Python会在先前的绘图上继续覆盖历史记录，但不会将其保存为所需的绘图

问题描述

我要保存两个单独的图形，每个图形应一起包含2个图.

问题在于第一个图形可以，但是第二个图形不会在新图上被覆盖，而在前一个图形上会被覆盖，但是在保存的图形中，我只找到其中​​一个图:

这是第一个数字，我正确地得到了第一个数字:

import scipy.stats as s
import numpy as np
import os
import pandas as pd
import openpyxl as pyx
import matplotlib
matplotlib.rcParams["backend"] = "TkAgg"
#matplotlib.rcParams['backend'] = "Qt4Agg"
#matplotlib.rcParams['backend'] = "nbAgg"
import matplotlib.pyplot as plt
import math

data = [336256, 620316, 958846, 1007830, 1080401]
pdf = array([ 0.00449982,  0.0045293 ,  0.00455894,  0.02397463,
    0.02395788,  0.02394114])

fig, ax = plt.subplots();
fig = plt.figure(figsize=(40,30))

x = np.linspace(np.min(data), np.max(data), 100);
plt.plot(x, s.exponweib.pdf(x, *s.exponweib.fit(data, 1, 1, loc=0, scale=2)))
plt.hist(data, bins = np.linspace(data[0], data[-1], 100), normed=True, alpha= 1)
text1= ' Weibull'
plt.savefig(text1+  '.png' )

datar =np.asarray(data)
mu, sigma = datar.mean() , datar.std() # mean and standard deviation

normal_std = np.sqrt(np.log(1 + (sigma/mu)**2))
normal_mean = np.log(mu) - normal_std**2 / 2
hs = np.random.lognormal(normal_mean, normal_std, 1000)
print(hs.max())    # some finite number
print(hs.mean())   # about 136519
print(hs.std())    # about 50405

count, bins, ignored = plt.hist(hs, 100, normed=True)
x = np.linspace(min(bins), max(bins), 10000)
pdfT = [];
for el in range (len(x)):
    pdfTmp = (math.exp(-(np.log(x[el]) - normal_mean)**2 / (2 * normal_std**2)))
    pdfT += [pdfTmp]


pdf = np.asarray(pdfT)

这是第二组:

fig, ax = plt.subplots();
fig = plt.figure(figsize=(40,40))

plt.plot(x, pdf, linewidth=2, color='r')
plt.hist(data, bins = np.linspace(data[0], data[-1], 100), normed=True, alpha= 1)

text= ' Lognormal '
plt.savefig(text+ '.png' )

第一个图将直方图与曲线一起保存.相反，第二个只保存曲线

更新1:查看这个问题，我发现清除绘图历史记录不会使图形混淆，但是仍然是我的第二组绘图，我的意思是对数法线不保存在一起，我只能得到曲线，而不能得到直方图.

解决方案

这是发生的，因为您已将normed设置为True，这意味着直方图下的面积被归一化为1.表示直方图条的实际高度很小(在这种情况下很小，以至于它们不可见)

如果您使用

n, bins, _ = plt.hist(data, bins = np.linspace(data[0], data[-1], 100), normed=True, alpha= 1)

n将包含垃圾箱的y值，您可以自己确认.
还可以查看 plt.hist 的文档.. >

因此，如果将normed设置为False，则直方图将可见.

箱数

import numpy as np
import matplotlib.pyplot as plt

rand_data = np.random.uniform(0, 1.0, 100)

fig = plt.figure()

ax_1 = fig.add_subplot(211)
ax_1.hist(rand_data, bins=10)

ax_2 = fig.add_subplot(212)
ax_2.hist(rand_data, bins=100)

plt.show()

将为您提供两个相似的图(由于随机):

显示箱的数量如何改变直方图.直方图直观地显示了数据在一个维度上的分布，因此不确定输入和箱的数量是什么意思.

I am saving two separate figures, that each should contain 2 plots together.

The problem is that the first figure is ok, but the second one, does not gets overwritten on the new plot but on the previous one, but in the saved figure, I only find one of the plots :

This is the first figure , and I get the first figure correctly :

import scipy.stats as s
import numpy as np
import os
import pandas as pd
import openpyxl as pyx
import matplotlib
matplotlib.rcParams["backend"] = "TkAgg"
#matplotlib.rcParams['backend'] = "Qt4Agg"
#matplotlib.rcParams['backend'] = "nbAgg"
import matplotlib.pyplot as plt
import math

data = [336256, 620316, 958846, 1007830, 1080401]
pdf = array([ 0.00449982,  0.0045293 ,  0.00455894,  0.02397463,
    0.02395788,  0.02394114])

fig, ax = plt.subplots();
fig = plt.figure(figsize=(40,30))

x = np.linspace(np.min(data), np.max(data), 100);
plt.plot(x, s.exponweib.pdf(x, *s.exponweib.fit(data, 1, 1, loc=0, scale=2)))
plt.hist(data, bins = np.linspace(data[0], data[-1], 100), normed=True, alpha= 1)
text1= ' Weibull'
plt.savefig(text1+  '.png' )

datar =np.asarray(data)
mu, sigma = datar.mean() , datar.std() # mean and standard deviation

normal_std = np.sqrt(np.log(1 + (sigma/mu)**2))
normal_mean = np.log(mu) - normal_std**2 / 2
hs = np.random.lognormal(normal_mean, normal_std, 1000)
print(hs.max())    # some finite number
print(hs.mean())   # about 136519
print(hs.std())    # about 50405

count, bins, ignored = plt.hist(hs, 100, normed=True)
x = np.linspace(min(bins), max(bins), 10000)
pdfT = [];
for el in range (len(x)):
    pdfTmp = (math.exp(-(np.log(x[el]) - normal_mean)**2 / (2 * normal_std**2)))
    pdfT += [pdfTmp]


pdf = np.asarray(pdfT)

This is the second set :

fig, ax = plt.subplots();
fig = plt.figure(figsize=(40,40))

plt.plot(x, pdf, linewidth=2, color='r')
plt.hist(data, bins = np.linspace(data[0], data[-1], 100), normed=True, alpha= 1)

text= ' Lognormal '
plt.savefig(text+ '.png' )

The first plot saves the histogram together with curve. instead the second one only saves the curve

update 1 : looking at This Question , I found out that clearing the plot history will help the figures don't mixed up , but still my second set of plots, I mean the lognormal do not save together, I only get the curve and not the histogram.

解决方案

This is happening, because you have set normed = True, which means that area under the histogram is normalized to 1. And since your bins are very wide, this means that the actual height of the histogram bars are very small (in this case so small that they are not visible)

If you use

n, bins, _ = plt.hist(data, bins = np.linspace(data[0], data[-1], 100), normed=True, alpha= 1)

n will contain the y-value of your bins and you can confirm this yourself.
Also have a look at the documentation for plt.hist.

So if you set normed to False, the histogram will be visible.

Edit: number of bins

import numpy as np
import matplotlib.pyplot as plt

rand_data = np.random.uniform(0, 1.0, 100)

fig = plt.figure()

ax_1 = fig.add_subplot(211)
ax_1.hist(rand_data, bins=10)

ax_2 = fig.add_subplot(212)
ax_2.hist(rand_data, bins=100)

plt.show()

will give you two plots similar (since its random) to:

which shows how the number of bins changes the histogram.A histogram visualises the distribution of your data along one dimension, so not sure what you mean by number of inputs and bins.

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