python学习笔记6-绘图

最新推荐文章于 2025-12-05 08:37:05 发布

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#python #学习

本文详细介绍NumPy和Matplotlib的使用方法，包括数组运算、矩阵操作、2D绘图、图表定制等功能，通过实例演示如何创建各种图表，如散点图、柱状图、高度图等。

绘图和可视化

参考:https://www.cnblogs.com/dudududu/p/9149762.html
https://www.runoob.com/numpy/numpy-tutorial.html
https://www.runoob.com/w3cnote/matplotlib-tutorial.html
https://www.cnblogs.com/zhizhan/p/5615947.html

这里学习numpy 和matplotlib一起用。

NumPy(Numerical Python) 是 Python 语言的一个扩展程序库，支持大量的维度数组与矩阵运算，此外也针对数组运算提供大量的数学函数库。
NumPy 的前身 Numeric 最早是由 Jim Hugunin 与其它协作者共同开发，2005 年，Travis Oliphant 在 Numeric 中结合了另一个同性质的程序库 Numarray 的特色，并加入了其它扩展而开发了 NumPy。NumPy 为开放源代码并且由许多协作者共同维护开发。
NumPy 是一个运行速度非常快的数学库，主要用于数组计算。

Matplotlib是一种2D的绘图库，它可以支持硬拷贝和跨系统的交互，它可以在Python脚本、IPython的交互环境下、Web应用程序中使用。该项目是由John Hunter于2002年启动的，其目的是为Python构建一个MATLAB式的绘图接口。如果结合使用一种GUI工具包（如IPython），Matplotlib还具有诸如缩放和平移等交互功能。它不仅支持各种操作系统上许多不同的GUI后端，而且还能将图片导出为各种常见的食量（vector）和光栅（raster）图：PDF、SVG、JPG、PNG、BMP、GIF等。

示例：

import matplotlib.pyplot as plt#约定俗成的写法plt
#也可以用pylab，功能大体上一样。
#首先定义两个函数（正弦&余弦）
import numpy as np

X=np.linspace(-np.pi,np.pi,256,endpoint=True)#-π to+π的256个值
C,S=np.cos(X),np.sin(X)
plt.plot(X,C)
plt.plot(X,S)
#在ipython的交互环境中需要这句话才能显示出来
plt.show()

简单说明：一般步骤为，定义x，y；定义图表名称；定义x轴，y轴名称；在图表中对x，y进行运算；最后展示。
（复杂一些的还有对图表本身的一些设置，如边界，注释，颜色等）

plt.axes([left, bottom, width, height])
在当前figure新增一个axes对象(相当于一个子图)，并设为当前活动。

axes不是数学上的座标轴，而是图形结构中的一个对象。所有绘图函数都是直接作用在当前axes对象上。

常用属性

#X 是一个 numpy 数组，包含了从 −π 到 +π 等间隔的 256 个值。
#C 和 S 则分别是这 256 个值对应的余弦和正弦函数值组成的 numpy 数组。
X = np.linspace(-np.pi, np.pi, 256,endpoint=True)
C,S = np.cos(X), np.sin(X)
x = np.arange(1, 11)
y = 2 * x + 5

x1=np.arange(1,11)#x1从1，10自增


plt.title("测试")#图表标题
plt.xlabel("x 轴")#x轴名称
plt.ylabel("y 轴")#y轴名称
plt.xlim(-4,40)#x轴上下限
plt.ylim(-5,50)#y轴上下限
#限制x轴坐标显示为-pi到pi
plt.xticks([-np.pi, -np.pi/2, 0, np.pi/2, np.pi],
       [r'$-\pi$', r'$-\pi/2$', r'$0$', r'$+\pi/2$', r'$+\pi$'])
#限制y轴坐标显示为-1到1
plt.yticks([-1, 0, +1],
       [r'$-1$', r'$0$', r'$+1$'])
plt.plot(x, y)#对x，y进行计算绘制
#图表中心设置成0，0
plt.subplot(1,1,1).spines['right'].set_color('none')
plt.subplot(1,1,1).spines['top'].set_color('none')
plt.subplot(1,1,1).xaxis.set_ticks_position('bottom')
plt.subplot(1,1,1).spines['bottom'].set_position(('data',0))
plt.subplot(1,1,1).yaxis.set_ticks_position('left')
plt.subplot(1,1,1).spines['left'].set_position(('data',0))
# 绘制余弦曲线，使用蓝色的、连续的、宽度为 1 （像素）的线条
plt.plot(X, C, color="blue", linewidth=1.0, linestyle="-")
plt.show()#显示，里面有2个图像

