content
stringlengths 5
1.03M
| input_ids
sequencelengths 4
823k
| ratio_char_token
float64 0.4
12.5
| token_count
int64 4
823k
|
|---|---|---|---|
using Base.Test
using QuantumOptics
# Set up operators
spinbasis = SpinBasis(1//2)
sx = sigmax(spinbasis)
sy = sigmay(spinbasis)
sx_dense = full(sx)
sy_dense = full(sy)
@test typeof(sx_dense) == DenseOperator
@test typeof(sparse(sx_dense)) == SparseOperator
@test sparse(sx_dense) == sx
b = FockBasis(3)
I = identity(b)
I_dense = dense_identity(b)
s = tensor(sx, sy)
s_dense = tensor(sx_dense, sy_dense)
@test typeof(I) == SparseOperator
@test typeof(I_dense) == DenseOperator
@test_approx_eq 0. norm((I_dense-full(I)).data)
@test_approx_eq 0. norm((s_dense - full(s)).data)
@test I == identity(destroy(b))
type A <: Operator
end
a = A()
@test_throws ArgumentError sparse(a)
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] | 2.255738 | 305 |
module FilePathsBase
using Dates
using Mmap
using Printf
using UUIDs
import Base: ==
export
# Types
AbstractPath,
Path,
SystemPath,
PosixPath,
WindowsPath,
Mode,
Status,
FileBuffer,
# Methods
cwd,
home,
hasparent,
parents,
isascendant,
isdescendant,
filename,
extension,
extensions,
exists,
absolute,
isabsolute,
mode,
created,
modified,
normalize,
canonicalize,
relative,
isrelative,
ismount,
islink,
cp,
mv,
sync,
tmpname,
tmpdir,
mktmp,
mktmpdir,
chown,
executable,
readable,
writable,
raw,
readpath,
walkpath,
diskusage,
# Macros
@p_str,
@__PATH__,
@__FILEPATH__,
# Constants
READ,
WRITE,
EXEC
export isexecutable
const PATH_TYPES = Type[]
function __init__()
# Register the default fallback path type based on the os.
register(Sys.iswindows() ? WindowsPath : PosixPath)
end
"""
AbstractPath
Defines an abstract filesystem path.
# Properties
- `segments::Tuple{Vararg{String}}` - path segments (required)
- `root::String` - path root (defaults to "/")
- `drive::String` - path drive (defaults to "")
- `separator::String` - path separator (defaults to "/")
# Required Methods
- `tryparse(::Type{T}, str::String)` - For parsing string representations of your path
- `read(path::T)`
- `write(path::T, data)`
- `exists(path::T` - whether the path exists
- `stat(path::T)` - File status describing permissions, size and creation/modified times
- `mkdir(path::T; kwargs...)` - Create a new directory
- `rm(path::T; kwags...)` - Remove a file or directory
- `readdir(path::T)` - Scan all files and directories at a specific path level
"""
abstract type AbstractPath end # Define the AbstractPath here to avoid circular include dependencies
"""
register(::Type{<:AbstractPath})
Registers a new path type to support using `Path("...")` constructor and p"..." string
macro.
"""
function register(T::Type{<:AbstractPath})
# We add the type to the beginning of our PATH_TYPES,
# so that they can take precedence over the Posix and
# Windows paths.
pushfirst!(PATH_TYPES, T)
end
"""
ispathtype(::Type{T}, x::AbstractString) where T <: AbstractPath
Return a boolean as to whether the string `x` fits the specified the path type.
"""
function ispathtype end
include("constants.jl")
include("utils.jl")
include("libc.jl")
include("mode.jl")
include("status.jl")
include("buffer.jl")
include("path.jl")
include("aliases.jl")
include("system.jl")
include("posix.jl")
include("windows.jl")
include("test.jl")
include("deprecates.jl")
end # end of module
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] | 2.694141 | 1,007 |
# This file is a part of InverseFunctions.jl, licensed under the MIT License (MIT).
"""
inverse(f)
Returns the inverse of a function `f`.
The following conditions must be satisfied:
* `inverse(f) β f` must be equivalent to `identity`.
* `inverse(f)(f(x)) β x`
* `inverse(inverse(f))` must be equivalent (ideally identical) to `f`.
`inverse` supports mapped/broadcasted functions (via `Base.Fix1`) and (on
Julia >=v1.6) function composition.
Example:
```julia
foo(x) = inv(exp(-x) + 1)
inv_foo(y) = log(y / (1 - y))
InverseFunctions.inverse(::typeof(foo)) = inv_foo
InverseFunctions.inverse(::typeof(inv_foo)) = foo
x = 4.2
@assert inverse(foo)(foo(x)) β x
@assert inverse(inverse(foo)) == foo
X = rand(10)
broadcasted_foo = Base.Fix1(broadcast, foo)
Y = broadcasted_foo(X)
@assert inverse(broadcasted_foo)(Y) β X
# Requires Julia >= v1.6:
bar = log β foo
@assert inverse(bar)(bar(x)) β x
```
"""
function inverse end
export inverse
inverse(::typeof(inverse)) = inverse
@static if VERSION >= v"1.6"
inverse(f::Base.ComposedFunction) = Base.ComposedFunction(inverse(f.inner), inverse(f.outer))
end
inverse(mapped_f::Base.Fix1{<:Union{typeof(map),typeof(broadcast)}}) = Base.Fix1(mapped_f.f, inverse(mapped_f.x))
inverse(::typeof(identity)) = identity
inverse(::typeof(inv)) = inv
inverse(::typeof(adjoint)) = adjoint
inverse(::typeof(transpose)) = transpose
inverse(::typeof(exp)) = log
inverse(::typeof(log)) = exp
inverse(::typeof(exp2)) = log2
inverse(::typeof(log2)) = exp2
inverse(::typeof(exp10)) = log10
inverse(::typeof(log10)) = exp10
inverse(::typeof(expm1)) = log1p
inverse(::typeof(log1p)) = expm1
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] | 2.525424 | 649 |
import LinearAlgebra.reflectorApply!
# this should suffice to handle simple qr()
const qr_mt_threshold = Ref(64.0)
mt_thresholds[:qr] = qr_mt_threshold;
function reflectorApply!(x::AbstractVector{DT}, Ο::Number, A::StridedMatrix{DT}
) where {DT <: Union{DoubleFloat{T},
Complex{DoubleFloat{T}}}} where T
has_offset_axes(x) && throw(ArgumentError("not implemented "
* "for offset axes"))
m, n = size(A)
if length(x) != m
throw(DimensionMismatch("reflector has length $(length(x)), "
* "which must match the first dimension of matrix A, $m"))
end
use_threads = (nthreads() > 1) &&
(Float64(m)*Float64(n) > qr_mt_threshold[])
if use_threads
@threads for j = 1:n
_mt_refl_loop1(Ο,A,x,m,j)
end
else
@inbounds begin
for j = 1:n
vAj = conj(Ο)*(A[1, j] + dot(view(x,2:m), view(A,2:m,j)))
A[1, j] -= vAj
axpy!( -vAj, view(x,2:m), view(A,2:m,j))
# for i = 2:m
# A[i,j] -= vAj * x[i]
# end
end
end
end
return A
end
function _mt_refl_loop1(Ο,A,x,m,j)
@inbounds begin
vAj = conj(Ο)*(A[1, j] + dot(uview(x,2:m), uview(A,2:m,j)))
A[1, j] -= vAj
# axpy!( -vAj, uview(x,2:m), uview(A,2:m,j))
for i = 2:m
A[i,j] -= vAj * x[i]
end
end
end
import LinearAlgebra.generic_matvecmul!
const gemv_mt_threshold = Ref(512.0)
mt_thresholds[:gemv] = gemv_mt_threshold;
const gemtv_mt_threshold = Ref(64.0)
mt_thresholds[:gemtv] = gemtv_mt_threshold;
function generic_matvecmul!(C::AbstractVector{DoubleFloat{T}}, tA, A::AbstractVecOrMat{DoubleFloat{T}}, B::AbstractVector{DoubleFloat{T}}) where {T <: AbstractFloat}
has_offset_axes(C, A, B) && throw(ArgumentError("offset axes are not supported"))
mB = length(B)
mA, nA = lapack_size(tA, A)
if mB != nA
throw(DimensionMismatch("matrix A has dimensions ($mA,$nA), vector B has length $mB"))
end
if mA != length(C)
throw(DimensionMismatch("result C has length $(length(C)), needs length $mA"))
end
if (tA == 'T') || (tA == 'C')
use_threads = (nthreads() > 1) &&
(Float64(mB)*Float64(mA) > gemtv_mt_threshold[])
if use_threads
liA = LinearIndices(A)
@threads for k = 1:mA
@inbounds C[k] = _dot(nA,A,liA[1,k],B,1,Vec{Npref,T})
end
else
@inbounds for k = 1:mA
C[k] = dot(uview(A,:,k),B)
end
end
else
fill!(C,zero(T))
use_threads = (nthreads() > 1) &&
(Float64(mB)*Float64(mA) > gemv_mt_threshold[])
if use_threads
nt = nthreads()
nd,nr = divrem(mA, nt)
liA = LinearIndices(A)
@threads for it=1:nt
# for it=1:nt # DEBUG
j1 = it*nd
j0 = j1-nd+1
_mt_gemv_loop1(nA,A,liA,B,C,j0,j1)
end
if nr > 0
j0 = nt*nd+1
@inbounds for i = 1:nA
_axpy!(nr,B[i],A,liA[j0,i],C,j0,Vec{Npref,T})
end
end
else
@inbounds begin
astride = stride(A,2)
for k=1:mB
ioff = (k-1)*astride
b = B[k]
for i = 1:mA
C[i] += A[ioff + i] * b
end
end
end
# liA = LinearIndices(A)
# @inbounds for i = 1:nA
# _axpy!(mA,B[i],A,liA[1,i],C,1,Vec{Npref,T})
# end
end
end
C
end
function _mt_gemv_loop1(nA,A::AbstractVecOrMat{DoubleFloat{T}},liA,
B,C,j0,j1) where {T}
# Julia v1.0+ compiler doesn't need help here
# nd = j1-j0+1
# @inbounds for i = 1:nA
# _axpy!(nd,B[i],A,liA[j0,i],C,j0,Vec{Npref,T})
# end
@inbounds for i=1:nA
b = B[i]
for j=j0:j1
C[j] += b * A[j,i]
end
end
end
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] | 1.641145 | 2,586 |
st = state(p=VariableSpec(),t=VariableSpec())
@test mass_volume(water,st(3.0, 300.0)) β 0.100_215_168E-2
@test mass_volume(water,st(3.0u"MPa", 300.0u"K")) β 0.100_215_168E-2u"m^3/kg"
@test mass_volume(water,st(80.0, 300.0)) β 0.971_180_894E-3
@test mass_volume(water,st(80.0u"MPa", 300.0u"K")) β 0.971_180_894E-3u"m^3/kg"
@test mass_volume(water,st(3.0, 500.0)) β 0.120_241_800E-2
@test mass_volume(water,st(3.0u"MPa", 500.0u"K")) β 0.120_241_800E-2u"m^3/kg"
@test mass_enthalpy(water,st(3.0, 300.0)) β 0.115_331_273E3
@test mass_enthalpy(water,st(3.0u"MPa", 300.0u"K")) β 0.115_331_273E3u"kJ/kg"
@test mass_enthalpy(water,st(80.0, 300.0)) β 0.184_142_828E3
@test mass_enthalpy(water,st(80.0u"MPa", 300.0u"K")) β 0.184_142_828E3u"kJ/kg"
@test mass_enthalpy(water,st(3.0, 500.0)) β 0.975_542_239E3
@test mass_enthalpy(water,st(3.0u"MPa", 500.0u"K")) β 0.975_542_239E3u"kJ/kg"
@test mass_internal_energy(water,st(3.0, 300.0)) β 0.112_324_818E3
@test mass_internal_energy(water,st(3.0u"MPa", 300.0u"K")) β 0.112_324_818E3u"kJ/kg"
@test mass_internal_energy(water,st(80.0, 300.0)) β 0.106_448_356E3
@test mass_internal_energy(water,st(80.0u"MPa", 300.0u"K")) β 0.106_448_356E3u"kJ/kg"
@test mass_internal_energy(water,st(3.0, 500.0)) β 0.971_934_985E3
@test mass_internal_energy(water,st(3.0u"MPa", 500.0u"K")) β 0.971_934_985E3u"kJ/kg"
@test mass_entropy(water,st(3.0, 300.0)) β 0.392_294_792
@test mass_entropy(water,st(3.0u"MPa", 300.0u"K")) β 0.392_294_792u"kJ/kg/K"
@test mass_entropy(water,st(80.0, 300.0)) β 0.368_563_852
@test mass_entropy(water,st(80.0u"MPa", 300.0u"K")) β 0.368_563_852u"kJ/kg/K"
@test mass_entropy(water,st(3.0, 500.0)) β 0.258_041_912E1
@test mass_entropy(water,st(3.0u"MPa", 500.0u"K")) β 0.258_041_912E1u"kJ/kg/K"
@test mass_cp(water,st(3.0, 300.0)) β 0.417_301_218E1
@test mass_cp(water,st(3.0u"MPa", 300.0u"K")) β 0.417_301_218E1u"kJ/kg/K"
@test mass_cp(water,st(80.0, 300.0)) β 0.401_008_987E1
@test mass_cp(water,st(80.0u"MPa", 300.0u"K")) β 0.401_008_987E1u"kJ/kg/K"
@test mass_cp(water,st(3.0, 500.0)) β 0.465_580_682E1
@test mass_cp(water,st(3.0u"MPa", 500.0u"K")) β 0.465_580_682E1u"kJ/kg/K"
@test sound_speed(water,st(3.0, 300.0)) β 0.150_773_921E4
@test sound_speed(water,st(3.0u"MPa", 300.0u"K")) β 0.150_773_921E4u"m/s"
@test sound_speed(water,st(80.0, 300.0)) β 0.163_469_054E4
@test sound_speed(water,st(80.0u"MPa", 300.0u"K")) β 0.163_469_054E4u"m/s"
@test sound_speed(water,st(3.0, 500.0)) β 0.124_071_337E4
@test sound_speed(water,st(3.0u"MPa", 500.0u"K")) β 0.124_071_337E4u"m/s"
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] | 1.787623 | 1,422 |
## MNIST data using Non-negative matrix Factorization
using MLDatasets
train_x, train_y = MNIST.traindata()
test_x, test_y = MNIST.testdata()
## Create the input for NMF
m = size(train_x,1)*size(train_x,2)
n = size(train_x,3)
X = Float64.(reshape(train_x, m, n))
A = copy(X') # input
## Show a random set of images
imgs = Gray.(reshape(A[1:25,:]',28,28,25))
p = plot([heatmap(imgs[:,:,i]') for i=1:25]...,framestyle=:none,yflip=true)
##
heatmap(Gray.(reshape(sum(A;dims=1)/size(A,1),28,28)'),framestyle=:none,yflip=true,colorbar=true)
##
#using ImageMagick
using NMF
using Images
## Make a rank 5 factorization
k = 5
W, H = NMF.nndsvd(A, k; variant= :ar)
# alginst = NMF.MultUpdate{Float64}(obj=:mse, maxiter=1000, verbose=false)
alginst = NMF.ALSPGrad{Float64}(maxiter=100)
r = NMF.solve!(alginst, A, W, H)
## show the images
imgs = Gray.(reshape(r.H',28,28,k))
p = plot([heatmap(imgs[:,:,i]) for i=1:k]...,framestyle=:none,yflip=true)
savefig("nmf-mnist-$k.pdf")
## Turn this into a method
function nmf(A,k)
W, H = NMF.nndsvd(A, k; variant= :ar)
alginst = NMF.ALSPGrad{Float64}(maxiter=100)
r = NMF.solve!(alginst, A, W, H)
imgs = Gray.(reshape(r.H',28,28,k))
plot([heatmap(imgs[:,:,i]) for i=1:k]...,framestyle=:none,yflip=true), r
savefig("nmf-mnist-$k.pdf")
end
nmf(A,8)
# alginst = NMF.MultUpdate{Float64}(obj=:mse, maxiter=1000, verbose=false)
##
nmf(A,15)
##
nmf(A,25)
##
nmf(A,50)
##
nmf(A,100)
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] | 2.133034 | 669 |
"""
SeisMP(in,operator,param,iter,lp,imax)
Robust Matching Pursuit algorithm via lp-norm inner product
# Arguments
- `in`: Noisy data
- `operator`: Any operator passed the dot test
- `param`: parameters for the operator
- `iter`: Max iterations for the matching pursuit
- `lp`: lp-norm space inner product
- `imax`: Max interation for the lp-norm inner product
"""
function SeisMP(in,operator,param,iter,lp,imax)
Madj=operator(in,param,-1,lp,imax);
x=zeros(size(Madj));
dr=zeros(size(in));
r=in
for i=1:iter
Madj=operator(r,param,-1,lp,imax);
ind=findmax(abs.(Madj))[2];
M=zeros(size(Madj));
M[ind[1],ind[2]]=Madj[ind[1],ind[2]];
g=operator(M,param,1,lp,imax);
alpha=LP_norm(vec(g),vec(r),lp,imax,1);
dr=dr+alpha*g
x=x+alpha*M;
r=r-alpha*g;
end
return dr
end
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] | 2.112195 | 410 |
################################################################################
#
# NfOrd.jl : Orders in number fields
#
# This file is part of hecke.
#
# Copyright (c) 2015, 2016: Claus Fieker, Tommy Hofmann
# All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
# * Redistributions of source code must retain the above copyright notice, this
# list of conditions and the following disclaimer.
#
# * Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
#
#
# Copyright (C) 2015, 2016 Tommy Hofmann
#
################################################################################
export isequationorder, nf, parent, basis, basis_mat, basis_mat_inv,
discriminant, degree, gen_index, index, is_index_divisor, deepcopy,
signature, minkowski_mat, norm_change_const, in, den, +, poverorder,
pmaximal_overorder
################################################################################
#
# Predicates
#
################################################################################
doc"""
isequationorder(O::NfOrd) -> Bool
> Returns whether $\mathcal O$ is the equation order.
"""
isequationorder(O::NfOrd) = O.isequationorder
################################################################################
#
# Ambient number field
#
################################################################################
doc"""
nf(O::NfOrd) -> AnticNumberField
> Returns the ambient number field of $\mathcal O$.
"""
nf(O::NfOrd) = O.nf
################################################################################
#
# Parent
#
################################################################################
doc"""
parent(O::NfOrd) -> NfOrdSet
> Returns the parent of $\mathcal O$, that is, the set of orders of the ambient
> number field.
"""
parent(O::NfOrd) = O.parent
################################################################################
#
# Basis
#
################################################################################
function basis_ord(O::NfOrd)
if isdefined(O, :basis_ord)
return O.basis_ord::Array{NfOrdElem{typeof(O)}, 1}
end
b = O.basis_nf
B = Array(NfOrdElem{typeof(O)}, length(b))
for i in 1:length(b)
v = fill(FlintZZ(0), length(b))
v[i] = FlintZZ(1)
B[i] = O(b[i], v; check = false)
end
O.basis_ord = B
return B::Array{NfOrdElem{typeof(O)}, 1}
end
doc"""
basis(O::NfOrd) -> Array{NfOrdElem, 1}
> Returns the $\mathbf Z$-basis of $\mathcal O$.
"""
function basis(O::NfOrd)
return basis_ord(O)
end
doc"""
basis(O::NfOrd, K::AnticNumberField) -> Array{nf_elem, 1}
> Returns the $\mathbf Z$-basis of $\mathcal O$ as elements of the ambient
> number field.
"""
function basis(O::NfOrd, K::AnticNumberField)
nf(O) != K && error()
return deepcopy(O.basis_nf)
end
################################################################################
#
# (Inverse) basis matrix
#
################################################################################
doc"""
basis_mat(O::NfOrd) -> FakeFmpqMat
> Returns the basis matrix of $\mathcal O$ with respect to the power basis
> of the ambient number field.
"""
function basis_mat(O::NfOrd)
if isdefined(O, :basis_mat)
return deepcopy(O.basis_mat)
end
A = O.basis_nf
O.basis_mat = FakeFmpqMat(basis_mat(A))
return deepcopy(O.basis_mat)
end
doc"""
basis_mat_inv(O::NfOrd) -> FakeFmpqMat
> Returns the inverse of the basis matrix of $\mathcal O$.
"""
function basis_mat_inv(O::NfOrd)
if isdefined(O, :basis_mat_inv)
return deepcopy(O.basis_mat_inv)
end
O.basis_mat_inv = inv(basis_mat(O))
return deepcopy(O.basis_mat_inv)
end
################################################################################
#
# Discriminant
#
################################################################################
doc"""
discriminant(O::NfOrd) -> fmpz
> Returns the discriminant of $\mathcal O$.
"""
function discriminant(O::NfOrd)
if isdefined(O, :disc)
return deepcopy(O.disc)
end
if isequationorder(O)
O.disc = num(discriminant(nf(O).pol))
return deepcopy(O.disc)
end
return discriminant(basis(O))
end
################################################################################
#
# Degree
#
################################################################################
doc"""
degree(O::NfOrd) -> Int
> Returns the degree of $\mathcal O$.
"""
degree(O::NfOrd) = degree(O.nf)
################################################################################
#
# (Generalized) index
#
################################################################################
doc"""
gen_index(O::NfOrd) -> fmpq
> Generalized index of $\mathcal O$ with respect to the ambient equation
> order $\mathbf Z[\alpha]$.
"""
function gen_index(O::NfOrd)
if isdefined(O, :gen_index)
return deepcopy(O.gen_index)
else
O.gen_index = QQ(basis_mat(O).den^degree(O), det(basis_mat(O).num))
return deepcopy(O.gen_index)
end
end
doc"""
index(O::NfOrd) -> fmpz
> Assuming that the order $\mathcal O$ contains the ambient equation order
> $\mathbf Z[\alpha]$, this function returns the index $[ \mathcal O : \mathbf ZZ]$.
"""
function index(O::NfOrd)
if isdefined(O, :index)
return deepcopy(O.index)
else
i = gen_index(O)
den(i) != 1 && error("Order does not contain the equation order")
O.index = num(i)
return deepcopy(O.index)
end
end
################################################################################
#
# Index divisor
#
################################################################################
doc"""
is_index_divisor(O::NfOrd, d::fmpz) -> Bool
is_index_divisor(O::NfOrd, d::Int) -> Bool
> Returns whether $d$ is a divisor of the index of $\mathcal O$.
"""
function is_index_divisor(O::NfOrd, d::Union{fmpz, Int})
i = index(O)
return i % d == 0
end
################################################################################
#
# Deepcopy
#
################################################################################
doc"""
deepcopy(O::NfOrd) -> NfOrd
> Makes a copy of $\mathcal O$.
"""
function deepcopy(O::NfOrd)
z = NfOrdGen()
for x in fieldnames(O)
# This is slow. Julia can't interfere the type of the right hand side.
# (According to @code_warntype)
if x != :nf && x != :parent && isdefined(O, x)
z.(x) = deepcopy(getfield(O, x))
end
end
z.nf = O.nf
z.parent = O.parent
return z
end
################################################################################
#
# Signature
#
################################################################################
doc"""
signature(O::NfOrd) -> Tuple{Int, Int}
> Returns the signature of the ambient number field of $\mathcal O$.
"""
function signature(x::NfOrd)
if x.signature[1] != -1
return x.signature
else
x.signature = signature(nf(x))
return x.signature
end
end
################################################################################
#
# Minkowski matrix
#
################################################################################
doc"""
minkowski_mat(O::NfOrd, abs_tol::Int = 64) -> arb_mat
> Returns the Minkowski matrix of $\mathcal O$.
> Thus if $\mathcal O$ has degree $d$, then the
> result is a matrix in $\operatorname{Mat}_{d\times d}(\mathbf R)$.
> The entries of the matrix are real balls of type `arb` with radius
> less then `2^-abs_tol`.
"""
function minkowski_mat(O::NfOrd, abs_tol::Int = 64)
if isdefined(O, :minkowski_mat) && O.minkowski_mat[2] > abs_tol
A = deepcopy(O.minkowski_mat[1])
else
T = Array(Array{arb, 1}, degree(O))
B = O.basis_nf
for i in 1:degree(O)
T[i] = minkowski_map(B[i], abs_tol)
end
p = maximum([ prec(parent(T[i][j])) for i in 1:degree(O), j in 1:degree(O) ])
M = ArbMatSpace(ArbField(p), degree(O), degree(O))()
for i in 1:degree(O)
for j in 1:degree(O)
M[i, j] = T[i][j]
end
end
O.minkowski_mat = (M, abs_tol)
A = deepcopy(M)
end
return A
end
################################################################################
#
# Inclusion of number field elements
#
################################################################################
# Check if a number field element is contained in O
# In this case, the second return value is the coefficient vector with respect
# to the basis of O
function _check_elem_in_order(a::nf_elem, O::NfOrd)
M = MatrixSpace(FlintZZ, 1, degree(O))()
t = FakeFmpqMat(M)
elem_to_mat_row!(t.num, 1, t.den, a)
x = t*basis_mat_inv(O)
v = Array(fmpz, degree(O))
for i in 1:degree(O)
v[i] = deepcopy(x.num[1,i])
end
return (x.den == 1, v)
end
doc"""
in(a::nf_elem, O::NfOrd) -> Bool
> Checks whether $a$ lies in $\mathcal O$.
"""
function in(a::nf_elem, O::NfOrd)
(x,y) = _check_elem_in_order(a,O)
return x
end
################################################################################
#
# Denominator in an order
#
################################################################################
doc"""
den(a::nf_elem, O::NfOrd) -> fmpz
> Returns the smallest positive integer $k$ such that $k \cdot a$ lies in O.
"""
function den(a::nf_elem, O::NfOrd)
d = den(a)
b = d*a
M = MatrixSpace(ZZ, 1, degree(O))()
elem_to_mat_row!(M, 1, fmpz(1), b)
t = FakeFmpqMat(M, d)
z = t*basis_mat_inv(O)
return z.den
end
##################################3#############################################
#
# Norm change constant
#
################################################################################
# For x = \sum_i x_i omega_i let |x|_1 = \sqrt(x_1^2 + ... + x_d^2).
# And let |x|_2 = sqrt(T_2(x))
# Then there exist c1, c2 such that
# |x|_2^2 <= c1 |x|_2^2, |x|_1^2 <= c2 |x|_1^2
# A suitable pair (c1, c2) can be determined using the Minkowski map/matrix
#
# Reference
# Fieker, Friedrichs
# On Reconstruction of Algebraic Numbers
# (in particular p. 288)
doc"""
norm_change_const(O::NfOrd) -> (Float64, Float64)
> Returns $(c_1, c_2) \in \mathbf R_{>0}^2$ such that for all
> $x = \sum_{i=1}^d x_i \omega_i \in \mathcal O$ we have
> $T_2(x) \leq c_1 \cdot \sum_i^d x_i^2$
> and
> $\sum_i^d x_i^2 \leq c_2 \cdot T_2(x)$,
> where $(\omega_i)_i$ is the $\mathbf Z$-basis of $\mathcal O$.
"""
function norm_change_const(O::NfOrd)
if O.norm_change_const[1] > 0
return O.norm_change_const
else
d = degree(O)
M = transpose(minkowski_mat(O, 64))
# I need to swap rows (really?)
# I don't think we have to swap rows, since permutation matrices are orthogonal
#r1, r2 = signature(O)
#for i in 2:2:r2
# swap_rows!(M, r1 + i, r1 + 2*r2 - i + 1)
#end
M = [ Float64(M[i, j]) for i in 1:rows(M), j in 1:cols(M) ]
N = transpose(M)*M
r = sort(eigvals(N))
# N = transpose(M)*M
# N = MatrixSpace(AcbField(prec(base_ring(N))), rows(N), cols(N))(N)
# chi = charpoly(PolynomialRing(base_ring(N), "x")[1], N)
# return chi
# r = roots(chi)
# # I want upper bound for the largest and lower bound for the smallest root
#
# tm = arf_struct(0, 0, 0, 0)
# ccall((:arf_init, :libarb), Void, (Ptr{arf_struct}, ), &tm)
# ccall((:arb_get_abs_ubound_arf, :libarb), Void, (Ptr{arf_struct}, Ptr{arb}), &tm, &real(r[end]))
# # 3 is round to infinity
# c1 = ccall((:arf_get_d, :libarb), Cdouble, (Ptr{arf_struct}, Cint), &tm, 3)
#
# ccall((:arb_get_abs_ubound_arf, :libarb), Void, (Ptr{arf_struct}, Ptr{arb}), &tm, &(inv(real(r[1]))))
# c2 = ccall((:arf_get_d, :libarb), Cdouble, (Ptr{arf_struct}, Cint), &tm, 3)
#
# ccall((:arf_clear, :libarb), Void, (Ptr{arf_struct}, ), &tm)
#
# z = (c1, c2)
z = (r[end], inv(r[1]))
O.norm_change_const = z
return z
end
end
################################################################################
#
# Addition of orders
#
################################################################################
doc"""
+(R::NfOrd, S::NfOrd) -> NfOrd
> Given two orders $R$, $S$ of $K$, this function returns the smallest order
> containing both $R$ and $S$. It is assumed that $R$, $S$ contain the ambient
> equation order and have coprime index.
"""
function +(a::NfOrd, b::NfOrd)
parent(a) != parent(b) && error("Orders must have same ambient number field")
gcd(index(a), index(b)) != 1 && error("Indices must be coprime")
aB = basis_mat(a)
bB = basis_mat(b)
d = degree(a)
c = sub(_hnf(vcat(bB.den*aB.num, aB.den*bB.num), :lowerleft), d + 1:2*d, 1:d)
O = Order(nf(a), FakeFmpqMat(c, aB.den*bB.den))
return O
end
################################################################################
#
# p-Overorder
#
################################################################################
function _poverorder(O::NfOrd, p::fmpz)
#OO = NfOrdGen(colon_ideal(pradical(O, p)))
OO = ring_of_multipliers(pradical(O, p))
#OO.basis_mat = hnf(OO.basis_mat)
return OO
end
function _poverorder(O::NfOrd, p::Integer)
return _poverorder(O, ZZ(p))
end
doc"""
poverorder(O::NfOrd, p::fmpz) -> NfOrd
poverorder(O::NfOrd, p::Integer) -> NfOrd
> This function tries to find an order that is locally larger than $\mathcal O$ at the prime $p$:
> If $p$ divides the index $[ \mathcal O_K : \mathcal O]$, this function will
> return an order $\tilde{\mathcal O}$ such that $v_p([ \mathcal O_K : \tilde{\mathcal O}]) < v_p([ \mathcal O_K : \mathcal O])$.
> Otherwise $\mathcal O$ is returned.
"""
function poverorder(O::NfOrd, p::fmpz)
if isequationorder(O)
return dedekind_poverorder(O, p)
else
return _poverorder(O, p)
end
end
function poverorder(O::NfOrd, p::Integer)
return poverorder(O::NfOrd, ZZ(p))
end
################################################################################
#
# p-maximal overorder
#
################################################################################
doc"""
pmaximal_overorder(O::NfOrd, p::fmpz) -> NfOrd
pmaximal_overorder(O::NfOrd, p::Integer) -> NfOrd
> This function finds a $p$-maximal order $\tilde{\mathcal O}$ containing $\mathcal O$.
> That is, the index $[ \mathcal O_K : \tilde{\mathcal O}]$ is not dividible by $p$.
"""
function pmaximal_overorder(O::NfOrd, p::fmpz)
@vprint :NfOrd 1 "computing p-maximal overorder for $p ... \n"
if rem(discriminant(O), p) != 0
return O
end
d = discriminant(O)
@vprint :NfOrd 1 "extending the order at $p for the first time ... \n"
OO = poverorder(O, p)
dd = discriminant(OO)
i = 1
while d != dd
i += 1
@vprint :NfOrd 1 "extending the order at $p for the $(i)th time ... \n"
d = dd
OO = poverorder(OO, p)
dd = discriminant(OO)
end
return OO
end
function pmaximal_overorder(O::NfOrd, p::Integer)
return pmaximal_overorder(O, ZZ(p))
end
function _MaximalOrder(O::NfOrd, primes::Array{fmpz, 1})
OO = deepcopy(O)
disc = abs(discriminant(O))
for i in 1:length(primes)
p = primes[i]
(j, disc) = valuation(disc, p)
if j == 1
continue
end
@vprint :NfOrd 1 "Computing p-maximal overorder for $p ..."
OO += pmaximal_overorder(O, p)
@vprint :NfOrd 1 "done\n"
end
return OO
end
function _MaximalOrder(O::NfOrd)
OO = deepcopy(O)
@vtime :NfOrd fac = factor(Nemo.abs(discriminant(O)))
for (p,j) in fac
if j == 1
continue
end
@vprint :NfOrd 1 "Computing p-maximal overorder for $p ..."
OO += pmaximal_overorder(O, p)
@vprint :NfOrd 1 "done\n"
end
return OO
end
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] | 2.785497 | 5,930 |
module Corpora
require("LoadEnvironment.jl")
require("DataTypes.jl")
## Using
using ArrayViews
using DataTypes
## Exports
export Token, DataToken, DummyToken
export Sentence
export read_linecorpus
export text
export get_context, initialize_contexts
export replace_vocab!
abstract Token;
immutable DummyToken <: Token
word::String;
count::Vector{CInt};
end
DummyToken(word::String) = DummyToken(word, [0])
immutable DataToken <: Token
word;
idx::Int64;
count::Vector{Int64};
vector::DenseArray{Float,1}
gradient::DenseArray{Float,1}
end
immutable Sentence
text::Vector{Token}
end
Sentence() = Sentence(Token[])
text(s::Vector{Token}) = join(map(x->x.word, s), " ")
text(sentence::Sentence) = text(sentence.text)
function readuntil_ws_eol(s::IO)
out = IOBuffer()
lineend = false
firstchar = true
while !eof(s)
c = read(s, Char)
if c == ' ' || c == '\n'
if !firstchar
if c == '\n'
lineend = true
end
break
end
else
firstchar = false
write(out, c)
end
end
return (lineend,takebuf_string(out))
end
function read_linecorpus(path::String, init_from_data, init_from_dummy,
init_params;
dummy_vocab=Dict{String,Token}(), unk="_UNK_",
limit_lines=0, skiprows=0, skipunk=true,
no_new_vocab=false, lock_tokens=false,
dummies_only=false)
# Estimate number of lines
lcounter = 0
open(path) do fin
for (iline, line) in enumerate(eachline(fin))
if iline < skiprows
continue
end
lcounter += 1
if limit_lines > 0 && lcounter >= limit_lines
break
end
end
end
data = Array(Sentence, lcounter)
n_words_total = 0
lcounter = 1
max_lensen = -1
open(path) do fin
iline = 1
sentence = Sentence()
data[lcounter] = sentence
lensen = 0
while !eof(fin)
(eol, token) = readuntil_ws_eol(fin)
if iline < skiprows
continue
end
## For every token in the text
# Check if it is already in the vocabulary
# If not: add it with count 1
# If it is: increase the counters
if !haskey(dummy_vocab, token)
if !no_new_vocab
#vocab[token] = inittoken(token, lock_tokens);
dummy_vocab[token] = DummyToken(token);
else
if skipunk
n_words_total += 1
continue
else
token = unk
end
end
end
dummytok = dummy_vocab[token]
push!(sentence.text,dummytok)
lensen += 1
dummytok.count[1] += 1
n_words_total += 1
if eol
if lensen > max_lensen
max_lensen = lensen
end
end
if eol
if (limit_lines > 0 && lcounter >= limit_lines)
break
end
if eof(fin)
break
end
lcounter += 1
sentence = Sentence()
data[lcounter] = sentence
lensen = 0
end
end
end
vocab = if dummies_only
dummy_vocab
else
v = Dict{String,Token}()
# Convert DummyTokens to real tokens
## Signal the model that the vocabulary has been acquired
init_params(length(dummy_vocab))
## Initialize token types
for (iword,(word, tok)) in enumerate(dummy_vocab)
if typeof(tok) == DataToken
v[word] = init_from_data(iword,tok)
else
v[word] = init_from_dummy(iword,tok)
end
end
## Replace the dummies by data tokens
for isentence in 1:size(data,1)
for itoken in 1:size(data[isentence].text,1)
data[isentence].text[itoken] = v[data[isentence].text[itoken].word]
end
end
v
end
return data, vocab, n_words_total, max_lensen;
end
function replace_vocab!(data::Vector{Sentence}, vocab::Dict{String,Token})
for isentence in 1:size(data,1)
for itoken in 1:size(data[isentence].text,1)
data[isentence].text[itoken] = vocab[data[isentence].text[itoken].word]
end
end
end
function get_context(sentence::Vector{Token}, pos::Int64, windowsize::Int64)
lensen = size(sentence,1)
windowsize_half = int64(windowsize/2)
window_start = max(1, pos - windowsize_half)
window_end = min(lensen, pos + windowsize_half)
window = Token[]
for i=window_start:window_end
if i != pos
push!(window, sentence[i])
end
end
return window
end
function initialize_contexts(data::Vector{Sentence}, windowsize::Int64,
minlensen=5)
for sentence in data
if size(sentence.text,1) < 5
continue
end
for pos=1:size(sentence.text,1)
empty!(sentence.text[pos].context)
append!(sentence.text[pos].context,
get_context(sentence.text, pos, windowsize))
end
end
end
end
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] | 1.852883 | 3,052 |
# moving average functions
@testset "Moving Averages" begin
Random.seed!(SEED)
@testset "Array" begin
x = cumsum(randn(N))
X = cumsum(randn(N, 2), dims=1)
tmp = sma(x)
@test size(tmp, 1) == N
@test size(tmp, 2) == 1
@test sum(isnan.(tmp)) != N
tmp = mama(x)
@test size(tmp, 1) == N
@test size(tmp, 2) == 2
@test sum(isnan.(tmp)) != N
tmp = ema(x)
@test size(tmp, 1) == N
@test size(tmp, 2) == 1
@test sum(isnan.(tmp)) != N
tmp = wma(x)
@test size(tmp, 1) == N
@test size(tmp, 2) == 1
@test sum(isnan.(tmp)) != N
tmp = hma(x)
@test size(tmp, 1) == N
@test size(tmp, 2) == 1
@test sum(isnan.(tmp)) != N
tmp = trima(x)
@test size(tmp, 1) == N
@test size(tmp, 2) == 1
@test sum(isnan.(tmp)) != N
tmp = mma(x)
@test size(tmp, 1) == N
@test size(tmp, 2) == 1
@test sum(isnan.(tmp)) != N
tmp = tema(x)
@test size(tmp, 1) == N
@test size(tmp, 2) == 1
@test sum(isnan.(tmp)) != N
tmp = dema(x)
@test size(tmp, 1) == N
@test size(tmp, 2) == 1
@test sum(isnan.(tmp)) != N
tmp = swma(x)
@test size(tmp, 1) == N
@test size(tmp, 2) == 1
@test sum(isnan.(tmp)) != N
tmp = kama(x)
@test size(tmp, 1) == N
@test size(tmp, 2) == 1
@test sum(isnan.(tmp)) != N
tmp = alma(x)
@test size(tmp, 1) == N
@test size(tmp, 2) == 1
@test sum(isnan.(tmp)) != N
tmp = zlema(x)
@test size(tmp, 1) == N
@test size(tmp, 2) == 1
@test sum(isnan.(tmp)) != N
tmp = vwma(X)
@test size(tmp, 1) == N
@test size(tmp, 2) == 1
tmp = vwap(X)
@test size(tmp, 1) == N
@test size(tmp, 2) == 1
tmp = hama(x)
@test size(tmp, 1) == N
@test size(tmp, 2) == 1
end
@testset "Temporal" begin
x = TS(cumsum(randn(N)))
X = TS(cumsum(randn(N, 2), dims=1))
# moving average functions
tmp = sma(x)
@test size(tmp, 1) == N
@test size(tmp, 2) == 1
tmp = mama(x)
@test size(tmp, 1) == N
@test size(tmp, 2) == 2
tmp = ema(x)
@test size(tmp, 1) == N
@test size(tmp, 2) == 1
tmp = wma(x)
@test size(tmp, 1) == N
@test size(tmp, 2) == 1
tmp = hma(x)
@test size(tmp, 1) == N
@test size(tmp, 2) == 1
tmp = trima(x)
@test size(tmp, 1) == N
@test size(tmp, 2) == 1
tmp = mma(x)
@test size(tmp, 1) == N
@test size(tmp, 2) == 1
tmp = tema(x)
@test size(tmp, 1) == N
@test size(tmp, 2) == 1
tmp = dema(x)
@test size(tmp, 1) == N
@test size(tmp, 2) == 1
tmp = swma(x)
@test size(tmp, 1) == N
@test size(tmp, 2) == 1
tmp = kama(x)
@test size(tmp, 1) == N
@test size(tmp, 2) == 1
tmp = alma(x)
@test size(tmp, 1) == N
@test size(tmp, 2) == 1
tmp = zlema(x)
@test size(tmp, 1) == N
@test size(tmp, 2) == 1
tmp = vwma(X)
@test size(tmp, 1) == N
@test size(tmp, 2) == 1
tmp = vwap(X)
@test size(tmp, 1) == N
@test size(tmp, 2) == 1
tmp = hama(x)
@test size(tmp, 1) == N
@test size(tmp, 2) == 1
end
end
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] | 1.719004 | 2,089 |
using Query
using DataFrames
using Test
@testset "Pipe Syntax" begin
df = DataFrame(a=[1,2,3], b=[3.,2.,1.], c=["a", "b", "c"])
df2 = df |> @query(i, begin
@where i.a>2
@select {i.c, i.b}
end) |> DataFrame
@test df2 isa DataFrame
@test size(df2) == (1,2)
@test df2[1,:c] == "c"
@test df2[1,:b] == 1.
end
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] | 1.863874 | 191 |
function transform_to_steps!(A::AbstractMatrix; epsilon = 1e-7, degenerate_exeption = true)
# @inbounds - Π·Π°ΠΌΠ΅ΡΠ½ΠΎΠ³ΠΎ ΡΡΠΊΠΎΡΠ΅Π½ΠΈΡ Π·Π΄Π΅ΡΡ ΡΡΠΎ Π½Π΅ Π΄Π°ΡΡ!!!
for k β 1:size(A, 1)
absval, Ξk = findmax(abs, @view(A[k:end,k]))
(degenerate_exeption && absval <= epsilon) && throw("ΠΡΡΠΎΠΆΠ΄Π΅Π½Π°Ρ ΠΌΠ°ΡΡΠΈΡΠ°")
Ξk > 1 && swap!(@view(A[k,k:end]), @view(A[k+Ξk-1,k:end]))
for i β k+1:size(A,1)
t = A[i,k]/A[k,k]
@. @views A[i,k:end] = A[i,k:end] - t * A[k,k:end]
# ΠΌΠ°ΠΊΡΠΎΡ @. Π·Π°ΠΌΠ΅Π½ΡΠ΅Ρ Π²ΡΠ΅ "ΡΠΊΠ²ΠΎΠ·Π½ΡΠ΅ ΡΠΎΡΠΊΠΈ"
# ΠΌΠ°ΠΊΡΠΎΡ @views Π·Π°ΠΌΠ΅Π½ΡΠ΅Ρ ΠΌΠ½ΠΎΠ³ΠΎΠΊΡΠ°ΡΠ½ΠΎΠ΅ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ ΠΌΠ°ΠΊΡΠΎΡΠ° @view
end
end
return A
end
function swap!(A,B)
for i in eachindex(A)
A[i], B[i] = B[i], A[i]
end
end
function swap_2!(A, i, iMax, k)
A[i, k], A[iMax, k] = A[iMax, k], A[i, k]
end
function transform_to_steps_2!(a::Matrix)
countSwaps = 1
c = a
eps = 0.000001
for i = 1:size(a, 1)
iMax = i
for j = i:size(a, 1)
if abs(c[j, i]) > abs(c[iMax, i])
iMax = j
end
end
if abs(c[iMax, i]) >= eps
for k = 1:size(a, 2)
swap_2!(c, i, iMax, k)
end
countSwaps = -countSwaps * (i != iMax ? 1 : -1)
for j = i+1:size(a, 1)
q = -c[j, i] / c[i, i]
for k = size(a, 2):-1:i
c[j, k] += q*c[i, k]
if abs(c[j, k]) < eps
c[j, k] = 0
end
end
end
end
end
if countSwaps > 0
c * -1
end
c
end
function transform_to_steps_edit!(A::AbstractMatrix; epsilon = 1e-7, degenerate_exeption = true)
ans = []
for k β 1:size(A, 1)
absval, Ξk = findmax(abs, @view(A[k:end,k]))
(degenerate_exeption && absval <= epsilon) && throw("ΠΡΡΠΎΠΆΠ΄Π΅Π½Π°Ρ ΠΌΠ°ΡΡΠΈΡΠ°")
Ξk > 1 && swap!(@view(A[k,k:end]), @view(A[k+Ξk-1,k:end]))
for i β k+1:size(A,1)
t = A[i,k]/A[k,k]
@. @views A[i,k:end] = A[i,k:end] - t * A[k,k:end]
end
end
for i = 1:size(A,1)
for j = i:size(A,2)
if abs(A[i, j]) > epsilon
push!(ans, j)
break;
end
end
end
return ans
end
function rank_(B::AbstractMatrix; epsilon = 1e-7, degenerate_exeption = true)
ans = []
A = copy(B)
for k β 1:size(A, 1)
absval, Ξk = findmax(abs, @view(A[k:end,k]))
(degenerate_exeption && absval <= epsilon) && throw("ΠΡΡΠΎΠΆΠ΄Π΅Π½Π°Ρ ΠΌΠ°ΡΡΠΈΡΠ°")
Ξk > 1 && swap!(@view(A[k,k:end]), @view(A[k+Ξk-1,k:end]))
for i β k+1:size(A,1)
t = A[i,k]/A[k,k]
@. @views A[i,k:end] = A[i,k:end] - t * A[k,k:end]
end
end
for i = 1:size(A,1)
for j = i:size(A,2)
if abs(A[i, j]) > epsilon
push!(ans, j)
break;
end
end
end
return length(ans)
end
function det_(a)
ans = 1
epsilon = 1e-7
s = 1
c = copy(a)
for k β 1:size(c, 1)
absval, Ξk = findmax(abs, @view(c[k:end,k]))
Ξk > 1 && swap!(@view(c[k,k:end]), @view(c[k+Ξk-1,k:end]))
if Ξk > 1
s*=-1
end
for i β k+1:size(c,1)
t = c[i,k]/c[k,k]
@. @views c[i,k:end] = c[i,k:end] - t * c[k,k:end]
end
end
for i = 1:size(c, 1)
ans*=c[i, i]
end
ans*s
end
function slau(A, b)
countSwaps = 1
a = [A b]
c = copy(a)
eps = 0.000001
for i = 1:size(a, 1)
iMax = i
for j = i:size(a, 1)
if abs(c[j, i]) > abs(c[iMax, i])
iMax = j
end
end
if abs(c[iMax, i]) >= eps
for k = 1:size(a, 2)
swap_2!(c, i, iMax, k)
end
countSwaps = -countSwaps * (i != iMax ? 1 : -1)
for j = i+1:size(a, 1)
q = -c[j, i] / c[i, i]
for k = size(a, 2):-1:i
c[j, k] += q*c[i, k]
if abs(c[j, k]) < eps
c[j, k] = 0
end
end
end
end
end
if countSwaps > 0
c * -1
end
c[size(a, 1), size(a, 1)+1] /= c[size(a, 1), size(a, 1)]
c[size(a, 1), size(a, 1)] = 1
v = []
for i = size(a, 1):-1:1
t = c[i, size(a, 1)+1]
for j = i+1:size(a, 1)
t-=c[j]*c[i,j]
end
c[size(a, 1), i] = t
end
c[size(a,1),1:size(a,1)]
end
function inv_(a)
c = copy(a)
for i = 1:size(c, 1)
for j = i+1:size(c, 1)
c[i,j], c[j,i] = c[j,i], c[i,j]
end
end
c
end
function transform_to_low_steps!(A::AbstractMatrix; epsilon = 1e-7, degenerate_exeption = true)
# @inbounds - Π·Π°ΠΌΠ΅ΡΠ½ΠΎΠ³ΠΎ ΡΡΠΊΠΎΡΠ΅Π½ΠΈΡ Π·Π΄Π΅ΡΡ ΡΡΠΎ Π½Π΅ Π΄Π°ΡΡ!!!
for k β size(A, 1):-1:1
absval, Ξk = findmax(abs, @view(A[k:end,k]))
(degenerate_exeption && absval <= epsilon) && throw("ΠΡΡΠΎΠΆΠ΄Π΅Π½Π°Ρ ΠΌΠ°ΡΡΠΈΡΠ°")
Ξk > 1 && swap!(@view(A[k,k:end]), @view(A[k+Ξk-1,k:end]))
for i β k-1:-1:1
t = A[i,k]/A[k,k]
@. @views A[i,k:end] = A[i,k:end] - t * A[k,k:end]
# ΠΌΠ°ΠΊΡΠΎΡ @. Π·Π°ΠΌΠ΅Π½ΡΠ΅Ρ Π²ΡΠ΅ "ΡΠΊΠ²ΠΎΠ½ΡΠ΅ ΡΠΎΡΠΊΠΈ"
# ΠΌΠ°ΠΊΡΠΎΡ @views Π·Π°ΠΌΠ΅Π½ΡΠ΅Ρ ΠΌΠ½ΠΎΠ³ΠΎΠΊΡΠ°ΡΠ½ΠΎΠ΅ ΠΏΡΠΈΠΌΠ΅Π½Π΅Π½ΠΈΠ΅ ΠΌΠ°ΠΊΡΠΎΡΠ° @view
end
end
return A
end
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] | 1.4956 | 3,636 |
# include("../../src/SFFM.jl")
using LinearAlgebra, Plots, SFFM
# #
# # ## define the model(s)
# include("exampleModelDef.jl")
## section 4.4: the sensitivity of the stationary distribution of X to rates r
Tfun(Ξ³β) = [
-(Ξ³β + Ξ³β) Ξ³β Ξ³β 0;
Ξ²β -(Ξ³β + Ξ²β) 0 Ξ³β;
Ξ²β 0 -(Ξ³β + Ξ²β) Ξ³β;
0 Ξ²β Ξ²β -(Ξ²β + Ξ²β);
]
let
c = 0
colours = [:green;:blue;:red]
shapes = [:diamond,:x,:+]
styles = [:solid,:dash,:dot]
q = plot(layout = (1,2))
for sp in 1:2
q = plot!(windowsize = (600,250), subplot = sp)
end
for Ξ³β in [11;16;22]
c = c+1
Ttemp = Tfun(Ξ³β)
tempModel = SFFM.Model( Ttemp, C, r, Bounds = approxModel.Bounds)
println("created tempModel with upper bound x=", tempModel.Bounds[1,end])
## mesh
Ξtemp = 0.4
Nodes = collect(approxBounds[1, 1]:Ξtemp:approxBounds[1, 2])
Basis = "lagrange"
nBases = 2
mesh = SFFM.DGMesh(
tempModel,
Nodes,
nBases,
Basis=Basis,
)
# compute the marginal via DG
All = SFFM.MakeAll( tempModel, mesh, approxType = "projection")
Ξ¨ = SFFM.PsiFun( All.D)
# the distribution of X when Y first returns to 0
ΞΎ = SFFM.MakeXi( All.B, Ξ¨)
marginalX, p, K = SFFM.MakeLimitDistMatrices(
All.B,
All.D,
All.R.RDict,
Ξ¨,
ΞΎ,
mesh,
tempModel,
)
println("For Ξ³β = ",
Ξ³β, ", Οβ° = ",
sum(p),
", ΟΒΉ = ",
sum(marginalX)-sum(p),
" and total prob is ",
sum(marginalX),
".",
)
tempDist = SFFM.Coeffs2Dist(
tempModel,
mesh,
marginalX,
SFFM.SFFMDensity,
)
temp = zeros(SFFM.NBases(mesh),SFFM.NIntervals(mesh),2)
temp[:,:,1] = tempDist.distribution[:,:,1]+tempDist.distribution[:,:,2]
temp[:,:,2] = tempDist.distribution[:,:,3]+tempDist.distribution[:,:,4]
q = plot!(
tempDist.x,
temp[:,:,1],
subplot = 1,
legend = false,
color = colours[c],
seriestype = :line,
linestyle = styles[c],
markershape = shapes[c],
xlabel = "x",
)
q = scatter!(
[-0.2 + (c-1)*0.2],
[sum(tempDist.pm)],
subplot = 2,
color = colours[c],
label = :none,
)
q = plot!(
tempDist.x[:,1],
temp[:,1,2],
subplot = 2,
color = colours[c],
label = "Ξ³β: "*string(Ξ³β),
linestyle = styles[c],
markershape = shapes[c],
)
q = plot!(
tempDist.x[:,2:end],
temp[:,2:end,2],
subplot = 2,
color = colours[c],
label = :none,
markershape = shapes[c],
linestyle = styles[c],
xlabel = "x",
)
println("")
end
titles = ["Phases 11 + 10" "Phases 01 + 00"]
for sp in 1:2
q = plot!(
subplot = sp,
xlims = (-0.5,8),
title = titles[sp],
ylabel = "Density / Probability",
grid = false,
)
end
display(q)
# savefig(pwd()*"/examples/paperNumerics/dump/sensitivityMarginalStationaryDistX.png")
end
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] | 1.677794 | 2,067 |
using Test
using StableRNGs
using ComplexMixtures, PDBTools
using Random
const CM = ComplexMixtures
include("./namd.jl")
include("./namd_chemfiles.jl")
include("./gromacs.jl")
include("./pdb.jl")
include("./merge.jl")
| [
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] | 2.619048 | 84 |
using Checkpointing
struct ReverseDiffADTool <: AbstractADTool end
struct ZygoteADTool <: AbstractADTool end
struct DiffractorADTool <: AbstractADTool end
struct EnzymeADTool <: AbstractADTool end
struct ForwardDiffADTool <: AbstractADTool end
function Checkpointing.jacobian(tobedifferentiated, F_H, ::ReverseDiffADTool)
return ReverseDiff.jacobian(tobedifferentiated, F_H)
end
function Checkpointing.jacobian(tobedifferentiated, F_H, ::ZygoteADTool)
return Zygote.jacobian(tobedifferentiated, F_H)[1]
end
function Checkpointing.jacobian(tobedifferentiated, F_H, ::ForwardDiffADTool)
return ForwardDiff.jacobian(tobedifferentiated, F_H)
end
function Checkpointing.jacobian(tobedifferentiated, F_H, ::EnzymeADTool)
function f(x,res)
y = tobedifferentiated(x)
copyto!(res,y)
return nothing
end
J = zeros(eltype(F_H), length(F_H), length(F_H))
x = zeros(eltype(F_H), length(F_H))
dx = zeros(eltype(F_H), length(F_H))
y = zeros(eltype(F_H), length(F_H))
dy = zeros(eltype(F_H), length(F_H))
for i in 1:length(F_H)
copyto!(x, F_H)
fill!(dx, 0)
fill!(y, 0)
dy[i] = 1.0
autodiff(f, Duplicated(x,dx), Duplicated(y, dy))
J[i,:] = dx[:]
end
return J
end
function Checkpointing.jacobian(tobedifferentiated, F_H, ::DiffractorADTool)
J = zeros(eltype(F_H), length(F_H), length(F_H))
for i in 1:length(F_H)
grad = Diffractor.gradient(x -> tobedifferentiated(x)[i], F_H)
J[i,:] = grad[:][1]
end
return J
end | [
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] | 2.23099 | 697 |
function registersymboluse(sym::Symbol, ctx::LintContext)
if sym == :end
# TODO: handle this special case elsewhere
return Any
end
lookupresult = nothing
let lu = lookup(ctx, sym)
if lu !== nothing
lookupresult = registeruse!(lu)
end
end
if lookupresult == nothing
if (!pragmaexists("Ignore use of undeclared variable $sym", ctx.current)
&& ctx.quoteLvl == 0)
msg(ctx, :E321, sym, "use of undeclared symbol")
end
Any
else
return lookupresult.typeactual
end
end
function lintglobal(ex::Expr, ctx::LintContext)
for sym in ex.args
if isa(sym, Symbol)
globalset!(ctx.current, sym, VarInfo(location(ctx), Any))
elseif expand_assignment(sym) !== nothing
ea = expand_assignment(sym)
lintassignment(Expr(:(=), ea[1], ea[2]), ctx; isGlobal=true)
else
msg(ctx, :E134, sym, "unknown global pattern")
end
end
end
function lintlocal(ex::Expr, ctx::LintContext)
for sube in ex.args
if isa(sube, Symbol)
# temporarily set to Union{} until rescued later? this is a safer
# choice for now.
set!(ctx.current, sube, VarInfo(location(ctx), Any))
elseif isexpr(sube, :(=))
lintassignment(sube, ctx; islocal = true)
elseif isexpr(sube, :(::))
sym = sube.args[1]
@checkisa(ctx, sym, Symbol)
set!(ctx.current, sym, VarInfo(location(ctx), parsetype(ctx, sube.args[2])))
else
msg(ctx, :E135, sube, "local declaration not understood by Lint")
end
end
end
function resolveLHSsymbol(ex, syms::Array{Any,1}, ctx::LintContext, assertions::Dict{Symbol,Any})
if isa(ex, Symbol)
push!(syms, ex)
elseif isa(ex, Expr)
if ex.head == :(::)
if isa(ex.args[1], Symbol)
assertions[ex.args[1]]=ex.args[2]
end
resolveLHSsymbol(ex.args[1], syms, ctx, assertions)
elseif ex.head == :tuple
for s in ex.args
resolveLHSsymbol(s, syms, ctx, assertions)
end
elseif ex.head == :(.) || # a.b = something
ex.head == :ref || # a[b] = something
ex.head == :($) # :($(esc(name)) = something)
push!(syms, ex)
lintexpr(ex, ctx)
return
else
msg(ctx, :I171, ex, "LHS in assignment not understood by Lint")
end
else
msg(ctx, :I171, ex, "LHS in assignment not understood by Lint")
end
end
function lintassignment(ex::Expr, ctx::LintContext; islocal = false, isConst=false, isGlobal=false, isForLoop=false) # is it a local decl & assignment?
lhs = ex.args[1]
# lower curly
rhstype = Any
if isexpr(lhs, :curly)
isConst = true
lhs = withincurly(lhs)
rhstype = Type
# TODO: lint the RHS too
else
lintexpr(ex.args[2], ctx)
end
syms = Any[]
assertions = Dict{Symbol, Any}()
resolveLHSsymbol(lhs, syms, ctx, assertions)
tuplelen = length(syms)
lhsIsTuple = Meta.isexpr(lhs, :tuple)
if rhstype == Any
rhstype = guesstype(ex.args[2], ctx)
end
if rhstype == Union{}
msg(ctx, :E539, lhs, "assigning an error to a variable")
elseif isForLoop && isa(rhstype, Type)
if rhstype <: Number
msg(ctx, :I672, "iteration works for a number but it may be a typo")
end
rhstype = StaticTypeAnalysis.eltype(rhstype)
if lhsIsTuple
computedlength = StaticTypeAnalysis.length(rhstype)
if (computedlength !== nothing
&& computedlength β tuplelen)
msg(ctx, :I474, rhstype, "iteration generates tuples, " *
"$tuplelen of $(computedlength) variables used")
end
end
elseif isa(rhstype, Type) && lhsIsTuple
computedlength = StaticTypeAnalysis.length(rhstype)
if computedlength !== nothing
if computedlength < tuplelen
msg(ctx, :E418, rhstype, "RHS is a tuple, $tuplelen of " *
"$(computedlength) variables used")
elseif computedlength > tuplelen
msg(ctx, :W546, rhstype, string(
"implicitly discarding values, $tuplelen of ",
computedlength, " used"))
end
end
end
for (symidx, s) in enumerate(syms)
if !isa(s, Symbol) # a.b or a[b]
if isexpr(s, [:(.), :ref])
containertype = guesstype(s.args[1], ctx)
if isa(unwrap_unionall(containertype), DataType) &&
!isabstract(containertype) &&
!unwrap_unionall(containertype).mutable
msg(ctx, :E525, s.args[1], "is of an immutable type $(containertype)")
end
end
continue
end
if string(s) == ctx.scope && !islocal
msg(ctx, :W355, ctx.scope, "conflicts with function name")
end
if s == :call
msg(ctx, :E332, s, "should not be used as a variable name")
end
# +=, -=, *=, etc.
if ex.head != :(=)
registersymboluse(s, ctx)
end
vi = VarInfo(location(ctx))
# @lintpragma("Ignore incompatible type comparison")
if isa(rhstype, Type) && !lhsIsTuple
rhst = rhstype
elseif isa(rhstype, Type)
rhst = StaticTypeAnalysis.typeof_nth(rhstype, symidx)
else
rhst = Any
end
try
if haskey(assertions, s)
dt = parsetype(ctx, assertions[s])
vi.typeactual = dt
# TODO: check that rhst is convertible to dt
elseif rhst != Any && !isForLoop
vi.typeactual = rhst
end
catch er
msg(ctx, :W251, ex, "$(er); Symbol=$(s); rhstype=$(rhst)")
end
if isGlobal || isConst || istoplevel(ctx.current)
globalset!(ctx.current, s, vi)
# TODO: guess type and use that type information
elseif islocal
localset!(ctx.current, s, vi)
else
set!(ctx.current, s, vi)
end
end
end
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] | 1.960049 | 3,254 |
using DynamicalSystemsBase
using DelayEmbeddings
using Test
using DelimitedFiles
println("\nTesting mdop_embedding.jl...")
