content
stringlengths 6
1.03M
| input_ids
sequencelengths 4
535k
| ratio_char_token
float64 0.68
8.61
| token_count
int64 4
535k
|
---|---|---|---|
<reponame>josePereiro/SimTools.jl
function run_OnDiskMonitor_tests()
n = 1000
m = SimTools.OnDiskMonitor(@__DIR__, "monitor_test.jld2"; dt = 1.0)
SimTools.reset!(m)
SimTools.clear_file(m)
@assert isempty(m.cache)
@assert !isfile(m.file)
# Watch task (It can be in another process)
tests_results = []
cvals = []
@async SimTools.watch(m) do ddat
isempty(ddat) && return
check = true
dvals = ddat[:vals]
for dval in dvals
check &= dval in cvals
end
push!(tests_results, check)
end
# Fake job to monitor
for i in 1:n
push!(cvals, i)
SimTools.record!(m) do dat
dat[:vals] = cvals
end
sleep(5.0/n)
end
SimTools.reset!(m)
sleep(1.0)
@test isempty(m.cache)
@test length(cvals) == n
@test all(tests_results)
SimTools.clear_file(m)
@test !isfile(m.file)
end
run_OnDiskMonitor_tests() | [
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] | 2.010373 | 482 |
"""
Subroutine dgpstep
This subroutine computes the gradient projection step
s = P[x + alpha*w] - x,
where P is the projection on the n-dimensional interval [xl,xu].
"""
function dgpstep(n,x,xl,xu,alpha,w,s)
# TODO
# This computation of the gradient projection step avoids
# rouding errors for the components that are feasible.
@inbounds for i=1:n
if x[i] + alpha*w[i] < xl[i]
s[i] = xl[i] - x[i]
elseif x[i] + alpha*w[i] > xu[i]
s[i] = xu[i] - x[i]
else
s[i] = alpha*w[i]
end
end
return
end
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] | 2.034483 | 290 |
<reponame>JuliaCollections/Tries.jl
using Compat.Test
using Tries
import Compat: Nothing
tests = ["trie"]
if length(ARGS) > 0
tests = ARGS
end
@testset "Tries" begin
for t in tests
fp = joinpath(dirname(@__FILE__), "test_$t.jl")
println("$fp ...")
include(fp)
end
end # @testset
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<filename>v1/src/systems/trace_test_pencil.jl
export TraceTestPencil
"""
TraceTestPencil(F, p₀, v) <: AbstractSystem
The system `H(x,t) := [F(x, p₀+x[end]v); l ⋅[x[1:end-1];1] * λ + τ*γ)`.
"""
struct TraceTestPencil{S<:AbstractSystem,T} <: AbstractSystem
F::TraceTestSystem{S,T}
l::Vector{T}
γ::T
end
function TraceTestPencil(F::TraceTestSystem, l)
TraceTestPencil(F, l, cis(2π * rand()))
end
struct TraceTestPencilCache{SC<:TraceTestSystemCache} <: AbstractSystemCache
cache::SC
end
function cache(F::TraceTestPencil, x, τ)
c = cache(F.F, x, F.l)
TraceTestPencilCache(c)
end
Base.size(F::TraceTestPencil) = size(F.F)
function evaluate!(u, F::TraceTestPencil, x, τ, c::TraceTestPencilCache)
λ = x[end]
evaluate!(u, F.F, x, F.l, c.cache)
u[end] = u[end] * λ + τ[1] * F.γ
u
end
function evaluate(F::TraceTestPencil, x, τ, c::TraceTestPencilCache)
evaluate!(evaluate(F.F, x, F.l, c.cache), F, x, τ, c)
end
function jacobian!(U, F::TraceTestPencil, x, τ, c::TraceTestPencilCache)
λ = x[end]
jacobian!(U, F.F, x, F.l, c.cache)
for j = 1:length(x)-1
U[end, j] *= λ
end
U[end, end] = F.l[end]
for i = 1:length(x)-1
U[end, end] += F.l[i] * x[i]
end
U
end
function jacobian(F::TraceTestPencil, x, τ, c::TraceTestPencilCache)
jacobian!(jacobian(F.F, x, F.l, c.cache), F, x, τ, c)
end
function evaluate_and_jacobian!(u, U, F::TraceTestPencil, x, τ, c::TraceTestPencilCache)
λ = x[end]
evaluate_and_jacobian!(u, U, F.F, x, F.l, c.cache)
u[end] = u[end] * λ + τ[1] * F.γ
for j = 1:length(x)-1
U[end, j] *= λ
end
U[end, end] = F.l[end]
for i = 1:length(x)-1
U[end, end] += F.l[i] * x[i]
end
nothing
end
function differentiate_parameters!(u, F::TraceTestPencil, x, τ, c::TraceTestPencilCache)
u .= zero(eltype(u))
u[end, 1] = F.γ
u
end
function differentiate_parameters(F::TraceTestPencil, x, τ, c::TraceTestPencilCache)
u = similar(c.cache.u, size(F)[1], 1)
differentiate_parameters!(u, F, x, τ, c)
end
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# --- PRIVATE METHODS --------------------------------------------------------------------------------------- #
function _compute_crr_index_array(numberOfLevels::Int64)
# ok, so lets build an index array -
number_items_per_level = [(i+1) for i=0:numberOfLevels]
tmp_array = Array{Int64,1}()
theta = 0
for value in number_items_per_level
for index = 1:value
push!(tmp_array,theta)
end
theta = theta + 1
end
N = sum(number_items_per_level[1:(numberOfLevels-1)])
index_array = Array{Int64,2}(undef,N,3)
for row_index = 1:N
#index_array[row_index,1] = tmp_array[row_index]
index_array[row_index,1] = row_index
index_array[row_index,2] = row_index + 1 + tmp_array[row_index]
index_array[row_index,3] = row_index + 2 + tmp_array[row_index]
end
return index_array
end
function _compute_full_binomial_index_array(numberOfLevels::Int64)
number_of_elements = (2^(Int(numberOfLevels))) - 1
index_array = Array{Int64,2}(undef,number_of_elements,3)
# populate the index array -
for index = 1:number_of_elements
# compute the child indexs -
left_child_index = 2*(index - 1) + 2
right_child_index = 2*(index - 1) + 3
# grab and go -
index_array[index,1] = index
index_array[index,2] = left_child_index
index_array[index,3] = right_child_index
end
# return -
return index_array
end
# array based -
function _build_binary_lattice_intrinsic_value_array(contractSet::Set{PSAbstractAsset}, binaryPriceArray::Array{Float64,1})::PSResult
# initialize -
intrinsic_value_array = similar(binaryPriceArray)
# ok, so lets go through each price, compute the intrinsic value - and then store
for (index,price) in enumerate(binaryPriceArray)
# ok, get the underlying price -
underlying_price_value = binaryPriceArray[index]
# compute the intrinsic value -
result = intrinsic_value(contractSet,underlying_price_value)
if (isa(result.value,Exception) == true)
return result
end
iv_value = result.value
# add the iv to the array - index should work out
intrinsic_value_array[index] = iv_value
end
# return -
return PSResult{Array{Float64,1}}(intrinsic_value_array)
end
function _build_binary_lattice_underlying_price_array(basePrice::Float64, volatility::Float64, timeToExercise::Float64;
numberOfLevels::Int64 = 100)::PSResult
# compute up and down perturbations -
Δt = (timeToExercise/numberOfLevels)
U = exp(volatility * √Δt)
D = 1 / U
# compute the index array =
index_array = _compute_crr_index_array(numberOfLevels);
max_element = index_array[end,end]
N = index_array[end,1]
# compute price array -
priceArray = zeros(max_element)
priceArray[1] = basePrice
for row_index = 1:N
# parent index -
parent_index = index_array[row_index,1]
left_child_index = index_array[row_index,2]
right_child_index = index_array[row_index,3]
# get the basePrice -
basePrice = priceArray[parent_index]
# compute the prices -
down_price = basePrice*D
priceArray[right_child_index] = down_price
up_price = basePrice*U
priceArray[left_child_index] = up_price
end
# return the price array -
return PSResult{Array{Float64,1}}(priceArray)
end
function _build_binary_lattice_option_value_array(intrinsicValueArray::Array{Float64,1}, latticeModel::PSBinaryLatticeModel;
earlyExcercise::Bool = true, numberOfLevels::Int64 = 100)::PSResult
# initialize -
contract_price_array = copy(intrinsicValueArray)
# get stuff from the lattice model -
volatility = latticeModel.volatility
timeToExercise = latticeModel.timeToExercise
riskFreeRate = latticeModel.riskFreeRate
dividendRate = latticeModel.dividendRate
# compute up and down perturbations -
Δt = (timeToExercise/numberOfLevels)
U = exp(volatility * √Δt)
D = 1 / U
p = (exp((riskFreeRate - dividendRate)*Δt) - D)/(U - D)
DF = exp(-riskFreeRate*Δt)
# create a index table -
index_table = _compute_crr_index_array(numberOfLevels)
N = index_table[end,1]
# ok, so now lets compute the value for the nodes -
for compute_index = 1:N
# get the indexs -
parent_node_index = index_table[compute_index,1]
child_left_index = index_table[compute_index,2]
child_right_index = index_table[compute_index,3]
# compute -
contract_price = DF*(p*contract_price_array[child_left_index]+(1-p)*contract_price_array[child_right_index])
if (earlyExcercise == false)
contract_price_array[parent_node_index] = contract_price
else
excercise_value = contract_price_array[parent_node_index]
contract_price_array[parent_node_index] = max(excercise_value,contract_price)
end
end
# setup the results tuple -
results_tuple = (option_index_table=index_table, option_contract_price_array=contract_price_array, U=U, D=D, PUP=p, PDOWN=(1-p), DF=DF)
# return -
return PSResult(results_tuple)
end
# ----------------------------------------------------------------------------------------------------------- #
# --- PUBLIC METHODS ---------------------------------------------------------------------------------------- #
function compute_underlying_price_distribution(timeStepIndex::Int64,latticeModel::PSBinaryLatticeModel, baseUnderlyingPrice::Float64,
movementFunction::Function)::PooksoftBase.PSResult
# initialize -
priceDistributionArray = Array{Float64,2}(undef,(timeStepIndex+1), 4)
riskFreeRate = latticeModel.riskFreeRate
kIndexVector = range(0,stop=timeStepIndex,step=1) |> collect
# compute the up and downmove values -
(u,d) = movementFunction(latticeModel)
# compute the probability p of an *up* move -
p = ((1 + riskFreeRate) - d)/(u - d)
# main loop -
for (index,k) in enumerate(kIndexVector)
# compute the price -
price = baseUnderlyingPrice*(u^k)*(d^(timeStepIndex - k))
# compute the probability -
prob = binomial(timeStepIndex,k)*(p^k)*((1 - p)^(timeStepIndex-k))
# package -
priceDistributionArray[index,1] = timeStepIndex
priceDistributionArray[index,2] = k
priceDistributionArray[index,3] = prob
priceDistributionArray[index,4] = price
end
# return -
return PSResult{Array{Float64,2}}(priceDistributionArray)
end
"""
option_contract_price(contractSet::Set{PSAbstractAsset}, latticeModel::PSBinaryLatticeModel, baseUnderlyingPrice::Float64;
earlyExercise::Bool = false)::PooksoftBase.PSResult
Estimate the price of a contract using a binary lattice pricing model.
"""
function option_contract_price(contractSet::Set{PSAbstractAsset}, latticeModel::PSBinaryLatticeModel, baseUnderlyingPrice::Float64;
earlyExercise::Bool = true)::PooksoftBase.PSResult
# initialize -
option_contract_price = 0.0
# we need to get some stuff from the lattice model -
volatility = latticeModel.volatility
timeToExercise = latticeModel.timeToExercise
numberOfLevels = latticeModel.numberOfLevels
# compute the price array -
result = _build_binary_lattice_underlying_price_array(baseUnderlyingPrice, volatility, timeToExercise;
numberOfLevels = numberOfLevels)
if (isa(result.value,Exception) == true)
return result
end
lattice_price_array = result.value
# compute the intrinsic value array -
result = _build_binary_lattice_intrinsic_value_array(contractSet,lattice_price_array)
if (isa(result.value,Exception) == true)
return result
end
iv_array = result.value
# ok, let's build the option value array -
result = _build_binary_lattice_option_value_array(iv_array, latticeModel;
earlyExcercise = earlyExercise, numberOfLevels = numberOfLevels)
if (isa(result.value,Exception) == true)
return result
end
cost_calc_tuple = result.value
# create a named tuple and return the results -
results_tuple = (lattice_price_array=lattice_price_array, intrinsic_value_array = iv_array, cost_calculation_result=cost_calc_tuple)
# return -
return PSResult(results_tuple)
end
# ----------------------------------------------------------------------------------------------------------- # | [
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1303
] | 2.599577 | 3,309 |
<reponame>UnofficialJuliaMirror/Merlin.jl-80f3d04f-b880-5e6d-8e06-6a7e799169ac<gh_stars>100-1000
export pack, unpack
function pack(xs::Vector{Var}; pad=0)
T = eltype(xs[1])
N = ndims(xs[1])
maxdims = zeros(Int, N)
for x in xs
s = size(x)
for i = 1:N
maxdims[i] < s[i] && (maxdims[i] = s[i])
end
end
y = fill(T(pad), maxdims..., length(xs))
st = stride(y, N+1)
yi = 1
for x in xs
copyto!(y, yi, x.data, 1)
yi += st
end
Var(y, (pack,xs))
end
pack(x::Node) = Node(pack, x)
function unpack(x::A, sizes::Vector) where A
ys = map(A, sizes)
for i = 1:length(sizes)
ys[i] = x[s...,i]
end
ys
end
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] | 1.775 | 400 |
<filename>AutomotiveDrivingModels/hdCSX/src/1d/definitions.jl<gh_stars>1-10
baremodule AgentClass
const MOTORCYCLE = 1
const CAR = 2
const TRUCK = 3
const PEDESTRIAN = 4
end
"""
Vehicle definition which contains a class and a bounding box.
"""
struct VehicleDef
class::Int64 # ∈ AgentClass
length::Float64
width::Float64
end
function VehicleDef(;
class::Int=AgentClass.CAR,
length::Float64=4.0,
width::Float64=1.8,
)
VehicleDef(class, length, width)
end
const NULL_VEHICLEDEF = VehicleDef(AgentClass.CAR, NaN, NaN)
function Base.show(io::IO, d::VehicleDef)
class = d.class == AgentClass.CAR ? "CAR" :
d.class == AgentClass.MOTORCYCLE ? "MOTORCYCLE" :
d.class == AgentClass.TRUCK ? "TRUCK" :
d.class == AgentClass.PEDESTRIAN ? "PEDESTRIAN" :
"UNKNOWN"
@printf(io, "VehicleDef(%s, %.3f, %.3f)", class, d.length, d.width)
end
Base.write(io::IO, ::MIME"text/plain", def::VehicleDef) = @printf(io, "%d %.16e %.16e", def.class, def.length, def.width)
function Base.read(io::IO, ::MIME"text/plain", ::Type{VehicleDef})
tokens = split(strip(readline(io)), ' ')
class = parse(Int, tokens[1])
length = parse(Float64, tokens[2])
width = parse(Float64, tokens[3])
return VehicleDef(class, length, width)
end | [
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] | 2.336842 | 570 |
#=
To improve:
-Only one possible best alignment calculated... Revise so that all possible alignments are calculated
-Improve speed
-More user input options?
=#
using ArgParse
cli = ArgParseSettings()
@add_arg_table cli begin
"--match"
help = "match score"
arg_type = Int
default = 1
"--mismatch"
help = "mismatch score"
arg_type = Int
default = -1
"--gap"
help = "gap score"
arg_type = Int
default = -1
end
arg = parse_args(ARGS, cli)
seq1 = "AGGTCC"
seq2 = "AGTTC"
match = arg["match"]
mismatch = arg["mismatch"]
gap = arg["gap"]
function solvenw(seq_1, seq_2, match, mismatch, gap)
#Info
println("""seq1: $(seq1)
seq2: $(seq2)
""")
println("""
match score = $(match)
mismatch score = $(mismatch)
gap score = $(gap)
""")
#Initializing Matrix
seq1_length = sizeof(seq1)
seq2_length = sizeof(seq2)
workspace = fill(0,(seq1_length+1,seq2_length+1))
#Initialization
for i in 2:seq1_length+1, j in 2:seq2_length+1
workspace[i,1] = workspace[i-1,1]+gap
workspace[1,j] = workspace[1,j-1]+gap
end
#Fill
for i in 2:seq1_length+1
for j in 2:seq2_length+1
left_gap = workspace[i,j-1]+gap
top_gap = workspace[i-1,j]+gap
if seq1[i-1] == seq2[j-1]
match_mismatch = workspace[i-1,j-1]+match
else
match_mismatch = workspace[i-1,j-1]+mismatch
end
workspace[i,j]=max(left_gap,top_gap,match_mismatch)
end
end
#Display
println("Matrix: ")
display(workspace)
#Trace Back
x = seq1_length+1
y = seq2_length+1
traceback = String[]
score = 0
while x!=1
trace_gap = workspace[x,y] - gap
if seq1[x-1] == seq2[y-1]
trace_diagnal = workspace[x,y] - match
local_mm = match
else
trace_diagnal = workspace[x,y] - mismatch
local_mm = mismatch
end
if trace_diagnal == workspace[x-1,y-1]
push!(traceback, "match/mismatch")
x = x-1
y = y-1
score += local_mm
elseif trace_gap == workspace[x,y-1]
push!(traceback,"left_gap")
y = y-1
score += gap
elseif trace_gap == workspace[x-1,y]
push!(traceback,"top_gap")
x = x-1
score += gap
end
end
println("\r", traceback)
aligned_seq1 = ""
aligned_seq2 = ""
counter_seq1 = 1
counter_seq2 = 1
for i in length(traceback):-1:1
if traceback[i] == "match/mismatch"
aligned_seq1 *= seq1[counter_seq1]
aligned_seq2 *= seq2[counter_seq2]
counter_seq1 += 1
counter_seq2 += 1
elseif traceback[i] == "left_gap"
aligned_seq1 *= '-'
aligned_seq2 *= seq2[counter_seq2]
counter_seq2 += 1
elseif traceback[i] == "top_gap"
aligned_seq2 *= '-'
aligned_seq1 *= seq1[counter_seq1]
counter_seq1 +=1
end
end
println(aligned_seq1)
println(aligned_seq2)
#Score
println("score = $(score)")
end
solvenw(seq1, seq2, match, mismatch, gap)
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] | 2.181675 | 1,266 |
using Test
using UAPIC
using FFTW
using LinearAlgebra
include("test_efd.jl")
include("test_poisson.jl")
include("test_particles.jl")
include("bupdate.jl")
| [
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] | 2.754386 | 57 |
module ArrowTypes
export ArrowType, NullType, PrimitiveType, ListType, FixedSizeListType, MapType, StructType, UnionType, DictEncodedType
abstract type ArrowType end
ArrowType(x::T) where {T} = ArrowType(T)
ArrowType(::Type{T}) where {T} = isprimitivetype(T) ? PrimitiveType() : StructType()
function arrowconvert end
arrowconvert(T, x) = convert(T, x)
arrowconvert(::Type{Union{T, Missing}}, x) where {T} = arrowconvert(T, x)
arrowconvert(::Type{Union{T, Missing}}, ::Missing) where {T} = missing
struct NullType <: ArrowType end
ArrowType(::Type{Missing}) = NullType()
struct PrimitiveType <: ArrowType end
ArrowType(::Type{<:Integer}) = PrimitiveType()
ArrowType(::Type{<:AbstractFloat}) = PrimitiveType()
ArrowType(::Type{Bool}) = PrimitiveType()
struct ListType <: ArrowType end
isstringtype(T) = false
isstringtype(::Type{Union{T, Missing}}) where {T} = isstringtype(T)
ArrowType(::Type{<:AbstractString}) = ListType()
isstringtype(::Type{<:AbstractString}) = true
ArrowType(::Type{Symbol}) = ListType()
isstringtype(::Type{Symbol}) = true
arrowconvert(::Type{Symbol}, x::String) = Symbol(x)
arrowconvert(::Type{String}, x::Symbol) = String(x)
ArrowType(::Type{<:AbstractArray}) = ListType()
struct FixedSizeListType <: ArrowType end
ArrowType(::Type{NTuple{N, T}}) where {N, T} = FixedSizeListType()
getsize(::Type{NTuple{N, T}}) where {N, T} = N
struct MapType <: ArrowType end
ArrowType(::Type{<:Dict}) = MapType()
struct StructType <: ArrowType end
ArrowType(::Type{<:NamedTuple}) = StructType()
@enum STRUCT_TYPES NAMEDTUPLE STRUCT # KEYWORDARGS
structtype(::Type{NamedTuple{N, T}}) where {N, T} = NAMEDTUPLE
structtype(::Type{T}) where {T} = STRUCT
struct UnionType <: ArrowType end
ArrowType(::Union) = UnionType()
struct DictEncodedType <: ArrowType end
"""
There are a couple places when writing arrow buffers where
we need to write a "dummy" value; it doesn't really matter
what we write, but we need to write something of a specific
type. So each supported writing type needs to define `default`.
"""
function default end
default(T) = zero(T)
default(::Type{Symbol}) = Symbol()
default(::Type{Char}) = '\0'
default(::Type{String}) = ""
function default(::Type{A}) where {A <: AbstractVector{T}} where {T}
a = similar(A, 1)
a[1] = default(T)
return a
end
default(::Type{NTuple{N, T}}) where {N, T} = ntuple(i -> default(T), N)
default(::Type{T}) where {T <: Tuple} = Tuple(default(fieldtype(T, i)) for i = 1:fieldcount(T))
default(::Type{Dict{K, V}}) where {K, V} = Dict{K, V}()
default(::Type{NamedTuple{names, types}}) where {names, types} = NamedTuple{names}(Tuple(default(fieldtype(types, i)) for i = 1:length(names)))
const JULIA_TO_ARROW_TYPE_MAPPING = Dict{Type, Tuple{String, Type}}(
Char => ("JuliaLang.Char", UInt32),
Symbol => ("JuliaLang.Symbol", String),
)
istyperegistered(::Type{T}) where {T} = haskey(JULIA_TO_ARROW_TYPE_MAPPING, T)
function getarrowtype!(meta, ::Type{T}) where {T}
arrowname, arrowtype = JULIA_TO_ARROW_TYPE_MAPPING[T]
meta["ARROW:extension:name"] = arrowname
meta["ARROW:extension:metadata"] = ""
return arrowtype
end
const ARROW_TO_JULIA_TYPE_MAPPING = Dict{String, Tuple{Type, Type}}(
"JuliaLang.Char" => (Char, UInt32),
"JuliaLang.Symbol" => (Symbol, String),
)
function extensiontype(meta)
if haskey(meta, "ARROW:extension:name")
typename = meta["ARROW:extension:name"]
if haskey(ARROW_TO_JULIA_TYPE_MAPPING, typename)
return ARROW_TO_JULIA_TYPE_MAPPING[typename][1]
else
@warn "unsupported ARROW:extension:name type: \"$typename\""
end
end
return nothing
end
function registertype!(juliatype::Type, arrowtype::Type, arrowname::String=string("JuliaLang.", string(juliatype)))
# TODO: validate that juliatype isn't already default arrow type
JULIA_TO_ARROW_TYPE_MAPPING[juliatype] = (arrowname, arrowtype)
ARROW_TO_JULIA_TYPE_MAPPING[arrowname] = (juliatype, arrowtype)
return
end
end # module ArrowTypes | [
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] | 2.574904 | 1,562 |
<filename>src/google/plugin_pb.jl<gh_stars>0
# syntax: proto2
using ProtoBuf
import ProtoBuf.meta
mutable struct Version <: ProtoType
major::Int32
minor::Int32
patch::Int32
suffix::AbstractString
Version(; kwargs...) = (o=new(); fillunset(o); isempty(kwargs) || ProtoBuf._protobuild(o, kwargs); o)
end #mutable struct Version
mutable struct CodeGeneratorRequest <: ProtoType
file_to_generate::Base.Vector{AbstractString}
parameter::AbstractString
proto_file::Base.Vector{ProtoBuf.GoogleProtoBuf.FileDescriptorProto}
compiler_version::Version
CodeGeneratorRequest(; kwargs...) = (o=new(); fillunset(o); isempty(kwargs) || ProtoBuf._protobuild(o, kwargs); o)
end #mutable struct CodeGeneratorRequest
const __fnum_CodeGeneratorRequest = Int[1,2,15,3]
meta(t::Type{CodeGeneratorRequest}) = meta(t, ProtoBuf.DEF_REQ, __fnum_CodeGeneratorRequest, ProtoBuf.DEF_VAL, true, ProtoBuf.DEF_PACK, ProtoBuf.DEF_WTYPES, ProtoBuf.DEF_ONEOFS, ProtoBuf.DEF_ONEOF_NAMES, ProtoBuf.DEF_FIELD_TYPES)
mutable struct CodeGeneratorResponse_File <: ProtoType
name::AbstractString
insertion_point::AbstractString
content::AbstractString
CodeGeneratorResponse_File(; kwargs...) = (o=new(); fillunset(o); isempty(kwargs) || ProtoBuf._protobuild(o, kwargs); o)
end #mutable struct CodeGeneratorResponse_File
const __fnum_CodeGeneratorResponse_File = Int[1,2,15]
meta(t::Type{CodeGeneratorResponse_File}) = meta(t, ProtoBuf.DEF_REQ, __fnum_CodeGeneratorResponse_File, ProtoBuf.DEF_VAL, true, ProtoBuf.DEF_PACK, ProtoBuf.DEF_WTYPES, ProtoBuf.DEF_ONEOFS, ProtoBuf.DEF_ONEOF_NAMES, ProtoBuf.DEF_FIELD_TYPES)
mutable struct CodeGeneratorResponse <: ProtoType
error::AbstractString
file::Base.Vector{CodeGeneratorResponse_File}
CodeGeneratorResponse(; kwargs...) = (o=new(); fillunset(o); isempty(kwargs) || ProtoBuf._protobuild(o, kwargs); o)
end #mutable struct CodeGeneratorResponse
const __fnum_CodeGeneratorResponse = Int[1,15]
meta(t::Type{CodeGeneratorResponse}) = meta(t, ProtoBuf.DEF_REQ, __fnum_CodeGeneratorResponse, ProtoBuf.DEF_VAL, true, ProtoBuf.DEF_PACK, ProtoBuf.DEF_WTYPES, ProtoBuf.DEF_ONEOFS, ProtoBuf.DEF_ONEOF_NAMES, ProtoBuf.DEF_FIELD_TYPES)
export Version, CodeGeneratorRequest, CodeGeneratorResponse_File, CodeGeneratorResponse
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] | 2.681499 | 854 |
<gh_stars>100-1000
using BenchmarkTools
SUITE = BenchmarkGroup()
for file in readdir(@__DIR__)
if startswith(file, "bench_") && endswith(file, ".jl")
SUITE[file[length("bench_") + 1:end - length(".jl")]] =
include(file)
end
end
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] | 2.285714 | 112 |
<gh_stars>0
module Cards
using Genie, Stipple, StippleUI, StippleUI.API
import Genie.Renderer.Html: HTMLString, normal_element, register_normal_element
export card, card_section, card_actions
register_normal_element("q__card", context = @__MODULE__)
register_normal_element("q__card__section", context = @__MODULE__)
register_normal_element("q__card__actions", context = @__MODULE__)
function card(args...;
wrap::Function = StippleUI.DEFAULT_WRAPPER,
kwargs...)
wrap() do
q__card(args...; kwargs...)
end
end
function card_section(args...; kwargs...)
q__card__section(args...; kwargs...)
end
function card_actions(args...; kwargs...)
q__card__actions(args...; kwargs...)
end
end
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<filename>src/_deriv2.jl
"""
Module: SymbolicDiff (Symbolic Operation for Arithmetic)
"""
"""
seval(f, (dvar1, dver2), env, cache)
Return the second derivative of expr f with respect to dvar1 and dver2
"""
function seval(f, dvar::Tuple{Symbol,Symbol})
seval(f, dvar, globalenv, SymbolicCache())
end
function seval(f, dvar::Tuple{Symbol,Symbol}, env::SymbolicEnv)
seval(f, dvar, env, SymbolicCache())
end
function seval(f, dvar::Tuple{Symbol,Symbol}, cache::SymbolicCache)
seval(f, dvar, globalenv, cache)
end
###
function seval(f, dvar::Tuple{SymbolicVariable{Tv},SymbolicVariable{Tv}}) where Tv
seval(f, (dvar[1].var, dvar[2].var))
end
function seval(f, dvar::Tuple{SymbolicVariable{Tv},SymbolicVariable{Tv}}, env::SymbolicEnv) where Tv
seval(f, (dvar[1].var, dvar[2].var), env)
end
function seval(f, dvar::Tuple{SymbolicVariable{Tv},SymbolicVariable{Tv}}, cache::SymbolicCache) where Tv
seval(f, (dvar[1].var, dvar[2].var), cache)
end
function seval(f, dvar::Tuple{SymbolicVariable{Tv},SymbolicVariable{Tv}}, env::SymbolicEnv, cache::SymbolicCache) where Tv
seval(f, (dvar[1].var, dvar[2].var), env, cache)
end
###
function seval(f::SymbolicValue{Tv}, dvar::Tuple{Symbol,Symbol}, env::SymbolicEnv, cache::SymbolicCache)::Tv where Tv
0
end
function seval(f::SymbolicVariable{Tv}, dvar::Tuple{Symbol,Symbol}, env::SymbolicEnv, cache::SymbolicCache)::Tv where Tv
0
end
function seval(f::AbstractNumberSymbolic{Tv}, dvar::Tuple{Symbol,Symbol}, env::SymbolicEnv, cache::SymbolicCache)::Tv where Tv
(dvar[1] in f.params) || (dvar[2] in f.params) || return Tv(0)
get(cache, (f,dvar)) do
retval = _eval(Val(f.op), f, dvar, env, cache)
cache[(f,dvar)] = retval
end
end
"""
_eval(::Val{xx}, dvar, f, env, cache)
Dispached function to evaluate the second derivative of f
"""
function _eval(::Val{:+}, f::SymbolicExpression{Tv}, dvar::Tuple{Symbol,Symbol}, env::SymbolicEnv, cache::SymbolicCache)::Tv where Tv
args = [seval(x, dvar, env, cache) for x = f.args]
+(args...)
end
function _eval(::Val{:-}, f::SymbolicExpression{Tv}, dvar::Tuple{Symbol,Symbol}, env::SymbolicEnv, cache::SymbolicCache)::Tv where Tv
args = [seval(x, dvar, env, cache) for x = f.args]
-(args...)
end
function _eval(::Val{:*}, f::SymbolicExpression{Tv}, dvar::Tuple{Symbol,Symbol}, env::SymbolicEnv, cache::SymbolicCache)::Tv where Tv
args = [seval(x, env, cache) for x = f.args]
dargs_a = [seval(x, dvar[1], env, cache) for x = f.args]
dargs_b = [seval(x, dvar[2], env, cache) for x = f.args]
dargs_ab = [seval(x, dvar, env, cache) for x = f.args]
ret = dargs_ab[1]
s = args[1]
s_a = dargs_a[1]
s_b = dargs_b[1]
for i = 2:length(args)
ret *= args[i]
ret += s * dargs_ab[i]
ret += s_a * dargs_b[i]
ret += s_b * dargs_a[i]
(i == length(args)) && break
s_a *= args[i]
s_b *= args[i]
s_a += s * dargs_a[i]
s_b += s * dargs_b[i]
s *= args[i]
end
ret
end
function _eval(::Val{:/}, f::SymbolicExpression{Tv}, dvar::Tuple{Symbol,Symbol}, env::SymbolicEnv, cache::SymbolicCache)::Tv where Tv
x,y = [seval(x, env, cache) for x = f.args]
dx_a,dy_a = [seval(x, dvar[1], env, cache) for x = f.args]
dx_b,dy_b = [seval(x, dvar[2], env, cache) for x = f.args]
dx_ab,dy_ab = [seval(x, dvar, env, cache) for x = f.args]
((dx_ab * y - dx_a * dy_b - dx_b * dy_a - x * dy_ab) * y + 2 * x * dy_a * dy_b) / y^3
end
function _eval(::Val{:^}, f::SymbolicExpression{Tv}, dvar::Tuple{Symbol,Symbol}, env::SymbolicEnv, cache::SymbolicCache)::Tv where Tv
x,y = [seval(x, env, cache) for x = f.args]
dx_a,dy_a = [seval(x, dvar[1], env, cache) for x = f.args]
dx_b,dy_b = [seval(x, dvar[2], env, cache) for x = f.args]
dx_ab,dy_ab = [seval(x, dvar, env, cache) for x = f.args]
f = x^y
f_a = f * (dy_a * x * log(x) + y * dx_a) / x
f_b = f * (dy_b * x * log(x) + y * dx_b) / x
f_ab = (f_b * (dy_a * x * log(x) + y * dx_a) - f_a * dx_b + f * (dy_ab * x * log(x) + dy_a * dx_b * (1 + log(x)) + dx_a * dy_b + y * dx_ab)) / x
f_ab
end
function _eval(::Val{:exp}, f::SymbolicExpression{Tv}, dvar::Tuple{Symbol,Symbol}, env::SymbolicEnv, cache::SymbolicCache)::Tv where Tv
x, = [seval(x, env, cache) for x = f.args]
dx_a, = [seval(x, dvar[1], env, cache) for x = f.args]
dx_b, = [seval(x, dvar[2], env, cache) for x = f.args]
dx_ab, = [seval(x, dvar, env, cache) for x = f.args]
exp(x) * (dx_b * dx_a + dx_ab)
end
function _eval(::Val{:log}, f::SymbolicExpression{Tv}, dvar::Tuple{Symbol,Symbol}, env::SymbolicEnv, cache::SymbolicCache)::Tv where Tv
x, = [seval(x, env, cache) for x = f.args]
dx_a, = [seval(x, dvar[1], env, cache) for x = f.args]
dx_b, = [seval(x, dvar[2], env, cache) for x = f.args]
dx_ab, = [seval(x, dvar, env, cache) for x = f.args]
(dx_ab * x - dx_a * dx_b) / x^2
end
function _eval(::Val{:sqrt}, f::SymbolicExpression{Tv}, dvar::Tuple{Symbol,Symbol}, env::SymbolicEnv, cache::SymbolicCache)::Tv where Tv
x, = [seval(x, env, cache) for x = f.args]
dx_a, = [seval(x, dvar[1], env, cache) for x = f.args]
dx_b, = [seval(x, dvar[2], env, cache) for x = f.args]
dx_ab, = [seval(x, dvar, env, cache) for x = f.args]
sqrt(x) * (dx_ab * 2 - dx_a * dx_b) / (4*x)
end
function _eval(::Val{:sum}, f::SymbolicExpression{Tv}, dvar::Tuple{Symbol,Symbol}, env::SymbolicEnv, cache::SymbolicCache)::Tv where Tv
dx_ab, = [seval(x, dvar, env, cache) for x = f.args]
sum(dx_ab)
end
function _eval(::Val{:dot}, f::SymbolicExpression{Tv}, dvar::Tuple{Symbol,Symbol}, env::SymbolicEnv, cache::SymbolicCache)::Tv where Tv
x,y = [seval(x, env, cache) for x = f.args]
dx_a,dy_a = [seval(x, dvar[1], env, cache) for x = f.args]
dx_b,dy_b = [seval(x, dvar[2], env, cache) for x = f.args]
dx_ab,dy_ab = [seval(x, dvar, env, cache) for x = f.args]
dot(x,dy_ab) + dot(dx_b,dy_a) + dot(dx_a,dy_b) + dot(dx_ab,y)
end
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] | 2.066644 | 2,911 |
"""
module ViewerGL
3D `interactive Viewer` for geometric and topological data.
Helper module for Julia's native ``OpenGL visualization``, forked from [Plasm.jl](https://github.com/plasm-language/pyplasm/tree/master/src/plasm.jl). To be used with geometric models and geometric expressions from [LinearAlgebraicRepresentation.jl](https://github.com/cvdlab/LinearAlgebraicRepresentation.jl), the simplest data structures for geometric and solid modeling :-)
"""
module ViewerGL
using LinearAlgebra,StaticArrays
using ModernGL
using GLFW
import Base:*
include("Point.jl")
include("Box.jl")
include("Matrix.jl")
include("Quaternion.jl")
include("Frustum.jl")
include("GLUtils.jl")
include("GLVertexBuffer.jl")
include("GLVertexArray.jl")
include("GLMesh.jl")
include("GLShader.jl")
include("GLPhongShader.jl")
include("Viewer.jl")
include("Geometry.jl")
include("GLText.jl")
include("GLColorBuffer.jl")
const GLFrame2 = GLAxis(Point3d(0,0,0),Point3d(1,1,0))
const GLFrame = GLAxis(Point3d(0,0,0),Point3d(1,1,1))
end # module
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<filename>examples/collisions/Simulate_SlidingAndRollingBall.jl
"""
module Simulate_SlidingAndRollingBall
Simulates a ball that is first sliding and then rolling on a table.
"""
module Simulate_SlidingAndRollingBall
using Modia3D
import Modia3D.ModiaMath
vmatSolids = Material(color="Red" , transparency=0.0) # material of solids
vmatTable = Material(color="Green" , transparency=0.1) # material of table
LxTable = 3.0
LyTable = 0.5
LzTable = 0.02
diameter = 0.06
solidBall = Solid(SolidSphere(diameter), "BilliardBall", vmatSolids; contactMaterial="BilliardBall")
solidTable = Solid(SolidBox(LxTable, LyTable, LzTable) , "BilliardTable", vmatTable; contactMaterial = "BilliardTable")
@assembly RollingBall() begin
world = Object3D()
table = Object3D(world, solidTable, fixed=true , r=[1.5, 0.0, -LzTable/2])
ball = Object3D(world, solidBall , fixed=false, r=[0.2, 0.0, diameter/2], v_start=[3.0, 0.0, 0.0], visualizeFrame=true )
end
gravField = UniformGravityField(g=9.81, n=[0,0,-1])
rollingBall = RollingBall(sceneOptions=SceneOptions(gravityField=gravField, visualizeFrames=false,
defaultFrameLength=0.1, enableContactDetection=true))
# Modia3D.visualizeAssembly!( rollingBall )
model = SimulationModel(rollingBall)
result = ModiaMath.simulate!(model; stopTime=0.5, tolerance=1e-8, log=false)
ModiaMath.plot(result, [ "ball.r[1]" "ball.v[1]"
"ball.r[3]" "ball.w[2]"])
println("... success of examples/collisions/Simulate_SlidingAndRollingBall.jl!")
end
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8,
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198,
5841,
544,
37372,
13,
29487,
7,
20274,
11,
685,
366,
1894,
13,
81,
58,
16,
30866,
220,
366,
1894,
13,
85,
58,
16,
30866,
198,
220,
220,
220,
220,
220,
220,
220,
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220,
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220,
220,
220,
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220,
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366,
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13,
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58,
18,
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366,
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13,
86,
58,
17,
60,
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8,
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35235,
7203,
986,
1943,
286,
6096,
14,
26000,
3279,
14,
8890,
5039,
62,
11122,
2530,
1870,
26869,
278,
23410,
13,
20362,
2474,
8,
198,
437,
198
] | 2.512658 | 632 |
"""
SteMDerivative(x::Vector{Float64}, y::Vector{Float64})
This function evaluates the numerical derivative according to the SteM
algorithm, [Camera et al. 2017](https://arxiv.org/abs/1606.03451):
- a linear regression over ``x`` and ``y`` is performed
- if the points obtained with the fit are close enough (less than 0.01 relative
difference) the linear ansatz is satisfied and the slope gives the derivative
- if the linear ansatz is not satisfied, the external couple of points and the
linear regression is performed again till the linear ansatz is satisfied
"""
function SteMDerivative(x::Vector{Float64}, y::Vector{Float64})
x_copy = deepcopy(x)
y_copy = deepcopy(y)
if minimum(y) == maximum(y)
der = 0
else
nonlinear = true
while nonlinear
coefficients = CustomRegression(x_copy, y_copy)
y_fit = zeros(length(x_copy))
percent_diff = zeros(length(x_copy))
y_fit .= coefficients[1] .+ x_copy .* coefficients[2]
percent_diff = abs.((y_copy .- y_fit) ./ y_copy)
if all(percent_diff .<= 0.01) || length(x_copy) < 3
nonlinear = false
der = coefficients[2]
else
pop!(x_copy)
pop!(y_copy)
popfirst!(y_copy)
popfirst!(x_copy)
end
end
end
return der
end
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220,
220,
886,
198,
220,
220,
220,
1441,
4587,
198,
437,
198
] | 2.29902 | 612 |
<filename>examples/loltools/featured-games.jl<gh_stars>1-10
using LOLTools.SpectatorV4
api_key = get(ENV, "RIOT_TOKEN", "")
platform = "kr"
featured = SpectatorV4.featured_games(api_key, platform)
# @info featured
@info sort(featured.gameList, by = x -> x.gameId)[1].participants
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function boardtest{Lib<:PolyhedraLibrary}(lib::Lib)
A1 = -eye(Int, 9) # x >= 0
b1 = zeros(Int, 9)
A2 = eye(Int, 9) # x <= 1
b2 = ones(Int, 9)
A3 = zeros(Int, 9, 9)
b3 = 3 * ones(Int, 9)
i = 1
for a = 1:3
for b = (a+1):3
for c = 1:3
for d = (c+1):3
ac = a + (c-1) * 3
ad = a + (d-1) * 3
bc = b + (c-1) * 3
bd = b + (d-1) * 3
A3[i, ac] = 1
A3[i, ad] = 1
A3[i, bc] = 1
A3[i, bd] = 1
i += 1
end
end
end
end
A = [A1; A2; A3]
b = [b1; b2; b3]
ine = SimpleHRepresentation(A, b)
poly = polyhedron(ine, lib)
@test !isempty(poly)
ext = SimpleVRepresentation(getgenerators(poly))
target = ones(Int, 9) * (3 // 4)
ok = false
for i = 1:size(ext.V, 1)
# In julia v0.4 [i,:] returns a row matrix and in v0.5 it is
# a 1D vector hence the use of vec
if vec(ext.V[i,:]) == target
ok = true
end
end
@test ok
cutA = ones(Int, 1, 9)
cutb = 6
Acut = [cutA; A]
bcut = [cutb; b]
inecut = SimpleHRepresentation(Acut, bcut)
polycut = polyhedron(inecut, lib)
@test !isempty(polycut)
#(isredundant, certificate) = isredundantinequality(polycut, 1)
#@test !isredundant
#@test certificate == target
@test !isredundantinequality(polycut, 1)
@test IntSet([]) == getredundantinequalities(polycut)
#(issredundant, scertificate) = isstronglyredundantinequality(polycut, 1)
#@test !issredundant
#@test scertificate == target
#@test IntSet([]) == getstronglyredundantinequalities(polycut)
end
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] | 2.029639 | 776 |
"""
Oscillator{T<:Float64}
Custom type to represent a SDOF oscillator. The type has two fields:
- `f_n` is the natural frequency of the oscillator
- `ζ_n` is the damping ratio
# Examples
```julia-repl
sdof = Oscillator( 1.0, 0.05 )
```
"""
struct Oscillator{T<:Float64}
f_n::T
ζ_n::T
end
"""
Oscillator(f_n)
Default initializer setting the damping ratio to 5% of critical
- `f_n` is the natural frequency of the oscillator (in Hz)
# Examples
```julia-repl
f_n = 2.0
sdof = Oscillator(f_n)
```
"""
Oscillator(f_n::T) where {T<:Float64} = Oscillator(f_n, 0.05)
"""
period(sdof::Oscillator)
Natural period (s) of the sdof Oscillator.
"""
function period(sdof::Oscillator)
return 1.0/sdof.f_n
end
@doc raw"""
transfer(f::T, sdof::Oscillator) where {T<:Real}
Compute the modulus of the transfer function for a SDOF system.
The transfer function is defined as:
```math
|H(f,f_n,\zeta_n)| = \frac{1}{\sqrt{ \left(1 - \beta^2 \right)^2 + \left(2\zeta_n\beta\right)^2 }}
```
where ``\beta`` is the tuning ratio defined by ``f/f_n``.
# Examples
```julia-repl
f = 2.0
sdof = Oscillator(1.0, 0.05)
Hf = transfer(f, sdof)
```
See also: [`squared_transfer`](@ref)
"""
function transfer(f, sdof::Oscillator)
# tuning ratio
β = f / sdof.f_n
return 1.0 / sqrt( (1.0 - β^2)^2 + (2sdof.ζ_n*β)^2 )
end
"""
squared_transfer(f, sdof::Oscillator)
Compute the square of the transfer function for a SDOF system, `sdof`, at frequency `f`.
# Examples
```julia-repl
f = 2.0
# create sdof with natural frequency f_n=1.0 and damping ζ=0.05
sdof = Oscillator( 1.0, 0.05 )
Hf2 = squared_transfer( f, sdof )
```
See also: [`transfer`](@ref)
"""
function squared_transfer(f, sdof::Oscillator)
# tuning ratio
β = f / sdof.f_n
return 1.0 / ( (1.0 - β^2)^2 + (2sdof.ζ_n*β)^2 )
end
"""
transfer(f::Vector{T}, sdof::Oscillator) where T<:Real
Computes the modulus of the transfer function of a SDOF for a vector of frequencies
- `f::Vector` is the vector of frequencies
- `sdof::Oscillator` is the oscillator instance
# Examples
```julia-repl
f = collect(range(0.1, stop=10.0, step=0.01))
sdof = Oscillator(1.0)
Hf = transfer(f, sdof)
```
"""
function transfer(f::Vector{T}, sdof::Oscillator) where T<:Real
# tuning ratio
Hf = similar(f)
for i in 1:length(f)
@inbounds Hf[i] = transfer(f[i], sdof)
end
return Hf
end
"""
transfer!(Hf::Vector{T}, f::Vector{T}, sdof::Oscillator) where T<:Real
Computes the modulus of the transfer function of a SDOF for a vector of frequencies in place
- `Hf::Vector` is the pre-allocated vector into which the results are stored
- `f::Vector` is the vector of frequencies
- `sdof::Oscillator` is the oscillator instance
# Examples
```julia-repl
f = collect(range(0.1, stop=10.0, step=0.01))
sdof = Oscillator(1.0)
Hf = similar(f)
transfer!(Hf, f, sdof)
```
"""
function transfer!(Hf::Vector{T}, f::Vector{T}, sdof::Oscillator) where T<:Real
for i in 1:length(f)
@inbounds Hf[i] = transfer(f[i],sdof)
end
return nothing
end
"""
squared_transfer!(Hf2::Vector{T}, f::Vector{T}, sdof::Oscillator) where T<:Real
Computes the square of the modulus of the transfer function of a SDOF for a vector of frequencies in place:
- `Hf2::Vector` is the pre-allocated vector into which the results are stored
- `f::Vector` is the vector of frequencies
- `sdof::Oscillator` is the oscillator instance
Inputs derive from the `Real` type and so are differentiable.
