sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
b5ec4338c4778c7766bbe1ddb28a0c59e4b53698af9dcac96b9ab2973dbf6de1 | Julia | 3,255 | 76 | using DelayDiffEq
using DifferentialEquations
using BenchmarkTools
using MRIgeneralizedBloch
using MRIgeneralizedBloch: apply_hamiltonian_gbloch!, hamiltonian_linear
using Test
## set variables
α = (.01:.01:1) * π
TRF = 500e-6 # s
ω1 = α ./ TRF # rad/s
m0s = 0.1
m0f = 1-m0s
R1f = 0.3 # 1/s
R1s = 2.0 # 1/s
R2f = 1 / 50... |
fa18922acb29e750d1e5c5a21088f7f7cbda64d70872a883ee762ca209922971 | Julia | 3,316 | 123 | using ArgParse,Distributions,Random,CSV,RCall,DrWatson
using LinearAlgebra,BayesianNetworkRegression,DataFrames
include("sim_utils.jl")
function parse_CL_args()
args = ArgParseSettings()
@add_arg_table! args begin
"--seed", "-s"
help = "random seed to use for simulations"
arg_type = Int
... |
d0b4d97722ffc502343be7338347879179b0d37142685bd0cf1b5eee7677cdc1 | Julia | 3,483 | 90 | using DelayDiffEq
using DifferentialEquations
using Test
using FiniteDifferences
using MRIgeneralizedBloch
using MRIgeneralizedBloch:
apply_hamiltonian_gbloch_superlorentzian!
## set parameters
ω1 = π / 500e-6
ω0 = 200.0
B1 = 1
m0s = 0.15
R1f = 0.3
R1s = 2
R2f = 1 / 65e-3
T2s = 10e-6
Rex = 30
TRF = 500e-6
M0 = 0.... |
b6c02fe01000a9a983f6b165011999db53370bebea06796636d1facda99e9378 | Julia | 3,487 | 112 | ##
using MRIgeneralizedBloch
using DifferentialEquations
using StaticArrays
using Test
R2slT = precompute_R2sl()
## choose random parameters
Npulse = 500
α = abs.(π/2 * sin.(π/2 * ((0:Npulse-1) / Npulse) .+ π/2))
TRF = 300e-6 .+ 200e-6 * cos.(π * (1:Npulse) / Npulse)
α[1] = π
TRF[1] = 500e-6
ω1 = α ./ TRF
isInversionP... |
da9930c9b3016489dc47836c891d7ade16cbbe2743f9dd569723487e4622772b | Julia | 3,618 | 92 | using MRIgeneralizedBloch
using Test
using FiniteDifferences
using DelayDiffEq
using DifferentialEquations
using SpecialFunctions
using QuadGK
## set parameters
TRF = 500e-6
α = π
NSideLobes = 1
f_ω1 = (t) -> sinc(2(NSideLobes+1) * t/TRF - (NSideLobes+1)) * α / (sinint((NSideLobes+1)π) * TRF/π / (NSideLobes+1))
@test... |
5e535a6b0bb335dea09cd3aa233017e421a371d8e0d42ec6888e71fb5d4743cc | Julia | 3,827 | 95 | using DelayDiffEq
using DifferentialEquations
using SpecialFunctions
using QuadGK
using FiniteDifferences
using Test
using MRIgeneralizedBloch
## set parameters
TRF = 10.24e-3
γ = 267.522e6 # rad/s/T
ω₁ᵐᵃˣ = 13e-6 * γ # rad/s
μ = 5 # rad
β = 674.1 # 1/s
f_ω1 = (t) -> ω₁ᵐᵃˣ .* sech.(β * (t - TRF/2)) # rad/s
f_ω0 = (t)... |
e59bb0cf7676294ea8d2f3461afb6e0f18d5ffd54cce77a597a17fef8b06047c | Julia | 3,882 | 157 | """
greens_lorentzian(κ)
Evaluate the Green's function corresponding to a Lorentzian lineshape at `κ = (t-τ)/T2s`.
# Examples
```jldoctest
julia> t = 100e-6;
julia> τ = 0;
julia> T2s = 10e-6;
julia> greens_lorentzian((t-τ)/T2s)
4.5399929762484854e-5
```
"""
greens_lorentzian(κ) = exp(-κ)
lineshape_lorentzian(... |
c4446829e6f3fe9603b1c3be4d0abccf34f382aa8e8262fd71e3107c9dc434a7 | Julia | 3,887 | 158 | """
greens_lorentzian(κ)
Evaluate the Green's function corresponding to a Lorentzian lineshape at `κ = (t-τ)/T2s`.
# Examples
```jldoctest
julia> t = 100e-6;
julia> τ = 0;
julia> T2s = 10e-6;
julia> greens_lorentzian((t-τ)/T2s)
4.5399929762484854e-5
```
"""
greens_lorentzian(κ) = exp(-κ)
lineshape_lorentzian(... |
5910495880c071d20ce2ad023fa0e3452053b7a53ed35eb9af552aa14a0f0b7d | Julia | 3,892 | 133 |
# fit the em model in this model dict to data, using experiment parmas
# if parallel, make sure you already loaded toolbox and cores, etc.
function fit_model_em(this_model,data, to_save_folder; full = false, parallel = false, emtol = 1e-3, maxiter = 100, run_loo = false, save_res = true, return_res = false)
#... |
161d7feae21bfdc5774ac4d76e03399c2e008c34e3a37e7eed2f69bc04a60479 | Julia | 3,924 | 70 | cd(dirname(@__FILE__)) #to go to the directory where this file is saved
using Plots
using Dates #For current time with Dates.now()
using CSV #To read data files
using DataFrames #To read csv data files
#Load parameters and functions of the brain model
include("BrainParameters.jl") #load parameter values
using ..Main.... |
a585f74a0cc2646aca169e5a7881312dae00f0e73e8ef76331aa7151cb63e97d | Julia | 3,972 | 93 | cd(dirname(@__FILE__)) #to go to the directory where this file is saved
using Plots
using Dates #For current time with Dates.now()
using CSV #To read data files
using DataFrames #To read csv data files
#Load parameters and functions of the brain model
include("BrainParameters.jl") #load parameter values
using ..Main.... |
2e995c9472fd3b86bde253f8d02c5116d0d6d578274ccc164180258e355312ce | Julia | 4,041 | 93 | cd(dirname(@__FILE__)) #to go to the directory where this file is saved
using Plots
using Dates #For current time with Dates.now()
using CSV #To read data files
using DataFrames #To read csv data files
#Load parameters and functions of the brain model
include("BrainParameters.jl") #load parameter values
using ..Main.... |
fa02bd0f8720012e8bc2f1b71b92e063cc10cc3bf52efa31ed0cd52b1fa3138d | Julia | 4,342 | 152 | """
create_lower_tri(vec,V)
Create a lower triangluar matrix from a vector of the form [12, ... 1V,23,...(V-1)V]
in the form [11 0............]
