hapsburg.PackagesSupport.simData

Attributes

sim

Classes

`Simulator`	Simulator Class for HMMs.
`BinomSim`	Bimomial Simulator.
`BinomSimCopyBoth`	Bimomial Simulator.
`SimRef`	Class for Simulating the Reference Strings
`BinomSimRefNoLD`	Simulate References under a Binomial Model.
`MultiVariateNormalLD`	Simulate References under a Multivariate Normal Model.

Module Contents

class hapsburg.PackagesSupport.simData.Simulator(refsim='BinomialNoLD')

Bases: object

Simulator Class for HMMs. This is the main Class, all specific Simulators can inherit from it and overwrite functions. Contains Parameters

l = 10000

lats = []

pure_stat = []

ob_stat = []

ref_states = []

ref_sim = 0

stats

set_ref_sim(sim): Set the Reference Simulator to sim

abstractmethod simulate_latent(): Simulate all l latent States

error_model(): Apply error model to all l latent States

simulate_full(): Return all Statistics

simulate_ref(): Generate the reference Data Set

apply_error(): Applies the Error Model

abstractmethod copyHaplo(): Copy from Haplotypes Refs via latent states

save_data(folder='../Simulated/Test0/'): Save the simulated Data to a Directory

save_parameters(folder='../Simulated/Test0/'): Save Parameters of Simulation

class hapsburg.PackagesSupport.simData.BinomSim(refsim='BinomialNoLD')

Bases: Simulator

Bimomial Simulator. Assume equally spaced SNPs. Copying: 0: From HW state. 1,…,n from Reference Data Set [n]

spacing = []

roh_in = 0.002

roh_out = 0.01

roh_jump = 0.1

set_cons_spacing(): Set the spacing of SNPs

abstractmethod set_params(): Set the parameters

save_parameters(folder): Save Parameters to parameter.csv in folder

first_state(): Generate the first latent state

sim_copying(): Simulate the Vector with States to copy from

jump_no_roh(): Generate length and next state not in ROH

jump_roh(): Generate length and next state in ROH

simulate_latent(): Simulate l latent States

copyHaplo(): Copy from Haplotypes Refs via latent states

class hapsburg.PackagesSupport.simData.BinomSimCopyBoth(refsim='BinomialNoLD')

Bases: Simulator

Bimomial Simulator. Assume equally spaced SNPs. Copy from two Individuals, even in non-ROH state

spacing = []

roh_in = 0.002

roh_out = 0.002

roh_jump = 0.1

set_cons_spacing(): Set the spacing of SNPs

abstractmethod set_params(): Set the parameters

jump_roh(): Generate length and next state in ROH

sim_copying_inroh(): Simulate Copying within ROH. Return Copy State [l]

sim_roh_inout(): Return 0 1 vector [l] wether in or out ROH state

simulate_latent(): Simulate and Return Vector with States to copy from

copyHaplo(): Copy from Haplotypes Refs via latent states

class hapsburg.PackagesSupport.simData.SimRef

Bases: object

Class for Simulating the Reference Strings

refs = []

haps = []

abstractmethod simulate_refs(): Simulate and return Reference Strings

set_params(**kwargs): Set Attributes with keyworded Arguments

plot_ld_r2(haps=[], l=200, fs=18): Plot R2 across pw. Distances of SNPs

class hapsburg.PackagesSupport.simData.BinomSimRefNoLD

Bases: SimRef

Simulate References under a Binomial Model. For the moment: Do not do any LD Structure

h = 2

k = 100

l = 1000

p = 0.5

simulate_refs(): Simulate References under a Binomial Model

class hapsburg.PackagesSupport.simData.MultiVariateNormalLD

Bases: SimRef

Simulate References under a Multivariate Normal Model. Simulate latent variable and push them through a logit function

l = 1000

k = 100

cov_dist = 200

logit(x): Define a Logit Function

simulate_refs(): Simulate References under the multivariate Normal Model

hapsburg.PackagesSupport.simData.sim