I am trying to plot some HDF data in matplotlib. After importing them using h5py, the data is stored in a form of array, like this:

```
array([[151, 176, 178],
[121, 137, 130],
[120, 125, 126])
```

In this case, x and y values are just the indexes of the array's fields, while z value is the value of specific field. In the (x,y,z) form it would look like:

```
(1,1,151)
(2,1,176)
(3,1,178)
(1,2,121)
...
```

and so on.

Is there an easy way to do a surface plot from this kind of data? I know I can change this to (x,y,z) tuples by iterating all over the array, but maybe it is not needed?

If you want a 3-d surface plot, you have to create the `meshgrid`

first. You can try:

```
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
X = np.arange(1, 10)
Y = np.arange(1, 10)
X, Y = np.meshgrid(X, Y)
R = np.sqrt(X**2 + Y**2)
Z = np.sin(R)
fig = plt.figure()
ax = fig.gca(projection='3d')
surf = ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap='hot', linewidth=0, antialiased=False)
ax.set_zlim(-1.01, 1.01)
fig.colorbar(surf, shrink=0.5, aspect=5)
plt.show()
```

which will generate,

However, if the only relevant information is in the z-values, you can simply use `imshow`

. Here, z-values are represented by their color. You can achieve this by:

```
im = plt.imshow(Z, cmap='hot')
plt.colorbar(im, orientation='horizontal')
plt.show()
```

Which will give,

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