89 lines
1.7 KiB
Markdown
89 lines
1.7 KiB
Markdown
# Meshgrid
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{:.no_toc}
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<nav markdown="1" class="toc-class">
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* TOC
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{:toc}
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</nav>
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## The goal
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Questions to [David Rotermund](mailto:davrot@uni-bremen.de)
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## [numpy.meshgrid](https://numpy.org/doc/stable/reference/generated/numpy.meshgrid.html)
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```python
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numpy.meshgrid(*xi, copy=True, sparse=False, indexing='xy')
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```
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> Return a list of coordinate matrices from coordinate vectors.
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>
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> Make N-D coordinate arrays for vectorized evaluations of N-D scalar/vector fields over N-D grids, given one-dimensional coordinate arrays x1, x2,…, xn.
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```python
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import numpy as np
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import matplotlib.pyplot as plt
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x = np.linspace(0, 1, 100)
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y = np.linspace(0, 1, 100)
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xv, yv = np.meshgrid(x, y)
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plt.imshow(xv, cmap="hot")
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plt.xlabel("x axis")
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plt.ylabel("y axis")
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plt.title("xv")
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plt.show()
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plt.imshow(yv, cmap="hot")
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plt.xlabel("x axis")
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plt.ylabel("y axis")
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plt.title("yv")
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plt.show()
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```
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An example:
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```python
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import numpy as np
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import matplotlib.pyplot as plt
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x = np.linspace(0, 1, 100)
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y = np.linspace(0, 1, 100)
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xv, yv = np.meshgrid(x, y)
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a = np.sin(xv * 2 * np.pi) * np.sin(yv * 8 * np.pi)
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plt.imshow(a, cmap="hot")
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plt.xlabel("x axis")
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plt.ylabel("y axis")
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plt.show()
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```
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**The question is if you really need a mesh or if just using broadcasting can do the job too.** I guess this depends on your need.
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```python
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import numpy as np
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import matplotlib.pyplot as plt
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x = np.linspace(0, 1, 100)[np.newaxis, :]
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y = np.linspace(0, 1, 100)[:, np.newaxis]
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a = np.sin(x * 2 * np.pi) * np.sin(y * 8 * np.pi)
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plt.imshow(a, cmap="hot")
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plt.xlabel("x axis")
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plt.ylabel("y axis")
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plt.show()
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```
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