content
stringlengths 5
1.03M
| input_ids
sequencelengths 4
823k
| ratio_char_token
float64 0.4
12.5
| token_count
int64 4
823k
|
|---|---|---|---|
using JuMP
using Dates
const OPTIMAL = JuMP.MathOptInterface.OPTIMAL
const INFEASIBLE = JuMP.MathOptInterface.INFEASIBLE
const UNBOUNDED = JuMP.MathOptInterface.DUAL_INFEASIBLE
function resolvePlne(model, verbose=1, nameOfPL="")
optimize!(model)
if verbose == 3
println(solution_summary(model, verbose=true))
return model
end
status = termination_status(model)
# if status == JuMP.MathOptInterface.OPTIMAL
# println("Valeur optimale = ", objective_value(model))
# end
if verbose >= 2
last = ""
for var in all_variables(model)
which = split(JuMP.name(var), "[")[1]
if which != last
last = which
println("\nVariables $(last)")
end
println(var, ": ", value(var))
end
end
if verbose >= 1
println("\nObjetive value: ", objective_value(model))
end
if ! isempty(nameOfPL)
write_to_file(model, nameOfPL*"_model_"*string(Dates.now())*".mps")
end
return model
end
function getModelVariables(model)
end
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] | 2.554377 | 377 |
module RandomFerns
using Distributions
using StatsBase
export Ensemble, kfoldCV_fern, kfoldCV_fernEnsemble, build_fern, apply_fern, build_ensemble, apply_ensemble
if VERSION.minor >= 4
typealias Range1{Int} Range{Int}
_int(x) = round(Int, x)
else
_int(x) = int(x)
end
include("utils.jl")
immutable Part
dist::MvNormal
mean::Any
end
immutable Fern
parts::Array{Part}
proj_matrix::Matrix
cuts::Vector
end
immutable Ensemble
ferns::Array{Fern}
end
function random_projection_matrix(method::Symbol, n_features::Int, n_components::Int)
P = zeros(n_components, n_features)
if method == :gaussian
dist = Normal(0, (1/sqrt(n_components)))
rand!(dist, P)
elseif method == :sparse
# todo
end
return P
end
function _get_index{T<:FloatingPoint}(x::Matrix{T}, cuts::Vector{T}, n::Int)
idx = @parallel (+) for i=1:n
c = x[:,i] .< cuts[i]
b = c .* 2^i
end
idx = idx + 1
return idx
end
### regression model ###
function apply_fern{T<:FloatingPoint}(model::Fern, features::Matrix{T})
yhat = zeros(size(features)[1])
w = zeros(size(features)[1])
# project unseen data
x = features * model.proj_matrix'
# get indices
idx = _get_index(x, model.cuts, size(model.proj_matrix)[1])
# we only need to consider those parts
uidx = unique(idx)
for i = 1:size(uidx)[1]
j = uidx[i]
s = idx .== j
if model.parts[j].mean == 0.0
yhat[s] = 0.0
w[s] = 0.0
else
yhat[s] = model.parts[j].mean
w[s] = pdf(model.parts[j].dist, x[s,:]') ./ pdf(model.parts[j].dist, mean(model.parts[j].dist))
end
end
return yhat, w
end
function apply_ensemble{T<:FloatingPoint}(model::Ensemble, features::Matrix{T})
n = size(model.ferns)[2]
yhat = zeros(size(features)[1])
wsum = zeros(size(features)[1])
for i=1:n
yy, w = apply_fern(model.ferns[i], features)
yhat = yhat + (yy .* w)
wsum = wsum + w
end
r = yhat ./ wsum
b = isnan(r)
r[b] = 0
return r
end
function build_fern{T<:FloatingPoint, U<:Real}(labels::Vector{T}, features::Matrix{U}, n_components=5)
# sanity check
if size(labels)[1] != size(features)[1]
println("number of instances in features and labels matrix not equal!")
return None
end
# get random projection and project features
p = random_projection_matrix(:gaussian, size(features)[2], n_components)
x = features * p'
# get cutting values
min_x = minimum(x, 1)
max_x = maximum(x, 1)
cuts = zeros(n_components)
for i = 1:n_components
d = Uniform(min_x[i], max_x[i])
cuts[i] = rand(d)
end
# estimate index
idx = _get_index(x, cuts, n_components)
max_idx = 2^(n_components+1)
# fill all parts with Distributions
parts = Array(Part, 1, max_idx)
for i = 1:max_idx
s = idx .== i
if (sum(s) == 0)
x_cov = eye(n_components)
x_mean = zeros(n_components)
y_mean = 0
else
c = mean_and_cov(x[s,:])
x_cov = c[2] .* eye(n_components)
x_mean = vec(c[1])
y_mean = mean(labels[s])
# do something if covariance matrix is not positive definite
if (det(x_cov) <= 0.0)
x_cov = eye(n_components) # todo: this is probably a bad idea
y_mean = 0.0
end
end
dist = MvNormal(x_mean, x_cov)
parts[i] = Part(dist, y_mean)
end
return Fern(parts, p, cuts)
end
function build_ensemble{T<:FloatingPoint, U<:Real}(labels::Vector{T}, features::Matrix{U}, n_components=5, n_ferns=10)
# sanity check
if size(labels)[1] != size(features)[1]
println("number of instances in features and labels matrix not equal!")
return None
end
ferns = Array(Fern, 1, n_ferns)
for i=1:n_ferns
ferns[i] = build_fern(labels, features, n_components)
end
return Ensemble(ferns)
end
end # module
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] | 2.316401 | 1,634 |
function add_kwargs!(ex::Expr; kwargs...)
ex.head === :call || throw(ArgumentError("expression is not a function call"))
isempty(ex.args) && throw(ArgumentError("expression body is empty"))
if !isempty(kwargs)
params = Expr(:parameters)
for (name, value) in kwargs
push!(params.args, Expr(:kw, name, value))
end
# Parameters need to come after the function name and before positional arguments
if length(ex.args) == 1
push!(ex.args, params)
else
insert!(ex.args, 2, params)
end
end
ex
end
function get_union_call(foo::Symbol, type_tuple::Expr; kwargs...)
# Get types from tuple and create a collection of symbols for use in the call.
types = get_types(get_body(type_tuple))
arg_names = [Symbol("x$j") for j in 1:length(types)]
# Generate the call.
typed_args = map((name, typ) -> :($name::$(unionise_type(typ))), arg_names, types)
call = add_kwargs!(Expr(:call, foo, typed_args...); kwargs...)
return replace_body(type_tuple, call), arg_names
end
"""
unionise_type(tp::Union{Symbol, Expr})
Returns an expression for the type union of `tp` and `Node{<:tp}`. e.g.
`unionise_type(:Real)` returns `:(Union{Real, Node{<:Real}})`.
"""
function unionise_type(tp::Union{Symbol, Expr})
(_tp, _info) = remove_vararg(tp)
tp_clean = (isa(_tp, Expr) && _tp.head == Symbol("<:")) ? _tp.args[1] : _tp
return replace_vararg(:(Union{$_tp, Node{<:$tp_clean}}), (_tp, _info))
end
"""
replace_body(unionall::Union{Symbol, Expr}, replacement::Union{Symbol, Expr})
Replace the body of an expression representing a `UnionAll`. e.g.
replace_body(:(Tuple{T, T} where T), :foo) returns the (nonsensical) expression
:(foo where T). If `unionall` is a `Symbol`, then `replacement` is returned.
"""
replace_body(unionall::Expr, replacement::Union{Symbol, Expr}) =
unionall.head == :where ?
Expr(:where, replace_body(unionall.args[1], replacement), unionall.args[2:end]...) :
replacement
replace_body(::Symbol, replacement::Union{Symbol, Expr}) = replacement
"""
get_body(x::Union{Symbol, Expr})
Get the body from an expression representing a `UnionAll`. e.g. :(Tuple{T, T} where T)
returns :(Tuple{T, T}). If `x` is a `Symbol`, then `x` is returned unaltered.
"""
get_body(unionall::Expr) = unionall.head == :where ? get_body(unionall.args[1]) : unionall
get_body(body::Symbol) = body
"""
get_types(type_tuple::Expr)
Return the types from a type-tuple expression. e.g. :(Tuple{Float64, Real}) returns a
vector [:Float64, :Real]
"""
get_types(type_tuple::Expr) = type_tuple.args[2:end]
"""
isa_vararg(symbol_or_expr)
Returns a bool indicating whether `symbol_or_expr` is a `Vararg`.
"""
isa_vararg(sym::Symbol) = (sym == :Vararg)
isa_vararg(expr::Expr) =
(expr.head == :curly && expr.args[1] == :Vararg) ?
true :
expr.head == :where ? isa_vararg(expr.args[1]) : false
"""
remove_vararg(typ::Union{Symbol, Expr})
Return the type contained by the `typ` if it's a `Vararg` and the `N` parameter if provided.
"""
remove_vararg(typ::Symbol) = isa_vararg(typ) ? (:Any, :Vararg) : (typ, :nothing)
function remove_vararg(typ::Expr)
if isa_vararg(typ)
body = get_body(typ)
new_typ = replace_body(typ, body.args[2])
vararg_info = length(body.args) == 3 ? body.args[3] : :Vararg
return new_typ, vararg_info
else
return (typ, :nothing)
end
end
"""
replace_vararg(typ::SymOrExpr, vararg_info::Tuple)
Convert `typ` to the `Vararg` containing elements of type `typ` specified by
`vararg_info`, which should be a `Tuple` returned from `remove_vararg`.
"""
replace_vararg(typ::SymOrExpr, vararg_info::Tuple) =
vararg_info[2] == :nothing ?
typ :
vararg_info[2] == :no_N || vararg_info[2] == :Vararg ?
replace_body(typ, :(Vararg{$(get_body(typ))})) :
replace_body(typ, :(Vararg{$(get_body(typ)), $(vararg_info[2])}))
"""
parse_kwargs(nt_expr) -> NamedTuple
Accepts an expression containing a `NamedTuple` literal and parses it into a `NamedTuple`
with expressions as values.
"""
function parse_kwargs(nt_expr)
if isempty(nt_expr.args) || nt_expr == Expr(:call, :NamedTuple)
return NamedTuple()
end
first_arg = first(nt_expr.args)
if first_arg isa Expr && first_arg.head == :parameters
return parse_kwargs_parameters(nt_expr)
elseif first_arg isa Expr && first_arg.head == :(=)
return parse_kwargs_tuple(nt_expr)
else
throw(ArgumentError("Unsupported expression $nt_expr for kwargs;"
* " they must be passed as a NamedTuple literal"))
end
end
function parse_kwargs_tuple(tup_expr)
nt_names = Tuple(first(ex.args) for ex in tup_expr.args)
return NamedTuple{nt_names}(last(ex.args) for ex in tup_expr.args)
end
function parse_kwargs_parameters(param_tuple_expr)
# code is the same even though the inner args are also different expression types
# (:kw vs :())
return parse_kwargs_tuple(param_tuple_expr.args[1])
end
"""
parse_is_node(bool_array_expr) -> Vector{Bool}
Accepts an expression containing a `Vector{Bool}` literal and parses it into a
`Vector{Bool}`.
"""
function parse_is_node(bool_array_expr)
if bool_array_expr.head != :vect
throw(ArgumentError("Unsupported expression $bool_array_expr for is_node; "
* "it must be passed as a `Vector{Bool}` literal (e.g., `[true, false]`)"))
end
return collect(Bool, bool_array_expr.args)
end
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] | 2.458886 | 2,262 |
module MultiPartParsing
import ..access_threaded, ..Request, ..Multipart, ..payload
using ..Parsers
export parse_multipart_form
const CR_BYTE = 0x0d # \r
const LF_BYTE = 0x0a # \n
const DASH_BYTE = 0x2d # -
const HTAB_BYTE = 0x09 # \t
const SPACE_BYTE = 0x20
const SEMICOLON_BYTE = UInt8(';')
const CRLFCRLF = (CR_BYTE, LF_BYTE, CR_BYTE, LF_BYTE)
"compare byte buffer `a` from index `i` to index `j` with `b` and check if they are byte-equal"
function byte_buffers_eq(a, i, j, b)
l = 1
@inbounds for k = i:j
a[k] == b[l] || return false
l += 1
end
return true
end
"""
find_multipart_boundary(bytes, boundaryDelimiter; start::Int=1)
Find the first and last index of the next boundary delimiting a part, and if
the discovered boundary is the terminating boundary.
"""
function find_multipart_boundary(bytes::AbstractVector{UInt8}, boundaryDelimiter::AbstractVector{UInt8}; start::Int=1)
# The boundary delimiter line is prepended with two '-' characters
# The boundary delimiter line starts on a new line, so must be preceded by a \r\n.
# The boundary delimiter line ends with \r\n, and can have "optional linear whitespace" between
# the end of the boundary delimiter, and the \r\n.
# The last boundary delimiter line has an additional '--' at the end of the boundary delimiter
# [RFC2046 5.1.1](https://tools.ietf.org/html/rfc2046#section-5.1.1)
i = start
end_index = i + length(boundaryDelimiter) + 1
while end_index <= length(bytes)
if bytes[i] == DASH_BYTE && bytes[i + 1] == DASH_BYTE && byte_buffers_eq(bytes, i + 2, end_index, boundaryDelimiter)
# boundary delimiter line start on a new line ...
if i > 1
(i == 2 || bytes[i-2] != CR_BYTE || bytes[i-1] != LF_BYTE) && error("boundary delimiter found, but it was not the start of a line")
# the CRLF preceding the boundary delimiter is "conceptually attached
# to the boundary", so account for this with the index
i -= 2
end
# need to check if there are enough characters for the CRLF or for two dashes
end_index < length(bytes)-1 || error("boundary delimiter found, but did not end with new line")
is_terminating_delimiter = bytes[end_index+1] == DASH_BYTE && bytes[end_index+2] == DASH_BYTE
is_terminating_delimiter && (end_index += 2)
# ... there can be arbitrary SP and HTAB space between the boundary delimiter ...
while end_index < length(bytes) && (bytes[end_index+1] in (HTAB_BYTE, SPACE_BYTE))
end_index += 1
end
# ... and ends with a new line
newlineEnd = end_index < length(bytes)-1 &&
bytes[end_index+1] == CR_BYTE &&
bytes[end_index+2] == LF_BYTE
if !newlineEnd
error("boundary delimiter found, but did not end with new line")
end
end_index += 2
return (is_terminating_delimiter, i, end_index)
end
i += 1
end_index += 1
end
error("boundary delimiter not found")
end
"""
find_multipart_boundaries(bytes, boundary; start=1)
Find the start and end indexes of all the parts of the multipart object. Ultimately this method is
looking for the data between the boundary delimiters in the byte array. A vector containing all
the start/end pairs is returned.
"""
function find_multipart_boundaries(bytes::AbstractVector{UInt8}, boundary::AbstractVector{UInt8}; start=1)
idxs = Tuple{Int, Int}[]
while true
(is_terminating_delimiter, i, end_index) = find_multipart_boundary(bytes, boundary; start = start)
push!(idxs, (i, end_index))
is_terminating_delimiter && break
start = end_index + 1
end
return idxs
end
"""
find_header_boundary(bytes)
Find the end of the multipart header in the byte array. Returns a Tuple with the
start index(1) and the end index. Headers are separated from the body by CRLFCRLF.
[RFC2046 5.1](https://tools.ietf.org/html/rfc2046#section-5.1)
[RFC822 3.1](https://tools.ietf.org/html/rfc822#section-3.1)
"""
function find_header_boundary(bytes::AbstractVector{UInt8})
length(CRLFCRLF) > length(bytes) && return nothing
l = length(bytes) - length(CRLFCRLF) + 1
i = 1
end_index = length(CRLFCRLF)
while (i <= l)
byte_buffers_eq(bytes, i, end_index, CRLFCRLF) && return (1, end_index)
i += 1
end_index += 1
end
error("no delimiter found separating header from multipart body")
end
const content_disposition_regex = Parsers.RegexAndMatchData[]
function content_disposition_regex_f()
r = Parsers.RegexAndMatchData(r"^Content-Disposition:[ \t]*form-data;[ \t]*(.*)\r\n"x)
Parsers.init!(r)
end
const content_disposition_flag_regex = Parsers.RegexAndMatchData[]
function content_disposition_flag_regex_f()
r = Parsers.RegexAndMatchData(r"""^
[ \t]*([!#$%&'*+\-.^_`|~[:alnum:]]+);?
"""x)
Parsers.init!(r)
end
const content_disposition_pair_regex = Parsers.RegexAndMatchData[]
function content_disposition_pair_regex_f()
r = Parsers.RegexAndMatchData(r"""^
[ \t]*([!#$%&'*+\-.^_`|~[:alnum:]]+)[ \t]*=[ \t]*"(.*?)";?
"""x)
Parsers.init!(r)
end
const content_type_regex = Parsers.RegexAndMatchData[]
function content_type_regex_f()
r = Parsers.RegexAndMatchData(r"(?i)Content-Type: (\S*[^;\s])"x)
Parsers.init!(r)
end
"""
parse_multipart_chunk(chunk)
Parse a single multi-part chunk into a Multipart object. This will decode
the header and extract the contents from the byte array.
"""
function parse_multipart_chunk(chunk)
startIndex, end_index = find_header_boundary(chunk)
header = SubString(unsafe_string(pointer(chunk, startIndex), end_index - startIndex + 1))
content = view(chunk, end_index+1:lastindex(chunk))
# find content disposition
re = access_threaded(content_disposition_regex_f, content_disposition_regex)
if !Parsers.exec(re, header)
@warn "Content disposition is not specified dropping the chunk." String(chunk)
return nothing # Specifying content disposition is mandatory
end
content_disposition = Parsers.group(1, re, header)
re_flag = access_threaded(content_disposition_flag_regex_f, content_disposition_flag_regex)
re_pair = access_threaded(content_disposition_pair_regex_f, content_disposition_pair_regex)
name = nothing
filename = nothing
while !isempty(content_disposition)
if Parsers.exec(re_pair, content_disposition)
key = Parsers.group(1, re_pair, content_disposition)
value = Parsers.group(2, re_pair, content_disposition)
if key == "name"
name = value
elseif key == "filename"
filename = value
else
# do stuff with other content disposition key-value pairs
end
content_disposition = Parsers.nextbytes(re_pair, content_disposition)
elseif Parsers.exec(re_flag, content_disposition)
# do stuff with content disposition flags
content_disposition = Parsers.nextbytes(re_flag, content_disposition)
else
break
end
end
name === nothing && return
re_ct = access_threaded(content_type_regex_f, content_type_regex)
contenttype = Parsers.exec(re_ct, header) ? Parsers.group(1, re_ct, header) : "text/plain"
return Multipart(filename, IOBuffer(content), contenttype, "", name)
end
"""
parse_multipart_body(body, boundary)::Vector{Multipart}
Parse the multipart body received from the client breaking it into the various
chunks which are returned as an array of Multipart objects.
"""
function parse_multipart_body(body::AbstractVector{UInt8}, boundary::AbstractString)::Vector{Multipart}
multiparts = Multipart[]
idxs = find_multipart_boundaries(body, codeunits(boundary))
length(idxs) > 1 || (return multiparts)
for i in 1:length(idxs)-1
chunk = view(body, idxs[i][2]+1:idxs[i+1][1]-1)
push!(multiparts, parse_multipart_chunk(chunk))
end
return multiparts
end
"""
parse_multipart_form(req::Request)::Vector{Multipart}
Parse the full mutipart form submission from the client returning and
array of Multipart objects containing all the data.
The order of the multipart form data in the request should be preserved.
[RFC7578 5.2](https://tools.ietf.org/html/rfc7578#section-5.2).
The boundary delimiter MUST NOT appear inside any of the encapsulated parts. Note
that the boundary delimiter does not need to have '-' characters, but a line using
the boundary delimiter will start with '--' and end in \r\n.
[RFC2046 5.1](https://tools.ietf.org/html/rfc2046#section-5.1.1)
"""
function parse_multipart_form(req::Request)::Union{Vector{Multipart}, Nothing}
# parse boundary from Content-Type
m = match(r"multipart/form-data; boundary=(.*)$", req["Content-Type"])
m === nothing && return nothing
boundary_delimiter = m[1]
# [RFC2046 5.1.1](https://tools.ietf.org/html/rfc2046#section-5.1.1)
length(boundary_delimiter) > 70 && error("boundary delimiter must not be greater than 70 characters")
return parse_multipart_body(payload(req), boundary_delimiter)
end
function __init__()
resize!(empty!(content_disposition_regex), Threads.nthreads())
resize!(empty!(content_disposition_flag_regex), Threads.nthreads())
resize!(empty!(content_disposition_pair_regex), Threads.nthreads())
resize!(empty!(content_type_regex), Threads.nthreads())
return
end
end # module MultiPartParsing
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4851,
7836,
37,
13,
198,
198,
58,
41150,
1238,
3510,
642,
13,
16,
16151,
5450,
1378,
31391,
13,
1155,
69,
13,
2398,
14,
6494,
14,
81,
16072,
1238,
3510,
2,
5458,
12,
20,
13,
16,
8,
198,
58,
41150,
23,
1828,
513,
13,
16,
16151,
5450,
1378,
31391,
13,
1155,
69,
13,
2398,
14,
6494,
14,
81,
16072,
23,
1828,
2,
5458,
12,
18,
13,
16,
8,
198,
37811,
198,
8818,
1064,
62,
25677,
62,
7784,
560,
7,
33661,
3712,
23839,
38469,
90,
52,
5317,
23,
30072,
198,
220,
220,
220,
4129,
7,
34,
7836,
4851,
7836,
37,
8,
1875,
4129,
7,
33661,
8,
11405,
1441,
2147,
628,
220,
220,
220,
300,
796,
4129,
7,
33661,
8,
532,
4129,
7,
34,
7836,
4851,
7836,
37,
8,
1343,
352,
198,
220,
220,
220,
1312,
796,
352,
198,
220,
220,
220,
886,
62,
9630,
796,
4129,
7,
34,
7836,
4851,
7836,
37,
8,
198,
220,
220,
220,
981,
357,
72,
19841,
300,
8,
198,
220,
220,
220,
220,
220,
220,
220,
18022,
62,
36873,
364,
62,
27363,
7,
33661,
11,
1312,
11,
886,
62,
9630,
11,
327,
7836,
4851,
7836,
37,
8,
11405,
1441,
357,
16,
11,
886,
62,
9630,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1312,
15853,
352,
198,
220,
220,
220,
220,
220,
220,
220,
886,
62,
9630,
15853,
352,
198,
220,
220,
220,
886,
198,
220,
220,
220,
4049,
7203,
3919,
46728,
2676,
1043,
27259,
13639,
422,
18540,
433,
1767,
4943,
198,
437,
198,
198,
9979,
2695,
62,
6381,
9150,
62,
260,
25636,
796,
23042,
364,
13,
3041,
25636,
1870,
23850,
6601,
21737,
198,
8818,
2695,
62,
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62,
260,
25636,
62,
69,
3419,
198,
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220,
220,
374,
796,
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13,
3041,
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1870,
23850,
6601,
7,
81,
1,
61,
19746,
12,
7279,
9150,
33250,
3467,
83,
60,
9,
687,
12,
7890,
26,
58,
3467,
83,
60,
9,
7,
15885,
19415,
81,
59,
77,
1,
87,
8,
198,
220,
220,
220,
23042,
364,
13,
15003,
0,
7,
81,
8,
198,
437,
198,
198,
9979,
2695,
62,
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9150,
62,
32109,
62,
260,
25636,
796,
23042,
364,
13,
3041,
25636,
1870,
23850,
6601,
21737,
198,
8818,
2695,
62,
6381,
9150,
62,
32109,
62,
260,
25636,
62,
69,
3419,
198,
220,
220,
220,
374,
796,
23042,
364,
13,
3041,
25636,
1870,
23850,
6601,
7,
81,
37811,
61,
198,
220,
220,
220,
685,
3467,
83,
60,
9,
26933,
0,
29953,
4,
5,
6,
9,
10,
59,
34507,
61,
62,
63,
91,
93,
58,
25,
282,
22510,
25,
11907,
10,
1776,
30,
198,
220,
220,
220,
37227,
87,
8,
198,
220,
220,
220,
23042,
364,
13,
15003,
0,
7,
81,
8,
198,
437,
198,
198,
9979,
2695,
62,
6381,
9150,
62,
24874,
62,
260,
25636,
796,
23042,
364,
13,
3041,
25636,
1870,
23850,
6601,
21737,
198,
8818,
2695,
62,
6381,
9150,
62,
24874,
62,
260,
25636,
62,
69,
3419,
198,
220,
220,
220,
374,
796,
23042,
364,
13,
3041,
25636,
1870,
23850,
6601,
7,
81,
37811,
61,
198,
220,
220,
220,
685,
3467,
83,
60,
9,
26933,
0,
29953,
4,
5,
6,
9,
10,
59,
34507,
61,
62,
63,
91,
93,
58,
25,
282,
22510,
25,
11907,
28988,
58,
3467,
83,
60,
9,
41888,
3467,
83,
60,
9,
18109,
15885,
10091,
8172,
30,
198,
220,
220,
220,
37227,
87,
8,
198,
220,
220,
220,
23042,
364,
13,
15003,
0,
7,
81,
8,
198,
437,
198,
198,
9979,
2695,
62,
4906,
62,
260,
25636,
796,
23042,
364,
13,
3041,
25636,
1870,
23850,
6601,
21737,
198,
8818,
2695,
62,
4906,
62,
260,
25636,
62,
69,
3419,
198,
220,
220,
220,
374,
796,
23042,
364,
13,
3041,
25636,
1870,
23850,
6601,
7,
81,
18109,
30,
72,
8,
19746,
12,
6030,
25,
357,
59,
50,
9,
58,
61,
26,
59,
82,
12962,
1,
87,
8,
198,
220,
220,
220,
23042,
364,
13,
15003,
0,
7,
81,
8,
198,
437,
198,
198,
37811,
198,
220,
220,
220,
21136,
62,
16680,
541,
433,
62,
354,
2954,
7,
354,
2954,
8,
198,
198,
10044,
325,
257,
2060,
5021,
12,
3911,
16058,
656,
257,
7854,
541,
433,
2134,
13,
220,
770,
481,
36899,
198,
1169,
13639,
290,
7925,
262,
10154,
422,
262,
18022,
7177,
13,
198,
37811,
198,
8818,
21136,
62,
16680,
541,
433,
62,
354,
2954,
7,
354,
2954,
8,
198,
220,
220,
220,
923,
15732,
11,
886,
62,
9630,
796,
1064,
62,
25677,
62,
7784,
560,
7,
354,
2954,
8,
198,
220,
220,
220,
13639,
796,
3834,
10100,
7,
13271,
8635,
62,
8841,
7,
29536,
7,
354,
2954,
11,
923,
15732,
828,
886,
62,
9630,
532,
923,
15732,
1343,
352,
4008,
198,
220,
220,
220,
2695,
796,
1570,
7,
354,
2954,
11,
886,
62,
9630,
10,
16,
25,
12957,
9630,
7,
354,
2954,
4008,
628,
220,
220,
220,
1303,
1064,
2695,
24665,
198,
220,
220,
220,
302,
796,
1895,
62,
16663,
276,
7,
11299,
62,
6381,
9150,
62,
260,
25636,
62,
69,
11,
2695,
62,
6381,
9150,
62,
260,
25636,
8,
198,
220,
220,
220,
611,
5145,
47,
945,
364,
13,
18558,
7,
260,
11,
13639,
8,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
40539,
366,
19746,
24665,
318,
407,
7368,
12047,
262,
16058,
526,
10903,
7,
354,
2954,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1441,
2147,
1303,
18291,
4035,
2695,
24665,
318,
13677,
198,
220,
220,
220,
886,
198,
220,
220,
220,
2695,
62,
6381,
9150,
796,
23042,
364,
13,
8094,
7,
16,
11,
302,
11,
13639,
8,
628,
220,
220,
220,
302,
62,
32109,
796,
1895,
62,
16663,
276,
7,
11299,
62,
6381,
9150,
62,
32109,
62,
260,
25636,
62,
69,
11,
2695,
62,
6381,
9150,
62,
32109,
62,
260,
25636,
8,
198,
220,
220,
220,
302,
62,
24874,
796,
1895,
62,
16663,
276,
7,
11299,
62,
6381,
9150,
62,
24874,
62,
260,
25636,
62,
69,
11,
2695,
62,
6381,
9150,
62,
24874,
62,
260,
25636,
8,
198,
220,
220,
220,
1438,
796,
2147,
198,
220,
220,
220,
29472,
796,
2147,
198,
220,
220,
220,
981,
5145,
271,
28920,
7,
11299,
62,
6381,
9150,
8,
198,
220,
220,
220,
220,
220,
220,
220,
611,
23042,
364,
13,
18558,
7,
260,
62,
24874,
11,
2695,
62,
6381,
9150,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1994,
796,
23042,
364,
13,
8094,
7,
16,
11,
302,
62,
24874,
11,
2695,
62,
6381,
9150,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1988,
796,
23042,
364,
13,
8094,
7,
17,
11,
302,
62,
24874,
11,
2695,
62,
6381,
9150,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
1994,
6624,
366,
3672,
1,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1438,
796,
1988,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
361,
1994,
6624,
366,
34345,
1,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
29472,
796,
1988,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
466,
3404,
351,
584,
2695,
24665,
1994,
12,
8367,
14729,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2695,
62,
6381,
9150,
796,
23042,
364,
13,
19545,
33661,
7,
260,
62,
24874,
11,
2695,
62,
6381,
9150,
8,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
361,
23042,
364,
13,
18558,
7,
260,
62,
32109,
11,
2695,
62,
6381,
9150,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
466,
3404,
351,
2695,
24665,
9701,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2695,
62,
6381,
9150,
796,
23042,
364,
13,
19545,
33661,
7,
260,
62,
32109,
11,
2695,
62,
6381,
9150,
8,
198,
220,
220,
220,
220,
220,
220,
220,
2073,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2270,
198,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
886,
628,
220,
220,
220,
1438,
24844,
2147,
11405,
1441,
628,
220,
220,
220,
302,
62,
310,
796,
1895,
62,
16663,
276,
7,
11299,
62,
4906,
62,
260,
25636,
62,
69,
11,
2695,
62,
4906,
62,
260,
25636,
8,
198,
220,
220,
220,
2695,
4906,
796,
23042,
364,
13,
18558,
7,
260,
62,
310,
11,
13639,
8,
5633,
23042,
364,
13,
8094,
7,
16,
11,
302,
62,
310,
11,
13639,
8,
1058,
366,
5239,
14,
25638,
1,
628,
220,
220,
220,
1441,
7854,
541,
433,
7,
34345,
11,
314,
9864,
13712,
7,
11299,
828,
2695,
4906,
11,
366,
1600,
1438,
8,
198,
437,
198,
198,
37811,
198,
220,
220,
220,
21136,
62,
16680,
541,
433,
62,
2618,
7,
2618,
11,
18645,
2599,
25,
38469,
90,
15205,
541,
433,
92,
198,
198,
10044,
325,
262,
18540,
433,
1767,
2722,
422,
262,
5456,
7163,
340,
656,
262,
2972,
198,
354,
14125,
543,
389,
4504,
355,
281,
7177,
286,
7854,
541,
433,
5563,
13,
198,
37811,
198,
8818,
21136,
62,
16680,
541,
433,
62,
2618,
7,
2618,
3712,
23839,
38469,
90,
52,
5317,
23,
5512,
18645,
3712,
23839,
10100,
2599,
25,
38469,
90,
15205,
541,
433,
92,
198,
220,
220,
220,
18540,
5889,
796,
7854,
541,
433,
21737,
198,
220,
220,
220,
4686,
34223,
796,
1064,
62,
16680,
541,
433,
62,
7784,
3166,
7,
2618,
11,
2438,
41667,
7,
7784,
560,
4008,
198,
220,
220,
220,
4129,
7,
312,
34223,
8,
1875,
352,
8614,
357,
7783,
18540,
5889,
8,
628,
220,
220,
220,
329,
1312,
287,
352,
25,
13664,
7,
312,
34223,
13219,
16,
198,
220,
220,
220,
220,
220,
220,
220,
16058,
796,
1570,
7,
2618,
11,
4686,
34223,
58,
72,
7131,
17,
48688,
16,
25,
312,
34223,
58,
72,
10,
16,
7131,
16,
45297,
16,
8,
198,
220,
220,
220,
220,
220,
220,
220,
4574,
0,
7,
16680,
541,
5889,
11,
21136,
62,
16680,
541,
433,
62,
354,
2954,
7,
354,
2954,
4008,
198,
220,
220,
220,
886,
198,
220,
220,
220,
1441,
18540,
5889,
198,
437,
628,
198,
37811,
198,
220,
220,
220,
21136,
62,
16680,
541,
433,
62,
687,
7,
42180,
3712,
18453,
2599,
25,
38469,
90,
15205,
541,
433,
92,
198,
198,
10044,
325,
262,
1336,
4517,
541,
433,
1296,
14498,
422,
262,
5456,
8024,
290,
198,
18747,
286,
7854,
541,
433,
5563,
7268,
477,
262,
1366,
13,
198,
198,
464,
1502,
286,
262,
18540,
433,
1296,
1366,
287,
262,
2581,
815,
307,
17232,
13,
198,
58,
41150,
2425,
3695,
642,
13,
17,
16151,
5450,
1378,
31391,
13,
1155,
69,
13,
2398,
14,
6494,
14,
81,
16072,
2425,
3695,
2,
5458,
12,
20,
13,
17,
737,
198,
198,
464,
18645,
46728,
2676,
17191,
5626,
1656,
2641,
597,
286,
262,
32652,
4817,
3354,
13,
5740,
198,
5562,
262,
18645,
46728,
2676,
857,
407,
761,
284,
423,
705,
19355,
3435,
11,
475,
257,
1627,
1262,
198,
1169,
18645,
46728,
2676,
481,
923,
351,
705,
438,
6,
290,
886,
287,
3467,
81,
59,
77,
13,
198,
58,
41150,
1238,
3510,
642,
13,
16,
16151,
5450,
1378,
31391,
13,
1155,
69,
13,
2398,
14,
6494,
14,
81,
16072,
1238,
3510,
2,
5458,
12,
20,
13,
16,
13,
16,
8,
198,
37811,
198,
8818,
21136,
62,
16680,
541,
433,
62,
687,
7,
42180,
3712,
18453,
2599,
25,
38176,
90,
38469,
90,
15205,
541,
433,
5512,
10528,
92,
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220,
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1303,
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18645,
422,
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6030,
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81,
1,
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14,
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8,
3,
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2147,
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18645,
62,
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320,
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285,
58,
16,
60,
628,
220,
220,
220,
1303,
685,
41150,
1238,
3510,
642,
13,
16,
13,
16,
16151,
5450,
1378,
31391,
13,
1155,
69,
13,
2398,
14,
6494,
14,
81,
16072,
1238,
3510,
2,
5458,
12,
20,
13,
16,
13,
16,
8,
198,
220,
220,
220,
4129,
7,
7784,
560,
62,
12381,
320,
2676,
8,
1875,
4317,
11405,
4049,
7203,
7784,
560,
46728,
2676,
1276,
407,
307,
3744,
621,
4317,
3435,
4943,
628,
220,
220,
220,
1441,
21136,
62,
16680,
541,
433,
62,
2618,
7,
15577,
2220,
7,
42180,
828,
18645,
62,
12381,
320,
2676,
8,
198,
437,
198,
198,
8818,
11593,
15003,
834,
3419,
198,
220,
220,
220,
47558,
0,
7,
28920,
0,
7,
11299,
62,
6381,
9150,
62,
260,
25636,
828,
14122,
82,
13,
77,
16663,
82,
28955,
198,
220,
220,
220,
47558,
0,
7,
28920,
0,
7,
11299,
62,
6381,
9150,
62,
32109,
62,
260,
25636,
828,
14122,
82,
13,
77,
16663,
82,
28955,
198,
220,
220,
220,
47558,
0,
7,
28920,
0,
7,
11299,
62,
6381,
9150,
62,
24874,
62,
260,
25636,
828,
14122,
82,
13,
77,
16663,
82,
28955,
198,
220,
220,
220,
47558,
0,
7,
28920,
0,
7,
11299,
62,
4906,
62,
260,
25636,
828,
14122,
82,
13,
77,
16663,
82,
28955,
198,
220,
220,
220,
1441,
198,
437,
198,
198,
437,
1303,
8265,
15237,
7841,
47,
945,
278,
198
] | 2.514123 | 3,859 |
using AutomotiveDrivingModels, AutomotivePOMDPs, POMDPs, ProgressMeter
params = UrbanParams(nlanes_main=1,
crosswalk_pos = [VecSE2(6, 0., pi/2), VecSE2(-6, 0., pi/2), VecSE2(0., -5., 0.)],
crosswalk_length = [14.0, 14., 14.0],
crosswalk_width = [4.0, 4.0, 3.1],
stop_line = 22.0)
env = UrbanEnv(params=params);
mdp = PedCarMDP(env = env, pos_res=3., vel_res=2., ped_birth=0.7, ped_type=VehicleDef(AgentClass.PEDESTRIAN, 1.0, 3.0))
egos = get_ego_states(mdp.env, mdp.pos_res, mdp.vel_res)
for (i, e) in enumerate(egos)
@assert ind2ego(mdp.env, i, mdp.pos_res, mdp.vel_res) == e
end
routes = AutomotivePOMDPs.get_car_routes(mdp.env)
for route in routes
cars = get_car_states(mdp.env, route, mdp.pos_res, mdp.vel_res)
for (i, c) in enumerate(cars)
@assert ind2car(mdp.env, i, route, mdp.pos_res, mdp.vel_res) == c
end
end
peds = get_ped_states(mdp.env, mdp.pos_res, mdp.vel_ped_res)
for (i, p) in enumerate(peds)
if ind2ped(mdp.env, i, mdp.pos_res, mdp.vel_ped_res) != p
println(i)
break
end
end
state_space = states(mdp)
@showprogress for (i, s) in enumerate(state_space)
@assert ind2state(mdp, i) == s
end
| [
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220,
220,
886,
201,
198,
437,
201,
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626,
62,
9124,
62,
411,
8,
14512,
279,
201,
198,
220,
220,
220,
220,
220,
220,
220,
44872,
7,
72,
8,
201,
198,
220,
220,
220,
220,
220,
220,
220,
2270,
201,
198,
220,
220,
220,
886,
201,
198,
437,
201,
198,
201,
198,
5219,
62,
13200,
796,
2585,
7,
9132,
79,
8,
201,
198,
201,
198,
31,
12860,
33723,
329,
357,
72,
11,
264,
8,
287,
27056,
378,
7,
5219,
62,
13200,
8,
201,
198,
220,
220,
220,
2488,
30493,
773,
17,
5219,
7,
9132,
79,
11,
1312,
8,
6624,
264,
201,
198,
437,
201,
198
] | 1.878261 | 690 |
module CI_Test
"""
TerribleGreek()
Map lowercase latin characters to symbols that kinda resemble them, ish...
"""
function TerribleGreek()
return Dict('a' => 'α',
'b' => 'β',
'c' => '≪',
'd' => 'δ',
'e' => '∈',
'f' => 'ɾ',
'g' => 'ʔ',
'h' => 'ƕ',
'i' => 'ι',
'j' => 'ȷ',
'k' => 'k',
'l' => 'ł',
'm' => 'μ',
'n' => 'π',
'o' => 'ø',
'p' => 'ɤ',
'q' => '♩',
'r' => 'ρ',
's' => '𝖲',
't' => 'τ',
'u' => '⟰',
'v' => '∇',
'w' => '≅',
'x' => 'Ξ',
'y' => '¥',
'z' => 'ζ',)
end
end # module
| [
21412,
14514,
62,
14402,
628,
198,
37811,
198,
220,
220,
220,
3813,
5547,
44059,
3419,
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198,
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2793,
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3042,
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284,
14354,
326,
17855,
22464,
606,
11,
318,
71,
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198,
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198,
8818,
3813,
5547,
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3419,
198,
220,
220,
220,
1441,
360,
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6,
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220,
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220,
220,
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220,
220,
220,
220,
220,
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220,
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112,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
69,
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122,
3256,
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220,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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6,
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134,
242,
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220,
220,
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220,
220,
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71,
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130,
243,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
72,
6,
5218,
705,
29945,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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115,
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220,
220,
220,
220,
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220,
220,
220,
220,
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220,
220,
220,
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220,
220,
220,
705,
80,
6,
5218,
705,
17992,
102,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
81,
6,
5218,
705,
33643,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
82,
6,
5218,
705,
47728,
244,
110,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
83,
6,
5218,
705,
32830,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
84,
6,
5218,
705,
158,
253,
108,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
85,
6,
5218,
705,
24861,
229,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
86,
6,
5218,
705,
35705,
227,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
87,
6,
5218,
705,
138,
252,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
88,
6,
5218,
705,
126,
98,
3256,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
705,
89,
6,
5218,
705,
138,
114,
3256,
8,
198,
437,
198,
198,
437,
1303,
8265,
198
] | 1.359209 | 657 |
# Normalized is basically wrapper like Symmetric
"""
Normalized(dist::Union{StringSemiMetric, StringMetric})
Creates a normalized distance. The normalized distance always return a Float64 between 0.0 and 1.0 (or a missing if one of the argument is missing).
A Normalized Distance has a keyword argument `max_dist` that defaults to 1.0. It returns 1.0 if the true distance is higher than `max_dist`.
### Examples
```julia-repl
julia> s1 = "New York Mets vs Atlanta"
julia> s2 = "Atlanta Braves vs New York Mets"
julia> Levenshtein()(s1, s2)
25
julia> StringDistances.Normalized(Levenshtein())(s1, s2)
0.8064
```
"""
struct Normalized{T <: Union{StringSemiMetric, StringMetric}} <: StringSemiMetric
dist::T
end
Normalized(dist::Normalized) = dist
# Consider all distances to be normalized by default
function (dist::Normalized)(s1, s2; max_dist = 1.0)
out = dist.dist(s1, s2; max_dist = max_dist)
max_dist !== nothing && out > max_dist && return 1.0
return out
end
function (dist::Normalized{<:Union{Hamming, DamerauLevenshtein}})(s1, s2; max_dist = 1.0)
(s1 === missing) | (s2 === missing) && return missing
s1, s2 = reorder(s1, s2)
len1, len2 = length(s1), length(s2)
len2 == 0 && return 0.0
out = dist.dist(s1, s2) / len2
max_dist !== nothing && out > max_dist && return 1.0
return out
end
function (dist::Normalized{<:Union{Levenshtein, OptimalStringAlignement}})(s1, s2; max_dist = 1.0)
(s1 === missing) | (s2 === missing) && return missing
s1, s2 = reorder(s1, s2)
len1, len2 = length(s1), length(s2)
len2 == 0 && return 0.0
if max_dist == 1.0
d = dist.dist(s1, s2)
else
d = dist.dist(s1, s2; max_dist = ceil(Int, len2 * max_dist))
end
out = d / len2
max_dist !== nothing && out > max_dist && return 1.0
return out
end
function (dist::Normalized{<:AbstractQGramDistance})(s1, s2; max_dist = 1.0)
(s1 === missing) | (s2 === missing) && return missing
# When string length < q for qgram distance, returns s1 == s2
s1, s2 = reorder(s1, s2)
len1, len2 = length(s1), length(s2)
len1 <= dist.dist.q - 1 && return Float64(s1 != s2)
if dist.dist isa QGram
out = dist.dist(s1, s2) / (len1 + len2 - 2 * dist.dist.q + 2)
else
out = dist.dist(s1, s2)
end
max_dist !== nothing && out > max_dist && return 1.0
return out
end | [
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] | 2.459236 | 969 |
module HelloDocs
export GreetUser
#Exported function:
"""
GreetUser()
Greet User.
"""
function GreetUser()
print("Hello User!")
end
#NON-exported function:
"""
greet()
Greet World.
"""
greet() = print("Hello World!")
#NON-exported function:
"""
GreetYou()
Greet You.
"""
GreetYou() = print("Hello You!")
#
end # module
| [
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] | 2.471429 | 140 |
### A Pluto.jl notebook ###
# v0.16.0
using Markdown
using InteractiveUtils
# ╔═╡ a82f22e4-f35b-461a-b481-1dff43722e44
using StaticArrays
# ╔═╡ d42f842d-6c2a-40db-b0c4-e936244a9e7c
using BenchmarkTools
# ╔═╡ 99b5c818-a825-4939-849e-1cade802f63d
using Measurements
# ╔═╡ d6564250-f646-40de-9463-a956af1a5b1d
using ForwardDiff
# ╔═╡ d2f4d622-8e35-4be3-b421-39b28a748cab
using CellListMap
# ╔═╡ f049ab19-7ecf-4c65-bf6d-21352c1fe767
using FastPow
# ╔═╡ 7c792b6b-b6ee-4e30-88d5-d0b8064f2734
begin
using Plots
plot_font = "Computer Modern"
default(
fontfamily=plot_font,
linewidth=2, framestyle=:box, label=:none, grid=false,
size=(400,400)
)
end
# ╔═╡ febe8c06-b3aa-4db1-a3ea-fdc2a81bdebd
using Printf
# ╔═╡ a756dd18-fac6-4527-944e-c16d8cc4bf95
begin
using PlutoUI
TableOfContents()
end
# ╔═╡ a87fad48-73c1-4a08-a6f1-aae759b3c6fc
md"""
# Simulações de partículas em Julia
Leandro Martínez
Instituto de Química - Universidade de Campinas (UNICAMP)
[http://m3g.iqm.unicamp.br](http://m3g.iqm.unicamp.br) -
[https://github.com/m3g](https://github.com/m3g)
"""
# ╔═╡ 172227c2-b27a-40db-91f4-9566c2f6cf52
md"""
# Resumo
- Elementos de uma simulação de partículas
- Performance vs. uma linguagem compilada convencional (Fortran)
- Explorando o caráter genérico das funções
- Simulações diferenciáveis e ajuste de parâmetros
- Using cell lists
- Uma implementação rápida e genérica das células ligadas
- A estratégia do Packmol
- Performance vs. NAMD
- Notas
"""
# ╔═╡ 4b484cf6-4888-4f04-b3fd-94862822b0c0
md"""
# Definindo o tipo de partícula
Definiremos, em princípio, um simples vetor em um espaço bidimensional, com coordenadas `x` e `y`. O ponto será definido com a ajuda do pacote `StaticArrays`, que fornece uma interface conveniente para este tipo de variável, e sua aritmética. A estrutura na memória de vetores destes pontos é idêntica àquela de matrizes `N×M`, onde `N` é a dimensão do espaço (aqui 2D) e `M` é o número de pontos. Julia é "column-major", então este é o formato mais eficiente para este tipo de cálculo.
