René's URL Explorer Experiment

{"@context":"https://schema.org","@type":"DiscussionForumPosting","headline":"Histogram missing in Matplotlib 2.1.0","articleBody":"\u003c!--To help us understand and resolve your issue, please fill out the form to the best of your ability.--\u003e\r\n\u003c!--You can feel free to delete the sections that do not apply.--\u003e\r\n\r\n### Bug report\r\n\r\n**Bug summary**\r\n\r\nHistogram missing in Matplotlib 2.1.0. See the following figures, one is from 2.1.0 and the other is from 2.0.2.\r\n\r\n\r\n\r\n\u003c!--A short 1-2 sentences that succinctly describes the bug--\u003e\r\n\r\n**Code for reproduction**\r\n\r\n\u003c!--A minimum code snippet required to reproduce the bug, also minimizing the number of dependencies required--\u003e\r\n\r\nThis code is exactly the same code we are using except the data is changed to random one. I suppose it will be minimal enough?\r\n\r\n```python\r\n#!/usr/bin/env python3\r\n\r\nimport numpy as np\r\nimport matplotlib\r\nimport matplotlib.pyplot as plt\r\nimport matplotlib.ticker as ticker\r\nimport collections\r\n\r\ndef plot_figure(distribution):\r\n    percentile = [95, 99]\r\n    percentile_color = 'yg'\r\n    mean_color = 'c'\r\n    bins = 100\r\n\r\n    legend_handle = []\r\n    legend_title = []\r\n\r\n    fig, (ax0, ax1) = plt.subplots(2, sharex=True)\r\n    fig.subplots_adjust(hspace=0)\r\n    mu, sigma = 100, 15\r\n    xs_downstream = mu + sigma*np.random.randn(10000)\r\n    xs_upstream = mu + sigma*np.random.randn(10000)\r\n    xmin = min(np.min(xs_downstream), np.min(xs_upstream))\r\n    xmax = max(np.max(xs_downstream), np.max(xs_upstream))\r\n\r\n    hist0 = ax0.hist(xs_downstream, bins=bins, range=(xmin, xmax), color='red', label='{} workload with Proxy'.format(distribution.capitalize()))\r\n    handles, labels = ax0.get_legend_handles_labels()\r\n    line = ax0.axvline(x=np.mean(xs_downstream), color=mean_color, linewidth=1)\r\n    legend_title.append('Mean')\r\n    legend_handle.append(line)\r\n\r\n    for percent, color in zip(percentile, percentile_color):\r\n        line = ax0.axvline(x=xs_downstream[int(percent / 100 * len(xs_downstream))], color=color, linewidth=1)\r\n        legend_title.append('{}th percentile'.format(percent))\r\n        legend_handle.append(line)\r\n\r\n    ax0.set_yscale('log')\r\n    legend_title += labels\r\n    legend_handle += handles\r\n\r\n    hist1 = ax1.hist(xs_upstream, bins=bins, range=(xmin, xmax), color='blue', label='{} workload without Proxy'.format(distribution.capitalize()))\r\n    handles, labels = ax1.get_legend_handles_labels()\r\n    legend_title += labels\r\n    legend_handle += handles\r\n    ax1.axvline(x=np.mean(xs_upstream), color=mean_color, linewidth=1)\r\n\r\n    for percent, color in zip(percentile, percentile_color):\r\n        ax1.axvline(x=xs_upstream[int(percent / 100 * len(xs_upstream))], color=color, linewidth=1)\r\n\r\n    ax1.set_yscale('log')\r\n    ax0.legend(legend_handle, legend_title)\r\n    ax1.set_xlabel('latency (ms)')\r\n\r\n    fig.savefig('latency-{}.pdf'.format(distribution))\r\n\r\ndef main():\r\n    plot_figure('zipf')\r\n    plot_figure('uniform')\r\n\r\nif __name__ == '__main__':\r\n    main()\r\n\r\n```\r\n\r\n**Actual outcome**\r\n\r\n\u003c!--The output produced by the above code, which may be a screenshot, console output, etc.--\u003e\r\n\r\n\u003cimg width=\"890\" alt=\"qq20171030-095657 2x\" src=\"https://user-images.githubusercontent.com/2470081/32174680-e8f9d1ba-bd58-11e7-86a7-b6b13041aea9.png\"\u003e\r\n\r\n**Expected outcome**\r\n\r\n\u003c!--A description of the expected outcome from the code snippet--\u003e\r\n\u003c!--If this used to work in an earlier version of Matplotlib, please note the version it used to work on--\u003e\r\n\r\n\r\n\u003cimg width=\"890\" alt=\"qq20171030-095722 2x\" src=\"https://user-images.githubusercontent.com/2470081/32174688-ebe0f0b6-bd58-11e7-9835-24679b787c75.png\"\u003e\r\n\r\nWorks well in 2.0.2.\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: Both macOS High Sierra and Ubuntu 17.10\r\n  * Matplotlib version: 2.1.0\r\n  * Matplotlib backend (`print(matplotlib.get_backend())`): `MacOSX`\r\n  * Python version: `3.6.3`\r\n  * Jupyter version (if applicable):\r\n  * Other libraries: \r\n\r\nInstalled using `pip3`\r\n\r\n\u003c!--Please tell us how you installed matplotlib and python e.g., from source, pip, conda--\u003e\r\n\u003c!--If you installed from conda, please specify which channel you used if not the default--\u003e\r\n\r\nThanks!\r\n","author":{"url":"https://github.com/abcdabcd987","@type":"Person","name":"abcdabcd987"},"datePublished":"2017-10-30T14:03:26.000Z","interactionStatistic":{"@type":"InteractionCounter","interactionType":"https://schema.org/CommentAction","userInteractionCount":2},"url":"https://github.com/9628/matplotlib/issues/9628"}