def get_model(info):
assert hasattr(info, "keys"), (
"The first argument must be a dictionary with the info needed for the model. "
"If you were trying to pass the name of an input file instead, "
"load it first with 'cobaya.input.load_input', "
"or, if you were passing a yaml string, load it with 'cobaya.yaml.yaml_load'."
)
# Configure the logger ASAP
# Just a dummy import before configuring the logger, until I fix root/individual level
import getdist
logger_setup(info.pop(_debug, _debug_default), info.pop(_debug_file, None))
# Create the full input information, including defaults for each module.
info = deepcopy(info)
ignored_info = {}
for k in list(info):
if k not in [
_params, _likelihood, _prior, _theory, _path_install, _timing
]:
ignored_info.update({k: info.pop(k)})
import logging
if ignored_info:
logging.getLogger(__name__.split(".")[-1]).warn(
"Ignored blocks/options: %r", list(ignored_info))
full_info = get_full_info(info)
if logging.root.getEffectiveLevel() <= logging.DEBUG:
logging.getLogger(__name__.split(".")[-1]).debug(
"Input info updated with defaults (dumped to YAML):\n%s",
yaml_dump(full_info, force_reproducible=False))
# Initialize the posterior and the sampler
return Model(full_info[_params],
full_info[_likelihood],
full_info.get(_prior),
full_info.get(_theory),
modules=info.get(_path_install),
timing=full_info.get(_timing))
def run(info):
assert hasattr(info, "keys"), (
"The first argument must be a dictionary with the info needed for the run. "
"If you were trying to pass the name of an input file instead, "
"load it first with 'cobaya.input.load_input', "
"or, if you were passing a yaml string, load it with 'cobaya.yaml.yaml_load'."
)
# Configure the logger ASAP
# Just a dummy import before configuring the logger, until I fix root/individual level
import getdist
logger_setup(info.get(_debug), info.get(_debug_file))
import logging
# Initialize output, if required
output = Output(output_prefix=info.get(_output_prefix),
resume=info.get(_resume),
force_output=info.pop(_force, None))
# Create the full input information, including defaults for each module.
full_info = get_full_info(info)
if output:
full_info[_output_prefix] = output.updated_output_prefix()
full_info[_resume] = output.is_resuming()
if logging.root.getEffectiveLevel() <= logging.DEBUG:
# Don't dump unless we are doing output, just in case something not serializable
# May be fixed in the future if we find a way to serialize external functions
if info.get(_output_prefix) and am_single_or_primary_process():
logging.getLogger(__name__.split(".")[-1]).info(
"Input info updated with defaults (dumped to YAML):\n%s",
yaml_dump(full_info))
# TO BE DEPRECATED IN >1.2!!! #####################
_force_reproducible = "force_reproducible"
if _force_reproducible in info:
info.pop(_force_reproducible)
logging.getLogger(__name__.split(".")[-1]).warn(
"Option '%s' is no longer necessary. Please remove it!" %
_force_reproducible)
# CHECK THAT THIS WARNING WORKS!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!
###################################################
# We dump the info now, before modules initialization, lest it is accidentally modified
# If resuming a sample, it checks that old and new infos are consistent
output.dump_info(info, full_info)
# Initialize the posterior and the sampler
with Model(full_info[_params],
full_info[_likelihood],
full_info.get(_prior),
full_info.get(_theory),
modules=info.get(_path_install),
timing=full_info.get(_timing),
allow_renames=False) as model:
with Sampler(full_info[_sampler],
model,
output,
resume=full_info.get(_resume),
modules=info.get(_path_install)) as sampler:
sampler.run()
# For scripted calls
return deepcopy(full_info), sampler.products()
def makeGrid(batchPath,
settingName=None,
settings=None,
read_only=False,
interactive=False,
install_reqs_at=None,
install_reqs_force=None):
print("Generating grid...")
batchPath = os.path.abspath(batchPath) + os.sep
if not settings:
if not settingName:
raise NotImplementedError(
"Re-using previous batch is work in progress...")
