Fixed minor distribution issues

README.rst

@@ -8,19 +8,19 @@ Introduction
 
 Many people currently analyzing DNase-seq data are using tools designed for ChIP-seq work, but may be inappropriate for DNase-seq data where one is less interested in the overlaps of sequenced fragments, but the site at which the cut occurs (the 5' most end of the aligned sequence fragment).
 
-We have developed :mod:`pyDNase` to interface with a sorted and indexed BAM file from a DNase-seq experiment, allowing efficient and easy random access of DNase-seq cut data from any genomic location, e.g.
+We have developed `pyDNase` to interface with a sorted and indexed BAM file from a DNase-seq experiment, allowing efficient and easy random access of DNase-seq cut data from any genomic location, e.g.
 
     >>> import pyDNase
     >>> reads = pyDNase.BAMHandler(pyDNase.example_reads())
     >>> reads["chr6,170863500,170863532,+"]
     {'+': array([0,0,0,1,0,0,1,1,2,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1,0,0,1,1,0,0,0,1]),
      '-': array([0,10,1,0,1,0,4,9,0,1,0,2,1,0,0,0,0,0,3,0,6,3,0,0,0,1,1,1,3,0,3,6])}
 
-Querying the ``BAMHandler`` object returns a dictionary containing numpy arrays with DNase cut counts on the positive reference strand (+), and cuts on the negative reference strand (-). :mod:`pyDNase` efficiently caches the cut data queried, so that multiple requests from the same genomic locations do not require repeated lookups from the BAM file (this can be disabled).
+Querying the ``BAMHandler`` object returns a dictionary containing numpy arrays with DNase cut counts on the positive reference strand (+), and cuts on the negative reference strand (-). `pyDNase` efficiently caches the cut data queried, so that multiple requests from the same genomic locations do not require repeated lookups from the BAM file (this can be disabled).
 
-:mod:`pyDNase` comes with several analysis scripts covering several common use cases of DNase-seq analysis, and also an implementation of the Wellington and Wellington 1D footprinting algorithms.
+`pyDNase` comes with several analysis scripts covering several common use cases of DNase-seq analysis, and also an implementation of the Wellington and Wellington 1D footprinting algorithms.
 
-to install :mod:`pyDNase` is as easy as::
+to install `pyDNase`, ensure NumPy_ is installed, and run::
 
     $ pip install pyDNase
 
@@ -41,7 +41,7 @@ Reference
 ---------
 
 .. note ::
-    If you use :mod:`pyDNase` or the Wellington algorithm in your work, please cite the following paper.
+    If you use `pyDNase` or the Wellington algorithm in your work, please cite the following paper.
     
     Piper et al. 2013. *Wellington: A novel method for the accurate identification of digital genomic footprints from DNase-seq data*, Nucleic Acids Research 2013; doi: 10.1093/nar/gkt850
 
@@ -50,3 +50,4 @@ License
 
 Copyright (C) 2013 Jason Piper. This work is licensed under the GNU GPLv3 license, see ``LICENCE.TXT`` for details.
 
+.. _NumPy: http://www.numpy.org/‎

docs/installation.rst

@@ -51,14 +51,20 @@ on their system. I have attempted to list them in the order that you need to ins
             $ curl https://raw.github.com/pypa/pip/master/contrib/get-pip.py | python
 
 #. Cython_
-    * Provided you installed the above, you should be able to simply run ::
+    * Provided you installed pip_, you should be able to simply run ::
 
         $ pip install Cython
 
 #. samtools_
     * **On OS X** the simplest way to install samtools_ is using the homebrew_ command ``brew tap homebrew/science``
    followed by ``brew install homebrew/science/samtools``.
     * **On Ubuntu** you can use ``sudo apt-get install samtools``
+
+#. NumPy_
+    * Provided you installed pip_, you should be able to simply run ::
+
+        $ pip install numpy
+
 
 Installing :mod:`pyDNase`
 ~~~~~~~~~~~~~~~~~~~~~~~~~

setup.py

@@ -1,5 +1,13 @@
 __author__ = 'Jason Piper'
 
+#Unfortunately, we have to ensure that the user has numpy is installed,
+#as pip is bad at installing numpy and scipy at the same time, and just breaks
+
+try:
+    import numpy
+except ImportError:
+    raise ImportError("Due to a quirk with pip, pyDNase requires numpy to be installed before starting setup")
+
 try:
     from setuptools import setup, Extension
 except ImportError:
@@ -22,13 +30,14 @@
     ],
 
     #Uses a custom version of clint that has a time estimator on the progress bar
-    dependency_links = ["http://github.com/jpiper/clint/tarball/develop#egg=clint-0.3.1dev"],
+    dependency_links = ["http://github.com/jpiper/clint/tarball/develop#egg=clint-0.3.0p"],
 
     install_requires=[
         "numpy",
         "scipy",
         "matplotlib",
         "pysam",
+        "clint==0.3.0p"
     ],
 
     package_data = {'pyDNase':["data/*"]},