Now ATIDLAS is standalone. Everything dynamic....

python/pyatidlas/external/boost/libs/numpy/cmake/FindNumPy.cmake

@@ -0,0 +1,90 @@
+# - Find the NumPy libraries
+# This module finds if NumPy is installed, and sets the following variables
+# indicating where it is.
+#
+# TODO: Update to provide the libraries and paths for linking npymath lib.
+#
+#  NUMPY_FOUND               - was NumPy found
+#  NUMPY_VERSION             - the version of NumPy found as a string
+#  NUMPY_VERSION_MAJOR       - the major version number of NumPy
+#  NUMPY_VERSION_MINOR       - the minor version number of NumPy
+#  NUMPY_VERSION_PATCH       - the patch version number of NumPy
+#  NUMPY_VERSION_DECIMAL     - e.g. version 1.6.1 is 10601
+#  NUMPY_INCLUDE_DIRS        - path to the NumPy include files
+
+#============================================================================
+# Copyright 2012 Continuum Analytics, Inc.
+#
+# MIT License
+#
+# Permission is hereby granted, free of charge, to any person obtaining
+# a copy of this software and associated documentation files
+# (the "Software"), to deal in the Software without restriction, including
+# without limitation the rights to use, copy, modify, merge, publish,
+# distribute, sublicense, and/or sell copies of the Software, and to permit
+# persons to whom the Software is furnished to do so, subject to
+# the following conditions:
+# 
+# The above copyright notice and this permission notice shall be included
+# in all copies or substantial portions of the Software.
+# 
+# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
+# OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL
+# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR
+# OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,
+# ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR
+# OTHER DEALINGS IN THE SOFTWARE.
+# 
+#============================================================================
+
+# Finding NumPy involves calling the Python interpreter
+if(NumPy_FIND_REQUIRED)
+    find_package(PythonInterp REQUIRED)
+else()
+    find_package(PythonInterp)
+endif()
+
+if(NOT PYTHONINTERP_FOUND)
+    set(NUMPY_FOUND FALSE)
+endif()
+
+execute_process(COMMAND "${PYTHON_EXECUTABLE}" "-c"
+    "import numpy as n; print(n.__version__); print(n.get_include());"
+    RESULT_VARIABLE _NUMPY_SEARCH_SUCCESS
+    OUTPUT_VARIABLE _NUMPY_VALUES
+    ERROR_VARIABLE _NUMPY_ERROR_VALUE
+    OUTPUT_STRIP_TRAILING_WHITESPACE)
+
+if(NOT _NUMPY_SEARCH_SUCCESS MATCHES 0)
+    if(NumPy_FIND_REQUIRED)
+        message(FATAL_ERROR
+            "NumPy import failure:\n${_NUMPY_ERROR_VALUE}")
+    endif()
+    set(NUMPY_FOUND FALSE)
+endif()
+
+# Convert the process output into a list
+string(REGEX REPLACE ";" "\\\\;" _NUMPY_VALUES ${_NUMPY_VALUES})
+string(REGEX REPLACE "\n" ";" _NUMPY_VALUES ${_NUMPY_VALUES})
+list(GET _NUMPY_VALUES 0 NUMPY_VERSION)
+list(GET _NUMPY_VALUES 1 NUMPY_INCLUDE_DIRS)
+
+# Make sure all directory separators are '/'
+string(REGEX REPLACE "\\\\" "/" NUMPY_INCLUDE_DIRS ${NUMPY_INCLUDE_DIRS})
+
+# Get the major and minor version numbers
+string(REGEX REPLACE "\\." ";" _NUMPY_VERSION_LIST ${NUMPY_VERSION})
+list(GET _NUMPY_VERSION_LIST 0 NUMPY_VERSION_MAJOR)
+list(GET _NUMPY_VERSION_LIST 1 NUMPY_VERSION_MINOR)
+list(GET _NUMPY_VERSION_LIST 2 NUMPY_VERSION_PATCH)
+string(REGEX MATCH "[0-9]*" NUMPY_VERSION_PATCH ${NUMPY_VERSION_PATCH})
+math(EXPR NUMPY_VERSION_DECIMAL
+    "(${NUMPY_VERSION_MAJOR} * 10000) + (${NUMPY_VERSION_MINOR} * 100) + ${NUMPY_VERSION_PATCH}")
+
+find_package_message(NUMPY
+    "Found NumPy: version \"${NUMPY_VERSION}\" ${NUMPY_INCLUDE_DIRS}"
+    "${NUMPY_INCLUDE_DIRS}${NUMPY_VERSION}")
+
+set(NUMPY_FOUND TRUE)
+

python/pyatidlas/external/boost/libs/numpy/cmake/FindPythonLibsNew.cmake

@@ -0,0 +1,213 @@
+# - Find python libraries
+# This module finds the libraries corresponding to the Python interpeter
+# FindPythonInterp provides.
+# This code sets the following variables:
+#
+#  PYTHONLIBS_FOUND           - have the Python libs been found
+#  PYTHON_PREFIX              - path to the Python installation
+#  PYTHON_LIBRARIES           - path to the python library
+#  PYTHON_INCLUDE_DIRS        - path to where Python.h is found
+#  PYTHON_SITE_PACKAGES       - path to installation site-packages
+#  PYTHON_IS_DEBUG            - whether the Python interpreter is a debug build
+#
+#  PYTHON_INCLUDE_PATH        - path to where Python.h is found (deprecated)
+#
+# A function PYTHON_ADD_MODULE(<name> src1 src2 ... srcN) is defined to build modules for python.
+
+#=============================================================================
+# Copyright 2001-2009 Kitware, Inc.
+# Copyright 2012 Continuum Analytics, Inc.
+#
+# All rights reserved.
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions
+# are met:
+#
+# * Redistributions of source code must retain the above copyright
+# notice, this list of conditions and the following disclaimer.
+#
+# * Redistributions in binary form must reproduce the above copyright
+# notice, this list of conditions and the following disclaimer in the
+# documentation and/or other materials provided with the distribution.
+#
+# * Neither the names of Kitware, Inc., the Insight Software Consortium,
+# nor the names of their contributors may be used to endorse or promote
+# products derived from this software without specific prior written
+# permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
+# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
+# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
+# # A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
+# HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
+# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
+# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
+# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
+# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
+# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+#=============================================================================
+# (To distribute this file outside of CMake, substitute the full
+#  License text for the above reference.)
+
+# Use the Python interpreter to find the libs.
+if(PythonLibsNew_FIND_REQUIRED)
+    find_package(PythonInterp REQUIRED)
+else()
+    find_package(PythonInterp)
+endif()
+
+if(NOT PYTHONINTERP_FOUND)
+    set(PYTHONLIBS_FOUND FALSE)
+    return()
+endif()
+
+# According to http://stackoverflow.com/questions/646518/python-how-to-detect-debug-interpreter
+# testing whether sys has the gettotalrefcount function is a reliable, cross-platform
+# way to detect a CPython debug interpreter.
+execute_process(COMMAND "${PYTHON_EXECUTABLE}" "-c"
+    "from distutils import sysconfig as s;import sys;import struct;
+print('.'.join(str(v) for v in sys.version_info));
+print(s.PREFIX);
+print(s.get_python_inc());
+print(s.get_python_inc(plat_specific=True));
+print(s.get_python_lib(plat_specific=True));
+print(s.get_config_var('SO'));
+print(hasattr(sys, 'gettotalrefcount')+0);
+print(struct.calcsize('@P'));
+"
+    RESULT_VARIABLE _PYTHON_SUCCESS
+    OUTPUT_VARIABLE _PYTHON_VALUES
+    ERROR_VARIABLE _PYTHON_ERROR_VALUE
+    OUTPUT_STRIP_TRAILING_WHITESPACE)
+
+if(NOT _PYTHON_SUCCESS MATCHES 0)
+    if(PythonLibsNew_FIND_REQUIRED)
+        message(FATAL_ERROR
+            "Python config failure:\n${_PYTHON_ERROR_VALUE}")
+    endif()
+    set(PYTHONLIBS_FOUND FALSE)
+    return()
+endif()
+
+# Convert the process output into a list
+string(REGEX REPLACE ";" "\\\\;" _PYTHON_VALUES ${_PYTHON_VALUES})
+string(REGEX REPLACE "\n" ";" _PYTHON_VALUES ${_PYTHON_VALUES})
+list(GET _PYTHON_VALUES 0 _PYTHON_VERSION_LIST)
+list(GET _PYTHON_VALUES 1 PYTHON_PREFIX)
+list(GET _PYTHON_VALUES 2 PYTHON_INCLUDE_DIR)
+list(GET _PYTHON_VALUES 3 PYTHON_PLATFORM_INCLUDE_DIR)
+list(GET _PYTHON_VALUES 4 PYTHON_SITE_PACKAGES)
+list(GET _PYTHON_VALUES 5 PYTHON_MODULE_EXTENSION)
+list(GET _PYTHON_VALUES 6 PYTHON_IS_DEBUG)
+list(GET _PYTHON_VALUES 7 PYTHON_SIZEOF_VOID_P)
+
+# Make sure the Python has the same pointer-size as the chosen compiler
+if(NOT ${PYTHON_SIZEOF_VOID_P} MATCHES ${CMAKE_SIZEOF_VOID_P})
+    if(PythonLibsNew_FIND_REQUIRED)
+        math(EXPR _PYTHON_BITS "${PYTHON_SIZEOF_VOID_P} * 8")
+        math(EXPR _CMAKE_BITS "${CMAKE_SIZEOF_VOID_P} * 8")
+        message(FATAL_ERROR
+            "Python config failure: Python is ${_PYTHON_BITS}-bit, "
+            "chosen compiler is  ${_CMAKE_BITS}-bit")
+    endif()
+    set(PYTHONLIBS_FOUND FALSE)
+    return()
+endif()
+
+# The built-in FindPython didn't always give the version numbers
+string(REGEX REPLACE "\\." ";" _PYTHON_VERSION_LIST ${_PYTHON_VERSION_LIST})
+list(GET _PYTHON_VERSION_LIST 0 PYTHON_VERSION_MAJOR)
+list(GET _PYTHON_VERSION_LIST 1 PYTHON_VERSION_MINOR)
+list(GET _PYTHON_VERSION_LIST 2 PYTHON_VERSION_PATCH)
+
+# Make sure all directory separators are '/'
+string(REGEX REPLACE "\\\\" "/" PYTHON_PREFIX ${PYTHON_PREFIX})
+string(REGEX REPLACE "\\\\" "/" PYTHON_INCLUDE_DIR ${PYTHON_INCLUDE_DIR})
+string(REGEX REPLACE "\\\\" "/" PYTHON_SITE_PACKAGES ${PYTHON_SITE_PACKAGES})
+
+# TODO: All the nuances of CPython debug builds have not been dealt with/tested.
+if(PYTHON_IS_DEBUG)
+    set(PYTHON_MODULE_EXTENSION "_d${PYTHON_MODULE_EXTENSION}")
+endif()
+
+if(CMAKE_HOST_WIN32)
+    set(PYTHON_LIBRARY
+        "${PYTHON_PREFIX}/libs/Python${PYTHON_VERSION_MAJOR}${PYTHON_VERSION_MINOR}.lib")
+elseif(APPLE)
+    set(PYTHON_LIBRARY
+        "${PYTHON_PREFIX}/lib/libpython${PYTHON_VERSION_MAJOR}.${PYTHON_VERSION_MINOR}.dylib")
+else()
+    if(${PYTHON_SIZEOF_VOID_P} MATCHES 8)
+        set(_PYTHON_LIBS_SEARCH "${PYTHON_PREFIX}/lib64" "${PYTHON_PREFIX}/lib")
+    else()
+        set(_PYTHON_LIBS_SEARCH "${PYTHON_PREFIX}/lib")
+    endif()
+    # Probably this needs to be more involved. It would be nice if the config
+    # information the python interpreter itself gave us were more complete.
+    find_library(PYTHON_LIBRARY
+        NAMES "python${PYTHON_VERSION_MAJOR}.${PYTHON_VERSION_MINOR}"
+        PATHS ${_PYTHON_LIBS_SEARCH}
+        NO_SYSTEM_ENVIRONMENT_PATH)
+endif()
+
+# For backward compatibility, set PYTHON_INCLUDE_PATH, but make it internal.
+SET(PYTHON_INCLUDE_PATH "${PYTHON_INCLUDE_DIR}" CACHE INTERNAL
+          "Path to where Python.h is found (deprecated)")
+
+MARK_AS_ADVANCED(
+  PYTHON_LIBRARY
+  PYTHON_INCLUDE_DIR
+)
+
+# We use PYTHON_INCLUDE_DIR, PYTHON_LIBRARY and PYTHON_DEBUG_LIBRARY for the
+# cache entries because they are meant to specify the location of a single
+# library. We now set the variables listed by the documentation for this
+# module.
+SET(PYTHON_INCLUDE_DIRS "${PYTHON_INCLUDE_DIR};${PYTHON_PLATFORM_INCLUDE_DIR}")
+SET(PYTHON_LIBRARIES "${PYTHON_LIBRARY}")
+SET(PYTHON_DEBUG_LIBRARIES "${PYTHON_DEBUG_LIBRARY}")
+
+
+# Don't know how to get to this directory, just doing something simple :P
+#INCLUDE(${CMAKE_CURRENT_LIST_DIR}/FindPackageHandleStandardArgs.cmake)
+#FIND_PACKAGE_HANDLE_STANDARD_ARGS(PythonLibs DEFAULT_MSG PYTHON_LIBRARIES PYTHON_INCLUDE_DIRS)
+find_package_message(PYTHON
+    "Found PythonLibs: ${PYTHON_LIBRARY}"
+    "${PYTHON_EXECUTABLE}${PYTHON_VERSION}")
+
+
+# PYTHON_ADD_MODULE(<name> src1 src2 ... srcN) is used to build modules for python.
+FUNCTION(PYTHON_ADD_MODULE _NAME )
+  GET_PROPERTY(_TARGET_SUPPORTS_SHARED_LIBS
+    GLOBAL PROPERTY TARGET_SUPPORTS_SHARED_LIBS)
+  OPTION(PYTHON_ENABLE_MODULE_${_NAME} "Add module ${_NAME}" TRUE)
+  OPTION(PYTHON_MODULE_${_NAME}_BUILD_SHARED
+    "Add module ${_NAME} shared" ${_TARGET_SUPPORTS_SHARED_LIBS})
+
+  # Mark these options as advanced
+  MARK_AS_ADVANCED(PYTHON_ENABLE_MODULE_${_NAME}
+    PYTHON_MODULE_${_NAME}_BUILD_SHARED)
+
+  IF(PYTHON_ENABLE_MODULE_${_NAME})
+    IF(PYTHON_MODULE_${_NAME}_BUILD_SHARED)
+      SET(PY_MODULE_TYPE MODULE)
+    ELSE(PYTHON_MODULE_${_NAME}_BUILD_SHARED)
+      SET(PY_MODULE_TYPE STATIC)
+      SET_PROPERTY(GLOBAL  APPEND  PROPERTY  PY_STATIC_MODULES_LIST ${_NAME})
+    ENDIF(PYTHON_MODULE_${_NAME}_BUILD_SHARED)
+
+    SET_PROPERTY(GLOBAL  APPEND  PROPERTY  PY_MODULES_LIST ${_NAME})
+    ADD_LIBRARY(${_NAME} ${PY_MODULE_TYPE} ${ARGN})
+    TARGET_LINK_LIBRARIES(${_NAME} ${PYTHON_LIBRARIES})
+
+    IF(PYTHON_MODULE_${_NAME}_BUILD_SHARED)
+      SET_TARGET_PROPERTIES(${_NAME} PROPERTIES PREFIX "${PYTHON_MODULE_PREFIX}")
+      SET_TARGET_PROPERTIES(${_NAME} PROPERTIES SUFFIX "${PYTHON_MODULE_EXTENSION}")
+    ELSE()
+    ENDIF()
+
+  ENDIF(PYTHON_ENABLE_MODULE_${_NAME})
+ENDFUNCTION(PYTHON_ADD_MODULE)
+

python/pyatidlas/external/boost/libs/numpy/cmake/README.txt

@@ -0,0 +1,9 @@
+The cmake files, FindNumPy.cmake and FindPythonLibsNew.cmake, in this
+folder came from the numexpr project at
+http://code.google.com/p/numexpr.
+
+The numexpr project was also a valuable resource in making many of the
+cmake constructs used in the cmake lists files written for
+Boost.NumPy. The boost-python-examples project at
+https://github.com/TNG/boost-python-examples was another helpful
+resource for understanding how to use cmake with boost.python.

python/pyatidlas/external/boost/libs/numpy/doc/CMakeLists.txt

@@ -0,0 +1,56 @@
+project(doc-html)
+file(GLOB NUMPY_DOC_DEPS conf.py *.rst)
+message( STATUS "NUMPY_DOC_DEPS=${NUMPY_DOC_DEPS}" )
+
+# add_custom_target(doc-html make -C ${PROJECT_SOURCE_DIR} html BUILDDIR=${PROJECT_BINARY_DIR}/_build)
+#
+# this custom target is a cross-platform python/sphinx way to
+# replicate what the above make command is doing.
+#
+add_custom_target(doc-html
+  ${SPHINX_BUILD}
+     -b html 
+     -c ${CMAKE_CURRENT_SOURCE_DIR}
+     -d .doctrees
+     ${CMAKE_CURRENT_SOURCE_DIR}
+     html
+  
+  DEPENDS ${NUMPY_DOC_DEPS}
+  WORKING_DIRECTORY ${PROJECT_BINARY_DIR}
+  COMMENT "Generating HTML Documentation"
+)
+
+SET_PROPERTY(TARGET doc-html PROPERTY FOLDER "doc")
+
+install(DIRECTORY ${PROJECT_BINARY_DIR}/html
+  DESTINATION share/doc/libboost_numpy 
+  OPTIONAL
+  )
+
+if (PDFLATEX_COMPILER)
+  project(doc-pdf)
+
+  add_custom_target(doc-pdf
+    ${SPHINX_BUILD}
+      -b latex 
+      -c ${CMAKE_CURRENT_SOURCE_DIR}
+      -d .doctrees
+      ${CMAKE_CURRENT_SOURCE_DIR}
+      latex
+    
+    COMMAND ${PDFLATEX_COMPILER} --include-directory=latex --output-directory=latex latex/BoostNumPy.tex 
+    COMMAND ${PDFLATEX_COMPILER} --include-directory=latex --output-directory=latex latex/BoostNumPy.tex 
+
+    DEPENDS ${NUMPY_DOC_DEPS}
+    WORKING_DIRECTORY ${PROJECT_BINARY_DIR}
+    COMMENT "Generating Latex-pdf Documentation"
+    )
+
+  SET_PROPERTY(TARGET doc-pdf PROPERTY FOLDER "doc")
+
+  install(FILES ${PROJECT_BINARY_DIR}/latex/BoostNumPy.pdf
+    DESTINATION share/doc/libboost_numpy
+    OPTIONAL
+    )
+
+endif()

python/pyatidlas/external/boost/libs/numpy/doc/Jamfile

@@ -0,0 +1,25 @@
+# Copyright David Abrahams 2006. Distributed under the Boost
+# Software License, Version 1.0. (See accompanying
+# file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
+project user-config : requirements <docutils-cmd>rst2html ;
+
+import docutils ;
+
+import path ;
+sources = tutorial.rst dtype.rst ndarray.rst fromdata.rst ufunc.rst ;
+bases = $(sources:S=) ;
+  
+# This is a path relative to the html/ subdirectory where the
+# generated output will eventually be moved.
+stylesheet = "--stylesheet=rst.css" ;
+
+for local b in $(bases)
+{
+    html $(b) : $(b).rst : 
+      
+    <docutils-html>"-gdt --source-url="./$(b).rst" --link-stylesheet --traceback --trim-footnote-reference-space --footnote-references=superscript "$(stylesheet)
+  ;
+}
+
+alias htmls : $(bases) ;
+stage . : $(bases) ;

python/pyatidlas/external/boost/libs/numpy/doc/Makefile

@@ -0,0 +1,132 @@
+# Makefile for Sphinx documentation
+#
+
+# You can set these variables from the command line.
+SPHINXOPTS    =
+SPHINXBUILD   = sphinx-build
+PAPER         =
+BUILDDIR      = _build
+
+# Internal variables.
+PAPEROPT_a4     = -D latex_paper_size=a4
+PAPEROPT_letter = -D latex_paper_size=letter
+ALLSPHINXOPTS   = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) .
+
+.PHONY: help clean html dirhtml singlehtml pickle json htmlhelp qthelp devhelp epub latex latexpdf text man changes linkcheck doctest
+
+all: html
+
+help:
+	@echo "Please use \`make <target>' where <target> is one of"
+	@echo "  html       to make standalone HTML files"
+	@echo "  dirhtml    to make HTML files named index.html in directories"
+	@echo "  singlehtml to make a single large HTML file"
+	@echo "  pickle     to make pickle files"
+	@echo "  json       to make JSON files"
+	@echo "  htmlhelp   to make HTML files and a HTML help project"
+	@echo "  qthelp     to make HTML files and a qthelp project"
+	@echo "  devhelp    to make HTML files and a Devhelp project"
+	@echo "  epub       to make an epub"
+	@echo "  latex      to make LaTeX files, you can set PAPER=a4 or PAPER=letter"
+	@echo "  latexpdf   to make LaTeX files and run them through pdflatex"
+	@echo "  text       to make text files"
+	@echo "  man        to make manual pages"
+	@echo "  changes    to make an overview of all changed/added/deprecated items"
+	@echo "  linkcheck  to check all external links for integrity"
+	@echo "  doctest    to run all doctests embedded in the documentation (if enabled)"
+
+clean:
+	-rm -rf $(BUILDDIR)/*
+
+html:
+	$(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html
+	@echo
+	@echo "Build finished. The HTML pages are in $(BUILDDIR)/html."
+
+dirhtml:
+	$(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml
+	@echo
+	@echo "Build finished. The HTML pages are in $(BUILDDIR)/dirhtml."
+
+singlehtml:
+	$(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml
+	@echo
+	@echo "Build finished. The HTML page is in $(BUILDDIR)/singlehtml."
+
+pickle:
+	$(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle
+	@echo
+	@echo "Build finished; now you can process the pickle files."
+
+json:
+	$(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json
+	@echo
+	@echo "Build finished; now you can process the JSON files."
+
+htmlhelp:
+	$(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) $(BUILDDIR)/htmlhelp
+	@echo
+	@echo "Build finished; now you can run HTML Help Workshop with the" \
+	      ".hhp project file in $(BUILDDIR)/htmlhelp."
+
+qthelp:
+	$(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) $(BUILDDIR)/qthelp
+	@echo
+	@echo "Build finished; now you can run "qcollectiongenerator" with the" \
+	      ".qhcp project file in $(BUILDDIR)/qthelp, like this:"
+	@echo "# qcollectiongenerator $(BUILDDIR)/qthelp/BoostNumPy.qhcp"
+	@echo "To view the help file:"
+	@echo "# assistant -collectionFile $(BUILDDIR)/qthelp/BoostNumPy.qhc"
+
+devhelp:
+	$(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp
+	@echo
+	@echo "Build finished."
+	@echo "To view the help file:"
+	@echo "# mkdir -p $$HOME/.local/share/devhelp/BoostNumPy"
+	@echo "# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/BoostNumPy"
+	@echo "# devhelp"
+
+epub:
+	$(SPHINXBUILD) -b epub $(ALLSPHINXOPTS) $(BUILDDIR)/epub
+	@echo
+	@echo "Build finished. The epub file is in $(BUILDDIR)/epub."
+
+latex:
+	$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
+	@echo
+	@echo "Build finished; the LaTeX files are in $(BUILDDIR)/latex."
+	@echo "Run \`make' in that directory to run these through (pdf)latex" \
+	      "(use \`make latexpdf' here to do that automatically)."
+
+latexpdf:
+	$(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex
+	@echo "Running LaTeX files through pdflatex..."
+	make -C $(BUILDDIR)/latex all-pdf
+	@echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex."
+
+text:
+	$(SPHINXBUILD) -b text $(ALLSPHINXOPTS) $(BUILDDIR)/text
+	@echo
+	@echo "Build finished. The text files are in $(BUILDDIR)/text."
+
+man:
+	$(SPHINXBUILD) -b man $(ALLSPHINXOPTS) $(BUILDDIR)/man
+	@echo
+	@echo "Build finished. The manual pages are in $(BUILDDIR)/man."
+
+changes:
+	$(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes
+	@echo
+	@echo "The overview file is in $(BUILDDIR)/changes."
+
+linkcheck:
+	$(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck
+	@echo
+	@echo "Link check complete; look for any errors in the above output " \
+	      "or in $(BUILDDIR)/linkcheck/output.txt."
+
+doctest:
+	$(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest
+	@echo "Testing of doctests in the sources finished, look at the " \
+	      "results in $(BUILDDIR)/doctest/output.txt."

