class CosmoPipeLikelihood(Likelihood):
def initialize(self):
self.pipeline = BasePipeline(config_block=self.config_file)
self.pipeline.setup()
def get_requirements(self):
return {}
def logp(self, **kwargs):
for key,val in kwargs.items():
self.pipeline.data_block[section_names.parameters,key] = val
self.pipeline.execute()
return self.pipeline.data_block[section_names.likelihood,'loglkl']
def clean(self):
pass
class CosmoPipeLikelihood(object):
def __init__(self, options):
self.pipeline = BasePipeline(
config_block=options.get_string('config_file'))
def setup(self):
self.pipeline.setup()
def execute(self, block):
for section, name in block.keys(
section=cosmosis_names.cosmological_parameters):
self.pipeline.data_block[section_names.parameters,
name] = block[section, name]
self.pipeline.execute()
block[cosmosis_names.likelihoods,
'cosmopipe_like'] = self.pipeline.data_block[
section_names.likelihood, 'loglkl']
def cleanup(self):
self.pipeline.cleanup()
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'])