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# Test that a hitran fixed-width file is correctly parsed
@testset "read_hitran" begin
println("Testing read_hitran...")
####
#### These tests check that the correct values & types are extracted from the file,
#### especially that molecule/isotope match and ν between ν_min and ν_max)
####
CO2_test_file = "test_profiles/testCO2.data"
test_ht = Absorption.read_hitran(CO2_test_file, mol=2, iso=1, ν_min=6000, ν_max=6400)
@test test_ht.mol == [2, 2, 2, 2] && test_ht.mol[1] isa Int64
@test test_ht.iso == [1, 1, 1, 1] && test_ht.iso[1] isa Int64
@test test_ht.νᵢ == [6000.542970, 6286.403343, 6317.417493, 6380.824116] && test_ht.νᵢ[1] isa Float64
@test test_ht.Sᵢ == [ 1.098E-28, 9.843E-30, 5.613E-27, 1.809E-30] && test_ht.Sᵢ[1] isa Float64
@test test_ht.Aᵢ == [ 9.993e-08, 1.179e-08, 1.324e-05, 1.601e-02] && test_ht.Aᵢ[1] isa Float64
@test test_ht.γ_air == [.0880, .0687, .0682, .0671] && test_ht.γ_air[1] isa Float64
@test test_ht.γ_self == [0.118, 0.087, 0.081, 0.073] && test_ht.γ_self[1] isa Float64
@test test_ht.E″ == [7.8043, 464.1717, 639.6004, 3798.2095] && test_ht.E″[1] isa Float64
@test test_ht.n_air == [0.77, 0.76, 0.76, 0.73] && test_ht.n_air[1] isa Float64
@test test_ht.δ_air == [-.004342, -.007362, -.007443, -.007669] && test_ht.δ_air[1] isa Float64
@test test_ht.global_upper_quanta == [" 4 1 1 03", " 2 2 2 12", " 2 2 2 12", " 4 2 2 12"] && test_ht.global_upper_quanta[1] isa String
@test test_ht.global_lower_quanta == [" 0 0 0 01", " 0 0 0 01", " 0 0 0 01", " 1 2 2 01"] && test_ht.global_lower_quanta[1] isa String
@test test_ht.local_upper_quanta == [" ", " ", " ", " "] && test_ht.local_upper_quanta[1] isa String
@test test_ht.local_lower_quanta == [" Q 4e ", " Q 34e ", " R 40e ", " R 51f "] && test_ht.local_lower_quanta[1] isa String
@test test_ht.ierr == ["367774", "367764", "367764", "367774"] && test_ht.ierr[1] isa String
@test test_ht.iref == ["2029 5 4 5 7", "2029 5 4 5 7", "2029 5 4 5 7", "2029 5 4 5 7"] && test_ht.iref[1] isa String
@test test_ht.line_mixing_flag == [" ", " ", " ", " "] && test_ht.line_mixing_flag[1] isa String
@test test_ht.g′ == [9.0, 69.0, 83.0, 105.0] && test_ht.g′[1] isa Float64
@test test_ht.g″ == [9.0, 69.0, 81.0, 103.0] && test_ht.g″[1] isa Float64
####
#### These tests check that the optional params work correctly
####
# Not specifying the molecule #
test_ht = Absorption.read_hitran(CO2_test_file, iso=1, ν_min=6000, ν_max=6400)
@test test_ht.mol == [1, 2, 2, 2, 2]
@test test_ht.iso == [1, 1, 1, 1, 1]
@test test_ht.νᵢ == [6286.403343, 6000.542970, 6286.403343, 6317.417493, 6380.824116]
@test test_ht.g″ == [69.0, 9.0, 69.0, 81.0, 103.0]
# Not specifying the isotope #
test_ht = Absorption.read_hitran(CO2_test_file, mol=2, ν_min=6000, ν_max=6400)
@test test_ht.mol == [2, 2, 2, 2, 2]
@test test_ht.iso == [2, 1, 1, 1, 1]
@test test_ht.νᵢ == [6000.542970, 6000.542970, 6286.403343, 6317.417493, 6380.824116]
@test test_ht.g″ == [9.0, 9.0, 69.0, 81.0, 103.0]
# Not specifying the molecule # OR isotope #
test_ht = Absorption.read_hitran(CO2_test_file, ν_min=6000, ν_max=6400)
@test test_ht.mol == [1, 2, 2, 2, 2, 2]
@test test_ht.iso == [1, 2, 1, 1, 1, 1]
@test test_ht.νᵢ == [6286.403343, 6000.542970, 6000.542970, 6286.403343, 6317.417493, 6380.824116]
@test test_ht.g″ == [69.0, 9.0, 9.0, 69.0, 81.0, 103.0]
# Not specifying ν_min
test_ht = Absorption.read_hitran(CO2_test_file, mol=2, iso=1, ν_max=6400)
@test length(test_ht.mol) == 9
# Not specifying ν_max
test_ht = Absorption.read_hitran(CO2_test_file, mol=2, iso=1, ν_min=6000)
@test length(test_ht.mol) == 7
# Not specifying ν_min OR ν_max
test_ht = Absorption.read_hitran(CO2_test_file, mol=2, iso=1)
@test length(test_ht.mol) == 12
end
# Test that absorption cross sections are calculated correctly
# (Not using pre-saved interpolator)
@testset "absorption_cross_section_hitran" begin
println("Testing absorption_cross_section_hitran...")
####
#### These tests check that for one molecule (CO2), over a temperature/
#### pressure grid and in a specific band (6000<ν<6400), our Voigt
#### broadening implementation closely matches the HAPI output under
#### the same conditions
####
# Temperature and pressure grids.
# Note that these are pre-defined for testing -- the values are defined in generateHapiTests.py
# To test against a different range, you must change the grids in that
# file and rerun it. Then, you can change it here.
pressures = 250:250:1250
temperatures = 100:75:400
# Get the test data
CO2_file = artifact("CO2")
test_ht = Absorption.read_hitran(CO2_file, mol=2, iso=1, ν_min=6000, ν_max=6400)
grid = 6000:0.01:6400;
# Threshold -- our value must be within ϵ of the HAPI value
ϵ = 3.6e-27
# Create a HitranModel
model = make_hitran_model(test_ht, Voigt(), CEF=HumlicekWeidemann32SDErrorFunction())
# Loop over every temperature/pressure combo and test that the results match HAPI
@showprogress 1 "Testing HAPI equivalence (On CO2 Band)..." for temp in temperatures
for pres in pressures
jl_cs = absorption_cross_section(model, grid, pres, temp)
py_cs = array_type(default_architecture)(readdlm("test_profiles/Voigt_CO2_T" * string(temp) * "_P" * string(pres) * ".csv"))
Δcs = abs.(jl_cs - py_cs)
@test maximum(Δcs) < ϵ
end
end
####
#### Now test HAPI equivalence with other molecules
####
names = ["H2O", "CO2", "O3", "N2O", "CO"]
molecules = [1, 2, 3, 4, 5]
isotopes = [1, 1, 1, 1, 1]
# Loop over every temperature/pressure combo and test that the results match HAPI
# (Doing this for other molecules)
@showprogress 1 "Testing HAPI equivalence (On Other Molecules)..." for name in names
pres = 1000
temp = 250
# Get the test data
test_ht = Absorption.read_hitran(artifact(name), iso=1, ν_min=6000, ν_max=6400)
# Create a HitranModel
model = make_hitran_model(test_ht, Voigt(), CEF=HumlicekWeidemann32SDErrorFunction())
jl_cs = absorption_cross_section(model, grid, pres, temp)
py_cs = array_type(default_architecture)(readdlm("test_profiles/Voigt_" * name * "_T250_P1000.csv"))
Δcs = abs.(jl_cs - py_cs)
@test maximum(Δcs) < ϵ
end
end
# Test that absorption cross sections if calculated using wavenumbers
@testset "absorption_cross_section_wavelengths" begin
println("Testing absorption_cross_section_wavelengths...")
hitran_data = read_hitran(artifact("CO2"), mol=2, iso=1, ν_min=6000, ν_max=6400)
model = make_hitran_model(hitran_data, Voigt(), wing_cutoff = 40, CEF=HumlicekWeidemann32SDErrorFunction(), architecture=CPU())
grid = 6000:0.01:6400
@test absorption_cross_section(model, grid, 1000.1, 296.1) ≈ reverse(absorption_cross_section(model, reverse(1e7 ./ collect(grid)), 1000.1, 296.1, wavelength_flag=true));
end
# Test that absorption cross sections are calculated correctly
# using a new interpolator
@testset "absorption_cross_section_interpolator" begin
println("Testing absorption_cross_section_interpolator...")
# Get the test data
test_ht = Absorption.read_hitran(artifact("CO2"), iso=1, ν_min=6000, ν_max=6400)
# Pressure and temperature grids
pressures = 250:250:1250
temperatures = 100:75:400
# Wavelength grid
ν_grid = 6000:0.01:6400
# Create the Interpolation Model
interp_model = make_interpolation_model(test_ht, Voigt(), ν_grid, pressures,
temperatures, CEF=HumlicekWeidemann32SDErrorFunction())
# Threshold -- our value must be within ϵ of the HAPI value
ϵ = 3.6e-27
# Loop over every temperature/pressure combo and test that the results match HAPI
for temp in temperatures
for pres in pressures
jl_cs = absorption_cross_section(interp_model, ν_grid, pres, temp)
py_cs = readdlm("test_profiles/Voigt_CO2_T" * string(temp) * "_P" * string(pres) * ".csv")
Δcs = abs.(jl_cs - py_cs)
@test maximum(Δcs) < ϵ
end
end
end
# Test that checks whether the cross-section auto-differentiation works
@testset "absorption_cross_section_autodiff" begin
println("Testing absorption_cross_section_autodiff...")
# Load HITRAN data
hitran_data = read_hitran(artifact("CO2"), mol=2, iso=1, ν_min=6000, ν_max=6400)
# Create the model with parameters
model = make_hitran_model(hitran_data, Voigt(), wing_cutoff = 40, CEF=HumlicekWeidemann32SDErrorFunction(), architecture=CPU())
# Compute the cross-section with autodifferentiation
value, derivs = absorption_cross_section(model, 6000:0.01:6400, 1000.1, 296.1, autodiff=true);
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] | 2.27943 | 4,001 |
using Printf
module ApolloniusProblems
using Polynomials
export Circle
struct Point{T<:Real}
x::T
y::T
end
xcoord(p::Point) = p.x
ycoord(p::Point) = p.y
struct Circle{T<:Real}
c::Point{T}
r::T
end
Circle(x::T, y::T, r::T) where T<:Real = Circle(Point(x, y), r)
radius(c::Circle) = c.r
center(c::Circle) = c.c
xcenter(c::Circle) = xcoord(center(c))
ycenter(c::Circle) = ycoord(center(c))
Base.show(io::IO, c::Circle) =
@printf(io, "centered at (%0.4f, %0.4f) with radius %0.4f",
xcenter(c), ycenter(c), radius(c))
function solve(ap::Vector{Circle{T}}, enc=()) where T<:Real
length(ap) == 3 || error("This Apollonius problem needs 3 circles.")
x = @. xcenter(ap)
y = @. ycenter(ap)
r = map(u -> ifelse(u ∈ enc, -1, 1), 1:3) .* radius.(ap)
@views begin
a = 2x[1] .- 2x[2:3]
b = 2y[1] .- 2y[2:3]
c = 2r[1] .- 2r[2:3]
d = (x[1] ^ 2 + y[1] ^ 2 - r[1] ^ 2) .- (x[2:3] .^ 2 .+ y[2:3] .^ 2 .- r[2:3] .^ 2)
end
u = Poly([-det([b d]), det([b c])] ./ det([a b]))
v = Poly([det([a d]), -det([a c])] ./ det([a b]))
w = Poly([r[1], 1.0]) ^ 2
s = (u - x[1]) ^ 2 + (v - y[1]) ^ 2 - w
r = filter(x -> iszero(imag(x)) && x > zero(x), roots(s))
length(r) < 2 || error("The solution is not unique.")
length(r) == 1 || error("There is no solution.")
r = r[1]
return Circle(polyval(u, r), polyval(v, r), r)
end
end # module ApolloniusProblem
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198,
437,
220,
1303,
8265,
5949,
692,
261,
3754,
40781,
198
] | 1.986301 | 730 |
using SpatialFields
using Base.Test
using Iterators
points = SVector{3, Float64}[[1; 0; 0], [0; 1; 0], [-1; 0; 0], [0; -1; 0], [0; 0; 1], [0; 0; -1]]
normals = SVector{3, Float64}[[1; -1; 0], [0; 1; 0], [-1; 0; 0], [0; -1; 0], [1; 0; 1], [0; 0; -1]]
field = HermiteRadialField(points, normals)
X = linspace(-2, 2)
Y = linspace(-2, 2)
Z = linspace(-2, 2)
for (i, point) in enumerate(points)
@test isapprox(field(point), 0, atol=1e-6)
@test isapprox(SpatialFields.gradient(field)(point), normals[i], atol=1e-6)
end
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72,
4357,
379,
349,
28,
16,
68,
12,
21,
8,
198,
437,
198
] | 2.095618 | 251 |
@testset "doc string generation" begin
IterationControl.@create_docs(SquareRooter,
header="header",
example="example",
body="body")
paragraphs = split(string(@doc SquareRooter), "\n")
@test paragraphs[2] == "header"
@test paragraphs[end-1] == "body"
end
true
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6624,
366,
2618,
1,
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437,
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] | 2.005464 | 183 |
@testset "Renewable data misspecification" begin
# See https://discourse.julialang.org/t/how-to-use-test-warn/15557/5 about testing for warning throwing
info_message = "The data doesn't include devices of type RenewableDispatch, consider changing the device models"
device_model = DeviceModel(RenewableDispatch, RenewableFullDispatch)
c_sys5 = PSB.build_system(PSITestSystems, "c_sys5")
model = DecisionModel(MockOperationProblem, DCPPowerModel, c_sys5)
@test_logs (:info, info_message) match_mode = :any mock_construct_device!(
model,
device_model,
)
end
@testset "Renewable DCPLossLess FullDispatch" begin
device_model = DeviceModel(RenewableDispatch, RenewableFullDispatch)
c_sys5_re = PSB.build_system(PSITestSystems, "c_sys5_re")
model = DecisionModel(MockOperationProblem, DCPPowerModel, c_sys5_re)
mock_construct_device!(model, device_model)
moi_tests(model, false, 72, 0, 72, 0, 0, false)
psi_checkobjfun_test(model, GAEVF)
end
@testset "Renewable ACPPower Full Dispatch" begin
device_model = DeviceModel(RenewableDispatch, RenewableFullDispatch)
c_sys5_re = PSB.build_system(PSITestSystems, "c_sys5_re")
model = DecisionModel(MockOperationProblem, ACPPowerModel, c_sys5_re;)
mock_construct_device!(model, device_model)
moi_tests(model, false, 144, 0, 144, 72, 0, false)
psi_checkobjfun_test(model, GAEVF)
end
@testset "Renewable DCPLossLess Constantpower_factor" begin
device_model = DeviceModel(RenewableDispatch, RenewableConstantPowerFactor)
c_sys5_re = PSB.build_system(PSITestSystems, "c_sys5_re")
model = DecisionModel(MockOperationProblem, DCPPowerModel, c_sys5_re)
mock_construct_device!(model, device_model)
moi_tests(model, false, 72, 0, 72, 0, 0, false)
psi_checkobjfun_test(model, GAEVF)
end
@testset "Renewable ACPPower Constantpower_factor" begin
device_model = DeviceModel(RenewableDispatch, RenewableConstantPowerFactor)
c_sys5_re = PSB.build_system(PSITestSystems, "c_sys5_re")
model = DecisionModel(MockOperationProblem, ACPPowerModel, c_sys5_re;)
mock_construct_device!(model, device_model)
moi_tests(model, false, 144, 0, 72, 0, 72, false)
psi_checkobjfun_test(model, GAEVF)
end
@testset "Renewable DCPLossLess FixedOutput" begin
device_model = DeviceModel(RenewableDispatch, FixedOutput)
c_sys5_re = PSB.build_system(PSITestSystems, "c_sys5_re")
model = DecisionModel(MockOperationProblem, DCPPowerModel, c_sys5_re;)
mock_construct_device!(model, device_model)
moi_tests(model, false, 0, 0, 0, 0, 0, false)
psi_checkobjfun_test(model, GAEVF)
end
@testset "Renewable ACPPowerModel FixedOutput" begin
device_model = DeviceModel(RenewableDispatch, FixedOutput)
c_sys5_re = PSB.build_system(PSITestSystems, "c_sys5_re")
model = DecisionModel(MockOperationProblem, ACPPowerModel, c_sys5_re;)
mock_construct_device!(model, device_model)
moi_tests(model, false, 0, 0, 0, 0, 0, false)
psi_checkobjfun_test(model, GAEVF)
end
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198,
220,
220,
220,
2746,
796,
26423,
17633,
7,
44,
735,
32180,
40781,
11,
7125,
10246,
789,
17633,
11,
269,
62,
17597,
20,
62,
260,
26,
8,
198,
220,
220,
220,
15290,
62,
41571,
62,
25202,
0,
7,
19849,
11,
3335,
62,
19849,
8,
198,
220,
220,
220,
6941,
72,
62,
41989,
7,
19849,
11,
3991,
11,
657,
11,
657,
11,
657,
11,
657,
11,
657,
11,
3991,
8,
198,
220,
220,
220,
46231,
62,
9122,
26801,
12543,
62,
9288,
7,
19849,
11,
402,
14242,
53,
37,
8,
198,
437,
198
] | 2.593857 | 1,172 |
"""
commjoin(c1::CommunityProfile, comms::CommunityProfile...)
Join multiple `CommunityProfile`s, creating a new `CommunityProfile`.
For now, sample names cannot overlap in any of the input profiles.
"""
function commjoin(c1::CommunityProfile, comms::CommunityProfile...)
length(intersect(samplenames(c1), samplenames.(comms)...)) == 0 || error("Duplicate sample names detected: $(intersect(samplenames(c1), samplenames.(comms)...))")
all_samples = vcat(samples(c1), samples.(comms)...)
sample_dict = dictionary(zip(all_samples, eachindex(all_samples)))
all_features = unique(vcat(features(c1), features.(comms)...))
feature_dict = dictionary(zip(all_features, eachindex(all_features)))
mat = spzeros(length(all_features), length(all_samples))
for comm in (c1, comms...)
for sample in samples(comm)
for feature in features(comm)
mat[feature_dict[feature], sample_dict[sample]] = comm[feature, sample]
end
end
end
return CommunityProfile(mat, all_features, all_samples)
end
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] | 2.794271 | 384 |
# Note that this script can accept some limited command-line arguments, run
# `julia build_tarballs.jl --help` to see a usage message.
using BinaryBuilder, Pkg
name = "blis"
version = v"0.9.0"
# Collection of sources required to complete build
sources = [
GitSource("https://github.com/flame/blis.git", "14c86f66b20901b60ee276da355c1b62642c18d2"),
DirectorySource("./bundled")
]
# Bash recipe for building across all platforms
script = raw"""
cd $WORKSPACE/srcdir
cd blis/
for i in ./config/*/*.mk; do
# Building in container forbids -march options <<< Settings overriden.
# sed -i "s/-march[^ ]*//g" $i
# Building in container forbids unsafe optimization.
sed -i "s/-ffast-math//g" $i
sed -i "s/-funsafe-math-optimizations//g" $i
done
case ${target} in
*"x86_64"*"linux"*)
export BLI_CONFIG=x86_64
export BLI_THREAD=openmp
;;
*"x86_64"*"w64"*)
# MinGW doesn't support savexmm instructions
# Build only for AMD processors.
export BLI_CONFIG=amd64
export BLI_THREAD=openmp
;;
*"x86_64"*"apple"*)
export BLI_CONFIG=x86_64
export BLI_THREAD=openmp
;;
*"x86_64"*"freebsd"*)
export BLI_CONFIG=x86_64
export BLI_THREAD=openmp
;;
*"aarch64"*"apple"*)
# Metaconfig arm64 is not needed here.
# All Mac processors should have equal or higher specs then firestorm
export BLI_CONFIG=firestorm
export BLI_THREAD=openmp
;;
*"aarch64"*"linux"*)
export BLI_CONFIG=arm64
export BLI_THREAD=openmp
;;
*"arm"*"linux"*)
export BLI_CONFIG=arm32
export BLI_THREAD=none
;;
*)
# Default (Generic) configuration without optimized kernel.
export BLI_CONFIG=generic
export BLI_THREAD=none
;;
esac
# For 64-bit builds, add _64 suffix to exported BLAS routines.
# This corresponds to ILP64 handling of OpenBLAS thus Julia.
if [ ${nbits} = 64 ]; then
patch frame/include/bli_macro_defs.h < ${WORKSPACE}/srcdir/patches/bli_macro_defs.h.f77suffix64.patch
fi
# Include A64FX in Arm64 metaconfig.
if [ ${BLI_CONFIG} = arm64 ]; then
# Add A64FX to the registry.
patch config_registry < ${WORKSPACE}/srcdir/patches/config_registry.metaconfig+a64fx.patch
# Unscreen Arm SVE code for metaconfig.
patch kernels/armsve/bli_kernels_armsve.h \
< ${WORKSPACE}/srcdir/patches/armsve_kernels_unscreen_arm_sve_h.patch
patch kernels/armsve/1m/old/bli_dpackm_armsve512_int_12xk.c \
< ${WORKSPACE}/srcdir/patches/armsve_kernels_unscreen_arm_sve_h.patch
patch kernels/armsve/1m/bli_dpackm_armsve256_int_8xk.c \
< ${WORKSPACE}/srcdir/patches/armsve_kernels_unscreen_arm_sve_h.patch
# Screen out A64FX sector cache.
patch config/a64fx/bli_cntx_init_a64fx.c \
< ${WORKSPACE}/srcdir/patches/a64fx_config_screen_sector_cache.patch
fi
# Import libblastrampoline-style nthreads setter.
cp ${WORKSPACE}/srcdir/nthreads64_.c frame/compat/nthreads64_.c
export BLI_F77BITS=${nbits}
./configure -p ${prefix} -t ${BLI_THREAD} -b ${BLI_F77BITS} ${BLI_CONFIG}
make -j${nproc}
make install
# Static library is not needed.
rm ${prefix}/lib/libblis.a
# Rename .dll for Windows targets.
if [[ "${target}" == *"x86_64"*"w64"* ]]; then
mkdir -p ${libdir}
mv ${prefix}/lib/libblis.4.dll ${libdir}/libblis.dll
fi
"""
# These are the platforms we will build for by default, unless further
# platforms are passed in on the command line
platforms = [
Platform("x86_64", "linux"; libc="musl"),
Platform("armv7l", "linux"; libc="glibc"),
Platform("x86_64", "windows"),
Platform("x86_64", "macos"),
Platform("x86_64", "linux"; libc="glibc"),
Platform("aarch64", "linux"; libc="glibc"),
Platform("aarch64", "macos"),
Platform("x86_64", "freebsd")
]
# The products that we will ensure are always built
products = [
LibraryProduct("libblis", :blis)
]
# Dependencies that must be installed before this package can be built
dependencies = [
# For OpenMP we use libomp from `LLVMOpenMP_jll` where we use LLVM as compiler (BSD
# systems), and libgomp from `CompilerSupportLibraries_jll` everywhere else.
Dependency(PackageSpec(name="CompilerSupportLibraries_jll", uuid="e66e0078-7015-5450-92f7-15fbd957f2ae"); platforms=filter(!Sys.isbsd, platforms)),
Dependency(PackageSpec(name="LLVMOpenMP_jll", uuid="1d63c593-3942-5779-bab2-d838dc0a180e"); platforms=filter(Sys.isbsd, platforms)),
]
# Build the tarballs, and possibly a `build.jl` as well.
build_tarballs(ARGS, name, version, sources, script, platforms, products, dependencies;
preferred_gcc_version=v"11", lock_microarchitecture=false, julia_compat="1.6")
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] | 2.371966 | 2,019 |
using Cbc
using PowerSimulations
using PowerSystems
using DataStructures
using InfrastructureSystems
const IS = InfrastructureSystems
const PSI = PowerSimulations
const PSY = PowerSystems
Cbc_optimizer = optimizer_with_attributes(Cbc.Optimizer)
abstract type TestOpProblem <: PSI.AbstractOperationsProblem end
system = build_c_sys5_re(; add_reserves = true)
solver = optimizer_with_attributes(Cbc.Optimizer)
devices = Dict{Symbol, DeviceModel}(
:Generators => DeviceModel(ThermalStandard, ThermalDispatch),
:Loads => DeviceModel(PowerLoad, StaticPowerLoad),
)
branches = Dict{Symbol, DeviceModel}(
:L => DeviceModel(Line, StaticLine),
:T => DeviceModel(Transformer2W, StaticBranch),
:TT => DeviceModel(TapTransformer, StaticBranch),
);
services = Dict{Symbol, ServiceModel}();
template = PSI.OperationsProblemTemplate(CopperPlatePowerModel, devices, branches, services);
operations_problem = PSI.OperationsProblem(
TestOpProblem,
template,
system;
optimizer = solver,
use_parameters = true,
);
set_services_template!(
operations_problem,
Dict(
:Reserve => ServiceModel(VariableReserve{ReserveUp}, RangeReserve),
:Down_Reserve => ServiceModel(VariableReserve{ReserveDown}, RangeReserve),
),
)
op_results = solve!(operations_problem)
re_results = PSI.run_economic_dispatch(system; optimizer = solver, use_parameters = true)
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] | 2.953488 | 473 |
@testset "315.count-of-smaller-numbers-after-self.jl" begin
f1 = count_smaller_method1
f2 = count_smaller_method2
@test f1([5, 2, 6, 1]) == f2([5, 2, 6, 1]) == [2, 1, 1, 0]
@test f1([2, 0, 1]) == f2([2, 0, 1]) == [2, 0, 0]
@test f1([-1]) == f2([-1]) == [0]
@test f1([-1, -1]) == f2([-1, -1]) == [0, 0]
@test f1([7, 6, 5, 4, 3, 2, 1]) == f2([7, 6, 5, 4, 3, 2, 1]) == [6, 5, 4, 3, 2, 1, 0]
end
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437,
198
] | 1.772152 | 237 |
using SpikingNetworks
parseParameters"""
taupre=20
taupost=20
tmax=50"""
parseParameters"""
N=100
groups=makeGroup([1,2],[N,N])
@group groups 1 tspike[i]=(i-1)*tmax/(N-1)
@group groups 2 tspike[i]=(N-i)*tmax/(N-1)
@connect groups 1 2 post==pre Apre[post,pre]=0.01
@connect groups 1 2 post==pre Apost[post,pre]=-0.01*1.05"""
parseEquations"""
dapre/dt=-apre/taupre
dapost/dt=-apost/taupost
dw/dt=0"""
net=init"""
N=200
apre=zeros(N,N)
apost=zeros(N,N)
w=zeros(N,N)
Apre=Apre
Apost=Apost"""
spike"t≂tspike"
plasticity"""
apre[j,i]+=Apre[j,i]
w[j,i]+=apost[j,i]
apost[i,j]+=Apost[i,j]
w[i,j]+=apre[i,j]"""
net.dt=0.5
@time run!(net,60/net.dt)
using SpikingNetwork: groups,tspike
index=sub2ind(size(net.w),groups[2],groups[1])
using MatlabPlot
mplot(tspike[groups[2]]-tspike[groups[1]],net.w[index])
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] | 1.992574 | 404 |
export AffineMap
"""
AffineMap{N<:Real, S<:LazySet{N}, NM, MAT<:AbstractMatrix{NM},
VN<:AbstractVector{NM}} <: AbstractAffineMap{N, S}
Type that represents an affine transformation ``M⋅X ⊕ v`` of a convex set ``X``.
### Fields
- `M` -- matrix/linear map
- `X` -- convex set
- `v` -- translation vector
### Notes
An affine map is the composition of a linear map and a translation. This type is
parametric in the coefficients of the linear map, `NM`, which may be different from
the numeric type of the wrapped set (`N`). However, the numeric type of the
translation vector should be `NM`.
### Examples
For the examples we create a ``3×2`` matrix, a two-dimensional unit square, and
a three-dimensional vector.
Then we combine them in an `AffineMap`.
```jldoctest constructors
julia> A = [1 2; 1 3; 1 4]; X = BallInf([0, 0], 1); b2 = [1, 2]; b3 = [1, 2, 3];
julia> AffineMap(A, X, b3)
AffineMap{Int64,BallInf{Int64,Array{Int64,1}},Int64,Array{Int64,2},Array{Int64,1}}([1 2; 1 3; 1 4], BallInf{Int64,Array{Int64,1}}([0, 0], 1), [1, 2, 3])
```
For convenience, `A` does not need to be a matrix but we also allow to use
`UniformScaling`s resp. scalars (interpreted as a scaling, i.e., a scaled
identity matrix).
Scaling by ``1`` is ignored and simplified to a pure `Translation`.
```jldoctest constructors
julia> using LinearAlgebra
julia> am = AffineMap(2I, X, b2)
AffineMap{Int64,BallInf{Int64,Array{Int64,1}},Int64,Diagonal{Int64,Array{Int64,1}},Array{Int64,1}}([2 0; 0 2], BallInf{Int64,Array{Int64,1}}([0, 0], 1), [1, 2])
julia> AffineMap(2, X, b2) == am
true
julia> AffineMap(1, X, b2)
Translation{Int64,Array{Int64,1},BallInf{Int64,Array{Int64,1}}}(BallInf{Int64,Array{Int64,1}}([0, 0], 1), [1, 2])
```
Applying a linear map to an `AffineMap` object combines the two maps into a new
`AffineMap` instance.
Again we can make use of the conversion for convenience.
```jldoctest constructors
julia> B = [2 0; 0 2]; am2 = B * am
AffineMap{Int64,BallInf{Int64,Array{Int64,1}},Int64,Array{Int64,2},Array{Int64,1}}([4 0; 0 4], BallInf{Int64,Array{Int64,1}}([0, 0], 1), [2, 4])
julia> 2 * am == am2
true
```
The application of an `AffineMap` to a `ZeroSet` or an `EmptySet` is simplified
automatically.
```jldoctest constructors
julia> AffineMap(A, ZeroSet{Int}(2), b3)
Singleton{Int64,Array{Int64,1}}([1, 2, 3])
julia> AffineMap(A, EmptySet{Int}(2), b3)
EmptySet{Int64}(2)
```
"""
struct AffineMap{N<:Real, S<:LazySet{N}, NM, MAT<:AbstractMatrix{NM},
VN<:AbstractVector{NM}} <: AbstractAffineMap{N, S}
M::MAT
X::S
v::VN
# default constructor with dimension match check
function AffineMap(M::MAT, X::S, v::VN) where {N<:Real, S<:LazySet{N}, NM,
MAT<:AbstractMatrix{NM},
VN<:AbstractVector{NM}}
@assert dim(X) == size(M, 2) "a matrix of size $(size(M)) cannot be " *
"applied to a set of dimension $(dim(X))"
@assert size(M, 1) == length(v) "a map with output dimension " *
"$(size(M, 1)) is incompatible with a translation vector of " *
"dimension $(length(v))"
return new{N, S, NM, MAT, VN}(M, X, v)
end
end
isoperationtype(::Type{<:AffineMap}) = true
isconvextype(::Type{<:AffineMap{N, S}}) where {N, S} = isconvextype(S)
# convenience constructor from a UniformScaling
function AffineMap(M::UniformScaling, X::LazySet, v::AbstractVector)
return AffineMap(M.λ, X, v)
end
# convenience constructor from a scalar
function AffineMap(α::N, X::LazySet, v::AbstractVector) where {N<:Real}
if α == one(N)
return Translation(X, v)
end
return AffineMap(Diagonal(fill(α, length(v))), X, v)
end
# simplification for a LinearMap
for MAP in (:AbstractMatrix, :Real)
@eval begin
function LinearMap(map::$MAP, am::AffineMap)
return AffineMap(map * am.M, am.X, map * am.v)
end
end
end
# ZeroSet is "almost absorbing" for the linear map (only the dimension changes)
# such that only the translation vector remains
function AffineMap(M::AbstractMatrix{N}, Z::ZeroSet{N}, v::AbstractVector{N}
) where {N<:Real}
@assert dim(Z) == size(M, 2) "a matrix of size $(size(M)) cannot be " *
"applied to a set of dimension $(dim(Z))"
@assert size(M, 1) == length(v) "a map with output dimension " *
"$(size(M, 1)) is incompatible with a translation vector of " *
"dimension $(length(v))"
return Singleton(v)
end
# EmptySet is absorbing for AffineMap
function AffineMap(M::AbstractMatrix{N}, ∅::EmptySet{N}, v::AbstractVector{N}
) where {N<:Real}
return ∅
end
# --- AbstractAffineMap interface functions ---
function matrix(am::AffineMap)
return am.M
end
function vector(am::AffineMap{N}) where {N<:Real}
return am.v
end
function set(am::AffineMap)
return am.X
end
# --- LazySet interface functions ---
function concretize(am::AffineMap)
return affine_map(am.M, concretize(am.X), am.v)
end
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] | 2.404931 | 2,109 |
# Code for Day 9 of Advent of Code: https://adventofcode.com/2018/day/9
# Julia Solution
#
# Copyright (C) 2018 Lawrence Woodman <lwoodman@vlifesystems.com>
# Licensed under an MIT licence. Please see LICENCE.md for details.
using Printf
mutable struct Game
numPlayers
lastMarble
current
marbles
Game(numPlayers, lastMarble) = new(numPlayers, lastMarble, -1, Dict())
Game() = new(0, 0, -1, Dict())
end
mutable struct Marble
prev
next
end
function start(g::Game)
g.marbles = Dict(0 => Marble(0,0))
g.current = 0
end
function next(g::Game, value)
g.marbles[value].next
end
function prev(g::Game, value)
g.marbles[value].prev
end
function prev7(g::Game)
v = g.current
for i in 1:7
v = prev(g, v)
end
v
end
function remove(g::Game, value)
prev = g.marbles[value].prev
next = g.marbles[value].next
g.marbles[prev].next = next
g.marbles[next].prev = prev
g.current = next
delete!(g.marbles, value)
end
function insert(g::Game, beforeValue, value)
before = g.marbles[beforeValue]
beforePrev = before.prev
g.marbles[value] = Marble(before.prev, beforeValue)
g.marbles[beforeValue].prev = value
g.marbles[beforePrev].next = value
g.current = value
end
function add(g::Game, value)
if value % 23 == 0
throw(ArgumentError("can't add multiple of 23"))
end
next1 = next(g, g.current)
next2 = next(g, next1)
insert(g, next2, value)
end
function getInput(filename)
game = Game()
open(filename) do file
for ln in eachline(file)
gameWords = split(ln)
game = Game(parse(Int32, gameWords[1]), parse(Int32, gameWords[7]))
break
end
end
game
end
function play(g::Game)
playerNum = 1
start(g)
scores = Dict()
for m in 1:g.lastMarble
if m % 23 == 0
removeValue = prev7(g)
remove(g, removeValue)
playerScore = get(scores, playerNum, 0)
scores[playerNum] = playerScore+m+removeValue
else
add(g, m)
end
playerNum = playerNum + 1
if playerNum >= g.numPlayers
playerNum = 0
end
end
highestScore = 0
for (p, s) in scores
if s > highestScore
highestScore = s
end
end
highestScore
end
function part1(game)
play(game)
end
function part2(game)
game.lastMarble = game.lastMarble*100
play(game)
end
game = getInput("day9.input")
@printf "part1: %d\n" part1(game)
@printf "part2: %d\n" part2(game)
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] | 2.510526 | 950 |
### A Pluto.jl notebook ###
# v0.14.5
using Markdown
using InteractiveUtils
# ╔═╡ 857d0150-b0ea-11eb-2bcb-b70a5fc0a6b7
md"""
# [Day 25: Let It Snow](https://adventofcode.com/2015/day/25)
Merry Christmas! Santa is booting up his weather machine; looks like you might get a [white Christmas](https://adventofcode.com/2015/day/1) after all.
The weather machine beeps! On the console of the machine is a copy protection message asking you to [enter a code from the instruction manual](https://en.wikipedia.org/wiki/Copy_protection#Early_video_games). Apparently, it refuses to run unless you give it that code. No problem; you'll just look up the code in the--
"Ho ho ho", Santa ponders aloud. "I can't seem to find the manual."
You look up the support number for the manufacturer and give them a call. Good thing, too - that 49th star wasn't going to earn itself.
"Oh, that machine is quite old!", they tell you. "That model went out of support six minutes ago, and we just finished shredding all of the manuals. I bet we can find you the code generation algorithm, though."
After putting you on hold for twenty minutes (your call is very important to them, it reminded you repeatedly), they finally find an engineer that remembers how the code system works.
The codes are printed on an infinite sheet of paper, starting in the top-left corner. The codes are filled in by diagonals: starting with the first row with an empty first box, the codes are filled in diagonally up and to the right. This process repeats until the [infinite paper is covered](https://en.wikipedia.org/wiki/Cantor's_diagonal_argument). So, the first few codes are filled in in this order:
```
| 1 2 3 4 5 6
---+---+---+---+---+---+---+
1 | 1 3 6 10 15 21
2 | 2 5 9 14 20
3 | 4 8 13 19
4 | 7 12 18
5 | 11 17
6 | 16
```
For example, the 12th code would be written to row `4`, column `2`; the 15th code would be written to row `1`, column `5`.
The voice on the other end of the phone continues with how the codes are actually generated. The first code is `20151125`. After that, each code is generated by taking the previous one, multiplying it by `252533`, and then keeping the remainder from dividing that value by `33554393`.
So, to find the second code (which ends up in row `2`, column `1`), start with the previous value, `20151125`. Multiply it by `252533` to get `5088824049625`. Then, divide that by `33554393`, which leaves a remainder of `31916031`. That remainder is the second code.
"Oh!", says the voice. "It looks like we missed a scrap from one of the manuals. Let me read it to you." You write down his numbers:
```
| 1 2 3 4 5 6
---+---------+---------+---------+---------+---------+---------+
1 | 20151125 18749137 17289845 30943339 10071777 33511524
2 | 31916031 21629792 16929656 7726640 15514188 4041754
3 | 16080970 8057251 1601130 7981243 11661866 16474243
4 | 24592653 32451966 21345942 9380097 10600672 31527494
5 | 77061 17552253 28094349 6899651 9250759 31663883
6 | 33071741 6796745 25397450 24659492 1534922 27995004
```
"Now remember", the voice continues, "that's not even all of the first few numbers; for example, you're missing the one at 7,1 that would come before 6,2. But, it should be enough to let your-- oh, it's time for lunch! Bye!" The call disconnects.
Santa looks nervous. Your puzzle input contains the message on the machine's console. **What code do you give the machine?**
"""
# ╔═╡ 36909b22-41bb-4b02-b2d7-a662da7ffbf3
puzzle_input = "To continue, please consult the code grid in the manual. Enter the code at row 2978, column 3083."
# ╔═╡ Cell order:
# ╟─857d0150-b0ea-11eb-2bcb-b70a5fc0a6b7
# ╟─36909b22-41bb-4b02-b2d7-a662da7ffbf3
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] | 2.94213 | 1,296 |
using JuMP
struct DummyOptimizer <: MOI.AbstractOptimizer end
MOI.is_empty(::DummyOptimizer) = true
MOI.supports_constraint(::DummyOptimizer, ::Type{<:MOI.AbstractFunction}, ::Type{<:MOI.AbstractSet}) = true
function MOI.optimize!(::DummyOptimizer, model::MOI.ModelLike)
return MOI.Utilities.identity_index_map(model), false
end
MOI.get(::DummyOptimizer, ::MOI.TerminationStatus) = MOI.OTHER_ERROR
MOI.get(::DummyOptimizer, ::MOI.RawStatusString) = "Dummy status"
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] | 2.571429 | 182 |
# Check whether the test is being run as standalone
if ~@isdefined Test
using Test
end
include("../src/setup_hierarchy.jl")
@testset "hierarchy matrix" begin
# Makes sure the hierarchy matrix generator is working as expected for a select set of orbital systems.
# Fully nested Keplerians
ϵ_nested::Matrix{Float64} = [-1 1 0 0;
-1 -1 1 0;
-1 -1 -1 1;
-1 -1 -1 -1;]
@test hierarchy([4,1,1,1]) == ϵ_nested
# Double Binary
ϵ_double::Matrix{Float64} = [-1 1 0 0;
0 0 -1 1;
-1 -1 1 1;
-1 -1 -1 -1;]
@test hierarchy([4,2,1]) == ϵ_double
# Double binary + top level body
ϵ_asym::Matrix{Float64} = [-1 1 0 0 0;
0 0 -1 1 0;
-1 -1 1 1 0;
-1 -1 -1 -1 1;
-1 -1 -1 -1 -1;]
@test hierarchy([5,2,1,1]) == ϵ_asym
# Test of specific ambiguous initialization case of a 'symmetric fully-nested' (top) and the double binary + tertiary binary (bottom)
ϵ_sym_full::Matrix{Float64} = [-1 1 0 0 0 0;
0 0 0 -1 1 0;
-1 -1 1 0 0 0;
0 0 0 -1 -1 1;
-1 -1 -1 1 1 1;
-1 -1 -1 -1 -1 -1;]
ϵ_asym_multi::Matrix{Float64}=[-1 1 0 0 0 0;
0 0 -1 1 0 0;
-1 -1 1 1 0 0;
0 0 0 0 -1 1;
-1 -1 -1 -1 1 1;
-1 -1 -1 -1 -1 -1;]
h_sym_full,h_asym_multi = hierarchy.([[6,2,2,1,2],[6,2,2,1]])
@test ϵ_sym_full == h_sym_full
@test ϵ_asym_multi == h_asym_multi
end | [
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] | 1.519288 | 1,348 |
# polyhelper function guesses what type of poly you want
export polyhelper
function polyhelper(y)
LGRPoly(y)
end
function polyhelper(x,y)
if length(x) < 6
vandpoly(x,y)
else
LagrangePoly(x,y) # test speed of this against fitting function of Polynomials package
end
end
#function polyhelper(x,y,b::Bound)
# if length(x) < 6
# vandpoly(x,y,b)
# else
# LagrangePoly(x,y,b)
# end
#end
# add automatically gen GlobalPoly of local polys
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] | 2.336493 | 211 |
NAME_EQUALS(s::State) = if length(s.name) >= 2
push!(s.boolean, pop!(s.name) == pop!(s.name))
end
NAME_DUP(s::State) = if !isempty(s.name)
push!(s.name, peek(s.name))
end
NAME_FLUSH(s::State) = empty!(s.name)
NAME_POP(s::State) = !isempty(s.name) && pop!(s.name)
NAME_QUOTE(s::State) = s.flag_quote_name = true
NAME_RAND(s::State) = push!(s.name, random_name(s))
NAME_RAND_BOUND_NAME(s::State) = push!(s.name, random_bound_name(s))
NAME_ROT(s::State) = if length(s.name) > 2
s.name[end], s.name[end-1], s.name[end-2] =
s.name[end-1], s.name[end-2], s.name[end]
end
NAME_SHOVE(s::State) = if !isempty(s.integer) && !isempty(s.name)
shove!(s.name, pop!(s.integer))
end
NAME_STACK_DEPTH(s::State) = push!(s.integer, length(s.name))
NAME_SWAP(s::State) = if length(s.name) >= 2
s.name[end], s.name[end-1] = s.name[end-1], s.name[end]
end
NAME_YANK(s::State) = if !isempty(s.integer) && !isempty(s.name)
yank!(s.name, pop!(s.integer))
end
NAME_YANKDUP(s::State) = if !isempty(s.integer) && !isempty(s.name)
yankdup!(s.name, pop!(s.integer))
end
NAME_ERC(s::State) = return
Push.register("NAME.=", NAME_EQUALS)
Push.register("NAME.DUP", NAME_DUP)
Push.register("NAME.FLUSH", NAME_FLUSH)
Push.register("NAME.POP", NAME_POP)
Push.register("NAME.QUOTE", NAME_QUOTE)
Push.register("NAME.RAND", NAME_RAND)
Push.register("NAME.RANDBOUNDNAME", NAME_RAND_BOUND_NAME)
Push.register("NAME.ROT", NAME_ROT)
Push.register("NAME.SHOVE", NAME_SHOVE)
Push.register("NAME.STACKDEPTH", NAME_STACK_DEPTH)
Push.register("NAME.SWAP", NAME_SWAP)
Push.register("NAME.YANK", NAME_YANK)
Push.register("NAME.YANKDUP", NAME_YANKDUP)
Push.register("NAME.ERC", NAME_ERC)
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] | 2.029412 | 884 |
@reexport module BlackJack
export BlackJackEnv
using ReinforcementLearningBase
using Random
const ACTIONS = [:hit, :stick]
const INDS = LinearIndices((
2, # player has ace or not
11, # player's hands sum, 12 ~ 21, plus other cases that the sum is greater than 21
10, # dealer's hands sum, 2~11
))
deal_card() = rand(1:13)
is_ace(x) = x == 1
value(x) = is_ace(x) ? 11 : min(x, 10)
mutable struct Hands
sum::Int
cards::Vector{Int}
n_usable_ace::Int
end
Hands() = Hands(0, [], false)
is_busted(h::Hands) = h.sum > 21
function Base.push!(h::Hands, x)
is_busted(h) && throw(ArgumentError("cards in hand are already busted!"))
push!(h.cards, x)
h.sum += value(x)
h.n_usable_ace += is_ace(x)
if h.n_usable_ace > 0 && is_busted(h)
h.sum -= 10
h.n_usable_ace -= 1
end
end
mutable struct BlackJackEnv <: AbstractEnv
player_hands::Hands
dealer_hands::Hands
is_end::Bool
reward::Float64
is_exploring_start::Bool
init::Union{Nothing,Tuple{Hands,Hands}}
end
RLBase.get_observation_space(env::BlackJackEnv) = DiscreteSpace(length(INDS))
RLBase.get_action_space(env::BlackJackEnv) = DiscreteSpace(2)
function BlackJackEnv(; is_exploring_start = false, init = nothing)
env = BlackJackEnv(
Hands(),
Hands(),
false,
0.0,
is_exploring_start,
init,
)
init_hands!(env)
env
end
function init_hands!(env::BlackJackEnv)
player_hands, dealer_hands = Hands(), Hands()
if env.is_exploring_start
player_hands.sum = rand(12:21)
player_hands.n_usable_ace = rand(Bool)
dealer_hands.sum = rand(2:11)
dealer_hands.n_usable_ace = dealer_hands.sum == 11 ? true : false
elseif env.init === nothing
while player_hands.sum <= 11
push!(player_hands, deal_card())
end
push!(dealer_hands, deal_card())
else
player_hands, dealer_hands = deepcopy(env.init)
end
env.player_hands, env.dealer_hands = player_hands, dealer_hands
end
function (env::BlackJackEnv)(a::Int)
if ACTIONS[a] == :hit
push!(env.player_hands, deal_card())
if is_busted(env.player_hands)
env.is_end = true
env.reward = -1.0
else
env.is_end = false
env.reward = 0.0
end
elseif ACTIONS[a] == :stick
while env.dealer_hands.sum < 17
push!(env.dealer_hands, deal_card())
end
if is_busted(env.dealer_hands)
env.reward = 1.0
else
if env.player_hands.sum > env.dealer_hands.sum
env.reward = 1.0
elseif env.player_hands.sum < env.dealer_hands.sum
env.reward = -1.0
else
env.reward = 0.0
end
end
env.is_end = true
end
nothing
end
function RLBase.reset!(env::BlackJackEnv)
env.is_end = false
env.reward = 0.0
init_hands!(env)
nothing
end
encode(env) =
INDS[
env.player_hands.n_usable_ace > 0 ? 1 : 2,
12 <= env.player_hands.sum <= 21 ? env.player_hands.sum - 10 : 1,
2 <= env.dealer_hands.sum <= 10 ? env.dealer_hands.sum : 1,
]
RLBase.observe(env::BlackJackEnv) =
(reward = env.reward, terminal = env.is_end, state = encode(env))
end | [
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] | 2.105927 | 1,586 |
struct EMR_MSM_Prediction{T<:AbstractFloat, S<:MSM_Timeseries{T}}
est::EMR_MSM_Estimate
pred_timeseries::AbstractArray{S,1}
start_ind::Array{Integer,1}
end
# TODO: enlarge existing prediction
function EMR_MSM_Prediction(
est::EMR_MSM_Estimate{T, M, S},
timesteps::Array{A,1},
num_samples::Integer = 1,
start_ind::Array{I,1} = length.(est.timeseries),
last_layer_residuals::Union{Nothing, Array{B,1}} = nothing;
rand_last_layer_res = true,
use_last_layer = true
) where {T<:AbstractFloat, M<:EMR_MSM_Model_Estimate{T},
S<:MSM_Timeseries{T}, A<:AbstractArray{T,1},
B<:AbstractArray{T,1}, I<:Integer}
pred_timeseries = [MSM_Timeseries(est.pred_timeseries[ts], timesteps[ts],
num_samples)
for ts in 1:length(est.pred_timeseries)]
EMR_MSM_Prediction(pred_timeseries, est, start_ind, last_layer_residuals;
rand_last_layer_res = rand_last_layer_res,
use_last_layer = use_last_layer)
end
function EMR_MSM_Prediction(
est::EMR_MSM_Estimate{T, M, S},
num_preds::Integer,
timestep::Array{T,1} = [one(T) for i in 1:length(est.timeseries)],
num_samples::Integer = 1,
start_ind::Array{I,1} = length.(est.timeseries),
last_layer_residuals::Union{Nothing, Array{B,1}} = nothing;
rand_last_layer_res = true,
use_last_layer = true
) where {T<:AbstractFloat, M<:EMR_MSM_Model_Estimate{T},
B<:AbstractArray{T,1}, S<:MSM_Timeseries{T}, I<:Integer}
pred_timeseries = [MSM_Timeseries(est.pred_timeseries[ts], num_preds, timestep[ts],
num_samples)
for ts in 1:length(est.pred_timeseries)]
EMR_MSM_Prediction(pred_timeseries, est, start_ind, last_layer_residuals;
rand_last_layer_res = rand_last_layer_res,
use_last_layer = use_last_layer)
end
# TODO use est.pred_ts not est.timeseries !!
function EMR_MSM_Prediction(pred_timeseries::Array{MSM_Timeseries_Point{T},1},
est::EMR_MSM_Estimate{T, EMR_MSM_Model_PointEstimate{T}, S,
MSM_Timeseries_Dist{T}},
start_ind::Array{I,1} = length.(est.timeseries),
last_layer_residuals::Union{Nothing, Array{B,1}} = nothing;
rand_last_layer_res = true,
use_last_layer = true
) where {T<:AbstractFloat, S<:MSM_Timeseries{T},
B<:AbstractArray{T,1}, I<:Integer}
EMR_MSM_Prediction(pred_timeseries,
EMR_MSM_Estimate(est.model,est.timeseries,
[rand(ps.ts_points) for ps in est.pred_timeseries]),
start_ind,
last_layer_residuals;
rand_last_layer_res = rand_last_layer_res,
use_last_layer = use_last_layer
)
end
function EMR_MSM_Prediction(pred_timeseries::Array{MSM_Timeseries_Point{T},1},
est::EMR_MSM_Estimate{T, EMR_MSM_Model_PointEstimate{T}, S,
MSM_Timeseries_Point{T}},
start_ind::Array{I,1} = length.(est.timeseries),
last_layer_residuals::Union{Nothing, Array{B,1}} = nothing;
rand_last_layer_res = true,
use_last_layer = true
) where {T<:AbstractFloat, S<:MSM_Timeseries{T},
B<:AbstractArray{T,1}, I<:Integer}
num_layers = layers(pred_timeseries[1])
num_params = params(pred_timeseries[1])
L_mats = est.model.RCorrs
A_mat = est.model.Lin_mat
B_mats = est.model.Quad_mats
F = est.model.F
μ = est.model.μ
Σ = est.model.Σ
for ts in 1:length(pred_timeseries)
r = residuals(pred_timeseries[ts])
x = values(pred_timeseries[ts])
x[1,:] = values(est.timeseries[ts])[start_ind[ts], :]
r[1,:,:] = residuals(est.pred_timeseries[ts])[start_ind[ts], :, :]
pred_timesteps = timesteps(pred_timeseries[ts])
for i=2:length(pred_timeseries[ts])
r[i,:,num_layers] = if isnothing(last_layer_residuals)
ifelse(use_last_layer,rand(MvNormal(μ, Σ)), zeros(T,num_params))
elseif rand_last_layer_res
last_layer_residuals[ts][rand(1:size(rand_last_layer_res,1)),:]
else
last_layer_residuals[ts][i,:]
end
for l = (num_layers-1):-1:1
r[i,:,l] = pred_timesteps[i]*(L_mats[l]*vcat(x[i-1,:],vec(r[i-1,:,1:l]))) +
r[i-1,:,l+1] + r[i-1,:,l]
end
x[i,:] = pred_timesteps[i]*(-A_mat*x[i-1,:] +
[transpose(x[i-1,:])*B_mats[:,:,k]*x[i-1,:] for k in 1:num_params] +
F) + r[i-1,:,1] + x[i-1,:]
end
end
EMR_MSM_Prediction{T, MSM_Timeseries_Point{T}}(est, pred_timeseries, start_ind)
end
function EMR_MSM_Prediction(pred_timeseries::Array{MSM_Timeseries_Dist{T},1},
est::EMR_MSM_Estimate{T, EMR_MSM_Model_DistEstimate{T}, S},
start_ind::Array{I,1} = length.(est.timeseries),
last_layer_residuals::Union{Nothing, Array{B,1}} = nothing;
rand_last_layer_res = true,
use_last_layer = true
) where {T<:AbstractFloat, S<:MSM_Timeseries{T}, B<:AbstractArray{T,1}, I<:Integer}
for ts in 1:length(pred_timeseries)
for which_ts in 1:samples(pred_timeseries[ts])
r_ind = rand(1:size(est.model))
EMR_MSM_Prediction(
[MSM_Timeseries_Point{T}(
view(pred_timeseries[ts].x,:,:,which_ts),
view(pred_timeseries[ts].residuals,:,:,:,which_ts),
view(pred_timeseries[ts].timesteps,:))],
EMR_MSM_Estimate(
est.model.estimates[r_ind],
[est.timeseries[ts]],
[est.pred_timeseries[ts].ts_points[r_ind]]
),
[start_ind[ts]],
if !isnothing(last_layer_residuals)
if typeof(last_layer_residuals[ts]) <: AbstractArray{T,3}
[last_layer_residuals[ts][:,:,which_ts]]
elseif typeof(last_layer_residuals[ts]) <: AbstractArray{T,2}
[last_layer_residuals[ts]]
else
nothing
end
else
nothing
end ; rand_last_layer_res = rand_last_layer_res,
use_last_layer = use_last_layer)
end
end
EMR_MSM_Prediction{T, MSM_Timeseries_Dist{T}}(
est, pred_timeseries, start_ind)
end
function write(output::String, pred::EMR_MSM_Prediction)
mkpath(output)
for i in 1:length(pred.pred_timeseries)
write(joinpath(output,"ts-"*string(i)), pred.pred_timeseries[i])
end
end
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] | 1.918171 | 3,434 |
module MarkovChains
# package code goes here
include("graphs.jl")
include("markov_chain.jl")
include("inftime_solve.jl")
include("fintime_solve.jl")
include("plot.jl")
struct CtmcSolution
prob::Vector{Float64}
cumtime::Vector{Float64}
end
export solve, state_prob, state_cumtime
function state_prob(sol::CtmcSolution, state::Integer)
return sol.prob[state]
end
function state_cumtime(sol::CtmcSolution, state::Integer)
return sol.cumtime[state]
end
function solve(chain, init_prob, time)
n = state_count(chain)
prob = Vector{Float64}(undef, n)
cumtime = Vector{Float64}(undef, n)
if isinf(time)
sol = inftime_solve(chain, init_prob)
for i in 1:n
prob[i] = state_prob(sol, i)
cumtime[i] = state_cumtime(sol, i)
end
else
sol = fintime_solve(chain, init_prob, time)
for i in 1:n
prob[i] = state_prob(sol, i)
cumtime[i] = state_cumtime(sol, i)
end
end
CtmcSolution(prob, cumtime)
end
export Graphs
end # module
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198,
198,
39344,
29681,
82,
198,
198,
437,
1303,
8265,
198
] | 2.188406 | 483 |
using Flux
using Random
using MinimalRLCore
Base.@kwdef struct EvaluateAgent{A, SB, Φ, P}
agent::A
prev_s::Φ
policy::AP
end
function EvaluateAgent(agent::DQNAgent, eval_policy::AP)
EvaluateAgent(agent,
zeros(eltype(agent.state_buffer),
size(agent.state_buffer)[1:end-1]...,
hist_length(agent.state_buffer)),
eval_policy)
end
function MinimalRLCore.start!(agent::EvaluateAgent{DQNAgent},
env_s_tp1,
rng::AbstractRNG=Random.GLOBAL_RNG)
agent.prev_s
agent.state_postproc(agent.state_preproc(env_s_tp1))
end
function MinimalRLCore.step!(agent::EvaluateAgent{DQNAgent},
env_s_tp1,
r,
terminal,
rng::AbstractRNG=Random.GLOBAL_RNG)
end
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437,
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] | 1.719557 | 542 |
export PolarizedModel, coherencymatrix
import ComradeBase: AbstractPolarizedModel, m̆, evpa, CoherencyMatrix, StokesVector
"""
$(TYPEDEF)
Wrapped model for a polarized model. This uses the stokes representation of the image.
"""
struct PolarizedModel{TI,TQ,TU,TV} <: AbstractPolarizedModel
I::TI
Q::TQ
U::TU
V::TV
end
@inline visanalytic(::Type{PolarizedModel{I,Q,U,V}}) where {I,Q,U,V} = visanalytic(I)*visanalytic(Q)*visanalytic(U)*visanalytic(V)
@inline imanalytic(::Type{PolarizedModel{I,Q,U,V}}) where {I,Q,U,V} = imanalytic(I)*imanalytic(Q)*imanalytic(U)*imanalytic(V)
@inline function intensity_point(pmodel::PolarizedModel, u, v)
I = intensity_point(pmodel.I, u, v)
Q = intensity_point(pmodel.Q, u, v)
U = intensity_point(pmodel.U, u, v)
V = intensity_point(pmodel.V, u, v)
return StokesVector(I,Q,U,V)
end
"""
$(SIGNATURES)
Computes the visibility in the stokes basis of the polarized model
"""
@inline function visibility(pimg::PolarizedModel, u, v)
si = visibility(pimg.I, u, v)
sq = visibility(pimg.Q, u, v)
su = visibility(pimg.U, u, v)
sv = visibility(pimg.V, u, v)
return StokesVector(si, sq, su, sv)
end
"""
$(SIGNATURES)
Finds the polarized intensity map of the polarized model pmodel.
"""
function intensitymap!(pimg::IntensityMap{<:StokesVector}, pmodel::PolarizedModel)
intensitymap!(stokes(pimg, :I), pmodel.I)
intensitymap!(stokes(pimg, :Q), pmodel.Q)
intensitymap!(stokes(pimg, :U), pmodel.U)
intensitymap!(stokes(pimg, :V), pmodel.V)
end
function intensitymap(pmodel::PolarizedModel, fovx::Real, fovy::Real, nx::Int, ny::Int; pulse=DeltaPulse())
imgI = intensitymap(pmodel.I, fovx, fovy, nx, ny)
imgQ = intensitymap(pmodel.Q, fovx, fovy, nx, ny)
imgU = intensitymap(pmodel.U, fovx, fovy, nx, ny)
imgV = intensitymap(pmodel.V, fovx, fovy, nx, ny)
pimg = StructArray{StokesVector{eltype(imgI)}}((imgI.im, imgQ.im, imgU.im, imgV.im))
return IntensityMap(pimg, fovx, fovy, pulse)
end
"""
$(SIGNATURES)
Computes the coherency matrix of the polarized model.
"""
@inline function coherencymatrix(pimg::PolarizedModel, u, v)
si = visibility(pimg, u, v)
return convert(CoherencyMatrix, si)
end
"""
$(SIGNATURES)
electric vector position angle or EVPA of the polarized model
"""
@inline function evpa(pimg, u, v)
sq = visibility(pimg.Q, u, v)
su = visibility(pimg.U, u, v)
return 1/2*angle(su/sq)
end
"""
$(SIGNATURES)
Computes the fractional linear polarization in the visibility domain
```math
\\breve{m} = \\frac{Q + iU}{I}
```
"""
@inline function m̆(pimg, u, v)
Q = visibility(pimg.Q, u, v)
U = visibility(pimg.U, u, v)
I = visibility(pimg.I, u, v)
return (Q+1im*U)/I
end
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##################################################################
## Chapter 1: Loading the damn thing.
#
# File for parsing the RubiRules and dumping them into a pretty printed JSON
# in your Rubin.jl/src dir.
#
# Here are the commands to download the artifacts:
# RUN THEM!
# using Pkg
# Pkg.instantiate()
##################################################################
using Rubin
using Test
using Artifacts
using FileTrees
using JSON3
using StructTypes
using Logging
using SymPy
using PyCall
using CSV
using DelimitedFiles
using DataFrames
# TODO mark as constants
rubi = artifact"Rubi"
rubitests = artifact"RubiTests"
rubidir = FileTree(rubi);
abstract type AbstractRubiParser end
struct RubiRules <: AbstractRubiParser end
struct RubiTests <: AbstractRubiParser end
## Design goals:
#1. Read Rules into a huge JSON array (with FileTrees.jl)
#2. Add metadata, facts, number, filename
#3. Save to a single file
## Steps:
# 1. Read artifact dir into a FileTree
# 2. Come up with a function to parse the files
# INPUT examples:
# (* ::Subsection::Closed:: *)
# (* 1.1.1.1 (a+b x)^m *)
# Int[1/x_, x_Symbol] := Log[x]
# Int[x_^m_., x_Symbol] := x^(m + 1)/(m + 1) /; FreeQ[m, x] && NeQ[m, -1]
# (Int[...]) := (...) /; (...)
# ^ lhs ^rhs ^facts
#
# BUT we can also have
# (* Int[Sqrt[a_.+b_.*x_]*(A_.+B_.*x_)/(Sqrt[c_.+d_.*x_]*Sqrt[e_.+f_.*x_ ]*Sqrt[g_.+h_.*x_]),x_Symbol] := B*Sqrt[a+b*x]*Sqrt[e+f*x]*Sqrt[g+h*x]/(f*h*Sqrt[c+d*x]) - B*(b*g-a*h)/(2*f*h)*Int[Sqrt[e+f*x]/(Sqrt[a+b*x]*Sqrt[c+d*x]*Sqrt[g+ h*x]),x] + B*(d*e-c*f)*(d*g-c*h)/(2*d*f*h)*Int[Sqrt[a+b*x]/((c+d*x)^(3/2)*Sqrt[ e+f*x]*Sqrt[g+h*x]),x] /; FreeQ[{a,b,c,d,e,f,g,h,A,B},x] && EqQ[2*A*d*f-B*(d*e+c*f),0] *)
# (* Int[...] := (...) ;/ (...) *)
# ^comment syntax around lhs := rhs ;/ facts *) closing comment syntax
# Which means we have 2 grammars:
# Case 1:
# (^Int.?) := (.+) /; (.+)
# Case 2:
# (* (Int.?) := (.+)(|\/; (.+))
#
## SOLUTION:
# With the help of Regex101.com:
# (Int.+) := (.+)(?: \/; \*\))
# ^glob lhs,
# ^glob rhs
# ^glob facts, if any exist, ignore if it doesn't
# Let's now write some tests for the regex.
regextest = [
"""(* ::Subsection::Closed:: *)""",
"""(* 1.1.1.1 (a+b x)^m *)""",
"""Int[1/x_, x_Symbol] := Log[x]""",
"""(* Int[Sqrt[a_.+b_.*x_]*(A_.+B_.*x_)/(Sqrt[c_.+d_.*x_]*Sqrt[e_.+f_.*x_ ]*Sqrt[g_.+h_.*x_]),x_Symbol] := B*Sqrt[a+b*x]*Sqrt[e+f*x]*Sqrt[g+h*x]/(f*h*Sqrt[c+d*x]) - B*(b*g-a*h)/(3*f*h)*Int[Sqrt[e+f*x]/(Sqrt[a+b*x]*Sqrt[c+d*x]*Sqrt[g+ h*x]),x] + B*(d*e-c*f)*(d*g-c*h)/(2*d*f*h)*Int[Sqrt[a+b*x]/((c+d*x)^(3/2)*Sqrt[ e+f*x]*Sqrt[g+h*x]),x] /; FreeQ[{a,b,c,d,e,f,g,h,A,B},x] && EqQ[2*A*d*f-B*(d*e+c*f),0] *)""",
"""(* Int[Sqrt[a_.+b_.*x_]*(A_.+B_.*x_)/(Sqrt[c_.+d_.*x_]*Sqrt[e_.+f_.*x_ ]*Sqrt[g_.+h_.*x_]),x_Symbol] := B*Sqrt[a+b*x]*Sqrt[e+f*x]*Sqrt[g+h*x]/(f*h*Sqrt[c+d*x]) - B*(b*g-a*h)/(2*f*h)*Int[Sqrt[e+f*x]/(Sqrt[a+b*x]*Sqrt[c+d*x]*Sqrt[g+ h*x]),x] + B*(d*e-c*f)*(d*g-c*h)/(2*d*f*h)*Int[Sqrt[a+b*x]/((c+d*x)^(3/2)*Sqrt[ e+f*x]*Sqrt[g+h*x]),x] /; FreeQ[{a,b,c,d,e,f,g,h,A,B},x] && EqQ[2*A*d*f-B*(d*e+c*f),0] *)""",
"""(* Int[1/(a_+b_.*x_^5),x_Symbol] := With[{r=Numerator[Rt[a/b,5]], s=Denominator[Rt[a/b,5]]}, r/(5*a)*Int[1/(r+s*x),x] + 2*r/(5*a)*Int[(r-1/4*(1-Sqrt[5])*s*x)/(r^2-1/2*(1-Sqrt[5])*r*s*x+s^ 2*x^2),x] + 2*r/(5*a)*Int[(r-1/4*(1+Sqrt[5])*s*x)/(r^2-1/2*(1+Sqrt[5])*r*s*x+s^ 2*x^2),x]] /; FreeQ[{a,b},x] && PosQ[a/b] *)""",
"""(* Int[1/Sqrt[a_+b_.*x_^3],x_Symbol] := With[{q=Rt[b/a,3]}, -Sqrt[2]*(1+Sqrt[3])*(1+Sqrt[3]+q*x)^2*Sqrt[(1+q^3*x^3)/(1+Sqrt[3]+ q*x)^4]/(3^(1/4)*q*Sqrt[a+b*x^3])* EllipticF[ArcSin[(-1+Sqrt[3]-q*x)/(1+Sqrt[3]+q*x)],-7-4*Sqrt[3]]] /; FreeQ[{a,b},x] && PosQ[a] *)""",
"""(* Int[Sqrt[c_+d_.*x_^2]/((a_+b_.*x_^2)*Sqrt[e_+f_.*x_^2]),x_Symbol] := Sqrt[c+d*x^2]*Sqrt[c*(e+f*x^2)/(e*(c+d*x^2))]/(a*Rt[d/c,2]*Sqrt[e+f* x^2])* EllipticPi[1-b*c/(a*d),ArcTan[Rt[d/c,2]*x],1-c*f/(d*e)] /; FreeQ[{a,b,c,d,e,f},x] && PosQ[d/c] *)"""];
## Where the 🌠 magic happens 🌠
intrulesregexfacts = r"(Int.+) := (.+) \/; (.+)(?: \*\))";
intrulesregexsimple = r"^(Int.+) := (.+)";
commenttest = """(* Int[Sqrt[a_.+b_.*x_]*(A_.+B_.*x_)/(Sqrt[c_.+d_.*x_]*Sqrt[e_.+f_.*x_ ]*Sqrt[g_.+h_.*x_]),x_Symbol] := B*Sqrt[a+b*x]*Sqrt[e+f*x]*Sqrt[g+h*x]/(f*h*Sqrt[c+d*x]) - B*(b*g-a*h)/(2*f*h)*Int[Sqrt[e+f*x]/(Sqrt[a+b*x]*Sqrt[c+d*x]*Sqrt[g+ h*x]),x] + B*(d*e-c*f)*(d*g-c*h)/(2*d*f*h)*Int[Sqrt[a+b*x]/((c+d*x)^(3/2)*Sqrt[ e+f*x]*Sqrt[g+h*x]),x] /; FreeQ[{a,b,c,d,e,f,g,h,A,B},x] && EqQ[2*A*d*f-B*(d*e+c*f),0] *)""";
## Regex tests
# Gotta make sure we are slurping up the things we care about, without too much cruft.
vregex = match.(intrulesregexsimple, regextest);
rulesregex = match.(intrulesregexfacts, regextest);
@test isnothing(vregex[1])
@test isnothing(vregex[2])
@test vregex[3].captures[1] == "Int[1/x_, x_Symbol]"
@test vregex[3].captures[2] == "Log[x]"
@test rulesregex[end].captures[3] == " FreeQ[{a,b,c,d,e,f},x] && PosQ[d/c]"
"""
# Structs for parsing
- Useful in conjunction with StructTypes.jl and dumping everything into a JSON
- Fields:
pathname: String
filename: String
rulenumber: Int
commented: Bool
lhs: String
rhs: String
givens: String (not atomize)
"""
Base.@kwdef struct IntRuleCapture
pathname::String = ""
filename::String = ""
rulenumber::Int = ""
comment::Bool = true
rhs::String = ""
lhs::String = ""
givens::String = ""
end
## Auxiliary functions for later
lhs(cap::RegexMatch) = cap.captures[1]
rhs(cap::RegexMatch) = cap.captures[2]
givens(cap::RegexMatch) = cap.captures[3]
iscommented(cap::RegexMatch) = startswith(cap.captures[1], '(')
# Input: A file
# Output: An array of structs like IntRuleCapture
# TODO: Figure out rule numbers
function Base.parse(file, ::Type{RubiRules})
regex = r"(Int.+) := (.+) \/; (.+)( \*\))?"