python/pyatidlas/external/boost/libs/numpy/doc/_static/boost.css

@@ -0,0 +1,66 @@
+/*=============================================================================
+    Copyright 2002 William E. Kempf
+    Distributed under the Boost Software License, Version 1.0. (See accompany-
+    ing file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
+=============================================================================*/
+
+H1
+{
+    FONT-SIZE: 200%;
+    COLOR: #00008B;
+}
+H2
+{
+    FONT-SIZE: 150%;
+}
+H3
+{
+    FONT-SIZE: 125%;
+}
+H4
+{
+    FONT-SIZE: 108%;
+}
+BODY
+{
+    FONT-SIZE: 100%;
+    BACKGROUND-COLOR: #ffffff;
+    COLOR: #000000;
+}
+PRE
+{
+    MARGIN-LEFT: 2em;
+    FONT-FAMILY: Courier,
+                 monospace;
+}
+CODE
+{
+    FONT-FAMILY: Courier,
+                 monospace;
+}
+CODE.as_pre
+{
+    white-space: pre;
+}
+.index
+{
+    TEXT-ALIGN: left;
+}
+.page-index
+{
+    TEXT-ALIGN: left;
+}
+.definition
+{
+    TEXT-ALIGN: left;
+}
+.footnote
+{
+    FONT-SIZE: 66%;
+    VERTICAL-ALIGN: super;
+    TEXT-DECORATION: none;
+}
+.function-semantics
+{
+    CLEAR: left;
+}

python/pyatidlas/external/boost/libs/numpy/doc/_static/style.css

@@ -0,0 +1,11 @@
+@import url(boost.css);
+
+#contents 
+{ 
+/*    border-bottom: solid thin black;*/
+}
+
+.highlight
+{
+  border: 1px solid #aaaaaa;    
+}

python/pyatidlas/external/boost/libs/numpy/doc/_templates/layout.html

@@ -0,0 +1,120 @@
+{%- macro navbar() %}
+    <div class="navbar" style="text-align:right;">
+      {#%- if parents|count > 0 %#}
+      {#{ parents[1].title }#}
+      {%- if prev %}
+      <a class="prev" title="{{ prev.title|striptags|e }}" href="{{ prev.link|e }}"><img src="{{ pathto('_static/prev.png', 1) }}" alt="prev"/></a>
+      {%- endif %}
+      {%- if parents %}
+      <a class="up" title="{{ parents[-1].title|striptags|e }}" href="{{ parents[-1].link|e }}"><img src="{{ pathto('_static/up.png', 1) }}" alt="up"/></a>
+      {%- endif %}
+      {%- if next %}
+      <a class="next" title="{{ next.title|striptags|e }}" href="{{ next.link|e }}"><img src="{{ pathto('_static/next.png', 1) }}" alt="next"/></a>
+      {%- endif %}
+      {#%- endif %#}
+    </div>
+{%- endmacro %}
+
+<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN"
+  "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd">
+<html xmlns="http://www.w3.org/1999/xhtml">
+  <head>
+    <meta http-equiv="Content-Type" content="text/html; charset=utf-8" />
+    {{ metatags }}
+    {%- if builder != 'htmlhelp' %}
+      {%- set titlesuffix = docstitle|e %}
+      {%- set titlesuffix = " - " + titlesuffix %}
+    {%- endif %}
+    <title>{{ title|striptags }}{{ titlesuffix }}</title>
+    {%- if builder == 'web' %}
+    <link rel="stylesheet" href="{{ pathto('index') }}?do=stylesheet{%
+      if in_admin_panel %}&admin=yes{% endif %}" type="text/css" />
+    {%- for link, type, title in page_links %}
+    <link rel="alternate" type="{{ type|e(true) }}" title="{{ title|e(true) }}" href="{{ link|e(true) }}" />
+    {%- endfor %}
+    {%- else %}
+    <link rel="stylesheet" href="{{ pathto('_static/style.css', 1) }}" type="text/css" />
+    <link rel="stylesheet" href="{{ pathto('_static/pygments.css', 1) }}" type="text/css" />
+
+    {%- endif %}
+    {%- if builder != 'htmlhelp' %}
+    <script type="text/javascript">
+      var DOCUMENTATION_OPTIONS = {
+          URL_ROOT:    '{{ pathto("", 1) }}',
+          VERSION:     '{{ release|e }}',
+          COLLAPSE_MODINDEX: false,
+          FILE_SUFFIX: '{{ file_suffix }}'
+      };
+    </script>
+    {%- for scriptfile in script_files %}
+    <script type="text/javascript" src="{{ pathto(scriptfile, 1) }}"></script>
+    {%- endfor %}
+    {%- if use_opensearch %}
+    <link rel="search" type="application/opensearchdescription+xml"
+          title="{% trans docstitle=docstitle|e %}Search within {{ docstitle }}{% endtrans %}"
+          href="{{ pathto('_static/opensearch.xml', 1) }}"/>
+    {%- endif %}
+    {%- if favicon %}
+    <link rel="shortcut icon" href="{{ pathto('_static/' + favicon, 1) }}"/>
+    {%- endif %}
+    {%- endif %}
+{%- block linktags %}
+    {%- if hasdoc('about') %}
+    <link rel="author" title="{{ _('About these documents') }}" href="{{ pathto('about') }}" />
+    {%- endif %}
+    <link rel="index" title="{{ _('Index') }}" href="{{ pathto('genindex') }}" />
+    <link rel="search" title="{{ _('Search') }}" href="{{ pathto('search') }}" />
+    {%- if hasdoc('copyright') %}
+    <link rel="copyright" title="{{ _('Copyright') }}" href="{{ pathto('copyright') }}" />
+    {%- endif %}
+    <link rel="top" title="{{ docstitle|e }}" href="{{ pathto('index') }}" />
+    {%- if parents %}
+    <link rel="up" title="{{ parents[-1].title|striptags }}" href="{{ parents[-1].link|e }}" />
+    {%- endif %}
+    {%- if next %}
+    <link rel="next" title="{{ next.title|striptags }}" href="{{ next.link|e }}" />
+    {%- endif %}
+    {%- if prev %}
+    <link rel="prev" title="{{ prev.title|striptags }}" href="{{ prev.link|e }}" />
+    {%- endif %}
+{%- endblock %}
+{%- block extrahead %} {% endblock %}
+  </head>
+  <body>
+    <div class="header">
+    <table border="0" cellpadding="7" cellspacing="0" width="100%" summary=
+    "header">
+      <tr>
+        <td valign="top" width="300">
+          <h3><a href="{{ pathto('index') }}"><img height="86" width="277"
+          alt="C++ Boost" src="{{ pathto('_static/' + logo, 1) }}" border="0"></a></h3>
+        </td>
+
+        <td valign="top">
+          <h1 align="center"><a href="{{ pathto('index') }}">Boost.NumPy</a></h1>
+<!--          <h2 align="center">CallPolicies Concept</h2>-->
+        </td>
+	<td>
+      {%- if pagename != "search" %}
+      <div id="searchbox" style="display: none">
+        <form class="search" action="{{ pathto('search') }}" method="get">
+          <input type="text" name="q" size="18" />
+          <input type="submit" value="{{ _('Search') }}" />
+          <input type="hidden" name="check_keywords" value="yes" />
+          <input type="hidden" name="area" value="default" />
+        </form>
+      </div>
+      <script type="text/javascript">$('#searchbox').show(0);</script>
+      {%- endif %}
+	</td>
+      </tr>
+    </table>
+    </div>
+    <hr/>
+    <div class="content">
+      {%- block top_navbar %}{{ navbar() }}{% endblock %}
+      {% block body %} {% endblock %}
+      {%- block bottom_navbar %}{{ navbar() }}{% endblock %}
+    </div>
+  </body>
+</html>

python/pyatidlas/external/boost/libs/numpy/doc/cmakeBuild.rst

@@ -0,0 +1,167 @@
+=============
+ CMake Build
+=============
+
+.. contents::
+   :local:
+
+Usage
+=====
+
+.. code-block:: bash
+   
+   $ mkdir build
+   $ cd build
+   $ cmake ..
+
+On my CentOs 6.3 linux system with a custom installation of boost, I
+needed to invoke cmake with a special option as shown here to get
+cmake to properly use the boost installation as referenced by the
+environment variable :envvar:`BOOST_ROOT` or :envvar:`BOOST_DIR`.
+
+.. code-block:: bash
+
+   $ cmake -D Boost_NO_BOOST_CMAKE=ON ..
+
+On windows I invoked cmake using:
+
+.. code-block:: bash
+
+   > cmake -G "Visual Studio 9 2008 Win64" ^
+      -D CMAKE_INSTALL_PREFIX=c:/pkg/x64-vc90 ^
+      -D CMAKE_PREFIX_PATH=c:/pkg/x64-vc90 ^
+      -D CMAKE_CONFIGURATION_TYPES="Debug;Release" ^
+      ..
+
+Once you have the cmake generated build files you may build
+Boost.NumPy. On linux you may build it using:
+
+.. code-block:: bash
+
+   $ make
+   $ make install
+
+On windows you may build it using:
+
+.. code-block:: bash
+
+   $ cmake --build . --config release
+   $ cmake --build . --config release --target install
+
+Note: You need to make sure that the cmake generator you use is
+compatible with your python installation. The cmake scripts try to be
+helpful, but the verification logic is incomplete. On both Linux and
+Windows, I am using the 64-bit python from Enthought. On windows it is
+built using Visual Studio 2008. I have also successfully used Visual
+Studio 2010 for Boost.NumPy extension modules, but the VS 2010
+generated executables that embed python do not run because of an
+apparent conflict with the runtimes.
+
+The build artifacts get installed to ``${CMAKE_INSTALL_PREFIX}``
+:file:`include` :file:`lib` and :file:`boost.numpy` where the first
+two are the conventional locations for header files and libraries (aka
+archives, shared objects, DLLs). The last one :file:`boost.numpy` is
+my guess at how to install the tests and examples in a place that is
+useful. But it is likely that this will need to be tweaked once other
+people start using it. Here is an outline of the installed files.
+
+::
+
+   boost.numpy/doc/BoostNumPy.pdf
+            |   |- html/index.html
+            |- example/demo_gaussian.py
+            |      |-  dtype.exe
+            |      |-  fromdata.exe
+            |      |-  gaussian.pyd
+            |      |-  ndarray.exe
+            |      |-  simple.exe
+            |      |-  ufunc.exe
+            |      |-  wrap.exe
+            |- test/dtype.py
+                |-  dtype_mod.pyd
+                |-  indexing.py
+                |-  indexing_mod.pyd
+                |-  ndarray.py
+                |-  ndarray_mod.pyd
+                |-  shapes.py
+                |-  shapes_mod.pyd
+                |-  templates.py
+                |-  templates_mod.pyd
+                |-  ufunc.py
+                |-  ufunc_mod.pyd
+
+
+You may develope and test without performing an install. The build
+binary directory is configured so the executables are in the
+:file:`build/bin` folder and the shared objects are in the
+:file:`build/lib` folder. If you want to test then you simply need to
+set the :envvar:`PYTHONPATH` environment variable to the lib folder
+containing the shared object files so that python can find the
+imported extension modules.
+
+Details
+=======
+
+I borrowed from the python ``numexpr`` project the two ``.cmake``
+files :file:`FindNumPy.cmake` and :file:`FindPythonLibsNew.cmake` in
+:file:`libs/numpy/cmake`.
+
+I followed a conventional structuring of the cmake
+:file:`CMakeLists.txt` input files where the one at the top level
+contains all of the configuration logic for the submodules that are
+built.
+
+If you want to also generate this documentation, invoke cmake with the
+additional argument ``-DBUILD_DOCS=ON`` and make sure that the sphinx
+package is in your path. You may build the documentation using ``make
+doc-html``. If pdflatex is also in your path, then there is an
+additonal target ``make doc-pdf`` that will generate the pdf manual.
+
+CMakeLists.txt Source Files
+===========================
+
+For reference the source code of each of the new
+:file:`CMakeLists.txt` files are included below.
+
+Top-Level
+---------
+
+:file:`Boost.NumPy/CMakeLists.txt` where the parent subdirectory
+:file:`Boost.NumPy` is ommited in directory references in the rest of
+this section.
+
+.. literalinclude:: ../../../CMakeLists.txt
+   :language: cmake
+   :linenos:
+
+Library Source
+--------------
+
+The file :file:`libs/numpy/src/CMakeLists.txt` contains the build of the :file:`boost_numpy library`.
+
+.. literalinclude:: ../src/CMakeLists.txt
+   :language: cmake
+   :linenos:
+
+Tests
+-----
+
+The file :file:`libs/numpy/test/CMakeLists.txt` contains the python tests.
+
+.. literalinclude:: ../test/CMakeLists.txt
+   :language: cmake
+   :linenos:
+
+Examples
+--------
+
+The file :file:`libs/numpy/example/CMakeLists.txt` contains simple
+examples (both an extension module and executables embedding python).
+
+.. literalinclude:: ../example/CMakeLists.txt
+   :language: cmake
+   :linenos:
+
+
+
+

python/pyatidlas/external/boost/libs/numpy/doc/conf.py

@@ -0,0 +1,219 @@
+# -*- coding: utf-8 -*-
+#
+# Boost.NumPy documentation build configuration file, created by
+# sphinx-quickstart on Thu Oct 27 09:04:58 2011.
+#
+# This file is execfile()d with the current directory set to its containing dir.
+#
+# Note that not all possible configuration values are present in this
+# autogenerated file.
+#
+# All configuration values have a default; values that are commented out
+# serve to show the default.
+
+import sys, os
+
+# If extensions (or modules to document with autodoc) are in another directory,
+# add these directories to sys.path here. If the directory is relative to the
+# documentation root, use os.path.abspath to make it absolute, like shown here.
+#sys.path.insert(0, os.path.abspath('.'))
+
+# -- General configuration -----------------------------------------------------
+
+# If your documentation needs a minimal Sphinx version, state it here.
+#needs_sphinx = '1.0'
+
+# Add any Sphinx extension module names here, as strings. They can be extensions
+# coming with Sphinx (named 'sphinx.ext.*') or your custom ones.
+extensions = []
+
+# Add any paths that contain templates here, relative to this directory.
+templates_path = ['_templates']
+
+# The suffix of source filenames.
+source_suffix = '.rst'
+
+# The encoding of source files.
+#source_encoding = 'utf-8-sig'
+
+# The master toctree document.
+master_doc = 'index'
+
+# General information about the project.
+project = u'Boost.NumPy'
+copyright = u'2011, Stefan Seefeld'
+
+# 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 = '1.0'
+# The full version, including alpha/beta/rc tags.
+release = '1.0'
+
+# The language for content autogenerated by Sphinx. Refer to documentation
+# for a list of supported languages.
+#language = None
+
+# There are two options for replacing |today|: either, you set today to some
+# non-false value, then it is used:
+#today = ''
+# Else, today_fmt is used as the format for a strftime call.
+#today_fmt = '%B %d, %Y'
+
+# List of patterns, relative to source directory, that match files and
+# directories to ignore when looking for source files.
+exclude_patterns = ['_build']
+
+# The reST default role (used for this markup: `text`) to use for all documents.
+#default_role = None
+
+# If true, '()' will be appended to :func: etc. cross-reference text.
+#add_function_parentheses = True
+
+# If true, the current module name will be prepended to all description
+# unit titles (such as .. function::).
+#add_module_names = True
+
+# If true, sectionauthor and moduleauthor directives will be shown in the
+# output. They are ignored by default.
+show_authors = False
+
+# The name of the Pygments (syntax highlighting) style to use.
+pygments_style = 'default'
+
+highlight_language = 'c++'
+
+# A list of ignored prefixes for module index sorting.
+#modindex_common_prefix = []
+
+
+# -- Options for HTML output ---------------------------------------------------
+
+# The theme to use for HTML and HTML Help pages.  See the documentation for
+# a list of builtin themes.
+html_theme = 'default'
+
+# Theme options are theme-specific and customize the look and feel of a theme
+# further.  For a list of options available for each theme, see the
+# documentation.
+#html_theme_options = {}
+
+# Add any paths that contain custom themes here, relative to this directory.
+#html_theme_path = []
+
+# The name for this set of Sphinx documents.  If None, it defaults to
+# "<project> v<release> documentation".
+#html_title = None
+
+# A shorter title for the navigation bar.  Default is the same as html_title.
+#html_short_title = None
+
+# The name of an image file (relative to this directory) to place at the top
+# of the sidebar.
+html_logo = '_static/boost.png'
+
+# The name of an image file (within the static path) to use as favicon of the
+# docs.  This file should be a Windows icon file (.ico) being 16x16 or 32x32
+# pixels large.
+#html_favicon = None
+
+# Add any paths that contain custom static files (such as style sheets) here,
+# relative to this directory. They are copied after the builtin static files,
+# so a file named "default.css" will overwrite the builtin "default.css".
+html_static_path = ['_static']
+
+# If not '', a 'Last updated on:' timestamp is inserted at every page bottom,
+# using the given strftime format.
+#html_last_updated_fmt = '%b %d, %Y'
+
+# If true, SmartyPants will be used to convert quotes and dashes to
+# typographically correct entities.
+#html_use_smartypants = True
+
+# Custom sidebar templates, maps document names to template names.
+#html_sidebars = {}
+
+# Additional templates that should be rendered to pages, maps page names to
+# template names.
+#html_additional_pages = {}
+
+# If false, no module index is generated.
+#html_domain_indices = True
+
+# If false, no index is generated.
+html_use_index = True
+
+# If true, the index is split into individual pages for each letter.
+#html_split_index = False
+
+# If true, links to the reST sources are added to the pages.
+#html_show_sourcelink = True
+
+# If true, "Created using Sphinx" is shown in the HTML footer. Default is True.
+#html_show_sphinx = True
+
+# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True.
+#html_show_copyright = True
+
+# If true, an OpenSearch description file will be output, and all pages will
+# contain a <link> tag referring to it.  The value of this option must be the
+# base URL from which the finished HTML is served.
+#html_use_opensearch = ''
+
+# This is the file name suffix for HTML files (e.g. ".xhtml").
+#html_file_suffix = None
+
+# Output file base name for HTML help builder.
+htmlhelp_basename = 'BoostNumPydoc'
+
+html_add_permalinks = False
+
+# -- Options for LaTeX output --------------------------------------------------
+
+# The paper size ('letter' or 'a4').
+#latex_paper_size = 'letter'
+
+# The font size ('10pt', '11pt' or '12pt').
+#latex_font_size = '10pt'
+
+# Grouping the document tree into LaTeX files. List of tuples
+# (source start file, target name, title, author, documentclass [howto/manual]).
+latex_documents = [
+  ('index', 'BoostNumPy.tex', u'Boost.NumPy Documentation',
+   u'Stefan Seefeld', 'manual'),
+]
+
+# The name of an image file (relative to this directory) to place at the top of
+# the title page.
+#latex_logo = None
+
+# For "manual" documents, if this is true, then toplevel headings are parts,
+# not chapters.
+#latex_use_parts = False
+
+# If true, show page references after internal links.
+#latex_show_pagerefs = False
+
+# If true, show URL addresses after external links.
+#latex_show_urls = False
+
+# Additional stuff for the LaTeX preamble.
+#latex_preamble = ''
+
+# Documents to append as an appendix to all manuals.
+#latex_appendices = []
+
+# If false, no module index is generated.
+#latex_domain_indices = True
+
+
+# -- Options for manual page output --------------------------------------------
+
+# One entry per manual page. List of tuples
+# (source start file, name, description, authors, manual section).
+man_pages = [
+    ('index', 'boostnumpy', u'Boost.NumPy Documentation',
+     [u'Stefan Seefeld'], 1)
+]

python/pyatidlas/external/boost/libs/numpy/doc/index.rst

@@ -0,0 +1,17 @@
+.. Boost.NumPy documentation master file, created by
+   sphinx-quickstart on Thu Oct 27 09:04:58 2011.
+   You can adapt this file completely to your liking, but it should at least
+   contain the root `toctree` directive.
+
+Welcome to Boost.NumPy's documentation!
+=======================================
+
+Contents:
+
+.. toctree::
+   :maxdepth: 2
+
+   Tutorial <tutorial/index>
+   Reference <reference/index>
+   cmakeBuild.rst
+

