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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alexlib

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:

alexlib

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 = []

alexlib

alexlib

alexlib

alexlib

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

alexlib

alexlib

alexlib

import streamlit as st
import numpy as np
import matplotlib.pyplot as plt
from scipy.integrate import odeint

def drag_coefficient(Re):
    """Calculate the drag coefficient using the empirical correlation."""
    if Re < 1:
        return 24 / Re
    elif Re < 1000: