Sunday, October 31, 2010

Buyer Beware: Microsoft Office 2011 for Macintosh, Academic License

I just finished reading the academic license for Microsoft's latest Office suite for OS X.  The academic license is very restrictive and may prove to make the $99 cost not economical.

The reason is that the academic license of Office is permanently installed on the first Mac it is installed onto.  Thus, if your computer goes out to pasture, you're out of luck and will need to get a new license.  Other licenses for Office 2011 allow a one-time transfer to another computer.

Update: Feb 16, 2011. Here's what I'm referring to within the academic license:
"'a. One Copy per Device. The software license is permanently assigned to the device on which the software is initially activated. That device is the “licensed device.”'

This does not appear on any other license.  In fact, other licenses have specific transfer rights.  The academic license does not.

I did not decipher this in the license itself, but be aware that the box says "One user, One Mac."  This seems to imply only the academic user that bought it can use it.  But I don't see how that could be enforceable without causing a lot of headaches for users.  The One Mac, in this case, REALLY means One Mac.  Not One Mac install at a time.

So, it may be better, if you need office on a Mac, to buy the Office for Home & Business edition if you want the software to have longevity.  Other people have lamented the fact that it can't be installed on a laptop and desktop simultaneously so long as you use only one at a time.  But Apple Pages has the same restriction.

What am I going to do?  Continue using Office 2007 in a Windows virtual machine.  But usually I rock it with MacTeX and Lyx, anyway.

Update, Nov 7 2010: I should note that the Microsoft Office 2010 for Windows Academic license is a full-blown license with no transfer restrictions.  It, apparently, can also be installed on a portable machine owned by the single user.  The license is also $20 less.  I have to wonder if the onerous restrictions in Office for Mac 2011 is purposeful: Microsoft would prefer you not use OSX, and so provides some justification via licensing.  Microsoft, those restrictions are a net negative, in my case. Sorry. 

Structural Geology and Python

As I journey through Structural Geology, I'm compiling (no pun intended) a set of python scripts.  These scripts, for example, include Mohr circle formulas, and calculations of apparent and true dip.  Direction cosines are only a matter of time.

This is a good study method.  Writing the scripts, while trivial, makes me reflect on why the formulas work.  Later on, they could be integrated into a GUI (perhaps using Tkinter or QT).  

These scripts might also be integrated later on into more complicated scripts designed to solve geologic problems.  I am writing them as modules and I am including docstrings as I go. 

Friday, October 29, 2010


libLAS is a library that processes the LAS LiDAR format.  It is now, apparently, available in the OSGEO4W repository.  OSGEO4W is a set of applications and libraries compiled for Windows by the most awesome OSGEO project; it is a complete environment to work with many raster (OGR) and vector formats (GDAL).  The packaging system also includes Python, which is great for geoprocessing scripts.  It also includes QGIS stable and experimental.  This OSGEO4W is the way to go if you're using Windows!

Anyway, libLAS as compiled with OSGEO4W can immediately process LAS formatted LiDAR data into OGR supported formats using las2ogr. For example, you could download LAS point cloud data and immediately get a GeoTIFF with all vertical reprojections applied.  Neat-o.

Of course, if you don't want to try installing OSGEO, you could try one of their Live DVDs or VMWare images.  But I highly doubt the LibLAS software has made it in version 4.01 (it was released August 26).

It is only a matter of time before libLAS and las2ogr makes it into OGR packages for OS X 10.6.

Happy geoprocessing and happy Halloween.  It's back to constructing Mohr circles and plotting lines and planes on stereonets.

Tuesday, October 19, 2010

OpenTopography, GDAL, and the San Andreas Fault is a resource for a limited, but massive, set of LIDAR point data. LIDAR is a LASER-based method to determine topography. It is pretty dang nice when you want to look at the details of topography at a very large scale.

GDAL can process the returned LIDAR data. I've been using GeoTIFFs, since it is lossless, and isn't an ESRI specified format. GDAL is open source.

gdal_contour, gdaldem are two GDAL tools you will want to use if you want to visualize LIDAR data.

LIDAR at Wallace Creek

This image shows a LIDAR DEM that has been processed using the GDAL toolset.  A hillshade has been generated using gdaldem and its defaults.  A 5 m contour interval is shown, but there is also a 1 m contour interval in the legend.  I turned it off because it obscured the hillshade.  Imagine doing a 1 m contour interval on USGS DEM data: not a bright idea!  This LIDAR data was downloaded at a scale of 1 m per cell (I haven't verified that is what I actually received, but it certainly looks great at incredibly large scales).  I must note what you probably have noted:  that is some awful labeling.  The labeling engine in QGIS (used to display this data) is still under serious work;  patience is necessary.  Until then,Inkscape is a good way to manually, and deliberately, label your maps.

Notice at the center of the image there is an offset of two stream channels.  Both have an offset at the same time.  And if you look closely, you can see a faint trace of the San Andreas Fault.  This is Wallace Creek!  So if you wanted proof that the San Andreas fault is a right-lateral strike-slip fault, here it is.  Please note that this creek has been beheaded by cummulative offset, not by a single event.  The old stream channel is towards the west (left).

It is worth noting that the trace of the fault really stands out when you generate a slope map.  Distinct breaks in slope are great for finding faults or landslides.  At such large scales, you can really understand the geomorphology of a region.