remove docs/package_readme

docs/README.md

@@ -12,14 +12,3 @@ $ make generate html
 
 Documentation is written to the _build/ directory. These files should not be
 checked into the repo.
-
-We have experimented with using the documentation system to generate the mhcflurry
-package level README, but this is not currently in use. To build the readme, run:
-
-```
-$ make readme
-```
-
-This will write `docs/package_readme/readme.generated.txt`. The main
-[README.rst](../README.rst) could be symlinked to this file at a later point.
-

docs/package_readme/readme.template.rst

-.. include:: /intro.rst
-    :start-line: 3
-
-.. include:: /commandline_tutorial.rst
-
-.. include:: /python_tutorial.rst
-
-.. include:: /models_supported_alleles.rst
-
-mhcflurry
-=========
-
-Open source neural network models for peptide-MHC binding affinity
-prediction
-
-The `adaptive immune
-system <https://en.wikipedia.org/wiki/Adaptive_immune_system>`__ depends
-on the presentation of protein fragments by
-`MHC <https://en.wikipedia.org/wiki/Major_histocompatibility_complex>`__
-molecules. Machine learning models of this interaction are used in
-studies of infectious diseases, autoimmune diseases, vaccine
-development, and cancer immunotherapy.
-
-MHCflurry supports Class I peptide/MHC binding affinity prediction using
-ensembles of allele-specific models. You can fit MHCflurry models to
-your own data or download models that we fit to data from
-`IEDB <http://www.iedb.org/home_v3.php>`__ and `Kim
-2014 <http://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-15-241>`__.
-Our combined dataset is available for download
-`here <https://github.com/hammerlab/mhcflurry/releases/download/pre-1.0.0-alpha/data_curated.tar.bz2>`__.
-
-Pan-allelic prediction is supported in principle but is not yet
-performing accurately. Infrastructure for modeling other aspects of
-antigen processing is also implemented but experimental.
-
-If you find MHCflurry useful in your research please cite:
-
-    O'Donnell, T. et al., 2017. MHCflurry: open-source class I MHC
-    binding affinity prediction. bioRxiv. Available at:
-    http://www.biorxiv.org/content/early/2017/08/09/174243.
-
-Setup (pip)
------------
-
-Install the package:
-
-::
-
-    pip install mhcflurry
-
-Then download our datasets and trained models:
-
-::
-
-    mhcflurry-downloads fetch
-
-From a checkout you can run the unit tests with:
-
-::
-
-    nosetests .
-
-The MHCflurry predictors are implemented in Python using
-`keras <https://keras.io>`__.
-
-MHCflurry works with both the tensorflow and theano keras backends. The
-tensorflow backend gives faster model-loading time but is undergoing
-more rapid development and sometimes hits issues. If you encounter
-tensorflow errors running MHCflurry, try setting this environment
-variable to switch to the theano backend:
-
-::
-
-    export KERAS_BACKEND=theano
-
-You may also needs to ``pip install theano``.
-
-Setup (conda)
--------------
-
-You can alternatively get up and running with a
-`conda <https://conda.io/docs/>`__ environment as follows:
-
-::
-
-    conda create -q -n mhcflurry-env python=3.6 'tensorflow>=1.1.0'
-    source activate mhcflurry-env
-
-Then continue as above:
-
-::
-
-    pip install mhcflurry
-    mhcflurry-downloads fetch
-
-If you wish to test your installation, you can install ``nose`` and run
-the tests from a checkout:
-
-::
-
-    pip install nose
-    nosetests .
-
-Making predictions from the command-line
-----------------------------------------
-
-.. code:: shell
-
-    $ mhcflurry-predict --alleles HLA-A0201 HLA-A0301 --peptides SIINFEKL SIINFEKD SIINFEKQ
-    allele,peptide,mhcflurry_prediction,mhcflurry_prediction_low,mhcflurry_prediction_high
-    HLA-A0201,SIINFEKL,5326.541919062165,3757.86675352994,7461.37693353508
-    HLA-A0201,SIINFEKD,18763.70298522213,13140.82000240037,23269.82139560844
-    HLA-A0201,SIINFEKQ,18620.10057358322,13096.425874678192,23223.148184869413
-    HLA-A0301,SIINFEKL,24481.726678691946,21035.52779725433,27245.371837497867
-    HLA-A0301,SIINFEKD,24687.529360239587,21582.590014592537,27749.39869616437
-    HLA-A0301,SIINFEKQ,25923.062203902562,23522.5793450799,28079.456657427705
-
-The predictions returned are affinities (KD) in nM. The
-``prediction_low`` and ``prediction_high`` fields give the 5-95
-percentile predictions across the models in the ensemble. The
-predictions above were generated with MHCflurry 0.9.2. Your exact
-predictions may vary slightly from these (up to about 1 nM) depending on
-the Keras backend in use and other numerical details. Different versions
-of MHCflurry can of course give results considerably different from
-these.
-
-You can also specify the input and output as CSV files. Run
-``mhcflurry-predict -h`` for details.
-
-Making predictions from Python
-------------------------------
-
-.. code:: python
-
-    >>> from mhcflurry import Class1AffinityPredictor
-    >>> predictor = Class1AffinityPredictor.load()
-    >>> predictor.predict_to_dataframe(peptides=['SIINFEKL'], allele='A0201')
-
-
-      allele   peptide   prediction  prediction_low  prediction_high
-      A0201  SIINFEKL  6029.084473     4474.103253      7771.297702
-
-See the
-`class1_allele_specific_models.ipynb <https://github.com/hammerlab/mhcflurry/blob/master/examples/class1_allele_specific_models.ipynb>`__
-notebook for an overview of the Python API, including fitting your own
-predictors.
-
-Scanning protein sequences for predicted epitopes
--------------------------------------------------
-
-The `mhctools <https://github.com/hammerlab/mhctools>`__ package
-provides support for scanning protein sequences to find predicted
-epitopes. It supports MHCflurry as well as other binding predictors.
-Here is an example:
-
-::
-
-    # First install mhctools if needed:
-    pip install mhctools
-
-    # Now generate predictions for protein sequences in FASTA format:
-    mhctools \
-        --mhc-predictor mhcflurry \
-        --input-fasta-file INPUT.fasta \
-        --mhc-alleles A02:01,A03:01 \
-        --mhc-peptide-lengths 8,9,10,11 \
-        --extract-subsequences \
-        --out RESULT.csv
-
-Details on the downloadable models
-----------------------------------
-
-Environment variables
----------------------
-
-The path where MHCflurry looks for model weights and data can be set
-with the ``MHCFLURRY_DOWNLOADS_DIR`` environment variable. This
-directory should contain subdirectories like "models_class1".
-
-.. |Build Status| image:: https://travis-ci.org/hammerlab/mhcflurry.svg?branch=master
-   :target: https://travis-ci.org/hammerlab/mhcflurry
-.. |Coverage Status| image:: https://coveralls.io/repos/github/hammerlab/mhcflurry/badge.svg?branch=master
-   :target: https://coveralls.io/github/hammerlab/mhcflurry?branch=master

docs/package_readme/readme_header.rst

-:orphan:
-
-.. image:: https://travis-ci.org/hammerlab/mhcflurry.svg?branch=master
-    :target: https://travis-ci.org/hammerlab/mhcflurry
-
-.. image:: https://coveralls.io/repos/github/hammerlab/mhcflurry/badge.svg?branch=master
-    :target: https://coveralls.io/github/hammerlab/mhcflurry
-
-mhcflurry
-===================
-
-Open source neural network models for peptide-MHC binding affinity prediction
-