Welcome to geog0111: Scientific Computing

UCL Geography: Level 7 course, Scientific Computing

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Chapter0_help

Chapter1_Python_introduction

Chapter1_Python_introduction_answers

Chapter2_Numpy_matplotlib

Chapter2_Numpy_matplotlib_answers

Chapter3_0_GDAL

Chapter3_1_GDAL

Chapter3_1_GDAL_answers

Chapter3_2_MODIS_download

Chapter3_2_MODIS_download_answers

Chapter3_3_GDAL_masking

Chapter3_4_GDAL_stacking_and_interpolating

Chapter3_4a_GDAL_stacking_and_interpolating-convolution

Chapter3_5_Movies

Chapter3_6A_GDAL_Reconciling_projections_prerequisites

Chapter3_6_GDAL_Reconciling_projections

Chapter4_Practical_Part1

Chapter5_Linear_models

Chapter5_Modelling_and_optimisation

Chapter6_NonLinear_Model_Fitting

Chapter6_NonLinear_Model_Fitting_Solutions

Chapter7_FittingPhenologyModels

Chapter7_FittingPhenologyModels_Solutions

Chapter8_Practical_Part2

Chapter9_Fire_and_Teleconnections

Chapter9_Fire_and_Teleconnections_Solution

Connection

Course information

Course Convenor

Prof P. Lewis

N.B. 2018-19 Course Convenors: Dr Qingling Wu and Dr. Jose Gomez-Dans

Course and Contributing Staff

Prof Philip Lewis

Dr. Jose Gomez-Dans

Dr Qingling Wu

Mr Feng Yin

Mr James Brennan

Purpose of this course

This course, geog0111 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:

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

Learning Outcomes

At the end of the module, students should:

• have an understanding of the Python programmibng language and experience of its use
• 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

Timetable

The course takes place over 10 weeks in term 1, 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.

The timetable is available on the UCL Academic Calendar

Assessment

Assessment is through two pieces of coursework, submitted in both paper form and electronically via Moodle.

See the Moodle page for more details.

Useful links

Course Moodle page

Python

Python is a high level programming language that is freely available, elatively easy to learn and portable acros

Contents:

• 1. Introduction to Python With ANSWERS
  • 1.1 Getting Started
• symbol meaning
  • 1.2 Text and looping
  • 1.3. Groups of things
• 1. Introduction to Python
  • 1.1 Getting Started
• symbol meaning
  • 1.2 Text and looping
  • 1.3. Groups of things
• 2. Manipulating and plotting data in Python: numpy, and matplotlib libraries With ANSWERS
  • 2.1 Functions
  • 2.2 numpy
  • 2.3 Plotting with Matplotlib
  • 2.4 Indexing and slicing arrays
  • 2.5 Reading data
• 2. Manipulating and plotting data in Python: numpy, and matplotlib libraries
  • 2.1 Functions
  • 2.2 numpy
  • 2.3 Plotting with Matplotlib
  • 2.4 Indexing and slicing arrays
  • 2.5 Reading data
• 3 Geospatial processing with gdal
  • 3.1 MODIS LAI product
  • 3.1.2 gdal
  • 3.2 Automatic downloading of NASA MODIS products
  • 3.3 GDAL masking
  • 3.4 GDAL stacking and interpoilation
• 3.5 Movies
  • 3.6 Summary
• 3 Geospatial processing with gdal
  • 3.1 MODIS LAI product
  • 3.1.2 gdal
  • 3.2 Automatic downloading of NASA MODIS products
  • 3.3 GDAL masking
  • 3.4 GDAL stacking and interpolating
  • 3.5 Summary
• 3 Geospatial processing with gdal
  • 3.1 MODIS LAI product
  • 3.1.2 gdal
  • 3.2 Automatic downloading of NASA MODIS products
  • 3.3 GDAL masking
  • 3.4 GDAL stacking and interpolating
  • 3.X Summary
• 3.2 Accessing MODIS Data products
  • 3.2.1 Introduction
  • 3.2.2 Accessing NASA MODIS URLs
  • 3.2.2.1 datetime
  • 3.2.3 MODIS filename format
  • 3.2.4 Saving binary data to a file
  • 3.2.4 downloading the data file
  • 3.2.5 Visualisation
  • 3.2.6 Summary
• 3.2 Accessing MODIS Data products
  • 3.2.1 Introduction
  • 3.2.2 Accessing NASA MODIS URLs
  • 3.2.2.1 datetime
  • 3.2.3 MODIS filename format
  • 3.2.4 Saving binary data to a file
  • 3.2.4 downloading the data file
  • 3.2.5 Visualisation
  • 3.2.6 Summary
• 3.3 GDAL, and OGR masking
  • 3.3.1 The MODIS LAI data
  • 3.3.2 MODIS dataset access
  • 3.3.3 Reading and displaying data
  • 3.3.4 The country borders dataset
• 3.4.4 Weighted interpolation
  • 3.4.4.1 Smoothing
  • 3.4.4.2 weighted smoothing
• 3.4 Stacking and interpolating data
  • 3.4.1 Introduction
  • 3.4.2 QA data
  • 3.4.3 A time series
  • 3.4.4 Weighted interpolation
  • 3.4.5 Making movies
• 3.5 LAI Movies
• 3.6 Reconciling projections
  • 3.6.A Introduction
  • 3.6.A.1 Requirements
• 3.6 Reconciling projections
  • 3.6.1 Introduction
  • 3.6.1.1 Projections
  • 3.6.1.2 Changing Projections
  • 3.6.2 Requirements
  • 3.6.3 Reconcile the datasets
  • 3.6.3.6 Putting this together
  • 3.6.6 Summary
• 4. Assessed Practical
  • 4.1 Introduction
  • 4.1.2 Purpose of the work
  • 4.3 Coursework
• Table of Contents
• Fitting to the Mauna Loa \(CO_2\) record
  • Introduction
  • Obtaining the data
  • Loading the data into Python
  • A model for \(CO_2\) concentration
  • Solving the problem using linear algebra
  • A model with seasonality
  • A phase shift
  • Prediction
  • Uncertainty
• 5 Modelling and optimisation
  • 5.1 Introduction
  • 5.2 Get datasets
  • 5.3 Interpretation of the data
• Fitting non-linear models
  • A synthetic experiment
  • Next: Real data
• Fitting non-linear models
  • A synthetic experiment
  • Fitting MODIS LAI data
• Fitting models of phenology to MODIS LAI data
  • The phenology model
  • Interpreting the QA LAI data
  • Selecting data from a raster file
  • Uncertainty
• Fitting models of phenology to MODIS LAI data
  • The phenology model
  • Interpreting the QA LAI data
  • Selecting data from a raster file
  • Uncertainty
• 8. Assessed Practical
  • 8.1 Introduction
  • 8.1.2 Purpose of the work
  • 8.2 The model
  • 8.3 Coursework
• Group project: Fire and teleconnections
  • In this Session..
  • Splitting the tasks
  • Model testing
  • Refinements
• Group project: Fire and teleconnections
  • In this Session..
  • Splitting the tasks
  • Solution: getting the data
  • Solution: getting hold of the teleconnection data
  • Solution: the model fitting part
• Connecting to notebooks from outside UCL
  • Remote access to your UCL shell
  • Actually accessing the Jupyter notebooks remotely

Indices and tables

• Index
• Module Index
• Search Page