Matplotlib-标记每个垃圾箱

我目前正在使用 Matplotlib 创建一个直方图:

enter image description here

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 轴的标签更有意义一点。

首先，这里的 x 轴滴答声似乎被限制在5次。无论我做什么，似乎都无法改变这一点——即使我添加更多 xticlabel，它也只使用前5个。我不确定 Matplotlib 是如何计算的，但我假设它是从范围/数据自动计算的？

有没有什么方法可以提高 x-tick 标签的分辨率 ——甚至可以达到每个条/箱一个的分辨率？

(理想情况下，我也希望秒被重新格式化为微秒/毫秒，但这是另一天的问题)。

其次，我希望 每个单独的酒吧标签-与实际数字在该垃圾箱，以及所有垃圾箱的百分比总数。

最终的输出可能是这样的:

enter image description here

Matplotlib 有这种可能吗？

干杯, 维克多

139160 次浏览
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最佳答案

Sure! To set the ticks, just, well... Set the ticks (see matplotlib.pyplot.xticks or ax.set_xticks). (Also, you don't need to manually set the facecolor of the patches. You can just pass in a keyword argument.)

For the rest, you'll need to do some slightly more fancy things with the labeling, but matplotlib makes it fairly easy.

As an example:

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()

enter image description here

小开

To add SI prefixes to your axis labels you want to use QuantiPhy. In fact, in its documentation it has an example that shows how to do this exact thing: MatPlotLib Example.

I think you would add something like this to your code:

from matplotlib.ticker import FuncFormatter
from quantiphy import Quantity


time_fmtr = FuncFormatter(lambda v, p: Quantity(v, 's').render(prec=2))
ax.xaxis.set_major_formatter(time_fmtr)
小开

One thing I wanted to add to the plots in the histogram with "density = True" was the relative frequency values for each bin, search but I couldn't find a function that would do that. A solution I made follows as image:

SAMPLE PLOT IMAGE

The function:

def label_densityHist(ax, n, bins, x=4, y=0.01, r=2, **kwargs):
"""
Add labels,relative value of bin, to each bin in a density histogram .
:param ax: Object axe of matplotlib
The axis to plot.
:param n: list, array of int, float
The values of the histogram bins.
:param bins: list, array of int, float
The edges of the bins.
:param x: int, float
Related the x position of the bin labels. The higher, the lower the value on the x-axis.
Default: 4
:param y: int, float
Related the y position of the bin labels. The higher, the greater the value on the y-axis.
Default: 0.01
:param r: int
Number of decimal places.
Default: 2
:param **kwargs: Text properties in matplotlib
:return: None




Example


import matplotlib.pyplot as plt
import numpy as np


dados = np.random.randn(100)


axe = plt.gca()
n, bins, _ = axe.hist(x=dados, edgecolor='black')
label_densityHist(axe,n, bins)
plt.show()


Example:
import matplotlib.pyplot as plt
import numpy as np




dados = np.random.randn(100)


axe = plt.gca()
n, bins, _ = axe.hist(x=dados, edgecolor='black')
label_densityHist(axe,n, bins, x=6, fontsize='large')
plt.show()




Reference:
[1]https://matplotlib.org/3.1.1/api/text_api.html#matplotlib.text.Text


"""


k = []
# calculate the relative frequency of each bin
for i in range(0,len(n)):
k.append((bins[i+1]-bins[i])*n[i])


# rounded
k = around(k,r); #print(k)


# plot the label/text to each bin
for i in range(0, len(n)):
x_pos = (bins[i + 1] - bins[i]) / x + bins[i]
y_pos = n[i] + (n[i] * y)
label = str(k[i]) # relative frequency of each bin
ax.text(x_pos, y_pos, label, kwargs)

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