def compute_tap_features(xtaps, ytaps, t, threshold=20):
"""
Elias Chaibub-Neto's tapping feature extraction methods.
Arno translated Elias's R code to Python.
Parameters
----------
xtaps : numpy array of integers
x coordinates of touch screen where tapped
ytaps : numpy array of integers
y coordinates of touch screen where tapped
t : numpy array of floats
time points of taps
threshold : integer
x offset threshold for left/right press event (pixels)
Return
------
T : class
many features stored in TapFeatures class
Examples
--------
>>> import numpy as np
>>> from mhealthx.extractors.tapping import compute_tap_features
>>> xtaps = np.round(200 * np.random.random(100))
>>> ytaps = np.round(300 * np.random.random(100))
>>> t = np.linspace(1, 100, 100) / 5.0
>>> threshold = 20
>>> T = compute_tap_features(xtaps, ytaps, t, threshold)
"""
import numpy as np
from mhealthx.extractors.tapping import compute_drift, \
compute_tap_intervals, compute_intertap_gap
from mhealthx.extractors.tapping import TapFeatures as T
from mhealthx.signals import signal_features
if isinstance(xtaps, list):
xtaps = np.array(xtaps)
if isinstance(ytaps, list):
ytaps = np.array(ytaps)
if isinstance(t, list):
t = np.array(t)
# Intertap intervals:
ipress, intervals = compute_tap_intervals(xtaps, t, threshold)
# Filter data:
t = t[ipress]
xtaps = xtaps[ipress]
ytaps = ytaps[ipress]
# Delta between fastest and slowest intertap intervals:
T.intertap_gap10, T.intertap_gap25, \
T.intertap_gap50 = compute_intertap_gap(intervals)
# Left and right taps and drift:
mean_x = np.mean(xtaps)
iL = np.where(xtaps < mean_x)
iR = np.where(xtaps >= mean_x)
xL = xtaps[iL]
yL = ytaps[iL]
xR = xtaps[iR]
yR = ytaps[iR]
driftL = compute_drift(xL, yL)
driftR = compute_drift(xR, yR)
# Number of taps:
T.num_taps = xtaps.size
T.num_taps_left = xL.size
T.num_taps_right = xR.size
# Time:
T.time_rng = t[-1] - t[0]
# Intertap interval statistics:
T.intertap_num, T.intertap_min, T.intertap_max, T.intertap_rng, \
T.intertap_avg, T.intertap_std, T.intertap_med, T.intertap_mad, \
T.intertap_kurt, T.intertap_skew, T.intertap_cvar, T.intertap_lower25, \
T.intertap_upper25, T.intertap_inter50, T.intertap_rms, \
T.intertap_entropy, T.intertap_tk_energy = signal_features(intervals)
# Tap statistics:
T.xL_num, T.xL_min, T.xL_max, T.xL_rng, T.xL_avg, T.xL_std, \
T.xL_med, T.xL_mad, T.xL_kurt, T.xL_skew, T.xL_cvar, \
T.xL_lower25, T.xL_upper25, T.xL_inter50, T.xL_rms, \
T.xL_entropy, T.xL_tk_energy = signal_features(xL)
T.xR_num, T.xR_min, T.xR_max, T.xR_rng, T.xR_avg, T.xR_std, \
T.xR_med, T.xR_mad, T.xR_kurt, T.xR_skew, T.xR_cvar, \
T.xR_lower25, T.xR_upper25, T.xR_inter50, T.xR_rms, \
T.xR_entropy, T.xR_tk_energy = signal_features(xR)
# T.yL_num, T.yL_min, T.yL_max, T.yL_rng, T.yL_avg, T.yL_std, \
# T.yL_med, T.yL_mad, T.yL_kurt, T.yL_skew, T.yL_cvar, \
# T.yL_lower25, T.yL_upper25, T.yL_inter50, T.yL_rms, \
# T.yL_entropy, T.yL_tk_energy = signal_features(yL)
# T.yR_num, T.yR_min, T.yR_max, T.yR_rng, T.yR_avg, T.yR_std, \
# T.yR_med, T.yR_mad, T.yR_kurt, T.yR_skew, T.yR_cvar, \
# T.yR_lower25, T.yR_upper25, T.yR_inter50, T.yR_rms, \
# T.yR_entropy, T.yR_tk_energy = signal_features(yR)
# Drift statistics:
T.driftL_num, T.driftL_min, T.driftL_max, T.driftL_rng, T.driftL_avg, \
T.driftL_std, T.driftL_med, T.driftL_mad, T.driftL_kurt, T.driftL_skew, \
T.driftL_cvar, T.driftL_lower25, T.driftL_upper25, T.driftL_inter50, \
T.driftL_rms, T.driftL_entropy, T.driftL_tk_energy = \
signal_features(driftL)
T.driftR_num, T.driftR_min, T.driftR_max, T.driftR_rng, T.driftR_avg, \
T.driftR_std, T.driftR_med, T.driftR_mad, T.driftR_kurt, T.driftR_skew, \
T.driftR_cvar, T.driftR_lower25, T.driftR_upper25, T.driftR_inter50, \
T.driftR_rms, T.driftR_entropy, T.driftR_tk_energy = \
signal_features(driftR)
return T
def run_signal_features(data, row, file_path, table_stem, save_rows=False):
"""
Extract various features from time series data.