@testset "Nichkawde method MDOP" begin
# For comparison reasons using Travis CI we carry out the integration on a UNIX
# OS and save the resulting time series
# # solve Mackey-Glass-Delay Diff.Eq. as in the Paper
# function mackey_glass(du,u,h,p,t)
# beta,n,gamma,tau = p
# hist = h(p, t-tau)[1]
# du[1] = (beta*hist)/(1+hist^n) - gamma * u[1]
# end
# # set parameters
# h(p,t) = 0
# tau_d = 44
# n = 10
# Ξ² = 0.2
# Ξ³ = 0.1
# Ξ΄t = 0.5
# p = (Ξ²,n,Ξ³,tau_d)
#
# # time span
# tspan = (0.0, 12000.0)
# u0 = [1.0]
#
# prob = DDEProblem(mackey_glass,u0,h,tspan,p; constant_lags=tau_d)
# alg = MethodOfSteps(Tsit5())
# sol = solve(prob,alg; adaptive=false, dt=Ξ΄t)
#
# s = [u[1] for u in sol.u]
# s = s[4001:end]
s = readdlm(joinpath(tsfolder, "1.csv"))
s = vec(s)
Y = Dataset(s)
theiler = 57
@testset "beta statistic" begin
## Test beta_statistic (core algorithm of mdop_embedding)
taus = 0:100
Ξ² = @inferred DelayEmbeddings.beta_statistic(Y, s, taus, theiler)
maxi, max_idx = findmax(Ξ²)
@test maxi>4.1
@test taus[max_idx]>=50
# # display results as in Fig. 3 of the paper
# using Plots
# plot(taus, Ξ², linewidth = 3, label = "1st embedding cycle")
# plot!(title = "Ξ²-statistic for Mackey Glass System as in Fig. 3 in the Paper")
# xlabel!("Ο")
# ylabel!("Ξ²-Statistic")
# # display results as in Fig. 3 of the paper
# using PyPlot
# pygui(true)
# figure()
# plot(taus, Ξ², linewidth = 3, label = "1st embedding cycle")
# scatter(max_idx-1,maxi, c="red")
# title("Ξ²-statistic for Mackey Glass System as in Fig. 3 in the Paper")
# xlabel("Ο")
# ylabel("Ξ²-Statistic")
# xticks(0:10:100)
# grid()
# test different tau range
taus2 = 1:4:100
Ξ²2 = @inferred beta_statistic(Y, s, taus2, theiler)
maxi2, max_idx2 = findmax(Ξ²2)
@test maxi2>4.1
@test taus2[max_idx2]>=40
# # display results as in Fig. 3 of the paper
# using Plots
# plot(taus2, Ξ²2, linewidth = 3, label = "1st embedding cycle")
# plot!(title = "coarse Ξ²-statistic for Mackey Glass System as in Fig. 3 in the Paper")
# xlabel!("Ο")
# ylabel!("Ξ²-Statistic")
end
@testset "mdop_embedding univariate" begin
taus = 0:100
Ξ² = DelayEmbeddings.beta_statistic(Y, s, taus, theiler)
Y, Ο_vals, ts_vals, FNNs, betas = mdop_embedding(s; Οs = taus, w = theiler)
# for different Οs
taus2 = 1:4:100
Y2, Ο_vals2, ts_vals2, FNNs2, betas2 = mdop_embedding(s; Οs = taus2, w = theiler)
@test round.(Ξ², digits=6) == round.(betas[:,1], digits=6)
@test size(Y,2) == 5
@test size(Y,2) == size(Y2,2)
@test sum(findall(x -> x != 1, ts_vals))==0
@test sum(abs.(diff(Ο_vals)) .< 10) == 0
# # display results as in Fig. 3 of the paper
# using Plots
# # Figure as in Fig.3 in the paper
# plot(taus, betas[:,1], linewidth = 3, label = "embedding cycle 1")
# plot!([taus[Ο_vals[2]+1]],[betas[Ο_vals[2]+1,1]], seriestype = :scatter, color="red", label = "")
# for i = 2:size(betas,2)
# plot!(taus, betas[:,i], linewidth = 3, label = "embedding cycle $i")
# plot!([taus[Ο_vals[i+1]+1]],[betas[Ο_vals[i+1]+1,i]], seriestype = :scatter, color="red", label = "")
# end
# plot!(title = "Ξ²-statistic's for each embedding cycle of Mackey Glass System as in Fig. 3")
# xlabel!("delay Ο")
# ylabel!("log10 Ξ²(Ο)")
#
# # Figure of coarse grained analysis
# taus22 = zeros(length(taus2))
# [taus22[i]=taus2[i] for i = 1: length(taus2)]
# plot(taus22, betas2[:,1], linewidth = 3, label = "embedding cycle 1")
# trueind = findall(x -> x == Ο_vals2[2], taus22)
# plot!(taus22[trueind],[betas2[trueind,1]], seriestype = :scatter, color="red", label = "")
# for i = 2:size(betas2,2)
# plot!(taus22, betas2[:,i], linewidth = 3, label = "embedding cycle $i")
# trueind = findall(x -> x == Ο_vals2[i+1], taus22)
# plot!(taus22[trueind],[betas2[trueind,i]], seriestype = :scatter, color="red", label = "")
# end
# plot!(title = "Ξ²-statistic's for each embedding cycle of Mackey Glass System as in Fig. 3")
# xlabel!("delay Ο")
# ylabel!("log10 Ξ²(Ο)")
end
@testset "estimate Ο max (Roessler)" begin
# For comparison reasons using Travis CI we carry out the integration on a UNIX
# OS and save the resulting time series
# roe = Systems.roessler([1.0, 0, 0]; a=0.2, b=0.2, c=5.7)
# sroe = trajectory(roe, 500; dt = 0.05, Ttr = 100.0)
# writedlm("2.csv", sroe)
sroe = readdlm(joinpath(tsfolder, "2.csv"))
tws = 25:32
Ο_m, L = @inferred mdop_maximum_delay(sroe[:, 2], tws)
@test Ο_m == 26
Ο_m, Ls = @inferred mdop_maximum_delay(Dataset(sroe[:, 1:2]), tws)
@test Ο_m == 26
# # reproduce Fig.2 of the paper
# tws = 1:2:101
# Ο_m, L = DelayEmbeddings.mdop_maximum_delay(s[:,2]; tw = tws, samplesize=1.0)
#
# using Plots
# twss = zeros(length(tws))
# [twss[cnt] = i for (cnt,i) in enumerate(tws)]
# plot(twss,L, label="")
# xlabel!("time window")
# ylabel!("L")
# tw=1:4:200
# tau_max, LL = DelayEmbeddings.mdop_maximum_delay(sroe; tw=tw)
#
# using Plots
# gui()
# twss = zeros(length(tw))
# [twss[cnt] = i for (cnt,i) in enumerate(tw)]
# plot(twss,LL, label="")
# xlabel!("time window")
# ylabel!("L")
end
@testset "mdop_embedding multivariate" begin
# For comparison reasons using Travis CI we carry out the integration on a UNIX
# OS and save the resulting time series
# roe = Systems.roessler([1.0, 0, 0]; a=0.2, b=0.2, c=5.7)
# sroe = trajectory(roe, 500; dt = 0.05, Ttr = 100.0)
# writedlm("2.csv", sroe)
sroe = readdlm(joinpath(tsfolder, "2.csv"))
tra = Dataset(sroe)
w1 = estimate_delay(sroe[:,1], "mi_min")
w2 = estimate_delay(sroe[:,2], "mi_min")
theiler = w2
taus = 0:26
mc = 10
Y, Ο_vals, ts_vals, FNNs, betas = mdop_embedding(sroe[:,1]; Οs = taus, w = theiler, max_num_of_cycles = mc)
max_idx, ts_number = @inferred DelayEmbeddings.choose_optimal_tau2(betas)
@test ts_number == 1
@test taus[max_idx] == Ο_vals[2]
Y2, Ο_vals2, ts_vals2, FNNs2, betas2 = mdop_embedding(tra; Οs = taus, w = theiler, max_num_of_cycles = mc)
ttra = standardize(tra)
b1 = DelayEmbeddings.beta_statistic(Dataset(ttra[:,ts_vals2[1]]), ttra[:,1], taus, theiler)
b2 = DelayEmbeddings.beta_statistic(Dataset(ttra[:,ts_vals2[1]]), ttra[:,2], taus, theiler)
b3 = DelayEmbeddings.beta_statistic(Dataset(ttra[:,ts_vals2[1]]), ttra[:,3], taus, theiler)
@test betas2[1][:,1] == b1
@test betas2[1][:,2] == b2
@test betas2[1][:,3] == b3
@test size(Y2,2) == 3
@test Ο_vals2[1] == Ο_vals2[2] == 0
@test Ο_vals2[3] == maximum(taus)
@test ts_vals2[2] == ts_vals2[3] == 2
@test ts_vals2[1] == 3
end
end
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] | 2.176875 | 3,200 |
function newbound_rounding(U,V,bmatchval)
U_sortperm = sortcolsperm(U,true)
V_sortperm = sortcolsperm(V,true)
nU = size(U,1)
nV = size(V,1)
r = size(U,2)
@assert r==size(V,2)
d = min(size(U_sortperm,1),size(V_sortperm,1))
U_weights = sort(U,1,rev=true)
V_weights = sort(V,1,rev=true)
# U_weights = U_weights[1:d,:]
# V_weights = V_weights[1:d,:]
#
# U_sortperm = U_sortperm[1:d,:]
# V_sortperm = V_sortperm[1:d,:]
P = spzeros(nU,nV)
allrecoveries = zeros(size(U,2))
for i = 1:size(U,2)
ui = U_weights[:,i]
vi = V_weights[:,i]
lastid_ui = findfirst(ui.<0)
lastid_vi = findfirst(vi.<0)
if lastid_ui == 0 && lastid_vi == 0
lastidpos = d
lneg = -1
elseif lastid_vi == 0
lastidpos = min(d,lastid_ui-1)
lneg = -1
elseif lastid_ui == 0
lastidpos = min(d,lastid_vi-1)
lneg = -1
else
lastidpos = min(lastid_ui,lastid_vi)-1
lneg = min(nU-lastid_ui,nV-lastid_vi)
end
ei1 = U_sortperm[1:lastidpos,i]
ej1 = V_sortperm[1:lastidpos,i]
ei2 = U_sortperm[nU-lneg:nU,i]
ej2 = V_sortperm[nV-lneg:nV,i]
ei = vcat(ei1,ei2)
ej = vcat(ej1,ej2)
# allrecoveries[i] = evaluate_erdosreyni_experiment(A,B,ei,ej)
P = P + sparse(ei,ej,1,nU,nV) #+ generate_b_match_overlapping(ei,ej,2,nU,nV)
# with bmatching:
# bmatchval = 20
for bm = 1:bmatchval
if !isempty(ej)
popfirst!(ej)
P = P + sparse(ei[1:end-bm],ej,1,nU,nV)
end
end
end
@show nnz(P)/prod(size(P))
return P
end
function newbound_rounding_lowrank_evaluation_relaxed(U,V,bmatchval)
# U = Float32.(U)
# V = Float32.(V)
U_sortperm = sortcolsperm(U,true)
V_sortperm = sortcolsperm(V,true)
nU = size(U,1)
nV = size(V,1)
r = size(U,2)
@assert r == size(V,2)
d = min(size(U_sortperm,1),size(V_sortperm,1))
U_weights = sort(U,dims=1,rev=true)
V_weights = sort(V,dims=1,rev=true)
# U_weights = U_weights[1:d,:]
# V_weights = V_weights[1:d,:]
#
# U_sortperm = U_sortperm[1:d,:]
# V_sortperm = V_sortperm[1:d,:]
# P = spzeros(nU,nV)
U1 = []
V1 = []
allrecoveries = zeros(size(U,2))
for i = 1:size(U,2)
ui = U_weights[:,i]
vi = V_weights[:,i]
lastid_ui = findfirst(ui.<0)
lastid_vi = findfirst(vi.<0)
lastid_ui = (lastid_ui === nothing) ? 0 : lastid_ui
lastid_vi = (lastid_vi === nothing) ? 0 : lastid_vi
if lastid_ui == 0 && lastid_vi == 0
lastidpos = d
lneg = -1
elseif lastid_vi == 0
lastidpos = min(d,lastid_ui-1)
lneg = -1
elseif lastid_ui == 0
lastidpos = min(d,lastid_vi-1)
lneg = -1
else
lastidpos = min(lastid_ui,lastid_vi)-1
lneg = min(nU-lastid_ui,nV-lastid_vi)
end
ei1 = U_sortperm[1:lastidpos,i]
ej1 = V_sortperm[1:lastidpos,i]
ei2 = U_sortperm[nU-lneg:nU,i]
ej2 = V_sortperm[nV-lneg:nV,i]
ei = vcat(ei1,ei2)
ej = vcat(ej1,ej2)
# allrecoveries[i] = evaluate_erdosreyni_experiment(A,B,ei,ej)
# P = P + sparse(ei,ej,1,nU,nV)
# P = P + sparse(ei,ej,1,nU,nV) #+ generate_b_match_overlapping(ei,ej,2,nU,nV)
# with bmatching:
append!(U1,ei)
append!(V1,ej)
# bmatchval = 6
#println("bmatchval is $bmatchval")
for bm = 1:bmatchval
if !isempty(ej)
popfirst!(ej)
# P = P + sparse(ei[1:end-bm],ej,1,nU,nV)
append!(U1,ei[1:end-bm])
append!(V1,ej)
end
end
end
all_matches = [U1 V1]
unique_matches = unique(all_matches,dims=1)
U1unique = unique_matches[:,1]
V1unique = unique_matches[:,2]
uo = U[U1unique,:]
vo = V[V1unique,:]
weights = vec(sum(uo.*vo,dims=2))
X = sparse(U1unique,V1unique,weights,nU,nV)
return X
end
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] | 1.843548 | 2,007 |
@testset "NMF" begin
## A simple NMF implementation, which is useful to test mark/wait
function step(X, W, H)
# H update
H = (H .* (W' * (X ./ (W * H)))
./ (sum(W; dims=1))')
# W update
W = (W .* ((X ./ (W * H)) * (H'))
./ (sum(H; dims=2)'))
# error estimate
X - W * H
end
for scale in (1:5:50)
ncol = 2001
nrow = 1002*scale
nfeatures = 12
X = rand(Float32, nrow, ncol)
W = rand(Float32, nrow, nfeatures)
H = rand(Float32, nfeatures, ncol)
cpu_res = step(X, W, H)
RX = ROCArray(X)
RW = ROCArray(W)
RH = ROCArray(H)
gpu_res = step(RX, RW, RH)
@test Array(gpu_res) β cpu_res
end
end
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] | 1.997041 | 338 |
N = 6
ug = SimpleWeightedGraph(N)
add_edge!(ug, 1, 2, 2); add_edge!(ug, 1, 3, 2); add_edge!(ug, 2, 3, 1)
add_edge!(ug, 3, 4, 5); add_edge!(ug, 2, 5, 2); add_edge!(ug, 3, 6, 2)
dg = SimpleWeightedDiGraph(N)
add_edge!(dg, 1, 3, 2); add_edge!(dg, 2, 3, 2); add_edge!(dg, 1, 6, 1)
add_edge!(dg, 2, 5, -2); add_edge!(dg, 3, 4, -2); add_edge!(dg, 3, 5, -1)
el_ug = Vector{Int64}[[2, 3], [1, 3, 5], [1, 2, 4, 6], [3], [2], [3]]
el_dg = Vector{Int64}[[3, 6], [3, 5], [4, 5], [], [], []]
@testset "weightedgraph" begin
fg = FeaturedGraph(ug)
@test adjacency_list(fg) == el_ug
fg = FeaturedGraph(dg)
@test adjacency_list(fg) == el_dg
end
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7,
40616,
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6624,
1288,
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198,
437,
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] | 1.881159 | 345 |
using OrdinaryDiffEq, DiffEqBase, DiffEqCallbacks, Test
using Random
Random.seed!(213)
CACHE_TEST_ALGS = [Euler(),Midpoint(),RK4(),SSPRK22(),SSPRK33(),
CarpenterKennedy2N54(), HSLDDRK64(),
CFRLDDRK64(), TSLDDRK74(),
CKLLSRK43_2(),
ParsaniKetchesonDeconinck3S32(),
BS3(),BS5(),DP5(),DP8(),Feagin10(),Feagin12(),Feagin14(),TanYam7(),
Tsit5(),TsitPap8(),Vern6(),Vern7(),Vern8(),Vern9(),OwrenZen3(),OwrenZen4(),OwrenZen5(),
AutoTsit5(Rosenbrock23())]
broken_CACHE_TEST_ALGS = [ORK256(), DGLDDRK73_C(),KenCarp4()]
using InteractiveUtils
NON_IMPLICIT_ALGS = filter((x)->isconcretetype(x) && !OrdinaryDiffEq.isimplicit(x()),union(subtypes(OrdinaryDiffEq.OrdinaryDiffEqAlgorithm),subtypes(OrdinaryDiffEq.OrdinaryDiffEqAdaptiveAlgorithm)))
f = function (du,u,p,t)
for i in 1:length(u)
du[i] = (0.3/length(u))*u[i]
end
end
condition = function (u,t,integrator)
1-maximum(u)
end
affect! = function (integrator)
u = integrator.u
resize!(integrator,length(u)+1)
maxidx = findmax(u)[2]
Ξ = rand()/5 + 0.25
u[maxidx] = Ξ
u[end] = 1-Ξ
nothing
end
callback = ContinuousCallback(condition,affect!)
u0 = [0.2]
tspan = (0.0,10.0)
prob = ODEProblem(f,u0,tspan)
println("Check for stochastic errors")
for i in 1:10
@test_nowarn sol = solve(prob,Tsit5(),callback=callback)
end
println("Check some other integrators")
sol = solve(prob,Rosenbrock23(chunk_size=1),callback=callback,dt=1/2)
@test length(sol[end]) > 1
sol = solve(prob,Rosenbrock32(chunk_size=1),callback=callback,dt=1/2)
@test length(sol[end]) > 1
@test_broken sol = solve(prob,KenCarp4(chunk_size=1),callback=callback,dt=1/2)
@test length(sol[end]) > 1
@test_broken sol = solve(prob,TRBDF2(chunk_size=1),callback=callback,dt=1/2)
@test length(sol[end]) > 1
for alg in CACHE_TEST_ALGS
@show alg
sol = solve(prob,alg,callback=callback,dt=1/2)
@test length(sol[end]) > 1
end
for alg in broken_CACHE_TEST_ALGS
@show alg
@test_broken length(solve(prob,alg,callback=callback,dt=1/2)[end]) > 1
end
sol = solve(prob,Rodas4(chunk_size=1),callback=callback,dt=1/2)
@test length(sol[end]) > 1
sol = solve(prob,Rodas5(chunk_size=1),callback=callback,dt=1/2)
@test length(sol[end]) > 1
# Force switching
function f2(du,u,p,t)
@assert length(u) == length(du) "length(u) = $(length(u)), length(du) = $(length(du)) at time $(t)"
for i in 1:length(u)
if t > 10
du[i] = -10000*u[i]
else
du[i] = 0.3*u[i]
end
end
return du
end
function condition2(u, t, integrator)
1-maximum(u)
end
function affect2!(integrator)
u = integrator.u
resize!(integrator,length(u)+1)
maxidx = findmax(u)[2]
Ξ = rand()
u[maxidx] = Ξ
u[end] = 1-Ξ
nothing
end
callback = ContinuousCallback(condition2,affect2!)
u0 = [0.2]
tspan = (0.0,20.0)
prob = ODEProblem(f2,u0,tspan)
sol = solve(prob, AutoTsit5(Rosenbrock23()), callback=callback)
@test length(sol[end]) > 1
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7,
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58,
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] | 2.238132 | 1,285 |
using Printf
println("hello world :-) !")
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function POMDPModelTools.gbmdp_handle_terminal(pomdp::Union{SubHuntPOMDP,DSubHuntPOMDP}, updater::Updater, b::ParticleCollection, s::SubState, a::Int, rng::AbstractRNG)
@assert isterminal(pomdp, s)
return ParticleCollection([s,s])
end
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"""
Shuffles the residues in each column, keeping fixed the gap positions
"""
function shuffle_residues_columnwise!(aln::Matrix{Residue})
nseq, nres = size(aln)
for i in 1:nres
@inbounds for j in 1:nseq
a = aln[j,i]
if a != GAP
k = rand(1:nseq)
b = aln[k,i]
while b == GAP
k = rand(1:nseq)
b = aln[k,i]
end
aln[k,i] = a
aln[j,i] = b
end
end
end
aln
end
# 0.00084 seconds faster than an implemetation similar to shuffle_residues_columnwise (PF00085)
"""
Shuffles the residues in each sequence, keeping fixed the gap positions
"""
function shuffle_residues_sequencewise!(aln::Matrix{Residue})
taln = transpose(aln)
shuffle_residues_columnwise!(taln)
transpose!(aln, taln)
end
"""
Shuffles the residues in each sequence
"""
function shuffle_sequencewise!(aln::Matrix{Residue})
nseq, nres = size(aln)
for i in 1:nseq
@inbounds for j in 1:nres
k = rand(1:nres)
aln[i,k], aln[i,j] = aln[i,j], aln[i,k]
end
end
aln
end
"""
Shuffles the residues in each column
"""
function shuffle_columnwise!(aln::Matrix{Residue})
nseq, nres = size(aln)
for i in 1:nres
@inbounds for j in 1:nseq
k = rand(1:nseq)
aln[k,i], aln[j,i] = aln[j,i], aln[k,i]
end
end
aln
end
for fun in [ :shuffle_columnwise!, :shuffle_sequencewise!, :shuffle_residues_sequencewise!, :shuffle_residues_columnwise! ]
@eval $(fun)(aln::AbstractMultipleSequenceAlignment) = $(fun)(aln.msa)
end
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] | 1.945602 | 864 |
# Use map_func to avoid float precision differences between the same vertex
function _push_cell_vertex!(vert,cell_verts, used_vertices,vertices,nextvert, map_func)
token = ht_keyindex2!(used_vertices, vert)
if token > 0 # reuse dofs
reuse_vert = used_vertices.vals[token]
push!(cell_verts, reuse_vert)
else # token <= 0, use new vertex
Base._setindex!(used_vertices, nextvert, vert, -token)
push!(cell_verts, nextvert)
push!(vertices, map_func(vert))
nextvert += 1
end
return nextvert
end
function _generate_2d_hex_centroids!(centroids::Vector{Tensors.Vec{2,T}},LL, n_centroid_rows, n_centroid_cols, hex_width, hex_heigth) where {T}
x_coord = LL[1]; y_coord = LL[2]
x_lim = n_centroid_cols*hex_width
y_lim = n_centroid_rows*hex_heigth
ss = 1
for j in 1:(2*n_centroid_rows)
for i in 1:(n_centroid_cols+1)
centroid = Tensors.Vec{2,T}((x_coord,y_coord))
push!(centroids, centroid)
(x_coord + hex_width) > x_lim ? break : x_coord = x_coord + hex_width
end
(ss > 0) ? x_coord = LL[1] + hex_width/2 : x_coord = LL[1]
ss = ss*(-1)
(y_coord + hex_heigth/4) > y_lim ? break : y_coord = y_coord + 3/4*hex_heigth;
end
return centroids
end | [
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886,
198,
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1441,
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305,
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198,
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] | 2.22242 | 562 |
module TestStoreApi
using ..MyPetStore
using Swagger
using Test
using Dates
function test(uri)
println("testing StoreApi...")
client = Swagger.Client(uri)
api = StoreApi(client)
println(" - getInventory")
inventory = getInventory(api)
@test isa(inventory, Dict{String,Int32})
@test !isempty(inventory)
println(" - placeOrder")
@test_throws Swagger.ValidationException Order(; id=10, petId=10, quantity=2, shipDate=DateTime(2017, 03, 12), status="invalid_status", complete=false)
order = Order(; id=10, petId=10, quantity=2, shipDate=DateTime(2017, 03, 12), status="placed", complete=false)
neworder = placeOrder(api, order)
@test neworder.id == 10
println(" - getOrderById")
@test_throws Swagger.ValidationException getOrderById(api, 0)
order = getOrderById(api, 10)
@test isa(order, Order)
@test order.id == 10
println(" - deleteOrder")
@test deleteOrder(api, 10) == nothing
nothing
end
end # module TestStoreApi
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] | 2.700535 | 374 |
#using Laplacians
function buildAPI(fileName, mod)
noDocString = "No documentation found."
v = names(mod)
fh = open(fileName,"w")
x = 0
for i in 1:length(v)
sym = v[i]
x = eval(mod,sym)
println(string(x))
println(fh, "### ", string(sym))
#= if (isa(x,Function))
println(string(functionloc(x)))
end
=#
docmd = @doc(x)
docstr = stringmime("text/plain", docmd )
if (length(docstr) < 23) || (docstr[1:23] != noDocString)
println(fh, docstr)
else
println("no docs for : ", sym)
end
extraInfo(fh, x)
println(fh, "\n")
end
close(fh)
end
function extraInfo(fh, x)
if isa(x,Function)
mt = methods(x)
println(fh,"\n```julia")
loc = " "
firstit = true
for meth in mt
str = string(meth)
ind = rsearchindex(str," at ")
println(fh, str[1:(ind-1)])
if firstit
loc = str[ind:end]
firstit = false
end
end
println(fh,"```\n")
println(fh, loc)
elseif isa(x,DataType)
str = Docs.typesummary(x)
writemime(fh, "text/plain", str)
end
end
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] | 1.813464 | 713 |
using Test
using QuantumLattices.Mathematics.VectorSpaces
using QuantumLattices.Mathematics.Combinatorics: Combinations
using QuantumLattices.Interfaces: dimension, β, rank, dims, inds
@testset "SimpleVectorSpace" begin
id1, id2, id3 = (1, 1), (1, 2), (1, 3)
vs = SimpleVectorSpace{'T'}(id1, id2)
@test vs == deepcopy(vs)
@test isequal(vs, deepcopy(vs))
@test vs|>size == (2,)
@test vs|>dimension == 2
@test vs|>collect == [id1, id2]
@test (vs[1] == id1) && (vs[2] == id2)
@test (searchsortedfirst(vs, id1) == 1) && (searchsortedfirst(vs, id2) == 2)
@test (findfirst(id1, vs) == 1) && (findfirst(id2, vs) == 2)
@test findfirst((id1, id2), vs) == (1, 2)
@test (id1 β vs) && (id2 β vs) && (id3 β vs)
@test HasTable(typeof(vs)) == HasTable(true)
@test TableSorted(typeof(vs)) == TableSorted(true)
@test IsMultiIndexable(typeof(vs)) == IsMultiIndexable(false)
@test vs == id1 β id2
@test vs β id3 == id1 β id2 β id3
@test id3 β vs == id3 β id1 β id2
@test (id2 β id3) β vs == id2 β id3 β id1 β id2
vs = SimpleVectorSpace{'F'}(id1, id2)
@test vs == deepcopy(vs)
@test isequal(vs, deepcopy(vs))
@test vs|>size == (2,)
@test vs|>dimension == 2
@test vs|>collect == [id1, id2]
@test (vs[1] == id1) && (vs[2] == id2)
@test (searchsortedfirst(vs, id1) == 1) && (searchsortedfirst(vs, id2) == 2)
@test (findfirst(id1, vs) == 1) && (findfirst(id2, vs) == 2)
@test findfirst((id1, id2), vs) == (1, 2)
@test (id1 β vs) && (id2 β vs) && (id3 β vs)
@test HasTable(typeof(vs)) == HasTable(true)
@test TableSorted(typeof(vs)) == TableSorted(false)
@test IsMultiIndexable(typeof(vs)) == IsMultiIndexable(false)
@test vs == id1 β id2
@test vs β id3 == id1 β id2 β id3
@test id3 β vs == id3 β id1 β id2
@test (id2 β id3) β vs == id2 β id3 β id1 β id2
end
@testset "SimpleIndices" begin
foi = SimpleIndices{'F'}(2, 2, 2)
@test HasTable(typeof(foi)) == HasTable(false)
@test IsMultiIndexable(typeof(foi)) == IsMultiIndexable(true)
@test MultiIndexOrderStyle(typeof(foi)) == MultiIndexOrderStyle('F')
@test dimension(foi) == 8
@test dims(foi) == (2, 2, 2)
@test rank(typeof(foi)) == 3
@test inds((1, 1, 1), foi) == (1, 1, 1)
@test Tuple((1, 1, 1), foi) == (1, 1, 1)
@test foi|>collect == [(1, 1, 1), (2, 1, 1), (1, 2, 1), (2, 2, 1), (1, 1, 2), (2, 1, 2), (1, 2, 2), (2, 2, 2)]
@test ((1, 1, 1) β foi) && ((1, 2, 3) β foi)
for (i, finds) in enumerate(foi)
@test findfirst(finds, foi) == i
@test searchsortedfirst(foi, foi[i]) == i
end
coi = SimpleIndices{'C'}(2, 2, 2)
@test HasTable(typeof(coi)) == HasTable(false)
@test IsMultiIndexable(typeof(coi)) == IsMultiIndexable(true)
@test MultiIndexOrderStyle(typeof(coi)) == MultiIndexOrderStyle('C')
@test dimension(coi) == 8
@test dims(coi) == (2, 2, 2)
@test rank(typeof(coi)) == 3
@test inds((1, 1, 1), coi) == (1, 1, 1)
@test Tuple((1, 1, 1), coi) == (1, 1, 1)
@test ((1, 1, 1) β coi) && ((1, 2, 3) β coi)
@test coi|>collect == [(1, 1, 1), (1, 1, 2), (1, 2, 1), (1, 2, 2), (2, 1, 1), (2, 1, 2), (2, 2, 1), (2, 2, 2)]
for (i, cinds) in enumerate(coi)
@test findfirst(cinds, coi) == i
@test searchsortedfirst(coi, coi[i]) == i
end
end
@testset "TabledIndices" begin
dims = (2, 2)
table = [(1, 1), (1, 2), (2, 1), (2, 2)]
toi = TabledIndices{'T'}(dims, table)
@test HasTable(typeof(toi)) == HasTable(true)
@test TableSorted(typeof(toi)) == TableSorted(true)
@test toi == TabledIndices{2}(DulPermutations, 2)
@test dimension(toi) == 4
@test toi|>collect == table
@test ((1, 1) β toi) && ((1, 3) β toi)
for i = 1:length(toi)
@test searchsortedfirst(toi, toi[i]) == i
end
table = [(1, 2), (2, 1), (2, 2), (1, 1)]
toi = TabledIndices{'F'}(dims, table)
@test HasTable(typeof(toi)) == HasTable(true)
@test TableSorted(typeof(toi)) == TableSorted(false)
@test dimension(toi) == 4
@test toi|>collect == table
@test ((1, 1) β toi) && ((1, 3) β toi)
for i = 1:length(toi)
@test searchsortedfirst(toi, toi[i]) == i
end
end
struct VSZNamedVectorSpace{NS, BS<:Tuple, VS<:Tuple{Vararg{Vector}}} <: NamedVectorSpace{:zip, NS, BS, VS}
contents::VS
end
@generated function VSZNamedVectorSpace{NS}(contents::Vector...) where NS
@assert (length(NS) == length(contents)) && isa(NS, Tuple{Vararg{Symbol}})
BS = Expr(:curly, :Tuple, [contents[i]|>eltype for i = 1:length(NS)]...)
return quote
@assert mapreduce(length, ==, contents)
VSZNamedVectorSpace{NS, $BS, typeof(contents)}(contents)
end
end
struct VSPNamedVectorSpace{NS, BS<:Tuple, VS<:Tuple{Vararg{Vector}}} <: NamedVectorSpace{:product, NS, BS, VS}
contents::VS
end
@generated function VSPNamedVectorSpace{NS}(contents::Vector...) where NS
@assert (length(NS) == length(contents)) && isa(NS, Tuple{Vararg{Symbol}})
BS = Expr(:curly, :Tuple, [contents[i]|>eltype for i = 1:length(NS)]...)
return :(VSPNamedVectorSpace{NS, $BS, typeof(contents)}(contents))
end
@testset "NamedVectorSpace" begin
@test IsMultiIndexable(NamedVectorSpace) == IsMultiIndexable(true)
@test MultiIndexOrderStyle(NamedVectorSpace) == MultiIndexOrderStyle('C')
nvs = VSZNamedVectorSpace{(:t, :U)}([1, 2], [8.0, 9.0])
@test nvs|>keys == nvs|>typeof|>keys == (:t, :U)
@test nvs|>values == ([1, 2], [8.0, 9.0])
@test nvs|>pairs|>collect == [:t=>[1, 2], :U=>[8.0, 9.0]]
@test eltype(nvs, 1) == eltype(nvs|>typeof, 1) == Int
@test eltype(nvs, 2) == eltype(nvs|>typeof, 2) == Float64
@test nvs|>typeof|>rank == 1
@test dims(nvs) == (2,)
elements = [(t = 1, U = 8.0), (t = 2, U = 9.0)]
for i = 1:dimension(nvs)
@test NamedTuple(inds(elements[i], nvs), nvs) == elements[i]
end
@test nvs|>collect == elements
nvs = VSPNamedVectorSpace{(:t, :U)}([1.0, 2.0], [8.0, 9.0])
@test nvs|>typeof|>rank == 2
@test dims(nvs) == (2, 2)
elements = [(t = 1.0, U = 8.0), (t = 1.0, U = 9.0), (t = 2.0, U = 8.0), (t = 2.0, U = 9.0)]
for i = 1:dimension(nvs)
@test NamedTuple(inds(elements[i], nvs), nvs) == elements[i]
end
@test nvs|>collect == elements
end
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] | 2.084242 | 3,027 |
module Axes
import ..Cubes: caxes, Cubes
using Dates
using Base.Iterators: take, drop
"""
abstract CubeAxis{T}
Supertype of all axes. Every `CubeAxis` is an 1D Cube itself and can be passed
to mapCube operations.
"""
abstract type CubeAxis{T,S} end
Base.size(x::CubeAxis)=(length(x.values),)
Base.size(x::CubeAxis,i)=i==1 ? length(x.values) : error("Axis has only a single dimension")
Base.ndims(x::CubeAxis)=1
"""
CategoricalAxis{T,S}
To represent axes that are categorical, where `T` is the element type.
The type parameter `S` denotes the axis name (a symbol).
The default constructor is:
CategoricalAxis(axname::String,values::Vector{T})
"""
struct CategoricalAxis{T,S,RT} <: CubeAxis{T,S}
values::RT
end
CategoricalAxis(s::Symbol,v)=CategoricalAxis{eltype(v),s,typeof(v)}(v)
CategoricalAxis(s::AbstractString,v)=CategoricalAxis(Symbol(s),v)
struct RangeAxis{T,S,R<:AbstractVector{T}} <: CubeAxis{T,S}
values::R
end
"""
RangeAxis{T,S,R}
To represent axes that are categorical, where `T` is the element type.
The type parameter `S` denotes the axis name (a symbol) and `R` the type of the
range which is used to represent the axis values.
The default constructor is:
RangeAxis(axname::String,values::Range{T})
"""
RangeAxis(s::Symbol,v::AbstractVector{T}) where T = RangeAxis{T,s,typeof(v)}(v)
RangeAxis(s::AbstractString,v)=RangeAxis(Symbol(s),v)
Base.length(a::CubeAxis)=length(a.values)
"""
axcopy(x,vals)
Makes a copy of a `CubeAxis` with the values `vals`
"""
axcopy(ax::RangeAxis,vals) = RangeAxis(axname(ax),vals)
axcopy(ax::CategoricalAxis,vals) = CategoricalAxis(axname(ax),vals)
axcopy(ax::RangeAxis) = RangeAxis(axname(ax),copy(ax.values))
axcopy(ax::CategoricalAxis) = CategoricalAxis(axname(ax),copy(ax.values))
Base.show(io::IO,a::RangeAxis)=print(io,rpad(Axes.axname(a),20," "),"Axis with ",length(a)," Elements from ",first(a.values)," to ",last(a.values))
function Base.show(io::IO,a::CategoricalAxis)
print(io,rpad(Axes.axname(a),20," "), "Axis with ", length(a), " elements: ")
if length(a.values)<10
for v in a.values
print(io,v," ")
end
else
for v in take(a.values,2)
print(io,v," ")
end
print(io,".. ")
for v in drop(a.values,length(a.values)-2)
print(io,v," ")
end
end
end
caxes(x::CubeAxis)=CubeAxis[x]
axname(::Type{<:CubeAxis{<:Any,U}}) where U = string(U)
axname(::CubeAxis{<:Any,U}) where U = string(U)
axsym(::CubeAxis{<:Any,S}) where S = S
get_step(r::AbstractRange)=step(r)
get_step(r::AbstractVector)=length(r)==0 ? zero(eltype(r)) : r[2]-r[1]
axVal2Index_ub(a::RangeAxis, v; fuzzy=false)=axVal2Index(a,v-abshalf(get_step(a.values)),fuzzy=fuzzy)
axVal2Index_lb(a::RangeAxis, v; fuzzy=false)=axVal2Index(a,v+abshalf(get_step(a.values)),fuzzy=fuzzy)
axVal2Index_ub(a::RangeAxis, v::Date; fuzzy=false)=axVal2Index(a,DateTime(v)-abshalf(get_step(a.values)),fuzzy=fuzzy)
axVal2Index_lb(a::RangeAxis, v::Date; fuzzy=false)=axVal2Index(a,DateTime(v)+abshalf(get_step(a.values)),fuzzy=fuzzy)
abshalf(a) = abs(a/2)
abshalf(a::Day) = abs(Millisecond(a)/2)
abshalf(a::Month) = iseven(Dates.value(a)) ? a/2 : Month(aΓ·2) + Day(15)
get_bb(ax::RangeAxis) = first(ax.values)-abshalf(get_step(ax.values)), last(ax.values)+abshalf(get_step(ax.values))
function axisfrombb(name,bb,n)
offs = (bb[2]-bb[1])/(2*n)
RangeAxis(name,range(bb[1]+offs,bb[2]-offs,length=n))
end
function axVal2Index(a::RangeAxis{<:Any,<:Any,<:AbstractRange},v;fuzzy=false)
dt = v-first(a.values)
r = round(Int,dt/step(a.values))+1
return max(1,min(length(a.values),r))
end
convert_time(T::Type{<:TimeType}, v::TimeType) = T(year(v), month(v), day(v), hour(v), minute(v), second(v))
convert_time(T::Type{<:TimeType}, v::Date) = T(year(v), month(v), day(v), 0, 0, 0)
convert_time(::Type{Date},v::TimeType) = Date(year(v),month(v),day(v))
convert_time(::Type{Date},v::Date) = Date(year(v),month(v),day(v))
function axVal2Index(a::RangeAxis{T},v;fuzzy=false) where T<:TimeType
vconverted = convert_time(T,v)
dd = map(i->abs((i-vconverted)),a.values)
mi,ind = findmin(dd)
return ind
end
function axVal2Index(a::RangeAxis{T,<:Any,<:AbstractRange},v;fuzzy=false) where T<:TimeType
vconverted = convert_time(T,v)
dd = map(i->abs((i-vconverted)),a.values)
mi,ind = findmin(dd)
return ind
end
function axVal2Index(axis::CategoricalAxis{String},v::String;fuzzy::Bool=false)
r=findfirst(isequal(v),axis.values)
if r===nothing
if fuzzy
r=findall(axis.values) do i
startswith(lowercase(i),lowercase(v[1:min(length(i),length(v))]))
end
if length(r)==1
return(r[1])
else
error("Could not find unique value of $v in $axis")
end
else
error("$v not found in $axis")
end
end
r
end
axVal2Index(x,v::CartesianIndex{1};fuzzy::Bool=false)=min(max(v.I[1],1),length(x))
function axVal2Index(x,v;fuzzy::Bool=false)
i = findfirst(isequal(v),x.values)
if isa(i,Nothing)
if fuzzy==true
dd = map(i->abs(i-v),x.values)
mi,ind = findmin(dd)
return ind
else
error("Value $v not found in x")
end
else
return i
end
end
abstract type AxisDescriptor end
struct ByName <: AxisDescriptor
name::String
end
struct ByInference <: AxisDescriptor end
struct ByValue <: AxisDescriptor
v::CubeAxis
end
struct ByFunction <: AxisDescriptor
f::Function
end
const VecOrTuple{S} = Union{Vector{<:S},Tuple{Vararg{<:S}}} where S
#findAxis(a::Any,c::VecOrTuple{<:CubeAxis}) = findAxis(get_descriptor(desc),c)
get_descriptor(a::String)=ByName(a)
get_descriptor(a::Symbol)=ByName(String(a))
get_descriptor(a::CubeAxis)=ByValue(a)
get_descriptor(a::Function)=ByFunction(a)
get_descriptor(a)=error("$a is not a valid axis description")
get_descriptor(a::AxisDescriptor)=a
function findAxis(bs::ByName,axlist::VecOrTuple{CubeAxis})
matchstr=bs.name
ism=findall(i->startswith(lowercase(axname(i)),lowercase(matchstr)),axlist)
isempty(ism) && return nothing
if length(ism)>1
f = axlist[ism[1]]
all(i->i==f,ism) || error("Multiple axes found matching string $matchstr")
return f
else
return ism[1]
end
end
function findAxis(bv::ByValue,axlist::VecOrTuple{CubeAxis})
v=bv.v
return findfirst(i->i==v,axlist)
end
function getAxis(desc,axlist::VecOrTuple{CubeAxis})
i = findAxis(desc,axlist)
if isa(i,Nothing)
return nothing
else
return axlist[i]
end
end
getOutAxis(desc,axlist,incubes,pargs,f) = getAxis(desc,unique(axlist))
function getOutAxis(desc::ByFunction,axlist,incubes,pargs,f)
outax = desc.f(incubes,pargs)
isa(outax,CubeAxis) || error("Axis Generation function $(desc.f) did not return an axis")
outax
end
import DataStructures: counter
function getOutAxis(desc::Tuple{ByInference},axlist,incubes,pargs,f)
inAxes = map(caxes,incubes)
inAxSmall = map(i->filter(j->in(j,axlist),i) |>collect,inAxes)
inSizes = map(i->(map(length,i)...,),inAxSmall)
intypes = map(eltype, incubes)
testars = map((s,it)->zeros(it,s...),inSizes, intypes)
map(testars) do ta
ta .= rand(Base.nonmissingtype(eltype(ta)),size(ta)...)
if eltype(ta) >: Missing
# Add some missings
randind = rand(1:length(ta),length(ta)Γ·10)
ta[randind] .= missing
end
end
resu = f(testars...,pargs...)
isa(resu,AbstractArray) || isa(resu,Number) || isa(resu,Missing) || error("Function must return an array or a number")
(isa(resu,Number) || isa(resu,Missing)) && return ()
outsizes = size(resu)
outaxes = map(outsizes,1:length(outsizes)) do s,il
if s>2
i = findall(i->i==s,length.(axlist))
if length(i)==1
return axlist[i[1]]
elseif length(i)>1
@info "Found multiple matching axes for output dimension $il"
end
end
return RangeAxis("OutAxis$(il)",1:s)
end
if !allunique(outaxes)
#TODO: fallback with axis renaming in this case
error("Could not determine unique output axes from output shape")
end
return (outaxes...,)
end
"""
getAxis(desc::String, c)
Given the string of an axis name and a cube, returns this axis of the cube.
"""
getAxis(desc,c)=getAxis(desc,caxes(c))
getAxis(desc::ByValue,axlist::Vector{T}) where {T<:CubeAxis}=desc.v
"Fallback method"
findAxis(desc,c)=findAxis(desc,caxes(c))
findAxis(a,axlist::VecOrTuple{CubeAxis}) = findAxis(get_descriptor(a),axlist)
renameaxis(r::RangeAxis{T,<:Any,V}, newname) where {T,V} = RangeAxis{T,Symbol(newname),V}(r.values)
renameaxis(r::CategoricalAxis{T,<:Any,V}, newname) where {T,V} = CategoricalAxis{T,Symbol(newname),V}(r.values)
import Base.==
import Base.isequal
==(a::CubeAxis,b::CubeAxis)=(a.values==b.values) && (axname(a)==axname(b))
isequal(a::CubeAxis, b::CubeAxis) = a==b
using YAXArrayBase: YAXArrayBase
#Implement yaxarray interface
YAXArrayBase.dimname(x::CubeAxis,_) = axname(x)
YAXArrayBase.dimvals(x::CubeAxis,_) = x.values
YAXArrayBase.iscontdim(::RangeAxis,_) = true
YAXArrayBase.iscontdim(::CategoricalAxis,_) = false
end
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] | 2.318088 | 3,870 |
struct Node
id::Union{Integer, Nothing}
alias::Union{String, Nothing}
label::String
properties::Dict
end
Node(alias::String, label::String, properties::Dict) = Node(nothing, alias, label, properties)
Node(label::String, properties::Dict) = Node(nothing, nothing, label, properties)
Node(alias::String, label::String) = Node(nothing, alias, label, Dict())
Node(label::String) = Node(nothing, nothing, label, Dict())
Node(id::Integer) = Node(id, nothing, "", Dict())
# TODO: should be moved to some common module
function prop_value_to_string(prop_value::Any) return "$prop_value" end
function prop_value_to_string(prop_value::String) return "\"$prop_value\"" end
function string(n::Node)
alias = ""
label = n.label
props_str = ""
if n.alias != nothing
alias = n.alias
end
if length(n.properties) != 0
props = ["$prop_name: " * prop_value_to_string(prop_value) for (prop_name, prop_value) in pairs(n.properties)]
props_str = "{" * join(props, ",") * "}"
end
"($alias:$label $props_str)"
end
function isequal(x::Node, y::Node)
if x.id != nothing && y.id != nothing && x.id == y.id
return true
else
if x.label == y.label && x.properties == y.properties
return true
end
end
return false
end
Base.:(==)(x::Node, y::Node) = isequal(x, y)
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] | 2.60076 | 526 |
uniform(key::JaxRNGKey, dims...; kwargs...) =
uniform(Float64, key, dims...; kwargs...)
uniform(T::Type{<:Number}, key::JaxRNGKey, dims...; kwargs...) =
uniform(T, key, Dims(dims); kwargs...)
function uniform(
T::Type{<:Number},
key::JaxRNGKey,
dims::Dims;
minval = 0.0,
maxval = 1.0,
)
_random.uniform(key, reverse(dims), jl_to_np_type(T); minval = minval, maxval = maxval)
end
function uniform(
T::Type{<:Integer},
key::JaxRNGKey,
dims::Dims;
minval::Integer = 0,
maxval::Integer = 1,
)
_random.randint(
key,
dims,
dtype = jl_to_np_type(T),
minval = minval,
maxval = maxval,
)
end
normal(key::JaxRNGKey, dims...; kwargs...) = uniform(Float64, key, dims...)
normal(T::Type{<:Number}, key::JaxRNGKey, dims...) = normal(T, key, Dims(dims))
function normal(T::Type{<:Number}, key::JaxRNGKey, dims::Dims)
_random.normal(key, reverse(dims), dtype = jl_to_np_type(T))
end
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] | 2.213429 | 417 |
# Various algorithms for computing quantile
function quantile_bisect(d::ContinuousUnivariateDistribution, p::Float64,
lx::Float64, rx::Float64, tol::Float64)
# find quantile using bisect algorithm
cl = cdf(d, lx)
cr = cdf(d, rx)
@assert cl <= p <= cr
while rx - lx > tol
m = 0.5 * (lx + rx)
c = cdf(d, m)
if p > c
cl = c
lx = m
else
cr = c
rx = m
end
end
return 0.5 * (lx + rx)
end
quantile_bisect(d::ContinuousUnivariateDistribution, p::Float64) =
quantile_bisect(d, p, minimum(d), maximum(d), 1.0e-12)
# if starting at mode, Newton is convergent for any unimodal continuous distribution, see:
# GΓΆknur Giner, Gordon K. Smyth (2014)
# A Monotonically Convergent Newton Iteration for the Quantiles of any Unimodal
# Distribution, with Application to the Inverse Gaussian Distribution
# http://www.statsci.org/smyth/pubs/qinvgaussPreprint.pdf
function quantile_newton(d::ContinuousUnivariateDistribution, p::Float64, xs::Float64=mode(d), tol::Float64=1e-12)
if 0.0 < p < 1.0
while true
x = xs + (p - cdf(d, xs)) / pdf(d, xs)
abs(x-xs) >= max(abs(x),abs(xs))*tol || return x
xs = x
end
elseif p == 0.0
return minimum(d)
elseif p == 1.0
return maximum(d)
else
return NaN
end
end
function cquantile_newton(d::ContinuousUnivariateDistribution, p::Float64, xs::Float64=mode(d), tol::Float64=1e-12)
if 0.0 < p < 1.0
while true
x = xs + (ccdf(d, xs)-p) / pdf(d, xs)
abs(x-xs) >= max(abs(x),abs(xs))*tol || return x
xs = x
end
elseif p == 1.0
return minimum(d)
elseif p == 0.0
return maximum(d)
else
return NaN
end
end
function invlogcdf_newton(d::ContinuousUnivariateDistribution, lp::Float64, xs::Float64=mode(d), tol::Float64=1e-12)
if -Inf < lp < 0.0
if lp < logcdf(d,xs)
while true
x = xs - exp(lp - logpdf(d,xs) + logexpm1(max(logcdf(d,xs)-lp,0.0)))
abs(x-xs) >= max(abs(x),abs(xs))*tol || return x
xs = x
end
else
while true
x = xs + exp(lp - logpdf(d,xs) + log1mexp(min(logcdf(d,xs)-lp,0.0)))
abs(x-xs) >= max(abs(x),abs(xs))*tol || return x
xs = x
end
end
elseif lp == -Inf
return minimum(d)
elseif lp == 0.0
return maximum(d)
else
return NaN
end
end
function invlogccdf_newton(d::ContinuousUnivariateDistribution, lp::Float64, xs::Float64=mode(d), tol::Float64=1e-12)
if -Inf < lp < 0.0
if lp < logccdf(d,xs)
while true
x = xs + exp(lp - logpdf(d,xs) + logexpm1(max(logccdf(d,xs)-lp,0.0)))
abs(x-xs) >= max(abs(x),abs(xs))*tol || return x
xs = x
end
else
while true
x = xs - exp(lp - logpdf(d,xs) + log1mexp(min(logccdf(d,xs)-lp,0.0)))
abs(x-xs) >= max(abs(x),abs(xs))*tol || return x
xs = x
end
end
elseif lp == -Inf
return maximum(d)
elseif lp == 0.0
return minimum(d)
else
return NaN
end
end
# A macro: specify that the quantile (and friends) of distribution D
# is computed using the newton method
macro quantile_newton(D)
esc(quote
quantile(d::$D, p::Float64) = quantile_newton(d,p)
cquantile(d::$D, p::Float64) = cquantile_newton(d,p)
invlogcdf(d::$D, lp::Float64) = invlogcdf_newton(d,lp)
invlogccdf(d::$D, lp::Float64) = invlogccdf_newton(d,lp)
end)
end
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] | 1.876427 | 2,015 |
# https://www.nature.com/articles/s41598-017-16178-8.pdf?proof=t
using MultidimensionalSpectroscopy, PyPlot, QuantumOptics, LinearAlgebra, FFTW, Colors,
Printf, DelimitedFiles, Random, Combinatorics, Distributions
QuantumOpticsBase.set_printing(standard_order=false,rounding_tol=1e-3)
# make sure to set script directory as pwd()
cd(@__DIR__)
mod_name = "A"
fn_log = "logs/" * mod_name * ".log"
# erase .log file
try
rm("logs/" * mod_name * ".log")
rm(fn_log)
catch
end
### -- select model parameters --- ###
mod_name = "parameters/" * mod_name
include( mod_name * ".params")
logger(String(read(mod_name * ".params")), fn_log)
pygui(true)
calc_2d = true
logger("\nDo 2D calculation: ", calc_2d, fn_log)
cmp = create_colormap("bright");
# use 2 level system to approximate Frenkel excitons ... seems to coarse, but well
Nlev = 3
b = NLevelBasis(Nlev)
# energy of TDBC Frenkel exciton bright state #TODO: add some details, such as J angles, etc.
Emon = E_monomer
Emonb = 2 * E_monomer + 0.1
# not important here, but in general for TDBC aggregates
# Ξ = .299 # shift monomer -> aggregate
# J = -Ξ / (2 * cos(pi /(N+1))) # nearest-neighbour coupling constant
# transitions and monomer Hamiltonian
ΟβΊ = transition(b,2,1)
Ο31 = transition(b,3,1)
Ο32 = transition(b,3,2)
Οβ» = transition(b,1,2)
Ο13 = transition(b,1,3)
Ο23 = transition(b,2,3)
H_mon = Emon * ΟβΊ * Οβ» + Emonb * Ο31*Ο13
ΟβΊ = ΟβΊ + Ο32
Οβ» = Οβ» + Ο23
logger("\nMonomer Hamiltonian:\n\n", dense(H_mon), fn_log)
# use N uncoupled TLSs
N = N_monomers # from .params file
B = b^N # basis for matter part of H
# random (normal dist) list of energy shifts (factors) scaled arbitrarily by scale. Used to account for static disorder of exciton transition energy
dE_n = energy_disorder # from .params file
# keep only factors that increase E_mon, since Frenkel exciton absorption spectrum is not Gaussian but favors high energy side. This could be explained by
# a maximum delocalisation length resulting in the lowest energy bright state beyond which no longer delocalisation is possible. Shorter delocalisation lengths,
# due to structural breaks in the exciton or other effecsts, lead to the lowest/bright transition being at higher energies
# idea1
#dE_n[dE_n.<=1] = 2 .- dE_n[dE_n.<=1] #BUG #CHECK This shifts polariton spectrum to higher E
# idea2, better since keeps all values
#dE_n = 1 .+ abs.(dE_n .- 1)
# can deactive random energy shifts
dE_n = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] .+ 1
# functions/methods to build matter H and transition operators, by embedding monomer H and Ο at site N
H_n(n) = embed(B,n,H_mon * dE_n[n]) # Hamiltonian at site n => Hβ with random, normally distributed energy offset dE
Ξ£βΊ_n(n) = embed(B,n,ΟβΊ) # raising operator at site n
Ξ£β»_n(n) = embed(B,n,Οβ») # lowering operator at site n
# need this workaroud to deal with a single TLS... #BUG
if N != 1
Ξ£βΊ = sum(Ξ£βΊ_n.(1:N))
Ξ£β» = sum(Ξ£β»_n.(1:N))
Ξ£βΊn = Ξ£βΊ_n.(1:N)
Ξ£β»n = Ξ£β»_n.(1:N)
Hexc = sum(H_n.(1:N)) # Hamiltonian of disorder TLSs ... NOT coupled to each other
else
Ξ£βΊ = ΟβΊ
Ξ£β» = Οβ»
Ξ£βΊn = ΟβΊ
Ξ£β»n = Οβ»
Hexc = H_mon # Hamiltonian of disorder TLSs ... NOT coupled to each other
end
logger("\nMatter Hamiltonian:\n\n", dense(Hexc), fn_log)
# create cavity basis
bcav = FockBasis(N_photonStates) #FACT: #CHECK need second FockState for ESA !?