"""
# ╔═╡ 8c444ee4-8c77-413a-bbeb-9e5ae2428876
struct Vec2D{T} <: FieldVector{2,T}
x::T
y::T
end
# ╔═╡ a0de01b5-779a-48c0-8d61-12b02a5f527e
md"""
Por conveniência, definiremos uma função simples que retorna um vetor aletório,
dado o intervalo de coordenadas desejado:
"""
# ╔═╡ 532eb3bc-5522-4348-afa5-5336ec6752c7
md"""
Ao definir a função acima, tomamos o cuidado de escrever um código genérico para o tipo de varíavel e dimensão, de forma que não precisamos redefir esta função mais tarde, quando realizarmos simulações com diferentes tipo de estrutura de dados.
"""
# ╔═╡ dc5f7484-3dc3-47a7-ad4a-30f97fc14d11
md"""
## Força entre um par de partículas
Inicialmente, a função de energia será um potencial suave, que é zero em distâncias maiores que um raio de corte, e cresce quadraticamente para distâncias menores que este raio.
Se $d = ||\vec{y}-\vec{x}||$ é o módulo da da posição relativa das duas partículas, temos o potencial:
$$u(\vec{x},\vec{y},c)=
\begin{cases}
(d-c)^2 &\textrm{se} & d\leq c \\
0 & \textrm{se} & d > c \\
\end{cases}$$
para o qual a força é:
$$\vec{f_x}(\vec{x},\vec{y},c)=
\begin{cases}
2(d-c)\frac{(\vec{y}-\vec{x})}{d} &\textrm{se} & d\leq c \\
\vec{0} & \textrm{se} & d > c \\
\end{cases}$$
e
$$\vec{f_y} = -\vec{f_x}$$.
"""
# ╔═╡ ab3ff21b-bf82-4d8c-abd1-c27418956ed8
md"""
O pacote padrão `LinearAlgegra` de Julia fornece uma função `norm`, e não há razão par não usá-la (ainda que uma versão manual é simples de escrever e tería performance similar):
"""
# ╔═╡ 7a1db355-bba9-4322-9fb4-a6d7b7bdd60d
import LinearAlgebra: norm
# ╔═╡ cc49ef04-08d8-42bb-9170-9db64e275a51
md"""
As funções de energia e força são simples de entender:
"""
# ╔═╡ d00e56f2-9d3a-4dd3-80eb-3201eff39b96
md"""
E, para o caso unidimensional, com um raio de corte definido, será:
"""
# ╔═╡ b5c09cd3-6063-4a36-96cd-2d128aa11b82
const cutoff = 5.
# ╔═╡ 7719b317-e85b-4583-b401-a8614d4b2373
md"""
A função que calcula a força sobre o pair de partículas correrá de forma ingênua sobre todos os pares de partículas não-repetidos. A função `forces!` vai receber os parâmetros necessários, de forma que poderemos modificar a função mais tarde.
Dentro da função `forces!`, a função `force_pair` vai receber quatro parâmetros: os índices das partículas e suas posições. Vamos usar os índices mais tarde.
"""
# ╔═╡ 144119ad-ab88-4165-883a-f2fc2464a838
md"""
Vamos criar alguns pontos para explicar como a função deve ser chamada.
"""
# ╔═╡ dd9e4332-2908-40ef-b461-6b571df56cf4
md"""
A função `force_pair` será passada para a função que calcula as forças na forma de uma *closure* (função de fechamento?), que vai capturar o valor do raio de corte. A *closure* também permite que ignoremos os índices das partículas, que são parâmetros esperados pela implementação interna da função dentro de `forces!`. Por exemplo:
"""
# ╔═╡ 017cd6a8-712d-4ec5-b31f-7f1f507764f2
md"""
O terceiro argumento de `forces!` é a *closure*, que pode ser lida como:
esta é uma função que, dados `(i,j,x,y)` retorna o valor de `fₓ(x,y,cutoff)`. Então, ela é consistente com a chamada interna de `fₓ` em `forces!`, e "captura" (*closes over*) o parâmetro adicional `cutoff` que é requerido pelo cálculo.
"""
# ╔═╡ b5206dd5-1f46-4437-929b-efd68393b12b
md"""
# Realizando uma simulação de partículas
Agora, dada a função que calcula as forças, podemos realizar uma simulação de partículas. Vamos usar um algoritmo simples de integração de Euler:
1. Calcular as forças no tempo $t$ dadas as posições $x$:
$f(t) = f(x)$
2. Atualizar as posições (usando $a = f/m$):
$x(t + dt) = x(t) + v(t)dt + a(t)dt^2/2$
3. Atualizar as velocidades:
$v(t+dt) = v(t) + a(t)dt$
4. Voltar ao passo 1.
## O código que realiza esta simulação é simples:
"""
# ╔═╡ eb5dc224-1491-11ec-1cae-d51c93cd292c
function md(
x0::Vector{T},
v0::Vector{T},
mass,dt,nsteps,isave,forces!
) where T
x = copy(x0)
v = copy(v0)
a = similar(x0)
f = similar(x0)
trajectory = [ copy(x0) ] # will store the trajectory
for step in 1:nsteps
# Compute forces
forces!(f,x)
# Accelerations
@. a = f / mass
# Update positions
@. x = x + v*dt + a*dt^2/2
# Update velocities
@. v = v + a*dt
# Save if required
if mod(step,isave) == 0
println("Saved trajectory at step: ",step)
push!(trajectory,copy(x))
end
end
return trajectory
end
# ╔═╡ 594ba1d6-2dae-4f20-9546-f52fac17c2f0
md"""
Ao usar um tipo de variável paramétrico (i. e. `Vector{T}`) garantimos que teremos um erro se as posições e velocidades não possuem o mesmo tipo de elemento.
A notação `@.` é muito comum em Julia, e significa que estamos fazendo um cálculo elemento por elemento, sendo apenas uma notação compacta para loops.
"""
# ╔═╡ 66c7d960-7e05-4613-84e8-2a40fe40dc3d
md"""
## Vamos rodar a simulação!
"""
# ╔═╡ e717a8d9-ccfb-4f89-b2a2-f244f108b48d
md"""
Aqui geramos um conjunto aleatório de posições e velocidades, e vamos usar massas iguais a `1.0` para todas as partículas.
"""
# ╔═╡ eab7b195-64d5-4587-8687-48a673ab091b
md"""
## Usando condições periódicas de contorno
Nossas partículas simplesmente explodem, já que as velocidades iniciais eram aleatórias e as partículas só interagem por potenciais repulsivos.
Podemos obter uma dinâmica mais interessante se colocarmos condições periódicas de contorno. Para isso, vamos modificar a forma de calcular as forças.
"""
# ╔═╡ 34dc72dc-4864-47c0-b730-183f67e7aea3
md"""
## Imagens mínimas
A função a seguir define como a imagem mínima relativa de duas partículas, de acordo com as condições periódicas de contorno, pode ser obtida (a operação se chama *wrap* em inglês), para uma caixa cúbica de lado `side`:
"""
# ╔═╡ 02d9bf3b-708c-4293-b198-9043b334ff7e
md"""
Isto permite que calculemos a força usando:
"""
# ╔═╡ 0a5282ee-c88a-4bcc-aca2-477f28e9e04d
md"""
Noss caixa terá lado `100`:
"""
# ╔═╡ b2a4a505-47ff-40bb-9a6d-a08d91c53217
const side = 100.
# ╔═╡ fcff6973-012a-40fc-a618-f6262266287a
md"""
Para executar a simulação com as novas forças periódicas, usaremos a mesma função `md`, apenas sendo necessário passar a nova função `fₓ` na definição de uma *closure*:
"""
# ╔═╡ 14867ffd-cde5-43f8-8399-01169ee29b73
md"""
Um detalhe relevante é que a função `fₓ` tem o mesmo nome. Como ela recebe o parâmetro `side` que não está presente na primeira definição da função, o múlitplo-despacho de Julia se encarregará de escolher o método correto.
"""
# ╔═╡ c4798182-de75-4b59-8be7-f7cf1051364d
md"""
Ao fazer a animação da trajetória, estamos envolvendo as coordenadas nas condições periódicas (*wrapping*):
"""
# ╔═╡ 22fb0386-d4fa-47b9-ac31-decf2731cbc1
md"""
## Performance
"""
# ╔═╡ 8e23a3ea-3039-4a5f-b37f-c4710153938e
md"""
A perforamnance em Julia pode ser medida com a macro `@time` ou com as macros do pacote `BenchmarkTools`. A compilação das funções ocorre quando um método é chamado pela primeira vez, e `BenchmarkTools` vai automaticamente descontar o tempo de compilação e fazer uma amostragem estatisticamente mais relevante.
"""
# ╔═╡ 2a3e7257-63ad-4761-beda-cec18b91f99c
md"""
Um tempo da ordem de `200ms` e alocações de `200kiB` não parecem ruins, mas também não significam nada isoladamente. O interessante é que este código é similar em performance a códigos em linguagens compiladas convencionais, como o Fortran, como mostra [este benchmark](https://github.com/m3g/2021_FortranCon/tree/main/benchmark_vs_fortran).
"""
# ╔═╡ 49b1f040-929a-4238-acd9-6554757b592c
md"""
# Explorando implementações genéricas
## Rodando uma simulação em 3D
Não precisamos fazer muito para rodar a simulação em três dimensões. Só é necessário definir nossos vetores em 3D:
"""
# ╔═╡ 26d5c6e9-a903-4792-a0e0-dec1a2e86a01
struct Vec3D{T} <: FieldVector{3,T}
x::T
y::T
z::T
end
# ╔═╡ 2aef27bd-dea6-4a93-9d0f-b9249c9dd2cd
md"""
Com isto, podemos rodar nossa simulação em 3D:
"""
# ╔═╡ b6dcb9a3-59e3-4eae-9399-fb072c704f1a
md"""
## Propagação automática de erros
Realizer uma simulação com dimensão variável não é a aplicação mais interessante nem a mais útil da programação genérica. Podemos, por exemplo, propagar os erros nas posições das partículas, definindo um tipo de partícula que carrega consigo tanto a posição como sua incerteza acumulada.
Um pequeno exemplo de como isto pode ser definido será mostrado aqui. Primeiro, criamos um tipo de variável que contém tanto a posição como sua incerteza:
"""
# ╔═╡ e4657169-1bb2-4d4a-ac9d-adc80499d07d
struct MyMeasurement{T}
x::T
Δx::T
end
# ╔═╡ 5d395353-5681-4780-983e-902fdb89eaf2
md"""
Vamos definir uma forma customizada de escrever esta variável na tela, para que fique mais bonita:
"""
# ╔═╡ e9376a4b-3d60-42eb-8681-cd2bcec13fe8
Base.show(io::IO,m::MyMeasurement) = println(io," $(m.x) ± $(m.Δx)")
# ╔═╡ c5cdd71f-5ded-482f-9205-c13f01a14d0b
m = MyMeasurement(1.0,0.1)
# ╔═╡ a0993a1f-60a6-45b5-815e-676c49a9f049
md"""
Agora, definimos a aritmética associada a esta variável. Por exemplo, a soma de duas variáveis do tipo `MyMeasurment` soma as incertezas. Mas a subtração delas também soma as incertezas. As outras incertezas são propagadas linearmente, de acordo com a primeira derivada da operação relativamente aos valores das variáveis:
"""
# ╔═╡ 4e7f8db4-b5cc-4a3e-9fa7-e62d8f2a36ac
begin
import Base: -, +, *, /, ^, sqrt
+(m1::MyMeasurement,m2::MyMeasurement) = MyMeasurement(m1.x+m2.x,m1.Δx+m2.Δx)
-(m1::MyMeasurement,m2::MyMeasurement) = MyMeasurement(m1.x-m2.x,m1.Δx+m2.Δx)
*(α,m::MyMeasurement) = MyMeasurement(α*m.x,sign(α)*α*m.Δx)
*(m::MyMeasurement,α) = α*m
/(m::MyMeasurement,α) = inv(α)*m
sqrt(m::MyMeasurement{T}) where T = MyMeasurement(sqrt(m.x),inv(2*sqrt(m.x))*m.Δx)
^(m::MyMeasurement{T},n) where T = MyMeasurement{T}(m.x^n,n*m.x^(n-1)*m.Δx)
end
# ╔═╡ f87e4036-8f82-41c7-90c1-daa5f677488d
function random_point(::Type{PointType},range) where PointType
dim = length(PointType)
T = eltype(PointType)
p = PointType(
range[begin] + rand(T)*(range[end]-range[begin]) for _ in 1:dim
)
return p
end
# ╔═╡ df33b999-4a42-4133-bf59-5a65240790cf
function energy(x::T,y::T,cutoff) where T
Δv = y - x
d = norm(Δv)
if d > cutoff
energy = zero(T)
else
energy = (d - cutoff)^2
end
return energy
end
# ╔═╡ 0f52365d-34f4-46ed-923e-3ea31c6db0ca
function fₓ(x::T,y::T,cutoff) where T
Δv = y - x
d = norm(Δv)
if d > cutoff
fₓ = zero(T)
else
fₓ = 2*(d - cutoff)*(Δv/d)
end
return fₓ
end
# ╔═╡ f58769a6-a656-42a3-8bc6-c204d4cfd897
function forces!(f::Vector{T},x::Vector{T},fₓ::F) where {T,F}
fill!(f,zero(T))
n = length(x)
for i in 1:n-1
for j in i+1:n
fᵢ = fₓ(i,j,x[i],x[j])
f[i] += fᵢ
f[j] -= fᵢ
end
end
return f
end
# ╔═╡ beeb3335-5c49-47de-a1d3-3eef5f9479f1
function wrap(x,side)
x = rem(x,side)
if x >= side/2
x -= side
elseif x < -side/2
x += side
end
return x
end
# ╔═╡ 0967b90d-ac88-476d-a57a-7c38dfa82204
function fₓ(x::T,y::T,cutoff,side) where T
Δv = wrap.(y - x, side)
d = norm(Δv)
if d > cutoff
fₓ = zero(T)
else
fₓ = 2*(d - cutoff)*(Δv/d)
end
return fₓ
end
# ╔═╡ 36da3e92-000c-4d4b-9abf-4cd588b3a354
md"""
Com estas definições, podemos aogra operar sobre estas variáveis do tipo `MyMeasurement` propagando as incertezas automaticamente:
"""
# ╔═╡ 70eb2e0a-a5c8-4975-8f6c-589035bea29c
sqrt((2*(m + 4*m)^2/3))
# ╔═╡ b4646a29-3efd-4bd1-bffc-3575559de937
md"""
E podemos também definir vetores 2D (ou 3D) com valores e incertezas, sem ser necessário modificar nossa definição de vetores:
"""
# ╔═╡ d32743c0-fc80-406f-83c5-4528e439589a
x = Vec2D(MyMeasurement(1.0,0.1),MyMeasurement(2.0,0.2))
# ╔═╡ 98478246-5940-4828-a8f1-9c9fa990676d
md"""
As operações sobre estes vetores, agora, propagam as incertezas também:
"""
# ╔═╡ 310f247e-3fe8-4621-ae0b-b5ee38d2ee89
2*x .+ sqrt.(x)
# ╔═╡ 8267220a-f06e-4761-b310-00f8ba44e4b1
md"""
Prpagar as incertezas em cenários mais gerais requer a definição de outras regras de progração (de derivação). Ao mesmo tempo, queremos considerar a correlação entre as variáveis, o que faz das regras propagação mais complicadas e caras computacionalmente.
Felizmente, alguns pacotes foram criados para fazer esta propagação de erros em casos gerais. Aqui, usaremos o pacote `Measurements`:
"""
# ╔═╡ ce916139-221a-462e-877f-88212663c05e
md"""
### Using `Measurements`
"""
# ╔═╡ 8e2903be-4975-4e14-84ed-6e712f47fe47
md"""
Usando `Measurements` não precisamos modificar nada nos nossos códigos anteriores. Apenas precisamos redefinir o conteúdo de nossas posições e velocidades, que agoram carregarão as incertezas de cada coordenada:
"""
# ╔═╡ 418f31bb-81d5-459b-b402-4fd4e3f4ab27
md"""
Vamos redefinir apenas o que signfica gerar coordenadas iniciais aleatórias, agora com incertezas:
"""
# ╔═╡ 05402cbd-78c6-4234-8680-c351c8c37778
function random_point(::Type{Vec2D{Measurement{T}}},range,Δ) where T
p = Vec2D(
range[begin] + rand(T)*(range[end]-range[begin]) ± rand()*Δ,
range[begin] + rand(T)*(range[end]-range[begin]) ± rand()*Δ
)
return p
end
# ╔═╡ 356ac5a4-c94e-42cb-a085-0198b29c7e52
x0 = [ random_point(Vec2D{Float64},(0,100)) for _ in 1:100]
# ╔═╡ d23b4a92-055e-4ed7-bd46-8a3c59312993
f = similar(x0)
# ╔═╡ e6e29d1e-9a93-49db-a358-6b66f0bc3433
forces!(
f,
x0,
(i,j,x,y) -> fₓ(x,y,cutoff) # closure
)
# ╔═╡ 3755a4f3-1842-4de2-965e-d294c06c54c7
trajectory = md((
x0 = [random_point(Vec2D{Float64},(-50,50)) for _ in 1:100 ],
v0 = [random_point(Vec2D{Float64},(-1,1)) for _ in 1:100 ],
mass = [ 1.0 for _ in 1:100 ],
dt = 0.1,
nsteps = 1000,
isave = 10,
forces! = (f,x) -> forces!(f,x, (i,j,p1,p2) -> fₓ(p1,p2,cutoff))
)...)
# ╔═╡ 985b4ffb-7964-4b50-8c2f-e5f45f352500
trajectory_periodic = md((
x0 = [random_point(Vec2D{Float64},(-50,50)) for _ in 1:100 ],
v0 = [random_point(Vec2D{Float64},(-1,1)) for _ in 1:100 ],
mass = [ 10.0 for _ in 1:100 ],
dt = 0.1,
nsteps = 1000,
isave = 10,
forces! = (f,x) -> forces!(f,x,(i,j,p1,p2) -> fₓ(p1,p2,cutoff,side))
)...)
# ╔═╡ 1ad401b5-20b2-489b-b2aa-92f729b1d725
@benchmark md($(
x0 = [random_point(Vec2D{Float64},-50:50) for _ in 1:100 ],
v0 = [random_point(Vec2D{Float64},-1:1) for _ in 1:100 ],
mass = [ 1.0 for _ in 1:100 ],
dt = 0.1,
nsteps = 1000,
isave = 10,
forces! = (f,x) -> forces!(f,x, (i,j,p1,p2) -> fₓ(p1,p2,cutoff,side))
)...)
# ╔═╡ 0546ee2d-b62d-4c7a-8172-ba87b3c1aea4
trajectory_periodic_3D = md((
x0 = [random_point(Vec3D{Float64},-50:50) for _ in 1:100 ],
v0 = [random_point(Vec3D{Float64},-1:1) for _ in 1:100 ],
mass = [ 1.0 for _ in 1:100 ],
dt = 0.1,
nsteps = 1000,
isave = 10,
forces! = (f,x) -> forces!(f,x,(i,j,p1,p2) -> fₓ(p1,p2,cutoff,side))
)...)
# ╔═╡ 4e97f24c-c237-4117-bc57-e4e88c8fb8d2
md"""
Which generates random points carrying an initial uncertainty we defined:
"""
# ╔═╡ b31da90d-7165-42de-b18d-90584affea03
random_point(Vec2D{Measurement{Float64}},(-50,50),1e-5)
# ╔═╡ 5f37640b-ffd9-4877-a78c-a699b2671919
md"""
Dadas estas novas coordenadas e velocidades, o mesmo código de simulação pod eser usado, mas agora propagando as incertezas automaticamente:
"""
# ╔═╡ 1d6eedfd-d013-4557-9cf2-103f8fb7b72a
md"""
A trajetória, claro, é a mesma (exceto que calculamos menos passos, porque propagar os erros é caro computacionalmente):
"""
# ╔═╡ c003a61d-a434-4d7b-9214-5b52aa044248
md"""
Mas temos, agora, uma estimativa do erro de cada posição, propagado ao longo do tempo desde a incerteza inicial nas medidas:
"""
# ╔═╡ 63eb391f-0238-434a-bc3a-2fa8ed41448e
md"""
### Movimento planetário
Esta propagação de incertezas é provavelmente mais interessante de ver em um movimento planetário:
"""
# ╔═╡ 7b9bb0fd-34a5-42e1-bc35-7259447b73d0
function gravitational_force(i,j,x,y,mass)
G = 0.00049823382528 # MKm³ / (10²⁴kg days²)
dr = y - x
r = norm(dr)
return G*mass[i]*mass[j]*dr/r^3
end
# ╔═╡ 6a4e0e2e-75c5-4cab-987d-3d6b62f9bb06
md"""
Note que agora precisamos dos índices das partículas na função, porque queremos passar também a informação sobre suas massas, que são diferentes.
Um conjunto inicial de posições e velocidades de planetas é algo que precisamos obter [experimentalmente](https://nssdc.gsfc.nasa.gov/planetary/factsheet/). Aqui, a distância está mediad em $10^6$ km, e o tempo está medido em dias. Então, a velocidade está medida em MKm por dia.
A incerteza inicial nas posições será tomada como o diâmetro de cada planeta. Neste exemplo não introduziremos incertezas sobre as velocidades.
"""
# ╔═╡ c91862dd-498a-4712-8e3d-b77e088cd470
planets_x0 = [
Vec2D( 0.0 ± 1.39 , 0. ± 1.39 ), # "Sun"
Vec2D( 57.9 ± 4.879e-3, 0. ± 4.879e-3), # "Mercury"
Vec2D(108.2 ± 12.104e-3, 0. ± 12.104e-3), # "Venus"
Vec2D(149.6 ± 12.756e-3, 0. ± 12.756e-3), # "Earth"
Vec2D(227.9 ± 6.792e-3, 0. ± 6.792e-3), # "Mars"
]
# ╔═╡ a08d6e6d-ddc4-40aa-b7c4-93ea03191415
planets_v0 = [
Vec2D(0. ± 0., 0.0 ± 0.), # "Sun"
Vec2D(0. ± 0., 4.10 ± 0.), # "Mercury"
Vec2D(0. ± 0., 3.02 ± 0.), # "Venus"
Vec2D(0. ± 0., 2.57 ± 0.), # "Earth"
Vec2D(0. ± 0., 2.08 ± 0.) # "Mars"
]
# ╔═╡ a356e2cc-1cb1-457a-986c-998cf1efe008
md"""
As massas estão medidas em $10^{24}$ kg:
"""
# ╔═╡ 57141f7c-9261-4dc5-98e4-b136a15f86fc
const masses = [ 1.99e6, 0.330, 4.87, 5.97, 0.642 ]
# ╔═╡ 055e32d7-073c-40db-a267-750636b9f786
md"""
Vamos ver os planetas orbitando o sol:
"""
# ╔═╡ aaa97ce4-a5ff-4332-89a2-843cee2e5b6d
trajectory_planets = md((
x0 = planets_x0,
v0 = planets_v0,
mass = masses,
dt = 1, # days
nsteps = 2*365, # two Earth years
isave = 1, # save every day
forces! = (f,x) -> forces!(
f,x, (i,j,p1,p2) -> gravitational_force(i,j,p1,p2,masses)
)
)...)
# ╔═╡ 93697e4d-369b-48e9-8b28-a0ff58604d02
md"""
Se você se perguntou porque os erros oscilam, é porque as trajetórias são periódicas. Quando todas as trajetórias inciadas no intervalo de posições associado às incertezas se cruzam, a posição é independente da posição inicial, portanto a incerteza é nula. Em outras palavras, a derivada da posição é zero em função da posição inicial, portanto a incerteza propagada linearmente é nula.
"""
# ╔═╡ c4344e64-aa22-4328-a97a-71e44bcd289f
md"""
Uma coisa que não está bonita, no entanto, é que em dois anos a Terra não completou duas revoluções completas em torno do Sol. Alguma coisa está errada com nossos dados. Podemos melhorar isso?
"""
# ╔═╡ 827bda6f-87d4-4d36-8d89-f144f4595240
md"""
## Podemos derivar tudo!
É supreendente (ao menos para mim) que nossa simulação é completamente diferenciável. Isto quer dizer que podemos ajustar os parâmetros da simulação, e os dados, usando algoritmos de otimização que requerem derivadas analíticas.
Aqui, especulamos que o que estava errado com nossos dados era a posição inicial da Terra. Isso fez com que a órbita fosse um pouco mais lenta do que deveria ser.
Vamos definir, então, uma função objetivo que retorna o deslocament da Terra relativamente a sua posição inicial (no primeiro dia) depois de 2 anos. Nosso objetivo é que a Terra volte à sua posição inicial.
"""
# ╔═╡ 1ff4077a-4742-4c5e-a8d6-c4699469a683
md"""
Primeiro, definimos uma função que exeuta uma simulação de *dois anos* da órbita da Terra. A simulação se inicia com uma posição `x` para a Terra, que é um parâmetro desta função. Vamos ser cuidadosos em definir todas as coordenadas com o mesmo tipo de variável que a o parâmetro `x` de entrada, de forma que a função permaneça genérica e os tipos todos consistentes:
"""
# ╔═╡ 4a75498d-8f4e-406f-8b01-f6a5f153919f
function earth_orbit(x::T=149.6,nsteps=2*3650,isave=20) where T
x0 = [
Vec2D( zero(T), zero(T)), # "Sun"
Vec2D( x, zero(T)) # "Earth"
]
v0 = [
Vec2D( zero(T), zero(T)), # "Sun"
Vec2D( zero(T), 2.57*one(T)), # "Earth"
]
masses = [ 1.99e6, 5.97 ]
trajectory = md((
x0 = x0,
v0 = v0,
mass = masses,
dt = 0.1, # days
nsteps = nsteps, # one Earth year
isave = isave,
forces! = (f,x) -> forces!(f,x,
(i,j,p1,p2) -> gravitational_force(i,j,p1,p2,masses)
)
)...)
return trajectory
end
# ╔═╡ 3ae783ce-d06e-4cc2-b8a3-94512e8f1490
md"""
Agora, definimos nossa função objetivo, que é o módulo do vetor diferença entre a posição inicial e a posição final da Terra (queremos que ela volte para a posição inicial):
"""
# ╔═╡ 13e7da81-8581-4f32-9fdb-2599dd36a12c
function error_in_orbit(x::T=149.6) where T
nsteps = 2*3650
traj = earth_orbit(x,nsteps,nsteps) # Save last point only
return norm(traj[end][2]-[x,0.])
end
# ╔═╡ 4870b1f3-3134-4ddc-a59d-fa806b456a23
md"""
Podemos ver que com os nossos dados atuais o resultado tem um erro significativo:
"""
# ╔═╡ fda6171c-9675-4f2e-b226-7ccf100529cd
error_in_orbit()
# ╔═╡ a862f8a3-0131-4644-bc90-246bf3120790
md"""
Queremos minimizar este erro, e resulta que nossa simulação é completamente diferenciável. Vamos usar o pacote `ForwarDiff` de diferenciação automática:
"""
# ╔═╡ eee3ac4b-4ddb-4699-b6e6-f0ffcc562c07
md"""
Este pacote pode ser usado sem modificações para calcualr as derivadas do erro da órbita relativamente à posição inicial `x` da Terra:
"""
# ╔═╡ 107aec28-ecb5-4007-95e5-25d0a7f0c465
ForwardDiff.derivative(error_in_orbit,149.6)
# ╔═╡ 1394e4c6-c371-47c0-8ca8-f0830d63d8ec
md"""
Para minimizar o erro, escreveremos um simples algorítmo de descida. Muitos pacotes fornecem algoritmos mais sofisticados de otimização, mas aqui vamos manter o código simples também para ilustrar que é possível implementar o algoritmo em Julia:
"""
# ╔═╡ 535716e6-9c1c-4324-a4cd-b1214df3c01d
function gradient_descent(x,f,g,tol,maxtrial)
itrial = 0
step = 1.0
fx = f(x)
gx = g(x)
while (abs(gx) > tol) && (itrial < maxtrial) && (step > 1e-10)
xtrial = x - gx*step
ftrial = f(xtrial)
if ftrial > fx
step = step / 2
else
x = xtrial
fx = ftrial
gx = g(x)
step = step * 2
end
itrial += 1
end
return x, gx, itrial
end
# ╔═╡ b8edfb4e-6780-4ce7-94c1-4073ff7fa832
md"""
A derivada do erro pode ser calculada usando a função `error_in_orbit` na forma de uma *closure*:
"""
# ╔═╡ b8320f78-323c-49a9-a9f9-2748d19ecb35
error_derivative(x) = ForwardDiff.derivative(error_in_orbit,x)
# ╔═╡ 92737d73-676c-4c96-a321-831ecaf37690
md"""
Agora podemos chamar a função `gradient_descent` diretamente:
"""
# ╔═╡ 931a9c5f-8f91-4e88-956b-50c0efc9c58b
best_x0 = gradient_descent(149.6,error_in_orbit,error_derivative,1e-4,1000)
# ╔═╡ b5b96082-efde-464f-bcd4-f2e0a84befcd
md"""
O resultado é razoável: o erro na órbita diminuiu significativamente:
"""
# ╔═╡ 7658a32c-d3da-4ec9-9d96-0d30bb18f08c
error_in_orbit(best_x0[1])
# ╔═╡ e61981d5-5448-45e9-81dc-320ac87ba813
md"""
Vamos como mudou nossa trajetória com as novas condições iniciais:
"""
# ╔═╡ 31e1bb51-c531-4c4a-8634-5caafb7e9e51
earth_traj_0 = earth_orbit(149.6)
# ╔═╡ b0b81da4-6788-45c4-b618-188a02b5e09c
earth_traj_best = earth_orbit(best_x0[1])
# ╔═╡ 47c205c3-ceae-4e12-9ade-753df1608deb
md"""
O ponto azul escuro é a trajetória corrigida, e o azul claro corresponde à trajetória original. Fomos capazes de otimizar *o ponto inicial* da trajetória com um método baseado no gradiente. Este conceito pode ser usado para ajustar parâmetros de simulações de vários tipos (simulações de partículas ou equações diferenciais em geral).
"""
# ╔═╡ 826693ff-9a9b-46b1-aeb3-767a5e6f9441
md"""
# Acelerando com CellListMap.jl
"""
# ╔═╡ d231842d-9b7a-4711-b71b-5d54041ebc1f
md"""
[`CellListMap.jl`](https://m3g.github.io/CellListMap.jl/stable/) é um pacote que implemnta [células ligadas](https://en.wikipedia.org/wiki/Cell_lists) de forma eficiente. Células ligadas são uma forma prática de reduzir o custo do cálculo de propriedades que dependem de distâncias entre partículas dentro de um raio de corte (interações de curto alcance ou listas de vizinhos). O pacote tem uma interface que premite calcular qualquer propriedade dependente da distância, como energias potenciais, forças, funções de distribuição, etc. Condições periódicas de contorno triclinicas (gerais) podem ser usadas, em duas ou três dimensões.
O algorítmo de células ligadas mais simples é bastante simples. Muitas otimizações podem ser feitas, no entanto, na construção das listas e no trato das condições periódicas de contorno, minimizando o número de distâncias desnecessárias calculadas. O cálculo também pode ser paralelizado.
"""
# ╔═╡ 53cedd26-3742-4c23-a8b8-8a1f2bdfa135
md"""
## O algoritmo ingênuo é muito lento O(n²)
"""
# ╔═╡ 889f837d-2e26-4261-b276-5fd91efdda6a
md"""
Com ~1k partículas, o número de pares de partículas já é da ordem de centenas de milhares. O algoritmo ingênuo O(n²) já lento demais. Simulações típicas envolvem dezenas ou centenas de milhares de partículas.
"""
# ╔═╡ 670a01e3-82f8-4c7f-8577-852081d91ed7
md"""
Para começar, vamos simular 1000 partículas:
"""
# ╔═╡ fce1e3e0-cdf7-453f-b913-964c10fa85a6
const n_large = 1000
# ╔═╡ 69a92ac6-833c-4605-b3d0-9400e4572886
md"""
Nosso sistema anterior tinha 100 partículas em um quadrado de lado 100. Vamos manter a densidade constante:
"""
# ╔═╡ 542a9ef5-d9ee-49bd-9d31-61e28b80b5cb
const box_side = sqrt(n_large / (100/100^2))
# ╔═╡ 8bada25c-b586-42b4-851d-232ccca8a456
md"""
Só precisamos gerar novas coordenadas e rodar a simulação:
"""
# ╔═╡ 7600c6dc-769e-4c77-8526-281a1bcec079
x0_large = [ Vec2D(box_side*rand(),box_side*rand()) for _ in 1:n_large ]
# ╔═╡ 29dbc47b-3697-4fdf-8f34-890ab4d0cdae
t_naive = @elapsed trajectory_periodic_large = md((
x0 = x0_large,
v0 = [random_point(Vec2D{Float64},(-1,1)) for _ in 1:n_large ],
mass = [ 10.0 for _ in 1:n_large ],
dt = 0.1,
nsteps = 1000,
isave = 10,
forces! = (f,x) -> forces!(f,x,(i,j,p1,p2) -> fₓ(p1,p2,cutoff,box_side))
)...)
# ╔═╡ 0ee7fc18-f41f-4179-a75e-1e1d56b2db29
md"""
Tempo de execução do algoritmo ingênuo: $t_naive seconds
"""
# ╔═╡ 0d0374ed-5150-40e6-b5a4-9a344b6ca47a
md"""
## Usando células ligadas
"""
# ╔═╡ f7cf613e-be9d-4f62-a778-cc4375eb99df
md"""
Nas células ligadas, as partículas são classificadas em células antes de qualquer cálculo de distâncias. As distâncias são calculadas apenas para partículas de células vizinhas. Se o tamanho das células é muito menor que o tamanho total do sistema, o número de distâncias calculado é drasticamente reduzido.
"""
# ╔═╡ 5be87c6f-5c31-4d14-a8cb-4e63ef39d538
begin
function cell_list_picture()
function square(c,side)
x = [ c[1]-side/2, c[1]+side/2, c[1]+side/2, c[1]-side/2, c[1]-side/2]
y = [ c[2]-side/2, c[2]-side/2, c[2]+side/2, c[2]+side/2, c[2]-side/2]
return x, y
end
plt = plot()
x,y=square([5,5],2)
plot!(
plt,x,y,seriestype=[:shape],
linewidth=2,fillalpha=0.05,color="green",label=""
)
x,y=square([5,5],6)
plot!(
plt,x,y,seriestype=[:shape],
linewidth=2,fillalpha=0.05,color="orange",label=""
)
lines = collect(2:2:8)
vline!(plt,lines,color="gray",label="",style=:dash)
hline!(plt,lines,color="gray",label="",style=:dash)
px = [ 0.1 + 9.8*rand() for i in 1:100 ]
py = [ 0.1 + 9.8*rand() for i in 1:100 ]
scatter!(plt,px,py,label="",alpha=0.20,color="blue")
fontsize=8
annotate!(plt,3,3,text("(i-1,j-1)",fontsize,:Courier))
annotate!(plt,5,3,text("(i-1,j)",fontsize,:Courier))
annotate!(plt,7,3,text("(i-1,j+1)",fontsize,:Courier))
annotate!(plt,3,5,text("(i,j-1)",fontsize,:Courier))
annotate!(plt,5,5,text("(i,j)",fontsize,:Courier))
annotate!(plt,7,5,text("(i,j+1)",fontsize,:Courier))
annotate!(plt,3,7,text("(i+1,j-1)",fontsize,:Courier))
annotate!(plt,5,7,text("(i+1,j)",fontsize,:Courier))
annotate!(plt,7,7,text("(i+1,j+1)",fontsize,:Courier))
plot!(
plt,size=(400,400),
xlim=(1.3,8.7),xticks=:none,
ylim=(1.3,8.7),yticks=:none,
framestyle=:box,
xlabel="x",ylabel="y",grid=false
)
return plt
end
cell_list_picture()
end
# ╔═╡ 0c07edd3-c0a1-4f72-a16a-74badb7a6123
md"""
Para usar o `CellListMap.jl`, precisamos definir o sistema, fornecendo os dados do tamanho da caixa e o raio de corte:
"""
# ╔═╡ 4fc5ef4d-e072-41f7-aef9-b42730c8313c
box = Box([box_side,box_side],cutoff)
# ╔═╡ 19c5cc9d-8304-4e36-a3ea-a1151f28f71d
md"""
As partículas são então classificadas nas células. Partículas virtuais são criadas nas fronteiras para lidar com as condições periódicas de contorno e evitar ter que calcular imagens mínimas durante o cálculo das interações por pares:
"""
# ╔═╡ 7dcadd85-2986-4e42-aa84-67128a8f666d
cl = CellList(x0_large,box)
# ╔═╡ 0b5c6ede-bceb-499a-a9a8-3c6a75ed340a
md"""
Só precisamos implementar a função que é avaliada *se* o par de partículas está dentro do raio de corte. Esta função só será chamada nesta condição. Aqui, a função vai atualizar o vetor de forças:
"""
# ╔═╡ 91b5eac1-4799-4a72-ac6a-e2b117b787d5
function fpair_cl(x,y,i,j,d2,f,box::Box)
Δv = y - x
d = sqrt(d2)
fₓ = 2*(d - box.cutoff)*(Δv/d)
f[i] += fₓ
f[j] -= fₓ
return f
end
# ╔═╡ 0f86ab3c-29aa-472b-8194-228c736ee940
md"""
A função que calcula as forças na nossa simulação vai, então, consistir na atualização das listas de células, seguida da chamada da função `map_pairwise!` de `CellListMap.jl`, que recebe como argumento a função que atualiza a força entre um par de partículas (`fpair_cl`, aqui), além do vetor de forças que deve ser atualizado, `f`, e as propriedades do sistema. Aqui vamos executar somente a versão serial:
"""
# ╔═╡ 0b8a2292-c0d6-44e4-b560-32d9d579a008
function forces_cl!(f::Vector{T},x,box::Box,cl::CellList,fpair::F) where {T,F}
fill!(f,zero(T))
cl = UpdateCellList!(x,box,cl,parallel=false)
map_pairwise!(
(x,y,i,j,d2,f) -> fpair(x,y,i,j,d2,f,box),
f, box, cl, parallel=false
)
return f
end
# ╔═╡ d6585cca-78bf-41d1-aea3-01d9831d76cb
md"""
Agora que temos uma definição apropriada da função que calcula as forças, podemos rodar a simulação novamente:
"""
# ╔═╡ 1b7b7d48-79d2-4317-9045-5b7e7bd073e5
t_cell_lists = @elapsed trajectory_cell_lists = md((
x0 = x0_large,
v0 = [random_point(Vec2D{Float64},(-1,1)) for _ in 1:n_large ],
mass = [ 10.0 for _ in 1:n_large ],
dt = 0.1,
nsteps = 1000,
isave = 10,
forces! = (f,x) -> forces_cl!(f,x,box,cl,fpair_cl)
)...)
# ╔═╡ 3f9dad58-294c-405c-bfc4-67855bb1e825
md"""
Tempo de execução usando `CellListMap.jl`: $t_cell_lists segundos (na segunda execução, a compilação demora cerca de 2 segundos).
"""
# ╔═╡ 6d61b58f-b88f-48f4-8bdd-0bb1a8bc1c82
md"""
Mesmo para um sistema pequeno como este, a simulação foi acelerada significativamente (em terno de $(round(Int,t_naive/t_cell_lists)) vezes).
"""
# ╔═╡ 76b8695e-64dc-44bc-8938-ce22c4a9e4d0
md"""
## Minimização da energia: A estratégia do Packmol
"""
# ╔═╡ 372637ff-9305-4d45-bf6e-e6531dadbd14
md"""
### O potencial de Lennard-Jones
Simulações de dinâmica moelcular geralmente envovlem o cálculo, para cada par de átomos, de um pontecial de Lennard-Jones, que tem a forma:
$$u(r) = \varepsilon\left(\frac{\sigma^{12}}{r^{12}} - 2\frac{\sigma^6}{r^6}\right)$$
As potências altas envolvidas fazem o cálculo desta função numericamente instável. Vamos tentar minimizar a energia de um conjunto de pontos gerado aleatoriamente.
Vamos definir uma função que soma a energia de todas as contribuições de pares de partículas, e usar a função `map_pairwise!` do `CellListMap.jl` para calcular a contribuição de todos os pares de partículas mais próximas que um dado raio de corte.
"""
# ╔═╡ 3b2c08a6-b27e-49be-a0b0-e5cb3d5546e0
md"""
O pacote `FastPow` expande as potências altas em produtos de potenciais menores, que são mais rápidas de calcular (com alguma perda de precisão, que não é importante aqui). Esta expansão poderia ser feita à mão, mas para que o código fique mais claro, e por conveniência, usaremos a macro `@fastpow`.
"""
# ╔═╡ 7280e368-c68a-48a5-91fe-93c76607c144
md"""
A função que calcula a energia associada a um par de partículas é:
"""
# ╔═╡ 755fae26-6db9-45a0-a60d-d0e9c063f8aa
function ulj_pair(r2,u,ε,σ)
@fastpow u += ε*(σ^12/r2^6 - 2*σ^6/r2^3)
return u
end
# ╔═╡ 9a8d8012-ba54-4d9b-8c4c-fe6358508f2a
md"""
E a função que calcula a energia total, mapeando esta função de pares para todos os pares de partículas mais próximos que o raio de corte é, então:
"""
# ╔═╡ ffbeae5f-8aec-4473-a446-5b73bd911733
function ulj(x,ε,σ,box::Box,cl::CellList)
cl = UpdateCellList!(x,box,cl,parallel=false)
u = map_pairwise!(
(x,y,i,j,d2,u) -> ulj_pair(d2,u,ε,σ),
zero(eltype(σ)), box, cl,
parallel=false
)
return u
end
# ╔═╡ 3738e40f-9596-469d-aa58-a4b28d8a22f8
md"""
As funções correspondentes para calcular as forças são:
"""
# ╔═╡ 5f1054b8-2337-43c1-a086-26233e95d42b
function flj_pair!(x,y,i,j,r2,f,ε,σ)
@fastpow ∂u∂x = 12*ε*(σ^12/r2^7 - σ^6/r2^4)*(y-x)
f[i] -= ∂u∂x
f[j] += ∂u∂x
return f
end
# ╔═╡ bd719619-bdd4-4c3c-8d66-1df0f210c595
function flj!(f::Vector{T},x,ε,σ,box,cl) where T
cl = UpdateCellList!(x,box,cl,parallel=false)
fill!(f,zero(T))
map_pairwise!(
(x,y,i,j,d2,f) -> flj_pair!(x,y,i,j,d2,f,ε,σ),
f, box, cl,
parallel=false
)
return f
end
# ╔═╡ f289955b-0239-4b8d-ba08-2edf0a7284c2
md"""
### Um sistema físico: Um gás de Neônio
Vamos simular um sistema com algum significado físico, que é mais interessante que apenas um conjunto de pontos quaisquer.
Vamos calcular a energia de um gás de Neônio com 10 mil partículas, com densidade $\sim 1$ partículas/ų, que é aproximadamente a densidade atômica na água líquida.
Os parâmetros de Lennard-Jones para o Neônio são:
"""
# ╔═╡ 878ab5f7-28c1-4832-9c58-cb36b360766f
const ε = 0.0441795 # kcal/mol
# ╔═╡ a0dcd888-059d-4abe-bb6b-958d2879101c
const σ = 2*1.64009 # Å
# ╔═╡ b1c5b7e5-cfbf-4d93-bd79-2924d957ae14
md"""
Definindo uma densidade adequada para estas dez mil partículas,
o número de pares de partículas dentro do raio de corte é da ordem do número típico de uma simulação de dinâmica molecular convencional (estamos na verdade simulando um gás de Neônio em alta pressão).
"""
# ╔═╡ cd1102ac-1500-4d79-be83-72ac9180c7ce
const n_Ne = 10_000
# ╔═╡ f21604f4-e4f7-4d43-b3d9-32f429df443e
const box_side_Ne = (10_000/0.1)^(1/3)
# ╔═╡ 10826a95-16f8-416d-b8c1-0ef3347c9b20
x0_Ne = [random_point(Vec3D{Float64},(0,box_side_Ne)) for _ in 1:n_Ne ]
# ╔═╡ c46a4f97-78e4-42fd-82b3-4dc6ce99abac
md"""
Dadas as posições iniciais, o tamanho da caixa e o raio de corte típico de 12Å, podemos inicializar as células ligadas:
"""
# ╔═╡ 7c433791-a653-4836-91e2-084355c01d90
const box_Ne = Box([box_side_Ne for _ in 1:3],12.)
