def core(cache: Path):
return mcmc.CoreCoevalModule(
redshift=9,
user_params={
"HII_DIM": 35,
"DIM": 70
},
cache_mcmc=False,
cache_dir=str(cache),
)
def test_forest(lc_core_lowz, lc_core_lowz_ctx):
lk = mcmc.LikelihoodForest(name="z5pt4")
with pytest.raises(mcmc.NotAChain):
assert lk._is_lightcone
core = mcmc.CoreForest(
redshift=5.4,
name="z5pt4",
user_params=lc_core_lowz.user_params,
flag_options=lc_core_lowz.flag_options,
n_realization=10,
)
with pytest.raises(NotImplementedError):
coeval_core_lowz = mcmc.CoreCoevalModule(
redshift=5.4,
max_redshift=8.0,
user_params=lc_core_lowz.user_params,
flag_options=lc_core_lowz.flag_options,
)
chain = mcmc.build_computation_chain([coeval_core_lowz, core],
lk,
setup=True)
lk.setup()
model = lk.reduce_data(chain.build_model_data())
with pytest.raises(ValueError):
lk_wrongz = mcmc.LikelihoodForest(name="z5pt5")
core_wrongz = mcmc.CoreForest(
redshift=5.5,
name="z5pt5",
user_params=lc_core_lowz.user_params,
flag_options=lc_core_lowz.flag_options,
n_realization=10,
)
chain = mcmc.build_computation_chain([lc_core_lowz, core_wrongz],
lk_wrongz,
setup=True)
lk_wrongz.setup()
model = lk_wrongz.reduce_data(chain.build_model_data())
mcmc.build_computation_chain([lc_core_lowz, core], lk, setup=True)
lk.setup()
model = lk.reduce_data(lc_core_lowz_ctx)
assert not np.all(model == 0)
def test_wrong_ctx_variable():
core = mcmc.CoreCoevalModule(
redshift=6,
user_params={
"HII_DIM": 35,
"BOX_LEN": 70
},
ctx_variables=("bad_key", "good key"),
)
lk = mcmc.Likelihood1DPowerCoeval(use_data=False)
chain = mcmc.build_computation_chain(core, lk, setup=False)
with pytest.raises(ValueError):
chain.build_model_data()
def test_bad_continuation(core, likelihood_coeval, default_params, tmpdirec):
"check if trying to continue a chain that isn't compatible with previous chain raises an error"
mcmc.run_mcmc(
core,
likelihood_coeval,
model_name="TESTBURNIN",
continue_sampling=False,
datadir=tmpdirec,
params=default_params,
walkersRatio=2,
burninIterations=0,
sampleIterations=1,
threadCount=1,
)
with pytest.raises(RuntimeError):
# core with different redshift
core = mcmc.CoreCoevalModule(
redshift=8,
user_params={
"HII_DIM": 35,
"DIM": 70
},
cache_mcmc=False,
cache_init=False,
)
mcmc.run_mcmc(
core,
likelihood_coeval,
model_name="TESTBURNIN",
continue_sampling=True,
datadir=tmpdirec,
params=default_params,
walkersRatio=2,
burninIterations=0,
sampleIterations=2,
threadCount=1,
)
def test_core_coeval_not_setup():
core = mcmc.CoreCoevalModule(redshift=9)
with pytest.raises(mcmc.NotAChain):
core.chain