# Examples
```julia-repl
f = collect(range(0.1, stop=10.0, step=0.01))
sdof = Oscillator(1.0)
Hf2 = similar(f)
squared_transfer!(Hf2, f, sdof)
```
"""
function squared_transfer!(Hf2::Vector{T}, f::Vector{T}, sdof::Oscillator) where T<:Real
for i in 1:length(f)
@inbounds Hf2[i] = squared_transfer(f[i],sdof)
end
return nothing
end
"""
squared_transfer!(Hf2::Vector, f::Vector, sdof::Oscillator)
Computes the square of the modulus of the transfer function of a SDOF for a vector of frequencies in place:
- `Hf2::Vector` is the pre-allocated vector into which the results are stored
- `f::Vector` is the vector of frequencies
- `sdof::Oscillator` is the oscillator instance
# Examples
```julia-repl
f = collect(range(0.1, stop=10.0, step=0.01))
sdof = Oscillator(1.0)
Hf2 = similar(f)
squared_transfer!(Hf2, f, sdof)
```
"""
function squared_transfer!(Hf2::Vector, f::Vector, sdof::Oscillator)
for i in 1:length(f)
Hf2[i] = squared_transfer(f[i],sdof)
end
return nothing
end
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197,
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] | 2.426978 | 1,883 |
using ModelingToolkit
using SteadyStateDiffEq
using Test
@parameters t r
@variables x(t)
@derivatives D'~t
eqs = [D(x) ~ x^2-r]
de = ODESystem(eqs)
for factor in [1e-1, 1e0, 1e10], u0_p in [(2.34,2.676),(22.34,1.632),(.3,15.676),(0.3,0.006)]
u0 = [x => factor*u0_p[1]]
p = [r => factor*u0_p[2]]
ss_prob = SteadyStateProblem(de,u0,p)
sol = solve(ss_prob,SSRootfind()).u[1]
@test abs(sol^2 - factor*u0_p[2]) < 1e-8
ss_prob = SteadyStateProblemExpr(de,u0,p)
sol_expr = solve(eval(ss_prob),SSRootfind()).u[1]
@test all(x->x==0,sol-sol_expr)
end
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] | 1.891447 | 304 |
<filename>src/multiTracer.jl
#= ============================================
Generate 𝐹 and ∇ₓ𝐹 from user input
============================================ =#
# TODO replace this with DiffEqOperators when possible
struct LinearOperators{T<:Tuple}
ops::T
end
LinearOperators(ops...) = LinearOperators((ops...,))
SparseArrays.blockdiag(As::LinearOperators...) = blockdiag([sum(A.ops) for A in As]...)
function LinearAlgebra.mul!(du, A::LinearOperators, u, α, β)
for (i, op) in enumerate(A.ops)
(i==1) ? mul!(du, op, u, α, β) : mul!(du, op, u, α, 1)
end
du
end
LinearAlgebra.factorize(A::LinearOperators) = factorize(sum(A.ops))
Base.:\(A::LinearOperators, u::AbstractArray) = factorize(A) \ u
Base.:+(A::LinearOperators, B::AbstractSparseArray) = LinearOperators(A.ops..., B)
Base.:+(B::AbstractSparseArray, A::LinearOperators) = A + B
function Base.:*(A::LinearOperators, u::AbstractArray) # has to be same type!
du = similar(u)
mul!(du, A, u, 1, 0)
end
export LinearOperators
# AIBECSFunction creates an ODEFunction from the Ts and Gs
AIBECSFunction(T::AbstractSparseArray, G::Function) = AIBECSFunction(p -> T, G, T.n)
AIBECSFunction(T::Function, G::Function, nb::Int) = AIBECSFunction(T, (G,), nb)
function AIBECSFunction(T::Function, Gs::Tuple, nb::Int)
nt = length(Gs) # if a single T is given, then nt is given by the number of Gs
Tidx = ones(Int64, nt) # and all the tracers share the same T
AIBECSFunction((T,), Gs, nb, nt, Tidx)
end
function AIBECSFunction(Ts::Tuple, Gs::Tuple, nb::Int, nt::Int=length(Ts), Tidx::AbstractVector=1:nt)
if all(isinplace(G, nt + 2) for G in Gs) # +2 to account for dx and p
iipAIBECSFunction(Ts, Gs, nb, nt, Tidx)
else
oopAIBECSFunction(Ts, Gs, nb, nt, Tidx)
end
end
function iipAIBECSFunction(Ts, Gs, nb, nt=length(Ts), Tidx=1:nt)
tracers(u) = state_to_tracers(u, nb, nt)
tracer(u, i) = state_to_tracer(u, nb, nt, i)
function G(du, u, p)
for j in 1:nt
Gs[j](tracer(du, j), tracers(u)..., p)
end
du
end
function f(du, u, p, t=0)
G(du, u, p)
for jT in eachindex(Ts)
op = Ts[jT](p)
for j in findall(Tidx .== jT)
mul!(tracer(du, j), op, tracer(u, j), -1, 1)
end
end
du
end
# Jacobian
∇ₓG(u, p) = inplace_local_jacobian(Gs, u, p, nt, nb)
function T(p)
uniqueTs = [Tⱼ(p) for Tⱼ in Ts]
blockdiag([uniqueTs[Tidx[j]] for j in 1:nt]...)
end
jac(u, p, t=0) = ∇ₓG(u, p) - T(p)
return ODEFunction{true}(f, jac=jac)
end
function oopAIBECSFunction(Ts, Gs, nb, nt=length(Ts), Tidx=1:nt)
tracers(u) = state_to_tracers(u, nb, nt)
tracer(u, i) = state_to_tracer(u, nb, nt, i)
G(u, p) = reduce(vcat, Gⱼ(tracers(u)..., p) for Gⱼ in Gs)
function f(u, p, t=0)
du = copy(G(u, p))
for jT in eachindex(Ts)
op = Ts[jT](p)
for j in findall(Tidx .== jT)
mul!(tracer(du, j), op, tracer(u, j), -1, 1)
end
end
du
end
# Jacobian
∇ₓG(u, p) = local_jacobian(Gs, u, p, nt, nb)
function T(p)
uniqueTs = [Tⱼ(p) for Tⱼ in Ts]
blockdiag([uniqueTs[Tidx[j]] for j in 1:nt]...)
end
jac(u, p, t=0) = ∇ₓG(u, p) - T(p)
return ODEFunction(f, jac=jac)
end
# AIBECSFunction calls itself to allow for both λ::Vector and p::APar
function AIBECSFunction(Ts, Gs, nb::Int, ::Type{P}) where {P <: APar}
AIBECSFunction(AIBECSFunction(Ts, Gs, nb), P)
end
function AIBECSFunction(fun::ODEFunction{false}, ::Type{P}) where {P <: APar}
jac(u, p::P, t=0) = fun.jac(u, p, t)
jac(u, λ::Vector, t=0) = fun.jac(u, λ2p(P, λ), t)
f(u, p::P, t=0) = fun.f(u, p, t)
f(u, λ::Vector, t=0) = fun.f(u, λ2p(P, λ), t)
return ODEFunction{false}(f, jac=jac)
end
function AIBECSFunction(fun::ODEFunction{true}, ::Type{P}) where {P <: APar}
jac(u, p::P, t=0) = fun.jac(u, p, t)
jac(u, λ::Vector, t=0) = fun.jac(u, λ2p(P, λ), t)
f(du, u, p::P, t=0) = fun.f(du, u, p, t)
f(du, u, λ::Vector, t=0) = fun.f(du, u, λ2p(P, λ), t)
return ODEFunction{true}(f, jac=jac)
end
export AIBECSFunction
"""
F, ∇ₓF = F_and_∇ₓF(Ts, Gs, nb)
Returns the state function `F` and its Jacobian, `∇ₓF`.
F, ∇ₓF = F_and_∇ₓF(T, Gs, nb)
Returns the state function `F` and its Jacobian, `∇ₓF` (with all tracers transported by single `T`).
This function is deprecated. Use
F = AIBECSFunction(Ts, Gs, nb)
instead.
You can then call `F(x,p)` for the tendencies, and `F(Val{:jac},x,p)` for the Jacobian.
"""
function F_and_∇ₓF(fun::ODEFunction)
Base.depwarn("""Deprecation:
F, ∇ₓF = F_and_∇ₓF(args...)
is deprecated. Use
F = AIBECSFunction(args...)
instead! (And then you can still call `F(x,p)`.)
""", :F_and_∇ₓF, force=true)
fun.f, fun.jac
end
F_and_∇ₓF(args...) = F_and_∇ₓF(AIBECSFunction(args...))
export F_and_∇ₓF
"""
localderivative(G, x, p)
localderivative(Gᵢ, xs, i, p)
localderivative(Gᵢ, dx, xs, i, p)
Returns the "local" derivative of `G` (or `Gᵢ`), i.e., equivalent to the vector
```
∇ₓG(x,p) * ones(size(x))
```
but using ForwardDiff's Jacobian instead.
"""
function localderivative(G, x, p) # for single tracer
return ForwardDiff.derivative(λ -> G(x .+ λ, p), 0.0)
end
function localderivative(Gᵢ, xs, j, p) # for multiple tracers
return ForwardDiff.derivative(λ -> Gᵢ(perturb_tracer(xs, j, λ)..., p), 0.0)
end
function localderivative(Gᵢ!, dx, xs, j, p) # if Gᵢ are in-place
return ForwardDiff.derivative((dx, λ) -> Gᵢ!(dx, perturb_tracer(xs, j, λ)..., p), dx, 0.0)
end
perturb_tracer(xs, j, λ) = (xs[1:j - 1]..., xs[j] .+ λ, xs[j + 1:end]...)
"""
F, L, NL, ∇ₓF, ∇ₓL, ∇ₓNL, T = split_state_function_and_Jacobian(Ts, Ls, NLs, nb)
Returns the state function `F` and its jacobian, `∇ₓF`, as well as a collection of split operators. This is experimental. Use at your own risk!
"""
function split_state_function_and_Jacobian(Ts::Tuple, Ls::Tuple, NLs::Tuple, nb)
nt = length(Ts)
tracers(x) = state_to_tracers(x, nb, nt)
T(p) = blockdiag([Tⱼ(p) for Tⱼ in Ts]...) # Big T (linear part)
NL(x, p) = reduce(vcat, NLⱼ(tracers(x)..., p) for NLⱼ in NLs) # nonlinear part
L(x, p) = reduce(vcat, Lⱼ(tracers(x)..., p) for Lⱼ in Ls) # nonlinear part
F(x, p) = NL(x, p) + L(x, p) - T(p) * x # full 𝐹(𝑥) = -T 𝑥 + 𝐺(𝑥)
∇ₓNL(x, p) = local_jacobian(NLs, x, p, nt, nb) # Jacobian of nonlinear part
∇ₓL(p) = local_jacobian(Ls, zeros(nt * nb), p, nt, nb) # Jacobian of nonlinear part
∇ₓF(x, p) = ∇ₓNL(x, p) + ∇ₓL(p) - T(p) # full Jacobian ∇ₓ𝐹(𝑥) = -T + ∇ₓ𝐺(𝑥)
return F, L, NL, ∇ₓF, ∇ₓL, ∇ₓNL, T
end
function split_state_function_and_Jacobian(T, L, NL, nb)
F(x, p) = NL(x, p) + L(x, p) - T(p) * x # full 𝐹(𝑥)
∇ₓNL(x, p) = sparse(Diagonal(localderivative(NL, x, p))) # Jacobian of nonlinear part
∇ₓL(p) = sparse(Diagonal(localderivative(L, zeros(nb), p))) # Jacobian of nonlinear part
∇ₓF(x, p) = ∇ₓNL(x, p) + ∇ₓL(p) - T(p) # full Jacobian ∇ₓ𝐹(𝑥) = -T + ∇ₓ𝐺(𝑥)
return F, L, NL, ∇ₓF, ∇ₓL, ∇ₓNL, T
end
export split_state_function_and_Jacobian
function local_jacobian(Gs, x, p, nt, nb)
return reduce(vcat, local_jacobian_row(Gⱼ, x, p, nt, nb) for Gⱼ in Gs)
end
function inplace_local_jacobian(Gs, x, p, nt, nb)
return reduce(vcat, inplace_local_jacobian_row(Gⱼ!, x, p, nt, nb) for Gⱼ! in Gs)
end
function local_jacobian_row(Gᵢ, x, p, nt, nb)
tracers(x) = state_to_tracers(x, nb, nt)
return reduce(hcat, sparse(Diagonal(localderivative(Gᵢ, tracers(x), j, p))) for j in 1:nt)
end
function inplace_local_jacobian_row(Gᵢ!, x, p, nt, nb)
tracers(x) = state_to_tracers(x, nb, nt)
dx = Vector{Float64}(undef, nb)
return reduce(hcat, sparse(Diagonal(localderivative(Gᵢ!, dx, tracers(x), j, p))) for j in 1:nt)
end
#= ============================================
Generate 𝑓 and derivatives from user input
============================================ =#
function generate_f(ωs, μx, σ²x, v, ωp, ::Type{T}) where {T <: APar}
nt, nb = length(ωs), length(v)
tracers(x) = state_to_tracers(x, nb, nt)
f(x, λorp) = ωp * mismatch(T, λorp) +
sum([ωⱼ * mismatch(xⱼ, μⱼ, σⱼ², v) for (ωⱼ, xⱼ, μⱼ, σⱼ²) in zip(ωs, tracers(x), μx, σ²x)])
return f
end
function generate_f(ωs, ωp, grd, obs, ::Type{T}; kwargs...) where {T <: APar}
nt, nb = length(ωs), count(iswet(grd))
tracers(x) = state_to_tracers(x, nb, nt)
Ms = [interpolationmatrix(grd, obsⱼ) for obsⱼ in obs]
cs = get(kwargs, :cs, (collect(identity for i in 1:nt)...,))
f(x, λorp) = ωp * mismatch(T, λorp) +
sum([ωⱼ * mismatch(xⱼ, grd, obsⱼ, M=Mⱼ, c=cⱼ) for (ωⱼ, xⱼ, obsⱼ, Mⱼ, cⱼ) in zip(ωs, tracers(x), obs, Ms, cs)])
return f
end
function generate_f(ωs, ωp, grd, modify::Function, obs, ::Type{T}) where {T <: APar}
nt, nb = length(ωs), count(iswet(grd))
Ms = [interpolationmatrix(grd, obsⱼ) for obsⱼ in obs]
iwets = [iswet(grd, obsⱼ) for obsⱼ in obs]
function f(x, λorp)
xs = unpack_tracers(x, grd)
return ωp * mismatch(T, λorp) + sum([ωᵢ * indirectmismatch(xs, grd, modify, obs, i, Mᵢ, iwetᵢ) for (i, (ωᵢ, Mᵢ, iwetᵢ)) in enumerate(zip(ωs, Ms, iwets))])
end
return f
end
function generate_∇ₓf(ωs, μx, σ²x, v)
nt, nb = length(ωs), length(v)
tracers(x) = state_to_tracers(x, nb, nt)
∇ₓf(x) = reduce(hcat, ωⱼ * ∇mismatch(xⱼ, μⱼ, σⱼ², v) for (ωⱼ, xⱼ, μⱼ, σⱼ²) in zip(ωs, tracers(x), μx, σ²x))
∇ₓf(x, p) = ∇ₓf(x)
return ∇ₓf
end
function generate_∇ₓf(ωs, grd, obs; kwargs...)
nt, nb = length(ωs), count(iswet(grd))
tracers(x) = state_to_tracers(x, nb, nt)
Ms = [interpolationmatrix(grd, obsⱼ) for obsⱼ in obs]
cs = get(kwargs, :cs, (collect(identity for i in 1:nt)...,))
∇ₓf(x) = reduce(hcat, ωⱼ * ∇mismatch(xⱼ, grd, obsⱼ, M=Mⱼ, c=cⱼ) for (ωⱼ, xⱼ, obsⱼ, Mⱼ, cⱼ) in zip(ωs, tracers(x), obs, Ms, cs))
∇ₓf(x, p) = ∇ₓf(x)
return ∇ₓf
end
function generate_∇ₓf(ωs, grd, modify::Function, obs)
nt, nb = length(ωs), count(iswet(grd))
Ms = [interpolationmatrix(grd, obsⱼ) for obsⱼ in obs]
iwets = [iswet(grd, obsⱼ) for obsⱼ in obs]
function ∇ₓf(x)
xs = unpack_tracers(x, grd)
sum([ωᵢ * ∇indirectmismatch(unpack_tracers(x, grd), grd, modify, obs, i, Mᵢ, iwetᵢ) for (i, (ωᵢ, Mᵢ, iwetᵢ)) in enumerate(zip(ωs, Ms, iwets))])
end
∇ₓf(x, p) = ∇ₓf(x)
return ∇ₓf
end
function f_and_∇ₓf(ωs, μx, σ²x, v, ωp, ::Type{T}) where {T <: APar}
generate_f(ωs, μx, σ²x, v, ωp, T), generate_∇ₓf(ωs, μx, σ²x, v)
end
function f_and_∇ₓf(ωs, ωp, grd, obs, ::Type{T}; kwargs...) where {T <: APar}
generate_f(ωs, ωp, grd, obs, T; kwargs...), generate_∇ₓf(ωs, grd, obs; kwargs...)
end
function f_and_∇ₓf(ωs, ωp, grd, modify::Function, obs, ::Type{T}) where {T <: APar}
generate_f(ωs, ωp, grd, modify, obs, T), generate_∇ₓf(ωs, grd, modify, obs)
end
export f_and_∇ₓf
"""
mismatch(x, xobs, σ²xobs, v)
Volume-weighted mismatch of modelled tracer `x` against observed mean, `xobs`, given observed variance, `σ²xobs`, and volumes `v`.
"""
function mismatch(x, xobs, σ²xobs, v)
δx = x - xobs
W = Diagonal(v ./ σ²xobs)
return 0.5 * transpose(δx) * W * δx / (transpose(xobs) * W * xobs)
end
mismatch(x, ::Missing, args...) = 0
"""
∇mismatch(x, xobs, σ²xobs, v)
Adjoint of the gradient of `mismatch(x, xobs, σ²xobs, v)`.
"""
function ∇mismatch(x, xobs, σ²xobs, v)
δx = x - xobs
W = Diagonal(v ./ σ²xobs)
return transpose(W * δx) / (transpose(xobs) * W * xobs)
end
∇mismatch(x, ::Missing, args...) = transpose(zeros(length(x)))
## new functions for more generic obs packages
# TODO Add an optional function argument to transform the data before computingn the mismatch
# Example if for isotope tracers X where one ususally wants to minimize the mismatch in δ or ε.
function mismatch(x, grd::OceanGrid, obs; c=identity, W=I, M=interpolationmatrix(grd, obs), iwet=iswet(grd, obs))
o = view(obs, iwet)
δx = M * c(x) - o
return 0.5 * transpose(δx) * W * δx / (transpose(o) * W * o)
end
mismatch(x, grd::OceanGrid, ::Missing; kwargs...) = 0
function ∇mismatch(x, grd::OceanGrid, obs; c=identity, W=I, M=interpolationmatrix(grd, obs), iwet=iswet(grd, obs))
∇c = Diagonal(ForwardDiff.derivative(λ -> c(x .+ λ), 0.0))
o = view(obs, iwet)
δx = M * c(x) - o
return transpose(W * δx) * M * ∇c / (transpose(o) * W * o)
end
∇mismatch(x, grd::OceanGrid, ::Missing; kwargs...) = transpose(zeros(length(x)))
# In case the mismatch is not based on the tracer but on some function of it
# TODO Add option for correlation matrix for Bayesian inferenece
function indirectmismatch(xs::Tuple, grd::OceanGrid, modify::Function, obs, i, M=interpolationmatrix(grd, obs[i]), iwet=iswet(grd, obs[i]))
x2 = modify(xs...)
out = 0.0
o = obs[i][iwet, :value]
δx = M * x2[i] - o
return 0.5 * transpose(δx) * δx / (transpose(o) * o)
end
function ∇indirectmismatch(xs::Tuple, grd::OceanGrid, modify::Function, obs, i, M=interpolationmatrix(grd, obs[i]), iwet=iswet(grd, obs[i]))
x2 = modify(xs...)
o = obs[i][iwet, :value]
δx = M * x2[i] - o
∇modᵢ = ∇modify(modify, xs, i)
return transpose(δx) * M * ∇modᵢ / (transpose(o) * o)
end
# TODO think of more efficient way to avoid recomputing ∇modify whole for each i
function ∇modify(modify, xs, i, j)
return sparse(Diagonal(ForwardDiff.derivative(λ -> modify(perturb_tracer(xs, j, λ)...)[i], 0.0)))
end
∇modify(modify, xs, i) = reduce(hcat, ∇modify(modify, xs, i, j) for j in 1:length(xs))
#= ============================================
multi-tracer norm
============================================ =#
function volumeweighted_norm(nt, v)
w = repeat(v, nt)
return nrm(x) = transpose(x) * Diagonal(w) * x
end
#= ============================================
unpacking of multi-tracers
============================================ =#
state_to_tracers(x, nb, nt) = ntuple(i -> state_to_tracer(x, nb, nt, i), nt)
state_to_tracer(x, nb, nt, i) = view(x, tracer_indices(nb, nt, i))
function state_to_tracers(x, grd)
nb = number_of_wet_boxes(grd)
nt = Int(round(length(x) / nb))
return state_to_tracers(x, nb, nt)
end
tracer_indices(nb, nt, i) = (i - 1) * nb + 1:i * nb
tracers_to_state(xs) = reduce(vcat, xs)
export state_to_tracers, state_to_tracer, tracers_to_state, tracer_indices
# Alias for better name
unpack_tracers = state_to_tracers
export unpack_tracers
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] | 1.962407 | 7,395 |
<gh_stars>1-10
"""
l1_adaptive_trend_filter{T<:Float64, S<:Int}(y::Vector{T}, components::Vector{S}; f::Vector{T} = Vector{T}(0), numλ::S=S(40), numγ::S=S(10), MAXITER::S=S(500), verbose::Bool=false,lower_bounds::Vector{T}=-10e+7*ones(T, TOTALCOMPONENTS), upper_bounds::Vector{T}=10e+7*ones(T, TOTALCOMPONENTS) )
The function `l1_adaptive_trend_filter` performs the filtering via coordinate descent and takes the following required inputs:
* y : Signal or time-series to be filtered; (Must be a vector of reals)
* components: List of integers corresponding to the types of components to be considered. (must be a vector of integers containing some of the following numbers: 1 = Step, 2 = Spike, 3 = Slope, 4 = Sine, 5 = Cossine )
Optional inputs:
* f: Vector of overcomplete frequencies (Default=ø);
* numλ: Size of the regularizer path for the parameter λ (Default=40);
* numγ: Size of the regularizer path for the parameter γ (Default=10);
* MAXITER: Maximum number of iterations (Default=500);
* verbose: Boolean flag for displaying progress of algorithm (Default=false);
* lower_bounds: List of lower bounds for each component type (Default=[-∞,-∞,-∞,-∞,-∞]);
* upper_bounds: List of upper bpunds for each component type (Default=[+∞,+∞,+∞,+∞,+∞]).
This function returns:
* β_path: Path of components coefficients;
* y_path: Path of filtered signals;
* β_best: Best components coefficients according to the EBIC criteria;
* y_best: Best filtered signal according to the EBIC criteria;
* λ_best: Best value for the λ regularizer according to the EBIC criteria;
* γ_best: Best value for the γ regularizer according to the EBIC criteria.
## Example
```
y = rand(18) # some inputs
components = [1,3] #(meaning we only consider step and slope components)
# run the algorithm
beta_path, y_path, beta_best, y_best, lambda_best, gamma_best = l1_adaptive_trend_filter(y,components)
```
"""
function l1_adaptive_trend_filter{T<:Float64, S<:Int}(
y::Vector{T}, components::Vector{S}; f::Vector{T} = Vector{T}(0), numλ::S=S(40), numγ::S=S(10), MAXITER::S=S(500), verbose::Bool=false,
lower_bounds::Vector{T}=-10e+7*ones(T, TOTALCOMPONENTS), upper_bounds::Vector{T}=10e+7*ones(T, TOTALCOMPONENTS) )
# subtracting the mean
y_mean = mean(y)
y = y - y_mean
# prepare check for dimension sizes
const N = length(y)
# iterator
IT = initIT_range(N, components, f, MAXITER=MAXITER)::iterator
# compute moments
const d = initData(IT, f, f)::dataCD
# inner products between response y and components
const xdy = initXDY(IT, y, d)
# build regularization path for λ (same path shape, different λs)
path, λ_max = compute_λ_path(IT, xdy, numλ, d)
# build regularization path for γ (unique path)
const Γ = compute_γ_path(IT, xdy, numγ, d)
# initialize weights
w = Vector{Float64}[]
for i in 1:TOTALCOMPONENTS
if in(i,IT.components)
push!(w, ones(IT.nelements[i]))
else
push!(w, ones(0))
end
end
# lasso pass
@fastmath β_path, y_path, β_best, y_best, λ_best, γ_best = coordinate_descent(
IT, d, xdy, path, λ_max, [1.0,] , y, lower_bounds, upper_bounds, w, verbose
)
# exclude the components the lasso has set to zero
update_components!(IT, w, β_best, d, xdy)
@fastmath β_path, y_path, β_best, y_best, λ_best, γ_best = coordinate_descent(
IT, d, xdy, path, λ_max, Γ, y, lower_bounds, upper_bounds, w, verbose
)
# adding back the mean
y_best = y_best + y_mean
if verbose
print(string(
"best regularization according to BIC was (λ=", round(λ_best, 3),
", γ=", round(γ_best, 3), ") \n"
))
end
return β_path, y_path, β_best, y_best, λ_best, γ_best
end
# coordinate descent algorithm for the regularization path (Λ x Γ)
function coordinate_descent(
IT::iterator, d::dataCD, xdy::Vector{Vector{Float64}}, path::Vector{Float64}, λ_max::Vector{Float64}, Γ::Vector{Float64},
y::Vector{Float64}, lower_bounds::Vector{Float64}, upper_bounds::Vector{Float64}, w::Vector{Vector{Float64}}, verbose::Bool; sparse::Int = 0
)
const N = IT.obs
# initializations
#if sparse == 1
# β_path, β_tilde, β, activeSet = initSparse(IT)
#else
β_path, β_tilde, β, activeSet = initDense(IT)
#end
# memory allocation
BIC = Inf::Float64
β_ols = 0.0::Float64
partial_fit = 0.0
β_best = 0.0
y_best = 0
λ_best = 0.0
γ_best = 0.0
path_iteration = 0
y_path = Vector{Float64}[]
#for i in 1:TOTALCOMPONENTS
# push!(w, zeros(IT.nelements[i]))
#end
# regularization path
@inbounds for γ in Γ
# clear warm-start
clear_warmstart!(β_tilde, IT)
@inbounds for path_iter in path
λ = path_iter * λ_max
change_flag = true
if verbose
path_iteration += 1
print(string(
"regularizers = (", round(λ, 3), ", ", round(γ, 3),
") path iteration = ", path_iteration, ".\n"
))
end
# loop until active set converges
@inbounds for iter in 1:IT.maxIter
if verbose
println(iter)
end
# convergence check
if !change_flag
break
end
change_flag = false
# cycle through every component
@inbounds for c1 in IT.components, j in IT.elements[c1]
# consider hoisting means and variaces, for better memoru use
# compute the partial fit with the components in the active set
partial_fit = 0.0
@inbounds for c2 in IT.components, l in IT.elements[c2]
@inbounds if activeSet[c2][l] && (c1, j) != (c2, l)
@inbounds partial_fit += GM2(c1, c2, j, l, d, N) * β_tilde[c2][l]
end
end
# univariate ordinary leasts squares coefficient
inner_prod_partial_residual = (xdy[c1][j] - partial_fit) / IT.obs
# weighted penalty
#w[c1][j] = 1.0 / (abs(β_ols)^γ)
# w[c1][j] = 1.0 / abs(inner_prod_partial_residual)
# # soft thresholding operator
# if abs(inner_prod_partial_residual) <= λ[c1] * w[c1][j]^γ
# if activeSet[c1][j]
# β_tilde[c1][j] = 0.0
# activeSet[c1][j] = false
# change_flag = true
# #println("$(c1) , $(j)")
# end
# else
# β_tilde[c1][j] = sign(inner_prod_partial_residual) * (abs(inner_prod_partial_residual) - λ[c1] * w[c1][j]^γ) / (d.σ[c1][j]^2)
# # projection onto the box constraints [lower_bound, upper_bound]
# β_tilde[c1][j] = max(β_tilde[c1][j], lower_bounds[c1])
# β_tilde[c1][j] = min(β_tilde[c1][j], upper_bounds[c1])
# if !activeSet[c1][j] #&& β_tilde[c1][j] != 0.0
# activeSet[c1][j] = true
# change_flag = true
# #println("$(c1) , $(j)")
# end
# end
change_flag = soft_treshold(j, inner_prod_partial_residual, λ[c1], γ, w[c1][j], activeSet[c1], β_tilde[c1], d.σ[c1][j], lower_bounds[c1], upper_bounds[c1])
end
end
push!(β_path, deepcopy(β_tilde))
# bayesian information criterion
BIC_new, y_hat = compute_BIC(y, β_tilde, activeSet, IT, d, xdy)
push!(y_path, copy(y_hat))
if verbose
print(string(" BIC = ", BIC_new))
end
# save the best fit so far
if BIC_new < BIC
BIC = BIC_new
y_best = copy(y_hat)
β_best = deepcopy(β_tilde)
λ_best = λ
γ_best = γ
end
end
end
return β_path, y_path, β_best, y_best, λ_best, γ_best
end
function clear_warmstart!(β_tilde::Vector{Vector{Float64}}, IT::iterator)
# clear warm-start
for i in IT.components
for j in IT.elements[i]
β_tilde[i][j] = 0.0
end
end
nothing
end
function soft_treshold(j::Int, residual::Float64, λ::Float64, γ::Float64, w::Float64, activeSet::Vector{Bool}, β_tilde::Vector{Float64},
var::Float64, lb::Float64, ub::Float64 )::Bool
if abs(residual) <= λ * w^γ
if activeSet[j]
β_tilde[j] = 0.0
activeSet[j] = false
return true
#println("$(c1) , $(j)")
else
return false
end
else
β_tilde[j] = sign(residual) * (abs(residual) - λ * w^γ) / (var^2)
# projection onto the box constraints [lower_bound, upper_bound]
β_tilde[j] = max(β_tilde[j], lb)
β_tilde[j] = min(β_tilde[j], ub)
if !activeSet[j] #&& β_tilde[c1][j] != 0.0
activeSet[j] = true
return true
#println("$(c1) , $(j)")
else
return false
end
end
return false
end
function update_components!(IT, w, β, d, xdy)
# clear given iterator
for c in IT.components
IT.elements[c] = Int[]
end
for c in IT.components
for j in 1:IT.nelements[c]
if β[c][j] != 0.0
# pre-compute weight
w[c][j] = abs(1.0 / β[c][j]) #/ (d.σ[c][j]^2)
β[c][j] = 0.0
# update iterator only with nonzero elements
push!(IT.elements[c], j)
end
end
end
return nothing
end
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] | 2.108545 | 4,459 |
# Surrogate functions
hinge(x, ϑ::Real = 1) = max(zero(x), 1 + ϑ * x)
quadratic(x, ϑ::Real = 1) = max(zero(x), 1 + ϑ * x)^2
# Objective functions
struct FNRate <: Objective end
Base.show(io::IO, ::FNRate) = print(io, "false-negative rate")
function objective(::FNRate, y, s::AbstractArray{T}, t, surrogate) where {T<:Real}
inds = zero(T) .+ (y .== 1)
return sum(surrogate.(t .- s) .* inds; dims = 2) ./ sum(inds; dims = 2)
end
struct FPRate <: Objective end
Base.show(io::IO, ::FPRate) = print(io, "false-positive rate")
function objective(::FPRate, y, s::AbstractArray{T}, t, surrogate) where {T<:Real}
inds = zero(T) .+ (y .== 1)
return sum(surrogate.(s .- t) .* inds; dims = 2) ./ sum(inds; dims = 2)
end
struct FNFPRate <: Objective
α::Real
end
function Base.show(io::IO, o::FNFPRate)
return print(io, "$(o.α)⋅false-negative + $(1-o.α)⋅false-positive rate")
end
function objective(o::FNFPRate, y, s, t, surrogate)
return o.α .* objective(FNRate(), y, s, t, surrogate) .+
(1 - o.α) .* objective(FPRate(), y, s, t, surrogate)
end
# Accuracy at Top formulation
struct AccAtTop
threshold_type::Threshold
objective_type::Objective
end
function Base.show(io::IO, m::AccAtTop)
println(io, "Accuracy at the top:")
println(io, " - threshold: $(m.threshold_type)")
print(io, " - objective function: $(m.objective_type)")
return
end
function objective(
m::AccAtTop,
y::AbstractMatrix,
s::AbstractMatrix;
surrogate = hinge,
agg = mean,
update_buffer = true,
)
if size(y) != size(s)
throw(DimensionMismatch("dimensions must match: y has dims $(size(y)), s has dims $(size(s))"))
end
ts = threshold(m.threshold_type, y, s; update_buffer)
return agg(objective(m.objective_type, y, s, ts, surrogate))
end
function predict(
m::AccAtTop,
y::AbstractMatrix,
s::AbstractMatrix;
ts = threshold(m.threshold_type, y, s; update_buffer = false),
)
if size(y) != size(s)
throw(DimensionMismatch("dimensions must match: y has dims $(size(y)), s has dims $(size(s))"))
end
if length(ts) != size(s, 1)
throw(DimensionMismatch("length of thresholds $(length(ts)) does not match first dim of scores $(size(s, 1))"))
end
return s .>= ts
end
# Basic models
DeepTopPush() = AccAtTop(Maximum(Neg), FNRate())
DeepTopPushK(K) = AccAtTop(Kth(K, Neg), FNRate())
PatMat(τ::Real) = AccAtTop(Quantile(τ, All; rev = true), FNRate())
PatMat(sampler) = AccAtTop(SampledQuantile(sampler, All; rev = true), FNRate())
PatMatNP(τ::Real) = AccAtTop(Quantile(τ, Neg; rev = true), FNRate())
PatMatNP(sampler) = AccAtTop(SampledQuantile(sampler, Neg; rev = true), FNRate())
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] | 2.422182 | 1,118 |
<reponame>angus-lewis/DFQExamples.jl
include((@__DIR__)*"/../preamble.jl")
include("reflecting_model/model_def.jl")
include("default_params.jl")
# look at time to exit of the interval [0,1]
# use the bounded fluid queue, but change the boundary to be at 1
model = BoundedFluidQueue(
model.T[1:2,1:2],rates(model)[1:2],[0.0 1.0],[1.0 0.0],1.0,
)
dg_model(order) = build_discretised_model(DGMesh{t},model,(1.0/3)/((order+1)/2),2)
# order1_model(order) = build_discretised_model(DGMesh{t},model,1.0/3/order,1)
# qbdrap_model(order) = build_discretised_model(FRAPMesh{t},model,1.0/3,order)
d0_map_point_mass(dq) = (interior_point_mass(eps(),1,dq).coeffs)
d0_map_exp(dq) = (SFMDistribution((x,i)->(i∈(1:2))*exp(-x)/(1-exp(-model.b))/2,dq).coeffs)
# change d0_map here to do the make_approximations for ohter initial confitions
# remember to change write directory below too
models = make_approximations(orders,approx_types,d0_map_exp,(args...)->args[1])
for k1 in keys(models)
for k2 in keys(models[k1])
models[k1][k2]["B"][1,:] = spzeros(1,size(models[k1][k2]["B"],2))
models[k1][k2]["B"][end,:] = spzeros(1,size(models[k1][k2]["B"],2))
droptol!(models[k1][k2]["B"],eps()*1000)
end
end
pth = mkpath((@__DIR__)*"/hitting_times/data/exp")
h = 0.005/3
t_vec = 0.0:h:10.0
hitting_times_cdf = zeros(length(t_vec),3)
hitting_times_cdf[:,1] = t_vec
for k1 in keys(models)
for k2 in keys(models[k1])
for (c,t) in enumerate(t_vec)
models[k1][k2]["coeffs_mapped"] =
DiscretisedFluidQueues._integrate(
models[k1][k2]["coeffs_mapped"],
models[k1][k2]["B"],
h,
StableRK4(h),
x->GeneralisedMUSCL.fun(x,GeneralisedMUSCL.generate_params(models[k1][k2]["dq"])...),
)
hitting_times_cdf[c,2:3] = models[k1][k2]["coeffs_mapped"][[1;end]]
end
CSV.write(
pth*"/order_"*string(k1)*"_model_"*string(k2)*".csv",
DataFrame(
t=hitting_times_cdf[:,1],
phase_2=hitting_times_cdf[:,2],
phase_1=hitting_times_cdf[:,3],
)
)
end
end
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] | 1.88955 | 1,177 |
<reponame>eviatarbach/ensemble_oscillation_correction
using Serialization
using Random
Random.seed!(1234)
include("models.jl")
include("integrators.jl")
include("enoc.jl")
using .Models
using .Integrators
using .enoc
M = 60
D = 3
k = 40
k_r = 30
window = 20
da = false
inflation = 1.0
osc_vars = 1:D
modes = 3:4
model = Models.chua_true
model_err = Models.chua_err
integrator = Integrators.rk4
outfreq = 1
Δt = 0.1
m = 20
cycles = 1000
record_length = 25000
ens_err_pct = 0.2
obs_err_pct = 0.1
transient = 3000
mp = 5
y0 = [-1.06095, 0.160678, 0.267729]
varimax = true
check_bounds = true
test_time = 10.0
y_fcst = true
α = 0.5
if !da
info, ssa_info = enoc.run(model=model, model_err=model_err, M=M, D=D, k=k,
k_r=k_r, modes=modes, osc_vars=osc_vars,
integrator=integrator, outfreq=outfreq, Δt=Δt,
m=m, cycles=1000, window=window,
record_length=record_length,
ens_err_pct=ens_err_pct, obs_err_pct=obs_err_pct,
transient=transient, y0=y0, mp=mp,
varimax=varimax, da=false, inflation=inflation,
check_bounds=check_bounds, y_fcst=y_fcst, α=α,
preload="chua")
mp = argmin(enoc.optimal_ens(info)[1])[1]
end
Random.seed!(1234)
info, ssa_info = enoc.run(model=model, model_err=model_err, M=M, D=D, k=k,
k_r=k_r, modes=modes, osc_vars=osc_vars,
integrator=integrator, outfreq=outfreq, Δt=Δt, m=m,
cycles=cycles, window=window,
record_length=record_length, ens_err_pct=ens_err_pct,
obs_err_pct=obs_err_pct, transient=transient, y0=y0,
mp=mp, varimax=varimax, da=da, inflation=inflation,
check_bounds=check_bounds, y_fcst=y_fcst, α=α,
preload="chua")
serialize(open(string("out_chua_", ARGS[1]), "w"), info)
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] | 1.739203 | 1,204 |
<filename>examples/lru_test.jl
# This file is a part of Julia. License is MIT: https://julialang.org/license
using .LRUExample
TestLRU = LRUExample.UnboundedLRU{String, String}()
TestBLRU = LRUExample.BoundedLRU{String, String}(1000)
get_str(i) = String(vcat(map(x->[x>>4; x&0x0F], reinterpret(UInt8, [Int32(i)]))...))
isbounded{L<:LRUExample.LRU}(::Type{L}) = any(map(n->n==:maxsize, fieldnames(L)))
isbounded{L<:LRUExample.LRU}(l::L) = isbounded(L)
nmax = round.(Int, logspace(2, 5, 4))
function lrutest()
#println("LRU consistency tests")
for lru in (TestLRU,TestBLRU)
for n in nmax
empty!(lru)
#@printf(" %s, %d items\n", lru, n)
#print(" Simple eviction: ")
for i in 1:n
str = get_str(i)
lru[str] = str
@assert lru.q[1].v == str
if isbounded(lru) && length(lru) >= lru.maxsize
tailstr = get_str(i-lru.maxsize+1)
@assert lru.q[end].v == tailstr
end
end
#println("pass")
#print(" Lookup, random access: ")
for i in 1:n
str = get_str(rand(1:n))
if haskey(lru, str) # the bounded LRUs can have cache misses
blah = lru[str]
@assert lru.q[1].v == blah
end
end
#println("pass")
end
empty!(lru)
end
end
lrutest()
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886,
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437,
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315,
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3419,
198
] | 1.783234 | 835 |
"""
ANLI
For details, see the [GitHub page](https://github.com/facebookresearch/anli) or read the [2019 paper](https://arxiv.org/pdf/1910.14599.pdf).
Available data:
```julia
ANLI.R1_train_jsonl()
ANLI.R1_dev_jsonl()
ANLI.R1_test_jsonl()
ANLI.R2_train_jsonl()
ANLI.R2_dev_jsonl()
ANLI.R2_test_jsonl()
ANLI.R3_train_jsonl()
ANLI.R3_dev_jsonl()
ANLI.R3_test_jsonl()
```
"""
module ANLI
import ...register_data
using DataDeps: @datadep_str, unpack
anli_file(file, files...) = joinpath(datadep"ANLI", "anli_v0.1", file, files...)
R1_train_jsonl() = anli_file("R1", "train.jsonl")
R1_dev_jsonl() = anli_file("R1", "dev.jsonl")
R1_test_jsonl() = anli_file("R1", "test.jsonl")
R2_train_jsonl() = anli_file("R2", "train.jsonl")
R2_dev_jsonl() = anli_file("R2", "dev.jsonl")
R2_test_jsonl() = anli_file("R2", "test.jsonl")
R3_train_jsonl() = anli_file("R3", "train.jsonl")
R3_dev_jsonl() = anli_file("R3", "dev.jsonl")
R3_test_jsonl() = anli_file("R3", "test.jsonl")
function __init__()
register_data(
"ANLI",
"""
ANLI is the Adversarial Natural Language Inference Benchmark.
ANLI is licensed under Creative Commons-Non Commercial 4.0.
""",
"https://dl.fbaipublicfiles.com/anli/anli_v0.1.zip",
"16ac929a7e90ecf9093deaec89cc81fe86a379265a5320a150028efe50c5cde8",
postfetch=unpack
)
end
end # module
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] | 2.081325 | 664 |
<reponame>Moelf/Hands-on-Design-Patterns-and-Best-Practices-with-Julia<filename>Chapter02/union_types.jl
# Union type example
using Dates: Date
# abstract types from previous section
abstract type Asset end
abstract type Property <: Asset end
abstract type Investment <: Asset end
abstract type Cash <: Asset end
abstract type House <: Property end
abstract type Apartment <: Property end
abstract type FixedIncome <: Investment end
abstract type Equity <: Investment end
# concrete types
struct Stock <: Equity
symbol::String
name::String
end
# new hierarchy
abstract type Art end
struct Painting <: Art
artist::String
title::String
end
# union type
struct BasketOfThings
things::Vector{Union{Painting,Stock}}
reason::String
end
#= REPL
julia> stock = Stock("AAPL", "Apple, Inc.",)
Stock("AAPL", "Apple, Inc.")
julia> monalisa = Painting("<NAME>", "Monalisa")
Painting("<NAME>", "Monalisa")
julia> things = Union{Painting,Stock}[stock, monalisa]
2-element Array{Union{Painting, Stock},1}:
Stock("AAPL", "Apple, Inc.")
Painting("<NAME>", "Monalisa")
julia> present = BasketOfThings(things, "Anniversary gift for my wife")
BasketOfThings(Union{Painting, Stock}[Stock("AAPL", "Apple, Inc."), Painting("<NAME>", "Monalisa")], "Anniversary gift for my wife")
=#
# easier to read :-)
const Thing = Union{Painting,Stock}
struct BasketOfThings
thing::Vector{Thing}
reason::String
end
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198,
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220,
1738,
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10100,
198,
437,
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] | 2.9389 | 491 |
<gh_stars>100-1000
let λ = Partition([5,4,2,2]), μ = Partition([2,1])
@test λ \ μ == SkewDiagram(λ, μ)
@test leglength(λ, μ) == leglength(λ \ μ) == 3
end
@test leglength(Partition(Int[]), Partition(Int[])) == -1
@test isrimhook(Partition([4,3,2]), Partition([2,2,2]))
@test !isrimhook(Partition([4,3,2]), Partition([2,2,1]))
@test !isrimhook(Partition([4,3,2]), Partition([1,1]))
let λ = Partition([5,4,2,1]), μ = Partition([4,3,2,2,1])
@test partitionsequence(λ) == [1, 0, 1, 0, 1, 1, 0, 1, 0]
@test character(λ, μ) == 0
end
let λ = μ = Partition([1])
@test leglength(λ, μ) == -1
@test character(λ, μ) == 1
end
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] | 2.108911 | 303 |
import PipeLayout: sorted_edges, min_spanning_tree, topology_from_mst, digraph_from_topology
using PipeLayout
using LightGraphs
@testset "compute MST from nodes" begin
coords = [0 1 0; 0 0 2]
nodes = Node[Node(coords[:,j]...) for j in 1:size(coords, 2)]
n = length(nodes)
@test n == 3
@testset "test sorted edges" begin
edges = sorted_edges(nodes)
m = n*(n-1)/2
@test m == 3
@test size(edges, 1) == 2
@test size(edges, 2) == m
@test edges[:, 1] == Node[nodes[1], nodes[2]]
@test edges[:, 2] == Node[nodes[1], nodes[3]]
@test edges[:, 3] == Node[nodes[2], nodes[3]]
end
@testset "look at resulting MST" begin
n = length(nodes)
m = n - 1 # tree
tree = nodes |> topology_from_mst |> digraph_from_topology
@test isa(tree, DiGraph)
@test is_directed(tree)
@test nv(tree) == n
@test ne(tree) == m
@test is_weakly_connected(tree)
@test has_edge(tree, 1, 2)
@test has_edge(tree, 1, 3)
end
end
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] | 2.076023 | 513 |
<gh_stars>0
# included from Potentials.jl
# part of the module JuLIP.Potentials
using JuLIP: JVec, JMat, neighbourlist
using LinearAlgebra: I
using JuLIP.Chemistry: atomic_number
using NeighbourLists
export ZeroPairPotential, PairSitePotential, ZBLPotential,
LennardJones, lennardjones,
Morse, morse
## TODO: kill this one?
grad(V::PairPotential, r::Real, R::JVec) = ((@D V(r)) / r) * R
evaluate!(tmp, V::PairPotential, r::Union{Number, JVec}) = V(r)
evaluate_d!(tmp, V::PairPotential, r::Union{Number, JVec}) = @D V(r)
evaluate_dd!(tmp, V::PairPotential, r::Union{Number, JVec}) = @DD V(r)
function evaluate!(tmp, V::PairPotential, R::AbstractVector{JVec{T}}) where {T}
Es = zero(T)
for i = 1:length(R)
Es += T(0.5) * evaluate!(tmp, V, norm(R[i]))
end
return Es
end
function evaluate_d!(dEs, tmp, V::PairPotential, R::AbstractVector{JVec{T}}) where {T}
for i = 1:length(R)
r = norm(R[i])
dEs[i] = (T(0.5) * evaluate_d!(tmp, V, r) / r) * R[i]
end
return dEs
end
function evaluate_dd!(hEs, tmp, V::PairPotential, R::AbstractVector{<: JVec})
n = length(R)
for i = 1:n
hEs[i,i] = 0.5 * _hess!(tmp, V, norm(R[i]), R[i])
end
return hEs
end
function _hess!(tmp, V::PairPotential, r::Number, R::JVec)
R̂ = R/r
P = R̂ * R̂'
dV = evaluate_d!(tmp, V, r) / r
ddV = evaluate_dd!(tmp, V, r)
return (ddV - dV) * P + dV * I
end
function precon!(hEs, tmp, V::PairPotential, R::AbstractVector{<: JVec}, innerstab=T(0.0))
n = length(R)
for i = 1:n
hEs[i,i] = precon!(tmp, V, norm(R[i]), R[i], innerstab)
end
return hEs
end
# an FF preconditioner for pair potentials
function precon!(tmp, V::PairPotential, r::T, R::JVec{T}, innerstab=T(0.1)
) where {T <: Number}
r = norm(R)
dV = evaluate_d!(tmp, V, r)
ddV = evaluate_dd!(tmp, V, r)
R̂ = R/r
return (1-innerstab) * (abs(ddV) * R̂ * R̂' + abs(dV / r) * (I - R̂ * R̂')) +
innerstab * (abs(ddV) + abs(dV / r)) * I
end
"""
`LennardJones(σ, e0):` constructs the 6-12 Lennard-Jones potential [wiki](https://en.wikipedia.org/wiki/Lennard-Jones_potential)
e0 * 4 * ( (σ/r)¹² - (σ/r)⁶ )
Constructor with kw arguments: `LennardJones(; kwargs...)`
* `e0, σ` : standard LJ parameters, default `e0 = 1.0, σ = 1.0`
* `r0` : equilibrium distance - if `r0` is specified then `σ` is ignored
* `a0` : FCC lattice parameter, if `a0` is specified then `σ` is ignored
(`r0, a0` cannot both be specified at the same time)
"""
LennardJones(σ, e0) = (@analytic r -> e0 * 4.0 * ((σ/r)^(12) - (σ/r)^(6)))
function ljparams(; σ=1.0, e0=1.0, r0 = nothing, a0 = nothing)
if r0 != nothing && a0 != nothing
error("`LenndardJones`: cannot specify both `r0` and `a0`")
end
if a0 != nothing # r0 = nn-dist = a0/sqrt(2) in FCC
r0 = a0 / sqrt(2)
end
if r0 != nothing # standard LJ is minimised at r = 2^(1/6)
σ = r0 / 2^(1/6)
end
return σ, e0
end
LennardJones(; kwargs...) = LennardJones(ljparams(;kwargs...)...)
"""
`lennardjones(; kwargs...)`
simplified constructor for `LennardJones` (note this is type unstable!)
In addition to the `kwargs` of `LennardJones`, this accepts also
* `rcut` : default `:auto` which gives `rcut = (1.9*σ, 2.7*σ)`. Use
`nothing` or `Inf` to specify no cutoff, or specify a tuple or
array with two elements specifying the lower and upper cut-off radii to be
used with `SplineCutoff`.
"""
function lennardjones(; rcut = :auto, kwargs...)
σ, e0 = ljparams(; kwargs...)
if (rcut == nothing || rcut == Inf)
return LennardJones(σ, e0)
elseif rcut == :auto
rcut = (1.9*σ, 2.7*σ)
end
return SplineCutoff(rcut[1], rcut[2]) * LennardJones(σ, e0)
end
"""
`Morse(A, e0, r0)` or `Morse(;A=4.0, e0=1.0, r0=1.0)`: constructs a
`PairPotential` for
```
e0 ( exp( -2 A (r/r0 - 1) ) - 2 exp( - A (r/r0 - 1) ) )
```
"""
Morse(A, e0, r0) = @analytic(
r -> e0 * ( exp(-(2.0*A) * (r/r0 - 1.0)) - 2.0 * exp(-A * (r/r0 - 1.0)) ) )
Morse(;A=4.0, e0=1.0, r0=1.0) = Morse(A, e0, r0)
"""
`morse(A=4.0, e0=1.0, r0=1.0, rcut=(1.9*r0, 2.7*r0))`
simplified constructor for `Morse` (type unstable)
"""
morse(;A=4.0, e0=1.0, r0=1.0, rcut=(1.9*r0, 2.7*r0)) = (
(rcut == nothing || rcut == Inf)
? Morse(A, e0, r0)
: SplineCutoff(rcut[1], rcut[2]) * Morse(A, e0, r0) )
"""
`ZeroPairPotential()`: creates a potential that just returns zero
"""
struct ZeroPairPotential <: PairPotential end
@pot ZeroPairPotential
evaluate(p::ZeroPairPotential, r::T) where {T <: Number} = T(0.0)
evaluate_d(p::ZeroPairPotential, r::T) where {T <: Number} = T(0.0)
evaluate_dd(p::ZeroPairPotential, r::T) where {T <: Number} = T(0.0)
cutoff(p::ZeroPairPotential) = Bool(0) # the weakest number type
# ------------------------------------------------------------------------
# TODO: write more docs + tests for ZBL
"""
Implementation of the ZBL potential to model close approach.
"""
struct ZBLPotential{TV} <: PairPotential
Z1::Int
Z2::Int
V::TV # analytic
end
@pot ZBLPotential
evaluate(V::ZBLPotential, r::Number) = evaluate(V.V, r::Number)
evaluate_d(V::ZBLPotential, r::Number) = evaluate_d(V.V, r::Number)
cutoff(::ZBLPotential) = Inf
ZBLPotential(Z1::Integer, Z2::Integer) =
let Z1=Z1, Z2=Z2
au = 0.8854 * 0.529 / (Z1^0.23 + Z2^0.23)
ϵ0 = 0.00552634940621
C = Z1*Z2/(4*π*ϵ0)
E1, E2, E3, E4 = 0.1818, 0.5099, 0.2802, 0.02817
A1, A2, A3, A4 = 3.2/au, 0.9423/au, 0.4028/au, 0.2016/au
V = @analytic(r -> C * (E1*exp(-A1*r) + E2*exp(-A2*r) +
E3*exp(-A4*r) + E4*exp(-A4*r) ) / r)
ZBLPotential(Z1, Z2, V)
end
ZBLPotential(Z::Integer) = ZBLPotential(Z, Z)
ZBLPotential(s1::Symbol, s2::Symbol) = ZBLPotential(atomic_number(s1), atomic_number(s2))
ZBLPotential(s::Symbol) = ZBLPotential(s, s)
Dict(V::ZBLPotential) = Dict("__id__" => "JuLIP_ZBLPotential",
"Z1" => V.Z1,
"Z2" => Z.Z2)
ZBLPotential(D::Dict) = ZBLPotential(D["Z1"], D["Z2"])
Base.convert(::Val{:JuLIP_ZBLPotential}, D::Dict) = ZBLPotential(D)
# ====================================================================
# A product of two pair potentials: primarily used for cutoff mechanisms
"product of two pair potentials"
mutable struct ProdPot{P1, P2} <: PairPotential
p1::P1
p2::P2
end
@pot ProdPot
import Base.*
*(p1::PairPotential, p2::PairPotential) = ProdPot(p1, p2)
@inline evaluate(p::ProdPot, r::Number) = p.p1(r) * p.p2(r)
evaluate_d(p::ProdPot, r::Number) = (p.p1(r) * (@D p.p2(r)) + (@D p.p1(r)) * p.p2(r))
evaluate_dd(p::ProdPot, r::Number) = (p.p1(r) * (@DD p.p2(r)) +
2 * (@D p.p1(r)) * (@D p.p2(r)) + (@DD p.p1(r)) * p.p2(r))
cutoff(p::ProdPot) = min(cutoff(p.p1), cutoff(p.p2))
# ====================================================================
"""
`struct WrappedPairPotential`
wraps a pairpotential using `FunctionWrappers` in order to allow
type-stable storage of multiple potentials. This is the main technique
required at the moment to work with multi-component systems.
