def plot_g_h_results(measurements, filtered_data,
title='', z_label='Measurements', **kwargs):
book_plots.plot_filter(filtered_data, **kwargs)
book_plots.plot_measurements(measurements, label=z_label)
book_plots.show_legend()
plt.title(title)
plt.gca().set_xlim(left=0,right=len(measurements))
def plot_dog_track(xs, measurement_var, process_var):
N = len(xs)
bp.plot_track([0, N-1], [1, N])
bp.plot_measurements(xs, label='Sensor')
bp.set_labels('variance = {}, process variance = {}'.format(
measurement_var, process_var), 'time', 'pos')
plt.ylim([0, N])
bp.show_legend()
plt.show()
def plot_gh_results(weights, estimates, predictions):
n = len(weights)
xs = list(range(n+1))
book_plots.plot_filter(xs, estimates, marker='o')
book_plots.plot_measurements(xs[1:], weights, color='k', label='Scale', lines=False)
book_plots.plot_track([0, n], [160, 160+n], c='k', label='Actual Weight')
book_plots.plot_track(xs[1:], predictions, c='r', label='Predictions', marker='v')
book_plots.show_legend()
book_plots.set_labels(x='day', y='weight (lbs)')
plt.xlim([0, n])
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()
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_gh_results(weights, estimates, predictions):
n = len(weights)
xs = list(range(n + 1))
book_plots.plot_filter(xs, estimates, marker='o')
book_plots.plot_measurements(xs[1:],
weights,
color='k',
label='Scale',
lines=False)
book_plots.plot_track([0, n], [160, 160 + n], c='k', label='Actual Weight')
book_plots.plot_track(xs[1:],
predictions,
c='r',
label='Predictions',
marker='v')
book_plots.show_legend()
book_plots.set_labels(x='day', y='weight (lbs)')
plt.xlim([0, n])
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()