[12 22 0.........]
[13 23 33 0.......]
[.................]
[1V .....(V-1)V VV]
# Arguments
- `vector`: vector containing ... |
2fbc9060caac80c657b66d4f428fa8f8b89c45df28f6e1c00dbf4a012612b58c | Julia | 4,348 | 147 | # julia EM model fitting, Nathaniel Daw 8/2019
###### setup
parallel = false # Run on multiple CPUs. If you are having trouble, set parallel = false: easier to debug
full = false # Maintain full covariance matrix (vs a diagional one) a the group level
emtol = 1e-3 # stopping condition (relative change) for EM
... |
fddc1dfc1cea9a875597ee82bb6c5d04b1fab1de27bc313a4971cf314f7b9436 | Julia | 4,394 | 90 | cd(dirname(@__FILE__)) #to go to the directory where this file is saved
using Plots
using Dates #For current time with Dates.now()
using CSV #To read data files
using DataFrames #To read csv data files
#Load parameters and functions of the brain model
include("BrainParameters.jl") #load parameter values
using ..Main.... |
0428f7a73f056fd59b1c5a3eb9fa69f50e5fe0feb166b0615f17e71213761b2c | Julia | 4,427 | 138 | ##
using MRIgeneralizedBloch
using DelayDiffEq
using DifferentialEquations
using SpecialFunctions
using QuadGK
using Test
## Pulse parameters
α = 0.8π
TRF = 100e-6
# define other parameters
B1 = 0.87
m0s = 0.2
m0f = 1 - m0s
R1f = 0.5
R1s = 3
R2f = 13
T2s = 12e-6
R2s = 1 / T2s
Rex = 17
# random initial m0
ms = 0.5m0s... |
4b87b61b156d217aca4dd9b25f06e42941eeddeb7f1afe8ed2c41189cb60c807 | Julia | 4,431 | 120 | ##
using MRIgeneralizedBloch
using DifferentialEquations
using StaticArrays
using Test
using FiniteDifferences
R2slT = precompute_R2sl()
## choose random parameters
Npulse = 500
α = abs.(π/2 * sin.(π/2 * ((0:Npulse-1) / Npulse) .+ π/2))
TRF = 300e-6 .+ 200e-6 * cos.(π * (1:Npulse) / Npulse)
α[1] = π
TRF[1] = 500e-6
ω1... |
53bb3c6891e57a607aa8c2f14791f64a42a5922f0138a5f5b78e7d773c4afe54 | Julia | 4,598 | 125 | ## Initial tests for BayesianNetworkRegression.jl on toy data
## global seed
rng = Xoshiro(1234)
X = [[0, 1, 0, 1,
1, 0, 1, 1,
0, 1, 0, 0,
0, 1, 0, 0],
[0, 1, 1, 1,
1, 0, 1, 1,
1, 1, 0, 0,
1, 1, 0, 0],
[0, 0, 1, 0,
0, 0, 1, 1,
1, 1, 0, 0,
0, 1, 0, 0],
[0, 0, 1,... |
41111215d41172326f4c7a88ab182d4c00a7ade3b6f74e3077c372bb0ad6c687 | Julia | 4,624 | 139 | ##
using MRIgeneralizedBloch
using DifferentialEquations
using SpecialFunctions
using QuadGK
using Test
## Pulse parameters
α = 0.8π
TRF = 100e-6
# define other parameters
B1 = 0.87
m0s = 0.2
m0f = 1 - m0s
R1f = 0.5
R1s = 3
R2f = 13
T2s = 12e-6
R2s = 1 / T2s
Rx = 17
# random initial m0
ms = m0s * rand()
mf = (1 - m0... |
ce5c8dcebb32e568f8b14f2e0164746d772f6ab9acdddf4a6b7c857d48ab3897 | Julia | 4,800 | 99 | cd(dirname(@__FILE__)) #to go to the directory where this file is saved
using Plots
using Dates #For current time with Dates.now()
using CSV #To read data files
using DataFrames #To read csv data files
#Load parameters and functions of the brain model
include("BrainParameters.jl") #load parameter values
using ..Main.... |
b91555f8f383c0123c686f15441c60d4a02e2dbb6f050300cb6ecbba153a9480 | Julia | 5,000 | 164 | using DifferentialEquations
using Test
using MRIgeneralizedBloch
using MRIgeneralizedBloch: hamiltonian_linear, propagator_linear_inversion_pulse
##
max_error = 1e-5
## set parameters
α = π
ω1 = α / 500e-6
ω0 = 200.0
B1 = 1.0
m0s = 0.15
R1f = 0.3
R2f = 1 / 65e-3
R1s = 2
T2s = 10e-6
Rx = 30.0
TRF = 500e-6
N = Inf
#... |
6c0745876e3acaa32ad1c11998327c764bd161075e42737a29d35fd660d89a73 | Julia | 5,029 | 222 | # julia EM model fitting, Nathaniel Daw 8/2019
# example likelihood functions
function qlik(params,data)
# basic rescorla wagner learning
beta = params[1]
lr = 0.5 + 0.5 * erf(params[2] / sqrt(2))
# It is important that we specify the type of Q below, as corresponding to the type
# of the input parameters
# Thi... |
bb6a6a71fbd0a5eae0cd31c93118e3c3dd8056755cc6af68b5208e6c8c74d2c8 | Julia | 5,051 | 114 | cd(dirname(@__FILE__)) #to go to the directory where this file is saved
using Plots
using Dates #For current time with Dates.now()
using CSV #To read data files
using DataFrames #To read csv data files
#Load parameters and functions of the brain model
include("BrainParameters.jl") #load parameter values
using ..Main.... |
55d95ae2f781cfa2da52a4aa427515a36725d120382703339828bd51dd005cce | Julia | 5,068 | 114 | cd(dirname(@__FILE__)) #to go to the directory where this file is saved
using Plots
using Dates #For current time with Dates.now()
using CSV #To read data files
using DataFrames #To read csv data files
#Load parameters and functions of the brain model
include("BrainParameters.jl") #load parameter values
using ..Main.... |
8c7387f59237c31af32d62be0608e3b3b31d946a4f18384e36db555a44cb2e2b | Julia | 5,106 | 176 | ###########################################################################
## THIS IMPLEMENTATION OF SAMPLING FROM THE GeneralizedInverseGaussian ##
## DISTRIBUTION IS TRANSLATED INTO JULIA DIRECTLY FROM ##
## Josef Leydold and Wolfgang Hormann's GIGrvg PACKAGE FOR R ##
################... |
0b0e58a9defb47c35b6c8034b8fcf41c8a7759132a0889636ba9099d0f752ba1 | Julia | 5,135 | 114 | cd(dirname(@__FILE__)) #to go to the directory where this file is saved