Otherwise, this is not advisable since it disables a range of
possible compiler optimisations.
"""
struct WrappedPairPotential <: SimplePairPotential
f::F64fun
f_d::F64fun
f_dd::F64fun
rcut::Float64
end
@pot WrappedPairPotential
cutoff(V::WrappedPairPotential) = V.rcut
# evaluate, etc are all derived from SimplePairPotential
function WrappedPairPotential(V::AnalyticFunction, rcut)
@assert (0 < rcut < Inf)
f, f_d, f_dd = let V=V, rc = rcut
(F64fun(r -> evaluate(V, r) * (r<rc)),
F64fun(r -> evaluate_d(V, r) * (r<rc)),
F64fun(r -> evaluate_dd(V, r) * (r<rc)))
end
return WrappedPairPotential(f, f_d, f_dd, cutoff(V))
end
function WrappedPairPotential(V::PairPotential)
@assert (0 < cutoff(V) < Inf)
f, f_d, f_dd = let V=V, rc = cutoff(V)
(F64fun(r -> evaluate(V, r) * (r<rc)),
F64fun(r -> evaluate_d(V, r) * (r<rc)),
F64fun(r -> evaluate_dd(V, r) * (r<rc)))
end
return WrappedPairPotential(f, f_d, f_dd, cutoff(V))
end
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] | 2.144702 | 3,794 |
<filename>src/fda.jl
struct Fda
T::Array{Float64}
P::Array{Float64}
Tcenters::Array{Float64}
eig::Vector{Float64}
sstot::Number
W::Matrix{Float64}
xmeans::Vector{Float64}
lev::AbstractVector
ni::AbstractVector
end
"""
fda(X, y; nlv, pseudo = false)
Factorial discriminant analysis (FDA).
* `X` : X-data.
* `y` : y-data (class membership).
* `nlv` : Nb. discriminant components.
* `pseudo` : If true, a MP pseudo-inverse is used (instead
of a usual inverse) for inverting W.
Eigen factorization of Inverse(W) * B.
The functions maximize the compromise p'Bp / p'Wp, i.e. max p'Bp with
constraint p'Wp = 1. Vectors p (columns of P) are the linear discrimant
coefficients "LD".
`X` is internally centered.
"""
function fda(X, y; nlv, pseudo = false)
fda!(copy(X), y; nlv = nlv, pseudo = pseudo)
end
function fda!(X, y; nlv, pseudo = false)
X = ensure_mat(X)
n, p = size(X)
xmeans = colmeans(X)
center!(X, xmeans)
res = matW(X, y)
lev = res.lev
nlev = length(lev)
ni = res.ni
res.W .= res.W * n / (n - nlev)
zres = matB(X, y)
!pseudo ? Winv = LinearAlgebra.inv!(cholesky!(Hermitian(res.W))) : Winv = pinv(res.W)
# Winv * B is not symmetric
fm = eigen!(Winv * zres.B; sortby = x -> -abs(x))
nlv = min(nlv, n, p, nlev - 1)
P = fm.vectors[:, 1:nlv]
eig = fm.values
P = real.(P)
eig = real.(eig)
sstot = sum(eig)
norm_P = sqrt.(diag(P' * res.W * P))
scale!(P, norm_P)
T = X * P
Tcenters = zres.ct * P
Fda(T, P, Tcenters, eig, sstot, res.W, xmeans, lev, ni)
end
"""
fdasvd(X, y; nlv, pseudo = false)
Factorial discriminant analysis (FDA).
* `X` : X-data.
* `y` : Univariate class membership.
* `nlv` : Nb. discriminant components.
* `pseudo` : If true, a MP pseudo-inverse is used (instead
of a usual inverse) for inverting W.
Weighted SVD factorization of the matrix of the class centers.
`X` is internally centered.
"""
function fdasvd(X, y; nlv, pseudo = false)
fdasvd!(copy(X), y; nlv = nlv, pseudo = pseudo)
end
function fdasvd!(X, y; nlv, pseudo = false)
X = ensure_mat(X)
n, p = size(X)
xmeans = colmeans(X)
center!(X, xmeans)
res = matW(X, y)
lev = res.lev
nlev = length(lev)
ni = res.ni
res.W .= res.W * n / (n - nlev)
!pseudo ? Winv = inv(res.W) : Winv = pinv(res.W)
ct = aggstat(X, y; fun = mean).res
Ut = cholesky!(Hermitian(Winv)).U'
Zct = ct * Ut
nlv = min(nlv, n, p, nlev - 1)
fm = pcasvd(Zct, ni; nlv = nlv)
Pz = fm.P
Tcenters = Zct * Pz
eig = (fm.sv).^2
sstot = sum(eig)
P = Ut * Pz[:, 1:nlv]
T = X * P
Tcenters = ct * P
Fda(T, P, Tcenters, eig, sstot, res.W, xmeans, lev, ni)
end
"""
summary(object::Fda, X)
Summarize the fitted model.
* `object` : The fitted model.
* `X` : The X-data that was used to fit the model.
"""
function Base.summary(object::Fda)
nlv = size(object.T, 2)
eig = object.eig[1:nlv]
pvar = eig ./ sum(object.eig)
cumpvar = cumsum(pvar)
explvar = DataFrame(lv = 1:nlv, var = eig, pvar = pvar,
cumpvar = cumpvar)
(explvar = explvar,)
end
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] | 2.097962 | 1,521 |
<gh_stars>0
using Test
using Manifolds
using Manifolds:
_derivative,
_derivative!,
differential,
differential!,
gradient,
gradient!,
_gradient,
_gradient!
using FiniteDifferences
using LinearAlgebra: Diagonal, dot
@testset "Differentiation backend" begin
fd51 = Manifolds.FiniteDifferencesBackend()
@testset "diff_backend" begin
@test diff_backend() isa Manifolds.FiniteDifferencesBackend
@test length(diff_backends()) == 1
@test diff_backends()[1] isa Manifolds.FiniteDifferencesBackend
@test length(fd51.method.grid) == 5
@test fd51.method.q == 1
fd71 = Manifolds.FiniteDifferencesBackend(central_fdm(7, 1))
@test diff_backend!(fd71) == fd71
@test diff_backend() == fd71
end
using ForwardDiff
fwd_diff = Manifolds.ForwardDiffBackend()
@testset "ForwardDiff" begin
@test diff_backend() isa Manifolds.FiniteDifferencesBackend
@test length(diff_backends()) == 2
@test diff_backends()[1] isa Manifolds.FiniteDifferencesBackend
@test diff_backends()[2] == fwd_diff
@test diff_backend!(fwd_diff) == fwd_diff
@test diff_backend() == fwd_diff
@test diff_backend!(fd51) isa Manifolds.FiniteDifferencesBackend
@test diff_backend() isa Manifolds.FiniteDifferencesBackend
diff_backend!(fwd_diff)
@test diff_backend() == fwd_diff
diff_backend!(fd51)
end
using FiniteDiff
finite_diff = Manifolds.FiniteDiffBackend()
@testset "FiniteDiff" begin
@test diff_backend() isa Manifolds.FiniteDifferencesBackend
@test length(diff_backends()) == 3
@test diff_backends()[3] == finite_diff
@test diff_backend!(finite_diff) == finite_diff
@test diff_backend() == finite_diff
@test diff_backend!(fd51) isa Manifolds.FiniteDifferencesBackend
@test diff_backend() isa Manifolds.FiniteDifferencesBackend
diff_backend!(finite_diff)
@test diff_backend() == finite_diff
diff_backend!(fd51)
end
@testset "gradient" begin
diff_backend!(fd51)
r2 = Euclidean(2)
c1(t) = [sin(t), cos(t)]
f1(x) = x[1] + x[2]^2
f2(x) = 3 * x[1] * x[2] + x[2]^3
@testset "Inference" begin
v = [-1.0, -1.0]
@test (@inferred _derivative(c1, 0.0, Manifolds.ForwardDiffBackend())) ≈
[1.0, 0.0]
@test (@inferred _derivative!(c1, v, 0.0, Manifolds.ForwardDiffBackend())) === v
@test v ≈ [1.0, 0.0]
@test (@inferred _derivative(c1, 0.0, finite_diff)) ≈ [1.0, 0.0]
@test (@inferred _gradient(f1, [1.0, -1.0], finite_diff)) ≈ [1.0, -2.0]
end
@testset for backend in [fd51, fwd_diff, finite_diff]
diff_backend!(backend)
@test _derivative(c1, 0.0) ≈ [1.0, 0.0]
v = [-1.0, -1.0]
@test _derivative!(c1, v, 0.0) === v
@test isapprox(v, [1.0, 0.0])
@test _gradient(f1, [1.0, -1.0]) ≈ [1.0, -2.0]
@test _gradient!(f1, v, [1.0, -1.0]) === v
@test v ≈ [1.0, -2.0]
end
diff_backend!(Manifolds.NoneDiffBackend())
@testset for backend in [fd51, Manifolds.ForwardDiffBackend()]
@test _derivative(c1, 0.0, backend) ≈ [1.0, 0.0]
@test _gradient(f1, [1.0, -1.0], backend) ≈ [1.0, -2.0]
end
diff_backend!(fd51)
end
end
rb_onb_default = RiemannianONBDiffBackend(
diff_backend(),
Manifolds.ExponentialRetraction(),
Manifolds.LogarithmicInverseRetraction(),
DefaultOrthonormalBasis(),
)
rb_onb_fd51 = RiemannianONBDiffBackend(
Manifolds.FiniteDifferencesBackend(),
Manifolds.ExponentialRetraction(),
Manifolds.LogarithmicInverseRetraction(),
DefaultOrthonormalBasis(),
)
rb_onb_fwd_diff = RiemannianONBDiffBackend(
Manifolds.ForwardDiffBackend(),
Manifolds.ExponentialRetraction(),
Manifolds.LogarithmicInverseRetraction(),
DefaultOrthonormalBasis(),
)
rb_onb_finite_diff = RiemannianONBDiffBackend(
Manifolds.FiniteDiffBackend(),
Manifolds.ExponentialRetraction(),
Manifolds.LogarithmicInverseRetraction(),
DefaultOrthonormalBasis(),
)
rb_onb_default2 = RiemannianONBDiffBackend(
diff_backend(),
Manifolds.ExponentialRetraction(),
Manifolds.LogarithmicInverseRetraction(),
CachedBasis(
DefaultOrthonormalBasis(),
[[0.0, -1.0, 0.0], [sqrt(2) / 2, 0.0, -sqrt(2) / 2]],
),
)
rb_proj = Manifolds.RiemannianProjectionGradientBackend(diff_backend())
@testset "rdiff_ functions" begin
@test Manifolds.rdifferential_backend() ===
Manifolds._current_rdifferential_backend.backend
@test Manifolds.rgradient_backend() === Manifolds._current_rgradient_backend.backend
tmp_diff = Manifolds.rdifferential_backend()
Manifolds.rdifferential_backend!(rb_onb_finite_diff)
@test Manifolds.rdifferential_backend() === rb_onb_finite_diff
Manifolds.rdifferential_backend!(tmp_diff)
tmp_grad = Manifolds.rgradient_backend()
Manifolds.rgradient_backend!(rb_onb_finite_diff)
@test Manifolds.rgradient_backend() === rb_onb_finite_diff
Manifolds.rgradient_backend!(tmp_grad)
end
@testset "Riemannian differentials" begin
s2 = Sphere(2)
p = [0.0, 0.0, 1.0]
q = [1.0, 0.0, 0.0]
c1(t) = geodesic(s2, q, p, t)
Xval = [-sqrt(2) / 2, 0.0, sqrt(2) / 2]
@test isapprox(s2, c1(π / 4), differential(s2, c1, π / 4), Xval)
X = similar(p)
differential!(s2, c1, X, π / 4)
@test isapprox(s2, c1(π / 4), X, Xval)
@testset for backend in [rb_onb_fd51, rb_onb_fwd_diff, rb_onb_finite_diff]
@test isapprox(s2, c1(π / 4), differential(s2, c1, π / 4, backend), Xval)
X = similar(p)
differential!(s2, c1, X, π / 4, backend)
@test isapprox(s2, c1(π / 4), X, Xval)
end
end
@testset "Riemannian gradients" begin
s2 = Sphere(2)
f1(p) = p[1]
q = [sqrt(2) / 2, 0, sqrt(2) / 2]
@test isapprox(s2, q, gradient(s2, f1, q), [0.5, 0.0, -0.5])
for backend in [rb_onb_default, rb_proj]
@test isapprox(s2, q, gradient(s2, f1, q, backend), [0.5, 0.0, -0.5])
end
X = similar(q)
gradient!(s2, f1, X, q)
@test isapprox(s2, q, X, [0.5, 0.0, -0.5])
for backend in [rb_onb_default, rb_proj]
gradient!(s2, f1, X, q, backend)
@test isapprox(s2, q, X, [0.5, 0.0, -0.5])
end
end
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] | 2.035512 | 3,182 |
import Celeste.ArgumentParse
@testset "argument parse" begin
@testset "positional argument" begin
parser = ArgumentParse.ArgumentParser(propagate_errors=true)
ArgumentParse.add_argument(parser, "my_arg")
@test_throws ArgumentParse.ArgumentParsingError ArgumentParse.parse_args(parser, String[])
parsed_args = ArgumentParse.parse_args(parser, ["hello"])
@test parsed_args["my_arg"] == "hello"
@test_throws ArgumentParse.ArgumentParsingError ArgumentParse.parse_args(parser, ["foo", "bar"])
end
@testset "positional argument type conversion" begin
parser = ArgumentParse.ArgumentParser(propagate_errors=true)
ArgumentParse.add_argument(parser, "my_arg", arg_type=Int)
parsed_args = ArgumentParse.parse_args(parser, ["123"])
@test parsed_args["my_arg"] == 123
end
@testset "keyword argument" begin
parser = ArgumentParse.ArgumentParser(propagate_errors=true)
ArgumentParse.add_argument(parser, "--my-arg")
parsed_args = ArgumentParse.parse_args(parser, String[])
@test !haskey(parsed_args, "my-arg")
parsed_args = ArgumentParse.parse_args(parser, ["--my-arg", "hello"])
@test parsed_args["my-arg"] == "hello"
end
@testset "keyword argument type conversion" begin
parser = ArgumentParse.ArgumentParser(propagate_errors=true)
ArgumentParse.add_argument(parser, "--my-arg", arg_type=Int)
parsed_args = ArgumentParse.parse_args(parser, ["--my-arg", "123"])
@test parsed_args["my-arg"] == 123
end
@testset "required keyword argument fails when missing" begin
parser = ArgumentParse.ArgumentParser(propagate_errors=true)
ArgumentParse.add_argument(parser, "--my-arg", required=true)
@test_throws ArgumentParse.ArgumentParsingError ArgumentParse.parse_args(parser, String[])
end
@testset "keyword argument default value when missing" begin
parser = ArgumentParse.ArgumentParser(propagate_errors=true)
ArgumentParse.add_argument(parser, "--my-arg", default="hi there")
parsed_args = ArgumentParse.parse_args(parser, String[])
@test parsed_args["my-arg"] == "hi there"
end
@testset "keyword argument :store_true" begin
parser = ArgumentParse.ArgumentParser(propagate_errors=true)
ArgumentParse.add_argument(parser, "--my-arg", action=:store_true)
parsed_args = ArgumentParse.parse_args(parser, String[])
@test parsed_args["my-arg"] == false
parsed_args = ArgumentParse.parse_args(parser, String["--my-arg"])
@test parsed_args["my-arg"] == true
end
@testset "help message" begin
parser = ArgumentParse.ArgumentParser(program_name="test.jl", propagate_errors=true)
ArgumentParse.add_argument(parser, "--my-keyword", default="hello")
ArgumentParse.add_argument(parser, "my_arg")
help_message = nothing
try
ArgumentParse.parse_args(parser, ["--help"])
@test false
catch exc
if !isa(exc, ArgumentParse.ShowHelp)
rethrow()
end
help_message = exc.message
end
@test help_message == "Usage: test.jl [--my-keyword my-keyword] my_arg"
end
end
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] | 2.766304 | 1,104 |
using DataDrivenDiffEq
using OrdinaryDiffEq
using Plots
gr()
# Define measurements from unstable system with known control input
X = [4 2 1 0.5 0.25; 7 0.7 0.07 0.007 0.0007]
U = [-4 -2 -1 -0.5]
B = Float32[1; 0]
# See fail with unknown input
sys = DMDc(X, U)
# But with a little more knowledge
sys = DMDc(X, U, B = B)
# Extract the DMD from inside DMDc
get_dynamics(sys)
# Acess all the other stuff
eigen(sys)
eigvals(sys)
eigvecs(sys)
isstable(sys)
# Get unforced dynamics
dudt_ = dynamics(sys)
prob = DiscreteProblem(dudt_, X[:, 1], (0., 10.))
sol_unforced = solve(prob, FunctionMap())
plot(sol_unforced)
sol_unforced[:,:]
# Create a system with cos control input to stabilize
dudt_ = dynamics(sys, control = (u, p, t) -> -0.5u[1])
prob = DiscreteProblem(dudt_, X[:, 1], (0., 10.))
sol = solve(prob, FunctionMap())
plot!(sol)
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] | 2.467456 | 338 |
<gh_stars>1-10
println("CHEM 324 Tutorials") | [
27,
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<reponame>hnlee77/ApproximateDPs.jl
using ApproximateDPs
const ADP = ApproximateDPs
using FlightSims
const FS = FlightSims
using Transducers
using Plots
using Random, LinearAlgebra, ComponentArrays
using DynamicPolynomials, UnPack
using DifferentialEquations, DataFrames
using NumericalIntegration: integrate
function initialise()
# setting
A, B = [-10 1; -0.002 -2], [0; 2]
Q, R = I, I
env = LinearSystem(A, B)
__t = 0.0
irl = CTLinearValueIterationIRL(Q, R)
env, irl
end
function train!(env, irl; Δt=0.01, tf=3.0, w_tol=1e-3)
@unpack A, B = env
args_linearsystem = (A, B)
linearsystem, integ = FSimZoo.LinearSystem_SingleIntegrator(args_linearsystem) # integrated system with scalar integrator ∫r
x0 = State(linearsystem, integ)([0.4, 4.0])
irl.V̂.param = zeros(size(irl.V̂.param)) # zero initialisation
û = ADP.ApproximateOptimalInput(irl, B)
_û = (X, p, t) -> û(X.x, p, t) # for integrated system
cb_train = ADP.update_params_callback(irl, w_tol)
cb = CallbackSet(cb_train)
running_cost = ADP.RunningCost(irl)
prob, df = sim(
x0,
apply_inputs(Dynamics!(linearsystem, integ, running_cost); u=_û);
tf=tf,
callback=cb,
savestep=Δt
)
ts = df.time
xs = df.sol |> Map(datum -> datum.linearsystem.state) |> collect
plot(ts, hcat(xs...)')
# ∫rs = df.sol |> Map(datum -> datum.integ.integral) |> collect
# plot(hcat(∫rs...)')
end
function main(; seed=1)
Random.seed!(seed)
env, irl = initialise()
train!(env, irl; w_tol = 1e-3)
end
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] | 2.120359 | 781 |
"""
rna(R::AbstractRecurrenceMatrix)
rna(args...; kwargs...)
Calculate a set of Recurrence Network parameters.
The input `R` can be a symmetric recurrence matrix that is
interpreted as the adjacency matrix of an undirected complex network,
such that linked vertices are neighboring points in the phase space.
Alternatively, the inputs can be a graph object or any
valid inputs to the `SimpleGraph` constructor of the
[LightGraphs](https://github.com/JuliaGraphs/LightGraphs.jl) package.
## Return
The returned value is a dictionary that contains the following entries,
with the corresponding global network properties[1, 2]:
* `:density`: edge density, approximately equivalent to the global recurrence rate in the phase space.
* `:transitivity`: network transitivity, which describes the
global clustering of points following Barrat's and Weigt's formulation [3].
* `:averagepath`: mean value of the shortest path lengths taken over
all pairs of connected vertices, related to the average separation
between points in the phase.
* `:diameter`: maximum value of the shortest path lengths between
pairs of connected vertices, related to the phase space diameter.
## References
[1]: <NAME> *et al.* "Recurrence networks — a novel paradigm for nonlinear time series analysis",
*New Journal of Physics* 12, 033025 (2010)
[DOI:10.1088/1367-2630/12/3/033025](https://doi.org/10.1088/1367-2630/12/3/033025)
[2]: <NAME> *et al.*, The geometry of chaotic dynamics — a complex network perspective,
*Eur. Phys. J.* B 84, 653–672 (2011)
[DOI:10.1140/epjb/e2011-10899-1](https://doi.org/10.1140/epjb/e2011-10899-1)
[3]: <NAME> & <NAME>, "On the properties of small-world network models",
*The European Physical Journal B* 13, 547–560 (2000)
[DOI:10.1007/s100510050067](https://doi.org/10.1007/s100510050067)
"""
function rna(args...; kwargs...)
graph = SimpleGraph(args...; kwargs...)
return Dict{Symbol, Float64}(
:density => density(graph),
:transitivity => global_clustering_coefficient(graph),
:averagepath => averagepath(graph),
:diameter => diameter(graph)
)
end
"""
averagepath(graph)
Calculates average minimum path length for a SimpleGraph.
Uses Donner, 2010, Eq. 26 on pg. 18.
"""
function averagepath(graph::SimpleGraph)
num_verts = nv(graph)
dist_mat = floyd_warshall_shortest_paths(graph).dists
@. dist_mat[dist_mat == typemax(dist_mat)] = 0.
return sum(dist_mat) / (num_verts * (num_verts - 1))
end
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67,
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2488,
13,
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62,
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58,
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62,
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7,
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62,
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13,
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7,
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24040,
1635,
357,
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62,
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532,
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] | 3.034188 | 819 |
# This file is auto-generated by AWSMetadata.jl
using AWS
using AWS.AWSServices: workmailmessageflow
using AWS.Compat
using AWS.UUIDs
"""
GetRawMessageContent()
Retrieves the raw content of an in-transit email message, in MIME format.
# Required Parameters
- `messageId`: The identifier of the email message to retrieve.
"""
get_raw_message_content(messageId; aws_config::AWSConfig=global_aws_config()) = workmailmessageflow("GET", "/messages/$(messageId)"; aws_config=aws_config)
get_raw_message_content(messageId, args::AbstractDict{String, <:Any}; aws_config::AWSConfig=global_aws_config()) = workmailmessageflow("GET", "/messages/$(messageId)", args; aws_config=aws_config)
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] | 3.113122 | 221 |
using InformationInequalities
using Test
@testset "InformationInequalities.jl" begin
# Write your tests here.
end
| [
3500,
6188,
40,
710,
13255,
871,
198,
3500,
6208,
198,
198,
31,
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198
] | 3.216216 | 37 |
<reponame>IngiOrn/AQFED.jl
using AQFED, Test
import AQFED.PLN: EtoreGobetPLNPricer, LeFlochLehmanPLNPricer, priceEuropean
import AQFED.TermStructure: CapitalizedDividend, Dividend, futureValue
import AQFED.Basket: DeelstraBasketPricer, DeelstraLBBasketPricer
@testset "EtoreGobetSingle" begin
spot = 100.0
vol = 0.3
discountRate = 0.0
divAmount = 7.0
tte = 1.0
ttd = 0.9 * tte
ttp = tte
isCall = true
discountFactor = exp(-discountRate * ttp)
dividends = Vector{CapitalizedDividend}(undef, 1)
#amount::T exDate::Float64 payDate::Float64 isProportional::bool isKnown::bool
dividends[1] =
CapitalizedDividend(Dividend(divAmount, ttd, ttd, false, false), exp((tte - ttd) * discountRate))
println("S Model Price Error")
eg2 = EtoreGobetPLNPricer(2)
eg3 = EtoreGobetPLNPricer(3)
ll2 = LeFlochLehmanPLNPricer(2)
ll3 = LeFlochLehmanPLNPricer(3)
d = DeelstraBasketPricer(1, 3)
dlb = DeelstraLBBasketPricer(1, 3)
refHHL = [43.24845580, 9.07480013, 1.06252880, 0.10473887]
refEg2 = [43.24846889, 9.07479039, 1.06253441, 0.10474035]
refEg3 = [43.24845582, 9.07480026, 1.06252875, 0.10473885]
for (i, strike) in enumerate(LinRange(50.0, 200.0, 4))
price = priceEuropean(
eg2,
isCall,
strike,
spot * exp(tte * discountRate),
vol * vol * tte,
tte,
discountFactor,
dividends,
)
println(strike, " EG-2 ", price, " ", price - refHHL[i])
@test isapprox(refEg2[i], price, atol = 1e-8)
price = priceEuropean(
eg3,
isCall,
strike,
spot * exp(tte * discountRate),
vol * vol * tte,
tte,
discountFactor,
dividends,
)
println(strike, " EG-3 ", price, " ", price - refHHL[i])
@test isapprox(refEg3[i], price, atol = 1e-8)
price = priceEuropean(
ll2,
isCall,
strike,
spot * exp(tte * discountRate),
vol * vol * tte,
tte,
discountFactor,
dividends,
)
println(strike, " LL-2 ", price, " ", price - refHHL[i])
price = priceEuropean(
ll3,
isCall,
strike,
spot * exp(tte * discountRate),
vol * vol * tte,
tte,
discountFactor,
dividends,
)
println(strike, " LL-3 ", price, " ", price - refHHL[i])
price = AQFED.Basket.priceEuropean(
d,
isCall,
strike,
spot * exp(tte * discountRate),
vol * vol * tte,
tte,
discountFactor,
dividends,
)
println(strike, " Deelstra ", price, " ", price - refHHL[i])
price = AQFED.Basket.priceEuropean(
dlb,
isCall,
strike,
spot * exp(tte * discountRate),
vol * vol * tte,
tte,
discountFactor,
dividends,
)
println(strike, " Deelstra-LB ", price, " ", price - refHHL[i])
end
end
@testset "MultipleVellekoop" begin
spot = 100.0
σ = 0.25
discountRate = 0.06
tte = 7.0
isCall = true
ttd = 0.9
dividends = [CapitalizedDividend(Dividend(6.0, ttd, ttd, false, false), exp((tte - ttd) * discountRate)),
CapitalizedDividend(Dividend(6.5, ttd + 1, ttd + 1, false, false), exp((tte - ttd - 1) * discountRate)),
CapitalizedDividend(Dividend(7.0, ttd + 2, ttd + 2, false, false), exp((tte - ttd - 2) * discountRate)),
CapitalizedDividend(Dividend(7.5, ttd + 3, ttd + 3, false, false), exp((tte - ttd - 3) * discountRate)),
CapitalizedDividend(Dividend(8.0, ttd + 4, ttd + 4, false, false), exp((tte - ttd - 4) * discountRate)),
CapitalizedDividend(Dividend(8.0, ttd + 5, ttd + 5, false, false), exp((tte - ttd - 5) * discountRate)),
CapitalizedDividend(Dividend(8.0, ttd + 6, ttd + 6, false, false), exp((tte - ttd - 6) * discountRate))]
rawForward = spot * exp(discountRate * tte)
df = exp(-discountRate * tte)
ll3 = LeFlochLehmanPLNPricer(3)
d = DeelstraBasketPricer(3, 3)
dlb = DeelstraLBBasketPricer(3, 3)
refPrices = [
34.19664044,
30.49396147,
27.21393776,
24.31297892,
21.74885502,
19.48228598,
17.47762235,
15.70300783,
14.13025908,
12.73460326,
11.49435473,
]
f = spot * exp(discountRate * tte) - sum(futureValue(cd) for cd in dividends)
for (i, strike) in enumerate(LinRange(50.0, 150.0, 11))
refVol = Black.impliedVolatility(isCall, refPrices[i], f, strike, tte, df)
price = priceEuropean(ll3, isCall, strike, rawForward, σ^2 * tte, tte, df, dividends)
vol = Black.impliedVolatility(isCall, price, f, strike, tte, df)
println(strike, " LL-3 ", price, " ", price / refPrices[i] - 1, " ", vol - refVol)
price = AQFED.Basket.priceEuropean(d, isCall, strike, rawForward, σ^2 * tte, tte, df, dividends)
vol = Black.impliedVolatility(isCall, price, f, strike, tte, df)
println(strike, " Deelstra ", price, " ", price / refPrices[i] - 1, " ", vol - refVol)
price = AQFED.Basket.priceEuropean(dlb, isCall, strike, rawForward, σ^2 * tte, tte, df, dividends)
vol = Black.impliedVolatility(isCall, price, f, strike, tte, df)
println(strike, " Deelstra-LB ", price, " ", price / refPrices[i] - 1, " ", vol - refVol)
end
end
@testset "MultipleGocsei" begin
spot = 100.0
σ = 0.25
discountRate = 0.03
divAmount = 2.0
tte = 10.0
isCall = true
ttd0 = 1.0 / 365
dividends = Vector{CapitalizedDividend}(undef, 20)
for i = 1:20
ttd = ttd0 + (i - 1) / 2
dividends[i] =
CapitalizedDividend(Dividend(divAmount, ttd, ttd, false, false), exp((tte - ttd) * discountRate))
end
rawForward = spot * exp(discountRate * tte)
df = exp(-discountRate * tte)
ll3 = LeFlochLehmanPLNPricer(3)
d = DeelstraBasketPricer(1, 3) #1,3 minimizes relative error in price, 3,3 minimizes volatility error
dlb = DeelstraLBBasketPricer(1, 3)
refPrices = [37.25772649, 28.59516740, 22.28192756, 17.61042951, 14.09731856, 11.41453970, 9.33695198]
f = spot * exp(discountRate * tte) - sum(futureValue(cd) for cd in dividends)
for (i, strike) in enumerate(LinRange(50.0, 200.0, 7))
refVol = Black.impliedVolatility(isCall, refPrices[i], f, strike, tte, df)
price = priceEuropean(ll3, isCall, strike, rawForward, σ^2 * tte, tte, df, dividends)
vol = Black.impliedVolatility(isCall, price, f, strike, tte, df)
println(strike, " LL-3 ", price, " ", price / refPrices[i] - 1, " ", vol - refVol)
price = AQFED.Basket.priceEuropean(d, isCall, strike, rawForward, σ^2 * tte, tte, df, dividends)
vol = Black.impliedVolatility(isCall, price, f, strike, tte, df)
println(strike, " Deelstra ", price, " ", price / refPrices[i] - 1, " ", vol - refVol)
price = AQFED.Basket.priceEuropean(dlb, isCall, strike, rawForward, σ^2 * tte, tte, df, dividends)
vol = Black.impliedVolatility(isCall, price, f, strike, tte, df)
println(strike, " Deelstra-LB ", price, " ", price / refPrices[i] - 1, " ", vol - refVol)
end
end
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] | 2.030715 | 3,679 |
<reponame>andreaskoher/CovidSurvey
using DrWatson
quickactivate(@__DIR__)
@show projectdir()
## ============================================================================
@info "load packages"
using CovidSurvey
using Random, Dates, Turing
using Base.Threads
using StatsPlots
using PrettyTables
using DataFrames
using BSON
using Memoization
using ReverseDiff
setadbackend(:reversediff)
Turing.setrdcache(true)
plotlyjs()
@info "number of threads available: $(nthreads())"
## ============================================================================
ps = ( warmup = 100,
model = "intdeaths",
steps = 100,
seed = nothing,
observ = nothing,
chains = 1,
preds = nothing,#"CF,CC,CR,CS",#"CS,MG,MA"
semipara=true,
rwstep = 7,
include= true,
std = false,
)
## ============================================================================
# load data
@info "load data"
data_params = (
observationsend = ps.observ
, predictors = ps.preds|> CovidSurvey.parse_predictors
# , cases_start = ps.cases
# , hospitmodel = Regional.HospitInit1(obs_stop="2020-07-01")
, rwstep = ps.rwstep
, epidemicstart = Date("2020-08-01")
, numimpute = 20
, include_early_dynamic = ps.include
, link = KLogistic(4.5)
, invlink = KLogit(4.5)
, predictive = false
, covariates_kwargs = Dict(
:semiparametric => ps.semipara,
:fname => [projectdir("data/contacts/int/smoothed_contact_rates_region=$(r).csv" ) for r in International.regions],#[projectdir("data", "mean_contact_rates_region=$r.csv" ) for r in 1:Regional.nregions],
:shift => -1,
:conditions => :date => x->x>Date("2020-11-10"), # >= 1000 cases / day
# :datecol => "date",
:aggregation => ps.rwstep,
:standardize => ps.std,
:normalize => true,
:mobility => [projectdir("data/mobility/mobility_country=$(r).csv") for r in International.regions]
)
)
data = International.load_data(; data_params... )
d = turing_data = data.turing_data;
## ============================================================================
@info "prepare model"
model = Regional.model_deaths
model = Regional.model_international_deaths
#!isnothing(ps.seed) && Random.seed!(ps.seed);
@info ps
m = model(turing_data; vectorize=true)
m();
## ============================================================================
@info "start sampling"
@time chain = let #460s; 366s
thinning = 1
if ps.chains > 1
sample(m, NUTS(ps.warmup, 0.9; max_depth=6), MCMCThreads(), ps.steps + ps.warmup, ps.chains; progress=true, thinning)
else
sample(m, NUTS(ps.warmup, 0.9; max_depth=6), ps.steps + ps.warmup; progress=true, thinning) #; max_depth=15
end
end
divervences = sum( chain[ps.warmup+1:end, :numerical_error, :] )
@show divervences
## ==========================================================================
@info "save"
fdir = projectdir("reports/tmp", "$(today())")
fname = Regional.save_results(fdir, ps, data, chain)
## ==========================================================================
@info "post processing"
Regional.postprocessing(fname; plot_results=false)
## ==========================================================================
## ==========================================================================
@info "post processing"
fdir, ps, ignores = Regional.parse_fname(fname)
p = Regional.PostProcessing2(fdir, ps, ignores, [], fname)
## ==========================================================================
@info "plot chain"
plot_results = true
Regional.plot_chains(p; plot_results)
## ==========================================================================
@info "meanplot"
Regional.plot_means(p; plot_results)
## ==========================================================================
# perform diagnostics
p = Regional.skip_warmup(p)
Regional.diagnostics(p)
data.cases
## ==========================================================================
@info "make predictions"
gp = Regional.generate_posterior(p);
## ==========================================================================
@info "plot regions"
Regional.plot_regions(p, gp; plot_results=true)
## ==========================================================================
@info "plot predictors"
# pgfplotsx()
# default(titlefontsize = 20, legendfontsize = 18, labelfontsize = 18, guidefontsize = 18, tickfontsize = 12, framestyle = :zerolines, yminorgrid = true)
Regional.plot_effects(p, gp; plot_results , grouped = false, effect_on_Rt = 0.)
Regional.plot_effects(p, gp; plot_results, grouped = false, effect_on_Rt = 2.)
Regional.plot_effects(p, gp; plot_results, grouped = false, effect_on_Rt = -0.5)
Regional.plot_effects(p, gp; plot_results, grouped = true, effect_on_Rt = 0.)
Regional.plot_effects(p, gp; plot_results, grouped = true, effect_on_Rt = 2.)
Regional.plot_effects(p, gp; plot_results, grouped = true, effect_on_Rt = -0.5)
## ===================================================================
TV = Vector{Float64}
V = Float64
vectorize = true
θ = turing_data
@unpack num_observations, num_total_days, num_regions, num_rt_steps, deathmodel, num_covariates, rwscale, include_early_dynamic,init_infected = θ
# If we don't want to predict the future, we only need to compute up-to time-step `num_obs_countries[m]`
num_time_steps = num_observations
############# 2.) time varying reproduction number
R0_std = truncated(Normal(.5, .2), 0.01, Inf) |> rand
R0s = filldist(truncated(Normal(0.8, R0_std), 0., 4.5), num_regions) |> rand
σ_rt = truncated(Normal(0.3*rwscale, .02*rwscale), 0, .5*rwscale) |> rand
n = first( num_rt_steps )
latent_Rts_z = filldist( CovidSurvey.RandomWalk( n ), num_regions) |> rand
latent_Rts = CovidSurvey.rescale( latent_Rts_z, σ_rt, d.invlink.(R0s) )
Rts = TV[TV(undef, num_time_steps[m]) for m in 1:num_regions]
if num_covariates > 0
grouped_effect = filldist( Laplace(0,0.2), num_covariates) |> rand
effect_std = filldist( GammaMeanCv(0.1, 0.5), num_covariates) |> rand
effects_z = filldist( MvNormal( num_covariates, 1.), num_regions) |> rand
effects = [ effects_z[:,m] .* effect_std .+ grouped_effect for m in 1:num_regions]
# grouped_effect = filldist( Exponential(0.2), num_covariates)
# effect_std = filldist( GammaMeanCv(0.1, 0.5), num_covariates)
# pooled_effects_distr_raw = Normal.( grouped_effect, effect_std )
# pooled_effects_distr = arraydist( truncated.( pooled_effects_distr_raw, zero(V), V(Inf) ) )
# effects = filldist( pooled_effects_distr, num_regions )
if θ.semiparametric
CovidSurvey.semiparametric!(Rts, θ, false, latent_Rts, R0s, σ_rt, effects)
else
CovidSurvey.mixed!(Rts, θ, false, latent_Rts, R0s, σ_rt, effects)
end
else
CovidSurvey.random_walks!(Rts, θ, false, latent_Rts, R0s, σ_rt)
end
plot(latent_Rts_z[:,3], ticks=:native)
plot(θ.link.(latent_Rts[:,1]), ticks=:native)
plot(d.dates[1], Rts[1], ticks=:native)
############ 3.) infection dynamics
ys = arraydist(Exponential.(init_infected)) |> rand
newly_infecteds = TV[TV(undef, num_time_steps[m]) for m in 1:num_regions]
CovidSurvey.infections!(newly_infecteds, θ, ys, Rts)
plot(d.dates[1], newly_infecteds[1], ticks=:native)
# infections!(newly_infecteds, cumulative_infecteds, effective_Rts, θ, τ, ys, Rts)
########### 4.) derive observables
μ_i2d = truncated(Normal(21.82, 1.01), 18, 24) |> rand
σ_i2d = truncated(Normal(4.26, 5.18), 1, 14) |> rand
idr = truncated(Normal(8/1000, 1/1000), 1/1000, 15/1000) |> rand
ϕ_d = truncated(Normal(50, 10), 20, Inf) |> rand
weekdayeffect_simplex = filldist(Dirichlet([7,7,7,7,7,3,1]), num_regions) |> rand
weekdayeffect = TV[TV(undef, 7) for m in 1:num_regions]
CovidSurvey.weekdayeffect!(weekdayeffect, weekdayeffect_simplex)
### holiday effect
holidayeffect = filldist(Beta(1,1), num_regions) |> rand
expected_daily_deaths = TV[TV(undef, num_time_steps[m]) for m in 1:num_regions]
deaths_observation_model = CovidSurvey.WeekdayHolidayObsModel2(
deathmodel, μ_i2d, σ_i2d, idr, ϕ_d, expected_daily_deaths, weekdayeffect, holidayeffect, vectorize
)
# deaths_observation_model = CovidSurvey.SimpleObsModel2(deathmodel, μ_i2d, σ_i2d, idr, ϕ_d, expected_daily_deaths, vectorize)
CovidSurvey.expected!(deaths_observation_model, newly_infecteds)
plot(d.dates[1], expected_daily_deaths[1], ticks=:native)
########### 4.) compare model to observations
## 4.1) observe hospitalizations
ℓ = zero(V)
ℓ += CovidSurvey.logpdf(deaths_observation_model, θ.deaths)
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] | 2.72693 | 3,186 |
<filename>src/unresolved/1232.check-if-it-is-a-straight-line.jl
# ---
# title: 1232. Check If It Is a Straight Line
# id: problem1232
# author: <NAME>
# date: 2020-10-31
# difficulty: Easy
# categories: Array, Math, Geometry
# link: <https://leetcode.com/problems/check-if-it-is-a-straight-line/description/>
# hidden: true
# ---
#
# You are given an array `coordinates`, `coordinates[i] = [x, y]`, where `[x,
# y]` represents the coordinate of a point. Check if these points make a
# straight line in the XY plane.
#
#
#
#
#
# **Example 1:**
#
# ![](https://assets.leetcode.com/uploads/2019/10/15/untitled-diagram-2.jpg)
#
#
#
# Input: coordinates = [[1,2],[2,3],[3,4],[4,5],[5,6],[6,7]]
# Output: true
#
#
# **Example 2:**
#
# **![](https://assets.leetcode.com/uploads/2019/10/09/untitled-diagram-1.jpg)**
#
#
#
# Input: coordinates = [[1,1],[2,2],[3,4],[4,5],[5,6],[7,7]]
# Output: false
#
#
#
#
# **Constraints:**
#
# * `2 <= coordinates.length <= 1000`
# * `coordinates[i].length == 2`
# * `-10^4 <= coordinates[i][0], coordinates[i][1] <= 10^4`
# * `coordinates` contains no duplicate point.
#
#
## @lc code=start
using LeetCode
## add your code here:
## @lc code=end
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28,
437,
198
] | 2.273723 | 548 |
# Use baremodule to shave off a few KB from the serialized `.ji` file
baremodule Ogg_jll
using Base
using Base: UUID
import JLLWrappers
JLLWrappers.@generate_main_file_header("Ogg")
JLLWrappers.@generate_main_file("Ogg", UUID("e7412a2a-1a6e-54c0-be00-318e2571c051"))
end # module Ogg_jll
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] | 2.416667 | 120 |
<gh_stars>0
# This file manages the tests for the `Exceptions.jl` package
# A flag to throw the first captured exception back
fatalerrors = length(ARGS) > 0 && ARGS[1] == "-f"
# A flag to hide additional LoadErrors
quiet = length(ARGS) > 0 && ARGS[1] == "-q"
# A flag to check if any error occurred
anyerrors = false
# Specify tests
tests_dir = joinpath(@__DIR__, "tests")
tests = ["exception.jl"]
# Print info
println("\e[1;32mRUNNING TESTS\e[0m for `Exceptions.jl`")
# Run tests
for test in joinpath.(tests_dir, tests)
# Run a test
try
include(test)
# If everything is fine, print about that
println("\e[1;32mPASSED\e[0m: $(basename(test))")
# If error occurred, note that
catch e
# Alter the global variable
global anyerrors = true
# Print about the fail
println("\e[1;31mFAILED\e[0m: $(basename(test))")
# Exit immediately is needed
if fatalerrors
rethrow(e)
# Show the error if needed
elseif !quiet
showerror(stdout, e, backtrace())
println()
end
end
end
# If any errors, throw an exception
anyerrors && throw("Some tests have failed.")
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14643,
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11405,
3714,
7203,
4366,
5254,
423,
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19570,
198
] | 2.462322 | 491 |
"""
Base.rand(randomwalk::RandomWalk{<:AbstractArray{<:DataType}, <:AbstractArray{<:Real},
<:Sampleable}, ntimesteps::Integer) -> Matrix{Int}
Simulate `randomwalk` for `ntimesteps` time-steps over an integer lattice, returning one
sample path (i.e. trajectory) of length `ntimesteps`.
The time steps represented in the sample path are `0, 1, ..., ntimesteps-1`, for we start
with an initial state.
##### Complexity
- Time complexity: `O(n)`.
"""
function Base.rand(randomwalk::RandomWalk{<:AbstractArray{<:DataType},
<:AbstractArray{<:Real},
<:Sampleable}, ntimesteps::Integer)
# allocate memory
realizationmatrix = Matrix{Int}(undef, ntimesteps,
length(randomwalk.initialstate))
realizationmatrix[1,:] = randomwalk.initialstate
# draw a random step
change = rand(randomwalk.stepsampler)
for step in 2:ntimesteps
realizationmatrix[step,:] = realizationmatrix[step-1,:] + change
change = rand(randomwalk.stepsampler)
end
return realizationmatrix
end
"""
Base.rand(randomwalk::RandomWalk{<:DataType, <:Real, <:Sampleable,
ntimesteps::Integer) -> Vector{Int}
Simulate a one-dimensional `randomwalk` for `ntimesteps` time-steps, returning one sample
path (i.e. trajectory) of length `ntimesteps`.
The time steps represented in the sample path are `0, 1, ..., ntimesteps-1`, for we start
with an initial state.
##### Complexity
- Time complexity: `O(n)`.
"""
function Base.rand(randomwalk::RandomWalk{<:DataType, <:Real, <:Sampleable},
ntimesteps::Integer)
# allocate memory
realizationvec = Vector{eltype(randomwalk)}(undef, ntimesteps)
realizationvec[1] = randomwalk.initialstate
# draw a random step
change = rand(randomwalk.stepsampler)[1]
for step in 2:ntimesteps
realizationvec[step] = realizationvec[step-1] + change
change = rand(randomwalk.stepsampler)[1]
end
return realizationvec
end
"""
Base.rand(randomwalk::RandomWalk{<:AbstractGraph, <:Integer, <:Sampleable},
ntimesteps::Integer) -> Vector{Int}
Simulate `randomwalk` for `ntimesteps` time-steps over a graph, returning one sample path
(i.e. trajectory) of length `ntimesteps`.
The time steps represented in the sample path are `0, 1, ..., ntimesteps-1`, for we start
with an initial state.
##### Complexity
- Time complexity: `O(n)`.
"""
function Base.rand(randomwalk::RandomWalk{<:AbstractGraph, <:Integer, <:Sampleable},
ntimesteps::Integer)
# allocate memory
realizationvec = Vector{eltype(randomwalk)}(undef, ntimesteps)
realizationvec[1] = randomwalk.initialstate
for step in 2:ntimesteps
# draw a neighbor at random
realizationvec[step] = NeighborSampler(randomwalk.statespace,
realizationvec[step-1]) |> rand
end
return realizationvec
end
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<filename>tutorials/Land/Soil/PhaseChange/freezing_front.jl
# # Modeling a freezing front in unsaturated soil
# Before reading this tutorial,
# we recommend that you look over the coupled energy
# and water [tutorial](../Coupled/equilibrium_test.md).
# That tutorial showed how to solve the heat equation for soil volumetric
# internal energy `ρe_int`, simultaneously
# with Richards equation for volumetric liquid water fraction `ϑ_l`, assuming zero
# volumetric ice fraction `θ_i` for all time, everywhere in the domain[^a].
# In this example, we add in a source term to the right hand side for both `θ_i`
# and `ϑ_l` which models freezing and thawing and conserves water mass during the process.
# The equations are
# ``
# \frac{∂ ρe_{int}}{∂ t} = ∇ ⋅ κ(θ_l, θ_i; ν, ...) ∇T + ∇ ⋅ ρe_{int_{liq}} K (T,θ_l, θ_i; ν, ...) \nabla h( ϑ_l, z; ν, ...)
# ``
# ``
# \frac{ ∂ ϑ_l}{∂ t} = ∇ ⋅ K (T,θ_l, θ_i; ν, ...) ∇h( ϑ_l, z; ν, ...) -\frac{F_T}{ρ_l}
# ``
# ``
# \frac{ ∂ θ_i}{∂ t} = \frac{F_T}{ρ_i}
# ``
# Here
# ``t`` is the time (s),
# ``z`` is the location in the vertical (m),
# ``ρe_{int}`` is the volumetric internal energy of the soil (J/m^3),
# ``T`` is the temperature of the soil (K),
# ``κ`` is the thermal conductivity (W/m/K),
# ``ρe_{int_{liq}}`` is the volumetric internal energy of liquid water (J/m^3),
# ``K`` is the hydraulic conductivity (m/s),
# ``h`` is the hydraulic head (m),
# ``ϑ_l`` is the augmented volumetric liquid water fraction,
# ``θ_i`` is the volumetric ice fraction,
# ``ν, ...`` denotes parameters relating to soil type, such as porosity, and
# ``F_T`` is the freeze-thaw term.
# To begin, we will show how to implement adding in this source term. After the results are obtained,
# we will [explain](#Discussion-and-Model-Explanation) how our model parameterizes this effect and
# compare the results with some analytic expections.
# We solve these equations in an effectively 1-d domain with ``z ∈ [-0.2,0]``,
# and with the following boundary and initial conditions:
# ``- κ ∇T(t, z = 0) = 28 W/m^2/K (T - 267.15K) ẑ``
# ``- κ ∇T(t, z= -0.2) = 0 ẑ ``
# `` T(t = 0, z) = 279.85 K``
# ``- K ∇h(t, z = 0) = 0 ẑ ``
# `` -K ∇h(t, z = -0.2) = 0 ẑ``
# `` ϑ_l(t = 0, z) = 0.33 ``.
# The problem setup and soil properties are chosen to match the lab experiment of [Mizoguchi1990](@cite), as detailed in [Hansson2004](@cite) and [DallAmico2011](@cite)].
