在直方图上方绘制密度函数 [英] Plot a density function above a histogram

问题描述

在Python中，我已经估算了分布模型密度的参数，我想在分布的直方图上方绘制密度函数.在 R 中它类似于使用选项 prop=TRUE.

将 numpy 导入为 np将matplotlib.mlab导入为mlab导入matplotlib.pyplot作为plt# 列表数据"的初始化# 估计参数，在我的例子中，是正态分布的均值和方差plt.hist(data，bins ="auto")#data是数据列表# 这里我想在直方图上方绘制密度plt.show()

我想最棘手的部分是使其变得合适.

我根据第一个答案尝试了这个:

  mean = np.mean(logdata)var = np.var(logdata)std = np.sqrt(var) # 标准差，被 numpy 用作方差的替代plt.hist(logdata，bins ="auto"，alpha = 0.5，label =donnéesempiriques")x = np.linspace(min(logdata), max(logdata), 100)plt.plot(x，mlab.normpdf(x，平均值，标准))plt.xlabel("log(tailed des fichiers)")plt.ylabel("nombre de fichiers")plt.legend(loc ='右上')plt.grid(真)plt.show()

但它不适合图表，这是它的外观:

** Edit 2 ** 与直方图函数中的选项 normed=True 一起使用.

解决方案

如果我理解正确，你就会得到一些数据的均值和标准差.您已经绘制了一个直方图，并希望在直方图上绘制正态分布线.可以使用 matplotlib.mlab.normpdf()生成此行，可以在

以上仅适用于 normed = True.如果这不是一个选项，我们可以定义我们自己的函数:

  def gauss_function(x，a，x0，sigma):返回一个 * np.exp(-(x - x0) ** 2/(2 * sigma ** 2))平均值 = 100西格玛= 5data = np.random.normal(mean,sigma,1000) # 生成假数据x = np.linspace(最小(数据)，最大(数据)，1000)测试= gauss_function(x，max(data)，平均值，sigma)plt.hist(data，bins ="auto")plt.plot(x, 测试)plt.show()

In Python, I have estimated the parameters for the density of a model of my distribution and I would like to plot the density function above the histogram of the distribution. In R it is similar to using the option prop=TRUE.

import numpy as np
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt

# initialization of the list "data"
# estimation of the parameter, in my case, mean and variance of a normal distribution

plt.hist(data, bins="auto") # data is the list of data
# here I would like to draw the density above the histogram
plt.show()

I guess the trickiest part is to make it fit.

Edit: I have tried this according to the first answer:

mean = np.mean(logdata)
var  = np.var(logdata)
std  = np.sqrt(var) # standard deviation, used by numpy as a replacement of the variance
plt.hist(logdata, bins="auto", alpha=0.5, label="données empiriques")
x = np.linspace(min(logdata), max(logdata), 100)
plt.plot(x, mlab.normpdf(x, mean, std))
plt.xlabel("log(taille des fichiers)")
plt.ylabel("nombre de fichiers")
plt.legend(loc='upper right')
plt.grid(True)
plt.show()

But it doesn't fit the graph, here is how it looks:

** Edit 2 ** Works with the option normed=True in the histogram function.

解决方案

If I understand you correctly you have the mean and standard deviation of some data. You have plotted a histogram of this and would like to plot the normal distribution line over the histogram. This line can be generated using matplotlib.mlab.normpdf(), the documentation can be found here.

import numpy as np
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt

mean = 100
sigma = 5

data = np.random.normal(mean,sigma,1000) # generate fake data
x = np.linspace(min(data), max(data), 100)

plt.hist(data, bins="auto",normed=True)
plt.plot(x, mlab.normpdf(x, mean, sigma))

plt.show()

Which gives the following figure:

Edit: The above only works with normed = True. If this is not an option, we can define our own function:

def gauss_function(x, a, x0, sigma):
    return a * np.exp(-(x - x0) ** 2 / (2 * sigma ** 2))

mean = 100
sigma = 5

data = np.random.normal(mean,sigma,1000) # generate fake data
x = np.linspace(min(data), max(data), 1000)

test = gauss_function(x, max(data), mean, sigma)

plt.hist(data, bins="auto")
plt.plot(x, test)

plt.show()

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