Coverage for python/lsst/meas/algorithms/coaddBoundedField.py: 24%

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1# This file is part of meas_algorithms. 

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/>. 

21 

22import numpy as np 

23 

24from lsst.geom import Point2D 

25from lsst.utils import continueClass 

26 

27from ._algorithmsLib import CoaddBoundedField 

28 

29__all__ = ["CoaddBoundedField"] 

30 

31 

32@continueClass 

33class CoaddBoundedField: # noqa: F811 

34 def evaluate(self, x, y=None): 

35 """Evaluate the CoaddBoundedField. 

36 

37 This accepts either a Point2D or an array of x and y 

38 positions. 

39 

40 When arrays are passed, this uses a vectorized version 

41 of CoaddBoundedField::evaluate(). If the coadd bounded 

42 field has throwOnMissing then this will return NaN 

43 for missing values; otherwise it will return 0.0. 

44 

45 Parameters 

46 ---------- 

47 x : `lsst.geom.Point2D` or `np.ndarray` 

48 Array of x values. 

49 y : `np.ndarray`, optional 

50 Array of y values. 

51 

52 Returns 

53 ------- 

54 values : `float` or `np.ndarray` 

55 Evaluated value or array of values. 

56 """ 

57 if isinstance(x, Point2D): 

58 return self._evaluate(x) 

59 

60 _x = np.atleast_1d(x).ravel() 

61 _y = np.atleast_1d(y).ravel() 

62 

63 if len(_x) != len(_y): 

64 raise ValueError("x and y arrays must be the same length.") 

65 

66 ra, dec = self.getCoaddWcs().pixelToSkyArray(_x, _y) 

67 

68 sums = np.zeros(len(_x)) 

69 wts = np.zeros_like(sums) 

70 

71 for elt in self.getElements(): 

72 ix, iy = elt.wcs.skyToPixelArray(ra, dec) 

73 in_box = elt.field.getBBox().contains(ix, iy) 

74 if elt.validPolygon: 

75 in_box &= elt.validPolygon.contains(ix, iy) 

76 sums[in_box] += elt.weight*elt.field.evaluate(ix[in_box], iy[in_box]) 

77 wts[in_box] += elt.weight 

78 

79 good = (wts > 0) 

80 values = np.zeros(len(_x)) 

81 values[good] = sums[good]/wts[good] 

82 

83 if self.getThrowOnMissing(): 

84 values[~good] = np.nan 

85 

86 return values