python/pyatidlas/external/boost/libs/numpy/doc/make.bat

@@ -0,0 +1,170 @@
+@ECHO OFF
+
+REM Command file for Sphinx documentation
+
+if "%SPHINXBUILD%" == "" (
+	set SPHINXBUILD=sphinx-build
+)
+set BUILDDIR=_build
+set ALLSPHINXOPTS=-d %BUILDDIR%/doctrees %SPHINXOPTS% .
+if NOT "%PAPER%" == "" (
+	set ALLSPHINXOPTS=-D latex_paper_size=%PAPER% %ALLSPHINXOPTS%
+)
+
+if "%1" == "" goto help
+
+if "%1" == "help" (
+	:help
+	echo.Please use `make ^<target^>` where ^<target^> is one of
+	echo.  html       to make standalone HTML files
+	echo.  dirhtml    to make HTML files named index.html in directories
+	echo.  singlehtml to make a single large HTML file
+	echo.  pickle     to make pickle files
+	echo.  json       to make JSON files
+	echo.  htmlhelp   to make HTML files and a HTML help project
+	echo.  qthelp     to make HTML files and a qthelp project
+	echo.  devhelp    to make HTML files and a Devhelp project
+	echo.  epub       to make an epub
+	echo.  latex      to make LaTeX files, you can set PAPER=a4 or PAPER=letter
+	echo.  text       to make text files
+	echo.  man        to make manual pages
+	echo.  changes    to make an overview over all changed/added/deprecated items
+	echo.  linkcheck  to check all external links for integrity
+	echo.  doctest    to run all doctests embedded in the documentation if enabled
+	goto end
+)
+
+if "%1" == "clean" (
+	for /d %%i in (%BUILDDIR%\*) do rmdir /q /s %%i
+	del /q /s %BUILDDIR%\*
+	goto end
+)
+
+if "%1" == "html" (
+	%SPHINXBUILD% -b html %ALLSPHINXOPTS% %BUILDDIR%/html
+	if errorlevel 1 exit /b 1
+	echo.
+	echo.Build finished. The HTML pages are in %BUILDDIR%/html.
+	goto end
+)
+
+if "%1" == "dirhtml" (
+	%SPHINXBUILD% -b dirhtml %ALLSPHINXOPTS% %BUILDDIR%/dirhtml
+	if errorlevel 1 exit /b 1
+	echo.
+	echo.Build finished. The HTML pages are in %BUILDDIR%/dirhtml.
+	goto end
+)
+
+if "%1" == "singlehtml" (
+	%SPHINXBUILD% -b singlehtml %ALLSPHINXOPTS% %BUILDDIR%/singlehtml
+	if errorlevel 1 exit /b 1
+	echo.
+	echo.Build finished. The HTML pages are in %BUILDDIR%/singlehtml.
+	goto end
+)
+
+if "%1" == "pickle" (
+	%SPHINXBUILD% -b pickle %ALLSPHINXOPTS% %BUILDDIR%/pickle
+	if errorlevel 1 exit /b 1
+	echo.
+	echo.Build finished; now you can process the pickle files.
+	goto end
+)
+
+if "%1" == "json" (
+	%SPHINXBUILD% -b json %ALLSPHINXOPTS% %BUILDDIR%/json
+	if errorlevel 1 exit /b 1
+	echo.
+	echo.Build finished; now you can process the JSON files.
+	goto end
+)
+
+if "%1" == "htmlhelp" (
+	%SPHINXBUILD% -b htmlhelp %ALLSPHINXOPTS% %BUILDDIR%/htmlhelp
+	if errorlevel 1 exit /b 1
+	echo.
+	echo.Build finished; now you can run HTML Help Workshop with the ^
+.hhp project file in %BUILDDIR%/htmlhelp.
+	goto end
+)
+
+if "%1" == "qthelp" (
+	%SPHINXBUILD% -b qthelp %ALLSPHINXOPTS% %BUILDDIR%/qthelp
+	if errorlevel 1 exit /b 1
+	echo.
+	echo.Build finished; now you can run "qcollectiongenerator" with the ^
+.qhcp project file in %BUILDDIR%/qthelp, like this:
+	echo.^> qcollectiongenerator %BUILDDIR%\qthelp\BoostNumPy.qhcp
+	echo.To view the help file:
+	echo.^> assistant -collectionFile %BUILDDIR%\qthelp\BoostNumPy.ghc
+	goto end
+)
+
+if "%1" == "devhelp" (
+	%SPHINXBUILD% -b devhelp %ALLSPHINXOPTS% %BUILDDIR%/devhelp
+	if errorlevel 1 exit /b 1
+	echo.
+	echo.Build finished.
+	goto end
+)
+
+if "%1" == "epub" (
+	%SPHINXBUILD% -b epub %ALLSPHINXOPTS% %BUILDDIR%/epub
+	if errorlevel 1 exit /b 1
+	echo.
+	echo.Build finished. The epub file is in %BUILDDIR%/epub.
+	goto end
+)
+
+if "%1" == "latex" (
+	%SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex
+	if errorlevel 1 exit /b 1
+	echo.
+	echo.Build finished; the LaTeX files are in %BUILDDIR%/latex.
+	goto end
+)
+
+if "%1" == "text" (
+	%SPHINXBUILD% -b text %ALLSPHINXOPTS% %BUILDDIR%/text
+	if errorlevel 1 exit /b 1
+	echo.
+	echo.Build finished. The text files are in %BUILDDIR%/text.
+	goto end
+)
+
+if "%1" == "man" (
+	%SPHINXBUILD% -b man %ALLSPHINXOPTS% %BUILDDIR%/man
+	if errorlevel 1 exit /b 1
+	echo.
+	echo.Build finished. The manual pages are in %BUILDDIR%/man.
+	goto end
+)
+
+if "%1" == "changes" (
+	%SPHINXBUILD% -b changes %ALLSPHINXOPTS% %BUILDDIR%/changes
+	if errorlevel 1 exit /b 1
+	echo.
+	echo.The overview file is in %BUILDDIR%/changes.
+	goto end
+)
+
+if "%1" == "linkcheck" (
+	%SPHINXBUILD% -b linkcheck %ALLSPHINXOPTS% %BUILDDIR%/linkcheck
+	if errorlevel 1 exit /b 1
+	echo.
+	echo.Link check complete; look for any errors in the above output ^
+or in %BUILDDIR%/linkcheck/output.txt.
+	goto end
+)
+
+if "%1" == "doctest" (
+	%SPHINXBUILD% -b doctest %ALLSPHINXOPTS% %BUILDDIR%/doctest
+	if errorlevel 1 exit /b 1
+	echo.
+	echo.Testing of doctests in the sources finished, look at the ^
+results in %BUILDDIR%/doctest/output.txt.
+	goto end
+)
+
+:end

python/pyatidlas/external/boost/libs/numpy/doc/reference/Jamfile

@@ -0,0 +1,25 @@
+# Copyright David Abrahams 2006. Distributed under the Boost
+# Software License, Version 1.0. (See accompanying
+# file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
+project user-config : requirements <docutils-cmd>rst2html ;
+
+import docutils ;
+
+import path ;
+sources = dtype.rst ndarray.rst multi_iter.rst unary_ufunc.rst binary_ufunc.rst ;
+bases = $(sources:S=) ;
+  
+# This is a path relative to the html/ subdirectory where the
+# generated output will eventually be moved.
+stylesheet = "--stylesheet=rst.css" ;
+
+for local b in $(bases)
+{
+    html $(b) : $(b).rst : 
+      
+    <docutils-html>"-gdt --source-url="./$(b).rst" --link-stylesheet --traceback --trim-footnote-reference-space --footnote-references=superscript "$(stylesheet)
+  ;
+}
+
+alias htmls : $(bases) ;
+stage . : $(bases) ;

python/pyatidlas/external/boost/libs/numpy/doc/reference/binary_ufunc.rst

@@ -0,0 +1,104 @@
+binary_ufunc
+============
+
+.. contents ::
+
+A ``binary_ufunc`` is a struct used as an intermediate step to broadcast two arguments so that a C++ function can be converted to a ufunc like function
+
+ ``<boost/numpy/ufunc.hpp>`` contains the ``binary_ufunc`` structure definitions
+
+
+synopsis
+--------
+
+::
+
+  namespace boost
+  {
+  namespace numpy 
+  {
+
+  template <typename TBinaryFunctor,
+            typename TArgument1=typename TBinaryFunctor::first_argument_type,
+            typename TArgument2=typename TBinaryFunctor::second_argument_type,
+            typename TResult=typename TBinaryFunctor::result_type>
+
+  struct binary_ufunc 
+  {
+
+    static python::object call(TBinaryFunctor & self, 
+                               python::object const & input1, 
+                               python::object const & input2,
+                               python::object const & output);
+
+    static python::object make(); 
+  };
+
+  }
+  }
+
+
+constructors
+------------
+
+::
+
+  struct example_binary_ufunc
+  {
+    typedef any_valid first_argument_type;
+    typedef any_valid second_argument_type;
+    typedef any_valid result_type;
+  };
+
+:Requirements: The ``any_valid`` type must be defined using typedef as a valid C++ type in order to use the struct methods correctly
+
+:Note: The struct must be exposed as a Python class, and an instance of the class must be created to use the ``call`` method corresponding to the ``__call__`` attribute of the Python object
+
+accessors
+---------
+
+::
+
+  template <typename TBinaryFunctor,
+            typename TArgument1=typename TBinaryFunctor::first_argument_type,
+            typename TArgument2=typename TBinaryFunctor::second_argument_type,
+            typename TResult=typename TBinaryFunctor::result_type>
+  static python::object call(TBinaryFunctor & self, 
+                             python::object const & input, 
+                             python::object const & output);
+
+:Requires: Typenames ``TBinaryFunctor`` and optionally ``TArgument1`` and ``TArgument2`` for argument type and ``TResult`` for result type
+
+:Effects: Passes a Python object to the underlying C++ functor after broadcasting its arguments
+
+::
+
+  template <typename TBinaryFunctor,
+            typename TArgument1=typename TBinaryFunctor::first_argument_type,
+            typename TArgument2=typename TBinaryFunctor::second_argument_type,
+            typename TResult=typename TBinaryFunctor::result_type>
+  static python::object make(); 
+
+:Requires: Typenames ``TBinaryFunctor`` and optionally ``TArgument1`` and ``TArgument2`` for argument type and ``TResult`` for result type
+
+:Returns: A Python function object to call the overloaded () operator in the struct (in typical usage)
+
+Example(s)
+----------
+
+::
+
+  struct BinarySquare
+  {
+    typedef double first_argument_type;
+    typedef double second_argument_type;
+    typedef double result_type;
+
+    double operator()(double a,double b) const { return (a*a + b*b) ; }
+  };
+
+  p::object ud = p::class_<BinarySquare, boost::shared_ptr<BinarySquare> >("BinarySquare").def("__call__", np::binary_ufunc<BinarySquare>::make());
+  p::object inst = ud();
+  result_array = inst.attr("__call__")(demo_array,demo_array) ;
+  std::cout << "Square of list with binary ufunc is " << p::extract <char const * > (p::str(result_array)) << std::endl ; 
+

python/pyatidlas/external/boost/libs/numpy/doc/reference/dtype.rst

@@ -0,0 +1,86 @@
+dtype
+=====
+
+.. contents ::
+
+A `dtype`_ is an object describing the type of the elements of an ndarray
+
+.. _dtype: http://docs.scipy.org/doc/numpy/reference/arrays.dtypes.html#data-type-objects-dtype
+
+ ``<boost/numpy/dtype.hpp>`` contains the method calls necessary to generate a python object equivalent to a numpy.dtype from builtin C++ objects, as well as to create custom dtypes from user defined types
+
+
+synopsis
+--------
+
+::
+
+  namespace boost 
+  {
+  namespace numpy 
+  {
+
+  class dtype : public python::object 
+  {
+    static python::detail::new_reference convert(python::object::object_cref arg, bool align);
+  public:
+
+    // Convert an arbitrary Python object to a data-type descriptor object.
+    template <typename T>
+    explicit dtype(T arg, bool align=false);
+
+    // Get the built-in numpy dtype associated with the given scalar template type.
+    template <typename T> static dtype get_builtin();
+
+    // Return the size of the data type in bytes.
+    int get_itemsize() const;
+  };
+
+  } 
+
+constructors
+------------
+
+::
+
+  template <typename T>
+  explicit dtype(T arg, bool align=false)
+
+:Requirements: ``T`` must be either :
+
+               * a built-in C++ typename convertible to object
+               * a valid python object or convertible to object
+
+:Effects: Constructs an object from the supplied python object / convertible 
+          to object / builtin C++ data type
+
+:Throws: Nothing
+
+::
+
+  template <typename T> static dtype get_builtin();
+  
+:Requirements: The typename supplied, ``T`` must be a builtin C++ type also supported by numpy
+
+:Returns: Numpy dtype corresponding to builtin C++ type
+
+accessors
+---------
+
+::
+
+  int get_itemsize() const;
+
+:Returns: the size of the data type in bytes.
+
+
+Example(s)
+----------
+
+::
+
+  namespace np = boost::numpy;
+  np::dtype dtype = np::dtype::get_builtin<double>();
+  p::tuple for_custom_dtype = p::make_tuple("ha",dtype);
+  np::dtype custom_dtype = np::dtype(list_for_dtype);
+

python/pyatidlas/external/boost/libs/numpy/doc/reference/index.rst

@@ -0,0 +1,14 @@
+Boost.NumPy Reference
+=====================
+
+Contents:
+
+.. toctree::
+   :maxdepth: 2
+
+   dtype
+   ndarray
+   unary_ufunc
+   binary_ufunc
+   multi_iter
+

python/pyatidlas/external/boost/libs/numpy/doc/reference/multi_iter.rst

@@ -0,0 +1,91 @@
+multi_iter
+==========
+
+.. contents ::
+
+A ``multi_iter`` is a Python object, intended to be used as an iterator  It should generally only be used in loops.
+
+ ``<boost/numpy/ufunc.hpp>`` contains the class definitions for ``multi_iter``
+
+
+synopsis
+--------
+
+::
+
+  namespace boost 
+  {
+  namespace numpy 
+  {
+
+  class multi_iter : public python::object 
+  {
+  public:
+    void next();
+    bool not_done() const;
+    char * get_data(int n) const;
+    int const get_nd() const;
+    Py_intptr_t const * get_shape() const;
+    Py_intptr_t const shape(int n) const;
+  };
+
+
+  multi_iter make_multi_iter(python::object const & a1);
+  multi_iter make_multi_iter(python::object const & a1, python::object const & a2);
+  multi_iter make_multi_iter(python::object const & a1, python::object const & a2, python::object const & a3);
+
+  }
+  }
+
+
+constructors
+------------
+
+::
+
+  multi_iter make_multi_iter(python::object const & a1);
+  multi_iter make_multi_iter(python::object const & a1, python::object const & a2);
+  multi_iter make_multi_iter(python::object const & a1, python::object const & a2, python::object const & a3);
+
+:Returns: A Python iterator object broadcasting over one, two or three sequences as supplied
+
+accessors
+---------
+
+::
+
+  void next();
+
+:Effects: Increments the iterator
+
+::
+
+  bool not_done() const;
+
+:Returns: boolean value indicating whether the iterator is at its end
+
+::
+
+  char * get_data(int n) const;
+
+:Returns: a pointer to the element of the nth broadcasted array.
+
+::
+
+  int const get_nd() const;
+
+:Returns: the number of dimensions of the broadcasted array expression
+
+::
+
+  Py_intptr_t const * get_shape() const;
+
+:Returns: the shape of the broadcasted array expression as an array of integers.
+
+::
+
+  Py_intptr_t const shape(int n) const;
+
+:Returns: the shape of the broadcasted array expression in the nth dimension.
+	    
+

python/pyatidlas/external/boost/libs/numpy/doc/reference/ndarray.rst

@@ -0,0 +1,377 @@
+ndarray
+=======
+
+.. contents ::
+
+A `ndarray`_ is an N-dimensional array which contains items of the same type and size, where N is the number of dimensions and is specified in the form of a ``shape`` tuple. Optionally, the numpy ``dtype`` for the objects contained may also be specified.
+
+.. _ndarray: http://docs.scipy.org/doc/numpy/reference/arrays.ndarray.html
+.. _dtype: http://docs.scipy.org/doc/numpy/reference/arrays.dtypes.html#data-type-objects-dtype
+
+ ``<boost/numpy/ndarray.hpp>`` contains the structures and methods necessary to move raw data between C++ and Python and create ndarrays from the data
+
+
+
+synopsis
+--------
+
+::
+
+  namespace boost 
+  {
+  namespace numpy 
+  {
+
+  class ndarray : public python::object 
+  {
+
+  public:
+  
+    enum bitflag 
+    {
+      NONE=0x0, C_CONTIGUOUS=0x1, F_CONTIGUOUS=0x2, V_CONTIGUOUS=0x1|0x2, 
+      ALIGNED=0x4, WRITEABLE=0x8, BEHAVED=0x4|0x8,
+      CARRAY_RO=0x1|0x4, CARRAY=0x1|0x4|0x8, CARRAY_MIS=0x1|0x8,
+      FARRAY_RO=0x2|0x4, FARRAY=0x2|0x4|0x8, FARRAY_MIS=0x2|0x8,
+      UPDATE_ALL=0x1|0x2|0x4, VARRAY=0x1|0x2|0x8, ALL=0x1|0x2|0x4|0x8
+    };
+
+    ndarray view(dtype const & dt) const;
+    ndarray astype(dtype const & dt) const;
+    ndarray copy() const;
+    int const shape(int n) const;
+    int const strides(int n) const;
+    char * get_data() const;
+    dtype get_dtype() const;
+    python::object get_base() const;
+    void set_base(object const & base);
+    Py_intptr_t const * get_shape() const;
+    Py_intptr_t const * get_strides() const;
+    int const get_nd() const;
+   
+    bitflag const get_flags() const;
+  
+    ndarray transpose() const;
+    ndarray squeeze() const;
+    ndarray reshape(python::tuple const & shape) const;
+    python::object scalarize() const;
+  };
+
+  ndarray zeros(python::tuple const & shape, dtype const & dt);
+  ndarray zeros(int nd, Py_intptr_t const * shape, dtype const & dt);
+
+  ndarray empty(python::tuple const & shape, dtype const & dt);
+  ndarray empty(int nd, Py_intptr_t const * shape, dtype const & dt);
+
+  ndarray array(python::object const & obj);
+  ndarray array(python::object const & obj, dtype const & dt);
+
+  template <typename Container>
+  ndarray from_data(void * data,dtype const & dt,Container shape,Container strides,python::object const & owner);
+  template <typename Container>
+  ndarray from_data(void const * data, dtype const & dt, Container shape, Container strides, python::object const & owner);
+
+  ndarray from_object(python::object const & obj, dtype const & dt,int nd_min, int nd_max, ndarray::bitflag flags=ndarray::NONE);
+  ndarray from_object(python::object const & obj, dtype const & dt,int nd, ndarray::bitflag flags=ndarray::NONE);
+  ndarray from_object(python::object const & obj, dtype const & dt, ndarray::bitflag flags=ndarray::NONE);
+  ndarray from_object(python::object const & obj, int nd_min, int nd_max,ndarray::bitflag flags=ndarray::NONE);
+  ndarray from_object(python::object const & obj, int nd, ndarray::bitflag flags=ndarray::NONE);
+  ndarray from_object(python::object const & obj, ndarray::bitflag flags=ndarray::NONE)
+
+  ndarray::bitflag operator|(ndarray::bitflag a, ndarray::bitflag b) ; 
+  ndarray::bitflag operator&(ndarray::bitflag a, ndarray::bitflag b);
+
+  }
+
+
+constructors
+------------
+
+::
+
+  ndarray view(dtype const & dt) const;
+
+:Returns: new ndarray with old ndarray data cast as supplied dtype
+
+::
+
+  ndarray astype(dtype const & dt) const;
+
+:Returns: new ndarray with old ndarray data converted to supplied dtype
+
+::
+
+  ndarray copy() const;
+  
+:Returns: Copy of calling ndarray object
+
+:: 
+
+  ndarray transpose() const;
+
+:Returns:  An ndarray with the rows and columns interchanged
+ 
+::
+
+  ndarray squeeze() const;
+
+:Returns:  An ndarray with all unit-shaped dimensions removed
+  
+::
+
+  ndarray reshape(python::tuple const & shape) const;
+
+:Requirements: The new ``shape`` of the ndarray must be supplied as a tuple
+
+:Returns:  An ndarray with the same data but reshaped to the ``shape`` supplied 
+
+
+::
+
+  python::object scalarize() const;
+
+:Returns: A scalar if the ndarray has only one element, otherwise it returns the entire array
+
+::
+
+  ndarray zeros(python::tuple const & shape, dtype const & dt);
+  ndarray zeros(int nd, Py_intptr_t const * shape, dtype const & dt);
+
+:Requirements: The following parameters must be supplied as required :
+
+		* the ``shape`` or the size of all dimensions, as a tuple
+		* the ``dtype`` of the data
+		* the ``nd`` size for a square shaped ndarray
+		* the ``shape`` Py_intptr_t 
+
+:Returns:  A new ndarray with the given shape and data type, with data initialized to zero.
+
+::
+
+  ndarray empty(python::tuple const & shape, dtype const & dt);
+  ndarray empty(int nd, Py_intptr_t const * shape, dtype const & dt);
+
+
+:Requirements: The following parameters must be supplied :
+
+		* the ``shape`` or the size of all dimensions, as a tuple
+		* the ``dtype`` of the data
+		* the ``shape`` Py_intptr_t 
+
+:Returns:  A new ndarray with the given shape and data type, with data left uninitialized.
+
+::
+
+  ndarray array(python::object const & obj);
+  ndarray array(python::object const & obj, dtype const & dt);
+
+:Returns:  A new ndarray from an arbitrary Python sequence, with dtype of each element specified optionally
+
+::
+
+  template <typename Container>
+  inline ndarray from_data(void * data,dtype const & dt,Container shape,Container strides,python::object const & owner)
+
+:Requirements: The following parameters must be supplied :
+
+		* the ``data`` which is a generic C++ data container
+		* the dtype ``dt`` of the data
+		* the ``shape`` of the ndarray as Python object
+		* the ``strides`` of each dimension of the array as a Python object
+		* the ``owner`` of the data, in case it is not the ndarray itself
+
+:Returns: ndarray with attributes and data supplied
+
+:Note: The ``Container`` typename must be one that is convertible to a std::vector or python object type
+
+::
+
+  ndarray from_object(python::object const & obj, dtype const & dt,int nd_min, int nd_max, ndarray::bitflag flags=ndarray::NONE);
+
+:Requirements: The following parameters must be supplied :
+
+		* the ``obj`` Python object to convert to ndarray
+		* the dtype ``dt`` of the data
+		* minimum number of dimensions ``nd_min`` of the ndarray as Python object
+		* maximum number of dimensions ``nd_max`` of the ndarray as Python object
+		* optional ``flags`` bitflags
+
+:Returns: ndarray constructed with dimensions and data supplied as parameters
+
+::
+
+  inline ndarray from_object(python::object const & obj, dtype const & dt, int nd, ndarray::bitflag flags=ndarray::NONE);
+
+:Requirements: The following parameters must be supplied :
+
+		* the ``obj`` Python object to convert to ndarray
+		* the dtype ``dt`` of the data
+		* number of dimensions ``nd`` of the ndarray as Python object
+		* optional ``flags`` bitflags
+
+:Returns: ndarray with dimensions ``nd`` x ``nd`` and suplied parameters
+
+::
+
+  inline ndarray from_object(python::object const & obj, dtype const & dt, ndarray::bitflag flags=ndarray::NONE)
+
+:Requirements: The following parameters must be supplied :
+
+		* the ``obj`` Python object to convert to ndarray
+		* the dtype ``dt`` of the data
+		* optional ``flags`` bitflags
+
+:Returns: Supplied Python object as ndarray
+
+::
+
+  ndarray from_object(python::object const & obj, int nd_min, int nd_max, ndarray::bitflag flags=ndarray::NONE);
+
+:Requirements: The following parameters must be supplied :
+
+		* the ``obj`` Python object to convert to ndarray
+		* minimum number of dimensions ``nd_min`` of the ndarray as Python object
+		* maximum number of dimensions ``nd_max`` of the ndarray as Python object
+		* optional ``flags`` bitflags
+
+:Returns: ndarray with supplied dimension limits and parameters
+
+:Note: dtype need not be supplied here
+
+::
+
+  inline ndarray from_object(python::object const & obj, int nd, ndarray::bitflag flags=ndarray::NONE);
+
+:Requirements: The following parameters must be supplied :
+
+		* the ``obj`` Python object to convert to ndarray
+		* the dtype ``dt`` of the data
+		* number of dimensions ``nd`` of the ndarray as Python object
+		* optional ``flags`` bitflags
+
+:Returns: ndarray of ``nd`` x ``nd`` dimensions constructed from the supplied object
+
+::
+
+  inline ndarray from_object(python::object const & obj, ndarray::bitflag flags=ndarray::NONE)
+
+:Requirements: The following parameters must be supplied :
+
+		* the ``obj`` Python object to convert to ndarray
+		* optional ``flags`` bitflags
+
+:Returns: ndarray of same dimensions and dtype as supplied Python object
+
+
+accessors
+---------
+
+::
+
+  int const shape(int n) const;
+
+:Returns: The size of the n-th dimension of the ndarray
+
+::
+
+  int const strides(int n) const;
+
+:Returns: The stride of the nth dimension.
+
+::
+
+  char * get_data() const;
+
+:Returns: Array's raw data pointer as a char
+
+:Note: This returns char so stride math works properly on it.User will have to reinterpret_cast it.
+
+::
+
+  dtype get_dtype() const;
+
+:Returns: Array's data-type descriptor object (dtype)
+
+
+::
+
+  python::object get_base() const;
+
+:Returns: Object that owns the array's data, or None if the array owns its own data.  
+
+
+::
+
+  void set_base(object const & base);
+
+:Returns: Set the object that owns the array's data. Exercise caution while using this
+
+
+::
+
+  Py_intptr_t const * get_shape() const;
+
+:Returns: Shape of the array as an array of integers
+
+
+::
+
+  Py_intptr_t const * get_strides() const;
+
+:Returns: Stride of the array as an array of integers
+
+
+::
+
+  int const get_nd() const;
+
+:Returns: Number of array dimensions
+
+
+::
+
+  bitflag const get_flags() const;
+
+:Returns: Array flags
+
+::
+
+  inline ndarray::bitflag operator|(ndarray::bitflag a, ndarray::bitflag b)
+
+:Returns: bitflag logically OR-ed as (a | b)
+
+::
+
+  inline ndarray::bitflag operator&(ndarray::bitflag a, ndarray::bitflag b)
+
+:Returns: bitflag logically AND-ed as (a & b)
+
+
+Example(s)
+----------
+
+::
+
+  p::object tu = p::make_tuple('a','b','c') ;
+  np::ndarray example_tuple = np::array (tu) ; 
+
+  p::list l ;
+  np::ndarray example_list = np::array (l) ; 
+
+  np::dtype dt = np::dtype::get_builtin<int>();
+  np::ndarray example_list1 = np::array (l,dt);
+
+  int data[] = {1,2,3,4} ;
+  p::tuple shape = p::make_tuple(4) ;
+  p::tuple stride = p::make_tuple(4) ; 
+  p::object own ;
+  np::ndarray data_ex = np::from_data(data,dt,shape,stride,own);
+
+  uint8_t mul_data[][4] = {{1,2,3,4},{5,6,7,8},{1,3,5,7}};
+  shape = p::make_tuple(3,2) ;
+  stride = p::make_tuple(4,2) ; 
+  np::dtype dt1 = np::dtype::get_builtin<uint8_t>();
+
+  np::ndarray mul_data_ex = np::from_data(mul_data,dt1, p::make_tuple(3,4),p::make_tuple(4,1),p::object());
+  mul_data_ex = np::from_data(mul_data,dt1, shape,stride,p::object());
+

