Merge pull request #117 from UDST/develop

v0.4.2 release

.travis.yml

@@ -1,40 +1,31 @@
 language: python
-sudo: false
+
 python:
-- '2.7'
-- '3.5'
-addons:
-  apt:
-    sources:
-      - ubuntu-toolchain-r-test
-    packages:
-      - g++-4.9
+  - '2.7'
+  - '3.5'
+  - '3.6'
+
+# This section can be removed when Python 3.7 is more cleanly supported in Travis
+matrix:
+  include:
+    - python: '3.7'
+      dist: xenial
+      sudo: true
+
 install:
-- if [[ "$TRAVIS_PYTHON_VERSION" == "2.7" ]]; then
-    wget http://repo.continuum.io/miniconda/Miniconda-latest-Linux-x86_64.sh -O miniconda.sh;
-  else
-    wget http://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh;
-  fi
-- bash miniconda.sh -b -p $HOME/miniconda
-- export PATH="$HOME/miniconda/bin:$PATH"
-- hash -r
-- conda config --set always_yes yes --set changeps1 no
-- conda update -q conda
-- conda info -a
-- |
-  conda create -q -n test-environment python=$TRAVIS_PYTHON_VERSION basemap matplotlib numpy pandas pip pytables requests cython scikit-learn "pytest<4.0"
-- source activate test-environment
-- pip install pytest-cov coveralls pycodestyle osmnet
-- CC=gcc-4.9 CXX=g++-4.9 python setup.py install
+  - pip install .
+  - pip install -r requirements-dev.txt
+  - pip list
+  - pip show pandana
+
 script:
-- pycodestyle pandana
-- python setup.py test --pytest-args "--cov pandana --cov-report term-missing"
+  - pycodestyle pandana
+  - python setup.py test --pytest-args "--cov pandana --cov-report term-missing"
+
 after_success:
-- coveralls
-- bin/build_docs.sh
-notifications:
-  slack:
-    secure: a6RjANmfIyE0s3iAz4LPy2wS0bOd+ijGlhh7CJf4bRwVnQPuihDTwzQiT92Uje1rHZVUTY0r5A7QzBcg7QcACs/b3hLQ6nYQ0kIm/beC5DfZUqlyHQAuRl6eK76cEg9Le7bX8OXrjWyfTs9jgH7Z2mRGutMieNXVYQG5wMlEKlU=
+  - coveralls
+  - bin/build_docs.sh
+
 env:
   global:
     secure: CMG0rjBgDBNy5FdfXawaaCCJm9ChzHk7e21ywVhIc1jbVS6lMn6bqwKJUnLaJAyjDhhZuxXTcHy+SALJgbzqLrH4GM5hOOL+8Rf4Jf9ESZzTBryvypRecVnUnk63SpJiq2Ki8maNrOcK1IBUAoFhFzptSgE4MDkxZ0LjsDAums8=

HISTORY.rst → CHANGELOG.md

@@ -1,3 +1,14 @@
+v0.4.2
+======
+
+2019/8/8
+
+* Speed of network aggregations is improved.
+* Support for aggregating integer values is restored.
+* Thread count and contraction hierarchy status messages are restored.
+* Code written for v0.3 will continue to run, now raising deprecation warnings instead of errors.
+* Compilation improvements for Mac.
+
 v0.4.1
 ======
 

MANIFEST.in

@@ -1,3 +1,9 @@
-include ez_setup.py
-include README.rst
+# files to include in the source distribution on pypi (setup.py and README.md are included automatically)
+
+include CHANGELOG.md
+include LICENSE.txt
+include requirements-dev.txt
+include setup.cfg
+
+recursive-include examples *.ipynb *.py
 recursive-include src *.h *.cpp

README.md

@@ -0,0 +1,46 @@
+[![Build Status](https://travis-ci.org/UDST/pandana.svg?branch=master)](https://travis-ci.org/UDST/pandana)
+[![Coverage Status](https://coveralls.io/repos/github/UDST/pandana/badge.svg?branch=master)](https://coveralls.io/github/UDST/pandana?branch=master)
+
+# Pandana
+
+Pandana is a Python package that uses [contraction hierarchies](https://en.wikipedia.org/wiki/Contraction_hierarchies) to perform rapid network calculations including shortest paths and accessibility buffers. The computations are parallelized for use on multi-core machines using an underlying C/C++ library. Pandana is tested on Mac, Linux, and Windows with Python 2.7, 3.6, and 3.7.
+
+Documentation: http://udst.github.io/pandana
+
+
+### Installation
+
+The easiest way to install Pandana is using the [Anaconda](https://www.anaconda.com/distribution/) package manager. Pandana's Anaconda distributions are pre-compiled and include multi-threading support on all platforms. 
+
+`conda install pandana --channel conda-forge`
+
+See the documentation for information about other [installation options](http://udst.github.io/pandana/installation.html).
+
+
+### Demo
+
+[Example.ipynb](https://github.com/UDST/pandana/blob/master/examples/Example.ipynb)
+
+The image below shows the distance to the _second_ nearest restaurant from each street intersection in the city of San Francisco. Pandana can calculate this in about half a second of computation time. 
+
+<img src="https://raw.githubusercontent.com/udst/pandana/master/docs/img/distance_to_restaurants.png" width=400>
+
+
+## Acknowledgments
+
+None of this would be possible without the help of Dennis Luxen and
+his [OSRM](https://github.com/DennisOSRM/Project-OSRM) project. Thank you Dennis!
+
+
+### Academic Literature
+
+A [complete description of the
+methodology](http://onlinepubs.trb.org/onlinepubs/conferences/2012/4thITM/Papers-A/0117-000062.pdf)
+was presented at the Transportation Research Board Annual Conference in 2012. Please cite this paper when referring
+to the methodology implemented by this library.
+
+
+### Related UDST libraries
+
+- [OSMnet](https://github.com/udst/osmnet)
+- [UrbanAccess](https://github.com/udst/urbanaccess)

bin/build_docs.sh

@@ -33,10 +33,6 @@ if [ "$TRAVIS_REPO_SLUG" == "UDST/pandana" ] && \
         [ "$TRAVIS_PULL_REQUEST" == "false" ] && \
         [ "$ACTUAL_TRAVIS_JOB_NUMBER" == "1" ]; then
 
-        echo "Installing dependencies"
-        conda install --yes --quiet sphinx numpydoc
-        pip install sphinx_rtd_theme
-
         echo "Building docs"
         cd docs
         make clean

docs/conf.py

@@ -50,16 +50,16 @@
 
 # General information about the project.
 project = 'pandana'
-copyright = '2017, UrbanSim Inc.'
+copyright = '2019, UrbanSim Inc.'
 
 # The version info for the project you're documenting, acts as replacement for
 # |version| and |release|, also used in various other places throughout the
 # built documents.
 #
 # The short X.Y version.
-version = '0.4.1'
+version = '0.4.2'
 # The full version, including alpha/beta/rc tags.
-release = '0.4.1'
+release = '0.4.2'
 
 # The language for content autogenerated by Sphinx. Refer to documentation
 # for a list of supported languages.

docs/index.rst

@@ -10,7 +10,7 @@ Pandana is a neologism representing Pandas Network Analysis.
 
 Pandana performs hundreds of thousands of network queries in under a second
 (for walking-scale distances) using a Pandas-like API.  The computations are
-parallelized for use on multi-core computers using an underlying C
+parallelized for use on multi-core computers using an underlying C/C++
 library.
 
 Contents