#bcav = FockBasis(1)
#BUG#TODO: Having 2 photon states messes up transforming between eigen and site basis in subspace
#IDEA MAYBE I need two 1 photon Fock states ?
Ecav = E_cavity # slightly red-tuned from excitonic transition, as main system in paper
a = destroy(bcav) # β one(bcav) + one(bcav) β destroy(bcav)
at = create(bcav) #β one(bcav) + one(bcav) β create(bcav)
# embed operators into full basis
A = a β one(Hexc)
At = at β one(Hexc)
Ξ£βΊ = one(a) β Ξ£βΊ
Ξ£β» = one(a) β Ξ£β»
Ξ£βΊn = [one(a) β Ξ£βΊn[i] for i in 1:N]
Ξ£β»n = [one(a) β Ξ£β»n[i] for i in 1:N]
# create cavity H
Hcav = Ecav * at * a
logger("\nCavity Hamiltonian:\n\n", dense(Hcav), fn_log)
# create basis of complete system
Bfull = bcav β B
# create Jaynes/Tavis-Cummings Hamiltonian for light-matter coupling g
g = 0.15 / sqrt(N) # should make splliting independent of N
#g = 0
H = embed(Bfull,1,Hcav) + one(Hcav) β Hexc + g * (Ξ£βΊ * A + Ξ£β» * At)
logger("\nFull Hamiltonian:\n\n", dense(H), fn_log)
# sort by increasing values of Hamiltonian (rough #CHECK ) ... sort into excitation sectors
idx = sortperm(real(diag((H).data)))
H.data = H.data[idx,idx]
A.data = A.data[idx,idx]
At.data = At.data[idx,idx]
Ξ£βΊ.data = Ξ£βΊ.data[idx,idx]
Ξ£β».data = Ξ£β».data[idx,idx]
for i in 1:N
Ξ£βΊn[i].data = Ξ£βΊn[i].data[idx,idx]
Ξ£β»n[i].data = Ξ£β»n[i].data[idx,idx]
end
logger("\nOrdered Hamiltonian:\n\n", dense(H), fn_log)
# transition operator
#ΞΌ = Ξ£βΊ + Ξ£β» # transitions between excitonic states only, #FACT: not possible induced by ext. EM field in cavity, every via optical mode
ΞΌ = At + A #+ At^2 + A^2 #FACT: transitions induced by external field
# density matrices
rho0 = dm(fockstate(bcav,0) β tensor([nlevelstate(b,1) for i in 1:N]))
rho1 = dm(fockstate(bcav,0) β tensor([nlevelstate(b,2) for i in 1:N]))
# transition operator from ground to first ES
ΞΌ12 = rho0 * ΞΌ + ΞΌ * rho0
# sort (see above)
ΞΌ12.data = ΞΌ12.data[idx,idx]
rho0.data = rho0.data[idx,idx]
rho1.data = rho1.data[idx,idx]
# transition from first ES to higher ES #FACT: should connect excitation on exciton site and excitation of exciton + cavity
ΞΌ23 = ΞΌ - ΞΌ12
# normalization of ΞΌ12 and ΞΌ23 ... seems not really necessary #DELETE
rho1 = ΞΌ12 * rho0 * ΞΌ12
#ΞΌ12 = ΞΌ12 / sqrt(tr(rho1))
#rho1 = ΞΌ12 * rho0 * ΞΌ12
rho2 = ΞΌ23 * rho1 * ΞΌ23
#ΞΌ23 = ΞΌ23 / sqrt(tr(rho2))
#ΞΌ23 = ΞΌ23
rho2 = ΞΌ23 * rho1 * ΞΌ23
# Lindblad dissipation operators
L1 = A # FACT: #CHECK only the relaxation channel from single exc. sector to GS is relevant ...
L2 = Ξ£β» # FACT: actually, having the elements that connect single and double exc. sector seems to ...
# FACT: broaden the ESA peaks (the only signal that considers single and double exc. sectors)
L3 = .5 * ((At*A) - (A*At)) # not sure if this one is relevant/meaningful/... #CHECK
#L3 = A - L1
#L4 = Ξ£β» - L2
# rates and list of Lindblad dissipation operators
Ξ = [.25, .000001] # Ξ[1] was 0.2 before
L = Ξ .* [L1, L2]
# plot energy levels
figure(); title("Energy level diagram")
plot_levels(H,0)
plot_levels(Hcav,-1)
plot_levels(Hexc,1)
plot_levels(H_mon,2)
xticks([-1, 0, 1, 2], ["cavity", "full", "matter", "monomer"])
##
# create subspace (single excitation, double excitation, ...) in eigenbasis/polariton basis
H_si, transf_op_si, P_si, L_si, rho0_si, ΞΌ12_si, ΞΌ23_si, Ξ£βΊ_si, Ξ£β»_si, At_si, A_si = create_subspace([H],"si", L, rho0, ΞΌ12, ΞΌ23, Ξ£βΊ, Ξ£β», At, A)
H_si = H_si[1]
H, transf_op, P, L, rho0, ΞΌ12, ΞΌ23, Ξ£βΊ, Ξ£β», At, A = create_subspace([H],"bi", L, rho0, ΞΌ12, ΞΌ23, Ξ£βΊ, Ξ£β», At, A)
H = H[1]
#FACT: Using "bi_lowest" does not give ESA that eliminates above diagonal cross-peak
#FACT: Neither does using "bi_polariton", which takes lowest and highest levels of double excitation sector
#FACT: only using "bi" adds ESA signal at above diagonal cross-peak that cancels out the GSB above diagonal cross-peak after some time T
Ξ£βΊn_si = [P_si * Ξ£βΊn[i] * P_si' for i in 1:N]
Ξ£β»n_si = [P_si * Ξ£β»n[i] * P_si' for i in 1:N]
Ξ£βΊn = [P * Ξ£βΊn[i] * P' for i in 1:N]
Ξ£β»n = [P * Ξ£β»n[i] * P' for i in 1:N]
logger("\nCHANGE TO SUBSPACE", fn_log)
logger("\n0th, 1st, 2nd Manifold", fn_log)
logger("\nCoupled (eigen) basis", fn_log)
logger("\nHamiltonian:\n\n", dense(H), fn_log)
logger("\nTransition operator gs <-> es:\n\n", dense(ΞΌ12), fn_log)
logger("\nTransition operator es <-> d-es:\n\n", dense(ΞΌ23), fn_log)
logger("\n0th, 1st, 2nd Manifold", fn_log)
logger("\nUncoupled (site) basis", fn_log)
logger("\nHamiltonian:\n\n", transf_op * H * transf_op', fn_log)
logger("\nTransition operator gs <-> es:\n\n", transf_op * ΞΌ12 * transf_op', fn_log)
logger("\nTransition operator es <-> d-es:\n\n", transf_op * ΞΌ23 * transf_op', fn_log)
logger("\n0th, 1st Manifold", fn_log)
logger("\nCoupled (eigen) basis", fn_log)
logger("\nHamiltonian:\n\n", dense(H_si), fn_log)
logger("\nTransition operator gs <-> es:\n\n", ΞΌ12_si, fn_log)
logger("\nTransition operator es <-> d-es:\n\n", ΞΌ23_si, fn_log)
logger("\nUncoupled (site) basis", fn_log)
logger("\nHamiltonian (o, 1, 2 Manifold; site):\n\n", transf_op_si * H_si * transf_op_si', fn_log)
logger("\nTransition operator gs <-> es:\n\n", transf_op_si * ΞΌ12_si * transf_op_si', fn_log)
logger("\nTransition operator es <-> d-es:\n\n", transf_op_si * ΞΌ23_si * transf_op_si', fn_log)
plot_levels(H_si,-0.05,col="r",ls="dashed")
plot_levels(H , 0.05,col="b",ls="dotted")
# transf_op can be used to convert between polariton and site basis #FACT: required prior sorting of operators into blocks of excitation sectors
# H_site = transf_op * H * transf_op'
# H_site_si = transf_op_si * H_si * transf_op_si'
#TODO: vary g instead of E_mon ... try to see effect
g = g .* dE_n
#Hk(i) = (Ecav * At_si * A_si) + (Emon * Ξ£βΊ_si * Ξ£β»_si) + g[1] * (Ξ£βΊ_si * A_si + Ξ£β»_si * At_si)
# time list
tlist = [0:1.1:200;]
logger("\nTime list:\n\n", tlist, fn_log)
# Lindblad time evolution
corr = timecorrelations.correlation(tlist, rho0_si, H_si, L_si, ΞΌ12_si, ΞΌ12_si)
# #TODO: test with varying g ...
#corr = sum([timecorrelations.correlation(tlist, rho0_si, Hk(i), L_si, ΞΌ12_si, ΞΌ12_si) for i in 1:1]) ./ 1
# zeropadding for smoother data only
zp = 10
corr = zeropad(corr,zp)
tnew, ~ = interpt(tlist,zp)
# calculate and plot spectrum
Ο, spec = timecorrelations.correlation2spectrum(tnew, corr; normalize_spec=true);
figure()
subplot(211)
plot(Ο,spec)
# load and plot experimental absorption spectrum
TDBC_cav = readdlm("TDBC_cav.dat")
TDBC_cav_E = TDBC_cav[:,2] ./ 8065 # convert from cm^-1 to eV
TDBC_cav_I = TDBC_cav[:,3] ./ 0.176 # normalize to 1 (in the visible)
plot(-TDBC_cav_E,TDBC_cav_I,"r:")
# TODO: spectral density ... so far only trial and error ... Seems like it needs to bridge the E gap between P+ and P- for relaxation to occur
width = .005
#Οq_TDBC = [0.12 0.336]
#ampl_TDBC = [0.12 3] / (N)
# from ... http://www.rsc.org/suppdata/c8/sc/c8sc00171e/c8sc00171e1.pdf
#Οq_TDBC = [40, 80, 120, 150, 185, 197] ./ 8065 #.* 0.02998 # cm-1 in Hz #./ 8065 # in eV instead
# => [0.00496 0.00992 0.01488 0.01860 0.02294 0.02443]
#ampl_TDBC = [14, 18, 25, 43, 42, 67, 60]
#Οq_TDBC = [0.0084, 0.0120, 0.1204, 0.1289, 0.1304, 0.1424, 0.303] #FACT: relaxation from UP to LP when Οq_TDBC bridges energy gap between UP and LP. However, no relaxation to dark states ... DUE TO LACK OF COUPLING ????
Οq_TDBC = Οq # from .params file
ampl_TDBC = Aq # from .params file
logger("Spectral frequencies: ", Οq_TDBC, fn_log)
logger("Spectral amplitudes: ", ampl_TDBC, fn_log)
logger("Spetral peak width: ", width, fn_log)
try # need to delete method first when working on it
local m = @which spectral_density(1)
Base.delete_method(m)
catch
end
function spectral_density(Ο) # power spectral density, thermal bath noise spectrum
#w = [(Ο .- Οq_TDBC[i]) ./ (.5 * width) for i in 1:length(Οq_TDBC)]
b = 1 / 0.0259 # eV k_B*T at T=300 K
f = .5 # 0 ... 1: relative importance of non-radiation vs. dephasing ... f = 0 > no dephasing
Ξ³ = 0.7 # from absorption linewidth Ξ³ = Ξ³_non-rad + Ξ³_dephasing
Ξ· = 1
Ξ· = f * Ξ³ / (2 * pi * 0.0259)
Ο_cut = .1
if Ο == 0
JΟ = .0
#elseif !isless(real(Ο),0)
#else
# w = [(Ο .- Οq_TDBC[i]) ./ (.5 * width) for i in 1:length(Οq_TDBC)]
# JΟ = sum([ampl_TDBC[i] ./ (1 .+ w[i].^2) for i in 1:length(Οq_TDBC)]) # probably most realistic ... ???
# #JΟ = 1
end
if !isless(real(Ο),0) && Ο != 0
w = [(Ο .- Οq_TDBC[i]) ./ (.5 * width) for i in 1:length(Οq_TDBC)]
JΟ = sum([ampl_TDBC[i] ./ (1 .+ w[i].^2) for i in 1:length(Οq_TDBC)])
JΟ = Ξ· * Ο * exp(-(Ο/Ο_cut)^2) + JΟ
JΟ = JΟ * (1/exp(b * Ο - 1) + 1)
elseif isless(real(Ο),0)
w = [(Ο .- Οq_TDBC[i]) ./ (.5 * width) for i in 1:length(Οq_TDBC)]
JΟa = sum([ampl_TDBC[i] ./ (1 .+ w[i].^2) for i in 1:length(Οq_TDBC)])
JΟ = Ξ· * -Ο * exp(-(-Ο/Ο_cut)^2)
JΟ = JΟ * (1/exp(b * -Ο - 1)) + JΟa
end
return JΟ
end
#plot spectral density
#figure()
subplot(212)
plot(collect(-1:.001:1),(spectral_density.(collect(-1:.001:1))))
# Rate and operator for redfield process (GS -> single exc sector)
Ξ_R = .23
# this means collective mode of all TLSs dephases ...
a_ops_si = [Ξ_R * Ξ£βΊ_si * Ξ£β»_si, spectral_density]
# ... and this: each TLS dephases individually
a_ops_si = vcat([[Ξ£βΊn_si[i] * Ξ£β»n_si[i], spectral_density] for i in 1:N]...)
R_si, ekets = timeevolution.bloch_redfield_tensor(H_si, [a_ops_si]; J=1 .* L_si)
# time evolution, Redfield
tout, rhot = timeevolution.master_bloch_redfield(tlist,ΞΌ12_si*rho0_si,R_si,H_si)
corr = expect(ΞΌ12_si,rhot)
# calculate and plot spectrum
corr = zeropad(corr,zp)
tnew, ~ = interpt(tlist,zp)
Ο, spec = timecorrelations.correlation2spectrum(tnew, corr; normalize_spec=true);
subplot(211)
plot(Ο,spec)
#FACT: J(0) > 0 induces pure dephasing and leads to broadening of ALL transitions
#FACT: J(Ο) > 0 for Ο > 0 leads to relaxation between states with Ei - Ef = Ο ...
#CHECK what role does the operator play ?
figure(figsize=(14,4));
subplot(131)
plot(tout,real(corr[1:length(tout)]))
tout, rhot = timeevolution.master_bloch_redfield(tlist,ΞΌ12_si*rho0_si*ΞΌ12_si,R_si,H_si)
rho1test = copy(rho0_si)
rho1test.data[1,1] = 0
rho1test.data[2,2] = 1
#tout, rhot = timeevolution.master_bloch_redfield(tlist,rho1test,R_si,H_si)
#corr_gs = expect(Ξ£βΊ_si*Ξ£β»_si,rhot)
corr_gs = expect(rho0_si,rhot)
#corr_es = expect(Ξ£β»_si*Ξ£βΊ_si,rhot)
#corr_es = expect(ΞΌ12_si * rho0_si * ΞΌ12_si,rhot)
corr_es_mat = expect(Ξ£βΊ_si * rho0_si * Ξ£β»_si / N,rhot)
corr_es_cav = expect(At_si * rho0_si * A_si, rhot)
plot(tout,real(corr_gs))
#plot(tout,corr_es) # == corr_es_cav
plot(tout,real(corr_es_cav))
plot(tout,real(corr_es_mat))
legend(["corr. function", "ground state", "cavity", "matter"])
rhot_site = [transf_op_si * i * transf_op_si' for i in rhot]
corr_es1_mat = expect(rho0_si, rhot_site)
#plot(tout,corr_es1_mat)
vecs = eigvecs(dense(H_si).data)
eivecs = [Ket(H_si.basis_l,vecs[:,i]) for i in 1:length(vecs[1,:])]
subplot(132);
title("eigen/coupled basis") # levels: 1 -> GS, 2 -> LP, 3 - N-1 -> ..., N -> UP
for i in 1:H_si.basis_l.shape[1]
local es_n = real(expect(dm(eivecs[i]),rhot))
plot(tout,es_n,linestyle="dashed", label=string(i)); #append!(leg,[string(i)])
ylim((-0.1, 1))
end
legend()
es_mat = zeros(length(tout))
subplot(133); title("uncoupled basis") # levels: 1 -> GS, 2 -> cav., 3 - N -> TLSs
for i in 1:H_si.basis_l.shape[1]
local es_n = real(expect(dm(eivecs[i]),rhot_site))
plot(tout,es_n,linestyle="dashed", label=string(i)); #append!(leg,[string(i)])
if i > 2
global es_mat = es_mat .+ es_n
end
end
plot(tout, es_mat, label="Ξ£(TLSs)")
legend()
# Rate and operator for redfield process (GS up to double exc sector)
a_ops = [Ξ_R * Ξ£βΊ * Ξ£β», spectral_density]
a_ops = vcat([[Ξ£βΊn[i] * Ξ£β»n[i], spectral_density] for i in 1:N]...)
R, ekets = timeevolution.bloch_redfield_tensor(H, [a_ops]; J=1 .* L)
# choose method to use for simulation of 2D spectra
method = "redfield"
# change parameters accordingly #TODO: simplify a bit
if method == "lindblad"
F = L
F_si = L_si
use_sub = true
H = H[1]
elseif method == "redfield"
F = R
F_si = R_si
use_sub = true
end
if calc_2d
## calculate (complex) 3rd order corr function (with T=0)
zp = 11 # zeropad up to 2^zp
## calculate 2D spectra at
T = [0, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 100, 200, 300] # energy eV -> t => [hbar / eV] ~ 0.66 fs
T = [0, 30]
spectra2d = Array{out2d}(undef, length(T))
# cannot plot inside cmds.jl when multithreading
#for i = 1:length(T)
# multithreading (run several T steps in parallel)!
Threads.@threads for i = 1:length(T)
spectra2d[i] = make2Dspectra(tlist,[rho0, rho0_si],[H, H_si],[F, F_si],[ΞΌ12, ΞΌ12_si],[ΞΌ23, ΞΌ23_si],T[i],
method;debug=true,use_sub=use_sub,
t2coh="kin",zp=zp)
end
## crop 2D data and increase dw
spectra2d = crop2d.(spectra2d,1.5;w_max=2.6,step=1)
spectra2d = round2d.(spectra2d)
end
if calc_2d
## plot 2D spectra for each(?) T
# what to plot
rep="abst"
scal="lin"
## make subplot layout
nplots = length(T);
ncols = Int32(ceil(sqrt(nplots)));
nrows = Int32(ceil(nplots / ncols));
# determine maximum value in dataset out2d[:].full2d[:,:]
maxi = Float64(maximum([maximum(real(spectra2d[i].full2d)) for i in 1:length(spectra2d)]))
# plot 2D spectra
fig, ax = subplots(nrows,ncols,sharex=true,sharey=true,figsize=(ncols*3.5,nrows*3))
if nplots == 1
ax = [ax]
end
fig.suptitle(rep * " 2D spectrum (" * scal * ". scaling)")
k = 0
for i = 1:nrows
for j = 1:ncols
global k += 1
if k > nplots
continue
end
sca(ax[j,i])
ax[j,i].set_aspect="equal"
plot2d(spectra2d[k].Ο,round.(spectra2d[k].full2d,digits=1);repr=rep,scaling=scal,norm=maxi)
title("2D spectrum at $(T[k]) fs")
end
end
tight_layout()
subplots_adjust(top=0.825)
## plot TA (summed 2D spectrum)
figure()
ta = [sum(spectra2d[i].full2d,dims=1) for i in 1:length(spectra2d)]
#[plot(out2d[1].Ο,ta[i]') for i in 1:length(ta)]
[plot(3e8 * 4.136e-15 ./ spectra2d[1].Ο ./ 1e-9,-ta[i]') for i in 1:length(ta)]
xlabel("Detection (Οβ)")
legend([string(i) * " fs" for i in T])
tight_layout()
end
# to plot time traces
# cmds.plot_timeTrace([real(i.full2d) for i in out2d],T,out2d[1].Ο,[1.925, 1.925, 2.24, 2.24],[1.925, 2.24, 2.24, 1.925])
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] | 2.139375 | 8,574 |
using Documenter, HomotopyContinuation
import LinearAlgebra
makedocs(
sitename = "Homotopy Continuation",
pages = [
"Introduction" => "index.md",
"Solving polynomial systems" => [
"solving.md",
"solver.md"
],
"Solving parametrized systems with monodromy" => "monodromy.md",
"Tracking paths" => [
"path_tracker.md",
"core_tracker.md",
],
"Homotopies" => "homotopies.md",
"Polynomial systems" => "systems.md",
"Reference" => "reference.md"
],
strict=false)
deploydocs(
repo = "github.com/JuliaHomotopyContinuation/HomotopyContinuation.jl.git"
)
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] | 2.081571 | 331 |
using TerminalLoggers: StickyMessages
@testset "Sticky messages without ANSI codes" begin
buf = IOBuffer()
# Without TTY, messages are just piped through
stickies = StickyMessages(buf, ansi_codes=false)
push!(stickies, :a=>"Msg\n")
@test String(take!(buf)) == "Msg\n"
push!(stickies, :a=>"Msg\n")
@test String(take!(buf)) == "Msg\n"
pop!(stickies, :a)
@test String(take!(buf)) == ""
end
@testset "Sticky messages with ANSI codes" begin
buf = IOBuffer()
dsize = (20, 80) # Intentionally different from default of 25 rows
# In TTY mode, we generate various escape codes.
stickies = StickyMessages(IOContext(buf, :displaysize=>dsize), ansi_codes=true)
push!(stickies, :a=>"Msg\n")
@test String(take!(buf)) == #scroll #csr #pos #msg #pos
"\e[20;1H\n\e[1;19r\e[20;1HMsg\e[19;1H"
push!(stickies, :a=>"MsgMsg\n")
@test String(take!(buf)) == #clear #msgpos #msg #repos
"\e[20;1H\e[J\e[20;1HMsgMsg\e[19;1H"
push!(stickies, :b=>"BBB\n")
@test String(take!(buf)) ==
#clear #scroll #csr #pos #msgs #pos
"\e[20;1H\e[J\e[19;1H\n\e[1;18r\e[19;1HMsgMsg\nBBB\e[18;1H"
pop!(stickies, :a)
@test String(take!(buf)) == #clear #csr #pos #msg #pos
"\e[19;1H\e[J\e[1;19r\e[20;1HBBB\e[18;1H"
pop!(stickies, :b)
@test String(take!(buf)) == #clear #csr #pos
"\e[20;1H\e[J\e[1;20r\e[19;1H"
pop!(stickies, :b)
@test String(take!(buf)) == ""
push!(stickies, :a=>"Ξ±Ξ²Ξ³\n")
@test String(take!(buf)) == #scroll #csr #pos #msg #pos
"\e[20;1H\n\e[1;19r\e[20;1HΞ±Ξ²Ξ³\e[19;1H"
push!(stickies, :b=>"msg\n")
take!(buf)
# Remove all two messages
empty!(stickies)
@test String(take!(buf)) == #clear #csr #pos
"\e[19;1H\e[J\e[1;20r\e[18;1H"
end
@noinline func_barrier(f) = f()
@testset "Sticky messages with ANSI codes" begin
buf = IOBuffer()
dsize = (20, 80) # Intentionally different from default of 25 rows
func_barrier() do
# Hide stickies variable behind a function barrier to make sure(er)
# that it can be GC'd.
stickies = StickyMessages(IOContext(buf, :displaysize=>dsize), ansi_codes=true)
push!(stickies, :a=>"a-msg\n")
push!(stickies, :b=>"b-msg\n")
take!(buf)
nothing
end
# Hack to force StickyMessages finalizer
GC.gc(true) # trigger finalizer
for i=1:1000
yield() # allow async cleanup
end
@test String(take!(buf)) == #clear #csr #pos
"\e[19;1H\e[J\e[1;20r\e[18;1H"
end
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] | 1.87073 | 1,493 |
using Documenter
using SymmetricSparseMatrices
makedocs(
sitename = "SymmetricSparseMatrices",
format = Documenter.HTML(),
modules = [SymmetricSparseMatrices]
)
deploydocs(repo = "github.com/victorsndvg/SymmetricSparseMatrices.jl.git")
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] | 2.631579 | 95 |
# # Collinear spin and magnetic systems
#
# In this example we consider iron in the BCC phase.
# To show that this material is ferromagnetic we will model it once
# allowing collinear spin polarization and once without
# and compare the resulting SCF energies. In particular
# the ground state can only be found if collinear spins are allowed.
#
# First we setup BCC iron without spin polarization
# using a single iron atom inside the unit cell.
using DFTK
a = 5.42352 # Bohr
lattice = a / 2 * [[-1 1 1];
[ 1 -1 1];
[ 1 1 -1]]
Fe = ElementPsp(:Fe, psp=load_psp("hgh/lda/Fe-q8.hgh"))
atoms = [Fe => [zeros(3)]];
# To get the ground-state energy we use an LDA model and rather moderate
# discretisation parameters.
kgrid = [3, 3, 3] # k-point grid (Regular Monkhorst-Pack grid)
Ecut = 15 # kinetic energy cutoff in Hartree
model_nospin = model_LDA(lattice, atoms, temperature=0.01)
basis_nospin = PlaneWaveBasis(model_nospin; kgrid, Ecut)
scfres_nospin = self_consistent_field(basis_nospin, tol=1e-6, mixing=KerkerMixing());
#-
scfres_nospin.energies
# Since we did not specify any initial magnetic moment on the iron atom,
# DFTK will automatically assume that a calculation with only spin-paired
# electrons should be performed. As a result the obtained ground state
# features no spin-polarization.
# Now we repeat the calculation, but give the iron atom an initial magnetic moment.
# For specifying the magnetic moment pass the desired excess of spin-up over spin-down
# electrons at each centre to the `Model` and the guess density functions.
# In this case we seek the state with as many spin-parallel
# ``d``-electrons as possible. In our pseudopotential model the 8 valence
# electrons are 2 pair of ``s``-electrons, 1 pair of ``d``-electrons
# and 4 unpaired ``d``-electrons giving a desired magnetic moment of `4` at the iron centre.
# The structure (i.e. pair mapping and order) of the `magnetic_moments` array needs to agree
# with the `atoms` array and `0` magnetic moments need to be specified as well.
magnetic_moments = [Fe => [4, ]];
# !!! tip "Units of the magnetisation and magnetic moments in DFTK"
# Unlike all other quantities magnetisation and magnetic moments in DFTK
# are given in units of the Bohr magneton ``ΞΌ_B``, which in atomic units has the
# value ``\frac{1}{2}``. Since ``ΞΌ_B`` is (roughly) the magnetic moment of
# a single electron the advantage is that one can directly think of these
# quantities as the excess of spin-up electrons or spin-up electron density.
#
# We repeat the calculation using the same model as before. DFTK now detects
# the non-zero moment and switches to a collinear calculation.
model = model_LDA(lattice, atoms, magnetic_moments=magnetic_moments, temperature=0.01)
basis = PlaneWaveBasis(model; Ecut, kgrid)
Ο0 = guess_density(basis, magnetic_moments)
scfres = self_consistent_field(basis, tol=1e-6; Ο=Ο0, mixing=KerkerMixing());
#-
scfres.energies
# !!! note "Model and magnetic moments"
# DFTK does not store the `magnetic_moments` inside the `Model`, but only uses them
# to determine the lattice symmetries. This step was taken to keep `Model`
# (which contains the physical model) independent of the details of the numerical details
# such as the initial guess for the spin density.
#
# In direct comparison we notice the first, spin-paired calculation to be
# a little higher in energy
println("No magnetization: ", scfres_nospin.energies.total)
println("Magnetic case: ", scfres.energies.total)
println("Difference: ", scfres.energies.total - scfres_nospin.energies.total);
# Notice that with the small cutoffs we use to generate the online
# documentation the calculation is far from converged.
# With more realistic parameters a larger energy difference of about
# 0.1 Hartree is obtained.
# The spin polarization in the magnetic case is visible if we
# consider the occupation of the spin-up and spin-down Kohn-Sham orbitals.
# Especially for the ``d``-orbitals these differ rather drastically.
# For example for the first ``k``-point:
iup = 1
idown = iup + length(scfres.basis.kpoints) Γ· 2
@show scfres.occupation[iup][1:7]
@show scfres.occupation[idown][1:7];
# Similarly the eigenvalues differ
@show scfres.eigenvalues[iup][1:7]
@show scfres.eigenvalues[idown][1:7];
# !!! note "k-points in collinear calculations"
# For collinear calculations the `kpoints` field of the `PlaneWaveBasis` object contains
# each ``k``-point coordinate twice, once associated with spin-up and once with down-down.
# The list first contains all spin-up ``k``-points and then all spin-down ``k``-points,
# such that `iup` and `idown` index the same ``k``-point, but differing spins.
# We can observe the spin-polarization by looking at the density of states (DOS)
# around the Fermi level, where the spin-up and spin-down DOS differ.
using Plots
plot_dos(scfres)
# Similarly the band structure shows clear differences between both spin components.
using Unitful
using UnitfulAtomic
plot_bandstructure(scfres; kline_density=6)
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] | 3.140663 | 1,628 |
module Bindings
using Pkg
using LinearAlgebra, LowRankApprox, Statistics, StaticArrays
using JuLIP
using ACE, ACEtb
using ACEtb.Bonds: BondCutoff, get_env, get_env_j, get_env_neighs, get_env_neighs_j, get_all_neighs, get_i_neighs, get_i_neighs_j, eval_bond, get_basis
using ACEtb.SlaterKoster
import ACEtb.SlaterKoster.CodeGeneration
using ACEtb.SlaterKoster: sk2cart
using ACEtb.Utils: read_json, write_json, h5read_SK
using ACEtb.Predictions: predict, train_and_predict
using ACEtb.TBhelpers
using ProgressMeter
using Random
using JSON
using HDF5
bohr2ang = 0.188972598857892E+01
hartree2ev = 27.211386024367243
export set_model, model_predict, acetb_greetings
acetb_dct = Dict()
Bondint_table = nothing
cutoff_func = nothing
saveh5_HH = nothing
saveh5_SS = nothing
saveh5_satoms = nothing
model_onsite_vals = nothing
function buildHS_test(H, S, istart, iend, natoms, coords, species, nnei, inei, ipair, norbs,
cutoff, cell, HH, SS, supercell_atoms; MPIproc=1, onsite_HS=nothing)
if(MPIproc == 1)
Nprg = iend-istart+1
if isa(stdout, Base.TTY)
prgres = Progress(Nprg, dt=0.25, desc="[ Info: | Calculating ... ",
barglyphs=BarGlyphs('|','β', ['β' ,'β' ,'β' ,'β
' ,'β', 'β'],' ','|',),
barlen=20)
end
end
invcell = inv(cell)
mesh = [9, 9, 9]
central_atom = prod(mesh) Γ· 2 + 1 # atom 365 for the 9Γ9Γ9=729 atom dataset
rcn = get_tbcells(supercell_atoms, invcell, mesh, central_atom)
if onsite_HS !== nothing
atoms = set_JuLIP_atoms(acetb_dct["elm_names"], natoms, coords, species, cell)
onsite_HS = onsite_HS(atoms)
end
for ia = istart:iend
isp = species[ia]
offset = ia == 1 ? 0 : sum(nnei[1:ia-1])
# Onsite blocks
io = ipair[offset + ia] + 1
nno = norbs[isp] * norbs[isp]
shift = convert(Array{Int64,1}, [0,0,0])
cid = find_row_in_matrix(shift, rcn)
# @show HH[cid,:,:]
# @show SS[cid,:,:]
if onsite_HS !== nothing
os_H, os_S = onsite_HS(ia)
H[io : io + nno - 1] = os_H
S[io : io + nno - 1] = os_S
else
H[io : io + nno - 1] = vcat(HH[cid,:,:]...) / hartree2ev
S[io : io + nno - 1] = vcat(SS[cid,:,:]...) / hartree2ev
end
# Offsite blocks
for nj = 1:nnei[ia]
jn = offset + ia + nj
ja = inei[jn]
jsp = species[ja]
ix = ipair[jn]
iy = ix + norbs[isp] * norbs[jsp]
ix += 1
Rij = transpose(coords[:,ja] - coords[:,ia])
if cutoff < norm(Rij)
continue
end
shift = convert(Array{Int64,1}, round.(invcell * Rij'))
shift = wrap_shift(shift, mesh)
cid = find_row_in_matrix(shift, rcn)
H[ix : iy] = vcat(HH[cid,:,:]...)
S[ix : iy] = vcat(SS[cid,:,:]...)
end
if(MPIproc == 1)
next!(prgres)
end
end
if(MPIproc == 1)
flush(stdout)
end
end
function buildHS(SKH_list, H, S, istart, iend, natoms, coords, cell, species, nnei, inei, ipair, i2a, norbs,
onsite_HS, Bondint_table, cutoff_func, cutoff; MPIproc=1)
WriteAllow = false
if(MPIproc == 1)
if Threads.threadid() == 1
WriteAllow = true
end
Nprg = iend-istart+1
if isa(stdout, Base.TTY)
prgres = Progress(Nprg, dt=0.25, desc="[ Info: | Calculating ... ",
barglyphs=BarGlyphs('|','β', ['β' ,'β' ,'β' ,'β
' ,'β', 'β'],' ','|',),
barlen=20)
end
end
atoms = set_JuLIP_atoms(acetb_dct["elm_names"], natoms, coords, species, cell)
onsite_HS = onsite_HS(atoms)
Threads.@threads for ia = istart:iend
isp = species[ia]
# @show ia, isp
offset = ia == 1 ? 0 : sum(nnei[1:ia-1])
# Onsite blocks
# io = ipair[offset + ia] + 1
# @show size(onsite_terms[isp])
# for ib = 1:norbs[isp]
# H[io] = onsite_terms[isp][ib]
# S[io] = 1.0
# io += norbs[isp] + 1
# end
io = ipair[offset + ia] + 1
nno = norbs[isp] * norbs[isp]
os_H, os_S = onsite_HS(ia)
# @show os_H, os_S
H[io : io + nno - 1] = os_H
S[io : io + nno - 1] = os_S
# Offsite blocks
lnb = length(SKH_list[isp].bonds)
for nj = 1:nnei[ia]
jn = offset + ia + nj
ja = inei[jn]
jsp = species[ja]
ix = ipair[jn]
iy = ix + norbs[isp] * norbs[jsp]
ix += 1
R0 = SVector((coords[:,ja] - coords[:,ia])...)
if cutoff < norm(R0)
continue
end
# Predictions
Renv = get_env(acetb_dct["julip_atoms"], R0, ia, cutoff_func)
VV = Bondint_table(R0,Renv)
# Set H and S
E = sk2cart(SKH_list[isp], R0, VV[1:lnb], WriteAllow=WriteAllow)
H[ix : iy] = vcat(E...) / hartree2ev # FIXME add units to metadata
ES = sk2cart(SKH_list[isp], R0, VV[lnb+1:end], WriteAllow=WriteAllow)
S[ix : iy] = vcat(ES...) / hartree2ev # FIXME add units to metadata
end
if(MPIproc == 1)
if isa(stdout, Base.TTY)
next!(prgres)
end
end
end
if(MPIproc == 1)
flush(stdout)
end
end
function buildHS_dftb_neigh(SKH_list, H, S, istart, iend, norbs, onsite_terms, Bondint_table, cutoff_func, cutoff; MPIproc=1)
tbcells = get_translation_cells(acetb_dct["julip_atoms"].cell, cutoff)
nnei, inei, i2a, icellvec, coords, species = get_neighbours(acetb_dct["julip_atoms"], tbcells, cutoff)
ipair = get_sparse_indexing(acetb_dct["julip_atoms"], nnei, inei, i2a, norbs)
inei = vcat(inei...)
ipair = vcat(ipair...)
coords = transpose(coords)
println("---")
println(size(nnei))
println(size(inei))
println(size(ipair))
println(size(coords))
WriteAllow = false
if(MPIproc == 1)
if Threads.threadid() == 1
WriteAllow = true
end
Nprg = iend-istart+1
if isa(stdout, Base.TTY)
prgres = Progress(Nprg, dt=0.25, desc="[ Info: | Calculating ... ",
barglyphs=BarGlyphs('|','β', ['β' ,'β' ,'β' ,'β
' ,'β', 'β'],' ','|',),
barlen=20)
end
end
Rt = get_i_neighs(1, length(acetb_dct["julip_atoms"]), coords, nnei, inei)
#Rt = get_all_neighs(acetb_dct["natoms"], coords, nnei, inei)
#Rt = get_i_neighs(1, natoms, coords, nnei, inei)
#Rt, jt = get_i_neighs_j(istart, iend, coords, nnei, inei)
Threads.@threads for ia = istart:iend
isp = species[ia]
offset = ia == 1 ? 0 : sum(nnei[1:ia-1])
# Onsite blocks
io = ipair[offset + ia] + 1
for ib = 1:norbs[isp]
H[io] = onsite_terms[isp][ib]
S[io] = 1.0
io += norbs[isp] + 1
end
lnb = length(SKH_list[isp].bonds)
# Offsite blocks
for nj = 1:nnei[ia]
jn = offset + ia + nj
ja = inei[jn]
jsp = species[ja]
ix = ipair[jn]
iy = ix + norbs[isp] * norbs[jsp]
ix += 1
R0 = Rt[ia][nj]
if cutoff < norm(R0)
continue
end
# Predictions
#Renv = get_env_neighs(vcat(Rt[ia],.-Rt[i2a[ja]]), R0, cutoff_func)
#Renv = get_env_neighs(Rt[ia], R0, cutoff_func)
#if(MPIproc == 1)
# Renv2, jl2 = get_env_neighs_j(Rt[ia], R0, cutoff_func)
# jlist2 = [ i2a[jt[ia][jj]] for jj in jl2 ]
#end
#Renv, jlist = get_env_j(acetb_dct["julip_atoms"], R0, ia, cutoff_func)
#if(MPIproc == 1)
# for j1 in jlist
# if j1 β jlist2
# println("ia: ",ia," ja: ",ja," j1: ",j1)
# end
# end
# for j2 in jlist2
# if j2 β jlist
# println("ia: ",ia," ja: ",ja," j2: ",j2)
# end
# end
#end
Renv = get_env(acetb_dct["julip_atoms"], R0, ia, cutoff_func)
VV = Bondint_table(R0,Renv)
# Set H and S
E = sk2cart(SKH_list[isp], R0, VV[1:lnb], WriteAllow=WriteAllow)
H[ix : iy] = vcat(E...)
ES = sk2cart(SKH_list[isp], R0, VV[lnb+1:end], WriteAllow=WriteAllow)
S[ix : iy] = vcat(ES...)
end
if(MPIproc == 1)
if isa(stdout, Base.TTY)
next!(prgres)
end
end
end
if(MPIproc == 1)
flush(stdout)
end
end
function get_list_str(str::Array{UInt8}, ln, stride)
slen = Int64(ln)
snames = []
nstr = String( copy(str[1:slen]) )
if ln == stride - 1
push!(snames,strip(nstr[1:ln-1]))
else
si = 1
sf = 0
for i = 1:ln
sf += 1
if sf == stride
elm = nstr[si:i-1]
elm = strip(elm)
push!(snames,elm)
si = i + 1
sf = 0
end
end
end
return snames
end
function get_specie_name(str::Array{UInt8}, ln, n)
slen = Int64(ln / n) - 1
snames = []
for i = 1:n
si = (i - 1) * slen + (i - 1) + i
sf = i * slen - 1 + (i - 1)
elm = String( copy(str[si:sf]) )
elm = strip(elm)
push!(snames,elm)
end
return snames
end
function set_JuLIP_atoms(elm_names, natoms, pos, species, cell)
atnums = []
#atmass = []
for i=1:natoms
sym = Symbol(elm_names[species[i]])
#am = atomic_mass(sym)
az = atomic_number(sym)
push!(atnums,az)
#push!(atmass,am)
end
return Atoms(; X = pos[:,1:natoms], Z = atnums, cell = cell,
pbc = [true, true, true])
end
function set_model(natoms, nspecies,
coords::Array{Float64},
latvecs::Array{Float64},
tcellv::Array{Float64},
rcellv::Array{Float64},
origin::Array{Float64},
species::Array{Int32},
nshells::Array{Int32},
norbe::Array{Int32},
norba::Array{Int32},
maxns, maxno, maxs,
maxo, toto,
angshell::Array{Int32},
ishell::Array{Int32},
posshell::Array{Int32},
lstr, specienames::Array{UInt8},
cutoff,
lfn, fnames::Array{UInt8},
MPIproc,
stat_jl::Array{Int32})
if(MPIproc == 1)
@info "βββ ACEtb : Julia set_model function."
end
try
global acetb_dct["natoms"] = natoms
global acetb_dct["ntypes"] = nspecies
pos = reshape(coords,3,:) ./ bohr2ang
cell = reshape(latvecs,3,:) ./ bohr2ang
tcells = reshape(tcellv,3,:) ./ bohr2ang
rcells = reshape(rcellv,3,:) ./ bohr2ang
global acetb_dct["latvecs"] = cell[:,:]
global acetb_dct["tcellv"] = tcells[:,:]
global acetb_dct["rcellv"] = rcells[:,:]
global acetb_dct["origin"] = origin[:]
global acetb_dct["norbe"] = norbe
elm_names = get_specie_name(specienames, lstr, nspecies)
global acetb_dct["elm_names"] = elm_names[:]
at = set_JuLIP_atoms(elm_names, natoms, pos, species, cell)
global acetb_dct["julip_atoms"] = at
SKH_list = set_SK_orbitals(nspecies,nshells,angshell)
global acetb_dct["SKH_list"] = SKH_list
model_files = get_list_str(fnames, lfn, 200)
global acetb_dct["model_files"] = model_files
if(MPIproc == 1)
@info "β Reading acetb.json file..."
end
filedata = read_json(model_files[1])
if(MPIproc == 1)
@info "β Reading is done."
end
global acetb_dct["file_data"] = filedata
cutoff_params = filedata["model"]["cutoff_params"]
fit_params = filedata["model"]["fit_params"]
Bpredict = true
Bfit = false
if haskey(filedata["model"], "predict")
predict_setting = filedata["model"]["predict"]
if predict_setting == 0
Bpredict = false
else
Bpredict = true
end
if haskey(filedata, "training_datasets")
trdata = filedata["training_datasets"]
end
end
if haskey(filedata["model"], "fit")
fit_setting = filedata["model"]["fit"]
if fit_setting == 0
Bfit = false
else
Bfit = true
end
end
if Bpredict
if Bfit
if(MPIproc == 1)
@info "β Fitting..."
end
Bint_table, cutf_func, train_dict = train_and_predict(trdata, elm_names, cutoff_params, fit_params; MPIproc=MPIproc)
if(MPIproc == 1)
@info "β Saving potential..."
write_json(model_files[1], train_dict)
end
global Bondint_table = Bint_table
global cutoff_func = cutf_func
if(MPIproc == 1)
@info "β Fitting is done."
end
else
if(MPIproc == 1)
@info "β Loading model..."
end
Bint_table, cutf_func = predict(filedata, cutoff_params, fit_params; MPIproc=MPIproc)
global Bondint_table = Bint_table
global cutoff_func = cutf_func
if(MPIproc == 1)
@info "β Model is loaded."
end
end
else
if(MPIproc == 1)
@info "β This mode is test only. It will not use model predictions."
@info "β Setting bond integral table..."
end
HSfile = filedata["HS_datasets"][1]
if(MPIproc == 1)
@info "β Reading saved H,S file:" HSfile
end
HSdata = h5read_SK(HSfile; get_HS=true, get_atoms=true, get_metadata=true, get_energies=true)
global saveh5_HH = permutedims(HSdata[2][1], [3, 2, 1])
global saveh5_SS = permutedims(HSdata[2][2], [3, 2, 1])
global saveh5_satoms = HSdata[6]
write_extxyz("saveh5_satoms.xyz", saveh5_satoms)
if(MPIproc == 1)
@info "β Setting H,S is done."
end
end
global onsite_vals = filedata["onsite-terms"]
stat_jl[1] = 0
catch
stat_jl[1] = 1
for (exc, bt) in Base.catch_stack()
if(MPIproc == 1)
showerror(stdout, exc, bt)
println()
end
end
end
if(MPIproc == 1)
@info "βββ ACEtb : Done in Julia module."
end
flush(stdout)
end
function ACE2tb_wrapper_predict_HS(model_ID, atoms; Ξ»=1e-3)
@show model_ID
dict_atoms = JuLIP.write_dict(atoms)
open("structure.json", "w") do f
write(f, JSON.json(dict_atoms))
end
cmd = setenv(`$(joinpath(Sys.BINDIR, Base.julia_exename())) --project=$(homedir())/.julia/dev/ACE2tb compute.jl $(model_ID) $(pwd())/structure.json $(pwd())/onsite_HS.h5`,
dir="$(homedir())/.julia/dev/ACE2tb/test")
@show cmd
run(cmd)
H, S = h5open("onsite_HS.h5") do f
read(f, "H"), read(f, "S")
end
println("Regularisation Ξ» = $Ξ»")
return ia -> (reshape(H[:, :, ia], :),
reshape(S[:, :, ia] + Ξ» * I, :))
end
function model_predict(iatf, iatl, natoms,
coords::Array{Float64},
latvecs::Array{Float64},
nspecies, nspecarr, species::Array{Int32},
nH, H::Array{Float64},
nS, S::Array{Float64},
nneigh::Array{Int32},
ineigh::Array{Int32},
ipair::Array{Int32},
i2a::Array{Int32}, cutoff,
MPIproc,
stat_jl::Array{Int32})
if(MPIproc == 1)
@info "βββ ACEtb : Julia model_predict function."
end
try
pos = reshape(coords,3,:) ./ bohr2ang
cell = reshape(latvecs,3,:) ./ bohr2ang
cutoff /= bohr2ang
elm_names = acetb_dct["elm_names"]
#at = set_JuLIP_atoms(elm_names, natoms, pos, species, cell)
SKH_list = acetb_dct["SKH_list"]
model_files = acetb_dct["model_files"]
filedata = acetb_dct["file_data"]
predict_params = filedata["model"]["predict"]
onsite_vals = filedata["onsite-terms"]
norbe = acetb_dct["norbe"]
if onsite_vals isa AbstractDict
for isp = 1:nspecies
sp = elm_names[species[isp]]
if onsite_vals[sp] isa AbstractVector
MPIproc == 1 && @info "β Using $(norbe[isp]) element diagonal onsite approximation for species $sp"
@assert length(onsite_vals[sp]) == norbe[isp]
onsite_HS = coords -> (ia -> (reshape(diagm(Float64.(onsite_vals[sp])), :), reshape(1.0*I(norbe[isp]) |> Matrix, :)))
elseif onsite_vals[sp] isa AbstractDict
MPIproc == 1 && @info "β Using $(norbe[isp]) Γ $(norbe[isp]) onsite block for species $sp"
onsite_HS = coords -> (ia -> (vcat(onsite_vals[sp]["H"]...), vcat(onsite_vals[sp]["S"]...)))
else
error("Invalid onsite specification for species $sp: $(onsite_vals[sp])")
end
end
elseif onsite_vals isa AbstractString
onsite_HS = coords -> ACE2tb_wrapper_predict_HS(onsite_vals, coords)
else
error("Invalid onsite specification $(onsite_vals)")
end
MPIproc == 1 && @info "β Calculating bond integrals..."
if predict_params == 0
buildHS_test(H, S, iatf, iatl, natoms, pos, species, nneigh, ineigh, ipair, norbe,
cutoff, cell, saveh5_HH, saveh5_SS, saveh5_satoms; MPIproc=MPIproc, onsite_HS=onsite_HS)
elseif predict_params == -1
buildHS_dftb_neigh(SKH_list, H, S, iatf, iatl, norbe, onsite_terms, Bondint_table, cutoff_func, cutoff; MPIproc=1)
else
buildHS(SKH_list, H, S, iatf, iatl, natoms, pos, cell, species, nneigh, ineigh, ipair, i2a, norbe, onsite_HS,
Bondint_table, cutoff_func, cutoff; MPIproc=MPIproc)
end
stat_jl[1] = 0
catch
stat_jl[1] = 1
for (exc, bt) in Base.catch_stack()
if(MPIproc == 1)
showerror(stdout, exc, bt)
println()
end
end
end
if(MPIproc == 1)
@info "βββ ACEtb : Done in Julia module."
end
flush(stdout)
end
function acetb_greetings()
ctx = Pkg.Operations.Context()
acetb_version = string(ctx.env.manifest[ctx.env.project.deps["ACEtb"]].version)
acetb_hash = split(string(ctx.env.project.deps["ACEtb"]),"-")[1]
if isa(stdout, Base.TTY)
println(" \033[31m\033[1m ___\033[38;5;208m ___\033[32m ___\033[34m _____\033[38;5;54m\033[1m _ \033[0m \n"*
" \033[31m\033[1m |_ \033[38;5;208m| _\033[32m| __\033[34m|_ _\033[38;5;54m\033[1m| |_ \033[0m\033[38;5;141m\033[1mVersion\033[0m \n"*
" \033[31m\033[1m | . \033[38;5;208m| |_\033[32m| __|\033[34m | | \033[38;5;54m\033[1m| . | \033[0m\033[38;5;200m\033[1mv",acetb_version,"\033[0m \n"*
" \033[31m\033[1m |___\033[38;5;208m|___\033[32m|___|\033[34m |__|\033[38;5;54m\033[1m|___| \033[0m\033[1m[",acetb_hash,"]\033[0m\n")
else
println(" ___ ___ ___ _____ _ \n"*
" |_ | _| __|_ _| |_ Version\n"*
" | . | |_| __| | | | . | v",acetb_version," \n"*
" |___|___|___| |__||___| [",acetb_hash,"]\n")
end
println(" βββββββββββββββββββββββββββββ\n"*
" β Developers: β\n"*
" βββββββββββββββββββββββββββββ€\n"*
" β GeneviΓ¨ve Dusson β\n"*
" β Berk Onat β\n"*
" β Reinhard Maurer β\n"*
" β Christoph Ortner β\n"*
" β James R. Kermode β\n"*
" βββββββββββββββββββββββββββββ\n")
println("Julia threads: ",Threads.nthreads())
flush(stdout)
end
end # End of module
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448,
8,
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437,
198,
198,
437,
1303,
5268,
286,
8265,
198
] | 1.769408 | 11,696 |
function isvalid(subject::Dict{String, String}, display = false)
if display
println("isvalid: $subject")
end
typeof(iban(subject["value"])) == Dict{String,String}
end
@testset "iban_random [one]" begin
@test iban_random(
CountryCode="BR"
)["CountryCode"] == "BR"
@test iban_random(
CountryCode="BR",
BankCode="18731592"
)["BankCode"] == "18731592"
@test iban_random(
CountryCode="BR",
BankCode="18731592",
BranchCode = "89800"
)["BranchCode"] == "89800"
@test iban_random(
CountryCode="BR",
BankCode="18731592",
BranchCode = "89800",
AccountNumber = "6427460610"
)["AccountNumber"] == "6427460610"
@test iban_random(
CountryCode="BR",
BankCode="18731592",
BranchCode = "89800",
AccountNumber = "6427460610",
AccountType = "X"
)["AccountType"] == "X"
@test iban_random(
CountryCode="BR",
BankCode="18731592",
BranchCode = "89800",
AccountNumber = "6427460610",
AccountType = "X",
OwnerAccountType = "m",
)["OwnerAccountType"] == "m"
end
@testset "iban_random [all]" begin
@test isvalid(iban_random()) == true
@test isvalid(iban_random(CountryCode="BR", BankCode="18731592")) == true
# all supported country codes
map(cc ->
(@test isvalid(iban_random(CountryCode=cc)) == true),
supported_countries()
)
end
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62,
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1678,
3419,
198,
220,
220,
220,
220,
220,
1267,
198,
437,
198
] | 2.159544 | 702 |
## cauchy
function cauchycircleS(cfs::AbstractVector,z::Number,s::Bool)
ret=zero(Complex{Float64})
if s
zm = one(Complex{Float64})
#odd coefficients are pos
@simd for k=1:2:length(cfs)
@inbounds ret += cfs[k]*zm
zm *= z
end
else
z=1./z
zm = z
#even coefficients are neg
@simd for k=2:2:length(cfs)
@inbounds ret -= cfs[k]*zm
zm *= z
end
end
ret
end
function stieltjes{DD<:Circle}(sp::Laurent{DD},f::AbstractVector,z,s::Bool)
d=domain(sp)
if !d.orientation
return -stieltjes(reverseorientation(Fun(f,sp)),z,!s)
end
@assert in(z,d)
-2Ο*im*cauchycircleS(f,mappoint(d,Circle(),z),s)
end
function stieltjes{DD<:Circle}(sp::Laurent{DD},f::AbstractVector,z::Number)
d=domain(sp)
if !d.orientation
return -stieltjes(reverseorientation(Fun(f,sp)),z)
end
z=mappoint(d,Circle(),z)
-2Ο*im*cauchycircleS(f,z,abs(z) < 1)
end
stieltjes{DD<:Circle}(sp::Laurent{DD},f,z::Vector)=[stieltjes(sp,f,zk) for zk in z]
stieltjes{DD<:Circle}(sp::Laurent{DD},f,z::Matrix)=reshape(stieltjes(sp,f,vec(z)),size(z,1),size(z,2))
stieltjes{DD<:Circle}(sp::Fourier{DD},f,z,s...)=stieltjes(Laurent(domain(sp)),coefficients(f,sp,Laurent(domain(sp))),z,s...)
# we implement cauchy Β±1 as canonical
hilbert{DD<:Circle}(sp::Laurent{DD},f,z)=(stieltjes(sp,f,z,true)+stieltjes(sp,f,z,false))/(-2Ο)
## stieltjesintegral and logkernel
function stieltjesintegral{DD<:Circle}(sp::Laurent{DD},f,z::Number,s...)
d=domain(sp)
@assert d==Circle() #TODO: radius
ΞΆ=Fun(d)
r=stieltjes(integrate(f-f[2]/ΞΆ),z,s...)
abs(z)<1?r:r+2Ο*im*f[2]*log(z)
end
stieltjesintegral{DD<:Circle}(sp::Fourier{DD},f,z::Number,s...)=stieltjesintegral(Fun(Fun(f,sp),Laurent),z,s...)
function logkernel{DD<:Circle}(sp::Fourier{DD},g,z::Number)
d=domain(sp)
c,r=d.center,d.radius
z=z-c
if abs(z) β€r
ret=2r*log(r)*g[1]
for j=2:2:length(g)
k=div(j,2)
ret+=-g[j]*sin(k*angle(z))*abs(z)^k/(k*r^(k-1))
end
for j=3:2:length(g)
k=div(j,2)
ret+=-g[j]*cos(k*angle(z))*abs(z)^k/(k*r^(k-1))
end
ret
else
ret=2r*logabs(z)*g[1]
for j=2:2:length(g)
k=div(j,2)
ret+=-g[j]*sin(k*angle(z))*r^(k+1)/(k*abs(z)^k)
end
for j=3:2:length(g)
k=div(j,2)
ret+=-g[j]*cos(k*angle(z))*r^(k+1)/(k*abs(z)^k)
end
ret
end
end
logkernel{DD<:Circle}(sp::Fourier{DD},g,z::Vector) = promote_type(eltype(g),eltype(z))[logkernel(sp,g,zk) for zk in z]
logkernel{DD<:Circle}(sp::Fourier{DD},g,z::Matrix) = reshape(promote_type(eltype(g),eltype(z))[logkernel(sp,g,zk) for zk in z],size(z))
logkernel{DD<:Circle}(sp::Laurent{DD},g,z)=logkernel(Fun(Fun(g,sp),Fourier),z)
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] | 1.729377 | 1,685 |
using VegaStreams
using Test
@testset "VegaStreams.jl" begin
vls = vegastream()
for row in enumerate(randn(100))
sleep(0.01)
push!(vls, row)
end
vls = vegastream(:point)
for (x, y) in enumerate(randn(100))
sleep(0.01)
push!(vls, (x=x, y=y))
end
end
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] | 1.895062 | 162 |
function read_gms_file(filename::AbstractString)
println("Reading $(filename) ...")
filepath = joinpath(Pkg.dir("toJuMP"),".gms","")
filepath = string(filepath,filename,".gms")
if isfile(filepath)
f = open(filepath, "r")
elseif isfile(filename)
f = open(filename, "r")
else
error("No gms file detected.")
end
#=
----------------------- Line Header Space ------------------
Example :: 4stufen.gms
Unkown ::
$offlisting
$if set nostart $goto modeldef
$label modeldef
$if not set MINLP $set MINLP MINLP Model Type
* -> comment
Null -> emptyline
Variables -> All Variable List, separated by ","
Positive Variable -> Non-negative variables list, separated by "," (assert)
Binary Variable -> Binary variables list, separated by "," (assert)
Equations -> Equations symbols, separated by ","
EquSymbols.. -> Equation content
VarSymbols.lo -> Lower Bound
VarSymbols.l -> Upper Bound ??