Parameters
----------
data : numpy array of floats
time series data
row : pandas Series
row to prepend, unaltered, to feature row
file_path : string
path to accelerometer file (from row)
table_stem : string
prepend to output table file
save_rows : Boolean
save individual rows rather than write to a single feature table?
Returns
-------
feature_row : pandas Series
row combining the original row with a row of signal feature values
feature_table : string
output table file (full path)
Examples
--------
>>> import pandas as pd
>>> from mhealthx.xio import read_accel_json
>>> from mhealthx.extract import run_signal_features
>>> input_file = '/Users/arno/DriveWork/mhealthx/mpower_sample_data/accel_walking_outbound.json.items-6dc4a144-55c3-4e6d-982c-19c7a701ca243282023468470322798.tmp'
>>> start = 150
>>> device_motion = False
>>> t, axyz, gxyz, uxyz, rxyz, sample_rate, duration = read_accel_json(input_file, start, device_motion)
>>> ax, data, az = axyz
>>> row = pd.Series({'a':[1], 'b':[2], 'c':[3]})
>>> file_path = '.'
>>> table_stem = './walking'
>>> save_rows = True
>>> feature_row, feature_table = run_signal_features(data, row, file_path, table_stem, save_rows)
"""
import pandas as pd
from mhealthx.signals import signal_features
from mhealthx.extract import make_row_table
# Extract different features from the data:
num, min, max, rng, avg, std, med, mad, kurt, skew, cvar, lower25, \
upper25, inter50, rms, entropy, tk_energy = signal_features(data)
# Create row of data:
row_data = pd.DataFrame({'num': num,
'min': min,
'max': max,
'rng': rng,
'avg': avg,
'std': std,
'med': med,
'mad': mad,
'kurt': kurt,
'skew': skew,
'cvar': cvar,
'lower25': lower25,
'upper25': upper25,
'inter50': inter50,
'rms': rms,
'entropy': entropy,
'tk_energy': tk_energy},
index=[0])
# Write feature row to a table or append to a feature table:
feature_row, feature_table = make_row_table(file_path, table_stem,
save_rows, row, row_data,
feature_row=None)
return feature_row, feature_table
def compute_tap_features(xtaps, ytaps, t, threshold=20):
"""
Elias Chaibub-Neto's tapping feature extraction methods.
Arno translated Elias's R code to Python.
Parameters
----------
xtaps : numpy array of integers
x coordinates of touch screen where tapped
ytaps : numpy array of integers
y coordinates of touch screen where tapped
t : numpy array of floats
time points of taps
threshold : integer
x offset threshold for left/right press event (pixels)
Return
------
T : class
many features stored in TapFeatures class
Examples
--------
>>> import numpy as np
>>> from mhealthx.extractors.tapping import compute_tap_features
>>> xtaps = np.round(200 * np.random.random(100))
>>> ytaps = np.round(300 * np.random.random(100))
>>> t = np.linspace(1, 100, 100) / 5.0
>>> threshold = 20
>>> T = compute_tap_features(xtaps, ytaps, t, threshold)
"""
import numpy as np
from mhealthx.extractors.tapping import compute_drift, \
compute_tap_intervals, compute_intertap_gap
from mhealthx.extractors.tapping import TapFeatures as T
from mhealthx.signals import signal_features
if isinstance(xtaps, list):
xtaps = np.array(xtaps)
if isinstance(ytaps, list):
ytaps = np.array(ytaps)
if isinstance(t, list):
t = np.array(t)
# Intertap intervals:
ipress, intervals = compute_tap_intervals(xtaps, t, threshold)
# Filter data:
t = t[ipress]
xtaps = xtaps[ipress]
ytaps = ytaps[ipress]
# Delta between fastest and slowest intertap intervals:
T.intertap_gap10, T.intertap_gap25, \
T.intertap_gap50 = compute_intertap_gap(intervals)
# Left and right taps and drift:
mean_x = np.mean(xtaps)
iL = np.where(xtaps < mean_x)
iR = np.where(xtaps >= mean_x)
xL = xtaps[iL]
yL = ytaps[iL]
xR = xtaps[iR]
yR = ytaps[iR]
driftL = compute_drift(xL, yL)
driftR = compute_drift(xR, yR)
# Number of taps:
T.num_taps = xtaps.size
T.num_taps_left = xL.size
T.num_taps_right = xR.size
# Time:
T.time_rng = t[-1] - t[0]
# Intertap interval statistics:
T.intertap_num, T.intertap_min, T.intertap_max, T.intertap_rng, \