特别的，如果在绘图时既引用了pyplot又引用了pylab，则在同一个plot绘制前，对pyplot画布属性的调整也会影响pylab的画布（即2者共用一个画布）。

常见图表

普通图

from pylab import *
#普通图
n = 256
X = np.linspace(-np.pi,np.pi,n,endpoint=True)
Y = np.sin(2*X)

plt.axes([0.025,0.025,0.95,0.95])

plt.plot (X, Y+1, color='blue', alpha=1.00)
plt.fill_between(X, 1, Y+1, color='blue', alpha=.25)

plt.plot (X, Y-1, color='blue', alpha=1.00)
plt.fill_between(X, -1, Y-1, (Y-1) > -1, color='blue', alpha=.25)
plt.fill_between(X, -1, Y-1, (Y-1) < -1, color='red',  alpha=.25)

plt.xlim(-np.pi,np.pi), plt.xticks([])
plt.ylim(-2.5,2.5), plt.yticks([])
# savefig('../figures/plot_ex.png',dpi=48)
plt.show()

散点图

# -----------------------------------------------------------------------------
# Copyright (c) 2015, Nicolas P. Rougier. All Rights Reserved.
# Distributed under the (new) BSD License. See LICENSE.txt for more info.
# -----------------------------------------------------------------------------
import numpy as np
import matplotlib.pyplot as plt

n = 1024
X = np.random.normal(0,1,n)
Y = np.random.normal(0,1,n)
T = np.arctan2(Y,X)

plt.axes([0.025,0.025,0.95,0.95])
plt.scatter(X,Y, s=75, c=T, alpha=.5)

plt.xlim(-1.5,1.5), plt.xticks([])
plt.ylim(-1.5,1.5), plt.yticks([])
# savefig('../figures/scatter_ex.png',dpi=48)
plt.show()

柱状图

# -----------------------------------------------------------------------------
# Copyright (c) 2015, Nicolas P. Rougier. All Rights Reserved.
# Distributed under the (new) BSD License. See LICENSE.txt for more info.
# -----------------------------------------------------------------------------
import numpy as np
import matplotlib.pyplot as plt

n = 12
X = np.arange(n)
Y1 = (1-X/float(n)) * np.random.uniform(0.5,1.0,n)
Y2 = (1-X/float(n)) * np.random.uniform(0.5,1.0,n)

plt.axes([0.025,0.025,0.95,0.95])
plt.bar(X, +Y1, facecolor='#9999ff', edgecolor='white')
plt.bar(X, -Y2, facecolor='#ff9999', edgecolor='white')

for x,y in zip(X,Y1):
    plt.text(x+0.4, y+0.05, '%.2f' % y, ha='center', va= 'bottom')

for x,y in zip(X,Y2):
    plt.text(x+0.4, -y-0.05, '%.2f' % y, ha='center', va= 'top')

plt.xlim(-.5,n), plt.xticks([])
plt.ylim(-1.25,+1.25), plt.yticks([])

# savefig('../figures/bar_ex.png', dpi=48)
plt.show()

高度图

# -----------------------------------------------------------------------------
# Copyright (c) 2015, Nicolas P. Rougier. All Rights Reserved.
# Distributed under the (new) BSD License. See LICENSE.txt for more info.
# -----------------------------------------------------------------------------
import numpy as np
import matplotlib.pyplot as plt

def f(x,y):
    return (1-x/2+x**5+y**3)*np.exp(-x**2-y**2)

n = 256
x = np.linspace(-3,3,n)
y = np.linspace(-3,3,n)
X,Y = np.meshgrid(x,y)

plt.axes([0.025,0.025,0.95,0.95])

plt.contourf(X, Y, f(X,Y), 8, alpha=.75, cmap=plt.cm.hot)
C = plt.contour(X, Y, f(X,Y), 8, colors='black', linewidth=.5)
plt.clabel(C, inline=1, fontsize=10)

plt.xticks([]), plt.yticks([])
# savefig('../figures/contour_ex.png',dpi=48)
plt.show()

