# Welcome to GeogG122: Scientific Computing¶

Prof P. Lewis

## Purpose of this course¶

This course, GeogG122 Scientific Computing, is a term 1 MSc module worth 15 credits (25% of the term 1 credits) that aims to:

• impart an understanding of scientific computing
• give students a grounding in the basic principles of algorithm development and program construction
• to introduce principles of computer-based image analysis and model development

It is open to students from a number of MSc courses run by the Department of Geography UCL, but the material should be of wider value to others wishing to make use of scientific computing.

The module will cover:

• Introduction to programming
• Computing for image analysis
• Computing in Python
• Computing for environmental modelling
• Data visualisation for scientific applications

## Learning Outcomes¶

At the end of the module, students should:

• have an understanding of algorithm development and be able to use widely used scientific computing software to manipulate datasets and accomplish analytical tasks
• have an understanding of the technical issues specific to image-based analysis, model implementation and scientific visualisation

If you follow the various ‘advanced’ sections of the course, you will gain a deeper understanding of the concepts and codes, and in addition:

• have a working knowledge of linux / unix operating systems and have

the knowledge and confidence to obtain, compile and install commonly available scientific software packages

## Timetable¶

The course takes place over 10 weeks in term 1, on Wednesdays usually from 10:00 to 13:00 (09:00-13:00 in the first two sessions) in the Geography Department Unix Computing Lab (PB110) in the Pearson Building, UCL.

Classes take place from the second week of term to the final week of term, other than Reading week. See UCL term dates for further information.

## Structure of the Course¶

#1  1  Oct 09:00-13:00 4 hrs Python 101
#2  8  Oct 09:00-13:00 4 hrs Plotting and Numerical Python
#3  15 Oct 10:00-13:00 4 hrs Geospatial Data
#4  22 Oct 10:00-13:00 3 hrs Interpolation
#5  29 Oct 10:00-13:00 3 hrs Practical
#6  12 Nov 10:00-13:00 3 hrs TBD
#7  19 Nov 10:00-13:00 3 hrs TBD
#8  26 Nov 10:00-13:00 3 hrs TBD
#9  3  Dec 10:00-13:00 3 hrs Group practical
#10 10 Dec 10:00-13:00 3 hrs Group practical


Total scheduled hours: 32 hours

Prof Philip Lewis

Rooms: Pearson Building Unix Lab, Room 110, 1st floor

In addition to the 10:300 session, I will be available 09:00-10:00 for help and questions (other than the first few weeks when we start at 09:00 anyway).

## Assessment¶

Assessment is through one piece of coursework that is submitted in both paper form and electronically via Moodle. See the Moodle page for more details.

Information on the assessment is directly available in a later section of the notes.

## Detailed Struucture of the Course¶

0.0 Introduction

A brief overview of the course (these notes):

[Introductory notes]

An introduction to some of the basics of the Unix operating system. The aim is to enable students to understand the directory structure and basic unix operations (e.g. copying and moving files) as well simple operations such as text editing.

Students should read and follow these notes in their own time* as we will not be going through them in class (unless requested).

1.0 Python 101

The aim of this section is to introduce you to some of the fundamental concepts in Python. Mainly, this is based around fundamental data types in Python (int, float, str, bool etc.) and ways to group them (tuple, list and dict). We then learn about how to loop over groups of things, which gives us control to iterate some process. We need to spend a little time on strings, as you will likely to quite a bit of string processing in Scientific Computing (e.g. reading/writing data to/from ASCII text files). Although some of the examples we use are very simple to explain a concept, the more developed ones should be directly applicable to the sort of programming you are likely to need to do. A set of exercises is developed throughout the chapter, with worked answers available to you once you have had a go yourself. In addition, a more advanced section of the chapter is available, that goes into some more detail and complications. This too has a set of exercises with worked examples.

2.0 Plotting and Numerical Python

In this session, we will introduce and use some packages that you will commonly use in scientific programming.

These are:

numpy: NumPy is the fundamental package for scientific computing with Python

matplotlib: Python 2D plotting library

We will also introduce some additional programming concepts, and set an exercise that you can do and get feedback on.