Model m / all / -> ??
m.limrow=0
m.
------------------------------------------------------------
=#
# Null Trackers
skip = 0
comment = 0
# Main data structure initialization
gms = oneProblem()
# -------------------------- Start Reading -------------------------- #
for l in eachline(f)
sl = lstrip(l, ' ')
if isempty(sl) || length(sl) == 1 # Skip empty line
skip += 1
elseif sl[1] == "*"
comment += 1
else
slt = split(sl, r" |,|\.")
slt = [slt[i] for i in 1:length(slt) if length(slt[i]) > 0]
if slt[1] in GMS_BLOCK_HEADER
read_block(f, gms, l) # Reading a general block
elseif slt[1] in gms.rows #ismatch(r"e\d", sl[1])
read_equation(f, gms, l) # Reading an equation block
elseif slt[1] in gms.cols #ismatch(r"\w\.\w", sl[1])
read_bounds(f, gms, l) # Reading a bound block
elseif slt[1] == "\$"
read_command(f, gms, l) # Reading a logical command line
elseif slt[1] == "Model"
read_model(f, gms, sl) # Reading a model command
elseif slt[1] == "Solve"
read_solve(f, gms, sl) # Reading a solve command line
else
# println("[Unkown]DUMPING >> ", l)
end
end
end
close(f)
return gms
end
function read_block(file::IOStream, gms::oneProblem, lInit::AbstractString; kwargs...)
sl = split(get_one_line(file, one_line=lInit), r" |,")
sl = [sl[i] for i in 1:length(sl) if !isempty(sl[i])] # Eliminate empty entries
blockIdentifier = sl[1] # This should always be true
for i in 1:length(sl)
if blockIdentifier == "Variables" && (i > 1)
push!(gms.cols, sl[i])
elseif blockIdentifier == "Positive" && (i > 2)
gms.colsType[sl[i]] = "Positive"
elseif blockIdentifier == "Negative" && (i > 2)
gms.colsType[sl[i]] = "Negative"
elseif blockIdentifier == "Binary" && (i > 2)
gms.colsType[sl[i]] = "Binary"
elseif blockIdentifier == "Semicont"
error("Currently don't support semi continous variables parsing.")
elseif blockIdentifier == "Integer" && (i > 2)
gms.colsType[sl[i]] = "Integer"
elseif blockIdentifier == "Equations" && (i > 1)
push!(gms.rows, sl[i])
end
end
return
end
function read_equation(file::IOStream, gms::oneProblem, lInit::AbstractString; kwargs...)
#=
Example Equation
#Single Line
e1.. objvar - x145 - x146 - x147 - x148 - x149 - x150 =E= 3271.22725820856;
# Multi Line
e38.. 109.0495*x1 + 100.462*x2 + 115.2937*x3 + 2.860271*x4 + 15.1404*x5
+ x44 - x556 =E= 101.1304;
=#
lhs = ""
rhs = ""
sense = ""
eN = string(strip(split(lInit, r" |,")[1], ['.', '\r', '\n']))
eS = split(eN, '.')
if eN in gms.rows
one_l = get_one_line(file, one_line=lInit)
one_l = replace(one_l, "$(eN)..", "")
if contains(one_l, "=E=")
gms.rowsSense[eN] = "E"
elseif contains(one_l, "=L=")
gms.rowsSense[eN] = "L"
elseif contains(one_l, "=G=")
gms.rowsSense[eN] = "G"
elseif contains(one_l, "=N=")
gms.rowsSense[eN] = "N"
else
error("NO sense detected in equation. $(one_l)")
end
sl = split(one_l, "=$(gms.rowsSense[eN])=", keep=false)
gms.rowsLHS[eN] = replace(sl[1], " ", "")
gms.rowsRHS[eN] = Float64(parse(sl[2]))
elseif length(eS) == 2 && eS[2] == "m"
@assert eS[1] in gms.rows
one_l = get_one_line(file, one_line=lInit)
mVal = parse(split(one_l, "=", keep=false)[2])
gms.m[eS[1]] = mVal
else
error("Non-indexed constraint detected. \n$(lInit)")
end
return
end
function read_bounds(file::IOStream, gms::oneProblem, lInit::AbstractString; kwargs...)
#=
.lo lower bound
.l level or primal value
.up upper bound
.m marginal or dual value
=#
@assert rstrip(strip(lInit, ['\n','\r']), ' ')[end] == ';' # Assumption :: alwasy one line
all_segs = split(lInit, ';', keep=false)
all_segs = [i for i in all_segs if !isempty(strip(i,' '))]
for lseg in all_segs
lseg = strip(lseg, [';', '\n', ' ', '\r'])
sl = split(lseg, "=")
sl = [sl[i] for i in 1:length(sl) if !isempty(sl[i])]
# There shouldn't be any space to begin with
sl[1] = strip(sl[1], [';', '\n', ' ', '\r'])
sl[2] = strip(sl[2], [';', '\n', ' ', '\r'])
@assert ismatch(r"\w.\w", sl[1])
boundVal = Float64(parse(sl[end]))
var = split(sl[1],".")[1]
attr = split(sl[1],".")[2]
if attr == "lo"
gms.lb[var] = boundVal
elseif attr == "l"
gms.l[var] = boundVal
elseif attr == "fx"
gms.fx[var] = boundVal
elseif attr == "up"
gms.ub[var] = boundVal
elseif attr == "m"
gms.m[var] = boundVal
elseif attr == "scale"
gms.scale[var] = boundVal
elseif attr == "prior"
gms.prior[var] = boundVal
elseif attr == "stage"
gms.prior[var] = boundVal
else
error("Unknown variable attribute. Consult gams hand-book for definition and report this issue.")
end
end
return
end
function read_model(file::IOStream, gms::oneProblem, lInit::AbstractString; kwargs...)
# Assume this will always be one line
@assert strip(lInit, ['\n','\r'])[end] == ';'
sl = split(strip(lInit,[';','\n','\r']), r" |,")
@assert sl[1] == "Model" # Already did outside
# Not sure if the printlnrmation stored here is useful or not.
for i in 1:length(sl)
if sl[i] == "Model"
gms.modelSymbol = sl[i+1]
end
end
return
end
function read_solve(file::IOStream, gms::oneProblem, lInit::AbstractString; kwargs...)
@assert strip(lInit, ['\n','\r'])[end] == ';'
sl = split(strip(lInit, [';','\n','\r']), r" |,")
@assert sl[1] == "Solve"
for i in 1:length(sl)
if sl[i] in PROBTYPE
gms.modelType = sl[i]
elseif sl[i] == "maximizing"
gms.objSense = sl[i]
elseif sl[i] == "minimizing"
gms.objSense = sl[i]
elseif sl[i] in gms.cols
gms.objective = sl[i]
end
end
end
function gms2jump(gmsPath::AbstractString;
mode::AbstractString="index",
ending="m=m",
quadNL::Bool=false,
loopifpossible::Bool=true,
outdir::AbstractString="")
probName = replace(splitdir(gmsPath)[end],".gms", "")
gms = read_gms_file(gmsPath)
write_julia_script(probName, gms, mode, loopifpossible=loopifpossible, quadNL=quadNL, outdir=outdir)
return
end
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function place_first_grid_order!(current, ticker, config, open_orders, bot_path, client, usr)
@info("NEW order found!")
id_str = open_orders["NEW"]["trigger"] * "--" * string(now())
quant = ceil(config["order_volume"] / current, digits=config["precision"])
if config["precision"] < 1
quant = string(Int(quant))
end
order = try client.order_limit_buy(
symbol=config["symbol"],
quantity=quant,
price=ticker);
catch
@info("order_limit_buy failed")
nothing
end
if order !== nothing
open_orders["BUY"]["EXTRA-0_" * id_str] = order
open_orders["NEW"] = Dict()
open(joinpath(bot_path, "open_orders.json"), "w") do f
JSON.print(f, open_orders, 4)
end
p = Dict()
p["ticker"] = ticker
create_notification("new cycle", config, usr, params = p)
end
return open_orders
end
export place_first_grid_order!
function place_buy_order_command(k, v, id_str, config, open_orders, closed_orders, bot_path, client, usr, fees)
quant = round(v["origQty"], digits=config["precision"])
new_id = k
if v["price"] != "market"
new_price = Pinguu.price2string(parse(Float64, v["price"]), config["sell_precision"])
order = try client.order_limit_buy(
symbol= config["symbol"],
quantity=quant,
price=new_price);
catch
nothing
end
else
order = try client.order_market_buy(
symbol= config["symbol"],
quantity=quant);
catch
nothing
end
end
if order !== nothing
order["time"] = string(now())
open_orders["BUY"][new_id] = order
else
open_orders["BUY"][new_id] = Dict()
open_orders["BUY"][new_id]["origQty"] = quant
open_orders["BUY"][new_id]["price"] = new_price
open_orders["BUY"][new_id]["status"] = "place order"
open_orders["BUY"][new_id]["time"] = string(now())
end
open(joinpath(bot_path, "open_orders.json"), "w") do f
JSON.print(f, open_orders, 4)
end
open(joinpath(bot_path, "trigger.json"), "w") do f
JSON.print(f, Dict(), 4)
end
end
export place_buy_order_command
function buy_executed(tmp, k, v, id_str, config, open_orders, closed_orders, bot_path, client, usr, fees)
#
## Cancel order, if not done yet
if tmp["status"] != client.ORDER_STATUS_FILLED && tmp["status"] != client.ORDER_STATUS_CANCELED
result = try client.cancel_order(
symbol=config["symbol"],
orderId=tmp["orderId"])
catch
nothing
end
if result == nothing
return nothing
end
end
extra_iteration = parse(Int64, split(split(k, "_")[1], "-")[2])
#
## Move to closed_orders
!(id_str in keys(closed_orders)) ? closed_orders[id_str] = Dict() : ""
tmp["time"] = string(now())
closed_orders[id_str][split(k, "_")[1]] = deepcopy(tmp);
open(joinpath(bot_path, "closed_orders.json"), "w") do f
JSON.print(f, closed_orders, 4)
end
#
## Remove from open_orders
delete!(open_orders["BUY"], k)
open(joinpath(bot_path, "open_orders.json"), "w") do f
JSON.print(f, open_orders, 4)
end
#
## Place EXTRA
new_id = "EXTRA-" * string(extra_iteration + 1) * "_" * id_str
last_price = parse(Float64, tmp["price"]) == 0.0 ? v["price"] : tmp["price"]
new_price = Pinguu.price2string(parse(Float64, last_price) * (1 - config["extra_order_step"]), config["sell_precision"], round_up=true)
quant = floor(config["order_volume"] * (config["extra_order_martingale"])^(extra_iteration + 1) / parse(Float64, new_price), digits=config["precision"])
if extra_iteration < parse(Float64, config["extra_order_count"])
open_orders["BUY"][new_id] = Dict()
open_orders["BUY"][new_id]["origQty"] = quant
open_orders["BUY"][new_id]["price"] = new_price
open_orders["BUY"][new_id]["status"] = "place order"
open_orders["BUY"][new_id]["time"] = string(now())
open(joinpath(bot_path, "open_orders.json"), "w") do f
JSON.print(f, open_orders, 4)
end
open(joinpath(bot_path, "trigger.json"), "w") do f
JSON.print(f, Dict(), 4)
end
end
#
## Cancel current take profit orders
if length(open_orders["SELL"]) > 0
for (k2, v2) in open_orders["SELL"]
if split(k2, "_")[end] == id_str
tmp_sell = try client.get_order(
symbol=config["symbol"],
orderId=v2["orderId"])
catch
try sleep(0.5)
client.get_order(
symbol=config["symbol"],
orderId=v2["orderId"])
catch nothing
end
end
if !(tmp_sell["status"] in [client.ORDER_STATUS_CANCELED, client.ORDER_STATUS_FILLED]) || tmp_sell == nothing
sleep(0.5)
tmp_sell = try client.cancel_order(
symbol=config["symbol"],
orderId=v2["orderId"])
catch
nothing
end
end
if tmp_sell !== nothing
if parse(Float64, v2["executedQty"]) > 0
random_id_str = "SELL-" * lpad(rand(1:1:10000), 5, "0")
while haskey(closed_orders[id_str], random_id_str)
random_id_str = "SELL-" * lpad(rand(1:1:10000), 5, "0")
end
closed_orders[id_str][random_id_str] = result
open(joinpath(bot_path, "closed_orders.json"), "w") do f
JSON.print(f, closed_orders, 4)
end
end
delete!(open_orders["SELL"], k2)
else
#
## Cancel failed
delete!(open_orders["SELL"], k2)
v2["status"] = "cancel order"
#
## Give different ID to be able to create a new "main" order later
random_id_str = "SELL-" * lpad(rand(1:1:10000), 5, "0")
open_orders[random_id_str] = v2
end
end
end
end
#
## Place command for take profit order
open_orders["SELL"]["SELL_" * id_str] = Dict()
open_orders["SELL"]["SELL_" * id_str]["status"] = "place order"
open_orders["SELL"]["SELL_" * id_str]["id_str"] = id_str
open(joinpath(bot_path, "open_orders.json"), "w") do f
JSON.print(f, open_orders, 4)
end
open(joinpath(bot_path, "trigger.json"), "w") do f
JSON.print(f, Dict(), 4)
end
#
## Create notification
p = Dict()
p["extra_iteration"] = extra_iteration
p["executedQty"] = tmp["executedQty"]
create_notification("buy exec", config, usr, params = p)
if extra_iteration >= parse(Float64, config["extra_order_count"])
create_notification("no volume", config, usr)
end
end
export buy_executed
function cancel_buy(k, v, id_str, config, open_orders, closed_orders, bot_path, client)
@info("Cancel Order")
if haskey(v, "orderId")
result = try client.cancel_order(
symbol=config["symbol"],
orderId=v["orderId"])
catch
nothing
end
if result !== nothing
delete!(open_orders["BUY"], k)
open(joinpath(bot_path, "open_orders.json"), "w") do f
JSON.print(f, open_orders, 4)
end
if parse(Float64, result["executedQty"]) > 0
random_id_str = "EXTRA-" * lpad(rand(100:1:10000), 5, "0")
while haskey(closed_orders[id_str], random_id_str)
random_id_str = "EXTRA-" * lpad(rand(100:1:10000), 5, "0")
end
closed_orders[id_str][random_id_str] = result
open(joinpath(bot_path, "closed_orders.json"), "w") do f
JSON.print(f, closed_orders, 4)
end
end
end
else
@info("Order has not been placed")
#
## Order not placed, yet
delete!(open_orders["BUY"], k)
open(joinpath(bot_path, "open_orders.json"), "w") do f
JSON.print(f, open_orders, 4)
end
end
open(joinpath(bot_path, "trigger.json"), "w") do f
JSON.print(f, Dict(), 4)
end
end
export cancel_buy
function buy_now(tmp, k, v, id_str, config, open_orders, closed_orders, bot_path, client, usr, fees)
extra_iteration = parse(Int64, split(split(k, "_")[1], "-")[2])
if extra_iteration < parse(Float64, config["extra_order_count"])
#
# Cancel current BUY order
if tmp["status"] != client.ORDER_STATUS_CANCELED
result = try client.cancel_order(
symbol=config["symbol"],
orderId=v["orderId"])
catch
nothing
end
if result == nothing
return nothing
end
else
result = tmp
end
#
# Check if that order was partially filled -> if so: move cancelled order to closed_orders
if parse(Float64, result["executedQty"]) > 0
result["time"] = string(now())
!haskey(closed_orders, id_str) ? closed_orders[id_str] = Dict() : ""
closed_orders[id_str][split(k, "_")[1]] = result
extra_iteration += 1
open(joinpath(bot_path, "closed_orders.json"), "w") do f
JSON.print(f, closed_orders, 4)
end
end
#
# Remove from open_orders
delete!(open_orders["BUY"], k)
#
# Get new order details
ticker = client.get_ticker(symbol=config["symbol"])["lastPrice"]
quant = floor(parse(Float64, v["origQty"]), digits=config["precision"])
new_id = "EXTRA-" * string(extra_iteration) * "_" * id_str
#
# Place new order
order = try client.order_limit_buy(
symbol=config["symbol"],
quantity=quant,
price=ticker)
catch
nothing
end
if order !== nothing
order["time"] = string(now())
# if parse(Float64, order["price"]) == 0
# order["price"] = Pinguu.price2string(parse(Float64, order["cummulativeQuoteQty"]) / parse(Float64, order["origQty"]), config["sell_precision"])
# end
open_orders["BUY"][new_id] = order
else
open_orders["BUY"][new_id] = Dict()
open_orders["BUY"][new_id]["origQty"] = quant
open_orders["BUY"][new_id]["price"] = ticker
open_orders["BUY"][new_id]["status"] = "place order"
open_orders["BUY"][new_id]["time"] = string(now())
end
open(joinpath(bot_path, "open_orders.json"), "w") do f
JSON.print(f, open_orders, 4)
end
end
open(joinpath(bot_path, "trigger.json"), "w") do f
JSON.print(f, Dict(), 4)
end
end
export buy_now
function partially_order(tmp, k, v, id_str, config, open_orders, closed_orders, bot_path, client, usr, fees, minutes)
if v["partial_time"] < string(now() - Minute(minutes))
result = try client.cancel_order(
symbol=config["symbol"],
orderId=tmp["orderId"])
catch
nothing
end
if result !== nothing
tmp["partial_time"] = v["partial_time"]
v = deepcopy(tmp)
v["status"] = "continue"
open_orders["BUY"][k] = v
open(joinpath(bot_path, "open_orders.json"), "w") do f
JSON.print(f, open_orders, 4)
end
p = Dict()
p["extra_iteration"] = extra_iteration
p["executedQty"] = tmp["executedQty"]
create_notification("partial buy exec", config, usr, params = p)
end
if tmp["status"] == client.ORDER_STATUS_CANCELED
v["status"] = "continue"
open_orders["BUY"][k] = v
open(joinpath(bot_path, "open_orders.json"), "w") do f
JSON.print(f, open_orders, 4)
end
end
end
end
export partially_order
function first_order_takes_too_long(tmp, k, v, id_str, config, open_orders, closed_orders, bot_path, client, usr, fees, minutes)
t = DateTime(split(id_str, "--")[end])
if now() - t > Minute(minutes)
result = try client.cancel_order(
symbol=config["symbol"],
orderId=v["orderId"])
catch
nothing
end
if result !== nothing
delete!(open_orders["BUY"], k)
open(joinpath(bot_path, "open_orders.json"), "w") do f
JSON.print(f, open_orders, 4)
end
Pinguu.create_notification("cycle closed", config, usr)
end
end
end
export first_order_takes_too_long
function add_fees_to_closed_order(order, config, bot_path, client)
for t in order["trades"]
sleep(0.5)
commission = parse(Float64, t["commission"])
commission_coin = parse(Float64, t["commission"])
if commission != 0
from = split(string(unix2datetime(t["time"]/1000)), ".")[1]
to = split(string(unix2datetime(t["time"]/1000) + Minute(1)), ".")[1]
#
## for commission
if t["commissionAsset"] == config["farm_coin"]
commission *= parse(Float64, t["price"])
elseif t["commissionAsset"] != config["base_coin"]
translator = string(t["commissionAsset"], config["base_coin"])
klines = client.get_historical_klines(translator, client.KLINE_INTERVAL_1MINUTE, from, to)
tt = Pinguu.klines2table(klines, time_warp=0)
commission *= tt[1].close
tmp_price_for_conversion = tt[1].close
end
#
## for commission_coin
if t["commissionAsset"] != config["farm_coin"]
translator = string(t["commissionAsset"], config["farm_coin"])
# Translator exists
klines = try client.get_historical_klines(translator, client.KLINE_INTERVAL_1MINUTE, from, to)
catch
false
end
if klines == false
# Translator exists in other order
translator = string(config["farm_coin"], t["commissionAsset"])
klines = try client.get_historical_klines(translator, client.KLINE_INTERVAL_1MINUTE, from, to)
catch
false
end
else
tt = Pinguu.klines2table(klines, time_warp=0)
weightedAvgPrice = 1/tt[1].close
end
if klines == false
# Translator does not exist e.g. DOGEBNB
weightedAvgPrice = tmp_price_for_conversion / parse(Float64, t["price"])
else
tt = Pinguu.klines2table(klines, time_warp=0)
weightedAvgPrice = 1/tt[1].close
end
if typeof(weightedAvgPrice) == String
weightedAvgPrice = parse(Float64, weightedAvgPrice)
end
commission_coin *= weightedAvgPrice
end
t["com"] = commission
t["com_coin"] = commission_coin
else
t["com"] = 0
t["com_coin"] = 0
end
end
return order
end
export add_fees_to_closed_order
function check_ongoing_closed(id_str, config, closed_orders, bot_path, client)
store = false
for (id, order) in closed_orders[id_str]
if !haskey(order, "trades")
trades = client.get_my_trades(orderId=order["orderId"], symbol=order["symbol"])
order["trades"] = trades
order = try add_fees_to_closed_order(order, config, bot_path, client)
catch
@info("Getting fees failed.")
return
end
store = true
end
end
if store
open(joinpath(bot_path, "closed_orders.json"), "w") do f
JSON.print(f, closed_orders, 4)
end
end
end
export check_ongoing_closed
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] | 1.946373 | 8,615 |
"""
```
type AltPolicy
```
Type defining an alternative policy rule.
### Fields
- `key::Symbol`: alternative policy identifier
- `eqcond::Function`: a version of `DSGE.eqcond` which computes the equilibrium
condition matrices under the alternative policy. Like `eqcond`, it should take
in one argument of type `AbstractModel` and return the `Ξ0`, `Ξ1`, `C`, `Ξ¨`,
and `Ξ ` matrices.
- `solve::Function`: a version of `DSGE.solve` which solves the model under the
alternative policy. Like `DSGE.solve`, it should take in one argument of type
`AbstractModel` and return the `TTT`, `RRR`, and `CCC` matrices.
- `forecast_init::Function`: a function that initializes forecasts under the
alternative policy rule. Specifically, it accepts a model, an `nshocks` x
`n_forecast_periods` matrix of shocks to be applied in the forecast, and a
vector of initial states for the forecast. It must return a new matrix of
shocks and a new initial state vector. If no adjustments to shocks or initial
state vectors are necessary under the policy rule, this field may be omitted.
- `color::Colorant`: color to plot this alternative policy in. Defaults to blue.
- `linestyle::Symbol`: line style for forecast plots under this alternative
policy. See options from `Plots.jl`. Defaults to `:solid`.
"""
type AltPolicy
key::Symbol
eqcond::Function
solve::Function
forecast_init::Function
color::Colorant
linestyle::Symbol
end
function AltPolicy(key::Symbol, eqcond_fcn::Function, solve_fcn::Function;
forecast_init::Function = identity,
color::Colorant = RGB(0., 0., 1.),
linestyle::Symbol = :solid)
AltPolicy(key, eqcond_fcn, solve_fcn, forecast_init, color, linestyle)
end
Base.string(a::AltPolicy) = string(a.key) | [
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] | 3.079932 | 588 |
cd(@__DIR__)
using Pkg
Pkg.activate(@__DIR__)
# Set Python executable to current and re-build PyCall if necessary
ENV["PYTHON"] = Sys.which("python")
try
Pkg.build("PyCall")
catch e
if !isa(e, Pkg.Types.PkgError)
throw(e)
end
end
# Precompile everything
Pkg.API.precompile()
# Use packages
# using SpineInterface # SpineOpt uses SpineInterface anyway hence commented
using SpineOpt
using Dates # For debugging with the time slice tools
using CSV # Because is useful sometimes
using DataFrames
using GEPPR # My GEP model to check the results, can get rid of at end
using JuMP
using Plots
using AxisArrays
using DataStructures
using JSON
using PyCall
# Useful vars
dataPath = abspath(@__DIR__, "data")
spineitemsPath = joinpath(
@__DIR__, "data", "case_study_b2", ".spinetoolbox", "items"
)
GEPPRDataPath = abspath(dataPath, "GEPPR_input_data")
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nsamples, nchains, nchainsatone = 200001, 16, 1
Tmax = 2.5
addprocs(nchains)
@info "workers are $(workers())"
@everywhere any(pwd() .== LOAD_PATH) || push!(LOAD_PATH, pwd())
@everywhere using Distributed
@everywhere import MCMC_Driver
## run McMC
@time MCMC_Driver.main(opt, d, Tmax=Tmax, nsamples=nsamples, nchains=nchains, nchainsatone=nchainsatone)
rmprocs(workers())
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x = [1.0 3.72e-19 0.00124827 6.89e-20 0.000302687; 0.999 9.37e-5 0.021975 0.000202742 0.0059907; 0.998 0.000188043 0.0240664 0.000249413 0.00676533; 0.997 0.000280359 0.0255829 0.000284137 0.00730148; 0.996 0.000375935 0.0267894 0.000313538 0.00776902; 0.995 0.000471427 0.0277863 0.00033875 0.00814893; 0.994 0.000567482 0.0287001 0.000364133 0.00850842; 0.993 0.000657252 0.0294574 0.00038607 0.00881441; 0.992 0.000752873 0.0301397 0.000408188 0.00909966; 0.991 0.000846283 0.0307407 0.000429906 0.00936091; 0.99 0.000942163 0.0313513 0.000450701 0.00959576; 0.989 0.00103629 0.0318999 0.000470316 0.00982234; 0.988 0.00113203 0.0324625 0.000489532 0.0100369; 0.987 0.00122773 0.0329567 0.000505444 0.0102623; 0.986 0.00131821 0.0334565 0.000523783 0.0104666; 0.985 0.00140661 0.0339376 0.000539904 0.01066; 0.984 0.00149244 0.034404 0.000556449 0.01085; 0.983 0.00158394 0.0348485 0.000572454 0.0110281; 0.982 0.00167742 0.0352564 0.00058828 0.0112065; 0.981 0.00177039 0.0356551 0.000603647 0.0113771; 0.98 0.00185793 0.0360538 0.000619117 0.0115431; 0.979 0.00195129 0.0364368 0.000633334 0.0117095; 0.978 0.00204573 0.0368142 0.000647966 0.0118651; 0.977 0.00213777 0.0371811 0.00066223 0.012026; 0.976 0.00222552 0.0375439 0.000676459 0.0121798; 0.975 0.00231716 0.0378828 0.000690862 0.0123308; 0.974 0.00241777 0.0381977 0.000704774 0.0124744; 0.973 0.00251057 0.0385258 0.000718623 0.0126127; 0.972 0.00260381 0.0388405 0.00073256 0.0127574; 0.971 0.00269342 0.0391768 0.000745919 0.012894; 0.97 0.00278951 0.0394771 0.00075933 0.0130366; 0.969 0.0028874 0.0397894 0.000774001 0.0131615; 0.968 0.00298291 0.0400657 0.000787949 0.0132864; 0.967 0.00306899 0.0403509 0.000801402 0.0134082; 0.966 0.00316129 0.0406593 0.000814029 0.0135368; 0.965 0.0032546 0.0409338 0.000827307 0.0136614; 0.964 0.00334883 0.0412121 0.000840839 0.0137847; 0.963 0.00344062 0.0414715 0.000853285 0.0139116; 0.962 0.00352747 0.041733 0.000865992 0.0140421; 0.961 0.00362503 0.0420045 0.000878152 0.0141627; 0.96 0.00371367 0.0422741 0.000891131 0.0142804; 0.959 0.00381083 0.0425311 0.000904478 0.0143951; 0.958 0.00390654 0.0427421 0.000916289 0.0145088; 0.957 0.00400001 0.0430091 0.000929041 0.014625; 0.956 0.00409551 0.0432567 0.000941844 0.0147402; 0.955 0.0041875 0.0435116 0.000954778 0.0148569; 0.954 0.00428007 0.0437421 0.000966663 0.0149588; 0.953 0.00437401 0.0439716 0.00097858 0.0150658; 0.952 0.00446909 0.0442029 0.000990899 0.0151731; 0.951 0.00455832 0.0444385 0.00100301 0.0152823; 0.95 0.00465346 0.0446483 0.001015 0.0153929; 0.949 0.00474747 0.0448879 0.00102672 0.0155026; 0.948 0.00484083 0.0451149 0.00103866 0.0156067; 0.947 0.00493377 0.0453335 0.00105091 0.015713; 0.946 0.00502778 0.0455505 0.0010625 0.0158157; 0.945 0.00512017 0.0457613 0.00107577 0.0159204; 0.944 0.00520837 0.0459837 0.00108795 0.0160205; 0.943 0.00530477 0.046193 0.00109979 0.0161132; 0.942 0.00539456 0.0464144 0.00111215 0.0162103; 0.941 0.00548804 0.0466327 0.00112363 0.0163077; 0.94 0.00557999 0.0468413 0.00113648 0.0164093; 0.939 0.00566732 0.0470441 0.001148 0.0165063; 0.938 0.00576016 0.047243 0.00116081 0.0166048; 0.937 0.00584624 0.0474359 0.00117287 0.0166994; 0.936 0.00593228 0.047632 0.00118444 0.0167913; 0.935 0.00602467 0.0478393 0.00119544 0.0168868; 0.934 0.0061212 0.0480362 0.00120693 0.0169851; 0.933 0.00621378 0.048236 0.00121812 0.017081; 0.932 0.00631099 0.0484257 0.00122957 0.0171741; 0.931 0.0064011 0.048605 0.00124167 0.0172687; 0.93 0.00649061 0.0487895 0.00125337 0.0173599; 0.929 0.00658459 0.0489837 0.00126469 0.0174521; 0.928 0.00667418 0.0491725 0.001276 0.0175432; 0.927 0.00676707 0.0493803 0.00128867 0.0176382; 0.926 0.006863 0.0495738 0.00130018 0.0177224; 0.925 0.00696173 0.0497547 0.00131161 0.01781; 0.924 0.00705182 0.049945 0.00132235 0.0178992; 0.923 0.00715009 0.0501409 0.0013343 0.0179842; 0.922 0.00723987 0.0503208 0.00134573 0.0180737; 0.921 0.00733536 0.050508 0.00135765 0.0181619; 0.92 0.0074357 0.0507083 0.00136939 0.0182525; 0.919 0.00752572 0.0508932 0.00138203 0.0183397; 0.918 0.00761856 0.0510768 0.00139322 0.0184242; 0.917 0.00770446 0.0512583 0.00140507 0.0185187; 0.916 0.00779229 0.0514328 0.00141642 0.0186001; 0.915 0.00788888 0.0516074 0.00142804 0.0186892; 0.914 0.00798609 0.0517857 0.00143907 0.0187802; 0.913 0.00807848 0.0519608 0.00145024 0.0188649; 0.912 0.00816983 0.0521452 0.00146136 0.0189494; 0.911 0.0082664 0.0523077 0.0014734 0.0190314; 0.91 0.00835513 0.0524876 0.00148483 0.0191154; 0.909 0.0084493 0.0526598 0.00149668 0.0191957; 0.908 0.00853732 0.0528396 0.00150889 0.0192754; 0.907 0.00863353 0.0530159 0.00152117 0.0193618; 0.906 0.0087338 0.0531883 0.00153298 0.019444; 0.905 0.0088278 0.0533478 0.00154484 0.0195258; 0.904 0.00892006 0.0535276 0.00155649 0.0196098; 0.903 0.00900906 0.0537065 0.00156733 0.0196951; 0.902 0.00910371 0.0538876 0.00157873 0.0197731; 0.901 0.00919626 0.0540592 0.00158999 0.0198547; 0.9 0.00929466 0.0542222 0.00160164 0.0199323; 0.899 0.00939184 0.0543904 0.00161331 0.0200053; 0.898 0.00947996 0.0545611 0.00162489 0.0200838; 0.897 0.0095765 0.0547434 0.00163662 0.0201577; 0.896 0.00967864 0.054902 0.00164774 0.0202375; 0.895 0.00977945 0.0550673 0.00165911 0.020314; 0.894 0.00987368 0.0552278 0.00167076 0.0203906; 0.893 0.00996604 0.0553922 0.00168219 0.020471; 0.892 0.01006 0.0555556 0.00169387 0.0205518; 0.891 0.0101555 0.0557089 0.00170512 0.0206261; 0.89 0.0102508 0.0558766 0.00171612 0.0206999; 0.889 0.0103476 0.0560367 0.00172738 0.0207834; 0.888 0.010437 0.0561793 0.00173861 0.0208616; 0.887 0.0105271 0.0563406 0.00174994 0.0209367; 0.886 0.0106303 0.0565014 0.00176107 0.0210167; 0.885 0.0107292 0.0566615 0.00177232 0.0210922; 0.884 0.0108182 0.0568358 0.0017833 0.0211717; 0.883 0.0109102 0.0569943 0.00179406 0.021247; 0.882 0.0110017 0.0571429 0.00180569 0.0213285; 0.881 0.0110965 0.0573018 0.00181718 0.0214076; 0.88 0.0111864 0.05747 0.00182775 0.0214835; 0.879 0.0112768 0.0576226 0.0018398 0.0215604; 0.878 0.011372 0.0577761 0.00185119 0.0216408; 0.877 0.0114619 0.0579322 0.00186231 0.0217124; 0.876 0.0115525 0.0580952 0.00187281 0.0217855; 0.875 0.0116468 0.0582456 0.00188339 0.0218622; 0.874 0.011738 0.0583987 0.00189419 0.0219416; 0.873 0.0118306 0.0585498 0.00190573 0.0220163; 0.872 0.0119205 0.0587172 0.00191684 0.022092; 0.871 0.012015 0.0588745 0.00192816 0.0221683; 0.87 0.0121039 0.0590164 0.00194001 0.022244; 0.869 0.0121975 0.0591716 0.00195175 0.0223207; 0.868 0.0122934 0.0593088 0.0019631 0.0223952; 0.867 0.0123912 0.0594729 0.00197516 0.0224693; 0.866 0.0124858 0.05962 0.00198685 0.0225428; 0.865 0.0125787 0.0597701 0.00199891 0.0226152; 0.864 0.0126798 0.059927 0.00201039 0.0226879; 0.863 0.0127779 0.0600733 0.00202192 0.0227621; 0.862 0.0128654 0.0602298 0.00203404 0.0228336; 0.861 0.0129579 0.0603884 0.00204564 0.0229143; 0.86 0.0130481 0.0605405 0.00205692 0.0229779; 0.859 0.0131458 0.0606786 0.00206909 0.0230511; 0.858 0.0132386 0.0608234 0.00208009 0.0231241; 0.857 0.0133361 0.0609679 0.00209155 0.0231976; 0.856 0.0134326 0.0611129 0.0021032 0.0232704; 0.855 0.013523 0.0612633 0.00211412 0.0233454; 0.854 0.0136267 0.0614086 0.00212478 0.0234192; 0.853 0.0137197 0.0615515 0.00213678 0.0234878; 0.852 0.0138166 0.0616966 0.00214751 0.0235588; 0.851 0.0139135 0.0618421 0.00215915 0.0236341; 0.85 0.0140141 0.0619878 0.00217059 0.0237083; 0.849 0.0141075 0.062135 0.00218123 0.023782; 0.848 0.0142 0.0622819 0.00219209 0.0238564; 0.847 0.0142933 0.0624276 0.00220346 0.0239332; 0.846 0.0143888 0.0625775 0.00221556 0.0240062; 0.845 0.0144808 0.0627237 0.00222706 0.0240821; 0.844 0.0145725 0.0628623 0.00223918 0.0241542; 0.843 0.0146642 0.0630113 0.00225003 0.0242279; 0.842 0.0147596 0.0631478 0.0022607 0.0243003; 0.841 0.0148579 0.0632962 0.00227194 0.0243741; 0.84 0.0149545 0.0634421 0.00228351 0.0244401; 0.839 0.015045 0.0635897 0.00229511 0.024509; 0.838 0.0151379 0.0637309 0.00230731 0.0245786; 0.837 0.0152323 0.0638836 0.0023187 0.0246497; 0.836 0.0153301 0.0640244 0.00233044 0.0247231; 0.835 0.0154261 0.0641791 0.00234198 0.024791; 0.834 0.0155206 0.0643089 0.00235367 0.024857; 0.833 0.0156209 0.0644456 0.00236563 0.0249322; 0.832 0.0157151 0.0645833 0.00237717 0.0250018; 0.831 0.0158131 0.064732 0.0023896 0.0250717; 0.83 0.0159051 0.064871 0.00240045 0.0251384; 0.829 0.0159974 0.0650014 0.00241146 0.0252028; 0.828 0.0160849 0.0651456 0.00242342 0.0252759; 0.827 0.0161767 0.0652897 0.00243588 0.0253455; 0.826 0.0162718 0.065438 0.00244799 0.0254166; 0.825 0.0163672 0.0655797 0.00245981 0.0254915; 0.824 0.0164628 0.0657172 0.0024712 0.0255578; 0.823 0.0165602 0.0658519 0.0024831 0.0256279; 0.822 0.0166586 0.0659928 0.00249465 0.0256997; 0.821 0.0167537 0.066141 0.00250647 0.0257683; 0.82 0.0168424 0.0662963 0.00251844 0.0258404; 0.819 0.0169416 0.0664508 0.00252995 0.0259103; 0.818 0.0170374 0.0666102 0.00254182 0.0259766; 0.817 0.0171325 0.0666667 0.00255408 0.0260441; 0.816 0.0172327 0.066843 0.00256488 0.026109; 0.815 0.0173295 0.0669913 0.00257723 0.0261751; 0.814 0.0174219 0.0671264 0.00258874 0.0262421; 0.813 0.0175175 0.0672586 0.0026013 0.0263107; 0.812 0.0176153 0.0673976 0.00261365 0.0263816; 0.811 0.0177108 0.0675263 0.00262564 0.0264466; 0.81 0.0177993 0.0676568 0.00263715 0.0265179; 0.809 0.0178987 0.0677884 0.00264883 0.0265831; 0.808 0.0179933 0.0679225 0.00266047 0.026655; 0.807 0.018092 0.0680556 0.00267243 0.0267237; 0.806 0.0181913 0.0681969 0.00268474 0.0267901; 0.805 0.0182862 0.0683333 0.00269703 0.0268602; 0.804 0.0183802 0.0684753 0.00270894 0.0269222; 0.803 0.0184745 0.0686037 0.00272135 0.0269937; 0.802 0.0185726 0.0687319 0.00273299 0.0270676; 0.801 0.0186802 0.0688604 0.00274498 0.0271307; 0.8 0.0187788 0.0690001 0.00275647 0.0271945; 0.799 0.0188727 0.0691363 0.00276851 0.0272601; 0.798 0.0189692 0.0692658 0.00278135 0.0273299; 0.797 0.019056 0.0694083 0.00279266 0.0273933; 0.796 0.0191552 0.0695452 0.0028057 0.0274667; 0.795 0.0192504 0.0696776 0.00281759 0.0275323; 0.794 0.0193492 0.0698133 0.00282882 0.0276044; 0.793 0.0194438 0.0699394 0.00284156 0.0276771; 0.792 0.0195501 0.0700667 0.00285385 0.0277432; 0.791 0.019637 0.0702076 0.00286655 0.0278116; 0.79 0.0197395 0.0703431 0.00287852 0.0278777; 0.789 0.0198383 0.0704762 0.00289081 0.0279422; 0.788 0.0199326 0.0706144 0.00290271 0.0280119; 0.787 0.0200293 0.0707469 0.00291517 0.0280772; 0.786 0.0201216 0.0708806 0.00292791 0.0281433; 0.785 0.0202183 0.0710227 0.0029407 0.0282132; 0.784 0.0203183 0.0711673 0.00295313 0.0282755; 0.783 0.0204113 0.0713013 0.00296509 0.0283417; 0.782 0.0205074 0.0714286 0.00297745 0.0284142; 0.781 0.0206027 0.0715542 0.00298977 0.028486; 0.78 0.0206958 0.0716884 0.00300192 0.0285541; 0.779 0.0207926 0.0718314 0.0030149 0.0286196; 0.778 0.0208862 0.0719697 0.00302628 0.0286868; 0.777 0.0209804 0.0720968 0.0030384 0.0287554; 0.776 0.0210695 0.0722298 0.00305059 0.0288195; 0.775 0.0211617 0.0723664 0.00306304 0.028881; 0.774 0.0212585 0.0725054 0.00307622 0.0289465; 0.773 0.0213514 0.0726492 0.00308935 0.0290088; 0.772 0.021449 0.0727873 0.00310206 0.0290791; 0.771 0.0215455 0.0729193 0.00311356 0.0291421; 0.77 0.0216454 0.0730605 0.00312526 0.0292099; 0.769 0.0217453 0.0731988 0.00313809 0.0292759; 0.768 0.0218381 0.0733333 0.0031507 0.0293396; 0.767 0.02193 0.0734537 0.00316284 0.029409; 0.766 0.0220231 0.0735805 0.00317594 0.029477; 0.765 0.0221251 0.0737095 0.00318773 0.0295439; 0.764 0.0222255 0.0738351 0.00320127 0.0296081; 0.763 0.0223217 0.0739755 0.00321375 0.0296771; 0.762 0.0224195 0.0741174 0.00322581 0.0297446; 0.761 0.0225176 0.0742576 0.00323822 0.0298116; 0.76 0.0226131 0.074386 0.00325089 0.0298801; 0.759 0.0227116 0.0745224 0.00326416 0.0299428; 0.758 0.0228073 0.0746478 0.00327658 0.0300128; 0.757 0.0229026 0.0747727 0.00328932 0.0300807; 0.756 0.0229991 0.0748984 0.0033027 0.0301528; 0.755 0.0230912 0.0750225 0.00331541 0.030219; 0.754 0.0231911 0.0751681 0.00332783 0.0302861; 0.753 0.0232899 0.0753015 0.00334109 0.0303578; 0.752 0.0233888 0.0754319 0.00335423 0.0304253; 0.751 0.0234812 0.0755611 0.00336607 0.0304896; 0.75 0.0235835 0.0756892 0.0033785 0.0305525; 0.749 0.0236882 0.0758203 0.00339124 0.0306205; 0.748 0.0237821 0.0759545 0.00340421 0.0306902; 0.747 0.0238815 0.0760916 0.00341764 0.0307551; 0.746 0.0239744 0.0762226 0.00342919 0.0308217; 0.745 0.0240706 0.0763582 0.00344257 0.0308887; 0.744 0.0241647 0.0764972 0.00345652 0.0309533; 0.743 0.0242634 0.0766304 0.00347052 0.0310153; 0.742 0.0243651 0.0767599 0.00348309 0.031077; 0.741 0.0244622 0.07689 0.00349571 0.031141; 0.74 0.0245588 0.0770323 0.00350916 0.031204; 0.739 0.024658 0.0771743 0.00352206 0.0312674; 0.738 0.0247568 0.0772947 0.00353519 0.0313313; 0.737 0.0248543 0.0774249 0.00354935 0.0314019; 0.736 0.0249524 0.0775549 0.00356248 0.0314709; 0.735 0.025048 0.0776868 0.00357584 0.0315391; 0.734 0.0251469 0.077798 0.00358827 0.0316052; 0.733 0.0252501 0.0779416 0.00360124 0.0316701; 0.732 0.0253466 0.0780689 0.00361527 0.0317348; 0.731 0.0254433 0.0781983 0.00362824 0.0317998; 0.73 0.0255375 0.0783333 0.00364205 0.0318654; 0.729 0.0256348 0.0784571 0.00365411 0.0319311; 0.728 0.025728 0.0785806 0.00366753 0.0319983; 0.727 0.0258291 0.0787224 0.00368132 0.0320661; 0.726 0.0259281 0.0788585 0.00369491 0.0321345; 0.725 0.0260256 0.07899 0.00370837 0.0322048; 0.724 0.0261215 0.079124 0.00372145 0.0322714; 0.723 0.0262181 0.0792436 0.00373454 0.0323347; 0.722 0.0263176 0.0793763 0.00374768 0.0323982; 0.721 0.0264164 0.0795133 0.00376109 0.0324671; 0.72 0.0265182 0.079643 0.00377457 0.0325294; 0.719 0.0266206 0.0797887 0.0037873 0.0325965; 0.718 0.0267235 0.0799318 0.00380104 0.0326621; 0.717 0.0268261 0.0800583 0.00381487 0.0327288; 0.716 0.0269255 0.080199 0.00382849 0.032793; 0.715 0.0270242 0.080336 0.003842 0.0328568; 0.714 0.0271227 0.0804634 0.00385521 0.0329231; 0.713 0.0272197 0.0806045 0.0038702 0.0329896; 0.712 0.0273185 0.0807292 0.00388387 0.0330573; 0.711 0.0274185 0.0808577 0.00389838 0.0331243; 0.71 0.0275233 0.0809766 0.00391221 0.0331912; 0.709 0.0276188 0.0811203 0.00392603 0.0332543; 0.708 0.0277144 0.0812519 0.00393975 0.0333252; 0.707 0.0278144 0.0813853 0.0039535 0.0333927; 0.706 0.0279142 0.0815152 0.00396635 0.0334577; 0.705 0.0280148 0.0816523 0.00397996 0.0335229; 0.704 0.0281089 0.0817833 0.0039939 0.0335861; 0.703 0.0282025 0.0819132 0.00400778 0.0336527; 0.702 0.0283023 0.0820484 0.00402117 0.0337199; 0.701 0.028401 0.0821743 0.00403495 0.0337842; 0.7 0.0284965 0.0823136 0.00404889 0.0338504; 0.699 0.0286011 0.0824453 0.00406233 0.0339177; 0.698 0.0287041 0.0825789 0.00407579 0.0339856; 0.697 0.0288032 0.0827178 0.00408978 0.0340573; 0.696 0.0288959 0.0828529 0.00410455 0.0341214; 0.695 0.0290006 0.0829852 0.00411777 0.0341856; 0.694 0.0291029 0.0831169 0.0041323 0.0342548; 0.693 0.0292082 0.083256 0.00414529 0.0343248; 0.692 0.0293125 0.0833501 0.00415952 0.034389; 0.691 0.0294145 0.0834979 0.00417214 0.0344535; 0.69 0.0295165 0.0836296 0.00418625 0.0345207; 0.689 0.0296165 0.0837577 0.00420014 0.0345834; 0.688 0.0297155 0.0838964 0.00421441 0.0346559; 0.687 0.0298176 0.0840319 0.00422825 0.0347212; 0.686 0.0299214 0.0841667 0.00424213 0.0347826; 0.685 0.0300217 0.0842915 0.0042557 0.0348495; 0.684 0.0301244 0.084423 0.00427003 0.0349186; 0.683 0.0302233 0.0845576 0.00428448 0.0349862; 0.682 0.0303194 0.0846924 0.00430026 0.0350506; 0.681 0.0304222 0.0848337 0.00431484 0.0351238; 0.68 0.0305235 0.0849628 0.00432891 0.0351936; 0.679 0.0306211 0.0851039 0.0043422 0.0352618; 0.678 0.0307173 0.0852378 0.0043566 0.0353291; 0.677 0.0308163 0.0853832 0.00437197 0.035398; 0.676 0.0309194 0.0855149 0.00438761 0.0354644; 0.675 0.0310164 0.0856583 0.00440186 0.0355314; 0.674 0.0311197 0.0857851 0.00441625 0.0356047; 0.673 0.0312194 0.0859176 0.00443116 0.03567; 0.672 0.0313172 0.0860623 0.00444704 0.0357387; 0.671 0.0314167 0.0861953 0.00446215 0.0358042; 0.67 0.0315155 0.0863277 0.00447678 0.0358764; 0.669 0.0316123 0.0864535 0.00449093 0.0359472; 0.668 0.03171 0.0865971 0.00450601 0.0360147; 0.667 0.0318058 0.0867252 0.00452176 0.0360864; 0.666 0.0319099 0.086859 0.00453649 0.0361518; 0.665 0.0320041 0.0870013 0.00455289 0.0362227; 0.664 0.0321034 0.0871369 0.00456764 0.0362887; 0.663 0.0322064 0.0872727 0.00458175 0.0363549; 0.662 0.0323087 0.0874038 0.00459677 0.0364247; 0.661 0.0324102 0.0875337 0.00461105 0.0364889; 0.66 0.0325026 0.0876731 0.00462662 0.0365629; 0.659 0.0325996 0.0878159 0.0046422 0.0366372; 0.658 0.0326988 0.0879525 0.00465778 0.0367079; 0.657 0.0327989 0.0880952 0.00467356 0.0367779; 0.656 0.0329007 0.0882353 0.00468867 0.0368442; 0.655 0.0329935 0.0883646 0.00470487 0.0369153; 0.654 0.0330914 0.0885057 0.00472078 0.0369825; 0.653 0.0331941 0.0886364 0.00473616 0.0370479; 0.652 0.0332971 0.0887701 0.00475115 0.0371179; 0.651 0.0334024 0.0889064 0.0047662 0.0371885; 0.65 0.0334994 0.0890594 0.00478089 0.037253; 0.649 0.033602 0.0891892 0.0047956 0.037327; 0.648 0.0337066 0.0893232 0.00480965 0.0373925; 0.647 0.0338036 0.0894498 0.00482521 0.0374629; 0.646 0.0339062 0.0895862 0.00483964 0.0375303; 0.645 0.0340057 0.0897253 0.00485462 0.0376051; 0.644 0.0341057 0.0898587 0.00487101 0.0376771; 0.643 0.0342137 0.0900014 0.00488589 0.0377435; 0.642 0.0343106 0.0901458 0.00490177 0.0378184; 0.641 0.0344095 0.0902778 0.00491741 0.0378926; 0.64 0.034509 0.0904184 0.00493272 0.0379657; 0.639 0.0346139 0.0905526 0.00494761 0.0380344; 0.638 0.0347067 0.0906964 0.00496429 0.0381067; 0.637 0.0348113 0.0908309 0.00498029 0.0381801; 0.636 0.0349119 0.0909616 0.00499547 0.0382539; 0.635 0.0350134 0.0911012 0.00501005 0.0383339; 0.634 0.0351167 0.0912383 0.00502656 0.0384059; 0.633 0.0352161 0.0913838 0.00504177 0.0384764; 0.632 0.0353126 0.0915344 0.0050586 0.038544; 0.631 0.0354161 0.0916667 0.00507499 0.0386174; 0.63 0.0355126 0.0918144 0.00509065 0.0386874; 0.629 0.0356189 0.091951 0.00510697 0.0387617; 0.628 0.0357256 0.0920858 0.00512156 0.0388338; 0.627 0.0358327 0.0922222 0.00513749 0.0389089; 0.626 0.0359406 0.0923561 0.0051527 0.0389753; 0.625 0.0360469 0.0924967 0.00516865 0.0390458; 0.624 0.0361515 0.0926387 0.00518462 0.0391199; 0.623 0.0362604 0.0927833 0.00520123 0.0391887; 0.622 0.0363627 0.0929167 0.00521731 0.0392607; 0.621 0.0364616 0.0930485 0.00523351 0.0393337; 0.62 0.0365725 0.0931942 0.00525054 0.0394083; 0.619 0.0366742 0.0933333 0.00526763 0.0394826; 0.618 0.0367855 0.0934626 0.00528421 0.0395598; 0.617 0.036894 0.0936032 0.00530011 0.0396314; 0.616 0.037001 0.09375 0.00531696 0.0397013; 0.615 0.0371023 0.0938962 0.00533345 0.0397774; 0.614 0.0372081 0.0940351 0.00535071 0.0398497; 0.613 0.0373068 0.0941785 0.00536822 0.03992; 0.612 0.0374185 0.0943164 0.00538505 0.0399954; 0.611 0.0375275 0.0944651 0.00540113 0.04007; 0.61 0.0376307 0.0945977 0.00541802 0.0401442; 0.609 0.0377382 0.0947493 0.00543557 0.0402172; 0.608 0.0378374 0.0948894 0.00545215 0.0402944; 0.607 0.0379424 0.095033 0.00546947 0.0403668; 0.606 0.0380535 0.0951754 0.00548575 0.0404398; 0.605 0.0381554 0.0953079 0.00550344 0.0405104; 0.604 0.0382561 0.0954457 0.00551963 0.0405907; 0.603 0.0383576 0.0955865 0.00553594 0.0406573; 0.602 0.0384525 0.0957265 0.00555248 0.0407351; 0.601 0.0385536 0.0958621 0.00556979 0.0408075; 0.6 0.038667 0.096008 0.00558901 0.0408831; 0.599 0.0387694 0.0961486 0.00560631 0.0409577; 0.598 0.0388797 0.0962927 0.00562191 0.0410325; 0.597 0.0389821 0.0964286 0.00563971 0.0411091; 0.596 0.0390885 0.096568 0.00565655 0.0411858; 0.595 0.0391954 0.0967033 0.0056738 0.0412617; 0.594 0.0393071 0.0968524 0.00569111 0.0413421; 0.593 0.0394194 0.0969851 0.00570838 0.0414143; 0.592 0.0395188 0.0971416 0.00572656 0.0414875; 0.591 0.0396154 0.0972802 0.00574414 0.0415657; 0.59 0.0397228 0.097412 0.00576128 0.0416451; 0.589 0.0398269 0.0975526 0.00577912 0.04172; 0.588 0.0399345 0.0976887 0.00579752 0.0417939; 0.587 0.0400427 0.0978355 0.00581668 0.0418646; 0.586 0.0401522 0.097992 0.00583388 0.0419404; 0.585 0.0402571 0.098133 0.00585197 0.0420203; 0.584 0.0403701 0.0982727 0.00586937 0.0420961; 0.583 0.0404746 0.0984177 0.00588796 0.0421739; 0.582 0.0405746 0.0985631 0.00590696 0.0422472; 0.581 0.0406735 0.0987085 0.00592545 0.0423183; 0.58 0.040785 0.0988506 0.00594262 0.0423999; 0.579 0.040896 0.0989914 0.00596072 0.0424768; 0.578 0.0410113 0.0991342 0.00597884 0.0425533; 0.577 0.0411129 0.0992811 0.00599621 0.042636; 0.576 0.0412159 0.0994211 0.00601471 0.0427125; 0.575 0.0413254 0.0995632 0.00603291 0.0427882; 0.574 0.0414325 0.0997115 0.00605225 0.0428671; 0.573 0.0415507 0.0998685 0.00606982 0.042948; 0.572 0.0416508 0.1 0.00608835 0.043026; 0.571 0.0417625 0.100121 0.00610718 0.0431079; 0.57 0.041868 0.100284 0.0061253 0.0431897; 0.569 0.0419713 0.100431 0.00614336 0.0432678; 0.568 0.0420764 0.10058 0.00616274 0.043347; 0.567 0.0421893 0.100728 0.00618105 0.0434253; 0.566 0.0423 0.100877 0.00620017 0.0435052; 0.565 0.042406 0.101028 0.00621809 0.0435848; 0.564 0.0425052 0.101176 0.00623731 0.0436627; 0.563 0.0426241 0.101324 0.00625775 0.0437408; 0.562 0.0427315 0.101475 0.00627662 0.0438249; 0.561 0.042841 0.101623 0.00629777 0.043902; 0.56 0.0429497 0.10177 0.00631597 0.0439858; 0.559 0.043053 0.101911 0.00633546 0.0440616; 0.558 0.0431609 0.102056 0.00635434 0.0441417; 0.557 0.0432742 0.102204 0.00637332 0.0442212; 0.556 0.0433834 0.102358 0.00639337 0.0443011; 0.555 0.043501 0.1025 0.00641177 0.0443786; 0.554 0.043611 0.102647 0.00643136 0.0444551; 0.553 0.043721 0.102792 0.00644931 0.0445337; 0.552 0.0438285 0.102941 0.00646863 0.0446147; 0.551 0.0439401 0.103082 0.00648642 0.0446953; 0.55 0.0440528 0.103245 0.00650557 0.0447802; 0.549 0.044161 0.103385 0.00652532 0.0448579; 0.548 0.0442759 0.10352 0.00654523 0.0449406; 0.547 0.0443796 0.103674 0.00656596 0.0450259; 0.546 0.0444886 0.103828 0.00658535 0.0451072; 0.545 0.0445964 0.103973 0.00660317 0.0451931; 0.544 0.0447155 0.104114 0.00662199 0.04528; 0.543 0.0448222 0.104268 0.00664136 0.0453607; 0.542 0.0449373 0.104409 0.00666254 0.04544; 0.541 0.0450497 0.104575 0.00668286 0.0455152; 0.54 0.0451603 0.104727 0.00670231 0.045598; 0.539 0.0452715 0.104867 0.00672093 0.045682; 0.538 0.0453813 0.105027 0.00674213 0.045768; 0.537 0.0454979 0.105175 0.00676252 0.0458506; 0.536 0.0456167 0.105323 0.00678459 0.0459314; 0.535 0.045734 0.105476 0.00680411 0.0460131; 0.534 0.0458478 0.10562 0.00682373 0.0460963; 0.533 0.0459582 0.105772 0.00684392 0.0461803; 0.532 0.046067 0.105923 0.00686503 0.0462657; 0.531 0.046177 0.106071 0.00688484 0.0463469; 0.53 0.0462894 0.106229 0.00690519 0.0464314; 0.529 0.0463997 0.106382 0.00692626 0.0465128; 0.528 0.0465119 0.106539 0.00694753 0.0465958; 0.527 0.0466351 0.106667 0.00696878 0.0466844; 0.526 0.0467497 0.106823 0.00699045 0.0467673; 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0.072 0.133999 0.232667 0.0324116 0.12778; 0.071 0.134474 0.233334 0.0326009 0.128267; 0.07 0.134945 0.234131 0.0328063 0.128747; 0.069 0.13541 0.234896 0.0330113 0.129267; 0.068 0.135922 0.235651 0.0332005 0.129792; 0.067 0.136403 0.236323 0.0334164 0.130293; 0.066 0.136886 0.237056 0.0336083 0.130822; 0.065 0.137372 0.237778 0.0338109 0.131387; 0.064 0.137905 0.238537 0.0340118 0.131907; 0.063 0.138444 0.239351 0.0342188 0.132457; 0.062 0.138964 0.240118 0.0344379 0.132982; 0.061 0.139499 0.240947 0.0346628 0.133534; 0.06 0.14002 0.241774 0.0348871 0.134112; 0.059 0.140552 0.242661 0.0351032 0.134687; 0.058 0.141073 0.243475 0.0353304 0.135291; 0.057 0.141646 0.24436 0.0355547 0.135876; 0.056 0.142219 0.245234 0.0358 0.136492; 0.055 0.142786 0.246159 0.0360511 0.13712; 0.054 0.143361 0.247115 0.0362843 0.13776; 0.053 0.143934 0.248037 0.0365201 0.138419; 0.052 0.144578 0.249026 0.0367862 0.139058; 0.051 0.1452 0.25 0.0370548 0.139722; 0.05 0.145834 0.250914 0.0373304 0.140372; 0.049 0.146498 0.251923 0.0376117 0.14104; 0.048 0.147159 0.252885 0.0379024 0.141737; 0.047 0.1478 0.253872 0.0382062 0.142434; 0.046 0.148482 0.254899 0.0384956 0.143194; 0.045 0.149155 0.255907 0.0388273 0.143967; 0.044 0.149841 0.256931 0.0391297 0.144705; 0.043 0.150539 0.258134 0.0394682 0.145469; 0.042 0.151291 0.25929 0.0398084 0.146261; 0.041 0.152042 0.260417 0.0401628 0.147013; 0.04 0.152751 0.261575 0.0404984 0.147823; 0.039 0.153546 0.262739 0.0408572 0.148646; 0.038 0.154329 0.26394 0.0412231 0.149509; 0.037 0.155119 0.265171 0.0416058 0.150421; 0.036 0.155904 0.266397 0.0420052 0.15131; 0.035 0.15671 0.267649 0.0424234 0.152204; 0.034 0.157574 0.2689 0.0428466 0.153133; 0.033 0.158458 0.270238 0.0432576 0.154157; 0.032 0.159396 0.271667 0.0437009 0.155223; 0.031 0.160313 0.273223 0.0441736 0.156293; 0.03 0.161296 0.27476 0.0446365 0.157343; 0.029 0.162241 0.276331 0.0451093 0.158423; 0.028 0.163318 0.277891 0.0455987 0.15962; 0.027 0.164388 0.279511 0.0461167 0.160761; 0.026 0.16549 0.281219 0.0466645 0.161906; 0.025 0.166588 0.28303 0.0472652 0.16314; 0.024 0.167775 0.284848 0.0478739 0.164443; 0.023 0.168988 0.286844 0.0484817 0.165785; 0.022 0.170269 0.288794 0.0491432 0.167174; 0.021 0.171499 0.290839 0.0498532 0.168675; 0.02 0.172853 0.293076 0.0505364 0.17036; 0.019 0.174288 0.295391 0.0512858 0.1719; 0.018 0.175805 0.297759 0.0520465 0.173459; 0.017 0.177334 0.300254 0.0528811 0.175214; 0.016 0.178881 0.30303 0.0537109 0.177139; 0.015 0.180708 0.305838 0.0546887 0.179112; 0.014 0.182571 0.308873 0.0557695 0.181351; 0.013 0.184555 0.312024 0.05675 0.183527; 0.012 0.186668 0.315558 0.0579006 0.186054; 0.011 0.189091 0.319324 0.059136 0.188671; 0.01 0.191631 0.323243 0.0604436 0.191475; 0.009 0.194385 0.327447 0.0619463 0.194415; 0.008 0.1973 0.332407 0.0635969 0.197726; 0.007 0.200614 0.337446 0.0655049 0.201308; 0.006 0.204318 0.343162 0.0676377 0.205533; 0.005 0.208466 0.350062 0.0702472 0.210387; 0.004 0.213596 0.358492 0.0732967 0.216264; 0.003 0.220008 0.368721 0.0770849 0.223663; 0.002 0.228465 0.382642 0.0825932 0.232994; 0.001 0.241961 0.405911 0.0911455 0.249059; 0.0 0.344619 0.633333 0.312688 0.392941]
