Matplotlib - 标记每个bin

Question

我目前正在使用Matplotlib来创建直方图:

import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as pyplot
...
fig = pyplot.figure()
ax = fig.add_subplot(1,1,1,)
n, bins, patches = ax.hist(measurements, bins=50, range=(graph_minimum, graph_maximum), histtype='bar')

#ax.set_xticklabels([n], rotation='vertical')

for patch in patches:
    patch.set_facecolor('r')

pyplot.title('Spam and Ham')
pyplot.xlabel('Time (in seconds)')
pyplot.ylabel('Bits of Ham')
pyplot.savefig(output_filename)

我想让x轴标签更有意义.

首先,这里的x轴刻度似乎限于五个刻度.无论我做什么,我似乎无法改变这一点 - 即使我添加更多xticklabels,它只使用前五个.我不确定Matplotlib如何计算这个,但我认为它是从范围/数据中自动计算的？

有没有什么办法可以提高x-tick标签的分辨率 - 甚至可以提高每个条形码/ bin 的分辨率？

(理想情况下,我也希望以微秒/毫秒重新格式化秒数,但这是另一天的问题).

其次,我想要标记每个单独的条形图 - 包含该条形图中的实际数字,以及所有条形图总数的百分比.

最终输出可能如下所示:

Matplotlib有可能吗？

干杯,维克多

Answer 1

当然!要设置滴答,只需,嗯...设置滴答(请参阅matplotlib.pyplot.xticks或ax.set_xticks).(另外,您不需要手动设置补丁的面部颜色.您只需传入关键字参数.)

对于其余部分,您需要使用标签做一些稍微更精美的事情,但matplotlib使它变得相当容易.

举个例子:

import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import FormatStrFormatter

data = np.random.randn(82)
fig, ax = plt.subplots()
counts, bins, patches = ax.hist(data, facecolor='yellow', edgecolor='gray')

# Set the ticks to be at the edges of the bins.
ax.set_xticks(bins)
# Set the xaxis's tick labels to be formatted with 1 decimal place...
ax.xaxis.set_major_formatter(FormatStrFormatter('%0.1f'))

# Change the colors of bars at the edges...
twentyfifth, seventyfifth = np.percentile(data, [25, 75])
for patch, rightside, leftside in zip(patches, bins[1:], bins[:-1]):
    if rightside < twentyfifth:
        patch.set_facecolor('green')
    elif leftside > seventyfifth:
        patch.set_facecolor('red')

# Label the raw counts and the percentages below the x-axis...
bin_centers = 0.5 * np.diff(bins) + bins[:-1]
for count, x in zip(counts, bin_centers):
    # Label the raw counts
    ax.annotate(str(count), xy=(x, 0), xycoords=('data', 'axes fraction'),
        xytext=(0, -18), textcoords='offset points', va='top', ha='center')

    # Label the percentages
    percent = '%0.0f%%' % (100 * float(count) / counts.sum())
    ax.annotate(percent, xy=(x, 0), xycoords=('data', 'axes fraction'),
        xytext=(0, -32), textcoords='offset points', va='top', ha='center')


# Give ourselves some more room at the bottom of the plot
plt.subplots_adjust(bottom=0.15)
plt.show()