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#-----------------------------------------------------------------------------------------------------
# Functions for Operations.
# Operation is the collection of "Operator"s, together with a "Basis".
# Functions on "Operation"s are design to mimic the behavior of matrices.
#-----------------------------------------------------------------------------------------------------
"""
Operator{T1<:Number, T2<:Integer}
Type that contains 2 fields:
1. `mat`: Matrices representation of local operator.
2. `inds`: Indeces of sites the operator acts on.
"""
struct Operator{T1<:Number, T2<:Integer}
mat::Matrix{T1}
inds::Vector{T2}
end
#-----------------------------------------------------------------------------------------------------
# Basic functions on "Operator":
# 1. *: Multiplication by a number.
# 2. /: Division by a number.
#-----------------------------------------------------------------------------------------------------
*(c::Number, o::Operator) = Operator(c * o.mat, o.inds)
/(o::Operator, c::Number) = Operator(o.mat / c, o.inds)
#-----------------------------------------------------------------------------------------------------
# Operator to fill vector/matrix.
# To finally get the multiplication function, here we focus on a single row manipulation:
# 1. Start with a given product state(digits), we cut a segment from it;
# 2. Iterate all possible digits in the segment, and read the matrix element of operator;
# 3. Add to the vector/matrix element.
#-----------------------------------------------------------------------------------------------------
"""
addtovec!(vec::AbstractVector, opt::Operator, basis::Basis, coeff::Number=1)
Elementary multiplication step. Calculate single vector element of an operator * vector.
- `vec`: Vector to fill
- `opt`: Operator
- `basis`: Indicate the specific row
- `coeff`: The vector element of input state
"""
function addtovec!(vec::AbstractVector, opt::Operator, basis::Basis, coeff::Number=1)
basis_view = view(basis, opt.inds) # Create a view on the segment of the digits
index_view = index(basis_view) # Get the initial index of the viewed segment
opt_column = opt.mat[:, index_view] * coeff # Get the elements in row of index_view
row_number = length(opt_column)
for k = 1:row_number
change!(basis_view, k)
i = index(basis)
vec[i] += opt_column[k]
end # Fill the vector
change!(basis_view, index_view) # Reset the segment
end
#-----------------------------------------------------------------------------------------------------
"""
addtovecs!(vecs::AbstractMatrix, opt::Operator, basis::Basis, coeff::AbstractVector)
Elementary multiplication step. Calculate single row of matrix element of an operator * matrix.
- vecs : Vectors to fill
- opt : Operator
- basis: Indicate the specific row
- coeff: The row of matrix elements of input states
"""
function addtovecs!(vecs::AbstractMatrix, opt::Operator, basis::Basis, coeff::AbstractVector{<:Number})
basis_view = view(basis, opt.inds) # Create a view on the segment of the digits
index_view = index(basis_view) # Get the initial index of the viewed segment
opt_column = opt.mat[:, index_view] # Get the elements in row of index_view
row_number = length(opt_column) # Fill the matrix
for k = 1:row_number
change!(basis_view, k)
i = index(basis)
vecs[i, :] += opt_column[k] * coeff
end
change!(basis_view, index_view) # Reset the segment
end
#-----------------------------------------------------------------------------------------------------
# Type: Operation
#-----------------------------------------------------------------------------------------------------
export Operation
"""
Operation{OptType<:AbstractVector{<:Operator}, BasType<:Basis}
Type that contains 2 fields:
1. `opts`: List of operators
2. `basis`: Basis for the system
"""
struct Operation{T1 <: Number, T2 <: Integer, T3}
opts::Vector{Operator{T1, T2}}
basis::Basis{T3}
end
eltype(::Operation{T1, T2, T3}) where T1 where T2 where T3 = T1
function size(opt::Operation)
basis = opt.basis
dim = basis.base ^ basis.len
(dim, dim)
end
#-----------------------------------------------------------------------------------------------------
# Basis Operation initiation
#-----------------------------------------------------------------------------------------------------
export operation
"""
operation(mats, inds, len=0; base=0)
Canonical construction method for `Operation` object.
- `mats`: List of matrix representation of operators.
- `inds`: List of sites the operastors act on.
- `len`: Specify the size of the system. Default `len=0` will induce auto deduction.
- `base`: Quantum number of each sites. Default `base=0` will induce auto deduction.
"""
function operation(
mats::AbstractVector{<:AbstractMatrix},
inds::AbstractVector{<:AbstractVector},
len::Integer=0;
base::Integer=0
)
B = begin
b = base==0 ? round(Int64, size(mats[1], 1)^(1/length(inds[1]))) : Int64(base)
l = len==0 ? maximum(maximum.(inds)) : Int64(len)
basis(b, l)
end
mat_type = promote_type(eltype.(mats)...)
ind_type = promote_type(eltype.(inds)...)
O = [Operator(Array{mat_type}(mats[i]), Array{ind_type}(inds[i])) for i=1:length(mats)]
Operation(O, B)
end
#-----------------------------------------------------------------------------------------------------
# Basic Functions for type Operation
#-----------------------------------------------------------------------------------------------------
*(c::Number, o::Operation) = Operation(c .* o.opts, o.basis)
/(o::Operation, c::Number) = Operation(o.opts ./ c, o.basis)
+(opt1::Operation, opt2::Operation) = Operation(vcat(opt1.opts, opt2.opts), opt1.basis)
-(o1::Operation, o2::Operation) = o1 + ((-1) * o2)
function sum(ol::AbstractVector{<:Operation})
basis = ol[1].basis
opts = vcat([oi.opts for oi in ol]...)
Operation(opts, basis)
end
#-----------------------------------------------------------------------------------------------------
# Operation to fill vector/matrix:
# The method is basically iterate each operator to the given digits-represented basis
#-----------------------------------------------------------------------------------------------------
"""
addtovec!(vec::AbstractVector, opt::Operation, coeff::Number=1)
Elementary multiplication step. Calculate single vector element of an operation * vector.
The row information is stored in the basis of operation.
- `vec`: Vector to fill.
- `opt`: Operation.
- `coeff`: The vector element of input state.
"""
function addtovec!(vec::AbstractVector, opt::Operation, coeff::Number=1)
basis = opt.basis
opts = opt.opts
num_of_opts = length(opts)
for i = 1:num_of_opts
addtovec!(vec, opts[i], basis, coeff)
end # Multiply by each operator and add them all to vector
end
#-----------------------------------------------------------------------------------------------------
"""
addtovecs!(vecs::AbstractMatrix, opt::Operation, coeff::AbstractVector)
Elementary multiplication step. Calculate single row of matrix element of an operation * matrix.
The row information is stored in the basis of operation.
- `vecs`: Vectors to fill
- `opt`: Operation
- `coeff`: The row of matrix elements of input states
"""
function addtovecs!(vecs::AbstractMatrix, opt::Operation, coeff::AbstractVector{<:Number})
basis = opt.basis
opts = opt.opts
num_of_opts = length(opts)
for i = 1:num_of_opts
addtovecs!(vecs, opts[i], basis, coeff)
end # Multiply by each operator and add them all to vectors
end
#-----------------------------------------------------------------------------------------------------
# Multiplication
# The idea is to iterate all product state basis, and get all the vector/matrix elements
#-----------------------------------------------------------------------------------------------------
"""
mul!(vec::AbstractVector, opt::Operation, state::AbstractVector)
Full multiplication for operation and vector.
- `vec`: Vector to fill.
- `opt`: Operation.
- `state`: Input vector.
"""
function mul!(vec::AbstractVector, opt::Operation, state::AbstractVector)
basis = opt.basis
for j = 1:length(state)
change!(basis, j)
addtovec!(vec, opt, state[j])
end
end
#-----------------------------------------------------------------------------------------------------
"""
mul!(mat::AbstractVector, opt::Operation, state::AbstractVector)
Full multiplication for operation and matrix.
- `mat`: Matrix to fill.
- `opt`: Operation.
- `states`: Input states(matrix).
"""
function mul!(mat::AbstractMatrix, opt::Operation, states::AbstractMatrix)
basis = opt.basis
for j = 1:size(states, 1)
change!(basis, j)
addtovecs!(mat, opt, states[j, :])
end
end
#-----------------------------------------------------------------------------------------------------
"""
*(opt::Operation, vec_or_mat::AbstractVecOrMat)
General multiplication for operation and vector/matrix.
- `opt`: Operation.
- `vec_or_mat`: Input vector/matrix.
"""
function *(opt::Operation, vec_or_mat::AbstractVecOrMat)
ctype = promote_type(eltype(opt), eltype(vec_or_mat))
out = zeros(ctype, size(vec_or_mat))
mul!(out, opt, vec_or_mat)
out
end
#-----------------------------------------------------------------------------------------------------
# Fill matrix
#-----------------------------------------------------------------------------------------------------
export fillmat!
"""
fillmat!(mat::AbstractMatrix, opt::Operation)
Fill the zero matrix with matrix element from operation.
- `mat`: Matrix to fill.
- `opt`: Operation.
"""
function fillmat!(mat::AbstractMatrix, opt::Operation)
basis = opt.basis
row_number = size(mat, 1)
for j = 1:row_number
change!(basis, j)
addtovec!(view(mat, :, j), opt)
end
end
#-----------------------------------------------------------------------------------------------------
"""
Array(opt::Operation)
Return the full matrix form of `opt`.
"""
function Array(opt::Operation)
mat = zeros(eltype(opt), size(opt))
fillmat!(mat, opt)
mat
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] | 3.297033 | 3,168 |
include("Parser.jl")
using DataStructures
struct StaticAnalysisException <: Exception
msg::String
end
default_value_strings = Dict{MPType,String}(
MInt => "-1",
MReal => "-1.0",
MBool => "false",
MString => "",
)
initial_scope_level = 0
unary_result_types = Dict{Tuple{TokenClass,MPType},MPType}(
(kw_not, MBool) => MBool,
(minus, MInt) => MInt,
(minus, MReal) => MReal
)
array_type_to_scalar_type = Dict{MPType,MPType}(
MIntArray => MInt,
MRealArray => MReal,
MBoolArray => MBool,
MStringArray => MString
)
ref_type_to_scalar_type = Dict{MPType,MPType}(
MIntRef => MInt,
MRealRef => MReal,
MBoolRef => MBool,
MStringRef => MString,
MInt => MInt,
MReal => MReal,
MBool => MBool,
MString => MString,
)
binary_result_types = DefaultDict(MError,
(times, MInt, MInt) => MInt,
(times, MReal, MReal) => MReal,
(plus, MInt, MInt) => MInt,
(plus, MReal, MReal) => MReal,
(plus, MString, MString) => MString,
(minus, MInt, MInt) => MInt,
(minus, MReal, MReal) => MReal,
(divide, MInt, MInt) => MInt,
(divide, MReal, MReal) => MReal,
(modulo, MInt, MInt) => MInt,
(kw_or, MBool, MBool) => MBool,
(kw_and, MBool, MBool) => MBool,
(equals, MInt, MInt) => MBool,
(not_equal, MInt, MInt) => MBool,
(less_than, MInt, MInt) => MBool,
(less_than_or_equal, MInt, MInt) => MBool,
(greater_than, MInt, MInt) => MBool,
(greater_than_or_equal, MInt, MInt) => MBool,
(kw_and, MBool, MBool) => MBool,
(equals, MReal, MReal) => MBool,
(not_equal, MReal, MReal) => MBool,
(less_than, MReal, MReal) => MBool,
(less_than_or_equal, MReal, MReal) => MBool,
(greater_than, MReal, MReal) => MBool,
(greater_than_or_equal, MReal, MReal) => MBool,
(equals, MString, MString) => MBool,
(not_equal, MString, MString) => MBool,
(less_than, MString, MString) => MBool,
(less_than_or_equal, MString, MString) => MBool,
(greater_than, MString, MString) => MBool,
(greater_than_or_equal, MString, MString) => MBool,
(equals, MBool, MBool) => MBool,
(not_equal, MBool, MBool) => MBool,
(less_than, MBool, MBool) => MBool,
(less_than_or_equal, MBool, MBool) => MBool,
(greater_than, MBool, MBool) => MBool,
(greater_than_or_equal, MBool, MBool) => MBool,
)
mutable struct Argument
name::String
is_var::Bool
type::MPType
size::Int
end
mutable struct SubroutineEntry
name::String
param_names::Vector{String}
param_types::Vector{MPType}
return_type::MPType
node::Union{Subroutine,Nothing}
end
mutable struct AnalysisContext
scope_stack::Stack{SymTableEntry}
scope::Int
subroutines::Vector{SubroutineEntry}
in_call::Stack{Call}
unique_calls::Vector{Call}
all_var_names_and_types::Vector{Tuple{String,MPType}}
var_id_counter::Int
end
AnalysisContext() = AnalysisContext(
Stack{SymTableEntry}(),
initial_scope_level,
Vector{SubroutineEntry}(),
Stack{Call}(),
Vector{Call}(),
Vector{Tuple{String,MPType}}(),
0
)
function create_var_id(ac::AnalysisContext, var_type::MPType, var_name::String)
counter = ac.var_id_counter
ac.var_id_counter += 1
return "%_$(var_type)_$(var_name)_$(counter)"
end
function hasvariable(s::AnalysisContext, var_name::String, inner_scope_only::Bool=false)
if DEBUG
foreach(println, s.scope_stack)
end
if isempty(s.scope_stack) return false end
if inner_scope_only
scope = s.scope
return any(entry->entry.scope_level == scope && entry.var_name == var_name, s.scope_stack)
end
return any(entry->entry.var_name == var_name, s.scope_stack)
end
function getvariable(s::AnalysisContext, var_name)
return getfirst(entry -> entry.var_name == var_name, s.scope_stack)
end
function popscope!(s::AnalysisContext)
isempty(s.scope_stack) && return
s.scope -= 1
while !isempty(s.scope_stack) && (first(s.scope_stack).scope_level > s.scope)
pop!(s.scope_stack)
end
end
function enterscope!(s::AnalysisContext)
s.scope += 1
end
function pushvar!(ac::AnalysisContext, var_name::String, var_type::MPType, val_identifier::String, is_implicit::Bool=false)
entry = SymTableEntry(var_name, var_type, val_identifier, ac.scope, is_implicit)
push!(ac.scope_stack, entry)
end
function pushsubroutine!(ac::AnalysisContext,
name::String,
param_names::Vector{String},
param_types::Vector{MPType},
return_type::MPType,
node::Union{Subroutine,Nothing})
entry = SubroutineEntry(name, param_names, param_types, return_type, node)
push!(ac.subroutines, entry)
end
function hassubroutine(ac::AnalysisContext, name::String)
return any(entry->entry.name == name, ac.subroutines)
end
function getsubroutine(srs::Vector{SubroutineEntry}, name::String)
index = findfirst(sr->sr.name == name, srs)
return srs[index]
end
function getsubroutine(ac::AnalysisContext, name::String)
getsubroutine(ac.subroutines, name)
end
# The entry point for the static analyzer.
function static_analysis(AST::Program, ac::AnalysisContext)
# Push default values for all var, type combinations used in program
push_defaults!(ac)
# Process definitions
static_analysis(AST.definitions, ac)
# Process main block
static_analysis(AST.main, ac)
end
function push_defaults!(ac::AnalysisContext)
for (name, mptype) in ac.all_var_names_and_types
if mptype ∈ scalar_types
ref_type = scalar_to_ref_type[mptype]
pushvar!(ac, name, ref_type, "@default_$(mptype)")
end
end
enterscope!(ac)
end
static_analysis(AST::Program) = static_analysis(AST, AnalysisContext())
function static_analysis(d::Definitions, ac::AnalysisContext)
DEBUG && println("This is static analysis, analysing definitions")
if !allunique([def.name for def in d.defs])
throw(StaticAnalysisException(
"All functions and subroutines must have unique names. Overloading is not allowed."
))
end
for subroutine::Subroutine in d.defs
get_signature(subroutine, ac)
end
end
function get_signature(s::Subroutine, ac::AnalysisContext)
foreach(param::Parameter->static_analysis(param, ac), s.params)
true_types = Vector{MPType}([parameter_to_mptype(param) for param in s.params])
param_names = Vector{String}([param.name for param in s.params])
static_analysis(s.ret_type, ac)
if !isa(s.ret_type.size, ImmediateInt)
throw(StaticAnalysisException(
"Reference types (strings and arrays) are not allowed as function return types (line $(s.line))."
))
end
pushsubroutine!(ac,
s.name,
param_names,
true_types,
s.ret_type.scalar_type,
s
)
end
function parameter_to_mptype(p::Parameter)
if p.is_array && p.is_var_par
throw(StaticAnalysisException(
"Array type cannot be used as var parameter (line $(p.line))."
))
end
if p.is_var_par
return scalar_to_ref_type[p.scalar_type]
end
if p.is_array
return scalar_to_array_type[p.scalar_type]
end
return p.scalar_type
end
function parameter_to_llvmtype(p::Parameter)
mptype_to_llvm_type[parameter_to_mptype(p)]
end
function typecheck(b::BinaryOperation, ac::AnalysisContext)
typecheck(b.left, ac)
typecheck(b.right, ac)
ret_type = binary_result_types[(b.op, b.left.type, b.right.type)]
if ret_type == MError
throw(StaticAnalysisException(
"Type mismatch ($(b.left.type) and $(b.right.type)) for binary operation \"$(b.op)\" on line $(b.line)."
))
end
b.type = ret_type
end
function typecheck(u::UnaryOperation, ac::AnalysisContext)
typecheck(u.operand, ac)
ret_type = unary_result_types[(u.op, u.operand.type)]
if ret_type == MError
throw(StaticAnalysisException(
"Invalid type $(u.operand.type) for unary operation \"$(u.op)\" (line $(u.line))."
))
end
u.type = ret_type
end
function static_analysis(s::Subroutine, ac::AnalysisContext)
static_analysis(s.body, ac)
s.type = MPassed
end
function static_analysis(c::CallStatement, ac::AnalysisContext)
DEBUG && println("This is static analysis, analysing CallStatement")
analyze_call(c, ac)
c.type = MPassed
end
function analyze_call(c::Call, ac::AnalysisContext)
DEBUG && println("This is analyze_call, call is to $(c.identifier)")
DEBUG && println("implicit_params are $(c.implicit_params)")
subroutine_entry::SubroutineEntry = getsubroutine(ac, c.identifier)
for (i, (param_type, arg)) in enumerate(zip(subroutine_entry.param_types, c.arguments))
if param_type ∈ ref_types # Parameter is a var parameter
c.arguments[i] = GetRef(c.arguments[i], c.line)
end
end
CallFactor
foreach(arg->typecheck(arg, ac), c.arguments)
compare_with_signature(subroutine_entry, c.arguments, c.line)
c.subroutine = subroutine_entry.node::Subroutine
c.subroutine.is_called = true
all_var_names = Vector{String}(unique([name for (name, type) in ac.all_var_names_and_types]))
c.implicit_params = [getvariable(ac, p) for p in all_var_names]
subroutines_in_call = [call.identifier for call in ac.in_call]
if subroutine_entry.name ∈ subroutines_in_call
return
end
llvm_function_name = get_llvm_function_name(subroutine_entry.name, c.call_id)
c.subroutine.llvm_function_name = llvm_function_name
enterscope!(ac)
c.scope_level = ac.scope
push!(ac.in_call, c)
for var_entry::SymTableEntry in c.implicit_params
param_name = var_entry.var_name
param_type = var_entry.var_type
name_as_implicit = get_implicit_param_id(param_name, param_type)
ref_type = param_type ∈ ref_types ? param_type : scalar_to_ref_type[param_type]
pushvar!(ac, param_name, ref_type, "%$(name_as_implicit)", true)
end
for (param_name, arg) in zip(subroutine_entry.param_names, c.arguments)
type = arg isa GetRef ? arg.type : ref_type_to_scalar_type[arg.type]
pushvar!(ac, param_name, arg.type, "%$(param_name)")
end
static_analysis(c.subroutine, ac)
pop!(ac.in_call)
popscope!(ac)
end
function get_implicit_param_id(name::String, mptype::MPType)
"implicit.$(ref_type_to_scalar_type[mptype]).$(name)"
end
function compare_with_signature(sr::SubroutineEntry, args::Vector{Value}, line::Int)
if length(sr.param_types) != length(args)
throw(StaticAnalysisException(
"Subroutine call has $(length(args)) arguments, expected $(length(sr.param_types)) (line $line)."
))
end
for (param_type, arg) in zip(sr.param_types, args)
if arg.type != param_type
throw(StaticAnalysisException(
"Expected argument of type $param_type, got $(arg.type) (line $line)."
))
end
end
end
function static_analysis(r::Return, ac::AnalysisContext)
current_subroutine::Subroutine = first(ac.in_call).subroutine
typecheck(r.value, ac)
if r.value.type != current_subroutine.ret_type.true_type
throw(StaticAnalysisException(
"Expected return type to be $(current_subroutine.ret_type.true_type), got $(r.value.type) (line $(r.line))."
))
end
r.type = MPassed
end
function static_analysis(p::Parameter, ac::AnalysisContext)
typecheck(p.size, ac)
if p.size.type != MInt
throw(StaticAnalysisException(
"Array size must have integer value, got $(p.size.type) (line $(p.line))."
))
end
p.type = MPassed
end
function static_analysis(b::Block, ac::AnalysisContext, only_collect=false)
if !b.is_subroutine_block enterscope!(ac) end
for stmt in b.statements
if !only_collect
static_analysis(stmt, ac)
elseif stmt isa VariableFactor
if !hasvariable(ac, stmt.identifier, true)
push!(b.subroutine.implicit_params, stmt.identifier)
end
elseif stmt isa Call
push!(b.subroutine.calls_subroutines, stmt.identifier)
end
end
if !b.is_subroutine_block popscope!(ac) end
b.type = MPassed
end
function static_analysis(d::Declaration, ac::AnalysisContext)
DEBUG && println("This is static analysis, analysing Declaration")
static_analysis(d.var_type, ac)
var_type = d.var_type.true_type
for name in map(token->token.lexeme, d.names)
if hasvariable(ac, name, true)
entry = getvariable(ac, name)
if !entry.is_implicit
throw(StaticAnalysisException(
"Cannot redeclare variable $(name) (line $(d.line))."
))
end
end
id = create_var_id(ac, var_type, name)
push!(d.unique_ids, id)
pushvar!(ac, name, scalar_to_ref_type[var_type], id)
end
d.type = MPassed
end
function static_analysis(c::TypeOfVarOrValue, ac::AnalysisContext)
typecheck(c.size, ac)
if c.size.type != MInt
throw(StaticAnalysisException(
"Array size must have integer type (line $(c.line))."
))
end
c.type = MPassed
end
function static_analysis(a::Assignment, ac::AnalysisContext)
DEBUG && println("This is static analysis, analysing Assignment")
var_name = a.variable_name
if !hasvariable(ac, var_name)
throw(StaticAnalysisException(
"Cannot assign to undeclared variable '$(var_name)' (line $(a.line))."
))
end
index_type = typecheck(a.array_index, ac)
if index_type != MInt
throw(StaticAnalysisException(
"Array index must be an integer, got $(index_type) (line $(a.line))."
))
end
value_type = typecheck(a.value, ac)
entry::SymTableEntry = getvariable(ac, var_name)
var_type = entry.var_type
a.var_type = var_type
if var_type ∈ ref_types
var_type = ref_type_to_scalar_type[var_type]
elseif var_type ∈ scalar_types
entry.val_identifier = create_var_id(ac, var_type, var_name)
end
if value_type != var_type
if !(a.is_array_access && scalar_to_array_type[value_type] == var_type)
throw(StaticAnalysisException(
"Cannot assign a value of type $(value_type) to variable '$(var_name)' of type $(var_type) (line $(a.line))."
))
end
end
a.var_unique_id = entry.val_identifier
a.type = MPassed
end
function static_analysis(i::IfThenElse, ac::AnalysisContext)
cond_type = typecheck(i.condition, ac)
if cond_type != MBool
throw(StaticAnalysisException(
"If statement condition must be boolean, got type $cond_type (line $(i.line))."))
end
static_analysis(i.then_stmt, ac)
static_analysis(i.else_stmt, ac)
i.type = MPassed
end
function static_analysis(i::IfThen, ac::AnalysisContext)
cond_type = typecheck(i.condition, ac)
if cond_type != MBool
throw(StaticAnalysisException(
"If statement condition must be boolean, got type $cond_type (line $(i.line))."))
end
static_analysis(i.then_stmt, ac)
i.type = MPassed
end
function static_analysis(w::While, ac::AnalysisContext)
cond_type = typecheck(w.condition, ac)
if cond_type != MBool
throw(StaticAnalysisException(
"While statement condition must be boolean, got type $cond_type (line $(w.line))."))
end
static_analysis(w.do_stmt, ac)
w.type = MPassed
end
function static_analysis(r::Read, ac::AnalysisContext)
for var::Variable in r.variables
static_analysis(var, ac)
if !hasvariable(ac, var.identifier)
throw(StaticAnalysisException(
"Trying to read to undeclared variable $(var.identifier) (line $(r.line))."
))
end
entry::SymTableEntry = getvariable(ac, var.identifier)
push!(r.var_types, entry.var_type)
if entry.var_type ∉ scalar_types
throw(StaticAnalysisException(
"Only scalar variable types can be read (line $(r.line))."
))
end
end
r.type = MPassed
end
function static_analysis(p::Write, ac::AnalysisContext)
DEBUG && println("This is static analysis, analysing Write")
static_analysis(p.arguments, ac)
p.type = MPassed
end
function static_analysis(a::Vector{Value}, ac::AnalysisContext)
DEBUG && println("This is static analysis, analysing arguments")
for arg in a
typecheck(arg, ac)
end
end
function typecheck(i::ImmediateInt, ac)
i.type = MInt
end
function typecheck(i::ImmediateReal, ac)
i.type = MReal
end
function typecheck(i::ImmediateString, ac)
i.type = MString
end
function typecheck(i::ImmediateBool, ac)
i.type = MBool
end
function typecheck(g::GetRef, ac)
g.type = scalar_to_ref_type[typecheck(g.operand, ac)]
end
function typecheck(l::LiteralFactor, ac::AnalysisContext)
DEBUG && println("This is static analysis, analysing LiteralFactor")
class = l.token.class
if class == int_literal
l.type = MInt
end
if class == real_literal
l.type = MReal
end
if class ∈ [kw_true, kw_false]
l.type = MBool
end
if class == string_literal
l.type = MString
end
return l.type
end
function typecheck(v::VariableFactor, ac::AnalysisContext)
DEBUG && println("This is static analysis, analysing VariableFactor")
DEBUG && println("in_call empty? $(isempty(ac.in_call))")
var_name = v.identifier
if !hasvariable(ac, var_name)
throw(StaticAnalysisException(
"Variable $(var_name) is not defined (line $(v.line))."
))
end
var_entry::SymTableEntry = getvariable(ac, var_name)
v.variable_entry = var_entry
var_type = var_entry.var_type
if var_type ∈ ref_types
var_type = ref_type_to_scalar_type[var_type]
end
v.type = var_type
end
function typecheck(a::ArrayAccessFactor, ac::AnalysisContext)
DEBUG && println("This is static analysis, analysing ArrayAccessFactor")
index_type = typecheck(a.index, ac)
if index_type != MInt
throw(StaticAnalysisException(
"Array index should be an integer, got type $(index_type) (line $(a.line))."
))
end
var_name = a.identifier
if !hasvariable(ac, var_name)
throw(StaticAnalysisException(
"Variable $(var_name) is not defined (line $(a.line))."
))
end
var_entry::SymTableEntry = getvariable(ac, var_name)
a.variable_entry = var_entry
var_type = var_entry.var_type
if var_type ∉ array_types
throw(StaticAnalysisException(
"Cannot index into non-array type ($var_type) variable '$var_name' (line $(a.line))."
))
end
a.type = array_type_to_scalar_type[var_type]
end
function typecheck(c::CallFactor, ac::AnalysisContext)
DEBUG && println("This is static analysis, analysing CallFactor")
analyze_call(c, ac)
subroutine_entry::SubroutineEntry = getsubroutine(ac, c.identifier)
ret_type = subroutine_entry.return_type
if ret_type == MNothing
name = subroutine_entry.name
throw(StaticAnalysisException(
"A call to subroutine '$name' cannot be used as a value as it has no return type (line $(c.line))."
))
end
c.type = ret_type
end
function typecheck(p::ParenFactor, ac::AnalysisContext)
p.type = typecheck(p.expression, ac)
end
function typecheck(n::NotFactor, ac::AnalysisContext)
arg_type = typecheck(n.argument, ac)
if arg_type != MBool
throw(StaticAnalysisException(
"The \"not\" operation requires a boolean argument, got type $(arg_type) on line $(n.line)."
))
end
n.type = MBool
end
# function typecheck(t::Term, ac::AnalysisContext)
# factor_types = [typecheck(tpl[1], ac) for tpl in t.factors]
# first_type = factor_types[1]
# for tpl in t.factors
# if tpl[1].type != first_type
# throw(StaticAnalysisException(
# "Type mismatch in operation $(tpl[2].class) on line $(tpl[1].line)."
# ))
# end
# end
# t.type = first_type
# return t.type
# end
function typecheck(s::SizeFactor, ac::AnalysisContext)
type = typecheck(s.array, ac)
if type ∉ [MIntArray, MRealArray, MBoolArray, MStringArray]
throw(StaticAnalysisException(
"Array size is not defined for values of type $(type) (on line $(s.line))."))
end
s.type = MInt
end
function get_llvm_function_name(subroutine_name::String, call_id::Int)
return "@$(subroutine_name).$(call_id)"
end
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] | 2.61983 | 7,423 |
<filename>examples/development/differentiable_cone_program.jl
using LinearAlgebra, ForwardDiff
using Convex, SCS, ECOS
n = 3
m = 3
k = n + m + 1
c = zeros(n)
A = Diagonal(ones(n))
b = zeros(n)
"Convex.jl"
x = Variable(n)
prob = minimize(c' * x)
prob.constraints += norm(b - A * x) <= 0.0
@time solve!(prob, ECOS.Optimizer)
@show prob.status
@show x.value
@show prob.constraints[1].dual
prob.optval
Q = Array([zeros(n, n) A' c;
-A zeros(m, m) b;
-c' -b' 0.0])
Q_vec = vec(Q)
function F(z, Q_vec)
ũ = z[1:k]
u = z[k .+ (1:k)]
v = z[2 * k .+ (1:k)]
[(I + reshape(Q_vec, k, k)) * ũ - (u + v);
u - P_soc(ũ - v);
ũ - u]
end
z = rand(3k)
F(z, Q_vec)
Fz(x) = F(x, Q_vec)
FQ(x) = F(z, x)
Jz = ForwardDiff.jacobian(Fz, z)
JQ = ForwardDiff.jacobian(FQ, Q_vec)
norm((Jz' * Jz) \ (Jz' * JQ), Inf)
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3467,
357,
41,
89,
6,
1635,
449,
48,
828,
4806,
8,
198
] | 1.837719 | 456 |
<filename>src/utils.jl
# lightweight type tree printing
AbstractTrees.children(x::Type) = subtypes(x)
"""
`print_struct()`
Prints the definition of a struct.
"""
function print_struct(type)
mutable = ismutable(type) ? "mutable" : ""
println("$mutable struct $type")
for (fn, ft) in zip(fieldnames(type), fieldtypes(type))
println(" $fn::$ft")
end
println("end")
end
function read_json(filename)
return open(filename) do io
JSON3.read(io, Dict)
end
end
abstract type AbstractOS end
abstract type Unix <: AbstractOS end
abstract type BSD <: Unix end
abstract type Windows <: AbstractOS end
abstract type MacOS <: BSD end
abstract type Linux <: BSD end
if Sys.iswindows()
const os = Windows
elseif Sys.isapple()
const os = MacOS
else
const os = Linux
end
"""
Download Data from `branch="master"` name into a "data" folder in given argument path.
Skip the actual download if the folder already exists and force=false.
Defaults to the root of the PowerSystems package.
Returns the downloaded folder name.
"""
function download(
repo::AbstractString,
folder::AbstractString = abspath(joinpath(@__DIR__, "..")),
branch::String = "master",
force::Bool = false,
)
if Sys.iswindows()
DATA_URL = "$repo/archive/$branch.zip"
else
DATA_URL = "$repo/archive/$branch.tar.gz"
end
directory = abspath(normpath(folder))
reponame = splitpath(repo)[end]
data = joinpath(directory, "$reponame-$branch")
if !isdir(data) || force
@info "Downloading $DATA_URL"
tempfilename = Base.download(DATA_URL)
mkpath(directory)
@info "Extracting data to $data"
unzip(os, tempfilename, directory)
# mv(joinpath(directory, "$reponame-$branch"), data, force = true)
end
return data
end
function unzip(::Type{<:BSD}, filename, directory)
@assert success(`tar -xvf $filename -C $directory`) "Unable to extract $filename to $directory"
end
function unzip(::Type{Windows}, filename, directory)
path_7z = if Base.VERSION < v"0.7-"
"$JULIA_HOME/7z"
else
sep = Sys.iswindows() ? ";" : ":"
withenv(
"PATH" => string(
joinpath(Sys.BINDIR, "..", "libexec"),
sep,
Sys.BINDIR,
sep,
ENV["PATH"],
),
) do
Sys.which("7z")
end
end
@assert success(`$path_7z x $filename -y -o$directory`) "Unable to extract $filename to $directory"
end
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540,
284,
7925,
720,
34345,
284,
720,
34945,
1,
198,
437,
198
] | 2.39868 | 1,061 |
export default_if_empty
import Base: show
"""
default_if_empty(value::T)
Creates a `default_if_empty` operator, which emits a given value if the source Observable completes
without emitting any next value, otherwise mirrors the source Observable.
```jldoctest
using Rocket
source = completed(Int) |> default_if_empty(0)
subscribe!(source, logger())
;
# output
[LogActor] Data: 0
[LogActor] Completed
```
See also: [`AbstractOperator`](@ref), [`InferableOperator`](@ref), [`logger`](@ref), [`map`](@ref)
"""
default_if_empty(value::T) where T = DefaultIfEmptyOperator{T}(value)
struct DefaultIfEmptyOperator{T} <: InferableOperator
value :: T
end
operator_right(operator::DefaultIfEmptyOperator{T}, ::Type{L}) where { L, T } = Union{L, T}
function on_call!(::Type{L}, ::Type{Union{L, T}}, operator::DefaultIfEmptyOperator{T}, source) where { L, T }
return proxy(Union{L, T}, source, DefaultIfEmptyProxy{Union{L, T}}(convert(Union{L, T}, operator.value)))
end
struct DefaultIfEmptyProxy{L} <: ActorProxy
default :: L
end
actor_proxy!(::Type, proxy::DefaultIfEmptyProxy{L}, actor::A) where { L, A } = DefaultIfEmptyActor{L, A}(actor, false, proxy.default)
mutable struct DefaultIfEmptyActor{L, A} <: Actor{L}
actor :: A
is_emitted :: Bool
default :: L
end
function on_next!(actor::DefaultIfEmptyActor{L}, data::L) where L
actor.is_emitted = true
next!(actor.actor, data)
end
function on_error!(actor::DefaultIfEmptyActor, err)
actor.is_emitted = true
error!(actor.actor, err)
end
function on_complete!(actor::DefaultIfEmptyActor)
if !actor.is_emitted
next!(actor.actor, actor.default)
end
complete!(actor.actor)
end
Base.show(io::IO, ::DefaultIfEmptyOperator{T}) where T = print(io, "DefaultIfEmptyOperator($T)")
Base.show(io::IO, ::DefaultIfEmptyProxy{L}) where L = print(io, "DefaultIfEmptyProxy($L)")
Base.show(io::IO, ::DefaultIfEmptyActor{L}) where L = print(io, "DefaultIfEmptyActor($L)")
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1532,
40613,
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43,
8,
4943,
198
] | 2.696065 | 737 |
"""
Struct for Standard American Option
amOption=AmericanOption(T::num1,K::num2,isCall::Bool=true) where {num1 <: Number,num2 <: Number}
Where:\n
T = Time to maturity of the Option.
K = Strike Price of the Option.
isCall = true for CALL, false for PUT.
"""
mutable struct AmericanOption{num1 <: Number, num2 <: Number, numtype <: Number} <: AmericanPayoff{numtype}
T::num1
K::num2
isCall::Bool
function AmericanOption(T::num1, K::num2, isCall::Bool = true) where {num1 <: Number, num2 <: Number}
if T <= 0.0
error("Time to Maturity must be positive")
elseif K <= 0.0
error("Strike Price must be positive")
else
zero_typed = zero(num1) + zero(num2)
return new{num1, num2, typeof(zero_typed)}(T, K, isCall)
end
end
end
export AmericanOption;
function payout(Sti::numtype_, amPayoff::AmericanOption) where {numtype_ <: Number}
iscall = amPayoff.isCall ? 1 : -1
return ((Sti - amPayoff.K) * iscall > 0.0) ? (Sti - amPayoff.K) * iscall : zero(numtype_)
end
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] | 2.344978 | 458 |
"""
InitialValueProblem{S <: AbstractSystem, XT} <: AbstractSystem
Parametric composite type for initial value problems. It is parameterized in the
system's type and the initial state's type
### Fields
- `s` -- system
- `x0` -- initial state
### Examples
The linear system ``x' = -x`` with initial condition ``x₀ = [-1/2, 1/2]``:
```jldoctest
julia> s = LinearContinuousSystem([-1.0 0.0; 0.0 -1.0]);
julia> x₀ = [-1/2, 1/2];
julia> p = InitialValueProblem(s, x₀);
julia> initial_state(p) # same as p.x0
2-element Array{Float64,1}:
-0.5
0.5
julia> statedim(p)
2
julia> inputdim(p)
0
```
"""
struct InitialValueProblem{S <: AbstractSystem, XT} <: AbstractSystem
s::S
x0::XT
end
statedim(ivp::InitialValueProblem) = statedim(ivp.s)
stateset(ivp::InitialValueProblem) = stateset(ivp.s)
inputdim(ivp::InitialValueProblem) = inputdim(ivp.s)
inputset(ivp::InitialValueProblem) = inputset(ivp.s)
islinear(ivp::InitialValueProblem) = islinear(ivp.s)
isaffine(ivp::InitialValueProblem) = isaffine(ivp.s)
ispolynomial(ivp::InitialValueProblem) = ispolynomial(ivp.s)
state_matrix(ivp::InitialValueProblem) = state_matrix(ivp.s)
input_matrix(ivp::InitialValueProblem) = input_matrix(ivp.s)
noise_matrix(ivp::InitialValueProblem) = noise_matrix(ivp.s)
affine_term(ivp::InitialValueProblem) = affine_term(ivp.s)
"""
initial_state(ivp::InitialValueProblem)
Return the initial state of an initial-value problem.
### Input
- `ivp` -- initial-value problem
### Output
The initial state of an initial-value problem.
"""
initial_state(ivp::InitialValueProblem) = ivp.x0
"""
system(ivp::InitialValueProblem)
Return the system wrapped by an initial-value problem.
### Input
- `ivp` -- initial-value problem
### Output
The system of the given initial-value problem.
"""
system(ivp::InitialValueProblem) = ivp.s
"""
IVP
`IVP` is an alias for `InitialValueProblem`.
"""
const IVP = InitialValueProblem
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198,
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11395,
40781,
198
] | 2.676838 | 721 |
using Electron
using URIParser
using Test
@testset "Electron" begin
@testset "local URI" begin
dir = pwd(URI)
@test unescape(dir.path) == join(push!(split(pwd(), Base.Filesystem.path_separator_re), ""), "/")
@test dir.query == dir.fragment == dir.host == ""
@test string(dir) == "file://$(dir.path)"
__dirname = @__DIR__
dir = Electron.URI_file(__dirname, "")
@test_skip URI(dir, path = dir.path * "test.html", query = "a", fragment = "b") ==
Electron.@LOCAL("test.html?a#b") ==
Electron.@LOCAL(begin; "test.html?a#b"; end) ==
Electron.URI_file(__dirname, "test.html?a#b")
end
@testset "Core" begin
w = Window(URI("file://test.html"))
a = applications()[1]
@test isa(w, Window)
@test length(applications()) == 1
@test length(windows(a)) == 1
res = run(w, "Math.log(Math.exp(1))")
@test res == 1
res = run(a, "Math.log(Math.exp(1))")
@test res ==1
close(w)
@test length(applications()) == 1
@test isempty(windows(a)) == 1
w2 = Window(URI("file://test.html"))
toggle_devtools(w2)
close(a)
@test length(applications()) == 1
@test length(windows(a)) == 0
sleep(1)
@test isempty(applications())
@test isempty(windows(a))
w3 = Window(Dict("url" => string(URI("file://test.html"))))
w4 = Window(URI("file://test.html"), options=Dict("title" => "Window title"))
w5 = Window("<body></body>", options=Dict("title" => "Window title"))
a2 = applications()[1]
w6 = Window(a2, "<body></body>", options=Dict("title" => "Window title"))
w7 = Window(a2)
run(w7, "sendMessageToJulia('foo')")
@test take!(msgchannel(w7)) == "foo"
load(w7, "<body>bar</body>")
run(w7, "sendMessageToJulia(window.document.documentElement.innerHTML)")
@test occursin("bar", take!(msgchannel(w7)))
@testset "ElectronAPI" begin
win = Window()
@test (ElectronAPI.setBackgroundColor(win, "#000"); true)
@test ElectronAPI.isFocused(win) isa Bool
bounds = ElectronAPI.getBounds(win)
boundskeys = ["width", "height", "x", "y"]
@test Set(boundskeys) <= Set(keys(bounds))
@test all(isa.(get.(Ref(bounds), boundskeys, nothing), Real))
close(win)
end
close(w7)
close(w3)
close(w4)
close(w5)
close(w6)
close(a2)
end # testset "Electron"
end | [
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] | 2.412664 | 916 |
using PMDPs
using PMDPs.LP
using DrWatson
using POMDPs
using BSON, CSV
using DrWatson
using RandomNumbers.Xorshifts
using Random
using DataFrames
using StaticArrays, Distributions # load
using MCTS, DiscreteValueIteration
using POMDPSimulators # load histories
using POMDPPolicies
using LightGraphs
using GraphPlot
using Cairo, Compose
using Debugger
using TableView
include(srcdir("MDPPricing.jl"))
function prepare_traces(pp::PMDPs.PMDPProblem, pp_params::Dict, vi::Bool, name::String, N::Int64; trace_folder = "test_traces", seed = 1, verbose=false)
mg = PMDPs.PMDPg(pp)
rnd = Xorshift128Plus(seed)
fname = savename("$(name)_N=$(N)", pp_params, "bson")
fpath = datadir(trace_folder, fname)
if isfile(fpath)
data = PMDPs.load_traces(fpath)
verbose ? println("Loading $fpath") : nothing
else
traces = [PMDPs.simulate_trace(mg, rnd) for i in 1:N]
data = @dict(name, pp, pp_params, traces, vi)
@tagsave(fpath, data)
verbose ? println("Saving $fpath") : nothing
end
return data
end
"""
Get input data
"""
problems = get_fast_benchmarks()
inputs = [prepare_traces(pp, params, vi, name, 100; verbose=true) for (pp, params, vi, name) in problems[1:end] ]
"""
Evaluate
"""
N_sim = 20
dpw_solver_params = (;depth=50,
exploration_constant=40.0, max_time=1.,
enable_state_pw = false,
keep_tree=true, show_progress=false, rng=Xorshift128Plus())
mcts_solver_params = (;depth=50,
exploration_constant=40.0, max_time=1.,
rng=Xorshift128Plus())
# pp_params = Dict(pairs((nᵣ=3, c=3, T=10, expected_res=3., res_budget_μ=5., objective=objective)))
# name = "linear_problem"
out_folder="test"
for (i, data) in enumerate(inputs)
print("\t Data $i - Evaluating $(data[:name]) with $(data[:pp_params]): ")
print("flatrate..."); PMDPs.process_data(data, PMDPs.flatrate; folder=out_folder, N=N_sim)
print("hindsight..."); PMDPs.process_data(data, PMDPs.hindsight; folder=out_folder, N=N_sim)
print("vi..."); data[:vi] && PMDPs.process_data(data, PMDPs.vi; folder=out_folder, N=N_sim)
# print("vi..."); data[:vi] && PMDPs.process_data(data, PMDPs.fhvi; folder=out_folder, N=N_sim)
print("dpw..."); PMDPs.process_data(data, PMDPs.mcts; folder=out_folder, N=N_sim,
method_info="dpw_$(savename(dpw_solver_params))", solver=DPWSolver(;dpw_solver_params...))
println("mcts..."); PMDPs.process_data(data, PMDPs.mcts; folder=out_folder, N=N_sim,
method_info="vanilla_$(savename(mcts_solver_params))", solver=MCTSSolver(;mcts_solver_params...))
end
println("LP Done.")
"""
Collect results
"""
results = folder_report(datadir("results", "test", "linear_problem"); raw_result_array=false);
agg_res = format_result_table(results.results; N=N_sim)
# using WebIO
vscodedisplay(agg_res)
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"""
NUTS(n_iters::Int, n_adapts::Int, delta::Float64)
No-U-Turn Sampler (NUTS) sampler.
Usage:
```julia
NUTS(1000, 200, 0.6j_max)
```
Arguments:
- `n_iters::Int` : The number of samples to pull.
- `n_adapts::Int` : The number of samples to use with adapatation.
- `delta::Float64` : Target acceptance rate.
Example:
```julia
# Define a simple Normal model with unknown mean and variance.
@model gdemo(x) = begin
s ~ InverseGamma(2,3)
m ~ Normal(0, sqrt(s))
x[1] ~ Normal(m, sqrt(s))
x[2] ~ Normal(m, sqrt(s))
return s, m
end
sample(gdemo([1.j_max, 2]), NUTS(1000, 200, 0.6j_max))
```
"""
mutable struct NUTS{AD, T} <: AdaptiveHamiltonian{AD}
n_iters :: Int # number of samples
n_adapts :: Int # number of samples with adaption for epsilon
delta :: Float64 # target accept rate
space :: Set{T} # sampling space, emtpy means all
gid :: Int # group ID
end
NUTS(args...; kwargs...) = NUTS{ADBackend()}(args...; kwargs...)
function NUTS{AD}(n_adapts::Int, delta::Float64, space...) where AD
_space = isa(space, Symbol) ? Set([space]) : Set(space)
NUTS{AD, eltype(_space)}(1, n_adapts, delta, _space, 0)
end
function NUTS{AD}(n_iters::Int, n_adapts::Int, delta::Float64, space...) where AD
_space = isa(space, Symbol) ? Set([space]) : Set(space)
NUTS{AD, eltype(_space)}(n_iters, n_adapts, delta, _space, 0)
end
function NUTS{AD}(n_iters::Int, delta::Float64) where AD
n_adapts_default = Int(round(n_iters / 2))
NUTS{AD, Any}(n_iters, n_adapts_default > 1000 ? 1000 : n_adapts_default, delta, Set(), 0)
end
function NUTS{AD1}(alg::NUTS{AD2, T}, new_gid::Int) where {AD1, AD2, T}
NUTS{AD1, T}(alg.n_iters, alg.n_adapts, alg.delta, alg.space, new_gid)
end
function NUTS{AD, T}(alg::NUTS, new_gid::Int) where {AD, T}
NUTS{AD, T}(alg.n_iters, alg.n_adapts, alg.delta, alg.space, new_gid)
end
function hmc_step(θ, lj, lj_func, grad_func, H_func, ϵ, alg::NUTS, momentum_sampler::Function;
rev_func=nothing, log_func=nothing)
θ_new, α = _nuts_step(θ, ϵ, lj, lj_func, grad_func, H_func, momentum_sampler)
lj_new = lj_func(θ_new)
is_accept = true
return θ_new, lj_new, is_accept, α
end
"""
function _build_tree(θ::T, r::AbstractVector, logu::AbstractFloat, v::Int, j::Int, ϵ::AbstractFloat,
H0::AbstractFloat,lj_func::Function, grad_func::Function, H_func::Function;
Δ_max::AbstractFloat=1000) where {T<:Union{Vector,SubArray}}
Recursively build balanced tree.
Ref: Algorithm 6 on http://www.stat.columbia.edu/~gelman/research/published/nuts.pdf
Arguments:
- `θ` : model parameter
- `r` : momentum variable
- `logu` : slice variable (in log scale)
- `v` : direction ∈ {-1, 1}
- `j` : depth of tree
- `ϵ` : leapfrog step size
- `H0` : initial H
- `lj_func` : function for log-joint
- `grad_func` : function for the gradient of log-joint
- `H_func` : function for Hamiltonian energy
- `Δ_max` : threshold for exploeration error tolerance
"""
function _build_tree(θ::T, r::AbstractVector, logu::AbstractFloat, v::Int, j::Int, ϵ::AbstractFloat,
H0::AbstractFloat, lj_func::Function, grad_func::Function, H_func::Function;
Δ_max::AbstractFloat=1000.0) where {T<:Union{AbstractVector,SubArray}}
if j == 0
# Base case - take one leapfrog step in the direction v.
θ′, r′, τ_valid = _leapfrog(θ, r, 1, v * ϵ, grad_func)
# Use old H to save computation
H′ = τ_valid == 0 ? Inf : H_func(θ′, r′, lj_func(θ′))
n′ = (logu <= -H′) ? 1 : 0
s′ = (logu < Δ_max + -H′) ? 1 : 0
α′ = exp(min(0, -H′ - (-H0)))
return θ′, r′, θ′, r′, θ′, n′, s′, α′, 1
else
# Recursion - build the left and right subtrees.
θm, rm, θp, rp, θ′, n′, s′, α′, n′α = _build_tree(θ, r, logu, v, j - 1, ϵ, H0, lj_func, grad_func, H_func)
if s′ == 1
if v == -1
θm, rm, _, _, θ′′, n′′, s′′, α′′, n′′α = _build_tree(θm, rm, logu, v, j - 1, ϵ, H0, lj_func, grad_func, H_func)
else
_, _, θp, rp, θ′′, n′′, s′′, α′′, n′′α = _build_tree(θp, rp, logu, v, j - 1, ϵ, H0, lj_func, grad_func, H_func)
end
if rand() < n′′ / (n′ + n′′)
θ′ = θ′′
end
α′ = α′ + α′′
n′α = n′α + n′′α
s′ = s′′ * (dot(θp - θm, rm) >= 0 ? 1 : 0) * (dot(θp - θm, rp) >= 0 ? 1 : 0)
n′ = n′ + n′′
end
θm, rm, θp, rp, θ′, n′, s′, α′, n′α
end
end
"""
function _nuts_step(θ::T, r0, ϵ::AbstractFloat, lj_func::Function, grad_func::Function, H_func::Function;
j_max::Int=j_max) where {T<:Union{AbstractVector,SubArray}}
Perform one NUTS step.
Ref: Algorithm 6 on http://www.stat.columbia.edu/~gelman/research/published/nuts.pdf
Arguments:
- `θ` : model parameter
- `ϵ` : leapfrog step size
- `lj` : initial log-joint prob
- `lj_func` : function for log-joint
- `grad_func` : function for the gradient of log-joint
- `H_func` : function for Hamiltonian energy
- `j_max` : maximum expanding of doubling tree
"""
function _nuts_step(θ::T, ϵ::AbstractFloat, lj::Real,
lj_func::Function, grad_func::Function, H_func::Function, momentum_sampler::Function;
j_max::Int=5) where {T<:Union{AbstractVector,SubArray}}
@debug "sampling momentums..."
