I have implemented a file-backed HashTable using numpy.memmap. It appears to be functioning correctly, however, I notice that on Linux both KSysGuard and SMART are reporting ridiculous IO Write amounts. About 50x the amount of data that should be written. I have not tested this on other operating systems. This is the code that creates the internal memory map And
Tag: numpy
Not able to import pandas and numpy
I am trying to run following simple script on Debian Stable Linux: But it is giving following error: Following versions of pandas and numpy are installed through Debian Repositories: Where is the problem and how can it be solved? Edit: I find that the same above file works perfectly in another folder! I am using correct filename for command. There
numpy.memmap: bogus memory allocation
I have a python3 script that operates with numpy.memmap arrays. It writes an array to newly generated temporary file that is located in /tmp: The size of the HDD is only 250G. Nevertheless, it can somehow generate 10T large files in /tmp, and the corresponding array still seems to be accessible. The output of the script is following: The file
python: numpy runs script twice
when I import numpy to my python script, the script is executed twice. Can someone tell me how I can stop this, since everything in my script takes twice as long? Here’s an example: And the output is: So, is my script first executed with normal python and then with numpy again? Maybe I should say that I have not
cv2.hough circles error on video
When I run cv2.HoughCircles() I am getting the error My code is GNU nano 2.2.6 File: cv.py Answer you are not checking if you circles is None. If you do that, it works: Output generated: