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Merge branch 'develop'
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Simon Ho committed Nov 12, 2018
2 parents bfa5409 + 7fef9a8 commit fdafe4d
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Showing 8 changed files with 288 additions and 202 deletions.
2 changes: 1 addition & 1 deletion docs/conf.py
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Original file line number Diff line number Diff line change
Expand Up @@ -39,7 +39,7 @@ def __getattr__(cls, name):

def get_version():
with open('../sensormotion/version.py') as f:
p = re.compile(r"'.*'$")
p = re.compile(r"\".*\"$")
return re.findall(p, f.read().strip())[0]

# -- General configuration ------------------------------------------------
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26 changes: 14 additions & 12 deletions sensormotion/gait.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,7 +10,7 @@
import numpy as np


def cadence(time, peak_times, time_units='ms'):
def cadence(time, peak_times, time_units="ms"):
"""
Calculate cadence of the current signal.
Expand Down Expand Up @@ -43,10 +43,10 @@ def cadence(time, peak_times, time_units='ms'):
n = step_count(peak_times)

# Convert duration to seconds
if time_units == 'ms':
if time_units == "ms":
duration = (time.max() - time.min()) / 1000
elif time_units == 's':
duration = (time.max() - time.min())
elif time_units == "s":
duration = time.max() - time.min()

steps_per_min = (n / duration) * 60

Expand Down Expand Up @@ -122,11 +122,12 @@ def step_regularity(autocorr_peak_values):
Stride regularity. Capped at 1.0 for both vertical and lateral axes.
"""

peaks_half = autocorr_peak_values[autocorr_peak_values.size//2:]
peaks_half = autocorr_peak_values[autocorr_peak_values.size // 2 :]

assert len(peaks_half) >= 3, \
'Not enough autocorrelation peaks detected. Plot the ' \
'autocorrelation signal to visually inspect peaks'
assert len(peaks_half) >= 3, (
"Not enough autocorrelation peaks detected. Plot the "
"autocorrelation signal to visually inspect peaks"
)

ac_lag0 = peaks_half[0] # autocorrelation value at lag 0
ac_d1 = peaks_half[1] # first dominant period i.e. a step (left-right)
Expand Down Expand Up @@ -175,11 +176,12 @@ def step_symmetry(autocorr_peak_values):
axis of interest.
"""

peaks_half = autocorr_peak_values[autocorr_peak_values.size//2:]
peaks_half = autocorr_peak_values[autocorr_peak_values.size // 2 :]

assert len(peaks_half) >= 3, \
'Not enough autocorrelation peaks detected. Plot the ' \
'autocorrelation signal to visually inspect peaks'
assert len(peaks_half) >= 3, (
"Not enough autocorrelation peaks detected. Plot the "
"autocorrelation signal to visually inspect peaks"
)

ac_d1 = peaks_half[1] # first dominant period i.e. a step (left-right)
ac_d2 = peaks_half[2] # second dominant period i.e. a stride (left-left)
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164 changes: 99 additions & 65 deletions sensormotion/pa.py
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Original file line number Diff line number Diff line change
Expand Up @@ -24,8 +24,16 @@
from scipy.integrate import simps, trapz


def convert_counts(x, time, time_scale='ms', epoch=60, rectify='full',
integrate='simpson', plot=False, fig_size=(10, 5)):
def convert_counts(
x,
time,
time_scale="ms",
epoch=60,
rectify="full",
integrate="simpson",
plot=False,
fig_size=(10, 5),
):
"""
Convert acceleration to physical activity (PA) counts.
Expand Down Expand Up @@ -70,31 +78,32 @@ def convert_counts(x, time, time_scale='ms', epoch=60, rectify='full',
PA count values for each epoch.
"""

assert len(x) == len(time), 'signal and time must be the same length'
assert np.all(np.diff(time) > 0), 'time signal is not fully ascending'
assert integrate == 'simpson' or integrate == 'trapezoid', \
'integrate method must either be simpson or trapezoid'
assert len(x) == len(time), "signal and time must be the same length"
assert np.all(np.diff(time) > 0), "time signal is not fully ascending"
assert (
integrate == "simpson" or integrate == "trapezoid"
), "integrate method must either be simpson or trapezoid"

x = np.asarray(x)
time = np.asarray(time)

# convert time to seconds
if time_scale == 'ms':
time = time/1000
elif time_scale == 's':
if time_scale == "ms":
time = time / 1000
elif time_scale == "s":
time = time

# calculate time diff
time = time - time[0]

assert max(time) > epoch, 'length of signal time shorter than epoch size'
assert max(time) > epoch, "length of signal time shorter than epoch size"

# rectify signal
x = sensormotion.signal.rectify_signal(x, rectify)

# interpolate missing times values to get exact epochs
boundary_count = int(max(time) / epoch) + 1
boundary_times = [i*epoch for i in range(boundary_count)]
boundary_times = [i * epoch for i in range(boundary_count)]
missing_times = np.setdiff1d(boundary_times, time) # epoch times to interp

x = np.append(x, np.interp(missing_times, time, x)) # interpolate x values
Expand All @@ -113,32 +122,36 @@ def convert_counts(x, time, time_scale='ms', epoch=60, rectify='full',

for i in range(len(counts)):
lower = boundary_idx[i]
upper = boundary_idx[i+1] + 1 # upper bound should be inclusive
upper = boundary_idx[i + 1] + 1 # upper bound should be inclusive

cur_signal = x[lower:upper]
cur_time = time[lower:upper]

if integrate == 'simpson':
if integrate == "simpson":
counts[i] = simps(cur_signal, cur_time)
elif integrate == 'trapezoid':
elif integrate == "trapezoid":
counts[i] = trapz(cur_signal, cur_time)

# plot counts
if plot:
f, ax = plt.subplots(1, 1, figsize=fig_size)

ax.bar(boundary_times[1:], counts, width=epoch-5)
ax.bar(boundary_times[1:], counts, width=epoch - 5)

plt.xticks(boundary_times[1:],
['{} - {}'.format(boundary_times[i], boundary_times[i+1])
for i, x in enumerate(boundary_times[1:])])
plt.xticks(
boundary_times[1:],
[
"{} - {}".format(boundary_times[i], boundary_times[i + 1])
for i, x in enumerate(boundary_times[1:])
],
)

for tick in ax.get_xticklabels():
tick.set_rotation(45)

plt.suptitle('Physical activity counts', size=16)
plt.xlabel('Time window (seconds)')
plt.ylabel('PA count')
plt.suptitle("Physical activity counts", size=16)
plt.xlabel("Time window (seconds)")
plt.ylabel("PA count")
plt.show()

return counts
Expand Down Expand Up @@ -207,49 +220,70 @@ def cut_points(x, set_name, n_axis, plot=False, fig_size=(10, 5)):
"""

# new cut-point sets should be added to this dictionary
sets = {'butte_preschoolers': {1: {'sedentary': [-np.inf, 239],
'light' : [240, 2119],
'moderate' : [2120, 4449],
'vigorous' : [4450, np.inf]},
3: {'sedentary': [-np.inf, 819],
'light' : [820, 3907],
'moderate' : [3908, 6111],
'vigorous' : [6112, np.inf]}
},
'freedson_adult' : {1: {'sedentary' : [-np.inf, 99],
'light' : [100, 1951],
'moderate' : [1952, 5724],
'vigorous' : [5725, 9498],
'very vigorous': [9499, np.inf]},
3: {'light' : [-np.inf, 2690],
'moderate' : [2691, 6166],
'vigorous' : [6167, 9642],
'very vigorous': [9643, np.inf]}
},
'freedson_children' : {1: {'sedentary' : [-np.inf, 149],
'light' : [150, 499],
'moderate' : [500, 3999],
'vigorous' : [4000, 7599],
'very vigorous': [7600, np.inf]}
},
'keadle_women' : {1: {'sedentary': [-np.inf, 99],
'light' : [100, 1951],
'moderate' : [1952, np.inf]},
3: {'sedentary': [-np.inf, 199],
'light' : [200, 2689],
'moderate' : [2690, np.inf]}
}
sets = {
"butte_preschoolers": {
1: {
"sedentary": [-np.inf, 239],
"light": [240, 2119],
"moderate": [2120, 4449],
"vigorous": [4450, np.inf],
},
3: {
"sedentary": [-np.inf, 819],
"light": [820, 3907],
"moderate": [3908, 6111],
"vigorous": [6112, np.inf],
},
},
"freedson_adult": {
1: {
"sedentary": [-np.inf, 99],
"light": [100, 1951],
"moderate": [1952, 5724],
"vigorous": [5725, 9498],
"very vigorous": [9499, np.inf],
},
3: {
"light": [-np.inf, 2690],
"moderate": [2691, 6166],
"vigorous": [6167, 9642],
"very vigorous": [9643, np.inf],
},
},
"freedson_children": {
1: {
"sedentary": [-np.inf, 149],
"light": [150, 499],
"moderate": [500, 3999],
"vigorous": [4000, 7599],
"very vigorous": [7600, np.inf],
}
},
"keadle_women": {
1: {
"sedentary": [-np.inf, 99],
"light": [100, 1951],
"moderate": [1952, np.inf],
},
3: {
"sedentary": [-np.inf, 199],
"light": [200, 2689],
"moderate": [2690, np.inf],
},
},
}

try:
cur_set = sets[set_name][n_axis]
print('Cut-point set: {} (axis count: {})...'.format(set_name, n_axis))
print("Cut-point set: {} (axis count: {})...".format(set_name, n_axis))

for i in cur_set:
print('{}: {} to {}'.format(i, cur_set[i][0], cur_set[i][1]))
print("{}: {} to {}".format(i, cur_set[i][0], cur_set[i][1]))
except KeyError:
print('Error: cut-point set not found. Make sure the set name and/or '
'number of axes are correct')
print(
"Error: cut-point set not found. Make sure the set name and/or "
"number of axes are correct"
)
raise

# categorize counts
Expand All @@ -271,19 +305,19 @@ def cut_points(x, set_name, n_axis, plot=False, fig_size=(10, 5)):

f, ax = plt.subplots(1, 1, figsize=fig_size)

ax.bar(range(1, len(x)+1), x)
ax.bar(range(1, len(x) + 1), x)

for line in boundaries[1:]:
if line[1] < max(x):
plt.axhline(line[1], linewidth=1, linestyle='--', color='k')
t = plt.text(0.4, line[1], line[0], backgroundcolor='w')
t.set_bbox(dict(facecolor='w', edgecolor='k'))
plt.axhline(line[1], linewidth=1, linestyle="--", color="k")
t = plt.text(0.4, line[1], line[0], backgroundcolor="w")
t.set_bbox(dict(facecolor="w", edgecolor="k"))

plt.xticks(range(1, len(x)+1))
plt.xticks(range(1, len(x) + 1))

plt.suptitle('Physical activity counts and intensity', size=16)
plt.xlabel('Epoch (length: 60 seconds)')
plt.ylabel('PA count')
plt.suptitle("Physical activity counts and intensity", size=16)
plt.xlabel("Epoch (length: 60 seconds)")
plt.ylabel("PA count")
plt.show()

return category, time_spent
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