using Plots
using Dates #For current time with Dates.now()
using CSV #To read data files
using DataFrames #To read csv data files
#Load parameters and functions of the brain model
include("BrainParameters.jl") #load parameter values
using ..Main.... |
00aa79b582e5b78629c9c004d387596ad32bf62addbc762145bee3348a3864bf | Julia | 5,150 | 151 | using MRIgeneralizedBloch
using Test
using LinearAlgebra
using StaticArrays
using FiniteDifferences
R2slT = precompute_R2sl()
function calc_CRB(ω1, TRF, w, grad_moment)
_, gradients = simulate_linearapprox(ω1 .* TRF, TRF, TR, ω0, B1, 1, m0s, R1f, R2f, Rex, R1s, T2s, R2slT; grad_list, grad_moment)
F = real.(gra... |
c8d47e01f660d16c19fd0c78da9861de4e927ad7e472b2636c6d465822afb2b1 | Julia | 5,237 | 134 | #md # [](@__BINDER_ROOT_URL__/build_literate/NLLS.ipynb)
# # Non-Linear Least Square Fitting
# This section gives a brief overview of the interface to fit the generalized Bloch model to [hybrid-state free precession](https://www.nature.com/articles/s42005-019-0174-0) data. We us... |
35064c05bb687064ebe4c447fc7904e0e996c6bc459e545c9f019e829ccbd1a0 | Julia | 5,390 | 135 | #md # [](@__BINDER_ROOT_URL__/build_literate/NLLS.ipynb)
# # Non-Linear Least Square Fitting
# This section gives a brief overview of the interface to fit the generalized Bloch model to [hybrid-state free precession](https://www.nature.com/articles/s42005-019-0174-0) data. We us... |
6dcdbef76947bd32326465bf9ec87d7de3a016c5524673415ede9ac26966d5c8 | Julia | 5,435 | 206 | using DataFrames,CSV
unreal_psrf = DataFrame()
real_psrf = DataFrame()
normed_psrf = DataFrame()
for flnm in readdir("results/simulation/local/unrealistic-results")
if !occursin("psrf",flnm)
continue
end
mu = flnm[last(findfirst("mu=",flnm))+1:last(findfirst("mu=",flnm))+3]
pi_ = flnm[last(fin... |
991a7da0f31a36b6d409a6c82b1aa65aad522d8f1d1d44b15c6884c7c6b972b3 | Julia | 5,672 | 155 | using DifferentialEquations
using Test
using MRIgeneralizedBloch
using MRIgeneralizedBloch: hamiltonian_linear, propagator_linear_crushed_pulse
using FiniteDifferences
##
max_error = 1e-5
## set parameters
α = π
ω1 = α / 500e-6
ω0 = 200.0
B1 = 1.0
M0 = 0.85
m0s = 0.15
R1f = 0.3
R2f = 1 / 65e-3
R1s = 2.
T2s = 10e-6
R... |
440bc216752ee0d63f8db0694378eb74211f6322b83cf109bb99de0f472a0850 | Julia | 5,673 | 136 | cd(dirname(@__FILE__)) #to go to the directory where this file is saved
using Plots
using Dates #For current time with Dates.now()
using CSV #To read data files
using DataFrames #To read csv data files
#Load parameters and functions of the brain model
include("BrainParameters.jl") #load parameter values
using ..Main.... |
b2e7b427bc45247d12bfbebc4b4ae235c85684a32cd8759a493fa8d2e74b1e2e | Julia | 5,733 | 145 | cd(dirname(@__FILE__)) #to go to the directory where this file is saved
using Plots
using Dates #For current time with Dates.now()
using CSV #To read data files
using DataFrames #To read csv data files
#Load parameters and functions of the brain model
include("BrainParameters.jl") #load parameter values
using ..Main.... |
5f8c7f4fdc5d255f86b363c60ce7564fcd63be79ab136936fdf3c4176f309d32 | Julia | 5,840 | 141 | cd(dirname(@__FILE__)) #to go to the directory where this file is saved
using Plots
using Dates #For current time with Dates.now()
using CSV #To read data files
using DataFrames #To read csv data files
#Load parameters and functions of the brain model
include("BrainParameters.jl") #load parameter values
using ..Main.... |
163aac555536bfc5475207e4f6395faebb41ac3c4471fc3cf4085cdde6f7a0ec | Julia | 6,070 | 130 | function d_hamiltonian_linear_dω1(B1, T, dR2sdω1)
H = @SMatrix [
0 0 B1 0 0 0;
0 0 0 0 0 0;
-B1 0 0 0 0 0;
0 0 0 -dR2sdω1 B1 0;
0 0 0 -B1 0 0;
0 0 0 0 0 0]
return H * T
end
functio... |
596d8f05f09c0d86db910492e575a672a2b2848c424e330629c5531e280b9b5a | Julia | 6,369 | 122 | cd(dirname(@__FILE__)) #to go to the directory where this file is saved
using Plots
using Dates #For current time with Dates.now()
using CSV #To read data files
using DataFrames #To read csv data files
#Load parameters and functions of the brain model
include("BrainParameters.jl") #load parameter values
using ..Main.... |
bfd2e1b2accecd1a0e24e3111c8e833aec701863cca0c27c032b5898335f05f7 | Julia | 6,418 | 109 | #md # [](@__BINDER_ROOT_URL__/build_literate/tutorial_pulsetrain.ipynb)
# # Balanced Hybrid-State Free Precession Pulse Sequence
# This section explains the interface for calculating the spin evolution during a train of RF pulses, assuming balanced gradient moments (cf. [Hybrid-... |
2c586b0d4d4626407f2047ed913bd08979a6e7d2fbaabe8578cd8d3d68b27323 | Julia | 6,422 | 181 | ##
using MRIgeneralizedBloch
using DifferentialEquations
using SpecialFunctions
using QuadGK
using LinearAlgebra
using Test
## Pulse parameters
α = 5π
TRF = 10.24e-3
# define other parameters
B1 = 0.87
m0s = 0.2
m0f = 1 - m0s
R1f = 0.5
R1s = 3
R2f = 13
T2s = 12e-6
R2s = 1 / T2s
Rx = 17
# random initial m0
ms = m0s *... |
3f9e02920c83b07a2be45cbb167e3955d4579a7479a626b77ea8faf1e1fa222c | Julia | 6,605 | 238 |
### This file generates the "true" results used in init-tests.jl, toy-generate-samples-test.jl, and test1-generate-samples-test.jl
import InteractiveUtils: versioninfo
