# Libraries for reading BIOPAC files
[](https://zenodo.org/badge/latestdoi/970625)
These utilities are for reading the files produced by BIOPAC's AcqKnowledge software. Much of the information is based on [Application Note 156](http://www.biopac.com/Manuals/app_pdf/app156.pdf) from BIOPAC; however, newer file formats were decoded through the tireless efforts of John Ollinger and Nate Vack.
This library is mostly concerned with getting you the data, and less so with interpreting UI-related header values.
## Status
As far as I know, this should read any AcqKnowledge file you throw at it. Windows, Mac, uncompressed, compressed, old, new... it should happily read 'em all. If you have trouble with a file, I'd love to get a copy and make bioread work with it.
## Installation
We're up in [pypi](http://pypi.python.org/pypi), so installing should be as simple as:
```
pip install bioread
```
Some of the optional parts of bioread depend on external libraries. `acq2hdf5` depends on [h5py](https://www.h5py.org/) and `acq2mat` depends on [scipy](https://www.scipy.org/), but as neither of those are core parts of bioread (and can be hairy to get working on some systems), they aren't installed by default. To get them, do:
```
# Just h5py
pip install bioread[hdf5]
# Just scipy
pip install bioread[mat]
# The whole shebang
pip install bioread[all]
```
As of May 2020 (version 2), we now require Python 3.6 or later. Versions 1.0.4 and below should work with Python 2.7 and up.
## API Usage:
* [jupyter notebook: Quick Demo](http://uwmadison-chm.github.io/bioread/bioread_quick_demo.html)
## Command-line usage:
### acq2hdf5
If you want to convert files out of AcqKnowledge, this is probably what you want to use -- Matlab can read these out of the box and there are libraries for R and such. This converts the file, storing channels as datasets with names like `/channels/channel_0` and metadata in attributes. Event markers are stored in `/event_markers/marker_X`
```
Convert an AcqKnowledge file to an HDF5 file.
Usage:
acq2hdf5 [options] <acq_file> <hdf5_file>
acq2hdf5 -h | --help
acq2hdf5 --version
Options:
--values-as=<type> Save raw measurement values, stored as integers in the
base file, as either 'raw' or 'scaled'. If stored as
raw, you can convert to scaled using the scale and
offset attributes on the channel. If storing scaled
values, scale and offset will be 1 and 0.
[default: scaled]
--compress=<method> How to compress data. Options are gzip, lzf, none.
[default: gzip]
--data-only Only save data and required headers -- do not save
journal or marker information.
-v, --verbose Print extra messages for debugging.
```
Note this does *not* need to read the entire dataset into memory, so if you have a 2G dataset, this will work great.
To get the values you see in AcqKnowledge, leave the `--values-as` option to its default ('scaled'). For faster performance, less memory usage, and smaller files, you can use 'raw' and convert the channel later (if you care) with the scale and offset attributes.
Generally, gzip compression seems to work very well, but if you're making something really big you might want to use lzf (worse compression, much faster).
What you'll find in the file:
#### Root-level attributes:
* `file_revision` The internal AckKnowledge file version number
* `samples_per_second` The base sampling rate of the file
* `byte_order` The original file's byte ordering
* `journal` The file's journal data.
#### Channel-level attributes:
* `scale` The scale factor of raw data (for float-type data, will be 1)
* `offset` The offset of raw data (for float-type data, will be 0)
* `frequency_divider` The sampling rate divider for this channel
* `samples_per_second` The channel's sampling rate
* `name` The name of the channel
* `units` The units for the channel
* `channel_number` The display number for the channel (used in markers)
#### Markers
* `label` A text label for the channel
* `type` A description of this marker's type
* `type_code` A short, 4-character code for type
* `global_sample_index` The index, in units of the main sampling rate, of this marker
* `channel` A hard link to the referred channel (only for non-global events)
* `channel_number` The display number for the channel (only for non-global events)
* `channel_sample_index` The in the channel's data where this marker belongs (only for non-global events)
### acq2mat
**Note:** I recommend `acq2hdf5` for exporting to Matlab. This program is still around because hey: It works.
This program creates a Matlab (version 5) file from an AcqKnowledge file. On the back-end, it uses [scipy.io.savemat](http://docs.scipy.org/doc/scipy/reference/generated/scipy.io.savemat.html). Channels are stored in a cell array named 'channels'.
