linear interpolation with grided data in python

Linear interpolation is a method to estimate values that lie between known values. In the context of gridded data, this technique can be useful for estimating values at arbitrary points within a grid based on the values at the grid nodes. In this tutorial, we'll explore how to perform linear interpolation on gridded data using Python, with a focus on NumPy and SciPy libraries.
Make sure you have the following libraries installed:
Linear interpolation assumes that the relationship between two adjacent points is a straight line. Given two known points (x0, y0) and (x1, y1), the interpolated value y at a point x between them can be calculated using the formula:
y=y0+
x1−x0
(x−x0)⋅(y1−y0)
Let's create a Python script to perform linear interpolation on gridded data. We'll use NumPy for array manipulation and SciPy for interpolation.
In this example, the linear_interpolation function takes the X and Y coordinates of the grid nodes, the gridded data values, and a set of points to interpolate. The function uses SciPy's griddata function to perform the linear interpolation.
Feel free to replace the random data in the linear_interpolation function with your actual gridded data.
Linear interpolation is a powerful technique for estimating values within a grid. With the help of Python libraries such as NumPy and SciPy, performing linear interpolation on gridded data becomes straightforward. This tutorial provides a basic implementation to get you started.
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