global metricp = DataFrame(pval = x[:, 1],
areametric = x[:, 2],
Dkmetric = x[:, 3],
invrsqmetric = x[:, 4],
meanDmetric = x[:, 5])
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405,
1954,
48712,
657,
13,
2999,
35378,
1558,
26,
657,
13,
5999,
657,
13,
486,
3270,
2713,
16,
657,
13,
15,
34287,
4869,
657,
13,
405,
1731,
405,
2231,
657,
13,
36629,
1485,
5705,
26,
657,
13,
23,
1959,
657,
13,
25150,
2079,
4524,
657,
13,
15,
2996,
405,
1415,
657,
13,
405,
1731,
1157,
3510,
657,
13,
36629,
1238,
2078,
26,
657,
13,
23,
2078,
657,
13,
486,
28688,
2920,
657,
13,
15,
2996,
1415,
3980,
657,
13,
405,
1731,
1954,
3682,
657,
13,
36629,
1983,
3270,
26,
657,
13,
23,
1983,
657,
13,
27037,
1558,
3134,
657,
13,
15,
2996,
2078,
5607,
657,
13,
405,
1731,
2327,
3459,
657,
13,
36629,
27712,
20,
26,
657,
13,
23,
2075,
657,
13,
27037,
1983,
1507,
657,
13,
15,
39111,
2548,
657,
13,
405,
1731,
2857,
2079,
657,
13,
15,
24970,
23055,
26,
657,
13,
47338,
657,
13,
27037,
2623,
4761,
657,
13,
3312,
2816,
44673,
657,
13,
405,
1731,
3270,
6659,
657,
13,
36629,
2920,
1314,
26,
657,
13,
23,
1731,
657,
13,
486,
27720,
2078,
657,
13,
15,
37680,
23628,
657,
13,
405,
23753,
1065,
657,
13,
36629,
2816,
3695,
26,
657,
13,
23,
1954,
657,
13,
486,
2996,
31418,
657,
13,
15,
2996,
5332,
1129,
657,
13,
405,
23045,
3132,
657,
13,
15,
11645,
26050,
26,
657,
13,
23,
1828,
657,
13,
486,
2791,
29796,
657,
13,
15,
2996,
2079,
2078,
657,
13,
405,
1731,
5824,
2996,
657,
13,
15,
11645,
39647,
26,
657,
13,
23,
2481,
657,
13,
27037,
2425,
2718,
657,
13,
15,
2791,
23756,
657,
13,
405,
9031,
33981,
657,
13,
15,
28676,
47521,
26,
657,
13,
6469,
657,
13,
486,
3104,
40090,
657,
13,
15,
2791,
1959,
5066,
657,
13,
405,
1495,
1507,
2598,
657,
13,
15,
25600,
26429,
26,
657,
13,
23,
1129,
657,
13,
486,
3388,
35218,
657,
13,
3312,
2414,
33042,
657,
13,
405,
1495,
1959,
3865,
657,
13,
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25191,
15197,
26,
657,
13,
23,
1507,
657,
13,
486,
2154,
31020,
657,
13,
15,
27310,
15377,
657,
13,
405,
24970,
24294,
657,
13,
36629,
5607,
2791,
26,
657,
13,
23,
1558,
657,
13,
29326,
1485,
1495,
657,
13,
15,
19060,
3134,
657,
13,
405,
13381,
26200,
657,
13,
2999,
1899,
39710,
26,
657,
13,
23,
1433,
657,
13,
29326,
1954,
1983,
657,
13,
15,
35809,
3559,
657,
13,
405,
1495,
2414,
3459,
657,
13,
45987,
14454,
26,
657,
13,
49503,
657,
13,
486,
4790,
25710,
657,
13,
15,
2791,
2079,
1485,
657,
13,
405,
1495,
3324,
1954,
657,
13,
45987,
1558,
4349,
26,
657,
13,
23,
1415,
657,
13,
29326,
3682,
1129,
657,
13,
15,
3134,
1065,
2414,
657,
13,
405,
1495,
3459,
4524,
657,
13,
45987,
1731,
2481,
26,
657,
13,
23,
1485,
657,
13,
486,
2425,
17430,
657,
13,
15,
3134,
1495,
4521,
657,
13,
405,
2075,
30273,
657,
13,
15,
29558,
15982,
26,
657,
13,
23,
1065,
657,
13,
486,
4304,
21395,
657,
13,
15,
3134,
2670,
4304,
657,
13,
405,
2075,
1485,
2996,
657,
13,
45987,
2548,
1433,
26,
657,
13,
23,
1157,
657,
13,
486,
3324,
15711,
657,
13,
3312,
2425,
29558,
657,
13,
405,
2075,
1495,
2414,
657,
13,
45987,
2598,
2791,
26,
657,
13,
6659,
657,
13,
486,
3324,
44821,
657,
13,
15,
3134,
2996,
3104,
657,
13,
405,
2075,
2718,
1314,
657,
13,
15,
22980,
21738,
26,
657,
13,
34583,
657,
13,
486,
3695,
44183,
657,
13,
15,
40179,
40353,
657,
13,
405,
2075,
2780,
5999,
657,
13,
45987,
3365,
3132,
26,
657,
13,
28362,
657,
13,
29326,
2079,
2091,
657,
13,
15,
3134,
5892,
1495,
657,
13,
405,
2075,
1899,
2857,
657,
13,
15,
25540,
2816,
26,
657,
13,
36928,
657,
13,
486,
1795,
5892,
657,
13,
3312,
1795,
37864,
657,
13,
405,
25674,
26660,
657,
13,
15,
25674,
24693,
26,
657,
13,
37988,
657,
13,
29159,
1129,
1485,
657,
13,
15,
3104,
38391,
657,
13,
405,
25022,
38652,
657,
13,
45987,
3720,
486,
26,
657,
13,
28256,
657,
13,
29159,
2078,
5237,
657,
13,
15,
3104,
24840,
657,
13,
405,
26276,
36809,
657,
13,
15,
25022,
31418,
26,
657,
13,
36088,
657,
13,
486,
5999,
30863,
657,
13,
15,
3104,
32576,
18,
657,
13,
405,
1983,
2919,
5824,
657,
13,
15,
26276,
23148,
26,
657,
13,
43564,
657,
13,
29159,
2857,
2231,
657,
13,
15,
3104,
1899,
2718,
657,
13,
405,
29807,
17059,
657,
13,
45987,
2079,
2718,
26,
657,
13,
30863,
657,
13,
29159,
3553,
2075,
657,
13,
15,
3104,
4790,
1129,
657,
13,
405,
27367,
22579,
657,
13,
15,
20233,
42548,
26,
657,
13,
41531,
657,
13,
486,
4521,
30863,
657,
13,
15,
34427,
31916,
657,
13,
405,
1983,
2598,
4089,
657,
13,
44698,
12952,
22,
26,
657,
13,
23,
657,
13,
29159,
3324,
3459,
657,
13,
3312,
12865,
486,
657,
13,
405,
23195,
33981,
657,
13,
44698,
41931,
26,
657,
13,
45455,
657,
13,
486,
46660,
1983,
657,
13,
3312,
24,
1485,
5066,
657,
13,
405,
1983,
3104,
4349,
657,
13,
44698,
2075,
486,
26,
657,
13,
43240,
657,
13,
486,
4531,
46589,
657,
13,
3312,
24,
2075,
3365,
657,
13,
405,
25870,
17059,
657,
13,
15,
27367,
22579,
26,
657,
13,
44673,
657,
13,
30484,
2713,
21,
657,
13,
3312,
46899,
5999,
657,
13,
405,
26050,
25540,
657,
13,
44698,
2670,
2091,
26,
657,
13,
41060,
657,
13,
30484,
1314,
4309,
657,
13,
3312,
3865,
37730,
657,
13,
405,
21033,
3553,
657,
13,
15,
28857,
28933,
26,
657,
13,
41544,
657,
13,
486,
5892,
33580,
657,
13,
3312,
24,
3134,
4304,
657,
13,
405,
2078,
1558,
3270,
657,
13,
44698,
4310,
1954,
26,
657,
13,
50242,
657,
13,
30484,
2682,
5892,
657,
13,
3312,
4089,
16945,
657,
13,
405,
2078,
2078,
6469,
657,
13,
44698,
1899,
2598,
26,
657,
13,
44750,
657,
13,
30484,
2598,
2548,
657,
13,
3312,
2079,
34626,
657,
13,
405,
30336,
21599,
657,
13,
44698,
3134,
4869,
26,
657,
13,
48156,
657,
13,
30484,
2816,
486,
657,
13,
2998,
405,
28933,
657,
13,
405,
26279,
27203,
657,
13,
44698,
4524,
2624,
26,
657,
13,
3720,
16,
657,
13,
486,
4846,
2718,
657,
13,
43509,
1238,
4304,
657,
13,
405,
2078,
2791,
2816,
657,
13,
15,
25870,
18298,
26,
657,
13,
3720,
657,
13,
486,
5607,
31010,
657,
13,
43509,
2682,
3132,
657,
13,
405,
2078,
3695,
4309,
657,
13,
15,
25870,
29331,
26,
657,
13,
40401,
657,
13,
486,
4089,
34741,
657,
13,
43509,
2857,
5237,
657,
13,
405,
27693,
2919,
16,
657,
13,
44698,
5824,
1828,
26,
657,
13,
22,
3459,
657,
13,
486,
2079,
39195,
657,
13,
15,
35402,
18444,
657,
13,
405,
24369,
28977,
657,
13,
46957,
486,
1129,
26,
657,
13,
41019,
657,
13,
44613,
31675,
657,
13,
43509,
4524,
3388,
657,
13,
405,
1959,
1314,
1558,
657,
13,
2999,
1795,
43571,
26,
657,
13,
46302,
657,
13,
15,
6999,
1433,
657,
13,
15,
32583,
37988,
657,
13,
405,
1959,
1983,
6420,
657,
13,
46957,
1415,
2091,
26,
657,
13,
41172,
657,
13,
15,
19004,
24839,
657,
13,
2998,
940,
24403,
657,
13,
405,
1959,
30120,
657,
13,
15,
32568,
19924,
26,
657,
13,
37688,
657,
13,
15,
22416,
24839,
657,
13,
2998,
1157,
45758,
657,
13,
405,
1959,
4310,
1485,
657,
13,
46957,
1983,
2816,
26,
657,
13,
50165,
657,
13,
15,
18638,
16616,
657,
13,
2998,
1485,
30273,
657,
13,
405,
1959,
17544,
24,
657,
13,
46957,
2682,
1558,
26,
657,
13,
46519,
657,
13,
33618,
1120,
4524,
657,
13,
2998,
1415,
27033,
657,
13,
405,
1959,
3324,
2231,
657,
13,
15,
30336,
23726,
26,
657,
13,
49703,
657,
13,
33618,
1899,
1983,
657,
13,
2998,
18742,
3682,
657,
13,
405,
1959,
4531,
3324,
657,
13,
46957,
34251,
26,
657,
13,
3695,
657,
13,
33618,
3388,
3365,
657,
13,
2998,
1433,
40353,
657,
13,
405,
6200,
17477,
657,
13,
46957,
2816,
3901,
26,
657,
13,
40393,
657,
13,
33618,
3720,
2075,
657,
13,
2998,
24839,
1415,
657,
13,
11245,
486,
2920,
657,
13,
15,
27033,
25272,
26,
657,
13,
39761,
657,
13,
33618,
3459,
5237,
657,
13,
2998,
38391,
22,
657,
13,
405,
1270,
2075,
2078,
657,
13,
46957,
3104,
3104,
26,
657,
13,
29331,
657,
13,
15,
22567,
36088,
657,
13,
2998,
22567,
3104,
657,
13,
405,
1270,
22842,
657,
13,
46957,
2425,
4051,
26,
657,
13,
39509,
657,
13,
2999,
940,
37381,
657,
13,
2998,
1828,
27728,
657,
13,
405,
1270,
1120,
3270,
657,
13,
15,
25270,
22186,
26,
657,
13,
34483,
657,
13,
2999,
18298,
1558,
657,
13,
2998,
24940,
2414,
657,
13,
405,
20548,
21288,
657,
13,
46957,
3459,
16,
26,
657,
13,
47582,
657,
13,
2999,
1065,
38905,
657,
13,
2998,
9031,
4051,
657,
13,
405,
1270,
4304,
1828,
657,
13,
46957,
5824,
2996,
26,
657,
13,
46871,
657,
13,
2999,
17059,
1415,
657,
13,
2998,
2075,
40256,
657,
13,
405,
1270,
4531,
2327,
657,
13,
48891,
405,
3459,
26,
657,
13,
43571,
657,
13,
2999,
1415,
2920,
657,
13,
2998,
25870,
4790,
657,
13,
11245,
940,
22136,
657,
13,
48891,
2998,
6420,
26,
657,
13,
46761,
657,
13,
2999,
1314,
30505,
657,
13,
2998,
1959,
24943,
657,
13,
11245,
1157,
32066,
657,
13,
48891,
1415,
2481,
26,
657,
13,
3324,
657,
13,
2999,
1433,
34229,
657,
13,
2998,
1270,
32417,
657,
13,
11245,
11623,
2075,
657,
13,
48891,
1238,
2079,
26,
657,
13,
22,
3388,
657,
13,
2999,
1558,
36625,
657,
13,
2998,
18,
26709,
657,
13,
11245,
1485,
34583,
657,
13,
48891,
1983,
3270,
26,
657,
13,
30610,
657,
13,
2999,
1507,
36626,
657,
13,
2998,
2091,
20370,
657,
13,
405,
3132,
35378,
657,
13,
48891,
2091,
4846,
26,
657,
13,
32059,
657,
13,
2999,
24943,
657,
13,
2998,
27712,
2718,
657,
13,
11245,
1433,
30336,
657,
13,
48891,
29416,
26,
657,
13,
22,
2791,
657,
13,
2999,
1238,
25667,
657,
13,
2998,
2327,
28256,
657,
13,
11245,
1558,
46438,
657,
13,
48891,
32883,
26,
657,
13,
29143,
657,
13,
44087,
1065,
4349,
657,
13,
2998,
20167,
3865,
657,
13,
11245,
1507,
46871,
657,
13,
48891,
4051,
2670,
26,
657,
13,
22,
2414,
657,
13,
15,
23148,
13381,
657,
13,
2998,
2548,
35273,
657,
13,
11245,
1264,
1983,
657,
13,
48891,
1899,
6659,
26,
657,
13,
49641,
657,
13,
44087,
2624,
1558,
657,
13,
2998,
2670,
38172,
657,
13,
405,
2624,
1485,
2425,
657,
13,
48891,
3134,
4869,
26,
657,
13,
48194,
657,
13,
2999,
1731,
22186,
657,
13,
2998,
42224,
4524,
657,
13,
405,
2624,
1495,
6659,
657,
13,
15,
26561,
27260,
26,
657,
13,
4304,
16,
657,
13,
2999,
1495,
24096,
657,
13,
2998,
32114,
4304,
657,
13,
11245,
23721,
1828,
657,
13,
15,
27728,
18298,
26,
657,
13,
4304,
657,
13,
15,
24909,
22042,
657,
13,
2998,
19,
21734,
657,
13,
405,
2624,
1120,
4531,
657,
13,
48891,
3459,
486,
26,
657,
13,
38314,
657,
13,
15,
24403,
18298,
657,
13,
2998,
2231,
24137,
657,
13,
11245,
18897,
1433,
657,
13,
48891,
5824,
2078,
26,
657,
13,
38569,
657,
13,
44087,
1795,
4790,
657,
13,
2998,
44578,
3695,
657,
13,
11245,
1983,
38431,
657,
13,
3070,
405,
12762,
26,
657,
13,
39251,
657,
13,
44087,
3829,
2075,
657,
13,
2998,
32883,
1983,
657,
13,
11245,
27693,
2624,
657,
13,
3070,
405,
36928,
26,
657,
13,
38219,
657,
13,
44087,
2079,
6420,
657,
13,
2998,
2780,
4089,
19,
657,
13,
11245,
1270,
1983,
657,
13,
3070,
25150,
2078,
26,
657,
13,
38172,
657,
13,
2999,
26895,
1065,
657,
13,
2998,
1120,
18182,
657,
13,
405,
2091,
1314,
3901,
657,
13,
15,
22709,
1129,
26,
657,
13,
41874,
657,
13,
45310,
1129,
1157,
657,
13,
46396,
1433,
6659,
657,
13,
405,
2091,
1983,
5999,
657,
13,
39101,
2078,
5333,
26,
657,
13,
44550,
657,
13,
45310,
2078,
2079,
657,
13,
46396,
18938,
20,
657,
13,
405,
31380,
14454,
657,
13,
39101,
2327,
3695,
26,
657,
13,
43665,
657,
13,
45310,
2548,
3459,
657,
13,
46396,
3559,
1129,
657,
13,
405,
2091,
4051,
1954,
657,
13,
15,
21288,
28592,
26,
657,
13,
48365,
657,
13,
45310,
2780,
1065,
657,
13,
46396,
3980,
1157,
657,
13,
405,
29211,
31980,
657,
13,
39101,
2780,
4846,
26,
657,
13,
2425,
657,
13,
45310,
3365,
2327,
657,
13,
46396,
3104,
5892,
657,
13,
405,
2091,
41172,
657,
13,
39101,
2816,
1495,
26,
657,
13,
22,
2920,
657,
13,
45310,
3104,
6469,
657,
13,
15,
38569,
22416,
657,
13,
405,
29626,
17464,
657,
13,
15,
20548,
21261,
26,
657,
13,
48246,
657,
13,
45310,
3695,
2481,
657,
13,
46396,
3865,
2231,
657,
13,
11245,
26429,
2481,
657,
13,
39101,
3388,
2999,
26,
657,
13,
48882,
657,
13,
45310,
3459,
1314,
657,
13,
2998,
31751,
1433,
657,
13,
405,
2682,
1558,
2414,
657,
13,
39101,
2425,
4349,
26,
657,
13,
22,
3510,
657,
13,
45310,
5607,
2598,
657,
13,
2998,
21,
1828,
2075,
657,
13,
405,
2682,
1959,
1129,
657,
13,
39101,
6469,
1558,
26,
657,
13,
50150,
657,
13,
15,
16102,
35402,
657,
13,
2998,
48250,
6469,
657,
13,
11245,
2598,
28676,
657,
13,
39101,
3459,
5774,
26,
657,
13,
22,
2598,
657,
13,
40839,
1433,
2857,
657,
13,
2998,
33300,
4761,
657,
13,
11245,
2231,
43193,
657,
13,
39101,
3865,
2091,
26,
657,
13,
22,
3559,
657,
13,
40839,
2075,
2682,
657,
13,
2998,
2791,
21288,
657,
13,
405,
2682,
2154,
4309,
657,
13,
3070,
8784,
4310,
26,
657,
13,
22,
3682,
657,
13,
40839,
2623,
4349,
657,
13,
2998,
42444,
2079,
657,
13,
405,
28978,
26895,
657,
13,
3070,
940,
3324,
26,
657,
13,
22,
3901,
657,
13,
40839,
3510,
1828,
657,
13,
2998,
40523,
657,
13,
405,
27371,
42875,
657,
13,
3070,
1157,
3901,
26,
657,
13,
4524,
657,
13,
40839,
2816,
3459,
657,
13,
2998,
2154,
32637,
657,
13,
11245,
29022,
1433,
657,
13,
3070,
1065,
3023,
26,
657,
13,
22,
2670,
657,
13,
40839,
38431,
657,
13,
2998,
22,
1558,
3559,
657,
13,
405,
2327,
17572,
21,
657,
13,
3070,
1065,
45385,
26,
657,
13,
22,
2548,
657,
13,
40839,
2425,
3104,
657,
13,
2998,
48555,
2857,
657,
13,
405,
2327,
2327,
1129,
657,
13,
3070,
16945,
1485,
26,
657,
13,
22,
2718,
657,
13,
40839,
5332,
3559,
657,
13,
2998,
4524,
21626,
657,
13,
405,
2327,
2920,
2327,
657,
13,
3070,
1415,
30484,
26,
657,
13,
49150,
657,
13,
40839,
3865,
1731,
657,
13,
2998,
38172,
2920,
657,
13,
405,
32066,
23045,
657,
13,
3070,
1415,
31495,
26,
657,
13,
22,
2327,
657,
13,
2999,
1120,
2780,
657,
13,
2998,
30610,
3104,
657,
13,
405,
2327,
2425,
5705,
657,
13,
3070,
1314,
37710,
26,
657,
13,
22,
2682,
657,
13,
36629,
1415,
3388,
657,
13,
2998,
3324,
4089,
657,
13,
405,
2327,
3459,
1983,
657,
13,
3070,
1433,
37841,
26,
657,
13,
49995,
657,
13,
36629,
1495,
486,
657,
13,
2998,
3720,
35218,
657,
13,
405,
2623,
486,
1731,
657,
13,
3070,
21940,
486,
26,
657,
13,
22,
2624,
657,
13,
36629,
2682,
2791,
657,
13,
2998,
1795,
40523,
657,
13,
405,
2623,
1314,
1983,
657,
13,
3070,
1558,
28978,
26,
657,
13,
22,
3132,
657,
13,
36629,
2598,
2091,
657,
13,
2998,
23,
29279,
657,
13,
405,
2623,
2078,
1731,
657,
13,
3070,
1558,
34808,
26,
657,
13,
4790,
657,
13,
36629,
4310,
2425,
657,
13,
2998,
23,
24840,
657,
13,
11245,
2414,
21261,
657,
13,
3070,
1507,
39111,
26,
657,
13,
48555,
657,
13,
15,
11645,
28978,
657,
13,
2998,
23,
2231,
4869,
657,
13,
405,
2623,
4051,
1157,
657,
13,
3070,
24943,
1157,
26,
657,
13,
48524,
657,
13,
15,
28676,
2078,
657,
13,
2998,
5332,
37988,
657,
13,
405,
32459,
44550,
657,
13,
3070,
21113,
18,
26,
657,
13,
47760,
657,
13,
15,
25600,
33551,
657,
13,
2998,
23,
4761,
1731,
657,
13,
405,
27412,
19924,
657,
13,
3070,
1238,
47159,
26,
657,
13,
22,
2075,
657,
13,
15,
25191,
30368,
657,
13,
2998,
3459,
38905,
657,
13,
405,
30803,
41289,
657,
13,
49959,
1485,
2231,
26,
657,
13,
45151,
657,
13,
2999,
1899,
11645,
657,
13,
2998,
44093,
657,
13,
11245,
32583,
2718,
657,
13,
49959,
1238,
2780,
26,
657,
13,
22,
1731,
657,
13,
45987,
1065,
1314,
657,
13,
2998,
24,
17464,
657,
13,
405,
36720,
18781,
657,
13,
3070,
24403,
1415,
26,
657,
13,
22,
1954,
657,
13,
15,
29119,
27057,
657,
13,
2998,
24,
1731,
2623,
657,
13,
405,
2718,
27712,
19,
657,
13,
3070,
1954,
30995,
26,
657,
13,
22,
1828,
657,
13,
15,
29558,
24096,
657,
13,
2998,
24,
2718,
5066,
657,
13,
405,
2718,
2857,
3104,
657,
13,
3070,
1954,
4089,
17,
26,
657,
13,
22,
2481,
657,
13,
15,
18897,
23237,
657,
13,
2998,
3865,
16945,
657,
13,
405,
32128,
14454,
657,
13,
3070,
1731,
46250,
26,
657,
13,
4761,
657,
13,
15,
22980,
24294,
657,
13,
2998,
24,
41813,
657,
13,
405,
26514,
33032,
657,
13,
3070,
1495,
27696,
26,
657,
13,
22,
1129,
657,
13,
15,
25540,
22136,
657,
13,
2998,
5607,
46660,
657,
13,
405,
30695,
4790,
657,
13,
3070,
25191,
2996,
26,
657,
13,
45720,
657,
13,
15,
25674,
22370,
657,
13,
2998,
2079,
36042,
657,
13,
405,
2548,
486,
3023,
657,
13,
3070,
25540,
2481,
26,
657,
13,
22,
1558,
657,
13,
15,
25022,
30057,
657,
13,
2919,
405,
46239,
657,
13,
405,
2548,
1415,
5774,
657,
13,
3070,
1983,
25270,
26,
657,
13,
22,
1433,
657,
13,
15,
26276,
13381,
657,
13,
2919,
486,
2079,
657,
13,
405,
2548,
2078,
2920,
657,
13,
3070,
1983,
6052,
26,
657,
13,
22,
1314,
657,
13,
15,
20233,
27877,
657,
13,
33057,
29211,
657,
13,
405,
2548,
3682,
657,
13,
3070,
26279,
3104,
26,
657,
13,
45722,
657,
13,
44698,
1065,
1983,
657,
13,
33057,
3510,
2682,
657,
13,
405,
2548,
2816,
2481,
657,
13,
3070,
1959,
25667,
26,
657,
13,
50055,
657,
13,
15,
29807,
24991,
657,
13,
33057,
1899,
2231,
657,
13,
405,
2548,
36680,
657,
13,
3070,
1959,
48712,
26,
657,
13,
49517,
657,
13,
15,
27367,
21652,
657,
13,
33057,
22,
32759,
657,
13,
405,
30460,
32220,
657,
13,
3070,
1270,
48638,
26,
657,
13,
22,
1157,
657,
13,
15,
28857,
21652,
657,
13,
33057,
5332,
3324,
657,
13,
405,
2548,
4089,
2548,
657,
13,
44427,
1065,
3559,
26,
657,
13,
4869,
657,
13,
15,
23195,
25429,
657,
13,
33057,
5607,
2791,
657,
13,
405,
2670,
1065,
2481,
657,
13,
44427,
1129,
1065,
26,
657,
13,
31495,
657,
13,
15,
27988,
20356,
657,
13,
2919,
14686,
3070,
657,
13,
405,
2670,
21719,
18,
657,
13,
44427,
1495,
3559,
26,
657,
13,
32583,
657,
13,
15,
27019,
18444,
657,
13,
2919,
11623,
1129,
657,
13,
405,
2670,
2670,
2425,
657,
13,
15,
20370,
22800,
26,
657,
13,
24038,
657,
13,
15,
25870,
18444,
657,
13,
2919,
20107,
4310,
657,
13,
405,
31010,
2327,
657,
13,
44427,
2670,
1983,
26,
657,
13,
35402,
657,
13,
15,
26050,
23726,
657,
13,
2919,
1314,
17827,
657,
13,
405,
2670,
2791,
2327,
657,
13,
44427,
2231,
3324,
26,
657,
13,
34801,
657,
13,
46957,
486,
2780,
657,
13,
2919,
20986,
1954,
657,
13,
405,
33372,
38565,
657,
13,
15,
27326,
23539,
26,
657,
13,
32869,
657,
13,
46957,
940,
4531,
657,
13,
2919,
23188,
2091,
657,
13,
11245,
2079,
2670,
657,
13,
44427,
3365,
5333,
26,
657,
13,
36809,
657,
13,
46957,
1238,
1495,
657,
13,
2919,
1129,
19924,
657,
13,
405,
7029,
39761,
657,
13,
44427,
2996,
1983,
26,
657,
13,
36680,
657,
13,
46957,
1270,
1954,
657,
13,
2919,
1238,
34137,
657,
13,
405,
32531,
17657,
657,
13,
15,
31496,
19104,
26,
657,
13,
41583,
657,
13,
46957,
21844,
657,
13,
2919,
24591,
3559,
657,
13,
405,
1821,
2682,
3865,
657,
13,
44427,
3695,
3682,
26,
657,
13,
22,
657,
13,
46957,
2920,
2996,
657,
13,
2919,
1954,
20809,
657,
13,
405,
1821,
2780,
4531,
657,
13,
44427,
25764,
19,
26,
657,
13,
47325,
657,
13,
15,
27033,
28555,
657,
13,
2919,
1731,
36625,
657,
13,
405,
29703,
25429,
657,
13,
15,
29626,
22413,
26,
657,
13,
39357,
657,
13,
46957,
2154,
3901,
657,
13,
2919,
1495,
40401,
657,
13,
405,
1821,
2425,
3720,
657,
13,
44427,
4089,
3980,
26,
657,
13,
40035,
657,
13,
46957,
1795,
2624,
657,
13,
2919,
1983,
23188,
657,
13,
405,
1821,
4531,
3695,
657,
13,
3070,
1821,
48638,
26,
657,
13,
38205,
657,
13,
46957,
4531,
3270,
657,
13,
2919,
26279,
1959,
657,
13,
22914,
940,
30505,
657,
13,
49841,
1065,
1415,
26,
657,
13,
37381,
657,
13,
48891,
830,
21,
657,
13,
2919,
27728,
4309,
657,
13,
22914,
1157,
29331,
657,
13,
49841,
1507,
3980,
26,
657,
13,
45214,
657,
13,
48891,
940,
1959,
657,
13,
2919,
36244,
3388,
657,
13,
22914,
1485,
1954,
657,
13,
49841,
1495,
2780,
26,
657,
13,
48528,
657,
13,
48891,
1238,
6469,
657,
13,
48290,
11645,
657,
13,
22914,
18781,
1959,
657,
13,
15,
32118,
23045,
26,
657,
13,
46589,
657,
13,
15,
31675,
11623,
657,
13,
2919,
27326,
486,
657,
13,
22914,
1314,
49234,
657,
13,
49841,
29769,
26,
657,
13,
49541,
657,
13,
15,
27696,
18781,
657,
13,
2919,
27371,
3720,
657,
13,
22914,
1558,
22291,
657,
13,
49841,
2231,
2327,
26,
657,
13,
3388,
657,
13,
15,
25710,
20986,
657,
13,
2919,
2623,
27137,
657,
13,
22914,
25096,
1495,
657,
13,
3070,
2231,
22745,
26,
657,
13,
40523,
657,
13,
15,
27137,
20986,
657,
13,
2919,
22318,
3324,
657,
13,
22914,
2167,
1415,
657,
13,
3070,
29334,
2682,
26,
657,
13,
34427,
657,
13,
15,
26561,
18742,
657,
13,
2919,
29769,
2414,
657,
13,
405,
3682,
1415,
3901,
657,
13,
15,
30557,
38605,
26,
657,
13,
39925,
657,
13,
15,
27728,
24096,
657,
13,
2919,
31552,
1129,
657,
13,
22914,
23815,
1495,
657,
13,
49841,
4761,
1065,
26,
657,
13,
33808,
657,
13,
48891,
5892,
1415,
657,
13,
2919,
35218,
3134,
657,
13,
22914,
1731,
26427,
657,
13,
49841,
3695,
2075,
26,
657,
13,
35978,
657,
13,
3070,
21601,
1558,
657,
13,
2919,
11785,
1314,
657,
13,
22914,
1495,
3553,
657,
13,
15,
28978,
33781,
26,
657,
13,
41580,
657,
13,
3070,
486,
25707,
657,
13,
2919,
2598,
1954,
657,
13,
22914,
1983,
11245,
657,
13,
15,
27371,
25096,
26,
657,
13,
47521,
657,
13,
39101,
1828,
2091,
657,
13,
2919,
2231,
37452,
657,
13,
22914,
2078,
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13,
31751,
657,
13,
15,
26514,
36243,
657,
13,
2931,
2857,
43134,
657,
13,
405,
4051,
2327,
3553,
657,
13,
15,
32531,
23628,
26,
657,
13,
28688,
657,
13,
15,
30695,
31020,
657,
13,
2931,
2780,
4531,
19,
657,
13,
22544,
2231,
23349,
657,
13,
36676,
1959,
2598,
26,
657,
13,
31980,
657,
13,
15,
2718,
5824,
1731,
657,
13,
2931,
1120,
2091,
657,
13,
405,
4051,
3388,
2857,
657,
13,
36676,
2623,
3104,
26,
657,
13,
33206,
657,
13,
3070,
28256,
2327,
657,
13,
2931,
48170,
4051,
657,
13,
22544,
32642,
2425,
657,
13,
15,
26429,
31952,
26,
657,
13,
32417,
657,
13,
15,
2548,
1314,
4051,
657,
13,
2931,
38612,
3720,
657,
13,
22544,
31938,
2598,
657,
13,
15,
26598,
13464,
26,
657,
13,
31916,
657,
13,
15,
2548,
1495,
5333,
657,
13,
2931,
4051,
33032,
657,
13,
405,
2816,
45192,
657,
13,
36676,
3270,
2998,
26,
657,
13,
35642,
657,
13,
15,
2548,
2327,
4304,
657,
13,
2931,
40486,
2996,
657,
13,
405,
2816,
2327,
5824,
657,
13,
36676,
2996,
4790,
26,
657,
13,
31418,
657,
13,
15,
2548,
2231,
1495,
657,
13,
2931,
3553,
22980,
657,
13,
405,
31046,
23045,
657,
13,
15,
30120,
35273,
26,
657,
13,
41706,
657,
13,
15,
2548,
2816,
2623,
657,
13,
2931,
29796,
2481,
657,
13,
405,
2816,
3388,
3720,
657,
13,
36676,
1795,
2425,
26,
657,
13,
21,
657,
13,
15,
2548,
28933,
657,
13,
2931,
8054,
23,
657,
13,
405,
2816,
4531,
486,
657,
13,
36676,
3459,
3132,
26,
657,
13,
43452,
657,
13,
15,
32220,
45214,
657,
13,
2931,
46841,
4521,
657,
13,
22544,
33206,
3132,
657,
13,
36676,
3865,
3324,
26,
657,
13,
41292,
657,
13,
15,
30460,
44673,
657,
13,
2931,
48602,
1983,
657,
13,
405,
43918,
26492,
657,
13,
3023,
15197,
1495,
26,
657,
13,
43239,
657,
13,
15,
2548,
4089,
2481,
657,
13,
2931,
2414,
27033,
657,
13,
405,
3980,
2670,
4869,
657,
13,
3023,
11442,
6420,
26,
657,
13,
45734,
657,
13,
15,
2670,
2919,
5332,
657,
13,
2931,
2996,
3104,
657,
13,
22544,
2996,
35916,
657,
13,
3023,
16817,
3365,
26,
657,
13,
35124,
657,
13,
15,
2670,
1129,
4051,
657,
13,
2931,
43798,
2091,
657,
13,
22544,
3134,
2548,
657,
13,
3023,
19420,
1558,
26,
657,
13,
46438,
657,
13,
15,
2670,
1270,
4869,
657,
13,
2931,
35978,
1731,
657,
13,
22544,
3388,
16243,
657,
13,
3023,
19880,
2481,
26,
657,
13,
49051,
657,
13,
15,
34626,
22913,
657,
13,
2931,
39357,
4349,
657,
13,
22544,
32583,
2548,
657,
13,
3023,
1415,
21139,
26,
657,
13,
45839,
657,
13,
15,
31010,
20356,
657,
13,
2931,
45722,
1433,
657,
13,
405,
3553,
2075,
3980,
657,
13,
3023,
18294,
2425,
26,
657,
13,
48952,
657,
13,
15,
34107,
21526,
657,
13,
2931,
4761,
30863,
657,
13,
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3553,
2598,
1415,
657,
13,
3023,
1314,
37680,
26,
657,
13,
3270,
657,
13,
15,
33372,
23815,
657,
13,
2931,
4524,
1065,
657,
13,
405,
37452,
12762,
657,
13,
3023,
1433,
36330,
26,
657,
13,
44169,
657,
13,
15,
31952,
26276,
657,
13,
2931,
38172,
2075,
657,
13,
22544,
40393,
1065,
657,
13,
3023,
23628,
26,
657,
13,
39118,
657,
13,
3070,
2079,
27712,
657,
13,
2931,
30610,
5774,
657,
13,
405,
41734,
43665,
657,
13,
3023,
21738,
2670,
26,
657,
13,
44617,
657,
13,
3023,
22914,
1983,
657,
13,
2931,
3695,
28567,
657,
13,
405,
3365,
1433,
3104,
657,
13,
3023,
1507,
27720,
26,
657,
13,
29796,
657,
13,
3023,
25150,
1828,
657,
13,
2931,
22,
41561,
657,
13,
405,
3365,
2091,
3459,
657,
13,
3023,
1129,
26429,
26,
657,
13,
38905,
657,
13,
36676,
1495,
4869,
657,
13,
2931,
23,
16945,
657,
13,
405,
38905,
24991,
657,
13,
3023,
1238,
22416,
26,
657,
13,
46352,
657,
13,
36676,
2718,
486,
657,
13,
2931,
23,
1983,
1983,
657,
13,
405,
3365,
3388,
2718,
657,
13,
3023,
1238,
4846,
16,
26,
657,
13,
46239,
657,
13,
36676,
2857,
3510,
657,
13,
2931,
5705,
22413,
657,
13,
405,
39118,
41060,
657,
13,
3023,
24591,
2670,
26,
657,
13,
46044,
657,
13,
36676,
3553,
3510,
657,
13,
2931,
23,
3980,
3132,
657,
13,
405,
3270,
3312,
4846,
657,
13,
3023,
24137,
4761,
26,
657,
13,
48630,
657,
13,
36676,
3134,
2327,
657,
13,
2931,
46951,
5332,
657,
13,
405,
3270,
1495,
2231,
657,
13,
3023,
1954,
24839,
26,
657,
13,
3365,
657,
13,
36676,
41172,
657,
13,
2931,
3459,
35638,
657,
13,
405,
46438,
29119,
657,
13,
3023,
1954,
17032,
26,
657,
13,
41734,
657,
13,
36676,
48712,
657,
13,
2931,
44093,
1415,
657,
13,
405,
3270,
1899,
4761,
657,
13,
3023,
23753,
3104,
26,
657,
13,
38907,
657,
13,
3023,
8784,
1485,
657,
13,
15,
2079,
1485,
3682,
657,
13,
405,
43239,
40353,
657,
13,
3023,
13381,
2091,
26,
657,
13,
49447,
657,
13,
3023,
1157,
18741,
657,
13,
15,
2079,
2078,
1157,
657,
13,
22544,
38565,
2481,
657,
13,
3023,
2075,
2623,
26,
657,
13,
37452,
657,
13,
3023,
1065,
19707,
657,
13,
15,
2079,
3682,
1157,
657,
13,
28041,
486,
38339,
657,
13,
3023,
1983,
11623,
26,
657,
13,
36189,
657,
13,
3023,
1485,
24970,
657,
13,
15,
2079,
3980,
2624,
657,
13,
405,
35642,
33551,
657,
13,
3023,
1983,
42980,
26,
657,
13,
46900,
657,
13,
3023,
21139,
1495,
657,
13,
15,
39647,
15363,
657,
13,
405,
1899,
4309,
1495,
657,
13,
3023,
2078,
46250,
26,
657,
13,
48638,
657,
13,
3023,
1314,
35378,
657,
13,
15,
34808,
35978,
657,
13,
405,
1899,
3388,
6469,
657,
13,
3023,
1959,
2780,
26,
657,
13,
48724,
657,
13,
3023,
1433,
33042,
657,
13,
16,
657,
13,
405,
1899,
3459,
2327,
657,
13,
3023,
1270,
2075,
26,
657,
13,
42875,
657,
13,
3023,
24096,
1495,
657,
13,
3064,
19244,
657,
13,
28041,
15982,
1507,
657,
13,
3023,
26717,
3720,
26,
657,
13,
3553,
657,
13,
3023,
1507,
3104,
657,
13,
3064,
30336,
657,
13,
28041,
1065,
4310,
657,
13,
3023,
36042,
5607,
26,
657,
13,
20,
3388,
657,
13,
3023,
24991,
1485,
657,
13,
3064,
50080,
657,
13,
28041,
1415,
29211,
657,
13,
3023,
39195,
3695,
26,
657,
13,
49211,
657,
13,
3023,
22745,
2414,
657,
13,
3064,
3365,
657,
13,
28041,
1433,
28857,
657,
13,
3023,
2091,
2857,
26,
657,
13,
20,
3134,
657,
13,
3023,
28727,
6052,
657,
13,
44318,
2078,
657,
13,
28041,
1507,
13348,
657,
13,
3023,
2682,
28592,
26,
657,
13,
20,
2791,
657,
13,
3023,
1954,
657,
13,
3064,
42802,
657,
13,
28041,
2167,
1558,
657,
13,
3023,
14877,
4309,
26,
657,
13,
47372,
657,
13,
3023,
1731,
3312,
657,
13,
8784,
46957,
657,
13,
405,
5237,
1507,
2931,
657,
13,
3023,
31128,
2780,
26,
657,
13,
20,
2414,
657,
13,
3023,
9031,
4309,
657,
13,
8784,
24096,
657,
13,
28041,
24693,
3132,
657,
13,
3023,
32459,
1983,
26,
657,
13,
46572,
657,
13,
3023,
2075,
28872,
657,
13,
8784,
33916,
657,
13,
405,
26704,
34483,
657,
13,
3023,
2718,
26200,
26,
657,
13,
43918,
657,
13,
3023,
27367,
1314,
657,
13,
8784,
32576,
657,
13,
28041,
1983,
39380,
657,
13,
3023,
2548,
21626,
26,
657,
13,
47915,
657,
13,
3023,
2078,
3901,
657,
13,
27956,
1954,
657,
13,
28041,
1959,
29331,
657,
13,
3023,
2670,
2999,
26,
657,
13,
3980,
657,
13,
3023,
1959,
38073,
657,
13,
8784,
3324,
657,
13,
405,
5066,
1314,
5607,
657,
13,
3023,
31952,
3365,
26,
657,
13,
38605,
657,
13,
3023,
1270,
4310,
657,
13,
8784,
35549,
657,
13,
28041,
27326,
3510,
657,
13,
3023,
29703,
1433,
26,
657,
13,
40486,
657,
13,
3023,
33400,
2931,
657,
13,
940,
1238,
3980,
657,
13,
28041,
32182,
2682,
657,
13,
43977,
1415,
1558,
26,
657,
13,
41948,
657,
13,
3023,
34159,
3682,
657,
13,
15377,
18638,
657,
13,
28041,
2718,
32148,
657,
13,
43977,
1828,
1065,
26,
657,
13,
37864,
657,
13,
3023,
28460,
2682,
657,
13,
940,
1954,
3365,
657,
13,
28041,
2670,
31496,
657,
13,
43977,
18938,
16,
26,
657,
13,
31046,
657,
13,
3023,
2327,
486,
657,
13,
940,
1495,
657,
13,
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2414,
1157,
3324,
657,
13,
43977,
2718,
4521,
26,
657,
13,
44218,
657,
13,
3023,
2623,
1157,
657,
13,
940,
2075,
2857,
657,
13,
405,
41813,
20809,
657,
13,
43977,
2231,
4349,
26,
657,
13,
48096,
657,
13,
3023,
2718,
2481,
657,
13,
40403,
5892,
657,
13,
405,
2414,
2920,
3132,
657,
13,
3023,
2231,
31496,
26,
657,
13,
40427,
657,
13,
3023,
2548,
26279,
657,
13,
940,
1959,
3901,
657,
13,
405,
2414,
3104,
5066,
657,
13,
15,
27260,
20198,
26,
657,
13,
43697,
657,
13,
3023,
2670,
21844,
657,
13,
940,
1270,
6469,
657,
13,
405,
34287,
41290,
657,
13,
43977,
3388,
4310,
26,
657,
13,
2816,
657,
13,
3023,
26598,
2078,
657,
13,
15197,
22995,
657,
13,
405,
17544,
41948,
657,
13,
15,
34825,
30863,
26,
657,
13,
44966,
657,
13,
43977,
25948,
657,
13,
940,
2091,
5332,
657,
13,
405,
2996,
1495,
2624,
657,
13,
15,
31115,
41734,
26,
657,
13,
49934,
657,
13,
43977,
1983,
3270,
657,
13,
940,
33394,
657,
13,
405,
2996,
2231,
1954,
657,
13,
15,
31911,
29703,
26,
657,
13,
20,
2857,
657,
13,
43977,
2718,
4846,
657,
13,
940,
2623,
4524,
657,
13,
405,
2996,
2996,
4846,
657,
13,
3023,
1120,
25191,
26,
657,
13,
49489,
657,
13,
43977,
2780,
4521,
657,
13,
940,
2548,
2078,
657,
13,
405,
2996,
5332,
2327,
657,
13,
40350,
940,
4761,
26,
657,
13,
45326,
657,
13,
3023,
33459,
2414,
657,
13,
940,
2670,
4790,
657,
13,
28041,
1899,
34125,
657,
13,
40350,
1129,
3132,
26,
657,
13,
47576,
657,
13,
15,
34825,
18742,
657,
13,
13464,
16562,
657,
13,
405,
39380,
19104,
657,
13,
40350,
2078,
26,
657,
13,
20,
3559,
657,
13,
15,
31115,
23148,
657,
13,
13464,
25022,
657,
13,
28041,
2414,
20809,
657,
13,
40350,
15277,
22,
26,
657,
13,
20,
3682,
657,
13,
15,
31911,
34770,
657,
13,
13464,
29416,
657,
13,
405,
27310,
24970,
657,
13,
40350,
2598,
26,
657,
13,
20,
3901,
657,
13,
3023,
1120,
38073,
657,
13,
940,
2231,
2425,
657,
13,
405,
35809,
27033,
657,
13,
15,
30505,
17827,
26,
657,
13,
4051,
657,
13,
40350,
1433,
3070,
657,
13,
940,
2857,
1983,
657,
13,
28041,
2154,
25667,
657,
13,
40350,
41292,
26,
657,
13,
20,
2670,
657,
13,
40350,
1983,
1314,
657,
13,
940,
2780,
3134,
657,
13,
405,
3134,
1238,
6052,
657,
13,
40350,
43950,
26,
657,
13,
49561,
657,
13,
40350,
2548,
1485,
657,
13,
940,
1120,
1983,
657,
13,
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3134,
3682,
1485,
657,
13,
40350,
30610,
26,
657,
13,
46096,
657,
13,
40350,
2920,
3720,
657,
13,
13348,
17430,
657,
13,
405,
42548,
22800,
657,
13,
40350,
25764,
21,
26,
657,
13,
44468,
657,
13,
15,
29228,
21940,
657,
13,
940,
4310,
1954,
657,
13,
405,
30924,
33459,
657,
13,
40350,
6052,
1415,
26,
657,
13,
44465,
657,
13,
15,
33032,
2682,
657,
13,
13348,
35435,
657,
13,
28041,
36088,
1157,
657,
13,
3023,
41706,
3132,
26,
657,
13,
20,
2682,
657,
13,
15,
29334,
29059,
657,
13,
940,
43918,
657,
13,
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2623,
23721,
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1959,
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657,
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2079,
16,
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2713,
47106,
2670,
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657,
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1129,
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657,
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2713,
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2425,
4846,
657,
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25257,
3980,
27936,
657,
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2713,
1959,
23516,
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19707,
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3312,
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19708,
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2327,
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26,
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3312,
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19708,
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657,
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2996,
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1959,
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33535,
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3312,
2079,
21315,
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20107,
3553,
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37466,
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2998,
405,
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657,
13,
1485,
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2780,
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2996,
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2996,
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26,
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17430,
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34801,
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2791,
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2425,
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3980,
657,
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2791,
1495,
2791,
26,
657,
13,
28460,
657,
13,
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32583,
19707,
657,
13,
20219,
46302,
657,
13,
486,
22047,
1495,
657,
13,
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2791,
2548,
3553,
26,
657,
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31496,
657,
13,
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31495,
32869,
657,
13,
1415,
657,
13,
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24403,
657,
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36879,
22172,
26,
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29211,
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2998,
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2598,
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2999,
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657,
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34229,
26,
657,
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27326,
657,
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2998,
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37680,
657,
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1415,
3023,
1983,
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24409,
2624,
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2791,
37688,
26,
657,
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31380,
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2998,
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26007,
657,
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3312,
2481,
657,
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1954,
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657,
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19244,
26,
657,
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20370,
657,
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2998,
19707,
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2919,
2078,
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1731,
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3312,
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2998,
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2998,
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6052,
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30924,
3553,
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2998,
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3388,
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2075,
40401,
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37601,
46660,
26,
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26582,
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2998,
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1959,
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25948,
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33916,
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2998,
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3388,
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5824,
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32637,
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2998,
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1959,
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2998,
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30368,
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2624,
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657,
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2998,
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3388,
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1270,
25022,
657,
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3312,
48894,
6420,
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2998,
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2598,
2598,
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2414,
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3312,
24,
1959,
4790,
26,
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27936,
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2998,
31794,
5607,
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18444,
42548,
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26717,
2425,
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3312,
5824,
38056,
26,
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33638,
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2998,
34825,
3682,
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2920,
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30273,
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4869,
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24,
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25838,
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2998,
3510,
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16616,
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30273,
1507,
3980,
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3312,
43587,
2624,
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2998,
2857,
44980,
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4310,
1433,
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27877,
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22318,
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2998,
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44218,
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23045,
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37804,
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2670,
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3459,
5607,
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2425,
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32583,
23195,
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21288,
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2998,
1899,
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] | 1.90807 | 24,388 |
"""
function vertex3(para, extK = [DiagTree.getK(para.totalLoopNum, 1), DiagTree.getK(para.totalLoopNum, 2)],
subdiagram = false; name = :Ξ3, chan = [PHr, PHEr, PPr, Alli], resetuid = false)
Generate 3-vertex diagrams using Parquet Algorithm.
With imaginary-time variables, all vertex3 generated has the same bosonic Tidx ``extT[1]=para.firstTauIdx`` and the incoming fermionic Tidx ``extT[2]=para.firstTauIdx+1``.
#Arguments
- `para` : parameters. It should provide internalLoopNum, interactionTauNum, firstTauIdx
- `extK` : basis of external loops as a vector [bosonic leg, fermionic in, fermionic out].
- `subdiagram` : a sub-vertex or not
- `name` : name of the vertex
- `chan` : vector of channels of the current 4-vertex.
- `resetuid` : restart uid count from 1
# Output
- A DataFrame with fields :response, :extT, :diagram, :hash.
