# python-linearpartition
Unofficial CPython binding to LinearPartition
### Installation
Use `pip` to install the module.
```bash
pip install linearpartition-unofficial
```
You may build from the source code for unsupported Python versions or platforms.
```bas
git clone --recursive https://github.com/ChangLabSNU/python-linearpartition
cd python-linearpartition
pip install .
```
### Usage
The module currently only has one function called `partition(seq)`.
The seq parameter should be an RNA sequence in *uppercase* letters,
and any `T` should be converted to `U` before passing it to the function.
```python
>>> import linearpartition as lp
>>> seq = 'UGUCGGGGUUGGCUGUCUGACA'
>>> bpmtx, fe = lp.partition(seq)
>>> fe
-7.216465644007023
>>> import pandas as pd
>>> pd.DataFrame(bpmtx).sort_values('prob', ascending=False).head()
i j prob
19 3 18 0.999201
18 2 19 0.998801
17 1 20 0.997717
21 5 16 0.996692
22 4 17 0.996508
```
### Functions
#### linearpartition.partition()
The `linearpartition.partition` function is a Python C extension function that
calls [LinearPartition](https://github.com/LinearFold/LinearPartition) to
perform a linear partitioning operation and get the base pairing probability
matrix.
```python
linearpartition.partition(seq, mode='eterna', beamsize=100, dangles=2)
```
##### Parameters
- `seq` (required): A string containing the RNA sequence to be analyzed.
The sequence must be in uppercase and only contain A, C, G, and U.
This parameter is required.
- `mode` (optional): The name of free energy parameters to use. Use
`'vienna'` for Vienna RNA parameters, or `'eterna'` for EternaFold
parameters.
- `beamsize` (optional): An integer representing the beam size for the
operation. Larger value requires more computational time and memory.
The default value is 100.
- `dangles` (optional): An integer representing the number of dangles for
the partitioning operation. The default value is 2.
##### Return Value
This function returns a tuple containing the result of the partitioning
operation and the free energy of the ensemble structure in kcal/mol.
### Author
Hyeshik Chang <hyeshik@snu.ac.kr>
### License
This Python binding is licensed under [the MIT-style license](LICENSE).
However, the compiled binary includes code from the LinearPartition
package, which is licensed for non-commercial use.
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"description": "# python-linearpartition\n\nUnofficial CPython binding to LinearPartition\n\n### Installation\n\nUse `pip` to install the module.\n\n```bash\npip install linearpartition-unofficial\n```\n\nYou may build from the source code for unsupported Python versions or platforms.\n\n```bas\ngit clone --recursive https://github.com/ChangLabSNU/python-linearpartition\ncd python-linearpartition\npip install .\n```\n\n### Usage\n\nThe module currently only has one function called `partition(seq)`.\nThe seq parameter should be an RNA sequence in *uppercase* letters,\nand any `T` should be converted to `U` before passing it to the function.\n\n```python\n>>> import linearpartition as lp\n>>> seq = 'UGUCGGGGUUGGCUGUCUGACA'\n>>> bpmtx, fe = lp.partition(seq)\n>>> fe\n-7.216465644007023\n>>> import pandas as pd\n>>> pd.DataFrame(bpmtx).sort_values('prob', ascending=False).head()\n i j prob\n19 3 18 0.999201\n18 2 19 0.998801\n17 1 20 0.997717\n21 5 16 0.996692\n22 4 17 0.996508\n```\n\n### Functions\n\n#### linearpartition.partition()\n\nThe `linearpartition.partition` function is a Python C extension function that\ncalls [LinearPartition](https://github.com/LinearFold/LinearPartition) to\nperform a linear partitioning operation and get the base pairing probability\nmatrix.\n\n```python\nlinearpartition.partition(seq, mode='eterna', beamsize=100, dangles=2)\n```\n\n##### Parameters\n\n- `seq` (required): A string containing the RNA sequence to be analyzed.\n The sequence must be in uppercase and only contain A, C, G, and U.\n This parameter is required.\n- `mode` (optional): The name of free energy parameters to use. Use\n `'vienna'` for Vienna RNA parameters, or `'eterna'` for EternaFold\n parameters.\n- `beamsize` (optional): An integer representing the beam size for the\n operation. Larger value requires more computational time and memory.\n The default value is 100.\n- `dangles` (optional): An integer representing the number of dangles for\n the partitioning operation. The default value is 2.\n\n##### Return Value\n\nThis function returns a tuple containing the result of the partitioning\noperation and the free energy of the ensemble structure in kcal/mol.\n\n### Author\n\nHyeshik Chang <hyeshik@snu.ac.kr>\n\n### License\n\nThis Python binding is licensed under [the MIT-style license](LICENSE).\nHowever, the compiled binary includes code from the LinearPartition\npackage, which is licensed for non-commercial use.\n",
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