Alex Liberzon alexlib

test_inpaint_equivalence.py
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Ran 2 tests in 1.908s

OK
(.conda) user@user-NUC8i7BEH:~/Documents/repos/OpenOpticalFlow_PIV_v1$ /home/user/Documents/repos/OpenOpticalFlow_PIV_v1/.conda/bin/python /home/user/Documents/repos/OpenOpticalFlow_PIV_v1/test_inpaint_equivalence.py
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Cython example of exposing C-computed arrays in Python without data copies

The goal of this example is to show how an existing C codebase for numerical computing (here c_code.c) can be wrapped in Cython to be exposed in Python.

The meat of the example is that the data is allocated in C, but exposed in Python without a copy using the PyArray_SimpleNewFromData numpy

	FROM ubuntu:20.04 as builder

	FROM python:3.10.7

	# Create a user to run the application
	RUN apt-get update && \
	apt-get -y install sudo

	RUN useradd -ms /bin/bash docker && echo "docker:docker" \| chpasswd && adduser docker sudo

	import numpy as np
	from scipy.interpolate import RBFInterpolator
	import matplotlib.pyplot as plt
	import matplotlib.colors as mcolors

	def process_frames_average(particles, N, corner_m, fps, max_frames=None):
	"""
	Process multiple frames to create average velocity maps.

	Args:

	import pandas as pd
	from flowtracks.scene import Scene
	import numpy as np

	h5_name = r'../corner_51.h5'

	particles = Scene(h5_name)
	ids = particles.trajectory_ids()

	dataframes = []