# if not pathIsGrid(batchPath):
# raise Exception('Need to give name of setting file if batchPath/config '
# 'does not exist')
# read_only = True
# sys.path.insert(0, batchPath + 'config')
# sys.modules['batchJob'] = batchjob # old name
# settings = __import__(IniFile(batchPath + 'config/config.ini').params['setting_file'].replace('.py', ''))
elif os.path.splitext(settingName)[-1].lower() in _yaml_extensions:
settings = yaml_load_file(settingName)
else:
raise NotImplementedError(
"Using a python script is work in progress...")
# In this case, info-as-dict would be passed
# settings = __import__(settingName, fromlist=['dummy'])
batch = batchjob.batchJob(batchPath, settings.get("yaml_dir", None))
# batch.skip = settings.get("skip", False)
batch.makeItems(settings, messages=not read_only)
if read_only:
for jobItem in [b for b in batch.jobItems]:
if not jobItem.chainExists():
batch.jobItems.remove(jobItem)
batch.save()
print('OK, configured grid with %u existing chains' %
(len(batch.jobItems)))
return batch
else:
batch.makeDirectories(setting_file=None)
batch.save()
infos = {}
modules_used = {}
# Default info
defaults = copy.deepcopy(settings)
grid_definition = defaults.pop("grid")
models_definitions = grid_definition["models"]
datasets_definitions = grid_definition["datasets"]
for jobItem in batch.items(wantSubItems=False):
# Model info
jobItem.makeChainPath()
try:
model_info = copy.deepcopy(models_definitions[jobItem.param_set]
or {})
except KeyError:
raise ValueError("Model '%s' must be defined." % jobItem.param_set)
model_info = merge_info(defaults, model_info)
# Dataset info
try:
dataset_info = copy.deepcopy(
datasets_definitions[jobItem.data_set.tag])
except KeyError:
raise ValueError("Data set '%s' must be defined." %
jobItem.data_set.tag)
# Combined info
combined_info = merge_info(defaults, model_info, dataset_info)
if "preset" in combined_info:
preset = combined_info.pop("preset")
combined_info = merge_info(create_input(**preset), combined_info)
combined_info[_output_prefix] = jobItem.chainRoot
# Requisites
modules_used = get_modules(modules_used, combined_info)
if install_reqs_at:
combined_info[_path_install] = os.path.abspath(install_reqs_at)
# Save the info (we will write it after installation:
# we need to install to add auto covmats
if jobItem.param_set not in infos:
infos[jobItem.param_set] = {}
infos[jobItem.param_set][jobItem.data_set.tag] = combined_info
# Installing requisites
if install_reqs_at:
print("Installing required code and data for the grid.")
from cobaya.log import logger_setup
logger_setup()
install_reqs(modules_used,
path=install_reqs_at,
force=install_reqs_force)
print("Adding covmats (if necessary) and writing input files")
for jobItem in batch.items(wantSubItems=False):
info = infos[jobItem.param_set][jobItem.data_set.tag]
# Covariance matrices
# We try to find them now, instead of at run time, to check if correctly selected
try:
sampler = list(info[_sampler])[0]
except KeyError:
raise ValueError("No sampler has been chosen")
if sampler == "mcmc" and info[_sampler][sampler].get("covmat", "auto"):
modules_path = install_reqs_at or info.get(_path_install, None)
if not modules_path:
raise ValueError(
"Cannot assign automatic covariance matrices because no "
"modules path has been defined.")
# Need full info for covmats: includes renames
full_info = get_full_info(info)
# Ideally, we use slow+sampled parameters to look for the covariance matrix
# but since for that we'd need to initialise a model, we approximate that set
# as theory+sampled
from itertools import chain
like_params = set(
chain(*[
list(like[_params])
for like in full_info[_likelihood].values()
]))
params_info = {
p: v
for p, v in full_info[_params].items()
if is_sampled_param(v) and p not in like_params
}
best_covmat = get_best_covmat(os.path.abspath(modules_path),
params_info, full_info[_likelihood])
info[_sampler][sampler]["covmat"] = os.path.join(
best_covmat["folder"], best_covmat["name"])
# Write the info for this job
try:
yaml_dump_file(jobItem.iniFile(), info, error_if_exists=True)
except IOError:
raise IOError(
"Can't write chain input file. Maybe the chain configuration "
"files already exists?")