python/pyatidlas/external/boost/libs/numpy/doc/reference/unary_ufunc.rst

@@ -0,0 +1,97 @@
+unary_ufunc
+===========
+
+.. contents ::
+
+A ``unary_ufunc`` is a struct used as an intermediate step to broadcast a single argument so that a C++ function can be converted to a ufunc like function
+
+ ``<boost/numpy/ufunc.hpp>`` contains the ``unary_ufunc`` structure definitions
+
+
+synopsis
+--------
+
+::
+
+  namespace boost 
+  {
+  namespace numpy 
+  {
+
+  template <typename TUnaryFunctor, 
+            typename TArgument=typename TUnaryFunctor::argument_type, 
+            typename TResult=typename TUnaryFunctor::result_type>
+  struct unary_ufunc 
+  {
+
+    static python::object call(TUnaryFunctor & self, 
+                               python::object const & input, 
+                               python::object const & output) ;
+
+    static python::object make(); 
+
+  };
+  }
+  }
+
+
+constructors
+------------
+
+::
+
+  struct example_unary_ufunc
+  {
+    typedef any_valid_type argument_type;
+    typedef any_valid_type result_type;
+  };
+
+:Requirements: The ``any_valid`` type must be defined using typedef as a valid C++ type in order to use the struct methods correctly
+
+:Note: The struct must be exposed as a Python class, and an instance of the class must be created to use the ``call`` method corresponding to the ``__call__`` attribute of the Python object
+
+accessors
+---------
+
+::
+
+  template <typename TUnaryFunctor, 
+            typename TArgument=typename TUnaryFunctor::argument_type,
+            typename TResult=typename TUnaryFunctor::result_type>
+  static python::object call(TUnaryFunctor & self, 
+                             python::object const & input, 
+                             python::object const & output);
+
+:Requires: Typenames ``TUnaryFunctor`` and optionally ``TArgument`` for argument type and ``TResult`` for result type
+
+:Effects: Passes a Python object to the underlying C++ functor after broadcasting its arguments
+
+::
+
+  template <typename TUnaryFunctor, 
+            typename TArgument=typename TUnaryFunctor::argument_type,
+            typename TResult=typename TUnaryFunctor::result_type>
+  static python::object make(); 
+
+:Requires: Typenames ``TUnaryFunctor`` and optionally ``TArgument`` for argument type and ``TResult`` for result type
+
+:Returns: A Python function object to call the overloaded () operator in the struct (in typical usage)
+
+
+
+Example(s)
+----------
+
+::
+
+  struct UnarySquare 
+  {
+    typedef double argument_type;
+    typedef double result_type;
+    double operator()(double r) const { return r * r;}
+  };
+
+  p::object ud = p::class_<UnarySquare, boost::shared_ptr<UnarySquare> >("UnarySquare").def("__call__", np::unary_ufunc<UnarySquare>::make());
+  p::object inst = ud();
+  std::cout << "Square of unary scalar 1.0 is " << p::extract <char const * > (p::str(inst.attr("__call__")(1.0))) << std::endl ; 
+

python/pyatidlas/external/boost/libs/numpy/doc/rst.css

@@ -0,0 +1,149 @@
+@import url("doc/src/boostbook.css");
+@import url("doc/src/docutils.css");
+/* Copyright David Abrahams 2006. Distributed under the Boost
+   Software License, Version 1.0. (See accompanying
+   file LICENSE_1_0.txt or copy at http://www.boost.org/LICENSE_1_0.txt)
+ */
+
+dl.docutils dt {
+  font-weight: bold }
+
+img.boost-logo {
+  border: none;
+  vertical-align: middle
+}
+
+pre.literal-block span.concept {
+  font-style: italic;
+}
+
+.nav { 
+display: inline;
+list-style-type: none;
+}
+
+.prevpage {
+padding-top: -5px;
+text-align: left;
+float: left;
+}
+
+.nextpage {
+padding-top: -20px;
+text-align: right;
+float: right;
+}
+
+div.small {
+   font-size: smaller }
+
+h2 a { 
+   font-size: 90%; 
+}
+h3 a { 
+   font-size: 80%; 
+}
+h4 a { 
+   font-size: 70%; 
+}
+h5 a { 
+   font-size: 60%; 
+}
+
+dl,table
+{
+   text-align: left;
+   font-size: 10pt; 
+   line-height: 1.15;
+}
+
+
+/*=============================================================================
+    Tables
+=============================================================================*/
+
+/* The only clue docutils gives us that tables are logically tables,
+   and not, e.g., footnotes, is that they have border="1".  Therefore
+   we're keying off of that.  We used to manually patch docutils to
+   add a "table" class to all logical tables, but that proved much too
+   fragile.
+*/
+
+    table[border="1"]
+    {
+        width: 92%;
+        margin-left: 4%;
+        margin-right: 4%;
+    }
+    
+    table[border="1"]
+    {
+        padding: 4px;
+    }
+    
+    /* Table Cells */
+    table[border="1"] tr td
+    {
+        padding: 0.5em;
+        text-align: left;
+        font-size: 9pt;
+    }
+
+    table[border="1"] tr th
+    {
+        padding: 0.5em 0.5em 0.5em 0.5em;
+        border: 1pt solid white;
+        font-size: 80%;
+    }
+
+    @media screen
+    {
+    
+    /* Tables */
+        table[border="1"] tr td
+        {
+            border: 1px solid #DCDCDC;
+        }
+    
+        table[border="1"] tr th
+        {
+            background-color: #F0F0F0;
+            border: 1px solid #DCDCDC;
+        }
+
+        pre, 
+        .screen
+        {
+            border: 1px solid #DCDCDC;
+        }
+    
+        td pre
+        td .screen
+        {
+            border: 0px
+        }
+    
+        .sidebar pre
+        {
+            border: 0px
+        }
+    
+    }
+
+    pre, 
+    .screen
+    {
+        font-size: 9pt;
+        display: block;
+        margin: 1pc 4% 0pc 4%;
+        padding: 0.5pc 0.5pc 0.5pc 0.5pc;
+    }
+
+    /* Program listings in tables don't get borders */
+    td pre, 
+    td .screen
+    {
+        margin: 0pc 0pc 0pc 0pc;
+        padding:  0pc 0pc 0pc 0pc;
+    }
+

python/pyatidlas/external/boost/libs/numpy/doc/tutorial/dtype.rst

@@ -0,0 +1,55 @@
+How to use dtypes
+=================
+
+Here is a brief tutorial to show how to create ndarrays with built-in python data types, and extract the types and values of member variables
+
+Like before, first get the necessary headers, setup the namespaces and initialize the Python runtime and numpy module::
+
+	#include <boost/numpy.hpp>
+	#include <iostream>
+
+	namespace p = boost::python;
+	namespace np = boost::numpy;
+
+	int main(int argc, char **argv)
+	{
+	  Py_Initialize();
+	  np::initialize();
+
+Next, we create the shape and dtype. We use the get_builtin method to get the numpy dtype corresponding to the builtin C++ dtype 
+Here, we will create a 3x3 array passing a tuple with (3,3) for the size, and double as the data type ::
+
+	  p::tuple shape = p::make_tuple(3, 3);
+	  np::dtype dtype = np::dtype::get_builtin<double>();
+	  np::ndarray a = np::zeros(shape, dtype);
+
+Finally, we can print the array using the extract method in the python namespace. 
+Here, we first convert the variable into a string, and then extract it as a C++ character array from the python string using the <char const \* > template ::
+
+	std::cout << "Original array:\n" << p::extract<char const *>(p::str(a)) << std::endl;
+
+We can also print the dtypes of the data members of the ndarray by using the get_dtype method for the ndarray ::
+
+	std::cout << "Datatype is:\n" << p::extract<char const *>(p::str(a.get_dtype())) << std::endl ;
+
+We can also create custom dtypes and build ndarrays with the custom dtypes
+
+We use the dtype constructor to create a custom dtype. This constructor takes a list as an argument.
+
+The list should contain one or more tuples of the format (variable name, variable type)
+
+So first create a tuple with a variable name and its dtype, double, to create a custom dtype ::
+
+  p::tuple for_custom_dtype = p::make_tuple("ha",dtype) ;
+
+Next, create a list, and add this tuple to the list. Then use the list to create the custom dtype ::
+
+  p::list list_for_dtype ;
+  list_for_dtype.append(for_custom_dtype) ;
+  np::dtype custom_dtype = np::dtype(list_for_dtype) ;
+
+We are now ready to create an ndarray with dimensions specified by \*shape\* and of custom dtpye ::
+
+  np::ndarray new_array = np::zeros(shape,custom_dtype);
+
+	}

python/pyatidlas/external/boost/libs/numpy/doc/tutorial/fromdata.rst

@@ -0,0 +1,51 @@
+How to access data using raw pointers
+=====================================
+
+One of the advantages of the ndarray wrapper is that the same data can be used in both Python and C++ and changes can be made to reflect at both ends.
+The from_data method makes this possible. 
+
+Like before, first get the necessary headers, setup the namespaces and initialize the Python runtime and numpy module::
+
+	#include <boost/numpy.hpp>
+	#include <iostream>
+
+	namespace p = boost::python;
+	namespace np = boost::numpy;
+
+	int main(int argc, char **argv)
+	{
+	  Py_Initialize();
+	  np::initialize();
+
+Create an array in C++ , and pass the pointer to it to the from_data method to create an ndarray::
+
+  int arr[] = {1,2,3,4} ; 
+  np::ndarray py_array = np::from_data(arr, np::dtype::get_builtin<int>() , p::make_tuple(4), p::make_tuple(4), p::object());
+
+Print the source C++ array, as well as the ndarray, to check if they are the same::
+
+  std::cout << "C++ array :" << std::endl ;
+  for (int j=0;j<4;j++)
+  {
+    std::cout << arr[j] << ' ' ;
+  }
+  std::cout << std::endl << "Python ndarray :" << p::extract<char const *>(p::str(py_array)) << std::endl;
+
+Now, change an element in the Python ndarray, and check if the value changed correspondingly in the source C++ array::
+
+  py_array[1] = 5 ; 
+  std::cout << "Is the change reflected in the C++ array used to create the ndarray ? " << std::endl ; 
+  for (int j = 0;j<4 ; j++)
+  {
+    std::cout << arr[j] << ' ' ;
+  }
+
+Next, change an element of the source C++ array and see if it is reflected in the Python ndarray::
+
+  arr[2] = 8 ;
+  std::cout << std::endl << "Is the change reflected in the Python ndarray ?" << std::endl << p::extract<char const *>(p::str(py_array)) << std::endl;
+
+}
+
+As we can see, the changes are reflected across the ends. This happens because the from_data method passes the C++ array by reference to create the ndarray, and thus uses the same locations for storing data.
+

python/pyatidlas/external/boost/libs/numpy/doc/tutorial/index.rst

@@ -0,0 +1,14 @@
+Boost.NumPy Tutorial
+====================
+
+Contents:
+
+.. toctree::
+   :maxdepth: 2
+
+   simple
+   dtype
+   ndarray
+   ufunc
+   fromdata
+

python/pyatidlas/external/boost/libs/numpy/doc/tutorial/ndarray.rst

@@ -0,0 +1,94 @@
+Creating ndarrays
+=================
+
+The Boost.Numpy library exposes quite a few methods to create ndarrays. ndarrays can be created in a variety of ways, include empty arrays and zero filled arrays.
+ndarrays can also be created from arbitrary python sequences as well as from data and dtypes. 
+
+This tutorial will introduce you to some of the ways in which you can create ndarrays. The methods covered here include creating ndarrays from arbitrary Python sequences, as well as from C++ containers, using both unit and non-unit strides
+
+First, as before, initialise the necessary namepaces and runtimes ::
+
+	#include <boost/numpy.hpp>
+	#include <iostream>
+
+	namespace p = boost::python;
+	namespace np = boost::numpy;
+
+	int main(int argc, char **argv)
+	{
+	  Py_Initialize();
+	  np::initialize();
+
+Let's now create an ndarray from a simple tuple. We first create a tuple object, and then pass it to the array method, to generate the necessary tuple ::
+
+  p::object tu = p::make_tuple('a','b','c') ;
+  np::ndarray example_tuple = np::array(tu) ; 
+
+Let's now try the same with a list. We create an empty list, add an element using the append method, and as before, call the array method ::
+
+  p::list l ;
+  l.append('a') ;
+  np::ndarray example_list = np::array (l) ;
+
+Optionally, we can also specify a dtype for the array ::
+
+  np::dtype dt = np::dtype::get_builtin<int>();
+  np::ndarray example_list1 = np::array (l,dt);
+
+We can also create an array by supplying data arrays and a few other parameters.
+
+First,create an integer array ::
+
+  int data[] = {1,2,3,4} ;
+
+Create a shape, and strides, needed by the function ::
+
+  p::tuple shape = p::make_tuple(4) ;
+  p::tuple stride = p::make_tuple(4) ; 
+
+Here, shape is 1x4 , and the stride is also 4. 
+A stride is the number of bytes that must be travelled to get to the next desired element while constructing the ndarray. Here, the size of the "int" is 32 bits and hence, the stride is 4 to access each element.
+
+The function also needs an owner, to keep track of the data array passed. Passing none is dangerous ::
+
+  p::object own ;
+
+The from_data function takes the data array, datatype,shape,stride and owner as arguments and returns an ndarray ::
+
+  np::ndarray data_ex1 = np::from_data(data,dt, shape,stride,own);
+
+Now let's print the ndarray we created ::
+
+  std::cout << "Single dimensional array ::" << std::endl << p::extract < char const * > (p::str(data_ex)) << std::endl ; 
+
+Let's make it a little more interesting. Lets make an 3x2 ndarray from a multi-dimensional array using non-unit strides
+
+First lets create a 3x4 array of 8-bit integers ::
+
+  uint8_t mul_data[][4] = {{1,2,3,4},{5,6,7,8},{1,3,5,7}};
+
+Now let's create an array of 3x2 elements, picking the first and third elements from each row . For that, the shape will be 3x2.
+The strides will be 4x2 i.e. 4 bytes to go to the next desired row, and 2 bytes to go to the next desired column ::
+
+  shape = p::make_tuple(3,2) ;
+  stride = p::make_tuple(4,2) ;
+ 
+Get the numpy dtype for the built-in 8-bit integer data type ::
+
+  np::dtype dt1 = np::dtype::get_builtin<uint8_t>();
+
+Now lets first create and print out the ndarray as is.
+Notice how we can pass the shape and strides in the function directly, as well as the owner. The last part can be done because we don't have any use to 
+manipulate the "owner" object ::
+
+  np::ndarray mul_data_ex = np::from_data(mul_data,dt1, p::make_tuple(3,4),p::make_tuple(4,1),p::object());
+  std::cout << "Original multi dimensional array :: " << std::endl << p::extract < char const * > (p::str(mul_data_ex)) << std::endl ; 
+
+Now create the new ndarray using the shape and strides and print out the array we created using non-unit strides ::
+
+  mul_data_ex = np::from_data(mul_data,dt1, shape,stride,p::object());
+  std::cout << "Selective multidimensional array :: "<<std::endl << p::extract < char const * > (p::str(mul_data_ex)) << std::endl ; 
+
+Note : The from_data method will throw "error_already_set" if the number of elements dictated by the shape and the corresponding strides don't match
+
+}

python/pyatidlas/external/boost/libs/numpy/doc/tutorial/simple.rst

@@ -0,0 +1,41 @@
+A simple tutorial on Arrays
+===========================
+
+Let's start with a simple tutorial to create and modify arrays.
+
+Get the necessary headers for numpy components and set up necessary namespaces::
+
+	#include <boost/numpy.hpp>
+	#include <iostream>
+
+	namespace p = boost::python;
+	namespace np = boost::numpy;
+
+Initialise the Python runtime, and the numpy module. Failure to call these results in segmentation errors::
+
+	int main(int argc, char **argv)
+	{
+	  Py_Initialize();
+	  np::initialize();
+
+
+Zero filled n-dimensional arrays can be created using the shape and data-type of the array as a parameter. Here, the shape is 3x3 and the datatype is the built-in float type::
+
+	  p::tuple shape = p::make_tuple(3, 3);
+	  np::dtype dtype = np::dtype::get_builtin<float>();
+	  np::ndarray a = np::zeros(shape, dtype);
+
+You can also create an empty array like this ::
+
+	np::ndarray b = np::empty(shape,dtype);
+	
+Print the original and reshaped array. The array a which is a list is first converted to a string, and each value in the list is extracted using extract< >::
+
+	  std::cout << "Original array:\n" << p::extract<char const *>(p::str(a)) << std::endl;
+
+	  // Reshape the array into a 1D array
+	  a = a.reshape(p::make_tuple(9));
+	  // Print it again.
+	  std::cout << "Reshaped array:\n" << p::extract<char const *>(p::str(a)) << std::endl;
+	}
+

python/pyatidlas/external/boost/libs/numpy/doc/tutorial/ufunc.rst

@@ -0,0 +1,116 @@
+Ufuncs
+======
+
+Ufuncs or universal functions operate on ndarrays element by element, and support array broadcasting, type casting, and other features.
+
+Lets try and see how we can use the binary and unary ufunc methods
+
+After the neccessary includes ::
+
+  #include <boost/numpy.hpp>
+  #include <iostream>
+  
+  namespace p = boost::python;
+  namespace np = boost::numpy;
+
+Now we create the structs necessary to implement the ufuncs. The typedefs *must* be made as the ufunc generators take these typedefs as inputs and return an error otherwise ::
+
+  struct UnarySquare 
+  {
+    typedef double argument_type;
+    typedef double result_type;
+  
+    double operator()(double r) const { return r * r;}
+  };
+ 
+  struct BinarySquare
+  {
+    typedef double first_argument_type;
+    typedef double second_argument_type;
+    typedef double result_type;
+  
+    double operator()(double a,double b) const { return (a*a + b*b) ; }
+  };
+
+Initialise the Python runtime and the numpy module :: 
+
+  int main(int argc, char **argv)
+ {
+  Py_Initialize();
+  np::initialize();
+
+Now expose the struct UnarySquare to Python as a class, and let ud be the class object. ::
+
+  p::object ud = p::class_<UnarySquare, boost::shared_ptr<UnarySquare> >("UnarySquare")
+    .def("__call__", np::unary_ufunc<UnarySquare>::make());
+
+Let inst be an instance of the class ud ::
+
+  p::object inst = ud();
+
+Use the "__call__" method to call the overloaded () operator and print the value ::
+
+  std::cout << "Square of unary scalar 1.0 is " << p::extract <char const * > (p::str(inst.attr("__call__")(1.0))) << std::endl ; 
+
+Create an array in C++ ::
+
+  int arr[] = {1,2,3,4} ; 
+
+
+..and use it to create the ndarray in Python ::
+
+  np::ndarray demo_array = np::from_data(arr, np::dtype::get_builtin<int>() , p::make_tuple(4), p::make_tuple(4), p::object());
+
+Print out the demo array :: 
+
+  std::cout << "Demo array is " << p::extract <char const * > (p::str(demo_array)) << std::endl ; 
+
+Call the "__call__" method to perform the operation and assign the value to result_array ::
+
+  p::object result_array = inst.attr("__call__")(demo_array) ;
+
+Print the resultant array ::
+ 
+ std::cout << "Square of demo array is " << p::extract <char const * > (p::str(result_array)) << std::endl ; 
+
+Lets try the same with a list ::
+
+  p::list li ;
+  li.append(3);
+  li.append(7);
+
+Print out the demo list ::
+
+  std::cout << "Demo list is " << p::extract <char const * > (p::str(li)) << std::endl ; 
+
+Call the ufunc for the list ::
+
+  result_array = inst.attr("__call__")(li) ;
+
+And print the list out ::
+
+  std::cout << "Square of demo list is " << p::extract <char const * > (p::str(result_array)) << std::endl ; 
+
+Now lets try Binary ufuncs. Again, expose the struct BinarySquare to Python as a class, and let ud be the class object ::
+
+  ud = p::class_<BinarySquare, boost::shared_ptr<BinarySquare> >("BinarySquare")
+    .def("__call__", np::binary_ufunc<BinarySquare>::make());
+
+And initialise ud ::
+
+  inst = ud();
+
+Print the two input lists ::
+
+  std::cout << "The two input list for binary ufunc are " << std::endl << p::extract <char const * > (p::str(demo_array)) << std::endl << p::extract <char const * >   (p::str(demo_array)) << std::endl ; 
+
+Call the binary ufunc taking demo_array as both inputs ::
+
+  result_array = inst.attr("__call__")(demo_array,demo_array) ;
+
+And print the output ::
+
+  std::cout << "Square of list with binary ufunc is " << p::extract <char const * > (p::str(result_array)) << std::endl ; 
+
+}
+  

