填充seaborn/matplotlib中两个正态分布之间的重叠区域 [英] Fill area of overlap between two normal distributions in seaborn / matplotlib

问题描述

我想填充两个正态分布之间的重叠区域.我已经有了 x 的最小值和最大值，但是我不知道如何设置 y 边界.

我查看了

导入reprexpy打印(reprexpy.SessionInfo())#>会话信息--------------------------------------------------------------------#>平台:Darwin-18.2.0-x86_64-i386-64bit(64位)#>的Python:3.6#>日期:2018-12-01#>包------------------------------------------------------------------------#>matplotlib == 2.1.2#>numpy == 1.15.4#>reprexpy == 0.1.1#>scipy==1.1.0#>seaborn==0.9.0

解决方案

虽然从 get_xydata 收集 pdf 数据很聪明，但您现在受制于 matplotlib 的渲染/分割算法.x1 和 x2 跨越不同的范围也使得比较 y1 和 y2 变得困难.

您可以通过自己拟合法线来避免这些问题，而不是让 sns.distplot 做到这一点.然后你可以更好地控制你的价值观寻找.

将 numpy 导入为 np导入 scipy.stats 作为统计信息导入matplotlib.pyplot作为plt规范 = stats.normpepe_calories = np.array([361，291，263，284，311，284，282，228，328，263，354，302，293，254, 297, 281, 307, 281, 262, 302, 244, 259, 273, 299, 278, 257,296、237、276、280、291、278、251、313、314、323、333、270、317，321, 307, 256, 301, 264, 221, 251, 307, 283, 300, 292, 344, 239,288、356、224、246、196、202、314、301、336、294、237、284、311，257, 255, 287, 243, 267, 253, 257, 320, 295, 295, 271, 322, 343,313, 293, 298, 272, 267, 257, 334, 276, 337, 325, 261, 344, 298,253, 302, 318, 289, 302, 291, 343, 310, 241])现代卡路里 = np.array([310, 315, 303, 360, 339, 416, 278, 326, 316, 314, 333, 317, 357,304、363、387、279、350、367、321、366、311、308、303、299、363，335, 357, 392, 321, 361, 285, 321, 290, 392, 341, 331, 338, 326,314、327、320、293、333、297、315、365、408、352、359、312、300，263, 358, 345, 360, 336, 378, 315, 354, 318, 300, 372, 305, 336,286、296、413、383、328、418、388、416、371、313、321、321、317，402、290、328、344、330、319、309、327、351、324、278、369、416，359，381，324，306，350，385，335，395，308])pepe_params = norm.fit(pepe_calories)Modern_params = norm.fit(modern_calories)xmin =分钟(pepe_calories.min()，modern_calories.min())xmax = max(pepe_calories.max()，modern_calories.max())x = np.linspace(xmin，xmax，100)pepe_pdf =规范(* pepe_params).pdf(x)modern_pdf =规范(* modern_params).pdf(x)y = np.minimum(modern_pdf, pepe_pdf)无花果，ax = plt.subplots()ax.plot(x, pepe_pdf, label="Pepe's", color='blue')ax.plot(x, modern_pdf, label="Modern", color='orange')ax.fill_between(x，y，color ='red'，alpha = 0.3)plt.legend()plt.show()

<小时>

如果说 sns.distplot (或某些其他绘图功能)绘制了一个您不想复制的绘图，那么您可以使用 get_xydata 这样:

将 numpy 导入为 np将 seaborn 作为 sns 导入导入 scipy.stats 作为统计信息导入matplotlib.pyplot作为pltpepe_calories = np.array([361，291，263，284，311，284，282，228，328，263，354，302，293，254, 297, 281, 307, 281, 262, 302, 244, 259, 273, 299, 278, 257,296, 237, 276, 280, 291, 278, 251, 313, 314, 323, 333, 270, 317,321, 307, 256, 301, 264, 221, 251, 307, 283, 300, 292, 344, 239,288、356、224、246、196、202、314、301、336、294、237、284、311，257, 255, 287, 243, 267, 253, 257, 320, 295, 295, 271, 322, 343,313, 293, 298, 272, 267, 257, 334, 276, 337, 325, 261, 344, 298,253、302、318、289、302、291、343、310、241])现代卡路里 = np.array([310, 315, 303, 360, 339, 416, 278, 326, 316, 314, 333, 317, 357,304、363、387、279、350、367、321、366、311、308、303、299、363，335、357、392、321、361、285、321、290、392、341、331、338、326，314, 327, 320, 293, 333, 297, 315, 365, 408, 352, 359, 312, 300,263, 358, 345, 360, 336, 378, 315, 354, 318, 300, 372, 305, 336,286、296、413、383、328、418、388、416、371、313、321、321、317，402, 290, 328, 344, 330, 319, 309, 327, 351, 324, 278, 369, 416,359, 381, 324, 306, 350, 385, 335, 395, 308])ax = sns.distplot(pepe_calories, fit_kws={"color":"blue"}, kde=False,fit=stats.norm, hist=None, label="Pepe's");ax = sns.distplot(modern_calories, fit_kws={"color":"orange"}, kde=False,fit = stats.norm，hist = None，label ="Modern");#从轴上获取两条线以生成阴影l1 = ax.lines [0]l2 = ax.lines [1]#从行中获取xy数据，以便我们可以进行阴影处理x1，y1 = l1.get_xydata().Tx2, y2 = l2.get_xydata().Txmin = max(x1.min()，x2.min())xmax = min(x1.max()，x2.max())x = np.linspace(xmin，xmax，100)y1 = np.interp(x，x1，y1)y2 = np.interp(x, x2, y2)y = np.minimum(y1, y2)ax.fill_between(x, y, color="red", alpha=0.3)plt.legend()plt.show()