# Non-translated old code
# if not start_at_bestfit:
# setMinimize(jobItem, ini)
# variant = '_minimize'
# ini.saveFile(jobItem.iniFile(variant))
## NOT IMPLEMENTED: start at best fit
## ini.params['start_at_bestfit'] = start_at_bestfit
# ---
# for deffile in settings.defaults:
# ini.defaults.append(batch.commonPath + deffile)
# if hasattr(settings, 'override_defaults'):
# ini.defaults = [batch.commonPath + deffile for deffile in settings.override_defaults] + ini.defaults
# ---
# # add ini files for importance sampling runs
# for imp in jobItem.importanceJobs():
# if getattr(imp, 'importanceFilter', None): continue
# if batch.hasName(imp.name.replace('_post', '')):
# raise Exception('importance sampling something you already have?')
# for minimize in (False, True):
# if minimize and not getattr(imp, 'want_minimize', True): continue
# ini = IniFile()
# updateIniParams(ini, imp.importanceSettings, batch.commonPath)
# if cosmomcAction == 0 and not minimize:
# for deffile in settings.importanceDefaults:
# ini.defaults.append(batch.commonPath + deffile)
# ini.params['redo_outroot'] = imp.chainRoot
# ini.params['action'] = 1
# else:
# ini.params['file_root'] = imp.chainRoot
# if minimize:
# setMinimize(jobItem, ini)
# variant = '_minimize'
# else:
# variant = ''
# ini.defaults.append(jobItem.iniFile())
# ini.saveFile(imp.iniFile(variant))
# if cosmomcAction != 0: break
if not interactive:
return batch
print('Done... to run do: cobaya-grid-run %s' % batchPath)
def post(info, sample=None):
logger_setup(info.get(_debug), info.get(_debug_file))
log = logging.getLogger(__name__.split(".")[-1])
try:
info_post = info[_post]
except KeyError:
log.error("No 'post' block given. Nothing to do!")
raise HandledException
if get_mpi_rank():
log.warning(
"Post-processing is not yet MPI-able. Doing nothing for rank > 1 processes."
)
return
# 1. Load existing sample
output_in = Output(output_prefix=info.get(_output_prefix), resume=True)
info_in = load_input(output_in.file_full) if output_in else deepcopy(info)
dummy_model_in = DummyModel(info_in[_params], info_in[_likelihood],
info_in.get(_prior, None),
info_in.get(_theory, None))
if output_in:
i = 0
while True:
try:
collection = Collection(dummy_model_in,
output_in,
name="%d" % (1 + i),
load=True,
onload_skip=info_post.get("skip", 0),
onload_thin=info_post.get("thin", 1))
if i == 0:
collection_in = collection
else:
collection_in._append(collection)
i += 1
except IOError:
break
elif sample:
if isinstance(sample, Collection):
sample = [sample]
collection_in = deepcopy(sample[0])
for s in sample[1:]:
try:
collection_in._append(s)
except:
log.error("Failed to load some of the input samples.")
raise HandledException
i = len(sample)
else:
log.error(
"Not output from where to load from or input collections given.")
raise HandledException
log.info("Loaded %d chain%s. Will process %d samples.", i,
"s" if i - 1 else "", collection_in.n())
if collection_in.n() <= 1:
log.error(
"Not enough samples for post-processing. Try using a larger sample, "
"or skipping or thinning less.")
