|
Size: 322
Comment:
|
Size: 1361
Comment:
|
| Deletions are marked like this. | Additions are marked like this. |
| Line 1: | Line 1: |
| = ScrumPy Documentation = | ## page was renamed from Software/ScrumPy/Doc |
| Line 3: | Line 3: |
| = ScrumPy - Metabolic Modelling in Python = <<TableOfContents>> |
|
| Line 4: | Line 6: |
=== Why Python ? === 'cause it's great. |
=== Metabolic Modelling === === Design Philosophy === === 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 Model Description Language == === Overview === [[SpyMDL|Overview]] === Identifiers === [[SpyMDL|Identifiers]] === Reactions === [[SpyMDL|Reactions]] === Directives === [[SpyMDL|Directives]] |
| Line 10: | Line 29: |
| === What ScrumPy Is === === What ScrumPy Isn't === === Quick examples === == ScrumPy model format == == Structural Analysis of Models == == Kinetic Analysis of Models == |
== Analysis of Models With ScrumPy == === The ScrumPy Modelling Environment === === 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 === == Secondary Analysis of Model Results == === Data sets === === Fitting and Optimisation === == Automatic Model Building == |
| Line 24: | Line 42: |
| == The Utility Package == === Dynamic Matrices === |
ScrumPy - Metabolic Modelling in Python
Contents
Introduction
Metabolic Modelling
Design Philosophy
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 Model Description Language
Overview
Identifiers
Reactions
Directives
Analysis of Models With ScrumPy
The ScrumPy Modelling Environment
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
Secondary Analysis of Model Results
Data sets
Fitting and Optimisation
Automatic Model Building
Bioinformatics Functions
The Utility Package