I want to fill the area overlapping between two normal distributions. I've got the x min and max, but I can't figure out how to set the y boundaries.

I've looked at the plt documentation and some examples. I think this related question and this one come close, but no luck. Here's what I have so far.

import numpy as np
import seaborn as sns
import scipy.stats as stats
import matplotlib.pyplot as plt

pepe_calories = np.array([361, 291, 263, 284, 311, 284, 282, 228, 328, 263, 354, 302, 293,
       254, 297, 281, 307, 281, 262, 302, 244, 259, 273, 299, 278, 257,
       296, 237, 276, 280, 291, 278, 251, 313, 314, 323, 333, 270, 317,
       321, 307, 256, 301, 264, 221, 251, 307, 283, 300, 292, 344, 239,
       288, 356, 224, 246, 196, 202, 314, 301, 336, 294, 237, 284, 311,
       257, 255, 287, 243, 267, 253, 257, 320, 295, 295, 271, 322, 343,
       313, 293, 298, 272, 267, 257, 334, 276, 337, 325, 261, 344, 298,
       253, 302, 318, 289, 302, 291, 343, 310, 241])



modern_calories = np.array([310, 315, 303, 360, 339, 416, 278, 326, 316, 314, 333, 317, 357,
       304, 363, 387, 279, 350, 367, 321, 366, 311, 308, 303, 299, 363,
       335, 357, 392, 321, 361, 285, 321, 290, 392, 341, 331, 338, 326,
       314, 327, 320, 293, 333, 297, 315, 365, 408, 352, 359, 312, 300,
       263, 358, 345, 360, 336, 378, 315, 354, 318, 300, 372, 305, 336,
       286, 296, 413, 383, 328, 418, 388, 416, 371, 313, 321, 321, 317,
       402, 290, 328, 344, 330, 319, 309, 327, 351, 324, 278, 369, 416,
       359, 381, 324, 306, 350, 385, 335, 395, 308])

ax = sns.distplot(pepe_calories, fit_kws={"color":"blue"}, kde=False,
        fit=stats.norm, hist=None, label="Pepe's");
ax = sns.distplot(modern_calories, fit_kws={"color":"orange"}, kde=False,
        fit=stats.norm, hist=None, label="Modern");

# Get the two lines from the axes to generate shading
l1 = ax.lines[0]
l2 = ax.lines[1]

# Get the xy data from the lines so that we can shade
x1 = l1.get_xydata()[:,0]
y1 = l1.get_xydata()[:,1]
x2 = l2.get_xydata()[:,0]
y2 = l2.get_xydata()[:,1]

x2min = np.min(x2)
x1max = np.max(x1)

ax.fill_between(x1,y1, where = ((x1 > x2min) & (x1 < x1max)), color="red", alpha=0.3)
#> <matplotlib.collections.PolyCollection at 0x1a200510b8>

plt.legend()
#> <matplotlib.legend.Legend at 0x1a1ff2e390>
plt.show()

Any ideas?

Created on 2018-12-01 by the reprexpy package

import reprexpy
print(reprexpy.SessionInfo())
#> Session info --------------------------------------------------------------------
#> Platform: Darwin-18.2.0-x86_64-i386-64bit (64-bit)
#> Python: 3.6
#> Date: 2018-12-01
#> Packages ------------------------------------------------------------------------
#> matplotlib==2.1.2
#> numpy==1.15.4
#> reprexpy==0.1.1
#> scipy==1.1.0
#> seaborn==0.9.0

解决方案

While gathering the pdf data from get_xydata is clever, you are now at the mercy of matplotlib's rendering / segmentation algorithm. Having x1 and x2 span different ranges also makes comparing y1 and y2 difficult.

