198 lines
7.0 KiB
Python
198 lines
7.0 KiB
Python
import numpy as np
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import pylab as plt
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from matplotlib.patches import Circle, Rectangle, Polygon, Arrow, FancyArrow
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import book_plots
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def create_predict_update_chart(box_bg = '#CCCCCC',
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arrow1 = '#88CCFF',
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arrow2 = '#88FF88'):
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plt.figure(figsize=(4,4), facecolor='w')
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ax = plt.axes((0, 0, 1, 1),
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xticks=[], yticks=[], frameon=False)
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#ax.set_xlim(0, 10)
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#ax.set_ylim(0, 10)
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pc = Circle((4,5), 0.5, fc=box_bg)
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uc = Circle((6,5), 0.5, fc=box_bg)
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ax.add_patch (pc)
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ax.add_patch (uc)
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plt.text(4,5, "Predict\nStep",ha='center', va='center', fontsize=14)
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plt.text(6,5, "Update\nStep",ha='center', va='center', fontsize=14)
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#btm
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ax.annotate('',
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xy=(4.1, 4.5), xycoords='data',
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xytext=(6, 4.5), textcoords='data',
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size=20,
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arrowprops=dict(arrowstyle="simple",
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fc="0.6", ec="none",
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patchB=pc,
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patchA=uc,
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connectionstyle="arc3,rad=-0.5"))
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#top
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ax.annotate('',
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xy=(6, 5.5), xycoords='data',
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xytext=(4.1, 5.5), textcoords='data',
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size=20,
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arrowprops=dict(arrowstyle="simple",
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fc="0.6", ec="none",
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patchB=uc,
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patchA=pc,
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connectionstyle="arc3,rad=-0.5"))
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ax.annotate('Measurement ($\mathbf{z_k}$)',
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xy=(6.3, 5.4), xycoords='data',
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xytext=(6,6), textcoords='data',
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size=18,
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arrowprops=dict(arrowstyle="simple",
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fc="0.6", ec="none"))
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ax.annotate('',
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xy=(4.0, 3.5), xycoords='data',
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xytext=(4.0,4.5), textcoords='data',
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size=18,
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arrowprops=dict(arrowstyle="simple",
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fc="0.6", ec="none"))
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ax.annotate('Initial\nConditions ($\mathbf{x_0}$)',
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xy=(4.0, 5.5), xycoords='data',
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xytext=(2.5,6.5), textcoords='data',
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size=18,
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arrowprops=dict(arrowstyle="simple",
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fc="0.6", ec="none"))
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plt.text (4,3.4,'State Estimate ($\mathbf{\hat{x}_k}$)',
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ha='center', va='center', fontsize=18)
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plt.axis('equal')
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#plt.axis([0,8,0,8])
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plt.show()
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def plot_estimate_chart_1():
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ax = plt.axes()
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ax.annotate('', xy=[1,159], xytext=[0,158],
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arrowprops=dict(arrowstyle='->', ec='r',shrinkA=6, lw=3,shrinkB=5))
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plt.scatter ([0], [158], c='b')
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plt.scatter ([1], [159], c='r')
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plt.xlabel('day')
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plt.ylabel('weight (lbs)')
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plt.show()
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def plot_estimate_chart_2():
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ax = plt.axes()
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ax.annotate('', xy=[1,159], xytext=[0,158],
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arrowprops=dict(arrowstyle='->',
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ec='r', lw=3, shrinkA=6, shrinkB=5))
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plt.scatter ([0], [158.0], c='k',s=128)
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plt.scatter ([1], [164.2], c='b',s=128)
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plt.scatter ([1], [159], c='r', s=128)
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plt.text (1.0, 158.8, "prediction ($x_t)$", ha='center',va='top',fontsize=18,color='red')
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plt.text (1.0, 164.4, "measurement ($z$)",ha='center',va='bottom',fontsize=18,color='blue')
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plt.text (0, 157.8, "estimate ($\hat{x}_{t-1}$)", ha='center', va='top',fontsize=18)
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plt.xlabel('day')
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plt.ylabel('weight (lbs)')
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plt.show()
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def plot_estimate_chart_3():
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ax = plt.axes()