# ╔═╡ 410b9da4-7848-4385-bffc-a3d9bd03cf19
const cl_Ne = CellList(x0_Ne,box_Ne)
# ╔═╡ c08fff28-520e-40af-951c-fe3c324f67e0
md"""
### Primeiro, vamos tentar minimizar a energia
"""
# ╔═╡ eb0f9080-2523-4633-be21-3a2281a1629e
md"""
A primeira coisa que fazemos em uma simulação de dinâmica molecular é minimizar a energia do sistema para eliminar maus contatos (afastar partículas que estão muito próximas).
"""
# ╔═╡ e3cc3c77-71ad-4006-8e27-fabaa1ae9cfb
md"""
A energia obtida (após 500 passos do método do gradiente), é:
"""
# ╔═╡ 739c9a8a-13a5-4a33-a441-f5bc6cb35e82
md"""
### Empacotando os átomos
"""
# ╔═╡ 4e059cb8-6dac-450d-9f46-b3e657d9c3cf
md"""
Para empacotar os átomos, o raio de corte precisa ser da ordem dos seus raios,
em lugar do raio de corte das interações de Lennard-Jones. Então, redefinimos as listas de células com um raio de corte de σ/2. Como apenas interações de muito curto alcance serão calculadas, e a função é bem comportada, a otimização é muito rápida:
"""
# ╔═╡ 5ff9c89a-d999-4af2-8e7e-fb99d4948c36
md"""
Vamos redefinir a caixa, com o raio de corte menor:
"""
# ╔═╡ 0f2f55f6-060b-475e-bef7-eaa99da4d99f
box_pack = Box([box_side_Ne for _ in 1:3],σ/2)
# ╔═╡ 415ad590-247b-4a5d-b21e-7af4d0c17493
cl_pack = CellList(x0_Ne,box_pack)
# ╔═╡ 53c2e16e-b7f5-4df2-96f4-08402b5f8979
md"""
Já havíamos definido uma função para calcular as forças de curto alcance (`forces_cl`), mas precisamos aqui a função de "energia" associada:
"""
# ╔═╡ 16cdbc18-e846-4d0a-b7e6-87f07c0c52d9
function u_pack(x,box::Box,cl::CellList)
cl = UpdateCellList!(x,box,cl,parallel=false)
u = map_pairwise!(
(x,y,i,j,d2,u) -> begin
u += (sqrt(d2) - box.cutoff)^2 # objective function
return u
end,
0., box, cl,
parallel=false
)
return u
end
# ╔═╡ 79169f89-fedc-466b-8170-fff99b98e147
md"""
Dadas as funções de energia o gradiente das funções de empacotamente, podemos resolver o problema:
"""
# ╔═╡ b5e258cd-5542-4a4c-ae0f-91c2fee426db
md"""
É importante notar que a otimização do empacotamento convergiu para um minimizador global (a valor da função é zero):
"""
# ╔═╡ b0dc1c2b-82b7-488d-8074-1ef9f59a15e5
md"""
A energia, por outro lado, não é necessariamente baixa:
"""
# ╔═╡ 97b8b15b-75c7-4321-999d-b067ed2a04f9
md"""
Em duas dimensões, esta é a diferença entre um conjunto de pontos gerado aleatoriamente, e um conjunto de pontos obtido após a solução do problema de empacotamento:
"""
# ╔═╡ 591e6a9c-444c-471f-a56b-4dfbc9111989
md"""
Mesmo a energia total sendo alta, temos a garantia de que nenhum par de átomos está mais próximo que o raio de corte mínimo tolerável para as interações repulsivas de curto alcance. Assim, esta configuração é adequada para iniciar uma simulação.
`Packmol` resolve este problema de empacotamento de para moléculas de geometria complexa, e ao mesmo tempo permite ao usuário a especificação de diferentes restrições geométricas que podem ser usadas para definir o arranjo das espécies no espaço.
"""
# ╔═╡ 1f265576-824a-4764-a738-685554068079
md"""
## Quão rápido é `CellListMap.jl`?
Uma ideia da eficiência da implementação das células ligadas em `CellListMap.jl` pode ser obtida comparando o tempo de uma simulação do gás de Neônio, com a mesma simulação feita por um pacote estabelecido de dinâmica molecular, como o [NAMD](https://www.ks.uiuc.edu/Research/namd/).
Podemos executar a simulação usando a mesma função `md` de antes, mas com a função de energia potencial de Lennard-Jones correta:
"""
# ╔═╡ 1e099e1f-6494-419b-8517-5bded3e18aa6
md"""
# Notas
1. Muitos tipos de distribuições podem ser obtidas usando o pacote `Distributions.jl`. O caráter geral das funções em Julia permite que um pacote como este seja usado para gerar distribuições de tipos próprios, como o `Vec2D` implementado aqui.
2. A integração do nosso código `md` é a mais simples de todas. O algoritmo padrão para integração de equações de movimento em dinâmica molecular é o `Velocity-Verlet`, e é este que está implementado no código que compara a eficiência dos `CellListMap.jl` em relação ao NAMD. Muitas outras alternativas estão disponíveis no pacote `DifferentialEquations.jl`.
3. A propagação de incertezas e a diferenciabilidade da simulação de partículas precisa ser vista com um pouco de cautela. Estes sistemas são tipicamente caóticos, portanto as incetezas aumentam de forma exponencial, e as derivadas são muito instáveis. Um blog post interessante sobre a sensibilidade destes cálculos pode está disponível [aqui](https://frankschae.github.io/post/shadowing/). Novamente, o pacote `DifferentialEquations.jl` possui ferramentas mais adequadas para lidar com a otimização de parâmetros das simulações nessas circunstâncias.
"""
# ╔═╡ 10c86547-d4f4-4c3f-8906-ac18ce93f3b6
md"""
# Agradecimentos
Agradeço a [Mosè Giordano](https://giordano.github.io/) por valiosas discussões a respeito do funcionamento pacote `Measurements`, e a [Chris Foster](https://github.com/c42f) por discussões a respeito da propagação de erros nas trajetórias e outras sugestões para o notebook. Também agradeço a vários outros membros dos fórums *discourse* de Julia e Fortran que contribuiram direta ou indiretamente com este trabalho. Agradeço finalmente ao comitê organizador da FortanCon2021 pelo convite, em particular a [Milan Curcic](https://milancurcic.com) e a [Ondřej Čertík](https://github.com/certik), e ao Prof. [Mendeli Vainstein](http://www.if.ufrgs.br/posfis-antigo/index.php/br/permanentes/431-mendeli-vainstein.html) pelo convite para apresentar o seminário na UFRGS.
"""
# ╔═╡ 2871aca3-e6b4-4a2d-868a-36562e9a274c
md"""
# Configurações do notebook
"""
# ╔═╡ 2a2e9155-1c77-46fd-8502-8431573f94d0
md"""
## Configurações padrão para os gráficos
"""
# ╔═╡ b557a646-8c3c-4dc7-8788-bf98aec8c5c0
md"""
Usamos `Printf` para formatar alguns dados.
"""
# ╔═╡ 260d5753-6cc2-4137-8a2c-8d8a47585ecf
md"""
Podemos definir esta variável como `false` e não fazer nenhum gráfico.
"""
# ╔═╡ a9981931-4cc9-4d16-a6d2-34b4071a84d7
const build_plots = true
# ╔═╡ 374f239b-6470-40ed-b068-a8ecaace4f09
build_plots && begin
r = 0:0.1:1.2*cutoff
plot(layout=(1,2),size=(600,300))
plot!(r,energy.(0.,r,cutoff),xlabel="Distance",ylabel="Energy",subplot=1)
plot!(r,fₓ.(0.,r,cutoff),xlabel="Distance",ylabel="Force",subplot=2)
end
# ╔═╡ 43e6b146-ee35-40f1-b540-3da22b9e1b1b
build_plots && scatter([(x.x, x.y) for x in x0])
# ╔═╡ 505ef5ab-f131-4ab3-a723-795b5eb5dc0f
build_plots && @gif for (step,x) in pairs(trajectory)
scatter([ (p.x,p.y) for p in x ], lims=(-250,250))
annotate!(130,-210,text("step: $step",plot_font,12,:left))
end
# ╔═╡ efc586a2-0946-4dc5-ab3a-3902a811f3ad
build_plots && @gif for (step,x) in pairs(trajectory_periodic)
scatter([ wrap.((p.x,p.y),100) for p in x ], lims=(-60,60))
annotate!(25,-50,text("step: $step",plot_font,12,:left))
end
# ╔═╡ 4a498c18-406f-4437-b378-aa9fdc75b919
build_plots && @gif for x in trajectory_periodic_3D
scatter([ wrap.((p.x,p.y,p.z),100) for p in x ], lims=(-60,60))
end
# ╔═╡ d87c22d1-d595-4d43-ab1c-f28d282a3485
build_plots && ( trajectory_2D_error = md((
x0 = [random_point(Vec2D{Measurement{Float64}},(-50,50),1e-5) for _ in 1:100 ],
v0 = [random_point(Vec2D{Measurement{Float64}},(-1,1),1e-5) for _ in 1:100 ],
mass = [ 1.0 for _ in 1:100 ],
dt = 0.1,
nsteps = 100,
isave = 1,
forces! = (f,x) -> forces!(f,x, (i,j,p1,p2) -> fₓ(p1,p2,cutoff,side))
)...) )
# ╔═╡ bf0a5303-f5ce-4711-b9ee-a12ce2d8a397
build_plots && @gif for x in trajectory_2D_error
positions = [ wrap.((p.x.val,p.y.val),100) for p in x ]
scatter(positions, lims=(-60,60))
end
# ╔═╡ e24ce081-e367-4feb-8a79-66b8654a0b3a
build_plots && @gif for x in trajectory_2D_error
histogram(
[ p.x.err for p in x ],
xlabel="Uncertainty in x",ylabel="Number of points",
bins=0:1e-4:20e-4,ylims=[0,50]
)
end
# ╔═╡ 1067527e-76b7-4331-b3ab-efd72fb99dfc
build_plots && @gif for (step,x) in pairs(trajectory_planets)
colors = [ :yellow, :grey, :brown, :blue, :red ]
positions = [ (p.x.val,p.y.val) for p in x ]
xerr = [ p.x.err for p in x ]
yerr = [ p.y.err for p in x ]
scatter(positions,lims=[-250,250], markercolor=colors, xerror=xerr, yerror=yerr)
annotate!(150,-210,text(@sprintf("%5i days",step),plot_font,12))
end
# ╔═╡ 4cef9cea-1e84-42b9-bff6-b9a8b3bfe8da
build_plots && @gif for step in eachindex(earth_traj_best)
colors = [ :yellow, :blue ]
positions0 = [ (p.x,p.y) for p in earth_traj_0[step] ]
positions_best = [ (p.x,p.y) for p in earth_traj_best[step] ]
scatter(positions0,lims=[-250,250], markercolor=colors, alpha=0.5)
scatter!(positions_best,lims=[-250,250], markercolor=colors)
scatter!(
(earth_traj_best[1][2].x,earth_traj_best[1][2].y),
markercolor=:white,alpha=0.5,
markersize=10
)
annotate!(150,-210,text(@sprintf("%5i days",2*step),plot_font,12))
end
# ╔═╡ e5b557d7-0952-4409-ae4c-a0c8ce736e03
build_plots && @gif for (step,x) in pairs(trajectory_periodic_large)
scatter(
[ wrap.((p.x,p.y),box_side) for p in x ],
lims=(-1.1*box_side/2,1.1*box_side/2)
)
end
# ╔═╡ 30d2f39e-5df2-4f38-8032-e5f8492ba335
build_plots && @gif for (step,x) in pairs(trajectory_cell_lists)
scatter(
[ wrap.((p.x,p.y),box_side) for p in x ],
lims=(-1.1*box_side/2,1.1*box_side/2)
)
end
# ╔═╡ 2634feff-7442-4d8f-b8e5-c11113136980
build_plots && begin
r_lj = 2.8:0.05:8
plot(
r_lj,ulj_pair.(r_lj.^2,0.,ε,σ),
xlabel="Distance / Å", ylabel="Potential energy / kcal/mol"
)
end
# ╔═╡ b4154fb7-e0b0-4211-8490-8a8fe47cd2da
md"""
## Método do gradiente para vetores
"""
# ╔═╡ 8ac7b1bf-c958-4eb5-8376-f802b372e796
function gradient_descent!(x::Vector{T},f,g!;tol=1e-3,maxtrial=500) where T
gnorm(x) = maximum(norm(v) for v in x)
itrial = 0
step = 1.0
xtrial = similar(x)
g = fill!(similar(x),zero(T))
fx = f(x)
g = g!(g,x)
while (gnorm(g) > tol) && (itrial < maxtrial)
@. xtrial = x - step*g
ftrial = f(xtrial)
if ftrial >= fx
step = step / 2
else
x .= xtrial
fx = ftrial
g = g!(g,x)
step = step * 2
end
@show itrial, step, fx, ftrial, gnorm(g)
itrial += 1
end
return x
end
# ╔═╡ 357c6621-b2b8-4f30-ba41-ffc1ae6f031b
t_min = @elapsed x_min = gradient_descent!(
copy(x0_Ne),
(x) -> ulj(x,ε,σ,box_Ne,cl_Ne),
(g,x) -> -flj!(g,x,ε,σ,box_Ne,cl_Ne)
)
# ╔═╡ 58eb5b4b-76ad-4f7a-b86b-0494a857dca1
ulj(x_min,ε,σ,box_Ne,cl_Ne)
# ╔═╡ 574047fa-6626-4cd0-8317-32118129711e
md"""
O exemplo aqui é somente ilustrativo, mas mostra um comportamento comum: a energia depois da minimização é ainda muito alta. O custo computacional e a instabilidade numérica do potencial verdadeiro, em curtas distâncias, faz da energia uma função difícil de minimizar.
**Tempo usado na mininimzação da energia: $t_min seconds**
Por causa disto [`Packmol`](http://m3g.iqm.unicamp.br/packmol) foi desenvolvido. Primeiro, resolvemos o problema de empacotar os átomos no espaço, garantindo que a menor distância entre eles seja maior que uma tolerância mínima. Aqui, isto consiste na minimização do nosso potencial suave original:
"""
# ╔═╡ 224336e2-522c-44af-b9a1-307e2ffff0f9
t_pack = @elapsed x_pack = gradient_descent!(
copy(x0_Ne),
(x) -> u_pack(x,box_pack,cl_pack),
(g,x) -> -forces_cl!(g,x,box_pack,cl_pack,fpair_cl)
)
# ╔═╡ 06526edf-911a-4ecc-a350-6d932ca56cd5
md"""
**Tempo necessário para o empacotamento: $t_pack seconds**
"""
# ╔═╡ c48210e0-1a04-4f84-a4e2-f6b5d34a603d
u_pack(x_pack,box_pack,cl_pack)
# ╔═╡ 0471b987-f987-4656-b961-285e32d0a5e1
ulj(x_pack,ε,σ,box_Ne,cl_Ne)
# ╔═╡ 339487cd-8ee8-4d1d-984b-b4c5ff00bae3
t_Ne = @elapsed trajectory_Ne = md((
x0 = x_pack,
v0 = [random_point(Vec3D{Float64},(-1,1)) for _ in 1:n_Ne ],
mass = [ 20.179 for _ in 1:n_Ne ],
dt = 0.01,
nsteps = 100,
isave = 10,
forces! = (f,x) -> flj!(f,x,ε,σ,box_Ne,cl_Ne)
)...)
# ╔═╡ 9cb29b01-7f49-4145-96d8-c8fd971fe1c8
build_plots && @gif for x in trajectory_Ne
scatter(
[ wrap.((p.x,p.y,p.z),box_side_Ne) for p in x ],
lims=(-1.1*box_side_Ne/2,1.1*box_side_Ne/2)
)
end
# ╔═╡ ac52a71b-1138-4f1b-99c3-c174d9f09187
md"""
A simulação demorou $t_Ne seconds.
Novamente, para entender exatamente o que isto significa, temos que fazer uma comparação apropriada. [Neste benchmark](https://github.com/m3g/2021_FortranCon/tree/main/celllistmap_vs_namd) fazemos duas simulações deste mesmo gás, com condições termodinâmicas apropriadas. O algoritmo implementado em Julia é similar ao que está descrito aqui, exceto que é feita uma termalização por reescalonamento de velocidades, e o algoritmo Velocity-Verlet de integração é usado (o que aumenta a estabilidade da simulação, mas não a performance relativamente ao método simples aqui usado). Os mesmo algoritmos são usados na simulação realizada com o NAMD. O resultado do benchmark foi obtido em um laptop com 4 processadores físicos (e 8 threads).
````
NAMD:
real 1m14,049s
user 8m59,065s
sys 0m1,130s
CellListMap:
real 1m21,054s
user 7m38,053s
sys 0m2,172s
````
Esta comparação claro que pode ser questionada: `NAMD` é um pacote de simulação de propósito geral, desenvolvido para ter eficiente em simulações massivamente paralelas. O `CellListMap.jl` é um pacote para computar qualquer propriedade dependente da distância, mas por enquanto otimizado apenas para computadores com memória compartilhada, e menos processadores.
No entanto, o benchmark mostra que é possível escrever códigos de alta peformance em Julia, e que `CellListMap.jl` é uma ferramenta poderosa para a simlação ou cálculo de propriedades e análises customizadas de simulações de partículas.
Uma das aplicações deste pacote consiste no cálculo de funções de distribuição de sistemas químicos de grande complexidade estruural, em [ComplexMixtures.jl](https://m3g.github.io/ComplexMixtures.jl/stable/).
"""
# ╔═╡ b5008faf-fd43-45dd-a5a1-7f51e0b4ede5
md"""
## Conteúdo
"""
# ╔═╡ 555d1f62-b95b-4377-a8e2-9e442ee7526d
# ╔═╡ f5510c1e-9b9f-49f0-bc7e-0fd8e79a5760
md"""
## Exemplo de empacotamento em 2D
"""
# ╔═╡ 5d9a40b5-4050-47d2-9855-e9b62d56e8df
side_test = 50
# ╔═╡ 7f556f7c-cdb0-4f91-a359-2f933bbc5b68
xtest = [ random_point(Vec2D{Float64},(-side_test,side_test)) for _ in 1:1000 ]
# ╔═╡ 0fc843d2-ac4f-4717-a298-92a476223112
tol_test = 2
# ╔═╡ fc7f665b-00d9-431b-a97e-d2ff7253221a
box_test = Box([side_test,side_test],tol_test)
# ╔═╡ aadf6e48-0cbf-4973-86a1-173b6648d1df
cl_test = CellList(xtest,box_test)
# ╔═╡ 452e1ea7-98be-4910-ba6b-c0881fb251b2
x_pack_test = gradient_descent!(
copy(xtest),
(x) -> u_pack(x,box_test,cl_test),
(g,x) -> -forces_cl!(g,x,box_test,cl_test,fpair_cl)
)
# ╔═╡ d9f254dc-ae4a-40b3-b682-f8a501e10a2d
build_plots && begin
plot(layout=(1,2))
scatter!([ wrap.(Tuple(p),side_test) for p in xtest ],subplot=1)
scatter!([ wrap.(Tuple(p),side_test) for p in x_pack_test ],subplot=2)
plot!(lims=(-1.1*side_test/2,1.1*side_test/2),aspect_ratio=1,size=(800,400))
end
# ╔═╡ 00000000-0000-0000-0000-000000000001
PLUTO_PROJECT_TOML_CONTENTS = """
[deps]
BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
CellListMap = "69e1c6dd-3888-40e6-b3c8-31ac5f578864"
FastPow = "c0e83750-1142-43a8-81cf-6c956b72b4d1"
ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
Measurements = "eff96d63-e80a-5855-80a2-b1b0885c5ab7"
Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
PlutoUI = "7f904dfe-b85e-4ff6-b463-dae2292396a8"
Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
[compat]
BenchmarkTools = "~1.2.0"
CellListMap = "~0.5.20"
FastPow = "~0.1.0"
ForwardDiff = "~0.10.19"
Measurements = "~2.6.0"
Plots = "~1.22.2"
PlutoUI = "~0.7.11"
StaticArrays = "~1.2.12"
"""
# ╔═╡ 00000000-0000-0000-0000-000000000002
PLUTO_MANIFEST_TOML_CONTENTS = """
# This file is machine-generated - editing it directly is not advised
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deps = ["LinearAlgebra"]
git-tree-sha1 = "84918055d15b3114ede17ac6a7182f68870c16f7"
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[[Base64]]
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[[BenchmarkTools]]
deps = ["JSON", "Logging", "Printf", "Profile", "Statistics", "UUIDs"]
git-tree-sha1 = "61adeb0823084487000600ef8b1c00cc2474cd47"
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[[Bzip2_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "19a35467a82e236ff51bc17a3a44b69ef35185a2"
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[[Cairo_jll]]
deps = ["Artifacts", "Bzip2_jll", "Fontconfig_jll", "FreeType2_jll", "Glib_jll", "JLLWrappers", "LZO_jll", "Libdl", "Pixman_jll", "Pkg", "Xorg_libXext_jll", "Xorg_libXrender_jll", "Zlib_jll", "libpng_jll"]
git-tree-sha1 = "f2202b55d816427cd385a9a4f3ffb226bee80f99"
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[[CellListMap]]
deps = ["DocStringExtensions", "LinearAlgebra", "Parameters", "ProgressMeter", "Random", "Setfield", "StaticArrays"]
git-tree-sha1 = "6307da1063024d02438f460b98a7539fc6270298"
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[[ColorTypes]]
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[[Colors]]
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git-tree-sha1 = "31d0151f5716b655421d9d75b7fa74cc4e744df2"
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deps = ["Unicode"]
uuid = "de0858da-6303-5e67-8744-51eddeeeb8d7"
[[Profile]]
deps = ["Printf"]
uuid = "9abbd945-dff8-562f-b5e8-e1ebf5ef1b79"
[[ProgressMeter]]
deps = ["Distributed", "Printf"]
git-tree-sha1 = "afadeba63d90ff223a6a48d2009434ecee2ec9e8"
uuid = "92933f4c-e287-5a05-a399-4b506db050ca"
version = "1.7.1"
[[Qt5Base_jll]]
deps = ["Artifacts", "CompilerSupportLibraries_jll", "Fontconfig_jll", "Glib_jll", "JLLWrappers", "Libdl", "Libglvnd_jll", "OpenSSL_jll", "Pkg", "Xorg_libXext_jll", "Xorg_libxcb_jll", "Xorg_xcb_util_image_jll", "Xorg_xcb_util_keysyms_jll", "Xorg_xcb_util_renderutil_jll", "Xorg_xcb_util_wm_jll", "Zlib_jll", "xkbcommon_jll"]
git-tree-sha1 = "ad368663a5e20dbb8d6dc2fddeefe4dae0781ae8"
uuid = "ea2cea3b-5b76-57ae-a6ef-0a8af62496e1"
version = "5.15.3+0"
[[REPL]]
deps = ["InteractiveUtils", "Markdown", "Sockets", "Unicode"]
uuid = "3fa0cd96-eef1-5676-8a61-b3b8758bbffb"
[[Random]]
deps = ["Serialization"]
uuid = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
[[RecipesBase]]
git-tree-sha1 = "44a75aa7a527910ee3d1751d1f0e4148698add9e"
uuid = "3cdcf5f2-1ef4-517c-9805-6587b60abb01"
version = "1.1.2"
[[RecipesPipeline]]
deps = ["Dates", "NaNMath", "PlotUtils", "RecipesBase"]
git-tree-sha1 = "7ad0dfa8d03b7bcf8c597f59f5292801730c55b8"
uuid = "01d81517-befc-4cb6-b9ec-a95719d0359c"
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[[Reexport]]
git-tree-sha1 = "45e428421666073eab6f2da5c9d310d99bb12f9b"
uuid = "189a3867-3050-52da-a836-e630ba90ab69"
version = "1.2.2"
[[Requires]]
deps = ["UUIDs"]
git-tree-sha1 = "4036a3bd08ac7e968e27c203d45f5fff15020621"
uuid = "ae029012-a4dd-5104-9daa-d747884805df"
version = "1.1.3"
[[SHA]]
uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce"
[[Scratch]]
deps = ["Dates"]
git-tree-sha1 = "0b4b7f1393cff97c33891da2a0bf69c6ed241fda"
uuid = "6c6a2e73-6563-6170-7368-637461726353"
version = "1.1.0"
[[Serialization]]
uuid = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
[[Setfield]]
deps = ["ConstructionBase", "Future", "MacroTools", "Requires"]
git-tree-sha1 = "fca29e68c5062722b5b4435594c3d1ba557072a3"
uuid = "efcf1570-3423-57d1-acb7-fd33fddbac46"
version = "0.7.1"
[[SharedArrays]]
deps = ["Distributed", "Mmap", "Random", "Serialization"]
uuid = "1a1011a3-84de-559e-8e89-a11a2f7dc383"
[[Showoff]]
deps = ["Dates", "Grisu"]
git-tree-sha1 = "91eddf657aca81df9ae6ceb20b959ae5653ad1de"
uuid = "992d4aef-0814-514b-bc4d-f2e9a6c4116f"
version = "1.0.3"
[[Sockets]]
uuid = "6462fe0b-24de-5631-8697-dd941f90decc"
[[SortingAlgorithms]]
deps = ["DataStructures"]
git-tree-sha1 = "b3363d7460f7d098ca0912c69b082f75625d7508"
uuid = "a2af1166-a08f-5f64-846c-94a0d3cef48c"
version = "1.0.1"
[[SparseArrays]]
deps = ["LinearAlgebra", "Random"]
uuid = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
[[SpecialFunctions]]
deps = ["ChainRulesCore", "LogExpFunctions", "OpenLibm_jll", "OpenSpecFun_jll"]
git-tree-sha1 = "ad42c30a6204c74d264692e633133dcea0e8b14e"
uuid = "276daf66-3868-5448-9aa4-cd146d93841b"
version = "1.6.2"
[[StaticArrays]]
deps = ["LinearAlgebra", "Random", "Statistics"]
git-tree-sha1 = "3240808c6d463ac46f1c1cd7638375cd22abbccb"
uuid = "90137ffa-7385-5640-81b9-e52037218182"
version = "1.2.12"
[[Statistics]]
deps = ["LinearAlgebra", "SparseArrays"]
uuid = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
[[StatsAPI]]
git-tree-sha1 = "1958272568dc176a1d881acb797beb909c785510"
uuid = "82ae8749-77ed-4fe6-ae5f-f523153014b0"
version = "1.0.0"
[[StatsBase]]
deps = ["DataAPI", "DataStructures", "LinearAlgebra", "Missings", "Printf", "Random", "SortingAlgorithms", "SparseArrays", "Statistics", "StatsAPI"]
git-tree-sha1 = "8cbbc098554648c84f79a463c9ff0fd277144b6c"
uuid = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
version = "0.33.10"
[[StructArrays]]
deps = ["Adapt", "DataAPI", "StaticArrays", "Tables"]
git-tree-sha1 = "2ce41e0d042c60ecd131e9fb7154a3bfadbf50d3"
uuid = "09ab397b-f2b6-538f-b94a-2f83cf4a842a"
version = "0.6.3"
[[TOML]]
deps = ["Dates"]
uuid = "fa267f1f-6049-4f14-aa54-33bafae1ed76"
[[TableTraits]]
deps = ["IteratorInterfaceExtensions"]
git-tree-sha1 = "c06b2f539df1c6efa794486abfb6ed2022561a39"
uuid = "3783bdb8-4a98-5b6b-af9a-565f29a5fe9c"
version = "1.0.1"
[[Tables]]
deps = ["DataAPI", "DataValueInterfaces", "IteratorInterfaceExtensions", "LinearAlgebra", "TableTraits", "Test"]
git-tree-sha1 = "1162ce4a6c4b7e31e0e6b14486a6986951c73be9"
uuid = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
version = "1.5.2"
[[Tar]]
deps = ["ArgTools", "SHA"]
uuid = "a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"
[[Test]]
deps = ["InteractiveUtils", "Logging", "Random", "Serialization"]
uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
[[URIs]]
git-tree-sha1 = "97bbe755a53fe859669cd907f2d96aee8d2c1355"
uuid = "5c2747f8-b7ea-4ff2-ba2e-563bfd36b1d4"
version = "1.3.0"
[[UUIDs]]
deps = ["Random", "SHA"]
uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
[[UnPack]]
git-tree-sha1 = "387c1f73762231e86e0c9c5443ce3b4a0a9a0c2b"
uuid = "3a884ed6-31ef-47d7-9d2a-63182c4928ed"
version = "1.0.2"
[[Unicode]]
uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"
[[Wayland_jll]]
deps = ["Artifacts", "Expat_jll", "JLLWrappers", "Libdl", "Libffi_jll", "Pkg", "XML2_jll"]
git-tree-sha1 = "3e61f0b86f90dacb0bc0e73a0c5a83f6a8636e23"
uuid = "a2964d1f-97da-50d4-b82a-358c7fce9d89"
version = "1.19.0+0"
[[Wayland_protocols_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Wayland_jll"]
git-tree-sha1 = "2839f1c1296940218e35df0bbb220f2a79686670"
uuid = "2381bf8a-dfd0-557d-9999-79630e7b1b91"
version = "1.18.0+4"
[[XML2_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Libiconv_jll", "Pkg", "Zlib_jll"]
git-tree-sha1 = "1acf5bdf07aa0907e0a37d3718bb88d4b687b74a"
uuid = "02c8fc9c-b97f-50b9-bbe4-9be30ff0a78a"
version = "2.9.12+0"
[[XSLT_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Libgcrypt_jll", "Libgpg_error_jll", "Libiconv_jll", "Pkg", "XML2_jll", "Zlib_jll"]
git-tree-sha1 = "91844873c4085240b95e795f692c4cec4d805f8a"
uuid = "aed1982a-8fda-507f-9586-7b0439959a61"
version = "1.1.34+0"
[[Xorg_libX11_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxcb_jll", "Xorg_xtrans_jll"]
git-tree-sha1 = "5be649d550f3f4b95308bf0183b82e2582876527"
uuid = "4f6342f7-b3d2-589e-9d20-edeb45f2b2bc"
version = "1.6.9+4"
[[Xorg_libXau_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "4e490d5c960c314f33885790ed410ff3a94ce67e"
uuid = "0c0b7dd1-d40b-584c-a123-a41640f87eec"
version = "1.0.9+4"
[[Xorg_libXcursor_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXfixes_jll", "Xorg_libXrender_jll"]
git-tree-sha1 = "12e0eb3bc634fa2080c1c37fccf56f7c22989afd"
uuid = "935fb764-8cf2-53bf-bb30-45bb1f8bf724"
version = "1.2.0+4"
[[Xorg_libXdmcp_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "4fe47bd2247248125c428978740e18a681372dd4"
uuid = "a3789734-cfe1-5b06-b2d0-1dd0d9d62d05"
version = "1.1.3+4"
[[Xorg_libXext_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"]
git-tree-sha1 = "b7c0aa8c376b31e4852b360222848637f481f8c3"
uuid = "1082639a-0dae-5f34-9b06-72781eeb8cb3"
version = "1.3.4+4"
[[Xorg_libXfixes_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"]
git-tree-sha1 = "0e0dc7431e7a0587559f9294aeec269471c991a4"
uuid = "d091e8ba-531a-589c-9de9-94069b037ed8"
version = "5.0.3+4"
[[Xorg_libXi_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXext_jll", "Xorg_libXfixes_jll"]
git-tree-sha1 = "89b52bc2160aadc84d707093930ef0bffa641246"
uuid = "a51aa0fd-4e3c-5386-b890-e753decda492"
version = "1.7.10+4"
[[Xorg_libXinerama_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXext_jll"]
git-tree-sha1 = "26be8b1c342929259317d8b9f7b53bf2bb73b123"
uuid = "d1454406-59df-5ea1-beac-c340f2130bc3"
version = "1.1.4+4"
[[Xorg_libXrandr_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libXext_jll", "Xorg_libXrender_jll"]
git-tree-sha1 = "34cea83cb726fb58f325887bf0612c6b3fb17631"
uuid = "ec84b674-ba8e-5d96-8ba1-2a689ba10484"
version = "1.5.2+4"
[[Xorg_libXrender_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"]
git-tree-sha1 = "19560f30fd49f4d4efbe7002a1037f8c43d43b96"
uuid = "ea2f1a96-1ddc-540d-b46f-429655e07cfa"
version = "0.9.10+4"
[[Xorg_libpthread_stubs_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "6783737e45d3c59a4a4c4091f5f88cdcf0908cbb"
uuid = "14d82f49-176c-5ed1-bb49-ad3f5cbd8c74"
version = "0.1.0+3"
[[Xorg_libxcb_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "XSLT_jll", "Xorg_libXau_jll", "Xorg_libXdmcp_jll", "Xorg_libpthread_stubs_jll"]
git-tree-sha1 = "daf17f441228e7a3833846cd048892861cff16d6"
uuid = "c7cfdc94-dc32-55de-ac96-5a1b8d977c5b"
version = "1.13.0+3"
[[Xorg_libxkbfile_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libX11_jll"]
git-tree-sha1 = "926af861744212db0eb001d9e40b5d16292080b2"
uuid = "cc61e674-0454-545c-8b26-ed2c68acab7a"
version = "1.1.0+4"
[[Xorg_xcb_util_image_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "0fab0a40349ba1cba2c1da699243396ff8e94b97"
uuid = "12413925-8142-5f55-bb0e-6d7ca50bb09b"
version = "0.4.0+1"
[[Xorg_xcb_util_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxcb_jll"]
git-tree-sha1 = "e7fd7b2881fa2eaa72717420894d3938177862d1"
uuid = "2def613f-5ad1-5310-b15b-b15d46f528f5"
version = "0.4.0+1"
[[Xorg_xcb_util_keysyms_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "d1151e2c45a544f32441a567d1690e701ec89b00"
uuid = "975044d2-76e6-5fbe-bf08-97ce7c6574c7"
version = "0.4.0+1"
[[Xorg_xcb_util_renderutil_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "dfd7a8f38d4613b6a575253b3174dd991ca6183e"
uuid = "0d47668e-0667-5a69-a72c-f761630bfb7e"
version = "0.3.9+1"
[[Xorg_xcb_util_wm_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
git-tree-sha1 = "e78d10aab01a4a154142c5006ed44fd9e8e31b67"
uuid = "c22f9ab0-d5fe-5066-847c-f4bb1cd4e361"
version = "0.4.1+1"
[[Xorg_xkbcomp_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxkbfile_jll"]
git-tree-sha1 = "4bcbf660f6c2e714f87e960a171b119d06ee163b"
uuid = "35661453-b289-5fab-8a00-3d9160c6a3a4"
version = "1.4.2+4"
[[Xorg_xkeyboard_config_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xkbcomp_jll"]
git-tree-sha1 = "5c8424f8a67c3f2209646d4425f3d415fee5931d"
uuid = "33bec58e-1273-512f-9401-5d533626f822"
version = "2.27.0+4"
[[Xorg_xtrans_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "79c31e7844f6ecf779705fbc12146eb190b7d845"
uuid = "c5fb5394-a638-5e4d-96e5-b29de1b5cf10"
version = "1.4.0+3"
[[Zlib_jll]]
deps = ["Libdl"]
uuid = "83775a58-1f1d-513f-b197-d71354ab007a"
[[Zstd_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "cc4bf3fdde8b7e3e9fa0351bdeedba1cf3b7f6e6"
uuid = "3161d3a3-bdf6-5164-811a-617609db77b4"
version = "1.5.0+0"
[[libass_jll]]
deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "HarfBuzz_jll", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"]
git-tree-sha1 = "5982a94fcba20f02f42ace44b9894ee2b140fe47"
uuid = "0ac62f75-1d6f-5e53-bd7c-93b484bb37c0"
version = "0.15.1+0"
[[libfdk_aac_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "daacc84a041563f965be61859a36e17c4e4fcd55"
uuid = "f638f0a6-7fb0-5443-88ba-1cc74229b280"
version = "2.0.2+0"
[[libpng_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"]
git-tree-sha1 = "94d180a6d2b5e55e447e2d27a29ed04fe79eb30c"
uuid = "b53b4c65-9356-5827-b1ea-8c7a1a84506f"
version = "1.6.38+0"
[[libvorbis_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Ogg_jll", "Pkg"]
git-tree-sha1 = "c45f4e40e7aafe9d086379e5578947ec8b95a8fb"
uuid = "f27f6e37-5d2b-51aa-960f-b287f2bc3b7a"
version = "1.3.7+0"
[[nghttp2_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d"
[[p7zip_jll]]
deps = ["Artifacts", "Libdl"]
uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0"
[[x264_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "4fea590b89e6ec504593146bf8b988b2c00922b2"
uuid = "1270edf5-f2f9-52d2-97e9-ab00b5d0237a"
version = "2021.5.5+0"
[[x265_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "ee567a171cce03570d77ad3a43e90218e38937a9"
uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76"
version = "3.5.0+0"
[[xkbcommon_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Wayland_jll", "Wayland_protocols_jll", "Xorg_libxcb_jll", "Xorg_xkeyboard_config_jll"]
git-tree-sha1 = "ece2350174195bb31de1a63bea3a41ae1aa593b6"
uuid = "d8fb68d0-12a3-5cfd-a85a-d49703b185fd"
version = "0.9.1+5"
"""
# ╔═╡ Cell order:
# ╟─a87fad48-73c1-4a08-a6f1-aae759b3c6fc
# ╟─172227c2-b27a-40db-91f4-9566c2f6cf52
# ╟─4b484cf6-4888-4f04-b3fd-94862822b0c0
# ╠═a82f22e4-f35b-461a-b481-1dff43722e44
# ╠═8c444ee4-8c77-413a-bbeb-9e5ae2428876
# ╟─a0de01b5-779a-48c0-8d61-12b02a5f527e
# ╠═f87e4036-8f82-41c7-90c1-daa5f677488d
# ╟─532eb3bc-5522-4348-afa5-5336ec6752c7
# ╟─dc5f7484-3dc3-47a7-ad4a-30f97fc14d11
# ╟─ab3ff21b-bf82-4d8c-abd1-c27418956ed8
# ╠═7a1db355-bba9-4322-9fb4-a6d7b7bdd60d
# ╟─cc49ef04-08d8-42bb-9170-9db64e275a51
# ╠═df33b999-4a42-4133-bf59-5a65240790cf
# ╠═0f52365d-34f4-46ed-923e-3ea31c6db0ca
# ╟─d00e56f2-9d3a-4dd3-80eb-3201eff39b96
# ╠═b5c09cd3-6063-4a36-96cd-2d128aa11b82
# ╟─374f239b-6470-40ed-b068-a8ecaace4f09
# ╟─7719b317-e85b-4583-b401-a8614d4b2373
# ╠═f58769a6-a656-42a3-8bc6-c204d4cfd897
# ╟─144119ad-ab88-4165-883a-f2fc2464a838
# ╠═356ac5a4-c94e-42cb-a085-0198b29c7e52
# ╟─43e6b146-ee35-40f1-b540-3da22b9e1b1b
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# ╠═d23b4a92-055e-4ed7-bd46-8a3c59312993
# ╠═e6e29d1e-9a93-49db-a358-6b66f0bc3433
# ╟─017cd6a8-712d-4ec5-b31f-7f1f507764f2
# ╟─b5206dd5-1f46-4437-929b-efd68393b12b
# ╠═eb5dc224-1491-11ec-1cae-d51c93cd292c
# ╟─594ba1d6-2dae-4f20-9546-f52fac17c2f0
# ╟─66c7d960-7e05-4613-84e8-2a40fe40dc3d
# ╟─e717a8d9-ccfb-4f89-b2a2-f244f108b48d
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"""
Area scaling factor for a function f with partial derivatives fx, fy
* fx::Function
* fy::Function
Returns:
* [Function] : Area scaling factor at x.
"""
function af(fx, fy)
return x -> norm(fx(x)×fy(x))
end
function af(f)
# Find partial derivatives
fx(x) = ForwardDiff.derivative(z -> f([z, x[2]]), x[1])
fy(x) = ForwardDiff.derivative(z -> f([x[1], z]), x[2])
# Compute area scaling factor
return x -> norm(fx(x)×fy(x))
end
"""
Derivative of area scaling factor for a function f with derivatives
* fx::Function
* fy::Function
* fxx::Function
* fxy::Function
* fyy::Function
Returns:
* [Function] : Derivative of area scaling factor at x.
"""
function Daf(af, fx, fy, fxx, fxy, fyy)
return x -> [
(fxx(x) × fy(x) - fxy(x) × fx(x)) ⋅ (fx(x) × fy(x)) / af(x)
(fxy(x) × fy(x) - fyy(x) × fx(x)) ⋅ (fx(x) × fy(x)) / af(x)
]
end
"""
Get q-map and derivative of a given function
* f::Function
Returns:
* q::Function, Dq::Function
"""
function Qmap(f)
# Find partial derivatives
fx(x) = ForwardDiff.derivative(z -> f([z, x[2]]), x[1])
fy(x) = ForwardDiff.derivative(z -> f([x[1], z]), x[2])
# Compute area scaling factor
a = af(fx, fy)
# Return q-map and derivative
q(x) = √a(x) * f(x)
Dq(x) = ForwardDiff.jacobian(q, x)
return q, Dq
end
"""
Get q-map and derivative of a given function with derivatives
* f::Function
* fx::Function
* fy::Function
Returns:
* q::Function, Dq::Function
"""
function Qmap(f, fx, fy)
a = af(fx, fy)
q(x) = √a(x) * f(x)
Dq(x) = ForwardDiff.jacobian(q, x)
return q, Dq
end
"""
Get q-map and derivative of a given function with derivatives
* f::Function
* fx::Function
* fy::Function
* fxx::Function
* fxy::Function
* fyy::Function
Returns:
* q::Function, Dq::Function
"""
function Qmap(f, fx, fy, fxx, fxy, fyy)
a = af(fx, fy)
Da = Daf(a, fx, fy, fxx, fxy, fyy)
Df(x) = [fx(x) fy(x)]
q(x) = √a(x) * f(x)
Dq(x) = 1. / (2. * √a(x)) * f(x) * Da(x)' + √a(x) * Df(x)
return q, Dq
end
"""
Compute the centroid of the parametric surface.
* f::Function
* af:: Function
Returns:
* [Array{Float}] : Vector value of surface centre
"""
function surface_centroid(f, af)
return hcubature(x -> f(x)*af(x), [0, 0], [1, 1])[1] / hcubature(af, [0, 0], [1, 1])[1]
end
"""
Translate function so that centroid coincides with the origin.
* f::Function
* af:: Function
Returns:
* [Function] : Translated Surface
"""
function centered(f, af)
c = surface_centroid(f, af)
return x -> f(x) - c
end
"""
Compute the parametric surface area.
* f::Function
* af:: Function
Returns:
* [Float] : Surface Area
"""
function surface_area(f, af)
return hcubature(af, [0, 0], [1, 1])[1]
end
"""
Scale surface to unit area.
* f::Function
* af:: Function
Returns:
* [Function] : Scaled Surface
"""
function scale_surface(f, af)
s = surface_area(f, af)
return x -> f(x) / s
end
"""
Translate and scale surface
* f::Function
Returns:
* [Function] : Scaled Surface
"""
function normalize_surface(f)
# Find partial derivatives
fx(x) = ForwardDiff.derivative(z -> f([z, x[2]]), x[1])
fy(x) = ForwardDiff.derivative(z -> f([x[1], z]), x[2])
# Compute area scaling factor
a = af(fx, fy)
c = surface_centroid(f, a)
s = surface_area(f, a)
return x -> (f(x) - c) / s
end
"""
Translate and scale surface
* f::Function
* af:: Function
Returns:
* [Function] : Scaled Surface
"""
function normalize_surface(f, af)
c = surface_centroid(f, af)
s = surface_area(f, af)
return x -> (f(x) - c) / s
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357,
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7,
87,
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269,
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] | 2.208625 | 1,716 |
using PGFPlots
using Test
@assert success(`lualatex -v`)
using NBInclude
@nbinclude(joinpath(dirname(@__FILE__), "..", "doc", "PGFPlots.ipynb"))
| [
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using Test
using MiniCheetahRobot
using RigidBodyDynamics
@testset "MiniCheetahRobot" begin
mechanism = MiniCheetahRobot.mechanism()
@test num_velocities(mechanism) == 21
meshdir = joinpath(MiniCheetahRobot.packagepath(), "cheetah_description", "meshes")
@test isdir(meshdir)
@test count(x -> endswith(x, ".obj"), readdir(meshdir)) == 4
end
| [
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@with_kw struct rtgM2{A,B,C} <: RTGmodel
model::A = rtgM2_model()
protein_lookup = get_protein_lookup(model)
p::C = init_p(model; idx_hill_coefs=[])
u::B = init_u(model, protein_lookup)
end
"""
This model is modified version of rtgM1.
Major changes:
1. Change input to be michaelis menten
2. Change the translocation to be Michaelis menten
3. Rtg1p and Rtg3p enter nucleus separately
"""
function rtgM2_model()
rtgM2 = @reaction_network begin
#=
This model is modified version of rtgM1.
