NumPy: Understanding Meshed Grids
<p>When I’m teaching my courses about scientific Python, students often have a hard time understanding the concept of meshed grids in NumPy and what’s the difference between the various options. As this appears to be a quite common problem and as the concept of meshed grids is vital for many scientific applications, I’m writing this post to settle these problems once and for all. ;-)</p>
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<h2>Daily Dose of Scientific Python</h2>
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<p>102 stories</p>
<p><img alt="" src="https://miro.medium.com/v2/resize:fill:175:175/0*gk6DNo1LvKaHUVKI" style="height:194px; width:194px" /></p>
<p><img alt="" src="https://miro.medium.com/v2/resize:fill:175:175/0*ftVVxPZ1LY4wtMBf" style="height:194px; width:194px" /></p>
<p><img alt="" src="https://miro.medium.com/v2/resize:fill:175:175/0*TaD_gJEI0bJRxCk8" style="height:194px; width:194px" /></p>
<h1>The Problem</h1>
<p>Consider the following code, where we define a one-dimensional equidistant grid <code>x</code>:</p>
<p><img alt="" src="https://miro.medium.com/v2/resize:fit:630/1*CHV9bFedH9Z0033IHasDXw.png" style="height:118px; width:700px" /></p>
<p>One of the beauties of NumPy is that you can easily apply functions to arrays. For example, we could calculate the sine function on that very grid by</p>
<p>Both <code>x</code> and <code>f</code> are one-dimensional arrays of length 100, as we would guess:</p>
<p><img alt="" src="https://miro.medium.com/v2/resize:fit:630/1*MCyyH8yjZ-hn8pw-s3HijQ.png" style="height:316px; width:700px" /></p>
<p>This is all intuitive. But what if we are working in multiple dimensions? Suppose we want to use a two-dimensional grid along the x and yaxes. How do we do that? Specifically, how can we calculate the function</p>
<p>on a two-dimensional grid?</p>
<h1>The Solution</h1>
<p>First, we define our one-dimensional coordinate values like in the one-dimensional case.</p>
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