# # Import necessary modules
# External (non - CliMA) modules
using MPI
using OrderedCollections
using StaticArrays
using Statistics
using Test
using DelimitedFiles
using Plots
# CliMA Parameters
using CLIMAParameters
struct EarthParameterSet <: AbstractEarthParameterSet end
const param_set = EarthParameterSet()
using CLIMAParameters.Planet: ρ_cloud_liq
using CLIMAParameters.Planet: ρ_cloud_ice
# ClimateMachine modules
using ClimateMachine
using ClimateMachine.Land
using ClimateMachine.Land.SoilWaterParameterizations
using ClimateMachine.Land.SoilHeatParameterizations
using ClimateMachine.Mesh.Topologies
using ClimateMachine.Mesh.Grids
using ClimateMachine.DGMethods
using ClimateMachine.DGMethods.NumericalFluxes
using ClimateMachine.DGMethods: BalanceLaw, LocalGeometry
using ClimateMachine.MPIStateArrays
using ClimateMachine.GenericCallbacks
using ClimateMachine.SystemSolvers
using ClimateMachine.ODESolvers
using ClimateMachine.VariableTemplates
using ClimateMachine.SingleStackUtils
using ClimateMachine.BalanceLaws:
BalanceLaw, Prognostic, Auxiliary, Gradient, GradientFlux, vars_state
using ClimateMachine.ArtifactWrappers
# # Preliminary set-up
# Get the parameter set, which holds constants used across CliMA models:
struct EarthParameterSet <: AbstractEarthParameterSet end
const param_set = EarthParameterSet();
# Initialize and pick a floating point precision:
ClimateMachine.init()
const FT = Float64;
# Load plot helpers:
const clima_dir = dirname(dirname(pathof(ClimateMachine)));
include(joinpath(clima_dir, "docs", "plothelpers.jl"));
# # Simulation specific parameters
N_poly = 1
nelem_vert = 20
zmax = FT(0)
zmin = FT(-0.2)
t0 = FT(0)
dt = FT(6)
timeend = FT(3600 * 50)
n_outputs = 50
every_x_simulation_time = ceil(Int, timeend / n_outputs)
Δ = abs(zmin - zmax) / FT(nelem_vert);
# # Soil properties.
# All are given in mks units.
ν = FT(0.535)
θ_r = FT(0.05)
S_s = FT(1e-3)
Ksat = FT(3.2e-6)
vg_α = 1.11
vg_n = 1.48;
ν_ss_quartz = FT(0.7)
ν_ss_minerals = FT(0.0)
ν_ss_om = FT(0.3)
ν_ss_gravel = FT(0.0);
κ_quartz = FT(7.7)
κ_minerals = FT(2.4)
κ_om = FT(0.25)
κ_liq = FT(0.57)
κ_ice = FT(2.29);
κ_solid = k_solid(ν_ss_om, ν_ss_quartz, κ_quartz, κ_minerals, κ_om)
κ_sat_frozen = ksat_frozen(κ_solid, ν, κ_ice)
κ_sat_unfrozen = ksat_unfrozen(κ_solid, ν, κ_liq);
ρp = FT(3200)
ρc_ds = FT((1 - ν) * 2.3e6);
soil_param_functions = SoilParamFunctions{FT}(
porosity = ν,
Ksat = Ksat,
S_s = S_s,
θ_r = θ_r,
ν_ss_gravel = ν_ss_gravel,
ν_ss_om = ν_ss_om,
ν_ss_quartz = ν_ss_quartz,
ρc_ds = ρc_ds,
ρp = ρp,
κ_solid = κ_solid,
κ_sat_unfrozen = κ_sat_unfrozen,
κ_sat_frozen = κ_sat_frozen,
);
# # Build the model
# Initial and Boundary conditions. The default initial condition for
# `θ_i` is zero everywhere, so we don't modify that. Furthermore, since
# the equation for `θ_i` does not involve spatial derivatives, we don't need
# to supply boundary conditions for it.
zero_flux = (aux, t) -> eltype(aux)(0.0)
surface_heat_flux =
(aux, t) -> eltype(aux)(28) * (aux.soil.heat.T - eltype(aux)(273.15 - 6))
T_init = aux -> eltype(aux)(279.85)
ϑ_l0 = (aux) -> eltype(aux)(0.33);
bc = LandDomainBC(
bottom_bc = LandComponentBC(
soil_heat = Neumann(zero_flux),
soil_water = Neumann(zero_flux),
),
surface_bc = LandComponentBC(
soil_heat = Neumann(surface_heat_flux),
soil_water = Neumann(zero_flux),
),
);
# Create the [`SoilWaterModel`](@ref ClimateMachine.Land.SoilWaterModel),
# [`SoilHeatModel`](@ref ClimateMachine.Land.SoilHeatModel),
# and the [`SoilModel`](@ref ClimateMachine.Land.SoilModel) instances.
# Note that we are allowing for the hydraulic conductivity to be affected by
# both temperature and ice fraction by choosing the following
# [`viscosity_factor`](@ref ClimateMachine.Land.SoilWaterParameterizations.viscosity_factor)
# and [`impedance_factor`](@ref ClimateMachine.Land.SoilWaterParameterizations.impedance_factor).
# To turn these off - the default - just remove these lines. These factors are explained more
# [here](../Water/hydraulic_functions.md).
soil_water_model = SoilWaterModel(
FT;
viscosity_factor = TemperatureDependentViscosity{FT}(),
moisture_factor = MoistureDependent{FT}(),
impedance_factor = IceImpedance{FT}(Ω = 7.0),
hydraulics = vanGenuchten{FT}(α = vg_α, n = vg_n),
initialϑ_l = ϑ_l0,
)
soil_heat_model = SoilHeatModel(FT; initialT = T_init);
m_soil = SoilModel(soil_param_functions, soil_water_model, soil_heat_model);
# Create the source term instance. Our phase change model requires
# knowledge of the vertical spacing, so we pass
# that information in via an attribute of the
# [`PhaseChange`](@ref ClimateMachine.Land.PhaseChange) structure.
freeze_thaw_source = PhaseChange{FT}(Δz = Δ);
# Sources are added as elements of a list of sources. Here we just add freezing
# and thawing.
sources = (freeze_thaw_source,);
# Next, we define the required `init_soil!` function, which takes the user
# specified functions of space for `T_init` and `ϑ_l0` and initializes the state
# variables of volumetric internal energy and augmented liquid fraction. This requires
# a conversion from `T` to `ρe_int`.
function init_soil!(land, state, aux, localgeo, time)
myFT = eltype(state)
ϑ_l = myFT(land.soil.water.initialϑ_l(aux))
θ_i = myFT(land.soil.water.initialθ_i(aux))
state.soil.water.ϑ_l = ϑ_l
state.soil.water.θ_i = θ_i
param_set = land.param_set
θ_l = volumetric_liquid_fraction(ϑ_l, land.soil.param_functions.porosity)
ρc_ds = land.soil.param_functions.ρc_ds
ρc_s = volumetric_heat_capacity(θ_l, θ_i, ρc_ds, param_set)
state.soil.heat.ρe_int = volumetric_internal_energy(
θ_i,
ρc_s,
land.soil.heat.initialT(aux),
param_set,
)
end;
# Lastly, package it all up in the `LandModel`:
m = LandModel(
param_set,
m_soil;
boundary_conditions = bc,
source = sources,
init_state_prognostic = init_soil!,
);
# # Build the simulation domain, solver, and callbacks
driver_config = ClimateMachine.SingleStackConfiguration(
"LandModel",
N_poly,
nelem_vert,
zmax,
param_set,
m;
zmin = zmin,
numerical_flux_first_order = CentralNumericalFluxFirstOrder(),
);
solver_config =
ClimateMachine.SolverConfiguration(t0, timeend, driver_config, ode_dt = dt);
dg = solver_config.dg
Q = solver_config.Q
state_types = (Prognostic(), Auxiliary(), GradientFlux())
dons_arr = Dict[dict_of_nodal_states(solver_config, state_types; interp = true)]
time_data = FT[0]
callback = GenericCallbacks.EveryXSimulationTime(every_x_simulation_time) do
dons = dict_of_nodal_states(solver_config, state_types; interp = true)
push!(dons_arr, dons)
push!(time_data, gettime(solver_config.solver))
nothing
end;
# # Run the simulation, and plot the output
ClimateMachine.invoke!(solver_config; user_callbacks = (callback,));
z = get_z(solver_config.dg.grid; rm_dupes = true);
output_dir = @__DIR__;
mkpath(output_dir);
export_plot(
z,
time_data[[1, 16, 31, 46]] ./ (60 * 60),
dons_arr[[1, 16, 31, 46]],
("soil.water.ϑ_l",),
joinpath(output_dir, "moisture_plot.png");
xlabel = "ϑ_l",
ylabel = "z (m)",
time_units = "hrs ",
)
# ![](moisture_plot.png)
export_plot(
z,
time_data[[1, 16, 31, 46]] ./ (60 * 60),
dons_arr[[1, 16, 31, 46]],
("soil.water.θ_i",),
joinpath(output_dir, "ice_plot.png");
xlabel = "θ_i",
ylabel = "z (m)",
time_units = "hrs ",
legend = :bottomright,
)
# ![](ice_plot.png)
export_plot(
z,
time_data[[1, 16, 31, 46]] ./ (60 * 60),
dons_arr[[1, 16, 31, 46]],
("soil.heat.T",),
joinpath(output_dir, "T_plot.png");
xlabel = "T (K)",
ylabel = "z (m)",
time_units = "hrs ",
)
# ![](T_plot.png)
# # Comparison to data
# This data was obtained by us from the figures of [Hansson2004](@cite), but was originally obtained
# by [Mizoguchi1990](@cite). No error bars were reported, and we haven't quantified the error in our
# estimation of the data from images.
dataset = ArtifactWrapper(
@__DIR__,
isempty(get(ENV, "CI", "")),
"mizoguchi",
ArtifactFile[ArtifactFile(
url = "https://caltech.box.com/shared/static/3xbo4rlam8u390vmucc498cao6wmqlnd.csv",
filename = "mizoguchi_all_data.csv",
),],
);
dataset_path = get_data_folder(dataset);
data = joinpath(dataset_path, "mizoguchi_all_data.csv")
ds = readdlm(data, ',')
hours = ds[:, 1][2:end]
vwc = ds[:, 2][2:end] ./ 100.0
depth = ds[:, 3][2:end]
mask_12h = hours .== 12
mask_24h = hours .== 24
mask_50h = hours .== 50;
plot_12h =
scatter(vwc[mask_12h], -depth[mask_12h], label = "", color = "purple")
plot!(
dons_arr[13]["soil.water.θ_i"] .+ dons_arr[13]["soil.water.ϑ_l"],
z,
label = "",
color = "green",
)
plot!(title = "12h")
plot!(xlim = [0.2, 0.55])
plot!(xticks = [0.2, 0.3, 0.4, 0.5])
plot!(ylabel = "Depth (m)");
plot_24h =
scatter(vwc[mask_24h], -depth[mask_24h], label = "Data", color = "purple")
plot!(
dons_arr[25]["soil.water.θ_i"] .+ dons_arr[25]["soil.water.ϑ_l"],
z,
label = "Simulation",
color = "green",
)
plot!(title = "24h")
plot!(legend = :bottomright)
plot!(xlim = [0.2, 0.55])
plot!(xticks = [0.2, 0.3, 0.4, 0.5]);
plot_50h =
scatter(vwc[mask_50h], -depth[mask_50h], label = "", color = "purple")
plot!(
dons_arr[51]["soil.water.θ_i"] .+ dons_arr[51]["soil.water.ϑ_l"],
z,
label = "",
color = "green",
)
plot!(title = "50h")
plot!(xlim = [0.2, 0.55])
plot!(xticks = [0.2, 0.3, 0.4, 0.5]);
plot(plot_12h, plot_24h, plot_50h, layout = (1, 3))
plot!(xlabel = "θ_l+θ_i")
savefig("mizoguchi_data_comparison.png")
# ![](mizoguchi_data_comparison.png)
# # Discussion and Model Explanation
# To begin, let's observe that the freeze thaw source term alone conserves water mass, as
# it satisfies
# ``
# ρ_l \partial_tϑ_l + ρ_i \partial_tθ_i = -F_T + F_T = 0
# ``
# Next, we describe how we define `F_T`.
# The Clausius-Clapeyron (CC) equation defines a pressure-temperature curve along which two
# phases can co-exist. It assumes that the phases are at equal temperature and pressures.
# For water in soil, however, the liquid water experiences pressure `ρ_l g ψ`, where
# `ψ` is the matric potential. A more general form of the CC equation allows for different
# pressures in the two phases. Usually the ice pressure is taken to be zero, which is reasonable
# for unsaturated freezing soils. In saturated soils, freezing can lead to heaving of the soil which
# we do not model. After that assumption is made, we obtain that, below freezing (``T < T_f``)
# ``
# \frac{dp_l}{ρ_l} = L_f \frac{dT}{T},
# ``
# or
# ``
# p_l = p_{l,0} + L_f ρ_l \frac{T-T_f}{T_f} \mathcal{H}(T_f-T)
# ``
# where we have assumed that assumed `T` is near the freezing point, and then
# performed a Taylor explansion of the logarithm,
# and we are ignoring the freezing point depression, which is small (less than one degree) for
# non-clay soils. What we have sketched is further explained in [DallAmico2011](@cite) and [KurylykWatanabe2013](@cite).
# What this implies is that above the freezing point, the pressure is equal to ``p_{l,0}``,
# which is independent of temperature. Once the temperature drops below the freezing point,
# the pressure drops. Since prior to freezing, the pressure ``p_{l,0}`` is equal to
# `ρ_l g ψ(θ_l)`, water undergoing freezing alone (without flowing) should satisfy ([DallAmico2011](@cite)):
# ``
# p_{l,0} = ρ_l g ψ(θ_l+ρ_iθ_i/ρ_l)
# ``
# where `ψ` is the matric potential function of van Genuchten. At each step, we know both
# the water and ice contents, as well as the temperature, and can then solve for
# ``
# θ_{l}^* = (ν-θ_r) ψ^{-1}(p_l/(ρ_l g)) + θ_r.
# ``
# For freezing, the freeze thaw function `F_T` is equal to
# ``
# F_T = \frac{1}{τ} ρ_l (θ_l-θ_{l}^*) \mathcal{H}(T_f-T) \mathcal{H}(θ_l-θ_{l}^*)
# ``
# which brings the `θ_l` to a value which satisfies `p_l = ρ_l g ψ(θ_l)`.
# This is why, in our simulation, we see the liquid
# water fraction approaches a constant around 0.075 in the frozen region, rather than the residual fraction
# of 0.019, or 0. This behavior is observed, for example, in the experiments of [Watanabe2011](@cite).
# Although this approach may indicate that we should replace the pressure head appearing in the
# diffusive water flux term in Richards equation ([DallAmico2011](@cite)), we do not do so at present. As such, we may not be modeling
# the flow of water around the freezing front properly. However, we still observe cryosuction, which
# is the flow of water towards the freezing front, from the unfrozen side. As the water freezes, the liquid
# water content drops,
# setting up a larger gradient in matric potential across the freezing front, which generates upward flow
# against gravity. This is evident because the total water content at the top is larger at the end of the
# simulation
# than it was at `t=0` (when it was 0.33).
# This model differs from others (e.g. [Painter2011](@cite), [Hansson2004](@cite), [DallAmico2011](@cite)) in that it requires us to set a timescale for the phase change, `τ`.
# In a first-order
# phase transition, the temperature is fixed while the necessary latent heat is either lost or gained by the
# system. Ignoring
# changes in internal energy due to flowing water, we would expect
# ``
# \partial_t ρe_{int} \approx (ρ_l c_l \partial_t θ_l + ρ_i c_i \partial_t θ_i) (T-T_0) -ρ_i L_f \partial_t θ_i
# ``
# ``
# = [(c_i-c_l) (T-T_0) -L_f]F_T \approx -L_f F_T
# ``
# or
# ``
# F_T ∼ \frac{κ|∇²T|}{L_f} ∼\frac{κ}{c̃ Δz²}\frac{c̃ |∂zT| Δz}{L_f}
# ``
# suggesting
# ``
# τ ∼ τ_{LTE}\frac{ρ_lL_f (ν-θ_r)}{c̃ |∂zT| Δz}
# ``
# with
# ``
# τ_{LTE}= c̃ Δz²/κ
# ``
# This is the value we use. This seems to work adequately for modeling freezing front propagation and
# cryosuction, via comparisons with [Mizoguchi1990](@cite), but we plan to revisit it in the future. For example,
# we do not see a strong temperature plateau at the freezing point ([Watanabe2011](@cite)),
# which we would expect while the phase change is occuring. Experimentally, this timescale also affects the abruptness of the freezing front, which our simulation softens.
# # References
# - [Mizoguchi1990](@cite)
# - [Hansson2004](@cite)
# - [DallAmico2011](@cite)
# - [KurylykWatanabe2013](@cite)
# - [Watanabe2011](@cite)
# - [Painter2011](@cite)
# [^a]:
# Note that `θ_i` is always treated as a prognostic variable
# in the `SoilWaterModel`, but with
# zero terms on the RHS unless freezing and thawing is turn on, as demonstrated in this
# tutorial. That means that the user could, in principle, set the initial condition to be nonzero
# (`θ_i(x, y, z ,t=0) = 0` is the default), which in turn would allow a nonzero `θ_i`
# profile to affect things like thermal conductivity, etc,
# in a consistent way. However, it would not be enforced that ``θ_l+θ_i \leq ν``, because there would
# be no physics linking the liquid and water content to each other, and they are independent
# variables in our model. We don't envision this being a common use case.
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] | 2.546769 | 6,917 |
module FractionalTransforms
using LinearAlgebra, DSP, ToeplitzMatrices, FFTW
include("frft.jl")
include("frst.jl")
include("frct.jl")
export frft
export freq_shear, time_shear, sinc_interp
export frst
export frct
end | [
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] | 2.569767 | 86 |
<filename>src/operations.jl
# Submodule for basic wavelets operations
###################################################
# Function that remove empty trailing dimensions
function trim_dim(x)
if size(x,3)==1
x = x[:,:,1];
if size(x,2)==1
x = x[:,1];
end
end
return x;
end # trim_dim
###################################################
# Function that make a subsampling of an array
# along dimension d
"""
...
subsampling(x,d=1,p=2) subsampling of an array x, along dimension d, by a
factor of p, for d≤3
...
"""
function subsampling(x,d=1,p=2)
if d==1
y = x[1:p:end,:,:];
elseif d==2
y = x[:,1:p:end,:];
elseif d==3
y = x[:,:,1:p:end];
else
error("Not implemented");
end
y = trim_dim(y);
return y;
end # subsampling
###################################################
# Function that make a upsampling of an array
# along dimension d
"""
...
upsampling(x,d=1,p=2) upsampling of an array x, along dimension d, by a
factor of p, for d≤3
...
"""
function upsampling(x,d=1,p=2)
if d==1
y = zeros(p*size(x,1),size(x,2),size(x,3));
y[1:p:end,:,:] = x;
elseif d==2
y = zeros(size(x,1),p*size(x,2),size(x,3));
y[:,1:p:end,:] = x;
elseif d==3
y = zeros(size(x,1),size(x,2),p*size(x,3));
y[:,:,1:p:end] = x;
else
error("Not implemented");
end
y = trim_dim(y);
return y;
end # upsampling
###################################################
# Function to compute the spatial domain circular
# convolution of two signals
"""
...
cconv(x,h,d=1) circular convolution along dimension d, of
signals x and h
...
"""
function cconvol(x,h,d=1)
# If p=length(h) is odd then h((p+1)/2) corresponds to the zero position.
# If p is even, h(p/2) corresponds to the zero position.
if d==2
return ( cconvol( x',h, 1 )' );
elseif d==3
# permute do not exists in python
return permute( cconvol( permute(x,[3, 2, 1]),h), [3 2 1]);
end
p = length(h);
if mod(p,2)==0
pc = p/2;
else
pc = (p+1)/2;
end
y = zeros(size(x));
for i=1:length(h)
y = y + h[i]*circshift(x,i-pc);
end
return y;
end # cconvol
#####################################
# Function that flips a vector
"""
...
reverse(x::Array) flips a vector
...
"""
function reverse(x)
return x[length(x):-1:1];
end # function x = reverse(x)
#####################################
# Function that computes the mirror
# filter
"""
...
mirror(h::Array) computes the mirror filter of h[n], namely
g[n] = (-1)^(1-n) h[1-n]
...
"""
function mirror(h)
cat(1, 0, h[length(h):-1:2]) .* ( (-1).^(1:length(h)) )
end #mirror
#####################################
# Function that clamps a value
"""
...
clamp(x::Array, a=0,b=1) clamps a value in [a,b](=[0,1] by default)
...
"""
function clamp(x,a=0,b=1)
y = max(x,a);
y = min(y,b);
return y;
end # clamp
#########################################
# Function to subselect dimensions in an
# array
function subselectdim(f,sel,d)
g = [];
if d==1
g = f[sel,:,:];
elseif d==2
g = f[:,sel,:];
elseif d==3
g = f[:,:,sel];
end
return trim_dim(g);
end
#########################################
# Function to assign value to elements
# of an array
function subassign(f,sel,g)
d = ndims(f);
if d==1
f[sel] = g;
elseif d==2
f[sel,sel] = g;
elseif d==3
f[sel,sel,sel] = g;
end
return f;
end # subassign
#########################################
# Function to select elements of an array
function subselect(f,sel)
d = ndims(f)
if d==1
return f[sel];
elseif d==2
return f[sel,sel];
elseif d==3
return f[sel,sel,sel];
end
return [];
end
##############################################################
# Function to perform wavelet orthognal transform of a signal
# f0, with Jmin minimum scale, dir dirección, and h a filter
"""
...
perform_wavortho_transform(f0:::Array, Jmin:::int,dir,h) computes
the wavelet transform of a signal f0, with Jmin as the minimum
scale in the direction dir∈{-1,1}
...
"""
function perform_wavortho_transf(f0,Jmin,dir,h)
n = size(f0,1);
Jmax = round(Int64,log2(n))-1;
g = [0; h[length(h):-1:2]] .* (-1).^(1:length(h));
f = copy(f0)
if dir==1
### FORWARD ###
for j=Jmax:-1:Jmin
sel = 1:2^(j+1);
a = subselect(f,sel);
for d=1:ndims(f)
a = cat(d, subsampling(cconvol(a,h,d),d), subsampling(cconvol(a,g,d),d) );
end
f = subassign(f,sel,a);
end
else
### FORWARD ###
for j=Jmin:Jmax
sel = 1:2^(j+1);
a = subselect(f,sel);
for d=1:ndims(f)
w = subselectdim(a,2^j+1:2^(j+1),d);
a = subselectdim(a,1:2^j,d);
a = cconvol(upsampling(a,d),reverse(h),d) + cconvol(upsampling(w,d),reverse(g),d);
end
f = subassign(f,sel,a);
end
end
return f;
end # perform_wavortho_transf
#####################################################
# Function that computes the signal to noise radio
"""
...
snr(x::Array,y:::Array) computes the siganl to noise radio
of a original clean signal x and a denoised signal y
...
"""
function snr(x,y)
return 20*log10(norm(x[:])/norm(x[:]-y[:]));
end # snr
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] | 2.269917 | 2,297 |
using ArcadeLearningEnvironment
using CGP
using Logging
using Images
function play_qbert()
game = Game("qbert", 0)
reward = 0
frames = 0
while ~game_over(game.ale)
inputs = get_inputs(game)
action = 0
if inputs[13076] > 0
action = 2
end
reward += act(game.ale, Cint(action))
frames += 1
screen = draw(game)
for pixel_i in -1:1:1
for pixel_j in -1:1:1
if ~(pixel_i == 0 && pixel_j == 0)
screen[56+pixel_i, 63+pixel_j] = RGB{Float64}(1.0, 1.0, 1.0)
end
end
end
save(@sprintf("qbert/frame_%06d.png", frames), screen)
end
close!(game)
reward
end
function random_breakout_expert(nin::Int64, nout::Int64)
# TODO: debug / rewrite without segmentation
c = HPCGPChromo(nin, nout)
pos = sort!(rand(15))
c.genes[1] = pos[1]
for out in nin+(1:nout)
c.genes[out] = pos[15]
end
c.genes[nin+4] = pos[13]
c.genes[nin+5] = pos[14]
CGP.set_genes!(c, nin+1, [
pos[2], rand(), rand(), CGP.Config.func2f(CGP.Config.f_const), 0.6])
CGP.set_genes!(c, nin+2, [
pos[3], rand(), rand(), CGP.Config.func2f(CGP.Config.f_const), 0.8])
CGP.set_genes!(c, nin+3, [
pos[4], pos[1], rand(), CGP.Config.func2f(CGP.Config.f_felzenszwalb), 0.8])
CGP.set_genes!(c, nin+4, [
pos[5], pos[4], pos[2], CGP.Config.func2f(CGP.Config.f_gt), rand()])
CGP.set_genes!(c, nin+5, [
pos[6], pos[4], pos[3], CGP.Config.func2f(CGP.Config.f_lt), rand()])
CGP.set_genes!(c, nin+6, [
pos[7], pos[4], pos[3], CGP.Config.func2f(CGP.Config.f_gt), rand()])
CGP.set_genes!(c, nin+7, [
pos[8], pos[5], pos[6], CGP.Config.func2f(CGP.Config.f_and), rand()])
CGP.set_genes!(c, nin+8, [
pos[9], pos[7], rand(), CGP.Config.func2f(CGP.Config.f_com), rand()])
CGP.set_genes!(c, nin+9, [
pos[10], pos[8], rand(), CGP.Config.func2f(CGP.Config.f_com), rand()])
CGP.set_genes!(c, nin+10, [
pos[11], pos[9], rand(), CGP.Config.func2f(CGP.Config.f_last), rand()])
CGP.set_genes!(c, nin+11, [
pos[12], pos[10], rand(), CGP.Config.func2f(CGP.Config.f_last), rand()])
CGP.set_genes!(c, nin+12, [
pos[13], pos[11], pos[12], CGP.Config.func2f(CGP.Config.f_gt), rand()])
CGP.set_genes!(c, nin+13, [
pos[14], pos[11], pos[12], CGP.Config.func2f(CGP.Config.f_lt), rand()])
CGP.set_genes!(c, nin+14, [
pos[15], rand(), rand(), CGP.Config.func2f(CGP.Config.f_zeros), rand()])
HPCGPChromo(c.genes, nin, nout)
end
function get_breakout_action(inputs::Array{Float64})
action = 2
bullet_box = inputs[94:189, 9:152];
slider_box = inputs[190:192, 9:152];
bullet_poses = find(bullet_box)
slider_poses = find(slider_box)
if length(bullet_poses) > 0 && length(slider_poses) > 0
bullet_pos = mean(map(x->ind2sub(bullet_box, x)[2], bullet_poses))
slider_pos = mean(map(x->ind2sub(slider_box, x)[2], slider_poses))
if bullet_pos < slider_pos
action = 5
else
action = 4
end
end
action
end
function play_breakout()
game = Game("breakout")
reward = 0
frames = 0
act(game.ale, game.actions[2])
life = lives(game.ale)
while ~game_over(game.ale)
action = game.actions[2]
if lives(game.ale) < life
life = lives(game.ale)
else
inputs = get_inputs(game)
action = game.actions[get_breakout_action(inputs)]
end
reward += act(game.ale, action)
frames += 1
screen = draw(game)
save(@sprintf("breakout/frame_%06d.png", frames), screen)
end
close!(game)
reward
end
# ACTION_MEANING = {
# 0 : "NOOP",
# 1 : "FIRE",
# 2 : "UP",
# 3 : "RIGHT",
# 4 : "LEFT",
# 5 : "DOWN",
# 6 : "UPRIGHT",
# 7 : "UPLEFT",
# 8 : "DOWNRIGHT",
# 9 : "DOWNLEFT",
# 10 : "UPFIRE",
# 11 : "RIGHTFIRE",
# 12 : "LEFTFIRE",
# 13 : "DOWNFIRE",
# 14 : "UPRIGHTFIRE",
# 15 : "UPLEFTFIRE",
# 16 : "DOWNRIGHTFIRE",
# 17 : "DOWNLEFTFIRE",
# }
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198
] | 1.904159 | 2,212 |
# Mld.jl
###### mld #####
"""
mld(v)
Compute the Mean log deviation of a vector `v`.
# Examples
```julia
julia> using Inequality
julia> mld([8, 5, 1, 3, 5, 6, 7, 6, 3])
0.1397460530936332
```
"""
mld(v::AbstractVector{<:Real})::Float64 = log(Statistics.mean(v[v .!= 0])) - Statistics.mean(log.(v[v .!= 0]))
###### weighted mld #####
"""
mld(v, w)
Compute the weighted Mean log deviation of a vector `v` using weights given by a weight vector `w`.
Weights must not be negative, missing or NaN. The weights and data vectors must have the same length.
# Examples
```julia
julia> using Inequality
julia> mld([8, 5, 1, 3, 5, 6, 7, 6, 3], collect(0.1:0.1:0.9))
0.10375545537468206
```
"""
function mld(v::AbstractVector{<:Real}, w::AbstractVector{<:Real})::Float64
checks_weights(v, w)
w = w[v .!= 0]
v = v[v .!= 0]
return -sum(w .* log.(v/mean(v, weights(w))) ) / sum(w)
end
function mld(v::AbstractVector{<:Real}, w::AbstractWeights)::Float64
checks_weights(v, w)
w = w[v .!= 0]
v = v[v .!= 0]
return -sum(w .* log.(v/mean(v, weights(w))))/ sum(w)
end
"""
wmld(v, w)
Compute the Mean log deviationof `v` with weights `w`. See also [`mld`](@mld)
"""
wmld(v::AbstractVector{<:Real}, w::AbstractVector{<:Real}) = mld(v, w)
wmld(v::AbstractVector{<:Real}, w::AbstractWeights) = mld(v, w) | [
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] | 2.322357 | 577 |
<reponame>darsnack/TensorBoardLogger.jl
"""
tags_match_re_im(old_tag, new_tag)
If `old_tag` and `new_tag` tag the real part and imaginary part of the same
quantity, such as `old_tag = "test/struct/re"` and `new_tag = "test/struct/im"`
then returns true. False otherwise
"""
function tags_match_re_im(old_tag, new_tag)
if old_tag[end-2:end] == "/re"
old_tag_parts = split(old_tag, "/")
new_tag_parts = split(new_tag, "/")
if ( all(old_tag_parts[1:end-1] .== new_tag_parts[1:end-1]) &&
new_tag_parts[end] == "im" )
return true
end
end
return false
end
"""
lookahead_deserialize(old_tag, old_val, summaries, state, type)
Looks ahead at state, and checks that the tag of `state` matches `old_tag`, and
if so, attempts to combine the value of `state` with that of `old_val`.
For example, if `old_tag = "someval/re"` then if `state` contains "someval/im" the
two values will be combined as real and immaginary part.
Returns a possibly modified tag, a possibily modified value, and the number
of states that it has looked ahead.
If the lookahead fails to find matching states, he returns (old_tag, old_val, 0)
"""
function lookahead_deserialize(old_tag, old_val, evs::Summary, state, typ)
res = old_tag, old_val, state
if typ == :histo
res = lookahead_deserialize_histogram_summary(old_tag, old_val, evs, state)
elseif typ === :image
res = lookahead_deserialize_image_summary(old_tag, old_val, evs, state)
elseif typ == :simple_value
res = lookahead_deserialize_simple_value_summary(old_tag, old_val, evs, state)
end
return res
end
# Lookahead for simple values.
# Checks the next entry if it is /re and /im, and combines them
function lookahead_deserialize_simple_value_summary(old_tag, old_val, evs::Summary,
state)
# prepare the default output (when no action is taken)
result = old_tag, old_val, state
# iterate to the next element
res = iterate(evs, state + 1)
res == nothing && return result
# if the next event is identified, check its type
(new_tag, summary), i_state = res
typ = summary_type(summary)
# if types match, check tags. If they match, return modified structure
if typ === :simple_value && tags_match_re_im(old_tag, new_tag)
val_im = summary.simple_value
result = new_tag[1:end-3], old_val + im*val_im, i_state - 1
else
return result
end
end
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220,
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198,
437,
198
] | 2.534343 | 990 |
###############################################################################
# Reset and Clear
###############################################################################
export @clearsnaps, @clearallsnaps, @resetallsnaps
brandnewsnaps!() = begin
global _num_trace_locations = 0
empty!(happysnaps)
empty!(location_counts)
empty!(watched_exprs)
end
macro resetallsnaps()
:(RickTracy.brandnewsnaps!()) |> esc
end
macro clearallsnaps()
:(empty!(RickTracy.happysnaps)) |> esc
end
clearsnaps(exprstr) = begin
#find snaps that match key, and remove them from the happysnaps vector
filter!((st)->st.exprstr == exprstr, happysnaps) #slow
end
macro clearsnaps(exprs...)
res = :()
for expr in exprs
exprstr = "$expr" #expr as a string
res = :($res; RickTracy.clearsnaps(exprstr))
end
res |> esc
end
macro clearunwatch(exprs...)
:(@unwatch exprs; @clearsnaps(exprs)) |> esc
end
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] | 2.747826 | 345 |
<gh_stars>1-10
# Script
const SIGHASH_ALL = UInt32(1)
const SIGHASH_NONE = UInt32(2)
const SIGHASH_SINGLE = UInt32(3)
const SCRIPT_TYPES = Dict([
("P2PKH", [0x6f, 0x00]),
("P2SH", [0xc4, 0x05])
])
# Block
GENESIS_BLOCK_HASH = Dict([
(false, hex2bytes("000000000019d6689c085ae165831e934ff763ae46a2a6c172b3f1b60a8ce26f")),
(true, hex2bytes("000000000933ea01ad0ee984209779baaec3ced90fa3f408719526f8d77f4943"))
])
# Network
USER_AGENT = "/bitcoin.jl:0.1/"
const DEFAULT = Dict([
("version", 70015),
("services", zero(UInt64)),
("ip", ip"0.0.0.0"),
("port", Dict([(false, 8333),
(true, 18333)])),
("rpcport", Dict([(false, 8332),
(true, 18332)])),
("start_height", zero(UInt32)),
("relay", true)
])
const IPV4_PREFIX = append!(fill(0x00, 10), [0xff, 0xff])
const NODE_URL = "btc.brane.cc"
const TX_DATA_TYPE = 1
const BLOCK_DATA_TYPE = 2
const FILTERED_BLOCK_DATA_TYPE = 3
const COMPACT_BLOCK_DATA_TYPE = 4
const DATA_MESSAGE_NAME = Dict([
(1, "MSG_TX"),
(2, "MSG_BLOCK"),
(3, "MSG_FILTERED_BLOCK"),
(4, "MSG_CMPCT_BLOCK"),
(5, "MSG_WITNESS_BLOCK"),
(6, "MSG_WITNESS_TX"),
(7, "MSG_FILTERED_WITNESS_BLOCK")])
const DATA_MESSAGE_TYPE = Dict([
("MSG_TX", 1),
("MSG_BLOCK", 2),
("MSG_FILTERED_BLOCK", 3),
("MSG_CMPCT_BLOCK", 4),
("MSG_WITNESS_BLOCK", 5),
("MSG_WITNESS_TX", 6),
("MSG_FILTERED_WITNESS_BLOCK", 7)])
"""
NETWORK_MAGIC is testnet if `true`
"""
const NETWORK_MAGIC = Dict([
(false, 0xd9b4bef9)
(true, 0x0709110b)
])
SERVICES_NAME = Dict([
(0x0000000000000001, "NODE_NETWORK"),
(0x0000000000000002, "NODE_GETUTXO"),
(0x0000000000000004, "NODE_BLOOM"),
(0x0000000000000008, "NODE_WITNESS"),
(0x0000000000000400, "NODE_NETWORK_LIMITED")
])
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] | 1.951768 | 933 |
module Datasource
using ...SimpleTasks.Types
import Base
export get, put
"""
get(datasource::DatasourceService, key::AbstractString; force::Bool=false)
Returns an IO reading the value in the datasource for the given key. Optionally
allow skipping any cached values.
"""
function get(datasource::DatasourceService, key::AbstractString;
override_cache::Bool=false)
error("get is not implemented for $datasource")
end
function Base.getindex(datasource::DatasourceService, key::AbstractString)
get(datasource, key)
end
"""
get{String <: AbstractString}(datasource::DatasourceService,
key::Array{AbstractString, 1};
Returns multiple IO's reading the value in the datasource for the given keys.
Optionally allow skipping any cached values.
"""
# Using parametrics because as of 0.4.6 can not promote Array{ASCIIString, 1}
# to Array{AbstractString, 1}
function get{String <: AbstractString}(datasource::DatasourceService,
keys::Array{String, 1}; override_cache::Bool=false)
return pmap((key) -> Datasource.get(datasource, key;
override_cache=override_cache), keys)
end
function Base.getindex{String <: AbstractString}(datasource::DatasourceService,
keys::Array{String, 1})
get(datasource, keys)
end
"""
put!(datasource::DatasourceService, key::AbstractString,
new_value::Union{IO, Void}=nothing; only_cache::Bool=false)
Put in a new value into the datasource for the given key. The new value can be
specified by the input IO or if no IO is specified, it will be pulled from the
cache. Optionally allow putting the new value into the cache only.
NOTE: The operation is undefined if only_cache = true and input new_value IO is
not specified.
"""
function put!(datasource::DatasourceService, key::AbstractString,
new_value::Union{IO, Void}=nothing; only_cache::Bool=false)
error("put! is not implemented for $datasource")
end
function Base.setindex!(datasource::DatasourceService, key::AbstractString,
new_value::Union{IO, Void}=nothing; only_cache::Bool=false)
put!(datasource, key, new_value)
end
"""
put!{String <: AbstractString, I <: IO}(
datasource::DatasourceService, keys::Array{String, 1},
new_values::Array{I, 1}; only_cache::Bool=false)
Put in new values into the datasource for the given keys. The new value can be
specified by the input IO or if no IO is specified, it will be pulled from the
cache. Optionally allow putting the new value into the cache only.
NOTE: The operation is undefined if only_cache = false and input new_value IO is
not specified.
"""
# Using parametrics because as of 0.4.6 can not promote Array{ASCIIString, 1}
# to Array{AbstractString, 1}
function put!{String <: AbstractString, I <: Union{IO, Void}}(
datasource::DatasourceService, keys::Array{String, 1},
new_values::Array{I, 1}; only_cache::Bool=false)
return pmap((index) -> Datasource.put!(datasource, keys[index],
new_values[index]; only_cache=only_cache), 1:length(keys))
end
function Base.setindex!{String <: AbstractString, I <: Union{IO, Void}}(
datasource::DatasourceService, keys::Array{String, 1},
new_values::Array{I, 1}; only_cache::Bool=false)
put!(datasource, keys, new_values)
end
function put!{String <: AbstractString}(
datasource::DatasourceService, keys::Array{String, 1};
only_cache::Bool=false)
return pmap((index) -> Datasource.put!(datasource, keys[index],
nothing; only_cache=only_cache), 1:length(keys))
end
"""
clear!(datasource::DatasourceService, key::AbstractString};
only_cache::Bool=false)
Delete the value from the datasource. Optionally only delete from cache.
Returns datasource and does nothing if key is not found (this is what Julia
`Dict` does)
"""
function Base.delete!(datasource::DatasourceService, key::AbstractString;
only_cache::Bool=false)
error("delete! is not implemented for $datasource")
end
"""
delete!{String <: AbstractString}(datasource::DatasourceService,
keys::Array{String, 1}; only_cache::Bool=false)
Delete multiple keys from the datasource. Optionally only delete from cache
Returns datasource and does nothing if key is not found (this is what Julia
`Dict` does)
"""
function Base.delete!{String <: AbstractString}(datasource::DatasourceService,
keys::Array{String, 1}; only_cache::Bool=false)
return pmap((key) -> Datasource.delete!(datasource, key;
only_cache=only_cache), keys)
end
"""
clear!()
Clear the datasource!
"""
function clear_cache(datasource::DatasourceService)
error("clear_cache! is not implemented for $datasource")
end
end # module Datasource
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] | 2.931832 | 1,599 |
@reexport module EmptyReservoirs
# Standard library
using Statistics
using Printf
using Dates
using RecipesBase
using Distributions
using Parameters
using StochasticPrograms
using Flux
using HydroModels
using HydroModels: AbstractHydroModel, River, Plant, Area, Scenario, Forecaster, forecast
import HydroModels: modelindices
using Plots: font, text, Shape
export
EmptyReservoirsData,
EmptyReservoirsScenario,
EmptyReservoirsSampler,
EmptyReservoirsModel
include("data.jl")
include("scenarios.jl")
include("indices.jl")
include("model.jl")
end
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] | 3.115385 | 182 |
<gh_stars>10-100
# This file is a part of ElasticArrays.jl, licensed under the MIT License (MIT).
function _split_dims(dims::NTuple{N,Integer}) where {N}
int_dims = Int.(dims)
Base.front(int_dims), int_dims[end]
end
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] | 2.511111 | 90 |
@inline function calcpoint(blockIdx, blockDim, threadIdx, size)
i = (blockIdx - 1) * blockDim + threadIdx
return i, Float32(i)
end
function kernel_texture_warp_native(dst::CuDeviceArray{<:Any,1}, texture::CuDeviceTexture{<:Any,1})
i, u = calcpoint(blockIdx().x, blockDim().x, threadIdx().x, size(dst)[1])
@inbounds dst[i] = texture[u]
return nothing
end
function kernel_texture_warp_native(dst::CuDeviceArray{<:Any,2}, texture::CuDeviceTexture{<:Any,2})
i, u = calcpoint(blockIdx().x, blockDim().x, threadIdx().x, size(dst)[1])
j, v = calcpoint(blockIdx().y, blockDim().y, threadIdx().y, size(dst)[2])
@inbounds dst[i,j] = texture[u,v]
return nothing
end
function kernel_texture_warp_native(dst::CuDeviceArray{<:Any,3}, texture::CuDeviceTexture{<:Any,3})
i, u = calcpoint(blockIdx().x, blockDim().x, threadIdx().x, size(dst)[1])
j, v = calcpoint(blockIdx().y, blockDim().y, threadIdx().y, size(dst)[2])
k, w = calcpoint(blockIdx().z, blockDim().z, threadIdx().z, size(dst)[3])
@inbounds dst[i,j,k] = texture[u,v,w]
return nothing
end
function fetch_all(texture)
dims = size(texture)
d_out = CuArray{eltype(texture)}(undef, dims...)
@cuda threads = dims kernel_texture_warp_native(d_out, texture)
d_out
end
@testset "CuTextureArray(::CuArray)" begin
testheight, testwidth, testdepth = 16, 16, 4
a1D = convert(Array{Float32}, 1:testheight)
a2D = convert(Array{Float32}, repeat(1:testheight, 1, testwidth) + repeat(0.01 * (1:testwidth)', testheight, 1))
a3D = convert(Array{Float32}, repeat(a2D, 1, 1, testdepth))
for k = 1:testdepth; a3D[:,:,k] .+= 0.0001 * k; end
d_a1D = CuArray(a1D)
d_a2D = CuArray(a2D)
d_a3D = CuArray(a3D)
texarr1D = CuTextureArray(d_a1D)
tex1D = CuTexture(texarr1D)
@test fetch_all(tex1D) == d_a1D
texarr2D = CuTextureArray(d_a2D)
tex2D = CuTexture(texarr2D)
@test fetch_all(tex2D) == d_a2D
texarr3D = CuTextureArray(d_a3D)
tex3D = CuTexture(texarr3D)
@test fetch_all(tex3D) == d_a3D
end
@testset "CuTextureArray(::Array)" begin
testheight, testwidth, testdepth = 16, 16, 4
a1D = convert(Array{Float32}, 1:testheight)
a2D = convert(Array{Float32}, repeat(1:testheight, 1, testwidth) + repeat(0.01 * (1:testwidth)', testheight, 1))
a3D = convert(Array{Float32}, repeat(a2D, 1, 1, testdepth))
for k = 1:testdepth; a3D[:,:,k] .+= 0.0001 * k; end
texarr1D = CuTextureArray(a1D)
copyto!(texarr1D, a1D)
tex1D = CuTexture(texarr1D)
@test Array(fetch_all(tex1D)) == a1D
texarr2D = CuTextureArray(a2D)
tex2D = CuTexture(texarr2D)
@test Array(fetch_all(tex2D)) == a2D
tex2D_dir = CuTexture(CuTextureArray(a2D))
@test Array(fetch_all(tex2D_dir)) == a2D
texarr3D = CuTextureArray(a3D)
tex3D = CuTexture(texarr3D)
@test Array(fetch_all(tex3D)) == a3D
end
@testset "CuTexture(::CuArray)" begin
testheight, testwidth, testdepth = 16, 16, 4
a1D = convert(Array{Float32}, 1:testheight)
a2D = convert(Array{Float32}, repeat(1:testheight, 1, testwidth) + repeat(0.01 * (1:testwidth)', testheight, 1))
d_a1D = CuArray(a1D)
d_a2D = CuArray(a2D)
# Strangely, this is not working
texwrap1D = CuTexture(d_a1D)
@test_broken fetch_all(texwrap1D) == d_a1D
# This works as long as d_a2D is well pitched
texwrap2D = CuTexture(d_a2D)
@test fetch_all(texwrap2D) == d_a2D
end
@testset "type support" begin
for T in (Int32, UInt32, Int16, UInt16, Int8, UInt8, Float32, Float16)
testheight, testwidth, testdepth = 32, 32, 4
a2D = rand(T, testheight, testwidth)
d_a2D = CuArray(a2D)
# Using CuTextureArray
tex_2D = CuTexture(d_a2D)
@test fetch_all(tex_2D) == d_a2D
# Wrapping CuArray
# This works as long as d_a2D is well pitched
texwrap_2D = CuTexture(d_a2D)
@test fetch_all(texwrap_2D) == d_a2D
end
end
@testset "multiple channels" begin
testheight, testwidth, testdepth = 16, 16, 4
a2D = [(Int32(i), Int32(j)) for i = 1:testheight, j = 1:testwidth]
d_a2D = CuArray(a2D)
texarr2D = CuTextureArray(d_a2D)
tex2D = CuTexture(texarr2D)
@test fetch_all(tex2D) == d_a2D
testheight, testwidth, testdepth = 16, 16, 4
a2D = [(Int16(i), Int16(j), Int16(i + j), Int16(i - j)) for i = 1:testheight, j = 1:testwidth]
d_a2D = CuArray(a2D)
texarr2D = CuTextureArray(d_a2D)
tex2D = CuTexture(texarr2D)
@test fetch_all(tex2D) == d_a2D
end
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] | 2.181643 | 2,070 |
#__precompile__()
module SparseUtils
import SparseArrays
import SparseArrays: SparseMatrixCSC, nnz
import LinearAlgebra
#import DataUtils
import StatsBase
import Printf
const Callable = Union{Function, Type}
export
c_to_julia_index,
c_to_julia_index!,
density,
nnzcounts,
summarystats,
nrows,
ncols,
prunecols!,
prunecols,
prunerows,
renumbercols,
renumberrows,
renumberrowscols,
hasemptycols,
hasemptyrows
include("findrepeated.jl")
include("nzval.jl")
include("ijv.jl")
include("csc.jl")
include("coo.jl")
include("coorenumber.jl")
include("abstract.jl")
include("countmap.jl")
import .NZVal, .IJV
end # module SparseUtils
# LocalWords: nnzcounts countmap sp
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using Turing, Test
import Random
dir = splitdir(splitdir(pathof(Turing))[1])[1]
include(dir*"/test/test_utils/AllUtils.jl")
@stage_testset "dynamichmc" "dynamichmc.jl" begin
import DynamicHMC
Random.seed!(100)
chn = sample(gdemo_default, DynamicNUTS(), 5000);
check_numerical(chn, [:s, :m], [49/24, 7/6], atol=0.2)
end
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] | 2.261745 | 149 |
module InteractiveErrors
# TODO: de-vendor once changes are upstreamed.
include("vendor/FoldingTrees/src/FoldingTrees.jl")
using .FoldingTrees
using REPL, REPL.TerminalMenus, InteractiveUtils, IterTools, Requires
export toggle, current_theme, set_theme!, reset_theme!, adjust_theme!
#
# Themes.
#
const DEFAULT_THEME = (
function_name = (bold = true,),
directory = (color = :light_black,),
filename = (color = :magenta, bold = true,),
line_number = (color = :green, bold = true),
user_stack = (color = :green, bold = true),
system_stack = (color = :red, bold = true),
stdlib_module = (color = :yellow,),
base_module = (color = :blue,),
core_module = (color = :light_black,),
package_module = (color = :cyan, bold = true,),
unknown_module = (color = :red,),
inlined_frames = (color = :light_black,),
toplevel_frames = (color = :light_black,),
repeated_frames = (color = :red,),
file_contents = (color = :light_black,),
signature = (color = :light_black, format = true, highlight = true),
source = (color = :normal, bold = true, highlight = true),
line_range = (before = 0, after = 5,),
charset = :unicode,
)
const THEME = Ref{Any}(DEFAULT_THEME)
current_theme() = THEME[]
set_theme!(nt::NamedTuple) = THEME[] = nt
set_theme!(; kws...) = set_theme!(_nt(kws))
_nt(kws) = NamedTuple{Tuple(keys(kws))}(values(kws))
reset_theme!() = set_theme!(DEFAULT_THEME)
adjust_theme!(nt::NamedTuple) = set_theme!(merge(current_theme(), nt))
adjust_theme!(; kws...) = adjust_theme!(_nt(kws))
get_theme(key) = get(NamedTuple, current_theme(), key)
get_theme(key, default) = get(current_theme(), key, default)
function style(str; kws...)
sprint(; context = :color => true) do io
printstyled(
io, str;
bold = get(kws, :bold, false),
color = get(kws, :color, :normal),
)
end
end
style(str, key::Symbol) = style(str; get_theme(key)...)
