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[
"MIT"
] | 0.2.3 | c886092ba2e14a4500a3e0b99b298d104fe60b46 | code | 2962 | # Main setup
using GeophysicalModelGenerator, LaMEM
"""
Creates a polygon with a layer
"""
function create_layer(model::Model; nx=50, A0_sin=1e-5, A0_rand=1e-4, z_cen = 0.0, H0=1e-2)
x = Vector(range(extrema(model.Grid.Grid.x_vec)..., length=nx))
W = model.Grid.Grid.W
poly_x = [x; x[end:-1:1]]
poly_z = [-H0/2 .+ A0_sin*cos.(2π*x/W); H0/2 .+ A0_sin*cos.(2π*x[end:-1:1]/W)];
poly_z .+= rand(2*nx)*A0_rand # add random noise
poly_z .+= z_cen # shift
# close polygon
push!(poly_x, poly_x[1])
push!(poly_z, poly_z[1])
return poly_x, poly_z
end
"""
"""
function create_model_setup(; nx=64, nz=64, W=0.2, H=0.2, Number_layers=1, H0=1e-2, A0_rand=1e-3, A0_sin=0, Spacing = 5e-2, eta_matrix=1e20, eta_fold=1e22,
OutFile="Folding", nstep_max=100, DirectPenalty=1e4, dt_max=0.25, ε=1e-15)
model = Model( # Define the grid
Grid(nel=(nx,nz), x=[-W/2, W/2], z=[-H/2 , H/2]),
# No slip lower boundary; the rest is free slip
BoundaryConditions(exx_strain_rates=[-ε]),
SolutionParams(eta_ref=eta_matrix),
# We use a multigrid solver with 4 levels:
Solver(SolverType="direct", DirectSolver="mumps",DirectPenalty=DirectPenalty,
PETSc_options=[ "-snes_ksp_ew",
"-snes_ksp_ew_rtolmax 1e-4",
]),
# Output filename
LaMEM.Output(out_file_name=OutFile),
# Timestepping etc
Time(nstep_max=nstep_max, nstep_out=5, time_end=100, dt_min=1e-8),
# Scaling:
Scaling(GEO_units(length=1km, stress=1e9Pa) )
)
# Add fold(s)
# compute center of folds
z_bot = 0 - (H0+Spacing)*floor((Number_layers+1))/2 + (H0+Spacing)
z_top = 0 + (H0+Spacing)*floor((Number_layers+1))/2 - (H0+Spacing)
z_center = Vector(z_bot:(H0+Spacing):z_top)
Phases = model.Grid.Phases[:,1,:]
for z_cen in z_center
poly_x, poly_z = create_layer(model, A0_sin=A0_sin, A0_rand=A0_rand, z_cen=z_cen, H0=H0) # fold polygon
# Determine points that are inside the fold
INSIDE=zeros(Bool, size(model.Grid.Grid)[1], size(model.Grid.Grid)[3]);
X = model.Grid.Grid.X[:,1,:]
Z = model.Grid.Grid.Z[:,1,:]
inpolygon!(INSIDE, poly_x, poly_z, X, Z; fast=false)
Phases[INSIDE] .= 1;
end
for i = 1:size(model.Grid.Grid)[2]
model.Grid.Phases[:,i,:] = Phases
end
# Add rheology
@info "Adding rheology" eta_matrix, eta_fold
matrix = Phase(Name="matrix", ID=0, eta=eta_matrix, rho=2700)
fold = Phase(Name="fold", ID=1, eta=eta_fold, rho=2700)
add_phase!(model, matrix, fold)
return model
end | InteractiveGeodynamics | https://github.com/JuliaGeodynamics/InteractiveGeodynamics.jl.git |
|
[
"MIT"
] | 0.2.3 | c886092ba2e14a4500a3e0b99b298d104fe60b46 | code | 2515 | using DelimitedFiles
"""
Returns an accordion menu containing the rheological parameters.
"""
function make_rheological_parameters()
return dbc_accordionitem(title="Rheological Parameters", [
make_accordion_item("log₁₀(η_fold [Pa⋅s]):", "viscosity_fold", "Logarithm of the viscosity of the slab.", 22.1, 16.0, 25.0),
dbc_row(html_p()),
make_accordion_item("(log₁₀(η_matrix [Pa⋅s]):", "viscosity_matrix", "Logarithm of the viscosity of the mantle", 20.0, 16.0, 23.0),
dbc_row(html_p()),
])
end
"""
Returns an accordion menu containing the rheological parameters.
"""
function make_geometry_parameters()
return dbc_accordionitem(title="Fold Geometry", [
make_accordion_item("# layers:", "nlayers", "Number of layers. Must be an integer greater/equal than 1", 1, 1),
dbc_row(html_p()),
make_accordion_item("Thickness layers [m]:", "ThicknessLayers", "Thickness of each of the layers", 1.0, 1.0),
dbc_row(html_p()),
make_accordion_item("Spacing layers [m]:", "SpacingLayers", "Distance between center layers", 2.0, 0.1),
dbc_row(html_p()),
make_accordion_item("Amplitude noise [m]:", "A0_rand", "Amplitude of the random noise on the layer interface [m]", 0.05, 0.0),
dbc_row(html_p()),
make_accordion_item("Amplitude sin [m]:", "A0_sin", "Amplitude of the sinusoidal perturbation on the layer interface [m]", 0.0, 0.0),
dbc_row(html_p()),
])
end
"""
Returns an accordion menu containing the simulation parameters.
"""
function make_simulation_parameters()
return dbc_accordionitem(title="Simulation Parameters", [
make_accordion_item("Thickness (m):", "thickness", "Model Thickness [m]", 30.0, 10.0),
dbc_row(html_p()),
make_accordion_item("Width (m):", "width", "Model Width [m]", 80.0, 10.0),
dbc_row(html_p()),
make_accordion_item("nx:", "nel_x", "Number of elements in the x-direction. Must be an integer greater than 2.", 128, 2),
dbc_row(html_p()),
make_accordion_item("nz:", "nel_z", "Number of elements in the z-direction. Must be an integer greater than 2.", 256, 2),
dbc_row(html_p()),
make_accordion_item("εbg [1/s]:", "e_bg", "Background strainrate of the deformation", 1e-15, -1e-13),
dbc_row(html_p()),
make_accordion_item("nt:", "n_timesteps", "Maximum number of timesteps. Must be an integer greater than 1.", 100, 1),
dbc_row(html_p()),
])
end
| InteractiveGeodynamics | https://github.com/JuliaGeodynamics/InteractiveGeodynamics.jl.git |
|
[
"MIT"
] | 0.2.3 | c886092ba2e14a4500a3e0b99b298d104fe60b46 | code | 12802 | module FreeSubductionTools
using Dash, DashBootstrapComponents
using PlotlyJS
using LaMEM
using UUIDs
using Interpolations
using GeophysicalModelGenerator
using HTTP
export subduction
pkg_dir = Base.pkgdir(FreeSubductionTools)
@show pkg_dir
include(joinpath(pkg_dir,"src/dash_tools.jl"))
include(joinpath(pkg_dir,"src/FreeSubduction/dash_functions_FreeSubduction.jl"))
include(joinpath(pkg_dir,"src/FreeSubduction/Setup.jl"))
"""
subduction(; host = HTTP.Sockets.localhost, port=8050, wait=false, width="80vw", height="45vh", cores=1)
This starts a free subduction GUI
Optional parameters
===
- `host` : IP address
- `port` : port number
- `wait` : if true, you will see the LaMEM output and figure windows are only shown after the simulation is finished
- `width` : relative width of main figure
- `height` : relative height of main figure
"""
function subduction(; host = HTTP.Sockets.localhost, port=8050, wait=false, width="80vw", height="50vh", cores=1)
pkg_dir = Base.pkgdir(FreeSubductionTools)
GUI_version = "0.1.3"
cmaps = read_colormaps(dir_colormaps=joinpath(pkg_dir,"src/assets/colormaps/"))
title_app = "Free Subduction"
# ParamFile = "RTI.dat"
OutFile = "FreeSubduction"
#app = dash(external_stylesheets=[dbc_themes.CYBORG])
app = dash(external_stylesheets = [dbc_themes.BOOTSTRAP, dbc_icons.BOOTSTRAP], prevent_initial_callbacks=false)
app.title = title_app
# Main code layout
app.layout = html_div() do
dbc_container(className="mxy-auto", fluid=true, [
make_title(title_app),
dbc_row([
dbc_col([
make_plot("",cmaps, width=width, height=height), # show graph
make_plot_controls(), # show media buttons
make_id_label(), # show user id
]),
dbc_col([
make_time_card(), # show simulation time info
make_menu(cmaps), # show menu with simulation parameters, rheological parameters, and plotting parameters
make_run_button() # show the run simulation button
])
]),
# Store a unique number of our session in the webpage
dcc_store(id="session-id", data=""),
# Store info related to the simulation and current timestep
dcc_store(id="current_timestep", data="0"),
dcc_store(id="last_timestep", data="0"),
dcc_store(id="update_fig", data="0"),
# Start an interval that updates the number every second
dcc_interval(id="session-interval", interval=100, n_intervals=0, disabled=true)
])
end
# This creates an initial session id that is unique for this session
# it will run on first start
callback!(app,
Dash.Output("session-id", "data"),
Dash.Output("label-id", "children"),
Input("session-id", "data")
) do session_id
session_id = UUIDs.uuid4()
str = "id=$(session_id), v=$(GUI_version)"
return String("$(session_id)"), str
end
# Call run button
callback!(app,
Dash.Output("session-interval", "disabled"),
Input("button-run", "n_clicks"),
Input("button-run", "disabled"),
Input("button-play", "n_clicks"),
State("slab_thickness", "value"),
State("crust_thickness", "value"),
State("nel_z", "value"),
State("n_timesteps", "value"),
State("switch-FreeSurf", "value"),
State("last_timestep", "data"),
State("plot_field", "value"),
State("session-id", "data"),
State("viscosity_slab", "value"),
State("viscosity_mantle", "value"),
State("viscosity_crust", "value"),
State("yield_stress_crust", "value"),
prevent_initial_call=true
) do n_run, active_run, n_play,
slab_thickness, crust_thickness, nel_z, n_timesteps,
free_surf,
last_timestep, plot_field, session_id,
η_slab,η_mantle,η_crust,yield_stress_crust
# print(layers)
# print(open_top)
trigger = get_trigger()
disable_interval = true
if trigger == "button-run.n_clicks"
cd(pkg_dir)
cur_dir = pwd()
base_dir = joinpath(pkgdir(FreeSubductionTools),"src","FreeSubduction")
η_slab = 10.0^η_slab
η_mantle = 10.0^η_mantle
η_crust = 10.0^η_crust
# We clicked the run button
user_dir = simulation_directory(session_id, clean=true)
cd(user_dir)
@show free_surf
if free_surf === nothing || free_surf == []
free_surface = true
else
free_surface = false
end
# Create the setup
model = create_model_setup(nz=nel_z, SlabThickness=slab_thickness, CrustThickness = crust_thickness, eta_slab=η_slab, eta_mantle=η_mantle, eta_crust=η_crust,
C_crust = yield_stress_crust,
OutFile=OutFile, nstep_max=n_timesteps,
free_surface=free_surface)
run_lamem(model, cores, wait=wait)
cd(cur_dir) # return to main directory
disable_interval = false
elseif trigger == "button-run.disabled"
last_t = parse(Int, last_timestep)
if active_run == true || last_t < n_timesteps
disable_interval = false
end
elseif trigger == "button-play.n_clicks"
last_t = parse(Int, last_timestep)
# @show last_t
disable_interval = false
end
return disable_interval
end
# deactivate the button
callback!(app,
Dash.Output("button-run", "disabled"),
Dash.Output("button-run", "color"),
Input("button-run", "n_clicks"),
Input("session-interval", "n_intervals"),
State("last_timestep", "data"),
State("current_timestep", "data"),
prevent_initial_call=true
) do n_run, n_inter, last_timestep, current_timestep
cur_t = parse(Int, current_timestep) # current timestep
last_t = parse(Int, last_timestep) # last timestep available on disk
if cur_t < last_t
button_run_disable = true
button_color = "danger"
else
button_run_disable = false
button_color = "primary"
end
return button_run_disable, button_color
end
# Check if *.pvd file on disk changed and a new timestep is available
callback!(app,
Dash.Output("last_timestep", "data"),
Dash.Output("update_fig", "data"),
Input("session-interval", "n_intervals"),
Input("button-run", "n_clicks"),
State("current_timestep", "data"),
State("update_fig", "data"),
State("session-id", "data"),
prevent_initial_call=true
) do n_inter, n_run, current_timestep, update_fig, session_id
trigger = get_trigger()
user_dir = simulation_directory(session_id, clean=false)
if trigger == "session-interval.n_intervals"
if has_pvd_file(OutFile, user_dir)
# Read LaMEM *.pvd file
Timestep, _, Time = read_LaMEM_simulation(OutFile, user_dir)
# Update the labels and data stored in webpage about the last timestep
last_time = "$(Timestep[end])"
update_fig = "$(parse(Int,update_fig)+1)"
else
last_time = "0"
update_fig = "0"
end
elseif trigger == "button-run.n_clicks"
last_time = "0"
update_fig = "0"
end
return last_time, update_fig
end
# Update the figure if the signal is given to do so
callback!(app,
Dash.Output("label-timestep", "children"),
Dash.Output("label-time", "children"),
Dash.Output("current_timestep", "data"),
Dash.Output("figure_main", "figure"),
Dash.Output("plot_field", "options"),
Dash.Output("contour_option", "options"),
Input("update_fig", "data"),
Input("current_timestep", "data"),
Input("button-run", "n_clicks"),
Input("button-start", "n_clicks"),
Input("button-last", "n_clicks"),
Input("button-forward", "n_clicks"),
Input("button-back", "n_clicks"),
Input("button-play", "n_clicks"),
State("last_timestep", "data"),
State("session-id", "data"),
State("plot_field", "value"),
State("switch-contour", "value"),
State("contour_option", "value"),
State("switch-velocity", "value"),
State("color_map_option", "value"),
prevent_initial_call=true
) do update_fig, current_timestep, n_run, n_start, n_last, n_back, n_forward, n_play, last_timestep, session_id,
plot_field, switch_contour, contour_field, switch_velocity, color_map_option
trigger = get_trigger()
# Get info about timesteps
cur_t = parse(Int, current_timestep) # current timestep
last_t = parse(Int, last_timestep) # last timestep available on disk
fig_cross = []
fields_available = ["phase"]
if trigger == "current_timestep.data" ||
trigger == "update_fig.data" ||
trigger == "button-start.n_clicks" ||
trigger == "button-last.n_clicks" ||
trigger == "button-back.n_clicks" ||
trigger == "button-forward.n_clicks" ||
trigger == "button-play.n_clicks"
user_dir = simulation_directory(session_id, clean=false)
if has_pvd_file(OutFile, user_dir)
Timestep, _, Time = read_LaMEM_simulation(OutFile, user_dir) # all timesteps
id = findall(Timestep .== cur_t)[1]
if trigger == "button-start.n_clicks" || trigger == "button-play.n_clicks"
cur_t = 0
id = 1
elseif trigger == "button-last.n_clicks"
cur_t = Timestep[end]
id = length(Timestep)
elseif (trigger == "button-forward.n_clicks") && (id < length(Timestep))
cur_t = Timestep[id+1]
id = id + 1
elseif (trigger == "button-back.n_clicks") && (id > 1)
cur_t = Timestep[id-1]
id = id - 1
end
# Load data
x, y, data, time, fields_available = get_data(OutFile, cur_t, plot_field, user_dir)
add_contours = active_switch(switch_contour)
if add_contours
x_con, y_con, data_con, _, _ = get_data(OutFile, cur_t, contour_field, user_dir)
else
x_con, y_con, data_con = x, y, data
end
# update the plot
add_velocity = active_switch(switch_velocity)
fig_cross = create_main_figure(OutFile, cur_t, x, y, data, x_con, y_con, data_con;
add_contours=add_contours, contour_field=contour_field,
add_velocity=add_velocity,
colorscale=color_map_option,
session_id=session_id,
field=plot_field, cmaps=cmaps)
if trigger == "current_timestep.data" || trigger == "update_fig.data" || trigger == "button-play.n_clicks"
if cur_t < last_t
cur_t = Timestep[id+1] # update current timestep
end
end
else
time = 0
end
elseif trigger == "button-run.n_clicks"
cur_t = 0
time = 0.0
end
# update the labels
label_timestep = "Timestep: $cur_t"
label_time = "Time: $time Myrs"
current_timestep = "$cur_t"
# @show current_timestep
println("Timestep ", current_timestep)
return label_timestep, label_time, current_timestep, fig_cross, fields_available, fields_available
end
#
callback!(app,
Dash.Output("contour_option", "disabled"),
Input("switch-contour", "value")) do switch_contour
if !isnothing(switch_contour)
if isempty(switch_contour)
disable_contours = true
else
disable_contours = false
end
else
disable_contours = true
end
return disable_contours
end
run_server(app, host, port, debug=false)
end
end
| InteractiveGeodynamics | https://github.com/JuliaGeodynamics/InteractiveGeodynamics.jl.git |
|
[
"MIT"
] | 0.2.3 | c886092ba2e14a4500a3e0b99b298d104fe60b46 | code | 2813 | # Main setup
using GeophysicalModelGenerator, LaMEM
function create_model_setup(; nz=64, SlabThickness=80, CrustThickness=15, eta_slab=2e23, eta_mantle=1e21, eta_crust=1e21,
C_crust=10, OutFile="FreeSubduction", nstep_max=100, DirectPenalty=1e4, free_surface=false, dt_max=0.25)
if free_surface
Air = 40
open_top_bound = 1
else
Air = 0
open_top_bound = 0
end
model = Model( # Define the grid
Grid(nel=(nz*4,nz), x=[-1200, 1200], z=[-660 ,Air]),
# No slip lower boundary; the rest is free slip
BoundaryConditions(noslip = [0, 0, 0, 0, 1, 0], open_top_bound=open_top_bound),
SolutionParams(eta_ref=eta_mantle),
# We use a multigrid solver with 4 levels:
Solver(SolverType="direct", DirectPenalty=DirectPenalty, DirectSolver="mumps",
PETSc_options=[ "-js_ksp_monitor",
"-snes_ksp_ew",
"-snes_ksp_ew_rtolmax 1e-4",
]),
# Free FreeSurface
FreeSurface(surf_use=open_top_bound),
# Output filename
LaMEM.Output(out_file_name=OutFile),
# Timestepping etc
Time(nstep_max=nstep_max, nstep_out=5, time_end=100, dt_min=1e-8, dt_max=dt_max),
# Scaling:
Scaling(GEO_units(length=1km, stress=1e9Pa) )
)
lith = LithosphericPhases(Layers=[CrustThickness,SlabThickness-CrustThickness], Phases=[2,3]);
# Add mantle
add_layer!(model, zlim=(-1000.0,0), phase=ConstantPhase(1))
# Add geometry
add_box!(model, xlim=(-900,200), zlim=(-SlabThickness,0), phase=lith)
# Add curved trench
trench = Trench(Start=(200.0,-100.0), End=(200.0,100.0), Thickness=SlabThickness, θ_max=45.0, Length=300, Lb=200);
add_slab!(model, trench, phase=lith);
# add stripes
add_stripes!(model,stripAxes=(1,0,1), stripeWidth=20, stripeSpacing=40, phase=ConstantPhase(3), stripePhase=ConstantPhase(4))
# Add rheology
@info "Adding rheology" eta_mantle, eta_crust, eta_slab
air = Phase(Name="Air", ID=0, eta=eta_mantle/10, rho=10)
mantle = Phase(Name="mantle", ID=1, eta=eta_mantle, rho=3200)
crust = Phase(Name="crust", ID=2, eta=eta_crust, rho=3280)
slab = Phase(Name="slab", ID=3, eta=eta_slab, rho=3280)
slab2 = Phase(Name="slab_stripe", ID=4, eta=eta_slab, rho=3280)
add_phase!(model, air, mantle, slab, slab2, crust)
return model
end | InteractiveGeodynamics | https://github.com/JuliaGeodynamics/InteractiveGeodynamics.jl.git |
|
[
"MIT"
] | 0.2.3 | c886092ba2e14a4500a3e0b99b298d104fe60b46 | code | 3475 | using DelimitedFiles
make_geometry_parameters() = nothing
"""
Returns an accordion menu containing the rheological parameters.
"""
function make_rheological_parameters()
return dbc_accordionitem(title="Rheological Parameters", [
make_accordion_item("log₁₀(η_slab [Pa⋅s]):", "viscosity_slab", "Logarithm of the viscosity of the slab.", 23.5, 16.0, 25.0),
dbc_row(html_p()),
make_accordion_item("(log₁₀(η_mantle [Pa⋅s]):", "viscosity_mantle", "Logarithm of the viscosity of the mantle", 21.0, 16.0, 23.0),
dbc_row(html_p()),
make_accordion_item("log₁₀(η_crust [Pa⋅s]):", "viscosity_crust", "Logarithm of the viscosity of the crust", 23.1, 16.0, 23.0),
dbc_row(html_p()),
make_accordion_item("σ_yield_crust [Pas]:", "yield_stress_crust", "Yield stress of the crust",1000, 1, 1000.0),
])
end
"""
Returns an accordion menu containing the simulation parameters.
"""
function make_simulation_parameters()
return dbc_accordionitem(title="Simulation Parameters", [
make_accordion_item("Slab Thickness (km):", "slab_thickness", "Full slab thickness given in kilometers.", 80.0, 1.0e-10),
dbc_row(html_p()),
make_accordion_item("Crust Thickness (km):", "crust_thickness", "Crust thickness given in kilometers.", 15.0, 1.0e-10),
dbc_row(html_p()),
#make_accordion_item("nx:", "nel_x", "Number of elements in the x-direction. Must be an integer greater than 2.", 64, 2),
#dbc_row(html_p()),
make_accordion_item("nz:", "nel_z", "Number of elements in the z-direction. Must be an integer greater than 2. nx=4*nz", 64, 2),
dbc_row(html_p()),
make_accordion_item("nt:", "n_timesteps", "Maximum number of timesteps. Must be an integer greater than 1.", 200, 1),
dbc_row(html_p()),
dbc_row([
dbc_checklist(options=["free slip upper boundary"],
id="switch-FreeSurf",
switch=true,
)
]),
# dbc_row(html_p()),
# dbc_row([
# dbc_checklist(options=["Layers"],
# id="switch-Layers",
# switch=true,
# )
# ])
])
end
#=
"""
Creates a setup with noisy temperature and one phase
"""
function CreateSetup(ParamFile, layered_overburden=false, Hi=-5.0, ampl_noise=0.1, ; args)
Grid = read_LaMEM_inputfile(ParamFile, args=args)
Phases = zeros(Int64, size(Grid.X));
Temp = zeros(Float64,size(Grid.X));
if layered_overburden
H_layer = 0.25;
for z_low = minimum(Grid.Z):2*H_layer:maximum(Grid.Z)
# print(z_low)
# z_low = -z_low
iz = (Grid.Z[1,1,:] .> z_low) .& (Grid.Z[1,1,:] .<= (z_low+H_layer) )
Phases[:,:,iz] .= 1;
end
end
z_int = [Hi + rand()*ampl_noise for _ in 1:Grid.nump_x]
# print(z_int)
# z_int = -z_int
for ix=1:Grid.nump_x, iy=1:Grid.nump_y
iz = Grid.Z[ix,iy,:] .< z_int[ix]
Phases[ix,iy,iz] .= 2;
end
# print(z_int)
Model3D = CartData(Grid, (Phases=Phases,Temp=Temp)) # Create LaMEM model
write_paraview(Model3D,"LaMEM_ModelSetup", verbose=false) # Save model to paraview (load with opening LaMEM_ModelSetup.vts in paraview)
save_LaMEM_markers_parallel(Model3D, directory="./markers", verbose=false) # save markers on one core
return nothing
end
=# | InteractiveGeodynamics | https://github.com/JuliaGeodynamics/InteractiveGeodynamics.jl.git |
|
[
"MIT"
] | 0.2.3 | c886092ba2e14a4500a3e0b99b298d104fe60b46 | code | 12719 | module ConvectionTools
using Dash, DashBootstrapComponents
using PlotlyJS
using LaMEM
using UUIDs
using Interpolations
using GeophysicalModelGenerator
using DelimitedFiles
using HTTP
export convection
pkg_dir = Base.pkgdir(ConvectionTools)
include(joinpath(pkg_dir,"src/dash_tools.jl"))
include(joinpath(pkg_dir,"src/RayleighBenardConvection/dash_functions_convection.jl"))
"""
convection(; host=HTTP.Sockets.localhost, port=8050)
This starts a convection GUI
"""
function convection(; host=HTTP.Sockets.localhost, port=8050, width="80vw", height="60vh")
pkg_dir = Base.pkgdir(ConvectionTools)
cmaps = read_colormaps(dir_colormaps=joinpath(pkg_dir,"src/assets/colormaps/"))
GUI_version = "0.1.3"
title_app = "Rayleigh-Benard convection"
ParamFile = "Convection.dat"
OutFile = "Convection"
base_dir = pwd()
cd(base_dir)
#app = dash(external_stylesheets=[dbc_themes.CYBORG])
app = dash(external_stylesheets = [dbc_themes.BOOTSTRAP, dbc_icons.BOOTSTRAP], prevent_initial_callbacks=false)
app.title = title_app
# Main code layout
app.layout = html_div() do
dbc_container(className="mxy-auto", fluid=true, [
make_title(title_app),
dbc_row([
dbc_col([
make_plot("",cmaps, width=width, height=height), # show graph
make_plot_controls(), # show media buttons
make_id_label(), # show user id
]),
dbc_col([
make_time_card(), # show simulation time info
make_menu(cmaps, show_field="temperature [°C]"), # show menu with simulation parameters, rheological parameters, and plotting parameters
make_run_button() # show the run simulation button
])
]),
# Store a unique number of our session in the webpage
dcc_store(id="session-id", data=""),
# Store info related to the simulation and current timestep
dcc_store(id="current_timestep", data="0"),
dcc_store(id="last_timestep", data="0"),
dcc_store(id="update_fig", data="0"),
# Start an interval that updates the number every second
dcc_interval(id="session-interval", interval=100, n_intervals=0, disabled=true)
])
end
# This creates an initial session id that is unique for this session
# it will run on first start
callback!(app,
Dash.Output("session-id", "data"),
Dash.Output("label-id", "children"),
Input("session-id", "data")
) do session_id
session_id = UUIDs.uuid4()
str = "id=$(session_id), v=$(GUI_version)"
return String("$(session_id)"), str
end
# Call run button
callback!(app,
Dash.Output("session-interval", "disabled"),
Input("button-run", "n_clicks"),
Input("button-run", "disabled"),
Input("button-play", "n_clicks"),
State("domain_width", "value"),
State("domain_height", "value"),
State("nel_x", "value"),
State("nel_z", "value"),
State("n_timesteps", "value"),
State("ΔT", "value"),
State("γ", "value"),
State("cohesion", "value"),
State("viscosity", "value"),
State("last_timestep", "data"),
State("plot_field", "value"),
State("session-id", "data"),
State("switch-FreeSurf","value"),
prevent_initial_call=true
) do n_run, active_run, n_play,
domain_width, domain_height, nel_x, nel_z, n_timesteps,
ΔT, γ, cohesion, viscosity,
last_timestep, plot_field, session_id, FreeSurface
trigger = get_trigger()
disable_interval = true
user_dir = simulation_directory(session_id, clean=false)
if trigger == "button-run.n_clicks"
cd(pkg_dir)
cur_dir = pwd()
base_dir = joinpath(pkgdir(ConvectionTools),"src","RayleighBenardConvection")
viscosity = 10.0^viscosity
cohesion *= 1.0e6
Δx = domain_width/nel_x # y-width
if FreeSurface === nothing
args = "-nstep_max $(n_timesteps) -eta_fk[0] $(viscosity) -gamma_fk[0] $γ -TRef_fk[0] $(ΔT/2) -ch[0] $(cohesion) -nel_x $nel_x -nel_z $nel_z -coord_x $(-domain_width/2),$(domain_width/2) -coord_z $(-domain_height),0 -coord_y $(-Δx/2),$(Δx/2) -temp_bot $ΔT"
else
args = "-nstep_max $(n_timesteps) -eta_fk[0] $(viscosity) -gamma_fk[0] $γ -TRef_fk[0] $(ΔT/2) -ch[0] $(cohesion) -nel_x $nel_x -coord_x $(-domain_width/2),$(domain_width/2) -coord_y $(-Δx/2),$(Δx/2) -temp_bot $ΔT"
end
println("args = ", args)
# We clicked the run button
user_dir = simulation_directory(session_id, clean=true)
cd(user_dir)
pfile = joinpath(base_dir,ParamFile)
CreateSetup(pfile, ΔT, args=args)
run_lamem(pfile, 1, args, wait=false)
disable_interval = false
cd(cur_dir) # return to main directory
elseif trigger == "button-run.disabled"
last_t = parse(Int, last_timestep)
if active_run == true || last_t < n_timesteps
disable_interval = false
end
elseif trigger == "button-play.n_clicks"
last_t = parse(Int, last_timestep)
# @show last_t
disable_interval = false
end
return disable_interval
end
# deactivate the button
callback!(app,
Dash.Output("button-run", "disabled"),
Dash.Output("button-run", "color"),
Input("button-run", "n_clicks"),
Input("session-interval", "n_intervals"),
State("last_timestep", "data"),
State("current_timestep", "data"),
prevent_initial_call=true
) do n_run, n_inter, last_timestep, current_timestep
cur_t = parse(Int, current_timestep) # current timestep
last_t = parse(Int, last_timestep) # last timestep available on disk
if cur_t < last_t
button_run_disable = true
button_color = "danger"
else
button_run_disable = false
button_color = "primary"
end
return button_run_disable, button_color
end
# Check if *.pvd file on disk changed and a new timestep is available
callback!(app,
Dash.Output("last_timestep", "data"),
Dash.Output("update_fig", "data"),
Input("session-interval", "n_intervals"),
Input("button-run", "n_clicks"),
State("current_timestep", "data"),
State("update_fig", "data"),
State("session-id", "data"),
prevent_initial_call=true
) do n_inter, n_run, current_timestep, update_fig, session_id
trigger = get_trigger()
user_dir = simulation_directory(session_id, clean=false)
if trigger == "session-interval.n_intervals"
if has_pvd_file(OutFile, user_dir)
# Read LaMEM *.pvd file
Timestep, _, Time = read_LaMEM_simulation(OutFile, user_dir)
# Update the labels and data stored in webpage about the last timestep
last_time = "$(Timestep[end])"
update_fig = "$(parse(Int,update_fig)+1)"
else
last_time = "0"
update_fig = "0"
end
elseif trigger == "button-run.n_clicks"
last_time = "0"
update_fig = "0"
end
return last_time, update_fig
end
# Update the figure if the signal is given to do so
callback!(app,
Dash.Output("label-timestep", "children"),
Dash.Output("label-time", "children"),
Dash.Output("current_timestep", "data"),
Dash.Output("figure_main", "figure"),
Dash.Output("plot_field", "options"),
Dash.Output("contour_option", "options"),
Input("update_fig", "data"),
Input("current_timestep", "data"),
Input("button-run", "n_clicks"),
Input("button-start", "n_clicks"),
Input("button-last", "n_clicks"),
Input("button-forward", "n_clicks"),
Input("button-back", "n_clicks"),
Input("button-play", "n_clicks"),
State("last_timestep", "data"),
State("session-id", "data"),
State("plot_field", "value"),
State("switch-contour", "value"),
State("contour_option", "value"),
State("switch-velocity", "value"),
State("color_map_option", "value"),
prevent_initial_call=true
) do update_fig, current_timestep, n_run, n_start, n_last, n_back, n_forward, n_play, last_timestep, session_id,
plot_field, switch_contour, contour_field, switch_velocity, color_map_option
trigger = get_trigger()
# Get info about timesteps
cur_t = parse(Int, current_timestep) # current timestep
last_t = parse(Int, last_timestep) # last timestep available on disk
fig_cross = []
fields_available = ["phase"]
if trigger == "current_timestep.data" ||
trigger == "update_fig.data" ||
trigger == "button-start.n_clicks" ||
trigger == "button-last.n_clicks" ||
trigger == "button-back.n_clicks" ||
trigger == "button-forward.n_clicks" ||
trigger == "button-play.n_clicks"
user_dir = simulation_directory(session_id, clean=false)
if has_pvd_file(OutFile, user_dir)
Timestep, _, Time = read_LaMEM_simulation(OutFile, user_dir) # all timesteps
id = findall(Timestep .== cur_t)[1]
if trigger == "button-start.n_clicks" || trigger == "button-play.n_clicks"
cur_t = 0
id = 1
elseif trigger == "button-last.n_clicks"
cur_t = Timestep[end]
id = length(Timestep)
elseif (trigger == "button-forward.n_clicks") && (id < length(Timestep))
cur_t = Timestep[id+1]
id = id + 1
elseif (trigger == "button-back.n_clicks") && (id > 1)
cur_t = Timestep[id-1]
id = id - 1
end
# Load data
x, y, data, time, fields_available = get_data(OutFile, cur_t, plot_field, user_dir)
add_contours = active_switch(switch_contour)
if add_contours
x_con, y_con, data_con, _, _ = get_data(OutFile, cur_t, contour_field, user_dir)
else
x_con, y_con, data_con = x, y, data
end
# update the plot
add_velocity = active_switch(switch_velocity)
fig_cross = create_main_figure(OutFile, cur_t, x, y, data, x_con, y_con, data_con;
add_contours=add_contours, contour_field=contour_field,
add_velocity=add_velocity,
colorscale=color_map_option,
session_id=session_id,
cmaps=cmaps,
field=plot_field)
if trigger == "current_timestep.data" || trigger == "update_fig.data" || trigger == "button-play.n_clicks"
if cur_t < last_t
cur_t = Timestep[id+1] # update current timestep
end
end
else
time = 0
end
elseif trigger == "button-run.n_clicks"
cur_t = 0
time = 0.0
end
# update the labels
label_timestep = "Timestep: $cur_t"
label_time = "Time: $time Myrs"
current_timestep = "$cur_t"
# @show current_timestep
println("Timestep ", cur_t)
return label_timestep, label_time, current_timestep, fig_cross, add_units(fields_available), add_units(fields_available)
end
#
callback!(app,
Dash.Output("contour_option", "disabled"),
Input("switch-contour", "value")) do switch_contour
if !isnothing(switch_contour)
if isempty(switch_contour)
disable_contours = true
else
disable_contours = false
end
else
disable_contours = true
end
return disable_contours
end
run_server(app, host, port, debug=false)
return app
end
end
| InteractiveGeodynamics | https://github.com/JuliaGeodynamics/InteractiveGeodynamics.jl.git |
|
[
"MIT"
] | 0.2.3 | c886092ba2e14a4500a3e0b99b298d104fe60b46 | code | 2753 | using DelimitedFiles
make_geometry_parameters() = nothing
"""
Returns an accordion menu containing the simulation parameters.
"""
function make_simulation_parameters()
return dbc_accordionitem(title="Simulation Parameters", [
make_accordion_item("Width (km):", "domain_width", "Width of the domain, given in kilometers.", 2000.0, 1.0e-10),
dbc_row(html_p()),
make_accordion_item("Height (km):", "domain_height", "Height of the domain, given in kilometers.", 1000.0, 1.0e-10),
dbc_row(html_p()),
make_accordion_item("nx:", "nel_x", "Number of elements in the x-direction. Must be an integer greater than 2.", 128, 2),
dbc_row(html_p()),
make_accordion_item("nz:", "nel_z", "Number of elements in the z-direction. Must be an integer greater than 2.", 64, 2),
dbc_row(html_p()),
make_accordion_item("nt:", "n_timesteps", "Maximum number of timesteps. Must be an integer greater than 1.", 250, 1),
])
end
"""
Returns an accordion menu containing the rheological parameters.
"""
function make_rheological_parameters()
return dbc_accordionitem(title="Rheological Parameters", [
make_accordion_item("ΔT:", "ΔT", "Temperature difference between the base and the top.", 2000.0, 1.0-10, 4_000.0),
dbc_row(html_p()),
make_accordion_item("η=η₀exp(-γ(T-½ΔT)), γ:", "γ", "Parameter for Frank-Kamenetzky viscosity (0.0 ≤ γ ≤ 0.1)", 0.001, 0.0, 0.1),
dbc_row(html_p()),
make_accordion_item("Yield stress (MPa):", "cohesion", "Maximum stress allowed in the model (0 ≤ Yield stress ≤ 1000) [MPa].", 500.0, 0.0, 1000.0),
dbc_row(html_p()),
make_accordion_item("η₀ (log₁₀(Pa⋅s)):", "viscosity", "Logarithm of the viscosity of the matrix at ΔT/2 (15 < η ≤ 25).", 21.0, 15.0, 25.0),
dbc_row(html_p()),
dbc_row([
dbc_checklist(options=["FreeSurf"],
id="switch-FreeSurf",
switch=true,
)
]),
])
end
"""
Creates a setup with noisy temperature and one phase
"""
function CreateSetup(ParamFile, ΔT=1000, ampl_noise=100; args)
Grid = read_LaMEM_inputfile(ParamFile, args=args)
Phases = zeros(Int64, size(Grid.X))
Temp = [ΔT / 2 + rand()*ampl_noise for _ in axes(Grid.X,1), _ in axes(Grid.X,2), _ in axes(Grid.X,3)]
Phases[Grid.Z.>0.0] .= 1
Temp[Grid.Z.>0.0] .= 0.0
Model3D = CartData(Grid, (Phases=Phases, Temp=Temp)) # Create LaMEM model
write_paraview(Model3D, "LaMEM_ModelSetup", verbose=false) # Save model to paraview (load with opening LaMEM_ModelSetup.vts in paraview)
save_LaMEM_markers_parallel(Model3D, directory="./markers", verbose=false) # save markers on one core
return nothing
end
| InteractiveGeodynamics | https://github.com/JuliaGeodynamics/InteractiveGeodynamics.jl.git |
|
[
"MIT"
] | 0.2.3 | c886092ba2e14a4500a3e0b99b298d104fe60b46 | code | 12371 | module RTITools
using Dash, DashBootstrapComponents
using PlotlyJS
using LaMEM
using UUIDs
using Interpolations
using GeophysicalModelGenerator
using HTTP
export rayleigh_taylor
pkg_dir = Base.pkgdir(RTITools)
include(joinpath(pkg_dir,"src/dash_tools.jl"))
include(joinpath(pkg_dir,"src/RayleighTaylorInstability/dash_functions_RTI.jl"))
"""
rayleigh_taylor(; host=HTTP.Sockets.localhost, port=8050)
This starts a rayleigh_taylor instability GUI
"""
function rayleigh_taylor(; host = HTTP.Sockets.localhost, port=8050)
pkg_dir = Base.pkgdir(RTITools)
GUI_version = "0.1.3"
cmaps = read_colormaps(dir_colormaps=joinpath(pkg_dir,"src/assets/colormaps/"))
title_app = "Rayleigh Taylor Instability"
ParamFile = "RTI.dat"
OutFile = "RTI"
#app = dash(external_stylesheets=[dbc_themes.CYBORG])
app = dash(external_stylesheets = [dbc_themes.BOOTSTRAP, dbc_icons.BOOTSTRAP], prevent_initial_callbacks=false)
app.title = title_app
# Main code layout
app.layout = html_div() do
dbc_container(className="mxy-auto", fluid=true, [
make_title(title_app),
dbc_row([
dbc_col([
make_plot("",cmaps), # show graph
make_plot_controls(), # show media buttons
make_id_label(), # show user id
]),
dbc_col([
make_time_card(), # show simulation time info
make_menu(cmaps), # show menu with simulation parameters, rheological parameters, and plotting parameters
make_run_button() # show the run simulation button
])
]),
# Store a unique number of our session in the webpage
dcc_store(id="session-id", data=""),
# Store info related to the simulation and current timestep
dcc_store(id="current_timestep", data="0"),
dcc_store(id="last_timestep", data="0"),
dcc_store(id="update_fig", data="0"),
# Start an interval that updates the number every second
dcc_interval(id="session-interval", interval=100, n_intervals=0, disabled=true)
])
end
# This creates an initial session id that is unique for this session
# it will run on first start
callback!(app,
Dash.Output("session-id", "data"),
Dash.Output("label-id", "children"),
Input("session-id", "data")
) do session_id
session_id = UUIDs.uuid4()
str = "id=$(session_id), v=$(GUI_version)"
return String("$(session_id)"), str
end
# Call run button
callback!(app,
Dash.Output("session-interval", "disabled"),
Input("button-run", "n_clicks"),
Input("button-run", "disabled"),
Input("button-play", "n_clicks"),
State("domain_width", "value"),
State("depth", "value"),
State("nel_x", "value"),
State("nel_z", "value"),
State("n_timesteps", "value"),
State("switch-FreeSurf", "value"),
State("switch-Layers", "value"),
State("last_timestep", "data"),
State("plot_field", "value"),
State("session-id", "data"),
State("viscosity_upper", "value"),
State("viscosity_lower", "value"),
State("density_upper", "value"),
State("density_lower", "value"),
prevent_initial_call=true
) do n_run, active_run, n_play,
domain_width, depth, nel_x, nel_z, n_timesteps,
open_top, layers,
last_timestep, plot_field, session_id,
η_up,η_lo,ρ_up,ρ_lo
# print(layers)
# print(open_top)
trigger = get_trigger()
disable_interval = true
if trigger == "button-run.n_clicks"
cur_dir = pwd()
base_dir = joinpath(pkgdir(RTITools),"src","RayleighTaylorInstability")
η_up = 10.0^η_up
η_lo = 10.0^η_lo
Hi_value = depth
W = domain_width
open_top_bound = active_switch(open_top)
# print(open_top)
addlayers = active_switch(layers)
# print(layers)c
args = "-nstep_max $(n_timesteps) -eta[0] $η_up -eta[1] $η_up -eta[2] $η_lo -rho[0] $ρ_up -rho[1] $ρ_up -rho[2] $ρ_lo -open_top_bound $(Int64(open_top_bound)) -nel_x $nel_x -nel_z $nel_z -coord_x $(-W/2),$(W/2)"
println("args = ", args)
# We clicked the run button
user_dir = simulation_directory(session_id, clean=true)
cd(user_dir)
pfile = joinpath(base_dir,ParamFile)
# Create the setup
CreateSetup(pfile, addlayers, Hi_value, args=args)
run_lamem(pfile, 1, args, wait=false)
cd(cur_dir) # return to main directory
disable_interval = false
elseif trigger == "button-run.disabled"
last_t = parse(Int, last_timestep)
if active_run == true || last_t < n_timesteps
disable_interval = false
end
elseif trigger == "button-play.n_clicks"
last_t = parse(Int, last_timestep)
# @show last_t
disable_interval = false
end
return disable_interval
end
# deactivate the button
callback!(app,
Dash.Output("button-run", "disabled"),
Dash.Output("button-run", "color"),
Input("button-run", "n_clicks"),
Input("session-interval", "n_intervals"),
State("last_timestep", "data"),
State("current_timestep", "data"),
prevent_initial_call=true
) do n_run, n_inter, last_timestep, current_timestep
cur_t = parse(Int, current_timestep) # current timestep
last_t = parse(Int, last_timestep) # last timestep available on disk
if cur_t < last_t
button_run_disable = true
button_color = "danger"
else
button_run_disable = false
button_color = "primary"
end
return button_run_disable, button_color
end
# Check if *.pvd file on disk changed and a new timestep is available
callback!(app,
Dash.Output("last_timestep", "data"),
Dash.Output("update_fig", "data"),
Input("session-interval", "n_intervals"),
Input("button-run", "n_clicks"),
State("current_timestep", "data"),
State("update_fig", "data"),
State("session-id", "data"),
prevent_initial_call=true
) do n_inter, n_run, current_timestep, update_fig, session_id
trigger = get_trigger()
user_dir = simulation_directory(session_id, clean=false)
if trigger == "session-interval.n_intervals"
if has_pvd_file(OutFile, user_dir)
# Read LaMEM *.pvd file
Timestep, _, Time = read_LaMEM_simulation(OutFile, user_dir)
# Update the labels and data stored in webpage about the last timestep
last_time = "$(Timestep[end])"
update_fig = "$(parse(Int,update_fig)+1)"
else
last_time = "0"
update_fig = "0"
end
elseif trigger == "button-run.n_clicks"
last_time = "0"
update_fig = "0"
end
return last_time, update_fig
end
# Update the figure if the signal is given to do so
callback!(app,
Dash.Output("label-timestep", "children"),
Dash.Output("label-time", "children"),
Dash.Output("current_timestep", "data"),
Dash.Output("figure_main", "figure"),
Dash.Output("plot_field", "options"),
Dash.Output("contour_option", "options"),
Input("update_fig", "data"),
Input("current_timestep", "data"),
Input("button-run", "n_clicks"),
Input("button-start", "n_clicks"),
Input("button-last", "n_clicks"),
Input("button-forward", "n_clicks"),
Input("button-back", "n_clicks"),
Input("button-play", "n_clicks"),
State("last_timestep", "data"),
State("session-id", "data"),
State("plot_field", "value"),
State("switch-contour", "value"),
State("contour_option", "value"),
State("switch-velocity", "value"),
State("color_map_option", "value"),
prevent_initial_call=true
) do update_fig, current_timestep, n_run, n_start, n_last, n_back, n_forward, n_play, last_timestep, session_id,
plot_field, switch_contour, contour_field, switch_velocity, color_map_option
trigger = get_trigger()
# Get info about timesteps
cur_t = parse(Int, current_timestep) # current timestep
last_t = parse(Int, last_timestep) # last timestep available on disk
fig_cross = []
fields_available = ["phase"]
if trigger == "current_timestep.data" ||
trigger == "update_fig.data" ||
trigger == "button-start.n_clicks" ||
trigger == "button-last.n_clicks" ||
trigger == "button-back.n_clicks" ||
trigger == "button-forward.n_clicks" ||
trigger == "button-play.n_clicks"
user_dir = simulation_directory(session_id, clean=false)
if has_pvd_file(OutFile, user_dir)
Timestep, _, Time = read_LaMEM_simulation(OutFile, user_dir) # all timesteps
id = findall(Timestep .== cur_t)[1]
if trigger == "button-start.n_clicks" || trigger == "button-play.n_clicks"
cur_t = 0
id = 1
elseif trigger == "button-last.n_clicks"
cur_t = Timestep[end]
id = length(Timestep)
elseif (trigger == "button-forward.n_clicks") && (id < length(Timestep))
cur_t = Timestep[id+1]
id = id + 1
elseif (trigger == "button-back.n_clicks") && (id > 1)
cur_t = Timestep[id-1]
id = id - 1
end
# Load data
x, y, data, time, fields_available = get_data(OutFile, cur_t, plot_field, user_dir)
add_contours = active_switch(switch_contour)
if add_contours
x_con, y_con, data_con, _, _ = get_data(OutFile, cur_t, contour_field, user_dir)
else
x_con, y_con, data_con = x, y, data
end
# update the plot
add_velocity = active_switch(switch_velocity)
fig_cross = create_main_figure(OutFile, cur_t, x, y, data, x_con, y_con, data_con;
add_contours=add_contours, contour_field=contour_field,
add_velocity=add_velocity,
colorscale=color_map_option,
session_id=session_id,
field=plot_field, cmaps=cmaps)
if trigger == "current_timestep.data" || trigger == "update_fig.data" || trigger == "button-play.n_clicks"
if cur_t < last_t
cur_t = Timestep[id+1] # update current timestep
end
end
else
time = 0
end
elseif trigger == "button-run.n_clicks"
cur_t = 0
time = 0.0
end
# update the labels
label_timestep = "Timestep: $cur_t"
label_time = "Time: $time Myrs"
current_timestep = "$cur_t"
# @show current_timestep
println("Timestep ", current_timestep)
return label_timestep, label_time, current_timestep, fig_cross, fields_available, fields_available
end
#
callback!(app,
Dash.Output("contour_option", "disabled"),
Input("switch-contour", "value")) do switch_contour
if !isnothing(switch_contour)
if isempty(switch_contour)
disable_contours = true
else
disable_contours = false
end
else
disable_contours = true
end
return disable_contours
end
run_server(app, host, port, debug=false)
end
end
| InteractiveGeodynamics | https://github.com/JuliaGeodynamics/InteractiveGeodynamics.jl.git |
|
[
"MIT"
] | 0.2.3 | c886092ba2e14a4500a3e0b99b298d104fe60b46 | code | 3382 | using DelimitedFiles
make_geometry_parameters() = nothing
"""
Returns an accordion menu containing the rheological parameters.
"""
function make_rheological_parameters()
return dbc_accordionitem(title="Rheological Parameters", [
make_accordion_item("η_up(log₁₀(Pa⋅s)):", "viscosity_upper", "Logarithm of the viscosity of the upper layers.", 21.0, 16.0, 23.0),
dbc_row(html_p()),
make_accordion_item("η_lo(log₁₀(Pa⋅s)):", "viscosity_lower", "Logarithm of the viscosity of the lower layer", 20.0, 16.0, 23.0),
dbc_row(html_p()),
make_accordion_item("ρ_up:", "density_upper", "Density of the upper layers.", 2800.0, 2.0, 5000.0),
dbc_row(html_p()),
make_accordion_item("ρ_lo:", "density_lower", "Density of the lower layer.", 2200.0, 2.0, 5000.0),
])
end
"""
Returns an accordion menu containing the simulation parameters.
"""
function make_simulation_parameters()
return dbc_accordionitem(title="Simulation Parameters", [
make_accordion_item("Width (km):", "domain_width", "Width of the domain, given in kilometers.", 10.0, 1.0e-10),
dbc_row(html_p()),
make_accordion_item("Depth of the interface (km):", "depth", "Depth of the interface, given in kilometers.", -2.5, -50.0),
dbc_row(html_p()),
make_accordion_item("nx:", "nel_x", "Number of elements in the x-direction. Must be an integer greater than 2.", 64, 2),
dbc_row(html_p()),
make_accordion_item("nz:", "nel_z", "Number of elements in the z-direction. Must be an integer greater than 2.", 32, 2),
dbc_row(html_p()),
make_accordion_item("nt:", "n_timesteps", "Maximum number of timesteps. Must be an integer greater than 1.", 50, 1),
dbc_row(html_p()),
dbc_row([
dbc_checklist(options=["FreeSurf"],
id="switch-FreeSurf",
switch=true,
)
]),
dbc_row(html_p()),
dbc_row([
dbc_checklist(options=["Layers"],
id="switch-Layers",
switch=true,
)
])
])
end
"""
Creates a setup with noisy temperature and one phase
"""
function CreateSetup(ParamFile, layered_overburden=false, Hi=-5.0, ampl_noise=0.1, ; args)
Grid = read_LaMEM_inputfile(ParamFile, args=args)
Phases = zeros(Int64, size(Grid.X));
Temp = zeros(Float64,size(Grid.X));
if layered_overburden
H_layer = 0.25;
for z_low = minimum(Grid.Z):2*H_layer:maximum(Grid.Z)
# print(z_low)
# z_low = -z_low
iz = (Grid.Z[1,1,:] .> z_low) .& (Grid.Z[1,1,:] .<= (z_low+H_layer) )
Phases[:,:,iz] .= 1;
end
end
z_int = [Hi + rand()*ampl_noise for _ in 1:Grid.nump_x]
# print(z_int)
# z_int = -z_int
for ix=1:Grid.nump_x, iy=1:Grid.nump_y
iz = Grid.Z[ix,iy,:] .< z_int[ix]
Phases[ix,iy,iz] .= 2;
end
# print(z_int)
Model3D = CartData(Grid, (Phases=Phases,Temp=Temp)) # Create LaMEM model
write_paraview(Model3D,"LaMEM_ModelSetup", verbose=false) # Save model to paraview (load with opening LaMEM_ModelSetup.vts in paraview)
save_LaMEM_markers_parallel(Model3D, directory="./markers", verbose=false) # save markers on one core
return nothing
end | InteractiveGeodynamics | https://github.com/JuliaGeodynamics/InteractiveGeodynamics.jl.git |
|
[
"MIT"
] | 0.2.3 | c886092ba2e14a4500a3e0b99b298d104fe60b46 | code | 12596 | module RisingSphereTools
using Dash, DashBootstrapComponents
using PlotlyJS
using LaMEM
using UUIDs
using Interpolations
using HTTP
export rising_sphere
pkg_dir = pkgdir(RisingSphereTools)
include(joinpath(pkg_dir,"src/dash_tools.jl"))
include(joinpath(pkg_dir,"src/RisingSphere/dash_functions_RisingSphere.jl"))
"""
rising_sphere(; host=HTTP.Sockets.localhost, port=8050)
This starts a rising sphere GUI
"""
function rising_sphere(; host=HTTP.Sockets.localhost, port=8050)
pkg_dir = pkgdir(RisingSphereTools)
GUI_version = "0.1.3"
cmaps = read_colormaps(dir_colormaps=joinpath(pkg_dir,"src/assets/colormaps/"))
title_app = "Rising Sphere example"
ParamFile = "RisingSphere.dat"
OutFile = "RiseSphere"
base_dir = pwd()
cd(base_dir)
# We also use a time-card that has maximum(Vz)
function make_time_card()
item = dbc_row([
html_p(),
dbc_card([
dbc_label(" Time : 0 Myrs", id="label-time"),
dbc_label(" Timestep : 0", id="label-timestep"),
dbc_label(" Maximum Vz : 0", id="label-max-vz"),
],
color="secondary",
class_name="mx-auto col-11",
outline=true),
html_p()])
return item
end
#app = dash(external_stylesheets=[dbc_themes.CYBORG])
app = dash(external_stylesheets = [dbc_themes.BOOTSTRAP], prevent_initial_callbacks=false)
app.title = title_app
# Main code layout
app.layout = html_div() do
dbc_container(className="mxy-auto", fluid=true, [
make_title(title_app),
dbc_row([
dbc_col([
make_plot("",cmaps), # show graph
make_plot_controls(), # show media buttons
make_id_label(), # show user id
]),
dbc_col([
make_time_card(), # show simulation time info
make_menu(cmaps), # show menu with simulation parameters, rheological parameters, and plotting parameters
make_run_button() # show the run simulation button
])
]),
# Store a unique number of our session in the webpage
dcc_store(id="session-id", data=""),
# Store info related to the simulation and current timestep
dcc_store(id="current_timestep", data="0"),
dcc_store(id="last_timestep", data="0"),
dcc_store(id="update_fig", data="0"),
# Start an interval that updates the number every second
dcc_interval(id="session-interval", interval=100, n_intervals=0, disabled=true)
])
end
# This creates an initial session id that is unique for this session
# it will run on first start
callback!(app,
Dash.Output("session-id", "data"),
Dash.Output("label-id", "children"),
Input("session-id", "data")
) do session_id
session_id = UUIDs.uuid4()
str = "id=$(session_id), v=$(GUI_version)"
return String("$(session_id)"), str
end
# Call run button
callback!(app,
Dash.Output("session-interval", "disabled"),
Input("button-run", "n_clicks"),
Input("button-run", "disabled"),
Input("button-play", "n_clicks"),
State("domain_width", "value"),
State("nel_x", "value"),
State("nel_z", "value"),
State("n_timesteps", "value"),
State("density_sphere", "value"),
State("density_matrix", "value"),
State("radius_sphere", "value"),
State("viscosity", "value"),
State("last_timestep", "data"),
State("plot_field", "value"),
State("session-id", "data"),
prevent_initial_call=true
) do n_run, active_run, n_play,
domain_width, nel_x, nel_z, n_timesteps,
sphere_density, matrix_density, sphere_radius, viscosity,
last_timestep, plot_field, session_id
trigger = get_trigger()
disable_interval = true
if trigger == "button-run.n_clicks"
cur_dir = pwd()
base_dir = joinpath(pkgdir(RisingSphereTools),"src","RisingSphere")
args = "-nstep_max $(n_timesteps) -radius[0] $sphere_radius -rho[0] $matrix_density -rho[1] $sphere_density -nel_x $nel_x -nel_z $nel_z -coord_x $(-domain_width/2),$(domain_width/2) -coord_z $(-domain_width/2),$(domain_width/2)"
# We clicked the run button
user_dir = simulation_directory(session_id, clean=true)
cd(user_dir)
pfile = joinpath(base_dir,ParamFile)
run_lamem(pfile, 1, args, wait=false)
disable_interval = false
cd(cur_dir) # return to main directory
elseif trigger == "button-run.disabled"
last_t = parse(Int, last_timestep)
if active_run == true || last_t < n_timesteps
disable_interval = false
end
elseif trigger == "button-play.n_clicks"
last_t = parse(Int, last_timestep)
@show last_t
disable_interval = false
end
return disable_interval
end
# deactivate the button
callback!(app,
Dash.Output("button-run", "disabled"),
Dash.Output("button-run", "color"),
Input("button-run", "n_clicks"),
Input("session-interval", "n_intervals"),
State("last_timestep", "data"),
State("current_timestep", "data"),
prevent_initial_call=true
) do n_run, n_inter, last_timestep, current_timestep
cur_t = parse(Int, current_timestep) # current timestep
last_t = parse(Int, last_timestep) # last timestep available on disk
if cur_t < last_t
button_run_disable = true
button_color = "danger"
else
button_run_disable = false
button_color = "primary"
end
return button_run_disable, button_color
end
# Check if *.pvd file on disk changed and a new timestep is available
callback!(app,
Dash.Output("last_timestep", "data"),
Dash.Output("update_fig", "data"),
Input("session-interval", "n_intervals"),
Input("button-run", "n_clicks"),
State("current_timestep", "data"),
State("update_fig", "data"),
State("session-id", "data"),
prevent_initial_call=true
) do n_inter, n_run, current_timestep, update_fig, session_id
trigger = get_trigger()
user_dir = simulation_directory(session_id, clean=false)
if trigger == "session-interval.n_intervals"
if has_pvd_file(OutFile, user_dir)
# Read LaMEM *.pvd file
Timestep, _, Time = read_LaMEM_simulation(OutFile, user_dir)
# Update the labels and data stored in webpage about the last timestep
last_time = "$(Timestep[end])"
update_fig = "$(parse(Int,update_fig)+1)"
else
last_time = "0"
update_fig = "0"
end
elseif trigger == "button-run.n_clicks"
last_time = "0"
update_fig = "0"
end
return last_time, update_fig
end
# Update the figure if the signal is given to do so
callback!(app,
Dash.Output("label-timestep", "children"),
Dash.Output("label-time", "children"),
Dash.Output("current_timestep", "data"),
Dash.Output("figure_main", "figure"),
Dash.Output("plot_field", "options"),
Dash.Output("contour_option", "options"),
Dash.Output("label-max-vz","children"),
Input("update_fig", "data"),
Input("current_timestep", "data"),
Input("button-run", "n_clicks"),
Input("button-start", "n_clicks"),
Input("button-last", "n_clicks"),
Input("button-forward", "n_clicks"),
Input("button-back", "n_clicks"),
Input("button-play", "n_clicks"),
State("last_timestep", "data"),
State("session-id", "data"),
State("plot_field", "value"),
State("switch-contour", "value"),
State("contour_option", "value"),
State("switch-velocity", "value"),
State("color_map_option", "value"),
prevent_initial_call=true
) do update_fig, current_timestep, n_run, n_start, n_last, n_back, n_forward, n_play, last_timestep, session_id,
plot_field, switch_contour, contour_field, switch_velocity, color_map_option
trigger = get_trigger()
# Get info about timesteps
cur_t = parse(Int, current_timestep) # current timestep
last_t = parse(Int, last_timestep) # last timestep available on disk
fig_cross = []
fields_available = ["phase"]
maxVz = 0
if trigger == "current_timestep.data" ||
trigger == "update_fig.data" ||
trigger == "button-start.n_clicks" ||
trigger == "button-last.n_clicks" ||
trigger == "button-back.n_clicks" ||
trigger == "button-forward.n_clicks" ||
trigger == "button-play.n_clicks"
user_dir = simulation_directory(session_id, clean=false)
if has_pvd_file(OutFile, user_dir)
Timestep, _, Time = read_LaMEM_simulation(OutFile, user_dir) # all timesteps
id = findall(Timestep .== cur_t)[1]
if trigger == "button-start.n_clicks" || trigger == "button-play.n_clicks"
cur_t = 0
id = 1
elseif trigger == "button-last.n_clicks"
cur_t = Timestep[end]
id = length(Timestep)
elseif (trigger == "button-forward.n_clicks") && (id < length(Timestep))
cur_t = Timestep[id+1]
id = id + 1
elseif (trigger == "button-back.n_clicks") && (id > 1)
cur_t = Timestep[id-1]
id = id - 1
end
# Load data
x, y, Vz, time, fields_available = get_data(OutFile, cur_t, "velocity_z", user_dir)
x, y, data, time, fields_available = get_data(OutFile, cur_t, plot_field, user_dir)
add_contours = active_switch(switch_contour)
if add_contours
x_con, y_con, data_con, _, _ = get_data(OutFile, cur_t, contour_field, user_dir)
else
x_con, y_con, data_con = x, y, data
end
# update the plot
add_velocity = active_switch(switch_velocity)
fig_cross = create_main_figure(OutFile, cur_t, x, y, data, x_con, y_con, data_con;
add_contours=add_contours, contour_field=contour_field,
add_velocity=add_velocity,
colorscale=color_map_option,
session_id=session_id,
field=plot_field, cmaps=cmaps)
if trigger == "current_timestep.data" || trigger == "update_fig.data" || trigger == "button-play.n_clicks"
if cur_t < last_t
cur_t = Timestep[id+1] # update current timestep
end
end
maxVz = maximum(Vz)
else
time = 0
end
elseif trigger == "button-run.n_clicks"
cur_t = 0
time = 0.0
end
# update the labels
label_timestep = "Timestep: $cur_t"
label_time = "Time : $time Myrs"
label_maxVz = "Maximum Vz : $maxVz cm/yr"
current_timestep = "$cur_t"
@show current_timestep
return label_timestep, label_time, current_timestep, fig_cross, fields_available, fields_available, label_maxVz
end
# Enable or disable contours
callback!(app,
Dash.Output("contour_option", "disabled"),
Input("switch-contour", "value")) do switch_contour
if !isnothing(switch_contour)
if isempty(switch_contour)
disable_contours = true
else
disable_contours = false
end
else
disable_contours = true
end
return disable_contours
end
run_server(app, host, port, debug=false)
return app
end
end | InteractiveGeodynamics | https://github.com/JuliaGeodynamics/InteractiveGeodynamics.jl.git |
|
[
"MIT"
] | 0.2.3 | c886092ba2e14a4500a3e0b99b298d104fe60b46 | code | 2348 | using DelimitedFiles
make_geometry_parameters() = nothing
"""
Returns an accordion menu containing the rheological parameters.
"""
function make_rheological_parameters()
return dbc_accordionitem(title="Rheological Parameters", [
make_accordion_item("ρₛ (kg/m³):", "density_sphere", "Density of the sphere in kg/m³ (0 < ρₛ ≤ 10_000.0).", 3000.0, 1.0e-10),
dbc_row(html_p()),
make_accordion_item("ρₘ (kg/m³):", "density_matrix", "Density of the matrix in kg/m³ (0 < ρₛ ≤ 10_000.0).", 3400.0, 1.0e-10),
dbc_row(html_p()),
make_accordion_item("rₛ (km):", "radius_sphere", "Radius of the sphere in kilometers (0 < rₛ ≤ Lₓ).", 0.1, 1.0e-10),
dbc_row(html_p()),
make_accordion_item("ηₘ (log₁₀(Pa⋅s)):", "viscosity", "Logarithm of the viscosity of the matrix (15 < ηₘ ≤ 25).", 25.0, 15.0, 25.0),
])
end
"""
Returns an accordion menu containing the simulation parameters.
"""
function make_simulation_parameters()
return dbc_accordionitem(title="Simulation Parameters", [
make_accordion_item("Lₓ (km):", "domain_width", "Width of the domain, given in kilometers.", 1.0, 1.0e-10),
dbc_row(html_p()),
make_accordion_item("nx:", "nel_x", "Number of elements in the x-direction. Must be an integer greater than 2.", 64, 2),
dbc_row(html_p()),
make_accordion_item("nz:", "nel_z", "Number of elements in the z-direction. Must be an integer greater than 2.", 64, 2),
dbc_row(html_p()),
make_accordion_item("nt:", "n_timesteps", "Maximum number of timesteps. Must be an integer greater than 1.", 30, 1),
])
end
"""
Creates a setup with noisy temperature and one phase
"""
function CreateSetup(ParamFile, ΔT=1000, ampl_noise=100; args)
Grid = read_LaMEM_inputfile(ParamFile, args=args)
Phases = zeros(Int64, size(Grid.X))
Temp = [ΔT / 2 + rand()*ampl_noise for _ in axes(Grid.X,1), _ in axes(Grid.X,2)]
Phases[Grid.Z.>0.0] .= 1
Temp[Grid.Z.>0.0] .= 0.0
Model3D = CartData(Grid, (Phases=Phases, Temp=Temp)) # Create LaMEM model
write_paraview(Model3D, "LaMEM_ModelSetup", verbose=false) # Save model to paraview (load with opening LaMEM_ModelSetup.vts in paraview)
save_LaMEM_markers_parallel(Model3D, directory="./markers", verbose=false) # save markers on one core
return nothing
end
| InteractiveGeodynamics | https://github.com/JuliaGeodynamics/InteractiveGeodynamics.jl.git |
|
[
"MIT"
] | 0.2.3 | c886092ba2e14a4500a3e0b99b298d104fe60b46 | code | 9969 | using GeoParams
using ForwardDiff, SparseArrays, SparseDiffTools, LinearAlgebra, Interpolations
av(x) = (x[2:end]+x[1:end-1])/2
"""
init_model(;nz=101, L=40e3, Geotherm=0, Ttop=400.0, Tbot=0.0, Δt=1e3*SecYear, MatParam=nothing)
Create initial model setup
"""
function init_model(;nz=101, L=40e3, Geotherm=0, Ttop=400.0, Tbot=0.0, Δt=1e3*SecYear, MatParam=nothing)
if isnothing(MatParam)
MatParam = (SetMaterialParams(Name="RockMelt", Phase=0,
Density = ConstantDensity(ρ=2700kg/m^3), # used in the parameterisation of Whittington
LatentHeat = ConstantLatentHeat(Q_L=2.55e5J/kg),
RadioactiveHeat = ExpDepthDependentRadioactiveHeat(H_0=0e-7Watt/m^3),
Conductivity = T_Conductivity_Whittington(), # T-dependent k
HeatCapacity = T_HeatCapacity_Whittington(), # T-dependent cp
Melting = MeltingParam_Assimilation() # Quadratic parameterization as in Tierney et al.
),
)
end
# Numerics
Told = zeros(nz)
T = zeros(nz)
ρ = zeros(nz)
Cp = zeros(nz)
dϕdT = zeros(nz)
ϕ = zeros(nz)
Hl = zeros(nz)
k = zeros(nz-1)
dz = L/(nz-1)
z = -L:dz:0
T = -Geotherm/1e3.*Vector(z) .+ Ttop
Phases = fill(0,nz)
Phases_c = fill(0,nz-1)
Params = (; Δt, k, ρ, Cp, dϕdT, ϕ, Hl, Told, Phases, Phases_c, MatParam, z)
N = (nz,)
BC = (; Ttop, Tbot)
Δ = (dz,)
return Params, BC, N, Δ, T, z
end
"""
Res!(F::AbstractArray, T::AbstractArray, Δ, N, BC)
"""
function Res!(F::AbstractVector{_T}, T::AbstractVector{_T}, Δ::NTuple, N::NTuple, BC::NamedTuple, Params::NamedTuple, MatParam) where _T<:Number
dz = Δ[1] # grid spacing
nz = N[1] # grid size
# Update material properties
args = (T = Params.Told .+273.15, )
args_c = (T = av(Params.Told) .+273.15, )
compute_conductivity!(Params.k, MatParam, Params.Phases_c, args_c)
compute_heatcapacity!(Params.Cp, MatParam, Params.Phases, args)
compute_density!(Params.ρ, MatParam, Params.Phases, args)
compute_dϕdT!(Params.dϕdT, MatParam, Params.Phases, args)
compute_meltfraction!(Params.ϕ, MatParam, Params.Phases, args)
compute_latent_heat!(Params.Hl, Params.MatParam, Params.Phases, args)
I = 2:nz-1
# ρ(Cp + Hₗ∂ϕ/∂T) ∂T/∂t = ∂/∂z(k ∂T/∂z)
F[2:end-1] = Params.ρ[I].*(Params.Cp[I] + Params.Hl[I].*Params.dϕdT[I]).*(T[I]-Params.Told[I])/Params.Δt - diff(Params.k .* diff(T)/dz)/dz;
F[1] = T[1] - BC.Tbot
F[nz] = T[nz] - BC.Ttop
return F
end
Res_closed! = (F,T) -> Res!(F, T, Δ, N, BC, Params, MatParam)
function LineSearch(func::Function, F, x, δx; α = [0.01 0.05 0.1 0.25 0.5 0.75 1.0])
Fnorm = zero(α)
N = length(x)
for i in eachindex(α)
func(F, x .+ α[i].*δx)
Fnorm[i] = norm(F)/N
end
_, i_opt = findmin(Fnorm)
return α[i_opt], Fnorm[i_opt]
end
"""
Usol = nonlinear_solution(Fup::Vector, U::Vector{<:AbstractArray}, J, colors; tol=1e-8, maxit=100)
Computes a nonlinear solution using a Newton method with line search.
`U` needs to be a vector of abstract arrays, which contains the initial guess of every field
`J` is the sparse jacobian matrix, and `colors` the coloring matrix, usually computed with `matrix_colors(J)`
"""
function nonlinear_solution(Fup::Vector, T::Vector, J, colors; tol=1e-8, maxit=100, verbose=true,
Δ, N, BC, Params, MatParam)
Res_closed! = (F,T) -> Res!(F, T, Δ, N, BC, Params, MatParam)
r = zero(Fup)
err = 1e3; it=0;
while err>tol && it<maxit
Res_closed!(r,T) # compute residual
forwarddiff_color_jacobian!(J, Res_closed!, T, colorvec = colors) # compute jacobian in an in-place manner
dT = J\-r # solve linear system:
α, err = LineSearch(Res_closed!, r, T, dT); # optimal step size
T += α*dT # update solution
it +=1;
if verbose; println(" Nonlinear iteration $it: error = $err, α=$α"); end
end
converged=false
return T, converged, it
end
function time_stepping(T, nt, Params, N, Δ, BC, MatParam; verbose=false, OutDir="test", OutFile="Thermal1D", PlotData=nothing)
# create a function with only 1 input parameter
CurDir = pwd()
if !isnothing(OutDir)
cd(OutDir)
end
# Initial sparsity pattern of matrix
nz = N[1]
J1 = Tridiagonal(ones(nz-1), ones(nz), ones(nz-1))
J1[1,2]=0; J1[2,1]=0; J1[nz-1,nz]=0; J1[nz,nz-1]=0
Jac = sparse(Float64.(abs.(J1).>0))
colors = matrix_colors(Jac)
io = open("$OutFile.pvd", "w")
time_yrs = 0.0
#Tline = Observable(Point2f.(T, Params.z/1e3))
# lines!(PlotData.ax1, Tline, color = :green)
PlotData.ax1.title="time=$(time_yrs)"
F = zero(T)
time = 0.0
SecYear = 3600*24*365.25
for it in 1:nt
T, converged, its = nonlinear_solution(F, T, Jac, colors, verbose=verbose, Δ=Δ, N=N, BC=BC, Params=Params, MatParam=MatParam)
Params.Told .= T
@show extrema(T), extrema(Params.ϕ)
time += Params.Δt
time_yrs = time/SecYear
# save file to disk
if mod(it,1)==0 & !isnothing(OutDir)
jldsave("test_$(it+10000).jld2"; Params.z, T, Params.ϕ, time)
writedlm(io, [it, time]) # update timestep in pvd file (really just a trick for the GUI)
end
if isnothing(OutDir)
empty!(PlotData.ax1)
lines!(PlotData.ax1, T, Params.z/1e3, color=:red)
PlotData.ax1.title = "$time_yrs years"
display(PlotData.fig)
end
@show time_yrs
end
if !isnothing(OutDir)
close(io)
end
cd(CurDir)
return T, Params.ϕ, time
end
crack_perp_displacement(z, d; r=5e3) = d.*(1.0 .- abs.(z)./(sqrt.(r^2 .+ z.^2)))
"""
Tadv = insert_sill!(T,z; Sill_thick=400, Sill_z0=-20e3, Sill_T=1200, SillType=:constant)
Adds a sill to the setup, using a 1D WENO5 advection scheme for a given temperature field `T` on a grid `z`.
Optional parameters are the sill thickness `Sill_thick`, the sill center `Sill_z0`, the sill temperature `Sill_T`.
Advection is done by `SillType`, which can be `:constant` (where rocks above/below are moved with constant displacement
or `:elastic`, where the displacement decreases with distance from the sill.
"""
function insert_sill(T,rocks, z; Sill_thick=400, Sill_z0=-20e3, Sill_T=1200, Sill_phase=1.0, SillType=:elastic)
# find points above & below sill emplacement level
z_shift = Vector(z) .- Sill_z0;
Displ = zero(z_shift)
# shift points above
id_above = findall(z_shift.>0)
id_below = findall(z_shift.<0)
# Assume constant displacement - in elastic case this should decrease with distance from sill
if SillType==:constant
Displ[id_above] .= Sill_thick
Displ[id_below] .= -Sill_thick
elseif SillType==:elastic
R = 5e3;
Displ[id_above] .= crack_perp_displacement(z_shift[id_above], Sill_thick; r=R)
Displ[id_below] .= -crack_perp_displacement(z_shift[id_below], Sill_thick; r=R)
end
# use WENO5 to advect the temperature field
T_adv = semilagrangian_advection(T, Displ, z)
# set sill temperature
ind = findall( abs.(z .- Sill_z0) .<= Sill_thick/2)
T_adv[ind] .= Sill_T
# use WENO5 to advect the rock field
rock_adv = semilagrangian_advection(rocks, Displ, z)
rock_adv[ind] .= Sill_phase
rock_adv = ceil.(rock_adv)
return T_adv, rock_adv
end
"""
Tadv = semilagrangian_advection(T, Displ, z)
Do semilagrangian_advection
"""
function semilagrangian_advection(T, Displ, z)
z_new = z + Displ # advect grid
interp_linear = linear_interpolation(z_new, T);
T_adv = interp_linear.(z)
return T_adv
end
#=
nz = 101
L = 40e3
Geotherm = 0; # K/km
Ttop = 400.0
Tbot = L/1e3*Geotherm
SecYear = 3600*24*365.25
Δt = 1e3*SecYear
MatParam = (SetMaterialParams(Name="RockMelt", Phase=0,
Density = ConstantDensity(ρ=2700kg/m^3), # used in the parameterisation of Whittington
LatentHeat = ConstantLatentHeat(Q_L=2.55e5J/kg),
RadioactiveHeat = ExpDepthDependentRadioactiveHeat(H_0=0e-7Watt/m^3),
Conductivity = T_Conductivity_Whittington(), # T-dependent k
HeatCapacity = T_HeatCapacity_Whittington(), # T-dependent cp
Melting = MeltingParam_Assimilation() # Quadratic parameterization as in Tierney et al.
