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Add CosmoPower P(k,z) #85

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Add CosmoPower P(k,z) #85

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0de4517
added filenames
itrharrison Nov 4, 2022
0b2ef60
added Pk methods and provides
itrharrison Nov 4, 2022
645bc5a
Merge branch 'master' into dev-84-cosmopower-pkz
itrharrison Nov 29, 2022
05642be
added pk_grid saved to state
itrharrison Nov 29, 2022
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77 changes: 71 additions & 6 deletions soliket/cosmopower.py
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Original file line number Diff line number Diff line change
Expand Up @@ -9,6 +9,7 @@

from cobaya.theories.cosmo import BoltzmannBase
from cobaya.typing import InfoDict
from cobaya.log import LoggedError

"""
Simple CosmoPower theory wrapper for Cobaya.
Expand All @@ -19,9 +20,13 @@
class CosmoPower(BoltzmannBase):
soliket_data_path: str = "soliket/data/CosmoPower"
network_path: str = "CP_paper/CMB"
network_path_pk: str = "CP_paper/PK"
cmb_tt_nn_filename: str = "cmb_TT_NN"
cmb_te_pcaplusnn_filename: str = "cmb_TE_PCAplusNN"
cmb_ee_nn_filename: str = "cmb_EE_NN"
pk_lin_nn_filename: str = "PKLIN_NN"
k_max: int = 1.
z: float = np.linspace(0.0, 2, 128)

extra_args: InfoDict = {}

Expand Down Expand Up @@ -51,29 +56,46 @@ def initialize(self):
restore=True,
restore_filename=os.path.join(base_path, self.cmb_ee_nn_filename),
)
self.cp_pk_nn = cp.cosmopower_NN(
restore=True,
restore_filename=os.path.join(self.soliket_data_path, self.network_path_pk, self.pk_lin_nn_filename),
)

if "lmax" not in self.extra_args:
self.extra_args["lmax"] = None

self.log.info(f"Loaded CosmoPower from directory {self.network_path}")

def calculate(self, state, want_derived=True, **params):
cmb_params = {}
network_params = {}

for par in self.renames:
if par in params:
cmb_params[par] = [params[par]]
network_params[par] = [params[par]]
else:
for r in self.renames[par]:
if r in params:
cmb_params[par] = [params[r]]
network_params[par] = [params[r]]
break

state["tt"] = self.cp_tt_nn.ten_to_predictions_np(cmb_params)[0, :]
state["te"] = self.cp_te_nn.predictions_np(cmb_params)[0, :]
state["ee"] = self.cp_ee_nn.ten_to_predictions_np(cmb_params)[0, :]
state["tt"] = self.cp_tt_nn.ten_to_predictions_np(network_params)[0, :]
state["te"] = self.cp_te_nn.predictions_np(network_params)[0, :]
state["ee"] = self.cp_ee_nn.ten_to_predictions_np(network_params)[0, :]
state["ell"] = self.cp_tt_nn.modes

# if "Pk_grid" in self.requested() or "Pk_interpolator" in self.requested():

network_params_z = {}

for k in network_params.keys():
network_params_z[k] = np.repeat(network_params[k], len(self.z))
network_params_z['z'] = self.z

var_pair=("delta_tot", "delta_tot")
nonlinear=True

state[("Pk_grid", bool(nonlinear)) + tuple(sorted(var_pair))] = self.cp_pk_nn.modes, self.z, self.cp_pk_nn.predictions_np(network_params_z)

def get_Cl(self, ell_factor=False, units="FIRASmuK2"):
cls_old = self.current_state.copy()

