!pip freeze

%pylab inline

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.head()

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

obinna['FP2.ForY'].plot(figsize=(18,10))

start = 500
stop = 3000
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

obinna.replace?

figure(figsize=(10, 8))
right_steps = {}
for i, heel in enumerate(right_strikes[:-1]):
    right_steps[i] = obinna[heel:right_strikes[i + 1]].apply(pandas.Series.interpolate)
    print("Time samples in step: {}".format(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)

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

mean_right_steps['FP1.ForY'].plot(figsize=(10, 8))

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

sensors = ['RKneeFlexion.Ang', 'RAnklePlantarFlexion.Ang']
controls = ['RKneeFlexion.Mom', 'RHipFlexion.Mom', 'RAnklePlantarFlexion.Mom']
solver = walk.SimpleControlSolver(right_steps, sensors, controls)

gains, sensors = solver.solve()

gains.shape

figure(figsize=(10, 8))
plot(gains.reshape(gains.shape[0], gains.shape[1] * gains.shape[2]), '.-')

solver.sensors = ['RKneeFlexion.Ang']
gains, sensors = solver.solve()

figure(figsize=(10, 8))
plot(gains.reshape(gains.shape[0], gains.shape[1] * gains.shape[2]), '.-')

A, b = solver.form_a_b()

A.shape, b.shape

from scipy.sparse.linalg import lsmr, lsqr

%%time
sol1 = lsmr(A, b)

%%time
sol2 = lsqr(A, b)

np.allclose(sol1[0], sol2[0])

sol1[0]

sol2[0]

%%time
sol3 = solver.least_squares(A, b)

sol3[0]

A = np.array([[1, 2],[2, 4], [0, 0]])
b = np.array([1, 2, 3])

%%timeit
np.linalg.lstsq(A, b)

%%timeit
lsqr(A,b)

%%timeit
lsmr(A, b)