),)
# Params, BC, N, Δ, T, z = init_model(nz=nz, L=L, Geotherm=Geotherm, Ttop=Ttop, Tbot=Tbot, Δt=Δt, MatParam=MatParam)
#N_2 = floor(Int64,(nz-1)/2)
#T[N_2-3:N_2+3] .+= 500
#Params.Told .= T
nt = 2
T, ϕ, t = time_stepping(T, nt, Params, N, Δ, BC, MatParam, verbose = false)
fig = make_subplots(
rows=1, cols=2,
column_widths=[0.6, 0.4],
row_heights=[1.0],
specs=[
Spec(kind= "xy") Spec(kind="xy")
]
)
add_trace!(
fig,
scatter(x=T,y=z/1e3),
row=1, col=1)
add_trace!(
fig,
scatter(x=ϕ,y=z/1e3),
row=1, col=2)
fig
=# | InteractiveGeodynamics | https://github.com/JuliaGeodynamics/InteractiveGeodynamics.jl.git |
|
[
"MIT"
] | 0.2.3 | c886092ba2e14a4500a3e0b99b298d104fe60b46 | code | 9532 | # GUI for GLMakie
using GLMakie
GLMakie.activate!()
GLMakie.closeall() # close any open screen
export sill_intrusion_1D
include("ThermalCode_1D.jl")
# Few helpers:
add_textbox(fig, label, value) = [Label(fig, label), Textbox(fig, stored_string = string(value), validator = typeof(value))]
add_togglebox(fig, label, active) = [Label(fig, label), Toggle(fig, active=active)]
get_valuebox(box::Vector) = parse(box[2].validator.val, box[2].stored_string.val)
"""
sill_intrusion_1D()
Interactive GLMakie App for 1D thermal intrusion model
"""
function sill_intrusion_1D()
fig = Figure(size=(900,900))
time_val = Observable(0.0)
Label(fig[0, 1:3], text = "1D sill injection in the crust", fontsize = 30)
ax1 = Axis(fig[1, 1], xlabel="Temperature [ᵒC]", ylabel="Depth [km]")
ax2 = Axis(fig[1, 2], xlabel="Melt fraction ϕ", title = @lift("t = $(round($time_val, digits = 2)) kyrs"))
ax3 = Axis(fig[2, 1:2], xlabel="Time [kyrs]", ylabel="Maximum Temperature [ᵒC]",ytickcolor=:red,ylabelcolor=:red,yticklabelcolor=:red)
ax4 = Axis(fig[2, 1:2], ylabel="Maximum melt fraction ϕ",ytickcolor=:blue,ylabelcolor=:blue,yticklabelcolor=:blue, yaxisposition = :right)
linkxaxes!(ax3, ax4)
fig[1:2, 3] = grid = GridLayout(tellwidth = false)
grid[1, 1:2] = but = Button(fig, label = " RUN SIMULATION ", buttoncolor = :lightgreen)
Box(grid[2:4, 1:2], color = :lightgrey, cornerradius = 10)
grid[2, 1:2] = Δz_box = add_textbox(fig,"Grid spacing Δz [m]:",20)
grid[3, 1:2] = nt_box = add_textbox(fig,"# timesteps nt:",150)
grid[4, 1:2] = Δt_yrs_box = add_textbox(fig,"timestep Δt [yrs]:",100.0)
Box(grid[5:7, 1:2], color = :lightblue, cornerradius = 10)
grid[5, 1:2] = H_box = add_textbox(fig,"Crustal thickness [km]:",40.0)
grid[6, 1:2] = Ttop_box = add_textbox(fig,"Ttop [ᵒC]:",0.0)
grid[7, 1:2] = γ_box = add_textbox(fig,"Geotherm [ᵒC/km]:",20.0)
Box(grid[8:12, 1:2], color = :lightyellow, cornerradius = 10)
grid[8, 1:2] = Tsill_box = add_textbox(fig,"Sill Temperature [ᵒC]:",1200.0)
grid[9, 1:2] = Sill_thick_box = add_textbox(fig,"Sill thickness [m]:",1000.0)
grid[10, 1:2] = Sill_interval_box = add_textbox(fig,"Sill injection interval [yrs]:",10000.0)
grid[11, 1:2] = Sill_interval_top_box = add_textbox(fig,"Top sill injection [km]:",10.0)
grid[12, 1:2] = Sill_interval_bot_box = add_textbox(fig,"Bottom sill injection [km]:",20.0)
Box(grid[13:15, 1:2], color = (:red,0.3), cornerradius = 10 )
grid[13, 1:2] = Ql_box = add_textbox(fig,"Latent heat [kJ/kg]:",255.0)
grid[14, 1:2] = menu_conduct = Menu(fig, options = ["T-dependent conductivity", "Constant conductivity 3 W/m/K"], default = "Constant conductivity 3 W/m/K")
grid[15, 1:2] = menu_melting = Menu(fig, options = ["MeltingParam_Assimilation", "MeltingParam_Basalt", "MeltingParam_Rhyolite"], default = "MeltingParam_Basalt")
Box(grid[16:17, 1:2], color = (:green,0.3), cornerradius = 10 )
grid[16, 1:2] = filename = [Label(fig, "filename:"), Textbox(fig, stored_string = "sim1.png")]
grid[17, 1:2] = but_save = Button(fig, label = " SAVE SCREENSHOT ", buttoncolor = (:lightgreen, 0.5))
on(but_save.clicks) do n
save(filename[2].stored_string.val, fig)
println("Save screenshot to $(joinpath(pwd(),filename[2].stored_string.val))")
end
rowsize!(fig.layout, 2, Relative(1/4))
SecYear = 3600*24*365.25
# Start the simulation
on(but.clicks) do n
# Retrieve data from GUI
SecYear = 3600*24*365.25
Δz = get_valuebox(Δz_box)
H = get_valuebox(H_box)
nz = floor(Int64, H*1e3/Δz)
nt = get_valuebox(nt_box)
γ = get_valuebox(γ_box)
Tsill = get_valuebox(Tsill_box)
Ttop = get_valuebox(Ttop_box)
Δt = get_valuebox(Δt_yrs_box)*SecYear
Silltop = get_valuebox(Sill_interval_top_box)
Sillbot = get_valuebox(Sill_interval_bot_box)
Sillthick = get_valuebox(Sill_thick_box)
Sill_int_yr = get_valuebox(Sill_interval_box)
Ql = get_valuebox(Ql_box)*1e3
conductivity = T_Conductivity_Whittington()
heatcapacity = T_HeatCapacity_Whittington()
if menu_conduct.selection[]=="Constant conductivity 3 W/m/K"
conductivity = ConstantConductivity(k=3.0)
heatcapacity = ConstantHeatCapacity()
end
melting = MeltingParam_Smooth3rdOrder()
if menu_melting.selection[]=="MeltingParam_Assimilation"
melting = MeltingParam_Assimilation()
elseif menu_melting.selection[]=="MeltingParam_Rhyolite"
melting = MeltingParam_Smooth3rdOrder(a=3043.0,b=−10552.0, c=12204.9,d=−4709.0)
end
MatParam = (SetMaterialParams(Name="RockMelt", Phase=0,
Density = ConstantDensity(ρ=2700kg/m^3), # used in the parameterisation of Whittington
LatentHeat = ConstantLatentHeat(Q_L=Ql*J/kg),
RadioactiveHeat = ExpDepthDependentRadioactiveHeat(H_0=0e-7Watt/m^3),
Conductivity = conductivity, # T-dependent k
HeatCapacity = heatcapacity, # T-dependent cp
Melting = melting # Quadratic parameterization as in Tierney et al.
),)
@info "parameters" nz, H, γ, Tsill, Ttop, nz
Tbot = Ttop + H*γ
# setup model
Params, BC, N, Δ, T, z = init_model(nz=nz, L=H*1e3, Geotherm=γ, Ttop=Ttop, Tbot=Tbot, Δt=Δt, MatParam=MatParam)
rocks = zero(T) # will later contain locations with injected sills
# add initial perturbation (if any)
T_cen = (Silltop + Sillbot)/2*1e3
ind = findall( abs.(z .+ T_cen) .< Sillthick/2)
if !isempty(ind)
T[ind] .= Tsill
rocks[ind] .= 1
end
Params.Told .= T
# create initial plot
PlotData = (;ax1, ax2, fig)
println("Running simulation $n")
# timestepping
F = zero(T)
time = 0.0
timevec =Observable([0.0, 1.0])
Tmaxvec =Observable([0.0, 1.0])
Tplot = Observable(T)
ϕplot = Observable(Params.ϕ)
empty!(ax1)
lines!(ax1, Tplot, z/1e3, color=:red)
ax1.limits=(minimum(T)-10, maximum(T)+10,extrema(z/1e3)...)
empty!(ax2)
lines!(ax2, ϕplot, z/1e3, color=:blue)
ax2.limits=(-1e-1,1+1e-1,extrema(z/1e3)...)
xlims!(ax3, 0, nt*Δt/SecYear/1e3)
xlims!(ax4, 0, nt*Δt/SecYear/1e3)
# Get initial sparsity pattern of matrix
nz = N[1]
J1 = Tridiagonal(ones(nz-1), ones(nz), ones(nz-1))
J1[1,2] = 0; J1[2,1]=0; J1[nz-1,nz]=0; J1[nz,nz-1]=0
Jac = sparse(Float64.(abs.(J1).>0))
colors = matrix_colors(Jac)
time_vec = Float64[]
Tmax_vec = Float64[]
ϕmax_vec = Float64[]
Sill_z0 = -20e3;
println("Injecting sill @ z=$Sill_z0")
# perform timestepping
crust_added = Sillthick/1e3
crust_added_numerics = sum(rocks)*Δz/1e3
@async for t = 1:nt
T, converged, its = nonlinear_solution(F, T, Jac, colors, verbose=false, Δ=Δ, N=N, BC=BC, Params=Params, MatParam=MatParam)
if mod(time/SecYear, Sill_int_yr)==0 && t>1
Sill_z0 = rand(-Sillbot*1e3:1:-Silltop*1e3)
T, rocks = insert_sill(T,rocks, z, Sill_thick=Sillthick, Sill_z0=Sill_z0, Sill_T=Tsill)
Params.Told .= T
crust_added += Sillthick/1e3
crust_added_numerics = sum(rocks)*Δz/1e3
println("Injecting sill @ z=$Sill_z0")
end
Params.Told .= T
time += Params.Δt
time_kyrs = time/SecYear/1e3
push!(time_vec, time_kyrs)
push!(Tmax_vec, maximum(T))
push!(ϕmax_vec, maximum(Params.ϕ))
# save file to disk
if mod(t,1)==0
Tplot[] = T
ϕplot[] = Params.ϕ
time_val[] = time_kyrs
empty!(ax2)
rock_low = Point2f.(zero(rocks), z/1e3)
rock_high = Point2f.(rocks, z/1e3)
band!(ax2, rock_low, rock_high, color=(:lightgrey,1.0))
lines!(ax2, Params.ϕ, z/1e3, color=:blue)
empty!(ax3)
lines!(ax3, time_vec, Tmax_vec, color=:red)
scatter!(ax3, time_vec[end], Tmax_vec[end], color=:red)
ylims!(ax3, minimum(Tmax_vec)-10,maximum(Tmax_vec)+10)
empty!(ax4)
lines!(ax4, time_vec, ϕmax_vec, color=:blue)
scatter!(ax4, time_vec[end], ϕmax_vec[end], color=:blue)
ylims!(ax4, 0, 1.01)
println("Timestep $t, $time_kyrs kyrs, nz=$(length(T)) pts; crust added: $crust_added ($crust_added_numerics) km")
end
end
end
display(fig)
return nothing
end
#sill_intrusion_1D() | InteractiveGeodynamics | https://github.com/JuliaGeodynamics/InteractiveGeodynamics.jl.git |
|
[
"MIT"
] | 0.2.3 | c886092ba2e14a4500a3e0b99b298d104fe60b46 | docs | 3670 | # InteractiveGeodynamics.jl
This package provides a range of graphical user interfaces (GUI's) to study and experiment with different geodynamic problems without need to program. It uses [julia](https://julialang.org) and [Dash](https://dash.plotly.com/julia) and automatically installs the required geodynamic software (such as [LaMEM](https://github.com/JuliaGeodynamics/LaMEM.jl)) in the background.
This is particularly useful for teaching.
We currently have the following examples included:
- `sill_intrusion_1D()` - 1D thermal model of sill intrusion in the crust
- `convection()` - 2D mantle (or magma chamber) convection
- `rayleigh_taylor()` - density driven instability
- `rising_sphere()` - rising stokes sphere example
- `subduction()` - subduction of a single plate
- `folding()` - folding of one or more viscous layers
### Getting started/requirements
Installing this is straightforward. Start julia
```julia
kausb$ julia
_
_ _ _(_)_ | Documentation: https://docs.julialang.org
(_) | (_) (_) |
_ _ _| |_ __ _ | Type "?" for help, "]?" for Pkg help.
| | | | | | |/ _` | |
| | |_| | | | (_| | | Version 1.9.3 (2023-08-24)
_/ |\__'_|_|_|\__'_| | Official https://julialang.org/ release
|__/ |
julia>
```
1) Go to the package manager by pressing `]` and type:
```julia
julia>]
(@v1.9) pkg> add InteractiveGeodynamics
```
2) Then download all required packages with
```julia
(@v1.9) pkg> instantiate
```
This can take some time the first time you do this. Note that step 1 & 2 only have to be done once. Go back to the main command window with backspace.
3) Start the GUI:
```julia
julia> using InteractiveGeodynamics
julia> convection()
[ Info: Listening on: 127.0.0.1:8050, thread id: 1
```
It will take a bit of time (to precompile/download all required packages). Next, open tye displayed web address in your browser (127.0.0.1:8050 in this case) and it will start a GUI. After pushing `Run`, you'll get something that looks like this:
```julia
julia>
Timestep 0
args = -nstep_max 250 -eta_fk[0] 1.0e21 -gamma_fk[0] 0.001 -TRef_fk[0] 1000.0 -ch[0] 5.0e8 -nel_x 128 -nel_z 64 -coord_x -1000.0,1000.0 -coord_z -1000,0 -coord_y -7.8125,7.8125 -temp_bot 2000
Timestep 1
Timestep 10
Timestep 15
Timestep 15
Timestep 20
Timestep 25
Timestep 30
```
### Running the examples
Running the other examples is straightforward. For example, the Rayleigh-Taylor example can be started with:
```julia
julia> using InteractiveGeodynamics
julia> rayleigh_taylor()
[ Info: Listening on: 127.0.0.1:8050, thread id: 1
Tiestep 0
args = -nstep_max 50 -eta[0] 1.0e21 -eta[1] 1.0e21 -eta[2] 1.0e20 -rho[0] 2800 -rho[1] 2800 -rho[2] 2200 -open_top_bound 0 -nel_x 32 -nel_z 16 -coord_x -5.0,5.0
Timestep 1
Timestep 5
Timestep 10
Timestep 15
Timestep 20
Timestep 25
```
### Available examples
We currently have the following LaMEM GUI's available, that can be started in the saame way:
- `convection()`
- `rising_sphere()`
- `rayleigh_taylor()`
- `subduction()` - subduction of a single plate
- `folding()` - folding of one or more viscous layers
### 1D thermal models following sill intrusion in the crust
We also have a GUI that relies on [GLMakie](docs.makie.org), which is an optional dependency.
This implies that the examples become available once you load `GLMakie` as well.
You can start this with:
```julia
julia> using InteractiveGeodynamics, GLMakie
julia> sill_intrusion_1D()
```
| InteractiveGeodynamics | https://github.com/JuliaGeodynamics/InteractiveGeodynamics.jl.git |
|
[
"MIT"
] | 0.2.3 | c886092ba2e14a4500a3e0b99b298d104fe60b46 | docs | 1108 | # Security Policy
We take security issues seriously. We appreciate all efforts
to responsibly disclose any security issues and will make every
effort to acknowledge contributions.
## Supported Versions
The current stable release following the interpretation of
[semantic versioning (SemVer)](https://julialang.github.io/Pkg.jl/dev/compatibility/#Version-specifier-format-1)
used in the Julia ecosystem is supported with security updates.
## Reporting a Vulnerability
To report a security issue, please use the GitHub Security Advisory
["Report a Vulnerability"](https://github.com/JuliaGeodynamics/InteractiveGeodynamics.jl/security/advisories/new)
tab.
We will send a response indicating the next steps in handling your report.
After the initial reply to your report, we will keep you informed of the
progress towards a fix and full announcement, and may ask for additional
information or guidance.
Please report security bugs in third-party modules directly to the person
or team maintaining the module.
Public notifications of vulnerabilities will be shared in community channels
such as Slack.
| InteractiveGeodynamics | https://github.com/JuliaGeodynamics/InteractiveGeodynamics.jl.git |
|
[
"MIT"
] | 0.1.1 | e72e157c08773595052733e8b39944a49b0109d9 | code | 530 | baremodule Terminators
function withtimeout end
function open end
function start end
function stop end
"""
Terminators.withtimeout(f, seconds::Number, [terminator])
Run `f()` with the timeout `seconds`. This process is terminated if `f()` does
not finish after `seconds` seconds.
"""
withtimeout
"""
Terminators.open() -> terminator
Terminators.open(f)
Create a terminator.
"""
open
module Internal
using ..Terminators: Terminators
include("internal.jl")
end # module Internal
end # baremodule Terminators
| Terminators | https://github.com/tkf/Terminators.jl.git |
|
[
"MIT"
] | 0.1.1 | e72e157c08773595052733e8b39944a49b0109d9 | code | 2203 | abstract type AbstractTerminator end
struct Terminator <: AbstractTerminator
proc::Base.Process
end
struct NullTerminator <: AbstractTerminator end
function check_supported()
# "OS" APIs used in ./server.jl
isdir("/proc/$(getpid())") || return false
try
read(`kill -l`)
catch
return false
end
return true
end
const IS_SUPPORTED = check_supported()
function Terminators.open(f)
p = Terminators.open()
try
f()
finally
close(p)
end
end
function Terminators.open()
IS_SUPPORTED || return NullTerminator()
julia = Base.julia_cmd()
script = joinpath(@__DIR__, "server.jl")
cmd = `$julia --startup-file=no $script -- $(getpid())`
proc = open(cmd; read = true, write = true)
return Terminator(proc)
end
function Base.close(p::Terminator)
close(p.proc)
wait(p.proc)
end
Base.close(::NullTerminator) = nothing
const LOCK = ReentrantLock()
const REF = Base.Ref{Union{Nothing,Terminator}}(nothing)
function global_terminator()
IS_SUPPORTED || return NullTerminator()
lock(LOCK) do
p = REF[]
p isa Terminator && return p
p = REF[] = Terminators.open()
atexit() do
close(p)
end
return p
end
end
const TIMER_ID = Ref{UInt}(0)
function wait_ack(p::Terminator, id::UInt)
ln = readline(p.proc)
@assert ln == "ack $id"
end
function Terminators.start(
timeout::Number,
p::AbstractTerminator = global_terminator();
label = "",
)
p isa NullTerminator && return UInt(0)
timeout = Float64(timeout)
id = TIMER_ID[] += 1
write(p.proc, "start $id $timeout $label\n")
flush(p.proc)
wait_ack(p, id)
return id
end
function Terminators.stop(id::UInt, p::AbstractTerminator = global_terminator())
p isa NullTerminator && return UInt(0)
write(p.proc, "stop $id 0 \n")
flush(p.proc)
wait_ack(p, id)
return id
end
function Terminators.withtimeout(
f,
timeout::Number,
p::AbstractTerminator = global_terminator();
kwargs...,
)
id = Terminators.start(timeout, p; kwargs...)
try
return f()
finally
Terminators.stop(id, p)
end
end
| Terminators | https://github.com/tkf/Terminators.jl.git |
|
[
"MIT"
] | 0.1.1 | e72e157c08773595052733e8b39944a49b0109d9 | code | 1497 | function terminator_loop(output::IO, input::IO, ppid::Int)
mypid = getpid()
timers = Dict{UInt,Timer}()
for ln in eachline(input)
@debug "[$mypid] GOT: $ln"
sop, sid, stime, label = split(ln; limit = 4, keepempty = true)
id = parse(UInt, sid)
time = parse(Float64, stime)
print(output, "ack $sid\n")
flush(output)
if sop == "start"
timer = Timer(time) do _
msglabel = label == "" ? "" : " for $label;"
@error "[$mypid] Timeout ($time)$msglabel terminating process $ppid"
isactive() = isdir("/proc/$ppid")
function signalling(sig, n = typemax(Int))
@info "[$mypid] Trying to terminate process $ppid with $sig"
for _ in 1:n
run(`kill -s $sig $ppid`)
sleep(0.1)
isactive() || return true
end
return false
end
signalling("SIGINT", 10) && return
signalling("SIGTERM", 10) && return
signalling("SIGHUP", 100) && return
signalling("SIGKILL")
end
timers[id] = timer
elseif sop == "stop"
close(pop!(timers, id))
else
error("unknown op: $sop")
end
end
end
if abspath(PROGRAM_FILE) == @__FILE__
terminator_loop(stdout, stdin, parse(Int, ARGS[1]))
end
| Terminators | https://github.com/tkf/Terminators.jl.git |
|
[
"MIT"
] | 0.1.1 | e72e157c08773595052733e8b39944a49b0109d9 | code | 49 | using TestFunctionRunner
TestFunctionRunner.@run
| Terminators | https://github.com/tkf/Terminators.jl.git |
|
[
"MIT"
] | 0.1.1 | e72e157c08773595052733e8b39944a49b0109d9 | code | 108 | module TerminatorsTests
include("utils.jl")
include("test_withtimeout.jl")
end # module TerminatorsTests
| Terminators | https://github.com/tkf/Terminators.jl.git |
|
[
"MIT"
] | 0.1.1 | e72e157c08773595052733e8b39944a49b0109d9 | code | 635 | module TestWithTimeout
using Terminators
using Test
using ..Utils: exec
function test_no_timeout()
ok = Ref(false)
Terminators.withtimeout(10) do
ok[] = true
end
@test ok[]
end
function test_timeout()
proc = Terminators.withtimeout(300) do
exec("""
using Terminators
Terminators.withtimeout(0.1) do
while true
$(VERSION ≥ v"1.4" ? "GC.safepoint()" : "yield()")
end
end
""")
end
@test !success(proc)
@test occursin("Timeout", proc.stderr)
@test occursin("terminating process", proc.stderr)
end
end # module
| Terminators | https://github.com/tkf/Terminators.jl.git |
|
[
"MIT"
] | 0.1.1 | e72e157c08773595052733e8b39944a49b0109d9 | code | 1696 | module Utils
struct CompletedProcess
stdout::String
stderr::String
proc::Base.Process
end
function open_stdin_new(f, cmd)
out = IOBuffer()
err = IOBuffer()
cmd = pipeline(cmd; stderr = err, stdout = out)
proc = open(cmd, write = true) do proc
f(proc)
return proc
end
completed = CompletedProcess(String(take!(out)), String(take!(err)), proc)
end
function open_stdin_old(f, cmd)
inp = Pipe()
out = Pipe()
err = Pipe()
cmd = pipeline(cmd; stdin = inp, stdout = out, stderr = err)
proc = run(cmd; wait = false)
close(out.in)
close(err.in)
outstr = Ref{String}()
errstr = Ref{String}()
@sync begin
@async outstr[] = read(out, String)
@async errstr[] = read(err, String)
try
f(inp)
finally
close(inp)
end
wait(proc)
end
return CompletedProcess(outstr[], errstr[], proc)
end
if VERSION < v"1.3-"
open_stdin(args...) = open_stdin_old(args...)
else
open_stdin(args...) = open_stdin_new(args...)
end
function exec(code)
julia = Base.julia_cmd()
script = "include_string(Main, read(stdin, String))"
cmd = `$julia --startup-file=no -e $script`
setup = Base.load_path_setup_code()
cmd = ignorestatus(cmd)
completed = open_stdin(cmd) do input
write(input, setup)
println(input)
write(input, code)
close(input)
end
@debug(
"Done `exec(code)`",
code = Text(code),
stdout = Text(completed.stdout),
stderr = Text(completed.stderr),
)
return completed
end
Base.success(c::CompletedProcess) = success(c.proc)
end # module
| Terminators | https://github.com/tkf/Terminators.jl.git |
|
[
"MIT"
] | 0.1.1 | e72e157c08773595052733e8b39944a49b0109d9 | docs | 170 | # Terminators.jl: adding timeout to your code
```julia
using Terminators
Terminators.withtimeout(1) do
sleep(3) # too slow, the process will be terminated
end
```
| Terminators | https://github.com/tkf/Terminators.jl.git |
|
[
"BSD-3-Clause"
] | 0.1.0 | 65c128d9fd34d1fd3e6886d1a31fd46cbb3b710c | code | 725 | #=
Original Author: Anastasia Yendiki
Copyright © 2022 The General Hospital Corporation (Boston, MA) "MGH"
Terms and conditions for use, reproduction, distribution and contribution
are found in the 'FreeSurfer Software License Agreement' contained
in the file 'LICENSE' found in the FreeSurfer distribution, and here:
https://surfer.nmr.mgh.harvard.edu/fswiki/FreeSurferSoftwareLicense
Reporting: [email protected]
=#
module FreeSurfer
include("mri.jl")
include("trk.jl")
include("view.jl")
include("dti.jl")
function __init__()
println("FREESURFER_HOME: " * (haskey(ENV, "FREESURFER_HOME") ?
ENV["FREESURFER_HOME"] : "not defined"))
end
end # module
| FreeSurfer | https://github.com/lincbrain/Fibers.jl.git |
|
[
"BSD-3-Clause"
] | 0.1.0 | 65c128d9fd34d1fd3e6886d1a31fd46cbb3b710c | code | 4076 | #=
Original Author: Anastasia Yendiki
Copyright © 2022 The General Hospital Corporation (Boston, MA) "MGH"
Terms and conditions for use, reproduction, distribution and contribution
are found in the 'FreeSurfer Software License Agreement' contained
in the file 'LICENSE' found in the FreeSurfer distribution, and here:
https://surfer.nmr.mgh.harvard.edu/fswiki/FreeSurferSoftwareLicense
Reporting: [email protected]
=#
using LinearAlgebra, Statistics
export DTI, dti_fit, dti_write
"Container for outputs of a DTI fit"
struct DTI
s0::MRI
eigval1::MRI
eigval2::MRI
eigval3::MRI
eigvec1::MRI
eigvec2::MRI
eigvec3::MRI
rd::MRI
md::MRI
fa::MRI
end
"""
dti_fit_ls(dwi::MRI, mask:MRI)
Fit tensors to DWIs and return a `DTI` structure.
"""
function dti_fit(dwi::MRI, mask::MRI)
dti_fit_ls(dwi::MRI, mask::MRI)
end
"""
dti_fit_ls(dwi::MRI, mask:MRI)
Perform least-squares fitting of tensors from DWIs and return a `DTI` structure.
"""
function dti_fit_ls(dwi::MRI, mask::MRI)
if isempty(dwi.bval)
error("Missing b-value table from input DWI structure")
end
if isempty(dwi.bvec)
error("Missing gradient table from input DWI structure")
end
ib0 = (dwi.bval .== minimum(dwi.bval))
A = hcat(dwi.bvec[:,1].^2, 2*dwi.bvec[:,1].*dwi.bvec[:,2],
2*dwi.bvec[:,1].*dwi.bvec[:,3], dwi.bvec[:,2].^2,
2*dwi.bvec[:,2].*dwi.bvec[:,3], dwi.bvec[:,3].^2)
A = hcat(-dwi.bval .* A, ones(size(A, 1), 1))
pA = pinv(A)
S0 = MRI(mask, 1)
Eval1 = MRI(mask, 1)
Eval2 = MRI(mask, 1)
Eval3 = MRI(mask, 1)
Evec1 = MRI(mask, 3)
Evec2 = MRI(mask, 3)
Evec3 = MRI(mask, 3)
Threads.@threads for iz in 1:size(dwi.vol, 3)
for iy in 1:size(dwi.vol, 2)
for ix in 1:size(dwi.vol, 1)
mask.vol[ix, iy, iz] == 0 && continue
# Only use positive DWI values to fit the model
ipos = dwi.vol[ix, iy, iz, :] .> 0
npos = sum(ipos)
if npos == length(dwi.bval)
D = pA * log.(dwi.vol[ix, iy, iz, :])
elseif npos > 6
sum(ipos .&& ib0) == 0 && continue
D = pinv(A[ipos, :]) * log.(dwi.vol[ix, iy, iz, ipos])
else
continue
end
S0.vol[ix, iy, iz] = exp(D[7])
E = eigen([D[1] D[2] D[3];
D[2] D[4] D[5];
D[3] D[5] D[6]])
Eval1.vol[ix, iy, iz] = E.values[3]
Eval2.vol[ix, iy, iz] = E.values[2]
Eval3.vol[ix, iy, iz] = E.values[1]
Evec1.vol[ix, iy, iz, :] = E.vectors[:, 3]
Evec2.vol[ix, iy, iz, :] = E.vectors[:, 2]
Evec3.vol[ix, iy, iz, :] = E.vectors[:, 1]
end
end
end
return DTI(S0, Eval1, Eval2, Eval3, Evec1, Evec2, Evec3,
dti_maps(Eval1, Eval2, Eval3)...)
end
"""
dti_maps(eigval1::MRI, eigval2::MRI, eigval3::MRI)
Compute radial diffusivity (RD), mean diffusivity (MD), and fractional
anisotropy (FA) maps from the 3 eigenvalues the diffusion tensors.
Return RD, MD, and FA maps as MRI structures.
"""
function dti_maps(eigval1::MRI, eigval2::MRI, eigval3::MRI)
rd = MRI(eigval1)
md = MRI(eigval1)
fa = MRI(eigval1)
imask = (eigval1.vol .!= 0)
rd.vol[imask] = eigval1.vol[imask] + eigval2.vol[imask]
md.vol[imask] = (rd.vol[imask] + eigval3.vol[imask]) / 3
rd.vol[imask] /= 2
fa.vol[imask] = sqrt.(((eigval1.vol[imask] - md.vol[imask]).^2 +
(eigval2.vol[imask] - md.vol[imask]).^2 +
(eigval3.vol[imask] - md.vol[imask]).^2) ./
(eigval1.vol[imask].^2 +
eigval2.vol[imask].^2 +
eigval3.vol[imask].^2) * 3/2)
return rd, md, fa
end
"""
dti_write(dti::DTI, basename::String)
Write the volumes from a `DTI` structure that was created by `dti_fit()`
to files whose names start with the specified base name.
"""
function dti_write(dti::DTI, basename::String)
for var in fieldnames(DTI)
mri_write(getfield(dti, var), basename * "_" * string(var) * ".nii.gz")
end
end
| FreeSurfer | https://github.com/lincbrain/Fibers.jl.git |
|
[
"BSD-3-Clause"
] | 0.1.0 | 65c128d9fd34d1fd3e6886d1a31fd46cbb3b710c | code | 50539 | #=
Original Author: Anastasia Yendiki
Copyright © 2022 The General Hospital Corporation (Boston, MA) "MGH"
Terms and conditions for use, reproduction, distribution and contribution
are found in the 'FreeSurfer Software License Agreement' contained
in the file 'LICENSE' found in the FreeSurfer distribution, and here:
https://surfer.nmr.mgh.harvard.edu/fswiki/FreeSurferSoftwareLicense
Reporting: [email protected]
=#
#=
File i/o for nii/mgh files based on MATLAB code by Doug Greve, Bruce Fischl:
MRIfspec.m
MRIread.m
MRIwrite.m
load_mgh.m
load_nifti.m
load_nifti_header.m
save_nifti.m
fsgettmppath.m
vox2ras_0to1.m
vox2ras_tkreg.m
vox2rasToQform.m
=#
using LinearAlgebra, Printf, DelimitedFiles
export MRI, NIfTIheader, get_tmp_path, mri_filename, mri_read, mri_write,
mri_read_bfiles, mri_read_bfiles!
"Container for header and image data of a volume stored in NIfTI format"
mutable struct NIfTIheader
# NIfTI standard header fields
sizeof_hdr::Int32
data_type::Vector{UInt8}
db_name::Vector{UInt8}
extents::Int32
session_error::Int16
regular::UInt8
dim_info::UInt8
dim::Vector{UInt16}
intent_p1::Float32
intent_p2::Float32
intent_p3::Float32
intent_code::Int16
datatype::Int16
bitpix::Int16
slice_start::Int16
pixdim::Vector{Float32}
vox_offset::Float32
scl_slope::Float32
scl_inter::Float32
slice_end::Int16
slice_code::Int8
xyzt_units::Int8
cal_max::Float32
cal_min::Float32
slice_duration::Float32
toffset::Float32
glmax::Int32
glmin::Int32
descrip::Vector{UInt8}
aux_file::Vector{UInt8}
qform_code::Int16
sform_code::Int16
quatern_b::Float32
quatern_c::Float32
quatern_d::Float32
quatern_x::Float32
quatern_y::Float32
quatern_z::Float32
srow_x::Vector{Float32}
srow_y::Vector{Float32}
srow_z::Vector{Float32}
intent_name::Vector{UInt8}
magic::Vector{UInt8}
# Additional fields
do_bswap::UInt8
sform::Matrix{Float32}
qform::Matrix{Float32}
vox2ras::Matrix{Float32}
vol::Array
end
"""
NIfTIheader()
Return an empty `NIfTIheader` structure
"""
NIfTIheader() = NIfTIheader(
Int32(0),
Vector{UInt8}(undef, 0),
Vector{UInt8}(undef, 0),
Int32(0),
Int16(0),
UInt8(0),
UInt8(0),
Vector{UInt16}(undef, 0),
Float32(0),
Float32(0),
Float32(0),
Int16(0),
Int16(0),
Int16(0),
Int16(0),
Vector{Float32}(undef, 0),
Float32(0),
Float32(0),
Float32(0),
Int16(0),
Int8(0),
Int8(0),
Float32(0),
Float32(0),
Float32(0),
Float32(0),
Int32(0),
Int32(0),
Vector{UInt8}(undef, 0),
Vector{UInt8}(undef, 0),
Int16(0),
Int16(0),
Float32(0),
Float32(0),
Float32(0),
Float32(0),
Float32(0),
Float32(0),
Vector{Float32}(undef, 0),
Vector{Float32}(undef, 0),
Vector{Float32}(undef, 0),
Vector{UInt8}(undef, 0),
Vector{UInt8}(undef, 0),
UInt8(0),
Matrix{Float32}(undef, 0, 0),
Matrix{Float32}(undef, 0, 0),
Matrix{Float32}(undef, 0, 0),
[]
)
"Container for header and image data of an MRI volume or volume series"
mutable struct MRI
vol::Array
ispermuted::Bool
niftihdr::NIfTIheader
fspec::String
pwd::String
flip_angle::Float32
tr::Float32
te::Float32
ti::Float32
vox2ras0::Matrix{Float32}
volsize::Vector{Int32}
height::Int32
width::Int32
depth::Int32
nframes::Int32
vox2ras::Matrix{Float32}
nvoxels::Int32
xsize::Float32
ysize::Float32
zsize::Float32
x_r::Float32
x_a::Float32
x_s::Float32
y_r::Float32
y_a::Float32
y_s::Float32
z_r::Float32
z_a::Float32
z_s::Float32
c_r::Float32
c_a::Float32
c_s::Float32
vox2ras1::Matrix{Float32}
Mdc::Matrix{Float32}
volres::Vector{Float32}
tkrvox2ras::Matrix{Float32}
bval::Vector{Number}
bvec::Matrix{Number}
end
"""
MRI()
Return an empty `MRI` structure
"""
MRI() = MRI(
[],
false,
NIfTIheader(),
"",
"",
Float32(0),
Float32(0),
Float32(0),
Float32(0),
Matrix{Float32}(undef, 0, 0),
Vector{Int32}(undef, 0),
Int32(0),
Int32(0),
Int32(0),
Int32(0),
Matrix{Float32}(undef, 0, 0),
Int32(0),
Float32(0),
Float32(0),
Float32(0),
Float32(0),
Float32(0),
Float32(0),
Float32(0),
Float32(0),
Float32(0),
Float32(0),
Float32(0),
Float32(0),
Float32(0),
Float32(0),
Float32(0),
Matrix{Float32}(undef, 0, 0),
Matrix{Float32}(undef, 0, 0),
Vector{Float32}(undef, 0),
Matrix{Float32}(undef, 0, 0),
Vector{Number}(undef, 0),
Matrix{Number}(undef, 0, 0)
)
"""
MRI(ref::MRI, nframes::Integer=ref.nframes)
Return an `MRI` structure whose header fields are populated based on a
reference `MRI` structure `ref`, and whose image array are populated with
zeros.
Optionally, the new `MRI` structure can be created with a different number of
frames (`nframes`) than the reference MRI structure.
"""
function MRI(ref::MRI, nframes::Integer=ref.nframes)
mri = MRI()
for var in fieldnames(MRI)
any(var .== (:vol, :fspec, :bval, :bvec)) && continue
setfield!(mri, var, getfield(ref, var))
end
mri.nframes = nframes
if nframes == 1
mri.vol = zeros(Float32, ref.volsize...)
else
mri.vol = zeros(Float32, ref.volsize..., nframes)
end
return mri
end
"""
get_tmp_path(tmpdir::String="")
Return path to a directory where temporary files can be stored.
Search for candidate directories in the following order:
1. `\$``TMPDIR`: Check if environment variable is defined and directory exists
2. `\$``TEMPDIR`: Check if environment variable is defined and directory exists
3. `/scratch`: Check if directory exists
4. `/tmp`: Check if directory exists
5. `tmpdir`: Check if `tmpdir` argument was passed and directory exists
If none of the above exist, use current directory (`./`) and print warning.
"""
function get_tmp_path(tmpdir::String="")
if haskey(ENV, "TMPDIR")
tmppath = ENV["TMPDIR"]
if isdir(tmppath)
return tmppath
end
end
if haskey(ENV, "TEMPDIR")
tmppath = ENV["TEMPDIR"]
if isdir(tmppath)
return tmppath
end
end
tmppath = "/scratch"
if isdir(tmppath)
return tmppath
end
tmppath = "/tmp"
if isdir(tmppath)
return tmppath
end
tmppath = tmpdir
if isdir(tmppath)
return tmppath
end
tmppath = "./"
println("WARNING: get_tmp_path could not find a temporary folder, " *
"using current folder")
return tmppath
end
"""
vox2ras_0to1(M0::Matrix)
Convert a 0-based vox2ras matrix `M0` to a 1-based vox2ras matrix such that:
Pxyz = M_0 * [c r s 1]' = M_1 * [c+1 r+1 s+1 1]'
"""
function vox2ras_0to1(M0::Matrix)
if size(M0) != (4,4)
error("Input must be a 4x4 matrix")
end
Q = zeros(4, 4)
Q[1:3, 4] = ones(3, 1)
M1 = inv(inv(M0)+Q)
return M1
end
"""
vox2ras_tkreg(voldim::Vector, voxres::Vector)
Return a 0-based vox2ras transform of a volume that is compatible with the
registration matrix produced by tkregister. May work with MINC xfm.
# Arguments
- voldim = [ncols; nrows; nslices ...]
- volres = [colres; rowres; sliceres ...]
"""
function vox2ras_tkreg(voldim::Vector, voxres::Vector)
if length(voldim) < 3 | length(voxres) < 3
error("Input vectors must have at least 3 elements")
end
T = zeros(4,4)
T[4,4] = 1
T[1,1] = -voxres[1]
T[1,4] = voxres[1] * voldim[1]/2
T[2,3] = voxres[3]
T[2,4] = -voxres[3] * voldim[3]/2
T[3,2] = -voxres[2]
T[3,4] = voxres[2] * voldim[2]/2
return T
end
"""
vox2ras_to_qform(vox2ras::Matrix)
Convert a vox2ras matrix to NIfTI qform parameters. The vox2ras should be 6 DOF.
Return the following NIfTI header fields:
- hdr.quatern_b
- hdr.quatern_c
- hdr.quatern_d
- hdr.qoffset_x
- hdr.qoffset_y
- hdr.qoffset_z
- hdr.pixdim[1]
From DG's vox2rasToQform.m:
This code mostly just follows CH's mriToNiftiQform() in mriio.c
"""
function vox2ras_to_qform(vox2ras::Matrix)
if size(vox2ras) != (4, 4)
error("vox2ras size=" * string(size(vox2ras)) * ", must be (4, 4)")
end
x = vox2ras[1,4]
y = vox2ras[2,4]
z = vox2ras[3,4]
d = sqrt.(sum(vox2ras[:, 1:3].^2; dims=1))
Mdc = vox2ras[1:3, 1:3] ./ repeat(d, 3)
if det(Mdc) == 0
error("vox2ras determinant is 0")
end
r11 = Mdc[1,1]
r21 = Mdc[2,1]
r31 = Mdc[3,1]
r12 = Mdc[1,2]
r22 = Mdc[2,2]
r32 = Mdc[3,2]
r13 = Mdc[1,3]
r23 = Mdc[2,3]
r33 = Mdc[3,3]
if det(Mdc) > 0
qfac = 1.0
else
r13 = -r13
r23 = -r23
r33 = -r33
qfac = -1.0
end
# From DG's vox2rasToQform.m: "following mat44_to_quatern()"
a = r11 + r22 + r33 + 1.0
if a > 0.5
a = 0.5 * sqrt(a)
b = 0.25 * (r32-r23) / a
c = 0.25 * (r13-r31) / a
d = 0.25 * (r21-r12) / a
else
xd = 1.0 + r11 - (r22+r33)
yd = 1.0 + r22 - (r11+r33)
zd = 1.0 + r33 - (r11+r22)
if xd > 1
b = 0.5 * sqrt(xd)
c = 0.25 * (r12+r21) / b
d = 0.25 * (r13+r31) / b
a = 0.25 * (r32-r23) / b
elseif yd > 1
c = 0.5 * sqrt(yd)
b = 0.25 * (r12+r21) / c
d = 0.25 * (r23+r32) / c
a = 0.25 * (r13-r31) / c
else
d = 0.5 * sqrt(zd)
b = 0.25 * (r13+r31) / d
c = 0.25 * (r23+r32) / d
a = 0.25 * (r21-r12) / d
end
if a < 0
a = -a
b = -b
c = -c
d = -d
end
end
return [b, c, d, x, y, z, qfac]
end
"""
mri_filename(fstring::String, checkdisk::Bool=true)
Return a valid file name, file stem, and file extension, given a string
`fstring` that can be either a file name or a file stem.
Valid extensions are: mgh, mgz, nii, nii.gz. Thus a file name is expected to
have the form stem.{mgh, mgz, nii, nii.gz}.
If `fstring` is a file name, then the stem and extension are determined from
`fstring`.
If `fstring` is a file stem and `checkdisk` is true (default), then the
file name and extension are determined by searching for a file on disk
named `fstring`.{mgh, mgz, nii, nii.gz}, where the possible extensions are
searched for in this order. If no such file is found, then empty strings are
returned.
"""
function mri_filename(fstring::String, checkdisk::Bool=true)
fname = ""
fstem = ""
fext = ""
# List of supported file extensions
extlist = ["mgh", "mgz", "nii", "nii.gz"]
idot = findlast(isequal('.'), fstring)
if isnothing(idot) && checkdisk
# Filename has no extension, check if file exists with a supported extension
for ext in extlist
name = fstring * '.' * ext
if isfile(name)
fname = name
fstem = fstring
fext = ext
end
end
else
# Filename has an extension, check if it is one of the supported ones
ext = lowercase(fstring[(idot+1):end]);
if cmp(ext, "gz") == 0
idot = findprev(isequal('.'), fstring, idot-1)
if !isnothing(idot)
ext = lowercase(fstring[(idot+1):end])
end
end
if any(cmp.(ext, extlist) .== 0)
fname = fstring
fstem = fstring[1:idot-1]
fext = ext
end
end
return fname, fstem, fext
end
"""
mri_read(infile::String, headeronly::Bool=false, permuteflag::Bool=false)
Read an image volume from disk and return an `MRI` structure similar to the
FreeSurfer MRI struct defined in mri.h.
# Arguments
- `infile::String`: Path to the input, which can be:
1. An MGH file, e.g., f.mgh or f.mgz
2. A NIfTI file, e.g., f.nii or f.nii.gz
3. A file stem, in which case the format and full file name are determined
by finding a file on disk named `infile`.{mgh, mgz, nii, nii.gz}
4. A Bruker scan directory, which is expected to contain the following files:
method, pdata/1/reco, pdata/1/2dseq
- `headeronly::Bool=false`: If true, the pixel data are not read in.
- `permuteflag::Bool==false`: If true, the first two dimensions of the image
volume are permuted in the .vol, .volsize, and .volres fields of the output
structure (this is the default behavior of the MATLAB version). The
`permuteflag` will not affect the vox2ras matrices, which always map indices
in the (column, row, slice) convention.
# Output
In the `MRI` structure:
- Times are in ms and angles are in radians.
- `vox2ras0`: This field contains the vox2ras matrix that converts 0-based
(column, row, slice) voxel indices to (x, y, z) coordinates. This is the also
the matrix that mri_write() uses to derive all geometry information
(e.g., direction cosines, voxel resolution, P0). Thus if any other geometry
fields of the structure are modified, the change will not be reflected in
the output volume.
- `vox2ras1`: This field contains the vox2ras matrix that converts 1-based
(column, row, slice) voxel indices to (x, y, z) coordinates.
- `niftihdr`: If the input file is NIfTI, this field contains a `NIfTIheader`
structure.
"""
function mri_read(infile::String, headeronly::Bool=false, permuteflag::Bool=false)
mri = MRI()
if isdir(infile) #------ Bruker -------#
mri = load_bruker(infile)
else
(fname, fstem, fext) = mri_filename(infile)
if isempty(fname)
error("Cannot determine format of " * infile)
end
mri.fspec = fname
mri.pwd = pwd()
if any(fext .== ["mgh", "mgz"]) #-------- MGH --------#
(mri.vol, M, mr_parms, volsz) =
load_mgh(fname, nothing, nothing, headeronly)
if !isempty(mr_parms)
(mri.tr, mri.flip_angle, mri.te, mri.ti) = mr_parms
end
if isempty(M)
error("Loading " * fname * " as MGH")
else
mri.vox2ras0 = M
end
mri.volsize = volsz[1:3]
mri.nframes = length(volsz) < 4 ? 1 : volsz[4]
elseif any(fext .== ["nii", "nii.gz"]) #------- NIfTI -------#
hdr = load_nifti(fname, headeronly)
if !headeronly && isempty(hdr.vol)
error("Loading " * fname * " as NIfTI")
end
# Compatibility with MRIread.m:
# When data have > 4 dims, put all data into dim 4.
volsz = hdr.dim[2:end]
volsz = volsz[volsz.>0]
volsz = Int.(volsz)
if headeronly
mri.vol = []
else
mri.vol = hdr.vol
if length(volsz) > 4
mri.vol = reshape(mri.vol, volsz[1], volsz[2], volsz[3], :)
end
end
hdr.vol = [] # Already have it above, so clear it
mri.niftihdr = hdr
mri.tr = hdr.pixdim[5] # Already in msec
mri.flip_angle = mri.te = mri.ti = 0
mri.vox2ras0 = hdr.vox2ras
mri.volsize = volsz[1:3]
mri.nframes = length(volsz) < 4 ? 1 : volsz[4]
else
error("File extension " * fext * " not supported")
end
# Optional DWI tables -----------------------------------------------#
bfile = fstem * ".bvals"
if ~isfile(bfile)
bfile = fstem * ".bval"
if ~isfile(bfile)
bfile = ""
end
end
gfile = fstem * ".bvecs"
if ~isfile(gfile)
gfile = fstem * ".bvec"
if ~isfile(gfile)
gfile = ""
end
end
if ~isempty(bfile) && ~isempty(gfile)
(b, g) = mri_read_bfiles(bfile, gfile)
if length(b) == mri.nframes
mri.bval = b
mri.bvec = g
end
end
end
# Dimensions not redundant when using header only
(mri.width, mri.height, mri.depth) = collect(mri.volsize)
# Set additional header fields related to volume geometry
mri_set_geometry!(mri)
# Permute volume from col-row-slice to row-col-slice, if desired
# (This is the default behavior in the MATLAB version, but not here)
if permuteflag
mri.vol = permutedims(mri.vol, [2; 1; 3:ndims(mri.vol)])
mri.volsize = mri.volsize[[2,1,3]]
mri.volres = mri.volres[[2,1,3]]
mri.ispermuted = true
end
return mri
end
"""
mri_set_geometry!(mri::MRI)
Set header fields related to volume geometry in an `MRI` structure.
These are redundant fields that can be derived from the `vox2ras0`,
`width`, `height`, `depth` fields. They are in the MRI struct defined
in mri.h, as well as the MATLAB version of this structure, so they have
been added here for completeness.
Note: mri_write() derives all geometry information (i.e., direction cosines,
voxel resolution, and P0 from vox2ras0. If any of the redundant geometry
elements below are modified, the change will not be reflected in the output
volume.
"""
function mri_set_geometry!(mri::MRI)
mri.vox2ras = mri.vox2ras0
mri.nvoxels = mri.width * mri.height * mri.depth # Number of voxels
mri.xsize = sqrt(sum(mri.vox2ras[:,1].^2)) # Col
mri.ysize = sqrt(sum(mri.vox2ras[:,2].^2)) # Row
mri.zsize = sqrt(sum(mri.vox2ras[:,3].^2)) # Slice
mri.x_r = mri.vox2ras[1,1]/mri.xsize # Col
mri.x_a = mri.vox2ras[2,1]/mri.xsize
mri.x_s = mri.vox2ras[3,1]/mri.xsize
mri.y_r = mri.vox2ras[1,2]/mri.ysize # Row
mri.y_a = mri.vox2ras[2,2]/mri.ysize
mri.y_s = mri.vox2ras[3,2]/mri.ysize
mri.z_r = mri.vox2ras[1,3]/mri.zsize # Slice
mri.z_a = mri.vox2ras[2,3]/mri.zsize
mri.z_s = mri.vox2ras[3,3]/mri.zsize
ic = [mri.width/2; mri.height/2; mri.depth/2; 1]
c = mri.vox2ras * ic
mri.c_r = c[1]
mri.c_a = c[2]
mri.c_s = c[3]
#--------------------------------------------------------------------#
# These are in the MATLAB version of this structure for convenience
# 1-based vox2ras: Good for doing transforms on julia indices
mri.vox2ras1 = vox2ras_0to1(mri.vox2ras)
# Matrix of direction cosines
mri.Mdc = mri.vox2ras[1:3, 1:3] *
Diagonal(1 ./ [mri.xsize, mri.ysize, mri.zsize])
# Vector of voxel resolutions
mri.volres = [mri.xsize, mri.ysize, mri.zsize]
mri.tkrvox2ras = vox2ras_tkreg(mri.volsize, mri.volres)
end
"""
load_bruker(indir::String, headeronly::Bool=false)
Read Bruker image data from disk and return an `MRI` structure similar to the
FreeSurfer MRI struct defined in mri.h.
# Arguments
- `indir::String`: Path to a Bruker scan directory (files called method,
pdata/1/reco, and pdata/1/2dseq are expected to be found in it)
- `headeronly::Bool=false`: If true, the pixel data are not read in.
"""
function load_bruker(indir::String, headeronly::Bool=false)
dname = abspath(indir)
methfile = dname * "/method"
recofile = dname * "/pdata/1/reco"
imgfile = dname * "/pdata/1/2dseq"
if any(.!isfile.([methfile, recofile, imgfile]))
error("Input directory must contain the files: " *
"method, pdata/1/reco, pdata/1/2dseq")
end
mri = MRI()
mri.fspec = imgfile
mri.pwd = pwd()
nb0 = 0
# Read information for the image header from the Bruker method file
io = open(methfile, "r")
while !eof(io)
ln = readline(io)
if startswith(ln, "##\$PVM_SpatResol=") # Voxel size
ln = readline(io)
ln = split(ln)
mri.volres = parse.(Float32, ln)
elseif startswith(ln, "##\$PVM_Matrix=") # Matrix size
ln = readline(io)
ln = split(ln)
mri.volsize = parse.(Float32, ln)
elseif startswith(ln, "##\$EchoTime=") # TE
ln = split(ln, "=")[2]
mri.te = parse(Float32, ln)
elseif startswith(ln, "##\$PVM_RepetitionTime=") # TR
ln = split(ln, "=")[2]
mri.tr = parse(Float32, ln)
elseif startswith(ln, "##\$PVM_DwAoImages=") # Number of b=0 volumes
ln = split(ln, "=")[2]
nb0 = parse(Int64, ln)
elseif startswith(ln, "##\$PVM_DwDir=") # Diffusion gradients
nval = split(ln, "(")[2]
nval = split(nval, ")")[1]
nval = split(nval, ",")
nval = prod(parse.(Int64, nval))
nread = 0
bvec = Vector{Float32}(undef, 0)
while nread < nval
ln = readline(io)
ln = split(ln)
nread += length(ln)
push!(bvec, parse.(Float32, ln)...)
end
mri.bvec = permutedims(reshape(bvec, 3, :), [2 1])
elseif startswith(ln, "##\$PVM_DwEffBval=") # b-values
nval = split(ln, "(")[2]
nval = split(nval, ")")[1]
nval = parse(Int64, nval)
nread = 0
bval = Vector{Float32}(undef, 0)
while nread < nval
ln = readline(io)
ln = split(ln)
nread += length(ln)
push!(bval, parse.(Float32, ln)...)
end
mri.bval = bval
end
end
close(io)
# Add b=0 volumes to the gradient table
# (The method file includes them in the list of b-values but not vectors)
for ib0 in 1:nb0
mri.bvec = vcat([0 0 0], mri.bvec)
end
# Read information about image binary data from Bruker reco file
io = open(recofile, "r")
data_type = Int32
int_offset = Vector{Float32}(undef, 0)
int_slope = Vector{Float32}(undef, 0)
byte_order = ""
while !eof(io)
ln = readline(io)
if startswith(ln, "##\$RECO_wordtype=") # Bytes per voxel
ln = split(ln, "=")[2]
if ln == "_32BIT_SGN_INT"
data_type = Int32
elseif ln == "_16BIT_SGN_INT"
data_type = Int16
elseif ln == "_8BIT_SGN_INT"
data_type = Int8
end
elseif startswith(ln, "##\$RECO_map_offset=") # Intensity offset
nval = split(ln, "(")[2]
nval = split(nval, ")")[1]
nval = parse(Int64, nval)
nread = 0
while nread < nval
ln = readline(io)
ln = split(ln)
nread += length(ln)
push!(int_offset, parse.(Float32, ln)...)
end
elseif startswith(ln, "##\$RECO_map_slope") # Intensity slope
nval = split(ln, "(")[2]
nval = split(nval, ")")[1]
nval = parse(Int64, nval)
nread = 0
while nread < nval
ln = readline(io)
ln = split(ln)
nread += length(ln)
push!(int_slope, parse.(Float32, ln)...)
end
elseif startswith(ln, "##\$RECO_byte_order=") # Byte order
byte_order = split(ln, "=")[2]
end
end
close(io)
mri.nframes = length(int_slope)
mri.vox2ras0 = Matrix(Diagonal([mri.volres; 1]))
if headeronly
return mri
end
# Read image data
io = open(imgfile, "r")
vol = read!(io, Array{data_type}(undef, (mri.volsize..., mri.nframes)))
close(io)
if byte_order == "littleEndian"
vol = ltoh.(vol)
else
vol = ntoh.(vol)
end
# Apply intensity offset and slope
vol = Int32.(vol)
if data_type == Float32
mri.vol = Float32.(vol)
else
mri.vol = Array{Float32}(undef, size(vol))
for iframe in 1:mri.nframes
mri.vol[:,:,:,iframe] = vol[:,:,:,iframe] ./ int_slope[iframe] .+
int_offset[iframe]
end
end
return mri
end
"""
load_mgh(fname::String, slices::Union{Vector{Unsigned}, Nothing}=nothing, frames::Union{Vector{Unsigned}, Nothing}=nothing, headeronly::Bool=false)
Load a .mgh or .mgz file from disk.
Return:
- The image data as an array
- The 4x4 vox2ras matrix that transforms 0-based voxel indices to coordinates
such that: [x y z]' = M * [i j k 1]'
- MRI parameters as a vector: [tr, flip_angle, te, ti]
- The image volume dimensions as a vector (useful when `headeronly` is true,
in which case the image array will be empty)
# Arguments
- `fname::String`: Path to the .mgh/.mgz file
- `slices::Vector`: 1-based slice numbers to load (default: read all slices)
- `frames::Vector`: 1-based frame numbers to load (default: read all frames)
- `headeronly::Bool=false`: If true, the pixel data are not read in.
"""
function load_mgh(fname::String, slices::Union{Vector{Unsigned}, Nothing}=nothing, frames::Union{Vector{Unsigned}, Nothing}=nothing, headeronly::Bool=false)
vol = M = mr_parms = volsz = []
# Unzip if it is compressed
ext = lowercase(fname[(end-1):end])
if cmp(ext, "gz") == 0
# Create unique temporary file name
tmpfile = tempname(get_tmp_path()) * ".load_mgh.mgh"
gzipped = true
if Sys.isapple()
cmd = `gunzip -c $fname`
else
cmd = `zcat $fname`
end
run(pipeline(cmd, stdout=tmpfile))
else
tmpfile = fname
gzipped = false
end
io = open(tmpfile, "r")
v = ntoh(read(io, Int32))
ndim1 = ntoh(read(io, Int32))
ndim2 = ntoh(read(io, Int32))
ndim3 = ntoh(read(io, Int32))
nframes = ntoh(read(io, Int32))
type = ntoh(read(io, Int32))
dof = ntoh(read(io, Int32))
if !isnothing(slices) && any(slices .> ndim3)
error("Some slices=" * string(slices) * " exceed nslices=" * string(dim3))
end
if !isnothing(frames) && any(frames .> nframes)
error("Some frames=" * string(frames) * " exceed nframes=" * string(nframes))
end
UNUSED_SPACE_SIZE = 256
USED_SPACE_SIZE = 3*4+4*3*4 # Space for RAS transform
unused_space_size = UNUSED_SPACE_SIZE-2
ras_good_flag = ntoh(read(io, Int16))
if ras_good_flag > 0
delta = ntoh.(read!(io, Vector{Float32}(undef, 3)))
Mdc = ntoh.(read!(io, Vector{Float32}(undef, 9)))
Mdc = reshape(Mdc, (3,3))
Pxyz_c = ntoh.(read!(io, Vector{Float32}(undef, 3)))
D = Diagonal(delta)
Pcrs_c = [ndim1; ndim2; ndim3]/2 # Should this be kept?
Pxyz_0 = Pxyz_c - Mdc*D*Pcrs_c
M = [Mdc*D Pxyz_0; 0 0 0 1]
ras_xform = [Mdc Pxyz_c; 0 0 0 1]
unused_space_size = unused_space_size - USED_SPACE_SIZE
end
skip(io, unused_space_size)
nv = ndim1 * ndim2 * ndim3 * nframes
volsz = [ndim1 ndim2 ndim3 nframes]
MRI_UCHAR = 0
MRI_INT = 1
MRI_LONG = 2
MRI_FLOAT = 3
MRI_SHORT = 4
MRI_BITMAP = 5
MRI_USHRT = 10
# Determine number of bytes per voxel
if type == MRI_FLOAT
nbytespervox = 4
dtype = Float32
elseif type == MRI_UCHAR
nbytespervox = 1
dtype = UInt8
elseif type == MRI_SHORT
nbytespervox = 2
dtype = Int16
elseif type == MRI_USHRT
nbytespervox = 2
dtype = UInt16
elseif type == MRI_INT
nbytespervox = 4
dtype = Int32
end
if headeronly
skip(io, nv*nbytespervox)
if !eof(io)
mr_parms = ntoh.(read!(io, Vector{Float32}(undef, 4)))
end
close(io)
if gzipped # Clean up
cmd = `rm -f $tmpfile`
run(cmd)
end
return vol, M, mr_parms, volsz
end
if isnothing(slices) && isnothing(frames) # Read the entire volume
vol = ntoh.(read!(io, Array{dtype}(undef, Tuple(volsz))))
else # Read a subset of slices/frames
isnothing(frames) && (frames = 1:nframes)
isnothing(slices) && (slices = 1:ndim3)
nvslice = ndim1 * ndim2
nvvol = nvslice * ndim3
filepos0 = position(io)
vol = zeros(dtype, ndim1, ndim2, length(slices), length(frames))
for iframe in 1:length(frames)
frame = frames(iframe)
for islice in 1:length(slices)
slice = slices(islice)
filepos = ((frame-1)*nvvol + (slice-1)*nvslice)*nbytespervox + filepos0
seek(io, filepos)
vol[:, :, islice, iframe] =
ntoh.(read!(io, Array{dtype}(undef, Tuple(volsz[1:2]))))
end
end
# Seek to just beyond the last slice/frame
filepos = (nframes*nvvol)*nbytespervox + filepos0;
seek(io, filepos)
end
if !eof(io)
mr_parms = ntoh.(read!(io, Vector{Float32}(undef, 4)))
end
close(io)
if gzipped # Clean up
cmd = `rm -f $tmpfile`
run(cmd)
end
return vol, M, mr_parms, volsz
end
"""
hdr = load_nifti_hdr(fname::String)
Load the header of a .nii volume from disk.
Return a `NIfTIheader` structure, where units have been converted to mm and
msec, the sform and qform matrices have been computed and stored in the .sform
and .qform fields, and the .vox2ras field has been set to the sform (if valid),
then the qform (if valid).
Assume that the input file is uncompressed (compression is handled in the
wrapper load_nifti()).
Handle data structures with more than 32k cols by setting the .glmin field to
ncols when hdr.dim[2] < 0. This is FreeSurfer specific, for handling surfaces.
"""
function load_nifti_hdr(fname::String)
hdr = NIfTIheader()
io = open(fname, "r")
hdr.sizeof_hdr = read(io, Int32)
hdr.data_type = read!(io, Vector{UInt8}(undef, 10))
hdr.db_name = read!(io, Vector{UInt8}(undef, 18))
hdr.extents = read(io, Int32)
hdr.session_error = read(io, Int16)
hdr.regular = read(io, UInt8)
hdr.dim_info = read(io, UInt8)
hdr.dim = read!(io, Vector{Int16}(undef, 8))
hdr.intent_p1 = read(io, Float32)
hdr.intent_p2 = read(io, Float32)
hdr.intent_p3 = read(io, Float32)
hdr.intent_code = read(io, Int16)
hdr.datatype = read(io, Int16)
hdr.bitpix = read(io, Int16)
hdr.slice_start = read(io, Int16)
hdr.pixdim = read!(io, Vector{Float32}(undef, 8))
hdr.vox_offset = read(io, Float32)
hdr.scl_slope = read(io, Float32)
hdr.scl_inter = read(io, Float32)
hdr.slice_end = read(io, Int16)
hdr.slice_code = read(io, Int8)
hdr.xyzt_units = read(io, Int8)
hdr.cal_max = read(io, Float32)
hdr.cal_min = read(io, Float32)
hdr.slice_duration = read(io, Float32)
hdr.toffset = read(io, Float32)
hdr.glmax = read(io, Int32)
hdr.glmin = read(io, Int32)
hdr.descrip = read!(io, Vector{UInt8}(undef, 80))
hdr.aux_file = read!(io, Vector{UInt8}(undef, 24))
hdr.qform_code = read(io, Int16)
hdr.sform_code = read(io, Int16)
hdr.quatern_b = read(io, Float32)
hdr.quatern_c = read(io, Float32)
hdr.quatern_d = read(io, Float32)
hdr.quatern_x = read(io, Float32)
hdr.quatern_y = read(io, Float32)
hdr.quatern_z = read(io, Float32)
hdr.srow_x = read!(io, Vector{Float32}(undef, 4))
hdr.srow_y = read!(io, Vector{Float32}(undef, 4))
hdr.srow_z = read!(io, Vector{Float32}(undef, 4))
hdr.intent_name = read!(io, Vector{UInt8}(undef, 16))
hdr.magic = read!(io, Vector{UInt8}(undef, 4))
close(io)
# If numbers have the wrong endian-ness, reverse byte order
if hdr.sizeof_hdr == bswap(Int32(348))
for var in fieldnames(NIfTIheader)
setfield!(hdr, var, bswap.(getfield(hdr, var)))
end
hdr.do_bswap = 1
end
# This is to accomodate structures with more than 32k cols
# FreeSurfer specific. See also mriio.c.
if hdr.dim[2] < 0
hdr.dim[2] = hdr.glmin
hdr.glmin = 0
end
# Look at xyz units and convert to mm if needed
xyzunits = hdr.xyzt_units & Int8(7) # Bitwise AND with 00000111
if xyzunits == 1
xyzscale = 1000.000 # meters
elseif xyzunits == 2
xyzscale = 1.000 # mm
elseif xyzunits == 3
xyzscale = .001 # microns
else
println("WARNING: xyz units code " * string(xyzunits) *
" is unrecognized, assuming mm")
xyzscale = 1 # just assume mm
end
hdr.pixdim[2:4] = hdr.pixdim[2:4] * xyzscale
hdr.srow_x = hdr.srow_x * xyzscale
hdr.srow_y = hdr.srow_y * xyzscale
hdr.srow_z = hdr.srow_z * xyzscale
# Look at time units and convert to msec if needed
tunits = hdr.xyzt_units & Int8(56) # Bitwise AND with 00111000
if tunits == 8
tscale = 1000.000 # seconds
elseif tunits == 16
tscale = 1.000 # msec
elseif tunits == 32
tscale = .001 # microsec
else
tscale = 0 # no time scale
end
hdr.pixdim[5] = hdr.pixdim[5] * tscale
# Change value in xyzt_units to reflect scale change
hdr.xyzt_units = Int8(2) | Int8(16) # Bitwise OR of 2=mm, 16=msec
# Sform matrix
hdr.sform = [hdr.srow_x';
hdr.srow_y';
hdr.srow_z';
0 0 0 1]
# Qform matrix
# (From DG's load_nifti_hdr.m: not quite sure how all this works,
# mainly just copied CH's code from mriio.c)
b = hdr.quatern_b
c = hdr.quatern_c
d = hdr.quatern_d
x = hdr.quatern_x
y = hdr.quatern_y
z = hdr.quatern_z
a = 1.0 - (b*b + c*c + d*d)
if abs(a) < 1.0e-7
a = 1.0 / sqrt(b*b + c*c + d*d)
b = b*a
c = c*a
d = d*a
a = 0.0
else
a = sqrt(a)
end
r11 = a*a + b*b - c*c - d*d
r12 = 2.0*b*c - 2.0*a*d
r13 = 2.0*b*d + 2.0*a*c
r21 = 2.0*b*c + 2.0*a*d
r22 = a*a + c*c - b*b - d*d
r23 = 2.0*c*d - 2.0*a*b
r31 = 2.0*b*d - 2*a*c
r32 = 2.0*c*d + 2*a*b
r33 = a*a + d*d - c*c - b*b
if hdr.pixdim[1] < 0.0
r13 = -r13
r23 = -r23
r33 = -r33
end
qMdc = [r11 r12 r13; r21 r22 r23; r31 r32 r33]
D = Diagonal(hdr.pixdim[2:4])
P0 = [x y z]'
hdr.qform = [qMdc*D P0; 0 0 0 1]
if hdr.sform_code != 0
# Use sform first
hdr.vox2ras = hdr.sform
elseif hdr.qform_code != 0
# Then use qform first
hdr.vox2ras = hdr.qform
else
println("WARNING: neither sform or qform are valid in " * fname)
D = Diagonal(hdr.pixdim[2:4])
P0 = [0 0 0]'
hdr.vox2ras = [D P0; 0 0 0 1]
end
return hdr
end
"""
load_nifti(fname::String, hdronly::Bool=false)
Load a NIfTI (.nii or .nii.gz) volume from disk and return a `NIfTIheader`
structure.
Handle compressed NIfTI (nii.gz) by issuing an external Unix call to
uncompress the file to a temporary file, which is then deleted.
The output structure contains:
- the image data, in the .vol field
- the units for each dimension of the volume [mm or msec], in the .pixdim field
- the sform and qform matrices, in the .sform and .qform fields
- the vox2ras matrix, which is the sform (if valid), otherwise the qform, in
the .vox2ras field
Handle data structures with more than 32k cols by looking at the .dim field.
If dim[2] = -1, then the .glmin field contains the numbfer of columns. This is
FreeSurfer specific, for handling surfaces. When the total number of spatial
voxels equals 163842, then reshape the volume to 163842x1x1xnframes. This is
for handling the 7th order icosahedron used by FS group analysis.
"""
function load_nifti(fname::String, hdronly::Bool=false)
# Unzip if it is compressed
ext = lowercase(fname[(end-1):end])
if cmp(ext, "gz") == 0
# Create unique temporary file name
tmpfile = tempname(get_tmp_path()) * ".load_nifti.nii"
gzipped = true
if Sys.isapple()
cmd = `gunzip -c $fname`
else
cmd = `zcat $fname`
end
run(pipeline(cmd, stdout=tmpfile))
else
tmpfile = fname
gzipped = false
end
# Read NIfTI header
hdr = load_nifti_hdr(tmpfile)
if isempty(hdr.vox2ras)
if gzipped # Clean up
cmd = `rm -f $tmpfile`
run(cmd)
end
return hdr
end
# Check for ico7
nspatial = prod(Int32.(hdr.dim[2:4]))
IsIco7 = (nspatial == 163842)
# If only header is desired, return now
if hdronly
if gzipped # Clean up
cmd = `rm -f $tmpfile`
run(cmd)
end
if IsIco7 # Reshape
hdr.dim[2] = 163842
hdr.dim[3] = 1
hdr.dim[4] = 1
end
return hdr
end
# Get volume dimensions
dim = hdr.dim[2:end]
while (dim[end] == 0) && !isempty(dim)
pop!(dim)
end
# Open to read the pixel data
io = open(tmpfile, "r")
# Get past the header
seek(io, Int64(round(hdr.vox_offset)))
if hdr.datatype == 2
dtype = UInt8
elseif hdr.datatype == 4
dtype = Int16
elseif hdr.datatype == 8
dtype = Int32
elseif hdr.datatype == 16
dtype = Float32
elseif hdr.datatype == 64
dtype = Float64
elseif hdr.datatype == 256
dtype = Int8
elseif hdr.datatype == 512
dtype = UInt16
elseif hdr.datatype == 768
dtype = UInt32
else
close(io)
if gzipped # Clean up
cmd = `rm -f $tmpfile`
run(cmd)
end
error("Data type " * string(hdr.datatype) * " not supported")
end
hdr.vol = read!(io, Array{dtype}(undef, Tuple(dim)))
close(io)
if gzipped # Clean up
cmd = `rm -f $tmpfile`
run(cmd)
end
# Check if end-of-file was reached
if !eof(io)
error(tmpfile * ", read a " * string(size(hdr.vol)) *
" volume but did not reach end of file")
end
# If needed, reverse order of bytes to correct endian-ness
if hdr.do_bswap == 1
hdr.vol = bswap.(hdr.vol)
end
if IsIco7
hdr.dim[2] = 163842
hdr.dim[3] = 1
hdr.dim[4] = 1
dim = hdr.dim[2:end]
hdr.vol = reshape(hdr.vol, Tuple(dim))
end
if hdr.scl_slope!=0 && !(hdr.scl_inter==0 && hdr.scl_slope==1)
# Rescaling is not needed if the slope==1 and intersect==0,
# skipping this preserves the numeric class of the data
hdr.vol = Float64(hdr.vol) .* hdr.scl_slope .+ hdr.scl_inter
end
return hdr
end
"""
mri_write(mri::MRI, outfile::String, datatype::DataType=Float32)
Write an MRI volume to disk. Return true is an error occurred (i.e., the
number of bytes written were not as expected based on the size of the volume).
# Arguments
- `mri::MRI`: A structure like that returned by `mri_read()`. The geometry
(i.e., direction cosines, voxel resolution, and P0) are all recomputed from
mri.vox2ras0. So, if a method has changed one of the other fields, e.g.,
mri.x_r, this change will not be reflected in the output volume.
- `outfile::String`: Path to the output file, which can be:
1. An MGH file, e.g., f.mgh or f.mgz (uncompressed or compressed)
2. A NIfTI file, e.g., f.nii or f.nii.gz (uncompressed or compressed).
- `datatype::DataType=Float32`: Only applies to NIfTI and can be UInt8, Int16,
Int32, Float32, Float64, Int8, UInt16, UInt32.
