Closed
Description
Bug report
Bug summary
Histogram missing in Matplotlib 2.1.0. See the following figures, one is from 2.1.0 and the other is from 2.0.2.
Code for reproduction
This code is exactly the same code we are using except the data is changed to random one. I suppose it will be minimal enough?
#!/usr/bin/env python3
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.ticker as ticker
import collections
def plot_figure(distribution):
percentile = [95, 99]
percentile_color = 'yg'
mean_color = 'c'
bins = 100
legend_handle = []
legend_title = []
fig, (ax0, ax1) = plt.subplots(2, sharex=True)
fig.subplots_adjust(hspace=0)
mu, sigma = 100, 15
xs_downstream = mu + sigma*np.random.randn(10000)
xs_upstream = mu + sigma*np.random.randn(10000)
xmin = min(np.min(xs_downstream), np.min(xs_upstream))
xmax = max(np.max(xs_downstream), np.max(xs_upstream))
hist0 = ax0.hist(xs_downstream, bins=bins, range=(xmin, xmax), color='red', label='{} workload with Proxy'.format(distribution.capitalize()))
handles, labels = ax0.get_legend_handles_labels()
line = ax0.axvline(x=np.mean(xs_downstream), color=mean_color, linewidth=1)
legend_title.append('Mean')
legend_handle.append(line)
for percent, color in zip(percentile, percentile_color):
line = ax0.axvline(x=xs_downstream[int(percent / 100 * len(xs_downstream))], color=color, linewidth=1)
legend_title.append('{}th percentile'.format(percent))
legend_handle.append(line)
ax0.set_yscale('log')
legend_title += labels
legend_handle += handles
hist1 = ax1.hist(xs_upstream, bins=bins, range=(xmin, xmax), color='blue', label='{} workload without Proxy'.format(distribution.capitalize()))
handles, labels = ax1.get_legend_handles_labels()
legend_title += labels
legend_handle += handles
ax1.axvline(x=np.mean(xs_upstream), color=mean_color, linewidth=1)
for percent, color in zip(percentile, percentile_color):
ax1.axvline(x=xs_upstream[int(percent / 100 * len(xs_upstream))], color=color, linewidth=1)
ax1.set_yscale('log')
ax0.legend(legend_handle, legend_title)
ax1.set_xlabel('latency (ms)')
fig.savefig('latency-{}.pdf'.format(distribution))
def main():
plot_figure('zipf')
plot_figure('uniform')
if __name__ == '__main__':
main()Actual outcome
Expected outcome
Works well in 2.0.2.
Matplotlib version
- Operating system: Both macOS High Sierra and Ubuntu 17.10
- Matplotlib version: 2.1.0
- Matplotlib backend (
print(matplotlib.get_backend())):MacOSX - Python version:
3.6.3 - Jupyter version (if applicable):
- Other libraries:
Installed using pip3
Thanks!