get_legend_handles_labels() for errorbar plot not working properly
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- Julia
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Description
Related to https://github.com/JuliaPy/PyPlot.jl/issues/401.
I'm simply trying to reverse the order of the legend entries of two errorbar plots.
Pure python (from within Julia):
```julia
py"""
import matplotlib.pyplot as plt
import numpy as np
plt.errorbar(np.random.rand(10), np.random.rand(10), np.random.rand(10), label='first', marker='o', linestyle="")
plt.errorbar(np.random.rand(10), np.random.rand(10), np.random.rand(10), label='second', marker='o', linestyle="")
h,l = plt.gca().get_legend_handles_labels()
plt.legend(reversed(h),reversed(l), frameon=True)
"""
```

Using PyPlot:
```julia
errorbar(rand(10), rand(10), rand(10), label="first", marker="o", linestyle="")
errorbar(rand(10), rand(10), rand(10), label="second", marker="o", linestyle="")
h,l = gca().get_legend_handles_labels()
legend(reverse(h),reverse(l), frameon=true)
```

Note that the handles are wrong (should be markers with error bars).
PyPlot + ugly fix:
```julia
errorbar(rand(10), rand(10), 0.1 .* rand(10), marker="o", linestyle="none", label="first")
errorbar(rand(10), rand(10), 0.1 .* rand(10), marker="o", linestyle="none", label="second")
py"""
import matplotlib.pyplot as plt
h, l = plt.gca().get_legend_handles_labels()
plt.legend(reversed(h), reversed(l), frameon=True)
"""
```

Presumably a PyObject conversion issue? In python, `h` is a container
```python
[,
]
```
In Julia I find
```julia
2-element Array{Tuple{PyObject,Tuple{PyObject,PyObject},Tuple{PyObject}},1}:
(PyObject , (PyObject , PyObject ), (PyObject ,))
(PyObject , (PyObject , PyObject ), (PyObject ,))
```
Contributor guide
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Research direction
Start by running the Julia examples in the issue and inspect the get_legend_handles_labels() entry point and its PyObject conversion. Compare the returned errorbar handles with Python's ErrorbarContainer objects, then verify that reversing the handles preserves error bars in the legend.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- data-visualization
- Issue type
- Bug
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Mostly clear
- Newbie friendliness
- 35/100