Major changes:
1. Change input to be michaelis menten
2. Change the translocation to be Michaelis menten
3. Rtg1p and Rtg3p enter nucleus separately
=#
#=
Input
=#
(1), s→s
#=
Modulation Layer
=#
(k1md*s*(k1mk+s), kn1md), Rtg2_ina_c ↔ Rtg2_act_c #[1]
(k2md, kn2md), Rtg2_act_c + Mks ↔ Rtg2Mks_c
(k3md, kn3md), Bmh + Mks ↔ BmhMks
#=
Activation Model:
=#
# NLSact: Nuclear Localization Signal (NLS) activation
(k2n + mm(BmhMks, k1, k1m)), Rtg13_a_c → Rtg13_i_c
(k2, k2n), Rtg3_i_c ↔ Rtg3_a_c
# Rtg1Binding: Rtg13 formation
(k3, kn3), Rtg1_c + Rtg3_a_c ↔ Rtg13_a_c
(k3, kn3), Rtg1_c + Rtg3_i_c ↔ Rtg13_i_c
(k3nu, kn3nu), Rtg1_n + Rtg3_a_n ↔ Rtg13_a_n #[3]
# Translocation
( mm(Rtg1_c, kt1, ktd1) , mm(Rtg1_c, kt1nu, ktd1nu)), Rtg1_c ↔ Rtg1_n #[2]
( mm(Rtg3_a_c, kt2, ktd2) , mm(Rtg3_a_n, kt2nu, ktd2nu)), Rtg3_a_c ↔ Rtg3_a_n #[2]
#(k4, kn4), Rtg13_a_c ↔ Rtg13_a_n # Deleted [2]
end k1mk k1md k1mk kn1md k2md kn2md k3md kn3md k1 k1m k2 k2n k3 kn3 k4 kn4 kt1 ktd1 kt1nu ktd1nu kt2 ktd2 kt2nu ktd2nu k3nu kn3nu
return rtgM2
end | [
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628,
220,
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220,
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374,
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] | 1.763029 | 1,017 |
using Documenter, Akagera
makedocs(
modules = [Akagera],
clean = false,
format = :html,
sitename = "Akagera.jl",
authors = "Colaholic",
pages = [
"Home" => "index.md",
"Manual" => "manual/tutorial.md",
"Library" => Any[
"lib/animator.md",
"lib/animecontainer.md"
]
],
html_canonical = "https://lcolaholicl.github.io/Akagera.j/stable"
)
deploydocs(
julia = "nightly",
repo = "github.com/lcolaholicl/Akagera.jl.git",
target = "build",
deps = nothing,
make = nothing,
)
| [
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220,
220,
787,
796,
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8,
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] | 2.024561 | 285 |
module stuff
using GaussianProcesses
end
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21412,
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274,
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] | 3.583333 | 12 |
using DiffEqDevTools, Test
@test stability_region(constructDormandPrince6(), initial_guess=-3.5) ≈ -3.95413 rtol=1e-3
@test stability_region(constructTsitourasPapakostas6(), initial_guess=-3.5) ≈ -3.95413 rtol=1e-3
@test stability_region(constructRadauIIA5(), initial_guess=12.) ≈ 11.84 rtol=1e-2
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] | 2.384 | 125 |
#=
Direct estimation of the probability of for a diffusion with X(0) = x₀ satisfying
X(T) ∈ B for the Muller potential.
=#
using Statistics
using HypothesisTests
using Printf
include("muller_setup.jl");
opts = MDOptions(n_iters=nΔt)
Random.seed!(100);
n_trials = 10^4;
n_in_set = 0;
for j in 1:n_trials
X = copy(x₀);
sample_trajectory!(X, sampler, options=opts);
global n_in_set+= f(X);
end
CI = confint(BinomialTest(n_in_set, n_trials));
@printf("95%% CI = (%g,%g)\n", CI[1], CI[2])
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] | 2.288991 | 218 |
# Copyright 2022 RelationalAI, Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import JSON3
import Tables
using RAI
using Test
@testset "api.jl" begin
include("api.jl")
end
# def output =
# 1, "foo", 3.4, :foo;
# 2, "bar", 5.6, :foo
const body = """{
"output": [{
"rel_key": {
"name": "output",
"values": [],
"keys": ["Int64", "String", "Float64", ":foo"],
"type": "RelKey"
},
"type": "Relation",
"columns": [[1, 2], ["foo", "bar"], [3.4, 5.6]]
}],
"problems": [],
"actions": [{
"name": "action1",
"type": "LabeledActionResult",
"result": {
"output": [],
"type": "QueryActionResult"}
}],
"debug_level": 0,
"aborted": false,
"type": "TransactionResult"
}
"""
@testset "JSON Response" begin
rsp = JSON3.read(body)
@test rsp.type == "TransactionResult"
@test rsp.aborted == false
@test length(rsp.problems) == 0
@test length(rsp.output) == 1
@test rsp.output[1].type == "Relation"
@test rsp.output[1].rel_key.type == "RelKey"
@test length(rsp.output[1].columns) == 3
@test length(rsp.output[1].columns[1]) == 2
@test rsp.output[1].columns[1] == [1, 2]
@test rsp.output[1].columns[2] == ["foo", "bar"]
@test rsp.output[1].columns[3] == [3.4, 5.6]
end
@testset "TransactionResult" begin
rsp = JSON3.read(body)
result = TransactionResult(rsp)
@test result.type == "TransactionResult"
@test result.aborted == false
@test length(result.problems) == 0
@test length(result.output) == 1
@test rsp.output[1].type == "Relation"
@test rsp.output[1].rel_key.type == "RelKey"
@test length(result.output[1].columns) == 3
@test length(result.output[1].columns[1]) == 2
@test result.output[1].columns[1] == [1, 2]
@test result.output[1].columns[2] == ["foo", "bar"]
@test result.output[1].columns[3] == [3.4, 5.6]
end
@testset "TransactionResult.relations" begin
rsp = JSON3.read(body)
result = TransactionResult(rsp)
@test length(result.relations) == 1
@test length(result.relations[1]) == 2
@test schema(result.relations[1]) == [Int64, String, Float64, Symbol]
@test getrow(result.relations[1], 1) == (1, "foo", 3.4, :foo)
@test getrow(result.relations[1], 2) == (2, "bar", 5.6, :foo)
@test result.relations[1][1] == (1, "foo", 3.4, :foo)
@test result.relations[1][2] == (2, "bar", 5.6, :foo)
end
# Instantiate `Relation` on the selected output.
@testset "Relation" begin
rsp = JSON3.read(body)
rel = Relation(rsp.output[1])
@test length(rel) == 2 # two rows
@test schema(rel) == [Int64, String, Float64, Symbol]
@test getrow(rel, 1) == (1, "foo", 3.4, :foo)
@test getrow(rel, 2) == (2, "bar", 5.6, :foo)
@test rel[1] == (1, "foo", 3.4, :foo)
@test rel[2] == (2, "bar", 5.6, :foo)
end
@testset "Tables.AbstractRow" begin
rsp = JSON3.read(body)
rel = Relation(rsp.output[1])
@test Tables.istable(typeof(rel))
@test Tables.rowaccess(typeof(rel))
@test Tables.rows(rel) === rel
row = first(rel)
@test eltype(rel) == typeof(row)
@test Tables.getcolumn(row, :Column1) == 1
@test Tables.getcolumn(row, 1) == 1
@test row.values == (1, "foo", 3.4, :foo)
@test row.Column1 == 1
@test row.Column2 == "foo"
@test row.Column3 == 3.4
@test row.Column4 == :foo
@test row[1] == 1
@test row[2] == "foo"
@test row[3] == 3.4
@test row[4] == :foo
@test Tables.columnnames(rel) == [:Column1, :Column2, :Column3, :Column4]
@test Tables.columnnames(row) == [:Column1, :Column2, :Column3, :Column4]
end
@testset "Tables.dictrowtable" begin
rsp = JSON3.read(body)
rel = Relation(rsp.output[1])
table = Tables.dictrowtable(rel)
row = first(table)
@test row.Column1 == 1
@test row.Column2 == "foo"
@test row.Column3 == 3.4
@test row.Column4 == :foo
end
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] | 2.330036 | 1,921 |
###### Periodic domains
abstract type PeriodicDomain{T} <: Domain{T} end
isperiodic(::PeriodicDomain) = true
canonicaldomain(::PeriodicDomain) = PeriodicSegment()
points(d::PeriodicDomain{T},n::Integer) where {T} =
fromcanonical.(Ref(d), fourierpoints(real(eltype(T)),n))
fourierpoints(n::Integer) = fourierpoints(Float64,n)
fourierpoints(::Type{T},n::Integer) where {T<:Number} = convert(T,π)*collect(0:2:2n-2)/n
function indomain(x, d::PeriodicDomain{T}) where T
y=tocanonical(d,x)
if !isapprox(fromcanonical(d,y),x)
return false
end
l=arclength(d)
if isinf(l)
abs(imag(y))<20eps(T)
else
abs(imag(y))/l<20eps(T)
end
end
first(d::PeriodicDomain) = fromcanonical(d,0)
last(d::PeriodicDomain) = fromcanonical(d,2π)
rand(d::PeriodicDomain,k...) = fromcanonical.(Ref(d),2π*rand(k...)-π)
checkpoints(d::PeriodicDomain) = fromcanonical.(Ref(d),[1.223972,3.14,5.83273484])
boundary(d::PeriodicDomain) = EmptyDomain()
for op in (:rdirichlet,:ldirichlet,:lneumann,:rneumann,:ivp,:bvp)
@eval $op(::PeriodicDomain) = error("Periodic domains do not have boundaries")
end
struct AnyPeriodicDomain <: PeriodicDomain{UnsetNumber} end
isambiguous(::AnyPeriodicDomain)=true
convert(::Type{D},::AnyDomain) where {D<:PeriodicDomain} = AnyPeriodicDomain()
include("PeriodicSegment.jl")
include("Circle.jl")
include("PeriodicLine.jl")
include("PeriodicCurve.jl")
include("Disk.jl")
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] | 2.353997 | 613 |
# This file contains functions that are related to the AMR capabilities of the DG solver
# Refine elements in the DG solver based on a list of cell_ids that should be refined
function refine!(dg::Dg1D{Eqn, MeshType, NVARS, POLYDEG}, mesh::TreeMesh,
cells_to_refine::AbstractArray{Int}) where {Eqn, MeshType, NVARS, POLYDEG}
# Return early if there is nothing to do
if isempty(cells_to_refine)
return
end
# Determine for each existing element whether it needs to be refined
needs_refinement = falses(nelements(dg.elements))
tree = mesh.tree
# The "Ref(...)" is such that we can vectorize the search but not the array that is searched
elements_to_refine = searchsortedfirst.(Ref(dg.elements.cell_ids[1:nelements(dg.elements)]),
cells_to_refine)
needs_refinement[elements_to_refine] .= true
# Retain current solution data
old_n_elements = nelements(dg.elements)
old_u = dg.elements.u
# Get new list of leaf cells
leaf_cell_ids = leaf_cells(tree)
# Initialize new elements container
elements = init_elements(leaf_cell_ids, mesh, Val(NVARS), Val(POLYDEG))
n_elements = nelements(elements)
# Loop over all elements in old container and either copy them or refine them
element_id = 1
for old_element_id in 1:old_n_elements
if needs_refinement[old_element_id]
# Refine element and store solution directly in new data structure
refine_element!(elements.u, element_id, old_u, old_element_id,
dg.amr_refine_right, dg.amr_refine_left, dg)
element_id += 2^ndims(dg)
else
# Copy old element data to new element container
@views elements.u[:, :, element_id] .= old_u[:, :, old_element_id]
element_id += 1
end
end
# Initialize new interfaces container
interfaces = init_interfaces(leaf_cell_ids, mesh, Val(NVARS), Val(POLYDEG), elements)
n_interfaces = ninterfaces(interfaces)
# Initialize boundaries
boundaries, n_boundaries_per_direction = init_boundaries(leaf_cell_ids, mesh, Val(NVARS), Val(POLYDEG), elements)
n_boundaries = nboundaries(boundaries)
# Sanity check
if isperiodic(mesh.tree)
@assert n_interfaces == 1*n_elements ("For 1D and periodic domains, n_surf must be the same as 1*n_elem")
end
# Update DG instance with new data
dg.elements = elements
dg.n_elements = n_elements
dg.n_elements_global = n_elements
dg.interfaces = interfaces
dg.n_interfaces = n_interfaces
dg.boundaries = boundaries
dg.n_boundaries = n_boundaries
dg.n_boundaries_per_direction = n_boundaries_per_direction
end
# Refine solution data u for an element, using L2 projection (interpolation)
function refine_element!(u, element_id, old_u, old_element_id,
refine_right, refine_left, dg::Dg1D)
# Store new element ids
left_id = element_id
right_id = element_id + 1
# Interpolate to left element
for i in 1:nnodes(dg)
acc = zero(get_node_vars(u, dg, i, element_id))
for k in 1:nnodes(dg)
acc += get_node_vars(old_u, dg, k, old_element_id) * refine_left[i, k]
end
set_node_vars!(u, acc, dg, i, left_id)
end
# Interpolate to right element
for i in 1:nnodes(dg)
acc = zero(get_node_vars(u, dg, i, element_id))
for k in 1:nnodes(dg)
acc += get_node_vars(old_u, dg, k, old_element_id) * refine_right[i, k]
end
set_node_vars!(u, acc, dg, i, right_id)
end
end
# Coarsen elements in the DG solver based on a list of cell_ids that should be removed
function coarsen!(dg::Dg1D{Eqn, MeshType, NVARS, POLYDEG}, mesh::TreeMesh,
child_cells_to_coarsen::AbstractArray{Int}) where {Eqn, MeshType, NVARS, POLYDEG}
# Return early if there is nothing to do
if isempty(child_cells_to_coarsen)
return
end
# Determine for each old element whether it needs to be removed
to_be_removed = falses(nelements(dg.elements))
# The "Ref(...)" is such that we can vectorize the search but not the array that is searched
elements_to_remove = searchsortedfirst.(Ref(dg.elements.cell_ids[1:nelements(dg.elements)]),
child_cells_to_coarsen)
to_be_removed[elements_to_remove] .= true
# Retain current solution data
old_n_elements = nelements(dg.elements)
old_u = dg.elements.u
# Get new list of leaf cells
leaf_cell_ids = leaf_cells(mesh.tree)
# Initialize new elements container
elements = init_elements(leaf_cell_ids, mesh, Val(NVARS), Val(POLYDEG))
n_elements = nelements(elements)
# Loop over all elements in old container and either copy them or coarsen them
skip = 0
element_id = 1
for old_element_id in 1:old_n_elements
# If skip is non-zero, we just coarsened 2^ndims elements and need to omit the following elements
if skip > 0
skip -= 1
continue
end
if to_be_removed[old_element_id]
# If an element is to be removed, sanity check if the following elements
# are also marked - otherwise there would be an error in the way the
# cells/elements are sorted
@assert all(to_be_removed[old_element_id:(old_element_id+2^ndims(dg)-1)]) "bad cell/element order"
# Coarsen elements and store solution directly in new data structure
coarsen_elements!(elements.u, element_id, old_u, old_element_id,
dg.amr_coarsen_right, dg.amr_coarsen_left, dg)
element_id += 1
skip = 2^ndims(dg) - 1
else
# Copy old element data to new element container
@views elements.u[:, :, element_id] .= old_u[:, :, old_element_id]
element_id += 1
end
end
# Initialize new interfaces container
interfaces = init_interfaces(leaf_cell_ids, mesh, Val(NVARS), Val(POLYDEG), elements)
n_interfaces = ninterfaces(interfaces)
# Initialize boundaries
boundaries, n_boundaries_per_direction = init_boundaries(leaf_cell_ids, mesh, Val(NVARS), Val(POLYDEG), elements)
n_boundaries = nboundaries(boundaries)
# Sanity check
if isperiodic(mesh.tree)
@assert n_interfaces == 1*n_elements ("For 1D and periodic domains, n_surf must be the same as 1*n_elem")
end
# Update DG instance with new data
dg.elements = elements
dg.n_elements = n_elements
dg.n_elements_global = n_elements
dg.interfaces = interfaces
dg.n_interfaces = n_interfaces
dg.boundaries = boundaries
dg.n_boundaries = n_boundaries
dg.n_boundaries_per_direction = n_boundaries_per_direction
end
# Coarsen solution data u for four elements, using L2 projection
function coarsen_elements!(u, element_id, old_u, old_element_id,
coarsen_right, coarsen_left, dg::Dg1D)
# Store old element ids
left_id = old_element_id
right_id = old_element_id + 1
for i in 1:nnodes(dg)
acc = zero(get_node_vars(u, dg, i, element_id))
# Project from left element
for k in 1:nnodes(dg)
acc += get_node_vars(old_u, dg, k, left_id) * coarsen_left[i, k]
end
# Project from right element
for k in 1:nnodes(dg)
acc += get_node_vars(old_u, dg, k, right_id) * coarsen_right[i, k]
end
# Update value
set_node_vars!(u, acc, dg, i, element_id)
end
end
# Calculate an AMR indicator value for each element/leaf cell
#
# The indicator value λ ∈ [-1,1] is ≈ -1 for cells that should be coarsened, ≈
# 0 for cells that should remain as-is, and ≈ 1 for cells that should be
# refined.
#
# Note: The implementation here implicitly assumes that we have an element for
# each leaf cell and that they are in the same order.
#
# FIXME: This is currently implemented for each test case - we need something
# appropriate that is both equation and test case independent
function calc_amr_indicator(dg::Dg1D, mesh::TreeMesh, time)
lambda = zeros(dg.n_elements)
if dg.amr_indicator === :gauss
base_level = 4
max_level = 6
threshold_high = 0.6
threshold_low = 0.1
# Iterate over all elements
for element_id in 1:dg.n_elements
# Determine target level from peak value
peak = maximum(dg.elements.u[:, :, element_id])
if peak > threshold_high
target_level = max_level
elseif peak > threshold_low
target_level = max_level - 1
else
target_level = base_level
end
# Compare target level with actual level to set indicator
cell_id = dg.elements.cell_ids[element_id]
actual_level = mesh.tree.levels[cell_id]
if actual_level < target_level
lambda[element_id] = 1.0
elseif actual_level > target_level
lambda[element_id] = -1.0
else
lambda[element_id] = 0.0
end
end
elseif dg.amr_indicator === :blast_wave
base_level = 4
max_level = 6
blending_factor_threshold = 0.01
# (Re-)initialize element variable storage for blending factor
if (!haskey(dg.element_variables, :amr_indicator_values) ||
length(dg.element_variables[:amr_indicator_values]) != dg.n_elements)
dg.element_variables[:amr_indicator_values] = Vector{Float64}(undef, dg.n_elements)
end
if (!haskey(dg.element_variables, :amr_indicator_values_tmp) ||
length(dg.element_variables[:amr_indicator_values_tmp]) != dg.n_elements)
dg.element_variables[:amr_indicator_values_tmp] = Vector{Float64}(undef, dg.n_elements)
end
alpha = dg.element_variables[:amr_indicator_values]
alpha_tmp = dg.element_variables[:amr_indicator_values_tmp]
calc_blending_factors!(alpha, alpha_tmp, dg.elements.u, dg.amr_alpha_max, dg.amr_alpha_min, dg.amr_alpha_smooth,
density_pressure, dg.thread_cache, dg)
# Iterate over all elements
for element_id in 1:dg.n_elements
if alpha[element_id] > blending_factor_threshold
target_level = max_level
else
target_level = base_level
end
# Compare target level with actual level to set indicator
cell_id = dg.elements.cell_ids[element_id]
actual_level = mesh.tree.levels[cell_id]
if actual_level < target_level
lambda[element_id] = 1.0
elseif actual_level > target_level
lambda[element_id] = -1.0
else
lambda[element_id] = 0.0
end
end
else
error("unknown AMR indicator '$(dg.amr_indicator)'")
end
return lambda
end
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# This file is a part of BitArraynge.jl, licensed under the MIT License (MIT).
println("building...")
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@doc raw"""
NelderMead(M, F [, p])
perform a nelder mead minimization problem for the cost funciton `F` on the
manifold `M`. If the initial population `p` is not given, a random set of
points is chosen.
This algorithm is adapted from the Euclidean Nelder-Mead method, see
[https://en.wikipedia.org/wiki/Nelder–Mead_method](https://en.wikipedia.org/wiki/Nelder–Mead_method)
and
[http://www.optimization-online.org/DB_FILE/2007/08/1742.pdf](http://www.optimization-online.org/DB_FILE/2007/08/1742.pdf).
# Input
* `M` – a manifold $\mathcal M$
* `F` – a cost function $F\colon\mathcal M\to\mathbb R$ to minimize
* `population` – (n+1 `random_point(M)`) an initial population of $n+1$ points, where $n$
is the dimension of the manifold `M`.
# Optional
* `stopping_criterion` – ([`StopAfterIteration`](@ref)`(2000)`) a [`StoppingCriterion`](@ref)
* `retraction` – (`exp`) a `retraction(M,x,ξ)` to use.
* `α` – (`1.`) reflection parameter ($\alpha > 0$)
* `γ` – (`2.`) expansion parameter ($\gamma$)
* `ρ` – (`1/2`) contraction parameter, $0 < \rho \leq \frac{1}{2}$,
* `σ` – (`1/2`) shrink coefficient, $0 < \sigma \leq 1$
and the ones that are passed to [`decorate_options`](@ref) for decorators.
# Output
* either `x` the last iterate or the complete options depending on the optional
keyword `return_options`, which is false by default (hence then only `x` is
returned).
"""
function NelderMead(M::MT,
F::Function,
population = [random_point(M) for i=1:(manifoldDimension(M)+1) ];
stopping_criterion::StoppingCriterion = StopAfterIteration(200000),
α = 1., γ = 2., ρ=1/2, σ = 1/2,
return_options=false,
kwargs... #collect rest
) where {MT <: Manifold,T}
p = CostProblem(M,F)
o = NelderMeadOptions(population, stopping_criterion;
α = α, γ = γ, ρ = ρ, σ = σ)
o = decorate_options(o; kwargs...)
resultO = solve(p,o)
if return_options
return resultO
else
return get_solver_result(resultO)
end
end
#
# Solver functions
#
function initialize_solver!(p::P,o::O) where {P <: CostProblem, O <: NelderMeadOptions}
# init cost and x
o.costs = get_cost.(Ref(p), o.population )
o.x = o.population[argmin(o.costs)] # select min
end
function step_solver!(p::P,o::O,iter) where {P <: CostProblem, O <: NelderMeadOptions}
m = mean(p.M, o.population)
ind = sortperm(o.costs) # reordering for cost and p, i.e. minimizer is at ind[1]
ξ =log( p.M, m, o.population[last(ind)])
# reflect last
xr = exp(p.M, m, - o.α * ξ )
Costr = get_cost(p,xr)
# is it better than the worst but not better than the best?
if Costr >= o.costs[first(ind)] && Costr < o.costs[last(ind)]
# store as last
o.population[last(ind)] = xr
o.costs[last(ind)] = Costr
end
# --- Expansion ---
if Costr < o.costs[first(ind)] # reflected is better than fist -> expand
xe = exp(p.M, m, - o.γ * o.α * ξ)
Coste = get_cost(p,xe)
if Coste < Costr # expanded successful
o.population[last(ind)] = xe
o.costs[last(ind)] = Coste
else # expansion failed but xr is still quite good -> store
o.population[last(ind)] = xr
o.costs[last(ind)] = Costr
end
end
# --- Contraction ---
if Costr > o.costs[ind[end-1]] # even worse than second worst
if Costr < o.costs[last(ind)] # but at least better tham last
# outside contraction
xc = exp(p.M, m, - o.ρ*ξ)
Costc = get_cost(p,xc)
if Costc < Costr # better than reflected -> store as last
o.population[last(ind)] = xr
o.costs[last(ind)] = Costr
end
else # even worse than last -> inside contraction
# outside contraction
xc = exp(p.M, m, o.ρ*ξ)
Costc = get_cost(p,xc)
if Costc < o.costs[last(ind)] # better than last ? -> store
o.population[last(ind)] = xr
o.costs[last(ind)] = Costr
end
end
end
# --- Shrink ---
for i=2:length(ind)
o.population[ind[i]] = shortest_geodesic(p.M, o.population[ind[1]], o.population[ind[i]], o.σ)
# update cost
o.costs[ind[i]] = get_cost(p, o.population[ind[i]])
end
# store best
o.x = o.population[ argmin(o.costs) ]
end
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] | 2.239273 | 1,981 |
# This file is auto-generated by AWSMetadata.jl
using AWS
using AWS.AWSServices: dynamodb_streams
using AWS.Compat
using AWS.UUIDs
"""
describe_stream(stream_arn)
describe_stream(stream_arn, params::Dict{String,<:Any})
Returns information about a stream, including the current status of the stream, its Amazon
Resource Name (ARN), the composition of its shards, and its corresponding DynamoDB table.
You can call DescribeStream at a maximum rate of 10 times per second. Each shard in the
stream has a SequenceNumberRange associated with it. If the SequenceNumberRange has a
StartingSequenceNumber but no EndingSequenceNumber, then the shard is still open (able to
receive more stream records). If both StartingSequenceNumber and EndingSequenceNumber are
present, then that shard is closed and can no longer receive more data.
# Arguments
- `stream_arn`: The Amazon Resource Name (ARN) for the stream.
# Optional Parameters
Optional parameters can be passed as a `params::Dict{String,<:Any}`. Valid keys are:
- `"ExclusiveStartShardId"`: The shard ID of the first item that this operation will
evaluate. Use the value that was returned for LastEvaluatedShardId in the previous
operation.
- `"Limit"`: The maximum number of shard objects to return. The upper limit is 100.
"""
function describe_stream(StreamArn; aws_config::AbstractAWSConfig=global_aws_config())
return dynamodb_streams(
"DescribeStream",
Dict{String,Any}("StreamArn" => StreamArn);
aws_config=aws_config,
feature_set=SERVICE_FEATURE_SET,
)
end
function describe_stream(
StreamArn,
params::AbstractDict{String};
aws_config::AbstractAWSConfig=global_aws_config(),
)
return dynamodb_streams(
"DescribeStream",
Dict{String,Any}(
mergewith(_merge, Dict{String,Any}("StreamArn" => StreamArn), params)
);
aws_config=aws_config,
feature_set=SERVICE_FEATURE_SET,
)
end
"""
get_records(shard_iterator)
get_records(shard_iterator, params::Dict{String,<:Any})
Retrieves the stream records from a given shard. Specify a shard iterator using the
ShardIterator parameter. The shard iterator specifies the position in the shard from which
you want to start reading stream records sequentially. If there are no stream records
available in the portion of the shard that the iterator points to, GetRecords returns an
empty list. Note that it might take multiple calls to get to a portion of the shard that
contains stream records. GetRecords can retrieve a maximum of 1 MB of data or 1000 stream
records, whichever comes first.
# Arguments
- `shard_iterator`: A shard iterator that was retrieved from a previous GetShardIterator
operation. This iterator can be used to access the stream records in this shard.
# Optional Parameters
Optional parameters can be passed as a `params::Dict{String,<:Any}`. Valid keys are:
- `"Limit"`: The maximum number of records to return from the shard. The upper limit is
1000.
"""
function get_records(ShardIterator; aws_config::AbstractAWSConfig=global_aws_config())
return dynamodb_streams(
"GetRecords",
Dict{String,Any}("ShardIterator" => ShardIterator);
aws_config=aws_config,
feature_set=SERVICE_FEATURE_SET,
)
end
function get_records(
ShardIterator,
params::AbstractDict{String};
aws_config::AbstractAWSConfig=global_aws_config(),
)
return dynamodb_streams(
"GetRecords",
Dict{String,Any}(
mergewith(_merge, Dict{String,Any}("ShardIterator" => ShardIterator), params)
);
aws_config=aws_config,
feature_set=SERVICE_FEATURE_SET,
)
end
"""
get_shard_iterator(shard_id, shard_iterator_type, stream_arn)
get_shard_iterator(shard_id, shard_iterator_type, stream_arn, params::Dict{String,<:Any})
Returns a shard iterator. A shard iterator provides information about how to retrieve the
stream records from within a shard. Use the shard iterator in a subsequent GetRecords
request to read the stream records from the shard. A shard iterator expires 15 minutes
after it is returned to the requester.
# Arguments
- `shard_id`: The identifier of the shard. The iterator will be returned for this shard ID.
- `shard_iterator_type`: Determines how the shard iterator is used to start reading stream
records from the shard: AT_SEQUENCE_NUMBER - Start reading exactly from the position
denoted by a specific sequence number. AFTER_SEQUENCE_NUMBER - Start reading right after
the position denoted by a specific sequence number. TRIM_HORIZON - Start reading at the
last (untrimmed) stream record, which is the oldest record in the shard. In DynamoDB
Streams, there is a 24 hour limit on data retention. Stream records whose age exceeds this
limit are subject to removal (trimming) from the stream. LATEST - Start reading just
after the most recent stream record in the shard, so that you always read the most recent
data in the shard.
- `stream_arn`: The Amazon Resource Name (ARN) for the stream.
# Optional Parameters
Optional parameters can be passed as a `params::Dict{String,<:Any}`. Valid keys are:
- `"SequenceNumber"`: The sequence number of a stream record in the shard from which to
start reading.
"""
function get_shard_iterator(
ShardId, ShardIteratorType, StreamArn; aws_config::AbstractAWSConfig=global_aws_config()
)
return dynamodb_streams(
"GetShardIterator",
Dict{String,Any}(
"ShardId" => ShardId,
"ShardIteratorType" => ShardIteratorType,
"StreamArn" => StreamArn,
);
aws_config=aws_config,
feature_set=SERVICE_FEATURE_SET,
)
end
function get_shard_iterator(
ShardId,
ShardIteratorType,
StreamArn,
params::AbstractDict{String};
aws_config::AbstractAWSConfig=global_aws_config(),
)
return dynamodb_streams(
"GetShardIterator",
Dict{String,Any}(
mergewith(
_merge,
Dict{String,Any}(
"ShardId" => ShardId,
"ShardIteratorType" => ShardIteratorType,
"StreamArn" => StreamArn,
),
params,
),
);
aws_config=aws_config,
feature_set=SERVICE_FEATURE_SET,
)
end
"""
list_streams()
list_streams(params::Dict{String,<:Any})
Returns an array of stream ARNs associated with the current account and endpoint. If the
TableName parameter is present, then ListStreams will return only the streams ARNs for that
table. You can call ListStreams at a maximum rate of 5 times per second.
# Optional Parameters
Optional parameters can be passed as a `params::Dict{String,<:Any}`. Valid keys are:
- `"ExclusiveStartStreamArn"`: The ARN (Amazon Resource Name) of the first item that this
operation will evaluate. Use the value that was returned for LastEvaluatedStreamArn in the
previous operation.
- `"Limit"`: The maximum number of streams to return. The upper limit is 100.
- `"TableName"`: If this parameter is provided, then only the streams associated with this
table name are returned.
"""
function list_streams(; aws_config::AbstractAWSConfig=global_aws_config())
return dynamodb_streams(
"ListStreams"; aws_config=aws_config, feature_set=SERVICE_FEATURE_SET
)
end
function list_streams(
params::AbstractDict{String}; aws_config::AbstractAWSConfig=global_aws_config()
)
return dynamodb_streams(
"ListStreams", params; aws_config=aws_config, feature_set=SERVICE_FEATURE_SET
)
end
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] | 2.885898 | 2,638 |
using DataFrames
using Query
using PyPlot
using LaTeXStrings
include("schema.jl")
include("plotting.jl")
const LEGEND_FIGURE_SIZE = (3.2, 3.5)
const PLOT_FIGURE_SIZE = (3, 2.5)
const COLOR_LIKELIHOOD_WEIGHTING_ONE_PARTICLE = "white"
const COLOR_GOLD_STANDARD = "gold"
const ANNOTATION_FONTSIZE = 11
const TITLE_FONTSIZE=13
function annotate_num_clusters_plot(data::DataFrame)
# draw filled circle for likelihood weighting with one particle
likelihood_weighting_one_particle = @from i in data begin
@where ((i.proposal_name == PRIOR_PROPOSAL_NAME) &&
(i.num_sweeps == 0) &&
(i.num_particles == 1))
@select {i.num_particles, i.num_clusters_estimate}
@collect DataFrame
end
add_filled_circle(likelihood_weighting_one_particle[COL_NUM_PARTICLES],
likelihood_weighting_one_particle[COL_NUM_CLUSTERS_ESTIMATE],
COLOR_LIKELIHOOD_WEIGHTING_ONE_PARTICLE)
# draw filled circle for gold-standard
gold_standard = @from i in data begin
@where ((i.proposal_name == OPTIMAL_PROPOSAL_NAME) &&
(i.num_sweeps == 4) &&
(i.num_particles == 100))
@select {i.num_particles, i.num_clusters_estimate}
@collect DataFrame
end
add_filled_circle(gold_standard[COL_NUM_PARTICLES],
gold_standard[COL_NUM_CLUSTERS_ESTIMATE],
COLOR_GOLD_STANDARD)
# draw arrow and text box for likelihood weighting with one particle
head = (0.07, 0.69)
base = (0.35, 0.87)
draw_arrow(base[1], base[2], head[1] - base[1], head[2] - base[2])
text(0.35, 0.9, "Appears accurate", ANNOTATION_FONTSIZE)
# draw horizontal dashed line for gold standard
xlim = get_xlim(data)
gold_standard_value = gold_standard[COL_NUM_CLUSTERS_ESTIMATE]
plt[:plot](xlim, [gold_standard_value, gold_standard_value], "k--")
end
function plot_num_clusters(data::DataFrame, fname::String, to_plot::Vector{Tuple{String,Int}})
plt[:figure](figsize=PLOT_FIGURE_SIZE)
for (proposal_name, num_sweeps) in to_plot
result = @from i in data begin
@where ((i.proposal_name == proposal_name) &&
(i.num_sweeps == num_sweeps))
@select {i.num_particles, i.num_clusters_estimate, i.num_clusters_stderr}
@collect DataFrame
end
plt[:plot](result[COL_NUM_PARTICLES], result[COL_NUM_CLUSTERS_ESTIMATE])
lower = result[COL_NUM_CLUSTERS_ESTIMATE] - result[COL_NUM_CLUSTERS_STDERR]
upper = result[COL_NUM_CLUSTERS_ESTIMATE] + result[COL_NUM_CLUSTERS_STDERR]
plt[:fill_between](result[COL_NUM_PARTICLES], lower, upper, alpha=0.3)
end
annotate_num_clusters_plot(data)
ax = plt[:gca]()
ax[:set_xscale]("log")
ax[:set_xlim](get_xlim(data))
plt[:ylabel]("Average number\nof clusters")
plt[:xlabel]("Number of particles")
plt[:title]("Heuristic diagnostic", fontsize=TITLE_FONTSIZE)
plt[:tight_layout](pad=0)
plt[:savefig](fname)
end
function annotate_aide_plot(data::DataFrame)
# draw filled circle for likelihood weighting with one particle
likelihood_weighting_one_particle = @from i in data begin
@where ((i.proposal_name == PRIOR_PROPOSAL_NAME) &&
(i.num_sweeps == 0) &&
(i.num_particles == 1))
@select {i.num_particles, i.aide_estimate}
@collect DataFrame
end
add_filled_circle(likelihood_weighting_one_particle[COL_NUM_PARTICLES],
likelihood_weighting_one_particle[COL_AIDE_ESTIMATE],
COLOR_LIKELIHOOD_WEIGHTING_ONE_PARTICLE)
# draw arrow and text box for likelihood weighting with one particle
head = (0.07, 0.93)
base = (0.23, 0.9)
draw_arrow(base[1], base[2], head[1] - base[1], head[2] - base[2])
text(0.25, 0.7, "Likelihood weighting\nwith 1 particle appears\nleast accurate", ANNOTATION_FONTSIZE)
# draw horizontal dashed line for zero KL divergence
xlim = get_xlim(data)
plt[:plot](xlim, [0., 0.], "k--")
end
function plot_aide(data::DataFrame, fname::String, to_plot::Vector{Tuple{String,Int}})
plt[:figure](figsize=PLOT_FIGURE_SIZE)
for (proposal_name, num_sweeps) in to_plot
result = @from i in data begin
@where ((i.proposal_name == proposal_name) &&
(i.num_sweeps == num_sweeps))
@select {i.num_particles, i.aide_estimate, i.aide_stderr}
@collect DataFrame
end
plt[:plot](result[COL_NUM_PARTICLES], result[COL_AIDE_ESTIMATE])
lower = result[COL_AIDE_ESTIMATE] - result[COL_AIDE_STDERR]
upper = result[COL_AIDE_ESTIMATE] + result[COL_AIDE_STDERR]
plt[:fill_between](result[COL_NUM_PARTICLES], lower, upper, alpha=0.3)
end
annotate_aide_plot(data)
ax = plt[:gca]()
ax[:set_xscale]("log")
ax[:set_xlim](get_xlim(data))
plt[:ylabel]("nats")
plt[:xlabel]("Number of particles")
plt[:title]("AIDE estimates", fontsize=TITLE_FONTSIZE)
plt[:tight_layout](pad=0)
plt[:savefig](fname)
end
function plot_legend(data::DataFrame, fname::String)
plt[:figure](figsize=LEGEND_FIGURE_SIZE)
plt[:plot]([], [], label="SMC, prior proposal\n0 rejuvenation sweeps")
plt[:plot]([], [], label="SMC, optimal proposal\n0 rejuvenation sweeps")
plt[:plot]([], [], label="SMC, optimal proposal\n4 rejuvenation sweeps")
plt[:scatter]([], [], color="gold", s=70, edgecolor="black", label="Gold-standard")
plt[:scatter]([], [], color="white", s=70, edgecolor="black", label="Likelihood-weighting\n(1 particle)")
plt[:legend](fontsize=13, ncol=1, frameon=false, borderpad=0, borderaxespad=0, loc="lower left", labelspacing=2)
plt[:axis]("off")
plt[:tight_layout](pad=0)
plt[:savefig](fname)
end
data_dir = "data"
plot_dir = "plots"
if !Base.Filesystem.isdir(plot_dir)
Base.Filesystem.mkdir(plot_dir)
end
data = readtable("$data_dir/sampler_data.csv")
# which proposals, and which number of sweeps to plot, as a function of number
# of particles
to_plot = [
(PRIOR_PROPOSAL_NAME, 0),
(OPTIMAL_PROPOSAL_NAME, 0),
(OPTIMAL_PROPOSAL_NAME, 4)]
plot_aide(data, "$plot_dir/aide.pdf", to_plot)
plot_num_clusters(data, "$plot_dir/num_clusters.pdf", to_plot)
plot_legend(data, "$plot_dir/legend.pdf")
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] | 2.180344 | 2,961 |
#****************************************************************************
# Molecular Dynamics Potentials (MDP)
# CESMIX-MIT Project
#
# Contributing authors: Ngoc-Cuong Nguyen (cuongng@mit.edu, exapde@gmail.com)
#****************************************************************************
function Singlea(xi, qi, ti, mu, eta, kappa)
u = 0.0;
return u;
end
# one-body bonded potentials
function Singleb(xi, qi, ti, mu, eta, kappa)
u = 0.0;
return u;
end
# two-body non-bonded potentials
function Paira(xij, qi, qj, ti, tj, mu, eta, kappa)
#u = 0.0;
r2 = xij[1]*xij[1] + xij[2]*xij[2] + xij[3]*xij[3];
r = sqrt(r2);
r3 = r2*r; r4 = r2*r2; r5 = r4*r; r6 = r2*r4;
r7 = r6*r; r12 = r6*r6;
# shitfted Lennard-Jones potential
R = eta[1]; # cut-off radius for shitfted Lennard-Jones potential
R2 = R*R;
R4 = R2*R2;
R6 = R4*R2;
R12 = R6*R6;
A = mu[1];
B = mu[2];
uLJ = (A/r12 - B/r6) - (A/R12 - B/R6);
# shielded Coulomb potential (cf. ReaxFF)
Rcut = eta[2]; # cut-off radius for Coulomb potential
tap0 = 1;
tap4 = -35/(Rcut^4);
tap5 = 84/(Rcut^5);
tap6 = -70/(Rcut^6);
tap7 = 20/(Rcut^7);
tap = tap0 + tap4*r4 + tap5*r5 + tap6*r6 + tap7*r7;
gamma = mu[3];
C = mu[4];
rs = (r3 + (1/gamma)^3)^(1/3);
uC = tap*C*qi[1]*qj[1]/rs;
# shielded Morse potential (cf. ReaxFF)
Rcut = eta[3]; # cut-off radius for shielded Morse potential
tap0 = 1;
tap4 = -35/(Rcut^4);
tap5 = 84/(Rcut^5);
tap6 = -70/(Rcut^6);
tap7 = 20/(Rcut^7);
tap = tap0 + tap4*r4 + tap5*r5 + tap6*r6 + tap7*r7;
gamma = mu[5];
D = mu[6];
a = mu[7];
re = mu[8];
p = mu[9];
rs = (r^p + (1/gamma)^p)^(1.0/p);
tm = a*(1 - rs/re);
uM = tap*D*(exp(tm) - 2*exp(0.5*tm));
u = [uLJ uC uM];
return u;
end
# two-body bonded potentials
function Pairb(xij, qi, qj, ti, tj, mu, eta, kappa)
#u = 0.0;
r2 = xij[1]*xij[1] + xij[2]*xij[2] + xij[3]*xij[3];
r = sqrt(r2);
# bonded Morse potential
D = mu[1];
a = mu[2];
rc = mu[3];
tm = 2*a*(rc - r);
uM = D*(exp(tm) - 2*exp(tm));
# bonded harmonic potential
K = mu[4];
r0 = mu[5];
rc = mu[6];
uH = K*((r-r0)^2 - (rc-r0)^2);
u = [uM uH];
return u;
end
# two-body bond order potentials (EAM-like potentials)
function Pairc(xij, qi, qj, ti, tj, rho, mu, eta, kappa)
#u = 0.0;
r2 = xij[1]*xij[1] + xij[2]*xij[2] + xij[3]*xij[3];
r = sqrt(r2);
# EAM Potential
a1 = mu[1];
m1 = mu[2];
a2 = mu[3];
m2 = mu[4];
rhoaa = (a1/r)^m1;
rhoab = (a2/r)^m2;
Fa = -sqrt(rho[1]);
ua = [rhoaa; rhoab; Fa];
u = [ua ua];
return u;
end
# three-body non-bonded potentials
function Tripleta(xij, xik, qi, qj, qk, ti, tj, tk, mu, eta, kappa)
u = 0.0;
return u;
end
# three-body bonded potentials
function Tripletb(xij, xik, qi, qj, qk, ti, tj, tk, mu, eta, kappa)
#u = 0.0;
xdot = xij[1]*xik[1] + xij[2]*xik[2] + xij[3]*xik[3];
rij2 = xij[1]*xij[1] + xij[2]*xij[2] + xij[3]*xij[3];
rik2 = xik[1]*xik[1] + xik[2]*xik[2] + xik[3]*xik[3];
rij = sqrt(rij2); # distance between atom i and atom j
rik = sqrt(rik2); # distance between atom i and atom k
# cos(theta), where theta is the angle between xij and xik
costhe = xdot/(rij*rik);
rjk2 = rij2 + rik2 - 2*rij*rik*costhe;
rjk = sqrt(rjk2); # distance between atom j and atom k
u1 = mu[1]*(costhe - mu[2])^2;
u2 = u1 + mu[3]*(mu[4] - rjk)^2;
u = [u1 u2];
return u;
end
# three-body bond porder potentials (Terssof-like potentials)
function Tripletc(xij, xik, qi, qj, qk, ti, tj, tk, rho, mu, eta, kappa)
#u = 0.0;
xdot = xij[1]*xik[1] + xij[2]*xik[2] + xij[3]*xik[3];
rij2 = xij[1]*xij[1] + xij[2]*xij[2] + xij[3]*xij[3];
rik2 = xik[1]*xik[1] + xik[2]*xik[2] + xik[3]*xik[3];
rij = sqrt(rij2); # distance between atom i and atom j
rik = sqrt(rik2); # distance between atom i and atom k
# cos(theta), where theta is the angle between xij and xik
costhe = xdot/(rij*rik);
B = mu[1];
lambda2 = mu[2];
lambda3 = mu[3];
gamma = mu[4];
c = mu[5];
d = mu[6];
cos0 = mu[7];
m = mu[8];
n = mu[9];
beta = mu[10];
fA = -B*exp(-lambda2*rij);
fB = gamma*(1 + c^2/d^2 - c^2/(d^2 + (costhe-cos0)^2))*exp(lambda3^m*(rij - rik)^m);
fC = gamma*(1 + c^2/d^2 - c^2/(d^2 + (costhe-cos0)^2))*exp(lambda3^m*(rij - rik)^m);
fD = (1 + beta^n*rho[1]^n)^(-1.0/(2*n));
u = [fB; fA; fD];
u = reshape(u,(3,1));
return u;
end
# four-body non-bonded potentials
function Quadrupleta(xij, xik, xil, qi, qj, qk, ql, ti, tj, tk, tl, mu, eta, kappa)
u = 0.0;
return u;
end
# four-body bonded potentials
function Quadrupletb(xij, xik, xil, qi, qj, qk, ql, ti, tj, tk, tl, mu, eta, kappa)
#u = 0.0;
# calculate the distance d from atom i to a plane defined by 3 atoms (j, k, l)
xjk = xik - xij;
xjl = xil - xij;
n1 = xjk[2]*xjl[3] - xjk[3]*xjl[2];
n2 = xjk[3]*xjl[1] - xjk[1]*xjl[3];
n3 = xjk[1]*xjl[2] - xjk[2]*xjl[1];
nn = sqrt(n1*n1 + n2*n2 + n3*n3);
d = (n1*xij[1] + n2*xij[2] + n3*xij[3])/nn;
u = mu[1]*d^2 + mu[2]*d^4;
return u;
end
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19,
26,
198,
220,
220,
220,
1441,
334,
26,
198,
437,
628
] | 1.726937 | 3,252 |
using Test
using Liga
include("testobjects.jl")
include("testoperations.jl")
| [
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198
] | 3.16 | 25 |
abstract type AbstractPhspIterator end
abstract type AbstractParticle end
abstract type AbstractIAEAParticle <: AbstractParticle end
abstract type Format end
struct FormatEGS <: Format end
struct FormatIAEA <: Format end
Format(p::AbstractIAEAParticle) = FormatIAEA()
Base.length(iter::AbstractPhspIterator) = iter.length
function Base.iterate(iter::AbstractPhspIterator)
seekstart(iter.io)
_iterate(iter)
end
function Base.iterate(iter::AbstractPhspIterator, state)
_iterate(iter)
end
@inline function _iterate(iter::AbstractPhspIterator)
io = iter.io
h = iter.header
P = ptype(h)
if eof(iter.io)
nothing
else
p = read_particle(io, h)
dummy_state = nothing
p, dummy_state
end
end
function Base.IteratorSize(iter::AbstractPhspIterator)
Base.HasLength()
end
function Base.IteratorSize(iter::Base.Iterators.Take{<:AbstractPhspIterator})
Base.IteratorSize(iter.xs)
end
function Base.close(iter::AbstractPhspIterator)
close(iter.io)
end
"""
ptype(h)
Return type of particles from header.
