def run_sdf_features(data, number_of_symbols, row, file_path, table_stem,
save_rows):
"""
Extract symbolic dynamic filtering features.
Parameters
----------
data : numpy array
number_of_symbols : integer
number of symbols for symbolic dynamic filtering method
Returns
-------
feature_row : pandas Series
row combining the original row with a row of SDF 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_sdf_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
>>> number_of_symbols = 4
>>> row = pd.Series({'a':[1], 'b':[2], 'c':[3]})
>>> file_path = '.'
>>> table_stem = './walking'
>>> save_rows = True
>>> feature_row, feature_table = run_sdf_features(data, number_of_symbols, row, file_path, table_stem, save_rows)
"""
import pandas as pd
from mhealthx.extract import make_row_table
from mhealthx.extractors.symbolic_dynamic_filtering import sdf_features
sdf = sdf_features(data, number_of_symbols, pi_matrix_flag=False)
# Create row of data:
row_data = pd.DataFrame({'SDF eigenvector 1': sdf[0]}, index=[0])
for isdf in range(1, len(sdf)):
hdr = 'SDF eigenvalue ' + str(isdf + 1)
df = pd.DataFrame({hdr: sdf[isdf]}, index=[0])
row_data = pd.concat([row_data, df],
axis=1,
join_axes=[row_data.index])
# 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 run_sdf_features(data, number_of_symbols, row, file_path, table_stem, save_rows):
"""
Extract symbolic dynamic filtering features.
Parameters
----------
data : numpy array
number_of_symbols : integer
number of symbols for symbolic dynamic filtering method
Returns
-------
feature_row : pandas Series
row combining the original row with a row of SDF 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_sdf_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
>>> number_of_symbols = 4
>>> row = pd.Series({'a':[1], 'b':[2], 'c':[3]})
>>> file_path = '.'
>>> table_stem = './walking'
>>> save_rows = True
>>> feature_row, feature_table = run_sdf_features(data, number_of_symbols, row, file_path, table_stem, save_rows)
"""
import pandas as pd
from mhealthx.extract import make_row_table
from mhealthx.extractors.symbolic_dynamic_filtering import sdf_features
sdf = sdf_features(data, number_of_symbols, pi_matrix_flag=False)
# Create row of data:
row_data = pd.DataFrame({'SDF eigenvector 1': sdf[0]}, index=[0])
for isdf in range(1, len(sdf)):
hdr = 'SDF eigenvalue ' + str(isdf + 1)
df = pd.DataFrame({hdr: sdf[isdf]}, index=[0])
row_data = pd.concat([row_data, df], axis=1,
join_axes=[row_data.index])
# 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 run_openSMILE(audio_file, command, flag1, flags, flagn, args, closing,
row, table_stem, save_rows):
"""
Run openSMILE to process audio file and store feature row to a table.
Steps ::
1. Run openSMILE's SMILExtract audio feature extraction command.
2. Construct a feature row from the original and openSMILE rows.
3. Write the feature row to a table or append to a feature table.
Parameters
----------
audio_file : string
full path to the input audio file
command : string
name of command: "SMILExtract"
flag1 : string
optional first command line flag
flags : string or list of strings
command line flags precede their respective args: ["-C", "-I", "-O"]
flagn : string
optional last command line flag
args : string or list of strings
command line arguments: ["config.conf", "input.wav", "output.csv"]
closing : string
closing string in command
row : pandas Series
row to prepend, unaltered, to feature 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 openSMILE feature values
feature_table : string
output table file (full path)
Examples
--------
>>> # openSMILE setup, with examples below:
>>>
>>> import os
>>> from mhealthx.extract import run_openSMILE
>>> command = 'SMILExtract'
>>> flag1 = '-I'
>>> flags = '-C'
>>> flagn = '-csvoutput'
>>> args = os.path.join('/software', 'openSMILE-2.1.0', 'config',
>>> 'IS13_ComParE.conf')
>>> closing = '-nologfile 1'
>>> save_rows = True
>>>
>>> # Example: openSMILE sample waveform
>>>
>>>
>>> audio_file = '../sample-data/input.wav'
>>> command = 'SMILExtract'
>>> flag1 = '-I'
>>> flags = '-C'
>>> flagn = '-csvoutput'
>>> args = os.path.join('/software', 'openSMILE-2.1.0', 'config',
>>> 'IS13_ComParE.conf')
>>> closing = '-nologfile 1'
>>> row = ''
>>> table_stem = ''
>>> save_rows = True
>>> feature_row, feature_table = run_openSMILE(audio_file, command, flag1, flags, flagn, args, closing,
>>> row, table_stem, save_rows)
>>>
>>> # Example: phonation data
>>>
>>> from mhealthx.xio import get_convert_audio
>>> from mhealthx.xio import extract_synapse_rows, read_file_from_synapse_table
>>> import synapseclient
>>> syn = synapseclient.Synapse()
>>> syn.login()
>>> synapse_table = 'syn4590865'
>>> row_series, row_files = extract_synapse_rows(synapse_table, save_path='.', limit=1, username='', password='')
>>> column_name = 'audio_audio.m4a' #, 'audio_countdown.m4a']
>>> convert_file_append = '.wav'
>>> convert_command = 'ffmpeg'
>>> convert_input_args = '-y -i'
>>> convert_output_args = '-ac 2'
>>> out_path = '.'