using BayesianNetworkRegression,Test,LinearAlgebra,Distributions
using CSV,DataFrames,StaticArrays,TypedTables,Random,Distributed,JLD2
## Initial tes... |
2e87221a8ebe062a20ed9babd8977f77f7bc6e90e2263ecbf16a66516ce7f84c | Julia | 6,606 | 109 | #md # [](@__BINDER_ROOT_URL__/build_literate/tutorial_pulsetrain.ipynb)
# # Balanced Hybrid-State Free Precession Pulse Sequence
# This section explains the interface for calculating the spin evolution during a train of RF pulses, assuming balanced gradient moments (cf. [Hybrid-... |
123459aedaa7c3b6940c07c1f87ca8384a7996fcdf3cad49c84bd45e6f0011ea | Julia | 6,671 | 147 | """
precompute_R2sl([;TRF_min=100e-6, TRF_max=500e-6, T2s_min=5e-6, T2s_max=21e-6, ω1_max=π/TRF_max, B1_max=1.5, greens=greens_superlorentzian])
Pre-compute and interpolate the linearized `R2sl(TRF, α, B1, T2s)` and its derivatives `dR2sldB1(TRF, α, B1, T2s)`, `R2sldT2s(TRF, α, B1, T2s)` etc. in the range specifie... |
411ede89241df7ffbf06cb3841ea308bb79df5ab0bcf858db8ceddb8dad920d4 | Julia | 6,673 | 182 | ##
using MRIgeneralizedBloch
using DelayDiffEq
using DifferentialEquations
using SpecialFunctions
using QuadGK
using LinearAlgebra
using Test
## Pulse parameters
α = 5π
TRF = 10.24e-3
# define other parameters
B1 = 0.87
m0s = 0.2
m0f = 1 - m0s
R1f = 0.5
R1s = 3
R2f = 13
T2s = 12e-6
R2s = 1 / T2s
Rex = 17
# random in... |
cfdc7dbe087e043f04d5a21fd829c2cfe047ff876d2bfbd13bf73efb3490ac62 | Julia | 6,678 | 222 | using CSV
using DataFrames
using DataFramesMeta
using CategoricalArrays
using Gadfly
using Statistics
using Distributions
using SpecialFunctions
using StatsFuns
using Optim
using ForwardDiff
using Cairo
using Fontconfig
cd("/Users/evanrussek/foraging/")
# basic real data clearning, etc, functions...
include("/Users/e... |
e3da435c19d7b5f3b50889869335957ee0bb5c4213d0d4cbaed1e71a9ff3e1ba | Julia | 6,840 | 147 | """
R2slInterpolants
Struct holding interpolated R2sl and its partial derivatives. Returned by
[`precompute_R2sl`](@ref). All fields are callable as `f(TRF, α, B1, T2s)`.
# Fields
- `R2sl`: R2sl(TRF, α, B1, T2s)
- `dR2sl_dT2s`: ∂R2sl/∂T2s
- `dR2sl_dB1`: ∂R2sl/∂B1
- `dR2sl_dω1`: ∂R2sl/∂ω1
- `dR... |
2d34ba7ad844332eb36d383aff65d570a69e0a7ace3a7fcdc85537536c3234ef | Julia | 6,993 | 172 | cd(dirname(@__FILE__)) #to go to the directory where this file is saved
using Plots
using Dates #For current time with Dates.now()
using CSV #To read data files
using DataFrames #To read csv data files
#Load parameters and functions of the brain model
include("BrainParameters.jl") #load parameter values
using ..Main.... |
801c96eda0cfb9fd0e947fb6bda2664c7ca6d7769b88ff4250c869583cd5f0f8 | Julia | 7,034 | 143 | #md # [](@__BINDER_ROOT_URL__/build_literate/Analyze_NMR_PreSat_Data.ipynb)
# # Continuous Wave Saturation Experiments
# The following code analyzes data from a steady-state experiment similar to the original work of [Henkelman et al](https://doi.org/10.1002/mrm.1910290607). In ... |
b9e660e7e7bbc72878ac7c9417e3312afa9582164df18c3a1cb7d11d8007e18a | Julia | 7,041 | 143 | #md # [](@__BINDER_ROOT_URL__/build_literate/Analyze_NMR_PreSat_Data.ipynb)
# # Continuous Wave Saturation Experiments
# The following code analyzes data from a steady-state experiment similar to the original work of [Henkelman et al](https://doi.org/10.1002/mrm.1910290607). In ... |
f26c59a66c056264ffa61f4ccd3c2408178472b6317f1bf7243a159e33ae83c1 | Julia | 7,045 | 174 | include("ImpedanceAnalyzer_Functions.jl")
function MagPhaseBoxDotImpedanceStats_AgainstKnown(ZMagVec::Vector,ZPhaseVec::Vector,KnownImpedance::Complex,newPlot,labelVal,CVal)
plt = [];
ZMagNormalized = ZMagVec ./ abs(KnownImpedance)
KnownRealVec = repeat([real(KnownImpedance)], Int(length(ZMagVec)))
... |
763adb7618af5fd0b28431cf6f841ed757d60f9a79bee2828660e8fd0166a195 | Julia | 7,086 | 117 | cd(dirname(@__FILE__)) #to go to the directory where this file is saved
using Plots
using Dates #For current time with Dates.now()
using CSV #To read data files
using DataFrames #To read csv data files
#Load parameters and functions of the brain model
include("BrainParameters.jl") #load parameter values
using ..Main.... |
a9b1833dea59cfd1aa3f3ba49ef2cb2b56c82ed2d34f8af501153033abcae554 | Julia | 7,280 | 181 | cd(dirname(@__FILE__)) #to go to the directory where this file is saved
using Plots
using Dates #For current time with Dates.now()
using CSV #To read data files
using DataFrames #To read csv data files
#Load parameters and functions of the brain model
include("BrainParameters.jl") #load parameter values
using ..Main.... |
e99b3f3576ac93f5e829f235a49686968342b2b89377cb2eebb566c3161ff9c4 | Julia | 7,505 | 243 | using Base: String, Int64, simd_outer_range
using ArgParse,Distributions,Random,CSV,RCall,DrWatson
using LinearAlgebra,BayesianNetworkRegression,DataFrames,PhyloNetworks
include("sim_utils.jl")
function parse_CL_args()
args = ArgParseSettings()
@add_arg_table! args begin
"--seed", "-s"
help = "rand... |
b2b2a46499e3241a36f909bb6c411176d234a64115f97408cfcd7a37105850a3 | Julia | 7,667 | 209 | # Old tests; need to include them as files into runtests.jl; one by one.
