Merge pull request #14 from ACCLAB/dev-update-deps

v0.1.2
ACCLAB · Jan 4, 2018 · 75d052f · 75d052f
2 parents c10e9e9 + 85d80e4
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diff --git a/README.md b/README.md
@@ -13,11 +13,11 @@ DABEST is a package for **D**ata **A**nalysis using **B**ootstrapped **EST**imat
 Python 3.6 is strongly recommended, although this has been tested with Python 2.7 and Python 3.5.
 
 In addition, the following packages are also required:
-- [numpy](https://www.numpy.org/) (1.13.1)
-- [scipy](https://www.scipy.org/) (1.0.0)
-- [matplotlib](https://www.matplotlib.org/) (2.0.2)
-- [seaborn](https://seaborn.pydata.org/) (0.8)
-- [pandas](https://pandas.pydata.org/) (0.21.0).
+- [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/) (0.22.x).
 
 To obtain these package dependencies easily, it is highly recommended to download the [Anaconda distribution](https://www.continuum.io/downloads) of Python.
 

diff --git a/dabest/__init__.py b/dabest/__init__.py
@@ -2,4 +2,4 @@
 from .api import plot
 from .bootstrap_tools import bootstrap
 
-__version__="0.1.1"
+__version__="0.1.2"
diff --git a/docs/source/_images/dataframe_out.png b/docs/source/_images/dataframe_out.png
diff --git a/docs/source/about.rst b/docs/source/about.rst
@@ -17,7 +17,7 @@ What license governs the use of ``dabest``?
 
 The ``dabest`` package in Python is licenced under the `BSD 3-clause Clear License <https://choosealicense.com/licenses/bsd-3-clause-clear/>`_.
 
-Copyright (c) 2016-2017, Joses W. Ho
+Copyright (c) 2016-2018, Joses W. Ho
 All rights reserved.
 
 Redistribution and use in source and binary forms, with or without

diff --git a/docs/source/conf.py b/docs/source/conf.py
@@ -48,17 +48,17 @@
 
 # General information about the project.
 project = 'dabest'
-copyright = '2017, Joses W. Ho'
+copyright = '2018, Joses W. Ho'
 author = 'Joses W. Ho'
 
 # The version info for the project you're documenting, acts as replacement for
 # |version| and |release|, also used in various other places throughout the
 # built documents.
 #
 # The short X.Y version.
-version = '0.1.1'
+version = '0.1'
 # The full version, including alpha/beta/rc tags.
-release = '0.1.1'
+release = '0.1.2'
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.
@@ -95,7 +95,7 @@
     'head_font_family': 'Palatino',
     'font_size': '19px',
 
-    # 'code_font_size': '15px',
+    'code_font_size': '12px',
 
     'github_banner': True,
     'github_button': True,

diff --git a/docs/source/getting-started.rst b/docs/source/getting-started.rst
@@ -12,11 +12,11 @@ Python 3.6 is strongly recommended. ``dabest`` has also been tested with Python
 
 In addition, the following packages are also required:
 
-* `numpy <https://www.numpy.org>`_ (1.13.1)
-* `scipy <https://www.scipy.org>`_ (1.0.0)
-* `matplotlib <https://www.matplotlib.org>`_ (2.0.2)
-* `pandas <https://pandas.pydata.org>`_ (0.21.0).
-* `seaborn <https://seaborn.pydata.org>`_ (0.8)
+* `numpy <https://www.numpy.org>`_ (1.13.x)
+* `scipy <https://www.scipy.org>`_ (1.0.x)
+* `matplotlib <https://www.matplotlib.org>`_ (2.0.x)
+* `pandas <https://pandas.pydata.org>`_ (0.22.x).
+* `seaborn <https://seaborn.pydata.org>`_ (0.8.x)
 
 To obtain these package dependencies easily, it is highly recommended to download the `Anaconda <https://www.continuum.io/downloads>`_ distribution of Python.
 
@@ -43,10 +43,6 @@ Then, navigate to the cloned repo in the command line and run
   $ pip install .
 
 
-3. Using ``conda``
-
-*This section will be updated once this is uploaded to conda.*
-
 -------
 Testing
 -------

diff --git a/docs/source/index.rst b/docs/source/index.rst
@@ -9,7 +9,7 @@ DABEST
 ---------------------------------------
 Data Analysis with Bootstrap ESTimation
 ---------------------------------------
-*version 0.1.1*
+*version 0.1.2*
 
