简单画线
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import matplotlib.pyplot as plt x = [1, 2, 3] y_1 = [50, 60, 70] y_2 = [20, 30, 40] plt.plot(x, y_1, marker='x') plt.plot(x, y_2, marker='^') plt.xlim([0, len(x)+1]) plt.ylim([0, max(y_1+y_2) + 10]) plt.xlabel('x-axis label') plt.ylabel('y-axis label') plt.title('Simple line plot') plt.legend(['sample 1', 'sample2'], loc='upper left') plt.show() |
带误差棒的线
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import matplotlib.pyplot as plt x = [1, 2, 3] y_1 = [50, 60, 70] y_2 = [20, 30, 40] y_1_err = [4.3, 4.5, 2.0] y_2_err = [2.3, 6.9, 2.1] x_labels = ["x1", "x2", "x3"] plt.errorbar(x, y_1, yerr=y_1_err, fmt='-x') plt.errorbar(x, y_2, yerr=y_2_err, fmt='-^') plt.xticks(x, x_labels) plt.xlim([0, len(x)+1]) plt.ylim([0, max(y_1+y_2) + 10]) plt.xlabel('x-axis label') plt.ylabel('y-axis label') plt.title('Line plot with error bars') plt.legend(['sample 1', 'sample2'], loc='upper left') plt.show() |
大尺度缩放
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import matplotlib.pyplot as plt x = [1, 2, 3] y_1 = [0.5,7.0,60.0] y_2 = [0.3,6.0,30.0] x_labels = ["x1", "x2", "x3"] plt.plot(x, y_1, marker='x') plt.plot(x, y_2, marker='^') plt.xticks(x, x_labels) plt.xlim([0,4]) plt.xlabel('x-axis label') plt.ylabel('y-axis label') plt.yscale('log') plt.title('Line plot with x-axis labels and log-scale') plt.legend(['sample 1', 'sample2'], loc='upper left') plt.show() |
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import numpy as np from matplotlib import pyplot as plt import math def pdf(x, mu=0, sigma=1): """ Calculates the normal distribution's probability density function (PDF). """ term1 = 1.0 / ( math.sqrt(2*np.pi) * sigma ) term2 = np.exp( -0.5 * ( (x-mu)/sigma )**2 ) return term1 * term2 x = np.arange(0, 100, 0.05) pdf1 = pdf(x, mu=5, sigma=2.5**0.5) pdf2 = pdf(x, mu=10, sigma=6**0.5) plt.plot(x, pdf1) plt.plot(x, pdf2) plt.title('Probability Density Functions') plt.ylabel('p(x)') plt.xlabel('random variable x') plt.legend(['pdf1 ~ N(5,2.5)', 'pdf2 ~ N(10,6)'], loc='upper right') plt.ylim([0,0.5]) plt.xlim([0,20]) plt.show() |
累加和
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import numpy as np import matplotlib.pyplot as plt A = np.arange(1, 11) B = np.random.randn(10) # 10 rand. values from a std. norm. distr. C = B.cumsum() fig, (ax0, ax1) = plt.subplots(ncols=2, sharex=True, sharey=True, figsize=(10,5)) ## A) via plt.step() ax0.step(A, C, label='cumulative sum') # cumulative sum via numpy.cumsum() ax0.scatter(A, B, label='actual values') ax0.set_ylabel('Y value') ax0.legend(loc='upper right') ## B) via plt.plot() ax1.plot(A, C, label='cumulative sum') # cumulative sum via numpy.cumsum() ax1.scatter(A, B, label='actual values') ax1.legend(loc='upper right') fig.text(0.5, 0.04, 'sample number', ha='center', va='center') fig.text(0.5, 0.95, 'Cumulative sum of 10 samples from a random normal distribution', ha='center', va='center') plt.show() |
Absolute Count
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import numpy as np import matplotlib.pyplot as plt A = np.arange(1, 11) B = np.random.randn(10) # 10 rand. values from a std. norm. distr. plt.figure(figsize=(10,5)) plt.step(np.sort(B), A) plt.ylabel('sample count') plt.xlabel('x value') plt.title('Number of samples at a certain threshold') plt.show() |
Colormaps
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import numpy as np import matplotlib.pyplot as plt fig, (ax0, ax1) = plt.subplots(1,2, figsize=(14, 7)) samples = range(1,16) # Default Color Cycle for i in samples: ax0.plot([0, 10], [0, i], label=i, lw=3) # Colormap colormap = plt.cm.Paired plt.gca().set_color_cycle([colormap(i) for i in np.linspace(0, 0.9, len(samples))]) for i in samples: ax1.plot([0, 10], [0, i], label=i, lw=3) # Annotation ax0.set_title('Default color cycle') ax1.set_title('plt.cm.Paired colormap') ax0.legend(loc='upper left') ax1.legend(loc='upper left') plt.show() |
标识样式
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import numpy as np import matplotlib.pyplot as plt markers = [ '.', # point ',', # pixel 'o', # circle 'v', # triangle down '^', # triangle up '<', # triangle_left '>', # triangle_right '1', # tri_down '2', # tri_up '3', # tri_left '4', # tri_right '8', # octagon 's', # square 'p', # pentagon '*', # star 'h', # hexagon1 'H', # hexagon2 '+', # plus 'x', # x 'D', # diamond 'd', # thin_diamond '|', # vline ] plt.figure(figsize=(13, 10)) samples = range(len(markers)) for i in samples: plt.plot([i-1, i, i+1], [i, i, i], label=markers[i], marker=markers[i], markersize=10) # Annotation plt.title('Matplotlib Marker styles', fontsize=20) plt.ylim([-1, len(markers)+1]) plt.legend(loc='lower right') plt.show() |
线样式
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import numpy as np import matplotlib.pyplot as plt linestyles = ['-.', '--', 'None', '-', ':'] plt.figure(figsize=(8, 5)) samples = range(len(linestyles)) for i in samples: plt.plot([i-1, i, i+1], [i, i, i], label='"%s"' %linestyles[i], linestyle=linestyles[i], lw=4 ) # Annotation plt.title('Matplotlib line styles', fontsize=20) plt.ylim([-1, len(linestyles)+1]) plt.legend(loc='lower right') plt.show() |