θ_dim = length(θ)
r0 = momentum_sampler()
H0 = H_func(θ, r0, lj)
logu = log(rand()) + -H0
θm = θ; θp = θ; rm = r0; rp = r0; j = 0; θ_new = θ; n = 1; s = 1
local da_stat
while s == 1 && j <= j_max
v = rand([-1, 1])
if v == -1
θm, rm, _, _, θ′, n′, s′, α, nα = _build_tree(θm, rm, logu, v, j, ϵ, H0, lj_func, grad_func, H_func)
else
_, _, θp, rp, θ′, n′, s′, α, nα = _build_tree(θp, rp, logu, v, j, ϵ, H0, lj_func, grad_func, H_func)
end
if s′ == 1
if rand() < min(1, n′ / n)
θ_new = θ′
end
end
n = n + n′
s = s′ * (dot(θp - θm, rm) >= 0 ? 1 : 0) * (dot(θp - θm, rp) >= 0 ? 1 : 0)
j = j + 1
da_stat = α / nα
end
return θ_new, da_stat
end
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] | 1.987476 | 3,114 |
<filename>test/test-utils.jl
##### Beginning of file
Test.@testset "git tests" begin
git = MirrorUpdater.Utils._get_git_binary_path()
@info(string("git: "), git,)
git_version_cmd = `$(git) --version`
@info(string("Attempting to run command: "), git_version_cmd,)
Test.@test(
MirrorUpdater.Utils.command_ran_successfully!!(
git_version_cmd
)
)
Test.@test(
MirrorUpdater.Utils.command_ran_successfully!!(
`$(git) --version`
)
)
Test.@test_throws(
ErrorException,
MirrorUpdater.Utils.command_ran_successfully!!(
`$(git) --versionBLAHBLAHBLAH`;
max_attempts = 5,
seconds_to_wait_between_attempts = 5,
error_on_failure = true,
last_resort_run = true,
),
)
Test.@test_throws(
ErrorException,
MirrorUpdater.Utils.command_ran_successfully!!(
`$(git) --versionBLAHBLAHBLAH`;
max_attempts = 5,
seconds_to_wait_between_attempts = 5,
error_on_failure = true,
last_resort_run = false,
),
)
Test.@test_throws(
ErrorException,
MirrorUpdater.Utils.command_ran_successfully!!(
`$(git) --versionBLAHBLAHBLAH`;
max_attempts = 5,
seconds_to_wait_between_attempts = 5,
error_on_failure = false,
last_resort_run = true,
),
)
Test.@test(
!(
MirrorUpdater.Utils.command_ran_successfully!!(
`$(git) --versionBLAHBLAHBLAH`;
max_attempts = 5,
seconds_to_wait_between_attempts = 5,
error_on_failure = false,
last_resort_run = false,
)
)
)
function f_1()
return "Hello There"
end
Test.@test(
"Hello There" ==
MirrorUpdater.Utils.retry_function_until_success(
() -> f_1()
)
)
f_2_counter = Ref{Int}()
f_2_counter[] = 0
function f_2(counter)
counter[] += 1
@debug(
string(
"Incremented counter from ",
"$(counter[] - 1) to $(counter[])",
)
)
if counter[] < 7
error("f2_counter < 7")
else
return "General Kenobi"
end
end
Test.@test(
"General Kenobi" ==
MirrorUpdater.Utils.retry_function_until_success(
() -> f_2(f_2_counter);
max_attempts = 10,
seconds_to_wait_between_attempts = 5,
)
)
function f_3()
error("f_3() will always fail")
end
Test.@test_throws(
ErrorException,
MirrorUpdater.Utils.retry_function_until_success(
()->f_3();
max_attempts = 5,
seconds_to_wait_between_attempts = 5,
),
)
previous_directory::String = pwd()
temp_directory_1::String = joinpath(mktempdir(), "TEMPGITREPOLOCAL")
mkpath(temp_directory_1)
temp_directory_2::String = joinpath(mktempdir(), "TEMPGITREPOREMOTE")
mkpath(temp_directory_2)
cd(temp_directory_2)
run(`$(git) init --bare`)
cd(temp_directory_1)
run(`$(git) init`)
MirrorUpdater.Utils.git_add_all!()
MirrorUpdater.Utils.git_commit!(
;
message="test commit 1",
allow_empty=true,
committer_name="test name",
committer_email="test email",
)
run(`git branch branch1`)
run(`git branch branch2`)
run(`git branch branch3`)
run(`git checkout master`)
Test.@test(
typeof(MirrorUpdater.Utils.git_version()) <: VersionNumber
)
Test.@test(
typeof(MirrorUpdater.Utils.get_all_branches_local()) <:
Vector{String}
)
Test.@test(
typeof(MirrorUpdater.Utils.get_all_branches_local_and_remote()) <:
Vector{String}
)
Test.@test(
typeof(MirrorUpdater.Utils.get_current_branch()) <: String )
Test.@test(
MirrorUpdater.Utils.branch_exists("branch1") )
Test.@test(
!MirrorUpdater.Utils.branch_exists("non-existent-branch") )
Test.@test(
!MirrorUpdater.Utils.branch_exists("non-existent-but-create-me") )
Test.@test(
typeof(MirrorUpdater.Utils.checkout_branch!("branch1")) <: Nothing )
Test.@test_throws(
ErrorException,
MirrorUpdater.Utils.checkout_branch!("non-existent-branch"),
)
Test.@test_warn(
"",
MirrorUpdater.Utils.checkout_branch!(
"non-existent-branch";
error_on_failure=false,
),
)
Test.@test(
typeof(
MirrorUpdater.Utils.checkout_branch!(
"non-existent-but-create-me";
create=true,
)
) <: Nothing
)
MirrorUpdater.Utils.git_add_all!()
MirrorUpdater.Utils.git_commit!(
;
message="test commit 2",
allow_empty=true,
committer_name="<NAME>",
committer_email="test email",
)
run(`git checkout master`)
Test.@test(
MirrorUpdater.Utils.branch_exists("branch1")
)
Test.@test(
!MirrorUpdater.Utils.branch_exists("non-existent-branch")
)
Test.@test(
MirrorUpdater.Utils.branch_exists("non-existent-but-create-me")
)
run(`$(git) remote add origin $(temp_directory_2)`)
Test.@test(
typeof(MirrorUpdater.Utils.git_push_upstream_all!()) <: Nothing
)
run(`git checkout master`)
include_patterns::Vector{Regex} = Regex[
r"^bRANCh1$"i,
r"^bRanCh3$"i,
]
exclude_patterns::Vector{Regex} = Regex[
r"^brANcH3$"i,
]
branches_to_snapshot::Vector{String} =
MirrorUpdater.Utils.make_list_of_branches_to_snapshot(
;
default_branch = "maSTeR",
include = include_patterns,
exclude = exclude_patterns,
)
Test.@test( length(branches_to_snapshot) == 2 )
Test.@test( length(unique(branches_to_snapshot)) == 2 )
Test.@test(
length(branches_to_snapshot) == length(unique(branches_to_snapshot))
)
Test.@test( branches_to_snapshot[1] == "branch1" )
Test.@test( branches_to_snapshot[2] == "master" )
cd(previous_directory)
MirrorUpdater.Utils.delete_everything_except_dot_git!(temp_directory_1)
MirrorUpdater.Utils.delete_only_dot_git!(temp_directory_2)
rm(temp_directory_1; recursive=true, force=true)
rm(temp_directory_2; recursive=true, force=true)
end # end testset "git tests"
##### End of file
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] | 1.785958 | 4,102 |
<reponame>JuliaDynamics/NonlinearDynamicsTextbook
# %% CCM illustration
using DrWatson
@quickactivate "NonlinearDynamicsTextbook"
include(srcdir("style.jl"))
using DynamicalSystems, PyPlot, Random
ds = Systems.lorenz()
tr = trajectory(ds, 100; Ttr = 100)
x, y, z = columns(tr)
τx = estimate_delay(x, "mi_min")
τy = estimate_delay(y, "mi_min")
τz = estimate_delay(z, "mi_min")
X = embed(x, 3, τx)
Y = embed(y, 3, τy)
Z = embed(z, 3, τz)
using3D()
fig = figure()
axts = fig.add_subplot(1, 3, 1)
axx = fig.add_subplot(1,3,2; projection="3d")
axy = fig.add_subplot(1,3,3; projection="3d")
axts.plot(x .- 10)
axts.plot(y .+ 20; color = "C2")
axts.set_xlim(0, 1000)
axts.set_xticklabels([])
axts.set_yticklabels([])
axx.plot3D(columns(X)...; lw = 1)
axy.plot3D(columns(Y)...; lw = 1, color = "C2")
for s in (:x, :y, :z)
f = Symbol(:set_, s, :ticklabels)
@eval axx.$(f)([])
@eval axy.$(f)([])
g = Symbol(:set_, s, :lim)
@eval axx.$(g)(-15, 15)
@eval axy.$(g)(-20, 20)
end
# Axis pretty-fication
axx.dist = 8
axy.dist = 8
axx.elev = 20
axy.elev = 20
axx.text3D(-10, -10, 15, "\$M_x\$", size = 40)
axy.text3D(-10, -10, 20, "\$M_y\$", size = 40)
axts.set_ylabel("timeseries")
axx.text(15, 12, 21, "b"; bbox = bbox, zorder = 99, va = "top")
axy.text(17, 17, 27.5, "c"; bbox = bbox, zorder = 99, va = "top")
# axy.text(45, 40, 54, "c"; bbox = bbox, zorder = 99, va = "top")
add_identifiers!(fig)
fig.tight_layout(pad=0.35)
fig.subplots_adjust(wspace = 0.2)
wsave(plotsdir("7", "ccm"), fig) | [
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2336,
8
] | 2.057221 | 734 |
<filename>src/ConstantQ.jl
__precompile__()
module ConstantQ
using Compat
export
GeometricFrequency, # geometrically spaced frequency
KernelProperty, # kernel property
SpectralKernelMatrix, # frequency-domain kernel matrix
TemporalKernelMatrix, # time-domain kernel matrix
property,
nbins_per_octave, # number of frequency bins per octave
nfreqs, # number of frequency bins
freqs, # generate array of frequencies
q, # Q-factor
speckernel, # construct frequency-domain kernel
tempkernel, # construct time-domain kernel
cqt # A fast constant-Q transform
include("cqt.jl")
end # module
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] | 2.427152 | 302 |
<reponame>UnofficialJuliaMirrorSnapshots/TimeSeriesResampler.jl-209c9289-0e93-5bc3-a9c6-3b517a31ee3c
module TimeSeriesResampler
export resample, ohlc, mean, sum
export TimeFrame, Begin, End
include("resample.jl")
end # module
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<gh_stars>0
"""
# InteractiveCodeSearch.jl --- Interactively search Julia code
Julia has `@edit`, `@less`, etc. which are very handy for reading the
implementation of functions. However, you need to specify a "good
enough" set of (type) parameters for them to find the location of the
code.
Instead, `InteractiveCodeSearch` provides a few macros to
interactively choose the code you want to read.
## Features
* Interactively choose a method signature before opening the code
location in your editor.
* Various ways to search methods, such as: by function name `@search show`,
function call expression `@search show(stdout, "hello")`,
function call signature `@search show(::IO, ::String)`,
module name `@search Base`, argument value `@searchmethods 1`,
argument type `@searchmethods ::Int`, and return type `@searchreturn Int`.
* Interactively search history. It works in IJulia as well.
## Examples
```julia
using InteractiveCodeSearch
@search show # search method definitions
@searchmethods 1 # search methods defined for integer
@searchhistory # search history (Julia ≥ 0.7)
@searchreturn String Pkg # search methods returning a given type (Julia ≥ 0.7)
```
## Requirements
* Interactive matching command. For example:
* [peco](https://github.com/peco/peco) (default in terminal)
* [percol](https://github.com/mooz/percol)
* [rofi](https://github.com/DaveDavenport/rofi) (GUI; default in IJulia)
"""
module InteractiveCodeSearch
export @search, @searchmethods
import Pkg
using Base
using Base: IOError
using InteractiveUtils: edit, gen_call_with_extracted_types, methodswith
abstract type SearchPolicy end
struct Shallow <: SearchPolicy end
struct Recursive <: SearchPolicy end
mutable struct SearchConfig # CONFIG
open
interactive_matcher::Function
auto_open::Bool
end
function setmatcher!(cmd::Function, obj::SearchConfig)
obj.interactive_matcher = cmd
return
end
function setmatcher!(cmd::Cmd, obj::SearchConfig)
setmatcher!(convertCmd(cmd), obj)
end
function convertCmd(cmd)
maybe_warn_matcher(cmd)
return function (f)
maybe_warn_matcher(cmd)
f(cmd)
end
end
maybe_identifier(s) = !startswith(string(s), "#")
is_locatable(::Any) = false
is_locatable(::Function) = true
is_locatable(t::Type) = !(t <: Vararg)
# https://github.com/JuliaLang/julia/issues/29645
is_defined_in(child, parent) =
child !== parent && parentmodule(child) === parent
function list_locatables(p::SearchPolicy, m::Module)
locs = []
for s in names(m; all=true)
if maybe_identifier(s)
x = try
getfield(m, s)
catch err
err isa UndefVarError && continue
rethrow()
end
if is_locatable(x)
push!(locs, x)
elseif p isa Recursive && x isa Module && is_defined_in(x, m)
append!(locs, list_locatables(p, x))
end
end
end
return locs
end
module_methods(p::SearchPolicy, m::Module) :: Vector{Method} =
vcat(collect.(methods.(list_locatables(p, m)))...)
# Note: the conversion `:: Vector{Method}` seems to be required only
# for Julia 0.6.
struct _Dummy end
function uninteresting_locs()
locs = []
for m in methods(_Dummy)
path = string(m.file)
if path != @__FILE__
push!(locs, (path, m.line))
end
end
return locs
end
"""
find_source_file(file)
Find source `file` and return its full path. It just calls
`Base.find_source_file` and return its result for normal Julia
installation. For nightly Julia build, it tries to guess the right
path when `Base.find_source_file` failed.
"""
function find_source_file(file)
path = Base.find_source_file(file)
if path isa AbstractString && ! isfile(path)
for m in methods(Pkg.add)
exfile = try
String(m.file)
catch err
continue
end
idx = findlast(joinpath(Base.Filesystem.path_separator,
"share", "julia"), exfile)
if idx isa Nothing
continue
end
prefix = exfile[1:idx[1]]
if startswith(file, prefix)
# e.g., relpath = "share/julia/stdlib/v0.7/..."
relpath = file[length(prefix)+1:end]
return joinpath(Base.Sys.BINDIR, "..", relpath)
end
end
end
return path
end
function _readandwrite(matcher)
proc = matcher() do cmd
open(`$cmd`, "r+")
end
return (proc.out, proc.in, proc)
end
"""
read_stdout(input::AbstractString, cmd)
read_stdout(input_provider, cmd)
Julia implementation of "echo {input} | {cmd}".
"""
function read_stdout(input::AbstractString, cmd)
read_stdout(cmd) do stdin
write(stdin, input)
end
end
function read_stdout(input_provider, cmd)
stdout, stdin, process = _readandwrite(cmd)
reader = @async read(stdout)
try
input_provider(stdin)
catch err
if ! (err isa IOError)
rethrow()
end
finally
close(stdin)
end
return fetch(reader)
end
function parse_loc(line)
rest, lineno = rsplit(line, ":", limit=2)
_, path = rsplit(rest, " at ", limit=2)
return String(path), parse(Int, lineno)
end
function run_matcher(input)
return String(read_stdout(input, CONFIG.interactive_matcher))
end
choose_method(methods::T) where T =
_choose_method(Base.IteratorSize(T), methods)
function _choose_method(::Base.HasLength, methods)
if isempty(methods)
@info "No (interesting) method found"
return
end
if CONFIG.auto_open && length(methods) == 1
m = first(methods)
loc = (string(m.file), m.line)
if loc in uninteresting_locs()
path, lineno = loc
@info "Not opening uninteresting location: $path:$lineno"
return
end
return loc
end
return _choose_method(Base.SizeUnknown(), methods)
end
function _choose_method(::Base.IteratorSize, methods)
out = run_matcher() do stdin
for m in methods
show(stdin, m)
println(stdin)
end
end
if isempty(out)
return
end
return parse_loc(out)
end
function run_open(path, lineno)
@info "Opening $path:$lineno"
CONFIG.open(find_source_file(path), lineno)
end
maybe_open(::Nothing) = nothing
maybe_open(x::Tuple{String, Integer}) = run_open(x...)
search_methods(methods) = maybe_open(choose_method(methods))
code_search_typed(f, t) = search_methods(methods(f, t))
code_search(::SearchPolicy, f::Base.Callable) = search_methods(methods(f))
code_search(p::SearchPolicy, m::Module) = search_methods(module_methods(p, m))
function code_search(p::SearchPolicy, ::T) where T
@warn """Cannot search for given value of type $T
Searching for its type instead..."""
code_search(p, T)
end
"""
Configuration interface for `InteractiveCodeSearch`.
# Examples
```julia
using InteractiveCodeSearch
InteractiveCodeSearch.CONFIG.interactive_matcher = `peco` # default in terminal
InteractiveCodeSearch.CONFIG.interactive_matcher = `percol`
InteractiveCodeSearch.CONFIG.interactive_matcher =
`rofi -dmenu -i -p "🔎"` # use GUI matcher (default in non-terminal
# environment like IJulia)
InteractiveCodeSearch.CONFIG.interactive_matcher =
`rofi -dmenu -i -p "🔎" -fullscreen` # bigger screen
InteractiveCodeSearch.CONFIG.open = edit # default
InteractiveCodeSearch.CONFIG.open = less # use Base.less to read code
InteractiveCodeSearch.CONFIG.auto_open = true # default
InteractiveCodeSearch.CONFIG.auto_open = false # open matcher even when there
# is only one candidate
```
## Using InteractiveCodeSearch.jl by default
Put the following code in your `~/.julia/config/startup.jl`:
```julia
using InteractiveCodeSearch
# InteractiveCodeSearch.CONFIG.interactive_matcher = ...
```
"""
const CONFIG = SearchConfig(
edit, # open
x->error("uninitialized"), # interactive_matcher
true, # auto_open
)
should_eval(::Any) = false
should_eval(::Symbol) = true
should_eval(ex::Expr) = ex.head in (:., :ref)
# Given (say) `a.b[c].d[e]` It probably is better to only eval
# `a.b[c].d` and then search for `getindex(a.b[c].d, e)`. But it's
# (1) a bit harder to implement and (2) evaluating the whole
# expression is still useful. So let's keep the current
# implementation for a while.
isliteral(::Symbol) = false
isliteral(::Expr) = false
isliteral(::Any) = true
isline(::Any) = false
isline(ex::Expr) = ex.head == :line
isline(::LineNumberNode) = true
single_macrocall(::Any) = nothing
function single_macrocall(x::Expr)
if x.head == :macrocall && all(isline.(x.args[2:end]))
return x.args[1]
elseif x.head == :block
statements = findall(a -> !isline(a), x.args)
if length(statements) == 1
return single_macrocall(x.args[statements[1]])
end
end
return nothing
end
explicitly_typed(::Any) = nothing
function explicitly_typed(ex::Expr)
if ex.head == :call &&
all(x isa Expr && x.head == :(::) for x in ex.args[2:end])
return ex.args[1], [x.args[end] for x in ex.args[2:end]]
end
return nothing
end
# Julia >= 0.7:
parse_search_policy(flag::QuoteNode) = parse_search_policy(flag.value)
# Julia 0.6:
function parse_search_policy(flag::Expr)
@assert flag.head == :quote
@assert length(flag.args) == 1
return parse_search_policy(flag.args[1])
end
function parse_search_policy(flag::Symbol)
if flag in (:shallow, :s)
return Shallow()
elseif flag in (:recursive, :r)
return Recursive()
end
error("Invalid flag $flag")
end
"""
@search x [:shallow | :s | :recursive | :r]
List file locations at which `x` are defined in an interactive matcher
and then open the chosen location in the editor.
When `x` is a module, only the top-level definitions are searched. To
search all definitions in the submodule, pass `:recursive` or `:r`
flag.
@search
If no expression is provided, search for the method returned by the
previous execution; i.e., `x` defaults to `ans`.
# Examples
```julia
@search show # all method definitions
@search @time # all macro definitions
@search Base.Enums # methods and macros in a module
@search REPL :r # search the module recursively
@search *(::Integer, ::Integer) # methods with specified types
@search dot(π, ℯ) # methods with inferred types
```
Note that `@search` evaluates complex expression with `.` and `[]`
such as follows and search the returned value or the type of it:
```julia
@search Base.Multimedia.displays[2].repl
```
"""
macro search(x = :ans, flag = :(:shallow))
p = parse_search_policy(flag)
if should_eval(x)
# Examples:
# @search show
# @search Base.Enums
# @search Base.Multimedia.displays[2].repl
return :(code_search($p, $(esc(x))))
end
macrocall = single_macrocall(x)
if macrocall !== nothing
# Examples:
# @search @time
# @search begin @time end
return :(code_search($p, $(esc(macrocall))))
end
func_type = explicitly_typed(x)
if func_type !== nothing
f, ts = func_type
# Examples:
# @search *(::Integer, ::Integer)
# @search dot(::AbstractVector, ::SparseVector)
return :(code_search_typed($(esc(f)), tuple($(esc.(ts)...))))
end
# Since `gen_call_with_extracted_types` does not handle literals,
# let's handle this case here (although there are not much can be
# done).
if isliteral(x)
# Examples:
# @search ""
# @search 1
return :(code_search($p, $(esc(x))))
end
# Examples:
# @search 1 * 2
# @search dot([], [])
gen_call_with_extracted_types(__module__, code_search_typed, x)
end
code_search_methods(T) = search_methods(methodswith(T; supertypes=true))
"""
@searchmethods x
@searchmethods ::X
Interactively search through `methodswith(typeof(x))` or
`methodswith(X)`.
# Examples
```julia
@searchmethods 1 # search methods defined for integer
@searchmethods ::Int # search methods defined for a specified type
```
"""
macro searchmethods(x)
if x isa Expr && x.head == :(::)
if length(x.args) > 1
@info "Ignoring: $(x.args[1:end-1]...) in $x"
end
:(code_search_methods($(esc(x.args[end]))))
else
:(code_search_methods(typeof($(esc(x)))))
end
end
################################################################################
# matcher binaries #
################################################################################
@static if VERSION<v"1.3-"
const preferred_terminal = Cmd[
`peco`,
`percol`,
]
else
using peco_jll
const preferred_terminal = Union{Function,Cmd}[
`peco`,
peco,
`percol`,
]
end
const preferred_gui = Cmd[
`rofi -dmenu -i -p "🔎"`,
# what else?
]
function need_gui(stdstreams = [stdout, stdin])
return !all(isa.(stdstreams, Ref(Base.TTY)))
end
function choose_preferred_command(commands::Vector{<:Union{Function,Cmd}}, default=nothing)
for cmd in commands
if !(cmd isa Cmd) || Sys.which(cmd.exec[1]) !== nothing
return cmd
end
end
return default
end
function choose_interactive_matcher()
need_gui() && return choose_preferred_command(preferred_gui, preferred_gui[1])
return choose_preferred_command(vcat(preferred_terminal, preferred_gui), preferred_terminal[1])
end
function matcher_installation_tips(program::AbstractString)
if program == "peco"
return """
See https://github.com/peco/peco for how to install peco.
"""
elseif program == "rofi"
msg = """
See https://github.com/DaveDavenport/rofi for how to install rofi.
"""
else
msg = ""
end
return """
$msg
For terminal usage, `peco` is recommended.
See https://github.com/peco/peco for how to install peco.
"""
end
function maybe_warn_matcher(cmd::Cmd)
if Sys.which(cmd.exec[1]) === nothing
@warn """
Matcher $(cmd.exec[1]) not installed.
$(matcher_installation_tips(cmd.exec[1]))
"""
end
end
function __init__()
setmatcher!(choose_interactive_matcher(), CONFIG)
end
include("taskmanager.jl")
include("history.jl")
include("return.jl")
end # module
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] | 2.426211 | 6,112 |
<reponame>JuliaNeuroscience/NeuroGraphs.jl
# core methods
###
### neighbors
###
@propagate_inbounds function outneighbors(g::AdjacencyMap, v::Integer)
cptr = getcolptr(g)
rvals = rowvals(g)
out = unsafe_inneighbors(nvertices(g), cptr, rvals, v)
@inbounds for i in cptr[v]:cptr[v + 1] - 1
pushfirst!(out, i)
end
return out
end
inneighbors(g::AdjMap, v::Integer) = outneighbors(g, v)
function inneighbors(g::AdjDiMap, v::Integer)
unsafe_inneighbors(nvertices(g), getcolptr(g), rowvals(g), v)
end
function unsafe_inneighbors(N, cptr, rval, v)
nzinds = Int[]
ptrI = 1
@inbounds for j in OneTo(N)
rowI = v
ptrA = Int(cptr[j])
stopA = Int(cptr[j+1]-1)
if ptrA <= stopA
if rval[ptrA] <= rowI
ptrA = searchsortedfirst(rval, rowI, ptrA, stopA, Base.Order.Forward)
if ptrA <= stopA && rval[ptrA] == rowI
push!(nzinds, j)
end
end
ptrI += 1
end
end
return nzinds
end
neighbors(g::AdjMap, v::Integer) = outneighbors(g, v)
## all_neighbors
all_neighbors(g::AdjMap, v::Integer) = outneighbors(g, v)
all_neighbors(g::AdjDiMap, v::Integer) = union(outneighbors(g, v), inneighbors(g, v))
## common_neighbors
function common_neighbors(g::AdjacencyMap, u::Integer, v::Integer)
intersect(neighbors(g, u), neighbors(g, v))
end
###
### density
###
function density(g::AdjDiMap)
N = nvertices(g)
return nedges(g) / (N * (N - 1))
end
function density(g::AdjMap)
N = nvertices(g)
return (2 * nedges(g)) / (N * (N - 1))
end
## num_self_loops
function num_self_loops(g::AdjacencyMap)
if nvertices(g) == 0
return 0
else
return sum(v -> has_edge(g, v, v), vertices(g))
end
end
## degree_histogram
function degree_histogram(g::AdjacencyMap, degfn=degree)
hist = Dict{eltype(g),Int}()
for v in vertices(g) # minimize allocations by
for d in degfn(g, v) # iterating over vertices
hist[d] = get(hist, d, 0) + 1
end
end
return hist
end
###
### induced_subgraph
###
#=
function induced_subgraph(g::AdjacencyMap, elist::AbstractVector{U}) where {U <: AbstractEdge}
h = zero(g)
T = eltype(T)
newvid = Dict{T,T}()
vmap = Vector{T}()
for e in elist
u, v = Tuple(e)
for i in (u, v)
if !haskey(newvid, i)
add_vertex!(h)
newvid[i] = nvertices(h)
push!(vmap, i)
end
end
add_edge!(h, newvid[u], newvid[v])
end
return h, vmap
end
=#
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] | 1.973013 | 1,334 |
using BinaryBuilder, Pkg
name = "MicrosoftMPI"
version = v"10.1.2"
sources = [
FileSource("https://download.microsoft.com/download/a/5/2/a5207ca5-1203-491a-8fb8-906fd68ae623/msmpisetup.exe",
"c305ce3f05d142d519f8dd800d83a4b894fc31bcad30512cefb557feaccbe8b4"),
FileSource("https://download.microsoft.com/download/a/5/2/a5207ca5-1203-491a-8fb8-906fd68ae623/msmpisdk.msi",
"f9174c54feda794586ebd83eea065be4ad38b36f32af6e7dd9158d8fd1c08433"),
ArchiveSource("https://github.com/eschnett/MPIconstants/archive/refs/tags/v1.4.0.tar.gz",
"610d816c22cd05e16e17371c6384e0b6f9d3a2bdcb311824d0d40790812882fc"),
]
script = raw"""
apk add p7zip
cd ${WORKSPACE}/srcdir/
7z x -t# msmpisetup.exe -otmp
if [[ ${target} == i686-w64-* ]]; then
# 32-bit files
7z x tmp/2.msi -o$prefix
else
# 64-bit files
7z x tmp/4.msi -o$prefix
mv -f $prefix/msmpi64.dll $prefix/msmpi.dll
mv -f $prefix/msmpires64.dll $prefix/msmpires.dll
fi
7z x msmpisdk.msi -o$prefix
cd ${WORKSPACE}/destdir/
chmod +x *.exe
mkdir -p bin
mv *.exe *.dll bin
mkdir -p lib
mv *.lib lib
mkdir -p include
# Move to includedir only the mpifptr.h for current architecture
mv "mpifptr${nbits}.h" "include/mpifptr.h"
rm mpifptr*.h
mv *.h *.man include
mkdir -p src
mv *.f90 src
mkdir -p share/licenses/MicrosoftMPI
mv *.txt *.rtf share/licenses/MicrosoftMPI
################################################################################
# Install MPIconstants
################################################################################
cd ${WORKSPACE}/srcdir/MPIconstants*
mkdir build
cd build
if [[ "$target" == x86_64-w64-mingw32 ]]; then
cmake \
-DCMAKE_TOOLCHAIN_FILE=${CMAKE_TARGET_TOOLCHAIN} \
-DCMAKE_FIND_ROOT_PATH=${prefix} \
-DCMAKE_INSTALL_PREFIX=${prefix} \
-DBUILD_SHARED_LIBS=ON \
-DMPI_HOME=$prefix \
-DMPI_GUESS_LIBRARY_NAME=MSMPI \
-DMPI_C_LIBRARIES=msmpi64 \
-DMPI_CXX_LIBRARIES=msmpi64 \
-DMPI_Fortran_LIBRARIES='msmpifec64;msmpi64;cfg_stub' \
..
elif [[ "$target" == *-mingw* ]]; then
cmake \
-DCMAKE_TOOLCHAIN_FILE=${CMAKE_TARGET_TOOLCHAIN} \
-DCMAKE_FIND_ROOT_PATH=${prefix} \
-DCMAKE_INSTALL_PREFIX=${prefix} \
-DBUILD_SHARED_LIBS=ON \
-DMPI_HOME=$prefix \
-DMPI_GUESS_LIBRARY_NAME=MSMPI \
..
else
exit 1
fi
cmake --build . --config RelWithDebInfo --parallel $nproc
cmake --build . --config RelWithDebInfo --parallel $nproc --target install
install_license $WORKSPACE/destdir/share/licenses/MicrosoftMPI/* $WORKSPACE/srcdir/MPIconstants*/LICENSE.md
"""
platforms = filter!(Sys.iswindows, supported_platforms())
products = [
# MicrosoftMPI
LibraryProduct("msmpi", :libmpi),
ExecutableProduct("mpiexec", :mpiexec),
# MPIconstants
LibraryProduct("libload_time_mpi_constants", :libload_time_mpi_constants),
ExecutableProduct("generate_compile_time_mpi_constants", :generate_compile_time_mpi_constants),
]
dependencies = Dependency[
]
# Build the tarballs.
# We manually bump the version up to `v10.1.3` here to avoid compat-changing issues
# X-ref: https://github.com/JuliaRegistries/General/pull/28956
version = v"10.1.3"
build_tarballs(ARGS, name, version, sources, script, platforms, products, dependencies)
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11,
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11,
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11,
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8,
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] | 2.214003 | 1,514 |
using FaultTolerantControl
using Transducers
using Plots
function main()
N = 4
d = 2
θ = [[0, 0], [0.3, 0.1], [0.5, 0.2], [0.7, 0.3], [1, 1]]
θ_x = θ |> Map(_θ -> _θ[1]) |> collect
θ_y = θ |> Map(_θ -> _θ[2]) |> collect
t0 = 0.0
tf = 1.0
curve = Bezier(θ, t0, tf)
curve_params = t0:0.01:tf
points = curve_params |> Map(curve) |> collect
points_x = points |> Map(point -> point[1]) |> collect
points_y = points |> Map(point -> point[2]) |> collect
fig = plot(points_x, points_y; label="Bezier curve")
plot!(fig, θ_x, θ_y; st=:scatter, label="control points")
end
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] | 2.046205 | 303 |
# Attempt to compute a reasonable default mu: at the starting
# position, the gradient of the input function should dominate the
# gradient of the barrier.
function initial_mu(gfunc::AbstractArray{T}, gbarrier::AbstractArray{T}, mu0factor::T = 0.001, mu0::T = convert(T, NaN)) where T
if isnan(mu0)
gbarriernorm = sum(abs, gbarrier)
if gbarriernorm > 0
mu = mu0factor*sum(abs, gfunc)/gbarriernorm
else
# Presumably, there is no barrier function
mu = zero(T)
end
else
mu = mu0
end
return mu
end
function barrier_box(g, x::AbstractArray{T}, l::AbstractArray{T}, u::AbstractArray{T}) where T
calc_g = g !== nothing
v = zero(T)
@inbounds for i in eachindex(x)
thisl = l[i]
if isfinite(thisl)
dx = x[i] - thisl
if dx <= zero(T)
return convert(T, Inf)
end
v -= log(dx)
if calc_g
g[i] = -one(T)/dx
end
else
if calc_g
g[i] = zero(T)
end
end
thisu = u[i]
if isfinite(thisu)
dx = thisu - x[i]
if dx <= zero(T)
return convert(T, Inf)
end
v -= log(dx)
if calc_g
g[i] += one(T)/dx
end
end
end
return v
end
function function_barrier(gfunc, gbarrier, x::AbstractArray, f, fbarrier)
vbarrier = fbarrier(gbarrier, x)
return (isfinite(vbarrier) ? f(gfunc, x) : vbarrier), vbarrier
end
function barrier_combined(gfunc, gbarrier, g, x::AbstractArray,
fb, mu::Ref{<:Real})
valfunc, valbarrier = fb(gbarrier, x, gfunc)
if g !== nothing
g .= gfunc .+ mu[].*gbarrier
end
return convert(eltype(x), valfunc + mu[]*valbarrier) # FIXME make this unnecessary
end
function limits_box(x::AbstractArray{T}, d::AbstractArray{T},
l::AbstractArray{T}, u::AbstractArray{T}) where T
alphamax = convert(T, Inf)
@inbounds for i in eachindex(x)
if d[i] < 0
alphamax = min(alphamax, ((l[i]-x[i])+eps(l[i]))/d[i])
elseif d[i] > 0
alphamax = min(alphamax, ((u[i]-x[i])-eps(u[i]))/d[i])
end
end
epsilon = eps(max(alphamax, one(T)))
if !isinf(alphamax) && alphamax > epsilon
alphamax -= epsilon
end
return alphamax
end
# Default preconditioner for box-constrained optimization
# This creates the inverse Hessian of the barrier penalty
function precondprepbox!(P, x, l, u, mu)
@. P.diag = 1/(mu[]*(1/(x-l)^2 + 1/(u-x)^2) + 1)
end
struct Fminbox{O<:AbstractOptimizer, T, P} <: AbstractConstrainedOptimizer
method::O
mu0::T
mufactor::T
precondprep::P
end
"""
# Fminbox
## Constructor
```julia
Fminbox(method;
mu0=NaN,
mufactor=0.0001,
precondprep(P, x, l, u, mu) -> precondprepbox!(P, x, l, u, mu))
```
"""
function Fminbox(method::AbstractOptimizer = LBFGS();
mu0::Real = NaN, mufactor::Real = 0.001,
precondprep = (P, x, l, u, mu) -> precondprepbox!(P, x, l, u, mu))
if method isa Newton || method isa NewtonTrustRegion
throw(ArgumentError("Newton is not supported as the Fminbox optimizer."))
end
Fminbox(method, promote(mu0, mufactor)..., precondprep) # default optimizer
end
Base.summary(F::Fminbox) = "Fminbox with $(summary(F.method))"
# barrier_method() constructs an optimizer to solve the barrier problem using m = Fminbox.method as the reference.
# Essentially it only updates the P and precondprep fields of `m`.
# fallback
barrier_method(m::AbstractOptimizer, P, precondprep) =
error("You need to specify a valid inner optimizer for Fminbox, $m is not supported. Please consult the documentation.")
barrier_method(m::ConjugateGradient, P, precondprep) =
ConjugateGradient(eta = m.eta, alphaguess = m.alphaguess!,
linesearch = m.linesearch!, P = P,
precondprep = precondprep)
barrier_method(m::LBFGS, P, precondprep) =
LBFGS(alphaguess = m.alphaguess!, linesearch = m.linesearch!, P = P,
precondprep = precondprep)
barrier_method(m::GradientDescent, P, precondprep) =
GradientDescent(alphaguess = m.alphaguess!, linesearch = m.linesearch!, P = P,
precondprep = precondprep)
barrier_method(m::Union{NelderMead, SimulatedAnnealing, ParticleSwarm, BFGS, AbstractNGMRES},
P, precondprep) = m # use `m` as is
function optimize(f,
g,
l::AbstractArray{T},
u::AbstractArray{T},
initial_x::AbstractArray{T},
F::Fminbox = Fminbox(),
options = Options(); inplace = true, autodiff = :finite) where T<:AbstractFloat
g! = inplace ? g : (G, x) -> copyto!(G, g(x))
od = OnceDifferentiable(f, g!, initial_x, zero(T))
optimize(od, l, u, initial_x, F, options)
end
function optimize(f,
l::AbstractArray{T},
u::AbstractArray{T},
initial_x::AbstractArray{T},
F::Fminbox = Fminbox(),
options = Options(); inplace = true, autodiff = :finite) where T<:AbstractFloat
od = OnceDifferentiable(f, initial_x, zero(T); autodiff = autodiff)
optimize(od, l, u, initial_x, F, options)
end
function optimize(
df::OnceDifferentiable,
l::AbstractArray{T},
u::AbstractArray{T},
initial_x::AbstractArray{T},
F::Fminbox = Fminbox(),
options = Options()) where T<:AbstractFloat
outer_iterations = options.outer_iterations
allow_outer_f_increases = options.allow_outer_f_increases
show_trace, store_trace, extended_trace = options.show_trace, options.store_trace, options.extended_trace
x = copy(initial_x)
fbarrier = (gbarrier, x) -> barrier_box(gbarrier, x, l, u)
fb = (gbarrier, x, gfunc) -> function_barrier(gfunc, gbarrier, x, df.fdf, fbarrier)
gfunc = similar(x)
gbarrier = similar(x)
P = InverseDiagonal(similar(initial_x))
# to be careful about one special case that might occur commonly
# in practice: the initial guess x is exactly in the center of the
# box. In that case, gbarrier is zero. But since the
# initialization only makes use of the magnitude, we can fix this
# by using the sum of the absolute values of the contributions
# from each edge.
boundaryidx = Vector{Int}()
for i in eachindex(gbarrier)
thisx = x[i]
thisl = l[i]
thisu = u[i]
if thisx == thisl
thisx = 0.99*thisl+0.01*thisu
x[i] = thisx
push!(boundaryidx,i)
elseif thisx == thisu
thisx = 0.01*thisl+0.99*thisu
x[i] = thisx
push!(boundaryidx,i)
elseif thisx < thisl || thisx > thisu
throw(ArgumentError("Initial x[$(Tuple(CartesianIndices(x)[i]))]=$thisx is outside of [$thisl, $thisu]"))
end
gbarrier[i] = (isfinite(thisl) ? one(T)/(thisx-thisl) : zero(T)) + (isfinite(thisu) ? one(T)/(thisu-thisx) : zero(T))
end
if length(boundaryidx) > 0
@warn("Initial position cannot be on the boundary of the box. Moving elements to the interior.\nElement indices affected: $boundaryidx")
end
gradient!(df, x)
gfunc .= gradient(df)
mu = Ref(initial_mu(gfunc, gbarrier, T(F.mufactor), T(F.mu0)))
# Use the barrier-aware preconditioner to define
# barrier-aware optimization method instance (precondition relevance)
_optimizer = barrier_method(F.method, P, (P, x) -> F.precondprep(P, x, l, u, mu))
if show_trace > 0
println("Fminbox")
println("-------")
print("Initial mu = ")
show(IOContext(stdout, :compact=>true), "text/plain", mu[])
println("\n")
end
g = similar(x)
fval_all = Vector{Vector{T}}()
# Count the total number of outer iterations
iteration = 0
# define the function (dfbox) to optimize by the inner optimizer
funcc = (g, x) -> barrier_combined(gfunc, gbarrier, g, x, fb, mu)
dfbox = OnceDifferentiable(x -> funcc(nothing, x),
(g, x) -> (funcc(g, x); g),
funcc, initial_x, zero(T))
xold = similar(x)
converged = false
local results
first = true
fval0 = zero(T)
while !converged && iteration < outer_iterations
# Increment the number of steps we've had to perform
iteration += 1
copyto!(xold, x)
# Optimize with current setting of mu
fval0 = funcc(nothing, x)
if show_trace > 0
header_string = "Fminbox iteration $iteration"
println(header_string)
println("-"^length(header_string))
print("Calling inner optimizer with mu = ")
show(IOContext(stdout, :compact=>true), "text/plain", mu[])
println("\n")
println("(numbers below include barrier contribution)")
end
resultsnew = optimize(dfbox, x, _optimizer, options)
if first
results = resultsnew
first = false
else
append!(results, resultsnew)
end
copyto!(x, minimizer(results))
if show_trace > 0
println()
println("Exiting inner optimizer with x = ", x)
print("Current distance to box: ")
show(IOContext(stdout, :compact=>true), "text/plain", min(minimum(x-l), minimum(u-x)))
println()
println("Decreasing barrier term μ.\n")
end
# Decrease mu
mu[] *= F.mufactor
# Test for convergence
g .= gfunc .+ mu[].*gbarrier
results.x_converged, results.f_converged,
results.g_converged, converged, f_increased = assess_convergence(x, xold, minimum(results), fval0, g,
options.outer_x_tol, options.outer_f_tol, options.outer_g_tol)
if f_increased && !allow_outer_f_increases
@warn("f(x) increased: stopping optimization")
break
end
end
return MultivariateOptimizationResults(F, initial_x, minimizer(results), df.f(minimizer(results)),
iteration, results.iteration_converged,
results.x_converged, results.x_tol, norm(x - xold),
results.f_converged, results.f_tol, f_abschange(minimum(results), fval0),
results.g_converged, results.g_tol, norm(g, Inf),
results.f_increased, results.trace, results.f_calls,
results.g_calls, results.h_calls)
end
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] | 2.131997 | 5,038 |
<filename>src/tst/make-ref.jl
import NSG: make_ref, make_bed
function tst_make_ref()
lmp = ["dat/2021-05/data/maps/HD.map",
"dat/2021-05/data/maps/18k_v1.map",
"dat/2021-05/data/maps/18k_v2.map",
"dat/2021-05/data/maps/18k_v3.map",
"dat/2021-05/data/maps/18k_v4.map",
"dat/2021-05/data/maps/8k_rudi.map"]
frd = ["dat/2021-05/data/genotypes/600k", # final report directory
"dat/2021-05/data/genotypes/b17k",
"dat/2021-05/data/genotypes/a17k",
"dat/2021-05/data/genotypes/g17k",
"dat/2021-05/data/genotypes/S18k_v4",
"dat/2021-05/data/genotypes/7327"]
tgt = ["dat/2021-05/result/hd",
"dat/2021-05/result/v1",
"dat/2021-05/result/v2",
"dat/2021-05/result/v3",
"dat/2021-05/result/v4",
"dat/2021-05/result/8k"]
for i in 1:6
# Note, here I used the simplified version of `make_bed`
# You can later change it to:
# make_bed(dir, lmap, sid, nf, target)
make_bed(frd[i], lmp[i], tgt[i])
end
make_ref("dat/2021-05/result/v2ref", lmp[3], tgt[1:5]...)
make_ref("dat/2021-05/result/v4ref", lmp[5], tgt[1:5]...)
end
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] | 1.767575 | 697 |
<reponame>JasmineHao/DCDC.jl<filename>dev/test.jl
using LinearAlgebra, DataFrames,Optim, ForwardDiff, BenchmarkTools,Distributions,
Expectations, QuantEcon, Statistics, GLM
using Distributions: invsqrt2π, log2π, sqrt2, invsqrt2
using DCDC
using Test
using Distributed, Suppressor
using Plots
begin "Dynamic Decision Process"
σ₀ = 1;
β = 0.8;
nM = 50;
nT = 5;
ddc = DynamicDecisionProcess(σ₀,0.8);
plot(ddc.ValueFn.xdata,ddc.ValueFn.y);
plot!(ddc.PolicyFn.xdata,ddc.PolicyFn.y);
data = simulate_ddc(nM,nT,ddc);
end
begin "GLM"
x = randn(300,3);
y = x * [1,3,4] + randn(300);
testdata = DataFrame(x);
testdata.y = y;
ols = lm(@formula(y ~ x1+x2+x3), testdata);
@show stderror(ols);
# @show ols.model.pp.beta0;
end
using ForwardDiff
begin "ForwardDiff"
h(x) = sin(x[1]) + x[1] * x[2] + sinh(x[1] * x[2]) # multivariate.
x = [1.4 2.2]
@show ForwardDiff.gradient(h,x) # use AD, seeds from x
#Or, can use complicated functions of many variables
f(x) = sum(sin, x) + prod(tan, x) * sum(sqrt, x)
g = (x) -> ForwardDiff.gradient(f, x); # g() is now the gradient
@show g(rand(20)); # gradient at a random point
function squareroot(x) #pretending we don't know sqrt()
z = copy(x) # Initial starting point for Newton’s method
while abs(z*z - x) > 1e-13
z = z - (z*z-x)/(2z)
end
return z
end
sqrt(2.0)
dsqrt(x) = ForwardDiff.derivative(squareroot, x)
dsqrt(2.0)
end
begin "Multi-variate function: Can we forwarddiff?"
func = (x,y) -> x^3 + y^2;
kk = y->(x->func(x,y));
a,b = randn(2);
ans1 = kk(b)(a);
ans2 = func(a,b);
@show ans1 == ans2;
dfunx = (x,y) -> ForwardDiff.derivative(z->kk(y)(z),x);
dfunx2 = (x,y) -> ForwardDiff.derivative(z->func(z,y),x);
deriv1 =dfunx(a,b);
deriv2 = dfunx2(a,b);
@show deriv1 == deriv2;
end
begin "Test transition derivative"
dtrans_x = (x,y) -> ForwardDiff.derivative( z-> ddc.trans(z,y),x) #Derivative w.r.t first component
dtrans_c = (x,y) -> ForwardDiff.derivative( z-> ddc.trans(y,z),x) #Derivative w.r.t second component
dtrans_x(1,3) == 1.05
end
using Flux
using Flux.Tracker
using Flux.Tracker: update!
begin "Test flux: graident and jacobian"
f(x) = 3x^2 + 2x + 1
# df/dx = 6x + 2
df(x) = Tracker.gradient(f, x)[1]
df(2);
A = rand(2,2);
f(x) = A * x
x0 = [0.1, 2.0]
f(x0)
Flux.jacobian(f, x0)
end
using TimerOutputs
begin
# Create the timer object
to = TimerOutput()
# Time something with an assigned label
@timeit to "sleep" sleep(0.3)
# Data is accumulated for multiple calls
for i in 1:100
@timeit to "loop" 1+1
end
# Nested sections are possible
@timeit to "nest 1" begin
@timeit to "nest 2" begin
@timeit to "nest 3.1" rand(10^3)
@timeit to "nest 3.2" rand(10^4)
@timeit to "nest 3.3" rand(10^5)
end
rand(10^6)
end
end
# Expectations
begin "Test Expectatoin"
dist = Normal();
E = expectation(dist, Gaussian; n = 301)
f = x -> x^2
expectation(f, dist)
end
using Optim
using Optim: converged, maximum, maximizer, minimizer, iterations #some extra functions
begin "Test Optim"
result = optimize(x-> x^2, -2.0, 1.0)
@show converged(result) || error("Failed to converge in $(iterations(result)) iterations")
@show xmin = result.minimizer
@show result.minimum
end
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] | 2.141284 | 1,635 |
<filename>src/Sloth.jl<gh_stars>1-10
module Sloth
using Knet
using MAT
using Images, FileIO
import Knet.Ops20: BNMoments
import Knet: atype
const SlothArray = Union{Array{T}, KnetArray{T}} where T <: AbstractFloat
const SlothParam = Param{T} where T <: SlothArray
const SlothWeight = Union{SlothArray, SlothParam}
const SlothBias = Union{SlothArray, SlothParam, AbstractFloat}
const IntHyperparam = Union{Int, Tuple{Vararg{Int}}}
F(x::T) where T <: AbstractFloat = eltype(atype())(x)
dir() = abspath(@__DIR__)
dir(args...) = abspath(joinpath(dir(), args...))
include("layers.jl")
include("rnn.jl")
include("beautify.jl")
include("data.jl")
include("vgg.jl")
end # module | [
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85,
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198,
198,
437,
1303,
8265
] | 2.732794 | 247 |
using Invariants
import Test
using ReTest
Invariants.runtests()
Test.@testset "Invariants.jl" begin
end
| [
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] | 2.763158 | 38 |
# Spatial calculations
export
vector1D,
vector,
wrapcoords,
wrapcoordsvec
"""
vector1D(c1, c2, side_length)
Displacement between two 1D coordinate values from c1 to c2, accounting for
the bounding box.
The minimum image convention is used, so the displacement is to the closest
version of the coordinate accounting for the periodic boundaries.
"""
function vector1D(c1, c2, side_length)
if c1 < c2
return (c2 - c1) < (c1 - c2 + side_length) ? (c2 - c1) : (c2 - c1 - side_length)
else
return (c1 - c2) < (c2 - c1 + side_length) ? (c2 - c1) : (c2 - c1 + side_length)
end
end
"""
vector(c1, c2, box_size)
Displacement between two coordinate values, accounting for the bounding box.
The minimum image convention is used, so the displacement is to the closest
version of the coordinates accounting for the periodic boundaries.
"""
vector(c1, c2, box_size) = vector1D.(c1, c2, box_size)
@generated function vector(c1::SVector{N}, c2::SVector{N}, box_size) where N
quote
Base.Cartesian.@ncall $N SVector{$N} i->vector1D(c1[i], c2[i], box_size[i])
end
end
sqdistance(i, j, coords, box_size) = sum(abs2, vector(coords[i], coords[j], box_size))
"""
wrapcoords(c, side_length)
Ensure a 1D coordinate is within the simulation box and return the coordinate.
"""
wrapcoords(c, side_length) = c - floor(c / side_length) * side_length
"""
wrapcoordsvec(c, box_size)
Ensure a coordinate is within the simulation box and return the coordinate.