"""
function mri_write(mri::MRI, outfile::String, datatype::DataType=Float32)
err = true
if isempty(mri.vol)
error("Input structure has empty vol field")
end
vsz = collect(size(mri.vol))
nvsz = length(vsz)
if nvsz < 4
vsz = [vsz; ones(eltype(vsz), 4-nvsz)]
end
if isempty(mri.volsize)
mri.volsize = vsz[1:3]
end
if mri.nframes == 0
mri.nframes = vsz[4]
end
if isempty(mri.vox2ras0)
mri.vox2ras0 = Diagonal(ones(4))
end
if isempty(mri.volres)
mri.volres = sqrt.((ones(3)' * (mri.vox2ras0[1:3,1:3].^2))')
end
(fname, fstem, fext) = mri_filename(outfile, false) # false = no checkdisk
if isempty(fname)
error("Cannot determine format of " * outfile)
end
if any(cmp.(fext, ["mgh", "mgz"]) .== 0) #-------- MGH --------#
M = mri.vox2ras0
mr_parms = [mri.tr, mri.flip_angle, mri.te, mri.ti]
if mri.ispermuted
err = save_mgh(permutedims(mri.vol, [2; 1; 3:ndims(mri.vol)]),
fname, M, mr_parms)
else
err = save_mgh(mri.vol, fname, M, mr_parms)
end
elseif any(cmp.(fext, ["nii", "nii.gz"]) .== 0) #------- NIfTI -------#
hdr = NIfTIheader()
hdr.db_name = zeros(UInt8, 18)
hdr.data_type = zeros(UInt8, 10)
if mri.ispermuted
hdr.dim = vcat(mri.volsize[[2,1,3]], mri.nframes)
else
hdr.dim = vcat(mri.volsize[1:3], mri.nframes)
end
if datatype == UInt8
hdr.datatype = 2
hdr.bitpix = 8
elseif datatype == Int16
hdr.datatype = 4
hdr.bitpix = 16
elseif datatype == Int32
hdr.datatype = 8
hdr.bitpix = 32
elseif datatype == Float32
hdr.datatype = 16
hdr.bitpix = 32
elseif datatype == Float64
hdr.datatype = 64
hdr.bitpix = 64
elseif datatype == Int8
hdr.datatype = 256
hdr.bitpix = 8
elseif datatype == UInt16
hdr.datatype = 512
hdr.bitpix = 16
elseif datatype == UInt32
hdr.datatype = 768
hdr.bitpix = 32
else
error("Data type " * string(datatype) * " not supported")
end
if mri.ispermuted
hdr.pixdim = vcat(0, mri.volres[[2,1,3]], mri.tr) # Physical units
else
hdr.pixdim = vcat(0, mri.volres[1:3], mri.tr) # Physical units
end
hdr.vox_offset = 348 # Will be set again
hdr.scl_slope = mri.niftihdr.scl_slope
hdr.scl_inter = mri.niftihdr.scl_inter
hdr.xyzt_units = Int8(2) | Int8(16) # Bitwise OR of 2=mm, 16=msec
hdr.cal_max = maximum(mri.vol)
hdr.cal_min = minimum(mri.vol)
hdr.descrip = collect(@sprintf("%-80s","FreeSurfer julia"))
hdr.aux_file = [UInt8(0)]
hdr.qform_code = 1 # 1=NIFTI_XFORM_SCANNER_ANAT
hdr.sform_code = 1 # 1=NIFTI_XFORM_SCANNER_ANAT
# Qform (must have 6 DOF)
(b, c, d, x, y, z, qfac) = vox2ras_to_qform(mri.vox2ras0)
hdr.pixdim[1] = qfac
hdr.quatern_b = b
hdr.quatern_c = c
hdr.quatern_d = d
hdr.quatern_x = x
hdr.quatern_y = y
hdr.quatern_z = z
# Sform (can be any affine)
hdr.srow_x = mri.vox2ras0[1,:]
hdr.srow_y = mri.vox2ras0[2,:]
hdr.srow_z = mri.vox2ras0[3,:]
hdr.intent_name = collect("huh?")
hdr.magic = collect("n+1")
if mri.ispermuted
hdr.vol = permutedims(mri.vol, [2; 1; 3:ndims(mri.vol)])
else
hdr.vol = mri.vol
end
err = save_nifti(hdr, fname)
else
error("File extension " * fext * " not supported")
end
if err
println("WARNING: Problem saving " * outfile)
end
# Optional DWI tables -----------------------------------------------#
if !isempty(mri.bval)
bfile = fstem * ".bvals"
writedlm(bfile, mri.bval, ' ')
end
if !isempty(mri.bvec)
gfile = fstem * ".bvecs"
writedlm(gfile, mri.bvec, ' ')
end
return err
end
"""
save_mgh(vol::Array, fname::String, M::Matrix=Diagonal(ones(4)), mr_parms::Vector=zeros(4))
Write an MRI volume to a .mgh or .mgz file. Return true is an error occurred
(i.e., the number of bytes written were not as expected based on the size of
the volume).
# Arguments
- `vol::Array`: the image data
- `fname::String`: path to the output file
- `M::Matrix`: the 4x4 vox2ras transform such that [x y z]' = M * [i j k 1]',
where the voxel indices (i, j, k) are 0-based.
- `mr_parms::Vector`: a vector of MRI parameters, [tr, flip_angle, te, ti]
"""
function save_mgh(vol::Array, fname::String, M::Matrix=Diagonal(ones(4)), mr_parms::Vector=zeros(4))
if size(M) != (4, 4)
error("M size=" * string(size(M)) * ", must be (4, 4)")
end
if length(mr_parms) != 4
error("mr_parms length=" * string(length(mr_parms)) * ", must be 4")
end
(ndim1, ndim2, ndim3, frames) = size(vol)
dtype = eltype(vol)
# The MATLAB version ingores these and always writes the data as float
# Here we use them properly
MRI_UCHAR = 0
MRI_INT = 1
MRI_LONG = 2
MRI_FLOAT = 3
MRI_SHORT = 4
MRI_BITMAP = 5
MRI_TENSOR = 6
MRI_USHRT = 10
# Determine number of bytes per voxel
if dtype == Float32
type = MRI_FLOAT
elseif dtype == UInt8
type = MRI_UCHAR
elseif dtype == Int32
type = MRI_INT
elseif dtype == Int64
type = MRI_LONG
elseif dtype == Int16
type = MRI_SHORT
elseif dtype == UInt16
type = MRI_USHRT
end
io = open(fname, "w")
nb = 0
# Write everything as big-endian
nb += write(io, hton(Int32(1))) # magic number
nb += write(io, hton(Int32(ndim1)))
nb += write(io, hton(Int32(ndim2)))
nb += write(io, hton(Int32(ndim3)))
nb += write(io, hton(Int32(frames)))
nb += write(io, hton(Int32(type)))
nb += write(io, hton(Int32(1))) # dof (not used)
UNUSED_SPACE_SIZE = 256
USED_SPACE_SIZE = (3*4+4*3*4) # Space for RAS transform
MdcD = M[1:3,1:3]
delta = sqrt.(sum(MdcD.^2; dims=1))
Mdc = MdcD ./ repeat(delta, 3)
Pcrs_c = [ndim1/2, ndim2/2, ndim3/2, 1]
Pxyz_c = M*Pcrs_c
Pxyz_c = Pxyz_c[1:3]
nb += write(io, hton(Int16(1))) # ras_good_flag = 1
nb += write(io, hton.(Float32.(delta)))
nb += write(io, hton.(Float32.(Mdc)))
nb += write(io, hton.(Float32.(Pxyz_c)))
unused_space_size = UNUSED_SPACE_SIZE-2
unused_space_size = unused_space_size - USED_SPACE_SIZE
nb += write(io, UInt8.(zeros(unused_space_size)))
nb += write(io, hton.(vol))
nb += write(io, hton.(Float32.(mr_parms)))
close(io)
err = (nb != (sizeof(Int32) * 7 +
sizeof(Int16) +
sizeof(UInt8) * unused_space_size +
sizeof(Float32) * 19 +
sizeof(dtype) * length(vol)))
ext = lowercase(fname[(end-1):end])
if cmp(ext, "gz") == 0
cmd = `gzip -f $fname`
run(cmd)
cmd = `mv $fname.gz $fname`
run(cmd)
end
return err
end
"""
save_nifti(hdr::NIfTIheader, fname::String)
Write an MRI volume to a .nii or .nii.gz file. Return true is an error occurred
(i.e., the number of bytes written were not as expected based on the size of
the volume).
# Arguments
- `hdr::NIfTIheader`: a NIfTI header structure that contains the image data in
its .vol field
- `fname::String`: path to the output file
Handle data structures with more than 32k cols by setting hdr.dim[2] = -1 and
hdr.glmin = ncols. This is FreeSurfer specific, for handling surfaces. The
exception to this is when the total number of spatial voxels equals 163842,
then the volume is reshaped to 27307x1x6xnframes. This is for handling the 7th
order icosahedron used by FS group analysis.
"""
function save_nifti(hdr::NIfTIheader, fname::String)
ext = lowercase(fname[(end-1):end])
if cmp(ext, "gz") == 0
gzip_needed = true
fname = fname[1:(end-3)]
else
gzip_needed = false
end
# Check for ico7
sz = size(hdr.vol)
if sz[1] == 163842
dim = (27307, 1, 6, size(hdr.vol,4))
hdr.vol = reshape(hdr.vol, dim)
end
io = open(fname, "w")
hdr.data_type = vcat(hdr.data_type,
repeat([UInt8(0)], 10-length(hdr.data_type)))
hdr.db_name = vcat(hdr.db_name,
repeat([UInt8(0)], 18-length(hdr.db_name)))
hdr.dim = ones(8)
if size(hdr.vol, 4) > 1
hdr.dim[1] = 4
else
hdr.dim[1] = 3
end
for k in (2:5)
hdr.dim[k] = size(hdr.vol, k-1)
end
# This is to accomodate structures with more than 32k cols
# FreeSurfer specific. See also mriio.c.
if hdr.dim[2] > 2^15
hdr.glmin = hdr.dim[2]
hdr.dim[2] = -1
end
hdr.pixdim = vcat(hdr.pixdim, zeros(8-length(hdr.pixdim)))
hdr.descrip = vcat(hdr.descrip,
repeat([UInt8(0)], 80-length(hdr.descrip)))
hdr.aux_file = vcat(hdr.aux_file,
repeat([UInt8(0)], 24-length(hdr.aux_file)))
hdr.intent_name = vcat(hdr.intent_name,
repeat([UInt8(0)], 16-length(hdr.intent_name)))
hdr.magic = vcat(hdr.magic,
repeat([UInt8(0)], 4-length(hdr.magic)))
hdr.vox_offset = 352 # not 348
nb = 0
nb += write(io, Int32(348))
nb += write(io, UInt8.(hdr.data_type))
nb += write(io, UInt8.(hdr.db_name))
nb += write(io, Int32(hdr.extents))
nb += write(io, Int16(hdr.session_error))
nb += write(io, UInt8(hdr.regular))
nb += write(io, UInt8(hdr.dim_info))
nb += write(io, Int16.(hdr.dim))
nb += write(io, Float32(hdr.intent_p1))
nb += write(io, Float32(hdr.intent_p2))
nb += write(io, Float32(hdr.intent_p3))
nb += write(io, Int16(hdr.intent_code))
nb += write(io, Int16(hdr.datatype))
nb += write(io, Int16(hdr.bitpix))
nb += write(io, Int16(hdr.slice_start))
nb += write(io, Float32.(hdr.pixdim))
nb += write(io, Float32(hdr.vox_offset))
nb += write(io, Float32(hdr.scl_slope))
nb += write(io, Float32(hdr.scl_inter))
nb += write(io, Int16(hdr.slice_end))
nb += write(io, Int8(hdr.slice_code))
nb += write(io, Int8(hdr.xyzt_units))
nb += write(io, Float32(hdr.cal_max))
nb += write(io, Float32(hdr.cal_min))
nb += write(io, Float32(hdr.slice_duration))
nb += write(io, Float32(hdr.toffset))
nb += write(io, Int32(hdr.glmax))
nb += write(io, Int32(hdr.glmin))
nb += write(io, UInt8.(hdr.descrip))
nb += write(io, UInt8.(hdr.aux_file))
nb += write(io, Int16(hdr.qform_code))
nb += write(io, Int16(hdr.sform_code))
nb += write(io, Float32(hdr.quatern_b))
nb += write(io, Float32(hdr.quatern_c))
nb += write(io, Float32(hdr.quatern_d))
nb += write(io, Float32(hdr.quatern_x))
nb += write(io, Float32(hdr.quatern_y))
nb += write(io, Float32(hdr.quatern_z))
nb += write(io, Float32.(hdr.srow_x))
nb += write(io, Float32.(hdr.srow_y))
nb += write(io, Float32.(hdr.srow_z))
nb += write(io, UInt8.(hdr.intent_name))
nb += write(io, UInt8.(hdr.magic))
# Pad to get to 352 bytes (header size is 348)
nb += write(io, UInt8.(zeros(4)))
if hdr.datatype == 2
dtype = UInt8
elseif hdr.datatype == 4
dtype = Int16
elseif hdr.datatype == 8
dtype = Int32
elseif hdr.datatype == 16
dtype = Float32
elseif hdr.datatype == 64
dtype = Float64
elseif hdr.datatype == 256
dtype = Int8
elseif hdr.datatype == 512
dtype = UInt16
elseif hdr.datatype == 768
dtype = UInt32
else
println("WARNING: data type " * string(hdr.datatype) *
" not supported, but writing as float")
dtype = Float32
end
nb += write(io, dtype.(hdr.vol))
close(io)
err = (nb != (sizeof(Float32) * 36 +
sizeof(Int32) * 4 +
sizeof(Int16) * 16 +
sizeof(Int8) * 2 +
sizeof(UInt8) * 158 +
sizeof(dtype) * length(hdr.vol)))
if gzip_needed
cmd = `gzip -f $fname`
run(cmd)
end
return err
end
"""
mri_read_bfiles(infile1::String, infile2::String)
Read a DWI b-value table and gradient table from text files `infile1` and
`infile2`. The two input files can be specified in any order. The gradient
table file must contain 3 times as many entries as the b-value table file.
Return the b-value table as a vector of size n and the gradient table as a
matrix of size (n, 3).
"""
function mri_read_bfiles(infile1::String, infile2::String)
tab = []
for infile in (infile1, infile2)
if ~isfile(infile)
error("Could not open " * infile)
end
push!(tab, readdlm(infile))
if ~all(isa.(tab[end], Number))
error("File " * infile * " contains non-numeric entries")
end
if size(tab[end], 2) > size(tab[end], 1)
tab[end] = permutedims(tab[end], 2:1)
end
if size(tab[end], 2) == 1
tab[end] = tab[end][:,1]
end
end
if size(tab[1], 1) != size(tab[2], 1)
error("Dimension mismatch between tables in " *
infile1 * " " * string(size(tab[1])) * " and " *
infile2 * " " * string(size(tab[2])))
end
if (size(tab[1], 2) == 1 && size(tab[2], 2) == 3) ||
(size(tab[1], 2) == 3 && size(tab[2], 2) == 1)
return tab[1], tab[2]
else
error("Wrong number of entries in tables " *
infile1 * " " * string(size(tab[1])) * " and " *
infile2 * " " * string(size(tab[2])))
end
end
"""
mri_read_bfiles!(dwi::MRI, infile1::String, infile2::String)
Set the .bval and .bvec fields of the MRI structure `dwi`, by reading a DWI
b-value table and gradient table from the text files `infile1` and `infile2`.
The two input files can be specified in any order. The gradient table file
must contain 3 times as many entries as the b-value table file.
Return the b-value table as a vector of size n and the gradient table as a
matrix of size (n, 3).
"""
function mri_read_bfiles!(dwi::MRI, infile1::String, infile2::String)
(tab1, tab2) = mri_read_bfiles(infile1, infile2)
if size(tab1, 1) != size(dwi.vol, 4)
error("Number of frames in volume (" * string(size(dwi.vol, 4)) *
") does not match dimensions of table in " *
infile1 * " " * string(size(tab1)))
end
if size(tab1, 2) == 1
dwi.bval = tab1
dwi.bvec = tab2
else
dwi.bval = tab2
dwi.bvec = tab1
end
return tab1, tab2
end
| FreeSurfer | https://github.com/lincbrain/Fibers.jl.git |
|
[
"BSD-3-Clause"
] | 0.1.0 | 65c128d9fd34d1fd3e6886d1a31fd46cbb3b710c | code | 8615 | #=
Original Author: Anastasia Yendiki
Copyright © 2022 The General Hospital Corporation (Boston, MA) "MGH"
Terms and conditions for use, reproduction, distribution and contribution
are found in the 'FreeSurfer Software License Agreement' contained
in the file 'LICENSE' found in the FreeSurfer distribution, and here:
https://surfer.nmr.mgh.harvard.edu/fswiki/FreeSurferSoftwareLicense
Reporting: [email protected]
=#
using LinearAlgebra
export Tract, trk_read, trk_write
"Container for header and streamline data stored in .trk format"
mutable struct Tract
# Header fields (.trk format version 2)
id_string::Vector{UInt8}
dim::Vector{Int16}
voxel_size::Vector{Float32}
origin::Vector{Float32}
n_scalars::Int16
scalar_name::Matrix{UInt8}
n_properties::Int16
property_name::Matrix{UInt8}
vox_to_ras::Matrix{Float32}
reserved::Vector{UInt8}
voxel_order::Vector{UInt8}
voxel_order_original::Vector{UInt8}
image_orientation_patient::Vector{Float32}
pad1::Vector{UInt8}
invert_x::UInt8
invert_y::UInt8
invert_z::UInt8
swap_xy::UInt8
swap_yz::UInt8
swap_zx::UInt8
n_count::Int32
version::Int32
hdr_size::Int32
# Streamline data fields
npts::Vector{Int32}
properties::Matrix{Float32}
xyz::Vector{Any}
scalars::Vector{Any}
end
"""
Tract()
Return an empty `Tract` structure
"""
Tract() = Tract(
Vector{UInt8}(undef, 0),
Vector{Int16}(undef, 0),
Vector{Float32}(undef, 0),
Vector{Float32}(undef, 0),
Int16(0),
Matrix{UInt8}(undef, 0, 0),
Int16(0),
Matrix{UInt8}(undef, 0, 0),
Matrix{Float32}(undef, 0, 0),
Vector{UInt8}(undef, 0),
Vector{UInt8}(undef, 0),
Vector{UInt8}(undef, 0),
Vector{Float32}(undef, 0),
Vector{UInt8}(undef, 0),
UInt8(0),
UInt8(0),
UInt8(0),
UInt8(0),
UInt8(0),
UInt8(0),
Int32(0),
Int32(0),
Int32(0),
Vector{Int32}(undef, 0),
Matrix{Float32}(undef, 0, 0),
[],
[]
)
"""
Tract(ref::MRI)
Return a `Tract` structure whose header fields are populated based on the
reference `MRI` structure `ref`
"""
function Tract(ref::MRI)
tr = Tract()
# In the following I must take into account that mri_read()
# has reversed the first 2 dimensions of volsize and volres,
# but it has not changed the vox2ras matrix
# Find orientation of image coordinate system
orient = Vector{Char}(undef, 3)
for idim in 1:3
(amax, imax) = findmax(abs.(ref.vox2ras[idim, 1:3]))
if imax == 1
if ref.vox2ras[idim, imax] > 0
orient[idim] = 'R'
else
orient[idim] = 'L'
end
elseif imax == 2
if ref.vox2ras[idim, imax] > 0
orient[idim] = 'A'
else
orient[idim] = 'P'
end
else
if ref.vox2ras[idim, imax] > 0
orient[idim] = 'S'
else
orient[idim] = 'I'
end
end
end
# Find patient-to-scanner coordinate transform:
# Take x and y vectors from vox2RAS matrix, convert to LPS,
# divide by voxel size
p2s = [-1 0 0; 0 -1 0; 0 0 1] * ref.vox2ras[1:3, 1:2] *
Diagonal([1, 1]./ref.volres[2,1])
tr.id_string = UInt8.(collect("TRACK\0"))
tr.dim = Int16.(ref.volsize[[2,1,3]])
tr.voxel_size = Float32.(ref.volres[[2,1,3]])
tr.origin = Float32.(zeros(3))
tr.n_scalars = Int16(0)
tr.scalar_name = UInt8.(zeros(10, 20))
tr.n_properties = Int16(0)
tr.property_name = UInt8.(zeros(10, 20))
tr.vox_to_ras = ref.vox2ras
tr.reserved = UInt8.(zeros(444))
tr.voxel_order = vcat(UInt8.(collect(orient)), UInt8(0))
tr.voxel_order_original = tr.voxel_order
tr.image_orientation_patient = Float32.(p2s[:])
tr.pad1 = UInt8.(zeros(2))
tr.invert_x = UInt8(0)
tr.invert_y = UInt8(0)
tr.invert_z = UInt8(0)
tr.swap_xy = UInt8(0)
tr.swap_yz = UInt8(0)
tr.swap_zx = UInt8(0)
tr.n_count = Int32(0)
tr.version = Int32(2)
tr.hdr_size = Int32(1000)
return tr
end
"""
trk_read(infile::String)
Read tractography streamlines from `infile` and return a `Tract` structure
Input file must be in .trk format, see:
http://trackvis.org/docs/?subsect=fileformat
"""
function trk_read(infile::String)
tr = Tract()
io = open(infile, "r")
# Read .trk header
tr.id_string = read!(io, Vector{UInt8}(undef, 6))
tr.dim = read!(io, Vector{Int16}(undef, 3))
tr.voxel_size = read!(io, Vector{Float32}(undef, 3))
tr.origin = read!(io, Vector{Float32}(undef, 3))
tr.n_scalars = read(io, Int16)
tr.scalar_name = read!(io, Matrix{UInt8}(undef, 20, 10))
tr.scalar_name = permutedims(tr.scalar_name, [2,1])
tr.n_properties = read(io, Int16)
tr.property_name = read!(io, Matrix{UInt8}(undef, 20, 10))
tr.property_name = permutedims(tr.property_name, [2,1])
tr.vox_to_ras = read!(io, Matrix{Float32}(undef, 4, 4))
tr.vox_to_ras = permutedims(tr.vox_to_ras, [2,1])
tr.reserved = read!(io, Vector{UInt8}(undef, 444))
tr.voxel_order = read!(io, Vector{UInt8}(undef, 4))
tr.voxel_order_original = read!(io, Vector{UInt8}(undef, 4))
tr.image_orientation_patient = read!(io, Vector{Float32}(undef, 6))
tr.pad1 = read!(io, Vector{UInt8}(undef, 2))
tr.invert_x = read(io, UInt8)
tr.invert_y = read(io, UInt8)
tr.invert_z = read(io, UInt8)
tr.swap_xy = read(io, UInt8)
tr.swap_yz = read(io, UInt8)
tr.swap_zx = read(io, UInt8)
tr.n_count = read(io, Int32)
tr.version = read(io, Int32)
tr.hdr_size = read(io, Int32)
# Read streamline data
tr.npts = Vector{Int32}(undef, tr.n_count)
tr.properties = Matrix{Float32}(undef, tr.n_properties, tr.n_count)
for istr in 1:tr.n_count # Loop over streamlines
tr.npts[istr] = read(io, Int32)
push!(tr.xyz, Matrix{Float32}(undef, 3, tr.npts[istr]))
push!(tr.scalars, Matrix{Float32}(undef, tr.n_scalars, tr.npts[istr]))
for ipt in 1:tr.npts[istr] # Loop over points on a streamline
# Divide by voxel size and make voxel coordinates 0-based
tr.xyz[istr][:, ipt] = read!(io, Vector{Float32}(undef, 3)) ./
tr.voxel_size .- .5
tr.scalars[istr][:, ipt] = read!(io, Vector{Float32}(undef, tr.n_scalars))
end
tr.properties[:, istr] = read!(io, Vector{Float32}(undef, tr.n_properties))
end
close(io)
return tr
end
"""
trk_write(tr::Tract, outfile::String)
Write a `Tract` structure to a file in the .trk format
Return true if an error occurred (i.e., the number of bytes written was not the
expected based on the size of the `Tract` structure)
"""
function trk_write(tr::Tract, outfile::String)
io = open(outfile, "w")
nb = 0
# Write .trk header
nb += write(io, UInt8.(tr.id_string))
nb += write(io, Int16.(tr.dim))
nb += write(io, Float32.(tr.voxel_size))
nb += write(io, Float32.(tr.origin))
nb += write(io, Int16(tr.n_scalars))
nb += write(io, UInt8.(permutedims(tr.scalar_name, [2,1])))
nb += write(io, Int16(tr.n_properties))
nb += write(io, UInt8.(permutedims(tr.property_name, [2,1])))
nb += write(io, Float32.(permutedims(tr.vox_to_ras, [2,1])))
nb += write(io, UInt8.(tr.reserved))
nb += write(io, UInt8.(tr.voxel_order))
nb += write(io, UInt8.(tr.voxel_order_original))
nb += write(io, Float32.(tr.image_orientation_patient))
nb += write(io, UInt8.(tr.pad1))
nb += write(io, UInt8(tr.invert_x))
nb += write(io, UInt8(tr.invert_y))
nb += write(io, UInt8(tr.invert_z))
nb += write(io, UInt8(tr.swap_xy))
nb += write(io, UInt8(tr.swap_yz))
nb += write(io, UInt8(tr.swap_zx))
nb += write(io, Int32(tr.n_count))
nb += write(io, Int32(tr.version))
nb += write(io, Int32(tr.hdr_size))
# Write streamline data
for istr in 1:tr.n_count # Loop over streamlines
nb += write(io, Int32(tr.npts[istr]))
for ipt in 1:tr.npts[istr] # Loop over points on a streamline
# Make voxel coordinates .5-based and multiply by voxel size
nb += write(io, Float32.((tr.xyz[istr][:, ipt] .+ .5) .* tr.voxel_size))
nb += write(io, Float32.(tr.scalars[istr][:, ipt]))
end
nb += write(io, Float32.(tr.properties[:, istr]))
end
close(io)
err = (nb != sizeof(UInt8) * 866 +
sizeof(Int32) * (3 + length(tr.npts)) +
sizeof(Int16) * 5 +
sizeof(Float32) * (28 + sum(length.(tr.xyz)) +
sum(length.(tr.scalars)) +
length(tr.properties)))
return err
end
| FreeSurfer | https://github.com/lincbrain/Fibers.jl.git |
|
[
"BSD-3-Clause"
] | 0.1.0 | 65c128d9fd34d1fd3e6886d1a31fd46cbb3b710c | code | 6132 | #=
Original Author: Anastasia Yendiki
Copyright © 2022 The General Hospital Corporation (Boston, MA) "MGH"
Terms and conditions for use, reproduction, distribution and contribution
are found in the 'FreeSurfer Software License Agreement' contained
in the file 'LICENSE' found in the FreeSurfer distribution, and here:
https://surfer.nmr.mgh.harvard.edu/fswiki/FreeSurferSoftwareLicense
Reporting: [email protected]
=#
using ColorTypes, DelimitedFiles, ImageInTerminal
export LUT, color_lut, show, view
"Container for segmentation and tract look-up tables"
mutable struct LUT
id::Vector{Int}
name::Vector{String}
rgb::Vector{RGB}
end
"""
LUT()
Return an empty `LUT` structure
"""
LUT() = LUT(
Vector{Int}(undef, 0),
Vector{String}(undef, 0),
Vector{RGB}(undef, 0)
)
"""
LUT(infile::String)
Read a look-up table from `infile` and return a `LUT` structure
The input file is assumed to have the format of FreeSurferColorLUT.txt
"""
function LUT(infile::String)
lut = LUT()
if ~isfile(infile)
error(infile * "is not a regular file")
end
tab = readdlm(infile; comments=true, comment_char='#')
lut.id = tab[:,1]
lut.name = tab[:,2]
lut.rgb = RGB.(tab[:,3]/255, tab[:,4]/255, tab[:,5]/255)
return lut
end
"The FreeSurfer color look-up table"
const global color_lut = haskey(ENV, "FREESURFER_HOME") ?
LUT(ENV["FREESURFER_HOME"]*"/FreeSurferColorLUT.txt") :
LUT()
"""
vol_to_rgb(vol::Array)
Convert an image array to an RGB/Gray array for display.
Determine how the image should be displayed:
- If all the values are IDs in the FreeSurfer color look-up table,
the image is assumed to be is a segmentation map and is converted
to RGB based on the FreeSurfer color look-up table.
- If the image has size 3 in any dimension, and the sum of squares
of the values in that dimension is approx. 1 everywhere, the image
is assumed to be a vector map and is converted to RGB based on
vector orientation.
- Otherwise, the image is assumed to be a generic intensity map and
is converted to grayscale.
Return an array of RGB/Gray values the same size as the input vol.
"""
function vol_to_rgb(vol::Array)
if isempty(color_lut.id)
error("FreeSurfer color look-up table is undefined")
end
if ~any(isnothing.(indexin(unique(vol), color_lut.id)))
# Assume the input is a segmentation map, get RGB of labels from LUT
return color_lut.rgb[indexin(vol, color_lut.id)]
end
dim3 = findall(size(vol) .== 3)
for idim in dim3
if all(isapprox.(sum(vol.^2; dims=idim), 1) .|| all(vol.==0, dims=idim))
# Assume the input is a vector map, get RGB based on vector orientation
return RGB.(abs.(selectdim(vol,idim,1)),
abs.(selectdim(vol,idim,2)),
abs.(selectdim(vol,idim,3)))
end
end
# Otherwise, assume the input is a generic intensity map
return Gray.(vol / maximum(vol))
end
"""
show(mri::MRI, mrimod::Union{MRI, Nothing}=nothing)
Show an `MRI` structure (an image slice and a summary of header info) in the
terminal window
# Arguments:
- `mri::MRI`: the main image to display
- `mrimod:MRI`: an optional image to modulate the main image by (e.g., an FA
map to modulate a vector map)
"""
function show(mri::MRI, mrimod::Union{MRI, Nothing}=nothing)
# Find non-empty slices in z dimension
iz = any(mri.vol .!= 0; dims=([1:2; 4:ndims(mri.vol)]))
iz = reshape(iz, length(iz))
iz = findall(iz)
# Keep only the middle non-empty slice in z dimension
iz = iz[Int(round(end/2))]
# Find non-empty slices in x, y dimensions
ix = any(mri.vol[:,:,iz,:] .!= 0; dims=(2:ndims(mri.vol)))
ix = reshape(ix, length(ix))
ix = findall(ix)
iy = any(mri.vol[:,:,iz,:] .!= 0; dims=([1; 3:ndims(mri.vol)]))
iy = reshape(iy, length(iy))
iy = findall(iy)
# Keep only the area containing non-empty slices in x, y dimensions
ix = ix[1]:ix[end]
iy = iy[1]:iy[end]
# Subsample to fit display in x, y dimensions
nsub = Int(ceil(length(iy) ./ displaysize()[2]))
ix = ix[1:nsub:end]
iy = iy[1:nsub:end]
# Convert image to RGB/Gray array
rgb = vol_to_rgb(mri.vol[ix, iy, iz, :])
# Keep first frame only
rgb = rgb[:, :, 1]
# Optional intensity modulation
if ~isnothing(mrimod)
if size(mrimod.vol)[1:3] != size(mri.vol)[1:3]
error("Dimension mismatch between main image " *
string(size(mri.vol)[1:3]) *
" and modulation image " *
string(size(mrimod.vol)[1:3]))
end
rgbmod = mrimod.vol[ix, iy, iz, 1] / maximum(mrimod.vol)
end
if hasfield(eltype(rgb), :r) # RGB
# Add α channel to make zero voxels transparent
rgb = RGBA.(getfield.(rgb, :r), getfield.(rgb, :g), getfield.(rgb, :b),
Float64.(rgb .!= RGB(0,0,0)))
# Modulate intensity with (optional) second image
if ~isnothing(mrimod)
rgb = RGBA.(getfield.(rgb, :r) .* rgbmod,
getfield.(rgb, :g) .* rgbmod,
getfield.(rgb, :b) .* rgbmod,
getfield.(rgb, :alpha))
end
else # Gray
# Add α channel to make zero voxels transparent
rgb = GrayA.(getfield.(rgb, :val), Float64.(rgb .!= Gray(0)))
# Modulate intensity with (optional) second image
if ~isnothing(mrimod)
rgb = GrayA.(getfield.(rgb, :val) .* rgbmod,
getfield.(rgb, :alpha))
end
end
# Display image
if mri.ispermuted
imshow(rgb)
else
imshow(permutedims(rgb, [2; 1]))
end
# Print image info
println()
if ~isempty(mri.fspec)
println("Read from: " * mri.fspec)
end
println("Volume dimensions: " * string(collect(size(mri.vol))))
println("Spatial resolution: " * string(Float64.(mri.volres)))
if ~isempty(mri.bval)
println("b-values: " * string(Float64.(unique(mri.bval))))
end
println("Intensity range: " * string(Float64.([minimum(mri.vol),
maximum(mri.vol)])))
end
"""
view(mri::MRI)
View an `MRI` structure in a slice viewer
"""
function view(mri::MRI)
#=
=#
end
| FreeSurfer | https://github.com/lincbrain/Fibers.jl.git |
|
[
"BSD-3-Clause"
] | 0.1.0 | 65c128d9fd34d1fd3e6886d1a31fd46cbb3b710c | docs | 1335 | <img src="https://user-images.githubusercontent.com/15318615/158502382-3d47c48b-e991-400b-8f7c-e745f32e9643.png" width=800>
### Import this package
Before starting julia and importing this package, it is recommended to define the environment variable FREESURFER_HOME. This will be used, e.g., to load the FreeSurfer color look-up table automatically.
```julia
julia> import FreeSurfer as fs
FREESURFER_HOME: /usr/local/freesurfer/dev
```
### Read .mgh, .mgz, .nii, .nii.gz volumes
```julia
julia> aa = fs.mri_read("/usr/local/freesurfer/dev/subjects/fsaverage/mri/aparc+aseg.mgz");
julia> fa = fs.mri_read("/usr/local/freesurfer/dev/trctrain/hcp/MGH35_HCP_FA_template.nii.gz");
```
### Show volume and header summary info
```julia
julia> fs.show(aa)
julia> fs.show(fa)
```
### Write .mgh, .mgz, .nii, .nii.gz volumes
```julia
julia> fs.mri_write(aa, "/tmp/aparc+aseg.nii.gz")
julia> fs.mri_write(fa, "/tmp/MGH35_HCP_FA_template.mgz")
```
### Read Bruker scan directories
```julia
julia> ph = fs.mri_read("/opt/nmrdata/PV-7.0.0/ayendiki/Phantom.cO1/5/");
```
### Read a .trk tractography streamline file
```julia
julia> tr = fs.trk_read("/usr/local/freesurfer/dev/trctrain/hcp/mgh_1001/syn/acomm.bbr.prep.trk");
```
### Write a .trk tractography streamline file
```julia
julia> fs.trk_write(tr, "/tmp/acomm.trk")
```
| FreeSurfer | https://github.com/lincbrain/Fibers.jl.git |
|
[
"MIT"
] | 0.1.1 | 4293935cfb1576a783192e81d965addff4d1b47a | code | 70 | using Pkg
pkg"add https://github.com/kirstvh/BioCCP"
pkg"precompile"
| BioCCP | https://github.com/kirstvh/BioCCP.jl.git |
|
[
"MIT"
] | 0.1.1 | 4293935cfb1576a783192e81d965addff4d1b47a | code | 8975 | module BioCCP
using Base: Integer
using Distributions
export expectation_minsamplesize, std_minsamplesize, success_probability,
expectation_fraction_collected, prob_occurrence_module
"""
exp_ccdf(n, T; p=ones(n)/n, m=1, r=1, normalize=true)
Calculates `1 - F(t)`, which is the complement of the success probability
`F(t)=P(T ≤ t)` (= probability that the expected minimum
number of designs T is smaller than `t` in order to
see each module at least `m` times). This function
serves as the integrand for calculating `E[T]`.
- `n`: number of modules in the design space
- `p`: vector with the probabilities/abundances of the different modules in the design space during library generation
- `T`: number of designs
- `m`: number of times each module has to observed in the sampled set of designs
- `r`: number of modules per design
- normalize: if true, normalize `p`
References:
- Doumas, A. V., & Papanicolaou, V. G. (2016). The coupon collector’s problem revisited:
generalizing the double Dixie cup problem of Newman and Shepp. ESAIM: Probability and Statistics, 20, 367-399.
- Boneh, A., & Hofri, M. (1997). The coupon-collector problem revisited—a survey of
engineering problems and computational methods. Stochastic Models, 13(1), 39-66.
## Examples
```julia-repl
julia> n = 100
julia> t = 500
julia> exp_ccdf(n, t; p=ones(n)/n, m=1, r=1, normalize=true)
0.4913906004535237
```
"""
function exp_ccdf(n, t; p=ones(n)/n, m=1, r=1, normalize=true)
@assert length(p) == n
# Normalize probabilities
if normalize
p=p ./ sum(p)
end
# Initialize probability P
P_cdf = 1
for i in 1:n
Sm = 0
for j in 1:m
Sm += ((p[i]*r*t)^(j-1))/factorial(big(j-1)) #formulas see paper reference [1]
end
P_cdf *= (1 - Sm*exp(-p[i]*r*t))
end
P = 1 - P_cdf
return P
end
"""
approximate_moment(n, fun; p=ones(n)/n, q=1, m=1, r=1,
steps=1000, normalize=true)
Calculates the q-th rising moment of `T[N]` (number of designs that are needed to collect
all modules `m` times). Integral is approximated by the Riemann sum.
Reference:
- Doumas, A. V., & Papanicolaou, V. G. (2016). The coupon collector’s problem revisited:
generalizing the double Dixie cup problem of Newman and Shepp. ESAIM: Probability
and Statistics, 20, 367-399.
## Examples
```julia-repl
julia> n = 100
julia> fun = exp_ccdf
julia> approximate_moment(n, fun; p=ones(n)/n, q=1, m=1, r=1,
steps=10000, normalize=true)
518.8175339489885
```
"""
function approximate_moment(n, fun; p=ones(n)/n, q=1, m=1, r=1,
steps=500, normalize=true)
@assert length(p) == n
a = 0; b = n*log(n)
ϵ = 0.001 # error tolerance
while fun(n, b; p=p, m=m, r=r, normalize=normalize) > ϵ
b += n
end
# integration exp_ccdf, see paper References [1]
# build in adaptive integration (exp_ccdf is a very steep function): minimize function evaluation at constant function value, only evaluate function at steep part
a = deepcopy(b)
while fun(n, a; p=p, m=m, r=r, normalize=normalize) < 1 - ϵ
a += -n/10
end
δ = (b-a)/steps; t = a:δ:b
qth_moment = q * sum(δ .* 1 .* (0:δ:a-δ).^[q-1]) + q * sum(δ .* fun.(n, t; p=p, m=m, r=r, normalize=normalize) .* t.^[q-1])
return qth_moment
end
"""
expectation_minsamplesize(n; p=ones(n)/n, m=1, r=1, normalize=true)
Calculates the expected minimum number of designs `E[T]` to observe each module at least `m` times.
- `n`: number of modules in the design space
- `p`: vector with the probabilities or abundances of the different modules
- `m`: number of times each module has to be observed in the sampled set of designs
- `r`: number of modules per design
- normalize: if true, normalize `p`
References:
- Doumas, A. V., & Papanicolaou, V. G. (2016). The coupon collector’s problem revisited:
generalizing the double Dixie cup problem of Newman and Shepp. ESAIM: Probability
and Statistics, 20, 367-399.
- Boneh, A., & Hofri, M. (1997). The coupon-collector problem revisited—a survey of
engineering problems and computational methods. Stochastic Models, 13(1), 39-66.
## Examples
```julia-repl
julia> n = 100
julia> expectation_minsamplesize(n; p=ones(n)/n, m=1, r=1, normalize=true)
519
```
"""
function expectation_minsamplesize(n::Integer; p=ones(n)/n, m::Integer=1, r=1, normalize=true)
@assert length(p) == n
@assert n > 0
@assert all(p .>= 0)
@assert m > 0
@assert r > 0
E = approximate_moment(n, exp_ccdf; p=p, q=1, m=m, r=r, normalize=normalize)
return Int(ceil(E))
end
"""
std_minsamplesize(n::Integer; p=ones(n)/n, m::Integer=1, r=1, normalize=true)
Calculates the standard deviation on the minimum number of designs to observe each module at least `m` times.
- `n`: number of modules in the design space
- `p`: vector with the probabilities or abundances of the different modules
- `m`: number of complete sets of modules that need to be collected
- `r`: number of modules per design
- normalize: if true, normalize `p`
## Examples
```julia-repl
julia> n = 100
julia> std_minsamplesize(n; p=ones(n)/n, m=1, r=1, normalize=true)
126
```
"""
function std_minsamplesize(n::Integer; p=ones(n)/n, m::Integer=1, r=1, normalize=true)
@assert length(p) == n
@assert n > 0
@assert all(p .>= 0)
@assert m > 0
@assert r > 0
M1 = approximate_moment(n, exp_ccdf; p=p, q=1, m=m, r=r, normalize=normalize)
M2 = approximate_moment(n, exp_ccdf; p=p, q=2, m=m, r=r, normalize=normalize)
var = M2 - M1 - M1^2
return Int(ceil(sqrt(var)))
end
"""
success_probability(n::Integer, t::Integer; p=ones(n)/n, m::Integer=1, r=1, normalize=true)
Calculates the success probability `F(t) = P(T ≤ t)` or the probability that
the minimum number of designs `T` to see each module at least `m` times
is smaller than `t`.
- `n`: number of modules in design space
- `t`: sample size/number of designs for which to calculate the success probability
- `p`: vector with the probabilities or abundances of the different modules
- `m`: number of complete sets of modules that need to be collected
- `r`: number of modules per design
- normalize: if true, normalize `p`
References:
- Boneh, A., & Hofri, M. (1997). The coupon-collector problem revisited—a survey of engineering problems and computational methods. Stochastic Models, 13(1), 39-66.
## Examples
```julia-repl
julia> n = 100
julia> t = 600
julia> success_probability(n, t; p=ones(n)/n, m=1, r=1, normalize=true)
0.7802171997092149
```
"""
function success_probability(n::Integer, t::Integer; p=ones(n)/n, m::Integer=1, r=1, normalize=true)
@assert length(p) == n
@assert n > 0
@assert all(p .>= 0)
@assert t >= 0
@assert m > 0
@assert r > 0
P_success = 1 - exp_ccdf(n, t; p=p, m=m, r=r, normalize=normalize)
return P_success
end
"""
expectation_fraction_collected(n::Integer, t::Integer; p=ones(n)/n, r=1, normalize=true)
Calculates the fraction of all modules that is expected to be observed
after collecting `t` designs.
- `n`: number of modules in design space
- `t`: sample size/number of designs
- `p`: vector with the probabilities or abundances of the different modules
- `r`: number of modules per design
- normalize: if true, normalize `p`
References:
- Boneh, A., & Hofri, M. (1997). The coupon-collector problem revisited—a survey of engineering problems and computational methods. Stochastic Models, 13(1), 39-66.
## Examples
```julia-repl
julia> n = 100
julia> t = 200
julia> expectation_fraction_collected(n, t; p=ones(n)/n, r=1, normalize=true)
0.8660203251420364
```
"""
function expectation_fraction_collected(n::Integer, t::Integer; p=ones(n)/n, r=1, normalize=true)
@assert length(p) == n
@assert n > 0
@assert all(p .>= 0)
@assert t >= 0
@assert r > 0
if normalize
p = p./sum(p)
end
frac = sum( (1-(1-p[i])^(t*r)) for i in 1:n )/n
return frac
end
"""
prob_occurrence_module(pᵢ, t::Integer, r, k::Integer)
Calculates probability that specific module with module probability `pᵢ`
has occurred `k` times after collecting `t` designs.
Sampling processes of individual modules are assumed to be independent Poisson processes.
- `pᵢ`: module probability
- `t`: sample size/number of designs
- `k`: number of occurrence
References:
- Boneh, A., & Hofri, M. (1997). The coupon-collector problem revisited—a survey of engineering problems and computational methods. Stochastic Models, 13(1), 39-66.
## Examples
```julia-repl
julia> pᵢ = 0.005
julia> t = 500
julia> k = 2
julia> r = 1
julia> prob_occurrence_module(pᵢ, t, r, k)
0.25651562069968376
```
"""
function prob_occurrence_module(pᵢ, t::Integer, r, k::Integer)
@assert pᵢ > 0 && pᵢ <= 1
@assert t >= 0
@assert r >= 0
@assert k >= 0
poisson = Poisson(pᵢ * t * r)
return pdf(poisson, k)
end
end
| BioCCP | https://github.com/kirstvh/BioCCP.jl.git |
|
[
"MIT"
] | 0.1.1 | 4293935cfb1576a783192e81d965addff4d1b47a | code | 247 | using Documenter
using BioCCP
makedocs(sitename="BioCCP",
format = Documenter.HTML(),
modules=[BioCCP],
pages=Any["BioCCP"=> "man/BioCCP.md"])
#=
deploydocs(
repo = "github.com/kirstvh/BioCCP.jl.git",
)
=#
| BioCCP | https://github.com/kirstvh/BioCCP.jl.git |
|
[
"MIT"
] | 0.1.1 | 4293935cfb1576a783192e81d965addff4d1b47a | code | 805080 | ### A Pluto.jl notebook ###
# v0.16.1
using Markdown
using InteractiveUtils
# ╔═╡ ee082fcf-54d1-4aea-9944-8d7f81c6dbf4
using BioCCP, Plots, PlutoUI, Distributions
# ╔═╡ 41beadc2-385e-42bf-9960-ab201242b400
md"*Loading the required packages...*"
# ╔═╡ 4d246460-af05-11eb-382b-590e60ba61f5
md"## BioCCP Case studies
In this notebook, the BioCCP.jl package will be applied to two real biological problems, based on two studies from literature. More specifically, we will
(1) illustrate how BioCCP can aid in determining an appropriate sample size in order to guarantee sufficient coverage of a CRISPR-Cas 9 guide RNA library. For this case study, we consider the paper 'Genome-wide CRISPR Screen in a Mouse Model of Tumor Growth and Metastasis' (Chen *et al.*, 2015) [^1].
(2) show how BioCCP assists in sample determination for screening of modular proteins. For this case study, we will use the paper 'Rapid and High-Throughput Evaluation of Diverse Configurations of Engineered Lysins Using the VersaTile Technique' (Duyvejonck *et al.*, 2021) [^2].
"
# ╔═╡ ad7e5e06-55b2-4752-9335-2364489932eb
md"##### 1. [Genome-wide CRISPR Screen in a Mouse Model of Tumor Growth and Metastasis (Chen *et al*, 2015)](https://www.sciencedirect.com/science/article/pii/S0092867415002044)
The study focuses on a genome-wide CRISPR-Cas9 screen in a mouse model of tumor evolution as a powerful tool to identify loss-of-function mutations that correlate with tumor growth and metastasis.
For this purpose, **over 60000 gRNAs** were designed, each targeting a protein-coding gene or microRNA precursor in the mouse genome.
Each gRNA sequence was cloned into a lentiviral vector. From this plasmid pool, single gRNA viruses were generated by transfection of a HEK293FT cell line. Then, **cancer cells (Cas9 KPD cells) were infected with the single gRNA designed lentiviruses**. The number of lentiviruses, and by consequence, the number of integrated sgRNA constructs per cancer cell, is determined by a Poisson distribution (see later). The integrated sgRNA construct results in production of the corresponding gRNA by the infected cell, which guides the Cas9 enzyme to execute the knockout in the target gene. Afterwards, these **gRNA-producing cancer cells are transplanted into the flanks of mice**, causing primary tumor formation *in situ*.
The question is **how many cells should be injected in total, so we can study the effect of each mutation on metastasis, or, how many cells must be transplanted into mice to observe all gRNAs at least once?**"
# ╔═╡ d6b7e9f5-3bda-4477-b92c-e32b836f1f0d
md"##### 1.1 Problem definition"
# ╔═╡ 50462b1a-f65e-4d91-8d8d-da9c93ad007c
Show(MIME"image/png"(), read("CRISPR_img.png"))
# ╔═╡ f3ef2715-da53-449e-b198-faeeb78ac83f
md"###### 🔹 Number of modules (*n*)
In BioCCP.jl terminology, we **consider each gRNA as a module**. There are 63090 gRNAs present in the gRNA plasmid library (*i.e.*, the number of gRNAs with more than 1 read as uncovered by deep sequencing the plasmid library (see below))."
# ╔═╡ 08054214-4045-486a-ac44-7a1a8aee9acf
n_gRNAs = 63090
# ╔═╡ 50d87809-df3b-4558-97ce-0a50fdfbcf69
md"###### 🔹 Module distribution (*p*)
The gRNA sequences were embedded into a plasmid library, with a single gRNA per lentiviral vector. Deep sequencing of this plasmid pool resulted in a number of reads for each gRNA, representing the abundance of each gRNA in the plasmid pool:
*Note: The log normalized reads were retrieved from the supplementary data accompanying the paper and converted to normalized reads for each gRNA.*"
# ╔═╡ 857da523-7c09-4230-9397-2dc0ef639007
reads_gRNA = [7.33862316
7.333862316
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]
# ╔═╡ 737c8375-2c49-4f32-b54c-1f15f8d451d5
md"which can be summarized in the following unimodal read distribution: "
# ╔═╡ 94bcc8de-a0be-47ab-a03a-b04c351ad6f0
begin
histogram(reads_gRNA, bar_edges=false, bins=100, size = (700, 320), orientation=:v, titlefont=font(10), xguidefont=font(9), yguidefont=font(9), label="")
xlabel!("Number of reads")
ylabel!("Density")
title!("Distribution of gRNA reads in plasmid pool")
end
# ╔═╡ 364c38ca-8637-4d26-8d64-525222d24033
md"Normalizing for the total number of reads in the plasmid pool gives us a **probability vector** *p*, **containing for each gRNA a probability to be randomly sampled from the plasmid library** (proportional to its abundance in the library):"
# ╔═╡ 5841ea1a-8d23-4fe7-8010-7d8512050c22
p_gRNA = reads_gRNA / sum(reads_gRNA)
# ╔═╡ e1444e8d-c0ae-408f-ab88-fd11459d9842
md"###### 🔹 Number of modules per design (*r*)
The single gRNA lentiviruses are used to infect cancer cells (Cas9 KPD cells). The Multiplicity of Infection (MOI), *i.e.*, the number of virions added per cell, amounts to 0.4: "
# ╔═╡ 5c906c4a-8785-4199-8aba-a35c855e77f6
MOI = 0.4
# ╔═╡ 449ad144-90d4-4f74-867f-939b489ddaad
md"The probability of a cell being infected by $k$ viral particles (= containing $k$ integrated RNA constructs) can be described by a Poisson distribution with λ = MOI:
$$P(k) = \frac{λ^k . e^{-λ}}{k!}$$"
# ╔═╡ 28dda5a1-546a-411f-9b8b-398e37195b13
λ = MOI
# ╔═╡ aad92f19-6935-4cbd-8e0f-d16a0d379386
poisson = Poisson(λ)
# ╔═╡ 69fe641d-afb9-45dc-9aa0-436177b02ac3
p_0gRNA = pdf(poisson, 0) # the probability that a cancer cell has 0 integrated gRNA constructs
# ╔═╡ e88e908f-be65-488f-a0dd-718a091f9d5c
p_1gRNA = pdf(poisson, 1) # the probability that a cancer cell has 1 integrated gRNA construct
# ╔═╡ 480d6282-ce97-4127-957d-d0b9fd37a7e4
p_2gRNA = pdf(poisson, 2) # the probability that a cancer cell has 2 integrated gRNA constructs
# ╔═╡ 55e0b804-a3ee-4667-8367-38b1b20b0096
md"
After selection on antibiotics, only cells that have received at least one sgRNA-expressing lentiviral integrant survive. The percentage of cells surviving can be written as:
$$P_{survival} = P(k>0) = 1-P(k=0)$$
"
# ╔═╡ d021a50b-3bd1-4125-a939-1bba5eace898
Pₛᵤᵣᵥᵢᵥₐₗ = 1 - pdf(poisson, 0)
# ╔═╡ 2bedff06-473e-49c0-9d8c-a0cde03ad554
md"With this information, we can calculate the **average number of gRNA integrands per cancer cell** *r*:"
# ╔═╡ fa7b1373-006c-48e8-917b-c750bce86e09
r = sum([k*pdf(poisson, k)/Pₛᵤᵣᵥᵢᵥₐₗ for k in 1:20])
# calculation of expected value: summation over number of integrated constructs multiplied by its probability (normalized by percentage of cells that survived)
# ╔═╡ 2ac080a7-af56-41ae-b913-8d65dedb9197
# ╔═╡ b4cc09e0-2a6a-4ab6-8153-cbc817dede2e
md"##### 1.2 Statistics for determining a minimum sample size using BioCCP.jl
"
# ╔═╡ 3466f636-7b06-42e0-b71e-55a7a2c74808
md"###### 🔹 Expected minimum number of cells
The **expected minimum number of cells** that need be injected into the mice so each gRNA is present in the experiment at least once (*m = 1*):"
# ╔═╡ 0a99939a-4edb-488d-bbdf-4a425c808607
m = 1
# ╔═╡ f35928d5-e8c2-4c9f-b4ba-9cb58eafb9e0
expectation_minsamplesize(n_gRNAs; p = p_gRNA, r = r, m = m)
# ╔═╡ b8fe484e-1189-4fd0-85b9-715e084d65da
std_minsamplesize(n_gRNAs; p = p_gRNA, r = r, m = m)
# ╔═╡ d4cd5ff7-88b8-4e5c-a540-04c51ad51f42
# ╔═╡ 224d556c-cd9d-4c00-9cde-1c3df7cfcdef
md"In the study of Chen *et al.*, 3 × 10⁷ cells are injected into the flanks of mice. This exceeds the expected minimum sample size to observe each gRNA at least once."
# ╔═╡ aa29947a-4dcb-48d8-9ad0-3828f8d4b8d2
sample_size_paper = 3*10^7
# ╔═╡ a27384c0-db19-4b06-8165-208d3fe77350
md"###### 🔹 Probability to observe each gRNA at least once w.r.t. sample size"
# ╔═╡ 7b43e546-07f0-45ce-9d2a-52894adad822
md"The **success probability curve**, describing the probability that the minimum number of cells that should be sampled to observe all gRNAs at least $m time(s) is smaller than or equal to a given sample size on the x-axis:"
# ╔═╡ ca7ecf8b-634e-4fa5-aaa8-102fa488c7a1
begin
sample_size_initial = 18*10^6
while (1 - success_probability(n_gRNAs, sample_size_initial; p = p_gRNA, r = r, m = m)) > 0.00005
global sample_size_initial += Int(ceil(10*n_gRNAs))
end
sample_sizes = Int.(ceil.(0:n_gRNAs*5:sample_size_initial))
successes = success_probability.(n_gRNAs, sample_sizes; p = p_gRNA, r = r, m = m)
plot(sample_sizes, successes, title = "Success probability to sample each gRNA at least $m time
in function of sample size", xlabel = "number of cells sampled", ylabel= "success probability", label = "", legend=:bottomright, size=(600,400), seriestype=:scatter, titlefont=font(10),xguidefont=font(9), yguidefont=font(9))
end
# ╔═╡ 60c3ce0d-85be-42f0-a869-1895abc096f3
md"When the number of cells is equal to $sample_size_paper, the probability that all gRNAs will be represented in the genome-wide screening experiment at least once, is:"
# ╔═╡ 7eb18559-e2c0-4c34-a2b2-4e3c3ab831a6
success_probability(63090, 3*10^7; p = p_gRNA, r = r, m = m)
# ╔═╡ ecf6292c-7cdd-4886-8a59-be8e4f2751b7
md"Let's investigate how many complete sets of gRNAs that are suffficiently covered by the sample size of the study by Chen *et al.*. Let's define sufficient coverage as a success probability of at least 95%."
# ╔═╡ 56f21296-fcd2-46f7-a130-4cb6940c1133
success_probability(63090, sample_size_paper; p = p_gRNA, r = r, m = 2)
# ╔═╡ 09d77a14-01f4-430c-9038-25b85b31a1c2
success_probability(63090, sample_size_paper; p = p_gRNA, r = r, m = 7)
# ╔═╡ 071cdabe-3443-490c-ab79-e270a0a1de68
success_probability(63090, sample_size_paper; p = p_gRNA, r = r, m = 10)
# ╔═╡ c59c5fb7-7291-46e2-88af-03ed38c7c3eb
begin
success_probabilities = []
for m in 1:15
push!(success_probabilities, success_probability(63090, sample_size_paper; p = p_gRNA, r = r, m = m))
end
plot(success_probabilities, seriestype=:scatter,
title="Coverage for sample size of $sample_size_paper cells",
xlabel="number of complete sets of gRNAs to observe (m)", ylabel="success probability", label="")
plot!([0, 15], [0.95, 0.95], label="95% success
probability", legend = :best, titlefont=font(10),xguidefont=font(9), yguidefont=font(9))
end
# ╔═╡ bb990e0c-df90-4635-80e4-e6639a62865a
md"We see the sample size of the paper covers 9 complete sets of gRNAs. Thereafter, the success probability drops below 95%."
# ╔═╡ 57309e16-8c1b-4268-b164-0b45b60b4fbd
# ╔═╡ ca947d4b-18e6-4bad-8d9b-b73324a5c40b
md"###### 🔹 Expected fraction of gRNAs observed w.r.t. sample size"
# ╔═╡ dc696281-7a5b-4568-a4c2-8dde90af43f0
md"The **fraction of the total number of available gRNAs that is expected to be observed** after injecting a given number of cells, is displayed by the curve below:"
# ╔═╡ 7968de5e-5ae8-4ab4-b089-c3d33475af2f
begin
global sample_size_initial_frac = 7*10^6
while (1 - expectation_fraction_collected(n_gRNAs, sample_size_initial_frac; p=p_gRNA, r = r)) > 0.000005
global sample_size_initial_frac += Int(ceil(10*n_gRNAs))
end
sample_sizes_frac = Int.(ceil.(collect(0 : n_gRNAs/2 : sample_size_initial_frac)))
fracs = expectation_fraction_collected.(n_gRNAs, sample_sizes_frac; p=p_gRNA, r = r)
plot(sample_sizes_frac, fracs,
title = "Expected observed fraction of the total number of gRNAs",
xlabel = "number of cells", seriestype=:scatter,
ylabel= "Expected observed fraction of gRNAs",
label = "",
size=(700,400), xguidefont=font(9), yguidefont=font(9), titlefont=font(10))
end
# ╔═╡ be8e6332-f79d-4d63-afae-51c2d829f998
md"When 50000 cells are injected, it is expected that, on average, approximately 53% of the gRNAs is represented:"
# ╔═╡ f0eaf96b-0bc0-4194-9a36-886cb1d66e00
expectation_fraction_collected(n_gRNAs, 5*10^4; p = p_gRNA, r = r)
# ╔═╡ 293386f0-aafe-4e94-8e16-3237659d6963
# ╔═╡ b2111d05-a153-4969-aeae-a7f0c01e3365
md"###### 🔹 Occurence of specific gRNA w.r.t. sample size"
# ╔═╡ 667fc6a8-b3ff-464d-903c-e215e7d2f472
pᵢ_gRNA = p_gRNA[1]
# ╔═╡ ead64d36-947a-4b9f-a0f7-a1821039f5b3
n_cells = 4*10^6
# ╔═╡ f92a6b6e-a556-45cb-a1ae-9f5fe791ffd2
md""" For the gRNA with the lowest abundance in the plasmid library, the probability that it is observed *k* times when injecting $n_cells cancer cells in the mice, can be described by the following curve:"""
# ╔═╡ 60fff6ab-3e19-4af7-b102-17d3d47494f3
begin
ed = Int(floor(n_cells*pᵢ_gRNA*r))
j = collect(0:1:2*ed)
x = prob_occurrence_module.(pᵢ_gRNA, n_cells, r, j)
plot(j,x, seriestype=[:scatter, :line], xlabel="number of times gRNA is represented",
ylabel="probability",
title=
"Probability that gRNA is represented k times in mouse model
when $n_cells cells injected",
label="",
size=((600,300)),titlefont=font(10),
xguidefont=font(9), yguidefont=font(9))
end
# ╔═╡ a041652b-365e-4594-9c48-c63d547b3295
mean, std = floor(n_cells * pᵢ_gRNA * r), floor(sqrt(n_cells * pᵢ_gRNA * r))
# ╔═╡ cc4c712d-6562-4d25-8b84-64458cda4198
md"The least abundant gRNA will be present on average 3 times with a standard deviation of 1, when 4x10⁶ cells are injected."
# ╔═╡ 752392c9-198a-4d5a-8ce7-7c154ce834ec
# ╔═╡ 1f2c0903-13af-48d5-bbf5-b6102043a288
# ╔═╡ a6014a30-6643-4504-8081-17bcc8be2615
md"##### 2. [Rapid and High-Throughput Evaluation of Diverse Configurations of Engineered Lysins Using the VersaTile Technique (Duyvejonck *et al.*, 2021)](https://www.mdpi.com/2079-6382/10/3/293)
This study aims engineering **modular endolysins** for obtaining optimal antibacterial properties.
###### 2.1 Problem definition
The endolysin engineered in the study can consists of different protein domains, e.g.:
- an outer membrane permeabilizing peptide (OMP),
- an enzymatically active domain (EAD),
- a cell wall binding domain (CBD),
- a peptide linker
Different variants of these domain types are available and combined with the VersaTile DNA assembly technique. The following picture represents an endolysin configuration that is considered in the paper:"
# ╔═╡ 45fd4574-679f-42b8-9aa5-b949d252e2e7
Show(MIME"image/png"(), read("Endolysin_img.png"))
# ╔═╡ 8032bbab-67a8-4058-873a-a33c4b39066f
md"In this library, there is an availability of
- 42 OMPs,
- 7 linkers,
- 6 CBDs, and
- 23 EADs.
This results in a total number of 42 x 7 x 6 x 23 = **40572 possible endolysins**."
# ╔═╡ 5f480d13-2dd1-451e-adf5-45d5d7749fdc
begin
n_OMP = 42;
n_linker = 7;
n_CBD = 6;
n_EAD = 23;
end
# ╔═╡ da16c262-4d76-4757-9e80-df6871546278
md"However, due to limitations in the screening technique, only **188 endolysins from this library were analysed** for their activity against *Klebsiella pneumoniae*. "
# ╔═╡ 8b0ca97d-e7fd-4720-80a9-87c43c1f78a9
# ╔═╡ a4e1a017-c37c-4a81-915c-96638964eda2
md"###### 2.2 Determining coverage using BioCCP.jl"
# ╔═╡ 12afa1a7-b4ed-4767-aff8-07dab6fa5d3c
endolysins_sample_size = 188
# ╔═╡ 99d3250d-2335-425e-b470-2f570cba7367
# ╔═╡ dad9f7f9-a5b0-44b2-b1a9-96b0fe9b3c7c
md"###### 🔹 Coverage OMP variants in endolysin library"
# ╔═╡ 16fd6703-d559-444c-8f42-26e314f29fb3
md"First, let's take a look at the coverage of the OMP variants in the screening experiment. The **probability that all available OMPs were observed at least once in this set**, will be calculated below. We assume that there is an equal probability for each OMP to be observed (optimistic scenario)."
# ╔═╡ 63eed55a-1dfe-4b3d-b7aa-e4736718b105
Float16(success_probability(n_OMP, endolysins_sample_size; p = ones(n_OMP)/n_OMP, m = 1))
# ╔═╡ 55631080-6d7d-4059-b8c1-32fa10ef8884
md"The OMPs are only guaranteed to be fully covered with 62 % probability at a sample size of 188 endolysins. "
# ╔═╡ efac7679-df64-4db2-8b8b-06febdc8a5f0
md"The success probability is way lower than 95%. Suppose we would execute a series of experiments in which we sample endolysins until all OMPs are observed at least once, what would be the **average required number of endolysins per experiment to see each OMP at least once** in the set of sampled endolysins?"
# ╔═╡ d26ab92f-3541-4eb6-9257-124266d6878a
expectation_minsamplesize(n_OMP; p = ones(n_OMP)/n_OMP, m = 1)
# ╔═╡ 0f98810b-c6c5-4f4b-9c40-0af0ec78c057
std_minsamplesize(n_OMP; p = ones(n_OMP)/n_OMP, m = 1)
# ╔═╡ 7a1bed30-ecb8-4747-bf83-578b77f48e59
md"The expected minimum number of endolysins to observe each OMP variant at least once is 182 (standard deviation: 51 endolysins)."
# ╔═╡ c0ccc9c2-278e-4c21-ac1f-53c73d209e38
md"Another research question we can ask ourselves is what **fraction of the total number of OMP variants is expected to be represented** for the set of 188 randomly sampled endolysins from the library."
# ╔═╡ 908644a9-a4db-41f7-9faf-1f3afb5dac79
expectation_fraction_collected(n_OMP, endolysins_sample_size; p = ones(n_OMP)/n_OMP)
# ╔═╡ 19f4c16d-2f59-45ba-8502-b86cacd9cd10
md"In other words, when over different sampling experiments, each time 188 endolysins are randomly sampled, on average a fraction of 98.9% of all OMP variants is expected to be observed."
# ╔═╡ ae2c1a89-cff9-4514-8744-f1620e46663d
# ╔═╡ feedcbb2-e6a0-4cb6-9d88-81554ba4ccbf
md"###### 🔹 Coverage linkers, CBDs and EADs in endolysin library"
# ╔═╡ a34b71fb-1da6-43db-981b-71e1a404e4d6
md"Now, let's investigate how well the other module types are covered. The **probability that all available linkers were observed at least once in this set** (assuming that there is an equal probability for each linker to be observed):"
# ╔═╡ ca9f4254-b38c-4de2-bafb-f97dd15a46bc
Float16(success_probability(n_linker, endolysins_sample_size; p = ones(n_linker)/n_linker))
# ╔═╡ cf19ee57-174d-42b9-8f9d-f4654907fc2a
md"The **probability that all available CBDs were observed at least once in this set** (assuming that there is an equal probability for each CBD to be observed):"
# ╔═╡ 63d90d97-ba80-449e-8d38-1bba52ab32ac
Float16(success_probability(n_CBD, endolysins_sample_size; p = ones(n_CBD)/n_CBD))
# ╔═╡ 062ceeb7-5d6d-48ce-8ad5-9e1d78a937e7
md"The **probability that all available EADs were observed at least once in this set** (assuming that there is an equal probability for each EAD to be observed):"
# ╔═╡ 04a2acf6-52e2-42d7-96a0-f3e31d1f6b58
Float16(success_probability(n_EAD, endolysins_sample_size; p = ones(n_EAD)/n_EAD))
# ╔═╡ 63dedbcb-7ea0-4c4e-bb4c-fa8e258f9a93
md"We can conclude the EADs, CBDs and linker domains were sufficiently covered by the sample size of $endolysins_sample_size endolysins."
# ╔═╡ fbffaab6-3154-49df-a226-d5810d0b7c38
md"""## References"""
# ╔═╡ 1f48143a-2152-4bb9-a765-a25e70c281a3
md"""[^1]: Chen, S., Sanjana, N. E., Zheng, K., Shalem, O., Lee, K., Shi, X., ... & Sharp, P. A. (2015). *Genome-wide CRISPR screen in a mouse model of tumor growth and metastasis.* Cell, 160(6), 1246-1260.
[^2]: Duyvejonck, L., Gerstmans, H., Stock, M., Grimon, D., Lavigne, R., & Briers, Y. (2021). *Rapid and High-Throughput Evaluation of Diverse Configurations of Engineered Lysins Using the VersaTile Technique.* Antibiotics, 10(3), 293.
"""
# ╔═╡ 00000000-0000-0000-0000-000000000001
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git-tree-sha1 = "1958272568dc176a1d881acb797beb909c785510"
uuid = "82ae8749-77ed-4fe6-ae5f-f523153014b0"
version = "1.0.0"
[[StatsBase]]
deps = ["DataAPI", "DataStructures", "LinearAlgebra", "Missings", "Printf", "Random", "SortingAlgorithms", "SparseArrays", "Statistics", "StatsAPI"]
git-tree-sha1 = "8cbbc098554648c84f79a463c9ff0fd277144b6c"
uuid = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
version = "0.33.10"
[[StatsFuns]]
deps = ["ChainRulesCore", "IrrationalConstants", "LogExpFunctions", "Reexport", "Rmath", "SpecialFunctions"]
git-tree-sha1 = "95072ef1a22b057b1e80f73c2a89ad238ae4cfff"
uuid = "4c63d2b9-4356-54db-8cca-17b64c39e42c"
version = "0.9.12"
[[StructArrays]]
deps = ["Adapt", "DataAPI", "StaticArrays", "Tables"]
git-tree-sha1 = "2ce41e0d042c60ecd131e9fb7154a3bfadbf50d3"
uuid = "09ab397b-f2b6-538f-b94a-2f83cf4a842a"
version = "0.6.3"
[[SuiteSparse]]
deps = ["Libdl", "LinearAlgebra", "Serialization", "SparseArrays"]
uuid = "4607b0f0-06f3-5cda-b6b1-a6196a1729e9"
[[TOML]]
deps = ["Dates"]
git-tree-sha1 = "44aaac2d2aec4a850302f9aa69127c74f0c3787e"
uuid = "fa267f1f-6049-4f14-aa54-33bafae1ed76"
version = "1.0.3"
[[TableTraits]]
deps = ["IteratorInterfaceExtensions"]
git-tree-sha1 = "c06b2f539df1c6efa794486abfb6ed2022561a39"
uuid = "3783bdb8-4a98-5b6b-af9a-565f29a5fe9c"
version = "1.0.1"
[[Tables]]
deps = ["DataAPI", "DataValueInterfaces", "IteratorInterfaceExtensions", "LinearAlgebra", "TableTraits", "Test"]
git-tree-sha1 = "fed34d0e71b91734bf0a7e10eb1bb05296ddbcd0"
uuid = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
version = "1.6.0"
[[Test]]
deps = ["Distributed", "InteractiveUtils", "Logging", "Random"]
uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
[[URIs]]
git-tree-sha1 = "97bbe755a53fe859669cd907f2d96aee8d2c1355"
uuid = "5c2747f8-b7ea-4ff2-ba2e-563bfd36b1d4"
version = "1.3.0"
[[UUIDs]]
deps = ["Random", "SHA"]
uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
[[Unicode]]
uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"
[[Wayland_jll]]
deps = ["Artifacts", "Expat_jll", "JLLWrappers", "Libdl", "Libffi_jll", "Pkg", "XML2_jll"]
git-tree-sha1 = "dc643a9b774da1c2781413fd7b6dcd2c56bb8056"
uuid = "a2964d1f-97da-50d4-b82a-358c7fce9d89"
version = "1.17.0+4"
[[Wayland_protocols_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Wayland_jll"]
git-tree-sha1 = "2839f1c1296940218e35df0bbb220f2a79686670"
uuid = "2381bf8a-dfd0-557d-9999-79630e7b1b91"
version = "1.18.0+4"
[[XML2_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Libiconv_jll", "Pkg", "Zlib_jll"]
git-tree-sha1 = "be0db24f70aae7e2b89f2f3092e93b8606d659a6"
uuid = "02c8fc9c-b97f-50b9-bbe4-9be30ff0a78a"
version = "2.9.10+3"
[[XSLT_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Libgcrypt_jll", "Libgpg_error_jll", "Pkg", "XML2_jll"]
git-tree-sha1 = "2b3eac39df218762d2d005702d601cd44c997497"
uuid = "aed1982a-8fda-507f-9586-7b0439959a61"
version = "1.1.33+4"
[[Xorg_libX11_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxcb_jll", "Xorg_xtrans_jll"]
git-tree-sha1 = "5be649d550f3f4b95308bf0183b82e2582876527"
uuid = "4f6342f7-b3d2-589e-9d20-edeb45f2b2bc"
version = "1.6.9+4"
[[Xorg_libXau_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "4e490d5c960c314f33885790ed410ff3a94ce67e"
uuid = "0c0b7dd1-d40b-584c-a123-a41640f87eec"
version = "1.0.9+4"
[[Xorg_libXcursor_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXfixes_jll", "Xorg_libXrender_jll"]
git-tree-sha1 = "12e0eb3bc634fa2080c1c37fccf56f7c22989afd"
uuid = "935fb764-8cf2-53bf-bb30-45bb1f8bf724"
version = "1.2.0+4"
[[Xorg_libXdmcp_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "4fe47bd2247248125c428978740e18a681372dd4"
uuid = "a3789734-cfe1-5b06-b2d0-1dd0d9d62d05"
version = "1.1.3+4"
[[Xorg_libXext_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"]
git-tree-sha1 = "b7c0aa8c376b31e4852b360222848637f481f8c3"
uuid = "1082639a-0dae-5f34-9b06-72781eeb8cb3"
version = "1.3.4+4"
[[Xorg_libXfixes_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"]
git-tree-sha1 = "0e0dc7431e7a0587559f9294aeec269471c991a4"
uuid = "d091e8ba-531a-589c-9de9-94069b037ed8"
version = "5.0.3+4"
[[Xorg_libXi_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXext_jll", "Xorg_libXfixes_jll"]
git-tree-sha1 = "89b52bc2160aadc84d707093930ef0bffa641246"
uuid = "a51aa0fd-4e3c-5386-b890-e753decda492"
version = "1.7.10+4"
[[Xorg_libXinerama_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXext_jll"]
git-tree-sha1 = "26be8b1c342929259317d8b9f7b53bf2bb73b123"
uuid = "d1454406-59df-5ea1-beac-c340f2130bc3"
version = "1.1.4+4"
[[Xorg_libXrandr_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXext_jll", "Xorg_libXrender_jll"]
git-tree-sha1 = "34cea83cb726fb58f325887bf0612c6b3fb17631"
uuid = "ec84b674-ba8e-5d96-8ba1-2a689ba10484"
version = "1.5.2+4"
[[Xorg_libXrender_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"]
git-tree-sha1 = "19560f30fd49f4d4efbe7002a1037f8c43d43b96"
uuid = "ea2f1a96-1ddc-540d-b46f-429655e07cfa"
version = "0.9.10+4"
[[Xorg_libpthread_stubs_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "6783737e45d3c59a4a4c4091f5f88cdcf0908cbb"
uuid = "14d82f49-176c-5ed1-bb49-ad3f5cbd8c74"
version = "0.1.0+3"
[[Xorg_libxcb_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "XSLT_jll", "Xorg_libXau_jll", "Xorg_libXdmcp_jll", "Xorg_libpthread_stubs_jll"]
git-tree-sha1 = "daf17f441228e7a3833846cd048892861cff16d6"
uuid = "c7cfdc94-dc32-55de-ac96-5a1b8d977c5b"
version = "1.13.0+3"
[[Xorg_libxkbfile_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"]
git-tree-sha1 = "926af861744212db0eb001d9e40b5d16292080b2"
uuid = "cc61e674-0454-545c-8b26-ed2c68acab7a"
version = "1.1.0+4"
[[Xorg_xcb_util_image_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "0fab0a40349ba1cba2c1da699243396ff8e94b97"
uuid = "12413925-8142-5f55-bb0e-6d7ca50bb09b"
version = "0.4.0+1"
[[Xorg_xcb_util_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxcb_jll"]
git-tree-sha1 = "e7fd7b2881fa2eaa72717420894d3938177862d1"
uuid = "2def613f-5ad1-5310-b15b-b15d46f528f5"
version = "0.4.0+1"
[[Xorg_xcb_util_keysyms_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "d1151e2c45a544f32441a567d1690e701ec89b00"
uuid = "975044d2-76e6-5fbe-bf08-97ce7c6574c7"
version = "0.4.0+1"
[[Xorg_xcb_util_renderutil_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "dfd7a8f38d4613b6a575253b3174dd991ca6183e"
uuid = "0d47668e-0667-5a69-a72c-f761630bfb7e"
version = "0.3.9+1"
[[Xorg_xcb_util_wm_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "e78d10aab01a4a154142c5006ed44fd9e8e31b67"
uuid = "c22f9ab0-d5fe-5066-847c-f4bb1cd4e361"
version = "0.4.1+1"
[[Xorg_xkbcomp_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxkbfile_jll"]
git-tree-sha1 = "4bcbf660f6c2e714f87e960a171b119d06ee163b"
uuid = "35661453-b289-5fab-8a00-3d9160c6a3a4"
version = "1.4.2+4"
[[Xorg_xkeyboard_config_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xkbcomp_jll"]
git-tree-sha1 = "5c8424f8a67c3f2209646d4425f3d415fee5931d"
uuid = "33bec58e-1273-512f-9401-5d533626f822"
version = "2.27.0+4"
[[Xorg_xtrans_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "79c31e7844f6ecf779705fbc12146eb190b7d845"
uuid = "c5fb5394-a638-5e4d-96e5-b29de1b5cf10"
version = "1.4.0+3"
[[Zlib_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "320228915c8debb12cb434c59057290f0834dbf6"
uuid = "83775a58-1f1d-513f-b197-d71354ab007a"
version = "1.2.11+18"
[[Zstd_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "2c1332c54931e83f8f94d310fa447fd743e8d600"
uuid = "3161d3a3-bdf6-5164-811a-617609db77b4"
version = "1.4.8+0"
[[libass_jll]]
deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"]
git-tree-sha1 = "acc685bcf777b2202a904cdcb49ad34c2fa1880c"
uuid = "0ac62f75-1d6f-5e53-bd7c-93b484bb37c0"
version = "0.14.0+4"
[[libfdk_aac_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "7a5780a0d9c6864184b3a2eeeb833a0c871f00ab"
uuid = "f638f0a6-7fb0-5443-88ba-1cc74229b280"
version = "0.1.6+4"
[[libpng_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"]
git-tree-sha1 = "6abbc424248097d69c0c87ba50fcb0753f93e0ee"
uuid = "b53b4c65-9356-5827-b1ea-8c7a1a84506f"
version = "1.6.37+6"
[[libvorbis_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Ogg_jll", "Pkg"]
git-tree-sha1 = "fa14ac25af7a4b8a7f61b287a124df7aab601bcd"
uuid = "f27f6e37-5d2b-51aa-960f-b287f2bc3b7a"
version = "1.3.6+6"
[[nghttp2_jll]]
deps = ["Libdl", "Pkg"]
git-tree-sha1 = "8e2c44ab4d49ad9518f359ed8b62f83ba8beede4"
uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d"
version = "1.40.0+2"
[[x264_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "d713c1ce4deac133e3334ee12f4adff07f81778f"
uuid = "1270edf5-f2f9-52d2-97e9-ab00b5d0237a"
version = "2020.7.14+2"
[[x265_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "487da2f8f2f0c8ee0e83f39d13037d6bbf0a45ab"
uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76"
version = "3.0.0+3"
[[xkbcommon_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Wayland_jll", "Wayland_protocols_jll", "Xorg_libxcb_jll", "Xorg_xkeyboard_config_jll"]
git-tree-sha1 = "ece2350174195bb31de1a63bea3a41ae1aa593b6"
uuid = "d8fb68d0-12a3-5cfd-a85a-d49703b185fd"
version = "0.9.1+5"
"""
# ╔═╡ Cell order:
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| BioCCP | https://github.com/kirstvh/BioCCP.jl.git |
|
[
"MIT"
] | 0.1.1 | 4293935cfb1576a783192e81d965addff4d1b47a | code | 54159 | ### A Pluto.jl notebook ###
# v0.16.1
using Markdown
using InteractiveUtils
# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).
macro bind(def, element)
quote
local el = $(esc(element))
global $(esc(def)) = Core.applicable(Base.get, el) ? Base.get(el) : missing
el
end
end
# ╔═╡ 56571409-a81d-4772-98fd-e85e883aa4e4
using Plots, PlutoUI, BioCCP
# ╔═╡ 20ce43cd-7634-4c94-afdf-d243415525cb
md" $(@bind date DateField())"
# ╔═╡ 4d246460-af05-11eb-382b-590e60ba61f5
md"## Collecting Coupons in combinatorial biotechnology
This notebook provides functions and corresponding visualizations to determine expected minimum sample sizes for combinatorial biotechnology experiments, based on the mathematical framework of the Coupon Collector Problem (references see [^1], [^2]).
