matplotlib—-1

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Matplotlib

import matplotlib

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import matplotlib.pyplot as plt
%matplotlib inline

style

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plt.figure() # 创建图形
# 创建两个子图中的第一个，设置坐标轴
plt.subplot(2, 1, 1) # (行、列、子图编号) 
plt.plot(x, np.sin(x)) 
# 创建两个子图中的第二个，设置坐标轴
plt.subplot(2, 1, 2) 
plt.plot(x, np.cos(x));

dn2IFP.png

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%matplotlib inline 
import matplotlib.pyplot as plt 
plt.style.use('seaborn-whitegrid') 
import numpy as np

fig = plt.figure() 
ax = plt.axes()

dnW3gU.png

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fig = plt.figure() 
ax = plt.axes() 
x = np.linspace(0, 10, 1000) 
ax.plot(x, np.sin(x))

#the same:
plt.plot(x, np.sin(x));

dnhEwj.png

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plt.plot(x, np.sin(x)) 
plt.plot(x, np.cos(x));

dn4tbQ.png

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plt.plot(x, np.sin(x - 0), color='blue') # 标准颜色名称
plt.plot(x, np.sin(x - 1), color='g') # 缩写颜色代码（rgbcmyk）
plt.plot(x, np.sin(x - 2), color='0.75') # 范围在0~1的灰度值
plt.plot(x, np.sin(x - 3), color='#FFDD44') # 十六进制（RRGGBB，00~FF）
plt.plot(x, np.sin(x - 4), color=(1.0,0.2,0.3)) # RGB元组，范围在0~1 
plt.plot(x, np.sin(x - 5), color='chartreuse'); # HTML颜色名称

styles of curve

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plt.plot(x, x + 0, linestyle='solid') 
plt.plot(x, x + 1, linestyle='dashed') 
plt.plot(x, x + 2, linestyle='dashdot') 
plt.plot(x, x + 3, linestyle='dotted'); 
# 你可以用下面的简写形式
plt.plot(x, x + 4, linestyle='-') # 实线
plt.plot(x, x + 5, linestyle='--') # 虚线
plt.plot(x, x + 6, linestyle='-.') # 点划线
plt.plot(x, x + 7, linestyle=':'); # 实点线

plt.plot(x, x + 0, '-g') # 绿色实线
plt.plot(x, x + 1, '--c') # 青色虚线
plt.plot(x, x + 2, '-.k') # 黑色点划线
plt.plot(x, x + 3, ':r'); # 红色实点线

limit the size

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plt.plot(x, np.sin(x)) 
plt.xlim(-1, 11) 
plt.ylim(-1.5, 1.5);

cut the blanket area

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plt.axis('tight');

make the x-axis and y-aix to be 1 to 1

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plt.plot(x, np.sin(x)) 
plt.axis('equal')

label

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plt.plot(x, np.sin(x)) 
plt.title("A Sine Curve") 
plt.xlabel("x")   #x-axis label
plt.ylabel("sin(x)")

plt.plot(x, np.sin(x), '-g', label='sin(x)') 
plt.plot(x, np.cos(x), ':b', label='cos(x)') 
plt.axis('equal') 
plt.legend()

scatter diagram

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%matplotlib inline 
import matplotlib.pyplot as plt 
plt.style.use('seaborn-whitegrid') 
import numpy as np

x = np.linspace(0, 10, 30) 
y = np.sin(x) 
plt.plot(x, y, 'o', color='black') #"o" means to use the point

dG4t1g.png

different values can be represented by different symbol:

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rng = np.random.RandomState(0) 
for marker in ['o', '.', ',', 'x', '+', 'v', '^', '<', '>', 's', 'd']: 
plt.plot(rng.rand(5), rng.rand(5), marker, 
label="marker='{0}'".format(marker)) 
plt.legend(numpoints=1) 
plt.xlim(0, 1.8)

dG42jJ.png

other function

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plt.plot(x, y, '-p', color='gray',  #"-" represented the curve
markersize=15, linewidth=4, 
markerfacecolor='white', 
markeredgecolor='gray', 
markeredgewidth=2) 
plt.ylim(-1.2, 1.2)

dGobgU.png

another way:scatter

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plt.scatter(x, y, marker='o')

whent the random points have different size

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rng = np.random.RandomState(0) 
x = rng.randn(100) 
y = rng.randn(100) 
colors = rng.rand(100) 
sizes = 1000 * rng.rand(100) 
plt.scatter(x, y, c=colors, s=sizes, alpha=0.3, cmap='viridis') 
plt.colorbar() # 显示颜色条

dGLe91.png

plt.scatter will spend more on make the picture more beautiful and accurate,so it costs more resource

errrorbar

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x = np.linspace(0, 10, 50) 
dy = 0.8 
y = np.sin(x) + dy * np.random.randn(50) 
plt.errorbar(x, y, yerr=dy, fmt='.k');

dGLo59.png

different style:

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plt.errorbar(x, y, yerr=dy, fmt='o', color='black', ecolor='lightgray', elinewidth=3, capsize=0)

Continuous error

fill_between

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plt.plot(xdata, ydata, 'or') 
plt.plot(xfit, yfit, '-', color='gray') 
plt.fill_between(xfit, yfit - dyfit, yfit + dyfit, 
color='gray', alpha=0.2) 
plt.xlim(0, 10)

dGxs1A.png

histogram

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data = np.random.randn(1000) 
plt.hist(data)

dGzngA.png

other factors:

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plt.hist(data, bins=30, normed=True, alpha=0.5, #alpha descides the level of transparence
         histtype='stepfilled', #histtype='stepfilled' makes the transparence change as dofferet data
         color='steelblue', edgecolor='none')

multivariate Gaussian distribution

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mean = [0, 0] 
cov = [[1, 1], [1, 2]] 
x, y = np.random.multivariate_normal(mean, cov, 10000).T

plt.hist2d(x, y, bins=30, cmap='Blues') 
cb = plt.colorbar() 
cb.set_label('counts in bin')

dJCYd0.png

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plt.hexbin(x, y, gridsize=30, cmap='Blues')  #change the squres to hexagon
cb = plt.colorbar(label='count in bin')

dJCwz4.png

configuration

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import matplotlib.pyplot as plt 
plt.style.use('classic') 

%matplotlib inline 
import numpy as np 

x = np.linspace(0, 10, 1000) 
fig, ax = plt.subplots() 
ax.plot(x, np.sin(x), '-b', label='Sine') 
ax.plot(x, np.cos(x), '--r', label='Cosine') 
ax.axis('equal') 
leg = ax.legend()

dJC6dx.png

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ax.legend(frameon=False, loc='lower center', ncol=2) #frameon controls the transparence of the box of label,loc controls the location,ncol control the number of columns

ax.legend(fancybox=True, framealpha=1, shadow=True, borderpad=1) #fancybox:filleted corner