Expand Down Expand Up @@ -102,5 +124,48 @@ def get_Cl(self, ell_factor=False, units="FIRASmuK2"):

return cls

# def must_provide(self, **requirements):

# super().must_provide(**requirements)

# for k, v in self._must_provide.items():
# if isinstance(k, tuple) and k[0] == "Pk_grid":
# v = deepcopy(v)
# self.add_P_k_max(v.pop("k_max"), units="1/Mpc")
# # NB: Actually, only the max z is used, and the actual sampling in z
# # for computing P(k,z) is controlled by `perturb_sampling_stepsize`
# # (default: 0.1). But let's leave it like this in case this changes
# # in the future.
# self.add_z_for_matter_power(v.pop("z"))
# if v["nonlinear"]:
# if "non_linear" not in self.extra_args:
# # this is redundant with initialisation, but just in case
# self.extra_args["non_linear"] = non_linear_default_code
# elif self.extra_args["non_linear"] == non_linear_null_value:
# raise LoggedError(
# self.log, ("Non-linear Pk requested, but `non_linear: "
# f"{non_linear_null_value}` imposed in "
# "`extra_args`"))
# pair = k[2:]
# if pair == ("delta_tot", "delta_tot"):
# self.collectors[k] = Collector(
# method="get_pk_and_k_and_z",
# kwargs=v,
# post=(lambda P, kk, z: (kk, z, np.array(P).T)))
# else:
# raise LoggedError(self.log, "NotImplemented in cosmopower: %r", pair)

# return needs


# def get_Pk_grid(self, params, var_pair=("delta_tot", "delta_tot"), nonlinear=False,
# extrap_kmin=None, extrap_kmax=None):

# return self.current_state['k'], self.current_state['pk_z'], self.current_state['pk']


def get_can_support_params(self):
return ["omega_b", "omega_cdm", "h", "logA", "ns", "tau_reio"]

def get_can_provide(self):
return ['Cl', 'Pk_grid', 'Pk_interpolator']
56 changes: 56 additions & 0 deletions soliket/tests/test_cosmopower.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,6 +22,30 @@
"H0": {"value": "lambda h: h * 100.0"},
}

des_params = {
'DES_DzL1': 0.001,
'DES_DzL2': 0.002,
'DES_DzL3': 0.001,
'DES_DzL4': 0.003,
'DES_DzL5': 0,
'DES_b1': 1.45,
'DES_b2': 1.55,
'DES_b3': 1.65,
'DES_b4': 1.8,
'DES_b5': 2.0,
'DES_DzS1': -0.001,
'DES_DzS2': -0.019,
'DES_DzS3': 0.009,
'DES_DzS4': -0.018,
'DES_m1': 0.012,
'DES_m2': 0.012,
'DES_m3': 0.012,
'DES_m4': 0.012,
'DES_AIA': 1,
'DES_alphaIA': 1,
'DES_z0IA': 0.62,
}

info_dict = {
"params": fiducial_params,
"likelihood": {
Expand All @@ -39,6 +63,27 @@
},
}

def lss_part_likelihood(_self):
results = _self.provider.get_Pk_interpolator().P(0.1, 1.0)
# results = _self.provider.get_Pk_grid()
return 1


info_dict_pk = {
"params": fiducial_params,
"likelihood": {'lss': {'external': lss_part_likelihood, 'requires': {'Pk_interpolator': {'z' : np.linspace(0.0, 2, 128),
'k_max': 1.}}}
},
"theory": {
"soliket.CosmoPower": {
"soliket_data_path": "soliket/data/CosmoPower",
"stop_at_error": True,
"provides": 'Pk_grid',
}
# 'camb': {"stop_at_error": True,}
}
}


@pytest.mark.skipif(not HAS_COSMOPOWER, reason='test requires cosmopower')
def test_cosmopower_theory():
Expand Down Expand Up @@ -77,3 +122,14 @@ def test_cosmopower_against_camb():

assert np.allclose(cp_cls['tt'][nanmask], camb_cls['tt'][nanmask], rtol=1.e-2)
assert np.isclose(logL_camb, logL_cp, rtol=1.e-1)


@pytest.mark.skipif(not HAS_COSMOPOWER, reason='test requires cosmopower')
def test_cosmopower_pkgrid():

model_cp = get_model(info_dict_pk)

logL_cp = float(model_cp.loglikes({})[0])

assert np.isfinite(logL_cp)