>>> username = ''
>>> password = ''
>>> for i in range(1):
>>> row = row_series[i]
>>> row, filepath = read_file_from_synapse_table(synapse_table, row,
>>> column_name, out_path, username, password)
>>> print(row)
>>> row, audio_file = get_convert_audio(synapse_table,
>>> row, column_name,
>>> convert_file_append,
>>> convert_command,
>>> convert_input_args,
>>> convert_output_args,
>>> out_path, username, password)
>>> table_stem = './phonation'
>>> feature_row, feature_table = run_openSMILE(audio_file, command,
>>> flag1, flags, flagn, args, closing,
>>> row, table_stem, save_rows)
"""
import os
import pandas as pd
from mhealthx.utilities import run_command
from mhealthx.extract import make_row_table
# Run openSMILE's SMILExtract audio feature extraction command.
feature_row = None
feature_table = None
if os.path.isfile(audio_file):
argn = ''.join([audio_file.strip('.wav'),'.csv'])
cline, args, arg1, argn = run_command(command=command,
flag1=flag1,
arg1=audio_file,
flags=flags,
args=args,
flagn=flagn,
argn=argn,
closing=closing)
# Extract row data from openSMILE:
try:
row_data = pd.read_csv(argn, sep=";")
except IOError as e:
import traceback; traceback.print_exc()
print("I/O error({0}): {1}".format(e.errno, e.strerror))
print("filename = ", argn)
# Write feature row to a table or append to a feature table:
else:
feature_row, feature_table = make_row_table(argn, table_stem,
save_rows, row,
row_data, feature_row)
return feature_row, feature_table
def run_quality(gx, gy, gz, row, file_path, table_stem, save_rows=False):
"""
Extract various features from time series data.
Parameters
----------
gx : list
x-axis gravity acceleration
gy : list
y-axis gravity acceleration
gz : list
z-axis gravity acceleration
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 quality measures
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_quality
>>> input_file = '/Users/arno/DriveWork/mhealthx/mpower_sample_data/deviceMotion_walking_outbound.json.items-a2ab9333-6d63-4676-977a-08591a5d837f5221783798792869048.tmp'
>>> device_motion = True
>>> start = 150
>>> t, axyz, gxyz, uxyz, rxyz, sample_rate, duration = read_accel_json(input_file, start, device_motion)
>>> #ax, ay, az = axyz
>>> gx, gy, gz = gxyz
>>> #rx, ry, rz = rxyz
>>> #uw, ux, uy, uz = wxyz
>>> row = pd.Series({'a':[1], 'b':[2], 'c':[3]})
>>> file_path = '.'
>>> table_stem = './walking'
>>> save_rows = True
>>> feature_row, feature_table = run_quality(gx, gy, gz, row, file_path, table_stem, save_rows)
"""
import pandas as pd
from mhealthx.signals import accelerometer_signal_quality
from mhealthx.extract import make_row_table
# Compute different quality measures from the data:
min_mse, vertical = accelerometer_signal_quality(gx, gy, gz)
# Create row of data:
row_data = pd.DataFrame({'min_mse': min_mse,
'vertical': vertical},
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 run_tap_features(xtaps, ytaps, t, threshold,
row, file_path, table_stem, save_rows=False):
"""
Run touch screen tap feature extraction methods.
