Fathom5's Awesome Software

Tell us a little overview of what this software does. Make it short and sweet. Here is an example:

Package haversine provides the great circle distance between two points on the surface of the earth. Points are identified by latitude and longitude, and distance results can be returned in nautical miles, statute miles, or kilometers. Earth great circle

The curvature of the earth dictates that the shortest distance between two points cannot be a straight line between the points. The Haversine formula provides an approximation for the shortest great circle route over the surface of earth that connects the two points. In this figure the dotted yellow line is the arc of a great circle. Image courtesy USGS.

Table of Contents

• QuickStart
• The Learning Platform
• Reporting Bugs and Issues
• Reporting Security Issues and Responsible Disclosure
• Contributing
• Platform, Build and Deployment Status
• License

Installation

If the installation is long then (DELETE THIS WHEN YOURE DONE)

Refer to the [Install Instructions][install] for instructions on installing this software

Otherwise add in the step by step guidelines like this: (DELETE THIS WHEN YOURE DONE)

Docker

Caliban executes your code inside a "container", managed by Docker. To get Docker:

• On MacOS, follow the installation instructions at Docker Desktop.
• On Linux, visit the Docker installation instructions.

Python 3.7

Make sure your Python version is up to date:

$ python --version
Python 3.7.7 # should be >=3.7.0

If you need to upgrade:

• On MacOS, install the latest Python version from python.org (direct link).
• On Linux, run sudo apt-get update && sudo apt-get install python3.7.

Documentation

Check out the Getting Started page for a quick overview.

The documentation is divided into several sections:

• Advanced Guides
• API Reference
• Diagrams

Dependency

This is where we will list all of the dependencies and the version we need.

software - version	documents
Golang 1.1	https://golang.org/doc/go1.1
node.js 12.18.0	https://nodejs.org/dist/latest-v12.x/docs/api/

QuickStart

Install Docker, then install Caliban via pip:

pip install caliban

Train your first machine learning model in 2 lines:

git clone https://github.com/google/caliban.git && cd caliban/tutorials/basic
caliban run --nogpu mnist.py

Next:

• See the Installation section for more detail
• Explore Caliban's more advanced features with "Getting Started with Caliban"
• Read the Overview for info on Caliban's subcommands.

Full documentation for this cool software lives at Read The Docs.

Example

The example below shows how far I'd have to walk in my boots to go visit my co-founder.

package main

import (
    "fmt"

    "github.com/FATHOM5/haversine"
)

func main() {
    austin := haversine.Coord{ // Austin, Texas
        Lat: 30.2672,
        Lon: -97.7431,
    }
    paloAlto := haversine.Coord{ // Palo Alto, California
        Lat: 37.4419,
        Lon: -122.1430,
    }

    nm := haversine.Distance(austin, paloAlto)

    fmt.Printf("%.1f miles is a long walk to Silicon Valley.\n", nm)

// Output: 1286.1 miles is a long walk to Silicon Valley.
}

References

• https://plus.maths.org/content/lost-lovely-haversine
• https://en.wikipedia.org/wiki/Haversine_formula
• Many thanks to the original author, umahmood, for a great package to begin this fork.

License

See LICENSE for rights and limitations (MIT).