The modeltes.jl from ggggggggg

Add use of package ARMA.jl to model TES noise

Functions in TESNoise.jl re-implement various computations that are already in ARMA.jl package. They should be replaced so that this work can be tested tracked and implemented in only one place.

ModelTES needs to be able to compute piled-up pulses

At some version in the past, one could use ModelTES to compute the temperature and current when there was more than one pulse, but either I am totally unable to find it, or the current API does not allow for multiple piled-up pulses. It needs to do so.

Cannot make multiple pulses work

Galen, I just merged my fork with yours, and I still get an error when I try either of the 2nd or 3rd biasesTES in the following code:

using ModelTES, PyPlot

bt1 = ModelTES.pholmes(48e-9, 0.20)
bt2 = ModelTES.lowEpix()
bt3 = ModelTES.LCLSII(52e-9)

sampletime = 1e-7
pulsetimes = [1e-4, 2e-4]

clf()
for bt in (bt1, bt2, bt3)
    outmany = ModelTES.pulses(6000, sampletime, bt, [1000,1000], pulsetimes);
    plot(outmany.I[1]-outmany.I)
end

Specifically, a NaN-based error:

ERROR: LoadError: DomainError:
 in nan_dom_err at ./math.jl:196 [inlined]
 in ^(::Float64, ::Float64) at ./math.jl:355
 in dT at /Users/fowlerj/.julia/v0.5/ModelTES/src/stochastic_integration.jl:7 [inlined]
 in (::ModelTES.BiasedTES{ModelTES.ShankRIT})(::Float64, ::Array{Float64,1}, ::Array{Float64,1}) at /Users/fowlerj/.julia/v0.5/ModelTES/src/stochastic_integration.jl:88
 in perform_step! at /Users/fowlerj/.julia/v0.5/OrdinaryDiffEq/src/integrators/low_order_rk_integrators.jl:224 [inlined]
 in perform_step! at /Users/fowlerj/.julia/v0.5/OrdinaryDiffEq/src/integrators/low_order_rk_integrators.jl:204 [inlined]
 in solve!(::OrdinaryDiffEq.ODEIntegrator{OrdinaryDiffEq.Tsit5,Array{Float64,1},Float64,Float64,Float64,Array{Array{Float64,1},1},DiffEqBase.ODESolution{Array{Array{Float64,1},1},Array{Float64,1},Array{Array{Array{Float64,1},1},1},DiffEqBase.ODEProblem{Array{Float64,1},Float64,true,ModelTES.BiasedTES{ModelTES.ShankRIT}},OrdinaryDiffEq.Tsit5,OrdinaryDiffEq.InterpolationData{ModelTES.BiasedTES{ModelTES.ShankRIT},Array{Array{Float64,1},1},Array{Float64,1},Array{Array{Array{Float64,1},1},1},OrdinaryDiffEq.Tsit5Cache{Array{Float64,1},Array{Float64,1},Array{Float64,1},Array{Float64,1},OrdinaryDiffEq.Tsit5ConstantCache{Float64,Float64}}}},Array{Float64,1},ModelTES.BiasedTES{ModelTES.ShankRIT},Void,OrdinaryDiffEq.Tsit5Cache{Array{Float64,1},Array{Float64,1},Array{Float64,1},Array{Float64,1},OrdinaryDiffEq.Tsit5ConstantCache{Float64,Float64}},OrdinaryDiffEq.DEOptions{Float64,Float64,Float64,Float64,OrdinaryDiffEq.#ODE_DEFAULT_NORM,DiffEqBase.CallbackSet{Tuple{},Tuple{DiffEqBase.DiscreteCallback{ModelTES.##11#15{Array{Float64,1}},ModelTES.##12#16{ModelTES.BiasedTES{ModelTES.ShankRIT},Dict{Float64,Int64}}}}},OrdinaryDiffEq.#ODE_DEFAULT_ISOUTOFDOMAIN,OrdinaryDiffEq.#ODE_DEFAULT_PROG_MESSAGE,OrdinaryDiffEq.#ODE_DEFAULT_UNSTABLE_CHECK,DataStructures.BinaryHeap{Float64,DataStructures.LessThan},Void}}) at /Users/fowlerj/.julia/v0.5/OrdinaryDiffEq/src/solve.jl:250
 in #solve#45(::Array{Any,1}, ::Function, ::DiffEqBase.ODEProblem{Array{Float64,1},Float64,true,ModelTES.BiasedTES{ModelTES.ShankRIT}}, ::OrdinaryDiffEq.Tsit5, ::Array{Any,1}, ::Array{Any,1}, ::Array{Any,1}, ::Type{Val{true}}) at /Users/fowlerj/.julia/v0.5/OrdinaryDiffEq/src/solve.jl:7
 in (::DiffEqBase.#kw##solve)(::Array{Any,1}, ::DiffEqBase.#solve, ::DiffEqBase.ODEProblem{Array{Float64,1},Float64,true,ModelTES.BiasedTES{ModelTES.ShankRIT}}, ::OrdinaryDiffEq.Tsit5, ::Array{Any,1}, ::Array{Any,1}, ::Array{Any,1}, ::Type{Val{true}}) at ./<missing>:0 (repeats 2 times)
 in #pulses#9(::Float64, ::OrdinaryDiffEq.Tsit5, ::Float64, ::Float64, ::Function, ::Int64, ::Float64, ::ModelTES.BiasedTES{ModelTES.ShankRIT}, ::Array{Int64,1}, ::Array{Float64,1}) at /Users/fowlerj/.julia/v0.5/ModelTES/src/ModelTES.jl:352
 in pulses(::Int64, ::Float64, ::ModelTES.BiasedTES{ModelTES.ShankRIT}, ::Array{Int64,1}, ::Array{Float64,1}) at /Users/fowlerj/.julia/v0.5/ModelTES/src/ModelTES.jl:342
 in macro expansion; at /Users/fowlerj/Microcal/Nonlinearity/Demos/swetz_bes_demo.jl:12 [inlined]
 in anonymous at ./<missing>:?
 in include_from_node1(::String) at ./loading.jl:488
 in include_from_node1(::String) at /Applications/Julia-0.5.app/Contents/Resources/julia/lib/julia/sys.dylib:?
while loading /Users/fowlerj/Microcal/Nonlinearity/Demos/swetz_bes_demo.jl, in expression starting on line 11

