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r3kste
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Feb 9, 2026
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@Sreekanth-M8 This looks good. As discussed, you can go ahead condense this solution to put as a comment to ask for initial feedback. There is just one minor comment about radial locator.
r3kste
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Feb 11, 2026
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Feb 11, 2026
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I ran test_polar.py and left some comments about the failing tests.
-
For Theta Tick, there is one test failing:
lib/matplotlib/tests/test_polar.py::test_polar_theta_limits. -
For Radial Tick, there are a lot of tests failing.
As mentioned below, it would be better if we stick with only changing Theta ticks in this PR (as that is what the issue also expects).
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@Sreekanth-M8 Sorry for the delay in the review. Apart from the below comments you can go ahead with implementing tests.
- See how Cartesian Axes Locators are tested and add similar tests for
ChoiceLocator. - Make sure previously existing tests with full circle plots still pass.
- Update the test image for
lib/matplotlib/tests/test_polar.py::test_polar_theta_limits
| """ | ||
|
|
||
| def __init__(self, base): | ||
| class ChoiceLocator(mticker.Locator): |
|
|
||
| def __init__(self, base): | ||
| class ChoiceLocator(mticker.Locator): | ||
| def __init__(self, base=None, choices=None): |
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- Should
basebe optional here, considering forThetaLocatorit was not. - I think we don't need to take
choicesas a parameter. Or is there a usecase to be able to control this?
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| if _is_full_circle_deg(lim[0], lim[1]): | ||
| return np.deg2rad(min(lim)) + np.arange(8) * 2 * np.pi / 8 | ||
| if (vmax - vmin > 60): | ||
| for ticks in self.choices: | ||
| in_range = (ticks >= vmin - tol) & (ticks <= vmax + tol) | ||
| ticks = ticks[in_range] | ||
| if len(ticks) > max_ticks: | ||
| continue | ||
| if len(ticks) == max_ticks: | ||
| if vmin != ticks[0]: | ||
| ticks[0] = vmin | ||
| if vmax != ticks[-1]: | ||
| ticks[-1] = vmax | ||
| return np.deg2rad(ticks) | ||
| if len(ticks) < max_ticks: | ||
| if vmin != ticks[0]: | ||
| if abs(ticks[0] - vmin) >= tick_interval: | ||
| ticks = np.concatenate(([vmin], ticks)) | ||
| else: | ||
| ticks[0] = vmin | ||
| if vmax != ticks[-1]: | ||
| if len(ticks) < max_ticks: | ||
| if abs(vmax - ticks[-1]) >= tick_interval: | ||
| ticks = np.concatenate((ticks, [vmax])) | ||
| else: | ||
| ticks[-1] = vmax | ||
| else: | ||
| ticks[-1] = vmax | ||
| return np.deg2rad(ticks) | ||
| else: | ||
| return np.deg2rad(self.base()) | ||
| ticks = self.choices[-1] | ||
| ticks = ticks[(ticks >= vmin - tol) & (ticks <= vmax + tol)] | ||
| return ticks |
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The logic here is pretty convoluted, but seems to be correct. Can you add comments for each case?
r3kste
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Mar 8, 2026
| ticks[-1] = vmax | ||
| return np.deg2rad(ticks) | ||
| else: | ||
| return np.deg2rad(self.base()) |
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PR summary
added ChoiceLocator as per 20012
PR checklist