The service name is postgresql
and it accepts the usual commands for a background service:
start
stop
restart
status
''' | |
Recover the Markdown content of an older Springseed note (*.note) file. Output | |
*.md file names will be the name of the Note file prepended by the Notebook | |
name, if available. | |
''' | |
import sys | |
import os | |
import re | |
import json |
chroma = require('./node_modules/chroma-js/chroma.min.js'); | |
function generatePalettes (type, reversed) { | |
var n, m; | |
var paletteNames; | |
var palettes = {}; | |
if (type === 'sequential') { | |
paletteNames = [ | |
'BuGn', 'BuPu', 'GnBu', 'OrRd', 'PuBu', 'PuBuGn', 'PuRd', 'RdPu', |
Apparently even a difference of the least precision of the upper left-hand corner causes an alignment error in PostGIS. Consider this example, where I am comparing a land cover raster subset by an MTBS raster and the MTBS raster itself (with the goal being to add the latter to the former, creating the "burned" land cover).
```
SELECT ST_MetaData(r1.rast)
FROM
(SELECT rast
FROM geowepp_burnedarea
WHERE geowepp_burnedarea.rid = 2) r1
UNION
SELECT ST_MetaData(r2.rast)
# Credit goes to my colleague Tyler (http://tylerickson.blogspot.com/2011/09/installing-gdal-in-python-virtual.html) | |
ENVDIR=/usr/local/pythonenv/ | |
ENVNAME=nasabaer-env | |
OWNER=arthur | |
# Install GEOS if necessary | |
# sudo apt-get libgeos-3.2.2 libgeos-dev | |
# Install GDAL on the system |
/** | |
Returns an object which can be used to calculate statistics on the | |
the passed numeric Array. | |
*/ | |
var Stats = function (arr) { | |
arr = arr || []; | |
// http://en.wikipedia.org/wiki/Mean#Arithmetic_mean_.28AM.29 | |
this.arithmeticMean = function () { | |
var i, sum = 0; |
country 2000 2001 2002 2003 2004 2005 2006 | |
Algeria 0 0 0 | |
Angola 96 66 98 254 80 86 62 | |
Benin 10 6 9 7 11 4 16 | |
Botswana 2 2 1 1 1 1 2 | |
Burkina Faso 23 35 32 37 31 37 56 | |
Burundi 9 6 7 6 9 25 5 | |
Cameroon 5 5 | |
Cape Verde 0 0 0 0 0 1 | |
Central African Republic 14 10 21 16 20 |
--- | |
title: "Aggregation in R" | |
author: "K. Arthur Endsley" | |
date: "August 4, 2016" | |
output: html_document | |
--- | |
Let's explore multiple ways to aggregate data in R. | |
We'll do this to answer two questions: |
''' | |
A module for machine learning on Landsat data; implemented and tested, | |
specifically, for learning water areas on an image. Performance so far: | |
Gaussian naive Bayes (where validation data chosen by the hydro mask): | |
Mean precision: Not water=0.9997, Water=0.3090 | |
Mean recall: Not water=0.9787, Water=0.9763 | |
Gaussian naive Bayes (where validation data inspected in Google Earth): | |
Mean precision: Not water=0.9675, Water=1.0000 |