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| === Python === ==== Knowing syntax vs programming ==== ==== Inbuilt types and classes ==== int, float, str, list and dict ==== Classes and sub-classes ==== ==== Repetiton and Decisions ==== ==== Functions ==== ==== Modules and Packages ==== ==== Standard libraries ==== Not all of them ! In practice math and random are the most commonly with {{{ScrumPy}}}. sys and os are also useful - any other suggestions ? |
Scrumpy is unusual, but not unique, in that the primary user interface is a language (it is an oversimplification to refer to it as a command line interface) rather than a more conventional GUI. The underlying reason for this choice is a simple one: A GUI restricts the user to only those actions which the programmer mpredicted the user might wish to perform. In some contexts this is not a problem, simple text editing and web-browsing being examples. However, in metabolic modelling (and scietific/research contexts in general) it is much harder for the programmer to predict what a user may wish to do. MORE HERE - Furthermore, in the twenty or so years in which I have been involved in the field, I have lost count of the number of presentations I've listened to for software (not only modelling or scientific) making the claim that the software is intuitive and user friendly, to the extent that this has become a mantra to be uttered at the begining of every presentation. Most of it has been unconvincing at best. === Tutorials === ==== Introduction to Python ==== [[ScrumPy/Doc/Tutorial/PyIntro | Introduction to Python]] ==== Introduction to ScrumPy ==== [[ScrumPy/Doc/Tutorial/ScrumPyIntro | Introduction to ScrumPy]] === Python Notes === [[ScrumPy/Doc/PyNotes|Python notes]] |
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| [[SpyMDL|Overview]] | [[SpyMDL#Overview|Overview]] |
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| [[SpyMDL|Identifiers]] | [[SpyMDL#Identifiers|Identifiers]] |
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| [[SpyMDL|Reactions]] | [[SpyMDL#Reactions|Reactions]] |
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| [[SpyMDL|Directives]] |
[[SpyMDL#Directives|Directives]] |
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| === The Matrix Class === Fully described in utility section - enough here to understand SMs, datasets and monitors. === Anatomy of a ScrumPy Model === === Kinetic Modelling === === Structural Modelling === === Linear Programming === |
==== Running ScrumPy ==== [[ScrumPy/Doc/ModEnv#RunSpy | Running ScrumPy]] ==== Loading Models ==== [[ScrumPy/Doc/ModEnv#LoadSpy | Loading Models]] ==== Structural Modelling ==== [[ScrumPy/Doc/StruMod | Structural Modelling ]] ==== Linear Programming ==== [[ScrumPy/Doc/LinProg | Linear Programming ]] ==== Kinetic Modelling ==== [[ScrumPy/Doc/KinMod | Kinetic Modelling ]] |
ScrumPy - Metabolic Modelling in Python
Contents
1. Introduction
1.1. Metabolic Modelling
1.2. Design Philosophy
Scrumpy is unusual, but not unique, in that the primary user interface is a language (it is an oversimplification to refer to it as a command line interface) rather than a more conventional GUI. The underlying reason for this choice is a simple one: A GUI restricts the user to only those actions which the programmer mpredicted the user might wish to perform. In some contexts this is not a problem, simple text editing and web-browsing being examples.
However, in metabolic modelling (and scietific/research contexts in general) it is much harder for the programmer to predict what a user may wish to do. MORE HERE -
Furthermore, in the twenty or so years in which I have been involved in the field, I have lost count of the number of presentations I've listened to for software (not only modelling or scientific) making the claim that the software is intuitive and user friendly, to the extent that this has become a mantra to be uttered at the begining of every presentation. Most of it has been unconvincing at best.
1.3. Tutorials
1.3.1. Introduction to Python
1.3.2. Introduction to ScrumPy
1.4. Python Notes
2. ScrumPy Model Description Language
2.1. Overview
2.2. Identifiers
2.3. Reactions
2.4. Directives
3. Analysis of Models With ScrumPy
3.1. The ScrumPy Modelling Environment
3.1.1. Running ScrumPy
3.1.2. Loading Models
3.1.3. Structural Modelling
3.1.4. Linear Programming
3.1.5. Kinetic Modelling
4. Secondary Analysis of Model Results
4.1. Data sets
4.2. Fitting and Optimisation
5. Automatic Model Building
6. Bioinformatics Functions
7. The Utility Package