#
# Stackframe Wrapping.
#
struct StackFrameWrapper
sf::StackTraces.StackFrame
n::Int
StackFrameWrapper(tuple) = new(tuple...)
end
function Base.show(io::IO, s::StackFrameWrapper)
func = style(s.sf.func, :function_name)
file = rewrite_path(s.sf.file)
dir, file = dirname(file), basename(file)
file = style(file, :filename)
dir = style(joinpath(dir, ""), :directory)
line = style(s.sf.line, :line_number)
repeated = s.n > 1 ? style("x $(s.n)", :repeated_frames) : ""
print(io, strip("$func $dir$file:$line $repeated"))
end
function rewrite_path(path)
fn(path, replacer) = replace(String(path), replacer; count = 1)
path = fn(path, normpath(Sys.BUILD_STDLIB_PATH) => "@stdlib")
path = fn(path, normpath(Sys.STDLIB) => "@stdlib")
path = fn(path, homedir() => "~")
return path
end
function find_source(file)
# Binary versions of Julia have the wrong stdlib path, fix it.
file = replace(string(file), normpath(Sys.BUILD_STDLIB_PATH) => Sys.STDLIB; count = 1)
return Base.find_source_file(file)
end
#
# Explorer.
#
struct CapturedError
err
bt
end
Base.show(io::IO, ce::CapturedError) = showerror(io, ce.err, ce.bt)
explore(err::CapturedError) = explore(stdout, err)
function explore(io::IO, err::CapturedError; interactive = true)
# Give a printout of the actual error message prior to launching tree
# explorer since it's probably useful to have.
println(io, sprint(showerror, err.err, context = :color => true))
# Use the default cleaning functionality from Base. No need to reinvent.
clean = Base.process_backtrace(err.bt)
wrapped = StackFrameWrapper.(clean)
toplevel = findfirst(s -> StackTraces.is_top_level_frame(s.sf), wrapped)
toplevel = toplevel === nothing ? length(wrapped) : toplevel
user_frames = wrapped[1:toplevel]
system_frames = wrapped[toplevel+1:end]
root = Node{Any}("(stacktrace)")
function make_nodes(root_node, frames; fold = false)
for (nth, frame_group) in enumerate(aggregate_modules(frames))
m = module_of(first(frame_group))
if m === :unknown
for frame in frame_group
fold!(Node{Any}(frame, root_node))
end
else
name =
m === :inlined ? style("[inlined]", :inlined_frames) :
m === :toplevel ? style("[top-level]", :toplevel_frames) :
is_from_stdlib(m) ? style("$(m)", :stdlib_module) :
is_from_base(m) ? style("$(m)", :base_module) :
is_from_core(m) ? style("$(m)", :core_module) :
is_from_package(m) ? style("$(m)", :package_module) :
style("$(m)", :unknown_module)
node = Node{Any}(name, root_node)
for frame in frame_group
current = Node{Any}(frame, node)
fold!(current)
# Formatted signature for the frame:
if !StackTraces.is_top_level_frame(frame.sf)
let lines = _formatted_signature(frame)
if !isempty(lines)
sig = Node{Any}(style("signature", :signature), current)
fold!(sig)
for line in lines
Node{Any}(line, sig)
end
end
end
end
# Source code for the frame:
let lines = _lines_around(frame)
if !isempty(lines)
src = Node{Any}(style("source", :source), current)
for line in lines
Node{Any}(line, src)
end
end
end
end
# Hide any of the following by default:
if m in (:inlined, :toplevel) || is_from_stdlib(m) || is_from_base(m) || is_from_core(m) || fold
fold!(node)
end
# Always open up the very first node, unless it's a toplevel.
if nth === 1 && m !== :toplevel
unfold!(node)
end
end
end
end
user_nodes = Node{Any}(style("(user)", :user_stack), root)
make_nodes(user_nodes, user_frames)
system_nodes = Node{Any}(style("(system)", :system_stack), root)
make_nodes(system_nodes, system_frames; fold = true)
fold!(system_nodes)
menu = TreeMenu(root; dynamic = true, maxsize = 30)
result = interactive ? TerminalMenus.request(menu; cursor = 3) : user_nodes
result === nothing && return
actions = [
"clipboard" => () -> (maybe_clipboard(sprint(showerror, err.err, err.bt[1:toplevel])); nothing),
"print" => () -> (showerror(io, err.err, err.bt[1:toplevel]); nothing),
"stacktrace" => () -> clean,
"exception" => () -> err.err,
"backtrace" => () -> err.bt,
]
data = result.data
extras = []
if isa(data, StackFrameWrapper)
file, line = data.sf.file, data.sf.line
file = find_source(file)
if file !== nothing && isfile(file)
file, line
extras = [
"edit" => () -> (edit(file, line); nothing),
"retry" => () -> true,
]
has_debugger() && push!(extras, "breakpoint" => () -> breakpoint(file, line))
push!(extras, "less" => () -> (less(file, line); nothing))
actions = vcat(extras, actions)
end
if isdefined(data.sf, :linfo)
mi = data.sf.linfo
if isa(mi, Core.MethodInstance)
extras = []
if has_cthulhu()
push!(extras, "ascend" => () -> ascend(mi))
push!(extras, "descend" => () -> descend(mi))
end
if has_jet()
push!(extras, "JET" => () -> report_call(mi))
end
actions = vcat(extras, actions)
end
end
end
result = interactive ? request(MultiSelectMenu(first.(actions); charset = get_theme(:charset, :unicode))) : collect(1:length(actions))
choice = sort(collect(result))
if !isempty(choice)
output = []
for (name, func) in actions[choice]
out = func()
out === nothing || push!(output, Symbol(name) => out)
end
isempty(output) || return NamedTuple{Tuple(first.(output))}(last.(output))
end
return nothing
end
function _lines_around(s::StackFrameWrapper)
file, line = s.sf.file, s.sf.line
file = find_source(file)
if file !== nothing && isfile(file)
lines = readlines(file)
range = get_theme(:line_range)
above = max(1, line - get(range, :before, 0))
below = min(line + get(range, :after, 5), length(lines))
highlighter = get(get_theme(:source), :highlight, true) === true ? highlight : s -> style(s, :file_contents)
return highlighter.(lines[above:below])
else
return String[]
end
end
function _formatted_signature(s::StackFrameWrapper)
str = String(rsplit(string(s.sf), " at "; limit = 2)[1])
str = replace(str, "#unused#" => "")
formatter = get(get_theme(:signature), :format, true) === true ? format_julia_source : identity
highlighter = get(get_theme(:signature), :highlight, true) === true ? highlight : s -> style(s, :file_contents)
fmt = highlighter(formatter(str))
return collect(eachline(IOBuffer(fmt)))
end
# Just give up when there is no clipboard available.
function maybe_clipboard(str)
try
clipboard(str)
catch err
@warn "Could not find a clipboard."
end
end
rootmodule(m::Module) = m === Base ? m : m === parentmodule(m) ? m : rootmodule(parentmodule(m))
rootmodule(::Any) = nothing
modulepath(m::Module) = string(pkgdir(m))
modulepath(other) = ""
is_from_stdlib(m) = startswith(modulepath(rootmodule(m)), Sys.STDLIB)
is_from_base(m) = rootmodule(m) === Base
is_from_core(m) = rootmodule(m) === Core
is_from_package(m) = (r = rootmodule(m); !is_from_core(r) && !is_from_base(r) && !is_from_stdlib(r))
module_of(sf) =
sf.sf.inlined ? :inlined :
sf.sf.func === Symbol("top-level scope") ? :toplevel :
isa(sf.sf.linfo, Core.MethodInstance) ? sf.sf.linfo.def.module :
:unknown
aggregate_modules(stacktrace) = IterTools.groupby(module_of, stacktrace)
#
# REPL hook.
#
const ENABLED = Ref(true)
"""
Turn interactive errors on or off.
"""
toggle() = ENABLED[] = !ENABLED[]
is_toggle_expr(expr) = Meta.isexpr(expr, :call, 1) && expr.args[1] === :toggle
is_retry(::Nothing) = false
is_retry(nt::NamedTuple) = haskey(nt, :retry) && nt.retry === true
maybe_retry(out, expr) = is_retry(out) ? Core.eval(Main, _ast_transforms(expr)) : out
function _ast_transforms(ast)
if isdefined(Base, :active_repl_backend)
for xf in Base.active_repl_backend.ast_transforms
ast = Base.invokelatest(xf, ast)
end
end
return ast
end
function wrap_errors(expr)
if ENABLED[] && !is_toggle_expr(expr)
quote
try
$(Expr(:toplevel, expr))
catch e
$(maybe_retry)($(explore)(($CapturedError)(e, catch_backtrace())), $(Expr(:quote, expr)))
end
end
else
expr
end
end
function setup_repl()
@async begin
done = false
for _ in 1:10
if isdefined(Base, :active_repl_backend)
backend = Base.active_repl_backend
if isdefined(backend, :ast_transforms)
pushfirst!(backend.ast_transforms, wrap_errors)
done = true
break
end
end
sleep(0.5)
end
done || @warn "Could not start `InteractiveErrors` REPL hook."
end
end
#
# Requires.
#
has_cthulhu(args...) = false
ascend(args...) = @warn "`import Cthulhu` to enable `ascend` action."
descend(args...) = @warn "`import Cthulhu` to enable `descend` action."
has_debugger(args...) = false
breakpoint(args...) = @warn "`import Debugger` to enable `breakpoint` action."
has_jet(args...) = false
report_call(args...) = @warn "`import JET` to enable `report_call` action."
has_juliaformatter(args...) = false
format_julia_source(source) = source
has_ohmyrepl(args...) = false
highlight(source) = style(source, :file_contents)
function requires()
@require Cthulhu = "f68482b8-f384-11e8-15f7-abe071a5a75f" begin
has_cthulhu() = true
ascend(mi::Core.MethodInstance) = Cthulhu.ascend(mi)
descend(mi::Core.MethodInstance) = Cthulhu.descend(mi)
end
@require Debugger = "31a5f54b-26ea-5ae9-a837-f05ce5417438" begin
has_debugger() = true
breakpoint(file::AbstractString, line::Integer) = Debugger.breakpoint(file, line)
end
@require JET = "c3a54625-cd67-489e-a8e7-0a5a0ff4e31b" begin
has_jet() = true
function report_call(mi::Core.MethodInstance)
func = Base.tuple_type_head(mi.specTypes).instance
sig = Base.tuple_type_tail(mi.specTypes)
result = JET.report_call(func, sig)
@info "Press return to continue."
readline()
return result
end
end
@require JuliaFormatter = "98e50ef6-434e-11e9-1051-2b60c6c9e899" begin
has_juliaformatter() = true
format_julia_source(source::String) = try JuliaFormatter.format_text(source); catch err; source; end
end
@require OhMyREPL = "5fb14364-9ced-5910-84b2-373655c76a03" begin
has_ohmyrepl() = true
function highlight(source::String)
O = OhMyREPL
tokens = collect(O.tokenize(source))
crayons = fill(O.Crayon(), length(tokens))
O.Passes.SyntaxHighlighter.SYNTAX_HIGHLIGHTER_SETTINGS(crayons, tokens, 0)
io = IOBuffer()
for (token, crayon) in zip(tokens, crayons)
print(io, crayon)
print(io, O.untokenize(token))
print(io, O.Crayon(reset = true))
end
return String(take!(io))
end
end
end
#
# Module Initialisation.
#
function __init__()
setup_repl()
requires()
end
end # module
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] | 2.116357 | 6,884 |
"""
transpose_to_axes(coord, b, n)
Convert from Hilbert index to a coordinatefor `b` bits
in `n` dimensions.
"""
function transpose_to_axes!(X::Vector{T}, b, n) where {T <: Integer}
N = T(2) << (b - 1)
# Gray decode by H^(H/2)
t = X[n] >> 1
for io = n:-1:2
X[io] ⊻= X[io - 1]
end
X[1] ⊻= t
# Undo excess work
Q = T(2)
while Q != N
P = Q - one(T)
for jo = n:-1:1
if (X[jo] & Q) != zero(T)
X[1] ⊻= P # invert
else # exchange
t = (X[1] ⊻ X[jo]) & P
X[1] ⊻= t
X[jo] ⊻= t
end
end
Q <<= 1
end
end
function axes_to_transpose!(X::Vector{T}, b, n) where {T <: Integer}
M = one(T) << (b - 1)
# Inverse undo
Q = M
while Q > one(T)
P = Q - one(T)
for io = 1:n
if (X[io] & Q) != zero(T)
X[1] ⊻= P
else
t = (X[1] ⊻ X[io]) & P
X[1] ⊻= t
X[io] ⊻= t
end
end
Q >>= 1
end
# Gray encode
for jo = 2:n
X[jo] ⊻= X[jo - 1]
end
t2 = zero(T)
Q = M
while Q > one(T)
if (X[n] & Q) != 0
t2 ⊻= (Q - one(T))
end
Q >>= one(T)
end
for ko = 1:n
X[ko] ⊻= t2
end
end
"""
Takes a vector of length `n` and places the bits of all `n` integers
into a single integer. The vector's 1st component is the most significant bit.
"""
function interleave_transpose(::Type{T}, X::Vector, b, n) where {T}
h = zero(T)
for i in 0:(b - 1)
for d in 1:n
ith_bit = (X[d] & (1<<i)) >> i
h |= ith_bit << (i * n + n - d)
end
end
h
end
"""
Takes a single integer and places its values into components of a vector,
bit-by-bit.
"""
function outerleave_transpose!(X::Vector{T}, h, b, n) where {T <: Integer}
X .= zero(T)
for i in 0:(b-1)
for d in 1:n
ith_bit = (h & (one(h) << (i * n + n - d))) >> (i * n + n - d)
X[d] |= ith_bit << i
end
end
end
"""
Takes a vector of length `n` and places the bits of all `n` integers
into a single integer. The vector's 1st component is the least significant bit.
"""
function interleave_transpose_low(::Type{T}, X::Vector{T}, b, n) where {T}
h = zero(T)
for i in 0:(b - 1)
for d in 1:n
h |= ((X[d] & (one(T)<<i))) << (i*(n - 1) + d - 1)
end
end
h
end
function outerleave_transpose_low!(X::Vector{T}, h, b, n) where {T <: Integer}
X .= zero(T)
for i in 0:(b-1)
for d in 1:n
X[d] |= (h & (one(T) << (i * n + d - 1))) >> (i * (n - 1) + d - 1)
end
end
end
"""
GlobalGray(b, n)
GlobalGray(T, b, n)
`T` is a data type for the Hilbert index. It can be signed or unsigned,
as long as it has at least `n * b` bits. `n` is the number of dimensions,
and `b` is the bits per dimension, so each axis value should be between
0 and ``2^b - 1``, inclusive, for the zero-based interface. They should be
between 1 and ``2^b``, inclusive, for the one-based interface.
The GlobalGray algorithm is an n-dimensional Hilbert curve with a simplified
implementation. It follows an article, "Programming the Hilbert Curve," by
<NAME>, 707 (2004), http://dx.doi.org/10.1063/1.1751381.
I call it "Global Gray" because the insight of the article
is that a single, global Gray code can be applied to all
np bits of a Hilbert length.
"""
struct GlobalGray{T} <: HilbertAlgorithm{T}
b::Int
n::Int
end
axis_type(gg::GlobalGray) = large_enough_unsigned(gg.b)
function GlobalGray(b, n)
ttype = large_enough_unsigned(b * n)
GlobalGray{ttype}(b, n)
end
function GlobalGray(::Type{T}, b, n) where {T}
GlobalGray{T}(b, n)
end
function encode_hilbert_zero!(g::GlobalGray{T}, X::Vector)::T where {T}
axes_to_transpose!(X, g.b, g.n)
interleave_transpose(T, X, g.b, g.n)
end
"""
encode_hilbert_zero(ha::HilbertAlgorithm{T}, X::Vector{A})
Takes an n-dimensional vector `X` and returns a single integer of type
`T` which orders `X` to improve spatial locality. The input `X` has multiple
axes and the output is called a Hilbert index. This version is zero-based,
so each axis counts from 0, and the smallest Hilbert index is 0.
"""
function encode_hilbert_zero(g::GlobalGray{T}, X::Vector)::T where {T}
Y = copy(X)
encode_hilbert_zero!(g, Y)
end
"""
decode_hilbert_zero!(ha::HilbertAlgorithm{T}}, X::Vector{A}, h::T)
Given a Hilbert index, `h`, computes an n-dimensional coordinate `X`. The type of
the Hilbert index is large enought to contain the bits of all dimensions of the
axis vector, `X`.
"""
function decode_hilbert_zero!(g::GlobalGray{T}, X::Vector, h::T) where {T}
outerleave_transpose!(X, h, g.b, g.n)
transpose_to_axes!(X, g.b, g.n)
end
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] | 2.101979 | 2,324 |
#=
Implement broadcast functionality for Sol
=#
Base.similar(sol::Sol{N}) where {N} =
Sol(similar(sol.x), similar(sol.α))
Broadcast.broadcasted(::typeof(identity), s::Sol{N}) where {N} = s
function Broadcast.materialize!(dest::Sol{N}, src::Sol{N}) where {N}
dest.x .= src.x
dest.α .= src.α
end
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] | 2.468254 | 126 |
<reponame>UnofficialJuliaMirrorSnapshots/MatLang.jl-05b439c0-bb3c-11e9-1d8d-1f0a9ebca87a
developing = false; # are you developing the package or just using?
if developing
# ] add Revise # if you don't have it
path = pwd()
if path[end-6:end] != "MatLang"
error("cd(to the MatLang path)")
end
push!(LOAD_PATH, ".")
using Revise
using MatLang
else
using MatLang
end
################################################################
################################################################
mzero0 = zerosM(:mat, 2) # same as zeros(2,2)
mzero1 = zerosM(:mat, 2, like = zerosM(Int32, 2, 2)) # like method
A=[2 1 2]
mzero2 = zerosM(2, like=A) # same as zeros(Int64,2)
mzero3 = zerosM(2) # same as zeros(2)
mzero4 = zerosM(Int32, 2, 2) # same as zeros(Int32,2,2)
# giving size as Tuple
mzero5 = zerosM((2, 2)) # = zerosM(2,2)
mzero6 = zerosM(Int32, (2, 2))
# giving size as an Array
## non-efficient Matlab way. Array should be Integer otherwise you will get errors.
mzero7 = zerosM([2, 2])
mzero8 = zerosM(Int32, [2, 2])
mzero9 = zerosM([2, 2], like = [2.5 3.0])
################################################################
mone0 = onesM(:mat, 2) # same as ones(2,2)
mone1 = onesM(:mat, 2, like = onesM(Int32, 2, 2)) # like method
A=[2 1 2]
mone2 = onesM(2, like=A) # same as ones(Int64,2)
mone3 = onesM(2) # same as ones(2)
mone4 = onesM(Int32, 2, 2) # same as ones(Int32,2,2)
# giving size as Tuple
mone5 = onesM((2, 2)) # = onesM(2,2)
mone6 = onesM(Int32, (2, 2))
# giving size as an Array
## non-efficient Matlab way. Array should be Integer otherwise you will get errors.
mone7 = onesM([2, 2])
mone8 = onesM(Int32, [2, 2])
mone9 = onesM([2, 2], like = [2.5 3.0])
################################################################
mrandom0 = randM(:mat, 2) # same as rand(2,2)
mrandom1 = randM(:mat, 2, like = randM(Int32, 2, 2)) # like method
A=[2 1 2]
mrandom2 = randM(2, like=A) # same as rand(Int64,2)
mrandom3 = randM(2) # same as rand(2)
mrandom4 = randM(Int32, 2, 2) # same as rand(Int32,2,2)
# giving size as Tuple
mrandom5 = randM((2, 2)) # = randM(2,2)
mrandom6 = randM(Int32, (2, 2))
# giving size as an Array
## non-efficient Matlab way. Array should be Integer otherwise you will get errors.
mrandom7 = randM([2, 2])
mrandom8 = randM(Int32, [2, 2])
mrandom9 = randM([2, 2], like = [2.5 3.0])
################################################################
mtrue0 = trueM(:mat, 2) # same as trues(2,2)
mtrue1 = trueM(2) # same as trues(2)
# giving size as Tuple
mtrue2 = trueM((2, 2)) # = trues(2,2)
# giving size as an Array
## non-efficient Matlab way. Array should be Integer otherwise you will get errors.
mtrue3 = trueM([2, 2])
################################################################
mfalse0 = falseM(:mat, 2) # same as falses(2,2)
mfalse1 = falseM(2) # same as falses(2)
# giving size as Tuple
mfalse2 = falseM((2, 2)) # = falses(2,2)
# giving size as an Array
## non-efficient Matlab way. Array should be Integer otherwise you will get errors.
mfalse3 = falseM([2, 2])
################################################################
mEye0 = eyeM(2); # [1 0 0; 0 1 0]
mEye1 = eyeM(2, 3); # [1 0 0; 0 1 0]
mEye2 = eyeM(Int32, 2, 3); # [1 0 0; 0 1 0]
mEye3 = eyeM(2, 3, like = zerosM(Int8, 2, 2))
mEye4 = eyeM((2, 2)) # giving size as a Tuple. In Julia we use (2,2) instead of giving it as [2,2]
mEye5 = eyeM(Int32, (2, 2)) # giving size as a Tuple. In Julia we use (2,2) instead of giving it as [2,2]
mEye6 = eyeM([2, 2]) # non-efficient Matlab way
mEye7 = eyeM(Int32, [2, 2]) # non-efficient Matlab way
s1 = size(ones(2, 3)) # getting size from another matrix or calculation
mEye8 = eyeM(s1) # giving size as a variable (Tuple).
################################################################
mDiag = diagM([2, 3, 4]) # [0 2 0 ; 0 3 0; 0 4]
mDiag2 = diagM([2, 3, 4], 1) # [0 2 3 0; 0 0 3 0; 0 0 0 4; 0 0 0 0]
mDiag3 = diagM([1 2 3; 4 5 6; 7 8 9]) # [1;5;9]
mDiag4 = diagM([1 2 3; 4 5 6; 7 8 9], 1) # [2;6]
################################################################
A1 = 3 * ones(2, 2);
A2 = 4 * ones(2, 2);
A3 = rand(3, 3);
mBlkdiag1 = blkdiagM(A1, A2, A3)
mBlkdiag2 = blkdiagM(ones(2, 2), 2 * ones(2, 2)) # [ones(2, 2) zeros(2, 2); zeros(2, 2) 2 * ones(2, 2)]
A1 = ones(2, 4);
A2 = 2 * ones(3, 2);
mBlkdiag3 = blkdiagM(A1, A2) # [ones(2,4) zeros(2,2); zeros(3,4) 2*ones(3,2)]
mBlkdiag1obj = blkdiagM(:obj, A1, A2, A3) # returning BlockDiagonal object
mBlkdiag2obj = blkdiagM(:obj, ones(2, 2), 2 * ones(2, 2)) # returning BlockDiagonal object for [ones(2, 2) zeros(2, 2); zeros(2, 2) 2 * ones(2, 2)]
################################################################
mCat1 = catM(1, ones(3, 3), zeros(3, 3)) # [ones(3, 3); zeros(3, 3)]
mCat2 = catM(2, ones(3, 3), zeros(3, 3)) # [ones(3, 3) zeros(3, 3)]
mCat3 = catM(1, ones(3, 3), zeros(3, 3), 2 * ones(3, 3)) # [ones(3, 3); zeros(3, 3); 2 * ones(3, 3)]
mCat4 = catM(3, ones(2, 2, 2), zeros(2, 2, 2)) # [ones(2, 2, 2) zeros(2, 2, 2)]
################################################################
mHCat1 = horzcatM(ones(3, 3), zeros(3, 3)) # [ones(3, 3) zeros(3, 3)]
################################################################
mVCat1 = vertcatM(ones(3, 3), zeros(3, 3)) # [ones(3, 3); zeros(3, 3)]
################################################################
V1 = [1 2 3 4];
mRepelem1 = repelemM(V1, 3) # [1 1 1 2 2 2 3 3 3 4 4 4]
# mRepelem2 = repelemM(V1, (3,2,1,1)) # [1 1 1 2 2 3 4]
# mRepelem3 = repelemM(V1, vec([3 2 1 1])) # [1 1 1 2 2 3 4]
################################################################
mRempat1 = repmatM(10, 3, 2) # [10 10; 10 10; 10 10]
V = [1; 2]
mRempat2 = repmatM(V, 3, 2) # [[1; 2] [1; 2]; [1; 2] [1; 2]; [1; 2] [1; 2]]
mRempat3 = repmatM(V, [3, 2]) # [[1; 2] [1; 2]; [1; 2] [1; 2]; [1; 2] [1; 2]]
mRempat4 = repmatM(V, (3, 2)) # [[1; 2] [1; 2]; [1; 2] [1; 2]; [1; 2] [1; 2]]
mRempat5 = repmatM(V, 2) # [[1; 2] [1; 2]; [1; 2] [1; 2]]
mRempat6 = repmatM(V, 1, 3) # [[1; 2] [1; 2] [1; 2]]
################################################################
mLinspace1 = linspaceM(1, 10) # 1:10
mLinspace2 = linspaceM(1, 10, 2) # 1:2:10 == 1:2:9
mLinspace3 = linspaceM(:arr, 1, 10) # collect(1:10)
mLinspace4 = linspaceM(:arr, 1, 10, 2) # collect(1:2:10)
################################################################
mLogspace1 = logspaceM(1, 5) # 50 logarithmically spaced points between 10^1 and 10^5
mLogspace2 = logspaceM(1, 5, 10) # 10 logarithmically spaced points between 10^1 and 10^5
mLogspace3 = logspaceM(1, 5, :equal) # [10.0^1, 10.0^2, 10.0^3, 10.0^4, 10.0^5] == 10.0.^(1:5)
################################################################
m1Meshgrid0, m2Meshgrid0 = meshgridM(1:2:5) # a 2-D rectangle spanning 1:2:5 in x and y direction == [[1;1;1][3;3;3] [5;5;5]] and [1 1 1; 3 3 3; 5 5 5]
m1Meshgrid1, m2Meshgrid1 = meshgridM(1:2:5, 1:2:5) # a 2-D rectangle spanning 1:2:5 in x and y direction == ([1 3 5;1 3 5; 1 3 5],[1 1 1; 3 3 3; 5 5 5])
m1Meshgrid2, m2Meshgrid2, m3Meshgrid2 = meshgridM(1:6, 20:25, 5:10) # a 3-D rectangle spanning 1:6 in x, 20:25 in y, and 5:10 in z
################################################################
m1Ndgrid0, m2Ndgrid0 = ndgridM(1:2:5, dim = 2) # a 2-D rectangle spanning 1:2:5 in x and y direction == [1 1 1; 3 3 3; 5 5 5] and [1 3 5;1 3 5; 1 3 5]
m1Ndgrid1, m2Ndgrid1 = ndgridM(1:2:5, 1:2:5) # a 2-D rectangle spanning 1:2:5 in x and y direction == ([1 1 1; 3 3 3; 5 5 5], [1 3 5;1 3 5; 1 3 5])
m1Ndgrid2, m2Ndgrid2, m3Ndgrid2 = ndgridM(1:6, 20:25, 5:10) # a 3-D rectangle spanning 1:6 in x, 20:25 in y, and 5:10 in z
################################################################
mFreqspace1 = freqspaceM(10, dim = 1) # 0:0.2:1
mFreqspace2 = freqspaceM(10, :whole, dim = 1) # 0:0.2:1.8
m1Freqspace3, m2Freqspace3 = freqspaceM(10, dim = 2) # returns two -1:0.2:0.8
m1Freqspace4, m2Freqspace4 = freqspaceM(10, :meshgrid, dim = 2) # returns mesgridM(-1:0.2:0.8, -1:0.2:0.8), which is two -1:0.2:0.8
################################################################
A1 = [2 3 4 5 6 7;
1 2 3 4 5 6]
nLength1 = lengthM(A1); # 6
A2 = ["Hi" 6;
"Goodbye" 5;
"Hello" 1]
nLength2 = lengthM(A2); # 3
################################################################
A1 = [2 3 4 5 6 7;
1 2 3 4 5 6]
mSize1 = sizeM(:arr, A1); # [2; 6]
tSize1 = sizeM(A1); # (2, 6)
nA1Size2 = sizeM(A1, 2); # 6
sizeM(A1, 2) == 6 # true
sizeM(:arr, A1, 2) == 6 # false
sizeM(:arr, A1, 2) == [6] # true
nA1Size1, nA1Size2 = sizeM(A1); # 2 and 6
A2 = rand(3, 5, 4)
mSize2 = sizeM(:arr, A2, 2, 3); # [5; 4]
tSize2 = sizeM(A2, 2, 3); # (5, 4)
################################################################
A = rand(3,4,5);
nDim1 = ndims(A) # 3
################################################################
nNumel1 = numelM(1:5) # 5
nNumel2 = numelM([1, 2, 3, 4]) # 4
nNumel3 = numelM([1 2; 3 4]) # 4
################################################################
bIsscalar1 = isscalarM(1) # true
bIsscalar2 = isscalarM(5 * ones(1, 1, 1)) # false
# bIsscalar3 = isscalarM("H") # gives error
#
# bIsscalar4 = isscalarM("Hi") # gives error
bIsscalar5 = isscalarM(["Hi"]) # false
bIsscalar6 = isscalarM(["Hi" "Bye"]) # false
bIsscalar7 = isscalarM('H') # true # becareful that in Julia, chars are always singular.
bIsscalar8 = isscalarM(true) # true
# Matlab way:
bIsscalarMat1 = isscalarM(:mat, 1) # true
bIsscalarMat2 = isscalarM(:mat, [1]) # true
bIsscalarMat3 = isscalarM(:mat, 5 * ones(1, 1, 1)) # true
bIsscalarMat4 = isscalarM(:mat, "H") # true
bIsscalarMat5 = isscalarM(:mat, "Hi") # false
bIsscalarMat6 = isscalarM(:mat, ["Hi"]) # true
bIsscalarMat7 = isscalarM(:mat, ["Hi" "Bye"]) # false
bIsscalarMat8 = isscalarM(:mat, 'H') # true # becareful that in Julia, chars are always singular.
bIsscalarMat9 = isscalarM(:mat, true) # true
################################################################
A1 = [1; 2; 3] # or [1, 2, 3]
bIsvector1 = isvectorM(A1) # true
A2 = [1 2 3]
bIsvector2 = isvectorM(A2) # false
bIsvector3 = isvectorM(ones(3, 1)) # false
bIsvector4 = isvectorM(ones(1, 3)) # false
bIsvector5 = isvectorM(ones(3)) # true
bIsvector6 = isvectorM(1) # false
bIsvector7 = isvectorM("Hi") # false
bIsvector8 = isvectorM(["Hi", "Bye"]) # true
bIsMatrix9 = isvectorM(["Hi" "Bye"]) # false
# Matlab Way:
bIsvectorMat1 = isvectorM(:mat, A1) # true
bIsvectorMat2 = isvectorM(:mat, A2) # true
bIsvectorMat3 = isvectorM(:mat, ones(3, 1)) # true
bIsvectorMat4 = isvectorM(:mat, ones(1, 3)) # true
bIsvectorMat5 = isvectorM(:mat, ones(3)) # true
bIsvectorMat6 = isvectorM(:mat, 1) # true
bIsvectorMat7 = isvectorM(:mat, "Hi") # true
bIsvectorMat8 = isvectorM(:mat, ["Hi", "Bye"]) # true
################################################################
A1 = [1 2 3; 3 5 6]
bIsMatrix1 = ismatrixM(A1) # true
A2 = [1;2;3] # or [1,2,3]
bIsMatrix2 = ismatrixM(A2) # false
bIsMatrix3 = ismatrixM(ones(3, 1)) # true
bIsMatrix4 = ismatrixM(ones(1, 3)) # true
bIsMatrix5 = ismatrixM(ones(3)) # false
bIsMatrix6 = ismatrixM("Hi") # false
bIsMatrix7 = ismatrixM(["Hi", "Bye"]) # false
bIsMatrix8 = ismatrixM(["Hi" "Bye"]) # true
# Matlab Way:
bIsMatrixMat1 = ismatrixM(:mat, A1) # true
bIsMatrixMat2 = ismatrixM(:mat, A2) # true
bIsMatrixMat3 = ismatrixM(:mat, ones(3, 1)) # true
bIsMatrixMat4 = ismatrixM(:mat, ones(1, 3)) # true
bIsMatrixMat5 = ismatrixM(:mat, ones(3)) # true
bIsMatrixMat6 = ismatrixM(:mat, "Hi") # true
bIsMatrixMat7 = ismatrixM(:mat, ["Hi", "Bye"]) # true
################################################################
A1 = [1; 2; 3] # or [1, 2, 3]
bIsrow1 = isrowM(A1) # false
A2 = [1 2 3]
bIsrow2 = isrowM(A2) # true
bIsrow3 = isrowM(ones(3, 1)) # false
bIsrow4 = isrowM(ones(1, 3)) # true
bIsrow5 = isrowM(ones(3)) # false
bIsrow6 = isrowM(1) # false
bIsrow7 = isrowM("Hi") # false
bIsrow8 = isrowM(["Hi", "Bye"]) # false
bIsrow9 = isrowM(["Hi" "Bye"]) # true
# Matlab Way:
bIsrowMat1 = isrowM(:mat, A1) # false
bIsrowMat2 = isrowM(:mat, A2) # true
bIsrowMat3 = isrowM(:mat, ones(3, 1)) # false
bIsrowMat4 = isrowM(:mat, ones(1, 3)) # true
bIsrowMat5 = isrowM(:mat, ones(3)) # false
bIsrowMat6 = isrowM(:mat, 1) # true
bIsrowMat7 = isrowM(:mat, "Hi") # true
bIsrowMat8 = isrowM(:mat, ["Hi", "Bye"]) # false
################################################################
A1 = [1; 2; 3] # or [1, 2, 3]
bIscolumn1 = iscolumnM(A1) # false
A2 = [1 2 3]
bIscolumn2 = iscolumnM(A2) # false
bIscolumn3 = iscolumnM(ones(3, 1)) # tue
bIscolumn4 = iscolumnM(ones(1, 3)) #false
bIscolumn5 = iscolumnM(ones(3)) # false
bIscolumn6 = iscolumnM(1) # false
bIscolumn7 = iscolumnM("Hi") # false
bIscolumn8 = iscolumnM(["Hi", "Bye"]) # false
bIscolumn9 = iscolumnM(["Hi" "Bye"]) # false
# Matlab Way:
bIscolumnMat1 = iscolumnM(:mat, A1) # true
bIscolumnMat2 = iscolumnM(:mat, A2) # false
bIscolumnMat3 = iscolumnM(:mat, ones(3, 1)) # true
bIscolumnMat4 = iscolumnM(:mat, ones(1, 3)) # false
bIscolumnMat5 = iscolumnM(:mat, ones(3)) # true
bIscolumnMat6 = iscolumnM(:mat, 1) # true
bIscolumnMat7 = iscolumnM(:mat, "Hi") # true
bIscolumnMat8 = iscolumnM(:mat, ["Hi", "Bye"]) # true
################################################################
mSort0 = sortM([5, 3, 19, 20, 1, 4]) # [1, 3, 4, 5, 19, 20]
A = [5, 3, 19, 20, 1, 4]
mSort1, iSort1 = sortM(A, I = true) # returning sort index
A[iSort1] == mSort1
mSort2 = sortM([5, 3, 19, 20, 1, 4], :descend) # [20,19,5,4,3,1]
mSort3 = sortM([1 5 3; 4 1 10]) # [1 1 3; 4 5 10]
mSort4 = sortM([1 5 3; 4 1 10], 2, :ascend) # [1 3 5; 1 4 10]
B = [1 5 3; 4 1 10]
mSort5, iSort5 = sortM(B, 2, :ascend, I = true) # [1 3 5; 1 4 10]
B[iSort5] == mSort5
A = zerosM(Integer, 2, 2, 2)
A[:, :, 1] = [2 3; 1 6]
A[:, :, 2] = [-1 9; 0 12]
mSort6 = sortM(A, 3) # 3D sort
################################################################
sFlip1 = flipM("Hi") # "iH"
mFlip2 = flipM([1; 2; 3; 4]) #[4;3;2;1]
mFlip3 = flipM([1 2; 3 4]) # flips every column: # [3 4; 1 2]
mFlip4 = flipM([1 2; 3 4], 2) # flip along dims 2: #[2 1; 4 3]
################################################################
mRot1 = rot90M([1 2; 3 4]) #[2 4; 1 3]
mRot2 = rot90M([1 2; 3 4], 3) # [3 1; 4 2]
################################################################
A1 = [2 3 4 5 6 7;
1 2 3 4 5 6]
mTranspose1 = transposeM(A1)
mTranspose2 = transposeM(:arr, A1)
################################################################
A1 = ones(2, 1, 2); # 3 dimensional
mSqueeze1 = squeezeM(A1) # [1 1; 1 1]
A2 = zeros(1, 4, 1);
A2[:, 1:4, ] = [5; 3; 6; 0]
mSqueeze2 = squeezeM(A2) # When it gets one dimensional, it is vertical.
mSqueeze2 == [5; 3; 6; 0] # true
mSqueeze2 == [5 3 6 0] # false
################################################################
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"""
equal_windows(N,window)
Create interval sequence given a length `N` and window `window`.
The last window chunk is not returned if leftover is less than `window`.
"""
function equal_windows(N::Int,window::Int)
leftover = mod(N,window)
1:window:N-leftover
end
"""
bootstrap_window(f,tokens,window)
Calculate `f` on `window`-size chunks of `tokens`.
"""
function bootstrap_window(f::Function,tokens::Vector{T},window=1000) where T
N = length(tokens)
Float64[f(tokens[start:start+window-1])
for start in equal_windows(N,window)]
end
"""
bootstrap_ci(xs)
Calculates a 95% confidence interval given bootstrap sequence `xs`.
"""
bootstrap_ci(xs) = 1.96*std(xs)/sqrt(length(xs))
"""
sttr(tokens,window)
Calculate the Standardized Type-Token Ratio on `tokens` given `window` size.
"""
sttr(tokens,window) = mean(bootstrap_window(ttr,tokens,window))
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function tcm_predictMNL(N, u, assortments)
M = size(assortments,1)
v = zeros(Float64,size(assortments))
for m = 1 : M
v[m,N] = 1.0
for p = 1 : N-1
v[m,p] = exp(u[p]) * assortments[m,p]
end
v[m,:] = v[m,:] / sum(v[m,:])
end
return v
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<reponame>queryverse/DataValues.jl<filename>test/array/test_primitives.jl<gh_stars>1-10
using Test
using DataValues
@testset "DataValueArray: Primitives" begin
n = rand(1:5)
siz = [ rand(2:5) for i in n ]
A = rand(siz...)
M = rand(Bool, siz...)
X = DataValueArray(A, M)
i = rand(1:length(X))
@test values(X, i) == X.values[i]
@test isna(X, i) == X.isna[i]
I = [ rand(1:size(X, i)) for i in 1:n ]
@test values(X, I...) == X.values[I...]
@test isna(X, I...) == X.isna[I...]
# ----- test Base.similar, Base.size ----------------------------------------#
x = DataValueArray{Int}((5, 2))
@test isa(x, DataValueMatrix{Int})
@test size(x) === (5, 2)
y = similar(x, DataValue{Int}, (3, 3))
@test isa(y, DataValueMatrix{Int})
@test size(y) === (3, 3)
z = similar(x, DataValue{Int}, (2,))
@test isa(z, DataValueVector{Int})
@test size(z) === (2,)
# test common use-patterns for 'similar'
dv = DataValueArray{Int}(2)
dm = DataValueArray{Int}(2, 2)
dt = DataValueArray{Int}(2, 2, 2)
similar(dv)
similar(dm)
similar(dt)
similar(dv, 2)
similar(dm, 2, 2)
similar(dt, 2, 2, 2)
# ----- test Base.copy/Base.copy! --------------------------------------------#
# copy
x = DataValueArray([1, 2, NA])
y = DataValueArray([3, NA, 5])
@test isequal(copy(x), x)
@test isequal(copyto!(y, x), x)
# copy!
function nonbits(dv)
ret = similar(dv, DataValue{Integer})
for ii = 1:length(dv)
if !dv.isna[ii]
ret[ii] = dv[ii]
end
end
ret
end
set1 = Any[DataValueArray([1, NA, 3]),
DataValueArray([NA, 5]),
DataValueArray([1, 2, 3, 4, 5]),
DataValueArray(Int[]),
DataValueArray([NA, 5, 3]),
DataValueArray([1, 5, 3])]
set2 = map(nonbits, set1)
for (dest, src, bigsrc, emptysrc, res1, res2) in Any[set1, set2]
@test isequal(copyto!(copy(dest), src), res1)
@test isequal(copyto!(copy(dest), 1, src), res1)
@test isequal(copyto!(copy(dest), 2, src, 2), res2)
@test isequal(copyto!(copy(dest), 2, src, 2, 1), res2)
@test isequal(copyto!(copy(dest), 99, src, 99, 0), dest)
@test isequal(copyto!(copy(dest), 1, emptysrc), dest)
@test_throws BoundsError copyto!(dest, 1, emptysrc, 1)
for idx in [0, 4]
@test_throws BoundsError copyto!(dest, idx, src)
@test_throws BoundsError copyto!(dest, idx, src, 1)
@test_throws BoundsError copyto!(dest, idx, src, 1, 1)
@test_throws BoundsError copyto!(dest, 1, src, idx)
@test_throws BoundsError copyto!(dest, 1, src, idx, 1)
end
@test_throws ArgumentError copyto!(dest, 1, src, 1, -1)
@test_throws BoundsError copyto!(dest, bigsrc)
@test_throws BoundsError copyto!(dest, 3, src)
@test_throws BoundsError copyto!(dest, 3, src, 1)
@test_throws BoundsError copyto!(dest, 3, src, 1, 2)
@test_throws BoundsError copyto!(dest, 1, src, 2, 2)
end
# ----- test Base.fill! ------------------------------------------------------#
X = DataValueArray{Int}(10, 2)
fill!(X, DataValue(10))
Y = DataValueArray{Float64}(10)
fill!(Y, rand(Float64))
@test X.values == fill(10, 10, 2)
@test isequal(X.isna, fill(false, 10, 2))
@test isequal(Y.isna, fill(false, 10))
fill!(X, DataValue())
@test isequal(X.isna, fill(true, 10, 2))
# ----- test Base.deepcopy ---------------------------------------------------#
Y1 = deepcopy(Y)
@test isequal(Y1, Y)
@assert !(Y === Y1)
# ----- test Base.resize! ----------------------------------------------------#
resize!(Y1, 20)
@test Y1.values[1:10] == Y.values[1:10]
@test Y1.isna[1:10] == Y.isna[1:10]
@test Y1.isna[11:20] == fill(true, 10)
resize!(Y1, 5)
@test Y1.values[1:5] == Y.values[1:5]
@test Y1.isna[1:5] == Y.isna[1:5]
# ----- test Base.reshape ----------------------------------------------------#
Y1 = reshape(copy(Y), length(Y), 1)
@test size(Y1) == (length(Y), 1)
@test all(i -> isequal(Y1[i], Y[i]), 1:length(Y))
Y2 = reshape(Y1, 1, length(Y1))
@test size(Y2) == (1, length(Y1))
@test all(i -> isequal(Y1[i], Y2[i]), 1:length(Y2))
# Test that arrays share the same data
Y2.values[1] += 1
Y2.isna[2] = true
@test all(i -> isequal(Y1[i], Y2[i]), 1:length(Y2))
# ----- test Base.ndims ------------------------------------------------------#
for n in 1:4
@test ndims(DataValueArray{Int}(collect(1:n)...)) == n
end
# ----- test Base.length -----------------------------------------------------#
@test length(DataValueArray{Int}(10)) == 10
@test length(DataValueArray{Int}(5, 5)) == 25
@test length(DataValueArray{Int}((3, 3, 3))) == 27
# ----- test Base.lastindex ------------------------------------------------------#
@test lastindex(DataValueArray(collect(1:10))) == 10
@test lastindex(DataValueArray([1, 2, nothing, 4, nothing])) == 5
# ----- test Base.find -------------------------------------------------------#
z = DataValueArray(rand(Bool, 10))
@test (LinearIndices(z))[findall(x -> x != 0, z)] == (LinearIndices(z.values))[findall(z.values)]
z = DataValueArray([false, true, false, true, false, true])
@test isequal((LinearIndices(z))[findall(x -> x != 0, z)], [2, 4, 6])
# ----- test dropna --------------------------------------------------------#
# dropna(X::DataValueVector)
z = DataValueArray([1, 2, 3, NA, 5, NA, 7, NA])
@test dropna(z) == [1, 2, 3, 5, 7]
# dropna(X::AbstractVector)
A = Any[1, 2, 3, DataValue(), 5, DataValue(), 7, DataValue()]
@test dropna(A) == [1, 2, 3, 5, 7]
# dropna(X::AbstractVector{<:DataValue})
B = [1, 2, 3, DataValue(), 5, DataValue(), 7, DataValue()]
@test dropna(B) == [1, 2, 3, 5, 7]
# assert dropna returns copy for !(DataValue <: eltype(X))
nullfree = [1, 2, 3, 4]
returned_copy = dropna(nullfree)
@test nullfree == returned_copy && !(nullfree === returned_copy)
# ----- test dropna! -------------------------------------------------------#
# for each, assert returned values are unwrapped and inplace change
# dropna!(X::DataValueVector)
@test dropna!(z) == [1, 2, 3, 5, 7]
@test isequal(z, DataValueArray([1, 2, 3, 5, 7]))
# dropna!(X::AbstractVector)
@test dropna!(A) == [1, 2, 3, 5, 7]
@test isequal(A, Any[1, 2, 3, 5, 7])
# dropna!(X::AbstractVector{<:DataValue})
@test dropna!(B) == [1, 2, 3, 5, 7]
@test isequal(B, DataValue[1, 2, 3, 5, 7])
# when no nulls present, dropna! returns input vector
returned_view = dropna!(nullfree)
@test nullfree == returned_view && nullfree === returned_view
# test that dropna! returns unwrapped values when DataValues are present
X = [false, 1, :c, "string", DataValue("I am not null"), DataValue()]
@test !any(x -> isa(x, DataValue), dropna!(X))
@test any(x -> isa(x, DataValue), X)
Y = Any[false, 1, :c, "string", DataValue("I am not null"), DataValue()]
@test !any(x -> isa(x, DataValue), dropna!(Y))
@test any(x -> isa(x, DataValue), Y)
# ----- test any(isna, X) --------------------------------------------------#
# any(isna, X::DataValueArray)
z = DataValueArray([1, 2, 3, NA, 5, NA, 7, NA])
@test any(isna, z) == true
@test any(isna, dropna(z)) == false
z = DataValueArray{Int}(10)
@test any(isna, z) == true
# any(isna, A::AbstractArray)
A2 = [DataValue(1), DataValue(2), DataValue(3)]
@test any(isna, A2) == false
push!(A2, DataValue{Int}())
@test any(isna, A2) == true
# any(isna, xs::NTuple)
@test any(isna, (DataValue(1), DataValue(2))) == false
@test any(isna, (DataValue{Int}(), DataValue(1), 3, 6)) == true
# any(isna, S::SubArray{T, N, U<:DataValueArray})
A = rand(10, 3, 3)
M = rand(Bool, 10, 3, 3)
X = DataValueArray(A, M)
i, j = rand(1:3), rand(1:3)
S = view(X, :, i, j)
@test any(isna, S) == any(isna, X[:, i, j])
X = DataValueArray(A)
S = view(X, :, i, j)
@test any(isna, S) == false
# ----- test all(isna, X) --------------------------------------------------#
# all(isna, X::DataValueArray)
z = DataValueArray{Int}(10)
@test all(isna, z) == true
z[1] = 10
@test all(isna, z) == false
# all(isna, X::AbstractArray{<:DataValue})
@test all(isna, DataValue{Int}[DataValue(), DataValue()]) == true
@test all(isna, DataValue{Int}[DataValue(1), DataValue()]) == false
# all(isna, X::Any)
@test all(isna, Any[DataValue(), DataValue()]) == true
@test all(isna, [1, 2]) == false
@test all(isna, 1:3) == false
# ----- test Base.isnan ------------------------------------------------------#
x = DataValueArray([1, 2, NaN, 4, 5, NaN, Inf, NA])
_x = map(isnan, x)
@test isequal(_x, [false, false, true, false,
false, true, false, false])
# ----- test Base.isfinite ---------------------------------------------------#
_x = map(isfinite, x)
@test isequal(_x, [true, true, false, true,
true, false, false, false])
# ----- test conversion methods ----------------------------------------------#
u = DataValueArray(collect(1:10))
v = DataValueArray{Int}(4, 4)
fill!(v, 4)
w = DataValueArray(['a', 'b', 'c', 'd', 'e', 'f', NA])
x = DataValueArray([(i, j, k) for i in 1:10, j in 1:10, k in 1:10])
y = DataValueArray([2, 4, 6, 8, 10])
z = DataValueArray([i * j for i in 1:10, j in 1:10])
_z = DataValueArray(reshape(collect(1:100), 10, 10),
convert(Array{Bool},
reshape([mod(j, 2) for i in 1:10, j in 1:10],
(10, 10))))
_x = DataValueArray([false, true, false, NA, false, true, NA])
a = [i * j * k for i in 1:2, j in 1:2, k in 1:2]
b = collect(1:10)
c = [i for i in 1:10, j in 1:10]
e = convert(DataValueArray, a)
f = convert(DataValueArray{Float64}, b)
g = convert(DataValueArray, c)
h = convert(DataValueArray{Float64}, g)
@test_throws DataValueException convert(Array{Char,1}, w)
@test convert(Array{Char,1},
DataValueArray(dropna(w))) == ['a', 'b', 'c', 'd', 'e', 'f']
@test_throws DataValueException convert(Array{Char}, w)
@test convert(Array{Float64}, u) == Float64[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
@test convert(Vector{Float64}, y) == Float64[2, 4, 6, 8, 10]
@test convert(Matrix{Float64}, z) == Float64[i * j for i in 1:10, j in 1:10]
@test_throws DataValueException convert(Array, w)
@test convert(Array, v) == [4 4 4 4; 4 4 4 4; 4 4 4 4; 4 4 4 4]
@test convert(Array, z) == [i * j for i in 1:10, j in 1:10]
@test convert(Array, u) == [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
@test convert(Array, _x, false) == [false, true, false, false,
false, true, false]
@test convert(Array{Int,1}, _x, 0) == [0, 1, 0, 0, 0, 1, 0]
@test convert(Vector, _x, false) == [false, true, false, false,
false, true, false]
@test sum(convert(Matrix, _z, 0)) == 2775
@test isequal(e[:, :, 1], DataValueArray([1 2; 2 4]))
@test isequal(f, DataValueArray(Float64[i for i in 1:10]))
@test isa(g, DataValueArray{Int,2})
@test isa(h, DataValueArray{Float64,2})
# Base.convert{T}(::Type{Vector}, X::DataValueVector{T})
X = DataValueArray([1, 2, 3, 4, 5])
@test convert(Vector, X) == [1, 2, 3, 4, 5]
push!(X, DataValue())
@test_throws DataValueException convert(Vector, X)
# Base.convert{T}(::Type{Matrix}, X::DataValueMatrix{T})
Y = DataValueArray([1 2; 3 4; 5 6; 7 8; 9 10])
@test convert(Matrix, Y) == [1 2; 3 4; 5 6; 7 8; 9 10]
Z = DataValueArray([1 2; 3 4; 5 6; 7 8; 9 NA])
@test_throws DataValueException convert(Matrix, Z)
# float(X::DataValueArray)
A = rand(Int, 20)
M = rand(Bool, 20)
X = DataValueArray(A, M)
@test isequal(map(float, X), DataValueArray(float(A), M))
# ----- test Base.hash (julia/base/hashing.jl:5) -----------------------------#
# Omitted for now, pending investigation into DataValueArray-specific
# method.
# TODO: reinstate testing once decision whether or not to implement
# DataValueArray-specific hash method is reached.
# ----- test unique (julia/base/set.jl:107) ----------------------------------#
x = DataValueArray([1, NA, -2, 1, NA, 4])
@assert isequal(unique(x), DataValueArray([1, NA, -2, 4]))
@assert isequal(unique(reverse(x)),
DataValueArray([4, NA, 1, -2]))
end
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] | 2.192735 | 5,946 |
#=
@author : <NAME> <<EMAIL>>
<NAME> <<EMAIL>>
=#
using Plots
pyplot()
using Distributions
using LaTeXStrings
# == Set parameters == #
srand(42) # reproducible results
n = 250 # Choice of n
k = 10000 # Number of draws of Y_n
dist = Exponential(1./2.) # Exponential distribution, lambda = 1/2
μ, s = mean(dist), std(dist)
# == Draw underlying RVs. Each row contains a draw of X_1,..,X_n == #
data = rand(dist, k, n)
# == Compute mean of each row, producing k draws of \bar X_n == #
sample_means = mean(data, 2)
# == Generate observations of Y_n == #
Y = sqrt(n) * (sample_means .- μ)
# == Plot == #
xmin, xmax = -3 * s, 3 * s
histogram(Y, nbins=60, alpha=0.5, xlims=(xmin, xmax),
norm=true, label="")
xgrid = linspace(xmin, xmax, 200)
plot!(xgrid, pdf.(Normal(0.0, s), xgrid), color=:black,
linewidth=2, label=LaTeXString("\$N(0, \\sigma^2=$(s^2))\$"),
legendfont=font(12))
| [
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220,
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8177,
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28,
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7,
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] | 2.347826 | 391 |
<filename>ray.jl
include("Vec3.jl")
struct ray
origin::Vec3;
direction::Vec3;
point_at_parameter(r::ray, t::Number) = r.origin + t*r.direction;
end
function color(r::ray)::Vec3
unit_direction = unit_vector(r.direction());
t = 0.5*(y(unit_direction) + 1.0);
return (1.0-t)*Vec3(1.0, 1.0, 1.0) + t*Vec3(0.5, 0.7, 1.0);
end
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13,
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198,
437,
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] | 2.017442 | 172 |
"""
u,v,η,sst = run_model()
runs ShallowWaters with default parameters as defined in src/DefaultParameters.jl
# Examples
```jldoc
julia> u,v,η,sst = run_model(Float64,nx=200,output=true)
```
"""
function run_model(::Type{T}=Float32; # number format
kwargs... # all additional parameters
) where {T<:Real}
P = Parameter(T=T;kwargs...)
return run_model(T,P)
end
function run_model(P::Parameter)
@unpack T = P
return run_model(T,P)
end
function run_model(::Type{T},P::Parameter) where {T<:Real}
@unpack Tprog = P
G = Grid{T,Tprog}(P)
C = Constants{T,Tprog}(P,G)
F = Forcing{T}(P,G)
S = ModelSetup{T,Tprog}(P,G,C,F)
Prog = initial_conditions(Tprog,S)
Diag = preallocate(T,Tprog,G)
Prog = time_integration(Prog,Diag,S)
return Prog
end
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1441,
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198,
437,
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] | 2.077114 | 402 |
struct IMDP <: MDP{Float64, Int} end
function POMDPs.transition(m::IMDP, s, a)
ImplicitDistribution(s, a) do s, a, rng
return s + a + rand(rng)
end
end
m = IMDP()
td = transition(m, 1.0, 1)
@test 2 <= rand(td) <= 3
impldist(m) = ImplicitDistribution(m) do m, rng
return rand(rng, m)
end
@test rand(impldist(Int)) isa Int
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] | 2.142857 | 161 |
<reponame>JuliaGaussianProcesses/AbstractGP
module AbstractGPs
using Distributions
using FillArrays
using LinearAlgebra
using PDMats: AbstractPDMat, ScalMat
using Reexport
@reexport using KernelFunctions
using Random
using Statistics
using StatsBase
using RecipesBase
using IrrationalConstants: log2π
using KernelFunctions: ColVecs, RowVecs
using ChainRulesCore: ChainRulesCore
export GP,
rand!,
mean,
cov,
var,
std,
mean_and_cov,
mean_and_var,
marginals,
logpdf,
elbo,
dtc,
posterior,
VFE,
DTC,
update_posterior,
LatentGP,
ColVecs,
RowVecs
# Various bits of utility functionality.
include("util/common_covmat_ops.jl")
# AbstractGP interface.
include("abstract_gp.jl")
# FiniteGP object that describes the projection of a GP at points x.
include("finite_gp_projection.jl")
# Basic GP object, e.g. to define the prior.
include("mean_function.jl")
include("base_gp.jl")
# Efficient exact posterior GP implementation.
include("exact_gpr_posterior.jl")
# Approximate sparse GP inference for Gaussian likelihood.
include("sparse_approximations.jl")
# LatentGP and LatentFiniteGP objects to accommodate GPs with non-Gaussian likelihoods.
include("latent_gp.jl")
# Plotting utilities.
include("util/plotting.jl")
# Testing utilities.
include("util/TestUtils.jl")
# Deprecations.
include("deprecations.jl")
end # module
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437,
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198
] | 2.814889 | 497 |
using Flase, Test
world = World(
v0 = 1.,
n_dogs = 120,
boxsize = 10.0,
motion = BrownianMotion(
noise = 0.5,
friction = 1.0
),
sheeps = DenseSheeps(
10,
n_sheeps = 10,
)
)
simulation = FiniteSimulation(;
dt = 0.2,
end_time = 100.0,
world = world,
plotter = UnicodePlotter()
)
@testset "Move items" begin
old_grid = copy(simulation.world.sheeps.grid)
@test simulation.time[] < simulation.t_sheep_boredom[]
@test Flase.move_sheep!( simulation ) == false
simulation.time[] = simulation.t_sheep_boredom[] + 1
@test Flase.move_sheep!( simulation ) == true
simulation.time[] = zero( simulation.time[] )
@test old_grid != simulation.world.sheeps.grid
end # testset
@testset "Interactions" begin
pos = simulation.world.dogs.member[3].position
@test all(pos .< Flase.getSheepCoords(simulation.world, pos))
end
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3669,
7,
14323,
1741,
13,
6894,
11,
1426,
4008,
198,
437,
198
] | 2.315136 | 403 |
seed!(12345)
function testSortedUniforms(low, high, h, N)
function cdf(x::Float64)
return x
end
samples = sampleSortedUniforms(N)
## check that they are sorted first
for j in 2:N
if samples[j] < samples[j-1]
return 0.0
end
end
i::Int64 = 0
function sampler()
i = i + 1
return samples[i]
end
return testGOFContinuous(cdf, sampler, low, high, h, N)
end
@test testSortedUniforms(0.01, 0.99, 0.01, 2^21) > 0.01
@test testSortedUniforms(0.02, 0.98, 0.02, 2^10) > 0.01
| [
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] | 2.221739 | 230 |
<gh_stars>1-10
using ITensors
using KrylovKit
using Random: seed!
seed!(1234)
struct TransferMatrix
A::ITensor
end
function (T::TransferMatrix)(v::ITensor)
A = addtags(T.A, "ket"; tags = "Link")
Adag = addtags(dag(T.A), "bra"; tags = "Link")
return noprime(A * v * Adag)
end
χ = 10
d = 2
l = Index(χ, "Link")
s = Index(d, "Site")
A = randomITensor(dag(l)', s, l)
T = TransferMatrix(A)
b = randomITensor(addtags(dag(l), "bra"), addtags(l, "ket"))
b += swaptags(dag(b), "bra", "ket")
# Solve Tx = b
x, con = linsolve(T, b; krylovdim = 80, tol = 1e-4)
@show con
@show norm(T(x) - b)
| [
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] | 2.162455 | 277 |
## ----------- Linear GradRule
# Defined by probGrad(h = hMin), which varies by college, and hMax where probGrad hits max (common to all colleges).
"""
$(SIGNATURES)
Switches for linear graduation rule. It can operate on h levels or gains.
