1""" 

2Grid of models. 

3""" 

4import numpy 

5from ..data import load_grid_image 

6from ..data.image_helper import reduce_image 

7 

8 

9class GridMapSIR: 

10 """ 

11 Implements a grid of models. 

12 

13 :param model: epidemic model like :class:`CovidSIR 

14 <aftercovid.model.CovidSIR>` 

15 :param name: picture name, must be a binary image 

16 :param grid_size: new size of the image 

17 :param gamma: propagation of the epidemics to the neighborhood 

18 """ 

19 

20 def __init__(self, model, name="france_bin.bmp", grid_size=(10, 12), 

21 gamma=0.1): 

22 self.raw_grid = load_grid_image(name) 

23 self.grid = reduce_image(self.raw_grid, grid_size) 

24 self.model = model 

25 self.gamma = gamma 

26 self._init() 

27 

28 def _init(self): 

29 """ 

30 Initializes the grid. 

31 """ 

32 self.grid_ = numpy.empty(self.grid.shape).tolist() 

33 edges = [] 

34 for i in range(self.grid.shape[0]): 

35 for j in range(self.grid.shape[1]): 

36 if self.grid[i, j] == 0: 

37 self.grid_[i][j] = None 

38 continue 

39 self.grid_[i][j] = self.model.copy() 

40 

41 for ii in range(-1, 2): 

42 for jj in range(-1, 2): 

43 if (i + ii >= 0 and j + jj >= 0 and 

44 i + ii < self.grid.shape[0] and 

45 j + jj < self.grid.shape[1] and 

46 self.grid[ii, jj] == 1): 

47 edges.append(((i, j), (i + ii, j + jj))) 

48 self.edges_ = edges 

49 

50 def __getitem__(self, name): 

51 """ 

52 Returns the sum of the metrics all over a grid. 

53 

54 :param name: quantity name 

55 :return: float 

56 """ 

57 g = self.metric(name) 

58 return g.sum() 

59 

60 def metric(self, metric="S"): 

61 """ 

62 Returns one metric for all models on the grid. 

63 

64 :param name: metric name 

65 :return: numpy array 

66 """ 

67 res = numpy.zeros(self.grid.shape, dtype=numpy.float64) 

68 for i in range(self.grid.shape[0]): 

69 for j in range(self.grid.shape[1]): 

70 if not self.grid[i, j]: 

71 continue 

72 res[i, j] = self.grid_[i][j][metric] 

73 return res