# Capture the lines that fit the regex, skip the empty ones, put it all into
# a big array. Gotta love Julia!
caps = [i for i in match.(regex, readlines(file)) if !isnothing(i)]
vlhs = lhs.(caps)
vrhs = rhs.(caps)
vgivens = givens.(caps)
vcomments = iscommented.(caps)
rubi = artifact"Rubi"
path = relpath(file, rubi) # Fix this global?
filename = splitpath(file)[end]
# Assemble and return 📦 ➡
[IntRuleCapture(pathname = path,
filename = filename,
rulenumber = 0,
lhs = vlhs[i],
rhs = vrhs[i],
givens = vgivens[i],
comment = vcomments[i]) for i in 1:length(caps)]
end
## Testing nothing's borked with finding the files and making a TreeFile of them
intfiles = rubidir["Rubi-4.16.1.0/Rubi/IntegrationRules"];
parsedstructs = FileTrees.load(intfiles) do file
parse(string(path(file)), RubiRules)
end;
vstructs = reducevalues(vcat, parsedstructs);
@test 7032 == length(vstructs)
"""
Loads the RubiRules into a vector.
See `IntRuleCapture` for the fields.
We use FileTrees.jl, so starting Julia with multiple threads should speed this up,
but it feels like it could be faster ... ?
"""
function load(::Type{RubiRules})
rubi = artifact"Rubi"
intfiles = joinpath(rubi, "Rubi-4.16.1.0", "Rubi", "IntegrationRules")
files = FileTree(intfiles)
parsedstructs = FileTrees.load(files) do file
parse(string(path(file)), RubiRules)
end
vstructs = reducevalues(vcat, parsedstructs)
7032 == length(vstructs) || error("Please alert @miguelraz, something has 💥")
vstructs
end
## Writing to JSON! At last!
# Defining this straight from the JSON3 documentation
StructTypes.StructType(::Type{IntRuleCapture}) = StructTypes.Struct();
# For debugging
#json = JSON3.write(vstructs);
"""
Write `vstructs` into a `intrules.json` file in Rubin.jl/src, where `vstructs`
is defined as
```julia
vstructs = load(RubiRules)
```
"""
function write(vstructs::Vector{IntRuleCapture}, ::Type{RubiRules})
# hat tip to Jacob Quinn and the #data gang
# Find the Rubin root dir in an OS independent way
targetpath = joinpath(pkgdir(Rubin), "src", "rubirules.json")
# JSON3 way to write to a file with 💃 pretty printing 💃
open(targetpath, "w") do f
JSON3.pretty(f, JSON3.write(vstructs))
println(f)
end
end
##################################################################
## Chapter 2:
# The friggin' tests
#
# GOALS: ⚽
#1. Read MathematicaSyntaxTestSuite into a huge JSON Array
#2. Add metadata, facts, number, filename, test number
#3. Save to a single file.
#
## Note: They claim 72944 tests total in the website,
# but we must care about the ones that are commented
#
# 💪 Currently at 72961 💪,
# with 1?? missing
# Wermer book missing
##################################################################
## Regex tests
# Tricky, gotta make sure we don't miss any of them!
inttests =
["{Sqrt[2*x + 1], x, 1, (1/3)*(1 + 2*x)^(3/2)}",
"(* {Sqrt[2*x + 1], x, 1, (1/3)*(1 + 2*x)^(3/2)} *)"];
inttestregex = r"{(.+),(.+),(.+),(.+)}";
res = match.(inttestregex, inttests);
@test res[1].captures[1] == "Sqrt[2*x + 1]"
@test res[1].captures[2] == " x"
@test res[1].captures[3] == " 1"
@test res[1].captures[4] == " (1/3)*(1 + 2*x)^(3/2)"
@test res[2].captures[1] == "Sqrt[2*x + 1]"
@test res[2].captures[2] == " x"
@test res[2].captures[3] == " 1"
@test res[2].captures[4] == " (1/3)*(1 + 2*x)^(3/2)"
## Header regex
# We'll likely need some metadata to alleviate pain when updating the codebase.
# This should help mitigate some of those aches.
headerregex = r"\(\*(.+)\*\)";
@test match(headerregex, "(*Integrands of the form x^m PolyLog[n, a x^q]*)").captures[1] == "Integrands of the form x^m PolyLog[n, a x^q]"
"""
Sample input: {Sqrt[2*x + 1], x, 1, (1/3)*(1 + 2*x)^(3/2)}
^query ^var ^steps ^optimal answer
# NOTE:
grep '(* {' | wc detects 284 commented cases at time of writing
# Fields:
- filename
- path
- header
- integrand
- variable
- steps
- optimal
- iscomment
"""
Base.@kwdef struct IntRuleTest
filename::String =""
path::String = ""
integrand::String = ""
variable::String = ""
steps::String = "" # Yeah sure, this could be an Int but I'm 🏈 punting.
optimal::String = ""
iscomment::Bool = false
end
# header: string - to print testset and @async regions
# tests: Vec{IntRuleTest} - to write all the tests
Base.@kwdef struct IntRuleTestSection
filename::String = ""
path::String = ""
header::String = ""
tests::Vector{IntRuleTest} = IntRuleTest[]
end
"""
1. Find first line that is not `skipme`, parse and push into the header
2. Slurp the subsequent integrals, capture them into a vector of `IntRuleTest`s
3. When you encounter a header that is different from the previous, put
all the previous IntRuleTests into a `IntTestSectionCapture` array
Headers: There is much metadata like `(* n > 0 *)` and `(*Integrands ... )`
We don't wanna throw that away if possible
# TODO: finer metadata with the headers in the file
So that testset nesting can happen
"""
function Base.parse(file, ::Type{RubiTests})
# TODO add nesting to subsubsections
# This regex globs up everything (*InsideTheirCommentSyntax*)
headerregex = r"\(\*(.+)\*\)"
# This regex globs up the 4 entries in {a,format,like,this}
# TODO Fix those 5 missing cases in issue
# inttestregex = r"{(.+),(.+),(.+),(.+)?}"
inttestregex = r"
{(.+), # captures integrand
\s?([a-z])\s?, # captures variable (single letter)
\s?((?:-?[0-9\s]+)|(?:If\[\$VersionNumber[>=<]{1,3}[-,\s\d]*\])) # captures steps (number or VersionNumber check)
(?:,(.+))?} # capturens optimal result if it exists
"x
rubi = artifact"RubiTests"
path = relpath(file)
header = ""
tests = IntRuleTest[]
for line in readlines(file)
# Skip the `(* ::Subsection...` and `(* ::Subsubsection...` lines
isempty(line) && continue
startswith(line, "(* ::") && continue
# If it's a test, push it
if occursin(r"{", line) && occursin(r"}", line)
# TODO Handle this better? We're losing information here
# Perhaps adding a hierarchy to the section or subsection is good
try
m = match(inttestregex, line).captures
catch
@warn "inttestregex did not capture int test line"
@warn line
@warn file
continue
end
m = match(inttestregex, line).captures
# We need to know 7 things:
# 1. integrand
# 2. variable to integrate
# 3. steps taken by rubi
# 4. optimal answer
# 5. if it's commented
# 6. path
# 7. filename
integrand, variable, steps, optimal = (m...,)
iscomment = startswith(line, "(* {")
path = relpath(file, rubi) # Fix this global?
filename = splitpath(file)[end]
#@info "pushed to test"
push!(tests, IntRuleTest(integrand = integrand, variable = variable,
path = path, filename = filename,
steps = steps, optimal = string(optimal),
iscomment = iscomment))
elseif startswith("(*", line) && endswith("*)", line)
# 🆙 Update the header
header = match(headerregex, line).captures[1]
else
continue
end
end
# Remember! Push the last section!
tests
end
## Test the test parsing function
# Because that is one chonky boii
#testfile = "/home/mrg/.julia/artifacts/1148cba18dae2f8939af8bc542233a48cc42cf19/MathematicaSyntaxTestSuite-4.16.0/5 Inverse trig functions/5.5 Inverse secant/5.5.2 Inverse secant functions.m";
testfile = "parsetest.m"
vtests = parse(testfile, RubiTests)
@test length(vtests) == 11
@test vtests[3].iscomment
@test vtests[9].iscomment
# TODO swap out the loads
function findtreefolder(::Type{RubiRules})
rubi = artifact"Rubi"
intfiles = joinpath(rubi, "Rubi-4.16.1.0", "Rubi", "IntegrationRules")
files = FileTree(intfiles)
end
function findtreefolder(::Type{RubiTests})
rubitests = artifact"RubiTests"
intfiles = joinpath(rubitests, "MathematicaSyntaxTestSuite-4.16.0")
files = FileTree(intfiles)
end
"""
Loads the RubiRules into a vector.
See `IntTestSections` for the fields.
"""
function load(::Type{RubiTests})
rubitests = artifact"RubiTests"
intfiles = joinpath(rubitests, "MathematicaSyntaxTestSuite-4.16.0")
fs = FileTree(intfiles)
# Wester problems has an irregular format, see issue # 3
rm(fs, r"LICENSE|README.md|Wester Problems.m")
@info fs
parsedstructs = FileTrees.load(fs) do file
parse(string(path(file)), RubiTests)
end
vtests = reducevalues(vcat, parsedstructs)
72961 == length(vtests) || error("Please alert @miguelraz, something has 💥")
vtests
end
vtests = load(RubiTests);
@test length(vtests) == 72961
# For JSON3 writing
StructTypes.StructType(::Type{IntRuleTest}) = StructTypes.Struct();
"""
Write `vtests` into a `inttests.json` file in Rubin.jl/src, where `vtests`
is defined as
```julia
vtests = load(RubiRules)
```
"""
function write(vtests::Vector{IntRuleTest}, ::Type{RubiTests})
# Hat tip to Jacob Quinn 🎩 and the #data gang
# Find the Rubin root dir in an OS independent way
targetpath = joinpath(pkgdir(Rubin), "src", "rubitests.json")
# JSON3 way to write to a file with 💃 pretty printing 💃
open(targetpath, "w") do f
JSON3.pretty(f, JSON3.write(vtests))
println(f)
end
end
##################################################################
## Chapter 3:
# Woflram -> Julia
#
# GOALS: ⚽
# 1. OUTPUT: tests/rubitests.jl with all the test files written in plain Julia
# - Format shown below
# 2. OUTPUT: src/rubirules.jl with all the rules written in plain Julia
# - Format: show below
#
# Approaches:
# 1. Make a small dict with Mathematica => Julia translations,
# THEN search and replace every string on both sides
# 2. use a while loop with the replacings, if it doesn't change go on.
# OOOPS
# - All fun and games until E^x and E^(x+2) showed up - unclear how to deal
# with parens 💩
# 3. Use a library like RBNF.jl
# 4. DERP! See if someone else has solved it first!
# Sympy has a Mathematica parser - from there it should be much simple.
# -
# Options:
#
# 1. Symbolics.jl vs Metatheory.jl .... vs Rust egraphs?
# # Consider how to propagate assumptions
# https://julialang.zulipchat.com/#narrow/stream/236639-symbolic-programming/topic/Integration/near/232350734
##################################################################
### Notes:
# Once I have a symbolic sympy expression, I can just turn it into a Julia
# Julia string with `str(y)`:
# sympy = pyimport("sympy")
# str = py"'Sin[x]^2'";
# mathparser = sympy.parsing.mathematica.mathematica
# y = maths(str)
# res = string(y) # 🚀
# @test "sin(x)^2" == res
## Parser hijinks:
# Ugh - the sympy.mathematica parser does not accept Int[ ... :(
# No worries 😅, we can pass in a PyDict with our prefs and have it work!
# function parsetosympy(s,d)
# s = pystring(PyObject(s))
# f = sympy.parsing.mathematica.mathematica
# f(s,d)
# end
## Parser hijinks: Sympy doesn't translate all functions :/
# Need to grep for the function names in rubirules.json and add them to the below dict
#mathfuncs = readlines("mathematicafunctions2.txt")
# mathfuncsdictionary = Dict(i * "*x]" => lowercase(i[1:end-1]) * "(*x)" for i in readlines("mathematicafunctions2.txt"))
# function mathfuncsdict()
# mathfuncs = readlines("mathematicafunctions2.txt")
# # ps =
# end
# note the *x to slurp all args
sympydictfixes = PyDict(Dict("Int[*x]" => "integrate(*x)",
"ProductLog[*x]" => "productlog(*x)",
"Gamma[*x]" => "gamma(*x)",
# Following convention from SpecialFunctions.jl
"EllipticF[*x]" => "ellipf(*x)",
# TODO add Fresnel, FresnelS, FresnelC
#"E^x" => "exp(x)",
#"E^(*x)" => "exp(*x)",
"Abs[*x]" => "abs(*x)",
"AppellF1[*x]" => "appelf1(*x)",
"CannotIntegrate[*x]" => "cannotintegrate(*x)",
"CoshIntegral[*x]" => "coshintegral(*x)",
"CosIntegral[*x]" => "cosintegral(*x)",
"Defer[*x]" => "defer(*x)",
"Derivative[*x]" => "derivative(*x)",
"EllipticE[*x]" => "ellipe(*x)",
"EllipticF[*x]" => "ellipf(*x)",
"EllipticK[*x]" => "ellipk(*x)",
"EllipticPi[*x]" => "ellippi(*x)",
"Erf[*x]" => "erf(*x)",
"Erfc[*x]" => "erfc(*x)",
"Erfi[*x]" => "erfi(*x)",
"Expand[*x]" => "expand(*x)",
"ExpIntegralE[*x]" => "expintegrale(*x)",
"ExpIntegralEi[*x]" => "expintegralei(*x)",
#"f[*x]" => f(*x)",
#"F[*x]" => f(*x)",
"F0[*x]" => "f0(*x)",
"FresnelC[*x]" => "fresnelc(*x)",
"FresnelS[*x]" => "fresnels(*x)",
#"g[*x]" => ,
#"Gamma[*x]" => ,
"Hypergeometric2F1[*x]" => "hypergeometric2f1(*x)",
"HypergeometricPFQ[*x]" => "hypergeometricpfq(*x)",
#"If[*x]" => if,
#"Log[*x]" => "log,
"LogGamma[*x]" => "loggamma(*x)",
"LogIntegral[*x]" => "logintegral(*x)",
"PolyGamma[*x]" => "polygamma(*x)",
"PolyLog[*x]" => "polylog(*x)",
"ProductLog[*x]" => "productlog(*x)",
#"sdx[*x]" => ,
"SinhIntegral[*x]" => "sinhintegral(*x)",
"SinIntegral[*x]" => "sinintegral(*x)",
"Unintegrable[*x]" => "unintegrable(*x)",
"Zeta[*x]" => "zeta(*x)",
));
funcdicts = Dict(
"Abs[*x]" => "abs(*x)",
"ActivateTrig[*x]" => "activatetrig(*x)",
"AlgebraicFunctionQ[*x]" => "algebraicfunctionq(*x)",
"Apart[*x]" => "apart(*x)",
"AppellF1[*x]" => "appellf1(*x)",
"ArcCos[*x]" => "arccos(*x)",
"ArcCosh[*x]" => "arccosh(*x)",
"ArcCot[*x]" => "arccot(*x)",
"ArcCoth[*x]" => "arccoth(*x)",
"ArcCsc[*x]" => "arccsc(*x)",
"ArcCsch[*x]" => "arccsch(*x)",
"ArcSec[*x]" => "arcsec(*x)",
"ArcSech[*x]" => "arcsech(*x)",
"ArcSin[*x]" => "arcsin(*x)",
"ArcSinh[*x]" => "arcsinh(*x)",
"ArcTan[*x]" => "arctan(*x)",
"ArcTanh[*x]" => "arctanh(*x)",
"AtomQ[*x]" => "atomq(*x)",
"BesselJ[*x]" => "besselj(*x)",
"BinomialDegree[*x]" => "binomialdegree(*x)",
"BinomialMatchQ[*x]" => "binomialmatchq(*x)",
"BinomialParts[*x]" => "binomialparts(*x)",
"BinomialQ[*x]" => "binomialq(*x)",
"Block[*x]" => "block(*x)",
"CalculusFreeQ[*x]" => "calculusfreeq(*x)",
"Cancel[*x]" => "cancel(*x)",
"CannotIntegrate[*x]" => "cannotintegrate(*x)",
"Coeff[*x]" => "coeff(*x)",
"Coefficient[*x]" => "coefficient(*x)",
"ComplexFreeQ[*x]" => "complexfreeq(*x)",
"Complex[*x]" => "complex(*x)",
"CosIntegral[*x]" => "cosintegral(*x)",
"Cos[*x]" => "cos(*x)",
"CoshIntegral[*x]" => "coshintegral(*x)",
"Cosh[*x]" => "cosh(*x)",
"Cot[*x]" => "cot(*x)",
"Coth[*x]" => "coth(*x)",
"Csc[*x]" => "csc(*x)",
"Csch[*x]" => "csch(*x)",
"D[*x]" => "d(*x)",
"DeactivateTrig[*x]" => "deactivatetrig(*x)",
"Denom[*x]" => "denom(*x)",
"Denominator[*x]" => "denominator(*x)",
"DerivativeDivides[*x]" => "derivativedivides(*x)",
"Derivative[*x]" => "derivative(*x)",
"Discriminant[*x]" => "discriminant(*x)",
"Dist[*x]" => "dist(*x)",
"Distrib[*x]" => "distrib(*x)",
"DistributeDegree[*x]" => "distributedegree(*x)",
"Divides[*x]" => "divides(*x)",
"EllipticE[*x]" => "elliptice(*x)",
"EllipticF[*x]" => "ellipticf(*x)",
"EllipticPi[*x]" => "ellipticpi(*x)",
"EqQ[*x]" => "eqq(*x)",
"Erf[*x]" => "erf(*x)",
"Erfc[*x]" => "erfc(*x)",
"Erfi[*x]" => "erfi(*x)",
"EulerIntegrandQ[*x]" => "eulerintegrandq(*x)",
"EveryQ[*x]" => "everyq(*x)",
"ExpIntegralE[*x]" => "expintegrale(*x)",
"ExpIntegralEi[*x]" => "expintegralei(*x)",
"Exp[*x]" => "exp(*x)",
"ExpandExpression[*x]" => "expandexpression(*x)",
"ExpandIntegrand[*x]" => "expandintegrand(*x)",
"ExpandLinearProduct[*x]" => "expandlinearproduct(*x)",
"ExpandToSum[*x]" => "expandtosum(*x)",
"ExpandTrigExpand[*x]" => "expandtrigexpand(*x)",
"ExpandTrigReduce[*x]" => "expandtrigreduce(*x)",
"ExpandTrigToExp[*x]" => "expandtrigtoexp(*x)",
"ExpandTrig[*x]" => "expandtrig(*x)",
"Expand[*x]" => "expand(*x)",
"Expon[*x]" => "expon(*x)",
"Exponent[*x]" => "exponent(*x)",
"F[*x]" => "f(*x)",
"F_[*x]" => "f_(*x)",
"Factor[*x]" => "factor(*x)",
"FalseQ[*x]" => "falseq(*x)",
"Floor[*x]" => "floor(*x)",
"FracPart[*x]" => "fracpart(*x)",
"FractionQ[*x]" => "fractionq(*x)",
"FractionalPart[*x]" => "fractionalpart(*x)",
"FreeFactors[*x]" => "freefactors(*x)",
"FreeQ[*x]" => "freeq(*x)",
"FresnelC[*x]" => "fresnelc(*x)",
"FresnelS[*x]" => "fresnels(*x)",
"FullSimplify[*x]" => "fullsimplify(*x)",
"FunctionOfExponentialFunction[*x]" => "functionofexponentialfunction(*x)",
"FunctionOfExponentialQ[*x]" => "functionofexponentialq(*x)",
"FunctionOfExponential[*x]" => "functionofexponential(*x)",
"FunctionOfLinear[*x]" => "functionoflinear(*x)",
"FunctionOfLog[*x]" => "functionoflog(*x)",
"FunctionOfQ[*x]" => "functionofq(*x)",
"FunctionOfSquareRootOfQuadratic[*x]" => "functionofsquarerootofquadratic(*x)",
"FunctionOfTrigOfLinearQ[*x]" => "functionoftrigoflinearq(*x)",
"FunctionOfTrig[*x]" => "functionoftrig(*x)",
"Function[*x]" => "function(*x)",
"GCD[*x]" => "gcd(*x)",
"G[*x]" => "g(*x)",
"G_[*x]" => "g_(*x)",
"Gamma[*x]" => "gamma(*x)",
"GeQ[*x]" => "geq(*x)",
"GeneralizedBinomialMatchQ[*x]" => "generalizedbinomialmatchq(*x)",
"GeneralizedBinomialQ[*x]" => "generalizedbinomialq(*x)",
"GeneralizedTrinomialDegree[*x]" => "generalizedtrinomialdegree(*x)",
"GeneralizedTrinomialMatchQ[*x]" => "generalizedtrinomialmatchq(*x)",
"GeneralizedTrinomialQ[*x]" => "generalizedtrinomialq(*x)",
"GtQ[*x]" => "gtq(*x)",
"H[*x]" => "h(*x)",
"H_[*x]" => "h_(*x)",
"HalfIntegerQ[*x]" => "halfintegerq(*x)",
"Head[*x]" => "head(*x)",
"Hold[*x]" => "hold(*x)",
"HyperbolicQ[*x]" => "hyperbolicq(*x)",
"Hypergeometric2F1[*x]" => "hypergeometric2f1(*x)",
"HypergeometricPFQ[*x]" => "hypergeometricpfq(*x)",
"IGeQ[*x]" => "igeq(*x)",
"IGtQ[*x]" => "igtq(*x)",
"ILeQ[*x]" => "ileq(*x)",
"ILtQ[*x]" => "iltq(*x)",
"Identity[*x]" => "identity(*x)",
"If[*x]" => "if(*x)",
"IndependentQ[*x]" => "independentq(*x)",
"InertTrigFreeQ[*x]" => "inerttrigfreeq(*x)",
"InertTrigQ[*x]" => "inerttrigq(*x)",
"IntBinomialQ[*x]" => "intbinomialq(*x)",
"IntHide[*x]" => "inthide(*x)",
"IntLinearQ[*x]" => "intlinearq(*x)",
"IntPart[*x]" => "intpart(*x)",
"IntQuadraticQ[*x]" => "intquadraticq(*x)",
"IntSum[*x]" => "intsum(*x)",
"Int[*x]" => "int(*x)",
"IntegerPart[*x]" => "integerpart(*x)",
"IntegerQ[*x]" => "integerq(*x)",
"IntegersQ[*x]" => "integersq(*x)",
"IntegralFreeQ[*x]" => "integralfreeq(*x)",
"Integral[*x]" => "integral(*x)",
"Integrate[*x]" => "integrate(*x)",
"InverseFunctionFreeQ[*x]" => "inversefunctionfreeq(*x)",
"InverseFunctionOfLinear[*x]" => "inversefunctionoflinear(*x)",
"InverseFunctionQ[*x]" => "inversefunctionq(*x)",
"KnownCotangentIntegrandQ[*x]" => "knowncotangentintegrandq(*x)",
"KnownSecantIntegrandQ[*x]" => "knownsecantintegrandq(*x)",
"KnownSineIntegrandQ[*x]" => "knownsineintegrandq(*x)",
"KnownTangentIntegrandQ[*x]" => "knowntangentintegrandq(*x)",
"LeQ[*x]" => "leq(*x)",
"LeafCount[*x]" => "leafcount(*x)",
"LinearMatchQ[*x]" => "linearmatchq(*x)",
"LinearPairQ[*x]" => "linearpairq(*x)",
"LinearQ[*x]" => "linearq(*x)",
"LogGamma[*x]" => "loggamma(*x)",
"LogIntegral[*x]" => "logintegral(*x)",
"Log[*x]" => "log(*x)",
"LtQ[*x]" => "ltq(*x)",
"MatchQ[*x]" => "matchq(*x)",
"MemberQ[*x]" => "memberq(*x)",
"MinimumMonomialExponent[*x]" => "minimummonomialexponent(*x)",
"Mod[*x]" => "mod(*x)",
"Module[*x]" => "module(*x)",
"NeQ[*x]" => "neq(*x)",
"NegQ[*x]" => "negq(*x)",
"NiceSqrtQ[*x]" => "nicesqrtq(*x)",
"NonfreeFactors[*x]" => "nonfreefactors(*x)",
"NonsumQ[*x]" => "nonsumq(*x)",
"NormalizeIntegrand[*x]" => "normalizeintegrand(*x)",
"NormalizePowerOfLinear[*x]" => "normalizepoweroflinear(*x)",
"NormalizePseudoBinomial[*x]" => "normalizepseudobinomial(*x)",
"Not[*x]" => "not(*x)",
"Numer[*x]" => "numer(*x)",
"Numerator[*x]" => "numerator(*x)",
"OddQ[*x]" => "oddq(*x)",
"PerfectSquareQ[*x]" => "perfectsquareq(*x)",
"PiecewiseLinearQ[*x]" => "piecewiselinearq(*x)",
"PolyGCD[*x]" => "polygcd(*x)",
"PolyGamma[*x]" => "polygamma(*x)",
"PolyLog[*x]" => "polylog(*x)",
"PolyQ[*x]" => "polyq(*x)",
"PolynomialDivide[*x]" => "polynomialdivide(*x)",
"PolynomialInQ[*x]" => "polynomialinq(*x)",
"PolynomialInSubst[*x]" => "polynomialinsubst(*x)",
"PolynomialQ[*x]" => "polynomialq(*x)",
"PolynomialQuotient[*x]" => "polynomialquotient(*x)",
"PolynomialRemainder[*x]" => "polynomialremainder(*x)",
"PosQ[*x]" => "posq(*x)",
"PowerOfLinearMatchQ[*x]" => "poweroflinearmatchq(*x)",
"PowerOfLinearQ[*x]" => "poweroflinearq(*x)",
"PowerQ[*x]" => "powerq(*x)",
"PowerVariableExpn[*x]" => "powervariableexpn(*x)",
"ProductLog[*x]" => "productlog(*x)",
"ProductQ[*x]" => "productq(*x)",
"PseudoBinomialPairQ[*x]" => "pseudobinomialpairq(*x)",
"QuadraticMatchQ[*x]" => "quadraticmatchq(*x)",
"QuadraticProductQ[*x]" => "quadraticproductq(*x)",
"QuadraticQ[*x]" => "quadraticq(*x)",
"QuotientOfLinearsParts[*x]" => "quotientoflinearsparts(*x)",
"QuotientOfLinearsQ[*x]" => "quotientoflinearsq(*x)",
"Quotient[*x]" => "quotient(*x)",
"RationalFunctionExpand[*x]" => "rationalfunctionexpand(*x)",
"RationalFunctionExponents[*x]" => "rationalfunctionexponents(*x)",
"RationalFunctionQ[*x]" => "rationalfunctionq(*x)",
"RationalQ[*x]" => "rationalq(*x)",
"RemoveContent[*x]" => "removecontent(*x)",
"ReplaceAll[*x]" => "replaceall(*x)",
"Rt[*x]" => "rt(*x)",
"Sec[*x]" => "sec(*x)",
"Sech[*x]" => "sech(*x)",
"ShowStep[*x]" => "showstep(*x)",
"Sign[*x]" => "sign(*x)",
"Simp[*x]" => "simp(*x)",
"SimplerIntegrandQ[*x]" => "simplerintegrandq(*x)",
"SimplerQ[*x]" => "simplerq(*x)",
"SimplerSqrtQ[*x]" => "simplersqrtq(*x)",
"SimplifyIntegrand[*x]" => "simplifyintegrand(*x)",
"Simplify[*x]" => "simplify(*x)",
"SinIntegral[*x]" => "sinintegral(*x)",
"Sin[*x]" => "sin(*x)",
"SinhIntegral[*x]" => "sinhintegral(*x)",
"Sinh[*x]" => "sinh(*x)",
"SplitProduct[*x]" => "splitproduct(*x)",
"Sqrt[*x]" => "sqrt(*x)",
"SubstForFractionalPowerOfLinear[*x]" => "substforfractionalpoweroflinear(*x)",
"SubstForFractionalPowerOfQuotientOfLinears[*x]" => "substforfractionalpowerofquotientoflinears(*x)",
"SubstForFractionalPowerQ[*x]" => "substforfractionalpowerq(*x)",
"SubstForInverseFunction[*x]" => "substforinversefunction(*x)",
"SubstFor[*x]" => "substfor(*x)",
"Subst[*x]" => "subst(*x)",
"SumQ[*x]" => "sumq(*x)",
"SumSimplerQ[*x]" => "sumsimplerq(*x)",
"Sum[*x]" => "sum(*x)",
"Tan[*x]" => "tan(*x)",
"Tanh[*x]" => "tanh(*x)",
"Together[*x]" => "together(*x)",
"TrigQ[*x]" => "trigq(*x)",
"TrigSimplifyQ[*x]" => "trigsimplifyq(*x)",
"TrigSimplify[*x]" => "trigsimplify(*x)",
"TrinomialMatchQ[*x]" => "trinomialmatchq(*x)",
"TrinomialQ[*x]" => "trinomialq(*x)",
"TryPureTanSubst[*x]" => "trypuretansubst(*x)",
"Unintegrable[*x]" => "unintegrable(*x)",
"With[*x]" => "with(*x)",
"Zeta[*x]" => "zeta(*x)",
"cos[*x]" => "cos(*x)",
"cot[*x]" => "cot(*x)",
"csc[*x]" => "csc(*x)",
"f[*x]" => "f(*x)",
"f_[*x]" => "f_(*x)",
"g[*x]" => "g(*x)",
"g_[*x]" => "g_(*x)",
"lst[*x]" => "lst(*x)",
"sec[*x]" => "sec(*x)",
"sin[*x]" => "sin(*x)",
"tan[*x]" => "tan(*x)",
"tmp[*x]" => "tmp(*x)",
"trig_[*x]" => "trig_(*x)",
"uu[*x]" => "uu(*x)",
);
# @test "integrate(x,x)" == parsetosympy("Int[x,x]",sympydictfixes)
# "integrate(x,x)"
## Parser hijinks:
# 1. Need to strip underscores and `x_Symbol`
### OOPS:
# It is clear that many of the "steps" field are being mangled horribly
# steps = [length(v.steps) != 2 for v in vtests]
# @test count(steps) == length(vtests)
## New parsing method ============================================================
# https://discourse.julialang.org/t/best-practices-for-converting-a-mathematica-expression-to-julia/18335
const sympy_parsing_mathematica = SymPy.PyCall.pyimport("sympy.parsing.mathematica")
mathematica2julia(s::AbstractString, substitutions::Pair{<:AbstractString,<:AbstractString}...) =
SymPy.walk_expression(sympy_parsing_mathematica."mathematica"(s, Dict(substitutions...)))
mathematica2julia(s::AbstractString) = mathematica2julia(s, m2j_subs...)
# Pull data from func_subs file to create substitution pairs
#= #TODO: functions to add
- Derivative
- Hypergeometric2F1
- f'[x]
- Double check functions that might come from SpecialFunctions.jl, etc.
=#
m2j_subs = [r[1] => r[2] for r in eachrow(readdlm("func_subs.csv", ';', String))]
function m2j(s::AbstractString; verbose=true)
try
return mathematica2julia(s)
catch e
verbose && @warn "Couldn't parse:\n$(s)\n"
# @error e
return "Parse Error"
end
end
@show m2j("Csc[x + Sin[x]]")
@show m2j("Sin[2*x]")
@show m2j("EllipticF[1, 0.5]")
@show m2j("Csc[x + Sin[x]]")
@show m2j("(2*I*Sqrt[2]*EllipticE[Pi/4 - (I*x)/2, 2] * Sqrt[Sinh[x]])/Sqrt[I*Sinh[x]]")
@show m2j("((-Gamma[-n, -Log[t]])*(-Log[t])^n)/Log[t]^n")
@show m2j("(c + d*x)^0*PolyGamma[n, a + b*x]")
@show m2j("(c + d*x)^1*ExpIntegralE[n, a + b*x]")
@show m2j("Hypergeometric2F1[1/2, 1/2, 3/2, 1/2)")
@show m2j("Derivative[n][f][x]")
@show m2j("f'[x]")
## Saving work as I go in a dataframe to make incremental changes
# Getting rid of any tests that don't work right now.
fname = "test/conversion_data.csv"
# df = DataFrame(vtests) # Initial creation
df = DataFrame(CSV.File(fname))
@show names(df)
@show size(df)
# filter!(x -> !x.iscomment, df);
# failstrgs = ["CannotIntegrate", "Unintegrable"]
# filter!(x -> !any(occursin.(failstrgs, x.optimal)), df);
# transform!(df, :integrand => ByRow(m2j) => :juliaintegrand) # slooooow
# @show size(df)
# CSV.write(fname, df)
# Save Point --------------------------------------------
# @show size(df)
# filter!(x -> x.juliaintegrand != "Parse Error", df);
# @show size(df)
# CSV.write(fname, df)
# Save Point --------------------------------------------
# filter!(x -> !occursin("Hypergeometric2F1", x.optimal), df); # Removes about 4000
# filter!(x -> !occursin("VersionNumber", x.optimal), df); # Removes about 200
# transform!(df, :optimal => ByRow(x -> m2j(x, verbose=false)) => :juliaoptimal) # slooooow
# filter!(x -> x.juliaoptimal != "Parse Error", df); # Removes about 3500
# filter!(x -> x.juliaoptimal != "0", df);
# @show size(df)
# CSV.write(fname, df)
# Save Point --------------------------------------------
create_local_path(s) = joinpath("test", split(s, "\\")[2:end-1]...)
create_fname(s) = replace(split(s, "\\")[end], ".m"=>".jl")
transform!(df, :path => ByRow(create_local_path) => :outputpath)
transform!(df, :path => ByRow(create_fname) => :outputfname)
# Makes all the directories, only need to run once if the folders are deleted
# unique(df.outputpath) .|> mkpath
## Write a test files =======================================================
preamble = """
using Rubin
using Tests
using Elliptic
using HypergeometricFunctions
using Polylogarithms
using SpecialFunctions\n
"""
gdf = groupby(df, :outputfname);
function fix_syntax(s)
s = replace(s, " "=> "") # Remove spaces
s = replace(s, "__" => ".")
end
for ((fn,), group) in pairs(gdf)
N,M = size(group)
@info "$N\ttests\tCreating File: $fn"
outpath = joinpath(first(group.outputpath), fn)
open(outpath, "w") do io
write(io, preamble)
for t in eachrow(group)
i = fix_syntax(t[:juliaintegrand])
v = t[:variable]
o = fix_syntax(t[:juliaoptimal])
write(io, "@test integrate($i, $v) == :($o)\n")
end
end
end
## TODO make a smaller test Suite in a single file
# TODO: create a smarter way to created abbreviated tests than just first
outpath = joinpath("test", "abbreviated_tests.jl")
@info "Creating File: $outpath"
N = 10
open(outpath, "w") do io
write(io, preamble)
for ((fn,), group) in pairs(gdf)
setname = first(group.outputfname)
@info "Creating test set: $setname"
# Begin test set
write(io, "@testset \"$setname\" begin\n")
for t in eachrow(first(group,N))
i = fix_syntax(t[:juliaintegrand])
v = t[:variable]
o = fix_syntax(t[:juliaoptimal])
write(io, "\t@test integrate($i, $v) == :($o)\n")
end
# End of test set
write(io, "end\n\n")
end
end
##################################################################
## Chapter 4: Parsing Utility Functions
#
# Mathematica pattern strings:
# ⋅ _ any single expression
# ⋅ x_ any single expression, to be named x
# ⋅ __ any sequence of one or more expressions
# ⋅ x__ sequence named x
# ⋅ x__h sequence of expressions, all of whose heads are h
# ⋅ ___ any sequence of zero or more expressions
# ⋅ x___ sequence of zero or more expressions named x
# ⋅ x___h sequence of zero or more expressions, all of whose heads are h
#
# Mathematica heads:
# ⋅ x_h an expression with head h
# ⋅ x_Integer an integer
# ⋅ x_Real an approximate real number
# ⋅ x_Complex a complex number
# ⋅ x_List a list
# ⋅ x_Symbol a symbol
#
# Conditions:
# ⋅ patt/;test is a pattern which matches only if the evaluation of test yields True.
# ⋅ lhs:>rhs/;test represents a rule which applies only if the evaluation of test yields True.
# ⋅ lhs:=rhs/;test is a definition to be used only if test yields True.
##################################################################
struct RubiUtility <: AbstractRubiParser end
"""
Sample input: IntHide[u_,x_Symbol] := Block[{\$ShowSteps=False,\$StepCounter=Null}, Int[u,x]]
# Fields:
- name
- args
- eqtype
- defintion
"""
Base.@kwdef struct UtilFuncMethod
name::String = ""
args::String = ""
eqtype::String = ""
defintion::String = ""
end
# Create util function type from regex matches automatically
UtilFuncMethod(re::RegexMatch) = UtilFuncMethod(re.captures...)
Base.show(io::IO, m::UtilFuncMethod) = print(io, "$(m.name)$(m.args)$(m.eqtype)$(m.defintion)")
# Needs for JSON3 to write a file
StructTypes.StructType(::Type{UtilFuncMethod}) = StructTypes.StringType()
"""
Sample input:
(* ::Subsection::Closed:: *)
(*TrigQ[u]*)
TrigQ::usage = "If u is an expression of the form F[v] where F is a circular trig function, TrigQ[u] returns True; else it returns False.";
\$TrigFunctions = {Sin, Cos, Tan, Cot, Sec, Csc};
TrigQ[u_] := MemberQ[\$TrigFunctions, If[AtomQ[u],u,Head[u]]]
# Fields:
- funcname
- funlist
- usage
- methods
"""
Base.@kwdef struct IntUtilityFunc
funcname::String = ""
funlist::String = ""
usage::String = ""
methods::Vector{UtilFuncMethod} = []
section::String = ""
end
# Pretty printing of functions with methods
function Base.show(io::IO, f::IntUtilityFunc)
println(io, "$(f.funcname)")
length(f.usage) > 0 && println("\tUsage:\t$(f.usage)")
# f.methods |> enumerate .|> t -> println(" Method $(t[1]): $(t[2])")
end
StructTypes.StructType(::Type{IntUtilityFunc}) = StructTypes.Struct()
function write(utilfuncs::Vector{IntUtilityFunc}, ::Type{RubiUtility})
targetpath = joinpath(pkgdir(Rubin), "src", "rubiutils.json")
open(targetpath, "w") do f
JSON3.pretty(f, JSON3.write(utilfuncs))
println(f)
end
end
##
# TODO: parse by section and subsection???
# Requires splitting methods that doesn't eat the secregex
# Handeling Item sections?
#
# TODO: Only 4 functions fail to match
# ⋅ IntegerPowerQ[u] - no brackets
# ⋅ FractionalPowerQ[u] - no brackets
# ⋅ ExpandBinomial[u,x] - broken up into sections???
# ⋅ DeactivateInverseHyperbolic[u,x] - commented out
#
function Base.parse(file, ::Type{RubiUtility})
secregex = r"\(\* ::[S].*\*\)" # match get eaten during the split operation
usageregex = r"""\S+::usage[\s="]+(.*)";"""
# Matches the usage lines and ... ^ captures usage string
funcheaderregex = r"\(\*(\w*\[.*\])\*\)\s?"
# Matches function header & ^ captures function name
# funlistregex = r"(^\$\w+)(\s?=\s?)(\{.*\};)"
funlistregex = r"\$\w+\s?=\s?\{(.*)\};" # w/o captures
# Matches lists of functions
# sec1nameregex = r"\(\* ::Section.*\n\(\*([\w\s]+)\*\)"
# sec2nameregex = r"\(\* ::Subsection.*\n\(\*([\w\s]+)\*\)"
secnameregex = r"\(\*([\w\s]{2,})\*\)"
# funclineregex = r"\n([A-Za-z0-9]+)(\[[:A-Za-z0-9_,\^\*\+\[\]]*\])(\s*[:=]+\s*)((?:.+\n)+)" # oneliner
funclineregex = r"
\n([A-Za-z0-9]+) # captures method name staring on new line
(\[[:A-Za-z0-9_,\^\*\+\[\]]*\]) # captures methods arguments
(\s*[:=]+\s*) # captures equality operator (:= or =)
((?:.+\n)+) # captures multile lines of definition
"x
utilcontents = read(file, String);
sections = split(utilcontents, secregex, keepempty=false)
secname = ""
utilfuncs = IntUtilityFunc[]
for sec in sections[2:end]
snm = match(secnameregex, sec) # Update section name
if !isnothing(snm)
secname = snm.captures[1]
end
fhm = match(funcheaderregex, sec) # Function header with name
isnothing(fhm) && continue
funcname = fhm.captures[1]
flm = match(funlistregex, sec) # Function list
funlist = isnothing(flm) ? "" : string(flm.match)
usm = match(usageregex, sec) # Usage string
usage = usm isa Nothing ? "" : usm.captures[1]
# Get function methods
!occursin(funclineregex, sec) && (@warn "No methods detected for: $funcname"; continue)
# create a list of methods using dispatch on Regex
methods = UtilFuncMethod.(eachmatch(funclineregex, sec))
# Push the utility function to the list
push!( utilfuncs, IntUtilityFunc(
funcname,
funlist,
usage,
methods,
secname
)
)
end
utilfuncs
end
function load(::Type{RubiUtility})
rubi = artifact"Rubi"
utilityfile = joinpath(rubi, "Rubi-4.16.1.0", "Rubi", "IntegrationUtilityFunctions.m")
parse(utilityfile, RubiUtility)
end
utilfuncs = load(RubiUtility);
@test length(utilfuncs) >= 92
# Writing the JSON file
# json = write(utilfuncs, RubiUtility);
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12543,
6359,
11,
6256,
72,
18274,
879,
1776,
198
] | 2.252299 | 17,396 |
# using Flux
using Pkg
Pkg.activate(".")
using JuliaRL
using Random
using ProgressMeter
function test_run(α=0.5/32, ϵ=0.01, γ=1.0, tilings=32, tiles=2; n_episodes=1000, seed=1002349)
# ϵ = 0.1
# α = 0.5/tilings
size_env_state = 2
num_actions = 3
lu = Learning.LinearRL.WatkinsQ(α)
policy = EpsilonGreedyQPolicy(ϵ, 1:3)
agent = LinearAgents.TileCoderAgent(lu, size_env_state, num_actions, tilings, tiles, γ, policy)
rng = Random.MersenneTwister(seed)
env = MountainCar(rng)
cumulative_reward_array = zeros(Int64, n_episodes)
# println("Here")
@showprogress 0.1 "Episode: " for episode = 1:n_episodes
# for episode = 1:n_episodes
terminal = false
num_steps = 0
cumulative_reward = 0
_, state = start!(env; rng=rng)
action = start!(agent, state; rng=rng)
while !terminal
# print(num_steps, "\r")
_, state_prime, reward, terminal = step!(env, action; rng=rng)
action = step!(agent, state_prime, reward, terminal; rng=rng)
# println(action)
# println(reward)
num_steps += 1
cumulative_reward += reward
end
cumulative_reward_array[episode] = cumulative_reward
end
return cumulative_reward_array
end
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] | 2.16092 | 609 |
"""
CosineKernel
The cosine kernel is a stationary kernel for a sinusoidal given by
```
κ(x,y) = cos( π * (x-y) )
```
"""
struct CosineKernel <: BaseKernel end
kappa(κ::CosineKernel, d::Real) = cospi(d)
metric(::CosineKernel) = Euclidean()
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function probLineSearch(func::AbstractPLSFunction, x₀::Vector{T}, α₀::T;
search_direction::Union{Nothing, Vector{T}}=nothing,
σ²_f::Union{Nothing, T}=nothing,
σ²_J::Union{Nothing, T, Array{T}}=nothing,
pars::PLSConstantParams{T}=PLSConstantParams(T),
history::PLSHistory=PLSHistory(α₀)) where T <: AbstractFloat
# Setup general x0 (no projection onto line yet)
x0 = evaluate(func, x₀; history=history)
# Get line search direction (if not specified)
(search_direction === nothing) && (search_direction = -x0.J)
# Project onto line
project_1d!(x0, search_direction) # project gradient from Jacobian, and gradient variance.
# User specified variances
!(σ²_f === nothing) && (x0.σ²_f = σ²_f)
!(σ²_J === nothing) && (x0.σ²_J = σ²_J)
# Set-up line search parameters
search = PLSSearchDefn(T, x₀, x0.y, α₀, search_direction, x0.σ²_f, x0.σ²_J)
# Run line search
x, gp = probLineSearch(func, x0, search, pars=pars, history=history)
return x, gp, search, history
end
function probLineSearch(func::AbstractPLSFunction, x0::PLSEvaluation{T}, search::PLSSearchDefn{T};
pars::PLSConstantParams{T}=PLSConstantParams(T), history::PLSHistory{T}=PLSHistory(T)) where T <: AbstractFloat
@unpack α₀, search_direction, denom = search
@unpack niter, c1, c2, wolfeThresh, verbosity = pars
# project current evaluation onto the search direction specified.
project_1d!(x0, search.search_direction)
x0.t = 0
# Need to learn normalisation factor from x0, then store for rest of proc.
norm∇_0 = abs(x0.∇y)
if !isapprox(norm∇_0, 1.0)
search.denom = norm∇_0 # magnitude for rescaling: |y′(0)|
search.x₀ = x0.x
search.f₀ = x0.y # offset for rescaling: y(0)
normalise_for_linesearch!(x0, search)
else
!isapprox(search.denom, 1.0) && (@warn "Perhaps x0 has been pre-normalised; and norm factor in searchpars≈1.0. This may cause problems.")
end
σ²_f, σ²_∇ = normalised_fun_var(search)
# @show sqrt(σ²_f)
# @show sqrt(σ²_∇)
gp = PLSPosterior(zeros(T, 1), zeros(T, 1), [x0.∇y], σ²_f, σ²_∇) # T, Y, ∇Y, ΣY, Σ∇
iterates = [x0]
tt = T(1.0) # initial step size (rescaled)
for i = 1:niter
#======================================================================
Evaluate chosen point.
=====================================================================#
x = evaluate(func, tt, search; history=history)
# @show x.x
push!(iterates, x)
gp = update(gp, x)
# @show gp.Ts
# @show gp.Y
# @show gp.∇Y
probWolfe(gp, tt, c1, c2)
# -- check last evaluated point for acceptance ------------------------
if probWolfe(gp, tt, c1, c2) > wolfeThresh # are we done?
# display("bark1")
push!(history.msg, "found acceptable point. (1).")
finalise(x, gp, search, pars, history);
return x, gp # done => x.x, x.y, x.J, x.∇_J, search.α₀, history.ewma_α
end
#======================================================================
Has updating the GP led to an existing point qualifying?
=====================================================================#
# ----------------- find minimum of posterior mean --------------------
minM, minT, minj = min_mean(gp) # of existing points
min∇ = d1m(gp, gp.Ts[minj])
# -------------- ∇ ≈ 0 with high probability? -------------------------
if abs(min∇) < 1e-5 && Vd(gp, minT) < 1e-4 # nearly deterministic
# display("bark2")
push!(history.msg, "found a point with almost zero gradient. Stopping, although Wolfe conditions not guaranteed.")
x = iterates[minj]
finalise(x, gp, search, pars, history)
return x, gp
end
# -------------- Wolfe Conditions satisfied? --------------------------
Tsort = sort(gp.Ts);
# ignore first point
wolfes_ix = 1 .+ findall(probWolfe.(gp, Tsort[2:end], c1, c2) .> wolfeThresh)
if !isempty(wolfes_ix)
# display("bark3")
push!(history.msg, "found acceptable point. (2).")
smallest_wolfe = argmin(m.(gp, Tsort[wolfes_ix]))
tt = Tsort[wolfes_ix[smallest_wolfe]] # choose point with smallest post. mean.
minj = findfirst(gp.Ts .== tt)
x = iterates[minj]
finalise(x, gp, search, pars, history)
return x, gp;
end
#======================================================================
Choose next evaluation using EI/ProbWolfe via candidates
=====================================================================#
# -- CANDIDATES 1: minimal means between all evaluations.--------------
# iterate through all `cells' (O[N]), check minimum mean locations.
Tcand = zeros(T, 0); # positions of candidate points
for cell = 1:length(gp)-1 # loop over cells
_cellT_ϵ = Tsort[cell] + T(1e-6) * (Tsort[cell+1] - Tsort[cell]);
cell_minT = cubicMinimum(gp, _cellT_ϵ);
# add point to candidate list if minimum lies in between T(cel) and T(cel+1)
if Tsort[cell] < cell_minT < Tsort[cell+1];
push!(Tcand, cell_minT);
# ~~PATHOLOGY~~
#= If unable to find minimum in first cell *AND* the estimated ∇ > 0
it is possible that (assuming the search direction is ok) the scale
is wrong, so return a point v. close to original, and reduce α₀.=#
elseif cell==1 && d1m(gp, 0) > 0
display("bark4")
push!(history.msg, "function seems very steep, reevaluating close to start.")
tt = T(0.01) * (Tsort[cell] + Tsort[cell+1]); # => Can lead to oscillation if T[1] is too far away.
# x = iterates[1]
# tt = T(0)
x = evaluate(func, tt, search; history=history)
# @show x.x
finalise(x, gp, search, pars, history) #; force_α= search.α₀ / 2)
# search.α₀ /= 10
# history.ewma_α /= 5
return x, gp # done
end
end
# -- CANDIDATES 2: one extrapolation step -----------------------------
push!(Tcand, maximum(gp.Ts) + search.extrap_amt);
# display(Tcand)
# display("before")
# -- DISCRIMINATION: EI and ProbWolfe ---------------------------------
# Calculate some statistics for EI.
μs = m(gp, Tcand) # means of candidate points
σs = __sqrt.(V(gp, Tcand)) # s.d. of candidate points
# display(μs)
# display(σs)
# display(minM)
# display(minT)
# display(minj)
EIcand = EI.(μs, σs, minM); # minM = η: best (lowest) GP mean of existing points
PPcand = probWolfe.(gp, Tcand, c1, c2)
idx_best = argmax(EIcand .* PPcand)
# If best point is found using extrapolation, extend extrapolation for next iter.
(idx_best == length(Tcand)) && (search.extrap_amt *= 2)
# display(EIcand)
# display(PPcand)
# display("Chosen value: $idx_best")
# Next point chosen for evaluation (next iter!):
tt = Tcand[idx_best];
# makePlot();
end
#======================================================================
!!! Not found an acceptable candidate in the niter limit.
=====================================================================#
x = evaluate(func, tt, search, history=history)
# @show x.x
push!(iterates, x)
gp = update(gp, x)
# -- check last evaluated point for acceptance ------------------------
if probWolfe(gp, tt, c1, c2) > wolfeThresh # are we done?
# display("bark5")
push!(history.msg, "found acceptable point. (3).")
finalise(x, gp, search, pars, history);
return x, gp # done => x.x, x.y, x.J, x.∇_J, search.α₀, history.ewma_α
end
# -- return point with lowest mean ----------------------------------------
# Code of Mahsereci and Hennig exclude t=0 from this, but I'm not sure I buy this.
minM, minT, minj = min_mean(gp)
# display("bark6")
(verbosity > 0) && @warn "reached evaluation limit. Returning 'best' known point."
push!(history.msg, "reached evaluation limit. Returning 'best' known point.")
x = iterates[minj]
finalise(x, gp, search, pars, history);
return x, gp
# tt = Tsort[argmin(m.(gp, Tsort[wolfes_ix]))] # choose point with smallest post. mean.
# minj = findfirst(gp.Ts .== tt)
# x = iterates[minj]
# finalise(x, gp, search, pars, history)
# makePlot();
end
function finalise(x::PLSEvaluation{T}, post::PLSPosterior{T}, searchpars::PLSSearchDefn{T},
pars::PLSConstantParams, history::PLSHistory{T}; force_α::Union{T,Nothing}=nothing) where T <: AbstractFloat
@unpack x₀ ,α₀, f₀, denom = searchpars
@unpack αGrowth, verbosity, c1, c2 = pars
# Transform `x` back to original frame of reference / undo [0,1] normalisation.
invert_normalise_for_linesearch!(x, searchpars)
# save step size
α = x.t*α₀
push!(history.αs, α)
# next initial step size is αGrowth (def=1.3x) larger than last accepted step size
searchpars.α₀ = something(force_α, α * αGrowth)
check_α = (force_α === nothing) # if new alpha not forced by input arg
# running average for reset in case the step size becomes very small
# this is a safeguard
gamma = 0.95;
history.ewma_α = gamma*history.ewma_α + (1-gamma)*something(force_α, α);
# reset NEXT initial step size to average step size if accepted step
# size is 100 times smaller or larger than average step size
if check_α && !(1e-2 * history.ewma_α < α < 1e2 * history.ewma_α)
push!(history.msg, "v large/small value of α, resetting alpha0.")
(verbosity > 0) && @warn "v large/small value of α, resetting alpha0. To avoid cycling, ensure passing PLSHistory object through."
searchpars.α₀ = history.ewma_α # reset step size
end
# reset search parameters for next iteration
# --- any extrapolation should be baked into α₀
searchpars.extrap_amt = 1
# --- update noise according to x
searchpars.σ²_f = x.σ²_f
searchpars.σ²_J = x.σ²_J
# Calculate acceptance statistics
minM, _minT, _minj = min_mean(post) # of existing points
push!(history.EI, EI(m(post, x.t), __sqrt(V(post, x.t)), minM))
push!(history.Wolfe, probWolfe(post, x.t, c1, c2))
history.recording = false
end
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] | 2.341215 | 4,610 |
# 1 -> 2
# |
# |
# v
# 3
station1 = EuclideanStation(1, EuclideanCoordinate(1, 4), "test", false)
station2 = EuclideanStation(2, EuclideanCoordinate(2, 4), "test", false)
station3 = EuclideanStation(4, EuclideanCoordinate(2, 2), "test", false)
line1 = Line("S1")
edge = ProcessedEdge(station1, station2, line1, 0, 1, false)
edge2 = ProcessedEdge(station2, station3, line1, 6, 1, false)
transit_map = InputGraph([station1, station2, station3],
[edge, edge2],
[line1])
result = optimize(GLPKSolverMIP(msg_lev = 3), transit_map, false)
@test result.stations[1].coordinate.y > result.stations[2].coordinate.y
@test result.stations[1].coordinate.x < result.stations[2].coordinate.x
@test result.stations[2].coordinate.x < result.stations[3].coordinate.x
@test result.stations[2].coordinate.y > result.stations[3].coordinate.y
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] | 2.546269 | 335 |
@testset "rules" begin
@testset "einsum" begin
_esum = @fn∇ 1 function (b, c, d)
@tensor a[B, A] := conj(b[A, C]) * c[C, D] * d[B, D]
end
for T in (ComplexF64, Float64)
b = randn(T, 3, 5)
c = randn(T, 5, 4)
d = randn(T, 4, 4)
test_rrule(_esum, b, c, d)
test_frule(_esum, b, c, d)
end
end
@testset "tensoropt" begin
_opt1 = @fn∇ 1 function (b, c, d)
@tensoropt (A => 1, C => χ) a[B, A] := b[A, C] * c[C, D] * d[B, D]
end
_opt2 = @fn∇ 1 function (b, c, d)
@tensoropt !(A, C) a[B, A] := b[A, C] * c[C, D] * d[B, D]
end
_opt3 = @fn∇ 1 function (b, c, d)
@tensoropt (A, C) a[B, A] := b[A, C] * c[C, D] * d[B, D]
end
for T in (ComplexF64, Float64)
b = randn(T, 3, 5)
c = randn(T, 5, 4)
d = randn(T, 4, 4)
test_rrule(_opt1, b, c, d)
test_rrule(_opt2, b, c, d)
test_rrule(_opt3, b, c, d)
test_frule(_opt1, b, c, d)
test_frule(_opt2, b, c, d)
test_frule(_opt3, b, c, d)
end
end
@testset "scalar in rhs" begin
_scar = @fn∇ 1 function (α, b, c)
@tensor a[B, A] := α * conj(b[A, C]) * c[C, B]
end
for T in (ComplexF64, Float64)
b = randn(T, 3, 5)
c = randn(T, 5, 4)
α = randn(T)
test_rrule(_scar, α, b, c)
test_frule(_scar, α, b, c)
end
end
@testset "add" begin
_add = @fn∇ 1 function (α, b, c, β, d, e, f)
@tensor a[B, A] :=
-α * conj(b[A, C]) * c[C, B] + conj(β) * d[A, C] * (-e[C, B] + 2 * f[C, B])
end
for T in (ComplexF64, Float64)
α = randn(T)
b = randn(T, 3, 5)
c = randn(T, 5, 4)
β = randn(T)
d = randn(T, 3, 5)
e = randn(T, 5, 4)
f = randn(T, 5, 4)
test_rrule(_add, α, b, c, β, d, e, f)
test_frule(_add, α, b, c, β, d, e, f)
end
end
@testset "trace" begin
_tr1 = @fn∇ 1 function (b, c)
@tensor a[C] := b[A, B, B', B', B] * c[A, A', A', C]
end
_tr2 = @fn∇ 1 function (b, c)
@tensor a[c] := b[a, b, bb, bb, b] * c[a, aa, aa, c]
end
for T in (ComplexF64, Float64)
b = randn(T, 3, 2, 3, 3, 2)
c = randn(T, 3, 3, 3, 2)
test_rrule(_tr1, b, c)
test_rrule(_tr2, b, c)
test_frule(_tr1, b, c)
test_frule(_tr2, b, c)
end
end
@testset "lhs is scalar" begin
_scal = @fn∇ 1 function (b, c, d)
@tensor a[] := b[A, B] * c[B, C] * d[C, A]
end
for T in (ComplexF64, Float64)
b = randn(T, 3, 2)
c = randn(T, 2, 4)
d = randn(T, 4, 3)
test_rrule(_scal, b, c, d)
test_frule(_scal, b, c, d)
end
end
@testset "conj" begin
_co1 = @fn∇ 1 function (b, c, d)
@tensor a[B, A] := conj(b[A, C]) * conj(c[C, D] * d[B, D])
end
_co2 = @fn∇ 1 function (b, c, d)
@tensor a[B, A] := conj(b[A, C] * c[C, D] * d[B, D])
end
_co3 = @fn∇ 1 function (b, c, d)
@tensor a[B, A] := conj(
1.23 * conj(conj(conj(b[A, C]))) * conj(c[C, D] * d[B, D])
)
end
for T in (ComplexF64, Float64)
b = randn(T, 3, 5)
c = randn(T, 5, 4)
d = randn(T, 4, 4)
α = randn(T)
β = randn(T)
test_rrule(_co1, b, c, d)
test_rrule(_co2, b, c, d)
test_rrule(_co3, b, c, d)
test_frule(_co1, b, c, d)
test_frule(_co2, b, c, d)
test_frule(_co3, b, c, d)
end
_co4 = @fn∇ 1 function (α, b, c, β, d, e, f)
@tensor a[B, A] :=
-α *
conj(conj(b[A, C]) * c[C, B] + conj(β) * d[A, C] * (-e[C, B] + 2 * f[C, B]))
end
for T in (ComplexF64, Float64)
α = randn(T)
b = randn(T, 3, 5)
c = randn(T, 5, 4)
β = randn(T)
d = randn(T, 3, 5)
e = randn(T, 5, 4)
f = randn(T, 5, 4)
test_rrule(_co4, α, b, c, β, d, e, f)
test_frule(_co4, α, b, c, β, d, e, f)
end
end
@testset "more" begin
T = ComplexF64
_tn1 = @fn∇ 1 function (x, ar, H)
return @tensoropt !(p, p1, p2) l[l, p, r] :=
x[l, p1, X] * ar[X, p2, Y] * conj(ar[r, p2', Y]) * H[p, p2', p1, p2]
end
_tn2 = @fn∇ 1 function (x, al, H)
return @tensoropt !(p, p1, p2) r[l, p, r] :=
conj(al[X, p1', l]) * al[X, p1, Y] * x[Y, p2, r] * H[p1', p, p1, p2]
end
a1 = randn(T, 3, 2, 3)
a2 = randn(T, 3, 2, 3)
x = randn(T, 3, 2, 3)
H = randn(T, 2, 2, 2, 2)
r = randn(T, 3, 2, 3)
test_rrule(_tn1, x, a1, a2, H)
test_rrule(_tn2, a1, a2, x, H)
test_frule(_tn1, x, a1, a2, H)
test_frule(_tn2, a1, a2, x, H)
_tn3 = @fn∇ 1 function (λ, o, a, b=a)
return @tensoropt !(p1, p2) _[l, r] :=
λ *
conj(b[1][X, p1', Y']) *
conj(b[2][Y', p2', l]) *
a[1][X, p1, Y] *
a[2][Y, p2, r] *
o[p1', p2', p1, p2]
end
_tn4 = @fn∇ 1 function (x, o, a, b=a)
return @tensoropt !(p1, p2) _[l, r] :=
a[1][l, p1, X] *
a[2][X, p2, Y] *
x[Y, Y'] *
conj(b[1][r, p1', X']) *
conj(b[2][X', p2', Y']) *
o[p1', p2', p1, p2]
end
a1 = randn(T, 3, 2, 3)
a2 = randn(T, 3, 2, 3)
b1 = randn(T, 3, 2, 3)
b2 = randn(T, 3, 2, 3)
x = randn(T, 3, 3)
λ = randn(T)
H = randn(T, 2, 2, 2, 2)
r = randn(T, 3, 3)
test_rrule(_tn3, λ, b1, b2, a1, a2, H)
test_rrule(_tn4, a1, a2, x, b1, b2, H)
test_frule(_tn3, λ, b1, b2, a1, a2, H)
test_frule(_tn4, a1, a2, x, b1, b2, H)
end
end
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] | 1.489676 | 4,262 |
export FinVolMesh, add_point!, add_face!, add_volume!, process_geometry!, mesh_smoothing!
using LinearAlgebra
"""
Mutable struct to define a mesh
* `ndim_space`, `ndim_mesh`: numbers of dimensions for the space in which the mesh is inserted and for the mesh
itself, respectively. A 2D, surface mesh defined as the surface of a sphere, for example, would yield `ndim_mesh=2, ndim_space=3`
* `points`: array of arrays of floats for corner points
* `point_connectivity`: array of arrays of ints, indicating points with which each point shares
a face. Used for mesh smoothing
* `point_fixed`: array of bools. Indicates whether or not each point in the mesh should be fixed when
performing mesh smoothing
* `point_weight`: weights for each point when performing elliptic mesh smoothing. Array of floats
* `faces`: array of arrays of ints for faces (point indices)
* `face_centers`: array of arrays of floats for face centers
* `face_normals`: array of arrays of floats indicating face normals (with norm equal to the face's area)
* `face_normal_unit`: the same as `face_normals`, but with unit norm
* `face_connectivity`: array of arrays of ints indicating, respectively, the volume behind the face and the volume ahead of it
(or more specifically their indices)
* `face_dists`: distances between the face and its adjacent volumes, in the same format as `face_connectivity`
* `volumes`: array of arrays of ints for faces (face indices)
* `volume_centers`: array of arrays of floats with positions for volume centers
* `volume_volumes`: volume of each finite volume (array of floats)
"""
mutable struct FinVolMesh{N}
ndim_space::Int64
ndim_mesh::Int64
points::Vector{Vector{Float64}}
point_connectivity::Vector{Vector{Int64}}
point_fixed::Vector{Bool}
point_weight::Vector{Float64}
faces::Vector{Vector{Int64}}
face_centers::Vector{Vector{Float64}}
face_normals::Vector{Vector{Float64}}
face_normal_unit::Vector{Vector{Float64}}
face_connectivity::Vector{Vector{Int64}}
face_dists::Vector{Vector{Float64}}
volumes::Vector{Vector{Int64}}
volume_centers::Vector{Vector{Float64}}
volume_volumes::Vector{Float64} # duh
end
"""
Jacobian space volume
"""
function Jac_volume(Jac::Matrix{Float64})
v=1.0
a=1.0
d=1.0
for i=1:size(Jac, 2)
for j=1:(i-1)
Jac[:, i].-=dot(Jac[:, i], Jac[:, j]).*Jac[:, j]
end
locn=norm(Jac[:, i])
Jac[:, i]./=(locn+1e-18)
if i==size(Jac, 2)
a=v
d=locn
end
v*=locn/i
end
Jac[:, end]*a, v, d
end
"""
Function to process the geometry of a finite volume mesh and obtain pertinent geometrical properties
* `msh`: the mesh at hand
"""
function process_geometry!(msh::FinVolMesh{N}) where N
for i=1:length(msh.points)
msh.point_connectivity[i]=[]
end
for i=1:length(msh.faces)
msh.face_centers[i].=0.0
msh.face_normals[i].=0.0
msh.face_normal_unit[i].=0.0
msh.face_dists[i].=0.0
msh.face_connectivity[i].=0
end
msh.volume_volumes.=0.0
for i=1:length(msh.faces)
for ptind in msh.faces[i]
msh.face_centers[i].+=msh.points[ptind]
end
msh.face_centers[i]./=length(msh.faces[i])
for j=1:length(msh.faces[i])
p1=msh.faces[i][j]
p2=msh.faces[i][j%length(msh.faces[i])+1]
if !(p1 in msh.point_connectivity[p2])
push!(msh.point_connectivity[p2], p1)
end
if !(p2 in msh.point_connectivity[p1])
push!(msh.point_connectivity[p1], p2)
end
end
end
for i=1:length(msh.volumes)
for fcind in msh.volumes[i]
msh.volume_centers[i].+=msh.face_centers[fcind]
end
msh.volume_centers[i]./=length(msh.volumes[i])
end
Jac=Matrix{Float64}(undef, N, msh.ndim_mesh)
for i=1:length(msh.volumes)
for fc in msh.volumes[i]
fcpts=msh.faces[fc]
nfc=length(fcpts)
freeside=1
if msh.face_connectivity[fc][freeside]!=0
freeside=2
end
if msh.face_connectivity[fc][freeside]!=0
throw(
error(
"process_geometry!:ERROR:face $(fc) connected to more than two volumes"
)
)
end
for j=1:(nfc-msh.ndim_mesh+1)
for k=1:(msh.ndim_mesh-1)
kind=k+j
Jac[:, k].=msh.points[fcpts[kind]].-msh.points[fcpts[1]]
end
if freeside==1
Jac[:, msh.ndim_mesh].=msh.face_centers[fc].-msh.volume_centers[i]
else
Jac[:, msh.ndim_mesh].=msh.volume_centers[i].-msh.face_centers[fc]
end
shat, v, d=Jac_volume(Jac)
msh.face_normals[fc].+=shat
msh.face_dists[fc][freeside]=d
msh.face_connectivity[fc][freeside]=i
msh.volume_volumes[i]+=v
end
if freeside==2
msh.face_normals[fc]./=2
end
end
end
for fc=1:length(msh.faces)
msh.face_normal_unit[fc].=msh.face_normals[fc]/norm(msh.face_normals[fc])
end
end
"""
Constructor for an empty mesh
* `ndim_mesh`: number of dimensions for the mesh, if differnet from space dimensionality.