On h levels: Grad prob is linear in h between hMin and hMax. Each college has a calibrated values for prob grad(h = hMin).
On h gains: Grad prob is linear in (h - h0). Each college has a calibrated value for prob grad(h gain = hMin)
"""
Base.@kwdef mutable struct GradRuleSwitchesLinear <: GradRuleSwitches
tFirstGrad :: TimeInt = 4
"Number of courses required for graduation"
minNcForGrad :: ncInt = 40
"Number of 2 year colleges (cannot graduate from those)"
n2Year :: ncInt = 1
"Number of colleges"
nColleges :: ncInt = 4
# Min and max grad prob when graduation is feasible.
gradProbMin :: Double = 0.05
gradProbMax :: Double = 0.95
# Grad prob depends on levels or gains
useHcLevel :: Bool = true
# Does grad rule vary by college
byCollege :: Bool = true
# h level (or h-h0 value) for which min grad prob is reached.
hMin :: Double = 2.0
# Are better colleges harder (or the other way around)?
betterHarder :: Bool = true
end
Base.@kwdef mutable struct GradRuleSetLinear <: GradRuleSet
@grad_rule_set_common
# Grad prob at h = hMin (or h-h0 = 0)
probHminV :: BoundedVector{Double}
# h level (or h-h0) for which min grad prob is reached.
hMin :: Double
# h (or h - h0) at which gradProbMax is reached (the same for all colleges)
hMax :: Double
switches :: GradRuleSwitchesLinear
end
## For one college
struct GradRuleLinear <: GradRule
# Can graduate after at least this many years
tFirstGrad :: TimeInt
# Grad prob at h = hMin or at (h-h0) = hMin
probHmin :: Double
# h level (or h-h0) for which min grad prob is reached.
hMin :: Double
# Grad prob reaches max at h = hMax (or at h - h0 = hMax)
hMax :: Double
gradProbMin :: Double
gradProbMax :: Double
# Min no of courses attempted (cumulative)
nMin :: ncInt
useHcLevel :: Bool
end
function Base.show(io :: IO, g :: GradRuleLinear)
print(io, "Linear graduation rule in")
if use_hc_level(g)
print(io, " h levels. ");
else
print(io, " h gains. ");
end
print(io, "hMin = $(h_min(g)), hMax = $(h_max(g))");
end
"""
$(SIGNATURES)
Table with settings. Columns are explanation and values.
All formatted into strings.
"""
function settings_table(gs :: GradRuleSwitchesLinear)
nc = n_colleges(gs);
tFirstGrad = Int(t_first_grad(gs, nc));
minNcToGrad = Int(min_courses_to_grad(gs, nc; modelUnits = false));
harderStr = gs.betterHarder ? "harder" : "easier";
return [
"Graduation rule" "Linear in h";
"Time to graduation" "$tFirstGrad";
"Min courses for graduation" "$minNcToGrad";
"Better colleges are" harderStr
]
end
function settings_list(gs :: GradRuleSwitchesLinear, st)
nc = n_colleges(gs);
tFirstGrad = symbol_entry(st, :tGradMin, Int(t_first_grad(gs, nc)));
minNcToGrad = symbol_entry(st, :nGrad, Int(min_courses_to_grad(gs, nc; modelUnits = false)));
probGradMin = symbol_entry(st, :probGradMin, grad_prob_min(gs));
probGradMax = symbol_entry(st, :probGradMax, grad_prob_max(gs));
return [tFirstGrad, minNcToGrad, probGradMin, probGradMax]
end
use_hc_level(switches :: GradRuleSwitchesLinear) =
switches.useHcLevel;
h_min(switches :: GradRuleSwitchesLinear) = switches.hMin;
h_min(gs :: GradRuleSetLinear) = h_min(gs.switches);
h_min(g :: GradRuleLinear) = g.hMin;
h_max(gs :: GradRuleSetLinear) = gs.hMax;
h_max(g :: GradRuleLinear) = g.hMax;
grad_colleges(gs :: GradRuleSetLinear) = grad_colleges(gs.switches);
grad_prob_hmin(g :: GradRuleLinear) = g.probHmin;
varies_by_college(switches :: GradRuleSwitchesLinear) =
switches.byCollege;
grad_rule_same!(switches :: GradRuleSwitchesLinear) =
switches.byCollege = false;
better_easier!(switches :: GradRuleSwitchesLinear) =
switches.betterHarder = false;
"""
$(SIGNATURES)
For all colleges.
Assumes college 1 is a 2 year college. Others are 4 year colleges.
"""
function make_grad_rule_set(objId :: ObjectId,
grSwitches :: GradRuleSwitchesLinear)
pProbHmin = init_prob_hmin(objId, grSwitches);
hMin = h_min(grSwitches);
if use_hc_level(grSwitches)
hMax = Double(4.0);
pHRatio = Param(:hMax, ldescription(:hGradMax), lsymbol(:hGradMax),
hMax, hMax, hMin + Double(1.0), hMin + Double(6.0), true);
else
# Change in h at which grad prob hits max
lb = hMin + Double(0.2);
hMax = lb + Double(0.8);
pHRatio = Param(:hMax, ldescription(:dhGradMax), lsymbol(:dhGradMax),
hMax, hMax, lb, Double(5.0), true);
end
pvec = ParamVector(objId, [pHRatio]);
gs = GradRuleSetLinear(objId = objId,
hMin = hMin, hMax = hMax, probHminV = pProbHmin,
switches = grSwitches, pvec = pvec);
@assert validate_grad_set(gs)
return gs
end
function validate_grad_set(gr :: GradRuleSetLinear)
isValid = (n_colleges(gr) > 1);
if any(grad_prob_at_hmin(gr) .>= gr.switches.gradProbMax)
isValid = false;
@warn "Grad prob at hMin > gradProbMax in $gr"
end
return isValid
end
# Only for colleges that produce graduates.
# Must be less than gradProbMax
function init_prob_hmin(parentId :: ObjectId,
grSwitches :: GradRuleSwitchesLinear)
objId = make_child_id(parentId, :probHminV);
if varies_by_college(grSwitches)
nc = length(grad_colleges(grSwitches));
else
# The same grad prob at hMin for all colleges.
nc = 1;
end
# The Bool argument implies a decreasing vector
pMax = grSwitches.gradProbMax - 0.05;
# The Bool argument means that better colleges are harder or easier
if grSwitches.betterHarder
slope = :decreasing;
else
slope = :increasing; # should be :nonmonotone ++++++
end
p = BoundedVector(objId, ParamVector(objId),
slope,
zero(Double), pMax,
fill(Double(0.5), nc));
set_pvector!(p; description = ldescription(:probHminV),
symbol = lsymbol(:probHminV));
return p
end
function grad_prob_at_hmin(gs :: GradRuleSetLinear, ic :: Integer)
if can_graduate(gs, ic)
if varies_by_college(gs)
# Index into list of colleges that can graduate (also into values of probH1V)
gIdx = findfirst(grad_colleges(gs) .== ic);
else
# There is only one value to retrieve. The same for all colleges.
gIdx = 1;
end
probH1 = ModelParams.values(gs.probHminV, gIdx);
else
probH1 = zero(Double);
end
return probH1
end
grad_prob_at_hmin(gs :: GradRuleSetLinear) =
[grad_prob_at_hmin(gs, ic) for ic = 1 : n_colleges(gs)];
"""
$(SIGNATURES)
Make grad rule for one college
"""
function make_grad_rule(gs :: GradRuleSetLinear, iCollege :: Integer)
probHmin = grad_prob_at_hmin(gs, iCollege);
g = GradRuleLinear(t_first_grad(gs, iCollege),
probHmin, h_min(gs), h_max(gs), grad_prob_min(gs), grad_prob_max(gs),
min_courses_to_grad(gs, iCollege),
use_hc_level(gs));
@assert validate_gr(g) "Invalid $g"
return g
end
# function make_test_grad_rule(switches :: GradRuleSwitchesLinear)
# return GradRuleLinear(t_first_grad(switches, 3), Double(0.5),
# h_min(switches), Double(4.0),
# switches.gradProbMin, switches.gradProbMax, switches.minNcForGrad,
# use_hc_level(switches))
# end
function validate_gr(g :: GradRuleLinear)
isValid = true;
if h_min(g) > h_max(g)
isValid = false;
@warn "h_min = $(h_min(g)) > h_max = $(h_max(g))"
end
return isValid
end
## Graduation probability at end of t
# One state
function grad_prob(g :: GradRuleLinear, t :: Integer, h :: Double, n :: T1,
h0 :: Double) where T1 <: Integer
if can_graduate(g, t)
if n >= min_courses_to_grad(g, t)
if use_hc_level(g)
# At h = 1: g.probH1. Then linear in up to g.hMax.
dh = h - h_min(g);
dhMax = h_max(g) - h_min(g);
else
# At dh = 0: g.probH1. Then linear in dh up to g.hMax.
dh = h - h0 - h_min(g);
dhMax = h_max(g) - h_min(g);
end
@assert dhMax > 0.0 "Negative dhMax: $dhMax in $g"
dProb = g.gradProbMax - grad_prob_hmin(g);
@assert dProb > 0.01 "Invalid dProb: $dProb"
gProb = grad_prob_hmin(g) + dh / dhMax * dProb;
gProb = min(g.gradProbMax, max(g.gradProbMin, gProb));
else
gProb = zero(Double);
end
else
gProb = zero(Double);
end
return gProb
end
# ----------------- | [
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12
] | 2.316253 | 3,864 |
# included in runtests.jl
using Test
using WebSockets
import WebSockets:HTTP,
base64encode,
throwto,
OPCODE_TEXT,
locked_write
import Base.BufferStream
include("client_server_functions.jl")
const FURL = "ws://127.0.0.1"
const FPORT = 8092
@info "Start a server with a ws handler that is unresponsive. \nClose from client side. The " *
" close handshake aborts after $(WebSockets.TIMEOUT_CLOSEHANDSHAKE) seconds..."
s = WebSockets.ServerWS(
req::HTTP.Request -> HTTP.Response(200),
(req::HTTP.Request, ws::WebSocket) -> begin
for i=1:16
sleep(1)
i < 11 && println(i)
end
return
end)
startserver(s, url=SURL, port=FPORT)
res = WebSockets.open((_) -> nothing, "$(FURL):$(FPORT)");
@test res.status == 101
close(s)
@info "Start a server with a ws handler that always reads guarded."
sleep(1)
s = WebSockets.ServerWS(
req -> HTTP.Response(200),
(req, ws_serv) -> begin
while isopen(ws_serv)
WebSockets.readguarded(ws_serv)
end
end);
startserver(s, url=SURL, port=FPORT)
sleep(1)
@info "Attempt to read guarded from a closing ws|client. Check for return false."
sleep(1)
WebSockets.open("$(FURL):$(FPORT)") do ws_client
close(ws_client)
data = WebSockets.readguarded(ws_client)
@test (UInt8[], false) == data
end;
sleep(1)
@info "Attempt to write guarded from a closing ws|client. Check for return false."
sleep(1)
WebSockets.open("$(FURL):$(FPORT)") do ws_client
close(ws_client)
data = WebSockets.writeguarded(ws_client, "writethis")
@test false == data
end;
sleep(1)
@info "Attempt to read from closing ws|client. Check caught error."
sleep(1)
try
WebSockets.open("$(FURL):$(FPORT)") do ws_client
close(ws_client)
read(ws_client)
end
catch err
@test typeof(err) <: ErrorException
@test err.msg == "Attempt to read from closed WebSocket|client. First isopen(ws), or use readguarded(ws)!"
end
sleep(1)
@info "Attempt to write to a closing ws|client (this takes some time, there is no check
in WebSockets against it). Check caught error."
sleep(1)
try
WebSockets.open("$(FURL):$(FPORT)") do ws_client
close(ws_client)
write(ws_client, "writethis")
end
catch err
show(err)
@test typeof(err) <: WebSocketClosedError
@test err.message == " while open ws|client: stream is closed or unusable"
end
close(s)
@info "Start a server. The wshandler use global channels for inspecting caught errors."
sleep(1)
chfromserv=Channel(2)
s = WebSockets.ServerWS(
req-> HTTP.Response(200),
ws_serv->begin
while isopen(ws_serv)
try
read(ws_serv)
catch err
put!(chfromserv, err)
put!(chfromserv, stacktrace(catch_backtrace())[1:2])
end
end
end);
startserver(s, url=SURL, port=FPORT)
sleep(3)
@info "Open a ws|client, close it out of protocol. Check server error on channel."
global res = WebSockets.open((ws)-> close(ws.socket), "$(FURL):$(FPORT)")
@test res.status == 101
sleep(1)
global err = take!(chfromserv)
@test typeof(err) <: WebSocketClosedError
@test err.message == " while read(ws|server) BoundsError(UInt8[], (1,))"
global stack_trace = take!(chfromserv)
if VERSION <= v"1.0.2"
# Stack trace on master is zero. Unknown cause.
@test length(stack_trace) == 2
end
close(s)
sleep(1)
@info "Start a server. Errors are output on built-in channel"
sleep(1)
s = WebSockets.ServerWS(
req-> HTTP.Response(200),
ws_serv->begin
while isopen(ws_serv)
read(ws_serv)
end
end);
startserver(s, url=SURL, port=FPORT)
sleep(3)
@info "Open a ws|client, close it out of protocol. Check server error on server.out channel."
sleep(1)
WebSockets.open((ws)-> close(ws.socket), "$(FURL):$(FPORT)");
global err = take!(s.out)
@test typeof(err) <: WebSocketClosedError
@test err.message == " while read(ws|server) BoundsError(UInt8[], (1,))"
sleep(1)
global stack_trace = take!(s.out);
if VERSION <= v"1.0.2"
# Stack trace on master is zero. Unknown cause.
@test length(stack_trace) in [5, 6]
end
while isready(s.out)
take!(s.out)
end
close(s)
startserver(s, url=SURL, port=FPORT)
sleep(3)
@info "Open ws|clients, close using every status code from RFC 6455 7.4.1\n" *
" Verify error messages on server.out reflect the codes."
sleep(1)
for (ke, va) in WebSockets.codeDesc
@info "Closing ws|client with reason ", ke, " ", va
sleep(0.3)
WebSockets.open((ws)-> close(ws, statusnumber = ke), "$(FURL):$(FPORT)")
wait(s.out)
global err = take!(s.out)
@test typeof(err) <: WebSocketClosedError
@test err.message == "ws|server respond to OPCODE_CLOSE $ke:$va"
wait(s.out)
stacktra = take!(s.out)
if VERSION <= v"1.0.2"
# Unknown cause, nighly behaves differently
@test length(stacktra) == 0
end
while isready(s.out)
take!(s.out)
end
sleep(1)
end
@info "Open a ws|client, close it using a status code from RFC 6455 7.4.1\n" *
" and also a custom reason string. Verify error messages on server.out reflect the codes."
sleep(1)
global va = 1000
@info "Closing ws|client with reason", va, " ", WebSockets.codeDesc[va], " and goodbye!"
WebSockets.open((ws)-> close(ws, statusnumber = va, freereason = "goodbye!"), "$(FURL):$(FPORT)")
wait(s.out)
global err = take!(s.out)
@test typeof(err) <: WebSocketClosedError
@test err.message == "ws|server respond to OPCODE_CLOSE 1000:goodbye!"
global stack_trace = take!(s.out)
sleep(1)
@info "Open a ws|client. Throw an InterruptException to it. Check that the ws|server\n " *
"error shows the reason for the close."
sleep(1)
function selfinterruptinghandler(ws)
task = @async WebSockets.open((ws)-> read(ws), "$(FURL):$(FPORT)")
sleep(3)
@async Base.throwto(task, InterruptException())
sleep(1)
nothing
end
WebSockets.open(selfinterruptinghandler, "$(FURL):$(FPORT)")
sleep(6)
global err = take!(s.out)
@test typeof(err) <: WebSocketClosedError
@test err.message == "ws|server respond to OPCODE_CLOSE 1006: while read(ws|client received InterruptException."
global stack_trace = take!(s.out)
close(s)
@info "Trigger check_upgrade WebSocketErrors "
let noupgrade, noconnectionupgrade, key
key = base64encode(rand(UInt8, 16))
noupgrade = WebSockets.Request("GET", "/", [
"Connection" => "Upgrade",
"Sec-WebSocket-Key" => key,
"Sec-WebSocket-Version" => "13"
])
@test_throws WebSockets.WebSocketError WebSockets.check_upgrade(noupgrade)
noconnectionupgrade = WebSockets.Request("GET", "/", [
"Upgrade" => "websocket",
"Sec-WebSocket-Key" => key,
"Sec-WebSocket-Version" => "13"
])
@test_throws WebSockets.WebSocketError WebSockets.check_upgrade(noconnectionupgrade)
end
sleep(2)
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] | 2.365229 | 2,968 |
isacidic(atom::Atom) =
(i = which_natural_aminoacid(atom)) == 0 ? false :
(natural_aminoacids[i].type == "Acidic")
isaliphatic(atom::Atom) =
(i = which_natural_aminoacid(atom)) == 0 ? false :
(natural_aminoacids[i].type == "Aliphatic")
isaromatic(atom::Atom) =
(i = which_natural_aminoacid(atom)) == 0 ? false :
(natural_aminoacids[i].type == "Aromatic")
isbasic(atom::Atom) =
(i = which_natural_aminoacid(atom)) == 0 ? false :
(natural_aminoacids[i].type == "Basic")
ischarged(atom::Atom) =
(i = which_natural_aminoacid(atom)) == 0 ? false : (natural_aminoacids[i].charge != 0)
ishydrophobic(atom::Atom) =
(i = which_natural_aminoacid(atom)) == 0 ? false :
(natural_aminoacids[i].hydrophobic == true)
isneutral(atom::Atom) =
(i = which_natural_aminoacid(atom)) == 0 ? false : (natural_aminoacids[i].charge == 0)
isnonpolar(atom::Atom) =
(i = which_natural_aminoacid(atom)) == 0 ? false :
(natural_aminoacids[i].polar == false)
ispolar(atom::Atom) =
(i = which_natural_aminoacid(atom)) == 0 ? false : (natural_aminoacids[i].polar == true)
isprotein(atom::Atom; newres = nothing) =
(atom.resname == newres || which_natural_aminoacid(atom) != 0) ? true : false
isprotein(residue::Residue; newres = nothing) =
isprotein(residue.atoms[residue.range[begin]]; newres = newres)
backbone_atoms = ["N", "CA", "C", "O"]
isbackbone(atom::Atom; backbone_atoms = backbone_atoms) =
which_natural_aminoacid(atom) == 0 ? false : (atom.name in backbone_atoms)
not_side_chain_atoms = ["N", "CA", "C", "O", "HN", "H", "HA", "HT1", "HT2", "HT3"]
issidechain(atom::Atom; not_side_chain_atoms = not_side_chain_atoms) =
which_natural_aminoacid(atom) == 0 ? false : (!(atom.name in not_side_chain_atoms))
water_residues = ["HOH", "OH2", "TIP3", "TIP3P", "TIP4P", "TIP5P", "TIP7P", "SPC", "SPCE"]
iswater(atom::Atom; water_residues = water_residues) =
(findfirst(isequal(atom.resname), water_residues) != nothing)
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] | 2.362336 | 839 |
# ---
# title: 1261. Find Elements in a Contaminated Binary Tree
# id: problem1261
# author: <NAME>
# date: 2020-10-31
# difficulty: Medium
# categories: Hash Table, Tree
# link: <https://leetcode.com/problems/find-elements-in-a-contaminated-binary-tree/description/>
# hidden: true
# ---
#
# Given a binary tree with the following rules:
#
# 1. `root.val == 0`
# 2. If `treeNode.val == x` and `treeNode.left != null`, then `treeNode.left.val == 2 * x + 1`
# 3. If `treeNode.val == x` and `treeNode.right != null`, then `treeNode.right.val == 2 * x + 2`
#
# Now the binary tree is contaminated, which means all `treeNode.val` have been
# changed to `-1`.
#
# You need to first recover the binary tree and then implement the
# `FindElements` class:
#
# * `FindElements(TreeNode* root)` Initializes the object with a contamined binary tree, you need to recover it first.
# * `bool find(int target)` Return if the `target` value exists in the recovered binary tree.
#
#
#
# **Example 1:**
#
# **![](https://assets.leetcode.com/uploads/2019/11/06/untitled-
# diagram-4-1.jpg)**
#
#
#
# **Input**
# ["FindElements","find","find"]
# [[[-1,null,-1]],[1],[2]]
# **Output**
# [null,false,true]
# **Explanation**
# FindElements findElements = new FindElements([-1,null,-1]);
# findElements.find(1); // return False
# findElements.find(2); // return True
#
# **Example 2:**
#
# **![](https://assets.leetcode.com/uploads/2019/11/06/untitled-diagram-4.jpg)**
#
#
#
# **Input**
# ["FindElements","find","find","find"]
# [[[-1,-1,-1,-1,-1]],[1],[3],[5]]
# **Output**
# [null,true,true,false]
# **Explanation**
# FindElements findElements = new FindElements([-1,-1,-1,-1,-1]);
# findElements.find(1); // return True
# findElements.find(3); // return True
# findElements.find(5); // return False
#
# **Example 3:**
#
# **![](https://assets.leetcode.com/uploads/2019/11/07/untitled-
# diagram-4-1-1.jpg)**
#
#
#
# **Input**
# ["FindElements","find","find","find","find"]
# [[[-1,null,-1,-1,null,-1]],[2],[3],[4],[5]]
# **Output**
# [null,true,false,false,true]
# **Explanation**
# FindElements findElements = new FindElements([-1,null,-1,-1,null,-1]);
# findElements.find(2); // return True
# findElements.find(3); // return False
# findElements.find(4); // return False
# findElements.find(5); // return True
#
#
#
#
# **Constraints:**
#
# * `TreeNode.val == -1`
# * The height of the binary tree is less than or equal to `20`
# * The total number of nodes is between `[1, 10^4]`
# * Total calls of `find()` is between `[1, 10^4]`
# * `0 <= target <= 10^6`
#
#
## @lc code=start
using LeetCode
## add your code here:
## @lc code=end
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] | 2.371212 | 1,188 |
push!(LOAD_PATH, "@stdlib")
import Pkg; Pkg.add("Conda");
using Conda
try
run(`which nvcc`)
ENV["GPU"] = 1
Pkg.build("ADCME")
catch
end
using ADCME
@info "Install Boost"
CONDA = get_conda()
run(`$CONDA install libboost==1.73.0=h3ff78a5_11`)
# run(`$CONDA install boost==1.73.0`)
@info "Install AMGCL"
UNZIP = joinpath(ADCME.BINDIR, "unzip")
if !isdir("$(@__DIR__)/amgcl")
download("https://github.com/ddemidov/amgcl/archive/master.zip", "$(@__DIR__)/amgcl.zip")
run(`$UNZIP -o $(@__DIR__)/amgcl.zip -d $(@__DIR__)`)
mv("$(@__DIR__)/amgcl-master","$(@__DIR__)/amgcl", force=true)
rm("$(@__DIR__)/amgcl.zip")
end
@info "Build Custom Operators"
change_directory("CustomOps/build")
require_file("build.ninja") do
ADCME.cmake()
end
ADCME.make()
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] | 2.113821 | 369 |
<filename>src/gradients/zygote.jl
import Base.getproperty
import Base.findmin
import Base.findmax
import Base.push!
using Zygote: @adjoint, @nograd
import Zygote.literal_getproperty
# ---- #
# Vec3 #
# ---- #
# Hack to avoid nothing in the gradient due to typemax
@adjoint bigmul(x::T) where {T} = bigmul(x), Δ -> (zero(T),)
@adjoint Vec3(a, b, c) = Vec3(a, b, c), Δ -> (Δ.x, Δ.y, Δ.z)
@adjoint literal_getproperty(v::Vec3, ::Val{:x}) =
getproperty(v, :x), Δ -> (Vec3(Δ, zero(v.y), zero(v.z)), nothing)
@adjoint literal_getproperty(v::Vec3, ::Val{:y}) =
getproperty(v, :y), Δ -> (Vec3(zero(v.x), Δ, zero(v.z)), nothing)
@adjoint literal_getproperty(v::Vec3, ::Val{:z}) =
getproperty(v, :z), Δ -> (Vec3(zero(v.x), zero(v.y), Δ), nothing)
@adjoint function dot(a::Vec3, b::Vec3)
dot(a, b), Δ -> begin
Δ = map(x -> isnothing(x) ? zero(eltype(a.x)) : x, Δ)
t1 = Δ * b
t2 = Δ * a
if length(a.x) != length(t1.x)
t1 = Vec3(sum(t1.x), sum(t1.y), sum(t1.z))
end
if length(b.x) != length(t2.x)
t2 = Vec3(sum(t2.x), sum(t2.y), sum(t2.z))
end
return (t1, t2)
end
end
# The purpose of this adjoint is to ensure type inference works
# in the backward pass
@adjoint function cross(a::Vec3{T}, b::Vec3{T}) where {T}
cross(a, b), Δ -> begin
∇a = zero(a)
∇b = zero(b)
x = (Δ.z .* b.y) .- (Δ.y .* b.z)
y = (Δ.x .* b.z) .- (Δ.z .* b.x)
z = (Δ.y .* b.x) .- (Δ.x .* b.y)
if length(a.x) == 1
∇a.x .= sum(x)
∇a.y .= sum(y)
∇a.z .= sum(z)
else
∇a.x .= x
∇a.y .= y
∇a.z .= z
end
x = (Δ.y .* a.z) .- (Δ.z .* a.y)
y = (Δ.z .* a.x) .- (Δ.x .* a.z)
z = (Δ.x .* a.y) .- (Δ.y .* a.x)
if length(b.x) == 1
∇b.x .= sum(x)
∇b.y .= sum(y)
∇b.z .= sum(z)
else
∇b.x .= x
∇b.y .= y
∇b.z .= z
end
return (∇a, ∇b)
end
end
@adjoint place(a::Vec3, cond) = place(a, cond), Δ -> (Vec3(Δ.x[cond], Δ.y[cond], Δ.z[cond]), nothing)
@adjoint place(a::Array, cond) = place(a, cond), Δ -> (Δ[cond], nothing)
@adjoint place_idx!(a::Vec3, b::Vec3, idx) = place_idx!(a, b, idx), Δ -> (zero(Δ), Vec3(Δ[idx]...), nothing)
# ----- #
# Light #
# ----- #
# -------------- #
# - PointLight - #
# -------------- #
@adjoint PointLight(color::Vec3, intensity, pos::Vec3) =
PointLight(color, intensity, pos), Δ -> (Δ.color, Δ.intensity, Δ.position)
@adjoint literal_getproperty(p::PointLight, ::Val{:color}) =
getproperty(p, :color), Δ -> (PointLight(Δ, zero(p.intensity), zero(p.position)), nothing)
@adjoint literal_getproperty(p::PointLight, ::Val{:intensity}) =
getproperty(p, :intensity), Δ -> (PointLight(zero(p.color), Δ, zero(p.position)), nothing)
@adjoint literal_getproperty(p::PointLight, ::Val{:position}) =
getproperty(p, :position), Δ -> (PointLight(zero(p.color), zero(p.intensity), Δ), nothing)
# ---------------- #
# - DistantLight - #
# ---------------- #
@adjoint DistantLight(color::Vec3, intensity, direction::Vec3) =
DistantLight(color, intensity, direction), Δ -> (Δ.color, Δ.intensity, Δ.direction)
@adjoint literal_getproperty(d::DistantLight, ::Val{:color}) =
getproperty(d, :color), Δ -> (DistantLight(Δ, zero(d.intensity), zero(d.direction)), nothing)
@adjoint literal_getproperty(d::DistantLight, ::Val{:intensity}) =
getproperty(d, :intensity), Δ -> (DistantLight(zero(d.color), Δ, zero(d.direction)), nothing)
@adjoint literal_getproperty(d::DistantLight, ::Val{:direction}) =
getproperty(d, :direction), Δ -> (DistantLight(zero(d.color), zero(d.intensity), Δ), nothing)
# -------- #
# Material #
# -------- #
@adjoint Material(color_ambient, color_diffuse, color_specular, specular_exponent,
reflection, texture_ambient, texture_diffuse, texture_specular,
uv_coordinates) =
Material(color_ambient, color_diffuse, color_specular, specular_exponent, reflection,
texture_ambient, texture_diffuse, texture_specular, uv_coordinates),
Δ -> (Δ.color_ambient, Δ.color_diffuse, Δ.color_specular, Δ.specular_exponent,
Δ.reflection, Δ.texture_ambient, Δ.texture_diffuse, Δ.texture_specular,
Δ.uv_coordinates)
@adjoint literal_getproperty(m::Material, ::Val{:color_ambient}) =
getproperty(m, :color_ambient), Δ -> (Material(Δ, zero(m.color_diffuse), zero(m.color_specular),
zero(m.specular_exponent), zero(m.reflection),
isnothing(m.texture_ambient) ? nothing : zero(m.texture_ambient),
isnothing(m.texture_diffuse) ? nothing : zero(m.texture_diffuse),
isnothing(m.texture_specular) ? nothing : zero(m.texture_specular),
isnothing(m.uv_coordinates) ? nothing : zero.(m.uv_coordinates)),
nothing)
@adjoint literal_getproperty(m::Material, ::Val{:color_diffuse}) =
getproperty(m, :color_diffuse), Δ -> (Material(zero(m.color_ambient), Δ, zero(m.color_specular),
zero(m.specular_exponent), zero(m.reflection),
isnothing(m.texture_ambient) ? nothing : zero(m.texture_ambient),
isnothing(m.texture_diffuse) ? nothing : zero(m.texture_diffuse),
isnothing(m.texture_specular) ? nothing : zero(m.texture_specular),
isnothing(m.uv_coordinates) ? nothing : zero.(m.uv_coordinates)),
nothing)
@adjoint literal_getproperty(m::Material, ::Val{:color_specular}) =
getproperty(m, :color_specular), Δ -> (Material(zero(m.color_ambient), zero(m.color_diffuse), Δ,
zero(m.specular_exponent), zero(m.reflection),
isnothing(m.texture_ambient) ? nothing : zero(m.texture_ambient),
isnothing(m.texture_diffuse) ? nothing : zero(m.texture_diffuse),
isnothing(m.texture_specular) ? nothing : zero(m.texture_specular),
isnothing(m.uv_coordinates) ? nothing : zero.(m.uv_coordinates)),
nothing)
@adjoint literal_getproperty(m::Material, ::Val{:specular_exponent}) =
getproperty(m, :specular_exponent), Δ -> (Material(zero(m.color_ambient), zero(m.color_diffuse),
zero(m.color_specular), Δ, zero(m.reflection),
isnothing(m.texture_ambient) ? nothing : zero(m.texture_ambient),
isnothing(m.texture_diffuse) ? nothing : zero(m.texture_diffuse),
isnothing(m.texture_specular) ? nothing : zero(m.texture_specular),
isnothing(m.uv_coordinates) ? nothing : zero.(m.uv_coordinates)),
nothing)
@adjoint literal_getproperty(m::Material, ::Val{:reflection}) =
getproperty(m, :reflection), Δ -> (Material(zero(m.color_ambient), zero(m.color_diffuse),
zero(m.color_specular), zero(m.specular_exponent), Δ,
isnothing(m.texture_ambient) ? nothing : zero(m.texture_ambient),
isnothing(m.texture_diffuse) ? nothing : zero(m.texture_diffuse),
isnothing(m.texture_specular) ? nothing : zero(m.texture_specular),
isnothing(m.uv_coordinates) ? nothing : zero.(m.uv_coordinates)),
nothing)
@adjoint literal_getproperty(m::Material, ::Val{:texture_ambient}) =
getproperty(m, :texture_ambient), Δ -> (Material(zero(m.color_ambient), zero(m.color_diffuse),
zero(m.color_specular), zero(m.specular_exponent),
zero(m.reflection), Δ,
isnothing(m.texture_diffuse) ? nothing : zero(m.texture_diffuse),
isnothing(m.texture_specular) ? nothing : zero(m.texture_specular),
isnothing(m.uv_coordinates) ? nothing : zero.(m.uv_coordinates)),
nothing)
@adjoint literal_getproperty(m::Material, ::Val{:texture_diffuse}) =
getproperty(m, :texture_diffuse), Δ -> (Material(zero(m.color_ambient), zero(m.color_diffuse),
zero(m.color_specular), zero(m.specular_exponent),
zero(m.reflection),
isnothing(m.texture_ambient) ? nothing : zero(m.texture_ambient), Δ,
isnothing(m.texture_specular) ? nothing : zero(m.texture_specular),
isnothing(m.uv_coordinates) ? nothing : zero.(m.uv_coordinates)),
nothing)
@adjoint literal_getproperty(m::Material, ::Val{:texture_specular}) =
getproperty(m, :texture_specular), Δ -> (Material(zero(m.color_ambient), zero(m.color_diffuse),
zero(m.color_specular), zero(m.specular_exponent),
zero(m.reflection),
isnothing(m.texture_ambient) ? nothing : zero(m.texture_ambient),
isnothing(m.texture_diffuse) ? nothing : zero(m.texture_diffuse), Δ,
isnothing(m.uv_coordinates) ? nothing : zero.(m.uv_coordinates)),
nothing)
@adjoint literal_getproperty(m::Material, ::Val{:uv_coordinates}) =
getproperty(m, :uv_coordinates), Δ -> (Material(zero(m.color_ambient), zero(m.color_diffuse),
zero(m.color_specular), zero(m.specular_exponent),
zero(m.reflection),
isnothing(m.texture_ambient) ? nothing : zero(m.texture_ambient),
isnothing(m.texture_diffuse) ? nothing : zero(m.texture_diffuse),
isnothing(m.texture_specular) ? nothing : zero(m.texture_specular),
Δ),
nothing)
# ------- #
# Objects #
# ------- #
# ---------- #
# - Sphere - #
# ---------- #
@adjoint Sphere(center, radius, material::Material) =
Sphere(center, radius, material), Δ -> (Δ.sphere, Δ.radius, Δ.material)
@adjoint literal_getproperty(s::Sphere, ::Val{:center}) =
getproperty(s, :center), Δ -> (Sphere(Δ, zero(s.radius), zero(s.material)), nothing)
@adjoint literal_getproperty(s::Sphere, ::Val{:radius}) =
getproperty(s, :radius), Δ -> (Sphere(zero(s.center), Δ, zero(s.material)), nothing)
@adjoint literal_getproperty(s::Sphere, ::Val{:material}) =
getproperty(s, :material), Δ -> (Sphere(zero(s.center), zero(s.radius), Δ), nothing)
# ------------ #
# - Triangle - #
# ------------ #
@adjoint Triangle(v1, v2, v3, material::Material) =
Triangle(v1, v2, v3, material), Δ -> (Δ.v1, Δ.v2, Δ.v3, Δ.material)
@adjoint literal_getproperty(t::Triangle, ::Val{:v1}) =
getproperty(t, :v1), Δ -> (Triangle(Δ, zero(t.v2), zero(t.v3), zero(t.material)), nothing)
@adjoint literal_getproperty(t::Triangle, ::Val{:v2}) =
getproperty(t, :v2), Δ -> (Triangle(zero(t.v1), Δ, zero(t.v3), zero(t.material)), nothing)
@adjoint literal_getproperty(t::Triangle, ::Val{:v3}) =
getproperty(t, :v3), Δ -> (Triangle(zero(t.v1), zero(t.v2), Δ, zero(t.material)), nothing)
@adjoint literal_getproperty(t::Triangle, ::Val{:material}) =
getproperty(t, :material), Δ -> (Triangle(zero(t.v1), zero(t.v2), zero(t.v3), Δ), nothing)
# ---------------- #
# - TriangleMesh - #
# ---------------- #
#=
@adjoint TriangleMesh(tm, mat, ftmp) =
TriangleMesh(tm, mat, ftmp), Δ -> (Δ.triangulated_mesh, Δ.material, Δ.ftmp)
@adjoint function literal_getproperty(t::TriangleMesh, ::Val{:triangulated_mesh})
tm = getproperty(t, :triangulated_mesh)
z = eltype(tm[1].v1.x)
mat = Material(PlainColor(rgb(z)), z)
return tm, Δ -> (TriangleMesh(Δ, mat, FixedTriangleMeshParams(IdDict(), [Vec3(z)])), nothing)
end
@adjoint function literal_getproperty(t::TriangleMesh, ::Val{:material})
mat = getproperty(t, :material)
z = eltype(t.triangulated_mesh[1].v1.x)
tm = [Triangle([Vec3(z)]...) for _ in 1:length(t.triangulated_mesh)]
return mat, Δ -> (TriangleMesh(tm, Δ, FixedTriangleMeshParams(IdDict(), [Vec3(z)])), nothing)
end
@adjoint function literal_getproperty(t::TriangleMesh, ::Val{:ftmp})
z = eltype(t.triangulated_mesh[1].v1.x)
mat = Material(PlainColor(rgb(z)), z)
tm = [Triangle([Vec3(z)]...) for _ in 1:length(t.triangulated_mesh)]
return getproperty(t, :ftmp), Δ -> (TriangleMesh(tm, mat, Δ), nothing)
end
@adjoint FixedTriangleMeshParams(isect, n) =
FixedTriangleMeshParams(isect, n), Δ -> (Δ.isect, Δ.n)
# The gradients for this params are never used so fill them with anything as long
# as they are consistent with the types
@adjoint literal_getproperty(ftmp::FixedTriangleMeshParams, ::Val{f}) where {f} =
getproperty(ftmp, f), Δ -> (FixedTriangleMeshParams(IdDict(), ftmp.normals[1:1]))
=#
# ------ #
# Camera #
# ------ #
@adjoint Camera(lf, la, vfov, focus, fp) =
Camera(lf, la, vfov, focus, fp), Δ -> (Δ.lookfrom, Δ.lookat, Δ.vfov,
Δ.focus, Δ.fixedparams)
@adjoint literal_getproperty(c::Camera, ::Val{:lookfrom}) =
getproperty(c, :lookfrom), Δ -> (Camera(Δ, zero(c.lookat), zero(c.vfov), zero(c.focus),
zero(c.fixedparams)), nothing)
@adjoint literal_getproperty(c::Camera, ::Val{:lookat}) =
getproperty(c, :lookat), Δ -> (Camera(zero(c.lookfrom), Δ, zero(c.vfov), zero(c.focus),
zero(c.fixedparams)), nothing)
@adjoint literal_getproperty(c::Camera, ::Val{:vfov}) =
getproperty(c, :vfov), Δ -> (Camera(zero(c.lookfrom), zero(c.lookat), Δ, zero(c.focus),
zero(c.fixedparams)), nothing)
@adjoint literal_getproperty(c::Camera, ::Val{:focus}) =
getproperty(c, :focus), Δ -> (Camera(zero(c.lookfrom), zero(c.lookat), zero(c.vfov), Δ,
zero(c.fixedparams)), nothing)
@adjoint literal_getproperty(c::Camera, ::Val{:fixedparams}) =
getproperty(c, :fixedparams), Δ -> (Camera(zero(c.lookfrom), zero(c.lookat), zero(c.vfov),
zero(c.focus), Δ), nothing)
@adjoint FixedCameraParams(vup, w, h) =
FixedCameraParams(vup, w, h), Δ -> (Δ.vup, Δ.width, Δ.height)
@adjoint literal_getproperty(fcp::FixedCameraParams, ::Val{f}) where {f} =
getproperty(fcp, f), Δ -> (zero(fcp), nothing)
# ------- #
# ImUtils #
# ------- #
@adjoint function zeroonenorm(x)
mini, indmin = findmin(x)
maxi, indmax = findmax(x)
res = (x .- mini) ./ (maxi - mini)
function ∇zeroonenorm(Δ)
∇x = similar(x)
fill!(∇x, 1 / (maxi - mini))
res1 = (x .- maxi) ./ (maxi - mini)^2
∇x[indmin] = sum(res1) - minimum(res1)
res2 = - res ./ (maxi - mini)
∇x[indmax] = sum(res2) - minimum(res2)
return (∇x .* Δ, )
end
return res, ∇zeroonenorm
end
# ----------------- #
# General Functions #
# ----------------- #
for func in (:findmin, :findmax)
@eval begin
@adjoint function $(func)(xs::AbstractArray; dims = :)
y = $(func)(xs, dims = dims)
function dfunc(Δ)
res = zero(xs)
res[y[2]] .= Δ[1]
return (res, nothing)
end
return y, dfunc
end
end
end
@adjoint reducehcat(x) = reduce(hcat, x), Δ -> ([Δ[:, i] for i in 1:length(x)], )
@adjoint push!(arr, val) = push!(arr, val), Δ -> (Δ[1:end-1], Δ[end])
@nograd fill
@nograd function update_index!(arr, i, j, val)
arr[i, j] = val
end
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] | 1.893308 | 9,101 |
<filename>src/callback.jl
# Callbacks functions
function fbs_trace(t, θ, loss, reg)
@printf("iter %5d : loss = %8.4f, reg = %8.4f, total = %8.4f, ",
t, loss, reg, loss+reg)
@printf("non zero = %d\n", sum(θ .!= 0.))
end
function fista_trace(t, θ, loss, reg, α)
@printf("iter %5d : loss = %8.4f, reg = %8.4f, total = %8.4f, ",
t, loss, reg, loss+reg)
@printf("α = %4.2f, non zero = %d\n",
α, sum(θ .!= 0.))
end
function fasta_trace(t, θ, loss, reg, λ, α, bt)
@printf("iter %5d : loss = %8.4f, reg = %8.4f, total = %8.4f, ",
t, loss, reg, loss+reg)
@printf("λ = %8.5f, α = %4.2f, backtracks = %2d, non zero = %d\n",
λ, α, bt, sum(θ .!= 0.))
end
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] | 1.825871 | 402 |
include("../utils.jl")
include("group_utils.jl")
@testset "Circle group" begin
G = CircleGroup()
@test repr(G) == "CircleGroup()"
@test base_manifold(G) === Circle{ℂ}()
@testset "identity overloads" begin
@test identity(G, Identity(G)) === Identity(G)
@test inv(G, Identity(G)) === Identity(G)
y = [Complex(0.0)]
@test identity!(G, y, [Complex(1.0)]) === y
@test y == [Complex(1.0)]
y = [1.0 * im]
v = [Complex(0.5)]
@test translate_diff(G, Identity(G), y, v) === v
end
@testset "scalar points" begin
pts = [1.0 + 0.0im, 0.0 + 1.0im, (1.0 + 1.0im) / √2]
vpts = [0.0 + 0.5im]
@test compose(G, pts[2], pts[1]) ≈ pts[2] * pts[1]
@test translate_diff(G, pts[2], pts[1], vpts[1]) ≈ pts[2] * vpts[1]
test_group(G, pts, vpts; test_diff = true, test_mutating = false)
end
@testset "vector points" begin
pts = [[1.0 + 0.0im], [0.0 + 1.0im], [(1.0 + 1.0im) / √2]]
vpts = [[0.0 + 0.5im]]
@test compose(G, pts[2], pts[1]) ≈ pts[2] .* pts[1]
@test translate_diff(G, pts[2], pts[1], vpts[1]) ≈ pts[2] .* vpts[1]
test_group(G, pts, vpts; test_diff = true, test_mutating = true)
end
@testset "Group forwards to decorated" begin
pts = [[1.0 + 0.0im], [0.0 + 1.0im], [(1.0 + 1.0im) / √2]]
test_manifold(
G,
pts,
test_forward_diff = false,
test_reverse_diff = false,
test_vector_spaces = false,
test_project_tangent = true,
test_musical_isomorphisms = false,
test_vector_transport = true,
is_mutating = true,
exp_log_atol_multiplier = 2.0,
is_tangent_atol_multiplier = 2.0,
)
end
end
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] | 1.86345 | 974 |
using OptimTools
using Base.Test
# test exact line search for quadratic
Q = 10*speye(40)
f(x) = 0.5*dot(x,Q*x)
df(x) = Q*x
d2f(x) = Q
x0 = randn(40)
exactLS = (f,df,fk,dfk,xk,pk)-> (-dot(pk,dfk)/dot(pk,Q*pk),1)
goldenLSf = (f,df,dk,dfk,xk,pk) -> goldenLS(f,xk,pk,0.0,5.0)
println("Test SD,NLCG,BFGS with exact line search ")
# steepest descent
x1,flag1,his1 = sd(f,df,x0,maxIter=1000,atol=1e-6,out=0,lineSearch=exactLS)
@test norm(x1)<1e-6*norm(x0)
x2,flag2,his2 = sd(f,df,x0,maxIter=1000,atol=1e-6,out=0, lineSearch=exactLS)
@test norm(x2)<1e-6
@test size(his2,1)==2 #should converge in one iteration
# nlcg
x1,flag1,his1 = nlcg(f,df,x0,maxIter=1000,atol=1e-6,out=0)
@test norm(x1)<1e-6*norm(x0)
x2,flag2,his2 = nlcg(f,df,x0,maxIter=1000,atol=1e-6,out=0, lineSearch=exactLS)
@test norm(x2)<1e-6
@test size(his2,1)==2 #should converge in one iteration
# bfgs
x1,flag1,his1 = bfgs(f,df,x0,maxIter=1000,atol=1e-6,out=0)
@test norm(x1)<1e-6*norm(x0)
x2,flag2,his2 = bfgs(f,df,x0,maxIter=1000,atol=1e-6,out=0, lineSearch=exactLS)
@test norm(x2)<1e-6
@test size(his2,1)==2 #should converge in one iteration
println("Done!")
println("Test SD,NLCG,BFGS with golden line search ")
# steepest descent
x1,flag1,his1 = sd(f,df,x0,maxIter=1000,atol=1e-6,out=0,lineSearch=goldenLSf)
@test norm(x1)<1e-6*norm(x0)
x2,flag2,his2 = sd(f,df,x0,maxIter=1000,atol=1e-6,out=0, lineSearch=goldenLSf)
@test norm(x2)<1e-6
# nlcg
x1,flag1,his1 = nlcg(f,df,x0,maxIter=1000,atol=1e-6,out=0)
@test norm(x1)<1e-6*norm(x0)
x2,flag2,his2 = nlcg(f,df,x0,maxIter=1000,atol=1e-6,out=0, lineSearch=goldenLSf)
@test norm(x2)<1e-6
# bfgs
x1,flag1,his1 = bfgs(f,df,x0,maxIter=1000,atol=1e-6,out=0)
@test norm(x1)<1e-6*norm(x0)
x2,flag2,his2 = bfgs(f,df,x0,maxIter=1000,atol=1e-6,out=0, lineSearch=goldenLSf)
@test norm(x2)<1e-6
println("Done!")
