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README.md

pfb-network-connectivity

PFB Bicycle Network Connectivity

Getting Started

Requirements:

  • Vagrant 1.8.3+
  • VirtualBox 4.3+
  • AWS CLI

Notes for Windows users

  1. Ensure all project files checkout with LF (unix) line endings. The easiest way is to run git config --global core.autocrlf false before checking out the project. Alternatively, you can checkout the project, then run git config core.autocrlf false within the project dir, then manually fix all remaining CRLF line endings before running vagrant up.
  2. Run all commands in a shell with administrator permissions. It's highly recommended to run all commands within the "Git for Windows" Git Bash shell, as that already includes an SSH client, and allows running the commands below as-is.
  3. Before starting the VM, ensure the ENV variable PFB_SHARED_FOLDER_TYPE=virtualbox is set. NFS is not supported on windows, so we need to ensure that Vagrant ignores our request for it.
  4. Do not use vagrant reload. In some cases it will create a new VM rather than autodetecting that the old one exists

Notes for non-Windows users

  1. An NFS daemon must be running on the host machine. This should be enabled by default on MacOS. Linux computers may require the installation of an additional package such as nfs-kernel-server on Ubuntu.

Setting up AWS credentials

Note: If you do not have AWS credentials, this step can be skipped if you just want to run local analyses. Continue below at Provisioning the VM

As noted above, ensure the AWS CLI is installed on your host machine. Once it is, you can configure your PFB account credentials by running:

aws configure --profile pfb

Provisioning the VM

First you'll need to copy the example ansible group_vars file:

cp deployment/ansible/group_vars/all.example deployment/ansible/group_vars/all

If you want to run the full development application and you've configured AWS credentials, copy the appropriate values at the links below into deployment/ansible/group_vars/all, choosing the resources with 'staging' in the name:

  • AWS Batch Job Queue: Copy the staging analysis job queue name to the equivalent group var setting.

If you don't have access to the console, or just want to run a local analysis, copying the values into group_vars/all can be skipped.

Run ./scripts/setup to install project dependencies and prepare the development environment. Then, SSH into the VM:

vagrant ssh

Once in the VM, if you added AWS credentials above, run the following commands to configure your development S3 buckets:

aws s3api create-bucket --bucket "${DEV_USER}-pfb-storage-us-east-1"
aws s3api put-bucket-policy --bucket "${DEV_USER}-pfb-storage-us-east-1" --policy "{\"Statement\":[{\"Effect\":\"Allow\",\"Principal\":\"*\",\"Action\":\"s3:GetObject\",\"Resource\":\"arn:aws:s3:::${DEV_USER}-pfb-storage-us-east-1/*\"}]}"

At this point, if you only intend to run the 'Bike Network Analysis', skip directly to Running the Analysis

To start the application containers:

./scripts/server

In order to use the API, you'll need to run migrations on the Django app server:

./scripts/django-manage migrate

This will add a default admin user that can log in to http://localhost:9200/api/ as:
systems+pfb@azavea.com / root

Ports

Port Service Notes
9200 Nginx
9202 Gunicorn
9203 Django Runserver Not running by default. Must be started manually via scripts/django-manage
9214 Postgresql Allows direct connections to the database where an analysis run is stored
9301 Gulp Gulp server for analysis angular app
9302 Browsersync Browsersync for analysis angular app
9400 Tilegarden Tilegarden development server
9401 Browsersync Node debugger for Tilegarden development server

Scripts

Name Description
setup Bring up a dev VM, and perform initial installation steps
update Re-build application Docker containers and run database migrations
server Start the application containers
console Start a bash shell on one of the running Docker containers
django-manage Run a Django management command on the django container

Running the Analysis

On creating a local anaylsis job in the admin UI, the Django logs will print the appropriate command to run in the VM console to actually run the analysis jobs locally.

See Running the Analysis Locally for details.

Verifying the Analysis

The output from the analysis run may be compared to previous output to see if it has changed. See the section below for the input parameters used to generate the verified output.

Build the docker container for the verification tool within the VM:

cd src/verifier
docker-compose build

Ensure the exported output from the analysis to check exists in the data/output directory. It will be there by default if the data directory was used for the neighborhood input shapefile.

To compare the analysis output for Boulder, run the verification tool with:

docker-compose run verifier boulder.csv

Any output in the verified_output directory may be used for comparison.

To compare to analysis output that has a non-default filename (analysis_neighborhood_score_inputs.csv), run the verification tool with the name of the file in data/output as the second argument:

docker-compose run verifier boulder.csv my_output_to_verify.csv

If there are any differences in the outputs, a summary of the differences will be output to console.

Verified Output Parameters

The analysis output in the verified_output directory was generated using the following input parameters and files:

Boulder:

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