bump version and update readme, update some scripts

README.md

@@ -7,7 +7,7 @@ Ligand-binding site prediction based on machine learning.
     <img src="http://siret.ms.mff.cuni.cz/krivak/p2rank/figures/points2_small.png" width="600">
 </p>
 
-[![version 2.1](https://img.shields.io/badge/version-2.1-green.svg)](/build.gradle)
+[![version 2.2](https://img.shields.io/badge/version-2.2-green.svg)](/build.gradle)
 [![Build Status](https://travis-ci.org/rdk/p2rank.svg?branch=master)](https://travis-ci.org/rdk/p2rank)
 [![License: MIT](http://img.shields.io/badge/license-MIT-blue.svg?style=flat)](/LICENSE.txt)
 
@@ -17,14 +17,16 @@ P2Rank is a stand-alone command line program that predicts ligand-binding pocket
 
 ### Requirements
 
-* Java 8 or newer
+* Java 8 to 15
 * PyMOL 1.7 (or newer) for viewing visualizations (optional)
 
+Program is tested on Linux, macOS and Windows. On Windows, using `bash` console is recommended to execute the program instead of `cmd` or `PowerShell`.
+
 ### Setup
 
-P2Rank requires no installation. Binary packages can be downloaded from the project website.
+P2Rank requires no installation. Binary packages are available as GitHub Releases.
 
-* **Download**: http://siret.ms.mff.cuni.cz/p2rank
+* **Download**: https://github.com/rdk/p2rank/releases
 * Source code: https://github.com/rdk/p2rank
 * Datasets: https://github.com/rdk/p2rank-datasets
 
@@ -36,17 +38,27 @@ P2Rank requires no installation. Binary packages can be downloaded from the proj
 
 See more usage examples below...
 
-### Build
+### Build from sources
 
 This project uses [Gradle](https://gradle.org/) build system via included Gradle wrapper. 
+On Windows use `bash` to execute build comands (`bash` is installed as a part of [Git for Windows](https://git-scm.com/download/win)). 
 
-Build with `./make.sh` or `./gradlew assemble`.
+```bash
+git clone https://github.com/rdk/p2rank.git && cd p2rank
+./make.sh
+./unit-tests.sh   # optionally you can run tests to check everything works on your machine
+```    
+Now you can run the program via:
+```bash
+distro/prank       # standard mode that logs to distro/log/prank.log
+./prank.sh         # development mode that logs to console
+``` 
 
 ### Algorithm
 
 P2Rank makes predictions by scoring and clustering points on the protein's solvent accessible surface. Ligandability score of individual points is determined by a machine learning based model trained on the dataset of known protein-ligand complexes. For more details see slides and publications.
 
-Slides introducing original version of the algotithm: http://bit.ly/p2rank_slides 
+Presentation slides introducing original version of the algotithm: [Slides (pdf)](http://bit.ly/p2rank_slides)  
 
 ### Publications
 
@@ -82,10 +94,10 @@ prank predict test.ds                             # run on whole dataset (contai
 prank predict -f test_data/1fbl.pdb               # run on single pdb file
 prank predict -f test_data/1fbl.pdb.gz            # run on single gzipped pdb file
 
