Coverage for python/lsst/analysis/tools/actions/keyedData/summaryStatistics.py: 68%

17 statements  

« prev     ^ index     » next       coverage.py v6.4.4, created at 2022-09-08 04:39 -0700

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 

23from lsst.pex.config import Field 

24 

25from ...interfaces import KeyedData 

26from ...statistics import sigmaMad 

27from ..scalar import CountAction, MedianAction, SigmaMadAction 

28from .keyedDataActions import KeyedScalars 

29 

30__all__ = ( 

31 "sigmaMad", 

32 "SummaryStatisticAction", 

33) 

34 

35 

36class SummaryStatisticAction(KeyedScalars): 

37 vectorKey = Field[str](doc="Column key to compute scalars") 

38 

39 def setDefaults(self): 

40 super().setDefaults() 

41 self.scalarActions.median = MedianAction(vectorKey=self.vectorKey) 

42 self.scalarActions.sigmaMad = SigmaMadAction(vectorKey=self.vectorKey) 

43 self.scalarActions.count = CountAction(vectorKey=self.vectorKey) 

44 

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

46 mask = kwargs.get("mask") 

47 return super().__call__(data, **(kwargs | dict(mask=mask)))