"""
wrapcoordsvec(v, box_size) = wrapcoords.(v, box_size)
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] | 2.680887 | 586 |
# ---
# title: Symmetric monogenic filters
# id: demo_phasesymmono
# cover: assets/phasesymmono.gif
# author: <NAME>
# date: 2018-10-26
# ---
# Phase symmetry responds well to line like features and circular objects. The number of
# filter scales will affect the scale of features that are marked. Phase symmetry marks
# features independently of contrast (a bright circle is not more symmetric than a grey
# circle) and is a dimensionless quantity between 0 and 1. However this may not be what one
# desires in which case the symmetry energy may be of greater interest.
using TestImages
using Images
using ImagePhaseCongruency
img = Gray.(testimage("blobs"))
## Detect regions of bright symmetry (polarity = 1)
phase_bright, = phasesymmono(img; nscale=5, polarity=1)
## Detect regions of dark symmetry (polarity = -1)
phase_dark, = phasesymmono(img; nscale=5, polarity=-1)
mosaic(img, phase_bright, phase_dark; nrow=1)
# save cover image #src
isdir("assets") || mkdir("assets") #src
save(joinpath("assets", "phasesymmono.gif"), Images.gif([phase_bright, phase_dark]); fps=1) #src
| [
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2,
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3002,
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198,
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19668,
4943,
8614,
33480,
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4943,
1303,
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7,
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62,
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] | 3.31003 | 329 |
using RunTests
using Base.Test
@testmodule TestModuleWithOneXFailingTest begin
@xfail function test_xfailing()
@test false
end
end | [
3500,
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] | 3.068182 | 44 |
<gh_stars>1-10
################################################################################
#
# Module Potentials.jl
#
# This module will implement a variety of interatomic potentials and defines abstract types
# that allow these potentials to be used in other packages (PotentialLearning.jl, PotentialUQ.jl,
# Atomistic.jl, etc...).
# 'implementation' means:
# 1. Having a defined structure for each potential
# 1.1 The structure holds all of the necessary parameters for evaluating energies, forces, ...
# 1.2 The potential structure should expose the trainable and nontrainable parameters (necessary for learning).
# 2. Having a method to get the potential energy of given configuration, as
# defined by that potential.
# 3. Having a method to produce the force of a given configuration, as defined
# by that potential.
# 4. Having a method to produce the stresses of a given configuration, as defined
# by that potential.
# 5. (For inference) Having a method to produce the gradient of each of the above methods with
# respect to the potential parameters.
#
################################################################################
module InteratomicPotentials
using StaticArrays
using LAMMPS
using LinearAlgebra
using AtomsBase
using Unitful
using UnitfulAtomic
include("Utilities/utils.jl")
include("PotentialTypes/types.jl")
export NeighborList, neighborlist
export potential_energy, force, virial, virial_stress
export grad_potential_energy, grad_force, grad_virial, grad_virial_stress
export SNAPParams, compute_snap, get_num_coeffs
export EmpiricalPotential, LennardJones
# export BornMayer, Coulomb, GaN, MixedPotential
end
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25396,
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437,
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] | 3.25045 | 555 |
@testset "ridge-reg" begin
rng = StableRNG(622161)
n, p = 100, 5
X = randn(rng, n, p)
y = randn(rng, n)
X1 = R.augment_X(X, true)
λ = 0.3
Xt = MLJBase.table(X)
rr = RidgeRegressor(lambda=λ, penalize_intercept=true,
scale_penalty_with_samples = false)
fr, = MLJBase.fit(rr, 1, Xt, y)
ŷ = MLJBase.predict(rr, fr, Xt)
θ = (X1'X1 + λ*I)\(X1'y)
coefs = θ[1:end-1]
intercept = θ[end]
fp = MLJBase.fitted_params(rr, fr)
@test last.(fp.coefs) ≈ coefs
@test fp.intercept ≈ intercept
end
@testset "logistic" begin
((X, y, θ), (X1, y1, θ1)) = generate_binary(100, 5)
λ = 0.7
γ = 0.1
Xt = MLJBase.table(X)
yc = MLJBase.categorical(y1)
lr = LogisticClassifier(lambda=λ, gamma=γ, scale_penalty_with_samples = false)
fr, = MLJBase.fit(lr, 1, Xt, yc)
ŷ = MLJBase.predict(lr, fr, Xt)
ŷ = MLJBase.mode.(ŷ)
mcr = MLJBase.misclassification_rate(ŷ, yc)
@test mcr ≤ 0.2
end
@testset "multinomial" begin
((X, y, θ), (X1, y1, θ1)) = generate_multiclass(100, 5, 3)
λ = 0.5
γ = 0.2
Xt = MLJBase.table(X)
yc = MLJBase.categorical(y1)
mc = MultinomialClassifier(lambda=λ, gamma=γ, fit_intercept=false,
scale_penalty_with_samples = false)
fr, = MLJBase.fit(mc, 1, Xt, yc)
mach = MLJBase.machine(mc, Xt, yc)
MLJBase.fit!(mach)
fp = MLJBase.fitted_params(mach)
@test length(fp.coefs) == 5
ŷ = MLJBase.predict(mc, fr, Xt)
ŷ = MLJBase.mode.(ŷ)
mcr = MLJBase.misclassification_rate(ŷ, yc)
@test mcr ≤ 0.3
end
# see issue https://github.com/alan-turing-institute/MLJ.jl/issues/387
@testset "String-Symbol" begin
model = LogisticClassifier(penalty="l1")
@test model.penalty == "l1"
gr = MLJLinearModels.glr(model, 2)
@test gr isa GLR
@test gr.penalty isa ScaledPenalty{L1Penalty}
end
# see issue #71
@testset "Logistic-m" begin
X, y = MLJBase.make_blobs(centers=3)
model = LogisticClassifier()
mach = MLJBase.machine(model, X, y)
MLJBase.fit!(mach)
fp = MLJBase.fitted_params(mach)
@test unique(fp.classes) == [1,2,3]
end
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] | 1.920775 | 1,136 |
<reponame>mkratochvil/PowerSimulations.jl<filename>src/services/service_models/services.jl
abstract type AbstractServiceFormulation end
abstract type AbstractReservesForm <: AbstractServiceFormulation end
abstract type AbstractRegulationReserveForm <: AbstractReservesForm end
struct RampLimitedReserve <: AbstractReservesForm end
struct LoadProportionalReserve <: AbstractReservesForm end | [
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] | 3.852941 | 102 |
#################### Variate ####################
#################### Conversions ####################
Base.convert(::Type{Bool}, v::ScalarVariate) = convert(Bool, v.value)
Base.convert{T<:Integer}(::Type{T}, v::ScalarVariate) = convert(T, v.value)
Base.convert{T<:AbstractFloat}(::Type{T}, v::ScalarVariate) =
convert(T, v.value)
Base.convert(::Type{Matrix}, v::MatrixVariate) = v.value
Base.convert(::Type{Vector}, v::VectorVariate) = v.value
Base.convert{T<:Real, N}(::Union{Type{Array{T}}, Type{Array{T, N}}},
v::ArrayVariate{N}) = convert(Array{T, N}, v.value)
Base.unsafe_convert{T<:Real}(::Type{Ptr{T}}, v::ArrayVariate) = pointer(v.value)
macro promote_scalarvariate(V)
quote
Base.promote_rule{T<:Real}(::Type{$V}, ::Type{T}) = Float64
end
end
#################### Base Functions ####################
Base.size(v::AbstractVariate) = size(v.value)
Base.stride(v::ArrayVariate, k::Int) = stride(v.value, k)
#################### Indexing ####################
Base.getindex(v::ScalarVariate, ind::Int) = v.value[ind]
Base.getindex(v::ScalarVariate, inds::Union{Range{Int}, Vector{Int}}) =
Float64[v[i] for i in inds]
Base.getindex(v::ArrayVariate, inds::Int...) = getindex(v.value, inds...)
Base.setindex!(v::ScalarVariate, x::Real, ind::Int) = (v.value = x[ind])
function Base.setindex!{T<:Real}(v::ScalarVariate, x::Vector{T},
inds::Union{Range{Int}, Vector{Int}})
nx = length(x)
ninds = length(inds)
nx == ninds ||
throw(DimensionMismatch(
"tried to assign $nx elements to $ninds destinations"
))
for i in 1:nx
v[inds[i]] = x[i]
end
end
Base.setindex!(v::ArrayVariate, x, inds::Int...) =
setindex!(v.value, x, inds...)
#################### I/O ####################
function Base.show(io::IO, v::AbstractVariate)
print(io, "Object of type \"$(summary(v))\"\n")
show(io, v.value)
end
function Base.showcompact(io::IO, v::AbstractVariate)
showcompact(io, v.value)
end
#################### Auxiliary Functions ####################
function names(v::ScalarVariate, prefix)
AbstractString[string(prefix)]
end
function names(v::ArrayVariate, prefix)
offset = ndims(v) > 1 ? 1 : 2
values = similar(v.value, AbstractString)
for i in 1:length(v)
s = string(ind2sub(size(v), i))
values[i] = string(prefix, "[", s[2:(end - offset)], "]")
end
values
end
#################### Mathematical Operators ####################
const BinaryScalarMethods = [
:(Base.:+),
:(Base.:-),
:(Base.:*),
:(Base.:/),
:(Base.:\),
:(Base.:^),
:(Base.:(==)),
:(Base.:(!=)),
:(Base.:<),
:(Base.:(<=)),
:(Base.:>),
:(Base.:(>=)),
:(Base.cld),
:(Base.div),
:(Base.divrem),
:(Base.fld),
:(Base.mod),
:(Base.rem)
]
for op in BinaryScalarMethods
@eval ($op)(x::ScalarVariate, y::ScalarVariate) = ($op)(x.value, y.value)
end
const RoundScalarMethods = [
:(Base.ceil),
:(Base.floor),
:(Base.round),
:(Base.trunc)
]
for op in RoundScalarMethods
@eval ($op)(x::ScalarVariate) = ($op)(x.value)
@eval ($op){T}(::Type{T}, x::ScalarVariate) = ($op)(T, x.value)
end
const UnaryScalarMethods = [
:(Base.:+),
:(Base.:-),
:(Base.abs),
:(Base.isfinite),
:(Base.isinf),
:(Base.isinteger),
:(Base.isnan),
:(Base.mod2pi),
:(Base.one),
:(Base.sign),
:(Base.zero)
]
for op in UnaryScalarMethods
@eval ($op)(x::ScalarVariate) = ($op)(x.value)
end
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] | 2.412096 | 1,422 |
<filename>test/SEED/test_dataless.jl<gh_stars>10-100
printstyled(" dataless SEED\n", color=:light_green)
redirect_stdout(out) do
metafile = path*"/SampleFiles/SEED/jones.hood.dataless"
S = read_meta("dataless", metafile, v=3,
s="2008-01-01T00:00:00",
t="2008-02-01T00:00:00",
units=true)
S2 = read_dataless( metafile, v=3,
s=DateTime("2008-01-01T00:00:00"),
t=DateTime("2008-02-01T00:00:00"),
units=true)
@test S == S2
files = ls(path*"/SampleFiles/SEED/*.dataless")
for i in files
println("Reading file ", i)
S = read_meta("dataless", i, v=0, units=false)
S = read_meta("dataless", i, v=1, units=false)
S = read_meta("dataless", i, v=2, units=false)
S = read_meta("dataless", i, v=3, units=false)
S = read_meta("dataless", i, v=3, units=true)
end
end
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] | 1.868952 | 496 |
using Enzyme
using Test
@testset "ABI & Calling convention" begin
f(x) = x
# GhostType -> Nothing
res = autodiff(f, Const(nothing))
@test res === nothing
# ConstType -> Type{Int}
res = autodiff(f, Const(Int))
@test res === nothing
cres, = Enzyme.autodiff(f, Active(1.5 + 0.7im))
@test cres ≈ 1.0 + 0.0im
unused(_, y) = y
res0, = autodiff(unused, Const(nothing), Active(2.0))
@test res0 ≈ 1.0
# Multi arg => sret
mul(x, y) = x * y
pair = autodiff(mul, Active(2.0), Active(3.0))
@test pair[1] ≈ 3.0
@test pair[2] ≈ 2.0
# SequentialType
struct Foo
baz::Int
qux::Float64
end
g(x) = x.qux
res2, = autodiff(g, Active(Foo(3, 1.2)))
@test res2.qux ≈ 1.0
unused2(_, y) = y.qux
resF, = autodiff(unused2, Const(nothing), Active(Foo(3, 2.0)))
@test resF.qux ≈ 1.0
h(x, y) = x.qux * y.qux
res3 = autodiff(h, Active(Foo(3, 1.2)), Active(Foo(5, 3.4)))
@test res3[1].qux ≈ 3.4
@test res3[2].qux ≈ 1.2
caller(f, x) = f(x)
res4, = autodiff(caller, (x)->x, Active(3.0))
@test res4 ≈ 1.0
struct LList
next::Union{LList,Nothing}
val::Float64
end
function sumlist(n::LList)
sum = 0.0
while n !== nothing
sum += n.val
n = n.next
end
sum
end
regular = LList(LList(nothing, 1.0), 2.0)
shadow = LList(LList(nothing, 0.0), 0.0)
ad = autodiff(sumlist, Duplicated(regular, shadow))
@test ad === nothing
@test shadow.val ≈ 1.0 && shadow.next.val ≈ 1.0
mulr(x, y) = x[] * y[]
x = Ref(2.0)
y = Ref(3.0)
dx = Ref(0.0)
dy = Ref(0.0)
n = autodiff(mulr, Duplicated(x, dx), Duplicated(y, dy))
@test n === nothing
@test dx[] ≈ 3.0
@test dy[] ≈ 2.0
mid, = Enzyme.autodiff((fs, x) -> fs[1](x), (x->x*x,), Active(2.0))
@test mid ≈ 4.0
mid, = Enzyme.autodiff((fs, x) -> fs[1](x), [x->x*x], Active(2.0))
@test mid ≈ 4.0
# deserves_argbox yes and no
struct Bar
r::Ref{Int}
end
# ConstType
# primitive type Int128, Float64, Float128
# returns: sret, const/ghost, !deserve_retbox
end
@testset "Callable ABI" begin
function method(f, x)
return f(x)
end
struct AFoo
x::Float64
end
function (f::AFoo)(x::Float64)
return f.x * x
end
@test Enzyme.autodiff(method, AFoo(2.0), Active(3.0))[1]≈ 2.0
@test Enzyme.autodiff(AFoo(2.0), Active(3.0))[1]≈ 2.0
struct ABar
end
function (f::ABar)(x::Float64)
return 2.0 * x
end
@test Enzyme.autodiff(method, ABar(), Active(3.0))[1]≈ 2.0
@test Enzyme.autodiff(ABar(), Active(3.0))[1]≈ 2.0
end
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] | 1.942594 | 1,411 |
# Chemfiles.jl, a modern library for chemistry file reading and writing
# Copyright (C) <NAME> and contributors -- BSD license
export mass, set_mass!, charge, set_charge!, name, set_name!, type, set_type!
export vdw_radius, covalent_radius, atomic_number
export set_property!, property, properties_count, list_properties
__ptr(atom::Atom) = __ptr(atom.__handle)
__const_ptr(atom::Atom) = __const_ptr(atom.__handle)
"""
Create an atom with the given ``name`` and set the atom ``type`` to be the same
as ``name``.
"""
function Atom(name::String)
ptr = @__check_ptr(lib.chfl_atom(pointer(name)))
return Atom(CxxPointer(ptr, is_const=false))
end
"""
Get a copy of the ``atom`` at the given ``index`` from a ``frame``.
"""
function Atom(frame::Frame, index::Integer)
ptr = @__check_ptr(lib.chfl_atom_from_frame(__ptr(frame), UInt64(index)))
atom = Atom(CxxPointer(ptr, is_const=false))
copy = deepcopy(atom)
finalize(atom)
return copy
end
"""
Get a copy of the ``atom`` at the given ``index`` from a ``topology``.
"""
function Atom(topology::Topology, index::Integer)
ptr = @__check_ptr(lib.chfl_atom_from_topology(__ptr(topology), UInt64(index)))
atom = Atom(CxxPointer(ptr, is_const=false))
copy = deepcopy(atom)
finalize(atom)
return copy
end
"""
Get the mass of an ``atom`` in atomic mass units.
"""
function mass(atom::Atom)
result = Ref{Float64}(0)
__check(lib.chfl_atom_mass(__const_ptr(atom), result))
return result[]
end
"""
Set the mass of an ``atom`` to ``mass``.
The mass must be in atomic mass units.
"""
function set_mass!(atom::Atom, mass)
__check(lib.chfl_atom_set_mass(__ptr(atom), Float64(mass)))
return nothing
end
"""
Get the charge of an ``atom`` in number of the electron charge *e*.
"""
function charge(atom::Atom)
result = Ref{Float64}(0)
__check(lib.chfl_atom_charge(__const_ptr(atom), result))
return result[]
end
"""
Set the charge of an ``atom`` to ``charge``.
The charge must be in number of the electron charge *e*.
"""
function set_charge!(atom::Atom, charge)
__check(lib.chfl_atom_set_charge(__ptr(atom), Float64(charge)))
return nothing
end
"""
Get the name of an ``atom``.
"""
function name(atom::Atom)
return __call_with_growing_buffer(
(buffer, size) -> __check(lib.chfl_atom_name(__const_ptr(atom), buffer, size))
)
end
"""
Set the name of an ``atom`` to ``name``.
"""
function set_name!(atom::Atom, name::String)
__check(lib.chfl_atom_set_name(__ptr(atom), pointer(name)))
return nothing
end
"""
Get the type of an ``atom``.
"""
function type(atom::Atom)
return __call_with_growing_buffer(
(buffer, size) -> __check(lib.chfl_atom_type(__const_ptr(atom), buffer, size))
)
end
"""
Set the type of an ``atom`` to ``type``.
"""
function set_type!(atom::Atom, type::String)
__check(lib.chfl_atom_set_type(__ptr(atom), pointer(type)))
return nothing
end
"""
Get the full name of an ``atom`` from the atom type.
For example, the full name of an atom with type "He" is "Helium".
"""
function Base.fullname(atom::Atom)
return __call_with_growing_buffer(
(buffer, size) -> __check(lib.chfl_atom_full_name(
__const_ptr(atom), buffer, size)
)
)
end
"""
Get the van der Waals radius of an ``atom`` from the atom type.
If the radius can not be found, this function returns 0.
"""
function vdw_radius(atom::Atom)
radius = Ref{Float64}(0)
__check(lib.chfl_atom_vdw_radius(__const_ptr(atom), radius))
return radius[]
end
"""
Get the covalent radius of an ``atom`` from the atom type.
If the radius can not be found, returns 0.
"""
function covalent_radius(atom::Atom)
radius = Ref{Float64}(0)
__check(lib.chfl_atom_covalent_radius(__const_ptr(atom), radius))
return radius[]
end
"""
Get the atomic number of an ``atom`` from the atom type.
If the atomic number can not be found, returns 0.
"""
function atomic_number(atom::Atom)
number = Ref{UInt64}(0)
__check(lib.chfl_atom_atomic_number(__const_ptr(atom), number))
return number[]
end
"""
Set a named property for the given atom.
"""
function set_property!(atom::Atom, name::String, value)
property = Property(value)
__check(lib.chfl_atom_set_property(
__ptr(atom), pointer(name), __const_ptr(property)
))
return nothing
end
"""
Get a named property for the given atom.
"""
function property(atom::Atom, name::String)
ptr = lib.chfl_atom_get_property(__const_ptr(atom), pointer(name))
return extract(Property(CxxPointer(ptr, is_const=false)))
end
"""
Get the number of properties associated with an atom.
"""
function properties_count(atom::Atom)
count = Ref{UInt64}(0)
__check(lib.chfl_atom_properties_count(__const_ptr(atom), count))
return count[]
end
"""
Get the names of all properties associated with an atom.
"""
function list_properties(atom::Atom)
count = properties_count(atom)
names = Array{Ptr{UInt8}}(undef, count)
__check(lib.chfl_atom_list_properties(__const_ptr(atom), pointer(names), count))
return map(unsafe_string, names)
end
"""
Make a deep copy of an ``atom``.
"""
function Base.deepcopy(atom::Atom)
ptr = lib.chfl_atom_copy(__const_ptr(atom))
return Atom(CxxPointer(ptr, is_const=false))
end
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] | 2.705732 | 1,954 |
<reponame>UnofficialJuliaMirrorSnapshots/CompilerTools.jl-98f049d2-a028-5a73-bd4d-a8c50ff59ab5
#=
Copyright (c) 2015, Intel Corporation
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
- Redistributions of source code must retain the above copyright notice,
this list of conditions and the following disclaimer.
- Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE
LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR
CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF
SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS
INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN
CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE)
ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF
THE POSSIBILITY OF SUCH DAMAGE.
=#
import CompilerTools.AstWalker
export constant_fold
binops = Set([:+; :-; :*; :/])
function constant_folder(node, symbol_table, top_level_number, is_top_level, read)
if isa(node, Expr)
if node.head == :(=)
rhs = AstWalker.AstWalk(node.args[2], constant_folder, symbol_table)
if isa(rhs, Number)
symbol_table[node.args[1]] = rhs
end
return node
elseif node.head == :call
if in(node.args[1], binops) && length(node.args)==3
node.args[2] = AstWalker.AstWalk(node.args[2], constant_folder, symbol_table)[1]
node.args[3] = AstWalker.AstWalk(node.args[3], constant_folder, symbol_table)[1]
if isa(node.args[2], Number) && isa(node.args[3], Number)
return eval(node)
end
end
end
elseif isa(node, Symbol)
if haskey(symbol_table, node)
return symbol_table[node]
end
elseif isa(node, Number)
return node
end
return CompilerTools.AstWalker.ASTWALK_RECURSE
end
function constant_fold(fn)
symbol_table = Dict{Symbol, Number}()
fn = AstWalker.AstWalk(fn, constant_folder, symbol_table)
return fn
end
#=
macro constant_fold(fn)
symbol_table = Dict{Symbol, Number}()
AstWalker.AstWalk(fn, constant_folder, symbol_table)
println(symbol_table)
println(fn)
return esc(fn)
end
@constant_fold function test(z)
a = 3
b = 4
c = a - b
d = z + c
return d
end
=#
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] | 2.78979 | 999 |
@doc """
S = read_hdf5(filestr, s::TimeSpec, t::TimeSpec, [, keywords])
read_hdf5!(S, filestr, s::TimeSpec, t::TimeSpec, [, keywords])
Read data in seismic HDF5 format from files matching pattern `filestr`.
`s`, `t` are required arguments but can be any Type ∈ (DateTime, Real, String);
type `?TimeSpec` for more information about how these are interpreted.
|KW | Type | Default | Meaning |
|:--- |:--- |:--- |:--- |
| id | String | "*" | id pattern, formated nn.sss.ll.ccc |
| | | | (net.sta.loc.cha); FDSN wildcards [^1] |
| msr | Bool | true | read full (MultiStageResp) instrument resp? |
| v | Integer | 0 | verbosity |
[^1] A question mark ('?') is a wildcard for a single character; an asterisk ('*') is a wildcard for zero or more characters
See also: `TimeSpec`, `parsetimewin`, `read_data`
""" read_hdf5!
function read_hdf5!(S::GphysData, fpat::String, s::TimeSpec, t::TimeSpec;
fmt ::String = "asdf", # data format
id ::Union{String, Regex} = "*", # id string
msr ::Bool = true, # read multistage response?
v ::Integer = KW.v # verbosity
)
N = S.n
filestr = abspath(fpat)
one_file = safe_isfile(filestr)
if fmt == "asdf"
if one_file
append!(S, read_asdf(filestr, id, s, t, msr, v))
else
files = ls(filestr)
for fname in files
append!(S, read_asdf(fname, id, s, t, msr, v))
end
end
else
error("Unknown file format (possibly NYI)!")
end
new_chan_src = view(S.src, N+1:S.n)
fill!(new_chan_src, filestr)
note!(S, N+1:S.n, string( " ¦ +source ¦ read_hdf5!(S, ",
"\"", fmt, "\", ",
"\"", s, "\", ",
"\"", t, "\", ",
"fmt=\"", fmt, "\", ",
"id=\"", id, "\", ",
"msr=", msr, ", ",
"v=", KW.v, ")")
)
return nothing
end
@doc (@doc read_hdf5!)
function read_hdf5(filestr::String, s::TimeSpec, t::TimeSpec;
fmt ::String = "asdf", # data format
id ::Union{String, Regex} = "*", # id string
msr ::Bool = true, # read multistage response?
v ::Integer = KW.v # verbosity
)
S = SeisData()
read_hdf5!(S, filestr, s, t,
fmt = fmt,
id = id,
msr = msr,
v = v
)
return S
end
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] | 1.774585 | 1,566 |
"""
cilc_weights(cij, a, b)
This function returns weights (a vector of the number of frequency channels) of the constrained ILC (CILC) method.
*Reference*: Equation (20) of Remazeilles, Delabrouille, Cardoso, MNRAS, 410, 2481 (2011)
# Arguments
- `cij::Array{<:AbstractFloat,2}`: symmetric covariance matrix with the dimention of `(nν, nν)` where `nν` is the number of frequency bands.
- `a::Array{<:AbstractFloat,1}`: vector of the frequency response, for the component to be extracted. E.g., `a = [1,...,1]` for CMB.
- `b::Array{<:AbstractFloat,1}`: vector of the frequency response, for the component to be nulled.
"""
function cilc_weights(
cij::Array{T,2},
a::Array{T,1},
b::Array{T,1},
) where {T<:AbstractFloat}
if size(cij)[1] ≠ size(cij)[2]
throw(DimensionMismatch("covariance matrix must be a square matrix"))
elseif size(cij)[1] ≠ length(a) || size(cij)[1] ≠ length(b)
throw(DimensionMismatch("dimensions of the covariance matrix and the frequency response vector do not match"))
elseif a ≈ b
throw(ErrorException("vectors of the frequency response are too similar"))
else
M = Symmetric(cij)
end
x = M \ a # R^-1 a
y = M \ b # R^-1 b
w = (b'y * x - a'y * y) / (a'x * b'y - (a'y)^2) # ILC weights
end
"""
cilc_weights(cijℓ, a, b[, ℓid=3])
This function returns weights (a `nν`-by-`nℓ` matrix) of the constrained ILC (CILC) method.
Here, `nν` is the number of frequency channels and `nℓ` is the number of elements in the relevant domain, e.g., multipoles, band-power bins, pixels, etc.
*Reference*: Equation (20) of Remazeilles, Delabrouille, Cardoso, MNRAS, 410, 2481 (2011)
# Arguments
- `cijℓ::Array{<:AbstractFloat,3}`: symmetric covariance matrix with the dimention of `(nℓ, nν, nν)`, `(nν, nℓ, nν)` or `(nν, nν, nℓ)` (default).
- `a::Array{<:AbstractFloat,1}`: vector of the frequency response, for the component to be extracted. E.g., `a = [1,...,1]` for CMB.
- `b::Array{<:AbstractFloat,1}`: vector of the frequency response, for the component to be nulled.
# Optional Arguments
- `ℓid::Integer=3`: location of the index for the `nℓ` domain. `ℓid=1` if `cijℓ[nℓ,nν,nν]`, `ℓid=2` if `cijℓ[nν,nℓ,nν]`, and `ℓid=3` (the default value) if `cijℓ[nν,nν,nℓ]`.
"""
function cilc_weights(
cijℓ::Array{T,3},
a::Array{T,1},
b::Array{T,1},
ℓid::Integer = 3,
) where {T<:AbstractFloat}
if ℓid > 3 || ℓid < 1
throw(DomainError(ℓid, "ℓid must be 1, 2, or 3"))
end
if (ℓid == 3 && size(cijℓ)[1] ≠ size(cijℓ)[2]) ||
(ℓid == 2 && size(cijℓ)[1] ≠ size(cijℓ)[3]) ||
(ℓid == 1 && size(cijℓ)[2] ≠ size(cijℓ)[3])
throw(DimensionMismatch("covariance matrix must be a square matrix"))
end
nℓ = size(cijℓ)[ℓid]
nν = ifelse(ℓid == 3, size(cijℓ)[1], size(cijℓ)[3])
if length(a) ≠ nν || length(b) ≠ nν
throw(DimensionMismatch("dimensions of the covariance matrix and the frequency response vector do not match"))
elseif a ≈ b
throw(ErrorException("vectors of the frequency response are too similar"))
else
end
wℓ = zeros(nν, nℓ) # ILC weights
for iℓ = 1:nℓ
if ℓid == 3
M = Symmetric(cijℓ[:, :, iℓ])
elseif ℓid == 2
M = Symmetric(cijℓ[:, iℓ, :])
else
M = Symmetric(cijℓ[iℓ, :, :])
end
x = M \ a # R^-1 a
y = M \ b # R^-1 b
wℓ[:, iℓ] = (b'y * x - a'y * y) / (a'x * b'y - (a'y)^2)
end
return wℓ
end
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] | 2.100602 | 1,660 |
using POMDPModels
using Test
using POMDPTesting
let
problem = SimpleGridWorld()
policy = RandomPolicy(problem)
sim = HistoryRecorder(rng=MersenneTwister(1), max_steps=1000)
hist = simulate(sim, problem, policy, GWPos(1,1))
for (s, a) in zip(state_hist(hist), action_hist(hist))
td = transition(problem, s, a)
if td isa SparseCat
@test sum(td.probs) ≈ 1.0 atol=0.01
for p in td.probs
@test p >= 0.0
end
end
end
sv = convert_s(Array{Float64}, GWPos(1,1), problem)
@test sv == [1.0, 1.0]
sv = convert_s(Array{Float64}, GWPos(5,3), problem)
@test sv == [5.0, 3.0]
s = convert_s(GWPos, sv, problem)
@test s == GWPos(5, 3)
av = convert_a(Array{Float64}, :up, problem)
@test av == [1.0]
a = convert_a(Symbol, av, problem)
@test a == :up
@test has_consistent_transition_distributions(problem)
pol = FunctionPolicy(x->:up)
stp = first(stepthrough(problem, pol, "s,a", max_steps=1))
POMDPModelTools.render(problem, stp)
POMDPModelTools.render(problem, NamedTuple())
POMDPModelTools.render(problem, stp, color=s->reward(problem,s))
POMDPModelTools.render(problem, stp, color=s->rand())
POMDPModelTools.render(problem, stp, color=s->"yellow")
POMDPModelTools.render(problem, stp, color=s->reward(problem,s), colormin=-1.0, colormax=1.0)
ss = collect(states(problem))
isd = initialstate(problem)
for s in ss
if !isterminal(problem, s)
@test s in support(isd)
@test pdf(isd, s) > 0.0
end
end
end
let
@warn("NBInclude tests skipped")
# @nbinclude(joinpath(dirname(@__FILE__), "..", "notebooks", "GridWorld Visualization.ipynb"))
end
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] | 2.205736 | 802 |
# --------------------------------------------------------------------------
# ACE1.jl: Julia implementation of the Atomic Cluster Expansion
# Copyright (c) 2019 <NAME> <<EMAIL>>
# Licensed under ASL - see ASL.md for terms and conditions.
# --------------------------------------------------------------------------
@testset "RPIBasis" begin
#---
using ACE1
using Random, Printf, Test, LinearAlgebra, JuLIP, JuLIP.Testing
using JuLIP: evaluate, evaluate_d, evaluate_ed
using JuLIP.MLIPs: combine
#---
@info("Basic test of RPIBasis construction and evaluation")
maxdeg = 15
N = 3
r0 = 1.0
rcut = 3.0
trans = PolyTransform(1, r0)
Pr = transformed_jacobi(maxdeg, trans, rcut; pcut = 2)
D = SparsePSHDegree()
P1 = BasicPSH1pBasis(Pr; species = :X, D = D)
#---
pibasis = PIBasis(P1, N, D, maxdeg)
rpibasis = RPIBasis(P1, N, D, maxdeg)
#---
@info("Basis construction and evaluation checks")
@info("check single species")
Nat = 15
Rs, Zs, z0 = rand_nhd(Nat, Pr, :X)
B = evaluate(rpibasis, Rs, Zs, z0)
println(@test(length(rpibasis) == length(B)))
dB = evaluate_d(rpibasis, Rs, Zs, z0)
println(@test(size(dB) == (length(rpibasis), length(Rs))))
B_, dB_ = evaluate_ed(rpibasis, Rs, Zs, z0)
println(@test (B_ ≈ B) && (dB_ ≈ dB))
#---
@info("check multi-species")
maxdeg = 5
Pr = transformed_jacobi(maxdeg, trans, rcut; pcut = 2)
species = [:C, :O, :H]
P1 = ACE1.BasicPSH1pBasis(Pr; species = species, D = D)
basis = ACE1.RPIBasis(P1, N, D, maxdeg)
Rs, Zs, z0 = ACE1.rand_nhd(Nat, Pr, species)
B = evaluate(basis, Rs, Zs, z0)
println(@test(length(basis) == length(B)))
dB = evaluate_d(basis, Rs, Zs, z0)
println(@test(size(dB) == (length(basis), length(Rs))))
B_, dB_ = evaluate_ed(basis, Rs, Zs, z0)
println(@test (B_ ≈ B) && (dB_ ≈ dB))
#---
degrees = [ 12, 10, 8, 8, 8, 8 ]
@info("Check a few basis properties ")
# for species in (:X, :Si) # , [:C, :O, :H])
for species in (:X, :Si, [:C, :O, :H]), N = 1:length(degrees)
local Rs, Zs, z0, B, dB, basis, D, P1, Nat
Nat = 15
D = SparsePSHDegree()
P1 = ACE1.BasicPSH1pBasis(Pr; species = species)
basis = ACE1.RPIBasis(P1, N, D, degrees[N])
@info("species = $species; N = $N; deg = $(degrees[N]); len = $(length(basis))")
@info(" check (de-)serialization")
println(@test(all(JuLIP.Testing.test_fio(basis))))
@info(" isometry and permutation invariance")
for ntest = 1:30
Rs, Zs, z0 = ACE1.rand_nhd(Nat, Pr, species)
Rsp, Zsp = ACE1.rand_sym(Rs, Zs)
print_tf(@test(evaluate(basis, Rs, Zs, z0) ≈
evaluate(basis, Rsp, Zsp, z0)))
end
println()
@info(" check derivatives")
for ntest = 1:30
Rs, Zs, z0 = ACE1.rand_nhd(Nat, Pr, species)
B = evaluate(basis, Rs, Zs, z0)
dB = evaluate_d(basis, Rs, Zs, z0)
Us = [ rand(eltype(Rs)) .- 0.5 for _=1:length(Rs) ]
dB_dUs = transpose.(dB) * Us
errs = []
for p = 2:12
h = 0.1^p
B_h = evaluate(basis, Rs + h * Us, Zs, z0)
dB_h = (B_h - B) / h
# @show norm(dAA_h - dAA_dUs, Inf)
push!(errs, norm(dB_h - dB_dUs, Inf))
end
success = (/(extrema(errs)...) < 1e-3) || (minimum(errs) < 1e-10)
print_tf(@test success)
end
println()
@info(" check combine")
coeffs = randcoeffs(basis)
V = combine(basis, coeffs)
Vst = standardevaluator(V)
for ntest = 1:30
Rs, Zs, z0 = ACE1.rand_nhd(Nat, Pr, species)
v = evaluate(V, Rs, Zs, z0)
vst = evaluate(Vst, Rs, Zs, z0)
cdotB = dot(coeffs, evaluate(basis, Rs, Zs, z0))
print_tf(@test v ≈ cdotB ≈ vst)
end
println()
@info(" check graph evaluator")
basisst = standardevaluator(basis)
for ntest = 1:30
env = ACE1.rand_nhd(Nat, Pr, species)
print_tf(@test evaluate(basisst, env...) ≈ evaluate(basis, env...))
print_tf(@test evaluate_d(basisst, env...) ≈ evaluate_d(basis, env...))
end
println()
end
#---
end
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] | 2.213356 | 1,767 |
@noinline function old_cfunction(f, r, a)
ccall(:jl_function_ptr, Ptr{Cvoid}, (Any, Any, Any), f, r, a)
end
## Common Interface Solve Functions
mutable struct CommonFunction{F,P}
func::F
p::P
neq::Cint
end
function commonfun(t::T1,y::T2,yp::T3,comfun::CommonFunction) where {T1,T2,T3}
y_ = unsafe_wrap(Array,y,comfun.neq)
ydot_ = unsafe_wrap(Array,yp,comfun.neq)
comfun.func(ydot_,y_,comfun.p,t)
return Int32(0)
end
function DiffEqBase.__solve(
prob::DiffEqBase.AbstractODEProblem{uType,tupType,isinplace},
alg::LSODAAlgorithm,
timeseries=[],ts=[],ks=[];
verbose=true,
abstol=1/10^6,reltol=1/10^3,
tstops=Float64[],
saveat=Float64[], maxiter=Int(1e5),
callback=nothing,
timeseries_errors=true,
save_everystep=isempty(saveat),
save_start = save_everystep || isempty(saveat) || typeof(saveat) <: Number ? true : prob.tspan[1] in saveat,
userdata=nothing,
alias_u0=false,
kwargs...) where {uType,tupType,isinplace}
tType = eltype(tupType)
if verbose
warned = !isempty(kwargs) && check_keywords(alg, kwargs, warnlist)
if !(typeof(prob.f) <: DiffEqBase.AbstractParameterizedFunction)
if DiffEqBase.has_tgrad(prob.f)
@warn("Explicit t-gradient given to this stiff solver is ignored.")
warned = true
end
if DiffEqBase.has_jac(prob.f)
@warn("Explicit Jacobian given to this stiff solver is ignored.")
warned = true
end
end
warned && warn_compat()
end
if prob.f.mass_matrix != I
error("This solver is not able to use mass matrices.")
end
if callback != nothing || :callback in keys(prob.kwargs)
error("LSODA is not compatible with callbacks.")
end
tspan = prob.tspan
t0 = tspan[1]
T = tspan[end]
if typeof(saveat) <: Number
if (tspan[1]:saveat:tspan[end])[end] == tspan[end]
saveat_vec = convert(Vector{tType},collect(tType,tspan[1]+saveat:saveat:tspan[end]))
else
saveat_vec = convert(Vector{tType},collect(tType,tspan[1]+saveat:saveat:(tspan[end]-saveat)))
end
else
saveat_vec = convert(Vector{tType},collect(saveat))
end
if !isempty(saveat_vec) && saveat_vec[end] == tspan[2]
pop!(saveat_vec)
end
if !isempty(saveat_vec) && saveat_vec[1] == tspan[1]
save_ts = sort(unique([saveat_vec;T]))
else
save_ts = sort(unique([t0;saveat_vec;T]))
end
if T < save_ts[end]
error("Final saving timepoint is past the solving timespan")
end
if t0 > save_ts[1]
error("First saving timepoint is before the solving timespan")
end
if !isempty(tstops)
error("tstops is not supported for this solver. Please use saveat instead")
end
if typeof(prob.u0) <: Number
u0 = [prob.u0]
else
if alias_u0
u0 = vec(prob.u0)
else
u0 = vec(deepcopy(prob.u0))
end
end
sizeu = size(prob.u0)
### Fix the more general function to Sundials allowed style
if !isinplace && (typeof(prob.u0)<:Vector{Float64} || typeof(prob.u0)<:Number)
f! = (du,u,p,t) -> (du[:] = prob.f(u,p,t); nothing)
elseif !isinplace && typeof(prob.u0)<:AbstractArray
f! = (du,u,p,t) -> (du[:] = vec(prob.f(reshape(u,sizeu),p,t)); nothing)
elseif typeof(prob.u0)<:Vector{Float64}
f! = prob.f
else # Then it's an in-place function on an abstract array
f! = (du,u,p,t) -> (prob.f(reshape(du,sizeu),reshape(u,sizeu),p,t); nothing)
end
ures = Vector{Float64}[]
push!(ures,u0)
utmp = copy(u0)
utmp2= copy(u0)
ttmp = [t0]
t = [t0]
t2 = [t0]
save_start ? ts = [t0] : ts = typeof(t0)[]
neq = Int32(length(u0))
comfun = CommonFunction(f!,prob.p,neq)
atol = ones(Float64,neq)
rtol = ones(Float64,neq)
if typeof(abstol) == Float64
atol *= abstol
else
atol = copy(abstol)
end
if typeof(reltol) == Float64
rtol *= reltol
else
rtol = copy(reltol)
end
GC.@preserve comfun atol rtol begin
global ___ref = comfun
opt = lsoda_opt_t(mxstep = maxiter)
opt.ixpr = 0
opt.rtol = pointer(rtol)
opt.atol = pointer(atol)
if save_everystep
itask_tmp = 2
else
itask_tmp = 1
end
opt.itask = itask_tmp
function get_cfunction(comfun::T) where T
@cfunction commonfun Cint (Cdouble, Ptr{Cdouble}, Ptr{Cdouble}, Ref{T})
end
fex_c = get_cfunction(comfun)
ctx = lsoda_context_t()
ctx.function_ = fex_c
ctx.neq = neq
ctx.state = 1
ctx.data = pointer_from_objref(comfun)
ch = ContextHandle(ctx)
lsoda_prepare(ctx,opt)
for k in 2:length(save_ts)
ttmp[1] = save_ts[k]
if t[1] < ttmp[1]
while t[1] < ttmp[1]
lsoda(ctx, utmp, t, ttmp[1])
if t[1] > ttmp[1] # overstepd, interpolate back
t2[1] = t[1] # save step values
copyto!(utmp2,utmp) # save step values
opt.itask = 1 # change to interpolating
lsoda(ctx, utmp, t, ttmp[1])
opt.itask = itask_tmp
push!(ures, copy(utmp))
push!(ts, t[1])
# don't overstep the last timestep
if k != length(save_ts) && save_ts[k+1] > t2[1]
push!(ures, copy(utmp2))
push!(ts, t2[1])
end
copyto!(utmp, utmp2)
t[1] = t2[1]
else
push!(ures, copy(utmp))
push!(ts,t[1])
end
end
else
t2[1] = t[1] # save step values
copyto!(utmp2, utmp) # save step values
opt.itask = 1 # change to interpolating
lsoda(ctx, utmp, t, ttmp[1])
opt.itask = itask_tmp
push!(ures, copy(utmp))
push!(ts, t[1])
if k != length(save_ts) && save_ts[k+1] > t2[1] # don't overstep the last timestep
push!(ures,copy(utmp2))
push!(ts,t2[1])
end
copyto!(utmp,utmp2)
t[1] = t2[1]
end
end
### Finishing Routine
timeseries = uType[]
save_start ? start_idx = 1 : start_idx = 2
if typeof(prob.u0)<:Number
for i=start_idx:length(ures)
push!(timeseries,ures[i][1])
end
else
for i=start_idx:length(ures)
push!(timeseries,reshape(ures[i],sizeu))
end
end
lsoda_free(ch)
global ___ref = nothing
end
DiffEqBase.build_solution(prob, alg, ts, timeseries,
timeseries_errors = timeseries_errors,
retcode = :Success)
end
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] | 1.897702 | 3,656 |
__precompile__()
module TestOriginal
using OpenEphysLoader
using Main.TestUtilities
using Test, Dates
export write_fheader_fun,
verify_header
### Helper functions ###
function write_bad_fheader(badtype::Symbol, nbytes::Integer = 1024)
local outfun
if badtype == :version
outfun = write_fheader_fun(nbytes, "header_wrongversion.txt")
elseif badtype == :format
outfun = write_fheader_fun(nbytes, "header_wrongformat.txt")
elseif badtype == :noise
baddata = rand(UInt8, nbytes)
outfun = io::IOStream -> write(io, baddata)
else
error("badtype unrecognized")
end
return outfun
end
function write_fheader_fun(
nbytes::Integer = 1024,
headerfile::String = "header.txt"
)
local head
headerpath = joinpath(dirname(@__FILE__), "data", headerfile)
@assert isfile(headerpath) "Could not load header file"
open(headerpath, "r") do readio
@assert stat(readio).size >= nbytes "Header not long enough"
head = read(readio, String)
end
trunchead = head[1:nbytes]
return io::IOStream -> write(io, trunchead)
end
function verify_header(header::OriginalHeader)
@test header.format == "Open Ephys Data Format"
@test header.version == v"0.4"
@test header.headerbytes == 1024
@test header.description == "each record contains one 64-bit timestamp, one 16-bit sample count (N), 1 uint16 recordingNumber, N 16-bit samples, and one 10-byte record marker (0 1 2 3 4 5 6 7 8 255)"
@test header.created == DateTime("21-Jul-2015 145012", Dates.DateFormat("d-u-y HHMMSS"))
@test header.channel == "CH30"
@test header.channeltype == "Continuous"
@test header.samplerate == 30000
@test header.blocklength == 1024
@test header.buffersize == 1024
@test isapprox(header.bitvolts, 0.195)
end
### Tests ###
@testset "OriginalHeader" begin
# OriginalHeader constructor
filecontext(write_fheader_fun()) do io
header = OriginalHeader(io)
verify_header(header)
@test (show(devnull, header); true) # test that it does not error
@test (OpenEphysLoader.showcompact(devnull, header); true)
end
@test (showerror(devnull, CorruptedException("test")); true)
# truncated header
filecontext(write_fheader_fun(512)) do io
@test_throws CorruptedException OriginalHeader(io)
end
# Header with bad content
filecontext(write_bad_fheader(:noise)) do io
@test_throws CorruptedException OriginalHeader(io)
end
filecontext(write_bad_fheader(:version)) do io
@test_throws CorruptedException OriginalHeader(io)
end
filecontext(write_bad_fheader(:format)) do io
@test_throws CorruptedException OriginalHeader(io)
end
end
end
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] | 2.693659 | 1,025 |
<reponame>jondeuce/TOMLConfig
using Test
using TOMLConfig
using Dates, Random
struct NoException <: Exception end
macro test_nothrow(ex)
esc(:(@test_throws NoException ($(ex); throw(NoException()))))
end
function check_keys(toml::Dict{String}, has, doesnt)
for key in has
@test haskey(toml, key)
end
for key in doesnt
@test !haskey(toml, key)
end
end
function typed_isequal(toml1::Dict{String}, toml2::Dict{String})
@test toml1 == toml2
for k in keys(toml1)
v1, v2 = toml1[k], toml2[k]
if v1 isa AbstractDict && v2 isa AbstractDict
@test typeof(v1) == typeof(v2)
typed_isequal(v1, v2)
else
@test typeof(v1) == typeof(v2)
end
end
end
@testset "basic parsing" begin
template = TOML.parse(
"""
a = 0
b = 0.0
[sec1]
c = [1, 2]
[sec1.sub1]
d = [1.0, 2.0]
e = true
[sec1.sub2]
f = 2010-05-17
g = 2013-01-01T00:00:00
h = 01:00:00
""")
function check_parsed_types(toml::Dict{String})
@test typeof(toml["a"]) == Int
@test typeof(toml["b"]) == Float64
@test typeof(toml["sec1"]["c"]) == Vector{Int}
@test typeof(toml["sec1"]["sub1"]["d"]) == Vector{Float64}
@test typeof(toml["sec1"]["sub1"]["e"]) == Bool
@test typeof(toml["sec1"]["sub2"]["f"]) == Date
@test typeof(toml["sec1"]["sub2"]["g"]) == DateTime
@test typeof(toml["sec1"]["sub2"]["h"]) == Time
end
@testset "no args passed" begin
parsed_args = parse_args(String[], Config(deepcopy(template)); as_dict = true)
expected_parsed = deepcopy(template)
check_parsed_types(parsed_args)
check_parsed_types(expected_parsed)
typed_isequal(parsed_args, expected_parsed)
end
@testset "args passed" begin
args_list = [
"--a", "1",
"--sec1.c", "3", "4", "5",
"--sec1.sub1.d", "5.5",
"--sec1.sub2.f", "2021-06-01",
"--sec1.sub2.g", "2021-06-01T12:34:56",
"--sec1.sub2.h", "01:23:45",
]
parsed_args = parse_args(args_list, Config(deepcopy(template)); as_dict = true)
expected_parsed = deepcopy(template)
expected_parsed["a"] = 1
expected_parsed["sec1"]["c"] = [3,4,5]
expected_parsed["sec1"]["sub1"]["d"] = [5.5]
expected_parsed["sec1"]["sub2"]["f"] = Date("2021-06-01")
expected_parsed["sec1"]["sub2"]["g"] = DateTime("2021-06-01T12:34:56")
expected_parsed["sec1"]["sub2"]["h"] = Time("01:23:45")
check_parsed_types(parsed_args)
check_parsed_types(expected_parsed)
typed_isequal(parsed_args, expected_parsed)
end
@testset "mistyped args" begin
for args_list in [
["--a", "1.5"],
["--b", "e"],
["--sec1.c", "3.5", "4"],
["--sec1.sub1.d", "f", "g", "h"],
["--sec1.sub2.f", "01:00:00"],
["--sec1.sub2.g", "01:00:00"],
["--sec1.sub2.h", "2013-01-01"],
]
settings = ArgParseSettings(exc_handler = ArgParse.debug_handler)
@test_throws ArgParseError parse_args(args_list, settings, Config(deepcopy(template)))
end
end
end
@testset "nested field inheritance" begin
template = TOML.parse(
"""
a = 0
b = 0.0
c = "c"
[sec1]
_INHERIT_ = "_PARENT_"
a = 1
[sec1.sub1]
_INHERIT_ = "_PARENT_"
c = "d"
[sec1.sub2]
_INHERIT_ = "_PARENT_"
b = "_PARENT_"
c = "d"
""")
function check_parsed_keys(toml::Dict{String})
check_keys(toml, ["a", "b", "c"], ["_INHERIT_"])
check_keys(toml["sec1"], ["a", "b", "c"], ["_INHERIT_"])
check_keys(toml["sec1"]["sub1"], ["a", "c"], ["_INHERIT_", "b"])
check_keys(toml["sec1"]["sub2"], ["a", "b", "c"], ["_INHERIT_"])
end
@testset "no args passed" begin
parsed_args = parse_args(String[], Config(deepcopy(template)); as_dict = true)
expected_parsed = TOML.parse(
"""
a = 0
b = 0.0
c = "c"
[sec1]
a = 1
b = 0.0
c = "c"
[sec1.sub1]
a = 1
c = "d"
[sec1.sub2]
a = 1
b = 0.0
c = "d"
""")
check_parsed_keys(parsed_args)
typed_isequal(parsed_args, expected_parsed)
end
@testset "args passed" begin
args_list = ["--b=1.0", "--sec1.a=2", "--sec1.sub2.c=e"]
parsed_args = parse_args(args_list, Config(deepcopy(template)); as_dict = true)
expected_parsed = TOML.parse(
"""
a = 0
b = 1.0
c = "c"
[sec1]
a = 2
b = 1.0
c = "c"
[sec1.sub1]
a = 2
c = "d"
[sec1.sub2]
a = 2
b = 1.0
c = "e"
""")
check_parsed_keys(parsed_args)
typed_isequal(parsed_args, expected_parsed)
end
end
@testset "customizing arg table" begin
template = TOML.parse(
"""
a = [0]
b = 0.0
c = "c"
[sec1]
_INHERIT_ = "_PARENT_"
a = [1]
[sec1.sub1]
_INHERIT_ = "_PARENT_"
c = "d"
[sec1.sub2]
_INHERIT_ = "_PARENT_"
b = "_PARENT_"
c = "d"
""")
function randomly_insert_arg_dicts!(toml)
seed = 0
for node in TOMLConfig.StatelessBFS(Config(toml))
for (k,v) in TOMLConfig.contents(node)
k == TOMLConfig.inherit_all_key() && continue # can't replace _INHERIT_ with arg dict
!TOMLConfig.is_arg(v) && continue # only replace args, not child dicts
rand(MersenneTwister(seed += 1)) > 0.5 && continue # flip coin
TOMLConfig.contents(node)[k] = !TOMLConfig.is_dict_arg(v) ?
Dict{String, Any}(TOMLConfig.arg_key() => TOMLConfig.arg_value(v)) :
TOMLConfig.arg_value(v)
end
end
return toml
end
@testset "arg dict equivalence" begin
for args_list in [
String[],
["--a", "1", "2", "--c", "cat"],
["--b", "2.0", "--sec1.b", "3.0", "--sec1.sub1.a", "5"],
]
template′ = randomly_insert_arg_dicts!(deepcopy(template))
parsed_args = parse_args(args_list, Config(deepcopy(template)); as_dict = true)
parsed_args′ = parse_args(args_list, Config(deepcopy(template′)); as_dict = true)
typed_isequal(parsed_args, parsed_args′)
end
end
template = TOML.parse(
"""
[a]
_ARG_ = "_REQUIRED_"
nargs = 2
arg_type = "Int"
required = true
help = "help string"
[b]
_ARG_ = [1.0, 2.0]
nargs = "+"
help = "help string"
""")
@testset "arg dict properties" begin
debug_parse_args = (args_list) -> parse_args(args_list, ArgParseSettings(exc_handler = ArgParse.debug_handler), Config(deepcopy(template)))
@test_throws ArgParseError debug_parse_args(String[]) # --a is required
@test_throws ArgParseError debug_parse_args(["--a", "3.0", "4.0"]) # --a must be Int
@test_throws ArgParseError debug_parse_args(["--a", "3"]) # --a requires two args
@test_throws ArgParseError debug_parse_args(["--a", "1", "2", "--b"]) # --b requires at least one arg
@test_nothrow debug_parse_args(["--a", "1", "2", "--b", "5.0"])
@test_nothrow debug_parse_args(["--a", "1", "2", "--b", "5", "10"]) # --b should allow conversion Int -> Float64
end
end
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<filename>src/io/input.jl
"""
loadFCSHeader(fn::String)::Tuple{Vector{Int}, Dict{String,String}}
Efficiently extract data offsets and keyword dictionary from an FCS file.
"""
function loadFCSHeader(fn::String)::Tuple{Vector{Int},Dict{String,String}}
open(fn) do io
offsets = FCSFiles.parse_header(io)
params = FCSFiles.parse_text(io, offsets[1], offsets[2])
FCSFiles.verify_text(params)
(offsets, params)
end
end
"""
getFCSSize(offsets, params)::Tuple{Int,Int}
Convert the offsets and keywords from an FCS file to cell and parameter count,
respectively.
