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()
Beispiel #8
0
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()
Beispiel #9
0
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()