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)
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 tap feature values
feature_table : string
output table file (full path)
Examples
--------
>>> import numpy as np
>>> import pandas as pd
>>> from mhealthx.extract import run_tap_features
>>> xtaps = np.round(200 * np.random.random(100))
>>> ytaps = np.round(300 * np.random.random(100))
>>> t = np.linspace(1, 100, 100) / 30.0
>>> threshold = 20
>>> row = pd.Series({'a':[1], 'b':[2], 'c':[3]})
>>> file_path = '.'
>>> table_stem = './tapping'
>>> save_rows = True
>>> feature_row, feature_table = run_tap_features(xtaps, ytaps, t, threshold, row, file_path, table_stem, save_rows)
"""
import pandas as pd
from mhealthx.extractors.tapping import compute_tap_features
from mhealthx.extract import make_row_table
# Extract different features from the data:
T = compute_tap_features(xtaps, ytaps, t, threshold)
# Create row of data:
row_data = pd.DataFrame({'num_taps': T.num_taps,
'num_taps_left': T.num_taps_left,
'num_taps_right': T.num_taps_right,
'time_rng': T.time_rng,
'intertap_gap10': T.intertap_gap10,
'intertap_gap25': T.intertap_gap25,
'intertap_gap50': T.intertap_gap50,
'intertap_num': T.intertap_num,
'intertap_min': T.intertap_min,
'intertap_max': T.intertap_max,
'intertap_rng': T.intertap_rng,
'intertap_avg': T.intertap_avg,
'intertap_std': T.intertap_std,
'intertap_med': T.intertap_med,
'intertap_mad': T.intertap_mad,
'intertap_kurt': T.intertap_kurt,
'intertap_skew': T.intertap_skew,
'intertap_cvar': T.intertap_cvar,
'intertap_lower25': T.intertap_lower25,
'intertap_upper25': T.intertap_upper25,
'intertap_inter50': T.intertap_inter50,
'intertap_rms': T.intertap_rms,
'intertap_entropy': T.intertap_entropy,
'intertap_tk_energy': T.intertap_tk_energy,
'xL_num': T.xL_num,
'xL_min': T.xL_min,
'xL_max': T.xL_max,
'xL_rng': T.xL_rng,
'xL_avg': T.xL_avg,
'xL_std': T.xL_std,
'xL_med': T.xL_med,
'xL_mad': T.xL_mad,
'xL_kurt': T.xL_kurt,
'xL_skew': T.xL_skew,
'xL_cvar': T.xL_cvar,
'xL_lower25': T.xL_lower25,
'xL_upper25': T.xL_upper25,
'xL_inter50': T.xL_inter50,
'xL_rms': T.xL_rms,
'xL_entropy': T.xL_entropy,
'xL_tk_energy': T.xL_tk_energy,
'xR_num': T.xR_num,
'xR_min': T.xR_min,
'xR_max': T.xR_max,
'xR_rng': T.xR_rng,
'xR_avg': T.xR_avg,
'xR_std': T.xR_std,
'xR_med': T.xR_med,
'xR_mad': T.xR_mad,
'xR_kurt': T.xR_kurt,
'xR_skew': T.xR_skew,
'xR_cvar': T.xR_cvar,
'xR_lower25': T.xR_lower25,
'xR_upper25': T.xR_upper25,
'xR_inter50': T.xR_inter50,
'xR_rms': T.xR_rms,
'xR_entropy': T.xR_entropy,
'xR_tk_energy': T.xR_tk_energy,
'driftL_min': T.driftL_min,
'driftL_max': T.driftL_max,
'driftL_rng': T.driftL_rng,
'driftL_avg': T.driftL_avg,
'driftL_std': T.driftL_std,
'driftL_med': T.driftL_med,
'driftL_mad': T.driftL_mad,
'driftL_kurt': T.driftL_kurt,
'driftL_skew': T.driftL_skew,
'driftL_cvar': T.driftL_cvar,
'driftL_lower25': T.driftL_lower25,
'driftL_upper25': T.driftL_upper25,
'driftL_inter50': T.driftL_inter50,
'driftL_rms': T.driftL_rms,
'driftL_entropy': T.driftL_entropy,
'driftL_tk_energy': T.driftL_tk_energy,
'driftR_min': T.driftR_min,
'driftR_max': T.driftR_max,
'driftR_rng': T.driftR_rng,
'driftR_avg': T.driftR_avg,
'driftR_std': T.driftR_std,
'driftR_med': T.driftR_med,
'driftR_mad': T.driftR_mad,
'driftR_kurt': T.driftR_kurt,
'driftR_skew': T.driftR_skew,
'driftR_cvar': T.driftR_cvar,
'driftR_lower25': T.driftR_lower25,
'driftR_upper25': T.driftR_upper25,
'driftR_inter50': T.driftR_inter50,
'driftR_rms': T.driftR_rms,
'driftR_entropy': T.driftR_entropy,
'driftR_tk_energy': T.driftR_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 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 run_pyGait(data, t, sample_rate, duration, threshold, order, cutoff,
distance, row, file_path, table_stem, save_rows=False):
"""
Run pyGait (replication of iGAIT) accelerometer feature extraction code.