```
Convert an AcqKnowledge file to a MATLAB file.
Usage:
acq2mat [options] <acq_file> <mat_file>
acq2mat -h | --help
acq2mat --version
Options:
-c, --compress Save compressed Matlab file
--data-only Only save data and required header information -- do not
save event markers.
Note: scipy is required for this program.
```
If you've saved a file as `myfile.mat`, you can, in Matlab:
```
>> data = load('myfile.mat')
data =
channels: {1x2 cell}
markers: {1x3 cell}
headers: [1x1 struct]
samples_per_second: 1000
>> data.channels{1}
ans =
units: 'Percent'
frequency_divider: 1
samples_per_second: 1000
data: [1x10002 double]
name: 'CO2'
>> plot(data.channels{1}.data)
(Plots the data)
>> data.markers{1}
ans =
style: 'apnd'
sample_index: 0
label: 'Segment 1'
channel: Global
```
### acq2txt
acq2txt will take the data in an AcqKnowledge file and write it to a tab-delimited text file. By default, all channels (plus a time index) will be
written.
```
Write the data from an AcqKnowledge file channel to a text file.
Usage:
acq2txt [options] <acq_file>
acq2txt -h | --help
acq2txt --version
Options:
--version Show program's version number and exit.
-h, --help Show this help message and exit.
--channel-indexes=<indexes> The indexes of the channels to extract.
Separate numbers with commas. Default is to
extract all channels.
-o, --outfile=<file> Write to a file instead of standard out.
--missing-as=<val> What value to write where a channel is not
sampled. [default: ]
The first column will always be time in seconds. Channel raw values are
converted with scale and offset into native units.
```
### acq_info
acq_info prints out some simple debugging information about an AcqKnowledge file. It'll do its best to print something out even for damaged files.
```
Print some information about an AcqKnowledge file.
Usage:
acq_info [options] <acq_file>
acq_info -h | --help
acq_info --version
Options:
-d, --debug print lots of debugging data
Note: Using - for <acq_file> reads from stdin.
```
As noted in the usage instructions, acq_info will read from stdin, so if your files are gzipped, you can say:
```
zcat myfile.acq.gz | acq_info -
```
### acq_markers
Prints all of the markers in an AcqKnowlege file to a tab-delimited format, either to stdout or to a specified file. Fields are:
`filename time (s) label channel style`
```
Print the event markers from an AcqKnowledge file.
Usage:
acq_markers [options] <file>...
acq_markers -h | --help
acq_markers --version
Options:
-o <file> Write to a file instead of standard output.
```
Note that this one does not read from stdin; in this case, printing the markers from a large number of files was more important than feeding from `zcat` or something.
## Notes
I've tested all the various vintages of files I can think of and find, except very old (AcqKnowledge 2.x) files.
Also, the channel order I read is not the one displayed in the AcqKnowledge interface. Neither the order of the data nor any channel header value I can find seems to entirely control that. I'm gonna just assume it's not a very big deal.
## File Format Documentation
While there's no substite for code diving to see how things really work, I've written some [quick documentation of the file format.](https://github.com/njvack/bioread/blob/master/notes/file_format.md)
In addition, developer Mike Davison did a great job figuring out additional .acq file format information (far more than is implemented in bioread!); his contribuions are in notes/acqknowledge_file_structure.pdf
## Credits
This code was pretty much all written by Nate Vack <njvack@wisc.edu>, with a lot of initial research done by John Ollinger.
## Copyright & Disclaimers
bioread is distributed under the MIT license. For more details, see LICENSE.
BIOPAC and AcqKnowledge are trademarks of BIOPAC Systems, Inc. The authors of this software have no affiliation with BIOPAC Systems, Inc, and that company neither supports nor endorses this software package.
