103 lines
3.5 KiB
Python
103 lines
3.5 KiB
Python
import matplotlib.pyplot as plt
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import numpy as np
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def create_data():
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# wikipedia:
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# Generally, males vary in total length from 250 to 390 cm and
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# weigh between 90 and 306 kg
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c = 6
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x = np.arange(2.2, 3.9, 0.05)
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y = c * x**3.0
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rng = np.random.RandomState(32281)
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noise = rng.randn(len(x))*50
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y += noise
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return x, y, c
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def plot_data(ax, x, y, c):
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ax.scatter(x, y, marker='o', color='b', s=40, zorder=10)
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xx = np.linspace(2.1, 3.9, 100)
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ax.plot(xx, c*xx**3.0, color='#CC0000', lw=2, zorder=5)
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for cc in [0.25*c, 0.5*c, 2.0*c, 4.0*c]:
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ax.plot(xx, cc*xx**3.0, color='#FF9900', lw=1.5, zorder=5)
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ax.spines["right"].set_visible(False)
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ax.spines["top"].set_visible(False)
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ax.yaxis.set_ticks_position('left')
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ax.xaxis.set_ticks_position('bottom')
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ax.tick_params(direction="out", width=1.25)
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ax.tick_params(direction="out", width=1.25)
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ax.set_xlabel('Size x / m')
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ax.set_ylabel('Weight y / kg')
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ax.set_xlim(2, 4)
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ax.set_ylim(0, 400)
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ax.set_xticks(np.arange(2.0, 4.1, 0.5))
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ax.set_yticks(np.arange(0, 401, 100))
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def plot_data_errors(ax, x, y, c):
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ax.spines["right"].set_visible(False)
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ax.spines["top"].set_visible(False)
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ax.yaxis.set_ticks_position('left')
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ax.xaxis.set_ticks_position('bottom')
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ax.tick_params(direction="out", width=1.25)
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ax.tick_params(direction="out", width=1.25)
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ax.set_xlabel('Size x / m')
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#ax.set_ylabel('Weight y / kg')
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ax.set_xlim(2, 4)
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ax.set_ylim(0, 400)
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ax.set_xticks(np.arange(2.0, 4.1, 0.5))
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ax.set_yticks(np.arange(0, 401, 100))
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ax.set_yticklabels([])
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ax.annotate('Error',
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xy=(x[28]+0.05, y[28]+60), xycoords='data',
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xytext=(3.4, 70), textcoords='data', ha='left',
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arrowprops=dict(arrowstyle="->", relpos=(0.9,1.0),
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connectionstyle="angle3,angleA=50,angleB=-30") )
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ax.scatter(x[:40], y[:40], color='b', s=10, zorder=0)
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inxs = [3, 10, 11, 17, 18, 21, 28, 30, 33]
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ax.scatter(x[inxs], y[inxs], color='b', s=40, zorder=10)
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xx = np.linspace(2.1, 3.9, 100)
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ax.plot(xx, c*xx**3.0, color='#CC0000', lw=2)
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for i in inxs :
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xx = [x[i], x[i]]
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yy = [c*x[i]**3.0, y[i]]
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ax.plot(xx, yy, color='#FF9900', lw=2, zorder=5)
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def plot_error_hist(ax, x, y, c):
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ax.spines["right"].set_visible(False)
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ax.spines["top"].set_visible(False)
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ax.yaxis.set_ticks_position('left')
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ax.xaxis.set_ticks_position('bottom')
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ax.tick_params(direction="out", width=1.25)
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ax.tick_params(direction="out", width=1.25)
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ax.set_xlabel('Squared error')
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ax.set_ylabel('Frequency')
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bins = np.arange(0.0, 1250.0, 100)
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ax.set_xlim(bins[0], bins[-1])
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#ax.set_ylim(0, 35)
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ax.set_xticks(np.arange(bins[0], bins[-1], 200))
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#ax.set_yticks(np.arange(0, 36, 10))
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errors = (y-(c*x**3.0))**2.0
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mls = np.mean(errors)
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ax.annotate('Mean\nsquared\nerror',
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xy=(mls, 0.5), xycoords='data',
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xytext=(800, 3), textcoords='data', ha='left',
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arrowprops=dict(arrowstyle="->", relpos=(0.0,0.2),
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connectionstyle="angle3,angleA=10,angleB=90") )
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ax.hist(errors, bins, color='#FF9900')
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if __name__ == "__main__":
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x, y, c = create_data()
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plt.xkcd()
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fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(7., 2.6))
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plot_data(ax1, x, y, c)
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plot_data_errors(ax2, x, y, c)
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#plot_error_hist(ax2, x, y, c)
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fig.set_facecolor("white")
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fig.tight_layout()
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fig.savefig("cubicerrors.pdf")
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plt.close()
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