def get_convert_audio(synapse_table,
row,
column_name,
convert_file_append='',
convert_command='ffmpeg',
convert_input_args='-y -i',
convert_output_args='-ac 2',
out_path='.',
username='',
password=''):
"""
Read data from a row of a Synapse table and convert audio file.
Calls ::
from mhealthx.xio import read_file_from_synapse_table
from mhealthx.xio import convert_audio_file
Parameters
----------
synapse_table : string or Schema
a synapse ID or synapse table Schema object
row : pandas Series or string
row of a Synapse table converted to a Series or csv file
column_name : string
name of file handle column
convert_file_append : string
append to file name to indicate converted file format (e.g., '.wav')
convert_command : string
executable command without arguments
convert_input_args : string
arguments preceding input file name for convert_command
convert_output_args : string
arguments preceding output file name for convert_command
out_path : string or None
a local path in which to store downloaded files.
username : string
Synapse username (only needed once on a given machine)
password : string
Synapse password (only needed once on a given machine)
Returns
-------
row : pandas Series
same as passed in: row of a Synapse table as a file or Series
new_file : string
full path to the converted file
Examples
--------
>>> 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=3, 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, new_file = get_convert_audio(synapse_table,
>>> row, column_name,
>>> convert_file_append,
>>> convert_command,
>>> convert_input_args,
>>> convert_output_args,
>>> out_path, username, password)
"""
from mhealthx.xio import read_file_from_synapse_table, convert_audio_file
row, file_path = read_file_from_synapse_table(synapse_table, row,
column_name, out_path,
username, password)
if convert_file_append:
renamed_file = file_path + convert_file_append
new_file = convert_audio_file(old_file=file_path,
new_file=renamed_file,
command=convert_command,
input_args=convert_input_args,
output_args=convert_output_args)
else:
new_file = None
return row, new_file
def get_tap(synapse_table,
row,
column_name,
start=0,
out_path='.',
username='',
password=''):
"""
Read screen tapping json data from Synapse table row.
Calls ::
from mhealthx.xio import read_file_from_synapse_table
from mhealthx.xio import read_tap_json
Parameters
----------
synapse_table : string or Schema
a synapse ID or synapse table Schema object
row : pandas Series or string
row of a Synapse table converted to a Series or csv file
column_name : string
name of file handle column
start : integer
starting index (remove beginning)
out_path : string or None
a local path in which to store downloaded files.
If None, stores them in (~/.synapseCache)
username : string
Synapse username (only needed once on a given machine)
password : string
Synapse password (only needed once on a given machine)
Returns
-------
tx : list
x-axis screen tap data
ty : list
y-axis screen tap data
t : list
time points for accelerometer data
sample_rate : float
sample rate
duration : float
duration of time series
row : pandas Series
same as passed in: row of a Synapse table as a file or Series
file_path : string
path to accelerometer file
Examples
--------
>>> from mhealthx.xio import extract_synapse_rows, read_file_from_synapse_table, get_tap
>>> import synapseclient
>>> syn = synapseclient.Synapse()
>>> syn.login()
>>> synapse_table = 'syn4590866'
>>> row_series, row_files = extract_synapse_rows(synapse_table, save_path='.', limit=3, username='', password='')
>>> column_name = 'deviceMotion_walking_outbound.json.items'
>>> start = 150
>>> out_path = None
>>> 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)
>>> tx, ty, t, sample_rate, duration, row, file_path = get_tap(synapse_table,
>>> row, column_name,
>>> start, out_path, username, password)
"""
from mhealthx.xio import read_file_from_synapse_table, read_tap_json
# Load row data and accelerometer json file (full path):
row, file_path = read_file_from_synapse_table(synapse_table, row,
column_name, out_path,
username, password)
# Read accelerometer json file:
t, tx, ty, button, sample_rate, duration = read_tap_json(file_path, start)
return tx, ty, t, sample_rate, duration, row, file_path
def get_accel(synapse_table,
row,
column_name,
start=0,
device_motion=True,
out_path='.',
username='',
password=''):
"""
Read accelerometer json data from Synapse table row.
Calls ::
from mhealthx.xio import read_file_from_synapse_table
from mhealthx.xio import read_accel_json
Parameters
----------
synapse_table : string or Schema
a synapse ID or synapse table Schema object
row : pandas Series or string
row of a Synapse table converted to a Series or csv file
column_name : string
name of file handle column
start : integer
starting index (remove beginning)
device_motion : Boolean
use deviceMotion vs. accelerometer json file?
out_path : string or None
a local path in which to store downloaded files.