Steps ::
1. Run pyGait accelerometer feature extraction.
2. Construct a feature row from the original and pyGait rows.
3. Write the feature row to a table or append to a feature table.
Parameters
----------
data : numpy array
accelerometer data along any (preferably forward walking) axis
t : list or numpy array
accelerometer time points
sample_rate : float
sample rate of accelerometer reading (Hz)
duration : float
duration of accelerometer reading (s)
threshold : float
ratio to the maximum value of the anterior-posterior acceleration
order : integer
order of the Butterworth filter
cutoff : integer
cutoff frequency of the Butterworth filter (Hz)
distance : float
estimate of distance traversed
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 pyGait 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_pyGait
>>> from mhealthx.extractors.pyGait import project_on_walking_direction
>>> 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, ay, az = axyz
>>> stride_fraction = 1.0/8.0
>>> threshold0 = 0.5
>>> threshold = 0.2
>>> order = 4
>>> cutoff = max([1, sample_rate/10])
>>> distance = None
>>> row = pd.Series({'a':[1], 'b':[2], 'c':[3]})
>>> file_path = '/fake/path'
>>> table_stem = './walking'
>>> save_rows = True
>>> px, py, pz = project_on_walking_direction(ax, ay, az, t, sample_rate, stride_fraction, threshold0, order, cutoff)
>>> feature_row, feature_table = run_pyGait(py, t, sample_rate, duration, threshold, order, cutoff, distance, row, file_path, table_stem, save_rows)
"""
import pandas as pd
from mhealthx.extractors.pyGait import heel_strikes, gait
from mhealthx.extract import make_row_table
# Extract features from data:
strikes, strike_indices = heel_strikes(data, sample_rate, threshold,
order, cutoff, False, t)
number_of_steps, cadence, velocity, avg_step_length, avg_stride_length,\
step_durations, avg_step_duration, sd_step_durations, strides, \
stride_durations, avg_number_of_strides, avg_stride_duration, \
sd_stride_durations, step_regularity, stride_regularity, \
symmetry = gait(strikes, data, duration, distance)
# Create row of data:
row_data = pd.DataFrame({'number_of_steps': number_of_steps,
'cadence': cadence,
'velocity': velocity,
'avg_step_length': avg_step_length,
'avg_stride_length': avg_stride_length,
'avg_step_duration': avg_step_duration,
'sd_step_durations': sd_step_durations,
'avg_number_of_strides': avg_number_of_strides,
'avg_stride_duration': avg_stride_duration,
'sd_stride_durations': sd_stride_durations,
'step_regularity': step_regularity,
'stride_regularity': stride_regularity,
'symmetry': symmetry},
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 run_openSMILE(audio_file, command, flag1, flags, flagn, args, closing,
row, table_stem, save_rows):
"""
Run openSMILE to process audio file and store feature row to a table.