raise HandledException
# 2. Compare old and new info: determine what to do
add = info_post.get("add", {})
remove = info_post.get("remove", {})
# Add a dummy 'one' likelihood, to absorb unused parameters
if not add.get(_likelihood):
add[_likelihood] = odict()
add[_likelihood].update({"one": None})
# Expand the "add" info
add = get_full_info(add)
# 2.1 Adding/removing derived parameters and changes in priors of sampled parameters
out = {_params: deepcopy(info_in[_params])}
for p in remove.get(_params, {}):
pinfo = info_in[_params].get(p)
if pinfo is None or not is_derived_param(pinfo):
log.error(
"You tried to remove parameter '%s', which is not a derived paramter. "
"Only derived parameters can be removed during post-processing.",
p)
raise HandledException
out[_params].pop(p)
mlprior_names_add = []
for p, pinfo in add.get(_params, {}).items():
pinfo_in = info_in[_params].get(p)
if is_sampled_param(pinfo):
if not is_sampled_param(pinfo_in):
# No added sampled parameters (de-marginalisation not implemented)
if pinfo_in is None:
log.error(
"You added a new sampled parameter %r (maybe accidentaly "
"by adding a new likelihood that depends on it). "
"Adding new sampled parameters is not possible. Try fixing "
"it to some value.", p)
raise HandledException
else:
log.error(
"You tried to change the prior of parameter '%s', "
"but it was not a sampled parameter. "
"To change that prior, you need to define as an external one.",
p)
raise HandledException
if mlprior_names_add[:1] != _prior_1d_name:
mlprior_names_add = (
[_minuslogprior + _separator + _prior_1d_name] +
mlprior_names_add)
elif is_derived_param(pinfo):
if p in out[_params]:
log.error(
"You tried to add derived parameter '%s', which is already "
"present. To force its recomputation, 'remove' it too.", p)
raise HandledException
elif is_fixed_param(pinfo):
# Only one possibility left "fixed" parameter that was not present before:
# input of new likelihood, or just an argument for dynamical derived (dropped)
if ((p in info_in[_params] and pinfo[_p_value] !=
(pinfo_in or {}).get(_p_value, None))):
log.error(
"You tried to add a fixed parameter '%s: %r' that was already present"
" but had a different value or was not fixed. This is not allowed. "
"The old info of the parameter was '%s: %r'", p,
dict(pinfo), p, dict(pinfo_in))
raise HandledException
else:
log.error("This should not happen. Contact the developers.")
raise HandledException
out[_params][p] = pinfo
# For the likelihood only, turn the rest of *derived* parameters into constants,
# so that the likelihoods do not try to compute them)
# But be careful to exclude *input* params that have a "derived: True" value
# (which in "full info" turns into "derived: 'lambda [x]: [x]'")
out_params_like = deepcopy(out[_params])
for p, pinfo in out_params_like.items():
if ((is_derived_param(pinfo) and not (_p_value in pinfo)
and p not in add.get(_params, {}))):
out_params_like[p] = {_p_value: np.nan, _p_drop: True}
parameterization_like = Parameterization(out_params_like,
ignore_unused_sampled=True)
# 2.2 Manage adding/removing priors and likelihoods
warn_remove = False
for level in [_prior, _likelihood]:
out[level] = getattr(dummy_model_in, level)
if level == _prior:
out[level].remove(_prior_1d_name)
for pdf in info_post.get("remove", {}).get(level, []) or []:
try:
out[level].remove(pdf)
warn_remove = True
except ValueError:
log.error(
"Trying to remove %s '%s', but it is not present. "
"Existing ones: %r", level, pdf, out[level])
raise HandledException
if warn_remove:
log.warning("You are removing a prior or likelihood pdf. "
"Notice that if the resulting posterior is much wider "
"than the original one, or displaced enough, "
"it is probably safer to explore it directly.")
if _prior in add:
mlprior_names_add += [
_minuslogprior + _separator + name for name in add[_prior]
]
out[_prior] += list(add[_prior])
prior_recompute_1d = (mlprior_names_add[:1] == [
_minuslogprior + _separator + _prior_1d_name
])
# Don't initialise the theory code if not adding/recomputing theory,
# theory-derived params or likelihoods
recompute_theory = info_in.get(_theory) and not (list(
add[_likelihood]) == ["one"] and not any([
is_derived_param(pinfo) for pinfo in add.get(_params, {}).values()
]))
if recompute_theory:
# Inherit from the original chain (needs input|output_params, renames, etc
theory = list(info_in[_theory].keys())[0]
info_theory_out = odict([[
theory,
recursive_update(deepcopy(info_in[_theory][theory]),
add.get(_theory, {theory: {}})[theory])
]])
else:
info_theory_out = None
chi2_names_add = [
_chi2 + _separator + name for name in add[_likelihood]
if name is not "one"
]
out[_likelihood] += [l for l in add[_likelihood] if l is not "one"]
if recompute_theory:
log.warn(
"You are recomputing the theory, but in the current version this does "
"not force recomputation of any likelihood or derived parameter, "
"unless explicitly removed+added.")