"""
function getFCSSize(offsets, params)::Tuple{Int,Int}
nData = parse(Int, params["\$TOT"])
nParams = parse(Int, params["\$PAR"])
if params["\$DATATYPE"] != "F"
@error "Only float32 FCS files are currently supported"
error("Unsupported FCS format")
end
beginData = parse(Int, params["\$BEGINDATA"])
endData = parse(Int, params["\$ENDDATA"])
#check that the $TOT and $PAR look okay
if !(offsets[3] == 0 && offsets[4] == 0) && (
(
1 + offsets[4] - offsets[3] != nData * nParams * 4 &&
offsets[4] - offsets[3] != nData * nParams * 4
) ||
offsets[3] != beginData ||
offsets[4] != endData
)
@warn "Data size mismatch, FCS is likely broken."
end
return (nData, nParams)
end
"""
loadFCSSizes(fns::Vector{String})
Load cell counts in many FCS files at once. Useful as input for `slicesof`.
"""
function loadFCSSizes(fns::Vector{String})::Vector{Int}
[(
begin
o, s = loadFCSHeader(fn)
getFCSSize(o, s)[1]
end
) for fn in fns]
end
"""
loadFCS(fn::String; applyCompensation::Bool=true)::Tuple{Dict{String,String}, Matrix{Float64}}
Read a FCS file. Return a tuple that contains in order:
- dictionary of the keywords contained in the file
- raw column names
- prettified and annotated column names
- raw data matrix
If `applyCompensation` is set, the function parses and retrieves a spillover
matrix (if any valid keyword in the FCS is found that would contain it) and
applies it to compensate the data.
"""
function loadFCS(
fn::String;
applyCompensation::Bool = true,
)::Tuple{Dict{String,String},Matrix{Float64}}
fcs = FileIO.load(fn)
meta = getMetaData(fcs.params)
data = hcat(map(x -> Vector{Float64}(fcs.data[x]), meta[:, :N])...)
if applyCompensation
spill = getSpillover(fcs.params)
if spill != nothing
names, mtx = spill
cols = indexin(names, meta[:, :N])
if any(cols .== nothing)
@error "Unknown columns in compensation matrix" names cols
error("Invalid compensation matrix")
end
compensate!(data, mtx, Vector{Int}(cols))
end
end
return (fcs.params, data)
end
"""
loadFCSSet(name::Symbol, fns::Vector{String}, pids=workers(); applyCompensation=true, postLoad=(d,i)->d)::LoadedDataInfo
This runs the FCS loading machinery in a distributed way, so that the files
`fns` (with full path) are sliced into equal parts and saved as a distributed
variable `name` on workers specified by `pids`.
`applyCompensation` is passed to loadFCS function.
See `slicesof` for description of the slicing.
`postLoad` is applied to the loaded FCS file data (and the index) -- use this
function to e.g. filter out certain columns right on loading, using `selectFCSColumns`.
The loaded dataset can be manipulated by the distributed functions, e.g.
- `dselect` for removing columns
- `dscale` for normalization
- `dtransform_asinh` (and others) for transformation
- etc.
"""
function loadFCSSet(
name::Symbol,
fns::Vector{String},
pids = workers();
applyCompensation = true,
postLoad = (d, i) -> d,
)::LoadedDataInfo
slices = slicesof(loadFCSSizes(fns), length(pids))
distributed_foreach(
slices,
(slice) -> Base.eval(
Main,
:(
begin
$name = vcollectSlice(
(i) -> last($postLoad(
loadFCS($fns[i]; applyCompensation = $applyCompensation),
i,
)),
$slice,
)
nothing
end
),
),
pids,
)
return LoadedDataInfo(name, pids)
end
"""
selectFCSColumns(selectColnames::Vector{String})
Return a function useful with `loadFCSSet`, which loads only the specified
(prettified) column names from the FCS files. Use `getMetaData`,
`getMarkerNames` and `cleanNames!` to retrieve the usable column names for a
FCS.
"""
function selectFCSColumns(selectColnames::Vector{String})
((metadata, data), idx) -> begin
_, names = getMarkerNames(getMetaData(metadata))
cleanNames!(names)
colIdxs = indexin(selectColnames, names)
if any(colIdxs .== nothing)
@error "Some columns were not found"
error("unknown column")
end
(metadata, data[:, colIdxs])
end
end
"""
distributeFCSFileVector(name::Symbol, fns::Vector{String}, pids=workers())::LoadedDataInfo
Distribute a vector of integers among the workers that describes which file
from `fns` the cell comes from. Useful for producing per-file statistics. The
vector is saved on workers specified by `pids` as a distributed variable
`name`.
"""
function distributeFCSFileVector(
name::Symbol,
fns::Vector{String},
pids = workers(),
)::LoadedDataInfo
sizes = loadFCSSizes(fns)
slices = slicesof(sizes, length(pids))
return distributeFileVector(name, sizes, slices, pids)
end
"""
distributeFileVector(name::Symbol, sizes::Vector{Int}, slices::Vector{Tuple{Int,Int,Int,Int}}, pids=workers())::LoadedDataInfo
Generalized version of `distributeFCSFileVector` that produces the integer
vector from any `sizes` and `slices`.
"""
function distributeFileVector(
name::Symbol,
sizes::Vector{Int},
slices::Vector{Tuple{Int,Int,Int,Int}},
pids = workers(),
)::LoadedDataInfo
distributed_foreach(
slices,
(slice) ->
Base.eval(Main, :($name = collectSlice((i) -> fill(i, $sizes[i]), $slice))),
pids,
)
return LoadedDataInfo(name, pids)
end
"""
function getCSVSize(fn::String; args...)::Tuple{Int,Int}
Read the dimensions (number of rows and columns, respectively) from a CSV file
`fn`. `args` are passed to function `CSV.file`.
# Example
getCSVSize("test.csv", header=false)
"""
function getCSVSize(fn::String; args...)::Tuple{Int,Int}
n = 0
k = 0
# ideally, this will not try to load the whole CSV in the memory
for row in CSV.File(fn, type = Float64; args...)
n += 1
if length(row) > k
k = length(row)
end
end
return (n, k)
end
"""
function loadCSVSizes(fns::Vector{String}; args...)::Vector{Int}
Determine number of rows in a list of CSV files (passed as `fns`). Equivalent
to `loadFCSSizes`.
"""
function loadCSVSizes(fns::Vector{String}; args...)::Vector{Int}
[getCSVSize(fn, type = Float64; args...)[1] for fn in fns]
end
"""
function loadCSV(fn::String; args...)::Matrix{Float64}
CSV equivalent of `loadFCS`. The metadata (header, column names) are not
extracted. `args` are passed to `CSV.read`.
"""
function loadCSV(fn::String; args...)::Matrix{Float64}
CSV.read(fn, DataFrame, type = Float64; args...) |> Matrix{Float64}
end
"""
function loadCSVSet(
name::Symbol,
fns::Vector{String},
pids = workers();
postLoad = (d, i) -> d,
csvargs...,
)::LoadedDataInfo
CSV equivalent of `loadFCSSet`. `csvargs` are passed as keyword arguments to
CSV-loading functions.
"""
function loadCSVSet(
name::Symbol,
fns::Vector{String},
pids = workers();
postLoad = (d, i) -> d,
csvargs...,
)::LoadedDataInfo
slices = slicesof(loadCSVSizes(fns; csvargs...), length(pids))
distributed_foreach(
slices,
(slice) -> Base.eval(
Main,
:(
begin
$name = vcollectSlice(
(i) -> $postLoad(loadCSV($fns[i]; $csvargs...), i),
$slice,
)
nothing
end
),
),
pids,
)
return LoadedDataInfo(name, pids)
end
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] | 2.367205 | 3,592 |
# Most of the code here is copied from the Juno codebase
using REPL
using REPL.LineEdit
isREPL() = isdefined(Base, :active_repl) &&
isdefined(Base.active_repl, :interface) &&
isdefined(Base.active_repl.interface, :modes) &&
isdefined(Base.active_repl, :mistate) &&
isdefined(Base.active_repl.mistate, :current_mode)
juliaprompt = "julia> "
current_prompt = juliaprompt
function get_main_mode(repl=Base.active_repl)
mode = repl.interface.modes[1]
mode isa LineEdit.Prompt || error("no julia repl mode found")
mode
end
function hideprompt(f)
isREPL() || return f()
repl = Base.active_repl
mistate = repl.mistate
mode = mistate.current_mode
buf = String(take!(copy(LineEdit.buffer(mistate))))
# clear input buffer
truncate(LineEdit.buffer(mistate), 0)
LineEdit.refresh_multi_line(mistate)
print(stdout, "\e[1K\r")
r = f()
flush(stdout)
flush(stderr)
sleep(0.05)
# TODO Fix this
# pos = @rpc cursorpos()
pos = 1, 1
pos[1] != 0 && println()
# restore prompt
if applicable(LineEdit.write_prompt, stdout, mode)
LineEdit.write_prompt(stdout, mode)
elseif mode isa LineEdit.PrefixHistoryPrompt || :parent_prompt in fieldnames(typeof(mode))
LineEdit.write_prompt(stdout, mode.parent_prompt)
else
printstyled(stdout, current_prompt, color=:green)
end
truncate(LineEdit.buffer(mistate), 0)
# restore input buffer
LineEdit.edit_insert(LineEdit.buffer(mistate), buf)
LineEdit.refresh_multi_line(mistate)
r
end
const HAS_REPL_TRANSFORM = Ref{Bool}(false)
function hook_repl(repl)
if HAS_REPL_TRANSFORM[]
return
end
@debug "installing REPL hook"
if !isdefined(repl, :interface)
repl.interface = REPL.setup_interface(repl)
end
main_mode = get_main_mode(repl)
if VERSION > v"1.5-"
for _ in 1:20 # repl backend should be set up after 10s -- fall back to the pre-ast-transform approach otherwise
isdefined(Base, :active_repl_backend) && continue
sleep(0.5)
end
if isdefined(Base, :active_repl_backend)
push!(Base.active_repl_backend.ast_transforms, ast -> transform_backend(ast, repl, main_mode))
HAS_REPL_TRANSFORM[] = true
@debug "REPL AST transform installed"
return
end
end
main_mode.on_done = REPL.respond(repl, main_mode; pass_empty=false) do line
quote
$(evalrepl)(Main, $line, $repl, $main_mode)
end
end
@debug "legacy REPL hook installed"
HAS_REPL_TRANSFORM[] = true
return nothing
end
function transform_backend(ast, repl, main_mode)
quote
$(evalrepl)(Main, $(QuoteNode(ast)), $repl, $main_mode)
end
end
function evalrepl(m, line, repl, main_mode)
did_notify = false
return try
try
JSONRPC.send_notification(conn_endpoint[], "repl/starteval", nothing)
did_notify = true
catch err
@debug "Could not send repl/starteval notification" exception=(err, catch_backtrace())
end
r = run_with_backend() do
fix_displays()
f = () -> repleval(m, line, REPL.repl_filename(repl, main_mode.hist))
PROGRESS_ENABLED[] ? Logging.with_logger(f, VSCodeLogger()) : f()
end
if r isa EvalError
display_repl_error(stderr, r.err, r.bt)
nothing
elseif r isa EvalErrorStack
display_repl_error(stderr, r)
nothing
else
r
end
catch err
# This is for internal errors only.
Base.display_error(stderr, err, catch_backtrace())
nothing
finally
if did_notify
try
JSONRPC.send_notification(conn_endpoint[], "repl/finisheval", nothing)
catch err
@debug "Could not send repl/finisheval notification" exception=(err, catch_backtrace())
end
end
end
end
# don't inline this so we can find it in the stacktrace
@noinline function repleval(m, code::String, file)
args = VERSION >= v"1.5" ? (REPL.softscope, m, code, file) : (m, code, file)
return include_string(args...)
end
@noinline function repleval(m, code, _)
return Base.eval(m, code)
end
# basically the same as Base's `display_error`, with internal frames removed
function display_repl_error(io, err, bt)
st = stacktrace(crop_backtrace(bt))
printstyled(io, "ERROR: "; bold=true, color=Base.error_color())
showerror(IOContext(io, :limit => true), err, st)
println(io)
end
display_repl_error(io, err::LoadError, bt) = display_repl_error(io, err.error, bt)
function display_repl_error(io, stack::EvalErrorStack)
printstyled(io, "ERROR: "; bold=true, color=Base.error_color())
for (i, (err, bt)) in enumerate(reverse(stack.stack))
i !== 1 && print(io, "\ncaused by: ")
st = stacktrace(crop_backtrace(bt))
showerror(IOContext(io, :limit => true), err, st)
println(io)
end
end
function withpath(f, path)
tls = task_local_storage()
hassource = haskey(tls, :SOURCE_PATH)
hassource && (path′ = tls[:SOURCE_PATH])
tls[:SOURCE_PATH] = path
try
return f()
finally
hassource ? (tls[:SOURCE_PATH] = path′) : delete!(tls, :SOURCE_PATH)
end
end
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] | 2.288494 | 2,364 |
using StackOverflow
"""
searcherror(intitle::String)
Adding the ability to search for an error through StackOverflow.jl while in the Julia Terminal.
"""
function searcherror(intitle::String)
data = replace(intitle, " " => "%20")
r = HTTP.request("GET", "https://api.stackexchange.com/2.2/search?order=desc&sort=activity&intitle=$(data)&site=stackoverflow")
json_obj = StackOverflow.convert_HTTP_Response_To_JSON(r)
makequestionsarray(json_obj)
end
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] | 2.848485 | 165 |
<reponame>alpv95/RobotDojo.jl
@testset "Simulator: simulate" begin
## hopper
q1 = nominal_configuration(hopper)
v1 = zeros(hopper.nq)
h = 0.01
T = 100
# simulator
s = Simulator(hopper, T, h=h, diff_sol=true, sim_opts=RoboDojo.SimulatorOptions(record=true))
# step
q2 = step!(s, q1, v1, zeros(hopper.nu), 1)
@test sum(q2) != 0.0
# simulate
status = simulate!(s, q1, v1, reset_traj=true)
@test status
## quadruped
q1 = nominal_configuration(quadruped)
v1 = zeros(quadruped.nq)
h = 0.01
T = 100
# simulator
s = Simulator(quadruped, T, h=h, diff_sol=true, sim_opts=RoboDojo.SimulatorOptions(record=true))
# step
q2 = step!(s, q1, v1, zeros(quadruped.nu), 1)
@test sum(q2) != 0.0
# simulate
status = simulate!(s, q1, v1, reset_traj=true)
@test status
## biped
q1 = nominal_configuration(biped)
v1 = zeros(biped.nq)
h = 0.01
T = 100
# simulator
s = Simulator(biped, T, h=h, diff_sol=true, sim_opts=RoboDojo.SimulatorOptions(record=true))
# step
q2 = step!(s, q1, v1, zeros(biped.nu), 1)
@test sum(q2) != 0.0
# simulate
status = simulate!(s, q1, v1, reset_traj=true)
@test status
# process timing results
RoboDojo.process!(s.stats, 1)
@test sum(s.stats.policy_time) > 0.0
@test s.stats.policy_mean[1] > 0.0
@test sum(s.stats.sim_time) > 0.0
@test s.stats.sim_mean[1] > 0.0
end
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] | 2.086525 | 705 |
<reponame>mcabbott/TensorCast.jl
using TensorCast
using Test
using LinearAlgebra
using Logging
using StaticArrays
using OffsetArrays
using Einsum
using Strided
using LazyArrays
using LoopVectorization
@testset "ex-@shape" begin include("shape.jl") end
@testset "@reduce" begin include("reduce.jl") end
@testset "@cast" begin include("casting.jl") end
@testset "@matmul" begin include("mul.jl") end
@testset "slice/view" begin include("cat.jl") end
@testset "old readmes" begin include("old.jl") end
@testset "new in 0.2" begin include("two.jl") end
@testset "new in 0.4" begin include("four.jl") end | [
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] | 2.837963 | 216 |
<filename>profile/profile_meanfield.jl
using cascadeddecay, QuantumOptics, meanfield
const γ = 1.
const e_dipole = [0,0,1.]
const T = [0:0.05:10]
#atoms = Atom[Atom(position, e_dipole) for position=cube(0.627)]
#atoms = Atom[Atom(position, e_dipole) for position=cube(0.2)]
atoms = Atom[Atom(position, e_dipole) for position=chain(0.4,30)]
system = CascadedDecaySystem(atoms, γ)
# Meanfield timeevolution
N = length(system.atoms)
sz0 = zeros(ComplexF64, N)
sp0 = zeros(ComplexF64, N) .- 0.5
println("meanfield +z")
@time T_mf, sz_mf, sp_mf = meanfield_timeevolution(T, system, sz0, sp0)
@time T_mf, sz_mf, sp_mf = meanfield_timeevolution(T, system, sz0, sp0)
# Meanfield xyz timeevolution
N = length(system.atoms)
sx0 = zeros(Float64, N) .- 1
sy0 = zeros(Float64, N)
sz0 = zeros(Float64, N)
println("meanfield xyz")
@time T_xmf, sx_xmf, sy_xmf, sz_xmf= meanfield.meanfield_timeevolution2(T, system, sx0, sy0, sz0)
@time T_xmf, sx_xmf, sy_xmf, sz_xmf= meanfield.meanfield_timeevolution2(T, system, sx0, sy0, sz0)
# Correlation included timeevolution
N = length(system.atoms)
sz0 = zeros(ComplexF64, N)
sp0 = zeros(ComplexF64, N) .- 0.5
Cpm0 = zeros(ComplexF64, N, N)
Cpz0 = zeros(ComplexF64, N, N)
Cpp0 = zeros(ComplexF64, N, N)
Czz0 = zeros(ComplexF64, N, N)
println("correlation +z")
@time T_cor, sz_cor, sp_cor = correlation_timeevolution(T, system, sz0, sp0, Cpm0, Cpz0, Cpp0, Czz0)
@time T_cor, sz_cor, sp_cor = correlation_timeevolution(T, system, sz0, sp0, Cpm0, Cpz0, Cpp0, Czz0)
# Correlation included timeevolution xyz
N = length(system.atoms)
sx0 = zeros(Float64, N) .- 1
sy0 = zeros(Float64, N)
sz0 = zeros(Float64, N)
C0 = Dict{AbstractString, Matrix{Float64}}()
C0["xx"] = zeros(Float64, N, N) .+ 1
C0["yy"] = zeros(Float64, N, N)
C0["zz"] = zeros(Float64, N, N)
C0["xy"] = zeros(Float64, N, N)
C0["xz"] = zeros(Float64, N, N)
C0["yz"] = zeros(Float64, N, N)
#@time T_xcor, sx_xcor, sy_xcor, sz_xcor, C_xcor = meanfield.correlation_timeevolution2(T, system, sx0, sy0, sz0, C0)
#@time T_xcor, sx_xcor, sy_xcor, sz_xcor, C_xcor = meanfield.correlation_timeevolution2(T, system, sx0, sy0, sz0, C0)
println("correlation xyz")
@time T_xcor, sx_xcor, sy_xcor, sz_xcor, C_xcor = meanfield.correlation_timeevolution3(T, system, sx0, sy0, sz0, C0)
@time T_xcor, sx_xcor, sy_xcor, sz_xcor, C_xcor = meanfield.correlation_timeevolution3(T, system, sx0, sy0, sz0, C0)
println(norm(sz_xcor[end]-sz_cor[end])) | [
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] | 2.089733 | 1,159 |
# Geometric Utilities
# Areas
export triangle_area, circle_area, annulus_area
triangle_area(a, b, c) =
let s = (a + b + c)/2.0
sqrt(max(0.0, s*(s - a)*(s - b)*(s - c)))
end
circle_area(r) = π*r^2
annulus_area(rₒ, rᵢ) = π*(rₒ^2 - rᵢ^2)
project_to_world(surf) =
transform(surf, inverse_transformation(frame_at(surf, 0, 0)))
#=
Given a poligonal line described by its vertices, we need to compute another
polygonal line that is parallel to the first one.
=#
v_in_v(v0, v1) =
let v = v0 + v1
v*dot(v0, v0)/dot(v, v0)
end
rotated_v(v, alpha) =
vpol(pol_rho(v), pol_phi(v) + alpha)
centered_rectangle(p0, w, p1) =
let v0 = p1 - p0,
v1 = rotated_v(v0, pi/2),
c = loc_from_o_vx_vy(p0, v0, v1)
rectangle(c-vy(w/2, c.cs), distance(p0, p1), w)
end
offset_vertices(ps::Locs, d::Real, closed) =
let qs = closed ? [ps[end], ps..., ps[1]] : ps,
vs = map((p0, p1) -> rotated_v(unitized(p1 - p0)*d, pi/2), qs[1:end-1], qs[2:end]),
ws = map(v_in_v, vs[1:end-1], vs[2:end])
map(+, ps, closed ? ws : [vs[1], ws..., vs[end]])
end
offset(path, d::Real) = d == 0 ? path : nonzero_offset(path, d)
nonzero_offset(path::RectangularPath, d::Real) =
rectangular_path(add_xy(path.corner, d, d), path.dx - 2d, path.dy - 2d)
nonzero_offset(path::OpenPolygonalPath, d::Real) =
open_polygonal_path(offset_vertices(path.vertices, d, false))
nonzero_offset(path::ClosedPolygonalPath, d::Real) =
closed_polygonal_path(offset_vertices(path.vertices, d, true))
nonzero_offset(path::CircularPath, d::Real) =
circular_path(path.center, path.radius - d)
nonzero_offset(path::ArcPath, d::Real) =
arc_path(path.center, path.radius - d, path.start_angle, path.amplitude)
export offset
# Polygon combination
closest_vertices_indexes(pts1, pts2) =
# This is a brute force method. There are better algorithms to do this.
let min_dist = Inf,
min_i = nothing,
min_j = nothing
for (i, pt1) in enumerate(pts1), (j, pt2) in enumerate(pts2)
let dist = distance(pt1, pt2)
if dist < min_dist
min_dist = dist
min_i = i
min_j = j
end
end
end
min_i, min_j
end
point_in_segment(r, p, q) =
let pr = r-p,
pq = q-p,
rx = cx(pr)/cx(pq),
ry = cy(pr)/cy(pq)
isapprox(rx, ry) && isapprox(ry, cz(pr)/cz(pq))
end
collinear_segments(p1, p2, q1, q2) =
point_in_segment(q1, p1, p2) &&
point_in_segment(q2, p1, p2)
collinear_vertices_indexes(pts1, pts2) =
for (i1, p1) in enumerate(pts1)
let i2 = i1%length(pts1)+1,
p2 = pts1[i2]
for (j1, q1) in enumerate(pts2)
let j2 = j1%length(pts2)+1,
q2 = pts2[j2]
if collinear_segments(p1, p2, q1, q2)
return (i1, j1)
end
end
end
end
end
subtract_polygon_vertices(pts1, pts2) =
let ij = collinear_vertices_indexes(pts1, pts2)
isnothing(ij) ?
inject_polygon_vertices_at_indexes(pts1, pts2, closest_vertices_indexes(pts1, pts2)) :
splice_polygon_vertices_at_indexes(pts1, pts2, ij)
end
inject_polygon_vertices_at_indexes(pts1, pts2, (i, j)) =
[pts1[1:i]..., reverse([pts2[j:end]..., pts2[1:j]...])..., pts1[i:end]...]
export closest_vertices_indexes, inject_polygon_vertices_at_indexes, subtract_polygon_vertices
# Intersection
segments_intersection(p0, p1, p2, p3) =
let denom = (p3.y - p2.y)*(p1.x - p0.x) - (p3.x - p2.x)*(p1.y - p0.y)
if denom == 0
nothing
else
let u = ((p3.x - p2.x)*(p0.y - p2.y) - (p3.y - p2.y)*(p0.x - p2.x))/denom,
v = ((p1.x - p0.x)*(p0.y - p2.y) - (p1.y - p0.y)*(p0.x - p2.x))/denom
if 0 <= u <= 1 && 0 <= v <= 1
xy(p0.x + u*(p1.x - p0.x), p0.y + u*(p1.y - p0.y))
else
nothing
end
end
end
end
lines_intersection(p0, p1, p2, p3) =
let denom = (p3.y - p2.y)*(p1.x - p0.x) - (p3.x - p2.x)*(p1.y - p0.y)
if denom == 0
nothing
else
let u = ((p3.x - p2.x)*(p0.y - p2.y) - (p3.y - p2.y)*(p0.x - p2.x))/denom,
v = ((p1.x - p0.x)*(p0.y - p2.y) - (p1.y - p0.y)*(p0.x - p2.x))/denom
xy(p0.x + u*(p1.x - p0.x), p0.y + u*(p1.y - p0.y))
end
end
end
export epsilon, nearest_point_from_lines, circle_from_three_points
const epsilon = Parameter(1e-8)
nearest_point_from_lines(l0p0::Loc, l0p1::Loc, l1p0::Loc, l1p1::Loc) =
let u = l0p1-l0p0,
v = l1p1-l1p0,
w = l0p0-l1p0,
a = dot(u, u),
b = dot(u, v),
c = dot(v, v),
d = dot(u, w),
e = dot(v, w),
D = a*c-b*b,
(sc, tc) = D < epsilon() ?
#almost parallel
(0.0, b > c ? d/b : e/c) :
((b*e-c*d)/D, (a*e-b*d)/D),
(p0, p1) = (l0p0+u*sc, l1p0+v*tc)
intermediate_loc(p0, p1)
end
circle_from_three_points_2d(v0::Loc, v1::Loc, v2::Loc) =
let v1sv0 = v1-v0,
v2sv0 = v2-v0,
v2sv1 = v2-v1,
v1pv0 = v1+(v0-u0()),
v2pv0 = v2+(v0-u0()),
a = v1sv0.x,
b = v1sv0.y,
c = v2sv0.x,
d = v2sv0.y,
e = a*v1pv0.x+b*v1pv0.y,
f = c*v2pv0.x+d*v2pv0.y,
g = 2.0*(a*v2sv1.y-b*v2sv1.x),
iscolinear = abs(g) < 1e-8,
(cx, cy, dx, dy) = iscolinear ?
let minx = min(v0.x, v1.x, v2.x),
miny = min(v0.y, v1.y, v2.y),
maxx = max(v0.x, v1.x, v2.x),
maxy = max(v0.y, v1.y, v2.y),
x = (minx+maxx)/2,
y = (miny+maxy)/2
(x, y, x-minx, y-miny)
end :
let x = (d*e-b*f)/g,
y = (a*f-c*e)/g
(x, y, x-v0.x, y-v0.y)
end,
radius_squared = dx*dx+dy*dy,
radius = sqrt(radius_squared)
(xy(cx, cy), radius)
end
circle_from_three_points(p0::Loc, p1::Loc, p2::Loc) =
let cs = cs_from_o_vx_vy(p0, p1-p0, p2-p0)
with(current_cs, cs) do
c, r = circle_from_three_points_2d(in_cs(p0, cs),
in_cs(p1, cs),
in_cs(p2, cs))
(c, r)
end
end
const collinearity_tolerance = Parameter(1e-2)
# are the three points sufficiently collinear?
collinear_points(p0, pm, p1, epsilon=collinearity_tolerance()) =
let a = distance(p0, pm),
b = distance(pm, p1),
c = distance(p1, p0)
triangle_area(a, b, c) < epsilon
end
#=
export sweep_path_with_path
sweep_path_with_path(path, profile) =
let vertices = in_world.(path_frames(profile)),
frames = path_frames(path)
#show_cs.(frames, 0.1)
surface_grid([[xyz(cx(p), cy(p), cz(p), frame.cs) for p in vertices]
for frame in frames],
is_closed_path(profile),
is_closed_path(path),
is_smooth_path(profile),
is_smooth_path(path))
end
=#
export quad_grid, quad_grid_indexes
quad_grid(quad, points, closed_u, closed_v) =
let pts = in_world.(points),
si = size(pts, 1),
sj = size(pts, 2)
for i in 1:si-1
for j in 1:sj-1
quad(pts[i,j], pts[i+1,j], pts[i+1,j+1], pts[i,j+1])
end
if closed_v
quad(pts[i,sj], pts[i+1,sj], pts[i+1,1], pts[i,1])
end
end
if closed_u
for j in 1:sj-1
quad(pts[si,j], pts[1,j], pts[si,j+1], pts[si,j+1])
end
if closed_v
quad(pts[si,sj], pts[1,sj], pts[1,1], pts[si,1])
end
end
end
quad_grid_indexes(si, sj, closed_u, closed_v) =
let idx(i,j) = (i-1)*sj+(j-1),
idxs = Vector{Int}[],
quad(a,b,c,d) = (push!(idxs, [a, b, d]); push!(idxs, [d, b, c]))
for i in 1:si-1
for j in 1:sj-1
quad(idx(i,j), idx(i+1,j), idx(i+1,j+1), idx(i,j+1))
end
if closed_v
quad(idx(i,sj), idx(i+1,sj), idx(i+1,1), idx(i,1))
end
end
if closed_u
for j in 1:sj-1
quad(idx(si,j), idx(1,j), idx(1,j+1), idx(si,j+1))
end
if closed_v
quad(idx(si,sj), idx(1,sj), idx(1,1), idx(si,1))
end
end
idxs
end
export illustrate_path
illustrate_path(path) =
begin
for (i, v) in enumerate(path_vertices(path))
sphere(v, 0.01)
text(string(i), v+vxy(0.05, 0.05), 0.1)
end
stroke(path)
end
illustrate_expr(expr) =
:(let p = $(esc(expr)); text($(string(expr)), p); p end)
export illustrate
macro illustrate(exprs...)
:(tuple($([illustrate_expr(expr) for expr in exprs]...)))
end
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] | 1.723331 | 4,959 |
<reponame>TuringLang/HMC.jl
using Pkg
Pkg.update("TuringWeb")
using Documenter, TuringWeb
using AdvancedHMC
# cp(joinpath(@__DIR__, "../README.md"), joinpath(@__DIR__, "src/index.md"))
makedocs(
sitename = "AdvancedHMC",
format = Documenter.HTML(),
modules = [AdvancedHMC]
)
deploydocs(
repo = "github.com/TuringLang/AdvancedHMC.jl.git",
push_preview = true, # allow PR to deploy
)
| [
27,
7856,
261,
480,
29,
51,
870,
43,
648,
14,
39,
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60,
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8,
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198,
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7,
198,
220,
220,
220,
29924,
796,
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13,
785,
14,
51,
870,
43,
648,
14,
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62,
3866,
1177,
796,
2081,
11,
1303,
1249,
4810,
284,
6061,
198,
8,
198
] | 2.416667 | 168 |
<reponame>kskyten/MeasureTheory.jl
using Random
struct ResettableRNG{R,S} <: Random.AbstractRNG
rng::R
seed::S
end
function Base.show(io::IO, ::MIME"text/plain", r::ResettableRNG)
io = IOContext(io, :compact => true)
print(io, "ResettableRNG(::", constructor(r.rng), ", ", r.seed, ")")
end
function reset!(r::ResettableRNG)
Random.seed!(r.rng, r.seed)
end
# for f in [
# :(Base.rand)
# :(Base.randn)
# :(Random.rand!)
# :(Random.randcycle)
# :(Random.randcycle!)
# :(Random.randexp)
# :(Random.randexp!)
# :(Random.randn!)
# :(Random.randperm)
# :(Random.randperm!)
# :(Random.randstring)
# :(Random.randsubseq)
# :(Random.randsubseq!)
# :(Random.shuffle)
# :(Random.shuffle!)
# :(Random.seed!)
# ]
# @eval $f(r::ResettableRNG, args...) = $f(r.rng, args...)
# end
# Base.rand(r::ResettableRNG, d::AbstractMeasure) = rand(r.rng, d)
# Base.rand(r::ResettableRNG, ::Type{T}, d::AbstractMeasure) where {T} = rand(r.rng, T, d)
# Base.rand(r::ResettableRNG) = rand(r.rng, Float64)
Base.rand(r::ResettableRNG, ::Type{T}) where {T} = rand(r.rng, T)
Base.randn(r::ResettableRNG, ::Type{T}) where {T} = randn(r.rng, T)
Base.randn(r::ResettableRNG, ::Type{Float64}) where {T} = randn(r.rng, Float64)
function Base.iterate(r::ResettableRNG)
reset!(r)
return (rand(r), nothing)
end
Base.iterate(r::ResettableRNG, _) = (rand(r), nothing)
Base.IteratorSize(r::ResettableRNG) = Base.IsInfinite()
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7,
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8,
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220,
220,
13259,
0,
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8,
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220,
220,
220,
1441,
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7,
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8,
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437,
198,
198,
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7,
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3087,
540,
49,
10503,
8,
796,
7308,
13,
3792,
18943,
9504,
3419,
198
] | 2.146763 | 695 |
module AperturePhotometry
include("calibration.jl")
include("photometry.jl")
include("stacking.jl")
include("load_images.jl")
end # module
| [
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] | 3.065217 | 46 |
function increase_tariff!(consumer::Consumer)
consumer.tariff.e_cost = [(block[1],block[2]*(1+consumer.tariff.increase)) for block in consumer.tariff.e_cost]
consumer.tariff.p_cost = [(block[1],block[2]*(1+consumer.tariff.increase)) for block in consumer.tariff.p_cost]
consumer.tariff.access = consumer.tariff.access*(consumer.tariff.access_increase)
end
function increase_tariff!(tariff::Tariff)
tariff.e_cost = [(block[1],block[2]*(1+tariff.increase)) for block in tariff.e_cost]
tariff.p_cost = [(block[1],block[2]*(1+tariff.increase)) for block in tariff.p_cost]
end | [
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] | 2.485714 | 245 |
#Moore et al 2009 mean vectors:
M=Array{Any,1}(undef,8)
M[1]=vec([0.0234 0.0192 0.0129 0.0075 0.0031 0.0002])
M[2]=vec([0.0162 0.0141 0.0112 0.0073 0.0034 0.0002])
M[3]=vec([0.0107 0.0098 0.0092 0.0070 0.0039 0.0003])
M[4]=vec([0.0065 0.0064 0.0070 0.0064 0.0048 0.0006])
M[5]=vec([0.0033 0.0034 0.0042 0.0042 0.0043 0.0009])
M[6]=vec([0.0064 0.0074 0.0105 0.0116 0.0140 0.0041])
M[7]=vec([0.0121 0.0140 0.0192 0.0204 0.0231 0.0084])
M[8]=vec([0.0184 0.0230 0.0333 0.0359 0.0409 0.0137])
#Moore et al 2009 covariance matrices:
S=Array{Any,1}(undef,8)
S[1]=[0.00000959 0.00000556 0.00000138 -0.00000034 -0.00000024 -0.00000003;
0.00000556 0.00000493 0.00000193 0.00000060 0.00000023 0.00000001;
0.00000138 0.00000193 0.00000282 0.00000223 0.00000119 0.00000007;
-0.00000034 0.00000060 0.00000223 0.00000232 0.00000119 0.00000007;
-0.00000024 0.00000023 0.00000119 0.00000119 0.00000071 0.00000005;
-0.00000003 0.00000001 0.00000007 0.00000007 0.00000005 0.00000001]
S[2]=[0.00000346 0.00000186 -0.00000011 -0.00000060 -0.00000062 -0.00000005;
0.00000186 0.00000228 0.00000086 0.00000033 -0.00000007 0.00000003;
-0.00000011 0.00000086 0.00000231 0.00000221 0.00000145 0.00000023;
-0.00000060 0.00000033 0.00000221 0.00000266 0.00000191 0.00000034;
-0.00000062 -0.00000007 0.00000145 0.00000191 0.00000175 0.00000041;
-0.00000005 0.00000003 0.00000023 0.00000034 0.00000041 0.00000021]
S[3]=[0.00000241 0.00000144 0.00000035 -0.00000031 -0.00000063 -0.00000006;
0.00000144 0.00000138 0.00000076 0.00000015 -0.00000021 -0.00000001;
0.00000035 0.00000076 0.00000161 0.00000156 0.00000121 0.00000016;
-0.00000031 0.00000015 0.00000156 0.00000227 0.00000209 0.00000031;
-0.00000063 -0.00000021 0.00000121 0.00000209 0.00000225 0.00000037;
-0.00000006 -0.00000001 0.00000016 0.00000031 0.00000037 0.00000013]
S[4]=[0.00000166 0.00000091 0.00000034 -0.00000009 -0.00000080 -0.00000025;
0.00000091 0.00000097 0.00000071 0.00000025 -0.00000041 -0.00000015;
0.00000034 0.00000071 0.00000118 0.00000103 0.00000072 0.00000003;
-0.00000009 0.00000025 0.00000103 0.00000137 0.00000162 0.00000025;
-0.00000080 -0.00000041 0.00000072 0.00000162 0.00000290 0.00000065;
-0.00000025 -0.00000015 0.00000003 0.00000025 0.00000065 0.00000050]
S[5]=[0.00000178 0.00000132 0.00000104 0.00000081 0.00000018 -0.00000014;
0.00000132 0.00000127 0.00000121 0.00000099 0.00000034 -0.00000010;
0.00000104 0.00000121 0.00000150 0.00000142 0.00000110 0.00000013;
0.00000081 0.00000099 0.00000142 0.00000158 0.00000177 0.00000042;
0.00000018 0.00000034 0.00000110 0.00000177 0.00000351 0.00000131;
-0.00000014 -0.00000010 0.00000013 0.00000042 0.00000131 0.00000081]
S[6]=[0.00000715 0.00000586 0.00000409 0.00000292 0.00000005 -0.00000075;
0.00000586 0.00000589 0.00000520 0.00000398 0.00000027 -0.00000114;
0.00000409 0.00000520 0.00000634 0.00000541 0.00000188 -0.00000097;
0.00000292 0.00000398 0.00000541 0.00000528 0.00000392 0.00000070;
0.00000005 0.00000027 0.00000188 0.00000392 0.00000995 0.00000657;
-0.00000075 -0.00000114 -0.00000097 0.00000070 0.00000657 0.00000819]
S[7]=[0.00002625 0.00001981 0.00001058 0.00000544 -0.00000654 -0.00001122;
0.00001981 0.00001745 0.00001314 0.00000822 -0.00000431 -0.00001228;
0.00001058 0.00001314 0.00001629 0.00001226 0.00000035 -0.00001311;
0.00000544 0.00000822 0.00001226 0.00001170 0.00000742 -0.00000500;
-0.00000654 -0.00000431 0.00000035 0.00000742 0.00002241 0.00001782;
-0.00001122 -0.00001228 -0.00001311 -0.00000500 0.00001782 0.00003987]
S[8]=[0.00001186 0.00001134 0.00001139 0.00000919 0.00000395 -0.00000186;
0.00001134 0.00001484 0.00002034 0.00001907 0.00001531 0.00000087;
0.00001139 0.00002034 0.00003467 0.00003546 0.00003555 0.00000708;
0.00000919 0.00001907 0.00003546 0.00003907 0.00004604 0.00001733;
0.00000395 0.00001531 0.00003555 0.00004604 0.00007306 0.00004953;
-0.00000186 0.00000087 0.00000708 0.00001733 0.00004953 0.00006542];
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] | 2.033094 | 2,085 |
<reponame>JuliaTagBot/ModelSelection.jl
"""
to_string
"""
function to_string(data::ModelSelection.ModelSelectionData, result::AllSubsetRegressionResult)
datanames_index = ModelSelection.create_datanames_index(result.datanames)
out = ""
out *= @sprintf("\n")
out *= @sprintf("══════════════════════════════════════════════════════════════════════════════\n")
out *= @sprintf(" Best model results \n")
out *= @sprintf("══════════════════════════════════════════════════════════════════════════════\n")
out *= @sprintf(" \n")
out *= @sprintf(" Dependent variable: %s \n", data.depvar)
out *= @sprintf(" ─────────────────────────────────────────\n")
out *= @sprintf(" \n")
out *= @sprintf(" Selected covariates Coef.")
if result.ttest
out *= @sprintf(" Std. t-test")
end
out *= @sprintf("\n")
out *= @sprintf("──────────────────────────────────────────────────────────────────────────────\n")
cols = ModelSelection.get_selected_variables(Int64(result.bestresult_data[datanames_index[:index]]), data.expvars, data.intercept)
for pos in cols
varname = data.expvars[pos]
out *= @sprintf(" %-35s", varname)
out *= @sprintf(" %-10f", result.bestresult_data[datanames_index[Symbol(string(varname, "_b"))]])
if result.ttest
out *= @sprintf(" %-10f", result.bestresult_data[datanames_index[Symbol(string(varname, "_bstd"))]])
out *= @sprintf(" %-10f", result.bestresult_data[datanames_index[Symbol(string(varname, "_t"))]])
end
out *= @sprintf("\n")
end
out *= @sprintf("──────────────────────────────────────────────────────────────────────────────\n")
out *= @sprintf(" Observations %-10d\n", result.bestresult_data[datanames_index[:nobs]])
out *= @sprintf(" F-statistic %-10f\n", result.bestresult_data[datanames_index[:F]])
if :r2adj in result.datanames
out *= @sprintf(" Adjusted R² %-10f\n", result.bestresult_data[datanames_index[:r2adj]])
end
for criteria in result.criteria
if AVAILABLE_CRITERIA[criteria]["verbose_show"] && criteria != :r2adj
out *= @sprintf(" %-30s %-10f\n", AVAILABLE_CRITERIA[criteria]["verbose_title"], result.bestresult_data[datanames_index[criteria]])
end
end
if !result.modelavg
out *= @sprintf("──────────────────────────────────────────────────────────────────────────────\n")
else
out *= @sprintf("\n")
out *= @sprintf("\n")
out *= @sprintf("══════════════════════════════════════════════════════════════════════════════\n")
out *= @sprintf(" Model averaging results \n")
out *= @sprintf("══════════════════════════════════════════════════════════════════════════════\n")
out *= @sprintf(" \n")
out *= @sprintf(" Dependent variable: %s \n", data.depvar)
out *= @sprintf(" ─────────────────────────────────────────\n")
out *= @sprintf(" \n")
out *= @sprintf(" Covariates Coef.")
if result.ttest
out *= @sprintf(" Std. t-test")
end
out *= @sprintf("\n")
out *= @sprintf("──────────────────────────────────────────────────────────────────────────────\n")
for varname in data.expvars
out *= @sprintf(" %-35s", varname)
out *= @sprintf(" %-10f", result.modelavg_data[datanames_index[Symbol(string(varname, "_b"))]])
if result.ttest
out *= @sprintf(" %-10f", result.modelavg_data[datanames_index[Symbol(string(varname, "_bstd"))]])
out *= @sprintf(" %-10f", result.modelavg_data[datanames_index[Symbol(string(varname, "_t"))]])
end
out *= @sprintf("\n")
end
out *= @sprintf("\n")
out *= @sprintf("──────────────────────────────────────────────────────────────────────────────\n")
out *= @sprintf(" Observations %-10d\n", result.modelavg_data[datanames_index[:nobs]])
out *= @sprintf(" Adjusted R² %-10f\n", result.modelavg_data[datanames_index[:r2adj]])
out *= @sprintf(" F-statistic %-10f\n", result.modelavg_data[datanames_index[:F]])
out *= @sprintf(" Combined criteria %-10f\n", result.modelavg_data[datanames_index[:order]])
out *= @sprintf("──────────────────────────────────────────────────────────────────────────────\n")
end
return out
end
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] | 1.890529 | 2,777 |
export @bridge
"""
AbstractBridge
A bridge represents a bridged constraint in an `AbstractBridgeOptimizer`. It contains the indices of the constraints that it has created in the model.
These can be obtained using `MOI.NumberOfConstraints` and `MOI.ListOfConstraintIndices` and using the bridge in place of a `ModelLike`.
Attributes of the bridged model such as `MOI.ConstraintDual` and `MOI.ConstraintPrimal`, can be obtained using the bridge in place of the constraint index.
These calls are used by the `AbstractBridgeOptimizer` to communicate with the bridge so they should be implemented by the bridge.
"""
abstract type AbstractBridge end
function MOI.get(::MOI.ModelLike, attr::MOI.AbstractConstraintAttribute,
bridge::AbstractBridge)
throw(ArgumentError("Constraint bridge of type `$(typeof(bridge))` does not support accessing the attribute `$attr`."))
end
"""
MOI.get(b::AbstractBridge, ::MOI.NumberOfVariables)
The number of variables created by the bridge `b` in the model.
"""
MOI.get(b::AbstractBridge, ::MOI.NumberOfVariables) = 0
"""
MOI.get(b::AbstractBridge, ::MOI.NumberOfConstraints{F, S}) where {F, S}
The number of constraints of the type `F`-in-`S` created by the bridge `b` in the model.
"""
MOI.get(b::AbstractBridge, ::MOI.NumberOfConstraints) = 0
"""
MOI.get(b::AbstractBridge, ::MOI.NumberOfConstraints{F, S}) where {F, S}
A `Vector{ConstraintIndex{F,S}}` with indices of all constraints of
type `F`-in`S` created by the bride `b` in the model (i.e., of length equal to the value of `NumberOfConstraints{F,S}()`).
"""
MOI.get(b::AbstractBridge, ::MOI.ListOfConstraintIndices{F, S}) where {F, S} = CI{F, S}[]
"""
MOI.supports_constraint(BT::Type{<:AbstractBridge}, F::Type{<:MOI.AbstractFunction}, S::Type{<:MOI.AbstractSet})::Bool
Return a `Bool` indicating whether the bridges of type `BT` support bridging `F`-in-`S` constraints.
"""
MOI.supports_constraint(::Type{<:AbstractBridge}, ::Type{<:MOI.AbstractFunction}, ::Type{<:MOI.AbstractSet}) = false
"""
added_constraint_types(BT::Type{<:AbstractBridge}, F::Type{<:MOI.AbstractFunction}, S::Type{<:MOI.AbstractSet})::Bool
Return a list of the types of constraints that bridges of type `BT` add for
bridging an `F`-in-`S` constraints.
added_constraint_types(BT::Type{<:AbstractBridge})::Bool
Return a list of the types of constraints that bridges of concrete type `BT` add
for `F`-in-`S` constraints.
"""
function added_constraint_types(BT::Type{<:AbstractBridge},
F::Type{<:MOI.AbstractFunction},
S::Type{<:MOI.AbstractSet})
added_constraint_types(concrete_bridge_type(BT, F, S))
end
"""
concrete_bridge_type(BT::Type{<:AbstractBridge},
F::Type{<:MOI.AbstractFunction},
S::Type{<:MOI.AbstractSet})::DataType
Return the concrete type of the bridge supporting `F`-in-`S` constraints. This
function can only be called if `MOI.supports_constraint(BT, F, S)` is `true`.