import InteractiveUtils: versioninfo
using BayesianNetworkRegression,Test,LinearAlgebra,Distributions
using CSV,DataFrames,StaticArrays,TypedTables,Random,Distributed
seed = 2358
η = 1.01
ζ = 1.0
ι = 1.0
R = 7
aΔ = 1.0
bΔ = 1.0
ν = 10
@test... |
188a7c2166932147555c3e0a503c324d16c2cd0aa809e6bc68142c6b97188864 | Julia | 8,028 | 135 | cd(dirname(@__FILE__)) #to go to the directory where this file is saved
using Plots
using Dates #For current time with Dates.now()
using CSV #To read data files
using DataFrames #To read csv data files
#Load parameters and functions of the brain model
include("BrainParameters.jl") #load parameter values
using ..Main.... |
4dcf1a006aeddfc0d1f1dad52e70aba84bba566f579b18798799650a2063acf0 | Julia | 8,228 | 292 | using DrWatson
@quickactivate
using LinearAlgebra
using CSV,ArgParse
using Random, DataFrames, StatsBase, InvertedIndices, ProgressMeter, Distributions
using StaticArrays,TypedTables
using BayesianNetworkRegression,DrWatson,MCMCDiagnosticTools,JLD2,Distributed,DrWatson
MIN_GEN = 10000 #10000 #20000
MAX_GEN = 200000 #... |
077afd24004a5f2111074878d9e300696dea7ed1d8b8ccb0b18da76722304700 | Julia | 8,270 | 165 | #md # [](@__BINDER_ROOT_URL__/build_literate/Linear_Approximation.ipynb)
# # Linear Approximation
# The following code demonstrates the linear approximation of the generalized Bloch model and replicates Figs. 7 and 8 in the paper.
# For this analysis we need the following packa... |
e6d7b8094c4b30032d8e9ed6e75d18b792ce6d0f373008b72f8edb3cef910541 | Julia | 8,374 | 166 | #md # [](@__BINDER_ROOT_URL__/build_literate/Linear_Approximation.ipynb)
# # Linear Approximation
# The following code demonstrates the linear approximation of the generalized Bloch model and replicates Figs. 7 and 8 in the paper.
# For this analysis we need the following packa... |
f94cca995181aa42a0e8f11367239bd3852d2f76c1e805ac909e9cb9df5c2020 | Julia | 8,399 | 149 | #md # [](@__BINDER_ROOT_URL__/build_literate/OCT.ipynb)
# # Optimal Control
# This section provides a brief introduction to the package's interface for sequence optimization. We use the [Cramer-Rao bound](https://en.wikipedia.org/wiki/Cramér–Rao_bound) (CRB) to assess a sequence... |
88dcba4366c9024eac03040aef86c2e10398a0ac931f2203e188af143ad26cc7 | Julia | 8,557 | 183 | cd(dirname(@__FILE__)) #to go to the directory where this file is saved
using Plots
using Dates #For current time with Dates.now()
using CSV #To read data files
using DataFrames #To read csv data files
#Load parameters and functions of the brain model
include("BrainParameters.jl") #load parameter values
using ..Main.... |
89c5c2f632712eb818d7af841f67051645be0c8b0a00aeb8fac95d378995d34e | Julia | 8,755 | 150 | #md # [](@__BINDER_ROOT_URL__/build_literate/OCT.ipynb)
# # Optimal Control
# This section provides a brief introduction to the package's interface for sequence optimization. We use the [Cramer-Rao bound](https://en.wikipedia.org/wiki/Cramér–Rao_bound) (CRB) to assess a sequence... |
da44fb49a1b0a1fee6f684c22ee3c742ac7bc150f3761a18de62c710177b9704 | Julia | 8,813 | 258 | using DifferentialEquations
using Test
using MRIgeneralizedBloch
using MRIgeneralizedBloch: apply_hamiltonian_freeprecession!, apply_hamiltonian_gbloch!
##
max_error = 5e-2
## set parameters
ω0 = 200.0
m0s = 0.15
R1f = 0.3
R2f = 1 / 65e-3
R1s = 2
T2s = 10e-6
Rx = 30.0
TE = 3.5e-3
mfun(p, t; idxs=nothing) = typeof(idx... |
af969a438ec5c8245509fc0dafca7a1f5788f772e08ff036e89b405a97e314f0 | Julia | 8,849 | 220 | ##
"""
hamiltonian_linear(ω1, B1, ω0, T, M0, m0s, R1f, R2f, Rex, R1s, R2s[, dR2sdT2s, dR2sdB1, grad_type])
Calculate the hamiltonian of the linear approximation of the generalized Bloch model.