"
# ╔═╡ 6183795b-62a0-4ed4-b8f9-ea522da956e2
begin
begin
function tocsv(raw)
t = string(raw)
t = split(t, "[")[2]
t = split(t, "]")[1]
return t
end
end
md""
end
# ╔═╡ a8c81622-194a-443a-891b-bfbabffccff1
begin
md"""
👇 **COMPLETE THE FIELDS BELOW** 👇\
*First, fill in the input parameters of your problem setting. Then, click outside the text field to update the report.*"""
end
# ╔═╡ e1b554a6-db6c-4d2a-9dd3-0a35095f4d8c
Show(MIME"image/png"(), read("BioCCP_scheme.png"))
# ╔═╡ 36a09fff-8b14-4d91-84e0-9ecabefa810a
# ╔═╡ 40ac5be1-6fc2-4fbc-b0ca-a021266b2247
begin
vec_n = [];
md"""🔹 **№ modules in design space** (`n`): $(@bind n_string TextField(default = "100")) $(@bind help_n Button("❓"))"""
end
# ╔═╡ 7b05669c-7abe-42a7-838c-61c06b261256
begin
help_n
switch_n = rem(length(vec_n), 2)
push!(vec_n, 1)
if switch_n == 1
md"""
= *How many different modules or building blocks are available to construct designs?*"""
end
end
# ╔═╡ 123d5b94-5772-42dc-bf74-d964d023b209
begin
vec_r = []
md"""
🔹 **Expected № modules per design** (`r`): $(@bind r NumberField(1:20)) $(@bind help_r Button("❓")) """
end
# ╔═╡ 30cb2ab3-ad67-405e-95a1-8feea223938a
begin
help_r
switch_r = rem(length(vec_r), 2)
push!(vec_r, 1)
if switch_r == 1
md"""
= *How many modules are combined in a single design, on average?*"""
end
end
# ╔═╡ c8164a38-fcf9-4f1b-b697-46c8ce978fce
begin
vec_m = []
md"""
**🔹 № times you want to observe each module** (`m`): $(@bind m NumberField(1:20)) $(@bind help_m Button("❓")) """
end
# ╔═╡ a5c3153f-0946-4af8-871c-634a71e8b7f1
begin
help_m
switch_m = rem(length(vec_m), 2)
push!(vec_m, 1)
if switch_m == 1
md"""
= *How many times do you want to observe each of the available modules in the total set of designs?*"""
end
end
# ╔═╡ d6c6be55-fc94-480a-bc58-ca67b0c44568
begin
vec_p = []
md"""🔹 **Abundances of modules during library generation** (`p`): $(@bind ps Select(["Equal", "Unequal"], default = "Equal")) $(@bind help_p Button("❓"))"""
end
# ╔═╡ b88fab57-9f78-4450-90af-62ab860620a0
begin
help_p
switch_p = rem(length(vec_p), 2)
push!(vec_p, 1)
if switch_p == 1
md"""
= *How are the abundances of the modules distributed during combinatorial generation of designs?
Is each module equally likely to be included in a design?*"""
end
end
# ╔═╡ 45507d48-d75d-41c9-a018-299e209f900e
begin
vec_p_unequal = []
n = parse(Int64, n_string);
if ps == "Equal"
distribution = "Equal"
end
if ps == "Unequal"
md""" ↳ **Specify distribution**:
$(@bind distribution Select(["Bell curve", "Zipf's law", "Custom vector"], default = " "))"""
end
end
# ╔═╡ a937e514-4c4a-4f76-b8e5-3c2031afd416
if ps == "Unequal"
md"""
*If the exact module probabilities are known, choose "Custom vector".*
*Otherwise, select:*
- *"Zipf's law" (when you expect a small number of modules to occur quite often, and a very large number of modules to occur at the statistical equivalent of zero, but, they do occur.)*
- *"Bell curve" (when you expect a large number of modules to occur at an average probability and a smaller number of modules to occur with a small or large probability)* """
end
# ╔═╡ b17f3b8a-61ee-4563-97cd-19ff049a8e1e
begin
if distribution == "Zipf's law" || distribution == "Bell curve"
md""" ↳ **Specify pₘₐₓ/pₘᵢₙ**: $(@bind pmaxpmin_string TextField(default = "4")) = *The ratio of the largest and smallest module probability*"""
end
end
# ╔═╡ 2639e3fb-ccbb-44de-bd15-1c5dbf6c1539
begin
if distribution == "Custom vector"
md""" **↳ Enter/load your custom abundances by changing the cell below 👇**"""
end
end
# ╔═╡ 464b67be-2dad-4315-a144-0b475414366f
if distribution == "Custom vector"
md""" $(@bind abundances_str TextField((30, 10), default=join(string.(rand(200:1:400, n)), "\n")))
*Make sure the number of abundances is equal to n!*"""
end
# ╔═╡ 1220c75b-303c-4b0a-84c4-a12ee834a5af
begin
function tonumbers(text)
text = split(text, "\n")
text = rstrip.(text)
text = text[text .!= ""]
text = parse.(Float64,text)
return text
end
if distribution == "Custom vector"
abundances = (tonumbers(abundances_str))
end
md""
end
# ╔═╡ f6ebf9fb-0a29-4cb4-a544-6c6e32bedcc4
md"""
🎯 **REPORT** 🎯
**💻 Module probabilities** $(@bind show_modprobs Select(["🔻 SHOW ", "🔺 HIDE "], default="🔻 SHOW ") ) \
*How the abundances of the modules are distributed during combinatorial library generation.*
"""
# ╔═╡ b0291e05-776e-49ce-919f-4ad7de4070af
begin
function p_power(n, k)
p = (1:n) .^ -k
return p ./ sum(p)
end
if ps == "Equal"
p_vec = ones(n)./sum(ones(n));
elseif ps == "Unequal"
if distribution == "Bell curve"
ratio = parse(Float64, pmaxpmin_string)
ab1 = 1
ab2 = ratio*ab1
μ = (ab1+ab2)/2
σ = (ab2-ab1)/6
#create fixed distribution of abundances according to percentiles of bell curve
n_perc_1 = Int(floor(n*0.34));
n_perc_2 = Int(floor(n*0.135));
n_perc_3 = Int(floor(n*0.0215));
#n_perc_4 = Int(floor(n*0.0013));
n_perc_rest = n - 2*n_perc_1 - 2*n_perc_2 - 2*n_perc_3 ;
p_vec_unnorm = vcat(fill(μ,2*n_perc_1+n_perc_rest), fill(μ+1.5*σ, n_perc_2), fill(μ-1.5*σ, n_perc_2), fill(μ+3*σ, n_perc_3), fill(μ-3*σ, n_perc_3) )
# normalize sum to 1
p_vec = sort(p_vec_unnorm ./ sum(p_vec_unnorm))
end
if distribution == "Custom vector"
p_vec_unnorm = abundances
p_vec = abundances ./ sum(abundances)
end
if distribution == "Zipf's law"
ratio = parse(Float64, pmaxpmin_string)
p_vec = p_power(n, log(ratio)/log(n))
p_vec = p_vec ./ sum(p_vec)
end
end
if show_modprobs == "🔻 SHOW "
scatter(p_vec, title = "Probability mass function", ylabel = "module probability pⱼ", xlabel = "module j", label="", size = (700, 400))
ylims!((0,2*maximum(p_vec)), titlefont=font(10), xguidefont=font(9), yguidefont=font(9))
end
end
# ╔═╡ 87c3f5cd-79bf-4ad8-b7f8-3e98ec548a9f
begin
if show_modprobs == "🔻 SHOW " && distribution == "Bell curve"
histogram(p_vec, normalize=:probability, bar_edges=false, size = (550, 320), orientation=:v, bins=[(μ - 3*σ)/sum(p_vec_unnorm), (μ - 2*σ)/sum(p_vec_unnorm), (μ-σ)/sum(p_vec_unnorm), (μ + σ)/sum(p_vec_unnorm), (μ + 2*σ)/sum(p_vec_unnorm), (μ + 3.2*σ)/sum(p_vec_unnorm)], titlefont=font(10), xguidefont=font(9), yguidefont=font(9), label="")
# if distribution == "Normally distributed"
# plot!(x->pdf(Normal(μ, σ), x), xlim=xlims())
# xlabel!("Abundance"); ylabel!("probability"); title!("Distribution of module abundances")
# end
xlabel!("Probability"); ylabel!("Relative frequency"); title!("Distribution of module probabilities")
end
end
# ╔═╡ 2313198e-3ac9-407b-b0d6-b79e02cefe35
begin
if show_modprobs == "🔻 SHOW " && distribution == "Bell curve"
md"""For $n_string modules of which the probabilities form a bell curve with ratio pₘₐₓ/pₘᵢₙ = $pmaxpmin_string , we follow the percentiles of a normal distribution to generate the probability vector.
We consider μ to be the mean module probability and σ to be the standard deviation of the module probabilities.
According to the percentiles
- 68% of the module probabilities lies in the interval [μ - σ, μ + σ],
- 95% of falls into the range [μ - 2σ, μ + 2σ] and
- 99.7% lies in [μ - 3σ, μ +3σ].
We use the ratio pₘₐₓ/pₘᵢₙ to fix the width of the interval [μ - 3σ, μ +3σ]. (We assume that pₘₐₓ = μ +3σ and pₘᵢₙ = μ - 3σ and calculate μ and σ from this assumption). In addition, we make sure the sum of the probability vector sums up to 1.
As a result, we get:
- $(n_perc_1+n_perc_rest) modules with a probability of $(µ/sum(p_vec_unnorm))
- $(n_perc_2) modules with a probability of $((μ+1.5*σ)/sum(p_vec_unnorm))
- $(n_perc_2) modules with a probability of $((μ-1.5*σ)/sum(p_vec_unnorm))
- $(n_perc_3) modules with a probability of $((μ+2.5*σ)/sum(p_vec_unnorm))
- $(n_perc_3) modules with a probability of $((μ-2.5*σ)/sum(p_vec_unnorm))"""
end
end
# ╔═╡ f098570d-799b-47e2-b692-476a4d95825b
if show_modprobs == "🔻 SHOW "
md"Each biological design in the design space is built by choosing $r module(s) (with replacement) out of a set of $n_string modules according to the module probabilities visualized above."
end
# ╔═╡ 85bfc3d5-447d-4078-af14-e3f369adfa71
# ╔═╡ caf67b2f-cc2f-4d0d-b619-6e1969fabc1a
md""" **💻 Expected minimum sample size** $(@bind show_E Select(["🔻 SHOW ", "🔺 HIDE "], default="🔺 SHOW "))
\
*The expected minimum number of designs to observe each module at least $m time(s) in the sampled set of designs.* """
# ╔═╡ 6f14a72c-51d3-4759-bb8b-10db1dc260f0
begin
if show_E == "🔻 SHOW "
E = Int(ceil(expectation_minsamplesize(n; p = p_vec, m=m, r = r)))
sd = Int(ceil(std_minsamplesize(n; p = p_vec, m=m, r = r)))
md"""
`Expected minimum sample size ` = **$E designs**\
`Standard deviation ` = **$sd designs** """
end
end
# ╔═╡ f1e180e5-82a7-4fab-b894-75be4627af5d
# ╔═╡ 22fe8006-0e81-4e0a-a460-28610a55cd97
md""" **💻 Success probability** $(@bind show_success Select(["🔻 SHOW ", "🔺 HIDE "], default="🔻 SHOW ") )\
*The probability that the minimum number of designs T is smaller than or equal to a given sample size t.* """
# ╔═╡ db4371e4-7f86-4db3-b076-12f6cd220b89
begin
if show_success == "🔻 SHOW "
sample_size_95 = 1
while 0.95 - success_probability(n, sample_size_95; p = p_vec,
r = r, m = m) > 0.00005
global sample_size_95 += Int(ceil(n/10))
end
md""" 👉 Enter your sample size of interest: $(@bind sample_size_1_string TextField(default=string(sample_size_95)))"""
end
#genereer tabel + download knop
end
# ╔═╡ 317995ed-bdf4-4f78-bd66-a39ffd1dc452
begin
if show_success == "🔻 SHOW "
sample_size_1 = parse(Int64, sample_size_1_string);
p_success = Float64(success_probability(n, sample_size_1; p = p_vec, m = m, r = r))
md"""
↳ `Success probability F(t)` = **$p_success**\
"""
end
end
# ╔═╡ 3039ac2b-656e-4c2b-9036-cb1d9cdc0790
# ╔═╡ ca5a4cef-df67-4a5e-8a86-75a9fe8c6f37
if show_success == "🔻 SHOW "
md"*A curve describing the success probability in function of sample size.*"
end
# ╔═╡ 9616af0e-810c-4e6a-bc67-cb70e5e620f5
# ╔═╡ 24f7aae7-d37a-4db5-ace0-c910b178da88
begin
if show_success == "🔻 SHOW "
sample_size_initial = 5
while (1 - success_probability(n, sample_size_initial; p = p_vec, r = r, m = m)) > 0.0005
global sample_size_initial += Int(ceil(n/10))
end
sample_sizes = Int.(ceil.(0:n/10:sample_size_initial))
successes = success_probability.(n, sample_sizes; p = p_vec, r = r, m = m)
plot(sample_sizes, successes, title = "Success probability in function of sample size",
xlabel = "sample size s",
ylabel="P(minimum sample size <= s)", label = "", legend=:bottomright, size=(600,400), seriestype=:scatter, titlefont=font(10),xguidefont=font(9), yguidefont=font(9))
end
end
# ╔═╡ 4902d817-3967-45cd-a283-b2872cf1b49c
if show_success == "🔻 SHOW "
DownloadButton(string("sample_size,", tocsv(sample_sizes), "\n", "success_probability,", tocsv(successes)), "successprobability_$date.csv")
end
# ╔═╡ 37f951ee-885c-4bbe-a05f-7c5e48ff4b6b
begin
#following one-sided version of Chebyshev's inequality.
function chebyshev_onesided_larger(X, μ, σ)
X_μ = X - μ
return σ^2 / (σ^2 + X_μ^2)
end
function chebyshev_onesided_smaller(X, μ, σ)
X_μ = μ - X
return σ^2 / (σ^2 + X_μ^2)
end
if show_success == "🔻 SHOW "
if sample_size_1 < E
compare = "smaller"
if sample_size_1 <= n/r
print_sentence = "P(minimum sample size ≤ $sample_size_1) = 0."
else
prob_chebyshev = chebyshev_onesided_smaller(sample_size_1, E, sd)
print_sentence = "P(minimum sample size ≤ $sample_size_1) ≤ $prob_chebyshev. "
end
elseif sample_size_1 > E
compare = "greater"
prob_chebyshev = chebyshev_onesided_larger(sample_size_1, E, sd)
print_sentence = "P(minimum sample size ≥ $sample_size_1) ≤ $prob_chebyshev. "
elseif sample_size_1==E
print_sentence = "P(minimum sample size ≤ $sample_size_1 OR minimum sample size ≥ $sample_size_1) ≤ 1."
end
md"""*Upper bound on probability that minimum sample size is smaller than given sample size t, according to Chebychev's inequality.*:
$print_sentence"""
end
end
# ╔═╡ 702b158b-4f1c-453f-9e70-c00ec22226c3
# ╔═╡ dc696281-7a5b-4568-a4c2-8dde90af43f0
md""" **💻 Expected observed fraction of the total number of modules** $(@bind show_satur Select(["🔻 SHOW ", "🔺 HIDE "], default="🔺 HIDE "))\
*The fraction of the total number of available modules that is expected to be observed after collecting a given number of designs.*"""
# ╔═╡ eb92ff7c-0140-468c-8b32-f15d1cf15913
if show_satur == "🔻 SHOW "
md"""
👉 Enter your sample size of interest: $(@bind sample_size_2_string TextField(default="50")) """
end
# ╔═╡ f0eaf96b-0bc0-4194-9a36-886cb1d66e00
begin
if show_satur == "🔻 SHOW "
sample_size_2 = Int(ceil(parse(Int64, sample_size_2_string)))
E_fraction = expectation_fraction_collected(n, sample_size_2; p = p_vec, r = r)
md""" ↳ `Expected fraction observed` = **$E_fraction**
"""
end
end
# ╔═╡ 8ce0d3d7-8081-4d08-9189-595e3dc1814f
# ╔═╡ 0099145a-5460-4549-9513-054bc1b04eea
if show_satur == "🔻 SHOW "
md""" *A curve describing the expected fraction of modules observed in function of sample size.* """
end
# ╔═╡ 7968de5e-5ae8-4ab4-b089-c3d33475af2f
begin
if show_satur == "🔻 SHOW "
global sample_size_initial_frac = 5
while (1 - expectation_fraction_collected(n, sample_size_initial_frac; p = p_vec, r = r)) > 0.0005
global sample_size_initial_frac += ceil(Int(n/10))
end
sample_sizes_frac = Int.(ceil.(collect(0 : n/10 : sample_size_initial_frac)))
fracs = expectation_fraction_collected.(n, sample_sizes_frac; p = p_vec, r = r)
plot(sample_sizes_frac, fracs, title = "Expected observed fraction of the total number of modules",
xlabel = "sample size", seriestype=:scatter,
ylabel= "Expected observed fraction", label = "", size=(700,400), xguidefont=font(9), yguidefont=font(9), titlefont=font(10))
end
end
# ╔═╡ 0b95ccff-4c7b-400d-be61-8ea056ccc87f
if show_satur == "🔻 SHOW "
DownloadButton(string("sample_size,", tocsv(sample_sizes_frac), "\n", "expected_observed_fraction,", tocsv(fracs)), "expectedobservedfraction_$date.csv")
end
# ╔═╡ 09fb9f1d-16e4-447c-a5c0-d153cec22665
# ╔═╡ f92a6b6e-a556-45cb-a1ae-9f5fe791ffd2
md""" **💻 Occurrence of a specific module** $(@bind show_occ Select(["🔻 SHOW ", "🔺 HIDE "], default="🔺 HIDE "))\
*How many times one can expect to have collected a specific module in a sample of a given size.*"""
# ╔═╡ ec2a065f-0dc7-44d4-a18b-6c6a228b3ffc
if show_occ == "🔻 SHOW " && distribution != "Zipf's law"
md""" 👉 Enter the probability of the module of interest: $(@bind p_string TextField(default="0.005"))\
"""
end
# ╔═╡ 0e39a993-bb2f-4897-bfe2-5128ec62bef9
if show_occ == "🔻 SHOW " && distribution == "Zipf's law"
md""" 👉 Enter the rank of the module of interest: $(@bind rank_string TextField(default="5"))\
"""
end
# ╔═╡ f3329934-d69b-48a0-9cf1-e9a5920cf414
if show_occ == "🔻 SHOW "
md""" 👉 Enter the sample size of interest: $(@bind sample_size_3_string TextField(default="500"))"""
end
# ╔═╡ a041652b-365e-4594-9c48-c63d547b3295
# ╔═╡ 6acb0a97-6469-499f-a5cf-6335d6aa909a
begin
if show_occ == "🔻 SHOW "
sample_size_3 = parse(Int64, sample_size_3_string)
if distribution != "Zipf's law"
pᵢ = parse(Float64, p_string)
ed = Int(floor(sample_size_3*pᵢ*r))
j = collect(0:1:3*ed)
x = prob_occurrence_module.(pᵢ, sample_size_3, r, j)
plot(j,x, seriestype=[:scatter, :line], xlabel="occurrences in sample",
ylabel="probability",
title="Probability on number of occurrences for specific module
(for sample size = $sample_size_3)",
size=((600,300)), label="",titlefont=font(10),
xguidefont=font(9), yguidefont=font(9))
else
rank = parse(Int64, rank_string)
pᵢ = p_vec[rank]
ed = Int(floor(sample_size_3*pᵢ*r))
j = collect(0:1:3*ed)
x = prob_occurrence_module.(pᵢ, sample_size_3, r, j)
plot(j,x, seriestype=[:scatter, :line], xlabel="occurrences in sample",
ylabel="probabilityS",
title="Probability on number of occurrences for specific module
(for sample size = $sample_size_3)",
size=((600,300)), label="",titlefont=font(10),
xguidefont=font(9), yguidefont=font(9))
end
end
end
# ╔═╡ 595423df-728b-43b1-ade4-176785c54be3
begin
if show_occ == "🔻 SHOW "
md""" ↳ `Expected number of times observed` ≈ **$ed**
"""
end
end
# ╔═╡ a2bf1368-20a9-42cd-af58-67397644d725
if show_occ == "🔻 SHOW "
DownloadButton(string("number_of_occurence,", tocsv(j), "\n", "probability,", tocsv(x)), "occurrencemodule_$date.csv")
end
# ╔═╡ fbffaab6-3154-49df-a226-d5810d0b7c38
md"""## References"""
# ╔═╡ 1f48143a-2152-4bb9-a765-a25e70c281a3
md"""[^1]: Doumas, A. V., & Papanicolaou, V. G. (2016). *The coupon collector’s problem revisited: generalizing the double Dixie cup problem of Newman and Shepp.* ESAIM: Probability and Statistics, 20, 367-399.
[^2]: Boneh, A., & Hofri, M. (1997). *The coupon-collector problem revisited—a survey of engineering problems and computational methods.* Stochastic Models, 13(1), 39-66.
"""
# ╔═╡ 00000000-0000-0000-0000-000000000001
PLUTO_PROJECT_TOML_CONTENTS = """
[deps]
BioCCP = "79e6b149-e254-49fe-a721-3c4960de1574"
Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
PlutoUI = "7f904dfe-b85e-4ff6-b463-dae2292396a8"
[compat]
BioCCP = "~0.1.0"
Plots = "~1.22.4"
PlutoUI = "~0.7.14"
"""
# ╔═╡ 00000000-0000-0000-0000-000000000002
PLUTO_MANIFEST_TOML_CONTENTS = """
# This file is machine-generated - editing it directly is not advised
[[Adapt]]
deps = ["LinearAlgebra"]
git-tree-sha1 = "84918055d15b3114ede17ac6a7182f68870c16f7"
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version = "3.3.1"
[[ArgTools]]
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version = "1.1.1"
[[Artifacts]]
deps = ["Pkg"]
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uuid = "56f22d72-fd6d-98f1-02f0-08ddc0907c33"
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[[Base64]]
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[[BioCCP]]
deps = ["Distributions"]
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uuid = "79e6b149-e254-49fe-a721-3c4960de1574"
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[[Bzip2_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "c3598e525718abcc440f69cc6d5f60dda0a1b61e"
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[[Cairo_jll]]
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git-tree-sha1 = "e2f47f6d8337369411569fd45ae5753ca10394c6"
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[[ChainRulesCore]]
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[[ColorSchemes]]
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[[ColorTypes]]
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[[Colors]]
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[[Compat]]
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git-tree-sha1 = "31d0151f5716b655421d9d75b7fa74cc4e744df2"
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[[CompilerSupportLibraries_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "8e695f735fca77e9708e795eda62afdb869cbb70"
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[[Contour]]
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[[DataAPI]]
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[[DataStructures]]
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[[DataValueInterfaces]]
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[[Dates]]
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[[DelimitedFiles]]
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[[Distributed]]
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[[Distributions]]
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git-tree-sha1 = "ff7890c74e2eaffbc0b3741811e3816e64b6343d"
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[[DocStringExtensions]]
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[[Downloads]]
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[[EarCut_jll]]
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[[Expat_jll]]
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[[FFMPEG]]
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[[FFMPEG_jll]]
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[[FillArrays]]
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[[FixedPointNumbers]]
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[[Fontconfig_jll]]
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[[Formatting]]
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[[FreeType2_jll]]
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[[FriBidi_jll]]
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[[GLFW_jll]]
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[[GR]]
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[[GR_jll]]
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[[GeometryBasics]]
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[[Gettext_jll]]
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[[Grisu]]
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[[JLLWrappers]]
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[[JpegTurbo_jll]]
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[[LAME_jll]]
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[[LibVPX_jll]]
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[[Libdl]]
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[[Libffi_jll]]
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[[Libgcrypt_jll]]
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[[MbedTLS]]
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[[MbedTLS_jll]]
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[[Zlib_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "320228915c8debb12cb434c59057290f0834dbf6"
uuid = "83775a58-1f1d-513f-b197-d71354ab007a"
version = "1.2.11+18"
[[Zstd_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "2c1332c54931e83f8f94d310fa447fd743e8d600"
uuid = "3161d3a3-bdf6-5164-811a-617609db77b4"
version = "1.4.8+0"
[[libass_jll]]
deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"]
git-tree-sha1 = "acc685bcf777b2202a904cdcb49ad34c2fa1880c"
uuid = "0ac62f75-1d6f-5e53-bd7c-93b484bb37c0"
version = "0.14.0+4"
[[libfdk_aac_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "7a5780a0d9c6864184b3a2eeeb833a0c871f00ab"
uuid = "f638f0a6-7fb0-5443-88ba-1cc74229b280"
version = "0.1.6+4"
[[libpng_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"]
git-tree-sha1 = "6abbc424248097d69c0c87ba50fcb0753f93e0ee"
uuid = "b53b4c65-9356-5827-b1ea-8c7a1a84506f"
version = "1.6.37+6"
[[libvorbis_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Ogg_jll", "Pkg"]
git-tree-sha1 = "fa14ac25af7a4b8a7f61b287a124df7aab601bcd"
uuid = "f27f6e37-5d2b-51aa-960f-b287f2bc3b7a"
version = "1.3.6+6"
[[nghttp2_jll]]
deps = ["Libdl", "Pkg"]
git-tree-sha1 = "8e2c44ab4d49ad9518f359ed8b62f83ba8beede4"
uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d"
version = "1.40.0+2"
[[x264_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "d713c1ce4deac133e3334ee12f4adff07f81778f"
uuid = "1270edf5-f2f9-52d2-97e9-ab00b5d0237a"
version = "2020.7.14+2"
[[x265_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "487da2f8f2f0c8ee0e83f39d13037d6bbf0a45ab"
uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76"
version = "3.0.0+3"
[[xkbcommon_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Wayland_jll", "Wayland_protocols_jll", "Xorg_libxcb_jll", "Xorg_xkeyboard_config_jll"]
git-tree-sha1 = "ece2350174195bb31de1a63bea3a41ae1aa593b6"
uuid = "d8fb68d0-12a3-5cfd-a85a-d49703b185fd"
version = "0.9.1+5"
"""
# ╔═╡ Cell order:
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# ╟─a041652b-365e-4594-9c48-c63d547b3295
# ╟─6acb0a97-6469-499f-a5cf-6335d6aa909a
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# ╟─00000000-0000-0000-0000-000000000001
# ╟─00000000-0000-0000-0000-000000000002
| BioCCP | https://github.com/kirstvh/BioCCP.jl.git |
|
[
"MIT"
] | 0.1.1 | 4293935cfb1576a783192e81d965addff4d1b47a | code | 501 | ### A Pluto.jl notebook ###
# v0.16.1
using Markdown
using InteractiveUtils
# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).
macro bind(def, element)
quote
local el = $(esc(element))
global $(esc(def)) = Core.applicable(Base.get, el) ? Base.get(el) : missing
el
end
end
# ╔═╡ e1a7f2da-a38b-4b3c-a238-076769e46408
#test
| BioCCP | https://github.com/kirstvh/BioCCP.jl.git |
|
[
"MIT"
] | 0.1.1 | 4293935cfb1576a783192e81d965addff4d1b47a | code | 9167 | module BioCCP
using Base: Integer
using Distributions
export expectation_minsamplesize, std_minsamplesize, success_probability,
expectation_fraction_collected, prob_occurrence_module
"""Computes the log of factorial(n), falls back on Stirling's approximation for `n` > 20"""
logfactorial(n) = n ≤ 20 ? log(factorial(n)) : 0.5log(2pi*n) + n * log(n/ℯ)
"""
exp_ccdf(n, T; p=ones(n)/n, m=1, r=1, normalize=true)
Calculates `1 - F(t)`, which is the complement of the success probability
`F(t)=P(T ≤ t)` (= probability that the expected minimum
number of designs `T` is smaller than `t` in order to
see each module at least `m` times). This function
serves as the integrand for calculating `E[T]`.
- `n`: number of modules in the design space
- `p`: vector with the probabilities/abundances of the different modules in the design space during library generation
- `T`: number of designs
- `m`: number of times each module has to observed in the sampled set of designs
- `r`: number of modules per design
- normalize: if true, normalize `p`
References:
- Doumas, A. V., & Papanicolaou, V. G. (2016). The coupon collector’s problem revisited:
generalizing the double Dixie cup problem of Newman and Shepp. ESAIM: Probability and Statistics, 20, 367-399.
- Boneh, A., & Hofri, M. (1997). The coupon-collector problem revisited—a survey of
engineering problems and computational methods. Stochastic Models, 13(1), 39-66.
## Examples
```julia-repl
julia> n = 100
julia> t = 500
julia> exp_ccdf(n, t; p=ones(n)/n, m=1, r=1, normalize=true)
0.4913906004535237
```
"""
function exp_ccdf(n, t; p=ones(n)/n, m=1, r=1, normalize=true)
@assert length(p) == n
# Normalize probabilities
if normalize
p ./= sum(p)
end
# Initialize probability P
P_cdf = 1.0
for i in 1:n
Sm = 0.0
for j in 1:m
# formulas see paper reference [1]
Sm += (j-1) * (log(p[i]) + log(r) + log(t)) - logfactorial(j-1) |> exp
end
P_cdf *= (1 - Sm * exp(-p[i]*r*t))
end
P = 1.0 - P_cdf
return P
end
"""
approximate_moment(n, fun; p=ones(n)/n, q=1, m=1, r=1,
steps=1000, normalize=true, ϵ = 1e-3)
Calculates the q-th rising moment of `T[N]` (number of designs that are needed to collect
all modules `m` times). Integral is approximated by the Riemann sum.
Reference:
- Doumas, A. V., & Papanicolaou, V. G. (2016). The coupon collector’s problem revisited:
generalizing the double Dixie cup problem of Newman and Shepp. ESAIM: Probability
and Statistics, 20, 367-399.
## Examples
```julia-repl
julia> n = 100
julia> fun = exp_ccdf
julia> approximate_moment(n, fun; p=ones(n)/n, q=1, m=1, r=1,
steps=10000, normalize=true)
518.8175339489885
```
"""
function approximate_moment(n, fun; p=ones(n)/n, q=1, m=1, r=1,
steps=500, normalize=true, ϵ=1e-3)
@assert length(p) == n
a = 0; b = n*log(n)
while fun(n, b; p=p, m=m, r=r, normalize=normalize) > ϵ
b += n
end
# integration exp_ccdf, see paper References [1]
# build in adaptive integration (exp_ccdf is a very steep function):
# minimize function evaluation at constant function value, only evaluate function at steep part
a = b
while fun(n, a; p=p, m=m, r=r, normalize=normalize) < 1 - ϵ
a += -n / 10
end
δ = (b-a)/steps; t = a:δ:b
qth_moment = q * sum(δ .* 1 .* (0:δ:a-δ).^[q-1]) +
q * sum(δ .* fun.(n, t; p=p, m=m, r=r, normalize=normalize) .* t.^[q-1])
return qth_moment
end
"""
expectation_minsamplesize(n; p=ones(n)/n, m=1, r=1, normalize=true)
Calculates the expected minimum number of designs `E[T]` to observe each module at least `m` times.
- `n`: number of modules in the design space
- `p`: vector with the probabilities or abundances of the different modules
- `m`: number of times each module has to be observed in the sampled set of designs
- `r`: number of modules per design
- normalize: if true, normalize `p`
References:
- Doumas, A. V., & Papanicolaou, V. G. (2016). The coupon collector’s problem revisited:
generalizing the double Dixie cup problem of Newman and Shepp. ESAIM: Probability
and Statistics, 20, 367-399.
- Boneh, A., & Hofri, M. (1997). The coupon-collector problem revisited—a survey of
engineering problems and computational methods. Stochastic Models, 13(1), 39-66.
## Examples
```julia-repl
julia> n = 100
julia> expectation_minsamplesize(n; p=ones(n)/n, m=1, r=1, normalize=true)
518
```
"""
function expectation_minsamplesize(n::Integer; p=ones(n)/n, m::Integer=1, r=1, normalize=true)
@assert length(p) == n
@assert n > 0
@assert all(p .>= 0)
@assert m > 0
@assert r > 0
E = approximate_moment(n, exp_ccdf; p=p, q=1, m=m, r=r, normalize=normalize)
return Int(ceil(E))
end
"""
std_minsamplesize(n::Integer; p=ones(n)/n, m::Integer=1, r=1, normalize=true)
Calculates the standard deviation on the minimum number of designs to observe each module at least `m` times.
- `n`: number of modules in the design space
- `p`: vector with the probabilities or abundances of the different modules
- `m`: number of complete sets of modules that need to be collected
- `r`: number of modules per design
- normalize: if true, normalize `p`
## Examples
```julia-repl
julia> n = 100
julia> std_minsamplesize(n; p=ones(n)/n, m=1, r=1, normalize=true)
126
```
"""
function std_minsamplesize(n::Integer; p=ones(n)/n, m::Integer=1, r=1, normalize=true)
@assert length(p) == n
@assert n > 0
@assert all(p .>= 0)
@assert m > 0
@assert r > 0
M1 = approximate_moment(n, exp_ccdf; p, m, r, normalize, q=1)
M2 = approximate_moment(n, exp_ccdf; p, m, r, normalize, q=2)
var = M2 - M1 - M1^2
return Int(ceil(sqrt(var)))
end
"""
success_probability(n::Integer, t::Integer; p=ones(n)/n, m::Integer=1, r=1, normalize=true)
Calculates the success probability `F(t) = P(T ≤ t)` or the probability that
the minimum number of designs `T` to see each module at least `m` times
is smaller than `t`.
- `n`: number of modules in design space
- `t`: sample size/number of designs for which to calculate the success probability
- `p`: vector with the probabilities or abundances of the different modules
- `m`: number of complete sets of modules that need to be collected
- `r`: number of modules per design
- normalize: if true, normalize `p`
References:
- Boneh, A., & Hofri, M. (1997). The coupon-collector problem revisited—a survey of engineering problems and computational methods. Stochastic Models, 13(1), 39-66.
## Examples
```julia-repl
julia> n = 100
julia> t = 600
julia> success_probability(n, t; p=ones(n)/n, m=1, r=1, normalize=true)
0.7802171997092149
```
"""
function success_probability(n::Integer, t::Integer; p=ones(n)/n, m::Integer=1, r=1, normalize=true)
@assert length(p) == n
@assert n > 0
@assert all(p .>= 0)
@assert t >= 0
@assert m > 0
@assert r > 0
P_success = 1.0 - exp_ccdf(n, t; p, m, r, normalize)
return P_success
end
"""
expectation_fraction_collected(n::Integer, t::Integer; p=ones(n)/n, r=1, normalize=true)
Calculates the fraction of all modules that is expected to be observed
after collecting `t` designs.
- `n`: number of modules in design space
- `t`: sample size/number of designs
- `p`: vector with the probabilities or abundances of the different modules
- `r`: number of modules per design
- normalize: if true, normalize `p`
References:
- Boneh, A., & Hofri, M. (1997). The coupon-collector problem revisited—a survey of engineering problems and computational methods. Stochastic Models, 13(1), 39-66.
## Examples
```julia-repl
julia> n = 100
julia> t = 200
julia> expectation_fraction_collected(n, t; p=ones(n)/n, r=1, normalize=true)
0.8660203251420364
```
"""
function expectation_fraction_collected(n::Integer, t::Integer; p=ones(n)/n, r=1, normalize=true)
@assert length(p) == n
@assert n > 0
@assert all(p .>= 0)
@assert t >= 0
@assert r > 0
if normalize
p ./= sum(p)
end
frac = sum( (1-(1-p[i])^(t*r)) for i in 1:n )/n
return frac
end
"""
prob_occurrence_module(pᵢ, t::Integer, r, k::Integer)
Calculates probability that specific module with module probability `pᵢ`
has occurred `k` times after collecting `t` designs.
Sampling processes of individual modules are assumed to be independent Poisson processes.
- `pᵢ`: module probability
- `t`: sample size/number of designs
- `k`: number of occurrence
References:
- Boneh, A., & Hofri, M. (1997). The coupon-collector problem revisited—a survey of engineering problems and computational methods. Stochastic Models, 13(1), 39-66.
## Examples
```julia-repl
julia> pᵢ = 0.005
julia> t = 500
julia> k = 2
julia> r = 1
julia> prob_occurrence_module(pᵢ, t, r, k)
0.25651562069968376
```
"""
function prob_occurrence_module(pᵢ, t::Integer, r, k::Integer)
@assert pᵢ > 0 && pᵢ <= 1
@assert t >= 0
@assert r >= 0
@assert k >= 0
poisson = Poisson(pᵢ * t * r)
return pdf(poisson, k)
end
end
| BioCCP | https://github.com/kirstvh/BioCCP.jl.git |
|
[
"MIT"
] | 0.1.1 | 4293935cfb1576a783192e81d965addff4d1b47a | code | 2632 | using Base: Int64
using BioCCP
@testset "BioCCP" begin
n = 20
# Equal probabilities
p_uniform = ones(n)/n
# Probabilities following Zipf's law
ρ = 10
α = exp(log(ρ)/(n-1))
p_zipf = collect(α.^-(1:n))
p_zipf = p_zipf ./ sum(p_zipf)
@testset "Expectation" begin
@test expectation_minsamplesize(n; p = p_uniform) isa Int64
@test expectation_minsamplesize(n; p = p_uniform) == Int(ceil(n*sum(1 ./ (1:n))))
@test expectation_minsamplesize(n; p = p_uniform) < expectation_minsamplesize(n; p = p_zipf)
@test expectation_minsamplesize(n; p = p_uniform, m = 1) < expectation_minsamplesize(n; p = p_uniform, m = 2)
@test Int(ceil(expectation_minsamplesize(n; p = p_uniform, m = 1, r = 1)/3)) == expectation_minsamplesize(n; p = p_uniform, m = 1, r = 3)
end
@testset "Standard deviation" begin
@test std_minsamplesize(n; p = p_uniform) isa Int64
@test std_minsamplesize(n; p = p_uniform) < std_minsamplesize(n; p = p_zipf)
@test std_minsamplesize(n; p = p_uniform, m = 1) < std_minsamplesize(n; p = p_uniform, m = 2)
end
t = 500
@testset "Success probability" begin
@test success_probability(n, t; p = p_uniform) isa Float64
@test success_probability(n, t; p = p_uniform) > success_probability(n, t; p = p_zipf)
@test success_probability(n, t; p = p_uniform, m = 1) > success_probability(n, t; p = p_uniform, m = 2)
@test all(success_probability(n, t; p = p_uniform) .< success_probability.(n, [t+10, t+100, t+1000]; p = p_uniform))
@test all(success_probability.(n, [1, 5, 10, 100, 500]) .<= 1)
@test all(success_probability.(n, [1, 5, 10, 100, 500]) .>= 0)
end
@testset "Expected fraction collected" begin
@test expectation_fraction_collected(n, t; p = p_uniform) isa Float64
@test expectation_fraction_collected(n, t; p = p_uniform) > expectation_fraction_collected(n, t; p = p_zipf)
@test all(expectation_fraction_collected(n, t; p = p_uniform) .< expectation_fraction_collected.(n, [t+10, t+100, t+1000]; p = p_uniform))
@test all(expectation_fraction_collected.(n, [1, 10, 100, 1000, 10000]) .<= 1)
@test all(expectation_fraction_collected.(n, [1, 10, 100, 1000, 10000]) .>= 0)
end
k = 2
pᵢ = 0.005
r = 3
@testset "Probability occurence" begin
@test prob_occurrence_module(pᵢ, t, r, k) isa Float64
@test all(prob_occurrence_module.(pᵢ, [0, 10, 100, 1000, 10000], r, k) .>= 0)
@test all(prob_occurrence_module.(pᵢ, [0, 10, 100, 1000, 10000], r, k) .<= 1)
end
end
| BioCCP | https://github.com/kirstvh/BioCCP.jl.git |
|
[
"MIT"
] | 0.1.1 | 4293935cfb1576a783192e81d965addff4d1b47a | code | 42 | using Test, BioCCP
include("BioCCP.jl")
| BioCCP | https://github.com/kirstvh/BioCCP.jl.git |
|
[
"MIT"
] | 0.1.1 | 4293935cfb1576a783192e81d965addff4d1b47a | docs | 8405 | [](https://doi.org/10.5281/zenodo.5547738)
# BioCCP.jl : Collecting Coupons in combinatorial biotechnology
## Intro
During the **combinatorial engineering of biosystems** such as proteins, genetic circuits and genomes, diverse libraries are generated by assembling and recombining modules. The variants with the optimal phenotypes are selected with screening techniques. However, when the number of available modules to compose biological designs increases, a combinatorial explosion of design possibilities arises, allowing only for a part of the libary to be analyzed. In this case, it is important for a researcher to get insight in which (minimum) sample size sufficiently covers the design space, *i.e.* what is the expected **minimum number of designs so that all modules are observed at least once**.
<p align="left">
<img url="https://github.com/kirstvh/BioCCP.jl/main/BioCCP_img.png" width="250"/>
</p>
## Functions
BioCCP contains functions for calculating (expected) minimum sample sizes and related statistics:
Function name | Short description
---------------- | -----------------
`expectation_minsamplesize` | Calculates the expected minimum number of designs to observe all modules at least *m* times
`std_minsamplesize` | Calculates standard deviation on the minimum number of designs
`success_probability` | Calculates the probability that the minimum number of designs *T* is smaller than or equal to a given sample size *t*
`expectation_fraction_collected` | Returns the fraction of the total number of modules in the design space that is expected to be observed for a given sample size *t*
`prob_occurrence_module` | Calculates for a module with specified module probability *p*, the probability that the module has occurred *k* times when a given number of designs has been collected
For more info about the implementation of the functions, please consult the [docs](https://kirstvh.github.io/BioCCP.jl/) or [source code](/src/BioCCP.jl).
## Pluto notebooks
### 1. Report-generating Pluto notebook
The [first Pluto notebook](/notebooks/BioCCP_Interactive_Notebook.jl) provides an interactive illustration of all functions in BioCCP and assembles a report for your specific design set-up.
Inputs |
---------------- |
Symbol | Short description
---------------- | -----------------
*n* | Total number of modules in the design space
*r* | The number of modules per design
*m* | The number of times each module has to be observed (default = 1) in the sampled set of designs
*p* (\*) | Probability distribution of the modules
> (\*)
> *When exact probabilities are known*, define your custom module probability/abundance vector or load them in the notebook from an external file.
> *When probabilities and/or their distribution are unknown*, you can either:
> > 1) Assume the probabilities of all modules to be equal (uniform distribution), or
> > 2) Assume the module probabilities to follow *Zipf's law*, specifying the ratio p<sub>max</sub>/p<sub>min</sub>, or
> > 3) Assume the histogram of the module probabilities to behave like a *bell curve*, specifying the ratio p<sub>max</sub>/p<sub>min</sub>
Using the inputs, a report for sample size determination is created using the [functions](https://kirstvh.github.io/BioCCP.jl/) described above. The report contains the following sections:
Report section | Short description
---------------- | -----------------
Module probabilities | This section shows a plot with the probability of each module in the design space during library generation.
Expected minimum sample size | This part displays the expected minimum number of designs **E**[_T_] and the standard deviation.
Success probability | In this section, the report calculates the probability *F(t)* that the minimum number of designs *T* is smaller than or equal to a given sample size *t*. Moreover, a curve describing the success probability *F(t)* in function of an increasing sample size *t* is available, to determine a minimum sample size according to a probability cut-off.
Expected observed fraction of the total number of modules | Here, the fraction of the total number of modules in the design space that is expected to be observed is computed for a given sample size *t*. A saturation curve, displaying the expected fraction of modules observed in function of increasing sample size, is provided.
Number of occurrence of a specific module | In this last part, you can specify the probability *p<sub>j</sub>* of a module of interest together with a particular sample size, to calculate a curve showing the probability for a module to occur *k* times (in function of *k*).
### 2. Case study Pluto notebook
The [second Pluto notebook](/notebooks/BioCCP_Case_Study.jl) contains two case studies, illustrating the application of the BioCCP.jl package to real biological problems, more specifically:
**(1)**   Studying the required sample size and related statistics for a genome-wide CRISPR experiment, based on a [study from Chen *et al.* (2015)](https://doi.org/10.1016/j.cell.2015.02.038) concerning tumour research in mouse models.
**(2)**   Determining coverage of a combinatorial protein engineering experiment, based on a [study from Duyvejonck *et al.* (2021)](https://doi.org/10.3390/antibiotics10030293) focusing on the development of endolysins as alternative antibiotics.
## Getting started
#### Launch Pluto notebook from Browser
The Pluto notebooks can be launched directly from your browser using Binder (no installation of Julia/packages required):
- Report-generating Pluto notebook:   [](https://mybinder.org/v2/gh/kirstvh/PlutoNotebooks/main?urlpath=pluto/open?path=/home/jovyan/notebooks/BioCCP_Interactive_Notebook.jl)
- Case study Pluto notebook:       [](https://kirstvh.github.io/BioCCP_Case_Study_html) → To skip the run time and have immediate access to the results, the link provides an html file of the executed case study notebook.
#### Execute functions in Julia
  **(1)**   [Install Julia](https://julialang.org/downloads/)
  **(2)**   Install BioCCP in the Julia REPL:
using Pkg; Pkg.add("BioCCP")
  **(3)**   Load the BioCCP package:
using BioCCP
Now you are ready for executing BioCCP functions in the Julia REPL.
#### Run the Pluto notebooks locally
Additionally, for using the Pluto notebooks, following steps need to be taken:
   In the Julia REPL, hit the following command to install the [Pluto package](https://github.com/fonsp/Pluto.jl):
using Pkg; Pkg.add(name="Pluto", version="0.16.1")
   Then start Pluto in the Julia REPL:
using Pluto; Pluto.run()
   Finally, open the notebook file ([report-generating notebook](/notebooks/BioCCP_Interactive_Notebook.jl) or [case study notebook](/notebooks/BioCCP_Case_Study.jl)).
## References
The implementation of formulas was based on the references below:
> Doumas, A. V., & Papanicolaou, V. G. (2016). *The coupon collector’s problem revisited: generalizing the double Dixie cup problem of Newman and Shepp.* ESAIM: Probability and Statistics, 20, 367-399. doi: https://doi.org/10.1051/ps/2016016
> Boneh, A., & Hofri, M. (1997). *The coupon-collector problem revisited—a survey of engineering problems and computational methods.* Stochastic Models, 13(1), 39-66. doi: https://doi.org/10.1080/15326349708807412
The case studies were based on the following references:
> Chen, S., Sanjana, N. E., Zheng, K., Shalem, O., Lee, K., Shi, X., ... & Sharp, P. A. (2015). *Genome-wide CRISPR screen in a mouse model of tumor growth and metastasis.* Cell, 160(6), 1246-1260. doi: https://doi.org/10.1016/j.cell.2015.02.038Get
> Duyvejonck, L., Gerstmans, H., Stock, M., Grimon, D., Lavigne, R., & Briers, Y. (2021). *Rapid and High-Throughput Evaluation of Diverse Configurations of Engineered Lysins Using the VersaTile Technique.* Antibiotics, 10(3), 293. doi: https://doi.org/10.3390/antibiotics10030293
| BioCCP | https://github.com/kirstvh/BioCCP.jl.git |
|
[
"MIT"
] | 0.1.1 | 4293935cfb1576a783192e81d965addff4d1b47a | docs | 7262 | # BioCCP
## Intro
BioCCP.jl applies the Coupon Collector Problem to **combinatorial biotechnology**, in particular to aid (expected) **minimum sample size** determination for screening experiments.
**Modular designs** are considered, created by randomly combining `r` modules from a set of `n` available modules (sampling with replacement). The module probabilities during the generation of the designs are specified by a probability/abundance vector `p`. Depending on how many complete sets of modules one wants to observe, parameter `m` can be increased from its default value of 1 to a higher value.
For a specific combinatorial design set-up of interest, a report with results regarding (expected) minimum sample sizes can be easily retrieved by using the provided Interactive Pluto notebook. Additionally, a Pluto notebook with case studies is provided to illustrate the usage of BioCCP onto real biological examples.
> **References:**
>
> Boneh, A., & Hofri, M. (1997). The coupon-collector problem revisited—a survey of engineering problems and computational methods. *Stochastic Models, 13*(1), 39-66.
>
> Doumas, A. V., & Papanicolaou, V. G. (2016). The coupon collector’s problem revisited: generalizing the double Dixie cup problem of Newman and Shepp. *ESAIM: Probability and Statistics, 20*, 367-399.
## Functions
<html lang="en"><head><meta charset="UTF-8"/><meta name="viewport" content="width=device-width, initial-scale=1.0"/></head><body><div id="documenter"><nav class="docs-sidebar"> <article class="docstring"><header><a class="docstring-binding" id="BioCCP.expectation_minsamplesize" href="#BioCCP.expectation_minsamplesize"><code><strong>BioCCP.expectation_minsamplesize</strong></code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia">expectation_minsamplesize(n::Integer; p=ones(n)/n, m::Integer=1, r=1, normalize=true)</code></pre><p>Calculates the expected minimum number of designs <code>E[T]</code> to observe each module at least <code>m</code> times.</p><ul><li><code>n</code>: number of modules in the design space</li><li><code>p</code>: vector with the probabilities or abundances of the different modules</li><li><code>m</code>: number of times each module has to be observed in the sampled set of designs </li><li><code>r</code>: number of modules per design</li><li>normalize: if true, normalize <code>p</code></li></ul>
<pre><code class="language-julia-repl">julia> n = 100
julia> expectation_minsamplesize(n; p = ones(n)/n, m = 1, r = 1, normalize = true)
518</code></pre></div></section>
</article><article class="docstring">
<header><a class="docstring-binding" id="BioCCP.std_minsamplesize" href="#BioCCP.std_minsamplesize"><code><strong>BioCCP.std_minsamplesize</strong></code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia">std_minsamplesize(n::Integer; p=ones(n)/n, m::Integer=1, r=1, normalize=true)</code></pre><p>Calculates the standard deviation on the minimum number of designs to observe each module at least <code>m</code> times.</p><ul><li><code>n</code>: number of modules in the design space</li><li><code>p</code>: vector with the probabilities or abundances of the different modules</li><li><code>m</code>: number of times each module has to be observed in the sampled set of designs </li><li><code>r</code>: number of modules per design</li><li>normalize: if true, normalize <code>p</code></li></ul>
<pre><code class="language-julia-repl">julia> n = 100
julia> std_minsamplesize(n; p = ones(n)/n, m = 1, r = 1, normalize = true)
126</code></pre></div></section>
</article><article class="docstring"><header><a class="docstring-binding" id="BioCCP.success_probability" href="#BioCCP.success_probability"><code><strong>BioCCP.success_probability</strong></code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia">success_probability(n::Integer, t::Integer; p=ones(n)/n, m::Integer=1, r=1, normalize=true)</code></pre><p>Calculates the success probability <code>F(t) = P(T ≤ t)</code> or the probability that the minimum number of designs <code>T</code> to see each module at least <code>m</code> times is smaller than or equal to <code>t</code>.</p><ul><li><code>n</code>: number of modules in design space</li><li><code>t</code>: sample size or number of designs for which to calculate the success probability </li><li><code>p</code>: vector with the probabilities or abundances of the different modules</li><li><code>m</code>: number of times each module has to be observed in the sampled set of designs </li><li><code>r</code>: number of modules per design</li><li>normalize: if true, normalize <code>p</code></li></ul>
<pre><code class="language-julia-repl">julia> n = 100
julia> t = 600
julia> success_probability(n, t; p = ones(n)/n, m = 1, r = 1, normalize = true)
0.7802171997092149</code></pre></div></section>
</article><article class="docstring"><header><a class="docstring-binding" id="BioCCP.expectation_fraction_collected" href="#BioCCP.expectation_fraction_collected"><code><strong>BioCCP.expectation_fraction_collected</strong></code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia">expectation_fraction_collected(n::Integer, t::Integer; p=ones(n)/n, r=1, normalize=true)</code></pre><p>Calculates the fraction of the total number of modules that is expected to be observed after collecting <code>t</code> designs.</p><ul><li><code>n</code>: number of modules in design space</li><li><code>t</code>: sample size or number of designs </li><li><code>p</code>: vector with the probabilities or abundances of the different modules </li><li><code>r</code>: number of modules per design</li><li>normalize: if true, normalize <code>p</code></li></ul>
<pre><code class="language-julia-repl">julia> n = 100
julia> t = 200
julia> expectation_fraction_collected(n, t; p = ones(n)/n, r = 1, normalize=true)
0.8660203251420364</code></pre></div></section>
</article><article class="docstring"><header><a class="docstring-binding" id="BioCCP.prob_occurrence_module" href="#BioCCP.prob_occurrence_module"><code><strong>BioCCP.prob_occurrence_module</strong></code></a> — <span class="docstring-category">Function</span></header><section><div><pre><code class="language-julia">prob_occurrence_module(p<sub>i</sub>, t::Integer, r, k::Integer)</code> </pre><p>Calculates the probability that specific module with module probability <code>p<sub>i</sub></code> has occurred <code>k</code> times after collecting <code>t</code> designs.</p><p>Sampling processes of individual modules are assumed to be independent Poisson processes.</p><ul><li><code>p<sub>i</sub></code>: module probability</li><li><code>t</code>: sample size or number of designs </li><li><code>r</code>: number of modules per design </li><li><code>k</code>: the number of occurrences </li></ul>
<pre><code class="language-julia-repl">julia> p<sub>i</sub> = 0.005
julia> t = 500
julia> r = 1
julia> k = 2
julia> prob_occurrence_module(p<sub>i</sub>, t, r, k)
0.25651562069968376</code></pre>
| BioCCP | https://github.com/kirstvh/BioCCP.jl.git |
|
[
"MIT"
] | 0.1.1 | 4293935cfb1576a783192e81d965addff4d1b47a | docs | 1310 | # BioCCP
## Intro
BioCCP.jl applies the Coupon Collector's Problem to **combinatorial biotechnology**, in particular to aid **minimum sample size** determination of screening experiments.
**Modular designs** are considered, created by randomly combining `r` modules from a set of `n`available modules. The module probabilities during the generation of the designs are specified by a probability/abundance vector `p`. Depending on how many complete sets of modules one wants to observe, parameter `m` can be increased from its default value of 1 to a higher value.
For a specific combinatorial design set-up of interest, a report with results regarding minimum sample sizes can be easily retrieved by using the provided Pluto notebook.
## Functions
```@docs
BioCCP.expectation_minsamplesize
BioCCP.std_minsamplesize
BioCCP.success_probability
BioCCP.expectation_fraction_collected
BioCCP.prob_occurrence_module
```
## References
- Doumas, A. V., & Papanicolaou, V. G. (2016). *The coupon collector’s problem revisited: generalizing the double Dixie cup problem of Newman and Shepp.* ESAIM: Probability and Statistics, 20, 367-399.
- Boneh, A., & Hofri, M. (1997). *The coupon-collector problem revisited—a survey of engineering problems and computational methods.* Stochastic Models, 13(1), 39-66.
| BioCCP | https://github.com/kirstvh/BioCCP.jl.git |
|
[
"MIT"
] | 0.1.1 | 4293935cfb1576a783192e81d965addff4d1b47a | docs | 1310 | # BioCCP
## Intro
BioCCP.jl applies the Coupon Collector's Problem to **combinatorial biotechnology**, in particular to aid **minimum sample size** determination of screening experiments.
**Modular designs** are considered, created by randomly combining `r` modules from a set of `n`available modules. The module probabilities during the generation of the designs are specified by a probability/abundance vector `p`. Depending on how many complete sets of modules one wants to observe, parameter `m` can be increased from its default value of 1 to a higher value.
For a specific combinatorial design set-up of interest, a report with results regarding minimum sample sizes can be easily retrieved by using the provided Pluto notebook.
## Functions
```@docs
BioCCP.expectation_minsamplesize
BioCCP.std_minsamplesize
BioCCP.success_probability
BioCCP.expectation_fraction_collected
BioCCP.prob_occurrence_module
```
## References
- Doumas, A. V., & Papanicolaou, V. G. (2016). *The coupon collector’s problem revisited: generalizing the double Dixie cup problem of Newman and Shepp.* ESAIM: Probability and Statistics, 20, 367-399.
- Boneh, A., & Hofri, M. (1997). *The coupon-collector problem revisited—a survey of engineering problems and computational methods.* Stochastic Models, 13(1), 39-66.
| BioCCP | https://github.com/kirstvh/BioCCP.jl.git |
|
[
"MIT"
] | 0.3.0 | 6e91cff8b0241d57750cca395a97d6919839a7b5 | code | 734 | module NumericalRepresentationTheoryPlotsExt
using NumericalRepresentationTheory, Plots
import Plots: plot, @recipe
@recipe function f(σ::Partition)
legend --> false
ratio --> 1.0
axis --> false
grid --> false
color --> :orange
ticks --> false
linewidth --> 2
ret = Shape[]
m = length(σ)
for j = 1:m, k = 1:σ[j]
push!(ret, Shape([k-1,k-1,k,k],[1-j,-j,-j,1-j]))
end
ret
end
function plot(mults::Dict{Partition,<:Integer}; kwds...)
ret = Any[]
M = mapreduce(maximum, max, keys(mults))
N = mapreduce(length, max, keys(mults))
for (σ,m) in sort(mults)
push!(ret, plot(σ; title="$m", xlims=(0,M), ylims=(-N,0)))
end
plot(ret...; kwds...)
end
end | NumericalRepresentationTheory | https://github.com/dlfivefifty/NumericalRepresentationTheory.jl.git |
|
[
"MIT"
] | 0.3.0 | 6e91cff8b0241d57750cca395a97d6919839a7b5 | code | 14468 | module NumericalRepresentationTheory
using Base, LinearAlgebra, Permutations, SparseArrays, BlockBandedMatrices, BlockArrays, FillArrays
import Base: getindex, size, setindex!, maximum, Int, length,
==, isless, copy, kron, hash, first, show, lastindex, |, Integer, BigInt
import LinearAlgebra: adjoint, transpose, eigen
import SparseArrays: blockdiag, AbstractSparseMatrixCSC
## Kronecker product of Sn
export Partition, YoungMatrix, partitions, youngtableaux, YoungTableau, ⊗, ⊕,
Representation, multiplicities, generators, standardrepresentation, randpartition,
blockdiagonalize, hooklength
# utility function
function kronpow(p,m)
ret = p
for k=1:m-1
ret = kron(ret,p)
end
ret
end
struct Partition
σ::Vector{Int}
function Partition(σ)
if !issorted(σ; lt=Base.:>)
error("input vector $σ should be sorted")
end
if !all(x -> x > 0, σ)
error("input vector $σ should be all positive")
end
new(σ)
end
end
Partition(σ::Int...) = Partition([σ...])
function isless(a::Partition, b::Partition)
n,m = Int(a), Int(b)
if n ≠ m
return n < m
end
M,N = length(a.σ), length(b.σ)
for k = 1:min(M,N)
if a.σ[k] ≠ b.σ[k]
return a.σ[k] < b.σ[k]
end
end
return false
end
(==)(a::Partition, b::Partition) = a.σ == b.σ
hash(a::Partition) = hash(a.σ)
copy(a::Partition) = Partition(copy(a.σ))
lastindex(a::Partition) = lastindex(a.σ)
function show(io::IO, σ::Partition)
print(io, "$(Int(σ)) = ")
for k = 1:length(σ)-1
print(io, "$(σ[k]) + ")
end
print(io, "$(σ[end])")
end
function transpose(σ::Partition)
cols = Vector{Int}(undef, maximum(σ))
j_end = 1
for k = length(σ):-1:1
for j = j_end:σ[k]
cols[j] = k
end
j_end = σ[k]+1
end
Partition(cols)
end
adjoint(σ::Partition) = transpose(σ)
getindex(σ::Partition, k::Int) = σ.σ[k]
setindex!(σ::Partition, v, k::Int) = setindex!(σ.σ, v, k)
for op in (:maximum, :length, :first)
@eval $op(σ::Partition) = $op(σ.σ)
end
Int(σ::Partition) = sum(σ.σ)
BigInt(σ::Partition) = BigInt(Int(σ))
Integer(σ::Partition) = Int(σ)
function partitions(N)
N == 1 && return [Partition([1])]
ret = Partition[]
part = partitions(N-1)
for p in part
if (length(p.σ) < 2 || p.σ[end-1] > p.σ[end])
p_new = copy(p)
p_new.σ[end] += 1
push!(ret, p_new)
end
push!(ret, Partition([p.σ ; 1]))
end
ret
end
struct YoungMatrix <: AbstractMatrix{Int}
data::Matrix{Int}
rows::Partition
columns::Partition
function YoungMatrix(data, rows, columns)
@assert size(data) == (length(rows), length(columns))
@assert rows == columns'
new(data, rows, columns)
end
end
YoungMatrix(data::Matrix{Int}, σ::Partition) = YoungMatrix(data, σ, σ')
YoungMatrix(::UndefInitializer, σ::Partition) = YoungMatrix(zeros(Int, length(σ), maximum(σ)), σ)
YoungMatrix(dat, σ::Vector{Int}) = YoungMatrix(dat, Partition(σ))
copy(Y::YoungMatrix) = YoungMatrix(copy(Y.data), Y.rows, Y.columns)
size(Y::YoungMatrix) = size(Y.data)
getindex(Y::YoungMatrix, k::Int, j::Int) = ifelse(k ≤ Y.columns[j] && j ≤ Y.rows[k], Y.data[k,j], 0)
function setindex!(Y::YoungMatrix, v, k::Int, j::Int)
@assert k ≤ Y.columns[j] && j ≤ Y.rows[k]
Y.data[k,j] = v
end
hash(Y::YoungMatrix) = hash(Y.data)
struct YoungTableau
partitions::Vector{Partition}
end
function isless(a::YoungTableau, b::YoungTableau)
if length(a.partitions) ≠ length(b.partitions)
return length(a.partitions) < length(b.partitions)
end
for (p,q) in zip(a.partitions, b.partitions)
p ≠ q && return isless(p,q)
end
return false
end
function YoungMatrix(Yt::YoungTableau)
ps = Yt.partitions
Y = YoungMatrix(undef, ps[end])
Y[1,1] = 1
for k=2:length(ps)
if length(ps[k]) > length(ps[k-1])
Y[length(ps[k]),1] = k
else
for j = 1:length(ps[k])
if ps[k][j] > ps[k-1][j]
Y[j,ps[k][j]] = k
break
end
end
end
end
Y
end
function lowerpartitions(σ)
p = Partition[]
n = length(σ)
if σ[n] > 1
p_new = copy(σ)
p_new[n] -= 1
push!(p, p_new)
else
push!(p, Partition(σ.σ[1:n-1]))
end
for k = n-1:-1:1
if σ[k] > σ[k+1]
p_new = copy(σ)
p_new[k] -= 1
push!(p, p_new)
end
end
p
end
function youngtableaux(σ::Partition)
σ == Partition([1]) && return [YoungTableau([σ])]
Yts = mapreduce(youngtableaux, vcat, lowerpartitions(σ))
sort!(map(Yt -> YoungTableau([Yt.partitions; σ]), Yts))
end
# function isyoungtableau(Y::YoungMatrix)
# Y[1,1] == 1 || return false
# for j=1:length(Y.cols), k=1:Y.cols[j]
#
# end
function hooklength(σ::Partition)
ret = BigInt(1)
m = length(σ)
for k = 1:m, j=1:σ[k]
ret_2 = 0
ret_2 += σ[k]-j
for p = k:m
σ[p] < j && break
ret_2 += 1
end
ret *= ret_2
end
factorial(BigInt(σ)) ÷ ret
end
# sample by Plancherel
function randpartition(N, m)
Σ = partitions(N)
l = hooklength.(Σ)
cs = cumsum(l)
rs = rand(1:cs[end], m)
p = (r -> findfirst(k -> k ≥ r, cs)).(rs)
Σ[p]
end
randpartition(N) = randpartition(N, 1)[1]
function irrepgenerator(σ::Partition, i::Int)
Is = Int[]; Js = Int[]; Vs = Float64[]
t = YoungMatrix.(youngtableaux(σ))
t_lookup = Dict(tuple.(t, 1:length(t)))
d = length(t)
for j = 1:d
Y = t[j]
ii = Base._to_subscript_indices(Y, Tuple(findfirst(isequal(i), Y))...)
ip = Base._to_subscript_indices(Y, Tuple(findfirst(isequal(i+1), Y))...)
if ii[1] == ip[1]
push!(Is, j)
push!(Js, j)
push!(Vs, 1)
elseif ii[2] == ip[2]
push!(Is, j)
push!(Js, j)
push!(Vs, -1)
else
Yt = copy(Y) # Tableau with swapped indices
Yt[ii...], Yt[ip...] = (Yt[ip...], Yt[ii...])
k = t_lookup[Yt]
# set entries to matrix [1/r sqrt(1-1/r^2); sqrt(1-1/r^2) -1/r]
push!(Is, j, j)
push!(Js, j, k)
ai = ii[2]-ii[1]
ap = ip[2]-ip[1]
r = ap - ai
push!(Vs, 1/r, sqrt(1-1/r^2))
end
end
sparse(Is, Js, Vs)
end
irrepgenerators(σ::Partition) = [irrepgenerator(σ, i) for i=1:Int(σ)-1]
struct Representation{MT}
generators::Vector{MT}
end
Representation(σ::Int...) = Representation(Partition(σ...))
Representation(σ::Partition) = Representation(irrepgenerators(σ))
Representation{MT}(ρ::Representation) where MT = Representation(convert.(MT, ρ.generators))
kron(A::Representation, B::Representation) = Representation(kron.(A.generators, B.generators))
_blockdiag(A::AbstractSparseMatrixCSC...) = blockdiag(A...)
_blockdiag(A::AbstractMatrix...) = blockdiag(map(sparse, A)...)
blockdiag(A::Representation...) = Representation(_blockdiag.(getfield.(A, :generators)...))
conjugate(ρ::Representation, Q) = Representation(map(g -> Q'*g*Q, ρ.generators))
⊗(A::Representation, B::Representation) = kron(A, B)
|(A::Representation, n::AbstractVector) = Representation(A.generators[n])
generators(R::Representation) = R.generators
size(R::Representation, k) = size(R.generators[1], k)
size(R::Representation) = size(R.generators[1])
diagm(A::Vector{<:Representation}) = Representation(blockdiag.(generators.(A)...))
⊕(A::Representation...) = Representation(blockdiag.(generators.(A)...))
function (R::Representation)(P)
if isempty(CoxeterDecomposition(P).terms)
# Identity
one(first(R.generators))
else
*(map(i -> R.generators[i], CoxeterDecomposition(P).terms)...)
end
end
# determine multiplicities of eigs on diagonal, assuming sorted
function eigmults(λ::Vector{Int})
mults = Vector{Int}()
n = length(λ)
tol = 0.01 # integer coefficents
c = 1
for k = 2:n
if λ[k] > λ[k-1]
push!(mults, c)
c = 0
end
c += 1
end
push!(mults, c)
mults
end
function gelfandbasis(gen::Vector{MT}) where MT<:Matrix
n = length(gen)+1
w = Vector{MT}(undef, n-1)
tmp = similar(gen[1])
a = similar(tmp)
for k = 1:n-1
a .= gen[k]
w[k] = copy(a)
for j = k-1:-1:1
mul!(tmp, a, gen[j])
mul!(a, gen[j], tmp)
w[k] .+= a
end
end
w
end
function gelfandbasis(gen::Vector{MT}) where MT
n = length(gen)+1
w = Vector{MT}(undef, n-1)
for k = 1:n-1
a = gen[k]
w[k] = a
for j = k-1:-1:1
a = gen[j]*a*gen[j]
w[k] += a
end
end
w
end
gelfand_reduce(R::Representation) = gelfand_reduce(convert.(Matrix, gelfandbasis(R.generators)))
function sortcontentsperm(Λ)
ps = contents2partition(Λ)
perm = Int[]
for pₖ in union(ps)
dₖ = hooklength(pₖ)
inds = findall(==(pₖ), ps)
aₖ = length(inds) ÷ dₖ # number of times repeated
for j = 1:aₖ
append!(perm, inds[j:aₖ:end])
end
end
perm
end
function gelfand_reduce(X)
λ̃, Q₁ = eigen(Symmetric(X[1]))
λ = round.(Int, λ̃)
if !(Q₁'Q₁ ≈ I)
error("The eigenvalue decomposition has failed")
end
if !(isapprox(λ, λ̃; atol=1E-7))
error("$λ̃ are not all approximately an integer")
end
length(X) == 1 && return reshape(λ,length(λ),1),Q₁
m = eigmults(λ)
c_m = [0;cumsum(m)]
Q = zero(Q₁)
Λ = Matrix{Int}(undef, length(λ), length(X))
Λ[:,1] = λ
for j=2:length(c_m)
j_inds = c_m[j-1]+1:c_m[j]
Qʲ = Q₁[:,j_inds] # deflated rows
Xⱼ = map(X -> Qʲ'*X*Qʲ, X[2:end])
Λⱼ, Qⱼ = gelfand_reduce(Xⱼ)
Q[j_inds,j_inds] = Qⱼ
Λ[j_inds,2:end] = Λⱼ
end
Λ, Q₁*Q
end
"""
singlemultreduce(ρ)
reduces a representation containing only a single irrep, multiple times.
"""
function singlemultreduce(ρ)
m = multiplicities(ρ)
@assert length(m) == 1
singlemultreduce(ρ, Representation(first(keys(m))))
end
"""
singlemultreduce(ρ, σ)
reduces a representation containing only a single irrep `σ`, multiple times.
"""
function singlemultreduce(ρ, σ)
m = size(σ,1)
n = size(ρ,1)
A = vcat((kron.(Ref(I(m)), ρ.generators) .- kron.(σ.generators, Ref(I(n))))...)
Q̃ = nullspace(convert(Matrix,A); atol=1E-10)*sqrt(m)
reshape(vec(Q̃), n, n)
end
# Creates vcat((kron.(Ref(I(m)), ρ.generators) .- kron.(σ.generators, Ref(I(ℓ))))...);
# but with the rows and columns corresponding to zero entries removed
function singlemultreducedkron(ρ, σ)
tol = 1E-10
m = size(σ,1)
ℓ = size(ρ,1)
μ = ℓ ÷ m
n = length(σ.generators)+1
B = zeros(m*(n-1)*ℓ, m*μ)
for κ=1:m, j= 1:μ, k = 1:n-1
B[range((k-1)*m*ℓ + (κ-1)*ℓ + 1; length=ℓ),(κ-1)*μ+j] = ρ.generators[k][:,(j-1)*m+κ]
B[range((k-1)*m*ℓ + (j-1)*m + κ; step=ℓ, length=m),(κ-1)*μ+j] -= σ.generators[k][:,κ]
end
# determine non-zero rows of A[:,jr]
kr = Int[]
for k = 1:size(B,1)
if maximum(abs,view(B,k,:)) > tol
push!(kr, k)
end
end
B[kr,:]
end
"""
singlemultreduce_blockdiag(ρ, σ)
reduces a representation containing only a single irrep `σ`, multiple times.
Unlike `singlemultreduce`, it is assumed that `ρ` comes from `gelfand_reduce` and hence
the returned `Q` is guaranteed to be block-diagonal.
