• Front Matter
• Introduction
  • What is simuPOP?
  • An overview of simuPOP concepts
  • Features
  • License, Distribution and Installation
  • How to read this user’s guide
  • Other help sources
• Loading and running simuPOP
  • Pythonic issues
    • from simuPOP import * v.s. import simuPOP
    • References and the clone()member function
    • Zero-based indexes, absolute and relative indexes
    • Ranges and iterators
    • Empty, ALL_AVAIL and dynamic values for parameters loci, reps, ancGen and subPops
    • User-defined functions and class WithArgs *
    • Exception handling *
  • Loading simuPOP modules
    • Short, long, binary, mutant and lineage modules and their optimized versions
    • Execution in multiple threads
    • Graphical user interface
  • Online help system
  • Debug-related functions and operators *
  • Random number generator *
• Individuals and Populations
  • Genotypic structure
    • Haploid, diploid and haplodiploid populations
    • Autosomes, sex chromosomes, mitochondrial, and other types of chromosomes *
    • Information fields
  • Individual
    • Access individual genotype
    • individual sex, affection status and information fields
  • Population
    • Access and change individual genotype
    • Subpopulations
    • Virtual subpopulations and virtual splitters *
    • Advanced virtual subpopulation splitters **
    • Access individuals and their properties
    • Attach arbitrary auxillary information using information fields
    • Keep track of ancestral generations
    • Change genotypic structure of a population
    • Remove or extract individuals and subpopulations from a population
    • Store arbitrary population information as population variables
    • Save and load a population
    • Import and export datasets in unsupported formats *
• simuPOP Operators
  • Introduction to operators
    • Apply operators to selected replicates and (virtual) subpopulations at selected generations
    • Applicable populations and (virtual) subpopulations
    • Dynamically determined loci (parameter loci) *
    • Write output of operators to one or more files
    • During-mating operators
    • Function form of an operator
  • Initialization
    • Initialize individual sex (operator InitSex)
    • Initialize genotype (operator InitGenotype)
    • Initialize information fields (operator InitInfo)
  • Expressions and statements
    • Output a Python string (operator PyOutput)
    • Execute Python statements (operator PyExec)
    • Evaluate and output Python expressions (operator PyEval)
    • Expression and statement involving individual information fields (operator InfoEval and InfoExec) *
    • Using functions in external modules in simuPOP expressions and statements
  • Demographic changes
    • Migration (operator Migrator)
      • Migration by probability
      • Migration by proportion and counts
      • Theoretical migration models
      • migrate from virtual subpopulations *
      • Arbitrary migration models **
    • Migration using backward migration matrix (operator BackwardMigrator)
    • Split subpopulations (operators SplitSubPops)
    • Merge subpopulations (operator MergeSubPops)
    • Resize subpopulations (operator ResizeSubPops)
    • Time-dependent migration rate
  • Genotype transmitters
    • Generic genotype transmitters (operators GenoTransmitter, CloneGenoTransmitter, MendelianGenoTransmitter, SelfingGenoTransmitter, HaplodiploidGenoTransmitter, and MitochondrialGenoTransmitter) *
    • Recombination (Operator Recombinator)
    • Gene conversion (Operator Recombinator) *
    • Tracking all recombination events **
  • Mutation
    • Mutation models specified by rate matrixes (MatrixMutator)
    • k-allele mutation model (KAlleleMutator)
    • Diallelic mutation models (SNPMutator)
    • Nucleotide mutation models (AcgtMutator)
    • Mutation model for microsatellite markers (StepwiseMutator)
    • Simulating arbitrary mutation models using a hybrid mutator (PyMutator)*
    • Mixed mutation models (MixedMutator) **
    • Context-dependent mutation models (ContextMutator)**
    • Manually-introduced mutations (PointMutator)
    • Apply mutation to (virtual) subpopulations *
    • Allele mapping **
    • Mutation rate and transition matrix of a MatrixMutator**
    • Infinite-sites model and other simulation techniques **
    • Recording and tracing individual mutants **
  • Penetrance
    • Map penetrance model (operator MapPenetrance)
    • Multi-allele penetrance model (operator MaPenetrance)
    • Multi-loci penetrance model (operator MlPenetrance)
    • Hybrid penetrance model (operator PyPenetrance)
  • Quantitative trait
    • A hybrid quantitative trait operator (operator PyQuanTrait)
  • Natural Selection
    • Natural selection through the selection of parents
    • Natural selection through the selection of offspring *
    • Are two selection scenarios equivalent? **
    • Map selector (operator MapSelector)
    • Multi-allele selector (operator MaSelector)
    • Multi-locus selection models (operator MlSelector)
    • A hybrid selector (operator PySelector)
    • Multi-locus random fitness effects (operator PyMlSelector)
    • Alternative implementations of natural selection
    • Frequency dependent or dynamic selection pressure *
    • Support for virtual subpopulations *
    • Natural selection in heterogeneous mating schemes **