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16,
68,
12,
21,
198,
198,
35235,
7203,
45677,
2474,
8,
198
] | 1.80773 | 1,009 |
module GlobalDiffEq
using Reexport
@reexport using DiffEqBase
import OrdinaryDiffEq, Richardson
abstract type GlobalDiffEqAlgorithm <: DiffEqBase.AbstractODEAlgorithm end
struct GlobalRichardson{A} <: GlobalDiffEqAlgorithm
alg::A
end
function DiffEqBase.__solve(prob::Union{DiffEqBase.AbstractODEProblem,DiffEqBase.AbstractDAEProblem},
alg::GlobalRichardson, args...;
dt,kwargs...)
opt = Dict(kwargs)
otheropts = delete!(copy(opt), :dt)
tstops = get(opt, :tstops, range(prob.tspan[1], stop=prob.tspan[2], step=dt))
local sol
val, err = Richardson.extrapolate(dt, rtol=get(opt, :reltol, 1e-3), atol=get(opt, :abstol, 1e-6), contract=0.5) do _dt
sol = solve(prob, alg.alg, args...; dt=_dt, adaptive=false, otheropts...)
sol.(tstops)
end
return sol
end
export GlobalRichardson
end
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] | 2.189655 | 406 |
<filename>test/NfRel/FracIdeal.jl<gh_stars>0
@testset "Relative fractional ideals" begin
Qx, x = FlintQQ["x"]
f = x^2 + 12*x - 92
K, a = number_field(f, "a")
OK = maximal_order(K)
Ky, y = K["y"]
g = y^2 - 54*y - 73
L, b = number_field(g, "b")
OL = maximal_order(L)
I = L(fmpq(1, 2))*OL
@test denominator(I) == fmpz(2)
@test Hecke.isintegral(I.den*I)
PM = basis_pmat(OL)
PM.matrix[1, 1] = K(fmpq(1, 2))
PM.matrix[2, 1] = K()
PM.matrix[2, 2] = K(fmpq(1, 3))
PM = pseudo_hnf(PM, :lowerleft)
J = frac_ideal(OL, PM)
@test denominator(J) == fmpz(6)
@test Hecke.isintegral(J.den*J)
end
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] | 1.952681 | 317 |
using ContactStructures
plotlyjs()
plot_along_xy_grid(-2:0.5:2,-2:0.5:2,0,standard_structure; markersize = 2.5,ε = 0.25)
plot!(xlims=(-3, 3), ylims=(-3,3), zlims=(-3,3),legend=false)
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] | 1.978947 | 95 |
<gh_stars>1-10
module Rank
using DataStructures
using Base: tail
export extrema_filter
# Max-min filter
# This is an implementation of the Lemire max-min filter
# http://arxiv.org/abs/cs.DS/0610046
# Monotonic wedge
immutable Wedge{T}
L::CircularDeque{T}
U::CircularDeque{T}
end
(::Type{Wedge{T}}){T}(n::Integer) = Wedge(CircularDeque{T}(n), CircularDeque{T}(n))
function Base.push!(W::Wedge, i::Integer)
push!(W.L, i)
push!(W.U, i)
W
end
function addtoback!(W::Wedge, A, i, J)
mn, mx = A[i, J]
@inbounds while !isempty(W.L) && mn < A[back(W.L), J][1]
pop!(W.L)
end
@inbounds while !isempty(W.U) && mx > A[back(W.U), J][2]
pop!(W.U)
end
push!(W.L, i)
push!(W.U, i)
W
end
function Base.empty!(W::Wedge)
empty!(W.L)
empty!(W.U)
W
end
@inline function getextrema(A, W::Wedge, J)
(A[front(W.L), J][1], A[front(W.U), J][2])
end
"""
extrema_filter(A, window) --> Array{(min,max)}
Calculate the running min/max over a window of width `window[d]` along
dimension `d`, centered on the current point. The returned array has
the same indices as the input `A`.
"""
function extrema_filter{T,N}(A::AbstractArray{T,N}, window::NTuple{N,Integer})
_extrema_filter!([(a,a) for a in A], window...)
end
extrema_filter(A::AbstractArray, window::AbstractArray) = extrema_filter(A, (window...,))
extrema_filter(A::AbstractArray, window) = error("`window` must have the same number of entries as dimensions of `A`")
extrema_filter{T,N}(A::AbstractArray{T,N}, window::Integer) = extrema_filter(A, ntuple(d->window, Val{N}))
function _extrema_filter!(A::Array, w1, w...)
if w1 > 1
a = first(A)
cache = ntuple(i->a, w1>>1)
_extrema_filter1!(A, w1, cache)
end
_extrema_filter!(permutedims(A, [2:ndims(A);1]), w...)
end
_extrema_filter!(A::Array) = A
# Extrema-filtering along "columns" (dimension 1). This implements Lemire
# Algorithm 1, with the following modifications:
# - multidimensional array support by looping over trailing dimensions
# - working with min/max pairs rather than plain values, to
# facilitate multidimensional processing
# - output for all points of the array, handling the edges as max-min
# over halfwindow on either side
function _extrema_filter1!{T}(A::AbstractArray{Tuple{T,T}}, window::Int, cache)
# Initialise the internal wedges
# U[1], L[1] are the location of the global (within the window) maximum and minimum
# U[2], L[2] are the maximum and minimum over (U1, end] and (L1, end], respectively
W = Wedge{Int}(window+1)
tmp = Array{Tuple{T,T}}(window)
c = z = first(cache)
inds = indices(A)
inds1 = inds[1]
halfwindow = window>>1
iw = min(last(inds1), first(inds1)+window-1)
for J in CartesianRange(tail(inds))
empty!(W)
# Leading edge. We can't overwrite any values yet in A because
# we'll need them again in later computations.
for i = first(inds1):iw
addtoback!(W, A, i, J)
c, cache = cyclecache(cache, getextrema(A, W, J))
end
# Process the rest of the "column"
for i = iw+1:last(inds1)
A[i-window, J] = c
if i == window+front(W.U)
shift!(W.U)
end
if i == window+front(W.L)
shift!(W.L)
end
addtoback!(W, A, i, J)
c, cache = cyclecache(cache, getextrema(A, W, J))
end
for i = last(inds1)-window+1:last(inds1)-1
if i >= first(inds1)
A[i, J] = c
end
if i == front(W.U)
shift!(W.U)
end
if i == front(W.L)
shift!(W.L)
end
c, cache = cyclecache(cache, getextrema(A, W, J))
end
A[last(inds1), J] = c
end
A
end
# This is slightly faster than a circular buffer
@inline cyclecache(b, x) = b[1], (Base.tail(b)..., x)
end
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] | 2.111581 | 1,891 |
# Functions
# Point
#rand(rng::Random.AbstractRNG, ::Random.SamplerType{Point{T, N}}) where N where T = Point{T, N}(NTuple{N, T}(rand(rng, T, N)))
distance(p1::Point{T, N}, p2::Point{T, N}) where N where T = sqrt(sum((p1.coordinates .- p2.coordinates).^2))
function area(arr::Array{Point{T, 2}, 1}) where T
length(arr) <= 2 && error("Area for two points cannot be computed!")
area = 0.0
x = 2
y = 3
flag = false
while(x <= length(arr) && y <= length(arr))
a = distance(arr[1], arr[x])
b = distance(arr[x], arr[y])
c = distance(arr[1], arr[y])
s = (a + b + c)/2.0
area += sqrt(s*(s - a)*(s - b)*(s - c))
if flag == false
flag = true
x = y + 1
else
flag = false
y = x + 1
end
end
return area
end
function convexHull(points::Array{Point{T,2},1}) where T
if length(points) < 3
error("Minimum three points required")
end
n = length(points)
function orientation(p::Point{T, 2}, q::Point{T, 2}, r::Point{T, 2}) where T <: Real
v = (q.coordinates[2] - p.coordinates[2])*(r.coordinates[1] - q.coordinates[1])- (q.coordinates[1] - p.coordinates[1])*(r.coordinates[2] - q.coordinates[2])
if v == 0 return 0 end
return v > 0 ? 1 : 2
end
hull = Array{Point{T, 2}, 1}(undef, 0)
l = 1
for i in 2:n
if points[i].coordinates[1] < points[l].coordinates[1]
l = i
end
end
p = l - 1
q = -1
while true
push!(hull, points[p + 1])
q = (p + 1) % n
for i in 0:(n - 1)
if(orientation(points[p + 1], points[i + 1], points[q+1]) == 2)
q = i
end
end
p = q
if p == l - 1
break
end
end
return hull
end
# Line
function distance(p::Point{T, N}, l::Line{T, N}) where N where T
t = dot(p.coordinates .- l.p.coordinates, l.v)/dot(l.v, l.v)
return norm(p.coordinates .- l.p.coordinates .- t*l.v)
end
distance(l::Line{T, N}, p::Point{T, N}) where N where T = distance(p::Point{T, N}, l::Line{T, N})
function distance(l1::Line{T, N}, l2::Line{T, N}; err = 1e-10) where N where T
if norm(l1.v .- l2.v) ≦ err
return distance(l1.p, l2)
else
return zero(T)
end
end
# Plane
function distance(p::Point{T, N}, q::Plane{T, N}) where N where T
x = distance(p, q.p)
y = distance(p, q.n)
return sqrt(x^2 - y^2)
end
function boundingbox(a::Array{Point{T, N}, 1}) where N where T
mn = Array{T,1}(undef, N)
mn .= typemax(T)
mx = Array{T,1}(undef, N)
mx .= typemin(T)
for i in 1:N, j in 1:length(a)
mn[i] = min(mn[i], a[j].coordinates[i])
mx[i] = max(mx[i], a[j].coordinates[i])
end
return (Point{T, N}(mn), Point{T, N}(mx))
end
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] | 1.905318 | 1,542 |
# functions to download the data
const FILESIZES = CSV.read(joinpath(DIR,"metadata","filesizes.csv"), DataFrame)
# _download is a modified MIT expat licensed code from HTTP.jl
# https://github.com/JuliaWeb/HTTP.jl/blob/668e7e68747bb333ebde13af8d16add5b82b3b8a/src/download.jl#L92
function _download(url::AbstractString,
file_path::AbstractString;
headers = [],
progress = nothing, # nothing or Progress or ParallelProgress
filesize = typemax(Int),
update_period = 1,
kw...)
if isnothing(progress)
update_period = Inf
end
HTTP.open("GET", url, headers; kw...) do stream
eof(stream) && return # don't do anything for streams we can't read (yet)
downloaded_bytes = 0
prev_time = Dates.now()
function report_callback()
prev_time = Dates.now()
completion_progress = downloaded_bytes
# we stay stuck at 99% if file is bigger than expected
update!(progress, min(completion_progress, filesize-1))
end
Base.open(file_path, "w") do fh
while !eof(stream)
downloaded_bytes += write(fh, readavailable(stream))
if !isinf(update_period)
if Dates.now() - prev_time > Dates.Millisecond(round(1000update_period))
report_callback()
end
end
end
end
if !isinf(update_period)
finish!(progress)
end
end
return
end
function get_file_size(siteid::Integer, compressed::Bool)
col = compressed ? ".csv.gz" : ".csv"
return ceil(Int, FILESIZES[siteid, col])
end
function download_site_csv(source, siteid, path_to_data_folder; kw...)
if source === :zenodo
return download_site_csv_from_zenodo(siteid, path_to_data_folder; kw...)
elseif source === :schneider
return download_site_csv_from_schneider(siteid, path_to_data_folder; kw...)
else
error("Source $(repr(source)) not found")
end
end
function download_sites_data(path_to_data_folder,
sitesid = 1:70;
source = :zenodo,
kw...)
for siteid in sitesid
download_site_csv(source, siteid, path_to_data_folder; kw...)
end
return
end
function download_pv(path_to_data_folder; kw...)
download_pv_csv_from_zenodo(path_to_data_folder; kw...)
return
end
function download_sites_data_parallel(path_to_data_folder,
sitesid = 1:70;
progress = true,
max_threads = 4,
compressed = true,
source = :zenodo,
kw...)
file_sizes = get_file_size.(sitesid, compressed)
if progress
ext = compressed ? ".csv.gz" : ".csv"
mprog = MultipleProgress(length(sitesid), file_sizes;
kws = [(:desc => "Downloading $j$ext ",) for j in sitesid],
desc = "Downloading files... ",
dt = 0.1)
end
i = firstindex(sitesid) - 1
@sync for p in 1:max_threads
@async while true
idx = (i += 1)
idx > lastindex(sitesid) && break
download_site_csv(source, sitesid[idx], path_to_data_folder;
progress = progress ? mprog[idx] : false,
compressed = compressed,
file_size = file_sizes[idx],
kw...)
end
end
progress && finish!(mprog)
return
end
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198
] | 1.90927 | 2,028 |
using HTTP
using JSON
struct Client
Query::Function
serverUrl::Function
headers::Function
serverAuth::Function
end
struct Result
Info
Data::String
end
"""
Queryclient(url::String,data::String; vars::Dict=Dict(),auth::String="Bearer <PASSWORD>", headers::Dict=Dict(),getlink::Bool=false,check::Bool=false,operationName::Union{String, Nothing}=nothing)
Execute a query with all available parameters
"""
function Queryclient(url::String,data::String;
vars::Dict=Dict(),auth::String="Bearer <PASSWORD>",
headers::Dict=Dict(),getlink::Bool=false,
check::Bool=false,operationName::Union{String, Nothing}=nothing)
if (check)
Validatequery(Parse(data))
end
if (getlink == true)
#------------
link =url*"?query="*HTTP.escapeuri(data)
if operationName !== nothing && length(operationName) > 0
link=link*"&operationName="*operationName
end
if length(vars)>0
link=link*"&variables="*HTTP.escapeuri(JSON.json(vars))
end
return link
#------------
else
myjson = Dict("query"=>data,"variables" => vars,"operationName" => operationName)
my_headers = HTTP.mkheaders(["Accept" => "application/json","Content-Type" => "application/json" ,"Authorization" => auth])
for (key, value) in headers
HTTP.setheader(my_headers,key => value )
end
r = HTTP.post(url,my_headers,JSON.json(myjson))
return Result(r,String(r.body))
end
end
"""
Queryclient(queryurl::String)
Execute the query in link format and return the result.
"""
function Queryclient(queryurl::String)
r=HTTP.get(queryurl)
return Result(r,String(r.body))
end
"""
GraphQLClient(url::String; auth::String="Bearer <PASSWORD>", headers::Dict=Dict())
Stores the parameters of the query for later use, returns a Client object
"""
function GraphQLClient(url::String; auth::String="Bearer 0<PASSWORD>", headers::Dict=Dict())
my_url::String= url
my_auth::String= auth
my_headersextras::Dict= headers
function serverUrl(url::String)
my_url = url
end
function setheaders(headers::Dict)
my_headersextras=headers
end
function serverAuth(auth::String)
my_auth= auth
end
function Query(data::String; vars::Dict=Dict(),getlink::Bool=false,check::Bool=false,operationName::Union{String, Nothing}=nothing)
return Queryclient(my_url,data,vars=vars,auth=my_auth,headers=my_headersextras,getlink=getlink,operationName=operationName,check=check)
end
return Client(Query,serverUrl,setheaders,serverAuth)
end | [
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] | 2.807298 | 877 |
<gh_stars>100-1000
using DataFrames, Dash, DashHtmlComponents, DashCoreComponents, PlotlyJS, UrlDownload
df2 = DataFrame(urldownload("https://raw.githubusercontent.com/plotly/datasets/master/country_indicators.csv"))
dropmissing!(df2)
rename!(df2, Dict(:"Year" => "year"))
available_indicators = unique(df2[!, "Indicator Name"])
years = unique(df2[!, "year"])
app = dash()
app.layout = html_div() do
html_div(
children = [
dcc_dropdown(
id = "xaxis-column",
options = [
(label = i, value = i) for i in available_indicators
],
value = "Fertility rate, total (births per woman)",
),
dcc_radioitems(
id = "xaxis-type",
options = [(label = i, value = i) for i in ["linear", "log"]],
value = "linear",
),
],
style = (width = "48%", display = "inline-block"),
),
html_div(
children = [
dcc_dropdown(
id = "yaxis-column",
options = [
(label = i, value = i) for i in available_indicators
],
value = "Life expectancy at birth, total (years)",
),
dcc_radioitems(
id = "yaxis-type",
options = [(label = i, value = i) for i in ["linear", "log"]],
value = "linear",
),
],
style = (width = "48%", display = "inline-block", float = "right"),
),
dcc_graph(id = "indicator-graphic"),
dcc_slider(
id = "year-slider-2",
min = minimum(years),
max = maximum(years),
marks = Dict([Symbol(v) => Symbol(v) for v in years]),
value = minimum(years),
step = nothing,
)
end
callback!(
app,
Output("indicator-graphic", "figure"),
Input("xaxis-column", "value"),
Input("yaxis-column", "value"),
Input("xaxis-type", "value"),
Input("yaxis-type", "value"),
Input("year-slider-2", "value"),
) do xaxis_column_name, yaxis_column_name, xaxis_type, yaxis_type, year_value
df2f = df2[df2.year .== year_value, :]
return Plot(
df2f[df2f[!, Symbol("Indicator Name")] .== xaxis_column_name, :Value],
df2f[df2f[!, Symbol("Indicator Name")] .== yaxis_column_name, :Value],
Layout(
xaxis_type = xaxis_type == "Linear" ? "linear" : "log",
xaxis_title = xaxis_column_name,
yaxis_title = yaxis_column_name,
yaxis_type = yaxis_type == "Linear" ? "linear" : "log",
hovermode = "closest",
),
kind = "scatter",
text = df2f[
df2f[Symbol("Indicator Name")] .== yaxis_column_name,
Symbol("Country Name"),
],
mode = "markers",
marker_size = 15,
marker_opacity = 0.5,
marker_line_width = 0.5,
marker_line_color = "white"
)
end
run_server(app, "0.0.0.0", debug = true)
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13,
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220,
220,
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198,
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198,
198,
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11,
366,
15,
13,
15,
13,
15,
13,
15,
1600,
14257,
796,
2081,
8,
198
] | 1.988823 | 1,521 |
using PENBBControl
using Test
@testset "PENBBControl.jl" begin
# Write your tests here.
end
| [
3500,
350,
1677,
33833,
756,
3225,
198,
3500,
6208,
198,
198,
31,
9288,
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1,
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198,
220,
220,
220,
1303,
19430,
534,
5254,
994,
13,
198,
437,
198
] | 2.621622 | 37 |
using RDatasets, DataFrames, FixedEffectModels, Base.Test
x = dataset("plm", "Cigar")
x[:pState] = pool(x[:State])
x[:pYear] = pool(x[:Year])
result = reg(Sales ~ NDI, x)
show(result)
predict(result, x)
residuals(result, x)
reg(Sales ~ CPI + (Price = Pimin), x)
result = reg(Sales ~ CPI + (Price = Pimin), x)
predict(result, x)
residuals(result, x)
model_response(result, x)
@test nobs(result) == 1380
@test_approx_eq vcov(result)[1] 3.5384578251636785
show(reg(Sales ~ Price |> pState, x))
show(reg(Sales ~ CPI + (Price = Pimin) |> pState, x))
result = reg(Sales ~ Price, x)
@test maxabs(residuals(result, x)[1:10] .- [-39.2637, -37.48801, -34.38801, -36.09743, -36.97446, -43.15547, -41.22573, -40.83648, -34.52427, -28.91617]) <= 1e-4
result = reg(Sales ~ Price |> pState, x, save = true)
@test maxabs(result.augmentdf[:residuals][1:10] .- [-22.08499, -20.33318, -17.23318, -18.97645, -19.85547, -26.1161, -24.20627, -23.87674, -17.62624, -12.01018]) <= 1e-4
| [
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13,
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13,
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11,
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13,
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11,
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13,
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11,
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532,
1065,
13,
486,
29159,
12962,
19841,
352,
68,
12,
19,
628,
628
] | 2.174779 | 452 |
using CosmoParticles
using FillArrays
using LazyArrays
using LinearAlgebra
using Rotations
using SortingAlgorithms
using StatsBase
using Test
using Unitful
const CP = CosmoParticles
@testset "CosmoParticles.jl" begin
@testset "Particles" begin
p = Particles(:dm)
p.id = sample(1:1000, 100; replace=false)
p.pos = rand(3, 100)
# different id and pos for copied Particles
pcid = rand(100)
pcpos = rand(3, 100)
# Particle specific
pc = Particles(:dm, :id => p.id, :pos => p.pos)
@test pc == p
pc = copy(p)
@test pc == p
@test pc !== p
@test pc.props == p.props
@test pc.props !== p.props
pc = empty(p)
@test pc.type === :dm
@test isempty(pc.props)
@test isempty(pc)
@test CP.particle_name(p) == "Particles"
@test issetequal(propertynames(p), [:type, :id, :pos])
@test issetequal(propertynames(p; private=true), [:type, :props, :id, :pos])
io = IOBuffer()
show(io, "text/plain", p)
@test String(take!(io)) == "dm: 100 Particles\n id pos"
# test AbstractParticles with basic implementation Particles
@test CP.get_props(p) === p.props
@test getproperty(p, :id) === p.props[:id]
@test getproperty(p, :type) === p.type === :dm
@test getproperty(p, :props) === p.props
@test p.id === p.props[:id]
pc = copy(p)
setproperty!(p, :pos, pcpos)
@test p.pos === pcpos
pc.id = pcid
@test pc.id === pcid
@test p.id !== pcid
@test_throws ErrorException("""
setfield!: immutable struct of type Particles cannot be changed""") p.type = :gas
@test p[:id] === p.props[:id]
pc = copy(p)
p[:id] = pcid
@test p.id === pcid
@test keys(p) == keys(p.props)
@test haskey(p, :id)
@test !haskey(p, :vel)
@test !haskey(p, :type)
@test values(p) == values(p.props)
@test issetequal(propertynames(p), [:type, :id, :pos])
# test default implementations for AbstractParticles
@test issetequal(Base.@invoke(propertynames(p::AbstractParticles)), [:id, :pos])
pc = deepcopy(p)
empty!(pc)
@test isempty(pc.props)
@test isempty(pc)
@test !isempty(p)
copy!(pc, p)
@test pc !== p && pc == p && !isempty(pc)
@test CP.particle_number(p) == 100
@test CP.particle_number(Particles(:gas)) == 0
@test CP.particle_number(Particles(:gas, Dict{Symbol,Any}(:mass => 3))) == 0
@test isnothing(Base.@invoke(CP.particle_name(p::AbstractParticles)))
io = IOBuffer()
CP.show_properties(io, "text/plain", p)
@test String(take!(io)) == "100 Particles\n id pos"
end
@testset "AllParticles" begin
dm = Particles(:dm)
dm.id = sample(1:1000, 100; replace=false)
dm.pos = rand(3, 100)
dm.mass = rand()
dm.test = rand()
gas = Particles(:gas)
gas.id = sample(1001:2000, 100; replace=false)
gas.pos = rand(3, 100)
gas.mass = rand(100)
gas.temp = rand(100)
gas.zs = rand(2, 100)
gas.test = rand(2, 100)
pcoll = ParticleCollection(dm, gas)
p = AllParticles(pcoll)
@test CP.particle_collection(p) === pcoll
# @test get_props(p) === p.props # not part of the API
if p.id[1] == dm.id[1]
@test p.id == vcat(dm.id, gas.id)
@test p.pos == hcat(dm.pos, gas.pos)
@test p.mass == vcat(fill(dm.mass, length(dm.id)), gas.mass)
@test isequal.(p.temp, vcat(fill(missing, length(dm.id)), gas.temp)) |> all
@test isequal.(p.zs, hcat(fill(missing, 2, length(dm.id)), gas.zs)) |> all
@test p.test == hcat(fill(dm.test, 2, length(dm.id)), gas.test)
else
@test p.id == vcat(gas.id, dm.id)
@test p.pos == hcat(gas.pos, dm.pos)
@test p.mass == vcat(gas.mass, fill(dm.mass, length(dm.id)))
@test isequal.(p.temp, vcat(gas.temp, fill(missing, length(dm.id)))) |> all
@test isequal.(p.zs, hcat(gas.zs, fill(missing, 2, length(dm.id)))) |> all
@test p.test == hcat(gas.test, fill(dm.test, 2, length(dm.id)))
end
@test_throws ErrorException p.id = rand(100)
@test_throws KeyError p.vel
@test issetequal(keys(p), [:id, :mass, :pos, :temp, :zs, :test])
@test keys(p) == keys(AllParticles(pcoll))
@test haskey(p, :id) && haskey(AllParticles(pcoll), :id)
@test !haskey(p, :vel) && !haskey(AllParticles(pcoll), :vel)
@test all(val === p[key] for (key, val) in zip(keys(p), values(p)))
@test all(val === p[key] for (key, val) in pairs(p))
@test_throws ErrorException copy(p)
@test_throws ErrorException copy!(p, AllParticles(pcoll))
@test_throws ErrorException copy!(p, dm)
pc = Particles(:all)
copy!(pc, p)
collect(values(p)) # materializes properties in Dict
@test pc.id == p.id
@test pc.mass == p.mass
@test pc.pos == p.pos
@test all(isequal.(pc.temp, p.temp))
@test all(isequal.(pc.zs, p.zs))
@test pc.test == p.test
copy!(pc, p, [:id, :mass, :temp])
@test haskey.((pc,), [:id, :mass, :temp]) |> all
@test !haskey(pc, :pos)
@test isa.([pc.id, pc.mass, pc.temp], Array) |> all
@test Particles(p, [:id, :mass, :temp]) == pc
@test_throws ErrorException empty(p)
@test_throws ErrorException empty!(p)
@test !isempty(p)
@test isempty(ParticleCollection() |> AllParticles)
@test isempty(ParticleCollection(Particles(:dm), Particles(:gas)) |> AllParticles)
@test p == AllParticles(pcoll)
@test CP.particle_name(p) == "Particles"
@test CP.particle_number(p) == 200
list = [:id, :mass, :pos, :temp, :zs, :test]
@test issetequal(propertynames(p), list)
@test issetequal(propertynames(p; private=true), [fieldnames(AllParticles) |> collect; list])
io = IOBuffer()
show(io, "text/plain", p)
@test String(take!(io)) == "all: 200 Particles\n id mass pos temp test zs"
@test CP.particle_number(AllParticles(ParticleCollection())) == 0
mask = isodd.(p.id)
ind = findall(mask)
@test_throws ErrorException p[mask]
@test_throws ErrorException p[ind]
# cover case where the dimension is not found from a vector or matrix
# and where only one particle subtype exists
pos = rand(3, 100)
mass = rand()
pc = ParticleCollection(Particles(:dm, :pos => pos, :mass => mass))
ap = pc.all
@test ap.pos == pos
@test ap.mass == fill(mass, 100)
end
@testset "Particle Collection" begin
dm = Particles(:dm)
dm.id = sample(1:1000, 100; replace=false)
dm.pos = rand(3, 100)
gas = Particles(:gas)
gas.id = sample(1001:2000, 100; replace=false)
gas.pos = rand(3, 100)
@testset "ParticleCollection" begin
pc = ParticleCollection()
@test pc isa ParticleCollection{Particles}
@test pc.particles == Dict{Symbol,Particles}()
@test ParticleCollection(Particles) == pc
pc = ParticleCollection(:dm => dm, :gas => gas)
@test pc isa ParticleCollection{Particles}
@test pc.particles == Dict{Symbol,Particles}(:dm => dm, :gas => gas)
@test ParticleCollection(dm, gas) == pc
@test redshift(pc) == 0
pc = ParticleCollection(dm)
pcc = copy(pc)
@test pcc.particles[:dm] === pc.particles[:dm]
pcc.particles[:dm] = gas
@test pcc.particles[:dm] !== pc.particles[:dm]
@test empty(pc) == ParticleCollection()
pc = ParticleCollection(dm, gas)
io = IOBuffer()
show(io, "text/plain", pc)
@test String(take!(io)) ==
"ParticleCollection\ndm: 100 Particles\n id pos\ngas: 100 Particles\n id pos"
end
@testset "RedshiftParticleCollection" begin
z = 0.5
pc = RedshiftParticleCollection(0.5)
@test pc isa RedshiftParticleCollection{Particles}
@test pc.particles == Dict{Symbol,Particles}()
@test pc.z == z
@test RedshiftParticleCollection(Particles, z) == pc
pc = RedshiftParticleCollection(z, :dm => dm, :gas => gas)
@test pc isa RedshiftParticleCollection{Particles}
@test pc.particles == Dict{Symbol,Particles}(:dm => dm, :gas => gas)
@test RedshiftParticleCollection(z, dm, gas) == pc
@test redshift(pc) == z
pc = RedshiftParticleCollection(z, dm)
pcc = copy(pc)
@test pcc.particles[:dm] === pc.particles[:dm]
pcc.particles[:dm] = gas
@test pcc.particles[:dm] !== pc.particles[:dm]
@test empty(pc) == RedshiftParticleCollection(z)
pc = RedshiftParticleCollection(z, dm, gas)
io = IOBuffer()
show(io, "text/plain", pc)
@test String(take!(io)) ==
"ParticleCollection at z = $z\ndm: 100 Particles\n id pos\ngas: 100 Particles\n id pos"
@test issetequal(propertynames(pc), [:z, :all, :dm, :gas])
@test issetequal(propertynames(pc; private=true), [:z, :all, :particles, :dm, :gas])
rpc = RedshiftParticleCollection(0, dm, gas)
pc = ParticleCollection(dm, gas)
@test rpc == pc && pc == rpc
rpc = RedshiftParticleCollection(z, dm, gas)
@test rpc != pc && pc != rpc
end
@testset "AbstractParticleCollection" begin
pc = ParticleCollection(dm, gas)
@test CP.get_particles(pc) === pc.particles
@test getproperty(pc, :dm) === pc.particles[:dm]
@test getproperty(pc, :particles) === pc.particles
@test pc.dm === pc.particles[:dm]
@test pc[:dm] === pc.particles[:dm]
@test_throws ErrorException(
"setfield!: immutable struct of type ParticleCollection cannot be changed",
) pc.particles = Dict{Symbol,Particles}()
pc.dm = gas
@test pc[:dm] === gas
pc[:dm] = dm
@test pc.dm === dm
@test pc.all == AllParticles(pc)
@test_throws ErrorException pc.all = dm
@test_throws KeyError pc[:all]
@test keys(pc) == keys(pc.particles)
@test haskey(pc, :dm)
@test !haskey(pc, :stars)
@test !haskey(pc, :particles)
@test values(pc) == values(pc.particles)
@test issetequal(propertynames(pc), [:all, :dm, :gas])
pcc = deepcopy(pc)
empty!(pcc)
@test isempty(pcc.particles)
@test isempty(pcc)
@test !isempty(pc)
copy!(pcc, pc)
@test pcc !== pc && pcc == pc && !isempty(pcc)
io = IOBuffer()
CP.show_properties(io, "text/plain", pc)
@test String(take!(io)) == "dm: 100 Particles\n id pos\ngas: 100 Particles\n id pos"
end
end
@testset "Utils" begin
@testset "Index particles" begin
p = Particles(:dm)
p.id = sample(1:1000, 100; replace=false)
p.pos = rand(3, 100)
p.mass = 4u"kg"
mask = rand(100) .> 0.5
ind = findall(mask)
@test CP._applyind(3, mask) == 3
@test CP._applyind(p.mass, mask) == p.mass
@test CP._applyind(p.id, mask) == p.id[mask]
@test CP._applyind(p.pos, mask) == p.pos[:, mask]
@test CP._applyind(3, ind) == 3
@test CP._applyind(p.mass, ind) == p.mass
@test CP._applyind(p.id, ind) == p.id[ind]
@test CP._applyind(p.pos, ind) == p.pos[:, ind]
a = rand(45)
b = rand(55)
c = ApplyArray(vcat, a, b)
@test CP._applyind(c, mask) == Array(c)[mask]
@test CP._applyind(c, ind) == Array(c)[ind]
a = rand(3, 45)
b = rand(3, 55)
c = ApplyArray(hcat, a, b)
@test CP._applyind(c, mask) == Array(c)[:, mask]
@test CP._applyind(c, ind) == Array(c)[:, ind]
pc = CP.applyind(p, mask)
@test pc.id == p.id[mask]
@test pc.pos == p.pos[:, mask]
@test pc.mass == p.mass
@test CP.applyind(p, ind) == pc
@test p[ind] == pc
@test p[mask] == pc
pcc = deepcopy(p)
CP.applyind!(pcc, mask)
@test pcc == pc
pcc = deepcopy(p)
CP.applyind!(pcc, ind)
@test pcc == pc
pc = CP.applyind(p, mask; affect=[:pos, :mass, :foo])
@test !haskey(pc, :id)
@test !haskey(pc, :foo)
@test pc.pos == p.pos[:, mask]
@test pc.mass == p.mass
pc = ParticleCollection(p)
ap = pc.all
@test_throws ErrorException CP.applyind(ap, ind)
@test_throws ErrorException CP.applyind!(ap, ind)
@test_throws ErrorException CP.applyind(ap, mask)
@test_throws ErrorException CP.applyind!(ap, mask)
end
@testset "Remove particles" begin
p = Particles(:dm)
p.id = sample(1:1000, 100; replace=false)
p.pos = rand(3, 100)
p.mass = 4u"kg"
mask = rand(100) .> 0.5
ind = findall(mask)
@test CP._removeind(3, ind) == 3
@test CP._removeind(p.mass, ind) == p.mass
@test CP._removeind(p.id, ind) == deleteat!(copy(p.id), ind)
posremoved = CP._removeind(p.pos, ind)
@test posremoved[1, :] == deleteat!(p.pos[1, :], ind)
@test posremoved[2, :] == deleteat!(p.pos[2, :], ind)
@test posremoved[3, :] == deleteat!(p.pos[3, :], ind)
a = rand(45)
b = rand(55)
c = ApplyArray(vcat, a, b)
@test CP._removeind(c, ind) == deleteat!(Array(c), ind)
a = rand(3, 45)
b = rand(3, 55)
c = ApplyArray(hcat, a, b)
cremoved = CP._removeind(c, ind)
carray = Array(c)
@test cremoved[1, :] == deleteat!(carray[1, :], ind)
@test cremoved[2, :] == deleteat!(carray[2, :], ind)
@test cremoved[3, :] == deleteat!(carray[3, :], ind)
pc = CP.applyind(p, .~mask)
@test CP.removeind(p, ind) == pc
pcc = deepcopy(p)
CP.removeind!(pcc, ind)
@test pcc == pc
affect = [:pos, :mass, :foo]
pc = CP.removeind(p, ind; affect)
@test pc == CP.applyind(p, .~mask; affect)
@test CP.deleteat(p, ind) == CP.removeind(p, ind)
pc = deepcopy(p)
@test CP.deleteat!(pc, ind) == CP.removeind(p, ind)
pc = ParticleCollection(p)
ap = pc.all
@test_throws Exception CP.removeind(ap, ind)
@test_throws Exception CP.removeind!(ap, ind)
end
@testset "Find all in" begin
a = sample(1:10000, 6000; replace=false)
set = sample(1:10000, 100; replace=false)
inboth = intersect(a, set)
ind = CP.findall_in(a, set)
@test length(ind) == length(inboth)
@test issetequal(a[ind], inboth)
@test CP.findall_in(a, Set(set)) == ind
sort!(a)
sort!(set)
ind = CP.findall_in(a, set)
@test length(ind) == length(inboth)
@test issetequal(a[ind], inboth)
@test CP.findall_in(a, Set(set)) == ind
@test CP.findall_in_sorted(a, set) == ind
alarge = collect(1:1_200_000)
alarge2 = collect(200_000:1_600_000)
setlarge = sample(1:1_200_000, 100_000; replace=false) |> sort!
