sgillies

"""Concurrent read-process-write example"""

import concurrent.futures
from itertools import islice
from time import sleep

import rasterio


CHUNK = 100

jqtrde

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

sgillies

chrishwiggins

Links from talk "what is Data Science?" (hackNY lecture 2016-06-15):

context

• google search trends: data science

• indeed job trends: data science

• drew's data science venn diagram, sept 2010

sgillies

A Rasterio dataset's index() method gives you the row, col index of the pixel containing the point x, y (in the dataset's CRS units).

Define a 1x1 pixel read window starting at that index (see the tiny_window() function below) and use it to read an ndarray. The one in this case has shape (3, 1, 1): 3 bands, 1 row, and 1 column. The values of the 3 bands at the point x, y for this case are 28, 29, and 27.

friggeri

haiku = ->
  adjs = [
    "autumn", "hidden", "bitter", "misty", "silent", "empty", "dry", "dark",
    "summer", "icy", "delicate", "quiet", "white", "cool", "spring", "winter",
    "patient", "twilight", "dawn", "crimson", "wispy", "weathered", "blue",
    "billowing", "broken", "cold", "damp", "falling", "frosty", "green",
    "long", "late", "lingering", "bold", "little", "morning", "muddy", "old",
    "red", "rough", "still", "small", "sparkling", "throbbing", "shy",
    "wandering", "withered", "wild", "black", "young", "holy", "solitary",
    "fragrant", "aged", "snowy", "proud", "floral", "restless", "divine",