for level in [_prior, _likelihood]:
for i, x_i in enumerate(out[level]):
if x_i in list(out[level])[i + 1:]:
log.error(
"You have added %s '%s', which was already present. If you "
"want to force its recomputation, you must also 'remove' it.",
level, x_i)
raise HandledException
# 3. Create output collection
if "suffix" not in info_post:
log.error("You need to provide a 'suffix' for your chains.")
raise HandledException
# Use default prefix if it exists. If it does not, produce no output by default.
# {post: {output: None}} suppresses output, and if it's a string, updates it.
out_prefix = info_post.get(_output_prefix, info.get(_output_prefix))
if out_prefix not in [None, False]:
out_prefix += "_" + _post + "_" + info_post["suffix"]
output_out = Output(output_prefix=out_prefix,
force_output=info.get(_force))
info_out = deepcopy(info)
info_out[_post] = info_post
# Updated with input info and extended (full) add info
info_out.update(info_in)
info_out[_post]["add"] = add
dummy_model_out = DummyModel(out[_params],
out[_likelihood],
info_prior=out[_prior])
if recompute_theory:
theory = list(info_theory_out.keys())[0]
if _input_params not in info_theory_out[theory]:
log.error(
"You appear to be post-processing a chain generated with an older "
"version of Cobaya. For post-processing to work, please edit the "
"'[root]__full.info' file of the original chain to add, inside the "
"theory code block, the list of its input parameters. E.g.\n----\n"
"theory:\n %s:\n input_params: [param1, param2, ...]\n"
"----\nIf you get strange errors later, it is likely that you did not "
"specify the correct set of theory parameters.\n"
"The full set of input parameters are %s.", theory,
list(dummy_model_out.parameterization.input_params()))
raise HandledException
prior_add = Prior(dummy_model_out.parameterization, add.get(_prior))
likelihood_add = Likelihood(add[_likelihood],
parameterization_like,
info_theory=info_theory_out,
modules=info.get(_path_install))
# Remove auxiliary "one" before dumping -- 'add' *is* info_out[_post]["add"]
add[_likelihood].pop("one")
if likelihood_add.theory:
# Make sure that theory.needs is called at least once, for adjustments
likelihood_add.theory.needs()
collection_out = Collection(dummy_model_out, output_out, name="1")
output_out.dump_info({}, info_out)
# 4. Main loop!
log.info("Running post-processing...")
last_percent = 0
for i, point in enumerate(collection_in.data.itertuples()):
log.debug("Point: %r", point)
sampled = [
getattr(point, param)
for param in dummy_model_in.parameterization.sampled_params()
]
derived = odict(
[[param, getattr(point, param, None)]
for param in dummy_model_out.parameterization.derived_params()])
inputs = odict([[
param,
getattr(
point, param,
dummy_model_in.parameterization.constant_params().get(
param,
dummy_model_out.parameterization.constant_params().get(
param, None)))
] for param in dummy_model_out.parameterization.input_params()])
# Solve inputs that depend on a function and were not saved
# (we don't use the Parameterization_to_input method in case there are references
# to functions that cannot be loaded at the moment)
for p, value in inputs.items():
if value is None:
func = dummy_model_out.parameterization._input_funcs[p]
args = dummy_model_out.parameterization._input_args[p]
inputs[p] = func(*[getattr(point, arg) for arg in args])
# Add/remove priors
priors_add = prior_add.logps(sampled)
if not prior_recompute_1d:
priors_add = priors_add[1:]
logpriors_add = odict(zip(mlprior_names_add, priors_add))
logpriors_new = [
logpriors_add.get(name, -getattr(point, name, 0))
for name in collection_out.minuslogprior_names
]
if log.getEffectiveLevel() <= logging.DEBUG:
log.debug("New set of priors: %r",
dict(zip(dummy_model_out.prior, logpriors_new)))
if -np.inf in logpriors_new:
continue
# Add/remove likelihoods
output_like = []
if likelihood_add:
# Notice "one" (last in likelihood_add) is ignored: not in chi2_names
loglikes_add = odict(