If no gradient is supplied, it returns a 6x6 (static) matrix with the dimensions (in this order) `[xf, yf, zf, xs, zs, 1]... |
a3524177c544cc28ef454e2f32b537b27ab427db5bda6f69c1c0087477d5281d | Julia | 8,983 | 210 |
"""
qM = fit_gBloch(data, α, TRF, TR;
reM0 = (-Inf, 1, Inf),
imM0 = (-Inf, 0, Inf),
m0s = ( 0, 0.2, 1),
R1f = ( 0, 0.3, Inf),
R2f = ( 0, 15, Inf),
Rx = ( 0, 20, Inf),
R1s = ( 0, 3, Inf),
T2s = (8e-6,1e-5,12e-6),
... |
d4ea977b71c4a712977faa3862f878372c17c5baa19b0bf779ba738888dfda52 | Julia | 9,039 | 270 | using CSV,ArgParse
using Random, DataFrames, StatsBase, InvertedIndices, ProgressMeter, Distributions
using StaticArrays,TypedTables
#using BayesianNetworkRegression,DrWatson,MCMCDiagnosticTools,JLD2,Distributed
using DrWatson,MCMCDiagnosticTools,JLD2,Distributed
include("../../BayesianNetworkRegression.jl/src/Bayesia... |
c930043429394e277be60bce643d44a17600955fb709889ea02c63f9f921effe | Julia | 9,336 | 278 | using LinearAlgebra
using CSV,ArgParse
using Random, DataFrames, StatsBase, InvertedIndices, ProgressMeter, Distributions
using StaticArrays,TypedTables
using BayesianNetworkRegression,DrWatson,MCMCDiagnosticTools,JLD2,Distributed
addprocs(3,exeflags=["--optimize=0","--math-mode=ieee","--check-bounds=yes"])
@everyw... |
ecd1ad5fbeb997a3733ed6c13333522c516717fe7938f2b2e0c0092ee7146891 | Julia | 9,377 | 275 | using LinearAlgebra
using CSV,ArgParse
using Random, DataFrames, StatsBase, InvertedIndices, ProgressMeter, Distributions
using StaticArrays,TypedTables
using BayesianNetworkRegression,DrWatson,MCMCDiagnosticTools,JLD2
#include("../BayesianNetworkRegression.jl/src/gelmandiag.jl")
function parse_CL_args()
args = ... |
baeff3dfface4ba95d3d7640faeade463b03ba90427d3c92348e4ba9813d78f2 | Julia | 9,518 | 177 | #md # [](@__BINDER_ROOT_URL__/build_literate/Simulation_ContinuousWave.ipynb)
# # Continuous Wave Simulation
# The following code replicates the continuous wave simulation of Fig. 2 and is slightly more comprehensive in the sense that all discussed models are simulated.
# For ... |
4d1d5b367fbfa21c1e536d2db3f6c3beea289174f23b5713b4a2bb03a8be5c9b | Julia | 9,613 | 178 | #md # [](@__BINDER_ROOT_URL__/build_literate/Simulation_ContinuousWave.ipynb)
# # Continuous Wave Simulation
# The following code replicates the continuous wave simulation of Fig. 2 and is slightly more comprehensive in the sense that all discussed models are simulated.
# For t... |
d52ce76c5cdeb8d189c7db13af15ec68a3d3c0e812fff52cadee10991c6c9438 | Julia | 9,676 | 247 | using Pkg
Pkg.develop(PackageSpec(path=pwd()))
Pkg.instantiate()
using Symbolics
using Symbolics: SConst
using MRIgeneralizedBloch
##
@variables ω1, B1, ω0, T, M0, m0s, R1f, R2f, Rex, R1s, R1a, T2s, R2s, dR2sdT2s, dR2sdB1, grad_type
@variables TRF, dR2sdB1, dR2sdω1, dR2sdTRF, dR2sdT2sdω1, dR2sdB1dω1, dR2sdT2sdTRF, dR2... |
128770cee826453c56c070575cd666d481cf0dc97dc593dadd833c86999a71ec | Julia | 9,793 | 288 | using CSV,ArgParse
using Random, DataFrames, StatsBase, InvertedIndices, ProgressMeter, Distributions
using StaticArrays,TypedTables
#using BayesianNetworkRegression,DrWatson,MCMCDiagnosticTools,JLD2,Distributed
using DrWatson,MCMCDiagnosticTools,JLD2,Distributed
include("../../BayesianNetworkRegression.jl/src/Bayesia... |
791552c6ba146bca4ec89a355dbf34289a791be2ac22a5667f3038fa999812ef | Julia | 9,793 | 288 | using CSV,ArgParse
using Random, DataFrames, StatsBase, InvertedIndices, ProgressMeter, Distributions
using StaticArrays,TypedTables
#using BayesianNetworkRegression,DrWatson,MCMCDiagnosticTools,JLD2,Distributed
using DrWatson,MCMCDiagnosticTools,JLD2,Distributed
include("../../BayesianNetworkRegression.jl/src/Bayesia... |
62eb68533736d3308f753ed818e63597a29b5c5139c3b3bd5dfc10998e203c53 | Julia | 9,808 | 285 | using DifferentialEquations
using SpecialFunctions
using QuadGK
using Test
using MRIgeneralizedBloch
## set parameters
max_error = 5e-2
TRF = 500e-6
α = π
# f_ω1(t) = α / 500e-6
NSideLobes = 1
f_ω1(t) = sinc(2(NSideLobes+1) * t/TRF - (NSideLobes+1)) * α / (sinint((NSideLobes+1)π) * TRF/π / (NSideLobes+1))
@test quad... |
72c96deee26d5496af294de2c017e17b2ebdba2bceacec608f80273f43ffc0ba | Julia | 9,825 | 281 | using DifferentialEquations
using SpecialFunctions
using QuadGK
using Test
using MRIgeneralizedBloch
## set parameters
max_error = 5e-2
TRF = 10.24e-3
γ = 267.522e6 # rad/s/T
ω₁ᵐᵃˣ = 13e-6 * γ # rad/s
μ = 5 # rad
β = 674.1 # 1/s
f_ω1(t) = ω₁ᵐᵃˣ .* sech.(β * (t - TRF/2)) # rad/s
f_ω0(t) = -μ * β * tanh.(β * (t - TRF/... |
0abd9513fc3bbd9f525e8d7b894444c91c67508008f01922df02c2242776546a | Julia | 9,931 | 327 | using DifferentialEquations
using Test
using MRIgeneralizedBloch
using MRIgeneralizedBloch: apply_hamiltonian_graham_superlorentzian!, apply_hamiltonian_linear!
