# ProteinNetPy 1.0.1
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[](https://proteinnetpy.readthedocs.io/en/latest/?badge=latest)
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A python library for working with [ProteinNet](https://github.com/aqlaboratory/proteinnet) text data, allowing you to easily load, stream and filter data, map functions across records and produce TensorFlow datasets.
For details of the dataset see the ProteinNet [Bioinformatics paper](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-2932-0).
Documentation for all functions of the module is available [here](https://proteinnetpy.readthedocs.io/en/latest/).
## Install
`pip install proteinnetpy`
Or install the development version from Github:
`pip install git+https://github.com:allydunham/proteinnetpy`
## Requirements
* Python 3
* Numpy
* Biopython
* TensorFlow (if using the `datasets` module)
## Basic Usage
The main object used in ProteinNetPy is the ProteinNetRecord, which allows access to the various record fields and methods for common manipulations, such as calculating a one-hot sequence representation or residue distance matrix.
It also supports most applicable operations like `len`, `str` etc.
While the `parser` module contains a generator to parse files, it is generally easier to use the `ProteinNetDataset` class from the data module:
```python
from proteinnetpy.data import ProteinNetDataset
data = ProteinNetDataset(path="path/to/proteinnet")
```
This class includes a `preload` argument, which determines if the dataset is loaded into memory or streamed.
It also supports filtering using the `filter_func` argument, which is passed a function that returns truthy values for a record to determine if it is kept in the dataset.
A range of common filters are included in the data module, as well as `combine_filters()`, which can applies all passed filters to each record.
Once a dataset has been loaded it can be iterated over to process data.
The `ProteinNetMap` class creates map objects that map a function over the dataset, including options to stream the map on each iteration or pre-calculate results.
They have a `generate` method that creates a generator object yielding the output of the function.
The `LabeledFunction` class is provided to create functions annotated with output types and shapes, used for automatically creating TensorFlow datasets.
The `mutation` module provides some example functions that return mutated records.
The following example code shows a typical simple usage, creating a streamed TensorFlow dataset from ProteinNet data:
```python
from proteinnetpy import data
from proteinnetpy import tfdataset
class MapFunction(data.LabeledFunction):
"""
Example ProteinNetMap function outputting a one-hot sequence and contact graph input data
and multiple alignment PSSM labels
"""
def __init__(self):
self.output_shapes = (([None, 20], [None, None]), [None, 20])
self.output_types = (('float32', 'float32'), 'int32')
def __call__(self, record):
return (record.get_one_hot_sequence().T, record.distance_matrix()), record.evolutionary.T
filter_func = data.make_length_filter(min_length=32, max_length=2000)
data = data.ProteinNetDataset(path="path/to/proteinnet", preload=False)
pn_map = data.ProteinNetMap(data, map=MapFunction(), static=False, filter_errors=True)
tf_dataset = tfdataset.proteinnet_tf_dataset(pn_map, batch_size=100, prefetch=400, shuffle_buffer=200)
```
Many more functions, arguments and uses are available, with detailed descriptions currently found in docstrings.
Full documentation will be generated from these for a future release.
## Scripts
The package also provides convenience scripts for processing ProteinNet datasets:
* add_angles_to_proteinnet - Add extra fields to a ProteinNet file with φ, ψ and χ backbone/torsion angles
* proteinnet_to_fasta - Extract a fasta file with the sequences from a ProteinNet file
* filter_proteinnet - Filter a ProteinNet file to include/exclude records from a list of IDs
Detailed usage instructions for each can be found using the `-h` argument.
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"description": "# ProteinNetPy 1.0.1\n<!-- badges: start -->\n[](https://zenodo.org/badge/latestdoi/267846791)\n[](https://proteinnetpy.readthedocs.io/en/latest/?badge=latest)\n<!-- badges: end -->\n\nA python library for working with [ProteinNet](https://github.com/aqlaboratory/proteinnet) text data, allowing you to easily load, stream and filter data, map functions across records and produce TensorFlow datasets.\nFor details of the dataset see the ProteinNet [Bioinformatics paper](https://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-019-2932-0).\nDocumentation for all functions of the module is available [here](https://proteinnetpy.readthedocs.io/en/latest/).\n\n## Install\n\n`pip install proteinnetpy`\n\nOr install the development version from Github:\n\n`pip install git+https://github.com:allydunham/proteinnetpy`\n\n## Requirements\n\n* Python 3\n* Numpy\n* Biopython\n* TensorFlow (if using the `datasets` module)\n\n## Basic Usage\n\nThe main object used in ProteinNetPy is the ProteinNetRecord, which allows access to the various record fields and methods for common manipulations, such as calculating a one-hot sequence representation or residue distance matrix.\nIt also supports most applicable operations like `len`, `str` etc.\nWhile the `parser` module contains a generator to parse files, it is generally easier to use the `ProteinNetDataset` class from the data module:\n\n```python\nfrom proteinnetpy.data import ProteinNetDataset\ndata = ProteinNetDataset(path=\"path/to/proteinnet\")\n```\n\nThis class includes a `preload` argument, which determines if the dataset is loaded into memory or streamed.\nIt also supports filtering using the `filter_func` argument, which is passed a function that returns truthy values for a record to determine if it is kept in the dataset.\nA range of common filters are included in the data module, as well as `combine_filters()`, which can applies all passed filters to each record.\n\nOnce a dataset has been loaded it can be iterated over to process data.\nThe `ProteinNetMap` class creates map objects that map a function over the dataset, including options to stream the map on each iteration or pre-calculate results.\nThey have a `generate` method that creates a generator object yielding the output of the function.\nThe `LabeledFunction` class is provided to create functions annotated with output types and shapes, used for automatically creating TensorFlow datasets.\nThe `mutation` module provides some example functions that return mutated records.\n\nThe following example code shows a typical simple usage, creating a streamed TensorFlow dataset from ProteinNet data:\n\n```python\nfrom proteinnetpy import data\nfrom proteinnetpy import tfdataset\n\nclass MapFunction(data.LabeledFunction):\n \"\"\"\n Example ProteinNetMap function outputting a one-hot sequence and contact graph input data\n and multiple alignment PSSM labels\n \"\"\"\n def __init__(self):\n self.output_shapes = (([None, 20], [None, None]), [None, 20])\n self.output_types = (('float32', 'float32'), 'int32')\n\n def __call__(self, record):\n return (record.get_one_hot_sequence().T, record.distance_matrix()), record.evolutionary.T\n\nfilter_func = data.make_length_filter(min_length=32, max_length=2000)\ndata = data.ProteinNetDataset(path=\"path/to/proteinnet\", preload=False)\npn_map = data.ProteinNetMap(data, map=MapFunction(), static=False, filter_errors=True)\n\ntf_dataset = tfdataset.proteinnet_tf_dataset(pn_map, batch_size=100, prefetch=400, shuffle_buffer=200)\n```\n\nMany more functions, arguments and uses are available, with detailed descriptions currently found in docstrings.\nFull documentation will be generated from these for a future release.\n\n## Scripts\n\nThe package also provides convenience scripts for processing ProteinNet datasets:\n\n* add_angles_to_proteinnet - Add extra fields to a ProteinNet file with \u03c6, \u03c8 and \u03c7 backbone/torsion angles\n* proteinnet_to_fasta - Extract a fasta file with the sequences from a ProteinNet file\n* filter_proteinnet - Filter a ProteinNet file to include/exclude records from a list of IDs\n\nDetailed usage instructions for each can be found using the `-h` argument.\n",
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