python/pyatidlas/external/boost/libs/numpy/example/CMakeLists.txt

@@ -0,0 +1,51 @@
+# custom macro with most of the redundant code for making a python example module
+macro( addPythonExe _name _srccpp )
+  ADD_EXECUTABLE(${_name} ${_srccpp})
+  
+  # make the pyd library link against boost_numpy python and boost
+  TARGET_LINK_LIBRARIES(${_name} boost_numpy ${PYTHON_LIBRARIES} ${Boost_LIBRARIES})
+  
+  # put the example target into a VS solution folder named example (should
+  # be a no-op for Linux)
+  SET_PROPERTY(TARGET ${_name} PROPERTY FOLDER "example")
+endmacro()
+
+macro( addPythonMod _name _srccpp )
+  PYTHON_ADD_MODULE(${_name} ${_srccpp})
+  
+  # make the pyd library link against boost_numpy python and boost
+  TARGET_LINK_LIBRARIES(${_name} boost_numpy ${PYTHON_LIBRARIES} ${Boost_LIBRARIES})
+  
+  # put the example target into a VS solution folder named example (should
+  # be a no-op for Linux)
+  SET_PROPERTY(TARGET ${_name} PROPERTY FOLDER "example")
+endmacro()
+
+addPythonMod(gaussian gaussian.cpp)
+addPythonExe(dtype dtype.cpp)
+addPythonExe(fromdata fromdata.cpp)
+addPythonExe(ndarray ndarray.cpp)
+addPythonExe(simple simple.cpp)
+addPythonExe(ufunc ufunc.cpp)
+addPythonExe(wrap wrap.cpp)
+
+# # installation logic (skip until it is better thought out)
+# set(DEST_EXAMPLE boost.numpy/example)
+#
+# # install executables demonstrating embedding python
+# install(TARGETS dtype fromdata ndarray simple ufunc wrap RUNTIME
+#   DESTINATION ${DEST_EXAMPLE}
+#   ${INSTALL_PERMSSIONS_RUNTIME}
+#   )
+#
+# # install extension module
+# install(TARGETS gaussian LIBRARY
+#   DESTINATION ${DEST_EXAMPLE}
+#   ${INSTALL_PERMSSIONS_RUNTIME}
+#   )
+#
+# # install source file using the extension module
+# install(FILES demo_gaussian.py 
+#   DESTINATION ${DEST_EXAMPLE}
+#   ${INSTALL_PERMSSIONS_SRC}
+#   )

python/pyatidlas/external/boost/libs/numpy/example/Jamfile

@@ -0,0 +1,20 @@
+# Copyright 2011 Stefan Seefeld.
+# Distributed under the Boost Software License, Version 1.0. (See
+# accompanying file LICENSE_1_0.txt or copy at
+# http://www.boost.org/LICENSE_1_0.txt)
+
+import python ;
+
+use-project /boost/numpy : ../src ;
+project /boost/numpy/example ;
+
+lib boost_python ;
+
+exe simple : simple.cpp ../src//boost_numpy boost_python /python//python ;
+exe dtype : dtype.cpp ../src//boost_numpy boost_python /python//python ;
+exe ndarray : ndarray.cpp ../src//boost_numpy boost_python /python//python ;
+exe hybrid : hybrid.cpp ../src//boost_numpy boost_python /python//python ;
+exe fromdata : fromdata.cpp ../src//boost_numpy boost_python /python//python ;
+exe ufunc : ufunc.cpp ../src//boost_numpy boost_python /python//python ;
+
+python-extension gaussian : gaussian.cpp ../src//boost_numpy boost_python ;

python/pyatidlas/external/boost/libs/numpy/example/SConscript

@@ -0,0 +1,27 @@
+# -*- python -*-
+
+# Copyright Jim Bosch 2010-2012.
+# Distributed under the Boost Software License, Version 1.0.
+#    (See accompanying file LICENSE_1_0.txt or copy at
+#          http://www.boost.org/LICENSE_1_0.txt)
+
+Import("env")
+
+import os
+
+example_env = env.Clone()
+lib_path = os.path.abspath(os.path.join("..", "src"))
+example_env.Append(LIBPATH=[lib_path])
+example_env.Append(RPATH=[lib_path])
+example_env.Append(LINKFLAGS = ["$__RPATH"])  # workaround for SCons bug #1644
+example_env.Append(LIBS=["boost_numpy"])
+
+example = []
+
+for name in ("ufunc", "dtype", "fromdata", "ndarray", "simple"):
+    example.extend(example_env.Program(name, "%s.cpp" % name))
+
+for name in ("gaussian",):
+    example.extend(example_env.SharedLibrary(name, "%s.cpp" % name, SHLIBPREFIX=""))
+
+Return("example")

python/pyatidlas/external/boost/libs/numpy/example/demo_gaussian.py

@@ -0,0 +1,37 @@
+# Copyright Jim Bosch 2010-2012.
+# Distributed under the Boost Software License, Version 1.0.
+#    (See accompanying file LICENSE_1_0.txt or copy at
+#          http://www.boost.org/LICENSE_1_0.txt)
+
+import numpy
+import gaussian
+
+mu = numpy.zeros(2, dtype=float)
+sigma = numpy.identity(2, dtype=float)
+sigma[0, 1] = 0.15
+sigma[1, 0] = 0.15
+
+g = gaussian.bivariate_gaussian(mu, sigma)
+
+r = numpy.linspace(-40, 40, 1001)
+x, y = numpy.meshgrid(r, r)
+
+z = g(x, y)
+
+s = z.sum() * (r[1] - r[0])**2
+print "sum (should be ~ 1):", s
+
+xc = (z * x).sum() / z.sum()
+print "x centroid (should be ~ %f): %f" % (mu[0], xc)
+
+yc = (z * y).sum() / z.sum()
+print "y centroid (should be ~ %f): %f" % (mu[1], yc)
+
+xx = (z * (x - xc)**2).sum() / z.sum()
+print "xx moment (should be ~ %f): %f" % (sigma[0,0], xx)
+
+yy = (z * (y - yc)**2).sum() / z.sum()
+print "yy moment (should be ~ %f): %f" % (sigma[1,1], yy)
+
+xy = 0.5 * (z * (x - xc) * (y - yc)).sum() / z.sum()
+print "xy moment (should be ~ %f): %f" % (sigma[0,1], xy)

python/pyatidlas/external/boost/libs/numpy/example/dtype.cpp

@@ -0,0 +1,49 @@
+// Copyright Ankit Daftery 2011-2012.
+// Distributed under the Boost Software License, Version 1.0.
+//    (See accompanying file LICENSE_1_0.txt or copy at
+//          http://www.boost.org/LICENSE_1_0.txt)
+
+/**
+ *  @brief An example to show how to create ndarrays with built-in python data types, and extract
+ *         the types and values of member variables
+ *
+ *  @todo Add an example to show type conversion.
+ *        Add an example to show use of user-defined types
+ *        
+ */
+
+#include <boost/numpy.hpp>
+#include <iostream>
+
+namespace p = boost::python;
+namespace np = boost::numpy;
+
+int main(int argc, char **argv)
+{
+  // Initialize the Python runtime.
+  Py_Initialize();
+  // Initialize NumPy
+  np::initialize();
+  // Create a 3x3 shape...
+  p::tuple shape = p::make_tuple(3, 3);
+  // ...as well as a type for C++ double
+  np::dtype dtype = np::dtype::get_builtin<double>();
+  // Construct an array with the above shape and type
+  np::ndarray a = np::zeros(shape, dtype);
+  // Print the array
+  std::cout << "Original array:\n" << p::extract<char const *>(p::str(a)) << std::endl;
+  // Print the datatype of the elements
+  std::cout << "Datatype is:\n" << p::extract<char const *>(p::str(a.get_dtype())) << std::endl ;
+  // Using user defined dtypes to create dtype and an array of the custom dtype
+  // First create a tuple with a variable name and its dtype, double, to create a custom dtype
+  p::tuple for_custom_dtype = p::make_tuple("ha",dtype) ;
+  // The list needs to be created, because the constructor to create the custom dtype
+  // takes a list of (variable,variable_type) as an argument
+  p::list list_for_dtype ;
+  list_for_dtype.append(for_custom_dtype) ;
+  // Create the custom dtype
+  np::dtype custom_dtype = np::dtype(list_for_dtype) ;
+  // Create an ndarray with the custom dtype
+  np::ndarray new_array = np::zeros(shape,custom_dtype);
+
+}

python/pyatidlas/external/boost/libs/numpy/example/fromdata.cpp

@@ -0,0 +1,48 @@
+// Copyright Ankit Daftery 2011-2012.
+// Distributed under the Boost Software License, Version 1.0.
+//    (See accompanying file LICENSE_1_0.txt or copy at
+//          http://www.boost.org/LICENSE_1_0.txt)
+
+/**
+ *  @brief An example to show how to access data using raw pointers.  This shows that you can use and
+ *         manipulate data in either Python or C++ and have the changes reflected in both.
+ */
+
+#include <boost/numpy.hpp>
+#include <iostream>
+
+namespace p = boost::python;
+namespace np = boost::numpy;
+
+
+int main(int argc, char **argv)
+{
+  // Initialize the Python runtime.
+  Py_Initialize();
+  // Initialize NumPy
+  np::initialize();
+  // Create an array in C++
+  int arr[] = {1,2,3,4} ; 
+  // Create the ndarray in Python
+  np::ndarray py_array = np::from_data(arr, np::dtype::get_builtin<int>() , p::make_tuple(4), p::make_tuple(4), p::object());
+  // Print the ndarray that we just created, and the source C++ array
+  std::cout << "C++ array :" << std::endl ;
+  for (int j=0;j<4;j++)
+  {
+    std::cout << arr[j] << ' ' ;
+  }
+  std::cout << std::endl << "Python ndarray :" << p::extract<char const *>(p::str(py_array)) << std::endl;
+  // Change an element in the python ndarray
+  py_array[1] = 5 ; 
+  // And see if the C++ container is changed or not
+  std::cout << "Is the change reflected in the C++ array used to create the ndarray ? " << std::endl ; 
+  for (int j = 0;j<4 ; j++)
+  {
+    std::cout << arr[j] << ' ' ;
+  }
+  // Conversely, change it in C++
+  arr[2] = 8 ;
+  // And see if the changes are reflected in the Python ndarray
+  std::cout << std::endl << "Is the change reflected in the Python ndarray ?" << std::endl << p::extract<char const *>(p::str(py_array)) << std::endl;
+
+}

python/pyatidlas/external/boost/libs/numpy/example/gaussian.cpp

@@ -0,0 +1,315 @@
+// Copyright Jim Bosch 2010-2012.
+// Distributed under the Boost Software License, Version 1.0.
+//    (See accompanying file LICENSE_1_0.txt or copy at
+//          http://www.boost.org/LICENSE_1_0.txt)
+
+#include <boost/numpy.hpp>
+
+#include <cmath>
+#include <memory>
+
+#ifndef M_PI
+#include <boost/math/constants/constants.hpp>
+const double M_PI = boost::math::constants::pi<double>();
+#endif
+
+namespace bp = boost::python;
+namespace bn = boost::numpy;
+
+/**
+ *  A 2x2 matrix class, purely for demonstration purposes.
+ *
+ *  Instead of wrapping this class with Boost.Python, we'll convert it to/from numpy.ndarray.
+ */
+class matrix2 {
+public:
+
+    double & operator()(int i, int j) {
+        return _data[i*2 + j];
+    }
+
+    double const & operator()(int i, int j) const {
+        return _data[i*2 + j];
+    }
+    
+    double const * data() const { return _data; }
+
+private:
+    double _data[4];
+};
+
+/**
+ *  A 2-element vector class, purely for demonstration purposes.
+ *
+ *  Instead of wrapping this class with Boost.Python, we'll convert it to/from numpy.ndarray.
+ */
+class vector2 {
+public:
+
+    double & operator[](int i) {
+        return _data[i];
+    }
+
+    double const & operator[](int i) const {
+        return _data[i];
+    }
+    
+    double const * data() const { return _data; }
+
+    vector2 operator+(vector2 const & other) const {
+        vector2 r;
+        r[0] = _data[0] + other[0];
+        r[1] = _data[1] + other[1];
+        return  r;
+    }
+
+    vector2 operator-(vector2 const & other) const {
+        vector2 r;
+        r[0] = _data[0] - other[0];
+        r[1] = _data[1] - other[1];
+        return  r;
+    }
+
+private:
+    double _data[2];
+};
+
+/**
+ *  Matrix-vector multiplication.
+ */
+vector2 operator*(matrix2 const & m, vector2 const & v) {
+    vector2 r;
+    r[0] = m(0, 0) * v[0] + m(0, 1) * v[1];
+    r[1] = m(1, 0) * v[0] + m(1, 1) * v[1];
+    return r;
+}
+
+/**
+ *  Vector inner product.
+ */
+double dot(vector2 const & v1, vector2 const & v2) {
+    return v1[0] * v2[0] + v1[1] * v2[1];
+}
+
+/**
+ *  This class represents a simple 2-d Gaussian (Normal) distribution, defined by a
+ *  mean vector 'mu' and a covariance matrix 'sigma'.
+ */
+class bivariate_gaussian {
+public:
+
+    vector2 const & get_mu() const { return _mu; }
+
+    matrix2 const & get_sigma() const { return _sigma; }
+
+    /**
+     *  Evaluate the density of the distribution at a point defined by a two-element vector.
+     */
+    double operator()(vector2 const & p) const {
+        vector2 u = _cholesky * (p - _mu);
+        return 0.5 * _cholesky(0, 0) * _cholesky(1, 1) * std::exp(-0.5 * dot(u, u)) / M_PI;
+    }
+
+    /**
+     *  Evaluate the density of the distribution at an (x, y) point.
+     */
+    double operator()(double x, double y) const {
+        vector2 p;
+        p[0] = x;
+        p[1] = y;
+        return operator()(p);
+    }
+
+    /**
+     *  Construct from a mean vector and covariance matrix.
+     */
+    bivariate_gaussian(vector2 const & mu, matrix2 const & sigma)
+        : _mu(mu), _sigma(sigma), _cholesky(compute_inverse_cholesky(sigma))
+    {}
+    
+private:
+
+    /**
+     *  This evaluates the inverse of the Cholesky factorization of a 2x2 matrix;
+     *  it's just a shortcut in evaluating the density.
+     */
+    static matrix2 compute_inverse_cholesky(matrix2 const & m) {
+        matrix2 l;
+        // First do cholesky factorization: l l^t = m
+        l(0, 0) = std::sqrt(m(0, 0));
+        l(0, 1) = m(0, 1) / l(0, 0);
+        l(1, 1) = std::sqrt(m(1, 1) - l(0,1) * l(0,1));
+        // Now do forward-substitution (in-place) to invert:
+        l(0, 0) = 1.0 / l(0, 0);
+        l(1, 0) = l(0, 1) = -l(0, 1) / l(1, 1);
+        l(1, 1) = 1.0 / l(1, 1);
+        return l;
+    }
+
+    vector2 _mu;
+    matrix2 _sigma;
+    matrix2 _cholesky;
+                        
+};
+
+/*
+ *  We have a two options for wrapping get_mu and get_sigma into NumPy-returning Python methods:
+ *   - we could deep-copy the data, making totally new NumPy arrays;
+ *   - we could make NumPy arrays that point into the existing memory.
+ *  The latter is often preferable, especially if the arrays are large, but it's dangerous unless
+ *  the reference counting is correct: the returned NumPy array needs to hold a reference that
+ *  keeps the memory it points to from being deallocated as long as it is alive.  This is what the
+ *  "owner" argument to from_data does - the NumPy array holds a reference to the owner, keeping it
+ *  from being destroyed.
+ *
+ *  Note that this mechanism isn't completely safe for data members that can have their internal
+ *  storage reallocated.  A std::vector, for instance, can be invalidated when it is resized,
+ *  so holding a Python reference to a C++ class that holds a std::vector may not be a guarantee
+ *  that the memory in the std::vector will remain valid.
+ */
+
+/**
+ *  These two functions are custom wrappers for get_mu and get_sigma, providing the shallow-copy
+ *  conversion with reference counting described above.
+ *
+ *  It's also worth noting that these return NumPy arrays that cannot be modified in Python;
+ *  the const overloads of vector::data() and matrix::data() return const references, 
+ *  and passing a const pointer to from_data causes NumPy's 'writeable' flag to be set to false.
+ */
+static bn::ndarray py_get_mu(bp::object const & self) {
+    vector2 const & mu = bp::extract<bivariate_gaussian const &>(self)().get_mu();
+    return bn::from_data(
+        mu.data(),
+        bn::dtype::get_builtin<double>(),
+        bp::make_tuple(2),
+        bp::make_tuple(sizeof(double)),
+        self
+    );  
+}
+static bn::ndarray py_get_sigma(bp::object const & self) {
+    matrix2 const & sigma = bp::extract<bivariate_gaussian const &>(self)().get_sigma();
+    return bn::from_data(
+        sigma.data(),
+        bn::dtype::get_builtin<double>(),
+        bp::make_tuple(2, 2),
+        bp::make_tuple(2 * sizeof(double), sizeof(double)),
+        self
+    );
+}
+
+/**
+ *  To allow the constructor to work, we need to define some from-Python converters from NumPy arrays
+ *  to the matrix/vector types.  The rvalue-from-python functionality is not well-documented in Boost.Python
+ *  itself; you can learn more from boost/python/converter/rvalue_from_python_data.hpp.
+ */
+
+/**
+ *  We start with two functions that just copy a NumPy array into matrix/vector objects.  These will be used
+ *  in the templated converted below.  The first just uses the operator[] overloads provided by
+ *  bp::object.
+ */
+static void copy_ndarray_to_mv2(bn::ndarray const & array, vector2 & vec) {
+    vec[0] = bp::extract<double>(array[0]);
+    vec[1] = bp::extract<double>(array[1]);
+}
+
+/**
+ *  Here, we'll take the alternate approach of using the strides to access the array's memory directly.
+ *  This can be much faster for large arrays.
+ */
+static void copy_ndarray_to_mv2(bn::ndarray const & array, matrix2 & mat) {
+    // Unfortunately, get_strides() can't be inlined, so it's best to call it once up-front.
+    Py_intptr_t const * strides = array.get_strides();
+    for (int i = 0; i < 2; ++i) {
+        for (int j = 0; j < 2; ++j) {
+            mat(i, j) = *reinterpret_cast<double const *>(array.get_data() + i * strides[0] + j * strides[1]);
+        }
+    }
+}
+
+/**
+ *  Here's the actual converter.  Because we've separated the differences into the above functions,
+ *  we can write a single template class that works for both matrix2 and vector2.
+ */
+template <typename T, int N>
+struct mv2_from_python {
+    
+    /**
+     *  Register the converter.
+     */
+    mv2_from_python() {
+        bp::converter::registry::push_back(
+            &convertible,
+            &construct,
+            bp::type_id< T >()
+        );
+    }
+
+    /**
+     *  Test to see if we can convert this to the desired type; if not return zero.
+     *  If we can convert, returned pointer can be used by construct().
+     */
+    static void * convertible(PyObject * p) {
+        try {
+            bp::object obj(bp::handle<>(bp::borrowed(p)));
+            std::auto_ptr<bn::ndarray> array(
+                new bn::ndarray(
+                    bn::from_object(obj, bn::dtype::get_builtin<double>(), N, N, bn::ndarray::V_CONTIGUOUS)
+                )
+            );
+            if (array->shape(0) != 2) return 0;
+            if (N == 2 && array->shape(1) != 2) return 0;
+            return array.release();
+        } catch (bp::error_already_set & err) {
+            bp::handle_exception();
+            return 0;
+        }
+    }
+
+    /**
+     *  Finish the conversion by initializing the C++ object into memory prepared by Boost.Python.
+     */
+    static void construct(PyObject * obj, bp::converter::rvalue_from_python_stage1_data * data) {
+        // Extract the array we passed out of the convertible() member function.
+        std::auto_ptr<bn::ndarray> array(reinterpret_cast<bn::ndarray*>(data->convertible));
+        // Find the memory block Boost.Python has prepared for the result.
+        typedef bp::converter::rvalue_from_python_storage<T> storage_t;
+        storage_t * storage = reinterpret_cast<storage_t*>(data);
+        // Use placement new to initialize the result.
+        T * m_or_v = new (storage->storage.bytes) T();
+        // Fill the result with the values from the NumPy array.
+        copy_ndarray_to_mv2(*array, *m_or_v);
+        // Finish up.
+        data->convertible = storage->storage.bytes;
+    }
+
+};
+
+
+BOOST_PYTHON_MODULE(gaussian) {
+    bn::initialize();
+
+    // Register the from-python converters
+    mv2_from_python< vector2, 1 >();
+    mv2_from_python< matrix2, 2 >();
+
+    typedef double (bivariate_gaussian::*call_vector)(vector2 const &) const;
+
+    bp::class_<bivariate_gaussian>("bivariate_gaussian", bp::init<bivariate_gaussian const &>())
+
+        // Declare the constructor (wouldn't work without the from-python converters).
+        .def(bp::init< vector2 const &, matrix2 const & >())
+
+        // Use our custom reference-counting getters
+        .add_property("mu", &py_get_mu)
+        .add_property("sigma", &py_get_sigma)
+
+        // First overload accepts a two-element array argument
+        .def("__call__", (call_vector)&bivariate_gaussian::operator())
+
+        // This overload works like a binary NumPy universal function: you can pass
+        // in scalars or arrays, and the C++ function will automatically be called
+        // on each element of an array argument.
+        .def("__call__", bn::binary_ufunc<bivariate_gaussian,double,double,double>::make())
+        ;
+}

python/pyatidlas/external/boost/libs/numpy/example/ndarray.cpp

@@ -0,0 +1,71 @@
+// Copyright Ankit Daftery 2011-2012.
+// Distributed under the Boost Software License, Version 1.0.
+//    (See accompanying file LICENSE_1_0.txt or copy at
+//          http://www.boost.org/LICENSE_1_0.txt)
+
+/**
+ *  @brief An example to show how to create ndarrays using arbitrary Python sequences.
+ *
+ *  The Python sequence could be any object whose __array__ method returns an array, or any
+ *  (nested) sequence.  This example also shows how to create arrays using both unit and
+ *  non-unit strides.      
+ */
+
+#include <boost/numpy.hpp>
+#include <iostream>
+
+namespace p = boost::python;
+namespace np = boost::numpy;
+
+#if _MSC_VER
+using boost::uint8_t;
+#endif
+
+int main(int argc, char **argv)
+{
+  // Initialize the Python runtime.
+  Py_Initialize();
+  // Initialize NumPy
+  np::initialize();
+  // Create an ndarray from a simple tuple
+  p::object tu = p::make_tuple('a','b','c') ;
+  np::ndarray example_tuple = np::array (tu) ; 
+  // and from a list
+  p::list l ;
+  np::ndarray example_list = np::array (l) ; 
+  // Optionally, you can also specify a dtype
+  np::dtype dt = np::dtype::get_builtin<int>();
+  np::ndarray example_list1 = np::array (l,dt);
+  // You can also create an array by supplying data.First,create an integer array
+  int data[] = {1,2,3,4} ;
+  // Create a shape, and strides, needed by the function
+  p::tuple shape = p::make_tuple(4) ;
+  p::tuple stride = p::make_tuple(4) ; 
+  // The function also needs an owner, to keep track of the data array passed. Passing none is dangerous
+  p::object own ;
+  // The from_data function takes the data array, datatype,shape,stride and owner as arguments
+  // and returns an ndarray
+  np::ndarray data_ex = np::from_data(data,dt,shape,stride,own);
+  // Print the ndarray we created
+  std::cout << "Single dimensional array ::" << std::endl << p::extract < char const * > (p::str(data_ex)) << std::endl ; 
+  // Now lets make an 3x2 ndarray from a multi-dimensional array using non-unit strides
+  // First lets create a 3x4 array of 8-bit integers
+  uint8_t mul_data[][4] = {{1,2,3,4},{5,6,7,8},{1,3,5,7}};
+  // Now let's create an array of 3x2 elements, picking the first and third elements from each row
+  // For that, the shape will be 3x2
+  shape = p::make_tuple(3,2) ;
+  // The strides will be 4x2 i.e. 4 bytes to go to the next desired row, and 2 bytes to go to the next desired column
+  stride = p::make_tuple(4,2) ; 
+  // Get the numpy dtype for the built-in 8-bit integer data type
+  np::dtype dt1 = np::dtype::get_builtin<uint8_t>();
+  // First lets create and print out the ndarray as is
+  np::ndarray mul_data_ex = np::from_data(mul_data,dt1, p::make_tuple(3,4),p::make_tuple(4,1),p::object());
+  std::cout << "Original multi dimensional array :: " << std::endl << p::extract < char const * > (p::str(mul_data_ex)) << std::endl ; 
+  // Now create the new ndarray using the shape and strides
+  mul_data_ex = np::from_data(mul_data,dt1, shape,stride,p::object());
+  // Print out the array we created using non-unit strides
+  std::cout << "Selective multidimensional array :: "<<std::endl << p::extract < char const * > (p::str(mul_data_ex)) << std::endl ; 
+
+}
+
+

