So I have been tracking this weird memory leak in my Python repo for a while now. Finally I was able to isolate the problem using the code below. The numpy arrays are generating data with a similar ...

This script: import numpy as np a = np.array([[0, 1], [1, 1], [1, 0]]) b = np.array([[1, 0], [1, 1], [1, -1]]) dot = a[:, 0]*b[:, 0] + a[:, 1]*b[:, 1] print(dot) produces elementwise dot product of ...

Let's assume there is an event and within this event there are multiple outcomes that can co-exist. An example would be a tennis game where A plays against B and B serves. A couple of possible ...

Could someone please help me understand the error in my code? I based it on the farmer example from the link provided, but I’m unable to figure out why the model isn’t being created. I have noted the ...

I thought numpy.load() writes directly into the array data memory. However, the results of the profiling functions (I profiled with memray and mprof) seems a bit strange to me... My array is 2GB big. ...

In a Python script, I've written: # etc. etc. input_file = args.input_file_path or sys.stdin arr = numpy.fromfile(input_file, dtype=numpy.dtype('f32')) when I run the script, I get: $ cat nums.fp32....

Ok, so, I'm trying to process some .bil files in Debian 12 I installed GDAL using: sudo apt install gdal-bin sudo apt-get install libgdal-dev And ogrinfo --version outputs: GDAL 3.6.2, released 2023/...

This is a follow up to my previous question. I want to efficiently compute Nth term of higher order generalized Fibonacci numbers, N is sufficiently large such that the Nth term is guaranteed to be ...

I have stored strings & their vector embeddings in a Sqlite DB file with the table name "query_metadata". Embeddings are stored as numpy bytes. The embeddings would be used for ...

I’m working with X-ray imaging data. I have a reference image containing a structured pattern, and a sample image where this pattern is slightly distorted due to the presence of a physical sample. My ...

import gensim import numpy import scipy print("gensim version:", gensim.__version__) print("numpy version:", numpy.__version__) print("scipy version:", scipy.__version__) ...

I have a Pandas Dataframe that I derive from a process like this: df1 = pd.DataFrame({'c1':['A','B','C','D','E'],'c2':[1,2,3,4,5]}) df2 = pd.DataFrame({'c1':['A','B','C'],'c2':[1,2,3],'c3': [np.array((...

The following python code allocates an 8GB numpy array, and writes into it. It kills the python interpreter, regardless of the size of the RAM of the machine (it happens on a server with 384GB of RAM)....

I have two numpy arrays a and b such that a.shape[:-1] and b.shape are broadcastable. With this constraint only, I want to calculate an array c according to the following: c = numpy.empty(numpy....

It was unexpected that x=np.empty((2,10,5)) x.shape >>> (2, 10, 5) x[0].shape, x[0,:,:].shape >>> ((10, 5), (10, 5)) mask = [True,True,True,False,False] x[0,:,mask].shape >>&...