Plot 2d coords

[nvaytet]

import scipp as sc
import numpy as np
import plopp as pp

# make some data
N = 10
M = 5
x = np.arange(N+1).astype(np.float64)
y = np.arange(M+1).astype(np.float64)
xx, yy = np.meshgrid(x, y[:-1])
z = np.random.random([M, N])
for i in range(M):
    xx[i] *= (i + 1.0)

da = sc.DataArray(data=sc.array(dims=['y', 'x'], values=z, unit='counts'),
 coords={'x': sc.array(dims=['y', 'x'], values=xx, unit='m'),
         'y': sc.array(dims=['y'], values=y, unit='m')})

pp.plot(da)

Fixes #3

[SimonHeybrock]

Tests like this don't follow what we have, e.g., learned from the "Software Engineering @ Google@ book. This is just checking some numbers, it is unclear what they mean (since we do not see the data setup), and probably the test will break if dense_data_array changes somehow?

[nvaytet]

True. So is doing assert np.array_equal(p._ax.get_xlim(), [da.coords['xx'].min().value, da.coords['xx'].max().value]) better? It still involves the xx dim, which may change in the dense data array.

[nvaytet]

On second thought, I think I will just remove this test. It sounds like it's going to break all the time, and it in fact has nothing to do with 2d coords, the problem was there also with 1d coords. It's the bin edges which is important.

[nvaytet]

@SimonHeybrock do you have a good idea on how we could do more of this with scipp instead of numpy? I think the axis parameter is a bit bug-prone, would be nice to just use dimension labels...

[SimonHeybrock]

This is going to break if that matches a dim name, use uuid.uuid4() (grep in scipp to see how we do this in other places).

[SimonHeybrock]

Can the repeat and broadcast be swapped? Is this expensive otherwise?

[nvaytet]

hmm would not be very easy I think. I can add a TODO note?

[SimonHeybrock]

Is this avoiding the potentially costly stuff below for "normal" coords?

[nvaytet]

yes