xio¶

Module: `xio`¶

Utility and I/O functions to read and write data files or Synapse tables.

Authors:

Arno Klein, 2015 (arno@sagebase.org) http://binarybottle.com

Functions¶

mhealthx.xio.convert_audio_file(old_file, new_file, command='ffmpeg', input_args='-i', output_args='-ac 2')¶

Convert audio file to new format.

old_file : string: full path to the input file
new_file : string: full path to the output file
command : string: executable command without arguments
input_args : string: arguments preceding input file name in command
output_args : string: arguments preceding output file name in command

new_file : string: full path to the output file

>>> from mhealthx.xio import convert_audio_file
>>> old_file = '/Users/arno/mhealthx_cache/mhealthx/feature_files/test.m4a'
>>> new_file = 'test.wav'
>>> command = 'ffmpeg'
>>> input_args = '-y -i'
>>> output_args = '-ac 2'
>>> new_file = convert_audio_file(old_file, new_file, command, input_args, output_args)

mhealthx.xio.extract_synapse_rows(synapse_table, save_path=None, limit=None, username='', password='')¶

Extract rows from a Synapse table.

synapse_table : string or Schema: a synapse ID or synapse table Schema object
save_path : string: save rows as separate files in this path, unless empty or None
limit : integer or None: limit to number of rows returned by the query
username : string: Synapse username (only needed once on a given machine)
password : string: Synapse password (only needed once on a given machine)

rows : list of pandas Series: each row of a Synapse table
row_files: list of strings: file names corresponding to each of the rows

>>> from mhealthx.xio import extract_synapse_rows
>>> import synapseclient
>>> syn = synapseclient.Synapse()
>>> syn.login()
>>> synapse_table = 'syn4590865'
>>> save_path = '.'
>>> limit = 3
>>> username = ''
>>> password = ''
>>> rows, row_files = extract_synapse_rows(synapse_table, save_path, limit, username='', password='')

mhealthx.xio.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

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)

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

>>> 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)

mhealthx.xio.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

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)

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

>>> 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)

mhealthx.xio.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

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)

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

>>> 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)

mhealthx.xio.read_accel_json(input_file, start=0, device_motion=True)¶

Read accelerometer or deviceMotion json file.

input_file : string: name of input accelerometer json file
start : integer: starting index (remove beginning)
device_motion : Boolean: use deviceMotion vs. accelerometer json file?

t : list: time points for accelerometer data
axyz : list of lists: x-, y-, and z-axis accelerometer data
gxyz : list of lists: x-, y-, and z-axis gravity (if deviceMotion)
wxyz : list of lists: w, x, y, z attitude quaternion (if deviceMotion)
rxyz : list of lists: x-, y-, and z-axis rotationRate (if deviceMotion)
sample_rate : float: sample rate
duration : float: duration of time series

>>> from mhealthx.xio import read_accel_json
>>> input_file = '/Users/arno/DriveWork/mhealthx/mpower_sample_data/deviceMotion_walking_outbound.json.items-90f7096a-84ac-4f29-a4d1-236ef92c3d262549858224214804657.tmp'
>>> start = 150
>>> device_motion = True
>>> t, axyz, gxyz, wxyz, rxyz, sample_rate, duration = read_accel_json(input_file, start, device_motion)

mhealthx.xio.read_file_from_synapse_table(synapse_table, row, column_name, out_path=None, username='', password='')¶

Read data from a row of a Synapse table.

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
out_path : string: 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)

row : pandas Series: same as passed in: row of a Synapse table as a file or Series
filepath : string: downloaded file (full path)

>>> from mhealthx.xio import extract_synapse_rows, read_file_from_synapse_table
>>> import synapseclient
>>> syn = synapseclient.Synapse()
>>> syn.login()
>>> synapse_table = 'syn4590865'
>>> save_path = '.'
>>> limit = 3
>>> username = ''
>>> password = ''
>>> rows, row_files = extract_synapse_rows(synapse_table, save_path, limit, username='', password='')
>>> column_name = 'audio_audio.m4a' #, 'audio_countdown.m4a']
>>> out_path = None
>>> for i in range(3):
>>>     row = rows[i]
>>>     row, filepath = read_file_from_synapse_table(synapse_table, row, column_name, out_path, username, password)
>>>     print(row)

mhealthx.xio.read_tap_json(input_file, start=0)¶

Read screen tap json file.

input_file : string: name of input screen tap json file
start : integer: starting index (remove beginning)

t : list: time points for tap data
tx : list: x coordinates of touch screen
ty : list: y coordinates of touch screen
button : list: buttons tapped
sample_rate : float: sample rate
duration : float: duration of time series

>>> from mhealthx.xio import read_tap_json
>>> input_file = '/Users/arno/DriveWork/mhealthx/mpower_sample_data/tapping_results.json.TappingSamples-49d2531d-dbda-4b6d-b403-f8763b8e05841011283015383434299.tmp'
>>> start = 0
>>> t, tx, ty, button, sample_rate, duration = read_tap_json(input_file, start)

mhealthx.xio.row_to_table(row_data, output_table)¶

Add row to table using nipype (thread-safe in multi-processor execution).

(Requires Python module lockfile)

row_data : pandas Series: row of data
output_table : string: add row to this table file

>>> import pandas as pd
>>> from mhealthx.xio import row_to_table
>>> row_data = pd.Series({'A': ['A0'], 'B': ['B0'], 'C': ['C0']})
>>> output_table = 'test.csv'
>>> row_to_table(row_data, output_table)

mhealthx.xio.write_wav(data, file_stem, file_append, sample_rate=44100, amplitude=32700)¶

Convert a list or array of numbers to a .wav format audio file.

After: http://blog.acipo.com/wave-generation-in-python/ and https://gist.github.com/Pretz/1773870 and http://codingmess.blogspot.com/2008/07/

how-to-make-simple-wav-file-with-python.html

data : list or array of floats or integers: input data to convert to audio file
file_stem : string: stem of file name of output audio file (including absolute path)
file_append : string: append string to file_stem for full output audio file path and name
sample_rate : integer: number of desired samples per second for audio file
amplitude : integer: maximum amplitude for audio file (32700 is within signed short)

wav_file : string: name of output .wav audio file

>>> from mhealthx.xio import write_wav
>>> import numpy as np
>>> from scipy.signal import resample
>>> file_stem = '/desk/temp'
>>> file_append = 'write_wav.wav'
>>> sample_rate = 44100
>>> amplitude = 32700
>>> data = np.random.random(500000)
>>> data /= np.max(np.abs(data))
>>> #data = resample(data, sample_rate/framerate)
>>> wav_file = write_wav(data, file_stem, file_append, sample_rate, amplitude)

xio¶

Module: xio¶

Functions¶

Module: `xio`¶