EDIT: added prefix / suffix value to interval arrays to make them the same length as their corresponding data arrays, as per @user1319128 ‘s suggestion and indeed interp does the job. For sure his solution was workable and good. I just couldn’t see it because I was tired and stupid. I am sure this is a fairly mundane application, but
Tag: numpy-ndarray
Filtering out rows in multidimensional numpy arrays
Let’s say that I have an array like this: I want to filter out all rows that include negative numbers in them. And, hopefully, get this: I tried this so far: But as you can see I get 1-dimensional array and I am getting unwanted rows (in this case is [2, 2]) How can I do this without using any
Numpy append 2D array in for loop over rows
I want to append a 2D array created within a for-loop vertically. I tried append method, but this won’t stack vertically (I wan’t to avoid reshaping the result later), and I tried the vstack() function, but this won’t work on an empty array. Does anyone know how to solve this? I can think of the following solution: Is there a
Numpy condition on a vector rather than an element
I have a numpy array that represents an image, it’s dimensions are (w, h, 4), 4 is for RGBA. Now I want to replace all white pixels with transparent pixels. I wish I could do something like np.where(pic == np.array([255, 255, 255, 255]), np.array([0, 0, 0, 0]), pic) but this exact code obviously doesn’t work: pic == something compares every
Efficiently render 3D numpy bitmap array (y, x, RGB) to window on macOS (using openCV or otherwise)
I’m rendering a dynamically changing numpy bitmap array and trying to improve my framerate. Currently I’m using openCV: This takes ~20ms, which is not bad. But can I do better? Setting this has no noticeable effect. But does openCV offer a better technique than imshow to make use of a GL drawing surface? And is there any viable alternative to
Numpy apply along axis based on row index
Trying to apply numpy inbuilt function apply_along_axis based on row index position OP: The function: Op: But is there a way to use this function based on row index position for example, if its a even row index then add 10 and if its a odd row index then add 50 Sample: Answer When iterating on array, directly or with
How to convert nested numpy arrays in a dictionary to JSON?
I’m parsing nested dictionaries, which have different degrees of how nested they are (dictionaries within dictionaries within dictionaries, etc.) I do not know beforehand to what degree the dictionaries are nested. The problem is, certain dictionary values are numpy.ndarrays. When I try to write the dictionary my_dictionary to JSON with I will get the following error: Naturally, one way to
python transform 1d array of probabilities to 2d array
I have an array of probabilities: and I want to make it 2d array: What is the best way to do so? Answer One idea is use numpy.hstack: Or use numpy.c_:
Loading the binary data to a NumPy array
I am having trouble reading the binary file. I have a NumPy array as, I wrote this array to a file in binary format. Now, I am unable to get back the data from the saved binary file. I tried using numpy.fromfile() but it didn’t work out for me. When I printed the data I got [0.00000000e+00 2.19335211e-13 8.33400000e+04 …
Whats the difference between `y = x` and `y = x[:]` with x a numpy-ndarray?
I was reading this question, about immutable numpy arrays, and in a comment to one of the answers someone shows that the given trick does not work when y = x[:] is used rather than y = x. (Python 3.7.2, numpy 1.16.2) What even is the difference between these two and why do they behave so differently in this specific