In [1]:
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import os, re, sys
# sklearn stuff
from sklearn.decomposition import PCA
# scipy stuff
from scipy.interpolate import interp1d
# my stuff
from preprocessingTR import *
%matplotlib inline
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base = './Data/'
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u_dict = generate_udict(base)
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frank = get_data(51, 5)
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accelerometer_cols = [x for x in frank.columns if 'data' in x]
# gyroscope_cols = [x for x in a.columns if 'gyr' in x]
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data = rot_trans(frank[accelerometer_cols].values)
acc_mag = (data ** 2).sum(axis=1) ** 0.5
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samples2 = acc_mag[500:5000]
starts = find_cycles(samples2, viz=True)
starts2 = merge_consecutive_starts(starts, 2)
plot_steps(samples2, starts2)
x,y,z=frank[accelerometer_cols].values[500:5000, :].T
plot_steps(x, starts2, main_title='$a_x$ values')
plot_steps(y, starts2, main_title='$a_y$ values')
plot_steps(z, starts2, main_title='$a_z$ values')
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In [10]:
feats = extract_feats(x, starts2, filter_short=True)
feats_interp = interpolate_features(feats)
plt.figure(figsize=(15,4))
for row in feats_interp:
plt.plot(row)
In [11]:
feats = extract_feats(y, starts2, filter_short=True)
feats_interp = interpolate_features(feats)
plt.figure(figsize=(15,4))
for row in feats_interp:
plt.plot(row)
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feats = extract_feats(z, starts2, filter_short=True)
feats_interp = interpolate_features(feats)
plt.figure(figsize=(15,4))
for row in feats_interp:
plt.plot(row)
In [26]:
d1 = get_data(51, 7)
d2 = get_data(51, 5)
accelerometer_cols = [x for x in d1.columns if 'data' in x]
data1 = rot_trans(d1[accelerometer_cols].values)
acc_mag1 = (data1 ** 2).sum(axis=1) ** 0.5
data2 = rot_trans(d2[accelerometer_cols].values)
acc_mag2 = (data2 ** 2).sum(axis=1) ** 0.5
samples1 = acc_mag1[500:5000]
samples2 = acc_mag2[500:5000]
starts1 = merge_consecutive_starts(find_cycles(samples1), 2)
starts2 = merge_consecutive_starts(find_cycles(samples2), 2)
fig, axarr = plt.subplots(1, 2)
fig.set_figheight(2)
fig.set_figwidth(15)
feats = extract_feats(acc_mag1[500:5000], starts1, filter_short=True)
feats_interp = interpolate_features(feats)
for row in feats_interp:
axarr[0].plot(row)
# feats = extract_feats(acc_mag2[500:5000], starts2, filter_short=True)
# feats_interp = interpolate_features(feats)
# for row in feats_interp:
# axarr[1].plot(row)
In [27]:
fig, axarr = plt.subplots(1, 1, sharex=True, sharey=True)
fig.set_figheight(5)
fig.set_figwidth(15)
for idx, row in enumerate(feats_interp[:1]):
axarr.plot(row)
plt.xticks([])
plt.yticks([])
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