"""
function vertex3(para, extK=[DiagTree.getK(para.totalLoopNum, 1), DiagTree.getK(para.totalLoopNum, 2)],
subdiagram=false; name=:Ξ3, chan=[PHr, PHEr, PPr, Alli], resetuid=false)
resetuid && uidreset()
@assert para.diagType == Ver3Diag
@assert para.innerLoopNum >= 1 "Only generates vertex corrections with more than one internal loops."
for k in extK
@assert length(k) >= para.totalLoopNum "expect dim of extK>=$(para.totalLoopNum), got $(length(k))"
end
extK = [k[1:para.totalLoopNum] for k in extK]
q, Kin = extK[1], extK[2]
Kout = length(extK) == 3 ? extK[3] : Kin .- q
@assert ((q β Kin) == false) && ((q β Kout) == false) "The bosonic q cann't be same as the fermionic k. Ohterwise the proper diagram check will fail!"
extK = [q, Kin, Kout]
if Proper in para.filter
para = reconstruct(para, transferLoop=q)
end
t0 = para.firstTauIdx
vertex3 = DataFrame()
# if para.innerLoopNum == 0
# push!(vertex3, (response = UpUp, extT = (t0, t0, t0), diagram = ver3diag))
# end
if (para.extra isa ParquetBlocks) == false
para = reconstruct(para, extra=ParquetBlocks())
end
K = zero(q)
LoopIdx = para.firstLoopIdx
K[LoopIdx] = 1.0
# extT = (t0, t0 + 1)
legK = [Kin, Kout, K, K .+ q]
######################## Ξ 0 = GG #########################################
for (oVer4, oGin, oGout) in orderedPartition(para.innerLoopNum - 1, 3, 0)
# ! Vertex4 must be in the first place, because we want to make sure that the TinL of the vertex4 start with t0+1
idx, maxLoop = findFirstLoopIdx([oVer4, oGin, oGout], LoopIdx + 1)
@assert maxLoop <= para.totalLoopNum "maxLoop = $maxLoop > $(para.totalLoopNum)"
Ver4Kidx, GinKidx, GoutKidx = idx
ver4t0 = para.hasTau ? para.firstTauIdx + 1 : para.firstTauIdx
idx, maxTau = findFirstTauIdx([oVer4, oGin, oGout], [Ver4Diag, GreenDiag, GreenDiag], ver4t0, para.interactionTauNum)
@assert maxTau <= para.totalTauNum "maxTau = $maxTau > $(para.totalTauNum)"
Ver4Tidx, GinTidx, GoutTidx = idx
if isValidG(para.filter, oGin) && isValidG(para.filter, oGout)
paraGin = reconstruct(para, diagType=GreenDiag, innerLoopNum=oGin,
firstLoopIdx=GinKidx, firstTauIdx=GinTidx)
paraGout = reconstruct(para, diagType=GreenDiag, innerLoopNum=oGout,
firstLoopIdx=GoutKidx, firstTauIdx=GoutTidx)
paraVer4 = reconstruct(para, diagType=Ver4Diag, innerLoopNum=oVer4,
firstLoopIdx=Ver4Kidx, firstTauIdx=Ver4Tidx)
ver4 = vertex4(paraVer4, legK, chan, true)
if isnothing(ver4) || isempty(ver4)
continue
end
if para.hasTau
@assert all(x -> x[INL] == ver4t0, ver4[:, :extT]) "The TinL of the inner Ξ4 must be firstTauIdx+1"
end
#transform extT coloum into extT for Vertex4 and the extT for Gin and Gout
df = transform(ver4, :extT => ByRow(x -> [(t0, x[INL], x[OUTL]), (t0, x[INR]), (x[OUTR], t0)]) => [:extT, :GinT, :GoutT])
groups = mergeby(df, [:response, :GinT, :GoutT, :extT], operator=Sum())
for v4 in eachrow(groups)
response = v4[:response]
@assert response == UpUp || response == UpDown
#type: Instant or Dynamic
ver3id = Ver3Id(para, response, k=extK, t=v4[:extT])
gin = green(paraGin, K, v4[:GinT], true, name=:Gin)
gout = green(paraGout, K .+ q, v4[:GoutT], true, name=:Gout)
@assert gin isa Diagram && gout isa Diagram
ver3diag = Diagram(ver3id, Prod(), [gin, gout, v4[:diagram]], name=name)
push!(vertex3, (response=response, extT=v4[:extT], diagram=ver3diag))
end
end
end
if isempty(vertex3)
return DataFrame(response=[], extT=[], diagram=[])
end
Factor = 1 / (2Ο)^para.loopDim
ver3 = mergeby(vertex3, [:response, :extT]; name=name, factor=Factor,
getid=g -> Ver3Id(para, g[1, :response], k=extK, t=g[1, :extT])
)
return ver3
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] | 2.131141 | 2,402 |
f = x->0;
u_exact = x->x;
a = 0;
b = 1;
n = 9;
problem = FiniteDifferenceBVPProblem(0, 1, n, f, DirichletBC(0), DirichletBC(1));
assemble_system!(problem);
u = solve_bvp(problem);
err = @. problem.x - u;
# check error
norm(err, Inf)< 1e-14 | [
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] | 2.141593 | 113 |
# This file is a part of TypeDBClient. License is MIT: https://github.com/Humans-of-Julia/TypeDBClient.jl/blob/main/LICENSE
struct ThingType <: AbstractThingType
label::Label
is_root::Bool
end
# Porting note: Java client calls into RequestBuilder but it really
# has nothing to to with requests... I think it's probably better
# migrating the function here.
function proto(t::AbstractThingType)
Proto._Type(
label = t.label.name,
encoding = encoding(t)
)
# return ThingTypeRequestBuilder.proto_thing_type(t.label, encoding(t))
end
# Contract: all subtypes of AbstractThingType should have these two fields
is_root(t::AbstractThingType) = t.is_root
label(t::AbstractThingType) = t.label
# ------------------------------------------------------------------------
# Remote functions
# ------------------------------------------------------------------------
function set_supertype(r::RemoteConcept{C,T}) where {
C <: AbstractThingType, T <: AbstractCoreTransaction
}
req = ThingTypeRequestBuilder.set_supertype_req(r.concept.label)
res = execute(r.transaction, req)
end
function get_supertype(r::RemoteConcept{C,T}) where {
C <: AbstractThingType, T <: AbstractCoreTransaction
}
req = TypeRequestBuilder.get_supertype_req(r.concept.label)
res = execute(r.transaction, req)
typ = res.type_res.type_get_supertype_res._type
return instantiate(typ)
end
function get_supertypes(r::RemoteConcept{C,T}) where {
C <: AbstractThingType, T <: AbstractCoreTransaction
}
req = TypeRequestBuilder.get_supertypes_req(r.concept.label)
res = execute(r.transaction, req)
typs = res.type_res_part.type_get_supertypes_res_part.types
return instantiate.(typs)
end
function get_subtypes(r::RemoteConcept{C,T}) where {
C <: AbstractThingType, T <: AbstractCoreTransaction
}
req = TypeRequestBuilder.get_subtypes_req(r.concept.label)
res = execute(r.transaction, req)
typs = res.type_res_part.type_get_subtypes_res_part.types
return instantiate.(typs)
end
function get_instances(r::RemoteConcept{C,T}) where {
C <: AbstractThingType, T <: AbstractCoreTransaction
}
req = ThingTypeRequestBuilder.get_instances_req(r.concept.label)
res = stream(r.transaction, req)
return instantiate.(collect(Iterators.flatten(
r.type_res_part.thing_type_get_instances_res_part.things for r in res)))
end
function set_abstract(r::RemoteConcept{C,T}) where {
C <: AbstractThingType, T <: AbstractCoreTransaction
}
req = ThingTypeRequestBuilder.set_abstract_req(r.concept.label)
execute(r.transaction, req)
end
function unset_abstract(r::RemoteConcept{C,T}) where {
C <: AbstractThingType, T <: AbstractCoreTransaction
}
req = ThingTypeRequestBuilder.unset_abstract_req(r.concept.label)
execute(r.transaction, req)
end
function set_plays(
r::RemoteConcept{C,T},
role_type::AbstractRoleType,
overridden_role_type::Optional{AbstractRoleType} = nothing
) where {C <: AbstractThingType, T <: AbstractCoreTransaction}
req = ThingTypeRequestBuilder.set_plays_req(
r.concept.label,
proto(role_type),
overridden_role_type
)
execute(r.transaction, req)
end
function set_owns(
r::RemoteConcept{C,T},
attribute_type::AbstractAttributeType,
is_key::Bool = false
) where {C <: AbstractThingType, T <: AbstractCoreTransaction}
req = ThingTypeRequestBuilder.set_owns_req(
r.concept.label,
is_key,
proto(attribute_type)
)
execute(r.transaction, req)
end
function get_owns(
r::RemoteConcept{C,T},
value_type::Optional{EnumType} = nothing,
keys_only::Bool = false
) where {C <: AbstractThingType, T <: AbstractCoreTransaction}
req = ThingTypeRequestBuilder.get_owns_req(r.concept.label, value_type, keys_only)
res = stream(r.transaction, req)
return instantiate.(collect(Iterators.flatten(
r.type_res_part.thing_type_get_owns_res_part.attribute_types for r in res)))
end
function get_plays(r::RemoteConcept{C,T}) where {
C <: AbstractThingType, T <: AbstractCoreTransaction
}
req = ThingTypeRequestBuilder.get_plays_req(r.concept.label)
res = stream(r.transaction, req)
return instantiate.(collect(Iterators.flatten(
r.type_res_part.thing_type_get_plays_res_part.roles for r in res)))
end
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] | 2.757768 | 1,577 |
## Apache Arrow
@info "Arrow.jl is available - including functionality to read / write JDF files"
using .Arrow
function read_table(::ArrowFormat, filename:: Union{AbstractString, IO}; kwargs...)
return Arrow.Table(filename; kwargs...)
end
function write_table!(::ArrowFormat, filename:: Union{AbstractString, IO}, table; kwargs...)
Arrow.write(filename, table; kwargs...)
nothing
end
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] | 3.2 | 125 |
import Knet.Ops20: elu, relu, selu, sigm, eluback, reluback, seluback, sigmback
import Base.Broadcast: broadcasted
import Knet
using Knet.KnetArrays: KnetArray, DevArray, Bcasted
using Knet.LibKnet8: @knet8
using CUDA: CuArray, CuPtr
using AutoGrad: AutoGrad, @primitive
# Specialize tanh gradient for DevArrays here. The others have been declared in Ops20 as generic gradients.
tanhback(dyi::T,yi::T) where {T<:Number} = dyi*(T(1)-yi*yi)
@primitive tanh(x::DevArray),dy,y tanhback.(dy,y)
@primitive tanhback(dy,y),ddx ddx.*(1 .- y.*y) ddx.*(-2 .* dy.*y)
for (R,P) in ((KnetArray,Ptr), (CuArray,CuPtr)), T in (Float32,Float64); S = sizeof(T) * 8
for f in ("elu","relu","selu","sigm")
J, F = Symbol(f), "$(f)_$S"; M = which(@__MODULE__,J)
@eval begin
function broadcasted(::typeof($J),x::$R{$T})
y = similar(x)
@knet8($F,(Cint,$P{$T},$P{$T}),length(y),x,y)
return y
end
# Bcasted methods -- only needed for KnetArray
($M).$J(x::Bcasted{<:$R{$T}}) = broadcasted($J, x.value) |> Bcasted
broadcasted(::typeof($J),x::Bcasted{<:$R{$T}}) = broadcasted($J, x.value) |> Bcasted
end
end
for f in ("eluback","reluback","seluback","sigmback","tanhback")
J, F = Symbol(f), "$(f)_$(S)_11"; M = which(@__MODULE__,J)
@eval begin
function broadcasted(::typeof($J),x::$R{$T},y::$R{$T})
z = similar(x)
@knet8($F,(Cint,$P{$T},$P{$T},$P{$T}),length(z),x,y,z)
return z
end
# Bcasted methods -- only needed for KnetArray
($M).$J(x::Bcasted{<:$R{$T}}, y::Bcasted{<:$R{$T}}) = broadcasted($J, x.value, y.value) |> Bcasted
($M).$J(x::$R{$T}, y::Bcasted{<:$R{$T}}) = broadcasted($J, x, y.value) |> Bcasted
($M).$J(x::Bcasted{<:$R{$T}}, y::$R{$T}) = broadcasted($J, x.value, y) |> Bcasted
broadcasted(::typeof($J),x::Bcasted{<:$R{$T}}, y::Bcasted{<:$R{$T}}) = broadcasted($J, x.value, y.value) |> Bcasted
broadcasted(::typeof($J),x::$R{$T}, y::Bcasted{<:$R{$T}}) = broadcasted($J, x, y.value) |> Bcasted
broadcasted(::typeof($J),x::Bcasted{<:$R{$T}}, y::$R{$T}) = broadcasted($J, x.value, y) |> Bcasted
end
end
end
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] | 1.869565 | 1,242 |
struct RootNode <: Node
children::Tuple{Any}
function RootNode(node)
new((node,))
end
end
function _traversal(node, visitfn::Function, visitfnargs::Union{Any, Nothing}, visited)
if haskey(visited, node)
return visited[node]
else
new_children = [_traversal(child, visitfn, visitfnargs, visited) for child in node.children]
result = visitfnargs !== nothing ?
visitfn(visited, node, visitfnargs, new_children...) :
visitfn(visited, node, new_children...)
visited[node] = result
return result
end
end
function _posttraversal(node, visitfn::Function, visitfnargs::Union{Any, Nothing}, visited)
if haskey(visited, node)
return visited[node]
else
result = visitfnargs !== nothing ?
visitfn(node, visitfnargs, (x -> _posttraversal(x, visitfn, visitfnargs, visited))) :
visitfn(node, (x -> _posttraversal(x, visitfn, visitfnargs, visited)))
visited[node] = result
return result
end
end
"""
traversal(node, pretraversalfn::Function, visitfn::Function,
pretraversalfnargs::Union{Any, Nothing}, visitfnargs::Union{Any, Nothing},
visited::Union{Dict, Nothing}, posttraversal=false)
Traverses an AST, calling visitfn on the nodes of the tree.
# Arguments
- `node`: the root of the tree to traverse. Assumes that each node has children
attribute.
- `pretraversalfn::Function` A function to be executed before the traversal
- `visitfn::Function` The function that is executed on each visit. If
posttraversal is false, and visitfnargs nothing, the function must have signature
`visitfn(visited, node, new_children...)`. If visitfnargs is not nothing, the
signature is `visitfn(visited, node, visitfnargs, new_children...). If
posttraversal is true, the signature must be `visitfn(node, fn)`,
or `visitfn(node, visitfn, fn)`, where `fn` is a function that calls traversal
on a node.
- `pretraversalfnargs::Union{Any, Nothing}` Arguments for the pretraversalfn.
- `visitfnargs::Union{Any, Nothing}` Arguments for traversalfn.
- `visited::Union{Dict, Nothing}` If not nothing, a dictionary with the results
of calling traversalfn on different nothing. If nothing, this dictionary is
created in traversal.
- `posttraversal=false` Determines the way traversal traverses each node. If
`posttraversal=false`, the traversal first processes the children of a node,
and then the parent node (example: [`togem(node::Node)`](@ref)). If
`posttraversal=true`, the visitfn first processes the parent and then calls
visitfn on its children if needed (example:
[`differentiate(A::AbstractTensor{0}, x::VariableTensor{0})`](@ref)).
"""
function traversal(node, pretraversalfn::Function, visitfn::Function,
pretraversalfnargs::Union{Any, Nothing}, visitfnargs::Union{Any, Nothing},
visited::Union{Dict, Nothing}, posttraversal::Bool)
if visited === nothing
visited = Dict{Any, Any}()
end
root = RootNode(node)
root = pretraversalfnargs !== nothing ? pretraversalfn(root, pretraversalfnargs...) : pretraversalfn(root)
root = posttraversal ? _posttraversal(
root, visitfn, visitfnargs, visited) : _traversal(
root, visitfn, visitfnargs, visited)
return root.children[1]
end
function traversal(node, pretraversalfn::Function, visitfn::Function,
pretraversalfnargs::Union{Any, Nothing}, visitfnargs::Union{Any, Nothing},
visited::Union{Dict, Nothing})
return traversal(node, pretraversalfn, visitfn, pretraversalfnargs,
visitfnargs, visited, false)
end | [
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] | 2.67052 | 1,384 |
using StatsBase, Distributions, IterTools #StatsPlots
function get_freq(h::Histogram, val)
x = searchsortedfirst.(h.edges, val)
h.weights[x...]
end
function get_bin(h::Histogram, val)
bin = searchsortedfirst.(h.edges, val)
bin[1] - 1
end
function get_probability(h::Histogram, val)
x = searchsortedfirst.(h.edges, val)
h.weights[x...]/sum(h.weights)
end
function mutual_information(X::Vector{Vector{T}}, Y::Vector{Vector{T}}) where T <: Number
hx = fit(Histogram, (X...,))
hy = fit(Histogram, (Y...,))
XY = vcat(X,Y)
hxy = fit(Histogram, (XY...,))
MI = 0.0
rx = create_matrix_range(size(hx.weights))
ry = create_matrix_range(size(hy.weights))
for x in Iterators.product(rx...)
p_x = get_bin_probability(hx, x...)
for y in Iterators.product(ry...)
p_y = get_bin_probability(hy, y...)
index_xy = (x..., y...)
p_xy = get_bin_probability(hxy, index_xy...)
if p_xy != 0
MI += p_xy*log(p_xy/(p_x*p_y))
end
end
end
MI
end
function mutual_information(p_x, p_y, p_xy)
MI = 0.0
for x = 1:length(p_x)
for y = 1:length(p_y)
prob_xy = p_xy[x, y]
prob_log = prob_xy/(p_x[x]*p_y[y])
if prob_xy != 0 && prob_log != NaN
MI += prob_xy*log(prob_log)
end
end
end
MI
end
function mutual_information_unit(X::Vector{T}, Y::Vector{T}) where T <: Int
max_X, max_Y = maximum(X), maximum(Y)
hx = fit(Histogram, (X), 0:max_X, closed=:right)
hy = fit(Histogram, (Y), 0:max_Y, closed=:right)
XY = hcat([X],[Y])
hxy = fit(Histogram, (X, Y), (0:max_X, 0:max_Y), closed=:right)
MI = 0.0
rx = create_matrix_range(size(hx.weights))
ry = create_matrix_range(size(hy.weights))
for x in Iterators.product(rx...)
p_x = get_bin_probability(hx, x...)
for y in Iterators.product(ry...)
p_y = get_bin_probability(hy, y...)
index_xy = (x..., y...)
p_xy = get_bin_probability(hxy, index_xy...)
if p_xy != 0
MI += p_xy*log(p_xy/(p_x*p_y))
end
end
end
MI
end
function mutual_information_unit(X::Vector{T}, Y::Vector{T}, lim_X, lim_Y) where T <: Int
min_X, max_X = lim_X
min_Y, max_Y = lim_Y
range_X = min_X:max_X
range_Y = min_Y:max_Y
hx = fit(Histogram, (X), range_X, closed=:right)
hy = fit(Histogram, (Y), range_Y, closed=:right)
XY = hcat([X],[Y])
hxy = fit(Histogram, (X, Y), (range_X, range_Y), closed=:right)
MI = 0.0
rx = create_matrix_range(size(hx.weights))
ry = create_matrix_range(size(hy.weights))
for x in Iterators.product(rx...)
p_x = get_bin_probability(hx, x...)
for y in Iterators.product(ry...)
p_y = get_bin_probability(hy, y...)
index_xy = (x..., y...)
p_xy = get_bin_probability(hxy, index_xy...)
if p_xy != 0
MI += p_xy*log(p_xy/(p_x*p_y))
end
end
end
MI
end
get_bin_probability(h::Histogram, x...) = h.weights[x...]/sum(h.weights)
function create_matrix_range(t::Tuple)
tuple_length = length(t)
ranges = []
for r = 1:tuple_length
range = 1:t[r]
push!(ranges, range)
end
ranges
end
function calculate_joint_probability(X::Vector{<:Number}, Y::Vector{<:Number}, lim_X, lim_Y)
min_X, max_X = lim_X
min_Y, max_Y = lim_Y
range_X = min_X:max_X
range_Y = min_Y:max_Y
p_x = fit(Histogram, (X), range_X, closed=:right)
p_y = fit(Histogram, (Y), range_Y, closed=:right)
p_xy = fit(Histogram, (X, Y), (range_X, range_Y), closed=:right)
p_X = p_x.weights/sum(p_x.weights)
p_Y = p_y.weights/sum(p_y.weights)
p_XY = p_xy.weights/sum(p_xy.weights)
return p_X, p_Y, p_XY
end
function calculate_joint_probability(X::Vector{<:Number}, Y::Vector{<:Number}, nbins)
p_x = fit(Histogram, (X), nbins=nbins[1])
p_y = fit(Histogram, (Y), nbins=nbins[2])
p_xy = fit(Histogram, (X, Y), nbins=nbins)
p_X = p_x.weights/sum(p_x.weights)
p_Y = p_y.weights/sum(p_y.weights)
p_XY = p_xy.weights/sum(p_xy.weights)
return p_X, p_Y, p_XY
end
include("layer_histogram.jl")
function calculate_joint_probability(X::Vector{<:Number}, Y::Vector{<:Number}, limits_X, limits_Y, nbins)
p_x = create_bin_histogram(X, nbins[1], limits_X[1], limits_X[2])
p_y = create_bin_histogram(Y, nbins[2], limits_Y[1], limits_Y[2])
p_xy = fit(Histogram, (X, Y), (p_x.edges[1], p_y.edges[1]))
p_X = p_x.weights/sum(p_x.weights)
p_Y = p_y.weights/sum(p_y.weights)
p_XY = p_xy.weights/sum(p_xy.weights)
return p_X, p_Y, p_XY
end
# Test
#=
X = randn(10000)
Y = randn(10000)
println("Mutual Information: I(X; Y) = ", mutual_information([X], [Y]))
println("Mutual Information: I(Y; X) = ", mutual_information([Y], [X]))
println("Mutual Information: I(X; (Y,X)) = ", mutual_information([X], [Y, X]))
println("Mutual Information: I(X; X) = ", mutual_information([X], [X]))
println("Mutual Information: I(Y; Y) = ", mutual_information([Y], [Y]))
=#
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] | 1.998455 | 2,589 |
# # Generating and manipulating a tetrahedral mesh
# In this tutorial, we will learn
#
# - How to generate a simple mesh.
# - How to inspect the mesh.
# - How to inspect the data stored in the mesh.
# - How to export the mesh and visualize it.
# The tutorial comes with a file containing representation of the mesh for
# visualization in the [Paraview](https://www.paraview.org/) format (VTK). One can
# visualize this mesh by loading the file `mymesh.vtu` with `paraview.exe`.
# One of the aims of this tutorial is to work with this mesh: to generate it,
# inspect it, and eventually to store it in the VTK format.
# We will generate the tetrahedral mesh inside a rectangular block.
# The block will have the dimensions shown below:
a, b, c = 2.0, 2.5, 3.0
# The tetrahedra will be generated in a regular pattern, with the number of
# edges per side of the block given as
na, nb, nc = 2, 2, 3
# The mesh will be generated by the package `MeshSteward`.
using MeshSteward: T4block
conn = T4block(a, b, c, na, nb, nc);
# The variable `conn` is an incidence relation. This will become the base
# relation of the mesh.
using MeshSteward: Mesh, attach!
# The mesh is first created.
m = Mesh()
# Then the ``(3, 0)`` incidence relation, which defines the tetrahedral elements in terms of the vertices at their corners, is attached to it.
attach!(m, conn);
# We can now inspect the mesh by printing its summary.
println(summary(m))
# We can see that the relation links the tetrahedral elements and the vertices.
# In addition to its being the only relation in the mesh (at the moment), it is
# also what is called the *base* incidence relation.
# The relations in the mesh are accessed by code. The base relation is
# described by the code:
using MeshSteward: basecode
@show irc = basecode(m);
# We can retrieve the base incidence relation from the mesh as
using MeshSteward: increl
conn = increl(m, basecode(m));
# We can access the data stored in the incidence relation as follows. For
# instance, the connectivity of the mesh can be accessed with regular array
# indexing. The numbers of the vertices for the first tetrahedron are:
@show conn[1]
# We can check that the incidence relation stores four vertices per tetrahedron as
using MeshCore: nentities
@show nentities(conn, 1) == 4
# We checked it for the first tetrahedron, but for this type of incidence
# relation all relations store the same number of entities.
# The vertex shape collection stores the locations of the vertices as an
# attribute.
using MeshCore: attribute
@show geom = attribute(conn.right, "geom");
# The coordinates of the vertices of the first tetrahedron can be accessed as
for j in 1:nentities(conn, 1)
k = conn[1, j]
println("Vertex $(j): global number $(k)")
println(" $(geom[k])")
end
# The mesh may be exported for viewing with the "Paraview" visualization
# program. Note that a file of this name is provided with the installation.
# When you run this tutorial you should get the same file. So a quick way of
# checking that things are working as expected is to visualize what you
# should get by loading the file `mymesh.vtu` with `paraview.exe`, and only
# then running the export in the two lines below and looking at the results again.
using MeshSteward: vtkwrite
vtkwrite("mymesh", conn)
# Start "Paraview", load the file `mymesh.vtu` and select for instance view as
# "Surface with Edges".
# Provided `paraview.exe` is installed and in the path where your operating
# system searches for executables, you may be able to run the following command
# to bring up the visualization program.
@async run(`paraview mymesh.vtu`)
# The executable will load the mesh. The mesh can then be visualized in
# different ways, for instance as a surface with edges.
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# Encoder structure : a downsampling is applied then two convolutions are done
struct Encoder
dspl
conv
end
@Flux.functor Encoder
(e::Encoder)(x) = x |> e.dspl |> e.conv
# Decoder structure : two convolutions are done then an upsampling is applied
struct Decoder
conv
uspl
end
@Flux.functor Decoder
(d::Decoder)(x) = x |> d.conv |> d.uspl
# Bridge structure :
# - downsampling
# - two convolutions
# - upsampling
struct Bridge
dspl
conv
uspl
end
@Flux.functor Bridge
(b::Bridge)(x) = x |> b.dspl |> b.conv |> b.uspl
# Encoder builders
function encoder(lvl)
enc = []
if lvl == 1
push!(enc, identity)
else
push!(enc, MaxPool((2, 2)))
end
push!(enc, Conv((3, 3), 1=>1, pad = 1))
push!(enc, Conv((3, 3), 1=>1, pad = 1))
enc
end
encoder(t::Symbol, lvl) = encoder(Val(t), lvl)
encoder(t::Val{:array}, lvl) = encoder(lvl)
encoder(t::Val{:chain}, lvl) = Chain(encoder(lvl)...)
encoder(t::Val{:struct}, lvl) = begin
enc = encoder(lvl)
Encoder(enc[1], Chain(enc[2], enc[3]))
end
# Decoder builders
function decoder(lvl)
dec = Any[Conv((3, 3), 2=>1, pad = 1), Conv((3, 3), 1=>1, pad = 1)]
if lvl == 1
push!(dec, identity)
else
push!(dec, Upsample(:bilinear, scale = 2))
end
dec
end
decoder(t::Symbol, lvl) = decoder(Val(t), lvl)
decoder(t::Val{:array}, lvl) = decoder(lvl)
decoder(t::Val{:chain}, lvl) = Chain(decoder(lvl)...)
decoder(t::Val{:struct}, lvl) = begin
dec = decoder(lvl)
Decoder(Chain(dec[1], dec[2]), dec[3])
end
# Bridge builders
bridge() = [MaxPool((2, 2)),
Conv((3, 3), 1=>1, pad = 1), Conv((3, 3), 1=>1, pad = 1),
Upsample(:bilinear, scale = 2)]
bridge(t::Symbol) = bridge(Val(t))
bridge(t::Val{:array}) = bridge()
bridge(t::Val{:chain}) = Chain(bridge()...)
bridge(t::Val{:struct}) = begin
bdg = bridge()
Bridge(bdg[1], Chain(bdg[2], bdg[3]), bdg[4])
end
@testset "uchain" begin
blkt = [:array :chain :struct]
for enct β blkt, dect β blkt, bdgt β blkt
enc, dec = [], []
for l β 1:2
push!(enc, encoder(enct, l))
push!(dec, decoder(dect, l))
end
bdg = bridge(bdgt)
model = uchain(encoders = enc, decoders = dec, bridge = bdg, connection = chcat)
x = rand(Float32, 32, 32, 1, 1)
@test (model(x) |> size) == (32, 32, 1, 1)
end
end | [
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220,
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886,
198,
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] | 2.140195 | 1,127 |
"""
IndependentMOKernel(k::Kernel) <: Kernel
A Multi-Output kernel which assumes each output is independent of the other.
"""
struct IndependentMOKernel{Tkernel<:Kernel} <: MOKernel
kernel::Tkernel
end
function (ΞΊ::IndependentMOKernel)((x, px)::Tuple{Any, Int}, (y, py)::Tuple{Any, Int})
if px == py
return ΞΊ.kernel(x, y)
else
return 0.0
end
end
function kernelmatrix(k::IndependentMOKernel, x::MOInput, y::MOInput)
@assert x.out_dim == y.out_dim
temp = k.kernel.(x.x, permutedims(y.x))
return cat((temp for _ in 1:y.out_dim)...; dims=(1,2))
end
function Base.show(io::IO, k::IndependentMOKernel)
print(io, string("Independent Multi-Output Kernel\n\t", string(k.kernel)))
end
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] | 2.489796 | 294 |
module TestIdealPoints
using Base.Test
import RollCallDataIO
import IdealPoints
import FiniteDiff
io = open(joinpath("data", "senate", "112.ord"), "r")
ord_file = read(RollCallDataIO.ORDFile, io)
close(io)
roll_call = convert(
RollCallDataIO.SparseRollCall,
RollCallDataIO.roll_call(ord_file),
)
n_legislators, n_bills = roll_call.n_legislators, roll_call.n_bills
for d in 1:2
a, b, c, misc = IdealPoints.ideal_points(ord_file, d)
ΞΈ = fill(
0.0,
d * n_legislators + d * n_bills + n_bills
)
nlp = IdealPoints.make_nlp(
d,
1.0,
n_legislators,
n_bills,
roll_call.legislators,
roll_call.bills,
roll_call.votes,
)
nlp_gr! = IdealPoints.make_nlp_gr(
d,
1.0,
n_legislators,
n_bills,
roll_call.legislators,
roll_call.bills,
roll_call.votes,
)
ΞΈstar = vcat(a, b, c)
@test nlp(ΞΈstar) < nlp(ΞΈ)
gr = copy(ΞΈ)
nlp_gr!(ΞΈ, gr)
@test norm(FiniteDiff.gradient(nlp, ΞΈ) - gr) < 1e-4
nlp_gr!(ΞΈstar, gr)
@test norm(FiniteDiff.gradient(nlp, ΞΈstar) - gr) < 1e-4
@test norm(gr) < 1e-4
# Check in a region around ΞΈstar that ΞΈstar produces a smaller nlp.
for _ in 1:10
for i in 1:length(ΞΈ)
ΞΈ[i] = ΞΈstar[i] + 0.01 * randn()
end
@test nlp(ΞΈstar) < nlp(ΞΈ)
nlp_gr!(ΞΈ, gr)
n1 = norm(gr)
nlp_gr!(ΞΈstar, gr)
n2 = norm(gr)
@test n2 < n1
end
end
end
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220,
220,
220,
220,
886,
198,
220,
220,
220,
886,
198,
437,
198
] | 1.679537 | 1,036 |
"""
graph_small_world(n::Integer, m::Integer, p::Real)
Small-world network model of Watts and Strogatz (1998).
# Arguments
* `n` β number of nodes,
* `k` β number of neighbors for each node, should be even,
* `p` β probability of random rewiring of each edge.
# References
Watts, D. J., & Strogatz, S. H. (1998).
Collective dynamics of βsmall-worldβ networks. Nature, 393(6684), 440β442.
"""
function graph_small_world(n::Integer, k::Integer, p::Real; kwargs...)
@assert(n > 5, "number of nodes (n) should be > 5")
@assert(0 < k < n / 2, "number of neighbors (k) is not in a valid range")
@assert(k % 2 == 0, "number of neighbors (k) should be even")
@assert(0 < p < 1, "rewiring probability (p) is not in (0, 1) interval")
g = Graph(n; kwargs...)
for i = 1:n, offset = 1:Int(k / 2)
j = (i + offset - 1) % n + 1
if rand() < p || hasedge(g, i, j)
while true
j = rand(1:n)
if j != i && !hasedge(g, i, j)
break
end
end
end
addedge!(g, i, j)
end
return g
end
@deprecate(graph_smallworld, graph_small_world)
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6894,
11,
4823,
62,
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62,
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] | 2.176796 | 543 |
IMGUI_DEMO_BUTTON_STATES = Dict(
"basic_button" => OnOffButtons.State("Button"),
"basic_button2" => Buttons.State("Button2"),
)
IMGUI_DEMO_RADIO_BUTTON_GROUP_STATES =
RadioButtonGroups.State("radio button group",
[RadioButtons.State("radio a"),
RadioButtons.State("radio b"),
RadioButtons.State("radio c")])
IMGUI_DEMO_COLOR_BUTTON_STATES = [
ColorButtons.State("click##1"),
ColorButtons.State("click##2"),
ColorButtons.State("click##3"),
ColorButtons.State("click##4"),
ColorButtons.State("click##5"),
ColorButtons.State("click##6"),
ColorButtons.State("click##7"),
]
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628
] | 2.062323 | 353 |
# Simple implementation for tensordot and einsum for Julia,
# giving max compatibility for ForwardDiff and Zygote.
module TensorAnyDiff
using LinearAlgebra
import OMEinsum: EinCode
import Zygote: @adjoint
export @einmm_str, execute, contract
include("tensordot.jl")
include("einengine.jl")
end
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437,
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] | 3.125 | 96 |
export rule
@rule AR(:ΞΈ, Marginalisation) (q_y_x::MultivariateNormalDistributionsFamily, q_Ξ³::GammaShapeRate, meta::ARMeta) = begin
order = getorder(meta)
F = getvform(meta)
myx, Vyx = mean(q_y_x), cov(q_y_x)
my, Vy = ar_slice(F, myx, 1:order), ar_slice(F, Vyx, 1:order, 1:order)
mx, Vx = ar_slice(F, myx, order+1:2order), ar_slice(F, Vyx, order+1:2order, order+1:2order)
Vyx = ar_slice(F, Vyx, order+1:2order, 1:order)
mΞ³ = mean(q_Ξ³)
D = mΞ³ * (Vx + mx * mx')
c = ar_unit(getvform(meta), order)
mΞΈ, VΞΈ = cholinv(D) * (Vyx + mx * my') * mΞ³ * c, cholinv(D)
return convert(promote_variate_type(F, NormalMeanVariance), mΞΈ, VΞΈ)
end
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590,
828,
285,
138,
116,
11,
569,
138,
116,
8,
198,
437,
198
] | 1.966006 | 353 |
## Initialization Filters
using LinearAlgebra, FFTW
generatefilters(type,dims;form=:list) = _generatefilters(Val(type),dims,Val(form))
# DCT
function _generatefilters(::Val{:DCT},dims,::Val{:list})
H = _generatefilters(Val(:DCT),dims,Val(:matrix))
return Array.(_filterlist(H,dims))
end
function _generatefilters(::Val{:DCT},dims,::Val{:matrix})
@assert length(dims) == 2 "Only 2D DCT is implemented" # todo
@assert all(i->i==first(dims),dims) "Only square DCT is implemented" # todo
temp = dct(Matrix(I,dims),1)
return kron(temp,temp)' / sqrt(prod(dims))
end
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1,
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7,
67,
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198,
437,
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] | 2.287879 | 264 |
@testset "Wagner + Section" begin
coupling = Coupling(Wagner(), Section())
# test provided jacobians
coupling_jacobian_tests(coupling)
# test consistency of input/output functions
coupling_io_tests(coupling)
end | [
31,
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] | 2.659341 | 91 |
using TcpInstruments
using Test
scope = initialize(AgilentDSOX4034A)
@info "Successfully connected $(scope.model) at $(scope.address)"
"""
Spec:
> scope_h = initialize("192.168.1.15")
Grab data from channel 1
> data_struct = get_data(scope_h, 1)
Grab data from channel 2 and 4
> data_struct = get_data(scope_h, [2,4])
Low Pass Filter
Turn on Low Pass Filter 25
> lpf_on(scope)
Check if low pass filter is on
> get_lpf_state(scope) == "1"
Turn on Low Pass Filter 25MHz
> lpf_off(scope)
> get_lpf_state(scope) == "0"
Impedance
> set_impedance_1Mohm(scope_h)
> get_impedance(scope_h) == ONEM
> set_impedance_50ohm(scope_h)
> get_impedance(scope_h) == FIFT
Terminate TCP connection
> terminate(scope_h)
"""
data = get_data(scope, 1)
data2 = get_data(scope, [1,2])
@info typeof(data2)
@test !isempty(data.volt)
@test !isempty(data.time)
@info get_lpf_state(scope)
lpf_on(scope)
@info get_lpf_state(scope)
lpf_off(scope)
@info get_lpf_state(scope)
@info get_impedance(scope)
set_impedance_1Mohm(scope)
@info get_impedance(scope)
set_impedance_50ohm(scope)
@info get_impedance(scope)
# plot(data)
terminate(scope)
@info "Successfully disconnected"
@info "Goodbye"
| [
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] | 2.437113 | 485 |
export rim
"""
`h, seed = rim([s,] xs, xr, L, T60, Nt, Fs)`
Randomized Image Source Method
#### Arguments:
* `s` : (Optional) Source signals
* `xs` : Source position in meters (must be a Tuple)
* `xr` : Microphone position in meters (must be a `Tuple` or a `Vector{Tuple}` for mic array)
* `Nt` : Time samples
* `L` : 3 element `Tuple` containing dimensions of the room
* `beta`/`T60` : 6 element `Tuple` containing reflection coefficients of walls/reverberation time
* `Nt` : length of the RIR
* `Fs` : sampling frequency
#### Keyword Arguments:
* `c = 343` : speed of sound
* `Rd = 1e-2` : random displacement (in meters)
* `N = (0,0,0)`: 3 element `Tuple` representing order of reflection when `N == (0;0;0)` full order is computed.
* `Tw = 20` : taps of fractional delay filter
* `Fc = 0.9` : cut-off frequency of fractional delay filter
#### Outputs:
* `h`: vector or matrix where each column is an impulse response or the sound pressure if `s` was specified corresponding to the microphone positions `xr`
* `seed`: randomization seed to preserve spatial properties when other RIR at different position are needed
"""
function rim(xs::NTuple{3,Number},
xr::NTuple{3,Number},
L::NTuple{3,Number},
beta::NTuple{6,Number},
Nt::Integer,
Fs::Number;
c::Number = 343,
Rd::Number = 1e-2,
seed::Integer = 1234,
N::NTuple{3,Integer} = (0,0,0),
Tw::Integer = 20,
Fc::Number = 0.9)
if any( xs .> (L[1], L[2], L[3]) ) || any( xs .< (0;0;0) )
error("xs outside domain")
end
if any( xr .> (L[1], L[2], L[3]) ) || any( xr .< (0, 0 , 0) )
error("xr outside domain")
end
if any(N .< 0 )
error("N should be positive")
end
Fsc = Fs/c
L = L.*(Fsc*2) #convert dimensions to indices
xr = xr.*Fsc
xs = xs.*Fsc
Rd = Rd *Fsc
h = zeros(Nt) # initialize output
beta = convert.(eltype(h), beta)
pos_is = zeros(3)
if( N == (0,0,0) )
N = floor.(Int,Nt./L).+1 # compute full order
end
Random.seed!(seed)
if Tw == 0
run_rim!(h, xs, xr, pos_is, L, beta, N, Rd, Nt)
else # with fractional delay
run_rim!(h, xs, xr, pos_is, L, beta, N, Rd, Fc, Tw, Nt)
end
h .*= Fsc
return h, seed
end
# with fractional delay
function run_rim!(h::AbstractVector{T},
xs::X, xr::X, pos_is::AbstractVector{T}, L::X,
beta::NTuple{6,T},
N::NN,
Rd::T, Fc::T, Tw::I, Nt::I
) where {T <: AbstractFloat, I<:Integer,
X <: NTuple{3,T}, NN <:NTuple{3,I}}
beta_pow = zeros(I,6)
for u = 0:1, v = 0:1, w = 0:1
for l = -N[1]:N[1], m = -N[2]:N[2], n = -N[3]:N[3]
if (l ==0 && m == 0 && n == 0 && u == 0 && v == 0 && w == 0)
# we have direct path, so
# no displacement is added
RD = 0.0
else
RD = Rd
end
# compute distance
pos_is[1] = xs[1]-2*u*xs[1]+l*L[1] + RD*(2*rand()-1)
pos_is[2] = xs[2]-2*v*xs[2]+m*L[2] + RD*(2*rand()-1)
pos_is[3] = xs[3]-2*w*xs[3]+n*L[3] + RD*(2*rand()-1)
# position of image source
d = norm( pos_is .-xr )+1
# instead of moving the source on a line
# as in the paper, we are moving the source
# in a cube with 2*Rd edge
id = round(Int64,d)
if (id > Nt || id < 1)
#if index not exceed length h
continue
end
A = get_amplitude!(beta, beta_pow, l,m,n, u,v,w, d)
add_delay!(h, d, A, Tw, Fc, Nt)
end
end
return h
end
# fractional delay
function add_delay!(h::Array{T}, d::T, A::T, Tw::I, Fc::T, Nt::I) where {
T <: AbstractFloat,
I <: Integer
}
# create time window
indx = max(ceil(I,d.-Tw/2),1):min(floor(I,d.+Tw/2),Nt)
# compute filtered impulse
A2 = A/2
@inbounds @simd for i in indx
h[i] += A2 *( 1.0 + cos(2*Ο*(i-d)/Tw) )*sinc(Fc*(i-d))
end
return h
end
function get_amplitude!(beta::NTuple{6,T},
beta_pow::Array{I},
l::I,m::I,n::I,
u::I,v::I,w::I,
d::T) where {I <: Integer,
T <: AbstractFloat}
beta_pow[1] = abs(l-u)
beta_pow[2] = abs(l)
beta_pow[3] = abs(m-v)
beta_pow[4] = abs(m)
beta_pow[5] = abs(n-w)
beta_pow[6] = abs(n)
A = one(T)
for i in eachindex(beta)
A *= beta[i]^beta_pow[i]
end
A /= (4*Ο*(d-1))
return A
end
# without fractional delay
function run_rim!(h::AbstractVector{T},
xs::X, xr::X, pos_is::AbstractVector{T}, L::X,
beta::NTuple{6,T},
N::NN,
Rd::T, Nt::I
) where {T <: AbstractFloat, I<:Integer,
X <: NTuple{3,T}, NN <:NTuple{3,I}}
beta_pow = zeros(I,6)
for u = 0:1, v = 0:1, w = 0:1
for l = -N[1]:N[1], m = -N[2]:N[2], n = -N[3]:N[3]
if (l ==0 && m == 0 && n == 0 && u == 0 && v == 0 && w == 0)
# we have direct path, so
# no displacement is added
RD = 0.0
else
RD = Rd
end
# compute distance
pos_is[1] = xs[1]-2*u*xs[1]+l*L[1] + RD*(2*rand()-1)
pos_is[2] = xs[2]-2*v*xs[2]+m*L[2] + RD*(2*rand()-1)
pos_is[3] = xs[3]-2*w*xs[3]+n*L[3] + RD*(2*rand()-1)
# position of image source
d = norm(pos_is .-xr)+1
# instead of moving the source on a line
# as in the paper, we are moving the source
# in a cube with 2*Rd edge
id = round(Int64,d)
if (id > Nt || id < 1)
#if index not exceed length h
continue
end
A = get_amplitude!(beta, beta_pow, l,m,n, u,v,w, d)
h[id] += A
end
end
return h
end
# with T60
function rim(xs::Union{ NTuple{3,Number}, Vector{NTuple{3, Number}} },
xr::Union{ NTuple{3,Number}, Vector{NTuple{3, Number}} },
L::NTuple{3,Number},
T60::Number, args...;
c::Number = 343,
kwargs...)
beta = RIM.revTime2beta(L, T60, c)
return rim(xs, xr, L, beta, args...; kwargs...)
end
# multiple mics
function rim(xs::NTuple{3,Number},
xr::Vector, args...;
kwargs...)
h = ()
seed = 1234
for xi in xr
hi, seed = rim(xs, xi, args...; kwargs...)
h = (h..., hi)
end
return hcat(h...), seed
end
# with source signal
function rim(s::AbstractVector,
args...;
kwargs...)
h, seed = rim(args...; kwargs...)
P = hcat([conv(s,h[:,i]) for i = 1:size(h,2)]...)
return P, seed
end
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] | 1.848915 | 3,733 |
module ExtractMacro
export @extract
"""
@extract obj : exprs...
Extracts fields from composite types. E.g.
```julia
@extract x : a b
# is translated to:
a = x.a
b = x.b
```
The colon is optional: `@extract x a b` is the same as above.
Destination variable names can be changed, and arbitrary functions (including indexing) applied, e.g.:
```julia
@extract x : q=b a1=abs(a[1]) ai=abs(a[i]) y=max(a[1],b)
# is translated to:
q = x.b
a1 = abs(x.a[1])
ai = abs(x.a[i])
y = max(x.a[1], x.b)
```
Notice that the `i` within the indexing expression is left untouched: indexing is special in this regard.
In order to explicitly avoid symbol manipulation on the right hand side, use `esc()`, e.g.:
```julia
@extract x : y=abs(a[1] + esc(b))
# is translated to:
y = abs(x.a[1] + b) # b is left untouched
```
"""
macro extract(obj, vars...)
ex = quote end
# next block is to allow this syntax
# @extract X : a b c
# (basically, we need to override the parsing precedence rules)
if Meta.isexpr(obj, [:(=), :(=>), :(:=)]) && Meta.isexpr(obj.args[1], :(:))
vars = Any[Expr(obj.head, obj.args[1].args[2:end]..., obj.args[2:end]...), vars...]
obj = obj.args[1].args[1]
elseif Meta.isexpr(obj, :(:))
vars = Any[obj.args[2], vars...]
obj = obj.args[1]
end
for v in vars
if isa(v, Symbol)
ex = quote
$ex
$(esc(v)) = $(prepend_obj(v, obj))
end
elseif isa(v, Expr)
if v.head β [:(=), :(=>), :(:=)]
error("invalid @extract argument: expression `$(v)` out of an assigment")
end
va = v.args
@assert length(va) == 2
ex = quote
$ex
$(esc(va[1])) = $(prepend_obj(va[2], obj))
end
end
end
ex
end
update_skiplist!(x, skip) = nothing
update_skiplist!(x::Symbol, skip) = push!(skip, x)
function update_skiplist!(x::Expr, skip)
Meta.isexpr(x, :tuple) || return
for a in x.args
update_skiplist!(a, skip)
end
end
prepend_obj(x, obj, skip=[]) = x
prepend_obj(s::Symbol, obj, skip=[]) = s β skip ? esc(s) : :($(esc(obj)).$s)
function prepend_obj(body::Expr, obj, skip=[])
if Meta.isexpr(body, :call)
if body.args[1] != :esc
return Expr(body.head, Expr(:escape, body.args[1]), map(x->prepend_obj(x, obj, skip), body.args[2:end])...)
else
@assert length(body.args) == 2
return Expr(:escape, body.args[2])
end
elseif Meta.isexpr(body, [:ref, :.])
return Expr(body.head, prepend_obj(body.args[1], obj, skip), map(esc, body.args[2:end])...)
elseif Meta.isexpr(body, [:comprehension, :generator])
inner_generator = Meta.isexpr(body.args[1], :generator)
args = inner_generator ? body.args[1].args : body.args
length(args) == 2 || error("unsupported expression")
Meta.isexpr(args[2], :(=)) || error("unsupported expression")
iter = prepend_obj(args[2].args[2], obj, skip)
var = args[2].args[1]
update_skiplist!(var, skip)
ex = prepend_obj(args[1], obj, skip)
genargs = Expr(:(=), esc(var), iter)
return inner_generator ?
Expr(body.head, Expr(:generator, ex, genargs)) :
Expr(body.head, ex, genargs)
else
return Expr(body.head, map(x->prepend_obj(x, obj, skip), body.args)...)
end
end
end # module
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] | 2.088782 | 1,667 |
map_str = ""
passports = []
open("day04/day04.in") do io
global map_str, passports
for line in readlines(io)
line = line * " "
if cmp(line, " ") == 0
push!(passports, map_str)
map_str = ""
else
map_str = map_str * line
end
end
end
pass_map = Dict(
"byr" => 2, "iyr" => 3, "eyr" => 5, "hgt" => 7, "hcl" => 11, "ecl" => 13,
"pid" => 17, "cid" => 19
)
pass_valid = Int64(0)
for passport in passports
global pass_valid
checkmult = Int64(1)
validity = true
for item in split(passport, ' ')
if occursin(":", item)
item_type, item_value = split(item, ":")
if length(item) > 2
checkmult *= pass_map[item_type]
end
if item_type == "byr"
item_value = parse(Int64, item_value)
validity &= 1920 <= item_value <= 2002
elseif item_type == "iyr"
item_value = parse(Int64, item_value)
validity &= 2010 <= item_value <= 2020
elseif item_type == "eyr"
item_value = parse(Int64, item_value)
validity &= 2020 <= item_value <= 2030
elseif item_type == "hgt"
if occursin("in", item_value)
item_value = split(item_value, "in")[1]
item_value = parse(Int64, item_value)
validity &= 59 <= item_value <= 76
elseif occursin("cm", item_value)
item_value = split(item_value, "cm")[1]
item_value = parse(Int64, item_value)
validity &= 150 <= item_value <= 193
else
validity = false
end
elseif item_type == "hcl"
validity &= item_value[1] == '#' && length(item_value) == 7
for ch in item_value[2:end]
validity &= '0' <= ch <= '9' || 'a' <= ch <= 'f'
end
elseif item_type == "ecl"
validity &= item_value in ["amb", "blu", "brn", "gry", "grn", "hzl", "oth"]
elseif item_type == "pid"
validity &= length(item_value) == 9
for ch in item_value[1:end]
validity &= '0' <= ch <= '9'
end
end
end
end
if (checkmult == 9699690 || checkmult == 510510 ) && validity
pass_valid += 1
end
end
println(pass_valid) | [
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62,
2536,
796,
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198,
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198,
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11,
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8,
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] | 1.82042 | 1,381 |
function _right_sweep_SVD!(Ο::CuMPS, Dcut::Int=typemax(Int))
Ξ£ = V = CUDA.ones(eltype(Ο), 1, 1)
for i β eachindex(Ο)
A = Ο[i]
C = Diagonal(Ξ£) * V'
# attach
@cutensor M[x, Ο, y] := C[x, Ξ±] * A[Ξ±, Ο, y]
@cast MΜ[(x, Ο), y] |= M[x, Ο, y]
# decompose
U, Ξ£, V = _truncate_svd(CUDA.svd(MΜ), Dcut)
# create new
d = physical_dim(Ο, i)
@cast A[x, Ο, y] |= U[(x, Ο), y] (Ο:d)
Ο[i] = A
end
end
function _left_sweep_SVD!(Ο::CuMPS, Dcut::Int=typemax(Int))
Ξ£ = U = CUDA.ones(eltype(Ο), 1, 1)
for i β length(Ο):-1:1
B = Ο[i]
C = U * Diagonal(Ξ£)
# attach
@cutensor M[x, Ο, y] := B[x, Ο, Ξ±] * C[Ξ±, y]
@cast MΜ[x, (Ο, y)] |= M[x, Ο, y]
# decompose
U, Ξ£, V = _truncate_svd(CUDA.svd(MΜ), Dcut)
# create new
d = physical_dim(Ο, i)
VV = conj.(transpose(V)) #hack - @cast does fail with allowscalar and Adjoint type
@cast B[x, Ο, y] |= VV[x, (Ο, y)] (Ο:d)
Ο[i] = B
end
end
function _left_sweep_var!!(Ο::CuMPS, env::Vector{<:CuMatrix}, Ο::CuMPS, Dcut::Int)
S = eltype(Ο)
# overwrite the overlap
env[end] = CUDA.ones(S, 1, 1)
for i β length(Ο):-1:1
L = env[i]
R = env[i+1]
# optimize site
M = Ο[i]
@cutensor MΜ[x, Ο, y] := L[x, Ξ²] * M[Ξ², Ο, Ξ±] * R[Ξ±, y] order = (Ξ±, Ξ²)
# right canonize it
@cast MM[x, (Ο, y)] |= MΜ[x, Ο, y]
Q = _truncate_qr(CUDA.qr(MM'), Dcut)'
Q = CuMatrix(Q) #hack @cast does not work with Adjoint
d = size(M, 2)
@cast B[x, Ο, y] |= Q[x, (Ο, y)] (Ο:d)
# update Ο and right environment
Ο[i] = B
A = Ο[i]
@cutensor RR[x, y] := A[x, Ο, Ξ±] * R[Ξ±, Ξ²] * conj(B[y, Ο, Ξ²]) order = (Ξ², Ξ±, Ο)
env[i] = RR
end
end
function _right_sweep_var!!(Ο::CuMPS, env::Vector{<:CuMatrix}, Ο::CuMPS, Dcut::Int)
S = eltype(Ο)
# overwrite the overlap
env[1] = CUDA.ones(S, 1, 1)
for i β eachindex(Ο)
L = env[i]
R = env[i+1]
# optimize site
M = Ο[i]
@cutensor MΜ[x, Ο, y] := L[x, Ξ²] * M[Ξ², Ο, Ξ±] * R[Ξ±, y] order = (Ξ±, Ξ²)
# left canonize it
@cast B[(x, Ο), y] |= MΜ[x, Ο, y]
Q = _truncate_qr(CUDA.qr(B), Dcut)
d = size(Ο[i], 2)
@cast A[x, Ο, y] |= Q[(x, Ο), y] (Ο:d)
# update Ο and left environment
Ο[i] = A
B = Ο[i]
@cutensor LL[x, y] := conj(A[Ξ², Ο, x]) * L[Ξ², Ξ±] * B[Ξ±, Ο, y] order = (Ξ±, Ξ², Ο)
env[i+1] = LL
end
return real(tr(env[end]))
end
function _qr_fix!(Q::CuMatrix, R::CuMatrix)
d = diag(R)
ph = d./abs.(d)
transpose(ph) .* Q
end
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1007,
3455,
7,
746,
8,
764,
9,
1195,
198,
437,
198
] | 1.608023 | 1,745 |
using Mdp
using Test
@testset "VI.jl" begin
include("test_vi.jl")
end
@testset "FH.jl" begin
include("test_fh.jl")
end
include("test_pricing_problem.jl")
@testset "PricingAlgorithmFH.jl" begin
include("test_pricing_algorithm_fh.jl")
end
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62,
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42289,
62,
69,
71,
13,
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198,
437,
198
] | 2.351852 | 108 |
module MolSimPy
export MatSciPy
include("MatSciPy.jl")
using .MatSciPy
using ASE: ASEAtoms
end # module
| [
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2953,
3150,
198,
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8265,
198
] | 2.4 | 45 |
"""
`BufferedInputStream{T}` provides buffered reading from a source of type `T`.
Any type `T` wrapped in a `BufferedInputStream` must implement:
BufferedStreams.readbytes!(source::T, buffer::Vector{UInt8}, from::Int, to::Int)
This function should:
* refill `buffer` starting at `from` and not filling past `to`.
* return the number of bytes read.
Failure to read any new data into the buffer is interpreted as eof.
"""
mutable struct BufferedInputStream{T} <: IO
source::T
buffer::Vector{UInt8}
# Position of the next byte to be read in buffer;
# `position β€ 0` indicates that the stream is closed.
position::Int
# Number of bytes available in buffer.
# I.e. buffer[1:available] is valid data.
available::Int
# If positive, preserve and move buffer[anchor:available]
# when refilling the buffer.
anchor::Int
# If `true`, buffered data `buffer[anchor:available]` is not shifted.
immobilized::Bool
end
function BufferedInputStream(source::T, bufsize::Integer = default_buffer_size) where T
if bufsize β€ 0
throw(ArgumentError("buffer size must be positive"))
end
return BufferedInputStream{T}(source, Vector{UInt8}(undef, bufsize), 1, 0, 0, false)
end
function Base.show(io::IO, stream::BufferedInputStream{T}) where T
bufsize = length(stream.buffer)
filled = stream.available - stream.position + 1
if isopen(stream)
print(io,
summary(stream), "(<",
_datasize(bufsize), " buffer, ",
round(Int, filled / bufsize * 100), "% filled",
stream.immobilized ? ", data immobilized" : "", ">)")
else
print(io, summary(stream), "(<closed>)")
end
end
"""
Refill the buffer, optionally moving and retaining part of the data.
"""
function fillbuffer!(stream::BufferedInputStream)
if eof(stream.source)
return 0
end
shiftdata!(stream)
margin = length(stream.buffer) - stream.available
if margin == 0
resize!(stream.buffer, length(stream.buffer) * 2)
end
nbytes = readbytes!(
stream.source,
stream.buffer,
stream.available + 1,
length(stream.buffer))
stream.available += nbytes
return nbytes
end
# Shift data to be kept.
function shiftdata!(stream::BufferedInputStream)
if stream.immobilized
return 0
else
if stream.anchor > 0 && stream.available - stream.anchor + 1 > 0
shift = stream.anchor - 1
n = stream.available - shift
copyto!(stream.buffer, 1, stream.buffer, stream.anchor, n)
stream.anchor -= shift
elseif stream.available - stream.position + 1 > 0
shift = stream.position - 1
n = stream.available - shift
copyto!(stream.buffer, 1, stream.buffer, stream.position, n)
else
# no data to be kept
@assert stream.position > stream.available
shift = stream.available
end
stream.position -= shift
stream.available -= shift
return shift
end
end
@inline function Base.eof(stream::BufferedInputStream)
if stream.position > stream.available
return fillbuffer!(stream) == 0
else
return false
end
end
@inline function Base.bytesavailable(stream::BufferedInputStream)
if eof(stream)
return 0
else
return stream.available - stream.position + 1
end
end
@inline function Base.readavailable(stream::BufferedInputStream)
read(stream, bytesavailable(stream))
end
@inline function Base.skip(stream::BufferedInputStream, n_::Integer)
n0 = n = convert(Int, n_)
if n < 0
throw(ArgumentError("n must be non-negative in skip(::BufferedInputStream, n)"))
end
while stream.position + n > stream.available + 1
n -= stream.available - stream.position + 1
stream.position = stream.available + 1
if fillbuffer!(stream) < 1
throw(EOFError())
end
end
stream.position += n
return n0
end
@inline function checkopen(stream::BufferedInputStream)
if !isopen(stream)
error("buffered input stream is already closed")
end
end
"""
Return the next byte from the input stream without advancing the position.
"""
@inline function peek(stream::BufferedInputStream)
checkopen(stream)
if stream.position > stream.available
if fillbuffer!(stream) < 1
throw(EOFError())
end
end
@inbounds c = stream.buffer[stream.position]
return c
end
"""
Fills `buffer` with bytes from `stream`'s buffer without advancing the
position.
Unless the buffer is empty, we do not re-fill it. Therefore the number of bytes
read is limited to the minimum of `nb` and the remaining bytes in the buffer.
