I have a list of numpy arrays that are of the following shapes:
(16, 250, 2)
(7, 250, 2)
(1, 250, 2)
I'm trying to stack them all together so they are a single numpy array of shape :
(24, 250, 2)
I tried using np.stack but I get the error:
ValueError: all input arrays must have the same shape
The trick is to use the right stacking method, in your case since you are stacking vertically you should use np.vstack
import numpy as np
a = np.random.random((16, 250, 2))
b = np.random.random((7, 250, 2))
c = np.random.random((1, 250, 2))
arr = np.vstack((a,b,c))
arr.shape
(24, 250, 2)
You can use np.concatenate: You will get this:-
a = np.random.rand(16,250,2)
b = np.random.rand(7,250,2)
c = np.random.rand(1,250,2)
print(np.shape(np.concatenate([a,b,c], axis=0))
Output:
(24,250,2)
Related
How do you reshape a (55, 11) numpy array to a (55, 11, 1) numpy array?
Attempts:
Simply doing numpy_array.reshape(-1, 1) without any loop produces a flat array that is not 3D.
The following for loop produces a "cannot
broadcast error":
for i in range(len(numpy_array)):
numpy_array[i] = numpy_array[i].reshape(-1, 1)
Maybe you are looking for numpy.expand_dims(https://numpy.org/doc/stable/reference/generated/numpy.expand_dims.html)?
import numpy
a = numpy.random.rand(55,11)
print(a.shape) # 55,11
print(numpy.expand_dims(a, 2).shape) # 55, 11, 1
Add a newaxis to the array
my_array = np.arange(55*11).reshape(55,11)
my_array.shape
# (55, 11)
# add new axis
new_array = my_array[...,None]
new_array.shape
# (55, 11, 1)
Can specify new shape in reshape too:
new_array = my_array.reshape(*my_array.shape, 1)
new_array.shape
# (55, 11, 1)
One of the answers recommends using expand_dims. That's a good answer, but if you look at its code, and strip off some generalities, all it is doing is:
In [409]: a = np.ones((2,3)); axis=(2,)
...: out_ndim = 2+1
...: shape_it = iter(a.shape)
...: shape = [1 if ax in axis else next(shape_it) for ax in range(out_ndim)]
In [410]: shape
Out[410]: [2, 3, 1]
followed by a return a.reshape(shape).
In other words, the function call is just hiding the obvious, expand a (x,y) to (x,y,1) with
a.reshape(x,y,1)
Are you seeking some 3d 'magic' akin to the -1 in numpy_array.reshape(-1, 1)?
Personally I like to use None to add dimensions, so prefer the other answer [...,None]. But functionally it's all the same.
lets assume we have a tensor representing an image of the shape (910, 270, 1) which assigned a number (some index) to each pixel with width=910 and height=270.
We also have a numpy array of size (N, 3) which maps a 3-tuple to an index.
I now want to create a new numpy array of shape (920, 270, 3) which has a 3-tuple based on the original tensor index and the mapping-3-tuple-numpy array. How do I do this assignment without for loops and other consuming iterations?
This would look simething like:
color_image = np.zeros((self._w, self._h, 3), dtype=np.int32)
self._colors = np.array(N,3) # this is already present
indexed_image = torch.tensor(920,270,1) # this is already present
#how do I assign it to this numpy array?
color_image[indexed_image.w, indexed_image.h] = self._colors[indexed_image.flatten()]
Assuming you have _colors, and indexed_image. Something that ressembles to:
>>> indexed_image = torch.randint(0, 10, (920, 270, 1))
>>> _colors = np.random.randint(0, 255, (N, 3))
A common way of converting a dense map to a RGB map is to loop over the label set:
>>> _colors = torch.FloatTensor(_colors)
>>> rgb = torch.zeros(indexed_image.shape[:-1] + (3,))
>>> for lbl in range(N):
... rgb[lbl == indexed_image[...,0]] = _colors[lbl]
My program creates a numpy array within a for loop. For example it creates array with shape (100*30*10), then (160*30*10) and then may be (120*30*10) . I have to append the above to an empty numpy array such that , at the end of the loop, it will be a numpy array with shape (380*30*10) (i.e sum of 100+160+120) . The second and third dimension doesnt change in the numpy array.