3.0 Geospatial Data

In this session, we will introduced the gdal geospatial module which can read a wide range of scientific data formats. You will find that using it to read data is quite similar to the work we did last week on netCDF datasets.

The main challenges are also much the same: very often, you need to be able to read data from a ‘stack’ of image files and generate a useful 3D (space and time) dataset from these. Once you have the data in such a form, there are many things we can do with it, and very many of these are convenient to do using array-based expressions such as in numpy (consider the simplicity of the expression absorbed = rad * (1 - albedo) from last week’s exercise).

That said, it can sometimes be quite an effort to prepare datasets in this form. Last week, we developed a ‘valid data’ mask from the GlobAlbedo dataset, as invalid data were stored as nan. Very often though, scientific datasets have more complex ‘Quality Control’ (QC) information, that gives per-pixel information describing the quality of the product at that location (e.g. it was very cloudy so the results are not so good).

To explore this, we will first consider the MODIS Leaf Area Index (LAI) product taht is mapped at 1 km resolution, every 8 days from the year 2000.

We will learn how to read in these data (in hdf format) using gdal, and how to interpret the QC information in such products to produce valid data masks. As an exercise, you will wrap some code around that to form a 3D masked array of the dataset.

Next, we will consider how to download such data. This should be a reinforcement of material from last week, but it is useful to know how to conveniently access NASA data products. A challenge in the exercise then is to download a different dataset (MODIS snow cover) for the UK, and form a masked 3D dataset from this.

Finally, we will introduce vector datasets and show you python tools that allow you (among many other things) to build a mask in the projection and sampling of your spatial dataset (MODIS LAI in this case).

There are many features and as many complexities to the Python tools we will deal with today, but in this material, we cover some very typical tasks you will want to do. They all revolve around generating masked 3D datasets from NASA MODIS datasets, which is a very useful form of global biophysical information over the last decade+. We also provide much material for further reading and use when you are more confident in your programming.

A final point here is that the material we cover today is very closely related to what you will need to do in the first section of your assessed practical that we will introduce next week, so you really need to get to grips with this now.

There is not as much ‘new’ material as in previous weeks now, but we assume that you have understood, and can make use of, material from those lectures.

4.0 Interpolation

In today’s session, we will be using some of the LAI datasets we examined last week (masked by national boundaries) and doing some analysis on them. First, we will examine how to improve our data reading function by extracting only the area we are interested in. This involves querying the ‘country’ mask to find its limits and passing this information through to the reader. Then we will look at methods to interpolate and smooth over gaps in datasets using various methods. Finally, we will look at fitting models to datasets, in this case a model describing LAI phenology.

[Course Notes]

4a ENVI

This session is rather apart from the rest and is included to allow students to familarise themselves with a package image processing environment (ENVI). This is an unsupervised session, that takes place during UCL Reading Week in Term 1.

[Course Notes]

5.0 Practical

This section describes the coursework you are to submit for assessment for this course.

[Practical for assessment]

6.0 ENSO

To finish the course, some practical applications. The first of these looks at predicting fire activity from from climatic data.

Using monthly fire count data from MODIS Terra, develop and test a predictive model for the number of fires per unit area per year driven by Sea Surface Temperature anomaly data.

[Course Notes]

7.0 P Theory

Another practical application involves a simple radiative transfer model applied to help interpret hyperspectral remote sensing data over an agricultural site.

Using hyperspectral image data over an agricultural area, use photon recollision theory to produce a map of Leaf Area Index.

[Course Notes]

## Using these notes¶

These notes and all associated files should be directly available for you in the directory ~/DATA/geogg122.

You should be able to just start firefox (or another browser) and type:

berlin% firefox &

berlin% cd ~/Data/geogg122

berlin% ipython notebook --pylab=inline

Then navigate to the section you want in the browser. This will allow you to run the sessions in the python notebook.

If you only want to view the notes (in html), then navigate to the pretty course notes link or the simple course notes link.

## Other ways to access the notes¶

There are several ways you can access this course material.