"""
function ptype end
ptype(T::Type) = error("$T has no ptype")
ptype(h) = ptype(typeof(h))
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] | 2.718605 | 430 |
if basename(pwd()) == "aoc"
cd("2017/23")
end
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] | 2.04 | 25 |
"""
find(registry, [; col = filter, ...])
Find entries in a registry that match a set of filters given as keyword
arguments. The keyword name must correspond to a field in `registry`,
while the value is a filter that is checked against each field value.
See [how to use registries](/documents/docs/using.md) for examples.
"""
function find(registry::Registry; filters...)
mask = [matchesentry(getfield(registry, :fields), row; filters...)
for row in getfield(registry, :data)]
return view(registry, mask)
end
Base.filter(registry::Registry; kwargs...) = find(registry; kwargs...)
function Base.findfirst(registry::Registry; filters...)
for row in getfield(registry, :data)
if matchesentry(getfield(registry, :fields), row; filters...)
return RegistryEntry(row, registry)
end
end
nothing
end
function matchesentry(fields, row; filters...)
return all(matchesentry(fields[col], row[col], f) for (col, f) in filters)
end
# The default for any function is to match against a value
matchesentry(f::Field, value, query) = isnothing(f.filterfn) ? value == query : f.filterfn(value, query)
matchesentry(field::Field, value::Missing, ::typeof(ismissing)) = true
matchesentry(field::Field, value::Missing, _) = false
# Passing a function as a query uses it as predicate
matchesentry(::Field, value, query::Function) = query(value)
matchesentry(::Field{<:Function}, value::Function, query::Function) = query(value)
# For strings, do fuzzy matching and allow regex
matchesentry(::Field, str::String, query::String) = occursin(query, str)
matchesentry(::Field, str::String, query::Regex) = match(query, str) !== nothing
# TODO: efficient `Base.findfirst`
@testset "find" begin
function testregistry()
registry = Registry((
id = Field(String, name="ID"),
name = Field(String, name="name"),
),
name = "Test registry")
push!(registry, (id = "id1", name = "name1"))
push!(registry, (id = "id2", name = "name2"))
return registry
end
@test_nowarn testregistry()
registry = testregistry()
@test length(find(registry, id = "id1")) == 1
@test length(find(registry, id = "1")) == 1
@test length(find(registry, id = r"1")) == 1
@test length(find(registry, id = "id")) == 2
@test length(find(registry, id = "id", name = "name")) == 2
@test length(find(registry, id = x -> startswith(x, "id"), name = "name")) == 2
@test NamedTuple(findfirst(registry, id = "id")) == (id = "id1", name = "name1")
@test isnothing(findfirst(registry, id = "404"))
@testset "filtering" begin
@testset "Fallback: equality" begin
@test matchesentry(Field(Symbol), :x, :x)
@test !matchesentry(Field(Symbol), :x, :y)
end
@testset "Predicates" begin
@test matchesentry(Field(Symbol), :x, ==(:x))
@test matchesentry(Field(Function), sum, ==(sum))
end
@testset "Missing values" begin
@test !matchesentry(Field(Symbol, optional = true), missing, :x)
@test matchesentry(Field(Symbol, optional = true), missing, ismissing)
end
@testset "String" begin
@test matchesentry(Field(String), "hello", "hel")
@test !matchesentry(Field(String), "hello", "yo")
@test matchesentry(Field(String), "hello", r"^h")
@test !matchesentry(Field(String), "hello", r"^h$l")
end
@testset "Row" begin
fields = (f1 = Field(String), f2 = Field(Symbol, optional = true))
@test matchesentry(fields, (f1 = "", f2 = :x))
@test matchesentry(fields, (f1 = "", f2 = :x), f2 = :x, f1 = "")
@test !matchesentry(fields, (f1 = "", f2 = :s), f2 = :x, f1 = "")
@test !matchesentry(fields, (f1 = "", f2 = :x), f2 = :x, f1 = "s")
end
end
end
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] | 2.471106 | 1,592 |
#######################
# static DSL function #
#######################
using FunctionalCollections: PersistentVector
struct Params
prob_outlier::Float64
slope::Float64
intercept::Float64
inlier_std::Float64
outlier_std::Float64
end
@gen (static) function datum(x::Float64, (grad)(params::Params))
is_outlier = @trace(bernoulli(params.prob_outlier), :z)
std = is_outlier ? params.inlier_std : params.outlier_std
y = @trace(normal(x * params.slope + params.intercept, std), :y)
return y
end
data_fn = Map(datum)
"""
my documentation
"""
@gen (static) function model(xs::Vector{Float64})
n = length(xs)
inlier_std = @trace(gamma(1, 1), :inlier_std)
outlier_std = @trace(gamma(1, 1), :outlier_std)
slope = @trace(normal(0, 2), :slope)
intercept = @trace(normal(0, 2), :intercept)
params::Params = Params(0.5, inlier_std, outlier_std, slope, intercept)
ys = @trace(data_fn(xs, fill(params, n)), :data)
return ys
end
@gen (static) function at_choice_example_1(i::Int)
ret = @trace(bernoulli(0.5), :x => i)
end
# @trace(choice_at(bernoulli)(0.5, i), :x)
@gen (static) function at_choice_example_2(i::Int)
ret = @trace(bernoulli(0.5), :x => i => :y)
end
# @trace(call_at(choice_at(bernoulli))(0.5, i, :y), :x)
@gen function foo(mu)
@trace(normal(mu, 1), :y)
end
@gen (static) function at_call_example_1(i::Int)
mu = 1.123
ret = @trace(foo(mu), :x => i)
end
@gen (static) function at_call_example_2(i::Int)
mu = 1.123
ret = @trace(foo(mu), :x => i => :y)
end
@testset "static DSL" begin
function get_node_by_name(ir, name::Symbol)
nodes = filter((node) -> (node.name == name), ir.nodes)
@assert length(nodes) == 1
nodes[1]
end
@testset "check IR" begin
#####################
# check IR of datum #
#####################
ir = Gen.get_ir(typeof(datum))
# argument nodes
@test length(ir.arg_nodes) == 2
x = ir.arg_nodes[1]
params = ir.arg_nodes[2]
@test x.name == :x
@test x.typ == :Float64
@test !x.compute_grad
@test params.name == :params
@test params.typ == :Params
@test params.compute_grad
# choice nodes and call nodes
@test length(ir.choice_nodes) == 2
@test length(ir.call_nodes) == 0
# is_outlier
is_outlier = ir.choice_nodes[1]
@test is_outlier.name == :is_outlier
@test is_outlier.addr == :z
@test is_outlier.typ == QuoteNode(Bool)
@test is_outlier.dist == bernoulli
@test length(is_outlier.inputs) == 1
# std
std = get_node_by_name(ir, :std)
@test isa(std, Gen.JuliaNode)
@test std.name == :std
@test std.typ == QuoteNode(Any)
@test length(std.inputs) == 2
in1 = std.inputs[1]
in2 = std.inputs[2]
@test (in1 === is_outlier && in2 === params) || (in2 === is_outlier && in1 === params)
# y
y = ir.choice_nodes[2]
@test y.name == :y
@test y.addr == :y
@test y.typ == QuoteNode(Float64)
@test y.dist == normal
@test length(y.inputs) == 2
@test y.inputs[2] === std
# y_mean
y_mean = y.inputs[1]
@test isa(y_mean, Gen.JuliaNode)
@test y_mean.typ == QuoteNode(Any)
@test length(y_mean.inputs) == 2
in1 = y_mean.inputs[1]
in2 = y_mean.inputs[2]
@test (in1 === x && in2 === params) || (in2 === x && in1 === params)
# prob outlier
prob_outlier = is_outlier.inputs[1]
@test isa(prob_outlier, Gen.JuliaNode)
@test length(prob_outlier.inputs) == 1
@test prob_outlier.inputs[1] === params
@test prob_outlier.typ == QuoteNode(Any)
@test ir.return_node === y
#####################
# check IR of model #
#####################
ir = Gen.get_ir(typeof(model))
@test length(ir.arg_nodes) == 1
xs = ir.arg_nodes[1]
@test xs.name == :xs
@test xs.typ == :(Vector{Float64})
@test !xs.compute_grad
# choice nodes and call nodes
@test length(ir.choice_nodes) == 4
@test length(ir.call_nodes) == 1
# inlier_std
inlier_std = ir.choice_nodes[1]
@test inlier_std.name == :inlier_std
@test inlier_std.addr == :inlier_std
@test inlier_std.typ == QuoteNode(Float64)
@test inlier_std.dist == gamma
@test length(inlier_std.inputs) == 2
# outlier_std
outlier_std = ir.choice_nodes[2]
@test outlier_std.name == :outlier_std
@test outlier_std.addr == :outlier_std
@test outlier_std.typ == QuoteNode(Float64)
@test outlier_std.dist == gamma
@test length(outlier_std.inputs) == 2
# slope
slope = ir.choice_nodes[3]
@test slope.name == :slope
@test slope.addr == :slope
@test slope.typ == QuoteNode(Float64)
@test slope.dist == normal
@test length(slope.inputs) == 2
# intercept
intercept = ir.choice_nodes[4]
@test intercept.name == :intercept
@test intercept.addr == :intercept
@test intercept.typ == QuoteNode(Float64)
@test intercept.dist == normal
@test length(intercept.inputs) == 2
# data
ys = ir.call_nodes[1]
@test ys.name == :ys
@test ys.addr == :data
@test ys.typ == QuoteNode(PersistentVector{Float64})
@test ys.generative_function == data_fn
@test length(ys.inputs) == 2
@test ys.inputs[1] == xs
# params
params = get_node_by_name(ir, :params)
@test isa(params, Gen.JuliaNode)
@test params.name == :params
@test params.typ == :Params
@test length(params.inputs) == 4
@test slope in params.inputs
@test intercept in params.inputs
@test inlier_std in params.inputs
@test outlier_std in params.inputs
# n
n = get_node_by_name(ir, :n)
@test isa(n, Gen.JuliaNode)
@test n.name == :n
@test n.typ == QuoteNode(Any)
@test length(n.inputs) == 1
@test n.inputs[1] === xs
# params_vec
params_vec = ys.inputs[2]
@test isa(params_vec, Gen.JuliaNode)
@test params_vec.typ == QuoteNode(Any)
@test length(params_vec.inputs) == 2
in1 = params_vec.inputs[1]
in2 = params_vec.inputs[2]
@test (in1 === params && in2 === n) || (in2 === params && in1 === n)
@test ir.return_node === ys
end
@testset "at_choice" begin
# at_choice_example_1
ir = Gen.get_ir(typeof(at_choice_example_1))
i = get_node_by_name(ir, :i)
ret = get_node_by_name(ir, :ret)
@test isa(ret, Gen.GenerativeFunctionCallNode)
@test ret.addr == :x
@test length(ret.inputs) == 2
@test isa(ret.inputs[1], Gen.JuliaNode) # () -> 0.5
@test ret.inputs[2] === i
at = ret.generative_function
@test isa(at, Gen.ChoiceAtCombinator)
@test at.dist == bernoulli
# at_choice_example_2
ir = Gen.get_ir(typeof(at_choice_example_2))
i = get_node_by_name(ir, :i)
ret = get_node_by_name(ir, :ret)
@test isa(ret, Gen.GenerativeFunctionCallNode)
@test ret.addr == :x
@test length(ret.inputs) == 3
@test isa(ret.inputs[1], Gen.JuliaNode) # () -> 0.5
@test isa(ret.inputs[2], Gen.JuliaNode) # () -> :y
@test ret.inputs[3] === i
at = ret.generative_function
@test isa(at, Gen.CallAtCombinator)
at2 = at.kernel
@test isa(at2, Gen.ChoiceAtCombinator)
@test at2.dist == bernoulli
end
@testset "at_call" begin
# at_call_example_1
ir = Gen.get_ir(typeof(at_call_example_1))
i = get_node_by_name(ir, :i)
ret = get_node_by_name(ir, :ret)
@test isa(ret, Gen.GenerativeFunctionCallNode)
@test ret.addr == :x
@test length(ret.inputs) == 2
@test isa(ret.inputs[1], Gen.JuliaNode) # () -> 0.5
@test ret.inputs[2] === i
at = ret.generative_function
@test isa(at, Gen.CallAtCombinator)
@test at.kernel == foo
#at_call_example_2
ir = Gen.get_ir(typeof(at_call_example_2))
i = get_node_by_name(ir, :i)
ret = get_node_by_name(ir, :ret)
@test isa(ret, Gen.GenerativeFunctionCallNode)
@test ret.addr == :x
@test length(ret.inputs) == 3
@test isa(ret.inputs[1], Gen.JuliaNode) # () -> 0.5
@test isa(ret.inputs[1], Gen.JuliaNode) # () -> 0.5
@test isa(ret.inputs[2], Gen.JuliaNode) # () -> :y
@test ret.inputs[3] === i
at = ret.generative_function
@test isa(at, Gen.CallAtCombinator)
at2 = at.kernel
@test isa(at2, Gen.CallAtCombinator)
@test at2.kernel == foo
end
@testset "tracked diff annotation" begin
@gen (static, diffs) function bar(x)
return x
end
@test Gen.get_options(typeof(bar)).track_diffs
@gen (static) function bar(x)
return x
end
@test !Gen.get_options(typeof(bar)).track_diffs
end
@testset "no julia cache annotation" begin
@gen (static, nojuliacache) function bar(x)
return x
end
@test !Gen.get_options(typeof(bar)).cache_julia_nodes
@gen (static) function bar(x)
return x
end
@test Gen.get_options(typeof(bar)).cache_julia_nodes
end
@testset "trainable parameters" begin
@gen (static) function foo()
@param theta::Float64
return theta
end
ir = Gen.get_ir(typeof(foo))
theta = get_node_by_name(ir, :theta)
@test isa(theta, Gen.TrainableParameterNode)
@test ir.return_node === theta
end
@testset "use of 'end'" begin
@gen (static) function foo()
x = [1, 2, 3, 4, 5, 6]
y = x[3:end]
return y
end
load_generated_functions()
@test foo() == [3, 4, 5, 6]
end
@testset "docstrings" begin
io = IOBuffer()
print(io, @doc model)
@test String(take!(io)) == "my documentation\n"
end
end # @testset "static DSL"
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] | 2.407594 | 3,582 |
using HelloCExecutable
using Test
@testset "hello_world" begin
@test hello_world() == "Hello, Goma!"
end | [
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] | 2.868421 | 38 |
module LorenzMapDefinition
using ..PwDynamicDefinition: PwMap
using ValidatedNumerics
export LorenzMap
struct LorenzMap
D::PwMap
end
LorenzMap(θ, α) = PwMap([x->θ*abs(x-0.5)^α, x->1-θ*abs(x-0.5)^α],
[@interval(0), @interval(0.5), @interval(1)])
import ..DynamicDefinition: derivative
end
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] | 2.090909 | 154 |
using Documenter, MIToS
include("literate.jl")
makedocs(
doctest = true,
format = :html,
sitename = "MIToS",
authors = "Diego Javier Zea",
modules = [MIToS],
pages = [
"Home" => "index.md",
"Installation.md",
"Example.md",
"Modules" => [ "MSA.md",
"Information.md",
"SIFTS.md",
"PDB.md",
"Pfam.md" ],
"Scripts.md",
"API" => [ "MSA_API.md",
"Information_API.md",
"SIFTS_API.md",
"PDB_API.md",
"Pfam_API.md",
"Utils_API.md" ],
"Cookbook" => [ "01_Change_B_factors.md",
"02_Linking_structural_and_evolutionary_information.md"]
]
)
deploydocs(
repo = "github.com/diegozea/MIToS.jl.git",
target = "build",
deps = nothing,
make = nothing
)
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] | 1.644781 | 594 |
##############################################################################################
module TestDeSolvers
##############################################################################################
##############################################################################################
# imports and exports
using DataAssimilationBenchmarks.DeSolvers, DataAssimilationBenchmarks.L96
using LsqFit
##############################################################################################
##############################################################################################
# Define test function for analytical verification of discretization error
VecA = Union{Vector{Float64}, SubArray{Float64, 1}}
ParamDict = Union{Dict{String, Array{Float64}}, Dict{String, Vector{Float64}}}
##############################################################################################
##############################################################################################
function exponentialODE(x::T, t::Float64, dx_params::ParamDict) where {T <: VecA}
# Wrapper for making a vector form of the exponential function for the DE solvers
[exp(t)]
end
##############################################################################################
# Define auxiliary function to compute the difference of the simulated integral versus
# the analytical value
function expDiscretizationError(step_model!, h)
# continuous time length of the integration
tanl = 0.1
# discrete integration steps
fore_steps = convert(Int64, tanl/h)
time_steps = LinRange(0, tanl, fore_steps + 1)
# initial data for the exponential function
x = [1.0]
# set the kwargs for the integration scheme
# with empty values for the uneccessary parameters
diffusion = 0.0
dx_params = Dict{String, Array{Float64}}()
kwargs = Dict{String, Any}(
"h" => h,
"diffusion" => diffusion,
"dx_params" => dx_params,
"dx_dt" => exponentialODE,
)
for i in 1:fore_steps
step_model!(x, time_steps[i], kwargs)
end
# find the absolute difference of the apprximate integral from the built-in exponential
abs(x[1] - exp(tanl))
end
##############################################################################################
# Define auxiliary function to compute the least-squares estimated order of convergence
function calculateOrderConvergence(step_model!)
# set step sizes in increasing order for log-10 log-10 analysis
h_range = [0.005, 0.01, 0.05, 0.1]
error_range = Vector{Float64}(undef, length(h_range))
# loop the discretization and calculate the errors
for i in 1:length(h_range)
error_range[i] = expDiscretizationError(step_model!, h_range[i])
end
# convert the error and the step sizes to log-10
h_range_log10 = log10.(h_range)
error_range_log10 = log10.(error_range)
function model_lsq_squares(x,p)
# define a function object to vary parameters p
@. p[1] + p[2]*x
end
# fit the best-fit line and return coefficients
fit = curve_fit(model_lsq_squares, h_range_log10, error_range_log10, [1.0, 1.0])
coef(fit)
end
##############################################################################################
# Wrapper function to be supplied to runtests
function testEMExponential()
coef = calculateOrderConvergence(em_step!)
if abs(coef[2] - 1.0) > 0.1
false
else
true
end
end
##############################################################################################
# Wrapper function to be supplied to runtests
function testRKExponential()
coef = calculateOrderConvergence(rk4_step!)
if abs(coef[2] - 4.0) > 0.1
false
else
true
end
end
##############################################################################################
# end module
end
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198,
220,
220,
220,
220,
220,
220,
220,
1303,
8160,
257,
2163,
2134,
284,
7565,
10007,
279,
198,
220,
220,
220,
220,
220,
220,
220,
2488,
13,
279,
58,
16,
60,
1343,
279,
58,
17,
60,
9,
87,
198,
220,
220,
220,
886,
628,
220,
220,
220,
1303,
4197,
262,
1266,
12,
11147,
1627,
290,
1441,
44036,
198,
220,
220,
220,
4197,
796,
12133,
62,
11147,
7,
19849,
62,
7278,
80,
62,
16485,
3565,
11,
289,
62,
9521,
62,
6404,
940,
11,
4049,
62,
9521,
62,
6404,
940,
11,
685,
16,
13,
15,
11,
352,
13,
15,
12962,
198,
220,
220,
220,
763,
891,
7,
11147,
8,
198,
437,
628,
198,
198,
29113,
29113,
14468,
7804,
4242,
2235,
198,
2,
27323,
2848,
2163,
284,
307,
14275,
284,
1057,
41989,
198,
198,
8818,
1332,
3620,
16870,
35470,
3419,
198,
220,
220,
220,
763,
891,
796,
15284,
18743,
3103,
332,
12745,
7,
368,
62,
9662,
8133,
628,
220,
220,
220,
611,
2352,
7,
1073,
891,
58,
17,
60,
532,
352,
13,
15,
8,
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13,
16,
198,
220,
220,
220,
220,
220,
220,
220,
3991,
198,
220,
220,
220,
2073,
198,
220,
220,
220,
220,
220,
220,
220,
2081,
198,
220,
220,
220,
886,
198,
437,
628,
198,
29113,
29113,
14468,
7804,
4242,
2235,
198,
2,
27323,
2848,
2163,
284,
307,
14275,
284,
1057,
41989,
198,
198,
8818,
1332,
49,
42,
16870,
35470,
3419,
198,
220,
220,
220,
763,
891,
796,
15284,
18743,
3103,
332,
12745,
7,
81,
74,
19,
62,
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220,
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611,
2352,
7,
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58,
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60,
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13,
15,
8,
1875,
657,
13,
16,
198,
220,
220,
220,
220,
220,
220,
220,
3991,
198,
220,
220,
220,
2073,
198,
220,
220,
220,
220,
220,
220,
220,
2081,
198,
220,
220,
220,
886,
198,
437,
628,
198,
29113,
29113,
14468,
7804,
4242,
2235,
198,
2,
886,
8265,
198,
198,
437,
198
] | 3.442994 | 1,149 |
# Mu' = f
struct SteadyStateProblem{uType,isinplace,P,F} <: AbstractSteadyStateProblem{uType,isinplace}
f::F
u0::uType
p::P
@add_kwonly function SteadyStateProblem{iip}(f::AbstractODEFunction{iip},
u0,p=nothing) where {iip}
new{typeof(u0),isinplace(f),typeof(p),typeof(f)}(f,u0,p)
end
function SteadyStateProblem{iip}(f,u0,p=nothing) where iip
SteadyStateProblem(ODEFunction{iip}(f),u0,p)
end
end
function SteadyStateProblem(f::AbstractODEFunction,u0,p=nothing;kwargs...)
SteadyStateProblem{isinplace(f)}(f,u0,p;kwargs...)
end
function SteadyStateProblem(f,u0,p=nothing;kwargs...)
SteadyStateProblem(ODEFunction(f),u0,p;kwargs...)
end
SteadyStateProblem(prob::AbstractODEProblem) =
SteadyStateProblem{isinplace(prob)}(prob.f,prob.u0,prob.p)
| [
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198,
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9012,
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7,
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65,
3712,
23839,
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8,
796,
198,
220,
220,
220,
220,
220,
45445,
88,
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40781,
90,
45763,
5372,
7,
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7,
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65,
13,
69,
11,
1676,
65,
13,
84,
15,
11,
1676,
65,
13,
79,
8,
198
] | 2.233062 | 369 |
include("./Functions.jl")
using Functions
val = BigInt(2) ^ BigInt(1000)
println(digitSum(val))
| [
17256,
7,
1911,
14,
24629,
2733,
13,
20362,
4943,
220,
198,
3500,
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5317,
7,
12825,
8,
198,
35235,
7,
27003,
13065,
7,
2100,
4008,
198
] | 2.648649 | 37 |
using Base.Test, EPInference, ExpFamily
# Proposing samples from a wide spherical gaussian.
srand(123)
C = [1.0 0.5; 0.5 1.0]
P = 2/3 * [2 -1; -1 2]
m = [1.0;-0.5]
gNP = GaussianNatParam(mean=m, cov=C)
gNPprop = ones(GaussianNatParam, 2)/20 # division INFLATES the variance
(samples,w) = impsampling(x->loglik(gNP,x), gNPprop, 10000)
@test isapprox(sum(samples[:,i]*w[i] for i in 1:length(w)), m, rtol=0.05)
| [
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58,
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7,
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285,
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2713,
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] | 2.147959 | 196 |
# Automatically generated using Clang.jl wrap_c, version 0.0.0
import Base.zero
export
unix,
linux,
AV_TIME_BASE,
AV_NOPTS_VALUE,
AV_FOURCC_MAX_STRING_SIZE,
AVMediaType,
AVMEDIA_TYPE_UNKNOWN,
AVMEDIA_TYPE_VIDEO,
AVMEDIA_TYPE_AUDIO,
AVMEDIA_TYPE_DATA,
AVMEDIA_TYPE_SUBTITLE,
AVMEDIA_TYPE_ATTACHMENT,
AVMEDIA_TYPE_NB,
AVPictureType,
AV_PICTURE_TYPE_NONE,
AV_PICTURE_TYPE_I,
AV_PICTURE_TYPE_P,
AV_PICTURE_TYPE_B,
AV_PICTURE_TYPE_S,
AV_PICTURE_TYPE_SI,
AV_PICTURE_TYPE_SP,
AV_PICTURE_TYPE_BI,
AV_BUFFER_FLAG_READONLY,
AVBuffer,
AVBufferRef,
AVBufferPool,
AV_DICT_MATCH_CASE,
AV_DICT_IGNORE_SUFFIX,
AV_DICT_DONT_STRDUP_KEY,
AV_DICT_DONT_STRDUP_VAL,
AV_DICT_DONT_OVERWRITE,
AV_DICT_APPEND,
AV_DICT_MULTIKEY,
AVDictionaryEntry,
AVDictionary,
AVFifoBuffer,
AV_NUM_DATA_POINTERS,
AV_FRAME_FLAG_CORRUPT,
AV_FRAME_FLAG_DISCARD,
AVFrameSideDataType,
AV_FRAME_DATA_PANSCAN,
AV_FRAME_DATA_A53_CC,
AV_FRAME_DATA_STEREO3D,
AV_FRAME_DATA_MATRIXENCODING,
AV_FRAME_DATA_DOWNMIX_INFO,
AV_FRAME_DATA_REPLAYGAIN,
AV_FRAME_DATA_DISPLAYMATRIX,
AV_FRAME_DATA_AFD,
AV_FRAME_DATA_MOTION_VECTORS,
AV_FRAME_DATA_SKIP_SAMPLES,
AV_FRAME_DATA_AUDIO_SERVICE_TYPE,
AV_FRAME_DATA_MASTERING_DISPLAY_METADATA,
AV_FRAME_DATA_GOP_TIMECODE,
AV_FRAME_DATA_SPHERICAL,
AV_FRAME_DATA_CONTENT_LIGHT_LEVEL,
AV_FRAME_DATA_ICC_PROFILE,
AV_FRAME_DATA_QP_TABLE_PROPERTIES,
AV_FRAME_DATA_QP_TABLE_DATA,
AV_FRAME_DATA_S12M_TIMECODE,
AVActiveFormatDescription,
AV_AFD_SAME,
AV_AFD_4_3,
AV_AFD_16_9,
AV_AFD_14_9,
AV_AFD_4_3_SP_14_9,
AV_AFD_16_9_SP_14_9,
AV_AFD_SP_4_3,
AVFrameSideData,
AVRational,
AVColorRange,
AVCOL_RANGE_UNSPECIFIED,
AVCOL_RANGE_MPEG,
AVCOL_RANGE_JPEG,
AVCOL_RANGE_NB,
AVColorPrimaries,
AVCOL_PRI_RESERVED0,
AVCOL_PRI_BT709,
AVCOL_PRI_UNSPECIFIED,
AVCOL_PRI_RESERVED,
AVCOL_PRI_BT470M,
AVCOL_PRI_BT470BG,
AVCOL_PRI_SMPTE170M,
AVCOL_PRI_SMPTE240M,
AVCOL_PRI_FILM,
AVCOL_PRI_BT2020,
AVCOL_PRI_SMPTE428,
AVCOL_PRI_SMPTEST428_1,
AVCOL_PRI_SMPTE431,
AVCOL_PRI_SMPTE432,
AVCOL_PRI_JEDEC_P22,
AVCOL_PRI_NB,
AVColorTransferCharacteristic,
AVCOL_TRC_RESERVED0,
AVCOL_TRC_BT709,
AVCOL_TRC_UNSPECIFIED,
AVCOL_TRC_RESERVED,
AVCOL_TRC_GAMMA22,
AVCOL_TRC_GAMMA28,
AVCOL_TRC_SMPTE170M,
AVCOL_TRC_SMPTE240M,
AVCOL_TRC_LINEAR,
AVCOL_TRC_LOG,
AVCOL_TRC_LOG_SQRT,
AVCOL_TRC_IEC61966_2_4,
AVCOL_TRC_BT1361_ECG,
AVCOL_TRC_IEC61966_2_1,
AVCOL_TRC_BT2020_10,
AVCOL_TRC_BT2020_12,
AVCOL_TRC_SMPTE2084,
AVCOL_TRC_SMPTEST2084,
AVCOL_TRC_SMPTE428,
AVCOL_TRC_SMPTEST428_1,
AVCOL_TRC_ARIB_STD_B67,
AVCOL_TRC_NB,
AVColorSpace,
AVCOL_SPC_RGB,
AVCOL_SPC_BT709,
AVCOL_SPC_UNSPECIFIED,
AVCOL_SPC_RESERVED,
AVCOL_SPC_FCC,
AVCOL_SPC_BT470BG,
AVCOL_SPC_SMPTE170M,
AVCOL_SPC_SMPTE240M,
AVCOL_SPC_YCGCO,
AVCOL_SPC_YCOCG,
AVCOL_SPC_BT2020_NCL,
AVCOL_SPC_BT2020_CL,
AVCOL_SPC_SMPTE2085,
AVCOL_SPC_CHROMA_DERIVED_NCL,
AVCOL_SPC_CHROMA_DERIVED_CL,
AVCOL_SPC_ICTCP,
AVCOL_SPC_NB,
AVChromaLocation,
AVCHROMA_LOC_UNSPECIFIED,
AVCHROMA_LOC_LEFT,
AVCHROMA_LOC_CENTER,
AVCHROMA_LOC_TOPLEFT,
AVCHROMA_LOC_TOP,
AVCHROMA_LOC_BOTTOMLEFT,
AVCHROMA_LOC_BOTTOM,
AVCHROMA_LOC_NB,
AVFrame,
ANONYMOUS_1,
AV_FRAME_CROP_UNALIGNED,
AV_LOG_QUIET,
AV_LOG_PANIC,
AV_LOG_FATAL,
AV_LOG_ERROR,
AV_LOG_WARNING,
AV_LOG_INFO,
AV_LOG_VERBOSE,
AV_LOG_DEBUG,
AV_LOG_TRACE,
AV_LOG_MAX_OFFSET,
AV_LOG_SKIP_REPEATED,
AV_LOG_PRINT_LEVEL,
ANONYMOUS_2,
AV_CLASS_CATEGORY_NA,
AV_CLASS_CATEGORY_INPUT,
AV_CLASS_CATEGORY_OUTPUT,
AV_CLASS_CATEGORY_MUXER,
AV_CLASS_CATEGORY_DEMUXER,
AV_CLASS_CATEGORY_ENCODER,
AV_CLASS_CATEGORY_DECODER,
AV_CLASS_CATEGORY_FILTER,
AV_CLASS_CATEGORY_BITSTREAM_FILTER,
AV_CLASS_CATEGORY_SWSCALER,
AV_CLASS_CATEGORY_SWRESAMPLER,
AV_CLASS_CATEGORY_DEVICE_VIDEO_OUTPUT,
AV_CLASS_CATEGORY_DEVICE_VIDEO_INPUT,
AV_CLASS_CATEGORY_DEVICE_AUDIO_OUTPUT,
AV_CLASS_CATEGORY_DEVICE_AUDIO_INPUT,
AV_CLASS_CATEGORY_DEVICE_OUTPUT,
AV_CLASS_CATEGORY_DEVICE_INPUT,
AV_CLASS_CATEGORY_NB,
AVClassCategory,
AVOptionRange,
AVOptionRanges,
AVOptionType,
AV_OPT_TYPE_FLAGS,
AV_OPT_TYPE_INT,
AV_OPT_TYPE_INT64,
AV_OPT_TYPE_DOUBLE,
AV_OPT_TYPE_FLOAT,
AV_OPT_TYPE_STRING,
AV_OPT_TYPE_RATIONAL,
AV_OPT_TYPE_BINARY,
AV_OPT_TYPE_DICT,
AV_OPT_TYPE_UINT64,
AV_OPT_TYPE_CONST,
AV_OPT_TYPE_IMAGE_SIZE,
AV_OPT_TYPE_PIXEL_FMT,
AV_OPT_TYPE_SAMPLE_FMT,
AV_OPT_TYPE_VIDEO_RATE,
AV_OPT_TYPE_DURATION,
AV_OPT_TYPE_COLOR,
AV_OPT_TYPE_CHANNEL_LAYOUT,
AV_OPT_TYPE_BOOL,
AVOption,
AVClass,
AV_PIX_FMT_FLAG_BE,
AV_PIX_FMT_FLAG_PAL,
AV_PIX_FMT_FLAG_BITSTREAM,
AV_PIX_FMT_FLAG_HWACCEL,
AV_PIX_FMT_FLAG_PLANAR,
AV_PIX_FMT_FLAG_RGB,
AV_PIX_FMT_FLAG_PSEUDOPAL,
AV_PIX_FMT_FLAG_ALPHA,
AV_PIX_FMT_FLAG_BAYER,
AV_PIX_FMT_FLAG_FLOAT,
AVComponentDescriptor,
AVPixFmtDescriptor,
AVPALETTE_SIZE,
AVPALETTE_COUNT,
AVPixelFormat,
AV_PIX_FMT_NONE,
AV_PIX_FMT_YUV420P,
AV_PIX_FMT_YUYV422,
AV_PIX_FMT_RGB24,
AV_PIX_FMT_BGR24,
AV_PIX_FMT_YUV422P,
AV_PIX_FMT_YUV444P,
AV_PIX_FMT_YUV410P,
AV_PIX_FMT_YUV411P,
AV_PIX_FMT_GRAY8,
AV_PIX_FMT_MONOWHITE,
AV_PIX_FMT_MONOBLACK,
AV_PIX_FMT_PAL8,
AV_PIX_FMT_YUVJ420P,
AV_PIX_FMT_YUVJ422P,
AV_PIX_FMT_YUVJ444P,
AV_PIX_FMT_UYVY422,
AV_PIX_FMT_UYYVYY411,
AV_PIX_FMT_BGR8,
AV_PIX_FMT_BGR4,
AV_PIX_FMT_BGR4_BYTE,
AV_PIX_FMT_RGB8,
AV_PIX_FMT_RGB4,
AV_PIX_FMT_RGB4_BYTE,
AV_PIX_FMT_NV12,
AV_PIX_FMT_NV21,
AV_PIX_FMT_ARGB,
AV_PIX_FMT_RGBA,
AV_PIX_FMT_ABGR,
AV_PIX_FMT_BGRA,
AV_PIX_FMT_GRAY16BE,
AV_PIX_FMT_GRAY16LE,
AV_PIX_FMT_YUV440P,
AV_PIX_FMT_YUVJ440P,
AV_PIX_FMT_YUVA420P,
AV_PIX_FMT_RGB48BE,
AV_PIX_FMT_RGB48LE,
AV_PIX_FMT_RGB565BE,
AV_PIX_FMT_RGB565LE,
AV_PIX_FMT_RGB555BE,
AV_PIX_FMT_RGB555LE,
AV_PIX_FMT_BGR565BE,
AV_PIX_FMT_BGR565LE,
AV_PIX_FMT_BGR555BE,
AV_PIX_FMT_BGR555LE,
AV_PIX_FMT_VAAPI_MOCO,
AV_PIX_FMT_VAAPI_IDCT,
AV_PIX_FMT_VAAPI_VLD,
AV_PIX_FMT_VAAPI,
AV_PIX_FMT_YUV420P16LE,
AV_PIX_FMT_YUV420P16BE,
AV_PIX_FMT_YUV422P16LE,
AV_PIX_FMT_YUV422P16BE,
AV_PIX_FMT_YUV444P16LE,
AV_PIX_FMT_YUV444P16BE,
AV_PIX_FMT_DXVA2_VLD,
AV_PIX_FMT_RGB444LE,
AV_PIX_FMT_RGB444BE,
AV_PIX_FMT_BGR444LE,
AV_PIX_FMT_BGR444BE,
AV_PIX_FMT_YA8,
AV_PIX_FMT_Y400A,
AV_PIX_FMT_GRAY8A,
AV_PIX_FMT_BGR48BE,
AV_PIX_FMT_BGR48LE,
AV_PIX_FMT_YUV420P9BE,
AV_PIX_FMT_YUV420P9LE,
AV_PIX_FMT_YUV420P10BE,
AV_PIX_FMT_YUV420P10LE,
AV_PIX_FMT_YUV422P10BE,
AV_PIX_FMT_YUV422P10LE,
AV_PIX_FMT_YUV444P9BE,
AV_PIX_FMT_YUV444P9LE,
AV_PIX_FMT_YUV444P10BE,
AV_PIX_FMT_YUV444P10LE,
AV_PIX_FMT_YUV422P9BE,
AV_PIX_FMT_YUV422P9LE,
AV_PIX_FMT_GBRP,
AV_PIX_FMT_GBR24P,
AV_PIX_FMT_GBRP9BE,
AV_PIX_FMT_GBRP9LE,
AV_PIX_FMT_GBRP10BE,
AV_PIX_FMT_GBRP10LE,
AV_PIX_FMT_GBRP16BE,
AV_PIX_FMT_GBRP16LE,
AV_PIX_FMT_YUVA422P,
AV_PIX_FMT_YUVA444P,
AV_PIX_FMT_YUVA420P9BE,
AV_PIX_FMT_YUVA420P9LE,
AV_PIX_FMT_YUVA422P9BE,
AV_PIX_FMT_YUVA422P9LE,
AV_PIX_FMT_YUVA444P9BE,
AV_PIX_FMT_YUVA444P9LE,
AV_PIX_FMT_YUVA420P10BE,
AV_PIX_FMT_YUVA420P10LE,
AV_PIX_FMT_YUVA422P10BE,
AV_PIX_FMT_YUVA422P10LE,
AV_PIX_FMT_YUVA444P10BE,
AV_PIX_FMT_YUVA444P10LE,
AV_PIX_FMT_YUVA420P16BE,
AV_PIX_FMT_YUVA420P16LE,
AV_PIX_FMT_YUVA422P16BE,
AV_PIX_FMT_YUVA422P16LE,
AV_PIX_FMT_YUVA444P16BE,
AV_PIX_FMT_YUVA444P16LE,
AV_PIX_FMT_VDPAU,
AV_PIX_FMT_XYZ12LE,
AV_PIX_FMT_XYZ12BE,
AV_PIX_FMT_NV16,
AV_PIX_FMT_NV20LE,
AV_PIX_FMT_NV20BE,
AV_PIX_FMT_RGBA64BE,
AV_PIX_FMT_RGBA64LE,
AV_PIX_FMT_BGRA64BE,
AV_PIX_FMT_BGRA64LE,
AV_PIX_FMT_YVYU422,
AV_PIX_FMT_YA16BE,
AV_PIX_FMT_YA16LE,
AV_PIX_FMT_GBRAP,
AV_PIX_FMT_GBRAP16BE,
AV_PIX_FMT_GBRAP16LE,
AV_PIX_FMT_QSV,
AV_PIX_FMT_MMAL,
AV_PIX_FMT_D3D11VA_VLD,
AV_PIX_FMT_CUDA,
AV_PIX_FMT_0RGB,
AV_PIX_FMT_RGB0,
AV_PIX_FMT_0BGR,
AV_PIX_FMT_BGR0,
AV_PIX_FMT_YUV420P12BE,
AV_PIX_FMT_YUV420P12LE,
AV_PIX_FMT_YUV420P14BE,
AV_PIX_FMT_YUV420P14LE,
AV_PIX_FMT_YUV422P12BE,
AV_PIX_FMT_YUV422P12LE,
AV_PIX_FMT_YUV422P14BE,
AV_PIX_FMT_YUV422P14LE,
AV_PIX_FMT_YUV444P12BE,
AV_PIX_FMT_YUV444P12LE,
AV_PIX_FMT_YUV444P14BE,
AV_PIX_FMT_YUV444P14LE,
AV_PIX_FMT_GBRP12BE,
AV_PIX_FMT_GBRP12LE,
AV_PIX_FMT_GBRP14BE,
AV_PIX_FMT_GBRP14LE,
AV_PIX_FMT_YUVJ411P,
AV_PIX_FMT_BAYER_BGGR8,
AV_PIX_FMT_BAYER_RGGB8,
AV_PIX_FMT_BAYER_GBRG8,
AV_PIX_FMT_BAYER_GRBG8,
AV_PIX_FMT_BAYER_BGGR16LE,
AV_PIX_FMT_BAYER_BGGR16BE,
AV_PIX_FMT_BAYER_RGGB16LE,
AV_PIX_FMT_BAYER_RGGB16BE,
AV_PIX_FMT_BAYER_GBRG16LE,
AV_PIX_FMT_BAYER_GBRG16BE,
AV_PIX_FMT_BAYER_GRBG16LE,
AV_PIX_FMT_BAYER_GRBG16BE,
AV_PIX_FMT_XVMC,
AV_PIX_FMT_YUV440P10LE,
AV_PIX_FMT_YUV440P10BE,
AV_PIX_FMT_YUV440P12LE,
AV_PIX_FMT_YUV440P12BE,
AV_PIX_FMT_AYUV64LE,
AV_PIX_FMT_AYUV64BE,
AV_PIX_FMT_VIDEOTOOLBOX,
AV_PIX_FMT_P010LE,
AV_PIX_FMT_P010BE,
AV_PIX_FMT_GBRAP12BE,
AV_PIX_FMT_GBRAP12LE,
AV_PIX_FMT_GBRAP10BE,
AV_PIX_FMT_GBRAP10LE,
AV_PIX_FMT_MEDIACODEC,
AV_PIX_FMT_GRAY12BE,
AV_PIX_FMT_GRAY12LE,
AV_PIX_FMT_GRAY10BE,
AV_PIX_FMT_GRAY10LE,
AV_PIX_FMT_P016LE,
AV_PIX_FMT_P016BE,
AV_PIX_FMT_D3D11,
AV_PIX_FMT_GRAY9BE,
AV_PIX_FMT_GRAY9LE,
AV_PIX_FMT_GBRPF32BE,
AV_PIX_FMT_GBRPF32LE,
AV_PIX_FMT_GBRAPF32BE,
AV_PIX_FMT_GBRAPF32LE,
AV_PIX_FMT_DRM_PRIME,
AV_PIX_FMT_OPENCL,
AV_PIX_FMT_GRAY14BE,
AV_PIX_FMT_GRAY14LE,
AV_PIX_FMT_GRAYF32BE,
AV_PIX_FMT_GRAYF32LE,
AV_PIX_FMT_NB,
AVSampleFormat,
AV_SAMPLE_FMT_NONE,
AV_SAMPLE_FMT_U8,
AV_SAMPLE_FMT_S16,
AV_SAMPLE_FMT_S32,
AV_SAMPLE_FMT_FLT,
AV_SAMPLE_FMT_DBL,
AV_SAMPLE_FMT_U8P,
AV_SAMPLE_FMT_S16P,
AV_SAMPLE_FMT_S32P,
AV_SAMPLE_FMT_FLTP,
AV_SAMPLE_FMT_DBLP,
AV_SAMPLE_FMT_S64,
AV_SAMPLE_FMT_S64P,
AV_SAMPLE_FMT_NB,
LIBAVUTIL_VERSION_MAJOR,
LIBAVUTIL_VERSION_MINOR,
LIBAVUTIL_VERSION_MICRO,
LIBAVUTIL_BUILD,
AES_CTR_KEY_SIZE,
AES_CTR_IV_SIZE,
AVAESCTR,
AVAudioFifo,
AVCAMELLIA,
AVCAST5,
AV_CH_FRONT_LEFT,
AV_CH_FRONT_RIGHT,
AV_CH_FRONT_CENTER,
AV_CH_LOW_FREQUENCY,
AV_CH_BACK_LEFT,
AV_CH_BACK_RIGHT,
AV_CH_FRONT_LEFT_OF_CENTER,
AV_CH_FRONT_RIGHT_OF_CENTER,
AV_CH_BACK_CENTER,
AV_CH_SIDE_LEFT,
AV_CH_SIDE_RIGHT,
AV_CH_TOP_CENTER,
AV_CH_TOP_FRONT_LEFT,
AV_CH_TOP_FRONT_CENTER,
AV_CH_TOP_FRONT_RIGHT,
AV_CH_TOP_BACK_LEFT,
AV_CH_TOP_BACK_CENTER,
AV_CH_TOP_BACK_RIGHT,
AV_CH_STEREO_LEFT,
AV_CH_STEREO_RIGHT,
AV_CH_WIDE_LEFT,
AV_CH_WIDE_RIGHT,
AV_CH_SURROUND_DIRECT_LEFT,
AV_CH_SURROUND_DIRECT_RIGHT,
AV_CH_LOW_FREQUENCY_2,
AV_CH_LAYOUT_NATIVE,
AV_CH_LAYOUT_MONO,
AV_CH_LAYOUT_STEREO,
AV_CH_LAYOUT_2POINT1,
AV_CH_LAYOUT_2_1,
AV_CH_LAYOUT_SURROUND,
AV_CH_LAYOUT_3POINT1,
AV_CH_LAYOUT_4POINT0,
AV_CH_LAYOUT_4POINT1,
AV_CH_LAYOUT_2_2,
AV_CH_LAYOUT_QUAD,
AV_CH_LAYOUT_5POINT0,
AV_CH_LAYOUT_5POINT1,
AV_CH_LAYOUT_5POINT0_BACK,
AV_CH_LAYOUT_5POINT1_BACK,
AV_CH_LAYOUT_6POINT0,
