pso']
del config.settings['fitting']['psf_iteration']
del config.settings['fitting']['sampling']
recipe = Recipe(config)
assert recipe.do_sampling is False
assert recipe.do_pso is False
assert recipe.reconstruct_psf is False
def test_get_recipe(self):
"""
Test `get_recipe` method.
:return:
:rtype:
"""
fitting_kwargs_list = self.recipe.get_recipe()
assert isinstance(fitting_kwargs_list, list)
config = deepcopy(self.config)
config.settings['fitting_kwargs_list'] = [{}, {}]
recipe = Recipe(config)
assert recipe.get_recipe()[:2] == [{}, {}]
config.settings['fitting_kwargs_list'] = None
recipe = Recipe(config)
assert isinstance(recipe.get_recipe(), list)
# check requirement to pass `kwargs_data_joint`
with pytest.raises(ValueError):
recipe.get_recipe(recipe_name='galaxy-galaxy')
with pytest.raises(ValueError):
recipe.get_recipe(recipe_name='tuna-salad')
# check that the first sequence is 'MCMC' when
# recipe 'skip' is used
assert recipe.get_recipe(recipe_name='skip')[0][0] == 'MCMC'
def test_get_power_law_model_index(self):
"""
Test `get_power_law_model_index` method.
:return:
:rtype:
"""
config = deepcopy(self.config)
config.settings['model']['lens'] = ['SERSIC', 'SPEMD']
assert Recipe(config)._get_power_law_model_index() == 1
config.settings['model']['lens'] = ['SERSIC', 'SPEP']
assert Recipe(config)._get_power_law_model_index() == 1
config.settings['model']['lens'] = ['PEMD', 'SERSIC']
assert Recipe(config)._get_power_law_model_index() == 0
config.settings['model']['lens'] = ['SERSIC']
assert Recipe(config)._get_power_law_model_index() is None
def test_get_external_shear_model_index(self):
"""
Test `_get_external_shear_model_index` method.
:return:
:rtype:
"""
config = deepcopy(self.config)
config.settings['model']['lens'] = ['SHEAR_GAMMA_PSI', 'SPEMD']
assert Recipe(config)._get_external_shear_model_index() == 0
config.settings['model']['lens'] = ['SERSIC', 'SHEAR']
assert Recipe(config)._get_external_shear_model_index() == 1
config.settings['model']['lens'] = ['SERSIC']
assert Recipe(config)._get_external_shear_model_index() is None
def test_get_shapelet_model_index(self):
"""
Test `get_power_law_model_index` method.
:return:
:rtype:
"""
config = deepcopy(self.config)
config.settings['model']['source_light'] = ['SERSIC', 'SHAPELETS']
assert Recipe(config)._get_shapelet_model_index() == 1
config.settings['model']['source_light'] = ['SERSIC']
assert Recipe(config)._get_shapelet_model_index() is None
def test_get_default_recipe(self):
"""
Test `get_default_recipe` method.
:return:
:rtype:
"""
self.recipe.reconstruct_psf = True
fitting_kwargs_list = self.recipe.get_default_recipe()
assert isinstance(fitting_kwargs_list, list)
def test_get_sampling_sequence(self):
"""
Test `get_sampling_sequence` method.
:return:
:rtype:
"""
self.recipe.do_sampling = True
fitting_kwargs_list = self.recipe.get_sampling_sequence()
assert isinstance(fitting_kwargs_list, list)
config = deepcopy(self.config)
config.settings['fitting']['sampling'] = True
config.settings['fitting']['sampler'] = 'not-a-sampler'
recipe = Recipe(config)
with pytest.raises(ValueError):
recipe.get_sampling_sequence()
# test initiating from given `init_samples`
config = deepcopy(self.config)
config.settings['fitting']['mcmc_settings']['init_samples'] = \
np.ones(20)
recipe = Recipe(config)
sequence = recipe.get_sampling_sequence()
npt.assert_array_equal(sequence[0][1]['init_samples'], np.ones(20))
npt.assert_raises(AssertionError, npt.assert_array_equal,
sequence[0][1]['init_samples'], np.zeros(20))
def test_get_galaxy_galaxy_recipe(self):
"""
Test `get_galaxy_galaxy_recipe` method.
:return:
:rtype:
"""
image = np.random.normal(size=(120, 120))
kwargs_data_joint = {
'multi_band_list': [[{
'image_data':
image,
'background_rms':
0.01,
'exposure_time':
np.ones_like(image),
'ra_at_xy_0':
0.,
'dec_at_xy_0':
0.,
'transform_pix2angle':
np.array([[-0.01, 0], [0, 0.01]])
}, {}, {}]],
'multi_band_type':
'multi-linear'
}
fitting_kwargs_list = self.recipe.get_galaxy_galaxy_recipe(
kwargs_data_joint)
assert isinstance(fitting_kwargs_list, list)
# test the recipe by running it fully
config = deepcopy(self.config)
config.settings['model']['source_light'] = ['SHAPELETS']
recipe = Recipe(config)
fitting_sequence = FittingSequence(
kwargs_data_joint,
config.get_kwargs_model(),
config.get_kwargs_constraints(),
config.get_kwargs_likelihood(),
config.get_kwargs_params(),
)
fitting_kwargs_list = recipe.get_recipe(
kwargs_data_joint=kwargs_data_joint, recipe_name='galaxy-galaxy')
fitting_sequence.fit_sequence(fitting_kwargs_list)
def test_get_arc_mask(self):
"""
Test `get_arc_mask` method.
:return:
:rtype:
"""
image = np.random.normal(size=(100, 100))
mask = self.recipe.get_arc_mask(image, mask=np.ones_like(image))
assert mask.shape == (100, 100)
def test_fix_params(self):
"""
Test `fix_params` method.
:return:
:rtype:
"""
test = self.recipe.fix_params('lens', [0])
assert set(test[1]['lens_add_fixed'][0][1]) == {
'theta_E', 'center_x', 'center_y', 'e1', 'gamma', 'e2'
}
test = self.recipe.fix_params('lens', [1])
assert set(test[1]['lens_add_fixed'][0][1]) == {'gamma_ext', 'psi_ext'}
test = self.recipe.fix_params('lens_light', [0])
assert set(test[1]['lens_light_add_fixed'][0][1]) == {
'e1', 'center_x', 'center_y', 'R_sersic', 'e2'
}
test = self.recipe.fix_params('source', [0])
assert set(test[1]['source_add_fixed'][0][1]) == {
'R_sersic', 'n_sersic', 'center_x', 'center_y', 'e1', 'e2'
}
with pytest.raises(ValueError):
self.recipe.fix_params('observer')
def test_unfix_params(self):
"""
Test `unfix_params` method.
:return:
:rtype:
"""
test = self.recipe.unfix_params('lens', [0])
assert set(test[1]['lens_remove_fixed'][0][1]) == {
'theta_E', 'center_x', 'center_y', 'e1', 'gamma', 'e2'
}
test = self.recipe.unfix_params('lens', [1])
assert set(
test[1]['lens_remove_fixed'][0][1]) == {'gamma_ext', 'psi_ext'}
test = self.recipe.unfix_params('lens_light', [0])
assert set(test[1]['lens_light_remove_fixed'][0][1]) == {
'e1', 'center_x', 'center_y', 'R_sersic', 'e2'
}
test = self.recipe.unfix_params('source', [0])
assert set(test[1]['source_remove_fixed'][0][1]) == {
'R_sersic', 'n_sersic', 'center_x', 'center_y', 'e1', 'e2'
}