"""
function singlemultreduce_blockdiag(ρ, σ)
tol = 1E-10
m = size(σ,1)
ℓ = size(ρ,1)
μ = ℓ ÷ m
# determine columns of `A` corresponding to non-zero entries of `Q`
jr = Int[]
for k = 1:m
append!(jr, range((k-1)*μ*m+k; step=m, length=μ))
end
Aₙ = singlemultreducedkron(ρ, σ)
V = svd(Aₙ).V[:,end-μ+1:end]*sqrt(m) # nullspace corresponds to last μ singular vectors
# now populate non-zero entries of `Q`
Q = BandedBlockBandedMatrix{Float64}(undef, Fill(m,μ), Fill(m,μ), (ℓ-1,ℓ-1), (0,0))
for j = 1:size(V,2)
sh = (j-1) * size(V,1)
for k = 1:size(V,1)
view(Q,:,Block(j))[jr[k]] = V[k,j]
end
end
Q
end
function blockdiagonalize(ρ::Representation)
Λ,Q = gelfand_reduce(ρ)
p = sortcontentsperm(Λ)
Q = Q[:,p]
Λ = Λ[p,:]
n = length(ρ.generators)+1
Q̃ = similar(Q)
# diagonalised generators
ρd = float.(zero.(ρ.generators))
c = contents2partition(Λ)
k = 0
for pⱼ in union(c)
j = findall(isequal(pⱼ), c)
Qⱼ = Q[:,j]
ρⱼ = conjugate(ρ, Qⱼ)
Q̃ⱼ = singlemultreduce_blockdiag(ρⱼ, Representation(pⱼ))
m = length(j)
Q̃[:,k+1:k+m] = Qⱼ * Q̃ⱼ
irrep = Representation(pⱼ)
for ℓ = 1:n-1
ρd[ℓ][k+1:k+m,k+1:k+m] = blockdiag(fill(irrep.generators[ℓ], m÷size(irrep,1))...)
end
k += m
end
Representation(ρd), Q̃
end
function contents2partition(part::Vector)
part = sort(part)
p = zeros(Int, 1-first(part))
k = 1
while k ≤ length(part)
pₖ = part[k]
rₖ = pₖ < 0 ? 1-pₖ : 1
p[rₖ] += 1
k += 1
while k ≤ length(part) && part[k] == pₖ
k += 1
rₖ += 1
p[rₖ] += 1
end
end
Partition(p)
end
function contents2partition(m::Matrix{Int})
ret = Vector{Partition}(undef, size(m,1))
for k=1:size(m,1)
ret[k] = contents2partition([0;vec(m[k,:])])
end
ret
end
# these are the multiplicities without dividing by dimension
function _multiplicities(parts::Vector{Partition})
dict = Dict{Partition,Int64}()
for part in parts
if !haskey(dict,part)
dict[part] = 0
end
dict[part] += 1
end
dict
end
function multiplicities(Λ::Vector{Partition})
mults = _multiplicities(Λ)
for σ in keys(mults)
mults[σ] = mults[σ] ÷ hooklength(σ)
end
mults
end
multiplicities(R::Representation) = multiplicities(contents2partition(gelfand_reduce(R)[1]))
## Representations
function perm(a,b,n)
ret = Matrix(I,n,n)
ret[a,a] = ret[b,b] = 0
ret[a,b] = ret[b,a] = 1
ret
end
standardrepresentation(n) = Representation(Matrix{Float64}[perm(k,k+1,n) for k=1:n-1])
include("canonicalprojection.jl")
end #module
| NumericalRepresentationTheory | https://github.com/dlfivefifty/NumericalRepresentationTheory.jl.git |
|
[
"MIT"
] | 0.3.0 | 6e91cff8b0241d57750cca395a97d6919839a7b5 | code | 276 | ###############
# This implements the "canonical projection" a la Hymabaccus 2020
##############
function canonicalprojection(σ::Partition, ρ)
n = Int(σ)
ρ_σ = Representation(σ)
n_σ = size(ρ_σ,1)
n_σ/factorial(n) * sum(tr(ρ_σ(g))ρ(g) for g in PermGen(n))
end | NumericalRepresentationTheory | https://github.com/dlfivefifty/NumericalRepresentationTheory.jl.git |
|
[
"MIT"
] | 0.3.0 | 6e91cff8b0241d57750cca395a97d6919839a7b5 | code | 8008 | using NumericalRepresentationTheory, Permutations, LinearAlgebra, SparseArrays, BlockBandedMatrices, Test
import NumericalRepresentationTheory: gelfandbasis, canonicalprojection, singlemultreduce, singlemultreduce_blockdiag, conjugate, gelfand_reduce, contents2partition, sortcontentsperm
@testset "Representations" begin
σ = Partition([3,3,2,1])
@test length(youngtableaux(σ)) == hooklength(σ)
@test all(isdiag,gelfandbasis(Representation(σ).generators)[3])
@test multiplicities(standardrepresentation(4))[Partition([3,1])] == 1
s = standardrepresentation(3)
ρ = s ⊗ s
@test multiplicities(ρ)[Partition([2,1])] == 3
ρ = Representation(Partition([3,2,1])) ⊗ Representation(Partition([2,2,2]))
@test multiplicities(ρ)[Partition([3,2,1])] == 2
ρ = Representation(Partition([3,2])) ⊗ Representation(Partition([2,2,1])) ⊗ Representation(Partition([3,1,1]))
λ,Q = blockdiagonalize(ρ)
for k = 1:length(λ.generators)
@test Q' * ρ.generators[k] * Q ≈ λ.generators[k]
end
@testset "Rotate irrep" begin
ρ = Representation(3,2,1,1)
λ,Q = blockdiagonalize(ρ)
@test Q ≈ -I || Q ≈ I
@test multiplicities(ρ)[Partition(3,2,1,1)] == 1
Q = qr(randn(size(ρ,1), size(ρ,1))).Q
ρ̃ = Representation(map(τ -> Q*τ*Q', ρ.generators))
@test multiplicities(ρ̃) == multiplicities(ρ)
@test abs.(blockdiagonalize(ρ̃)[2]) ≈ abs.(Matrix(Q))
end
@testset "Rico Bug" begin
ρ = Representation(2,1)
g = Permutation([1,2,3])
@test ρ(g) == I(2)
end
@testset "group by rep" begin
s = standardrepresentation(4)
ρ = s ⊗ s
Λ = gelfand_reduce(ρ)[1]
p = sortcontentsperm(Λ)
@test contents2partition(Λ[p,:]) == [fill(Partition(2,1,1),3); fill(Partition(2,2),2); fill(Partition(3,1),9); fill(Partition(4),2)]
end
@testset "singlemultreduce_blockdiag" begin
σ = Representation(3,2,1)
ρ = blockdiag(σ, σ)
Q = qr(randn(size(ρ))).Q
ρ = conjugate(ρ, Q)
Λ, Q̃ = gelfand_reduce(ρ)
p = sortcontentsperm(Λ)
ρ = conjugate(ρ, Q̃[:,p])
Q = singlemultreduce(ρ)
Q̃ = singlemultreduce_blockdiag(ρ, σ)
@test Q̃'Q̃ ≈ I
for (σ_k,g) in zip(blockdiag(σ,σ).generators,ρ.generators)
@test Q̃'g*Q̃ ≈ Q'g*Q ≈ σ_k
end
ρ = blockdiag(σ, σ)
Q = qr(randn(size(ρ))).Q
ρ = conjugate(ρ, Q)
λ,Q = blockdiagonalize(ρ)
for (σ_k,g) in zip(blockdiag(σ,σ).generators,ρ.generators)
@test Q'g*Q ≈ σ_k
end
end
@testset "reduce matrix" begin
σ = Representation(2,2,1)
ρ = blockdiag(σ, σ, σ)
Q = qr(randn(size(ρ))).Q
ρ = conjugate(ρ, Q)
m = size(σ,1)
ℓ = size(ρ,1)
μ = ℓ ÷ m
n = length(ρ.generators)+1
jr = Int[]
for κ = 1:m
append!(jr, range((κ-1)*μ*m+κ; step=m, length=μ))
end
@time A = vcat((kron.(Ref(I(m)), ρ.generators) .- kron.(σ.generators, Ref(I(ℓ))))...);
B = zeros(m*(n-1)*ℓ, length(jr));
@time for κ=1:m, j= 1:μ, k = 1:n-1
B[range((k-1)*m*ℓ + (κ-1)*ℓ + 1; length=ℓ),(κ-1)*μ+j] = ρ.generators[k][:,(j-1)*m+κ]
B[range((k-1)*m*ℓ + (j-1)*m + κ; step=ℓ, length=m),(κ-1)*μ+j] -= σ.generators[k][:,κ]
end
@test B ≈ A[:,jr]
end
end
@testset "Canonical Projection" begin
@testset "standard" begin
ρ = standardrepresentation(4)
@test rank(canonicalprojection(Partition(4), ρ)) == 1
@test rank(canonicalprojection(Partition(3,1), ρ)) == 3
P_1 = canonicalprojection(Partition(4), ρ)
P_2 = canonicalprojection(Partition(3,1), ρ)
@test P_1^2 ≈ P_1
@test P_2^2 ≈ P_2
Q_1 = qr(P_1).Q[:,1:1]
@test Q_1' ≈ Q_1'*P_1
Q_2 = qr(P_2).Q[:,1:3]
@test Q_2' ≈ Q_2'*P_2
Q = [Q_1 Q_2]'
@test Q'Q ≈ I
ρ_2 = Representation(3,1)
# we don't guarantee the ordering
@test_broken Q*ρ.generators[1]*Q' ≈ blockdiag(sparse(I(1)),ρ_2.generators[3])
@test_broken Q*ρ.generators[2]*Q' ≈ blockdiag(sparse(I(1)),ρ_2.generators[2])
@test_broken Q*ρ.generators[3]*Q' ≈ blockdiag(sparse(I(1)),ρ_2.generators[1])
end
@testset "tensor" begin
s = standardrepresentation(4)
ρ = s ⊗ s
λ,Q = blockdiagonalize(ρ)
@test rank(canonicalprojection(Partition(4), ρ)) == 2
@test rank(canonicalprojection(Partition(3,1), ρ)) == 3*3
@test rank(canonicalprojection(Partition(2,2), ρ)) == 2
@test rank(canonicalprojection(Partition(1,1,1,1), ρ)) == 0
P_4 = canonicalprojection(Partition(4), ρ)
@test P_4^2 ≈ P_4
P_31 = canonicalprojection(Partition(3,1), ρ)
@test P_31^2 ≈ P_31
P_22 = canonicalprojection(Partition(2,2), ρ)
@test P_22^2 ≈ P_22
P_211 = canonicalprojection(Partition(2,1,1), ρ)
@test P_211^2 ≈ P_211
@test P_4*Q[:,end-1:end] ≈ Q[:,end-1:end]
@test norm(P_4*Q[:,1:end-2]) ≤ 1E-15
@test norm(P_31*Q[:,end-1:end]) ≤ 1E-15
@test P_31*Q[:,end-10:end-2] ≈ Q[:,end-10:end-2]
@test norm(P_31*Q[:,1:end-11]) ≤ 1E-15
@test norm(P_22*Q[:,1:3]) ≤ 1E-14
@test P_22*Q[:,4:5] ≈ Q[:,4:5]
@test norm(P_22*Q[:,6:end]) ≤ 1E-14
@test norm(P_211*Q[:,4:end]) ≤ 1E-14
@test P_211*Q[:,1:3] ≈ Q[:,1:3]
Q_31 = svd(P_31).U[:,1:9]
@test Q_31'*P_31 ≈ Q_31'
@test Q_31'*Q_31 ≈ I
σ = Partition(3,1)
n = Int(σ)
ρ_31 = Representation(σ)
n_σ = size(ρ_31,1)
p_11 = n_σ/factorial(n) * sum(ρ_31(inv(g))[1,1]*ρ(g) for g in PermGen(n))
@test p_11^2 ≈ p_11
# sects a single representation
@test rank(p_11 * P_31) == rank(p_11) == 3
V_31_11 = svd(p_11).U[:,1:3]
# V_31_11 is a basis
Q̃_31 = hcat([Matrix(svd(hcat([(
p_α1 = n_σ/factorial(n) * sum(ρ_31(inv(g))[1,α]*ρ(g) for g in PermGen(n));
p_α1 * V_31_11[:,j]) for α = 1:3]...)).U) for j = 1:3]...)
@test Q̃_31'Q̃_31 ≈ I
# Q̃_31 spans same space as columns of Q
@test norm(Q̃_31'Q[:,end-1:end]) ≤ 1E-14
@test rank(Q̃_31'Q[:,end-10:end-2]) == 9
@test norm(Q̃_31'Q[:,1:end-11]) ≤ 1E-14
# this shows the action of ρ acts on each column space separately
@test Q̃_31'ρ.generators[1]*Q̃_31 ≈ Diagonal(Q̃_31'ρ.generators[1]*Q̃_31)
@test Q̃_31'ρ.generators[2]*Q̃_31 ≈ blockdiag(sparse((Q̃_31'ρ.generators[2]*Q̃_31)[1:3,1:3]), sparse((Q̃_31'ρ.generators[2]*Q̃_31)[4:6,4:6]), sparse((Q̃_31'ρ.generators[2]*Q̃_31)[7:end,7:end]))
@test Q̃_31'ρ.generators[3]*Q̃_31 ≈ blockdiag(sparse((Q̃_31'ρ.generators[3]*Q̃_31)[1:3,1:3]), sparse((Q̃_31'ρ.generators[3]*Q̃_31)[4:6,4:6]), sparse((Q̃_31'ρ.generators[3]*Q̃_31)[7:end,7:end]))
# replace basis
Q̃ = copy(Q)
Q̃[:,3:3 +8] = Q̃_31
ρ_22 = Representation(2,2)
ρ_211 = Representation(2,1,1)
@test_skip Q'*ρ.generators[1]*Q ≈ blockdiag(sparse(I(2)),fill(ρ_31.generators[1],3)..., ρ_22.generators[1], ρ_211.generators[1])
@test_skip Q'*ρ.generators[2]*Q ≈ blockdiag(sparse(I(2)),fill(ρ_31.generators[2],3)..., ρ_22.generators[2], ρ_211.generators[2])
@test_skip Q'*ρ.generators[3]*Q ≈ blockdiag(sparse(I(2)),fill(ρ_31.generators[3],3)..., ρ_22.generators[3], ρ_211.generators[3])
@test_skip Q̃'*ρ.generators[1]*Q̃ ≈ blockdiag(sparse(I(2)),fill(ρ_31.generators[1],3)..., ρ_22.generators[1], ρ_211.generators[1])
@test_skip Q̃'*ρ.generators[2]*Q̃ ≈ blockdiag(sparse(I(2)),fill(ρ_31.generators[2],3)..., ρ_22.generators[1], ρ_211.generators[1])
end
end
# basis = gelfandbasis(Representation(Partition([3,2,1])).generators)
# Λ = Matrix{Int}(undef, size(basis[1],1), length(basis))
# for k in axes(Λ,1), j in axes(Λ,2)
# Λ[k,j] = round(Int,basis[j][k,k])
# end
| NumericalRepresentationTheory | https://github.com/dlfivefifty/NumericalRepresentationTheory.jl.git |
|
[
"MIT"
] | 0.3.0 | 6e91cff8b0241d57750cca395a97d6919839a7b5 | docs | 3027 | # NumericalRepresentationTheory.jl
A Julia package for representation theory of the symmetric group
This package supports basic representation theory of the symmetric group. One can form irreducible representations (irreps) by specifying the corresponding permutation, combine representations via direct sum and Kronecker product, and also calculate the resulting irrep multipliciplities. For example, the following code calculates the Kronecker coefficients of two irreps of S₇, specified by the partitions `5+1+1` and `2+2+2+1`:
```julia
julia> using NumericalRepresentationTheory, Permutations, Plots
julia> R₁ = Representation(5,1,1);
julia> g = Permutation([7,2,1,3,4,6,5]); Matrix(R₁(g)) # Matrix representation of a specific permutation
15×15 Array{Float64,2}:
-0.2 -0.0408248 -0.0527046 … 0.0 0.0 0.0
0.163299 0.241667 0.31199 0.0 0.0 0.0
0.0 -0.161374 0.0138889 0.0 0.0 0.0
0.0 0.0 -0.157135 0.467707 0.810093 0.0
0.0 0.0 0.0 0.270031 -0.155902 -0.881917
-0.966092 0.0493007 0.0636469 … 0.0 0.0 0.0
0.0 -0.190941 0.0164336 0.0 0.0 0.0
0.0 0.0 -0.185924 0.0790569 0.136931 0.0
0.0 0.0 0.0 0.0456435 -0.0263523 -0.149071
0.0 0.935414 -0.0805076 0.0 0.0 0.0
0.0 0.0 0.91084 … 0.0968246 0.167705 0.0
0.0 0.0 0.0 0.0559017 -0.0322749 -0.182574
0.0 0.0 0.0 0.125 0.216506 0.0
0.0 0.0 0.0 0.0721688 -0.0416667 -0.235702
0.0 0.0 0.0 -0.816497 0.471405 -0.333333
julia> R₂ = Representation(2,2,2,1);
julia> R = R₁ ⊗ R₂; # Tensor product representation
julia> multiplicities(R) # Returns a dictionary whose keys are partitions and values are the multiplicities
Dict{Partition, Int64} with 8 entries:
7 = 2 + 2 + 2 + 1 => 1
7 = 4 + 2 + 1 => 1
7 = 3 + 1 + 1 + 1 + 1 => 1
7 = 3 + 2 + 2 => 1
7 = 3 + 3 + 1 => 1
7 = 2 + 2 + 1 + 1 + 1 => 1
7 = 4 + 1 + 1 + 1 => 1
7 = 3 + 2 + 1 + 1 => 2
julia> plot(multiplicities(R)) # We can also plot
```
<img src=https://github.com/dlfivefifty/NumericalRepresentationTheory.jl/raw/master/images/mults.png width=500 height=400>
In addition, one can find an orthogonal transformation that reduces a representation to irreducibles:
```julia
julia> ρ,Q = blockdiagonalize(R); # Q'R(g)*Q ≈ ρ(g) where ρ is a direct sum (block diagonal) of irreducibles.
julia> Q'R(g)*Q ≈ ρ(g)
true
julia> ρ(g) ≈ (Representation(4,2,1) ⊕ Representation(4,1,1,1) ⊕ Representation(3,3,1) ⊕ Representation(3,2,2) ⊕ Representation(3,2,1,1) ⊕ Representation(3,2,1,1) ⊕ Representation(3,1,1,1,1) ⊕ Representation(2,2,2,1) ⊕ Representation(2,2,1,1,1))(g)
true
``` | NumericalRepresentationTheory | https://github.com/dlfivefifty/NumericalRepresentationTheory.jl.git |
|
[
"MIT"
] | 0.2.2 | e250e1b3a478ad99132e3993ed881c7debd0f559 | code | 633 | using Documenter, Presentation, DocumenterMarkdown
cp(joinpath(@__DIR__, "../README.md"), joinpath(@__DIR__, "./src/index.md"), force=true, follow_symlinks=true)
makedocs(
sitename="Presentation.jl documentation",
format = Markdown()
)
deploydocs(
repo = "github.com/kdheepak/Presentation.jl.git",
deps = Deps.pip(
"mkdocs==0.17.5",
"mkdocs-material==2.9.4",
"python-markdown-math",
"pygments",
"pymdown-extensions",
),
make = () -> run(`mkdocs build`),
target = "site",
)
| Presentation | https://github.com/kdheepak/Presentation.jl.git |
|
[
"MIT"
] | 0.2.2 | e250e1b3a478ad99132e3993ed881c7debd0f559 | code | 933 | Presentation.Slide(
"""
# Presentation.jl
- Introduction
- Installation
- REPL
"""
)
Presentation.Slide(
"""
## Introduction
- Allows presenting Julia code from the terminal
- Allows defined predefined code to run
"""
)
Presentation.Slide(
"""
## Installation
- Run `Pkg.add("https://github.com/kdheepak/Presentation.jl")`
- Add `using Presentation` to `~/.juliarc.jl`
- Create slides in `slideshow.jl`
- Run `julia -i slideshow.jl`
"""
)
Presentation.Slide(
"""
## REPL
- Move forward slides by using Ctrl+f
- Move back slides by using Ctrl+b
- Input predefined expressions on a slide by using Ctrl+e
- Slide(..., [
:(1+1)
:(println("hello world"))
quote
function add(x, y)
x+y
end
end
]
)
""", [
:(1+1)
:(println("hello world"))
quote
function add(x, y)
x+y
end
end
]
)
| Presentation | https://github.com/kdheepak/Presentation.jl.git |
|
[
"MIT"
] | 0.2.2 | e250e1b3a478ad99132e3993ed881c7debd0f559 | code | 849 | module Presentation
import Pandoc
import REPL
import REPL: Terminals
import Base: read
using Crayons
using Highlights
using Highlights.Tokens
using Highlights.Lexers
using TerminalExtensions
using Markdown
export render, next, previous, current_slide
abstract type PandocMarkdown end
abstract type JuliaMarkdown end
include("lexers.jl")
include("utils.jl")
include("slideshow.jl")
include("renderer.jl")
TERMINAL = nothing # Contains reference to the built in Terminal
SLIDES = nothing # Contains reference to a `Slides` object
render() = render(SLIDES)
next() = next(SLIDES)
previous() = previous(SLIDES)
current_slide() = current_slide(SLIDES)
filename() = filename(SLIDES)
function __init__()
global TERMINAL
TERMINAL = REPL.Terminals.TTYTerminal(get(ENV, "TERM", Sys.iswindows() ? "" : "dumb"), stdin, stdout, stderr)
end
end
| Presentation | https://github.com/kdheepak/Presentation.jl.git |
|
[
"MIT"
] | 0.2.2 | e250e1b3a478ad99132e3993ed881c7debd0f559 | code | 49 |
abstract type PythonLexer <: AbstractLexer end
| Presentation | https://github.com/kdheepak/Presentation.jl.git |
|
[
"MIT"
] | 0.2.2 | e250e1b3a478ad99132e3993ed881c7debd0f559 | code | 8356 | const CODEBLOCK_FOREGROUND = get(ENV, "PRESENTATION_JL_CODEBLOCK_FOREGROUND", 0x909090) |> UInt32
const CODEBLOCK_BACKGROUND = get(ENV, "PRESENTATION_JL_CODEBLOCK_BACKGROUND", 0xf0f0f0) |> UInt32
const PRESENTATION_JL_LEXERS = Dict(
"julia" => JuliaLexer,
# "python" => PythonLexer,
)
draw_border(io, x, y, w, h) = draw_border(io, x, y, w, h, Crayon())
function draw_border(io, x, y, w, h, c)
cmove(x, y) ; print(io, c(repeat("━", w)))
cmove(x, y + h) ; print(io, c(repeat("━", w)))
for i in 1:h
cmove(x, y + i) ; print(io, c("┃"))
cmove(x + w, y + i) ; print(io, c("┃"))
end
cmove(x, y) ; print(io, c("┏"))
cmove(x + w, y) ; print(io, c("┓"))
cmove(x, y + h) ; print(io, c("┗"))
cmove(x + w, y + h) ; print(io, c("┛"))
end
"""
Syntax Highlighter
"""
function Highlights.Format.render(io::IO, ::MIME"text/ansi", tokens::Highlights.Format.TokenIterator)
for (str, id, style) in tokens
fg = style.fg.active ? map(Int, (style.fg.r, style.fg.g, style.fg.b)) : :nothing
bg = style.bg.active ? map(Int, (style.bg.r, style.bg.g, style.bg.b)) : :nothing
crayon = Crayon(
foreground = fg,
background = bg,
bold = style.bold,
italics = style.italic,
underline = style.underline,
)
print(io, crayon, str, inv(crayon))
end
end
## Render functions
render(e) = render(stdout, e)
render(io, e::Pandoc.RawBlock) = nothing
function render(io, e::Pandoc.Link)
iob = IOBuffer()
for se in e.content
render(iob, se)
end
title = String(take!(iob))
url = e.target.url
# This seems to be an iTerm2 only feature
print(io, "$ESC]8;;$url$ESC\\$title$ESC]8;;$ESC\\")
end
function render(io, e::Pandoc.Strikeout)
c = Crayon(strikethrough = true)
print(io, c)
for ec in e.content
render(io, ec)
end
print(io, inv(c))
end
function render(io, e::Pandoc.Emph)
c = Crayon(italics = true)
print(io, c)
for ec in e.content
render(io, ec)
end
print(io, inv(c))
end
function render(io, e::Pandoc.Strong)
c = Crayon(bold = true)
print(io, c)
for ec in e.content
render(io, ec)
end
print(io, inv(c))
end
function render(io, es::Vector{Pandoc.Block})
x, y = getXY()
cmove(x + 4, y)
for e in es
render(io, e)
end
cmove(x, getY())
end
function render(io, e::Pandoc.SoftBreak)
end
function render(io, e::Pandoc.Plain)
x, y = getXY()
w, h = canvassize()
oldX, oldY = getXY()
print(io, "▶ ")
for se in e.content
render(io, se)
newX, newY = getXY()
if newX > w * 7/8
cmove(x, newY + 1)
oldX, oldY = newX, newY
elseif newY > oldY
cmove(x, newY)
oldX, oldY = newX, newY
end
end
cmove(x, getY() + 2)
end
function render(io, e::Pandoc.OrderedList)
x, y = getXY()
for items in e.content
render(io, items)
end
end
function render(io, e::Pandoc.BulletList)
x, y = getXY()
cmove(x, getY() + 1)
for items in e.content
render(io, items)
end
cmove(x, getY() + 1)
end
function render(io, e::Pandoc.Image)
w, h = canvassize()
x, y = getXY()
cmove(round(Int, w / 3), y + 2)
data = read(abspath(joinpath(dirname(filename()), e.target.url)))
TerminalExtensions.iTerm2.display_file(
data;
io=io,
width="$(round(Int, w/3))",
filename="image",
inline=true,
preserveAspectRatio=true
)
title = e.target.title
cmove(round(Int, w / 2) - round(Int, length(title) / 2), getY() + 2)
print(io, "$title")
end
render(io, e::Pandoc.Element) = error("Not implemented renderer for $e")
hex2rgb(c) = convert.(Int, ((c >> 16) % 0x100, (c >> 8) % 0x100, c % 0x100))
function render(io, e::Pandoc.CodeBlock)
w = round(Int, getW() * 6 / 8)
x, y = getXY()
if length(e.attr.classes) > 0
lexer = get(PRESENTATION_JL_LEXERS, e.attr.classes[1], AbstractLexer)
if lexer == AbstractLexer
content = e.content
else
iob = IOBuffer()
highlight(iob, MIME("text/ansi"), e.content, lexer)
content = String(take!(iob))
end
else
content = e.content
end
c = Crayon(background = hex2rgb(CODEBLOCK_BACKGROUND))
cmove(x, y)
print(io, c(repeat(" ", w)))
y += 1
# draw background
save_y = y
split_content = split(content, '\n')
for (i, code_line) in enumerate(split_content)
cmove(x, y)
print(io, c(repeat(" ", w)))
print(io, Crayon(background = hex2rgb(CODEBLOCK_FOREGROUND))(" "))
y += 1
end
cmove(x, y)
print(io, c(repeat(" ", w)))
print(io, Crayon(background = hex2rgb(CODEBLOCK_FOREGROUND))(" "))
y += 1
cmove(x+1, y)
println(io, Crayon(foreground = hex2rgb(CODEBLOCK_FOREGROUND))(repeat("▀", w)))
# write code blocks
y = save_y
for code_line in split_content
cmove(x+2, y)
println(io, c(code_line))
y += 1
end
end
render(io, e::Pandoc.Str) = print(io, e.content)
render(io, e::Pandoc.Space) = print(io, " ")
render(io, e::Pandoc.Code) = print(io, "`", Crayon(foreground=:red)("$(e.content)"), "`")
render(io, h::Pandoc.Header) = render(io, h, Val{h.level}())
function render(io, e::Pandoc.Header, level::Val{1})
c = Crayon(bold = true)
w, h = canvassize()
x, y = round(Int, w / 2), round(Int, h / 2)
iob = IOBuffer()
for se in e.content
render(iob, se)
end
t = String(take!(iob))
lines = wrap(t)
for line in lines
cmove(x - round(Int, length(line) / 2), y)
print(io, c(line))
y += 1
end
m = maximum(length.(lines))
draw_border(io, x - round(Int, m / 2) - 2, y - length(lines) - 1, m + 3, length(lines) + 1)
cmove(round(Int, w / 8), getY() + 4)
end
function render(io, e::Pandoc.Header, level::Val{2})
c = Crayon(bold = true)
w, h = canvassize()
x, y = round(Int, w / 2), round(Int, h / 8)
iob = IOBuffer()
for se in e.content
render(iob, se)
end
t = String(take!(iob))
lines = wrap(t)
for line in lines
cmove(x - round(Int, length(line) / 2), y)
print(io, c(line))
y += 1
end
m = maximum(length.(lines))
draw_border(io, x - round(Int, m / 2) - 2, y - length(lines) - 1, m + 3, length(lines) + 1)
cmove(round(Int, w / 8), getY() + 4)
end
function render(io, e::Pandoc.Para)
w, h = canvassize()
cmove(round(Int, w / 8), getY() + 2)
oldX, oldY = getXY()
for se in e.content
render(io, se)
newX, newY = getXY()
if newX > w * 7/8
cmove(round(Int, w / 8), newY + 1)
oldX, oldY = newX, newY
elseif newY > oldY
cmove(round(Int, w / 8), newY)
oldX, oldY = newX, newY
end
end
cmove(round(Int, w / 8), getY() + 2)
end
function render(io, s::Slides)
clear()
width, height = canvassize()
x, y = round(Int, width / 2), round(Int, height / 4)
cmove(x, y)
for e in current_slide(s)
render(io, e)
end
cmove_bottom()
end
function render(io, md::Markdown.MD, filename)
d = convert(Pandoc.Document, md)
render(io, d, filename)
end
function render(io, d::Pandoc.Document, filename::AbstractString="")
global SLIDES
s = Slides(d, filename)
SLIDES = s
return render(io, s)
end
render(io, filename::AbstractString) = Pandoc.exists() ? render(io, PandocMarkdown, filename) : render(io, JuliaMarkdown, filename)
render(io, ::Type{T}, filename::AbstractString) where T = render(io, read(T, filename), filename)
Base.read(::Type{PandocMarkdown}, filename::AbstractString) = Pandoc.parse_file(filename)
Base.read(::Type{JuliaMarkdown}, filename::AbstractString) = Markdown.parse_file(filename)
| Presentation | https://github.com/kdheepak/Presentation.jl.git |
|
[
"MIT"
] | 0.2.2 | e250e1b3a478ad99132e3993ed881c7debd0f559 | code | 1337 | const Slide = Vector{Pandoc.Element}
mutable struct Slides
current_slide::Int
content::Vector{Slide}
filename::String
end
function Slides(d::Pandoc.Document, filename::String)
content = Pandoc.Element[]
slides = Slides(1, Slide[], filename)
for e in d.blocks
if typeof(e) == Pandoc.Header && e.level == 1 && length(content) == 0
push!(content, e)
elseif typeof(e) == Pandoc.Header && e.level == 1 && length(content) != 0
push!(slides.content, content)
content = Pandoc.Element[]
push!(content, e)
elseif typeof(e) == Pandoc.Header && e.level == 2 && length(content) == 0
push!(content, e)
elseif typeof(e) == Pandoc.Header && e.level == 2 && length(content) != 0
push!(slides.content, content)
content = Pandoc.Element[]
push!(content, e)
else
push!(content, e)
end
end
push!(slides.content, content)
return slides
end
current_slide(s::Slides) = s.content[s.current_slide]
filename(s::Slides) = s.filename
function next(s::Slides)
if s.current_slide < length(s.content)
s.current_slide += 1
end
render(s)
end
function previous(s::Slides)
if s.current_slide > 1
s.current_slide -= 1
end
render(s)
end
| Presentation | https://github.com/kdheepak/Presentation.jl.git |
|
[
"MIT"
] | 0.2.2 | e250e1b3a478ad99132e3993ed881c7debd0f559 | code | 5390 | import Base.Libc: strerror
mean(x::Number, y::Number) = (x + y) / 2
const ESC = '\x1B'
# Encoded terminal modes from SSH.jl
const NCCS = Sys.islinux() ? 32 : 20
const tcflag_t = Sys.islinux() ? Cuint : Culong
const speed_t = tcflag_t
mutable struct termios
c_iflag::tcflag_t
c_oflag::tcflag_t
c_cflag::tcflag_t
c_lflag::tcflag_t
@static if Sys.islinux()
c_line::UInt8
end
c_cc::NTuple{NCCS, UInt8}
c_uispeed::speed_t
c_ospeed::speed_t
end
TERM = Ref{termios}(
@static if Sys.islinux() termios(
0,
0,
0,
0,
0,
Tuple([0 for _ in 1:NCCS]),
0,
0
)
else
termios(
0,
0,
0,
0,
Tuple([0 for _ in 1:NCCS]),
0,
0
)
end
);
RESTORE = deepcopy(TERM);
const ICANON = Sys.islinux() ? 0o0000002 : 0x00000100
const ECHO = Sys.islinux() ? 0o0000010 : 0x00000008
const TCSANOW = 0
const O_RDWR = Base.Filesystem.JL_O_RDWR
const O_NOCTTY = Base.Filesystem.JL_O_NOCTTY
const OS_HANDLE = Base.OS_HANDLE
function getColor(t::Terminals.TTYTerminal)
fd = ccall(:jl_uv_file_handle, Base.OS_HANDLE, (Ptr{Cvoid},), stdin.handle)
systemerror("tcgetattr",
ccall(:tcgetattr, Cint, (Cint, Ptr{Cvoid}), fd, TERM) == -1)
systemerror("tcgetattr",
ccall(:tcgetattr, Cint, (Cint, Ptr{Cvoid}), fd, RESTORE) == -1)
TERM[].c_lflag &= ~(ICANON|ECHO)
systemerror("tcsetattr",
ccall(:tcsetattr, Cint, (Cint, Cint, Ptr{Cvoid}), fd, TCSANOW, TERM) == -1
)
write(stdout, "\e]11;?\a")
buf = UInt8[]
while true
ch = read(stdin, 1)[1]
if Char(ch) == ';'
break
end
end
@assert Char(read(stdin, 1)[1]) == 'r'
@assert Char(read(stdin, 1)[1]) == 'g'
@assert Char(read(stdin, 1)[1]) == 'b'
@assert Char(read(stdin, 1)[1]) == ':'
r = parse(UInt8, "0x$(String(read(stdin, 2)))")
@assert Char(read(stdin, 1)[1]) == '/'
g = parse(UInt8, "0x$(String(read(stdin, 2)))")
@assert Char(read(stdin, 1)[1]) == '/'
b = parse(UInt8, "0x$(String(read(stdin, 2)))")
systemerror("tcsetattr",
ccall(:tcsetattr, Cint, (Cint, Cint, Ptr{Cvoid}), fd, TCSANOW, RESTORE) == -1
)
return r, g, b
end
function getXY(t::Terminals.TTYTerminal)
fd = ccall(:jl_uv_file_handle, Base.OS_HANDLE, (Ptr{Cvoid},), stdin.handle)
systemerror("tcgetattr",
ccall(:tcgetattr, Cint, (Cint, Ptr{Cvoid}), fd, TERM) == -1)
systemerror("tcgetattr",
ccall(:tcgetattr, Cint, (Cint, Ptr{Cvoid}), fd, RESTORE) == -1)
TERM[].c_lflag &= ~(ICANON|ECHO)
systemerror("tcsetattr",
ccall(:tcsetattr, Cint, (Cint, Cint, Ptr{Cvoid}), fd, TCSANOW, TERM) == -1
)
write(stdout, "$(Terminals.CSI)6n")
buf = UInt8[]
while true
ch = read(stdin, 1)[1]
if Char(ch) == 'R'
break
end
push!(buf, ch)
end
systemerror("tcsetattr",
ccall(:tcsetattr, Cint, (Cint, Cint, Ptr{Cvoid}), fd, TCSANOW, RESTORE) == -1
)
r, c = split(String(buf[3:end]), ";")
return parse(Int, c), parse(Int, r)
end
abstract type Terminal end
abstract type JPEG end
size(::Type{Terminal}) = Base.displaysize(stdout) |> reverse
function size(::Type{JPEG}, filename)
fhandle = open(filename)
seek(fhandle, 0) # Read 0xff next
size = 2
ftype = 0
while ! ( 0xc0 <= ftype <= 0xcf )
read(fhandle, size)
byte = read(fhandle, 1)
while byte[1] == 0xff
byte = read(fhandle, 1)
end
ftype = byte[1]
size = read(fhandle, 2)[2] - 2
end
read(fhandle, 1)
h1, h2, w1, w2 = read(fhandle, 4)
return Int(UInt16(w1)<<8 + w2), Int(UInt16(h1)<<8 + h2)
end
canvassize() = size(Terminal)
pos() = pos(TERMINAL)
getXY() = getXY(TERMINAL)
getColor() = getColor(TERMINAL)
getX() = getX(TERMINAL)
getY() = getY(TERMINAL)
getW() = canvassize()[1]
getH() = canvassize()[2]
cmove(x, y) = cmove(TERMINAL, x, y)
clear() = clear(TERMINAL)
cmove_bottom() = cmove(1, getH() - 2)
pos(t::Terminals.TTYTerminal) = (getX(t), getY(t))
function getX(t::Terminals.TTYTerminal)
x, y = getXY(t)
return x
end
function getY(t::Terminals.TTYTerminal)
x, y = getXY(t)
return y
end
@eval cmove(t::Terminals.TTYTerminal, x::Int, y::Int) = print("$(Terminals.CSI)$(y);$(x)H")
@eval function clear(t::Terminals.TTYTerminal)
r = getH()
print("$(Terminals.CSI)$(r);1H")
print("$(Terminals.CSI)2J")
end
wrap(s) = wrap(s, round(Int, getW() * 3 / 4))
function wrap(s, w::Int)
length(s) < w && return String[s]
i = findprev(" ", s, w)
i = (i == nothing) ? w : i[1]
first, remaining = s[1:i], s[i+1:end]
first = first
remaining = remaining
return vcat(String[first], wrap(remaining, w))
end
| Presentation | https://github.com/kdheepak/Presentation.jl.git |
|
[
"MIT"
] | 0.2.2 | e250e1b3a478ad99132e3993ed881c7debd0f559 | code | 102 | using Test
using Presentation
if get(ENV, "CI", "") == ""
include("test_presentation.jl")
end
| Presentation | https://github.com/kdheepak/Presentation.jl.git |
|
[
"MIT"
] | 0.2.2 | e250e1b3a478ad99132e3993ed881c7debd0f559 | code | 230 |
@testset "test renderer" begin
iob = IOBuffer()
Presentation.render(iob, joinpath(@__DIR__, "sample.md"))
@test String(take!(iob)) == "\e[1mPresentation.jl\e[22m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┃┃┃┃┏┓┗┛"
end
| Presentation | https://github.com/kdheepak/Presentation.jl.git |
|
[
"MIT"
] | 0.2.2 | e250e1b3a478ad99132e3993ed881c7debd0f559 | docs | 614 | # Presentation.jl
## Installation
```
(v1.1) pkg> add Presentation
```
## Usage
```julia
using Presentation
render("/path/to/filename.md")
```
Use `next()` or `previous()` to move between slides.
## Motivation
The motivation was to build a presentation tool that uses the text interface in a terminal.
Other similar tools:
- [mdp](https://github.com/visit1985/mdp)
- [patat](https://github.com/jaspervdj/patat)
- [qov](https://github.com/chunqiuyiyu/qov)
- [vtmc](https://github.com/jclulow/vtmc)
| Presentation | https://github.com/kdheepak/Presentation.jl.git |
|
[
"MIT"
] | 0.2.2 | e250e1b3a478ad99132e3993ed881c7debd0f559 | docs | 305 | # Usage
To use [Presentation.jl](https://github.com/kdheepak/Presentation.jl.git) in Julia, first add it:
```
(v1.1)> add Presentation
```
Then you can run the following:
```
julia> using Presentation
julia> render("/path/to/file.md")
```
You move through slides by using `next()` and `previous()`.
| Presentation | https://github.com/kdheepak/Presentation.jl.git |
|
[
"MIT"
] | 0.2.2 | e250e1b3a478ad99132e3993ed881c7debd0f559 | docs | 919 | # Presentation.jl
## Terminal Presentation
- Use Markdown for presenting
- Support **bold** and *italics*
## This is a very long slide title name I wonder how this will be rendered will it be rendered correctly?
Lorem ipsum dolor sit amet...
Lorem **ipsum** dolor sit amet, *consectetuer* adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat. Ut wisi enim ad minim veniam, quis nostrud exerci tation ullamcorper suscipit lobortis nisl ut aliquip ex ea commodo consequat. Duis splople autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi.
## No body
## Images
## Interactive julia session
- Run julia code from within presentation
| Presentation | https://github.com/kdheepak/Presentation.jl.git |
|
[
"MIT"
] | 0.2.2 | e250e1b3a478ad99132e3993ed881c7debd0f559 | docs | 2337 | # Presentation.jl
## Installation
You can install `Presentation.jl` by using the following command:
```
(v1.1) pkg> add https://github.com/kdheepak/Presentation.jl
```
## Bulleted list
This is a list of items:
- Item 1
- Sub Item 1
- Sub sub item 1
- Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
- Sub Item 2
- Item 2
- Item 3
## Numbered list
This is a list of items:
1. Item 1
1. Sub Item 1
2. Item 2
3. Item 3
## Bold and Italics and Strikethrough
Supports _italics_ and **bold** formatting as well as combined **asterisks and _underscores_**.
And if your font is configured correctly, it also supports strikethrough uses two tildes. ~~Scratch this.~~
## Presentation.jl is a presentation framework written in Julia for presenting slides in the terminal in interactive manner
Supports sensible wrapping of long text in a paragraph.
Lorem ipsum dolor sit amet, consectetur adipiscing elit, sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.
## Syntax highlighting for Julia code
`julia` code is always syntax highlighted:
```julia
# function to calculate the volume of a sphere
function sphere_vol(r)
# julia allows Unicode names (in UTF-8 encoding)
# so either "pi" or the symbol π can be used
return 4/3*pi*r^3
end
# functions can also be defined more succinctly
quadratic(a, sqr_term, b) = (-b + sqr_term) / 2a
```
## Images
Supports inline images  in the terminal.
## Gifs
And gifs!
## Link
This is a [link](https://github.com/kdheepak/Presentation.jl) to the repository.
| Presentation | https://github.com/kdheepak/Presentation.jl.git |
|
[
"MIT"
] | 0.4.2 | 59107c179a586f0fe667024c5eb7033e81333271 | code | 181 | using Documenter, GeoFormatTypes
makedocs(;
modules = [GeoFormatTypes],
sitename = "GeoFormatTypes.jl",
)
deploydocs(;
repo="github.com/JuliaGeo/GeoFormatTypes.jl",
)
| GeoFormatTypes | https://github.com/JuliaGeo/GeoFormatTypes.jl.git |
|
[
"MIT"
] | 0.4.2 | 59107c179a586f0fe667024c5eb7033e81333271 | code | 10069 | module GeoFormatTypes
# Use the README as the module docs
@doc let
path = joinpath(dirname(@__DIR__), "README.md")
include_dependency(path)
read(path, String)
end GeoFormatTypes
export GeoFormat
export CoordinateReferenceSystemFormat, EPSG, ProjString, ProjJSON, CoordSys, GML
export GeometryFormat, GeoJSON, KML
export MixedFormat, GML
export AbstractWellKnownText, WellKnownText, WellKnownText2, ESRIWellKnownText, WellKnownBinary
const PROJ_PREFIX = "+proj="
const EPSG_PREFIX = "EPSG:"
# TODO more verification that types are wrapping the right format.
"""
FormatMode
Traits to indicate the format type, such as `Geom`, `CRS` or `MixedFormat`.
"""
abstract type FormatMode end
"""
Geom <: FormatMode
Geom()
Trait specifying that a format object, like [`WellKnownText`](@ref),
contains geometry data.
"""
struct Geom <: FormatMode end
"""
CRS <: FormatMode
CRS()
Trait specifying that a format object, like [`WellKnownText`](@ref),
contains only coordinate reference system data.
"""
struct CRS <: FormatMode end
"""
MixedFormatMode <: FormatMode
Abstract subtype of [`FormatMode`](@ref) where both
geometry and coordinate reference system data are or may be present.
"""
abstract type MixedFormatMode <: FormatMode end
"""
Extended <: MixedFormatMode <: FormatMode
Extended()
Trait specifying that a mixed format object, like [`WellKnownText`](@ref),
contains both geometry and coordinate reference system.
"""
struct Extended <: MixedFormatMode end
"""
Unknown <: MixedFormatMode <: FormatMode
Unknown()
Trait specifying that for a mixed format object, like [`WellKnownText`](@ref),
it is unknown whether it stores geometry or coordinate reference system data, or both.
"""
struct Unknown <: MixedFormatMode end
"""
val(f::GeoFormat)
Get the contained value of a GeoFormat type.
"""
function val end
"""
GeoFormat
Abstract supertype for geospatial data formats
"""
abstract type GeoFormat end
# Convert from the same type does nothing.
Base.convert(::Type{T1}, source::T2) where {T1<:GeoFormat,T2<:T1} = source
# Convert uses the `mode` trait to distinguish crs form geometry conversion
Base.convert(target::Type{T1}, source::T2; kwargs...) where {T1<:GeoFormat,T2<:GeoFormat} = begin
sourcemode = mode(source)
targetmode = mode(target)
convertmode = if targetmode isa Geom
if sourcemode isa Union{MixedFormatMode,Geom}
Geom() # Geom is the default if both formats are mixed
else
throw(ArgumentError("cannot convert $(typeof(source)) to $target"))
end
elseif targetmode isa CRS
if sourcemode isa Union{MixedFormatMode,CRS}
CRS()
else
throw(ArgumentError("cannot convert $(typeof(source)) to $target"))
end
else # targetmode isa MixedFormatMode
# MixedFormatMode to MixedFormatMode defaults to Geom
if sourcemode isa Union{MixedFormatMode,Geom}
Geom()
else
CRS()
end
end
convert(target, convertmode, source; kwargs...)
end
"""
CoordinateReferenceSystemFormat <: GeoFormat
Formats representing coordinate reference systems
"""
abstract type CoordinateReferenceSystemFormat <: GeoFormat end
"""
GeometryFormat <: GeoFormat
Formats representing geometries. These wrappers simply mark string
formats that may optionally be converted to Geoetry objects at a later point.
"""
abstract type GeometryFormat <: GeoFormat end
"""
MixedFormat <: GeoFormat
Formats that may hold either or both coordinate reference systems and geometries.
"""
abstract type MixedFormat{X} <: GeoFormat end
val(x::GeoFormat) = x.val
mode(format::GeoFormat) = mode(typeof(format))
mode(::Type{<:GeometryFormat}) = Geom()
mode(::Type{<:CoordinateReferenceSystemFormat}) = CRS()
mode(::Type{<:MixedFormat}) = Unknown()
mode(::Type{<:MixedFormat{M}}) where {M} = M()
# Most GeoFormat types wrap String or have a constructor for string inputs
Base.convert(::Type{String}, input::GeoFormat) = val(input)
Base.convert(::Type{T}, input::AbstractString) where {T<:GeoFormat} = T(convert(String, (input)))
"""
ProjString <: CoordinateReferenceSystemFormat
ProjString(x::String)
Wrapper for Proj strings. String input must start with "$PROJ_PREFIX".
"""
struct ProjString <: CoordinateReferenceSystemFormat
val::String
ProjString(input::String) = begin
startswith(input, PROJ_PREFIX) ||
throw(ArgumentError("Not a Proj string: $input does not start with $PROJ_PREFIX"))
new(input)
end
end
"""
ProjJSON <: CoordinateReferenceSystemFormat
ProjJSON(x::Dict{String,<:Any})
ProjJSON(x::String)
Wrapper for [PROJJSON](https://proj.org/specifications/projjson.html).
"""
struct ProjJSON <: CoordinateReferenceSystemFormat
val::Union{String,Dict{String,<:Any}}
ProjJSON(input::Dict{String,<:Any}) = begin
haskey(input, "type") ||
throw(ArgumentError("Not a ProjJSON: $input does not have the required key 'type'"))
new(input)
end
ProjJSON(input::String) = begin
occursin("type", input) ||
throw(ArgumentError("Not a ProjJSON: $input does not have the required key 'type'"))
new(input)
end
end
"""
CoordSys <: CoordinateReferenceSystemFormat
CoordSys(val)
Wrapper for a Mapinfo CoordSys string.
"""
struct CoordSys <: CoordinateReferenceSystemFormat
val::String
end
"""
AbstractWellKnownText <: MixedFormat
Well known text has a number of versions and standards, and can hold
either coordinate reference systems or geometric data in string format.
"""
abstract type AbstractWellKnownText{X} <: MixedFormat{X} end
"""
WellKnownText <: AbstractWellKnownText
WellKnownText(val)
WellKnownText(mode, val)
Wrapper for Well-known text (WKT) v1, following the OGC standard.
These may hold CRS or geometry data.
These may hold CRS or geometry data. The default mode is `Mixed()`,
and conversions to either type will be attempted where possible.
A specific type can be specified if it is known, e.g.:
```julia
geom = WellKnownText(Geom(), geom_string)
```
"""
struct WellKnownText{X} <: AbstractWellKnownText{X}
mode::X
val::String
end
WellKnownText(val) = WellKnownText(Unknown(), val)
"""
WellKnownText2 <: AbstractWellKnownText
WellKnownText2(val)
WellKnownText2(mode, val)
Wrapper for Well-known text v2 objects, following the new OGC standard.
These may hold CRS or geometry data. The default mode is `Unknown()`,
and conversions to either type will be attempted where possible.
A specific type can be specified if it is known, e.g.:
```julia
crs = WellKnownText2(CRS(), crs_string)
```
"""
struct WellKnownText2{X} <: AbstractWellKnownText{X}
mode::X
val::String
end
WellKnownText2(val) = WellKnownText2(Unknown(), val)
"""
ESRIWellKnownText <: AbstractWellKnownText
ESRIWellKnownText(x::String)
ESRIWellKnownText(::CRS, x::String)
ESRIWellKnownText(::Geom, x::String)
Wrapper for Well-known text strings, following the ESRI standard.
These may hold CRS or geometry data. The default mode is `Unknown`,
and conversions to either type will be attempted where possible.
A specific type can be specified if it is known, e.g:
```julia
crs = ESRIWellKnownText(CRS(), crs_string)
```
"""
struct ESRIWellKnownText{X} <: AbstractWellKnownText{X}
mode::X
val::String
end
ESRIWellKnownText(val) = ESRIWellKnownText(Unknown(), val)
"""
WellKnownBinary <: MixedFormat
Wrapper for Well-known binary (WKB) objects.
These may hold CRS or geometry data. The default mode is `Unknown`,
and conversions to either type will be attempted where possible.
A specific type can be specified if it is known, e.g:
```julia
crs = WellKnownBinary(CRS(), crs_blob)
```
"""
struct WellKnownBinary{X,T} <: MixedFormat{X}
mode::X
val::T
end
WellKnownBinary(val) = WellKnownBinary(Unknown(), val)
Base.convert(::Type{String}, input::WellKnownBinary) =
error("`convert` to `String` is not defined for `WellKnownBinary`")
"""
EPSG <: CoordinateReferenceSystemFormat
EPSG(input)
EPSG code representing a coordinate reference system from the
EPSG spatial reference system registry.
String input must start with "$EPSG_PREFIX". `EPSG` can be converted to an `Int` or `String`
using `convert`, or another `CoordinateReferenceSystemFormat` when ArchGDAL.jl is loaded.
"""
struct EPSG{N} <: CoordinateReferenceSystemFormat
val::NTuple{N,Int}
end
EPSG(input::Vararg{Int}) = EPSG(input)
function EPSG(input::AbstractString)
startswith(input, EPSG_PREFIX) || throw(ArgumentError("String $input does no start with $EPSG_PREFIX"))
code = Tuple(parse.(Int, split(input[findlast(EPSG_PREFIX, input).stop+1:end], "+")))
EPSG(code)
end
val(input::EPSG{1}) = input.val[1] # backwards compatible
Base.convert(::Type{Int}, input::EPSG{1}) = val(input)
Base.convert(::Type{String}, input::EPSG) = string(EPSG_PREFIX, join(input.val, "+"))
Base.convert(::Type{EPSG}, input::Int) = EPSG((input,))
"""
KML <: GeometryFormat
Wrapper object for "Keyhole Markup Language" (KML) strings.
See: https://www.ogc.org/standards/kml/
Can be converted to a `String`. Conversion to crs will convert from `EPSG(4326)`,
which is the default for KML.
"""
struct KML <: GeometryFormat
val::String
end
# We know KML always has a crs of EPSG(4326)
Base.convert(::Type{T}, ::KML) where {T<:CoordinateReferenceSystemFormat} = convert(T, EPSG(4326))
"""
GML <: MixedFormat
Wrapper for Geography Markup Language string.
These contain geometry data, but may also have embedded crs information.
`GML` can be converted to either a `GeometryFormat` or `CoordinateReferenceSystemFormat`.
"""
struct GML{X} <: MixedFormat{X}
mode::X
val::String
end
GML(val) = GML(Unknown(), val)
"""
GeoJSON <: GeometryFormat
Wrapper for a GeoJSON `String` or `Dict`.
Conversion between `Dict` and `String` values is not yet handled.
"""
struct GeoJSON{T} <: GeometryFormat
val::T
end
end # module
| GeoFormatTypes | https://github.com/JuliaGeo/GeoFormatTypes.jl.git |
|
[
"MIT"
] | 0.4.2 | 59107c179a586f0fe667024c5eb7033e81333271 | code | 6138 | using GeoFormatTypes, Test
using GeoFormatTypes: Geom, CRS, Extended, Unknown
@testset "Test construcors" begin
@test_throws ArgumentError ProjString("+lat_ts=56.5 +ellps=GRS80")
@test_throws ArgumentError ProjJSON(Dict("fype" => 1))
@test_throws ArgumentError ProjJSON("fype")
@test_throws ArgumentError EPSG("ERROR:4326")
@test EPSG("EPSG:4326") == EPSG(4326)
@test EPSG("EPSG:4326+3855") == EPSG((4326, 3855))
end
@testset "Test constructors" begin
@test ProjString("+proj=test") isa ProjString
@test ProjJSON(Dict("type" => "GeographicCRS")) isa ProjJSON
@test ProjJSON("type: GeographicCRS") isa ProjJSON
@test EPSG(4326) isa EPSG
@test EPSG((4326, 3855)) isa EPSG
@test WellKnownText("test") isa WellKnownText{Unknown}
@test WellKnownBinary([1, 2, 3, 4]) isa WellKnownBinary{Unknown}
@test WellKnownText2("test") isa WellKnownText2{Unknown}
@test ESRIWellKnownText("test") isa ESRIWellKnownText{Unknown}
@test WellKnownText(Extended(), "test") isa WellKnownText{Extended}
@test WellKnownBinary(Extended(), [1, 2, 3, 4]) isa WellKnownBinary{Extended}
@test WellKnownText2(CRS(), "test") isa WellKnownText2{CRS}
@test ESRIWellKnownText(Geom(), "test") isa ESRIWellKnownText{Geom}
@test GML("test") isa GML{Unknown}
@test GML(Geom(), "test") isa GML{Geom}
@test GML(CRS(), "test") isa GML{CRS} # Probably doesn't actually exist
@test KML("test") isa KML
@test GeoJSON("test") isa GeoJSON
end
@testset "Test conversion to string or int" begin
@test convert(String, ProjString("+proj=test")) == "+proj=test"
@test convert(String, EPSG(4326)) == "EPSG:4326"
@test convert(Int, EPSG(4326)) == 4326
@test_throws MethodError convert(Int, EPSG(4326, 3855))
@test convert(String, EPSG(4326, 3855)) == "EPSG:4326+3855"
@test convert(String, WellKnownText("test")) == "test"
@test convert(String, WellKnownText2("test")) == "test"
@test convert(String, ESRIWellKnownText("test")) == "test"
@test convert(String, GML("test")) == "test"
@test convert(String, KML("test")) == "test"
@test convert(String, GeoJSON("test")) == "test"
end
@testset "Test val" begin
@test GeoFormatTypes.val(EPSG(4326)) == 4326
@test GeoFormatTypes.val(EPSG(4326, 3855)) == (4326, 3855)
end
# `convert` placeholder methods
Base.convert(target::Type{<:GeoFormat}, mode::Union{Geom,Type{Geom}}, source::GeoFormat; kwargs...) =
(:geom, kwargs...)
Base.convert(target::Type{<:GeoFormat}, mode::Union{CRS,Type{CRS}}, source::GeoFormat; kwargs...) =
(:crs, kwargs...)
@testset "Test convert mode allocation" begin
@testset "Test identical type is passed through unchanged" begin
@test convert(WellKnownText, WellKnownText(Extended(), "test")) == WellKnownText(Extended(), "test")
@test convert(ProjString, ProjString("+proj=test")) == ProjString("+proj=test")
end
@testset "Test conversions are assigned to crs or geom correctly" begin
@test convert(WellKnownText, WellKnownText2(CRS(), "test")) == (:crs,)
@test convert(WellKnownText2, WellKnownText(CRS(), "test")) == (:crs,)
@test convert(WellKnownBinary, WellKnownText(CRS(), "test")) == (:crs,)
@test convert(ProjString, WellKnownText(CRS(), "test")) == (:crs,)
@test convert(EPSG, ProjString("+proj=test")) == (:crs,)
@test convert(CoordSys, ProjString("+proj=test")) == (:crs,)
@test convert(GeoJSON, WellKnownText(Geom(), "test")) == (:geom,)
@test convert(KML, WellKnownText(Geom(), "test")) == (:geom,)
@test convert(GML, WellKnownText(Geom(), "test")) == (:geom,)
@test convert(ESRIWellKnownText, WellKnownText(Geom(), "test")) == (:geom,)
@test convert(WellKnownBinary, WellKnownText(Geom(), "test")) == (:geom,)
@test convert(WellKnownText2, WellKnownText(Geom(), "test")) == (:geom,)
@test convert(WellKnownText2, WellKnownText(Geom(), "test")) == (:geom,)
@test convert(WellKnownText, WellKnownText2(Geom(), "test")) == (:geom,)
@test convert(GeoJSON, WellKnownText(Extended(), "test")) == (:geom,)
@test convert(KML, WellKnownText(Extended(), "test")) == (:geom,)
@test convert(GML, WellKnownText(Extended(), "test")) == (:geom,)
@test convert(ESRIWellKnownText, WellKnownText(Extended(), "test")) == (:geom,)
@test convert(WellKnownBinary, WellKnownText(Extended(), "test")) == (:geom,)
@test convert(WellKnownText2, WellKnownText(Extended(), "test")) == (:geom,)
@test convert(WellKnownText2, WellKnownText(Extended(), "test")) == (:geom,)
@test convert(WellKnownText, WellKnownText2(Extended(), "test")) == (:geom,)
@test convert(GeoJSON, WellKnownText(Unknown(), "test")) == (:geom,)
@test convert(KML, WellKnownText(Unknown(), "test")) == (:geom,)
@test convert(GML, WellKnownText(Unknown(), "test")) == (:geom,)
@test convert(ESRIWellKnownText, WellKnownText(Unknown(), "test")) == (:geom,)
@test convert(WellKnownBinary, WellKnownText(Unknown(), "test")) == (:geom,)
@test convert(WellKnownText2, WellKnownText(Unknown(), "test")) == (:geom,)
@test convert(WellKnownText2, WellKnownText(Unknown(), "test")) == (:geom,)
@test convert(WellKnownText, WellKnownText2(Unknown(), "test")) == (:geom,)
end
@testset "Test kargs pass through convert" begin
@test convert(WellKnownText, WellKnownText2(CRS(), "test"); order=:trad) == (:crs, :order => :trad,)
@test convert(GML, WellKnownText(Extended(), "test"); order=:custom) == (:geom, :order => :custom)
end
@testset "Test conversions that are not possible throw an error" begin
@test_throws ArgumentError convert(KML, ProjString("+proj=test"))
@test_throws ArgumentError convert(GeoJSON, ProjString("+proj=test"))
@test_throws ArgumentError convert(ProjString, WellKnownText(Geom(), "test"))
@test_throws ArgumentError convert(CoordSys, WellKnownText(Geom(), "test"))
@test_throws ArgumentError convert(EPSG, WellKnownText(Geom(), "test"))
end
end
| GeoFormatTypes | https://github.com/JuliaGeo/GeoFormatTypes.jl.git |
|
[
"MIT"
] | 0.4.2 | 59107c179a586f0fe667024c5eb7033e81333271 | docs | 2420 | # GeoFormatTypes
[](https://JuliaGeo.github.io/GeoFormatTypes.jl/stable)
[](https://JuliaGeo.github.io/GeoFormatTypes.jl/dev)
[](https://github.com/JuliaGeo/GeoFormatTypes.jl/actions?query=workflow%3ACI)
GeoFormatTypes defines wrapper types to make it easy to pass and dispatch on geographic formats
like Well Known Text or GeoJSON between packages. This way information about
what format is contained is kept for later use, - instead of passing a `String`
or `Int` that could mean anything.
Wrapper types also allow methods such as `convert` to work with data in multiple
formats, instead of defining lists of format-specific handling methods.
Currently ArchGDAL.jl is privileged to define `convert` methods for
GeoFormatTypes.jl objects, using GDAL. When it is loaded, objects can be
converted from one format to another:
```julia
julia> using GeoFormatTypes, ArchGDAL
julia> convert(WellKnownText, EPSG(4326))
WellKnownText{GeoFormatTypes.CRS, String}(GeoFormatTypes.CRS(), "GEOGCS[\"WGS 84\",DATUM[\"WGS_1984\",SPHEROID[\"WGS 84\",6378137,298.257223563,AUTHORITY[\"EPSG\",\"7030\"]],AUTHORITY[\"EPSG\",\"6326\"]],PRIMEM[\"Greenwich\",0,AUTHORITY[\"EPSG\",\"8901\"]],UNIT[\"degree\",0.0174532925199433,AUTHORITY[\"EPSG\",\"9122\"]],AXIS[\"Latitude\",NORTH],AXIS[\"Longitude\",EAST],AUTHORITY[\"EPSG\",\"4326\"]]")
```
ArchGDAL.jl is not a direct dependency of GeoFormatTypes.jl, so small packages
that handle geospatial formats in some way can depend on GeoFormatTypes.jl
without worry about large dependencies.
One complexity of `GeoFormat` objects is that some formats can hold either CRS
(Coordinate Reference System) or geometric data, or even both at the same time.
This is handled using the `CRS`, `Geom` and `Mixed` traits. When the contents
are explicitly known to be e.g. crs data, then `CRS` can be used, for example
with all types of well known text:
```julia
crs = WellKnownText2(CRS(), crs_string)
```
If the contents are not known, the default `Mixed()` will mostly do the right
thing anyway - it can be converted to either CRS or geometry formats using
`convert`, given that it is actually possible to do with the contained data.
We thank Julia Computing for supporting contributions to this package.
| GeoFormatTypes | https://github.com/JuliaGeo/GeoFormatTypes.jl.git |
|
[
"MIT"
] | 0.4.2 | 59107c179a586f0fe667024c5eb7033e81333271 | docs | 65 | # GeoFormatTypes.jl
```@autodocs
Modules = [GeoFormatTypes]
```
| GeoFormatTypes | https://github.com/JuliaGeo/GeoFormatTypes.jl.git |
|
[
"Apache-2.0"
] | 0.3.3 | d639d537c14127a70c8bd3e0158ecbccfb2a54a3 | code | 195 | clima_formatter_options = (
indent = 4,
margin = 120,
always_for_in = true,
whitespace_typedefs = true,
whitespace_ops_in_indices = true,
remove_extra_newlines = false,
)
| RandomFeatures | https://github.com/CliMA/RandomFeatures.jl.git |
|
[
"Apache-2.0"
] | 0.3.3 | d639d537c14127a70c8bd3e0158ecbccfb2a54a3 | code | 2585 | #!/usr/bin/env julia
#
# This is an adapted version of format.jl from JuliaFormatter with the
# following license:
#
# MIT License
# Copyright (c) 2019 Dominique Luna
#
# Permission is hereby granted, free of charge, to any person obtaining a
# copy of this software and associated documentation files (the
# "Software"), to deal in the Software without restriction, including
# without limitation the rights to use, copy, modify, merge, publish,
# distribute, sublicense, and/or sell copies of the Software, and to permit
# persons to whom the Software is furnished to do so, subject to the
# following conditions:
#
# The above copyright notice and this permission notice shall be included
# in all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
# OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN
# NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM,
# DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR
# OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE
# USE OR OTHER DEALINGS IN THE SOFTWARE.
#
using Pkg
Pkg.activate(@__DIR__)
Pkg.instantiate()
using JuliaFormatter
include("clima_formatter_options.jl")
help = """
Usage: climaformat.jl [flags] [FILE/PATH]...
Formats the given julia files using the CLIMA formatting options. If paths
are given it will format the julia files in the paths. Otherwise, it will
format all changed julia files.
-v, --verbose
Print the name of the files being formatted with relevant details.
-h, --help
Print this message.
"""
function parse_opts!(args::Vector{String})
i = 1
opts = Dict{Symbol, Union{Int, Bool}}()
while i ≤ length(args)
arg = args[i]
if arg[1] != '-'
i += 1
continue
end
if arg == "-v" || arg == "--verbose"
opt = :verbose
elseif arg == "-h" || arg == "--help"
opt = :help
else
error("invalid option $arg")
end
if opt in (:verbose, :help)
opts[opt] = true
deleteat!(args, i)
end
end
return opts
end
opts = parse_opts!(ARGS)
if haskey(opts, :help)
write(stdout, help)
exit(0)
end
if isempty(ARGS)
filenames = readlines(`git ls-files "*.jl"`)
else
filenames = ARGS
end
format(filenames; clima_formatter_options..., opts...)
| RandomFeatures | https://github.com/CliMA/RandomFeatures.jl.git |
|
[
"Apache-2.0"
] | 0.3.3 | d639d537c14127a70c8bd3e0158ecbccfb2a54a3 | code | 1485 | # reference in tree version of RandomFeatures
prepend!(LOAD_PATH, [joinpath(@__DIR__, "..")])
using Documenter,
RandomFeatures, RandomFeatures.Samplers, RandomFeatures.Features, RandomFeatures.Methods, RandomFeatures.Utilities
# Gotta set this environment variable when using the GR run-time on CI machines.
# This happens as examples will use Plots.jl to make plots and movies.
# See: https://github.com/jheinen/GR.jl/issues/278
ENV["GKSwstype"] = "100"
api = [
"Samplers" => "API/Samplers.md",
"Features" => "API/Features.md",
"Methods" => "API/Methods.md",
"Utilities" => "API/Utilities.md",
]
pages = [
"Home" => "index.md",
"Installation instructions" => "installation_instructions.md",
"Scalar method" => "setting_up_scalar.md",
"Vector method" => "setting_up_vector.md",
"Bottlenecks and performance tips" => "parallelism.md",
"Contributing" => "contributing.md",
"API" => api,
]
format = Documenter.HTML(collapselevel = 1, prettyurls = !isempty(get(ENV, "CI", "")))
makedocs(
sitename = "RandomFeatures.jl",
authors = "CliMA Contributors",
format = format,
pages = pages,
modules = [RandomFeatures],
doctest = true,
clean = true,
checkdocs = :none,
)
if !isempty(get(ENV, "CI", ""))
deploydocs(
repo = "github.com/CliMA/RandomFeatures.jl.git",
versions = ["stable" => "v^", "v#.#.#", "dev" => "dev"],
push_preview = true,
devbranch = "main",
)
end
| RandomFeatures | https://github.com/CliMA/RandomFeatures.jl.git |
|
[
"Apache-2.0"
] | 0.3.3 | d639d537c14127a70c8bd3e0158ecbccfb2a54a3 | code | 9776 | # Example to learn hyperparameters of simple 1d-1d regression example.
# This example matches test/Methods/runtests.jl testset: "Fit and predict: 1D -> 1D"
# The (approximate) optimal values here are used in those tests.
using StableRNGs
using Distributions
using StatsBase
using LinearAlgebra
using Random
using Dates
PLOT_FLAG = true
println("plot flag: ", PLOT_FLAG)
if PLOT_FLAG
using Plots, ColorSchemes
end
using EnsembleKalmanProcesses
const EKP = EnsembleKalmanProcesses
using RandomFeatures.ParameterDistributions
using RandomFeatures.DataContainers
using RandomFeatures.Samplers
using RandomFeatures.Features
using RandomFeatures.Methods
using RandomFeatures.Utilities
seed = 2024
ekp_seed = 99999
rng = StableRNG(seed)
## Functions of use
function RFM_from_hyperparameters(
rng::AbstractRNG,
l::Real,
regularizer::Real,
n_features::Int,
batch_sizes::Dict,
feature_type::String,
)
μ_c = 0.0
σ_c = l
pd = constrained_gaussian("xi", μ_c, σ_c, -Inf, Inf)
feature_sampler = FeatureSampler(pd, rng = rng)
# Learn hyperparameters for different feature types
if feature_type == "fourier"
sf = ScalarFourierFeature(n_features, feature_sampler)
elseif feature_type == "neuron"
sf = ScalarNeuronFeature(n_features, feature_sampler)
elseif feature_type == "sigmoid"
sf = ScalarFeature(n_features, feature_sampler, Sigmoid())
end
return RandomFeatureMethod(sf, batch_sizes = batch_sizes, regularization = regularizer)
end
function calculate_mean_and_coeffs(
rng::AbstractRNG,
l::Real,
noise_sd::Real,
n_features::Int,
batch_sizes::Dict,
io_pairs::PairedDataContainer,
feature_type::String,
)
regularizer = noise_sd^2
n_train = Int(floor(0.8 * size(get_inputs(io_pairs), 2))) # 80:20 train test
n_test = size(get_inputs(io_pairs), 2) - n_train
# split data into train/test randomly
itrain = reshape(get_inputs(io_pairs)[1, 1:n_train], 1, :)
otrain = reshape(get_outputs(io_pairs)[1, 1:n_train], 1, :)
io_train_cost = PairedDataContainer(itrain, otrain)
itest = reshape(get_inputs(io_pairs)[1, (n_train + 1):end], 1, :)
otest = reshape(get_outputs(io_pairs)[1, (n_train + 1):end], 1, :)
# build and fit the RF
rfm = RFM_from_hyperparameters(rng, l, regularizer, n_features, batch_sizes, feature_type)
fitted_features = fit(rfm, io_train_cost)
test_batch_size = get_batch_size(rfm, "test")
batch_inputs = batch_generator(itest, test_batch_size, dims = 2) # input_dim x batch_size
#we want to calc lambda/m * coeffs^2 in the end
pred_mean, features = predictive_mean(rfm, fitted_features, DataContainer(itest))
scaled_coeffs = sqrt(1 / n_features) * get_coeffs(fitted_features)
return pred_mean, scaled_coeffs
end
function estimate_mean_and_coeffnorm_covariance(
rng::AbstractRNG,
l::Real,
noise_sd::Real,
n_features::Int,
batch_sizes::Dict,
io_pairs::PairedDataContainer,
feature_type::String,
n_samples::Int;
repeats::Int = 1,
)
n_train = Int(floor(0.8 * size(get_inputs(io_pairs), 2))) # 80:20 train test
n_test = size(get_inputs(io_pairs), 2) - n_train
means = zeros(n_test, n_samples)
coeffl2norm = zeros(1, n_samples)
for i in 1:n_samples
for j in 1:repeats
m, c = calculate_mean_and_coeffs(rng, l, noise_sd, n_features, batch_sizes, io_pairs, feature_type)
means[:, i] += m' / repeats
coeffl2norm[1, i] += sqrt(sum(c .^ 2)) / repeats
end
end
Γ = cov(vcat(means, coeffl2norm), dims = 2)
return Γ
end
function calculate_ensemble_mean_and_coeffnorm(
rng::AbstractRNG,
lvec::AbstractVector,
noise_sd::Real,
n_features::Int,
batch_sizes::Dict,
io_pairs::PairedDataContainer,
feature_type::String;
repeats::Int = 1,
)
N_ens = length(lvec)
n_train = Int(floor(0.8 * size(get_inputs(io_pairs), 2))) # 80:20 train test
n_test = size(get_inputs(io_pairs), 2) - n_train
means = zeros(n_test, N_ens)
coeffl2norm = zeros(1, N_ens)
for (i, l) in zip(collect(1:N_ens), lvec)
for j in collect(1:repeats)
m, c = calculate_mean_and_coeffs(rng, l, noise_sd, n_features, batch_sizes, io_pairs, feature_type)
means[:, i] += m' / repeats
coeffl2norm[1, i] += sqrt(sum(c .^ 2)) / repeats
end
end
return vcat(means, coeffl2norm)
end
## Begin Script, define problem setting
println("starting script")
date_of_run = Date(2024, 4, 10)
# Target function
ftest(x::AbstractVecOrMat) = exp.(-0.5 * x .^ 2) .* (x .^ 4 - x .^ 3 - x .^ 2 + x .- 1)
n_data = 20 * 2
noise_sd = 0.1
x = rand(rng, Uniform(-3, 3), n_data)
noise = rand(rng, Normal(0, noise_sd), n_data)
y = ftest(x) + noise
io_pairs = PairedDataContainer(reshape(x, 1, :), reshape(y, 1, :), data_are_columns = true) #matrix input
xtestvec = collect(-3:0.01:3)
ntest = length(xtestvec) #extended domain
xtest = DataContainer(reshape(xtestvec, 1, :), data_are_columns = true)
ytest = ftest(get_data(xtest))
## Define Hyperpriors for EKP
μ_l = 10.0
σ_l = 10.0
prior_lengthscale = constrained_gaussian("lengthscale", μ_l, σ_l, 0.0, Inf)
priors = prior_lengthscale
# estimate the noise from running many RFM sample costs at the mean values
batch_sizes = Dict("train" => 100, "test" => 100, "feature" => 100)
n_train = Int(floor(0.8 * n_data))
n_test = n_data - n_train
n_samples = n_test + 1 # > n_test
n_features = 80
repeats = 1
feature_types = ["fourier", "neuron", "sigmoid"]
lengthscales = zeros(length(feature_types))
for (idx, type) in enumerate(feature_types)
println("estimating noise in observations... ")
internal_Γ = estimate_mean_and_coeffnorm_covariance(
rng,
μ_l, # take mean values
noise_sd,
n_features,
batch_sizes,
io_pairs,
type,
n_samples,
repeats = repeats,
)
Γ = internal_Γ
Γ[1:n_test, 1:n_test] += noise_sd^2 * I
Γ[(n_test + 1):end, (n_test + 1):end] += I
println("Finished estimation.")