A 2D, surface mesh defined as the surface of a sphere, for example, would yield `ndim_mesh=2, ndim_space=3`
"""
function FinVolMesh{N}(; ndim_mesh::Union{Int64, Nothing}=nothing) where N
FinVolMesh{N}(
N,
something(ndim_mesh, N),
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[],
[]
)
end
"""
Add a point to a mesh
* `msh`: the mesh
* `pt`: a vector with the point at hand
* `fixed`: a boolean identifying whether the point should be fixed during mesh smoothing
* `weight`: a weight for mesh smoothing
* `geps`: if set to a non-zero value, is set as a minimum threshold for merging with other pre-existing points
* return: the new point's index
"""
function add_point!(msh::FinVolMesh{N}, pt::Vector{Float64}; fixed::Bool=false, weight::Float64=1.0, geps::Float64=0.0) where N
@assert N==length(pt)
if geps>0.0
for (i, ept) in enumerate(msh.points)
if norm(ept.-pt)<geps
return i
end
end
end
push!(msh.points, pt)
push!(msh.point_fixed, fixed)
push!(msh.point_weight, weight)
push!(msh.point_connectivity, [])
length(msh.points)
end
function _is_face_eq(f1, f2)
if length(f1)!=length(f2)
return false
end
for p1 in f1
if !(p1 in f2)
return false
end
end
true
end
"""
Add a face to a mesh
* `msh`: the mesh at hand
* `pts`: a vector of point indices
* `univocal`: if true (default), allows for merging with equal, previously set faces
* return: the new face's index
"""
function add_face!(msh::FinVolMesh{N}, pts::Vector{Int64}; univocal::Bool=true) where N
for (i, fc) in enumerate(msh.faces)
if _is_face_eq(fc, pts)
return i
end
end
push!(msh.faces, pts)
push!(msh.face_centers, zeros(Float64, N))
push!(msh.face_normals, zeros(Float64, N))
push!(msh.face_normal_unit, zeros(Float64, N))
push!(msh.face_connectivity, zeros(Int64, 2))
push!(msh.face_dists, zeros(Float64, 2))
length(msh.faces)
end
"""
Add volume to a mesh
* `msh`: the mesh
* `fcs`: vector of face indices
* return: an index for the volume
"""
function add_volume!(msh::FinVolMesh{N}, fcs::Vector{Int64}) where N
push!(msh.volumes, fcs)
push!(msh.volume_centers, zeros(Float64, N))
push!(msh.volume_volumes, 0.0)
length(msh.volumes)
end
"""
Function to apply elliptic equation mesh smoothing. Must be used after `process_geometry!`, which is
also automatically re-ran after changes are made
* `msh`: the mesh at hand
* `niter`: number of iterations (defaults to 100)
* `relaxation_parameter`: relaxation parameter for iteration
"""
function mesh_smoothing!(msh::FinVolMesh{N}; niter::Int64=100, relaxation_parameter::Float64=1.0) where N
pos=zeros(Float64, N)
w=0.0
for nit=1:niter
for (i, (pt, cons)) in enumerate(zip(msh.points, msh.point_connectivity))
if !msh.point_fixed[i]
w=msh.point_weight[i]
pos.=pt.*w
for c in cons
pos.+=msh.points[c].*msh.point_weight[c]
w+=msh.point_weight[c]
end
pos./=w
msh.points[i].=relaxation_parameter.*pos.+(1.0-relaxation_parameter).*msh.points[i]
w/=(length(cons)+1)
msh.point_weight[i]=(1.0-relaxation_parameter)*msh.point_weight[i]+relaxation_parameter*w
end
end
end
process_geometry!(msh)
end
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62,
17143,
2357,
9,
86,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
886,
628,
220,
220,
220,
1429,
62,
469,
15748,
0,
7,
907,
71,
8,
198,
437,
198
] | 2.080073 | 4,396 |
export RESP_wont_read
function resp_unit_split(buf::Array{UInt8,1}, L::Int64)
i = 1
while i+2 < L
if buf[i] == 0x20 && buf[i+1] == 0x2d && buf[i+2] == 0x20
return String(buf[1:i-1])
end
i = i+1
end
return String(buf[1:L])
end
# *** FIX ME ==> version in dataless.jl is better
function parse_resp_date!(buf::Array{UInt8,1}, L::Int64, T::Array{Int16,1})
is_u8_digit(buf[1]) || return typemax(Int64)
fill!(T, zero(Int16))
i = 0
j = 1 # counter to start position in buf
k = 1 # counter to y,j,h,m,s
while i ≤ L
i += 1
if i > L
T[k] = buf_to_int(buf, i-1, j)
T[k] = parse(Int16, String(buf[j:i-1]))
break
elseif buf[i] in (0x2c, 0x2e, 0x3a)
T[k] = buf_to_int(buf, i-1, j)
k += 1
k > 6 && break
j = i+1
end
end
return mktime(T)
end
function close_resp_channel!(S::SeisData, C::SeisChannel, ts::Int64, te::Int64, fname::String, ns::Int64)
L = length(C.resp.fs)
for f in fieldnames(MultiStageResp)
deleteat!(getfield(C.resp, f), (ns+1):L)
end
i = findid(S, C.id)
if i != 0
t0 = isempty(S.t[i]) ? ts : S.t[i][1,2]
if ts ≤ t0 < te
S.resp[i] = C.resp
S.gain[i] = C.gain
note!(S, i, string("seed_resp, ", fname, ", overwrote :gain, :resp"))
end
else
# ObsPy does this but I don't think it's safe; stage fs is not stored uniformly.
if length(C.resp.fs) > 0
fac = C.resp.fac[end]
C.fs = C.resp.fs[end] * (fac > 0 ? 1/fac : 1.0)
end
push!(S, C)
end
# fill!(delay, zero(Float64))
# fill!(fg, zero(Float64))
# fill!(fs_in, zero(Float64))
# fill!(gain, zero(Float64))
# fill!(corr, zero(Float64))
# fill!(fac, zero(Int64))
# fill!(os, zero(Int64))
return nothing
end
function add_stage!(C::SeisChannel, n::Int64, tfc::UInt8)
if C.resp.stage[n] == nothing
C.resp.stage[n] =
if tfc == 0x41
PZResp64()
elseif tfc == 0x44
CoeffResp()
else
GenResp()
end
return nothing
end
end
function read_seed_resp!(S::GphysData, fpat::String;
units::Bool=false)
buf = BUF.buf
C = SeisChannel(resp = MultiStageResp(12))
R = C.resp
blk = zero(UInt64)
# Containers for parts of the response
# nstg = 12
# delay = zeros(Float64, nstg) # Estimated delay (seconds) (047F08, 057F07)
# corr = zeros(Float64, nstg) # Correction applied (seconds) (047F09, 057F08)
# fg = zeros(Float64, nstg) # Frequency of gain (058F05)
# fs_in = zeros(Float64, nstg) # Input sample rate (047F05, 057F04)
# gain = zeros(Float64, nstg) # Gain (058F04)
# fac = zeros(Int64, nstg) # Decimation factor (047F06, 057F05)
# os = zeros(Int64, nstg) # Decimation offset (047F07, 057F06)
seq_n = zero(Int64) # Stage sequence number (053F04, 054F04, 057F03, 058F03, 060F04, 061F03, 062F04)
seq_n_old = zero(Int64) # Last sequence number
d_arr = zeros(Int16, 6) # Date
nmax = zero(Int64)
# Array containers
X = Float64[]
D = Float64[]
Z = ComplexF64[]
P = ComplexF64[]
# Unit strings containers
units_out = "" # Response out units lookup (041F07, 043F07, 044F07, 053F06, 054F06, 061F07, 062F06)
if safe_isfile(fpat)
files = [fpat]
else
files = ls(fpat)
end
for (nf, file) in enumerate(files)
# Counters, etc.
c = 0x00
i = 1
read_state = 0x00
tfc = 0x00
ts = zero(Int64) # Start date (052F22)
te = zero(Int64) # End date (052F23)
ND = zero(Int64) # Number of denominators (044F11, 054F10)
NN = zero(Int64) # Number of coefficients (041F08, 044F08, 054F07, 061F08, 062F14)
NZ = zero(Int64) # Number of zeroes (043F10, 053F09)
NP = zero(Int64) # Number of poles (043F15, 053F14)
a0 = one(Float64) # A0 normalization factor (043F08, 048F05, 053F07)
f0 = one(Float64) # Normalization frequency (043F09, 048F06, 053F08)
id = UInt8[]
units_in = "" # Response in units lookup (041F06, 043F06, 044F06, 053F05, 054F05, 061F06, 062F05)
io = open(file, "r")
chip = false
while !eof(io)
c = read(io, UInt8)
# comment line ------------------------------
if c == 0x23
while c != 0x0a
c = read(io, UInt8)
end
end
# start of blockette line -------------------
if c == 0x42
#= format: %c%3i%c%2i, 'B', blockette_#, 'F', field_#
Here, I map the blockette and field numbers (along with 'F') to a UInt64.
This mapping should be unique; for any blockette blk, I expect that
String(reinterpret(UInt8, [blk]))[1:6] = bbbFff, where bbb is
blockette # and ff is field #
=#
blk = UInt64(read(io, UInt8))
blk |= UInt64(read(io, UInt8)) << 8
blk |= UInt64(read(io, UInt8)) << 16
blk |= UInt64(read(io, UInt8)) << 24
blk |= UInt64(read(io, UInt8)) << 32
blk |= UInt64(read(io, UInt8)) << 40
read_state = 0x01
# Coefficient parsing ==================================================
if blk in (0x0000393046313630, 0x0000393046313430)
# Numerator coefficients (061F09, 041F09)
X = Array{Float64,1}(undef, NN)
for n = 1:NN
# To first non-whitespace
if n > 1
skip(io, 7)
end
c = read(io, UInt8)
while c == 0x20
c = read(io, UInt8)
end
j = get_coeff_n(io, c, buf)
c = skip_whitespace(io, c)
# To next newline
k = 1
while c != 0x0a
buf[k] = c
k += 1
c = read(io, UInt8)
end
store_dbl!(X, buf, k, j)
end
elseif blk in (0x0000313146333430, 0x0000303146333530, 0x0000363146333430, 0x0000353146333530)
# Complex zeros (043F11, 053F10) / Complex poles (043F16, 053F15)
if blk in (0x0000313146333430, 0x0000303146333530)
N = NZ
Z = Array{ComplexF64,1}(undef, N)
is_p = false
else
N = NP
P = Array{ComplexF64,1}(undef, N)
is_p = true
end
for n = 1:N
# coefficient number
if n > 1
skip(io, 10)
else
skip(io, 3)
end
c = skip_whitespace(io, c)
j = get_coeff_n(io, c, buf)
# real part
c = skip_whitespace(io, c)
k = 1
while c != 0x20
buf[k] = c
k += 1
c = read(io, UInt8)
end
xr = buf_to_double(buf, k)
# imaginary part
c = skip_whitespace(io, c)
k = 1
while c != 0x20
buf[k] = c
k += 1
c = read(io, UInt8)
end
xi = buf_to_double(buf, k)
# store
if is_p
P[j] = complex(xr, xi)
else
Z[j] = complex(xr, xi)
end
# rest irrelevant
c = to_newline(io, c)
end
elseif blk in (0x0000383046343530, 0x0000313146343530, 0x0000353146323630)
# Numerator coefficients (054F08) / Denominator coefficients (054F11) / Polynomial coefficients (B062F15)
if blk == 0x0000383046343530
N = NN
X = Array{Float64,1}(undef, N)
is_n = true
else
N = ND
D = Array{Float64,1}(undef, N)
is_n = false
end
for n = 1:N
# To first non-whitespace
if n > 1
skip(io, 10)
else
skip(io, 3)
end
c = skip_whitespace(io, c)
j = get_coeff_n(io, c, buf)
c = skip_whitespace(io, c)
# To next whitesoace
k = 1
while c != 0x20
buf[k] = c
k += 1
c = read(io, UInt8)
end
# Store
if is_n
store_dbl!(X, buf, k, j)
else
store_dbl!(D, buf, k, j)
end
c = to_newline(io, c)
end
else
# To separator
while c != 0x3a
c = read(io, UInt8)
end
# To first non-whitespace
c = skip_whitespace(io, c)
end
end
# header info --------------------------------
if read_state == 0x01
i = 1
while c != 0x0a
buf[i] = c
i += 1
eof(io) && break
c = read(io, UInt8)
end
i -= 1
# header parsing =======================================================
if blk in (0x0000383046333430, 0x0000373046333530)
# A0 normalization factor (043F08, 053F07)
a0 = buf_to_double(buf, i)
elseif blk == 0x0000343046323530
# Channel (052F04)
ni = min(i,3)
L = length(id)
append!(id, zeros(UInt8, ni+1))
id[L+1] = 0x2e
unsafe_copyto!(id, L+2, buf, 1, ni)
setfield!(C, :id, String(id))
# println("Starting channel ", C.id)
chip = true
elseif blk in (0x0000383046373530, 0x0000393046373430)
# Correction applied (seconds) (057F08) / Response correction (047F09)
store_dbl!(R.corr, buf, i, seq_n)
elseif blk in (0x0000353046373530, 0x0000363046373430)
# Decimation factor (057F05) / Response decimation factor (047F06)
store_int!(R.fac, buf, i, seq_n)
elseif blk in (0x0000363046373530, 0x0000373046373430)
# Decimation offset (057F06) / Response decimation offset (047F07)
store_int!(R.os, buf, i, seq_n)
elseif blk == 0x0000333246323530
# End date (052F23)
te = parse_resp_date!(buf, i, d_arr)
elseif blk in (0x0000373046373530, 0x0000383046373430)
# Estimated delay (seconds) (057F07) / Response delay (047F08)
store_dbl!(R.delay, buf, i, seq_n)
elseif blk in (0x0000353046383530, 0x0000363046383430)
# Frequency of gain (058F05) / Frequency of sensitivity (048F06)
# println("blk = ", String(reinterpret(UInt8, [blk])), ", buf = ", String(buf[1:i]))
store_dbl!(R.fg, buf, i, seq_n)
elseif blk in (0x0000343046383530, 0x0000353046383430)
# Gain (058F04) / Sensitivity (048F05)
if seq_n > 0
store_dbl!(R.gain, buf, i, seq_n)
else
C.gain = buf_to_double(buf, i)
end
elseif blk in (0x0000343046373530, 0x0000353046373430)
# Input sample rate (057F04) / Response input sample rate (047F05)
store_dbl!(R.fs, buf, i, seq_n)
elseif blk == 0x0000333046323530
# Location (052F03)
push!(id, 0x2e)
for j = 1:min(i,2)
if buf[j] != 0x3f
push!(id, buf[j])
end
end
elseif blk == 0x0000363146303530
# Network (050F16)
ni = min(i,2)
prepend!(id, zeros(UInt8, ni))
copyto!(id, 1, buf, 1, ni)
elseif blk in (0x0000393046333430, 0x0000383046333530)
# Normalization frequency (043F09, 053F08)
f0 = buf_to_double(buf, i)
elseif blk in (0x0000313146343430, 0x0000303146343530)
# Number of denominators (044F11, 054F10)
ND = buf_to_int(buf, i)
elseif blk in (0x0000383046313430, 0x0000383046343430, 0x0000373046343530, 0x0000383046313630, 0x0000343146323630)
# Number of numerators (041F08, 044F08, 054F07, 061F08) / Number of coefficients (062F14)
NN = buf_to_int(buf, i)
elseif blk in (0x0000353146333430, 0x0000343146333530)
# Number of poles (043F15, 053F14)
NP = buf_to_int(buf, i)
elseif blk in (0x0000303146333430, 0x0000393046333530)
# Number of zeroes (043F10, 053F09)
NZ = buf_to_int(buf, i)
elseif blk in (0x0000363046313430, 0x0000363046333430, 0x0000363046343430, 0x0000353046333530, 0x0000353046343530, 0x0000363046313630, 0x0000353046323630)
# Response in units lookup (041F06, 043F06, 044F06, 053F05, 054F05, 061F06, 062F05)
if units || isempty(C.units)
units_in = fix_units(resp_unit_split(buf, i))
if seq_n > 0 && units == true
C.resp.i[seq_n] = units_in
end
end
elseif blk in (0x0000373046313430, 0x0000373046333430, 0x0000373046343430, 0x0000363046333530, 0x0000363046343530, 0x0000373046313630, 0x0000363046323630)
# Response out units lookup (041F07, 043F07, 044F07, 053F06, 054F06, 061F07, 062F06)
if units
units_out = fix_units(resp_unit_split(buf, i))
if seq_n > 0
C.resp.o[seq_n] = units_out
end
end
elseif blk in (0x0000343046333530, 0x0000343046343530, 0x0000333046373530, 0x0000333046383530, 0x0000343046303630, 0x0000333046313630, 0x0000343046323630)
# Stage sequence number (053F04, 054F04, 057F03, 058F03, 060F04, 061F03, 062F04)
seq_n = buf_to_int(buf, i)
nmax = max(nmax, seq_n)
#= Here is where we dump everything to seq_n_old.
Fortunately RESP files list stage 0 last, which makes stupid tricks
like this viable.
=#
if seq_n_old != seq_n
# initialize stage seq_n
if seq_n > 0
if length(C.resp.stage) < seq_n
append!(C.resp, MultiStageResp(6))
end
end
# dump to seq_n_old
if seq_n_old > 0
if isa(C.resp.stage[seq_n_old], CoeffResp)
C.resp.stage[seq_n_old].b = X
C.resp.stage[seq_n_old].a = D
X = Float64[]
D = Float64[]
elseif isa(C.resp.stage[seq_n_old], PZResp64)
C.resp.stage[seq_n_old].a0 = a0
C.resp.stage[seq_n_old].f0 = f0
C.resp.stage[seq_n_old].z = Z
C.resp.stage[seq_n_old].p = P
Z = ComplexF64[]
P = ComplexF64[]
a0 = one(Float64)
f0 = one(Float64)
end
end
seq_n_old = seq_n
end
# trigger new channel on seq_n
if blk in (0x0000343046333530, 0x0000343046343530, 0x0000343046323630)
if seq_n > 0 && tfc != 0x00
add_stage!(C, seq_n, tfc)
tfc = 0x00
end
end
elseif blk == 0x0000323246323530
# Start date (052F22)
ts = parse_resp_date!(buf, i, d_arr)
elseif blk == 0x0000333046303530
# Station (050F03)
if chip
# close_resp_channel!(S, C, ts, te, file, corr, delay, fg, fs_in, gain, fac, os, length(C.resp.stage))
close_resp_channel!(S, C, ts, te, file, nmax)
C = SeisChannel(resp = MultiStageResp(12))
R = C.resp
chip = false
seq_n = zero(Int64)
seq_n_old = zero(Int64)
tfc = 0x00
units_in = ""
nmax = zero(Int64)
end
ni = min(i,5)
id = zeros(UInt8, ni+1)
unsafe_copyto!(id, 2, buf, 1, ni)
id[1] = 0x2e
elseif blk in (0x0000353046333430, 0x0000353046343430, 0x0000333046333530, 0x0000333046343530, 0x0000333046323630)
# Response type (043F05, 044F05) / Transfer function type (053F03, 054F03, 062F03)
tfc = buf[1]
# trigger on response type
if blk in (0x0000353046333430, 0x0000353046343430)
if seq_n > 0
add_stage!(C, seq_n, tfc)
tfc = 0x00
end
end
elseif blk in (0x0000353046313430, 0x0000353046313630)
# Symmetry type (041F05, 061F05) tells us we have a digital filter
tfc = 0x44
if seq_n > 0
add_stage!(C, seq_n, tfc)
tfc = 0x00
end
end
if seq_n == 1 && isempty(C.units) && !isempty(units_in)
C.units = units_in
units_in = ""
end
read_state = 0x00
end
end
close(io)
close_resp_channel!(S, C, ts, te, file, nmax)
if nf != lastindex(files)
C = SeisChannel(resp = MultiStageResp(12))
R = C.resp
chip = false
seq_n = zero(Int64)
seq_n_old = zero(Int64)
tfc = 0x00
units_in = ""
nmax = zero(Int64)
end
end
return S
end
function read_seed_resp(fpat::String;
units::Bool=false)
S = SeisData()
read_seed_resp!(S, fpat, units=units)
return S
end
"""
RESP_wont_read()
The following is a list of breaking SEED RESP issues that we've encountered
in real data. Files with these issues don't read correctly into any known
program (e.g., ObsPy, SAC, SeisIO).
| Network | Station(s) | Problem(s)
| :---- | :---- | :----
| CN | (broadbands) | B058F05-06 contain units; should be B053F05-06
"""
function RESP_wont_read()
return nothing
end
| [
39344,
47203,
62,
86,
756,
62,
961,
198,
198,
8818,
1217,
62,
20850,
62,
35312,
7,
29325,
3712,
19182,
90,
52,
5317,
23,
11,
16,
5512,
406,
3712,
5317,
2414,
8,
198,
220,
1312,
796,
352,
198,
220,
981,
1312,
10,
17,
1279,
406,
198,
220,
220,
220,
611,
42684,
58,
72,
60,
6624,
657,
87,
1238,
11405,
42684,
58,
72,
10,
16,
60,
6624,
657,
87,
17,
67,
11405,
42684,
58,
72,
10,
17,
60,
6624,
657,
87,
1238,
198,
220,
220,
220,
220,
220,
1441,
10903,
7,
29325,
58,
16,
25,
72,
12,
16,
12962,
198,
220,
220,
220,
886,
198,
220,
220,
220,
1312,
796,
1312,
10,
16,
198,
220,
886,
198,
220,
1441,
10903,
7,
29325,
58,
16,
25,
43,
12962,
198,
437,
198,
198,
2,
17202,
44855,
11948,
6624,
29,
2196,
287,
4818,
282,
408,
13,
20362,
318,
1365,
198,
8818,
21136,
62,
4363,
62,
4475,
0,
7,
29325,
3712,
19182,
90,
52,
5317,
23,
11,
16,
5512,
406,
3712,
5317,
2414,
11,
309,
3712,
19182,
90,
5317,
1433,
11,
16,
30072,
198,
220,
318,
62,
84,
23,
62,
27003,
7,
29325,
58,
16,
12962,
8614,
1441,
2170,
368,
897,
7,
5317,
2414,
8,
198,
220,
6070,
0,
7,
51,
11,
6632,
7,
5317,
1433,
4008,
198,
220,
1312,
796,
657,
198,
220,
474,
796,
352,
1303,
3753,
284,
923,
2292,
287,
42684,
198,
220,
479,
796,
352,
1303,
3753,
284,
331,
11,
73,
11,
71,
11,
76,
11,
82,
198,
220,
981,
1312,
41305,
406,
198,
220,
220,
220,
1312,
15853,
352,
198,
220,
220,
220,
611,
1312,
1875,
406,
198,
220,
220,
220,
220,
220,
309,
58,
74,
60,
796,
42684,
62,
1462,
62,
600,
7,
29325,
11,
1312,
12,
16,
11,
474,
8,
198,
220,
220,
220,
220,
220,
309,
58,
74,
60,
796,
21136,
7,
5317,
1433,
11,
10903,
7,
29325,
58,
73,
25,
72,
12,
16,
60,
4008,
198,
220,
220,
220,
220,
220,
2270,
198,
220,
220,
220,
2073,
361,
42684,
58,
72,
60,
287,
357,
15,
87,
17,
66,
11,
657,
87,
17,
68,
11,
657,
87,
18,
64,
8,
198,
220,
220,
220,
220,
220,
309,
58,
74,
60,
796,
42684,
62,
1462,
62,
600,
7,
29325,
11,
1312,
12,
16,
11,
474,
8,
198,
220,
220,
220,
220,
220,
479,
15853,
352,
198,
220,
220,
220,
220,
220,
479,
1875,
718,
11405,
2270,
198,
220,
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] | 1.807317 | 9,622 |
using StaticPolynomials
using Base.Test
using BenchmarkTools
@polyvar x y z
@testset "monomials" begin
m = x^2
@test isa(m, Monomial)
@test variables(m) == (x,)
@test exponents(m) == (2,)
m = x * y
@test isa(m, Monomial)
@test variables(m) == (x, y)
@test exponents(m) == (1, 1)
m = y * x
@test isa(m, Monomial)
@test variables(m) == (x, y)
@test exponents(m) == (1, 1)
m = y * x^2 * y^3
@test isa(m, Monomial)
@test variables(m) == (x, y)
@test exponents(m) == (2, 4)
@test x * y < x^2
@test (@benchmark $(x * y) < $(x^2)).allocs == 0
end
@testset "terms" begin
t = @inferred 3x
@test isa(t, Term)
@test variables(t) == (x,)
@test coefficient(t) == 3
@test monomial(t) == convert(Monomial, x)
t = @inferred x * 3
@test isa(t, Term)
@test variables(t) == (x,)
@test coefficient(t) == 3
@test monomial(t) == convert(Monomial, x)
t = @inferred (2x) * y * x
@test isa(t, Term)
@test variables(t) == (x, y)
@test coefficient(t) == 2
@test exponents(t) == (2, 1)
end
@testset "polynomials" begin
p = @inferred x + 1
@test isa(p, Polynomial)
@test p.terms[2] == Term(x)
@test variables(p) == (x,)
p = @inferred x^2 + x * x
@test isa(p, Polynomial)
@test length(terms(p)) == 1
@test coefficient(terms(p)[1]) == 2
p = @inferred (1 + x) + (y + 2)
@test coefficient(terms(p)[1]) == 3
@test exponents(terms(p)[1]) == (0, 0)
@test variables(p) == (x, y)
@test coefficient(terms(p)[2]) == 1
@test exponents(terms(p)[2]) == (0, 1)
@test coefficient(terms(p)[3]) == 1
@test exponents(terms(p)[3]) == (1, 0)
p = @inferred x^2 + y + x * x + 3 * x * y + x * y^2
@test length(terms(p)) == 4
@test coefficient.(terms(p)) == [1, 3, 2, 1]
@test exponents.(terms(p)) == [(0, 1), (1, 1), (2, 0), (1, 2)]
end
function testmonomials(var, degree)
[var^i for i in 0:degree]
end
@testset "monomial vector" begin
ms = @inferred testmonomials(x, 3)
@test eltype(ms) == Monomial{1, (x,)}
@test (@benchmark testmonomials($x, 3)).allocs == 1
end
include("substitution.jl")
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] | 2.142296 | 1,019 |
using CSV
using DataFrames
using DelimitedFiles
using YAML
using Query
using SLiDE
# ******************************************************************************************
# READ BLUENOTE OUTPUT RESULTS TO CHECK.
# ******************************************************************************************
y = read_file(joinpath("dev","buildstream","check_share.yml"));
bluenote = Dict(k => sort(read_file(joinpath(y["SharePath"]..., ensurearray(v["name"])...)))
for (k,v) in y["ShareInput"])
bluenote = Dict(Symbol(k) => edit_with(v, Rename.(names(v), Symbol.(y["ShareInput"][k]["col"])))
for (k,v) in bluenote)
# ******************************************************************************************
# READ SETS AND SLiDE SUPPLY/USE DATA.
# ******************************************************************************************
y = read_file(joinpath("dev","buildstream","setlist.yml"));
set = Dict(Symbol(k) => sort(read_file(joinpath(y["SetPath"]..., ensurearray(v)...)))[:,1]
for (k,v) in y["SetInput"])
# Define edits to perform on each input DataFrame.
x = Dict()
x[:gsp] = [
Map(joinpath("crosswalk","gsp.csv"), [:gsp_code], [:windc_code], [:si], [:s], :left);
Map(joinpath("..","coresets","yr.csv"), [:yr], [:yr], [:yr], [:yr], :left);
Map(joinpath("..","coresets","r","state.csv"), [:r], [:r], [:r], [:r], :left);
Drop.([:yr,:r,:s], missing, "==");
Order([:yr,:r,:gdpcat,:s,:si,:value], [Int, String, String, String, Int, Float64])
];
x[:pce] = [
Map(joinpath("crosswalk","pce.csv"), [:pg], [:g], [:pg], [:g], :left);
Map(joinpath("..","coresets","yr.csv"), [:yr], [:yr], [:yr], [:yr], :left);
Map(joinpath("..","coresets","r","state.csv"), [:r], [:r], [:r], [:r], :left);
Drop.([:yr,:r,:g], missing, "==");
Order([:yr,:r,:g,:value], [Int, String, String, Float64])
];
x[:utd] = [
Add(:share, 0.0);
Map(joinpath("crosswalk","naics.csv"), [:naics_code], [:windc_code], [:n], [:s], :left);
Map(joinpath("..","coresets","yr.csv"), [:yr], [:yr], [:yr], [:yr], :left);
Map(joinpath("..","coresets","r","state.csv"), [:r], [:r], [:r], [:r], :left);
Drop.([:yr,:r], missing, "==");
Order([:yr,:r,:s,:t,:value,:n], [Int, String, String, String, Float64, Int])
];
x[:cfs] = [
Map(joinpath("crosswalk","sctg.csv"), [:sctg_code], [:windc_code], [:sg], [:g], :left);
Drop(:g, missing, "==")
# Order([:orig_state,:dest_state,:n,:g,:units,:value],[String,String,Int,String,String,Float64])
]
# Read share info.
shr = Dict()
shr[:cfs] = read_file(joinpath("data", "output", "cfs_state.csv"))
shr[:gsp] = read_file(joinpath("data", "output", "gsp_state.csv"))
shr[:pce] = read_file(joinpath("data", "output", "pce.csv"))
shr[:sgf] = read_file(joinpath("data", "output", "sgf.csv"))
shr[:utd] = read_file(joinpath("data", "output", "utd.csv"))
[global shr[k] = edit_with(shr[k], x[k]) for k in keys(shr) if k in keys(x)]
function filter_with(df::DataFrame, set::Dict)
cols = find_oftype(df, Not(AbstractFloat))
cols_sets = intersect(cols, collect(keys(set)));
vals_sets = [set[k] for k in cols_sets]
list_sets = NamedTuple{Tuple(cols_sets,)}(vals_sets,)
df = fill_zero(list_sets, df);
return df
end
# # ******************************************************************************************
# """
# ## PCE Shares
# """
# shr[:pce][!,:share] .= shr[:pce][:,:value] ./ sum_over(shr[:pce], :r; keepkeys = true)
# # PCE Share. Regional shares of final consumption
# #! pce_map(r,g,yr) = sum(map(g,pg), pce_raw_units(yr,r,pg,"millions of us dollars (USD)"));
# #! pce_shr(yr,r,g) = pce_map(r,g,yr) / sum(r.local, pce_map(r,g,yr));
# # ******************************************************************************************
# # # Share of total trade by region.
# # #! usatrd_(yr,r,s,t) = sum(map(n,s), usatrd(r,n,yr,t));
# # #! usatrd_shr(yr,r,s,t)$(NOT notinc(s) and sum(r.local, usatrd_(yr,r,s,t)))
# # #! = usatrd_(yr,r,s,t) / sum(r.local, usatrd_(yr,r,s,t));
# # #! usatrd_shr(yr,r,s,t)$(NOT notinc(s) AND NOT sum(r.local, usatrd_(yr,r,s,t)))
# # #! = sum(yr.local, usatrd_(yr,r,s,t)) / sum((r.local,yr.local), usatrd_(yr,r,s,t));
# # shr[:utd] = fill_zero(sum_over(shr[:utd], :n; values_only = false))
# # shr[:utd][!,:share] .= shr[:utd][:,:value] ./ sum_over(shr[:utd], :r; keepkeys = true)
# # shr[:utd][isnan.(shr[:utd][:,:share]),:share] .= (sum_over(shr[:utd], :yr; keepkeys = true) ./
# # sum_over(shr[:utd], [:yr,:r]; keepkeys = true))[isnan.(shr[:utd][:,:share])]
# # # Sectors not included in USA Trade Data.
# # #! notinc(s) = yes$(NOT sum(n, map(n,s)));
# # shr[:notinc] = DataFrame(s = setdiff(set[:i], shr[:utd][:,:s]))
# # """
# # ## GSP Shares
# # """
# # df = fill_zero(copy(shr[:sgf]))
# # Labor share of value added from national dataset
# #! PARAMETER lshr_0(yr,s) "Labor share of value added from national dataset";
# #! lshr_0(yr,s)$(va_0(yr,'compen',s) + va_0(yr,'surplus',s)) =
# #! va_0(yr,'compen',s) / (va_0(yr,'compen',s) + va_0(yr,'surplus',s));
# # !!!! I would move to partitionbea.jl
# io[:va0] = edit_with(io[:va0], Rename(:j,:s))
# io[:lshr0] = io[:va0] |> @filter(_.va == set[:va][1]) |> DataFrame
# io[:lshr0] = edit_with(io[:lshr0], Drop(:va, "all", "=="))
# io[:lshr0][!,:value] = io[:va0][io[:va0][:,:va] .== "compen",:value] ./
# (io[:va0][io[:va0][:,:va] .== "compen",:value] + io[:va0][io[:va0][:,:va] .== "surplus",:value])
# # Calculated gross state product.
# shr[:gsp] = fill_zero(sum_over(shr[:gsp], :si; values_only = false))
# shr[:gsp][!,:calc] .= shr[:gsp][:,:value]
# shr[:gsp][shr[:gsp][:,:gdpcat] .== "gdp",:calc] .=
# sum_over(shr[:gsp][findall(in(["cmp","gos","taxsbd"]), shr[:gsp][:,:gdpcat]),:], :gdpcat)
# shr[:gsp][!,:diff] = shr[:gsp][:,:calc] - shr[:gsp][:,:value]
# #! region_shr(yr,r,s)$(sum(r.local, gsp0(yr,r,s,'Reported'))) =
# #! gsp0(yr,r,s,'Reported') / sum(r.local, gsp0(yr,r,s,'Reported'));
# #! region_shr(yr,r,'use')$sum((r.local,s), region_shr(yr,r,s)) =
# #! sum(s, region_shr(yr,r,s)) / sum((r.local,s), region_shr(yr,r,s));
# #! region_shr(yr,r,'oth')$sum((r.local,s), region_shr(yr,r,s)) =
# #! sum(s, region_shr(yr,r,s)) / sum((r.local,s), region_shr(yr,r,s));
# """
# Regional share of value added.
# """
# shr[:region] = shr[:gsp][shr[:gsp][:,:gdpcat] .== "gdp", [:yr,:r,:s,:value]]
# shr[:region][!,:share] .= shr[:region][:,:value] ./ sum_over(shr[:region], :r; keepkeys = true)
# # !!!! Not matching
# for SECT in ["use", "oth"]
# df_temp = fill_zero((yr = set[:yr], r = set[:r], s = SECT))
# df_temp[!,:share] .= sum_over(shr[:region], :s) ./
# sum_over(sum_over(shr[:region], :r; keepkeys = true, values_only = false), :s)
# global shr[:region] = [shr[:region]; df_temp]
# end
# # ******************************************************************************************
# # # # CFS
# # # df_cfs = copy(shr[:cfs])
# # # shr[:cfs] = sort(edit_with(shr[:cfs], x[:cfs]))
# # # # Local supply-demand. Trade that remains within the same region.
# # # #! PARAMETER d0(r,g) "Local supply-demand (CFS)";
# # # #! d0_(r,n,sg) = cfs2012_units(r,r,n,sg,"millions of us dollars (USD)");
# # # #! d0(r,g) = sum(map(sg,g), sum(n, d0_(r,n,sg)));
# # # shr[:d0] = shr[:cfs][shr[:cfs][:,:orig_state] .== shr[:cfs][:,:dest_state],:]
# # # shr[:d0] = edit_with(shr[:d0], Rename(:orig_state,:r))[:,[:r,:n,:g,:units,:value]]
# # # shr[:d0] = sum_over(shr[:d0], :n; values_only = false)
# # # # Interstate trade (CFS)
# # # #! PARAMETER mrt0(r,r,g) "Interstate trade (CFS)";
# # # #! mrt0_(r,rr,n,sg)$(NOT SAMEAS(r,rr)) = cfs2012_units(r,rr,n,sg,"millions of us dollars (USD)");
# # # #! mrt0(r,rr,g) = sum(map(sg,g), sum(n, mrt0_(r,rr,n,sg)));
# # # shr[:mrt0] = shr[:cfs][shr[:cfs][:,:orig_state] .!= shr[:cfs][:,:dest_state],:]
# # # shr[:mrt0] = sum_over(shr[:mrt0], :n; values_only = false)
# # # # National exports (CFS)
# # # #! PARAMETER xn0(r,g) "National exports (CFS)";
# # # #! xn0(r,g) = sum(rr, mrt0(r,rr,g));
# # # shr[:x0] = sum_over(shr[:mrt0], :dest_state; values_only = false)
# # # shr[:x0] = edit_with(shr[:x0], Rename(:orig_state, :r))
# # # # National demand (CFS)
# # # # PARAMETER mn0(r,g) "National demand (CFS)";
# # # # mn0(r,g) = sum(rr, mrt0(rr,r,g));
# # # shr[:mn0] = sum_over(shr[:mrt0], :orig_state; values_only = false)
# # # shr[:mn0] = edit_with(shr[:mn0], Rename(:dest_state, :r))
# # # # Regional purchase coefficient
# # # # PARAMETER rpc(*,g) "Regional purchase coefficient";
# # # # rpc(r,g)$(d0(r,g) + mn0(r,g)) = d0(r,g) / (d0(r,g) + mn0(r,g));
# # # shr[:mn0], shr[:d0] = fill_zero(shr[:mn0], shr[:d0]);
# # # shr[:rpc] = shr[:d0]
# # # shr[:rpc][!,:value] .= shr[:d0][:,:value] ./ (shr[:mn0][:,:value] + shr[:d0][:,:value])
# # # UTI = 0.9
# # # shr[:rpc] = [shr[:rpc];
# # # DataFrame(permute((
# # # r = set[:r], g = :uti, units = "billions of us dollard (USD)", value = UTI)))]
# # # # # # ii_zero = shr[:mn0] + shr[:d0] .== 0.0
# # # # # # df = copy(shr[:cfs])
# # # # # # first(df,3) | [
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8
] | 2.186837 | 4,148 |
# sample library (unusable) that shows you how to code up your own Aetherlang libraries in Julia.
# NOTE that for your library to be installed it should be _SAMPLE_namespace_modifyin the "Libs" folder in a folder named exactly as your library and have a .jl file named exactly as your library.
# NOTE that for a library to be observable from Aetherlang, its name should be in all-lowercase style. this library's name is uppercase on purpose.
const TYPE = "_SAMPLE.type" # in case your library defines some types,
# you need to create constant strings representing this type in Aetherlang type system.
# this string is used when calling (typeof obj)
# constructor for AetherlangObject of your type
AetherlangObject(arg::MyType) = AetherlangObject{MyType}(arg, Ref(TYPE))
# all functions defined for Aetherlang use should have either the AE_ prefix or the specific prefix named exactly as your library (_SAMPLE_ in this case).
function AE_sample_function(line::Ref{UInt}, ns::Namespace, args...)::AetherlangObject
# you can specify the number of arguments if needed instead of using args...
# NOTE: arguments come as AetherlangObjects. use arg.dataref unwrap data. use arg.type[] to get a string representing object's type in Aetherlang
# NOTE: you are provided with mutable arguments line::Ref{UInt} and ns::Namespace but please don't change them, as it would ruin the paradigm and some features may not work properly
#=
CODE
=#
AetherlangObject()
end
_SAMPLE_namespace_modify = Namespace(
"name_of_the_function" => AetherlangObject(AE_sample_function)
)
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7,
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] | 3.393814 | 485 |
struct {T<:Number} Monomial
c::T
deg::Int
end
| [
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] | 2.076923 | 26 |
using TableIOInterface
using Test
@testset "TableIOInterface.jl" begin
fnames = ["test.csv", "test.jdf", "test.parquet", "test.arrow", "test.xlsx", "test.json",
"test.zip", "test.sqlite"]
@testset "File type ID" begin
expected_file_types = [TableIOInterface.CSVFormat, TableIOInterface.JDFFormat, TableIOInterface.ParquetFormat,
TableIOInterface.ArrowFormat, TableIOInterface.ExcelFormat, TableIOInterface.JSONFormat,
TableIOInterface.ZippedFormat, TableIOInterface.SQLiteFormat]
for (i, fname) ∈ enumerate(fnames)
file_type = get_file_type(fname)
@test file_type == expected_file_types[i]()
end
end
@testset "Sample code generation" begin
expected_sample_code = [
"""df_test_csv = let
import TableIO
import DataFrames
DataFrames.DataFrame(TableIO.read_table(joinpath(dirname(@__FILE__), "my_notebook.assets", "test.csv")); copycols=false)
end""",
"""df_test_jdf = let
import TableIO
import DataFrames
DataFrames.DataFrame(TableIO.read_table(joinpath(dirname(@__FILE__), "my_notebook.assets", "test.jdf")); copycols=false)
end""",
"""df_test_parquet = let
import TableIO
import DataFrames
DataFrames.DataFrame(TableIO.read_table(joinpath(dirname(@__FILE__), "my_notebook.assets", "test.parquet")); copycols=false)
end""",
"""df_test_arrow = let
import TableIO
import DataFrames
DataFrames.DataFrame(TableIO.read_table(joinpath(dirname(@__FILE__), "my_notebook.assets", "test.arrow")); copycols=false)
end""",
"""df_test_xlsx = let
import TableIO
import DataFrames
df_test_xlsx = DataFrames.DataFrame(TableIO.read_table(joinpath(dirname(@__FILE__), "my_notebook.assets", "test.xlsx"),
# "Sheet1", # uncomment this to specify the sheet name to be imported
); copycols=false)
end""",
"""df_test_json = let
import TableIO
import DataFrames
DataFrames.DataFrame(TableIO.read_table(joinpath(dirname(@__FILE__), "my_notebook.assets", "test.json")); copycols=true)
end""",
"""df_test_zip = let
import TableIO
import DataFrames
df_test_zip = DataFrames.DataFrame(TableIO.read_table(joinpath(dirname(@__FILE__), "my_notebook.assets", "test.zip"),
# "filename_in_archive", # uncomment this to specify a specific file name in the archive
); copycols=false)
end""",
"""df_test_sqlite = let
import TableIO
import DataFrames
df_test_sqlite = DataFrames.DataFrame(TableIO.read_table(joinpath(dirname(@__FILE__), "my_notebook.assets", "test.sqlite"),
"tablename", # define the table name here
); copycols=false)
end""",
]
for (i, fname) ∈ enumerate(fnames)
sample_code = get_example_code("my_notebook.assets", fname)
@test sample_code == expected_sample_code[i]
#println(sample_code)
end
end
end
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] | 1.988142 | 1,771 |
using ReachabilityBenchmarks, MathematicalSystems, Reachability, Plots
include(@relpath "heat_model.jl")
include(@relpath "heat_specifications.jl")
S = heat_model()
X0, options = heat_specification()
options = Dict(options)
# initial value problem
problem = InitialValueProblem(S, X0)
# partition with 1D blocks
partition = [i:i for i in 1:200]
# reachability algorithms
algorithm_reach = BFFPSV18(:δ => 1e-3, :vars => [133], :partition => partition)
algorithm_check = BFFPSV18(:δ => 1e-3, :vars => [133], :partition => partition)
# check property
options[:mode] = "check"
solution = solve(problem, options; op=algorithm_check)
@assert solution.satisfied
# compute flowpipe
options[:mode] = "reach"
solution = solve(problem, options; op=algorithm_reach)
# project flowpipe to time and x₁₃₃
solution.options[:plot_vars] = [0, 133]
solution_proj = project(solution)
# plot projection
plot(solution_proj)
savefig("heat")
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] | 2.817629 | 329 |
#########################################################################
using AutoPreallocation, BenchmarkTools
foo() = ones(1, 2096) * ones(2096, 1024) * ones(1024,1)
@btime foo()
const foo_res, foo_record = record_allocations(foo)
@btime avoid_allocations($foo_record, foo)
#########################################################################
using Test
twos(dims) = 2*ones(dims)
const twos_res, twos_record = record_allocations(twos, (3,6))
@test_throws TypeError avoid_allocations(twos_record, twos, (3,6,9))
# If the type is the same and only size differs AutoPreallocation right now won't even
# error, it will just silently return the wrong result
avoid_allocations(twos_record, twos, (30,60))
###############################################################################
function bar(x)
ys = fill(10.0, 30)
for ii in 1:100
push!(ys, x)
end
return ys
end
@btime bar(3.14);
const _, bar_record = record_allocations(bar, 3.14);
reinitialize!(bar_record);
@btime avoid_allocations($bar_record, bar, 42.0);
reinitialize!(bar_record);
@btime avoid_allocations($bar_record, bar, 24601);
############################################
function mat(x)
out = zeros(2,2)
out[1, 1] = x
out[2, 2] = x
return out
end
const mat1, mat_record = record_allocations(mat, 1);
(mat1,)
mat2 = avoid_allocations(mat_record, mat, 2);
(mat1, mat2) # Notice mat1 has changed
mat3 = avoid_allocations(mat_record, mat, 3);
(mat1, mat2, mat3) # Notice: mat1 and mat2 have changed
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] | 2.904762 | 525 |
struct CopositiveInnerVariableBridge{T, VB} <: AbstractVariableBridge
variable_bridge::VB
# TODO store GreaterThan constraints
end
function add_variable_bridge(::Type{CopositiveInnerVariableBridge{T, VB}},
model::MOI.ModelLike, set::CopositiveInner) where {T, VB}
func, variable_bridge = add_variable_bridge(VB, model, set.psd_inner)
F = MOI.ScalarAffineFunction{T}
# If `func isa F` then `convert` won't create a copy so we explictly copy it.
# It is less costly to copy it before, e.g. if `q is MOI.SingleVariable`.
Q = F[convert(F, copy(q)) for q in func]
k = 0
for j in 1:side_dimension(set)
for i in 1:(j-1)
k += 1
# TODO: these should be deleted when the bridge is deleted
Nij = MOI.SingleVariable(MOI.add_variable(model))
MOI.add_constraint(model, Nij, MOI.GreaterThan(zero(T)))
MOIU.operate!(+, T, Q[k], Nij)
end
k += 1 # for diagonal entry (i, i)
end
return Q, CopositiveInnerVariableBridge{T, VB}(variable_bridge)
end
function MOIB.added_constraint_types(::Type{CopositiveInnerVariableBridge{T, VB}}) where {T, VB}
added = MOIB.added_constraint_types(VB)
push!(added, (MOI.SingleVariable, MOI.GreaterThan{T}))
return added
end
function variable_bridge_type(::Type{CopositiveInner{S}}, T::Type) where S
return CopositiveInnerVariableBridge{T, variable_bridge_type(S, T)}
end
function MOI.delete(model::MOI.ModelLike, bridge::CopositiveInnerVariableBridge)
# TODO remove GreaterThan constraints
MOI.delete(model, bridge.variable_bridge)
end
function MOI.get(model::MOI.ModelLike,
attr::Union{MomentMatrixAttribute, GramMatrixAttribute},
bridge::CopositiveInnerVariableBridge)
return MOI.get(modle, attr, bridge.variable_bridge)
end
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] | 2.377551 | 784 |
module Kepler
export rv_model
kepler_f(M, E, e) = E - e*sin(E) - M
kepler_fp(E, e) = one(E) - e*cos(E)
kepler_fpp(E,e) = e*sin(E)
kepler_fppp(E,e) = e*cos(E)
function kepler_solve_ea(n, e, t)
M = mod(n*t, 2.0*pi)
if M < pi
E = M + 0.85*e
else
E = M - 0.85*e
end
f = kepler_f(M, E, e)
while abs(f) > 1e-8
fp = kepler_fp(E,e)
disc = sqrt(abs(16.0*fp*fp - 20.0*f*kepler_fpp(E,e)))
if fp > 0.0
d = -5.0*f/(fp + disc)
else
d = -5.0*f/(fp - disc)
end
E += d
f = kepler_f(M, E, e)
end
E
end
function kepler_solve_ta(n, e, t)
E = kepler_solve_ea(n, e, t)
f = 2.0*atan(sqrt((one(e)+e)/(one(e)-e))*tan(E/2.0))
if f < 0.0
f += 2.0*pi
end
f
end
"""
`rv_model(ts, K, e, omega, chi, P)`
Returns the radial velocity at times `ts` for the system with
semi-amplitude `K`, eccentricity `e`, argument of pericentre `omega`,
phase at a `t=0` of `2*pi*chi`, and period `P`. These parameter
arguments can also be vectorised, in which case the returned array has
size `(ntimes, nplanets)`.
"""
function rv_model(ts::Array{Float64, 1},
K::Float64,
e::Float64,
omega::Float64,
chi::Float64,
P::Float64)
nts = size(ts, 1)
rvs = zeros(nts)
t0 = -chi*P
n = 2.0*pi/P
ecw = e*cos(omega)
for i in 1:nts
t = ts[i]
f = kepler_solve_ea(n, e, t-t0)
rvs[i] = K*(cos(f + omega) + ecw)
end
rvs
end
function rv_model(ts::Array{Float64, 1},
Ks::Array{Float64, 1},
es::Array{Float64, 1},
omegas::Array{Float64, 1},
chis::Array{Float64, 1},
Ps::Array{Float64, 1})
npl = size(Ks, 1)
nts = size(ts, 1)
rvs = zeros(nts, npl)
for j in 1:npl
rvs[:,j] = rv_model(ts, Ks[j], es[j], omegas[j], chis[j], Ps[j])
end
rvs
end
end
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] | 1.704013 | 1,196 |
export Canvas, canvas, png, svg, pdf
mutable struct Canvas
size::Tuple{Int, Int}
toplayer::Layer
rect::Shape{Rect}
color::Colors.Colorant
end
function canvas(
width, height, toplayer::Union{Layer,Nothing}=nothing; color::Union{String, Colors.Colorant}="white")
l = if isnothing(toplayer)
layer() +
Visible(true) +
Markersize(3) +
Marker(:o) +
Fill("transparent") +
Stroke("black") +
Linewidth(1) +
Linestyle(:solid) +
Textfill("black")
else
toplayer
end
r = rect!(l, (0, 0), width, height, deg(0)) + Visible(false)
color = typeof(color) <: String ? parse(Colors.Colorant, color) : color
Canvas((width, height), l, r, color), l
end
function png(c::Canvas, filename::String; px_per_pt=1/0.75)
csurface = draw_rgba(c, px_per_pt=px_per_pt)
bufdata = UInt8[]
iobuf = IOBuffer(bufdata, read=true, write=true)
Cairo.write_to_png(csurface, iobuf)
Cairo.finish(csurface)
Cairo.destroy(csurface)
write(filename, bufdata)
nothing
end
function svg(c::Canvas, filename::String)
draw_svg(c, filename)
end
function pdf(c::Canvas, filename::String)
csurface = draw_pdf(c, filename)
Cairo.finish(csurface)
Cairo.destroy(csurface)
nothing
end
function Base.show(io::IO, ::MIME"image/svg+xml", c::Canvas)
svgbuffer = draw_svg(c)
print(io, String(take!(svgbuffer)))
end
# function Base.show(io::IO, ::MIME"image/png", c::Canvas)
# mktempdir() do path
# p = joinpath(path, "layered.png")
# png(c, p)
# write(io, read(p))
# end
# end
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] | 2.137004 | 781 |
### A Pluto.jl notebook ###
# v0.16.1
using Markdown
using InteractiveUtils
# ╔═╡ c4cccb7a-7d16-4dca-95d9-45c4115cfbf0
using BenchmarkTools, CSV, DataFrames, Distances, Distributions, HypothesisTests, HTTP, KernelDensity, LinearAlgebra, MCMCChains, MLDataUtils, Plots, PlutoUI, Random, RDatasets, StateSpaceModels, Statistics, StatsBase, StatsPlots, Turing, UrlDownload
# ╔═╡ 09a9d9f9-fa1a-4192-95cc-81314582488b
html"""
<div style="
position: absolute;
width: calc(100% - 30px);
border: 50vw solid #2e3440;
border-top: 200px solid #2e3440;
border-bottom: none;
box-sizing: content-box;
left: calc(-50vw + 15px);
top: -100px;
height: 100px;
pointer-events: none;
"></div>
<div style="
height: 200px;
width: 100%;
background: #2e3440;
color: #fff;
padding-top: 50px;
">
<span style="
font-family: Vollkorn, serif;
font-weight: 700;
font-feature-settings: 'lnum', 'pnum';
"> <p style="
font-size: 1.5rem;
opacity: 0.8;
">ATS 872: Lecture 5</p>
<p style="text-align: center; font-size: 1.8rem;">
Bayesian basics and regression
</p>
<style>
body {
overflow-x: hidden;
}
</style>"""
# ╔═╡ 41eb90d1-9262-42b1-9eb2-d7aa6583da17
html"""
<style>
main {
max-width: 850px;
}
</style>
"""
# ╔═╡ aa69729a-0b08-4299-a14c-c9eb2eb65d5c
md" # Introduction "
# ╔═╡ a681c4c5-1451-44d6-be27-f9003340883f
md"""
> These lecture notes draw heavily from the notes of [Joshua Chen](https://joshuachan.org/papers/BayesMacro.pdf), [Gary Koop](https://sites.google.com/site/garykoop/) and [Jamie Cross](https://github.com/Jamie-L-Cross/Bayes/blob/master/5_Linear_Regression_Normal.ipynb)
"""
# ╔═╡ 000021af-87ce-4d6d-a315-153cecce5091
md"
We will recap some of the basics on Bayesian econometrics.
"
# ╔═╡ 2eb626bc-43c5-4d73-bd71-0de45f9a3ca1
#TableOfContents() # Uncomment to see TOC
# ╔═╡ bbcafd74-97f4-4b8f-bffa-937812d9a2eb
Random.seed!(0)
# ╔═╡ d65de56f-a210-4428-9fac-20a7888d3627
md" Packages used for this notebook are given above. Check them out on **Github** and give a star ⭐ if you want."
# ╔═╡ 38d2167d-2ca2-4398-9406-db418cc4bf17
md""" ## General overview """
# ╔═╡ 3f0c1adc-3efd-4ade-9a65-a208d8457b85
md" Today we will be looking at the following topics,
- Recap of Bayesian basics
- Gibbs sampling routine for our Normal model with unknown $\sigma^{2}$
- The Bayesian approach to linear regression
- Gibbs sampling and regression analysis
- AR model example
- Regression with `Turing.jl`
"
# ╔═╡ 040c011f-1653-446d-8641-824dc82162eb
md" ## Bayesian methods recap "
# ╔═╡ f3823457-8757-4665-86a8-bf536d80e24d
md"""
**Please read** the following section carefully. It is from the notes by Joshua Chen and it explains in clear detail what we have been trying to do up to this point. This first section is a quick recap of many of the concepts we have covered so far. You will notice that we adopt matrix notation from now on. This takes some getting used to, but in the end it is worth it.
As we have established, the fundamental organizing principle in Bayesian econometrics is **Bayes' theorem**. It forms the unifying principle on how Bayesians estimate model parameters, conduct inference, compare models and so forth.
Bayes' theorem states that for events $A$ and $B$, the conditional probability of $A$ given $B$ is:
$$\mathbb{P}(A \mid B)=\frac{\mathbb{P}(A) \mathbb{P}(B \mid A)}{\mathbb{P}(B)}$$
where $\mathbb{P}(A)$ and $\mathbb{P}(B)$ are the marginal probabilities for events $A$ and $B$, respectively. This expression tells us how our view about event $A$ should change in light of information in event $B$.
To apply Bayes' theorem to estimation and inference, we first introduce some notation. Notice that we are going to start talking about vectors and matrices. Which means that matrix algebra will feature heavily from now on.
Suppose we have a model that is characterized by the likelihood function $p(\mathbf{y} \mid \boldsymbol{\theta})$, where $\boldsymbol{\theta}$ is a vector of model parameters. Intuitively, the likelihood function specifies how the observed data are generated from the model given a particular set of parameters.
Now, suppose we have obtained an observed sample $\mathbf{y}=\left(y_{1}, \ldots, y_{T}\right)^{\prime}$, and we would like to learn about $\boldsymbol{\theta}$. How should we proceed? The goal of Bayesian methods is to obtain the posterior distribution $p(\boldsymbol{\theta} \mid \mathbf{y})$ that summaries all the information about the parameter vector $\boldsymbol{\theta}$ given the data.
Applying Bayes' theorem, the posterior distribution can be computed as
$$p(\boldsymbol{\theta} \mid \mathbf{y})=\frac{p(\boldsymbol{\theta}) p(\mathbf{y} \mid \boldsymbol{\theta})}{p(\mathbf{y})} \propto p(\boldsymbol{\theta}) p(\mathbf{y} \mid \boldsymbol{\theta})$$
where $p(\boldsymbol{\theta})$ is the prior distribution and
$$p(\mathbf{y})=\int p(\boldsymbol{\theta}) p(\mathbf{y} \mid \boldsymbol{\theta}) \mathrm{d} \boldsymbol{\theta}$$
is the marginal likelihood. Bayes' theorem says that knowledge of $\boldsymbol{\theta}$ comes from two sources: the prior distribution and an observed sample $y_{1}, \ldots, y_{T}$ summarized by the likelihood. The prior distribution $p(\boldsymbol{\theta})$ incorporates our subjective beliefs about $\boldsymbol{\theta}$ before we look at the data.
The posterior distribution $p(\boldsymbol{\theta} \mid \mathbf{y})$ characterizes all relevant information about $\boldsymbol{\theta}$ given the data. For example, if we wish to obtain a point estimate of $\boldsymbol{\theta}$, we might compute the posterior mean $\mathbb{E}(\boldsymbol{\theta} \mid \mathbf{y})$. To characterize the uncertainty about $\boldsymbol{\theta}$, we might report the posterior standard deviations of the parameters. For instance, for the $i$ th element of $\boldsymbol{\theta}$, we can compute $\sqrt{\operatorname{Var}\left(\theta_{i} \mid \mathbf{y}\right)}$
In principle, these quantities can be computed given the posterior distribution $p(\boldsymbol{\theta} \mid \mathbf{y}) .$ In practice, however, they are often not available analytically. In those cases we would require simulation to approximate those quantities of interest.
To outline the main idea, suppose we obtain $R$ independent draws from $p(\boldsymbol{\theta} \mid \mathbf{y})$, say, $\boldsymbol{\theta}^{(1)}, \ldots, \boldsymbol{\theta}^{(R)}$. If we assume the first moment exists, i.e., $\mathbb{E}(\boldsymbol{\theta} \mid \mathbf{y})<\infty$, then by the weak law of large numbers the sample mean $\widehat{\boldsymbol{\theta}}=R^{-1} \sum_{r=1}^{R} \boldsymbol{\theta}^{(r)}$ converges in probability to $\mathbb{E}(\boldsymbol{\theta} \mid \mathbf{y})$ as $R$ tends to infinity. Since we can control the simulation size, we could approximate the posterior mean arbitrarily well - if we are patient enough. Similarly, other moments or quantiles can be estimated using the sample analogs.
Hence, estimation and inference become essentially a computational problem of obtaining draws from the posterior distribution. In general, sampling from arbitrary distribution is a difficult problem. Fortunately, there is now a large family of algorithms generally called Markov chain Monte Carlo (MCMC) methods to sample from complex distributions.
The basic idea behind these algorithms is to construct a Markov chain so that its limiting distribution is the target distribution - in our case the target is the posterior distribution. By construction, samples from the MCMC algorithms are autocorrelated. Fortunately, similar convergence theorems - called ergodic theorems-hold for these correlated samples. Under some weak regularity conditions, we can use draws from these MCMC algorithms to estimate any functions of the parameters arbitrary well, provided that the population analogs exist.
"""
# ╔═╡ f95ccee4-a2d3-4492-b869-551e61acf995
md"""
## Normal model with unknown $\sigma^{2}$
"""
# ╔═╡ 6c027ac7-ef68-4582-ba17-8f34a879a21d
md"""
We have already covered the normal model with known variance in great detail in a previous lecture. We also covered the case of a normal model with unknown variance in theory. However, now we will introduce our knowledge of Markov chain Monte Carlo methods to estimate the posterior distribution of a normal model with unknown variance. We will use Gibbs sampling to achieve this. You might notice that the notation is slightly different here, but the ideas are the same.
"""
# ╔═╡ 66c936a3-055d-42a0-bcad-5176d10e5994
md"""
### Prior and likelihood selection
"""
# ╔═╡ 3aad749e-1256-4f93-b119-4717d2b95607
md"""
The model in question is
$$y_{n} \mid \mu, \sigma^{2} \sim \mathcal{N}\left(\mu, \sigma^{2}\right), \quad n=1, \ldots, N$$
where both $\mu$ and $\sigma^{2}$ are now unknown. We assume the following prior for $\mu: \mathcal{N}\left(\mu_{0}, \sigma_{0}^{2}\right)$.
As for $\sigma^{2}$, which takes only positive values, a convenient prior is the **inverse-gamma prior**. It turns out that this prior is conjugate for the likelihood of the model.
A random variable $X$ is said to have an inverse-gamma distribution with shape parameter $\alpha>0$ and scale parameter $\beta>0$ if its density is given by
$$f(x ; \alpha, \beta)=\frac{\beta^{\alpha}}{\Gamma(\alpha)} x^{-(\alpha+1)} \mathrm{e}^{-\beta / x}$$
We write $X \sim \mathcal{I} G(\alpha, \beta)$. This is the parameterization we use throughout these notes. There are other common parameterizations for the inverse-gamma distribution when comparing derivations and results across books and papers, it is important to first determine the parameterization used.
For $X \sim \mathcal{I} G(\alpha, \beta)$, its mean and variance are given by
$$\mathbb{E} X=\frac{\beta}{\alpha-1}$$
for $\alpha>1$, and
$$\operatorname{Var}(X)=\frac{\beta^{2}}{(\alpha-1)^{2}(\alpha-2)}$$
for $\alpha>2$.
"""
# ╔═╡ 83ae194c-e04f-4615-9b71-7c389513898c
md"""
### Posterior derivation
"""
# ╔═╡ 727a703f-1233-4098-86f3-3192e4de08d4
md"""
Given the likelihood and priors, we can now derive the joint posterior distribution of $\left(\mu, \sigma^{2}\right) .$ Again, by Bayes' theorem, the joint posterior density is given by
$$\begin{aligned}
p\left(\mu, \sigma^{2} \mid \mathbf{y}\right) & \propto p\left(\mu, \sigma^{2}, \mathbf{y}\right) \\
& \propto p(\mu) p\left(\sigma^{2}\right) p\left(\mathbf{y} \mid \mu, \sigma^{2}\right) \\
& \propto \mathrm{e}^{-\frac{1}{2 \sigma_{0}^{2}}\left(\mu-\mu_{0}\right)^{2}}\left(\sigma^{2}\right)^{-\left(\nu_{0}+1\right)} \mathrm{e}^{-\frac{S_{0}}{\sigma^{2}}} \prod_{n=1}^{N}\left(\sigma^{2}\right)^{-\frac{1}{2}} \mathrm{e}^{-\frac{1}{2 \sigma^{2}}\left(y_{n}-\mu\right)^{2}}
\end{aligned}$$
"""
# ╔═╡ 14f11fc0-dc9b-4b77-b2da-ae534b911cd6
md"""
Even though we have an explicit expression for the joint posterior density, it is not obvious how we can compute analytically various quantities of interest, such as, $\mathbb{E}(\mu \mid \mathbf{y})$, the posterior mean of $\mu$ or $\operatorname{Var}\left(\sigma^{2} \mid \mathbf{y}\right)$, the posterior variance of $\sigma^{2}$. One way forward is to use Monte Carlo simulation to approximate those quantities.
"""
# ╔═╡ d92d80ef-256c-443a-a81c-8d5f02e01e66
md"""
### Quick detour on Gibbs sampling
"""
# ╔═╡ 48c98f0d-d880-4abc-91d3-6c79be5fcf8a
md"""
Generally, if we wanted to approximate for example $\operatorname{Var}\left(\sigma^{2} \mid \mathbf{y}\right)$, we first obtain draws from the posterior distribution $\left(\mu, \sigma^{2} \mid \mathbf{y}\right), \operatorname{say},\left(\mu^{(1)}, \sigma^{2(1)}\right), \ldots,\left(\mu^{(R)}, \sigma^{2(R)}\right) .$ Then, we compute
$$\frac{1}{R} \sum_{r=1}^{R}\left(\sigma^{2(r)}-\bar{\sigma}^{2}\right)^{2}$$
where $\bar{\sigma}^{2}$ is the mean of $\sigma^{2(1)}, \ldots, \sigma^{2(R)}$
Now the problem becomes: How do we sample from the posterior distribution? This brings us to Markov chain Monte Carlo (MCMC) methods, which are a broad class of algorithms for sampling from arbitrary probability distributions. This is achieved by constructing a Markov chain such that its limiting distribution is the desired distribution. Below we discuss one such method, called Gibbs sampling.