AV_CH_LAYOUT_6POINT0_FRONT,
AV_CH_LAYOUT_HEXAGONAL,
AV_CH_LAYOUT_6POINT1,
AV_CH_LAYOUT_6POINT1_BACK,
AV_CH_LAYOUT_6POINT1_FRONT,
AV_CH_LAYOUT_7POINT0,
AV_CH_LAYOUT_7POINT0_FRONT,
AV_CH_LAYOUT_7POINT1,
AV_CH_LAYOUT_7POINT1_WIDE,
AV_CH_LAYOUT_7POINT1_WIDE_BACK,
AV_CH_LAYOUT_OCTAGONAL,
AV_CH_LAYOUT_HEXADECAGONAL,
AV_CH_LAYOUT_STEREO_DOWNMIX,
AVMatrixEncoding,
AV_MATRIX_ENCODING_NONE,
AV_MATRIX_ENCODING_DOLBY,
AV_MATRIX_ENCODING_DPLII,
AV_MATRIX_ENCODING_DPLIIX,
AV_MATRIX_ENCODING_DPLIIZ,
AV_MATRIX_ENCODING_DOLBYEX,
AV_MATRIX_ENCODING_DOLBYHEADPHONE,
AV_MATRIX_ENCODING_NB,
AVBPrint,
AVDES,
AVDownmixType,
AV_DOWNMIX_TYPE_UNKNOWN,
AV_DOWNMIX_TYPE_LORO,
AV_DOWNMIX_TYPE_LTRT,
AV_DOWNMIX_TYPE_DPLII,
AV_DOWNMIX_TYPE_NB,
AVDownmixInfo,
AVSubsampleEncryptionInfo,
AVEncryptionInfo,
AVEncryptionInitInfo,
AV_HASH_MAX_SIZE,
AVHashContext,
AVHWDeviceType,
AV_HWDEVICE_TYPE_NONE,
AV_HWDEVICE_TYPE_VDPAU,
AV_HWDEVICE_TYPE_CUDA,
AV_HWDEVICE_TYPE_VAAPI,
AV_HWDEVICE_TYPE_DXVA2,
AV_HWDEVICE_TYPE_QSV,
AV_HWDEVICE_TYPE_VIDEOTOOLBOX,
AV_HWDEVICE_TYPE_D3D11VA,
AV_HWDEVICE_TYPE_DRM,
AV_HWDEVICE_TYPE_OPENCL,
AV_HWDEVICE_TYPE_MEDIACODEC,
AVHWDeviceInternal,
AVHWDeviceContext,
AVHWFramesInternal,
AVHWFramesContext,
AVHWFrameTransferDirection,
AV_HWFRAME_TRANSFER_DIRECTION_FROM,
AV_HWFRAME_TRANSFER_DIRECTION_TO,
AVHWFramesConstraints,
ANONYMOUS_3,
AV_HWFRAME_MAP_READ,
AV_HWFRAME_MAP_WRITE,
AV_HWFRAME_MAP_OVERWRITE,
AV_HWFRAME_MAP_DIRECT,
AVCUDADeviceContextInternal,
AVCUDADeviceContext,
AVD3D11VADeviceContext,
AVD3D11FrameDescriptor,
AVD3D11VAFramesContext,
ANONYMOUS_4,
AV_DRM_MAX_PLANES,
AVDRMObjectDescriptor,
AVDRMPlaneDescriptor,
AVDRMLayerDescriptor,
AVDRMFrameDescriptor,
AVDRMDeviceContext,
AVDXVA2DeviceContext,
AVDXVA2FramesContext,
AVMediaCodecDeviceContext,
AVQSVDeviceContext,
AVQSVFramesContext,
ANONYMOUS_5,
AV_VAAPI_DRIVER_QUIRK_USER_SET,
AV_VAAPI_DRIVER_QUIRK_RENDER_PARAM_BUFFERS,
AV_VAAPI_DRIVER_QUIRK_ATTRIB_MEMTYPE,
AV_VAAPI_DRIVER_QUIRK_SURFACE_ATTRIBUTES,
# AVVAAPIDeviceContext,
# AVVAAPIFramesContext,
# AVVAAPIHWConfig,
# AVVDPAUDeviceContext,
AVMasteringDisplayMetadata,
AVContentLightMetadata,
AVMotionVector,
AV_OPT_FLAG_ENCODING_PARAM,
AV_OPT_FLAG_DECODING_PARAM,
AV_OPT_FLAG_AUDIO_PARAM,
AV_OPT_FLAG_VIDEO_PARAM,
AV_OPT_FLAG_SUBTITLE_PARAM,
AV_OPT_FLAG_EXPORT,
AV_OPT_FLAG_READONLY,
AV_OPT_FLAG_BSF_PARAM,
AV_OPT_FLAG_FILTERING_PARAM,
AV_OPT_FLAG_DEPRECATED,
AV_OPT_SEARCH_CHILDREN,
AV_OPT_SEARCH_FAKE_OBJ,
AV_OPT_ALLOW_NULL,
AV_OPT_MULTI_COMPONENT_RANGE,
AV_OPT_SERIALIZE_SKIP_DEFAULTS,
AV_OPT_SERIALIZE_OPT_FLAGS_EXACT,
ANONYMOUS_6,
AV_OPT_FLAG_IMPLICIT_KEY,
av_pixelutils_sad_fn,
AVRC4,
AVReplayGain,
AVSphericalProjection,
AV_SPHERICAL_EQUIRECTANGULAR,
AV_SPHERICAL_CUBEMAP,
AV_SPHERICAL_EQUIRECTANGULAR_TILE,
AVSphericalMapping,
AV_STEREO3D_FLAG_INVERT,
AVStereo3DType,
AV_STEREO3D_2D,
AV_STEREO3D_SIDEBYSIDE,
AV_STEREO3D_TOPBOTTOM,
AV_STEREO3D_FRAMESEQUENCE,
AV_STEREO3D_CHECKERBOARD,
AV_STEREO3D_SIDEBYSIDE_QUINCUNX,
AV_STEREO3D_LINES,
AV_STEREO3D_COLUMNS,
AVStereo3DView,
AV_STEREO3D_VIEW_PACKED,
AV_STEREO3D_VIEW_LEFT,
AV_STEREO3D_VIEW_RIGHT,
AVStereo3D,
AVTEA,
AVThreadMessageQueue,
AVThreadMessageFlags,
AV_THREAD_MESSAGE_NONBLOCK,
AV_TIMECODE_STR_SIZE,
AVTimecodeFlag,
AV_TIMECODE_FLAG_DROPFRAME,
AV_TIMECODE_FLAG_24HOURSMAX,
AV_TIMECODE_FLAG_ALLOWNEGATIVE,
AVTimecode,
AVTreeNode,
AVTWOFISH,
AVXTEA
const unix = 1
const linux = 1
const FF_LAMBDA_SHIFT = 7
const FF_LAMBDA_SCALE = 1 << FF_LAMBDA_SHIFT
const FF_QP2LAMBDA = 118
const FF_LAMBDA_MAX = 256 * 128 - 1
const FF_QUALITY_SCALE = FF_LAMBDA_SCALE
const AV_NOPTS_VALUE = reinterpret(Int64, 0x8000000000000000)
const AV_TIME_BASE = 1000000
struct AVRational
num::Cint
den::Cint
end
zero(::Type{AVRational}) = AVRational(0, 1)
const AV_TIME_BASE_Q = AVRational(1, AV_TIME_BASE)
# Skipping MacroDefinition: av_int_list_length ( list , term ) av_int_list_length_for_size ( sizeof ( * ( list ) ) , list , term )
const AV_FOURCC_MAX_STRING_SIZE = 32
# Skipping MacroDefinition: av_fourcc2str ( fourcc ) av_fourcc_make_string ( ( char [ AV_FOURCC_MAX_STRING_SIZE ] ) { 0 } , fourcc )
# begin enum AVMediaType
const AVMediaType = Cint
const AVMEDIA_TYPE_UNKNOWN = -1 |> Int32
const AVMEDIA_TYPE_VIDEO = 0 |> Int32
const AVMEDIA_TYPE_AUDIO = 1 |> Int32
const AVMEDIA_TYPE_DATA = 2 |> Int32
const AVMEDIA_TYPE_SUBTITLE = 3 |> Int32
const AVMEDIA_TYPE_ATTACHMENT = 4 |> Int32
const AVMEDIA_TYPE_NB = 5 |> Int32
# end enum AVMediaType
# begin enum AVPictureType
const AVPictureType = UInt32
const AV_PICTURE_TYPE_NONE = 0 |> UInt32
const AV_PICTURE_TYPE_I = 1 |> UInt32
const AV_PICTURE_TYPE_P = 2 |> UInt32
const AV_PICTURE_TYPE_B = 3 |> UInt32
const AV_PICTURE_TYPE_S = 4 |> UInt32
const AV_PICTURE_TYPE_SI = 5 |> UInt32
const AV_PICTURE_TYPE_SP = 6 |> UInt32
const AV_PICTURE_TYPE_BI = 7 |> UInt32
# end enum AVPictureType
const AV_BUFFER_FLAG_READONLY = 1 << 0
struct AVBuffer
end
struct AVBufferRef
buffer::Ptr{AVBuffer}
data::Ptr{UInt8}
size::Cint
end
struct AVBufferPool
end
const AV_DICT_MATCH_CASE = 1
const AV_DICT_IGNORE_SUFFIX = 2
const AV_DICT_DONT_STRDUP_KEY = 4
const AV_DICT_DONT_STRDUP_VAL = 8
const AV_DICT_DONT_OVERWRITE = 16
const AV_DICT_APPEND = 32
const AV_DICT_MULTIKEY = 64
struct AVDictionaryEntry
key::Cstring
value::Cstring
end
struct AVDictionary
end
struct AVFifoBuffer
buffer::Ptr{UInt8}
rptr::Ptr{UInt8}
wptr::Ptr{UInt8}
_end::Ptr{UInt8}
rndx::UInt32
wndx::UInt32
end
const AV_NUM_DATA_POINTERS = 8
const AV_FRAME_FLAG_CORRUPT = 1 << 0
const AV_FRAME_FLAG_DISCARD = 1 << 2
const FF_DECODE_ERROR_INVALID_BITSTREAM = 1
const FF_DECODE_ERROR_MISSING_REFERENCE = 2
# begin enum AVFrameSideDataType
const AVFrameSideDataType = UInt32
const AV_FRAME_DATA_PANSCAN = 0 |> UInt32
const AV_FRAME_DATA_A53_CC = 1 |> UInt32
const AV_FRAME_DATA_STEREO3D = 2 |> UInt32
const AV_FRAME_DATA_MATRIXENCODING = 3 |> UInt32
const AV_FRAME_DATA_DOWNMIX_INFO = 4 |> UInt32
const AV_FRAME_DATA_REPLAYGAIN = 5 |> UInt32
const AV_FRAME_DATA_DISPLAYMATRIX = 6 |> UInt32
const AV_FRAME_DATA_AFD = 7 |> UInt32
const AV_FRAME_DATA_MOTION_VECTORS = 8 |> UInt32
const AV_FRAME_DATA_SKIP_SAMPLES = 9 |> UInt32
const AV_FRAME_DATA_AUDIO_SERVICE_TYPE = 10 |> UInt32
const AV_FRAME_DATA_MASTERING_DISPLAY_METADATA = 11 |> UInt32
const AV_FRAME_DATA_GOP_TIMECODE = 12 |> UInt32
const AV_FRAME_DATA_SPHERICAL = 13 |> UInt32
const AV_FRAME_DATA_CONTENT_LIGHT_LEVEL = 14 |> UInt32
const AV_FRAME_DATA_ICC_PROFILE = 15 |> UInt32
const AV_FRAME_DATA_QP_TABLE_PROPERTIES = 16 |> UInt32
const AV_FRAME_DATA_QP_TABLE_DATA = 17 |> UInt32
const AV_FRAME_DATA_S12M_TIMECODE = 18 |> UInt32
# end enum AVFrameSideDataType
# begin enum AVActiveFormatDescription
const AVActiveFormatDescription = UInt32
const AV_AFD_SAME = 8 |> UInt32
const AV_AFD_4_3 = 9 |> UInt32
const AV_AFD_16_9 = 10 |> UInt32
const AV_AFD_14_9 = 11 |> UInt32
const AV_AFD_4_3_SP_14_9 = 13 |> UInt32
const AV_AFD_16_9_SP_14_9 = 14 |> UInt32
const AV_AFD_SP_4_3 = 15 |> UInt32
# end enum AVActiveFormatDescription
struct AVFrameSideData
_type::AVFrameSideDataType
data::Ptr{UInt8}
size::Cint
metadata::Ptr{AVDictionary}
buf::Ptr{AVBufferRef}
end
# begin enum AVColorRange
const AVColorRange = UInt32
const AVCOL_RANGE_UNSPECIFIED = 0 |> UInt32
const AVCOL_RANGE_MPEG = 1 |> UInt32
const AVCOL_RANGE_JPEG = 2 |> UInt32
const AVCOL_RANGE_NB = 3 |> UInt32
# end enum AVColorRange
# begin enum AVColorPrimaries
const AVColorPrimaries = UInt32
const AVCOL_PRI_RESERVED0 = 0 |> UInt32
const AVCOL_PRI_BT709 = 1 |> UInt32
const AVCOL_PRI_UNSPECIFIED = 2 |> UInt32
const AVCOL_PRI_RESERVED = 3 |> UInt32
const AVCOL_PRI_BT470M = 4 |> UInt32
const AVCOL_PRI_BT470BG = 5 |> UInt32
const AVCOL_PRI_SMPTE170M = 6 |> UInt32
const AVCOL_PRI_SMPTE240M = 7 |> UInt32
const AVCOL_PRI_FILM = 8 |> UInt32
const AVCOL_PRI_BT2020 = 9 |> UInt32
const AVCOL_PRI_SMPTE428 = 10 |> UInt32
const AVCOL_PRI_SMPTEST428_1 = 10 |> UInt32
const AVCOL_PRI_SMPTE431 = 11 |> UInt32
const AVCOL_PRI_SMPTE432 = 12 |> UInt32
const AVCOL_PRI_JEDEC_P22 = 22 |> UInt32
const AVCOL_PRI_NB = 23 |> UInt32
# end enum AVColorPrimaries
# begin enum AVColorTransferCharacteristic
const AVColorTransferCharacteristic = UInt32
const AVCOL_TRC_RESERVED0 = 0 |> UInt32
const AVCOL_TRC_BT709 = 1 |> UInt32
const AVCOL_TRC_UNSPECIFIED = 2 |> UInt32
const AVCOL_TRC_RESERVED = 3 |> UInt32
const AVCOL_TRC_GAMMA22 = 4 |> UInt32
const AVCOL_TRC_GAMMA28 = 5 |> UInt32
const AVCOL_TRC_SMPTE170M = 6 |> UInt32
const AVCOL_TRC_SMPTE240M = 7 |> UInt32
const AVCOL_TRC_LINEAR = 8 |> UInt32
const AVCOL_TRC_LOG = 9 |> UInt32
const AVCOL_TRC_LOG_SQRT = 10 |> UInt32
const AVCOL_TRC_IEC61966_2_4 = 11 |> UInt32
const AVCOL_TRC_BT1361_ECG = 12 |> UInt32
const AVCOL_TRC_IEC61966_2_1 = 13 |> UInt32
const AVCOL_TRC_BT2020_10 = 14 |> UInt32
const AVCOL_TRC_BT2020_12 = 15 |> UInt32
const AVCOL_TRC_SMPTE2084 = 16 |> UInt32
const AVCOL_TRC_SMPTEST2084 = 16 |> UInt32
const AVCOL_TRC_SMPTE428 = 17 |> UInt32
const AVCOL_TRC_SMPTEST428_1 = 17 |> UInt32
const AVCOL_TRC_ARIB_STD_B67 = 18 |> UInt32
const AVCOL_TRC_NB = 19 |> UInt32
# end enum AVColorTransferCharacteristic
# begin enum AVColorSpace
const AVColorSpace = UInt32
const AVCOL_SPC_RGB = 0 |> UInt32
const AVCOL_SPC_BT709 = 1 |> UInt32
const AVCOL_SPC_UNSPECIFIED = 2 |> UInt32
const AVCOL_SPC_RESERVED = 3 |> UInt32
const AVCOL_SPC_FCC = 4 |> UInt32
const AVCOL_SPC_BT470BG = 5 |> UInt32
const AVCOL_SPC_SMPTE170M = 6 |> UInt32
const AVCOL_SPC_SMPTE240M = 7 |> UInt32
const AVCOL_SPC_YCGCO = 8 |> UInt32
const AVCOL_SPC_YCOCG = 8 |> UInt32
const AVCOL_SPC_BT2020_NCL = 9 |> UInt32
const AVCOL_SPC_BT2020_CL = 10 |> UInt32
const AVCOL_SPC_SMPTE2085 = 11 |> UInt32
const AVCOL_SPC_CHROMA_DERIVED_NCL = 12 |> UInt32
const AVCOL_SPC_CHROMA_DERIVED_CL = 13 |> UInt32
const AVCOL_SPC_ICTCP = 14 |> UInt32
const AVCOL_SPC_NB = 15 |> UInt32
# end enum AVColorSpace
# begin enum AVChromaLocation
const AVChromaLocation = UInt32
const AVCHROMA_LOC_UNSPECIFIED = 0 |> UInt32
const AVCHROMA_LOC_LEFT = 1 |> UInt32
const AVCHROMA_LOC_CENTER = 2 |> UInt32
const AVCHROMA_LOC_TOPLEFT = 3 |> UInt32
const AVCHROMA_LOC_TOP = 4 |> UInt32
const AVCHROMA_LOC_BOTTOMLEFT = 5 |> UInt32
const AVCHROMA_LOC_BOTTOM = 6 |> UInt32
const AVCHROMA_LOC_NB = 7 |> UInt32
# end enum AVChromaLocation
zero(::Type{NTuple{N,P}}) where {N,P<:Ptr} = (N == 1 ? (C_NULL,) : (zero(NTuple{N-1,P})..., C_NULL))
zero(::Type{NTuple{N,T}}) where {N,T} = (N == 1 ? (zero(T),) : (zero(NTuple{N-1,T})..., zero(T)))
struct AVFrame
data::NTuple{8, Ptr{UInt8}}
linesize::NTuple{8, Cint}
extended_data::Ptr{Ptr{UInt8}}
width::Cint
height::Cint
nb_samples::Cint
format::Cint
key_frame::Cint
pict_type::AVPictureType
sample_aspect_ratio::AVRational
pts::Int64
pkt_pts::Int64
pkt_dts::Int64
coded_picture_number::Cint
display_picture_number::Cint
quality::Cint
opaque::Ptr{Cvoid}
error::NTuple{8, UInt64}
repeat_pict::Cint
interlaced_frame::Cint
top_field_first::Cint
palette_has_changed::Cint
reordered_opaque::Int64
sample_rate::Cint
channel_layout::UInt64
buf::NTuple{8, Ptr{AVBufferRef}}
extended_buf::Ptr{Ptr{AVBufferRef}}
nb_extended_buf::Cint
side_data::Ptr{Ptr{AVFrameSideData}}
nb_side_data::Cint
flags::Cint
color_range::AVColorRange
color_primaries::AVColorPrimaries
color_trc::AVColorTransferCharacteristic
colorspace::AVColorSpace
chroma_location::AVChromaLocation
best_effort_timestamp::Int64
pkt_pos::Int64
pkt_duration::Int64
metadata::Ptr{AVDictionary}
decode_error_flags::Cint
channels::Cint
pkt_size::Cint
qscale_table::Ptr{Int8}
qstride::Cint
qscale_type::Cint
qp_table_buf::Ptr{AVBufferRef}
hw_frames_ctx::Ptr{AVBufferRef}
opaque_ref::Ptr{AVBufferRef}
crop_top::Csize_t
crop_bottom::Csize_t
crop_left::Csize_t
crop_right::Csize_t
private_ref::Ptr{AVBufferRef}
end
# begin enum ANONYMOUS_1
const ANONYMOUS_1 = UInt32
const AV_FRAME_CROP_UNALIGNED = 1 |> UInt32
# end enum ANONYMOUS_1
# Skipping MacroDefinition: AV_IS_INPUT_DEVICE ( category ) ( ( ( category ) == AV_CLASS_CATEGORY_DEVICE_VIDEO_INPUT ) || ( ( category ) == AV_CLASS_CATEGORY_DEVICE_AUDIO_INPUT ) || ( ( category ) == AV_CLASS_CATEGORY_DEVICE_INPUT ) )
# Skipping MacroDefinition: AV_IS_OUTPUT_DEVICE ( category ) ( ( ( category ) == AV_CLASS_CATEGORY_DEVICE_VIDEO_OUTPUT ) || ( ( category ) == AV_CLASS_CATEGORY_DEVICE_AUDIO_OUTPUT ) || ( ( category ) == AV_CLASS_CATEGORY_DEVICE_OUTPUT ) )
const AV_LOG_QUIET = -8
const AV_LOG_PANIC = 0
const AV_LOG_FATAL = 8
const AV_LOG_ERROR = 16
const AV_LOG_WARNING = 24
const AV_LOG_INFO = 32
const AV_LOG_VERBOSE = 40
const AV_LOG_DEBUG = 48
const AV_LOG_TRACE = 56
const AV_LOG_MAX_OFFSET = AV_LOG_TRACE - AV_LOG_QUIET
# Skipping MacroDefinition: AV_LOG_C ( x ) ( ( x ) << 8 )
const AV_LOG_SKIP_REPEATED = 1
const AV_LOG_PRINT_LEVEL = 2
# begin enum ANONYMOUS_2
const ANONYMOUS_2 = UInt32
const AV_CLASS_CATEGORY_NA = 0 |> UInt32
const AV_CLASS_CATEGORY_INPUT = 1 |> UInt32
const AV_CLASS_CATEGORY_OUTPUT = 2 |> UInt32
const AV_CLASS_CATEGORY_MUXER = 3 |> UInt32
const AV_CLASS_CATEGORY_DEMUXER = 4 |> UInt32
const AV_CLASS_CATEGORY_ENCODER = 5 |> UInt32
const AV_CLASS_CATEGORY_DECODER = 6 |> UInt32
const AV_CLASS_CATEGORY_FILTER = 7 |> UInt32
const AV_CLASS_CATEGORY_BITSTREAM_FILTER = 8 |> UInt32
const AV_CLASS_CATEGORY_SWSCALER = 9 |> UInt32
const AV_CLASS_CATEGORY_SWRESAMPLER = 10 |> UInt32
const AV_CLASS_CATEGORY_DEVICE_VIDEO_OUTPUT = 40 |> UInt32
const AV_CLASS_CATEGORY_DEVICE_VIDEO_INPUT = 41 |> UInt32
const AV_CLASS_CATEGORY_DEVICE_AUDIO_OUTPUT = 42 |> UInt32
const AV_CLASS_CATEGORY_DEVICE_AUDIO_INPUT = 43 |> UInt32
const AV_CLASS_CATEGORY_DEVICE_OUTPUT = 44 |> UInt32
const AV_CLASS_CATEGORY_DEVICE_INPUT = 45 |> UInt32
const AV_CLASS_CATEGORY_NB = 46 |> UInt32
# end enum ANONYMOUS_2
const AVClassCategory = Cint
struct AVOptionRange
str::Cstring
value_min::Cdouble
value_max::Cdouble
component_min::Cdouble
component_max::Cdouble
is_range::Cint
end
struct AVOptionRanges
range::Ptr{Ptr{AVOptionRange}}
nb_ranges::Cint
nb_components::Cint
end
# begin enum AVOptionType
const AVOptionType = UInt32
const AV_OPT_TYPE_FLAGS = 0 |> UInt32
const AV_OPT_TYPE_INT = 1 |> UInt32
const AV_OPT_TYPE_INT64 = 2 |> UInt32
const AV_OPT_TYPE_DOUBLE = 3 |> UInt32
const AV_OPT_TYPE_FLOAT = 4 |> UInt32
const AV_OPT_TYPE_STRING = 5 |> UInt32
const AV_OPT_TYPE_RATIONAL = 6 |> UInt32
const AV_OPT_TYPE_BINARY = 7 |> UInt32
const AV_OPT_TYPE_DICT = 8 |> UInt32
const AV_OPT_TYPE_UINT64 = 9 |> UInt32
const AV_OPT_TYPE_CONST = 10 |> UInt32
const AV_OPT_TYPE_IMAGE_SIZE = 11 |> UInt32
const AV_OPT_TYPE_PIXEL_FMT = 12 |> UInt32
const AV_OPT_TYPE_SAMPLE_FMT = 13 |> UInt32
const AV_OPT_TYPE_VIDEO_RATE = 14 |> UInt32
const AV_OPT_TYPE_DURATION = 15 |> UInt32
const AV_OPT_TYPE_COLOR = 16 |> UInt32
const AV_OPT_TYPE_CHANNEL_LAYOUT = 17 |> UInt32
const AV_OPT_TYPE_BOOL = 18 |> UInt32
# end enum AVOptionType
struct AVOption
name::Cstring
help::Cstring
offset::Cint
_type::AVOptionType
default_val::Int64
min::Cdouble
max::Cdouble
flags::Cint
unit::Cstring
end
struct AVClass
class_name::Cstring
item_name::Ptr{Cvoid}
option::Ptr{AVOption}
version::Cint
log_level_offset_offset::Cint
parent_log_context_offset::Cint
child_next::Ptr{Cvoid}
child_class_next::Ptr{Cvoid}
category::AVClassCategory
get_category::Ptr{Cvoid}
query_ranges::Ptr{Cvoid}
end
# Skipping MacroDefinition: DECLARE_ALIGNED ( n , t , v ) t __attribute__ ( ( aligned ( n ) ) ) v
# Skipping MacroDefinition: DECLARE_ASM_ALIGNED ( n , t , v ) t av_used __attribute__ ( ( aligned ( n ) ) ) v
# Skipping MacroDefinition: DECLARE_ASM_CONST ( n , t , v ) static const t av_used __attribute__ ( ( aligned ( n ) ) ) v
# Skipping MacroDefinition: av_malloc_attrib __attribute__ ( ( __malloc__ ) )
# Skipping MacroDefinition: av_alloc_size ( ... )
const AV_PIX_FMT_FLAG_BE = 1 << 0
const AV_PIX_FMT_FLAG_PAL = 1 << 1
const AV_PIX_FMT_FLAG_BITSTREAM = 1 << 2
const AV_PIX_FMT_FLAG_HWACCEL = 1 << 3
const AV_PIX_FMT_FLAG_PLANAR = 1 << 4
const AV_PIX_FMT_FLAG_RGB = 1 << 5
const AV_PIX_FMT_FLAG_PSEUDOPAL = 1 << 6
const AV_PIX_FMT_FLAG_ALPHA = 1 << 7
const AV_PIX_FMT_FLAG_BAYER = 1 << 8
const AV_PIX_FMT_FLAG_FLOAT = 1 << 9
const FF_LOSS_RESOLUTION = 0x0001
const FF_LOSS_DEPTH = 0x0002
const FF_LOSS_COLORSPACE = 0x0004
const FF_LOSS_ALPHA = 0x0008
const FF_LOSS_COLORQUANT = 0x0010
const FF_LOSS_CHROMA = 0x0020
struct AVComponentDescriptor
plane::Cint
step::Cint
offset::Cint
shift::Cint
depth::Cint
step_minus1::Cint
depth_minus1::Cint
offset_plus1::Cint
end
struct AVPixFmtDescriptor
name::Cstring
nb_components::UInt8
log2_chroma_w::UInt8
log2_chroma_h::UInt8
flags::UInt64
comp::NTuple{4, AVComponentDescriptor}
alias::Cstring
end
const AVPALETTE_SIZE = 1024
const AVPALETTE_COUNT = 256
# begin enum AVPixelFormat
const AVPixelFormat = Cint
const AV_PIX_FMT_NONE = -1 |> Int32
const AV_PIX_FMT_YUV420P = 0 |> Int32
const AV_PIX_FMT_YUYV422 = 1 |> Int32
const AV_PIX_FMT_RGB24 = 2 |> Int32
const AV_PIX_FMT_BGR24 = 3 |> Int32
const AV_PIX_FMT_YUV422P = 4 |> Int32
const AV_PIX_FMT_YUV444P = 5 |> Int32
const AV_PIX_FMT_YUV410P = 6 |> Int32
const AV_PIX_FMT_YUV411P = 7 |> Int32
const AV_PIX_FMT_GRAY8 = 8 |> Int32
const AV_PIX_FMT_MONOWHITE = 9 |> Int32
const AV_PIX_FMT_MONOBLACK = 10 |> Int32
const AV_PIX_FMT_PAL8 = 11 |> Int32
const AV_PIX_FMT_YUVJ420P = 12 |> Int32
const AV_PIX_FMT_YUVJ422P = 13 |> Int32
const AV_PIX_FMT_YUVJ444P = 14 |> Int32
const AV_PIX_FMT_UYVY422 = 15 |> Int32
const AV_PIX_FMT_UYYVYY411 = 16 |> Int32
const AV_PIX_FMT_BGR8 = 17 |> Int32
const AV_PIX_FMT_BGR4 = 18 |> Int32
const AV_PIX_FMT_BGR4_BYTE = 19 |> Int32
const AV_PIX_FMT_RGB8 = 20 |> Int32
const AV_PIX_FMT_RGB4 = 21 |> Int32
const AV_PIX_FMT_RGB4_BYTE = 22 |> Int32
const AV_PIX_FMT_NV12 = 23 |> Int32
const AV_PIX_FMT_NV21 = 24 |> Int32
const AV_PIX_FMT_ARGB = 25 |> Int32
const AV_PIX_FMT_RGBA = 26 |> Int32
const AV_PIX_FMT_ABGR = 27 |> Int32
const AV_PIX_FMT_BGRA = 28 |> Int32
const AV_PIX_FMT_GRAY16BE = 29 |> Int32
const AV_PIX_FMT_GRAY16LE = 30 |> Int32
const AV_PIX_FMT_YUV440P = 31 |> Int32
const AV_PIX_FMT_YUVJ440P = 32 |> Int32
const AV_PIX_FMT_YUVA420P = 33 |> Int32
const AV_PIX_FMT_RGB48BE = 34 |> Int32
const AV_PIX_FMT_RGB48LE = 35 |> Int32
const AV_PIX_FMT_RGB565BE = 36 |> Int32
const AV_PIX_FMT_RGB565LE = 37 |> Int32
const AV_PIX_FMT_RGB555BE = 38 |> Int32
const AV_PIX_FMT_RGB555LE = 39 |> Int32
const AV_PIX_FMT_BGR565BE = 40 |> Int32
const AV_PIX_FMT_BGR565LE = 41 |> Int32
const AV_PIX_FMT_BGR555BE = 42 |> Int32
const AV_PIX_FMT_BGR555LE = 43 |> Int32
const AV_PIX_FMT_VAAPI_MOCO = 44 |> Int32
const AV_PIX_FMT_VAAPI_IDCT = 45 |> Int32
const AV_PIX_FMT_VAAPI_VLD = 46 |> Int32
const AV_PIX_FMT_VAAPI = 46 |> Int32
const AV_PIX_FMT_YUV420P16LE = 47 |> Int32
const AV_PIX_FMT_YUV420P16BE = 48 |> Int32
const AV_PIX_FMT_YUV422P16LE = 49 |> Int32
const AV_PIX_FMT_YUV422P16BE = 50 |> Int32
const AV_PIX_FMT_YUV444P16LE = 51 |> Int32
const AV_PIX_FMT_YUV444P16BE = 52 |> Int32
const AV_PIX_FMT_DXVA2_VLD = 53 |> Int32
const AV_PIX_FMT_RGB444LE = 54 |> Int32
const AV_PIX_FMT_RGB444BE = 55 |> Int32
const AV_PIX_FMT_BGR444LE = 56 |> Int32
const AV_PIX_FMT_BGR444BE = 57 |> Int32
const AV_PIX_FMT_YA8 = 58 |> Int32
const AV_PIX_FMT_Y400A = 58 |> Int32
const AV_PIX_FMT_GRAY8A = 58 |> Int32
const AV_PIX_FMT_BGR48BE = 59 |> Int32
const AV_PIX_FMT_BGR48LE = 60 |> Int32
const AV_PIX_FMT_YUV420P9BE = 61 |> Int32
const AV_PIX_FMT_YUV420P9LE = 62 |> Int32
const AV_PIX_FMT_YUV420P10BE = 63 |> Int32
const AV_PIX_FMT_YUV420P10LE = 64 |> Int32
const AV_PIX_FMT_YUV422P10BE = 65 |> Int32
const AV_PIX_FMT_YUV422P10LE = 66 |> Int32
const AV_PIX_FMT_YUV444P9BE = 67 |> Int32
const AV_PIX_FMT_YUV444P9LE = 68 |> Int32
const AV_PIX_FMT_YUV444P10BE = 69 |> Int32
const AV_PIX_FMT_YUV444P10LE = 70 |> Int32
const AV_PIX_FMT_YUV422P9BE = 71 |> Int32
const AV_PIX_FMT_YUV422P9LE = 72 |> Int32
const AV_PIX_FMT_GBRP = 73 |> Int32
const AV_PIX_FMT_GBR24P = 73 |> Int32
const AV_PIX_FMT_GBRP9BE = 74 |> Int32
const AV_PIX_FMT_GBRP9LE = 75 |> Int32
const AV_PIX_FMT_GBRP10BE = 76 |> Int32
const AV_PIX_FMT_GBRP10LE = 77 |> Int32
const AV_PIX_FMT_GBRP16BE = 78 |> Int32
const AV_PIX_FMT_GBRP16LE = 79 |> Int32
const AV_PIX_FMT_YUVA422P = 80 |> Int32
const AV_PIX_FMT_YUVA444P = 81 |> Int32
const AV_PIX_FMT_YUVA420P9BE = 82 |> Int32
const AV_PIX_FMT_YUVA420P9LE = 83 |> Int32
const AV_PIX_FMT_YUVA422P9BE = 84 |> Int32
const AV_PIX_FMT_YUVA422P9LE = 85 |> Int32
const AV_PIX_FMT_YUVA444P9BE = 86 |> Int32
const AV_PIX_FMT_YUVA444P9LE = 87 |> Int32
const AV_PIX_FMT_YUVA420P10BE = 88 |> Int32
const AV_PIX_FMT_YUVA420P10LE = 89 |> Int32
const AV_PIX_FMT_YUVA422P10BE = 90 |> Int32
const AV_PIX_FMT_YUVA422P10LE = 91 |> Int32
const AV_PIX_FMT_YUVA444P10BE = 92 |> Int32
const AV_PIX_FMT_YUVA444P10LE = 93 |> Int32
const AV_PIX_FMT_YUVA420P16BE = 94 |> Int32
const AV_PIX_FMT_YUVA420P16LE = 95 |> Int32
const AV_PIX_FMT_YUVA422P16BE = 96 |> Int32
const AV_PIX_FMT_YUVA422P16LE = 97 |> Int32
const AV_PIX_FMT_YUVA444P16BE = 98 |> Int32
const AV_PIX_FMT_YUVA444P16LE = 99 |> Int32
const AV_PIX_FMT_VDPAU = 100 |> Int32
const AV_PIX_FMT_XYZ12LE = 101 |> Int32
const AV_PIX_FMT_XYZ12BE = 102 |> Int32
const AV_PIX_FMT_NV16 = 103 |> Int32
const AV_PIX_FMT_NV20LE = 104 |> Int32
const AV_PIX_FMT_NV20BE = 105 |> Int32
const AV_PIX_FMT_RGBA64BE = 106 |> Int32
const AV_PIX_FMT_RGBA64LE = 107 |> Int32
const AV_PIX_FMT_BGRA64BE = 108 |> Int32
const AV_PIX_FMT_BGRA64LE = 109 |> Int32
const AV_PIX_FMT_YVYU422 = 110 |> Int32
const AV_PIX_FMT_YA16BE = 111 |> Int32
const AV_PIX_FMT_YA16LE = 112 |> Int32
const AV_PIX_FMT_GBRAP = 113 |> Int32
const AV_PIX_FMT_GBRAP16BE = 114 |> Int32
const AV_PIX_FMT_GBRAP16LE = 115 |> Int32
const AV_PIX_FMT_QSV = 116 |> Int32
const AV_PIX_FMT_MMAL = 117 |> Int32
const AV_PIX_FMT_D3D11VA_VLD = 118 |> Int32
const AV_PIX_FMT_CUDA = 119 |> Int32
const AV_PIX_FMT_0RGB = 120 |> Int32
const AV_PIX_FMT_RGB0 = 121 |> Int32
const AV_PIX_FMT_0BGR = 122 |> Int32
const AV_PIX_FMT_BGR0 = 123 |> Int32
const AV_PIX_FMT_YUV420P12BE = 124 |> Int32
const AV_PIX_FMT_YUV420P12LE = 125 |> Int32
const AV_PIX_FMT_YUV420P14BE = 126 |> Int32
const AV_PIX_FMT_YUV420P14LE = 127 |> Int32
const AV_PIX_FMT_YUV422P12BE = 128 |> Int32
const AV_PIX_FMT_YUV422P12LE = 129 |> Int32
const AV_PIX_FMT_YUV422P14BE = 130 |> Int32
const AV_PIX_FMT_YUV422P14LE = 131 |> Int32
const AV_PIX_FMT_YUV444P12BE = 132 |> Int32
const AV_PIX_FMT_YUV444P12LE = 133 |> Int32
const AV_PIX_FMT_YUV444P14BE = 134 |> Int32
const AV_PIX_FMT_YUV444P14LE = 135 |> Int32
const AV_PIX_FMT_GBRP12BE = 136 |> Int32
const AV_PIX_FMT_GBRP12LE = 137 |> Int32
const AV_PIX_FMT_GBRP14BE = 138 |> Int32
const AV_PIX_FMT_GBRP14LE = 139 |> Int32
const AV_PIX_FMT_YUVJ411P = 140 |> Int32
const AV_PIX_FMT_BAYER_BGGR8 = 141 |> Int32
const AV_PIX_FMT_BAYER_RGGB8 = 142 |> Int32
const AV_PIX_FMT_BAYER_GBRG8 = 143 |> Int32
const AV_PIX_FMT_BAYER_GRBG8 = 144 |> Int32
const AV_PIX_FMT_BAYER_BGGR16LE = 145 |> Int32
const AV_PIX_FMT_BAYER_BGGR16BE = 146 |> Int32
const AV_PIX_FMT_BAYER_RGGB16LE = 147 |> Int32
const AV_PIX_FMT_BAYER_RGGB16BE = 148 |> Int32
const AV_PIX_FMT_BAYER_GBRG16LE = 149 |> Int32
const AV_PIX_FMT_BAYER_GBRG16BE = 150 |> Int32
const AV_PIX_FMT_BAYER_GRBG16LE = 151 |> Int32
const AV_PIX_FMT_BAYER_GRBG16BE = 152 |> Int32
const AV_PIX_FMT_XVMC = 153 |> Int32
const AV_PIX_FMT_YUV440P10LE = 154 |> Int32
const AV_PIX_FMT_YUV440P10BE = 155 |> Int32
const AV_PIX_FMT_YUV440P12LE = 156 |> Int32
const AV_PIX_FMT_YUV440P12BE = 157 |> Int32
const AV_PIX_FMT_AYUV64LE = 158 |> Int32
const AV_PIX_FMT_AYUV64BE = 159 |> Int32
const AV_PIX_FMT_VIDEOTOOLBOX = 160 |> Int32
const AV_PIX_FMT_P010LE = 161 |> Int32
const AV_PIX_FMT_P010BE = 162 |> Int32
const AV_PIX_FMT_GBRAP12BE = 163 |> Int32
const AV_PIX_FMT_GBRAP12LE = 164 |> Int32
const AV_PIX_FMT_GBRAP10BE = 165 |> Int32
const AV_PIX_FMT_GBRAP10LE = 166 |> Int32
const AV_PIX_FMT_MEDIACODEC = 167 |> Int32
const AV_PIX_FMT_GRAY12BE = 168 |> Int32
const AV_PIX_FMT_GRAY12LE = 169 |> Int32
const AV_PIX_FMT_GRAY10BE = 170 |> Int32
const AV_PIX_FMT_GRAY10LE = 171 |> Int32
const AV_PIX_FMT_P016LE = 172 |> Int32
const AV_PIX_FMT_P016BE = 173 |> Int32
const AV_PIX_FMT_D3D11 = 174 |> Int32
const AV_PIX_FMT_GRAY9BE = 175 |> Int32
const AV_PIX_FMT_GRAY9LE = 176 |> Int32
const AV_PIX_FMT_GBRPF32BE = 177 |> Int32
const AV_PIX_FMT_GBRPF32LE = 178 |> Int32
const AV_PIX_FMT_GBRAPF32BE = 179 |> Int32
const AV_PIX_FMT_GBRAPF32LE = 180 |> Int32
const AV_PIX_FMT_DRM_PRIME = 181 |> Int32
const AV_PIX_FMT_OPENCL = 182 |> Int32
const AV_PIX_FMT_GRAY14BE = 183 |> Int32
const AV_PIX_FMT_GRAY14LE = 184 |> Int32
const AV_PIX_FMT_GRAYF32BE = 185 |> Int32
const AV_PIX_FMT_GRAYF32LE = 186 |> Int32
const AV_PIX_FMT_NB = 187 |> Int32
# end enum AVPixelFormat
macro AV_PIX_FMT_NE(be, le)
Symbol("AV_PIX_FMT_"*string(le))
end
const AV_PIX_FMT_RGB32 = @AV_PIX_FMT_NE(ARGB, BGRA)
const AV_PIX_FMT_RGB32_1 = @AV_PIX_FMT_NE(RGBA, ABGR)
const AV_PIX_FMT_BGR32 = @AV_PIX_FMT_NE(ABGR, RGBA)
const AV_PIX_FMT_BGR32_1 = @AV_PIX_FMT_NE(BGRA, ARGB)
const AV_PIX_FMT_0RGB32 = @AV_PIX_FMT_NE(0RGB, BGR0)
const AV_PIX_FMT_0BGR32 = @AV_PIX_FMT_NE(0BGR, RGB0)
const AV_PIX_FMT_GRAY9 = @AV_PIX_FMT_NE(GRAY9BE, GRAY9LE)
const AV_PIX_FMT_GRAY10 = @AV_PIX_FMT_NE(GRAY10BE, GRAY10LE)
const AV_PIX_FMT_GRAY12 = @AV_PIX_FMT_NE(GRAY12BE, GRAY12LE)
const AV_PIX_FMT_GRAY14 = @AV_PIX_FMT_NE(GRAY14BE, GRAY14LE)
const AV_PIX_FMT_GRAY16 = @AV_PIX_FMT_NE(GRAY16BE, GRAY16LE)
const AV_PIX_FMT_YA16 = @AV_PIX_FMT_NE(YA16BE, YA16LE)
const AV_PIX_FMT_RGB48 = @AV_PIX_FMT_NE(RGB48BE, RGB48LE)
const AV_PIX_FMT_RGB565 = @AV_PIX_FMT_NE(RGB565BE, RGB565LE)
const AV_PIX_FMT_RGB555 = @AV_PIX_FMT_NE(RGB555BE, RGB555LE)
const AV_PIX_FMT_RGB444 = @AV_PIX_FMT_NE(RGB444BE, RGB444LE)
const AV_PIX_FMT_RGBA64 = @AV_PIX_FMT_NE(RGBA64BE, RGBA64LE)
const AV_PIX_FMT_BGR48 = @AV_PIX_FMT_NE(BGR48BE, BGR48LE)
const AV_PIX_FMT_BGR565 = @AV_PIX_FMT_NE(BGR565BE, BGR565LE)
const AV_PIX_FMT_BGR555 = @AV_PIX_FMT_NE(BGR555BE, BGR555LE)
const AV_PIX_FMT_BGR444 = @AV_PIX_FMT_NE(BGR444BE, BGR444LE)
const AV_PIX_FMT_BGRA64 = @AV_PIX_FMT_NE(BGRA64BE, BGRA64LE)
const AV_PIX_FMT_YUV420P9 = @AV_PIX_FMT_NE(YUV420P9BE, YUV420P9LE)
const AV_PIX_FMT_YUV422P9 = @AV_PIX_FMT_NE(YUV422P9BE, YUV422P9LE)
const AV_PIX_FMT_YUV444P9 = @AV_PIX_FMT_NE(YUV444P9BE, YUV444P9LE)
const AV_PIX_FMT_YUV420P10 = @AV_PIX_FMT_NE(YUV420P10BE, YUV420P10LE)
const AV_PIX_FMT_YUV422P10 = @AV_PIX_FMT_NE(YUV422P10BE, YUV422P10LE)
const AV_PIX_FMT_YUV440P10 = @AV_PIX_FMT_NE(YUV440P10BE, YUV440P10LE)
const AV_PIX_FMT_YUV444P10 = @AV_PIX_FMT_NE(YUV444P10BE, YUV444P10LE)
const AV_PIX_FMT_YUV420P12 = @AV_PIX_FMT_NE(YUV420P12BE, YUV420P12LE)
const AV_PIX_FMT_YUV422P12 = @AV_PIX_FMT_NE(YUV422P12BE, YUV422P12LE)
const AV_PIX_FMT_YUV440P12 = @AV_PIX_FMT_NE(YUV440P12BE, YUV440P12LE)
const AV_PIX_FMT_YUV444P12 = @AV_PIX_FMT_NE(YUV444P12BE, YUV444P12LE)
const AV_PIX_FMT_YUV420P14 = @AV_PIX_FMT_NE(YUV420P14BE, YUV420P14LE)
const AV_PIX_FMT_YUV422P14 = @AV_PIX_FMT_NE(YUV422P14BE, YUV422P14LE)
const AV_PIX_FMT_YUV444P14 = @AV_PIX_FMT_NE(YUV444P14BE, YUV444P14LE)
const AV_PIX_FMT_YUV420P16 = @AV_PIX_FMT_NE(YUV420P16BE, YUV420P16LE)
const AV_PIX_FMT_YUV422P16 = @AV_PIX_FMT_NE(YUV422P16BE, YUV422P16LE)
const AV_PIX_FMT_YUV444P16 = @AV_PIX_FMT_NE(YUV444P16BE, YUV444P16LE)
const AV_PIX_FMT_GBRP9 = @AV_PIX_FMT_NE(GBRP9BE, GBRP9LE)
const AV_PIX_FMT_GBRP10 = @AV_PIX_FMT_NE(GBRP10BE, GBRP10LE)
const AV_PIX_FMT_GBRP12 = @AV_PIX_FMT_NE(GBRP12BE, GBRP12LE)
const AV_PIX_FMT_GBRP14 = @AV_PIX_FMT_NE(GBRP14BE, GBRP14LE)
const AV_PIX_FMT_GBRP16 = @AV_PIX_FMT_NE(GBRP16BE, GBRP16LE)
const AV_PIX_FMT_GBRAP10 = @AV_PIX_FMT_NE(GBRAP10BE, GBRAP10LE)
const AV_PIX_FMT_GBRAP12 = @AV_PIX_FMT_NE(GBRAP12BE, GBRAP12LE)
const AV_PIX_FMT_GBRAP16 = @AV_PIX_FMT_NE(GBRAP16BE, GBRAP16LE)
const AV_PIX_FMT_BAYER_BGGR16 = @AV_PIX_FMT_NE(BAYER_BGGR16BE, BAYER_BGGR16LE)
const AV_PIX_FMT_BAYER_RGGB16 = @AV_PIX_FMT_NE(BAYER_RGGB16BE, BAYER_RGGB16LE)
const AV_PIX_FMT_BAYER_GBRG16 = @AV_PIX_FMT_NE(BAYER_GBRG16BE, BAYER_GBRG16LE)
const AV_PIX_FMT_BAYER_GRBG16 = @AV_PIX_FMT_NE(BAYER_GRBG16BE, BAYER_GRBG16LE)
const AV_PIX_FMT_GBRPF32 = @AV_PIX_FMT_NE(GBRPF32BE, GBRPF32LE)
const AV_PIX_FMT_GBRAPF32 = @AV_PIX_FMT_NE(GBRAPF32BE, GBRAPF32LE)
const AV_PIX_FMT_GRAYF32 = @AV_PIX_FMT_NE(GRAYF32BE, GRAYF32LE)
const AV_PIX_FMT_YUVA420P9 = @AV_PIX_FMT_NE(YUVA420P9BE, YUVA420P9LE)
const AV_PIX_FMT_YUVA422P9 = @AV_PIX_FMT_NE(YUVA422P9BE, YUVA422P9LE)
const AV_PIX_FMT_YUVA444P9 = @AV_PIX_FMT_NE(YUVA444P9BE, YUVA444P9LE)
const AV_PIX_FMT_YUVA420P10 = @AV_PIX_FMT_NE(YUVA420P10BE, YUVA420P10LE)
const AV_PIX_FMT_YUVA422P10 = @AV_PIX_FMT_NE(YUVA422P10BE, YUVA422P10LE)
const AV_PIX_FMT_YUVA444P10 = @AV_PIX_FMT_NE(YUVA444P10BE, YUVA444P10LE)
const AV_PIX_FMT_YUVA420P16 = @AV_PIX_FMT_NE(YUVA420P16BE, YUVA420P16LE)
const AV_PIX_FMT_YUVA422P16 = @AV_PIX_FMT_NE(YUVA422P16BE, YUVA422P16LE)
const AV_PIX_FMT_YUVA444P16 = @AV_PIX_FMT_NE(YUVA444P16BE, YUVA444P16LE)
const AV_PIX_FMT_XYZ12 = @AV_PIX_FMT_NE(XYZ12BE, XYZ12LE)
const AV_PIX_FMT_NV20 = @AV_PIX_FMT_NE(NV20BE, NV20LE)
const AV_PIX_FMT_AYUV64 = @AV_PIX_FMT_NE(AYUV64BE, AYUV64LE)
const AV_PIX_FMT_P010 = @AV_PIX_FMT_NE(P010BE, P010LE)
const AV_PIX_FMT_P016 = @AV_PIX_FMT_NE(P016BE, P016LE)
# begin enum AVColorPrimaries
const AVColorPrimaries = UInt32
const AVCOL_PRI_RESERVED0 = 0 |> UInt32
const AVCOL_PRI_BT709 = 1 |> UInt32
const AVCOL_PRI_UNSPECIFIED = 2 |> UInt32
const AVCOL_PRI_RESERVED = 3 |> UInt32
const AVCOL_PRI_BT470M = 4 |> UInt32
const AVCOL_PRI_BT470BG = 5 |> UInt32
const AVCOL_PRI_SMPTE170M = 6 |> UInt32
const AVCOL_PRI_SMPTE240M = 7 |> UInt32
const AVCOL_PRI_FILM = 8 |> UInt32
const AVCOL_PRI_BT2020 = 9 |> UInt32
const AVCOL_PRI_SMPTE428 = 10 |> UInt32
const AVCOL_PRI_SMPTEST428_1 = 10 |> UInt32
const AVCOL_PRI_SMPTE431 = 11 |> UInt32
const AVCOL_PRI_SMPTE432 = 12 |> UInt32
const AVCOL_PRI_JEDEC_P22 = 22 |> UInt32
const AVCOL_PRI_NB = 23 |> UInt32
# end enum AVColorPrimaries
# begin enum AVColorTransferCharacteristic
const AVColorTransferCharacteristic = UInt32
const AVCOL_TRC_RESERVED0 = 0 |> UInt32
const AVCOL_TRC_BT709 = 1 |> UInt32
const AVCOL_TRC_UNSPECIFIED = 2 |> UInt32
const AVCOL_TRC_RESERVED = 3 |> UInt32
const AVCOL_TRC_GAMMA22 = 4 |> UInt32
const AVCOL_TRC_GAMMA28 = 5 |> UInt32
const AVCOL_TRC_SMPTE170M = 6 |> UInt32
const AVCOL_TRC_SMPTE240M = 7 |> UInt32
const AVCOL_TRC_LINEAR = 8 |> UInt32
const AVCOL_TRC_LOG = 9 |> UInt32
const AVCOL_TRC_LOG_SQRT = 10 |> UInt32
const AVCOL_TRC_IEC61966_2_4 = 11 |> UInt32
const AVCOL_TRC_BT1361_ECG = 12 |> UInt32
const AVCOL_TRC_IEC61966_2_1 = 13 |> UInt32
const AVCOL_TRC_BT2020_10 = 14 |> UInt32
const AVCOL_TRC_BT2020_12 = 15 |> UInt32
const AVCOL_TRC_SMPTE2084 = 16 |> UInt32
const AVCOL_TRC_SMPTEST2084 = 16 |> UInt32
const AVCOL_TRC_SMPTE428 = 17 |> UInt32
const AVCOL_TRC_SMPTEST428_1 = 17 |> UInt32
const AVCOL_TRC_ARIB_STD_B67 = 18 |> UInt32
const AVCOL_TRC_NB = 19 |> UInt32
# end enum AVColorTransferCharacteristic
# begin enum AVColorSpace
const AVColorSpace = UInt32
const AVCOL_SPC_RGB = 0 |> UInt32
const AVCOL_SPC_BT709 = 1 |> UInt32
const AVCOL_SPC_UNSPECIFIED = 2 |> UInt32
const AVCOL_SPC_RESERVED = 3 |> UInt32
const AVCOL_SPC_FCC = 4 |> UInt32
const AVCOL_SPC_BT470BG = 5 |> UInt32
const AVCOL_SPC_SMPTE170M = 6 |> UInt32
const AVCOL_SPC_SMPTE240M = 7 |> UInt32
const AVCOL_SPC_YCGCO = 8 |> UInt32
const AVCOL_SPC_YCOCG = 8 |> UInt32
const AVCOL_SPC_BT2020_NCL = 9 |> UInt32
const AVCOL_SPC_BT2020_CL = 10 |> UInt32
const AVCOL_SPC_SMPTE2085 = 11 |> UInt32
const AVCOL_SPC_CHROMA_DERIVED_NCL = 12 |> UInt32
const AVCOL_SPC_CHROMA_DERIVED_CL = 13 |> UInt32
const AVCOL_SPC_ICTCP = 14 |> UInt32
const AVCOL_SPC_NB = 15 |> UInt32
# end enum AVColorSpace
# begin enum AVColorRange
const AVColorRange = UInt32
const AVCOL_RANGE_UNSPECIFIED = 0 |> UInt32
const AVCOL_RANGE_MPEG = 1 |> UInt32
const AVCOL_RANGE_JPEG = 2 |> UInt32
