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from botocore.credentials import RefreshableCredentials
from botocore.session import get_session
from boto3 import Session
def assumed_session(role_arn, session_name, session=None):
"""STS Role assume a boto3.Session
With automatic credential renewal.
View StyleTransfer-Mapbox.ipynb
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jacquestardie /
Created Jul 17, 2018 — forked from swalkinshaw/
Designing a GraphQL API

Tutorial: Designing a GraphQL API

This tutorial was created by Shopify for internal purposes. We've created a public version of it since we think it's useful to anyone creating a GraphQL API.

It's based on lessons learned from creating and evolving production schemas at Shopify over almost 3 years. The tutorial has evolved and will continue to change in the future so nothing is set in stone.

import random
import time
import json
import threading
import click
def send_message(msg, async=True):
"""Logs the msg (presumably a JSON object) to firehose
jacquestardie /
Created Apr 14, 2018
Stanford Big Data Hackathon - Tools for working with Satellite Imagery


We're very much Python powered!

  • rasterio - Reading and writing geospatial data (rasters)
  • shapely - Manipulation and analysis of geometric objects (vectors)
  • fiona - Reading and writing geospatial data (vectors)

The combination of ^ with other standard Python utilities provide a powerful & flexible means of achieving almost any sort of geospatial process you can imagine.

jacquestardie / shh.rb
Created Mar 8, 2018 — forked from robinsloan/shh.rb
Disable RTs from all the people you follow on Twitter.
View shh.rb
require "rubygems"
require "twitter"
# get these from
OAUTH_TOKEN = "blee"
TWITTER_USER = "your_username" # needs to be the one associated with keys above
jacquestardie /
Created Jan 25, 2018
A nice little geojson renderer for Jupyter Lab.
$ pip install jupyterlab
$ jupyter labextension install @jupyterlab/geojson-extension
jacquestardie /
Last active Aug 16, 2018
Maptime Presentation

Hi, and welcome!

We love pixels. We build our tools such that they allow us to work with pixels at a wide range of scales, from individual landsat scenes, to global mosaics, via command line programs or as python libraries we can integrate into large processing pipelines.

Today we'll walk through:

  • processing an individual Landsat scene using rasterio, a tool which serves as the foundation for most of our work
  • uploading the outputs of ^ to Mapbox, where we can visualize it on top of mapbox.satellite, and serve it globally at crazy speed
jacquestardie /
Created Nov 13, 2017
Stanford's Stats 385 - Theories of Deep Learning
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