def weighted_region_space_sampling(discretizor, n, weights=None):
"""Select points weighting regions.
Parameters
----------
discretizor: pySpatialTools.Discretization.BaseDiscretizor object
the discretization information.
weights: numpy.ndarray
the weights of each region.
n: int
the number of points we want to sample.
Returns
-------
points_s: numpy.ndarray, shape (n, 2)
the coordinates of the sampled points.
"""
## 0. Compute variables needed
regions = discretizor.regions_id
# regions.shape[0] == weights.shape[0]
## 1. Compute regions selected
regions = weighted_sampling_with_repetition(regions, n, weights)
## 2. Compute uniform selection from the selected points
t_regions = np.unique(regions)
points_s = -1 * np.ones((n, 2))
# Region by region
for r in t_regions:
# Compute limits of the region
limits_r = discretizor.get_limits(r)
# Compute how many points of this region you want
logi = regions == r
inds = np.where(logi)[0]
n_r = logi.sum()
j = 0
while j != n_r:
# Generate points randomly in the limits
point_j = uniform_points_sampling(limits_r, 1)
# Reject if there is not in the region, accept otherwise
boolean = discretizor.belong_region(point_j, r)
if boolean:
points_s[inds[j]] = point_j
j += 1
return points_s
def weighted_region_points_sampling(discretizor, points, n, weights=None):
"""Select points weighting regions.
Parameters
----------
discretizor: pySpatialTools.Neighbourhood.discretizor object
the discretization information.
weights: numpy.ndarray
the weights of each region.
n: int
the number of points we want to sample.
Returns
-------
indices: numpy.ndarray, shape(n)
the indices of the samples.
"""
## 0. Compute variables needed
points_c = discretizor.discretize(points)
p_cats = np.unique(points_c)
if weights is not None:
if len(p_cats) < len(weights):
weights = weights[p_cats]
elif len(p_cats) < len(weights):
pass
## 1. Compute regions selected
regions = weighted_sampling_with_repetition(p_cats, n, weights)
## 2. Compute uniform selection from the selected points
indices = -1*np.ones(n)
for i in xrange(n):
logi = points_c == regions[i]
n_i = logi.sum()
j = np.random.randint(0, n_i)
indices[i] = np.where(logi)[0][j]
indices = indices.astype(int)
return indices