-prank predict -threads 8          test.ds         # specify no. of working threads for parallel processing
-prank predict -o output_here      test.ds         # explicitly specify output directory
-prank predict -c predict2.groovy  test.ds         # specify configuration file (predict2.groovy uses 
-                                                    different prediction model and combination of parameters)
+prank predict -threads 8              test.ds     # specify no. of working threads for parallel processing
+prank predict -o output_here          test.ds     # explicitly specify output directory
+prank predict -c conservation.groovy  test.ds     # specify configuration file (conservation.groovy  
+                                                  # uses different prediction model and parameters)
 ~~~
 
 ### Evaluate prediction model
@@ -135,13 +147,8 @@ P2Rank is also able to rescore pockets predicted by other methods
 ~~~bash
 prank rescore test_data/fpocket.ds
 prank rescore fpocket.ds                 # test_data/ is default 'dataset_base_dir'
-prank rescore fpocket.ds -o output_dir   # test_output/ is default 'output_base_dir'
-~~~
-
-### Evaluate rescoring model
-
-~~~
-prank eval-rescore fpocket.ds
+prank rescore fpocket.ds -o output_dir   # test_output/ is default 'output_base_dir'       
+prank eval-rescore fpocket.ds            # evaluate rescoring model
 ~~~
 
 ## Comparison with Fpocket

build.gradle

@@ -4,7 +4,7 @@ apply plugin: 'idea'
 
 
 group = 'cz.siret'
-version = '2.2-rc.1'
+version = '2.2'
 
 description = 'Ligand binding site prediction based on machine learning.'
 

changelog.md

@@ -1,24 +0,0 @@
-
-# Changelog
-
-This files track major changes in development versions of P2RANK since version 2.0-dev.6.
-
-
-## 2.0-dev.7
-
-- "chem" and "volsite" feature sets 
-    - original features were moved to those two feature sets and can be turned off through `-estra_features` param
-- FasterForest 
-    -  streamlined implementation of FastRandomForest (~ 0.75x time, 0.5x memory, same algorithm)
-- AUC (area under ROC curve) and AUPRC (area under Precision-Recall curve) metrics
-    - can be turned on with `-stats_collect_predictions 1` param    
-- improved logging
-    - possible to log to a file inside the output directory 
-    - related params: `-log_to_file 1`, `-zip_log_file 1`, `-log_to_console 1`, `-log_level WARN`
-     
-    
- ## 2.0-dev.6   
-    
-    
-
-

commit.sh

@@ -1,4 +1,4 @@
-#!/bin/bash
+#!/usr/bin/env bash
 
 MSG="$1"
 if [[ -z "$1" ]]; then

distro/README.md

@@ -7,7 +7,7 @@ Ligand-binding site prediction based on machine learning.
     <img src="http://siret.ms.mff.cuni.cz/krivak/p2rank/figures/points2_small.png" width="600">
 </p>
 
-[![version 2.1](https://img.shields.io/badge/version-2.1-green.svg)](/build.gradle)
+[![version 2.2](https://img.shields.io/badge/version-2.2-green.svg)](/build.gradle)
 [![Build Status](https://travis-ci.org/rdk/p2rank.svg?branch=master)](https://travis-ci.org/rdk/p2rank)
 [![License: MIT](http://img.shields.io/badge/license-MIT-blue.svg?style=flat)](/LICENSE.txt)
 
@@ -17,14 +17,16 @@ P2Rank is a stand-alone command line program that predicts ligand-binding pocket
 
 ### Requirements
 
-* Java 8 or newer
+* Java 8 to 15
 * PyMOL 1.7 (or newer) for viewing visualizations (optional)
 
+Program is tested on Linux, macOS and Windows. On Windows, using `bash` console is recommended to execute the program instead of `cmd` or `PowerShell`.
+
 ### Setup
 
-P2Rank requires no installation. Binary packages can be downloaded from the project website.
+P2Rank requires no installation. Binary packages are available as GitHub Releases.
 
-* **Download**: http://siret.ms.mff.cuni.cz/p2rank
+* **Download**: https://github.com/rdk/p2rank/releases
 * Source code: https://github.com/rdk/p2rank
 * Datasets: https://github.com/rdk/p2rank-datasets
 
@@ -36,17 +38,27 @@ P2Rank requires no installation. Binary packages can be downloaded from the proj
 
 See more usage examples below...
 
-### Build
+### Build from sources
 
 This project uses [Gradle](https://gradle.org/) build system via included Gradle wrapper. 
+On Windows use `bash` to execute build comands (`bash` is installed as a part of [Git for Windows](https://git-scm.com/download/win)). 
 
-Build with `./make.sh` or `./gradlew assemble`.
+```bash
+git clone https://github.com/rdk/p2rank.git && cd p2rank
+./make.sh
+./unit-tests.sh   # optionally you can run tests to check everything works on your machine
+```    
+Now you can run the program via:
+```bash
+distro/prank       # standard mode that logs to distro/log/prank.log
+./prank.sh         # development mode that logs to console
+``` 
 
 ### Algorithm
 
 P2Rank makes predictions by scoring and clustering points on the protein's solvent accessible surface. Ligandability score of individual points is determined by a machine learning based model trained on the dataset of known protein-ligand complexes. For more details see slides and publications.
 
-Slides introducing original version of the algotithm: http://bit.ly/p2rank_slides 
+Presentation slides introducing original version of the algotithm: [Slides (pdf)](http://bit.ly/p2rank_slides)  
 
 ### Publications
 
@@ -82,10 +94,10 @@ prank predict test.ds                             # run on whole dataset (contai
 prank predict -f test_data/1fbl.pdb               # run on single pdb file
 prank predict -f test_data/1fbl.pdb.gz            # run on single gzipped pdb file
 
-prank predict -threads 8          test.ds         # specify no. of working threads for parallel processing
-prank predict -o output_here      test.ds         # explicitly specify output directory
-prank predict -c predict2.groovy  test.ds         # specify configuration file (predict2.groovy uses 
-                                                    different prediction model and combination of parameters)
+prank predict -threads 8              test.ds     # specify no. of working threads for parallel processing
+prank predict -o output_here          test.ds     # explicitly specify output directory
+prank predict -c conservation.groovy  test.ds     # specify configuration file (conservation.groovy  
+                                                  # uses different prediction model and parameters)
 ~~~
 