zip(chi2_names_add,
likelihood_add.logps(inputs, _derived=output_like)))
output_like = dict(zip(likelihood_add.output_params, output_like))
else:
loglikes_add = dict()
loglikes_new = [
loglikes_add.get(name, -0.5 * getattr(point, name, 0))
for name in collection_out.chi2_names
]
if log.getEffectiveLevel() <= logging.DEBUG:
log.debug("New set of likelihoods: %r",
dict(zip(dummy_model_out.likelihood, loglikes_new)))
if output_like:
log.debug("New set of likelihood-derived parameters: %r",
output_like)
if -np.inf in loglikes_new:
continue
# Add/remove derived parameters and change priors of sampled parameters
for p in add[_params]:
if p in dummy_model_out.parameterization._directly_output:
derived[p] = output_like[p]
elif p in dummy_model_out.parameterization._derived_funcs:
func = dummy_model_out.parameterization._derived_funcs[p]
args = dummy_model_out.parameterization._derived_args[p]
derived[p] = func(*[
getattr(point, arg, output_like.get(arg, None))
for arg in args
])
if log.getEffectiveLevel() <= logging.DEBUG:
log.debug(
"New derived parameters: %r",
dict([[
p, derived[p]
] for p in dummy_model_out.parameterization.derived_params()
if p in add[_params]]))
# Save to the collection (keep old weight for now)
collection_out.add(sampled,
derived=derived.values(),
weight=getattr(point, _weight),
logpriors=logpriors_new,
loglikes=loglikes_new)
# Display progress
percent = np.round(i / collection_in.n() * 100)
if percent != last_percent and not percent % 5:
last_percent = percent
progress_bar(log, percent, " (%d/%d)" % (i, collection_in.n()))
if not collection_out.data.last_valid_index():
log.error(
"No elements in the final sample. Possible causes: "
"added a prior or likelihood valued zero over the full sampled domain, "
"or the computation of the theory failed everywhere, etc.")
raise HandledException
# Reweight -- account for large dynamic range!
# Prefer to rescale +inf to finite, and ignore final points with -inf.
# Remove -inf's (0-weight), and correct indices
difflogmax = max(collection_in[_minuslogpost] -
collection_out[_minuslogpost])
collection_out.data[_weight] *= np.exp(collection_in[_minuslogpost] -
collection_out[_minuslogpost] -
difflogmax)
collection_out.data = (
collection_out.data[collection_out.data.weight > 0].reset_index(
drop=True))
collection_out._n = collection_out.data.last_valid_index() + 1
# Write!
collection_out._out_update()
log.info("Finished! Final number of samples: %d", collection_out.n())
return info_out, {"sample": collection_out}
def body_of_test(modules,
best_fit,
info_likelihood,
info_theory,
ref_chi2,
best_fit_derived=None,
extra_model={}):
# Create base info
theo = list(info_theory)[0]
# In Class, theta_s is exact, but different from the approximate one cosmomc_theta
# used by Planck, so we take H0 instead
planck_base_model_prime = deepcopy(planck_base_model)
planck_base_model_prime.update(extra_model or {})
if theo == "classy":
planck_base_model_prime["hubble"] = "H"
best_fit_derived = deepcopy(best_fit_derived) or {}
best_fit_derived.pop("H0", None)
info = create_input(planck_names=True,
theory=theo,
**planck_base_model_prime)
# Add specifics for the test: theory, likelihoods and derived parameters
info = recursive_update(info, {_theory: info_theory})
info[_theory][theo]["use_planck_names"] = True
info = recursive_update(info, {_likelihood: info_likelihood})
info[_params].update({p: None for p in best_fit_derived or {}})
# We need FULL info, to get the likelihoods nuisance parameters
info = get_full_info(info)
# Notice that get_full_info adds an aux internal-only _params property to the likes
for lik in info[_likelihood]:
info[_likelihood][lik].pop(_params, None)
info[_path_install] = process_modules_path(modules)
info[_debug] = True
# Create the model and compute likelihood and derived parameters at best fit
model = get_model(info)
best_fit_values = {
p: best_fit[p]
for p in model.parameterization.sampled_params()