Specifically, suppose we wish to sample from the target distribution $p(\boldsymbol{\Theta})=p\left(\boldsymbol{\theta}_{1}, \ldots, \boldsymbol{\theta}_{n}\right)$. A Gibbs sampler constructs a Markov chain $\boldsymbol{\Theta}^{(1)}, \boldsymbol{\Theta}^{(2)}, \ldots$ using the full conditional distributions $p\left(\boldsymbol{\theta}_{i} \mid \boldsymbol{\theta}_{1}, \ldots, \boldsymbol{\theta}_{i-1}, \boldsymbol{\theta}_{i+1}, \ldots, \boldsymbol{\theta}_{n}\right)$ as the transition kernels. Under certain regularity conditions, the limiting distribution of the Markov chain thus constructed is the target distribution.
Operationally, we start from an initial state $\boldsymbol{\Theta}^{(0)}$. Then, we repeat the following steps from $r=1$ to $R$ :
Given the current state $\boldsymbol{\Theta}=\boldsymbol{\Theta}^{(r)}$, generate $\mathbf{Y}=\left(\mathbf{Y}_{1}, \ldots, \mathbf{Y}_{n}\right)$ as follows:
1. Draw $\mathbf{Y}_{1} \sim p\left(\mathbf{y}_{1} \mid \boldsymbol{\theta}_{2}, \ldots, \boldsymbol{\theta}_{n}\right)$.
2. Draw $\mathbf{Y}_{i} \sim p\left(\mathbf{y}_{i} \mid \mathbf{Y}_{1}, \ldots, \mathbf{Y}_{i-1}, \boldsymbol{\theta}_{i+1}, \ldots, \boldsymbol{\theta}_{n}\right), i=2, \ldots, n-1$
3. Draw $\mathbf{Y}_{n} \sim p\left(\mathbf{y}_{n} \mid \mathbf{Y}_{1}, \ldots, \mathbf{Y}_{n-1}\right)$
It is important to note that the Markov chain $\boldsymbol{\Theta}^{(1)}, \boldsymbol{\Theta}^{(2)}, \ldots$ does not converge to a fixed vector of constants. Rather, it is the distribution of $\boldsymbol{\Theta}^{(r)}$ that converges to the target distribution.
In practice, one typically discards the first $R_{0}$ draws to eliminate the effect of the initial state $\boldsymbol{\Theta}^{(0)}$. The discarded draws are often refereed to as the 'burn-in'. There are a number of convergence diagnostics to test if the Markov chain has converged to the limiting distribution. One popular test is the Geweke's convergence diagnostics.
"""
# ╔═╡ d811cfd9-3bdd-4830-8e9d-ecd4d7d2c890
md"""
### Posterior and Gibbs sampling
"""
# ╔═╡ d3ceb4ea-6d45-4545-be09-8446f103c2e5
md"
Now, after this discussion of Gibbs sampling, we return to the estimation of the two parameter normal model. To construct a Gibbs sampler to draw from the posterior distribution $p\left(\mu, \sigma^{2} \mid \mathbf{y}\right)$, we need to derive two conditional distributions: $p\left(\mu \mid \mathbf{y}, \sigma^{2}\right)$ and $p\left(\sigma^{2} \mid \mathbf{y}, \mu\right)$.
To derive the first conditional distribution, note that given $\sigma^{2}$, this is the same normal model with known variance discussed in last section. Thus, using the same derivation before, we have
$$\begin{aligned}
p\left(\mu \mid \mathbf{y}, \sigma^{2}\right) & \propto p(\mu) p\left(\mathbf{y} \mid \mu, \sigma^{2}\right) \\
& \propto \mathrm{exp}{\left[-\frac{1}{2}\left(\left(\frac{1}{\sigma_{0}^{2}}+\frac{N}{\sigma^{2}}\right) \mu^{2}-2 \mu\left(\frac{\mu_{0}}{\sigma_{0}^{2}}+\frac{N \bar{y}}{\sigma^{2}}\right)\right)\right]}
\end{aligned}$$
Hence, $\left(\mu \mid \mathbf{y}, \sigma^{2}\right) \sim \mathcal{N}\left(\widehat{\mu}, D_{\mu}\right)$, where
$$D_{\mu}=\left(\frac{1}{\sigma_{0}^{2}}+\frac{N}{\sigma^{2}}\right)^{-1}, \quad \widehat{\mu}=D_{\mu}\left(\frac{\mu_{0}}{\sigma_{0}^{2}}+\frac{N \bar{y}}{\sigma^{2}}\right)$$
Next, we derive the conditional distribution of $\sigma^{2}$ :
$$\begin{aligned}
p\left(\sigma^{2} \mid \mathbf{y}, \mu\right) & \propto p\left(\sigma^{2}\right) p\left(\mathbf{y} \mid \mu, \sigma^{2}\right) \\
& \propto\left(\sigma^{2}\right)^{-\left(\nu_{0}+1\right)} \mathrm{e}^{-\frac{S_{0}}{\sigma^{2}}}\left(\sigma^{2}\right)^{-N / 2} \mathrm{e}^{-\frac{1}{2 \sigma^{2}} \sum_{n=1}^{N}\left(y_{n}-\mu\right)^{2}} \\
& \propto\left(\sigma^{2}\right)^{-\left(\nu_{0}+N / 2+1\right)} \mathrm{e}^{-\frac{S_{0}+\sum_{n=1}^{N}\left(y_{n}-\mu\right)^{2} / 2}{\sigma^{2}}}
\end{aligned}$$
Is this a known distribution? It turns out that this is an inverse-gamma distribution. Comparing this expression with the generic inverse-gamma distribution, we have
$$\left(\sigma^{2} \mid \mathbf{y}, \mu\right) \sim \mathcal{I} G\left(\nu_{0}+\frac{N}{2}, S_{0}+\frac{1}{2} \sum_{n=1}^{N}\left(y_{n}-\mu\right)^{2}\right)$$
To summarize, the Gibbs sampler for the two-parameter model is given as below. Pick some initial values $\mu^{(0)}$ and $\sigma^{2(0)}>0 .$ Then, repeat the following steps from $r=1$ to $R$ :
1. Draw $\mu^{(r)} \sim p\left(\mu \mid \mathbf{y}, \sigma^{2(r-1)}\right)$.
2. Draw $\sigma^{2(r)} \sim p\left(\sigma^{2} \mid \mathbf{y}, \mu^{(r)}\right)$
After discarding the first $R_{0}$ draws as burn-in, we can use the draws $\mu^{\left(R_{0}+1\right)}, \ldots, \mu^{(R)}$ and $\sigma^{2\left(R_{0}+1\right)}, \ldots, \sigma^{2(R)}$ to compute various quantities of interest. For example, we can use
$$\frac{1}{R-R_{0}} \sum_{r=R_{0}+1}^{R} \mu^{(r)}$$
as an estimate of $\mathbb{E}(\mu \mid \mathbf{y})$.
"
# ╔═╡ 5cb5ab74-da6b-439a-bea4-75a3d0e43c63
md" ### Practical implementation "
# ╔═╡ bade1741-5e84-413a-9c67-932bd6748c49
md"""
As an illustration, the following code first generates a dataset of $N=100$ observations from the two-parameter normal model with $\mu=3$ and $\sigma^{2}=0.1$. Then, it implements a Gibbs sampler to sequentially to sample from the two conditional distributions: $p\left(\mu \mid \mathbf{y}, \sigma^{2}\right)$ and $p\left(\sigma^{2} \mid \mathbf{y}, \mu\right)$.
"""
# ╔═╡ b94db7f0-9f38-4761-a3c3-4d6fc4729ae9
begin
# Parameters for first example
nsim = 10_000
burnin = 1000
μ = 3.0 # True μ
σ2 = 0.1 # True σ2
N = 100
μ_0 = 0.0
σ2_0 = 100
ν_0 = 3.0
Σ_0 = 0.5
μ_1 = 0.0 # Initial mu for Gibbs sampler
σ2_1 = 1.0 # Initial sigma for Gibbs sampler
# Additional parameters for second example
T = 500
β = [1.0 5.0]
β_0 = [0.0 0.0]
end;
# ╔═╡ 1a6c859c-e3e7-4ad9-9299-091b6b1d2bbf
function data_gen(μ, σ2, N)
μ .+ sqrt(σ2) .* randn(N, 1)
end;
# ╔═╡ 0980d7a1-129b-4724-90fb-b46e3088d2d6
new_data = data_gen(μ, σ2, N)
# ╔═╡ bf12f6f6-f9a7-4d04-a5ac-1da94f03f8c3
histogram(new_data)
# ╔═╡ 0919cb0d-ba03-49c8-b2b9-53a467c39f87
function gibbs(nsim, burnin, μ, σ2, N, μ_0, σ2_0, ν_0, Σ_0, μ_1, σ2_1)
y = data_gen(μ, σ2, N) # Generated data
store_θ = zeros(nsim, 2) # Initialise the store_θ array
# Start the Gibbs sampling procedure
for i in 1:nsim + burnin
# Sample μ
D_μ = 1 / (1 / σ2_0 .+ N / σ2_1)
μ_hat = D_μ .* (μ_0 / σ2_0 .+ sum(y) / σ2_1)
μ_1 = μ_hat .+ sqrt(D_μ) .* randn() # Affine transformation is also normal (more MATLAB)
#μ_1 = rand(Normal(μ_hat, D_μ)) # Julian
# Sample σ2
σ2_1 = rand(InverseGamma(ν_0 .+ N/2, 1/(Σ_0 .+ sum((y .- μ_1).^2) / 2)))
if i > burnin
isave = i .- burnin
store_θ[isave, :] = [μ_1, σ2_1]
end
end
mean(store_θ[:, 1]), mean(store_θ[:, 2]), store_θ
end;
# ╔═╡ 343202b3-23b5-4600-b912-7db4ab58deaf
post_gibbs = gibbs(nsim, burnin, μ, σ2, N, μ_0, σ2_0, ν_0, Σ_0, μ_1, σ2_1); # posterior mean of μ and σ^2
# ╔═╡ 3aeab073-c98a-4213-a835-3098233ba90c
md" Let us see what this looks like when we plot the posterior. "
# ╔═╡ afc56ef2-d5d2-4624-9ad9-11f15429607f
begin
x_l = collect(1:(nsim-burnin));
histogram(x_l, post_gibbs[3][:,1], normalize=:pdf, legend = false)
end
# ╔═╡ fed7288d-5051-4ed0-babd-8eac00a634d2
histogram(x_l, post_gibbs[3][:,2], normalize=:pdf, legend = false)
# ╔═╡ 41365136-5d0e-4a4e-808f-d0e90a14c5dd
md""" We can also draw nice contour plots... """
# ╔═╡ 3335094d-a67b-471c-834d-e22089933104
begin
gr()
const N₁ = 100_000
const μ₁ = [post_gibbs[1], post_gibbs[1]]
const Σ = [1 post_gibbs[2]; post_gibbs[2] 1]
const mvnormal = MvNormal(μ₁, Σ)
data = rand(mvnormal, N₁)'
x₁ = 0:0.01:6
y₁ = 0:0.01:6
dens_mvnormal = [pdf(mvnormal, [i, j]) for i in x₁, j in y₁]
contour(x₁, y₁, dens_mvnormal, xlabel="X", ylabel="Y", fill=true, fillcolour = :ice)
end
# ╔═╡ 330bd7cb-7102-41c2-b7a4-f053669960c3
md""" Or even fancy surface plots """
# ╔═╡ 80e6619b-ac42-453b-8f38-850b2b99d000
surface(x₁, y₁, dens_mvnormal, fillcolour = :ice, backgroundinside = :ghostwhite)
# ╔═╡ 82b96729-33c2-49b0-b908-562faf903a1e
md"""
## Bayesian linear regression
"""
# ╔═╡ 1f2c9795-0b2c-4a14-9f28-1fef68f6b467
md"""
The workhorse model in econometrics is the normal linear regression model. Virtually all other more flexible models are built upon this foundation. It is therefore vital to fully understand how one estimates this model. In this section we will provide the details in deriving the likelihood and the posterior sampler.
"""
# ╔═╡ 70193cca-ce19-49ee-aa0c-06997affe2a6
md""" ### Linear regression in matrix notation """
# ╔═╡ 1703eb19-aeca-4ebe-a9b3-18dfbf4efdfe
md"""
To start, suppose we have data on a dependent variable $y_{t}$ for $t=1, \ldots, T$. Then, consider the following linear regression model:
$$y_{t}=\beta_{1}+x_{2, t} \beta_{2}+\cdots+x_{k, t} \beta_{k}+\varepsilon_{t}$$
where $\varepsilon_{1}, \ldots, \varepsilon_{T}$ are assumed to be iid $\mathcal{N}\left(0, \sigma^{2}\right)$, and $x_{1, t}, x_{2, t}, \ldots, x_{k, t}$ are the $k$ regressors (explanatory variables) and $\beta_{1}, \ldots, \beta_{k}$ are the associated regression coefficients.
Note that in this construction we set $x_{1, t} = 1$ for all $t = 1, \ldots, T$ to allow for an intercept. In addition, if we replace the $x_{i, t}$ values to be lags of the dependent variable then this would be an autoregressive model.
To derive the likelihood, it is more convenient to write this in matrix notation. In particular, we stack the observations over $t=1, \ldots, T$ so that each row represents the observation at time $t$. Let $\mathbf{y}=\left(y_{1}, \ldots, y_{T}\right)^{\prime}$ and $\boldsymbol{\beta}=\left(\beta_{1}, \ldots, \beta_{k}\right)^{\prime} .$ Finally, we have that $\varepsilon$ is a $T \times 1$ vector that is stacked in the same way as $\mathbf{Y}$. Then, rewrite the whole system of $T$ equations as:
$$\left(\begin{array}{c}
y_{1} \\
y_{2} \\
\vdots \\
y_{T}
\end{array}\right)=\left(\begin{array}{cccc}
1 & x_{2,1} & \cdots & x_{k, 1} \\
1 & x_{2,2} & \cdots & x_{k, 2} \\
\vdots & \vdots & \ddots & \vdots \\
1 & x_{2, T} & \cdots & x_{k, T}
\end{array}\right)\left(\begin{array}{c}
\beta_{1} \\
\vdots \\
\beta_{k}
\end{array}\right)+\left(\begin{array}{c}
\varepsilon_{1} \\
\varepsilon_{2} \\
\vdots \\
\varepsilon_{T}
\end{array}\right)$$
Make sure the dimensions above make sense to you. For our example, $\mathbf{y}$ is a $T \times 1$ vector, $\mathbf{X}$ is a $T \times k$ matrix, $\boldsymbol{\beta}$ is a $k \times 1$ vector and $\varepsilon$ is a $T \times 1$ vector.
Our system can be written more succinctly as,
$$\mathbf{y}=\mathbf{X} \boldsymbol{\beta}+\varepsilon$$
Since we assume that $\varepsilon_{1}, \ldots, \varepsilon_{T}$ are iid $\mathcal{N}\left(0, \sigma^{2}\right), \varepsilon$ has a multivariate normal distribution with mean vector $\mathbf{0}_{T}$ and covariance matrix $\mathbf{\Sigma} = \sigma^{2} \mathbf{I}_{T}$, where $\mathbf{0}_{T}$ is a $T \times 1$ vector of zeros and $\mathbf{I}_{T}$ is the $T \times T$ identity matrix. That is,
$$\varepsilon \sim \mathcal{N}\left(0, \sigma^{2} \mathbf{I}_{T}\right)$$
Now we move on to the derivation of the likelihood function.
"""
# ╔═╡ 9ffed7a8-f8d3-4c1a-affa-5d6646683829
md"""
### Estimation
"""
# ╔═╡ 993c9137-d708-4feb-8a85-51d85c38fc8d
md"""
The first thing that we do in the estimation procedure is derive the likelihood function. This function is defined as the joint density of the data given the parameters. In our case, the likelihood function is the joint density of $\mathbf{y}$ given $\boldsymbol{\beta}$ and $\sigma^{2}$. To derive the likelihood of the normal linear regression model, we use two useful results.
First, an affine transformation, i.e. a linear transformation followed by a translation of a normal random vector, is also a normal random vector. You can find a discussion on this results [here](https://math.stackexchange.com/questions/332441/affine-transformation-applied-to-a-multivariate-gaussian-random-variable-what). Now, $\mathbf{y}$ is an affine transformation of $\varepsilon$, which is assumed to have a multivariate normal distribution. Thus, $\mathbf{y}$ also has a normal distribution. Since a normal distribution is uniquely determined by its mean vector and covariance matrix, it suffices to compute the mean and covariance matrix of $\mathbf{y}$.
This brings us to the next useful result: suppose $\mathbf{u}$ has a mean vector $\boldsymbol{\mu}_{\mathrm{u}}$ and covariance matrix $\boldsymbol{\Sigma}_{\mathbf{u}}$. Let $\mathbf{v}=\mathbf{A} \mathbf{u}+\mathbf{c}$ for constant matrices $\mathbf{A}$ and $\mathbf{c} .$ Then the mean vector and covariance matrix of $\mathbf{v}$ are given by
$$\mathbb{E} \mathbf{v}=\mathbf{A} \boldsymbol{\mu}_{\mathbf{u}}+\mathbf{c}, \quad \operatorname{Cov}(\mathbf{u})=\mathbf{A} \boldsymbol{\Sigma}_{\mathbf{u}} \mathbf{A}^{\prime}$$
Using this result, it is easy to see that given $\boldsymbol{\beta}$ and $\sigma^{2}$,
$$\mathbb{E} \mathbf{y}=\mathbf{X} \boldsymbol{\beta}, \quad \operatorname{Cov}(\mathbf{y})=\sigma^{2} \mathbf{I}_{T}$$
Putting it all together, we have
$$\mathbf{y} \mid \boldsymbol{\beta}, \sigma^{2} \sim \mathcal{N}\left(\mathbf{X} \boldsymbol{\beta}, \sigma^{2} \mathbf{I}_{T}\right)$$
This means that estimating the linear regression model is the same as estimating the parameters of a multivariate Nomral distribution with unknown mean and covariance. In order to estimate the model parameters with Bayesian methods we need to apply Bayes' rule.
$$\begin{align}
p(\boldsymbol{\beta},\sigma^2 \mid \mathbf{y}) \propto p(\mathbf{y} \mid \boldsymbol{\beta},\sigma^2)p(\boldsymbol{\beta},\sigma^2)
\end{align}$$
The posterior is comprised of the likelihood function $p(\mathbf{y} \mid\boldsymbol{\beta},\sigma^2)$ and the joint prior distribution $p(\boldsymbol{\beta},\sigma^2)$.
"""
# ╔═╡ dcf7adfb-0d6e-4459-a3cc-6a20cc632c72
md"""
#### Likelihood function
"""
# ╔═╡ a541e83e-22c5-48c7-9beb-705d26f602ee
md"""
For this model the likelihood function is then given by:
$$\begin{aligned}
p\left(\mathbf{y} \mid \boldsymbol{\beta}, \sigma^{2}\right) &=\left|2 \pi \sigma^{2} \mathbf{I}_{T}\right|^{-\frac{1}{2}} \mathrm{e}^{-\frac{1}{2}(\mathbf{y}-\mathbf{X} \boldsymbol{\beta})^{\prime}\left(\sigma^{2} \mathbf{I}_{T}\right)^{-1}(\mathbf{y}-\mathbf{X} \boldsymbol{\beta})} \\
&=\left(2 \pi \sigma^{2}\right)^{-\frac{T}{2}} \mathrm{e}^{-\frac{1}{2 \sigma^{2}}(\mathbf{y}-\mathbf{X} \boldsymbol{\beta})^{\prime}(\mathbf{y}-\mathbf{X} \boldsymbol{\beta})}
\end{aligned}$$
where $|\cdot|$ denotes the determinant. Also note that the second equality follows from the result that for an $n \times n$ matrix $\mathbf{A}$ and scalar $c,|c \mathbf{A}|=c^{n}|\mathbf{A}|$. In our example this means that $|\sigma^{2} \mathbf{I}_{t}| = (\sigma^{2})^{T}$. For this equation we also have that $\sigma^{2} \mathbf{I}_{t} = \frac{1}{\sigma^{2}}$.
"""
# ╔═╡ 5b442d42-b6ff-4677-afff-a00cfdb00fbc
md"""
#### Priors
"""
# ╔═╡ 8a8dea4a-3f49-4095-a1a0-d4d3c803c171
md"""
In specifying priors we need to take a stance on the dependence between the model parameters $\boldsymbol{\beta}$ and $\sigma^{2}$.
If we believe that the regression coefficients might depend on the error variance, i.e. $p(\boldsymbol{\beta},\sigma^2)=p(\boldsymbol{\beta}|\sigma^2)p(\sigma^2)$, then it turns out (see [Chapter 2 of Koop's Bayesian Econometrics](https://www.amazon.com/Bayesian-Econometrics-Gary-Koop/dp/0470845678)) that the natural conjugate prior for $\boldsymbol{\beta}$ and $\sigma^2$ is the [Normal-Gamma distribution](https://en.wikipedia.org/wiki/Normal-gamma_distribution) denoted by
$$\boldsymbol{\beta},\sigma^2 \sim NG(m_0,v_0,\nu_0,S_0)$$
in which $m_0,v_0,\nu_0$ and $S_0$ are hyperparameters.
If we believe that the regression coefficients and the error variance are independent, i.e. $p(\boldsymbol{\beta},\sigma^2)=p(\boldsymbol{\beta})p(\sigma^2)$, then it's convenient to specify the following conjugate priors
1. Multivariate Normal prior for $\boldsymbol{\beta}$, i.e. $\boldsymbol{\beta}\sim N(\mathbf{m}_0,\mathbf{V}_0)$
2. Inverse-Gamma prior for $\sigma^2$, i.e. $\sigma^2\sim IG(\nu_0,S_0)$
The explicit prior densities are given by,
$$\begin{aligned}
p(\boldsymbol{\beta}) &=(2 \pi)^{-\frac{k}{2}}\left|\mathbf{V}_{0}\right|^{-\frac{1}{2}} \mathrm{e}^{-\frac{1}{2}\left(\boldsymbol{\beta}-\boldsymbol{m}_{0}\right)^{\prime} \mathbf{V}_{\boldsymbol{0}}^{-1}\left(\boldsymbol{\beta}-\boldsymbol{m}_{0}\right)} \\
p\left(\sigma^{2}\right) &=\frac{S_{0}^{\nu_{0}}}{\Gamma\left(\nu_{0}\right)}\left(\sigma^{2}\right)^{-\left(\nu_{0}+1\right)} \mathrm{e}^{-\frac{S_{0}}{\sigma^{2}}}
\end{aligned}$$
We will assume the latter is true (i.e., that parameters are independent).
"""
# ╔═╡ be364d9e-c04d-47fd-ac2f-2be93bdd3f87
md"""
#### Posterior
"""
# ╔═╡ 4a985421-7513-407a-8edf-01df7b405c55
md"""
To get the posterior, we combine the prior and likelihood
$$\begin{align}
p(\boldsymbol{\beta},\sigma^2|\mathbf{y}) &\propto p(\mathbf{y}|\boldsymbol{\beta},\sigma^2)p(\boldsymbol{\beta})p(\sigma^2)\\
&\propto \underset{p(\mathbf{y}|\boldsymbol{\beta},\sigma^2)}{\underbrace{(\sigma^2)^{-\frac{T}{2}}\exp(-\frac{1}{2\sigma^2}(\mathbf{y}-\mathbf{X}\boldsymbol{\beta})'(\mathbf{y}-\mathbf{X}\boldsymbol{\beta}))}}\underset{p(\boldsymbol{\beta})}{\underbrace{\exp(-\frac{1}{2}(\boldsymbol{\beta}-\mathbf{m}_0)'\mathbf{V}_0^{-1}(\boldsymbol{\beta}-\mathbf{m}_0))}}\underset{p(\sigma^2)}{\underbrace{(\sigma^2)^{-(\nu_0+1)}\exp(-\frac{S_0}{\sigma^2})}}\\
&\propto (\sigma^2)^{-(\frac{T}{2}\nu_0+1)}\exp(-\frac{1}{2\sigma^2}(S_0 + (\mathbf{y}-\mathbf{X}\boldsymbol{\beta})'(\mathbf{y}-\mathbf{X}\boldsymbol{\beta})) -\frac{1}{2}(\boldsymbol{\beta}-\mathbf{m}_0)'\mathbf{V}_0^{-1}(\boldsymbol{\beta}-\mathbf{m}_0)))\\
\end{align}$$
This final distribution is an unknown distribution. However, we know that we can use a two-block Gibbs sampler to estimate the parameters of a univariate Normal distribution (from above). Now we need to see whether we can extend this to parameters of a multivariate Normal distribution.
"""
# ╔═╡ 5b3bd2d1-2697-4516-aa58-99b3d229e5f3
md"""
#### Gibbs sampling (full derivation)
"""
# ╔═╡ 8cf7fa3c-aaf2-4684-869e-d099218fe6e9
md"""
Now, we derive a Gibbs sampler for the normal linear regression. Specifically, we need to derive the two conditional densities $p\left(\sigma^{2} \mid \mathbf{y}, \boldsymbol{\beta}\right)$ and $p\left(\boldsymbol{\beta} \mid \mathbf{y}, \sigma^{2}\right)$. The full conditional distributions are
$p(\boldsymbol{\beta} \mid \mathbf{y},\sigma^2)\propto p(\mathbf{y} \mid \boldsymbol{\beta},\sigma^2)p(\boldsymbol{\beta})$
$p(\sigma^2 \mid \mathbf{y},\boldsymbol{\beta})\propto p(\mathbf{y} \mid \boldsymbol{\beta},\sigma^2) p(\sigma^2)$
**First block:**
First, we can show that the conditional density $p\left(\sigma^{2} \mid \mathbf{y}, \boldsymbol{\beta}\right)$ is inverse-gamma:
$$\begin{aligned}
p\left(\sigma^{2} \mid \mathbf{y}, \boldsymbol{\beta}\right) & \propto p\left(\mathbf{y} \mid \boldsymbol{\beta}, \sigma^{2}\right) p\left(\sigma^{2}\right) \\
& \propto\left(\sigma^{2}\right)^{-\frac{T}{2}} \mathrm{e}^{-\frac{1}{2 \sigma^{2}}(\mathbf{y}-\mathbf{X} \boldsymbol{\beta})^{\prime}(\mathbf{y}-\mathbf{X} \boldsymbol{\beta})} \times\left(\sigma^{2}\right)^{-\left(\nu_{0}+1\right)} \mathrm{e}^{-\frac{S_{0}}{\sigma^{2}}} \\
&=\left(\sigma^{2}\right)^{-\left(\frac{T}{2}+\nu_{0}+1\right)} \mathrm{e}^{-\frac{1}{\sigma^{2}}\left(S_{0}+\frac{1}{2}(\mathbf{y}-\mathbf{X} \boldsymbol{\beta})^{\prime}(\mathbf{y}-\mathbf{X} \boldsymbol{\beta})\right)}
\end{aligned}$$
We recognize this as the kernel of an inverse-gamma density. In fact, we have
$$\left(\sigma^{2} \mid \mathbf{y}, \boldsymbol{\beta}\right) \sim \mathcal{I} G\left(\nu_{0}+\frac{T}{2}, S_{0}+\frac{1}{2}(\mathbf{y}-\mathbf{X} \boldsymbol{\beta})^{\prime}(\mathbf{y}-\mathbf{X} \boldsymbol{\beta})\right)$$
In other words it is the the kernel of an inverse-gamma density with scale parameter $\nu = \frac{T}{2}+\nu_0$ and shape parameter $S = S_0 + (\mathbf{y}-\mathbf{X}\boldsymbol{\beta})'(\mathbf{y}-\mathbf{X}\boldsymbol{\beta})$. Thus
$$\sigma^2 \mid \mathbf{y},\boldsymbol{\beta} \sim \mathcal{I} G(\nu,S)$$
**Second block:**
Next, we derive the conditional density $p\left(\boldsymbol{\beta} \mid \mathbf{y}, \sigma^{2}\right)$. To that end, first note that the likelihood involves the quadratic term $(\mathbf{y}-\mathbf{X} \boldsymbol{\beta})^{\prime}(\mathbf{y}-\mathbf{X} \boldsymbol{\beta})$, which can be expanded as
$$\begin{aligned}
(\mathbf{y}-\mathbf{X} \boldsymbol{\beta})^{\prime}(\mathbf{y}-\mathbf{X} \boldsymbol{\beta}) &=\mathbf{y}^{\prime} \mathbf{y}-\mathbf{y}^{\prime} \mathbf{X} \boldsymbol{\beta}-\boldsymbol{\beta}^{\prime} \mathbf{X}^{\prime} \mathbf{y}+\boldsymbol{\beta}^{\prime} \mathbf{X}^{\prime} \mathbf{X} \boldsymbol{\beta} \\
&=\boldsymbol{\beta}^{\prime} \mathbf{X}^{\prime} \mathbf{X} \boldsymbol{\beta}-2 \boldsymbol{\beta}^{\prime} \mathbf{X}^{\prime} \mathbf{y}+\mathbf{y}^{\prime} \mathbf{y}
\end{aligned}$$
where we have used the fact that $\mathbf{y}^{\prime} \mathbf{X} \boldsymbol{\beta}$ is a scalar, and therefore it is equal to its transpose:
$$\mathbf{y}^{\prime} \mathbf{X} \boldsymbol{\beta}=\left(\boldsymbol{\beta}^{\prime} \mathbf{X}^{\prime} \mathbf{y}\right)^{\prime}=\boldsymbol{\beta}^{\prime} \mathbf{X}^{\prime} \mathbf{y}$$
Similarly, the quadratic term in the prior can be expanded as
$$\left(\boldsymbol{\beta}-\boldsymbol{m}_{0}\right)^{\prime} \mathbf{V}_{\boldsymbol{\beta}}^{-1}\left(\boldsymbol{\beta}-\boldsymbol{m}_{0}\right)=\boldsymbol{\beta}^{\prime} \mathbf{V}_{\boldsymbol{0}}^{-1} \boldsymbol{\beta}-2 \boldsymbol{\beta}^{\prime} \mathbf{V}_{\boldsymbol{0}}^{-1} \boldsymbol{m}_{0}+\boldsymbol{m}_{0}^{\prime} \mathbf{V}_{\boldsymbol{0}}^{-1} \boldsymbol{m}_{0}$$
For notational convenience, let $\frac{1}{\sigma^{2}} = \Sigma^{-1}$ where $\Sigma = \sigma^{2}\mathbf{I}_{T}$. The conditional density $p\left(\boldsymbol{\beta} \mid \mathbf{y}, \sigma^{2}\right)$ is then given by
$$\begin{align}
p(\boldsymbol{\beta} \mid \mathbf{y},\sigma^2)&\propto p(\mathbf{y} \mid \boldsymbol{\beta},\sigma^2)p(\boldsymbol{\beta})\\
&\propto\exp(-\frac{1}{2}((\mathbf{y}-\mathbf{X}\boldsymbol{\beta})'\boldsymbol{\Sigma}^{-1}(\mathbf{y}-\mathbf{X}\boldsymbol{\beta}))\exp(-\frac{1}{2}(\boldsymbol{\beta}-\mathbf{m}_0)'\mathbf{V}_0^{-1}(\boldsymbol{\beta}-\mathbf{m}_0))\\
&\propto\exp(-\frac{1}{2}((\boldsymbol{\beta}'\mathbf{X}'\boldsymbol{\Sigma}^{-1}\mathbf{X}\boldsymbol{\beta}-2\boldsymbol{\beta}'\mathbf{X}'\boldsymbol{\Sigma}^{-1}\mathbf{y}))\exp(-\frac{1}{2}(\boldsymbol{\beta}'\mathbf{V}_0^{-1}\boldsymbol{\beta}-2\boldsymbol{\beta}'\mathbf{V}_0^{-1}\mathbf{m}_0))\\
&=\exp(-\frac{1}{2}(\boldsymbol{\beta}'(\mathbf{X}'\boldsymbol{\Sigma}^{-1}\mathbf{X}+\mathbf{V}_0^{-1})\boldsymbol{\beta}-2\boldsymbol{\beta}'(\mathbf{X}'\boldsymbol{\Sigma}^{-1}\mathbf{y} + \mathbf{V}_0^{-1}\mathbf{m}_0))\\
\end{align}$$
Since the exponent is quadratic in $\boldsymbol{\beta}, p\left(\boldsymbol{\beta} \mid \mathbf{y}, \sigma^{2}\right)$ is a multivariate normal density,
$$\boldsymbol{\beta} \mid \mathbf{y}, \sigma^{2} \sim \mathcal{N}\left(\widehat{\boldsymbol{\beta}}, \mathbf{D}_{\boldsymbol{\beta}}\right)$$
for some mean vector $\widehat{\boldsymbol{\beta}}$ and covariance matrix $\mathbf{D}_{\beta}$. Next, we derive explicit expressions for $\widehat{\boldsymbol{\beta}}$ and $\mathbf{D}_{\boldsymbol{\beta}}$
The kernel of $\mathcal{N}\left(\widehat{\boldsymbol{\beta}}, \mathbf{D}_{\boldsymbol{\beta}}\right)$ is simply
$$p(\boldsymbol{\beta} \mid \mathbf{y}, \sigma^{2}) \propto \mathrm{exp}\left({-\frac{1}{2}\left(\boldsymbol{\beta}^{\prime} \mathbf{D}_{\boldsymbol{\beta}}^{-1} \boldsymbol{\beta}-2 \boldsymbol{\beta}^{\prime} \mathbf{D}_{\boldsymbol{\beta}}^{-1} \widehat{\boldsymbol{\beta}}\right)}\right)$$
Comparing this with the kernel in a previous expression, we have that
$$\mathbf{D}_{\boldsymbol{\beta}}=\left(\mathbf{V}_{0}^{-1}+\frac{1}{\sigma^{2}} \mathbf{X}^{\prime} \mathbf{X}\right)^{-1}, \quad \widehat{\boldsymbol{\beta}}=\mathbf{D}_{\beta}\left(\mathbf{V}_{0}^{-1} \boldsymbol{m}_{0}+\frac{1}{\sigma^{2}} \mathbf{X}^{\prime} \mathbf{y}\right)$$
Even though one can use the built-in functionality in _Julia_ to sample from $\mathcal{N}(\boldsymbol{\mu}, \mathbf{\Sigma})$, it is instructive to see how it can be done by simply transforming independent standard normal random variables.
"""
# ╔═╡ 0999d280-3d93-4a0d-a487-215a3f068fee
md"""
### Sampling from a Multivariate Normal distribution
"""
# ╔═╡ f43f8154-5bd3-4a33-8316-991417549d32
md"""
To generate $R$ independent draws from $\mathcal{N}(\boldsymbol{\mu}, \mathbf{\Sigma})$ of dimension $n$, carry out the following steps:
1. Compute the lower Cholesky factorization $\mathbf{\Sigma}=\mathbf{B B}^{\prime}$.
2. Generate $\mathbf{Z}=\left(Z_{1}, \ldots, Z_{n}\right)^{\prime}$ by drawing $Z_{1}, \ldots, Z_{n} \sim \mathcal{N}(0,1)$.
3. Return $\mathbf{U}=\boldsymbol{\mu}+\mathbf{B Z}$.
4. Repeat Steps 2 and 3 independently $R$ times.
We can show what this process looks like below.
"""
# ╔═╡ 6ec571a0-6407-439a-b7f3-fb15085d73bf
begin
## Sample from a Multivariate Normal Distribution
# Set up Normal random vector
n = 2 # Dimension of Normal random vector
mu = ones(n);
A = randn(n, n);
Sig = A' * A; # positive definite matrix
# Simulate
R = 10000; # Simulation size
U = zeros(R, 2); # storage matrix
for i in 1:R
U[i,:] = transpose(mu + (cholesky(Sig).L) * rand(Normal(0, 1), n));
end
# Summary statistics
muhat = mean(U, dims=1);
Sighat = cov(U);
end;
# ╔═╡ 3752db0a-3c4f-48e9-89ec-c14927584a1d
md""" In _Julia_ we could have simply run the following command to sample from this distribution """
# ╔═╡ 6e288af8-8f7d-4d4a-9fc3-4bb7df212e8e
rand(MvNormal(mu, Sig));
# ╔═╡ fd643db4-ad7f-4203-b7ed-96c730343013
md""" However, it is computationially more efficient to use the first method. """
# ╔═╡ c5c4da02-8b6b-4318-b625-ce4f31703c79
md"""
### Gibbs Sampler for Regression
"""
# ╔═╡ 428eb291-d516-4b56-91e7-df3a92cd3a4f
md"""
Pick some initial values $\boldsymbol{\beta}^{(0)}=\mathbf{a}_{0}$ and $\sigma^{2(0)}=b_{0}>0 .$ Then, repeat the following steps from $r=1$ to $R:$
1. Draw $\sigma^{2(r)} \sim p\left(\sigma^{2} \mid \mathbf{y}, \boldsymbol{\beta}^{(r-1)}\right)$ (inverse-gamma).
2. Draw $\boldsymbol{\beta}^{(r)} \sim p\left(\boldsymbol{\beta} \mid \mathbf{y}, \sigma^{2(r)}\right)$ (multivariate normal).
Next we show how to implement this process in Julia.
"""
# ╔═╡ 223f4b6f-8321-4142-9dfa-1afe371d40ac
md"""
### Practical implementation
"""
# ╔═╡ edaf930a-4933-4047-8854-bbb02ea9c39c
md"""
As an example, the following code first generates a sample of $T=500$ observations from a normal linear regression model. It then implements the Gibbs sampler in our second algorithm, where the sampler is initialized using the least squares estimate. The posterior means of the model parameters are stored in the variable `theta_hat` and the corresponding $95$ percent credible intervals are stored in `theta_CI`.
"""
# ╔═╡ c9c8d57b-3010-4056-aaa3-a0354cf456fd
function data_gen2(T, β, σ2)
X = [ones(T, 1) 1 .+ randn(T, 1)]
return X * β' .+ sqrt(σ2) .* randn(T, 1)
end;
# ╔═╡ bb10296b-1b93-46d9-bd5f-c11a4ed8bcae
y_dg = data_gen2(T, β, σ2)
# ╔═╡ bfd8fab5-64fb-4bee-aee6-e9691cc887f8
mean(y_dg)
# ╔═╡ 1c38646c-b750-4402-bc70-571670cd7acd
var(y_dg)
# ╔═╡ 000cf50f-d3f8-452b-b661-11545c2ec0c4
function get_prior(ν_0)
Vβ_0 = I(2) ./ 100 # Prior for beta
Σ_0 = 1 * (ν_0 - 1) # Prior for Sig2
return Vβ_0, Σ_0
end;
# ╔═╡ 7b70270e-2991-40ee-97a9-082691e68701
function ols_est(T, β, σ2)
y = data_gen2(T, β, σ2) # Generated data
X = [ones(T, 1) 1 .+ randn(T, 1)]
β_1 = (X' * X) \ (X' * y)
σ2_1 = sum((y .- X * β_1) .^ 2) / T
return β_1, σ2_1
end;
# ╔═╡ 791939ae-3e00-4d6b-968c-5a82a78f55f8
function gibbs_linear(nsim, burnin, T, β, σ2, β_0, ν_0)
y = data_gen2(T, β, σ2) # Generated data
X = [ones(T, 1) 1 .+ randn(T, 1)]
Vβ_0, Σ_0 = get_prior(ν_0)
β_1, σ2_1 = ols_est(T, β, σ2)
# Initialise Markov Chain
store_θ = zeros(nsim, 3)
# Start the Gibbs sampling procedure
for i in 1:nsim + burnin
# Sample from beta
D_β = (Vβ_0 .+ X' * X / σ2_1) \ I(2)
β_hat = D_β * (Vβ_0 * β_0' .+ X' * y ./ σ2_1)
# Refer to section on sampling from multivariate normal
C = cholesky(Hermitian(D_β)).L
β_1 = β_hat .+ C * randn(2,1)
# Sample from σ2
e = y - X * β_1
sig2 = rand(InverseGamma(ν_0 .+ T/2, Σ_0 + (e' * e / 2)[1]))
if i > burnin
isave = i .- burnin
store_θ[isave, :] = [β_1' σ2_1]
end
end
mean(store_θ[:, 1]), mean(store_θ[:, 2]), store_θ
end;
# ╔═╡ 9d1361c3-6de1-4326-85f8-4a272856d16b
posterior_beta, posterior_sig2, store_θ = gibbs_linear(nsim, burnin, T, β, σ2, β_0, ν_0);
# ╔═╡ 01f5e6cd-1efa-49a2-8d0d-b5536bdd7388
begin
x_n = collect(1:(nsim-burnin));
pg1 = histogram(x_n, store_θ[:,1], normalize=:pdf, title = "Posterior: Beta", legend = false)
pg2 = histogram(x_n, store_θ[:,3], normalize=:pdf, title = "Posterior: Sigma2", legend = false)
plot(pg1,pg2, layout = (1,2), legend = false)
end
# ╔═╡ 442f79e7-425b-43fd-a760-099f5005d4b1
md"""
## Regression with `Turing.jl`
"""
# ╔═╡ b58c1e30-da22-4ff2-93e7-6c2bf2e3a7e3
md""" Let us start with trying to replicate our linear regression from the previous section in `Turing.jl`. """
# ╔═╡ 5e9d778f-dd29-46ca-85ee-0eb2b4e4d027
@model linear_regression_1(X, y) = begin
#priors
α ~ Normal(mean(y), 2.5 * std(y))
β ~ Normal(TDist(3), predictors)
σ ~ InverseGamma(2)
#likelihood
y ~ MvNormal(α .+ X * β, σ)
end;
# ╔═╡ d5e73cc5-0a28-4a84-ba7e-7d493c53114b
md"""
We will be doing a quick regression using a dataset from the `RDatasets` package. You can pick whatever you find interesting. I wanted it to be related to economics in some way, so I chose the `Ecdat` dataset, which is supposed to be relevant for econometrics
"""
# ╔═╡ f3109dad-1b97-4f68-babd-65751142486a
ic_data = RDatasets.dataset("Ecdat", "Icecream")
# ╔═╡ 692704dd-4be7-4c51-a421-2273f2b0711b
md" First we split the dataset into two subsets. One for the training model and the other for evaluation. "
# ╔═╡ 8d35cc9d-e37a-4a42-83af-8f48ffa67252
# Split our dataset 70%/30% into training/test sets.
trainset, testset = splitobs(shuffleobs(ic_data), 0.7)
# ╔═╡ 71172cf3-be3e-416b-a35f-9ba724c528f0
md" Convert everything to matrix form, not DataFrames. "
# ╔═╡ bd6bcc2d-dd6d-4140-a62f-61a0c27bcda1
begin
# Turing requires data in matrix form.
target = :Cons
train = Matrix(select(trainset, Not(target)))
test = Matrix(select(testset, Not(target)))
train_target = trainset[:, target]
test_target = testset[:, target]
# Standardize the features.
μ₂, σ₂ = rescale!(train; obsdim = 1)
rescale!(test, μ₂, σ₂; obsdim = 1)
# Standardize the targets.
μtarget, σtarget = rescale!(train_target; obsdim = 1)
rescale!(test_target, μtarget, σtarget; obsdim = 1);
end;
# ╔═╡ 0b23e994-d1c7-466e-bb40-e8b1db183885
md""" #### Model specification """
# ╔═╡ 8c803ed9-051b-45a0-8e8a-c3754b0a1f10
# Bayesian linear regression.
@model function linear_regression(x, y)
# Set variance prior.
σ₂ ~ truncated(Normal(0, 100), 0, Inf)
# Set intercept prior.
intercept ~ Normal(0, sqrt(3))
# Set the priors on our coefficients.
nfeatures = size(x, 2)
coefficients ~ MvNormal(nfeatures, sqrt(10))
# Calculate all the mu terms.
mu = intercept .+ x * coefficients
y ~ MvNormal(mu, sqrt(σ₂))
end;
# ╔═╡ 7cf8df8f-aa2d-41f9-95b7-b50fc191cb9d
model = linear_regression(train, train_target);
# ╔═╡ 7967b6e7-8391-4489-9e36-fe281dfe3fcc
chain = sample(model, NUTS(0.65), 2_000);
# ╔═╡ beaa1dfc-e2a7-4125-a45e-bfba93f8f02a
plot(chain)
# ╔═╡ 3006d4f8-1cca-4947-8dc6-3b77b278fbb8
md"""
## Autoregressive model
"""
# ╔═╡ 22bdce87-a1f2-440b-965a-391148b010ac
md""" As you know from the first semester, the autoregressive model is a workhorse model in economics and finance. This model is represented by
$$y_t = \rho_0 + \rho_1 y_{t-1} + \dots + \rho_p y_{t-p} + e_t, \quad e_t\sim N(0,\sigma^2)$$
where $\rho_0$ is an unknown real-valued constant and $\rho_i$ for $i = 1, \ldots, p$ are the **unknown** coefficients and $\sigma^2 \geq 0$ is the **unknown** error variance. The number of lagged terms determines the order of the AR process. We estimate an AR(1) model on simulated data as follows.
"""
# ╔═╡ 54d73a3a-5804-44cf-b771-7313790129e4
begin
## Simulate Data for AR(1) process
true_beta0 = 0; # true intercept
true_beta1 = 0.6; # true AR(1) coefficient
true_sig2 = 1; # true variance
T_1 = 1000; # no. of dates
y0 = true_beta0/(1 - true_beta1); # initial condition
y = zeros(T_1); # storage vector
y[1] = y0;
for t = 2:T_1
y[t] = true_beta0 + true_beta1*y[t-1] + rand(Normal(0,sqrt(true_sig2)));
end
data_new = y;
x_1 = collect(1:1:T_1);
plot(x_1, data_new, label="Simulated Data")
end
# ╔═╡ f539d376-0f23-4667-9b26-25848b5c635a
begin
# Define key variables in linear regression model
p = 1; # AR order
y0_new = data_new[1:p]; # Initial conditions
y_new = data_new[p+1:end]; # Observations
T_new = size(y_new, 1); # Dates after removing initial conditions
X = [ones(T_new) data_new[1:end-1]]; # intercept and first lag
x_new = collect(1:1:T_new);
plot(x_new, y_new, label="Data")
plot!(x_new, X[:,2], label="First lag")
end
# ╔═╡ 78f29328-9670-4f77-a0d0-4386c6aa71ae
function gibbs_AR(X, T, y)
# Controls
nburn = 1000;
ndraws = nburn + 10000;
# Prior for beta
k = size(X,2); # number of regressors
pri_m = zeros(k); # prior mean
pri_v = Diagonal(10*ones(k)); # prior covariance
pri_beta = MvNormal(pri_m,pri_v); # prior distribution
# Prior for sig2
pri_nu = 3; # prior shape parameter
pri_S = 1*(pri_nu-1); # prior scale parameter - sets E(pri_sig2) = 1
pri_sig2 = InverseGamma(pri_S,pri_S); # prior distribution
# Storage
s_beta = zeros(ndraws-nburn,k);
s_sig2 = zeros(ndraws-nburn,1);
# Deterministic terms in posterior
post_nu = pri_nu + T/2; # posterior shape parameter for sig2
inv_priV = (pri_v)\I(k); # inverse prior covariance for beta
XpX = X'*X;
Xpy = X'*y;
# Initial conditions
MC_beta = XpX\Xpy;
MC_sig2 = (y-X*MC_beta)'*(y-X*MC_beta)/T;
# Markov chain
for loop in 1:ndraws
# Draw beta
post_v = (XpX/MC_sig2 + inv_priV)\I(k);
post_m = post_v*(Xpy/MC_sig2 + inv_priV*pri_m);
#MC_beta = post_m + (cholesky(post_v).L)*rand(Normal(0,1),k);
MC_beta = post_m + (cholesky(Hermitian(post_v)).L)*rand(Normal(0,1),k);
# Draw sig2
post_S = pri_S +0.5*(y-X*MC_beta)'*(y-X*MC_beta);
MC_sig2 = rand(InverseGamma(post_nu,post_S));
# Store
if loop > nburn
count_loop = loop - nburn;
s_beta[count_loop,:] = transpose(MC_beta);
s_sig2[count_loop] = MC_sig2;
end
end
## Summarize results
# Compute posterior mean using Monte Carlo Integration
post_beta = mean(s_beta,dims=1);
post_sig2 = mean(s_sig2);
post_beta, post_sig2, s_beta, s_sig2
end;
# ╔═╡ 2502b96c-7bdc-4749-b92e-f171f60a4508
begin
nburn = 1000;
ndraws = nburn + 10000;
post_beta, post_sig2, s_beta, s_sig2 = gibbs_AR(X, T_new, y_new)
end;
# ╔═╡ 01b84089-7a99-4bc4-93f4-5259fabde2cb
begin
# Trace plots
x_t = collect(1:(ndraws-nburn))
p1a = plot(x_t,s_beta[:,1], title = "Markov chain: beta0", legend = false);
p1b = plot(x_t,s_beta[:,2], title = "Markov chain: beta1", legend = false);
p1c = plot(x_t,s_sig2, title = "Markov chain: sig2", label="posterior draws");
end;
# ╔═╡ 82fb8ff1-8017-4554-ba79-98045b1fccf3
begin
# Plot marginal posterior distributions
x_h = collect(1:(ndraws-nburn));
histogram(x_h, s_beta[:,1], normalize=:pdf, title = "Posterior: beta0", legend = false)
plot!([true_beta0], seriestype="vline", legend = false)
p2a = plot!([post_beta[1]], seriestype="vline", legend = false)
histogram(x_h, s_beta[:,2], normalize=:pdf, title = "Posterior: beta1", legend = false)
plot!([true_beta1], seriestype="vline", legend = false)
p2b = plot!([post_beta[2]], seriestype="vline", legend = false)
histogram(x_h, s_sig2, normalize=:pdf, title = "Posterior: sig2", label="Empirical pdf")
plot!([true_sig2], seriestype="vline", label="True value")
p2c = plot!([post_sig2], seriestype="vline", label="MC mean")
plot(p2a,p2b,p2c,layout = (1,3), legend = false)
end
# ╔═╡ 4b4dde8d-e34d-44ac-9010-8ca22feae0e2
md""" We can extend this example to a moving average model and thereby also the more general class of ARMA models.
"""
# ╔═╡ c5d09e13-540d-4993-834b-dd1565a06f1a
md""" ### Autoregressive model with `Turing.jl` """
# ╔═╡ 8e9d13a9-46f6-406e-b53c-a1f763178b5a
md""" Instead of necesarilly writing our own routine for the Gibbs sampler, we can use the functionality of `Turing.jl` to generate estimates of the parameters of interest. Consider the following code for the AR(1) case. """
# ╔═╡ 1f9ae2e6-bb0c-45cb-8992-684e14892b36
@model function ar1(ys=missing)
if ys === missing
ys = Vector{Real}(undef, 10)
end
n = length(ys)
# Uninformative priors
# alpha ~ Uniform(-2,2)
# beta ~ Uniform(-2,2)
# sigma ~ Uniform(0,2)
# Informative priors
beta_0 ~ Normal(2,2)
beta_1 ~ Normal(2,2)
sig2 ~ InverseGamma(2,2)
ys[1] ~ Normal(beta_0 + beta_1, sig2)
for i in 2:n
ys[i] ~ Normal(beta_0 + beta_1 * ys[i-1], sig2)
end
end;
# ╔═╡ 1e62adf7-17ba-4f2d-b401-c3822d266d81
model_new = ar1(y_new)
# ╔═╡ 78156e4d-e593-4e8c-8d4b-6567a3671aff
chs = sample(model_new,NUTS(), 10_000)
# ╔═╡ 48a26331-e569-4f15-b271-9a2a3b640878
plot(chs)
# ╔═╡ d66073c1-1de0-4532-8686-d56255350193
md""" Let us look at one last thing for this section with respect to ARIMA models. The purpose here is to show you how to load data and manipulate with the DataFrames package. """
# ╔═╡ 7eaf829b-7398-4026-8eae-d84cbbb80fe0
md""" ### ARIMA forecasting with `StateSpaceModels.jl` """
# ╔═╡ 5a4eb3d4-ccde-4b88-bc45-75fe85c6bdb1
md""" Generally if you are going to be doing ARIMA forecasts I would recommend the `forecast` package in R. It has a lot of nice functionality. This is not a Bayesian package, but not everything you do has to be Bayesian.
If you want to go with a Bayesian approach one normally has to frame the model in a state space setting. I will make a quick reference below to state space models in another lecture. State space modelling is also a precursor to the work on TVP-VARs and the Kalman filter. However, due to time considerations, we won't be able to do all of the work indicated.
I suggest the `bsts` or `bayesforecast` pacakges in R for the purpose of Bayesian structural time series modelling."""
# ╔═╡ 34307b89-0a10-4339-9356-fd21fd877e95
md" Let us look at some financial data for our next example. Ice cream data might not have been the best bet for **real / serious** econometric analysis... Only you can decide if this is the case. "
# ╔═╡ d81f725d-a250-44e8-98ba-5c0efecdb19c
df = urldownload("https://raw.githubusercontent.com/inertia7/timeSeries_sp500_R/master/data/data_master_1.csv") |> DataFrame
# ╔═╡ dfbe27b2-bfdd-40e9-a63c-3115c308de75
s = df.sp_500;
# ╔═╡ 455a97c8-db6e-42d8-82b1-9c21f2794ab7
plot(s, legend = false, lw = 1.5, alpha = 0.8)
# ╔═╡ 3f656347-0b61-4b7e-b52f-c4f34d724f5c
begin
# Split into training and test sets.
train_percentage = 0.95
s_train = s[1:floor(Int, train_percentage*length(s))]
N₂ = length(s_train)
end;
# ╔═╡ 536ecb71-f33f-402d-8d6f-16291aa9530f
md" We can test for stationarity with a Dickey Fuller test. "
# ╔═╡ ccea5b69-dfb1-47ee-bd54-7b58d87f09f5
ADFTest(s_train, Symbol("constant"), 5)
# ╔═╡ d90046ec-2bb6-4224-a26d-65550d86dc96
md" We observe stationarity and the easiest way to resolve this is by taking a first difference. "
# ╔═╡ 6c7f89c4-cbeb-42c6-aba4-cff79a68ab8f
begin
s_diff = diff(s_train)
plot(s_diff, legend = false, lw = 1.5, alpha = 0.8)
end
# ╔═╡ 1c1d4271-6cec-4d86-be3b-1e89417154ee
ADFTest(s_diff, Symbol("constant"), 5)
# ╔═╡ 9b5622fa-9caf-4f54-a15d-f8b731dc842b
md" Seems stationary, so let's move to our next step. We want to figure out the number of MA and AR terms by using ACF and PACF plots. You should have this type of process in the first semester. "
# ╔═╡ 596fdda0-0d0e-4e45-8fa2-5618122a08b6
begin
total_lags = 20
s1 = plot(line = :stem, collect(1:total_lags), autocor(s_diff, collect(1:total_lags)), title = "ACF", ylim = [-0.3,0.5], lw = 10)
s2 = plot(line = :stem, collect(1:total_lags), pacf(s_diff, collect(1:total_lags)), title = "PACF", ylim = [-0.3,0.5], lw = 10)
plot(s1, s2, layout = (2, 1), legend = false)
end
# ╔═╡ e680e488-f1e2-4da8-8ef4-612f60cd030b
md" For the sake of argument, let us say that we can then model with with an AR(0, 1, 1) model."
