Changing the font in Carbon Emacs

My current monospaced font of choice is Deja Vu Sans Mono. It is pretty easy to change the default font in Carbon Emacs. It is not so easy to change the default font on Windows- or Linux-based Emacs22, which is the current release version. It will be relatively straightforward in Emacs23 via the xft package. But, for your Mac folks out there…

Installing a Font on Mac OS X

1. Download the font (Mac will accept almost any format).
2. Drag the font file into /System/Fonts

Figure out the long name of your font face in Carbon Emacs

• M-x mac-font-panel-mode. Then pick your font
• M-x describe-font and copy the long name.

Set the default font

There are a couple of ways to do this.

Customization

• M-x customize-face RET default RET
• Fill in the fields of the font with the long name.

.emacs.d file

(set-default-font "-apple-inconsolata-medium-r-normal--11-110-72-72-m-110-iso10646-1")

Monospaced fonts for the uninitiated

Programmers tend to use different fonts for code than what we see in books an on the web. Word processors typically use proportional fonts, where the horizontal space allowed for narrow letters like "l" is less than the space allowed for wide letters like "W". In code, we want the columns of each line to be aligned. So, programmers tend to use monospaced fonts, where each letter takes up the same amount of horizontal space. The most commonly known fixed width-font is Courier. Another common fixed-width font for Windows and Microsoft products in Consolas. The Code Project has an exhaustive list of monospaced fonts.

One of the key attributes to look for in a monospaced font is the distinction between the number one and the lowercase letter "l" and the number zero and the uppercase "O". When you are trying to read variable names that have mixtures of numbers and letters, you will be thankful for a font that distinguishes between these.

Extracting Raster Values from Points in R and GRASS

A common task in GIS analysis is to extract the value of a remotely sensed environmental variable at a point location. For instance we may wish to extract the elevation of a field plot from a digital elevation model. The elevation data is a raster (i.e. grid) and the plots are a point shapefile (or a simple text file of X, Y locations). The command for doing this in ArcGIS is ExtractValuesToPoints available in the Spatial Analyst package. Situations may arise where ArcGIS is not the most efficient way of extracting these values. So, here, I provide a brief overview of how to extract raster values to points in R and GRASS.

Extract Values to Points in R

This is strikingly easy is R. My work usually requires more statistical sophistication than is available in ArcGIS. As a result, I have completely switched to doing the extraction in R. I known I am going to end in R eventually, and it is easier to automate than writing a long python script in ArcGIS.

Data required

For the purpose of this exercise. All the data must be have the same spatial projection.

gr.asc

an ESRI ASCII grid. This could also be an ArcGIS binary grid if you know how to use RGDAL. That perhaps will be another post.

pt.shp

a point shapefile.

You also need the maptools and sp packages.

The Code

library(maptools) # autoloads sp gr <- readAsciiGrid("www.unc.edu/~toddjobe/data/gr.asc") pt <- readShapePoints("www.unc.edu/~toddjobe/data/pt.shp") overlay(gr, pt)

coordinates gr.asc 1497 (569292, 1224170) 6094.6080 539 (567718, 1227840) 7964.5331 1023 (564565, 1225810) -293.6599 663 (562462, 1227260) -5351.7297 69 (563675, 1229710) -2923.9394 716 (563062, 1227180) -4241.8255 339 (567636, 1228780) 4781.6488 509 (561722, 1227870) -3958.7312 805 (560981, 1226690) 139.9091 155 (560884, 1229220) -2719.7353

That is it. Fast, and easy.

Extracting Values in GRASS

Extracting raster values in GRASS is somewhat faster than in R, but it takes a little bit more planning in that you have to explicitly create the column that the raster values will go into.

Data Required

• gr : A GRASS grid
• pt : A GRASS point dataset

The Code

The basic flow of this is that you create an empty column in the point dataset with the right data type (i.e. varchar(10) string of length 10, double precision floating point numbers, int integers). Then, fill the column with the raster values.

v.db.addcol map=pt columns="grval double precision" v.what.rast vector=pt raster=gr column=grval