python/pyatidlas/external/boost/libs/numpy/example/simple.cpp

@@ -0,0 +1,32 @@
+// Copyright 2011 Stefan Seefeld.
+// Distributed under the Boost Software License, Version 1.0. (See
+// accompanying file LICENSE_1_0.txt or copy at
+// http://www.boost.org/LICENSE_1_0.txt)
+
+#include <boost/numpy.hpp>
+#include <iostream>
+
+namespace p = boost::python;
+namespace np = boost::numpy;
+
+int main(int argc, char **argv)
+{
+  // Initialize the Python runtime.
+  Py_Initialize();
+  // Initialize NumPy
+  np::initialize();
+  // Create a 3x3 shape...
+  p::tuple shape = p::make_tuple(3, 3);
+  // ...as well as a type for C++ float
+  np::dtype dtype = np::dtype::get_builtin<float>();
+  // Construct an array with the above shape and type
+  np::ndarray a = np::zeros(shape, dtype);
+  // Construct an empty array with the above shape and dtype as well
+  np::ndarray b = np::empty(shape,dtype);
+  // Print the array
+  std::cout << "Original array:\n" << p::extract<char const *>(p::str(a)) << std::endl;
+  // Reshape the array into a 1D array
+  a = a.reshape(p::make_tuple(9));
+  // Print it again.
+  std::cout << "Reshaped array:\n" << p::extract<char const *>(p::str(a)) << std::endl;
+}

python/pyatidlas/external/boost/libs/numpy/example/ufunc.cpp

@@ -0,0 +1,86 @@
+// Copyright Ankit Daftery 2011-2012.
+// Distributed under the Boost Software License, Version 1.0.
+//    (See accompanying file LICENSE_1_0.txt or copy at
+//          http://www.boost.org/LICENSE_1_0.txt)
+
+/**
+ *  @brief An example to demonstrate use of universal functions or ufuncs
+ *        
+ *
+ * @todo Calling the overloaded () operator is in a roundabout manner, find a simpler way
+ *       None of the methods like np::add, np::multiply etc are supported as yet
+ */
+
+#include <boost/numpy.hpp>
+#include <iostream>
+
+namespace p = boost::python;
+namespace np = boost::numpy;
+
+
+// Create the structs necessary to implement the ufuncs 
+// The typedefs *must* be made
+
+struct UnarySquare 
+{
+  typedef double argument_type;
+  typedef double result_type;
+
+  double operator()(double r) const { return r * r;}
+};
+
+struct BinarySquare
+{
+  typedef double first_argument_type;
+  typedef double second_argument_type;
+  typedef double result_type;
+
+  double operator()(double a,double b) const { return (a*a + b*b) ; }
+};
+
+int main(int argc, char **argv)
+{
+  // Initialize the Python runtime.
+  Py_Initialize();
+  // Initialize NumPy
+  np::initialize();
+  // Expose the struct UnarySquare to Python as a class, and let ud be the class object
+  p::object ud = p::class_<UnarySquare, boost::shared_ptr<UnarySquare> >("UnarySquare")
+    .def("__call__", np::unary_ufunc<UnarySquare>::make());
+  // Let inst be an instance of the class ud
+  p::object inst = ud();
+  // Use the "__call__" method to call the overloaded () operator and print the value
+  std::cout << "Square of unary scalar 1.0 is " << p::extract <char const * > (p::str(inst.attr("__call__")(1.0))) << std::endl ; 
+  // Create an array in C++
+  int arr[] = {1,2,3,4} ; 
+  // ..and use it to create the ndarray in Python
+  np::ndarray demo_array = np::from_data(arr, np::dtype::get_builtin<int>() , p::make_tuple(4), p::make_tuple(4), p::object());
+  // Print out the demo array
+  std::cout << "Demo array is " << p::extract <char const * > (p::str(demo_array)) << std::endl ; 
+  // Call the "__call__" method to perform the operation and assign the value to result_array
+  p::object result_array = inst.attr("__call__")(demo_array) ;
+  // Print the resultant array
+  std::cout << "Square of demo array is " << p::extract <char const * > (p::str(result_array)) << std::endl ; 
+  // Lets try the same with a list
+  p::list li ;
+  li.append(3);
+  li.append(7);
+  // Print out the demo list
+  std::cout << "Demo list is " << p::extract <char const * > (p::str(li)) << std::endl ; 
+  // Call the ufunc for the list
+  result_array = inst.attr("__call__")(li) ;
+  // And print the list out
+  std::cout << "Square of demo list is " << p::extract <char const * > (p::str(result_array)) << std::endl ; 
+  // Now lets try Binary ufuncs
+  // Expose the struct BinarySquare to Python as a class, and let ud be the class object
+  ud = p::class_<BinarySquare, boost::shared_ptr<BinarySquare> >("BinarySquare")
+    .def("__call__", np::binary_ufunc<BinarySquare>::make());
+  // Again initialise inst as an instance of the class ud
+  inst = ud();
+  // Print the two input listsPrint the two input lists
+  std::cout << "The two input list for binary ufunc are " << std::endl << p::extract <char const * > (p::str(demo_array)) << std::endl << p::extract <char const * > (p::str(demo_array)) << std::endl ; 
+  // Call the binary ufunc taking demo_array as both inputs
+  result_array = inst.attr("__call__")(demo_array,demo_array) ;
+  std::cout << "Square of list with binary ufunc is " << p::extract <char const * > (p::str(result_array)) << std::endl ; 
+}
+  

python/pyatidlas/external/boost/libs/numpy/example/wrap.cpp

@@ -0,0 +1,132 @@
+// Copyright Jim Bosch 2011-2012.
+// Distributed under the Boost Software License, Version 1.0.
+//    (See accompanying file LICENSE_1_0.txt or copy at
+//          http://www.boost.org/LICENSE_1_0.txt)
+
+/**
+ *  A simple example showing how to wrap a couple of C++ functions that
+ *  operate on 2-d arrays into Python functions that take NumPy arrays
+ *  as arguments.
+ *        
+ *  If you find have a lot of such functions to wrap, you may want to
+ *  create a C++ array type (or use one of the many existing C++ array
+ *  libraries) that maps well to NumPy arrays and create Boost.Python
+ *  converters.  There's more work up front than the approach here,
+ *  but much less boilerplate per function.  See the "Gaussian" example
+ *  included with Boost.NumPy for an example of custom converters, or
+ *  take a look at the "ndarray" project on GitHub for a more complete,
+ *  high-level solution.
+ *
+ *  Note that we're using embedded Python here only to make a convenient
+ *  self-contained example; you could just as easily put the wrappers
+ *  in a regular C++-compiled module and imported them in regular
+ *  Python.  Again, see the Gaussian demo for an example.
+ */
+
+#include <boost/numpy.hpp>
+#include <boost/scoped_array.hpp>
+#include <iostream>
+
+namespace p = boost::python;
+namespace np = boost::numpy;
+
+// This is roughly the most efficient way to write a C/C++ function that operates
+// on a 2-d NumPy array - operate directly on the array by incrementing a pointer
+// with the strides.
+void fill1(double * array, int rows, int cols, int row_stride, int col_stride) {
+    double * row_iter = array;
+    double n = 0.0; // just a counter we'll fill the array with.
+    for (int i = 0; i < rows; ++i, row_iter += row_stride) {
+        double * col_iter = row_iter;
+        for (int j = 0; j < cols; ++j, col_iter += col_stride) {
+            *col_iter = ++n;
+        }
+    }
+}
+
+// Here's a simple wrapper function for fill1.  It requires that the passed
+// NumPy array be exactly what we're looking for - no conversion from nested
+// sequences or arrays with other data types, because we want to modify it
+// in-place.
+void wrap_fill1(np::ndarray const & array) {
+    if (array.get_dtype() != np::dtype::get_builtin<double>()) {
+        PyErr_SetString(PyExc_TypeError, "Incorrect array data type");
+        p::throw_error_already_set();
+    }
+    if (array.get_nd() != 2) {
+        PyErr_SetString(PyExc_TypeError, "Incorrect number of dimensions");
+        p::throw_error_already_set();
+    }
+    fill1(reinterpret_cast<double*>(array.get_data()),
+          array.shape(0), array.shape(1),
+          array.strides(0) / sizeof(double), array.strides(1) / sizeof(double));
+}
+
+// Another fill function that takes a double**.  This is less efficient, because
+// it's not the native NumPy data layout, but it's common enough in C/C++ that
+// it's worth its own example.  This time we don't pass the strides, and instead
+// in wrap_fill2 we'll require the C_CONTIGUOUS bitflag, which guarantees that
+// the column stride is 1 and the row stride is the number of columns.  That
+// restricts the arrays that can be passed to fill2 (it won't work on most
+// subarray views or transposes, for instance).
+void fill2(double ** array, int rows, int cols) {
+    double n = 0.0; // just a counter we'll fill the array with.
+    for (int i = 0; i < rows; ++i) {
+        for (int j = 0; j < cols; ++j) {
+            array[i][j] = ++n;
+        }
+    }    
+}
+// Here's the wrapper for fill2; it's a little more complicated because we need
+// to check the flags and create the array of pointers.
+void wrap_fill2(np::ndarray const & array) {
+    if (array.get_dtype() != np::dtype::get_builtin<double>()) {
+        PyErr_SetString(PyExc_TypeError, "Incorrect array data type");
+        p::throw_error_already_set();
+    }
+    if (array.get_nd() != 2) {
+        PyErr_SetString(PyExc_TypeError, "Incorrect number of dimensions");
+        p::throw_error_already_set();
+    }
+    if (!(array.get_flags() & np::ndarray::C_CONTIGUOUS)) {
+        PyErr_SetString(PyExc_TypeError, "Array must be row-major contiguous");
+        p::throw_error_already_set();
+    }
+    double * iter = reinterpret_cast<double*>(array.get_data());
+    int rows = array.shape(0);
+    int cols = array.shape(1);
+    boost::scoped_array<double*> ptrs(new double*[rows]);
+    for (int i = 0; i < rows; ++i, iter += cols) {
+        ptrs[i] = iter;
+    }
+    fill2(ptrs.get(), array.shape(0), array.shape(1));
+}
+
+BOOST_PYTHON_MODULE(example) {
+    np::initialize();  // have to put this in any module that uses Boost.NumPy
+    p::def("fill1", wrap_fill1);
+    p::def("fill2", wrap_fill2);
+}
+
+int main(int argc, char **argv)
+{
+    // This line makes our module available to the embedded Python intepreter.
+    PyImport_AppendInittab("example", &initexample); 
+
+    // Initialize the Python runtime.
+    Py_Initialize();
+
+    PyRun_SimpleString(
+        "import example\n"
+        "import numpy\n"
+        "z1 = numpy.zeros((5,6), dtype=float)\n"
+        "z2 = numpy.zeros((4,3), dtype=float)\n"
+        "example.fill1(z1)\n"
+        "example.fill2(z2)\n"
+        "print z1\n"
+        "print z2\n"
+    );
+    Py_Finalize();
+}
+
+

python/pyatidlas/external/boost/libs/numpy/src/CMakeLists.txt

@@ -0,0 +1,27 @@
+add_library(boost_numpy ${LIBRARY_TYPE}
+  # header files
+  ../../../boost/numpy/dtype.hpp
+  ../../../boost/numpy/internal.hpp
+  ../../../boost/numpy/invoke_matching.hpp
+  ../../../boost/numpy/matrix.hpp
+  ../../../boost/numpy/ndarray.hpp
+  ../../../boost/numpy/numpy_object_mgr_traits.hpp
+  ../../../boost/numpy/scalars.hpp
+  ../../../boost/numpy/ufunc.hpp
+
+  # source files (in current directory)
+  dtype.cpp
+  scalars.cpp
+  ndarray.cpp
+  matrix.cpp
+  ufunc.cpp
+  numpy.cpp
+)
+
+TARGET_LINK_LIBRARIES(boost_numpy ${Boost_LIBRARIES})
+
+install(TARGETS boost_numpy 
+  ARCHIVE DESTINATION lib 
+  LIBRARY DESTINATION lib 
+  PERMISSIONS OWNER_READ OWNER_WRITE OWNER_EXECUTE GROUP_READ GROUP_EXECUTE WORLD_READ WORLD_EXECUTE
+)

python/pyatidlas/external/boost/libs/numpy/src/Jamfile

@@ -0,0 +1,64 @@
+import python ;
+#import numpy ;
+import regex ;
+using python ;
+
+libraries = ;
+
+if [ python.configured ]
+{
+
+project boost/numpy
+  : source-location .
+#  : requirements
+  ;
+
+lib boost_python ;
+
+rule numpy-includes ( properties * )
+{
+  import feature ;
+  local python-interpreter = [ feature.get-values python.interpreter : $(properties) ] ;
+  if $(python-interpreter)
+  {
+    local full-cmd =
+      $(python-interpreter)" -c \"from numpy.distutils import misc_util; print ':'.join(misc_util.get_numpy_include_dirs())\" " ;
+    local output = [ SHELL $(full-cmd) ] ;
+    local includes = [ regex.split $(output) ":" ] ;
+    return <include>$(includes) ;
+  }
+}
+
+lib boost_numpy
+  : # sources
+    dtype.cpp
+    scalars.cpp
+    ndarray.cpp
+    matrix.cpp
+    ufunc.cpp
+    numpy.cpp
+  : # requirements
+    <library>/python//python_for_extensions
+    #<library>/boost/python//boost_python
+    <library>boost_python
+    <conditional>@numpy-includes
+  : # default build
+    <link>shared
+  : # usage requirements
+    <conditional>@numpy-includes
+  ;
+
+libraries += boost_numpy ;  
+
+}
+else if ! ( --without-python in [ modules.peek : ARGV ] )
+{
+    message boost_numpy
+        : "warning: No python installation configured and autoconfiguration"
+        : "note: failed.  See http://www.boost.org/libs/python/doc/building.html"
+        : "note: for configuration instructions or pass --without-python to"
+        : "note: suppress this message and silently skip all Boost.NumPy targets"
+        ;
+}
+
+#boost-install $(libraries) ;

python/pyatidlas/external/boost/libs/numpy/src/SConscript

@@ -0,0 +1,12 @@
+# -*- python -*-
+
+# Copyright Jim Bosch 2010-2012.
+# Distributed under the Boost Software License, Version 1.0.
+#    (See accompanying file LICENSE_1_0.txt or copy at
+#          http://www.boost.org/LICENSE_1_0.txt)
+
+Import("env")
+
+lib = env.SharedLibrary("boost_numpy", Glob("*.cpp"))
+
+Return("lib")

python/pyatidlas/external/boost/libs/numpy/src/dtype.cpp

@@ -0,0 +1,164 @@
+// Copyright Jim Bosch 2010-2012.
+// Distributed under the Boost Software License, Version 1.0.
+//    (See accompanying file LICENSE_1_0.txt or copy at
+//          http://www.boost.org/LICENSE_1_0.txt)
+
+#define BOOST_NUMPY_INTERNAL
+#include <boost/numpy/internal.hpp>
+
+#define DTYPE_FROM_CODE(code) \
+    dtype(python::detail::new_reference(reinterpret_cast<PyObject*>(PyArray_DescrFromType(code))))
+
+#define BUILTIN_INT_DTYPE(bits)                                         \
+    template <> struct builtin_int_dtype< bits, false > {               \
+        static dtype get() { return DTYPE_FROM_CODE(NPY_INT ## bits); } \
+    };                                                                  \
+    template <> struct builtin_int_dtype< bits, true > {                \
+        static dtype get() { return DTYPE_FROM_CODE(NPY_UINT ## bits); } \
+    };                                                                  \
+    template dtype get_int_dtype< bits, false >();                      \
+    template dtype get_int_dtype< bits, true >()
+
+#define BUILTIN_FLOAT_DTYPE(bits)                                       \
+    template <> struct builtin_float_dtype< bits > {                    \
+        static dtype get() { return DTYPE_FROM_CODE(NPY_FLOAT ## bits); } \
+    };                                                                  \
+    template dtype get_float_dtype< bits >()
+
+#define BUILTIN_COMPLEX_DTYPE(bits)                                     \
+    template <> struct builtin_complex_dtype< bits > {                  \
+        static dtype get() { return DTYPE_FROM_CODE(NPY_COMPLEX ## bits); } \
+    };                                                                  \
+    template dtype get_complex_dtype< bits >()
+
+namespace boost { namespace python { namespace converter {
+NUMPY_OBJECT_MANAGER_TRAITS_IMPL(PyArrayDescr_Type, numpy::dtype)
+}}} // namespace boost::python::converter
+
+namespace boost { namespace numpy {
+
+namespace detail {
+
+dtype builtin_dtype<bool,true>::get() { return DTYPE_FROM_CODE(NPY_BOOL); }
+
+template <int bits, bool isUnsigned> struct builtin_int_dtype;
+template <int bits> struct builtin_float_dtype;
+template <int bits> struct builtin_complex_dtype;
+
+template <int bits, bool isUnsigned> dtype get_int_dtype() {
+    return builtin_int_dtype<bits,isUnsigned>::get();
+}
+template <int bits> dtype get_float_dtype() { return builtin_float_dtype<bits>::get(); }
+template <int bits> dtype get_complex_dtype() { return builtin_complex_dtype<bits>::get(); }
+
+BUILTIN_INT_DTYPE(8);
+BUILTIN_INT_DTYPE(16);
+BUILTIN_INT_DTYPE(32);
+BUILTIN_INT_DTYPE(64);
+BUILTIN_FLOAT_DTYPE(32);
+BUILTIN_FLOAT_DTYPE(64);
+BUILTIN_COMPLEX_DTYPE(64);
+BUILTIN_COMPLEX_DTYPE(128);
+#if NPY_BITSOF_LONGDOUBLE > NPY_BITSOF_DOUBLE
+template <> struct builtin_float_dtype< NPY_BITSOF_LONGDOUBLE > {
+    static dtype get() { return DTYPE_FROM_CODE(NPY_LONGDOUBLE); }
+};
+template dtype get_float_dtype< NPY_BITSOF_LONGDOUBLE >();
+template <> struct builtin_complex_dtype< 2 * NPY_BITSOF_LONGDOUBLE > {
+    static dtype get() { return DTYPE_FROM_CODE(NPY_CLONGDOUBLE); }
+};
+template dtype get_complex_dtype< 2 * NPY_BITSOF_LONGDOUBLE >();
+#endif
+
+} // namespace detail
+
+python::detail::new_reference dtype::convert(python::object const & arg, bool align) {
+  PyArray_Descr* obj=NULL;
+  if (align) {
+    if (PyArray_DescrAlignConverter(arg.ptr(), &obj) < 0)
+      python::throw_error_already_set();
+  } else {
+    if (PyArray_DescrConverter(arg.ptr(), &obj) < 0)
+      python::throw_error_already_set();
+  }
+  return python::detail::new_reference(reinterpret_cast<PyObject*>(obj));
+}
+
+int dtype::get_itemsize() const { return reinterpret_cast<PyArray_Descr*>(ptr())->elsize;}
+
+bool equivalent(dtype const & a, dtype const & b) {
+    return PyArray_EquivTypes(
+        reinterpret_cast<PyArray_Descr*>(a.ptr()),
+        reinterpret_cast<PyArray_Descr*>(b.ptr())
+    );
+}
+
+namespace {
+
+namespace pyconv = boost::python::converter;
+
+template <typename T>
+class array_scalar_converter {
+public:
+
+  static PyTypeObject const * get_pytype() {
+	// This implementation depends on the fact that get_builtin returns pointers to objects
+	// NumPy has declared statically, and that the typeobj member also refers to a static
+	// object.  That means we don't need to do any reference counting.
+	// In fact, I'm somewhat concerned that increasing the reference count of any of these
+	// might cause leaks, because I don't think Boost.Python ever decrements it, but it's
+	// probably a moot point if everything is actually static.
+	return reinterpret_cast<PyArray_Descr*>(dtype::get_builtin<T>().ptr())->typeobj;
+  }
+
+  static void * convertible(PyObject * obj) {
+	if (obj->ob_type == get_pytype()) {
+	  return obj;
+	} else { 
+	  return 0;
+	}
+  }
+
+  static void convert(PyObject * obj, pyconv::rvalue_from_python_stage1_data* data) {
+	void * storage = reinterpret_cast<pyconv::rvalue_from_python_storage<T>*>(data)->storage.bytes;
+	// We assume std::complex is a "standard layout" here and elsewhere; not guaranteed by
+	// C++03 standard, but true in every known implementation (and guaranteed by C++11).
+	PyArray_ScalarAsCtype(obj, reinterpret_cast<T*>(storage));
+	data->convertible = storage;
+  }
+
+  static void declare() {
+	pyconv::registry::push_back(
+	  &convertible, &convert, python::type_id<T>()
+#ifndef BOOST_PYTHON_NO_PY_SIGNATURES
+	  , &get_pytype
+#endif
+	);
+  }
+
+};
+
+} // anonymous
+
+void dtype::register_scalar_converters() {
+  array_scalar_converter<bool>::declare();
+  array_scalar_converter<npy_uint8>::declare();
+  array_scalar_converter<npy_int8>::declare();
+  array_scalar_converter<npy_uint16>::declare();
+  array_scalar_converter<npy_int16>::declare();
+  array_scalar_converter<npy_uint32>::declare();
+  array_scalar_converter<npy_int32>::declare();
+  array_scalar_converter<npy_uint64>::declare();
+  array_scalar_converter<npy_int64>::declare();
+  array_scalar_converter<float>::declare();
+  array_scalar_converter<double>::declare();
+  array_scalar_converter< std::complex<float> >::declare();
+  array_scalar_converter< std::complex<double> >::declare();
+#if NPY_BITSOF_LONGDOUBLE > NPY_BITSOF_DOUBLE
+  array_scalar_converter<long double>::declare();
+  array_scalar_converter< std::complex<long double> >::declare();
+#endif
+}
+
+} // namespace boost::numpy
+} // namespace boost