# ╔═╡ b8adc2e6-2e1c-4f00-a92f-81a110b123e9
begin
arima_model = SARIMA(s_diff; order = (0, 0, 1))
StateSpaceModels.fit!(arima_model)
end
# ╔═╡ 6b56be36-2951-4562-be94-ad263b155082
results(arima_model)
# ╔═╡ a1411a18-3c64-46b1-b752-e3af0265eaf9
forec = forecast(arima_model, 24)
# ╔═╡ 99ebe7a6-35c1-46ce-9d73-685d451741f7
plot(arima_model, forec; legend = false)
# ╔═╡ 00000000-0000-0000-0000-000000000001
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git-tree-sha1 = "daf17f441228e7a3833846cd048892861cff16d6"
uuid = "c7cfdc94-dc32-55de-ac96-5a1b8d977c5b"
version = "1.13.0+3"
[[deps.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"
[[deps.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"
[[deps.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"
[[deps.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"
[[deps.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"
[[deps.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"
[[deps.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"
[[deps.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"
[[deps.Xorg_xtrans_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "79c31e7844f6ecf779705fbc12146eb190b7d845"
uuid = "c5fb5394-a638-5e4d-96e5-b29de1b5cf10"
version = "1.4.0+3"
[[deps.Zlib_jll]]
deps = ["Libdl"]
uuid = "83775a58-1f1d-513f-b197-d71354ab007a"
[[deps.Zstd_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "cc4bf3fdde8b7e3e9fa0351bdeedba1cf3b7f6e6"
uuid = "3161d3a3-bdf6-5164-811a-617609db77b4"
version = "1.5.0+0"
[[deps.ZygoteRules]]
deps = ["MacroTools"]
git-tree-sha1 = "8c1a8e4dfacb1fd631745552c8db35d0deb09ea0"
uuid = "700de1a5-db45-46bc-99cf-38207098b444"
version = "0.2.2"
[[deps.libass_jll]]
deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "HarfBuzz_jll", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"]
git-tree-sha1 = "5982a94fcba20f02f42ace44b9894ee2b140fe47"
uuid = "0ac62f75-1d6f-5e53-bd7c-93b484bb37c0"
version = "0.15.1+0"
[[deps.libblastrampoline_jll]]
deps = ["Artifacts", "Libdl", "OpenBLAS_jll"]
uuid = "8e850b90-86db-534c-a0d3-1478176c7d93"
[[deps.libfdk_aac_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "daacc84a041563f965be61859a36e17c4e4fcd55"
uuid = "f638f0a6-7fb0-5443-88ba-1cc74229b280"
version = "2.0.2+0"
[[deps.libpng_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"]
git-tree-sha1 = "94d180a6d2b5e55e447e2d27a29ed04fe79eb30c"
uuid = "b53b4c65-9356-5827-b1ea-8c7a1a84506f"
version = "1.6.38+0"
[[deps.libvorbis_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Ogg_jll", "Pkg"]
git-tree-sha1 = "c45f4e40e7aafe9d086379e5578947ec8b95a8fb"
uuid = "f27f6e37-5d2b-51aa-960f-b287f2bc3b7a"
version = "1.3.7+0"
[[deps.nghttp2_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d"
[[deps.p7zip_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0"
[[deps.x264_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "4fea590b89e6ec504593146bf8b988b2c00922b2"
uuid = "1270edf5-f2f9-52d2-97e9-ab00b5d0237a"
version = "2021.5.5+0"
[[deps.x265_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "ee567a171cce03570d77ad3a43e90218e38937a9"
uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76"
version = "3.5.0+0"
[[deps.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:
# ╟─09a9d9f9-fa1a-4192-95cc-81314582488b
# ╟─41eb90d1-9262-42b1-9eb2-d7aa6583da17
# ╟─aa69729a-0b08-4299-a14c-c9eb2eb65d5c
# ╟─a681c4c5-1451-44d6-be27-f9003340883f
# ╟─000021af-87ce-4d6d-a315-153cecce5091
# ╠═c4cccb7a-7d16-4dca-95d9-45c4115cfbf0
# ╠═2eb626bc-43c5-4d73-bd71-0de45f9a3ca1
# ╠═bbcafd74-97f4-4b8f-bffa-937812d9a2eb
# ╟─d65de56f-a210-4428-9fac-20a7888d3627
# ╟─38d2167d-2ca2-4398-9406-db418cc4bf17
# ╟─3f0c1adc-3efd-4ade-9a65-a208d8457b85
# ╟─040c011f-1653-446d-8641-824dc82162eb
# ╟─f3823457-8757-4665-86a8-bf536d80e24d
# ╟─f95ccee4-a2d3-4492-b869-551e61acf995
# ╟─6c027ac7-ef68-4582-ba17-8f34a879a21d
# ╟─66c936a3-055d-42a0-bcad-5176d10e5994
# ╟─3aad749e-1256-4f93-b119-4717d2b95607
# ╟─83ae194c-e04f-4615-9b71-7c389513898c
# ╟─727a703f-1233-4098-86f3-3192e4de08d4
# ╟─14f11fc0-dc9b-4b77-b2da-ae534b911cd6
# ╟─d92d80ef-256c-443a-a81c-8d5f02e01e66
# ╟─48c98f0d-d880-4abc-91d3-6c79be5fcf8a
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# ╠═b94db7f0-9f38-4761-a3c3-4d6fc4729ae9
# ╠═1a6c859c-e3e7-4ad9-9299-091b6b1d2bbf
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# ╠═bf12f6f6-f9a7-4d04-a5ac-1da94f03f8c3
# ╠═0919cb0d-ba03-49c8-b2b9-53a467c39f87
# ╠═343202b3-23b5-4600-b912-7db4ab58deaf
# ╟─3aeab073-c98a-4213-a835-3098233ba90c
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# ╟─70193cca-ce19-49ee-aa0c-06997affe2a6
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# ╟─9ffed7a8-f8d3-4c1a-affa-5d6646683829
# ╟─993c9137-d708-4feb-8a85-51d85c38fc8d
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# ╠═6ec571a0-6407-439a-b7f3-fb15085d73bf
# ╟─3752db0a-3c4f-48e9-89ec-c14927584a1d
# ╠═6e288af8-8f7d-4d4a-9fc3-4bb7df212e8e
# ╟─fd643db4-ad7f-4203-b7ed-96c730343013
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# ╟─223f4b6f-8321-4142-9dfa-1afe371d40ac
# ╟─edaf930a-4933-4047-8854-bbb02ea9c39c
# ╠═c9c8d57b-3010-4056-aaa3-a0354cf456fd
# ╠═bb10296b-1b93-46d9-bd5f-c11a4ed8bcae
# ╠═bfd8fab5-64fb-4bee-aee6-e9691cc887f8
# ╠═1c38646c-b750-4402-bc70-571670cd7acd
# ╠═000cf50f-d3f8-452b-b661-11545c2ec0c4
# ╠═7b70270e-2991-40ee-97a9-082691e68701
# ╠═791939ae-3e00-4d6b-968c-5a82a78f55f8
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# ╠═5e9d778f-dd29-46ca-85ee-0eb2b4e4d027
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# ╠═f3109dad-1b97-4f68-babd-65751142486a
# ╟─692704dd-4be7-4c51-a421-2273f2b0711b
# ╠═8d35cc9d-e37a-4a42-83af-8f48ffa67252
# ╟─71172cf3-be3e-416b-a35f-9ba724c528f0
# ╠═bd6bcc2d-dd6d-4140-a62f-61a0c27bcda1
# ╟─0b23e994-d1c7-466e-bb40-e8b1db183885
# ╠═8c803ed9-051b-45a0-8e8a-c3754b0a1f10
# ╠═7cf8df8f-aa2d-41f9-95b7-b50fc191cb9d
# ╠═7967b6e7-8391-4489-9e36-fe281dfe3fcc
# ╠═beaa1dfc-e2a7-4125-a45e-bfba93f8f02a
# ╟─3006d4f8-1cca-4947-8dc6-3b77b278fbb8
# ╟─22bdce87-a1f2-440b-965a-391148b010ac
# ╟─54d73a3a-5804-44cf-b771-7313790129e4
# ╟─f539d376-0f23-4667-9b26-25848b5c635a
# ╠═78f29328-9670-4f77-a0d0-4386c6aa71ae
# ╠═2502b96c-7bdc-4749-b92e-f171f60a4508
# ╠═82fb8ff1-8017-4554-ba79-98045b1fccf3
# ╟─4b4dde8d-e34d-44ac-9010-8ca22feae0e2
# ╟─c5d09e13-540d-4993-834b-dd1565a06f1a
# ╟─8e9d13a9-46f6-406e-b53c-a1f763178b5a
# ╠═1f9ae2e6-bb0c-45cb-8992-684e14892b36
# ╠═1e62adf7-17ba-4f2d-b401-c3822d266d81
# ╠═78156e4d-e593-4e8c-8d4b-6567a3671aff
# ╠═48a26331-e569-4f15-b271-9a2a3b640878
# ╟─d66073c1-1de0-4532-8686-d56255350193
# ╟─7eaf829b-7398-4026-8eae-d84cbbb80fe0
# ╟─5a4eb3d4-ccde-4b88-bc45-75fe85c6bdb1
# ╟─34307b89-0a10-4339-9356-fd21fd877e95
# ╠═d81f725d-a250-44e8-98ba-5c0efecdb19c
# ╠═dfbe27b2-bfdd-40e9-a63c-3115c308de75
# ╠═455a97c8-db6e-42d8-82b1-9c21f2794ab7
# ╠═3f656347-0b61-4b7e-b52f-c4f34d724f5c
# ╟─536ecb71-f33f-402d-8d6f-16291aa9530f
# ╠═ccea5b69-dfb1-47ee-bd54-7b58d87f09f5
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# ╠═6c7f89c4-cbeb-42c6-aba4-cff79a68ab8f
# ╠═1c1d4271-6cec-4d86-be3b-1e89417154ee
# ╟─9b5622fa-9caf-4f54-a15d-f8b731dc842b
# ╟─596fdda0-0d0e-4e45-8fa2-5618122a08b6
# ╟─e680e488-f1e2-4da8-8ef4-612f60cd030b
# ╠═b8adc2e6-2e1c-4f00-a92f-81a110b123e9
# ╠═6b56be36-2951-4562-be94-ad263b155082
# ╠═a1411a18-3c64-46b1-b752-e3af0265eaf9
# ╠═99ebe7a6-35c1-46ce-9d73-685d451741f7
# ╟─00000000-0000-0000-0000-000000000001
# ╟─00000000-0000-0000-0000-000000000002
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2,
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22,
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12,
22,
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12,
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2,
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2,
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7280,
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65,
24,
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12,
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66,
17,
12,
2920,
65,
15,
12,
65,
24,
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12,
43918,
69,
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64,
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2,
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7280,
16,
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17,
66,
24,
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12,
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12,
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12,
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12,
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69,
21,
65,
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198,
2,
2343,
243,
253,
7280,
41583,
6052,
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12,
344,
1129,
12,
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12,
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15,
66,
12,
3312,
39647,
21223,
17,
64,
21,
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2,
2343,
243,
253,
7280,
1558,
3070,
1765,
1129,
12,
64,
31047,
12,
19,
68,
1350,
12,
64,
24,
65,
18,
12,
1507,
7568,
19881,
19,
891,
67,
5036,
198,
2,
2343,
243,
253,
7280,
24,
487,
276,
22,
64,
23,
12,
69,
23,
67,
18,
12,
19,
66,
16,
64,
12,
2001,
64,
12,
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67,
21,
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2,
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7280,
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12,
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12,
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67,
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66,
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23,
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2,
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243,
253,
7280,
67,
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22,
324,
21855,
12,
15,
67,
21,
68,
12,
19,
33459,
12,
64,
18,
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12,
21,
64,
1238,
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21,
2624,
66,
4761,
198,
2,
2343,
243,
253,
7280,
64,
20,
3901,
68,
5999,
68,
12,
1828,
66,
20,
12,
2780,
66,
22,
12,
24,
1350,
65,
12,
34801,
67,
2075,
69,
31418,
1453,
198,
2,
2343,
243,
253,
7280,
20,
65,
39506,
67,
3682,
12,
65,
21,
487,
12,
24669,
22,
12,
2001,
69,
12,
64,
405,
12993,
9945,
405,
69,
15630,
198,
2,
2343,
243,
253,
7280,
23,
64,
23,
2934,
64,
19,
64,
12,
18,
69,
2920,
12,
1821,
3865,
12,
64,
16,
64,
15,
12,
67,
19,
67,
18,
66,
43564,
66,
27192,
198,
2,
2343,
243,
253,
7280,
1350,
26780,
67,
24,
68,
12,
66,
3023,
67,
12,
2857,
16344,
12,
330,
17,
69,
12,
17,
1350,
6052,
65,
1860,
18,
69,
5774,
198,
2,
2343,
243,
253,
7280,
19,
64,
4089,
4051,
2481,
12,
2425,
1485,
12,
30120,
64,
12,
23,
276,
69,
12,
486,
7568,
22,
65,
26598,
66,
2816,
198,
2,
2343,
243,
253,
7280,
20,
65,
18,
17457,
17,
67,
16,
12,
2075,
5607,
12,
2231,
1433,
12,
7252,
3365,
12,
2079,
65,
18,
67,
23539,
68,
20,
69,
18,
198,
2,
2343,
243,
253,
7280,
23,
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22,
13331,
18,
66,
12,
64,
1878,
17,
12,
38472,
19,
12,
23,
3388,
68,
12,
67,
15,
2079,
28727,
5036,
21,
68,
24,
198,
2,
2343,
243,
253,
7280,
15,
17032,
67,
21033,
12,
18,
67,
6052,
12,
19,
64,
15,
67,
12,
64,
35133,
12,
23349,
64,
18,
69,
15,
3104,
39071,
198,
2,
2343,
243,
253,
7280,
69,
3559,
69,
23,
21526,
12,
20,
17457,
18,
12,
19,
64,
2091,
12,
5999,
1433,
12,
2079,
1415,
17430,
2920,
67,
2624,
198,
2,
2343,
243,
254,
28670,
21,
721,
42875,
64,
15,
12,
21,
30120,
12,
47106,
64,
12,
65,
22,
69,
18,
12,
21855,
8628,
5332,
67,
4790,
19881,
198,
2,
2343,
243,
253,
7280,
22318,
17,
9945,
15,
64,
12,
18,
66,
19,
69,
12,
2780,
68,
24,
12,
4531,
721,
12,
66,
19442,
1983,
46352,
64,
16,
67,
198,
2,
2343,
243,
254,
28670,
21,
68,
25270,
1878,
23,
12,
23,
69,
22,
67,
12,
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67,
19,
64,
12,
24,
16072,
18,
12,
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11848,
22,
7568,
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68,
23,
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198,
2,
2343,
243,
253,
7280,
16344,
41813,
9945,
19,
12,
324,
22,
69,
12,
19,
22416,
12,
65,
22,
276,
12,
4846,
66,
43916,
2682,
18938,
18,
198,
2,
2343,
243,
253,
7280,
66,
20,
66,
19,
6814,
2999,
12,
23,
65,
21,
65,
12,
3559,
1507,
12,
65,
26704,
12,
344,
19,
69,
34125,
3070,
66,
3720,
198,
2,
2343,
243,
253,
7280,
40173,
1765,
33551,
12,
67,
47493,
12,
19,
65,
3980,
12,
6420,
68,
22,
12,
7568,
18,
64,
5892,
10210,
18,
64,
19,
69,
198,
2,
2343,
243,
253,
7280,
22047,
69,
19,
65,
21,
69,
12,
5999,
2481,
12,
19,
23726,
12,
24,
7568,
64,
12,
16,
8635,
38056,
67,
1821,
330,
198,
2,
2343,
243,
253,
7280,
276,
1878,
45418,
64,
12,
2920,
2091,
12,
1821,
2857,
12,
3459,
4051,
12,
11848,
65,
2999,
18213,
24,
66,
2670,
66,
198,
2,
2343,
243,
254,
28670,
66,
24,
66,
23,
67,
3553,
65,
12,
18938,
15,
12,
1821,
3980,
12,
46071,
18,
12,
64,
15,
32182,
12993,
29228,
16344,
198,
2,
2343,
243,
254,
28670,
11848,
940,
27137,
65,
12,
16,
65,
6052,
12,
3510,
67,
24,
12,
17457,
20,
69,
12,
66,
1157,
64,
19,
276,
23,
15630,
3609,
198,
2,
2343,
243,
254,
28670,
65,
16344,
23,
36434,
20,
12,
2414,
21855,
12,
19,
20963,
12,
64,
1453,
21,
12,
68,
38819,
16,
535,
46660,
69,
23,
198,
2,
2343,
243,
254,
28670,
16,
66,
2548,
27720,
66,
12,
65,
15426,
12,
25644,
17,
12,
15630,
2154,
12,
3553,
1433,
2154,
10210,
22,
330,
67,
198,
2,
2343,
243,
254,
28670,
830,
12993,
1120,
69,
12,
67,
18,
69,
23,
12,
37730,
65,
12,
65,
47159,
12,
15363,
2231,
66,
17,
721,
15,
66,
19,
198,
2,
2343,
243,
254,
28670,
22,
65,
2154,
20233,
68,
12,
1959,
6420,
12,
1821,
1453,
12,
5607,
64,
24,
12,
2919,
2075,
6420,
68,
39925,
486,
198,
2,
2343,
243,
254,
28670,
3720,
1129,
2670,
3609,
12,
18,
68,
405,
12,
19,
67,
21,
65,
12,
38956,
66,
12,
20,
64,
6469,
64,
3695,
69,
2816,
69,
23,
198,
2,
2343,
243,
254,
28670,
24,
67,
1485,
5333,
66,
18,
12,
21,
2934,
16,
12,
3559,
2075,
12,
5332,
69,
23,
12,
19,
64,
1983,
2078,
3980,
67,
1433,
65,
198,
2,
2343,
243,
254,
28670,
486,
69,
20,
68,
21,
10210,
12,
16,
891,
64,
12,
2920,
64,
17,
12,
23,
67,
15,
67,
12,
65,
2816,
2623,
65,
1860,
22,
30460,
198,
2,
2343,
243,
253,
7280,
486,
65,
40675,
4531,
12,
22,
64,
2079,
12,
19,
15630,
19,
12,
6052,
69,
19,
12,
20,
25191,
36434,
2934,
17,
21101,
198,
2,
2343,
243,
253,
7280,
39506,
69,
3720,
68,
22,
12,
32114,
65,
12,
3559,
16344,
12,
64,
40761,
12,
15,
2079,
69,
4059,
20,
67,
19,
65,
16,
198,
2,
2343,
243,
253,
7280,
65,
3365,
66,
16,
68,
1270,
12,
6814,
1828,
12,
19,
487,
17,
12,
6052,
68,
22,
12,
21,
66,
17,
19881,
17,
68,
18,
64,
22,
68,
18,
198,
2,
2343,
243,
254,
28670,
20,
68,
24,
67,
39761,
69,
12,
1860,
1959,
12,
3510,
6888,
12,
5332,
1453,
12,
15,
1765,
17,
65,
19,
68,
19,
67,
44698,
198,
2,
2343,
243,
253,
7280,
67,
20,
68,
4790,
535,
20,
12,
15,
64,
2078,
12,
19,
64,
5705,
12,
7012,
22,
68,
12,
22,
67,
43134,
66,
4310,
16562,
65,
198,
2,
2343,
243,
254,
28670,
69,
18,
14454,
47984,
12,
16,
65,
5607,
12,
19,
69,
3104,
12,
65,
397,
67,
12,
2996,
2425,
16562,
1731,
4521,
64,
198,
2,
2343,
243,
253,
7280,
3388,
1983,
3023,
1860,
12,
19,
1350,
22,
12,
19,
66,
4349,
12,
64,
46636,
12,
1828,
4790,
69,
17,
65,
2998,
1157,
65,
198,
2,
2343,
243,
254,
28670,
23,
67,
2327,
535,
24,
67,
12,
68,
2718,
64,
12,
19,
64,
3682,
12,
5999,
1878,
12,
23,
69,
2780,
487,
64,
3134,
22800,
198,
2,
2343,
243,
253,
7280,
22,
1157,
4761,
12993,
18,
12,
1350,
18,
68,
12,
35218,
65,
12,
64,
2327,
69,
12,
24,
7012,
22,
1731,
66,
49351,
69,
15,
198,
2,
2343,
243,
254,
28670,
17457,
21,
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535,
17,
67,
12,
1860,
21,
67,
12,
19,
15187,
12,
64,
5237,
69,
12,
5333,
64,
15,
66,
1983,
15630,
6814,
16,
198,
2,
2343,
243,
253,
7280,
15,
65,
1954,
68,
42691,
12,
67,
16,
66,
22,
12,
42199,
68,
12,
11848,
1821,
12,
68,
23,
65,
16,
9945,
1507,
2548,
5332,
198,
2,
2343,
243,
254,
28670,
23,
66,
43564,
276,
24,
12,
2713,
16,
65,
12,
2231,
64,
15,
12,
23,
68,
23,
64,
12,
66,
22318,
19,
65,
15,
64,
16,
69,
940,
198,
2,
2343,
243,
254,
28670,
22,
12993,
23,
7568,
23,
69,
12,
7252,
17,
67,
12,
3901,
69,
24,
12,
3865,
65,
22,
12,
65,
1120,
16072,
26492,
21101,
24,
67,
198,
2,
2343,
243,
254,
28670,
3720,
3134,
65,
21,
68,
22,
12,
23,
37710,
12,
2598,
4531,
12,
24,
68,
2623,
12,
5036,
30368,
67,
5036,
18,
69,
535,
198,
2,
2343,
243,
254,
28670,
1350,
7252,
16,
7568,
66,
12,
68,
17,
64,
22,
12,
19,
11623,
12,
64,
2231,
68,
12,
19881,
7012,
6052,
69,
23,
69,
2999,
64,
198,
2,
2343,
243,
253,
7280,
6200,
21,
67,
19,
69,
23,
12,
16,
13227,
12,
2920,
2857,
12,
23,
17896,
21,
12,
18,
65,
3324,
65,
25870,
69,
11848,
23,
198,
2,
2343,
243,
253,
7280,
1828,
17457,
344,
5774,
12,
64,
16,
69,
17,
12,
25644,
65,
12,
24,
2996,
64,
12,
2670,
1157,
2780,
65,
20943,
330,
198,
2,
2343,
243,
253,
7280,
4051,
67,
4790,
64,
18,
64,
12,
20,
36088,
12,
2598,
12993,
12,
65,
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] | 2.044256 | 59,133 |
include(joinpath(@__DIR__, "..", "src/dynamics", "flamingo", "visuals.jl"))
T = Float64
vis = Visualizer()
open(vis)
s = get_simulation("flamingo", "flat_2D_lc", "flat")
model = s.model
env = s.env
const ContactImplicitMPC = Main
ref_traj_ = deepcopy(ContactImplicitMPC.get_trajectory(s.model, s.env,
joinpath(module_dir(), "src/dynamics/flamingo/gaits/gait_forward_36_4.jld2"),
load_type = :split_traj_alt))
ref_traj = deepcopy(ref_traj_)
H = ref_traj.H
h = ref_traj.h
N_sample = 5
H_mpc = 15
h_sim = h / N_sample
H_sim = 1000 #35000
p = pratt_policy(model, h_sim)
# q1_ref = copy(ref_traj.q[2])
# q0_ref = copy(ref_traj.q[1])
q1_ref = [0.0, 0.849, -0.00, 0.1, 0.295, -0.3, 0.1, π/2, π/2]
q0_ref = [0.0, 0.849, -0.00, 0.1, 0.295, -0.3, 0.1, π/2, π/2]
q1_sim = SVector{model.dim.q}(q1_ref)
q0_sim = SVector{model.dim.q}(copy(q1_sim - (q1_ref - q0_ref) / N_sample))
@assert norm((q1_sim - q0_sim) / h_sim - (q1_ref - q0_ref) / h) < 1.0e-8
sim = simulator(s, q0_sim, q1_sim, h_sim, H_sim,
p = p,
ip_opts = InteriorPointOptions(
undercut = Inf,
γ_reg = 0.0,
# verbose = true,
r_tol = 1.0e-8,
κ_tol = 1.0e-8),
sim_opts = SimulatorOptions(warmstart = true),
)
@time status = simulate!(sim, verbose = true)
l = 9
lu = 1
plt = plot(layout=(3,1), legend=false)
plot!(plt[1,1], hcat(Vector.(vcat([fill(ref_traj.q[i], N_sample) for i=1:H]...))...)',
color=:red, linewidth=3.0)
plot!(plt[1,1], hcat(Vector.([q[l:l] for q in sim.traj.q])...)', color=:blue, linewidth=1.0)
plot!(plt[2,1], hcat(Vector.(vcat([fill(ref_traj.u[i][lu:lu], N_sample) for i=1:H]...))...)',
color=:red, linewidth=3.0)
plot!(plt[3,1], hcat(Vector.(vcat([fill(ref_traj.γ[i][1:nc], N_sample) for i=1:H]...))...)',
color=:red, linewidth=3.0)
plot!(plt[2,1], hcat(Vector.([u[lu:lu] for u in sim.traj.u]*N_sample)...)', color=:blue, linewidth=1.0)
# plot!(plt[3,1], hcat(Vector.([γ[1:nc] for γ in sim.traj.γ]*N_sample)...)', color=:blue, linewidth=1.0)
# plot!(plt[3,1], hcat(Vector.([b[1:nb] for b in sim.traj.b]*N_sample)...)', color=:red, linewidth=1.0)
plot_surface!(vis, env, xlims=[-0.5, 1.5], ylims = [-0.5, 0.5])
plot_lines!(vis, model, sim.traj.q)
# anim = visualize_robot!(vis, model, sim.traj, sample=10)
anim = visualize_meshrobot!(vis, model, sim.traj, sample=2)
anim = visualize_force!(vis, model, env, sim.traj, anim=anim, h=h_sim, sample=10)
# filename = "flamingo_100_steps"
# MeshCat.convert_frames_to_video(
# "/home/simon/Downloads/$filename.tar",
# "/home/simon/Documents/$filename.mp4", overwrite=true)
#
# convert_video_to_gif(
# "/home/simon/Documents/$filename.mp4",
# "/home/simon/Documents/$filename.gif", overwrite=true)
#
#
# settransform!(vis["/Cameras/default"],
# compose(Translation(0.0, 0.5, -1.0),LinearMap(RotZ(-pi / 2.0))))
#
# # Ghost
# flamingo_ghost!(vis, sim, x -> 0.0)
#
# # Animation
# flamingo_animation!(vis, sim, x -> 0.0)
qref = [0.0, 0.849, -0.00, 0.1, 0.295, -0.3, 0.1, π/2, π/2]
set_robot!(vis, model, qref)
qref = [0.0, 0.849, -0.00, -0.3, 0.1, 0.1, 0.295, π/2, π/2]
set_robot!(vis, model, qref)
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] | 1.985852 | 1,555 |
# License for this file: MIT (expat)
# Copyright 2017-2018, DLR Institute of System Dynamics and Control
#
# This file is part of module
# ModiaMath.Frames (ModiaMath/Frames/_module.jl)
#
"""
const ModiaMath.Quaternion = SVector{4,Float64}
Describes the rotation from a frame 1 into a frame 2 with a quaternion vector.
If `e` is the (normalized) axis of rotation to rotate frame 1 into frame 2
(either resolved in frame 1 or frame 2) and `angle`
is the rotation angle for this rotation then the quaternion vector
`q::ModiaMath.Quaternions` is defined as:
```julia
q = [e*sin(angle/2),
cos(angle/2]
```
"""
const Quaternion = SVector{4,Float64}
"""
const ModiaMath.NullQuaternion = Quaternion(0,0,0,1)
Constant Quaternion vector of a null rotation (= no rotation from frame 1 to frame 2)
"""
NullQuaternion = Quaternion(0.0, 0.0, 0.0, 1.0)
"""
ModiaMath.assertQuaternion(q::AbstractVector)
Assert that vector `q` has the properties of a `Quaternion` vector
(has 4 elements, `norm(q) = 1`)
"""
function assertQuaternion(q::AbstractVector)
@assert(length(q) == 4)
@assert(abs(norm(q) - 1.0) <= 1e-10)
end
"""
R = ModiaMath.from_q(q::ModiaMath.Quaternion)
Return RotationMatrix `R` from Quaternion `q`.
"""
from_q(q::Quaternion) = RotationMatrix(2.0 * (q[1] * q[1] + q[4] * q[4]) - 1.0,
2.0 * (q[2] * q[1] - q[3] * q[4]),
2.0 * (q[3] * q[1] + q[2] * q[4]),
2.0 * (q[1] * q[2] + q[3] * q[4]),
2.0 * (q[2] * q[2] + q[4] * q[4]) - 1.0,
2.0 * (q[3] * q[2] - q[1] * q[4]),
2.0 * (q[1] * q[3] - q[2] * q[4]),
2.0 * (q[2] * q[3] + q[1] * q[4]),
2.0 * (q[3] * q[3] + q[4] * q[4]) - 1.0)
const p4limit = 0.1
const c4limit = 4.0 * p4limit * p4limit
"""
q = ModiaMath.from_R(R::ModiaMath.RotationMatrix;
q_guess = NullQuaternion)
Return `Quaternion q` from `RotationMatrix R`.
From the two possible solutions `q` the one is returned that is closer
to `q_guess` (note, `q` and `-q` define the same rotation).
"""
function from_R(R::RotationMatrix;
q_guess::Quaternion=NullQuaternion)::Quaternion
#=
Below it is guaranteed that c1>=0, c2>=0, c3>=0, c4>=0 and
that not all of them can be zero at the same time
(e.g., if 3 of them are zero, the 4th variable is 1).
Since the sqrt(..) has to be performed on one of these variables,
it is applied on a variable which is far enough from zero.
This guarantees that the sqrt(..) is never taken near zero
and therefore the derivative of sqrt(..) can never be infinity.
There is an ambiguity for quaternions, since q and -q
lead to the same RotationMatrix. This ambiguity
is resolved here by selecting the q that is closer to
the input argument q_guess.
=#
c1::Float64 = 1.0 + R[1,1] - R[2,2] - R[3,3]
c2::Float64 = 1.0 + R[2,2] - R[1,1] - R[3,3]
c3::Float64 = 1.0 + R[3,3] - R[1,1] - R[2,2]
c4::Float64 = 1.0 + R[1,1] + R[2,2] + R[3,3]
paux::Float64 = 0.0
paux4::Float64 = 0.0
if c4 > c4limit || (c4 > c1 && c4 > c2 && c4 > c3)
paux = sqrt(c4) / 2
paux4 = 4 * paux
q = Quaternion((R[2,3] - R[3,2]) / paux4,
(R[3,1] - R[1,3]) / paux4,
(R[1,2] - R[2,1]) / paux4,
paux)
elseif c1 > c2 && c1 > c3 && c1 > c4
paux = sqrt(c1) / 2
paux4 = 4 * paux
q = Quaternion(paux,
(R[1,2] + R[2,1]) / paux4,
(R[1,3] + R[3,1]) / paux4,
(R[2,3] - R[3,2]) / paux4)
elseif c2 > c1 && c2 > c3 && c2 > c4
paux = sqrt(c2) / 2
paux4 = 4 * paux
q = Quaternion((R[1,2] + R[2,1]) / paux4,
paux,
(R[2,3] + R[3,2]) / paux4,
(R[3,1] - R[1,3]) / paux4)
else
paux = sqrt(c3) / 2
paux4 = 4 * paux
q = Quaternion((R[1,3] + R[3,1]) / paux4,
(R[2,3] + R[3,2]) / paux4,
paux,
(R[1,2] - R[2,1]) / paux4)
end
return dot(q, q_guess) >= 0 ? q : -q
end
from_R(R::AbstractMatrix; q_guess::AbstractVector=NullQuaternion)::Quaternion =
from_R(RotationMatrix(R); q_guess=Quaternion(q_guess))
"""
q = ModiaMath.qrot1(angle; q_guess = NullQuaternion)
Return Quaternion `q` that rotates with angle `angle` along the x-axis of frame 1.
From the two possible solutions `q` the one is returned that is closer
to `q_guess` (note, `q` and `-q` define the same rotation).
"""
@inline function qrot1(angle::Number; q_guess::Quaternion=NullQuaternion)::Quaternion
q = Quaternion(sin(angle / 2), 0.0, 0.0, cos(angle / 2))
return dot(q, q_guess) >= 0 ? q : -q
end
"""
q = ModiaMath.qrot2(angle; q_guess = NullQuaternion)
Return Quaternion `q` that rotates with angle `angle` along the y-axis of frame 1.
From the two possible solutions `q` the one is returned that is closer
to `q_guess` (note, `q` and `-q` define the same rotation).
"""
@inline function qrot2(angle::Number; q_guess::Quaternion=NullQuaternion)::Quaternion
q = Quaternion(0.0, sin(angle / 2), 0.0, cos(angle / 2))
return dot(q, q_guess) >= 0 ? q : -q
end
"""
q = ModiaMath.qrot3(angle; q_guess = NullQuaternion)
Return Quaternion `q` that rotates with angle `angle` along the z-axis of frame 1.
From the two possible solutions `q` the one is returned that is closer
to `q_guess` (note, `q` and `-q` define the same rotation).
"""
@inline function qrot3(angle::Number; q_guess::Quaternion=NullQuaternion)::Quaternion
q = Quaternion(0.0, 0.0, sin(angle / 2), cos(angle / 2))
return dot(q, q_guess) >= 0 ? q : -q
end
absoluteRotation(q1::Quaternion, q_rel::Quaternion)::Quaternion =
(@SMatrix [ q_rel[4] q_rel[3] -q_rel[2] q_rel[1];
-q_rel[3] q_rel[4] q_rel[1] q_rel[2];
q_rel[2] -q_rel[1] q_rel[4] q_rel[3];
-q_rel[1] -q_rel[2] -q_rel[3] q_rel[4]]) * q1
"""
q = ModiaMath.qrot123(angle1, angle2, angle3)
Return Quaternion `q` by rotating with angle1 along the x-axis of frame 1,
then with angle2 along the y-axis of this frame and then with angle3 along
the z-axis of this frame.
From the two possible solutions `q` the one is returned that is closer
to `q_guess` (note, `q` and `-q` define the same rotation).
"""
qrot123(angle1::Number, angle2::Number, angle3::Number)::Quaternion = absoluteRotation(absoluteRotation(qrot1(angle1), qrot2(angle2)), qrot3(angle3))
"""
q = ModiaMath.qrot_e(e, angle; q_guess = NullQuaternion)
Return Quaternion `q` that rotates with angle `angle` along unit axis `e`.
This function assumes that `norm(e) == 1`.
From the two possible solutions `q` the one is returned that is closer
to `q_guess` (note, `q` and `-q` define the same rotation).
"""
@inline function qrot_e(e::Vector3D, angle::Number; q_guess::Quaternion=NullQuaternion)::Quaternion
sa = sin(angle / 2)
q = Quaternion(e[1] * sa, e[2] * sa, e[3] * sa, cos(angle / 2))
return dot(q, q_guess) >= 0 ? q : -q
end
qrot_e(e::AbstractVector, angle::Number)::Quaternion = qrot_e(Vector3D(e), convert(Float64, angle))
"""
q = ModiaMath.qrot_nxy(nx, ny)
It is assumed that the two input vectors `nx` and `ny` are resolved in frame 1 and
are directed along the x and y axis of frame 2.
The function returns the Quaternion `q` to rotate from frame 1 to frame 2.
The function is robust in the sense that it returns always a Quaternion `q`,
even if `ny` is not orthogonal to `nx` or if one or both vectors have zero length.
This is performed in the following way:
If `nx` and `ny` are not orthogonal to each other, first a unit vector `ey` is
determined that is orthogonal to `nx` and is lying in the plane spanned by
`nx` and `ny`. If `nx` and `ny` are parallel or nearly parallel to each other
or `ny` is a vector with zero or nearly zero length, a vector `ey` is selected
arbitrarily such that `ex` and `ey` are orthogonal to each other.
If both `nx` and `ny` are vectors with zero or nearly zero length, an
arbitrary Quaternion `q` is returned.
# Example
```julia
using Unitful
import ModiaMath
q1 = ModiaMath.qrot1(90u"°")
q2 = ModiaMath.qrot_nxy([1 , 0, 0], [0 , 0, 1 ])
q3 = ModiaMath.qrot_nxy([0.9, 0, 0], [1.1, 0, 1.1])
isapprox(q1,q2) # returns true
isapprox(q1,q3) # returns true
```
"""
qrot_nxy(nx, ny)::Quaternion = from_R(rot_nxy(nx, ny))
resolve1(q::Quaternion, v2::Vector3D)::Vector3D =
2 * ((q[4] * q[4] - 0.5) * v2 + dot(q[1:3], v2) * q[1:3] + q[4] * cross(q[1:3], v2))
resolve2(q::Quaternion, v1::Vector3D)::Vector3D =
2 * ((q[4] * q[4] - 0.5) * v1 + dot(q[1:3], v1) * q[1:3] - q[4] * cross(q[1:3], v1))
relativeRotation(q1::Quaternion, q2::Quaternion)::Quaternion =
(@SMatrix [ q1[4] q1[3] -q1[2] -q1[1];
-q1[3] q1[4] q1[1] -q1[2];
q1[2] -q1[1] q1[4] -q1[3];
q1[1] q1[2] q1[3] q1[4]]) * q2
inverseRotation(q::Quaternion)::Quaternion = Quaternion(-q[1], -q[2], -q[3], q[4])
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] | 2.070307 | 4,523 |
using PETSc2
import MPI
MPI.Init()
PetscInitialize(["-ksp_monitor","-malloc","-malloc_debug","-malloc_dump"]);
#
# Create a vector and put values in it
comm = MPI.COMM_WORLD
comm_size = MPI.Comm_size(MPI.COMM_WORLD)
comm_rank = MPI.Comm_rank(MPI.COMM_WORLD)
sys_size_local = 3
println("my comm rank = ", comm_rank)
b = PetscVec(comm);
PetscVecSetType(b,"mpi");
PetscVecSetSizes(b,sys_size_local, comm_size*sys_size_local);
x = PetscVec(comm);
PetscVecSetType(x,"mpi");
PetscVecSetSizes(x,sys_size_local, comm_size*sys_size_local);
rhs = zeros(sys_size_local)
for i=1:sys_size_local
idxm = [(comm_rank)*10 + i] # index
PetscVecSetValues(b,idxm,[ convert(Float64,i)],INSERT_VALUES);
rhs[i] = i
end
PetscVecAssemblyBegin(b);
println("began vector assembly")
PetscVecAssemblyEnd(b);
println("finished vector assembly")
PetscView(b);
println("finished assembling vector")
#
# Create a matrix
A_julia = [1.0 2.0 3; 4 5 7; 7 8 9]
println("rhs = ", rhs)
println("A_julia = ", A_julia)
x_julia = A_julia\rhs
println("x_julia = ", x_julia)
A = PetscMat(comm);
PetscMatSetType(A,"mpiaij");
PetscMatSetSizes(A,sys_size_local,sys_size_local,comm_size*sys_size_local,comm_size*sys_size_local);
PetscSetUp(A);
for i=1:sys_size_local
for j = 1:sys_size_local
idxm = [(comm_rank)*sys_size_local + i] # row index
idxn = [(comm_rank)*sys_size_local + j] # column index
PetscMatSetValues(A,idxm, idxn, [A_julia[i,j]],INSERT_VALUES);
end
end
println("finished setting matrix values")
PetscMatAssemblyBegin(A,MAT_FINAL_ASSEMBLY);
PetscMatAssemblyEnd(A,MAT_FINAL_ASSEMBLY);
println("finished assembling matrix")
PetscView(A);
println("finished viewing matrix")
println("setting up linear solve")
# setup linear solve
ksp = KSP(comm)
KSPSetOperators(ksp, A, A)
KSPSetFromOptions(ksp)
println("performing linear solve")
KSPSolve(ksp, b, x)
println("finished performing linear solve")
PetscView(x)
# copy solution back to Julia
x_copy = zeros(sys_size_local)
idx = collect(0:2)
PetscVecGetValues(x, sys_size_local, idx, x_copy)
println("x_copy = ", x_copy)
err_norm = norm(x_copy - x_julia)
println("err norm = ", err_norm)
PetscDestroy(b)
PetscDestroy(A)
PetscDestroy(ksp)
# remove references to all Petsc variables
# perform solution
#A_julia\rhs
#println("x = ", x)
#PetscFinalize()
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] | 2.376025 | 976 |
@testset "1579.remove-max-number-of-edges-to-keep-graph-fully-traversable.jl" begin
@test max_num_edges_to_remove(
4, [[3, 1, 2], [3, 2, 3], [1, 1, 3], [1, 2, 4], [1, 1, 2], [2, 3, 4]]
) == 2
@test max_num_edges_to_remove(4, [[3, 2, 3], [1, 1, 2], [2, 3, 4]]) == -1
@test max_num_edges_to_remove(4, [[3, 1, 2], [3, 2, 3], [1, 1, 4], [2, 1, 4]]) == 0
@test max_num_edges_to_remove(4, [[3, 2, 3], [1, 1, 2], [1, 3, 4]]) == -1
end
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] | 1.824 | 250 |
export assign, assignlattice
function assign(n,sigma)
magicnumber = 8
K = Int64(ceil(sqrt(n*sigma/magicnumber)))
println("We use $K x $K cells.")
return assign(n,sigma,K,K)
end
function assignlattice(n,sigma)
magicnumber = 8
K = Int64(ceil(sqrt(n*sigma/magicnumber)))
println("We use $K x $K cells.")
return assignlattice(n,sigma,K,K)
end
function assignlattice(n,sigma,nx,ny)
x0,x1 = 0.0,1.0
y0,y1 = 0.0,1.0
m = Int64(floor(sqrt(n)))
edgesx = range(x0,stop=x1,length=m+1)
edgesy = range(y0,stop=y1,length=m+1)
x = zeros(m*m)
y = zeros(m*m)
r = ones(m*m)
count = 1
for i=1:m
for j=1:m
x[count] = edgesx[i]
y[count] = edgesy[j]
r[count] = 2*sigma/(m*m)
count += 1
end
end
delta1 = edgesx[2]-edgesx[1]
radii = r[1]
delta2 = delta1-2*radii
println("Lattice distance: $delta1")
println("Boundary distance $delta2")
println("Radii: $radii")
return assign(x0,x1,nx,y0,y1,ny,x,y,r)
end
function assign(n,sigma,nx,ny)
x0,x1 = 0.0,1.0
y0,y1 = 0.0,1.0
x = x0.+(x1-x0)*rand(n)
y = y0.+(y1-y0)*rand(n)
r = ones(n)*(sigma/n)/2;
return assign(x0,x1,nx,y0,y1,ny,x,y,r)
end
function assign(x0,x1,nx,y0,y1,ny,x,y,r)
T = typeof(x[1])
collectionx = [zeros(T,0) for i=1:nx,j=1:ny]
collectiony = [zeros(T,0) for i=1:nx,j=1:ny]
collectionr = [zeros(T,0) for i=1:nx,j=1:ny]
lx = x1-x0
ly = y1-y0
edgesx = range(x0,stop=x1,length=nx+1)
edgesy = range(y0,stop=y1,length=ny+1)
@assert length(x) == length(y) == length(r)
no = length(x)
for n=1:no
i = findindex(x[n],edgesx)
j = findindex(y[n],edgesy)
# Obstacle is in this cell but we have to check the neighbors
for nexti in [-1,0,1]
for nextj in [-1,0,1]
shiftedx = x[n]+nexti*r[n]
shiftedy = y[n]+nextj*r[n]
shiftedxperiodic = x0+mod(shiftedx-x0,lx)
shiftedyperiodic = y0+mod(shiftedy-y0,ly)
shiftedi = mmod(i+nexti,nx)
shiftedj = mmod(j+nextj,ny)
if (edgesx[shiftedi]<=shiftedxperiodic<edgesx[shiftedi+1]) & (edgesy[shiftedj]<=shiftedyperiodic<edgesy[shiftedj+1])
deltax = ifelse(shiftedx<x0,lx,ifelse(shiftedx>x1,-lx,0))
deltay = ifelse(shiftedy<y0,ly,ifelse(shiftedy>y1,-ly,0))
append!(collectionx[shiftedi,shiftedj],x[n]+deltax)
append!(collectiony[shiftedi,shiftedj],y[n]+deltay)
append!(collectionr[shiftedi,shiftedj],r[n])
end
end
end
end
Collection = Array{Obstacles,2}(undef,nx,ny)
for i=1:nx
for j=1:ny
Collection[i,j] = Obstacles(a2sa(collectionx[i,j]),
a2sa(collectiony[i,j]),
a2sa(collectionr[i,j]),
length(collectionx[i,j]))
end
end
Meta = Metadata(nx,ny,edgesx,edgesy)
return Collection, Meta
end
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] | 1.672907 | 1,923 |
const NGROUP = 6
const MAXOVERLAY = 500
const MAXLINE = 100
const MAXLINEPNT = 1000
const MAXPLANEGRID = 200
@enum mjtCatBit begin # bitflags for vGeom category
CAT_STATIC = 1 # model elements in body 0
CAT_DYNAMIC = 2 # model elements in all other bodies
CAT_DECOR = 4 # decorative geoms
CAT_ALL = 7 # select all categories
end
@enum mjtMouse begin # mouse interaction mode
MOUSE_NONE = 0 # no action
MOUSE_ROTATE_V # rotate, vertical plane
MOUSE_ROTATE_H # rotate, horizontal plane
MOUSE_MOVE_V # move, vertical plane
MOUSE_MOVE_H # move, horizontal plane
MOUSE_ZOOM # zoom
MOUSE_SELECT # selection
end
@enum mjtPertBit begin # mouse perturbations
PERT_TRANSLATE = 1 # translation
PERT_ROTATE = 2 # rotation
end
@enum mjtCamera begin # abstract camera type
CAMERA_FREE = 0 # free camera
CAMERA_TRACKING # tracking camera; uses trackbodyid
CAMERA_FIXED # fixed camera; uses fixedcamid
CAMERA_USER # user is responsible for setting OpenGL camera
end
@enum mjtLabel begin # object labeling
LABEL_NONE = 0 # nothing
LABEL_BODY # body labels
LABEL_JOINT # joint labels
LABEL_GEOM # geom labels
LABEL_SITE # site labels
LABEL_CAMERA # camera labels
LABEL_LIGHT # light labels
LABEL_TENDON # tendon labels
LABEL_ACTUATOR # actuator labels
LABEL_CONSTRAINT # constraint labels
LABEL_SKIN # skin labels
LABEL_SELECTION # selected object
LABEL_SELPNT # coordinates of selection point
LABEL_CONTACTFORCE # magnitude of contact force
NLABEL # number of label types
end
@enum mjtFrame begin # frame visualization
FRAME_NONE = 0 # no frames
FRAME_BODY # body frames
FRAME_GEOM # geom frames
FRAME_SITE # site frames
FRAME_CAMERA # camera frames
FRAME_LIGHT # light frames
FRAME_WORLD # world frame
NFRAME # number of visualization frames
end
@enum mjtVisFlag begin # flags enabling model element visualization
VIS_CONVEXHULL = 0 # mesh convex hull
VIS_TEXTURE # textures
VIS_JOINT # joints
VIS_ACTUATOR # actuators
VIS_CAMERA # cameras
VIS_LIGHT # lights
VIS_TENDON # tendons
VIS_RANGEFINDER # rangefinder sensors
VIS_CONSTRAINT # point constraints
VIS_INERTIA # equivalent inertia boxes
VIS_SCLINERTIA # scale equivalent inertia boxes with mass
VIS_PERTFORCE # perturbation force
VIS_PERTOBJ # perturbation object
VIS_CONTACTPOINT # contact points
VIS_CONTACTFORCE # contact force
VIS_CONTACTSPLIT # split contact force into normal and tanget
VIS_TRANSPARENT # make dynamic geoms more transparent
VIS_AUTOCONNECT # auto connect joints and body coms
VIS_COM # center of mass
VIS_SELECT # selection point
VIS_STATIC # static bodies
VIS_SKIN # skin
NVISFLAG # number of visualization flags
end
@enum mjtRndFlag begin # flags enabling rendering effects
RND_SHADOW = 0 # shadows
RND_WIREFRAME # wireframe
RND_REFLECTION # reflections
RND_ADDITIVE # additive transparency
RND_SKYBOX # skybox
RND_FOG # fog
RND_HAZE # haze
RND_SEGMENT # segmentation with random color
RND_IDCOLOR # segmentation with segid color
NRNDFLAG # number of rendering flags
end
@enum mjtStereo begin # type of stereo rendering
STEREO_NONE = 0 # no stereo; use left eye only
STEREO_QUADBUFFERED # quad buffered; revert to side-by-side if no hardware support
STEREO_SIDEBYSIDE # side-by-side
end
struct mjvPerturb
select::Cint
skinselect::Cint
active::Cint
refpos::SVector{3, mjtNum}
refquat::SVector{4, mjtNum}
localpos::SVector{3, mjtNum}
scale::mjtNum
end
struct mjvCamera
_type::Cint
fixedcamid::Cint
trackbodyid::Cint
lookat::SVector{3, mjtNum}
distance::mjtNum
azimuth::mjtNum
elevation::mjtNum
end
struct mjvGLCamera
pos::SVector{3, Cfloat}
forward::SVector{3, Cfloat}
up::SVector{3, Cfloat}
frustum_center::Cfloat
frustum_bottom::Cfloat
frustum_top::Cfloat
frustum_near::Cfloat
frustum_far::Cfloat
end
struct mjvGeom
_type::Cint
dataid::Cint
objtype::Cint
objid::Cint
category::Cint
texid::Cint
texuniform::Cint
texcoord::Cint
segid::Cint
texrepeat::SVector{2, Cfloat}
size::SVector{3, Cfloat}
pos::SVector{3, Cfloat}
mat::SVector{9, Cfloat}
rgba::SVector{4, Cfloat}
emission::Cfloat
specular::Cfloat
shininess::Cfloat
reflectance::Cfloat
label::SVector{100, UInt8}
camdist::Cfloat
rbound::Cfloat
transparent::mjtByte
end
struct mjvLight
pos::SVector{3, Cfloat}
dir::SVector{3, Cfloat}
attenuation::SVector{3, Cfloat}
cutoff::Cfloat
exponent::Cfloat
ambient::SVector{3, Cfloat}
diffuse::SVector{3, Cfloat}
specular::SVector{3, Cfloat}
headlight::mjtByte
directional::mjtByte
castshadow::mjtByte
end
struct mjvOption
label::Cint
frame::Cint
geomgroup::SVector{NGROUP, mjtByte}
sitegroup::SVector{NGROUP, mjtByte}
jointgroup::SVector{NGROUP, mjtByte}
tendongroup::SVector{NGROUP, mjtByte}
actuatorgroup::SVector{NGROUP, mjtByte}
flags::SVector{Int(NVISFLAG), mjtByte}
end
struct mjvScene
maxgeom::Cint
ngeom::Cint
geoms::Ptr{mjvGeom}
geomorder::Ptr{Cint}
nskin::Cint
skinfacenum::Ptr{Cint}
skinvertadr::Ptr{Cint}
skinvertnum::Ptr{Cint}
skinvert::Ptr{Cfloat}
skinnormal::Ptr{Cfloat}
nlight::Cint
lights::SVector{8, mjvLight}
camera::SVector{2, mjvGLCamera}
enabletransform::mjtByte
translate::SVector{3, Cfloat}
rotate::SVector{4, Cfloat}
scale::Cfloat
stereo::Cint
flags::SVector{5, mjtByte}
end
struct mjvFigure
flg_legend::Cint
flg_ticklabel::SVector{2, Cint}
flg_extend::Cint
flg_barplot::Cint
flg_selection::Cint
flg_symmetric::Cint
legendoff::Cint
gridsize::SVector{2, Cint}
selection::Cint
highlight::SVector{2, Cint}
gridrgb::SVector{3, Cfloat}
gridwidth::Cfloat
figurergba::SVector{4, Cfloat}
panergba::SVector{4, Cfloat}
legendrgba::SVector{4, Cfloat}
textrgb::SVector{3, Cfloat}
range::SVector{2, SVector{2, Cfloat}}
xlabel::SVector{100, UInt8}
title::SVector{100, UInt8}
xformat::SVector{20, UInt8}
yformat::SVector{20, UInt8}
minwidth::SVector{20, UInt8}
linepnt::SVector{MAXLINE, Cint}
linergb::SVector{MAXLINE, SVector{3, Cfloat}}
linewidth::SVector{MAXLINE, Cfloat}
linedata::SVector{MAXLINE, SVector{2*MAXLINEPNT, Cfloat}}
linename::SVector{MAXLINE, SVector{100, UInt8}}
xaxispixel::SVector{2, Cint}
yaxispixel::SVector{2, Cint}
xaxisdata::SVector{2, Cint}
yaxisdata::SVector{2, Cint}
end
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] | 2.099354 | 3,563 |
# RELOG: Reverse Logistics Optimization
# Copyright (C) 2020, UChicago Argonne, LLC. All rights reserved.
# Released under the modified BSD license. See COPYING.md for more details.
using Geodesy
abstract type Node end
mutable struct Arc
source::Node
dest::Node
values::Dict{String,Float64}
end
mutable struct ProcessNode <: Node
index::Int
location::Plant
incoming_arcs::Vector{Arc}
outgoing_arcs::Vector{Arc}
end
mutable struct ShippingNode <: Node
index::Int
location::Union{Plant,CollectionCenter}
product::Product
incoming_arcs::Vector{Arc}
outgoing_arcs::Vector{Arc}
end
mutable struct Graph
process_nodes::Vector{ProcessNode}
plant_shipping_nodes::Vector{ShippingNode}
collection_shipping_nodes::Vector{ShippingNode}
arcs::Vector{Arc}
name_to_process_node_map::Dict{Tuple{AbstractString,AbstractString},ProcessNode}
end
function Base.show(io::IO, instance::Graph)
print(io, "RELOG graph with ")
print(io, "$(length(instance.process_nodes)) process nodes, ")
print(io, "$(length(instance.plant_shipping_nodes)) plant shipping nodes, ")
print(io, "$(length(instance.collection_shipping_nodes)) collection shipping nodes, ")
print(io, "$(length(instance.arcs)) arcs")
end
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] | 2.875566 | 442 |
#### Entrainment-Detrainment kernels
"""
entr_detr(
m::AtmosModel{FT},
entr::EntrainmentDetrainment,
state::Vars,
aux::Vars,
t::Real,
ts,
env,
i,
) where {FT}
Returns the dynamic entrainment and detrainment rates,
as well as the turbulent entrainment rate, following
Cohen et al. (JAMES, 2020), given:
- `m`, an `AtmosModel`
- `entr`, an `EntrainmentDetrainment` model
- `state`, state variables
- `aux`, auxiliary variables
- `t`, the time
- `ts`, NamedTuple of thermodynamic states
- `env`, NamedTuple of environment variables
- `i`, index of the updraft
"""
function entr_detr(
m::AtmosModel{FT},
entr::EntrainmentDetrainment,
state::Vars,
aux::Vars,
t::Real,
ts,
env,
i,
) where {FT}
# Alias convention:
gm = state
en = state.turbconv.environment
up = state.turbconv.updraft
en_aux = aux.turbconv.environment
up_aux = aux.turbconv.updraft
N_up = n_updrafts(m.turbconv)
ρ_inv = 1 / gm.ρ
a_up_i = up[i].ρa * ρ_inv
lim_ϵ = entr.lim_ϵ
lim_amp = entr.lim_amp
w_min = entr.w_min
# precompute vars
w_up_i = up[i].ρaw / up[i].ρa
sqrt_tke = sqrt(max(en.ρatke, 0) * ρ_inv / env.a)
# ensure far from zero
Δw = filter_w(w_up_i - env.w, w_min)
w_up_i = filter_w(w_up_i, w_min)
Δb = up_aux[i].buoyancy - en_aux.buoyancy
D_ε, D_δ, M_δ, M_ε =
nondimensional_exchange_functions(m, entr, state, aux, t, ts, env, i)
# I am commenting this out for now, to make sure there is no slowdown here
Λ_w = abs(Δb / Δw)
Λ_tke = entr.c_λ * abs(Δb / (max(en.ρatke * ρ_inv, 0) + w_min))
λ = lamb_smooth_minimum(
SVector(Λ_w, Λ_tke),
m.turbconv.mix_len.smin_ub,
m.turbconv.mix_len.smin_rm,
)
# compute limiters
εt_lim = εt_limiter(w_up_i, lim_ϵ, lim_amp)
ε_lim = ε_limiter(a_up_i, lim_ϵ, lim_amp)
δ_lim = δ_limiter(a_up_i, lim_ϵ, lim_amp)
# compute entrainment/detrainment components
ε_trb =
2 * a_up_i * entr.c_t * sqrt_tke /
max((w_up_i * a_up_i * m.turbconv.pressure.H_up), entr.εt_min)
ε_dyn = λ / w_up_i * (D_ε + M_ε) * ε_lim
δ_dyn = λ / w_up_i * (D_δ + M_δ) * δ_lim
ε_dyn = min(max(ε_dyn, FT(0)), FT(1))
δ_dyn = min(max(δ_dyn, FT(0)), FT(1))
ε_trb = min(max(ε_trb, FT(0)), FT(1))
return ε_dyn, δ_dyn, ε_trb
end;
"""
ε_limiter(a_up::FT, ϵ::FT) where {FT}
Returns the asymptotic value of entrainment
needed to ensure boundedness of the updraft
area fraction between 0 and 1, given
- `a_up`, the updraft area fraction
- `ϵ`, a minimum threshold value
"""
ε_limiter(a_up::FT, ϵ::FT, lim_amp::FT) where {FT} =
FT(1) + lim_amp * exp(-a_up^2 / (2 * ϵ)) - exp(-(FT(1) - a_up)^2 / (2 * ϵ))
"""
δ_limiter(a_up::FT, ϵ::FT) where {FT}
Returns the asymptotic value of detrainment
needed to ensure boundedness of the updraft
area fraction between 0 and 1, given
- `a_up`, the updraft area fraction
- `ϵ`, a minimum threshold value
"""
δ_limiter(a_up::FT, ϵ::FT, lim_amp::FT) where {FT} =
FT(1) - exp(-a_up^2 / (2 * ϵ)) + lim_amp * exp(-(FT(1) - a_up)^2 / (2 * ϵ))
"""
εt_limiter(w_up_i::FT, ϵ::FT) where {FT}
Returns the asymptotic value of turbulent
entrainment needed to ensure positiveness
of the updraft vertical velocity, given
- `w_up_i`, the updraft vertical velocity
- `ϵ`, a minimum threshold value
"""
εt_limiter(w_up_i::FT, ϵ::FT, lim_amp::FT) where {FT} =
FT(1) + lim_amp * exp(-w_up_i^2 / (2 * ϵ))
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] | 2.056593 | 1,714 |
# Get status of the registration
function status(r::HTTP.Request)
r.method == "GET" || return json(405; error="Method not allowed")
# Only allow authenticated users
state = getcookie(r, "state")
if isempty(state) || !haskey(USERS, state)
return json(400; error="Missing or invalid state cookie")
end
id = getquery(r, "id")
haskey(REGISTRATIONS, id) || return json(404; state="unknown")
regstate = REGISTRATIONS[id]
regstate.state === :pending || delete!(REGISTRATIONS, id)
return json(; state=regstate.state, message=regstate.msg)
end
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] | 2.8125 | 208 |
@testset "Capillary pressure" begin
# Struct
sys = Swalbe.SysConst(Lx=5, Ly=5, n=3, m=2, γ=1.0, hmin=0.1, hcrit=0.1)
state = Swalbe.Sys(sys, "CPU")
state.height .= reshape(collect(1.0:25),5,5)
# With arguments struct
f = reshape(collect(1.0:25),5,5)
f_float = reshape(collect(1.0f0:25.0f0),5,5)
res = zeros(5,5)
@testset "No contact angle" begin
Swalbe.filmpressure!(res, f, 1.0, 0.0, 3, 2, 0.1, 0.1)
Swalbe.filmpressure!(state, sys, 0.0)
sol = [-30.0 -5.0 -5.0 -5.0 20;
-25.0 0.0 0.0 0.0 25.0;
-25.0 0.0 0.0 0.0 25.0;
-25.0 0.0 0.0 0.0 25.0;
-20.0 5.0 5.0 5.0 30.0;
]
@test all(isapprox.(res, sol; atol=1e-10))
@test all(isapprox.(state.pressure, sol; atol=1e-10))
Swalbe.filmpressure!(res, f, 0.0)
@test all(isapprox.(res, 0.01 .* sol; atol=1e-10))
end
@testset "No height gradient" begin
nograd = ones(5,5)
state.height .= 1.0
sys2 = Swalbe.SysConst(Lx=5, Ly=5, n=3, m=2, γ=1.0, hmin=0.1, hcrit=0.0, θ=1/2)
Swalbe.filmpressure!(res, nograd, 1.0, 1/2, 3, 2, 0.1, 0.0)
Swalbe.filmpressure!(state, sys2, 1/2)
@test all(isapprox.(res, -2(0.1^2-0.1); atol=1e-10))
@test all(isapprox.(state.pressure, -2(0.1^2-0.1); atol=1e-10))
Swalbe.filmpressure!(state, sys2)
@test all(isapprox.(state.pressure, -2(0.1^2-0.1); atol=1e-10))
end
@testset "Gradient and contact angle" begin
state.height .= reshape(collect(1.0:25),5,5)
sys2 = Swalbe.SysConst(Lx=5, Ly=5, n=3, m=2, γ=1.0, hmin=0.1, hcrit=0.0)
Swalbe.filmpressure!(state, sys2, 1/2)
Swalbe.filmpressure!(res, f, 1.0, 1/2, 3, 2, 0.1, 0.0)
sol = [30.0 5.0 5.0 5.0 -20;
25.0 0.0 0.0 0.0 -25.0;
25.0 0.0 0.0 0.0 -25.0;
25.0 0.0 0.0 0.0 -25.0;
20.0 -5.0 -5.0 -5.0 -30.0]
@test all(isapprox.(res, -1 .* (sol .+ 20 .* ((0.1 ./ f).^3 .- (0.1 ./ f).^2)); atol=1e-10))
@test all(isapprox.(state.pressure, -1 .* (sol .+ 20 .* ((0.1 ./ f).^3 .- (0.1 ./ f).^2)); atol=1e-10))
end
@testset "Gradient and contact angle Float32" begin
Swalbe.filmpressure!(res, f_float, 1.0f0, 0.5f0, 3, 2, 0.1f0, 0.0f0)
sol = [30.0f0 5.0f0 5.0f0 5.0f0 -20f0;
25.0f0 0.0f0 0.0f0 0.0f0 -25.0f0;
25.0f0 0.0f0 0.0f0 0.0f0 -25.0f0;
25.0f0 0.0f0 0.0f0 0.0f0 -25.0f0;
20.0f0 -5.0f0 -5.0f0 -5.0f0 -30.0f0]
@test all(isapprox.(res, -1 .* (sol .+ 20 .* ((0.1f0 ./ f_float).^3 .- (0.1f0 ./ f_float).^2)); atol=1e-6))
end
dgrad = zeros(5,5,8)
@testset "No contact angle circshift!" begin
state.height .= reshape(collect(1.0:25),5,5)
Swalbe.filmpressure!(state, sys, 0.0)
Swalbe.filmpressure!(res, f, dgrad, 1.0, 0.0, 3, 2, 0.1, 0.1)
sol = [-30.0 -5.0 -5.0 -5.0 20;
-25.0 0.0 0.0 0.0 25.0;
-25.0 0.0 0.0 0.0 25.0;
-25.0 0.0 0.0 0.0 25.0;
-20.0 5.0 5.0 5.0 30.0;
]
@test all(isapprox.(res, sol; atol=1e-10))
@test all(isapprox.(state.pressure, sol; atol=1e-10))
end
end
@testset "Capillary pressure 1D" begin
# Struct
sys = Swalbe.SysConst_1D(L=30, n=3, m=2, γ=1.0, hmin=0.1, hcrit=0.1)
state = Swalbe.Sys(sys)
state.height .= collect(1.0:30)
sys2 = Swalbe.SysConst_1D(L=30, n=3, m=2, γ=1.0, hmin=0.1, hcrit=0.0, θ=1/2)
state2 = Swalbe.Sys(sys2)
# Without the struct
f = collect(1.0:30)
f_float = collect(1.0f0:30.0f0)
res = zeros(30)
dummy = zeros(30,3)
@testset "No contact angle" begin
Swalbe.filmpressure!(res, f, dummy, 1.0, 0.0, 3, 2, 0.1, 0.1)
Swalbe.filmpressure!(state, sys, 0.0)
# println("My result: $res")
sol = zeros(30)
sol[1] = 30
sol[end] = -30
@test all(res .== -sol)
@test all(state.pressure .== -sol)
end
@testset "No height gradient" begin
nograd = ones(30)
state2.height .= 1.0
Swalbe.filmpressure!(res, nograd, dummy, 1.0, 1/2, 3, 2, 0.1, 0.0)
Swalbe.filmpressure!(state2, sys2, 1/2)
@test all(isapprox.(res, -2(0.1^2-0.1); atol=1e-10))
@test all(isapprox.(state2.pressure, -2(0.1^2-0.1); atol=1e-10))
end
@testset "Gradient and contact angle" begin
Swalbe.filmpressure!(res, f, dummy, 1.0, 1/2, 3, 2, 0.1, 0.0)
Swalbe.filmpressure!(state, sys2, 1/2)
sol = zeros(30)
sol[1] = 30
sol[end] = -30
@test all(isapprox.(res, -1 .* (sol .+ 20 .* ((0.1 ./ f).^3 .- (0.1 ./ f).^2)); atol=1e-10))
@test all(isapprox.(state.pressure, -1 .* (sol .+ 20 .* ((0.1 ./ f).^3 .- (0.1 ./ f).^2)); atol=1e-10))
# TODO: Fix me
# state2.height .= sol
# Swalbe.filmpressure!(state2, sys2)
# @test all(isapprox.(state2.pressure, -1 .* (sol .+ 20 .* ((0.1 ./ f).^3 .- (0.1 ./ f).^2)); atol=1e-10))
end
end
@testset "Capillary pressure 1D with Gamma" begin
f = collect(1.0:30)
res = zeros(30)
rho = fill(0.1, 30)
dummy = zeros(30,2)
@testset "No Gamma no theta" begin
Swalbe.filmpressure!(res, f, dummy, rho, 1.0, 0.0, 3, 2, 0.1, 0.1)
# println("My result: $res")
sol = zeros(30)
sol[1] = 30
sol[end] = -30
@test all(res .== -sol)
end
@testset "No Gamma no laplacian" begin
nograd = ones(30)
Swalbe.filmpressure!(res, nograd, dummy, rho, 1.0, 1/2, 3, 2, 0.1, 0.0)
@test all(isapprox.(res, -2(0.1^2-0.1); atol=1e-10))
end
@testset "Gamma but rho=0 no laplacian" begin
nograd = ones(30)
Swalbe.filmpressure!(res, nograd, dummy, zeros(30), 1.0, 1/2, 3, 2, 0.1, 0.0)
@test all(isapprox.(res, -2(0.1^2-0.1); atol=1e-10))
end
@testset "Gamma, rho, gradient and contact angle" begin
rho = fill(0.1, 30)
Swalbe.filmpressure!(res, f, dummy, rho, 1.0, 1/2, 3, 2, 0.1, 0.0, Gamma=0.1)
sol = zeros(30)
sol[1] = 30
sol[end] = -30
@test all(isapprox.(res, -(1 + 0.01) .* (sol .+ 20 .* ((0.1 ./ f).^3 .- (0.1 ./ f).^2)); atol=1e-10))
end
end
@testset "Power function" begin
arg = 2
argsimple = 5.0
argsimplefp32 = 5.0f0
argvec = [2.0 3.0 4.0]
n = [3 6]
r = [8 64]
r1 = [125.0 15625.0]
r2 = [125.0f0 15625.0f0]
r3 = [8.0 27.0 64.0; 64.0 729.0 4096.0]
for i in 1:2
res0 = Swalbe.power_broad(arg, n[i])
@test res0 == r[i]
res0 = Swalbe.power_broad(argsimple, n[i])
@test res0 == r1[i]
res1 = Swalbe.power_broad(argsimplefp32, n[i])
@test res1 == r2[i]
res2 = Swalbe.power_broad.(argvec, n[i])
for j in 1:3
@test res2[j] .== r3[i,j]
end
end
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] | 1.649885 | 4,330 |
using Documenter, Konig
makedocs(;
modules=[Konig],
format=:html,
pages=[
"Introduction" => "index.md",
],
repo="https://github.com/SyxP/Konig.jl/blob/{commit}{path}#L{line}",
sitename="Konig.jl",
authors="Syx Pek",
assets=[],
)
deploydocs(
repo = "github.com/SyxP/Konig.jl.git",
)
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] | 2.024691 | 162 |
let part1 = 0, part2 = 1
# This reads the input in a transposed format — rows here are columns in
# the file.
chart = hcat(map(collect, readlines("input.txt"))...)
nrow, ncol = size(chart)
for (run, rise) in [(1, 1), (3, 1), (5, 1), (7, 1), (1, 2)]
trees, row = 0, 1
for col in 1:rise:ncol
if chart[row, col] == '#'
# only count the tree for part1 if the slope is (3, 1)
part1 += (run == 3 && rise == 1)
# For part2
trees += 1
end
# # We could use the following if statement which doesn't require
# # as much mental overhead.
# row += run
#
# # This is the only place where we have to think about the indexing.
# # If we were using 0-indexing, we'd need `row ≥ nrow`.
# if row > nrow
# row -= nrow
# end
# By default, Julia arrays are 1-indexed, so we have to subtract 1
# before we `%` and add one after. I like this better because its
# short, fairly clear if you know how `%` works, and doesn't
# require the overhead of an if-statement.
row = (row + run - 1) % nrow + 1
end
part2 *= trees
end
@info "Solution" part1 part2
end
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] | 2.06577 | 669 |
module RocketBuilder
const datadir = joinpath(@__DIR__, "..", "data")
include("db.jl")
include("tank-editor.jl")
include("engine-editor.jl")
end
| [
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] | 2.740741 | 54 |
export gpu,cpu, NodalCorr
mutable struct NodalCorr <: NoiseData
n::Int64
id::Array{String,1} # id
name::Array{String,1} # name
loc::Array{<:InstrumentPosition,1} # loc
fs::Float64 # fs
src::String # src
t::Array{Array{Int64,2},1} # time
corr::AbstractArray{Float32, 2} # actual data
function NodalCorr(
n ::Int64,
id ::Array{String,1},
name ::Array{String,1},
loc ::Array{<:InstrumentPosition,1},
fs ::Float64,
src ::String,
t ::Array{Array{Int64,2},1} ,
corr ::AbstractArray{<:AbstractFloat,2},
)
return new(n, id, name, loc, fs, src, t, corr)
end
end
NodalCorr(;
n ::Int64 = 0,
id ::Array{String,1} = Array{String}(undef,0),
name ::Array{String,1} = Array{String}(undef,0),
loc ::Array{InstrumentPosition,1} = Array{InstrumentPosition}(undef,0),
fs ::Float64 = zero(Float64),
src ::String = "",
t ::Array{Array{Int64,2},1} = Array{Array{Int64,2},1}(undef,0),
corr ::AbstractArray{<:AbstractFloat,2} = Array{Float32,2}(undef, 0, 2),
) = CorrData(n, id, name, loc, fs, src, t, corr)
# functions for adapting NodalData to GPU
cpu(m::NodalData) = adapt(Array,m)
gpu(x::NodalData) = use_cuda[] ? cu(x) : x
# function for adapting NodalCorr to GPU
cpu(m::NodalCorr) = adapt(Array,m)
gpu(x::NodalCorr) = use_cuda[] ? cu(x) : x
Adapt.adapt_structure(to, N::NodalData) = NodalData(
N.n,
N.ox,
N.oy,
N.oz,
N.info,
N.id,
N.name,
N.loc,
N.fs,
N.gain,
N.resp,
N.units,
N.src,
N.misc,
N.notes,
N.t,
adapt(to,N.data),
)
Adapt.adapt_structure(to, N::NodalCorr) = NodalCorr(
N.n,
N.id,
N.name,
N.loc,
N.fs,
N.gain,
N.resp,
N.units,
N.src,
N.t,
adapt(to,N.corr),
)
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] | 1.709754 | 1,261 |
using Markdown # hide
# ## All-KNN
# By Eric S. Téllez
#
ENV["GKSwstype"] = "nul" # hide
using SimilaritySearch
# Computing the $k$ nearest neighbors of a dataset (all vs all) is a useful task to take knowledge
# of a given dataset. This is a core task for some clustering algorithms and non-linear dimensional reduction
# algorithms.
#
# Given a metric database $(X, dist)$ and a relatively small $k$ value, the goal is
# to compute $\{ knn(x) \mid x \in X \}$ taking into account that each $x_i \in X$, and therefore, $x_i$ should be
# removed from the $i$-th $knn$ result set.
#
# Solving `allknn` fast and accuratelly is the goal of this example.
const dim = 16
k = 15
db = MatrixDatabase(randn(Float32, dim, 10^5))
dist = SqL2Distance()
G = SearchGraph(; dist, db)
opt = OptimizeParameters(kind=MinRecall(0.9))
itime = @elapsed (index!(G); optimize!(G, opt))
nothing # hide
# Now we can solve all $k$nn
allknntime = @elapsed knns, dists = allknn(G, k; parallel=true)
nothing # hide
# ## Differences between `allknn(G, k)` and `searchbatch(G, X, k)`
#
# We can solve similarly with `searchbatch` but self-references should be removed later, and more important,
# `allknn` use special pivoting/boosting strategies that yields to faster searches.
searchtime = @elapsed sknns, sdists = searchbatch(G, db, k; parallel=true)
nothing # hide
# ## About the cost of construction + `allknn` instead of a brute force computation.
# `allknn` for `ExhaustiveSearch` doesn't perform any optimization but removes self references.
etime = @elapsed eknns, edists = allknn(ExhaustiveSearch(; db, dist), k; parallel=true)
nothing # hide
# ## Comparing solution times
#
# indexing, allknn, and searchbatch times
itime, allknntime, searchtime
# full cost `allknn`
itime + allknntime
# full cost `searchbatch`
itime + searchtime
# exhaustive `allknn`
etime
# ## Quality
# macro recall of `allknn`
macrorecall(eknns, knns)
# macro recall of `searchbatch`
macrorecall(eknns, sknns)
# ## Final notes:
# Exhaustive search will fetch the exact solution but it has a higher cost and this could be more
# notorious as dataset's size increases.
# Also note that even when we pass `parallel=true` it runs in a single thread due to the html generation pipeline.
#
#- Using BeutifulMakie style
# ## Dependencies
using Pkg # hide
Pkg.status(["SimilaritySearch"]) # hide
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2,
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7,
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18925,
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8,
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198
] | 3.089122 | 763 |
using VennABERS
using Test
@testset "VennABERS.jl" begin
# Write your tests here.
end
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] | 2.6 | 35 |
function ConfidenceInterval(arg0::jdouble, arg1::jdouble, arg2::jdouble)
return ConfidenceInterval((jdouble, jdouble, jdouble), arg0, arg1, arg2)
end
function get_confidence_level(obj::ConfidenceInterval)
return jcall(obj, "getConfidenceLevel", jdouble, ())
end
function get_lower_bound(obj::ConfidenceInterval)
return jcall(obj, "getLowerBound", jdouble, ())
end
function get_upper_bound(obj::ConfidenceInterval)
return jcall(obj, "getUpperBound", jdouble, ())
end
function to_string(obj::ConfidenceInterval)
return jcall(obj, "toString", JString, ())
end
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] | 2.881188 | 202 |
#!/usr/bin/env julia
# This file is a part of SIS3316.jl, licensed under the MIT License (MIT).
using ArgParse
using SIS3316
using ROOTFramework
function sis3316_to_root end
export sis3316_to_root
mktemp_custom(parent=tempdir(), format="tmpXXXXXX") = begin
b = joinpath(parent, format)
p = ccall(:mkstemp, Int32, (Ptr{UInt8},), b) # modifies b
systemerror(:mktemp, p == -1)
return (b, fdio(p, true))
end
sis3316_to_root(input_io::IO, output_tfile::TFile; evt_merge_window::AbstractFloat = 100e-9, waveform_format = :none) = begin
_time(x::Pair{Int64, SIS3316.RawChEvent}) = time(x.second)
bindings = TTreeBindings()
const info_idx = bindings[:info_idx] = Ref{Int32}(0)
const info_time = bindings[:info_time] = Ref{Float64}(0)
const raw_pp_ch = bindings[:raw_pp_ch] = Int32[]
const raw_pp_mca = bindings[:raw_pp_mca] = Int32[]
const raw_pp_trig_max = bindings[:raw_pp_trig_max] = Int32[]
const raw_pp_peak_pos = bindings[:raw_pp_peak_pos] = Int32[]
const raw_pp_peak_height = bindings[:raw_pp_peak_height] = Int32[]
const raw_pp_acc = map(i -> bindings[Symbol("raw_pp_acc_$i")] = Int32[], 1:8)
const raw_trig_ch = bindings[:raw_trig_ch] = Int32[]
const raw_trig_trel = bindings[:raw_trig_trel] = Float64[]
const raw_trig_pileup = bindings[:raw_trig_pileup] = Int32[]
const raw_trig_overflow = bindings[:raw_trig_overflow] = Int32[]
const raw_wf_ch = Int32[]
const raw_wf_smpl_n = Int32[]
const raw_wf_smpl_v = Int32[]
if waveform_format == :none
elseif waveform_format == :integers
bindings[:raw_wf_ch] = raw_wf_ch
bindings[:raw_wf_smpl_n] = raw_wf_smpl_n
bindings[:raw_wf_smpl_v] = raw_wf_smpl_v
else
error("Unknown waveform format $waveform_format.")
end
const ch_sized_vecs = Vector[raw_pp_ch, raw_pp_mca, raw_pp_trig_max,
raw_pp_peak_pos, raw_pp_peak_height,
raw_trig_ch, raw_trig_trel, raw_trig_pileup, raw_trig_overflow
]
append!(ch_sized_vecs, raw_pp_acc)
for v in ch_sized_vecs sizehint!(v, 16) end
ttree = create_ttree!(output_tfile, "data", "Data")
ttoutput = TTreeOutput(ttree, bindings)
const reader = eachchunk(input_io, SIS3316.UnsortedEvents)
local evtno = 0
for unsorted in reader
const sorted = sortevents(unsorted, merge_window = evt_merge_window)
const evtv = Vector{Pair{Int64, SIS3316.RawChEvent}}()
const timestamps = Vector{Float64}()
const energynull = SIS3316.EnergyValues(0, 0)
const mawnull = SIS3316.MAWValues(0, 0, 0)
const psanull = SIS3316.PSAValue(0, 0)
const flagsnull = SIS3316.EvtFlags(false,false,false,false)
for evt in sorted
evtno += 1
resize!(evtv, length(evt))
copy!(evtv, evt)
sort!(evtv, by = first)
resize!(timestamps, length(evtv))
map!(_time, timestamps, evtv)
const starttime = isempty(timestamps) ? zero(Float64) : minimum(timestamps)
info_idx.x = evtno
info_time.x = starttime
for v in ch_sized_vecs empty!(v) end
empty!(raw_wf_ch)
empty!(raw_wf_smpl_n)
empty!(raw_wf_smpl_v)
for (ch, chevt) in evtv
push!(raw_pp_ch, ch)
push!(raw_pp_mca, get(chevt.energy, energynull).maximum)
push!(raw_pp_trig_max, get(chevt.trig_maw, mawnull).maximum)
push!(raw_pp_peak_pos, get(chevt.peak_height, psanull).index)
push!(raw_pp_peak_height, get(chevt.peak_height, psanull).value)
for i in eachindex(raw_pp_acc)
push!(raw_pp_acc[i], get(chevt.accsums, i, 0))
end
push!(raw_trig_ch, ch)
push!(raw_trig_trel, time(chevt) - starttime)
push!(raw_trig_pileup, chevt.pileup_flag + 2 * get(chevt.flags, flagsnull).pileup + 4 * get(chevt.flags, flagsnull).repileup)
push!(raw_trig_overflow, 1 * get(chevt.flags, flagsnull).overflow + 2 * get(chevt.flags, flagsnull).underflow)
if !isempty(chevt.samples)
if waveform_format == :integers
push!(raw_wf_ch, ch)
push!(raw_wf_smpl_n, length(chevt.samples))
append!(raw_wf_smpl_v, chevt.samples)
end
end
end
push!(ttoutput)
end
end
end
sis3316_to_root(input_fname::AbstractString; evt_merge_window::AbstractFloat = 100e-9, waveform_format = :none) = begin
const fnexpr = r"(.*)\.dat(\.[^.]+)?"