# Create EKI
N_ens = 50
N_iter = 20
initial_params = construct_initial_ensemble(priors, N_ens; rng_seed = ekp_seed)
data = vcat(y[(n_train + 1):end], 0.0)
ekiobj = [EKP.EnsembleKalmanProcess(initial_params, data, Γ, Inversion())]
err = zeros(N_iter)
for i in 1:N_iter
#get parameters:
constrained_u = transform_unconstrained_to_constrained(priors, get_u_final(ekiobj[1]))
lvec = constrained_u[1, :]
g_ens = calculate_ensemble_mean_and_coeffnorm(
rng,
lvec,
noise_sd,
n_features,
batch_sizes,
io_pairs,
type,
repeats = repeats,
)
EKP.update_ensemble!(ekiobj[1], g_ens)
err[i] = get_error(ekiobj[1])[end] #mean((params_true - mean(params_i,dims=2)).^2)
println(
"Iteration: " *
string(i) *
", Error: " *
string(err[i]) *
", with parameter mean" *
string(mean(transform_unconstrained_to_constrained(priors, get_u_final(ekiobj[1])), dims = 2)[:, 1]),
" and sd ",
string(sqrt.(var(transform_unconstrained_to_constrained(priors, get_u_final(ekiobj[1])), dims = 2))[:, 1]),
)
end
lengthscales[idx] = transform_unconstrained_to_constrained(priors, mean(get_u_final(ekiobj[1]), dims = 2))[1, 1]
end
println("****")
println("Optimal lengthscales: ", feature_types, " = ", lengthscales)
println("****")
#run an actual experiment
n_features_test = 1000
n_data_test = 300
x_test = rand(rng, Uniform(-3, 3), (1, n_data_test))
noise_test = rand(rng, Normal(0, noise_sd), (1, n_data_test))
y_test = ftest(x_test) + noise_test
io_pairs_test = PairedDataContainer(x_test, y_test)
μ_c = 0.0
σ_c = lengthscales
regularizer = noise_sd^2
rfms = Any[]
fits = Any[]
for (idx, sd, feature_type) in zip(collect(1:length(σ_c)), σ_c, feature_types)
pd = constrained_gaussian("xi", 0.0, sd, -Inf, Inf)
feature_sampler = FeatureSampler(pd, rng = copy(rng))
if feature_type == "fourier"
sf = ScalarFourierFeature(n_features_test, feature_sampler)
elseif feature_type == "neuron"
sf = ScalarNeuronFeature(n_features_test, feature_sampler)
elseif feature_type == "sigmoid"
sf = ScalarFeature(n_features_test, feature_sampler, Sigmoid())
end
push!(rfms, RandomFeatureMethod(sf, batch_sizes = batch_sizes, regularization = regularizer))
push!(fits, fit(rfms[end], io_pairs_test))
end
if PLOT_FLAG
figure_save_directory = joinpath(@__DIR__, "output", string(date_of_run))
if !isdir(figure_save_directory)
mkpath(figure_save_directory)
end
#plot slice through one dimensions, others fixed to 0
xplt = reshape(collect(-3:0.01:3), 1, :)
yplt = ftest(xplt)
clrs = map(x -> get(colorschemes[:hawaii], x), [0.25, 0.5, 0.75])
plt = plot(
xplt',
yplt',
show = false,
color = "black",
linewidth = 5,
size = (600, 600),
legend = :topleft,
label = "Target",
)
for (idx, rfm, fit, feature_type, clr) in zip(collect(1:length(σ_c)), rfms, fits, feature_types, clrs)
pred_mean, pred_cov = predict(rfm, fit, DataContainer(xplt))
pred_cov = pred_cov[1, 1, :] #just variances
pred_cov = max.(pred_cov, 0.0) #not normally needed..
plot!(
xplt',
pred_mean',
ribbon = [2 * sqrt.(pred_cov); 2 * sqrt.(pred_cov)]',
label = feature_type,
color = clr,
)
end
savefig(plt, joinpath(figure_save_directory, "Fit_and_predict_1D.pdf"))
end
| RandomFeatures | https://github.com/CliMA/RandomFeatures.jl.git |
|
[
"Apache-2.0"
] | 0.3.3 | d639d537c14127a70c8bd3e0158ecbccfb2a54a3 | code | 10884 | # Example to learn hyperparameters of simple 1d-1d regression example.
# This example matches test/Methods/runtests.jl testset: "Fit and predict: 1D -> 1D"
# The (approximate) optimal values here are used in those tests.
using StableRNGs
using Distributions
using StatsBase
using LinearAlgebra
using Random
using Dates
PLOT_FLAG = true
println("plot flag: ", PLOT_FLAG)
if PLOT_FLAG
using Plots, ColorSchemes
end
using EnsembleKalmanProcesses
const EKP = EnsembleKalmanProcesses
using RandomFeatures.ParameterDistributions
using RandomFeatures.DataContainers
using RandomFeatures.Samplers
using RandomFeatures.Features
using RandomFeatures.Methods
using RandomFeatures.Utilities
seed = 2024
ekp_seed = 99999
rng = StableRNG(seed)
## Functions of use
function RFM_from_hyperparameters(
rng::AbstractRNG,
l::Real,
regularizer::Real,
n_features::Int,
batch_sizes::Dict,
feature_type::String,
)
μ_c = 0.0
σ_c = l
pd = constrained_gaussian("xi", μ_c, σ_c, -Inf, Inf)
feature_sampler = FeatureSampler(pd, rng = rng)
# Learn hyperparameters for different feature types
if feature_type == "fourier"
sf = ScalarFourierFeature(n_features, feature_sampler)
elseif feature_type == "neuron"
sf = ScalarNeuronFeature(n_features, feature_sampler)
elseif feature_type == "sigmoid"
sf = ScalarFeature(n_features, feature_sampler, Sigmoid())
end
return RandomFeatureMethod(sf, batch_sizes = batch_sizes, regularization = regularizer)
end
function calculate_mean_cov_and_coeffs(
rng::AbstractRNG,
l::Real,
noise_sd::Real,
n_features::Int,
batch_sizes::Dict,
io_pairs::PairedDataContainer,
feature_type::String,
)
regularizer = noise_sd^2
n_train = Int(floor(0.8 * size(get_inputs(io_pairs), 2))) # 80:20 train test
n_test = size(get_inputs(io_pairs), 2) - n_train
# split data into train/test randomly
itrain = reshape(get_inputs(io_pairs)[1, 1:n_train], 1, :)
otrain = reshape(get_outputs(io_pairs)[1, 1:n_train], 1, :)
io_train_cost = PairedDataContainer(itrain, otrain)
itest = reshape(get_inputs(io_pairs)[1, (n_train + 1):end], 1, :)
otest = reshape(get_outputs(io_pairs)[1, (n_train + 1):end], 1, :)
# build and fit the RF
rfm = RFM_from_hyperparameters(rng, l, regularizer, n_features, batch_sizes, feature_type)
fitted_features = fit(rfm, io_train_cost)
test_batch_size = get_batch_size(rfm, "test")
batch_inputs = batch_generator(itest, test_batch_size, dims = 2) # input_dim x batch_size
#we want to calc lambda/m * coeffs^2 in the end
pred_mean, pred_cov = predict(rfm, fitted_features, DataContainer(itest))
scaled_coeffs = sqrt(1 / n_features) * get_coeffs(fitted_features)
return pred_mean, pred_cov, scaled_coeffs
end
function estimate_mean_cov_and_coeffnorm_covariance(
rng::AbstractRNG,
l::Real,
noise_sd::Real,
n_features::Int,
batch_sizes::Dict,
io_pairs::PairedDataContainer,
feature_type::String,
n_samples::Int;
repeats::Int = 1,
)
n_train = Int(floor(0.8 * size(get_inputs(io_pairs), 2))) # 80:20 train test
n_test = size(get_inputs(io_pairs), 2) - n_train
means = zeros(n_test, n_samples)
covs = zeros(n_test, n_samples)
coeffl2norm = zeros(1, n_samples)
for i in 1:n_samples
for j in 1:repeats
m, v, c = calculate_mean_cov_and_coeffs(rng, l, noise_sd, n_features, batch_sizes, io_pairs, feature_type)
means[:, i] += m[1, :] / repeats
covs[:, i] += v[1, 1, :] / repeats
coeffl2norm[1, i] += sqrt(sum(c .^ 2)) / repeats
end
end
Γ = cov(vcat(means, covs, coeffl2norm), dims = 2)
approx_σ2 = Diagonal(mean(covs, dims = 2)[:, 1]) # approx of \sigma^2I +rf var
return Γ, approx_σ2
end
function calculate_ensemble_mean_cov_and_coeffnorm(
rng::AbstractRNG,
lvec::AbstractVector,
noise_sd::Real,
n_features::Int,
batch_sizes::Dict,
io_pairs::PairedDataContainer,
feature_type::String;
repeats::Int = 1,
)
N_ens = length(lvec)
n_train = Int(floor(0.8 * size(get_inputs(io_pairs), 2))) # 80:20 train test
n_test = size(get_inputs(io_pairs), 2) - n_train
means = zeros(n_test, N_ens)
covs = zeros(n_test, N_ens)
coeffl2norm = zeros(1, N_ens)
for (i, l) in zip(collect(1:N_ens), lvec)
for j in collect(1:repeats)
m, v, c = calculate_mean_cov_and_coeffs(rng, l, noise_sd, n_features, batch_sizes, io_pairs, feature_type)
means[:, i] += m[1, :] / repeats
covs[:, i] += v[1, 1, :] / repeats
coeffl2norm[1, i] += sqrt(sum(c .^ 2)) / repeats
end
end
return vcat(means, covs, coeffl2norm), Diagonal(mean(covs, dims = 2)[:, 1]) # approx of +\sigma^2I
end
## Begin Script, define problem setting
println("starting script")
date_of_run = Date(2022, 9, 14)
# Target function
ftest(x::AbstractVecOrMat) = exp.(-0.5 * x .^ 2) .* (x .^ 4 - x .^ 3 - x .^ 2 + x .- 1)
n_data = 20 * 4
noise_sd = 0.1
x = rand(rng, Uniform(-3, 3), n_data)
noise = rand(rng, Normal(0, noise_sd), n_data)
y = ftest(x) + noise
io_pairs = PairedDataContainer(reshape(x, 1, :), reshape(y, 1, :), data_are_columns = true) #matrix input
xtestvec = collect(-3:0.01:3)
ntest = length(xtestvec) #extended domain
xtest = DataContainer(reshape(xtestvec, 1, :), data_are_columns = true)
ytest = ftest(get_data(xtest))
## Define Hyperpriors for EKP
μ_l = 10.0
σ_l = 10.0
prior_lengthscale = constrained_gaussian("lengthscale", μ_l, σ_l, 0.0, Inf)
priors = prior_lengthscale
# estimate the noise from running many RFM sample costs at the mean values
batch_sizes = Dict("train" => 100, "test" => 100, "feature" => 100)
n_train = Int(floor(0.8 * n_data))
n_test = n_data - n_train
n_samples = n_test + 1 # > n_test
n_features = 80
@assert(!(n_features == n_train))
repeats = 1
feature_types = ["fourier", "neuron", "sigmoid"]
lengthscales = zeros(length(feature_types))
for (idx, type) in enumerate(feature_types)
println("estimating noise in observations... ")
internal_Γ, approx_σ2 = estimate_mean_cov_and_coeffnorm_covariance(
rng,
μ_l, # take mean values
noise_sd,
n_features,
batch_sizes,
io_pairs,
type,
n_samples,
repeats = repeats,
)
Γ = internal_Γ
Γ[1:n_test, 1:n_test] += approx_σ2 #RF prediction of noise
Γ[(n_test + 1):(2 * n_test), (n_test + 1):(2 * n_test)] += I
Γ[(2 * n_test + 1):end, (2 * n_test + 1):end] += I
println(
"Estimated variance. Tr(cov) = ",
tr(Γ[1:n_test, 1:n_test]),
" + ",
tr(Γ[(n_test + 1):(2 * n_test), (n_test + 1):(2 * n_test)]),
" + ",
tr(Γ[(2 * n_test + 1):end, (2 * n_test + 1):end]),
)
# Create EKI
N_ens = 20
N_iter = 20
initial_params = construct_initial_ensemble(priors, N_ens; rng_seed = ekp_seed)
data = vcat(y[(n_train + 1):end], noise_sd^2 * ones(n_test), 0.0)
ekiobj = [EKP.EnsembleKalmanProcess(initial_params, data, Γ, Inversion())]
err = zeros(N_iter)
for i in 1:N_iter
#get parameters:
constrained_u = transform_unconstrained_to_constrained(priors, get_u_final(ekiobj[1]))
lvec = constrained_u[1, :]
g_ens, approx_σ2_ens = calculate_ensemble_mean_cov_and_coeffnorm(
rng,
lvec,
noise_sd,
n_features,
batch_sizes,
io_pairs,
type,
repeats = repeats,
)
#replace Γ in loop
# Γ_tmp = internal_Γ
# Γ_tmp[1:n_test,1:n_test] += approx_σ2_ens
# Γ_tmp[n_test+1:2*n_test, n_test+1:2*n_test] += I
# Γ_tmp[2*n_test+1:end,2*n_test+1:end] += I
# ekiobj[1] = EKP.EnsembleKalmanProcess(initial_params, data, Γ, Inversion())
EKP.update_ensemble!(ekiobj[1], g_ens)
err[i] = get_error(ekiobj[1])[end] #mean((params_true - mean(params_i,dims=2)).^2)
println(
"Iteration: " *
string(i) *
", Error: " *
string(err[i]) *
", with parameter mean" *
string(mean(transform_unconstrained_to_constrained(priors, get_u_final(ekiobj[1])), dims = 2)[:, 1]),
" and sd ",
string(sqrt.(var(transform_unconstrained_to_constrained(priors, get_u_final(ekiobj[1])), dims = 2))[:, 1]),
)
end
lengthscales[idx] = transform_unconstrained_to_constrained(priors, mean(get_u_final(ekiobj[1]), dims = 2))[1, 1]
end
println("****")
println("Optimal lengthscales: ", feature_types, " = ", lengthscales)
println("****")
#run an actual experiment
n_features_test = 1000
n_data_test = 300
x_test = rand(rng, Uniform(-3, 3), (1, n_data_test))
noise_test = rand(rng, Normal(0, noise_sd), (1, n_data_test))
y_test = ftest(x_test) + noise_test
io_pairs_test = PairedDataContainer(x_test, y_test)
μ_c = 0.0
σ_c = lengthscales
regularizer = noise_sd^2
rfms = Any[]
fits = Any[]
for (idx, sd, feature_type) in zip(collect(1:length(σ_c)), σ_c, feature_types)
pd = constrained_gaussian("xi", 0.0, sd, -Inf, Inf)
feature_sampler = FeatureSampler(pd, rng = copy(rng))
if feature_type == "fourier"
sf = ScalarFourierFeature(n_features_test, feature_sampler)
elseif feature_type == "neuron"
sf = ScalarNeuronFeature(n_features_test, feature_sampler)
elseif feature_type == "sigmoid"
sf = ScalarFeature(n_features_test, feature_sampler, Sigmoid())
end
push!(rfms, RandomFeatureMethod(sf, batch_sizes = batch_sizes, regularization = regularizer))
push!(fits, fit(rfms[end], io_pairs_test, decomposition_type = "qr"))
end
if PLOT_FLAG
figure_save_directory = joinpath(@__DIR__, "output", string(date_of_run))
if !isdir(figure_save_directory)
mkpath(figure_save_directory)
end
#plot slice through one dimensions, others fixed to 0
xplt = reshape(collect(-3:0.01:3), 1, :)
yplt = ftest(xplt)
clrs = map(x -> get(colorschemes[:hawaii], x), [0.25, 0.5, 0.75])
plt = plot(
xplt',
yplt',
show = false,
color = "black",
linewidth = 5,
size = (600, 600),
legend = :topleft,
label = "Target",
)
for (idx, rfm, fit, feature_type, clr) in zip(collect(1:length(σ_c)), rfms, fits, feature_types, clrs)
pred_mean, pred_cov = predict(rfm, fit, DataContainer(xplt))
pred_cov[1, 1, :] = max.(pred_cov[1, 1, :], 0.0)
plot!(
xplt',
pred_mean',
ribbon = [2 * sqrt.(pred_cov[1, 1, :]); 2 * sqrt.(pred_cov[1, 1, :])]',
label = feature_type,
color = clr,
)
end
savefig(plt, joinpath(figure_save_directory, "Fit_and_predict_1D.pdf"))
end
| RandomFeatures | https://github.com/CliMA/RandomFeatures.jl.git |
|
[
"Apache-2.0"
] | 0.3.3 | d639d537c14127a70c8bd3e0158ecbccfb2a54a3 | code | 10472 | # Example to learn hyperparameters of simple 1d-1d regression example.
# This example matches test/Methods/runtests.jl testset: "Fit and predict: 1D -> 1D"
# The (approximate) optimal values here are used in those tests.
using StableRNGs
using Distributions
using StatsBase
using LinearAlgebra
using Random
using Dates
PLOT_FLAG = true
println("plot flag: ", PLOT_FLAG)
if PLOT_FLAG
using Plots, ColorSchemes
end
using EnsembleKalmanProcesses
const EKP = EnsembleKalmanProcesses
using RandomFeatures.ParameterDistributions
using RandomFeatures.DataContainers
using RandomFeatures.Samplers
using RandomFeatures.Features
using RandomFeatures.Methods
using RandomFeatures.Utilities
seed = 2024
ekp_seed = 99999
rng = StableRNG(seed)
## Functions of use
function RFM_from_hyperparameters(
rng::AbstractRNG,
l::Real,
regularizer::Real,
n_features::Int,
batch_sizes::Dict,
feature_type::String,
)
μ_c = 0.0
σ_c = l
pd = constrained_gaussian("xi", μ_c, σ_c, -Inf, Inf)
feature_sampler = FeatureSampler(pd, rng = rng)
# Learn hyperparameters for different feature types
if feature_type == "fourier"
sf = ScalarFourierFeature(n_features, feature_sampler)
elseif feature_type == "neuron"
sf = ScalarNeuronFeature(n_features, feature_sampler)
elseif feature_type == "sigmoid"
sf = ScalarFeature(n_features, feature_sampler, Sigmoid())
end
return RandomFeatureMethod(sf, batch_sizes = batch_sizes, regularization = regularizer)
end
function calculate_mean_cov_and_coeffs(
rng::AbstractRNG,
l::Real,
noise_sd::Real,
n_features::Int,
batch_sizes::Dict,
io_pairs::PairedDataContainer,
feature_type::String,
)
regularizer = noise_sd^2
n_train = Int(floor(0.8 * size(get_inputs(io_pairs), 2))) # 80:20 train test
n_test = size(get_inputs(io_pairs), 2) - n_train
# split data into train/test randomly
itrain = reshape(get_inputs(io_pairs)[1, 1:n_train], 1, :)
otrain = reshape(get_outputs(io_pairs)[1, 1:n_train], 1, :)
io_train_cost = PairedDataContainer(itrain, otrain)
itest = reshape(get_inputs(io_pairs)[1, (n_train + 1):end], 1, :)
otest = reshape(get_outputs(io_pairs)[1, (n_train + 1):end], 1, :)
# build and fit the RF
rfm = RFM_from_hyperparameters(rng, l, regularizer, n_features, batch_sizes, feature_type)
fitted_features = fit(rfm, io_train_cost)
test_batch_size = get_batch_size(rfm, "test")
batch_inputs = batch_generator(itest, test_batch_size, dims = 2) # input_dim x batch_size
#we want to calc lambda/m * coeffs^2 in the end
pred_mean, pred_cov = predict(rfm, fitted_features, DataContainer(itest))
scaled_coeffs = sqrt(1 / n_features) * get_coeffs(fitted_features)
return pred_mean, pred_cov, scaled_coeffs
end
function estimate_mean_cov_and_coeffnorm_covariance(
rng::AbstractRNG,
l::Real,
noise_sd::Real,
n_features::Int,
batch_sizes::Dict,
io_pairs::PairedDataContainer,
feature_type::String,
n_samples::Int;
repeats::Int = 1,
)
n_train = Int(floor(0.8 * size(get_inputs(io_pairs), 2))) # 80:20 train test
n_test = size(get_inputs(io_pairs), 2) - n_train
means = zeros(n_test, n_samples)
covs = zeros(n_test, n_samples)
coeffl2norm = zeros(1, n_samples)
for i in 1:n_samples
for j in 1:repeats
m, v, c = calculate_mean_cov_and_coeffs(rng, l, noise_sd, n_features, batch_sizes, io_pairs, feature_type)
means[:, i] += m[1, :] / repeats
covs[:, i] += v[1, 1, :] / repeats
coeffl2norm[1, i] += sqrt(sum(c .^ 2)) / repeats
end
end
Γ = cov(vcat(means, coeffl2norm), dims = 2)
approx_σ = Diagonal(mean(covs, dims = 2)[:, 1]) # approx of \sigma^2I +rf var
return Γ, approx_σ
end
function calculate_ensemble_mean_cov_and_coeffnorm(
rng::AbstractRNG,
lvec::AbstractVector,
noise_sd::Real,
n_features::Int,
batch_sizes::Dict,
io_pairs::PairedDataContainer,
feature_type::String;
repeats::Int = 1,
)
N_ens = length(lvec)
n_train = Int(floor(0.8 * size(get_inputs(io_pairs), 2))) # 80:20 train test
n_test = size(get_inputs(io_pairs), 2) - n_train
means = zeros(n_test, N_ens)
covs = zeros(n_test, N_ens)
coeffl2norm = zeros(1, N_ens)
for (i, l) in zip(collect(1:N_ens), lvec)
for j in collect(1:repeats)
m, v, c = calculate_mean_cov_and_coeffs(rng, l, noise_sd, n_features, batch_sizes, io_pairs, feature_type)
means[:, i] += m[1, :] / repeats
covs[:, i] += v[1, 1, :] ./ repeats
coeffl2norm[1, i] += sqrt(sum(c .^ 2)) / repeats
end
end
return vcat(means, coeffl2norm), Diagonal(mean(covs, dims = 2)[:, 1]) # approx of +\sigma^2I
end
## Begin Script, define problem setting
println("starting script")
date_of_run = Date(2024, 4, 10)
# Target function
ftest(x::AbstractVecOrMat) = exp.(-0.5 * x .^ 2) .* (x .^ 4 - x .^ 3 - x .^ 2 + x .- 1)
n_data = 20 * 4
noise_sd = 0.1
x = rand(rng, Uniform(-3, 3), n_data)
noise = rand(rng, Normal(0, noise_sd), n_data)
y = ftest(x) + noise
io_pairs = PairedDataContainer(reshape(x, 1, :), reshape(y, 1, :), data_are_columns = true) #matrix input
xtestvec = collect(-3:0.01:3)
ntest = length(xtestvec) #extended domain
xtest = DataContainer(reshape(xtestvec, 1, :), data_are_columns = true)
ytest = ftest(get_data(xtest))
## Define Hyperpriors for EKP
μ_l = 10.0
σ_l = 10.0
prior_lengthscale = constrained_gaussian("lengthscale", μ_l, σ_l, 0.0, Inf)
priors = prior_lengthscale
# estimate the noise from running many RFM sample costs at the mean values
batch_sizes = Dict("train" => 100, "test" => 100, "feature" => 100)
n_train = Int(floor(0.8 * n_data))
n_test = n_data - n_train
n_samples = n_test + 1 # > n_test
n_features = 80
repeats = 1
feature_types = ["fourier", "neuron", "sigmoid"]
lengthscales = zeros(length(feature_types))
for (idx, type) in enumerate(feature_types)
println("estimating noise in observations... ")
internal_Γ, approx_σ = estimate_mean_cov_and_coeffnorm_covariance(
rng,
μ_l, # take mean values
noise_sd,
n_features,
batch_sizes,
io_pairs,
type,
n_samples,
repeats = repeats,
)
Γ = internal_Γ
# Γ = zeros(size(internal_Γ))
Γ[1:n_test, 1:n_test] += approx_σ #RF prediction of noise
Γ[(n_test + 1):end, (n_test + 1):end] += I
println("Finished estimation.")
# Create EKI
N_ens = 50
N_iter = 20
initial_params = construct_initial_ensemble(priors, N_ens; rng_seed = ekp_seed)
data = vcat(y[(n_train + 1):end], 0.0)
ekiobj = [EKP.EnsembleKalmanProcess(initial_params, data, Γ, Inversion())]
err = zeros(N_iter)
for i in 1:N_iter
#get parameters:
constrained_u = transform_unconstrained_to_constrained(priors, get_u_final(ekiobj[1]))
lvec = constrained_u[1, :]
g_ens, approx_σ_ens = calculate_ensemble_mean_cov_and_coeffnorm(
rng,
lvec,
noise_sd,
n_features,
batch_sizes,
io_pairs,
type,
repeats = repeats,
)
#replace Γ in loop
Γ_tmp = internal_Γ
#Γ_tmp = zeros(size(internal_Γ))
Γ_tmp[1:n_test, 1:n_test] += approx_σ_ens
Γ_tmp[(n_test + 1):end, (n_test + 1):end] += I
# ekiobj[1] = EKP.EnsembleKalmanProcess(initial_params, data, Γ, Inversion())
EKP.update_ensemble!(ekiobj[1], g_ens)
err[i] = get_error(ekiobj[1])[end] #mean((params_true - mean(params_i,dims=2)).^2)
println(
"Iteration: " *
string(i) *
", Error: " *
string(err[i]) *
", with parameter mean " *
string(mean(transform_unconstrained_to_constrained(priors, get_u_final(ekiobj[1])), dims = 2)[:, 1]),
" and sd ",
string(sqrt.(var(transform_unconstrained_to_constrained(priors, get_u_final(ekiobj[1])), dims = 2))[:, 1]),
)
end
lengthscales[idx] = transform_unconstrained_to_constrained(priors, mean(get_u_final(ekiobj[1]), dims = 2))[1, 1]
end
println("****")
println("Optimal lengthscales: ", feature_types, " = ", lengthscales)
println("****")
#run an actual experiment
n_features_test = 1000
n_data_test = 300
x_test = rand(rng, Uniform(-3, 3), (1, n_data_test))
noise_test = rand(rng, Normal(0, noise_sd), (1, n_data_test))
y_test = ftest(x_test) + noise_test
io_pairs_test = PairedDataContainer(x_test, y_test)
μ_c = 0.0
σ_c = lengthscales
regularizer = noise_sd^2
rfms = Any[]
fits = Any[]
for (idx, sd, feature_type) in zip(collect(1:length(σ_c)), σ_c, feature_types)
pd = constrained_gaussian("xi", 0.0, sd, -Inf, Inf)
feature_sampler = FeatureSampler(pd, rng = copy(rng))
if feature_type == "fourier"
sf = ScalarFourierFeature(n_features_test, feature_sampler)
elseif feature_type == "neuron"
sf = ScalarNeuronFeature(n_features_test, feature_sampler)
elseif feature_type == "sigmoid"
sf = ScalarFeature(n_features_test, feature_sampler, Sigmoid())
end
push!(rfms, RandomFeatureMethod(sf, batch_sizes = batch_sizes, regularization = regularizer))
push!(fits, fit(rfms[end], io_pairs_test))
end
if PLOT_FLAG
figure_save_directory = joinpath(@__DIR__, "output", string(date_of_run))
if !isdir(figure_save_directory)
mkpath(figure_save_directory)
end
#plot slice through one dimensions, others fixed to 0
xplt = reshape(collect(-3:0.01:3), 1, :)
yplt = ftest(xplt)
clrs = map(x -> get(colorschemes[:hawaii], x), [0.25, 0.5, 0.75])
plt = plot(
xplt',
yplt',
show = false,
color = "black",
linewidth = 5,
size = (600, 600),
legend = :topleft,
label = "Target",
)
for (idx, rfm, fit, feature_type, clr) in zip(collect(1:length(σ_c)), rfms, fits, feature_types, clrs)
pred_mean, pred_cov = predict(rfm, fit, DataContainer(xplt))
pred_cov[1, 1, :] = max.(pred_cov[1, 1, :], 0.0)
plot!(
xplt',
pred_mean',
ribbon = [2 * sqrt.(pred_cov[1, 1, :]); 2 * sqrt.(pred_cov[1, 1, :])]',
label = feature_type,
color = clr,
)
end
savefig(plt, joinpath(figure_save_directory, "Fit_and_predict_1D.pdf"))
end
| RandomFeatures | https://github.com/CliMA/RandomFeatures.jl.git |
|
[
"Apache-2.0"
] | 0.3.3 | d639d537c14127a70c8bd3e0158ecbccfb2a54a3 | code | 12092 | # Example to learn hyperparameters of simple 1d-1d regression example.
# This example matches test/Methods/runtests.jl testset: "Fit and predict: 1D -> 1D"
# The (approximate) optimal values here are used in those tests.
using StableRNGs
using Distributions
using JLD2
using StatsBase
using LinearAlgebra
using Random
using Dates
PLOT_FLAG = true
println("plot flag:", PLOT_FLAG)
if PLOT_FLAG
using Plots
end
using EnsembleKalmanProcesses
const EKP = EnsembleKalmanProcesses
using EnsembleKalmanProcesses.Localizers
using RandomFeatures.ParameterDistributions
using RandomFeatures.DataContainers
using RandomFeatures.Samplers
using RandomFeatures.Features
using RandomFeatures.Methods
using RandomFeatures.Utilities
seed = 2024
ekp_seed = 99999
rng = StableRNG(seed)
## Functions of use
function RFM_from_hyperparameters(
rng::AbstractRNG,
l::Union{Real, AbstractVecOrMat},
regularizer::Real,
n_features::Int,
batch_sizes::Dict,
input_dim::Int,
)
μ_c = 0.0
if isa(l, Real)
σ_c = fill(l, input_dim)
elseif isa(l, AbstractVector)
if length(l) == 1
σ_c = fill(l[1], input_dim)
else
σ_c = l
end
else
isa(l, AbstractMatrix)
σ_c = l[:, 1]
end
pd = ParameterDistribution(
Dict(
"distribution" => VectorOfParameterized(map(sd -> Normal(μ_c, sd), σ_c)),
"constraint" => repeat([no_constraint()], input_dim),
"name" => "xi",
),
)
feature_sampler = FeatureSampler(pd, rng = rng)
# Learn hyperparameters for different feature types
sf = ScalarFourierFeature(n_features, feature_sampler)
return RandomFeatureMethod(sf, batch_sizes = batch_sizes, regularization = regularizer)
end
function calculate_mean_and_coeffs(
rng::AbstractRNG,
l::Union{Real, AbstractVecOrMat},
noise_sd::Real,
n_features::Int,
batch_sizes::Dict,
io_pairs::PairedDataContainer,
)
regularizer = noise_sd^2
n_train = Int(floor(0.8 * size(get_inputs(io_pairs), 2))) # 80:20 train test
n_test = size(get_inputs(io_pairs), 2) - n_train
# split data into train/test randomly
itrain = get_inputs(io_pairs)[:, 1:n_train]
otrain = get_outputs(io_pairs)[:, 1:n_train]
io_train_cost = PairedDataContainer(itrain, otrain)
itest = get_inputs(io_pairs)[:, (n_train + 1):end]
otest = get_outputs(io_pairs)[:, (n_train + 1):end]
input_dim = size(itrain, 1)
# build and fit the RF
rfm = RFM_from_hyperparameters(rng, l, regularizer, n_features, batch_sizes, input_dim)
fitted_features = fit(rfm, io_train_cost)
test_batch_size = get_batch_size(rfm, "test")
batch_inputs = batch_generator(itest, test_batch_size, dims = 2) # input_dim x batch_size
#we want to calc 1/var(y-mean)^2 + lambda/m * coeffs^2 in the end
pred_mean, features = predictive_mean(rfm, fitted_features, DataContainer(itest))
scaled_coeffs = sqrt(1 / n_features) * get_coeffs(fitted_features)
return pred_mean, scaled_coeffs
end
function estimate_mean_and_coeffnorm_covariance(
rng::AbstractRNG,
l::Union{Real, AbstractVecOrMat},
noise_sd::Real,
n_features::Int,
batch_sizes::Dict,
io_pairs::PairedDataContainer,
n_samples::Int;
repeats::Int = 1,
)
n_train = Int(floor(0.8 * size(get_inputs(io_pairs), 2))) # 80:20 train test
n_test = size(get_inputs(io_pairs), 2) - n_train
means = zeros(n_test, n_samples)
coeffl2norm = zeros(1, n_samples)
for i in 1:n_samples
for j in 1:repeats
m, c = calculate_mean_and_coeffs(rng, l, noise_sd, n_features, batch_sizes, io_pairs)
means[:, i] += m' / repeats
coeffl2norm[1, i] += sqrt(sum(c .^ 2)) / repeats
end
end
# println("take covariances")
println(mean(vcat(means, coeffl2norm), dims = 2)[end])
Γ = cov(vcat(means, coeffl2norm), dims = 2)
return Γ
end
function calculate_ensemble_mean_and_coeffnorm(
rng::AbstractRNG,
lvecormat::AbstractVecOrMat,
noise_sd::Real,
n_features::Int,
batch_sizes::Dict,
io_pairs::PairedDataContainer;
repeats::Int = 1,
)
if isa(lvecormat, AbstractVector)
lmat = reshape(lvecormat, 1, :)
else
lmat = lvecormat
end
N_ens = size(lmat, 2)
n_train = Int(floor(0.8 * size(get_inputs(io_pairs), 2))) # 80:20 train test
n_test = size(get_inputs(io_pairs), 2) - n_train
means = zeros(n_test, N_ens)
coeffl2norm = zeros(1, N_ens)
for i in collect(1:N_ens)
for j in collect(1:repeats)
l = lmat[:, i]
m, c = calculate_mean_and_coeffs(rng, l, noise_sd, n_features, batch_sizes, io_pairs)
means[:, i] += m' / repeats
coeffl2norm[1, i] += sqrt(sum(c .^ 2)) / repeats
end
end
return vcat(means, coeffl2norm)
end
## Begin Script, define problem setting
println("Begin script")
date_of_run = Date(2024, 4, 10)
input_dim = 6
println("Number of input dimensions: ", input_dim)
# radial
# ftest_nd_to_1d(x::AbstractMatrix) = mapslices(column -> 1/(1+exp(-0.25*norm(column)^2)), x, dims=1)
# not radial - different scale in each dimension
ftest_nd_to_1d(x::AbstractMatrix) =
mapslices(column -> exp(-0.1 * norm([i * c for (i, c) in enumerate(column)])^2), x, dims = 1)
n_data = 30 * input_dim
#x = hcat(rand(rng, Uniform(-0.5,0.5), (input_dim, Int(floor(n_data/4)))),rand(rng, Uniform(-3,3), (input_dim, Int(n_data - floor(n_data/4)))))
#x = x[:,shuffle(1:size(x,2))]
x = rand(rng, MvNormal(zeros(input_dim), I), n_data)
noise_sd = 1e-6
noise = rand(rng, Normal(0, noise_sd), (1, n_data))
y = ftest_nd_to_1d(x) + noise
io_pairs = PairedDataContainer(x, y)
## Define Hyperpriors for EKP
μ_l = 1.0
σ_l = 5.0
# prior for radial problem
#prior_lengthscale = constrained_gaussian("lengthscale", μ_l, σ_l, 0.0, Inf)
# prior for non radial problem
prior_lengthscale = constrained_gaussian("lengthscale", μ_l, σ_l, 0.0, Inf, repeats = input_dim)
priors = prior_lengthscale
# estimate the noise from running many RFM sample costs at the mean values
batch_sizes = Dict("train" => 600, "test" => 600, "feature" => 600)
n_features = 100
n_train = Int(floor(0.8 * n_data))
n_test = n_data - n_train
repeats = 8
CALC_TRUTH = true
println("RHKS norm type: norm of coefficients")
if CALC_TRUTH
sample_multiplier = 1
n_samples = Int(floor(((1 + n_test) + 1) * sample_multiplier))
println("Estimating output covariance with ", n_samples, " samples")
internal_Γ = estimate_mean_and_coeffnorm_covariance(
rng,
μ_l, # take mean values
noise_sd,
n_features,
batch_sizes,
io_pairs,
n_samples,
repeats = repeats,
)
save("calculated_truth_cov.jld2", "internal_Γ", internal_Γ)
else
println("Loading truth covariance from file...")
internal_Γ = load("calculated_truth_cov.jld2")["internal_Γ"]
end
Γ = internal_Γ
Γ[1:n_test, 1:n_test] += noise_sd^2 * I
Γ[(n_test + 1):end, (n_test + 1):end] += I
println("Estimated covariance. Tr(cov) = ", tr(Γ[1:n_test, 1:n_test]), " + ", tr(Γ[(n_test + 1):end, (n_test + 1):end]))
#println("noise in observations: ", Γ)
# Create EKI
N_ens = 50
N_iter = 30
update_cov_step = 10
initial_params = construct_initial_ensemble(priors, N_ens; rng_seed = ekp_seed)
data = vcat(y[(n_train + 1):end], 0.0)
loc_method = SEC(0.2)
ekiobj = [EKP.EnsembleKalmanProcess(initial_params, data, Γ, Inversion(), localization_method = loc_method)]
err = zeros(N_iter)
println("Begin EKI iterations:")
Δt = [1.0]
for i in 1:N_iter
#get parameters:
lvec = transform_unconstrained_to_constrained(priors, get_u_final(ekiobj[1]))
g_ens =
calculate_ensemble_mean_and_coeffnorm(rng, lvec, noise_sd, n_features, batch_sizes, io_pairs, repeats = repeats)
if i == update_cov_step # one update to the
constrained_u = transform_unconstrained_to_constrained(priors, get_u_final(ekiobj[1]))
println("Estimating output covariance with ", n_samples, " samples")
internal_Γ_new = estimate_mean_and_coeffnorm_covariance(
rng,
mean(constrained_u, dims = 2)[:, 1], # take mean values
noise_sd,
n_features,
batch_sizes,
io_pairs,
n_samples,
repeats = repeats,
)
Γ_new = internal_Γ_new
Γ_new[1:n_test, 1:n_test] += noise_sd^2 * I
Γ_new[(n_test + 1):end, (n_test + 1):end] += I
println(
"Estimated covariance. Tr(cov) = ",
tr(Γ_new[1:n_test, 1:n_test]),
" + ",
tr(Γ_new[(n_test + 1):end, (n_test + 1):end]),
)
ekiobj[1] = EKP.EnsembleKalmanProcess(
get_u_final(ekiobj[1]),
data,
Γ_new,
Inversion(),
localization_method = loc_method,
)
end
EKP.update_ensemble!(ekiobj[1], g_ens, Δt_new = Δt[1])
err[i] = get_error(ekiobj[1])[end] #mean((params_true - mean(params_i,dims=2)).^2)
constrained_u = transform_unconstrained_to_constrained(priors, get_u_final(ekiobj[1]))
println(
"Iteration: " *
string(i) *
", Error: " *
string(err[i]) *
", for parameter means: " *
string(mean(constrained_u, dims = 2)),
" and sd :" * string(sqrt.(var(constrained_u, dims = 2))),
)
Δt[1] *= 1.0
end
#run actual experiment
# override following parameters for actual run
n_features_test = 5000
n_data_test = 300 * input_dim
#x_test = hcat(rand(rng, Uniform(-0.5,0.5), (input_dim, Int(floor(n_data_test/4)))),rand(rng, Uniform(-3,3), (input_dim, Int(n_data_test - floor(n_data_test/4)))))
#x_test = x_test[:,shuffle(1:size(x_test,2))]
x_test = rand(rng, MvNormal(zeros(input_dim), I), n_data_test)
noise_test = rand(rng, Normal(0, noise_sd), (1, n_data_test))
y_test = ftest_nd_to_1d(x_test) + noise_test
io_pairs_test = PairedDataContainer(x_test, y_test)
# get feature distribution
final_lvec = mean(transform_unconstrained_to_constrained(priors, get_u_final(ekiobj[1])), dims = 2)
μ_c = 0.0
if size(final_lvec, 1) == 1
σ_c = repeat([final_lvec[1, 1]], input_dim)
else
σ_c = final_lvec[:, 1]
end
regularizer = noise_sd^2
pd = ParameterDistribution(
Dict(
"distribution" => VectorOfParameterized(map(sd -> Normal(μ_c, sd), σ_c)),
"constraint" => repeat([no_constraint()], input_dim),
"name" => "xi",
),
)
feature_sampler = FeatureSampler(pd, rng = copy(rng))
sff = ScalarFourierFeature(n_features_test, feature_sampler)
#second case with batching
rfm_batch = RandomFeatureMethod(sff, batch_sizes = batch_sizes, regularization = regularizer)
fitted_batched_features = fit(rfm_batch, io_pairs_test)
if PLOT_FLAG
#plot slice through one dimensions, others fixed to 0
xrange = collect(-3:0.01:3)
xslice = zeros(input_dim, length(xrange))
figure_save_directory = joinpath(@__DIR__, "output", string(date_of_run))
if !isdir(figure_save_directory)
mkpath(figure_save_directory)
end
for direction in 1:input_dim
xslicenew = copy(xslice)
xslicenew[direction, :] = xrange
yslice = ftest_nd_to_1d(xslicenew)
pred_mean_slice, pred_cov_slice = predict(rfm_batch, fitted_batched_features, DataContainer(xslicenew))
pred_cov_slice = max.(pred_cov_slice[1, 1, :], 0.0)
plt = plot(
xrange,
yslice',
show = false,
color = "black",
linewidth = 5,
size = (600, 600),
legend = :topleft,
label = "Target",
)
plot!(
xrange,
pred_mean_slice',
ribbon = [2 * sqrt.(pred_cov_slice); 2 * sqrt.(pred_cov_slice)]',
label = "Fourier",
color = "blue",
)
savefig(
plt,
joinpath(
figure_save_directory,
"Fit_and_predict_ND_" * string(direction) * "of" * string(input_dim) * ".pdf",
),
)
end
end
| RandomFeatures | https://github.com/CliMA/RandomFeatures.jl.git |
|
[
"Apache-2.0"
] | 0.3.3 | d639d537c14127a70c8bd3e0158ecbccfb2a54a3 | code | 13864 | # Example to learn hyperparameters of simple 1d-1d regression example.
# This example matches test/Methods/runtests.jl testset: "Fit and predict: 1D -> 1D"
# The (approximate) optimal values here are used in those tests.
using StableRNGs
using Distributions
using JLD2
using StatsBase
using LinearAlgebra
using Random
using Dates
PLOT_FLAG = true
println("plot flag:", PLOT_FLAG)
if PLOT_FLAG
using Plots
end
using EnsembleKalmanProcesses
const EKP = EnsembleKalmanProcesses
using EnsembleKalmanProcesses.Localizers
using RandomFeatures.ParameterDistributions
using RandomFeatures.DataContainers
using RandomFeatures.Samplers
using RandomFeatures.Features
using RandomFeatures.Methods
using RandomFeatures.Utilities
seed = 2024
ekp_seed = 99999
rng = StableRNG(seed)
## Functions of use
function RFM_from_hyperparameters(
rng::AbstractRNG,
l::Union{Real, AbstractVecOrMat},
regularizer::Real,
n_features::Int,
batch_sizes::Dict,
input_dim::Int,
)
μ_c = 0.0
if isa(l, Real)
σ_c = fill(l, input_dim)
elseif isa(l, AbstractVector)
if length(l) == 1
σ_c = fill(l[1], input_dim)
else
σ_c = l
end
else
isa(l, AbstractMatrix)
σ_c = l[:, 1]
end
pd = ParameterDistribution(
Dict(
"distribution" => VectorOfParameterized(map(sd -> Normal(μ_c, sd), σ_c)),
"constraint" => repeat([no_constraint()], input_dim),
"name" => "xi",
),
)
feature_sampler = FeatureSampler(pd, rng = rng)
# Learn hyperparameters for different feature types
sf = ScalarFourierFeature(n_features, feature_sampler)
return RandomFeatureMethod(sf, batch_sizes = batch_sizes, regularization = regularizer)
end
function calculate_mean_cov_and_coeffs(
rng::AbstractRNG,
l::Union{Real, AbstractVecOrMat},
noise_sd::Real,
n_features::Int,
batch_sizes::Dict,
io_pairs::PairedDataContainer,
)
regularizer = noise_sd^2
n_train = Int(floor(0.8 * size(get_inputs(io_pairs), 2))) # 80:20 train test
n_test = size(get_inputs(io_pairs), 2) - n_train
# split data into train/test randomly
itrain = get_inputs(io_pairs)[:, 1:n_train]
otrain = get_outputs(io_pairs)[:, 1:n_train]
io_train_cost = PairedDataContainer(itrain, otrain)
itest = get_inputs(io_pairs)[:, (n_train + 1):end]
otest = get_outputs(io_pairs)[:, (n_train + 1):end]
input_dim = size(itrain, 1)
# build and fit the RF
rfm = RFM_from_hyperparameters(rng, l, regularizer, n_features, batch_sizes, input_dim)
fitted_features = fit(rfm, io_train_cost)
test_batch_size = get_batch_size(rfm, "test")
batch_inputs = batch_generator(itest, test_batch_size, dims = 2) # input_dim x batch_size
#we want to calc 1/var(y-mean)^2 + lambda/m * coeffs^2 in the end
pred_mean, pred_cov = predict(rfm, fitted_features, DataContainer(itest))
scaled_coeffs = sqrt(1 / n_features) * get_coeffs(fitted_features)
return pred_mean, pred_cov, scaled_coeffs
end
function estimate_mean_cov_and_coeffnorm_covariance(
rng::AbstractRNG,
l::Union{Real, AbstractVecOrMat},
noise_sd::Real,
n_features::Int,
batch_sizes::Dict,
io_pairs::PairedDataContainer,
n_samples::Int;
repeats::Int = 1,
)
n_train = Int(floor(0.8 * size(get_inputs(io_pairs), 2))) # 80:20 train test
n_test = size(get_inputs(io_pairs), 2) - n_train
means = zeros(n_test, n_samples)
covs = zeros(n_test, n_samples)
coeffl2norm = zeros(1, n_samples)
for i in 1:n_samples
for j in 1:repeats
m, v, c = calculate_mean_cov_and_coeffs(rng, l, noise_sd, n_features, batch_sizes, io_pairs)
means[:, i] += m[1, :] / repeats
covs[:, i] += v[1, 1, :] / repeats
coeffl2norm[1, i] += sqrt(sum(c .^ 2)) / repeats
end
end
# println("take covariances")
Γ = cov(vcat(means, covs, coeffl2norm), dims = 2)
approx_σ2 = Diagonal(mean(covs, dims = 2)[:, 1]) # approx of \sigma^2I +rf var
return Γ, approx_σ2
end
function calculate_ensemble_mean_cov_and_coeffnorm(
rng::AbstractRNG,
lvecormat::AbstractVecOrMat,
noise_sd::Real,
n_features::Int,
batch_sizes::Dict,
io_pairs::PairedDataContainer;
repeats::Int = 1,
)
if isa(lvecormat, AbstractVector)
lmat = reshape(lvecormat, 1, :)
else
lmat = lvecormat
end
N_ens = size(lmat, 2)
n_train = Int(floor(0.8 * size(get_inputs(io_pairs), 2))) # 80:20 train test
n_test = size(get_inputs(io_pairs), 2) - n_train
means = zeros(n_test, N_ens)
covs = zeros(n_test, N_ens)
coeffl2norm = zeros(1, N_ens)
for i in collect(1:N_ens)
for j in collect(1:repeats)
l = lmat[:, i]
m, v, c = calculate_mean_cov_and_coeffs(rng, l, noise_sd, n_features, batch_sizes, io_pairs)
means[:, i] += m[1, :] / repeats
covs[:, i] += v[1, 1, :] / repeats
coeffl2norm[1, i] += sqrt(sum(c .^ 2)) / repeats
end
end
return vcat(means, covs, coeffl2norm), Diagonal(mean(covs, dims = 2)[:, 1]) # approx of \sigma^2I
end
## Begin Script, define problem setting
println("Begin script")
date_of_run = Date(2024, 4, 10)
input_dim_list = [8]
for input_dim in input_dim_list
println("Number of input dimensions: ", input_dim)
# radial
# ftest_nd_to_1d(x::AbstractMatrix) = mapslices(column -> 1/(1+exp(-0.25*norm(column)^2)), x, dims=1)
# not radial - different scale in each dimension
ftest_nd_to_1d(x::AbstractMatrix) =
mapslices(column -> exp(-0.1 * norm([i * c for (i, c) in enumerate(column)])^2), x, dims = 1)
n_data = 20 * input_dim
x = rand(rng, MvNormal(zeros(input_dim), 0.5 * I), n_data)
noise_sd = 1e-3
noise = rand(rng, Normal(0, noise_sd), (1, n_data))
y = ftest_nd_to_1d(x) + noise
io_pairs = PairedDataContainer(x, y)
## Define Hyperpriors for EKP
μ_l = 5.0
σ_l = 10.0
# prior for non radial problem
prior_lengthscale = constrained_gaussian("lengthscale", μ_l, σ_l, 0.0, Inf, repeats = input_dim)
priors = prior_lengthscale
# estimate the noise from running many RFM sample costs at the mean values
batch_sizes = Dict("train" => 600, "test" => 600, "feature" => 600)
n_train = Int(floor(0.8 * n_data))
n_test = n_data - n_train
n_features = Int(floor(1.2 * n_data))
# RF will perform poorly when n_features is close to n_train
@assert(!(n_features == n_train)) #
repeats = 1
CALC_TRUTH = true
println("RHKS norm type: norm of coefficients")
if CALC_TRUTH
sample_multiplier = 1
n_samples = 2 * Int(floor(((1 + n_test) + 1) * sample_multiplier))
println("Estimating output covariance with ", n_samples, " samples")
internal_Γ, approx_σ2 = estimate_mean_cov_and_coeffnorm_covariance(
rng,
μ_l, # take mean values
noise_sd,
n_features,
batch_sizes,
io_pairs,
n_samples,
repeats = repeats,
)
save("calculated_truth_cov.jld2", "internal_Γ", internal_Γ)
else
println("Loading truth covariance from file...")
internal_Γ = load("calculated_truth_cov.jld2")["internal_Γ"]
end
Γ = internal_Γ
Γ[1:n_test, 1:n_test] += approx_σ2
Γ[(n_test + 1):(2 * n_test), (n_test + 1):(2 * n_test)] += I
Γ[(2 * n_test + 1):end, (2 * n_test + 1):end] += I
println(
"Estimated variance. Tr(cov) = ",
tr(Γ[1:n_test, 1:n_test]),
" + ",
tr(Γ[(n_test + 1):(2 * n_test), (n_test + 1):(2 * n_test)]),
" + ",
tr(Γ[(2 * n_test + 1):end, (2 * n_test + 1):end]),
)
#println("noise in observations: ", Γ)
# Create EKI
N_ens = 10 * input_dim
N_iter = 30
update_cov_step = Inf
initial_params = construct_initial_ensemble(priors, N_ens; rng_seed = ekp_seed)
params_init = transform_unconstrained_to_constrained(priors, initial_params)[1, :]
println("Prior gives parameters between: [$(minimum(params_init)),$(maximum(params_init))]")
data = vcat(y[(n_train + 1):end], noise_sd^2 * ones(n_test), 0.0)
ekiobj = [EKP.EnsembleKalmanProcess(initial_params, data, Γ, Inversion())]
err = zeros(N_iter)
println("Begin EKI iterations:")
Δt = [1.0]
for i in 1:N_iter
#get parameters:
lvec = transform_unconstrained_to_constrained(priors, get_u_final(ekiobj[1]))
g_ens, _ = calculate_ensemble_mean_cov_and_coeffnorm(
rng,
lvec,
noise_sd,
n_features,
batch_sizes,
io_pairs,
repeats = repeats,
)
if i % update_cov_step == 0 # one update to the
constrained_u = transform_unconstrained_to_constrained(priors, get_u_final(ekiobj[1]))
println("Estimating output covariance with ", n_samples, " samples")
internal_Γ_new, approx_σ2_new = estimate_mean_cov_and_coeffnorm_covariance(
rng,
mean(constrained_u, dims = 2)[:, 1], # take mean values
noise_sd,
n_features,
batch_sizes,
io_pairs,
n_samples,
repeats = repeats,
)
Γ_new = internal_Γ_new
Γ_new[1:n_test, 1:n_test] += approx_σ2_new
Γ_new[(n_test + 1):(2 * n_test), (n_test + 1):(2 * n_test)] += I
Γ_new[(2 * n_test + 1):end, (2 * n_test + 1):end] += I
println(
"Estimated variance. Tr(cov) = ",
tr(Γ_new[1:n_test, 1:n_test]),
" + ",
tr(Γ_new[(n_test + 1):(2 * n_test), (n_test + 1):(2 * n_test)]),
" + ",
tr(Γ_new[(2 * n_test + 1):end, (2 * n_test + 1):end]),
)
ekiobj[1] = EKP.EnsembleKalmanProcess(get_u_final(ekiobj[1]), data, Γ_new, Inversion())
end
EKP.update_ensemble!(ekiobj[1], g_ens, Δt_new = Δt[1])
err[i] = get_error(ekiobj[1])[end] #mean((params_true - mean(params_i,dims=2)).^2)
constrained_u = transform_unconstrained_to_constrained(priors, get_u_final(ekiobj[1]))
println(
"Iteration: " *
string(i) *
", Error: " *
string(err[i]) *
", for parameter means: \n" *
string(mean(constrained_u, dims = 2)),
"\n and sd :\n" * string(sqrt.(var(constrained_u, dims = 2))),
)
Δt[1] *= 1.0
end
#run actual experiment
# override following parameters for actual run
n_data_test = 300 * input_dim
n_features_test = Int(floor(1.2 * n_data_test))
println("number of training data: ", n_data_test)
println("number of features: ", n_features_test)
x_test = rand(rng, MvNormal(zeros(input_dim), 0.5 * I), n_data_test)
noise_test = rand(rng, Normal(0, noise_sd), (1, n_data_test))
y_test = ftest_nd_to_1d(x_test) + noise_test
io_pairs_test = PairedDataContainer(x_test, y_test)
# get feature distribution
final_lvec = mean(transform_unconstrained_to_constrained(priors, get_u_final(ekiobj[1])), dims = 2)
println("**********")
println("Optimal lengthscales: $(final_lvec)")
println("**********")
μ_c = 0.0
if size(final_lvec, 1) == 1
σ_c = repeat([final_lvec[1, 1]], input_dim)
else
σ_c = final_lvec[:, 1]
end
regularizer = noise_sd^2
pd = ParameterDistribution(
Dict(
"distribution" => VectorOfParameterized(map(sd -> Normal(μ_c, sd), σ_c)),
"constraint" => repeat([no_constraint()], input_dim),
"name" => "xi",
),
)
feature_sampler = FeatureSampler(pd, rng = copy(rng))
sff = ScalarFourierFeature(n_features_test, feature_sampler)
#second case with batching
rfm_batch = RandomFeatureMethod(sff, batch_sizes = batch_sizes, regularization = regularizer)
fitted_batched_features = fit(rfm_batch, io_pairs_test)
if PLOT_FLAG
#plot slice through one dimensions, others fixed to 0
xrange = collect(-3:0.01:3)
xslice = zeros(input_dim, length(xrange))
figure_save_directory = joinpath(@__DIR__, "output", string(date_of_run))
if !isdir(figure_save_directory)
mkpath(figure_save_directory)
end
for direction in 1:input_dim
xslicenew = copy(xslice)
xslicenew[direction, :] = xrange
yslice = ftest_nd_to_1d(xslicenew)
pred_mean_slice, pred_cov_slice = predict(rfm_batch, fitted_batched_features, DataContainer(xslicenew))
pred_cov_slice = max.(pred_cov_slice, 0.0)
plt = plot(
xrange,
yslice',
show = false,
color = "black",
linewidth = 5,
size = (600, 600),
legend = :topleft,
label = "Target",
)
plot!(
xrange,
pred_mean_slice',
ribbon = [2 * sqrt.(pred_cov_slice[1, 1, :]); 2 * sqrt.(pred_cov_slice[1, 1, :])]',
label = "Fourier",
color = "blue",
)
savefig(
plt,
joinpath(
figure_save_directory,
"Fit_and_predict_ND_" * string(direction) * "of" * string(input_dim) * ".pdf",
),
)
savefig(
plt,
joinpath(
figure_save_directory,
"Fit_and_predict_ND_" * string(direction) * "of" * string(input_dim) * ".png",
),
)
end
end
end
| RandomFeatures | https://github.com/CliMA/RandomFeatures.jl.git |
|
[
"Apache-2.0"
] | 0.3.3 | d639d537c14127a70c8bd3e0158ecbccfb2a54a3 | code | 12562 | # Example to learn hyperparameters of simple 1d-1d regression example.
# This example matches test/Methods/runtests.jl testset: "Fit and predict: 1D -> 1D"
# The (approximate) optimal values here are used in those tests.
using StableRNGs
using Distributions
using JLD2
using StatsBase
using LinearAlgebra
using Random
using Dates
PLOT_FLAG = true
println("plot flag:", PLOT_FLAG)
if PLOT_FLAG
using Plots
end
using EnsembleKalmanProcesses
const EKP = EnsembleKalmanProcesses
using EnsembleKalmanProcesses.Localizers
using RandomFeatures.ParameterDistributions
using RandomFeatures.DataContainers
using RandomFeatures.Samplers
using RandomFeatures.Features
using RandomFeatures.Methods
using RandomFeatures.Utilities
seed = 2024
ekp_seed = 99999
rng = StableRNG(seed)
## Functions of use
function RFM_from_hyperparameters(
rng::AbstractRNG,
l::Union{Real, AbstractVecOrMat},
regularizer::Real,
n_features::Int,
batch_sizes::Dict,
input_dim::Int,
)
μ_c = 0.0
if isa(l, Real)
σ_c = fill(l, input_dim)
elseif isa(l, AbstractVector)
if length(l) == 1
σ_c = fill(l[1], input_dim)
else
σ_c = l
end
else
isa(l, AbstractMatrix)
σ_c = l[:, 1]
end
pd = ParameterDistribution(
Dict(
"distribution" => VectorOfParameterized(map(sd -> Normal(μ_c, sd), σ_c)),
"constraint" => repeat([no_constraint()], input_dim),
"name" => "xi",
),
)
feature_sampler = FeatureSampler(pd, rng = rng)
# Learn hyperparameters for different feature types
sf = ScalarFourierFeature(n_features, feature_sampler)
return RandomFeatureMethod(sf, batch_sizes = batch_sizes, regularization = regularizer)
end
function calculate_mean_cov_and_coeffs(
rng::AbstractRNG,
l::Union{Real, AbstractVecOrMat},
noise_sd::Real,
n_features::Int,
batch_sizes::Dict,
io_pairs::PairedDataContainer,
)
regularizer = noise_sd^2
n_train = Int(floor(0.8 * size(get_inputs(io_pairs), 2))) # 80:20 train test
n_test = size(get_inputs(io_pairs), 2) - n_train
# split data into train/test randomly
itrain = get_inputs(io_pairs)[:, 1:n_train]
otrain = get_outputs(io_pairs)[:, 1:n_train]
io_train_cost = PairedDataContainer(itrain, otrain)
itest = get_inputs(io_pairs)[:, (n_train + 1):end]
otest = get_outputs(io_pairs)[:, (n_train + 1):end]
input_dim = size(itrain, 1)
# build and fit the RF
rfm = RFM_from_hyperparameters(rng, l, regularizer, n_features, batch_sizes, input_dim)
fitted_features = fit(rfm, io_train_cost)
test_batch_size = get_batch_size(rfm, "test")
batch_inputs = batch_generator(itest, test_batch_size, dims = 2) # input_dim x batch_size
#we want to calc 1/var(y-mean)^2 + lambda/m * coeffs^2 in the end
pred_mean, pred_cov = predict(rfm, fitted_features, DataContainer(itest))
scaled_coeffs = sqrt(1 / n_features) * get_coeffs(fitted_features)
return pred_mean, pred_cov, scaled_coeffs
end
function estimate_mean_cov_and_coeffnorm_covariance(
rng::AbstractRNG,
l::Union{Real, AbstractVecOrMat},
noise_sd::Real,
n_features::Int,
batch_sizes::Dict,
io_pairs::PairedDataContainer,
n_samples::Int;
repeats::Int = 1,
)
n_train = Int(floor(0.8 * size(get_inputs(io_pairs), 2))) # 80:20 train test
n_test = size(get_inputs(io_pairs), 2) - n_train
means = zeros(n_test, n_samples)
covs = zeros(n_test, n_samples)
coeffl2norm = zeros(1, n_samples)
for i in 1:n_samples
for j in 1:repeats
m, v, c = calculate_mean_cov_and_coeffs(rng, l, noise_sd, n_features, batch_sizes, io_pairs)
means[:, i] += m[1, :] / repeats
covs[:, i] += v[1, 1, :] / repeats
coeffl2norm[1, i] += sqrt(sum(c .^ 2)) / repeats
end
end
# println("take covariances")
Γ = cov(vcat(means, coeffl2norm), dims = 2)
approx_σ = Diagonal(mean(covs, dims = 2)[:, 1]) # approx of \sigma^2I +rf var
return Γ, approx_σ
end
function calculate_ensemble_mean_cov_and_coeffnorm(
rng::AbstractRNG,
lvecormat::AbstractVecOrMat,
noise_sd::Real,
n_features::Int,
batch_sizes::Dict,
io_pairs::PairedDataContainer;
repeats::Int = 1,
)
if isa(lvecormat, AbstractVector)
lmat = reshape(lvecormat, 1, :)
else
lmat = lvecormat
end
N_ens = size(lmat, 2)
n_train = Int(floor(0.8 * size(get_inputs(io_pairs), 2))) # 80:20 train test
n_test = size(get_inputs(io_pairs), 2) - n_train
means = zeros(n_test, N_ens)
covs = zeros(n_test, N_ens)
coeffl2norm = zeros(1, N_ens)
for i in collect(1:N_ens)
for j in collect(1:repeats)
l = lmat[:, i]
m, v, c = calculate_mean_cov_and_coeffs(rng, l, noise_sd, n_features, batch_sizes, io_pairs)
means[:, i] += m[1, :] / repeats
covs[:, i] += v[1, 1, :] / repeats
coeffl2norm[1, i] += sqrt(sum(c .^ 2)) / repeats
end
end
return vcat(means, coeffl2norm), Diagonal(mean(covs, dims = 2)[:, 1]) # approx of +\sigma^2I
end
## Begin Script, define problem setting
println("Begin script")
date_of_run = Date(2024, 4, 10)
input_dim = 8
println("Number of input dimensions: ", input_dim)
# not radial - different scale in each dimension
ftest_nd_to_1d(x::AbstractMatrix) =
mapslices(column -> exp(-0.1 * norm([i * c for (i, c) in enumerate(column)])^2), x, dims = 1)
n_data = 100 * input_dim
x = rand(rng, MvNormal(zeros(input_dim), 0.5 * I), n_data)
noise_sd = 1e-3
noise = rand(rng, Normal(0, noise_sd), (1, n_data))
y = ftest_nd_to_1d(x) + noise
io_pairs = PairedDataContainer(x, y)
## Define Hyperpriors for EKP
μ_l = 5.0
σ_l = 5.0
# prior for non radial problem
prior_lengthscale = constrained_gaussian("lengthscale", μ_l, σ_l, 0.0, Inf, repeats = input_dim)
priors = prior_lengthscale
# estimate the noise from running many RFM sample costs at the mean values
batch_sizes = Dict("train" => 600, "test" => 600, "feature" => 600)
n_features = Int(floor(1.2 * n_data))
n_train = Int(floor(0.8 * n_data))
n_test = n_data - n_train
repeats = 1
CALC_TRUTH = true
println("RHKS norm type: norm of coefficients")
if CALC_TRUTH
sample_multiplier = 1
n_samples = Int(floor(((1 + n_test) + 1) * sample_multiplier))
println("Estimating output covariance with ", n_samples, " samples")
internal_Γ, approx_σ = estimate_mean_cov_and_coeffnorm_covariance(
rng,
μ_l, # take mean values
noise_sd,
n_features,
batch_sizes,
io_pairs,
n_samples,
repeats = repeats,
)
save("calculated_truth_cov.jld2", "internal_Γ", internal_Γ, "approx_σ", approx_σ)
else
println("Loading truth covariance from file...")
internal_Γ = load("calculated_truth_cov.jld2")["internal_Γ"]
approx_σ = load("calculated_truth_cov.jld2")["approx_σ"]
end
Γ = internal_Γ
Γ[1:n_test, 1:n_test] += approx_σ
Γ[(n_test + 1):end, (n_test + 1):end] += I
println("Estimated covariance. Tr(cov) = ", tr(Γ[1:n_test, 1:n_test]), " + ", tr(Γ[(n_test + 1):end, (n_test + 1):end]))
#println("noise in observations: ", Γ)
# Create EKI
N_ens = 10 * input_dim
N_iter = 30
update_cov_step = Inf
initial_params = construct_initial_ensemble(priors, N_ens; rng_seed = ekp_seed)
data = vcat(y[(n_train + 1):end], 0.0)
loc_method = SEC(0.2)
ekiobj = [EKP.EnsembleKalmanProcess(initial_params, data, Γ, Inversion(), localization_method = loc_method)]
err = zeros(N_iter)
println("Begin EKI iterations:")
Δt = [1.0]
for i in 1:N_iter
#get parameters:
lvec = transform_unconstrained_to_constrained(priors, get_u_final(ekiobj[1]))
g_ens, approx_σ_ens = calculate_ensemble_mean_cov_and_coeffnorm(
rng,
lvec,
noise_sd,
n_features,
batch_sizes,
io_pairs,
repeats = repeats,
)
if i % update_cov_step == 0 # one update to the
constrained_u = transform_unconstrained_to_constrained(priors, get_u_final(ekiobj[1]))
println("Estimating output covariance with ", n_samples, " samples")
internal_Γ_new, approx_σ_new = estimate_mean_cov_and_coeffnorm_covariance(
rng,
mean(constrained_u, dims = 2)[:, 1], # take mean values
noise_sd,
n_features,
batch_sizes,
io_pairs,
n_samples,
repeats = repeats,
)
Γ_new = internal_Γ_new
Γ_new[1:n_test, 1:n_test] += approx_σ_new
Γ_new[(n_test + 1):end, (n_test + 1):end] += I
println(
"Estimated covariance. Tr(cov) = ",
tr(Γ_new[1:n_test, 1:n_test]),
" + ",
tr(Γ_new[(n_test + 1):end, (n_test + 1):end]),
)
ekiobj[1] = EKP.EnsembleKalmanProcess(
get_u_final(ekiobj[1]),
data,
Γ_new,
Inversion(),
localization_method = loc_method,
)
end
EKP.update_ensemble!(ekiobj[1], g_ens, Δt_new = Δt[1])
err[i] = get_error(ekiobj[1])[end] #mean((params_true - mean(params_i,dims=2)).^2)
constrained_u = transform_unconstrained_to_constrained(priors, get_u_final(ekiobj[1]))
println(
"Iteration: " *
string(i) *
", Error: " *
string(err[i]) *
", for parameter means: " *
string(mean(constrained_u, dims = 2)),
" and sd :" * string(sqrt.(var(constrained_u, dims = 2))),
)
Δt[1] *= 1.0
end
#run actual experiment
# override following parameters for actual run
n_data_test = 200 * input_dim
n_features_test = Int(floor(1.2 * n_data_test))
println("number of training data: ", n_data_test)
println("number of features: ", n_features_test)
#x_test = hcat(rand(rng, Uniform(-0.5,0.5), (input_dim, Int(floor(n_data_test/4)))),rand(rng, Uniform(-3,3), (input_dim, Int(n_data_test - floor(n_data_test/4)))))
#x_test = x_test[:,shuffle(1:size(x_test,2))]
x_test = rand(rng, MvNormal(zeros(input_dim), 0.5 * I), n_data_test)
noise_test = rand(rng, Normal(0, noise_sd), (1, n_data_test))
y_test = ftest_nd_to_1d(x_test) + noise_test
io_pairs_test = PairedDataContainer(x_test, y_test)
# get feature distribution
final_lvec = mean(transform_unconstrained_to_constrained(priors, get_u_final(ekiobj[1])), dims = 2)
μ_c = 0.0
if size(final_lvec, 1) == 1
σ_c = repeat([final_lvec[1, 1]], input_dim)
else
σ_c = final_lvec[:, 1]
end
regularizer = noise_sd^2
pd = ParameterDistribution(
Dict(
"distribution" => VectorOfParameterized(map(sd -> Normal(μ_c, sd), σ_c)),
"constraint" => repeat([no_constraint()], input_dim),
"name" => "xi",
),
)
feature_sampler = FeatureSampler(pd, rng = copy(rng))
sff = ScalarFourierFeature(n_features_test, feature_sampler)
#second case with batching
rfm_batch = RandomFeatureMethod(sff, batch_sizes = batch_sizes, regularization = regularizer)
fitted_batched_features = fit(rfm_batch, io_pairs_test)
if PLOT_FLAG
#plot slice through one dimensions, others fixed to 0
xrange = collect(-3:0.01:3)
xslice = zeros(input_dim, length(xrange))
figure_save_directory = joinpath(@__DIR__, "output", string(date_of_run))
if !isdir(figure_save_directory)
mkpath(figure_save_directory)
end
for direction in 1:input_dim
xslicenew = copy(xslice)
xslicenew[direction, :] = xrange
yslice = ftest_nd_to_1d(xslicenew)
pred_mean_slice, pred_cov_slice = predict(rfm_batch, fitted_batched_features, DataContainer(xslicenew))
pred_cov_slice = max.(pred_cov_slice[1, 1, :], 0.0)
plt = plot(
xrange,
yslice',
show = false,
color = "black",
linewidth = 5,
size = (600, 600),
legend = :topleft,
label = "Target",
)
plot!(
xrange,
pred_mean_slice',
ribbon = [2 * sqrt.(pred_cov_slice); 2 * sqrt.(pred_cov_slice)]',
label = "Fourier",
color = "blue",
)
savefig(
plt,
joinpath(
figure_save_directory,
"Fit_and_predict_ND_" * string(direction) * "of" * string(input_dim) * ".pdf",
),
)
savefig(
plt,
joinpath(
figure_save_directory,
"Fit_and_predict_ND_" * string(direction) * "of" * string(input_dim) * ".png",
),
)
end
end
| RandomFeatures | https://github.com/CliMA/RandomFeatures.jl.git |
|
[
"Apache-2.0"
] | 0.3.3 | d639d537c14127a70c8bd3e0158ecbccfb2a54a3 | code | 20614 | # Example to learn hyperparameters of simple 1d-1d regression example.