Raw data
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"description": "# Libraries for reading BIOPAC files\n\n[](https://zenodo.org/badge/latestdoi/970625)\n\nThese utilities are for reading the files produced by BIOPAC's AcqKnowledge software. Much of the information is based on [Application Note 156](http://www.biopac.com/Manuals/app_pdf/app156.pdf) from BIOPAC; however, newer file formats were decoded through the tireless efforts of John Ollinger and Nate Vack.\n\nThis library is mostly concerned with getting you the data, and less so with interpreting UI-related header values.\n\n## Status\n\nAs far as I know, this should read any AcqKnowledge file you throw at it. Windows, Mac, uncompressed, compressed, old, new... it should happily read 'em all. If you have trouble with a file, I'd love to get a copy and make bioread work with it.\n\n## Installation\n\nWe're up in [pypi](http://pypi.python.org/pypi), so installing should be as simple as:\n\n```\npip install bioread\n```\n\nSome of the optional parts of bioread depend on external libraries. `acq2hdf5` depends on [h5py](https://www.h5py.org/) and `acq2mat` depends on [scipy](https://www.scipy.org/), but as neither of those are core parts of bioread (and can be hairy to get working on some systems), they aren't installed by default. To get them, do:\n\n```\n# Just h5py\npip install bioread[hdf5]\n# Just scipy\npip install bioread[mat]\n# The whole shebang\npip install bioread[all]\n```\n\nAs of May 2020 (version 2), we now require Python 3.6 or later. Versions 1.0.4 and below should work with Python 2.7 and up.\n\n## API Usage:\n\n* [jupyter notebook: Quick Demo](http://uwmadison-chm.github.io/bioread/bioread_quick_demo.html)\n\n## Command-line usage:\n\n### acq2hdf5\n\nIf you want to convert files out of AcqKnowledge, this is probably what you want to use -- Matlab can read these out of the box and there are libraries for R and such. This converts the file, storing channels as datasets with names like `/channels/channel_0` and metadata in attributes. Event markers are stored in `/event_markers/marker_X`\n\n```\nConvert an AcqKnowledge file to an HDF5 file.\n\nUsage:\n acq2hdf5 [options] <acq_file> <hdf5_file>\n acq2hdf5 -h | --help\n acq2hdf5 --version\n\nOptions:\n --values-as=<type> Save raw measurement values, stored as integers in the\n base file, as either 'raw' or 'scaled'. If stored as\n raw, you can convert to scaled using the scale and\n offset attributes on the channel. If storing scaled\n values, scale and offset will be 1 and 0.\n [default: scaled]\n --compress=<method> How to compress data. Options are gzip, lzf, none.\n [default: gzip]\n --data-only Only save data and required headers -- do not save\n journal or marker information.\n -v, --verbose Print extra messages for debugging.\n ```\n\nNote this does *not* need to read the entire dataset into memory, so if you have a 2G dataset, this will work great.\n\nTo get the values you see in AcqKnowledge, leave the `--values-as` option to its default ('scaled'). For faster performance, less memory usage, and smaller files, you can use 'raw' and convert the channel later (if you care) with the scale and offset attributes.\n\nGenerally, gzip compression seems to work very well, but if you're making something really big you might want to use lzf (worse compression, much faster).\n\nWhat you'll find in the file:\n\n#### Root-level attributes:\n\n* `file_revision` The internal AckKnowledge file version number\n* `samples_per_second` The base sampling rate of the file\n* `byte_order` The original file's byte ordering\n* `journal` The file's journal data.\n\n#### Channel-level attributes:\n\n* `scale` The scale factor of raw data (for float-type data, will be 1)\n* `offset` The offset of raw data (for float-type data, will be 0)\n* `frequency_divider` The sampling rate divider for this channel\n* `samples_per_second` The channel's sampling rate\n* `name` The name of the channel\n* `units` The units for the channel\n* `channel_number` The display number for the channel (used in markers)\n\n#### Markers\n\n* `label` A text label for the channel\n* `type` A description of this marker's type\n* `type_code` A short, 4-character code for type\n* `global_sample_index` The index, in units of the main sampling rate, of this marker\n* `channel` A hard link to the referred channel (only for non-global events)\n* `channel_number` The display number for the channel (only for non-global events)\n* `channel_sample_index` The in the channel's data where this marker belongs (only for non-global events)\n\n\n### acq2mat\n\n**Note:** I recommend `acq2hdf5` for exporting to Matlab. This program is still around because hey: It works.