  • Tagging operators
    • Inheritance tagger (operator InheritTagger)
    • Summarize parental informatin fields (operator SummaryTagger)
    • Tracking parents (operator ParentsTagger)
    • Tracking index of offspring within families (operator OffspringTagger)
    • Assign unique IDs to individuals (operator IdTagger)
    • Tracking Pedigrees (operator PedigreeTagger)
    • A hybrid tagger (operator PyTagger)
    • Tagging that involves other parental information
  • Statistics calculation (operator Stat)
    • How statistics calculation works
    • defdict datatype
    • Support for virtual subpopulations
    • Counting individuals by sex and affection status
    • Number of segregating and fixed sites
    • Allele count and frequency
    • Genotype count and frequency
    • Homozygote and heterozygote count and frequency
    • Haplotype count and frequency
    • Summary statistics of information fields
    • Linkage disequilibrium
    • Genetic association
    • population structure
    • Hardy-Weinberg equilibrium test
    • Measure of Inbreeding
    • Effective population size
    • Other statistics
    • Support for sex and customized chromosome types
  • Conditional operators
    • Conditional operator (operator IfElse) *
    • Conditionally terminate an evolutionary process (operator TerminateIf)
    • Conditional removal of individuals (operator DiscardIf)
  • Miscellaneous operators
    • An operator that does nothing (operator NoneOp)
    • dump the content of a population (operator Dumper)
    • Save a population during evolution (operator SavePopulation)
    • Pause and resume an evolutionary process (operator Pause) *
    • Measuring execution time of operators (operator TicToc) *
  • Hybrid and Python operators
    • Hybrid operators
    • Python operator PyOperator *
    • During-mating Python operator *
    • Define your own operators *
• Evolving populations
  • Mating Schemes
    • Control the size of the offspring generation
    • Advanced use of demographic functions *
    • Determine the number of offspring during mating
    • Dynamic population size determined by number of offspring *
    • Determine sex of offspring
    • Monogamous mating
    • Polygamous mating
    • Asexual random mating
    • Mating in haplodiploid populations
    • Self-fertilization
    • Heterogeneous mating schemes *
    • Conditional mating schemes
  • Simulator
    • Add, access and remove populations from a simulator
    • Number of generations to evolve
    • Evolve populations in a simulator
  • Non-random and customized mating schemes *
    • The structure of a homogeneous mating scheme *
    • Offspring generators *
    • Genotype transmitters *
    • A Python parent chooser *
    • Using C++ to implement a parent chooser **
  • Age structured populations with overlapping generations **
  • Tracing allelic lineage *
  • Pedigrees
    • Create a pedigree object
    • Locate close and remote relatives of each individual
    • Identify pedigrees (related individuals)
    • Save and load pedigrees
  • Evolve a population following a specified pedigree structure **
  • Simulation of mitochondrial DNAs (mtDNAs) *
• Utility Modules
  • Module simuOpt (function simuOpt.setOptions)
  • Module simuPOP.utils
    • Trajectory simulation (classes Trajectory and TrajectorySimulator)
      • Forward-time trajectory simulations (function simulateForwardTrajectory)
      • Backward-time trajectory simulations (function simulateBackwardTrajectory).
    • Graphical or text-based progress bar (class ProgressBar)
    • Display population variables (function viewVars)
    • Import simuPOP population from files in GENEPOP, PHYLIP and FSTAT formats (function importPopulation)
    • Export simuPOP population to files in STRUCTURE, GENEPOP, FSTAT, Phylip, PED, MAP, MS, and CSV formats (function export and operator Exporter)
    • Export simuPOP population in csv format (function saveCSV, deprecated)
  • Module simuPOP.demography
    • Predefined migration models
    • Uniform interface of demographic models
    • Demographic models defined by outcomes
    • Demographic models defined by population changes (events)
    • Predefined demographic models for human populations
    • Demographic model without predefined generations to evolve *
  • Module simuPOP.sampling
    • Introduction
    • Sampling individuals randomly (class RandomSampler, functions drawRandomSample and drawRandomSamples)
    • Sampling cases and controls (class CaseControlSampler, functions CaseControlSample and CaseControlSamples)
    • Sampling Pedigrees (functions indexToID and plotPedigree)
    • Sampling affected sibpairs (class AffectedSibpairSampler, functions drawAffectedSibpairSample(s))
    • Sampling nuclear families (class NuclearFamilySampler, functions drawNuclearFamilySample and drawNuclearFamilySamples)
    • Sampling three-generation families (class ThreeGenFamilySampler, functions drawThreeGenFamilySample and drawThreeGenFamilySamples)
    • Sampling different types of samples (class CombinedSampler, functions drawCombinedSample and drawCombinedSamples)
    • Sampling from subpopulations and virtual subpopulations *
  • Module simuPOP.gsl
• A real world example
  • Simulation scenario
  • Demographic model
  • Mutation and selection models
  • Output statistics
  • Initialize and evolve the population
  • Option handling