灰度图

# -----------------------------------------------------------------------------
# Copyright (c) 2015, Nicolas P. Rougier. All Rights Reserved.
# Distributed under the (new) BSD License. See LICENSE.txt for more info.
# -----------------------------------------------------------------------------
import numpy as np
import matplotlib.pyplot as plt

def f(x,y):
    return (1-x/2+x**5+y**3)*np.exp(-x**2-y**2)

n = 10
x = np.linspace(-3,3,3.5*n)
y = np.linspace(-3,3,3.0*n)
X,Y = np.meshgrid(x,y)
Z = f(X,Y)

plt.axes([0.025,0.025,0.95,0.95])
plt.imshow(Z,interpolation='bicubic', cmap='bone', origin='lower')
plt.colorbar(shrink=.92)

plt.xticks([]), plt.yticks([])
# savefig('../figures/imshow_ex.png', dpi=48)
plt.show()

饼状图

# -----------------------------------------------------------------------------
# Copyright (c) 2015, Nicolas P. Rougier. All Rights Reserved.
# Distributed under the (new) BSD License. See LICENSE.txt for more info.
# -----------------------------------------------------------------------------
import numpy as np
import matplotlib.pyplot as plt

n = 20
Z = np.ones(n)
Z[-1] *= 2

plt.axes([0.025, 0.025, 0.95, 0.95])

plt.pie(Z, explode=Z*.05, colors=['%f' % (i/float(n)) for i in range(n)],
        wedgeprops={"linewidth": 1, "edgecolor": "black"})
plt.gca().set_aspect('equal')
plt.xticks([]), plt.yticks([])

# savefig('../figures/pie_ex.png',dpi=48)
plt.show()

量场图

# -----------------------------------------------------------------------------
# Copyright (c) 2015, Nicolas P. Rougier. All Rights Reserved.
# Distributed under the (new) BSD License. See LICENSE.txt for more info.
# -----------------------------------------------------------------------------
import numpy as np
import matplotlib.pyplot as plt

n = 8
X,Y = np.mgrid[0:n,0:n]
T = np.arctan2(Y-n/2.0, X-n/2.0)
R = 10+np.sqrt((Y-n/2.0)**2+(X-n/2.0)**2)
U,V = R*np.cos(T), R*np.sin(T)

plt.axes([0.025,0.025,0.95,0.95])
plt.quiver(X,Y,U,V,R, alpha=.5)
plt.quiver(X,Y,U,V, edgecolor='k', facecolor='None', linewidth=.5)

plt.xlim(-1,n), plt.xticks([])
plt.ylim(-1,n), plt.yticks([])

# savefig('../figures/quiver_ex.png',dpi=48)
plt.show()

网格图

# -----------------------------------------------------------------------------
# Copyright (c) 2015, Nicolas P. Rougier. All Rights Reserved.
# Distributed under the (new) BSD License. See LICENSE.txt for more info.
# -----------------------------------------------------------------------------
import numpy as np
import matplotlib.pyplot as plt

ax = plt.axes([0.025,0.025,0.95,0.95])

ax.set_xlim(0,4)
ax.set_ylim(0,3)
ax.xaxis.set_major_locator(plt.MultipleLocator(1.0))
ax.xaxis.set_minor_locator(plt.MultipleLocator(0.1))
ax.yaxis.set_major_locator(plt.MultipleLocator(1.0))
ax.yaxis.set_minor_locator(plt.MultipleLocator(0.1))
ax.grid(which='major', axis='x', linewidth=0.75, linestyle='-', color='0.75')
ax.grid(which='minor', axis='x', linewidth=0.25, linestyle='-', color='0.75')
ax.grid(which='major', axis='y', linewidth=0.75, linestyle='-', color='0.75')
ax.grid(which='minor', axis='y', linewidth=0.25, linestyle='-', color='0.75')
ax.set_xticklabels([])
ax.set_yticklabels([])

# savefig('../figures/grid_ex.png',dpi=48)
plt.show()

多重网格

# -----------------------------------------------------------------------------
# Copyright (c) 2015, Nicolas P. Rougier. All Rights Reserved.
# Distributed under the (new) BSD License. See LICENSE.txt for more info.
# -----------------------------------------------------------------------------
import numpy as np
import matplotlib.pyplot as plt

fig = plt.figure()
fig.subplots_adjust(bottom=0.025, left=0.025, top = 0.975, right=0.975)

plt.subplot(2,1,1)
plt.xticks([]), plt.yticks([])

plt.subplot(2,3,4)
plt.xticks([]), plt.yticks([])

plt.subplot(2,3,5)
plt.xticks([]), plt.yticks([])

plt.subplot(2,3,6)
plt.xticks([]), plt.yticks([])