Steps ::
1. Run openSMILE's SMILExtract audio feature extraction command.
2. Construct a feature row from the original and openSMILE rows.
3. Write the feature row to a table or append to a feature table.
Parameters
----------
audio_file : string
full path to the input audio file
command : string
name of command: "SMILExtract"
flag1 : string
optional first command line flag
flags : string or list of strings
command line flags precede their respective args: ["-C", "-I", "-O"]
flagn : string
optional last command line flag
args : string or list of strings
command line arguments: ["config.conf", "input.wav", "output.csv"]
closing : string
closing string in command
row : pandas Series
row to prepend, unaltered, to feature 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 openSMILE feature values
feature_table : string
output table file (full path)
Examples
--------
>>> # openSMILE setup, with examples below:
>>>
>>> import os
>>> from mhealthx.extract import run_openSMILE
>>> command = 'SMILExtract'
>>> flag1 = '-I'
>>> flags = '-C'
>>> flagn = '-csvoutput'
>>> args = os.path.join('/software', 'openSMILE-2.1.0', 'config',
>>> 'IS13_ComParE.conf')
>>> closing = '-nologfile 1'
>>> save_rows = True
>>>
>>> # Example: phonation data
>>>
>>> from mhealthx.xio import get_convert_audio
>>> from mhealthx.xio import extract_synapse_rows, read_file_from_synapse_table
>>> import synapseclient
>>> syn = synapseclient.Synapse()
>>> syn.login()
>>> synapse_table = 'syn4590865'
>>> row_series, row_files = extract_synapse_rows(synapse_table, save_path='.', limit=1, username='', password='')
>>> column_name = 'audio_audio.m4a' #, 'audio_countdown.m4a']
>>> convert_file_append = '.wav'
>>> convert_command = 'ffmpeg'
>>> convert_input_args = '-y -i'
>>> convert_output_args = '-ac 2'
>>> out_path = '.'
>>> username = ''
>>> password = ''
>>> for i in range(1):
>>> row = row_series[i]
>>> row, filepath = read_file_from_synapse_table(synapse_table, row,
>>> column_name, out_path, username, password)
>>> print(row)
>>> row, audio_file = get_convert_audio(synapse_table,
>>> row, column_name,
>>> convert_file_append,
>>> convert_command,
>>> convert_input_args,
>>> convert_output_args,
>>> out_path, username, password)
>>> table_stem = './phonation'
>>> feature_row, feature_table = run_openSMILE(audio_file, command,
>>> flag1, flags, flagn, args, closing,
>>> row, table_stem, save_rows)
"""
import os
import pandas as pd
from mhealthx.utilities import run_command
from mhealthx.extract import make_row_table
# Run openSMILE's SMILExtract audio feature extraction command.
feature_row = None
feature_table = None
if os.path.isfile(audio_file):
argn = ''.join([audio_file.strip('.wav'),'.csv'])
cline, args, arg1, argn = run_command(command=command,
flag1=flag1,
arg1=audio_file,
flags=flags,
args=args,
flagn=flagn,
argn=argn,
closing=closing)
# Extract row data from openSMILE:
try:
row_data = pd.read_csv(argn, sep=";")
except IOError as e:
import traceback; traceback.print_exc()
print("I/O error({0}): {1}".format(e.errno, e.strerror))
print("filename = ", argn)
# Write feature row to a table or append to a feature table:
else:
feature_row, feature_table = make_row_table(argn, table_stem,
save_rows, row,
row_data, feature_row)
return feature_row, feature_table
def run_quality(gx, gy, gz, row, file_path, table_stem, save_rows=False):
"""
Extract various features from time series data.
Parameters
----------
gx : list
x-axis gravity acceleration
gy : list
y-axis gravity acceleration
gz : list
z-axis gravity acceleration
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 quality measures
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_quality
>>> input_file = '/Users/arno/DriveWork/mhealthx/mpower_sample_data/deviceMotion_walking_outbound.json.items-a2ab9333-6d63-4676-977a-08591a5d837f5221783798792869048.tmp'
>>> device_motion = True
>>> start = 150
>>> t, axyz, gxyz, uxyz, rxyz, sample_rate, duration = read_accel_json(input_file, start, device_motion)
>>> #ax, ay, az = axyz
>>> gx, gy, gz = gxyz
>>> #rx, ry, rz = rxyz
>>> #uw, ux, uy, uz = wxyz
>>> row = pd.Series({'a':[1], 'b':[2], 'c':[3]})
>>> file_path = '.'
