e-mission is a project to gather data about user travel patterns using phone apps, and use them to provide an personalized carbon footprint, and aggregate them to make data available to urban planners and transportation engineers.

If you are here to use Zephyr, which uses the e-mission platform to evaluate power/accuracy tradeoffs for background sensed data, please see the zephyr-specific README and examples.

It has two components, the backend server and the phone apps. This is the backend server - the phone apps are available in the e-mission-phone repo

The current build status is:

Issues: Since this repository is part of a larger project, all issues are tracked in the central docs repository. If you have a question, as suggested by the open source guide, please file an issue instead of sending an email. Since issues are public, other contributors can try to answer the question and benefit from the answer.

The backend in turn consists of two parts - a summary of their code structure is shown below. - The webapp supports a REST API, and accesses data from the database to fulfill the queries. A set of background scripts pull the data from external sources, and preprocessing results ensures reasonable performance.

Installation:

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• For deployers (i.e. if you want to primarily use the system as opposed to modify/develop it, the docker installation is probably the easiest way to get started.
• For builders (i.e. if you want to write new scripts or modify existing scripts) the manual install will make it easier to edit files directly on your local filesystem. Make sure to use a POSIX-compliant CLI; you may want to look into gitbash or similar on Windows.

Additional Documentation:

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Additional documentation has been moved to its own repository e-mission-docs. Specific e-mission-server additional documentation can be found here: https://github.com/e-mission/e-mission-docs/tree/master/docs/e-mission-server

Deployment:

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• If you just want to run the server, you can use our docker image.
• Alternatively, you can follow the manual installation instructions to pull from the repo to the server, change the config files slightly and just not change any code.

Development:

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In order to test out changes to the webapp, you should make the changes locally, test them and then push the tested changes to a repository. Note that if the changes are to the server code, you need to restart the server after making changes.

Loading test data

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You may also want to load some test data. Note that for the docker install, you will need to run these scripts from the docker image after opening a shell

Quick start

1. Sample timeline data used for unit testing the server code (checked in with consent of users) is at emission/tests/data/real_examples. You can load the data using the load_timeline_for_day_and_user script, like so

   $ ./e-mission-py.bash bin/debug/load_timeline_for_day_and_user.py emission/tests/data/real_examples/shankari_2015-07-22 test_july_22
    storage not configured, falling back to sample, default configuration
    Connecting to database URL localhost
    emission/tests/data/real_examples/shankari_2015-07-22
    Loading file emission/tests/data/real_examples/shankari_2015-07-22
    After registration, test_july_22 -> 908eb622-be3f-4cf4-bf04-1b7e610bea1c
   

   This will load the data as a new user with email test_july_22 and a newly created uuid. You can run the intake pipeline for this user like so

   $ ./e-mission-py.bash bin/debug/intake_single_user.py -e test_july_22
    storage not configured, falling back to sample, default configuration
    Connecting to database URL localhost
    analysis.debug.conf.json not configured, falling back to sample, default configuration
    google maps key not configured, falling back to nominatim
    nominatim not configured either, place decoding must happen on the client
    transit stops query not configured, falling back to default
    2018-05-22T19:56:36.262694-07:00**********UUID 908eb622-be3f-4cf4-bf04-1b7e610bea1c: moving to long term**********
    2018-05-22T19:56:36.281071-07:00**********UUID 908eb622-be3f-4cf4-bf04-1b7e610bea1c: filter accuracy if needed**********
    2018-05-22T19:56:36.293284-07:00**********UUID 908eb622-be3f-4cf4-bf04-1b7e610bea1c: segmenting into trips**********
    2018-05-22T19:56:45.741950-07:00**********UUID 908eb622-be3f-4cf4-bf04-1b7e610bea1c: segmenting into sections**********
    2018-05-22T19:56:45.777937-07:00**********UUID 908eb622-be3f-4cf4-bf04-1b7e610bea1c: smoothing sections**********
    2018-05-22T19:56:45.784900-07:00**********UUID 908eb622-be3f-4cf4-bf04-1b7e610bea1c: cleaning and resampling timeline**********
    2018-05-22T19:56:46.055701-07:00**********UUID 908eb622-be3f-4cf4-bf04-1b7e610bea1c: inferring transportation mode**********
    2018-05-22T19:56:46.063397-07:00**********UUID 908eb622-be3f-4cf4-bf04-1b7e610bea1c: checking active mode trips to autocheck habits**********
    2018-05-22T19:56:46.067243-07:00**********UUID 908eb622-be3f-4cf4-bf04-1b7e610bea1c: storing views to cache**********
   

   If you have the phone app installed, you can log in using test_july_22 as the email, and select July 22 2015 to see the data for that date.

Note that loading the data retains the object IDs. This means that if you load the same data twice with different user IDs, then only the second one will stick. In other words, if you load the file as [email protected] and then load the same file as [email protected], you will only have data for [email protected] in the database. This can be overwritten using the --make-new flag - e.g.

$ ./e-mission-py.bash bin/debug/load_timeline_for_day_and_user.py -n /tmp/data-collection-eval/results_dec_2015/ucb.sdb.android.1/timeseries/active_day_2.2015-11-27 [email protected]

Other data sources

1. Get your own data. You can export your timeline for a particular day via email (Profile -> Download json dump) and then load and view it as above.

2. Request access to anonymized data for research purposes by sending email to @shankari. You will be asked to consent to data retention and usage policies and will get an encrypted timeline with data from multiple users, one file per user. More information is at https://github.com/e-mission/e-mission-docs/blob/master/docs/e-mission-server/requesting_data_as_a_collaborator.md

3. Sample timeline data from the test phones can be retrieved using the bin/public/request_public_data.py script. You can see the inputs to pass to the script by using

   $ bin/public/request_public_data.py --help
   

4. The script loads the data into your mongodb instance using the test phone UUIDs. If you want to play with the raw data, you are good.

