GDAL 
Libspatialindex (only for network model)
PostgreSQL (only for production website)
RabbitMQ (only for distributed system)

numpy/scipy
shapely

# setup system level packages
sudo apt-get -y install git python-pip python-dev libgdal-dev build-essential
# for scipy
sudo apt-get -y install gfortran libopenblas-dev liblapack-dev

# libspatialindex (only required for network model)
mkdir -p tmp
cd tmp
git clone git://github.com/libspatialindex/libspatialindex.git
sudo apt-get install -y automake libtool
(cd libspatialindex && ./autogen.sh && ./configure && make && sudo make install)
sudo ldconfig
cd

git clone https://github.com/SEL-Columbia/networkplanner.git np
# git clone git@github.com:SEL-Columbia/networkplanner.git np

# install virtualenv and virtualenvwrapper (see http://virtualenvwrapper.readthedocs.org)
# YMMV 
# This is optional if you don't mind intermingled python packages on your system
sudo apt-get -y install python-virtualenv
sudo pip install virtualenvwrapper
# setup your bashrc to source virtualenvwrapper
if ! grep 'virtualenvwrapper\.sh' .bashrc; 
then
cat >> .bashrc << EOF
. `which virtualenvwrapper.sh`
EOF
fi
. `which virtualenvwrapper.sh` # source it for the session (not needed for future sessions)
if ! workon np > /dev/null
then
    mkvirtualenv --system-site-packages np # from here on, working in np virtualenv
    cd np 
    setvirtualenvproject # from here on you can enter 'workon np' to work in the np virtualenv
fi

# setup project specific python packages
pip install -r requirements.txt

# only required for network model
pip install -r requirements_network.txt 

# NOTE:  Depending on your default user environment variables, you may need to set
# the LD_LIBRARY_PATH in order for networkplanner to reference libspatialindex properly
# Here are the contents of my $VIRTUAL_ENV/bin/postactivate file:

export OLD_LD_LIBRARY_PATH=$LD_LIBRARY_PATH
export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH

# and the $VIRTUAL_ENV/bin/predeactivate file:
�
export LD_LIBRARY_PATH=$OLD_LD_LIBRARY_PATH
unset OLD_LD_LIBRARY_PATH

# Run metric model on a set of demand nodes (using mvMax5 model)
# The output can be loaded as an R or Pandas dataframe for analysis
python utilities/build_demand.py mvMax5 sample_metric_params.json test_data/sample_demand_nodes.csv > sample_demand_out.csv

# Run full scenario (including network builder) on a set of demand nodes
# (the results will end up in the mv5_run directory as spec'd by the 5th param)
python utilities/run_scenario.py mvMax5 sample_metric_params.json modKruskal network_params.json mv5_run test_data/sample_demand_nodes.csv

# Create a dot graph for the dependencies of the MiniGrid RecurringCost 
# variable of the mvMax4 model 
# The dot file can be passed to graphviz utilities to render the graphs as png, svg, pdf...
python utilities/model_var_dependencies.py mvMax4 costMiniGrid.MiniGridSystemRecurringCostPerYear mv4_mg_rec.dot class

# Create a flat list of class, option/section, alias name mappings of all 
# variables "under" Metric for mvMax5
python utilities/write_variable_fields.py mvMax5 Metric > mapping_mvmax5.csv

# Run the development server via paster 
# 'ds' for development server
./restart ds 

# Generate the docs
./restart docs

# start interactive python session for working with 
# web app in development mode
paster shell development.ini

# Process scenarios submitted

deployment/cluster-queue-reset.sh