>>> table_stem = './walking'
>>> save_rows = True
>>> feature_row, feature_table = run_quality(gx, gy, gz, row, file_path, table_stem, save_rows)
"""
import pandas as pd
from mhealthx.signals import accelerometer_signal_quality
from mhealthx.extract import make_row_table
# Compute different quality measures from the data:
min_mse, vertical = accelerometer_signal_quality(gx, gy, gz)
# Create row of data:
row_data = pd.DataFrame({'min_mse': min_mse,
'vertical': vertical},
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 run_tap_features(xtaps, ytaps, t, threshold,
row, file_path, table_stem, save_rows=False):
"""
Run touch screen tap feature extraction methods.
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)
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 tap feature values
feature_table : string
output table file (full path)
Examples
--------
>>> import numpy as np
>>> import pandas as pd
>>> from mhealthx.extract import run_tap_features
>>> xtaps = np.round(200 * np.random.random(100))
>>> ytaps = np.round(300 * np.random.random(100))
>>> t = np.linspace(1, 100, 100) / 30.0
>>> threshold = 20
>>> row = pd.Series({'a':[1], 'b':[2], 'c':[3]})
>>> file_path = '.'
>>> table_stem = './tapping'
>>> save_rows = True
>>> feature_row, feature_table = run_tap_features(xtaps, ytaps, t, threshold, row, file_path, table_stem, save_rows)
"""
import pandas as pd
from mhealthx.extractors.tapping import compute_tap_features
from mhealthx.extract import make_row_table
# Extract different features from the data:
T = compute_tap_features(xtaps, ytaps, t, threshold)
# Create row of data:
row_data = pd.DataFrame({'num_taps': T.num_taps,
'num_taps_left': T.num_taps_left,
'num_taps_right': T.num_taps_right,
'time_rng': T.time_rng,
'intertap_gap10': T.intertap_gap10,
'intertap_gap25': T.intertap_gap25,
'intertap_gap50': T.intertap_gap50,
'intertap_num': T.intertap_num,
'intertap_min': T.intertap_min,
'intertap_max': T.intertap_max,
'intertap_rng': T.intertap_rng,
'intertap_avg': T.intertap_avg,
'intertap_std': T.intertap_std,
'intertap_med': T.intertap_med,
'intertap_mad': T.intertap_mad,
'intertap_kurt': T.intertap_kurt,
'intertap_skew': T.intertap_skew,
'intertap_cvar': T.intertap_cvar,
'intertap_lower25': T.intertap_lower25,
'intertap_upper25': T.intertap_upper25,
'intertap_inter50': T.intertap_inter50,
'intertap_rms': T.intertap_rms,
'intertap_entropy': T.intertap_entropy,
'intertap_tk_energy': T.intertap_tk_energy,
'xL_num': T.xL_num,
'xL_min': T.xL_min,
'xL_max': T.xL_max,
'xL_rng': T.xL_rng,
'xL_avg': T.xL_avg,
'xL_std': T.xL_std,
'xL_med': T.xL_med,
'xL_mad': T.xL_mad,
'xL_kurt': T.xL_kurt,
'xL_skew': T.xL_skew,
'xL_cvar': T.xL_cvar,
'xL_lower25': T.xL_lower25,
'xL_upper25': T.xL_upper25,
'xL_inter50': T.xL_inter50,
'xL_rms': T.xL_rms,
'xL_entropy': T.xL_entropy,
'xL_tk_energy': T.xL_tk_energy,
'xR_num': T.xR_num,
'xR_min': T.xR_min,
'xR_max': T.xR_max,
'xR_rng': T.xR_rng,
'xR_avg': T.xR_avg,
'xR_std': T.xR_std,
'xR_med': T.xR_med,
'xR_mad': T.xR_mad,
'xR_kurt': T.xR_kurt,
'xR_skew': T.xR_skew,
'xR_cvar': T.xR_cvar,
'xR_lower25': T.xR_lower25,
'xR_upper25': T.xR_upper25,
'xR_inter50': T.xR_inter50,
'xR_rms': T.xR_rms,
'xR_entropy': T.xR_entropy,
'xR_tk_energy': T.xR_tk_energy,
'driftL_min': T.driftL_min,
'driftL_max': T.driftL_max,
'driftL_rng': T.driftL_rng,
'driftL_avg': T.driftL_avg,
'driftL_std': T.driftL_std,
'driftL_med': T.driftL_med,
'driftL_mad': T.driftL_mad,
'driftL_kurt': T.driftL_kurt,
'driftL_skew': T.driftL_skew,
'driftL_cvar': T.driftL_cvar,
'driftL_lower25': T.driftL_lower25,
'driftL_upper25': T.driftL_upper25,
