finitediff

Computes the derivatives in X and Y from a regular grid using finite differences

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Requirements

pip install numpy
pip install matplotlib

Installation

import numpy as np
import matplotlib.pyplot as plt

Functions list

1. compute_derivative_x Compute the derivative in the X-direction using finite differences

Arguments:

data: 2D array of shape (n, m) containing the data.

delta_x: The known spacing between data points in the x-direction.

derivative_type: String specifying the type of derivative calculation. Can be 'left', 'right', or 'center'.

Returns: derivative 2D array of shape (n, m) containing the numerical derivative in the x-direction.

2. compute_derivative_y Compute the derivative in the Y-direction using finite differences

Arguments:

data: 2D array of shape (n, m) containing the data.

delta_y: The known spacing between data points in the y-direction.

derivative_type: String specifying the type of derivative calculation. Can be 'left', 'right', or 'center'.

Returns: derivative 2D array of shape (n, m) containing the numerical derivative in the y-direction

3. quick_plotContourMap Plots a contour map with the result of the derivative by finite difference

Usage examples

Sample 1

n = 150
m = 100

data = np.random.random((n, m))

delta_x = 0.35 
delta_y = 0.25

x_derivative = compute_derivative_x(data, delta_x, 'center')
quick_plotContourMap(x_derivative)

Sample 2

n = 20
m = 18

data = np.random.random((n, m))

delta_x = 0.15
delta_y = 0.12

y_derivative = compute_derivative_y(data, delta_y, 'right')
quick_plotContourMap(y_derivative)