const fnbase = match(fnexpr, basename(input_fname))[1]
const output_fname = "$(fnbase).root"
if !ispath(output_fname)
output_tmpname, tmpio = mktemp_custom(pwd(), "$(output_fname).tmp-XXXXXX")
close(tmpio)
open(SIS3316.DecompessIO, input_fname) do input_io
open(TFile, output_tmpname, "recreate") do output_tfile
sis3316_to_root(input_io, output_tfile, evt_merge_window = evt_merge_window, waveform_format = waveform_format)
end
end
mv(output_tmpname, output_fname, remove_destination = false)
else
info("Output file \"$(output_fname)\" already exists, skipping \"$(input_fname)\".")
end
end
sis3316_to_root() = begin
argsettings = ArgParseSettings()
@add_arg_table argsettings begin
"--merge-within", "-m"
help = "time window for merging events (no merging for negative values)"
arg_type = Float64
default = 100e-9
"--waveform-format", "-w"
help = "waveform format (\"none\" or \"integers\")"
arg_type = AbstractString
default = "integers"
"inputs"
help = "input files"
nargs = '*'
end
const parsed_args = parse_args(argsettings)
for input_fname in parsed_args["inputs"]
try
info("Converting \"$(input_fname)\"")
@time sis3316_to_root(
input_fname,
evt_merge_window = parsed_args["merge-within"],
waveform_format = Symbol(parsed_args["waveform-format"])
)
catch err
print_with_color(:red, STDERR, "ERROR: $err\n")
end
end
end
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] | 2.006897 | 3,190 |
using LightGraphs
using MatrixDepot
function symmetrize(A)
println("Symmetrizing ")
tic()
if !issymmetric(A)
println(STDERR, "the matrix is not symmetric using A+A'")
A = A + A'
end
if isa(A, Base.LinAlg.Symmetric)
A.data.nzval = abs(A.data.nzval)
else
A.nzval = abs(A.nzval)
end
#= spA = abs(sparse(A)) =#
toc()
return A
end
function loadmat(matname)
println("Reading MTX for $matname")
tic()
try
info = matrixdepot(matname, :get)
end
A = matrixdepot(matname, :read)
A = symmetrize(A)
g = Graph(A)
toc()
println(STDERR, "size(A) = $(size(A))")
return g
end
function vec2tree(v::Vector{Int})
nv = length(v)
I = Vector{Int}()
J = Vector{Int}()
for i in 1:nv
if v[i] > 0
push!(I, i)
push!(J, v[i])
end
end
#= @assert length(I) == length(J) =#
ncolumns = max(maximum(J), maximum(I))
nentries = length(J)
n = ncolumns
return sparse(I,J, fill(1, nentries), n,n)
end
names = ["Newman/football" , "Newman/cond-mat-2003", "SNAP/amazon0302", "SNAP/roadNet-CA"]
for matname in names
g = loadmat(matname)
seed = 1
println("bfs_tree original")
# woah bfs_tree allocates a lot of memory
@time tdg = LightGraphs.bfs_tree(g, seed)
println(tdg)
# preallocating the output tree reduces the number of allocations.
# much faster
println("bfs_tree!(vector)")
visitor = LightGraphs.TreeBFSVisitorVector(zeros(Int, nv(g)))
@time tvec = LightGraphs.bfs_tree!(visitor, g, seed)
println("converting to Sparse")
@time m = vec2tree(visitor.tree)
println("converting to DiGraph")
@time h = DiGraph(m)
end
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198,
437,
198
] | 2.129584 | 818 |
apply(ψ::Vector{<:Complex}, m::AbstractMatrix) = m*ψ
"""Tailored apply for XGate"""
function _apply(ψ::Vector{<:Complex}, cg::CircuitGate{1,XGate}, N::Int)
i = cg.iwire[1]
χ = reshape(ψ, 2^(i-1), 2, 2^(N-i))
χ = χ[:, [2, 1], :]
return reshape(χ, :)
end
"""Tailored apply for YGate"""
function _apply(ψ::Vector{<:Complex}, cg::CircuitGate{1,YGate}, N::Int)
i = cg.iwire[1]
ψs = reshape(ψ, 2^(i-1), 2, 2^(N-i))
χ = similar(ψs)
χ[:, 1, :] .= -im.*ψs[:, 2, :]
χ[:, 2, :] .= im.*ψs[:, 1, :]
return reshape(χ, :)
end
"""Tailored apply for ZGate"""
function _apply(ψ::Vector{<:Complex}, cg::CircuitGate{1,ZGate}, N::Int)
i = cg.iwire[1]
ψs = reshape(ψ, 2^(i-1), 2, 2^(N-i))
χ = similar(ψs)
χ[:, 1, :] .= ψs[:, 1, :]
χ[:, 2, :] .= .-ψs[:, 2, :]
return reshape(χ, :)
end
"""Tailored apply for HadamardGate"""
function _apply(ψ::Vector{<:Complex}, cg::CircuitGate{1,HadamardGate}, N::Int)
i = cg.iwire[1]
ψs = reshape(ψ, 2^(i-1), 2, 2^(N-i))
χ = similar(ψs)
χ[:, 1, :] .= (ψs[:, 1, :] .+ ψs[:, 2, :]) ./ sqrt(2)
χ[:, 2, :] .= (ψs[:, 1, :] .- ψs[:, 2, :]) ./ sqrt(2)
return reshape(χ, :)
end
"""Tailored apply for SGate"""
function _apply(ψ::Vector{<:Complex}, cg::CircuitGate{1,SGate}, N::Int)
i = cg.iwire[1]
ψs = reshape(ψ, 2^(i-1), 2, 2^(N-i))
χ = similar(ψs)
χ[:, 1, :] .= ψs[:, 1, :]
χ[:, 2, :] .= im .* ψs[:, 2, :]
return reshape(χ, :)
end
"""Tailored apply for SdagGate"""
function _apply(ψ::Vector{<:Complex}, cg::CircuitGate{1,SdagGate}, N::Int)
i = cg.iwire[1]
ψs = reshape(ψ, 2^(i-1), 2, 2^(N-i))
χ = similar(ψs)
χ[:, 1, :] .= ψs[:, 1, :]
χ[:, 2, :] .= -im .* ψs[:, 2, :]
return reshape(χ, :)
end
"""Tailored apply for TGate"""
function _apply(ψ::Vector{<:Complex}, cg::CircuitGate{1,TGate}, N::Int)
i = cg.iwire[1]
ψs = reshape(ψ, 2^(i-1), 2, 2^(N-i))
χ = similar(ψs)
χ[:, 1, :] .= ψs[:, 1, :]
χ[:, 2, :] .= Base.exp(im*π/4) .* ψs[:, 2, :]
return reshape(χ, :)
end
"""Tailored apply for TdagGate"""
function _apply(ψ::Vector{<:Complex}, cg::CircuitGate{1,TdagGate}, N::Int)
i = cg.iwire[1]
ψs = reshape(ψ, 2^(i-1), 2, 2^(N-i))
χ = similar(ψs)
χ[:, 1, :] .= ψs[:, 1, :]
χ[:, 2, :] .= Base.exp(-im*π/4) .* ψs[:, 2, :]
return reshape(χ, :)
end
"""Tailored apply for RzGate"""
function _apply(ψ::Vector{<:Complex}, cg::CircuitGate{1,RzGate}, N::Int)
i = cg.iwire[1]
θ = cg.gate.θ[]
ψs = reshape(ψ, 2^(i-1), 2, 2^(N-i))
χ = similar(ψs)
χ[:, 1, :] .= Base.exp(-im*θ/2).*ψs[:, 1, :]
χ[:, 2, :] .= Base.exp( im*θ/2).*ψs[:, 2, :]
return reshape(χ, :)
end
"""Tailored apply for PhaseShiftGate"""
function _apply(ψ::Vector{<:Complex}, cg::CircuitGate{1,PhaseShiftGate}, N::Int)
i = cg.iwire[1]
ψs = reshape(ψ, 2^(i-1), 2, 2^(N-i))
χ = similar(ψs)
χ[:, 1, :] .= ψs[:, 1, :]
χ[:, 2, :] .= Base.exp(im*cg.gate.ϕ[]) .* ψs[:, 2, :]
return reshape(χ, :)
end
"""Tailored apply for a general single qubit gate"""
function _apply(ψ::Vector{<:Complex}, cg::CircuitGate{1,T}, N::Int) where {T<:AbstractGate}
i = cg.iwire[1]
ψs = reshape(ψ, 2^(i-1), 2, 2^(N-i))
χ = similar(ψs)
U = matrix(cg.gate)
χ[:, 1, :] .= U[1, 1] .* ψs[:, 1, :] .+ U[1, 2] .* ψs[:, 2, :]
χ[:, 2, :] .= U[2, 1] .* ψs[:, 1, :] .+ U[2, 2] .* ψs[:, 2, :]
return reshape(χ, :)
end
#
"""Tailored apply for SwapGate"""
function _apply(ψ::Vector{<:Complex}, cg::CircuitGate{2,SwapGate}, N::Int)
i, j = cg.iwire
i, j = i < j ? (i, j) : (j, i) # sort them
ψs = reshape(ψ, 2^(i-1), 2, 2^(j-i-1), 2, 2^(N-j))
χ = similar(ψs)
χ[:, 1, :, 1, :] .= ψs[:, 1, :, 1, :]
χ[:, 2, :, 2, :] .= ψs[:, 2, :, 2, :]
χ[:, 1, :, 2, :] .= ψs[:, 2, :, 1, :]
χ[:, 2, :, 1, :] .= ψs[:, 1, :, 2, :]
return reshape(χ, :)
end
"""Tailored apply for a general ControlledGate"""
function _apply(ψ::Vector{<:Complex}, cg::CircuitGate{M,ControlledGate{G}}, N::Int) where {M,G}
χ = copy(ψ) # TODO: copy only what's needed
χ = reshape(χ, fill(2, N)...)
T = target_wires(cg.gate)
itarget = cg.iwire[1:T]
icontrol = cg.iwire[T+1:M]
new_iwire = Tuple(i - sum(icontrol .< i) for i in itarget)
slice_target = Tuple(i in icontrol ? 2 : Colon() for i in 1:N)
new_cg = CircuitGate(new_iwire, cg.gate.U)
χ[slice_target...] = reshape(apply(reshape(χ[slice_target...], :), new_cg),
fill(2, N-M+T)...)
return reshape(χ, :)
end
"""
apply(ψ::Vector{<:Complex}, cg::CircuitGate{M,G}) where {M,G}
Apply a [`CircuitGate`](@ref) to a quantum state vector `ψ`.
# Examples
```jldoctest
julia> cg = circuit_gate(1, HadamardGate());
julia> ψ = [1 0];
julia> apply(ψ, cg)
2-element Array{Complex{Float64},1}:
0.7071067811865475 + 0.0im
0.7071067811865475 + 0.0im
```
"""
function apply(ψ::Vector{<:Complex}, cg::CircuitGate{M,G}) where {M,G}
l = length(ψ)::Int
N = Qaintessent.intlog2(l)
l == 2^N || error("Vector length must be a power of 2")
req_wires(cg) <= N || error("CircuitGate requires a minimum of $(req_wires(cg)) qubits, input vector `ψ` has $N qubits")
_apply(ψ, cg, N)
end
function _apply(ψ::Vector{<:Complex}, cg::CircuitGate{M,G}, N::Int) where {M,G}
U = matrix(cg.gate)
ψs = reshape(ψ, fill(2, N)...)
χ = similar(ψs)
it = binary_digits(M, 0)
m = binary_digits(M, 0)
for i in 1:2^M
binary_digits!(it, i - 1)
# cannot use .= here since broadcasting fails for scalar numbers
χ[sliced_index(it, cg.iwire, N)...] = sum(U[i, j] .* ψs[sliced_index(binary_digits!(m, j - 1), cg.iwire, N)...] for j in 1:2^M)
end
return reshape(χ, :)
end
"""
apply(ψ::Vector{<:Complex}, cgs::Vector{<:CircuitGate})
Apply a sequence of [`CircuitGate`](@ref)(s) to a quantum state vector `ψ`.
# Examples
```jldoctest
julia> cgs = [circuit_gate(1, HadamardGate()),
circuit_gate(1, X),
circuit_gate(1, Y)];
julia> ψ = [1 0];
julia> apply(ψ, cgs)
2-element Array{Complex{Float64},1}:
0.0 - 0.7071067811865475im
0.0 + 0.7071067811865475im
```
"""
function apply(ψ::Vector{<:Complex}, cgs::Vector{<:CircuitGate})
length(cgs) != 0 || error("Vector of length 0 cannot be applied")
l = length(ψ)
N = Qaintessent.intlog2(l)
l == 2^N || error("Vector length must be a power of 2")
req = maximum(req_wires.(cgs))
req <= N || error("CircuitGates require a minimum of $req qubits, input vector `ψ` has $N qubits")
for cg in cgs
ψ = _apply(ψ, cg, N)
end
return ψ
end
"""
apply(ψ::Vector{<:Complex}, m::Moment)
returns state vector of `N` qubits after applying a `Moment{N}` object to a quantum state vector of `N` qubits `ψ`
"""
function apply(ψ::Vector{<:Complex}, m::Moment)
Nmoment = req_wires(m)
l = length(ψ)
N = Qaintessent.intlog2(l)
l == 2^N || error("Vector length must be a power of 2")
Nmoment <= N || error("Moment affecting $Nmoment qubits applied to $N qubits")
_apply(ψ, m, N)
end
function _apply(ψ::Vector{<:Complex}, m::Moment, N::Int)
for gate in m
ψ = _apply(ψ, gate, N)
end
return ψ
end
function apply(ψ::Vector{<:Complex}, m::Vector{Moment})
length(m) != 0 || error("Vector of length 0 cannot be applied")
Nmoment = maximum(req_wires.(m))
l = length(ψ)
N = Qaintessent.intlog2(l)
l == 2^N || error("Vector length must be a power of 2")
Nmoment <= N || error("Moment affecting $Nmoment qubits applied to $N qubits")
for moment in m
ψ = _apply(ψ, moment, N)
end
return ψ
end
"""
apply(ψ::Vector{<:Complex}, m::MeasurementOperator{M,G}) where {M,G<:AbstractGate}
returns state vector of `N` qubits after applying a `Moment{N}` object to a quantum state vector of `N` qubits `ψ`
"""
function apply(ψ::Vector{<:Complex}, m::MeasurementOperator{M,G}) where {M,G<:AbstractGate}
Nmoment = num_wires(m)
l = length(ψ)::Int
N = Qaintessent.intlog2(l)
l == 2^N || error("Vector length must be a power of 2")
Nmoment <= N || error("MeasurementOperator affecting $Nmoment qubits applied to $N qubits")
c = circuit_gate((m.iwire...), m.operator)
_apply(ψ, c, N)
end
function apply(ψ::Vector{<:Complex}, m::MeasurementOperator{M,G}) where {M,G<:AbstractMatrix}
Nmoment = num_wires(m)
l = length(ψ)::Int
N = Qaintessent.intlog2(l)
l == 2^N || error("Vector length must be a power of 2")
Nmoment <= N || error("MeasurementOperator affecting $Nmoment qubits applied to $N qubits")
apply(ψ, m.operator)
end
"""
apply(ψ::Vector{<:Complex}, c::Circuit{N}) where {N}
returns list of expectation values from measurement operators in `c.meas` after applying circuit gates in `c.cgc` on state vector of `N` qubits `ψ`
"""
function apply(ψ::Vector{<:Complex}, c::Circuit{N}) where {N}
length(ψ)::Int == 2^N || error("Size of vector `ψ` must match Circuit size of $(2^N)")
length(c) != 0 || error("Circuit does not contain any gates")
length(c.meas) != 0 || error("Circuit does not contain any measurement operators")
ψl = copy(ψ)
for moment in c.moments
ψl = _apply(ψl, moment, N)
end
ψr = apply.((ψl,), c.meas)
return real.(dot.((ψl,), ψr))
end
(c::Circuit)(ψ) = apply(ψ, c)
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] | 1.99701 | 4,682 |
module BSplineKit
export Derivative
using Reexport
include("BandedTensors/BandedTensors.jl")
@reexport using .BandedTensors
include("DifferentialOps/DifferentialOps.jl")
using .DifferentialOps
include("BoundaryConditions/BoundaryConditions.jl")
@reexport using .BoundaryConditions
include("BSplines/BSplines.jl")
@reexport using .BSplines
include("Splines/Splines.jl")
@reexport using .Splines
include("Recombinations/Recombinations.jl")
@reexport using .Recombinations
include("Collocation/Collocation.jl")
@reexport using .Collocation
include("Galerkin/Galerkin.jl")
@reexport using .Galerkin
include("SplineInterpolations/SplineInterpolations.jl")
@reexport using .SplineInterpolations
include("SplineApproximations/SplineApproximations.jl")
@reexport using .SplineApproximations
end
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26568,
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198,
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2381,
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4943,
198,
31,
631,
87,
634,
1262,
764,
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500,
9492,
16104,
602,
198,
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17256,
7203,
26568,
500,
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320,
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26568,
500,
4677,
13907,
320,
602,
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20362,
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634,
1262,
764,
26568,
500,
4677,
13907,
320,
602,
198,
198,
437,
198
] | 2.919708 | 274 |
n = rand(1:10)
@test matrixdepot("parter", n) == matrixdepot("parter", Float64, n)
A = matrixdepot("cauchy", [1.:n;] + 0.5, -[1.:n;])
@test_approx_eq matrixdepot("parter", n) A
println("'parter' passed test...")
| [
77,
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7,
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10378,
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353,
1600,
299,
8,
317,
198,
35235,
7203,
6,
1845,
353,
6,
3804,
1332,
9313,
8,
198
] | 2.276596 | 94 |
module scordelis_lo_dsg3_verification
using Test
using LinearAlgebra
using FinEtools
using FinEtoolsDeforLinear
using FinEtoolsFlexStructures.FESetShellT3Module: FESetShellT3
using FinEtoolsFlexStructures.FEMMShellT3FFModule
using FinEtoolsFlexStructures.RotUtilModule: initial_Rfield, update_rotation_field!
function _execute(n = 8, visualize = true)
# analytical solution for the vertical deflection and the midpoint of the
# free edge
analyt_sol=-0.3024;
# Parameters:
E=4.32e8;
nu=0.0;
thickness = 0.25; # geometrical dimensions are in feet
R = 25.0;
L = 50.0;
formul = FEMMShellT3FFModule
tolerance = R/n/1000
fens, fes = T3block(40/360*2*pi,L/2,n,n);
fens.xyz = xyz3(fens)
for i in 1:count(fens)
a=fens.xyz[i, 1]; y=fens.xyz[i, 2];
fens.xyz[i, :] .= (R*sin(a), y, R*(cos(a)-1))
end
mater = MatDeforElastIso(DeforModelRed3D, E, nu)
sfes = FESetShellT3()
accepttodelegate(fes, sfes)
femm = formul.make(IntegDomain(fes, TriRule(1), thickness), mater)
femm.mult_el_size = 0.2
femm.drilling_stiffness_scale = 1.0
stiffness = formul.stiffness
associategeometry! = formul.associategeometry!
# Construct the requisite fields, geometry and displacement
# Initialize configuration variables
geom0 = NodalField(fens.xyz)
u0 = NodalField(zeros(size(fens.xyz,1), 3))
Rfield0 = initial_Rfield(fens)
dchi = NodalField(zeros(size(fens.xyz,1), 6))
# Apply EBC's
# rigid diaphragm
l1 = selectnode(fens; box = Float64[-Inf Inf 0 0 -Inf Inf], inflate = tolerance)
for i in [1,3,5]
setebc!(dchi, l1, true, i)
end
# plane of symmetry perpendicular to Y
l1 = selectnode(fens; box = Float64[-Inf Inf L/2 L/2 -Inf Inf], inflate = tolerance)
for i in [2,4,6]
setebc!(dchi, l1, true, i)
end
# plane of symmetry perpendicular to X
l1 = selectnode(fens; box = Float64[0 0 -Inf Inf -Inf Inf], inflate = tolerance)
for i in [1,5,6]
setebc!(dchi, l1, true, i)
end
applyebc!(dchi)
numberdofs!(dchi);
# Assemble the system matrix
associategeometry!(femm, geom0)
K = stiffness(femm, geom0, u0, Rfield0, dchi);
# Midpoint of the free edge
nl = selectnode(fens; box = Float64[sin(40/360*2*pi)*25 sin(40/360*2*pi)*25 L/2 L/2 -Inf Inf], inflate = tolerance)
lfemm = FEMMBase(IntegDomain(fes, TriRule(3)))
fi = ForceIntensity(FFlt[0, 0, -90, 0, 0, 0]);
F = distribloads(lfemm, geom0, dchi, fi, 3);
# Solve
U = K\F
scattersysvec!(dchi, U[:])
resultpercent = dchi.values[nl, 3][1]/analyt_sol*100
return resultpercent
end
function test_convergence()
formul = FEMMShellT3FFModule
results = [
66.54771615057949,
85.54615143134853,
89.85075281481419,
92.50616661644985,
95.40469210310079,
97.65376880486126
]
for (n, res) in zip([4, 8, 10, 12, 16, 24], results)
v = _execute(n, false)
@test isapprox(res, v, rtol = 1.0e-4)
# @show v
end
return true
end
test_convergence()
end # module
"""
The geometry consists of a “hook” in the form of a curved strip rigidly clamped
at one end and loaded with a unit in-plane shear along the width at the other
end. It has two circular segments that are connected at the tangent point. The
smaller segment has a mean radius of 0.3556 m (14 inches) and spans 60° from
the clamped end to the tangent point. The larger segment spans 150° from the
tangent point to the free end and has a mean radius of 1.1684 m (46 inches).
The hook is 0.0508 m (2 inches) thick and 0.508 m (20 inches) wide, modeled as
linear elastic with an elastic modulus of 22.77 MPa (3300 psi) and a Poisson's
ratio of 0.35. In most tests the shear force is applied through the use of a
distributing coupling constraint. The coupling constraint provides coupling
between a reference node on which the load is prescribed and the nodes located
on the free end. The distributed nodal loads on the free end are equivalent to
a uniformly distributed load of 8.7563 N/m (0.05 lb/in). In two of the tests an
equivalent shear force is applied as a distributed shear traction instead.
"""
module raasch_dsg3_verification
using Test
using LinearAlgebra
using FinEtools
using FinEtoolsDeforLinear
using FinEtoolsFlexStructures.FESetShellT3Module: FESetShellT3
using FinEtoolsFlexStructures.FEMMShellT3FFModule
using FinEtoolsFlexStructures.RotUtilModule: initial_Rfield, update_rotation_field!
function _execute(input = "raasch_s4_1x9.inp", visualize = true)
E = 3300.0;
nu = 0.35;
thickness = 2.0;
tolerance = thickness/2
# analytical solution for the vertical deflection under the load
analyt_sol = 5.02;
R = 46.0;
formul = FEMMShellT3FFModule
output = import_ABAQUS(joinpath(input))
fens = output["fens"]
fes = output["fesets"][1]
connected = findunconnnodes(fens, fes);
fens, new_numbering = compactnodes(fens, connected);
fes = renumberconn!(fes, new_numbering);
fens, fes = Q4toT3(fens, fes)
# plots = cat(plot_space_box([[0 0 -R/2]; [R/2 R/2 R/2]]),
# plot_nodes(fens),
# plot_midsurface(fens, fes);
# dims = 1)
# pl = render(plots)
mater = MatDeforElastIso(DeforModelRed3D, E, nu)
# Report
# @info "Raasch hook, formulation=$(formul)"
# @info "Mesh: $input"
sfes = FESetShellT3()
accepttodelegate(fes, sfes)
femm = formul.make(IntegDomain(fes, TriRule(1), thickness), mater)
femm.mult_el_size = 0.2
femm.drilling_stiffness_scale = 1.0
stiffness = formul.stiffness
associategeometry! = formul.associategeometry!
# Construct the requisite fields, geometry and displacement
# Initialize configuration variables
geom0 = NodalField(fens.xyz)
u0 = NodalField(zeros(size(fens.xyz,1), 3))
Rfield0 = initial_Rfield(fens)
dchi = NodalField(zeros(size(fens.xyz,1), 6))
# Apply EBC's
# Clamped end
l1 = selectnode(fens; box = Float64[0 0 -Inf Inf -Inf Inf], inflate = tolerance)
for i in [1,2,3,4,5,6]
setebc!(dchi, l1, true, i)
end
applyebc!(dchi)
numberdofs!(dchi);
# Assemble the system matrix
associategeometry!(femm, geom0)
K = stiffness(femm, geom0, u0, Rfield0, dchi);
# Load
bfes = meshboundary(fes)
l1 = selectelem(fens, bfes, box = [97.9615 97.9615 -16 -16 0 20], inflate = tolerance)
lfemm = FEMMBase(IntegDomain(subset(bfes, l1), GaussRule(1, 2)))
fi = ForceIntensity(FFlt[0, 0, 0.05, 0, 0, 0]);
F = distribloads(lfemm, geom0, dchi, fi, 3);
# @infiltrate
# Solve
U = K\F
scattersysvec!(dchi, U[:])
nl = selectnode(fens; box = Float64[97.9615 97.9615 -16 -16 0 0], inflate = tolerance)
targetu = dchi.values[nl, 3][1]
# @info "Target: $(round(targetu, digits=8)), $(round(targetu/analyt_sol, digits = 4)*100)%"
return targetu/analyt_sol*100
end
function test_convergence()
formul = FEMMShellT3FFModule
results = [
91.7059961843231,
95.9355786538892,
97.19276899988246,
98.38896641657374
]
for (m, res) in zip(["1x9", "3x18", "5x36", "10x72"], results)
v = _execute("raasch_s4_" * m * ".inp", false)
@test isapprox(res, v, rtol = 1.0e-4)
# @show v
end
return true
end
test_convergence()
end # module
"""
The initially twisted cantilever beam is one of the standard test
problems for verifying the finite-element accuracy [1]. The beam is
clamped at one end and loaded either with unit in-plane or
unit out-of-plane force at the other. The centroidal axis of the beam is
straight at the undeformed configuration, while its cross-sections are
twisted about the centroidal axis from 0 at the clamped end to pi/2 at
the free end.
Reference:
Zupan D, Saje M (2004) On "A proposed standard set of problems to test
finite element accuracy": the twisted beam. Finite Elements in Analysis
and Design 40: 1445-1451.
Thicker cross section (t = 0.32)
# Loading in the Z direction
dir = 3; uex = 0.005424534868469; # Harder: 5.424e-3;
# Loading in the Y direction
dir = 2; uex = 0.001753248285256; # Harder: 1.754e-3;
Thinner cross section (t = 0.0032)
# Loading in the Z direction
dir = 3; uex = 0.005256;
# Loading in the Y direction
dir = 2; uex = 0.001294;
MacNeal, R. H., and R. L. Harder, “A Proposed Standard Set of Problems to Test Finite Element Accuracy,” Finite Elements in Analysis Design, vol. 11, pp. 3–20, 1985.
Simo, J. C., D. D. Fox, and M. S. Rifai, “On a Stress Resultant Geometrically Exact Shell Model. Part II: The Linear Theory; Computational Aspects,” Computational Methods in Applied Mechanical Engineering, vol. 73, pp. 53–92, 1989.
"""
module twisted_beam_dsg3_verification
using Test
using FinEtools
using FinEtoolsDeforLinear
using FinEtoolsFlexStructures.FESetShellT3Module: FESetShellT3
using FinEtoolsFlexStructures.FEMMShellT3FFModule
using FinEtoolsFlexStructures.RotUtilModule: initial_Rfield, update_rotation_field!
params_thicker_dir_3 = (t = 0.32, force = 1.0, dir = 3, uex = 0.005424534868469);
params_thicker_dir_2 = (t = 0.32, force = 1.0, dir = 2, uex = 0.001753248285256);
params_thinner_dir_3 = (t = 0.0032, force = 1.0e-6, dir = 3, uex = 0.005256);
params_thinner_dir_2 = (t = 0.0032, force = 1.0e-6, dir = 2, uex = 0.001294);
function _execute(t = 0.32, force = 1.0, dir = 3, uex = 0.005424534868469, nL = 8, nW = 2, visualize = true)
E = 0.29e8;
nu = 0.22;
W = 1.1;
L = 12.0;
formul = FEMMShellT3FFModule
tolerance = W/nW/100
fens, fes = T3block(L,W,nL,nW,:a);
fens.xyz = xyz3(fens)
for i in 1:count(fens)
a=fens.xyz[i,1]/L*(pi/2); y=fens.xyz[i,2]-(W/2); z=fens.xyz[i,3];
fens.xyz[i,:]=[fens.xyz[i,1],y*cos(a)-z*sin(a),y*sin(a)+z*cos(a)];
end
mater = MatDeforElastIso(DeforModelRed3D, E, nu)
sfes = FESetShellT3()
accepttodelegate(fes, sfes)
femm = formul.make(IntegDomain(fes, TriRule(1), t), mater)
# femm.mult_el_size = 0.2
# femm.drilling_stiffness_scale = 1.0
stiffness = formul.stiffness
associategeometry! = formul.associategeometry!
# Construct the requisite fields, geometry and displacement
# Initialize configuration variables
geom0 = NodalField(fens.xyz)
u0 = NodalField(zeros(size(fens.xyz,1), 3))
Rfield0 = initial_Rfield(fens)
dchi = NodalField(zeros(size(fens.xyz,1), 6))
# Apply EBC's
# Clamped end
l1 = selectnode(fens; box = Float64[0 0 -Inf Inf -Inf Inf], inflate = tolerance)
for i in 1:6
setebc!(dchi, l1, true, i)
end
applyebc!(dchi)
numberdofs!(dchi);
# Assemble the system matrix
associategeometry!(femm, geom0)
K = stiffness(femm, geom0, u0, Rfield0, dchi);
# Load
nl = selectnode(fens; box = Float64[L L 0 0 0 0], tolerance = tolerance)
loadbdry = FESetP1(reshape(nl, 1, 1))
lfemm = FEMMBase(IntegDomain(loadbdry, PointRule()))
v = FFlt[0, 0, 0, 0, 0, 0]
v[dir] = force
fi = ForceIntensity(v);
F = distribloads(lfemm, geom0, dchi, fi, 3);
# Solve
U = K\F
scattersysvec!(dchi, U[:])
result = dchi.values[nl, dir][1]/uex*100
return result
end
function test_convergence()
formul = FEMMShellT3FFModule
results = [
39.709921740907355,
68.87306876326497,
86.01944734315117,
95.04101960524827,
53.10262177376127,
83.8593790803426,
94.91359387874728,
98.21549248655576,
48.16757753755567,
79.43420077873479,
92.54464819755955,
96.85008269135751,
50.577029703967334,
80.34160167730624,
92.48675665271801,
96.7096641005938,
]
for n in [2, 4, 8, 16, ]
v = _execute(params_thicker_dir_2..., 2*n, n, false)
# println(v, ",")
@test isapprox(v, popat!(results, 1), rtol = 1.0e-3)
end
for n in [2, 4, 8, 16, ]
v = _execute(params_thicker_dir_3..., 2*n, n, false)
# println(v, ",")
@test isapprox(v, popat!(results, 1), rtol = 1.0e-3)
end
for n in [2, 4, 8, 16, ]
v = _execute(params_thinner_dir_2..., 2*n, n, false)
# println(v, ",")
@test isapprox(v, popat!(results, 1), rtol = 1.0e-3)
end
for n in [2, 4, 8, 16, ]
v = _execute(params_thinner_dir_3..., 2*n, n, false)
# println(v, ",")
@test isapprox(v, popat!(results, 1), rtol = 1.0e-3)
end
return true
end
test_convergence()
end # module
"""
MODEL DESCRIPTION
Z-section cantilever under torsional loading.
Linear elastic analysis, Young's modulus = 210 GPa, Poisson's ratio = 0.3.
All displacements are fixed at X=0.
Torque of 1.2 MN-m applied at X=10. The torque is applied by two
uniformly distributed shear loads of 0.6 MN at each flange surface.
Objective of the analysis is to compute the axial stress at X = 2.5 from fixed end.
NAFEMS REFERENCE SOLUTION
Axial stress at X = 2.5 from fixed end (point A) at the midsurface is -108 MPa.
"""
module LE5_Z_cantilever_dsg3_verification
using Test
using LinearAlgebra
using FinEtools
using FinEtoolsDeforLinear
using FinEtoolsFlexStructures.FESetShellT3Module: FESetShellT3
using FinEtoolsFlexStructures.FEMMShellT3FFModule
using FinEtoolsFlexStructures.RotUtilModule: initial_Rfield, update_rotation_field!
function _execute(input = "nle5xf3c.inp", nrefs = 0, visualize = true)
E = 210e9;
nu = 0.3;
L = 10.0;
thickness = 0.1
formul = FEMMShellT3FFModule
tolerance = thickness/1000
output = import_ABAQUS(joinpath(dirname(@__FILE__()), input))
fens = output["fens"]
fes = output["fesets"][1]
connected = findunconnnodes(fens, fes);
fens, new_numbering = compactnodes(fens, connected);
fes = renumberconn!(fes, new_numbering);
for r in 1:nrefs
fens, fes = T3refine(fens, fes)
end
mater = MatDeforElastIso(DeforModelRed3D, E, nu)
# Report
# @info "Mesh: $input, nrefs = $nrefs"
sfes = FESetShellT3()
accepttodelegate(fes, sfes)
femm = formul.make(IntegDomain(fes, TriRule(1), thickness), mater)
femm.mult_el_size = 0.2
femm.drilling_stiffness_scale = 1.0
stiffness = formul.stiffness
associategeometry! = formul.associategeometry!
# Construct the requisite fields, geometry and displacement
# Initialize configuration variables
geom0 = NodalField(fens.xyz)
u0 = NodalField(zeros(size(fens.xyz,1), 3))
Rfield0 = initial_Rfield(fens)
dchi = NodalField(zeros(size(fens.xyz,1), 6))
# plots = cat(plot_space_box([[0 0 -L/2]; [L/2 L/2 L/2]]),
# plot_nodes(fens),
# plot_midsurface(fens, fes);
# dims = 1)
# pl = render(plots)
# Apply EBC's
# plane of symmetry perpendicular to X
l1 = selectnode(fens; box = Float64[0 0 -Inf Inf -Inf Inf], inflate = tolerance)
for i in [1,2,3,]
setebc!(dchi, l1, true, i)
end
applyebc!(dchi)
numberdofs!(dchi);
# @show dchi.nfreedofs
# Assemble the system matrix
associategeometry!(femm, geom0)
K = stiffness(femm, geom0, u0, Rfield0, dchi);
# Load
nl = selectnode(fens; box = Float64[10.0 10.0 1.0 1.0 0 0], tolerance = tolerance)
loadbdry1 = FESetP1(reshape(nl, 1, 1))
lfemm1 = FEMMBase(IntegDomain(loadbdry1, PointRule()))
fi1 = ForceIntensity(FFlt[0, 0, +0.6e6, 0, 0, 0]);
nl = selectnode(fens; box = Float64[10.0 10.0 -1.0 -1.0 0 0], tolerance = tolerance)
loadbdry2 = FESetP1(reshape(nl, 1, 1))
lfemm2 = FEMMBase(IntegDomain(loadbdry2, PointRule()))
fi2 = ForceIntensity(FFlt[0, 0, -0.6e6, 0, 0, 0]);
F = distribloads(lfemm1, geom0, dchi, fi1, 3) + distribloads(lfemm2, geom0, dchi, fi2, 3);
# @infiltrate
# Solve
U = K\F
scattersysvec!(dchi, U[:])
return minimum(dchi.values[:, 3]), maximum(dchi.values[:, 3])
end
function test_convergence()
formul = FEMMShellT3FFModule
# @info "LE5 Z-cantilever, formulation=$(formul)"
for n in [0, ]
res = _execute("nle5xf3c.inp", n, false)
@test res[1] ≈ (-0.01568028415401719, 0.015401663810490641)[1]
@test res[2] ≈ (-0.01568028415401719, 0.015401663810490641)[2]
end
return true
end
# res = test_st3dsg("nle5xf3c.inp", 0, false)
# @test res[1] ≈ (-0.01568028415401719, 0.015401663810490641)[1]
# @test res[2] ≈ (-0.01568028415401719, 0.015401663810490641)[2]
test_convergence()
end # module
"""
The barrel vault (Scordelis-Lo) roof is one of the benchmarks for linear elastic
analysis of shells.
The candidate element's usefulness in irregular geometries (and most practical
cases involve a high degree of geometric irregularity) is tested. As would be
expected,the irregular mesh results are not as good as those provided by a
regular mesh with the same number of variables.
Problem description
The physical basis of the problem is a deeply arched roof supported only
by diaphragms at its curved edges (an aircraft hanger), deforming under its own
weight. It is interesting to observe that the geometry is such that the
centerpoint of the roof moves upward under the self-weight (downwardly directed)
load. Perhaps this is one reason why the problem is not straightforward
numerically.
"""
module barrel_vault_test
using Test
using LinearAlgebra
using FinEtools
using FinEtoolsDeforLinear
using FinEtoolsFlexStructures.FESetShellT3Module: FESetShellT3
using FinEtoolsFlexStructures.FESetShellQ4Module: FESetShellQ4
using FinEtoolsFlexStructures.FEMMShellT3FFModule
using FinEtoolsFlexStructures.RotUtilModule: initial_Rfield, update_rotation_field!
using FinEtools.MeshExportModule.VTKWrite: vtkwrite
function _execute(input = "barrelvault_s3r_fineirreg.inp", visualize = true)
E = 3.0e6;
nu = 0.0;
thickness = 3.0;
tolerance = thickness/20
# analytical solution for the vertical deflection under the load
analyt_sol = -0.3024*12;
R = 25.0*12;
L = 50.0*12;
formul = FEMMShellT3FFModule
output = import_ABAQUS(joinpath(dirname(@__FILE__()), input))
fens = output["fens"]
fes = output["fesets"][1]
# boundingbox(fens.xyz)
connected = findunconnnodes(fens, fes);
fens, new_numbering = compactnodes(fens, connected);
fes = renumberconn!(fes, new_numbering);
fens, fes = mergenodes(fens, fes, thickness/10)
# plots = cat(plot_space_box([[0 0 -R/2]; [R/2 R/2 R/2]]),
# plot_nodes(fens),
# plot_midsurface(fens, fes);
# dims = 1)
# pl = render(plots)
mater = MatDeforElastIso(DeforModelRed3D, E, nu)
# @info "Mesh: $input"
sfes = FESetShellT3()
accepttodelegate(fes, sfes)
femm = formul.make(IntegDomain(fes, TriRule(1), thickness), mater)
femm.mult_el_size = 0.2
femm.drilling_stiffness_scale = 0.1
stiffness = formul.stiffness
associategeometry! = formul.associategeometry!
# Construct the requisite fields, geometry and displacement
# Initialize configuration variables
geom0 = NodalField(fens.xyz)
u0 = NodalField(zeros(size(fens.xyz,1), 3))
Rfield0 = initial_Rfield(fens)
dchi = NodalField(zeros(size(fens.xyz,1), 6))
# vtkwrite("barrel_vault_test-mesh.vtu", fens, fes)
# bfes = meshboundary(fes)
# vtkwrite("barrel_vault_test-boundary-mesh.vtu", fens, bfes)
# Apply EBC's
# Rigid diaphragm end
l1 = selectnode(fens; box = Float64[-Inf Inf -Inf Inf 0 0], inflate = tolerance)
for i in [1,2]
setebc!(dchi, l1, true, i)
end
# plane of symmetry perpendicular to Z
l1 = selectnode(fens; box = Float64[-Inf Inf -Inf Inf L/2 L/2], inflate = tolerance)
for i in [3,4,5]
setebc!(dchi, l1, true, i)
end
# plane of symmetry perpendicular to Y (apex)
l1 = selectnode(fens; box = Float64[-Inf Inf 0 0 -Inf Inf], inflate = tolerance)
for i in [2,4,6]
setebc!(dchi, l1, true, i)
end
applyebc!(dchi)
numberdofs!(dchi);
# Assemble the system matrix
associategeometry!(femm, geom0)
K = stiffness(femm, geom0, u0, Rfield0, dchi);
# Load
# Midpoint of the free edge
nl = selectnode(fens; box = Float64[-Inf Inf sin(40/360*2*pi)*R sin(40/360*2*pi)*R L/2 L/2], inflate = tolerance)
lfemm = FEMMBase(IntegDomain(fes, TriRule(3)))
fi = ForceIntensity(FFlt[-0.625, 0, 0, 0, 0, 0]);
F = distribloads(lfemm, geom0, dchi, fi, 3);
# @infiltrate
# Solve
U = K\F
scattersysvec!(dchi, U[:])
targetu = dchi.values[nl, 1][1]
# @info "Solution: $(round(targetu, digits=8)), $(round(targetu/analyt_sol, digits = 4)*100)%"
# Generate a graphical display of resultants
cylindrical!(csmatout::FFltMat, XYZ::FFltMat, tangents::FFltMat, fe_label::FInt) = begin
r = -vec(XYZ); r[3] = 0.0
csmatout[:, 3] .= vec(r)/norm(vec(r))
csmatout[:, 2] .= (0.0, 0.0, 1.0)
cross3!(view(csmatout, :, 1), view(csmatout, :, 2), view(csmatout, :, 3))
return csmatout
end
ocsys = CSys(3, 3, cylindrical!)
test_results = [
(-1520.6449167366522, 14.067403309095397)
(-73.8262145426215, 425.93651541819503)
(-0.005341121492547284, 953.0929383629322)
]
scalars = []
for nc in 1:3
fld = fieldfromintegpoints(femm, geom0, dchi, :moment, nc, outputcsys = ocsys)
# fld = elemfieldfromintegpoints(femm, geom0, dchi, :moment, nc)
push!(scalars, ("m$nc", fld.values))
@test isapprox(minimum(fld.values), test_results[nc][1], rtol = 0.01)
@test isapprox(maximum(fld.values), test_results[nc][2], rtol = 0.01)
end
# vtkwrite("$(input)-m.vtu", fens, fes; scalars = scalars, vectors = [("u", dchi.values[:, 1:3])])
test_results = [
(-306.83173146926623, 309.5860993742647),
(-1011.4832977998705, 2167.0403478574167),
(-687.3500043290137, 69.38703021678862),
]
scalars = []
for nc in 1:3
fld = fieldfromintegpoints(femm, geom0, dchi, :membrane, nc, outputcsys = ocsys)
# fld = elemfieldfromintegpoints(femm, geom0, dchi, :moment, nc)
push!(scalars, ("n$nc", fld.values))
@test isapprox(minimum(fld.values), test_results[nc][1], rtol = 0.01)
@test isapprox(maximum(fld.values), test_results[nc][2], rtol = 0.01)
end
# vtkwrite("$(input)-n.vtu", fens, fes; scalars = scalars, vectors = [("u", dchi.values[:, 1:3])])
test_results = [
(-13.784764125688811, 21.165312065421237),
(-7.4216963152916255, 25.679801383392967)
]
scalars = []
for nc in 1:2
fld = fieldfromintegpoints(femm, geom0, dchi, :shear, nc, outputcsys = ocsys)
# fld = elemfieldfromintegpoints(femm, geom0, dchi, :moment, nc)
push!(scalars, ("q$nc", fld.values))
@test isapprox(minimum(fld.values), test_results[nc][1], rtol = 0.01)
@test isapprox(maximum(fld.values), test_results[nc][2], rtol = 0.01)
end
# vtkwrite("$(input)-q.vtu", fens, fes; scalars = scalars, vectors = [("u", dchi.values[:, 1:3])])
# Visualization
if !visualize
return true
end
scattersysvec!(dchi, (R/2)/maximum(abs.(U)).*U)
update_rotation_field!(Rfield0, dchi)
plots = cat(plot_space_box([[0 0 -R]; [R R R]]),
plot_nodes(fens),
plot_midsurface(fens, fes; x = geom0.values, u = dchi.values[:, 1:3], R = Rfield0.values);
dims = 1)
pl = render(plots)
return true
end
function test_convergence()
# @info "Scordelis-Lo Abaqus model"
_execute("barrelvault_stri3_irreg.inp", false)
# _execute("barrelvault_s3r_fineirreg.inp", false)
return true
end
test_convergence()
end # module
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] | 2.223299 | 10,730 |
# Predefined functions and values
const ϵ = 0.5
const ξ = 0.01
invfun(x) = 1/x
function rademacherDistribution!(v)
o = one(eltype(v))
v .= Base.rand.(Ref(-o:2*o:o))
end
| [
2,
14322,
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5499,
290,
3815,
198,
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18074,
113,
796,
657,
13,
20,
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78,
25,
17,
9,
78,
25,
78,
4008,
198,
437,
198
] | 2.182927 | 82 |
export create_guild,
get_guild,
modify_guild,
delete_guild,
get_guild_channels,
create_guild_channel,
modify_guild_channel_positions,
get_guild_member,
list_guild_members,
add_guild_member,
modify_guild_member,
modify_current_user_nick,
add_guild_member_role,
remove_guild_member_role,
remove_guild_member,
get_guild_bans,
get_guild_ban,
create_guild_ban,
remove_guild_ban,
get_guild_roles,
create_guild_role,
modify_guild_role_positions,
modify_guild_role,
delete_guild_role,
get_guild_prune_count,
begin_guild_prune,
get_guild_voice_regions,
get_guild_invites,
get_guild_integrations,
create_guild_integration,
modify_guild_integration,
delete_guild_integration,
sync_guild_integration,
get_guild_embed,
modify_guild_embed,
get_vanity_url,
get_guild_widget_image
"""
create_guild_scheduled_event(c::Client, guild::Integer; kwargs...)
Create a Guild Scheduled Event.
More details [here](https://discord.com/developers/docs/resources/guild-scheduled-event#create-guild-scheduled-event)
"""
function create_guild_scheduled_event(c::Client, guild::Integer, s::ScheduledEvent; kwargs...)
return Response{ScheduledEvent}(c, :POST, "/guilds/$(guild)/scheduled-events"; body=s)
end
"""
modify_guild_scheduled_event(c::Client, guild::Integer; kwargs...)
Modify a Guild Scheduled Event.
More details [here](https://discord.com/developers/docs/resources/guild-scheduled-event#modify-guild-scheduled-event)
"""
function modify_guild_scheduled_event(c::Client, guild::Integer, s::ScheduledEvent; kwargs...)
return Response{ScheduledEvent}(c, :PATCH, "/guilds/$(guild)/scheduled-events/$(s.id)"; body=s)
end
"""
delete_guild_scheduled_event(c::Client, guild::Integer; kwargs...)
Delete a Guild Scheduled Event.
More details [here](https://discord.com/developers/docs/resources/guild-scheduled-event#delete-guild-scheduled-event)
"""
function delete_guild_scheduled_event(c::Client, guild::Integer, s::ScheduledEvent)
return Response(c, :DELETE, "/guilds/$(guild)/scheduled-events/$(s.id)")
end
"""
get_guild_scheduled_event(c::Client, guild::Integer; kwargs...)
Get a Guild Scheduled Event.
More details [here](https://discord.com/developers/docs/resources/guild-scheduled-event#get-guild-scheduled-event)
"""
function get_guild_scheduled_event(c::Client, guild::Integer, s::Integer; kwargs...)
return Response{ScheduledEvent}(c, :GET, "/guilds/$(guild)/scheduled-events/$(s.id)", body=kwargs)
end
"""
create_guild(c::Client; kwargs...) -> Guild
Create a [`Guild`](@ref).
More details [here](https://discordapp.com/developers/docs/resources/guild#create-guild).
"""
function create_guild(c::Client; kwargs...)
return Response{Guild}(c, :POST, "/guilds"; body=kwargs)
end
"""
get_guild(c::Client, guild::Integer) -> Guild
Get a [`Guild`](@ref).
"""
function get_guild(c::Client, guild::Integer)
return Response{Guild}(c, :GET, "/guilds/$guild")
end
"""
modify_guild(c::Client, guild::Integer; kwargs...) -> Guild
Edit a [`Guild`](@ref).
More details [here](https://discordapp.com/developers/docs/resources/guild#modify-guild).
"""
function modify_guild(c::Client, guild::Integer; kwargs...)
return Response{Guild}(c, :PATCH, "/guilds/$guild"; body=kwargs)
end
"""
delete_guild(c::Client, guild::Integer)
Delete a [`Guild`](@ref).
"""
function delete_guild(c::Client, guild::Integer)
return Response(c, :DELETE, "/guilds/$guild")
end
"""
get_guild_channels(c::Client, guild::Integer) -> Vector{DiscordChannel}
Get the [`DiscordChannel`](@ref)s in a [`Guild`](@ref).
"""
function get_guild_channels(c::Client, guild::Integer)
return Response{Vector{DiscordChannel}}(c, :GET, "/guilds/$guild/channels")
end
"""
create_guild_channel(c::Client, guild::Integer; kwargs...) -> DiscordChannel
Create a [`DiscordChannel`](@ref) in a [`Guild`](@ref).
More details [here](https://discordapp.com/developers/docs/resources/guild#create-guild-channel).
"""
function create_guild_channel(c::Client, guild::Integer; kwargs...)
return Response{DiscordChannel}(c, :POST, "/guilds/$guild/channels"; body=kwargs)
end
"""
modify_guild_channel_positions(c::Client, guild::Integer, positions...)
Modify the positions of [`DiscordChannel`](@ref)s in a [`Guild`](@ref).
More details [here](https://discordapp.com/developers/docs/resources/guild#modify-guild-channel-positions).
"""
function modify_guild_channel_positions(c::Client, guild::Integer, positions...)
return Response(c, :PATCH, "/guilds/$guild/channels"; body=positions)
end
"""
get_guild_member(c::Client, guild::Integer, user::Integer) -> Member
Get a [`Member`](@ref) in a [`Guild`](@ref).
"""
function get_guild_member(c::Client, guild::Integer, user::Integer)
return Response{Member}(c, :GET, "/guilds/$guild/members/$user")
end
"""
list_guild_members(c::Client, guild::Integer; kwargs...) -> Vector{Member}
Get a list of [`Member`](@ref)s in a [`Guild`](@ref).
More details [here](https://discordapp.com/developers/docs/resources/guild#list-guild-members).
"""
function list_guild_members(c::Client, guild::Integer; kwargs...)
return Response{Vector{Member}}(c, :GET, "/guilds/$guild/members"; kwargs...)
end
"""
add_guild_member(c::Client; kwargs...) -> Member
Add a [`User`](@ref) to a [`Guild`](@ref).
More details [here](https://discordapp.com/developers/docs/resources/guild#add-guild-member).
"""
function add_guild_member(c::Client, guild::Integer, user::Integer; kwargs...)
return Response{Member}(c, :PUT, "/guilds/$guild/members/$user"; body=kwargs)
end
"""
modify_guild__member(c::Client, guild::Integer, user::Integer; kwargs...)
Modify a [`Member`](@ref) in a [`Guild`](@ref).
More details [here](https://discordapp.com/developers/docs/resources/guild#modify-guild-member).
"""
function modify_guild_member(c::Client, guild::Integer, user::Integer; kwargs...)
return Response(c, :PATCH, "/guilds/$guild/members/$user"; body=kwargs)
end
"""
modify_current_user_nick(c::Client, guild::Intger; kwargs...) -> String
Modify the [`Client`](@ref) user's nickname in a [`Guild`](@ref).
More details [here](https://discordapp.com/developers/docs/resources/guild#modify-current-user-nick).
"""
function modify_current_user_nick(c::Client, guild::Integer; kwargs...)
return Response{String}(c, :PATCH, "/guilds/$guild/members/@me/nick"; body=kwargs)
end
"""
add_guild_member_role(c::Client, guild::Integer, user::Integer, role::Integer)
Add a [`Role`](@ref) to a [`Member`](@ref).
"""
function add_guild_member_role(c::Client, guild::Integer, user::Integer, role::Integer)
return Response(c, :PUT, "/guilds/$guild/members/$user/roles/$role")
end
"""
remove_guild_member_role(c::Client, guild::Integer, user::Integer, role::Integer)
Remove a [`Role`](@ref) from a [`Member`](@ref).
"""
function remove_guild_member_role(c::Client, guild::Integer, user::Integer, role::Integer)
return Response(c, :DELETE, "/guilds/$guild/members/$user/roles/$role")
end
"""
remove_guild_member(c::Client, guild::Integer, user::Integer)
Kick a [`Member`](@ref) from a [`Guild`](@ref).
"""
function remove_guild_member(c::Client, guild::Integer, user::Integer)
return Response(c, :DELETE, "/guilds/$guild/members/$user")
end
"""
get_guild_bans(c::Client, guild::Integer) -> Vector{Ban}
Get a list of [`Ban`](@ref)s in a [`Guild`](@ref).
"""
function get_guild_bans(c::Client, guild::Integer)
return Response{Vector{Ban}}(c, :GET, "/guilds/$guild/bans")
end
"""
get_ban(c::Client, guild::Integer, user::Integer) -> Ban
Get a [`Ban`](@ref) in a [`Guild`](@ref).
"""
function get_guild_ban(c::Client, guild::Integer, user::Integer)
return Response{Ban}(c, :GET, "/guilds/$guild/bans/$user")
end
"""
create_guild_ban(c::Client, guild::Integer, user::Integer; kwargs...)
Ban a [`Member`](@ref) from a [`Guild`](@ref).
More details [here](https://discordapp.com/developers/docs/resources/guild#create-guild-ban).
"""
function create_guild_ban(c::Client, guild::Integer, user::Integer; kwargs...)
return Response(c, :PUT, "/guilds/$guild/bans/$user"; kwargs...)
end
"""
remove_guild_ban(c::Client, guild::Integer, user::Integer)
Unban a [`User`](@ref) from a [`Guild`](@ref).
"""
function remove_guild_ban(c::Client, guild::Integer, user::Integer)
return Response(c, :DELETE, "/guilds/$guild/bans/$user")
end
"""
get_guild_roles(c::Client, guild::Integer) -> Vector{Role}
Get a [`Guild`](@ref)'s [`Role`](@ref)s.
"""
function get_guild_roles(c::Client, guild::Integer)
return Response{Vector{Role}}(c, :GET, "/guilds/$guild/roles")
end
"""
create_guild_role(c::Client, guild::Integer; kwargs) -> Role
Create a [`Role`](@ref) in a [`Guild`](@ref).
More details [here](https://discordapp.com/developers/docs/resources/guild#create-guild-role).
"""
function create_guild_role(c::Client, guild::Integer; kwargs...)
return Response{Role}(c, :POST, "/guilds/$guild/roles"; body=kwargs)
end
"""
modify_guild_role_positions(c::Client, guild::Integer, positions...) -> Vector{Role}
Modify the positions of [`Role`](@ref)s in a [`Guild`](@ref).
More details [here](https://discordapp.com/developers/docs/resources/guild#modify-guild-role-positions).
"""
function modify_guild_role_positions(c::Client, guild::Integer, positions...)
return Response{Vector{Role}}(c, :PATCH, "/guilds/$guild/roles"; body=positions)
end
"""
modify_guild_role(c::Client, guild::Integer, role::Integer; kwargs) -> Role
Modify a [`Role`](@ref) in a [`Guild`](@ref).
More details [here](https://discordapp.com/developers/docs/resources/guild#modify-guild-role).
"""
function modify_guild_role(c::Client, guild::Integer, role::Integer; kwargs...)
return Response{Role}(c, :PATCH, "/guilds/$guild/roles/$role"; body=kwargs)
end
"""
delete_guild_role(c::Client, guild::Integer, role::Integer)
Delete a [`Role`](@ref) from a [`Guild`](@ref).
"""
function delete_guild_role(c::Client, guild::Integer, role::Integer)
return Response(c, :DELETE, "/guilds/$guild/roles/$role")
end
"""
get_guild_prune_count(c::Client, guild::Integer; kwargs...) -> Dict
Get the number of [`Member`](@ref)s that would be removed from a [`Guild`](@ref) in a prune.
More details [here](https://discordapp.com/developers/docs/resources/guild#get-guild-prune-count).
"""
function get_guild_prune_count(c::Client, guild::Integer; kwargs...)
return Response{Dict}(c, :GET, "/guilds/$guild/prune"; kwargs...)
end
"""
begin_guild_prune(c::Client, guild::Integer; kwargs...) -> Dict
Begin pruning [`Member`](@ref)s from a [`Guild`](@ref).
More details [here](https://discordapp.com/developers/docs/resources/guild#begin-guild-prune).
"""
function begin_guild_prune(c::Client, guild::Integer; kwargs...)
return Response{Dict}(c, :POST, "/guilds/$guild/prune"; kwargs...)
end
"""
get_guild_voice_regions(c::Client, guild::Integer) -> Vector{VoiceRegion}
Get a list of [`VoiceRegion`](@ref)s for the [`Guild`](@ref).
"""
function get_guild_voice_regions(c::Client, guild::Integer)
return Response{Vector{VoiceRegion}}(c, :GET, "/guilds/$guild/regions")
end
"""
get_guild_invites(c::Client, guild::Integer) -> Vector{Invite}
Get a list of [`Invite`](@ref)s to a [`Guild`](@ref).
"""
function get_guild_invites(c::Client, guild::Integer)
return Response{Vector{Invite}}(c, :GET, "/guilds/$guild/invites")
end
"""
get_guild_integrations(c::Client, guild::Integer) -> Vector{Integration}
Get a list of [`Integration`](@ref)s for a [`Guild`](@ref).
"""
function get_guild_integrations(c::Client, guild::Integer)
return Response{Vector{Integration}}(c, :GET, "/guilds/$guild/integrations")
end
"""
create_guild_integration(c::Client, guild::Integer; kwargs...)
Create/attach an [`Integration`](@ref) to a [`Guild`](@ref).
More details [here](https://discordapp.com/developers/docs/resources/guild#create-guild-integration).
"""
function create_guild_integration(c::Client, guild::Integer; kwargs...)
return Response{Integration}(c, :POST, "/guilds/$guild/integrations"; body=kwargs)
end
"""
modify_guild_integration(c::Client, guild::Integer, integration::Integer; kwargs...)
Modify an [`Integration`](@ref) in a [`Guild`](@ref).
More details [here](https://discordapp.com/developers/docs/resources/guild#modify-guild-integration).
"""
function modify_guild_integration(
c::Client,
guild::Integer,
integration::Integer;
kwargs...,
)
return Response(c, :PATCH, "/guilds/$guild/integrations/$integration"; body=kwargs)
end
"""
delete_guild_integration(c::Client, guild::Integer, integration::Integer)
Delete an [`Integration`](@ref) from a [`Guild`](@ref).
"""
function delete_guild_integration(c::Client, guild::Integer, integration::Integer)
return Response(c, :DELETE, "/guilds/$guild/integrations/$integration")
end
"""
sync_guild_integration(c::Client, guild::Integer, integration::Integer)
Sync an [`Integration`](@ref) in a [`Guild`](@ref).
"""
function sync_guild_integration(c::Client, guild::Integer, integration::Integer)
return Response(c, :POST, "/guilds/$guild/integrations/$integration/sync")
end
"""
get_guild_embed(c::Client, guild::Integer) -> GuildEmbed
Get a [`Guild`](@ref)'s [`GuildEmbed`](@ref).
"""
function get_guild_embed(c::Client, guild::Integer)
return Response{GuildEmbed}(c, :GET, "/guilds/$guild/embed")
end
"""
modify_guild_embed(c::Client, guild::Integer; kwargs...) -> GuildEmbed
Modify a [`Guild`](@ref)'s [`GuildEmbed`](@ref).
More details [here](https://discordapp.com/developers/docs/resources/guild#modify-guild-embed).
"""
function modify_guild_embed(c::Client, guild::Integer; kwargs...)
return Response{GuildEmbed}(c, :PATCH, "/guilds/$guild/embed"; body=kwargs)
end
"""
get_vanity_url(c::Client, guild::Integer) -> Invite
Get a [`Guild`](@ref)'s vanity URL, if it supports that feature.
"""
function get_vanity_url(c::Client, guild::Integer)
return Response{Invite}(c, :GET, "/guilds/$guild/vanity-url")
end
"""
get_guild_widget_image(c::Client, guild::Integer; kwargs...) -> Vector{UInt8}
Get a [`Guild`](@ref)'s widget image in PNG format.
More details [here](https://discordapp.com/developers/docs/resources/guild#get-guild-widget-image).