# plt.savefig('../figures/multiplot_ex.png',dpi=48)
plt.show()

极轴图

# -----------------------------------------------------------------------------
# Copyright (c) 2015, Nicolas P. Rougier. All Rights Reserved.
# Distributed under the (new) BSD License. See LICENSE.txt for more info.
# -----------------------------------------------------------------------------
import numpy as np
import matplotlib.pyplot as plt

ax = plt.axes([0.025,0.025,0.95,0.95], polar=True)

N = 20
theta = np.arange(0.0, 2*np.pi, 2*np.pi/N)
radii = 10*np.random.rand(N)
width = np.pi/4*np.random.rand(N)
bars = plt.bar(theta, radii, width=width, bottom=0.0)

for r,bar in zip(radii, bars):
    bar.set_facecolor( plt.cm.jet(r/10.))
    bar.set_alpha(0.5)

ax.set_xticklabels([])
ax.set_yticklabels([])
# savefig('../figures/polar_ex.png',dpi=48)
plt.show()

3d图

# -----------------------------------------------------------------------------
# Copyright (c) 2015, Nicolas P. Rougier. All Rights Reserved.
# Distributed under the (new) BSD License. See LICENSE.txt for more info.
# -----------------------------------------------------------------------------
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D

fig = plt.figure()
ax = Axes3D(fig)
X = np.arange(-4, 4, 0.25)
Y = np.arange(-4, 4, 0.25)
X, Y = np.meshgrid(X, Y)
R = np.sqrt(X**2 + Y**2)
Z = np.sin(R)

ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap=plt.cm.hot)
ax.contourf(X, Y, Z, zdir='z', offset=-2, cmap=plt.cm.hot)
ax.set_zlim(-2,2)

# savefig('../figures/plot3d_ex.png',dpi=48)
plt.show()

手稿图

import numpy as np
import matplotlib.pyplot as plt

eqs = []
eqs.append((r"$W^{3\beta}_{\delta_1 \rho_1 \sigma_2} = U^{3\beta}_{\delta_1 \rho_1} + \frac{1}{8 \pi 2} \int^{\alpha_2}_{\alpha_2} d \alpha^\prime_2 \left[\frac{ U^{2\beta}_{\delta_1 \rho_1} - \alpha^\prime_2U^{1\beta}_{\rho_1 \sigma_2} }{U^{0\beta}_{\rho_1 \sigma_2}}\right]$"))
eqs.append((r"$\frac{d\rho}{d t} + \rho \vec{v}\cdot\nabla\vec{v} = -\nabla p + \mu\nabla^2 \vec{v} + \rho \vec{g}$"))
eqs.append((r"$\int_{-\infty}^\infty e^{-x^2}dx=\sqrt{\pi}$"))
eqs.append((r"$E = mc^2 = \sqrt{{m_0}^2c^4 + p^2c^2}$"))
eqs.append((r"$F_G = G\frac{m_1m_2}{r^2}$"))


plt.axes([0.025,0.025,0.95,0.95])

for i in range(24):
    index = np.random.randint(0,len(eqs))
    eq = eqs[index]
    size = np.random.uniform(12,32)
    x,y = np.random.uniform(0,1,2)
    alpha = np.random.uniform(0.25,.75)
    plt.text(x, y, eq, ha='center', va='center', color="#11557c", alpha=alpha,transform=plt.gca().transAxes, fontsize=size,clip_on=True)

plt.xticks([]), plt.yticks([])
# savefig('../figures/text_ex.png',dpi=48)
plt.show()

子图示例
axes（）

import matplotlib.pyplot as plt
import numpy as np

# create some data to use for the plot
dt = 0.001
t = np.arange(0.0, 10.0, dt)
r = np.exp(-t[:1000]/0.05)               # impulse response
x = np.random.randn(len(t))
s = np.convolve(x, r)[:len(x)]*dt  # colored noise

# the main axes is subplot(111) by default
plt.plot(t, s)
plt.axis([0, 1, 1.1*np.amin(s), 2*np.amax(s)])
plt.xlabel('time (s)')
plt.ylabel('current (nA)')
plt.title('Gaussian colored noise')

# this is an inset axes over the main axes
a = plt.axes([.65, .6, .2, .2])
n, bins, patches = plt.hist(s, 400, density=1)
plt.title('Probability')
plt.xticks([])
plt.yticks([])

# this is another inset axes over the main axes
a = plt.axes([0.2, 0.6, .2, .2])
plt.plot(t[:len(r)], r)
plt.title('Impulse response')
plt.xlim(0, 0.2)
plt.xticks([])
plt.yticks([])

plt.show()