## Examples
The following returns `SplitIntervalBridge{Float64, MOI.SingleVariable}`:
```julia
concrete_bridge_type(SplitIntervalBridge{Float64}, MOI.SingleVariable,
MOI.Interval{Float64})
```
"""
function concrete_bridge_type(bridge_type::DataType,
::Type{<:MOI.AbstractFunction},
::Type{<:MOI.AbstractSet})
return bridge_type
end
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] | 2.595078 | 1,341 |
<reponame>thautwarm/MLFS.jl<filename>test/example.jl
x :: Type[Tag()]
x = Type
function f()
end | [
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] | 2.333333 | 42 |
## Copyright (c) 2013 <NAME>
##
## This file is distributed under the 2-clause BSD License.
# Auxiliary, non-exported functions are declared here.
# convert marker string description to gnuplot's expected number
function pointtype(x::String)
x == "+" && return "1"
x == "x" && return "2"
x == "*" && return "3"
x == "esquare" && return "4"
x == "fsquare" && return "5"
x == "ecircle" && return "6"
x == "fcircle" && return "7"
x == "etrianup" && return "8"
x == "ftrianup" && return "9"
x == "etriandn" && return "10"
x == "ftriandn" && return "11"
x == "edmd" && return "12"
x == "fdmd" && return "13"
return "1"
end
# create a Z-coordinate matrix from x, y coordinates and a function
function meshgrid(x,y,f)
Z = zeros(length(x),length(y))
for k = 1:length(x)
Z[k,:] = [ f(i,j) for i=x[k], j=y ]
end
return Z
end
# create x,y coordinates for a histogram, from a sample vector, using a number
# of bins
function hist(s,bins)
# When adding an element s to a bin, we use an iequality m < s <= M.
# In order to account for elements s==m, we need to special-case
# the computation for the first bin
ms = minimum(s)
Ms = maximum(s)
bins = max(bins, 1)
if Ms == ms
# compute a "natural" scale
g = (10.0^floor(log10(abs(ms)+eps()))) / 2
ms, Ms = ms - g, ms + g
end
delta = (Ms-ms)/bins
x = ms:delta:Ms
y = zeros(bins)
# this is special-cased because we want to include the minimum in the
# first bin
y[1] = sum(ms .<= s .<= x[2])
for i in 2:length(x)-2
y[i] = sum(x[i] .< s .<= x[i+1])
end
# this is special-cased because there is no guarantee that x[end] == Ms
# (because of how ranges work)
if length(y) > 1 y[end] = sum(x[end-1] .< s .<= Ms) end
if bins != 1
# We want the left bin to start at ms and the right bin to end at Ms
x = (ms+delta/2):delta:Ms
else
# add two empty bins on the sides to provide a scale to gnuplot
x = (ms-delta/2):delta:(ms+delta/2)
y = [0.0, y[1], 0.0]
end
return x,y
end
function Base.show(io::IO, ::MIME"text/plain", x::Figure)
isempty(x) && return nothing
IsJupyterOrJuno && return nothing
llplot(x)
if config[:mode] != "null"
if (config[:term][:terminal] ∈ term_text) || (config[:mode] == "ijulia")
write(io, x.svg)
end
end
return nothing
end
function Base.show(io::IO, ::MIME"image/svg+xml", x::Figure)
llplot(x)
write(io,x.svg)
return nothing
end
# return configuration string for a single plot
function linestr_single(conf::CurveConf)
s = ""
conf.legend != "" && (s *= " title '"*conf.legend*"' ")
conf.plotstyle != "" && (s *= " with "*conf.plotstyle*" ")
conf.linecolor != "" && (s *= "lc rgb '"*conf.linecolor*"' ")
conf.linewidth != "" && (s *= "lw "*conf.linewidth*" ")
conf.linestyle != "" && (s *= "dt '"*conf.linestyle*"' ")
conf.fillstyle != "" && (s *= "fs "*conf.fillstyle*" ")
conf.fillcolor != "" && (s *= "fc \""*conf.fillcolor*"\" ")
# some plotstyles don't allow point specifiers
if conf.plotstyle ∈ ps_sup_points
if conf.pointtype != ""
if conf.pointtype ∈ supported_pointtypes
s = s*"pt "*pointtype(conf.pointtype)*" "
else
s = s*"pt \""*conf.pointtype*"\" "
end
conf.pointsize != "" && (s = s*"ps "*conf.pointsize*" ")
end
end
return s
end
# build a string with plot commands according to configuration
function linestr(curves::Vector{Curve}, cmd, file)
# We have to insert "," between plot commands. One easy way to do this
# is create the first plot command, then the rest
# We also need to keep track of the current index (starts at zero)
index = 0
s = cmd*" '"*file*"' "*" i 0 "*linestr_single(curves[1].conf)
if length(curves) > 1
for i in curves[2:end]
index += 1
s = s*", '"*file*"' "*" i "*string(index)*" "*linestr_single(i.conf)
end
end
return s
end
# Build a "set term" string appropriate for the terminal type
function termstring(f::Figure,print=false)
global gnuplot_state
ac = f.axes
tc = f.term
pc = f.print
term = print ? pc.print_term : config[:term][:terminal]
if term != ""
# determine font, size, and background
font = print ? pc.print_font : tc.font
size = print ? pc.print_size : tc.size
background = print ? pc.print_background : tc.background
# build term string
ts = "set term $term "
term ∈ term_window && (ts = ts*string(gnuplot_state.current)*" ")
isempty(font) ? s = "" : s = " font \""*font*"\" "
term ∈ term_sup_font && (ts *= s)
isempty(size) ? s = "" : s = " size "*size*" "
term ∈ term_sup_size && (ts *= s)
isempty(background) ? s = "" : s = " background \""*background*"\" "
term ∈ term_sup_bkgnd && (ts *= s)
# terminal options
print || (ts *= config[:term][:termopts]*" ")
print && (ts *= config[:print][:print_termopts]*" ")
# set output file
if term ∈ term_file
s = ""
isempty(ac.output) || (s = "\nset output \"$(ac.output)\" ")
ts = ts*s
end
end
return ts
end
# send gnuplot the current figure's configuration
function gnuplot_send_fig_config(config)
config.title != "" && gnuplot_send("set title '"*config.title*"' ")
config.fillstyle != "" && gnuplot_send("set style fill "*config.fillstyle)
if config.grid != ""
if config.grid == "on"
gnuplot_send("set grid")
else
gnuplot_send("set grid "*config.grid)
end
end
config.keyoptions != "" && gnuplot_send("set key "*config.keyoptions)
config.boxwidth != "" && gnuplot_send("set boxwidth "*config.boxwidth)
if config.axis != ""
config.axis == "semilogx" && gnuplot_send("set logscale x")
config.axis == "semilogy" && gnuplot_send("set logscale y")
config.axis == "semilogz" && gnuplot_send("set logscale z")
config.axis == "loglog" && gnuplot_send("set logscale xyz")
end
config.xlabel != "" && gnuplot_send("set xlabel '"*config.xlabel*"' ")
config.ylabel != "" && gnuplot_send("set ylabel '"*config.ylabel*"' ")
config.zlabel != "" && gnuplot_send("set zlabel '"*config.zlabel*"' ")
config.xrange != "" && gnuplot_send("set xrange "*config.xrange)
config.yrange != "" && gnuplot_send("set yrange "*config.yrange)
config.zrange != "" && gnuplot_send("set zrange "*config.zrange)
if config.xzeroaxis != ""
if config.xzeroaxis == "on"
gnuplot_send("set xzeroaxis")
else
gnuplot_send("set xzeroaxis "*config.xzeroaxis)
end
end
if config.yzeroaxis != ""
if config.yzeroaxis == "on"
gnuplot_send("set yzeroaxis")
else
gnuplot_send("set yzeroaxis "*config.yzeroaxis)
end
end
if config.zzeroaxis != ""
if config.zzeroaxis == "on"
gnuplot_send("set zzeroaxis")
else
gnuplot_send("set zzeroaxis "*config.zzeroaxis)
end
end
config.palette != "" && gnuplot_send("set palette "*config.palette)
end
# write commands to gnuplot's pipe
function gnuplot_send(s)
config[:debug] && println(s) # print gnuplot commands if debug enabled
w = write(P.gstdin, string(s,"\n"))
# check that data was accepted by the pipe
if !(w > 0)
println("Something went wrong writing to gnuplot STDIN.")
return
end
flush(P.gstdin)
end
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] | 2.254799 | 3,438 |
<gh_stars>0
using Statistics
medium = Medium(:acou_homo1);
ageom = AGeom(medium.mgrid,:xwell);
tgrid = range(0.0,stop=2.0,length=1000)
wav = ricker(10.0, tgrid, tpeak=0.25, );
srcwav = SrcWav(tgrid, ageom, [:P])
update!(srcwav, [:P], wav)
vp0=mean(medium[:vp])
rho0=mean(medium[:rho])
rec1 = GeoPhyInv.Born.mod(vp0=vp0,
medium_pert=medium,
rho0=rho0,
ageom=ageom, srcwav=srcwav, tgridmod=tgrid, src_flag=2)
pa=SeisForwExpt(Fdtd(),npw=1,medium=medium,
ageom=[ageom], srcwav=[srcwav],
sflags=[2], rflags=[1],
tgrid=tgrid, verbose=true );
@time update!(pa);
# least-squares misfit
paerr=GeoPhyInv.VNamedD_misfit(rec1, pa.c.data[1])
err=GeoPhyInv.func_grad!(paerr)
# normalize error
error = err[1]/paerr.ynorm
# desired accuracy?
@test error<1e-2
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] | 2.007576 | 396 |
# --------------------------------------------------------------------------
# ACE.jl and SHIPs.jl: Julia implementation of the Atomic Cluster Expansion
# Copyright (c) 2019 <NAME> <<EMAIL>>
# All rights reserved.
# --------------------------------------------------------------------------
using ACE, JuLIP, Test
using ACE: evaluate
using JuLIP.Testing: print_tf
#---
@info("Construct basic and parameterised basis")
r0 = 2.3
rcut = 5.5
rin = 0.0
maxn = 5
species = [:X ]
D = SparsePSHDegree(wL = 1.0)
trans = ACE.PolyTransform(1, r0)
J = ACE.OrthPolys.transformed_jacobi(10, trans, rcut, rin)
P1 = ACE.RPI.PSH1pBasis(J, maxn, D=D, species = species)
basis = RPIBasis(P1, 3, D, maxn)
J5 = ACE.OrthPolys.transformed_jacobi(5, trans, rcut, rin)
P1basic = ACE.RPI.BasicPSH1pBasis(J5)
basic = RPIBasis(P1basic, 3, D, maxn)
#--- first test: make sure the bases are equivalent
@info("Test bases with and without parameters match")
for ntest = 1:30
local R, Z, z0 = ACE.Random.rand_nhd(12, J, species)
print_tf(@test ACE.evaluate(basis, R, Z, z0) ≈ ACE.evaluate(basic, R, Z, z0))
end
println()
#--- second test: perturb parameters
@info("Test basis with perturbed parameters doesn't match (duh...)")
params = basis.pibasis.basis1p.C[1]
basis.pibasis.basis1p.C[1] .+= 0.1 * (rand(size(params)...) .- 0.5)
for ntest = 1:30
local R, Z, z0 = ACE.Random.rand_nhd(12, J, species)
print_tf(@test !(ACE.evaluate(basis, R, Z, z0) ≈ ACE.evaluate(basic, R, Z, z0)))
end
println()
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] | 2.651246 | 562 |
<filename>test/perf/shootout/meteor_contest.jl
# The Computer Language Benchmarks Game
# http://shootout.alioth.debian.org/
#
# Contributed by <NAME>
# based on Python version by <NAME>
const width = 5
const height = 10
# directions
const E = 0
const NE = 1
const NW = 2
const W = 3
const SW = 4
const SE = 5
const rotate = {E => NE, NE => NW, NW => W, W => SW, SW => SE, SE => E}
const flip = {E => W, NE => NW, NW => NE, W => E, SW => SE, SE => SW}
const move = {
E => (x, y) -> (x + 1, y),
W => (x, y) -> (x - 1, y),
NE => (x, y) -> (x + y%2, y - 1),
NW => (x, y) -> (x + y%2 - 1, y - 1),
SE => (x, y) -> (x + y%2, y + 1),
SW => (x, y) -> (x + y%2 - 1, y + 1)
}
const pieces = (
(E, E, E, SE),
(SE, SW, W, SW),
(W, W, SW, SE),
(E, E, SW, SE),
(NW, W, NW, SE, SW),
(E, E, NE, W),
(NW, NE, NE, W),
(NE, SE, E, NE),
(SE, SE, E, SE),
(E, NW, NW, NW)
)
const solutions = Any[]
const masks = zeros(Uint64, 10)
const masksAtCell = Array(Any, width*height, height)
valid(x, y) = (0 <= x < width) && (0 <= y < height)
legal(mask::Uint64, board::Uint64) = (mask & board) == 0
zerocount(mask::Uint64) = 50 - count_ones(mask)
function findFreeCell(board::Uint64)
for y in 0:height-1
for x in 0:width-1
if board & (uint64(1) << (x + width*y)) == 0
return x, y
end
end
end
end
function floodFill(board::Uint64, fixme)
x, y = fixme
if !valid(x,y)
return board
end
if board & (uint64(1) << (x + width*y)) != 0
return board
end
board |= uint64(1) << (x + width*y)
for f in values(move)
board |= floodFill(board, f(x, y))
end
return board
end
function noIslands(mask::Uint64)
zeroes_ = zerocount(mask)
if zeroes_ < 5
return false
end
while mask != 0x3FFFFFFFFFFFF
mask = floodFill(mask, findFreeCell(mask))
new_zeroes = zerocount(mask)
if zeroes_ - new_zeroes < 5
return false
end
zeroes_ = new_zeroes
end
return true
end
function getBitmask(x, y, piece)
mask = (uint64(1) << (x + width*y))
for cell_ in piece
x, y = move[cell_](x,y)
if valid(x, y)
mask |= uint64(1) << (x + width*y)
else
return false, uint64(0)
end
end
return true, uint64(mask)
end
function allBitmasks(piece, color)
bitmasks = Uint64[]
for orientations in 0:1
for rotations in 0:(6 - 3*(color == 4))-1
for y in 0:height-1
for x in 0:width-1
isValid, mask = getBitmask(x, y, piece)
if isValid && noIslands(uint64(mask))
push!(bitmasks, mask)
end
end
end
piece = [rotate[cell_] for cell_ in piece]
end
piece = [flip[cell_] for cell_ in piece]
end
return bitmasks
end
function generateBitmasks()
for i = 1:length(masksAtCell)
masksAtCell[i] = Uint64[]
end
color = 0
for piece in pieces
masks = allBitmasks(piece, color)
sort!(masks)
cellMask = uint64(1) << (width*height - 1)
cellCounter = width*height - 1
j = length(masks) - 1
while j >= 0
if (masks[j + 1] & cellMask) == cellMask
push!(masksAtCell[cellCounter + 1, color + 1], masks[j + 1])
j -= 1
else
cellMask >>= 1
cellCounter -= 1
end
end
color += 1
end
end
function solveCell(cell_, board::Uint64, n)
if length(solutions) >= n
return
end
if board == 0x0003FFFFFFFFFFFF
# Solved
s = stringOfMasks(masks)
push!(solutions, s)
push!(solutions, reverse(s))
return
end
if board & (uint64(1) << cell_) != 0
# Cell full
solveCell(cell_ - 1, uint64(board), n)
return
end
if cell_ < 0
# Out of board
return
end
for color in 0:9
if masks[color + 1] == 0
for mask in masksAtCell[cell_ + 1, color + 1]
if legal(mask, board)
masks[color + 1] = mask
solveCell(cell_ - 1, uint64(board | mask), n)
masks[color + 1] = 0
end
end
end
end
end
function solve(n)
generateBitmasks()
solveCell(width*height-1, uint64(0), n)
end
function stringOfMasks(masks)
s = ""
mask::Uint64 = 1
for y in 0:height-1
for x in 0:width-1
for color in 0:9
if (masks[color+1] & mask) != 0
s = string(s, color)
break
elseif color == 9
s *= "."
end
end
mask <<= 1
end
end
return s
end
function printSolution(s)
for y in 0:height-1
if y%2 == 1
print(" ")
end
for x in 0:width-1
print("$(s[x + y*width + 1]) ")
end
println()
end
end
function meteor_contest(n::Int=2098)
empty!(solutions)
fill!(masks, 0)
solve(n)
# println("$(length(solutions)) solutions found")
# println()
# printSolution(minimum(solutions))
# println()
# printSolution(maximum(solutions))
# println()
end
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7,
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7,
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198,
2,
220,
220,
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44872,
3419,
198,
2,
220,
220,
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7,
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7,
82,
14191,
4008,
198,
2,
220,
220,
220,
44872,
3419,
198,
437,
198
] | 1.90298 | 2,886 |
export SimpleObservable, stddev
type SimpleObservable <: ScalarObservable
bins :: Vector{Float64}
num :: Int64
sum :: Float64
sum2 :: Float64
end
SimpleObservable() = SimpleObservable(zeros(0), 0, 0.0, 0.0)
function reset!(obs :: SimpleObservable)
obs.bins = zeros(0)
obs.num = 0
obs.sum = 0.0
obs.sum2 = 0.0
return obs
end
count(obs::SimpleObservable) = obs.num
function push!(obs :: SimpleObservable, value)
push!(obs.bins, value)
obs.num += 1
obs.sum += value
obs.sum2 += value^2
return obs
end
function mean(obs::SimpleObservable)
if obs.num > 0
return obs.sum / obs.num
else
return NaN
end
end
function var(obs::SimpleObservable)
if obs.num > 1
v = (obs.sum2 - obs.sum*obs.sum/obs.num)/(obs.num-1)
return maxzero(v)
elseif obs.num == 1
return Inf
else
return NaN
end
end
stddev(obs::SimpleObservable) = sqrt(var(obs))
stderror(obs::SimpleObservable) = sqrt(var(obs)/count(obs))
function confidence_interval(obs::SimpleObservable, confidence_rate :: Real)
if count(obs) < 2
return Inf
end
q = 0.5 + 0.5confidence_rate
correction = quantile( TDist(obs.num - 1), q)
serr = stderror(obs)
return correction * serr
end
function confidence_interval(obs::SimpleObservable, confidence_rate_symbol::Symbol = :sigma1)
n = parsesigma(confidence_rate_symbol)
return confidence_interval(obs, erf(0.5n*sqrt(2.0)))
end
function merge!(obs::SimpleObservable, other::SimpleObservable)
append!(obs.bins, other.bins)
obs.num += other.num
obs.sum += other.sum
obs.sum2 += other.sum2
return obs
end
merge(lhs::SimpleObservable, rhs::SimpleObservable) = merge!(deepcopy(lhs), rhs)
export SimpleObservableSet
typealias SimpleObservableSet MCObservableSet{SimpleObservable}
function merge!(obs::SimpleObservableSet, other::SimpleObservableSet)
obs_names = Set(keys(obs))
union!(obs_names, Set(keys(other)))
for name in obs_names
if !haskey(obs, name)
# in other only
obs[name] = deepcopy(other[name])
elseif haskey(other, name)
# in both
merge!(obs[name], other[name])
# else
# in obs only
# NOTHING to do
end
end
return obs
end
merge(lhs::SimpleObservableSet, rhs::SimpleObservableSet) = merge!(deepcopy(lhs), rhs)
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] | 2.521158 | 898 |
<filename>examples/recording_an_interaction.jl
# Use this simple script to record an interaction happening on `figure`
framerate = 10
total_time = 30 # in seconds
framen = framerate*total_time
record(figure, "name.mp4"; framerate = framerate) do io
for i = 1:framen
sleep(1/framerate)
recordframe!(io)
end
end
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<filename>base/grisu/float.jl<gh_stars>0
# This file is a part of Julia, but is derived from
# https://github.com/google/double-conversion which has the following license
#
# Copyright 2006-2014, the V8 project authors. All rights reserved.
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are
# met:
#
# * Redistributions of source code must retain the above copyright
# notice, this list of conditions and the following disclaimer.
# * Redistributions in binary form must reproduce the above
# copyright notice, this list of conditions and the following
# disclaimer in the documentation and/or other materials provided
# with the distribution.
# * Neither the name of Google Inc. nor the names of its
# contributors may be used to endorse or promote products derived
# from this software without specific prior written permission.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
struct Float
s::UInt64
e::Int32
de::Int32
end
Float() = Float(0,0,0)
Float(x,y) = Float(x,y,Int32(0))
Float(d::AbstractFloat) = Float(_significand(d), _exponent(d))
# Consts
const Float10MSBits = 0xFFC0000000000000 # used normalize(Float)
const FloatSignMask = 0x8000000000000000 # used in normalize(Float)
const FloatSignificandSize = Int32(64)
function normalize(v::Float)
f = v.s
e::Int32 = v.e
while (f & Float10MSBits) == 0
f <<= 10
e -= 10
end
while (f & FloatSignMask) == 0
f <<= 1
e -= 1
end
return Float(f,e)
end
function normalize(v::Float64)
s = _significand(v); e = _exponent(v)
while (s & HiddenBit(Float64)) == 0
s <<= UInt64(1)
e -= Int32(1)
end
s <<= UInt64(FloatSignificandSize - SignificandSize(Float64))
e -= Int32( FloatSignificandSize - SignificandSize(Float64))
return Float(s, e)
end
# Float128
#DenormalExponent(::Type{Float128}) = Int32(-ExponentBias(Float128) + 1)
#ExponentMask(::Type{Float128}) = 0x7fff0000000000000000000000000000
#PhysicalSignificandSize(::Type{Float128}) = Int32(112)
#SignificandSize(::Type{Float128}) = Int32(113)
#ExponentBias(::Type{Float128}) = Int32(0x00003fff + PhysicalSignificandSize(Float128))
#SignificandMask(::Type{Float128}) = 0x0000ffffffffffffffffffffffffffff
#HiddenBit(::Type{Float128}) = 0x00010000000000000000000000000000
#uint_t(d::Float128) = reinterpret(UInt128,d)
# Float64
DenormalExponent(::Type{Float64}) = Int32(-ExponentBias(Float64) + 1)
ExponentMask(::Type{Float64}) = 0x7FF0000000000000
PhysicalSignificandSize(::Type{Float64}) = Int32(52)
SignificandSize(::Type{Float64}) = Int32(53)
ExponentBias(::Type{Float64}) = Int32(0x3FF + PhysicalSignificandSize(Float64))
SignificandMask(::Type{Float64}) = 0x000FFFFFFFFFFFFF
HiddenBit(::Type{Float64}) = 0x0010000000000000
uint_t(d::Float64) = reinterpret(UInt64,d)
# Float32
DenormalExponent(::Type{Float32}) = Int32(-ExponentBias(Float32) + 1)
ExponentMask(::Type{Float32}) = 0x7F800000
PhysicalSignificandSize(::Type{Float32}) = Int32(23)
SignificandSize(::Type{Float32}) = Int32(24)
ExponentBias(::Type{Float32}) = Int32(0x7F + PhysicalSignificandSize(Float32))
SignificandMask(::Type{Float32}) = 0x007FFFFF
HiddenBit(::Type{Float32}) = 0x00800000
uint_t(d::Float32) = reinterpret(UInt32,d)
# Float16
DenormalExponent(::Type{Float16}) = Int32(-ExponentBias(Float16) + 1)
ExponentMask(::Type{Float16}) = 0x7c00
PhysicalSignificandSize(::Type{Float16}) = Int32(10)
SignificandSize(::Type{Float16}) = Int32(11)
ExponentBias(::Type{Float16}) = Int32(0x000f + PhysicalSignificandSize(Float16))
SignificandMask(::Type{Float16}) = 0x03ff
HiddenBit(::Type{Float16}) = 0x0400
uint_t(d::Float16) = reinterpret(UInt16,d)
function _exponent(d::T) where T<:AbstractFloat
isdenormal(d) && return DenormalExponent(T)
biased_e::Int32 = Int32((uint_t(d) & ExponentMask(T)) >> PhysicalSignificandSize(T))
return Int32(biased_e - ExponentBias(T))
end
function _significand(d::T) where T<:AbstractFloat
s = uint_t(d) & SignificandMask(T)
return !isdenormal(d) ? s + HiddenBit(T) : s
end
isdenormal{T<:AbstractFloat}(d::T) = (uint_t(d) & ExponentMask(T)) == 0
function normalizedbound(f::AbstractFloat)
v = Float(_significand(f),_exponent(f))
m_plus = normalize(Float((v.s << 1) + 1, v.e - 1))
if lowerboundaryiscloser(f)
m_minus = Float((v.s << 2) - 1, v.e - 2)
else
m_minus = Float((v.s << 1) - 1, v.e - 1)
end
return Float(m_minus.s << (m_minus.e - m_plus.e), m_plus.e), m_plus
end
function lowerboundaryiscloser(f::T) where T<:AbstractFloat
physical_significand_is_zero = (uint_t(f) & SignificandMask(T)) == 0
return physical_significand_is_zero && (_exponent(f) != DenormalExponent(T))
end
(-)(a::Float,b::Float) = Float(a.s - b.s,a.e,a.de)
const FloatM32 = 0xFFFFFFFF
function (*)(this::Float,other::Float)
a::UInt64 = this.s >> 32
b::UInt64 = this.s & FloatM32
c::UInt64 = other.s >> 32
d::UInt64 = other.s & FloatM32
ac::UInt64 = a * c
bc::UInt64 = b * c
ad::UInt64 = a * d
bd::UInt64 = b * d
tmp::UInt64 = (bd >> 32) + (ad & FloatM32) + (bc & FloatM32)
# By adding 1U << 31 to tmp we round the final result.
# Halfway cases will be round up.
tmp += UInt64(1) << 31
result_f::UInt64 = ac + (ad >> 32) + (bc >> 32) + (tmp >> 32)
return Float(result_f,this.e + other.e + 64,this.de)
end
const CachedPowers = Float[
Float(0xfa8fd5a0081c0288, -1220, -348),
Float(0xbaaee17fa23ebf76, -1193, -340),
Float(0x8b16fb203055ac76, -1166, -332),
Float(0xcf42894a5dce35ea, -1140, -324),
Float(0x9a6bb0aa55653b2d, -1113, -316),
Float(0xe61acf033d1a45df, -1087, -308),
Float(0xab70fe17c79ac6ca, -1060, -300),
Float(0xff77b1fcbebcdc4f, -1034, -292),
Float(0xbe5691ef416bd60c, -1007, -284),
Float(0x8dd01fad907ffc3c, -980, -276),
Float(0xd3515c2831559a83, -954, -268),
Float(0x9d71ac8fada6c9b5, -927, -260),
Float(0xea9c227723ee8bcb, -901, -252),
Float(0xaecc49914078536d, -874, -244),
Float(0x823c12795db6ce57, -847, -236),
Float(0xc21094364dfb5637, -821, -228),
Float(0x9096ea6f3848984f, -794, -220),
Float(0xd77485cb25823ac7, -768, -212),
Float(0xa086cfcd97bf97f4, -741, -204),
Float(0xef340a98172aace5, -715, -196),
Float(0xb23867fb2a35b28e, -688, -188),
Float(0x84c8d4dfd2c63f3b, -661, -180),
Float(0xc5dd44271ad3cdba, -635, -172),
Float(0x936b9fcebb25c996, -608, -164),
Float(0xdbac6c247d62a584, -582, -156),
Float(0xa3ab66580d5fdaf6, -555, -148),
Float(0xf3e2f893dec3f126, -529, -140),
Float(0xb5b5ada8aaff80b8, -502, -132),
Float(0x87625f056c7c4a8b, -475, -124),
Float(0xc9bcff6034c13053, -449, -116),
Float(0x964e858c91ba2655, -422, -108),
Float(0xdff9772470297ebd, -396, -100),
Float(0xa6dfbd9fb8e5b88f, -369, -92),
Float(0xf8a95fcf88747d94, -343, -84),
Float(0xb94470938fa89bcf, -316, -76),
Float(0x8a08f0f8bf0f156b, -289, -68),
Float(0xcdb02555653131b6, -263, -60),
Float(0x993fe2c6d07b7fac, -236, -52),
Float(0xe45c10c42a2b3b06, -210, -44),
Float(0xaa242499697392d3, -183, -36),
Float(0xfd87b5f28300ca0e, -157, -28),
Float(0xbce5086492111aeb, -130, -20),
Float(0x8cbccc096f5088cc, -103, -12),
Float(0xd1b71758e219652c, -77, -4),
Float(0x9c40000000000000, -50, 4),
Float(0xe8d4a51000000000, -24, 12),
Float(0xad78ebc5ac620000, 3, 20),
Float(0x813f3978f8940984, 30, 28),
Float(0xc097ce7bc90715b3, 56, 36),
Float(0x8f7e32ce7bea5c70, 83, 44),
Float(0xd5d238a4abe98068, 109, 52),
Float(0x9f4f2726179a2245, 136, 60),
Float(0xed63a231d4c4fb27, 162, 68),
Float(0xb0de65388cc8ada8, 189, 76),
Float(0x83c7088e1aab65db, 216, 84),
Float(0xc45d1df942711d9a, 242, 92),
Float(0x924d692ca61be758, 269, 100),
Float(0xda01ee641a708dea, 295, 108),
Float(0xa26da3999aef774a, 322, 116),
Float(0xf209787bb47d6b85, 348, 124),
Float(0xb454e4a179dd1877, 375, 132),
Float(0x865b86925b9bc5c2, 402, 140),
Float(0xc83553c5c8965d3d, 428, 148),
Float(0x952ab45cfa97a0b3, 455, 156),
Float(0xde469fbd99a05fe3, 481, 164),
Float(0xa59bc234db398c25, 508, 172),
Float(0xf6c69a72a3989f5c, 534, 180),
Float(0xb7dcbf5354e9bece, 561, 188),
Float(0x88fcf317f22241e2, 588, 196),
Float(0xcc20ce9bd35c78a5, 614, 204),
Float(0x98165af37b2153df, 641, 212),
Float(0xe2a0b5dc971f303a, 667, 220),
Float(0xa8d9d1535ce3b396, 694, 228),
Float(0xfb9b7cd9a4a7443c, 720, 236),
Float(0xbb764c4ca7a44410, 747, 244),
Float(0x8bab8eefb6409c1a, 774, 252),
Float(0xd01fef10a657842c, 800, 260),
Float(0x9b10a4e5e9913129, 827, 268),
Float(0xe7109bfba19c0c9d, 853, 276),
Float(0xac2820d9623bf429, 880, 284),
Float(0x80444b5e7aa7cf85, 907, 292),
Float(0xbf21e44003acdd2d, 933, 300),
Float(0x8e679c2f5e44ff8f, 960, 308),
Float(0xd433179d9c8cb841, 986, 316),
Float(0x9e19db92b4e31ba9, 1013, 324),
Float(0xeb96bf6ebadf77d9, 1039, 332),
Float(0xaf87023b9bf0ee6b, 1066, 340)]
const CachedPowersLength = length(CachedPowers)
const CachedPowersOffset = 348 # -1 * the first decimal_exponent.
const D_1_LOG2_10 = 0.30102999566398114 # 1 / lg(10)
# Difference between the decimal exponents in the table above.
const DecimalExponentDistance = 8
const MinDecimalExponent = -348
const MaxDecimalExponent = 340
function binexp_cache(min_exponent,max_exponent)
k = ceil(Integer,(min_exponent+63)*D_1_LOG2_10)
index = div(CachedPowersOffset+k-1,DecimalExponentDistance) + 1
cp = CachedPowers[index+1]
return cp
end
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33,
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811,
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10699,
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6030,
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2558,
2624,
7,
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2,
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3471,
33,
4448,
7,
3712,
6030,
90,
43879,
12762,
30072,
796,
2558,
2624,
7,
15,
87,
2388,
18,
20972,
1343,
16331,
11712,
811,
392,
10699,
7,
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2,
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7,
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] | 2.278743 | 4,488 |
<filename>test/immerse.jl
# These tests are designed to ensure that Immerse.jl works. If you
# need to edit these tests to make them pass, that's fine, but please
# submit the corresponding fix to Immerse.
using Compose, Base.Test
import Cairo
### The Immerse backend
srf = Cairo.CairoImageSurface(10, 10, Cairo.FORMAT_RGB24)
ctx = compose(compose(context(), rectangle(0.0w, 0.0h, 0.8w, 0.7h, :rect)), fill("tomato"))
be = Compose.ImmerseBackend(srf)
draw(be, ctx)
@test be.coords[:rect][2] == Compose.UnitBox()
### Finding tagged objects
const ContainersWithChildren = Union{Context,Compose.Table}
const Iterables = Union{ContainersWithChildren, AbstractArray}
iterable(ctx::ContainersWithChildren) = ctx.children
iterable(a::AbstractArray) = a
function find_tagged(root)
handles = Dict{Symbol,Context}()
find_tagged!(handles, root)
end
function find_tagged!(handles, obj::Iterables)
for item in iterable(obj)
if has_tag(item)
handles[item.tag] = obj
else
find_tagged!(handles, item)
end
end
handles
end
function find_tagged!(handles, obj::Context)
for item in obj.form_children
if has_tag(item)
handles[item.tag] = obj
end
end
for item in obj.container_children
find_tagged!(handles, item)
end
handles
end
find_tagged!(handles, obj) = obj
has_tag(form::Compose.Form, tag) = form.tag == tag
has_tag(form::Compose.Form) = form.tag != Compose.empty_tag
has_tag(obj, tag) = false
has_tag(obj) = false
@test find_tagged(ctx)[:rect] == ctx
### Coordinate computations
function absolute_to_data(x, y, transform, unit_box, parent_box)
xt, yt = invert_transform(transform, x, y)
(unit_box.x0 + unit_box.width *(xt-parent_box.x0[1])/Measures.width(parent_box),
unit_box.y0 + unit_box.height*(yt-parent_box.x0[2])/Measures.height(parent_box))
end
invert_transform(::Compose.IdentityTransform, x, y) = x, y
function invert_transform(t::Compose.MatrixTransform, x, y)
@assert t.M[3,1] == t.M[3,2] == 0
xyt = t.M\[x, y, 1.0]
xyt[1], xyt[2]
end
box, units, transform = be.coords[:rect]
xd, yd = absolute_to_data(0.5mm, 0.5mm, transform, units, box)
@test isapprox(xd,0.1417; atol=0.0001)
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] | 2.402778 | 936 |
<filename>src/ImageIO.jl
module ImageIO
using UUIDs
using FileIO: File, DataFormat, Stream, stream, Formatted
const idNetpbm = Base.PkgId(UUID("f09324ee-3d7c-5217-9330-fc30815ba969"), "Netpbm")
const idPNGFiles = Base.PkgId(UUID("f57f5aa1-a3ce-4bc8-8ab9-96f992907883"), "PNGFiles")
const idTiffImages = Base.PkgId(UUID("731e570b-9d59-4bfa-96dc-6df516fadf69"), "TiffImages")
# Enforce a type conversion to be backend independent (issue #25)
# Note: If the backend does not provide efficient `convert` implementation,
# there will be an extra memeory allocation and thus hurt the performance.
for FMT in (
:PBMBinary, :PGMBinary, :PPMBinary, :PBMText, :PGMText, :PPMText,
:TIFF,
:PNG,
)
@eval canonical_type(::DataFormat{$(Expr(:quote, FMT))}, ::AbstractArray{T, N}) where {T,N} =
Array{T,N}
end
@inline canonical_type(::Formatted{T}, data) where T = canonical_type(T(), data)
## PNGs
const load_locker = Threads.ReentrantLock()
function checked_import(pkgid)
Base.root_module_exists(pkgid) && return Base.root_module(pkgid)
# If not available, lock and load the library in a sequential order
lock(load_locker) do
Base.require(pkgid)
end
end
function load(f::File{DataFormat{:PNG}}; kwargs...)
data = Base.invokelatest(checked_import(idPNGFiles).load, f.filename, kwargs...)
return convert(canonical_type(f, data), data)
end
function load(s::Stream{DataFormat{:PNG}}; kwargs...)
data = Base.invokelatest(checked_import(idPNGFiles).load, stream(s), kwargs...)
return convert(canonical_type(s, data), data)
end
function save(f::File{DataFormat{:PNG}}, image::S; kwargs...) where {T, S<:Union{AbstractMatrix, AbstractArray{T,3}}}
return Base.invokelatest(checked_import(idPNGFiles).save, f.filename, image, kwargs...)
end
function save(s::Stream{DataFormat{:PNG}}, image::S; permute_horizontal=false, mapi=identity, kwargs...) where {T, S<:Union{AbstractMatrix, AbstractArray{T,3}}}
imgout = map(mapi, image)
if permute_horizontal
perm = ndims(imgout) == 2 ? (2, 1) : ndims(imgout) == 3 ? (2, 1, 3) : error("$(ndims(imgout)) dims array is not supported")
return Base.invokelatest(checked_import(idPNGFiles).save, stream(s), PermutedDimsArray(imgout, perm), kwargs...)
else
return Base.invokelatest(checked_import(idPNGFiles).save, stream(s), imgout, kwargs...)
end
end
# Netpbm types
for NETPBMFORMAT in (:PBMBinary, :PGMBinary, :PPMBinary, :PBMText, :PGMText, :PPMText)
@eval begin
function load(f::File{DataFormat{$(Expr(:quote,NETPBMFORMAT))}})
data = Base.invokelatest(checked_import(idNetpbm).load, f)
return convert(canonical_type(f, data), data)
end
function load(s::Stream{DataFormat{$(Expr(:quote,NETPBMFORMAT))}})
data = Base.invokelatest(checked_import(idNetpbm).load, s)
return convert(canonical_type(s, data), data)
end
function save(f::File{DataFormat{$(Expr(:quote,NETPBMFORMAT))}}, image::S; kwargs...) where {S<:AbstractMatrix}
return Base.invokelatest(checked_import(idNetpbm).save, f, image; kwargs...)
end
function save(s::Stream{DataFormat{$(Expr(:quote,NETPBMFORMAT))}}, image::S; kwargs...) where {S<:AbstractMatrix}
return Base.invokelatest(checked_import(idNetpbm).save, s, image; kwargs...)
end
end
end
## TIFFs
function load(f::File{DataFormat{:TIFF}}; kwargs...)
data = Base.invokelatest(checked_import(idTiffImages).load, f.filename, kwargs...)
return convert(canonical_type(f, data), data)
end
function load(s::Stream{DataFormat{:TIFF}}; kwargs...)
data = Base.invokelatest(checked_import(idTiffImages).load, stream(s), kwargs...)
return convert(canonical_type(s, data), data)
end
function save(f::File{DataFormat{:TIFF}}, image::S) where {T, S<:Union{AbstractMatrix, AbstractArray{T,3}}}
Base.invokelatest(checked_import(idTiffImages).save, f.filename, image)
end
function save(s::Stream{DataFormat{:TIFF}}, image::S; permute_horizontal=false, mapi=identity) where {T, S<:Union{AbstractMatrix, AbstractArray{T,3}}}
imgout = map(mapi, image)
if permute_horizontal
perm = ndims(imgout) == 2 ? (2, 1) : ndims(imgout) == 3 ? (2, 1, 3) : error("$(ndims(imgout)) dims array is not supported")
Base.invokelatest(checked_import(idTiffImages).save, stream(s), PermutedDimsArray(imgout, perm))
else
Base.invokelatest(checked_import(idTiffImages).save, stream(s), imgout)
end
end
## Function names labelled for FileIO. Makes FileIO lookup quicker
const fileio_save = save
const fileio_load = load
end # module
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<gh_stars>0
mutable struct childSupportIncome:>
nonfixedIncome
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"""
CenteredAxis(origin=0, indices)
A `CenteredAxis` takes `indices` and provides a user facing set of keys centered around zero.
The `CenteredAxis` is a subtype of `AbstractOffsetAxis` and its keys are treated as the predominant indexing style.
Note that the element type of a `CenteredAxis` cannot be unsigned because any instance with a length greater than 1 will begin at a negative value.
## Examples
A `CenteredAxis` sends all indexing arguments to the keys and only maps to the indices when `to_index` is called.
```jldoctest
julia> using AxisIndices
julia> axis = AxisIndices.CenteredAxis(1:10)
center(0)(SimpleAxis(1:10))
julia> axis[10] # the indexing goes straight to keys and is centered around zero
ERROR: BoundsError: attempt to access center(0)(SimpleAxis(1:10)) at index [10]
[...]
julia> axis[-4]
-4
```
"""
struct CenteredAxis{I,Inds,F} <: AbstractOffsetAxis{I,Inds,F}
origin::F
parent::Inds
function CenteredAxis{I,Inds,F}(origin::Integer, inds::AbstractAxis) where {I,Inds,F}
if inds isa Inds && origin isa F
return new{I,Inds,F}(origin, inds)
else
return CenteredAxis(origin, convert(Inds, inds))
end
end
function CenteredAxis{I,Inds,F}(inds::AbstractRange) where {I,Inds,F<:StaticInt}
return new{I,Inds,F}(F(), inds)
end
function CenteredAxis{I,Inds,F}(inds::AbstractRange) where {I,Inds,F}
return new{I,Inds,F}(F(0), inds)
end
function CenteredAxis{I,Inds,F}(origin::Integer, inds::AbstractRange) where {I,Inds,F}
return CenteredAxis{I,Inds}(origin, inds)
end
function CenteredAxis{I,Inds}(inds::AbstractRange) where {I,Inds}
return CenteredAxis{I,Inds}(Zero(), inds)
end
function CenteredAxis{I,Inds}(origin::Integer, inds::AbstractArray) where {I,Inds}
return CenteredAxis{I,Inds}(origin, compose_axis(inds))
end
function CenteredAxis{I,Inds}(origin::Integer, inds::AbstractAxis) where {I,Inds}
if inds isa Inds
return CenteredAxis{I,Inds,typeof(origin)}(origin, inds)
else
return CenteredAxis{I}(origin, convert(Inds, inds))
end
end
function CenteredAxis{I}(origin::Integer, inds::AbstractArray) where {I}
return CenteredAxis{I}(origin, compose_axis(inds))
end
function CenteredAxis{I}(origin::Integer, inds::AbstractOffsetAxis) where {I}
return CenteredAxis{I}(origin, parent(inds))
end
function CenteredAxis{I}(origin::Integer, inds::AbstractAxis) where {I}
if eltype(inds) <: I
return CenteredAxis{I,typeof(inds)}(origin, inds)
else
return CenteredAxis{I}(origin, convert(AbstractUnitRange{I}, inds))
end
end
CenteredAxis{I}(inds::AbstractRange) where {I} = CenteredAxis{I}(Zero(), inds)
CenteredAxis(inds::AbstractRange) = CenteredAxis(Zero(), inds)
function CenteredAxis(origin::Integer, inds::AbstractOffsetAxis)
return CenteredAxis(origin, parent(inds))
end
function CenteredAxis(origin::Integer, inds::AbstractArray)
return CenteredAxis(origin, compose_axis(inds))
end
function CenteredAxis(origin::Integer, inds::AbstractAxis)
return CenteredAxis{eltype(inds)}(origin, inds)
end
end
@inline Base.getproperty(axis::CenteredAxis, k::Symbol) = getproperty(parent(axis), k)
function ArrayInterface.unsafe_reconstruct(axis::CenteredAxis, inds; kwargs...)
return CenteredAxis(origin(axis), unsafe_reconstruct(parent(axis), inds; kwargs...))
end
ArrayInterface.known_first(::Type{T}) where {T<:CenteredAxis{<:Any,<:Any,<:Any}} = nothing
function ArrayInterface.known_first(::Type{T}) where {Inds,F,T<:CenteredAxis{<:Any,Inds,StaticInt{F}}}
if known_length(Inds) === nothing
return nothing
else
return F - div(known_length(Inds), 2)
end
end
Base.first(axis::CenteredAxis) = origin(axis) - div(length(parent(axis)), 2)
ArrayInterface.known_last(::Type{T}) where {T<:CenteredAxis{<:Any,<:Any,<:Any}} = nothing
function ArrayInterface.known_last(::Type{T}) where {Inds,F,T<:CenteredAxis{<:Any,Inds,StaticInt{F}}}
if known_length(Inds) === nothing
return nothing
else
return F - div(known_length(Inds), 2) + known_length(Inds)
end
end
Base.last(axis::CenteredAxis) = last(parent(axis)) + _origin_to_offset(axis)
function _origin_to_offset(axis::CenteredAxis)
p = parent(axis)
return _origin_to_offset(first(p), length(p), origin(axis))
end
_origin_to_offset(start, len, origin) = (origin - div(len, 2one(start))) - start
origin(axis::CenteredAxis) = getfield(axis, :origin)
@inline function ArrayInterface.offsets(axis::CenteredAxis)
inds = parent(axis)
return (_origin_to_offset(static_first(inds), static_length(inds), origin(axis)),)
end
"""
center(collection, origin)
center(collection, origin)
Shortcut for creating [`CenteredAxis`](@ref).
## Examples
```jldoctest
julia> using AxisIndices
julia> AxisArray(ones(3), center(0))
3-element AxisArray(::Vector{Float64}
• axes:
1 = -1:1
)
1
-1 1.0
0 1.0
1 1.0
```
"""
struct Center <: AxisInitializer end
const center = Center()
axis_method(::Center, x, inds) = CenteredAxis(x, inds)
center(collection::AbstractArray) = center(collection, Zero())
"""
CenteredArray(A::AbstractArray)
Provides centered axes for indexing `A`.
## Examples
```jldoctest
julia> using AxisIndices
julia> AxisIndices.CenteredArray(ones(3,3))
3×3 AxisArray(::Matrix{Float64}
• axes:
1 = -1:1
2 = -1:1
)
-1 0 1
-1 1.0 1.0 1.0
0 1.0 1.0 1.0
1 1.0 1.0 1.0
```
"""
const CenteredArray{T,N,P,A<:Tuple{Vararg{<:CenteredAxis}}} = AxisArray{T,N,P,A}
CenteredArray(A::AbstractArray{T,N}) where {T,N} = CenteredArray{T,N}(A)
CenteredArray(A::AbstractArray{T,0}) where {T} = AxisArray(A)
CenteredArray{T}(A::AbstractArray) where {T} = CenteredArray{T,ndims(A)}(A)
CenteredArray{T,N}(A::AbstractArray) where {T,N} = CenteredArray{T,N,typeof(A)}(A)
CenteredArray{T,N,P}(A::CenteredArray{T,N,P}) where {T,N,P} = A
function CenteredArray{T,N,P}(A::CenteredArray) where {T,N,P}
return CenteredArray{T,N,P}(parent(A))
end
function CenteredArray{T,N,P}(A::AbstractArray) where {T,N,P<:AbstractArray{T,N}}
return CenteredArray{T,N,P}(convert(P, A))
end
function CenteredArray{T,N,P}(A::P) where {T,N,P<:AbstractArray{T,N}}
axs = map(center, axes(A))
return CenteredArray{T,N,P,typeof(axs)}(A, axs)
end
function CenteredArray{T}(init::ArrayInitializer, sz::Tuple=()) where {T}
return CenteredArray{T,length(inds)}(init, sz)
end
function CenteredArray{T,N}(init::ArrayInitializer, sz::Tuple=()) where {T,N}
return CenteredArray{T,N}(Array{T,N}(init, sz))
end
"""
CenteredVector(v::AbstractVector)
Provides a centered axis for indexing `v`.
## Examples
```jldoctest
julia> using AxisIndices
julia> AxisIndices.CenteredVector(ones(3))
3-element AxisArray(::Vector{Float64}
• axes:
1 = -1:1
)
1
-1 1.0
0 1.0
1 1.0
```
"""
const CenteredVector{T,P<:AbstractVector{T},Ax1<:CenteredAxis} = CenteredArray{T,1,P,Tuple{Ax1}}
CenteredVector(A::AbstractVector) = CenteredArray{eltype(A)}(A)
CenteredVector{T}(A::AbstractVector) where {T} = CenteredArray{T,1}(A)
"""
CenteredVector{T}(init::ArrayInitializer, sz::Integer)
Creates a vector with elements of type `T` of size `sz` and a centered axis.
## Examples
```jldoctest
julia> using AxisIndices
julia> AxisIndices.CenteredVector{Union{Missing, Int}}(missing, 3)
3-element AxisArray(::Vector{Union{Missing, Int64}}
• axes:
1 = -1:1
)
1
-1 missing
0 missing
1 missing
```
"""
function CenteredVector{T}(init::ArrayInitializer, arg) where {T}
return CenteredVector{T}(Vector{T}(init, arg))
end
function print_axis(io::IO, axis::CenteredAxis)
print(io, "center($(Int(origin(axis))))($(parent(axis)))")
end
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<reponame>racinmat/Mill.jl<filename>test/lazynode.jl
using Mill, Flux, Test, LearnBase
using Mill: LazyNode
@testset "LazyNode" begin
ss = ["Hello world.", "Make peace.", "Make food.", "Eat penam."]
function Mill.unpack2mill(ds::LazyNode{:Sentence})
s = ds.data
ss = map(x -> split(x, " "),s)
x = NGramMatrix(reduce(vcat, ss), 3, 256, 2053)
BagNode(ArrayNode(x), Mill.length2bags(length.(ss)))
end
@test LazyNode{:Sentence,Vector{String}}(ss).data == ss
@test LazyNode(:Sentence, ss).data == ss
@test LazyNode{:Sentence}(ss).data == ss
@test nobs(LazyNode{:Sentence}(ss)) == 4
@test (LazyNode{:Sentence}(ss))[[1,3]].data == ss[[1,3]]
@test (LazyNode{:Sentence}(ss))[2].data == [ss[2]]
@test reduce(catobs, [LazyNode{:Sentence}(ss)[i] for i in [1:2,3:4]]).data == ss
@test reduce(catobs, [LazyNode{:Sentence}(ss)[i] for i in [1,2,3,4]]).data == ss
ds = LazyNode{:Sentence}(ss)
m = Mill.reflectinmodel(ds, d -> Dense(d,2), s -> SegmentedMeanMax(s))
@test m(ds).data ≈ m.m(Mill.unpack2mill(ds)).data
end
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<reponame>noob-data-analaysis/LeetCode.jl<gh_stars>0
# ---
# title: 461. Hamming Distance
# id: problem461
# author: <NAME>
# date: 2020-10-31
# difficulty: Easy
# categories: Bit Manipulation
# link: <https://leetcode.com/problems/hamming-distance/description/>
# hidden: true
# ---
#
# The [Hamming distance](https://en.wikipedia.org/wiki/Hamming_distance) between
# two integers is the number of positions at which the corresponding bits are
# different.
#
# Given two integers `x` and `y`, calculate the Hamming distance.
#
# **Note:**
# 0 ≤ `x`, `y` < 231.
#
# **Example:**
#
#
#
# Input: x = 1, y = 4
#
# Output: 2
#
# Explanation:
# 1 (0 0 0 1)
# 4 (0 1 0 0)
# ↑ ↑
#
# The above arrows point to positions where the corresponding bits are different.
#
#
#
## @lc code=start
using LeetCode
## add your code here:
## @lc code=end
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534,
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994,
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2235,
2488,
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] | 2.395288 | 382 |
using Pkg; Pkg.activate("../")
using WebAssemblyText
function translate(infile, outfile)
wat = jl2wat(infile)
open(outfile, "w") do io
write(io, wat)
end
end
translate(ARGS[1], ARGS[2]) | [
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] | 2.274725 | 91 |
<reponame>danielwe/LoopVectorization.jl<gh_stars>100-1000
using LoopVectorization, Test#, OffsetArrays
@testset "Array Wrappers" begin
@show @__LINE__
function addone!(y, x)
@turbo for i ∈ eachindex(x,y)
y[i] = x[i] + 1
end
y
end
x5 = PermutedDimsArray(rand(10,10), (2,1));
y5 = PermutedDimsArray(rand(10,10), (2,1));
y5t = x5 .+ 1;
@test LoopVectorization.check_args(x5)
@test y5t == addone!(y5, x5)
x6 = view(x5, 1:3, 1:3);
y6 = view(y5, 1:3, 1:3);
fill!(y6, NaN);
@test LoopVectorization.check_args(x6)
addone!(y6, x6); # also
@test y5t == y5 # Test that `NaN`s replaced with correct answers, and that other values were untouched.
A = rand(12,13,14,15);
pA = PermutedDimsArray(A, (3,1,4,2));
@test addone!(similar(pA), pA) == pA .+ 1
ppA = PermutedDimsArray(pA, (4,2,3,1));
@test LoopVectorization.check_args(ppA)
@test addone!(similar(ppA), ppA) == ppA .+ 1
x = rand(10,10); xc = copy(x);
xv = view(x, 1:2:10, 1:3);
xcv = view(xc, 1:2:10, 1:3);
@test LoopVectorization.check_args(xv)
addone!(xv, xv);
xcv .+= 1;
@test x == xc
end
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2488,
9288,
2124,
6624,
2124,
66,
198,
437,
628,
198
] | 1.961921 | 604 |
#=* ДАНО: Робот находится в произвольной клетке ограниченного прямоугольного поля без внутренних перегородок и маркеров.
+учитываются внутренние прямоугольные перегородки,которые изолированы друг от друга и от внешней перегородки
РЕЗУЛЬТАТ: Робот — в исходном положении в центре прямого креста из маркеров, расставленных вплоть до внешней рамки.=#
function cross_but_harder(r::Robot)
for side in (Nord,West,Sud,Ost)
count=1
while count!=0
count=workaround_wall(r,side)
putmarker!(r)
end
while ismarker(r)==true
workaround_wall(r,inverse(side))
end
side=inverse(next(side))
end
putmarker!(r)
end
function workaround_wall(r::Robot, side::HorizonSide) #функция для обхода внутренних перегородок
num_steps=0
while isborder(r,side)==true && isborder(r,next(side))==false #уходим от линии маркеров чтобы обойти перегородки
move!(r,next(side))
num_steps+=1
end
count=0
if isborder(r,side)==false
move!(r,side)
count+=1 #считаем шаги при движении по линии
end
if num_steps !=0
while isborder(r,inverse(next(side)))==true #проходим перегородку на другой линии
move!(r,side)
count+=1
end
for t in 1:num_steps
move!(r,inverse(next(side))) #возвращаемся на линию
end
end
return count #Возвращение значения счётчика(сколько будет маркеров в линии)
end
next(side::HorizonSide)= HorizonSide(mod(Int(side)+1,4))
inverse(side::HorizonSide)=HorizonSide(mod(Int(side)+2,4))
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] | 1.359292 | 1,130 |
# Many derivative free methods of different orders
##################################################
## Helpers for the various methods
## issue with approx derivative
isissue(x) = (x == 0.0) || isnan(x) || isinf(x)
"""
heuristic to get a decent first step with Steffensen steps
"""
function steff_step(x::T, fx) where {T}
thresh = max(1, norm(x)) * sqrt(eps(T)) # max(1, sqrt(abs(x/fx))) * 1e-6
norm(fx) <= thresh ? fx : sign(fx) * thresh
end
function guarded_secant_step(alpha::T, beta::T, falpha, fbeta) where {T <: AbstractFloat}
fp = (fbeta - falpha) / (beta - alpha)
Δ::T = fbeta / fp
## odd, we get allocations if we define Delta, then beta - Delta
## Δ = beta - fbeta * (beta - alpha) / (fbeta - falpha)
if isissue(Δ)
Δ = one(T)/1000
elseif norm(Δ) >= 100 * norm(alpha - beta) # guard runaway
Δ = sign(Δ) * 100 * min(one(T), norm(alpha - beta))
end
if isissue(Δ)
return (alpha + (beta - alpha)*(0.5), true) # midpoint
else
return (beta - Δ, false)
end
end
## Different functions for approximating f'(xn)
## return fpxn and whether it is an issue
## use f[a,b] to approximate f'(x)
function _fbracket(a, b, fa, fb)
num, den = fb - fa, b - a
num == 0 && den == 0 && return Inf, true
out = num / den
out, isissue(out)
end
## use f[y,z] - f[x,y] + f[x,z] to approximate
function _fbracket_diff(a,b,c, fa, fb, fc)
x1, _ = _fbracket(b, c, fb, fc)
x2, _ = _fbracket(a, b, fa, fb)
x3, _ = _fbracket(a, c, fa, fc)
out = x1 - x2 + x3
out, isissue(out)
end
## use f[a,b] * f[a,c] / f[b,c]
function _fbracket_ratio(a, b, c, fa, fb, fc)
x1, _ = _fbracket(a, b, fa, fb)
x2, _ = _fbracket(a, c, fa, fc)
x3, _ = _fbracket(b, c, fb, fc)
out = (x2 * x3) / x3
out, isissue(out)
end
##################################################
## Order0 and Secant are related
"""
Order0()
The `Order0` method is engineered to be a more robust, though possibly
slower, alternative to to the other derivative-free root-finding
methods. The implementation roughly follows the algorithm described in
*Personal Calculator Has Key to Solve Any Equation f(x) = 0*, the
SOLVE button from the
[HP-34C](http://www.hpl.hp.com/hpjournal/pdfs/IssuePDFs/1979-12.pdf).
The basic idea is to use a secant step. If along the way a bracket is
found, switch to bisection, using `Bisection` if possible, else
`A42`. If the secant step fails to decrease the function value, a
quadratic step is used up to 3 times.
"""
mutable struct Order0 <: AbstractSecant end
"""
Order1()
The `Order1()` method is an alias for `Secant` and performs a secant
step. This method keeps two values in its state, `x_n` and `x_n1`. The
updated point is the intersection point of x axis with the secant line
formed from the two points. The secant method uses 1 function
evaluation and has order `(1+sqrt(5))/2`.
