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Use scipy chi2 & ncx2 #29

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Use scipy chi2 & ncx2 #29

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23 changes: 6 additions & 17 deletions titrate/statistics.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,7 @@

import numpy as np
from gammapy.modeling import Fit
from scipy.stats import kstwo, norm
from scipy.stats import chi2, kstwo, ncx2, norm

from titrate.datasets import AsimovMapDataset

Expand Down Expand Up @@ -116,24 +116,14 @@ def asympotic_approximation_pdf(
self, ts_val, poi_val, same=True, poi_true_val=None
):
if same:
return (
1
/ (2 * np.sqrt(2 * np.pi * ts_val))
* np.exp(-0.5 * (np.sqrt(ts_val)) ** 2)
)
return 0.5 * chi2.pdf(ts_val, df=1)

if not isinstance(self.dataset, AsimovMapDataset):
raise AsimovApproximationError(
"`dataset` must be an `AsimovMapDataset` in order to use the"
" `asympotic_approximation`"
)
return (
1
/ (2 * np.sqrt(2 * np.pi * ts_val))
* np.exp(
-0.5 * (np.sqrt(ts_val) - (poi_val - poi_true_val) / self.sigma()) ** 2
)
)
return ncx2.pdf(ts_val, df=1, nc=((poi_val - poi_true_val) / self.sigma()) ** 2)

def asympotic_approximation_cdf(
self, ts_val, poi_val, same=True, poi_true_val=None
Expand Down Expand Up @@ -266,7 +256,7 @@ def asympotic_approximation_pdf(
* (ts_val + poi_val**2 / sigma**2) ** 2
/ (2 * poi_val / sigma) ** 2
),
1 / (2 * np.sqrt(2 * np.pi * ts_val)) * np.exp(-0.5 * ts_val),
0.5 * chi2.pdf(ts_val, df=1),
)

return np.where(
Expand All @@ -279,9 +269,8 @@ def asympotic_approximation_pdf(
** 2
/ (2 * poi_val / sigma) ** 2
),
1
/ (2 * np.sqrt(2 * np.pi * ts_val))
* np.exp(-0.5 * (np.sqrt(ts_val) - (poi_val - poi_true_val) / sigma) ** 2),
0.5
* ncx2.pdf(ts_val, df=1, nc=((poi_val - poi_true_val) / self.sigma()) ** 2),
)

def asympotic_approximation_cdf(
Expand Down
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