René's URL Explorer Experiment

{"@context":"https://schema.org","@type":"DiscussionForumPosting","headline":"[Wishlist/candidate for gallery?] Diverging colormap taking into account asymmetry of the data about vcenter","articleBody":"I have been using a different approach to achieve what #12419 tries to address (`DivergingNorm`).\r\nThe limitation of using something like `DivergingNorm`  is that it doesn't allow for using color range proportional to the extent of the vmax, vmin w.r.t. `vcenter` (please see the example below). I have been using the following hackish approach to get over this limitation. This approach also allows for using colorbar extensions with the ability to specify different colors for `data \u003e vmax` and/or `data \u003c vmin` [based on my understanding, this won't be possible if `DivergingNorm` is used]. \r\n\r\nWould this be a useful addition to the gallery? Does it look like a functionality that I should think of adding to Matplotlib? (Also, please let me know if there is an easier way of achieving the same result).\r\n\r\n```python\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport matplotlib.colors as mcolors\r\nimport copy\r\n\r\ndef div_proportional_colormap(cmap='coolwarm', vmin=-1., vmax=1.,\r\n                              vcenter=0., cmap_name='my_cmap'):\r\n    \"\"\" Given a diverging colormap, this returns a diverging\r\n        colormap with color ranges on either side proportional to\r\n        the extent of vmax, vmin w.r.t. vcenter i.e. vmax-vcenter, vcenter-vmin\r\n    \"\"\"\r\n    if isinstance(cmap, str):\r\n\t    cmap = cm.get_cmap(cmap)\r\n\r\n    max_delta_v= max(vcenter-vmin, vmax-vcenter)\r\n    cmap_min = 0.5-0.5*(vcenter-vmin)/max_delta_v\r\n    cmap_max = 0.5+0.5*(vmax-vcenter)/max_delta_v\r\n    my_colors = cmap(np.linspace(cmap_min, cmap_max, 512))\r\n    my_cmap = mcolors.LinearSegmentedColormap.from_list(cmap_name, my_colors)\r\n    return my_cmap\r\n\r\nx = np.linspace(-2, 7)\r\ny = np.linspace(-1*np.pi, np.pi)\r\nX, Y = np.meshgrid(x, y)\r\nZ = x * np.sin(Y)**2\r\n\r\nfig, (ax1, ax2) = plt.subplots(ncols=2)\r\nmy_cmap1 = div_proportional_colormap(cmap=plt.cm.coolwarm,\r\n                                     vmin=-2., vmax=6., vcenter=0.,\r\n                                     cmap_name='my_cmap1')\r\nmy_cmap2 = copy.copy(my_cmap1)\r\nmy_cmap2.set_over('orange')\r\n\r\nimg1 = ax1.pcolormesh(Z, cmap=my_cmap1, vmin=-2, vmax=6)\r\ncbar1 = fig.colorbar(img1, ax=ax1)\r\n\r\nimg2 = ax2.pcolormesh(Z, cmap=my_cmap2, vmin=-2, vmax=6)\r\ncbar2 = fig.colorbar(img2, ax=ax2, extend='max')\r\nplt.show()\r\n```\r\n![figure_1](https://user-images.githubusercontent.com/15175620/46628759-b31f0200-cb0c-11e8-87b0-af7b6ba85f52.png)\r\n","author":{"url":"https://github.com/pharshalp","@type":"Person","name":"pharshalp"},"datePublished":"2018-10-08T19:12:36.000Z","interactionStatistic":{"@type":"InteractionCounter","interactionType":"https://schema.org/CommentAction","userInteractionCount":5},"url":"https://github.com/12449/matplotlib/issues/12449"}