python/pyatidlas/external/boost/libs/numpy/src/matrix.cpp

@@ -0,0 +1,68 @@
+// Copyright Jim Bosch 2010-2012.
+// Distributed under the Boost Software License, Version 1.0.
+//    (See accompanying file LICENSE_1_0.txt or copy at
+//          http://www.boost.org/LICENSE_1_0.txt)
+
+#define BOOST_NUMPY_INTERNAL
+#include <boost/numpy/internal.hpp>
+#include <boost/numpy/matrix.hpp>
+
+namespace boost 
+{
+namespace numpy 
+{ 
+namespace detail 
+{
+inline python::object get_matrix_type() 
+{
+  python::object module = python::import("numpy");
+  return module.attr("matrix");
+}
+} // namespace boost::numpy::detail
+} // namespace boost::numpy
+
+namespace python
+{
+namespace converter 
+{
+
+PyTypeObject const * object_manager_traits<numpy::matrix>::get_pytype()
+{
+  return reinterpret_cast<PyTypeObject*>(numpy::detail::get_matrix_type().ptr());
+}
+
+} // namespace boost::python::converter
+} // namespace boost::python
+
+namespace numpy 
+{
+
+python::object matrix::construct(python::object const & obj, dtype const & dt, bool copy) 
+{
+  return numpy::detail::get_matrix_type()(obj, dt, copy);
+}
+
+python::object matrix::construct(python::object const & obj, bool copy) 
+{
+  return numpy::detail::get_matrix_type()(obj, object(), copy);
+}
+
+matrix matrix::view(dtype const & dt) const 
+{
+  return matrix(python::detail::new_reference
+    (PyObject_CallMethod(this->ptr(), const_cast<char*>("view"), const_cast<char*>("O"), dt.ptr())));
+}
+
+matrix matrix::copy() const 
+{
+  return matrix(python::detail::new_reference
+    (PyObject_CallMethod(this->ptr(), const_cast<char*>("copy"), const_cast<char*>(""))));
+}
+
+matrix matrix::transpose() const 
+{
+  return matrix(python::extract<matrix>(ndarray::transpose()));
+}
+
+} // namespace boost::numpy
+} // namespace boost

python/pyatidlas/external/boost/libs/numpy/src/ndarray.cpp

@@ -0,0 +1,281 @@
+// Copyright Jim Bosch 2010-2012.
+// Distributed under the Boost Software License, Version 1.0.
+//    (See accompanying file LICENSE_1_0.txt or copy at
+//          http://www.boost.org/LICENSE_1_0.txt)
+
+#define BOOST_NUMPY_INTERNAL
+#include <boost/numpy/internal.hpp>
+#include <boost/scoped_array.hpp>
+
+namespace boost 
+{ 
+namespace python 
+{
+namespace converter 
+{
+NUMPY_OBJECT_MANAGER_TRAITS_IMPL(PyArray_Type, numpy::ndarray)
+} // namespace boost::python::converter
+} // namespace boost::python
+
+namespace numpy 
+{
+namespace detail 
+{
+
+ndarray::bitflag numpy_to_bitflag(int const f) 
+{
+  ndarray::bitflag r = ndarray::NONE;
+  if (f & NPY_C_CONTIGUOUS) r = (r | ndarray::C_CONTIGUOUS);
+  if (f & NPY_F_CONTIGUOUS) r = (r | ndarray::F_CONTIGUOUS);
+  if (f & NPY_ALIGNED) r = (r | ndarray::ALIGNED);
+  if (f & NPY_WRITEABLE) r = (r | ndarray::WRITEABLE);
+  return r;
+}
+
+int const bitflag_to_numpy(ndarray::bitflag f) 
+{
+  int r = 0;
+  if (f & ndarray::C_CONTIGUOUS) r |= NPY_C_CONTIGUOUS;
+  if (f & ndarray::F_CONTIGUOUS) r |= NPY_F_CONTIGUOUS;
+  if (f & ndarray::ALIGNED) r |= NPY_ALIGNED;
+  if (f & ndarray::WRITEABLE) r |= NPY_WRITEABLE;
+  return r;
+}
+
+bool is_c_contiguous(std::vector<Py_intptr_t> const & shape,
+		     std::vector<Py_intptr_t> const & strides,
+		     int itemsize)
+{
+  std::vector<Py_intptr_t>::const_reverse_iterator j = strides.rbegin();
+  int total = itemsize;
+  for (std::vector<Py_intptr_t>::const_reverse_iterator i = shape.rbegin(); i != shape.rend(); ++i, ++j) 
+  {
+    if (total != *j) return false;
+    total *= (*i);
+  }
+  return true;
+}
+
+bool is_f_contiguous(std::vector<Py_intptr_t> const & shape,
+		     std::vector<Py_intptr_t> const & strides,
+		     int itemsize)
+{
+  std::vector<Py_intptr_t>::const_iterator j = strides.begin();
+  int total = itemsize;
+  for (std::vector<Py_intptr_t>::const_iterator i = shape.begin(); i != shape.end(); ++i, ++j)
+  {
+    if (total != *j) return false;
+    total *= (*i);
+  }
+  return true;
+}
+
+bool is_aligned(std::vector<Py_intptr_t> const & strides,
+		int itemsize)
+{
+  for (std::vector<Py_intptr_t>::const_iterator i = strides.begin(); i != strides.end(); ++i) 
+  {
+    if (*i % itemsize) return false;
+  }
+  return true;
+}
+
+inline PyArray_Descr * incref_dtype(dtype const & dt) 
+{
+  Py_INCREF(dt.ptr());
+  return reinterpret_cast<PyArray_Descr*>(dt.ptr());
+}
+
+ndarray from_data_impl(void * data, 
+		       dtype const & dt, 
+		       python::object const & shape,
+		       python::object const & strides,
+		       python::object const & owner,
+		       bool writeable)
+{
+  std::vector<Py_intptr_t> shape_(len(shape));
+  std::vector<Py_intptr_t> strides_(len(strides));
+  if (shape_.size() != strides_.size()) 
+  {
+    PyErr_SetString(PyExc_ValueError, "Length of shape and strides arrays do not match.");
+    python::throw_error_already_set();
+  }
+  for (std::size_t i = 0; i < shape_.size(); ++i) 
+  {
+    shape_[i] = python::extract<Py_intptr_t>(shape[i]);
+    strides_[i] = python::extract<Py_intptr_t>(strides[i]);
+  }
+  return from_data_impl(data, dt, shape_, strides_, owner, writeable);
+}
+
+ndarray from_data_impl(void * data, 
+		       dtype const & dt, 
+		       std::vector<Py_intptr_t> const & shape,
+		       std::vector<Py_intptr_t> const & strides,
+		       python::object const & owner,
+		       bool writeable)
+{
+  if (shape.size() != strides.size()) 
+  {
+    PyErr_SetString(PyExc_ValueError, "Length of shape and strides arrays do not match.");
+    python::throw_error_already_set();
+  }
+  int itemsize = dt.get_itemsize();
+  int flags = 0;
+  if (writeable) flags |= NPY_WRITEABLE;
+  if (is_c_contiguous(shape, strides, itemsize)) flags |= NPY_C_CONTIGUOUS;
+  if (is_f_contiguous(shape, strides, itemsize)) flags |= NPY_F_CONTIGUOUS;
+  if (is_aligned(strides, itemsize)) flags |= NPY_ALIGNED;
+  ndarray r(python::detail::new_reference
+    (PyArray_NewFromDescr(&PyArray_Type,
+			  incref_dtype(dt),
+			  shape.size(),
+			  const_cast<Py_intptr_t*>(&shape.front()),
+			  const_cast<Py_intptr_t*>(&strides.front()),
+			  data,
+			  flags,
+			  NULL)));
+    r.set_base(owner);
+    return r;
+}
+
+} // namespace detail
+
+ndarray ndarray::view(dtype const & dt) const
+{
+  return ndarray(python::detail::new_reference
+    (PyObject_CallMethod(this->ptr(), const_cast<char*>("view"), const_cast<char*>("O"), dt.ptr())));
+}
+    
+ndarray ndarray::astype(dtype const & dt) const
+{
+  return ndarray(python::detail::new_reference
+    (PyObject_CallMethod(this->ptr(), const_cast<char*>("astype"), const_cast<char*>("O"), dt.ptr())));
+}
+
+ndarray ndarray::copy() const 
+{
+  return ndarray(python::detail::new_reference
+    (PyObject_CallMethod(this->ptr(), const_cast<char*>("copy"), const_cast<char*>(""))));
+}
+
+dtype ndarray::get_dtype() const 
+{
+  return dtype(python::detail::borrowed_reference(get_struct()->descr));
+}
+
+python::object ndarray::get_base() const 
+{
+  if (get_struct()->base == NULL) return object();
+  return python::object(python::detail::borrowed_reference(get_struct()->base));
+}
+
+void ndarray::set_base(object const & base) 
+{
+  Py_XDECREF(get_struct()->base);
+  if (base != object()) 
+  {
+    Py_INCREF(base.ptr());
+    get_struct()->base = base.ptr();
+  }
+  else
+  {
+    get_struct()->base = NULL;
+  }
+}
+
+ndarray::bitflag const ndarray::get_flags() const 
+{
+  return numpy::detail::numpy_to_bitflag(get_struct()->flags);
+}
+
+ndarray ndarray::transpose() const 
+{
+  return ndarray(python::detail::new_reference
+    (PyArray_Transpose(reinterpret_cast<PyArrayObject*>(this->ptr()), NULL)));
+}
+
+ndarray ndarray::squeeze() const 
+{
+  return ndarray(python::detail::new_reference
+    (PyArray_Squeeze(reinterpret_cast<PyArrayObject*>(this->ptr()))));
+}
+
+ndarray ndarray::reshape(python::tuple const & shape) const 
+{
+  return ndarray(python::detail::new_reference
+    (PyArray_Reshape(reinterpret_cast<PyArrayObject*>(this->ptr()), shape.ptr())));
+}
+
+python::object ndarray::scalarize() const 
+{
+  Py_INCREF(ptr());
+  return python::object(python::detail::new_reference(PyArray_Return(reinterpret_cast<PyArrayObject*>(ptr()))));
+}
+
+ndarray zeros(python::tuple const & shape, dtype const & dt) 
+{
+  int nd = len(shape);
+  boost::scoped_array<Py_intptr_t> dims(new Py_intptr_t[nd]);
+  for (int n=0; n<nd; ++n) dims[n] = python::extract<Py_intptr_t>(shape[n]);
+  return ndarray(python::detail::new_reference
+                 (PyArray_Zeros(nd, dims.get(), detail::incref_dtype(dt), 0)));
+}
+
+ndarray zeros(int nd, Py_intptr_t const * shape, dtype const & dt) 
+{
+  return ndarray(python::detail::new_reference
+    (PyArray_Zeros(nd, const_cast<Py_intptr_t*>(shape), detail::incref_dtype(dt), 0)));
+}
+
+ndarray empty(python::tuple const & shape, dtype const & dt) 
+{
+  int nd = len(shape);
+  boost::scoped_array<Py_intptr_t> dims(new Py_intptr_t[nd]);
+  for (int n=0; n<nd; ++n) dims[n] = python::extract<Py_intptr_t>(shape[n]);
+  return ndarray(python::detail::new_reference
+                 (PyArray_Empty(nd, dims.get(), detail::incref_dtype(dt), 0)));    
+}
+
+ndarray empty(int nd, Py_intptr_t const * shape, dtype const & dt)
+{
+  return ndarray(python::detail::new_reference
+    (PyArray_Empty(nd, const_cast<Py_intptr_t*>(shape), detail::incref_dtype(dt), 0)));
+}
+
+ndarray array(python::object const & obj) 
+{
+  return ndarray(python::detail::new_reference
+    (PyArray_FromAny(obj.ptr(), NULL, 0, 0, NPY_ENSUREARRAY, NULL)));
+}
+
+ndarray array(python::object const & obj, dtype const & dt) 
+{
+  return ndarray(python::detail::new_reference
+    (PyArray_FromAny(obj.ptr(), detail::incref_dtype(dt), 0, 0, NPY_ENSUREARRAY, NULL)));
+}
+
+ndarray from_object(python::object const & obj, dtype const & dt, int nd_min, int nd_max, ndarray::bitflag flags)
+{
+  int requirements = detail::bitflag_to_numpy(flags);
+  return ndarray(python::detail::new_reference
+    (PyArray_FromAny(obj.ptr(),
+		     detail::incref_dtype(dt),
+		     nd_min, nd_max,
+		     requirements,
+		     NULL)));
+}
+
+ndarray from_object(python::object const & obj, int nd_min, int nd_max, ndarray::bitflag flags) 
+{
+  int requirements = detail::bitflag_to_numpy(flags);
+  return ndarray(python::detail::new_reference
+    (PyArray_FromAny(obj.ptr(),
+		     NULL,
+		     nd_min, nd_max,
+		     requirements,
+		     NULL)));
+}
+
+}
+}

python/pyatidlas/external/boost/libs/numpy/src/numpy.cpp

@@ -0,0 +1,34 @@
+// Copyright Jim Bosch 2010-2012.
+// Distributed under the Boost Software License, Version 1.0.
+//    (See accompanying file LICENSE_1_0.txt or copy at
+//          http://www.boost.org/LICENSE_1_0.txt)
+
+#define BOOST_NUMPY_INTERNAL_MAIN
+#include <boost/numpy/internal.hpp>
+#include <boost/numpy/dtype.hpp>
+
+namespace boost 
+{
+namespace numpy 
+{
+
+#if PY_MAJOR_VERSION >= 3
+int
+#else
+void
+#endif
+do_import_array()
+{
+  import_array();
+}
+
+void initialize(bool register_scalar_converters) 
+{
+  do_import_array();
+  import_ufunc();
+  if (register_scalar_converters)
+	dtype::register_scalar_converters();
+}
+
+}
+}

python/pyatidlas/external/boost/libs/numpy/src/scalars.cpp

@@ -0,0 +1,40 @@
+// Copyright Jim Bosch 2010-2012.
+// Distributed under the Boost Software License, Version 1.0.
+//    (See accompanying file LICENSE_1_0.txt or copy at
+//          http://www.boost.org/LICENSE_1_0.txt)
+
+#define BOOST_NUMPY_INTERNAL
+#include <boost/numpy/internal.hpp>
+
+namespace boost 
+{ 
+namespace python 
+{
+namespace converter 
+{
+NUMPY_OBJECT_MANAGER_TRAITS_IMPL(PyVoidArrType_Type, numpy::void_)
+} // namespace boost::python::converter
+} // namespace boost::python
+
+namespace numpy 
+{
+
+void_::void_(Py_ssize_t size)
+  : object(python::detail::new_reference
+      (PyObject_CallFunction((PyObject*)&PyVoidArrType_Type, const_cast<char*>("i"), size)))
+{}
+
+void_ void_::view(dtype const & dt) const 
+{
+  return void_(python::detail::new_reference
+    (PyObject_CallMethod(this->ptr(), const_cast<char*>("view"), const_cast<char*>("O"), dt.ptr())));
+}
+
+void_ void_::copy() const 
+{
+  return void_(python::detail::new_reference
+    (PyObject_CallMethod(this->ptr(), const_cast<char*>("copy"), const_cast<char*>(""))));
+}
+
+}
+}

python/pyatidlas/external/boost/libs/numpy/src/ufunc.cpp

@@ -0,0 +1,69 @@
+// Copyright Jim Bosch 2010-2012.
+// Distributed under the Boost Software License, Version 1.0.
+//    (See accompanying file LICENSE_1_0.txt or copy at
+//          http://www.boost.org/LICENSE_1_0.txt)
+
+#define BOOST_NUMPY_INTERNAL
+#include <boost/numpy/internal.hpp>
+#include <boost/numpy/ufunc.hpp>
+
+namespace boost 
+{
+namespace python 
+{
+namespace converter 
+{
+NUMPY_OBJECT_MANAGER_TRAITS_IMPL(PyArrayMultiIter_Type, numpy::multi_iter)
+} // namespace boost::python::converter
+} // namespace boost::python
+
+namespace numpy 
+{
+
+multi_iter make_multi_iter(python::object const & a1) 
+{
+  return multi_iter(python::detail::new_reference(PyArray_MultiIterNew(1, a1.ptr())));
+}
+
+multi_iter make_multi_iter(python::object const & a1, python::object const & a2) 
+{
+  return multi_iter(python::detail::new_reference(PyArray_MultiIterNew(2, a1.ptr(), a2.ptr())));
+}
+
+multi_iter make_multi_iter(python::object const & a1, python::object const & a2, python::object const & a3) 
+{
+  return multi_iter(python::detail::new_reference(PyArray_MultiIterNew(3, a1.ptr(), a2.ptr(), a3.ptr())));
+}
+
+void multi_iter::next() 
+{
+  PyArray_MultiIter_NEXT(ptr());
+}
+
+bool multi_iter::not_done() const 
+{
+  return PyArray_MultiIter_NOTDONE(ptr());
+}
+
+char * multi_iter::get_data(int i) const 
+{
+  return reinterpret_cast<char*>(PyArray_MultiIter_DATA(ptr(), i));
+}
+
+int const multi_iter::get_nd() const 
+{
+  return reinterpret_cast<PyArrayMultiIterObject*>(ptr())->nd;
+}
+
+Py_intptr_t const * multi_iter::get_shape() const 
+{
+  return reinterpret_cast<PyArrayMultiIterObject*>(ptr())->dimensions;
+}
+
+Py_intptr_t const multi_iter::shape(int n) const 
+{
+  return reinterpret_cast<PyArrayMultiIterObject*>(ptr())->dimensions[n];
+}
+
+}
+}

python/pyatidlas/external/boost/libs/numpy/test/CMakeLists.txt

@@ -0,0 +1,65 @@
+project(BoostNumpyTests)
+
+if (WIN32)
+  set(runCmakeTest runCmakeTest.bat)
+  foreach(cfg ${CMAKE_CONFIGURATION_TYPES})
+    message( STATUS "configuring runCmakeTest for cfg=${cfg}" )
+    CONFIGURE_FILE( ${runCmakeTest}.in ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/${cfg}/${runCmakeTest} @ONLY )
+  endforeach()
+else()
+  set(runCmakeTest runCmakeTest.sh)
+  CONFIGURE_FILE( ${runCmakeTest}.in ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/${runCmakeTest} @ONLY )
+endif()
+
+set( TEST_SOURCE_DIR ${PROJECT_SOURCE_DIR} )
+set( TestCommand ${CMAKE_RUNTIME_OUTPUT_DIRECTORY}/${runCmakeTest} )
+
+# custom macro with most of the redundant code for making a python test
+macro( addPythonTest _name _srcpy )
+  # make the pyd library link against boost_numpy python and boost
+  TARGET_LINK_LIBRARIES(${_name} boost_numpy ${PYTHON_LIBRARIES} ${Boost_LIBRARIES})
+  
+  # make a test of the module using the python source file in the test directory
+  ADD_TEST(${_name} ${TestCommand} ${TEST_SOURCE_DIR}/${_srcpy})
+
+  # set the regex to use to recognize a failure since `python testfoo.py`
+  # does not seem to return non-zero with a test failure
+  set_property(TEST ${_name} PROPERTY FAIL_REGULAR_EXPRESSION "ERROR\\:" "ImportError\\: DLL load failed\\: " )
+
+  # put the test target into a VS solution folder named test (should
+  # be a no-op for Linux)
+  SET_PROPERTY(TARGET ${_name} PROPERTY FOLDER "test")
+endmacro()
+
+PYTHON_ADD_MODULE(dtype_mod dtype_mod.cpp)
+addPythonTest( dtype_mod dtype.py )
+
+PYTHON_ADD_MODULE(indexing_mod indexing_mod.cpp)
+addPythonTest( indexing_mod indexing.py )
+
+PYTHON_ADD_MODULE(ndarray_mod ndarray_mod.cpp)
+addPythonTest( ndarray_mod ndarray.py )
+
+PYTHON_ADD_MODULE(shapes_mod shapes_mod.cpp)
+addPythonTest( shapes_mod shapes.py )
+
+PYTHON_ADD_MODULE(templates_mod templates_mod.cpp)
+addPythonTest( templates_mod templates.py )
+
+PYTHON_ADD_MODULE(ufunc_mod ufunc_mod.cpp)
+addPythonTest( ufunc_mod ufunc.py )
+
+# installation logic (skip until it is better thought out)
+# set(DEST_TEST boost.numpy/test)
+#
+# # copy the extension modules to DEST_TEST
+# install(TARGETS dtype_mod indexing_mod ndarray_mod shapes_mod templates_mod ufunc_mod LIBRARY
+#   DESTINATION ${DEST_TEST}
+#   ${INSTALL_PERMSSIONS_RUNTIME}
+#   )
+#
+# # copy the source test python modules to DEST_TEST too
+# install(FILES dtype.py indexing.py ndarray.py shapes.py templates.py ufunc.py
+#   DESTINATION ${DEST_TEST}
+#   ${INSTALL_PERMSSIONS_SRC}
+#   )

python/pyatidlas/external/boost/libs/numpy/test/Jamfile

@@ -0,0 +1,23 @@
+import testing ;
+import python ;
+
+use-project /boost/numpy : ../src ;
+project /boost/numpy/test ;
+
+rule numpy-test ( name : sources * : requirements * )
+{
+    sources ?= $(name).py $(name)_mod.cpp ;
+    return [ testing.make-test run-pyd : $(sources) /boost/numpy//boost_numpy
+        : $(requirements) : $(name) ] ;
+}
+
+test-suite numpy
+  :
+
+    [ numpy-test templates ]
+    [ numpy-test ufunc ]
+    [ numpy-test shapes ]
+    [ numpy-test ndarray ]
+    [ numpy-test indexing ]
+
+  ;

python/pyatidlas/external/boost/libs/numpy/test/SConscript

@@ -0,0 +1,35 @@
+# -*- python -*-
+
+# Copyright Jim Bosch 2010-2012.
+# Distributed under the Boost Software License, Version 1.0.
+#    (See accompanying file LICENSE_1_0.txt or copy at
+#          http://www.boost.org/LICENSE_1_0.txt)
+
+Import("env")
+
+import os
+import sys
+
+test_env = env.Clone()
+lib_path = os.path.abspath(os.path.join("..", "src"))
+test_env.Append(LIBPATH=[lib_path])
+test_env.Append(RPATH=[lib_path])
+test_env.Append(LINKFLAGS = ["$__RPATH"])  # workaround for SCons bug #1644t
+test_env.Append(LIBS=["boost_numpy"])
+
+test = []
+
+def RunPythonUnitTest(target, source, env):
+    if not env.Execute('%s %s' % (sys.executable, source[0].abspath)):
+        env.Execute(Touch(target))
+
+def PythonUnitTest(env, script, dependencies):
+    run = env.Command(".%s.succeeded" % str(script), script, RunPythonUnitTest)
+    env.Depends(run, dependencies)
+    return run
+
+for name in ("dtype", "ufunc", "templates", "ndarray", "indexing", "shapes"):
+    mod = test_env.LoadableModule("%s_mod" % name, "%s_mod.cpp" % name, LDMODULEPREFIX="")
+    test.extend(PythonUnitTest(test_env, "%s.py" % name, mod))
+
+Return("test")

