irfpy.util.confidence¶
Created on Tue Jun 4 19:17:23 2019
A Unified Approach to the Classical Statistical Analysis of Small Signals Feldman and Cousins
DOI: 10.1103/PhysRevD.57.3873
@author: Martin Wieser
Swedish Institute of Space Physics Bengt Hultqvists väg 1 SE-981 92 Kiruna Sweden
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irfpy.util.confidence.scalar_confinterval68(n0, background)[source]¶ Returns 68% confidence interval for n0-backgorund. n0 must be an integer >=0, background is a float number. This function only accepts scalars and not numpy arrays.
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irfpy.util.confidence.scalar_confinterval90(n0, background)[source]¶ Returns 90% confidence interval for n0-backgorund. n0 must be an integer >=0, background is a float number >= 0.0. This function only accepts scalars and not numpy arrays.
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irfpy.util.confidence.scalar_confinterval95(n0, background)[source]¶ Returns 95% confidence interval for n0-backgorund. n0 must be an integer >=0, background is a float number >= 0.0. This function only accepts scalars and not numpy arrays.
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irfpy.util.confidence.scalar_confinterval99(n0, background)[source]¶ Returns 99% confidence interval for n0-backgorund. n0 must be an integer >=0, background is a float number >= 0.0. This function only accepts scalars and not numpy arrays.
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irfpy.util.confidence.scalar_sensitivity68(background)[source]¶ returns 68% an upper limit for the signal assuming there are is zero counts measured and a background is present.
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irfpy.util.confidence.scalar_sensitivity90(background)[source]¶ returns 90% an upper limit for the signal assuming there are is zero counts measured and a background is present.
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irfpy.util.confidence.scalar_sensitivity95(background)[source]¶ returns 95% an upper limit for the signal assuming there are is zero counts measured and a background is present.