These notes are created in ipython notebooks.

The course is all stored online in github, so you can just navigate to that site and download the files as you like.

Probably the easiest option is to access the html version of the notes from notebook links in the README on github or above on this page. You can access notebook links, which are guaranteed to be up to date, or html links, which should be (but not guaranteed).

Another option is to access individual notebooks online through the IPython Notebook Viewer.

For example, to view the notebook Chapter0_Introduction/f2_intro.html, you use a link to the github file.

Provided you have a relatively up to date version of ipython and a few other tools such as pandoc you can convert your own notebooks to other formats using ipython, e.g.:

berlin% ipython nbconvert --to html f2_intro.html

You can also convert the notebooks to other formats though you might need some other tools as well for this. If you have a working copy of LaTeX on your system (e.g. MacTeX on OS X), you can convert the notebooks to pdf format:

berlin% ipython nbconvert --to latex --post PDF f2_intro.html

### Obtaining the course material¶

Alternatively, you can obtain the whole course from github.

1. using git
use the command

git, if available: | Create a place on the system that you want to work in (N.B., don’t type berlin%: that represents the command line prompt), e.g.:

berlin% cd ~/DATA
berlin% git clone https://github.com/profLewis/geogg122.git
berlin% cd ~/DATA/geogg122

This will create a directory ~/Data/msc/geogg122 which has the current versions of the notebooks for the course and associated files.

If the course notes change at all (e.g. are updated), you can update your copy with:

berlin% git pull

To find out more about using git, type git --help, get help online or download and use a gui tool.

If you set up an account on github, you can fork the course repository to make your own version of the course notes, and add in your own comments and examples, if that helps you learn or remember things.

1. using a zip file

berlin% mkdir -p ~/Data/msc
berlin% cd ~/Data/msc

berlin% wget -O geogg122.zip https://github.com/profLewis/geogg122/archive/master.zip | berlin% unzip geogg122.zip | berlin% cd ~/Data/msc/geogg122-master

You can directly use the notebooks, or you can open the html files, e.g. opening

file:///home/plewis/Data/geogg122/Chapter1_Unix/f3_1_unix_intro.html in a browser (obviously changing the username and path as appropriate).

### Using the course material¶

Once you have copied the course material as described above (and have changed directory to where you have put the course (e.g. ~/DATA/geogg122-master or ~/DATA/geogg122) then cd to the chapter you want, e.g.:

berlin% cd ~/DATA/geogg122/Chapter0_Introduction

and you can start the notebooks with:

berlin% ipython notebook

This should launch a web browser with the address http://127.0.0.1:8888/ or similar with links to the notebooks you have available.

To load a specific notebook, you can type e.g.:

berlin% ipython notebook f1_index.html

### Python¶

For most users wanting to install a working python environment, Anaconda appears to be far easier and overall quite nice to use: https://store.continuum.io/cshop/anaconda/. Comes with Python notebooks, spyder and a wealth of other things not in some other releases.

### System access¶

You should be able to install python on a windows operating system and so could run most of the class material from any windows computer that you have. As we have noted above, you can download all of the class notes as python notebooks or other formats (such as html).

For windows users, it’s probably best if you just use http://mobaxterm.mobatek.net/ to connect to the UCL system (you don’t need exceed, it’s free, got SFTP, etc).

For linux and OS X machines, it’s very straightforward as you already have a unix system. For OS X, you can find the terminal in the Utilities folder under Applications. For X windows on OS X, you may need to install this if you have a recent version of the operating system.

Another approach is to use the UCL WTS system, where you have access to some software called exceed to allow you to log on to the system.

Using any of these (or other!) approaches, you want to be able to use the command ssh (or similar) to log on to the gateway machine shankly.geog.ucl.ac.uk:

This will normally be:

From there, you should log in (with ssh) to another computer in the lab (or else everyone will be on the same computer).

An alternative gateway, if shankly is down or busy is lyon.geog.ucl.ac.uk.

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← Welcome to GeogG122’s documentation!

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→ 1. Introduction to Python