"""
function get_guild_widget_image(c::Client, guild::Integer; kwargs...)
return Response{Vector{UInt8}}(c, :GET, "/guilds/$guild/widget.png"; kwargs...)
end
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19806,
3712,
46541,
11,
264,
3712,
50,
1740,
6309,
9237,
26,
479,
86,
22046,
23029,
198,
220,
220,
220,
1441,
18261,
90,
50,
1740,
6309,
9237,
92,
7,
66,
11,
1058,
47,
11417,
11,
12813,
70,
3547,
82,
32624,
7,
70,
3547,
20679,
1416,
704,
6309,
12,
31534,
32624,
7,
82,
13,
312,
8,
8172,
1767,
28,
82,
8,
198,
437,
198,
198,
37811,
198,
220,
220,
220,
12233,
62,
70,
3547,
62,
1416,
704,
6309,
62,
15596,
7,
66,
3712,
11792,
11,
19806,
3712,
46541,
26,
479,
86,
22046,
23029,
198,
198,
38727,
257,
16446,
27774,
6309,
8558,
13,
198,
5167,
3307,
685,
1456,
16151,
5450,
1378,
15410,
585,
13,
785,
14,
16244,
364,
14,
31628,
14,
37540,
14,
70,
3547,
12,
1416,
704,
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12,
15596,
2,
33678,
12,
70,
3547,
12,
1416,
704,
6309,
12,
15596,
8,
198,
37811,
198,
8818,
12233,
62,
70,
3547,
62,
1416,
704,
6309,
62,
15596,
7,
66,
3712,
11792,
11,
19806,
3712,
46541,
11,
264,
3712,
50,
1740,
6309,
9237,
8,
198,
220,
220,
220,
1441,
18261,
7,
66,
11,
1058,
7206,
2538,
9328,
11,
12813,
70,
3547,
82,
32624,
7,
70,
3547,
20679,
1416,
704,
6309,
12,
31534,
32624,
7,
82,
13,
312,
8,
4943,
198,
437,
628,
198,
37811,
198,
220,
220,
220,
651,
62,
70,
3547,
62,
1416,
704,
6309,
62,
15596,
7,
66,
3712,
11792,
11,
19806,
3712,
46541,
26,
479,
86,
22046,
23029,
198,
198,
3855,
257,
16446,
27774,
6309,
8558,
13,
198,
5167,
3307,
685,
1456,
16151,
5450,
1378,
15410,
585,
13,
785,
14,
16244,
364,
14,
31628,
14,
37540,
14,
70,
3547,
12,
1416,
704,
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12,
15596,
2,
1136,
12,
70,
3547,
12,
1416,
704,
6309,
12,
15596,
8,
198,
37811,
198,
8818,
651,
62,
70,
3547,
62,
1416,
704,
6309,
62,
15596,
7,
66,
3712,
11792,
11,
19806,
3712,
46541,
11,
264,
3712,
46541,
26,
479,
86,
22046,
23029,
198,
220,
220,
220,
1441,
18261,
90,
50,
1740,
6309,
9237,
92,
7,
66,
11,
1058,
18851,
11,
12813,
70,
3547,
82,
32624,
7,
70,
3547,
20679,
1416,
704,
6309,
12,
31534,
32624,
7,
82,
13,
312,
42501,
1767,
28,
46265,
22046,
8,
198,
437,
198,
198,
37811,
198,
220,
220,
220,
2251,
62,
70,
3547,
7,
66,
3712,
11792,
26,
479,
86,
22046,
23029,
4613,
16446,
198,
198,
16447,
257,
685,
63,
38,
3547,
63,
16151,
31,
5420,
737,
198,
5167,
3307,
685,
1456,
16151,
5450,
1378,
15410,
585,
1324,
13,
785,
14,
16244,
364,
14,
31628,
14,
37540,
14,
70,
3547,
2,
17953,
12,
70,
3547,
737,
198,
37811,
198,
8818,
2251,
62,
70,
3547,
7,
66,
3712,
11792,
26,
479,
86,
22046,
23029,
198,
220,
220,
220,
1441,
18261,
90,
38,
3547,
92,
7,
66,
11,
1058,
32782,
11,
12813,
70,
3547,
82,
8172,
1767,
28,
46265,
22046,
8,
198,
437,
198,
198,
37811,
198,
220,
220,
220,
651,
62,
70,
3547,
7,
66,
3712,
11792,
11,
19806,
3712,
46541,
8,
4613,
16446,
198,
198,
3855,
257,
685,
63,
38,
3547,
63,
16151,
31,
5420,
737,
198,
37811,
198,
8818,
651,
62,
70,
3547,
7,
66,
3712,
11792,
11,
19806,
3712,
46541,
8,
198,
220,
220,
220,
1441,
18261,
90,
38,
3547,
92,
7,
66,
11,
1058,
18851,
11,
12813,
70,
3547,
82,
32624,
70,
3547,
4943,
198,
437,
198,
198,
37811,
198,
220,
220,
220,
13096,
62,
70,
3547,
7,
66,
3712,
11792,
11,
19806,
3712,
46541,
26,
479,
86,
22046,
23029,
4613,
16446,
198,
198,
18378,
257,
685,
63,
38,
3547,
63,
16151,
31,
5420,
737,
198,
5167,
3307,
685,
1456,
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5450,
1378,
15410,
585,
1324,
13,
785,
14,
16244,
364,
14,
31628,
14,
37540,
14,
70,
3547,
2,
4666,
1958,
12,
70,
3547,
737,
198,
37811,
198,
8818,
13096,
62,
70,
3547,
7,
66,
3712,
11792,
11,
19806,
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46541,
26,
479,
86,
22046,
23029,
198,
220,
220,
220,
1441,
18261,
90,
38,
3547,
92,
7,
66,
11,
1058,
47,
11417,
11,
12813,
70,
3547,
82,
32624,
70,
3547,
8172,
1767,
28,
46265,
22046,
8,
198,
437,
198,
198,
37811,
198,
220,
220,
220,
12233,
62,
70,
3547,
7,
66,
3712,
11792,
11,
19806,
3712,
46541,
8,
198,
198,
38727,
257,
685,
63,
38,
3547,
63,
16151,
31,
5420,
737,
198,
37811,
198,
8818,
12233,
62,
70,
3547,
7,
66,
3712,
11792,
11,
19806,
3712,
46541,
8,
198,
220,
220,
220,
1441,
18261,
7,
66,
11,
1058,
7206,
2538,
9328,
11,
12813,
70,
3547,
82,
32624,
70,
3547,
4943,
198,
437,
198,
198,
37811,
198,
220,
220,
220,
651,
62,
70,
3547,
62,
354,
8961,
7,
66,
3712,
11792,
11,
19806,
3712,
46541,
8,
4613,
20650,
90,
15642,
585,
29239,
92,
198,
198,
3855,
262,
685,
63,
15642,
585,
29239,
63,
16151,
31,
5420,
8,
82,
287,
257,
685,
63,
38,
3547,
63,
16151,
31,
5420,
737,
198,
37811,
198,
8818,
651,
62,
70,
3547,
62,
354,
8961,
7,
66,
3712,
11792,
11,
19806,
3712,
46541,
8,
198,
220,
220,
220,
1441,
18261,
90,
38469,
90,
15642,
585,
29239,
11709,
7,
66,
11,
1058,
18851,
11,
12813,
70,
3547,
82,
32624,
70,
3547,
14,
354,
8961,
4943,
198,
437,
198,
198,
37811,
198,
220,
220,
220,
2251,
62,
70,
3547,
62,
17620,
7,
66,
3712,
11792,
11,
19806,
3712,
46541,
26,
479,
86,
22046,
23029,
4613,
39462,
29239,
198,
198,
16447,
257,
685,
63,
15642,
585,
29239,
63,
16151,
31,
5420,
8,
287,
257,
685,
63,
38,
3547,
63,
16151,
31,
5420,
737,
198,
5167,
3307,
685,
1456,
16151,
5450,
1378,
15410,
585,
1324,
13,
785,
14,
16244,
364,
14,
31628,
14,
37540,
14,
70,
3547,
2,
17953,
12,
70,
3547,
12,
17620,
737,
198,
37811,
198,
8818,
2251,
62,
70,
3547,
62,
17620,
7,
66,
3712,
11792,
11,
19806,
3712,
46541,
26,
479,
86,
22046,
23029,
198,
220,
220,
220,
1441,
18261,
90,
15642,
585,
29239,
92,
7,
66,
11,
1058,
32782,
11,
12813,
70,
3547,
82,
32624,
70,
3547,
14,
354,
8961,
8172,
1767,
28,
46265,
22046,
8,
198,
437,
198,
198,
37811,
198,
220,
220,
220,
13096,
62,
70,
3547,
62,
17620,
62,
1930,
1756,
7,
66,
3712,
11792,
11,
19806,
3712,
46541,
11,
6116,
23029,
198,
198,
5841,
1958,
262,
6116,
286,
685,
63,
15642,
585,
29239,
63,
16151,
31,
5420,
8,
82,
287,
257,
685,
63,
38,
3547,
63,
16151,
31,
5420,
737,
198,
5167,
3307,
685,
1456,
16151,
5450,
1378,
15410,
585,
1324,
13,
785,
14,
16244,
364,
14,
31628,
14,
37540,
14,
70,
3547,
2,
4666,
1958,
12,
70,
3547,
12,
17620,
12,
1930,
1756,
737,
198,
37811,
198,
8818,
13096,
62,
70,
3547,
62,
17620,
62,
1930,
1756,
7,
66,
3712,
11792,
11,
19806,
3712,
46541,
11,
6116,
23029,
198,
220,
220,
220,
1441,
18261,
7,
66,
11,
1058,
47,
11417,
11,
12813,
70,
3547,
82,
32624,
70,
3547,
14,
354,
8961,
8172,
1767,
28,
1930,
1756,
8,
198,
437,
198,
198,
37811,
198,
220,
220,
220,
651,
62,
70,
3547,
62,
19522,
7,
66,
3712,
11792,
11,
19806,
3712,
46541,
11,
2836,
3712,
46541,
8,
4613,
10239,
198,
198,
3855,
257,
685,
63,
27608,
63,
16151,
31,
5420,
8,
287,
257,
685,
63,
38,
3547,
63,
16151,
31,
5420,
737,
198,
37811,
198,
8818,
651,
62,
70,
3547,
62,
19522,
7,
66,
3712,
11792,
11,
19806,
3712,
46541,
11,
2836,
3712,
46541,
8,
198,
220,
220,
220,
1441,
18261,
90,
27608,
92,
7,
66,
11,
1058,
18851,
11,
12813,
70,
3547,
82,
32624,
70,
3547,
14,
30814,
32624,
7220,
4943,
198,
437,
198,
198,
37811,
198,
220,
220,
220,
1351,
62,
70,
3547,
62,
30814,
7,
66,
3712,
11792,
11,
19806,
3712,
46541,
26,
479,
86,
22046,
23029,
4613,
20650,
90,
27608,
92,
198,
198,
3855,
257,
1351,
286,
685,
63,
27608,
63,
16151,
31,
5420,
8,
82,
287,
257,
685,
63,
38,
3547,
63,
16151,
31,
5420,
737,
198,
5167,
3307,
685,
1456,
16151,
5450,
1378,
15410,
585,
1324,
13,
785,
14,
16244,
364,
14,
31628,
14,
37540,
14,
70,
3547,
2,
4868,
12,
70,
3547,
12,
30814,
737,
198,
37811,
198,
8818,
1351,
62,
70,
3547,
62,
30814,
7,
66,
3712,
11792,
11,
19806,
3712,
46541,
26,
479,
86,
22046,
23029,
198,
220,
220,
220,
1441,
18261,
90,
38469,
90,
27608,
11709,
7,
66,
11,
1058,
18851,
11,
12813,
70,
3547,
82,
32624,
70,
3547,
14,
30814,
8172,
479,
86,
22046,
23029,
198,
437,
198,
198,
37811,
198,
220,
220,
220,
751,
62,
70,
3547,
62,
19522,
7,
66,
3712,
11792,
26,
479,
86,
22046,
23029,
4613,
10239,
198,
198,
4550,
257,
685,
63,
12982,
63,
16151,
31,
5420,
8,
284,
257,
685,
63,
38,
3547,
63,
16151,
31,
5420,
737,
198,
5167,
3307,
685,
1456,
16151,
5450,
1378,
15410,
585,
1324,
13,
785,
14,
16244,
364,
14,
31628,
14,
37540,
14,
70,
3547,
2,
2860,
12,
70,
3547,
12,
19522,
737,
198,
37811,
198,
8818,
751,
62,
70,
3547,
62,
19522,
7,
66,
3712,
11792,
11,
19806,
3712,
46541,
11,
2836,
3712,
46541,
26,
479,
86,
22046,
23029,
198,
220,
220,
220,
1441,
18261,
90,
27608,
92,
7,
66,
11,
1058,
30076,
11,
12813,
70,
3547,
82,
32624,
70,
3547,
14,
30814,
32624,
7220,
8172,
1767,
28,
46265,
22046,
8,
198,
437,
198,
198,
37811,
198,
220,
220,
220,
13096,
62,
70,
3547,
834,
19522,
7,
66,
3712,
11792,
11,
19806,
3712,
46541,
11,
2836,
3712,
46541,
26,
479,
86,
22046,
23029,
198,
198,
5841,
1958,
257,
685,
63,
27608,
63,
16151,
31,
5420,
8,
287,
257,
685,
63,
38,
3547,
63,
16151,
31,
5420,
737,
198,
5167,
3307,
685,
1456,
16151,
5450,
1378,
15410,
585,
1324,
13,
785,
14,
16244,
364,
14,
31628,
14,
37540,
14,
70,
3547,
2,
4666,
1958,
12,
70,
3547,
12,
19522,
737,
198,
37811,
198,
8818,
13096,
62,
70,
3547,
62,
19522,
7,
66,
3712,
11792,
11,
19806,
3712,
46541,
11,
2836,
3712,
46541,
26,
479,
86,
22046,
23029,
198,
220,
220,
220,
1441,
18261,
7,
66,
11,
1058,
47,
11417,
11,
12813,
70,
3547,
82,
32624,
70,
3547,
14,
30814,
32624,
7220,
8172,
1767,
28,
46265,
22046,
8,
198,
437,
198,
198,
37811,
198,
220,
220,
220,
13096,
62,
14421,
62,
7220,
62,
17172,
7,
66,
3712,
11792,
11,
19806,
3712,
5317,
1362,
26,
479,
86,
22046,
23029,
4613,
10903,
198,
198,
5841,
1958,
262,
685,
63,
11792,
63,
16151,
31,
5420,
8,
2836,
338,
21814,
287,
257,
685,
63,
38,
3547,
63,
16151,
31,
5420,
737,
198,
5167,
3307,
685,
1456,
16151,
5450,
1378,
15410,
585,
1324,
13,
785,
14,
16244,
364,
14,
31628,
14,
37540,
14,
70,
3547,
2,
4666,
1958,
12,
14421,
12,
7220,
12,
17172,
737,
198,
37811,
198,
8818,
13096,
62,
14421,
62,
7220,
62,
17172,
7,
66,
3712,
11792,
11,
19806,
3712,
46541,
26,
479,
86,
22046,
23029,
198,
220,
220,
220,
1441,
18261,
90,
10100,
92,
7,
66,
11,
1058,
47,
11417,
11,
12813,
70,
3547,
82,
32624,
70,
3547,
14,
30814,
14,
31,
1326,
14,
17172,
8172,
1767,
28,
46265,
22046,
8,
198,
437,
198,
198,
37811,
198,
220,
220,
220,
751,
62,
70,
3547,
62,
19522,
62,
18090,
7,
66,
3712,
11792,
11,
19806,
3712,
46541,
11,
2836,
3712,
46541,
11,
2597,
3712,
46541,
8,
198,
198,
4550,
257,
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63,
47445,
63,
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31,
5420,
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284,
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685,
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27608,
63,
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5420,
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8818,
751,
62,
70,
3547,
62,
19522,
62,
18090,
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66,
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11,
19806,
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46541,
11,
2836,
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46541,
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2597,
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46541,
8,
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220,
220,
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18261,
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1058,
30076,
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12813,
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82,
32624,
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14,
30814,
32624,
7220,
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4781,
62,
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19806,
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27914,
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19806,
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220,
4781,
62,
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19806,
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45390,
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19806,
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2836,
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30814,
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7220,
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198,
198,
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198,
220,
220,
220,
651,
62,
70,
3547,
62,
65,
504,
7,
66,
3712,
11792,
11,
19806,
3712,
46541,
8,
4613,
20650,
90,
30457,
92,
198,
198,
3855,
257,
1351,
286,
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30457,
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287,
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38,
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198,
8818,
651,
62,
70,
3547,
62,
65,
504,
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66,
3712,
11792,
11,
19806,
3712,
46541,
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198,
220,
220,
220,
1441,
18261,
90,
38469,
90,
30457,
11709,
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66,
11,
1058,
18851,
11,
12813,
70,
3547,
82,
32624,
70,
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14,
65,
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198,
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198,
198,
37811,
198,
220,
220,
220,
651,
62,
3820,
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66,
3712,
11792,
11,
19806,
3712,
46541,
11,
220,
2836,
3712,
46541,
8,
4613,
10274,
198,
198,
3855,
257,
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30457,
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5420,
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287,
257,
685,
63,
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3547,
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5420,
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37811,
198,
8818,
651,
62,
70,
3547,
62,
3820,
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66,
3712,
11792,
11,
19806,
3712,
46541,
11,
2836,
3712,
46541,
8,
198,
220,
220,
220,
1441,
18261,
90,
30457,
92,
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66,
11,
1058,
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11,
12813,
70,
3547,
82,
32624,
70,
3547,
14,
65,
504,
32624,
7220,
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198,
437,
198,
198,
37811,
198,
220,
220,
220,
2251,
62,
70,
3547,
62,
3820,
7,
66,
3712,
11792,
11,
19806,
3712,
46541,
11,
2836,
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46541,
26,
479,
86,
22046,
23029,
198,
198,
30457,
257,
685,
63,
27608,
63,
16151,
31,
5420,
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422,
257,
685,
63,
38,
3547,
63,
16151,
31,
5420,
737,
198,
5167,
3307,
685,
1456,
16151,
5450,
1378,
15410,
585,
1324,
13,
785,
14,
16244,
364,
14,
31628,
14,
37540,
14,
70,
3547,
2,
17953,
12,
70,
3547,
12,
3820,
737,
198,
37811,
198,
8818,
2251,
62,
70,
3547,
62,
3820,
7,
66,
3712,
11792,
11,
19806,
3712,
46541,
11,
2836,
3712,
46541,
26,
479,
86,
22046,
23029,
198,
220,
220,
220,
1441,
18261,
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66,
11,
1058,
30076,
11,
12813,
70,
3547,
82,
32624,
70,
3547,
14,
65,
504,
32624,
7220,
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479,
86,
22046,
23029,
198,
437,
198,
198,
37811,
198,
220,
220,
220,
4781,
62,
70,
3547,
62,
3820,
7,
66,
3712,
11792,
11,
19806,
3712,
46541,
11,
2836,
3712,
46541,
8,
198,
198,
3118,
3820,
257,
685,
63,
12982,
63,
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31,
5420,
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422,
257,
685,
63,
38,
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63,
16151,
31,
5420,
737,
198,
37811,
198,
8818,
4781,
62,
70,
3547,
62,
3820,
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66,
3712,
11792,
11,
19806,
3712,
46541,
11,
2836,
3712,
46541,
8,
198,
220,
220,
220,
1441,
18261,
7,
66,
11,
1058,
7206,
2538,
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11,
12813,
70,
3547,
82,
32624,
70,
3547,
14,
65,
504,
32624,
7220,
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198,
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198,
198,
37811,
198,
220,
220,
220,
651,
62,
70,
3547,
62,
305,
829,
7,
66,
3712,
11792,
11,
19806,
3712,
46541,
8,
4613,
20650,
90,
47445,
92,
198,
198,
3855,
257,
685,
63,
38,
3547,
63,
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31,
5420,
33047,
82,
685,
63,
47445,
63,
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31,
5420,
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82,
13,
198,
37811,
198,
8818,
651,
62,
70,
3547,
62,
305,
829,
7,
66,
3712,
11792,
11,
19806,
3712,
46541,
8,
198,
220,
220,
220,
1441,
18261,
90,
38469,
90,
47445,
11709,
7,
66,
11,
1058,
18851,
11,
12813,
70,
3547,
82,
32624,
70,
3547,
14,
305,
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4943,
198,
437,
198,
198,
37811,
198,
220,
220,
220,
2251,
62,
70,
3547,
62,
18090,
7,
66,
3712,
11792,
11,
19806,
3712,
46541,
26,
479,
86,
22046,
8,
4613,
20934,
198,
198,
16447,
257,
685,
63,
47445,
63,
16151,
31,
5420,
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287,
257,
685,
63,
38,
3547,
63,
16151,
31,
5420,
737,
198,
5167,
3307,
685,
1456,
16151,
5450,
1378,
15410,
585,
1324,
13,
785,
14,
16244,
364,
14,
31628,
14,
37540,
14,
70,
3547,
2,
17953,
12,
70,
3547,
12,
18090,
737,
198,
37811,
198,
8818,
2251,
62,
70,
3547,
62,
18090,
7,
66,
3712,
11792,
11,
19806,
3712,
46541,
26,
479,
86,
22046,
23029,
198,
220,
220,
220,
1441,
18261,
90,
47445,
92,
7,
66,
11,
1058,
32782,
11,
12813,
70,
3547,
82,
32624,
70,
3547,
14,
305,
829,
8172,
1767,
28,
46265,
22046,
8,
198,
437,
198,
198,
37811,
198,
220,
220,
220,
13096,
62,
70,
3547,
62,
18090,
62,
1930,
1756,
7,
66,
3712,
11792,
11,
19806,
3712,
46541,
11,
6116,
23029,
4613,
20650,
90,
47445,
92,
198,
198,
5841,
1958,
262,
6116,
286,
685,
63,
47445,
63,
16151,
31,
5420,
8,
82,
287,
257,
685,
63,
38,
3547,
63,
16151,
31,
5420,
737,
198,
5167,
3307,
685,
1456,
16151,
5450,
1378,
15410,
585,
1324,
13,
785,
14,
16244,
364,
14,
31628,
14,
37540,
14,
70,
3547,
2,
4666,
1958,
12,
70,
3547,
12,
18090,
12,
1930,
1756,
737,
198,
37811,
198,
8818,
13096,
62,
70,
3547,
62,
18090,
62,
1930,
1756,
7,
66,
3712,
11792,
11,
19806,
3712,
46541,
11,
6116,
23029,
198,
220,
220,
220,
1441,
18261,
90,
38469,
90,
47445,
11709,
7,
66,
11,
1058,
47,
11417,
11,
12813,
70,
3547,
82,
32624,
70,
3547,
14,
305,
829,
8172,
1767,
28,
1930,
1756,
8,
198,
437,
198,
198,
37811,
198,
220,
220,
220,
13096,
62,
70,
3547,
62,
18090,
7,
66,
3712,
11792,
11,
19806,
3712,
46541,
11,
2597,
3712,
46541,
26,
479,
86,
22046,
8,
4613,
20934,
198,
198,
5841,
1958,
257,
685,
63,
47445,
63,
16151,
31,
5420,
8,
287,
257,
685,
63,
38,
3547,
63,
16151,
31,
5420,
737,
198,
5167,
3307,
685,
1456,
16151,
5450,
1378,
15410,
585,
1324,
13,
785,
14,
16244,
364,
14,
31628,
14,
37540,
14,
70,
3547,
2,
4666,
1958,
12,
70,
3547,
12,
18090,
737,
198,
37811,
198,
8818,
13096,
62,
70,
3547,
62,
18090,
7,
66,
3712,
11792,
11,
19806,
3712,
46541,
11,
2597,
3712,
46541,
26,
479,
86,
22046,
23029,
198,
220,
220,
220,
1441,
18261,
90,
47445,
92,
7,
66,
11,
1058,
47,
11417,
11,
12813,
70,
3547,
82,
32624,
70,
3547,
14,
305,
829,
32624,
18090,
8172,
1767,
28,
46265,
22046,
8,
198,
437,
198,
198,
37811,
198,
220,
220,
220,
12233,
62,
70,
3547,
62,
18090,
7,
66,
3712,
11792,
11,
19806,
3712,
46541,
11,
2597,
3712,
46541,
8,
198,
198,
38727,
257,
685,
63,
47445,
63,
16151,
31,
5420,
8,
422,
257,
685,
63,
38,
3547,
63,
16151,
31,
5420,
737,
198,
37811,
198,
8818,
12233,
62,
70,
3547,
62,
18090,
7,
66,
3712,
11792,
11,
19806,
3712,
46541,
11,
2597,
3712,
46541,
8,
198,
220,
220,
220,
1441,
18261,
7,
66,
11,
1058,
7206,
2538,
9328,
11,
12813,
70,
3547,
82,
32624,
70,
3547,
14,
305,
829,
32624,
18090,
4943,
198,
437,
198,
198,
37811,
198,
220,
220,
220,
651,
62,
70,
3547,
62,
1050,
1726,
62,
9127,
7,
66,
3712,
11792,
11,
19806,
3712,
46541,
26,
479,
86,
22046,
23029,
4613,
360,
713,
198,
198,
3855,
262,
1271,
286,
685,
63,
27608,
63,
16151,
31,
5420,
8,
82,
326,
561,
307,
4615,
422,
257,
685,
63,
38,
3547,
63,
16151,
31,
5420,
8,
287,
257,
778,
1726,
13,
198,
5167,
3307,
685,
1456,
16151,
5450,
1378,
15410,
585,
1324,
13,
785,
14,
16244,
364,
14,
31628,
14,
37540,
14,
70,
3547,
2,
1136,
12,
70,
3547,
12,
1050,
1726,
12,
9127,
737,
198,
37811,
198,
8818,
651,
62,
70,
3547,
62,
1050,
1726,
62,
9127,
7,
66,
3712,
11792,
11,
19806,
3712,
46541,
26,
479,
86,
22046,
23029,
198,
220,
220,
220,
1441,
18261,
90,
35,
713,
92,
7,
66,
11,
1058,
18851,
11,
12813,
70,
3547,
82,
32624,
70,
3547,
14,
1050,
1726,
8172,
479,
86,
22046,
23029,
198,
437,
198,
198,
37811,
198,
220,
220,
220,
2221,
62,
70,
3547,
62,
1050,
1726,
7,
66,
3712,
11792,
11,
19806,
3712,
46541,
26,
479,
86,
22046,
23029,
4613,
360,
713,
198,
198,
44140,
778,
46493,
685,
63,
27608,
63,
16151,
31,
5420,
8,
82,
422,
257,
685,
63,
38,
3547,
63,
16151,
31,
5420,
737,
198,
5167,
3307,
685,
1456,
16151,
5450,
1378,
15410,
585,
1324,
13,
785,
14,
16244,
364,
14,
31628,
14,
37540,
14,
70,
3547,
2,
27471,
12,
70,
3547,
12,
1050,
1726,
737,
198,
37811,
198,
8818,
2221,
62,
70,
3547,
62,
1050,
1726,
7,
66,
3712,
11792,
11,
19806,
3712,
46541,
26,
479,
86,
22046,
23029,
198,
220,
220,
220,
1441,
18261,
90,
35,
713,
92,
7,
66,
11,
1058,
32782,
11,
12813,
70,
3547,
82,
32624,
70,
3547,
14,
1050,
1726,
8172,
479,
86,
22046,
23029,
198,
437,
198,
198,
37811,
198,
220,
220,
220,
651,
62,
70,
3547,
62,
38888,
62,
2301,
507,
7,
66,
3712,
11792,
11,
19806,
3712,
46541,
8,
4613,
20650,
90,
35708,
47371,
92,
198,
198,
3855,
257,
1351,
286,
685,
63,
35708,
47371,
63,
16151,
31,
5420,
8,
82,
329,
262,
685,
63,
38,
3547,
63,
16151,
31,
5420,
737,
198,
37811,
198,
8818,
651,
62,
70,
3547,
62,
38888,
62,
2301,
507,
7,
66,
3712,
11792,
11,
19806,
3712,
46541,
8,
198,
220,
220,
220,
1441,
18261,
90,
38469,
90,
35708,
47371,
11709,
7,
66,
11,
1058,
18851,
11,
12813,
70,
3547,
82,
32624,
70,
3547,
14,
2301,
507,
4943,
198,
437,
198,
198,
37811,
198,
220,
220,
220,
651,
62,
70,
3547,
62,
16340,
2737,
7,
66,
3712,
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] | 2.60843 | 5,575 |
# SPDX-License-Identifier: X11
# 2020-12-07
# Day 1, Part 1
function main()
input = parse.(Int, readlines())
nums = falses(2020)
for x ∈ input
nums[x] = true
end
for x ∈ input
if 2020 - x > 0 && nums[2020 - x]
println(x * (2020 - x))
return
end
end
end
main()
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] | 2.238806 | 134 |
include(Pkg.dir("FinancialAnalytics/src/helpers.jl"))
using MarketData
r = percentchange(cl)
facts("keyword checking is correct") do
context("simple returns selected") do
@fact sum(keyword_check(r,false,false).values) => -0.8392600357038683
end
context("lsum(og returns selected") do
@fact sum(keyword_check(percentchange(cl,method="log"),false,true).values) => -0.8392600357038683
end
context("ssum(imple prices selected") do
@fact sum(keyword_check(cl,true,false).values) => -0.8392600357038683
end
context("lsum(og prices selected") do
@fact sum(keyword_check(basecall(cl,log),true,true).values) => -0.8392600357038683
end
end
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] | 2.65098 | 255 |
"""
Parameters
Abstract type representing a generic set of paramters for finding optimal
two-stage designs.
"""
abstract type Parameters end
# make parameters iterable for automatic broadcasting
Base.size(::Parameters) = ()
Base.length(par::Parameters) = 1
Base.getindex(par::Parameters, i) = par
"""
label(par::Parameters)
Return the label of a [`Parameters`](@ref)-object.
"""
label(par::Parameters) = try par.label catch error("not implemented") end
"""
null(par::Parameters)
Return the response rate under the null hypothesis of a [`Parameters`](@ref)-object.
"""
null(par::Parameters) = try par.p0 catch error("not implemented") end
"""
mtoer(par::Parameters)
Return the maximal type one error rate of a [`Parameters`](@ref)-object.
"""
mtoer(par::Parameters) = try par.mtoer catch error("not implemented") end
"""
mcrv(par::Parameters)
Return the minimal clinically relevant response rate of a [`Parameters`](@ref)-object.
"""
mcrv(par::Parameters) = try par.pmcrv catch error("not implemented") end
"""
samplespace(par::Parameters)
Return the [`SampleSpace`](@ref)-object of a [`Parameters`](@ref)-object.
"""
samplespace(par::Parameters) = try par.samplespace catch error("not implemeted") end
"""
maxsamplesize(par::Parameters)
Return the maximal overall sample size of a [`Parameters`](@ref)-object.
"""
maxsamplesize(par::Parameters) = error("not implemented")
"""
isgroupsequential(par::Parameters)::Bool
Returns `true` if the undrlying [`SampleSpace`](@ref) is constraint to
group-sequential designs.
"""
isgroupsequential(par::Parameters)::Bool = error("not implemented")
isunimodal(par::Parameters)::Bool = true
"""
allowsstoppingforfutility(par::Parameters)::Bool
Returns `true` if the undrlying [`SampleSpace`](@ref) allows stopping-for-futlity.
"""
allowsstoppingforfutility(par::Parameters) = error("not implemented")
"""
NoParameters <: Parameters
This dummy object is used is a [`Design`](@ref) is created manually without
providing information on the respective optimality criterion.
"""
struct NoParameters <: Parameters end
null(par::NoParameters) = error("NoParameters do not have null hypothesis")
mtoer(par::NoParameters) = error("NoParameters do not have mtoer")
samplespace(par::NoParameters) = error("NoParameters do not have sample space")
maxsamplesize(par::NoParameters) = error("NoParameters do not have maximal sample size")
Base.show(io::IO, object::NoParameters) = print("NoParameters")
"""
PointAlternative <: Parameters
Abstract type representing a generic set of paramters for finding optimal
two-stage designs which are characterized by a point alternative.
"""
abstract type PointAlternative <: Parameters end
alternative(par::PointAlternative) = try par.p1 catch error("not implemented") end
mcrv(par::PointAlternative) = try par.p1 catch error("not implemented") end
mtter(par::PointAlternative) = try par.beta catch error("not implemented") end
"""
VagueAlternative <: Parameters
Abstract type representing a generic set of paramters for finding optimal
two-stage designs which are characterized by a prior distribution over the
response probability.
"""
abstract type VagueAlternative <: Parameters end
prior(par::VagueAlternative, p::T) where {T<:Real} = try par.prior(p) catch error("not implemented") end
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] | 3.42229 | 978 |
#
# This file is part of module
# ModiaMedia (ModiaMedia/src/ModiaMedia.jl)
#
### Data structures that are additionally available for media <: PartialMixtureMedium -----------------------
"""
state = ThermodynamicState_pTX(p,T,X)
Generate a `ThermodynamicState_pT <: MixtureThermodynamicState` object containing
pressure `p` [Pa], temperature `T` [K], and a vector of mass fractions `X` as states.
"""
mutable struct ThermodynamicState_pTX <: MixtureThermodynamicState
p::Float64
T::Float64
X::Vector{Float64}
end
"R = gas_constant(medium,state) - return gas constant for `medium` from `state` in [J/mol.K]"
# should this be MixtureMedium?
# should this take MixtureThermodynamicState?
gas_constant(m::AbstractMedium, state::ThermodynamicState) = undefinedFunction("gas constant", m)
# moleToMassFractions: use inner constructor with fluid information.
# massToMoleFractions: use inner constructor with fluid information from abstract medium, since we have that type. | [
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] | 3.31 | 300 |
using Pkg
const HOME = ENV["HOME"]
using Printf
Pkg.activate("$HOME/HelioseismicKernels")
using MPI
MPI.Init()
using HelioseismicKernels
const comm = MPI.COMM_WORLD
const xobs1 = Point2D(pi/2, pi/3)
const xobs2 = Point2D(pi/2, pi/2)
const ℓ_range = 1:100
try
HelioseismicKernels.kernel_uₛₜ(comm, TravelTimes(), xobs1, xobs2, los_radial(), save = false, ℓ_range = 1:1)
HelioseismicKernels.kernel_uₛₜ(comm, TravelTimes(), xobs1, xobs2, los_earth(), save = false, ℓ_range = 1:1)
s_max = 25
tstart = time()
HelioseismicKernels.kernel_uₛₜ(comm,
TravelTimes(), xobs1, xobs2, los_earth(),
s_max = s_max, t_max = s_max, ℓ_range = ℓ_range)
tend = time()
Δt = round(Int, tend - tstart)
if MPI.Comm_rank(comm) == 0
@printf "los\t%d\t%d\n" s_max Δt
end
tstart = time()
HelioseismicKernels.kernel_uₛₜ(comm,
TravelTimes(), xobs1, xobs2, los_radial(),
s_max = s_max, t_max = s_max, ℓ_range = ℓ_range)
tend = time()
Δt = round(Int, tend - tstart)
if MPI.Comm_rank(comm) == 0
@printf "rad\t%d\t%d\n" s_max Δt
end
finally
MPI.Finalize()
end
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366,
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4,
67,
59,
83,
4,
67,
59,
77,
1,
264,
62,
9806,
37455,
83,
198,
220,
220,
220,
886,
198,
198,
69,
3289,
198,
220,
220,
220,
4904,
40,
13,
19006,
1096,
3419,
198,
437,
198
] | 1.974138 | 580 |
module TestDoctest
import DataTools
using Documenter: doctest
using Test
@testset "doctest" begin
doctest(DataTools; manual = false)
end
end # module
| [
21412,
6208,
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##################################################
# Component types
abstract type UCComponentType end
abstract type RequiredConstraints <: UCComponentType end
abstract type SystemConstraints <: UCComponentType end
abstract type GenerationLimits <: UCComponentType end
abstract type PiecewiseProduction <: UCComponentType end
abstract type UpDownTime <: UCComponentType end
abstract type ReserveConstraints <: UCComponentType end
abstract type RampLimits <: UCComponentType end
abstract type StartupCosts <: UCComponentType end
abstract type ShutdownCosts <: UCComponentType end
##################################################
# Components
"""
Generic component of the unit commitment problem.
Elements
===
* `name`: name of the component
* `description`: gives a brief summary of what the component adds
* `type`: reference back to the UCComponentType being modeled
* `vars`: required variables
* `constrs`: constraints that are created by this function
* `add_component`: function to add constraints and update the objective to capture this component
* `params`: extra parameters the component might use
"""
mutable struct UCComponent
"Name of the component."
name::String
"Description of what the component adds."
description::String
"Which part of the unit commitment problem is modeled by this component."
type::Type{<:UCComponentType}
"Variables that are needed for the component (subset of `var_list`)."
vars::Union{Array{Symbol},Nothing}
"Equations that are modified for the component (subset of `constr_list`)."
constrs::Union{Array{Symbol},Nothing}
"Function to add constraints and objective coefficients needed for this component to the model. Signature should be (component, mip, model)."
add_component::Union{Function,Nothing}
"Extra parameters for the component."
params::Any
end # struct UCComponent
export UCComponent
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"""
This functions returns an AxisArray of project parameters
included in CEM for price projection and endogeneous Economic Dispatch.
"""
function make_parameter_vector(
structs::Vector, id::Symbol, parameter::Symbol)
ids = getproperty.(structs, id)
vals = getproperty.(structs, parameter)
return AxisArrays.AxisArray(vals, ids)
end
"""
This functions returns an array of market parameters
included in CEM for price projection and endogeneous Economic Dispatch.
"""
function make_parameter_vector(
structs::Vector{<:MarketCollection}, market::Symbol, parameter::Symbol)
markets = getproperty.(structs, market)
return getproperty.(markets, parameter)
end
"""
This functions returns a matrix of demand parameters
included in CEM for price projection and endogeneous Economic Dispatch.
"""
function make_demand_matrix(structs::Vector{<:MarketCollection}, market::Symbol)
markets = getproperty.(structs, market)
matrix = hcat(getproperty.(markets, :demand)...)
return permutedims(matrix)
end
"""
This functions returns capacity market demand curve parameters
included in CEM for price projection and endogeneous Economic Dispatch.
"""
function make_capacity_demand_vectors(capmarkets::Vector{CapacityMarket})
break_points = getproperty.(capmarkets, :break_points)
price_points = getproperty.(capmarkets, :price_points)
num_segments = length.(break_points) .- 1
segment_size = [zeros(num_segments[i]) for i in 1:length(capmarkets)]
segment_grad = [zeros(num_segments[i]) for i in 1:length(capmarkets)]
for p in 1:length(capmarkets)
for segment in 1:num_segments[p]
segment_size[p][segment] = break_points[p][segment + 1] - break_points[p][segment]
if segment_size[p][segment] != 0
segment_grad[p][segment] = (price_points[p][segment + 1] - price_points[p][segment]) / segment_size[p][segment]
end
end
end
return segment_size, segment_grad, price_points, num_segments
end
"""
This functions returns operating reserve demand curve parameters
included in CEM for price projection and endogeneous Economic Dispatch.
"""
function make_ORDC_vectors(reserveup_markets::Vector{ReserveUpMarket})
break_points = getproperty.(reserveup_markets, :break_points)
price_points_raw = getproperty.(reserveup_markets, :price_points)
zones = AxisArrays.axisvalues(break_points[1])[1]
num_segments = AxisArrays.AxisArray(Array{Int64, 2}(undef, length(zones), length(break_points)), zones, 1:length(break_points))
segment_size = AxisArrays.AxisArray(Array{Vector{Float64}, 2}(undef, length(zones), length(break_points)), zones, 1:length(break_points))
segment_grad = AxisArrays.AxisArray(Array{Vector{Float64}, 2}(undef, length(zones), length(break_points)), zones, 1:length(break_points))
price_points = AxisArrays.AxisArray(Array{Vector{Float64}, 2}(undef, length(zones), length(break_points)), zones, 1:length(break_points))
for z in zones
for p in 1:length(break_points)
num_segments[z, p] = length(break_points[p][z]) - 1
segment_size[z, p] = zeros(num_segments[z, p])
segment_grad[z, p] = zeros(num_segments[z, p])
price_points[z, p] = price_points_raw[p][z]
for segment in 1:num_segments[z, p]
segment_size[z, p][segment] = break_points[p][z][segment + 1] - break_points[p][z][segment]
if segment_size[z, p][segment] != 0
segment_grad[z, p][segment] = (price_points[z, p][segment + 1] - price_points[z, p][segment]) / segment_size[z, p][segment]
end
end
end
end
return segment_size, segment_grad, price_points, num_segments
end
"""
This functions returns capacity market demand curve parameters
included in the actual clearing of capacity markets.
"""
function make_capacity_demand(capmarket::CapacityMarket)
break_points = getproperty(capmarket, :break_points)
price_points = getproperty(capmarket, :price_points)
num_segments = length(break_points) - 1
segment_size = zeros(num_segments)
segment_grad = zeros(num_segments)
for segment in 1:num_segments
segment_size[segment] = break_points[segment + 1] - break_points[segment]
segment_grad[segment] = (price_points[segment + 1] - price_points[segment]) / segment_size[segment]
end
return segment_size, segment_grad, price_points
end
"""
This functions returns capital cost multiplier curve parameters
included in CEM for price projection and endogeneous Economic Dispatch.
"""
function make_capital_cost_curve(options_by_type::Dict{String, Vector{String}},
annualized_cap_cost::AxisArrays.AxisArray{Float64, 2},
basecostunits::AxisArrays.AxisArray{Int64, 1},
maxnewoptions::AxisArrays.AxisArray{Int64, 1},
capital_cost_multiplier::Float64)
invperiods = size(annualized_cap_cost, 2)
types = collect(keys(options_by_type))
num_segments = AxisArrays.AxisArray(round.(Int, 2 * ones(length(types), invperiods)), types, 1:invperiods)
segment_size = AxisArrays.AxisArray(Array{Vector{Float64}, 2}(undef, length(types), invperiods), types, 1:invperiods)
segment_grad = AxisArrays.AxisArray(Array{Vector{Float64}, 2}(undef, length(types), invperiods), types, 1:invperiods)
costpoints = AxisArrays.AxisArray(Array{Vector{Float64}, 2}(undef, length(types), invperiods), types, 1:invperiods)
for t in types
for p in 1:invperiods
segment_size[t, p] = zeros(num_segments[t, p])
segment_grad[t, p] = zeros(num_segments[t, p])
costpoints[t, p] = zeros(num_segments[t, p])
if length(options_by_type[t]) >= 1
total_max_new_options = sum(maxnewoptions[g] for g in options_by_type[t])
g = options_by_type[t][1]
segment_size[t, p][1] = basecostunits[g]
segment_size[t, p][2] = total_max_new_options - basecostunits[g]
costpoints[t, p] = [annualized_cap_cost[g, p], annualized_cap_cost[g, p]]
segment_grad[t, p][1] = 0.0
segment_grad[t, p][2] = costpoints[t, p][2] * capital_cost_multiplier
end
end
end
return segment_size, segment_grad, costpoints, num_segments
end
"""
This function updates the expected capacity factors of projects which are not in Option phase.
"""
function update_capacity_factors!(project::P,
scenario_name::String,
capacity_factors::Dict{String, Array{Float64, 2}}) where P <: Project{<: BuildPhase}
for product in get_products(project)
set_capacity_factors!(product, scenario_name, capacity_factors[get_name(project)])
end
return
end
"""
This function updates the expected capacity factors of Option projects.
"""
function update_capacity_factors!(project::P,
scenario_name::String,
capacity_factors::Dict{String, Array{Float64, 2}}) where P <: Project{Option}
tech = get_tech(project)
for product in get_products(project)
set_capacity_factors!(product, scenario_name, capacity_factors["option_$(get_type(tech))_$(get_zone(tech))"])
end
return
end
"""
This function updates the expected accepted capacity percentage of projects which are not in Option phase.
"""
function update_capacity_accepted_perc!(project::P,
scenario_name::String,
capacity_accepted_perc::Dict{String, Array{Float64, 1}}) where P <: Project{<: BuildPhase}
for product in get_products(project)
set_accepted_perc!(product, scenario_name, capacity_accepted_perc[get_name(project)])
end
return
end
"""
This function updates the expected accepted capacity percentage of Option projects.
"""
function update_capacity_accepted_perc!(project::P,
scenario_name::String,
capacity_accepted_perc::Dict{String, Array{Float64, 1}}) where P <: Project{Option}
tech = get_tech(project)
for product in get_products(project)
set_accepted_perc!(product, scenario_name, capacity_accepted_perc["option_$(get_type(tech))_$(get_zone(tech))"])
end
return
end
"""
This function does nothing if the product is not of Capacity type.
"""
function update_initial_capacity_revenues!(project::P,
product::T,
initial_capacity_prices::Vector{Float64},
year::Int64) where {P <: Project{<: BuildPhase}, T <: Product}
return
end
"""
This function updates the capacity market revenues of project at the start of the simulation.
"""
function update_initial_capacity_revenues!(project::P,
product::Capacity,
initial_capacity_prices::Vector{Float64},
year::Int64) where P <: Project{<: BuildPhase}
capacity_revenue = initial_capacity_prices[year] * get_maxcap(project) * get_project_derating(project)
finance_data = get_finance_data(project)
set_realized_profit!(get_finance_data(project),
get_name(product),
year,
capacity_revenue)
for scenario_name in keys(get_scenario_profit(finance_data))
update_forward_profit!(product, finance_data, scenario_name, year, capacity_revenue)
end
return
end
"""
This function does nothing if the product is not of Capacity or REC type.
"""
function update_bid!(product::T,
capacity_market_bid::Float64,
rec_market_bid::Float64,
energy_production::Float64) where T <: Product
return
end
"""
This function updates the Capacity market bid of projects.
"""
function update_bid!(product::Capacity,
capacity_market_bid::Float64,
rec_market_bid::Float64,
energy_production::Float64)
set_capacity_bid!(product, capacity_market_bid)
return
end
"""
This function updates the REC market bid of projects.
"""
function update_bid!(product::REC,
capacity_market_bid::Float64,
rec_market_bid::Float64,
energy_production::Float64)
set_rec_bid!(product, rec_market_bid)
set_rec_certificates!(product, energy_production)
return
end
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] | 2.383834 | 4,528 |
#######################################################################################
# Tangent Dynamics (aka linearized dynamics) #
#######################################################################################
# IIP Tangent Space dynamics
function create_tangent(@nospecialize(f::F), @nospecialize(jacobian::JAC), J::JM,
::Val{true}, ::Val{k}) where {F, JAC, JM, k}
J = deepcopy(J)
tangentf = (du, u, p, t) -> begin
uv = @view u[:, 1]
f(view(du, :, 1), uv, p, t)
jacobian(J, uv, p, t)
mul!((@view du[:, 2:(k+1)]), J, (@view u[:, 2:(k+1)]))
nothing
end
return tangentf
end
# for the case of autodiffed systems, a specialized version is created
# so that f! is not called twice in ForwardDiff
function create_tangent_iad(f::F, J::JM, u, p, t, ::Val{k}) where {F, JM, k}
let
J = deepcopy(J)
cfg = ForwardDiff.JacobianConfig(
(du, u) -> f(du, u, p, p), deepcopy(u), deepcopy(u)
)
tangentf = (du, u, p, t) -> begin
uv = @view u[:, 1]
ForwardDiff.jacobian!(
J, (du, u) -> f(du, u, p, t), view(du, :, 1), uv, cfg, Val{false}()
)
mul!((@view du[:, 2:k+1]), J, (@view u[:, 2:k+1]))
nothing
end
return tangentf
end
end
# OOP Tangent Space dynamics
function create_tangent(f::F, jacobian::JAC, J::JM,
::Val{false}, ::Val{k}) where {F, JAC, JM, k}
ws_index = SVector{k, Int}(2:(k+1)...)
tangentf = TangentOOP{F, JAC, k}(f, jacobian, ws_index)
return tangentf
end
struct TangentOOP{F, JAC, k} <: Function
f::F
jacobian::JAC
ws::SVector{k, Int}
end
function (tan::TangentOOP)(u, p, t)
@inbounds s = u[:, 1]
du = tan.f(s, p, t)
J = tan.jacobian(s, p, t)
@inbounds dW = J*u[:, tan.ws]
return hcat(du, dW)
end
#######################################################################################
# Parallel Dynamics #
#######################################################################################
# Create equations of motion of evolving states in parallel
function create_parallel(ds::DS{true}, states)
st = [Vector(s) for s in states]
L = length(st)
paralleleom = (du, u, p, t) -> begin
for i in 1:L
@inbounds ds.f(du[i], u[i], p, t)
end
end
return paralleleom, st
end
function create_parallel(ds::DS{false}, states)
D = dimension(ds)
st = [SVector{D}(s) for s in states]
L = length(st)
# The following may be inneficient
paralleleom = (du, u, p, t) -> begin
for i in 1:L
@inbounds du[i] = ds.f(u[i], p, t)
end
end
return paralleleom, st
end
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] | 2.134644 | 1,322 |
#########################################################################################################################################
######### knotvector.jl contains all basic functionality in order to construct and manipulate B-spline knotvectors ######################
#########################################################################################################################################
#
# Functions:
# buildvector() - construct a knotvector using a vector with increasing values of type T and a vector # specifying the knot multiplicity
# uniqueknots() - decompose a knotvector into an increasing vector of Float64, the knotmultiplicity, and vectors specifying indexing into the knotvector
# globalrefine() - put k new knots inside the non-zero knotspans of the knotvector
# findspan() - determine the knot span index of a point 'u' in the knotvector 'kts'
# findnonzerospan() - find span of all non-zero span elements in knotvector kts
# getelementknotvectors() - determine the local knotvectors of each non-zero-span element in the knovector
#
#########################################################################################################################################
#
# File part of the Julia IsoGeometric Analysis toolbox
# R.R.Hiemstra
# 14-09-2015
#
########################################################################################################################################
#########################################################################################################################################
# typealiases
const Degree = Int
const KnotVector = Vector{Float64}
"""
construct a knotvector using a vector with increasing values of type T and a vector specifying the knot multiplicity
## input:
uvals :: Vector{T} - vector with increasing values of type T
mult :: Vector{Int} - vector specifying the knot multiplicity
## output:
kts :: Vector{T} - knotvector with knot values of type T
"""
function buildvector(uvals::Array{T,1},mult::Array{Int64,1}) where {T<:Number}
# allocate space
v = zeros(T,sum(mult))
# set values
ind = 1
for i in 1:length(uvals)
v[ind:ind+mult[i]-1] = fill(uvals[i],mult[i])
ind = ind + mult[i]
end
return v
end
"""
decompose a knotvector into an increasing vector of Float64, the knotmultiplicity, and vectors specifying indexing into the knotvector
## input:
kts :: Vector{Float64} - knotvector
## output:
ukts :: Vector{Float64} - unique knot values in increasing order
mult :: Vector{Int} - corresponding knot multiplicity
inda :: Vector{Int} - indexing such that kts==ukts[inda]
indb :: Vector{Int} - indexing such that kts[indb]==ukts
"""
function uniqueknots(knots::Array{Float64,1})
n = length(knots)
mult = ones(Int64,n)
uknots = zeros(Float64,n)
inda = zeros(Int64,n)
indc = zeros(Int64,n)
j = 1; uknots[1] = knots[1]; inda[1] = 1; indc[1] = 1
for i in 2:n
if knots[i]==knots[i-1]
mult[j] += 1
else
j+=1
uknots[j] = knots[i]
indc[j] = i
end
inda[i] = j
end
return uknots[1:j], mult[1:j], inda, indc[1:j]
end
function uniqueknots(kts)
n = length(kts)
temp = ntuple(i -> uniqueknots(kts[i]), n)
return ukts, umult, inda, indc = ntuple(j -> ntuple(i -> temp[i][j], n), 4)
end
"""
global refine knotvector: put k new knots in non-zero knotspans
## input:
kts :: Vector{Float64} - knotvector
k :: Int - integer prescribing the number of new knots to put in current non-zero knot-spans
## output:
newkts :: Vector{Float64} - refined knotvector
"""
function globalrefine(kts::Vector{Float64},k::Int)
# get unique knots and multiplicity
ukts, umult, inda, indc = uniqueknots(kts)
n = length(kts); m = length(ukts)
# compute new knotvector
newkts = zeros(n + k*(m-1)); j = 1
for i in 1:m-1
newkts[j:j+umult[i]-1] = ukts[i]
j += umult[i]-1
newkts[j:j+k+1] = linspace(ukts[i],ukts[i+1],k+2)
j += k+1
end
newkts[j:j+umult[m]-1] = ukts[m]
return newkts
end
"""
Determine the knot span index of a point 'u' in th knotvector 'kts'
## input:
p :: Int - polynomial degree
kts :: Vector{Float64} - knotvector
u :: Float64 - evaluation point
## output:
span :: Int - knot span index of a point 'u' in th knotvector 'kts'
"""
function findspan(p::Integer, kts::Vector{Float64}, u::Float64)
if u<kts[1] || u>kts[end]
error("Boundserror findspan(p::Integer, kts::Vector{Float64}, u::Float64)")
end
if u==kts[end]
span = length(kts)-1
while kts[span]==kts[span+1]
span+=-1
end
else
low = 0
high = length(kts)
mid = round(Int64,(low + high)/2)
while u < kts[mid] || u >= kts[mid + 1]
if u < kts[mid]
high = mid
else
low = mid
end
mid = round(Int64,(low + high)/2)
end
span = round(Int64,(low + high)/2)
end
return span
end
"""
Determine the knot span index of a vector of points 'u' in the knotvector 'kts'
## input:
p :: Int - polynomial degree
kts :: Vector{Float64} - knotvector
u :: Vector{Float64} - evaluation point
## output:
span :: Vector{Int} - vector with knot span index of each point u[i] in the knotvector 'kts'
"""
function findspan(p::Integer, kts::Vector{Float64}, u::Vector{Float64})
# spline space dimension
n = length(kts)-p-1
# allocate space for span
m = length(u)
span = zeros(Int64,m)
span[1] = findspan(p,kts,u[1])
span[m] = findspan(p,kts,u[m])
# find span at u[i]
for i in 2:m-1
span[i] = span[i-1]
while u[i] > kts[span[i]+1]
span[i] += 1
end
end
return span
end
function findfunswithcompactsupp(p::Int,kts::Vector{Float64},a::Float64,b::Float64)
n = length(kts); k = 1
while a!=kts[k] && k<n
k+=1
end
a = k
while b>=kts[k] && k<=n
k+=1
end
b = k-2-p
return a:b
end
"""
find span of all non-zero span elements in knotvector 'kts'
## input:
p :: Int - polynomial degree
kts :: Vector{Float64} - knotvector
## output:
span :: Vector{Int} - vector with knot span index of each non-zero-span elements in the knotvector 'kts'
"""
function findnonzerospan(p::Int,kts::Vector{Float64})
m = length(kts)
span = zeros(Int64,m)
count = 1
for j in p+1:m-p-1
if kts[j]!=kts[j+1]
span[count] = j
count+=1
end
end
return span[1:count-1]
end
findnonzerospan(kts::NTuple{N,Vector{Float64}}) where {N} = ntuple(length(kts),(j)->findnonzerospan(kts[j]))
"find the elements in support of basisfunctions"
function findsupp(p::Int, kts::Vector{Float64})
span = findnonzerospan(p,kts)
n = dimsplinespace(p,kts)
m = length(span)
graph = zeros(Bool,m,n)
for k in 1:m
graph[k,span[k]-p:span[k]] = true
end
return [[1:m][graph[:,k]] for k in 1:n]
end
"""
get element knot vectors from global knot partition
## input:
p :: Int - polynomial degree
kts :: Vector{Float64} - knotvector
span :: Vector{Int} - span-index of each non-zero knot-span in the knotvector
## output:
ekts :: Vector{Vector{Float64}} - vector knotvectors local to each element in knotvector kts
"""
getelementknotvectors(p::Int, kts::Vector{Float64}, span::Vector{Int}) = [kts[span[i]-p:span[i]+1+p] for i in 1:length(span)]
"""
get element knot vectors from global knot partition
## input:
p :: Int - polynomial degree
kts :: Vector{Float64} - knotvector
## output:
vkts :: Vector{Vector{Float64}} - vector knotvectors local to each element in knotvector kts
"""
getelementknotvectors(p::Int, kts::Vector{Float64}) = getelementknotvectors(p,kts,findnonzerospan(p,kts))
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] | 2.412777 | 3,428 |
module MechGluecode
using Requires
export value, ODE_DEFAULT_NORM, UNITLESS_ABS2, Unitfu, norm
function __init__()
@require MechanicalUnits = "e6be9192-89dc-11e9-36e6-5dbcb28f419e" begin
@require Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80" begin
@info "Plots => using MechGluePlots"
@eval using MechGluePlots
end
@require Interpolations = "a98d9a8b-a2ab-59e6-89dd-64a1c18fca59" begin
@info "Interpolations => using MechGlueInterpolations"
@eval using MechGlueInterpolations
end
@require ModelingToolkit = "961ee093-0014-501f-94e3-6117800e7a78" begin
#@info "ModelingToolkit => using MechGlueModelingToolkit"
#@eval using MechGlueModelingToolkit
end
@require RecursiveArrayTools = "731186ca-8d62-57ce-b412-fbd966d074cd" begin
# @info "RecursiveArrayTools => using MechGlueRecursiveArrayTools"
# @eval using MechGlueDiffEqBase
# @info "RecursiveArrayTools => using MechGlueRecursiveArrayTools"
# @eval using MechGlueRecursiveArrayTools
end
@require DiffEqBase = "2b5f629d-d688-5b77-993f-72d75c75574e" begin
@info "DiffEqBase => using MechGlueDiffEqBase"
@eval using MechGlueDiffEqBase
end
end
@info "MechGluecode init"
end
end
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] | 2.074581 | 657 |
#################################################################################
# Comparison of Zig-Zag for diffusion bridges with tailored Poisson rates #
# and with adapted Poisson rate. Reference: https://arxiv.org/abs/2001.05889. #
#################################################################################
using ZigZagBoomerang, SparseArrays, LinearAlgebra
#using CairoMakie
include("../../src/faberschauder.jl")
include("../../src/asvar.jl")
include("../../src/pdmp.jl")
const ZZB = ZigZagBoomerang
using CSV
using DataFrames
using Statistics
# Drift
b(x) = α * sin(x)
# First derivative
b′(x) = α * cos(x)
# Second derivative
b″(x) = -α * sin(x)
####################################################################
# Overloading Poisson times in order to have tighter upperbounds #
####################################################################
struct MyBound
c::Float64
end
function ZZB.adapt!(b::Vector{MyBound}, i, x)
b[i] = MyBound(b[i].c * x)
end
"""
poisson_time(a, b, c, u)
Obtaining waiting time for inhomogeneous Poisson Process
with rate of the form λ(t) = a + (b + c*t)^+, where `c`,`a`> 0 ,`b` ∈ R, `u` uniform random variable
"""
function ZZB.poisson_time((a, b, c)::NTuple{3}, u = rand()) # formula (22)
if b > 0
return (-(b + a) + sqrt((b + a)^2 - 2.0 * c * log(u))) / c # positive solution of quadratic equation c*0.5 x^2 + (b + a) x + log(u) = 0
elseif a * b / c <= log(u)
return -log(u) / a
else
return (-(a + b) + sqrt((a + b)^2 - 2.0 * c * (b * b * 0.5 / c + log(u)))) / c # positive solution of quadratic equation c*0.5 x^2 + (b + a) x + log(u) + b*b*0.5/c = 0
end
end
ZZB.sλ̄((a, b, c)::NTuple{3}, Δt) = a + ZZB.pos(b + c * Δt)
"""
abc(G, i, x, θ, c, Flow)
Returns the constant term `a` and linear term `b` when computing the Poisson times
from the upper upper bounding rates λᵢ(t) = max(a + b*t)^2. The factors `a` and `b`
can be function of the current position `x`, velocity `θ`, tuning parameter `c` and
the Graph `G`
"""
function ZZB.ab(G, i, x, θ, c::Vector{MyBound}, F::ZigZag)
if i == 1
a = c[i].c + T^(1.5)*0.5*(α^2 + α) * abs(θ[i]) # initial point
b1 = θ[i]*(x[i] - x[end])
b2 = θ[i]*(θ[i] - θ[end])
elseif i == (2 << L) + 1
a = c[i].c + T^(1.5)*0.5*(α^2 + α) * abs(θ[i]) # final point
b1 = θ[i]*(x[i] - x[1])
b2 = θ[i]*(θ[i] - θ[1])
else
l = lvl(i, L)
a = c[i].c + T^(1.5) / 2^((L - l) * 1.5 + 2) * (α^2 + α) * abs(θ[i]) # formula (22)
b1 = x[i] * θ[i]
b2 = θ[i] * θ[i]
end
return a, b1, b2
end
function ∇ϕmoving_var1(t, ξ, θ, i, t′, F, L, T) # formula (17)
if i == (2 << L) + 1 # final point
s = T * (rand())
x = dotψmoving(t, ξ, θ, t′, s, F, L, T)
return -b(ξ[end]*sqrt(T))*sqrt(T) + 0.5 * sqrt(T) * s * (2b(x) * b′(x) + b″(x)) + ξ[i] - ξ[1]
elseif i == 1 # initial point
s = T * (rand())
x = dotψmoving(t, ξ, θ, t′, s, F, L, T)
return 0.5 * T^(1.5) * (1 - s / T) * (2b(x) * b′(x) + b″(x)) + ξ[1]
else
l = lvl(i, L)
k = (i - 1) ÷ (2 << l)
δ = T / (1 << (L - l))
res = 0.0
ave_fact = 1/(l+1)
for _ in 1:l+1
s = δ * (k + rand())
x = dotψmoving(t, ξ, θ, t′, s, F, L, T)
res += ave_fact * 0.5 * δ * Λ(s, L - l, T) * (2b(x) * b′(x) + b″(x))
end
return res + ξ[i]
end
end
function ∇ϕmoving_var2(t, ξ, θ, i, t′, F, L, T) # formula (17)
if i == (2 << L) + 1 # final point
s = T * (rand())
x = dotψmoving(t, ξ, θ, t′, s, F, L, T)
return -b(ξ[end]*sqrt(T))*sqrt(T) + 0.5 * sqrt(T) * s * (2b(x) * b′(x) + b″(x)) + ξ[i] - ξ[1]
elseif i == 1 # initial point
s = T * (rand())
x = dotψmoving(t, ξ, θ, t′, s, F, L, T)
return 0.5 * T^(1.5) * (1 - s / T) * (2b(x) * b′(x) + b″(x)) + ξ[1]
else
l = lvl(i, L)
Δi = T / (1 << L) # subintervals
k = (i - 1) ÷ (2 << l)
δ = T / (1 << (L - l))
res = 0.0
ave_fact = 1/(l+1)
for jj in 1:l+1 # number of points proportional to the length
s = δ * k + Δi*(jj - 1 + rand()) #
@assert δ * k < s < δ * (k+1)
x = dotψmoving(t, ξ, θ, t′, s, F, L, T)
res += ave_fact * 0.5 * δ * Λ(s, L - l, T) * (2b(x) * b′(x) + b″(x))
end
return res + ξ[i]
end
end
##
function run_zz(df, T′)
println("zigzag var 1")
n = (2 << L) + 1
ξ0 = 0randn(n)
u, v = 0.0, 0.0 # initial and fianl point
ξ0[1] = u / sqrt(T)
ξ0[end] = v / sqrt(T)
θ0 = rand((-1.0, 1.0), n)
θ0[end] = θ0[1] = 0.0 # fix final point
Γ = sparse(1.0I, n, n)
c = [MyBound(0.0) for i in 1:n]
#Improved algorithm
zz_time = @elapsed((trace, (t, ξ, θ), (acc, num), c) = spdmp(∇ϕmoving_var1, 0.0, ξ0, θ0, T′, c, ZigZag(Γ, ξ0 * 0),
SelfMoving(), L, T, adapt = false))
t_trace = getindex.(trace.events, 1)
t_trace = [0.0, t_trace...]
x_trace, v_trace = resize(trace)
burn1 = 100.0
i1 = findfirst(x -> x > burn1, t_trace)
xT_2 = sqrt(T)/2*getindex.(x_trace[i1:end], Int((length(ξ0)+1)/2))
ave_xT_2_var = cumsum(((xT_2[1:end-1] + xT_2[2:end])/2).*diff(t_trace[i1:end]))./(t_trace[i1+1:end] .- burn1)
for batches in [50]
ess = ess_pdmp_components(t_trace[i1:end] .- burn1, x_trace[i1:end], v_trace[i1:end], n_batches = batches)
push!(df, Dict(:sampler => "ZZ_var", :alpha => α, :T => T′, :nbatches => batches,
:stat => "ess_mean", :y => sum(ess[2:end-1])/(length(ess)-2), :runtime => zz_time))
push!(df, Dict(:sampler => "ZZ_var", :alpha => α, :T => T′, :nbatches => batches,
:stat => "ess_median", :y => median(ess[2:end-1]), :runtime => zz_time))
push!(df, Dict(:sampler => "ZZ_var", :alpha => α, :T => T′, :nbatches => batches,
:stat => "ess_min", :y => minimum(ess[2:end-1]), :runtime => zz_time))
push!(df, Dict(:sampler => "ZZ_var", :alpha => α, :T => T′, :nbatches => batches,
:stat => "ess_x_T2", :y => ess[Int((length(ξ0)+1)/2)], :runtime => zz_time))
end
println("standard zigzag")
n = (2 << L) + 1
ξ0 = 0randn(n)
u, v = 0.0, 0.0 # initial and fianl point
ξ0[1] = u / sqrt(T)
ξ0[end] = v / sqrt(T)
θ0 = rand((-1.0, 1.0), n)
θ0[end] = θ0[1] = 0.0 # fix final point
Γ = sparse(1.0I, n, n)
c = [MyBound(0.0) for i in 1:n]
#Improved algorithm
zz_time = @elapsed((trace, (t, ξ, θ), (acc, num), c) = spdmp(∇ϕmoving, 0.0, ξ0, θ0, T′, c, ZigZag(Γ, ξ0 * 0),
SelfMoving(), L, T, adapt = false))
t_trace1 = getindex.(trace.events, 1)
t_trace1 = [0.0, t_trace1...]
x_trace, v_trace = resize(trace)
i1 = findfirst(x -> x > burn1, t_trace1)
xT_2 = sqrt(T)/2*getindex.(x_trace[i1:end], Int((length(ξ0)+1)/2))
ave_xT_2 = cumsum(((xT_2[1:end-1] + xT_2[2:end])/2).*diff(t_trace1[i1:end]))./(t_trace1[i1+1:end] .- burn1)
for batches in [50]
ess = ess_pdmp_components(t_trace1[i1:end] .- burn1, x_trace[i1:end], v_trace[i1:end], n_batches = batches)
push!(df, Dict(:sampler => "ZZ", :alpha => α, :T => T′, :nbatches => batches,
:stat => "ess_mean", :y => sum(ess[2:end-1])/(length(ess)-2), :runtime => zz_time))
push!(df, Dict(:sampler => "ZZ", :alpha => α, :T => T′, :nbatches => batches,
:stat => "ess_median", :y => median(ess[2:end-1]), :runtime => zz_time))
push!(df, Dict(:sampler => "ZZ", :alpha => α, :T => T′, :nbatches => batches,
:stat => "ess_min", :y => minimum(ess[2:end-1]), :runtime => zz_time))
push!(df, Dict(:sampler => "ZZ", :alpha => α, :T => T′, :nbatches => batches,
:stat => "ess_x_T2", :y => ess[Int((length(ξ0)+1)/2)], :runtime => zz_time))
end
println("zigzag var 2")
n = (2 << L) + 1
ξ0 = 0randn(n)
u, v = 0.0, 0.0 # initial and fianl point
ξ0[1] = u / sqrt(T)
ξ0[end] = v / sqrt(T)
θ0 = rand((-1.0, 1.0), n)
θ0[end] = θ0[1] = 0.0 # fix final point
Γ = sparse(1.0I, n, n)
c = [MyBound(0.0) for i in 1:n]
#Improved algorithm
zz_time = @elapsed((trace, (t, ξ, θ), (acc, num), c) = spdmp(∇ϕmoving_var2, 0.0, ξ0, θ0, T′, c, ZigZag(Γ, ξ0 * 0),
SelfMoving(), L, T, adapt = false))
t_trace3 = getindex.(trace.events, 1)
t_trace3 = [0.0, t_trace3...]
