!pip freeze

%pylab inline

ls ../data

import pandas

coordinates = pandas.read_csv('../data/CAREN_Trial04_dof.txt', delimiter='\t', index_col="TimeStamp")
speeds = pandas.read_csv('../data/CAREN_Trial04_dofvel.txt', delimiter='\t', index_col="TimeStamp")
generalized_forces = pandas.read_csv('../data/CAREN_Trial04_jointmoment.txt', delimiter='\t', index_col="TimeStamp")
ground_reaction_forces = pandas.read_csv('../data/CAREN_Trial04_grf.txt', delimiter='\t', index_col="TimeStamp")
raw_data = pandas.read_csv('../data/CAREN_Trial04.txt', delimiter='\t', index_col="TimeStamp")

for df in [coordinates, speeds, generalized_forces, ground_reaction_forces, raw_data]:
    print(df)
    print('=' * 50)

coordinates['RKneeFlexion'].plot(figsize=(12.0, 8.0))

raw_data['TreadmillSpeed'].plot(figsize=(12.0, 8.0))

ground_reaction_forces['LFx'][500:1000].plot(figsize=(12.0, 8.0))

ground_reaction_forces['LFy'][500:1000].plot(figsize=(12.0, 8.0))

ground_reaction_forces['LFz'][500:1000].plot(figsize=(12.0, 8.0))

coordinates['RKneeFlexion'].plot(figsize=(12.0, 8.0))

raw_data['TreadmillSpeed'].to_csv('treadmill-speed.csv')

!head treadmill-speed.csv

import numpy as np

speed_array = np.loadtxt('treadmill-speed.csv', delimiter=',')

speed_array[:, 0]

from dtk import walk
reload(walk)

walk.gait_landmarks_from_grf(ground_reaction_forces.index.values.astype(float),
 ground_reaction_forces['RFy'], ground_reaction_forces['LFy'], do_plot=True, min_time=15.0, threshold=5.0)

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

obinna.head()

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

obinna['FP2.ForY'][1000:2000].plot(figsize=(12,10))

start = 1100
stop = 2000
right_strikes, left_strikes, right_toe_off, left_toe_off = \
    walk.gait_landmarks_from_grf(obinna.index.values.astype(float)[start:stop],
                                 obinna['FP1.ForY'][start:stop],
                                 obinna['FP2.ForY'][start:stop],
                                 do_plot=True, min_time=15.0, threshold=28.0)

obinna['FP2.ForY'].to_csv('../data/example_vertical_grf.csv')

first_foot_down = obinna[right_strikes[0]:right_toe_off[1]]

second_foot_down = obinna[right_strikes[1]:right_toe_off[2]]

first_foot_down['FP1.ForY'].shape

second_foot_down['FP1.ForY'].shape

from dtk import process

# this is giving different answers depending on the guess value
process.find_timeshift(first_foot_down['FP1.ForY'][:-2].values,
                       second_foot_down['FP1.ForY'].values,
                       100,
                       guess=0.01,
                       plot=True)

figure(figsize=(10, 8))
right_steps = {}
for i, heel in enumerate(right_strikes[:-1]):
    right_steps[i] = obinna[heel:right_toe_off[i + 1]]
    print(len(right_steps[i]))
    time = process.time_vector(len(right_steps[i]), 100)
    right_steps[i].index = time
    plot(time, right_steps[i]['FP1.ForY'],)
#right_steps = pandas.Panel(right_steps)

right_steps = pandas.Panel(right_steps)

mean_right_steps = right_steps.mean(axis='items')

mean_right_steps['FP1.ForY'].plot()

right_steps = right_steps.fillna(method='pad')

from dtk import walk

solver = walk.SimpleControlSolver(right_steps)

sensors = ['RKneeFlexion.Ang', 'RHipFlexion.Ang','RAnklePlantarFlexion.Ang']
sensors = ['RKneeFlexion.Ang', 'RAnklePlantarFlexion.Ang']
controls = ['RKneeFlexion.Mom', 'RHipFlexion.Mom', 'RAnklePlantarFlexion.Mom']
gains, sensors = solver.solve(sensors, controls)

gains.shape

figure(figsize=(10, 8))
plot(gains.reshape(79, 6))