Model Tools ========================================= Groups and assignments -------------------------- The computer labs related to Model Tools are done in groups of two students. To support the management of marks in Blackboard, please self-subscribe to a group in Blackboard (Course Content -> Lab Groups). For all Labs, you need to answer questions given in the computer practical. You need to do so in Blackboard, using the specific Community related to the Lab. Only for the Agent-Based Modelling Lab, you need to write down your answers in a document and upload this document to Blackboard when finished. In the group of two students, enter the answers to the questions using one of your Blackboard logins; for all Labs and questions use the same Blackboard account. The staff knows the composition of the groups and it will be marked for both of you. The Labs are meant to acquire a deeper understanding of the concepts and how these are applied on data; they are thus relevant for preparing for the exam as well. They also learn you how to use the tools, which is important for the case study at the end of the course. The Labs are thus only marked as fullfilled/not-fulfilled as they are considered as a preparation for other course components for which you recieve a mark on a scale 1-10. Software installation ---------------------- Before you start, you have to install the software that is used in this course. We mainly use PCRaster and Python, including related other software. Follow the instructions at https://pcraster.geo.uu.nl/pcraster/latest/documentation/pcraster_project/install.html. It is important you install with the command given there for on-site or online courses (so including campo, spyder, etc). All software runs in Python and if you follow the instructions above you can use Spyder (which is installed together with the other software) as the Scientific Python Development Environment, that is, as editor for your Python scripts. Start up Spyder from Miniconda (or Conda). If you need to run Aguila 'from the command prompt', use the Miniconda command prompt. You can follow these instructions to install the software on your own desktop or laptop. The same instructions apply for installation of the software in the computer rooms in the V. Meinesz building. In addition please follow these instructions: - Login with a special account for these rooms. Login name is lspmGN where you replace GN with the number of your Lab Group in Blackboard (see above). For instance: lspm02, lspm24, etc. Note that lspm2 will not work, you need to use lspm02 if your group number is 2. You find the password in the Announcements channel of the Blackboard page of our course. - You do NOT need to install Miniconda or Anaconda, it is already installed. Directly start the Anaconda command prompt. - Install the software following the instructions on the PCRaster page (link is above) - The software will be installed on your user directory on the local drive. If you move to another computer, you need to install it on that computer again. So it is recommended to work always on the same computer. - The installation takes 10-15 minutes so start it right away. Python programming, week 1 ------------------------------ Key topics ~~~~~~~~~~~~ - Principles of computer programming - Python programming - Introduction to object orientation Literature for exam ~~~~~~~~~~~~~~~~~~~~ Think Python, An introduction to software design, 2nd Edition (!), A. Downey, 2015, Green Tea Press, Needham, 222 pp. Chapters 1, 2, 3, 5, 6, 7, 8, 10, and 14. Online at http://greenteapress.com/wp/think-python-2e/ or order a print from blackboard. e-Lectures ~~~~~~~~~~~ e-Lecture `Programming Python 3 - 01 Introduction `_ e-Lecture `Programming Python 3 - 02 Variables, expressions, statements `_ e-Lecture `Programming Python 3 - 03 Functions `_ e-Lecture `Programming Python 3 - 04 Conditionals and user intervention `_ e-Lecture `Programming Python 3 - 05 Fruitful functions and program development `_ e-Lecture `Programming Python 3 - 06 Strings `_ e-Lecture `Programming Python 3 - 07 Lists `_ e-Lecture `Programming Python 3 - 08 Files `_ e-Lecture slides `Python programming, pdf <_static/sheets/python_3.pdf>`_ .. e-Lecture `Programming Python - 01 Introduction `_ .. e-Lecture `Programming Python - 02 Variables, expressions, statements `_ .. e-Lecture `Programming Python - 03 Functions `_ .. e-Lecture `Programming Python - 04 Conditionals and user intervention `_ .. e-Lecture `Programming Python - 05 Fruitful functions and program development `_ .. e-Lecture `Programming Python - 06 Strings `_ .. e-Lecture `Programming Python - 07 Lists `_ .. e-Lecture `Programming Python - 08 Files `_ .. e-Lecture slides `Python programming, pdf <_static/sheets/python.pdf>`_ Computer lab ~~~~~~~~~~~~~~~~~~~~ Available in Blackboard. In Blackboard, go to 'Communities', select the community 'PCRaster Python - Programming'. Enter answers to questions posed in computer lab inside the Blackboard community. Map Algebra, week 2 ---------------------- Key topics ~~~~~~~~~~~~ - Static modelling with PCRaster Python - Local operations and neighbourhood operations e-Lectures ~~~~~~~~~~~ e-Lecture `Introduction to Map Algebra `_ e-Lecture `Map Algebra Operations `_ e-Lecture slides `Map Algebra, pdf <_static/sheets/karssenbergMapAlgebra.pdf>`_ Computer lab ~~~~~~~~~~~~~~ Map algebra course, available in Blackboard. In Blackboard, go to 'Communities', select the community 'PCRaster Python - Map Algebra'. Enter answers to questions posed in computer lab inside the Blackboard community. Dynamic modelling, week 3 ---------------------------------------- Key topics ~~~~~~~~~~~~~ - Dynamic modelling with PCRaster Python, field-based only Computer lab ~~~~~~~~~~~~~~ Available in Blackboard (Community PCRaster Python - Dynamic modelling). Enter answers to questions posed in computer lab inside the Blackboard community. Reading material (not for exam) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Karssenberg, D., De Jong, K. and Van der Kwast, J., 2007, Modelling landscape dynamics with Python. International Journal of Geographical Information Science, 21, pp. 483-495. `Link. `_ This article explains how you can construct dynamic models using the PCRaster Python framework. Lectures, e-Lectures ~~~~~~~~~~~~~~~~~~~~~ e-Lecture `Dynamic Modelling with PCRaster Python, part 1 `_ e-Lecture `Dynamic Modelling with PCRaster Python, part 2 `_ e-Lecture `Dynamic Modelling with PCRaster Python, part 3 `_ e-Lecture slides `PCRaster Python, pdf <_static/sheets/pcrasterPython03.pdf>`_ Stochastic Modelling, week 4 ----------------------------------------------------------------- Key topics ~~~~~~~~~~~ - Defining probability distributions as inputs to models - Monte Carlo simulation Computer lab ~~~~~~~~~~~~~~ Available in Blackboard (Community PCRaster Python - Stochastic Modelling). Important note: you do not need to do the section 'Dynamic stochastic modelling: infiltration model'. Enter answers to questions posed in computer lab inside the Blackboard community. Agent-based modelling, week 5 --------------------------------------------- Key topics ~~~~~~~~~~~~ - Static modelling with agents - Spatio-temporal modelling with agents e-Lectures ~~~~~~~~~~~ e-Lecture `Campo: spatial agent-based modelling `_ e-Lecture slides `Campo: spatial agent-based modelling, pdf <_static/sheets/campo_introduction.pdf>`_ Computer lab ~~~~~~~~~~~~~~ Campo course, available at http://campo.computationalgeography.org. Write down answers to questions posed in computer lab and upload these as a text document (e.g. MS Word, PDF) to Blackboard, there is a link in the Assignments section of our course. Calibration, week 6-7 --------------------------------------------- Key topics ~~~~~~~~~~~~ - Sensitivity analysis - Brute force calibration e-Lectures ~~~~~~~~~~~ No e-Lectures Computer lab ~~~~~~~~~~~~~~ Available in Blackboard (Community PCRaster Python - Calibration). Enter answers to questions posed in computer lab inside the Blackboard community.