Coverage for python/lsst/pipe/tasks/statistic.py: 90%
55 statements
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« prev ^ index » next coverage.py v7.5.0, created at 2024-04-27 03:58 -0700
1# This file is part of pipe_tasks.
2#
3# Developed for the LSST Data Management System.
4# This product includes software developed by the LSST Project
5# (https://www.lsst.org).
6# See the COPYRIGHT file at the top-level directory of this distribution
7# for details of code ownership.
8#
9# This program is free software: you can redistribute it and/or modify
10# it under the terms of the GNU General Public License as published by
11# the Free Software Foundation, either version 3 of the License, or
12# (at your option) any later version.
13#
14# This program is distributed in the hope that it will be useful,
15# but WITHOUT ANY WARRANTY; without even the implied warranty of
16# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
17# GNU General Public License for more details.
18#
19# You should have received a copy of the GNU General Public License
20# along with this program. If not, see <https://www.gnu.org/licenses/>.
22__all__ = ['Statistic', 'Count', 'Median', 'Percentile', 'StandardDeviation', 'SigmaIQR', 'SigmaMAD',
23 'Statistics']
25from abc import ABCMeta, abstractmethod
26from astropy.stats import mad_std
27from dataclasses import dataclass
28import numpy as np
29from scipy.stats import iqr
32class Statistic(metaclass=ABCMeta):
33 """Compute a statistic from a list of values.
34 """
35 # TODO: Make this a property after upgrade to Python 3.9
36 @classmethod
37 @abstractmethod
38 def name(cls):
39 pass
41 @abstractmethod
42 def value(self, values):
43 """Return the value of the statistic given a set of values.
45 Parameters
46 ----------
47 values : `Collection` [`float`]
48 A set of values to compute the statistic for.
49 Returns
50 -------
51 statistic : `float`
52 The value of the statistic.
53 """
54 pass
57class Count(Statistic):
58 @classmethod
59 def name(cls):
60 return "count"
62 """The median of a set of values."""
63 def value(self, values):
64 return len(values)
67class Median(Statistic):
68 @classmethod
69 def name(cls):
70 return "median"
72 """The median of a set of values."""
73 def value(self, values):
74 return np.median(values)
77@dataclass(frozen=True)
78class Percentile(Statistic):
79 """An arbitrary percentile.
81 Parameters
82 ----------
83 percentile : `float`
84 A valid percentile (0 <= p <= 100).
85 """
86 percentile: float
88 @classmethod
89 def name(cls):
90 return "percentile"
92 def value(self, values):
93 return np.percentile(values, self.percentile)
96class StandardDeviation(Statistic):
97 """The standard deviation (sigma)."""
98 @classmethod
99 def name(cls):
100 return "std"
102 def value(self, values):
103 return np.std(values)
106class SigmaIQR(Statistic):
107 """The re-scaled inter-quartile range (sigma equivalent)."""
108 @classmethod
109 def name(cls):
110 return "sigma_iqr"
112 def value(self, values):
113 return iqr(values, scale='normal')
116class SigmaMAD(Statistic):
117 """The re-scaled median absolute deviation (sigma equivalent)."""
118 @classmethod
119 def name(cls):
120 return "sigma_mad"
122 def value(self, values):
123 return mad_std(values)
126Statistics = {
127 stat.name(): stat
128 for stat in (Count, Median, Percentile, StandardDeviation, SigmaIQR, SigmaMAD)
129}