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Bilinear Binning with Numpy
def bilinear_bincount_numpy(points, intensities):
"""Bilinear weighting of points onto a grid.
Extent of grid given by min and max of points in each dimension
points should have shape (N, 2)
intensity should have shape (N,)
"""
floor = np.floor(points)
ceil = floor + 1
floored_indices = np.array(floor, dtype=int)
low0, low1 = floored_indices.min(0)
high0, high1 = floored_indices.max(0)
floored_indices = floored_indices - (low0, low1)
shape = (high0 - low0 + 2, high1-low1 + 2)
upper_diff = ceil - points
lower_diff = points - floor
w1 = np.prod((upper_diff), axis=1)
w2 = upper_diff[:,0]*lower_diff[:,1]
w3 = lower_diff[:,0]*upper_diff[:,1]
w4 = np.prod((lower_diff), axis=1)
shifts = np.array([[0, 0], [0, 1], [1, 0], [1, 1]])
indices = floored_indices[:, None] + shifts
indices = (indices * (shape[1], 1)).sum(-1)
weights = np.array([w1, w2, w3, w4]).T
weight_bins = np.bincount(indices.flatten(), weights=weights.flatten(), minlength = np.prod(shape))
intens_bins = np.bincount(indices.flatten(), weights=(intensities[:, None]*weights).flatten(), minlength = np.prod(shape))
weight_image = weight_bins.reshape(shape)
intens_image = intens_bins.reshape(shape)
return intens_image, weight_image
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