You can avoid these problems by fitting the normals yourself instead of letting sns.distplot do it. Then you have more control over the values you are looking for.

import numpy as np
import scipy.stats as stats
import matplotlib.pyplot as plt
norm = stats.norm

pepe_calories = np.array([361, 291, 263, 284, 311, 284, 282, 228, 328, 263, 354, 302, 293,
       254, 297, 281, 307, 281, 262, 302, 244, 259, 273, 299, 278, 257,
       296, 237, 276, 280, 291, 278, 251, 313, 314, 323, 333, 270, 317,
       321, 307, 256, 301, 264, 221, 251, 307, 283, 300, 292, 344, 239,
       288, 356, 224, 246, 196, 202, 314, 301, 336, 294, 237, 284, 311,
       257, 255, 287, 243, 267, 253, 257, 320, 295, 295, 271, 322, 343,
       313, 293, 298, 272, 267, 257, 334, 276, 337, 325, 261, 344, 298,
       253, 302, 318, 289, 302, 291, 343, 310, 241])



modern_calories = np.array([310, 315, 303, 360, 339, 416, 278, 326, 316, 314, 333, 317, 357,
       304, 363, 387, 279, 350, 367, 321, 366, 311, 308, 303, 299, 363,
       335, 357, 392, 321, 361, 285, 321, 290, 392, 341, 331, 338, 326,
       314, 327, 320, 293, 333, 297, 315, 365, 408, 352, 359, 312, 300,
       263, 358, 345, 360, 336, 378, 315, 354, 318, 300, 372, 305, 336,
       286, 296, 413, 383, 328, 418, 388, 416, 371, 313, 321, 321, 317,
       402, 290, 328, 344, 330, 319, 309, 327, 351, 324, 278, 369, 416,
       359, 381, 324, 306, 350, 385, 335, 395, 308])


pepe_params = norm.fit(pepe_calories)
modern_params = norm.fit(modern_calories)

xmin = min(pepe_calories.min(), modern_calories.min())
xmax = max(pepe_calories.max(), modern_calories.max())
x = np.linspace(xmin, xmax, 100)

pepe_pdf = norm(*pepe_params).pdf(x)
modern_pdf = norm(*modern_params).pdf(x)
y = np.minimum(modern_pdf, pepe_pdf)

fig, ax = plt.subplots()
ax.plot(x, pepe_pdf, label="Pepe's", color='blue')
ax.plot(x, modern_pdf, label="Modern", color='orange')
ax.fill_between(x, y, color='red', alpha=0.3)
plt.legend()
plt.show()

---

If, let's say, sns.distplot (or some other plotting function) made a plot that you did not want to have to reproduce, then you could use the data from get_xydata this way:

import numpy as np
import seaborn as sns
import scipy.stats as stats
import matplotlib.pyplot as plt

pepe_calories = np.array([361, 291, 263, 284, 311, 284, 282, 228, 328, 263, 354, 302, 293,
       254, 297, 281, 307, 281, 262, 302, 244, 259, 273, 299, 278, 257,
       296, 237, 276, 280, 291, 278, 251, 313, 314, 323, 333, 270, 317,
       321, 307, 256, 301, 264, 221, 251, 307, 283, 300, 292, 344, 239,
       288, 356, 224, 246, 196, 202, 314, 301, 336, 294, 237, 284, 311,
       257, 255, 287, 243, 267, 253, 257, 320, 295, 295, 271, 322, 343,
       313, 293, 298, 272, 267, 257, 334, 276, 337, 325, 261, 344, 298,
       253, 302, 318, 289, 302, 291, 343, 310, 241])



modern_calories = np.array([310, 315, 303, 360, 339, 416, 278, 326, 316, 314, 333, 317, 357,
       304, 363, 387, 279, 350, 367, 321, 366, 311, 308, 303, 299, 363,
       335, 357, 392, 321, 361, 285, 321, 290, 392, 341, 331, 338, 326,
       314, 327, 320, 293, 333, 297, 315, 365, 408, 352, 359, 312, 300,
       263, 358, 345, 360, 336, 378, 315, 354, 318, 300, 372, 305, 336,
       286, 296, 413, 383, 328, 418, 388, 416, 371, 313, 321, 321, 317,
       402, 290, 328, 344, 330, 319, 309, 327, 351, 324, 278, 369, 416,
       359, 381, 324, 306, 350, 385, 335, 395, 308])

ax = sns.distplot(pepe_calories, fit_kws={"color":"blue"}, kde=False,
        fit=stats.norm, hist=None, label="Pepe's");
ax = sns.distplot(modern_calories, fit_kws={"color":"orange"}, kde=False,
        fit=stats.norm, hist=None, label="Modern");

# Get the two lines from the axes to generate shading
l1 = ax.lines[0]
l2 = ax.lines[1]

# Get the xy data from the lines so that we can shade
x1, y1 = l1.get_xydata().T
x2, y2 = l2.get_xydata().T

xmin = max(x1.min(), x2.min())
xmax = min(x1.max(), x2.max())
x = np.linspace(xmin, xmax, 100)
y1 = np.interp(x, x1, y1)
y2 = np.interp(x, x2, y2)
y = np.minimum(y1, y2)
ax.fill_between(x, y, color="red", alpha=0.3)

plt.legend()
plt.show()

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