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# Meshgrid
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## The goal
Questions to [David Rotermund ](mailto:davrot@uni-bremen.de )
## [numpy.meshgrid](https://numpy.org/doc/stable/reference/generated/numpy.meshgrid.html)
```python
numpy.meshgrid(*xi, copy=True, sparse=False, indexing='xy')
```
> Return a list of coordinate matrices from coordinate vectors.
>
> 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.
```python
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0, 1, 100)
y = np.linspace(0, 1, 100)
xv, yv = np.meshgrid(x, y)
plt.imshow(xv, cmap="hot")
plt.xlabel("x axis")
plt.ylabel("y axis")
plt.title("xv")
plt.show()
plt.imshow(yv, cmap="hot")
plt.xlabel("x axis")
plt.ylabel("y axis")
plt.title("yv")
plt.show()
```
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An example:
```python
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0, 1, 100)
y = np.linspace(0, 1, 100)
xv, yv = np.meshgrid(x, y)
a = np.sin(xv * 2 * np.pi) * np.sin(yv * 8 * np.pi)
plt.imshow(a, cmap="hot")
plt.xlabel("x axis")
plt.ylabel("y axis")
plt.show()
```
**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.
```python
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0, 1, 100)[np.newaxis, :]
y = np.linspace(0, 1, 100)[:, np.newaxis]
a = np.sin(x * 2 * np.pi) * np.sin(y * 8 * np.pi)
plt.imshow(a, cmap="hot")
plt.xlabel("x axis")
plt.ylabel("y axis")
plt.show()
```
