!pip freeze

%pylab inline

rcParams['figure.figsize'] = 16, 8

import pandas
from dtk import walk, process

obinna = pandas.read_csv('../data/obinna-walking.txt', delimiter='\t',
                         index_col="TimeStamp", na_values='0.000000')

obinna

[col for col in obinna.columns if col.endswith('.Ang') or col.endswith('ForY') or col.endswith('.Mom')]

obinna['FP2.ForY'].plot()

start = 500
stop = 3500

data = walk.WalkingData(obinna.iloc[start:stop].copy())

to_diff = [col for col in obinna.columns if col.endswith('.Ang')]
new_names = [name.split('.')[0] + '.Rate' for name in to_diff]

data.time_derivative(to_diff, new_names)

data.raw_data[new_names]

right_strikes, left_strikes, right_offs, left_offs = \
    data.grf_landmarks('FP2.ForY', 'FP1.ForY', do_plot=True, threshold=28.0)

right_steps = data.split_at('right', num_samples=20)
data.plot_steps('FP2.ForY', 'RKneeFlexion.Ang', 'RKneeFlexion.Rate', 'RKneeFlexion.Mom', marker='o')

mean_right_steps = right_steps.mean(axis='items')
mean_right_steps[['FP2.ForY', 'RKneeFlexion.Ang', 'RKneeFlexion.Rate', 'RKneeFlexion.Mom']].plot(subplots=True)

sensors = ['RKneeFlexion.Ang',
           'RKneeFlexion.Rate',
           'RHipFlexion.Ang',
           'RHipFlexion.Rate',
           'LKneeFlexion.Ang',
           'LKneeFlexion.Rate',
           'LHipFlexion.Ang',
           'LHipFlexion.Rate']

controls = ['RKneeFlexion.Mom',
            'RHipFlexion.Mom',
            'LKneeFlexion.Mom',
            'LHipFlexion.Mom']

solver = walk.SimpleControlSolver(right_steps, sensors, controls)

gains, sensors = solver.solve()

gains.shape

solver.plot_gains(gains)