"""
function peekbytes!(stream::BufferedInputStream,
buffer::AbstractArray{UInt8},
nb=length(buffer))
checkopen(stream)
if stream.position > stream.available
if fillbuffer!(stream) < 1
throw(EOFError())
end
end
nb = min(nb, stream.available - stream.position + 1)
copyto!(buffer, 1, stream.buffer, stream.position, nb)
return nb
end
@inline function Base.read(stream::BufferedInputStream, ::Type{UInt8})
checkopen(stream)
if stream.position > stream.available
if fillbuffer!(stream) < 1
throw(EOFError())
end
end
@inbounds c = stream.buffer[stream.position]
stream.position += 1
return c
end
# fast multi-byte data readers
for T in [Int16, UInt16, Int32, UInt32, Int64, UInt64, Int128, UInt128, Float16, Float32, Float64]
@eval begin
@inline function Base.read(stream::BufferedInputStream, ::Type{$(T)})
checkopen(stream)
if !ensurebuffered!(stream, $(sizeof(T)))
throw(EOFError())
end
ptr::Ptr{$(T)} = pointer(stream)
ret = unsafe_load(ptr)
stream.position += $(sizeof(T))
return ret
end
end
end
if isdefined(Base, :unsafe_read)
function Base.unsafe_read(stream::BufferedInputStream, ptr::Ptr{UInt8}, nb::UInt)
p = ptr
p_end = ptr + nb
while p < p_end
if ensurebuffered!(stream, 1)
n = min(p_end - p, available_bytes(stream))
ccall(:memcpy, Cvoid, (Ptr{Cvoid}, Ptr{Cvoid}, Csize_t), p, pointer(stream), n)
p += n
stream.position += n
else
throw(EOFError())
end
end
return nothing
end
end
# Special purpose readuntil for plain bytes.
function Base.readuntil(stream::BufferedInputStream, delim::UInt8)
checkopen(stream)
anchor!(stream)
while true
p0 = pointer(stream.buffer, stream.position)
p1 = ccall(:memchr, Ptr{UInt8}, (Ptr{UInt8}, Cint, Csize_t),
p0, delim, stream.available - stream.position + 1)
if p1 != C_NULL
stream.position += p1 - p0
break
else
stream.position = stream.available + 1
nb = fillbuffer!(stream)
if nb == 0
chunk = takeanchored!(stream)
return chunk
end
end
end
chunk = stream.buffer[upanchor!(stream):stream.position]
stream.position += 1
return chunk
end
function readbytes!(stream::BufferedInputStream,
buffer::AbstractArray{UInt8},
nb=length(buffer))
return readbytes!(stream, buffer, 1, nb)
end
function readbytes!(stream::BufferedInputStream,
buffer::AbstractArray{UInt8},
from::Int, to::Int)
p = from
while !eof(stream) && p β€ to
@assert ensurebuffered!(stream, 1)
n = min(to - p + 1, stream.available - stream.position + 1)
copyto!(buffer, p, stream.buffer, stream.position, n)
p += n
stream.position += n
end
return p - from
end
function Base.ismarked(stream::BufferedInputStream)
return stream.anchor > 0
end
function Base.mark(stream::BufferedInputStream)
stream.anchor = stream.position
return stream.anchor
end
function Base.unmark(stream::BufferedInputStream)
if !ismarked(stream)
return false
end
stream.anchor = 0
return true
end
function Base.reset(stream::BufferedInputStream)
if !ismarked(stream)
error("buffered stream is not marked")
end
anchor = stream.anchor
stream.position = anchor
unmark(stream)
return anchor
end
"""
Return true if the stream is anchored.
"""
function isanchored(stream::BufferedInputStream)
return stream.anchor > 0
end
"""
Set the buffer's anchor to its current position.
"""
function anchor!(stream::BufferedInputStream)
stream.anchor = stream.position
end
"""
Remove and return a buffer's anchor.
"""
function upanchor!(stream::BufferedInputStream)
anchor = stream.anchor
stream.anchor = 0
return anchor
end
"""
Copy and return a byte array from the anchor up to, but not including the
current position, also removing the anchor.
"""
function takeanchored!(stream::BufferedInputStream)
if stream.position - 1 > stream.available
throw(EOFError())
end
chunk = stream.buffer[stream.anchor:stream.position - 1]
upanchor!(stream)
return chunk
end
function Base.position(stream::BufferedInputStream)
return position(stream.source) - stream.available + stream.position - 1
end
function Base.seek(stream::BufferedInputStream{T}, pos::Integer) where T
if applicable(seek, stream.source, pos)
upanchor!(stream)
source_position = position(stream.source)
# is the new position within the buffer?
if source_position - stream.available <= pos <= source_position
stream.position = 1 + pos - (source_position - stream.available)
else
seek(stream.source, pos)
stream.position = 1
stream.available = 0
end
else
throw(ArgumentError(
string("Can't seek in input stream with source of type ", T)))
# TODO: Allow seeking forwards by just reading and discarding input
end
end
function Base.isopen(stream::BufferedInputStream)
return stream.position > 0
end
function Base.close(stream::BufferedInputStream)
if !isopen(stream)
return
end
if applicable(close, stream.source)
close(stream.source)
end
stream.position = 0
empty!(stream.buffer)
return
end
function Base.pointer(stream::BufferedInputStream, index::Integer=1)
return pointer(stream.buffer, stream.position + index - 1)
end
@inline function available_bytes(stream::BufferedInputStream)
return stream.available - stream.position + 1
end
@inline function ensurebuffered!(stream::BufferedInputStream, nb::Integer)
if available_bytes(stream) < nb
fillbuffer!(stream)
if available_bytes(stream) < nb
return false
end
end
return true
end
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] | 2.483958 | 4,457 |
## DeltaF/F
function deltaFF(fs::AxisArray,Fo::Array{Float64,1},B=0.0)
length(Fo) != size(fs)[2] ? error("Fo vector should be the same length as the number of ROIs in the series.") :
Fo = permutedims(Fo)
newF = (fs .- Fo)./(Fo .-B)
AxisArray(newF,AxisArrays.axes(fs)...)
end
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] | 2.291339 | 127 |
# ******************************************************************************************
# Notices:
#
# Copyright Β© 2022 United States Government as represented by the Administrator of the
# National Aeronautics and Space Administration. All Rights Reserved.
#
# Disclaimers
#
# No Warranty: THE SUBJECT SOFTWARE IS PROVIDED "AS IS" WITHOUT ANY WARRANTY OF ANY KIND,
# EITHER EXPRESSED, IMPLIED, OR STATUTORY, INCLUDING, BUT NOT LIMITED TO, ANY WARRANTY
# THAT THE SUBJECT SOFTWARE WILL CONFORM TO SPECIFICATIONS, ANY IMPLIED WARRANTIES OF
# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, OR FREEDOM FROM INFRINGEMENT, ANY
# WARRANTY THAT THE SUBJECT SOFTWARE WILL BE ERROR FREE, OR ANY WARRANTY THAT DOCUMENTATION,
# IF PROVIDED, WILL CONFORM TO THE SUBJECT SOFTWARE. THIS AGREEMENT DOES NOT, IN ANY MANNER,
# CONSTITUTE AN ENDORSEMENT BY GOVERNMENT AGENCY OR ANY PRIOR RECIPIENT OF ANY RESULTS,
# RESULTING DESIGNS, HARDWARE, SOFTWARE PRODUCTS OR ANY OTHER APPLICATIONS RESULTING FROM
# USE OF THE SUBJECT SOFTWARE. FURTHER, GOVERNMENT AGENCY DISCLAIMS ALL WARRANTIES AND
# LIABILITIES REGARDING THIRD-PARTY SOFTWARE, IF PRESENT IN THE ORIGINAL SOFTWARE, AND
# DISTRIBUTES IT "AS IS."
#
# Waiver and Indemnity: RECIPIENT AGREES TO WAIVE ANY AND ALL CLAIMS AGAINST THE UNITED
# STATES GOVERNMENT, ITS CONTRACTORS AND SUBCONTRACTORS, AS WELL AS ANY PRIOR RECIPIENT.
# IF RECIPIENT'S USE OF THE SUBJECT SOFTWARE RESULTS IN ANY LIABILITIES, DEMANDS, DAMAGES,
# EXPENSES OR LOSSES ARISING FROM SUCH USE, INCLUDING ANY DAMAGES FROM PRODUCTS BASED ON,
# OR RESULTING FROM, RECIPIENT'S USE OF THE SUBJECT SOFTWARE, RECIPIENT SHALL INDEMNIFY AND
# HOLD HARMLESS THE UNITED STATES GOVERNMENT, ITS CONTRACTORS AND SUBCONTRACTORS, AS WELL
# AS ANY PRIOR RECIPIENT, TO THE EXTENT PERMITTED BY LAW. RECIPIENT'S SOLE REMEDY FOR ANY
# SUCH MATTER SHALL BE THE IMMEDIATE, UNILATERAL TERMINATION OF THIS AGREEMENT.
# ******************************************************************************************
"""
Reset MDP.
"""
function reset!(mdp::ASTMDP)
reset!(mdp.sim)
reset!(mdp.reward.heuristic)
return nothing
end
"""
Return set of available actions as sampleable object. # TODO: not type-stable.
"""
function actions(mdp::ASTMDP{<:State, SampleAction})
env = environment(mdp.sim)
return mdp.flatten ? Flat(env) : env
end
actions(::ASTMDP{<:State, SeedAction}) = UInt32
"""
Return observation of environment.
"""
observe(mdp::ASTMDP{ObservableState, <:Action}) = Float32.(observe(mdp.sim))
"""
Step simulation and return log probability of transition.
"""
function evaluate!(mdp::ASTMDP{<:State, A}, a::A) where A <: SampleAction
env = environment(mdp.sim)
val = a.sample
logp = logprob(env, val, mdp.reward.marginalize)
step!(mdp.sim, val)
return logp
end
function evaluate!(mdp::ASTMDP{<:State, A}, a::A) where A <: SeedAction
copy!(RNG_TEMP[], mdp.rng)
Random.seed!(mdp.rng, a.seed)
logp = step!(mdp.rng, mdp.sim)
copy!(mdp.rng, RNG_TEMP[])
return logp
end
"""
Apply raw action to environment and return reward.
"""
act!(mdp::ASTMDP{<:State, <:Action}, action) = act!(mdp, convert_a(mdp, action))
"""
Apply converted action to environment and return reward.
"""
function act!(mdp::ASTMDP{<:State, A}, a::A) where A <: Action
# rewards (partial application)
event = mdp.episodic ? isterminal(mdp.sim) && isevent(mdp.sim) : isevent(mdp.sim)
bonus = event ? mdp.reward.event_bonus : 0.0
heur = mdp.reward.heuristic(mdp)
rew = custom_reward(mdp.sim, a)
# stepping and likelihood evaluation
logp = evaluate!(mdp, a)
# final reward calculation
return mdp.reward.reward_function(logp, bonus, heur(mdp)) + rew(mdp.sim)
end
"""
Return Boolean indicating termination status.
"""
terminated(mdp::ASTMDP) = isterminal(mdp.sim) || !mdp.episodic && isevent(mdp.sim)
# Connection between AdaStress internal functions and CommonRLInterface
CommonRLInterface.reset!(mdp::AbstractASTMDP) = reset!(mdp)
CommonRLInterface.actions(mdp::AbstractASTMDP) = actions(mdp)
CommonRLInterface.observe(mdp::AbstractASTMDP) = observe(mdp)
CommonRLInterface.act!(mdp::AbstractASTMDP, action) = act!(mdp, action)
CommonRLInterface.terminated(mdp::AbstractASTMDP) = terminated(mdp)
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] | 2.917921 | 1,462 |
open(IO ->write(IO, read("file1", String)), "file2", "w")
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@testset "FairEvaluate Benchmarking" begin
model = ConstantClassifier()
X, y = @load_toydata
results = fairevaluate([model, model], X, y, grp=:Sex, priv_grps=["M"])
@test length(results)==5
@test size(results["measures"])==(1,)
@test size(results["pvalues"])==(1, 2, 2)
@test size(results["classifier_names"])==(2,)
@test size(results["results"])==(1, 2, 6)
@test size(results["tstats"])==(1, 2, 2)
end
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abstract AbstractGeometry
import Base: size
include("getNodeProperty.jl")
include("cuboidRoom.jl")
#include("LShapedRoom.jl")
size(f::AbstractGeometry) = (f.Nx,f.Ny,f.Nz)
function Base.show(io::IO, f::AbstractGeometry)
println(io, "geometry : ", fun_name(f))
println(io, "dimensions : ", fun_dim(f))
println(io, "samples : ", fun_Nxyz(f))
println(io, "ΞΎ : ", fun_ΞΎ(f))
println(io, "spatial step : ", fun_X(f))
println(io, "sampling frq.: ", fun_Fs(f))
end
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] | 2.205357 | 224 |
export show_plan
### DAG-based graphing
function write_node(io, t::Thunk, c)
f = isa(t.f, Function) ? "$(t.f)" : "fn"
println(io, "n_$(t.id) [label=\"$f - $(t.id)\"];")
c
end
dec(x) = Base.dec(x, 0, false)
function write_node(io, t, c, id=dec(hash(t)))
l = replace(node_label(t), "\""=>"")
println(io, "n_$id", " [label=\"$l\"];")
c
end
function node_label(t)
iob = IOBuffer()
Base.show(iob, t)
String(take!(iob))
end
function node_label(x::T) where T<:AbstractArray
"$T\nShape: $(size(x))\nSize: $(pretty_size(sizeof(x)))"
end
global _part_labels = Dict()
function write_node(io, t::Chunk, c)
_part_labels[t]="part_$c"
c+1
end
function node_id(t::Thunk)
t.id
end
function node_id(t)
dec(hash(t))
end
function node_id(t::Chunk)
_part_labels[t]
end
function write_dag(io, t::Thunk)
!istask(t) && return
deps = dependents(t)
c=1
for k in keys(deps)
c = write_node(io, k, c)
end
for (k, v) in deps
for dep in v
if isa(k, Union{Chunk, Thunk})
println(io, "$(node_id(k)) -> $(node_id(dep))")
end
end
end
end
### Timespan-based graphing
pretty_time(ts::Timespan) = pretty_time(ts.finish-ts.start)
function pretty_time(t)
r(t) = round(t; digits=3)
if t > 1000^3
"$(r(t/(1000^3))) s"
elseif t > 1000^2
"$(r(t/(1000^2))) ms"
elseif t > 1000
"$(r(t/1000)) us"
else
"$(r(t)) ns"
end
end
function pretty_size(sz)
if sz > 1024^4
"$(sz/(1024^4)) TB (terabytes)"
elseif sz > 1024^3
"$(sz/(1024^3)) GB (gigabytes)"
elseif sz > 1024^2
"$(sz/(1024^2)) MB (megabytes)"
elseif sz > 1024
"$(sz/1024) KB (kilobytes)"
else
"$sz B (bytes)"
end
end
function write_node(io, ts::Timespan, c)
f, res_type, res_sz = ts.timeline
f = isa(f, Function) ? "$f" : "fn"
t_comp = pretty_time(ts)
sz_comp = pretty_size(res_sz)
println(io, "n_$(ts.id) [label=\"$f - $(ts.id)\nCompute: $t_comp\nResult Type: $res_type\nResult Size: $sz_comp\"]")
c
end
function write_edge(io, ts_comm::Timespan, logs)
f, id = ts_comm.timeline
# FIXME: We should print these edges too
id === nothing && return
t_comm = pretty_time(ts_comm)
print(io, "n_$id -> n_$(ts_comm.id[1]) [label=\"Comm: $t_comm")
ts_idx = findfirst(x->x.category==:move &&
ts_comm.id==x.id &&
id==x.timeline[2], logs)
if ts_idx !== nothing
ts_move = logs[ts_idx]
t_move = pretty_time(ts_move)
print(io, "\nMove: $t_move")
end
println(io, "\"];")
end
write_edge(io, from::String, to::String) = println(io, "n_$from -> n_$to")
getargs!(d, t) = nothing
function getargs!(d, t::Thunk)
d[t.id] = [filter(!istask, [t.inputs...,])...,]
foreach(i->getargs!(d, i), t.inputs)
end
function write_dag(io, logs::Vector, t=nothing)
argmap = Dict{Int,Vector}()
getargs!(argmap, t)
c = 1
for ts in filter(x->x.category==:compute, logs)
c = write_node(io, ts, c)
end
argnodemap = Dict{Int,Vector{String}}()
argids = IdDict{Any,String}()
for id in keys(argmap)
nodes = String[]
arg_c = 1
for arg in argmap[id]
name = "$(id)_arg_$(arg_c)"
if !isimmutable(arg)
if arg in keys(argids)
name = argids[arg]
else
argids[arg] = name
c = write_node(io, arg, c, name)
end
push!(nodes, name)
else
c = write_node(io, arg, c, name)
push!(nodes, name)
end
end
argnodemap[id] = nodes
end
for ts in filter(x->x.category==:comm, logs)
write_edge(io, ts, logs)
end
for id in keys(argnodemap)
for arg in argnodemap[id]
write_edge(io, arg, string(id))
end
end
end
function show_plan(io::IO, t)
print(io, """digraph {
graph [layout=dot, rankdir=TB];""")
write_dag(io, t)
println(io, "}")
end
function show_plan(io::IO, logs::Vector{Timespan}, t::Thunk)
print(io, """digraph {
graph [layout=dot, rankdir=TB];""")
write_dag(io, logs, t)
println(io, "}")
end
function show_plan(t::Union{Thunk,Vector{Timespan}})
io = IOBuffer()
show_plan(io, t)
return String(take!(io))
end
function show_plan(logs::Vector{Timespan}, t::Thunk)
io = IOBuffer()
show_plan(io, logs, t)
return String(take!(io))
end
function show_plan(c)
t = thunkize(Context(), stage(Context(), c))
show_plan(t)
end
function show_plan(t::Tuple)
show_plan(TupleCompute(t))
end
# function printing
argname(x::Symbol) = x
function argname(x)
@assert x.head == :(::)
x.args[1]
end
function show_statement(io, x)
if x.head == :return
x = x.args[1]
end
print(io, x)
end
function fnbody(io, x)
body = x.args[3]
statements = filter(x -> !isa(x, LineNumberNode), body.args)
for s in statements
show_statement(io, s)
end
end
function show_ast(io, f)
ast = Base.uncompressed_ast(f.code)
args = map(x -> string(argname(x)), ast.args[1])
write(io, "(", join(args, ','), ") -> ")
fnbody(io, ast)
end
function showfn(io, f::Function)
if isa(f, Function)
show(io, f)
else
show_ast(io, f)
end
end
showfn(io, f) = show(io, f)
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] | 1.952111 | 2,819 |
using Plots
using StatsBase
f1(point)=[0.00 0.00 ; 0.00 0.16]*point
f2(point)=[0.85 0.04 ; -0.04 0.85]*point + [0.00 ; 1.60]
f3(point)=[0.20 -0.26 ; 0.23 0.22]*point + [0.00 ; 1.60]
f4(point)=[-0.15 0.28 ; 0.26 0.24]*point + [0.00 ; 0.44]
f_list=[f1, f2, f3, f4]
p=100
num=500000
finalPoints=[]
for number_of_points in 1:num
(number_of_points==1) ? (point=point=[0.0; 0.0]) : (point=[rand();rand()])
for number_of_iteration in 1:p
weights=[0.01,0.85,0.07,0.07]
items=[f1, f2, f3, f4]
point=sample(items, Weights(weights))(point)
end
push!(finalPoints, point)
end
scatter(hcat(finalPoints...)[1,:], hcat(finalPoints...)[2,:],markersize=0.7, color=:green, xlabel="x", ylabel="y", legend=false)
savefig("C:\\Users\\Narges\\Documents\\GitHub\\computational_physics\\chapter2\\report\\RandomBarnsleyFern.png")
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] | 2.065693 | 411 |
using Test, JuMP
const MOIT = MOI.Test
const MOIB = MOI.Bridges
function _model(optimizer::MOI.AbstractOptimizer)
MOI.empty!(optimizer)
return direct_model(optimizer)
end
function _model(factory::OptimizerFactory)
return Model(factory)
end
#"""
# @test_suite setname subsets
#
#Defines a function `setname_test(model, config, exclude)` that runs the tests
#defined in the dictionary `setname_tests` with the model `model` and config
#`config` except the tests whose dictionary key is in `exclude`. If `subsets` is
#`true` then each test runs in fact multiple tests hence the `exclude` argument
#is passed as it can also contains test to be excluded from these subsets of
#tests.
#"""
macro test_suite(setname, subsets=false)
testname = Symbol(string(setname) * "_test")
testdict = Symbol(string(testname) * "s")
if subsets
runtest = :( f(model, config, exclude) )
else
runtest = :( f(model, config) )
end
esc(:(
function $testname(model::Union{$MOI.ModelLike, OptimizerFactory},
config::$MOI.Test.TestConfig,
exclude::Vector{String} = String[])
for (name,f) in $testdict
if name in exclude
continue
end
@testset "$name" begin
$runtest
end
end
end
))
end
function test_noc(model, F, S, n)
@test MOI.get(model, MOI.NumberOfConstraints{F, S}()) == n
@test length(MOI.get(model, MOI.ListOfConstraintIndices{F, S}())) == n
@test ((F, S) in MOI.get(model, MOI.ListOfConstraints())) == !iszero(n)
end
# Test deletion of bridge
function test_delete_bridge(model::Model,
cref::ConstraintRef{Model,
MOI.ConstraintIndex{F, S}},
nvars::Int, nocs::Tuple) where {F, S}
@test num_variables(model) == nvars
test_noc(model, F, S, 1)
for noc in nocs
test_noc(model, noc...)
end
@test is_valid(model, cref)
delete(model, cref)
@test_throws MOI.InvalidIndex(index(cref)) delete(model, cref)
@test !is_valid(model, cref)
test_noc(model, F, S, 0)
# As the bridge has been removed, if the constraints it has created where not removed, it wouldn't be there to decrease this counter anymore
@test num_variables(model) == nvars
for noc in nocs
test_noc(model, noc...)
end
end
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] | 2.197705 | 1,133 |
using Dates, Printf
using Plots
using LongwaveModePropagator
using LMPTools
# Ground
tx = TRANSMITTER[:NAA]
rx = Receiver("Boulder", 40.01, -105.244, 0.0, VerticalDipole())
sigmas = [get_sigma(y, x) for y in LAT, x in LON]
heatmap(LON, LAT, sigmas, clims=(0, 0.11))
epsilons = [get_epsilon(y, x) for y in LAT, x in LON]
heatmap(LON, LAT, epsilons, clims=(0, 20))
pts = [forward(tx.longitude, tx.latitude, az, d) for d in distances]
heatmap(LON, LAT, sigmas, clims=(0, 0.11), xlims=(-110, -65), ylims=(35, 48))
scatter!(getfield.(pts, :lon), getfield.(pts, :lat))
# Magnetic field
# Compare to LWPC
tx = Transmitter{VerticalDipole}("fdtdnaa", 44.646, -67.281, VerticalDipole(), Frequency(24e3), 100e3)
rx = Receiver("Boulder", 40.01, -105.244, 0.0, VerticalDipole())
grounds, distances = groundsegments(tx.latitude, tx.longitude, rx.latitude, rx.longitude;
require_permittivity_change=false)
bfields = igrf(tx, rx, 2020, distances)
species = Species(LMP.QE, LMP.ME, z->waitprofile(z, 82, 0.55), electroncollisionfrequency)
ground = GROUND[10]
wvg = SegmentedWaveguide([HomogeneousWaveguide(bfields[i], species, ground, distances[i]) for i in 1:length(distances)])
sampler = GroundSampler(0:5e3:2000e3, Fields.Ez)
e, a, p = propagate(wvg, tx, sampler)
ld, la, lp = readlog(LMPTools.project_path("data", "bfield.log"))
plot(sampler.distance/1000, a, label="LMP",
ylabel="Amplitude (dB)", xlabel="Range (km)", linewidth=1.5)
plot!(ld, la, label="LWPC", linewidth=1.5)
# Zenith angle
dt = DateTime(2021, 2, 1)
lats = 15:89
lons = -160:-60
szas = [zenithangle(la, lo, dt) for la in lats, lo in lons]
heatmap(lons, lats, szas,
color=cgrad(:starrynight, [50, 70, 80, 85, 90, 95, 100, 110, 130]/180, rev=true), clims=(0, 180))
function gpmap(file, lats::AbstractRange, lons::AbstractRange, data)
size(data) == (length(lats), length(lons)) || throw(ArgumentError("data not compatible with lats, lons"))
issorted(lons) && issorted(lats) || throw(ArgumentError("lats and lons must be sorted"))
Ξ΄lat = step(lats)/2
Ξ΄lon = step(lons)/2
open(file, "w") do f
for j in eachindex(lons)
for i in eachindex(lats)
str = @sprintf("%f, %f, %f\n", lons[j]-Ξ΄lon, lats[i]-Ξ΄lat, szas[i,j])
write(f, str)
str = @sprintf("%f, %f, %f\n", lons[j]-Ξ΄lon, lats[i]+Ξ΄lat, szas[i,j])
write(f, str)
str = @sprintf("%f, %f, %f\n", lons[j]+Ξ΄lon, lats[i]+Ξ΄lat, szas[i,j])
write(f, str)
str = @sprintf("%f, %f, %f\n", lons[j]+Ξ΄lon, lats[i]-Ξ΄lat, szas[i,j])
write(f, str)
write(f, "\n")
end
end
end
end
dt = DateTime(2021, 2, 1)
lats = 35:60
lons = -110:-75
szas = [zenithangle(la, lo, dt) for la in lats, lo in lons]
gpmap("szas.csv", lats, lons, szas)
function gppm3d(file, lats, lons, data)
lon=vec([lo for la in lats, lo in lons])
lat=vec([la for la in lats, lo in lons])
sza=vec(szas)
open(file, "w") do f
for j in eachindex(lons)
for i in eachindex(lats)
str = @sprintf("%f, %f, %f\n", lons[j], lats[i], szas[i,j])
write(f, str)
end
write(f, "\n")
end
end
end
dt = DateTime(2021, 2, 1)
lats = 40:65
lons = -145:-60
szas = [zenithangle(la, lo, dt) for la in lats, lo in lons]
gppm3d("szas_3col.csv", lats, lons, szas)
# Ionospheres
dt = DateTime(2021, 2, 1)
lats = 15:89
lons = -160:-60
x = [ferguson(la, zenithangle(la, lo, dt), dt) for la in lats, lo in lons]
heatmap(lons, lats, getindex.(x,1), color=:amp, clims=(68, 90))
dt = DateTime(2020, 3, 1, 2)
szas = [zenithangle(la, lo, dt) for la in lats, lo in lons]
heatmap(lons, lats, szas,
color=cgrad(:starrynight, [50, 70, 80, 85, 90, 95, 100, 110, 130]/180, rev=true), clims=(0, 180))
hprimes, betas = flatlinearterminator(szas)
heatmap(lons, lats, hprimes, color=:amp, clims=(68, 90))
heatmap(lons, lats, betas, color=:tempo, clims=(0.2, 0.6))
ionos = smoothterminator.(szas)
heatmap(lons, lats, getindex.(ionos,1), color=:amp, clims=(68, 90))
heatmap(lons, lats, getindex.(ionos,2), color=:tempo, clims=(0.2, 0.6))
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] | 2.025359 | 2,090 |
# Note on genome representation - p_int is a vector of integers of length n_gene,encoding gene type.
# p_float is an n_gene x 8 array encoding the real values for each gene (each row is a gene)
# Hence p_int[1] is the associated type of gene p_float[1,:]
function initialise_grn(p_float::Array{Float64,2},p_int::Vector{Int64},grn_config::FGRNConfig)
n_rec = 0
n_beh = 0
n_reg = 0
n_env = 0
n = grn_config.protein_size
for i in 1:size(p_int,1)
if p_int[i] == 1
n_rec += 1
elseif p_int[i] == 2
n_beh += 1
elseif p_int[i] == 3
n_reg += 1
else
n_env +=1
end
end
grn = ( rec_coding = ones(Int,n,n,n_rec),
beh_coding = ones(Int,n,n,n_beh),
reg_coding = ones(Int,n,n,n_reg),
env_coding = ones(Int,n,n,n_env),
rec_prom = ones(Int,n,n,n_rec),
beh_prom = ones(Int,n,n,n_beh),
reg_prom = ones(Int,n,n,n_reg),
env_prom = ones(Int,n,n,n_env),
rec_cod_thresh = zeros(Float64,n_rec),
rec_aff_thresh = zeros(Float64,n_rec),
rec_id = ones(Int,n_rec),
beh_cod_thresh = zeros(Float64,n_beh),
beh_aff_thresh = zeros(Float64,n_beh),
beh_id = ones(Int,n_beh),
reg_cod_thresh = zeros(Float64,n_reg),
reg_aff_thresh = zeros(Float64,n_reg),
reg_id = ones(Int,n_reg),
env_cod_thresh = zeros(Float64,n_env),
env_aff_thresh = zeros(Float64,n_env),
env_id = ones(Int,n_env),
Ct = grn_config.Ct, # assume average affinity = 0.3 ish, average affinity th - 0.5 as uniform
Cs = grn_config.Cs, # assume average affinity = 0.3 ish, average affinity th - 0.5 as uniform
Cw = grn_config.Cw,
Cp = grn_config.Cp,
Ci = grn_config.Ci,
)
n_rec = 0
n_beh = 0
n_reg = 0
n_env = 0
for i in 1:size(p_int,1)
coding = mandelbrot_protein(p_float[i,1:3],grn_config.mandel_thresh,grn_config.max_iter,n)
promoter = mandelbrot_protein(p_float[i,4:6],grn_config.mandel_thresh,grn_config.max_iter,n)
threshold = p_float[i,7]
affinity = p_float[i,8]
if p_int[i] == 1
n_rec += 1
grn.rec_coding[:,:,n_rec] = coding
grn.rec_prom[:,:,n_rec] = promoter
grn.rec_cod_thresh[n_rec] = threshold
grn.rec_aff_thresh[n_rec] = affinity
grn.rec_id[n_rec] = i
elseif p_int[i] == 2
n_beh += 1
grn.beh_coding[:,:,n_beh] = coding
grn.beh_prom[:,:,n_beh] = promoter
grn.beh_cod_thresh[n_beh] = threshold
grn.beh_aff_thresh[n_beh] = affinity
grn.beh_id[n_beh] = i
elseif p_int[i] == 3
n_reg += 1
grn.reg_coding[:,:,n_reg] = coding
grn.reg_prom[:,:,n_reg] = promoter
grn.reg_cod_thresh[n_reg] = threshold
grn.reg_aff_thresh[n_reg] = affinity
grn.reg_id[n_reg] = i
else
n_env += 1
grn.env_coding[:,:,n_env] = coding
grn.env_prom[:,:,n_env] = promoter
grn.env_cod_thresh[n_env] = threshold
grn.env_aff_thresh[n_env] = affinity
grn.env_id[n_env] = i
end
end
return grn
end
function calculate_affinity(protein_a::Array{Int,2}, protein_b::Array{Int,2})
return convert(Float64,sum(@. abs(protein_a - protein_b))) / (size(protein_a,1)*size(protein_a,1)*255)
end
function bind_probability(affinity::Float64,affinity_th::Float64,config::FGRNConfig)
return (1 + tanh((affinity_th - affinity - config.Ct)/config.Cs))/2
end
function update_rule(product_conc::Float64,original_conc::Float64,config::FGRNConfig)
return product_conc*tanh((product_conc - 0.5)/config.Cw)/config.Ci - original_conc/config.Cp
end
function updateConcentrations!(dc::Array{Float64,1},grn::NamedTuple, concentrations::Array{Float64,1},grn_config::FGRNConfig)
product = zeros(Int,grn_config.protein_size,grn_config.protein_size)
product_conc = zeros(Float64,grn_config.protein_size,grn_config.protein_size)
fill!(dc, zero(Float64))
nProteins::Int = 0
affinity = 0
@inbounds for j in 1:grn_config.protein_size
@inbounds for i in 1:grn_config.protein_size
for k in 1:size(grn.rec_coding,3)
if (concentrations[grn.rec_id[k]] > 0.5*grn.rec_cod_thresh[k]) && (grn.rec_coding[i,j,k] > product[i,j])
product[i,j] = grn.rec_coding[i,j,k]
product_conc[i,j] = concentrations[grn.rec_id[k]]
nProteins+=1
end
end
for k in 1:size(grn.reg_coding,3)
if (concentrations[grn.reg_id[k]] > 0.5*grn.reg_cod_thresh[k]) && (grn.reg_coding[i,j,k] > product[i,j])
product[i,j] = grn.reg_coding[i,j,k]
product_conc[i,j] = concentrations[grn.reg_id[k]]
nProteins+=1
end
end
for k in 1:size(grn.env_coding,3)
if (concentrations[grn.env_id[k]] > 0.5*grn.env_cod_thresh[k]) && (grn.env_coding[i,j,k] > product[i,j])
product[i,j] = grn.env_coding[i,j,k]
product_conc[i,j] = concentrations[grn.env_id[k]]
nProteins+=1
end
end
end
end
product_conc = sum(product_conc)/(grn_config.protein_size*grn_config.protein_size)
if nProteins != 0
@inbounds for k in 1:size(grn.rec_prom,3)
dc[grn.rec_id[k]] = 0
end
@inbounds for k in 1:size(grn.beh_prom,3)
affinity = calculate_affinity(grn.beh_prom[:,:,k],product)
if affinity > grn.beh_aff_thresh[k]
dc[grn.beh_id[k]] = update_rule((1 - affinity)*product_conc,concentrations[grn.beh_id[k]],grn_config)
else
dc[grn.beh_id[k]] = 0
end
end
@inbounds for k in 1:size(grn.reg_prom,3)
affinity = calculate_affinity(grn.reg_prom[:,:,k],product)
if affinity > grn.reg_aff_thresh[k]
dc[grn.reg_id[k]] = update_rule((1 - affinity)*product_conc,concentrations[grn.reg_id[k]],grn_config)
else
dc[grn.reg_id[k]] = 0
end
end
@inbounds for k in 1:size(grn.env_prom,3)
affinity = calculate_affinity(grn.env_prom[:,:,k],product)
if affinity > grn.env_aff_thresh[k]
dc[grn.env_id[k]] = update_rule((1 - affinity)*product_conc,concentrations[grn.env_id[k]],grn_config)
else
dc[grn.env_id[k]] = 0
end
end
else
fill!(dc, zero(Float64))
end
@. concentrations = concentrations + dc;
end | [
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] | 1.685279 | 4,531 |
type Grouped{T, I}
source::T
group_indices::I
group_levels
groupbys
predicate_aliases
end
type GroupLevels
levels::Vector{Vector}
end
Base.indices(group_levels::GroupLevels, j) = group_levels.levels[j]
function Base.isequal(grouped1::Grouped, grouped2::Grouped)
isequal(grouped1.source, grouped2.source) || return false
isequal(grouped1.group_indices, grouped2.group_indices) || return false
isequal(grouped1.groupbys, grouped2.groupbys) || return false
return true
end
function Base.hash(grouped::Grouped)
h = hash(grouped.source) + 1
h = hash(grouped.indices, h)
h = hash(grouped.groupbys, h)
return @compat UInt(h)
end
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] | 2.544776 | 268 |
@testset "Appender Error" begin
db = DBInterface.connect(DuckDB.DB)
con = DBInterface.connect(db)
@test_throws DuckDB.QueryException DuckDB.Appender(db, "nonexistanttable")
@test_throws DuckDB.QueryException DuckDB.Appender(con, "t")
end
@testset "Appender Usage" begin
db = DBInterface.connect(DuckDB.DB)
DBInterface.execute(db, "CREATE TABLE integers(i INTEGER)")
appender = DuckDB.Appender(db, "integers")
DuckDB.close(appender)
DuckDB.close(appender)
appender = DuckDB.Appender(db, "integers")
for i in 0:9
DuckDB.append(appender, i)
DuckDB.end_row(appender)
end
DuckDB.flush(appender)
results = DBInterface.execute(db, "SELECT * FROM integers")
df = DataFrame(results)
@test names(df) == ["i"]
@test size(df, 1) == 10
@test df.i == [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
end
# @testset "Appender API" begin
# # Open the database
# db = DuckDB.open(":memory:")
# con = DuckDB.connect(db)
#
# # Create the table the data is appended to
# DuckDB.execute(con, "CREATE TABLE dtypes(bol BOOLEAN, tint TINYINT, sint SMALLINT, int INTEGER, bint BIGINT, utint UTINYINT, usint USMALLINT, uint UINTEGER, ubint UBIGINT, float FLOAT, double DOUBLE, date DATE, time TIME, vchar VARCHAR, nullval INTEGER)")
#
# # Create the appender
# appender = DuckDB.appender_create(con, "dtypes")
#
# # Append the different data types
# DuckDB.duckdb_append_bool(appender, true)
# DuckDB.duckdb_append_int8(appender, 1)
# DuckDB.duckdb_append_int16(appender, 2)
# DuckDB.duckdb_append_int32(appender, 3)
# DuckDB.duckdb_append_int64(appender, 4)
# DuckDB.duckdb_append_uint8(appender, 1)
# DuckDB.duckdb_append_uint16(appender, 2)
# DuckDB.duckdb_append_uint32(appender, 3)
# DuckDB.duckdb_append_uint64(appender, 4)
# DuckDB.duckdb_append_float(appender, 1.0)
# DuckDB.duckdb_append_double(appender, 2.0)
# DuckDB.duckdb_append_date(appender, 100)
# DuckDB.duckdb_append_time(appender, 200)
# DuckDB.duckdb_append_varchar(appender, "Foo")
# DuckDB.duckdb_append_null(appender)
# # End the row of the appender
# DuckDB.duckdb_appender_end_row(appender)
# # Destroy the appender and flush the data
# DuckDB.duckdb_appender_destroy(appender)
#
# # Retrive the data from the table and store it in a vector
# df = DuckDB.toDataFrame(con, "select * from dtypes;")
# data = Matrix(df)
#
# # Test if the correct types have been appended to the table
# @test data[1] === true
# @test data[2] === Int8(1)
# @test data[3] === Int16(2)
# @test data[4] === Int32(3)
# @test data[5] === Int64(4)
# @test data[6] === UInt8(1)
# @test data[7] === UInt16(2)
# @test data[8] === UInt32(3)
# @test data[9] === UInt64(4)
# @test data[10] === Float32(1.0)
# @test data[11] === Float64(2.0)
# @test data[12] === Dates.Date("1970-04-11")
# @test data[13] === Dates.Time(0, 0, 0, 0, 200)
# @test data[14] === "Foo"
# @test data[15] === missing
#
# # Disconnect and close the database
# DuckDB.disconnect(con)
# DuckDB.close(db)
# end
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] | 2.239238 | 1,417 |
module WhipRest
using HttpServer
include("beans.jl")
export ServerConfig, ResponseConfig, WhipRestServer, RestMessage, GET, POST, PUT, PATCH, DELETE, createServer, startServer
function createServer(config::ServerConfig)
restServer = WhipRestServer(config.port, Dict{String,Any}(), Dict{String,Any}(), Dict{String,Any}(), Dict{String,Any}(), Dict{String,Any}())
#Iterate over all the request response mappings
for i = 1:length(config.responseConfigurations)
responseConfiguration = config.responseConfigurations[i]
#get the correct mappings set based on HTTP method
mappings = getMappings(restServer, responseConfiguration.method)
#Put the response handler into map for the given path
mappings[responseConfiguration.path] = responseConfiguration.responseHandler
end
return restServer
end
function startServer(whipServer::WhipRestServer)
#Create a new HTTP handler
http = HttpHandler() do req::Request, res::Response
#Get the mappings of path to action based on HTTP method
mappings = getMappings(whipServer, getHTTP_METHOD(req.method))
try
#Extract the path from the request resource
path = getPath(req.resource)
#Try to find the function for the mapping
responseHandler = mappings[String(path)]
#Call the handler for this path with the content after the path
params = getParams(req.resource)
Response(responseHandler(RestMessage(params, req.data)))
catch error
if isa(error, KeyError)
#No mapping was found for this path
Response("404")
end
end
end
server = Server(http)
run(server, whipServer.port)
end
function getPath(resource::String)
#Get the entire resource from the first / to the ? or the end of string
paramIndex = search(resource, '?')
if paramIndex==0
#Trim the / off the end if it exists
if resource[end] == '/'
path = resource[1:end-1]
else
path = resource[1:end]
end
else
path = resource[1:paramIndex-1]
end
return path
end
function getParams(resource::String)
paramIndex = search(resource, '?')
paramsMap = Dict{String,String}()
if paramIndex==0
return paramsMap
else
params = split(resource[paramIndex+1:end], '&')
for i = 1:length(params)
pair = split(params[i], '=')
paramsMap[pair[1]] = String(pair[2])
end
return paramsMap
end
end
function getHTTP_METHOD(method::String)
if method == "GET"
return GET
elseif method== "POST"
return POST
elseif method == "PUT"
return PUT
elseif method == "PATCH"
return PATCH
elseif method == "DELETE"
return DELETE
end
end
function getMappings(whipServer::WhipRestServer, method::HTTP_METHOD)
if GET == method
return whipServer.getMappings
elseif POST == method
return whipServer.postMappings
elseif PUT == method
return whipServer.putMappings
elseif PACTH == method
return whipServer.patchMappings
elseif DELETE == method
return whipServer.deleteMappings
end
end
end #end module WhipRest
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] | 2.678275 | 1,206 |
ENV["GKSwstype"]="100" #src
# # GEMPIC
#
# ### Geometric ElectroMagnetic Particle-In-Cell Methods
#
# https://arxiv.org/abs/1609.03053
#
# Michael Kraus, Katharina Kormann, Philip J. Morrison, Eric SonnendrΓΌcker
#
# Framework for Finite Element Particle-in-Cell methods based on
# the discretization of the underlying Hamiltonian structure of the
# Vlasov-Maxwell system.
#
# Install the GEMPIC package
using Pkg
Pkg.add("GEMPIC");
using ProgressMeter, Plots, GEMPIC
#md # ---
# # Strong Landau Damping
#
# The physical parameters
kx, Ξ± = 0.5, 0.5
xmin, xmax = 0, 2Ο/kx
domain = [xmin, xmax, xmax - xmin]
#md # --
# The numerical parameters
βt = 0.05
nx = 32
n_particles = 100000
mesh = GEMPIC.Mesh( xmin, xmax, nx)
spline_degree = 3
#md # ---
# Initialize particles
mass, charge = 1.0, 1.0
particle_group = GEMPIC.ParticleGroup{1,2}( n_particles, mass, charge, 1)
sampler = LandauDamping( Ξ±, kx)
sample!(sampler, particle_group)
#md # --
# Particle-mesh coupling operators
kernel_smoother1 = ParticleMeshCoupling( domain, [nx], n_particles,
spline_degree-1, :galerkin)
kernel_smoother0 = ParticleMeshCoupling( domain, [nx], n_particles,
spline_degree, :galerkin)
# Allocate electrostatic fields and Maxwell solver
rho = zeros(Float64, nx)
efield_poisson = zeros(Float64, nx)
maxwell_solver = Maxwell1DFEM(domain, nx, spline_degree)
solve_poisson!( efield_poisson, particle_group,
kernel_smoother0, maxwell_solver, rho)
#md # ---
# ### Charge density
xg = LinRange(xmin, xmax, nx)
sval = eval_uniform_periodic_spline_curve(spline_degree-1, rho)
plot( xg, sval, label="Ο")
#md savefig("rho.svg"); nothing # hide
#md # 
#md # ---
# ### Electric field
sval = eval_uniform_periodic_spline_curve(spline_degree-1, efield_poisson)
plot( xg, sval, label="efield")
#md savefig("ex.svg"); nothing # hide
#md # 
#md # ---
# Initialize the arrays for the spline coefficients of the fields
efield_dofs = [copy(efield_poisson), zeros(Float64, nx)]
bfield_dofs = zeros(Float64, nx)
propagator = HamiltonianSplitting( maxwell_solver,
kernel_smoother0,
kernel_smoother1,
particle_group,
efield_dofs,
bfield_dofs,
domain);
efield_dofs_n = propagator.e_dofs
thdiag = TimeHistoryDiagnostics( particle_group, maxwell_solver,
kernel_smoother0, kernel_smoother1 );
#md # ---
# ## Loop over time
steps, Ξt = 500, 0.05
@showprogress 1 for j = 1:steps # loop over time
## Strang splitting
strang_splitting!(propagator, Ξt, 1)
## Diagnostics
solve_poisson!( efield_poisson, particle_group,
kernel_smoother0, maxwell_solver, rho)
write_step!(thdiag, j * Ξt, spline_degree,
efield_dofs, bfield_dofs,
efield_dofs_n, efield_poisson)
end
#md nothing # hide
#md # ---
# ## Diagnostics stored in a dataframe
using DataFrames
first(thdiag.data, 5)
#md # ---
plot(thdiag.data[!,:Time], log.(thdiag.data[!,:PotentialEnergyE1]))
#md savefig("mod_e.svg"); nothing # hide
#md # 
@test true #src
#md # Next [Conclusion](/Numkin2019/06/build/index.html)
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] | 2.100487 | 1,642 |
# Constans for the game
const BLOCK_SIZE = 20
const WIDTH = 640
const HEIGHT = 480
@enum Direction begin
UP = 1
RIGHT = 2
DOWN = 3
LEFT = 4
end
const MODELS_PATH = "saved_models" | [
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] | 2.512821 | 78 |
# ------------------------------------------------------------------
# Licensed under the MIT License. See LICENSE in the project root.
# ------------------------------------------------------------------
"""
Neighborhood
A neighborhood is a geometry that is not attached to any specific
point in the space, and is free to slide over a domain of interest.
"""
abstract type Neighborhood end
"""
MetricBall
A metric ball is a neighborhood that can be expressed in terms of a
metric and a range. They are useful for fast searches with tree
data structures.
"""
abstract type MetricBall <: Neighborhood end
"""
metric(ball)
Return the metric of the norm `ball`.
"""
function metric(::MetricBall) end
# ----------------
# IMPLEMENTATIONS
# ----------------
include("neighborhoods/normball.jl")
include("neighborhoods/ellipsoid.jl")
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] | 4.096618 | 207 |
#This is an additional wrapper to generate julia function definitions
# from the netcdf.h header file. This is necessary because it looks like
# Clang.jl had problems translating size_t and ptrdiff_t to the correct
# Julia types
#A small translator for c types:
tdict = Dict(
"int"=>"Cint",
"size_t"=>"Csize_t",
"char"=>"Cchar",
"NC_Dispatch"=>"Cvoid",
"nc_type"=>"Cint",
"nc_vlen_t"=>"nc_vlen_t",
"void"=>"Cvoid",
"long long"=>"Clonglong",
"ptrdiff_t"=>"Cptrdiff_t",
"unsigned int"=>"Cuint",
"float"=>"Cfloat",
"unsigned char"=>"Cuchar",
"signed char"=>"Cchar",
"short"=>"Cshort",
"long"=>"Clong",
"double"=>"Cdouble",
"unsigned short"=>"Cushort",
"unsigned long"=>"Culong",
"unsigned long long"=>"Culonglong",
)
"Parses the signature from the c function argument and returns the Julia type as well as the argument name"
function parsesig(s,tdict)
s = strip(replace(s,"const"=>""))
nstar = count(isequal('*'),s)
s = replace(s,"*"=>"")
if endswith(s,"[]")
nstar += 1
s = replace(s,"[]"=>"")
end
spl = split(strip(s)," ", keepempty=false)
if length(spl) == 1
return tdict[spl[1]],"x"
end
if length(spl)>2
spl = (join(spl[1:end-1]," "), spl[end])
end
t, argname = spl
if nstar == 2
return "Ptr{Ptr{$(tdict[t])}}", argname
elseif nstar == 1
return "Ptr{$(tdict[t])}", argname[2:end]
else
return tdict[t], argname
end
end
"Write the function with name `fname` and signature `sig` to the IO stream `outp`"
function writefun(outp,fname,sig)
print(outp,"function ")
print(outp,fname)
print(outp,"(")
for (t,name) in sig
print(outp,name)
# if parse(t) <: Integer
# print(outp,"::Integer")
# end
print(outp,", ")
end
print(outp, ")\n")
println(outp, " check(ccall(")
println(outp, " (:$(fname), libnetcdf),")
println(outp, " Cint,")
print(outp, " (")
foreach(i->print(outp,string(i[1],", ")),sig)
println(outp,"),")
foreach(i->println(outp," $(i[2]),"),sig)
println(outp, " ))")
println(outp, "end")
end
include(joinpath(@__DIR__,"..","src","netcdf_constants.jl"))
function parseheader(input, output)
cheader = eachline(input) |> collect;
open(output,"w") do outp
ii = 1
while true
i1 = findnext(startswith("EXTERNL int"), cheader,ii)
i1 === nothing && break
i2 = findnext(contains(";"), cheader,i1)
cdef = join(cheader[i1:i2])
sig = match(r"int\s*(\w+)\((.*)\)",cdef)
if sig !== nothing
fname = sig.captures[1]
sig = parsesig.(split(sig.captures[2],","), Ref(tdict))
writefun(outp,fname,sig)
end
ii = i2+1
end
end
end
input = eachline(`locate netcdf.h`) |> first
output = joinpath(@__DIR__, "..", "src","netcdf_c.jl")
parseheader(input, output) | [
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] | 2.083737 | 1,445 |
using Documenter, Arena
makedocs(
modules = [Arena],
format = :html,
sitename = "Arena.jl",
pages = Any[
"Home" => "index.md",
"Examples" => "examples.md",
"Functions" => "func_ref.md"
]
# html_prettyurls = !("local" in ARGS),
)
deploydocs(
repo = "github.com/dehann/Arena.jl.git",
target = "build",
deps = nothing,
make = nothing,
julia = "0.6",
osname = "linux"
)
| [
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] | 2.045662 | 219 |
# Exercise 1
using LinearAlgebra
using StaticArrays
using BenchmarkTools
N = 3
A = rand(N, N)
x = rand(N)
@btime $A * $x # the $ in front of variable names is sometimes important
@btime inv($A)
# Benchmark
A_s = @SMatrix rand(N,N)
x_s = @SVector rand(N)
@btime $A_s * $x_s
@btime inv($A_s);
# very fast matrix multiplication, use $ and denote as static vectors
@btime $A_s * $x_s
@show supertypes(typeof(A_s))
@show supertypes(typeof(A));
# Exercise 2
Ξ£ = [0.4 0.3;
0.3 0.45]
G = I
R = 0.5 * Ξ£
gain(Ξ£, G, R) = Ξ£ * G' * inv(G * Ξ£ * G' + R)
@btime gain($Ξ£, $G, $R)
# Benchmark
Ξ£_s = @SMatrix [0.4 0.3; 0.3 0.45]
R_s = 0.5 * Ξ£_s
@show typeof(R) #static array, too
@btime gain($Ξ£_s, $G, $R_s);
# Exercise 3
using Polynomials
p = Polynomial([2, -5, 2], :x) # :x just gives a symbol for display
@show p
@show typeof(p)
pβ² = derivative(p) # gives the derivative of p, another polynomial
@show p(0.1), pβ²(0.1) # call like a function
@show roots(p); # find roots such that p(x) = 0
# plot p and p'
r = range(-2.0, 2.0, length = 20)
p_r = p.(r)
pβ²_r = pβ².(r)
plot(r[1:end-1],pβ²_r[1:end-1],label="Dp(x)")
plot!(r,p_r,label="p(x)")
# Exercise 4
function newtonsmethod(p::Polynomial, x_0; tolerance = 1E-7, maxiter = 100)
pβ² = derivative(p)
#initialise loop
iter = 1
normdiff = Inf
x_old = x_0
while normdiff > tolerance && iter < maxiter
x_new = x_old - p(x_old)/ pβ²(x_old)
normdiff = norm(x_new - x_old)
iter += 1 #continuation
x_old = x_new
end
return (root = x_old, normdiff = normdiff, iter = iter )
end
# Compute roots
p = Polynomial([2, -5, 2], :x) # :x just gives a symbol for display
@show newtonsmethod(p, 5); # find roots such that p(x) = 0
@show roots(p)
@btime newtonsmethod(p, 5);
@btime roots(p);
# Exercise 5
function trapezoidal(f::AbstractArray{Float64,1}, x::AbstractArray{Float64,1})
sol = similar(x)
del = diff(x)
for i in 2:length(x)
sol[i] = (f_x[i]+f_x[i-1])/2*del[i-1]
end
return sum(sol)
end
x = range(0.0,1.0,length=9)
f(x) = x^2
f_x = f.(x)
int = trapezoidal(f_x,x)
function trapezoidal2(f::AbstractArray{Float64}, x::AbstractRange{Float64})
sol = similar(x)
del = step(x)
for i in 2:length(x)
sol[i] = (f_x[i]+ f_x[i-1])/2*del
end
return sum(sol)
end
x = range(0.0,1.0,length=1100)
f(x) = x^2
f_x = f.(x)
integral = trapezoidal2(f_x,x)
f(x) = x^3
xΜ = 1.0
xΜ² = 0.0
N = 1100;
function trapezoidal3(f, xΜ²::Real , xΜ::Real , N::Int64)
x = range(xΜ², xΜ, length = N)
f_x = f.(x)
#implementation the same as trapezodal2
sol = similar(x)
del = step(x)
for i in 2:length(x)
sol[i] = (f_x[i]+ f_x[i-1])/2*del
end
return sum(sol)
end
integral = trapezoidal3(f ,xΜ² , xΜ , N)
# Exercise 6
using ForwardDiff
function f(a, b; N::Int64 = 50)
r = range(a, b, length=N) # one
return mean(r)
end
Df(x) = ForwardDiff.derivative(y -> f(0.0, y), x)
@show f(0.0, 3.0)
@show f(0.0, 3.1)
Df(3.0)
function f(a, b; N::Int64 = 50)
r = range(a, b, length=N) # one
return mean(r)
end
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] | 2.022757 | 1,538 |
"""
Const <: Kernel
Constant kernel
```math
k(x,x') = ΟΒ²
```
with signal standard deviation ``Ο``.
