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Treat yadism matching data as level-2 closure test data #56

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merged 6 commits into from
Oct 18, 2022
Merged

Treat yadism matching data as level-2 closure test data #56

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1c526f9
Incrase the variance of matching pseudodata
RoyStegeman Oct 7, 2022
5db6400
freeze the level1 shift of the matching pseudodata
RoyStegeman Oct 14, 2022
5b3bd29
Merge branch 'main' into increase_matching_variance
Radonirinaunimi Oct 15, 2022
b1840dc
Move L1 noise of matching to loader
Radonirinaunimi Oct 17, 2022
e774de7
Merge branch 'main' into increase_matching_variance
Radonirinaunimi Oct 17, 2022
e1d101c
fix left-over comment concerning L1 in load_data
Radonirinaunimi Oct 17, 2022
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21 changes: 16 additions & 5 deletions src/nnusf/data/loader.py
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Original file line number Diff line number Diff line change
Expand Up @@ -200,7 +200,16 @@ def n_data(self):
@property
def central_values(self) -> np.ndarray:
"""Return the dataset central values."""
return self.table["data"].values
if self.name.endswith("_MATCHING"):
cholesky = np.linalg.cholesky(self.covariance_matrix)
np_rng_state = np.random.get_state()
np.random.seed(pow(self.table.shape[0], 3))
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random_samples = np.random.randn(self.table.shape[0])
np.random.set_state(np_rng_state)
shift_data = cholesky @ random_samples
return self.table["data"].values + shift_data
else:
return self.table["data"].values

@property
def covmat(self) -> np.ndarray:
Expand Down Expand Up @@ -246,19 +255,21 @@ def build_covariance_matrix(
"""
if "_MATCHING" in dataset_name:
sv_variations = []
for variation in pathlib.Path(f"{commondata_path}/matching/").iterdir():
for variation in pathlib.Path(
f"{commondata_path}/matching/"
).iterdir():
if dataset_name in variation.stem:
# central scale
# central scale
if "xif1_xir1" in variation.stem:
nrep_predictions = np.load(variation)
else:
sv_variations.append(np.load(variation))
# build th shift
th_shift = (sv_variations - nrep_predictions[:,0]).T
th_shift = (sv_variations - nrep_predictions[:, 0]).T
# build covaraince
pdf_covmat = np.cov(nrep_predictions[mask_predictions])
th_covamt = np.cov(th_shift[mask_predictions])
covmat = np.sqrt(th_covamt ** 2 + pdf_covmat ** 2)
covmat = np.sqrt(th_covamt**2 + pdf_covmat**2)
return clip_covmat(covmat, dataset_name)
else:
diagonal = np.power(unc_df["stat"], 2)
Expand Down