5. If you want to run the existing pipeline, you need to either enable the pipeline for test phones, OR re-load the data as a normal user.

   1. To enable the pipeline for test phones, edit emission/pipeline/scheduler.py to add the test phones to the TEMP_HANDLED_PUBLIC_PHONES array.
   2. To reload the data as a normal user, save the data as a timeline file using bin/debug/extract_timeline_for_day_and_user.py with one of the test phone UUIDs and the time range that you downloaded the data for. This saves the data into a json file. You can then load the data using the bin/debug/load_timeline_for_day_and_user.py script. It requires a timeline file and a user that the timeline is being loaded as. If you wish to view this timeline in the UI after processing it, you need to login with this email.

            $ cd ..../e-mission-server
            $ ./e-mission-py.bash bin/debug/load_timeline_for_day_and_user.py /tmp/data-collection-eval/results_dec_2015/ucb.sdb.android.1/timeseries/active_day_2.2015-11-27 [email protected]

Creating fake user data

You may need a larger or more diverse set of data than the given test data supplies. To create it you can run the trip generation script included in the project.

The script works by creating random noise around starting and ending points of trips.

You can fill out options for the new data in emission/simulation/input.json. The different options are as follows

• radius - the number of kilometers of randomization around starting and ending points (the amount of noise)
• starting centroids - addresses you want trips to start around, as well as a weight defining the relative probability a trip will start there
• ending centroids - addresses you want trips to end around, as well as a weight defining the relative probability a trip will end there
• modes - the relative probability a user will take a trip with the given mode
• number of trips - the amount of trips the simulation should create

run the script with

$ python emission/simulation/trip_gen.py <user_name>

Because this user data is specifically designed to test our tour model creation, you can create fake tour models easily by running the make_tour_model_from_fake_data function in emission/storage/decorations/tour_model_queries.py

Running the analysis pipeline

Once you have loaded the timeline, you probably want to segment it into trips and sections, smooth the sections, generate a timeline, etc. We have a unified script to do all of those, called the intake pipeline. You can run it like this.

$ ./e-mission-py.bash bin/debug/intake_single_user.py -u <uuid>

You can also use the user's email id with the -e option. See the help message for details. Once the script is done running, places, trips, sections and stops would have been generated and stored in their respective mongodb tables, and the timelines for the last 7 days have been stored in the usercache.

We also do some modelling on the generated data. This is much more time-intensive than the intake, but also does not need to run at the same frequency as the intake pipeline. So it is pulled out to its own pipeline. If you want to work on the modelling, you need to run this pipeline as well.

$ ./e-mission-py.bash emission/pipeline/model_stage.py

Experimenting with loaded data

Some examples of how to retrieve and experiment with loaded/analysed data are in the Timeseries_Sample.ipynb

Running unit tests

1. Make sure that the anaconda python is in your path

    $ which python
    /Users/shankari/OSS/anaconda/bin/python
   

2. Run all tests.

    $ ./runAllTests.sh
   

3. If you get import errors, you may need to add the current directory to PYTHONPATH.

    $ PYTHONPATH=. ./runAllTests.sh
   

Analysis

Several exploratory analysis scripts are checked in as ipython notebooks into https://github.com/e-mission/e-mission-explore/. All data in the notebooks is from members of the research team who have provided permission to use it. The results in the notebooks cannot be replicated in the absence of the raw data, but they can be run on data collected from your own instance as well.

The notebooks are occasionally modified and simplified as code is moved out of them into utility functions. Original versions of the notebooks can be obtained by looking at other notebooks with the same name, or by looking at the history of the notebooks.

JS Testing

From the webapp directory

$ npm install karma --save-dev
$ npm install karma-jasmine karma-chrome-launcher --save-dev

Write tests in www/js/test If you're interested in having karma in your path and globally set, run

$ npm install -g karma-cli

To run tests if you have karma globally set, run

$ karma start my.conf.js 

in the webapp directory. If you didn't run the -g command, you can run tests with

$ ./node_modules/karma/bin/karma start

in the webapp directory

TROUBLESHOOTING:

1. If a python execution fails to import a module, make sure to add current directory to your PYTHONPATH.

2. If starting the server gives a CONNECTION_ERROR, make sure MongoDB is actively running when you attempt to start the server.

3. After running MongoDB, if you get an error that says dbpath does not exist (on Windows) or Data directory /data/db not found (on Mac), make sure to manually create the data directory as follows.

    on Windows
    % md c:\data\db\  
   

or

    on Mac (the user account running mongod must have read and write permissions for the data directory)
    $ mkdir -p /data/db
    $ chmod 777 /data/db

Design decisions:

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This site is currently designed to support travel behavior tracking and aggregation. There is a fair amount of backend work that is more complex than just reading and writing data from a database. So we are not using any of the specialized web frameworks such as django or rails.

Instead, we have focused on developing the backend code and exposing it via a simple API. I have maintained separation between the backend code and the API glue so that we can swap out the API glue later if needed.

The API glue is currently Bottle, which is a single file webapp framework. I chose Bottle because it was simple, didn't use a lot of space, and because it wasn't heavy weight, could easily be replaced with something more heavyweight later.

The front-end is javascript based. In order to be consistent with the phone, it also uses angular + ionic. javascript components are largely managed using bower.

Deployment:

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This is fairly complex and is under active change as we have more projects deploy their own servers with various configurations. So I have moved it to the e-mission-server section in the e-mission-docs repo: https://github.com/e-mission/e-mission-docs/blob/master/docs/e-mission-server/deploying_your_own_server_to_production.md