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adds option to use pygt3x to read gt3x files #29

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adds option to use pygt3x to read gt3x files #29

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6450a2a
adds option to use pygt3x to read gt3x files
Trybnetic Sep 11, 2024
9f68a45
Merge branch 'main' into pygt3x
Trybnetic Sep 17, 2024
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38 changes: 24 additions & 14 deletions paat/io.py
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Original file line number Diff line number Diff line change
Expand Up @@ -470,7 +470,7 @@
return time_data.flatten()


def read_gt3x(file, rescale=True, pandas=True, metadata=False):
def read_gt3x(file, rescale=True, pandas=True, metadata=False, use_pygt3x=False):
"""
Reads a .gt3x file and returns the tri-axial acceleration values together
with the corresponding time stamps and all meta data.
Expand All @@ -485,6 +485,8 @@
boolean indicating whether the data should be returned as a pandas DataFrame
metadata : boolean (optional)
boolean indicating whether the full metadata should be returned
use_pygt3x : boolean (optional)
boolean indicating whether to use ActiGraph's Pygt3x library to read the file.

Returns
-------
Expand All @@ -503,24 +505,32 @@
a dict containing all meta data produced by ActiGraph

"""
with tempfile.TemporaryDirectory() as tmpdirname:
# unzip .gt3x file and get the file location of the binary log.bin (which contains the raw data) and the info.txt which contains the meta-data
log_bin, info_txt = _unzip_gt3x_file(file=file, save_location=tmpdirname)
if use_pygt3x:
with FileReader(file) as reader:
values = reader.to_pandas()

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time = pd.to_datetime(values.index, unit="s").values
values = values.values

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# get meta data from info.txt file
meta = _extract_info(info_txt)
meta = read_metadata(file)

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else:
with tempfile.TemporaryDirectory() as tmpdirname:
# unzip .gt3x file and get the file location of the binary log.bin (which contains the raw data) and the info.txt which contains the meta-data
log_bin, info_txt = _unzip_gt3x_file(file=file, save_location=tmpdirname)

meta = _format_meta_data(meta)
# get meta data from info.txt file
meta = _extract_info(info_txt)
meta = _format_meta_data(meta)

# extract acceleration data from the log file
values, time_data = _extract_log(log_bin, meta['Acceleration_Scale'], meta['Sample_Rate'], use_scaling=rescale)
# extract acceleration data from the log file
values, time_data = _extract_log(log_bin, meta['Acceleration_Scale'], meta['Sample_Rate'], use_scaling=rescale)

# create time array
time = _create_time_array(time_data, hz=meta['Sample_Rate'])
# create time array
time = _create_time_array(time_data, hz=meta['Sample_Rate'])

# Add additional keys to meta (Note: they are important to later reconstruct the time vector)
meta["Number_Of_Samples"] = values.shape[0]
meta["Start_Time"] = time[0].astype(int)
# Add additional keys to meta (Note: they are important to later reconstruct the time vector)
meta["Number_Of_Samples"] = values.shape[0]
meta["Start_Time"] = time[0].astype(int)

if pandas:
data = pd.DataFrame(values, columns=["Y", "X", "Z"], index=time)
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