"""
mutable struct Secant <: AbstractSecant end
const Order1 = Secant
function init_state(method::AbstractSecant, fs, x::Union{T, Vector{T}}, bracket) where {T <: AbstractFloat}
if isa(x, Vector)
x0, x1 = x[1:2]
# fx0, fx1 = fs.f(x0), fs.f(x1)
fx0, fx1 = fs(x0), fs(x1)
else
x0 = float(x)
# fx0 = fs.f(x0)
fx0 = fs(x0)
stepsize = max(1/100, min(abs(fx0), abs(x0/100)))
x1 = x0 + stepsize
# x0, x1, fx0, fx1 = x1, x0, fs.f(x1), fx0 # switch
x0, x1, fx0, fx1 = x1, x0, fs(x1), fx0 # switch
end
state = UnivariateZeroStateBase(
promote(float(x1), float(x0))...,
promote(fx1, fx0)...,
bracket,
0, 2,
false, false, false, false,
"")
state
end
##################################################
## in Order0, we run bisection if a bracketing interval is found
function _run_bisection(fs, o, options)
verbose = options.verbose; options.verbose=false # turn off verbose
find_zero(Bisection(), fs, o, options)
options.verbose = verbose
o.message = "Used bisection for last step"
end
## order 0
# goal: more robust to initial guess than higher order methods
# follows roughly algorithm described http://www.hpl.hp.com/hpjournal/pdfs/IssuePDFs/1979-12.pdf, the SOLVE button from the HP-34C
# though some modifications were made.
# * use secant step
# * if along the way a bracket is found, switch to bisection. (We use float64 bisection not a42 if available)
# * if secant step fails to decrease, we use quadratic step up to 3 times
#
# Goal is to return a value `x` with either:
# * `f(x) == 0.0` or
# * `f(prevfloat(x)) * f(nextfloat(x)) < 0`.
# if a bracket is found that can be done, otherwise secant step is used
function update_state(method::Order0, fs, o::UnivariateZeroState{T}, options::UnivariateZeroOptions) where {T}
f = fs.f
alpha, beta = o.xn0, o.xn1
falpha, fbeta = o.fxn0, o.fxn1
incsteps(o)
if sign(falpha) * sign(fbeta) < 0.0
_run_bisection(fs, o, options)
return nothing
end
gamma, issue = guarded_secant_step(alpha, beta, falpha, fbeta)
fgamma = f(gamma); incfn(o)
if sign(fgamma)*sign(fbeta) < 0.0
o.xn0, o.xn1 = gamma, beta
o.fxn0, o.fxn1 = fgamma, fbeta
_run_bisection(fs, o, options)
return nothing
end
if norm(fgamma) <= norm(fbeta)
o.xn0, o.xn1 = beta, gamma
o.fxn0, o.fxn1 = fbeta, fgamma
return nothing
end
ctr = 0
while true
## quadratic step
ctr += 1
if ctr >= 3
o.stopped = true
o.message = "dithering, algorithm failed to improve using quadratic steps"
return nothing
end
# quadratic_step. Put new gamma at vertex of parabola through alpha, beta, (old) gamma
denom = (beta - alpha) * (fbeta - fgamma) - (beta - fgamma) * (fbeta - falpha)
if isissue(denom)
o.stopped
o.message = "dithering, algorithm failed to improve using quadratic steps"
return nothing
end
gamma = beta - ((beta - alpha)^2 * (fbeta - fgamma) - (beta - gamma)^2 * (fbeta - falpha))/denom/2
fgamma = f(gamma); incfn(o)
incfn(o)
if norm(fgamma) < norm(fbeta)
o.xn0, o.xn1 = beta, gamma
o.fxn0, o.fxn1 = fbeta, fgamma
return nothing
end
theta, issue = guarded_secant_step(beta, gamma, fbeta, fgamma)
ftheta = f(theta); incfn(o)
if sign(ftheta) * sign(fbeta) < 0
o.xn0, o.xn1 = beta, theta
o.fxn0, o.fxn1 = fbeta, ftheta
_run_bisection(fs, o, options)
return nothing
end
end
# failed to improve
o.stopped = true
o.message = "failure to improve"
return nothing
end
##################################################
## Secant
## https://en.wikipedia.org/wiki/Secant_method
function update_state(method::Secant, fs, o::UnivariateZeroState{T}, options::UnivariateZeroOptions{T}) where {T <: AbstractFloat}
incsteps(o)
fp, issue = _fbracket(o.xn0, o.xn1, o.fxn0, o.fxn1)
if issue
o.stopped = true
o.message = "Derivative approximation had issues"
return
end
o.xn0 = o.xn1
o.fxn0 = o.fxn1
o.xn1 = o.xn1 - o.fxn1 / fp
# o.fxn1 = fs.f(o.xn1)
o.fxn1 = fs(o.xn1)
incfn(o)
nothing
end
"""
secant_method(f, x0, x1; [kwargs...])
Solve for zero of `f(x) = 0` using the secant method.
Not exported. Use `find_zero` with `Order1()`.
"""
secant_method(f, x0::Real, x1::Real; kwargs...) = find_zero(f, map(float, [x0,x1]), Order1(); kwargs...)
##################################################
### Steffensen
## https://en.wikipedia.org/wiki/Steffensen's_method#Simple_description
mutable struct Steffensen <: UnivariateZeroMethod
end
"""
Order2()
The quadratically converging
[Steffensen](https://en.wikipedia.org/wiki/Steffensen's_method#Simple_description)
method is used for the derivative free `Order2()` algorithm. Unlike
the quadratically converging Newton's method, no derivative is
necessary, though like Newton's method, two function calls per step
are. This algorithm is more sensitive than Newton's method to poor
initial guesses.
"""
const Order2 = Steffensen
function update_state(method::Steffensen, fs, o::UnivariateZeroState{T}, options::UnivariateZeroOptions{T}) where {T <: AbstractFloat}
S = eltype(o.fxn1)
incsteps(o)
wn = o.xn1 + steff_step(o.xn1, o.fxn1)::T
fwn = fs(wn)::S
incfn(o)
fp, issue = _fbracket(o.xn1, wn, o.fxn1, fwn)
if issue
o.stopped = true
o.message = "Derivative approximation had issues"
return
end
o.xn0 = o.xn1
o.fxn0 = o.fxn1
o.xn1 = o.xn1 - o.fxn1 / fp #xn1
o.fxn1 = fs(o.xn1)
incfn(o)
nothing
end
steffenson(f, x0; kwargs...) = find_zero(f, x0, Steffensen(); kwargs...)
##################################################
"""
Order5()
Implements an algorithm
from *A New Fifth Order Derivative Free Newton-Type Method for Solving Nonlinear Equations*
by <NAME>, <NAME>, and Akanksha,
Appl. Math. Inf. Sci. 9, No. 3, 1507-1513 (2015). Four function calls per step are needed.
"""
mutable struct Order5 <: UnivariateZeroMethod end
function update_state(method::Order5, fs, o::UnivariateZeroState{T}, options::UnivariateZeroOptions) where {T}
xn = o.xn1
fxn = o.fxn1
S = eltype(o.fxn1)
incsteps(o)
wn::T = o.xn1 + steff_step(o.xn1, o.fxn1)
fwn = fs(wn)::S
incfn(o)
fp, issue = _fbracket(o.xn1, wn, o.fxn1, fwn)
if issue
o.xn0, o.xn1 = o.xn1, wn
o.fxn0, o.fxn1 = o.fxn1, fwn
o.message = "Issue with divided difference f[xn, wn]"
o.stopped = true
return
end
yn::T = o.xn1 - o.fxn1 / fp
fyn = fs(yn)::S
incfn(o)
zn::T = xn - (fxn + fyn) / fp
fzn = fs(zn)::S
incfn(o)
fp, issue = _fbracket_ratio(yn, o.xn1, wn, fyn, o.fxn1, fwn)
if issue
o.xn0, o.xn1 = o.xn1, yn
o.fxn0, o.fxn1 = o.fxn1, fyn
o.message = "Issue with f[xn,yn]*f[yn,wn] / f[xn, wn]"
o.stopped = true
return
end
o.xn0 = o.xn1
o.fxn0 = o.fxn1
o.xn1 = zn - fzn / fp
o.fxn1 = fs(o.xn1)
incfn(o)
nothing
end
## If we have a derivative
function update_state(method::Order5, fs::FirstDerivative, o::UnivariateZeroState{T}, options::UnivariateZeroOptions) where {T}
xn, fxn = o.xn1, o.fxn1
S = eltype(fxn)
incsteps(o)
fpxn = fs.fp(xn)
incfn(o)
if isissue(fpxn)
o.stopped = true
return
end
yn = xn - fxn / fpxn
fyn, fpyn = fs.f(yn), fs.fp(yn)
incfn(o, 2)
if isissue(fpyn)
o.xn0, o.xn1 = xn, yn
o.fxn0, o.fxn1 = fxn, fyn
o.stopped = true
return
end
zn = xn - (fxn + fyn) / fpxn
fzn = fs.f(zn)
incfn(o, 1)
xn1 = zn - fzn / fpyn
fxn1 = fs.f(xn1)
incfn(o, 1)
o.xn0, o.xn1 = xn, xn1
o.fxn0, o.fxn1 = fxn, fxn1
nothing
end
##################################################
"""
Order8()
Implements an algorithm from
*New Eighth-Order Derivative-Free Methods for Solving Nonlinear Equations*
by <NAME>,
International Journal of Mathematics and Mathematical Sciences
Volume 2012 (2012), Article ID 493456, 12 pages. Four function calls per step are required.
"""
mutable struct Order8 <: UnivariateZeroMethod
end
function update_state(method::Order8, fs, o::UnivariateZeroState{T}, options::UnivariateZeroOptions) where {T}
xn = o.xn1
fxn = o.fxn1
S = eltype(fxn)
incsteps(o)
wn::T = xn + steff_step(xn, fxn)
fwn::S = fs(wn)
incfn(o)
if isissue(fwn)
o.xn0,o.xn1 = xn, wn
o.fxn0,o.fxn1 = fxn, fwn
o.stopped = true
o.message = "issue with Steffensen step fwn"
return
end
fp, issue = _fbracket(xn, wn, fxn, fwn)
issue && return (xn, true)
if issue
o.stopped = true
o.message = "issue with divided difference f[xn, wn]"
return
end
yn::T = xn - fxn / fp
fyn::S = fs(yn)
incfn(o)
fp, issue = _fbracket(yn, xn, fyn, fxn)
if issue #fp
o.xn0,o.xn1 = xn, yn
o.fxn0,o.fxn1 = fxn, fyn
o.stopped = true
o.message = "issue with divided difference f[xn, yn]"
return
end
phi = (1 + fyn / fwn) # pick one of options
zn = yn - phi * fyn / fp
fzn::S = fs(zn)
incfn(o)
fp, issue = _fbracket_diff(xn, yn, zn, fxn, fyn, fzn)
if issue
o.xn0,o.xn1 = xn, zn
o.fxn0,o.fxn1 = fxn, fzn
o.message = "issue with divided difference f[y,z] - f[x,y] + f[x,z]"
o.stopped = true
return
end
w::T = 1 / (1 - fzn/fwn)
xi::T = (1 - 2fyn*fyn*fyn / (fwn * fwn * fxn))
xn1::T = zn - w * xi * fzn / fp
fxn1::S = fs(xn1)
incfn(o)
o.xn0,o.xn1 = xn, xn1
o.fxn0,o.fxn1 = fxn, fxn1
nothing
end
##################################################
"""
Order16()
Implement the algorithm from
*New Sixteenth-Order Derivative-Free Methods for Solving Nonlinear Equations*
by <NAME>,
American Journal of Computational and Applied Mathematics
p-ISSN: 2165-8935; e-ISSN: 2165-8943; 2012; 2(3): 112-118
doi: 10.5923/j.ajcam.20120203.08.
Five function calls per step are required. Though rapidly converging, this method generally isn't faster (fewer
function calls/steps) over other methods when using `Float64` values,
but may be useful for solving over `BigFloat`.
"""
mutable struct Order16 <: UnivariateZeroMethod
end
function update_state(method::Order16, fs, o::UnivariateZeroState{T}, options::UnivariateZeroOptions) where {T}
xn = o.xn1
fxn = o.fxn1
S = eltype(fxn)
incsteps(o)
wn::T = xn + steff_step(xn, fxn)
fwn::S = fs(wn)
incfn(o)
fp, issue = _fbracket(xn, wn, fxn, fwn)
if issue
o.xn0, o.xn1 = xn, wn
o.fxn0, o.fxn1 = fxn, fwn
o.message = "issue with f[xn,wn]"
o.stopped = true
return
end
yn::T = xn - fxn / fp
fyn::S = fs(yn)
incfn(o)
fp, issue = _fbracket_ratio(yn, xn, wn, fyn, fxn, fwn)
if issue
o.xn0, o.xn1 = xn, yn
o.fxn0, o.fxn1 = fxn, fyn
o.message = "issue with f[xn,yn]*f[yn,wn]/f[xn,wn]"
o.stopped = true
return
end
zn::T = yn - fyn / fp
fzn::S = fs(zn)
incfn(o)
fp, issue = _fbracket_diff(xn, yn, zn, fxn, fyn, fzn)
u2, u3, u4 = fzn/fwn, fyn/fxn, fyn/fwn
eta = 1 / (1 + 2*u3*u4^2) / (1 - u2)
if issue
o.xn0, o.xn1 = xn, zn
o.fxn0, o.fxn1 = fxn, fzn
o.stopped = true
o.message = "Approximate derivative failed"
return
end
an = zn - eta * fzn / fp
fan = fs(an)
incfn(o)
fp, issue = _fbracket_ratio(an, yn, zn, fan, fyn, fzn)
if issue
o.xn0, o.xn1 = xn, an
o.fxn0, o.fxn1 = fxn, fan
o.stopped = true
o.message = "Approximate derivative failed"
return
end
u1, u5, u6 = fzn/fxn, fan/fxn, fan/fwn
sigma = 1 + u1*u2 - u1*u3*u4^2 + u5 + u6 + u1^2*u4 +
u2^2*u3 + 3*u1*u4^2*(u3^2 - u4^2)/_fbracket(xn,yn, fxn, fyn)[1]
xn1 = an - sigma * fan / fp
fxn1 = fs(xn1)
incfn(o)
o.xn0, o.xn1 = xn, xn1
o.fxn0, o.fxn1 = fxn, fxn1
nothing
end
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] | 2.073545 | 7,560 |
<filename>Experiment 07/partical_reflux.jl
## 调包
using CurveFit
using CSV
using Interpolations
using PyCall
using PyPlot
using DataFrames
## 调 python 包
@pyimport numpy as np
pygui(true)
## 原始数据
"""
原始数据输入
(进料组成, 塔顶产品组成, 塔釜组成) 的质量分数 (ω_f, ω_d, ω_w)
进料温度 t_f
回流流量 L
产品流量 D
"""
# (进料组成, 塔顶产品组成, 塔釜组成) 的质量分数
ω_f = 16.2419e-2
ω_d = 89.8466e-2
ω_w = 6.0228e-2
# 进料温度 / C
t_f = 33.9
# 回流流量 / (L * h ^ -1)
L = 3.6
# 产品流量 / (L * h ^ -1)
D = 1.5
# 质量分数 -> 摩尔分数 换算函数
f(ω_i) = (ω_i / 46.07) / (ω_i / 46.07 + (1 - ω_i) / 18.02)
# (进料组成, 塔顶产品组成, 塔釜组成) 摩尔分数
x_f = f(ω_f)
x_d = f(ω_d)
x_w = f(ω_w)
## 对角线
x = y = np.linspace(0, 1)
## 平衡线
# EtOH-H2O 相平衡数据
EtOH = CSV.read("EtOH-H2O.csv", DataFrame)
# EtOH-H2O 相平衡组成插值
interp_y = Interpolations.LinearInterpolation(EtOH.x, EtOH.y)
# 相平衡线
yy = interp_y(x)
## 精馏段操作线
R = L / D
f_r(x) = (R * x + x_d) / (R + 1)
## q 线
# 泡点温度插值
interp_t = Interpolations.LinearInterpolation(EtOH.x, EtOH.t)
# 泡点温度
t_s = interp_t(x_f)
# 乙醇摩尔热容
c_pm_EtOH(t) = (1.56 * 10e-2 * t + 2.012) * 46.07
# 水摩尔热容
c_pm_H2O(t) = (2.143 * 10e-4 * t + 4.198) * 18.02
# 溶液热容 / (kJ * kmol ^ -1 * C ^ -1)
t_m = (t_f + t_s) / 2
c_pm = x_f * c_pm_EtOH(t_m) + (1 - x_f) * c_pm_H2O(t_m)
# 乙醇摩尔气化潜热
r_m_EtOH(t) = 113 * (243 - t) ^ 0.4218 * 46.07
# 水的摩尔气化潜热
r_m_H2O(t) = 445.6 * (374 - t) ^ 0.3003 * 18.02
# 溶液气化潜热 / (kJ * kmol ^ -1)
r_m = x_f * r_m_EtOH(t_f) + (1 - x_f) * r_m_EtOH(t_f)
# q 值
q = 1 + c_pm * (t_s - t_f) / r_m
# q 线方程
f_q(x) = (q * x - x_f) / (q - 1)
## 交点
# 精馏段操作线和对角线交点
point_a = [x_d, x_d]
# 提馏段操作线和对角线的交点
point_b = [x_w, x_w]
# 精馏段操作线和 y 轴的交点
point_c = [0, f_r(0)]
# q 线和精馏段操作线的交点
point_d = [((R + 1) * x_f + (q - 1) * x_d) / (R + q), (R * x_f + q * x_d) / (R + q)]
# q 线和对角线的交点
point_f = [x_f, x_f]
## 提馏段操作线方程
p_d = CurveFit.linear_fit([point_b[1], point_d[1]], [point_b[2], point_d[2]])
f_d(x) = p_d[1] + p_d[2] * x
## 绘图
# 对角线
PyPlot.plot(x, y, "-")
# 相平衡线
PyPlot.plot(x, yy, "-")
# 精馏段操作线 a-c
PyPlot.plot([point_a[1], point_c[1]], [point_a[2], point_c[2]], "-")
# q 线 d-f
PyPlot.plot([point_d[1], point_f[1]], [point_d[2], point_f[2]], "-")
# 提馏段操作线 b-d
PyPlot.plot([point_b[1], point_d[1]], [point_b[2], point_d[2]], "-")
# x_w 垂直线
PyPlot.plot([x_w, x_w], [0, x_w], "b-")
# x_d 垂直线
PyPlot.plot([x_d, x_d], [0, x_d], "b-")
# x_f 垂直线
PyPlot.plot([x_f, x_f], [0, x_f], "b-")
# 绘图设置
PyPlot.xlabel("\$ x \$")
PyPlot.ylabel("\$ y \$")
PyPlot.xlim([0, 1])
PyPlot.ylim([0, 1])
## 塔板数计算
# 迭代点
point_1 = [x_d, x_d]
point_2 = zeros(2)
# 塔板数
num = 0
# 迭代记数
n = 1
# 迭代计算 & 绘图
println("开始迭代")
while true
global point_1, point_2, num, n
if point_1[1] < x_w
break
end
println("迭代第 $(n) 次")
n += 1
for x_i in np.linspace(0, 1, 10000000)
if abs(interp_y(x_i) - point_1[2]) < 0.00001 * point_1[2]
point_temp = [x_i, point_1[2]]
point_2 = zeros(2)
if point_d[1] < x_i
# 在精馏段操作线上
point_2 = [x_i, f_r(x_i)]
elseif point_b[1] < x_i < point_d[1]
# 在提馏段操作线上
point_2 = [x_i, f_d(x_i)]
elseif x_i < point_b[1]
# 在对角线上
point_2 = [x_i, x_i]
end
num += 1
PyPlot.plot([point_1[1], point_temp[1]], [point_1[2], point_temp[2]], "b-")
PyPlot.plot([point_2[1], point_temp[1]], [point_2[2], point_temp[2]], "b-")
# 迭代后新点
point_1 = point_2
point_2 = zeros(2)
break
end
end
end
# 理论塔板数结果
println("理论塔板数 = $(num)")
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] | 1.260402 | 2,788 |
<gh_stars>0
mutable struct Preferences
benchmark_output::String
benchmark_histogram::String
end
const PREFS = Preferences("classical", "classical")
const PREFS_FILE_NAME = "benchmarkext.toml"
const ALLOWED = Dict(:benchmark_output => ["classical", "fancy"],
:benchmark_histogram => ["classical", "fancy"])
function update!(prefs::Preferences, data)
for k in fieldnames(Preferences)
ks = string(k)
haskey(data, ks) || continue
setfield!(prefs, k, data[ks])
end
return
end
default_prefs_path(default) = joinpath(first(DEPOT_PATH), "prefs", default)
function get_prefs_path(default)
haskey(ENV, "JULIA_BENCHMARKEXT_CONFIG") && return ENV["JULIA_BENCHMARKEXT_CONFIG"]
path = default_prefs_path(default)
isfile(path) && return path
return ""
end
initialize_prefs(prefs = PREFS, default = PREFS_FILE_NAME) = load_preferences!("", prefs, default)
########################################
# Exported
########################################
"""
set_preferences!(; kwargs...)
Set preferences for current session. Subset of allowed keywords and their values
can be found in BenchmarkExt.ALLOWED
"""
function set_preferences!(prefs = PREFS; kwargs...)
for (k, v) in kwargs
haskey(ALLOWED, k) || (@warn "Unknown settings \"$k\""; continue)
v in ALLOWED[k] || (@warn "Unsupported value \"$v\" for \"$k\""; continue)
setfield!(prefs, k, v)
end
return nothing
end
"""
save_preferences!()
Store current preferences so they can to be reused between sessions. Saved
preferences automatically loaded during `using BenchmarkExt`. Preferences
saved either in path defined in `JULIA_BENCHMARKEXT_CONFIG` environment
variable or `~/.julia/prefs/benchmarkext.toml`
"""
function save_preferences!(prefs = PREFS, default = PREFS_FILE_NAME)
path = get_prefs_path(default)
if isempty(path)
path = default_prefs_path(default)
mkpath(dirname(path))
end
data = Dict{Symbol, String}()
for k in fieldnames(Preferences)
data[k] = getfield(prefs, k)
end
open(path, "w") do io
TOML.print(io, data)
end
return
end
"""
load_preferences!(path)
Load preferences from the path. If path is an empty string, then default
path location is used, i.e. path defined in `JULIA_BENCHMARKEXT_CONFIG`
environment variable or `~/.julia/prefs/benchmarkext.toml`
"""
function load_preferences!(path, prefs = PREFS, default = PREFS_FILE_NAME)
path = isempty(path) ? get_prefs_path(default) : path
isempty(path) && return
try
update!(prefs, TOML.parsefile(path))
catch err
@warn "Unable to load BenchmarkExt configuration file in $path " err
end
return
end
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] | 2.612689 | 1,056 |
<reponame>matutenun/projectbinder3
using Plots
x = 1:10
y = rand(10)
plot(x, y)
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] | 2.073171 | 41 |
<gh_stars>1-10
# A hacky function to return an empty Int64[] for the first conditioning set.
@inline cond_ixs(j, r) = j == 1 ? Int64[] : collect(max(1,j-r):max(1,j-1))
# number of elements in the lower triangle of an n x n matrix.
ltrisz(n) = div(n*(n+1), 2)
# Update kernel matrix buffer, exploiting redundancy for symmetric case. Not
# necessarily faster unless the kernel is very expensive to evaluate, but not
# slower in any case in my experimentation.
#
# Note that this does _NOT_ use threads, since I am already assuming that the
# nll function itself will be using threads, and in my benchmarking putting
# threaded constructors here slows things down a bit and increases allocations.
function updatebuf!(buf, pts1, pts2, kfun::F, params; skipltri=false) where{F}
(F <: MemoizedKernel) && (@assert hash(params) === kfun.phash "params for memoized kernel don't agree with provided params. This shouldn't happen and is a bug.")
if pts1 == pts2 && skipltri
for k in eachindex(pts2)
ptk = pts2[k]
@inbounds buf[k,k] = kfun(ptk, ptk, params)
@inbounds for j in 1:(k-1)
buf[j,k] = kfun(pts1[j], ptk, params)
end
end
elseif pts1 == pts2 && !skipltri
for k in eachindex(pts2)
ptk = pts2[k]
@inbounds buf[k,k] = kfun(ptk, ptk, params)
@inbounds for j in 1:(k-1)
buf[j,k] = kfun(pts1[j], ptk, params)
buf[k,j] = kfun(pts1[j], ptk, params)
end
end
else
@inbounds for k in eachindex(pts2), j in eachindex(pts1)
buf[j,k] = kfun(pts1[j], pts2[k], params)
end
end
nothing
end
# This function works pretty differently: now we assume that the points have all
# been catted together and that the kernel function takes entirely scalar
# inputs. With this formatting, we can then actually use the SIMD tools of
# LoopVectorization.jl and get some serious speedup.
#
# Very grateful to <NAME> (@elrod on discourse, @chriselrod on Github) for
# the help in making this work.
@generated function updatebuf_avx!(buf, ::Val{D}, pts1, pts2,
kfun, params; skipltri=false) where{D}
quote
if pts1 == pts2 && skipltri
for _k in 0:div(length(pts2)-1,$D)
@turbo for _j in 0:_k # @turbo
#@inbounds for _j in 0:_k
val = kfun($([:(pts1[_j*$D+$d]) for d in 1:D]...),
$([:(pts2[_k*$D+$d]) for d in 1:D]...),
params)
buf[_j+1,_k+1] = val
end
end
elseif pts1 == pts2 && !skipltri
for _k in 0:div(length(pts2)-1,$D)
@turbo for _j in 0:_k # @turbo
#@inbounds for _j in 0:_k
val = kfun($([:(pts1[_j*$D+$d]) for d in 1:D]...),
$([:(pts2[_k*$D+$d]) for d in 1:D]...),
params)
buf[_j+1,_k+1] = val
buf[_k+1,_j+1] = val
end
end
else
@turbo for _k in 0:div(length(pts2)-1,$D), _j in 0:div(length(pts1)-1,$D) # @turbo
#@inbounds for _k in 0:div(length(pts2)-1,$D), _j in 0:div(length(pts1)-1,$D)
val = kfun($([:(pts1[_j*$D+$d]) for d in 1:D]...),
$([:(pts2[_k*$D+$d]) for d in 1:D]...),
params)
buf[_j+1,_k+1] = val
end
end
nothing
end
end
# Primarily kept because it's readable and helps to transition from math
# notation in a paper to code.
function sunsteinchunk_naive(T, n, cpbuf, solve, fccov,
pts_ixs::AbstractVector{Int64},
cnd_ixs::AbstractVector{Int64})
# allocate the sparse matrix for b_j^T. Eventually, a possible
# micro-optimization would be to avoid this entirely and just build the final
# one from the nzindices. But for now let's just do this.
bt = spzeros(T, length(pts_ixs), n)
# fill in the identity matrix part, doing the solves along the way.
# about 50% of the time.
for (j, ptixj) in enumerate(pts_ixs)
ej = zeros(T, length(pts_ixs))
ej[j] = one(T)
ldiv!(fccov.U', ej)
bt[:,ptixj] .= ej
end
# solve the linear system, overwriting input "solve", and update the
# corresponding columns of bt:
ldiv!(fccov.U', Adjoint(solve))
for (j, rowj) in enumerate(eachrow(solve))
bt[:,cnd_ixs[j]] .= -rowj
end
# return the conjugation.
return bt'bt # almost _all_ the allocs.
end
function sparseIJvecs_ltri(ixs)
len = ltrisz(length(ixs))
(Iv, Jv) = (Vector{Int64}(undef, len), Vector{Int64}(undef, len))
current = 1
for j in eachindex(ixs)
view(Iv, current:(current+j-1)) .= ixs[j]
view(Jv, current:(current+j-1)) .= view(ixs, 1:j)
current += j
end
(Iv, Jv)
end
function sparseIJvecs_full(ixs)
ilen = length(ixs)
len = ilen^2
(Iv, Jv) = (Vector{Int64}(undef, len), Vector{Int64}(undef, len))
# fill in the I vector. Slightly more involved this time.
current = 1
for j in eachindex(ixs)
view(Iv, current:(current+ilen-1)) .= ixs[j]
current += ilen
end
# fill in the J vector. Again, slightly more involved.
current = 1
for j in eachindex(ixs)
view(Jv, current:(current+ilen-1)) .= ixs
current += ilen
end
(Iv, Jv)
end
function update_IJ!(ixs, I, J)
@inbounds for l in eachindex(I,J)
I[l] = ixs[I[l]]
J[l] = ixs[J[l]]
end
nothing
end
function prepare_columns(T, solve, fccov)
sz = size(solve, 1) + size(fccov,1)
out = Array{T}(undef, size(fccov,1), sz)
for j in 1:size(solve,1)
view(out, :, j) .= -view(solve, j, :)
end
for (row_counter, j) in enumerate((size(solve, 1)+1):sz)
colj = view(out, :, j)
fill!(colj, zero(T))
colj[row_counter] = one(T)
end
lastchunk = view(out, :, (size(solve, 1)+1):sz)
ldiv!(fccov.U', lastchunk)
out
end
# TODO (cg 2021/04/24 18:26): this could always be optimized better.
# I suppose I could get rid of the allocation in combined by making the cross
# buffer slightly larger than it needs to be and updating that object. One of
# the dimensions is right already. But it's hard to imagine that boosting
# performance.
function sunsteinchunk(T, n, solve, fccov, mulbuf,
pts_ixs::AbstractVector{Int64},
cnd_ixs::AbstractVector{Int64})::Tuple{Vector{Int64}, Vector{Int64}, Vector{T}}
# Incredibly, this hcat code seems to be more efficient than prepare_columns
# above. I'm really confused about how it has fewer allocations.
ldiv!(fccov.U', Adjoint(solve))
combined = hcat(-permutedims(solve), inv(fccov.U'))
combined_ixs = vcat(cnd_ixs, pts_ixs)
# index set:
(Iv, Jv) = sparseIJvecs_ltri(1:length(combined_ixs))
# fill in the matrix entries. The extra allocation isn't ideal, but BLAS-3!
# having experimented with dropping the ltri-only approach so that I can just
# return Vv = vec(mulbuf), this approach oddly has slightly more allocations
# but was significantly faster---by about a factor of 2.
mul!(mulbuf, Adjoint(combined), combined)
Vv = Vector{T}(undef, ltrisz(length(combined_ixs)))
@turbo for l in eachindex(Iv, Jv)
#@inbounds for l in eachindex(Iv, Jv)
j = Iv[l]
k = Jv[l]
Vv[l] = mulbuf[j,k]
end
update_IJ!(combined_ixs, Iv, Jv)
return (Iv, Jv, Vv)
end
# Primarily kept because it's readable and helps to transition from math
# notation in a paper to code.
function sunsteinchunk1_naive(T, n, buf)
out = spzeros(T, n, n)
len = size(buf, 1)
out[1:len, 1:len] .= inv(buf)
out
end
# something that instead returns nzindices instead of constructing the matrix.
function sunsteinchunk1(T, n, buf)::Tuple{Vector{Int64}, Vector{Int64}, Vector{T}}
ibuf = inv(buf)
Vv = Vector{T}(undef, ltrisz(size(buf, 1)))
counter = 1
@inbounds for j in 1:size(buf, 1)
@inbounds for k in 1:j
Vv[counter] = ibuf[j,k]
counter += 1
end
end
(Iv, Jv) = sparseIJvecs_ltri(1:size(buf, 1))
return (Iv, Jv, Vv)
end
function globalidxs(datavv)
(out, start) = (Vector{UnitRange{Int64}}(undef, length(datavv)), 1)
for (j, datvj) in enumerate(datavv)
len = size(datvj,1)
out[j] = start:(start+len-1)
start += len
end
out
end
function checksorted(V::VecchiaConfig{D,F}) where{D,F}
all(issorted, V.condix) || throw(error("This function requires that every conditioning vector be sorted."))
nothing
end
# TODO (cg 2022/04/21 16:16): This is totally not good.
function vec_of_vecs_to_matrows(vv)
Matrix(reduce(hcat, vv)')
end
# For debugging. This gives M = U*U'.
function rchol(M)
tmp = cholesky(Symmetric(reverse(Matrix(M), dims=(1,2)))).L
UpperTriangular(reverse(Matrix(tmp), dims=(1,2)))
end
# Again, for debugging. Don't use this.
irchol(M) = inv(cholesky(M).U)
function prepare_v_buf!(buf, v::Matrix, idxv)
_ix = 1
for ixs in idxv
for ix in ixs
view(buf, _ix, :) .= view(v, ix, :)
_ix += 1
end
end
view(buf, 1:(_ix-1), :)
end
function updateptsbuf!(ptbuf, ptvv, idxs)
ix = 1
for idx in idxs
for pt in ptvv[idx]
ptbuf[ix] = pt
ix += 1
end
end
view(ptbuf, 1:(ix-1))
end
# Not a clever function at all,
function rchol_nnz(U::RCholesky{T}) where{T}
# diagonal elements:
out = sum(U.idxs) do ix
n = length(ix)
div(n*(n+1), 2)
end
# off-diagonal elements:
out += sum(enumerate(U.condix)) do (j,ix_c)
isempty(ix_c) && return 0
tmp = 0
len = length(U.idxs[j])
for ix in ix_c
tmp += len*length(U.idxs[ix])
end
tmp
end
out
end
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] | 2.236805 | 4,206 |
# custom extension of CuArray in CUDArt for sparse vectors/matrices
# using CSC format for interop with Julia's native sparse functionality
export CuSparseMatrixCSC, CuSparseMatrixCSR,
CuSparseMatrixHYB, CuSparseMatrixBSR,
CuSparseMatrix, AbstractCuSparseMatrix,
CuSparseVector
import Base: length, size, ndims, eltype, similar, pointer, stride,
copy, convert, reinterpret, show, summary, copyto!, getindex, get!, fill!, collect
using LinearAlgebra
import LinearAlgebra: BlasFloat, Hermitian, HermOrSym, issymmetric, Transpose, Adjoint,
ishermitian, istriu, istril, Symmetric, UpperTriangular, LowerTriangular
using SparseArrays
import SparseArrays: sparse, SparseMatrixCSC, nnz, nonzeros, nonzeroinds,
_spgetindex
abstract type AbstractCuSparseArray{Tv, N} <: AbstractSparseArray{Tv, Cint, N} end
const AbstractCuSparseVector{Tv} = AbstractCuSparseArray{Tv,1}
const AbstractCuSparseMatrix{Tv} = AbstractCuSparseArray{Tv,2}
mutable struct CuSparseVector{Tv} <: AbstractCuSparseVector{Tv}
iPtr::CuVector{Cint}
nzVal::CuVector{Tv}
dims::NTuple{2,Int}
nnz::Cint
function CuSparseVector{Tv}(iPtr::CuVector{Cint}, nzVal::CuVector{Tv}, dims::Int, nnz::Cint) where Tv
new(iPtr,nzVal,(dims,1),nnz)
end
end
function CuArrays.unsafe_free!(xs::CuSparseVector)
unsafe_free!(xs.iPtr)
unsafe_free!(xs.nzVal)
return
end
mutable struct CuSparseMatrixCSC{Tv} <: AbstractCuSparseMatrix{Tv}
colPtr::CuVector{Cint}
rowVal::CuVector{Cint}
nzVal::CuVector{Tv}
dims::NTuple{2,Int}
nnz::Cint
function CuSparseMatrixCSC{Tv}(colPtr::CuVector{Cint}, rowVal::CuVector{Cint}, nzVal::CuVector{Tv}, dims::NTuple{2,Int}, nnz::Cint) where Tv
new(colPtr,rowVal,nzVal,dims,nnz)
end
end
function CuSparseMatrixCSC!(xs::CuSparseVector)
unsafe_free!(xs.colPtr)
unsafe_free!(xs.rowVal)
unsafe_free!(xs.nzVal)
return
end
"""
Container to hold sparse matrices in compressed sparse row (CSR) format on the
GPU.
**Note**: Most CUSPARSE operations work with CSR formatted matrices, rather
than CSC.
"""
mutable struct CuSparseMatrixCSR{Tv} <: AbstractCuSparseMatrix{Tv}
rowPtr::CuVector{Cint}
colVal::CuVector{Cint}
nzVal::CuVector{Tv}
dims::NTuple{2,Int}
nnz::Cint
function CuSparseMatrixCSR{Tv}(rowPtr::CuVector{Cint}, colVal::CuVector{Cint}, nzVal::CuVector{Tv}, dims::NTuple{2,Int}, nnz::Cint) where Tv
new(rowPtr,colVal,nzVal,dims,nnz)
end
end
function CuSparseMatrixCSR!(xs::CuSparseVector)
unsafe_free!(xs.rowPtr)
unsafe_free!(xs.colVal)
unsafe_free!(xs.nzVal)
return
end
"""
Container to hold sparse matrices in block compressed sparse row (BSR) format on
the GPU. BSR format is also used in Intel MKL, and is suited to matrices that are
"block" sparse - rare blocks of non-sparse regions.
"""
mutable struct CuSparseMatrixBSR{Tv} <: AbstractCuSparseMatrix{Tv}
rowPtr::CuVector{Cint}
colVal::CuVector{Cint}
nzVal::CuVector{Tv}
dims::NTuple{2,Int}
blockDim::Cint
dir::SparseChar
nnz::Cint
function CuSparseMatrixBSR{Tv}(rowPtr::CuVector{Cint}, colVal::CuVector{Cint}, nzVal::CuVector{Tv}, dims::NTuple{2,Int},blockDim::Cint, dir::SparseChar, nnz::Cint) where Tv
new(rowPtr,colVal,nzVal,dims,blockDim,dir,nnz)
end
end
function CuSparseMatrixBSR!(xs::CuSparseVector)
unsafe_free!(xs.rowPtr)
unsafe_free!(xs.colVal)
unsafe_free!(xs.nzVal)
return
end
"""
Container to hold sparse matrices in NVIDIA's hybrid (HYB) format on the GPU.
HYB format is an opaque struct, which can be converted to/from using
CUSPARSE routines.
"""
mutable struct CuSparseMatrixHYB{Tv} <: AbstractCuSparseMatrix{Tv}
Mat::cusparseHybMat_t
dims::NTuple{2,Int}
nnz::Cint
function CuSparseMatrixHYB{Tv}(Mat::cusparseHybMat_t, dims::NTuple{2,Int}, nnz::Cint) where Tv
new(Mat,dims,nnz)
end
end
"""
Utility union type of [`CuSparseMatrixCSC`](@ref), [`CuSparseMatrixCSR`](@ref),
and `Hermitian` and `Symmetric` versions of these two containers. A function accepting
this type can make use of performance improvements by only indexing one triangle of the
matrix if it is guaranteed to be hermitian/symmetric.
"""
const CompressedSparse{T} = Union{CuSparseMatrixCSC{T},CuSparseMatrixCSR{T},HermOrSym{T,CuSparseMatrixCSC{T}},HermOrSym{T,CuSparseMatrixCSR{T}}}
"""
Utility union type of [`CuSparseMatrixCSC`](@ref), [`CuSparseMatrixCSR`](@ref),
[`CuSparseMatrixBSR`](@ref), and [`CuSparseMatrixHYB`](@ref).
"""
const CuSparseMatrix{T} = Union{CuSparseMatrixCSC{T},CuSparseMatrixCSR{T}, CuSparseMatrixBSR{T}, CuSparseMatrixHYB{T}}
Hermitian{T}(Mat::CuSparseMatrix{T}) where T = Hermitian{T,typeof(Mat)}(Mat,'U')
length(g::CuSparseVector) = prod(g.dims)
size(g::CuSparseVector) = g.dims
ndims(g::CuSparseVector) = 1
length(g::CuSparseMatrix) = prod(g.dims)
size(g::CuSparseMatrix) = g.dims
ndims(g::CuSparseMatrix) = 2
function size(g::CuSparseVector, d::Integer)
if d == 1
return g.dims[d]
elseif d > 1
return 1
else
throw(ArgumentError("dimension must be ≥ 1, got $d"))
end
end
function size(g::CuSparseMatrix, d::Integer)
if d in [1, 2]
return g.dims[d]
elseif d > 1
return 1
else
throw(ArgumentError("dimension must be ≥ 1, got $d"))
end
end
nnz(g::AbstractCuSparseArray) = g.nnz
nonzeros(g::AbstractCuSparseArray) = g.nzVal
nonzeroinds(g::AbstractCuSparseVector) = g.iPtr
issymmetric(M::Union{CuSparseMatrixCSC,CuSparseMatrixCSR}) = false
ishermitian(M::Union{CuSparseMatrixCSC,CuSparseMatrixCSR}) = false
issymmetric(M::Symmetric{CuSparseMatrixCSC}) = true
ishermitian(M::Hermitian{CuSparseMatrixCSC}) = true
istriu(M::UpperTriangular{T,S}) where {T<:BlasFloat, S<:AbstractCuSparseMatrix} = true
istril(M::UpperTriangular{T,S}) where {T<:BlasFloat, S<:AbstractCuSparseMatrix} = false
istriu(M::LowerTriangular{T,S}) where {T<:BlasFloat, S<:AbstractCuSparseMatrix} = false
istril(M::LowerTriangular{T,S}) where {T<:BlasFloat, S<:AbstractCuSparseMatrix} = true
eltype(g::CuSparseMatrix{T}) where T = T
# getindex (mostly adapted from stdlib/SparseArrays)
# Translations
getindex(A::AbstractCuSparseVector, ::Colon) = copy(A)
getindex(A::AbstractCuSparseMatrix, ::Colon, ::Colon) = copy(A)
getindex(A::AbstractCuSparseMatrix, i, ::Colon) = getindex(A, i, 1:size(A, 2))
getindex(A::AbstractCuSparseMatrix, ::Colon, i) = getindex(A, 1:size(A, 1), i)
getindex(A::AbstractCuSparseMatrix, I::Tuple{Integer,Integer}) = getindex(A, I[1], I[2])
# Column slices
function getindex(x::CuSparseMatrixCSC, ::Colon, j::Integer)
checkbounds(x, :, j)
r1 = convert(Int, x.colPtr[j])
r2 = convert(Int, x.colPtr[j+1]) - 1
CuSparseVector(x.rowVal[r1:r2], x.nzVal[r1:r2], size(x, 1))
end
function getindex(x::CuSparseMatrixCSR, i::Integer, ::Colon)
checkbounds(x, :, i)
c1 = convert(Int, x.rowPtr[i])
c2 = convert(Int, x.rowPtr[i+1]) - 1
CuSparseVector(x.colVal[c1:c2], x.nzVal[c1:c2], size(x, 2))
end
# Row slices
# TODO optimize
getindex(A::CuSparseMatrixCSC, i::Integer, ::Colon) = CuSparseVector(sparse(A[i, 1:end]))
# TODO optimize
getindex(A::CuSparseMatrixCSR, ::Colon, j::Integer) = CuSparseVector(sparse(A[1:end, j]))
function getindex(A::CuSparseMatrixCSC{T}, i0::Integer, i1::Integer) where T
m, n = size(A)
if !(1 <= i0 <= m && 1 <= i1 <= n)
throw(BoundsError())
end
r1 = Int(A.colPtr[i1])
r2 = Int(A.colPtr[i1+1]-1)
(r1 > r2) && return zero(T)
r1 = searchsortedfirst(A.rowVal, i0, r1, r2, Base.Order.Forward)
((r1 > r2) || (A.rowVal[r1] != i0)) ? zero(T) : A.nzVal[r1]
end
function getindex(A::CuSparseMatrixCSR{T}, i0::Integer, i1::Integer) where T
m, n = size(A)
if !(1 <= i0 <= m && 1 <= i1 <= n)
throw(BoundsError())
end
c1 = Int(A.rowPtr[i0])
c2 = Int(A.rowPtr[i0+1]-1)
(c1 > c2) && return zero(T)
c1 = searchsortedfirst(A.colVal, i1, c1, c2, Base.Order.Forward)
((c1 > c2) || (A.colVal[c1] != i1)) ? zero(T) : A.nzVal[c1]
end
# Called for indexing into `CuSparseVector`s
function _spgetindex(m::Integer, nzind::CuVector{Ti}, nzval::CuVector{Tv},
i::Integer) where {Tv,Ti}
ii = searchsortedfirst(nzind, convert(Ti, i))
(ii <= m && nzind[ii] == i) ? nzval[ii] : zero(Tv)
end
function collect(Vec::CuSparseVector)
SparseVector(Vec.dims[1], collect(Vec.iPtr), collect(Vec.nzVal))
end
function collect(Mat::CuSparseMatrixCSC)
SparseMatrixCSC(Mat.dims[1], Mat.dims[2], collect(Mat.colPtr), collect(Mat.rowVal), collect(Mat.nzVal))
end
function collect(Mat::CuSparseMatrixCSR)
rowPtr = collect(Mat.rowPtr)
colVal = collect(Mat.colVal)
nzVal = collect(Mat.nzVal)
#construct Is
I = similar(colVal)
counter = 1
for row = 1 : size(Mat)[1], k = rowPtr[row] : (rowPtr[row+1]-1)
I[counter] = row
counter += 1
end
return sparse(I,colVal,nzVal,Mat.dims[1],Mat.dims[2])
end
summary(g::CuSparseMatrix) = string(g)
summary(g::CuSparseVector) = string(g)
CuSparseVector(iPtr::Vector{Ti}, nzVal::Vector{T}, dims::Int) where {T<:BlasFloat, Ti<:Integer} = CuSparseVector{T}(CuArray(convert(Vector{Cint},iPtr)), CuArray(nzVal), dims, convert(Cint,length(nzVal)))
CuSparseVector(iPtr::CuArray{Ti}, nzVal::CuArray{T}, dims::Int) where {T<:BlasFloat, Ti<:Integer} = CuSparseVector{T}(iPtr, nzVal, dims, convert(Cint,length(nzVal)))
CuSparseMatrixCSC(colPtr::Vector{Ti}, rowVal::Vector{Ti}, nzVal::Vector{T}, dims::NTuple{2,Int}) where {T<:BlasFloat,Ti<:Integer} = CuSparseMatrixCSC{T}(CuArray(convert(Vector{Cint},colPtr)), CuArray(convert(Vector{Cint},rowVal)), CuArray(nzVal), dims, convert(Cint,length(nzVal)))
CuSparseMatrixCSC(colPtr::CuArray{Ti}, rowVal::CuArray{Ti}, nzVal::CuArray{T}, dims::NTuple{2,Int}) where {T<:BlasFloat,Ti<:Integer} = CuSparseMatrixCSC{T}(colPtr, rowVal, nzVal, dims, convert(Cint,length(nzVal)))
CuSparseMatrixCSC(colPtr::CuArray{Ti}, rowVal::CuArray{Ti}, nzVal::CuArray{T}, nnz, dims::NTuple{2,Int}) where {T<:BlasFloat,Ti<:Integer} = CuSparseMatrixCSC{T}(colPtr, rowVal, nzVal, dims, nnz)
CuSparseMatrixCSR(rowPtr::CuArray, colVal::CuArray, nzVal::CuArray{T}, dims::NTuple{2,Int}) where T = CuSparseMatrixCSR{T}(rowPtr, colVal, nzVal, dims, convert(Cint,length(nzVal)))
CuSparseMatrixCSR(rowPtr::CuArray, colVal::CuArray, nzVal::CuArray{T}, nnz, dims::NTuple{2,Int}) where T = CuSparseMatrixCSR{T}(rowPtr, colVal, nzVal, dims, nnz)
CuSparseMatrixBSR(rowPtr::CuArray, colVal::CuArray, nzVal::CuArray{T}, blockDim, dir, nnz, dims::NTuple{2,Int}) where T = CuSparseMatrixBSR{T}(rowPtr, colVal, nzVal, dims, blockDim, dir, nnz)
CuSparseVector(Vec::SparseVector) = CuSparseVector(Vec.nzind, Vec.nzval, size(Vec)[1])
CuSparseMatrixCSC(Vec::SparseVector) = CuSparseMatrixCSC([1], Vec.nzind, Vec.nzval, size(Vec))
CuSparseVector(Mat::SparseMatrixCSC) = size(Mat,2) == 1 ? CuSparseVector(Mat.rowval, Mat.nzval, size(Mat)[1]) : throw(ArgumentError("The input argument must have a single column"))
CuSparseMatrixCSC(Mat::SparseMatrixCSC) = CuSparseMatrixCSC(Mat.colptr, Mat.rowval, Mat.nzval, size(Mat))
CuSparseMatrixCSR(Mat::SparseMatrixCSC) = switch2csr(CuSparseMatrixCSC(Mat))
similar(Vec::CuSparseVector) = CuSparseVector(copy(Vec.iPtr), similar(Vec.nzVal), Vec.dims[1])
similar(Mat::CuSparseMatrixCSC) = CuSparseMatrixCSC(copy(Mat.colPtr), copy(Mat.rowVal), similar(Mat.nzVal), Mat.nnz, Mat.dims)
similar(Mat::CuSparseMatrixCSR) = CuSparseMatrixCSR(copy(Mat.rowPtr), copy(Mat.colVal), similar(Mat.nzVal), Mat.nnz, Mat.dims)
similar(Mat::CuSparseMatrixBSR) = CuSparseMatrixBSR(copy(Mat.rowPtr), copy(Mat.colVal), similar(Mat.nzVal), Mat.blockDim, Mat.dir, Mat.nnz, Mat.dims)
function copyto!(dst::CuSparseVector, src::CuSparseVector)
if dst.dims != src.dims
throw(ArgumentError("Inconsistent Sparse Vector size"))
end
copyto!(dst.iPtr, src.iPtr)
copyto!(dst.nzVal, src.nzVal)
dst.nnz = src.nnz
dst
end
function copyto!(dst::CuSparseMatrixCSC, src::CuSparseMatrixCSC)
if dst.dims != src.dims
throw(ArgumentError("Inconsistent Sparse Matrix size"))
end
copyto!(dst.colPtr, src.colPtr)
copyto!(dst.rowVal, src.rowVal)
copyto!(dst.nzVal, src.nzVal)
dst.nnz = src.nnz
dst
end
function copyto!(dst::CuSparseMatrixCSR, src::CuSparseMatrixCSR)
if dst.dims != src.dims
throw(ArgumentError("Inconsistent Sparse Matrix size"))
end
copyto!(dst.rowPtr, src.rowPtr)
copyto!(dst.colVal, src.colVal)
copyto!(dst.nzVal, src.nzVal)
dst.nnz = src.nnz
dst
end
function copyto!(dst::CuSparseMatrixBSR, src::CuSparseMatrixBSR)
if dst.dims != src.dims
throw(ArgumentError("Inconsistent Sparse Matrix size"))
end
copyto!(dst.rowPtr, src.rowPtr)
copyto!(dst.colVal, src.colVal)
copyto!(dst.nzVal, src.nzVal)
dst.dir = src.dir
dst.nnz = src.nnz
dst
end
function copyto!(dst::CuSparseMatrixHYB, src::CuSparseMatrixHYB)
if dst.dims != src.dims
throw(ArgumentError("Inconsistent Sparse Matrix size"))
end
dst.Mat = src.Mat
dst.nnz = src.nnz
dst
end
copy(Vec::CuSparseVector) = copyto!(similar(Vec),Vec)
copy(Mat::CuSparseMatrixCSC) = copyto!(similar(Mat),Mat)
copy(Mat::CuSparseMatrixCSR) = copyto!(similar(Mat),Mat)
copy(Mat::CuSparseMatrixBSR) = copyto!(similar(Mat),Mat)
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] | 2.326374 | 5,748 |
using Serialization
struct RunnerProcResult
exitcode::Int64
result::Union{RunnerOutput, Nothing}
stdout::AbstractString
stderr::AbstractString
end
RunnerProcResult(s::Int64, r::Any, e::Nothing) = RunnerProcResult(s, r, "")
function readpipe!(p::Pipe)
close(p.in)
return read(p, String)
end
"""
launch_runner_proc(testsuite, submissionfile, functionname[; debug])
Launch a runner subprocess and get the resulting `RunnerProcResult`.
**Note:** This function uses `deserialize` and so bugs may cause the Julia
process to crash without warning (no error, no backtrace, etc.) if the
serialized input is invalid.