##
max_error = 5e-2
## set parameters
ω1 = π / 500e-6
ω0 = 200.0
B1 = 1.0
m0s = 0.15
R1f = 0.3
R1s = 2.0
R2f = 1 / 65e-3
T2s = 10e-6
Rx = 30.0
TRF = 500e-6
... |
8b98f0d72553b0f8d3bf35741067ae7c5f0826b9b9bc093a9d3db8c7b353f3e8 | Julia | 10,204 | 407 | using ArgParse,RCall,TickTock
using BayesianNetworkRegression,StatsBase,DataFrames
include("../old-src/plot_output.jl")
function parse_CL_args()
args = ArgParseSettings()
@add_arg_table! args begin
"--nburn", "-b"
help="Number of burn-in Gibbs samples to take"
arg_type = Int
defaul... |
ad28089dcc76db47fc239ddf47f81aba29d4ef97a9d570f9a443744273dd0144 | Julia | 10,323 | 353 | # julia EM model fitting, Nathaniel Daw 8/2019
#### basic fitting routine
function em(data,subs,X,betas,sigma::Vector,likfun; emtol=1e-4, parallel=false, startx = [], maxiter=100, quiet=false, full=false)
if full
return em(data,subs,X,betas,Matrix(Diagonal(sigma)),likfun; emtol=emtol, parallel=parallel, startx = s... |
3050761e83f2ae1b7d43a78c2bcc9839c298e3c3e8f917921fbdc5ede42bd140 | Julia | 10,433 | 312 | using LinearAlgebra
using CSV,ArgParse
using Random, DataFrames, StatsBase, InvertedIndices, ProgressMeter, Distributions
using StaticArrays,TypedTables
#using BayesianNetworkRegression,DrWatson,MCMCDiagnosticTools,JLD2,Distributed
using DrWatson,MCMCDiagnosticTools,JLD2,Distributed
include("../../BayesianNetworkRegr... |
59832604bc161541613ebe124773e284ebdd4b375e1a4081f284efe9d0492f0a | Julia | 10,437 | 312 | using LinearAlgebra
using CSV,ArgParse
using Random, DataFrames, StatsBase, InvertedIndices, ProgressMeter, Distributions
using StaticArrays,TypedTables
#using BayesianNetworkRegression,DrWatson,MCMCDiagnosticTools,JLD2,Distributed
using DrWatson,MCMCDiagnosticTools,JLD2,Distributed
include("../../BayesianNetworkRegr... |
b78b2b2864ede41e8121fe34571ad2334bed43acbd42f24d75c24d3fedd6a7df | Julia | 10,652 | 321 | using LinearAlgebra
using CSV,ArgParse
using Random, DataFrames, StatsBase, InvertedIndices, ProgressMeter, Distributions
using StaticArrays,TypedTables
#using BayesianNetworkRegression,DrWatson,MCMCDiagnosticTools,JLD2,Distributed
using DrWatson,MCMCDiagnosticTools,JLD2,Distributed
include("../../BayesianNetworkRegr... |
19bd3754c31050628c73a5f67a4c78fb3e9709f1133845a46b63e0da2c633dac | Julia | 10,746 | 400 | using DifferentialEquations
using Test
using MRIgeneralizedBloch
using MRIgeneralizedBloch:
apply_hamiltonian_gbloch_superlorentzian!
##
max_error = 5e-2
## set parameters
ω1 = π / 500e-6
ω0 = 200.0
B1 = 1.0
m0s = 0.15
R1f = 0.3
R1s = 2.0
R2f = 1 / 65e-3
T2s = 10e-6
Rx = 30.0
TRF = 500e-6
mfun(p, t; idxs = nothin... |
2fe3c885be968f806e1a32c751996e173fa2c62950dfbfc0f445ff611bc1a198 | Julia | 10,929 | 192 | ############################################################################
# main call function
############################################################################
"""
calculatesignal_linearapprox(α, TRF, TR, ω0, B1, m0s, R1f, R2f, Rx, R1s, T2s, R2slT[; grad_list=[undef], rfphase_increment=[π], m0=:perio... |
3b61626d061aa687ead0225ebf1c72beb2ac18c63996ccc1befb7339e40a8e72 | Julia | 11,220 | 228 | #md # [](@__BINDER_ROOT_URL__/build_literate/Simulation_Pulse.ipynb)
# # RF-Pulse Simulation
# The following code replicates the RF-pulse simulation of Fig. 3 and plots the ``z^s``-magnetization at the end of respective pulse.
# For these simulations we need the following packa... |
8c81f2c39b11e5884af0c8326deb5d3a910611926db3f87d08a50bd43d684157 | Julia | 11,434 | 229 | #md # [](@__BINDER_ROOT_URL__/build_literate/Simulation_Pulse.ipynb)
# # RF-Pulse Simulation
# The following code replicates the RF-pulse simulation of Fig. 3 and plots the ``z^s``-magnetization at the end of respective pulse.
# For these simulations we need the following packa... |
9f0f9fe8a0b08e41533ddaefdeac9b0f4b0c4490a117a5234aa0381e17813c3e | Julia | 11,821 | 310 | using LinearAlgebra
using CSV,ArgParse
using Random, DataFrames, StatsBase, InvertedIndices, ProgressMeter, Distributions
using StaticArrays,TypedTables
#using BayesianNetworkRegression,DrWatson,MCMCDiagnosticTools,JLD2,Distributed
using DrWatson,MCMCDiagnosticTools,JLD2,Distributed
include("../../BayesianNetworkRegr... |
db76222ba12b3afbe316139add3877a4db444bbd9c7d2c7510e45e194ed3579a | Julia | 12,205 | 301 | ####################################################################
# Bounded ω1 and TRF
####################################################################
"""
ω1, TRF = get_bounded_omega1_TRF(x)
Transform a vector of length `2Npulses` with values in the range `[-Inf, Inf]` into two vectors of length `Npulses`,... |
76cb47b13c521ee0564c661e5ed175714068a94d60bbabc11d04e10887643927 | Julia | 12,319 | 275 |
"""
qM = fit_gBloch(data, α, TRF, TR;
reM0 = (-Inf, 1, Inf),
imM0 = (-Inf, 0, Inf),
m0s = ( 0, 0.2, 1),
R1f = ( 0, 0.3, Inf),
R2f = ( 0, 15, Inf),
Rex = ( 0, 20, Inf),
R1s = ( 0, 3, Inf),
T2s = (8e-6,1e-5,12e-6),
... |
ab3d0aab38f0a1a6a11d69cac3f8b8e194fae69c0e7cc64ea37bd5b77166a2d8 | Julia | 12,379 | 210 | ############################################################################
# main call function
############################################################################
"""
simulate_linearapprox(α, TRF, TR, ω0, B1, M0, m0s, R1f, R2f, Rex, R1s, T2s, R2slT[; grad_list=nothing, rfphase_increment=π, m0=:periodic,... |
562dc7c96312ebdb1071630de58a69627bb6d3404d724571f7c82d39ca23ddb1 | Julia | 12,618 | 377 | ####################################################################