# This example matches test/Methods/runtests.jl testset: "Fit and predict: 1D -> 1D"
# The (approximate) optimal values here are used in those tests.
using StableRNGs
using Distributions
using JLD2
using StatsBase
using LinearAlgebra
using Random
using Dates
PLOT_FLAG = true
println("plot flag:", PLOT_FLAG)
if PLOT_FLAG
using Plots
end
using EnsembleKalmanProcesses
const EKP = EnsembleKalmanProcesses
using EnsembleKalmanProcesses.Localizers
using RandomFeatures.ParameterDistributions
using RandomFeatures.DataContainers
using RandomFeatures.Samplers
using RandomFeatures.Features
using RandomFeatures.Methods
using RandomFeatures.Utilities
seed = 2024
ekp_seed = 99999
rng = StableRNG(seed)
function flat_to_chol(x::AbstractArray)
choldim = Int(floor(sqrt(2 * length(x))))
cholmat = zeros(choldim, choldim)
for i in 1:choldim
for j in 1:i
cholmat[i, j] = x[sum(0:(i - 1)) + j]
end
end
return cholmat
end
function posdef_correct(mat::AbstractMatrix, tol = 1e8 * eps())
mat += permutedims(mat, (2, 1))
mat *= 0.5 # symmetrize
return mat + (abs(minimum(eigvals(mat))) + tol) * I #add to diag
end
## Functions of use
function RFM_from_hyperparameters(
rng::RNG,
l::RorM,
lambda::L,
n_features::Int,
batch_sizes::Dict{S, Int},
input_dim::Int,
output_dim::Int,
) where {
RNG <: AbstractRNG,
RorM <: Union{Real, AbstractVecOrMat},
S <: AbstractString,
L <: Union{AbstractMatrix, UniformScaling, Real},
}
# l = [input_dim params + output_dim params]
μ_c = 0.0
if input_dim > 1 && output_dim > 1
cholU = flat_to_chol(l[1:Int(0.5 * input_dim * (input_dim + 1))])
cholV = flat_to_chol(
l[(Int(0.5 * input_dim * (input_dim + 1)) + 1):Int(
0.5 * input_dim * (input_dim + 1) + 0.5 * output_dim * (output_dim + 1),
)],
)
U = l[end - 1] * (cholU * permutedims(cholU, (2, 1)) + l[end - 1] * I)
V = l[end] * (cholV * permutedims(cholV, (2, 1)) + l[end] * I)
elseif input_dim == 1 && output_dim > 1
U = ones(1, 1)
cholV = flat_to_chol(l[2:(Int(0.5 * output_dim * (output_dim + 1)) + 1)])
V = l[1] * (cholV * permutedims(cholV, (2, 1)) + l[1] * I)
elseif input_dim > 1 && output_dim == 1
cholU = flat_to_chol(l[1:Int(0.5 * input_dim * (input_dim + 1))])
U = l[end] * (holU * permutedims(cholU, (2, 1)) + l[end] * I)
V = ones(1, 1)
end
M = zeros(input_dim, output_dim) # n x p mean
if !isposdef(U)
println("U not posdef")
U = posdef_correct(U)
end
if !isposdef(V)
println("V not posdef")
V = posdef_correct(V)
end
representation = "covariance" # "covariance"
if representation == "precision"
Uinv = inv(U)
Vinv = inv(V)
if !isposdef(Uinv)
println("Uinv not posdef")
U = posdef_correct(Uinv)
end
if !isposdef(Vinv)
println("Vinv not posdef")
V = posdef_correct(Vinv)
end
elseif representation == "covariance"
nothing
else
throw(ArgumentError("representation must be \"covariance\" else \"precision\". Got $representation"))
end
pd = ParameterDistribution(
Dict(
"distribution" => Parameterized(MatrixNormal(M, U, V)),
"constraint" => repeat([no_constraint()], input_dim * output_dim),
"name" => "xi",
),
)
feature_sampler = FeatureSampler(pd, output_dim, rng = rng)
vff = VectorFourierFeature(n_features, output_dim, feature_sampler)
return RandomFeatureMethod(vff, batch_sizes = batch_sizes, regularization = lambda)
end
function calculate_mean_cov_and_coeffs(
rng::RNG,
l::RorM,
lambda::L,
n_features::Int,
batch_sizes::Dict{S, Int},
io_pairs::PairedDataContainer,
mean_store::Matrix{FT},
cov_store::Array{FT, 3},
buffer::Array{FT, 3};
decomp_type::S = "chol",
) where {
RNG <: AbstractRNG,
FT <: AbstractFloat,
S <: AbstractString,
RorM <: Union{Real, AbstractVecOrMat},
L <: Union{AbstractMatrix, UniformScaling, Real},
}
n_train = Int(floor(0.8 * size(get_inputs(io_pairs), 2))) # 80:20 train test
n_test = size(get_inputs(io_pairs), 2) - n_train
# split data into train/test randomly
itrain = get_inputs(io_pairs)[:, 1:n_train]
otrain = get_outputs(io_pairs)[:, 1:n_train]
io_train_cost = PairedDataContainer(itrain, otrain)
itest = get_inputs(io_pairs)[:, (n_train + 1):end]
otest = get_outputs(io_pairs)[:, (n_train + 1):end]
input_dim = size(itrain, 1)
output_dim = size(otrain, 1)
# build and fit the RF
rfm = RFM_from_hyperparameters(rng, l, lambda, n_features, batch_sizes, input_dim, output_dim)
if decomp_type == "chol"
fitted_features = fit(rfm, io_train_cost, decomposition_type = "cholesky")
else
fitted_features = fit(rfm, io_train_cost, decomposition_type = "svd")
end
test_batch_size = get_batch_size(rfm, "test")
batch_inputs = batch_generator(itest, test_batch_size, dims = 2) # input_dim x batch_size
#we want to calc 1/var(y-mean)^2 + lambda/m * coeffs^2 in the end
# pred_mean, pred_cov = predict(rfm, fitted_features, DataContainer(itest))
predict!(rfm, fitted_features, DataContainer(itest), mean_store, cov_store, buffer)
# sizes (output_dim x n_test), (output_dim x output_dim x n_test)
scaled_coeffs = 1 / sqrt(n_features) * norm(get_coeffs(fitted_features))
if decomp_type == "chol"
chol_fac = get_decomposition(get_feature_factors(fitted_features)).L
complexity = 2 * sum(log(chol_fac[i, i]) for i in 1:size(chol_fac, 1))
else
svd_singval = get_decomposition(get_feature_factors(fitted_features)).S
complexity = sum(log, svd_singval) # note this is log(abs(det))
end
complexity = sqrt(complexity) # complexity must be positive
println("sample_complexity", complexity)
return scaled_coeffs, complexity
end
function estimate_mean_and_coeffnorm_covariance(
rng::RNG,
l::RorM,
lambda::L,
n_features::Int,
batch_sizes::Dict{S, Int},
io_pairs::PairedDataContainer,
n_samples::Int,
y;
repeats::Int = 1,
) where {
RNG <: AbstractRNG,
S <: AbstractString,
RorM <: Union{Real, AbstractVecOrMat},
L <: Union{AbstractMatrix, UniformScaling, Real},
}
n_train = Int(floor(0.8 * size(get_inputs(io_pairs), 2))) # 80:20 train test
n_test = size(get_inputs(io_pairs), 2) - n_train
output_dim = size(get_outputs(io_pairs), 1)
means = zeros(output_dim, n_samples, n_test)
mean_of_covs = zeros(output_dim, output_dim, n_test)
moc_tmp = similar(mean_of_covs)
mtmp = zeros(output_dim, n_test)
buffer = zeros(n_test, output_dim, n_features)
complexity = zeros(1, n_samples)
coeffl2norm = zeros(1, n_samples)
for i in 1:n_samples
for j in 1:repeats
c, cplxty =
calculate_mean_cov_and_coeffs(rng, l, lambda, n_features, batch_sizes, io_pairs, mtmp, moc_tmp, buffer)
# m output_dim x n_test
# v output_dim x output_dim x n_test
# c n_features
# cplxty 1
# update vbles needed for cov
means[:, i, :] .+= (y - mtmp) ./ repeats
coeffl2norm[1, i] += sqrt(sum(abs2, c)) / repeats
complexity[1, i] += cplxty / repeats
# update vbles needed for mean
@. mean_of_covs += moc_tmp / (repeats * n_samples)
end
end
means = permutedims(means, (3, 2, 1))
mean_of_covs = permutedims(mean_of_covs, (3, 1, 2))
approx_σ2 = zeros(n_test * output_dim, n_test * output_dim)
blockmeans = zeros(n_test * output_dim, n_samples)
for i in 1:n_test
id = ((i - 1) * output_dim + 1):(i * output_dim)
approx_σ2[id, id] = mean_of_covs[i, :, :] # this ordering, so we can take a mean/cov in dims = 2.
blockmeans[id, :] = permutedims(means[i, :, :], (2, 1))
end
sample_mat = vcat(blockmeans, coeffl2norm, complexity)
Γ = cov(sample_mat, dims = 2)
if !isposdef(approx_σ2)
println("approx_σ2 not posdef")
approx_σ2 = posdef_correct(approx_σ2)
end
return Γ, approx_σ2
return Γ, approx_σ2
end
function calculate_ensemble_mean_and_coeffnorm(
rng::RNG,
lvecormat::VorM,
lambda::L,
n_features::Int,
batch_sizes::Dict{S, Int},
io_pairs::PairedDataContainer,
y;
repeats::Int = 1,
) where {
RNG <: AbstractRNG,
S <: AbstractString,
VorM <: AbstractVecOrMat,
L <: Union{AbstractMatrix, UniformScaling, Real},
}
if isa(lvecormat, AbstractVector)
lmat = reshape(lvecormat, 1, :)
else
lmat = lvecormat
end
N_ens = size(lmat, 2)
n_train = Int(floor(0.8 * size(get_inputs(io_pairs), 2))) # 80:20 train test
n_test = size(get_inputs(io_pairs), 2) - n_train
output_dim = size(get_outputs(io_pairs), 1)
means = zeros(output_dim, N_ens, n_test)
mean_of_covs = zeros(output_dim, output_dim, n_test)
buffer = zeros(n_test, output_dim, n_features)
complexity = zeros(1, N_ens)
coeffl2norm = zeros(1, N_ens)
moc_tmp = similar(mean_of_covs)
mtmp = zeros(output_dim, n_test)
for i in collect(1:N_ens)
for j in collect(1:repeats)
l = lmat[:, i]
c, cplxty =
calculate_mean_cov_and_coeffs(rng, l, lambda, n_features, batch_sizes, io_pairs, mtmp, moc_tmp, buffer)
# m output_dim x n_test
# v output_dim x output_dim x n_test
# c n_features
means[:, i, :] += (y - mtmp) ./ repeats
@. mean_of_covs += moc_tmp / (repeats * N_ens)
coeffl2norm[1, i] += sqrt(sum(c .^ 2)) / repeats
complexity[1, i] += cplxty / repeats
end
end
means = permutedims(means, (3, 2, 1))
mean_of_covs = permutedims(mean_of_covs, (3, 1, 2))
blockcovmat = zeros(n_test * output_dim, n_test * output_dim)
blockmeans = zeros(n_test * output_dim, N_ens)
for i in 1:n_test
id = ((i - 1) * output_dim + 1):(i * output_dim)
blockcovmat[id, id] = mean_of_covs[i, :, :]
blockmeans[id, :] = permutedims(means[i, :, :], (2, 1))
end
if !isposdef(blockcovmat)
println("blockcovmat not posdef")
blockcovmat = posdef_correct(blockcovmat)
end
return vcat(blockmeans, coeffl2norm, complexity), blockcovmat
end
@time begin
## Begin Script, define problem setting
println("Begin script")
date_of_run = Date(2024, 4, 10)
input_dim = 1
output_dim = 3
println("Number of input dimensions: ", input_dim)
println("Number of output dimensions: ", output_dim)
function ftest_1d_to_3d(x::M) where {M <: AbstractMatrix}
out = zeros(3, size(x, 2))
out[1, :] = mapslices(column -> sin(norm([i * c for (i, c) in enumerate(column)])^2), x, dims = 1)
out[2, :] = mapslices(column -> exp(-0.1 * norm([i * c for (i, c) in enumerate(column)])^2), x, dims = 1)
out[3, :] = mapslices(
column ->
norm([i * c for (i, c) in enumerate(column)]) * sin(1 / norm([i * c for (i, c) in enumerate(column)])^2) -
1,
x,
dims = 1,
)
return out
end
#problem formulation
n_data = 100
x = rand(rng, MvNormal(zeros(input_dim), I), n_data)
# diagonal noise
# cov_mat = Diagonal((5e-2)^2 * ones(output_dim))
#cov_mat = (5e-2)^2*I(output_dim)
# correlated noise
cov_mat = convert(Matrix, Tridiagonal((5e-3) * ones(2), (2e-2) * ones(3), (5e-3) * ones(2)))
noise_dist = MvNormal(zeros(output_dim), cov_mat)
noise = rand(rng, noise_dist, n_data)
# simple regularization
#lambda = exp((1 / output_dim) * sum(log.(eigvals(cov_mat)))) * I(output_dim)
# more complex
lambda = cov_mat
y = ftest_1d_to_3d(x) + noise
io_pairs = PairedDataContainer(x, y)
## Define Hyperpriors for EKP
μ_l = 5.0
σ_l = 5.0
# prior for non radial problem
n_l = Int(0.5 * input_dim * (input_dim + 1)) + Int(0.5 * output_dim * (output_dim + 1))
n_l += (input_dim > 1 && output_dim > 1) ? 2 : 0
prior_lengthscale = constrained_gaussian("lengthscale", μ_l, σ_l, 0.0, Inf, repeats = n_l)
priors = prior_lengthscale
println("number of hyperparameters to train: ", n_l)
# estimate the noise from running many RFM sample costs at the mean values
batch_sizes = Dict("train" => 500, "test" => 500, "feature" => 500)
n_train = Int(floor(0.8 * n_data))
n_test = n_data - n_train
n_features_opt = Int(floor(2 * n_train))
#n_features = Int(floor(2 * n_data))
# RF will perform poorly when n_features is close to n_train
@assert(!(n_features_opt == n_train)) #
repeats = 1
CALC_TRUTH = true
println("RHKS norm type: norm of coefficients")
if CALC_TRUTH
sample_multiplier = 1
n_samples = (n_test * output_dim + 2) * sample_multiplier
println("Estimating output covariance with ", n_samples, " samples")
internal_Γ, approx_σ2 = estimate_mean_and_coeffnorm_covariance(
rng,
repeat([μ_l], n_l), # take mean values
lambda,
n_features_opt,
batch_sizes,
io_pairs,
n_samples,
y[:, (n_train + 1):end],
repeats = repeats,
)
save("calculated_truth_cov.jld2", "internal_Γ", internal_Γ, "approx_σ2", approx_σ2)
else
println("Loading truth covariance from file...")
internal_Γ = load("calculated_truth_cov.jld2")["internal_Γ"]
approx_σ2 = load("calculated_truth_cov.jld2")["approx_σ2"]
end
Γ = internal_Γ
for i in 1:(n_test - 1)
Γ[((i - 1) * output_dim + 1):(i * output_dim), ((i - 1) * output_dim + 1):(i * output_dim)] += lambda[:, :]
end
Γ[(n_test * output_dim + 1):end, (n_test * output_dim + 1):end] += I
println(
"Estimated variance. Tr(cov) = ",
tr(Γ[1:(n_test * output_dim), 1:(n_test * output_dim)]),
" + ",
Γ[end - 1, end - 1],
" + ",
Γ[end, end],
)
println("is EKP noise positive definite? ", isposdef(Γ))
#println("noise in observations: ", Γ)
# Create EKI
N_ens = 10 * input_dim
N_iter = 10
update_cov_step = Inf
initial_params = construct_initial_ensemble(priors, N_ens; rng_seed = ekp_seed)
params_init = transform_unconstrained_to_constrained(priors, initial_params)#[1, :]
println("Prior gives parameters between: [$(minimum(params_init)),$(maximum(params_init))]")
data = zeros(size(Γ, 1))
ekiobj = [EKP.EnsembleKalmanProcess(initial_params, data[:], Γ, Inversion())]
err = zeros(N_iter)
println("Begin EKI iterations:")
Δt = [1.0]
for i in 1:N_iter
#get parameters:
lvec = transform_unconstrained_to_constrained(priors, get_u_final(ekiobj[1]))
g_ens, _ = calculate_ensemble_mean_and_coeffnorm(
rng,
lvec,
lambda,
n_features_opt,
batch_sizes,
io_pairs,
y[:, (n_train + 1):end],
repeats = repeats,
)
if i % update_cov_step == 0 # to update cov if required
constrained_u = transform_unconstrained_to_constrained(priors, get_u_final(ekiobj[1]))
println("Estimating output covariance with ", n_samples, " samples")
internal_Γ_new, approx_σ2_new = estimate_mean_and_coeffnorm_covariance(
rng,
mean(constrained_u, dims = 2)[:, 1], # take mean values
lambda,
n_features_opt,
batch_sizes,
io_pairs,
n_samples,
y[:, (n_train + 1):end],
repeats = repeats,
)
Γ_new = internal_Γ_new
# Γ_new[1:(n_test * output_dim), 1:(n_test * output_dim)] += approx_σ2_new
# Γ_new[(n_test * output_dim + 1):end, (n_test * output_dim + 1):end] += I
println(
"Estimated variance. Tr(cov) = ",
tr(Γ_new[1:n_test, 1:n_test]),
" + ",
tr(Γ_new[(n_test * output_dim + 1):end, (n_test * output_dim + 1):end]),
)
ekiobj[1] = EKP.EnsembleKalmanProcess(get_u_final(ekiobj[1]), data[:], Γ_new, Inversion())
end
EKP.update_ensemble!(ekiobj[1], g_ens, Δt_new = Δt[1])
err[i] = get_error(ekiobj[1])[end] #mean((params_true - mean(params_i,dims=2)).^2)
constrained_u = transform_unconstrained_to_constrained(priors, get_u_final(ekiobj[1]))
println(
"Iteration: " *
string(i) *
", Error: " *
string(err[i]) *
", for parameter means: \n" *
string(mean(constrained_u, dims = 2)),
"\n and sd :\n" * string(sqrt.(var(constrained_u, dims = 2))),
)
Δt[1] *= 1.0
end
#run actual experiment
# override following parameters for actual run
n_data_test = 100 * input_dim
n_features_test = Int(floor(2 * n_data_test))
println("number of training data: ", n_data_test)
println("number of features: ", n_features_test)
x_test = rand(rng, MvNormal(zeros(input_dim), 0.5 * I), n_data_test)
noise_test = rand(rng, noise_dist, n_data_test)
y_test = ftest_1d_to_3d(x_test) + noise_test
io_pairs_test = PairedDataContainer(x_test, y_test)
# get feature distribution
final_lvec = get_ϕ_mean_final(priors, ekiobj[1])
println("**********")
println("Optimal lengthscales: $(final_lvec)")
println("**********")
rfm = RFM_from_hyperparameters(rng, final_lvec, lambda, n_features_test, batch_sizes, input_dim, output_dim)
fitted_features = fit(rfm, io_pairs_test, decomposition_type = "cholesky")
if PLOT_FLAG
# learning on Normal(0,1) dist, forecast on (-2,2)
xrange = reshape(collect(-2.01:0.02:2.01), 1, :)
yrange = ftest_1d_to_3d(xrange)
pred_mean_slice, pred_cov_slice = predict(rfm, fitted_features, DataContainer(xrange))
for i in 1:output_dim
pred_cov_slice[i, i, :] = max.(pred_cov_slice[i, i, :], 0.0)
end
figure_save_directory = joinpath(@__DIR__, "output", string(date_of_run))
if !isdir(figure_save_directory)
mkpath(figure_save_directory)
end
#plot diagonal
xplot = xrange[:]
plt = plot(
xplot,
yrange[1, :],
show = false,
color = "black",
linewidth = 5,
size = (600, 600),
legend = :topleft,
label = "Target",
)
plot!(
xplot,
yrange[2, :],
show = false,
color = "black",
linewidth = 5,
size = (600, 600),
legend = :topleft,
label = "Target",
)
plot!(
xplot,
yrange[3, :],
show = false,
color = "black",
linewidth = 5,
size = (600, 600),
legend = :topleft,
label = "Target",
)
scatter!(x_test[:], y_test[1, :], color = "blue", label = "", marker = :x)
plot!(
xplot,
pred_mean_slice[1, :],
ribbon = [2 * sqrt.(pred_cov_slice[1, 1, :]); 2 * sqrt.(pred_cov_slice[1, 1, :])],
label = "Fourier",
color = "blue",
)
scatter!(x_test[:], y_test[2, :], color = "red", label = "", marker = :x)
plot!(
xplot,
pred_mean_slice[2, :],
ribbon = [2 * sqrt.(pred_cov_slice[2, 2, :]); 2 * sqrt.(pred_cov_slice[2, 2, :])],
label = "Fourier",
color = "red",
)
scatter!(x_test[:], y_test[3, :], color = "green", label = "", marker = :x)
plot!(
xplot,
pred_mean_slice[3, :],
ribbon = [2 * sqrt.(pred_cov_slice[3, 3, :]); 2 * sqrt.(pred_cov_slice[3, 3, :])],
label = "Fourier",
color = "green",
)
savefig(plt, joinpath(figure_save_directory, "Fit_and_predict_1D_to_MD.pdf"))
savefig(plt, joinpath(figure_save_directory, "Fit_and_predict_1D_to_MD.png"))
end
end
| RandomFeatures | https://github.com/CliMA/RandomFeatures.jl.git |
|
[
"Apache-2.0"
] | 0.3.3 | d639d537c14127a70c8bd3e0158ecbccfb2a54a3 | code | 1391 | module Features
include("ScalarFunctions.jl")
import StatsBase: sample
using EnsembleKalmanProcesses.ParameterDistributions,
DocStringExtensions, RandomFeatures.Samplers, Tullio, LoopVectorization
abstract type RandomFeature end
include("ScalarFeatures.jl")
include("VectorFeatures.jl")
export RandomFeature
export sample,
get_scalar_function, get_feature_sampler, get_feature_sample, get_n_features, get_feature_parameters, get_output_dim
"""
$(TYPEDSIGNATURES)
samples the random feature distribution
"""
function sample(rf::RF) where {RF <: RandomFeature}
sampler = get_feature_sampler(rf)
m = get_n_features(rf)
return sample(sampler, m)
end
# methods
"""
$(TYPEDSIGNATURES)
gets the `n_features` field
"""
get_n_features(rf::RF) where {RF <: RandomFeature} = rf.n_features
"""
$(TYPEDSIGNATURES)
gets the `scalar_function` field
"""
get_scalar_function(rf::RF) where {RF <: RandomFeature} = rf.scalar_function
"""
$(TYPEDSIGNATURES)
gets the `feature_sampler` field
"""
get_feature_sampler(rf::RF) where {RF <: RandomFeature} = rf.feature_sampler
"""
$(TYPEDSIGNATURES)
gets the `feature_sample` field
"""
get_feature_sample(rf::RF) where {RF <: RandomFeature} = rf.feature_sample
"""
$(TYPEDSIGNATURES)
gets the `feature_parameters` field
"""
get_feature_parameters(rf::RF) where {RF <: RandomFeature} = rf.feature_parameters
end #module
| RandomFeatures | https://github.com/CliMA/RandomFeatures.jl.git |
|
[
"Apache-2.0"
] | 0.3.3 | d639d537c14127a70c8bd3e0158ecbccfb2a54a3 | code | 18405 | module Methods
import StatsBase: sample, fit, predict, predict!
using LinearAlgebra,
DocStringExtensions,
RandomFeatures.Features,
RandomFeatures.Utilities,
EnsembleKalmanProcesses.DataContainers,
Tullio,
LoopVectorization
export RandomFeatureMethod,
Fit,
get_random_feature,
get_batch_sizes,
get_batch_size,
get_regularization,
get_tullio_threading,
sample,
get_feature_factors,
get_coeffs,
fit,
predict,
predict!,
predictive_mean,
predictive_cov,
predictive_mean!,
predictive_cov!,
predict_prior,
predict_prior_mean,
predict_prior_cov
"""
$(TYPEDEF)
Holds configuration for the random feature fit
$(TYPEDFIELDS)
"""
struct RandomFeatureMethod{S <: AbstractString, USorM <: Union{UniformScaling, AbstractMatrix}}
"The random feature object"
random_feature::RandomFeature
"A dictionary specifying the batch sizes. Must contain \"train\", \"test\", and \"feature\" keys"
batch_sizes::Dict{S, Int}
"A positive definite matrix used during the fit method to regularize the linear solve, interpreted as the inverse of the observational noise covariance"
regularization::USorM
"Use multithreading provided by Tullio"
tullio_threading::Bool
end
"""
$(TYPEDSIGNATURES)
Basic constructor for a `RandomFeatureMethod`.
"""
function RandomFeatureMethod(
random_feature::RandomFeature;
regularization::USorMorR = 1e12 * eps() * I,
batch_sizes::Dict{S, Int} = Dict("train" => 0, "test" => 0, "feature" => 0),
tullio_threading = true,
regularization_inverted::Bool = false,
) where {S <: AbstractString, USorMorR <: Union{<:Real, AbstractMatrix{<:Real}, UniformScaling}}
if !all([key ∈ keys(batch_sizes) for key in ["train", "test", "feature"]])
throw(ArgumentError("batch_sizes keys must contain all of \"train\", \"test\", and \"feature\""))
end
# ToDo store cholesky factors
if isa(regularization, Real)
if regularization <= 0
@info "input regularization <=0 is invalid, using regularization = 1e12*eps()"
λ = 1e12 * eps() * I
else
λ = regularization * I
end
else
if !isposdef(regularization) #check positive definiteness
tol = 1e12 * eps() #MAGIC NUMBER
λ = posdef_correct(regularization, tol = tol)
@warn "input regularization matrix is not positive definite, replacing with nearby positive definite matrix"
else
λ = regularization
end
end
# we work with inverted regularization matrix
if regularization_inverted == false
if cond(regularization) > 10^8
@warn "The provided regularization is poorly conditioned: κ(reg) = cond(regularization). Imprecision or SingularException during inversion may occur."
end
λinv = inv(λ)
else
λinv = λ
end
return RandomFeatureMethod{S, typeof(λ)}(random_feature, batch_sizes, λinv, tullio_threading)
end
"""
$(TYPEDSIGNATURES)
gets the `random_feature` field
"""
get_random_feature(rfm::RandomFeatureMethod) = rfm.random_feature
"""
$(TYPEDSIGNATURES)
gets the `batch_sizes` field
"""
get_batch_sizes(rfm::RandomFeatureMethod) = rfm.batch_sizes
"""
$(TYPEDSIGNATURES)
gets the `regularization` field, this is the inverse of the provided matrix if keyword `regularization_inverted = false`
"""
get_regularization(rfm::RandomFeatureMethod) = rfm.regularization
"""
$(TYPEDSIGNATURES)
gets the `tullio_threading` field
"""
get_tullio_threading(rfm::RandomFeatureMethod) = rfm.tullio_threading
"""
$(TYPEDSIGNATURES)
samples the random_feature field
"""
sample(rfm::RandomFeatureMethod) = sample(get_random_feature(rfm))
"""
$(TYPEDSIGNATURES)
get the specified batch size from `batch_sizes` field
"""
get_batch_size(rfm::RandomFeatureMethod, key::S) where {S <: AbstractString} = get_batch_sizes(rfm)[key]
"""
$(TYPEDEF)
Holds the coefficients and matrix decomposition that describe a set of fitted random features.
$(TYPEDFIELDS)
"""
struct Fit{V <: AbstractVector, USorM <: Union{UniformScaling, AbstractMatrix}}
"The `LinearAlgreba` matrix decomposition of `(1 / m) * Feature^T * regularization^-1 * Feature + I`"
feature_factors::Decomposition
"Coefficients of the fit to data"
coeffs::V
"output-dim regularization used during fit"
regularization::USorM
end
"""
$(TYPEDSIGNATURES)
gets the `feature_factors` field
"""
get_feature_factors(f::Fit) = f.feature_factors
"""
$(TYPEDSIGNATURES)
gets the `coeffs` field
"""
get_coeffs(f::Fit) = f.coeffs
"""
$(TYPEDSIGNATURES)
gets the `regularization` field (note this is the outputdim regularization)
"""
get_regularization(f::Fit) = f.regularization
"""
$(TYPEDSIGNATURES)
Fits a `RandomFeatureMethod` to input-output data, optionally provide a preferred `LinearAlgebra` matrix decomposition.
Returns a `Fit` object.
"""
function fit(
rfm::RandomFeatureMethod,
input_output_pairs::PairedDataContainer;
decomposition_type::S = "cholesky",
) where {S <: AbstractString}
(input, output) = get_data(input_output_pairs)
input_dim, n_data = size(input)
output_dim = size(output, 1) # for scalar features this is 1
train_batch_size = get_batch_size(rfm, "train")
rf = get_random_feature(rfm)
tullio_threading = get_tullio_threading(rfm)
n_features = get_n_features(rf)
#data are columns, batch over samples
λinv = get_regularization(rfm)
Phi = build_features(rf, input)
FT = eltype(Phi)
PhiTλinv = zeros(size(Phi))
PhiTλinvY = zeros(n_features)
PhiTλinvPhi = zeros(n_features, n_features)
if !tullio_threading
if isa(λinv, UniformScaling)
PhiTλinv = Phi * λinv.λ
else
@tullio threads = 10^9 PhiTλinv[n, q, i] = Phi[n, p, i] * λinv[p, q]
end
@tullio threads = 10^9 PhiTλinvY[j] = PhiTλinv[n, p, j] * output[p, n]
@tullio threads = 10^9 PhiTλinvPhi[i, j] = PhiTλinv[n, p, i] * Phi[n, p, j] # BOTTLENECK
else
if isa(λinv, UniformScaling)
PhiTλinv = Phi * λinv.λ
else
@tullio PhiTλinv[n, q, i] = Phi[n, p, i] * λinv[p, q]
end
@tullio PhiTλinvY[j] = PhiTλinv[n, p, j] * output[p, n]
@tullio PhiTλinvPhi[i, j] = PhiTλinv[n, p, i] * Phi[n, p, j] # BOTTLENECK
end
# alternative using svd - turns out to be slower and more mem intensive
@. PhiTλinvPhi /= FT(n_features)
# solve the linear system
# (PhiTλinvPhi + I) * beta = PhiTλinvY
# in-place add I (as we don't use PhiTPhi again after this)
for i in 1:size(PhiTλinvPhi, 1)
PhiTλinvPhi[i, i] += 1.0
end
feature_factors = Decomposition(PhiTλinvPhi, decomposition_type)
# bottleneck for small problems only (much quicker than PhiTPhi for big problems)
coeffs = linear_solve(feature_factors, PhiTλinvY, tullio_threading = tullio_threading) #n_features x n_samples x dim_output
return Fit{typeof(coeffs), typeof(λinv)}(feature_factors, coeffs, λinv)
end
"""
$(TYPEDSIGNATURES)
Makes a prediction of mean and (co)variance of fitted features on new input data
"""
function predict(rfm::RandomFeatureMethod, fit::Fit, new_inputs::DataContainer; kwargs...)
pred_mean, features = predictive_mean(rfm, fit, new_inputs; kwargs...)
pred_cov = predictive_cov(rfm, fit, new_inputs, features; kwargs...)
return pred_mean, pred_cov
end
"""
$(TYPEDSIGNATURES)
Makes a prediction of mean and (co)variance of fitted features on new input data, overwriting the provided stores.
- mean_store:`output_dim` x `n_samples`
- cov_store:`output_dim` x `output_dim` x `n_samples`
- buffer:`n_samples` x `output_dim` x `n_features`
"""
function predict!(
rfm::RandomFeatureMethod,
fit::Fit,
new_inputs::DataContainer,
mean_store::M,
cov_store::A,
buffer::A;
kwargs...,
) where {M <: AbstractMatrix{<:AbstractFloat}, A <: AbstractArray{<:AbstractFloat, 3}}
#build features once only
features = predictive_mean!(rfm, fit, new_inputs, mean_store; kwargs...)
predictive_cov!(rfm, fit, new_inputs, cov_store, buffer, features; kwargs...)
nothing
end
"""
$(TYPEDSIGNATURES)
Makes a prediction of mean and (co)variance with unfitted features on new input data
"""
function predict_prior(rfm::RandomFeatureMethod, new_inputs::DataContainer; kwargs...)
prior_mean, features = predict_prior_mean(rfm, new_inputs; kwargs...)
prior_cov = predict_prior_cov(rfm, new_inputs, features; kwargs...)
return prior_mean, prior_cov
end
"""
$(TYPEDSIGNATURES)
Makes a prediction of mean with unfitted features on new input data
"""
function predict_prior_mean(rfm::RandomFeatureMethod, new_inputs::DataContainer; kwargs...)
rf = get_random_feature(rfm)
n_features = get_n_features(rf)
coeffs = ones(n_features)
return predictive_mean(rfm, coeffs, new_inputs; kwargs...)
end
function predict_prior_mean(
rfm::RandomFeatureMethod,
new_inputs::DataContainer,
prebuilt_features::A;
kwargs...,
) where {A <: AbstractArray{<:AbstractFloat, 3}}
rf = get_random_feature(rfm)
n_features = get_n_features(rf)
coeffs = ones(n_features)
return predictive_mean(rfm, coeffs, new_inputs, prebuilt_features; kwargs...)
end
"""
$(TYPEDSIGNATURES)
Makes a prediction of (co)variance with unfitted features on new input data
"""
function predict_prior_cov(rfm::RandomFeatureMethod, new_inputs::DataContainer; kwargs...)
inputs = get_data(new_inputs)
rf = get_random_feature(rfm)
features = build_features(rf, inputs) # bsize x output_dim x n_features
return predict_prior_cov(rfm, new_inputs, features; kwargs...), features
end
function predict_prior_cov(
rfm::RandomFeatureMethod,
new_inputs::DataContainer,
prebuilt_features::A;
tullio_threading = true,
kwargs...,
) where {A <: AbstractArray{<:AbstractFloat, 3}}
#TODO optimize with woodbury as with other predictive_cov
inputs = get_data(new_inputs)
test_batch_size = get_batch_size(rfm, "test")
rf = get_random_feature(rfm)
output_dim = get_output_dim(rf)
n_features = get_n_features(rf)
FT = eltype(prebuilt_features)
if !tullio_threading
@tullio threads = 10^9 cov_outputs[p, q, n] :=
prebuilt_features[n, p, m] * prebuilt_features[l, q, m] - prebuilt_features[l, q, m] # output_dim, output_dim, size(inputs, 2)
else
@tullio cov_outputs[p, q, n] :=
prebuilt_features[n, p, m] * prebuilt_features[l, q, m] - prebuilt_features[l, q, m] # output_dim, output_dim, size(inputs, 2)
end
@. cov_outputs /= FT(n_features)
return cov_outputs
end
"""
$(TYPEDSIGNATURES)
Makes a prediction of mean of fitted features on new input data.
Returns a `output_dim` x `n_samples` array.
"""
predictive_mean(rfm::RandomFeatureMethod, fit::Fit, new_inputs::DataContainer; kwargs...) =
predictive_mean(rfm, get_coeffs(fit), new_inputs; kwargs...)
predictive_mean(
rfm::RandomFeatureMethod,
fit::Fit,
new_inputs::DataContainer,
prebuilt_features::A;
kwargs...,
) where {A <: AbstractArray{<:AbstractFloat, 3}} =
predictive_mean(rfm, get_coeffs(fit), new_inputs, prebuilt_features; kwargs...)
function predictive_mean(
rfm::RandomFeatureMethod,
coeffs::V,
new_inputs::DataContainer;
kwargs...,
) where {V <: AbstractVector}
inputs = get_data(new_inputs)
rf = get_random_feature(rfm)
features = build_features(rf, inputs)
return predictive_mean(rfm, coeffs, new_inputs, features; kwargs...), features
end
function predictive_mean(
rfm::RandomFeatureMethod,
coeffs::V,
new_inputs::DataContainer,
prebuilt_features::A;
kwargs...,
) where {V <: AbstractVector{<:AbstractFloat}, A <: AbstractArray{<:AbstractFloat, 3}}
inputs = get_data(new_inputs)
rf = get_random_feature(rfm)
n_samples = size(inputs, 2)
output_dim = get_output_dim(rf)
mean_store = zeros(output_dim, n_samples)
predictive_mean!(rfm, coeffs, new_inputs, mean_store, prebuilt_features; kwargs...)
return mean_store
end
"""
$(TYPEDSIGNATURES)
Makes a prediction of mean of fitted features on new input data.
Writes into a provided `output_dim` x `n_samples` array: `mean_store`.
"""
predictive_mean!(
rfm::RandomFeatureMethod,
fit::Fit,
new_inputs::DataContainer,
mean_store::M;
kwargs...,
) where {M <: Matrix{<:AbstractFloat}} = predictive_mean!(rfm, get_coeffs(fit), new_inputs, mean_store; kwargs...)
predictive_mean!(
rfm::RandomFeatureMethod,
fit::Fit,
new_inputs::DataContainer,
mean_store::M,
features::A;
kwargs...,
) where {M <: Matrix{<:AbstractFloat}, A <: AbstractArray{<:AbstractFloat, 3}} =
predictive_mean!(rfm, get_coeffs(fit), new_inputs, mean_store, features; kwargs...)
function predictive_mean!(
rfm::RandomFeatureMethod,
coeffs::V,
new_inputs::DataContainer,
mean_store::M;
kwargs...,
) where {V <: AbstractVector{<:AbstractFloat}, M <: Matrix{<:AbstractFloat}}
inputs = get_data(new_inputs)
rf = get_random_feature(rfm)
features = build_features(rf, inputs)
predictive_mean!(rfm, coeffs, new_inputs, mean_store, features; kwargs...)
return features
end
function predictive_mean!(
rfm::RandomFeatureMethod,
coeffs::V,
new_inputs::DataContainer,
mean_store::M,
prebuilt_features::A;
tullio_threading = true,
kwargs...,
) where {V <: AbstractVector{<:AbstractFloat}, M <: Matrix{<:AbstractFloat}, A <: AbstractArray{<:AbstractFloat, 3}}
inputs = get_data(new_inputs)
rf = get_random_feature(rfm)
tullio_threading = get_tullio_threading(rfm)
output_dim = get_output_dim(rf)
n_features = get_n_features(rf)
if !(size(mean_store) == (output_dim, size(inputs, 2)))
throw(
DimensionMismatch(
"provided storage for output expected to be size ($(output_dim),$(size(inputs,2))) got $(size(mean_store))",
),
)
end
if !tullio_threading
@tullio threads = 10^9 mean_store[p, n] = prebuilt_features[n, p, m] * coeffs[m]
else
@tullio mean_store[p, n] = prebuilt_features[n, p, m] * coeffs[m]
end
FT = eltype(prebuilt_features)
@. mean_store /= FT(n_features)
nothing
end
"""
$(TYPEDSIGNATURES)
Makes a prediction of (co)variance of fitted features on new input data.
Returns a `output_dim` x `output_dim` x `n_samples` array
"""
function predictive_cov(
rfm::RandomFeatureMethod,
fit::Fit,
new_inputs::DataContainer,
prebuilt_features::A;
kwargs...,
) where {A <: AbstractArray{<:AbstractFloat, 3}}
inputs = get_data(new_inputs)
n_samples = size(inputs, 2)
rf = get_random_feature(rfm)
output_dim = get_output_dim(rf)
n_features = get_n_features(rf)
cov_store = zeros(output_dim, output_dim, n_samples)
buffer = zeros(n_samples, output_dim, n_features)
predictive_cov!(rfm, fit, new_inputs, cov_store, buffer, prebuilt_features; kwargs...)
return cov_store
end
function predictive_cov(rfm::RandomFeatureMethod, fit::Fit, new_inputs::DataContainer; kwargs...)
rf = get_random_feature(rfm)
inputs = get_data(new_inputs)
features = build_features(rf, inputs) # build_features gives bsize x output_dim x n_features
return predictive_cov(rfm, fit, new_inputs, features; kwargs...), features
end
"""
$(TYPEDSIGNATURES)
Makes a prediction of (co)variance of fitted features on new input data.
Writes into a provided `output_dim` x `output_dim` x `n_samples` array: `cov_store`, and uses provided `n_samples` x `output_dim` x `n_features` buffer.
"""
function predictive_cov!(
rfm::RandomFeatureMethod,
fit::Fit,
new_inputs::DataContainer,
cov_store::A,
buffer::A,
prebuilt_features::A;
tullio_threading = true,
kwargs...,
) where {A <: AbstractArray{<:AbstractFloat, 3}}
# unlike in mean case, we must perform a linear solve for coefficients at every test point.
# thus we return both the covariance and the input-dep coefficients
# note the covariance here is a posterior variance in 1d outputs, it is not the posterior covariance
inputs = get_data(new_inputs)
test_batch_size = get_batch_size(rfm, "test")
features_batch_size = get_batch_size(rfm, "feature")
rf = get_random_feature(rfm)
tullio_threading = get_tullio_threading(rfm)
n_features = get_n_features(rf)
output_dim = get_output_dim(rf)
coeffs = get_coeffs(fit)
PhiTλinvPhi_factors = get_feature_factors(fit)
inv_decomp = get_inv_decomposition(PhiTλinvPhi_factors) # (get the inverse of this)
if !(size(cov_store) == (output_dim, output_dim, size(inputs, 2)))
throw(
DimensionMismatch(
"provided storage for output expected to be size ($(output_dim),$(output_dim),$(size(inputs,2))) got $(size(cov_store))",
),
)
end
FT = eltype(prebuilt_features)
# Bishop Nasrabadi 2006 - efficient computation of cov:
if !(size(buffer) == (size(inputs, 2), output_dim, n_features))
throw(
DimensionMismatch(
"provided storage for tmp buffer expected to be size ($(size(inputs,2)),$(output_dim),$(n_features)), got $(size(buffer))",
),
)
end
if !tullio_threading
@tullio threads = 10^9 buffer[n, p, o] = prebuilt_features[n, p, m] * inv_decomp[m, o] # = betahat(x')
@tullio threads = 10^9 cov_store[p, q, n] = buffer[n, p, o] * prebuilt_features[n, q, o]
else
@tullio buffer[n, p, o] = prebuilt_features[n, p, m] * inv_decomp[m, o] # = betahat(x')
@tullio cov_store[p, q, n] = buffer[n, p, o] * prebuilt_features[n, q, o]
end
@. cov_store /= FT(n_features)
nothing
end
function predictive_cov!(
rfm::RandomFeatureMethod,
fit::Fit,
new_inputs::DataContainer,
cov_store::A,
buffer::A;
kwargs...,
) where {A <: AbstractArray{<:AbstractFloat, 3}}
rf = get_random_feature(rfm)
inputs = get_data(new_inputs)
features = build_features(rf, inputs)
predictive_cov!(rfm, fit, new_inputs, cov_store, buffer, features; kwargs...)
return features
end
# TODO
# function posterior_cov(rfm::RandomFeatureMethod, u_input, v_input)
#
# end
end # module
| RandomFeatures | https://github.com/CliMA/RandomFeatures.jl.git |
|
[
"Apache-2.0"
] | 0.3.3 | d639d537c14127a70c8bd3e0158ecbccfb2a54a3 | code | 551 | """
# Imported modules:
$(IMPORTS)
# Exports:
$(EXPORTS)
"""
module RandomFeatures
using Statistics, LinearAlgebra, DocStringExtensions
using Tullio, LoopVectorization
# importing parameter distirbutions
import EnsembleKalmanProcesses: ParameterDistributions, DataContainers
export ParameterDistributions, DataContainers
#auxiliary modules
include("Utilities.jl") # some additional tools
include("Samplers.jl") # samples a distribution
include("Features.jl") # builds a feature from the samples
include("Methods.jl") # fits to data
end # module
| RandomFeatures | https://github.com/CliMA/RandomFeatures.jl.git |
|
[
"Apache-2.0"
] | 0.3.3 | d639d537c14127a70c8bd3e0158ecbccfb2a54a3 | code | 4808 | module Samplers
import StatsBase: sample
using Random, Distributions, DocStringExtensions, EnsembleKalmanProcesses.ParameterDistributions
export Sampler, FeatureSampler, get_parameter_distribution, get_rng, sample
"""
$(TYPEDEF)
Wraps the parameter distributions used to sample random features
$(TYPEDFIELDS)
"""
struct Sampler{RNG <: AbstractRNG}
"A probability distribution, possibly with constraints"
parameter_distribution::ParameterDistribution
"A random number generator state"
rng::RNG
end
"""
$(TYPEDSIGNATURES)
basic constructor for a `Sampler`
"""
function FeatureSampler(
parameter_distribution::ParameterDistribution,
bias_distribution::Union{ParameterDistribution, Nothing};
rng::RNG = Random.GLOBAL_RNG,
) where {RNG <: AbstractRNG}
if isnothing(bias_distribution) # no bias
return Sampler(parameter_distribution, rng)
else
pd = combine_distributions([parameter_distribution, bias_distribution])
return Sampler{RNG}(pd, rng)
end
end
"""
$(TYPEDSIGNATURES)
one can conveniently specify the bias as a uniform-shift `uniform_shift_bounds` with `output_dim` dimensions
"""
function FeatureSampler(
parameter_distribution::ParameterDistribution,
output_dim::Int;
uniform_shift_bounds::V = [0, 2 * pi],
rng::RNG = Random.GLOBAL_RNG,
) where {RNG <: AbstractRNG, V <: AbstractVector}
# adds a uniform distribution to the parameter distribution
if output_dim == 1
unif_dict = Dict(
"distribution" => Parameterized(Uniform(uniform_shift_bounds[1], uniform_shift_bounds[2])),
"constraint" => no_constraint(),
"name" => "bias",
)
else
unif_dict = Dict(
"distribution" => VectorOfParameterized(
repeat([Uniform(uniform_shift_bounds[1], uniform_shift_bounds[2])], output_dim),
),
"constraint" => repeat([no_constraint()], output_dim),
"name" => "bias",
)
end
unif_pd = ParameterDistribution(unif_dict)
pd = combine_distributions([parameter_distribution, unif_pd])
return Sampler{RNG}(pd, rng)
end
FeatureSampler(parameter_distribution::ParameterDistribution; kwargs...) =
FeatureSampler(parameter_distribution, 1; kwargs...)
"""
$(TYPEDSIGNATURES)
gets the `parameter_distribution` field
"""
get_parameter_distribution(s::Sampler) = s.parameter_distribution
"""
$(TYPEDSIGNATURES)
gets the `rng` field
"""
get_rng(s::Sampler) = s.rng
"""
$(TYPEDSIGNATURES)
samples the distribution within `s`, `n_draws` times using a random number generator `rng`. Can be called without `rng` (defaults to `s.rng`) or `n_draws` (defaults to `1`)
"""
function sample(rng::RNG, s::Sampler, n_draws::Int) where {RNG <: AbstractRNG}
pd = get_parameter_distribution(s)
# TODO: Support for Matrix Distributions, Flattening them for now.
if any([length(size(get_distribution(d))) > 1 for d in pd.distribution])
# samps is [ [in x out] x samples, [out x samples]]
samps = [sample(rng, d, n_draws) for d in pd.distribution]
# Faster than cat(samp[1]..., dims = 3)
samp_xi = zeros(size(samps[1][1], 1), size(samps[1][1], 2), length(samps[1]))
for i in 1:n_draws
samp_xi[:, :, i] = samps[1][i]
end
samp_xi = reshape(samp_xi, size(samp_xi, 1) * size(samp_xi, 2), size(samp_xi, 3)) # stacks in+in+... to make a (in x out) x samples
samp_bias = samps[2] # out x samples
# Faster than cat(samp_xi, samp_bias, dims = 1)
samp = zeros(size(samp_xi, 1) + size(samp_bias, 1), size(samp_xi, 2))
samp[1:size(samp_xi, 1), :] = samp_xi
samp[(size(samp_xi, 1) + 1):end, :] = samp_bias
else
# Faster than cat([sample(rng, d, n_draws) for d in pd.distribution]..., dims = 1) for many distributions
batches = batch(pd) # get indices of dist
n = ndims(pd)
samp = zeros(n, n_draws)
for (i, d) in enumerate(pd.distribution)
samp[batches[i], :] = sample(rng, d, n_draws)
end
end
constrained_samp = transform_unconstrained_to_constrained(pd, samp)
#now create a Samples-type distribution from the samples
s_names = get_name(pd)
s_slices = batch(pd) # e.g.,"xi","bias" [1:3,4:6]
s_samples = [Samples(constrained_samp[slice, :]) for slice in s_slices]
s_constraints = [repeat([no_constraint()], size(slice, 1)) for slice in s_slices]
return combine_distributions([
ParameterDistribution(ss, sc, sn) for (ss, sc, sn) in zip(s_samples, s_constraints, s_names)
])
end
sample(s::Sampler, n_draws::Int) = sample(s.rng, s, n_draws)
sample(rng::RNG, s::Sampler) where {RNG <: AbstractRNG} = sample(rng, s, 1)
sample(s::Sampler) = sample(s.rng, s, 1)
end # module
| RandomFeatures | https://github.com/CliMA/RandomFeatures.jl.git |
|
[
"Apache-2.0"
] | 0.3.3 | d639d537c14127a70c8bd3e0158ecbccfb2a54a3 | code | 3973 | export ScalarFeature, ScalarFourierFeature, ScalarNeuronFeature
export build_features
"""
$(TYPEDEF)
Contains information to build and sample RandomFeatures mapping from N-D -> 1-D
$(TYPEDFIELDS)
"""
struct ScalarFeature{S <: AbstractString, SF <: ScalarFunction} <: RandomFeature
"Number of features"
n_features::Int
"Sampler of the feature distribution"
feature_sampler::Sampler
"ScalarFunction mapping R -> R"
scalar_function::SF
"Current `Sample` from sampler"
feature_sample::ParameterDistribution
"hyperparameters in Feature (and not in Sampler)"
feature_parameters::Union{Dict{S}, Nothing}
end
# common constructors
"""
$(TYPEDSIGNATURES)
basic constructor for a `ScalarFeature'
"""
function ScalarFeature(
n_features::Int,
feature_sampler::Sampler,
scalar_fun::SF;
feature_parameters::Dict{S} = Dict("sigma" => 1),
) where {S <: AbstractString, SF <: ScalarFunction}
if "xi" ∉ get_name(get_parameter_distribution(feature_sampler))
throw(
ArgumentError(
" Named parameter \"xi\" not found in names of parameter_distribution. " *
" \n Please provide the name \"xi\" to the distribution used to sample the features",
),
)
end
if "sigma" ∉ keys(feature_parameters)
@info(" Required feature parameter key \"sigma\" not defined, continuing with default value \"sigma\" = 1 ")
feature_parameters["sigma"] = 1.0
end
samp = sample(feature_sampler, n_features)
return ScalarFeature{S, SF}(n_features, feature_sampler, scalar_fun, samp, feature_parameters)
end
#these call the above constructor
"""
$(TYPEDSIGNATURES)
Constructor for a `ScalarFeature` with cosine features
"""
function ScalarFourierFeature(
n_features::Int,
sampler::Sampler;
feature_parameters::Dict{S} = Dict("sigma" => sqrt(2.0)),
) where {S <: AbstractString}
return ScalarFeature(n_features, sampler, Cosine(); feature_parameters = feature_parameters)
end
"""
$(TYPEDSIGNATURES)
Constructor for a `ScalarFeature` with activation-function features (default ReLU)
"""
function ScalarNeuronFeature(
n_features::Int,
sampler::Sampler;
activation_fun::SA = Relu(),
kwargs...,
) where {SA <: ScalarActivation}
return ScalarFeature(n_features, sampler, activation_fun; kwargs...)
end
"""
$(TYPEDSIGNATURES)
builds features (possibly batched) from an input matrix of size (input dimension, number of samples) output of dimension (number of samples, 1, number features)
"""
function build_features(
rf::ScalarFeature,
inputs::M, # input_dim x n_sample
batch_feature_idx::V,
) where {M <: AbstractMatrix, V <: AbstractVector}
# inputs = permutedims(inputs_t, (2, 1)) # n_sample x input_dim
# build: sigma * scalar_function(xi . input + b)
samp = get_feature_sample(rf)
xi = get_distribution(samp)["xi"][:, batch_feature_idx] # dim_inputs x n_features
# features = inputs * xi[:, batch_feature_idx] # n_samples x n_features
@tullio features[n, b] := inputs[d, n] * xi[d, b] # n_samples x output_dim x n_feature_batch
is_biased = "bias" ∈ get_name(samp)
if is_biased
bias = get_distribution(samp)["bias"][1, batch_feature_idx] # 1 x n_features
@tullio features[n, b] += bias[b]
end
sf = get_scalar_function(rf)
features .= apply_scalar_function.(Ref(sf), features) # BOTTLENECK OF build_features.
sigma = get_feature_parameters(rf)["sigma"] # scalar
@. features *= sigma
#consistent output shape with vector case, by putting output_dim = 1 in middle dimension
return reshape(features, size(features, 1), 1, size(features, 2))
end
build_features(rf::ScalarFeature, inputs::M) where {M <: AbstractMatrix} =
build_features(rf, inputs, collect(1:get_n_features(rf)))
"""
$(TYPEDSIGNATURES)
gets the output dimension (equals 1 for scalar-valued features)
"""
get_output_dim(rf::ScalarFeature) = 1
| RandomFeatures | https://github.com/CliMA/RandomFeatures.jl.git |
|
[
"Apache-2.0"
] | 0.3.3 | d639d537c14127a70c8bd3e0158ecbccfb2a54a3 | code | 3412 | # list of scalar function - here usage is e.g.,
# SA = Relu()
# apply_scalar_function(SA,r)
using SpecialFunctions
using DocStringExtensions
import Base.@kwdef
export ScalarFunction,
ScalarActivation,
Cosine,
Relu,
Lrelu,
Gelu,
Elu,
Selu,
Heaviside,
SmoothHeaviside,
Sawtooth,
Softplus,
Tansig,
Sigmoid
export apply_scalar_function
"""
$(TYPEDEF)
Type of a function mapping 1D -> 1D
"""
abstract type ScalarFunction end
"""
$(TYPEDSIGNATURES)
apply the scalar function `sf` pointwise to vectors or matrices
"""
apply_scalar_function(sf::SF, r::A) where {SF <: ScalarFunction, A <: AbstractArray} =
apply_scalar_function.(Ref(sf), r) # Ref(sf) treats sf as a scalar for the broadcasting
"""
$(TYPEDEF)
"""
struct Cosine <: ScalarFunction end
function apply_scalar_function(sf::Cosine, r::FT) where {FT <: AbstractFloat}
return cos(r)
end
# specific set used for neurons
"""
$(TYPEDEF)
Type of scalar activation functions
"""
abstract type ScalarActivation <: ScalarFunction end
"""
$(TYPEDEF)
"""
struct Relu <: ScalarActivation end
function apply_scalar_function(sa::Relu, r::FT) where {FT <: AbstractFloat}
return max(0, r)
end
"""
$(TYPEDEF)
"""
struct Gelu <: ScalarActivation end
function apply_scalar_function(sa::Gelu, r::FT) where {FT <: AbstractFloat}
cdf = 0.5 * (1.0 + erf(r / sqrt(2.0)))
return r * cdf
end
"""
$(TYPEDEF)
"""
struct Heaviside <: ScalarActivation end
function heaviside(x, y)
if x < 0
return 0
elseif x == 0
return y
else
return 1
end
end
function apply_scalar_function(sa::Heaviside, r::FT) where {FT <: AbstractFloat}
return heaviside(r, 0.5)
end
"""
$(TYPEDEF)
"""
struct Sawtooth <: ScalarActivation end
function apply_scalar_function(sa::Sawtooth, r::FT) where {FT <: AbstractFloat}
return max(0, min(2 * r, 2 - 2 * r))
end
"""
$(TYPEDEF)
"""
struct Softplus <: ScalarActivation end
function apply_scalar_function(sa::Softplus, r::FT) where {FT <: AbstractFloat}
return log(1 + exp(-abs(r))) + max(r, 0)
end
"""
$(TYPEDEF)
"""
struct Tansig <: ScalarActivation end
function apply_scalar_function(sa::Tansig, r::FT) where {FT <: AbstractFloat}
return tanh(r)
end
"""
$(TYPEDEF)
"""
struct Sigmoid <: ScalarActivation end
function apply_scalar_function(sa::Sigmoid, r::FT) where {FT <: AbstractFloat}
return 1 / (1 + exp(-r))
end
"""
$(TYPEDEF)
"""
@kwdef struct Elu{FT <: AbstractFloat} <: ScalarActivation
alpha::FT = 1.0
end
function apply_scalar_function(sa::Elu, r::FT) where {FT <: AbstractFloat}
return r > 0 ? r : sa.alpha * (exp(r) - 1.0)
end
"""
$(TYPEDEF)
"""
@kwdef struct Lrelu{FT <: AbstractFloat} <: ScalarActivation
alpha::FT = 0.01
end
function apply_scalar_function(sa::Lrelu, r::FT) where {FT <: AbstractFloat}
return r > 0 ? r : sa.alpha * r
end
"""
$(TYPEDEF)
"""
@kwdef struct Selu{FT <: AbstractFloat} <: ScalarActivation
alpha::FT = 1.67326
lambda::FT = 1.0507
end
function apply_scalar_function(sa::Selu, r::FT) where {FT <: AbstractFloat}
return r > 0 ? sa.lambda * r : sa.lambda * sa.alpha * (exp(r) - 1.0)
end
"""
$(TYPEDEF)
"""
@kwdef struct SmoothHeaviside{FT <: AbstractFloat} <: ScalarActivation
epsilon::FT = 0.01
end
function apply_scalar_function(sa::SmoothHeaviside, r::FT) where {FT <: AbstractFloat}
return 1 / 2 + (1 / pi) * atan(r / sa.epsilon)
end
| RandomFeatures | https://github.com/CliMA/RandomFeatures.jl.git |
|
[
"Apache-2.0"
] | 0.3.3 | d639d537c14127a70c8bd3e0158ecbccfb2a54a3 | code | 6234 | module Utilities
using LinearAlgebra, DocStringExtensions, Tullio, LoopVectorization
export batch_generator,
Decomposition,
StoredInvType,
Factor,
PseInv,
get_decomposition,
get_inv_decomposition,
get_full_matrix,
get_parametric_type,
linear_solve,
posdef_correct
"""
$(TYPEDSIGNATURES)
produces batched sub-array views of size `batch_size` along dimension `dims`.
!!! note
this creates views not copies. Modifying a batch will modify the original!
"""
function batch_generator(array::A, batch_size::Int; dims::Int = 1) where {A <: AbstractArray}
if batch_size == 0
return [array]
end
n_batches = Int(ceil(size(array, dims) / batch_size))
batch_idx = [
i < n_batches ? collect(((i - 1) * batch_size + 1):(i * batch_size)) :
collect(((i - 1) * batch_size + 1):size(array, dims)) for i in 1:n_batches
]
return [selectdim(array, dims, b) for b in batch_idx]
end
# Decomposition/linear solves for feature matrices
"""
posdef_correct(mat::AbstractMatrix; tol::Real=1e8*eps())
Makes square matrix `mat` positive definite, by symmetrizing and bounding the minimum eigenvalue below by `tol`
"""
function posdef_correct(mat::AbstractMatrix; tol::Real = 1e12 * eps())
mat = deepcopy(mat)
if !issymmetric(mat)
out = 0.5 * (mat + permutedims(mat, (2, 1))) #symmetrize
if isposdef(out)
# very often, small numerical errors cause asymmetry, so cheaper to add this branch
return out
end
else
out = mat
end
if !isposdef(out)
nugget = abs(minimum(eigvals(out)))
for i in 1:size(out, 1)
out[i, i] += nugget + tol # add to diag
end
end
return out
end
"""
$(TYPEDEF)
Type used as a flag for the stored Decomposition type
"""
abstract type StoredInvType end
"""
$(TYPEDEF)
"""
abstract type Factor <: StoredInvType end
"""
$(TYPEDEF)
"""
abstract type PseInv <: StoredInvType end
"""
$(TYPEDEF)
Stores a matrix along with a decomposition `T=Factor`, or pseudoinverse `T=PseInv`, and also computes the inverse of the Factored matrix (for several predictions this is actually the most computationally efficient action)
$(TYPEDFIELDS)
"""
struct Decomposition{T, M <: AbstractMatrix, MorF <: Union{AbstractMatrix, Factorization}}
"The original matrix"
full_matrix::M
"The matrix decomposition, or pseudoinverse"
decomposition::MorF
"The matrix decomposition of the inverse, or pseudoinverse"
inv_decomposition::M
end
function Decomposition(
mat::M,
method::S;
nugget::R = 1e12 * eps(),
) where {M <: AbstractMatrix, S <: AbstractString, R <: Real}
# TODOs
# 1. Originally I used f = getfield(LinearAlgebra, Symbol(method)) but this is slow for evaluation so defining svd and cholesky is all we have now. I could maybe do dispatch here to make this a bit more slick.
# 2. I have tried using the in-place methods, but so far these have not made enough difference to be worthwhile, I think at some-point they would be, but the original matrix would be needed for matrix regularization. They are not the bottleneck in the end
if method == "pinv"
invmat = pinv(mat)
return Decomposition{PseInv, M, M}(mat, invmat, invmat)
elseif method == "svd"
fmat = svd(mat)
return Decomposition{Factor, typeof(mat), Base.return_types(svd, (typeof(mat),))[1]}(mat, fmat, inv(fmat))
elseif method == "cholesky"
if !isposdef(mat)
# @info "Random Feature system not positive definite. Performing cholesky factorization with a close positive definite matrix"
mat = posdef_correct(mat, tol = nugget)
end
fmat = cholesky(mat)
return Decomposition{Factor, typeof(mat), Base.return_types(cholesky, (typeof(mat),))[1]}(mat, fmat, inv(fmat))
else
throw(
ArgumentError(
"Only factorization methods \"pinv\", \"cholesky\" and \"svd\" implemented. got " * string(method),
),
)
end
end
"""
$(TYPEDSIGNATURES)
get `decomposition` field
"""
get_decomposition(d::Decomposition) = d.decomposition
"""
$(TYPEDSIGNATURES)
get `inv_decomposition` field
"""
get_inv_decomposition(d::Decomposition) = d.inv_decomposition
"""
$(TYPEDSIGNATURES)
get `full_matrix` field
"""
get_full_matrix(d::Decomposition) = d.full_matrix
"""
$(TYPEDSIGNATURES)
get the parametric type
"""
get_parametric_type(d::Decomposition{T, M}) where {T, M <: Union{AbstractMatrix, Factorization}} = T
"""
$(TYPEDSIGNATURES)
Solve the linear system based on `Decomposition` type
"""
function linear_solve(
d::Decomposition,
rhs::A,
::Type{Factor};
tullio_threading = true,
) where {A <: AbstractArray{<:AbstractFloat, 3}}
# return get_decomposition(d) \ permutedims(rhs (3,1,2))
# for prediction its far more worthwhile to store the inverse (in cases seen thus far)
x = similar(rhs)#zeros(N, P, M)
if !tullio_threading
@tullio threads = 10^9 x[n, p, i] = get_inv_decomposition(d)[i, j] * rhs[n, p, j]
else
@tullio x[n, p, i] = get_inv_decomposition(d)[i, j] * rhs[n, p, j]
end
return x
end
function linear_solve(
d::Decomposition,
rhs::A,
::Type{PseInv};
tullio_threading = true,
) where {A <: AbstractArray{<:AbstractFloat, 3}}
# return get_decomposition(d) * rhs
if !tullio_threading
@tullio threads = 10^9 x[n, p, m] := get_decomposition(d)[m, i] * rhs[n, p, i]
else
@tullio x[n, p, m] := get_decomposition(d)[m, i] * rhs[n, p, i]
end
return x
end
function linear_solve(d::Decomposition, rhs::A, ::Type{Factor}; kwargs...) where {A <: AbstractVector{<:AbstractFloat}}
# return get_decomposition(d) \ rhs
return get_inv_decomposition(d) * rhs
end
function linear_solve(d::Decomposition, rhs::A, ::Type{PseInv}; kwargs...) where {A <: AbstractVector{<:AbstractFloat}}
#get_decomposition(d) * rhs
return get_decomposition(d) * rhs
end
linear_solve(d::Decomposition, rhs::A; tullio_threading = true) where {A <: AbstractArray} =
linear_solve(d, rhs, get_parametric_type(d), tullio_threading = tullio_threading)
end
| RandomFeatures | https://github.com/CliMA/RandomFeatures.jl.git |
|
[
"Apache-2.0"
] | 0.3.3 | d639d537c14127a70c8bd3e0158ecbccfb2a54a3 | code | 4434 | export VectorFeature, VectorFourierFeature, VectorNeuronFeature
export build_features
"""
$(TYPEDEF)
Contains information to build and sample RandomFeatures mapping from N-D -> M-D
$(TYPEDFIELDS)
"""
struct VectorFeature{S <: AbstractString, SF <: ScalarFunction} <: RandomFeature
"Number of features"
n_features::Int
"Dimension of output"
output_dim::Int
"Sampler of the feature distribution"
feature_sampler::Sampler
"ScalarFunction mapping R -> R"
scalar_function::SF
"Current `Sample` from sampler"
feature_sample::ParameterDistribution
"hyperparameters in Feature (and not in Sampler)"
feature_parameters::Union{Dict{String}, Nothing}
end
"""
$(TYPEDSIGNATURES)
gets the output dimension (equals 1 for scalar-valued features)
"""
get_output_dim(rf::VectorFeature) = rf.output_dim
# common constructors
"""
$(TYPEDSIGNATURES)
basic constructor for a `VectorFeature'
"""
function VectorFeature(
n_features::Int,
output_dim::Int,
feature_sampler::Sampler,
scalar_fun::SF;
feature_parameters::Dict{S} = Dict("sigma" => 1),
) where {S <: AbstractString, SF <: ScalarFunction}
if "xi" ∉ get_name(get_parameter_distribution(feature_sampler))
throw(
ArgumentError(
" Named parameter \"xi\" not found in names of parameter_distribution. " *
" \n Please provide the name \"xi\" to the distribution used to sample the features",
),
)
end
if "sigma" ∉ keys(feature_parameters)
@info(" Required feature parameter key \"sigma\" not defined, continuing with default value \"sigma\" = 1 ")
feature_parameters["sigma"] = 1.0
end
samp = sample(feature_sampler, n_features)
return VectorFeature{S, SF}(n_features, output_dim, feature_sampler, scalar_fun, samp, feature_parameters)
end
#these call the above constructor
"""
$(TYPEDSIGNATURES)
Constructor for a `VectorFeature` with cosine features
"""
function VectorFourierFeature(
n_features::Int,
output_dim::Int,
sampler::Sampler;
feature_parameters::Dict{S} = Dict("sigma" => sqrt(2.0)),
) where {S <: AbstractString}
return VectorFeature(n_features, output_dim, sampler, Cosine(); feature_parameters = feature_parameters)
end
"""
$(TYPEDSIGNATURES)
Constructor for a `VectorFeature` with activation-function features (default ReLU)
"""
function VectorNeuronFeature(
n_features::Int,
output_dim::Int,
sampler::Sampler;
activation_fun::ScalarActivation = Relu(),
kwargs...,
)
return VectorFeature(n_features, output_dim, sampler, activation_fun; kwargs...)
end
"""
$(TYPEDSIGNATURES)
builds features (possibly batched) from an input matrix of size (input dimension,number of samples) output of dimension (number of samples, output dimension, number features)
"""
function build_features(
rf::VectorFeature,
inputs::M, # input_dim x n_sample
batch_feature_idx::V,
) where {M <: AbstractMatrix, V <: AbstractVector}
# build: sigma * scalar_function(xi * input + b)
samp = get_feature_sample(rf)
input_dim = size(inputs, 1)
output_dim = get_output_dim(rf)
#TODO: What we want:
# xi = get_distribution(samp)["xi"][:,:,batch_feature_idx] # input_dim x output_dim x n_feature_batch
# for now, as matrix distributions aren't yet supported, xi is flattened, so we reshape
xi_flat = get_distribution(samp)["xi"][:, batch_feature_idx] # (input_dim x output_dim) x n_feature_batch
sampler = get_feature_sampler(rf)
pd = get_parameter_distribution(sampler)
xi = reshape(xi_flat, input_dim, output_dim, size(xi_flat, 2))
features = zeros(size(inputs, 2), size(xi, 2), size(xi, 3))
@tullio features[n, p, b] = inputs[d, n] * xi[d, p, b] # n_samples x output_dim x n_feature_batch
is_biased = "bias" ∈ get_name(samp)
if is_biased
bias = get_distribution(samp)["bias"][:, batch_feature_idx] # dim_output x n_features
@tullio features[n, p, b] += bias[p, b]
end
sf = get_scalar_function(rf)
features .= apply_scalar_function.(Ref(sf), features) # BOTTLENECK OF build_features.