Unfortunately, now that you installed a package with a totally inscrutable interface, I'm helpless here. And I promised Dan some kind of demo plot quickly. Ideas??

NoiseModel or model_covariance: one doesn't properly handle sampleTime argument

In some tests, I'm finding that the following

using ModelTES, ARMA
t = ModelTES.LCLSII()
ΔT = 1e-6
ntotal = 2000 # How many lags to compute

nm = NoiseModel(t, ΔT, 5e-23)
wrong_covar = ARMA.model_covariance(nm, ntotal)
nm2 = ARMA.ARMAModel(nm.thetacoef * sqrt(0.5/ΔT), nm.phicoef)
correct_covar = ARMA.model_covariance(nm2, ntotal)

The noise model underestimates the covariance by a factor of 2ΔT. Bad!

Make compatible with Julia 0.7 and 1.0

I'm planning to tag a release, which can be the last one compatible with Julia 0.6, and then start a branch to remove the new deprecations in julia 0.7.

ggggggggg / modeltes.jl Goto Github PK

modeltes.jl's People

Contributors

Watchers

Forkers

modeltes.jl's Issues

Add use of package ARMA.jl to model TES noise

ModelTES needs to be able to compute piled-up pulses

Cannot make multiple pulses work

NoiseModel or model_covariance: one doesn't properly handle sampleTime argument

Make compatible with Julia 0.7 and 1.0

Recommend Projects

React

Vue.js

Typescript

TensorFlow

Django

Laravel

D3

Recommend Topics

javascript

web

server

Machine learning

Visualization

Game

Recommend Org

Facebook

Microsoft

Google

Alibaba

D3

Tencent