x_trace3, v_trace3 = resize(trace)
i1 = findfirst(x -> x > burn1, t_trace3)
xT_3 = sqrt(T)/2*getindex.(x_trace3[i1:end], Int((length(ξ0)+1)/2))
ave_xT_3 = cumsum(((xT_3[1:end-1] + xT_3[2:end])/2).*diff(t_trace3[i1:end]))./(t_trace3[i1+1:end] .- burn1)
for batches in [50]
ess = ess_pdmp_components(t_trace3[i1:end] .- burn1, x_trace3[i1:end], v_trace3[i1:end], n_batches = batches)
push!(df, Dict(:sampler => "ZZ_var2", :alpha => α, :T => T′, :nbatches => batches,
:stat => "ess_mean", :y => sum(ess[2:end-1])/(length(ess)-2), :runtime => zz_time))
push!(df, Dict(:sampler => "ZZ_var2", :alpha => α, :T => T′, :nbatches => batches,
:stat => "ess_median", :y => median(ess[2:end-1]), :runtime => zz_time))
push!(df, Dict(:sampler => "ZZ_var2", :alpha => α, :T => T′, :nbatches => batches,
:stat => "ess_min", :y => minimum(ess[2:end-1]), :runtime => zz_time))
push!(df, Dict(:sampler => "ZZ_var2", :alpha => α, :T => T′, :nbatches => batches,
:stat => "ess_x_T2", :y => ess[Int((length(ξ0)+1)/2)], :runtime => zz_time))
end
return df, (ave_xT_2, t_trace1), (ave_xT_2_var, t_trace), (ave_xT_3, t_trace3)
end
function resize(zz_trace)
x0 = zz_trace.x0
v0 = zz_trace.θ0
x_trace = [deepcopy(x0)]
v_trace = [deepcopy(v0)]
for i in eachindex(zz_trace.events)
if i == 1
x0 = x0 + v0*zz_trace.events[i][1]
else
x0 = x0 + v0*(zz_trace.events[i][1] - zz_trace.events[i-1][1])
end
v0[zz_trace.events[i][2]] = zz_trace.events[i][4]
x0[zz_trace.events[i][2]] = zz_trace.events[i][3]
# if !(v0[zz_trace.events[i][2]] == zz_trace.events[i][4])
# println("vel: $(v0[zz_trace.events[i][2]]) vs $(zz_trace.events[i][4])")
# error("")
# end
# if !(|x0[zz_trace.events[i][2]]| zz_trace.events[i][4])
# println("pos: $(x0[zz_trace.events[i][2]]) vs $(zz_trace.events[i][3])")
# error("")
# end
push!(x_trace, deepcopy(x0))
push!(v_trace, deepcopy(v0))
end
x_trace, v_trace
end
using DataFrames, Random
df = DataFrame(sampler = String[], alpha = Float64[], T = Float64[],
nbatches = Int64[], stat = String[], y = Float64[], runtime = Float64[])
function data_collection(df)
Random.seed!(0)
T′ = 25000.0
zz_var = []
zz_var1 = []
zz = []
for α′ in [0.1, 0.3, 0.5]
global α = α′
global L = 6
global T = 50.0
df, x1, x2, x3 = run_zz(df, T′)
push!(zz, x1)
push!(zz_var, x2)
push!(zz_var1, x3)
end
return df, zz, zz_var, zz_var1
end
using CSV, JLD2
df, zz, zz_var1, zz_var2 = data_collection(df)
#CSV.write("./scripts/zz_diff_bridges/compare/benchamrk_zz_final.csv", df)
@save "./scripts/compare/covenrgence_zz.jld2" zz
@save "./scripts/compare/covenrgence_zz_var.jld2" zz_var1
@save "./scripts/compare/covenrgence_zz_var1.jld2" zz_var2
error("")
#
# using Makie
# using CairoMakie
#
# T′ = 1000
# α =0.3
# n = (2 << L) + 1
# ξ0 = 0randn(n)
# u, v = 0.0, 0.0 # initial and fianl point
# ξ0[1] = u / sqrt(T)
# ξ0[end] = v / sqrt(T)
# θ0 = rand((-1.0, 1.0), n)
# θ0[end] = θ0[1] = 0.0 # fix final point
# Γ = sparse(1.0I, n, n)
# c = [MyBound(0.0) for i in 1:n]
# zz_time = @elapsed((trace, (t, ξ, θ), (acc, num), c) = spdmp(∇ϕmoving, 0.0, ξ0, θ0, T′, c, ZigZag(Γ, ξ0 * 0),
# SelfMoving(), L, T, adapt = false))
# t_trace = getindex.(trace.events, 1)
# t_trace = [0.0, t_trace...]
# x_trace, v_trace = resize(trace)
# ess = ess_pdmp_components(t_trace, x_trace, v_trace, n_batches = 50)
# #plot
# ts, ξs = splitpairs(discretize(trace, T′/n))
# S = T*(0:n)/(n+1)
# p1 = lines(S, [dotψ(ξ, s, L, T) for s in S], linewidth=0.3)
# for ξ in ξs[1:5:end]
# lines!(p1, S, [dotψ(ξ, s, L, T) for s in S], linewidth=0.3)
# end
# display(p1)
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] | 1.886291 | 6,543 |
module FHColoredLightbeam
using ..Ahorn, Maple
using Random
@mapdef Entity "FrostHelper/ColoredLightbeam" ColoredLightbeam(x::Integer, y::Integer, width::Integer=32, height::Integer=24, flag::String="", rotation::Integer=0, color::String="ccffff")
const colors = sort(collect(keys(Ahorn.XNAColors.colors)))
function lightbeamFinalizer(entity::ColoredLightbeam)
x, y = Ahorn.position(entity)
nx, ny = Int.(entity.nodes[1])
theta = atan(y - ny, x - nx) + pi / 2
magnitude = sqrt((y - ny)^2 + (x - nx)^2)
entity.rotation = round(Int, rad2deg(theta))
entity.height = round(Int, max(24, magnitude))
end
Ahorn.editingOptions(entity::ColoredLightbeam) = Dict{String, Any}(
"color" => colors
)
const placements = Ahorn.PlacementDict(
"Colored Lightbeam (Frost Helper)" => Ahorn.EntityPlacement(
ColoredLightbeam,
"line",
Dict{String, Any}(),
lightbeamFinalizer
)
)
Ahorn.minimumSize(entity::ColoredLightbeam) = 32, 24
Ahorn.resizable(entity::ColoredLightbeam) = true, true
function rotate(point::Tuple{Number, Number}, theta::Number)
res = [
cos(theta) -sin(theta);
sin(theta) cos(theta)
] * [point[1]; point[2]]
return (res[1], res[2])
end
function getSelectionRect(entity::ColoredLightbeam)
x, y = Ahorn.position(entity)
theta = deg2rad(get(entity.data, "rotation", 0))
width = round(Int, get(entity.data, "width", 32))
height = round(Int, get(entity.data, "height", 24))
points = Tuple{Number, Number}[
(-width / 2, 0),
(width / 2, 0),
(-width / 2, height - 4),
(width / 2, height - 4)
]
rotated = rotate.(points, theta)
tlx, tly = minimum(p -> p[1], rotated), minimum(p -> p[2], rotated)
brx, bry = maximum(p -> p[1], rotated), maximum(p -> p[2], rotated)
return Ahorn.Rectangle(x + tlx, y + tly, brx - tlx, bry - tly)
end
function Ahorn.selection(entity::ColoredLightbeam)
return getSelectionRect(entity)
end
texture = "util/lightbeam"
function renderLightbeam(ctx::Ahorn.Cairo.CairoContext, entity::ColoredLightbeam)
x, y = Ahorn.position(entity)
lightbeam = Ahorn.getSprite(texture, "Gameplay")
theta = deg2rad(get(entity.data, "rotation", 0))
width = round(Int, get(entity.data, "width", 32))
height = round(Int, get(entity.data, "height", 24))
rng = Ahorn.getSimpleEntityRng(entity)
Ahorn.Cairo.save(ctx)
Ahorn.translate(ctx, x, y)
Ahorn.rotate(ctx, theta)
Ahorn.translate(ctx, width / 2, 0)
Ahorn.rotate(ctx, pi / 2)
Ahorn.scale(ctx, (height - 4) / lightbeam.width, 1)
Ahorn.set_antialias(ctx, 1)
Ahorn.Cairo.save(ctx)
for i in 0:width - 1
Ahorn.drawImage(ctx, texture, 0, i, alpha=0.3)
end
for i in 0:4:width - 1
num = i * 0.6
lineWidth = 4.0 + sin(num * 0.5 + 1.2) * 4.0
length = height + sin(num * 0.25) * 8
alpha = 0.6 + sin(num + 0.8) * 0.3
offset = sin((num + i * 32) * 0.1 + sin(num * 0.05 + i * 0.1) * 0.25) * (width / 2.0 - lineWidth / 2.0)
# Not part of official calculations, but makes the render a bit less boring
offsetMultiplier = (rand(rng) - 0.5) * 2
for j in 0:3
Ahorn.drawImage(ctx, texture, 0, offset * offsetMultiplier + width / 2 + j, alpha=alpha / 1.5)
end
end
Ahorn.Cairo.restore(ctx)
end
Ahorn.renderAbs(ctx::Ahorn.Cairo.CairoContext, entity::ColoredLightbeam, room::Maple.Room) = renderLightbeam(ctx, entity)
end | [
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] | 2.328704 | 1,512 |
# Alias of Model{K,Kₑ,Kₘ, K², K₊₁,K₊₂, AK₊₁,AK₊₂} for full 3D Maxwell's equations.
# The type parameters AK₊₁ and AK₊₂ specify device-specific arrays types (e.g., CuArray) and
# are user-defined in the constructor.
const ModelFull{AK₊₁,AK₊₂} = Model{3,3,3, 9, 4,5, AK₊₁,AK₊₂}
# Convenience constructor
function ModelFull(grid::Grid; Atype::Type=Array)
cmpₛ = SInt(1,2,3) # shapes in 3D space
cmpₑ = SInt(1,2,3) # E-field with x-, y-, and y-components
cmpₘ = SInt(1,2,3) # H-field with x-, y-, and y-components
return ModelFull{Atype{ComplexF,4},Atype{ComplexF,5}}(;grid, cmpₛ, cmpₑ, cmpₘ)
end
# Assign the material parameters on the grid and smooth them.
function calc_matparams!(mdl::ModelFull)
# Take necessary fields of mdl.
grid = mdl.grid
isbloch = grid.isbloch
N = grid.N
l = grid.l
σ = grid.σ
lg = grid.ghosted.l
τl = grid.ghosted.τl
∆τ = grid.ghosted.∆τ
boundft = mdl.boundft
gₑ = ft2gt.(EE,boundft)
gₘ = ft2gt.(HH,boundft)
εarr = mdl.εarr
µarr = mdl.μarr
oind2shp = mdl.oind2shp
oind2εind = mdl.oind2εind
oind2μind = mdl.oind2μind
cmpₑ = mdl.cmpₑ
cmpₘ = mdl.cmpₘ
εind2ε = sub_pind2matprm(mdl.εind2ε, cmpₑ)
μind2μ = sub_pind2matprm(mdl.μind2μ, cmpₘ)
# Create temporary storages.
εxx_oind3d = create_oind_array(N)
εyy_oind3d = create_oind_array(N)
εzz_oind3d = create_oind_array(N)
εoo_oind3d = create_oind_array(N)
μxx_oind3d = create_oind_array(N)
μyy_oind3d = create_oind_array(N)
μzz_oind3d = create_oind_array(N)
μoo_oind3d = create_oind_array(N)
# Assign the material parameters.
assign_param!(εarr, (μxx_oind3d,μyy_oind3d,μzz_oind3d), oind2shp, oind2εind, εind2ε, gₑ, τl, isbloch) # diagonal entries of ε tensors
assign_param!(εarr, tuple(μoo_oind3d), oind2shp, oind2εind, εind2ε, gₑ, τl, isbloch) # off-diagonal entries of ε tensors
assign_param!(μarr, (εxx_oind3d,εyy_oind3d,εzz_oind3d), oind2shp, oind2μind, μind2μ, gₘ, τl, isbloch) # diagonal entries of μ tensors
assign_param!(μarr, tuple(εoo_oind3d), oind2shp, oind2μind, μind2μ, gₘ, τl, isbloch) # off-diagonal entries of μ tensors
# Smooth the material parameters.
smooth_param!(εarr, (εxx_oind3d,εyy_oind3d,εzz_oind3d), oind2shp, oind2εind, εind2ε, gₑ, l, lg, σ, ∆τ, mdl.ise˔shp) # diagonal entries of ε tensors
smooth_param!(εarr, tuple(εoo_oind3d), oind2shp, oind2εind, εind2ε, gₑ, l, lg, σ, ∆τ, mdl.ise˔shp) # off-diagonal entries of ε tensors
smooth_param!(μarr, (μxx_oind3d,μyy_oind3d,μzz_oind3d), oind2shp, oind2μind, μind2μ, gₘ, l, lg, σ, ∆τ, mdl.ish˔shp) # diagonal entries of μ tensors
smooth_param!(μarr, tuple(μoo_oind3d), oind2shp, oind2μind, μind2μ, gₘ, l, lg, σ, ∆τ, mdl.ish˔shp) # off-diagonal entries of μ tensors
return nothing
end
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] | 1.876576 | 1,507 |
@inline function always_assert(condition::Bool, message::String)
if !condition
throw(AlwaysAssertionError(message))
end
return nothing
end
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using Test, ModelConstructors
include(joinpath(dirname(@__FILE__), "../../src/includeall.jl"))
# TEST USING SLM OBJECT THAT IT MATCHES THE DESIRED OUTPUT
rp = joinpath(dirname(@__FILE__), "../reference/SLM")
in_inc = load(joinpath(rp, "eval.jld2"))
in_dec = load(joinpath(rp, "eval_decrease.jld2"))
in_sin = load(joinpath(rp, "eval_sine.jld2"))
in_sinnoineq = load(joinpath(rp, "eval_sinnoineq.jld2"))
slms = Vector{AbstractSLM}(undef, 0)
for (i, in_data) in zip(1:4, [in_inc, in_dec, in_sin, in_sinnoineq])
if i == 1
push!(slms, SLM(vec(in_data["w"]), vec(in_data["p_sol"]); knots = Int(in_data["knots"]),
increasing = true, concave_down = true, left_value = in_data["p0_norun"],
right_value = in_data["p1_norun"], min_value = in_data["p0_norun"] - 1e-3,
max_value = in_data["p1_norun"] + 1e-3))
elseif i == 2
push!(slms, SLM(vec(in_data["w"]), vec(in_data["rev_p_sol"]); knots = Int(in_data["knots"]),
decreasing = true, concave_up = true, right_value = in_data["p0_norun"],
left_value = in_data["p1_norun"], min_value = in_data["p0_norun"] - 1e-3,
max_value = in_data["p1_norun"] + 1e-3))
elseif i == 3
push!(slms, SLM(vec(in_data["x"]), vec(in_data["y"]); knots = Int(in_data["knots"]),
increasing_intervals = in_data["inc_int"], decreasing_intervals = in_data["dec_int"],
concave_up_intervals = in_data["cu_int"], concave_down_intervals = in_data["cd_int"],
right_value = in_data["rightvalue"], left_value = in_data["leftvalue"]))
else
push!(slms, SLM(vec(in_data["x"]), vec(in_data["y"]); knots = Int(in_data["knots"]),
right_value = in_data["rightvalue"], left_value = in_data["leftvalue"]))
end
end
@testset "eval of SLM" begin
for (i, in_data) in zip(1:4, [in_inc, in_dec, in_sin, in_sinnoineq])
if i == 1
@test @test_matrix_approx_eq eval(slms[i], vec(in_data["w"])) in_data["phat"]
@test @test_matrix_approx_eq eval(slms[i], vec(in_data["w"]), 0) in_data["phat"]
elseif i == 2
@test @test_matrix_approx_eq eval(slms[i], vec(in_data["w"])) in_data["phat"]
@test @test_matrix_approx_eq eval(slms[i], vec(in_data["w"]), 0) in_data["phat"]
elseif i == 3
@test @test_matrix_approx_eq eval(slms[i], vec(in_data["xhat"])) in_data["yhat"]
@test @test_matrix_approx_eq eval(slms[i], vec(in_data["xhat"]), 0) in_data["yhat"]
@test @test_matrix_approx_eq eval(slms[i], vec(in_data["xhat"]), 1) in_data["yphat"]
@test @test_matrix_approx_eq eval(slms[i], vec(in_data["xhat"]), 2) in_data["ypphat"]
@test @test_matrix_approx_eq eval(slms[i], vec(in_data["xhat"]), 3) in_data["yppphat"]
else
@test @test_matrix_approx_eq eval(slms[i], vec(in_data["xhat"])) in_data["yhat"]
@test @test_matrix_approx_eq eval(slms[i], vec(in_data["xhat"]), 0) in_data["yhat"]
end
end
end
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] | 1.944307 | 1,616 |
using Test
using FluxRM
if !haskey(ENV, "FLUX_URI")
flux_available = try
success(`flux env`)
catch
false
end
if !flux_available
@error "flux-core is not installed, skipping further tests"
exit()
end
@info "relaunching under Flux"
current_file = @__FILE__ # bug in 1.5 can't be directly interpolated
jlcmd = `$(Base.julia_cmd()) $(current_file)`
cmd = `flux start -o,-Slog-forward-level=7 -- $jlcmd`
@test success(pipeline(cmd, stdout=stdout, stderr=stderr))
exit()
end
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1082,
81,
4008,
198,
220,
220,
220,
8420,
3419,
198,
437,
628
] | 2.2875 | 240 |
using MAGMA
using Test, LinearAlgebra, Random
@testset "singular_value_decomposition" begin
include("svds/runtests.jl")
end
| [
3500,
28263,
5673,
198,
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11,
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2348,
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11,
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198,
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31,
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62,
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21370,
9310,
14,
81,
2797,
3558,
13,
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4943,
198,
437,
198
] | 2.804348 | 46 |
function is_isymata_mode()
Symata.is_loaded_IJulia() && _is_isymata_mode()
end
## `set_jupyter_input_prompt_color` is not very useful now because
## it changes the color for the entire notebook, rather than the current
## cell.
"""
set_jupyter_input_prompt_color(c)
Set the color of the Jupyter prompt.
"""
function set_jupyter_input_prompt_color(c)
display("text/javascript",string("\$(\"div.input_prompt\").css({\"color\": \"", c, "\"})"))
end
"""
isymata()
Enter Symata mode from within Jupyter (IPython).
From within Symata, `Julia()` exits Symata
and returns to Julia mode.
"""
function isymata()
if Symata.isymata_inited()
invokelatest(set_isymata_mode)
else
init_isymata()
if Symata.isymata_inited()
invokelatest(set_isymata_mode)
else
error("Unable to initialize isymata in IJulia")
end
end
# set_jupyter_input_prompt_color("blue") # This now applies to all cells. So we can't switch between
nothing
end
"""
init_isymata()
Check if `IJulia` is loaded. If so, install an "execute_request" handler that
will translate input and output for `Symata`. See `isymata`.
"""
function init_isymata()
if isdefined(Main, :IJulia)
try
_init_isymata()
Symata._set_historylength(10) # IJulia stores history, as well.
Core.eval(Symata, :(do_we_print_Out_label = false)) # prevents symataevaluate from printing results.
catch e
@warn("Initializing isymata failed")
rethrow(e)
return
end
else
@warn("IJulia not loaded. Try 'using IJulia'. Try 'Pkg.add(\"IJulia\") if IJulia is not installed.")
return
end
Symata.isymata_inited(true)
Symata.setkerneloptions(:output_style, :JupyterForm) # this is only set when initing. The user can change it
return nothing
end
function import_IJulia()
eval(:(const IJulia = Main.IJulia))
end
function _symata_code_hook(code::AbstractString)
if Symata.iscomment(code)
return ""
else
return "@Symata.symfull " * " begin\n" * code * "\nend"
end
end
function _init_isymata()
Symata.populate_builtins()
load_ijulia_handlers()
import_IJulia()
# create struct with hooks used in modal handlers
symata_mode = invokelatest(IJulia.JupyterMode,
:symata, # language mode name
_symata_code_hook, # to transform input
helpcode -> Symata.print_doc(strip(helpcode)),
symata_format_output, # to transform output
Symata.symata_completions # to choose symbols for completion
)
## install Symata-language hooks, switch to Symata mode, install modal handlers.
invokelatest(IJulia.install_ijulia_mode, :symata, symata_mode)
invokelatest(IJulia.set_ijulia_mode, :symata)
invokelatest(IJulia.set_handler, "execute_request", IJulia.execute_request_modal)
invokelatest(IJulia.set_handler, "complete_request", IJulia.complete_request_modal)
eval(quote
set_isymata_mode() = IJulia.set_ijulia_mode(:symata)
set_julia_mode() = IJulia.set_ijulia_mode(:julia)
_is_isymata_mode() = IJulia.get_current_ijulia_modename() == :symata
end)
return nothing
end
function symata_format_output(ex)
if Symata.isymata_mma_mode()
if Symata.using_ijulia_output()
newex = Symata.mxpr(:TeXForm, ex)
return try
str = Symata.symata_expr_to_mma_string(newex)
Symata.MyLaTeXString("\$\$ " * str * " \$\$")
catch
@warn("Unable to format expression in Mathematic syntax.")
Symata.using_ijulia_output() ? Symata.latex_display(Symata.wrapout(ex)) : Symata.wrapout(ex)
end
else
# SymataSyntax.MmaOutString(symata_expr_to_mma_string(ex))
end
else
return Symata.using_ijulia_output() ? Symata.latex_display(Symata.wrapout(ex)) : Symata.wrapout(ex)
end
end
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13,
271,
62,
14578,
62,
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] | 2.209178 | 1,874 |
using TcpInstruments
using Test
m = initialize(KeysightDMM34465A)
V = get_voltage(m)
@info get_voltage V
@test V isa Float64
A = get_current(m)
@info get_current A
@test A isa Float64
Ω = get_resistance(m; wire=2)
@info "get_resistance(; wire=2)" Ω
@test Ω isa Float64
Ω = get_resistance(m; wire=4)
@info "get_resistance(; wire=4)" Ω
@test Ω isa Float64
channel = get_channel(m)
@info get_channel channel
@test channel in ["FRON", "REAR"]
@info "°" get_tc_temperature(m) get_temp_unit(m)
set_temp_unit_kelvin(m)
@info get_tc_temperature(m) get_temp_unit(m)
# set_tc_type(m; type="K")
set_temp_unit_farenheit(m)
@info get_tc_temperature(m) get_temp_unit(m)
set_temp_unit_celsius(m)
@info get_tc_temperature(m) get_temp_unit(m)
terminate(m)
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] | 2.185507 | 345 |
export Lens, set, get, modify
export @lens
export set, get, modify
export setproperties
import Base: get
using Base: setindex, getproperty
"""
Lens
A `Lens` allows to access or replace deeply nested parts of complicated objects.
# Example
```jldoctest
julia> using Setfield
julia> struct T;a;b; end
julia> obj = T("AA", "BB")
T("AA", "BB")
julia> lens = @lens _.a
(@lens _.a)
julia> get(obj, lens)
"AA"
julia> set(obj, lens, 2)
T(2, "BB")
julia> obj
T("AA", "BB")
julia> modify(lowercase, obj, lens)
T("aa", "BB")
```
# Interface
Concrete subtypes of `Lens` have to implement
* `set(obj, lens, val)`
* `get(obj, lens)`
These must be pure functions, that satisfy the three lens laws:
```jldoctest; output = false, setup = :(using Setfield; obj = (a="A", b="B"); lens = @lens _.a; val = 2; val1 = 10; val2 = 20)
@assert get(set(obj, lens, val), lens) == val
# You get what you set.
@assert set(obj, lens, get(obj, lens)) == obj
# Setting what was already there changes nothing.
@assert set(set(obj, lens, val1), lens, val2) == set(obj, lens, val2)
# The last set wins.
# output
```
See also [`@lens`](@ref), [`set`](@ref), [`get`](@ref), [`modify`](@ref).
"""
abstract type Lens end
"""
modify(f, obj, l::Lens)
Replace a deeply nested part `x` of `obj` by `f(x)`. See also [`Lens`](@ref).
"""
function modify end
"""
get(obj, l::Lens)
Access a deeply nested part of `obj`. See also [`Lens`](@ref).
"""
function get end
"""
set(obj, l::Lens, val)
Replace a deeply nested part of `obj` by `val`. See also [`Lens`](@ref).
"""
function set end
@inline function modify(f, obj, l::Lens)
old_val = get(obj, l)
new_val = f(old_val)
set(obj, l, new_val)
end
struct IdentityLens <: Lens end
get(obj, ::IdentityLens) = obj
set(obj, ::IdentityLens, val) = val
struct PropertyLens{fieldname} <: Lens end
function get(obj, l::PropertyLens{field}) where {field}
getproperty(obj, field)
end
@generated function set(obj, l::PropertyLens{field}, val) where {field}
Expr(:block,
Expr(:meta, :inline),
:(setproperties(obj, ($field=val,)))
)
end
@generated constructor_of(::Type{T}) where T =
getfield(parentmodule(T), nameof(T))
function assert_hasfields(T, fnames)
for fname in fnames
if !(fname in fieldnames(T))
msg = "$T has no field $fname"
throw(ArgumentError(msg))
end
end
end
"""
setproperties(obj, patch)
Return a copy of `obj` with attributes updates accoring to `patch`.
# Examples
```jldoctest
julia> using Setfield
julia> struct S;a;b;c; end
julia> s = S(1,2,3)
S(1, 2, 3)
julia> setproperties(s, (a=10,c=4))
S(10, 2, 4)
julia> setproperties((a=1,c=2,b=3), (a=10,c=4))
(a = 10, c = 4, b = 3)
```
"""
function setproperties end
@generated function setproperties(obj, patch)
assert_hasfields(obj, fieldnames(patch))
args = map(fieldnames(obj)) do fn
if fn in fieldnames(patch)
:(patch.$fn)
else
:(obj.$fn)
end
end
Expr(:block,
Expr(:meta, :inline),
Expr(:call,:(constructor_of($obj)), args...)
)
end
@generated function setproperties(obj::NamedTuple, patch)
# this function is only generated to force the following check
# at compile time
assert_hasfields(obj, fieldnames(patch))
Expr(:block,
Expr(:meta, :inline),
:(merge(obj, patch))
)
end
struct ComposedLens{LO, LI} <: Lens
outer::LO
inner::LI
end
compose() = IdentityLens()
compose(l::Lens) = l
compose(::IdentityLens, ::IdentityLens) = IdentityLens()
compose(::IdentityLens, l::Lens) = l
compose(l::Lens, ::IdentityLens) = l
compose(outer::Lens, inner::Lens) = ComposedLens(outer, inner)
function compose(l1::Lens, ls::Lens...)
# We can build _.a.b.c as (_.a.b).c or _.a.(b.c)
# The compiler prefers (_.a.b).c
compose(l1, compose(ls...))
end
"""
lens₁ ∘ lens₂
compose([lens₁, [lens₂, [lens₃, ...]]])
Compose lenses `lens₁`, `lens₂`, ..., `lensₙ` to access nested objects.
# Example
```jldoctest
julia> using Setfield
julia> obj = (a = (b = (c = 1,),),);
julia> la = @lens _.a
lb = @lens _.b
lc = @lens _.c
lens = la ∘ lb ∘ lc
(@lens _.a.b.c)
julia> get(obj, lens)
1
```
"""
Base.:∘(l1::Lens, l2::Lens) = compose(l1, l2)
function get(obj, l::ComposedLens)
inner_obj = get(obj, l.outer)
get(inner_obj, l.inner)
end
function set(obj,l::ComposedLens, val)
inner_obj = get(obj, l.outer)
inner_val = set(inner_obj, l.inner, val)
set(obj, l.outer, inner_val)
end
struct IndexLens{I <: Tuple} <: Lens
indices::I
end
Base.@propagate_inbounds function get(obj, l::IndexLens)
getindex(obj, l.indices...)
end
Base.@propagate_inbounds function set(obj, l::IndexLens, val)
setindex(obj, val, l.indices...)
end
"""
ConstIndexLens{I}
Lens with index stored in type parameter. This is useful for type-stable
[`get`](@ref) and [`set`](@ref) operations on tuples and named tuples.
This lens can be constructed by, e.g., `@lens _[\$1]`. Complex expression
must be wrapped with `\$(...)` like `@lens _[\$(length(xs))]`.
# Examples
```jldoctest
julia> using Setfield
julia> get((1, 2.0), @lens _[\$1])
1
julia> Base.promote_op(get, typeof.(((1, 2.0), @lens _[\$1]))...)
Int64
julia> Base.promote_op(get, typeof.(((1, 2.0), @lens _[1]))...) !== Int
true
```
"""
struct ConstIndexLens{I} <: Lens end
Base.@propagate_inbounds get(obj, ::ConstIndexLens{I}) where I = obj[I...]
Base.@propagate_inbounds set(obj, ::ConstIndexLens{I}, val) where I =
setindex(obj, val, I...)
@generated function set(obj::Union{Tuple, NamedTuple},
::ConstIndexLens{I},
val) where I
if length(I) == 1
n, = I
args = map(1:length(obj.types)) do i
i == n ? :val : :(obj[$i])
end
quote
$(Expr(:meta, :inline))
($(args...),)
end
else
quote
throw(ArgumentError($(string(
"A `Tuple` and `NamedTuple` can only be indexed with one ",
"integer. Given: $I"))))
end
end
end
Base.@deprecate get(lens::Lens, obj) get(obj, lens)
Base.@deprecate set(lens::Lens, obj, val) set(obj, lens, val)
Base.@deprecate modify(f, lens::Lens, obj) modify(f, obj, lens)
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] | 2.267214 | 2,803 |
# BED Reader
# ==========
mutable struct Reader <: BioCore.IO.AbstractReader
state::BioCore.Ragel.State
index::Union{GenomicFeatures.Indexes.Tabix,Nothing}
function Reader(input::BufferedStreams.BufferedInputStream, index=nothing)
return new(BioCore.Ragel.State(file_machine.start_state, input), index)
end
end
"""
BED.Reader(input::IO; index=nothing)
BED.Reader(input::AbstractString; index=:auto)
Create a data reader of the BED file format.
The first argument specifies the data source. When it is a filepath that ends
with *.bgz*, it is considered to be block compression file format (BGZF) and the
function will try to find a tabix index file (<filename>.tbi) and read it if
any. See <http://www.htslib.org/doc/tabix.html> for bgzip and tabix tools.
Arguments
---------
- `input`: data source
- `index`: path to a tabix file
"""
function Reader(input::IO; index=nothing)
if isa(index, AbstractString)
index = GenomicFeatures.Indexes.Tabix(index)
end
return Reader(BufferedStreams.BufferedInputStream(input), index)
end
function Reader(filepath::AbstractString; index=:auto)
if isa(index, Symbol) && index != :auto
throw(ArgumentError("invalid index argument: ':$(index)'"))
end
if endswith(filepath, ".bgz")
input = BGZFStreams.BGZFStream(filepath)
if index == :auto
index = GenomicFeatures.Indexes.findtabix(filepath)
end
else
input = open(filepath)
end
return Reader(input, index=index)
end
function Base.eltype(::Type{Reader})
return Record
end
function BioCore.IO.stream(reader::Reader)
return reader.state.stream
end
function GenomicFeatures.eachoverlap(reader::Reader, interval::GenomicFeatures.Interval)
if reader.index === nothing
throw(ArgumentError("index is null"))
end
return GenomicFeatures.Indexes.TabixOverlapIterator(reader, interval)
end
const record_machine, file_machine = (function ()
cat = Automa.RegExp.cat
rep = Automa.RegExp.rep
opt = Automa.RegExp.opt
record = let
chrom = re"[ -~]*"
chrom.actions[:enter] = [:mark]
chrom.actions[:exit] = [:record_chrom]
chromstart = re"[0-9]+"
chromstart.actions[:enter] = [:mark]
chromstart.actions[:exit] = [:record_chromstart]
chromend = re"[0-9]+"
chromend.actions[:enter] = [:mark]
chromend.actions[:exit] = [:record_chromend]
name = re"[ -~]*"
name.actions[:enter] = [:mark]
name.actions[:exit] = [:record_name]
score = re"[0-9]+"
score.actions[:enter] = [:mark]
score.actions[:exit] = [:record_score]
strand = re"[+\-.?]"
strand.actions[:enter] = [:record_strand]
thickstart = re"[0-9]+"
thickstart.actions[:enter] = [:mark]
thickstart.actions[:exit] = [:record_thickstart]
thickend = re"[0-9]+"
thickend.actions[:enter] = [:mark]
thickend.actions[:exit] = [:record_thickend]
itemrgb = cat(re"[0-9]+", opt(cat(',', re"[0-9]+", ',', re"[0-9]+")))
itemrgb.actions[:enter] = [:mark]
itemrgb.actions[:exit] = [:record_itemrgb]
blockcount = re"[0-9]+"
blockcount.actions[:enter] = [:mark]
blockcount.actions[:exit] = [:record_blockcount]
# comma-separated values
csv(x) = cat(rep(cat(x, ',')), opt(x))
blocksizes = let
blocksize = re"[0-9]+"
blocksize.actions[:enter] = [:mark]
blocksize.actions[:exit] = [:record_blocksizes_blocksize]
csv(blocksize)
end
blocksizes.actions[:exit] = [:record_blocksizes]
blockstarts = let
blockstart = re"[0-9]+"
blockstart.actions[:enter] = [:mark]
blockstart.actions[:exit] = [:record_blockstarts_blockstart]
csv(blockstart)
end
blockstarts.actions[:exit] = [:record_blockstarts]
cat(
chrom, '\t',
chromstart, '\t',
chromend,
opt(cat('\t', name,
opt(cat('\t', score,
opt(cat('\t', strand,
opt(cat('\t', thickstart,
opt(cat('\t', thickend,
opt(cat('\t', itemrgb,
opt(cat('\t', blockcount,
opt(cat('\t', blocksizes,
opt(cat('\t', blockstarts)))))))))))))))))))
end
record.actions[:enter] = [:anchor]
record.actions[:exit] = [:record]
newline = let
lf = re"\n"
lf.actions[:enter] = [:countline]
cat(opt('\r'), lf)
end
file = rep(cat(record, newline))
return map(Automa.compile, (record, file))
end)()
#=
write("bed.dot", Automa.machine2dot(file_machine))
run(`dot -Tsvg -o bed.svg bed.dot`)
=#
const record_actions = Dict(
:record_chrom => :(record.chrom = (mark:p-1) .- offset; record.ncols += 1),
:record_chromstart => :(record.chromstart = (mark:p-1) .- offset; record.ncols += 1),
:record_chromend => :(record.chromend = (mark:p-1) .- offset; record.ncols += 1),
:record_name => :(record.name = (mark:p-1) .- offset; record.ncols += 1),
:record_score => :(record.score = (mark:p-1) .- offset; record.ncols += 1),
:record_strand => :(record.strand = p - offset; record.ncols += 1),
:record_thickstart => :(record.thickstart = (mark:p-1) .- offset; record.ncols += 1),
:record_thickend => :(record.thickend = (mark:p-1) .- offset; record.ncols += 1),
:record_itemrgb => :(record.itemrgb = (mark:p-1) .- offset; record.ncols += 1),
:record_blockcount => :(record.blockcount = (mark:p-1) .- offset; record.ncols += 1),
:record_blocksizes_blocksize => :(push!(record.blocksizes, (mark:p-1) .- offset)),
:record_blocksizes => :(record.ncols += 1),
:record_blockstarts_blockstart => :(push!(record.blockstarts, (mark:p-1) .- offset)),
:record_blockstarts => :(record.ncols += 1),
:record => :(record.filled = 1:p-1),
:countline => :(),
:mark => :(mark = p),
:anchor => :())
eval(
BioCore.ReaderHelper.generate_index_function(
Record,
record_machine,
:(offset = mark = 0),
record_actions))
eval(
BioCore.ReaderHelper.generate_read_function(
Reader,
file_machine,
:(offset = mark = 0),
merge(record_actions, Dict(
:record => quote
BioCore.ReaderHelper.resize_and_copy!(record.data, data, BioCore.ReaderHelper.upanchor!(stream):p-1)
record.filled = (offset+1:p-1) .- offset
found_record = true
@escape
end,
:countline => :(linenum += 1),
:anchor => :(BioCore.ReaderHelper.anchor!(stream, p); offset = p - 1)))))
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] | 2.151926 | 3,245 |
using AsciiDocTools
using Test
@testset "AsciiDocTools.jl" begin
# Write your tests here.
end
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] | 2.675676 | 37 |
using Weave
path = "C:\\Users\\Jasmine\\.julia\\dev\\DCDC\\doc"
weave(joinpath(path,"BandwidthSelection.jmd"),doctype="pandoc",out_path=path)
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] | 2.383333 | 60 |
include("run_sim.jl")
T = 100000
p_E_range = 0.5
p_H_range = 0.02
λ_E_range = 0.5:0.5:(10-0.5)
λ_H_range = 2
transitions_list = [tr_chains_d1, tr_chains_d10,
tr_chains_d50, tr_chains_d100]
table1 = run_multiple_sim(λ_E_range, λ_H_range, p_E_range, p_H_range, transitions_list);
writetable("csv/fig_4.1.csv", table1)
T = 100000
p_E_range = 0.5
p_H_range = 0.02
λ_H_range = 0.5:0.5:(10-0.5)
λ_E_range = 2
transitions_list = [tr_chains_d1, tr_chains_d10,
tr_chains_d50, tr_chains_d100]
table2 = run_multiple_sim(λ_E_range, λ_H_range, p_E_range, p_H_range, transitions_list);
writetable("csv/fig_4.2.csv", table2)
T = 1000000
p_E_range = 0.5
p_H_range = [0.1, 0.05, 0.01, 0.005, 0.001, 0.0005, 0.0001, 0.00005, 0.00001]
λ_E_range = 1
λ_H_range = 2
transitions_list = [tr_chains_d1, tr_chains_d10,
tr_chains_d50, tr_chains_d100]
table3 = run_multiple_sim(λ_E_range, λ_H_range, p_E_range, p_H_range, transitions_list);
table3[:norm_d1] = table3[:tr_chains_d1] .* table3[:p_H]
table3[:norm_d10] = table3[:tr_chains_d10] .* table3[:p_H]
table3[:norm_d50] = table3[:tr_chains_d50] .* table3[:p_H]
table3[:norm_d100] = table3[:tr_chains_d100] .* table3[:p_H]
table3[:log_p_H] = - log(table3[:p_H])
writetable("csv/fig_4.3.csv", table3)
T = 1000000
p_E_range = 0.1:0.1:1
p_H_range = 0.01
λ_E_range = 1
λ_H_range = 2
transitions_list = [tr_chains_d1, tr_chains_d10,
tr_chains_d50, tr_chains_d100]
table4 = run_multiple_sim(λ_E_range, λ_H_range, p_E_range, p_H_range, transitions_list);
writetable("csv/fig_4.4.csv", table4)
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] | 1.885613 | 848 |
module BPlot
export plotb
using PyPlot
import Main.BMath
function plotb(points::Vector; precision=10, Title="Math Plot", xlbl="X", ylbl="Y")
xvals = Float64[]
yvals = Float64[]
for point in points
if BMath.dim_count(point) != 1
error("Cannot Plot Due to way to many dimensions! Needs to be 1")
end
c = BMath.cartesian(point)
push!(xvals, round(c[1], digits=precision))
push!(yvals, round(c[2], digits=precision))
end
pygui(true)
scatter(xvals, yvals, 1)
title(Title)
xlabel(xlbl)
ylabel(ylbl)
end
function plotb(inputs::Vector, points::Vector; precision=10, Title="Math Plot", xlbl="Variable", ylbl="X_Comp", zlbl="Y_Comp")
xvals = Float64[]
yvals = Float64[]
zvals = Float64[]
for i in 1:length(points)
point = points[i]
if BMath.dim_count(point) != 1
error("Cannot Plot Due to way to many dimensions! Needs to be 1")
end
c = BMath.cartesian(point)
push!(xvals, round(inputs[i], digits=precision))
push!(yvals, round(c[1], digits=precision))
push!(zvals, round(c[2], digits=precision))
end
pygui(true)
using3D()
fig = plt.figure()
ax = fig.add_subplot(projection="3d")
ax.set_title(Title)
ax.set_xlabel(xlbl)
ax.set_ylabel(ylbl)
ax.set_zlabel(zlbl)
ax.scatter(xvals, yvals, zvals, s=1)
end
end
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] | 1.942543 | 818 |
struct Connection
weight::AbstractArray
adjacency::AbstractArray
end
function randomConnection(n_source::Int, n_target::Int, p::AbstractFloat;
min_weight=0, max_weight=1, amplitude=nothing, seed=nothing)
if !isnothing(seed)
Random.seed!(seed)
end
adjacency = rand(n_source, n_target) .< p
if isnothing(amplitude)
weights = rand(n_source, n_target) .* (max_weight - min_weight) .+ min_weight
else
weights = ones(n_source, n_target) .* amplitude
end
weights .*= adjacency
return Connection(weights, adjacency)
end
function randomConnection(n::Int, p::AbstractFloat; min_weight=0, max_weight=1,
amplitude=nothing, seed=nothing)
randomConnection(n, n, p; min_weight, max_weight, amplitude, seed)
end
function EI(Nₑ::Integer, Nᵢ::Integer;
E_E::Tuple=(0, 1), E_I::Tuple=(0, 1), I_E::Tuple=(0, 1), I_I::Tuple=(0, 1),
adjacency::Union{Nothing,AbstractMatrix}=nothing, shuffle::Bool=false, seed=nothing
)
excitatory = [ones(Bool, Nₑ); zeros(Bool, Nᵢ)]
if shuffle
if !isnothing(seed)
Random.seed!(seed)
end
shuffle!(excitatory)
end
inhibitory = .!excitatory
min_weight = (excitatory * transpose(excitatory)) .* E_E[1]
min_weight += (excitatory * transpose(inhibitory)) .* E_I[1]
min_weight -= (inhibitory * transpose(excitatory)) .* abs(I_E[2])
min_weight -= (inhibitory * transpose(inhibitory)) .* abs(I_I[2])
max_weight = (excitatory * transpose(excitatory)) .* E_E[2]
max_weight += (excitatory * transpose(inhibitory)) .* E_I[2]
max_weight -= (inhibitory * transpose(excitatory)) .* abs(I_E[1])
max_weight -= (inhibitory * transpose(inhibitory)) .* abs(I_I[1])
if !isnothing(adjacency)
min_weight .*= adjacency
max_weight .*= adjacency
end
return excitatory, inhibitory, min_weight, max_weight
end
function EI(n::Integer, EI_rate::AbstractFloat;
E_E::Tuple=(0, 1), E_I::Tuple=(0, 1), I_E::Tuple=(0, 1), I_I::Tuple=(0, 1),
adjacency::Union{Nothing,AbstractMatrix}=nothing, shuffle::Bool=false, seed=nothing
)
if EI_rate > 1
Nₑ = Int(ceil(EI_rate * n / (EI_rate + 1)))
else
Nₑ = Int(ceil(EI_rate * n))
end
Nᵢ = n - Nₑ
return EI(Nₑ, Nᵢ; E_E, E_I, I_E, I_I, adjacency, shuffle, seed)
end
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] | 2.238462 | 1,040 |
function solve()
a, b = [parse(Int, x) for x in split(readline())]
c, d = [parse(Int, x) for x in split(readline())]
a*d - b*c
end
println(solve())
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@testset "Multiple Drivers" begin
println("VITUIX MULTIPLE DRIVER TESTS")
Hs = load_vituix_FIRs((joinpath(test_dir, "FIR", wav) for wav in ["T.wav", "M.wav", "W.wav"])...; Δt=-371.519ms, names=[:FIR_T, :FIR_M, :FIR_W])
template_hor = Regex(raw"VituixCAD_PolarFR hor (.*)\.frd")
template_ver = Regex(raw"VituixCAD_PolarFR ver (.*)\.frd")
matchFIR = true
test_set = [
(src=[
Dict(:name => "T", :spinorama => "T/polar_noXO", :transform => Dict(:H => Hs[:, :FIR_T], :y => 0.0)),
Dict(:name => "M", :spinorama => "M/polar_noXO", :transform => Dict(:H => Hs[:, :FIR_M], :y => -0.118)),
Dict(:name => "W", :spinorama => "W/polar_noXO", :transform => Dict(:H => Hs[:, :FIR_W], :y => -0.348)),
], target="total/polar", freqrange=(0Hz, 20kHz), T=Dict(), max_mag=14, mae_mag=0.2, max_phi=180.0, mae_phi=2.5),
(src=[
Dict(:name => "T", :spinorama => "T/polar_noXO", :transform => Dict(:H => Hs[:, :FIR_T], :r => 10.0, :t => 15.0)),
Dict(:name => "M", :spinorama => "M/polar_noXO", :transform => Dict(:H => Hs[:, :FIR_M], :x => 0.095, :y => -0.118, :z => 0.150, :r => 12.0, :t => -15.0)),
Dict(:name => "W", :spinorama => "W/polar_noXO", :transform => Dict(:H => Hs[:, :FIR_W], :x => -0.15, :y => -0.348, :z => -0.035, :r => -90.0, :t => 3.0)),
], target="total_rotated_tilted2/polar", freqrange=(0Hz, 20kHz), T=Dict(), max_mag=21.0, mae_mag=0.7, max_phi=180.0, mae_phi=10.5),
]
for matchFIR in [true, false]
println("match FIR: $matchFIR")
for e in test_set
sources = String[]
drivers = []
for s in e.src
push!(sources, s[:spinorama])
push!(drivers, Dict(
:spinorama => load_spinorama_from_FRDs(joinpath(test_dir, s[:spinorama]), template_hor=template_hor, template_ver=template_ver),
:transform => s[:transform]
))
end
println(" \"$(sources)\" -> \"$(e.target)\"")
target_spinorama = load_spinorama_from_FRDs(joinpath(test_dir, e.target), template_hor=template_hor, template_ver=template_ver)
print("\tA:")
@test test_transform(drivers, target_spinorama, e.freqrange, compare_magdb, e.max_mag, e.mae_mag, matchFIR)
print("\tΦ:")
@test test_transform(drivers, target_spinorama, e.freqrange, compare_phasedeg, e.max_phi, e.mae_phi, matchFIR)
end
end
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] | 1.911243 | 1,352 |
function registerdrivers(f::Function)
GDAL.allregister()
try
f()
finally
GDAL.destroydrivermanager()
end
end
"Fetch driver by index"
getdriver(i::Integer) = GDAL.getdriver(i-1)
"Fetch a driver based on the short name (such as `GTiff`)."
getdriver(name::AbstractString) = GDAL.getdriverbyname(name)
"""
Destroy a `GDALDriver`.
This is roughly equivelent to deleting the driver, but is guaranteed to take
place in the GDAL heap. It is important this that function not be called on a
driver that is registered with the `GDALDriverManager`.
"""
destroy(drv::Driver) = GDAL.destroydriver(drv)
"Register a driver for use."
register(drv::Driver) = GDAL.registerdriver(drv)
"Deregister the passed drv."
deregister(drv::Driver) = GDAL.deregisterdriver(drv)
"Return the list of creation options of the driver [an XML string]"
options(drv::Driver) = GDAL.getdrivercreationoptionlist(drv)
"Return the short name of a driver (e.g. `GTiff`)"
getshortname(drv::Driver) = GDAL.getdrivershortname(drv)
"Return the long name of a driver (e.g. `GeoTIFF`), or empty string."
getlongname(drv::Driver) = GDAL.getdriverlongname(drv)
"Fetch the number of registered drivers."
ndriver() = GDAL.getdrivercount()
"Returns a listing of all registered drivers"
function drivers()
dlist = Dict{ASCIIString,ASCIIString}()
for i in 1:ndriver()
dlist[shortname(getdriver(i))] = longname(getdriver(i))
end
dlist
end
"""
Identify the driver that can open a raster file.
This function will try to identify the driver that can open the passed filename
by invoking the Identify method of each registered `GDALDriver` in turn. The
first driver that successful identifies the file name will be returned. If all
drivers fail then `NULL` is returned.
"""
identifydriver(filename::AbstractString) = GDAL.identifydriver(filename, C_NULL)
"""
Validate the list of creation options that are handled by a drv.
This is a helper method primarily used by `Create()` and `CreateCopy()` to
validate that the passed in list of creation options is compatible with the
`GDAL_DMD_CREATIONOPTIONLIST` metadata item defined by some drivers.
See also: `GDALGetDriverCreationOptionList()`
If the `GDAL_DMD_CREATIONOPTIONLIST` metadata item is not defined, this
function will return `TRUE`. Otherwise it will check that the keys and values
in the list of creation options are compatible with the capabilities declared
by the `GDAL_DMD_CREATIONOPTIONLIST` metadata item. In case of incompatibility
a (non fatal) warning will be emited and `FALSE` will be returned.
### Parameters
* `hDriver` the handle of the driver with whom the lists of creation option
must be validated
* `options` the list of creation options. An array of strings, whose last
element is a `NULL` pointer
### Returns
`TRUE` if the list of creation options is compatible with the `Create()` and
`CreateCopy()` method of the driver, `FALSE` otherwise.
"""
function validate{T <: AbstractString}(drv::Driver, options::Vector{T})
Bool(GDAL.validatecreationoptions(drv,
Ptr{Ptr{UInt8}}(pointer(options))))
end
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] | 3.124378 | 1,005 |
# Consider creating Source module in source.jl under source/ directory. Such a module can
# be used by
# include("source/source.jl")
# importall (or using?) .Source
# See "importall .LinAlg" in julia-0.6/base/sysimg.jl.
# When we discretize a source, a source located in the interval that begins or ends with a
# ghost point is cumbersome. In such a case, we have to assign current density at the ghost
# point, which is impossible because there is no entry in the current density array that
# is assigned to ghost points.
#
# Depending on the boundary condition, we have to handle such a case differently. Suppose
# we are assiging a point source of the primal field type in the direction parallel to (but
# slightly away from) the boundary. We assign a portion of the source current at the
# nonghost point adjacent to the ghost point. The question is how to handle the portion of
# the source current to be assigned to the ghost point.
#
# - For Bloch, we have to assign a point source at the nonghost point corresponding to the
# ghost point. Note that this should be additive to any existing current density at the
# nonghost point. (The same situation as two point sources in neighboring grid intervals:
# at the shared grid point, the contributions from the two point sources are added. The
# first and last grid intervals are adjacent through the Bloch boundary condition.)
#
# - For the symmetry boundary, putting current density component parallel to the boundary
# effectively puts another anti-symmetric current density component at the mirror symmetry
# position. Usually these two current density components are sufficiently well-separated
# and their contributions do not overlap. However, in the situation considered here, they
# are actually in neighboring cell intervals that share a point on the symmetry boundary.
#
# If a point source is in a cell interval between two grid points A and B, the method to
# assign current densities to A and B is the "sliding" method. In this method, imagine the
# point source location changes gradually from A to B. When it is exactly at A, current
# density is assigned only to A, and the magnitude J_A of the current density is determined
# by dividing the current of the point source by the grid cell area occupied by A. We can
# similarly calculate the current density J_B to assign to B in the case where the point
# source is exactly at B. (Note that J_A ≠ J_B in general even though the current of the
# point source is the same, because the area occupied by A and B can be different if the
# grid is nonuniform.) Now, if the point source location changes gradually from A to B, the
# current density to assign to A should decrease linearly from J_A to 0, and the current
# density to assign to B should increase linearly from 0 to J_B. Therefore, if the point
# source is located between A and B such that the ratio of the distances to A and B is
# x:(1-x), then the current densities to assign to A and B should be J_A * (1-x) and J_B * x,
# respectively. Note that these becomes J_A and 0 for x = 0 (when the point source is at A)
# and 0 and J_B for x = 1 (when the point source is at B).
#
# The situation of the symmetry boundary condition discussed earlier, where the two point
# sources with opposite directions are placed at the mirror symmetry locations around the
# symmetry boundary, can be thought of as assigning the current densities for the two
# individual point sources using the above described recipe. Note that as the two point
# sources approach to the symmetry boundary, the portions of the two point sources' currents
# assigned at the shared point on the boundary exactly cancel each other. Therefore, we
# always assign zero current density on the ghost point, which is at the symmetry boundary.
# This makes us to simply ignore assiging current density to the point on the symmetry
# boundary. The portion of the total current to assign to the grid point adjacent
# to the symmetry boundary is as usual, and this portion becomes zero as the point source
# approaches the symmetry boundary. This makes sense, because if the point source is
# exactly on the symmetry boundary, being parallel to the boundary, then there must be no
# current in the system at all.
#
#
#
# Now, consider assignment of a point source corresponding to the dual field parallel to the
# boundary. For Bloch, we proceed similarly. For the symmetry boundary, now the image
# source put behind the boundary is in the same direction as the original source in the
# domain. Also, when the source is sufficiently close to the boundary, the actual and
# image sources are in a single dual grid cell interval, whose the middle point is the
# boundary point and the end points are dual grid points, which are referred to as A and B
# as before for convenience below, instead of the two adjacent cell intervals. Suppose A is
# within the domain and B is outside the domain. We don't have to assign a portion of the
# total current of the real point source to B ourselves, which is behind the symmetry
# boundary: this is something automatically done by imposing symmetry. However, if the
# image point source existing behind the symmetry boundary assigns some portion of its total
# current to A, which is within the domain, that must be assigned ourselves.
#
# Because the two point sources are at mirror-symmetric locations, the reduction in the
# amount of the current assigned to A by the point source moving away from A towards the
# symmetry boundary is exactly compensated by the increase in the amount of the current
# assigned to A by the image point source to the symmetry boundary from behind the boundary.
# Therefore, the net effect is the constant amount of current assigend to the dual grid
# point, no matter where the point source is located inside this cell interval containing
# the symmetry boundary.
# Note that we need to support both Je and Jm to realize unidirectional sources.
export Source
export add_src!
abstract type Source{K,Kf} end # K: shape dimension; Kf: field dimension
add_src!(jKd::AbsArrNumber, gt₀::AbsVec{GridType}, bounds::Tuple2{AbsVecReal},
l::Tuple2{NTuple{K,AbsVecReal}}, ∆l::Tuple2{NTuple{K,AbsVecReal}},
isbloch::AbsVecBool, srcs::Source...) where {K} =
add_src!(jKd, SVec{K}(gt₀), (float.(l[nPR]),float.(l[nDL])), (float.(∆l[nPR]), float.(∆l[nDL])), SVec{K}(isbloch), srcs...)
# About the determination of gt_cmp
# In the concrete implementation of add_src!() for each concrete Source type, gt_cmp, which
# is the grid type of the J field component to set up (typically indicated by the
# nw-component) is determined by
#
# gt_cmp = src.isfield˔shp ? gt₀ : gt_w(nw, gt₀)
#
# gt_cmp is used to obtain the locations of the field by
#
# lcmp = t_ind(l, gt_cmp)
#
# So, why do we choose gt₀ when the field subspace is orthogonal to the shape subspace?
#
# gt₀ represents the grid type of the corners of Yee's voxel whose edges are composed of the
# field lines. The w-component of the field is located a half grid point away from these
# voxel corners along the w-direction. This picture describes how to determine gt_cmp when
# the field subspace is equal to the shape subspace.
#
# However, when the field subspace is orthogonal to the shape subspace, the half grid point
# shift to apply to the w-component occurs in the direction normal to the shape subspace.
# Along that direction, the grid lines are not defined, because l is defined in the shape
# subspace. Therefore, the half grid point shift does not have any effect in determining
# the locations of the w-component, and we can use gt₀ as the grid type in determining the
# field locations.
#
# For example, In the TE equation, the field subspace for the H-field is the z-direction and
# the shape subspace is the xy-plane. When the boundary field types are the E-field type
# for both the x- and y-directions, gt₀ for the H-field (Hz) is [DUAL,DUAL]. We can use
# [DUAL,DUAL] in determining the xy-locations of the H-field, as the half grid point shift
# along the z-direction for the H-field does not affect the locations of the H-field.
function add_src!(jKd::AbsArrNumber{K₊₁}, # (K+1)-dimensional array of Je (electric) or Jm (magnetic); first K dimensions specify location; last 1 dimension specify field component
gt₀::SVec{K,GridType}, # grid type of voxel corners; generated by ft2gt.(ft, boundft)
bounds::Tuple2{SFloat{K}}, # bounds[NEG][k] = boundary of domain at negative end in k-direction
l::Tuple2{NTuple{K,VecFloat}}, # l[PRIM][k] = primal vertex locations in k-direction
∆l::Tuple2{NTuple{K,VecFloat}}, # ∆l[PRIM][k] = (∆l at primal vertices in w) == diff(l[DUAL][k] including ghost point)
isbloch::SBool{K}, # Bloch boundary conditions
src::Source{K,Kf}... # sources
) where {K,K₊₁,Kf}
for src = srcs
add_src!(j3d, ft, boundft, bounds, l, ∆l, isbloch, src)
end
end
# I could have defined distweights with more abstractly typed arguments, such as c::Real,
# bounds::AbsVecReal, l::AbsVecReal, ∆l::AbsVecReal, and have let the type inference system
# create distweights for each distinct set of types. However, then I cannot control the
# behavior of the function perfectly, because the two numbers used in division could be
# integer or float. Therefore, I implement the kernel for a specific type later, and here I
# define a wrapper that converts the arguments into those specific type.
#
# Another advantage of this approach is that even though several different versions of the
# wrapper will be generated, the kernel will be generated only once for the specific type.
# This makes the amount of compiled code minimal.
#
# Note that the situation here is a bit different from create_curl. There, I wanted to
# create a curl operator of differnt types, such as Float, ComplexF, and even Int. Therefore,
# I had to allow generation of different kernel code for different argument types.
distweights(c::Real, gt::GridType, bounds::AbsVecReal, l::AbsVecReal, ∆l::AbsVecReal, isbloch::Bool) =
distweights(float(c), gt, SFloat{2}(bounds), float(l), float(∆l), isbloch)
# In a given Cartesian direction, if the source is located between two grid points,
# calculate the weights to distribute over the two points. The output is in the form of
# ([ind₁, ind₂], [wt₁, wt₂]), where wtₖ is the weight assigned to the grid point at indₖ.
# Note that the physical dimension of the weight is 1/(length).
#
# If the source is placed exactly at one grid point, weight distribution is unnecessary and
# the output satisfies ind₂ = ind₁ and wt₂ = 0. Note that the output type remains the same
# as before, which is a useful feature for achieving type stability of the function calling
# this function.
#
# This function works for both longitudinal and transversal discretization with respect to
# polarization. Luckily, the consideration of the boundary conditions discussed at the
# beginning of this file works for both cases. For example, suppose we are distributing
# along the x-grid an electric current source. For Bloch, it is obvious that both
# polarizations are treated equally. For the symmetry boundary condition, we can handle
# both polarization with the same code by determining whether the boundary condition is
# "zeroing" the source on the boundary or not. Specifically, the zeroing boundary condition
# is defined as follows (see the definition of `zeroing_bc` in the function definition as
# well):
# - if the boundary condition is zeroing the source, the source put beyond the boundary is
# antisymmetric to the source put within the boundary, so the two sources are superposed
# destructively on the boundary (note the two sources are in adjacent cells), and
# - if the boundary condition is not zeroing the source, the source put beyond the boundary
# is symmetric to the source put within the boundary, so the two sources are superposed
# constructively on the boundary (note the two sources are in the same cell).
#
# Therefore,
# - For Jy and Jz (parallel to the x-boundary), the zeroing boundary condition is the
# symmetric E-field boundary condition. Because the x-location of Jy and Jz is the E-field
# grid location, we know that a symmetric boundary is the E-field boundary if the x-location
# of Jy and Jz are primal.
# - For Jx (normal to the x-boundary), the zeroing boundary condition is the symmetric
# H-field boundary condition. Because the x-location of Jx is the H-field grid location, we
# know again that a symmetric boundary is the H-field boundary if the x-location of Jx is
# primal.
#
# Note that regardless of the polarization of the source, the boundary condition is zeroing
# if the location type is primal.
#
# In the implementation, assume that the source "value" to distribute is something that does
# not change with discretization (e.g., Id of a point source and K of a plane source). The
# returned weights are the factors to multiply to this source value.
#
# When distributing a source between two points on the opposite sides of the Bloch boundary,
# the Bloch phase factor is not multiplied. The source we are trying to distribute by this
# function is a delta-function-like source in the axis of consideration (e.g., axis normal
# to a plane source). It feels a bit strange to impose phase difference between what were
# originally a single point. Also, I think it would be much easier to multiply the Bloch
# phase factors to the entire 3D array of source when all the sources are assigned and
# properly distributed with weight factors. That way, we don't have to implement
# multiplication of the Bloch phase factors in every source type. However, this decision
# can change later if needed.
function distweights(c::Float, # location of point (c means center)
gt::GridType, # type of the grid planes
bounds::SFloat{2}, # [lneg, lpos]: negative- and positive-end boundaries of domain
l::AbsVecFloat, # location of grid planes without ghost points
∆l::AbsVecFloat, # size of grid cell centered at l (not lg)
isbloch::Bool) # boundary condition
bounds[1]≤c≤bounds[2] || throw(ArgumentError("c = $c must be within bounds = $bounds."))
L = bounds[2] - bounds[1]
N = length(l)
# Below, what if N = 1? I think N = 1 will work fine for Bloch, for which indn = indp
# = 1 and therefore the contribution from indn and indp will be added in source array
# construction, and for the symmetry boundary that is incongruent with the field type,
# for which indn = indp = 1 again but only indn will be considered and the current
# density to set will remain constant regardless of the source position... Wait, we have
# contribution from both boundaries in this case, so maybe we should consider one real
# point source and two imaginary point sources...
#
# Anyway, I think the main reason to use N = 1 is for 2D simulation, and in that case
# we use Bloch in that direction. So, I think it should be OK to allow N = 1 only for
# Bloch.
N > 1 || isbloch || throw(ArgumentError("length(l) = $N must be > 1 for symmetry boundary (= non-Bloch)."))
# Test if c is in the range where the result is not affected by the boundary.
# - For l[1:end] being the primal grid locations except for the ghost point,
# - for Bloch, l[1] ≤ c < l[end], and
# - for symmetry, l[2] ≤ c < l[end]. (Note the minimum boundary beng l[2], not l[1].)
# - For l[1:end] being the dual grid locations except for the ghost point,
# - for Bloch, l[1] ≤ c < l[end], and
# - for symmetry, l[1] ≤ c < l[end].
# Therefore, only the case with the primal field type and symmetry boundary condition is
# different from other cases. I will call this a case with a boundary condition
# "congruent" with the field type, because it is the boundary that is effective for the
# field type of interest.
zeroing_bc = gt==PRIM && !isbloch
indn = zeroing_bc ? 2 : 1
indp = N
# Below, note that c < l[indp] is used rather than c ≤ l[indp]. If c = l[indp], then
# ind₁ = findlast(x->x≤0, l.-c) is indp, and ind₂ = ind₁ + 1 is not well-defined when
# indp = N. The use of c < l[indp] avoids such a case.
bc_!eff = l[indn] ≤ c < l[indp] # boundary condition does not affect result
if bc_!eff
ind₁ = findlast(x->x≤0, l.-c) # because l is sorted and c ≥ l[1], l[1]-c ≤ 0 and thus ind₁ ≠ nothing
ind₂ = ind₁ + 1
∆c1 = c - l[ind₁]
∆lc = l[ind₂] - l[ind₁]
else # boundary affects result
if c < l[indn] # cannot happen for PRIM && Bloch
ind₁ = indn
ind₂ = N
bnd = bounds[1]
else # c ≥ l[indp] (not >)
ind₁ = indp
ind₂ = 1
bnd = bounds[2]
end
∆c1 = abs(l[ind₁] - c)
if gt == PRIM
∆lc = abs(l[ind₁] - bnd)
else # gt == DUAL
∆lc = isbloch ? L-abs(l[ind₁]-l[ind₂]) : 2abs(l[ind₁]-bnd)
end
end
r = ∆c1 / ∆lc
# Below, scale wt₁ and wt₂ with 1-r and r, respectively,
# - if c is in the region where the result is not affected by the boundary (bc_!eff), or
# - even if c is in the region where the result is affected by the boundary,
# - if the boundary is Bloch, or
# - if the boundary is symmetry it is congruent with field type (zeroing_bc = true).
wt₁ = 1.0 / ∆l[ind₁]
(bc_!eff || isbloch || zeroing_bc) && (wt₁ *= 1.0-r)
# Return only one weight factor
# - if c is exactly at a grid point, or
# - even if c is not exactly at a grid point, if it is in the region where the result is
# affected by the boundary but the boundary is non-Bloch (= symmetry).
if c==l[ind₁] || (!bc_!eff && !isbloch)
# We want to return only one weight factor in this case, but in order to keep the
# output SVec type the same (SVec{2} rather than SVec{1}), introduce a
# dummy second weight factor.
ind₂ = ind₁
wt₂ = 0.0
else
# Return two weight factors. Make sure all the cases handled by this else block
# have ind₂ defined already.
wt₂ = 1.0 / ∆l[ind₂]
(bc_!eff || isbloch) && (wt₂ *= r)
end
ind = SVec(ind₁, ind₂)
wt = SVec(wt₁, wt₂)
return (ind, wt)
end
# funciton calc_blochphase()
# function j3d2j()
# When defining concrete source types below, try to use the following order of arguments in
# the constructor:
# - geometry (e.g., direction normal to a plane)
# - location (e.g., location for a point, intercept for a plane)
# - polarization
# - strength (e.g., current dipole, current, sheet current density)
# I think I can define a "uniform source" that populates a uniform current inside a given
# geometrical object specified with a GeometryPrimitives type. This can cover various
# primitive source types, such as a point source, line source, and a plane source. The
# source assignment function will accept a vector of sources, assign these sources similarly
# as assign_param! in assignment.jl (i.e., iterate over a vector of shapes and assign points
# included in each shape). A main difference is that I have to find voxels that overlap with
# the shape, instead of points in the shape. Once such voxels are found, I need to assign
# source current density to the edges of the voxels. (I have to think about subvoxel
# smoothing in a voxel that is partially included in the shape.)
#
# Even more generally, I think I can create a single source that accepts a geometry and
# source strength function. The uniform sources are a special case with a constant
# source strength.
include("pointsrc.jl")
include("planesrc.jl")
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4943,
198
] | 3.238281 | 6,165 |
function format(x)
io = IOBuffer()
show(IOContext(io, :compact => true, :typeinfo => typeof(x)), x)
String(take!(io))
end
function row(r, i; format = TableWidgets.format)
fields = propertynames(r)
node("tr",
node("th", format(i)),
(node("td", format(getproperty(r, field))) for field in fields)...)
end
rendertable(t; kwargs...) = render_row_iterator(Tables.rows(t); kwargs...)
rendertable(t, r::Integer; kwargs...) = render_row_iterator(Iterators.take(Tables.rows(t), r); kwargs...)
function render_row_iterator(t;
format = TableWidgets.format, className = "is-striped is-hoverable", row = TableWidgets.row)
fr, lr = Iterators.peel(t)
names = propertynames(fr)
headers = node("tr", node("th", ""), (node("th", string(n)) for n in names)...) |> node("thead")
first_row = row(fr, 1; format = format)
body = node("tbody", first_row, (row(r, i+1; format = format) for (i, r) in enumerate(lr))...)
className = "table interact-widget $className"
n = slap_design!(node("table", headers, body, className = className))
end
"""
`head(t, r=6)`
Show first `r` rows of table `t` as HTML table.
"""
function head(t, r = 6; kwargs...)
t isa AbstractObservable || (t = Observable{Any}(t))
r isa AbstractObservable || (r = Observable{Int}(r))
h = @map rendertable(&t, &r; kwargs...)
Widget{:head}([:rows => r, :head => h], output = t, layout = i -> i[:head])
end
function toggled(wdg::AbstractWidget; readout = true, label = "Show")
toggled_wdg = togglecontent(wdg, label = label, value = readout)
Widget{:toggled}([:toggle => toggled_wdg], output = observe(wdg), layout = i -> i[:toggle])
end
"""
`dataeditor(t, rows; label = "Show table")`
Create a textbox to preprocess a table: displays the result using `toggled(head(t, rows))`.
"""
function dataeditor(t, args...; readout = true, label = "Show table", kwargs...)
(t isa AbstractObservable) || (t = Observable{Any}(t))
output = Observable{Any}(t[])
wdg = Widget{:dataeditor}(output = output)
wdg[:input] = t
wdg[:display] = toggled(head(observe(wdg), args...; kwargs...); readout = readout, label = label)
wdg[:text] = textarea(placeholder = "Write transformation to apply to the table")
parsetext!(wdg; text = observe(wdg, :text), on = (observe(wdg, :text)), default = identity)
wdg[:apply] = button("Apply")
wdg[:reset] = button("Reset", className = "is-danger")
@map! observe(wdg) begin
&wdg[:apply]
(wdg[:function][])(&t)
end
@on begin
&wdg[:reset]
wdg[:text][] = ""
observe(wdg)[] = t[]
end
@layout! wdg Widgets.div(:text, hbox(:apply, hskip(1em), :reset), :display)
end
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368,
828,
1058,
42503,
828,
1058,
13812,
8,
198,
437,
198
] | 2.446348 | 1,109 |
using Pkg
packages = [
"IJulia",
"Plots",
"PyPlot"]
for package=packages
println("installing $package")
Pkg.add(package)
end
Pkg.update()
Pkg.precompile() | [
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] | 2.269231 | 78 |
using StatsPlots
using IndexedTables
# This demonstrates the PROCESS by plotting a correlation matrix I THINK.
include(joinpath(WATERFALL_DIR,"src","includes.jl"))
begin value=:Value; label=:Label; sample=:Sample end
distribution=:normal
fuzziness=(0.01,0.3)
numsample=1000
ncor=2
kwargs = (label=label, distribution=distribution, fuzziness=fuzziness,
numsample=numsample,
ncor=ncor,
)
df = CSV.read(joinpath(DATA_DIR,"pvrd2-investment-metrics.csv"), DataFrames.DataFrame)
df[!,:Label] .= string.(titlecase.(df[:,:Process])," (",df[:,:Step],")")
df[!,:Units] .= "Efficiency (%)"
label=:Process
df = DataFrames.transform(fuzzify(df; kwargs...))
dfw = DataFrames.unstack(DataFrames.select(df, [sample,label,value]), label, value)
steps = size(dfw,2)-1
cols = propertynames(dfw)[2:end]
vcorr = random_correlation(steps, ncor)
# @df iris corrplot(cols(1:4), grid = false)
StatsPlots.gr(size=(800,800))
iis = sum(hcat([vcat(sum(LinearAlgebra.UnitLowerTriangular(vcorr); dims=ii)...) for ii in 1:2]...); dims=2).!==2.0
iis = unique([[ii for ii in collect(2:steps+1) .* iis if ii!==0]; steps+1])
@df dfw StatsPlots.corrplot(cols(iis),
grid=false,
compact=true,
label =["$i" for i=iis],
)
# using StatsPlots
# using IndexedTables
# import RDatasets
# df = DataFrames.DataFrame(a = 1:10, b = 10 .* rand(10), c = 10 .* rand(10))
# @df df plot(:a, [:b :c], colour = [:red :blue])
# @df df scatter(:a, :b, markersize = 4 .* log.(:c .+ 0.1))
# t = table(1:10, rand(10), names = [:a, :b]) # IndexedTable
# @df t scatter(2 .* :b)
# @df df plot(:a, cols(2:3), colour = [:red :blue])
# # SCHOOL
# # school = RDatasets.dataset("mlmRev","Hsb82")
# # @df school density(:MAch, group = (:Sx, :Sector), legend = :topleft) | [
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] | 2.40638 | 721 |