 .. image:: _images/f1.png
 
@@ -28,4 +28,5 @@ Contents
    getting-started
    tutorial
    api
+   release-notes
    about
diff --git a/docs/source/release-notes.rst b/docs/source/release-notes.rst
@@ -0,0 +1,20 @@
+.. _Release Notes:
+
+=============
+Release Notes
+=============
+
+
+0.1
+------
+
+0.1.2
+Update dependencies to
+
+* numpy v1.13
+* scipy v1.0
+* pandas v0.22
+* seaborn v0.8
+
+0.1.1
+`Update LICENSE to BSD-3 Clear. <https://github.com/ACCLAB/DABEST-python/commit/615c4cbb9145cf7b9451bf1840a20475ebcb2e99>`_
diff --git a/docs/source/tutorial.rst b/docs/source/tutorial.rst
@@ -1,4 +1,8 @@
 .. _Tutorial:
+.. highlight:: python
+  :linenothreshold: 2
+  :dedent: 4
+
 ========
 Tutorial
 ========
@@ -17,9 +21,9 @@ This is known as a 'wide' dataset. See this `writeup <https://sejdemyr.github.io
   dataset = list()
 
   for seed in [10,11,12,13,14,15]:
-      # fix the seed so we get the same numbers each time.
-      np.random.seed(seed)
-      dataset.append(np.random.randn(40))
+        # fix the seed so we get the same numbers each time.
+        np.random.seed(seed)
+        dataset.append(np.random.randn(40))
 
   df = pd.DataFrame(dataset).T
   cols = ['Control','Group1','Group2','Group3','Group4','Group5']
@@ -41,24 +45,27 @@ Gardner-Altman comparison plot (two independent groups)
 We can easily use ``dabest.plot()`` to create a **Gardner-Altman mean difference plot** to compare and compute the mean difference between two independent samples::
 
   f1, b1 = dabest.plot(df,
-                     idx=('Control','Group1'),
-                     color_col='Gender',
-                     # Pass the length and width of the image, in inches.
-                     fig_size=(4,6)
-                    )
+                 idx=('Control','Group1'),
+                 color_col='Gender',
+                 # Pass the length and width of the image, in inches.
+                 fig_size=(4,6)
+                )
 .. image:: _images/f1.png
 
-*output table here*
 
 A few things to note:
 
-- The ``dabest.plot()`` function will return **two objects**: a matplotlib ``Figure`` and a pandas ``DataFrame``. (In the Jupyter Notebook, with the ``magic`` command ``%matplotlib inline``, the figure should automatically appear.)
-
-- Note how the *Ns* (appended to the group names in the xtick labels) indicate the number of datapoints being plotted, and used to calculate the contrasts.
+- The ``dabest.plot()`` function will return **two objects**: a matplotlib ``Figure`` and a pandas ``DataFrame``. (In the Jupyter Notebook, with the ``magic`` command ``%matplotlib inline``, the figure should automatically appear.) In the above example, the Figure is assigned to the variable ``f1``, while the DataFrame is assigned to ``b1``.
 
 - ``dabest.plot()`` will automatically drop any NaNs (aka empty cells) in the data.
 
-- The pandas ``DataFrame`` returned by plot contains the pairwise comparisons made in the course of generating the plot, with confidence intervals (95% by default) and relevant *P* values.
+- The *Ns* (appended to the group names in the xtick labels) indicate the number of datapoints being plotted, and used to calculate the contrasts.
+
+- The pandas ``DataFrame`` returned by plot contains the pairwise comparisons made in the course of generating the plot, with confidence intervals (95% by default) and relevant *P* values. The DataFrame produced is shown below, and can be accessed by normal pandas commands.
+
+.. image:: _images/dataframe_out.png
+    :width: 900px
+    :align: center
 
 Gardner-Altman comparison plot (two paired groups)
 -------------------------------------------------------

diff --git a/setup.py b/setup.py
@@ -17,24 +17,29 @@ def check_dependencies():
 
     try:
         import numpy
+        if int(numpy.__version__.split('.')[1])<=12:
+            to_install.append('numpy==1.13')
     except ImportError:
         to_install.append('numpy==1.13')
+
     try:
         import scipy
+        if int(scipy.__version__.split('.')[0])==0:
+            to_install.append('scipy==1.0')
     except ImportError:
-        to_install.append('scipy==1.0.0')
-    try:
-        import matplotlib
-    except ImportError:
-        to_install.append('matplotlib==2.1')
+        to_install.append('scipy==1.0')
+
     try:
         import pandas
-        if int(pandas.__version__.split('.')[1])<21:
-            to_install.append('pandas==0.21')
+        if int(pandas.__version__.split('.')[1])<=21:
+            to_install.append('pandas==0.22')
     except ImportError:
-        to_install.append('pandas==0.21')
+        to_install.append('pandas==0.22')
+
     try:
         import seaborn
+        if int(seaborn.__version__.split('.')[1])<=7:
+            to_install.append('seaborn==0.8')
     except ImportError:
         to_install.append('seaborn==0.8')
 
@@ -46,7 +51,7 @@ def check_dependencies():
     setup(name='dabest',
     author='Joses W. Ho',
     author_email='[email protected]',
-    version='0.1.1',
+    version='0.1.2',
     description='Data Analysis and Visualization using Bootstrapped Estimation.',
     packages=find_packages(),
     install_requires=installs,