How can I do the above in python. I tried the following.
np_model = np.append(np_model,np_temp1)
print("Appended model shape is",np_model.shape)
np_label = np.append(np_label,np_temp2)
print("Appended label shape is",np_label.shape)
The np_model is an empty array which I have defined as np_model = np.empty(1,30,10) and np_label as np_label = np.empty(1 ,str)
np_temp1 corresponds to array within each for loop like 100*30*10,120*30*10 etc and np_temp2 is a string with "item1","item2" etc
The np_label is a string numpy array with 1 label corresponding to np_temp1.shape[0]. But the result I get in np_model is flattened array with size 380*30*10 = 1140000
Any help is appreciated.
you can use numpy concatenate function, append the output numpy(s) to a list and then feed it to the concatenate function:
empty_list = []
x = np.zeros([10, 20, 4])
y = np.zeros([12, 20, 4])
empty_list.append(x)
empty_list.append(y)
z = np.concatenate(empty_list, axis=0)
print(x.shape, y.shape, z.shape)
(10, 20, 4) (12, 20, 4) (22, 20, 4)
As #Nullman suggested in comment(np.vstack)
You can create empty array like this >>> np_model = np.empty((0,30,10))
>>> np_model = np.empty((0,30,10))
>>> a = np.random.rand(100,30,10)
>>> b = np.random.rand(160,30,10)
>>> c = np.random.rand(120,30,10)
# It can done by one-line like`np_model = np.vstack((a,b,c))`
# but i guess you have loop dependency here
>>> np_model = np.vstack((np_model,a))
>>> np_model = np.vstack((np_model,b))
>>> np_model = np.vstack((np_model,c))
>>> np_model.shape
(380, 30, 10)
To specifically answer your question towards starting with an empty array, that'd be my solution, solely using np.concatenate:
import numpy as np
# Some arrays to append in a loop
arrays = (
np.random.rand(100, 30, 10),
np.random.rand(160, 30, 10),
np.random.rand(120, 30, 10)
)
# Initial empty array
array = np.zeros((0, 30, 10))
# Appending arrays in loop
for a in arrays:
array = np.concatenate((array, a), axis=0)
# Output shape
print(array.shape)
Output:
(380, 30, 10)
Hope that helps!
----------------------------------------
System information
----------------------------------------
Platform: Windows-10-10.0.16299-SP0
Python: 3.8.1
NumPy: 1.18.1
----------------------------------------
I have a list with numpy.ndarrays - each of shape (33,1,8,45,3)
Problem that when i concatenate the list using a = np.concatenate(list)
The output shape of a becomes
print a.shape
(726,1,8,45,3)
instead of shape (22,33,1,8,45,3).
How do I cleanly concatenate the list, without having to change the input.
You can use numpy.array() or numpy.stack():
import numpy
a = [numpy.random.rand(33,1,8,45,3) for i in range(22)]
b = numpy.array(a)
b.shape # (22, 33, 1, 8, 45, 3)
c = numpy.stack(a, axis=0)
c.shape # (22, 33, 1, 8, 45, 3)
np.concatenate:
Join a sequence of arrays along an existing axis.
np.stack:
Stack a sequence of arrays along a new axis.
a = np.ones((3, 4))
b = np.stack([a, a])
print(b.shape) # (2, 3, 4)
So I have 2 images, X and Y, as numpy arrays, each of shape (3, 30, 30): that is, 3 channels (RGB), each of height and width 30 pixels. I'd like to pair them up into a numpy array to get a specific output shape:
my_pair = pair_up_images(X, Y)
my_pair.shape = (2, 3, 30, 30)
Such that I can get the original images by slicing:
my_pair[0] == X
my_pair[1] == Y
After a few attempts, I keep getting either:
my_pair.shape = (2,) #By converting the images into lists and adding them.
This works as well, but the next step in the pipeline just requires a shape (2, 3, 30, 30)
my_pair.shape = (6, 30, 30) # using np.vstack
my_pair.shape = (3, 60, 30) # using np.hstack
Thanks!
Simply:
Z = np.array([X, Y])
Z.shape
Out[62]: (2, 3, 30, 30)