Coverage for python/lsst/analysis/tools/actions/vector/calcBinnedStats.py: 53%

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1# This file is part of analysis_tools. 

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/>. 

21from __future__ import annotations 

22 

23__all__ = ("CalcBinnedStatsAction",) 

24 

25from functools import cached_property 

26from typing import cast 

27 

28import numpy as np 

29from lsst.pex.config import Field 

30from lsst.pex.config.configurableActions import ConfigurableActionField 

31 

32from ...interfaces import KeyedData, KeyedDataAction, KeyedDataSchema, Scalar, Vector 

33from ..keyedData.summaryStatistics import SummaryStatisticAction 

34from .selectors import RangeSelector 

35 

36 

37class CalcBinnedStatsAction(KeyedDataAction): 

38 key_vector = Field[str](doc="Vector on which to compute statistics") 

39 name_prefix = Field[str](doc="Field name to append stat names to") 

40 name_suffix = Field[str](doc="Field name to append to stat names") 

41 selector_range = ConfigurableActionField[RangeSelector](doc="Range selector") 

42 

43 def getInputSchema(self, **kwargs) -> KeyedDataSchema: 

44 yield (self.key_vector, Vector) 

45 yield from self.selector_range.getInputSchema() 

46 

47 def getOutputSchema(self) -> KeyedDataSchema: 

48 return ( 

49 (self.name_mask, Vector), 

50 (self.name_median, Scalar), 

51 (self.name_sigmaMad, Scalar), 

52 (self.name_count, Scalar), 

53 (self.name_select_maximum, Scalar), 

54 (self.name_select_median, Scalar), 

55 (self.name_select_minimum, Scalar), 

56 ("range_maximum", Scalar), 

57 ("range_minimum", Scalar), 

58 ) 

59 

60 @cached_property 

61 def name_count(self): 

62 return f"{self.name_prefix}count{self.name_suffix}" 

63 

64 @cached_property 

65 def name_mask(self): 

66 return f"{self.name_prefix}mask{self.name_suffix}" 

67 

68 @cached_property 

69 def name_median(self): 

70 return f"{self.name_prefix}median{self.name_suffix}" 

71 

72 @cached_property 

73 def name_select_maximum(self): 

74 return f"{self.name_prefix}select_maximum{self.name_suffix}" 

75 

76 @cached_property 

77 def name_select_median(self): 

78 return f"{self.name_prefix}select_median{self.name_suffix}" 

79 

80 @cached_property 

81 def name_select_minimum(self): 

82 return f"{self.name_prefix}select_minimum{self.name_suffix}" 

83 

84 @cached_property 

85 def name_sigmaMad(self): 

86 return f"{self.name_prefix}sigmaMad{self.name_suffix}" 

87 

88 def __call__(self, data: KeyedData, **kwargs) -> KeyedData: 

89 results = {} 

90 mask = self.selector_range(data, **kwargs) 

91 results[self.name_mask] = mask 

92 kwargs["mask"] = mask 

93 

94 action = SummaryStatisticAction(vectorKey=self.key_vector) 

95 # this is sad, but pex_config seems to have broken behavior that 

96 # is dangerous to fix 

97 action.setDefaults() 

98 

99 for name, value in action(data, **kwargs).items(): 

100 results[getattr(self, f"name_{name}")] = value 

101 

102 values = cast(Vector, data[self.selector_range.key][mask]) # type: ignore 

103 valid = np.sum(np.isfinite(values)) > 0 

104 results[self.name_select_maximum] = cast(Scalar, float(np.nanmax(values)) if valid else np.nan) 

105 results[self.name_select_median] = cast(Scalar, float(np.nanmedian(values)) if valid else np.nan) 

106 results[self.name_select_minimum] = cast(Scalar, float(np.nanmin(values)) if valid else np.nan) 

107 results["range_maximum"] = self.selector_range.maximum 

108 results["range_minimum"] = self.selector_range.minimum 

109 

110 return results