T.intertap_avg, T.intertap_std, T.intertap_med, T.intertap_mad, \
T.intertap_kurt, T.intertap_skew, T.intertap_cvar, T.intertap_lower25, \
T.intertap_upper25, T.intertap_inter50, T.intertap_rms, \
T.intertap_entropy, T.intertap_tk_energy = signal_features(intervals)
# Tap statistics:
T.xL_num, T.xL_min, T.xL_max, T.xL_rng, T.xL_avg, T.xL_std, \
T.xL_med, T.xL_mad, T.xL_kurt, T.xL_skew, T.xL_cvar, \
T.xL_lower25, T.xL_upper25, T.xL_inter50, T.xL_rms, \
T.xL_entropy, T.xL_tk_energy = signal_features(xL)
T.xR_num, T.xR_min, T.xR_max, T.xR_rng, T.xR_avg, T.xR_std, \
T.xR_med, T.xR_mad, T.xR_kurt, T.xR_skew, T.xR_cvar, \
T.xR_lower25, T.xR_upper25, T.xR_inter50, T.xR_rms, \
T.xR_entropy, T.xR_tk_energy = signal_features(xR)
# T.yL_num, T.yL_min, T.yL_max, T.yL_rng, T.yL_avg, T.yL_std, \
# T.yL_med, T.yL_mad, T.yL_kurt, T.yL_skew, T.yL_cvar, \
# T.yL_lower25, T.yL_upper25, T.yL_inter50, T.yL_rms, \
# T.yL_entropy, T.yL_tk_energy = signal_features(yL)
# T.yR_num, T.yR_min, T.yR_max, T.yR_rng, T.yR_avg, T.yR_std, \
# T.yR_med, T.yR_mad, T.yR_kurt, T.yR_skew, T.yR_cvar, \
# T.yR_lower25, T.yR_upper25, T.yR_inter50, T.yR_rms, \
# T.yR_entropy, T.yR_tk_energy = signal_features(yR)
# Drift statistics:
T.driftL_num, T.driftL_min, T.driftL_max, T.driftL_rng, T.driftL_avg, \
T.driftL_std, T.driftL_med, T.driftL_mad, T.driftL_kurt, T.driftL_skew, \
T.driftL_cvar, T.driftL_lower25, T.driftL_upper25, T.driftL_inter50, \
T.driftL_rms, T.driftL_entropy, T.driftL_tk_energy = \
signal_features(driftL)
T.driftR_num, T.driftR_min, T.driftR_max, T.driftR_rng, T.driftR_avg, \
T.driftR_std, T.driftR_med, T.driftR_mad, T.driftR_kurt, T.driftR_skew, \
T.driftR_cvar, T.driftR_lower25, T.driftR_upper25, T.driftR_inter50, \
T.driftR_rms, T.driftR_entropy, T.driftR_tk_energy = \
signal_features(driftR)
return T
def run_signal_features(data, row, file_path, table_stem, save_rows=False):
"""
Extract various features from time series data.
Parameters
----------
data : numpy array of floats
time series data
row : pandas Series
row to prepend, unaltered, to feature row
file_path : string
path to accelerometer file (from row)
table_stem : string
prepend to output table file
save_rows : Boolean
save individual rows rather than write to a single feature table?
Returns
-------
feature_row : pandas Series
row combining the original row with a row of signal feature values
feature_table : string
output table file (full path)
Examples
--------
>>> import pandas as pd
>>> from mhealthx.xio import read_accel_json
>>> from mhealthx.extract import run_signal_features
>>> input_file = '/Users/arno/DriveWork/mhealthx/mpower_sample_data/accel_walking_outbound.json.items-6dc4a144-55c3-4e6d-982c-19c7a701ca243282023468470322798.tmp'
>>> start = 150
>>> device_motion = False
>>> t, axyz, gxyz, uxyz, rxyz, sample_rate, duration = read_accel_json(input_file, start, device_motion)
>>> ax, data, az = axyz
>>> row = pd.Series({'a':[1], 'b':[2], 'c':[3]})
>>> file_path = '.'
>>> table_stem = './walking'
>>> save_rows = True
>>> feature_row, feature_table = run_signal_features(data, row, file_path, table_stem, save_rows)
"""
import pandas as pd
from mhealthx.signals import signal_features
from mhealthx.extract import make_row_table
# Extract different features from the data:
num, min, max, rng, avg, std, med, mad, kurt, skew, cvar, lower25, \
upper25, inter50, rms, entropy, tk_energy = signal_features(data)
# Create row of data:
row_data = pd.DataFrame({'num': num,
'min': min,
'max': max,
'rng': rng,
'avg': avg,
'std': std,
'med': med,
'mad': mad,
'kurt': kurt,
'skew': skew,
'cvar': cvar,
'lower25': lower25,
'upper25': upper25,
'inter50': inter50,
'rms': rms,
'entropy': entropy,
'tk_energy': tk_energy},
index=[0])
# Write feature row to a table or append to a feature table:
feature_row, feature_table = make_row_table(file_path, table_stem,
save_rows, row, row_data,
feature_row=None)
return feature_row, feature_table