README: update python usage
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README.md
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README.md
@ -200,110 +200,38 @@ will write the single channel with id `usi16` to the file
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### Python
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...tbc...
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To be able to use the Python module _tdm_reaper_ it first has to be build locally
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or installed on the system. In the Python interpreter simply do:
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## !!! Deprecated !!!
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The makefile provides targets for using the library both as native C++ extension
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and as Python module. The package supports usage on Linux and MacOSX.
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The tdm_ripper module is built on these platforms by
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```Shell
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# Linux
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pip install Cython
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make install
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```Python
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import tdm_reaper
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```
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and
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to import the module. The TDM files are provided by creating an instance of
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the _tdm_reaper_ class:
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```Shell
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# macOS
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pip install Cython
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make install_osx
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```Python
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# create 'tdm_reaper' instance object
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try :
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jack = tdm_reaper.tdmreaper(b'samples/SineData.tdm',b'samples/SineData.tdx')
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except RuntimeError as e:
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print("failed to load/decode TDM files: " + str(e))
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```
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## Usage
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After initializing the _tdm_reaper_ object it can be used to extract any of the
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available data. For instance, to list the included channelgroups and channels:
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Although the package is built upon a C++ core, which decodes the data, it may be
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used as a Python module, as well, by interfacing the C++ library with a Cython
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wrapper.
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```Python
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# list ids of channelgroups
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grpids = jack.get_channelgroup_ids()
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### C++ core
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- In order to parse the XML tree of the .tdm file, the library employs pugixml:
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https://pugixml.org/ and https://github.com/zeux/pugixml
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- The package currently supports the following datatypes:
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- eInt8Usi: 8 byte
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- eInt16Usi: 16 byte
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- eInt32Usi: 32 byte
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- eInt64Usi: 64 byte
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- eUInt8Usi: 8 byte
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- eUInt16Usi: 16 byte
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- eUInt32Usi: 32 byte
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- eUInt64Usi: 64 byte
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- eFloat32Usi: 32 byte
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- eFloat64Usi: 64 byte
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- The core of the library takes care of the decoding by reinterpretation of the
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binary in the buffer as the required datatype implemented by
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# list ids of channels
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chnids = jack.get_channel_ids()
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```
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```C++
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uint8_t *dfcast = reinterpret_cast<uint8_t*>(&df);
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for ( int i = 0; i < (int)sizeof(double); i++ )
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{
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dfcast[i] = (int)bych[i];
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}
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```
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where for instance df is the resulting float and bych contains the binary
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data as an array of chars.
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- main.cpp contains an example of how the C++ library might be used to provide
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the channels and groups of the dataset. It is simply build by
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```Shell
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make
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```
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- extract_all.cpp takes the .tdm, the .tdx file and some output directory as arguments
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to provide all given information in .csv format without any logging. To build:
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```Shell
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make extall
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```
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For instance, the executable accepts the following arguments:
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```Shell
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./extract_all samples/SineData.tdm samples/SineData.tdx data/
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```
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### Python module
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- The library may also be used as a Python module and supports the use of
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group channels in NumPy arrays as shown in example.py .
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- To extract all available information and data in the TDM files without any
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further interaction, the use of extract_all.py is recommended. To exhibit the
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required arguments:
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```Shell
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python extract_all.py --help
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```
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- The same functionality may be obtained from an existing python script by
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importing the tdm_ripper module and calling the extract_all function. For
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instance
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```Python
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import tdm_ripper as td
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files = td.extract_all(b"samples/SineData.tdm",b"samples/SineData.tdx",b"data/",b"my_tdm")
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```
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where the arguments "data/" and "my_tdm" are optional. "data/" specifies the
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directory where all .csv output is dumped while "my_tdm" represents a name
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prefix for all csv. files.
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Note, that all string arguments must be converted to bytes before passing to
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the argument list by prepending "b".
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For a full example and to see how the actual data is extracted see the example
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`python/usage.py`.
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## References
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