issue_comments: 1008210008

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https://github.com/pydata/xarray/pull/5622#issuecomment-1008210008 https://api.github.com/repos/pydata/xarray/issues/5622 1008210008 IC_kwDOAMm_X848GBBY 14371165 2022-01-09T01:47:20Z 2022-01-09T01:47:20Z MEMBER

Gotten a little sidetracked with line plots for a while now. I'm annoyed that all the primitives are different for each plotting type, e.g Collections, Line2D, list of Line2D, etc. It makes it hard to use similar code paths. So I've been trying out LineCollection a little which behaves very similarly to ax.scatter. Being able to use hue and size the same way as in scatter seems like a big improvement to me.

In the example below you can clearly see that we have two curves where the hue changes somehow over time. But I'm having trouble understanding how to determine what's supposed to be a line. For example if x and y were 4d arrays how should it be split? scatter uses ravel to get around these hard questions. Maybe you can ravel just certain dimensions?

Like if y(a,b,c,d) and x(a, b) maybe you can * ravel along a,b and create a single line * Will this require sorting as unsorted lines usually looks bad? Does it make sense at that point? * new lines will be generated along remaining dims; c, d. * How do you label these new lines, since color and linewidth can now be used along lines as well? Using linestyle along c? Those are finite though. And what about the d dim and possibly other remaining dims? Maybe they should just remain a mystery? You probably could play around with hue or size to see those changes.

Not sure how to do that in xarray though. Thoughts, ideas or other examples are appreciated.

```python import matplotlib as mpl import matplotlib.pyplot as plt import matplotlib.dates as mdates import numpy as np from matplotlib.collections import LineCollection import numpy as np np.random.seed(42) dates = np.arange("2017-01-01", "2017-06-20", dtype="datetime64[D]" ) y = np.cumsum(np.random.normal(size=len(dates))) y2 = np.cumsum(np.random.normal(size=len(dates))) c = np.cumsum(np.random.normal(size=len(dates))) c2 = np.cumsum(np.random.normal(size=len(dates))) s = 1 + np.minimum(np.cumsum(np.random.normal(size=len(dates))), 0) s2 = 1 + np.minimum(np.cumsum(np.random.normal(size=len(dates))), 0) fig, ax = plt.subplots() #convert dates to numbers first inxval = mdates.date2num(dates) points = np.array([inxval, y]).T.reshape(-1,1,2) segments = np.concatenate([points[:-1],points[1:]], axis=1) lc = LineCollection(segments, cmap="plasma", linewidth=s) lc.set_array(c) p = ax.add_collection(lc) points2 = np.array([inxval, y2]).T.reshape(-1,1,2) segments2 = np.concatenate([points2[:-1],points2[1:]], axis=1) lc2 = LineCollection(segments2, cmap="plasma", linewidth=s2) lc2.set_array(c2) p = ax.add_collection(lc2) fig.colorbar(p, ax=ax) ax.xaxis_date() ax.autoscale_view() plt.show() ``` 
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