groundhog

Our mission:

Things go up. And down. We might be able to make money by telling people when that happened.

Our motto:

If you ain't first, you're last.

Building the container

We'll put the container up on Dockerhub soon. For now, you can build locally from this repo.

docker build -t groundhog -f Dockerfile .

Running the app

Run this command to kick up a local version of the app

docker run -p 5005:5005 --name groundhog_server groundhog

You can now hit the API on localhost. Run the following command in a separate terminal to see the help docs.

curl http://localhost:5005

To stop the container by name (if you used the --name tag when launching it), do the following:

docker stop groundhog_server

R client

We provide an R client for the service. Given a data.table with GPS information and (optionally) bearing, this client will use groundhog to enrich that dataset with elevation and slope features.

You can install the package from source

R CMD INSTALL r-client/

To test it out, spin up a local version of the service, then run this example:

library(data.table)

someDT <- data.table::data.table(
    longitude = runif(10, -110, -109)
    , latitude = runif(10, 45, 46)
    , dateTime = seq.POSIXt(from = as.POSIXct("2017-01-01", tz = "UTC")
                             , to = as.POSIXct("2017-01-15", tz = "UTC")
                             , length.out = 10)
    , assetId = c(rep("ABC", 5), rep("DEF", 5))
)

groundhog::AppendSlopeFeatures(someDT, hostName = "localhost", port = 5005)

Python client

We provide a Python client for the service. Given a pandas DataFrame with GPS information and (optionally) bearing, this client will use groundhog to enrich that dataset with elevation and slope features.

You can install the package from source

pushd py-client
    pip install .
popd

To test it out, spin up a local version of the service, then run this example:

import pandas as pd
import numpy as np
import groundhog as gh

some_df = pd.DataFrame({
    "longitude": -110 + np.random.rand(10),
    "latitude": 45 + np.random.rand(10),
    "dateTime": pd.date_range(pd.datetime.today(), periods=10),
    "assetId": ["ABC"]*5 + ["DEF"]*5
    })

gh.append_slope_features(some_df, host_name="localhost", port=5005)

Background

This project is built on top of srtm.py, a Python library that makes the SRTM data accessible and easy to query.