python面积图之曲线图的填充 python面积图之曲线图的填充

基础用法
案例
复杂的fille_between(案例来源官网)
where和interpolate

where
interpolate
step

用法：

Axes.fill_between(x, y1, y2=0, where=None, interpolate=False, step=None, *, data=https://www.it610.com/article/None, **kwargs)

参数说明：

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基础用法

import matplotlib.pyplot as pltimport numpy as npx = [1, 2, 3, 4, 5]y1 = [1, 4, 9, 16, 25]y2 = [9, 9, 9, 9, 9]fig, (ax1, ax2) = plt.subplots(1,2)ax1.fill_between(x, y1, alpha=.5, linewidth=0)ax1.set_title('填充x,y1之间')ax2.fill_between(x, y2, alpha=.5, linewidth=1)ax2.set_title('填充x,y2之间')plt.show()

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当然这样时没有多大意义的,只是想展示出一个比较明确的填充,类似于区域全部填充颜色

案例

import matplotlib.pyplot as pltimport numpy as npnp.random.seed(1)x = np.linspace(0, 8, 16)y1 = 3 + 4*x/8 + np.random.uniform(0.0, 0.5, len(x))y2 = 1 + 2*x/8 + np.random.uniform(0.0, 0.5, len(x))fig, ax = plt.subplots()ax.fill_between(x, y1, y2, alpha=.5, linewidth=0)ax.plot(x, (y1 + y2)/2, linewidth=2)ax.set(xlim=(0, 8), xticks=np.arange(1, 8),ylim=(0, 8), yticks=np.arange(1, 8))plt.show()

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复杂的fille_between(案例来源官网)

import numpy as npimport matplotlib.pyplot as pltNsteps, Nwalkers = 100, 250t = np.arange(Nsteps)# an (Nsteps x Nwalkers) array of random walk stepsS1 = 0.004 + 0.02*np.random.randn(Nsteps, Nwalkers)S2 = 0.002 + 0.01*np.random.randn(Nsteps, Nwalkers)# an (Nsteps x Nwalkers) array of random walker positionsX1 = S1.cumsum(axis=0)X2 = S2.cumsum(axis=0)# Nsteps length arrays empirical means and standard deviations of both# populations over timemu1 = X1.mean(axis=1)sigma1 = X1.std(axis=1)mu2 = X2.mean(axis=1)sigma2 = X2.std(axis=1)# plot it!fig, ax = plt.subplots(1)ax.plot(t, mu1, lw=2, label='mean population 1')ax.plot(t, mu2, lw=2, label='mean population 2')ax.fill_between(t, mu1+sigma1, mu1-sigma1, facecolor='C0', alpha=0.4)ax.fill_between(t, mu2+sigma2, mu2-sigma2, facecolor='C1', alpha=0.4)ax.set_title(r'random walkers empirical $\mu$ and $\pm \sigma$ interval')ax.legend(loc='upper left')ax.set_xlabel('num steps')ax.set_ylabel('position')ax.grid()

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where和interpolate
where
定义从何处排除要填充的某些水平区域。填充区域由坐标x[其中]定义。更准确地说，如果其中[i]和其中[i+1]，则在x[i]和x[i+1]之间填充。请注意，此定义意味着where中两个假值之间的孤立真值不会导致填充。由于相邻的假值，真实位置的两侧仍保持未填充状态。

import numpy as npimport matplotlib.pyplot as pltfig, ax = plt.subplots()x = np.arange(0, 4 * np.pi, 0.01)y = np.sin(x)ax.plot(x, y, color='black')ax.fill_between(x, y, 0, where=(x>4)&(x<5),color='cyan', alpha=0.5)plt.show()

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interpolate
在语义上，where通常用于y1>y2或类似的词。默认情况下，定义填充区域的多边形节点将仅放置在x阵列中的位置。这样的多边形无法描述上述靠近交点的语义。包含交叉点的x截面仅被剪裁。
将“插值”设置为True将计算实际交点，并将填充区域延伸到此点。

import numpy as npimport matplotlib.pyplot as pltx = np.array([0, 1, 2, 3])y1 = np.array([0.8, 0.8, 0.2, 0.2])y2 = np.array([0, 0, 1, 1])fig, (ax1, ax2) = plt.subplots(2, 1, sharex=True)ax1.set_title('interpolation=False')ax1.plot(x, y1, 'o--')ax1.plot(x, y2, 'o--')ax1.fill_between(x, y1, y2, where=(y1 > y2), color='C0', alpha=0.3)ax1.fill_between(x, y1, y2, where=(y1 < y2), color='C1', alpha=0.3)ax2.set_title('interpolation=True')ax2.plot(x, y1, 'o--')ax2.plot(x, y2, 'o--')ax2.fill_between(x, y1, y2, where=(y1 > y2), color='C0', alpha=0.3,interpolate=True)ax2.fill_between(x, y1, y2, where=(y1 <= y2), color='C1', alpha=0.3,interpolate=True)fig.tight_layout()

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step
包含参数为三个{‘pre’,‘post’,‘mid’}
如果填充应为阶跃函数，即x之间的常数，则定义阶跃。该值确定阶跃发生的位置：

“pre”：y值从每个x位置持续向左，即间隔（x[i-1]，x[i]]的值为y[i]。
“post”：y值从每个x位置持续向右，即区间[x[i]，x[i+1]）的值为y[i]。
“mid”：步数出现在x位置的中间。

import numpy as npimport matplotlib.pyplot as plta = np.linspace(0,2*3.14,50) b = np.sin(a) plt.figsize=((12,6))plt.subplot(131)plt.fill_between(a, b, 0, where = (a > 2) & (a < 5), color = 'green', step='pre') plt.plot(a,b)plt.title('step=pre')plt.subplot(132)plt.fill_between(a, b, 0, where = (a > 2) & (a < 5), color = 'cyan', step='post') plt.plot(a,b)plt.title('step=post')plt.subplot(133)plt.fill_between(a, b, 0, where = (a > 2) & (a < 5), color = 'red', step='mid') plt.plot(a,b)plt.title('step=mid')plt.show()

【python面积图之曲线图的填充】