Merge pull request #17 from ACCLAB/v0.1.4

v0.1.4

Author: josesho

.gitignore

@@ -45,6 +45,7 @@ nosetests.xml
 coverage.xml
 *.cover
 .hypothesis/
+.pytest_cache/*
 
 # Translations
 *.mo
@@ -109,8 +110,12 @@ venv.bak/
 dev*
 
 # DS_Store
-.DS_Store
+*.DS_Store
 **/.DS_Store
 
 # font list
 fontList.json
+fontList.py3k.cache
+
+# tex folders
+tex.cache/

.travis.yml

@@ -1,9 +1,25 @@
 language: python
 
+# matrix:
+#     include:
+#         - python: 2.7
+#           dist: trusty
+#           sudo: false
+#         - python: 3.5
+#           dist: trusty
+#           sudo: false
+#         - python: 3.6
+#           dist: trusty
+#           sudo: false
+#         - python: 3.7
+#           dist: xenial
+#           sudo: true
+
 env:
-  - PYTHON=3.6
-  - PYTHON=3.5
-  - PYTHON=2.7
+  - PYTHON=2.7 BACKEND=agg
+  - PYTHON=3.5 BACKEND=agg
+  - PYTHON=3.6 BACKEND=agg
+  - PYTHON=3.7 BACKEND=agg
 
 before_install:
   - wget https://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh
@@ -18,7 +34,7 @@ install:
   - conda update conda --yes
   - conda create -n testenv --yes pip python=$PYTHON matplotlib
   - source activate testenv
-  - pip install pytest==3.3
+  - pip install pytest==3.6
   - pip install .
 
-script: pytest -s
+script: pytest

README.md

@@ -1,23 +1,33 @@
 # DABEST (Python)
-[![Travis CI](https://travis-ci.org/ACCLAB/DABEST-python.svg?branch=master)](https://travis-ci.org/ACCLAB/DABEST-python)
-[![PyPI](https://img.shields.io/pypi/v/dabest.svg)](https://pypi.python.org/pypi/dabest/0.1.3)
+[![Travis CI](https://travis-ci.org/ACCLAB/DABEST-python.svg)](https://travis-ci.org/ACCLAB/DABEST-python)
+[![PyPI](https://badge.fury.io/py/dabest.svg)](https://pypi.python.org/pypi/dabest)
 
 ## About
 
-DABEST is a package for **D**ata **A**nalysis using **B**ootstrapped **EST**imation.
+DABEST is a package for **D**ata **A**nalysis using **B**ootstrap-Coupled **EST**imation.
 
-![Gardner-Altman plot](https://acclab.github.io/DABEST-python-docs/_images/f1.png)
+[Estimation statistics](https://en.wikipedia.org/wiki/Estimation_statistics) is a [simple framework](https://thenewstatistics.com/itns/) that avoids the [pitfalls](https://www.nature.com/articles/nmeth.3288) of significance testing. It uses familiar statistical concepts: means, mean differences, and error bars. More importantly, it focuses on the effect size of one's experiment/intervention, as opposed to a false dichotomy engendered by *P* values.
+
+An estimation plot has two key features.
+
+1. It presents all datapoints as a swarmplot, which orders each point to display the underlying distribution.
+
+2. It presents the effect size as a **bootstrap 95% confidence interval** on a **separate but aligned axes**.
+
+![The five kinds of estimation plots](docs/source/_images/showpiece.png?raw=true "The five kinds of estimation plots.")
+
+DABEST powers [estimationstats.com](https://www.estimationstats.com/), allowing everyone access to high-quality estimation plots.
 
 ## Requirements
 
-Python 3.6 is strongly recommended, although this has been tested with Python 2.7 and Python 3.5.
+DABEST has been tested on Python 2.7, 3.5, 3.6, and 3.7.
 
 In addition, the following packages are also required:
-- [numpy](https://www.numpy.org/) (1.13.x)
-- [scipy](https://www.scipy.org/) (1.0.x)
-- [matplotlib](https://www.matplotlib.org/) (2.0.x)
-- [seaborn](https://seaborn.pydata.org/) (0.8.x)
-- [pandas](https://pandas.pydata.org/) (0.23.x).
+- [numpy](https://www.numpy.org) (1.15)
+- [scipy](https://www.scipy.org) (1.1)
+- [matplotlib](https://www.matplotlib.org) (2.2)
+- [seaborn](https://seaborn.pydata.org) (0.9)
+- [pandas](https://pandas.pydata.org) (0.23).
 
 To obtain these package dependencies easily, it is highly recommended to download the [Anaconda distribution](https://www.continuum.io/downloads) of Python.
 
@@ -34,18 +44,25 @@ or -->
 ```shell
 pip install --upgrade dabest
 ```
-You can also clone this repo locally (see intstructions [here](https://help.github.com/articles/cloning-a-repository/)).
+You can also clone this repo locally (see intstructions [here](https://help.github.com/articles/cloning-a-repository)).
 
 Then, navigate to the cloned repo in the command line and run
 
 ```shell
 pip install .
 ```
 
-
 ## Usage
 
-Please refer to the [documentation](https://acclab.github.io/DABEST-python-docs/index.html).
+Please refer to the [documentation](https://acclab.github.io/DABEST-python-docs).
+
+## How to cite
+
+**Moving beyond P values: Everyday data analysis with estimation plots**
+
+*Joses Ho, Tayfun Tumkaya, Sameer Aryal, Hyungwon Choi, Adam Claridge-Chang*
+
+[https://doi.org/10.1101/377978](https://doi.org/10.1101/377978)
 
 
 ## Matlab version
@@ -55,6 +72,21 @@ There is also a [Matlab version](https://github.com/ACCLAB/DABEST-Matlab) of DAB
 
 ## R version
 
-There isn't an implementation of DABEST in R, and there are no current plans to create one.
+It is possible to use the R package `reticulate` to run Python code. Please take a look at this [tutorial](https://acclab.github.io/DABEST-python-docs/dabest-r.html) on how to use `reticulate` to analyse data in R.
+
+
+## Testing
+
+To test DABEST, you will need to install [pytest](https://docs.pytest.org/en/latest).
+
+Run `pytest` in the root directory of the source distribution. This runs the test suite in the folder `dabest/tests`. The test suite will ensure that the bootstrapping functions and the plotting functions perform as expected.
+
+
+## Bugs
+
+Please report any bugs on the [Github issue tracker](https://github.com/ACCLAB/DABEST-python/issues/new).
+
+
+## Contributing
 
-However, it is possible to use the R package `reticulate` to run Python code. Please take a look at this [tutorial](https://acclab.github.io/DABEST-python-docs/dabest-r.html) on how to use `reticulate` to analyse data in R.
+All contributions are welcome. Please fork the [Github repo](https://github.com/ACCLAB/DABEST-python) and open a pull request.

dabest/__init__.py

@@ -2,4 +2,4 @@
 from .api import plot
 from .bootstrap_tools import bootstrap
 
-__version__="0.1.3"
+__version__="0.1.4"