If None, stores them in (~/.synapseCache)
username : string
Synapse username (only needed once on a given machine)
password : string
Synapse password (only needed once on a given machine)
Returns
-------
t : list
time points for accelerometer data
ax : list
x-axis acceleration
ay : list
y-axis acceleration
az : list
z-axis acceleration
gx : list
x-axis gravity acceleration
gy : list
y-axis gravity acceleration
gz : list
z-axis gravity acceleration
rx : list
x-axis rotationRate
ry : list
y-axis rotationRate
rz : list
z-axis rotationRate
uw : list
w of attitude quaternion
ux : list
x of attitude quaternion
uy : list
y of attitude quaternion
uz : list
z of attitude quaternion
sample_rate : float
sample rate
duration : float
duration of time series
row : pandas Series
same as passed in: row of a Synapse table as a file or Series
file_path : string
path to accelerometer file
Examples
--------
>>> from mhealthx.xio import extract_synapse_rows, read_file_from_synapse_table, get_accel
>>> import synapseclient
>>> syn = synapseclient.Synapse()
>>> syn.login()
>>> synapse_table = 'syn4590866'
>>> row_series, row_files = extract_synapse_rows(synapse_table, save_path='.', limit=3, username='', password='')
>>> column_name = 'deviceMotion_walking_outbound.json.items'
>>> device_motion = True
>>> start = 150
>>> out_path = None
>>> 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)
>>> t, ax, ay, az, gx, gy, gz, rx, ry, rz, uw, ux, uy, uz, sample_rate, duration, row, file_path = get_accel(synapse_table,
>>> row, column_name,
>>> start, device_motion,
>>> out_path, username, password)
"""
from mhealthx.xio import read_file_from_synapse_table, read_accel_json
# Load row data and accelerometer json file (full path):
row, file_path = read_file_from_synapse_table(synapse_table, row,
column_name, out_path,
username, password)
# Read accelerometer json file:
t, axyz, gxyz, wxyz, rxyz, sample_rate, \
duration, = read_accel_json(file_path, start, device_motion)
ax, ay, az = axyz
gx, gy, gz = gxyz
rx, ry, rz = rxyz
uw, ux, uy, uz = wxyz
return t, ax, ay, az, gx, gy, gz, rx, ry, rz, uw, ux, uy, uz, \
sample_rate, duration, row, file_path
def get_convert_audio(synapse_table, row, column_name,
convert_file_append='', convert_command='ffmpeg',
convert_input_args='-y -i', convert_output_args='-ac 2',
out_path='.', username='', password=''):
"""
Read data from a row of a Synapse table and convert audio file.
Calls ::
from mhealthx.xio import read_file_from_synapse_table
from mhealthx.xio import convert_audio_file
Parameters
----------
synapse_table : string or Schema
a synapse ID or synapse table Schema object
row : pandas Series or string
row of a Synapse table converted to a Series or csv file
column_name : string
name of file handle column
convert_file_append : string
append to file name to indicate converted file format (e.g., '.wav')
convert_command : string
executable command without arguments
convert_input_args : string
arguments preceding input file name for convert_command
convert_output_args : string
arguments preceding output file name for convert_command
out_path : string or None
a local path in which to store downloaded files.
username : string
Synapse username (only needed once on a given machine)
password : string
Synapse password (only needed once on a given machine)
Returns
-------
row : pandas Series
same as passed in: row of a Synapse table as a file or Series
new_file : string
full path to the converted file
Examples
--------
>>> 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=3, 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, new_file = get_convert_audio(synapse_table,
>>> row, column_name,
>>> convert_file_append,
>>> convert_command,
>>> convert_input_args,
>>> convert_output_args,
>>> out_path, username, password)
"""
from mhealthx.xio import read_file_from_synapse_table, convert_audio_file
row, file_path = read_file_from_synapse_table(synapse_table, row,
column_name, out_path,
username, password)
if convert_file_append:
renamed_file = file_path + convert_file_append
new_file = convert_audio_file(old_file=file_path,
new_file=renamed_file,
command=convert_command,
input_args=convert_input_args,
output_args=convert_output_args)
else:
new_file = None
return row, new_file
def get_tap(synapse_table, row, column_name, start=0,
out_path='.', username='', password=''):
"""
Read screen tapping json data from Synapse table row.
Calls ::
from mhealthx.xio import read_file_from_synapse_table
from mhealthx.xio import read_tap_json
Parameters
----------
synapse_table : string or Schema
a synapse ID or synapse table Schema object
row : pandas Series or string
row of a Synapse table converted to a Series or csv file
column_name : string
name of file handle column
start : integer
starting index (remove beginning)
out_path : string or None
a local path in which to store downloaded files.
