def test_demo3():
config_fn_ini = os.path.join(demo_dir, 'demo3.ini')
config_fn_yaml = os.path.join(demo_dir, 'demo3.yaml')
config = ConfigBlock(config_fn_ini)
with open(config_fn_yaml, 'w') as file:
yaml.dump(config.data, file)
for config_fn in [config_fn_ini, config_fn_yaml]:
graph_fn = os.path.join(demo_dir, 'pipe3.ps')
mapping_proj = ['ell_0', 'ell_2', 'ell_4']
make_data_covariance(data_fn=data_fn,
covariance_fn=covariance_fn,
mapping_proj=mapping_proj)
pipeline = BasePipeline(config_block=config_fn)
pipeline.plot_pipeline_graph(graph_fn)
pipeline.setup()
pipeline.execute_parameter_values(a=0.)
loglkl = pipeline.data_block[section_names.likelihood, 'loglkl']
pipeline.execute_parameter_values(a=4.)
assert pipeline.data_block[section_names.likelihood,
'loglkl'] != loglkl
pipeline.cleanup()
def test_demo4():
config_fn = os.path.join(demo_dir, 'demo4.ini')
graph_fn = os.path.join(demo_dir, 'pipe4.ps')
mapping_proj = ['ell_0', 'ell_2', 'ell_4']
make_data_covariance(data_fn=data_fn,
covariance_fn=covariance_fn,
mapping_proj=mapping_proj)
pipeline = BasePipeline(config_block=config_fn)
pipeline.plot_pipeline_graph(graph_fn)
pipeline.setup()
pipeline.data_block[section_names.parameters, 'a'] = 0.
del pipeline.data_block[section_names.parameters, 'a_model1']
del pipeline.data_block[section_names.parameters, 'b_model1']
def test_error():
ok = False
try:
pipeline.execute()
except BlockError:
ok = True
assert ok
test_error()
pipeline.data_block[section_names.parameters, 'a_model1'] = 0.
test_error()
pipeline.data_block[section_names.parameters, 'b_model1'] = 0.
assert (section_names.common, 'y_data1') in pipeline.data_block
assert (section_names.common, 'y_data2') not in pipeline.data_block
pipeline.cleanup()
def test_internal():
os.chdir(base_dir)
mapping_proj = ['ell_0', 'ell_2', 'ell_4']
make_data_covariance(data_fn=data_fn,
covariance_fn=covariance_fn,
mapping_proj=mapping_proj)
try: # not finished
main(config='demo3.ini')
except CosmosisConfigurationError:
pass
def test_external():
os.chdir(base_dir)
mapping_proj = ['ell_0', 'ell_2', 'ell_4']
make_data_covariance(data_fn=data_fn,
covariance_fn=covariance_fn,
mapping_proj=mapping_proj)
info = yaml_load_file('./test_cobaya.yaml')
updated_info, sampler = run(info)
assert 'a' in updated_info['params']
assert 'sample' in sampler.products()
def test_external():
os.chdir(base_dir)
mapping_proj = ['ell_0', 'ell_2', 'ell_4']
make_data_covariance(data_fn=data_fn,
covariance_fn=covariance_fn,
mapping_proj=mapping_proj)
ini = Inifile('test_cosmosis.ini')
pipeline = LikelihoodPipeline(ini)
data = pipeline.run_parameters([0.2])
assert data['likelihoods', 'cosmopipe_like'] != 0.
from cosmosis.samplers.emcee.emcee_sampler import EmceeSampler
from cosmosis.output.in_memory_output import InMemoryOutput
output = InMemoryOutput()
sampler = EmceeSampler(ini, pipeline, output)
sampler.config()
while not sampler.is_converged():
sampler.execute()
def test_demo2():
config_fn = os.path.join(demo_dir, 'demo2.ini')
graph_fn = os.path.join(demo_dir, 'pipe2.ps')
mapping_proj = ['ell_0', 'ell_2', 'ell_4']
make_data_covariance(data_fn=data_fn,
covariance_fn=covariance_fn,
mapping_proj=mapping_proj)
pipeline = BasePipeline(config_block=config_fn)
pipeline.plot_pipeline_graph(graph_fn)
pipeline.setup()
pipeline.data_block[section_names.parameters, 'a'] = 0.
pipeline.execute()
loglkl = pipeline.data_block[section_names.likelihood, 'loglkl']
pipeline.data_block[section_names.parameters, 'a'] = 4.
pipeline.execute()
assert pipeline.data_block[section_names.likelihood, 'loglkl'] != loglkl
pipeline.cleanup()
def test_demo3b():
config_fn = os.path.join(demo_dir, 'demo3.ini')
graph_fn = os.path.join(demo_dir, 'pipe3b.ps')
mapping_proj = ['ell_0', 'ell_2', 'ell_4']
make_data_covariance(data_fn=data_fn,
covariance_fn=covariance_fn,
mapping_proj=mapping_proj)
config_block = ConfigBlock(config_fn)
data1 = BaseModule.from_library(name='data1',
options=SectionBlock(
config_block, 'data1'))
model1 = FlatModel(name='model1')
data2 = BaseModule.from_library(name='data2',
options=SectionBlock(
config_block, 'data2'))
model2 = BaseModule.from_library(name='model2',
options=SectionBlock(
config_block, 'model2'))
cov = BaseModule.from_library(name='cov',
options=SectionBlock(config_block, 'cov'))
like1 = BaseLikelihood(name='like1', modules=[data1, model1])
like2 = BaseLikelihood(name='like2', modules=[data2, model2])
like = JointGaussianLikelihood(name='like',
join=[like1, like2],
modules=[cov])
pipeline = BasePipeline(modules=[like])
pipeline.plot_pipeline_graph(graph_fn)
pipeline.setup()
pipeline.data_block[section_names.parameters, 'a'] = 0.
pipeline.execute()
loglkl = pipeline.data_block[section_names.likelihood, 'loglkl']
pipeline.data_block[section_names.parameters, 'a'] = 4.
pipeline.execute()
assert pipeline.data_block[section_names.likelihood, 'loglkl'] != loglkl
pipeline.cleanup()
def test_demo1():
os.chdir(base_dir)
config_fn = os.path.join(demo_dir, 'demo1.ini')
graph_fn = os.path.join(demo_dir, 'pipe1.ps')
mapping_proj = ['ell_0', 'ell_2', 'ell_4']
make_data_covariance(data_fn=data_fn,
covariance_fn=covariance_fn,
mapping_proj=mapping_proj)
pipeline = BasePipeline(config_block=config_fn)
pipeline.plot_pipeline_graph(graph_fn)
pipeline.setup()
pipeline.data_block[section_names.parameters, 'a'] = 0.
pipeline.execute()
loglkl = pipeline.data_block[section_names.likelihood, 'loglkl']
pipeline.data_block[section_names.parameters, 'a'] = 4.
pipeline.execute()
assert pipeline.data_block[section_names.likelihood, 'loglkl'] != loglkl
pipeline.cleanup()
graph_fn = os.path.join(demo_dir, 'inheritance.ps')
BaseModule.plot_inheritance_graph(graph_fn, exclude=['AffineModel'])