This is a collection of information on PostgreSQL and PostGIS for what I tend to use most often.
A personal diary of DataFrame munging over the years.
Convert Series datatype to numeric (will error if column has non-numeric values)
(h/t @makmanalp)
# -*- encoding: UTF-8 -*- | |
import os | |
import httplib2 | |
# pip install --upgrade google-api-python-client | |
from oauth2client.file import Storage | |
from apiclient.discovery import build | |
from oauth2client.client import OAuth2WebServerFlow |
-
Single-line comments are started with
//
. Multi-line comments are started with/*
and ended with*/
. -
C# uses braces (
{
and}
) instead of indentation to organize code into blocks. If a block is a single line, the braces can be omitted. For example,
This is my default career advice for people starting out in geo/GIS, especially remote sensing, adapted from a response to a letter in 2013.
I'm currently about to start a Geography degree at the University of [Redacted] at [Redacted] with a focus in GIS, and I've been finding that I have an interest in working with imagery. Obviously I should take Remote Sensing and other similar classes, but I'm the type of person who likes to self learn as well. So my question is this: What recommendations would you give to a student who is interested in working with imagery? Are there any self study paths that you could recommend?
I learned on my own and on the job, and there are a lot of important topics in GIS that I don’t know anything about, so I can’t give comprehensive advice. I haven’t arrived anywhere; I’m just ten minutes ahead in the convoy we’re both in. Take these recommendations critically.
Find interesting people. You’ll learn a lot more from a great professor (or mentor, or friend, or conference) o
This is a small collection of scripts showing how to use require.js. It's only one of several ways of setting up a require.js project, but it's enough to get started.
At its core, require.js is about three things:
- Dependency management
- Modularity
- Dynamic script loading
The following files show how these are achieved.
<!DOCTYPE html> | |
<html lang="en"> | |
<head> | |
<meta charset="utf-8"> | |
<title>Marker Fade</title> | |
<meta name="viewport" content="width=device-width, initial-scale=1.0"> | |
<link rel="stylesheet" href="http://cdn.leafletjs.com/leaflet-0.4.5/leaflet.css" /> | |
<!--[if lte IE 8]> | |
<link rel="stylesheet" href="http://cdn.leafletjs.com/leaflet-0.4.5/leaflet.ie.css" /> |
<snippet> | |
<!-- put this file in /packages/User/<Folder Name>/console_log.sublime-snippet then restart your Sublime Text 2 --> | |
<content><![CDATA[console.log($1);$0]]></content> | |
<tabTrigger>conl</tabTrigger> | |
<scope>text.html,source.js</scope> | |
<description>console.log()</description> | |
</snippet> | |
<snippet> | |
<!-- put this in another file /packages/User/<Folder Name>/console_dir.sublime-snippet then restart your Sublime Text 2 --> |
Latency Comparison Numbers (~2012) | |
---------------------------------- | |
L1 cache reference 0.5 ns | |
Branch mispredict 5 ns | |
L2 cache reference 7 ns 14x L1 cache | |
Mutex lock/unlock 25 ns | |
Main memory reference 100 ns 20x L2 cache, 200x L1 cache | |
Compress 1K bytes with Zippy 3,000 ns 3 us | |
Send 1K bytes over 1 Gbps network 10,000 ns 10 us | |
Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD |
This post examines the features of [R Markdown](http://www.rstudio.org/docs/authoring/using_markdown) | |
using [knitr](http://yihui.name/knitr/) in Rstudio 0.96. | |
This combination of tools provides an exciting improvement in usability for | |
[reproducible analysis](http://stats.stackexchange.com/a/15006/183). | |
Specifically, this post | |
(1) discusses getting started with R Markdown and `knitr` in Rstudio 0.96; | |
(2) provides a basic example of producing console output and plots using R Markdown; | |
(3) highlights several code chunk options such as caching and controlling how input and output is displayed; | |
(4) demonstrates use of standard Markdown notation as well as the extended features of formulas and tables; and | |
(5) discusses the implications of R Markdown. |