"""
mutable struct Const{T} <: Kernel where {T<:Real}
"Signal variance"
Ο2::T
"Priors for kernel parameters"
priors::Array
end
"""
Constant kernel function
Const(lΟ::T)
# Arguments
- `lΟ::Real`: signal standard deviation (given on log scale)
"""
Const(lΟ::T) where T = Const{T}(exp(2 * lΟ), [])
function set_params!(cons::Const, hyp::AbstractVector)
length(hyp) == 1 || throw(ArgumentError("Constant kernel has one parameter, received $(length(hyp))."))
cons.Ο2 = exp(2.0*hyp[1])
end
get_params(cons::Const{T}) where T = T[log(cons.Ο2)/2.0]
get_param_names(cons::Const) = [:lΟ]
num_params(cons::Const) = 1
cov(cons::Const) = cons.Ο2
function cov(cons::Const, x::AbstractVector, y::AbstractVector)
return cov(cons)
end
@inline dk_dlΟ(cons::Const) = 2.0*cov(cons)
@inline function dKij_dΞΈp(cons::Const, X1, X2, i::Int, j::Int, p::Int, dim::Int)
if p == 1
return dk_dlΟ(cons)
else
return NaN
end
end
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] | 2.349345 | 458 |
using Test
using JustSayIt
using PyCall
import JustSayIt: recorder, active_recorder_id, start_recording, stop_recording, pause_recording, restart_recording
# Test setup
const SAMPLEDIR_CMD = joinpath("samples", "commands")
@testset "$(basename(@__FILE__))" begin
@testset "1. start/stop/pause/restart recording from audio input cmd" begin
id = "help"
cmd = `julia -e 'using JustSayIt; JustSayIt.read_wav(joinpath("samples","commands","help.wav"))'`
@test isa(start_recording(;audio_input_cmd=cmd, id=id), Base.Process)
@test active_recorder_id() == id
@test isa(recorder(id), Base.Process)
pause_recording()
restart_recording()
@test isa(recorder(id), Base.Process)
stop_recording(id=id)
end;
@static if !Sys.iswindows() && !Sys.islinux()
@testset "2. start/stop/pause/restart recording from mic" begin
id = "mic"
@test isa(start_recording(id=id), PyObject)
@test active_recorder_id() == id
@test isa(recorder(id), PyObject)
pause_recording()
restart_recording()
@test isa(recorder(id), PyObject)
stop_recording(id=id)
end;
end
end;
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] | 2.223022 | 556 |
nodes_OMIB = [
Bus(
1, #number
"Bus 1", #Name
"REF", #BusType (REF, PV, PQ)
0, #Angle in radians
1.06, #Voltage in pu
(min = 0.94, max = 1.06), #Voltage limits in pu
69,
nothing,
nothing,
), #Base voltage in kV
Bus(2, "Bus 2", "PV", 0, 1.045, (min = 0.94, max = 1.06), 69, nothing, nothing),
]
static_gen = ThermalStandard(
name = "TestGen",
available = true,
status = true,
bus = nodes_OMIB[2],
active_power = 0.40,
reactive_power = 0.010,
rating = 0.5,
prime_mover = PrimeMovers.ST,
fuel = ThermalFuels.COAL,
active_power_limits = (min = 0.0, max = 0.40),
reactive_power_limits = (min = -0.30, max = 0.30),
time_limits = nothing,
ramp_limits = nothing,
operation_cost = ThreePartCost((0.0, 1400.0), 0.0, 4.0, 2.0),
base_power = 1.0,
)
branch_OMIB = [
Line(
"Line1", #name
true, #available
0.0, #active power flow initial condition (from-to)
0.0, #reactive power flow initial condition (from-to)
Arc(from = nodes_OMIB[1], to = nodes_OMIB[2]), #Connection between buses
0.01, #resistance in pu
0.05, #reactance in pu
(from = 0.0, to = 0.0), #susceptance in pu
18.046, #rate in MW
1.04,
),
] #angle limits (-min and max)
@testset "Dynamic Machines" begin
Basic = BaseMachine(R = 0.0, Xd_p = 0.2995, eq_p = 1.05)
@test Basic isa PowerSystems.DynamicComponent
GENROU = RoundRotorQuadratic(
R = 0.0,
Td0_p = 7.4,
Td0_pp = 0.03,
Tq0_p = 0.06,
Tq0_pp = 0.033,
Xd = 0.8979,
Xq = 0.646,
Xd_p = 0.2995,
Xq_p = 0.646,
Xd_pp = 0.23,
Xl = 0.1,
Se = (0.1, 0.5),
)
@test GENROU isa PowerSystems.DynamicComponent
GENROE = RoundRotorExponential(
R = 0.0,
Td0_p = 7.4,
Td0_pp = 0.03,
Tq0_p = 0.06,
Tq0_pp = 0.033,
Xd = 0.8979,
Xq = 0.646,
Xd_p = 0.2995,
Xq_p = 0.646,
Xd_pp = 0.23,
Xl = 0.1,
Se = (0.1, 0.5),
)
@test GENROE isa PowerSystems.DynamicComponent
GENSAL = SalientPoleQuadratic(
R = 0.0,
Td0_p = 7.4,
Td0_pp = 0.03,
Tq0_pp = 0.033,
Xd = 0.8979,
Xq = 0.646,
Xd_p = 0.2995,
Xd_pp = 0.23,
Xl = 0.1,
Se = (0.1, 0.5),
)
@test GENSAL isa PowerSystems.DynamicComponent
GENSAE = SalientPoleExponential(
R = 0.0,
Td0_p = 7.4,
Td0_pp = 0.03,
Tq0_pp = 0.033,
Xd = 0.8979,
Xq = 0.646,
Xd_p = 0.2995,
Xd_pp = 0.23,
Xl = 0.1,
Se = (0.1, 0.5),
)
@test GENSAE isa PowerSystems.DynamicComponent
oneDoneQ = OneDOneQMachine(
R = 0.0,
Xd = 0.8979,
Xq = 0.646,
Xd_p = 0.2995,
Xq_p = 0.04,
Td0_p = 7.4,
Tq0_p = 0.033,
)
@test oneDoneQ isa PowerSystems.DynamicComponent
AndersonFouad = AndersonFouadMachine(
R = 0.0,
Xd = 0.8979,
Xq = 0.646,
Xd_p = 0.2995,
Xq_p = 0.646,
Xd_pp = 0.23,
Xq_pp = 0.4,
Td0_p = 7.4,
Tq0_p = 0.01, #Data not available in Milano: Used 0.01
Td0_pp = 0.03,
Tq0_pp = 0.033,
)
@test AndersonFouad isa PowerSystems.DynamicComponent
KundurMachine = SimpleFullMachine(
R = 0.003, #Example 3.1 and 4.1 of Kundur
R_f = 0.0006,
R_1d = 0.0284, #RD in Machowski
R_1q = 0.0062, #RQ on Machowski
L_d = 1.81,
L_q = 1.76,
L_ad = 1.66, #k*M_f or k*M_D in Machowski
L_aq = 1.61, #k*M_Q in Machowski
L_f1d = 1.66, #L_fD in Machowski. Assumed to be equal to L_ad
L_ff = 1.825,
L_1d = 0.1713, #L_D in Machowski
L_1q = 0.7525, #L_Q in Machowski
)
@test KundurMachine isa PowerSystems.DynamicComponent
KundurFullMachine = FullMachine(
R = 0.003, #Example 3.1 and 4.1 of Kundur
R_f = 0.0006,
R_1d = 0.0284, #RD in Machowski
R_1q = 0.0062, #RQ on Machowski
L_d = 1.81,
L_q = 1.76,
L_ad = 1.66, #k*M_f or k*M_D in Machowski
L_aq = 1.61, #k*M_Q in Machowski
L_f1d = 1.66, #L_fD in Machowski. Assumed to be equal to L_ad
L_ff = 1.825,
L_1d = 0.1713, #L_D in Machowski
L_1q = 0.7525, #L_Q in Machowski
)
@test KundurFullMachine isa PowerSystems.DynamicComponent
Mach2_benchmark = OneDOneQMachine(
R = 0.0,
Xd = 1.3125,
Xq = 1.2578,
Xd_p = 0.1813,
Xq_p = 0.25,
Td0_p = 5.89,
Tq0_p = 0.6,
)
@test Mach2_benchmark isa PowerSystems.DynamicComponent
end
################ Shaft Data #####################
@testset "Dynamic Shaft" begin
BaseShaft = SingleMass(H = 5.148, D = 2.0)
@test BaseShaft isa PowerSystems.DynamicComponent
FiveShaft = FiveMassShaft(
H = 5.148,
H_hp = 0.3348,
H_ip = 0.7306,
H_lp = 0.8154,
H_ex = 0.0452,
D = 2.0,
D_hp = 0.5180,
D_ip = 0.2240,
D_lp = 0.2240,
D_ex = 0.1450,
D_12 = 0.0518,
D_23 = 0.0224,
D_34 = 0.0224,
D_45 = 0.0145,
K_hp = 33.07,
K_ip = 28.59,
K_lp = 44.68,
K_ex = 21.984,
)
@test FiveShaft isa PowerSystems.DynamicComponent
end
################# PSS Data #####################
@testset "Dynamic PSS" begin
no_pss = PSSFixed(V_pss = 0.0)
@test no_pss isa PowerSystems.DynamicComponent
end
################ TG Data #####################
@testset "Dynamic Turbine Governor Constructors" begin
fixed_tg = TGFixed(efficiency = 1.0)
@test fixed_tg isa PowerSystems.DynamicComponent
typeI_tg = TGTypeI(
R = 0.02,
Ts = 0.1,
Tc = 0.45,
T3 = 0.0,
T4 = 0.0,
T5 = 50.0,
valve_position_limits = (min = 0.3, max = 1.2),
)
@test typeI_tg isa PowerSystems.DynamicComponent
typeII_tg = TGTypeII(R = 0.05, T1 = 0.3, T2 = 0.1, Ο_limits = (min = 0.1, max = 1.0))
@test typeII_tg isa PowerSystems.DynamicComponent
gast_tg = GasTG(
R = 0.05,
T1 = 0.40,
T2 = 0.10,
T3 = 2.0,
AT = 1.0,
Kt = 2.0,
V_lim = (0.417, 0.8),
D_turb = 0.0,
)
@test gast_tg isa PowerSystems.DynamicComponent
end
################ AVR Data #####################
@testset "Dynamic AVR Constructors" begin
proportional_avr = AVRSimple(Kv = 5000.0)
@test proportional_avr isa PowerSystems.DynamicComponent
fixed_avr = AVRFixed(Vf = 1.05, V_ref = 1.0)
@test fixed_avr isa PowerSystems.DynamicComponent
typeI_avr = AVRTypeI(
Ka = 200.0,
Ke = 1.0,
Kf = 0.0012,
Ta = 0.02,
Te = 0.19,
Tf = 1.0,
Tr = 0.001,
Va_lim = (0.0, 0.0),
Ae = 0.0006,
Be = 0.9,
)
@test typeI_avr isa PowerSystems.DynamicComponent
ac1a_avr = ESAC1A(
Tr = 0.0,
Tb = 0.0,
Tc = 0.0,
Ka = 200,
Ta = 0.5,
Va_lim = (-7.0, 7.0),
Te = 1.333,
Kf = 0.02,
Tf = 0.8,
Kc = 0.0,
Kd = 0.0,
Ke = 1.0,
E_sat = (0.0, 0.0),
Se = (0.0, 0.0),
Vr_lim = (-99.0, 99.0),
)
@test ac1a_avr isa PowerSystems.DynamicComponent
mod_ac1a_avr = EXAC1(
Tr = 0.0,
Tb = 0.0,
Tc = 0.0,
Ka = 400,
Ta = 0.5,
Vr_lim = (-5.2477, 5.2477),
Te = 1.1,
Kf = 0.035,
Tf = 1.0,
Kc = 0.2469,
Kd = 0.5,
Ke = 1.0,
E_sat = (2.707, 3.6102),
Se = (0.0366, 0.1831),
)
@test mod_ac1a_avr isa PowerSystems.DynamicComponent
st1a_avr = ESST1A(
UEL_flags = 1,
PSS_flags = 1,
Tr = 0.0,
Vi_lim = (-99.0, 99.0),
Tc = 2.5,
Tb = 13.25,
Tc1 = 0.0,
Tb1 = 0.0,
Ka = 200.0,
Ta = 0.1,
Va_lim = (-9.5, 9.5),
Vr_lim = (-9.5, 9.5),
Kc = 0.0,
Kf = 0.0,
Tf = 1.0,
K_lr = 0.0,
I_lr = 999.0,
)
@test st1a_avr isa PowerSystems.DynamicComponent
gen2_avr_benchmark = AVRTypeII(
K0 = 20.0,
T1 = 0.2,
T2 = 0.063,
T3 = 0.35,
T4 = 0.01,
Te = 0.314,
Tr = 0.001,
Va_lim = (-5.0, 5.0),
Ae = 0.0039,
Be = 1.555,
)
@test gen2_avr_benchmark isa PowerSystems.DynamicComponent
end
######################### Generators ########################
@testset "Dynamic Generators" begin
#Components for the test
Basic = BaseMachine(R = 0.0, Xd_p = 0.2995, eq_p = 1.05)
BaseShaft = SingleMass(H = 5.148, D = 2.0)
fixed_avr = AVRFixed(Vf = 1.05, V_ref = 1.0)
proportional_avr = AVRSimple(Kv = 5000.0)
fixed_tg = TGFixed(efficiency = 1.0)
no_pss = PSSFixed(V_pss = 0.0)
oneDoneQ = OneDOneQMachine(
R = 0.0,
Xd = 0.8979,
Xq = 0.646,
Xd_p = 0.2995,
Xq_p = 0.04,
Td0_p = 7.4,
Tq0_p = 0.033,
)
Gen1AVR = DynamicGenerator(
name = get_name(static_gen),
Ο_ref = 1.0,
machine = Basic,
shaft = BaseShaft,
avr = proportional_avr,
prime_mover = fixed_tg,
pss = no_pss,
)
@test Gen1AVR isa PowerSystems.Component
Gen1AVRnoAVR = DynamicGenerator(
name = get_name(static_gen),
Ο_ref = 1.0,
machine = Basic,
shaft = BaseShaft,
avr = fixed_avr,
prime_mover = fixed_tg,
pss = no_pss,
)
@test Gen1AVRnoAVR isa PowerSystems.Component
Gen2AVRnoAVR = DynamicGenerator(
name = get_name(static_gen),
Ο_ref = 1.0,
machine = oneDoneQ,
shaft = BaseShaft,
avr = fixed_avr,
prime_mover = fixed_tg,
pss = no_pss,
)
@test Gen2AVRnoAVR isa PowerSystems.Component
Gen2AVR = DynamicGenerator(
name = get_name(static_gen),
Ο_ref = 1.0,
machine = oneDoneQ,
shaft = BaseShaft,
avr = proportional_avr,
prime_mover = fixed_tg,
pss = no_pss,
)
@test Gen2AVR isa PowerSystems.Component
sys = System(100)
for bus in nodes_OMIB
add_component!(sys, bus)
end
for lines in branch_OMIB
add_component!(sys, lines)
end
# Names must be the same.
Gen1AVR.name = "bad_name"
@test_throws ArgumentError add_component!(sys, Gen1AVR, static_gen)
Gen1AVR.name = get_name(static_gen)
# static_injector must be passed.
@test_throws ArgumentError add_component!(sys, Gen1AVR)
# static_injector must be attached to the system.
@test_throws ArgumentError add_component!(sys, Gen1AVR, static_gen)
add_component!(sys, static_gen)
@test isnothing(get_dynamic_injector(static_gen))
add_component!(sys, Gen1AVR, static_gen)
dynamics = collect(get_components(DynamicGenerator, sys))
@test length(dynamics) == 1
@test dynamics[1] == Gen1AVR
@test get_dynamic_injector(static_gen) == Gen1AVR
@test get_base_power(static_gen) == get_base_power(Gen1AVR)
remove_component!(sys, Gen1AVR)
@test isnothing(get_dynamic_injector(static_gen))
add_component!(sys, Gen2AVR, static_gen)
@test get_dynamic_injector(static_gen) === Gen2AVR
@test get_base_power(static_gen) == get_base_power(Gen2AVR)
# Rule: Can't set the pair injector if the current injector is already set.
@test_throws ArgumentError set_dynamic_injector!(static_gen, Gen1AVR)
# Rule: Can't remove a static injector if it is attached to a dynamic injector.
@test_throws ArgumentError remove_component!(sys, static_gen)
#Rule: Can't add saturation if Se(1.2) <= Se(1.0)
@test_throws IS.ConflictingInputsError PSY.get_quadratic_saturation((0.5, 0.1))
@test_throws IS.ConflictingInputsError PSY.get_quadratic_saturation((0.1, 0.1))
@test_throws IS.ConflictingInputsError PSY.get_avr_saturation((0.2, 0.1), (0.1, 0.1))
@test length(collect(get_dynamic_components(Gen2AVR))) == 5
end
@testset "Forward functions" begin
GENROU = RoundRotorQuadratic(
R = 0.0,
Td0_p = 7.4,
Td0_pp = 0.03,
Tq0_p = 0.06,
Tq0_pp = 0.033,
Xd = 0.8979,
Xq = 0.646,
Xd_p = 0.2995,
Xq_p = 0.646,
Xd_pp = 0.23,
Xl = 0.1,
Se = (0.1, 0.5),
)
test_accessors(GENROU)
GENROE = RoundRotorExponential(
R = 0.0,
Td0_p = 7.4,
Td0_pp = 0.03,
Tq0_p = 0.06,
Tq0_pp = 0.033,
Xd = 0.8979,
Xq = 0.646,
Xd_p = 0.2995,
Xq_p = 0.646,
Xd_pp = 0.23,
Xl = 0.1,
Se = (0.1, 0.5),
)
test_accessors(GENROE)
GENSAL = SalientPoleQuadratic(
R = 0.0,
Td0_p = 7.4,
Td0_pp = 0.03,
Tq0_pp = 0.033,
Xd = 0.8979,
Xq = 0.646,
Xd_p = 0.2995,
Xd_pp = 0.23,
Xl = 0.1,
Se = (0.1, 0.5),
)
test_accessors(GENSAL)
GENSAE = SalientPoleExponential(
R = 0.0,
Td0_p = 7.4,
Td0_pp = 0.03,
Tq0_pp = 0.033,
Xd = 0.8979,
Xq = 0.646,
Xd_p = 0.2995,
Xd_pp = 0.23,
Xl = 0.1,
Se = (0.1, 0.5),
)
test_accessors(GENSAE)
#Test GENROU
@test get_R(GENROU) == 0.0
@test get_Td0_p(GENROU) == 7.4
@test get_Td0_pp(GENROU) == 0.03
@test get_Tq0_p(GENROU) == 0.06
@test get_Tq0_pp(GENROU) == 0.033
@test get_Xd(GENROU) == 0.8979
@test get_Xq(GENROU) == 0.646
@test get_Xd_p(GENROU) == 0.2995
@test get_Xq_p(GENROU) == 0.646
@test get_Xd_pp(GENROU) == 0.23
@test get_Xl(GENROU) == 0.1
@test get_Se(GENROU) == (0.1, 0.5)
@test get_states(GENROU) == [:eq_p, :ed_p, :Ο_kd, :Ο_kq]
@test get_n_states(GENROU) == 4
@test abs(get_saturation_coeffs(GENROU)[1] - 0.8620204102886729) <= 1e-6
@test abs(get_saturation_coeffs(GENROU)[2] - 5.252551286084112) <= 1e-6
#Test GENROE
@test get_R(GENROE) == 0.0
@test get_Td0_p(GENROE) == 7.4
@test get_Td0_pp(GENROE) == 0.03
@test get_Tq0_p(GENROE) == 0.06
@test get_Tq0_pp(GENROE) == 0.033
@test get_Xd(GENROE) == 0.8979
@test get_Xq(GENROE) == 0.646
@test get_Xd_p(GENROE) == 0.2995
@test get_Xq_p(GENROE) == 0.646
@test get_Xd_pp(GENROE) == 0.23
@test get_Xl(GENROE) == 0.1
@test get_Se(GENROE) == (0.1, 0.5)
@test get_states(GENROE) == [:eq_p, :ed_p, :Ο_kd, :Ο_kq]
@test get_n_states(GENROE) == 4
@test abs(get_saturation_coeffs(GENROE)[1] - 8.827469119589406) <= 1e-6
@test abs(get_saturation_coeffs(GENROE)[2] - 0.1) <= 1e-6
#Test GENSAL
@test get_R(GENSAL) == 0.0
@test get_Td0_p(GENSAL) == 7.4
@test get_Td0_pp(GENSAL) == 0.03
@test get_Tq0_pp(GENSAL) == 0.033
@test get_Xd(GENSAL) == 0.8979
@test get_Xq(GENSAL) == 0.646
@test get_Xd_p(GENSAL) == 0.2995
@test get_Xd_pp(GENSAL) == 0.23
@test get_Xl(GENSAL) == 0.1
@test get_Se(GENSAL) == (0.1, 0.5)
@test get_states(GENSAL) == [:eq_p, :Ο_kd, :Οq_pp]
@test get_n_states(GENSAL) == 3
@test abs(get_saturation_coeffs(GENSAL)[1] - 0.8620204102886729) <= 1e-6
@test abs(get_saturation_coeffs(GENSAL)[2] - 5.252551286084112) <= 1e-6
#Test GENSAE
@test get_R(GENSAE) == 0.0
@test get_Td0_p(GENSAE) == 7.4
@test get_Td0_pp(GENSAE) == 0.03
@test get_Tq0_pp(GENSAE) == 0.033
@test get_Xd(GENSAE) == 0.8979
@test get_Xq(GENSAE) == 0.646
@test get_Xd_p(GENSAE) == 0.2995
@test get_Xd_pp(GENSAE) == 0.23
@test get_Xl(GENSAE) == 0.1
@test get_Se(GENSAE) == (0.1, 0.5)
@test get_states(GENSAE) == [:eq_p, :Ο_kd, :Οq_pp]
@test get_n_states(GENSAE) == 3
@test abs(get_saturation_coeffs(GENSAE)[1] - 8.827469119589406) <= 1e-6
@test abs(get_saturation_coeffs(GENSAE)[2] - 0.1) <= 1e-6
end
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] | 1.777288 | 9,070 |
abstract type StaticInjection <: Device end
function supports_services(::T) where {T <: Device}
return true
end
function get_services(::Device)
return Vector{Service}()
end
get_dynamic_injector(d::StaticInjection) = nothing
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## PARTE 2
cd(@__DIR__)
input = map(line -> parse.(Bool, collect(line)), eachline("input.txt"));
##
function keep_only(pred, input)
res = deepcopy(input)
for j in axes(input[1], 1)
length(res) == 1 && break
pattern = pred(length(res))(foldl((s, row) -> s+row[j], res; init=0))
filter!(row -> row[j] == pattern, res)
end
first(res)
end
##
oxygen_b = keep_only(d -> >=(cld(d, 2)), input)
CO2_b = keep_only(d -> <(cld(d, 2)), input)
n = length(oxygen_b)
oxygen, CO2 = foldl(0:n-1; init=(0,0)) do (o, c), i
o |= oxygen_b[n-i] << i
c |= CO2_b[n-i] << i
o, c
end
oxygen * CO2
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] | 2.052632 | 304 |
using LinearAlgebra, ForwardDiff, Distributions
using Convex, SCS, ECOS
using IterativeSolvers
# problem setup
"Convex.jl"
P = [1.0 0.0 0.0;
0.0 1.0 0.0]
D = [P; -P]
v = [1.0; 1.0; 0.0]
p = 4
c = D * v
y = -1.0e-8
Ξ² = Variable(p)
prob = minimize(c' * Ξ²)
prob.constraints += Ξ²' * ones(p) <= y
prob.constraints += Ξ² >= 0.0
@time solve!(prob, ECOS.Optimizer)
@show prob.status
@show prob.constraints
@show D' * Ξ².value
# @show prob.constraints[1].dual
# @show prob.constraints[2].dual
A = [-1.0 * Diagonal(ones(p)); ones(1,p)]
b = [zeros(p); y]
m = size(A, 1)
k = p + m + 1
Q = Array([zeros(p, p) A' c;
-A zeros(m, m) b;
-c' -b' 0.0])
function ΞΊ(z)
# z_proj = zero(z)
# z_proj[1:N] = z[1:N]
# z_proj[N .+ (1:p)] = max.(0.0, z[N .+ (1:p)])
z_proj = max.(0.0, z)
return z_proj
# return z
end
function Pc(z)
z_proj = zero(z)
z_proj[1:p] = z[1:p]
z_proj[p .+ (1:m)] = ΞΊ(z[p .+ (1:m)])
z_proj[p + m + 1] = max(0.0, z[p + m + 1])
return z_proj
end
function F(z)
uΜ = z[1:k]
u = z[k .+ (1:k)]
v = z[2 * k .+ (1:k)]
[(I + Q) * uΜ - (u + v);
u - Pc(uΜ - v);
uΜ - u]
end
function Ju(z)
uΜ = z[1:k]
u = z[k .+ (1:k)]
v = z[2k .+ (1:k)]
JP = zeros(m, m)
dif = uΜ[p .+ (1:m)] - v[p .+ (1:m)]
for i = 1:m
if dif[i] >= 0.0
JP[i, i] = 1.0
else
JP[i, i] = 0.0
end
end
if z[k] - z[2k] >= 0.0
β = 1.0
else
β = 0.0
end
[-I zeros(p, m) zeros(p) I zeros(p, m) zeros(p) I zeros(p, m) zeros(p);
zeros(m, p) -JP zeros(m) zeros(m, p) I zeros(m) zeros(m, p) JP zeros(m);
[zeros(1, p) zeros(1, m) -β zeros(1, p) zeros(1, m) 1.0 zeros(1, p) zeros(1, m) β]]
end
function J(z)
[(I + Q) -I -I
Ju(z);
I -I zeros(k, k)]
end
uΜ = zeros(k)
u = zeros(k)
v = zeros(k)
uΜ[end] = 1.0
u[end] = 1.0
v[end] = 1.0
z = [uΜ; u; v]
z = rand(3k)
F(z)
rank(Ju(z))
J(z)
rank(ForwardDiff.jacobian(F, z))
norm(J(z) - ForwardDiff.jacobian(F, z), Inf)
gmres(J(z), -F(z))
# (J(z)' * J(z)) \ (J(z)' * F(z))
# Ξ = zero(z)
# Ξ[k] = 1.0
# Ξ[2k] = 1.0
# Ξ[3k] = 1.0
# gmres!(Ξ, J(z), -1.0 * F(z), abstol = 1.0)
function solve()
uΜ = zeros(k)
u = zeros(k)
v = zeros(k)
uΜ[end] = 1.0
u[end] = 1.0
v[end] = 1.0
z = [uΜ; u; v]
# z = rand(3k)
# Ξ = zero(z)
extra_iters = 0
for i = 1:500
_F = F(z)
_J = J(z)
Ξ = gmres(_J, 1.0 * _F, abstol = 1.0e-12, maxiter = i + extra_iters)
iter = 0
Ξ± = 1.0
while norm(F(z - Ξ± * Ξ))^2.0 >= (1.0 - 0.001 * Ξ±) * norm(_F)^2.0 && Ξ± > 1.0e-4
Ξ± = 0.5 * Ξ±
# println(" Ξ± = $Ξ±")
iter += 1
if iter > 100
@error "line search fail"
# return z
x = z[k .+ (1:p)]
y = z[k + p .+ (1:m)]
Ο = z[k + k]
ΞΊ = z[3k]
println("Ο = $Ο")
println("ΞΊ = $ΞΊ")
return x ./ Ο, z
end
end
if Ξ± <= 1.0e-4
extra_iters += 1
end
println("iter ($i) - norm: $(norm(F(z)))")
z .-= Ξ± * Ξ
end
# return z
x = z[k .+ (1:p)]
y = z[k + p .+ (1:m)]
Ο = z[k + k]
ΞΊ = z[3k]
println("Ο = $Ο")
println("ΞΊ = $ΞΊ")
return x ./ Ο, z
end
x_sol, z_sol = solve()
x_sol
b - A * x_sol
norm(x_sol - Ξ².value)
function FΞΈ(z, ΞΈ)
A = reshape(ΞΈ[1:m * p], m, p)
b = ΞΈ[m * p .+ (1:m)]
c = ΞΈ[m * p + m .+ (1:p)]
Q = Array([zeros(eltype(ΞΈ), p, p) A' c;
-A zeros(eltype(ΞΈ), m, m) b;
-c' -b' 0.0])
uΜ = z[1:k]
u = z[k .+ (1:k)]
v = z[2 * k .+ (1:k)]
[(I + Q) * uΜ - (u + v);
u - Pc(uΜ - v);
uΜ - u]
end
ΞΈ[m * p + m]
ΞΈ = [vec(A); b; c]
_F(y) = FΞΈ(z_sol, y)
_F(ΞΈ)
ForwardDiff.jacobian(_F, ΞΈ)
rank(J(z_sol))
J(z_sol)
((J(z_sol)' * J(z_sol)) \ (J(z_sol)' * ForwardDiff.jacobian(_F, ΞΈ)))[k .+ (1:p), m * p + (m-1) .+ (1:1+p)] ./ z_sol[2k]
# (J(z_sol) \ ForwardDiff.jacobian(_F, ΞΈ))[k .+ (1:p), m * p + (m-1) .+ (1:1+p)] ./ z_sol[2k]
# minimum(ForwardDiff.jacobian(_F, ΞΈ)[:, m * p + (m-1) .+ (1:1+p)])
#
# maximum(J(z_sol))
# minimum(J(z_sol))
#
# pinv(J(z_sol))
# F(z_sol)
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] | 1.578154 | 2,719 |
using DataFrames
using CSV
using Plots
#%%
file = open("./lezione5/Si_data_2.csv")
df=CSV.read(file, header=false, delim=" ")
plot(df[1],df[2])
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] | 2.301587 | 63 |
### A Pluto.jl notebook ###
# v0.17.2
# using Markdown
# using InteractiveUtils
# βββ‘ 35da0c45-6d91-4ad9-811f-5d633684208e
begin
using StaticArrays
using LinearAlgebra
using Unitful
using Unitful.DefaultSymbols
using SatelliteToolbox: Ellipsoid
using Parameters
import Proj4
using DocStringExtensions
end
# βββ‘ c3a5e97e-00ed-44af-9c67-fa8198900fbd
#=β ββ‘ notebook_exclusive
begin
using PlutoDevMacros
using PlutoUtils
using PlutoTest
end
β ββ‘ notebook_exclusive =#
# βββ‘ e2f50ace-ad14-4e7c-af74-abf3ca9df9fb
#=β ββ‘ notebook_exclusive
md"""
# Packages
"""
β ββ‘ notebook_exclusive =#
# βββ‘ 82c0d6f9-373d-4866-81eb-9cd2d0981310
#=β ββ‘ notebook_exclusive
ToC()
β ββ‘ notebook_exclusive =#
# βββ‘ 2e6bcf9b-002f-4fbc-80c1-0cfd2ab1d253
#=β ββ‘ notebook_exclusive
md"""
# Exports
"""
β ββ‘ notebook_exclusive =#
# βββ‘ 1e3da0c9-2f96-4637-9093-ac7f10c1ad27
#=β ββ‘ notebook_exclusive
md"""
# Helper Functions
"""
β ββ‘ notebook_exclusive =#
# βββ‘ 48c73104-fe4c-4543-942a-6f23b0fd2547
const Point{N} = Union{Tuple{Vararg{<:Number, N}},StaticVector{N,<:Number}}
# βββ‘ 48c73104-fe4c-4543-0001-6f23b0fd2547
const Point2D = Point{2}
# βββ‘ 48c73104-fe4c-4543-0002-6f23b0fd2547
const Point3D = Point{3}
# βββ‘ c4402f72-67ac-4630-a651-da81c1df71bf
"""
ExtraOutput
Struct used inside `SatView` to use multiple dispatch to give more than the standard output.
See for example [`get_range`](@ref)
"""
struct ExtraOutput end
# βββ‘ e796d5be-e011-45d3-ad90-58769feb5e85
#=β ββ‘ notebook_exclusive
md"""
## Show/Print
"""
β ββ‘ notebook_exclusive =#
# βββ‘ d890aff1-dbd0-451c-bf14-bde9758c3be0
function _print_angle(io,val,displayname,last=false)
print(io,"$displayname=")
print(io,round(val;digits=2) * rad)
print(io," (")
print(io,round(rad2deg(val);digits=2) * Β°)
print(io,")")
last || print(io,", ")
end
# βββ‘ fdbbc8d9-83d6-413e-aff8-aca18f24dfea
function _print_length(io,val,displayname,last=false)
print(io,"$displayname=")
mval = val < 1000 ? round(val;digits=2) * m : round(val/1000;digits=2) * km
print(io,mval)
last || print(io,", ")
end
# βββ‘ cc0cae75-ba10-4a62-b0ef-22259e40a083
#=β ββ‘ notebook_exclusive
md"""
## Spherical Ellipsoid
"""
β ββ‘ notebook_exclusive =#
# βββ‘ 855553e3-491b-4e8a-a482-95855697e063
"""
SphericalEllipsoid(r = 6371e3)
Define a spherical ellipsoid of radius `r` to be used for the various transformation in [`SatView`](@ref). Defaults to 6371km radius
"""
SphericalEllipsoid(r = 6371e3) = Ellipsoid(r,0.0)
# βββ‘ 1f7a7093-ce8e-461f-8b91-69266de86748
#=β ββ‘ notebook_exclusive
md"""
## geod_geodesic
"""
β ββ‘ notebook_exclusive =#
# βββ‘ f83fc65f-5f7b-498d-9ed3-0762565ad710
Proj4.geod_geodesic(e::Ellipsoid) = Proj4.geod_geodesic(e.a, e.f)
# βββ‘ 4714c6ae-27d9-47db-b12e-126283b10606
#=β ββ‘ notebook_exclusive
md"""
# EarthModel
"""
β ββ‘ notebook_exclusive =#
# βββ‘ f9cc2880-bab1-4be3-b67b-d43508df8d3b
begin
"""
$(TYPEDEF)
Geometrical model of the Earth ellipsoid to be used for all the view angles between satellite and points on earth.
# Fields
$(TYPEDFIELDS)
A single instance of this structure should be used for all satellites of a given simulation/constellation.\\
Changes to any of the two fields (via `setproperty!`) will trigger an automatic recomputation of the other field.\\
When called without arguments, it defaults to a spherical earth with a radius of 6371 km.
See also: [`Ellipsoid`](@ref), [`SphericalEllipsoid`](@ref), [`SatView`](@ref)
"""
@with_kw mutable struct EarthModel
"Ellipsoid structure, used for the various point of view conversion."
ellipsoid::Ellipsoid{Float64} = SphericalEllipsoid()
"Extended geod structure, used for the inverse geodesic problem to compute distance and azimuth between points on earth. (Relies on GeographicLib)."
geod::Proj4.geod_geodesic = Proj4.geod_geodesic(ellipsoid)
end
EarthModel(e::Ellipsoid) = EarthModel(;ellipsoid = e)
end
# βββ‘ 1f2b9b74-de46-401d-8a46-0434b9f9aca1
function Base.setproperty!(value::EarthModel, name::Symbol, x)
if name === :ellipsoid
setfield!(value, name, x)
setfield!(value, :geod, Proj4.geod_geodesic(x))
else
setfield!(value, name, x)
setfield!(value, :ellipsoid, Ellipsoid(x.a, x.f))
end
end
# βββ‘ 6a5cb372-60cb-4ffc-b4f0-22e4016104e7
#=β ββ‘ notebook_exclusive
md"""
# Angle Types
"""
β ββ‘ notebook_exclusive =#
# βββ‘ e832c1b7-8c04-4146-90f6-1628e91fea2a
const UnitfulAngleType = Union{typeof(Β°),typeof(rad)}
# βββ‘ 64cf1b8b-6686-4eeb-a3cc-786300ea7c7d
const UnitfulAngleQuantity = Quantity{<:Real,<:Any,<:UnitfulAngleType}
# βββ‘ 1d023a0c-a93a-451c-a894-1d1f6a4b78a9
#=β ββ‘ notebook_exclusive
md"""
# SatViewCoordinate types
"""
β ββ‘ notebook_exclusive =#
# βββ‘ b8ce87d4-4768-4e8a-a16e-9e68b00b6617
abstract type SatViewCoordinate end
# βββ‘ e3c221c6-6c4a-4b5f-93a6-30d3508ac9d2
#=β ββ‘ notebook_exclusive
md"""
## LLA
"""
β ββ‘ notebook_exclusive =#
# βββ‘ 8368ae01-ce53-449e-87dd-8dfa3f29f8f4
#=β ββ‘ notebook_exclusive
md"""
Here we want to define a structure that contains useful informations and functions to perform conversions between the view from the satellite based on it's orbital position and points on ground
"""
β ββ‘ notebook_exclusive =#
# βββ‘ f951805e-515a-475f-893f-bb8b968e425c
#=β ββ‘ notebook_exclusive
md"""
## ERA
"""
β ββ‘ notebook_exclusive =#
# βββ‘ 86ae20a9-e69c-4d63-9119-395449e9ac09
#=β ββ‘ notebook_exclusive
md"""
ERA stands for Elevation, Range and Azimuth and is used to express the position of a satellite relative to an observer in spherical coordinates.
The elevation is the angle of the pointing with respect to the local horizon of the observer, meaning the plane where the observer is located that is perpendicular to the gravity vector acting on the observe (or in an alternative definition, the plane where the observer is located that is parallel to the tangent plane to the earth ellipsoid at the given lat and lon positions of the observer.
"""
β ββ‘ notebook_exclusive =#
# βββ‘ 9be2fd5c-4b6c-4e13-b2aa-fb7120a504b7
begin
"""
Elevation, Range and Azimuth for a target point on space as seen from a source point on or above the earth surface
# Fields
- `el::Float64`: Elevation view angle (`0 <= el <= Ο/2`) between source and target point [rad].
- `r::Float64`: Range (`r >= 0`) between the source and target points [m].
- `az::Float64`: Azimuth view angle between source and target point [rad], computed from West to North from the source point perspective. Values provided are automatically converted between -Ο and Ο
# Constructors
ERA(el::Real,r::Real,az::Real)
ERA(el::UnitfulAngleQuantity,r::Real,az::Real)
ERA(el::UnitfulAngleQuantity,r::Real,az::UnitfulAngleQuantity)
ERA(el,r::Unitful.Length,az)
where `UnitfulAngleQuantity` is a `Unitful.Quantity` of unit either `u"rad"` or `u"Β°"`.
"""
@with_kw_noshow struct ERA <: SatViewCoordinate
el::Float64 # Elevation in radians
r::Float64 # Range in meters
az::Float64 # Azimuth in radians
function ERA(el::Real,r::Real,az::Real)
(isnan(el) || isnan(r) || isnan(az)) && return new(NaN,NaN,NaN)
@assert el >= 0 && el <= Ο/2 "Elevation should be between 0 and Ο/2"
@assert r >= 0 "Range must be positive"
new(el,r,rem2pi(az,RoundNearest))
end
end
# Define a constructor that takes combinations of real numbers and angles/lengths
ERA(el::UnitfulAngleQuantity,r::Real,az::Real) = ERA(
uconvert(u"rad",el) |> ustrip,
r,
az,
)
ERA(el::UnitfulAngleQuantity,r::Real,az::UnitfulAngleQuantity) = ERA(
el,
r,
uconvert(u"rad",az) |> ustrip,
)
ERA(el,r::Unitful.Length,az) = ERA(
el,
uconvert(u"m",r) |> ustrip,
az,
)
# Show
function Base.show(io::IO,era::ERA)
print(io,"ERA(")
_print_angle(io,era.el,"el",false)
_print_length(io,era.r,"r",false)
_print_angle(io,era.az,"az",true)
print(io,")")
end
end
# βββ‘ 16782c72-ecb1-48ec-8510-78e2e0689a10
function Base.isapprox(x1::ERA, x2::ERA)
x1.el β x2.el && return false
# Don't care about different azimuth if elevation is 90Β°
x2.el β Ο/2 && x1.az β x2.az && return false
x1.r β x2.r && return false
return true
end
# βββ‘ 7344190c-7989-4b55-b7be-357f7d6b7370
Base.isnan(era::ERA) = isnan(era.el)
# βββ‘ f207d849-ebff-4e6c-95bb-50693cb7c9b6
begin
"""
Identify a point on or above earth using geodetic coordinates
# Fields
- `lat::Float64`: Latitude (`-Ο/2 <= lat <= Ο/2`) of the point [rad].
- `lon::Float64`: Longitude of the point [rad].
- `alt::Float64`: Altitude of the point above the reference earth ellipsoid [m].
# Constructors
LLA(lat::Real,lon::Real,alt::Real)
LLA(lat::UnitfulAngleQuantity,lon::Real,alt::Real)
LLA(lat::UnitfulAngleQuantity,lon::UnitfulAngleQuantity,alt::Real)
LLA(lat,lon,alt::Unitful.Length)
LLA(lat,lon) # Defaults to 0.0 altitude
where `UnitfulAngleQuantity` is a `Unitful.Quantity` of unit either `u"rad"` or `u"Β°"`.
"""
@with_kw_noshow struct LLA <: SatViewCoordinate
lat::Float64 # Latitude in radians
lon::Float64 # Longitude in radians
alt::Float64 # Altitude in meters
function LLA(lat::Real,lon::Real,alt::Real)
(isnan(lat) || isnan(lon) || isnan(alt)) && return new(NaN,NaN,NaN)
l2 = rem2pi(lon,RoundNearest)
@assert abs(lat) <= Ο/2 "Latitude should be between -Ο/2 and Ο/2"
new(lat,l2,alt)
end
end
# Constructor without altitude, assume it is 0
LLA(lat,lon) = LLA(lat,lon,0.0)
# Define a constructor that takes combinations of real numbers and angles/lengths
LLA(lat::UnitfulAngleQuantity,lon::Real,alt::Real) = LLA(
uconvert(u"rad",lat) |> ustrip,
lon,
alt)
LLA(lat::UnitfulAngleQuantity,lon::UnitfulAngleQuantity,alt::Real) = LLA(
lat,
uconvert(u"rad",lon) |> ustrip,
alt)
LLA(lat,lon,alt::Unitful.Length) = LLA(
lat,
lon,
uconvert(u"m",alt) |> ustrip)
end
# βββ‘ 3033d5c1-d8e0-4d46-0001-7dec4ff7afbd
# Show method for LLA
function Base.show(io::IO,lla::LLA)
print(io,"LLA(")
_print_angle(io,lla.lat,"lat",false)
_print_angle(io,lla.lon,"lon",false)
_print_length(io,lla.alt,"alt",true)
print(io,")")
end
# βββ‘ 3033d5c1-d8e0-4d46-a4b9-7dec4ff7afbd
function Base.isapprox(x1::LLA, x2::LLA)
x1.alt β x2.alt && return false
# Don't care about different longitude if latitude is Β±90Β°
abs(x1.lat) β Ο/2 && abs(x2.lat) β Ο/2 && return true
# Return true if all the lat and lon are matching
x1.lat β x2.lat && (x1.lon β x2.lon || abs(x1.lon) β abs(x2.lon) β Ο) && return true
return false
end
# βββ‘ 528beffe-0707-4661-8970-def1b1e00ea5
Base.isnan(lla::LLA) = isnan(lla.lat)
# βββ‘ 11938cb6-46b3-0001-96c0-ef6424d1d0db
#=β ββ‘ notebook_exclusive
md"""
# Inverse Geodesic Problem
"""
β ββ‘ notebook_exclusive =#
# βββ‘ 11938cb6-46b3-0002-96c0-ef6424d1d0db
"""
geod_inverse(geod::Proj4.geod_geodesic, lonlat1::AbstractVector{Cdouble}, lonlat2::AbstractVector{Cdouble})
geod_inverse(geod::Proj4.geod_geodesic, lla1::LLA, lla2::LLA)
Solve the inverse geodesic problem.
# Args
- `g` β the geod_geodesic object specifying the ellipsoid.
- `lonlat1` β point 1 (degrees), where lat β [-90, 90], lon β [-540, 540)
- `lonlat2` β point 2 (degrees), where lat β [-90, 90], lon β [-540, 540)
# Outputs
- `dist` β distance between point 1 and point 2 (meters).
- `azi1` β azimuth at point 1 (degrees) β [-180, 180)
- `azi2` β (forward) azimuth at point 2 (degrees) β [-180, 180)
# Remarks
If either point is at a pole, the azimuth is defined by keeping the longitude fixed,
writing lat = 90 +/- eps, and taking the limit as eps -> 0+.
"""
function geod_inverse(geod::Proj4.geod_geodesic, lonlat1::AbstractVector{Cdouble}, lonlat2::AbstractVector{Cdouble})
dist = Ref{Cdouble}()
azi1 = Ref{Cdouble}()
azi2 = Ref{Cdouble}()
ccall((:geod_inverse, Proj4.libproj), Cvoid, (Ptr{Cvoid},Cdouble,Cdouble,Cdouble,
Cdouble,Ptr{Cdouble},Ptr{Cdouble},Ptr{Cdouble}),
pointer_from_objref(geod), lonlat1[2], lonlat1[1], lonlat2[2], lonlat2[1], dist, azi1, azi2)
dist[], azi1[], azi2[]
end
# βββ‘ 11938cb6-46b3-0003-96c0-ef6424d1d0db
function geod_inverse(geod::Proj4.geod_geodesic, lla1::LLA, lla2::LLA)
lonlat1 = rad2deg.(SA_F64[lla1.lon,lla1.lat])
lonlat2 = rad2deg.(SA_F64[lla2.lon,lla2.lat])
geod_inverse(geod,lonlat1,lonlat2)
end
# βββ‘ 4c06d21c-ac14-4522-bf25-2e0a1ed2d6b9
begin
export Ellipsoid, SphericalEllipsoid
export EarthModel
export UnitfulAngleQuantity, UnitfulAngleType, Β°
export km
export LLA, ERA
export geod_inverse
end
# βββ‘ 11938cb6-46b3-499b-96c0-ef6424d1d0db
#=β ββ‘ notebook_exclusive
md"""
# Tests
"""
β ββ‘ notebook_exclusive =#
# βββ‘ d2c248b1-c48e-437b-a910-edcc59b4424f
#=β ββ‘ notebook_exclusive
md"""
## LLA
"""
β ββ‘ notebook_exclusive =#
# βββ‘ 7b306ed5-4bda-465d-abf2-4d07cb4642c1
#=β ββ‘ notebook_exclusive
@test LLA(10Β°,10Β°,1000) β LLA((10+100*eps())*Β°,10Β°,1000)
β ββ‘ notebook_exclusive =#
# βββ‘ c45f6aac-bff3-4c98-8bd5-c91a98c9eef7
#=β ββ‘ notebook_exclusive
@test LLA(90Β°,10Β°,1000) β LLA(90Β°,130Β°,1000)
β ββ‘ notebook_exclusive =#
# βββ‘ 1f1505a6-c6af-4fdd-9583-6e783e76de4f
#=β ββ‘ notebook_exclusive
@test LLA(40Β°,-180Β°,1000) β LLA(40Β°,180Β°,1000)
β ββ‘ notebook_exclusive =#
# βββ‘ 980e48bd-9dd1-4195-8086-40785a7f43e1
#=β ββ‘ notebook_exclusive
@test LLA(10Β°,10Β°,1000) !== LLA((10+100*eps())*Β°,10Β°,1000)
β ββ‘ notebook_exclusive =#
# βββ‘ fc146750-46b2-4084-a6d2-0d91c0e104e6
#=β ββ‘ notebook_exclusive
LLA(1,1,NaN) |> isnan
β ββ‘ notebook_exclusive =#
# βββ‘ fc3e96a6-7af8-4ef0-a4ca-b33509aa512f
#=β ββ‘ notebook_exclusive
md"""
## ERA
"""
β ββ‘ notebook_exclusive =#
# βββ‘ ed2f2fa3-be19-4e2d-ad28-36cb053ed6bd
#=β ββ‘ notebook_exclusive
@test ERA(10Β°,1000,20Β°) == ERA(10Β°,1km,deg2rad(20)*rad)
β ββ‘ notebook_exclusive =#
# βββ‘ 6151e4d1-a7a6-4fa7-bc77-0e7f3b2a3cc0
#=β ββ‘ notebook_exclusive
@test ERA(90Β°,1000,20Β°) β ERA(90Β°,1km,deg2rad(90)*rad)
β ββ‘ notebook_exclusive =#
# βββ‘ b5449c6a-9c43-4f9c-81f7-f01277acb109
#=β ββ‘ notebook_exclusive
ERA(1,1,NaN) |> isnan
β ββ‘ notebook_exclusive =#
# βββ‘ 00000000-0000-0000-0000-000000000001
PLUTO_PROJECT_TOML_CONTENTS = """
[deps]
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"""
# βββ‘ 00000000-0000-0000-0000-000000000002
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deps = ["Artifacts", "JLLWrappers", "LibCURL_jll", "LibSSH2_jll", "Libdl", "Libtiff_jll", "MbedTLS_jll", "Pkg", "SQLite_jll", "Zlib_jll", "nghttp2_jll"]
git-tree-sha1 = "2435e91710d7f97f53ef7a4872bf1f948dc8e5f8"
uuid = "58948b4f-47e0-5654-a9ad-f609743f8632"
version = "700.202.100+0"
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deps = ["OrderedCollections", "UnPack"]
git-tree-sha1 = "34c0e9ad262e5f7fc75b10a9952ca7692cfc5fbe"
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version = "0.12.3"
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[[deps.Pkg]]
deps = ["Artifacts", "Dates", "Downloads", "LibGit2", "Libdl", "Logging", "Markdown", "Printf", "REPL", "Random", "SHA", "Serialization", "TOML", "Tar", "UUIDs", "p7zip_jll"]
uuid = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
[[deps.PlutoDevMacros]]
deps = ["MacroTools", "PlutoHooks"]
git-tree-sha1 = "6ab70183795e4ad00ecd406f2d05740328c9b905"
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git-tree-sha1 = "f297787f7d7507dada25f6769fe3f08f6b9b8b12"
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deps = ["HypertextLiteral", "InteractiveUtils", "Markdown", "Test"]
git-tree-sha1 = "92b8ae1eee37c1b8f70d3a8fb6c3f2d81809a1c5"
uuid = "cb4044da-4d16-4ffa-a6a3-8cad7f73ebdc"
version = "0.2.0"
[[deps.PlutoUI]]
deps = ["AbstractPlutoDingetjes", "Base64", "Dates", "Hyperscript", "HypertextLiteral", "IOCapture", "InteractiveUtils", "JSON", "Logging", "Markdown", "Random", "Reexport", "UUIDs"]
git-tree-sha1 = "1e0cb51e0ccef0afc01aab41dc51a3e7f781e8cb"
uuid = "7f904dfe-b85e-4ff6-b463-dae2292396a8"
version = "0.7.20"
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deps = ["Chain", "Glob", "HypertextLiteral", "InteractiveUtils", "Markdown", "PlutoDevMacros", "PlutoHooks", "PlutoTest", "PlutoUI", "PrettyTables", "Reexport", "Requires", "UUIDs"]
git-tree-sha1 = "3d3856ecfea340b4ee0c77e5c3228dd1b4478ae1"
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deps = ["Crayons", "Formatting", "Markdown", "Reexport", "Tables"]
git-tree-sha1 = "d940010be611ee9d67064fe559edbb305f8cc0eb"
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uuid = "de0858da-6303-5e67-8744-51eddeeeb8d7"
[[deps.Proj4]]
deps = ["CEnum", "CoordinateTransformations", "PROJ_jll", "StaticArrays"]
git-tree-sha1 = "5f15f1c647b563e49f655fbbfd4e2ade24bd3c64"
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deps = ["InteractiveUtils", "Markdown", "Sockets", "Unicode"]
uuid = "3fa0cd96-eef1-5676-8a61-b3b8758bbffb"
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deps = ["SHA", "Serialization"]
uuid = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
[[deps.Ratios]]
deps = ["Requires"]
git-tree-sha1 = "01d341f502250e81f6fec0afe662aa861392a3aa"
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git-tree-sha1 = "45e428421666073eab6f2da5c9d310d99bb12f9b"
uuid = "189a3867-3050-52da-a836-e630ba90ab69"
version = "1.2.2"
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deps = ["Crayons", "LinearAlgebra", "Printf", "StaticArrays"]
git-tree-sha1 = "d526371cec370888f485756a4bf8284ab531860b"
uuid = "74f56ac7-18b3-5285-802d-d4bd4f104033"
version = "1.0.1"
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deps = ["Dates", "FileIO", "HTTP"]
git-tree-sha1 = "54527375d877a64c55190fb762d584f927d6d7c3"
uuid = "cbe49d4c-5af1-5b60-bb70-0a60aa018e1b"
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deps = ["UUIDs"]
git-tree-sha1 = "4036a3bd08ac7e968e27c203d45f5fff15020621"
uuid = "ae029012-a4dd-5104-9daa-d747884805df"
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[[deps.SHA]]
uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce"
[[deps.SQLite_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"]
git-tree-sha1 = "e601efdf2ce1df61147a1d1fed83cc428af1b53a"
uuid = "76ed43ae-9a5d-5a62-8c75-30186b810ce8"
version = "3.36.1+0"
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git-tree-sha1 = "0a2c0f1565a51487fe58c28f528675dba1008432"
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deps = ["Distributed", "Mmap", "Random", "Serialization"]
uuid = "1a1011a3-84de-559e-8e89-a11a2f7dc383"
[[deps.Sockets]]
uuid = "6462fe0b-24de-5631-8697-dd941f90decc"
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deps = ["LinearAlgebra", "Random"]
uuid = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
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deps = ["LinearAlgebra", "Random", "Statistics"]
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deps = ["LinearAlgebra", "SparseArrays"]
uuid = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
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deps = ["Dates"]
uuid = "fa267f1f-6049-4f14-aa54-33bafae1ed76"
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deps = ["IteratorInterfaceExtensions"]
git-tree-sha1 = "c06b2f539df1c6efa794486abfb6ed2022561a39"
uuid = "3783bdb8-4a98-5b6b-af9a-565f29a5fe9c"
version = "1.0.1"
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uuid = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
version = "1.6.0"
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uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
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git-tree-sha1 = "cc4bf3fdde8b7e3e9fa0351bdeedba1cf3b7f6e6"
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deps = ["Artifacts", "Libdl"]
uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0"
"""
# βββ‘ Cell order:
# ββe2f50ace-ad14-4e7c-af74-abf3ca9df9fb
# β β35da0c45-6d91-4ad9-811f-5d633684208e
# β βc3a5e97e-00ed-44af-9c67-fa8198900fbd
# β β82c0d6f9-373d-4866-81eb-9cd2d0981310
# ββ2e6bcf9b-002f-4fbc-80c1-0cfd2ab1d253
# β β4c06d21c-ac14-4522-bf25-2e0a1ed2d6b9
# ββ1e3da0c9-2f96-4637-9093-ac7f10c1ad27
# β β48c73104-fe4c-4543-942a-6f23b0fd2547
# β β48c73104-fe4c-4543-0001-6f23b0fd2547
# β β48c73104-fe4c-4543-0002-6f23b0fd2547
# β βc4402f72-67ac-4630-a651-da81c1df71bf
# ββe796d5be-e011-45d3-ad90-58769feb5e85
# β βd890aff1-dbd0-451c-bf14-bde9758c3be0
# β βfdbbc8d9-83d6-413e-aff8-aca18f24dfea
# ββcc0cae75-ba10-4a62-b0ef-22259e40a083
# β β855553e3-491b-4e8a-a482-95855697e063
# ββ1f7a7093-ce8e-461f-8b91-69266de86748
# β βf83fc65f-5f7b-498d-9ed3-0762565ad710
# ββ4714c6ae-27d9-47db-b12e-126283b10606
# β βf9cc2880-bab1-4be3-b67b-d43508df8d3b
# β β1f2b9b74-de46-401d-8a46-0434b9f9aca1
# ββ6a5cb372-60cb-4ffc-b4f0-22e4016104e7
# β βe832c1b7-8c04-4146-90f6-1628e91fea2a
# β β64cf1b8b-6686-4eeb-a3cc-786300ea7c7d
# ββ1d023a0c-a93a-451c-a894-1d1f6a4b78a9
# β βb8ce87d4-4768-4e8a-a16e-9e68b00b6617
# ββe3c221c6-6c4a-4b5f-93a6-30d3508ac9d2
# ββ8368ae01-ce53-449e-87dd-8dfa3f29f8f4
# β βf207d849-ebff-4e6c-95bb-50693cb7c9b6
# β β3033d5c1-d8e0-4d46-0001-7dec4ff7afbd
# β β3033d5c1-d8e0-4d46-a4b9-7dec4ff7afbd
# β β528beffe-0707-4661-8970-def1b1e00ea5
# ββf951805e-515a-475f-893f-bb8b968e425c
# ββ86ae20a9-e69c-4d63-9119-395449e9ac09
# β β9be2fd5c-4b6c-4e13-b2aa-fb7120a504b7
# β β16782c72-ecb1-48ec-8510-78e2e0689a10
# β β7344190c-7989-4b55-b7be-357f7d6b7370
# ββ11938cb6-46b3-0001-96c0-ef6424d1d0db
# ββ11938cb6-46b3-0002-96c0-ef6424d1d0db
# ββ11938cb6-46b3-0003-96c0-ef6424d1d0db
# ββ11938cb6-46b3-499b-96c0-ef6424d1d0db
# ββd2c248b1-c48e-437b-a910-edcc59b4424f
# β β7b306ed5-4bda-465d-abf2-4d07cb4642c1
# β βc45f6aac-bff3-4c98-8bd5-c91a98c9eef7
# β β1f1505a6-c6af-4fdd-9583-6e783e76de4f
# β β980e48bd-9dd1-4195-8086-40785a7f43e1
# β βfc146750-46b2-4084-a6d2-0d91c0e104e6
# ββfc3e96a6-7af8-4ef0-a4ca-b33509aa512f
# β βed2f2fa3-be19-4e2d-ad28-36cb053ed6bd
# β β6151e4d1-a7a6-4fa7-bc77-0e7f3b2a3cc0
# β βb5449c6a-9c43-4f9c-81f7-f01277acb109
# ββ00000000-0000-0000-0000-000000000001
# ββ00000000-0000-0000-0000-000000000002
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] | 1.923491 | 16,913 |
using Blosc2:Lib
@testset "storage" begin
s = Storage()
@test s.contiguous == false
@test isnothing(s.urlpath)
@test isnothing(s.cparams)
@test isnothing(s.dparams)
Blosc2.with_preserved_blosc2_storage(s) do bs
@test bs.contiguous == false
@test bs.urlpath == C_NULL
@test bs.cparams == C_NULL
@test bs.dparams == C_NULL
@test bs.io == C_NULL
end
s = Storage(contiguous = true,
urlpath = "test",
cparams = CompressionParams(Int32),
dparams = DecompressionParams(nthreads = 2)
)
@test s.contiguous == true
@test s.urlpath == "test"
@test s.cparams.typesize == 4
@test s.dparams.nthreads == 2
Blosc2.with_preserved_blosc2_storage(s) do bs
@test bs.contiguous == true
@test unsafe_string(bs.urlpath) == "test"
@test bs.cparams != C_NULL
@test bs.dparams != C_NULL
@test unsafe_load(bs.cparams.typesize) == 4
@test unsafe_load(bs.dparams.nthreads) == 2
@test bs.io == C_NULL
end
end
@testset "superchunk create" begin
schunk = SChunk(Storage())
@test clevel(schunk) == 5
@test typesize(schunk) == 8
schunk = SChunk(Storage(cparams = CompressionParams(Int16, level = 9)))
@test clevel(schunk) == 9
@test typesize(schunk) == 2
end
@testset "schunk append buffer" begin
rm("test.bl2", force = true, recursive = true)
schunk = SChunk(Storage(urlpath = "test.bl2"))
n = 100000
data = rand(1:1000, n)
r = GC.@preserve data begin
unsafe_append_buffer!(schunk, pointer(data), sizeof(data))
end
@test r == 1
data2 = rand(1:1000, floor(Int64, n/2))
r = GC.@preserve data begin
unsafe_append_buffer!(schunk, pointer(data2), sizeof(data2))
end
@test r == 2
@test nchunks(schunk) == 2
res = Vector{Int64}(undef, n)
r = GC.@preserve res begin
unsafe_decompress_chunk(schunk, 2, pointer(res), sizeof(res))
end
@test r == length(data2)
@test res[1:r] == data2
r = GC.@preserve res begin
unsafe_decompress_chunk(schunk, 1, pointer(res), sizeof(res))
end
@test r == n
@test res == data
@test_throws BoundsError unsafe_decompress_chunk(schunk, 3, pointer(res), sizeof(res))
rm("test.bl2", force = true, recursive = true)
end
@testset "schunk unsafe chunk manipulations" begin
schunk = SChunk(Storage())
n = 100000
data = rand(1:1000, n)
r = GC.@preserve data begin
unsafe_append_buffer!(schunk, pointer(data), sizeof(data))
end
println(chunksize(schunk))
@test r == 1
data2 = rand(1:1000, n)
chunk = compress(data2)
r = GC.@preserve chunk begin
unsafe_append_chunk!(schunk, pointer(chunk))
end
@test r == 2
data3 = rand(1:1000, n)
chunk = compress(data3)
r = GC.@preserve chunk begin
unsafe_insert_chunk!(schunk, 2, pointer(chunk))
end
@test r == 3
res = Vector{Int64}(undef, n)
r = GC.@preserve res begin
unsafe_decompress_chunk(schunk, 2, pointer(res), sizeof(res))
end
@test res == data3
data4 = rand(1:1000, n)
chunk = compress(data4)
r = GC.@preserve chunk begin
unsafe_update_chunk!(schunk, 2, pointer(chunk))
end
@test r == 3
r = GC.@preserve res begin
unsafe_decompress_chunk(schunk, 2, pointer(res), sizeof(res))
end
@test res == data4
@test delete_chunk!(schunk, 2) == 2
r = GC.@preserve res begin
unsafe_decompress_chunk(schunk, 2, pointer(res), sizeof(res))
end
@test res == data2
chunk2 = unsafe_get_chunk(schunk, 1)
@test decompress(Int64, chunk2) == data
end | [
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