René's URL Explorer Experiment

{"@context":"https://schema.org","@type":"DiscussionForumPosting","headline":"colorbar shows no ticks for decreasing norms","articleBody":"### Bug report\r\n\r\n**Bug summary**\r\n\r\nWhen using a decreasing Norm, colorbars show no ticks.\r\n\r\n**Code for reproduction**\r\n\r\nThis example uses a PowerNorm-like norm which first applies the power, then does linear rescaling to (0, 1) (as opposed to matplotlib's PowerNorm which first linearly rescales to (0, 1) then applies the power) -- as argued in #10234 the behavior here is often more desirable, though that's not the point of this bug report.\r\n\r\nPNorms with a negative gamma are decreasing (the builtin PowerNorm wouldn't work here because the first linear rescaling step would put the minimum to zero, which doesn't like being raised to a negative power...).\r\n\r\n```python\r\nfrom matplotlib import pyplot as plt\r\nfrom matplotlib.colors import Normalize\r\nimport numpy as np\r\n\r\n\r\nclass PNorm(Normalize):\r\n    def __init__(self, vmin=None, vmax=None, clip=False, *, gamma):\r\n        super().__init__(vmin=vmin, vmax=vmax, clip=clip)\r\n        self.gamma = gamma\r\n\r\n    def __call__(self, value, clip=None):\r\n        if clip is None:\r\n            clip = self.clip\r\n        result, is_scalar = self.process_value(value)\r\n        self.autoscale_None(result)\r\n        vmin, vmax = self.vmin, self.vmax\r\n        gamma = self.gamma\r\n        if vmin \u003e vmax:\r\n            raise ValueError(\"minvalue must be less than or equal to maxvalue\")\r\n        elif vmin == vmax:\r\n            result.fill(0)\r\n        else:\r\n            if clip:\r\n                mask = np.ma.getmask(result)\r\n                result = np.ma.array(np.clip(result.filled(vmax), vmin, vmax),\r\n                                     mask=mask)\r\n            resdat = result.data\r\n            resdat = resdat ** gamma\r\n            resdat -= min(vmin ** gamma, vmax ** gamma)\r\n            resdat /= abs(vmax ** gamma - vmin ** gamma)\r\n            result = np.ma.array(resdat, mask=result.mask, copy=False)\r\n        if is_scalar:\r\n            result = result[0]\r\n        return result\r\n\r\n    def inverse(self, value):\r\n        if not self.scaled():\r\n            raise ValueError(\"Not invertible until scaled\")\r\n        if np.iterable(value):\r\n            return np.vectorize(self.inverse)(value)\r\n        else:\r\n            vmin, vmax = self.vmin, self.vmax\r\n            gamma = self.gamma\r\n            return (value * abs(vmax ** gamma - vmin ** gamma)\r\n                    + min(vmin ** gamma, vmax ** gamma)) ** (1 / gamma)\r\n\r\n\r\nfig, axs = plt.subplots(1, 2)\r\nim = axs[0].imshow(np.arange(1., 101.).reshape((10, 10)), norm=PNorm(gamma=2))\r\nfig.colorbar(im, ax=axs[0])\r\nim = axs[1].imshow(np.arange(1., 101.).reshape((10, 10)), norm=PNorm(gamma=-2))\r\nfig.colorbar(im, ax=axs[1])\r\n\r\nplt.show()\r\n```\r\n\r\n(as a side note, creating new norms is a bit ridiculously verbose)\r\n\r\n**Actual outcome**\r\n\r\nleft is gamma=2, right is gamma=-2.\r\n\r\n![test](https://user-images.githubusercontent.com/1322974/47671876-ddfef200-dbb0-11e8-996f-c8957c720419.png)\r\n\r\n**Expected outcome**\r\n\r\nSome ticks on the right (gamma=-2) colorbar.\r\n\r\n**Matplotlib version**\r\n\u003c!--Please specify your platform and versions of the relevant libraries you are using:--\u003e\r\n  * Operating system: Arch Linux\r\n  * Matplotlib version: 3.0.1\r\n  * Matplotlib backend (`print(matplotlib.get_backend())`): any\r\n  * Python version: 3.7","author":{"url":"https://github.com/anntzer","@type":"Person","name":"anntzer"},"datePublished":"2018-10-29T18:31:04.000Z","interactionStatistic":{"@type":"InteractionCounter","interactionType":"https://schema.org/CommentAction","userInteractionCount":11},"url":"https://github.com/12665/matplotlib/issues/12665"}