Coverage for local_installation_linux/mumott/methods/utilities/grids_on_the_sphere.py: 58%

1import numpy as np 

2from numpy.typing import NDArray 

3 

4 

5def TOMCAT_7(half_sphere: bool = True) -> NDArray: 

6 r""" The seven sensitivity directions used in publications by the TOMCAT group. 

7 

8 Parameters 

9 ---------- 

10 half_sphere 

11 Whether to use points only covering the half sphere or the full sphere. 

12 

13 Returns 

14 --------- 

15 directions 

16 Array with shape ``(N, 3)`` containing the coordinated of the ``N`` unit vectors. 

17 """ 

18 

19 # Hard coded {100} and {111} cubic directions in the +z hemisphere 

20 directions = [[1.0, 0.0, 0.0], 

21 [0.0, 1.0, 0.0], 

22 [0.0, 0.0, 1.0], 

23 [1.0, 1.0, 1.0], 

24 [1.0, -1.0, 1.0], 

25 [-1.0, 1.0, 1.0], 

26 [-1.0, -1.0, 1.0]] 

27 

28 directions = np.stack(directions) 

29 

30 if not half_sphere: 

31 directions = np.concatenate((directions, -directions), axis=0) 

32 

33 # Normalize 

34 norm = np.linalg.norm(directions, axis=1) 

35 directions = directions/norm[:, np.newaxis] 

36 

37 return directions 

38 

39 

40def football(half_sphere=True) -> NDArray: 

41 r""" 16 sensitivity directions uniformly distributed on the half sphere. 

42 

43 Parameters 

44 ---------- 

45 half_sphere 

46 Whether to use points only covering the half sphere or the full sphere. 

47 

48 Returns 

49 --------- 

50 directions 

51 Array with shape ``(N, 3)`` containing the coordinated of the ``N`` unit vectors. 

52 """ 

53 

54 # football face centers in the +z hemisphere 

55 C0 = 3*(np.sqrt(5) - 1)/4 

56 C1 = 9*(9 + np.sqrt(5))/76 

57 C2 = 9*(7 + 5 * np.sqrt(5))/76 

58 C3 = 3*(1 + np.sqrt(5))/4 

59 

60 directions = np.array([[0.0, C0, C3], 

61 [0.0, -C0, C3], 

62 [C3, 0.0, C0], 

63 [-C3, 0.0, C0], 

64 [C0, C3, 0.0], 

65 [-C0, C3, 0.0], 

66 [C1, 0.0, C2], 

67 [-C1, 0.0, C2], 

68 [C2, C1, 0.0], 

69 [-C2, C1, 0.0], 

70 [0.0, C2, C1], 

71 [0.0, -C2, C1], 

72 [1.5, 1.5, 1.5], 

73 [1.5, -1.5, 1.5], 

74 [-1.5, 1.5, 1.5], 

75 [-1.5, -1.5, 1.5]]) 

76 

77 directions = np.stack(directions) 

78 

79 if not half_sphere: 79 ↛ 80line 79 didn't jump to line 80, because the condition on line 79 was never true

80 directions = np.concatenate((directions, -directions), axis=0) 

81 

82 # Normalize 

83 norm = np.linalg.norm(directions, axis=1) 

84 directions = directions / norm[:, np.newaxis] 

85 

86 return directions