inboth = intersect(alarge, setlarge)
inboth2 = intersect(alarge2, setlarge)
ind = CP.findall_in(alarge, setlarge)
@test length(ind) == length(inboth)
@test issetequal(alarge[ind], inboth)
@test CP.findall_in(alarge, Set(setlarge)) == ind
ind = CP.findall_in(alarge2, setlarge)
@test length(ind) == length(inboth2)
@test issetequal(alarge2[ind], inboth2)
@test CP.findall_in(alarge2, Set(setlarge)) == ind
# test empty arrays
@test CP.findall_in_sorted([], []) |> isempty
@test CP.findall_in_sorted(a, []) |> isempty
@test CP.findall_in_sorted([], set) |> isempty
@test CP.findall_in([], Set([])) |> isempty
@test CP.findall_in(a, Set([])) |> isempty
@test CP.findall_in([], Set(set)) |> isempty
end
@testset "Unitful operations" begin
a = rand(100)
au = a * u"m"
af = convert(Vector{Float32}, a)
afu = af * u"m"
b = rand()
@test CP.product_preserve_type(a, b) == a .* b
@test CP.product_preserve_type(au, b) == au .* b
@test CP.product_preserve_type(af, b) == af .* convert(Float32, b)
@test CP.product_preserve_type(afu, b) == afu .* convert(Float32, b)
ac = copy(a)
CP.product_preserve_type!(ac, b)
@test ac == a .* b
auc = copy(au)
CP.product_preserve_type!(auc, b)
@test auc == au .* b
afc = copy(af)
CP.product_preserve_type!(afc, b)
@test afc == af .* convert(Float32, b)
afuc = copy(afu)
CP.product_preserve_type!(afuc, b)
@test afuc == afu .* convert(Float32, b)
end
@testset "Lazy ustrip" begin
@test CP.ustrip_lazy(3) == 3
a = rand(4)
b = rand(3)
au = a * u"m"
bu = b * u"m"
@test CP.ustrip_lazy(a) === a
@test CP.ustrip_lazy(au) == a
@test CP.ustrip_lazy(Diagonal(au)) == Diagonal(a)
@test CP.ustrip_lazy(Bidiagonal(au, bu, :U)) == Bidiagonal(a, b, :U)
@test CP.ustrip_lazy(Tridiagonal(bu, au, bu)) == Tridiagonal(b, a, b)
@test CP.ustrip_lazy(SymTridiagonal(au, bu)) == SymTridiagonal(a, b)
auview = @view au[1:3]
@test CP.ustrip_lazy(auview) == a[1:3]
c = vcat(a, b)
cu = ApplyArray(vcat, au, bu)
@test CP.ustrip_lazy(cu) == c
c = fill(2.5, 4)
cu = Fill(2.5u"m", 4)
@test CP.ustrip_lazy(cu) == c
auf = rand(Float32, 4) * u"m"
au_strip = CP.ustrip_lazy(u"km", au)
auf_strip = CP.ustrip_lazy(u"km", auf)
@test au_strip ≈ ustrip(au ./ 1000)
@test eltype(au_strip) === Float64
@test auf_strip ≈ ustrip(auf ./ 1000)
@test eltype(auf_strip) === Float32
auc = copy(au)
aufc = copy(auf)
auc_strip = CP.ustrip_lazy!(u"km", auc)
aufc_strip = CP.ustrip_lazy!(u"km", aufc)
@test auc_strip == au_strip
@test aufc_strip == auf_strip
@test CP.ustrip_lazy(auc) == au_strip
@test CP.ustrip_lazy(aufc) == auf_strip
auc = copy(au)
aufc = copy(auf)
auc_conv = CP.uconvert_lazy!(u"km", auc)
aufc_conv = CP.uconvert_lazy!(u"km", aufc)
@test auc_conv ≈ uconvert.(u"km", au)
@test aufc_conv ≈ uconvert.(u"km", auf)
@test CP.ustrip_lazy(auc) == CP.ustrip_lazy(auc_conv)
@test CP.ustrip_lazy(aufc) == CP.ustrip_lazy(aufc_conv)
auc = copy(au)
aufc = copy(auf)
auc_conv = CP.uconvert_lazy!(u"m", auc)
aufc_conv = CP.uconvert_lazy!(u"m", aufc)
@test auc_conv ≈ uconvert.(u"m", au)
@test aufc_conv ≈ uconvert.(u"m", auf)
@test CP.ustrip_lazy(auc) == CP.ustrip_lazy(auc_conv)
@test CP.ustrip_lazy(aufc) == CP.ustrip_lazy(aufc_conv)
au = rand(4) * u"°C"
auf = rand(Float32, 4) * u"°C"
auc = copy(au)
aufc = copy(auf)
auc_conv = CP.uconvert_lazy!(u"K", auc)
aufc_conv = CP.uconvert_lazy!(u"K", aufc)
@test auc_conv ≈ uconvert.(u"K", au)
@test aufc_conv ≈ uconvert.(u"K", auf)
@test CP.ustrip_lazy(auc) == CP.ustrip_lazy(auc_conv)
@test CP.ustrip_lazy(aufc) == CP.ustrip_lazy(aufc_conv)
end
end
@testset "Transformations" begin
@testset "Rotation" begin
rotmat = rand(RotMatrix{3})
a = rand(3, 100)
au = a * u"m"
arot = rotmat * a
aurot = rotmat * au
@test CP.matrix_rotate(a, rotmat) == arot
@test CP.matrix_rotate(au, rotmat) == aurot
ac = copy(a)
CP.matrix_rotate!(ac, rotmat)
@test ac == arot
auc = copy(au)
CP.matrix_rotate!(auc, rotmat)
@test auc == aurot
ac = similar(a)
CP.matrix_rotate!(ac, a, rotmat)
@test ac == arot
auc = similar(au)
CP.matrix_rotate!(auc, au, rotmat)
@test auc == aurot
@testset "Particle Rotation" begin
p = Particles(:dm)
p.pos = copy(au)
p.vel = copy(a)
pc = rotate(p, rotmat, :pos)
@test p.pos == au
@test pc.pos == aurot
@test p.vel === pc.vel == a
pc = rotate(p, rotmat)
@test p.pos == au
@test pc.pos == aurot
@test p.vel == a
@test pc.vel == arot
pc = deepcopy(p)
rotate!(pc, rotmat, :vel)
@test pc.pos == au
@test pc.vel == arot
pc = deepcopy(p)
rotate!(pc, rotmat)
@test pc.pos == aurot
@test pc.vel == arot
end
@testset "Particle Collection Rotation" begin
dm = Particles(:dm)
dm.pos = copy(au)
dm.vel = copy(a)
gas = Particles(:gas)
gas.pos = copy(au)
gas.vel = copy(a)
pc = ParticleCollection(dm, gas)
pcc = rotate(pc, rotmat, :pos)
@test pc.dm.pos == pc.gas.pos == au
@test pcc.dm.pos == pcc.gas.pos == aurot
@test pc.dm.vel === pcc.dm.vel == a
pcc = rotate(pc, rotmat)
@test pc.dm.pos == pc.gas.pos == au
@test pcc.dm.pos == pcc.gas.pos == aurot
@test pc.dm.vel == pc.gas.vel == a
@test pcc.dm.vel == pcc.gas.vel == arot
pcc = deepcopy(pc)
rotate!(pcc, rotmat, :vel)
@test pcc.dm.pos == pcc.gas.pos == au
@test pcc.dm.vel == pcc.gas.vel == arot
pcc = deepcopy(pc)
rotate!(pcc, rotmat)
@test pcc.dm.pos == pcc.gas.pos == aurot
@test pcc.dm.vel == pcc.gas.vel == arot
pccc = deepcopy(pc)
ap = pccc.all
@test_throws ErrorException rotate(ap, rotmat)
rotate!(ap, rotmat)
@test pccc == pcc
end
end
@testset "Translations" begin
da = rand(3)
dau = rand(3) * u"m"
a = rand(3, 100)
au = a * u"m"
atrans = a .+ da
autrans = au .+ dau
@testset "Particle Translations" begin
p = Particles(:dm)
p.pos = copy(au)
p.vel = copy(a)
pc = translate(p, dau)
@test p.pos == au
@test pc.pos == autrans
@test p.vel === pc.vel == a
pc = translate(p, da, :vel)
@test p.pos === pc.pos == au
@test p.vel == a
@test pc.vel == atrans
pc = deepcopy(p)
translate!(pc, dau)
@test pc.pos == autrans
@test pc.vel == a
pc = deepcopy(p)
translate!(pc, da, :vel)
@test pc.pos == au
@test pc.vel == atrans
# periodic translations
period, period_half = 10, 5
CP._translate_periodic(1, 2, period) == 1
CP._translate_periodic(8, 2, period, period_half) == -2
CP._translate_periodic(2, 8, period, period_half) == 12
o = oneunit(eltype(dau))
pc = translate_periodic(p, dau, o)
@test all(dau .- 0.5o .≤ minimum(pc.pos; dims=2)) &&
all(maximum(pc.pos; dims=2) .≤ dau .+ 0.5o)
pc = deepcopy(p)
translate_periodic!(pc, dau, o)
@test all(dau .- 0.5o .≤ minimum(pc.pos; dims=2)) &&
all(maximum(pc.pos; dims=2) .≤ dau .+ 0.5o)
pcc = translate_periodic_to_center(p, dau, o)
@test pcc == translate(pc, -dau)
pcc = deepcopy(p)
translate_periodic_to_center!(pcc, dau, o)
@test pcc == translate(pc, -dau)
end
@testset "Particle Collection Translations" begin
dm = Particles(:dm)
dm.pos = copy(au)
dm.vel = copy(a)
gas = Particles(:gas)
gas.pos = copy(au)
gas.vel = copy(a)
pc = ParticleCollection(dm, gas)
pcc = translate(pc, dau)
@test pc.dm.pos == pc.gas.pos == au
@test pcc.dm.pos == pcc.gas.pos == autrans
@test pc.dm.vel === pcc.dm.vel == a
pcc = translate(pc, da, :vel)
@test pc.dm.pos === pcc.dm.pos == au
@test pc.dm.vel == pc.gas.vel == a
@test pcc.dm.vel == pcc.gas.vel == atrans
pcc = deepcopy(pc)
translate!(pcc, dau)
@test pcc.dm.pos == pcc.gas.pos == autrans
@test pcc.dm.vel == pcc.gas.vel == a
pcc = deepcopy(pc)
translate!(pcc, da, :vel)
@test pcc.dm.pos == pcc.gas.pos == au
@test pcc.dm.vel == pcc.dm.vel == atrans
pccc = deepcopy(pc)
ap = pccc.all
@test_throws ErrorException translate(ap, da)
translate!(ap, dau)
@test pccc == translate(pc, dau)
pccc = deepcopy(pc)
ap = pccc.all
translate!(ap, da, :vel)
@test pccc == pcc
o = oneunit(eltype(dau))
pcc = translate_periodic(pc, dau, o)
@test pcc.dm == translate_periodic(pc.dm, dau, o)
@test pcc.gas == translate_periodic(pc.gas, dau, o)
pccc = deepcopy(pc)
translate_periodic!(pccc, dau, o)
@test pccc == pcc
pcc = translate_periodic_to_center(pc, dau, o)
@test pcc.dm == translate_periodic_to_center(pc.dm, dau, o)
@test pcc.gas == translate_periodic_to_center(pc.gas, dau, o)
pccc = deepcopy(pc)
translate_periodic_to_center!(pccc, dau, o)
@test pccc == pcc
end
end
@testset "Comoving" begin
z = 1.5
a = rand(Float32, 3, 100)
au = a * u"m"
n = rand(Float32, 100)
nu = n * u"m^-3"
m = copy(n)
propexp = [(:pos, 1), (:vel, 1), (:n, -3)]
acom = a .* (1 + z)
aucom = au .* (1 + z)
ncom = n .* (1 + z)^-3
nucom = nu .* (1 + z)^-3
aphys = a ./ (1 + z)
auphys = au ./ (1 + z)
nphys = n ./ (1 + z)^-3
nuphys = nu ./ (1 + z)^-3
@testset "Particle Comoving" begin
p = Particles(:dm)
p.pos = a
p.n = n
p.mass = m
@test a .* CP.factor_to_comoving(z, 1) ≈ acom
@test n .* CP.factor_to_comoving(z, -3) ≈ ncom
@test a .* CP.factor_to_physical(z, 1) ≈ aphys
@test n .* CP.factor_to_physical(z, -3) ≈ nphys
pc = to_comoving(p, z; propexp)
@test pc.pos ≈ acom
@test pc.n ≈ ncom
@test pc.mass ≈ m
pcc = deepcopy(p)
to_comoving!(pcc, z; propexp)
@test pcc == pc
pc = to_physical(p, z; propexp)
@test pc.pos ≈ aphys
@test pc.n ≈ nphys
@test pc.mass ≈ m
pcc = deepcopy(p)
to_physical!(pcc, z; propexp)
@test pcc == pc
p = Particles(:dm)
p.pos = au
p.n = nu
p.mass = m
pc = to_comoving(p, z; propexp)
@test pc.pos ≈ aucom
@test pc.n ≈ nucom
@test pc.mass ≈ m
pcc = deepcopy(p)
to_comoving!(pcc, z; propexp)
@test pcc == pc
pc = to_physical(p, z; propexp)
@test pc.pos ≈ auphys
@test pc.n ≈ nuphys
@test pc.mass ≈ m
pcc = deepcopy(p)
to_physical!(pcc, z; propexp)
@test pcc == pc
end
@testset "Particle Collection Comoving" begin
dm = Particles(:dm)
dm.pos = copy(au)
dm.n = copy(n)
dm.mass = copy(m)
gas = Particles(:gas)
gas.pos = copy(a)
gas.n = copy(nu)
gas.mass = copy(m)
pc = RedshiftParticleCollection(z, dm, gas)
pcc = to_comoving(pc; propexp)
@test pcc.dm == to_comoving(dm, z; propexp)
@test pcc.gas == to_comoving(gas, z; propexp)
pccc = deepcopy(pc)
to_comoving!(pccc; propexp)
@test pccc == pcc
pcc = to_physical(pc; propexp)
@test pcc.dm == to_physical(dm, z; propexp)
@test pcc.gas == to_physical(gas, z; propexp)
pccc = deepcopy(pc)
to_physical!(pccc; propexp)
@test pccc == pcc
pccc = deepcopy(pc)
ap = pccc.all
@test_throws ErrorException to_comoving(ap, z; propexp)
@test_throws ErrorException to_physical(ap, z; propexp)
to_comoving!(ap, z; propexp)
@test pccc == to_comoving(pc; propexp)
pccc = deepcopy(pc)
ap = pccc.all
to_physical!(ap, z; propexp)
@test pccc == to_physical(pc; propexp)
end
end
end
@testset "Misc Operations" begin
ids = sample(1:1000, 100; replace=false)
a = rand(3, 100)
bu = rand(100) * u"kg"
p = Particles(:dm)
p.id = ids
p.pos = copy(a)
p.mass = copy(bu)
@testset "Particle Sorting" begin
pc = sort(p, :id)
@test issorted(pc.id)
@test p.id == ids
ind = searchsortedfirst(pc.id, p.id[1])
@test pc.pos[:, ind] == p.pos[:, 1]
@test pc.mass[ind] == p.mass[1]
pc = sort(p, :mass; affect=[:mass, :pos], alg=RadixSort)
@test issorted(pc.mass)
@test haskey(pc, :pos)
@test !haskey(pc, :id)
pc = sort(p, :mass; affect=[:pos], alg=RadixSort)
@test !haskey(pc, :mass)
pc = deepcopy(p)
sort!(pc, :id; alg=RadixSort)
@test issorted(pc.id)
ind = searchsortedfirst(pc.id, p.id[1])
@test pc.pos[:, ind] == p.pos[:, 1]
@test pc.mass[ind] == p.mass[1]
end
@testset "Particle Collection Sorting" begin
dm = p
gas = Particles(:gas, deepcopy(p.props))
pc = ParticleCollection(dm, gas)
pcc = sort(pc, :id)
@test pc != pcc
@test pcc.dm == sort(dm, :id)
@test pcc.gas == sort(gas, :id)
affect = [:mass, :pos]
pcc = sort(pc, :mass; affect, alg=RadixSort)
@test pcc.dm == sort(dm, :mass; affect)
@test pcc.gas == sort(gas, :mass; affect)
affect = [(:dm, [:mass, :pos]), (:gas, [:mass])]
pcc = sort(pc, :mass; affect, alg=QuickSort)
@test pcc.dm == sort(dm, :mass; affect=[:mass, :pos])
@test pcc.gas == sort(gas, :mass; affect=[:mass])
pcc = deepcopy(pc)
sort!(pcc, :id; alg=RadixSort)
@test pcc == sort(pc, :id)
ap = pc.all
@test_throws ErrorException sort(ap, :id; affect)
@test_throws ErrorException sort!(ap, :id; alg=RadixSort)
end
@testset "Particle Filtering" begin
massmin = 0.5u"kg"
pc = filter(p -> p.mass .> massmin, p)
@test all(pc.mass .> massmin)
@test p.mass == bu
ind = findfirst(>(massmin), p.mass)
@test pc.pos[:, 1] == p.pos[:, ind]
@test pc.id[1] == p.id[ind]
@test filter(p -> findall(p.mass .> massmin), p) == pc
pc = filter(p -> p.mass .> massmin, p; affect=[:id, :mass])
@test all(pc.mass .> massmin)
@test haskey(pc, :id)
@test !haskey(pc, :pos)
pc = deepcopy(p)
filter!(p -> p.mass .> massmin, pc)
@test all(pc.mass .> massmin)
@test p.mass == bu
ind = findfirst(>(massmin), p.mass)
@test pc.pos[:, 1] == p.pos[:, ind]
@test pc.id[1] == p.id[ind]
ids_wanted = sample(1:1000, 100; replace=false)
pc = filter(p; ids=ids_wanted)
@test all(in.(pc.id, (ids_wanted,)))
@test !any(in.(setdiff(ids, pc.id), (ids_wanted,)))
ind = findfirst(in(ids_wanted), p.id)
@test pc.pos[:, 1] == p.pos[:, ind]
@test pc.mass[1] == p.mass[ind]
@test filter(p; ids=Set(ids_wanted)) == pc
pc = filter(p; ids=ids_wanted, affect=[:id, :mass])
@test all(in.(pc.id, (ids_wanted,)))
@test !any(in.(setdiff(ids, pc.id), (ids_wanted,)))
@test haskey(pc, :mass)
@test !haskey(pc, :pos)
pc = deepcopy(p)
filter!(pc; ids=ids_wanted)
@test all(in.(pc.id, (ids_wanted,)))
@test !any(in.(setdiff(ids, pc.id), (ids_wanted,)))
ind = findfirst(in(ids_wanted), p.id)
@test pc.pos[:, 1] == p.pos[:, ind]
@test pc.mass[1] == p.mass[ind]
@test filter!(deepcopy(p); ids=Set(ids_wanted)) == pc
end
@testset "Particle Collection Filtering" begin
dm = p
gas = Particles(:gas, deepcopy(p.props))
pc = ParticleCollection(dm, gas)
massmin = 0.5u"kg"
f = p -> p.mass .> massmin
pcc = filter(f, pc)
@test pcc.dm == filter(f, dm)
@test filter(p -> findall(p.mass .> massmin), pc) == pcc
affect = [:id, :mass]
pcc = filter(f, pc; affect)
@test pcc.dm == filter(f, dm; affect)
affect = [(:dm, [:id, :mass])]
pcc = filter(f, pc; affect)
@test pcc.dm == filter(f, dm; affect=[:id, :mass])
@test !haskey(pcc, :gas)
pcc = deepcopy(pc)
filter!(f, pcc)
@test pcc == filter(f, pc)
ids_wanted = sample(1:1000, 100; replace=false)
pcc = filter(pc; ids=ids_wanted)
@test pcc.dm == filter(dm; ids=ids_wanted)
@test filter(pc; ids=Set(ids_wanted)) == pcc
affect = [:id, :mass]
pcc = filter(pc; ids=ids_wanted, affect)
@test pcc.dm == filter(dm; ids=ids_wanted, affect)
affect = [(:gas, [:id])]
pcc = filter(pc; ids=ids_wanted, affect)
@test !haskey(pcc, :dm)
@test pcc.gas == filter(gas; ids=ids_wanted, affect=[:id])
pcc = deepcopy(pc)
filter!(pcc; ids=ids_wanted)
@test pcc == filter(pcc; ids=ids_wanted)
@test filter!(deepcopy(pc); ids=Set(ids_wanted)) == pcc
ap = pc.all
@test_throws ErrorException filter(f, ap; affect)
@test_throws ErrorException filter!(f, ap)
@test_throws ErrorException filter(ap; ids=ids_wanted)
@test_throws ErrorException filter!(ap; ids=ids_wanted)
end
@testset "Particle Removing" begin
ids_remove = sample(1:1000, 100; replace=false)
ids_keep = setdiff(1:1000, ids_remove)
pc_check = filter(p; ids=ids_keep)
@test delete(p; ids=ids_remove) == pc_check
@test delete(p; ids=Set(ids_remove)) == pc_check
pc = delete(p; ids=ids_remove, affect=[:id, :mass])
@test pc == filter(p; ids=ids_keep, affect=[:id, :mass])
pc = deepcopy(p)
delete!(pc; ids=ids_remove)
@test pc == pc_check
@test delete!(deepcopy(p); ids=Set(ids_remove)) == pc_check
end
@testset "Particle Collection Removing" begin
dm = p
gas = Particles(:gas, deepcopy(p.props))
pc = ParticleCollection(dm, gas)
ids_remove = sample(1:1000, 100; replace=false)
pcc = delete(pc; ids=ids_remove)
@test pcc.dm == delete(dm; ids=ids_remove)
@test delete(pc; ids=Set(ids_remove)) == pcc
affect = [:id, :mass]
pcc = delete(pc; ids=ids_remove, affect)
@test pcc.dm == delete(dm; ids=ids_remove, affect)
affect = [(:gas, [:id])]
pcc = delete(pc; ids=ids_remove, affect)
@test !haskey(pcc, :dm)
@test pcc.gas == delete(gas; ids=ids_remove, affect=[:id])
pcc = deepcopy(pc)
delete!(pcc; ids=ids_remove)
@test pcc == delete(pcc; ids=ids_remove)
@test delete!(deepcopy(pc); ids=Set(ids_remove)) == pcc
ap = pc.all
@test_throws Exception delete(ap; ids=ids_remove)
@test_throws Exception delete!(ap; ids=ids_remove)
end
end
@testset "Geometry" begin
# create square/cube/hypercube of positions between 0 and 1
pos2 = reshape(Iterators.product(0:0.1:1, 0:0.1:1) |> collect, :)
pos2 = reduce(hcat, getindex.(pos2, i) for i in eachindex(pos2[1]))'
pos3 = reshape(Iterators.product(0:0.1:1, 0:0.1:1, 0:0.1:1) |> collect, :)
pos3 = reduce(hcat, getindex.(pos3, i) for i in eachindex(pos3[1]))'
pos4 = reshape(Iterators.product(0:0.1:1, 0:0.1:1, 0:0.1:1, 0:0.1:1) |> collect, :)
pos4 = reduce(hcat, getindex.(pos4, i) for i in eachindex(pos4[1]))'
@testset "Hyperrectangle" begin
hrect = CosmoHyperrectangle(0.5rand(4), 0.5(1 .+ rand(4)))
@test hrect isa CosmoHyperrectangle{Float64,4}
hrect = CosmoHyperrectangle([0, 0, 0] .// 100, [15, 25, 20] .// 100)
@test hrect isa CosmoHyperrectangle{Float64,3}
@test hrect isa CosmoCuboid{Float64}
# construct hyperrectangles from center and side lengths
@test CosmoHyperrectangle([75, 125, 100] .// 1000, ([15, 25, 20] .// 100)...) == hrect
@test CosmoCuboid([75, 125, 100] .// 1000, ([15, 25, 20] .// 100)...) == hrect
@test CosmoRectangle([75, 125] .// 1000, ([15, 25] .// 100)...) ==
CosmoRectangle([0, 0] .// 100, [15, 25] .// 100)
# test constructors
@test CosmoHyperrectangle([0, 0, 0], [0.15, 0.25, 0.20]) isa CosmoCuboid{Float64}
@test CosmoHyperrectangle{Float64,3}([0, 0, 0.0], [0.15, 0.25, 0.20]) isa CosmoCuboid{Float64}
@test CosmoHyperrectangle{Float64,3}([0, 0, 0], [0.15, 0.25, 1 // 10]) isa CosmoCuboid{Float64}
@test_throws AssertionError CosmoHyperrectangle{Float64,4}([0, 0, 0.0], [0.15, 0.25, 0.20])
@test CosmoCuboid([0, 0, 0], [0.15, 0.25, 0.20]) isa CosmoCuboid{Float64}
@test_throws AssertionError CosmoCuboid([0, 0], [0.15, 0.25])
@test CosmoRectangle([0, 0], [0.25, 0.20]) isa CosmoRectangle{Float64}
@test_throws AssertionError CosmoRectangle([0, 0, 0], [0.15, 0.25, 0.20])
@test_throws AssertionError CosmoRectangle([0, 0], [0.15, 0.25, 0.20])
@test CosmoCuboid{Float64}([0, 0, 0.0], [0.15, 0.25, 0.20]) isa CosmoCuboid{Float64}
@test CosmoCuboid{Float32}([0, 0, 0], [0.15, 0.25, 1 // 10]) isa CosmoCuboid{Float32}
@test CosmoRectangle{Float64}([0, 0.0], [0.25, 0.20]) isa CosmoRectangle{Float64}
@test CosmoRectangle{Float64}([0, 0], [0.25, 0.20]) isa CosmoRectangle{Float64}
@test CosmoHyperrectangle([0, 0], 0.15, 0.25) isa CosmoRectangle{Float64}
@test CosmoHyperrectangle([0, 0, 0], 0.15, 0.25, 0.20) isa CosmoCuboid{Float64}
@test CosmoHyperrectangle([0, 0, 0, 0], 0.15, 0.25, 0.20, 0.54) isa CosmoHyperrectangle{Float64,4}
@test_throws Exception CosmoHyperrectangle([0, 0], 0.15, 0.25, 0.54)
r = 5 // 2
@test CosmoHypercube([0, 0, 0, 0], r) == CosmoHyperrectangle([-r, -r, -r, -r], [r, r, r, r])
@test CosmoCube([0, 0, 0], r) == CosmoCuboid([-r, -r, -r], [r, r, r])
@test CosmoSquare([0, 0], r) == CosmoRectangle([-r, -r], [r, r])
cuboid = CosmoCuboid([0, 0, 0] .// 100, [15, 25, 19] .// 100)
@test CP.geometry_enclosing_center(cuboid) ≈ [15, 25, 19] .// 200 |> float
@test CP.geometry_enclosing_center(CosmoCube(zeros(Int, 3), 4 // 1)) == zeros(3)
@test all(CP.geometry_enclosing_corners(cuboid) .≈ (zeros(3), [15, 25, 19] .// 100 |> float))
pos3in = pos3[:, CP.mask_in(pos3, cuboid)]
@test all(0 .≤ pos3in[1, :] .≤ 0.15) &&
all(0 .≤ pos3in[2, :] .≤ 0.25) &&
all(0 .≤ pos3in[3, :] .≤ 0.19)
rect = CosmoRectangle([0, 0] .// 100, [25, 19] .// 100)
pos2in = pos2[:, CP.mask_in(pos2, rect)]
@test all(0 .≤ pos2in[1, :] .≤ 0.25) && all(0 .≤ pos2in[2, :] .≤ 0.19)
hrect = CosmoHyperrectangle([0, 0, 0, 0] .// 100, [15, 25, 19, 35] .// 100)
pos4in = pos4[:, CP.mask_in(pos4, hrect)]
@test all(0 .≤ pos4in[1, :] .≤ 0.15) &&
all(0 .≤ pos4in[2, :] .≤ 0.25) &&
all(0 .≤ pos4in[3, :] .≤ 0.19) &&
all(0 .≤ pos4in[4, :] .≤ 0.35)
@test_throws AssertionError CP.mask_in(pos3, hrect)
end
@testset "Hypersphere" begin
hsphere = CosmoHypersphere(0.5rand(4), 0.5)
@test hsphere isa CosmoHypersphere{Float64,4}
hsphere = CosmoHypersphere([0, 0, 0] .// 100, 15 // 100)
@test hsphere isa CosmoHypersphere{Float64,3}
@test hsphere isa CosmoSphere{Float64}
@test hsphere == CosmoHypersphere([0, 0, 0], 15 // 100)
# test constructors
@test CosmoHypersphere([0.15, 0.25, 0.20], 2) isa CosmoSphere{Float64}
@test CosmoHypersphere{Float64,3}([0.15, 0.25, 0.20], 0.2) isa CosmoSphere{Float64}
@test CosmoHypersphere{Float32,3}([0.15, 0.25, 0.20], 0.2) isa CosmoSphere{Float32}
@test_throws AssertionError CosmoHypersphere{Float64,4}([0.15, 0.25, 0.20], 0.2)
@test CosmoSphere([0.15, 0.25, 0.20], 0.2) isa CosmoSphere{Float64}
@test_throws AssertionError CosmoSphere([0.15, 0.25], 0.3)
@test CosmoCircle([0.25, 0.20], 0.2) isa CosmoCircle{Float64}
@test_throws AssertionError CosmoCircle([0.15, 0.25, 0.20], 0.2)
@test CosmoSphere{Float64}([0.15, 0.25, 0.20], 0.2) isa CosmoSphere{Float64}
@test CosmoSphere{Float32}([0.15, 0.25, 0.20], 1) isa CosmoSphere{Float32}
@test CosmoCircle{Float64}([0.25, 0.20], 0.2) isa CosmoCircle{Float64}
r = 12 // 100
center = [10, 20, 30] .// 100
sphere = CosmoSphere(center, r)
@test CP.geometry_enclosing_center(sphere) ≈ center |> float
@test all(
CP.geometry_enclosing_corners(sphere) .≈
([-2, 8, 18] .// 100 |> float, [22, 32, 42] .// 100 |> float),
)
pos3in = pos3[:, CP.mask_in(pos3, sphere)]
@test all(sum(abs2, pos3in .- center; dims=1) .≤ r^2)
pos3notin = pos3[:, .~CP.mask_in(pos3, sphere)]
@test all(sum(abs2, pos3notin .- center; dims=1) .> r^2)
center = [20, 30] .// 100
circle = CosmoCircle(center, r)
pos2in = pos2[:, CP.mask_in(pos2, circle)]
@test all(sum(abs2, pos2in .- center; dims=1) .≤ r^2)
center = [10, 20, 30, 40] .// 100
hsphere = CosmoHypersphere(center, r)
pos4in = pos4[:, CP.mask_in(pos4, hsphere)]
@test all(sum(abs2, pos4in .- center; dims=1) .≤ r^2)
sphere = CosmoSphere(zeros(3), r)
@test CosmoSphere(r) == sphere
pos3in = pos3[:, CP.mask_in(pos3, sphere)]
@test all(sum(abs2, pos3in; dims=1) .≤ r^2)
circle = CosmoCircle(zeros(2), r)
@test CosmoCircle(r) == circle
pos2in = pos2[:, CP.mask_in(pos2, circle)]
@test all(sum(abs2, pos2in; dims=1) .≤ r^2)
hsphere = CosmoHypersphere(zeros(4), r)
@test CosmoHypersphere(4, r) == hsphere
pos4in = pos4[:, CP.mask_in(pos4, hsphere)]
@test all(sum(abs2, pos4in; dims=1) .≤ r^2)
@test_throws AssertionError CP.mask_in(pos3, hsphere)
end
@testset "Cylinder" begin
scyl = CosmoStandingCylinder(0.5rand(3), 0.5, 0.5)
@test scyl isa CosmoStandingCylinder{Float64}
scyl = CosmoStandingCylinder([1, 2, 3] .// 10, 2 // 10, 1 // 10)
@test scyl isa CosmoStandingCylinder{Float64}
@test CosmoStandingCylinder(rand(3), 1, 1 // 10) isa CosmoStandingCylinder{Float64}
@test CosmoStandingCylinder{Float32}(rand(3), 1, 1 // 10) isa CosmoStandingCylinder{Float32}
@test_throws AssertionError CosmoStandingCylinder(rand(4), 1, 1 // 10)
center = [1, 2, 3] .// 10
h = 24 // 100
r = 15 // 100
scyl = CosmoStandingCylinder(center, h, r)
@test CP.geometry_enclosing_center(scyl) ≈ center |> float
@test all(
CP.geometry_enclosing_corners(scyl) .≈
([-5, 5, 18] .// 100 |> float, [25, 35, 42] .// 100 |> float),
)
pos3in = pos3[:, CP.mask_in(pos3, scyl)]
@test @views all(sum(abs2, pos3in[1:2, :] .- center[1:2]; dims=1) .≤ r^2)
@test @views all(18 // 100 .≤ pos3in[3, :] .≤ 42 // 100)
pos3notin = pos3[:, .~CP.mask_in(pos3, scyl)]
@test @views all(
vec(sum(abs2, pos3notin[1:2, :] .- center[1:2]; dims=1) .> r^2) .|
(18 // 100 .> pos3notin[3, :]) .|
(pos3notin[3, :] .> 42 // 100),
)
@test_throws AssertionError CP.mask_in(pos2, scyl)
cyl = CosmoCylinder(0.5rand(3), 0.5rand(3), 0.5)
@test cyl isa CosmoCylinder{Float64}
cyl = CosmoCylinder([1, 2, 3] .// 10, [2, 3, 4] .// 10, 1 // 10)
@test cyl isa CosmoCylinder{Float64}
@test CosmoCylinder(rand(3), rand(3), 1 // 10) isa CosmoCylinder{Float64}
@test CosmoCylinder(rand(3), [0, 0, 0], 1 // 10) isa CosmoCylinder{Float64}
@test CosmoCylinder{Float32}(rand(3), [0, 0, 0], 1 // 10) isa CosmoCylinder{Float32}
@test_throws AssertionError CosmoCylinder(rand(4), rand(3), 0.1)
@test_throws AssertionError CosmoCylinder(rand(3), rand(2), 0.1)
center = center
startpos = [10, 20, 18] .// 100
endpos = [10, 20, 42] .// 100
r = r
cyl = CosmoCylinder(startpos, endpos, r)
@test CP.geometry_enclosing_center(cyl) ≈ center |> float
@test all(CP.geometry_enclosing_corners(cyl) .≈ ([-5, 5, 18] .// 100, [25, 35, 42] .// 100))
# right mask for standing cylinder
@test scyl == cyl
@test CP.mask_in(pos3, scyl) == CP.mask_in(pos3, cyl)
@test_throws AssertionError CP.mask_in(pos2, cyl)
@test CosmoStandingCylinder(cyl) == scyl
@test CosmoCylinder(scyl) == cyl
# same result switching random start and end points of the cylinder
p1 = rand(3)
p2 = rand(3)
r = 0.15
c1 = CosmoCylinder(p1, p2, r)
c2 = CosmoCylinder(p2, p1, r)
@test CP.geometry_enclosing_center(c1) ≈ CP.geometry_enclosing_center(c2)
@test all(CP.geometry_enclosing_corners(c1) .≈ CP.geometry_enclosing_corners(c2))
@test CP.mask_in(pos3, c1) == CP.mask_in(pos3, c2)
@test_throws AssertionError CosmoStandingCylinder(c1)
scyl = CosmoStandingCylinder([1, 2, 3] .// 10, 2 // 10, 1 // 10)
@test scyl == CosmoStandingCylinder([1, 2, 3] .// 10, 2 // 10, 1 // 10)
@test scyl ≈ CosmoCylinder([1, 2, 2] .// 10, [1, 2, 4] .// 10, 1 // 10)
@test CosmoCylinder([1, 2, 2] .// 10, [1, 2, 4] .// 10, 1 // 10) ≈ scyl
end
@testset "Filtering" begin
p = Particles(:dm)
p.pos = pos3
p.mass = rand(size(pos3, 2))
pu = Particles(:dm)
pu.pos = pos3 .* u"m"
pu.mass = rand(size(pos3, 2))
center = rand(3)
sph = CosmoSphere(center, 0.2)
sphu = CosmoSphere(center * u"m", 0.2u"m")
mask = CP.mask_in(pos3, sph)
@test filter(p, sph) == p[mask]
@test filter(pu, sphu) == pu[mask]
pc = filter(p, sph; affect=[:pos])
@test pc.pos == p.pos[:, mask]
@test !haskey(pc, :mass)
pc = deepcopy(p)
filter!(pc, sph)
@test pc == p[mask]
# Particle collection
dm = p
gas = Particles(:gas, deepcopy(p.props))
pc = ParticleCollection(dm, gas)
pcc = filter(pc, sph)
@test pcc.dm == dm[mask]
pcc = filter(pc, sph; affect=[:pos])
@test pcc.dm == filter(dm, sph; affect=[:pos])
pcc = filter(pc, sph; affect=[(:dm, [:pos, :mass])])
@test pcc.dm == filter(dm, sph; affect=[:pos, :mass])
@test !haskey(pcc, :gas)
pcc = deepcopy(pc)
filter!(pcc, sph)
@test pcc == filter(pc, sph)
ap = pc.all
@test_throws ErrorException filter(ap, sph)
@test_throws ErrorException filter!(ap, sph)
end
end
@testset "Properties" begin
@testset "Norm" begin
a2 = rand(2, 100)
a3 = rand(3, 100)
a4 = rand(4, 100)
n2 = norm.(eachcol(a2))
n3 = norm.(eachcol(a3))
n4 = norm.(eachcol(a4))
@test colnorm(a2) ≈ n2
@test colnorm(a3) ≈ n3
@test colnorm(a4) ≈ n4
@test colnorm(a2 * u"m") ≈ n2 * u"m"
@test colnorm(a3 * u"m") ≈ n3 * u"m"
@test colnorm(a4 * u"m") ≈ n4 * u"m"
@test colnorm2(a2) ≈ n2 .^ 2
@test colnorm2(a3) ≈ n3 .^ 2
@test colnorm2(a4) ≈ n4 .^ 2
@test colnorm2(a2 * u"m") ≈ (n2 * u"m") .^ 2
@test colnorm2(a3 * u"m") ≈ (n3 * u"m") .^ 2
@test colnorm2(a4 * u"m") ≈ (n4 * u"m") .^ 2
am2 = similar(a2, Union{Float64,Missing})
am3 = similar(a3, Union{Float64,Missing})
am4 = similar(a4, Union{Float64,Missing})
am2 .= a2
am3 .= a3
am4 .= a4
am2[:, 1] .= missing
am3[:, 1] .= missing
am4[:, 1] .= missing
@test all(colnorm(am2) .≈ n2) |> ismissing
@test all(colnorm(am3) .≈ n3) |> ismissing
@test all(colnorm(am4) .≈ n4) |> ismissing
@test all(colnorm(am2 * u"m") .≈ n2 * u"m") |> ismissing
@test all(colnorm(am3 * u"m") .≈ n3 * u"m") |> ismissing
@test all(colnorm(am4 * u"m") .≈ n4 * u"m") |> ismissing
@test all(colnorm2(am2) .≈ n2 .^ 2) |> ismissing
@test all(colnorm2(am3) .≈ n3 .^ 2) |> ismissing
@test all(colnorm2(am4) .≈ n4 .^ 2) |> ismissing
@test all(colnorm2(am2 * u"m") .≈ (n2 * u"m") .^ 2) |> ismissing
@test all(colnorm2(am3 * u"m") .≈ (n3 * u"m") .^ 2) |> ismissing
@test all(colnorm2(am4 * u"m") .≈ (n4 * u"m") .^ 2) |> ismissing
end
@testset "Norm around Origin" begin
a2 = rand(2, 100)
a3 = rand(3, 100)
a4 = rand(4, 100)
o2 = rand(2)
o3 = rand(3)
o4 = rand(4)
n2 = norm.(eachcol(a2 .- o2))
n3 = norm.(eachcol(a3 .- o3))
n4 = norm.(eachcol(a4 .- o4))
@test colnorm(a2, o2) ≈ n2
@test colnorm(a3, o3) ≈ n3
@test colnorm(a4, o4) ≈ n4
@test colnorm(a2 * u"m", o2 * u"m") ≈ n2 * u"m"
@test colnorm(a3 * u"m", o3 * u"m") ≈ n3 * u"m"
@test colnorm(a4 * u"m", o4 * u"m") ≈ n4 * u"m"
@test colnorm2(a2, o2) ≈ n2 .^ 2
@test colnorm2(a3, o3) ≈ n3 .^ 2
@test colnorm2(a4, o4) ≈ n4 .^ 2
@test colnorm2(a2 * u"m", o2 * u"m") ≈ (n2 * u"m") .^ 2
@test colnorm2(a3 * u"m", o3 * u"m") ≈ (n3 * u"m") .^ 2
@test colnorm2(a4 * u"m", o4 * u"m") ≈ (n4 * u"m") .^ 2
am2 = similar(a2, Union{Float64,Missing})
am3 = similar(a3, Union{Float64,Missing})
am4 = similar(a4, Union{Float64,Missing})
am2 .= a2
am3 .= a3
am4 .= a4
am2[:, 1] .= missing
am3[:, 1] .= missing
am4[:, 1] .= missing
@test all(colnorm(am2, o2) .≈ n2) |> ismissing
@test all(colnorm(am3, o3) .≈ n3) |> ismissing
@test all(colnorm(am4, o4) .≈ n4) |> ismissing
@test all(colnorm(am2 * u"m", o2 * u"m") .≈ n2 * u"m") |> ismissing
@test all(colnorm(am3 * u"m", o3 * u"m") .≈ n3 * u"m") |> ismissing
@test all(colnorm(am4 * u"m", o4 * u"m") .≈ n4 * u"m") |> ismissing
@test all(colnorm2(am2, o2) .≈ n2 .^ 2) |> ismissing
@test all(colnorm2(am3, o3) .≈ n3 .^ 2) |> ismissing
@test all(colnorm2(am4, o4) .≈ n4 .^ 2) |> ismissing
@test all(colnorm2(am2 * u"m", o2 * u"m") .≈ (n2 * u"m") .^ 2) |> ismissing
@test all(colnorm2(am3 * u"m", o3 * u"m") .≈ (n3 * u"m") .^ 2) |> ismissing
@test all(colnorm2(am4 * u"m", o4 * u"m") .≈ (n4 * u"m") .^ 2) |> ismissing
end
@testset "Dot Product" begin
a2 = rand(2, 100)
a3 = rand(3, 100)
a4 = rand(4, 100)
b2 = rand(2, 100)
b3 = rand(3, 100)
b4 = rand(4, 100)
c2 = reduce(hcat, [dot(ai, bi) for (ai, bi) in zip(eachcol(a2), eachcol(b2))]) |> vec
c3 = reduce(hcat, [dot(ai, bi) for (ai, bi) in zip(eachcol(a3), eachcol(b3))]) |> vec
c4 = reduce(hcat, [dot(ai, bi) for (ai, bi) in zip(eachcol(a4), eachcol(b4))]) |> vec
for (a, b, c) in zip([a2, a3, a4], [b2, b3, b4], [c2, c3, c4])
@test coldot(a, b) ≈ c
@test coldot(a * u"m", b) ≈ c * u"m"
@test coldot(a, b * u"m") ≈ c * u"m"
@test coldot(a * u"m", b * u"m") ≈ c * u"m^2"
am = similar(a, Union{Float64,Missing})
am .= a
am[:, 1] .= missing
bm = similar(b, Union{Float64,Missing})
bm .= b
bm[:, 1] .= missing
@test all(coldot(am, b) .≈ c) |> ismissing
@test all(coldot(a * u"m", bm) .≈ c * u"m") |> ismissing
@test all(coldot(a, bm * u"m") .≈ c * u"m") |> ismissing
@test all(coldot(am * u"m", bm) .≈ c * u"m") |> ismissing
@test all(coldot(am * u"m", bm * u"m") .≈ c * u"m^2") |> ismissing
end
am = similar(a3, Union{Float64,Nothing})
am .= a3
@test coldot(am, b3) ≈ c3
end
@testset "Cross Product" begin
a = rand(3, 100)
b = rand(3, 100)
au = a * u"m"
bu = b * u"m"
c = reduce(hcat, [cross(ai, bi) for (ai, bi) in zip(eachcol(a), eachcol(b))])
@test colcross(a, b) ≈ c
@test colcross(au, b) ≈ c * u"m"
@test colcross(a, bu) ≈ c * u"m"
@test colcross(au, bu) ≈ c * u"m^2"
am = similar(a, Union{Float64,Missing})
am .= a
am[:, 1] .= missing
bm = similar(b, Union{Float64,Missing})
bm .= b
bm[:, 1] .= missing
@test all(colcross(am, b) .≈ c) |> ismissing
@test all(colcross(au, bm) .≈ c * u"m") |> ismissing
@test all(colcross(a, bm * u"m") .≈ c * u"m") |> ismissing
@test all(colcross(am * u"m", bm) .≈ c * u"m") |> ismissing
@test all(colcross(am * u"m", bm * u"m") .≈ c * u"m^2") |> ismissing
am = similar(a, Union{Float64,Nothing})
am .= a
@test colcross(am, b) ≈ c
end
@testset "Angular momentum" begin
a = rand(3, 100)
b = rand(3, 100)
au = a * u"m"
bu = b * u"m"
for m in [rand(), rand(100)]
mu = m * u"kg"
j = m' .* colcross(a, b)
@test angmom(a, b, m) ≈ j
@test angmom(au, b, m) ≈ j * u"m"
@test angmom(a, bu, m) ≈ j * u"m"
@test angmom(au, bu, m) ≈ j * u"m^2"
@test angmom(a, b, mu) ≈ j * u"kg"
@test angmom(au, b, mu) ≈ j * u"m*kg"
@test angmom(a, bu, mu) ≈ j * u"m*kg"
@test angmom(au, bu, mu) ≈ j * u"m^2*kg"
for func in [angmomtot, angmomtot_stable]
@test func(a, b, m) ≈ sum(j; dims=2)
@test func(au, b, m) ≈ sum(j * u"m"; dims=2)
@test func(a, bu, m) ≈ sum(j * u"m"; dims=2)
@test func(au, bu, m) ≈ sum(j * u"m^2"; dims=2)
@test func(a, b, mu) ≈ sum(j * u"kg"; dims=2)
@test func(au, b, mu) ≈ sum(j * u"m*kg"; dims=2)
@test func(a, bu, mu) ≈ sum(j * u"m*kg"; dims=2)
@test func(au, bu, mu) ≈ sum(j * u"m^2*kg"; dims=2)
end
origin = rand(3)
velorigin = rand(3)
jorigin = m' .* colcross(a .- origin, b)
@test angmom(au, bu, mu; origin=origin * u"m") ≈ jorigin * u"m^2*kg"
@test angmomtot(au, bu, mu; origin=origin * u"m") ≈ sum(jorigin * u"m^2*kg"; dims=2)
jorigin = m' .* colcross(a, b .- velorigin)
@test angmom(au, bu, mu; velorigin=velorigin * u"m") ≈ jorigin * u"m^2*kg"
@test angmomtot(au, bu, mu; velorigin=velorigin * u"m") ≈ sum(jorigin * u"m^2*kg"; dims=2)
@test angmomtot_stable(au, bu, mu; velorigin=velorigin * u"m") ≈
sum(jorigin * u"m^2*kg"; dims=2)
jorigin = m' .* colcross(a .- origin, b .- velorigin)
@test angmom(au, bu, mu; origin=origin * u"m", velorigin=velorigin * u"m") ≈
jorigin * u"m^2*kg"
@test angmomtot(au, bu, mu; origin=origin * u"m", velorigin=velorigin * u"m") ≈
sum(jorigin * u"m^2*kg"; dims=2)
@test angmomtot_stable(au, bu, mu; origin=origin * u"m", velorigin=velorigin * u"m") ≈
sum(jorigin * u"m^2*kg"; dims=2)
am = similar(a, Union{Float64,Missing})
am .= a
am[:, 1] .= missing
bm = similar(b, Union{Float64,Missing})
bm .= b
bm[:, 1] .= missing
@test all(angmom(am, b, m) .≈ j) |> ismissing
@test all(angmom(au, bm, m) .≈ j * u"m") |> ismissing
@test all(angmom(a, bm * u"m", m) .≈ j * u"m") |> ismissing
@test all(angmom(am * u"m", bm, m) .≈ j * u"m") |> ismissing
@test all(angmom(am * u"m", bm * u"m", m) .≈ j * u"m^2") |> ismissing
@test all(angmom(am, b, mu) .≈ j * u"kg") |> ismissing
@test all(angmom(au, bm, mu) .≈ j * u"m*kg") |> ismissing
@test all(angmom(a, bm * u"m", mu) .≈ j * u"m*kg") |> ismissing
@test all(angmom(am * u"m", bm, mu) .≈ j * u"m*kg") |> ismissing
@test all(angmom(am * u"m", bm * u"m", mu) .≈ j * u"m^2*kg") |> ismissing
am = similar(a, Union{Float64,Nothing})
am .= a
@test angmom(am, b, m) ≈ j
end
end
@testset "Particles properties" begin
dm = Particles(:dm, :pos => rand(3, 100), :vel => rand(Float32, 3, 100), :mass => 2)
gas = Particles(
:gas,
:pos => rand(3, 100),
:vel => rand(Float32, 3, 100),
:mass => rand(100),
:temp => rand(100),
:test => 1,
:mass2 => Fill(2, 100),
)
dmu = deepcopy(dm)
gasu = deepcopy(gas)
dmu.pos *= u"m"
dmu.vel *= u"km/s"
dmu.mass *= u"kg"
gasu.pos *= u"m"
gasu.vel *= u"km/s"
gasu.mass *= u"kg"
gasu.mass2 *= u"kg"
gasu.temp *= u"K"
for (dm, gas) in zip([dm, dmu], [gas, gasu])
cp = ParticleCollection(dm, gas)
ap = cp.all
p = Particles(ap)
@test meanpos(dm) == mean(dm.pos; dims=2)
@test meanpos(dm; massweighted=false) == mean(dm.pos; dims=2)
@test meanpos(gas) ≈ sum(gas.pos .* gas.mass'; dims=2) / sum(gas.mass)
@test meanpos(gas; massweighted=false) == mean(gas.pos; dims=2)
@test meanpos(ap) ≈ meanpos(p)
@test meanprop(dm, :pos) == mean(dm.pos; dims=2)
@test meanprop(gas, :pos; massprop=:mass2) == mean(gas.pos; dims=2)
@test meanprop(gas, :temp) ≈ sum(gas.temp .* gas.mass) / sum(gas.mass)
@test meanprop(gas, :temp; massweighted=false) == mean(gas.temp)
@test meanprop(gas, :temp; massprop=:mass2) == mean(gas.temp)
@test meanprop(dm, :mass) == dm.mass
@test meanprop(dm, :mass; massweighted=false) == dm.mass
@test meanprop(gas, :test) == gas.test
@test meanprop(gas, :mass2) == gas.mass2[1]
@test meanprop(gas, :mass2; massweighted=false) == gas.mass2[1]
@test meanprop(gas, :mass2; massprop=:mass2) == gas.mass2[1]
@test meanprop(ap, :mass) ≈ meanvel(p, :mass)
@test meanvel(dm) == mean(dm.vel; dims=2)
@test meanvel(dm; massweighted=false) == mean(dm.vel; dims=2)
@test meanvel(gas) ≈ sum(gas.vel .* gas.mass'; dims=2) / sum(gas.mass)
@test meanvel(gas; massweighted=false) == mean(gas.vel; dims=2)
@test meanvel(ap) ≈ meanvel(p)
@test sumprop(dm, :pos) == sum(dm.pos; dims=2)
@test sumprop(gas, :pos) == sum(gas.pos; dims=2)
@test sumprop(dm, :mass) == CP.particle_number(dm) * dm.mass
@test sumprop(gas, :mass2) == sum(gas.mass2)
@test sumprop(ap, :pos) == sumprop(p, :pos)
@test angmom(dm; angmomprop=:j) == angmom(dm.pos, dm.vel, dm.mass)
@test angmomtot(dm; angmomprop=:j) == angmomtot(dm.pos, dm.vel, dm.mass)
@test angmomtot_stable(dm; angmomprop=:j) == angmomtot_stable(dm.pos, dm.vel, dm.mass)
dm.j = angmom(dm)
@test angmom(dm; angmomprop=:j) ≈ angmom(dm.pos, dm.vel, dm.mass)
@test angmomtot(dm; angmomprop=:j) ≈ angmomtot(dm.pos, dm.vel, dm.mass)
@test angmomtot_stable(dm; angmomprop=:j) ≈ angmomtot_stable(dm.pos, dm.vel, dm.mass)
@test angmom(gas; angmomprop=:j) == angmom(gas.pos, gas.vel, gas.mass)
@test angmomtot(gas; angmomprop=:j) == angmomtot(gas.pos, gas.vel, gas.mass)
@test angmomtot_stable(gas; angmomprop=:j) == angmomtot_stable(gas.pos, gas.vel, gas.mass)
origin = 2 * dm.pos[:, 1]
velorigin = 2 * dm.vel[:, 1]
for func in [angmom, angmomtot, angmomtot_stable]
@test func(gas; origin) == func(gas.pos, gas.vel, gas.mass; origin)
@test func(gas; velorigin) == func(gas.pos, gas.vel, gas.mass; velorigin)
@test func(gas; origin, velorigin) == func(gas.pos, gas.vel, gas.mass; origin, velorigin)
end
end
end
end
end
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] | 1.752604 | 38,117 |
# # Instructions for Assignment06
#
# ## Introduction
#
# Note: this file is runnable in its current state,
# but is incomplete.
# You can run the file from the command line,
# or use the VS Code julia extension to run individual lines.
## To view additional instructions for this assignment, #jl
## see https://wellesley-bisc195.github.io/BISC195.jl/stable/Assignments/assignment06 #jl
## To view a rendered version of this document, #jl
## see https://wellesley-bisc195.github.io/BISC195.jl/stable/Assignments/assignment06_code #jl
# ## Question 1 - Reading FASTA files
#
# A common way to interact with biological sequence data
# is in a format called [`FASTA`](https://en.wikipedia.org/wiki/FASTA_format).
# This is a very simple format, where each "record" (sequence) contains:
#
# 1. A "header" line, always starting with `>`, that contains a description of the sequence
# 2. The sequence, which can be split across multiple lines
#
# For example, in the `data/` directory of this assignment,
# there is a file called `cov2_genomes.fasta`,
# which contains some genome sequences of Sars-CoV2
# isolated from humans early in the pandemic.
# Use the shell to take a look at the first 10 lines (`head`)
# of the file.
#
# Now, let's work on building a julia function
# to help us use this file.
"""
count_records(path)
Given a path to a `fasta` file,
counts and returns the number of records it contains.
Example
≡≡≡≡≡≡≡≡≡
julia> count_records("data/cov2_genomes.fasta")
10
"""
function count_records(path)
## Your code here.
end
# Hint - If you're having trouble, the easiest thing to do
# is probably to count lines that start with `>`.
# ## Question 2 - Split the headers
#
# It's nice to know the number of records the file contains,
# but it's more useful to actually be able to read and store
# those records.
#
# Let's look again at the headers for these sequences.
# This can be accomplished from the terminal using `grep`.
# The simplest way to use `grep` is with 2 arguments -
# the first argument specifies a pattern to look for,
# and the second is the path to a file (or group of files).
#
# `grep` will then print to the screen all of the lines
# from the file that match that pattern.
# To see all of the header lines, for example,
#
# ```sh
# $ grep '>' data/cov2_genomes.fasta
# ```
#
# Each header has 3 components, separated by `|`
# Write a function that divides the header
# into these 3 components, and returns them as a tuple.
"""
fasta_header(header)
Divides a fasta header into its component parts,
removing any leading or trailing spaces.
Example
≡≡≡≡≡≡≡≡≡
julia> fasta_header(">M0002 |China|Homo sapiens")
("M0002", "China", "Homo sapiens")
julia> fasta_header("AAATTC")
Error: Invalid header (headers must start with '>')
julia> fasta_header(">Another sequence")
("Another sequence",)
julia> fasta_header(">headers| can | have| any number | of | info blocks")
("headers", "can", "have", "any number", "of", "info blocks")
"""
function fasta_header(header)
startswith(header, '>') || error("Invalid header (headers must start with '>')")
## Your code here
end
# A couple of things to note about this function if you're strugling:
#
# 1. Notice that, even though each header must start with a `>`,
# the first item in the returned Tuple should not
# 2. The return item should always be a Tuple, even if there's only 1 item
# 3. The `split()` function can divide a `String` by a delimeter.
# 4. You may need to do some searching on the internet.
# ## Question 3 - parse the file
#
# Alright - let's actually parse the file.
# For now, the easiest thing to do
# is to return two vectors,
# one containing the parsed identifiers as tuples,
# and one containing the sequences as strings.
#
# One thing to keep in mind
# is that the sequences in a fasta file
# can be spread out over multiple lines,
# but they'll be much easier to work with as a single string,
# so your function should have a way to join multiple lines
# (remember the `join()` function?).
#
# I've included a few example files in the "data" directory
# with some example files to test your functions on.
# The examples in the docstring below
# assume that your working directory is the assignment directory.
"""
function parse_fasta(path)
Reads a fasta-formated file and returns 2 vectors,
one containing parsed headers,
the other containing the entire sequence as a `String`.
Note: function does not validate DNA sequences for correctness.
Example
≡≡≡≡≡≡≡≡≡
julia> ex1 = parse_fasta("data/ex1.fasta");
julia> ex1 isa Tuple
true
julia> ex1[1]
2-element Array{Tuple{String,String},1}:
("ex1.1", "easy")
("ex1.2", "multiline")
julia> ex1[2]
2-element Array{String,1}:
"AATTATAGC"
"CGCCCCCCAGTCGGATT"
julia> ex2 = parse_fasta("data/ex2.fasta");
julia> ex2[1]
4-element Array{Tuple{String,String},1}:
("ex2.1", "oneper")
("ex2.2", "wrong")
("ex2.3", "wronger")
("ex2.4", "wrongest")
julia> ex2[2]
4-element Array{String,1}:
"ATCCGT"
"ATCGTGGaact"
"ATCGTGGaact"
"this isn't a dna string,but parse it anyway"
"""
function parse_fasta(path)
## Think through the components you need
## Does it make sense to define any containers at the beginning?
## How will you loop through the file?
## What do you need to get from each line?
end
# Tips:
#
# 1. Build the function in parts
# - first, see if you can build the vector of headers,
# ignoring the sequences
# - next, see if you can collect the sequences,
# even if they're not joined together properly
# 2. Work on pieces of the function in the REPL.
# Make sure each expression does what you think it does.
#
# For example, if you make a `for` loop to go through each line of the file,
# add `@info` statements to check the value of each variable
# in each loop.
# ## Question 4 - Sequence Statistics
#
# We now have most of the pieces necessary to get some summary statistics
# of CoV2 genomes.
# Note: You may want to grab some code from previous assignments.
#
# Calculate the mean sequence length and standard deviation
# of the CoV2 geneomes in `data/cov2_genomes.fasta`.
## helper code goes here
mean_cov2_length = 0.0
std_cov2_length = 0.0
# Calculate the mean and standard deviation of GC content
# of the CoV2 genomes in `data/cov2_genomes.fasta`.
# GC content should be a number between 0 and 1.
# Note: if you already parsed the fasta file above
# and assigned the results to variables,
# there's no need to re-parse it.
## helper code goes here
mean_cov2_gc = 0.0
std_cov2_gc = 0.0
# ## Bonus Question
#
# Write a function that will read any proper fasta file
# and returns a tuple with the mean and standard deviation
# of the sequence length and gc content.
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] | 3.042347 | 2,267 |
<gh_stars>0
using Test, Pkg
using MPI
if haskey(Pkg.installed(), "CuArrays")
using CuArrays
ArrayType = CuArray
else
ArrayType = Array
end
MPI.Init()
comm = MPI.COMM_WORLD
size = MPI.Comm_size(comm)
rank = MPI.Comm_rank(comm)
for T in Base.uniontypes(MPI.MPIDatatype)
# Allocating version
a = ArrayType(fill(T(rank), size))
b = MPI.Alltoall(a, 1, comm)
@test b isa ArrayType{T}
@test b == ArrayType{T}(0:size-1)
# Non Allocating version
a = ArrayType(fill(T(rank),size))
b = ArrayType{T}(undef, size*1)
MPI.Alltoall!(a, b, 1, comm)
@test b == ArrayType{T}(0:size-1)
# IN_PLACE version
a = ArrayType{T}(fill(T(rank),size))
MPI.Alltoall!(MPI.IN_PLACE, a, 1, comm)
@test a == ArrayType{T}(0:size-1)
end
MPI.Finalize()
@test MPI.Finalized()
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] | 2.128272 | 382 |
<reponame>rcalxrc08/FinancialFFT.jl
#TODO: CHANGE NAME
function pricer(mcProcess::FinancialMonteCarlo.BaseProcess, zero_rate::FinancialMonteCarlo.AbstractZeroRateCurve, method::LewisMethod, abstractPayoff::FinancialMonteCarlo.EuropeanOption, ::FinancialMonteCarlo.CudaMode)
T = abstractPayoff.T
K = abstractPayoff.K
A = method.A
N = method.N
S0 = mcProcess.underlying.S0
r = FinancialMonteCarlo.integral(zero_rate.r, T) / T
d = FinancialMonteCarlo.integral(FinancialMonteCarlo.dividend(mcProcess), T) / T
cf = FinancialFFT.CharactheristicFunction(mcProcess, T)
corr = FinancialFFT.CharactheristicExponent(-1im, mcProcess, T)
CharFunc(v) = cf(v) * exp(-v * 1im * corr)
x__ = log(S0 / K) + (r - d) * T
func_(z) = real_mod(exp(-z * 1im * x__) * CharFunc(-z - 1im * 0.5) / (z^2 + 0.25))
int_1 = midpoint_definite_integral_cu(func_, -A, A, N)
price = S0 * (1 - exp(-x__ / 2) * int_1 / (2 * pi)) * exp(-d * T)
return call_to_put(price, mcProcess.underlying, zero_rate, abstractPayoff)
end | [
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] | 2.395402 | 435 |
<filename>src/dnn/compat.jl<gh_stars>100-1000
# Compatibility shims until users upgrade to new NNlib format
function conv!(y::CuArray{T}, x::CuArray{T}, w::CuArray{T}; pad=0, stride=1, flipkernel=0, dilation=1, kwargs...) where {T<:CUDNNFloat}
cdims = DenseConvDims(x, w; padding=pad, stride=stride, flipkernel=flipkernel, dilation=dilation)
return conv!(y, x, w, cdims; kwargs...)
end
function ∇conv_filter!(dw::CuArray{T}, dy::CuArray{T}, x::CuArray{T}; pad=0, stride=1, flipkernel=0, dilation=1, kwargs...) where {T<:CUDNNFloat}
cdims = DenseConvDims(x, dw; padding=pad, stride=stride, flipkernel=flipkernel, dilation=dilation)
# NOTE!!! This compat shim re-arranges the argument order!
return ∇conv_filter!(dw, x, dy, cdims; kwargs...)
end
function maxpool!(y::CuArray{T}, x::CuArray{T}, k; pad=map(_->0,k), stride=k) where {T<:CUDNNFloat}
pdims = PoolDims(x, k; padding=pad, stride=stride)
return maxpool!(y, x, pdims)
end
function meanpool!(y::CuArray{T}, x::CuArray{T}, k; pad=map(_->0,k), stride=k) where {T<:CUDNNFloat}
pdims = PoolDims(x, k; padding=pad, stride=stride)
return meanpool!(y, x, pdims)
end
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] | 2.364754 | 488 |