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ax.annotate('', xy=[1,159], xytext=[0,158],
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arrowprops=dict(arrowstyle='->',
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ec='r', lw=3, shrinkA=6, shrinkB=5))
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ax.annotate('', xy=[1,159], xytext=[1,164.2],
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arrowprops=dict(arrowstyle='-',
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ec='k', lw=1, shrinkA=8, shrinkB=8))
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est_y = ((164.2-158)*.8 + 158)
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plt.scatter ([0,1], [158.0,est_y], c='k',s=128)
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plt.scatter ([1], [164.2], c='b',s=128)
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plt.scatter ([1], [159], c='r', s=128)
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plt.text (1.0, 158.8, "prediction ($x_t)$", ha='center',va='top',fontsize=18,color='red')
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plt.text (1.0, 164.4, "measurement ($z$)",ha='center',va='bottom',fontsize=18,color='blue')
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plt.text (0, 157.8, "estimate ($\hat{x}_{t-1}$)", ha='center', va='top',fontsize=18)
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plt.text (0.95, est_y, "new estimate ($\hat{x}_{t}$)", ha='right', va='center',fontsize=18)
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plt.xlabel('day')
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plt.ylabel('weight (lbs)')
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plt.show()
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def plot_hypothesis():
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plt.errorbar([1, 2, 3], [170, 161, 169],
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xerr=0, yerr=10, fmt='bo', capthick=2, capsize=10)
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plt.plot([1, 3], [180, 160], color='g', ls='--')
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plt.plot([1, 3], [170, 170], color='g', ls='--')
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plt.plot([1, 3], [160, 175], color='g', ls='--')
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plt.plot([1, 2, 3], [180, 152, 179], color='g', ls='--')
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plt.xlim(0,4); plt.ylim(150, 185)
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plt.xlabel('day')
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plt.ylabel('lbs')
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plt.tight_layout()
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plt.show()
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def plot_hypothesis2():
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plt.errorbar(range(1, 11), [169, 170, 169,171, 170, 171, 169, 170, 169, 170],
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xerr=0, yerr=6, fmt='bo', capthick=2, capsize=10)
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plt.plot([1, 10], [169, 170.5], color='g', ls='--')
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plt.xlim(0, 11); plt.ylim(150, 185)
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plt.xlabel('day')
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plt.ylabel('lbs')
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plt.show()
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def plot_hypothesis3():
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weights = [158.0, 164.2, 160.3, 159.9, 162.1, 164.6,
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169.6, 167.4, 166.4, 171.0, 171.2, 172.6]
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plt.errorbar(range(1, 13), weights,
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xerr=0, yerr=6, fmt='o', capthick=2, capsize=10)
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plt.xlim(0, 13); plt.ylim(145, 185)
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plt.xlabel('day')
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plt.ylabel('weight (lbs)')
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plt.show()
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def plot_hypothesis4():
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weights = [158.0, 164.2, 160.3, 159.9, 162.1, 164.6,
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169.6, 167.4, 166.4, 171.0, 171.2, 172.6]
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ave = np.sum(weights) / len(weights)
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plt.errorbar(range(1,13), weights, label='weights',
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yerr=6, fmt='o', capthick=2, capsize=10)
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plt.plot([1, 12], [ave,ave], c='r', label='hypothesis')
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plt.xlim(0, 13); plt.ylim(145, 185)
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plt.xlabel('day')
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plt.ylabel('weight (lbs)')
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book_plots.show_legend()
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plt.show()
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def plot_hypothesis5():
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weights = [158.0, 164.2, 160.3, 159.9, 162.1, 164.6,
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169.6, 167.4, 166.4, 171.0, 171.2, 172.6]
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xs = range(1, len(weights)+1)
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line = np.poly1d(np.polyfit(xs, weights, 1))
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plt.errorbar(range(1, 13), weights, label='weights',
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yerr=5, fmt='o', capthick=2, capsize=10)
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plt.plot (xs, line(xs), c='r', label='hypothesis')
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plt.xlim(0, 13); plt.ylim(145, 185)
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plt.xlabel('day')
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plt.ylabel('weight (lbs)')
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book_plots.show_legend()
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plt.show()
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def plot_g_h_results(measurements, filtered_data,
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title='', z_label='Measurements', **kwargs):
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book_plots.plot_filter(filtered_data, **kwargs)
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book_plots.plot_measurements(measurements, label=z_label)
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book_plots.show_legend()
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plt.title(title)
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plt.gca().set_xlim(left=0,right=len(measurements))
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if __name__ == '__main__':
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create_predict_update_chart() |