docs edits

docs/Ethos.rst

@@ -0,0 +1,36 @@
+
+.. _ Ethos:
+
+
+How is it different from *pyrad*?
+-------------------------------
+ipyrad_ is a complete re-write of pyrad_ with an expanded focus on speed and flexibility.
+While we continue in the minimalist ethos of pyrad_ which emphasized a simple
+installation procedure and ease-of-use, ipyrad_ differs in offering an additional 
+interactive interface through which to access data and results with simple Python scripts.
+We continue to support a command line interface (CLI_) that will be familiar
+to legacy pyrad_ users, but the real power of ipyrad_ comes from its
+implementation as a Python module which allows users to design complex
+assemblies that construct multiple data sets under multiple sets
+of parameter settings; to directly access assembly statistics; to plot assembly results;
+and to perform interactive downstream analyses.
+
+
+Features
+--------
+Major new features and improvements include:
+
+    - New assembly methods: *de novo*, reference alignment, 
+         or hybrid (*de novo* & reference).
+    - Parallel implementation using ipyparallel_ which utilizes MPI 
+         allowing greater use of HPC clusters.
+    - Better checkpointing. If your job is ever interrupted you should 
+         be able to simply restart the
+      script and continue from where it left off.
+    - Faster code (speed comparisons forthcoming with publication).
+    - Write highly reproducible documented code with Jupyter Notebooks (see Notebook_workflow_).
+    - No external installations: vsearch, muscle and all other 
+         dependencies are installed with ipyrad_ using conda (see Installation_).
+
+
+.. include:: global.rst

docs/Features.rst

@@ -0,0 +1,40 @@
+
+.. _Features:
+
+
+What does *ipyrad* do?
+----------------------
+*ipyrad_* can be used to assemble RADseq data sets using `*de novo* assembly`_,
+`reference mapping assembly`_, or `*hybrid assembly*`_ -- a combination
+of the two approaches. Assembled data sets can be output in a large variety of
+`formats`_, facilitating downstream genomic analyses for both population
+genetic and phylogenetic studies. It also includes methods for visualizing
+data and results, inferring population genetic statistics, and inferring genomic introgression.
+
+
+How is it different from *pyrad*?
+-------------------------------
+
+*ipyrad* is a complete re-write of pyrad_ with an expanded focus on speed and flexibility.
+While we continue in the minimalist ethos of pyrad_ which emphasized a simple
+installation procedure and ease-of-use, ipyrad_ offers an additional interactive
+interface with which to access data and results through simple Python scripts.
+We continue to support a command line interface (CLI_) that will be familiar
+to legacy pyrad_ users, but the real power of ipyrad_ comes from its
+implementation as a Python module which allows users to design complex
+assemblies that construct multiple data sets under multiple sets
+of parameter settings; to directly access assembly statistics; to plot assembly results;
+and to perform interactive downstream analyses.
+
+
+Major new features and improvements include:
+
+    - New assembly methods: *de novo*, reference alignment, or hybrid (*de novo* & reference).
+    - Parallel implementation using ipyparallel_ which utilizes MPI allowing greater use of HPC clusters.
+    - Better checkpointing. If your job is ever interrupted you should be able to simply restart the
+      script and continue from where it left off.
+    - Faster code (speed comparisons forthcoming with publication).
+    - Write highly reproducible documented code with Jupyter Notebooks (see Notebook_workflow_).
+    - No external installations: vsearch, muscle and all other dependencies are installed with ipyrad_
+      using conda (see Installation_).
+

docs/Installation.rst

@@ -5,9 +5,9 @@
 Installation with conda
 -----------------------
 The easiest way to install *ipyrad* and all of its dependencies is 
-to use `*conda*`_, which is a command line program for installing Python
+to use conda_, which is a command line program for installing Python
 packages. If you do not have *conda* installed, follow these 
-`instructions`_ to install either *Anaconda* or *Miniconda* 
+instructions_ to install either *Anaconda* or *Miniconda* 
 for Python2.7. If you're working on an HPC system you can install
 *conda* in your home directory even without administrative privileges. 
 

docs/index.rst

@@ -8,56 +8,21 @@
 
 *ipyrad*: interactive assembly and analysis of RADseq data sets
 -------------------------------------------------
-Welcome to *ipyrad_*, an interactive toolkit for assembly and analysis of 
-restriction-site associated genomic data sets (e.g., RAD, ddRAD, GBS) with 
-the following goals:
+Welcome to *ipyrad*, an interactive toolkit for assembly and analysis of 
+restriction-site associated genomic data sets (e.g., RAD, ddRAD, GBS) for 
+population genetic or phylogenetic studies, with the following goals:
 
-- Provide an easy-to-use and intuitive workflow to convert raw data to formatted output data files.
-- Provide a framework for producing complex assemblies within a simple and reproducible framework.
-- Provide visualization and checks on the quality of data assemblies. 
-- Provide interactive access to assembled data and statistics for downstream analyses and plotting.
+- Provide an easy-to-use and intuitive workflow to convert raw data to formatted output files.
+- Offer a range of fast and parallelized assembly methods.
+- Create a `reproducible framework`_ for designing complex assembly procedures.
+- Allow visualization and checks on the quality of data assemblies. 
+- Enable interactive_ access to assembled data and statistics.
 
-Read more about our broader goals behind *ipyrad* here_. 
+Read more about the broader goals behind *ipyrad* here_. 
 
-.. here_ Ethos.rst
-
-
-What does _ipyrad_ do?
-----------------------
-`*ipyrad*`_ can be used to assemble RADseq data sets using either `*de novo* assembly`_, 
-`reference mapping assembly`_, or `*hybrid assembly*`_ -- a combination
-of the two approaches. Assembled data sets are output in a huge variety of 
-`output formats`_ facilitating downstream genomic analyses for both population
-genetic analyses as well as for phylogenetic studies of highly divergent species. 
-*ipyrad* also includes methods for visualizing data and results, inferring 
-population genetic statistics, and inferring genomic introgression. 
-
-
-How is it different from *pyrad*?
--------------------------------
-
-ipyrad_ is a complete re-write of pyrad_ with an expanded focus on speed and flexibility.
-While we continue in the minimalist ethos of pyrad_ which emphasized a simple 
-installation procedure and ease-of-use, ipyrad_ offers an additional interactive 
-interface with which to access data and results through simple Python scripts. 
-We continue to support a command line interface (CLI_) that will be familiar
-to legacy pyrad_ users, but the real power of ipyrad_ comes from its 
-implementation as a Python module which allows users to design complex 
-assemblies that construct multiple data sets under multiple sets
-of parameter settings; to directly access assembly statistics; to plot assembly results;
-and to perform interactive downstream analyses. 
-
-
-Major new features and improvements include: 
-
-    - New assembly methods: *de novo*, reference alignment, or hybrid (*de novo* & reference).  
-    - Parallel implementation using ipyparallel_ which utilizes MPI allowing greater use of HPC clusters.   
-    - Better checkpointing. If your job is ever interrupted you should be able to simply restart the
-      script and continue from where it left off.  
-    - Faster code (speed comparisons forthcoming with publication).  
-    - Write highly reproducible documented code with Jupyter Notebooks (see Notebook_workflow_).    
-    - No external installations: vsearch, muscle and all other dependencies are installed with ipyrad_ 
-      using conda (see Installation_).   
+.. here_ :: Ethos.rst
+.. `reproducible framework`_ :: Notebooks.rst
+.. interactive_ :: interactive.rst
 
 
 Documentation