 ### Evaluate prediction model
@@ -135,13 +147,8 @@ P2Rank is also able to rescore pockets predicted by other methods
 ~~~bash
 prank rescore test_data/fpocket.ds
 prank rescore fpocket.ds                 # test_data/ is default 'dataset_base_dir'
-prank rescore fpocket.ds -o output_dir   # test_output/ is default 'output_base_dir'
-~~~
-
-### Evaluate rescoring model
-
-~~~
-prank eval-rescore fpocket.ds
+prank rescore fpocket.ds -o output_dir   # test_output/ is default 'output_base_dir'       
+prank eval-rescore fpocket.ds            # evaluate rescoring model
 ~~~
 
 ## Comparison with Fpocket

distro/prank

@@ -1,4 +1,4 @@
-#!/bin/bash
+#!/usr/bin/env bash
 
 #
 # Set maximum memory for JVM heap

experiment.sh

@@ -1,4 +1,4 @@
-#!/bin/bash
+#!/usr/bin/env bash
 
 #
 # run experiment ant push results to p2rank-results git repo

logc.sh

@@ -1,4 +1,4 @@
-#!/bin/sh
+#!/usr/bin/env bash
 
 # color lines of log output
 

make-clean.sh

@@ -1,3 +1,3 @@
-#!/bin/bash
+#!/usr/bin/env bash
 
 ./gradlew clean assemble

make-distro.sh

@@ -1,6 +1,8 @@
-#!/bin/bash
+#!/usr/bin/env bash
 
-# creates binary distribution package
+#
+# creates binary distribution package for release
+#
 
 set -e
 

make.sh

@@ -1,3 +1,3 @@
-#!/bin/bash
+#!/usr/bin/env bash
 
 ./gradlew assemble

prank.sh

@@ -1,4 +1,4 @@
-#!/bin/bash
+#!/usr/bin/env bash
 
 #
 # script for running development builds in-place from this directory

push.sh

@@ -1,4 +1,4 @@
-#!/bin/bash
+#!/usr/bin/env bash
 
 set -e
 

tests.sh

@@ -3,5 +3,11 @@
 #
 # run test sets
 #
+# Examples:
+#
+# ./tests.sh quick   # fast set of basic tests
+# ./tests.sh all     # comprehensive set of tests that include training an evaluation on real datasets
+#                    # datasets from https://github.com/rdk/p2rank-datasets have to be downloaded first
+#
 
 misc/test-scripts/testsets.sh $@

unit-tests.sh

@@ -0,0 +1,6 @@
+#!/usr/bin/env bash
+
+#
+# run unit tests
+#
+./gradlew test

update.sh

@@ -1,4 +1,4 @@
-#!/bin/bash
+#!/usr/bin/env bash
 
 set -e  # fail fast