Vectorizing string formatting across NumPy array

Vectorizing string formatting across NumPy array

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Vectorizing string formatting across NumPy array
Tag : python , By : Robert MacGregor
Date : November 29 2020, 04:01 AM

Any of those help I have two integer arrays that I want to combine, per element, into a single array of strings of the form 'a[i]_b[i]'. That is, I have
fv = np.frompyfunc("{}_{}".format, 2, 1)
result = fv(a, b)  # array(['1_4', '2_5', '3_6'], dtype=object)
In [2]: a = np.arange(100000)

In [3]: b = np.arange(100000) + a.size

In [4]: fv = np.frompyfunc("{}_{}".format, 2, 1)

In [5]: def f(a, b): return np.array(["{}_{}".format(a,b) for a,b in zip(a,b)], dtype=object)

In [6]: %timeit f(a,b)
370 ms ± 12.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

In [7]: %timeit fv(a,b)
137 ms ± 1.48 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)

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Vectorizing operation on numpy array

Tag : python , By : Jesenko Mehmedbasic
Date : March 29 2020, 07:55 AM
will help you The problem is that just by using partial it doesn't make the existence of the other arguments go away for the sake of vectorize. The function underlying the partial object will be vectorizedWarpAffine.pyfunc, which will keep track of whatever pre-bound arguments you'd like it to use when calling vectorizedWarpAffine.pyfunc.func (which is still a multi-argumented function).
You can see it like this (after you import inspect):
In [19]: inspect.getargspec(vectorizedWarpAffine.pyfunc.func)
Out[19]: ArgSpec(args=['M', 'size', 'img'], varargs=None, keywords=None, defaults=None)
vectorizedWarpAffine = np.vectorize(partialWarpAffine, 
                                    excluded=set((0, 1)))
In [21]: vectorizedWarpAffine(data[:, 0])
OpenCV Error: Assertion failed (cn <= 4 && ssize.area() > 0) in remapBilinear, file -------src-dir-------/opencv-, line 2296
error                                     Traceback (most recent call last)
<ipython-input-21-3fb586393b75> in <module>()
----> 1 vectorizedWarpAffine(data[:, 0])

/home/ely/anaconda/lib/python2.7/site-packages/numpy/lib/function_base.pyc in __call__(self, *args, **kwargs)
   1570             vargs.extend([kwargs[_n] for _n in names])
-> 1572         return self._vectorize_call(func=func, args=vargs)
   1574     def _get_ufunc_and_otypes(self, func, args):

/home/ely/anaconda/lib/python2.7/site-packages/numpy/lib/function_base.pyc in _vectorize_call(self, func, args)
   1628         """Vectorized call to `func` over positional `args`."""
   1629         if not args:
-> 1630             _res = func()
   1631         else:
   1632             ufunc, otypes = self._get_ufunc_and_otypes(func=func, args=args)

/home/ely/anaconda/lib/python2.7/site-packages/numpy/lib/function_base.pyc in func(*vargs)
   1565                     the_args[_i] = vargs[_n]
   1566                 kwargs.update(zip(names, vargs[len(inds):]))
-> 1567                 return self.pyfunc(*the_args, **kwargs)
   1569             vargs = [args[_i] for _i in inds]

/home/ely/programming/np_vect.py in <lambda>(M, size, img)
     10 size = (96, 96) # output image size
---> 12 warpAffine = lambda M, size, img : cv2.warpAffine(img, M, size) # re-order function parameters
     13 partialWarpAffine = partial(warpAffine, M, size)

error: -------src-dir-------/opencv- error: (-215) cn <= 4 && ssize.area() > 0 in function remapBilinear
vectorizedWarpAffine = np.vectorize(warpAffine, 
                                    excluded=(0, 1), 
In [29]: vectorizedWarpAffine(M, size, data[:, 0])
array([[[ array([[ 0.,  0.,  0., ...,  0.,  0.,  0.],
       [ 0.,  0.,  0., ...,  0.,  0.,  0.],
       [ 0.,  0.,  0., ...,  0.,  0.,  0.],

Nested For Loops Numpy Array: Is vectorizing possible?

Tag : python , By : Epora75
Date : March 29 2020, 07:55 AM
With these it helps Vectorizing won't help you much here, but avoiding repeated work will:
patterns = [re.compile('\\b'+name[idx]+'\\b') for idx in index]
for i, row in enumerate(data):
    for j, patt in enumerate(patterns):
        x[i, j] = len(patt.findall(row[1]))
        y[i, j] = len(patt.findall(row[2]))

Vectorizing NumPy covariance for 3D array

Tag : python , By : suresh
Date : March 29 2020, 07:55 AM
wish helps you Hacked into numpy.cov source code and tried using the default parameters. As it turns out, np.cov(x[i,:,:]) would be simply :
N = x.shape[2]
m = x[i,:,:]
m -= np.sum(m, axis=1, keepdims=True) / N
cov = np.dot(m, m.T)  /(N - 1)
N = x.shape[2]
m1 = x - x.sum(2,keepdims=1)/N
y_out = np.einsum('ijk,ilk->ijl',m1,m1) /(N - 1)
In [155]: def original_app(x):
     ...:     n = x.shape[0]
     ...:     y = np.zeros((n,2,2))
     ...:     for i in np.arange(x.shape[0]):
     ...:         y[i]=np.cov(x[i,:,:])
     ...:     return y
     ...: def proposed_app(x):
     ...:     N = x.shape[2]
     ...:     m1 = x - x.sum(2,keepdims=1)/N
     ...:     out = np.einsum('ijk,ilk->ijl',m1,m1)  / (N - 1)
     ...:     return out

In [156]: # Setup inputs
     ...: n = 10000
     ...: x = np.random.rand(n,2,4)

In [157]: np.allclose(original_app(x),proposed_app(x))
Out[157]: True  # Results verified

In [158]: %timeit original_app(x)
1 loops, best of 3: 610 ms per loop

In [159]: %timeit proposed_app(x)
100 loops, best of 3: 6.32 ms per loop

Vectorizing code of looping through NumPy 3D array

Tag : python , By : davidg
Date : March 29 2020, 07:55 AM
I wish did fix the issue. If img and truth_table are arrays where truth_table is of broadcastable shape:
img[truth_table == 12] = 0

Vectorizing Numpy 3D and 2D array operation

Tag : python , By : Aki Björklund
Date : March 29 2020, 07:55 AM
Does that help I'm trying to create K MxN matrices in Python, stored in a (M,N,K) numpy array, C, from two matrices, A and B, with shapes (K, M) and (K,N) respectively. The first matrix is computed as C0 = a0.T x b0, where a0 is the first row of A and b1 is the first row of B, the second matrix as C1 = a1.T x b0 and so on.
In [235]: A = np.random.random((10,800)) 
     ...: B = np.random.random((10,500)) 
     ...: C = np.zeros((800,500,10)) 
     ...: for k in range(10): 
     ...:     C[:,:,k] = A[k,:][:,None] @ B[k,:][None,:] 
In [236]: C.shape                                                                    
Out[236]: (800, 500, 10)
In [237]: np.allclose((A[:,:,None]@B[:,None,:]).transpose(1,2,0), C)                 
Out[237]: True
In [238]: np.allclose((A[:,:,None]*B[:,None,:]).transpose(1,2,0), C)                 
Out[238]: True
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