arthur-e

Managing the PostgreSQL Service

The service name is postgresql and it accepts the usual commands for a background service:

• start
• stop
• restart
• status

arthur-e

'''
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

arthur-e

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',

arthur-e

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)

arthur-e

# 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

arthur-e

Introduction

Drivers

These are the exact names that OGR recognizes; in parentheses are the file extensions, not to be included in the driver name:

• ESRI Shapefile (*.shp)
• KML (*.kml)
• AVCE00 (*.e00)
• AVCBin

arthur-e

/**
    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;

arthur-e

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

arthur-e

---
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:

arthur-e

'''
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