const AVCOL_RANGE_NB = 3 |> UInt32
# end enum AVColorRange
# begin enum AVChromaLocation
const AVChromaLocation = UInt32
const AVCHROMA_LOC_UNSPECIFIED = 0 |> UInt32
const AVCHROMA_LOC_LEFT = 1 |> UInt32
const AVCHROMA_LOC_CENTER = 2 |> UInt32
const AVCHROMA_LOC_TOPLEFT = 3 |> UInt32
const AVCHROMA_LOC_TOP = 4 |> UInt32
const AVCHROMA_LOC_BOTTOMLEFT = 5 |> UInt32
const AVCHROMA_LOC_BOTTOM = 6 |> UInt32
const AVCHROMA_LOC_NB = 7 |> UInt32
# end enum AVChromaLocation
# begin enum AVSampleFormat
const AVSampleFormat = Cint
const AV_SAMPLE_FMT_NONE = -1 |> Int32
const AV_SAMPLE_FMT_U8 = 0 |> Int32
const AV_SAMPLE_FMT_S16 = 1 |> Int32
const AV_SAMPLE_FMT_S32 = 2 |> Int32
const AV_SAMPLE_FMT_FLT = 3 |> Int32
const AV_SAMPLE_FMT_DBL = 4 |> Int32
const AV_SAMPLE_FMT_U8P = 5 |> Int32
const AV_SAMPLE_FMT_S16P = 6 |> Int32
const AV_SAMPLE_FMT_S32P = 7 |> Int32
const AV_SAMPLE_FMT_FLTP = 8 |> Int32
const AV_SAMPLE_FMT_DBLP = 9 |> Int32
const AV_SAMPLE_FMT_S64 = 10 |> Int32
const AV_SAMPLE_FMT_S64P = 11 |> Int32
const AV_SAMPLE_FMT_NB = 12 |> Int32
# end enum AVSampleFormat
# Skipping MacroDefinition: AV_VERSION_INT ( a , b , c ) ( ( a ) << 16 | ( b ) << 8 | ( c ) )
# Skipping MacroDefinition: AV_VERSION_DOT ( a , b , c ) a ## . ## b ## . ## c
# Skipping MacroDefinition: AV_VERSION ( a , b , c ) AV_VERSION_DOT ( a , b , c )
# Skipping MacroDefinition: AV_VERSION_MAJOR ( a ) ( ( a ) >> 16 )
# Skipping MacroDefinition: AV_VERSION_MINOR ( a ) ( ( ( a ) & 0x00FF00 ) >> 8 )
# Skipping MacroDefinition: AV_VERSION_MICRO ( a ) ( ( a ) & 0xFF )
const LIBAVUTIL_VERSION_MAJOR = 56
const LIBAVUTIL_VERSION_MINOR = 22
const LIBAVUTIL_VERSION_MICRO = 100
# Skipping MacroDefinition: LIBAVUTIL_VERSION_INT AV_VERSION_INT ( LIBAVUTIL_VERSION_MAJOR , LIBAVUTIL_VERSION_MINOR , LIBAVUTIL_VERSION_MICRO )
# Skipping MacroDefinition: LIBAVUTIL_VERSION AV_VERSION ( LIBAVUTIL_VERSION_MAJOR , LIBAVUTIL_VERSION_MINOR , LIBAVUTIL_VERSION_MICRO )
# const LIBAVUTIL_BUILD = LIBAVUTIL_VERSION_INT
# Skipping MacroDefinition: LIBAVUTIL_IDENT "Lavu" AV_STRINGIFY ( LIBAVUTIL_VERSION )
# Skipping MacroDefinition: FF_API_VAAPI ( LIBAVUTIL_VERSION_MAJOR < 57 )
# Skipping MacroDefinition: FF_API_FRAME_QP ( LIBAVUTIL_VERSION_MAJOR < 57 )
# Skipping MacroDefinition: FF_API_PLUS1_MINUS1 ( LIBAVUTIL_VERSION_MAJOR < 57 )
# Skipping MacroDefinition: FF_API_ERROR_FRAME ( LIBAVUTIL_VERSION_MAJOR < 57 )
# Skipping MacroDefinition: FF_API_PKT_PTS ( LIBAVUTIL_VERSION_MAJOR < 57 )
# Skipping MacroDefinition: FF_API_CRYPTO_SIZE_T ( LIBAVUTIL_VERSION_MAJOR < 57 )
# Skipping MacroDefinition: FF_API_FRAME_GET_SET ( LIBAVUTIL_VERSION_MAJOR < 57 )
# Skipping MacroDefinition: FF_API_PSEUDOPAL ( LIBAVUTIL_VERSION_MAJOR < 57 )
const AES_CTR_KEY_SIZE = 16
const AES_CTR_IV_SIZE = 8
struct AVAESCTR
end
struct AVAudioFifo
end
struct AVCAMELLIA
end
struct AVCAST5
end
const AV_CH_FRONT_LEFT = 0x00000001
const AV_CH_FRONT_RIGHT = 0x00000002
const AV_CH_FRONT_CENTER = 0x00000004
const AV_CH_LOW_FREQUENCY = 0x00000008
const AV_CH_BACK_LEFT = 0x00000010
const AV_CH_BACK_RIGHT = 0x00000020
const AV_CH_FRONT_LEFT_OF_CENTER = 0x00000040
const AV_CH_FRONT_RIGHT_OF_CENTER = 0x00000080
const AV_CH_BACK_CENTER = 0x00000100
const AV_CH_SIDE_LEFT = 0x00000200
const AV_CH_SIDE_RIGHT = 0x00000400
const AV_CH_TOP_CENTER = 0x00000800
const AV_CH_TOP_FRONT_LEFT = 0x00001000
const AV_CH_TOP_FRONT_CENTER = 0x00002000
const AV_CH_TOP_FRONT_RIGHT = 0x00004000
const AV_CH_TOP_BACK_LEFT = 0x00008000
const AV_CH_TOP_BACK_CENTER = 0x00010000
const AV_CH_TOP_BACK_RIGHT = 0x00020000
const AV_CH_STEREO_LEFT = 0x20000000
const AV_CH_STEREO_RIGHT = 0x40000000
const AV_CH_WIDE_LEFT = UInt64(0x0000000080000000)
const AV_CH_WIDE_RIGHT = UInt64(0x0000000100000000)
const AV_CH_SURROUND_DIRECT_LEFT = UInt64(0x0000000200000000)
const AV_CH_SURROUND_DIRECT_RIGHT = UInt64(0x0000000400000000)
const AV_CH_LOW_FREQUENCY_2 = UInt64(0x0000000800000000)
const AV_CH_LAYOUT_NATIVE = UInt64(0x8000000000000000)
const AV_CH_LAYOUT_MONO = AV_CH_FRONT_CENTER
const AV_CH_LAYOUT_STEREO = AV_CH_FRONT_LEFT | AV_CH_FRONT_RIGHT
const AV_CH_LAYOUT_2POINT1 = AV_CH_LAYOUT_STEREO | AV_CH_LOW_FREQUENCY
const AV_CH_LAYOUT_2_1 = AV_CH_LAYOUT_STEREO | AV_CH_BACK_CENTER
const AV_CH_LAYOUT_SURROUND = AV_CH_LAYOUT_STEREO | AV_CH_FRONT_CENTER
const AV_CH_LAYOUT_3POINT1 = AV_CH_LAYOUT_SURROUND | AV_CH_LOW_FREQUENCY
const AV_CH_LAYOUT_4POINT0 = AV_CH_LAYOUT_SURROUND | AV_CH_BACK_CENTER
const AV_CH_LAYOUT_4POINT1 = AV_CH_LAYOUT_4POINT0 | AV_CH_LOW_FREQUENCY
const AV_CH_LAYOUT_2_2 = (AV_CH_LAYOUT_STEREO | AV_CH_SIDE_LEFT) | AV_CH_SIDE_RIGHT
const AV_CH_LAYOUT_QUAD = (AV_CH_LAYOUT_STEREO | AV_CH_BACK_LEFT) | AV_CH_BACK_RIGHT
const AV_CH_LAYOUT_5POINT0 = (AV_CH_LAYOUT_SURROUND | AV_CH_SIDE_LEFT) | AV_CH_SIDE_RIGHT
const AV_CH_LAYOUT_5POINT1 = AV_CH_LAYOUT_5POINT0 | AV_CH_LOW_FREQUENCY
const AV_CH_LAYOUT_5POINT0_BACK = (AV_CH_LAYOUT_SURROUND | AV_CH_BACK_LEFT) | AV_CH_BACK_RIGHT
const AV_CH_LAYOUT_5POINT1_BACK = AV_CH_LAYOUT_5POINT0_BACK | AV_CH_LOW_FREQUENCY
const AV_CH_LAYOUT_6POINT0 = AV_CH_LAYOUT_5POINT0 | AV_CH_BACK_CENTER
const AV_CH_LAYOUT_6POINT0_FRONT = (AV_CH_LAYOUT_2_2 | AV_CH_FRONT_LEFT_OF_CENTER) | AV_CH_FRONT_RIGHT_OF_CENTER
const AV_CH_LAYOUT_HEXAGONAL = AV_CH_LAYOUT_5POINT0_BACK | AV_CH_BACK_CENTER
const AV_CH_LAYOUT_6POINT1 = AV_CH_LAYOUT_5POINT1 | AV_CH_BACK_CENTER
const AV_CH_LAYOUT_6POINT1_BACK = AV_CH_LAYOUT_5POINT1_BACK | AV_CH_BACK_CENTER
const AV_CH_LAYOUT_6POINT1_FRONT = AV_CH_LAYOUT_6POINT0_FRONT | AV_CH_LOW_FREQUENCY
const AV_CH_LAYOUT_7POINT0 = (AV_CH_LAYOUT_5POINT0 | AV_CH_BACK_LEFT) | AV_CH_BACK_RIGHT
const AV_CH_LAYOUT_7POINT0_FRONT = (AV_CH_LAYOUT_5POINT0 | AV_CH_FRONT_LEFT_OF_CENTER) | AV_CH_FRONT_RIGHT_OF_CENTER
const AV_CH_LAYOUT_7POINT1 = (AV_CH_LAYOUT_5POINT1 | AV_CH_BACK_LEFT) | AV_CH_BACK_RIGHT
const AV_CH_LAYOUT_7POINT1_WIDE = (AV_CH_LAYOUT_5POINT1 | AV_CH_FRONT_LEFT_OF_CENTER) | AV_CH_FRONT_RIGHT_OF_CENTER
const AV_CH_LAYOUT_7POINT1_WIDE_BACK = (AV_CH_LAYOUT_5POINT1_BACK | AV_CH_FRONT_LEFT_OF_CENTER) | AV_CH_FRONT_RIGHT_OF_CENTER
const AV_CH_LAYOUT_OCTAGONAL = ((AV_CH_LAYOUT_5POINT0 | AV_CH_BACK_LEFT) | AV_CH_BACK_CENTER) | AV_CH_BACK_RIGHT
const AV_CH_LAYOUT_HEXADECAGONAL = (((((((AV_CH_LAYOUT_OCTAGONAL | AV_CH_WIDE_LEFT) | AV_CH_WIDE_RIGHT) | AV_CH_TOP_BACK_LEFT) | AV_CH_TOP_BACK_RIGHT) | AV_CH_TOP_BACK_CENTER) | AV_CH_TOP_FRONT_CENTER) | AV_CH_TOP_FRONT_LEFT) | AV_CH_TOP_FRONT_RIGHT
const AV_CH_LAYOUT_STEREO_DOWNMIX = AV_CH_STEREO_LEFT | AV_CH_STEREO_RIGHT
# begin enum AVMatrixEncoding
const AVMatrixEncoding = UInt32
const AV_MATRIX_ENCODING_NONE = 0 |> UInt32
const AV_MATRIX_ENCODING_DOLBY = 1 |> UInt32
const AV_MATRIX_ENCODING_DPLII = 2 |> UInt32
const AV_MATRIX_ENCODING_DPLIIX = 3 |> UInt32
const AV_MATRIX_ENCODING_DPLIIZ = 4 |> UInt32
const AV_MATRIX_ENCODING_DOLBYEX = 5 |> UInt32
const AV_MATRIX_ENCODING_DOLBYHEADPHONE = 6 |> UInt32
const AV_MATRIX_ENCODING_NB = 7 |> UInt32
# end enum AVMatrixEncoding
struct AVBPrint
end
struct AVDES
round_keys::NTuple{3, NTuple{16, UInt64}}
triple_des::Cint
end
# begin enum AVDownmixType
const AVDownmixType = UInt32
const AV_DOWNMIX_TYPE_UNKNOWN = 0 |> UInt32
const AV_DOWNMIX_TYPE_LORO = 1 |> UInt32
const AV_DOWNMIX_TYPE_LTRT = 2 |> UInt32
const AV_DOWNMIX_TYPE_DPLII = 3 |> UInt32
const AV_DOWNMIX_TYPE_NB = 4 |> UInt32
# end enum AVDownmixType
struct AVDownmixInfo
preferred_downmix_type::AVDownmixType
center_mix_level::Cdouble
center_mix_level_ltrt::Cdouble
surround_mix_level::Cdouble
surround_mix_level_ltrt::Cdouble
lfe_mix_level::Cdouble
end
struct AVSubsampleEncryptionInfo
bytes_of_clear_data::UInt32
bytes_of_protected_data::UInt32
end
struct AVEncryptionInfo
scheme::UInt32
crypt_byte_block::UInt32
skip_byte_block::UInt32
key_id::Ptr{UInt8}
key_id_size::UInt32
iv::Ptr{UInt8}
iv_size::UInt32
subsamples::Ptr{AVSubsampleEncryptionInfo}
subsample_count::UInt32
end
struct AVEncryptionInitInfo
system_id::Ptr{UInt8}
system_id_size::UInt32
key_ids::Ptr{Ptr{UInt8}}
num_key_ids::UInt32
key_id_size::UInt32
data::Ptr{UInt8}
data_size::UInt32
next::Ptr{AVEncryptionInitInfo}
end
const AV_HASH_MAX_SIZE = 64
struct AVHashContext
end
# begin enum AVHWDeviceType
const AVHWDeviceType = UInt32
const AV_HWDEVICE_TYPE_NONE = 0 |> UInt32
const AV_HWDEVICE_TYPE_VDPAU = 1 |> UInt32
const AV_HWDEVICE_TYPE_CUDA = 2 |> UInt32
const AV_HWDEVICE_TYPE_VAAPI = 3 |> UInt32
const AV_HWDEVICE_TYPE_DXVA2 = 4 |> UInt32
const AV_HWDEVICE_TYPE_QSV = 5 |> UInt32
const AV_HWDEVICE_TYPE_VIDEOTOOLBOX = 6 |> UInt32
const AV_HWDEVICE_TYPE_D3D11VA = 7 |> UInt32
const AV_HWDEVICE_TYPE_DRM = 8 |> UInt32
const AV_HWDEVICE_TYPE_OPENCL = 9 |> UInt32
const AV_HWDEVICE_TYPE_MEDIACODEC = 10 |> UInt32
# end enum AVHWDeviceType
struct AVHWDeviceInternal
end
struct AVHWDeviceContext
av_class::Ptr{AVClass}
internal::Ptr{AVHWDeviceInternal}
_type::AVHWDeviceType
hwctx::Ptr{Cvoid}
free::Ptr{Cvoid}
user_opaque::Ptr{Cvoid}
end
struct AVHWFramesInternal
end
struct AVHWFramesContext
av_class::Ptr{AVClass}
internal::Ptr{AVHWFramesInternal}
device_ref::Ptr{AVBufferRef}
device_ctx::Ptr{AVHWDeviceContext}
hwctx::Ptr{Cvoid}
free::Ptr{Cvoid}
user_opaque::Ptr{Cvoid}
pool::Ptr{AVBufferPool}
initial_pool_size::Cint
format::AVPixelFormat
sw_format::AVPixelFormat
width::Cint
height::Cint
end
# begin enum AVHWFrameTransferDirection
const AVHWFrameTransferDirection = UInt32
const AV_HWFRAME_TRANSFER_DIRECTION_FROM = 0 |> UInt32
const AV_HWFRAME_TRANSFER_DIRECTION_TO = 1 |> UInt32
# end enum AVHWFrameTransferDirection
struct AVHWFramesConstraints
valid_hw_formats::Ptr{AVPixelFormat}
valid_sw_formats::Ptr{AVPixelFormat}
min_width::Cint
min_height::Cint
max_width::Cint
max_height::Cint
end
# begin enum ANONYMOUS_3
const ANONYMOUS_3 = UInt32
const AV_HWFRAME_MAP_READ = 1 |> UInt32
const AV_HWFRAME_MAP_WRITE = 2 |> UInt32
const AV_HWFRAME_MAP_OVERWRITE = 4 |> UInt32
const AV_HWFRAME_MAP_DIRECT = 8 |> UInt32
# end enum ANONYMOUS_3
struct AVCUDADeviceContextInternal
end
struct AVCUDADeviceContext
cuda_ctx::Cint
stream::Cint
internal::Ptr{AVCUDADeviceContextInternal}
end
struct AVD3D11VADeviceContext
device::Ptr{Cint}
device_context::Ptr{Cint}
video_device::Ptr{Cint}
video_context::Ptr{Cint}
lock::Ptr{Cvoid}
unlock::Ptr{Cvoid}
lock_ctx::Ptr{Cvoid}
end
struct AVD3D11FrameDescriptor
texture::Ptr{Cint}
index::Cint
end
struct AVD3D11VAFramesContext
texture::Ptr{Cint}
BindFlags::Cint
MiscFlags::Cint
end
# begin enum ANONYMOUS_4
const ANONYMOUS_4 = UInt32
const AV_DRM_MAX_PLANES = 4 |> UInt32
# end enum ANONYMOUS_4
struct AVDRMObjectDescriptor
fd::Cint
size::Csize_t
format_modifier::UInt64
end
struct AVDRMPlaneDescriptor
object_index::Cint
offset::Cptrdiff_t
pitch::Cptrdiff_t
end
struct AVDRMLayerDescriptor
format::UInt32
nb_planes::Cint
planes::NTuple{4, AVDRMPlaneDescriptor}
end
struct AVDRMFrameDescriptor
nb_objects::Cint
objects::NTuple{4, AVDRMObjectDescriptor}
nb_layers::Cint
layers::NTuple{4, AVDRMLayerDescriptor}
end
struct AVDRMDeviceContext
fd::Cint
end
struct AVDXVA2DeviceContext
devmgr::Ptr{Cint}
end
struct AVDXVA2FramesContext
surface_type::Cint
surfaces::Ptr{Ptr{Cint}}
nb_surfaces::Cint
decoder_to_release::Ptr{Cint}
end
struct AVMediaCodecDeviceContext
surface::Ptr{Cvoid}
end
struct AVQSVDeviceContext
session::Cint
end
struct AVQSVFramesContext
surfaces::Ptr{Cint}
nb_surfaces::Cint
frame_type::Cint
end
# begin enum ANONYMOUS_5
const ANONYMOUS_5 = UInt32
const AV_VAAPI_DRIVER_QUIRK_USER_SET = 1 |> UInt32
const AV_VAAPI_DRIVER_QUIRK_RENDER_PARAM_BUFFERS = 2 |> UInt32
const AV_VAAPI_DRIVER_QUIRK_ATTRIB_MEMTYPE = 4 |> UInt32
const AV_VAAPI_DRIVER_QUIRK_SURFACE_ATTRIBUTES = 8 |> UInt32
# end enum ANONYMOUS_5
# struct AVVAAPIDeviceContext
# display::VADisplay
# driver_quirks::UInt32
# end
# struct AVVAAPIFramesContext
# attributes::Ptr{VASurfaceAttrib}
# nb_attributes::Cint
# surface_ids::Ptr{VASurfaceID}
# nb_surfaces::Cint
# end
# struct AVVAAPIHWConfig
# config_id::VAConfigID
# end
# struct AVVDPAUDeviceContext
# device::VdpDevice
# get_proc_address::Ptr{VdpGetProcAddress}
# end
struct AVMasteringDisplayMetadata
display_primaries::NTuple{3, NTuple{2, AVRational}}
white_point::NTuple{2, AVRational}
min_luminance::AVRational
max_luminance::AVRational
has_primaries::Cint
has_luminance::Cint
end
struct AVContentLightMetadata
MaxCLL::UInt32
MaxFALL::UInt32
end
struct AVMotionVector
source::Int32
w::UInt8
h::UInt8
src_x::Int16
src_y::Int16
dst_x::Int16
dst_y::Int16
flags::UInt64
motion_x::Int32
motion_y::Int32
motion_scale::UInt16
end
const AV_OPT_FLAG_ENCODING_PARAM = 1
const AV_OPT_FLAG_DECODING_PARAM = 2
const AV_OPT_FLAG_AUDIO_PARAM = 8
const AV_OPT_FLAG_VIDEO_PARAM = 16
const AV_OPT_FLAG_SUBTITLE_PARAM = 32
const AV_OPT_FLAG_EXPORT = 64
const AV_OPT_FLAG_READONLY = 128
const AV_OPT_FLAG_BSF_PARAM = 1 << 8
const AV_OPT_FLAG_FILTERING_PARAM = 1 << 16
const AV_OPT_FLAG_DEPRECATED = 1 << 17
const AV_OPT_SEARCH_CHILDREN = 1 << 0
const AV_OPT_SEARCH_FAKE_OBJ = 1 << 1
const AV_OPT_ALLOW_NULL = 1 << 2
const AV_OPT_MULTI_COMPONENT_RANGE = 1 << 12
# Skipping MacroDefinition: av_opt_set_int_list ( obj , name , val , term , flags ) ( av_int_list_length ( val , term ) > INT_MAX / sizeof ( * ( val ) ) ? AVERROR ( EINVAL ) : av_opt_set_bin ( obj , name , ( const uint8_t * ) ( val ) , av_int_list_length ( val , term ) * sizeof ( * ( val ) ) , flags ) )
const AV_OPT_SERIALIZE_SKIP_DEFAULTS = 0x00000001
const AV_OPT_SERIALIZE_OPT_FLAGS_EXACT = 0x00000002
# begin enum ANONYMOUS_6
const ANONYMOUS_6 = UInt32
const AV_OPT_FLAG_IMPLICIT_KEY = 1 |> UInt32
# end enum ANONYMOUS_6
const av_pixelutils_sad_fn = Ptr{Cvoid}
struct AVRC4
state::NTuple{256, UInt8}
x::Cint
y::Cint
end
struct AVReplayGain
track_gain::Int32
track_peak::UInt32
album_gain::Int32
album_peak::UInt32
end
# begin enum AVSphericalProjection
const AVSphericalProjection = UInt32
const AV_SPHERICAL_EQUIRECTANGULAR = 0 |> UInt32
const AV_SPHERICAL_CUBEMAP = 1 |> UInt32
const AV_SPHERICAL_EQUIRECTANGULAR_TILE = 2 |> UInt32
# end enum AVSphericalProjection
struct AVSphericalMapping
projection::AVSphericalProjection
yaw::Int32
pitch::Int32
roll::Int32
bound_left::UInt32
bound_top::UInt32
bound_right::UInt32
bound_bottom::UInt32
padding::UInt32
end
const AV_STEREO3D_FLAG_INVERT = 1 << 0
# begin enum AVStereo3DType
const AVStereo3DType = UInt32
const AV_STEREO3D_2D = 0 |> UInt32
const AV_STEREO3D_SIDEBYSIDE = 1 |> UInt32
const AV_STEREO3D_TOPBOTTOM = 2 |> UInt32
const AV_STEREO3D_FRAMESEQUENCE = 3 |> UInt32
const AV_STEREO3D_CHECKERBOARD = 4 |> UInt32
const AV_STEREO3D_SIDEBYSIDE_QUINCUNX = 5 |> UInt32
const AV_STEREO3D_LINES = 6 |> UInt32
const AV_STEREO3D_COLUMNS = 7 |> UInt32
# end enum AVStereo3DType
# begin enum AVStereo3DView
const AVStereo3DView = UInt32
const AV_STEREO3D_VIEW_PACKED = 0 |> UInt32
const AV_STEREO3D_VIEW_LEFT = 1 |> UInt32
const AV_STEREO3D_VIEW_RIGHT = 2 |> UInt32
# end enum AVStereo3DView
struct AVStereo3D
_type::AVStereo3DType
flags::Cint
view::AVStereo3DView
end
struct AVTEA
end
struct AVThreadMessageQueue
end
# begin enum AVThreadMessageFlags
const AVThreadMessageFlags = UInt32
const AV_THREAD_MESSAGE_NONBLOCK = 1 |> UInt32
# end enum AVThreadMessageFlags
const AV_TIMECODE_STR_SIZE = 23
# begin enum AVTimecodeFlag
const AVTimecodeFlag = UInt32
const AV_TIMECODE_FLAG_DROPFRAME = 1 |> UInt32
const AV_TIMECODE_FLAG_24HOURSMAX = 2 |> UInt32
const AV_TIMECODE_FLAG_ALLOWNEGATIVE = 4 |> UInt32
# end enum AVTimecodeFlag
const AVTimecode =
struct AVTreeNode
end
struct AVTWOFISH
end
struct AVXTEA
key::NTuple{16, UInt32}
end
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886,
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198,
198,
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317,
8898,
1546,
198,
220,
220,
220,
2835,
62,
13083,
3712,
11251,
29291,
90,
18,
11,
24563,
29291,
90,
1433,
11,
471,
5317,
2414,
11709,
198,
220,
220,
220,
15055,
62,
8906,
3712,
34,
600,
198,
437,
198,
198,
2,
2221,
33829,
14661,
8048,
19816,
6030,
198,
9979,
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8048,
19816,
6030,
796,
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5317,
2624,
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62,
41925,
8895,
55,
62,
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62,
4944,
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29,
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43,
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29,
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62,
41925,
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62,
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62,
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43,
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513,
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29,
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5317,
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198,
9979,
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62,
41925,
8895,
55,
62,
25216,
62,
32819,
796,
604,
930,
29,
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5317,
2624,
198,
2,
886,
33829,
14661,
8048,
19816,
6030,
198,
198,
7249,
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8048,
19816,
12360,
198,
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220,
220,
9871,
62,
2902,
19816,
62,
4906,
3712,
10116,
8048,
19816,
6030,
198,
220,
220,
220,
3641,
62,
19816,
62,
5715,
3712,
34,
23352,
198,
220,
220,
220,
3641,
62,
19816,
62,
5715,
62,
75,
2213,
83,
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34,
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198,
220,
220,
220,
4573,
62,
19816,
62,
5715,
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34,
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198,
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220,
220,
4573,
62,
19816,
62,
5715,
62,
75,
2213,
83,
3712,
34,
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198,
220,
220,
220,
300,
5036,
62,
19816,
62,
5715,
3712,
34,
23352,
198,
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198,
198,
7249,
14661,
7004,
39873,
27195,
13168,
12360,
198,
220,
220,
220,
9881,
62,
1659,
62,
20063,
62,
7890,
3712,
52,
5317,
2624,
198,
220,
220,
220,
9881,
62,
1659,
62,
24326,
62,
7890,
3712,
52,
5317,
2624,
198,
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198,
198,
7249,
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27195,
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12360,
198,
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220,
7791,
3712,
52,
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8194,
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9967,
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220,
14267,
62,
26327,
62,
9967,
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52,
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198,
220,
220,
220,
1994,
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312,
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46745,
90,
52,
5317,
23,
92,
198,
220,
220,
220,
1994,
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62,
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21628,
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1080,
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1994,
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46745,
90,
52,
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23,
11709,
198,
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220,
220,
997,
62,
2539,
62,
2340,
3712,
52,
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220,
220,
1994,
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62,
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52,
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220,
1366,
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1306,
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31768,
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92,
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198,
198,
9979,
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39,
11211,
62,
22921,
62,
33489,
796,
5598,
198,
198,
7249,
14661,
26257,
21947,
198,
437,
198,
198,
2,
2221,
33829,
14661,
39,
54,
24728,
6030,
198,
9979,
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39,
54,
24728,
6030,
796,
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5317,
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39,
54,
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62,
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45,
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54,
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8898,
4537,
52,
796,
352,
930,
29,
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5317,
2624,
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9979,
14661,
62,
39,
54,
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27389,
62,
25216,
62,
43633,
5631,
796,
362,
930,
29,
471,
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9979,
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62,
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54,
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40,
2246,
3727,
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886,
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6030,
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220,
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39,
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220,
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220,
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90,
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28632,
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92,
198,
220,
220,
220,
3335,
62,
49464,
3712,
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90,
10116,
39,
54,
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21947,
92,
198,
220,
220,
220,
289,
86,
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90,
34,
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92,
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220,
220,
220,
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90,
34,
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92,
198,
220,
220,
220,
2836,
62,
404,
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3712,
46745,
90,
34,
19382,
92,
198,
220,
220,
220,
5933,
3712,
46745,
90,
10116,
28632,
27201,
92,
198,
220,
220,
220,
4238,
62,
7742,
62,
7857,
3712,
34,
600,
198,
220,
220,
220,
5794,
3712,
32,
8859,
7168,
26227,
198,
220,
220,
220,
1509,
62,
18982,
3712,
32,
8859,
7168,
26227,
198,
220,
220,
220,
9647,
3712,
34,
600,
198,
220,
220,
220,
6001,
3712,
34,
600,
198,
437,
198,
198,
2,
2221,
33829,
14661,
39,
54,
19778,
43260,
35,
4154,
198,
9979,
14661,
39,
54,
19778,
43260,
35,
4154,
796,
471,
5317,
2624,
198,
9979,
14661,
62,
39,
54,
10913,
10067,
62,
5446,
15037,
24302,
62,
17931,
23988,
2849,
62,
10913,
2662,
796,
657,
930,
29,
471,
5317,
2624,
198,
9979,
14661,
62,
39,
54,
10913,
10067,
62,
5446,
15037,
24302,
62,
17931,
23988,
2849,
62,
10468,
796,
352,
930,
29,
471,
5317,
2624,
198,
2,
886,
33829,
14661,
39,
54,
19778,
43260,
35,
4154,
198,
198,
7249,
14661,
39,
54,
35439,
3103,
2536,
6003,
198,
220,
220,
220,
4938,
62,
36599,
62,
687,
1381,
3712,
46745,
90,
32,
8859,
7168,
26227,
92,
198,
220,
220,
220,
4938,
62,
2032,
62,
687,
1381,
3712,
46745,
90,
32,
8859,
7168,
26227,
92,
198,
220,
220,
220,
949,
62,
10394,
3712,
34,
600,
198,
220,
220,
220,
949,
62,
17015,
3712,
34,
600,
198,
220,
220,
220,
3509,
62,
10394,
3712,
34,
600,
198,
220,
220,
220,
3509,
62,
17015,
3712,
34,
600,
198,
437,
198,
198,
2,
2221,
33829,
3537,
40508,
44,
20958,
62,
18,
198,
9979,
3537,
40508,
44,
20958,
62,
18,
796,
471,
5317,
2624,
198,
9979,
14661,
62,
39,
54,
10913,
10067,
62,
33767,
62,
15675,
796,
352,
930,
29,
471,
5317,
2624,
198,
9979,
14661,
62,
39,
54,
10913,
10067,
62,
33767,
62,
18564,
12709,
796,
362,
930,
29,
471,
5317,
2624,
198,
9979,
14661,
62,
39,
54,
10913,
10067,
62,
33767,
62,
41983,
18564,
12709,
796,
604,
930,
29,
471,
5317,
2624,
198,
9979,
14661,
62,
39,
54,
10913,
10067,
62,
33767,
62,
17931,
23988,
796,
807,
930,
29,
471,
5317,
2624,
198,
2,
886,
33829,
3537,
40508,
44,
20958,
62,
18,
198,
198,
7249,
14661,
34,
8322,
2885,
1990,
501,
21947,
37693,
198,
437,
198,
198,
7249,
14661,
34,
8322,
2885,
1990,
501,
21947,
198,
220,
220,
220,
269,
15339,
62,
49464,
3712,
34,
600,
198,
220,
220,
220,
4269,
3712,
34,
600,
198,
220,
220,
220,
5387,
3712,
46745,
90,
10116,
34,
8322,
2885,
1990,
501,
21947,
37693,
92,
198,
437,
198,
198,
7249,
317,
8898,
18,
35,
1157,
53,
2885,
1990,
501,
21947,
198,
220,
220,
220,
3335,
3712,
46745,
90,
34,
600,
92,
198,
220,
220,
220,
3335,
62,
22866,
3712,
46745,
90,
34,
600,
92,
198,
220,
220,
220,
2008,
62,
25202,
3712,
46745,
90,
34,
600,
92,
198,
220,
220,
220,
2008,
62,
22866,
3712,
46745,
90,
34,
600,
92,
198,
220,
220,
220,
5793,
3712,
46745,
90,
34,
19382,
92,
198,
220,
220,
220,
12116,
3712,
46745,
90,
34,
19382,
92,
198,
220,
220,
220,
5793,
62,
49464,
3712,
46745,
90,
34,
19382,
92,
198,
437,
198,
198,
7249,
317,
8898,
18,
35,
1157,
19778,
24564,
1968,
273,
198,
220,
220,
220,
11743,
3712,
46745,
90,
34,
600,
92,
198,
220,
220,
220,
6376,
3712,
34,
600,
198,
437,
198,
198,
7249,
317,
8898,
18,
35,
1157,
53,
8579,
859,
274,
21947,
198,
220,
220,
220,
11743,
3712,
46745,
90,
34,
600,
92,
198,
220,
220,
220,
41211,
40053,
3712,
34,
600,
198,
220,
220,
220,
29882,
40053,
3712,
34,
600,
198,
437,
198,
198,
2,
2221,
33829,
3537,
40508,
44,
20958,
62,
19,
198,
9979,
3537,
40508,
44,
20958,
62,
19,
796,
471,
5317,
2624,
198,
9979,
14661,
62,
7707,
44,
62,
22921,
62,
6489,
1565,
1546,
796,
604,
930,
29,
471,
5317,
2624,
198,
2,
886,
33829,
3537,
40508,
44,
20958,
62,
19,
198,
198,
7249,
14661,
7707,
44,
10267,
24564,
1968,
273,
198,
220,
220,
220,
277,
67,
3712,
34,
600,
198,
220,
220,
220,
2546,
3712,
34,
7857,
62,
83,
198,
220,
220,
220,
5794,
62,
4666,
7483,
3712,
52,
5317,
2414,
198,
437,
198,
198,
7249,
14661,
7707,
44,
3646,
1531,
24564,
1968,
273,
198,
220,
220,
220,
2134,
62,
9630,
3712,
34,
600,
198,
220,
220,
220,
11677,
3712,
34,
457,
4372,
733,
62,
83,
198,
220,
220,
220,
7078,
3712,
34,
457,
4372,
733,
62,
83,
198,
437,
198,
198,
7249,
14661,
7707,
5805,
2794,
24564,
1968,
273,
198,
220,
220,
220,
5794,
3712,
52,
5317,
2624,
198,
220,
220,
220,
299,
65,
62,
22587,
3712,
34,
600,
198,
220,
220,
220,
13016,
3712,
11251,
29291,
90,
19,
11,
14661,
7707,
44,
3646,
1531,
24564,
1968,
273,
92,
198,
437,
198,
198,
7249,
14661,
7707,
44,
19778,
24564,
1968,
273,
198,
220,
220,
220,
299,
65,
62,
48205,
3712,
34,
600,
198,
220,
220,
220,
5563,
3712,
11251,
29291,
90,
19,
11,
14661,
7707,
44,
10267,
24564,
1968,
273,
92,
198,
220,
220,
220,
299,
65,
62,
75,
6962,
3712,
34,
600,
198,
220,
220,
220,
11685,
3712,
11251,
29291,
90,
19,
11,
14661,
7707,
5805,
2794,
24564,
1968,
273,
92,
198,
437,
198,
198,
7249,
14661,
7707,
12740,
1990,
501,
21947,
198,
220,
220,
220,
277,
67,
3712,
34,
600,
198,
437,
198,
198,
7249,
317,
8898,
55,
11731,
17,
24728,
21947,
198,
220,
220,
220,
1614,
76,
2164,
3712,
46745,
90,
34,
600,
92,
198,
437,
198,
198,
7249,
317,
8898,
55,
11731,
17,
35439,
21947,
198,
220,
220,
220,
4417,
62,
4906,
3712,
34,
600,
198,
220,
220,
220,
16649,
3712,
46745,
90,
46745,
90,
34,
600,
11709,
198,
220,
220,
220,
299,
65,
62,
11793,
32186,
3712,
34,
600,
198,
220,
220,
220,
875,
12342,
62,
1462,
62,
20979,
3712,
46745,
90,
34,
600,
92,
198,
437,
198,
198,
7249,
14661,
13152,
43806,
721,
24728,
21947,
198,
220,
220,
220,
4417,
3712,
46745,
90,
34,
19382,
92,
198,
437,
198,
198,
7249,
14661,
48,
50,
8898,
1990,
501,
21947,
198,
220,
220,
220,
6246,
3712,
34,
600,
198,
437,
198,
198,
7249,
14661,
48,
50,
53,
35439,
21947,
198,
220,
220,
220,
16649,
3712,
46745,
90,
34,
600,
92,
198,
220,
220,
220,
299,
65,
62,
11793,
32186,
3712,
34,
600,
198,
220,
220,
220,
5739,
62,
4906,
3712,
34,
600,
198,
437,
198,
198,
2,
2221,
33829,
3537,
40508,
44,
20958,
62,
20,
198,
9979,
3537,
40508,
44,
20958,
62,
20,
796,
471,
5317,
2624,
198,
9979,
14661,
62,
11731,
17614,
62,
7707,
38757,
62,
10917,
4663,
42,
62,
29904,
62,
28480,
796,
352,
930,
29,
471,
5317,
2624,
198,
9979,
14661,
62,
11731,
17614,
62,
7707,
38757,
62,
10917,
4663,
42,
62,
49,
10619,
1137,
62,
27082,
2390,
62,
19499,
5777,
4877,
796,
362,
930,
29,
471,
5317,
2624,
198,
9979,
14661,
62,
11731,
17614,
62,
7707,
38757,
62,
10917,
4663,
42,
62,
1404,
5446,
9865,
62,
44,
3620,
25216,
796,
604,
930,
29,
471,
5317,
2624,
198,
9979,
14661,
62,
11731,
17614,
62,
7707,
38757,
62,
10917,
4663,
42,
62,
50,
4261,
49836,
62,
1404,
5446,
9865,
3843,
1546,
796,
807,
930,
29,
471,
5317,
2624,
198,
2,
886,
33829,
3537,
40508,
44,
20958,
62,
20,
198,
198,
2,
2878,
14661,
11731,
2969,
2389,
1990,
501,
21947,
198,
2,
220,
220,
220,
220,
3359,
3712,
53,
2885,
271,
1759,
198,
2,
220,
220,
220,
220,
4639,
62,
421,
343,
591,
3712,
52,
5317,
2624,
198,
2,
886,
198,
198,
2,
2878,
14661,
11731,
2969,
5064,
859,
274,
21947,
198,
2,
220,
220,
220,
220,
12608,
3712,
46745,
90,
53,
1921,
333,
2550,
8086,
822,
92,
198,
2,
220,
220,
220,
220,
299,
65,
62,
1078,
7657,
3712,
34,
600,
198,
2,
220,
220,
220,
220,
4417,
62,
2340,
3712,
46745,
90,
53,
1921,
333,
2550,
2389,
92,
198,
2,
220,
220,
220,
220,
299,
65,
62,
11793,
32186,
3712,
34,
600,
198,
2,
886,
198,
198,
2,
2878,
14661,
11731,
17614,
39,
54,
16934,
198,
2,
220,
220,
220,
220,
4566,
62,
312,
3712,
53,
2246,
261,
5647,
2389,
198,
2,
886,
198,
198,
2,
2878,
14661,
8898,
4537,
8322,
1990,
501,
21947,
198,
2,
220,
220,
220,
220,
3335,
3712,
53,
26059,
24728,
198,
2,
220,
220,
220,
220,
651,
62,
36942,
62,
21975,
3712,
46745,
90,
53,
26059,
3855,
2964,
66,
20231,
92,
198,
2,
886,
198,
198,
7249,
14661,
18254,
278,
23114,
9171,
14706,
198,
220,
220,
220,
3359,
62,
19795,
3166,
3712,
11251,
29291,
90,
18,
11,
24563,
29291,
90,
17,
11,
317,
13024,
864,
11709,
198,
220,
220,
220,
2330,
62,
4122,
3712,
11251,
29291,
90,
17,
11,
317,
13024,
864,
92,
198,
220,
220,
220,
949,
62,
75,
7230,
590,
3712,
10116,
49,
864,
198,
220,
220,
220,
3509,
62,
75,
7230,
590,
3712,
10116,
49,
864,
198,
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] | 1.97435 | 30,760 |
# This file is a part of TypeDBClient. License is MIT: https://github.com/Humans-of-Julia/TypeDBClient.jl/blob/main/LICENSE
abstract type AbstractTypeDBStub end
mutable struct Core_TypeDBStub <: AbstractTypeDBStub
blockingStub::Proto.TypeDBBlockingStub
asyncStub::Proto.TypeDBStub
end
function Core_TypeDBStub(channel::gRPCClient.gRPCChannel)
blockingStub = Proto.TypeDBBlockingStub(channel)
asyncStub = Proto.TypeDBStub(channel)
return Core_TypeDBStub(blockingStub,asyncStub)
end
mutable struct Cluster_TypeDBStub <: AbstractTypeDBStub
blockingStub::Proto.TypeDBBlockingStub
end
function Cluster_TypeDBStub(channel::gRPCClient.gRPCChannel)
blockingStub = Proto.TypeDBBlockingStub(channel)
return Cluster_TypeDBStub(blockingStub)
end
function ensure_connected(stub::T) where {T<:AbstractTypeDBStub}
throw(TypeDBClientException(GENERAL_UNKOWN_ERROR,"Isn't implemented yet"))
end
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] | 2.754491 | 334 |
# Global method definition needs to be at top level in .7
"""
Takes a PowerSystems dictionary and return an array of PowerSystems struct for
Bus, Generator, Branch and load
"""
function ps_dict2ps_struct(data::Dict{String,Any})
generators = Array{G where {G<:Generator},1}()
storages = Array{S where {S<:Storage},1}()
buses = Array{Bus,1}()
branches = Array{B where {B<:Branch},1}()
loads = Array{E where {E<:ElectricLoad},1}()
shunts = Array{FixedAdmittance,1}()
loadZones = Array{D where {D<:Device},1}()
services = Array{S where {S<:Service},1}()