}
likes, derived = model.loglikes(best_fit_values)
likes = dict(zip(list(model.likelihood), likes))
derived = dict(zip(list(model.parameterization.derived_params()), derived))
# Check value of likelihoods
for like in info[_likelihood]:
chi2 = -2 * likes[like]
assert abs(chi2 - ref_chi2[like]) < ref_chi2["tolerance"], (
"Testing likelihood '%s': | %.2f - %.2f | = %.2f >= %.2f" %
(like, chi2, ref_chi2[like], abs(chi2 - ref_chi2[like]),
ref_chi2["tolerance"]))
print("Testing likelihood '%s': | %.2f - %.2f | = %.2f >= %.2f" %
(like, chi2, ref_chi2[like], abs(chi2 - ref_chi2[like]),
ref_chi2["tolerance"]))
# Check value of derived parameters
not_tested = []
not_passed = []
for p in best_fit_derived or {}:
if best_fit_derived[p][0] is None or p not in best_fit_derived:
not_tested += [p]
continue
rel = (abs(derived[p] - best_fit_derived[p][0]) /
best_fit_derived[p][1])
if rel > tolerance_derived * (2 if p in
("YHe", "Y_p", "DH", "sigma8",
"s8omegamp5", "thetastar") else 1):
not_passed += [(p, rel)]
print("Derived parameters not tested because not implemented: %r" %
not_tested)
assert not not_passed, "Some derived parameters were off. Fractions of test tolerance: %r" % not_passed
def run(info):
assert hasattr(info, "items"), (
"The agument of `run` must be a dictionary with the info needed for the run. "
"If you were trying to pass an input file instead, load it first with "
"`cobaya.input.load_input`.")
# Import names
from cobaya.conventions import _likelihood, _prior, _params
from cobaya.conventions import _theory, _sampler, _path_install
from cobaya.conventions import _debug, _debug_file, _output_prefix
# Configure the logger ASAP
from cobaya.log import logger_setup
logger_setup(info.get(_debug), info.get(_debug_file))
# Debug (lazy call)
import logging
if logging.root.getEffectiveLevel() <= logging.DEBUG:
# Don't dump unless we are doing output, just in case something not serializable
# May be fixed in the future if we find a way to serialize external functions
if info.get(_output_prefix):
from cobaya.yaml import yaml_dump
logging.getLogger(__name__.split(".")[-1]).debug(
"Input info (dumped to YAML):\n%s", yaml_dump(info))
# Import general classes
from cobaya.prior import Prior
from cobaya.sampler import get_Sampler as Sampler
# Import the functions and classes that need MPI wrapping
from cobaya.mpi import import_MPI
# Likelihood = import_MPI(".likelihood", "LikelihoodCollection")
from cobaya.likelihood import LikelihoodCollection as Likelihood
# Initialise output, if requiered
do_output = info.get(_output_prefix)
if do_output:
Output = import_MPI(".output", "Output")
output = Output(info)
else:
from cobaya.output import Output_dummy
output = Output_dummy(info)
# Create the full input information, including defaults for each module.
from cobaya.input import get_full_info
full_info = get_full_info(info)
if logging.root.getEffectiveLevel() <= logging.DEBUG:
# Don't dump unless we are doing output, just in case something not serializable
# May be fixed in the future if we find a way to serialize external functions
if info.get(_output_prefix):
logging.getLogger(__name__.split(".")[-1]).debug(
"Updated info (dumped to YAML):\n%s", yaml_dump(full_info))
# We dump the info now, before modules initialization, lest it is accidentaly modified
output.dump_info(info, full_info)
# Set the path of the installed modules, if given
from cobaya.tools import set_path_to_installation
set_path_to_installation(info.get(_path_install))
# Initialise parametrization, likelihoods and prior
from cobaya.parametrization import Parametrization
with Parametrization(full_info[_params]) as par:
with Prior(par, full_info.get(_prior)) as prior:
with Likelihood(full_info[_likelihood], par,
full_info.get(_theory)) as lik:
with Sampler(full_info[_sampler], par, prior, lik,
output) as sampler:
sampler.run()
# For scripted calls
return deepcopy(full_info), sampler.products()