daviddoji/Kalman-and-Bayesian-Filters-in-Python
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Changes to bring page count down.

Browse Source 
Made plots smaller, moved legends outside of plot (so smallersize
is still readable), moved plotting code into external modules.
This commit is contained in:
Roger Labbe
2015-04-18 14:52:41 -07:00
parent f6855f1bb6
commit 0086fcd201
6 changed files with 343 additions and 338 deletions
Download Patch File Download Diff File Expand all files Collapse all files

75
code/gh_internal.py
View File

@@ -1,6 +1,7 @@
import numpy as np
import pylab as plt
from matplotlib.patches import Circle, Rectangle, Polygon, Arrow, FancyArrow
import book_plots
def create_predict_update_chart(box_bg = '#CCCCCC',
arrow1 = '#88CCFF',
@@ -119,6 +120,80 @@ def plot_estimate_chart_3():
plt.ylabel('weight (lbs)')
plt.show()
def plot_hypothesis():
plt.errorbar([1, 2, 3], [170, 161, 169],
xerr=0, yerr=10, fmt='bo', capthick=2, capsize=10)
plt.plot([1, 3], [180, 160], color='g', ls='--')
plt.plot([1, 3], [170, 170], color='g', ls='--')
plt.plot([1, 3], [160, 175], color='g', ls='--')
plt.plot([1, 2, 3], [180, 152, 179], color='g', ls='--')
plt.xlim(0,4); plt.ylim(150, 185)
plt.xlabel('day')
plt.ylabel('lbs')
plt.tight_layout()
plt.show()
def plot_hypothesis2():
plt.errorbar(range(1, 11), [169, 170, 169,171, 170, 171, 169, 170, 169, 170],
xerr=0, yerr=6, fmt='bo', capthick=2, capsize=10)
plt.plot([1, 10], [169, 170.5], color='g', ls='--')
plt.xlim(0, 11); plt.ylim(150, 185)
plt.xlabel('day')
plt.ylabel('lbs')
plt.show()
def plot_hypothesis3():
weights = [158.0, 164.2, 160.3, 159.9, 162.1, 164.6,
169.6, 167.4, 166.4, 171.0, 171.2, 172.6]
plt.errorbar(range(1, 13), weights,
xerr=0, yerr=6, fmt='o', capthick=2, capsize=10)
plt.xlim(0, 13); plt.ylim(145, 185)
plt.xlabel('day')
plt.ylabel('weight (lbs)')
plt.show()
def plot_hypothesis4():
weights = [158.0, 164.2, 160.3, 159.9, 162.1, 164.6,
169.6, 167.4, 166.4, 171.0, 171.2, 172.6]
ave = np.sum(weights) / len(weights)
plt.errorbar(range(1,13), weights, label='weights',
yerr=6, fmt='o', capthick=2, capsize=10)
plt.plot([1, 12], [ave,ave], c='r', label='hypothesis')
plt.xlim(0, 13); plt.ylim(145, 185)
plt.xlabel('day')
plt.ylabel('weight (lbs)')
book_plots.show_legend()
plt.show()
def plot_hypothesis5():
weights = [158.0, 164.2, 160.3, 159.9, 162.1, 164.6,
169.6, 167.4, 166.4, 171.0, 171.2, 172.6]
xs = range(1, len(weights)+1)
line = np.poly1d(np.polyfit(xs, weights, 1))
plt.errorbar(range(1, 13), weights, label='weights',
yerr=5, fmt='o', capthick=2, capsize=10)
plt.plot (xs, line(xs), c='r', label='hypothesis')
plt.xlim(0, 13); plt.ylim(145, 185)
plt.xlabel('day')
plt.ylabel('weight (lbs)')
book_plots.show_legend()
plt.show()
def plot_g_h_results(measurements, filtered_data,
title='', z_label='Scale', ):
book_plots.plot_measurements(measurements, label=z_label)
book_plots.plot_filter(filtered_data)
book_plots.show_legend()
plt.title(title)
plt.gca().set_xlim(left=0,right=len(measurements))
plt.show()
if __name__ == '__main__':
create_predict_update_chart()