When I pip install (or pip install –upgrade) packages that require numpy, they have a tendency to uninstall my existing numpy+mkl (which has a high enough version to satisfy the numpy version requirement). Afterwards, they install numpy without +mkl, which causes problems for other packages that do require MKL. An example for which this happens is gym (which has ‘numpy>=1.10.4’
Tag: numpy
PyQt5 QImage from Numpy Array
Considering the following code I get the following error TypeError: arguments did not match any overloaded call: QImage(): too many arguments QImage(QSize, QImage.Format): argument 1 has unexpected type ‘numpy.ndarray’ QImage(int, int, QImage.Format): argument 1 has unexpected type ‘numpy.ndarray’ QImage(bytes, int, int, QImage.Format): argument 1 has unexpected type ‘numpy.ndarray’ QImage(sip.voidptr, int, int, QImage.Format): argument 1 has unexpected type ‘numpy.ndarray’ QImage(bytes, int,
ValueError: special directives must be the first entry
why this error appears and what does it mean exactly? It appears on this code (I put only the part of machine learning, because the code is so long): The error is the following: Thank you in advance! Answer Looking at what the code is doing I don’t think you should have np.c_ there at all. The model is trained
Pandas finding local max and min
I have a pandas data frame with two columns one is temperature the other is time. I would like to make third and fourth columns called min and max. Each of these columns would be filled with nan’s except where there is a local min or max, then it would have the value of that extrema. Here is a sample
Reconstruct Image from patches/tiles in python
I have a task where I have tiled an Input image using the code: After that I calculated the sum of all tiles and sort them in ascending order. I’ll take the sum.sorted[-20] as a threshold and set all tiles below that threshold to 0 in order to neglect background. So far everything works fine. Now I need to reconstruct
Why does saving an image with OpenCV result in a black image?
So, I want to create a 500×500 white image using python Numpy library, though I can easily do it in photoshop. The code below is valid and the image is white (because I saved the image using cv2.imsave function and later I opened it with windows photos viewer). But when I try to display it using the cv2.imshow function a
cv2.imshow() giving black screen
I’m converting a image (numpy array) into a string. Then I’m converting this string back to a numpy array of the original dimensions. Hence both the numpy arrays are equal- infact numpy.array_equals() also returns True for the arrays being equal. When I call cv2.imshow() on the original numpy array, it prints the image. But when I call cv2.imshow() on the
Interpolate/Resize 3D array
I have a 3D array holding voxels from a mri dataset. The model could be stretched along one or more directions. E.g. the voxel size (x,y,z) could be 0.5×0.5×2 mm. Now I want to resample the 3D array into an array holding 1,1,1 mm voxels. For this I need to make the x/y dimensions smaller and the z dimension bigger
How to create 2d array with numpy random.choice for every rows?
I’m trying to create a 2d array (which is a six column and lots of rows) with numpy random choice with unique values between 1 and 50 for every row not all of the array But this raises an error. Is it possible to make this with an one liner without a loop Edit Okey i get the answer. These
Why doesn’t Python have a “__req__” (reflected equality) method?
I have a little helper class: This lets me do sweet magic like: without having to use a list comprehension (as in np.array(x in (2,3) for x in arr). (I maintain a UI that lets (trusted) users type in arbitrary code, and a == AnyOf(1,2,3) is a lot more palatable than a list comprehension to the non-technically savvy user.) However!