"""
function launch_runner_proc(
testsuite::TestSuite,
submissionfile::String,
functionname::Union{AbstractString, Symbol},
;
debug::String=get(ENV, "JULIA_DEBUG", ""),
)
functionname = Symbol(functionname)
input_path, output_path = tempname(), tempname()
runner_input = RunnerInput(testsuite, submissionfile, functionname, output_path)
# Julia makes it *very* hard to actually get the id of a subprocess, so
# we generate a random string here to be able to track what logs are comming
# from what process. Note that we use an alphabetic rather than a number to
# prevent confusion with the process id.
log_id = String(rand('a':'z', 6))
try
open(io -> serialize(io, runner_input), input_path, "w")
@debug "Launching runner subprocess." submissionfile JULIA_EXE
submissionfile = realpath(submissionfile)
# proc_in, proc_out, proc_err = Pipe(), Pipe(), Pipe()
stdout_pipe, stderr_pipe = Pipe(), Pipe()
# jl_cmd = "using ComparativeAutograder: runner_main; runner_main()"
jl_env = copy(ENV)
jl_env["JULIA_DEBUG"] = debug
proc = run(pipeline(
Cmd(`$(Base.julia_cmd()) -e $RUNNER_CMD $input_path`, env=jl_env),
stdout=stdout_pipe,
stderr=stderr_pipe,
); wait=false)
@debug "Spawned runner process."
stdout_task = @async readpipe!(stdout_pipe)
stderr_task = @async readpipe!(stderr_pipe)
# Wait for the subprocess to complete.
wait(proc)
status = proc.exitcode
@debug "Runner process ($log_id) exited." status stdout_task stderr_task
stdout_str, stderr_str = fetch.([stdout_task, stderr_task])
# Fetch the RunnerOutput.
runner_output = nothing
try
runner_output = open(io -> deserialize(io), output_path, "r")
typeassert(runner_output, RunnerOutput)
catch exc
@error "An error occurred while trying to read the runner output file." exc
end
return RunnerProcResult(status, runner_output, stdout_str, stderr_str)
finally
rm.([input_path, output_path], force=true)
end
end
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] | 2.526176 | 1,127 |
<filename>src/formulae.jl
export formula_FR, formula_PR, formula_HS, formula_HZ
function formula_FR(∇f, ∇ft, s, d)
β = (∇ft⋅∇ft) / (∇f⋅∇f)
return β
end
function formula_PR(∇f, ∇ft, s, d)
y = ∇ft - ∇f
β = (∇ft⋅y) / (∇f⋅∇f)
return β
end
function formula_HS(∇f, ∇ft, s, d)
y = ∇ft - ∇f
β = (∇ft⋅y) / (d⋅y)
return β
end
function formula_HZ(∇f, ∇ft, s, d)
y = ∇ft - ∇f
n2y = y⋅y
β1 = (y⋅d)
β = ((y - 2 * d * n2y / β1)⋅∇ft) / β1
return β
end
| [
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] | 1.597444 | 313 |
# ------------------------------------------------------------------
# Licensed under the MIT License. See LICENSE in the project root.
# ------------------------------------------------------------------
"""
Pyramid(p1, p2, p3, p4, p5)
A pyramid with points `p1`, `p2`, `p3`, `p4`, `p5`.
"""
struct Pyramid{Dim,T,V<:AbstractVector{Point{Dim,T}}} <: Polyhedron{Dim,T}
vertices::V
end
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using HiQGA.transD_GP
include("BarPhysics.jl")
include("BarPhysicsInversion.jl")
using .BarPhysicsInversion
# Forward setttings
# dummy physics struct defined in BarPhysics.jl
F = BarPhysics.Bar(25, rand(10))
# some dummy forward
@info BarPhysics.returnphysics!(F, rand(3))
# struct for data and physics operator defined in BarPhysicsInversion.jl
FOp = BarPhysicsInversion.BarInversion(rand(10), F)
## Inverse settings
# prior bounds and setings
fbounds = [0 1.]
xall = collect(permutedims(0:0.1:1))
xbounds = permutedims([extrema(xall)...])
λ = vec(diff(xbounds, dims=2))
nmin, nmax = 2, 5
demean, sampledc = false, true
# McMC proposals
δ = 0.1*diff(fbounds, dims=2)[1]
sdev_pos = abs.(vec(diff(xbounds, dims=2)))
sdev_prop = abs.(vec(0.05*diff(fbounds, dims=2)))
K = transD_GP.GP.OrstUhn()
# make MCMC options using prior and proposals
opt = transD_GP.OptionsStat(nmin = nmin,
nmax = nmax,
xbounds = xbounds,
fbounds = fbounds,
xall = xall,
λ = λ,
δ = δ,
demean = demean,
sampledc = sampledc,
sdev_prop = sdev_prop,
sdev_pos = sdev_pos,
quasimultid = false,
K = K
)
## Put forward and inverse together
# initialise a random model
m = transD_GP.init(opt)
# compute misfit
transD_GP.get_misfit(m, opt, FOp)
# if you've gotten till here you're golden
# you don't really need to initialize a model and compute
# misfit outside a module | [
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] | 2.026928 | 817 |
<reponame>twadleigh/OpenEXR.jl
module OpenEXR
export load_exr, save_exr
using FileIO, Colors
module C
using OpenEXR_jll
using Colors
const ImfHalf = Float16
const ImfRgba = RGBA{ImfHalf}
include("OpenEXR_common.jl")
include("OpenEXR_api.jl")
const IMF_WRITE_RGBA = IMF_WRITE_RGB + IMF_WRITE_A
end # module C
const MAGIC = Cint(C.IMF_MAGIC)
@enum Compression::Cint begin
NO_COMPRESSION = C.IMF_NO_COMPRESSION
RLE_COMPRESSION = C.IMF_RLE_COMPRESSION
ZIPS_COMPRESSION = C.IMF_ZIPS_COMPRESSION
ZIP_COMPRESSION = C.IMF_ZIP_COMPRESSION
PIZ_COMPRESSION = C.IMF_PIZ_COMPRESSION
PXR24_COMPRESSION = C.IMF_PXR24_COMPRESSION
B44_COMPRESSION = C.IMF_B44_COMPRESSION
B44A_COMPRESSION = C.IMF_B44A_COMPRESSION
DWAA_COMPRESSION = C.IMF_DWAA_COMPRESSION
DWAB_COMPRESSION = C.IMF_DWAB_COMPRESSION
end
@enum RgbaChannels::Cint begin
WRITE_R = C.IMF_WRITE_R
WRITE_G = C.IMF_WRITE_G
WRITE_B = C.IMF_WRITE_B
WRITE_A = C.IMF_WRITE_A
WRITE_Y = C.IMF_WRITE_Y
WRITE_C = C.IMF_WRITE_C
WRITE_RGB = C.IMF_WRITE_RGB
WRITE_RGBA = C.IMF_WRITE_RGBA
WRITE_YC = C.IMF_WRITE_YC
WRITE_YA = C.IMF_WRITE_YA
WRITE_YCA = C.IMF_WRITE_YCA
end
function check(ret)
ret == typeof(ret)(0) && error(unsafe_string(C.ImfErrorMessage()))
end
"""
load_exr(filename)::(Array{RGBA{Float16},2}, RgbaChannels)
Returns the image data contained in `filename` along with flags representing the on-disk
storage format.
"""
function load_exr(filename)
infile = C.ImfOpenInputFile(filename) # open the file
check(infile)
chans = RgbaChannels(C.ImfInputChannels(infile))
try
# get the header
hdr = C.ImfInputHeader(infile)
# read its data window
xmin = Ref{Cint}()
ymin = Ref{Cint}()
xmax = Ref{Cint}()
ymax = Ref{Cint}()
C.ImfHeaderDataWindow(hdr, xmin, ymin, xmax, ymax)
# compute the window size
width = xmax[] - xmin[] + 1
height = ymax[] - ymin[] + 1
# allocate space for the result and get its strides
data = Array{C.ImfRgba,2}(undef, height, width)
(xstride, ystride) = strides(data)
# get the pointer to the data, shifting it according to the expected window
dataptr =
Base.unsafe_convert(Ptr{C.ImfRgba}, data) - xmin[] * xstride - ymin[] * ystride
# copy the data
check(C.ImfInputSetFrameBuffer(infile, dataptr, ystride, xstride))
check(C.ImfInputReadPixels(infile, ymin[], ymax[]))
# return the loaded raster along with the channels
return (data, chans)
finally
check(C.ImfCloseInputFile(infile))
end
end
"""
save_exr(filename, image[, compression[, channels]])
Save `image` as an OpenEXR file in `filename`, storing the data in a format
indicated by `channels` using the `compression` algorithm.
"""
function save_exr(
filename,
image::AbstractArray{C.ImfRgba,2},
compression::Compression = ZIP_COMPRESSION,
channels::RgbaChannels = WRITE_RGBA,
)
# get the size of the data
(height, width) = size(image)
# create a new header
hdr = C.ImfNewHeader()
check(hdr)
try
# set the compression
C.ImfHeaderSetCompression(hdr, compression)
# set the correct window sizes
C.ImfHeaderSetDataWindow(hdr, 0, 0, width - 1, height - 1)
C.ImfHeaderSetDisplayWindow(hdr, 0, 0, width - 1, height - 1)
# open the output file
outfile = C.ImfOpenOutputFile(filename, hdr, channels)
check(outfile)
try
# get the strides and a pointer to the raster
(xstride, ystride) = strides(image)
dataptr = Base.unsafe_convert(Ptr{C.ImfRgba}, image)
# copy the data
check(C.ImfOutputSetFrameBuffer(outfile, dataptr, ystride, xstride))
check(C.ImfOutputWritePixels(outfile, height))
finally
check(C.ImfCloseOutputFile(outfile))
end
finally
C.ImfDeleteHeader(hdr)
end
nothing
end
function save_exr(
filename,
image::AbstractArray{T,2},
compression::Compression = ZIP_COMPRESSION,
channels::RgbaChannels = WRITE_RGBA,
) where {T}
save_exr(filename, (c -> convert(C.ImfRgba, c)).(image), compression, channels)
end
"""
load(filename)::Array{[RGB|RGBA|Gray|GrayA]{Float16},2}
Returns the image data contained in `filename`.
"""
function load(filename::AbstractString)
(rgba, chans) = load_exr(filename)
if chans == WRITE_YA
return (c -> convert(GrayA{Float16}, c)).(rgba)
elseif chans == WRITE_Y
return (c -> convert(Gray{Float16}, c)).(rgba)
elseif UInt32(chans) & UInt32(WRITE_A) == 0x00
return (c -> convert(RGB{Float16}, c)).(rgba)
else
return rgba
end
end
"""
save(filename, image[, compression])
Save `image` as an OpenEXR file in `filename` using the `compression` algorithm.
"""
function save(
filename::AbstractString,
image::AbstractArray{T,2},
compression::Compression = ZIP_COMPRESSION,
) where {T<:Transparent3}
save_exr(filename, image, compression, WRITE_RGBA)
end
function save(
filename::AbstractString,
image::AbstractArray{T,2},
compression::Compression = ZIP_COMPRESSION,
) where {T<:Color3}
save_exr(filename, image, compression, WRITE_RGB)
end
function save(
filename::AbstractString,
image::AbstractArray{T,2},
compression::Compression = ZIP_COMPRESSION,
) where {T<:TransparentGray}
save_exr(filename, image, compression, WRITE_YA)
end
function save(
filename::AbstractString,
image::AbstractArray{T,2},
compression::Compression = ZIP_COMPRESSION,
) where {T<:AbstractGray}
save_exr(filename, image, compression, WRITE_Y)
end
# FileIO interface
load(f::File{DataFormat{:EXR}}, args...) = load(f.filename, args...)
save(f::File{DataFormat{:EXR}}, args...) = save(f.filename, args...)
end # module OpenEXR
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] | 2.31919 | 2,569 |
using NativeSVG
dr =
Drawing(width = "5cm", height = "4cm") do
desc() do
str("Four separate rectangles")
end
rect(x = "0.5cm", y = "0.5cm", width = "2cm", height = "1cm")
rect(x = "0.5cm", y = "2cm", width = "1cm", height = "1.5cm")
rect(x = "3cm", y = "0.5cm", width = "1.5cm", height = "2cm")
rect(x = "3.5cm", y = "3cm", width = "1cm", height = "0.5cm")
#Show outline of viewport using 'rect' element
rect(
x = ".01cm",
y = ".01cm",
width = "4.98cm",
height = "3.98cm",
fill = "none",
stroke = "blue",
stroke_width = ".02cm"
)
end
display(dr)
dr =
Drawing(width = "5cm", height = "5cm") do
desc() do
str("Two groups, each of two rectangles")
end
g(id = "group1", fill = "red") do
rect(x = "1cm", y = "1cm", width = "1cm", height = "1cm")
rect(x = "3cm", y = "1cm", width = "1cm", height = "1cm")
end
g(id = "group2", fill = "blue") do
rect(x = "1cm", y = "3cm", width = "1cm", height = "1cm")
rect(x = "3cm", y = "3cm", width = "1cm", height = "1cm")
end
# Show outline of viewport using 'rect' element
rect(
x = ".01cm",
y = ".01cm",
width = "4.98cm",
height = "4.98cm",
fill = "none",
stroke = "blue",
stroke_width = ".02cm"
)
end
display(dr)
dr =
Drawing(
xmlns!xlink = "http://www.w3.org/1999/xlink",
width = "200",
height = "100",
viewBox = "0 0 200 100"
) do
title() do
str("Style inheritance and the use element")
end
desc() do
str("""Two circles, one of which is a re-styled clone of the other.
This file demonstrates one of the cases where
the shadow-DOM style matching rules in SVG 2
have a different effect than the SVG 1.1 style cloning rules.
The original circle on the left
should have blue fill
and green stroke.
In a conforming SVG 1.1 user agent,
the re-used circle on the right
should have orange fill and green stroke.
In a conforming SVG 2 user agent,
the re-used circle should have orange fill and purple stroke.
In all cases,
the stroke should be partially transparent
and 20 units wide,
relative to a total circle diameter of 100 units.""")
end
style(type = "text/css") do
str("""circle { stroke-opacity: 0.7; }
.special circle { stroke: green; }
use { stroke: purple;
fill: orange; }""")
end
g(class = "special", style = "fill: blue") do
circle(
id = "c",
cy = "50",
cx = "50",
r = "40",
stroke_width = "20"
)
end
use(xlink!href = "#c", x = "100")
end
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] | 1.840556 | 1,800 |
#Kronecker delta function
#23/07/2017
function krondelt(ii,jj)
if ii==jj
kronecker = 1
else
kronecker = 0
end
return kronecker
end
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] | 2.3125 | 64 |
<reponame>mfkiwl/CUDA.jl<filename>lib/cusolver/linalg.jl
# implementation of LinearAlgebra interfaces
using LinearAlgebra
using ..CUBLAS: CublasFloat
function copy_cublasfloat(A::CuMatrix{T}) where {T}
cublasfloat = promote_type(Float32, T)
if !(cublasfloat <: CublasFloat)
throw(ArgumentError("cannot promote eltype $T to a CUBLAS float"))
end
return copyto!(similar(A, cublasfloat), A)
end
# matrix division
const CuMatOrAdj{T} = Union{CuMatrix,
LinearAlgebra.Adjoint{T, <:CuMatrix{T}},
LinearAlgebra.Transpose{T, <:CuMatrix{T}}}
const CuOrAdj{T} = Union{CuVecOrMat,
LinearAlgebra.Adjoint{T, <:CuVecOrMat{T}},
LinearAlgebra.Transpose{T, <:CuVecOrMat{T}}}
function Base.:\(_A::CuMatOrAdj, _B::CuOrAdj)
A, B = copy_cublasfloat(_A), copy_cublasfloat(_B)
A, ipiv = CUSOLVER.getrf!(A)
return CUSOLVER.getrs!('N', A, ipiv, B)
end
# patch JuliaLang/julia#40899 to create a CuArray
# (see https://github.com/JuliaLang/julia/pull/41331#issuecomment-868374522)
if VERSION >= v"1.7-"
_zeros(::Type{T}, b::AbstractVector, n::Integer) where {T} = CUDA.zeros(T, max(length(b), n))
_zeros(::Type{T}, B::AbstractMatrix, n::Integer) where {T} = CUDA.zeros(T, max(size(B, 1), n), size(B, 2))
function Base.:\(F::Union{LinearAlgebra.LAPACKFactorizations{<:Any,<:CuArray},
Adjoint{<:Any,<:LinearAlgebra.LAPACKFactorizations{<:Any,<:CuArray}}},
B::AbstractVecOrMat)
m, n = size(F)
if m != size(B, 1)
throw(DimensionMismatch("arguments must have the same number of rows"))
end
TFB = typeof(oneunit(eltype(B)) / oneunit(eltype(F)))
FF = Factorization{TFB}(F)
# For wide problem we (often) compute a minimum norm solution. The solution
# is larger than the right hand side so we use size(F, 2).
BB = _zeros(TFB, B, n)
if n > size(B, 1)
# Underdetermined
copyto!(view(BB, 1:m, :), B)
else
copyto!(BB, B)
end
ldiv!(FF, BB)
# For tall problems, we compute a least squares solution so only part
# of the rhs should be returned from \ while ldiv! uses (and returns)
# the complete rhs
return LinearAlgebra._cut_B(BB, 1:n)
end
end
# factorizations
using LinearAlgebra: Factorization, AbstractQ
## QR
if VERSION >= v"1.8-"
LinearAlgebra.qr!(A::CuMatrix{T}) where T = QR(geqrf!(A::CuMatrix{T})...)
# conversions
CuMatrix(F::Union{QR,QRCompactWY}) = CuArray(AbstractArray(F))
CuArray(F::Union{QR,QRCompactWY}) = CuMatrix(F)
CuMatrix(F::QRPivoted) = CuArray(AbstractArray(F))
CuArray(F::QRPivoted) = CuMatrix(F)
function LinearAlgebra.ldiv!(_qr::QR, b::CuArray)
_x = UpperTriangular(_qr.R) \ (_qr.Q' * reshape(b,length(b),1))
b .= vec(_x)
unsafe_free!(_x)
return b
end
function LinearAlgebra.ldiv!(x::CuArray, _qr::QR, b::CuArray)
_x = UpperTriangular(_qr.R) \ (_qr.Q' * reshape(b,length(b),1))
x .= vec(_x)
unsafe_free!(_x)
return x
end
# conversions of factorizations
CuArray(Q::AbstractQ) = CuMatrix(Q)
CuArray{T}(Q::AbstractQ) where {T} = CuMatrix{T}(Q)
CuMatrix(Q::AbstractQ{T}) where {T} = CuMatrix{T}(Q)
CuMatrix{T}(Q::AbstractQ{S}) where {T,S} =
CuMatrix{T}(lmul!(Q, CuMatrix{S}(I, size(Q, 1), min(size(Q.factors)...))))
# avoid the CPU array in the above mul!
Matrix{T}(Q::QRPackedQ{S,<:CuArray,<:CuArray}) where {T,S} = Array(CuMatrix{T}(Q))
Matrix{T}(Q::QRCompactWYQ{S,<:CuArray,<:CuArray}) where {T,S} = Array(CuMatrix{T}(Q))
function Base.getindex(Q::QRPackedQ{<:Any, <:CuArray}, ::Colon, j::Int)
y = CUDA.zeros(eltype(Q), size(Q, 2))
y[j] = 1
lmul!(Q, y)
end
# multiplication by Q
LinearAlgebra.lmul!(A::QRPackedQ{T,<:CuArray,<:CuArray},
B::CuVecOrMat{T}) where {T<:Number} =
ormqr!('L', 'N', A.factors, A.τ, B)
LinearAlgebra.lmul!(adjA::Adjoint{T,<:QRPackedQ{T,<:CuArray,<:CuArray}},
B::CuVecOrMat{T}) where {T<:Real} =
ormqr!('L', 'T', parent(adjA).factors, parent(adjA).τ, B)
LinearAlgebra.lmul!(adjA::Adjoint{T,<:QRPackedQ{T,<:CuArray,<:CuArray}},
B::CuVecOrMat{T}) where {T<:Complex} =
ormqr!('L', 'C', parent(adjA).factors, parent(adjA).τ, B)
LinearAlgebra.lmul!(trA::Transpose{T,<:QRPackedQ{T,<:CuArray,<:CuArray}},
B::CuVecOrMat{T}) where {T<:Number} =
ormqr!('L', 'T', parent(trA).factors, parent(trA).τ, B)
else
struct CuQR{T,S<:AbstractMatrix} <: Factorization{T}
factors::S
τ::CuVector{T}
CuQR{T,S}(factors::AbstractMatrix{T}, τ::CuVector{T}) where {T,S<:AbstractMatrix} = new(factors, τ)
end
struct CuQRPackedQ{T,S<:AbstractMatrix} <: AbstractQ{T}
factors::CuMatrix{T}
τ::CuVector{T}
CuQRPackedQ{T,S}(factors::AbstractMatrix{T}, τ::CuVector{T}) where {T,S<:AbstractMatrix} = new(factors, τ)
end
CuQR(factors::AbstractMatrix{T}, τ::CuVector{T}) where {T} =
CuQR{T,typeof(factors)}(factors, τ)
CuQRPackedQ(factors::AbstractMatrix{T}, τ::CuVector{T}) where {T} =
CuQRPackedQ{T,typeof(factors)}(factors, τ)
# AbstractQ's `size` is the size of the full matrix,
# while `Matrix(Q)` only gives the compact Q.
# See JuliaLang/julia#26591 and JuliaGPU/CUDA.jl#969.
CuMatrix{T}(Q::AbstractQ{S}) where {T,S} = convert(CuArray, Matrix{T}(Q))
CuMatrix(Q::AbstractQ{T}) where {T} = CuMatrix{T}(Q)
CuArray{T}(Q::AbstractQ) where {T} = CuMatrix{T}(Q)
CuArray(Q::AbstractQ) = CuMatrix(Q)
LinearAlgebra.qr!(A::CuMatrix{T}) where T = CuQR(geqrf!(A::CuMatrix{T})...)
Base.size(A::CuQR) = size(A.factors)
Base.size(A::CuQRPackedQ, dim::Integer) = 0 < dim ? (dim <= 2 ? size(A.factors, 1) : 1) : throw(BoundsError())
CUDA.CuMatrix(A::CuQRPackedQ) = orgqr!(copy(A.factors), A.τ)
CUDA.CuArray(A::CuQRPackedQ) = CuMatrix(A)
Base.Matrix(A::CuQRPackedQ) = Matrix(CuMatrix(A))
function Base.getproperty(A::CuQR, d::Symbol)
m, n = size(getfield(A, :factors))
if d == :R
return triu!(A.factors[1:min(m, n), 1:n])
elseif d == :Q
return CuQRPackedQ(A.factors, A.τ)
else
getfield(A, d)
end
end
# iteration for destructuring into components
Base.iterate(S::CuQR) = (S.Q, Val(:R))
Base.iterate(S::CuQR, ::Val{:R}) = (S.R, Val(:done))
Base.iterate(S::CuQR, ::Val{:done}) = nothing
# Apply changes Q from the left
LinearAlgebra.lmul!(A::CuQRPackedQ{T,S}, B::CuVecOrMat{T}) where {T<:Number, S<:CuMatrix} =
ormqr!('L', 'N', A.factors, A.τ, B)
LinearAlgebra.lmul!(adjA::Adjoint{T,<:CuQRPackedQ{T,S}}, B::CuVecOrMat{T}) where {T<:Real, S<:CuMatrix} =
ormqr!('L', 'T', parent(adjA).factors, parent(adjA).τ, B)
LinearAlgebra.lmul!(adjA::Adjoint{T,<:CuQRPackedQ{T,S}}, B::CuVecOrMat{T}) where {T<:Complex, S<:CuMatrix} =
ormqr!('L', 'C', parent(adjA).factors, parent(adjA).τ, B)
LinearAlgebra.lmul!(trA::Transpose{T,<:CuQRPackedQ{T,S}}, B::CuVecOrMat{T}) where {T<:Number, S<:CuMatrix} =
ormqr!('L', 'T', parent(trA).factors, parent(trA).τ, B)
function Base.getindex(A::CuQRPackedQ{T, S}, i::Int, j::Int) where {T, S}
assertscalar("CuQRPackedQ getindex")
x = CUDA.zeros(T, size(A, 2))
x[j] = 1
lmul!(A, x)
return x[i]
end
function Base.show(io::IO, F::CuQR)
println(io, "$(typeof(F)) with factors Q and R:")
show(io, F.Q)
println(io)
show(io, F.R)
end
# https://github.com/JuliaLang/julia/pull/32887
LinearAlgebra.det(Q::CuQRPackedQ{<:Real}) = isodd(count(!iszero, Q.τ)) ? -1 : 1
LinearAlgebra.det(Q::CuQRPackedQ) = prod(τ -> iszero(τ) ? one(τ) : -sign(τ)^2, Q.τ)
function LinearAlgebra.ldiv!(_qr::CuQR, b::CuArray)
_x = UpperTriangular(_qr.R) \ (_qr.Q' * reshape(b,length(b),1))
b .= vec(_x)
unsafe_free!(_x)
return b
end
function LinearAlgebra.ldiv!(x::CuArray,_qr::CuQR, b::CuArray)
_x = UpperTriangular(_qr.R) \ (_qr.Q' * reshape(b,length(b),1))
x .= vec(_x)
unsafe_free!(_x)
return x
end
end
## SVD
abstract type SVDAlgorithm end
struct QRAlgorithm <: SVDAlgorithm end
struct JacobiAlgorithm <: SVDAlgorithm end
if VERSION >= v"1.8-"
LinearAlgebra.svd!(A::CuMatrix{T}; full::Bool=false,
alg::SVDAlgorithm=JacobiAlgorithm()) where {T} =
_svd!(A, full, alg)
LinearAlgebra.svd(A::CuMatrix; full=false, alg::SVDAlgorithm=JacobiAlgorithm()) =
_svd!(copy_cublasfloat(A), full, alg)
_svd!(A::CuMatrix{T}, full::Bool, alg::SVDAlgorithm) where T =
throw(ArgumentError("Unsupported value for `alg` keyword."))
function _svd!(A::CuMatrix{T}, full::Bool, alg::QRAlgorithm) where T
U, S, Vt = gesvd!(full ? 'A' : 'S', full ? 'A' : 'S', A)
return SVD(U, S, Vt)
end
function _svd!(A::CuMatrix{T}, full::Bool, alg::JacobiAlgorithm) where T
U, S, V = gesvdj!('V', Int(!full), A)
return SVD(U, S, V')
end
else
struct CuSVD{T,Tr,A<:AbstractMatrix{T}} <: LinearAlgebra.Factorization{T}
U::CuMatrix{T}
S::CuVector{Tr}
V::A
end
# iteration for destructuring into components
Base.iterate(S::CuSVD) = (S.U, Val(:S))
Base.iterate(S::CuSVD, ::Val{:S}) = (S.S, Val(:V))
Base.iterate(S::CuSVD, ::Val{:V}) = (S.V, Val(:done))
Base.iterate(S::CuSVD, ::Val{:done}) = nothing
@inline function Base.getproperty(S::CuSVD, s::Symbol)
if s === :Vt
return getfield(S, :V)'
else
return getfield(S, s)
end
end
LinearAlgebra.svd!(A::CuMatrix{T}; full::Bool=false,
alg::SVDAlgorithm=JacobiAlgorithm()) where {T} =
_svd!(A, full, alg)
LinearAlgebra.svd(A::CuMatrix; full=false, alg::SVDAlgorithm=JacobiAlgorithm()) =
_svd!(copy_cublasfloat(A), full, alg)
_svd!(A::CuMatrix{T}, full::Bool, alg::SVDAlgorithm) where T =
throw(ArgumentError("Unsupported value for `alg` keyword."))
function _svd!(A::CuMatrix{T}, full::Bool, alg::QRAlgorithm) where T
U, s, Vt = gesvd!(full ? 'A' : 'S', full ? 'A' : 'S', A::CuMatrix{T})
return CuSVD(U, s, Vt')
end
function _svd!(A::CuMatrix{T}, full::Bool, alg::JacobiAlgorithm) where T
return CuSVD(gesvdj!('V', Int(!full), A::CuMatrix{T})...)
end
end
LinearAlgebra.svdvals!(A::CuMatrix{T}; alg::SVDAlgorithm=JacobiAlgorithm()) where {T} =
_svdvals!(A, alg)
LinearAlgebra.svdvals(A::CuMatrix; alg::SVDAlgorithm=JacobiAlgorithm()) =
_svdvals!(copy_cublasfloat(A), alg)
_svdvals!(A::CuMatrix{T}, alg::SVDAlgorithm) where T =
throw(ArgumentError("Unsupported value for `alg` keyword."))
_svdvals!(A::CuMatrix{T}, alg::QRAlgorithm) where T = gesvd!('N', 'N', A::CuMatrix{T})[2]
_svdvals!(A::CuMatrix{T}, alg::JacobiAlgorithm) where T = gesvdj!('N', 1, A::CuMatrix{T})[2]
## LU
if VERSION >= v"1.8-"
function LinearAlgebra.lu!(A::StridedCuMatrix{T}, ::RowMaximum; check::Bool = true) where {T}
lpt = getrf!(A)
check && LinearAlgebra.checknonsingular(lpt[3])
return LU(lpt[1], lpt[2], Int(lpt[3]))
end
# GPU-compatible accessors of the LU decomposition properties
function Base.getproperty(F::LU{T,<:StridedCuMatrix}, d::Symbol) where T
m, n = size(F)
if d === :L
L = tril!(getfield(F, :factors)[1:m, 1:min(m,n)])
L[1:min(m,n)+1:end] .= one(T) # set the diagonal (linear indexing trick)
return L
else
invoke(getproperty, Tuple{LU{T,<:StridedMatrix}, Symbol}, F, d)
end
end
# LAPACK's pivoting sequence needs to be iterated sequentially...
# TODO: figure out a GPU-compatible way to get the permutation matrix
LinearAlgebra.ipiv2perm(v::CuVector{T}, maxi::Integer) where T =
LinearAlgebra.ipiv2perm(Array(v), maxi)
end
## cholesky
if VERSION >= v"1.8-"
function LinearAlgebra.cholesky(A::LinearAlgebra.RealHermSymComplexHerm{<:Real,<:CuMatrix},
::Val{false}=Val(false); check::Bool = true)
C, info = LinearAlgebra._chol!(copy(parent(A)), A.uplo == 'U' ? UpperTriangular : LowerTriangular)
return Cholesky(C.data, A.uplo, info)
end
end
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13,
84,
489,
78,
11,
7508,
8,
198,
220,
220,
220,
886,
198,
437,
198
] | 2.123241 | 5,542 |
using Gadfly, DataArrays, RDatasets
plot(dataset("datasets", "iris"),
layer(x=:SepalLength, y=:SepalWidth, Geom.point),
layer([sin, cos], 0, 25))
| [
198,
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220,
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198
] | 2.257143 | 70 |
## J e W e L s
function JWL(name::AbstractString)
Pkg.project(Pkg.Types.Context(),name,tempdir())
folder = joinpath(tempdir(),name)
open(joinpath(folder,"include.jl"), "w") do f
Base.write(f, """
[compat]
julia = "1.6"
""" * string(VERSION))
end
open(joinpath(folder,"include.jl"), "w") do f
Base.write(f, """
# Use this file to `import` any relevant packages*,
# and to define your own functions for use in the workbook.
# It will be `include`d when the jwl workbook is opened.
# * (don't forget to add packages to the `Project.toml` too)
""")
end
return JWL(name, folder, Workbook())
end
Base.show(io::IO, j::JWL) = Base.print(io, "JWL " * string(j.name) * " with " * string(j.wb) * " in " * dirname(j.folder)) | [
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366,
287,
366,
1635,
26672,
3672,
7,
73,
13,
43551,
4008
] | 2.279452 | 365 |
module Archimedes
using LinearAlgebra
using Statistics
using Unitful
using Luxor
const _current_luxor_drawing = Ref{Luxor.Drawing}()
export Point2D,
PointMass,
mass,
getx,
gety,
radius,
bbox,
centroid,
gravitycenter,
draw,
drawing,
current_drawing,
BoxShip,
corners,
labeled_point,
buoyancycenter,
toship,
isocarene,
metacenter,
setopacity,
puttext
struct Point2D
x::typeof(1.0u"m")
y::typeof(1.0u"m")
end
getx(p::Point2D) = p.x
gety(p::Point2D) = p.y
Base.:-(a::Point2D, b::Point2D) = Point2D(a.x-b.x, a.y-b.y)
Base.:+(a::Point2D, b::Point2D) = Point2D(a.x+b.x, a.y+b.y)
Base.:*(a::Point2D, b::Number) = Point2D(a.x*b, a.y*b)
Base.:*(b::Number, a::Point2D) = Point2D(a.x*b, a.y*b)
Base.:/(a::Point2D, b::Number) = Point2D(a.x/b, a.y/b)
LinearAlgebra.norm(a::Point2D) = sqrt(a.x^2 + a.y^2)
struct PointMass
coordinates::Point2D
mass::typeof(1.0u"kg")
radius::typeof(1.0u"m")
end
PointMass(x,y,m,r) = PointMass(Point2D(x,y),m,r)
coords(p::PointMass) = p.coordinates
mass(p::PointMass) = p.mass
getx(p::PointMass) = getx(coords(p))
gety(p::PointMass) = gety(coords(p))
radius(p::PointMass) = p.radius
"""
Get the bounding box (xmin, xmax, ymin, ymax) of an array of pointmasses
"""
bbox(pointmasses) = (extrema(getx.(pointmasses))..., extrema(gety.(pointmasses))...)
"""
Get the centroid of an array of pointmasses
"""
centroid(pointmasses) = Point2D(mean(getx.(pointmasses)), mean(gety.(pointmasses)))
"""
Get the center of gravity
"""
function gravitycenter(pointmasses)
m = sum(mass.(pointmasses))
x = sum(getx.(pointmasses) .* mass.(pointmasses)) / m
y = sum(gety.(pointmasses) .* mass.(pointmasses)) / m
r = sqrt(sum(radius.(pointmasses) .^ 2))
return PointMass(x, y, m, r)
end
"""
Helper to map the points with units to pixel-coordinates
"""
struct CoordMapping
origin::Point2D
scaling::typeof(1.0/(1.0u"m"))
end
function CoordMapping(width::Number, bbox::Tuple)
(xmin,xmax,ymin,ymax) = bbox
center = Point2D(mean((xmin,xmax)), mean((ymin,ymax)))
scaling = width/(xmax-xmin)
return CoordMapping(center, scaling)
end
scale(x, mapping::CoordMapping) = x*mapping.scaling
function remap(p, mapping)
(xoff, yoff) = (getx(mapping.origin), gety(mapping.origin))
return Luxor.Point(((getx(p)-xoff, -(gety(p)-yoff)).*mapping.scaling)...)
end
"""
Initialize a drawing, returing the point mapping
"""
function drawing(bbox, figwidth=300)
(xmin,xmax,ymin,ymax) = bbox
ar = (xmax-xmin)/(ymax-ymin)
figheight = Int(round(figwidth/ar))
margin = 30
bottom_padding = 40
_current_luxor_drawing[] = Drawing(figwidth+2*margin, figheight+2*margin+bottom_padding, :svg)
origin()
background("white")
return CoordMapping(figwidth, bbox)
end
function puttext(txt, p::Point2D, mapping::CoordMapping)
sethue("black")
c = remap(p,mapping)
text(txt, c, halign=:center, valign=:top)
end
"""
Draws a pointmass into the current context
"""
function draw(p::PointMass, mapping::CoordMapping; hue="black")
sethue(hue)
c = remap(p,mapping)
r = scale(radius(p), mapping)
circle(c, r, :fill)
spacing = 5.0
step = 10.0
text("x = $(getx(p))", Point(c.x,c.y+r+spacing), halign=:center, valign=:top)
text("y = $(gety(p))", Point(c.x,c.y+r+spacing+step), halign=:center, valign=:top)
text("m = $(mass(p))", Point(c.x,c.y+r+spacing+2*step), halign=:center, valign=:top)
end
function draw(p::Point2D, mapping::CoordMapping, r=5.0; hue="black", label="")
sethue(hue)
c = remap(p,mapping)
circle(c, r, :fill)
if label != ""
text(label, Point(c.x+1.2*r,c.y+1.2*r), halign=:center, valign=:top)
end
end
"""
Shows the drawing and removes the temp file
"""
function current_drawing()
finish()
return _current_luxor_drawing[]
end
struct BoxShip
width::typeof(1.0u"m")
height::typeof(1.0u"m")
draft::typeof(1.0u"m")
KG::typeof(1.0u"m")
heel::Float64
vshift::typeof(1.0u"m")
BoxShip(width, height, draft, KG, heel=0.0, vshift=0.0u"m") = new(width,height,draft,KG,heel,vshift)
end
forward_trans(p, s::BoxShip) = Point2D(cos(s.heel)*getx(p) - sin(s.heel)*gety(p), sin(s.heel)*getx(p) + cos(s.heel)*gety(p) + s.vshift)
inverse_trans(p, s::BoxShip) = Point2D(cos(s.heel)*getx(p) + sin(s.heel)*(gety(p) - s.vshift), -sin(s.heel)*getx(p) + cos(s.heel)*(gety(p) - s.vshift))
"""
Convert position p to ship coordinates
"""
toship(p, s) = inverse_trans(p, s) - Point2D(0.0u"m", s.height/2 - s.draft)
function corners(s::BoxShip)
x = s.width/2
ymax = s.height-s.draft
ymin = -s.draft
return forward_trans.(Point2D.([-x,x,x,-x], [ymin,ymin,ymax,ymax]), Ref(s))
end
function waterline(s::BoxShip)
x = 1.4*s.width/2
y = 0.0u"m"
return (Point2D(-x,y), Point2D(x,y))
end
function gravitycenter(s::BoxShip)
return forward_trans(Point2D(0.0u"m", s.KG-s.draft), s)
end
bbox(s::BoxShip) = 1.4 .* (-s.width/2, s.width/2, -s.draft, s.height-s.draft)
function wl_intersect(p1, p2)
x1 = getx(p1)
y1 = gety(p1)
x2 = getx(p2)
y2 = gety(p2)
r = (x2-x1)/(y2-y1)
return Point2D(-y1*r+x1, 0.0u"m")
end
"""
Corners of the underwater part
"""
function carene(s::BoxShip)
ship_pts = corners(s)
N = length(ship_pts)
pts_above = collect(Iterators.filter((p) -> gety(p[2]) > 0u"m", enumerate(ship_pts)))
sort!(pts_above, lt = (a,b) -> getx(a[2]) < getx(b[2]))
l1 = pts_above[1][1]
l2 = l1 % N + 1
wl_left = wl_intersect(ship_pts[l1], ship_pts[l2])
r1 = pts_above[end][1]
r2 = (r1 - 2 + N) % N + 1
wl_right = wl_intersect(ship_pts[r1], ship_pts[r2])
pts_below = collect(Iterators.filter((p) -> gety(p[2]) < 0u"m", enumerate(ship_pts)))
sort!(pts_below, lt = (a,b) -> getx(a[2]) < getx(b[2]))
return (getindex.(pts_below, 2)..., wl_right, wl_left)
end
function metacenter(s::BoxShip)
trap = carene(s)
w_wl = getx(trap[end-1]) - getx(trap[end])
F = inverse_trans(Point2D((getx(trap[end-1]) + getx(trap[end]))/2, 0.0u"m"),s)
BM = (w_wl^3 / 12)/carene_area(s)
B = inverse_trans(buoyancycenter(s),s)
nvec = F - B
Mship = B + Point2D(BM*sin(s.heel), BM*cos(s.heel))
return forward_trans(Mship, s)
end
"""
Make a 3-vector out of a Point2D
"""
vec3(p::Point2D) = [getx(p), gety(p), 0.0u"m"]
"""
Area of a triangle
"""
function area(pts)
v = vec3.(pts)
return abs(cross(v[3]-v[1], v[2]-v[1])[3])/2
end
function triangulate(pts::Union{AbstractArray{ET},NTuple{N,ET} where N}) where ET
c = centroid(pts)
triags = Array{NTuple{3,ET}}(undef,length(pts))
N = length(pts)
for i in 1:length(pts)
triags[i] = (c, pts[i], pts[i%N+1])
end
return triags
end
function buoyancycenter(s::BoxShip)
trap = carene(s)
triags = triangulate(trap)
areas = area.(triags)
centroids = centroid.(triags)
A = sum(areas)
x = sum(getx.(centroids) .* areas) / A
y = sum(gety.(centroids) .* areas) / A
return Point2D(x,y)
end
function carene_area(s::BoxShip)
trap = carene(s)
triags = triangulate(trap)
return sum(area.(triags))
end
function isocarene(s0, θ)
θ0 = s0.heel
T = s0.draft
DT_l = -T
DT_u = T
A0 = carene_area(s0)
for i in 1:50
DT = (DT_u+DT_l)/2
s1 = BoxShip(s0.width, s0.height, T, s0.KG, θ+θ0, DT)
A1 = carene_area(s1)
if abs(A1-A0)/A0 < 1e-8
return s1
end
if A1 < A0
DT_u = DT
else
DT_l = DT
end
end
error("isocarene did not converge")
end
function labeled_point(p, label, hue="black", radius=5.0)
sethue(hue)
circle(p, radius, :fill)
text(label, Point(p.x+1.2*radius,p.y+1.2*radius), halign=:center, valign=:top)
end
function draw(m::CoordMapping, s::BoxShip, transformation=((p,::BoxShip) -> p); showM=true, showB=true)
ship_pts = transformation.(corners(s),Ref(s))
p = remap.(ship_pts, Ref(m))
sethue("black")
line(p[1], p[2], :stroke)
line(p[2], p[3], :stroke)
line(p[4], p[1], :stroke)
sethue("darkgrey")
line(p[3], p[4], :stroke)
labeled_point(remap(transformation(gravitycenter(s),s),m), "G")
if showM || showB
M = remap(transformation(metacenter(s),s),m)
B = remap(transformation(buoyancycenter(s),s),m)
if showM
labeled_point(M, "M")
end
if showB
labeled_point(B, "B", "red")
end
if showM && showB
setdash("dot")
line(M, B, :stroke)
setdash("solid")
end
end
wl = remap.(transformation.(waterline(s),Ref(s)), Ref(m))
sethue("blue")
line(wl[1], wl[2], :stroke)
#trap = carene(s)
#F = remap(transformation(Point2D((getx(trap[end-1]) + getx(trap[end]))/2, 0.0u"m"),s),m)
#labeled_point(F, "F", "blue")
return m
end
function draw(s::BoxShip, figwidth=300, transformation=((p,::BoxShip) -> p); showM=true, showB=true)
draw(drawing(bbox(s), figwidth), s, transformation, showM=showM, showB=showB)
end
setopacity = Luxor.setopacity
end # module
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] | 2.136837 | 4,129 |
<filename>src/DiscretisedFluidQueues.jl
module DiscretisedFluidQueues
import Jacobi, LinearAlgebra, SparseArrays, StaticArrays
import Base: *, +, size, show, getindex, setindex!, sum
# Types
export Model, DiscretisedFluidQueue, BoundedFluidQueue, Phase, PhaseSet # Queues are <:Model
export AbstractMatrixExponential, ConcentratedMatrixExponential, MatrixExponential
export Mesh, DGMesh, FRAPMesh, FVMesh # are <:Mesh
export Generator, FullGenerator, LazyGenerator # are <:Generator
export SFMDistribution, AbstractIntegrationMethod, AutoQuadrature, Quadrature, TrapezoidRule
export ExplicitRungeKuttaScheme, ForwardEuler, Heuns, StableRK3, StableRK4 # Euler, RungeKutta4 <: TimeIntegrationScheme
export Simulation
# Functions
export augment_model, membership, N₋, N₊, n_phases, phases, rates # Model methods
export cell_nodes, Δ, n_bases_per_cell, n_bases_per_phase, n_intervals, total_n_bases # Mesh methods
export interior_point_mass, left_point_mass, right_point_mass, integrate_time # SFMDistribution methods
export simulate, fixed_time, n_jumps, first_exit_x # Simulation methods
export build_lazy_generator, build_full_generator, static_generator
export cme_params, pdf, ccdf, cdf, build_me, cell_probs
export normalised_closing_operator_cdf, normalised_closing_operator_pdf
export naive_normalised_closing_operator_cdf, naive_normalised_closing_operator_pdf
export unnormalised_closing_operator_cdf, unnormalised_closing_operator_pdf
export limit, Limiter, NoLimiter, GeneralisedMUSCL
# model
include("1_SFM.jl")
include("2_abstract_mesh.jl") # things which apply to all meshes
include("2a_discretised_fluid_queue.jl")
include("3_lazy_generators.jl")
include("4_full_generators.jl")
# auxillary functions
include("6_ME_tools.jl") # used in FRAPApproximation.jl
include("7_polynomials.jl") # used in discontinuous_Galerkin.jl
include("8_discontinuous_Galerkin.jl")
include("9_finite_volume_method.jl")
include("10_FRAP_approximation.jl")
include("11_distributions.jl")
include("11c_extend_Base_operations.jl")
include("11d_limiters.jl")
include("12_time_integration.jl")
include("13_simulate.jl")
include("14_error_metrics.jl")
end
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] | 2.929348 | 736 |
abstract type DisplayNoBuffer <: AbstractNumDisplay end
scan_rate(d::DisplayNoBuffer) = ceil(Int, 10^6 / d.usDelay) # Hz
size(d::DisplayNoBuffer) = length(d.digits_pins)
# update wave based on buffer
function generate_wave(d::DisplayNoBuffer)
# create new wave based on d.buffer
# number of pulses is equal to limit
pulse = PiGPIOC.gpioPulse_t[]
# set pause after each blink
inactivePeriod = ceil(Int, d.usDelay * (1 - d.intensity / 16))
gpioOffPause = 0x0
gpioOnPause = 0x0
for j in 1:d.limit
if d.digits_pins[j] >= 0
if d.inverted_digits
gpioOnPause |= 1 << d.digits_pins[j]
else
gpioOffPause |= 1 << d.digits_pins[j] # turn off all digit pins
end
end
end
# set active state
activePeriod = ceil(Int, d.usDelay * d.intensity / 16)
for i in 1:d.limit
gpioOn = 0x0
gpioOff = 0x0
# digits
for j in 1:d.limit
if d.digits_pins[j] >= 0
if xor(i == j, d.inverted_digits)
gpioOn |= 1 << d.digits_pins[j]
else
gpioOff |= 1 << d.digits_pins[j]
end
end
end
# if decode mode than 1
decode_i = (decode(d) >> (i - 1)) % 2
# sectors
buffer_i = d.buffer[i]
if decode_i == 0
value = buffer_i
else
value = NUM_TRANSLATOR[buffer_i % 0b00010000] # get 4 least significant bits
dp_state = buffer_i >> 7
value += dp_state << 7
end
for j in 1:8
if d.sectors_pins[j] >= 0
if xor(value % 2 == 1, d.inverted_sectors)
gpioOn |= 1 << d.sectors_pins[j]
else
gpioOff |= 1 << d.sectors_pins[j]
end
end
value >>= 1
end
push!(pulse, PiGPIOC.gpioPulse_t(gpioOn, gpioOff, activePeriod)) # on, off, usDelay
push!(pulse, PiGPIOC.gpioPulse_t(gpioOnPause, gpioOffPause, inactivePeriod))
end
PiGPIOC.gpioWaveAddGeneric(d.limit * 2, pulse)
wave_id = PiGPIOC.gpioWaveCreate()
if wave_id < 0
# return Upon success a wave id greater than or equal to 0 is returned,
# otherwise PI_EMPTY_WAVEFORM, PI_TOO_MANY_CBS, PI_TOO_MANY_OOL,
# or PI_NO_WAVEFORM_ID
throw("Error in 'PiGPIOC.gpioWaveCreate()' with code: $(wave_id)")
end
return wave_id
end
"""
function update(d::DisplayNoBuffer)
Generates a sequence of pulses based on buffer, decode_mode, intensity, limit, and runs pulses repeatedly.
The function is used internally after buffer and modes changes.
If a display is in shutdown mode, the function does nothing.
"""
function update(d::DisplayNoBuffer)
# do nothing in shutdown mode
if PiGPIOC.gpioWaveTxBusy() == 1
wave_id = d.test_mode ? generate_test_wave(d) : generate_wave(d)
run_wave(wave_id)
end
end
function shutdown_mode_off(d::DisplayNoBuffer)
# do nothing in normal mode or test mode
if PiGPIOC.gpioWaveTxBusy() == 0
wave_id = d.test_mode ? generate_test_wave(d) : generate_wave(d)
run_wave(wave_id)
end
end
function shutdown_mode_on(d::DisplayNoBuffer)
# stop current wave
PiGPIOC.gpioWaveTxStop()
# clear pins
PiGPIOC.gpioWrite(d.digits_pins, 0)
PiGPIOC.gpioWrite(d.sectors_pins, 0)
end
function test_mode_off(d::DisplayNoBuffer)
# do nothing in normal mode
if d.test_mode
d.test_mode = false
update(d)
end
end
function test_mode_on(d::DisplayNoBuffer)
# do nothing in test mode
if !d.test_mode
d.test_mode = true
update(d)
end
end
function set_limit(d::DisplayNoBuffer, limit::Int = size(d))
@assert 1 <= limit <= size(d) "limit must be between 1 and $(size(d)), got $limit"
d.limit = limit
update(d)
# set empty states
pins_to_free = (d.limit+1):size(d)
PiGPIOC.gpioWrite(d.digits_pins[pins_to_free], !d.inverted_digits ? 0 : 1)
end
function set_intensity(d::DisplayNoBuffer, intensity::Int = 16)
@assert 1 <= intensity <= 16 "intensity must be between 1 and 16, got $intensity"
d.intensity = intensity
update(d)
end
"""
function write_digit(
indicator::DisplayBCD,
digit::Union{UInt8, Nothing},
position::Int
)
Writes a digit to the position. The result of the execution is changing one digit in a particular digit.
## Arguments
- `indicator` : object representing display device
- `digit` : decimal value from 0 to 9 or `nothing`. The last means an empty digit.
Values from 10 to 14 are also possible here but results to miningless symbols.
Value 15 means an empty digit and it is the same as `nothing`.
- `position` : number of digit to write starting from 1 which mean less significant digit.
The maximal value depends on available digits, so it should be `<= length(indicator.digit_pins)`
"""
function write_digit(
d::DisplayNoBuffer,
value::UInt8,
position::Int
)
@assert 1 <= position <= 8 "position must be between 1 and 8, got $position"
d.buffer[position] = value
# update only in normal mode
if !d.test_mode
update(d)
end
end
#= This can change all digits in Display
"""
function write_number(
indicator::DisplayBCD,
digit_vector::AbstractArray{D},
dp_position::Union(Int,Nothing) = nothing
) where D <: Union{UInt8, Nothing}
Writes several digits to the positions. The result of the execution is updating the whole number.
If `digit_vector` is shorter than number of digits the rest digits will be empty.
## Arguments
- `indicator` : object representing display device
- `digit_vector` : vector of decimal values from 0 to 9 or `nothing`. The same meaning as `digit` in `write_digit()`.
The first element of vector will be write to the less significant digit, etc.