If None, stores them in (~/.synapseCache)
username : string
Synapse username (only needed once on a given machine)
password : string
Synapse password (only needed once on a given machine)
Returns
-------
tx : list
x-axis screen tap data
ty : list
y-axis screen tap data
t : list
time points for accelerometer data
sample_rate : float
sample rate
duration : float
duration of time series
row : pandas Series
same as passed in: row of a Synapse table as a file or Series
file_path : string
path to accelerometer file
Examples
--------
>>> from mhealthx.xio import extract_synapse_rows, read_file_from_synapse_table, get_tap
>>> import synapseclient
>>> syn = synapseclient.Synapse()
>>> syn.login()
>>> synapse_table = 'syn4590866'
>>> row_series, row_files = extract_synapse_rows(synapse_table, save_path='.', limit=3, username='', password='')
>>> column_name = 'deviceMotion_walking_outbound.json.items'
>>> start = 150
>>> out_path = None
>>> 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)
>>> tx, ty, t, sample_rate, duration, row, file_path = get_tap(synapse_table,
>>> row, column_name,
>>> start, out_path, username, password)
"""
from mhealthx.xio import read_file_from_synapse_table, read_tap_json
# Load row data and accelerometer json file (full path):
row, file_path = read_file_from_synapse_table(synapse_table, row,
column_name, out_path,
username, password)
# Read accelerometer json file:
t, tx, ty, button, sample_rate, duration = read_tap_json(file_path, start)
return tx, ty, t, sample_rate, duration, row, file_path
def get_accel(synapse_table, row, column_name, start=0, device_motion=True,
out_path='.', username='', password=''):
"""
Read accelerometer json data from Synapse table row.
Calls ::
from mhealthx.xio import read_file_from_synapse_table
from mhealthx.xio import read_accel_json
Parameters
----------
synapse_table : string or Schema
a synapse ID or synapse table Schema object
row : pandas Series or string
row of a Synapse table converted to a Series or csv file
column_name : string
name of file handle column
start : integer
starting index (remove beginning)
device_motion : Boolean
use deviceMotion vs. accelerometer json file?
out_path : string or None
a local path in which to store downloaded files.
If None, stores them in (~/.synapseCache)
username : string
Synapse username (only needed once on a given machine)
password : string
Synapse password (only needed once on a given machine)
Returns
-------
t : list
time points for accelerometer data
ax : list
x-axis acceleration
ay : list
y-axis acceleration
az : list
z-axis acceleration
gx : list
x-axis gravity acceleration
gy : list
y-axis gravity acceleration
gz : list
z-axis gravity acceleration
rx : list
x-axis rotationRate
ry : list
y-axis rotationRate
rz : list
z-axis rotationRate
uw : list
w of attitude quaternion
ux : list
x of attitude quaternion
uy : list
y of attitude quaternion
uz : list
z of attitude quaternion
sample_rate : float
sample rate
duration : float
duration of time series
row : pandas Series
same as passed in: row of a Synapse table as a file or Series
file_path : string
path to accelerometer file
Examples
--------
>>> from mhealthx.xio import extract_synapse_rows, read_file_from_synapse_table, get_accel
>>> import synapseclient
>>> syn = synapseclient.Synapse()
>>> syn.login()
>>> synapse_table = 'syn4590866'
>>> row_series, row_files = extract_synapse_rows(synapse_table, save_path='.', limit=3, username='', password='')
>>> column_name = 'deviceMotion_walking_outbound.json.items'
>>> device_motion = True
>>> start = 150
>>> out_path = None
>>> 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)
>>> t, ax, ay, az, gx, gy, gz, rx, ry, rz, uw, ux, uy, uz, sample_rate, duration, row, file_path = get_accel(synapse_table,
>>> row, column_name,
>>> start, device_motion,
>>> out_path, username, password)
"""
from mhealthx.xio import read_file_from_synapse_table, read_accel_json
# Load row data and accelerometer json file (full path):
row, file_path = read_file_from_synapse_table(synapse_table, row,
column_name, out_path,
username, password)
# Read accelerometer json file:
t, axyz, gxyz, wxyz, rxyz, sample_rate, \
duration, = read_accel_json(file_path, start, device_motion)
ax, ay, az = axyz
gx, gy, gz = gxyz
rx, ry, rz = rxyz
uw, ux, uy, uz = wxyz
return t, ax, ay, az, gx, gy, gz, rx, ry, rz, uw, ux, uy, uz, \
sample_rate, duration, row, file_path