'driftL_inter50': T.driftL_inter50,
'driftL_rms': T.driftL_rms,
'driftL_entropy': T.driftL_entropy,
'driftL_tk_energy': T.driftL_tk_energy,
'driftR_min': T.driftR_min,
'driftR_max': T.driftR_max,
'driftR_rng': T.driftR_rng,
'driftR_avg': T.driftR_avg,
'driftR_std': T.driftR_std,
'driftR_med': T.driftR_med,
'driftR_mad': T.driftR_mad,
'driftR_kurt': T.driftR_kurt,
'driftR_skew': T.driftR_skew,
'driftR_cvar': T.driftR_cvar,
'driftR_lower25': T.driftR_lower25,
'driftR_upper25': T.driftR_upper25,
'driftR_inter50': T.driftR_inter50,
'driftR_rms': T.driftR_rms,
'driftR_entropy': T.driftR_entropy,
'driftR_tk_energy': T.driftR_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 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 run_pyGait(data, t, sample_rate, duration, threshold, order, cutoff,
distance, row, file_path, table_stem, save_rows=False):
"""
Run pyGait (replication of iGAIT) accelerometer feature extraction code.
Steps ::
1. Run pyGait accelerometer feature extraction.
2. Construct a feature row from the original and pyGait rows.
3. Write the feature row to a table or append to a feature table.
Parameters
----------
data : numpy array
accelerometer data along any (preferably forward walking) axis
t : list or numpy array
accelerometer time points
sample_rate : float
sample rate of accelerometer reading (Hz)
duration : float
duration of accelerometer reading (s)
threshold : float
ratio to the maximum value of the anterior-posterior acceleration
order : integer
order of the Butterworth filter
cutoff : integer
cutoff frequency of the Butterworth filter (Hz)
distance : float
estimate of distance traversed
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 pyGait 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_pyGait
>>> from mhealthx.extractors.pyGait import project_on_walking_direction
>>> 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, ay, az = axyz
>>> stride_fraction = 1.0/8.0
>>> threshold0 = 0.5
>>> threshold = 0.2
>>> order = 4
>>> cutoff = max([1, sample_rate/10])
>>> distance = None
>>> row = pd.Series({'a':[1], 'b':[2], 'c':[3]})
>>> file_path = '/fake/path'
>>> table_stem = './walking'
>>> save_rows = True
>>> px, py, pz = project_on_walking_direction(ax, ay, az, t, sample_rate, stride_fraction, threshold0, order, cutoff)
>>> feature_row, feature_table = run_pyGait(py, t, sample_rate, duration, threshold, order, cutoff, distance, row, file_path, table_stem, save_rows)
"""
import pandas as pd
from mhealthx.extractors.pyGait import heel_strikes, gait
from mhealthx.extract import make_row_table
# Extract features from data:
strikes, strike_indices = heel_strikes(data, sample_rate, threshold,
order, cutoff, False, t)
number_of_steps, cadence, velocity, avg_step_length, avg_stride_length,\
step_durations, avg_step_duration, sd_step_durations, strides, \
stride_durations, avg_number_of_strides, avg_stride_duration, \
sd_stride_durations, step_regularity, stride_regularity, \
symmetry = gait(strikes, data, duration, distance)
# Create row of data:
row_data = pd.DataFrame({'number_of_steps': number_of_steps,
'cadence': cadence,
'velocity': velocity,
'avg_step_length': avg_step_length,
'avg_stride_length': avg_stride_length,
'avg_step_duration': avg_step_duration,
'sd_step_durations': sd_step_durations,
'avg_number_of_strides': avg_number_of_strides,
'avg_stride_duration': avg_stride_duration,
'sd_stride_durations': sd_stride_durations,
'step_regularity': step_regularity,
'stride_regularity': stride_regularity,
'symmetry': symmetry},
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