# Bounded ω1 and TRF
####################################################################
"""
ω1, TRF = get_bounded_ω1_TRF(x; ω1_min = 0, ω1_max = 2e3π, TRF_min = 100e-6, TRF_max = 500e-6)
Transform a vector of length `2Npulses` with values in th... |
829e4f6d13166f758bf102f839b3cc5b4156f71aade4f0c8ee5b13d8a1ead2aa | Julia | 13,172 | 426 |
using SpecialFunctions # for erf
using StatsFuns
using DataFrames
using DataFramesMeta
using Distributions
#####################################################
#####################################################
# HELPER FUNCTIONS TO COMPUTE LIKELIHOOD #########
#################################################... |
073e81fac43f276dc73b919c457b41432c86daafb203a803397e99027e4d34e7 | Julia | 13,239 | 271 | cd(dirname(@__FILE__)) #to go to the directory where this file is saved
using Plots
using Dates #For current time with Dates.now()
using CSV #To read data files
using DataFrames #To read csv data files
#Load parameters and functions of the brain model
include("BrainParameters.jl") #load parameter values
using ..Main.... |
96aa778e92baa805ff629cc13f60e883d49f036183997e05efa797691eeeb6fb | Julia | 13,251 | 271 | cd(dirname(@__FILE__)) #to go to the directory where this file is saved
using Plots
using Dates #For current time with Dates.now()
using CSV #To read data files
using DataFrames #To read csv data files
#Load parameters and functions of the brain model
include("BrainParameters.jl") #load parameter values
using ..Main.... |
a5ea5533d980811034793c17ae41d2a0c1193d5e8ea066f9822553cc0b7b8249 | Julia | 13,265 | 376 | cd(dirname(@__FILE__)) #to go to the directory where this file is saved
using Distributions
using Plots
using LazySets, Polyhedra #For convex hull
using Dates #For current time with Dates.now()
include("BrainParameters.jl")
using ..Main.BrainParameters
include("BrainInitialConditions.jl") #load initial conditions
usi... |
5f75076ef04c617a0f704f4ee2b6aaf7008753d03650de2a40c33bb69deda6b5 | Julia | 13,482 | 274 | cd(dirname(@__FILE__)) #to go to the directory where this file is saved
using Plots
using Dates #For current time with Dates.now()
using CSV #To read data files
using DataFrames #To read csv data files
#Load parameters and functions of the brain model
include("BrainParameters.jl") #load parameter values
using ..Main.... |
fae007494f65ded1c1e6fbd3b8c5b078d320717098230e221f27de96dd77f3db | Julia | 13,803 | 429 | ### want to use this to do the UWS and the Probability Sampling...
include("$project_folder/model_fitting/likfuns2.jl")
using Distributions
using StatsFuns
function binomial_pdf(n,N, p)
#print(n)
return binomial(N, n)*(p^n)*(1-p)^(N-n)
end
function ordered_probit_pmf(k, n_in, c_in, beta_in)
# printl... |
b3dc9dd3c0027b198bc3125899e648d57e4aa5ae44928e1c47e45170ba24e10b | Julia | 13,833 | 289 | cd(dirname(@__FILE__)) #to go to the directory where this file is saved
using Plots
using Dates #For current time with Dates.now()
using CSV #To read data files
using DataFrames #To read csv data files
#Load parameters and functions of the brain model
include("BrainParameters.jl") #load parameter values
using ..Main.... |
95c8326bbbc2ad8ef11a629bb877578c20f694e2c80d0c525f3e907ca1af70a4 | Julia | 14,771 | 196 | #md # [](@__BINDER_ROOT_URL__/build_literate/tutorial_singlepulse.ipynb)
# # Simulation of a Single RF Pulse
# The core of generalized Bloch model is implemented in the function [`apply_hamiltonian_gbloch!(∂m∂t, m, mfun, p, t)`](@ref), which calculates the derivative `∂m/∂t` fo... |
3cfd538fa5820a159014990bacd3a1ae2ed66b1b0494160d886dbee18a405171 | Julia | 14,938 | 197 | #md # [](@__BINDER_ROOT_URL__/build_literate/tutorial_singlepulse.ipynb)
# # Simulation of a Single RF Pulse
# The core of generalized Bloch model is implemented in the function [`apply_hamiltonian_gbloch!(∂m∂t, m, mfun, p, t)`](@ref), which calculates the derivative `∂m/∂t` fo... |
1b6703f45a796c917774fe307996c304f61d8734a9dbc8cd8e82ebf289644a4a | Julia | 14,972 | 352 | """
CRB, grad_ω1, grad_TRF = CRB_gradient_OCT(ω1, TRF, TR, ω0, B1, m0s, R1f, R2f, Rx, R1s, T2s, R2slT, grad_list, weights; isInversionPulse = [true; falses(length(ω1)-1)])
Calculate the Cramer-Rao bound of a pulse sequence along with the derivatives wrt. `ω1` and `TRF`.
# Arguments
- `ω1::Vector{Real}`: Control v... |
442ac8c5b5ea422aea288c86a3f56f930a75f55233f76cf9be6df6770ff4047d | Julia | 15,331 | 300 | """
simulate_gbloch_ide(α, TRF, TR, ω0, B1, M0, m0s, R1f, R2f, Rex, R1s, T2s[; grad_list=nothing, Ncyc=2, output=:complexsignal])
Calculate the signal or magnetization evolution with the full generalized Bloch model assuming a super-Lorentzian lineshape (slow).
The simulation assumes a sequence of rectangular RF-... |
faf4bd43df63ee3828f491efd645144004d1eac37b4a488f566e27ef5b897c15 | Julia | 16,438 | 306 | """
calculatesignal_gbloch_ide(α, TRF, TR, ω0, B1, m0s, R1f, R2f, Rx, R1s, T2s[; grad_list, Ncyc=2, output=:complexsignal])
Calculate the signal or magnetization evolution with the full generalized Bloch model assuming a super-Lorentzian lineshape (slow).
The simulation assumes a sequence of rectangular RF-pulses... |
6a8aeccd6b7e4a4831d4162f2808b5fe0858bb93c9af387b0050f8f42e51d411 | Julia | 19,826 | 162 | function hamiltonian_linear(ω1, B1, ω0, T, M0, m0s, R1f, R2f, Rex, R1s, R2s, dR2sdT2s, dR2sdB1, grad_type::grad_M0)
@inbounds begin
begin
begin
SMatrix{11, 11}((*)((*)(-1, R2f), T), (*)(T, ω0), (*)((*)((*)(-1, B1), T), ω1), 0, 0, 0, 0, 0, 0, 0, 0, (*)((*)(-1, T), ω0)... |
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