python/pyatidlas/external/boost/libs/numpy/test/dtype.py

@@ -0,0 +1,60 @@
+#!/usr/bin/env python
+
+# Copyright Jim Bosch & Ankit Daftery 2010-2012.
+# Distributed under the Boost Software License, Version 1.0.
+#    (See accompanying file LICENSE_1_0.txt or copy at
+#          http://www.boost.org/LICENSE_1_0.txt)
+
+import dtype_mod
+import unittest
+import numpy
+
+class DtypeTestCase(unittest.TestCase):
+    
+    def assertEquivalent(self, a, b):
+        return self.assert_(dtype_mod.equivalent(a, b), "%r is not equivalent to %r")
+
+    def testIntegers(self):
+        for bits in (8, 16, 32, 64):
+            s = getattr(numpy, "int%d" % bits)
+            u = getattr(numpy, "uint%d" % bits)
+            fs = getattr(dtype_mod, "accept_int%d" % bits)
+            fu = getattr(dtype_mod, "accept_uint%d" % bits)
+            self.assertEquivalent(fs(s(1)), numpy.dtype(s))
+            self.assertEquivalent(fu(u(1)), numpy.dtype(u))
+            # these should just use the regular Boost.Python converters
+            self.assertEquivalent(fs(True), numpy.dtype(s))
+            self.assertEquivalent(fu(True), numpy.dtype(u))
+            self.assertEquivalent(fs(int(1)), numpy.dtype(s))
+            self.assertEquivalent(fu(int(1)), numpy.dtype(u))
+            self.assertEquivalent(fs(long(1)), numpy.dtype(s))
+            self.assertEquivalent(fu(long(1)), numpy.dtype(u))
+        for name in ("bool_", "byte", "ubyte", "short", "ushort", "intc", "uintc"):
+            t = getattr(numpy, name)
+            ft = getattr(dtype_mod, "accept_%s" % name)
+            self.assertEquivalent(ft(t(1)), numpy.dtype(t))
+            # these should just use the regular Boost.Python converters
+            self.assertEquivalent(ft(True), numpy.dtype(t))
+            if name != "bool_":
+                self.assertEquivalent(ft(int(1)), numpy.dtype(t))
+                self.assertEquivalent(ft(long(1)), numpy.dtype(t))
+
+
+    def testFloats(self):
+        f = numpy.float32
+        c = numpy.complex64
+        self.assertEquivalent(dtype_mod.accept_float32(f(numpy.pi)), numpy.dtype(f))
+        self.assertEquivalent(dtype_mod.accept_complex64(c(1+2j)), numpy.dtype(c))
+        f = numpy.float64
+        c = numpy.complex128
+        self.assertEquivalent(dtype_mod.accept_float64(f(numpy.pi)), numpy.dtype(f))
+        self.assertEquivalent(dtype_mod.accept_complex128(c(1+2j)), numpy.dtype(c))
+        if hasattr(numpy, "longdouble"):
+            f = numpy.longdouble
+            c = numpy.clongdouble
+            self.assertEquivalent(dtype_mod.accept_longdouble(f(numpy.pi)), numpy.dtype(f))
+            self.assertEquivalent(dtype_mod.accept_clongdouble(c(1+2j)), numpy.dtype(c))
+            
+
+if __name__=="__main__":
+    unittest.main()

python/pyatidlas/external/boost/libs/numpy/test/dtype_mod.cpp

@@ -0,0 +1,48 @@
+// Copyright Jim Bosch & Ankit Daftery 2010-2012.
+// Distributed under the Boost Software License, Version 1.0.
+//    (See accompanying file LICENSE_1_0.txt or copy at
+//          http://www.boost.org/LICENSE_1_0.txt)
+
+#include <boost/numpy.hpp>
+#include <boost/cstdint.hpp>
+
+namespace p = boost::python;
+namespace np = boost::numpy;
+
+template <typename T>
+np::dtype accept(T) {
+  return np::dtype::get_builtin<T>();
+}
+
+BOOST_PYTHON_MODULE(dtype_mod) 
+{
+  np::initialize();
+  // wrap dtype equivalence test, since it isn't available in Python API.
+  p::def("equivalent", np::equivalent);
+  // integers, by number of bits
+  p::def("accept_int8", accept<boost::int8_t>);
+  p::def("accept_uint8", accept<boost::uint8_t>);
+  p::def("accept_int16", accept<boost::int16_t>);
+  p::def("accept_uint16", accept<boost::uint16_t>);
+  p::def("accept_int32", accept<boost::int32_t>);
+  p::def("accept_uint32", accept<boost::uint32_t>);
+  p::def("accept_int64", accept<boost::int64_t>);
+  p::def("accept_uint64", accept<boost::uint64_t>);
+  // integers, by C name according to NumPy
+  p::def("accept_bool_", accept<bool>);
+  p::def("accept_byte", accept<signed char>);
+  p::def("accept_ubyte", accept<unsigned char>);
+  p::def("accept_short", accept<short>);
+  p::def("accept_ushort", accept<unsigned short>);
+  p::def("accept_intc", accept<int>);
+  p::def("accept_uintc", accept<unsigned int>);
+  // floats and complex
+  p::def("accept_float32", accept<float>);
+  p::def("accept_complex64", accept< std::complex<float> >);
+  p::def("accept_float64", accept<double>);
+  p::def("accept_complex128", accept< std::complex<double> >);
+  if (sizeof(long double) > sizeof(double)) {
+      p::def("accept_longdouble", accept<long double>);
+      p::def("accept_clongdouble", accept< std::complex<long double> >);
+  }
+}

python/pyatidlas/external/boost/libs/numpy/test/indexing.py

@@ -0,0 +1,55 @@
+#!/usr/bin/env python
+
+# Copyright Jim Bosch & Ankit Daftery 2010-2012.
+# Distributed under the Boost Software License, Version 1.0.
+#    (See accompanying file LICENSE_1_0.txt or copy at
+#          http://www.boost.org/LICENSE_1_0.txt)
+
+import unittest
+import numpy
+import indexing_mod
+
+class TestIndexing(unittest.TestCase):
+
+    def testSingle(self):
+        x = numpy.arange(0,10)
+        for i in range(0,10):
+            numpy.testing.assert_equal(indexing_mod.single(x,i), i)
+        for i in range(-10,0):
+            numpy.testing.assert_equal(indexing_mod.single(x,i),10+i)
+
+    def testSlice(self):
+        x = numpy.arange(0,10)
+        sl = slice(3,8)
+        b = [3,4,5,6,7]
+        numpy.testing.assert_equal(indexing_mod.slice(x,sl), b)
+
+    def testStepSlice(self):
+        x = numpy.arange(0,10)
+        sl = slice(3,8,2)
+        b = [3,5,7]
+        numpy.testing.assert_equal(indexing_mod.slice(x,sl), b)
+
+    def testIndex(self):
+        x = numpy.arange(0,10)
+        chk = numpy.array([3,4,5,6])
+        numpy.testing.assert_equal(indexing_mod.indexarray(x,chk),chk)
+        chk = numpy.array([[0,1],[2,3]])
+        numpy.testing.assert_equal(indexing_mod.indexarray(x,chk),chk)
+        x = numpy.arange(9).reshape(3,3)
+        y = numpy.array([0,1])
+        z = numpy.array([0,2])
+        chk = numpy.array([0,5])
+        numpy.testing.assert_equal(indexing_mod.indexarray(x,y,z),chk)
+        x = numpy.arange(0,10)
+        b = x>4
+        chk = numpy.array([5,6,7,8,9])
+        numpy.testing.assert_equal(indexing_mod.indexarray(x,b),chk)
+        x = numpy.arange(9).reshape(3,3)
+        b = numpy.array([0,2])
+        sl = slice(0,3)
+        chk = numpy.array([[0,1,2],[6,7,8]])
+        numpy.testing.assert_equal(indexing_mod.indexslice(x,b,sl),chk)
+
+if __name__=="__main__":
+    unittest.main()

python/pyatidlas/external/boost/libs/numpy/test/indexing_mod.cpp

@@ -0,0 +1,27 @@
+// Copyright Jim Bosch & Ankit Daftery 2010-2012.
+// Distributed under the Boost Software License, Version 1.0.
+//    (See accompanying file LICENSE_1_0.txt or copy at
+//          http://www.boost.org/LICENSE_1_0.txt)
+
+#include <boost/numpy.hpp>
+#include <boost/python/slice.hpp>
+
+namespace p = boost::python;
+namespace np = boost::numpy;
+
+p::object single(np::ndarray ndarr, int i) { return ndarr[i];}
+p::object slice(np::ndarray ndarr, p::slice sl) { return ndarr[sl];}
+p::object indexarray(np::ndarray ndarr, np::ndarray d1) { return ndarr[d1];}
+p::object indexarray_2d(np::ndarray ndarr, np::ndarray d1,np::ndarray d2) { return ndarr[p::make_tuple(d1,d2)];}
+p::object indexslice(np::ndarray ndarr, np::ndarray d1, p::slice sl) { return ndarr[p::make_tuple(d1, sl)];}
+
+BOOST_PYTHON_MODULE(indexing_mod) 
+{
+  np::initialize();
+  p::def("single", single);
+  p::def("slice", slice);
+  p::def("indexarray", indexarray);
+  p::def("indexarray", indexarray_2d);
+  p::def("indexslice", indexslice);
+
+}

python/pyatidlas/external/boost/libs/numpy/test/ndarray.py

@@ -0,0 +1,79 @@
+#!/usr/bin/env python
+
+# Copyright Jim Bosch & Ankit Daftery 2010-2012.
+# Distributed under the Boost Software License, Version 1.0.
+#    (See accompanying file LICENSE_1_0.txt or copy at
+#          http://www.boost.org/LICENSE_1_0.txt)
+
+import ndarray_mod
+import unittest
+import numpy
+
+class TestNdarray(unittest.TestCase):
+    
+    def testNdzeros(self):    
+        for dtp in (numpy.int16, numpy.int32, numpy.float32, numpy.complex128):
+            v = numpy.zeros(60, dtype=dtp)
+            dt = numpy.dtype(dtp)
+            for shape in ((60,),(6,10),(4,3,5),(2,2,3,5)):
+                a1 = ndarray_mod.zeros(shape,dt)
+                a2 = v.reshape(a1.shape)
+                self.assertEqual(shape,a1.shape)
+                self.assert_((a1 == a2).all())
+
+    def testNdzeros_matrix(self):    
+        for dtp in (numpy.int16, numpy.int32, numpy.float32, numpy.complex128):
+            dt = numpy.dtype(dtp)
+            shape = (6, 10)
+            a1 = ndarray_mod.zeros_matrix(shape, dt)
+            a2 = numpy.matrix(numpy.zeros(shape, dtype=dtp))
+            self.assertEqual(shape,a1.shape)
+            self.assert_((a1 == a2).all())
+            self.assertEqual(type(a1), type(a2))
+
+    def testNdarray(self):    
+        a = range(0,60)
+        for dtp in (numpy.int16, numpy.int32, numpy.float32, numpy.complex128):
+            v = numpy.array(a, dtype=dtp)
+            dt = numpy.dtype(dtp)
+            a1 = ndarray_mod.array(a)
+            a2 = ndarray_mod.array(a,dt)
+            self.assert_((a1 == v).all())
+            self.assert_((a2 == v).all())
+            for shape in ((60,),(6,10),(4,3,5),(2,2,3,5)):
+                a1 = a1.reshape(shape)
+                self.assertEqual(shape,a1.shape)
+                a2 = a2.reshape(shape)
+                self.assertEqual(shape,a2.shape)
+
+    def testNdempty(self):
+        for dtp in (numpy.int16, numpy.int32, numpy.float32, numpy.complex128):
+            dt = numpy.dtype(dtp)
+            for shape in ((60,),(6,10),(4,3,5),(2,2,3,5)):
+                a1 = ndarray_mod.empty(shape,dt)
+                a2 = ndarray_mod.c_empty(shape,dt)
+                self.assertEqual(shape,a1.shape)
+                self.assertEqual(shape,a2.shape)
+
+    def testTranspose(self):
+        for dtp in (numpy.int16, numpy.int32, numpy.float32, numpy.complex128):
+            dt = numpy.dtype(dtp)
+            for shape in ((6,10),(4,3,5),(2,2,3,5)):
+                a1 = numpy.empty(shape,dt)
+                a2 = a1.transpose()
+                a1 = ndarray_mod.transpose(a1)    
+                self.assertEqual(a1.shape,a2.shape)
+
+    def testSqueeze(self):
+        a1 = numpy.array([[[3,4,5]]])
+        a2 = a1.squeeze()
+        a1 = ndarray_mod.squeeze(a1)
+        self.assertEqual(a1.shape,a2.shape)
+
+    def testReshape(self):
+        a1 = numpy.empty((2,2))
+        a2 = ndarray_mod.reshape(a1,(1,4))
+        self.assertEqual(a2.shape,(1,4))
+
+if __name__=="__main__":
+    unittest.main()

python/pyatidlas/external/boost/libs/numpy/test/ndarray_mod.cpp

@@ -0,0 +1,45 @@
+// Copyright Jim Bosch & Ankit Daftery 2010-2012.
+// Distributed under the Boost Software License, Version 1.0.
+//    (See accompanying file LICENSE_1_0.txt or copy at
+//          http://www.boost.org/LICENSE_1_0.txt)
+
+#include <boost/numpy.hpp>
+
+namespace p = boost::python;
+namespace np = boost::numpy;
+
+np::ndarray zeros(p::tuple shape, np::dtype dt) { return np::zeros(shape, dt);}
+np::ndarray array2(p::object obj, np::dtype dt) { return np::array(obj,dt);}
+np::ndarray array1(p::object obj) { return np::array(obj);}
+np::ndarray empty1(p::tuple shape, np::dtype dt) { return np::empty(shape,dt);}
+
+np::ndarray c_empty(p::tuple shape, np::dtype dt) 
+{
+  // convert 'shape' to a C array so we can test the corresponding
+  // version of the constructor
+  unsigned len = p::len(shape);
+  Py_intptr_t *c_shape = new Py_intptr_t[len];
+  for (unsigned i = 0; i != len; ++i)
+    c_shape[i] = p::extract<Py_intptr_t>(shape[i]);
+  np::ndarray result = np::empty(len, c_shape, dt);
+  delete [] c_shape;
+  return result;
+}
+
+np::ndarray transpose(np::ndarray arr) { return arr.transpose();}
+np::ndarray squeeze(np::ndarray arr) { return arr.squeeze();}
+np::ndarray reshape(np::ndarray arr,p::tuple tup) { return arr.reshape(tup);}
+
+BOOST_PYTHON_MODULE(ndarray_mod) 
+{
+  np::initialize();
+  p::def("zeros", zeros);
+  p::def("zeros_matrix", zeros, np::as_matrix<>());
+  p::def("array", array2);
+  p::def("array", array1);
+  p::def("empty", empty1);
+  p::def("c_empty", c_empty);
+  p::def("transpose", transpose);
+  p::def("squeeze", squeeze);
+  p::def("reshape", reshape);
+}

python/pyatidlas/external/boost/libs/numpy/test/runCmakeTest.bat.in

@@ -0,0 +1,4 @@
+set local
+set PYTHONPATH=@CMAKE_LIBRARY_OUTPUT_DIRECTORY@/@cfg@;%PYTHONPATH%
+python %1
+endlocal

python/pyatidlas/external/boost/libs/numpy/test/runCmakeTest.sh.in

@@ -0,0 +1,3 @@
+#!/bin/bash
+export PYTHONPATH=@CMAKE_LIBRARY_OUTPUT_DIRECTORY@:${PYTHONPATH}
+python $1

python/pyatidlas/external/boost/libs/numpy/test/shapes.py

@@ -0,0 +1,21 @@
+#!/usr/bin/env python
+
+# Copyright Jim Bosch & Ankit Daftery 2010-2012.
+# Distributed under the Boost Software License, Version 1.0.
+#    (See accompanying file LICENSE_1_0.txt or copy at
+#          http://www.boost.org/LICENSE_1_0.txt)
+
+import shapes_mod
+import unittest
+import numpy
+
+class TestShapes(unittest.TestCase):
+    
+    def testShapes(self):
+        a1 = numpy.array([(0,1),(2,3)])
+        a1_shape = (1,4)
+        a1 = shapes_mod.reshape(a1,a1_shape)
+        self.assertEqual(a1_shape,a1.shape)
+
+if __name__=="__main__":
+    unittest.main()

python/pyatidlas/external/boost/libs/numpy/test/shapes_mod.cpp

@@ -0,0 +1,21 @@
+// Copyright Jim Bosch & Ankit Daftery 2010-2012.
+// Distributed under the Boost Software License, Version 1.0.
+//    (See accompanying file LICENSE_1_0.txt or copy at
+//          http://www.boost.org/LICENSE_1_0.txt)
+
+#include <boost/numpy.hpp>
+
+namespace p = boost::python;
+namespace np = boost::numpy;
+
+np::ndarray reshape(np::ndarray old_array, p::tuple shape)
+{
+  np::ndarray local_shape =  old_array.reshape(shape);
+  return local_shape;
+}
+
+BOOST_PYTHON_MODULE(shapes_mod)
+{
+  np::initialize();
+  p::def("reshape", reshape);
+}

python/pyatidlas/external/boost/libs/numpy/test/templates.py

@@ -0,0 +1,28 @@
+#!/usr/bin/env python
+
+# Copyright Jim Bosch & Ankit Daftery 2010-2012.
+# Distributed under the Boost Software License, Version 1.0.
+#    (See accompanying file LICENSE_1_0.txt or copy at
+#          http://www.boost.org/LICENSE_1_0.txt)
+
+import templates_mod
+import unittest
+import numpy
+
+class TestTemplates(unittest.TestCase):
+
+    def testTemplates(self):
+        for dtype in (numpy.int16, numpy.int32, numpy.float32, numpy.complex128):
+            v = numpy.arange(12, dtype=dtype)
+            for shape in ((12,), (4, 3), (2, 6)):
+                a1 = numpy.zeros(shape, dtype=dtype)
+                a2 = v.reshape(a1.shape)
+                templates_mod.fill(a1)
+                self.assert_((a1 == a2).all())
+        a1 = numpy.zeros((12,), dtype=numpy.float64)
+        self.assertRaises(TypeError, templates_mod.fill, a1)
+        a1 = numpy.zeros((12,2,3), dtype=numpy.float32)
+        self.assertRaises(TypeError, templates_mod.fill, a1)
+
+if __name__=="__main__":
+    unittest.main()

python/pyatidlas/external/boost/libs/numpy/test/templates_mod.cpp

@@ -0,0 +1,62 @@
+// Copyright Jim Bosch & Ankit Daftery 2010-2012.
+// Distributed under the Boost Software License, Version 1.0.
+//    (See accompanying file LICENSE_1_0.txt or copy at
+//          http://www.boost.org/LICENSE_1_0.txt)
+
+#include <boost/numpy.hpp>
+#include <boost/mpl/vector.hpp>
+#include <boost/mpl/vector_c.hpp>
+
+namespace p = boost::python;
+namespace np = boost::numpy;
+
+struct ArrayFiller 
+{
+
+  typedef boost::mpl::vector< short, int, float, std::complex<double> > TypeSequence;
+  typedef boost::mpl::vector_c< int, 1, 2 > DimSequence;
+
+  explicit ArrayFiller(np::ndarray const & arg) : argument(arg) {}
+
+  template <typename T, int N>
+  void apply() const 
+  {
+    if (N == 1) 
+    {
+      char * p = argument.get_data();
+      int stride = argument.strides(0);
+      int size = argument.shape(0);
+      for (int n = 0; n != size; ++n, p += stride) 
+	*reinterpret_cast<T*>(p) = static_cast<T>(n);
+    }
+    else
+    {
+      char * row_p = argument.get_data();
+      int row_stride = argument.strides(0);
+      int col_stride = argument.strides(1);
+      int rows = argument.shape(0);
+      int cols = argument.shape(1);
+      int i = 0;
+      for (int n = 0; n != rows; ++n, row_p += row_stride) 
+      {
+	char * col_p = row_p;
+	for (int m = 0; m != cols; ++i, ++m, col_p += col_stride)
+	  *reinterpret_cast<T*>(col_p) = static_cast<T>(i);
+      }
+    }
+  }
+
+  np::ndarray argument;
+};
+
+void fill(np::ndarray const & arg) 
+{
+  ArrayFiller filler(arg);
+  np::invoke_matching_array<ArrayFiller::TypeSequence, ArrayFiller::DimSequence >(arg, filler);
+}
+
+BOOST_PYTHON_MODULE(templates_mod)
+{
+  np::initialize();
+  p::def("fill", fill);
+}

python/pyatidlas/external/boost/libs/numpy/test/ufunc.py

@@ -0,0 +1,57 @@
+#!/usr/bin/env python
+
+# Copyright Jim Bosch & Ankit Daftery 2010-2012.
+# Distributed under the Boost Software License, Version 1.0.
+#    (See accompanying file LICENSE_1_0.txt or copy at
+#          http://www.boost.org/LICENSE_1_0.txt)
+
+import ufunc_mod
+import unittest
+import numpy
+from numpy.testing.utils import assert_array_almost_equal
+
+class TestUnary(unittest.TestCase):
+
+    def testScalar(self):
+        f = ufunc_mod.UnaryCallable()
+        assert_array_almost_equal(f(1.0), 2.0)
+        assert_array_almost_equal(f(3.0), 6.0)
+
+    def testArray(self):
+        f = ufunc_mod.UnaryCallable()
+        a = numpy.arange(5, dtype=float)
+        b = f(a)
+        assert_array_almost_equal(b, a*2.0) 
+        c = numpy.zeros(5, dtype=float)
+        d = f(a,output=c)
+        self.assert_(c is d)
+        assert_array_almost_equal(d, a*2.0) 
+
+    def testList(self):
+        f = ufunc_mod.UnaryCallable()
+        a = range(5)
+        b = f(a)
+        assert_array_almost_equal(b/2.0, a) 
+
+class TestBinary(unittest.TestCase):
+
+    def testScalar(self):
+        f = ufunc_mod.BinaryCallable()
+        assert_array_almost_equal(f(1.0, 3.0), 11.0) 
+        assert_array_almost_equal(f(3.0, 2.0), 12.0) 
+
+    def testArray(self):
+        f = ufunc_mod.BinaryCallable()
+        a = numpy.random.randn(5)
+        b = numpy.random.randn(5)
+        assert_array_almost_equal(f(a,b), (a*2+b*3)) 
+        c = numpy.zeros(5, dtype=float)
+        d = f(a,b,output=c)
+        self.assert_(c is d)
+        assert_array_almost_equal(d, a*2 + b*3) 
+        assert_array_almost_equal(f(a, 2.0), a*2 + 6.0) 
+        assert_array_almost_equal(f(1.0, b), 2.0 + b*3) 
+        
+
+if __name__=="__main__":
+    unittest.main()

python/pyatidlas/external/boost/libs/numpy/test/ufunc_mod.cpp

@@ -0,0 +1,35 @@
+// Copyright Jim Bosch & Ankit Daftery 2010-2012.
+// Distributed under the Boost Software License, Version 1.0.
+//    (See accompanying file LICENSE_1_0.txt or copy at
+//          http://www.boost.org/LICENSE_1_0.txt)
+
+#include <boost/numpy.hpp>
+
+namespace p = boost::python;
+namespace np = boost::numpy;
+
+struct UnaryCallable 
+{
+  typedef double argument_type;
+  typedef double result_type;
+
+  double operator()(double r) const { return r * 2;}
+};
+
+struct BinaryCallable 
+{
+  typedef double first_argument_type;
+  typedef double second_argument_type;
+  typedef double result_type;
+
+  double operator()(double a, double b) const { return a * 2 + b * 3;}
+};
+
+BOOST_PYTHON_MODULE(ufunc_mod) 
+{
+  np::initialize();
+  p::class_<UnaryCallable>("UnaryCallable")
+    .def("__call__", np::unary_ufunc<UnaryCallable>::make());
+  p::class_< BinaryCallable>("BinaryCallable")
+    .def("__call__", np::binary_ufunc<BinaryCallable>::make());
+}