Here is a sampler that creates a sample cut from an array of any dimensionality. It uses functions to control where to start the cut and for how wide the cut should be along any axis.
Here is an explanation of the parameters:
arr - the input numpy array. loc_sampler_fn - this is the function you want to use to set the corner of the box. If you want the corner of the box to be sampled uniformly from the anywhere along the axis, use np.random.uniform. If you want the corner to be closer to the center of the array, use np.random.normal. However, we need to tell the function what range to sample over. This brings us to the next parameter. loc_dim_param - this passes the size of each axis to loc_sampler_fn. If we are using np.random.uniform for the location sampler, we want to sample from the entire range of the axis. np.random.uniform has two parameters: low and high, so by passing the length of the axis to high it samples uniformly over the entire axis. In other words, if the axis has length 120 we want np.random.uniform(low=0, high=120), so we would set loc_dim_param='high'. loc_params - this passes any additional parameters to loc_sampler_fn. Keeping with the example, we need to pass low=0 to np.random.uniform, so we pass the dictionary loc_params={'low':0}.
From here, it is basically identical for the shape of the box. If you want the box height and width to be uniformly sampled from 3 to 10, pass in shape_sampler_fn=np.random.uniform, with shape_dim_param=None since we are not using the size of the axis for anything, and shape_params={'low':3, 'high':11}.
def box_sampler(arr,
loc_sampler_fn,
loc_dim_param,
loc_params,
shape_sampler_fn,
shape_dim_param,
shape_params):
'''
Extracts a sample cut from `arr`.
Parameters:
-----------
loc_sampler_fn : function
The function to determine the where the minimum coordinate
for each axis should be placed.
loc_dim_param : string or None
The parameter in `loc_sampler_fn` that should use the axes
dimension size
loc_params : dict
Parameters to pass to `loc_sampler_fn`.
shape_sampler_fn : function
The function to determine the width of the sample cut
along each axis.
shape_dim_param : string or None
The parameter in `shape_sampler_fn` that should use the
axes dimension size.
shape_params : dict
Parameters to pass to `shape_sampler_fn`.
Returns:
--------
(slices, x) : A tuple of the slices used to cut the sample as well as
the sampled subsection with the same dimensionality of arr.
slice :: list of slice objects
x :: array object with the same ndims as arr
'''
slices = []
for dim in arr.shape:
if loc_dim_param:
loc_params.update({loc_dim_param: dim})
if shape_dim_param:
shape_params.update({shape_dim_param: dim})
start = int(loc_sampler_fn(**loc_params))
stop = start + int(shape_sampler_fn(**shape_params))
slices.append(slice(start, stop))
return slices, arr[slices]
Example for a uniform cut on a 2D array with widths between 3 and 9:
a = np.random.randint(0, 1+1, size=(100,150))
box_sampler(a,
np.random.uniform, 'high', {'low':0},
np.random.uniform, None, {'low':3, 'high':10})
# returns:
([slice(49, 55, None), slice(86, 89, None)],
array([[0, 0, 1],
[0, 1, 1],
[0, 0, 0],
[0, 0, 1],
[1, 1, 1],
[1, 1, 0]]))
Examples for taking 2x2x2 chunks from a 10x20x30 3D array:
a = np.random.randint(0,2,size=(10,20,30))
box_sampler(a, np.random.uniform, 'high', {'low':0},
np.random.uniform, None, {'low':2, 'high':2})
# returns:
([slice(7, 9, None), slice(9, 11, None), slice(19, 21, None)],
array([[[0, 1],
[1, 0]],
[[0, 1],
[1, 1]]]))
Update based on the comments.
For your specific purpose, it looks like you want a rectangular sample where the starting corner is uniformly sampled from anywhere in the array, and the the width of the sample along each axis is uniformly sampled, but can be limited.
Here is a function that generates these samples. min_width and max_width can accept iterables of integers (such as a tuple) or a single integer.
def uniform_box_sampler(arr, min_width, max_width):
'''
Extracts a sample cut from `arr`.
Parameters:
-----------
arr : array
The numpy array to sample a box from
min_width : int or tuple
The minimum width of the box along a given axis.
If a tuple of integers is supplied, it my have the
same length as the number of dimensions of `arr`
max_width : int or tuple
The maximum width of the box along a given axis.
If a tuple of integers is supplied, it my have the
same length as the number of dimensions of `arr`
Returns:
--------
(slices, x) : A tuple of the slices used to cut the sample as well as
the sampled subsection with the same dimensionality of arr.
slice :: list of slice objects
x :: array object with the same ndims as arr
'''
if isinstance(min_width, (tuple, list)):
assert len(min_width)==arr.ndim, 'Dimensions of `min_width` and `arr` must match'
else:
min_width = (min_width,)*arr.ndim
if isinstance(max_width, (tuple, list)):
assert len(max_width)==arr.ndim, 'Dimensions of `max_width` and `arr` must match'
else:
max_width = (max_width,)*arr.ndim
slices = []
for dim, mn, mx in zip(arr.shape, min_width, max_width):
fn = np.random.uniform
start = int(np.random.uniform(0,dim))
stop = start + int(np.random.uniform(mn, mx+1))
slices.append(slice(start, stop))
return slices, arr[slices]
Example of generating a box cut that starts uniformly anywhere in the array, the height is a random uniform draw from 1 to 4 and the width is a random uniform draw from 2 to 6 (just to show). In this case, the size of the box was 3 by 4, starting at the 66th row and 19th column.
x = np.random.randint(0,2,size=(100,100))
uniform_box_sampler(x, (1,2), (4,6))
# returns:
([slice(65, 68, None), slice(18, 22, None)],
array([[1, 0, 0, 0],
[0, 0, 1, 1],
[0, 1, 1, 0]]))
..lacks the ability to sample a patch at a random location.a + (b - a)*(0, 1). I don't want this to confuse the primary objective...distribution here is not that important right now.