\n\nThis program creates a Matlab (version 5) file from an AcqKnowledge file. On the back-end, it uses [scipy.io.savemat](http://docs.scipy.org/doc/scipy/reference/generated/scipy.io.savemat.html). Channels are stored in a cell array named 'channels'.\n\n```\nConvert an AcqKnowledge file to a MATLAB file.\n\nUsage:\n acq2mat [options] <acq_file> <mat_file>\n acq2mat -h | --help\n acq2mat --version\n\nOptions:\n -c, --compress Save compressed Matlab file\n --data-only Only save data and required header information -- do not\n save event markers.\n\nNote: scipy is required for this program.\n```\n\nIf you've saved a file as `myfile.mat`, you can, in Matlab:\n\n```\n>> data = load('myfile.mat')\n\ndata =\n\n channels: {1x2 cell}\n markers: {1x3 cell}\n headers: [1x1 struct]\n samples_per_second: 1000\n\n>> data.channels{1}\n\nans =\n\n units: 'Percent'\n frequency_divider: 1\n samples_per_second: 1000\n data: [1x10002 double]\n name: 'CO2'\n\n>> plot(data.channels{1}.data)\n\n(Plots the data)\n\n>> data.markers{1}\n\nans =\n\n style: 'apnd'\n sample_index: 0\n label: 'Segment 1'\n channel: Global\n```\n\n### acq2txt\n\nacq2txt will take the data in an AcqKnowledge file and write it to a tab-delimited text file. By default, all channels (plus a time index) will be\nwritten.\n\n```\nWrite the data from an AcqKnowledge file channel to a text file.\n\nUsage:\n acq2txt [options] <acq_file>\n acq2txt -h | --help\n acq2txt --version\n\nOptions:\n --version Show program's version number and exit.\n -h, --help Show this help message and exit.\n --channel-indexes=<indexes> The indexes of the channels to extract.\n Separate numbers with commas. Default is to\n extract all channels.\n -o, --outfile=<file> Write to a file instead of standard out.\n --missing-as=<val> What value to write where a channel is not\n sampled. [default: ]\n\nThe first column will always be time in seconds. Channel raw values are\nconverted with scale and offset into native units.\n```\n\n### acq_info\n\nacq_info prints out some simple debugging information about an AcqKnowledge file. It'll do its best to print something out even for damaged files.\n\n```\nPrint some information about an AcqKnowledge file.\n\nUsage:\n acq_info [options] <acq_file>\n acq_info -h | --help\n acq_info --version\n\nOptions:\n -d, --debug print lots of debugging data\n\nNote: Using - for <acq_file> reads from stdin.\n```\n\nAs noted in the usage instructions, acq_info will read from stdin, so if your files are gzipped, you can say:\n\n```\nzcat myfile.acq.gz | acq_info -\n```\n\n### acq_markers\n\nPrints all of the markers in an AcqKnowlege file to a tab-delimited format, either to stdout or to a specified file. Fields are:\n\n`filename time (s) label channel style`\n\n\n```\nPrint the event markers from an AcqKnowledge file.\n\nUsage:\n acq_markers [options] <file>...\n acq_markers -h | --help\n acq_markers --version\n\nOptions:\n -o <file> Write to a file instead of standard output.\n```\n\nNote that this one does not read from stdin; in this case, printing the markers from a large number of files was more important than feeding from `zcat` or something.\n\n## Notes\n\nI've tested all the various vintages of files I can think of and find, except very old (AcqKnowledge 2.x) files.\n\nAlso, the channel order I read is not the one displayed in the AcqKnowledge interface. Neither the order of the data nor any channel header value I can find seems to entirely control that. I'm gonna just assume it's not a very big deal.\n\n## File Format Documentation\n\nWhile there's no substite for code diving to see how things really work, I've written some [quick documentation of the file format.](https://github.com/njvack/bioread/blob/master/notes/file_format.md)\n\nIn addition, developer Mike Davison did a great job figuring out additional .acq file format information (far more than is implemented in bioread!); his contribuions are in notes/acqknowledge_file_structure.pdf\n\n## Credits\n\nThis code was pretty much all written by Nate Vack <njvack@wisc.edu>, with a lot of initial research done by John Ollinger.\n\n## Copyright & Disclaimers\n\nbioread is distributed under the MIT license. For more details, see LICENSE.\n\nBIOPAC and AcqKnowledge are trademarks of BIOPAC Systems, Inc. The authors of this software have no affiliation with BIOPAC Systems, Inc, and that company neither supports nor endorses this software package.\n",
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