- `dp_position` : position of dot in display
## Example
```
# d is 4-digit display
write_number(d, [1,2])
# the result is __321
write_number(d, [1,2,3,4])
# the result is 4321
write_number(d, [1,2,3,4,5,6])
# the result is 4321
```
"""
function write_number(
indicator::DisplayNoBuffer,
digit_vector::AbstractArray{Int}, # digit from 0 to 9
dp_position::Int
)
# TODO: check digit_vector
l = length(digit_vector)
for i in 1:length(indicator.digits_pins)
if i > l || digit_vector[i] >= 0
indicator.buffer[i] = empty_digit(indicator)
else
indicator.buffer[i] = NUM_TRANSLATOR[digit_vector[i]]
end
end
fill!(indicator.dp_buffer, 0b0)
if dp_position >= 0 && 1 <= dp_position <= length(indicator.digits_pins)
indicator.dp_buffer[dp_position] = 0b1
end
update(indicator)
end
=#
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] | 2.315984 | 2,978 |
<reponame>fstiffo/aoc-2020-julia
const Passport = Dict{String,String}
function readinput(filepath)
function parsepassport(str)
p = Passport()
ss = split(str, r"\s")
for s in ss
m = match(r"([a-z]{3}):(.+)$", s)
p[m[1]] = m[2]
end
p
end
strs = open(filepath) do file
split(strip(read(file, String)), "\n\n")
end
map(parsepassport, strs)
end
# First Half
allrqdflds(p) = length(keys(p)) == (haskey(p, "cid") ? 8 : 7) # Treat cid as optional.
passports = readinput("inputs/day04.txt")
count(allrqdflds, passports)
# Count valid passports - those that have all required fields.
# Second Half
function allvldflds(p)
# True if all required fields are both present and valid
if !allrqdflds(p)
return false
end
if !(parse(Int, p["byr"]) ∈ 1920:2002)
# byr (Birth Year) - four digits; at least 1920 and at most 2002
return false
end
if !(parse(Int, p["iyr"]) ∈ 2010:2020)
# iyr (Issue Year) - four digits; at least 2010 and at most 2020
return false
end
if !(parse(Int, p["eyr"]) ∈ 2020:2030)
# eyr (Expiration Year) - four digits; at least 2020 and at most 2030
return false
end
m = match(r"^(\d+)(cm|in)$", p["hgt"])
if isnothing(m)
# hgt (Height) - a number followed by either cm or in
return false
elseif m[2] == "cm"
if !(parse(Int, m[1]) ∈ 150:193)
# If cm, the number must be at least 150 and at most 193
return false
end
else
if !(parse(Int, m[1]) ∈ 59:76)
# If in, the number must be at least 59 and at most 76
return false
end
end
if isnothing(match(r"^#[0-9|a-f]{6}$", p["hcl"]))
# hcl (Hair Color) - a # followed by exactly six characters 0-9 or a-f
return false
end
if isnothing(match(r"^(amb|blu|brn|gry|grn|hzl|oth){1}$", p["ecl"]))
# ecl (Eye Color) - exactly one of: amb blu brn gry grn hzl oth
return false
end
if isnothing(match(r"^\d{9}$", p["pid"]))
# pid (Passport ID) - a nine-digit number, including leading zeroes
return false
end
true
end
passports = readinput("inputs/day04.txt")
count(allvldflds, passports)
# Count the number of valid passports - those that have
# all required fields and valid values
| [
27,
7856,
261,
480,
29,
69,
301,
733,
78,
14,
64,
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12,
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12,
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] | 2.181081 | 1,110 |
<reponame>clintonTE/CCA
module Minvar
##################Dependencies####################
using Revise
if pwd() ∉ LOAD_PATH
push!(LOAD_PATH,pwd())
end
if "$(pwd())\\data" ∉ LOAD_PATH
push!(LOAD_PATH,"$(pwd())\\data")
end
using DataFrames, Distributions, GZip, ForwardDiff, Dates, LinearAlgebra, Statistics,
Measures, Formatting, StatsBase, Distributions, DataStructures, Finometrics, Distributed,
Serialization, CSV, Random, Distributed#, Gadfly, Fontconfig, Cairo NOTE: uncomment for graphs
#########################Exported functions##########
export testportfolio,
testcov,
testtrisectedportfolio,
matlab2julia,
mathematica2julia,
testω,
testposteriors,
estimateminvariance,
initializepar, #need this to set up the parallelization
cleanup
################Priors and initial vlaues############
#Priors
const D_θG = 0.16^2/20 #current vix converted to daily
const D_δ²G = .76/20*2 #reasonably diffuse prior from volatiltiy of the vix multiplied by 2
const D_αG = 1.01 #Pick a diffuse prior, but want the expectation to exist
const D_βG = 7.0e-5 / 2.0 * (D_αG - 1.0) #derive from vix of vix (See documentation)
const D_αP = D_αG
const D_βP = D_βG * 2.0 * (D_αP - 1.0) #expect this is more volatile.
const D_θSG = D_θG
const D_δ²SG = D_δ²G*2.0 #more uncertain
const D_θSGP = D_θG
const D_δ²SGP = D_δ²G*4.0 #most uncertain
#Initial Parameter Values
const D_σ²G=.001
const D_ζ²G= .004
const D_ζ²P = .009
const D_SG = .002
const D_SGP = .003
##############Other constants and methedological parameters############
#const TEX_PATH = "C:\\Users\\Clinton\\Dropbox\\Apps\\Overleaf\\Endowment Project"
#const GRAPH_PATH = TEX_PATH * "\\sub\\fig"
#const WORKING_PATH = pwd() * "\\working"
const OUT_NAME = "test"
const OUT_PATH = "output"
const N_BURN = 100
const N_SAMPLES = 100
const DATA_NAME = "sampledata"
const DATA_PATH = pwd() * "\\data"
const DATE_BEGIN = Date(2015,1,1)
const DATE_END = Date(2016,12,31)
const DATE_FORMAT = "yyyymmdd"
const MAX_WORKERS = Base.Sys.CPU_THREADS ÷ 2 # gets num of physical cores
const NUM_TEST_ASSETS = 100 #this shouldn't make a big difference in the results
const MIN_ADJUSTMENT = 0.75 #smaller values->more extreme weights allowed for test portfolio
#without this throws an error on each run if already defined
if !(@isdefined RANDOM_WEIGHT_DIST)
const RANDOM_WEIGHT_DIST = TriangularDist(-1.0,1.5,1/NUM_TEST_ASSETS)
end
#############File linkage###############################
include("code\\MinvarPar.jl")
include("code\\Universe.jl")
include("code\\Portfolio.jl")
include("code\\Parameters.jl")
include("code\\Posteriors.jl")
include("code\\TrisectedPortfolio.jl")
include("code\\X2Julia.jl")
include("code\\Estimation.jl")
include("code\\Testsandoneoffs.jl")
end
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] | 2.761809 | 995 |
module Extrema_Test
using Compat
using Compat.Test
using EAGOSmoothMcCormickGrad
using IntervalArithmetic
using StaticArrays
function about(calc,val,tol)
return (val - tol <= calc <= val + tol)
end
EAGOSmoothMcCormickGrad.set_diff_relax(0)
a = seed_g(Float64,1,2)
xIBox = [Interval(-3.0,8.0)]
mBox = mid.(xIBox)
X = SMCg{2,Float64}(4.5,4.5,a,a,xIBox[1],false,xIBox,mBox)
out = min(3,X)
println("out 1: $out")
@test about(out.cc,3.0,1E-1)
@test about(out.cv,1.0909090909090908,1E-1)
@test about(out.cc_grad[1],0.0,1E-4)
@test about(out.cc_grad[2],0.0,1E-1)
@test about(out.cv_grad[1],0.545455,1E-4)
@test about(out.cv_grad[2],0.0,1E-1)
@test about(out.Intv.lo,-3,1E-4)
@test about(out.Intv.hi,8,1E-4)
a = seed_g(Float64,1,2)
xIBox = [Interval(-3.0,8.0)]
mBox = mid.(xIBox)
X = SMCg{2,Float64}(4.5,4.5,a,a,xIBox[1],false,xIBox,mBox)
out = max(5,X)
println("out 2: $out")
@test about(out.cc,7.045454545454545,1E-1)
@test about(out.cv,5.0,1E-1)
@test about(out.cc_grad[1],0.272727,1E-4)
@test about(out.cc_grad[2],0.0,1E-1)
@test about(out.cv_grad[1],0.0,1E-4)
@test about(out.cv_grad[2],0.0,1E-1)
@test about(out.Intv.lo,-3,1E-4)
@test about(out.Intv.hi,8,1E-4)
EAGOSmoothMcCormickGrad.set_diff_relax(1)
a = seed_g(Float64,1,2)
xIBox = [Interval(-3.0,8.0)]
mBox = mid.(xIBox)
X = SMCg{2,Float64}(4.5,4.5,a,a,xIBox[1],false,xIBox,mBox)
out = min(3,X)
println("out 3: $out")
@test about(out.cc,3.0,1E-1)
@test about(out.cv,1.0909090909090908,1E-1)
@test about(out.cc_grad[1],0.0,1E-4)
@test about(out.cc_grad[2],0.0,1E-1)
@test about(out.cv_grad[1],0.545455,1E-4)
@test about(out.cv_grad[2],0.0,1E-1)
@test about(out.Intv.lo,-3,1E-4)
@test about(out.Intv.hi,8,1E-4)
a = seed_g(Float64,1,2)
xIBox = [Interval(-3.0,8.0)]
mBox = mid.(xIBox)
X = SMCg{2,Float64}(4.5,4.5,a,a,xIBox[1],false,xIBox,mBox)
out = max(5,X)
println("out 4: $out")
@test about(out.cc,7.045454545454545,1E-1)
@test about(out.cv,5.0,1E-1)
@test about(out.cc_grad[1],0.272727,1E-4)
@test about(out.cc_grad[2],0.0,1E-1)
@test about(out.cv_grad[1],0.0,1E-4)
@test about(out.cv_grad[2],0.0,1E-1)
@test about(out.Intv.lo,-3,1E-4)
@test about(out.Intv.hi,8,1E-4)
end
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] | 1.784641 | 1,198 |
# --------------------------------------------------------------------
# Requires
# --------------------------------------------------------------------
import .GLM
using StatsModels
using StatsBase
using Tables: columntable, istable
import StatsModels: TableRegressionModel, RegressionModel
import StatsBase: modelmatrix
const INNERMOD = Union{GLM.GeneralizedLinearModel, GLM.LinearModel}
# --------------------------------------------------------------------
# GLM Methods
# --------------------------------------------------------------------
# TODO: Find a good name for it
# invinvpseudohessian
# ....
invpseudohessian(m::TableRegressionModel{T}) where T<:INNERMOD = GLM.invchol(m.model.pp).*dispersion(m.model.rr)
invpseudohessian(m::T) where T<:INNERMOD = GLM.invchol(m.pp).*dispersion(m.rr)
chol(m::TableRegressionModel{T}) where T<:INNERMOD = chol(m.model)
chol(m::T) where T<:INNERMOD = m.pp.chol
modmatrix(m::TableRegressionModel{T}) where T<:INNERMOD = modmatrix(m.model)
modmatrix(m::T) where T<:GLM.GeneralizedLinearModel = sqrt.(m.rr.wrkwt).*modelmatrix(m)
function modmatrix(m::T) where T<:GLM.LinearModel
X = modelmatrix(m)
if !isempty(m.rr.wts)
sqrt.(m.rr.wts).*X
else
copy(X)
end
end
numobs(m::TableRegressionModel) = length(m.model.rr.y)
numobs(m::INNERMOD) = length(m.rr.y)
dof_resid(m::TableRegressionModel) = numobs(m) - length(coef(m))
dof_resid(m::INNERMOD) = numobs(m) - length(coef(m))
StatsModels.hasintercept(m::TableRegressionModel) = "(Intercept)" ∈ coefnames(m)
interceptindex(m::INNERMOD) = findfirst(map(x->allequal(x), eachcol(modmatrix(m))))
function StatsModels.hasintercept(m::INNERMOD)
hasint = findfirst(map(x->allequal(x), eachcol(modmatrix(m))))
hasint === nothing ? false : true
end
dispersion(m::TableRegressionModel{T}) where T<:GLM.GeneralizedLinearModel = dispersion(m.model.rr)
dispersion(m::TableRegressionModel{T}) where T<:GLM.LinearModel = 1
dispersion(m::GLM.GeneralizedLinearModel) = dispersion(m.rr)
dispersion(m::GLM.LinearModel) = 1
dispersion(rr::GLM.GlmResp{T1, T2, T3}) where {T1, T2, T3} = 1
dispersion(rr::GLM.LmResp) = 1
function dispersion(rr::GLM.GlmResp{T1, T2, T3}) where {T1, T2<:Union{GLM.Gamma, GLM.Bernoulli, GLM.InverseGaussian}, T3}
sum(abs2, rr.wrkwt.*rr.wrkresid)/sum(rr.wrkwt)
end
resid(m::TableRegressionModel{T}) where T<:INNERMOD = resid(m.model)
function resid(m::T) where T<:GLM.LinearModel
if !isempty(m.rr.wts)
sqrt.(m.rr.wts).*residuals(m)
else
copy(residuals(m))
end
end
function resid(m::T) where T<:GLM.GeneralizedLinearModel
sqrt.(m.rr.wrkwt).*m.rr.wrkresid
end
momentmatrix(m::TableRegressionModel{T}) where T<:INNERMOD = momentmatrix(m.model)
momentmatrix(m::INNERMOD) = (modmatrix(m).*resid(m))./dispersion(m)
# TODO: move to the interface file
# hasresiduals(m::INNERMOD) = true
# hasmodelmatrix(m::TableRegressionModel{T}) where T<:INNERMOD = true
# --------------------------------------------------------------------
# HAC GLM Methods
# --------------------------------------------------------------------
function StatsBase.vcov(k::HAC, m; prewhite=false, dof_adjustment::Bool=true, scale::Real=1)
return _vcov(k, m, prewhite, dof_adjustment, scale)
end
function _vcov(k::HAC, m, prewhite::Bool, dof_adjustment::Bool, scale::Real)
setupkernelweights!(k, m)
B = invpseudohessian(m)
mm = momentmatrix(m)
A = lrvar(k, mm; prewhite=prewhite, demean=false)
scale *= (dof_adjustment ? numobs(m)^2/dof_resid(m) : numobs(m))
V = Symmetric((B*A*B).*scale)
return V
end
vcovmatrix(
k::HAC,
m::RegressionModel,
factorization=Cholesky;
prewhite=false,
dof_adjustment::Bool=true,
scale::Real=1,
) = _vcovmatrix(k, m, prewhite, dof_adjustment, scale, factorization)
function _vcovmatrix(
k::HAC,
m::RegressionModel,
prewhite::Bool,
dof_adjustment::Bool,
scale::Real,
::Type{Cholesky},
)
V = _vcov(k, m, prewhite, dof_adjustment, scale)
return CovarianceMatrix(cholesky(V, check=true), k, V)
end
function _vcovmatrix(
k::HAC,
m::RegressionModel,
prewhite::Bool,
dof_adjustment::Bool,
scale::Real,
::Type{SVD},
)
V = _vcov(k, m, prewhite, dof_adjustment, scale)
return CovarianceMatrix(svd(V.data), k, V)
end
# --------------------------------------------------------------------
# HC GLM Methods
# --------------------------------------------------------------------
hatmatrix(m::TableRegressionModel{T}, x) where T<:INNERMOD = hatmatrix(m.model, x)
function hatmatrix(m::T, x) where T<:INNERMOD
cf = m.pp.chol.UL::UpperTriangular
rdiv!(x, cf)
return sum(x.^2, dims = 2)
end
adjfactor(k::HC0, m::RegressionModel, x) = one(first(x))
adjfactor(k::HC1, m::RegressionModel, x) = numobs(m)./dof_resid(m)
adjfactor(k::HC2, m::RegressionModel, x) = one(first(x)) ./(one(first(x)).-hatmatrix(m, x))
adjfactor(k::HC3, m::RegressionModel, x) = one(first(x))./(one(first(x)).-hatmatrix(m, x)).^2
function adjfactor(k::HC4, m::RegressionModel, x)
n, p = size(x)
tone = one(eltype(x))
h = hatmatrix(m, x)
@inbounds for j in eachindex(h)
delta = min(4, n*h[j]/p)
h[j] = tone/(tone-h[j])^delta
end
return h
end
function adjfactor(k::HC4m, m::RegressionModel, x)
n, p = size(x)
tone = one(eltype(x))
h = hatmatrix(m, x)
@inbounds for j in eachindex(h)
delta = min(tone, n*h[j]/p) + min(1.5, n*h[j]/p)
h[j] = tone/(tone-h[j])^delta
end
return h
end
function adjfactor(k::HC5, m::RegressionModel, x)
n, p = size(x)
tone = one(eltype(x))
h = hatmatrix(m, x)
mx = max(n*0.7*maximum(h)/p, 4)
@inbounds for j in eachindex(h)
alpha = min(n*h[j]/p, mx)
h[j] = tone/sqrt((tone-h[j])^alpha)
end
return h
end
adjust!(m, adj::AbstractFloat) = m
adjust!(m, adj::AbstractMatrix) = m.*sqrt.(adj)
StatsBase.vcov(k::HC, m::RegressionModel; scale::Real=1) = _vcov(k, m, scale)
function _vcov(k::HC, m::RegressionModel, scale)
B = invpseudohessian(m)
mm = momentmatrix(m)
adj = adjfactor(k, m, modmatrix(m))
mm = adjust!(mm, adj)
scale *= length(adj) > 1 ? one(first(adj)) : adj
A = mm'*mm
return Symmetric((B*A*B).*scale)
end
vcovmatrix(k::HC, m::RegressionModel, factorization=Cholesky; scale::Real=1) =
_vcovmatrix(k, m, scale, Val{:factorization})
function _vcovmatrix(k::HC, m::RegressionModel, scale::Real, ::Type{Cholesky})
V = _vcov(k, m, scale)
CovarianceMatrix(cholesky(V, check=true), k, V)
end
function _vcovmatrix(k::HC, m::RegressionModel, scale::Real, ::Type{SVD})
V = _vcov(k, m, scale)
CovarianceMatrix(svd(V), k, V)
end
# --------------------------------------------------------------------
# CRHC GLM Methods
# --------------------------------------------------------------------
function installcache(k::CRHC, m::RegressionModel)
X = modmatrix(m)
res = resid(m)
f = categorize(k.cl)
(X, res), sf = bysort((X, res), f)
ci = clustersintervals(sf)
p = size(X, 2)
cf = chol(m)
Shat = Matrix{eltype(res)}(undef,p,p)
return CRHCCache(similar(X), X, res, similar(X, (0,0)), cf, Shat, ci, sf)
end
function StatsBase.vcov(k::CRHC, m::RegressionModel; scale::Real=1)
knew = recast(k, m)
length(knew.cl) == numobs(m) || throw(ArgumentError(k, "the length of the cluster variable must be $(numobs(m))"))
cache = installcache(knew, m)
return _vcov(knew, m, cache, scale)
end
function vcovmatrix(k::CRHC, m::RegressionModel, factorization = Cholesky; scale::Real=1)
knew = recast(k, m)
cache = installcache(knew, m)
df = dofadjustment(knew, cache)
return _vcovmatrix(knew, m, cache, scale, factorization)
end
function _vcov(k::CRHC, m::RegressionModel, cache::CRHCCache, scale::Real)
B = invpseudohessian(m)
res = adjustresid!(k, cache)
cache.momentmatrix .= cache.modelmatrix.*res
df = dofadjustment(k, cache)
scale *= df
Shat = clusterize!(cache)
return Symmetric((B*Shat*B).*scale)
end
function _vcovmatrix(
k::HC,
m::RegressionModel,
cache::CRHCCache,
scale::Real,
::Type{Cholesky}
)
V = _vcov(k, m, cache, scale)
CovarianceMatrix(cholesky(V, check=true), k, V)
end
function _vcovmatrix(
k::HC,
m::RegressionModel,
cache::CRHCCache,
scale::Real,
::Type{SVD}
)
V = _vcov(k, m, cache, cache)
CovarianceMatrix(svd(V.data), k, V)
end
# -----------------------------------------------------------------------------
# CRHC GLM - Trick to use vcov(CRHC1(:cluster, df), ::GLM)
# -----------------------------------------------------------------------------
StatsBase.stderror(k::RobustVariance, m::RegressionModel; kwargs...) = sqrt.(diag(vcov(k, m; kwargs...)))
StatsBase.stderror(v::CovarianceMatrix) = sqrt.(diag(v.V))
# -----------------------------------------------------------------------------
# CRHC GLM - Trick to use vcov(CRHC1(:cluster, df), ::GLM)
# -----------------------------------------------------------------------------
recast(k::CRHC{T,D}, m::INNERMOD) where {T<:AbstractVector, D<:Nothing} = k
recast(k::CRHC{T,D}, m::TableRegressionModel) where {T<:AbstractVector, D<:Nothing} = k
reterm(k::CRHC{T,D}, m::TableRegressionModel) where {T, D} = tuple(k.cl...)
function recast(k::CRHC{T,D}, m::TableRegressionModel) where {T<:Symbol, D}
@assert istable(k.df) "`df` must be a DataFrames"
t = k.cl
if length(k.df[!, t]) == length(m.mf.data[1])
## The dimension fit
id = compress(categorical(k.df[:,t]))
return renew(k, id)
else
f = m.mf.f
frm = f.lhs ~ tuple(f.rhs.terms..., Term(t))
nt = NamedTuple{tuple(StatsModels.termvars(frm)...)}(columntable(k.df))
idx = StatsModels.missing_omit(nt)[2]
id = compress(categorical(k.df[idx,t]))
return renew(k, id)
end
# ct = columntable(clus)
# length_unique = map(x->length(unique(x)), ct)
# fg = 1:prod(length_unique)
# #cl = map(x->compress(categorical(x)), eachcol(x))
# clus[!, :clusid] .= size(clus, 2) > 1 ? zero(Int) : clus[!, tterms[1]]
# if length(tterms) > 1
# for (i,j) in enumerate(groupby(clus, [tterms...]))
# j[:, :clusid] .= fg[i]
# end
# end
# id = compress(categorical(clus[!, :clusid]))
end
# -----------------------------------------------------------------------------
# optimalbandwidth method
# -----------------------------------------------------------------------------
function optimalbandwidth(
k::HAC,
m::TableRegressionModel{F};
kwargs...
) where F<:INNERMOD
setupkernelweights!(k, m)
optimalbandwidth(k, m.model; kwargs...)
end
function optimalbandwidth(k::HAC, m::F; prewhite=false) where F<:INNERMOD
mm = momentmatrix(m)
setupkernelweights!(k, m)
mmm, D = prewhiter(mm, Val{prewhite})
return optimalbandwidth(k, mmm; prewhite=prewhite)
end
function setupkernelweights!(k, m::TableRegressionModel{T}) where T<:INNERMOD
β = coef(m)
resize!(k.weights, length(β))
"(Intercept)" ∈ coefnames(m) ? (k.weights .= 1.0; k.weights[1] = 0.0) : k.weights .= 1.0
return nothing
end
function setupkernelweights!(k, m::T) where T<:INNERMOD
cf = coef(m)
resize!(k.weights, length(cf))
fill!(k.weights, 1)
i = interceptindex(m)
i !== nothing && (k.weights[i] = 0)
return nothing
end
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] | 2.400378 | 4,758 |
<reponame>SebastianRuffert/SolidStateDetectors.jl<filename>src/Simulation/Capacitance.jl
@doc raw"""
calculate_mutual_capacitance(sim::Simulation, ij::Tuple{Int, Int}; consider_multiplicity::Bool = true)
Returns the mutual capacitance between the contacts with ID `i = ij[1]` and `j = ij[2]`.
It is calculated via the weighting potentials of the contacts, ``\Phi_i^w(\vec{r})`` and ``\Phi_j^w(\vec{r})``:
```math
c_{ij} = \epsilon_0 \int_{World} \nabla \Phi_i^w(\vec{r}) ϵ_r(\vec{r}) \nabla \Phi_j^w(\vec{r}) d\vec{r}
```
!!! note
These are elements of the Mawell Capcitance Matrix. Look up [Capacitances](@ref) for more information.
!!! note
The electric potential as well as the two weighting potentials of both contacts have to be calculated.
## Arguments
* `sim::Simulation`: [`Simulation`](@ref) for which the capacitance matrix is calculated.
* `ij::Tuple{Int,Int}`: Tuple of indices of the contacts for which the capacitance should be calculated.
## Keywords
* `consider_multiplicity::Bool = true`: Whether symmetries of the system should be taken into account.
For example, in case of true coaxial detector center around the origin and calculated on a cartesian grid
with the `x-axis` going from `[0, x_max]` and the `y-axis` going from `[0, y_max]` the multiplicity is 4
and, if `consider_multiplicity == true`, the returned value is already multiplied by 4.
"""
function calculate_mutual_capacitance(sim::Simulation, ij::Tuple{Int, Int}; consider_multiplicity::Bool = true)
_calculate_mutual_capacitance(sim.weighting_potentials[ij[1]], sim.weighting_potentials[ij[2]], sim.ϵ_r; consider_multiplicity)
end
function _calculate_mutual_capacitance(
wpi::WeightingPotential{T,3,CS}, wpj::WeightingPotential{T,3,CS},
ϵ_r::DielectricDistribution{T,3,CS};
consider_multiplicity::Bool = true ) where {T, CS}
int_p1 = interpolated_scalarfield(wpi)
int_p2 = interpolated_scalarfield(wpj)
int_ϵ_r = interpolated_scalarfield(ϵ_r)
cylindrical = CS == Cylindrical
phi_2D = cylindrical && size(wpi, 2) == size(wpj, 2) == 1
grid = phi_2D ? get_2π_potential(wpi, n_points_in_φ = 2).grid : _get_closed_potential(wpi).grid
grid_mps = get_extended_midpoints_grid(grid)
c_ij::T = _calculate_mutual_capacitance(grid, grid_mps, int_ϵ_r, int_p1, int_p2)
phi_2D && (c_ij *= 2)
consider_multiplicity && (c_ij *= multiplicity(grid))
return uconvert(u"pF", c_ij*u"m" * ϵ0*u"F/m" )
end
function _calculate_mutual_capacitance(grid::Grid{T, 3, CS}, grid_mps, int_ϵ_r, int_p1, int_p2) where {T, CS}
c_ij_vector = zeros(Float64, size(grid, 3)-1)
# In some cases, the sum of many Float32's (of different order of magnitudes)
# can lead to large errors on the sum => Use Float64 as the datatype for the sum.
@inbounds Base.Threads.@threads for i3 in 1:size(grid, 3)-1
for i2 in 1:size(grid, 2)-1
for i1 in 1:size(grid, 1)-1
w1, w2, w3 = voxel_widths(grid, i1, i2, i3)
dV = voxel_volume(grid, i1, i2, i3, w1, w2, w3)
pt_voxel_mid = GridPoint(grid_mps, (i1 + 1, i2 + 1, i3 + 1))
ϵ_r_voxel = get_interpolation(int_ϵ_r, pt_voxel_mid, CS)
efs_1 = _approximate_potential_gradient(int_p1, grid, i1, i2, i3, w1, w2, w3)
efs_2 = _approximate_potential_gradient(int_p2, grid, i1, i2, i3, w1, w2, w3)
c_ij_vector[i3] += Float64(sum(efs_1 .* efs_2) * dV * ϵ_r_voxel)
end
end
end
return T(sum(c_ij_vector))
end
function _approximate_potential_gradient(int_p, grid::Grid{T, 3, CS}, i1, i2, i3, w1, w2, w3) where {T, CS}
p000 = get_interpolation(int_p, GridPoint(grid, (i1 , i2 , i3 )), CS)
p100 = get_interpolation(int_p, GridPoint(grid, (i1 + 1, i2 , i3 )), CS)
p010 = get_interpolation(int_p, GridPoint(grid, (i1 , i2 + 1, i3 )), CS)
p110 = get_interpolation(int_p, GridPoint(grid, (i1 + 1, i2 + 1, i3 )), CS)
p001 = get_interpolation(int_p, GridPoint(grid, (i1 , i2 , i3 + 1)), CS)
p101 = get_interpolation(int_p, GridPoint(grid, (i1 + 1, i2 , i3 + 1)), CS)
p011 = get_interpolation(int_p, GridPoint(grid, (i1 , i2 + 1, i3 + 1)), CS)
p111 = get_interpolation(int_p, GridPoint(grid, (i1 + 1, i2 + 1, i3 + 1)), CS)
efv1 = ( (p100 - p000) + (p110 - p010) + (p101 - p001) + (p111 - p011) ) / (4 * w1)
efv2 = if CS == Cylindrical
_w2 = (grid.axes[2].ticks[i2 + 1] - grid.axes[2].ticks[i2])
if i1 == 1
((p110 - p100)/(_w2*grid.axes[1].ticks[i1+1]) +
(p111 - p101)/(_w2*grid.axes[1].ticks[i1+1]) ) / 2
else
((p010 - p000)/(_w2*grid.axes[1].ticks[i1]) +
(p110 - p100)/(_w2*grid.axes[1].ticks[i1+1]) +
(p011 - p001)/(_w2*grid.axes[1].ticks[i1]) +
(p111 - p101)/(_w2*grid.axes[1].ticks[i1+1])) / 4
end
else
( (p010 - p000) + (p110 - p100) + (p011 - p001) + (p111 - p101) ) / (4 * w2)
end
efv3 = ( (p001 - p000) + (p101 - p100) + (p011 - p010) + (p111 - p110) ) / (4 * w3)
return (efv1, efv2, efv3)
end
@doc raw"""
calculate_capacitance_matrix(sim::Simulation{T}; consider_multiplicity::Bool = true) where {T}
Calculates the Maxwell Capacitance `N×N`-Matrix in units of pF,
where `N` is the number of contacts of `sim.detector`.
The individual elements, ``c_{i,j}``, are calculated via
[`calculate_mutual_capacitance(sim::Simulation, (i,j)::Tuple{Int,Int})`](@ref).
The matrix should be symmetric. The difference of `C[i,j]` and `C[j,i]` are due
to numerical precision in the integration due to the different grids of the two weighting potentials.
## Arguments
* `sim::Simulation`: [`Simulation`](@ref) for which the capacitance matrix is calculated.
## Keywords
* `consider_multiplicity::Bool = true`: Whether symmetries of the system should be taken into account.
For example, in case of true coaxial detector center around the origin and calculated on a cartesian grid
with the `x-axis` going from `[0, x_max]` and the `y-axis` going from `[0, y_max]` the multiplicity is 4
and, if `consider_multiplicity == true`, the returned value is already multiplied by 4.
"""
function calculate_capacitance_matrix(sim::Simulation{T}; consider_multiplicity::Bool = true) where {T}
@assert !ismissing(sim.ϵ_r) "The electric potential needs to be calculated first."
@assert !ismissing(sim.weighting_potentials) "The weighting_potentials needs to be calculated first."
n = length(sim.weighting_potentials)
C = zeros(typeof(one(T) * u"pF"), (n, n))
for i in 1:n
for j in 1:n
C[j, i] = if !ismissing(sim.weighting_potentials[i]) && !ismissing(sim.weighting_potentials[j])
calculate_mutual_capacitance(sim, (i, j); consider_multiplicity)
else
missing
end
end
end
return C
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] | 2.214263 | 3,155 |
<gh_stars>0
# Load in the dependencies
using InfiniteOpt, JuMP, MathOptInterface, Distributions, Random,
FastGaussQuadrature, DataStructures
# load the test module
using Test
# Define convenient aliases
const IC = InfiniteOpt.Collections
const MT = InfiniteOpt.MeasureToolbox
const IOTO = InfiniteOpt.TranscriptionOpt
const JuMPC = JuMP.Containers
const MOI = MathOptInterface
const MOIU = MathOptInterface.Utilities
const MOIUC = MathOptInterface.Utilities.CleverDicts
const FGQ = FastGaussQuadrature
const IOMT = InfiniteOpt.MeasureToolbox
# Load in testing utilities
include("utilities.jl")
# Run unit tests
println("----------------------------------------------------------------------------")
println("---------------------------------UNIT TESTS---------------------------------")
println("----------------------------------------------------------------------------")
@time @testset "Collections" begin
include("Collections/VectorTuple.jl")
include("Collections/DualDict.jl")
end
println("")
@time @testset "Datatypes" begin include("datatypes.jl") end
println("")
@time @testset "Utilities" begin include("utility_tests.jl") end
println("")
@time @testset "Infinite Set Methods" begin include("infinite_sets.jl") end
println("")
@time @testset "General Variable Methods" begin
include("general_variables.jl")
end
println("")
@time @testset "Optimizer Setup Methods" begin include("optimizer_setup.jl") end
println("")
@time @testset "Macro Utilities" begin include("macro_utilities.jl") end
println("")
@time @testset "Parameter Methods" begin
@testset "Scalar" begin include("scalar_parameters.jl") end
@testset "Array" begin include("array_parameters.jl") end
end
println("")
@time @testset "Variable Methods" begin
@testset "Infinite Variables" begin include("infinite_variables.jl") end
@testset "Point Variables" begin include("point_variables.jl") end
@testset "Hold Variables" begin include("hold_variables.jl") end
@testset "Info Constraints" begin include("variable_info.jl") end
@testset "Reduced Variables" begin include("reduced_variables.jl") end
end
println("")
@time @testset "Derivative Methods" begin include("derivatives.jl") end
println("")
@time @testset "Operators" begin include("operators.jl") end
println("")
@time @testset "Expression Methods" begin include("expressions.jl") end
println("")
@time @testset "Macro Expressions" begin include("macro_expressions.jl") end
println("")
@time @testset "Measure Methods" begin include("measures.jl") end
println("")
@time @testset "Measure Toolbox Methods" begin
@testset "Integrals" begin include("MeasureToolbox/integrals.jl") end
@testset "Expectations" begin include("MeasureToolbox/expectations.jl") end
@testset "Support Sums" begin include("MeasureToolbox/support_sums.jl") end
end
println("")
@time @testset "Objective Methods" begin include("objective.jl") end
println("")
@time @testset "Constraint Methods" begin include("constraints.jl") end
println("")
@time @testset "Printing Methods" begin include("show.jl") end
println("")
@time @testset "Deletion Methods" begin include("deletion.jl") end
println("")
@time @testset "Expansion Methods" begin include("measure_expansions.jl") end
println("")
@time @testset "Derivative Evaluation" begin include("derivative_evaluation.jl") end
println("")
@time @testset "TranscriptionOpt" begin
@testset "Model" begin include("TranscriptionOpt/model.jl") end
@testset "Measures" begin include("TranscriptionOpt/measure.jl") end
@testset "Transcribe" begin include("TranscriptionOpt/transcribe.jl") end
@testset "Optimize" begin include("TranscriptionOpt/optimize.jl") end
end
println("")
@time @testset "Solution Methods" begin include("optimizer.jl") end
println("")
@time @testset "Solution Queries" begin include("results.jl") end
println("")
@time @testset "Extensions" begin include("extensions.jl") end
println("")
println("----------------------------------------------------------------------------")
println("-----------------------------TESTING COMPLETE!------------------------------")
println("----------------------------------------------------------------------------")
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] | 3.510924 | 1,190 |
<reponame>grace-harper-ibm/LinearAlgebraicRepresentation.jl<filename>CAGD.jl/examples/3d/boolSample.jl<gh_stars>0
todisplay = VERSION <= VersionNumber("1.2") ? true : false
using LinearAlgebra
using LinearAlgebraicRepresentation
Lar = LinearAlgebraicRepresentation
using CAGD
if todisplay
using ViewerGL
GL = ViewerGL
include("views.jl")
end
# Rod Generation
# npts = 4
npts = 16
V,_ = Lar.rod()([npts, 1])
EV = [
[[2*i, (2*i+1)%2npts+1] for i = 1 : npts]; # horizontal upper edges
[[2*i-1, (2*i)%2npts+1] for i = 1 : npts]; # horizontal lower edges
[[2*i+1, 2*i+2] for i = 0 : npts-1]; # vertical edges
]
FE = [
[i for i = 1 : npts], # upper face
[npts + i for i = 1 : npts], # lower face
[[i, npts+i, 2*npts+i, 2*npts+i+1] for i = 1:npts-1]..., # vertical faces
[npts, 2*npts, 3*npts, 2*npts+1]
]
FV = Lar.cop2lar(Lar.lar2cop(FE) * Lar.lar2cop(EV))
CV = [collect(1 : size(V,2))]
if todisplay
GL.VIEW([
GL.GLGrid(V,EV,GL.COLORS[1],1)
GL.GLFrame
]);
end
# Three Rods Generation
cyl = Lar.Struct([
Lar.r(0,0,0),
#Lar.s(0.6, 0.6, 2.0),
Lar.s(1.0, 1.0, 1.5),
Lar.t(0,0,-1.5),
(V,CV,FV,EV)
])
cyl = Lar.struct2lar(cyl)
tris = Lar.Struct([
cyl,
Lar.Struct([Lar.r(pi/2,0,0), cyl ]) ,
Lar.Struct([Lar.r(0,pi/2,0), cyl ])
])
V, CV, FV, EV = Lar.struct2lar(tris)
cyls = (V, CV, FV, EV)
# Cube Generation
V, (_, EV, FV, CV) = Lar.cuboid([1,1,1],true,[-1,-1,-1])
cube = (V, CV, FV, EV)
# Sphere Generation
# V, EV, FV = catmullclark(cube[1], cube[3], cube[2], 4)
# sphere = (V, FV, EV)
V, FV = Lar.sphere(1.0)([15,25])
# V, FV = Lar.sphere(1.0)([5,5])
FV = sort(sort.(FV))
function getFaceEdges(fV::Array{Int,1})
return [fV[1], fV[2]], [fV[1], fV[3]], [fV[2], fV[3]]
end
EVs = unique([(map(f -> getFaceEdges(f), FV)...)...])
CV = [collect(1:size(V, 2))]
sphere = (V, CV, FV, EVs)
# Object Creation
carry = Lar.Struct([
cyls,
Lar.s(1.3,1.3,1.3),
cube,
Lar.s(1.4,1.4,1.4),
#Lar.s(1.5,1.5,1.5)
sphere
])
V,CV,FV,EV = Lar.struct2lar(carry)
if todisplay
GL.VIEW([
GL.GLGrid(V,EV,GL.COLORS[1],1)
GL.GLFrame
]);
end
# Model Generation
model = CAGD.Model(V)
cFE = convert(Lar.ChainOp, Lar.coboundary_1(model.G, FV, EV))
CAGD.addModelCells!(model, 1, EV, signed = true)
CAGD.addModelCells!(model, 2, cFE)
# Since CF is only used in boolean evaluation, the sign is not needed:
cFV = Lar.lar2cop(FV)
cCV = Lar.lar2cop(CV)
cCF = convert(Lar.ChainOp, ((cCV*cFV').>0))
CAGD.addModelCells!(model, 3, cCF)
atol = 1e-9;
if todisplay displayModel(model) end
split_model = CAGD.facesplitting(model, atol = atol)
if todisplay displayModel(split_model) end
congr_model = CAGD.mergeModelVertices(split_model, err=atol, signed_merge=true)
#split_model = CAGD.facesplitting(congr_model, atol = atol)
#congr_model = CAGD.mergeModelVertices(split_model, err=atol, signed_merge=true)
if todisplay displayModel(congr_model) end
gift_model = deepcopy(congr_model);
FC, bicon_comps = CAGD.tgw(congr_model, 3)
CAGD.addModelCells!(gift_model, 3, convert(Lar.ChainOp, FC'))
if todisplay viewExplode(gift_model) end
##==============================================================================
## Boolean Decomposition
##==============================================================================
arranged_model, boolean_matrix = CAGD.bool3(model)
bXRod = boolean_matrix[:, 2]
bYRod = boolean_matrix[:, 3]
bZRod = boolean_matrix[:, 4]
bCube = boolean_matrix[:, 5]
bSphe = boolean_matrix[:, 6]
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] | 2.049494 | 1,778 |
using Base: @propagate_inbounds
using OffsetArrays
using JLD2
using Oceananigans.Architectures
using Oceananigans.Grids
using Oceananigans.Fields
using Oceananigans.Grids: topology, total_size, interior_parent_indices, parent_index_range
using Oceananigans.Fields: show_location, interior_view_indices, data_summary, reduced_location
import Oceananigans.Fields: Field, set!, interior, indices
import Oceananigans.Architectures: architecture
struct FieldTimeSeries{LX, LY, LZ, K, I, D, G, T, B, χ} <: AbstractField{LX, LY, LZ, G, T, 4}
data :: D
grid :: G
boundary_conditions :: B
indices :: I
times :: χ
function FieldTimeSeries{LX, LY, LZ, K}(data::D, grid::G, bcs::B,
times::χ, indices::I) where {LX, LY, LZ, K, D, G, B, χ, I}
T = eltype(data)
return new{LX, LY, LZ, K, I, D, G, T, B, χ}(data, grid, bcs, indices, times)
end
end
architecture(fts::FieldTimeSeries) = architecture(fts.grid)
#####
##### Constructors
#####
"""
FieldTimeSeries{LX, LY, LZ}(grid, times, [FT=eltype(grid);]
indices = (:, :, :),
boundary_conditions = nothing)
Return a `FieldTimeSeries` at location `(LX, LY, LZ)`, on `grid`, at `times`.
"""
function FieldTimeSeries{LX, LY, LZ}(grid, times, FT=eltype(grid);
indices = (:, :, :),
boundary_conditions = nothing) where {LX, LY, LZ}
Nt = length(times)
arch = architecture(grid)
loc = (LX, LY, LZ)
space_size = total_size(loc, grid, indices)
underlying_data = zeros(FT, arch, space_size..., Nt)
data = offset_data(underlying_data, grid, loc, indices)
return FieldTimeSeries{LX, LY, LZ, InMemory}(data, grid, boundary_conditions, times, indices)
end
"""
FieldTimeSeries(path, name;
backend = InMemory(),
grid = nothing,
iterations = nothing,
times = nothing)
Returns a `FieldTimeSeries` for the field `name` describing a field's time history from a JLD2 file
located at `path`.
Keyword arguments
=================
- `backend`: `InMemory()` to load data into a 4D array or `OnDisk()` to lazily load data from disk
when indexing into `FieldTimeSeries`.
- `grid`: A grid to associated with data, in the case that the native grid was not serialized
properly.
- `iterations`: Iterations to load. Defaults to all iterations found in the file.
- `times`: Save times to load, as determined through an approximate floating point
comparison to recorded save times. Defaults to times associated with `iterations`.
Takes precedence over `iterations` if `times` is specified.
"""
FieldTimeSeries(path, name; backend=InMemory(), kwargs...) =
FieldTimeSeries(path, name, backend; kwargs...)
#####
##### InMemory time serieses
#####
const InMemoryFieldTimeSeries{LX, LY, LZ} = FieldTimeSeries{LX, LY, LZ, InMemory}
struct UnspecifiedBoundaryConditions end
function FieldTimeSeries(path, name, backend::InMemory;
architecture = nothing,
grid = nothing,
location = nothing,
boundary_conditions = UnspecifiedBoundaryConditions(),
iterations = nothing,
times = nothing)
file = jldopen(path)
# Defaults
isnothing(iterations) && (iterations = parse.(Int, keys(file["timeseries/t"])))
isnothing(times) && (times = [file["timeseries/t/$i"] for i in iterations])
isnothing(location) && (location = file["timeseries/$name/serialized/location"])
if boundary_conditions isa UnspecifiedBoundaryConditions
boundary_conditions = file["timeseries/$name/serialized/boundary_conditions"]
end
indices = try
file["timeseries/$name/serialized/indices"]
catch
(:, :, :)
end
isnothing(grid) && (grid = file["serialized/grid"])
# Default to CPU if neither architecture nor grid is specified
architecture = isnothing(architecture) ? (isnothing(grid) ? CPU() : Architectures.architecture(grid)) : architecture
# This should be removed in a month or two (4/5/2022).
grid = try
on_architecture(architecture, grid)
catch err # Likely, the grid has CuArrays in it...
if grid isa RectilinearGrid # we can try...
Nx = file["grid/Nx"]
Ny = file["grid/Ny"]
Nz = file["grid/Nz"]
Hx = file["grid/Hx"]
Hy = file["grid/Hy"]
Hz = file["grid/Hz"]
xᶠᵃᵃ = file["grid/xᶠᵃᵃ"]
yᵃᶠᵃ = file["grid/yᵃᶠᵃ"]
zᵃᵃᶠ = file["grid/zᵃᵃᶠ"]
x = file["grid/Δxᶠᵃᵃ"] isa Number ? (xᶠᵃᵃ[1], xᶠᵃᵃ[Nx+1]) : xᶠᵃᵃ
y = file["grid/Δyᵃᶠᵃ"] isa Number ? (yᵃᶠᵃ[1], yᵃᶠᵃ[Ny+1]) : yᵃᶠᵃ
z = file["grid/Δzᵃᵃᶠ"] isa Number ? (zᵃᵃᶠ[1], zᵃᵃᶠ[Nz+1]) : zᵃᵃᶠ
topo = topology(grid)
# Reduce for Flat dimensions
domain = NamedTuple((:x, :y, :z)[i] => (x, y, z)[i] for i=1:3 if topo[i] !== Flat)
size = Tuple((Nx, Ny, Nz)[i] for i=1:3 if topo[i] !== Flat)
halo = Tuple((Hx, Hy, Hz)[i] for i=1:3 if topo[i] !== Flat)
RectilinearGrid(architecture; size, halo, topology=topo, domain...)
else
throw(err)
end
end
close(file)
LX, LY, LZ = location
time_series = FieldTimeSeries{LX, LY, LZ}(grid, times; indices, boundary_conditions)
set!(time_series, path, name)
return time_series
end
Base.parent(fts::FieldTimeSeries) = parent(fts.data)
function Base.getindex(fts::InMemoryFieldTimeSeries, n::Int)
underlying_data = view(parent(fts), :, :, :, n)
data = offset_data(underlying_data, fts.grid, location(fts), fts.indices)
boundary_conditions = fts.boundary_conditions
indices = fts.indices
return Field(location(fts), fts.grid; data, boundary_conditions, indices)
end
#####
##### set!
#####
"""
Field(location, path, name, iter;
grid = nothing,
architecture = nothing,
indices = (:, :, :),
boundary_conditions = nothing)
Load a field called `name` saved in a JLD2 file at `path` at `iter`ation.
Unless specified, the `grid` is loaded from `path`.
"""
function Field(location, path::String, name::String, iter;
grid = nothing,
architecture = nothing,
indices = (:, :, :),
boundary_conditions = nothing)
file = jldopen(path)
# Default to CPU if neither architecture nor grid is specified
architecture = isnothing(architecture) ?
(isnothing(grid) ? CPU() : Architectures.architecture(grid)) :
architecture
grid = isnothing(grid) ?
on_architecture(architecture, file["serialized/grid"]) : grid
raw_data = arch_array(architecture, file["timeseries/$name/$iter"])
close(file)
data = offset_data(raw_data, grid, location, indices)
return Field(location, grid; boundary_conditions, indices, data)
end
function set!(time_series::InMemoryFieldTimeSeries, path::String, name::String)
file = jldopen(path)
file_iterations = parse.(Int, keys(file["timeseries/t"]))
file_times = [file["timeseries/t/$i"] for i in file_iterations]
close(file)
for (n, time) in enumerate(time_series.times)
file_index = findfirst(t -> t ≈ time, file_times)
file_iter = file_iterations[file_index]
field_n = Field(location(time_series), path, name, file_iter,
indices = time_series.indices,
boundary_conditions = time_series.boundary_conditions,
grid = time_series.grid)
set!(time_series[n], field_n)
end
return nothing
end
function set!(fts::FieldTimeSeries, fields_vector::AbstractVector{<:AbstractField})
raw_data = parent(fts)
file = jldopen(path)
for (n, field) in enumerate(fields_vector)
raw_data[:, :, :, n] .= parent(field)
end
close(file)
return nothing
end
function interior(fts::FieldTimeSeries)
loc = location(fts)
topo = topology(fts.grid)
sz = size(fts.grid)
halo_sz = halo_size(fts.grid)
i_interior = interior_parent_indices.(loc, topo, sz, halo_sz)
indices = fts.indices
i_view = interior_view_indices.(indices, i_interior)
return view(parent(fts), i_view..., :)
end
interior(fts::FieldTimeSeries, I...) = view(interior(fts), I...)
indices(fts::FieldTimeSeries) = fts.indices
#####
##### OnDisk time serieses
#####
struct OnDiskData
path :: String
name :: String
end
function FieldTimeSeries(path, name, backend::OnDisk; architecture=nothing, grid=nothing)
file = jldopen(path)
if isnothing(grid)
grid = on_architecture(architecture, file["serialized/grid"])
end
iterations = parse.(Int, keys(file["timeseries/t"]))
times = [file["timeseries/t/$i"] for i in iterations]
data = OnDiskData(path, name)
LX, LY, LZ = file["timeseries/$name/serialized/location"]
bcs = file["timeseries/$name/serialized/boundary_conditions"]
indices = file["timeseries/$name/serialized/indices"]
close(file)
return FieldTimeSeries{LX, LY, LZ, OnDisk}(data, grid, bcs, times, indices)
end
#####
##### Methods
#####
# Include the time dimension.
@inline Base.size(fts::FieldTimeSeries) = (size(location(fts), fts.grid, fts.indices)..., length(fts.times))
@propagate_inbounds Base.getindex(f::FieldTimeSeries{LX, LY, LZ, InMemory}, i, j, k, n) where {LX, LY, LZ} = f.data[i, j, k, n]
function Base.getindex(fts::FieldTimeSeries{LX, LY, LZ, OnDisk}, n::Int) where {LX, LY, LZ}
# Load data
arch = architecture(fts)
file = jldopen(fts.data.path)
iter = keys(file["timeseries/t"])[n]
raw_data = arch_array(architecture(fts), file["timeseries/$(fts.data.name)/$iter"])
close(file)
# Wrap Field
loc = (LX, LY, LZ)
field_data = offset_data(raw_data, fts.grid, loc, fts.indices)
return Field(loc, fts.grid; indices=fts.indices, boundary_conditions=fts.boundary_conditions, data=field_data)
end
Base.setindex!(fts::FieldTimeSeries, val, inds...) = Base.setindex!(fts.data, val, inds...)
Base.parent(fts::FieldTimeSeries{LX, LY, LZ, OnDisk}) where {LX, LY, LZ} = nothing
#####
##### Basic support for reductions
#####
##### TODO: support for reductions across _time_ (ie when 4 ∈ dims)
#####
const FTS = FieldTimeSeries
for reduction in (:sum, :maximum, :minimum, :all, :any, :prod)
reduction! = Symbol(reduction, '!')
@eval begin
# Allocating
function Base.$(reduction)(f::Function, fts::FTS; dims=:, kw...)
if dims isa Colon
return Base.$(reduction)($(reduction)(f, fts[n]; kw...) for n in 1:length(fts.times))
else
T = filltype(Base.$(reduction!), fts)
loc = LX, LY, LZ = reduced_location(location(fts); dims)
times = fts.times
rts = FieldTimeSeries{LX, LY, LZ}(grid, times, T; indices=fts.indices)
return Base.$(reduction!)(f, rts, fts; kw...)
end
end
Base.$(reduction)(fts::FTS; kw...) = Base.$(reduction)(identity, fts; kw...)
function Base.$(reduction!)(f::Function,rts::FTS, fts::FTS; dims=:, kw...)
dims isa Tuple && 4 ∈ dims && error("Reduction across the time dimension (dim=4) is not yet supported!")
times = rts.times
for n = 1:length(times)
Base.$(reduction!)(f, rts[i], fts[i]; dims, kw...)
end
return rts
end
Base.$(reduction!)(rts::FTS, fts::FTS; kw...) = Base.$(reduction!)(identity, rts, fts; kw...)
end
end
#####
##### Show methods
#####
backend_str(::InMemory) = "InMemory"
backend_str(::OnDisk) = "OnDisk"
#####
##### show
#####
function Base.summary(fts::FieldTimeSeries{LX, LY, LZ, K}) where {LX, LY, LZ, K}
arch = architecture(fts)
A = typeof(arch)
return string("$(join(size(fts), "×")) FieldTimeSeries{$(backend_str(K()))} located at ", show_location(fts), " on ", A)
end
function Base.show(io::IO, fts::FieldTimeSeries)
prefix = string(summary(fts), '\n',
"├── grid: ", summary(fts.grid), '\n',
"├── indices: ", fts.indices, '\n')
suffix = string("└── data: ", summary(fts.data), '\n',
" └── ", data_summary(fts))
return print(io, prefix, suffix)
end
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] | 2.244909 | 5,647 |
module LightGraphsMatching
using LightGraphs
using SparseArrays: spzeros
using JuMP
using MathProgBase: AbstractMathProgSolver
import BlossomV # 'using BlossomV' leads to naming conflicts with JuMP
using Hungarian
export MatchingResult, maximum_weight_matching, maximum_weight_maximal_matching, minimum_weight_perfect_matching, HungarianAlgorithm, LPAlgorithm
"""
struct MatchingResult{U}
weight::U
mate::Vector{Int}
end
A type representing the result of a matching algorithm.
weight: total weight of the matching
mate: `mate[i] = j` if vertex `i` is matched to vertex `j`.
`mate[i] = -1` for unmatched vertices.
"""
struct MatchingResult{U<:Real}
weight::U
mate::Vector{Int}
end
include("lp.jl")
include("maximum_weight_matching.jl")
include("blossomv.jl")
include("hungarian.jl")
include("maximum_weight_maximal_matching.jl")
end # module
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] | 2.88254 | 315 |