sigma = get_feature_parameters(rf)["sigma"] # scalar
@. features *= sigma
return features # n_sample x n_feature_batch x output_dim
end
build_features(rf::VectorFeature, inputs::M) where {M <: AbstractMatrix} =
build_features(rf, inputs, collect(1:get_n_features(rf)))
| RandomFeatures | https://github.com/CliMA/RandomFeatures.jl.git |
|
[
"Apache-2.0"
] | 0.3.3 | d639d537c14127a70c8bd3e0158ecbccfb2a54a3 | code | 852 | using Test
TEST_PLOT_FLAG = !isempty(get(ENV, "TEST_PLOT_FLAG", ""))
if TEST_PLOT_FLAG
using Plots, ColorSchemes
end
function include_test(_module)
println("Starting tests for $_module")
t = @elapsed include(joinpath(_module, "runtests.jl"))
println("Completed tests for $_module, $(round(Int, t)) seconds elapsed")
return nothing
end
@testset "RandomFeatures" begin
all_tests = isempty(ARGS) || "all" in ARGS ? true : false
function has_submodule(sm)
any(ARGS) do a
a == sm && return true
first(split(a, '/')) == sm && return true
return false
end
end
for submodule in ["Utilities", "Samplers", "Features", "Methods"]
if all_tests || has_submodule(submodule) || "RandomFeatures" in ARGS
include_test(submodule)
end
end
end
| RandomFeatures | https://github.com/CliMA/RandomFeatures.jl.git |
|
[
"Apache-2.0"
] | 0.3.3 | d639d537c14127a70c8bd3e0158ecbccfb2a54a3 | code | 13248 | using Test
using StableRNGs
using StatsBase
using LinearAlgebra
using Random
using Distributions
using Tullio
using RandomFeatures.ParameterDistributions
using RandomFeatures.Samplers
using RandomFeatures.Features
seed = 2202
@testset "Features" begin
@testset "ScalarFunctions" begin
af_list = [
Relu(),
Lrelu(),
Gelu(),
Elu(),
Selu(),
Heaviside(),
SmoothHeaviside(),
Sawtooth(),
Softplus(),
Tansig(),
Sigmoid(),
]
# very rough tests that these are activation functions
for af in af_list
@test isa(af, ScalarActivation)
x_test_neg = collect(-1:0.1:-0.1)
x_test_pos = collect(0:0.1:1)
println("Testing ", af)
@test all(apply_scalar_function(af, x_test_neg) .<= log(2)) # small for negative x
if !isa(af, Sawtooth)
@test all(
apply_scalar_function(af, x_test_pos[2:end]) -
apply_scalar_function(af, x_test_pos[1:(end - 1)]) .>= 0,
) # monotone increasing for positive x
else
x_test_0_0pt5 = collect(0:0.1:0.5)
x_test_0pt5_1 = collect(0.5:0.1:1)
@test all(
apply_scalar_function(af, x_test_0_0pt5[2:end]) -
apply_scalar_function(af, x_test_0_0pt5[1:(end - 1)]) .>= 0,
)
@test all(
apply_scalar_function(af, x_test_0pt5_1[2:end]) -
apply_scalar_function(af, x_test_0pt5_1[1:(end - 1)]) .<= 0,
)
end
end
# others
sf = Features.Cosine() # as Distributions also has a Cosine()
println("Testing ", sf)
@test isa(sf, ScalarFunction)
x_test = collect(-1:0.1:1)
@test all(abs.(apply_scalar_function(sf, x_test) - cos.(x_test)) .< 2 * eps())
end
@testset "Scalar: Constructors" begin
n_features = 20
relu = Relu()
rng = StableRNG(seed)
#setup sampler xi distributions
μ_c = 0.0
σ_c = 2.0
pd_err = constrained_gaussian("test", μ_c, σ_c, -Inf, Inf)
feature_sampler_err = FeatureSampler(pd_err, rng = copy(rng))
pd = constrained_gaussian("xi", μ_c, σ_c, -Inf, Inf)
feature_sampler = FeatureSampler(pd, rng = copy(rng))
# postive constraints for sigma
sigma_fixed_err = Dict("not sigma" => 10.0)
# Error checks
@test_throws ArgumentError ScalarFeature(
n_features,
feature_sampler_err, # causes error
relu,
)
@test_logs (
:info,
" Required feature parameter key \"sigma\" not defined, continuing with default value \"sigma\" = 1 ",
) ScalarFeature(n_features, feature_sampler, relu, feature_parameters = sigma_fixed_err)
# ScalarFeature and getters
feature_sampler = FeatureSampler(pd, rng = copy(rng)) # to reset the rng
sf_test = ScalarFeature(n_features, feature_sampler, relu)
@test get_n_features(sf_test) == n_features
@test get_feature_parameters(sf_test) == Dict("sigma" => 1.0)
@test get_output_dim(sf_test) == 1
@test get_feature_parameters(sf_test)["sigma"] == sqrt(1)
test_sample = sample(copy(rng), feature_sampler, n_features)
sf_test_sample = get_feature_sample(sf_test)
@test get_distribution(sf_test_sample)["xi"] == get_distribution(test_sample)["xi"]
@test get_distribution(sf_test_sample)["bias"] == get_distribution(test_sample)["bias"]
@test get_all_constraints(sf_test_sample) == get_all_constraints(test_sample)
@test get_name(sf_test_sample) == get_name(test_sample)
sf_test_sampler = get_feature_sampler(sf_test)
sff_test = ScalarFourierFeature(n_features, feature_sampler)
snf_test = ScalarNeuronFeature(n_features, feature_sampler)
@test isa(get_scalar_function(sff_test), Features.Cosine)
@test get_feature_parameters(sff_test)["sigma"] == sqrt(2.0)
@test isa(get_scalar_function(snf_test), Relu)
end
@testset "Scalar: build features" begin
n_features = 20
rng = StableRNG(seed)
μ_c = 0.0
σ_c = 2.0
pd = constrained_gaussian("xi", μ_c, σ_c, -Inf, Inf)
feature_sampler_1d = FeatureSampler(pd, rng = copy(rng))
sigma_value = 10.0
sigma_fixed = Dict("sigma" => sigma_value)
sff_1d_test = ScalarFourierFeature(n_features, feature_sampler_1d, feature_parameters = sigma_fixed)
# 1D input space -> 1D output space
inputs_1d = reshape(collect(-1:0.01:1), (1, length(collect(-1:0.01:1))))
n_samples_1d = length(inputs_1d)
features_1d = build_features(sff_1d_test, inputs_1d)
rng1 = copy(rng)
samp_xi = reshape(sample(rng1, pd, n_features), (1, n_features))
samp_unif = reshape(rand(rng1, Uniform(0, 2 * pi), n_features), (1, n_features))
inputs_1d_T = permutedims(inputs_1d, (2, 1))
rf_test = sigma_value * cos.(inputs_1d_T * samp_xi .+ samp_unif)
@test size(features_1d) == (n_samples_1d, 1, n_features) # we store internally with output_dim = 1
@test all(abs.(rf_test - features_1d[:, 1, :]) .< 10 * eps()) # sufficiently big to deal with inaccuracy of cosine
# 10D input space -> 1D output space
# generate a bunch of random samples as data points
n_samples = 200
inputs_10d = rand(MvNormal(zeros(10), convert(Matrix, SymTridiagonal(2 * ones(10), 0.5 * ones(9)))), n_samples) # 10 x n_samples
# 10D indep gaussians on input space as feature distribution
pd_10d = ParameterDistribution(
Dict(
"distribution" => VectorOfParameterized(repeat([Normal(μ_c, σ_c)], 10)),
"constraint" => repeat([no_constraint()], 10),
"name" => "xi",
),
)
feature_sampler_10d = FeatureSampler(pd_10d, rng = copy(rng))
sff_10d_test = ScalarNeuronFeature(n_features, feature_sampler_10d, feature_parameters = sigma_fixed)
features_10d = build_features(sff_10d_test, inputs_10d)
rng2 = copy(rng)
samp_xi = reshape(sample(rng2, pd_10d, n_features), (10, n_features))
samp_unif = reshape(rand(rng2, Uniform(0, 2 * pi), n_features), (1, n_features))
inputs_10d_T = permutedims(inputs_10d, (2, 1))
rf_test2 = sigma_value * max.(inputs_10d_T * samp_xi .+ samp_unif, 0)
@test size(features_10d) == (n_samples, 1, n_features) # we store internall with output_dim = 1
@test all(abs.(rf_test2 - features_10d[:, 1, :]) .< 1e3 * eps()) # sufficiently big to deal with inaccuracy of relu
end
@testset "Vector: Constructors" begin
n_features = 20
input_dim = 5
output_dim = 2
relu = Relu()
rng = StableRNG(seed)
#just to test error flag
μ_c = 0.0
σ_c = 2.0
pd_err = constrained_gaussian("test", μ_c, σ_c, -Inf, Inf, repeats = output_dim)
feature_sampler_err = FeatureSampler(pd_err, output_dim, rng = copy(rng))
#setup sampler xi distributions:
dist = MatrixNormal(zeros(input_dim, output_dim), Diagonal(ones(input_dim)), Diagonal(ones(output_dim))) #produces 5 x 2 matrix samples
pd = ParameterDistribution(
Dict(
"distribution" => Parameterized(dist),
"constraint" => repeat([no_constraint()], input_dim * output_dim), #flattened
"name" => "xi",
),
)
feature_sampler = FeatureSampler(pd, output_dim, rng = copy(rng))
# postive constraints for sigma
sigma_fixed_err = Dict("not sigma" => 10.0)
# Error checks
@test_throws ArgumentError VectorFeature(
n_features,
output_dim,
feature_sampler_err, # causes error
relu,
)
@test_logs (
:info,
" Required feature parameter key \"sigma\" not defined, continuing with default value \"sigma\" = 1 ",
) VectorFeature(n_features, output_dim, feature_sampler, relu, feature_parameters = sigma_fixed_err)
# VectorFeature and getters
feature_sampler = FeatureSampler(pd, output_dim, rng = copy(rng)) # to reset the rng
vf_test = VectorFeature(n_features, output_dim, feature_sampler, relu)
@test get_n_features(vf_test) == n_features
@test get_feature_parameters(vf_test) == Dict("sigma" => 1.0)
@test get_output_dim(vf_test) == output_dim
@test get_feature_parameters(vf_test)["sigma"] == sqrt(1)
test_sample = sample(copy(rng), feature_sampler, n_features)
vf_test_sample = get_feature_sample(vf_test)
@test get_distribution(vf_test_sample)["xi"] == get_distribution(test_sample)["xi"]
@test get_distribution(vf_test_sample)["bias"] == get_distribution(test_sample)["bias"]
@test get_all_constraints(vf_test_sample) == get_all_constraints(test_sample)
@test get_name(vf_test_sample) == get_name(test_sample)
vf_test_sampler = get_feature_sampler(vf_test)
vff_test = VectorFourierFeature(n_features, output_dim, feature_sampler)
vnf_test = VectorNeuronFeature(n_features, output_dim, feature_sampler)
@test isa(get_scalar_function(vff_test), Features.Cosine)
@test get_feature_parameters(vff_test)["sigma"] == sqrt(2.0)
@test isa(get_scalar_function(vnf_test), Relu)
end
@testset "Vector: build features" begin
n_features = 20
input_dim = 5
output_dim = 2
rng = StableRNG(seed)
#setup sampler xi distributions:
dist = MatrixNormal(zeros(input_dim, output_dim), Diagonal(ones(input_dim)), Diagonal(ones(output_dim))) #produces 5 x 2 matrix samples
pd = ParameterDistribution(
Dict(
"distribution" => Parameterized(dist),
"constraint" => repeat([no_constraint()], input_dim * output_dim),
"name" => "xi",
),
)
feature_sampler_5d = FeatureSampler(pd, output_dim, rng = copy(rng))
sigma_value = 10.0
sigma_fixed = Dict("sigma" => sigma_value)
vff_5d_2d_test =
VectorFourierFeature(n_features, output_dim, feature_sampler_5d, feature_parameters = sigma_fixed)
# and a flat one
dist = MvNormal(zeros(input_dim * output_dim), Diagonal(ones(input_dim * output_dim))) #produces 10-length vector samples
pd = ParameterDistribution(
Dict(
"distribution" => Parameterized(dist),
"constraint" => repeat([no_constraint()], input_dim * output_dim),
"name" => "xi",
),
)
feature_sampler_10dflat = FeatureSampler(pd, output_dim, rng = copy(rng))
vff_10dflat_test =
VectorFourierFeature(n_features, output_dim, feature_sampler_10dflat, feature_parameters = sigma_fixed)
# 5D input space -> 2D output space
n_samples = 200
inputs_5d_2d = rand(Uniform(-1, 1), (input_dim, n_samples))
features_5d_2d = build_features(vff_5d_2d_test, inputs_5d_2d)
rng1 = copy(rng)
samp_flat = sample(rng1, feature_sampler_5d, n_features)
samp_xi_flat = get_distribution(samp_flat)["xi"]
# as we flatten the samples currently in the sampler.sample. reshape with dist.
samp_xi = reshape(samp_xi_flat, (input_dim, output_dim, size(samp_xi_flat, 2))) # in x out x n_feature_batch
@tullio features[n, p, b] := inputs_5d_2d[d, n] * samp_xi[d, p, b]
samp_bias = get_distribution(samp_flat)["bias"]
@tullio features[n, p, b] += samp_bias[p, b]
rf_test = sigma_value * cos.(features)
@test size(features_5d_2d) == (n_samples, output_dim, n_features) # we store internally with output_dim = 1
@test all(abs.(rf_test - features_5d_2d) .< 1e3 * eps()) # sufficiently big to deal with inaccuracy of cosine
features_10dflat = build_features(vff_10dflat_test, inputs_5d_2d)
rng1 = copy(rng)
samp_flat = sample(rng1, feature_sampler_10dflat, n_features)
samp_xi_flat = get_distribution(samp_flat)["xi"]
# as we flatten the samples currently in the sampler.sample. reshape with dist.
samp_xi = reshape(samp_xi_flat, (input_dim, output_dim, size(samp_xi_flat, 2))) # in x out x n_feature_batch
@tullio features[n, p, b] := inputs_5d_2d[d, n] * samp_xi[d, p, b]
samp_bias = get_distribution(samp_flat)["bias"]
@tullio features[n, p, b] += samp_bias[p, b]
rf_test = sigma_value * cos.(features)
# rf_test = sigma_value * cos.(inputs_5d_2d_T * samp_xi .+ samp_unif)
@test size(features_10dflat) == (n_samples, output_dim, n_features) # we store internally with output_dim = 1
@test all(abs.(rf_test - features_10dflat) .< 1e3 * eps()) # sufficiently big to deal with inaccuracy of cosine
end
end
| RandomFeatures | https://github.com/CliMA/RandomFeatures.jl.git |
|
[
"Apache-2.0"
] | 0.3.3 | d639d537c14127a70c8bd3e0158ecbccfb2a54a3 | code | 30931 | using Test
using Distributions
using StableRNGs
using StatsBase
using LinearAlgebra
using Random
using RandomFeatures.Utilities
using RandomFeatures.Samplers
using RandomFeatures.Features
using RandomFeatures.Methods
using RandomFeatures.DataContainers
using RandomFeatures.ParameterDistributions
seed = 2023
tol = 1e3 * eps()
@testset "Methods" begin
@testset "construction of RFM" begin
rng = StableRNG(seed)
#specify features
μ_c = 0.0
σ_c = 2.0
pd = constrained_gaussian("xi", μ_c, σ_c, -Inf, Inf)
feature_sampler = FeatureSampler(pd, rng = copy(rng))
n_features = 100
sigma_fixed = Dict("sigma" => 10.0)
sff = ScalarFourierFeature(n_features, feature_sampler, feature_parameters = sigma_fixed)
# configure the method, and fit
batch_sizes_err = Dict("train" => 100, "test" => 100, "NOT_FEATURES" => 100)
batch_sizes = Dict("train" => 100, "test" => 100, "feature" => 100)
lambda_warn = -1
lambda = 1e-4
lambdamat_warn = ones(3, 3) # not pos def
L = [0.5 0; 1.3 0.3]
lambdamat = L * permutedims(L, (2, 1)) #pos def
@test_throws ArgumentError RandomFeatureMethod(sff, regularization = lambda, batch_sizes = batch_sizes_err)
rfm_warn = RandomFeatureMethod(sff, regularization = lambda_warn, batch_sizes = batch_sizes)
@test get_regularization(rfm_warn) ≈ inv(1e12 * eps() * I) # inverted internally
rfm_warn2 = RandomFeatureMethod(
sff,
regularization = lambdamat_warn,
batch_sizes = batch_sizes,
regularization_inverted = true,
) #don't invert, just make PD
reg_new = get_regularization(rfm_warn2)
@test isposdef(reg_new)
@test minimum(eigvals(reg_new)) > 1e12 * eps()
rfm = RandomFeatureMethod(
sff,
regularization = lambdamat,
batch_sizes = batch_sizes,
regularization_inverted = true,
)
@test get_regularization(rfm) ≈ lambdamat
rfm = RandomFeatureMethod(sff, regularization = lambdamat, batch_sizes = batch_sizes)
@test get_regularization(rfm) ≈ inv(lambdamat)
rfm = RandomFeatureMethod(sff, regularization = lambda, batch_sizes = batch_sizes)
@test get_batch_sizes(rfm) == batch_sizes
rf_test = get_random_feature(rfm)
@test get_tullio_threading(rfm) == true
rfm = RandomFeatureMethod(sff, tullio_threading = false)
@test get_tullio_threading(rfm) == false
#too arduous right now to check rf_test == sff will wait until "==" is overloaded for ParameterDistribution
rfm_default = RandomFeatureMethod(sff)
@test get_batch_sizes(rfm_default) == Dict("train" => 0, "test" => 0, "feature" => 0)
@test get_regularization(rfm_default) ≈ inv(1e12 * eps() * I)
end
@testset "Fit and predict: 1-D -> 1-D" begin
rng_base = StableRNG(seed)
# looks like a 4th order polynomial near 0, then is damped to 0 toward +/- inf
ftest(x::AbstractVecOrMat) = exp.(-0.5 * x .^ 2) .* (x .^ 4 - x .^ 3 - x .^ 2 + x .- 1)
exp_range = [1, 2, 4]
n_data_exp = 20 * exp_range
priorL2err = zeros(length(exp_range), 3)
priorweightedL2err = zeros(length(exp_range), 3)
L2err = zeros(length(exp_range), 3)
weightedL2err = zeros(length(exp_range), 3)
# values with 1/var learning in examples/Learn_hyperparameters/1d_to_1d_regression_direct_withcov.jl
σ_c_vec = [2.5903560156755194, 1.9826946095752571, 2.095420236641444]
σ_c_snf_vec = [9.606414682837055, 4.406586351058134, 2.756419855446525]
σ_c_ssf_vec = [2.2041952067873742, 3.0205667976224384, 4.307656997874708]
for (exp_idx, n_data, σ_c, σ_c_snf, σ_c_ssf) in
zip(1:length(exp_range), n_data_exp, σ_c_vec, σ_c_snf_vec, σ_c_ssf_vec)
rng = copy(rng_base)
#problem formulation
x = rand(rng, Uniform(-3, 3), n_data)
noise_sd = 0.1
noise = rand(rng, Normal(0, noise_sd), n_data)
y = ftest(x) + noise
io_pairs = PairedDataContainer(reshape(x, 1, :), reshape(y, 1, :), data_are_columns = true) #matrix input
xtestvec = collect(-3:0.01:3)
ntest = length(xtestvec) #extended domain
xtest = DataContainer(reshape(xtestvec, 1, :), data_are_columns = true)
ytest_nonoise = ftest(get_data(xtest))
# specify feature distributions
# NB we optimize hyperparameter values σ_c in examples/Learn_hyperparameters/1d_to_1d_regression.jl
# Such values may change with different ftest and different noise_sd
n_features = 400
μ_c = 0.0
pd = constrained_gaussian("xi", μ_c, σ_c, -Inf, Inf)
feature_sampler = FeatureSampler(pd, rng = copy(rng))
sff = ScalarFourierFeature(n_features, feature_sampler)
pd_snf = constrained_gaussian("xi", μ_c, σ_c_snf, -Inf, Inf)
feature_sampler_snf = FeatureSampler(pd_snf, rng = copy(rng))
snf = ScalarNeuronFeature(n_features, feature_sampler_snf)
pd_ssf = constrained_gaussian("xi", μ_c, σ_c_ssf, -Inf, Inf)
feature_sampler_ssf = FeatureSampler(pd_ssf, rng = copy(rng))
ssf = ScalarFeature(n_features, feature_sampler_ssf, Sigmoid())
#first case without batches
lambda = noise_sd^2
rfm = RandomFeatureMethod(sff, regularization = lambda)
fitted_features = fit(rfm, io_pairs)
decomp = get_feature_factors(fitted_features)
@test get_parametric_type(decomp) == Factor
@test typeof(get_decomposition(decomp)) <: Cholesky
coeffs = get_coeffs(fitted_features)
#second case with batching
batch_sizes = Dict("train" => 100, "test" => 100, "feature" => 100)
rfm_batch = RandomFeatureMethod(sff, batch_sizes = batch_sizes, regularization = lambda)
rfm_nothread = RandomFeatureMethod(sff, regularization = lambda, tullio_threading = false)
fitted_batched_features = fit(rfm_batch, io_pairs)
coeffs_batched = get_coeffs(fitted_batched_features)
@test coeffs ≈ coeffs_batched
fitted_features_nothread = fit(rfm_nothread, io_pairs)
coeffs_nothread = get_coeffs(fitted_features_nothread)
@test coeffs ≈ coeffs_nothread
# test prediction with different features
pred_mean, pred_cov = predict(rfm_batch, fitted_batched_features, xtest)
if exp_idx == 1
pmtmp = zeros(size(pred_mean)) # p x n
pctmp = zeros(size(pred_cov)) # p x p x n
buffer = zeros(size(pctmp, 3), size(pctmp, 1), n_features) # n x p x m
predict!(rfm_batch, fitted_batched_features, xtest, pmtmp, pctmp, buffer)
@test all(isapprox.(pred_mean, pmtmp, atol = tol))
@test all(isapprox.(pred_cov, pctmp, atol = tol))
end
rfm_relu = RandomFeatureMethod(snf, batch_sizes = batch_sizes, regularization = lambda)
fitted_relu_features = fit(rfm_relu, io_pairs)
pred_mean_relu, pred_cov_relu = predict(rfm_relu, fitted_relu_features, xtest)
rfm_sig = RandomFeatureMethod(ssf, batch_sizes = batch_sizes, regularization = lambda)
fitted_sig_features = fit(rfm_sig, io_pairs)
pred_mean_sig, pred_cov_sig = predict(rfm_sig, fitted_sig_features, xtest)
prior_mean, prior_cov = predict_prior(rfm_batch, xtest) # predict inputs from unfitted features
prior_mean_relu, prior_cov_relu = predict_prior(rfm_relu, xtest)
prior_mean_sig, prior_cov_sig = predict_prior(rfm_sig, xtest)
pred_mean_nothread, pred_cov_nothread =
predict(rfm_nothread, fitted_features_nothread, xtest, tullio_threading = false)
@test all(isapprox.(pred_mean_nothread, pred_mean, atol = tol))
@test all(isapprox.(pred_cov_nothread, pred_cov, atol = tol))
pmr_nothread = similar(pred_mean)
pcr_nothread = similar(pred_cov)
buffer = zeros(size(pred_cov, 3), size(pred_cov, 1), n_features) # n x p x m
predict!(
rfm_nothread,
fitted_features_nothread,
xtest,
pmr_nothread,
pcr_nothread,
buffer,
tullio_threading = false,
)
@test all(isapprox.(pmr_nothread, pred_mean, atol = tol))
@test all(isapprox.(pcr_nothread, pred_cov, atol = tol))
# enforce positivity
prior_cov = max.(0, prior_cov)
prior_cov_relu = max.(0, prior_cov_relu)
prior_cov_sig = max.(0, prior_cov_sig)
pred_cov = max.(0, pred_cov)
pred_cov_relu = max.(0, pred_cov_relu)
pred_cov_sig = max.(0, pred_cov_sig)
# added Plots for these different predictions:
if TEST_PLOT_FLAG
clrs = map(x -> get(colorschemes[:hawaii], x), [0.25, 0.5, 0.75])
plt = plot(
get_data(xtest)',
ytest_nonoise',
show = false,
color = "black",
linewidth = 5,
size = (600, 600),
legend = :topleft,
label = "Target",
)
plot!(
get_data(xtest)',
pred_mean',
ribbon = [2 * sqrt.(pred_cov[1, 1, :]); 2 * sqrt.(pred_cov[1, 1, :])]',
label = "Fourier",
color = clrs[1],
)
plot!(
get_data(xtest)',
pred_mean_relu',
ribbon = [2 * sqrt.(pred_cov_relu[1, 1, :]); 2 * sqrt.(pred_cov_relu[1, 1, :])]',
label = "Relu",
color = clrs[2],
)
plot!(
get_data(xtest)',
pred_mean_sig',
ribbon = [2 * sqrt.(pred_cov_sig[1, 1, :]); 2 * sqrt.(pred_cov_sig[1, 1, :])]',
label = "Sigmoid",
color = clrs[3],
)
scatter!(x, y, markershape = :x, label = "", color = "black", markersize = 6)
savefig(plt, joinpath(@__DIR__, "Fit_and_predict_1D_" * string(exp_range[exp_idx]) * ".pdf"))
end
priorL2err[exp_idx, :] += [
sqrt(sum((ytest_nonoise - prior_mean) .^ 2)),
sqrt(sum((ytest_nonoise - prior_mean_relu) .^ 2)),
sqrt(sum((ytest_nonoise - prior_mean_sig) .^ 2)),
]
priorweightedL2err[exp_idx, :] += [
sqrt(sum(1 ./ (prior_cov .+ noise_sd^2) .* (ytest_nonoise - prior_mean) .^ 2)),
sqrt(sum(1 ./ (prior_cov_relu .+ noise_sd^2) .* (ytest_nonoise - prior_mean_relu) .^ 2)),
sqrt(sum(1 ./ (prior_cov_sig .+ noise_sd^2) .* (ytest_nonoise - prior_mean_sig) .^ 2)),
]
L2err[exp_idx, :] += [
sqrt(sum((ytest_nonoise - pred_mean) .^ 2)),
sqrt(sum((ytest_nonoise - pred_mean_relu) .^ 2)),
sqrt(sum((ytest_nonoise - pred_mean_sig) .^ 2)),
]
weightedL2err[exp_idx, :] += [
sqrt(sum(1 ./ (pred_cov .+ noise_sd^2) .* (ytest_nonoise - pred_mean) .^ 2)),
sqrt(sum(1 ./ (pred_cov_relu .+ noise_sd^2) .* (ytest_nonoise - pred_mean_relu) .^ 2)),
sqrt(sum(1 ./ (pred_cov_sig .+ noise_sd^2) .* (ytest_nonoise - pred_mean_sig) .^ 2)),
]
end
println("Prior for 1d->1d:")
println("L2 errors: fourier, neuron, sigmoid")
println(priorL2err)
#println("weighted L2 errors: fourier, neuron, sigmoid")
#println(priorweightedL2err)
println("Posterior for 1d->1d, with increasing data:")
println("L2 errors: fourier, neuron, sigmoid")
println(L2err)
@test all([all(L2err[i, :] .< L2err[i - 1, :]) for i in 2:size(L2err, 1)])
## This test is too brittle for small data
#println("weighted L2 errors: fourier, neuron, sigmoid")
#println(weightedL2err)
#@test all([all(weightedL2err[i,:] .< weightedL2err[i-1,:]) for i=2:size(weightedL2err,1)])
end # testset "Fit and predict"
@testset "Fit and predict: d-D -> 1-D" begin
rng = StableRNG(seed + 1)
input_dim = 6
n_features = 3000
ftest_nd_to_1d(x::AbstractMatrix) =
mapslices(column -> exp(-0.1 * norm([i * c for (i, c) in enumerate(column)])^2), x, dims = 1)
#problem formulation
n_data = 2000
x = rand(rng, MvNormal(zeros(input_dim), I), n_data)
noise_sd = 1e-6
lambda = noise_sd^2
noise = rand(rng, Normal(0, noise_sd), (1, n_data))
y = ftest_nd_to_1d(x) + noise
io_pairs = PairedDataContainer(x, y)
n_test = 500
xtestvec = rand(rng, MvNormal(zeros(input_dim), I), n_test)
xtest = DataContainer(xtestvec)
ytest_nonoise = ftest_nd_to_1d(get_data(xtest))
# specify features
# note the σ_c and sigma values come from `examples/Learn_hyperparameters/nd_to_1d_regression_direct_matchingcov.jl`
μ_c = 0.0
σ_c = [
0.4234088946781989,
0.8049531151024479,
2.0175064410998393,
1.943714718437188,
2.9903379860220314,
3.3332086723624266,
]
pd = ParameterDistribution(
Dict(
"distribution" => VectorOfParameterized(map(sd -> Normal(μ_c, sd), σ_c)),
"constraint" => repeat([no_constraint()], input_dim),
"name" => "xi",
),
)
feature_sampler = FeatureSampler(pd, rng = copy(rng))
sff = ScalarFourierFeature(n_features, feature_sampler)
#second case with batching
batch_sizes = Dict("train" => 500, "test" => 500, "feature" => 500)
rfm_batch = RandomFeatureMethod(sff, batch_sizes = batch_sizes, regularization = lambda)
fitted_batched_features = fit(rfm_batch, io_pairs)
# test prediction with different features
prior_mean, prior_cov = predict_prior(rfm_batch, xtest) # predict inputs from unfitted features
# test other prior methods.
prior_cov2, features_tmp = predict_prior_cov(rfm_batch, xtest)
prior_mean2 = predict_prior_mean(rfm_batch, xtest, features_tmp)
@test all(isapprox.(prior_cov, prior_cov2, atol = tol))
@test all(isapprox.(prior_mean, prior_mean2, atol = tol))
priorL2err = sqrt(sum((ytest_nonoise - prior_mean) .^ 2))
priorweightedL2err = sqrt(sum(1 ./ (prior_cov .+ noise_sd^2) .* (ytest_nonoise - prior_mean) .^ 2))
println("Prior for nd->1d")
println("L2 error: ", priorL2err)
#println("weighted L2 error: ", priorweightedL2err)
pred_mean, pred_cov = predict(rfm_batch, fitted_batched_features, xtest)
# test in-place calculations
pmtmp = ones(size(pred_mean))
pctmp = ones(size(pred_cov))
buffer = zeros(size(pctmp, 3), size(pctmp, 1), n_features) # n x p x m
predict!(rfm_batch, fitted_batched_features, xtest, pmtmp, pctmp, buffer)
@test all(isapprox.(pred_mean, pmtmp, atol = tol))
@test all(isapprox.(pred_cov, pctmp, atol = tol))
L2err = sqrt(sum((ytest_nonoise - pred_mean) .^ 2))
weightedL2err = sqrt(sum(1 ./ (pred_cov .+ noise_sd^2) .* (ytest_nonoise - pred_mean) .^ 2))
println("Posterior for nd->1d")
println("L2 error: ", L2err)
#println("weighted L2 error: ", weightedL2err)
@test L2err < priorL2err
#@test weightedL2err < priorweightedL2err
if TEST_PLOT_FLAG
#plot slice through one dimensions, others fixed to 0
xrange = collect(-3:0.01:3)
xslice = zeros(input_dim, length(xrange))
for direction in 1:input_dim
xslicenew = copy(xslice)
xslicenew[direction, :] = xrange
yslice = ftest_nd_to_1d(xslicenew)
pred_mean_slice, pred_cov_slice = predict(rfm_batch, fitted_batched_features, DataContainer(xslicenew))
pred_cov_slice[1, 1, :] = max.(pred_cov_slice[1, 1, :], 0.0)
plt = plot(
xrange,
yslice',
show = false,
color = "black",
linewidth = 5,
size = (600, 600),
legend = :topleft,
label = "Target",
)
plot!(
xrange,
pred_mean_slice',
ribbon = [2 * sqrt.(pred_cov_slice[1, 1, :]); 2 * sqrt.(pred_cov_slice[1, 1, :])]',
label = "Fourier",
color = "blue",
)
savefig(
plt,
joinpath(@__DIR__, "Fit_and_predict_d-D_" * string(direction) * "of" * string(input_dim) * ".pdf"),
)
end
end
end
@testset "Fit and predict: 1-D -> M-D" begin
rng = StableRNG(seed + 2)
input_dim = 1
output_dim = 3
n_features = 300
function ftest_1d_to_3d(x::AbstractMatrix)
out = zeros(3, size(x, 2))
out[1, :] = mapslices(column -> sin(norm([i * c for (i, c) in enumerate(column)])^2), x, dims = 1)
out[2, :] = mapslices(column -> exp(-0.1 * norm([i * c for (i, c) in enumerate(column)])^2), x, dims = 1)
out[3, :] = mapslices(
column ->
norm([i * c for (i, c) in enumerate(column)]) * sin(1 / norm([i * c for (i, c) in enumerate(column)])^2) -
1,
x,
dims = 1,
)
return out
end
#utility
function flat_to_chol(x::AbstractArray)
choldim = Int(floor(sqrt(2 * length(x))))
cholmat = zeros(choldim, choldim)
for i in 1:choldim
for j in 1:i
cholmat[i, j] = x[sum(0:(i - 1)) + j]
end
end
return cholmat
end
#problem formulation
n_data = 200
x = rand(rng, MvNormal(zeros(input_dim), I), n_data)
# run three sims, one with diagonal noise, one with multivariate using ID reg, one with multivariate using cov reg.
# TODO make non-diagonal lambdamat stable for hyperparameter learning.
exp_names = ["diagonal", "correlated-lambdaconst", "diagonal-lambdamat", "correlated-lambdamat"]
cov_mats = [
Diagonal((5e-2)^2 * ones(output_dim)),
convert(
Matrix,
Tridiagonal((5e-3) * ones(output_dim - 1), (2e-2) * ones(output_dim), (5e-3) * ones(output_dim - 1)),
),
Diagonal((5e-2)^2 * ones(output_dim)),
convert(
Matrix,
Tridiagonal((5e-3) * ones(output_dim - 1), (2e-2) * ones(output_dim), (5e-3) * ones(output_dim - 1)),
),
]
lambdas = [
exp((1 / output_dim) * sum(log.(eigvals(cov_mats[1])))) * I, #det(C)^{1/m}*I
exp((1 / output_dim) * sum(log.(eigvals(cov_mats[2])))) * I,
cov_mats[3],
cov_mats[4],
]
# use learnt hyperparameters from nd_to_md_regression_direct_withcov.jl
hps = [
[
0.4593643339085826,
3.4402666569048286,
0.7969152294959352,
1.6851794464936298,
3.4527613874722656,
5.854154858415093,
2.302845969371132,
],
[
0.5262518700390091,
3.101856977561892,
0.26825394655980433,
0.7441825061473302,
2.82685046470828,
1.6584531227433983,
1.7630307816260378,
],
[
1.0743245054960715,
2.431492517819835,
1.3719239685183025,
2.1201669372745564,
5.5003047613684934,
4.331847045019546,
2.2668423707079453,
],
[
0.3498665262324442,
2.9456636865429298,
0.22302714358146644,
4.1288684250215555,
0.43381960299341393,
5.405698886196184,
0.5746603230965016,
],
]
for (cov_mat, lambda, hp, exp_name) in zip(cov_mats, lambdas, hps, exp_names)
println(exp_name)
U = ones(1, 1)
cholV = flat_to_chol(hp[2:(Int(0.5 * output_dim * (output_dim + 1)) + 1)])
V = hp[1] * (cholV * permutedims(cholV, (2, 1)) + hp[1] * I)
noise_dist = MvNormal(zeros(output_dim), cov_mat)
noise = rand(rng, noise_dist, n_data)
y = ftest_1d_to_3d(x) + noise
io_pairs = PairedDataContainer(x, y)
n_test = 200
# xtestvec = rand(rng, MvNormal(zeros(input_dim), I), n_test)
xtestvec = rand(rng, Uniform(-2.01, 2.01), (1, n_test))
xtest = DataContainer(xtestvec)
ytest_nonoise = ftest_1d_to_3d(get_data(xtest))
# numbers from examples/hyperparameter_learning/nd_to_md_regression_direct_withcov.jl
M = zeros(input_dim, output_dim)
dist = MatrixNormal(M, U, V) #produces matrix samples
pd = ParameterDistribution(
Dict(
"distribution" => Parameterized(dist),
"constraint" => repeat([no_constraint()], input_dim * output_dim),
"name" => "xi",
),
)
feature_sampler = FeatureSampler(pd, output_dim, rng = copy(rng))
vff = VectorFourierFeature(n_features, output_dim, feature_sampler)
batch_sizes = Dict("train" => 500, "test" => 500, "feature" => 500)
rfm_batch = RandomFeatureMethod(vff, batch_sizes = batch_sizes, regularization = lambda)
fitted_batched_features = fit(rfm_batch, io_pairs)
#quick test for the m>np case (changes the regularization)
if exp_name == "diagonal-lambdamat"
vff_tmp = VectorFourierFeature(n_test * output_dim + 1, output_dim, feature_sampler) #m > np
rfm_tmp = RandomFeatureMethod(vff_tmp, regularization = lambda)
# @test_logs (:info,) fit(rfm_tmp, io_pairs) loop vec throws some warning - messing this test up
fit_tmp = fit(rfm_tmp, io_pairs)
@test fit_tmp.regularization ≈ inv(lambda) # exp(1.0 / output_dim * log(det(lambda))) * I
end
# test prediction L^2 error of mean
prior_mean, prior_cov = predict_prior(rfm_batch, xtest) # predict inputs from unfitted features
# 2 x n, 2 x 2 x n
priorL2err = sqrt.(sum((ytest_nonoise - prior_mean) .^ 2))
priorweightedL2err = [0.0]
for i in 1:n_test
diff = reshape(ytest_nonoise[:, i] - prior_mean[:, i], :, 1)
priorweightedL2err .+= sum(permutedims(diff, (2, 1)) * inv(prior_cov[:, :, i] + cov_mat) * diff)
end
priorweightedL2err = sqrt.(priorweightedL2err)[:]
println("Prior for 1d->3d")
println("L2 error: ", priorL2err)
# println("weighted L2 error: ", priorweightedL2err)
pred_mean, pred_cov = predict(rfm_batch, fitted_batched_features, xtest)
# and other internal methods not called by predict
pred_cov_tmp, features_tmp = predictive_cov(rfm_batch, fitted_batched_features, xtest)
pred_mean_tmp = predictive_mean(rfm_batch, fitted_batched_features, xtest, features_tmp)
@test all(isapprox.(pred_mean, pred_mean_tmp, atol = tol))
@test all(isapprox.(pred_cov, pred_cov_tmp, atol = tol))
if exp_name ∈ ["correlated-lambdaconst", "diagonal-lambdamat"]
pmtmp = similar(pred_mean)
pctmp = similar(pred_cov)
buffer = zeros(size(pctmp, 3), size(pctmp, 1), n_features) # n x p x m
predict!(rfm_batch, fitted_batched_features, xtest, pmtmp, pctmp, buffer)
@test all(isapprox.(pred_mean, pmtmp, atol = tol))
@test all(isapprox.(pred_cov, pctmp, atol = tol))
# now create features in cov and pass to mean (check these methods)
pmtmp2 = similar(pred_mean)
pctmp2 = similar(pred_cov)
features_tmp = predictive_cov!(rfm_batch, fitted_batched_features, xtest, pctmp2, buffer)
predictive_mean!(rfm_batch, fitted_batched_features, xtest, pmtmp2, features_tmp)
@test all(isapprox.(pred_mean, pmtmp2, atol = tol))
@test all(isapprox.(pred_cov, pctmp2, atol = tol))
end
# no threading
if exp_name ∈ ["correlated-lambdamat", "diagonal-lambdamat"]
rfm_nothread = RandomFeatureMethod(vff, regularization = lambda, tullio_threading = false)
fitted_features_nothread = fit(rfm_nothread, io_pairs)
coeffs = get_coeffs(fitted_batched_features)
coeffs_nothread = get_coeffs(fitted_features_nothread)
@test coeffs ≈ coeffs_nothread
# it appears there is a significant difference in the linear algebra with and without threads here.
tol_tmp = 1e-10
prior_mean_nothread, prior_cov_nothread = predict_prior(rfm_nothread, xtest, tullio_threading = false) # predict inputs from unfitted features
@test all(isapprox.(prior_mean_nothread, prior_mean, atol = tol_tmp))
@test all(isapprox.(prior_cov_nothread, prior_cov, atol = tol_tmp))
pred_mean_nothread, pred_cov_nothread =
predict(rfm_nothread, fitted_features_nothread, xtest, tullio_threading = false)
@test all(isapprox.(pred_mean_nothread, pred_mean, atol = tol_tmp))
@test all(isapprox.(pred_cov_nothread, pred_cov, atol = tol_tmp))
pmr_nothread = similar(pred_mean)
pcr_nothread = similar(pred_cov)
buffer = zeros(size(pred_cov, 3), size(pred_cov, 1), n_features) # n x p x m
predict!(
rfm_nothread,
fitted_features_nothread,
xtest,
pmr_nothread,
pcr_nothread,
buffer,
tullio_threading = false,
)
@test all(isapprox.(pmr_nothread, pred_mean, atol = tol_tmp))
@test all(isapprox.(pcr_nothread, pred_cov, atol = tol_tmp))
end
#println(pred_mean)
L2err = sqrt.(sum((ytest_nonoise - pred_mean) .^ 2))
weightedL2err = [0.0]
#for i in 1:n_test
# diff = reshape(ytest_nonoise[:, i] - pred_mean[:, i], :, 1)
# weightedL2err .+= sum(permutedims(diff, (2, 1)) * inv(pred_cov[:, :, i] + cov_mat) * diff)
#end
#weightedL2err = sqrt.(weightedL2err)[:]
println("Posterior for 1d->3d")
println("L2 error: ", L2err)
#println("weighted L2 error: ", weightedL2err)
@test L2err < priorL2err
#@test weightedL2err < priorweightedL2err
if TEST_PLOT_FLAG
# learning on Normal(0,1) dist, forecast on (-2.01,2.01)
xrange = reshape(collect(-2.01:0.02:2.01), 1, :)
yrange = ftest_1d_to_3d(xrange)
pred_mean_slice, pred_cov_slice = predict(rfm_batch, fitted_batched_features, DataContainer(xrange))
for i in 1:output_dim
pred_cov_slice[i, i, :] = max.(pred_cov_slice[i, i, :], 0.0)
end
#plot diagonal
xplot = xrange[:]
plt = plot(
xplot,
yrange[1, :],
show = false,
color = "black",
linewidth = 5,
size = (600, 600),
legend = :topleft,
label = "Target",
)
plot!(
xplot,
yrange[2, :],
show = false,
color = "black",
linewidth = 5,
size = (600, 600),
legend = :topleft,
label = "Target",
)
plot!(
xplot,
yrange[3, :],
show = false,
color = "black",
linewidth = 5,
size = (600, 600),
legend = :topleft,
label = "Target",
)
scatter!(x[:], y[1, :], color = "blue", label = "", marker = :x)
plot!(
xplot,
pred_mean_slice[1, :],
ribbon = [2 * sqrt.(pred_cov_slice[1, 1, :]); 2 * sqrt.(pred_cov_slice[1, 1, :])],
label = "Fourier",
color = "blue",
)
scatter!(x[:], y[2, :], color = "red", label = "", marker = :x)
plot!(
xplot,
pred_mean_slice[2, :],
ribbon = [2 * sqrt.(pred_cov_slice[2, 2, :]); 2 * sqrt.(pred_cov_slice[2, 2, :])],
label = "Fourier",
color = "red",
)
scatter!(x[:], y[3, :], color = "green", label = "", marker = :x)
plot!(
xplot,
pred_mean_slice[3, :],
ribbon = [2 * sqrt.(pred_cov_slice[3, 3, :]); 2 * sqrt.(pred_cov_slice[3, 3, :])],
label = "Fourier",
color = "green",
)
savefig(plt, joinpath(@__DIR__, "Fit_and_predict_1D_to_3D_" * exp_name * ".pdf"))
end
end
end
end
| RandomFeatures | https://github.com/CliMA/RandomFeatures.jl.git |
|
[
"Apache-2.0"
] | 0.3.3 | d639d537c14127a70c8bd3e0158ecbccfb2a54a3 | code | 3798 | using Test
using Distributions
using StableRNGs
using StatsBase
using LinearAlgebra
using Random
using RandomFeatures.ParameterDistributions
using RandomFeatures.Samplers
seed = 2022
@testset "Samplers" begin
# create a Gaussian(0,4) distribution with EKP's ParameterDistribution constructors
μ_c = -4.0
σ_c = 1.0
pd = constrained_gaussian("xi", μ_c, σ_c, -Inf, 0.0)
#1d output space
fsampler = FeatureSampler(pd) # takes a uniform with shift as the bias
unif_pd = ParameterDistribution(
Dict("distribution" => Parameterized(Uniform(0, 2 * π)), "constraint" => no_constraint(), "name" => "bias"),
)
fsamplerbias = FeatureSampler(pd, unif_pd)#provide bias distribution explicitly
full_pd = combine_distributions([pd, unif_pd])
@test get_parameter_distribution(fsampler) == full_pd
@test get_parameter_distribution(fsamplerbias) == full_pd
#5d input - 3d output-space
output_dim = 3
pd_3d = constrained_gaussian("xi", μ_c, σ_c, -Inf, 0.0, repeats = 5)
fsampler_3d = FeatureSampler(pd_3d, output_dim) # 3d output space
unif_pd_3d = ParameterDistribution(
Dict(
"distribution" => VectorOfParameterized(repeat([Uniform(0, 2 * π)], output_dim)),
"constraint" => repeat([no_constraint()], output_dim),
"name" => "bias",
),
)
fsamplerbias_3d = FeatureSampler(pd_3d, unif_pd_3d)#provide bias distribution explicitly
full_pd_3d = combine_distributions([pd_3d, unif_pd_3d])
@test get_parameter_distribution(fsampler_3d) == full_pd_3d
@test get_parameter_distribution(fsamplerbias_3d) == full_pd_3d
# and other option for settings
no_bias = nothing
sampler_no_bias = FeatureSampler(pd, no_bias)
@test pd == get_parameter_distribution(sampler_no_bias)
# test method: sample
function sample_to_Sample(pd::ParameterDistribution, samp::AbstractMatrix)
constrained_samp = transform_unconstrained_to_constrained(pd, samp)
#now create a Samples-type distribution from the samples
s_names = get_name(pd)
s_slices = batch(pd) # e.g., [1, 2:3, 4:9]
s_samples = [Samples(constrained_samp[slice, :]) for slice in s_slices]
s_constraints = [repeat([no_constraint()], size(slice, 1)) for slice in s_slices]
return combine_distributions([
ParameterDistribution(ss, sc, sn) for (ss, sc, sn) in zip(s_samples, s_constraints, s_names)
])
end
# first with global rng
Random.seed!(seed)
sample1 = sample(fsampler) # produces a Samples ParameterDistribution
Random.seed!(seed)
@test sample1 == sample_to_Sample(full_pd, sample(full_pd))
n_samples = 40
Random.seed!(seed)
sample2 = sample(fsampler, n_samples)
Random.seed!(seed)
@test sample2 == sample_to_Sample(full_pd, sample(full_pd, n_samples))
# now with two explicit rng's
rng1 = Random.MersenneTwister(seed)
sampler_rng1 = FeatureSampler(pd, rng = copy(rng1))
@test get_rng(sampler_rng1) == copy(rng1)
sample3 = sample(sampler_rng1)
@test !(get_rng(sampler_rng1) == copy(rng1))
@test sample3 == sample_to_Sample(full_pd, sample(copy(rng1), full_pd, 1))
sampler_rng1 = FeatureSampler(pd, rng = copy(rng1))
sample4 = sample(sampler_rng1, n_samples)
@test sample4 == sample_to_Sample(full_pd, sample(copy(rng1), full_pd, n_samples))
#this time override use rng2 to override the default Random.GLOBAL_RNG at the point of sampling
rng2 = StableRNG(seed)
sample5 = sample(copy(rng2), fsampler)
sample5 == sample_to_Sample(full_pd, sample(copy(rng2), full_pd, 1))
sample6 = sample(copy(rng2), fsampler, n_samples)
@test sample6 == sample_to_Sample(full_pd, sample(copy(rng2), full_pd, n_samples))
end
| RandomFeatures | https://github.com/CliMA/RandomFeatures.jl.git |
|
[
"Apache-2.0"
] | 0.3.3 | d639d537c14127a70c8bd3e0158ecbccfb2a54a3 | code | 2596 | using Test
#using Distributions
#using StableRNGs
#using StatsBase
using LinearAlgebra
#using Random
using RandomFeatures.Utilities
@testset "Utilities" begin
# batch generator
x = [i + j for i in 1:10, j in 1:234]
batch_size = 10
x1 = batch_generator(x, batch_size) #default dims=1
@test length(x1) == Int(ceil(size(x, 1) / batch_size))
@test x1[1] == x
x2 = batch_generator(x, 10, dims = 2)
@test length(x2) == Int(ceil(size(x, 2) / batch_size))
for i in 1:length(x2)
@test x2[i] == (
i < length(x2) ? x[:, ((i - 1) * batch_size + 1):(i * batch_size)] : x[:, ((i - 1) * batch_size + 1):end]
)
end
x3 = batch_generator(x, 0) #default dims=1
@test length(x3) == 1
@test x3[1] == x
# Decomposition - only pinv, chol and svd available
M = 30
x = [i + j for i in 1:M, j in 1:M]
x = 1 ./ (x + x') + 1e-3 * I
# internally RHS are stored with three indices.
# (n_samples, output_dim, n_features)
b = ones(1, 1, M)
xsolve = zeros(size(b))
xsolve[1, 1, :] = x \ b[1, 1, :]
xsvd = Decomposition(x, "svd")
@test get_decomposition(xsvd) == svd(x)
@test get_parametric_type(xsvd) == Factor
@test get_full_matrix(xsvd) == x
@test get_inv_decomposition(xsvd) ≈ inv(svd(x))
xpinv = Decomposition(x, "pinv")
@test isposdef(x)
xchol = Decomposition(x, "cholesky")
@test_throws ArgumentError Decomposition(x, "qr")
xbad = [1.0 1.0; 1.0 0.0] # not pos def
xbadchol = Decomposition(xbad, "cholesky")
@test isposdef(get_full_matrix(xbadchol))
xsvdsolve = linear_solve(xsvd, b)
xcholsolve = linear_solve(xchol, b)
xpinvsolve = linear_solve(xpinv, b)
@test xsvdsolve ≈ xsolve
@test xcholsolve ≈ xsolve
@test xpinvsolve ≈ xsolve
xsvdvecsolve = linear_solve(xsvd, vec(b))
xcholvecsolve = linear_solve(xchol, vec(b))
xpinvvecsolve = linear_solve(xpinv, vec(b))
@test xsvdvecsolve ≈ vec(xsolve)
@test xcholvecsolve ≈ vec(xsolve)
@test xpinvvecsolve ≈ vec(xsolve)
y = Float64[x for i in 1:M, x in 1:M]
ypinv = Decomposition(y, "pinv")
@test get_decomposition(ypinv) == pinv(y)
@test get_parametric_type(ypinv) == PseInv
@test get_full_matrix(ypinv) == y
# to show pinv gets the right solution in a singular problem
@test_throws SingularException y \ b[1, 1, :]
ysolve = zeros(size(b))
ysolve[1, 1, :] = pinv(y) * b[1, 1, :]
@test linear_solve(ypinv, b) ≈ ysolve
ysolvevec = pinv(y) * vec(b)
@test linear_solve(ypinv, vec(b)) ≈ ysolvevec
end
| RandomFeatures | https://github.com/CliMA/RandomFeatures.jl.git |
|
[
"Apache-2.0"
] | 0.3.3 | d639d537c14127a70c8bd3e0158ecbccfb2a54a3 | docs | 1588 | # RandomFeatures.jl
RandomFeatures is a Julia implementation of random features for high-dimensional function emulation. It builds on an earlier Python implementation by Nick Nelsen, Maya Mutic, and Oliver Dunbar ([RandomFeatureKernel](https://github.com/odunbar/RandomFeatureKernel)).
| **Documentation** | [![dev][docs-latest-img]][docs-latest-url] |
|----------------------|--------------------------------------------------|
| **DOI** | [![DOI][zenodo-img]][zenodo-latest-url] |
| **Docs Build** | [![docs build][docs-bld-img]][docs-bld-url] |
| **Unit tests** | [![unit tests][unit-tests-img]][unit-tests-url] |
| **Code Coverage** | [![codecov][codecov-img]][codecov-url] |
[zenodo-img]: https://zenodo.org/badge/523830850.svg
[zenodo-latest-url]: https://zenodo.org/badge/latestdoi/523830850
[docs-latest-img]: https://img.shields.io/badge/docs-latest-blue.svg
[docs-latest-url]: https://CliMA.github.io/RandomFeatures.jl/dev/
[docs-bld-img]: https://github.com/CliMA/RandomFeatures.jl/actions/workflows/Docs.yml/badge.svg?branch=main
[docs-bld-url]: https://github.com/CliMA/RandomFeatures.jl/actions/workflows/Docs.yml
[unit-tests-img]: https://github.com/CliMA/RandomFeatures.jl/actions/workflows/Tests.yml/badge.svg?branch=main
[unit-tests-url]: https://github.com/CliMA/RandomFeatures.jl/actions/workflows/Tests.yml
[codecov-img]: https://codecov.io/gh/CliMA/RandomFeatures.jl/branch/main/graph/badge.svg
[codecov-url]: https://codecov.io/gh/CliMA/RandomFeatures.jl
### Requirements
Julia version 1.6+
| RandomFeatures | https://github.com/CliMA/RandomFeatures.jl.git |
|
[
"Apache-2.0"
] | 0.3.3 | d639d537c14127a70c8bd3e0158ecbccfb2a54a3 | docs | 6505 | # Contributing
Thank you for considering contributing to `RandomFeatures`! We encourage opening issues and pull requests (PRs).
## What to contribute?
- The easiest way to contribute is by using `RandomFeatures`, identifying
problems and opening issues;
- You can try to tackle an existing [issue](https://github.com/CliMA/RandomFeatures.jl/issues). It is best to outline your proposed solution in the issue thread before implementing it in a PR;
- Write an example or tutorial. It is likely that other users may find your use of `RandomFeatures` insightful;
- Improve documentation or comments if you found something hard to use;
- Implement a new feature if you need it. We strongly encourage opening an issue to make sure the administrators are on board before opening a PR with an unsolicited feature addition.
## Using `git`
If you are unfamiliar with `git` and version control, the following guides
will be helpful:
- [Atlassian (bitbucket) `git`
tutorials](https://www.atlassian.com/git/tutorials). A set of tips and tricks
for getting started with `git`.
- [GitHub's `git` tutorials](https://try.github.io/). A set of resources from
GitHub to learn `git`.
### Forks and branches
Create your own fork of `RandomFeatures` [on
GitHub](https://github.com/CliMA/RandomFeatures.jl) and check out your copy:
```
$ git clone https://github.com/<your-username>/RandomFeatures.jl.git
$ cd RandomFeatures.jl
```
Now you have access to your fork of `RandomFeatures` through `origin`. Create a branch for your feature; this will hold your contribution:
```
$ git checkout -b <branchname>
```
#### Some useful tips
- When you start working on a new feature branch, make sure you start from
main by running: `git checkout main` and `git pull`.
- Create a new branch from main by using `git checkout -b <branchname>`.
### Develop your feature
Make sure you add tests for your code in `test/` and appropriate documentation in the code and/or
in `docs/`. Before committing your changes, you can verify their behavior by running the tests, the examples, and building the documentation [locally](https://clima.github.io/RandomFeatures.jl/previews/PR157/installation_instructions/). In addition, make sure your feature follows the formatting guidelines by running
```
julia --project=.dev .dev/climaformat.jl .
```
from the `RandomFeatures.jl` directory.
### Squash and rebase
When your PR is ready for review, clean up your commit history by squashing
and make sure your code is current with `RandomFeatures.jl` main by rebasing. The general rule is that a PR should contain a single commit with a descriptive message.
To make sure you are up to date with main, you can use the following workflow:
```
$ git checkout main
$ git pull
$ git checkout <name_of_local_branch>
$ git rebase main
```
This may create conflicts with the local branch. The conflicted files will be outlined by git. To resolve conflicts,
we have to manually edit the files (e.g. with vim). The conflicts will appear between >>>>, ===== and <<<<<.
We need to delete these lines and pick what version we want to keep.
To squash your commits, you can use the following command:
```
$ git rebase -i HEAD~n
```
where `n` is the number of commits you need to squash into one. Then, follow the instructions in the terminal. For example, to squash 4 commits:
```
$ git rebase -i HEAD~4
```
will open the following file in (typically) vim:
```
pick 01d1124 <commit message 1>
pick 6340aaa <commit message 2>
pick ebfd367 <commit message 3>
pick 30e0ccb <commit message 4>
# Rebase 60709da..30e0ccb onto 60709da
#
# Commands:
# p, pick = use commit
# e, edit = use commit, but stop for amending
# s, squash = use commit, but meld into previous commit
#
# If you remove a line here THAT COMMIT WILL BE LOST.
# However, if you remove everything, the rebase will be aborted.
##
```
We want to keep the first commit and squash the last 3. We do so by changing the last three commits to `squash` and then do `:wq` on vim.
```
pick 01d1124 <commit message 1>
squash 6340aaa <commit message 2>
squash ebfd367 <commit message 3>
squash 30e0ccb <commit message 4>
# Rebase 60709da..30e0ccb onto 60709da
#
# Commands:
# p, pick = use commit
# e, edit = use commit, but stop for amending
# s, squash = use commit, but meld into previous commit
#
# If you remove a line here THAT COMMIT WILL BE LOST.
# However, if you remove everything, the rebase will be aborted.
```
Then in the next screen that appears, we can just delete all messages that
we do not want to show in the commit. After this is done and we are back to
the console, we have to force push. We need to force push because we rewrote
the local commit history.
```
$ git push -u origin <name_of_local_branch> --force
```
You can find more information about squashing [here](https://github.com/edx/edx-platform/wiki/How-to-Rebase-a-Pull-Request#squash-your-changes).
### Unit testing
Currently a number of checks are run per commit for a given PR.
- `JuliaFormatter` checks if the PR is formatted with `.dev/climaformat.jl`.
- `Documentation` rebuilds the documentation for the PR and checks if the docs
are consistent and generate valid output.
- `Unit Tests` run subsets of the unit tests defined in `tests/`, using `Pkg.test()`.
The tests are run in parallel to ensure that they finish in a reasonable time.
The tests only run the latest commit for a PR, branch and will kill any stale jobs on push.
These tests are only run on linux (Ubuntu LTS).
Unit tests are run against every new commit for a given PR,
the status of the unit-tests are not checked during the merge
process but act as a sanity check for developers and reviewers.
Depending on the content changed in the PR, some CI checks that
are not necessary will be skipped. For example doc only changes
do not require the unit tests to be run.
### The merge process
We use [`bors`](https://bors.tech/) to manage merging PR's in the the `RandomFeatures` repo.
If you're a collaborator and have the necessary permissions, you can type
`bors try` in a comment on a PR to have integration test suite run on that
PR, or `bors r+` to try and merge the code. Bors ensures that all integration tests
for a given PR always pass before merging into `main`. The integration tests currently run example cases in `examples/`. Any breaking changes will need to also update the `examples/`, else bors will fail.
| RandomFeatures | https://github.com/CliMA/RandomFeatures.jl.git |
|
[
"Apache-2.0"
] | 0.3.3 | d639d537c14127a70c8bd3e0158ecbccfb2a54a3 | docs | 1507 | # RandomFeatures
A julia package to construct and apply random feature methods for regression.
RandomFeatures can be viewed as an approximation of kernel methods. They can be used both as a substitution in Kernel ridge regression and Gaussian Process regresison.
Module | Purpose
---------------|-----------------------------------------------------------------------------------------
RandomFeatures | Container of all tools
Samplers | Samplers for constrained probability distributions
Features | Builds feature functions from input data
Methods | Fits features to output data, and prediction on new inputs
Utilities | Utilities to aid batching, and matrix decompositions
## Highlights
- A flexible probability distribution backend with which to sample features, with a comprehensive API
- A library of modular scalar functions to choose from
- Methods for solving ridge regression or Gaussian Process regression problem, with functions for producing predictive means and (co)variances using fitted features.
- Examples that demonstrate using the package `EnsembleKalmanProcesses.jl` to optimize hyperparameters of the probability distribution.
## Authors
`RandomFeatures.jl` is being developed by the [Climate Modeling
Alliance](https://clima.caltech.edu). The main developers are Oliver R. A. Dunbar and Thomas Jackson, with acknowledgement that the code was based on a python repository developed by Oliver R. A. Dunbar, Maya Mutic, and Nicholas H. Nelsen.
| RandomFeatures | https://github.com/CliMA/RandomFeatures.jl.git |
|
[
"Apache-2.0"
] | 0.3.3 | d639d537c14127a70c8bd3e0158ecbccfb2a54a3 | docs | 2440 | # Installation
RandomFeatures.jl is a not a registered Julia package. To install perform the following in the `julia` command prompt
```julia
julia> ]
(v1.8) pkg> add https://github.com/CliMA/RandomFeatures.jl
(v1.8) pkg> instantiate
```
This will install the latest version of the package Git repository
You can run the tests via the package manager by:
```julia
julia> ]
(v1.8) pkg> test RandomFeatures
```
### Cloning the repository
If you are interested in getting your hands dirty and modifying the code then, you can also
clone the repository and then instantiate, e.g.,
```
> cd RandomFeatures.jl
> julia --project -e 'using Pkg; Pkg.instantiate()'
```
!!! info "Do I need to clone the repository?"
Most times, cloning the repository in not necessary. If you only want to use the package's
functionality, adding the packages as a dependency on your project is enough.
### [Running the test suite](@id test-suite)
You can run the package's tests:
```
> julia --project -e 'using Pkg; Pkg.test()'
```
Alternatively, you can do this from within the repository:
```
> julia --project
julia> ]
(RandomFeatures) pkg> test
```
!!! note "Plot outputs"
Tests will output plots by setting the environment variable `TEST_PLOT_FLAG`. For example,
```
julia> ENV["TEST_PLOT_FLAG"] = true
```
### Building the documentation locally
Once the project is built, you can build the project documentation under the `docs/` sub-project:
```
> julia --project=docs/ -e 'using Pkg; Pkg.develop(PackageSpec(path=pwd())); Pkg.instantiate()'
> julia --project=docs/ docs/make.jl
```
The locally rendered HTML documentation can be viewed at `docs/build/index.html`
### Running repository examples
We have a selection of examples, found within the `examples/` directory to demonstrate different use of our toolbox.
Each example directory contains a `Project.toml`
To build with the latest `RandomFeatures.jl` release:
```
> cd examples/example-name/
> julia --project -e 'using Pkg; Pkg.instantiate()'
> julia --project example-file-name.jl
```
If you wish to run a local modified version of `RandomFeatures.jl` then try the following (starting from the `RandomFeatures.jl` package root)
```
> cd examples/example-name/
> julia --project
> julia> ]
> (example-name)> rm RandomFeatures.jl
> (example-name)> dev ../..
> (example-name)> instantiate
```
followed by
```
> julia --project example-file-name.jl
```
| RandomFeatures | https://github.com/CliMA/RandomFeatures.jl.git |
|
[
"Apache-2.0"
] | 0.3.3 | d639d537c14127a70c8bd3e0158ecbccfb2a54a3 | docs | 2934 | # Explicit bottlenecks
### Explicit bottlenecks
- By far the highest computational demand for high-dimensional and/or large data systems is the building of the system matrix, particularly the multiplication ``\Phi^T\Phi`` (for scalar) and ``\Phi_{n,i,m}\Phi_{n,j,m}``. These can be accelerated by multithreading (see below)
- For large number of features, the inversion `inv(factorization(system_matrix))` is noticeable, though typically still small when in the regime of `n_features << n_samples * output_dim`.
- For the vector case, the square output-dimensional regularization matrix ``\Lambda`` must be inverted. For high-dimensional spaces, diagonal approximation will avoid this.
- Prediction bottlenecks are largely due to allocations and matrix multiplications. Please see our `predict!()` methods which allow for users to pass in preallocated of arrays. This is very beneficial for repeated predictions.
- For systems without enough regularization, positive definiteness may need to be enforced. If done too often, it has non-negligible cost, as it involves calculating eigenvalues of the non p.d. matrix (particularly `abs(min(eigval)`) that is then added to the diagonal. It is better to add more regularization into ``\Lambda``
### Implicit bottlenecks
- The optimization of hyperparameters is a costly operation that may require construction and evaluation of thousands of `RandomFeatureMethod`s. The dimensionality (i.e. complexity) of this task will depend on how many free parameters are taken to be within a distribution though. ``\mathcal{O}(1000s)`` parameters may take even hours to optimize (on multiple threads).
# Parallelism/memory
- We make use of [`Tullio.jl`](https://github.com/mcabbott/Tullio.jl) which comes with in-built memory management. We are phasing out our own batches in favour of using this for now.
- [`Tullio.jl`](https://github.com/mcabbott/Tullio.jl) comes with multithreading routines, Simply call the code with `julia --project -t n_threads` to take advantage of this. Depending on problem size you may wish to use your own external threading, Tullio will greedily steal threads in this case. To prevent this interference we provide a keyword argument:
```julia
RandomFeatureMethod(... ; tullio_threading=false) # serial threading during the build and fit! methods
predict(...; tullio_threading=false) # serial threading for prediction
predict!(...; tullio_threading=false) # serial threading for in-place prediction
```
An example where `tullio_threading=false` is useful is when optimizing hyperparameters with ensemble methods (see our examples), here one could use threading/multiprocessing approaches across ensemble members to make better use of the embarassingly parallel framework (e.g. see this page for [EnsembleKalmanProcessess: Parallelism and HPC](https://clima.github.io/EnsembleKalmanProcesses.jl/dev/parallel_hpc/).
!!! note
We do not yet have GPU functionality
| RandomFeatures | https://github.com/CliMA/RandomFeatures.jl.git |
|
[
"Apache-2.0"
] | 0.3.3 | d639d537c14127a70c8bd3e0158ecbccfb2a54a3 | docs | 7190 | # Setting up a Scalar Random Feature Method
A basic creation of sigmoid-based scalar-valued random feature method is given as follows
```julia
# user inputs required:
# paired input-output data - io_pairs::PairedDataContainer
# parameter distribution - pd::ParameterDistribution
# number of features - n_features::Int
feature_sampler = FeatureSampler(pd)
sigmoid_sf = ScalarFeature(n_features, feature_sampler, Sigmoid())
rfm = RandomFeatureMethod(sigmoid_sf)
fitted_features = fit(rfm, io_pairs)
```
Prediction at new inputs are made with
``` julia
# user inputs required
# new test inputs - i_test::DataContainer
predicted_mean, predicted_var = predict(rfm, fitted_features, i_test)
```
We see the core objects
- [`ParameterDistribution`](https://clima.github.io/EnsembleKalmanProcesses.jl/stable/parameter_distributions/): a flexible container for constructing constrained parameter distributions, (from [`EnsembleKalmanProcesses.jl`](https://clima.github.io/EnsembleKalmanProcesses.jl/stable/))
- [`(Paired)DataContainer`](https://clima.github.io/EnsembleKalmanProcesses.jl/stable/internal_data_representation): consistent storage objects for input-output pairs or just inputs, (from [`EnsembleKalmanProcesses.jl`](https://clima.github.io/EnsembleKalmanProcesses.jl/stable))
- `FeatureSampler`: Builds the random feature distributions from a parameter distribution
- `ScalarFeature`: Builds a feature from the random feature distributions
- `RandomFeatureMethod`: Sets up the learning problem (with e.g. batching, regularization)
- `Fit`: Stores fitted features from the `fit` method
!!! note "See some examples!"
Running the [test suite](@ref test-suite) with `TEST_PLOT_FLAG = true` will produce some ``1``-D``\to 1``-D and ``d``-D ``\to 1``-D example fits produced by [`test/Methods/runtests.jl`](https://github.com/CliMA/RandomFeatures.jl/tree/main/test/Methods). These use realistic optional arguments and distributions.
## `ParameterDistributions`
The simplest construction of parameter distributions is by using the `constrained_gaussian` construction.
```julia
using RandomFeatures.ParameterDistributions
```
#### **Recommended** univariate and product distribution build
The easiest constructors are for univariate and products
```julia
# constrained_gaussian("xi", desired_mean, desired_std, lower_bound, upper_bound)
one_dim_pd = constrained_gaussian("xi", 10, 5, -Inf, Inf) # Normal distribution
five_dim_pd = constrained_gaussian("xi", 10, 5, 0, Inf, repeats = 5) # Log-normal (approx mean 10 & approx std 5) in each of the five dimensions
```
#### Simple multivariate distribution
Simple unconstrained distribution is created as follows.
```julia
using Distributions
μ = zeros(3)
Σ = SymTridiagonal(2 * ones(3), ones(2))
three_dim_pd = ParameterDistribution(
Dict("distribution" => Parameterized(MvNormal(μ,Σ)), # the distribution
"constraint" => repeat([no_constraint()],3), # constraints
"name" => "xi",
),
)
```
!!! note " xi? "
The name of the distribution of the features **must** be `"xi"`
!!! note "Further distributions"
Combined distributions can be made using the `VectorOfParameterized`, or histogram-based distributions with `Samples`. Extensive documentation of distributions and constraints is found [here](https://clima.github.io/EnsembleKalmanProcesses.jl/stable/parameter_distributions/).
## `Sampler`
The random feature distribution ``\mathcal{D}`` is built of two distributions, the user-provided ``\mathcal{D}_\xi`` (`"xi"`) and a bias distribution (`"bias"`). The bias distribution is one-dimensional, and commonly uniformly distributed, so we provide an additional constructor for this case
```math
\theta = (\xi,b) \sim \mathcal{D} = (\mathcal{D}_\xi, \mathcal{U}([c_\ell,c_u]))
```
Defaults ``c_\ell = 0, c_u = 2\pi``. In the code this is built as
```julia
sampler = FeatureSampler(
parameter_distribution;
uniform_shift_bounds = [0, 2 * π],
rng = Random.GLOBAL_RNG
)
```
A random number generator can be provided. The second argument can be replaced with a general ``1``-D `ParameterDistribution` with a name-field `"bias"`.
## Features: `ScalarFeature` ``d``-D ``\to 1``-D
Given ``x\in\mathbb{R}^n`` input data, and ``m`` (`n_features`) features, `Features` produces samples of
```math
\Phi(x;\theta_j) = \sigma f(\xi_j\cdot x + b_j),\qquad \theta_j=(\xi_j,b_j) \sim \mathcal{D}\qquad \mathrm{for}\ j=1,\dots,m
```
Note that ``\Phi \in \mathbb{R}^{n,m,p}``. Choosing``f`` as a cosine to produce fourier features
```julia
sf = ScalarFourierFeature(
n_features,
sampler;
kwargs...
)
```
``f`` as a neuron activation produces a neuron feature (`ScalarActivation` listed [here](@ref scalar-functions))
```julia
sf = ScalarNeuronFeature(
n_features,
sampler;
activation_fun = Relu(),
kwargs...
)
```
The keyword `feature_parameters = Dict("sigma" => a)`, can be included to set the value of ``\sigma``.
## Method
The `RandomFeatureMethod` sets up the training problem to learn coefficients ``\beta\in\mathbb{R}^m`` from input-output training data ``(x,y)=\{(x_i,y_i)\}_{i=1}^n``, ``y_i \in \mathbb{R}`` and parameters ``\theta = \{\theta_j\}_{j=1}^m``:
```math
(\frac{1}{\lambda m}\Phi^T(x;\theta) \Phi(x;\theta) + I) \beta = \frac{1}{\lambda}\Phi^T(x;\theta)y
```
Where ``\lambda>0`` is a regularization parameter that effects the `+I`. The equation is written in this way to be consistent with the vector-output case.
```julia
rfm = RandomFeatureMethod(
sf;
regularization = 1e12 * eps() * I,
batch_sizes = ("train" => 0, "test" => 0, "feature" => 0),
)
```
One can select batch sizes to balance the space-time (memory-process) trade-off. when building and solving equations by setting values of `"train"`, test data `"test"` and number of features `"feature"`. The default is no batching (`0`).
!!! warning "Conditioning"
The problem is ill-conditioned without regularization.
If you encounters a Singular or Positive-definite exceptions, try increasing `regularization`
The solve for ``\beta`` occurs in the `fit` method. In the `Fit` object we store the system matrix, its factorization, and the inverse of the factorization. For many applications this is most efficient representation, as predictions (particularly of the covariance) are then only a matrix multiplication.
```julia
fitted_features = fit(
rfm,
io_pairs; # (x,y)
decomposition = "cholesky",
)
```
The decomposition is based off the [`LinearAlgebra.Factorize`](https://docs.julialang.org/en/v1/stdlib/LinearAlgebra/#man-linalg-factorizations) functions. For performance we have implemented only ("cholesky", "svd", and for ``\Lambda=0`` (not recommended) the method defaults to "pinv")
## Hyperparameters
[Coming soon]
!!! note
Hyperparameter selection is very important for a good random feature fit.
The hyperparameters are the parameters appearing in the random feature distribution ``\mathcal{D}``. We have an examples where an ensemble-based algorithm is used to optimize such parameters in [`examples/Learn_hyperparameters/`](https://github.com/CliMA/RandomFeatures.jl/tree/main/examples/Learn_hyperparameters)
| RandomFeatures | https://github.com/CliMA/RandomFeatures.jl.git |
|
[
"Apache-2.0"
] | 0.3.3 | d639d537c14127a70c8bd3e0158ecbccfb2a54a3 | docs | 7623 | # Setting up a Vector Random Feature Method
A basic creation of vector-valued Fourier-based random feature method is given as:
```julia
# user inputs required:
# paired input-output data - io_pairs::PairedDataContainer
# parameter distribution - pd::ParameterDistribution
# number of features - n_features::Int
feature_sampler = FeatureSampler(pd)
fourier_vf = VectorFourierFeature(n_features, feature_sampler)
rfm = RandomFeatureMethod(fourier_vf)
fitted_features = fit(rfm, io_pairs)
```
Prediction at new inputs are made with
``` julia
# user inputs required
# new test inputs - i_test::DataContainer
predicted_mean, predicted_cov = predict(rfm, fitted_features, i_test)
```
We see the core objects
- [`ParameterDistribution`](https://clima.github.io/EnsembleKalmanProcesses.jl/stable/parameter_distributions/): a flexible container for constructing constrained parameter distributions, (from [`EnsembleKalmanProcesses.jl`](https://clima.github.io/EnsembleKalmanProcesses.jl/stable/))
- [`(Paired)DataContainer`](https://clima.github.io/EnsembleKalmanProcesses.jl/stable/internal_data_representation): consistent storage objects for input-output pairs or just inputs, (from [`EnsembleKalmanProcesses.jl`](https://clima.github.io/EnsembleKalmanProcesses.jl/stable))
- `FeatureSampler`: Builds the random feature distributions from a parameter distribution
- `VectorFourierFeature`: Special constructor of a Cosine-based `VectorFeature` from the random feature distributions
- `RandomFeatureMethod`: Sets up the learning problem (with e.g. batching, regularization)
- `Fit`: Stores fitted features from the `fit` method
!!! note "See some examples!"
Running the [test suite](@ref test-suite) with `TEST_PLOT_FLAG = true` produces a ``1``-D``\to p``-D example produced by [`test/Methods/runtests.jl`](https://github.com/CliMA/RandomFeatures.jl/tree/main/test/Methods). These use realistic optional arguments and distributions.
## `ParameterDistributions`
The simplest construction of parameter distributions is by using the `constrained_gaussian` construction.
```julia
using RandomFeatures.ParameterDistributions
```
#### **Recommended** univariate and product distribution build
The easiest constructors are for univariate and products
```julia
# constrained_gaussian("xi", desired_mean, desired_std, lower_bound, upper_bound)
one_dim_pd = constrained_gaussian("xi", 10, 5, -Inf, Inf) # Normal distribution
five_dim_pd = constrained_gaussian("xi", 10, 5, 0, Inf, repeats = 5) # Log-normal (approx mean 10 & approx std 5) in each of the five dimensions
```
#### Simple matrixvariate distribution
Simple unconstrained distribution is created as follows.
```julia
using Distributions
d = 2
p = 5
M = zeros(d,p)
U = Diagonal(ones(d))
V = SymTridiagonal(2 * ones(p), ones(p - 1))
two_by_five_dim_pd = ParameterDistribution(
Dict("distribution" => Parameterized(MatrixNormal(M, U, V)), # the distribution
"constraint" => repeat([no_constraint()], d * p), # flattened constraints
"name" => "xi",
),
)
```
!!! note " xi? "
The name of the distribution of the features **must** be `"xi"`
!!! note "Further distributions"
Combined distributions can be made using the `VectorOfParameterized`, or histogram-based distributions with `Samples`. Extensive documentation of distributions and constraints is found [`here`](https://clima.github.io/EnsembleKalmanProcesses.jl/stable/parameter_distributions/).
## `Sampler`
The random feature distribution ``\mathcal{D}`` is built of two distributions, the user-provided ``\mathcal{D}_\Xi`` (`"xi"`) and a bias distribution (`"bias"`). The bias distribution is p-dimensional, and commonly uniformly distributed, so we provide an additional constructor for this case
```math
\theta = (\Xi,B) \sim \mathcal{D} = (\mathcal{D}_\Xi, \mathcal{U}([c_\ell,c_u]^p))
```
Defaults ``c_\ell = 0, c_u = 2\pi``. In the code this is built as
```julia
sampler = FeatureSampler(
parameter_distribution,
output_dim;
uniform_shift_bounds = [0,2*π],
rng = Random.GLOBAL_RNG
)
```
A random number generator can be provided. The second argument can be replaced with a general ``p``-D `ParameterDistribution` with a name-field `"bias"`.
## Features: `VectorFeature` ``d``-D ``\to p``-D
Given ``x\in\mathbb{R}^n`` input data, and ``m`` features, `Features` produces samples of
```math
(\Phi(x;\theta_j))_\ell = (\sigma f(\Xi_j x + B_j))_\ell,\qquad \theta_j=(\Xi_j,B_j) \sim \mathcal{D}\qquad \mathrm{for}\ j=1,\dots,m \ \text{and} \ \ell=1,\dots,p.
```
Note that ``\Phi \in \mathbb{R}^{n,m,p}``. Choosing ``f`` as a cosine produces fourier features
```julia
vf = VectorFourierFeature(
n_features,
output_dim,
sampler;
kwargs...
)
```
``f`` as a neuron activation produces a neuron feature (`ScalarActivation` listed [here](@ref scalar-functions))
```julia
vf = VectorNeuronFeature(
n_features,
output_dim,
sampler;
activation_fun = Relu(),
kwargs...
)
```
The keyword `feature_parameters = Dict("sigma" => a)`, can be included to set the value of ``\sigma``.
## Method
The `RandomFeatureMethod` sets up the training problem to learn coefficients ``\beta\in\mathbb{R}^m`` from input-output training data ``(x,y)=\{(x_i,y_i)\}_{i=1}^n``, ``y_i \in \mathbb{R}^p`` and parameters ``\theta = \{\theta_j\}_{j=1}^m``. Regularization is provided through ``\Lambda``, a user-provided `p-by-p` positive-definite regularization matrix (or scaled identity). In Einstein summation notation the method solves the following system
```math
(\frac{1}{m}\Phi_{n,i,p}(x;\theta) \, [I_{n,m} \otimes \Lambda^{-1}_{p,q}]\, \Phi_{n,j,q}(x;\theta) + I_{i,j}) \beta_j = \Phi(x;\theta)_{n,i,p}\,[ I_{n,m} \otimes \Lambda^{-1}_{p,q}]\, y_{n,p}
```
So long as ``\Lambda`` is easily invertible then this system is efficiently solved.
```julia
rfm = RandomFeatureMethod(
vf;
regularization = 1e12 * eps() * I,
batch_sizes = ("train" => 0, "test" => 0, "feature" => 0),
)
```
One can select batch sizes to balance the space-time (memory-process) trade-off. when building and solving equations by setting values of `"train"`, test data `"test"` and number of features `"feature"`. The default is no batching (`0`).
!!! warning "Conditioning"
The problem is ill-conditioned without regularization.
If you encounters a Singular or Positive-definite exceptions or warnings, try increasing the constant scaling `regularization`.
The solve for ``\beta`` occurs in the `fit` method. In the `Fit` object we store the system matrix, its factorization, and the inverse of the factorization. For many applications this is most efficient representation, as predictions (particularly of the covariance) are then only a matrix multiplication.
```julia
fitted_features = fit(
rfm,
io_pairs; # (x,y)
decomposition = "cholesky",
)
```
The decomposition is based off the [`LinearAlgebra.Factorize`](https://docs.julialang.org/en/v1/stdlib/LinearAlgebra/#man-linalg-factorizations) functions. For performance we have implemented only ("cholesky" (default), "svd", and for ``\Lambda=0.0`` (not recommended) the method defaults to "pinv").
## Hyperparameters
[Coming soon]
!!! note
Hyperparameter selection is very important for a good random feature fit.
The hyperparameters are the parameters appearing in the random feature distribution ``\mathcal{D}``. We have an examples where an ensemble-based algorithm is used to optimize such parameters in [`examples/Learn_hyperparameters/`](https://github.com/CliMA/RandomFeatures.jl/tree/main/examples/Learn_hyperparameters)
| RandomFeatures | https://github.com/CliMA/RandomFeatures.jl.git |
|
[
"Apache-2.0"
] | 0.3.3 | d639d537c14127a70c8bd3e0158ecbccfb2a54a3 | docs | 892 | # Features
```@meta
CurrentModule = RandomFeatures.Features
```
```@docs
get_scalar_function
get_feature_sampler
get_feature_sample
get_n_features
get_feature_parameters
get_output_dim
sample(rf::RandomFeature)
```
# [Scalar Features](@id scalar-features)
```@docs
ScalarFeature
ScalarFourierFeature
ScalarNeuronFeature
build_features(rf::ScalarFeature,inputs::AbstractMatrix,atch_feature_idx::AbstractVector{Int})
```
# [Vector Features](@id vector-features)
```@docs
VectorFeature
VectorFourierFeature
VectorNeuronFeature
build_features(rf::VectorFeature,inputs::AbstractMatrix,atch_feature_idx::AbstractVector{Int})
```
# [Scalar Functions](@id scalar-functions)
```@docs
ScalarFunction
ScalarActivation
apply_scalar_function
```
```@docs
Cosine
Relu
Lrelu
Gelu
Elu
Selu
Heaviside
SmoothHeaviside
Sawtooth
Softplus
Tansig
Sigmoid
``` | RandomFeatures | https://github.com/CliMA/RandomFeatures.jl.git |
|
[
"Apache-2.0"
] | 0.3.3 | d639d537c14127a70c8bd3e0158ecbccfb2a54a3 | docs | 446 | # Methods
```@meta
CurrentModule = RandomFeatures.Methods
```
```@docs
RandomFeatureMethod
Fit
get_random_feature
get_batch_sizes
get_batch_size
get_regularization
sample(rfm::RandomFeatureMethod)
get_feature_factors
get_coeffs
fit
predict
predict!
predictive_mean
predictive_mean!
predictive_cov
predictive_cov!
predict_prior
predict_prior_mean
predict_prior_cov
```
| RandomFeatures | https://github.com/CliMA/RandomFeatures.jl.git |
|
[
"Apache-2.0"
] | 0.3.3 | d639d537c14127a70c8bd3e0158ecbccfb2a54a3 | docs | 188 | # Samplers
```@meta
CurrentModule = RandomFeatures.Samplers
```
```@docs
Sampler
FeatureSampler
get_parameter_distribution
get_rng
sample(rng::AbstractRNG, s::Sampler, n_draws::Int)
```
| RandomFeatures | https://github.com/CliMA/RandomFeatures.jl.git |
|
[
"Apache-2.0"
] | 0.3.3 | d639d537c14127a70c8bd3e0158ecbccfb2a54a3 | docs | 257 | # Utilities
```@meta
CurrentModule = RandomFeatures.Utilities
```
## Batching
```@docs
batch_generator
```
## Matrix Decomposition
```@docs
Decomposition
StoredInvType
Factor
PseInv
get_decomposition
get_full_matrix
get_parametric_type
linear_solve
```
| RandomFeatures | https://github.com/CliMA/RandomFeatures.jl.git |
|
[
"MIT"
] | 0.1.0 | 6eba29d96bef815c6fda407240c0c4ba358f0f6f | code | 596 | using OpenADMIXTURE
using Documenter
makedocs(;
modules=[OpenADMIXTURE],
authors="Seyoon Ko <[email protected]> and contributors",
repo="https://github.com/OpenMendel/OpenADMIXTURE.jl/blob/{commit}{path}#L{line}",
sitename="OpenADMIXTURE.jl",
format=Documenter.HTML(;
prettyurls=get(ENV, "CI", "false") == "true",
canonical="https://OpenMendel.github.io/OpenADMIXTURE.jl",
assets=String[],
),
pages=[
"Home" => "index.md",
],
warnonly=true
)
deploydocs(;
repo="github.com/OpenMendel/OpenADMIXTURE.jl",
devbranch = "main"
)
| OpenADMIXTURE | https://github.com/OpenMendel/OpenADMIXTURE.jl.git |
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