# TODO: should we raise an exception in the following?
if haskey(data, "bus")
buses = PowerSystems.bus_dict_parse(data["bus"])
else
@warn "key 'bus' not found in PowerSystems dictionary, this will result in an empty Bus array"
end
if haskey(data, "gen")
(generators, storage) = PowerSystems.gen_dict_parser(data["gen"])
else
@warn "key 'gen' not found in PowerSystems dictionary, this will result in an empty Generators and Storage array"
end
if haskey(data, "branch")
branches = PowerSystems.branch_dict_parser(data["branch"], branches)
else
@warn "key 'branch' not found in PowerSystems dictionary, this will result in an empty Branches array"
end
if haskey(data, "load")
loads = PowerSystems.load_dict_parser(data["load"])
else
@warn "key 'load' not found in PowerSystems dictionary, this will result in an empty Loads array"
end
if haskey(data, "loadzone")
loadZones = PowerSystems.loadzone_dict_parser(data["loadzone"])
else
@warn "key 'loadzone' not found in PowerSystems dictionary, this will result in an empty LoadZones array"
end
if haskey(data, "shunt")
shunts = PowerSystems.shunt_dict_parser(data["shunt"])
else
@warn "key 'shunt' not found in PowerSystems dictionary, this will result in an empty Shunts array"
end
if haskey(data, "dcline")
branches = PowerSystems.dclines_dict_parser(data["dcline"], branches)
else
@warn "key 'dcline' not found in PowerSystems dictionary, this will result in an empty DCLines array"
end
if haskey(data, "services")
services = PowerSystems.services_dict_parser(data["services"],generators)
else
@warn "key 'services' not found in PowerSystems dictionary, this will result in an empty services array"
end
return sort!(buses, by = x -> x.number), generators, storage, sort!(branches, by = x -> x.connectionpoints.from.number), loads, loadZones, shunts, services
end
function _retrieve(dict::T, key_of_interest::String, output = Dict(), path = []) where T<:AbstractDict
iter_result = Base.iterate(dict)
last_element = length(path)
while iter_result !== nothing
((key,value), state) = iter_result
if key == key_of_interest
output[value] = !haskey(output,value) ? path[1:end] : push!(output[value],path[1:end])
end
if value isa AbstractDict
push!(path,key)
_retrieve(value, key_of_interest, output, path)
path = path[1:last_element]
end
iter_result = Base.iterate(dict, state)
end
return output
end
function _retrieve(dict::T, type_of_interest, output = Dict(), path = []) where T<:AbstractDict
iter_result = Base.iterate(dict)
last_element = length(path)
while iter_result !== nothing
((key,value), state) = iter_result
if typeof(value) <: type_of_interest
output[key] = !haskey(output,value) ? path[1:end] : push!(output[value],path[1:end])
end
if value isa AbstractDict
push!(path,key)
_retrieve(value, type_of_interest, output, path)
path = path[1:last_element]
end
iter_result = Base.iterate(dict, state)
end
return output
end
function _access(nesteddict::T,keylist) where T<:AbstractDict
if !haskey(nesteddict,keylist[1])
@error "$(keylist[1]) not found in dict"
end
if length(keylist) > 1
nesteddict = _access(nesteddict[keylist[1]],keylist[2:end])
else
nesteddict = nesteddict[keylist[1]]
end
end
function _get_device(name, collection, devices = [])
if isa(collection,Array)
fn = fieldnames(typeof(collection[1]))
if :name in fn
[push!(devices,d) for d in collection if d.name == name]
end
else
fn = fieldnames(typeof(collection))
for f in fn
_get_device(name,getfield(collection,f),devices)
end
end
return devices
end
"""
Arg:
Dataframes which includes a timerseries columns of either:
Year, Month, Day, Period
or
DateTime
or
nothing (creates a today referenced DateTime Column)
Returns:
Dataframe with a DateTime columns
"""
function read_datetime(df; kwargs...)
if [c for c in [:Year,:Month,:Day,:Period] if c in names(df)] == [:Year,:Month,:Day,:Period]
if Dates.Hour(DataFrames.maximum(df[:Period])) <= Dates.Hour(25)
df[:DateTime] = collect(Dates.DateTime(df[1,:Year],df[1,:Month],df[1,:Day],(df[1,:Period]-1)) :Dates.Hour(1) :
Dates.DateTime(df[end,:Year],df[end,:Month],df[end,:Day],(df[end,:Period]-1)))
elseif (Dates.Minute(5) * DataFrames.maximum(df[:Period]) >= Dates.Minute(1440))& (Dates.Minute(5) * DataFrames.maximum(df[:Period]) <= Dates.Minute(1500))
df[:DateTime] = collect(Dates.DateTime(df[1,:Year],df[1,:Month],df[1,:Day],floor(df[1,:Period]/12),Int(df[1,:Period])-1) :Dates.Minute(5) :
Dates.DateTime(df[end,:Year],df[end,:Month],df[end,:Day],floor(df[end,:Period]/12)-1,5*(Int(df[end,:Period])-(floor(df[end,:Period]/12)-1)*12) -5))
else
@error "I don't know what the period length is, reformat timeseries"
end
DataFrames.deletecols!(df, [:Year,:Month,:Day,:Period])
elseif :DateTime in names(df)
df[:DateTime] = Dates.DateTime(df[:DateTime])
else
if :startdatetime in keys(kwargs)
startdatetime = kwargs[:startdatetime]
else
@warn "No reference date given, assuming today"
startdatetime = Dates.today()
end
df[:DateTime] = collect(Dates.DateTime(startdatetime):Dates.Hour(1):Dates.DateTime(startdatetime)+Dates.Hour(size(df)[1]-1))
end
return df
end
"""
Arg:
Device dictionary - Generators
Dataframe contains device Realtime/Forecast TimeSeries
Returns:
Device dictionary with timeseries added
"""
function add_time_series(Device_dict::Dict{String,Any}, df::DataFrames.DataFrame)
for (device_key,device) in Device_dict
if device_key in map(string, names(df))
ts_raw = df[Symbol(device_key)]
if maximum(ts_raw) <= 1.0
@info "assumed time series is a scaling factor for $device_key"
Device_dict[device_key]["scalingfactor"] = TimeSeries.TimeArray(df[:DateTime],ts_raw)
else
@info "assumed time series is MW for $device_key"
Device_dict[device_key]["scalingfactor"] = TimeSeries.TimeArray(df[:DateTime],ts_raw/device["tech"]["installedcapacity"])
end
end
end
return Device_dict
end
function add_time_series(Device_dict::Dict{String,Any}, ts_raw::TimeSeries.TimeArray)
"""
Arg:
Device dictionary - Generators
Dict contains device Realtime/Forecast TimeSeries.TimeArray
Returns:
Device dictionary with timeseries added
"""
name = get(Device_dict, "name", "")
if name == ""
throw(DataFormatError("input dict to add_time_series in wrong format"))
end
if maximum(values(ts_raw)) > 1.0
@warn "Time series for $name has values > 1.0, expected values in range {0.0,1.0}"
end
Device_dict["scalingfactor"] = ts_raw
return Device_dict
end
"""
Arg:
Load dictionary
LoadZones dictionary
Dataframe contains device Realtime/Forecast TimeSeries
Returns:
Device dictionary with timeseries added
"""
function add_time_series_load(data::Dict{String,Any}, df::DataFrames.DataFrame)
load_dict = data["load"]
load_names = [string(l["name"]) for (k,l) in load_dict]
ts_names = [string(n) for n in names(df) if n != :DateTime]
write_sf_by_lz = false
lzkey = [k for k in ["loadzone","load_zone"] if haskey(data,k)][1]
if lzkey in keys(data)
load_zone_dict = data[lzkey]
z_names = [string(z["name"]) for (k,z) in load_zone_dict]
if length([n for n in z_names if n in ts_names]) > 0
write_sf_by_lz = true
end
end
assigned_loads = []
if write_sf_by_lz
@info "assigning load scaling factors by load_zone"
# TODO: make this faster/better
for (l_key,l) in load_dict
for (lz_key,lz) in load_zone_dict
if l["bus"] in lz["buses"]
ts_raw = df[lz_key]/lz["maxactivepower"]
load_dict[l_key]["scalingfactor"] = TimeSeries.TimeArray(df[:DateTime],ts_raw)
push!(assigned_loads,l_key)
end
end
end
else
@info "assigning load scaling factors by bus"
for (l_key,l) in load_dict
load_dict[l_key]["scalingfactor"] = TimeSeries.TimeArray(df[:DateTime],df[Symbol(l["name"])])
push!(assigned_loads,l["name"])
end
end
for l in [l for l in load_names if !(l in assigned_loads)]
@warn "No load scaling factor assigned for $l" maxlog=PS_MAX_LOG
end
return load_dict
end
## - Parse Dict to Struct
function bus_dict_parse(dict::Dict{Int,Any})
Buses = [Bus(b["number"],b["name"], b["bustype"],b["angle"],b["voltage"],b["voltagelimits"],b["basevoltage"]) for (k_b,b) in dict ]
return Buses
end
## - Parse Dict to Array
function gen_dict_parser(dict::Dict{String,Any})
Generators = Array{G where {G<:Generator},1}()
Storage_gen = Array{S where {S<:Storage},1}()
for (gen_type_key,gen_type_dict) in dict
if gen_type_key =="Thermal"
for (thermal_key,thermal_dict) in gen_type_dict
push!(Generators,ThermalDispatch(string(thermal_dict["name"]),
Bool(thermal_dict["available"]),
thermal_dict["bus"],
TechThermal(thermal_dict["tech"]["activepower"],
thermal_dict["tech"]["activepowerlimits"],
thermal_dict["tech"]["reactivepower"],
thermal_dict["tech"]["reactivepowerlimits"],
thermal_dict["tech"]["ramplimits"],
thermal_dict["tech"]["timelimits"]),
EconThermal(thermal_dict["econ"]["capacity"],
thermal_dict["econ"]["variablecost"],
thermal_dict["econ"]["fixedcost"],
thermal_dict["econ"]["startupcost"],
thermal_dict["econ"]["shutdncost"],
thermal_dict["econ"]["annualcapacityfactor"])
))
end
elseif gen_type_key =="Hydro"
for (hydro_key,hydro_dict) in gen_type_dict
push!(Generators,HydroCurtailment(string(hydro_dict["name"]),
hydro_dict["available"],
hydro_dict["bus"],
TechHydro( hydro_dict["tech"]["installedcapacity"],
hydro_dict["tech"]["activepower"],
hydro_dict["tech"]["activepowerlimits"],
hydro_dict["tech"]["reactivepower"],
hydro_dict["tech"]["reactivepowerlimits"],
hydro_dict["tech"]["ramplimits"],
hydro_dict["tech"]["timelimits"]),
hydro_dict["econ"]["curtailcost"],
hydro_dict["scalingfactor"]
))
end
elseif gen_type_key =="Renewable"
for (ren_key,ren_dict) in gen_type_dict
if ren_key == "PV"
for (pv_key,pv_dict) in ren_dict
push!(Generators,RenewableCurtailment(string(pv_dict["name"]),
Bool( pv_dict["available"]),
pv_dict["bus"],
pv_dict["tech"]["installedcapacity"],
EconRenewable(pv_dict["econ"]["curtailcost"],
pv_dict["econ"]["interruptioncost"]),
pv_dict["scalingfactor"]
))
end
elseif ren_key == "RTPV"
for (rtpv_key,rtpv_dict) in ren_dict
push!(Generators,RenewableFix(string(rtpv_dict["name"]),
Bool(rtpv_dict["available"]),
rtpv_dict["bus"],
rtpv_dict["tech"]["installedcapacity"],
rtpv_dict["scalingfactor"]
))
end
elseif ren_key == "WIND"
for (wind_key,wind_dict) in ren_dict
push!(Generators,RenewableCurtailment(string(wind_dict["name"]),
Bool(wind_dict["available"]),
wind_dict["bus"],
wind_dict["tech"]["installedcapacity"],
EconRenewable(wind_dict["econ"]["curtailcost"],
wind_dict["econ"]["interruptioncost"]),
wind_dict["scalingfactor"]
))
end
end
end
elseif gen_type_key =="Storage"
for (storage_key,storage_dict) in gen_type_dict
push!(Storage_gen,GenericBattery(string(storage_dict["name"]),
Bool(storage_dict["available"]),
storage_dict["bus"],
storage_dict["energy"],
storage_dict["capacity"],
storage_dict["activepower"],
storage_dict["inputactivepowerlimits"],
storage_dict["outputactivepowerlimits"],
storage_dict["efficiency"],
storage_dict["reactivepower"],
storage_dict["reactivepowerlimits"]
))
end
end
end
return (Generators, Storage_gen)
end
# - Parse Dict to Array
function branch_dict_parser(dict::Dict{String,Any},Branches::Array{B,1}) where {B<:Branch}
for (branch_key,branch_dict) in dict
if branch_key == "Transformers"
for (trans_key,trans_dict) in branch_dict
if trans_dict["tap"] ==1.0
push!(Branches,Transformer2W(string(trans_dict["name"]),
Bool(trans_dict["available"]),
trans_dict["connectionpoints"],
trans_dict["r"],
trans_dict["x"],
trans_dict["primaryshunt"],
trans_dict["rate"]
))
elseif trans_dict["tap"] !=1.0
alpha = "α" in keys(trans_dict) ? trans_dict["α"] : 0.0
if alpha !=0.0 #TODO : 3W Transformer
push!(Branches,PhaseShiftingTransformer(string(trans_dict["name"]),
Bool(trans_dict["available"]),
trans_dict["connectionpoints"],
trans_dict["r"],
trans_dict["x"],
trans_dict["primaryshunt"],
trans_dict["tap"],
trans_dict["α"],
trans_dict["rate"]
))
else
push!(Branches,TapTransformer(string(trans_dict["name"]),
Bool(trans_dict["available"]),
trans_dict["connectionpoints"],
trans_dict["r"],
trans_dict["x"],
trans_dict["primaryshunt"],
trans_dict["tap"],
trans_dict["rate"]
))
end
end
end
else branch_key == "Lines"
for (line_key,line_dict) in branch_dict
push!(Branches,Line(string(line_dict["name"]),
Bool(line_dict["available"]),
line_dict["connectionpoints"],
line_dict["r"],
line_dict["x"],
line_dict["b"],
float(line_dict["rate"]),
line_dict["anglelimits"]
))
end
end
end
return Branches
end
function load_dict_parser(dict::Dict{String,Any})
Loads =Array{L where {L<:ElectricLoad},1}()
for (load_key,load_dict) in dict
push!(Loads,PowerLoad(string(load_dict["name"]),
Bool(load_dict["available"]),
load_dict["bus"],
load_dict["maxactivepower"],
load_dict["maxreactivepower"],
load_dict["scalingfactor"]
))
end
return Loads
end
function loadzone_dict_parser(dict::Dict{Int64,Any})
LoadZs =Array{D where {D<:Device},1}()
for (lz_key,lz_dict) in dict
push!(LoadZs,LoadZones(lz_dict["number"],
string(lz_dict["name"]),
lz_dict["buses"],
lz_dict["maxactivepower"],
lz_dict["maxreactivepower"]
))
end
return LoadZs
end
function shunt_dict_parser(dict::Dict{String,Any})
Shunts = Array{FixedAdmittance,1}()
for (s_key,s_dict) in dict
push!(Shunts,FixedAdmittance(string(s_dict["name"]),
Bool(s_dict["available"]),
s_dict["bus"],
s_dict["Y"]
)
)
end
return Shunts
end
function dclines_dict_parser(dict::Dict{String,Any},Branches::Array{Branch,1})
for (dct_key,dct_dict) in dict
if dct_key == "HVDCLine"
for (dcl_key,dcl_dict) in dct_dict
push!(Branches,HVDCLine(string(dcl_dict["name"]),
Bool(dcl_dict["available"]),
dcl_dict["connectionpoints"],
dcl_dict["activepowerlimits_from"],
dcl_dict["activepowerlimits_to"],
dcl_dict["reactivepowerlimits_from"],
dcl_dict["reactivepowerlimits_to"],
dcl_dict["loss"]
))
end
elseif dct_key == "VSCDCLine"
for (dcl_key,dcl_dict) in dct_dict
push!(Branches,VSCDCLine(string(dcl_dict["name"]),
Bool(dcl_dict["available"]),
dcl_dict["connectionpoints"],
dcl_dict["rectifier_taplimits"],
dcl_dict["rectifier_xrc"],
dcl_dict["rectifier_firingangle"],
dcl_dict["inverter_taplimits"],
dcl_dict["inverter_xrc"],
dcl_dict["inverter_firingangle"]
))
end
end
end
return Branches
end
function services_dict_parser(dict::Dict{String,Any},generators::Array{Generator,1})
Services = Array{D where {D <: Service},1}()
for (k,d) in dict
contributingdevices = Array{D where {D<:PowerSystems.Device},1}()
[PowerSystems._get_device(dev,generators) for dev in d["contributingdevices"]] |> (x->[push!(contributingdevices,d[1]) for d in x if length(d)==1])
push!(Services,ProportionalReserve(d["name"],
contributingdevices,
Float64(d["timeframe"]),
TimeSeries.TimeArray(Dates.today(),ones(1)) #TODO : fix requirement
))
end
return Services
end
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] | 1.723714 | 13,743 |
using Gadfly
VTR = readdlm("VTR.dat")
# Central wave number for incoming wave packet
k = 200
# Theoretically predicted transmission and reflection coefficients
T = (V0) -> k^2 < 2V0 ? 0.0 : 4k*√(k^2 - 2V0) / (k+√(k^2 - 2V0))^2
R = (V0) -> k^2 < 2V0 ? 1.0 : (k-√(k^2 - 2V0))^2 / (k+√(k^2 - 2V0))^2
Vs = [5e3:100:5e4]
p = plot(
layer(
x = VTR[:,1],
y = VTR[:,2],
Geom.line,
Geom.point,
color = ["Transmission Probability, Experimental"]
),
layer(
x = VTR[:,1],
y = VTR[:,3],
Geom.line,
Geom.point,
color = ["Reflection Probability, Experimental"]
),
layer(
x = Vs,
y = map(T, Vs),
Geom.line,
color = ["Transmission Probability, Theoretical"]
),
layer(
x = Vs,
y = map(R, Vs),
Geom.line,
color = ["Reflection Probability, Theoretical"]
),
Guide.xlabel("Potential Energy at Step Function"),
Guide.ylabel("Probability"),
Guide.title("Transmission probability across step potential vs. magnitude of potential for k = 200, σ = 0.05")
)
draw(SVG("VTR.svg", 20cm, 12cm), p)
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8291,
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3691,
13,
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2785,
329,
479,
796,
939,
11,
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225,
796,
657,
13,
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8,
198,
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7,
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5446,
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21370,
70,
1600,
1160,
11215,
11,
1105,
11215,
828,
279,
8,
198
] | 2.036907 | 569 |
using Gadfly
using BlackBoxOptimizationBenchmarking
const BBOB = BlackBoxOptimizationBenchmarking
##
function plot_fun(f)
r = 5
c, n, nline = Geom.contour(levels=exp10.(range(-6, stop=10, length=40))), 800, 1000
if any(f .== [BBOB.F16,BBOB.F17,BBOB.F18,BBOB.F19])
c, n, nline = Geom.contour(levels=exp10.(range(-6, stop=8, length=20))), 500, 1000
end
p1 = plot(
layer(x=[f.x_opt[1]],y=[f.x_opt[2]],Geom.point,Theme(default_color=colorant"red")),
layer(
z=(x,y)->f([x,y])-f.f_opt, x=range(-r, stop=r, length=n), y=range(-r, stop=r, length=n),
c,
),
Coord.cartesian(xmin=-r,xmax=r,ymin=-r,ymax=r),
Guide.title(string(f))
)
# line plot
x = collect(range(-r, stop=r, length=nline))
p2 = plot(
layer(x=x,y=[ f([xi,f.x_opt[2],f.x_opt[3]]) for xi in x]-f.f_opt, Geom.line),
layer(x=x,y=[ f([f.x_opt[1],xi,f.x_opt[3]]) for xi in x]-f.f_opt, Geom.line,Theme(default_color=colorant"orange")),
layer(x=x,y=[ f([f.x_opt[1],f.x_opt[2],xi]) for xi in x]-f.f_opt, Geom.line,Theme(default_color=colorant"red")),
Coord.cartesian(xmin=-r,xmax=r),
Guide.title(string(f)),
Scale.y_log10
)
p1,p2
end
outdir = joinpath(Pkg.dir(),"BBOB","data","plots","functions")
for i in 20:length(enumerate(BBOB.BBOBFunction))
f = getfield(BBOB,Symbol("F$i"))
info(f)
p1,p2 = plot_fun(f)
draw(PDF(joinpath(outdir,"F$i.pdf"),16cm,16cm),p1)
draw(SVG(joinpath(outdir,"F$i.svg"),16cm,16cm),p1)
draw(PDF(joinpath(outdir,"1D","F$i.pdf"),16cm,12cm),p2)
draw(SVG(joinpath(outdir,"1D","F$i.svg"),16cm,12cm),p2)
end
## | [
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828,
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17,
8,
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437,
198,
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2235
] | 1.854645 | 915 |
### A Pluto.jl notebook ###
# v0.12.21
using Markdown
using InteractiveUtils
# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).
macro bind(def, element)
quote
local el = $(esc(element))
global $(esc(def)) = Core.applicable(Base.get, el) ? Base.get(el) : missing
el
end
end
# ╔═╡ 4c148672-8b79-11eb-0133-6de14d906224
using HeroIcons, PlutoUI
# ╔═╡ 39d69132-8b7a-11eb-06cf-bfa0618705d8
begin
opts = map(x -> replace(x, ".svg" => ""), readdir(
joinpath(@__DIR__, "..", "deps", "heroicons-0.4.2", "optimized", "outline")
))
md"""
# Choose an Icon to View
$(@bind icon Select(opts))
"""
end
# ╔═╡ 4c30529a-8b7d-11eb-314d-1ba4c5db4103
HeroIcon(icon, css="height:3em; width:3em")
# ╔═╡ 555361a0-8b7d-11eb-189e-31e44fd4fd2d
md"# In HTML"
# ╔═╡ a3b68f84-8b7d-11eb-0562-1911d449f75d
let
s = repr("text/html", HeroIcon(icon, css="height:1em; width:1em; display:inline-block;"))
HTML("""<h4>$s This is inside an H4 Tag</h4>""")
end
# ╔═╡ 9355cd84-8b7e-11eb-0278-05736b4c3983
md"# In Markdown"
# ╔═╡ 9792e224-8b7e-11eb-2816-f767eac59cf4
md"""
$(HeroIcon(icon, css="height: 2em; width:2em"))
"""
# ╔═╡ Cell order:
# ╠═4c148672-8b79-11eb-0133-6de14d906224
# ╟─39d69132-8b7a-11eb-06cf-bfa0618705d8
# ╟─4c30529a-8b7d-11eb-314d-1ba4c5db4103
# ╟─555361a0-8b7d-11eb-189e-31e44fd4fd2d
# ╠═a3b68f84-8b7d-11eb-0562-1911d449f75d
# ╟─9355cd84-8b7e-11eb-0278-05736b4c3983
# ╠═9792e224-8b7e-11eb-2816-f767eac59cf4
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] | 1.923636 | 825 |
#_______________________________________________________________________________
# Writting results
# requirements fulfillement
RequirSheet = getSheet( CPworkbook, "Requirements" )
if RequirSheet.ptr === Ptr{Void}( 0 )
RequirSheet = createSheet( CPworkbook, "Requirements" )
end
rowIdx = 0
Row = createRow(RequirSheet, rowIdx)
rowIdx += 1
cellIdx = 1
for i in 1:length(MPObjectives)
obj = ""
for j in 1:length(MPObjectives[i].Objectives)
obj = obj * MPObjectives[i].Objectives[j]
end
Cell=createCell(Row, cellIdx); setCellValue(Cell, obj)
cellIdx += 1
end
for i in 1:NBYears
Row = createRow(RequirSheet, rowIdx)
rowIdx += 1
cellIdx = 0
Cell=createCell(Row, cellIdx); setCellValue(Cell, i)
cellIdx += 1
for j in 1:length(MPObjectives)
Cell=createCell(Row, cellIdx); setCellValue(Cell, round(Int, RequirementFulfillment[i, j]))
cellIdx += 1
end
end
# Initial manpower distribution
InitDistSheet = getSheet( CPworkbook, "Initial distribution" )
if InitDistSheet.ptr === Ptr{Void}( 0 )
InitDistSheet = createSheet( CPworkbook, "Initial distribution" )
end
rowIdx = 0
Row = createRow(InitDistSheet, rowIdx)
rowIdx += 1
cellIdx = 2
#for i in 1:length(GuyCareerPaths)
# Cell=createCell(Row, cellIdx); setCellValue(Cell, i)
# cellIdx += 1
#end
index = 1
for i in 1:length(InitManpower)
Row = createRow(InitDistSheet, rowIdx)
rowIdx += 1
cellIdx = 0
Cell=createCell(Row, cellIdx); setCellValue(Cell, InitManpower[i].Academiclvl)
cellIdx += 1
Cell=createCell(Row, cellIdx); setCellValue(Cell, InitManpower[i].Seniority)
cellIdx += 1
for j in 1:length(GuyCareerPaths)
if j in InitMPPartCP[i]
if sol.sol[index] != 0
Cell=createCell(Row, cellIdx); setCellValue(Cell, j); cellIdx += 1
Cell=createCell(Row, cellIdx); setCellValue(Cell, sol.sol[index]); cellIdx += 1
end
index += 1
end
# Cell=createCell(Row, cellIdx);
# if j in InitMPPartCP[i]
# setCellValue(Cell, sol.sol[index])
# index += 1
# else
# setCellValue(Cell, 0)
end
end
# Annual Recruitment
RecruitmentSheet = getSheet( CPworkbook, "Perforemed Recruitment" )
if RecruitmentSheet.ptr === Ptr{Void}( 0 )
RecruitmentSheet = createSheet( CPworkbook, "Perforemed Recruitment" )
end
rowIdx = 0
#Row = createRow(RecruitmentSheet, rowIdx)
rowIdx += 1
#cellIdx = 1
#for i in 1:length(GuyCareerPaths)
# Cell=createCell(Row, cellIdx); setCellValue(Cell, i)
# cellIdx += 1
#end
#Cell=createCell(Row, cellIdx); setCellValue(Cell, "sum")
for i in 1:NBYears
sum = 0
Row = createRow(RecruitmentSheet, rowIdx)
rowIdx += 1
cellIdx = 0
Cell=createCell(Row, cellIdx); setCellValue(Cell, i)
cellIdx += 2
for j in 1:length(GuyCareerPaths)
if sol.sol[InitMPDivisionNb + (i - 1)*length(GuyCareerPaths) + j] != 0
sum += sol.sol[InitMPDivisionNb + (i - 1)*length(GuyCareerPaths) + j]
Cell=createCell(Row, cellIdx); setCellValue(Cell, j)
cellIdx += 1
Cell=createCell(Row, cellIdx); setCellValue(Cell, sol.sol[InitMPDivisionNb + (i - 1)*length(GuyCareerPaths) + j])
cellIdx += 1
end
end
Cell=createCell(Row, 1); setCellValue(Cell, sum)
cellIdx += 1
end
# rectuirment Variables
# rectuirmentVariables = getSheet( CPworkbook, "rectuirmentVariables" )
# if rectuirmentVariables.ptr === Ptr{Void}( 0 )
# rectuirmentVariables = createSheet( CPworkbook, "rectuirmentVariables" )
# end
# Deviation Variables
DeviationVariables = getSheet( CPworkbook, "DeviationVariables" )
if DeviationVariables.ptr === Ptr{Void}( 0 )
DeviationVariables = createSheet( CPworkbook, "DeviationVariables" )
end
rowIdx = 0
Row = createRow(DeviationVariables, rowIdx)
rowIdx += 1
cellIdx = 0
Cell=createCell(Row, cellIdx); setCellValue(Cell, rowIdx)
cellIdx += 1
for i in 1:(length(GuyCareerPaths) * NBYears)
Cell=createCell(Row, cellIdx); setCellValue(Cell, sol.sol[InitMPDivisionNb + i])
cellIdx += 1
if i%length(GuyCareerPaths) == 0
Row = createRow(DeviationVariables, rowIdx)
rowIdx += 1
cellIdx = 0
Cell=createCell(Row, cellIdx); setCellValue(Cell, rowIdx)
cellIdx += 1
end
end
#Positive deviation
rowIdx = 0
Row = createRow(DeviationVariables, rowIdx)
rowIdx += 1
cellIdx = 0
Cell=createCell(Row, cellIdx); setCellValue(Cell, rowIdx)
cellIdx += 1
for i in 1:(length(MPObjectives) * NBYears)
Cell=createCell(Row, cellIdx); setCellValue(Cell, sol.sol[InitMPDivisionNb + AnnualRecDivNb + i])
cellIdx += 1
if i%length(MPObjectives) == 0
Row = createRow(DeviationVariables, rowIdx)
rowIdx += 1
cellIdx = 0
Cell=createCell(Row, cellIdx); setCellValue(Cell, rowIdx)
cellIdx += 1
end
end
# Negative deviation
rowIdx += 2
Row = createRow(DeviationVariables, rowIdx)
rowIdx += 1
cellIdx = 0
Cell=createCell(Row, cellIdx); setCellValue(Cell, rowIdx)
cellIdx += 1
for i in 1:(length(MPObjectives) * NBYears)
Cell=createCell(Row, cellIdx); setCellValue(Cell, sol.sol[InitMPDivisionNb + AnnualRecDivNb + (length(MPObjectives) * NBYears) + i])
cellIdx += 1
if i%length(MPObjectives) == 0
Row = createRow(DeviationVariables, rowIdx)
rowIdx += 1
cellIdx = 0
Cell=createCell(Row, cellIdx); setCellValue(Cell, rowIdx)
cellIdx += 1
end
end
#try rm(joinpath( dirname( Base.source_path() ), "..", "..", "..", "GuyCPResultsAll.xlsx" )) end
write(joinpath( dirname( Base.source_path() ), "..", "..", "..", "GuyCPResultsAllPop.xlsx" ), CPworkbook)
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] | 2.293296 | 2,506 |
# This file was generated by the Julia Swagger Code Generator
# Do not modify this file directly. Modify the swagger specification instead.
mutable struct AzureReachabilityReport <: SwaggerModel
aggregationLevel::Any # spec type: Union{ Nothing, String } # spec name: aggregationLevel
providerLocation::Any # spec type: Union{ Nothing, AzureReachabilityReportLocation } # spec name: providerLocation
reachabilityReport::Any # spec type: Union{ Nothing, Vector{AzureReachabilityReportItem} } # spec name: reachabilityReport
function AzureReachabilityReport(;aggregationLevel=nothing, providerLocation=nothing, reachabilityReport=nothing)
o = new()
validate_property(AzureReachabilityReport, Symbol("aggregationLevel"), aggregationLevel)
setfield!(o, Symbol("aggregationLevel"), aggregationLevel)
validate_property(AzureReachabilityReport, Symbol("providerLocation"), providerLocation)
setfield!(o, Symbol("providerLocation"), providerLocation)
validate_property(AzureReachabilityReport, Symbol("reachabilityReport"), reachabilityReport)
setfield!(o, Symbol("reachabilityReport"), reachabilityReport)
o
end
end # type AzureReachabilityReport
const _property_map_AzureReachabilityReport = Dict{Symbol,Symbol}(Symbol("aggregationLevel")=>Symbol("aggregationLevel"), Symbol("providerLocation")=>Symbol("providerLocation"), Symbol("reachabilityReport")=>Symbol("reachabilityReport"))
const _property_types_AzureReachabilityReport = Dict{Symbol,String}(Symbol("aggregationLevel")=>"String", Symbol("providerLocation")=>"AzureReachabilityReportLocation", Symbol("reachabilityReport")=>"Vector{AzureReachabilityReportItem}")
Base.propertynames(::Type{ AzureReachabilityReport }) = collect(keys(_property_map_AzureReachabilityReport))
Swagger.property_type(::Type{ AzureReachabilityReport }, name::Symbol) = Union{Nothing,eval(Base.Meta.parse(_property_types_AzureReachabilityReport[name]))}
Swagger.field_name(::Type{ AzureReachabilityReport }, property_name::Symbol) = _property_map_AzureReachabilityReport[property_name]
function check_required(o::AzureReachabilityReport)
(getproperty(o, Symbol("aggregationLevel")) === nothing) && (return false)
(getproperty(o, Symbol("providerLocation")) === nothing) && (return false)
(getproperty(o, Symbol("reachabilityReport")) === nothing) && (return false)
true
end
function validate_property(::Type{ AzureReachabilityReport }, name::Symbol, val)
end
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"
SelectionOperator represents the method that selects a portion of the population
for crossover and mutation.
# Fields
- `_method::Function`: selection method between individuals.
- `_nSelectedParents::Real`: number of selected individuals. Its values can either
be an integer number, which directly represents the number of individuals that
are going to be picked for selection, or a floating point number, which then
represents the percentage of the population that is going to be selected.
- `needsComparison::Bool = true`: tells wether the user wants the system to compare
between the individuals in order to select between them.
- `_samplingWithRep::Bool`: tells wether the user wants the system to perform the
selection with replacement (`true`) or not (`false`).
- `_varArgs::Array{Any}`: arguments of the selection method, if necessary.
"
struct SelectionOperator
_method::Function
_nSelectedParents::Real
_needsComparison::Bool
_samplingWithRep::Bool
_varArgs::Array{Any}
end # struct
"""
getMethod(selector::SelectionOperator)::Function
Returns the method for select the parents.
"""
getMethod(selector::SelectionOperator)::Function = selector._method
# function
"""
needsComparison(selector::SelectionOperator)::Bool
Indicates wether the selection method needs to compare between individuals
or not.
"""
needsComparison(selector::SelectionOperator)::Bool = selector._needsComparison
# function
"""
getDefaultSelector()::Tuple{Function, Array}
Returns the default selection method, which is [`tournamentSelector`](@ref) between
4 individuals.
"""
function getDefaultSelector()::Tuple{Function, Array}
return tournamentSelector, [4]
end # function
"""
getNParents(selector::SelectionOperator)::Real
Returns the attribute that represents the number of parents that are going to be selected.
"""
getNSelectedParents(selector::SelectionOperator)::Real = selector._nSelectedParents
# function
"""
samplingWithReplacement(selector::SelectionOperator)::Bool
"""
samplingWithReplacement(selector::SelectionOperator)::Bool = selector._samplingWithRep
# function
"""
getFunctionArgs(selector::SelectionOperator)::Array
Obtains the aditional arguments associated to selector method.
"""
getFunctionArgs(selector::SelectionOperator)::Array = selector._varArgs
# function
"""
selectParentsWithReplacement(selector::SelectionOperator, population::Array{Individual},
evaluator::Evaluator, rng::Random.AbstractRNG)::Array{Individual}
Performs a selection between individuals with replacement using the comparison
function of the `Evaluator`.
"""
function selectParentsWithReplacement(selector::SelectionOperator, population::Array{Individual},
evaluator::Evaluator, rng::Random.AbstractRNG)::Array{Individual}
nSelectedParents = getNSelectedParents(selector)
compare = getCompareFunction(evaluator)
popSize = length(population)
parents = Array{Individual}(undef, nSelectedParents)
method = getMethod(selector)
functionArgs = getFunctionArgs(selector)
for i=eachindex(parents)
if needsComparison(selector)
index = method(population, compare, rng, functionArgs...)
else
index = method(population, rng, functionArgs...)
end
parents[i] = population[index]
end
return parents
end # function
"""
selectParentsWithoutReplacement(selector::SelectionOperator,
population::Array{Individual},
evaluator::Evaluator,
rng::Random.AbstractRNG)::Array{Individual}
Performs a selection between individuals without replacement using the comparison
function of the `Evaluator`.
"""
function selectParentsWithoutReplacement(selector::SelectionOperator,
population::Array{Individual},
evaluator::Evaluator,
rng::Random.AbstractRNG)::Array{Individual}
nSelectedParents = getNSelectedParents(selector)
compare = getCompareFunction(evaluator)
popSize = length(population)
copyPopulation = copyPop(population)
indexes = collect(1:popSize)
method = getMethod(selector)
functionArgs = getFunctionArgs(selector)
for i=1:nSelectedParents
if needsComparison(selector)
index = method(copyPopulation, compare, rng, functionArgs...)
else
index = method(copyPopulation, rng, functionArgs...)
end
copyPopulation[index] = copyPopulation[end]
pop!(copyPopulation)
indexes[index], indexes[end-i+1] = indexes[end-i+1], indexes[index]
end
parents = population[indexes[end-nSelectedParents+1:end]]
return parents
end # function
"""
selectParents_(selector::SelectionOperator, population::Array{Individual},
evaluator::Evaluator, rng::Random.AbstractRNG)
Performs the selection of individuals given by the selection method and using
the comparison function between individuals of the `Evaluator`.
"""
function selectParents_(selector::SelectionOperator, population::Array{Individual},
evaluator::Evaluator, rng::Random.AbstractRNG)
if samplingWithReplacement(selector)
selectParentsWithReplacement(selector, population, evaluator, rng)
else
selectParentsWithoutReplacement(selector, population, evaluator, rng)
end
end # function
"""
randomSelector(population::Array{Individual}, rng::Random.AbstractRNG)
Performs a random selection between the individuals of a population.
# Mandatory Arguments
- `population::Array{Individual}`: population of individuals that are going to
be selected.
- `rng::Random.AbstractRNG`: random number generator of the experiment.
# Returns
The index of the selected individual.
See also: [`setSelector`](@ref)
"""
function randomSelector(population::Array{Individual}, rng::Random.AbstractRNG)
rand(rng, UInt16)%(length(population)) + 1
end # function
"""
tournamentSelector(population::Array{Individual}, compare::Function,
rng::Random.AbstractRNG, k::Integer)
Performs a tournament selection between the individuals of a population.
# Mandatory Arguments
- `population::Array{Individual}`: population of individuals that are going to
be selected.
- `compare::Function`: `Evaluator`'s comparison method between individuals to
decide which ones win the tournament.
- `rng::Random.AbstractRNG`: random number generator of the experiment.
# Arguments
- `k::Integer`: number of individuals that compete in the tournament. If `k`
equals 1, it will be a random selection between the individuals (see
[`randomSelector`](@ref)).
# Returns
The index of the selected individual.
See also: [`setSelector`](@ref)
"""
function tournamentSelector(population::Array{Individual}, compare::Function,
rng::Random.AbstractRNG, k::Integer)
nInd = length(population) # Number of individuals of the population
bestIndex = rand(rng, UInt16) % nInd + 1
best = population[bestIndex]
for i=2:k
currentIndex = rand(rng, UInt16) % nInd + 1
current = population[currentIndex]
bestIndex = compare(best, current) ? bestIndex : currentIndex
best = population[bestIndex]
end
return bestIndex
end # function
"""
function tournamentSelector(fitnesses::Array, compare::Function, rng::Random.AbstractRNG, k::Integer)
nInd = length(fitnesses) # Number of individuals of the population
bestIndex = rand(rng, UInt) % nInd + 1
best = fitnesses[bestIndex]
for i=2:k
currentIndex = rand(rng, UInt) % nInd + 1
current = fitnesses[currentIndex]
bestIndex = compare(best, current) ? bestIndex : currentIndex
best = fitnesses[bestIndex]
end
return bestIndex
end
"""
function rouletteSelector(fitnesses::Array, compare::Function, rng::Random.AbstractRNG, k::Integer=-1)
minimize = compare(1,2)
popSize = length(fitnesses)
if minimize
max = maximum(fitnesses)
fitnesses = map(x->abs(x-max), fitnesses)
end
if k < 0
k = popSize
indexes = collect(1:popSize)
else
if k < 50
indexes = randomIndexSelection(popSize, k, rng)
else
indexes = randomIndexSelection2(popSize, k, rng)
end
fitnesses = fitnesses[indexes]
end
sumFitness = sum(fitnesses)
selected = 1
random = rand(rng, 0:sumFitness)
random -= fitnesses[1]
while random >= 0
selected += 1
random -= fitnesses[selected]
end
return indexes[selected]
end
function rankingSelector(fitnesses::Array, compare::Function, rng::Random.AbstractRNG, k::Integer=-1)
minimize = compare(1,2)
popSize = length(fitnesses)
if minimize
newFitnesses = copy(fitnesses)
max = maximum(newFitnesses)
newFitnesses = map(x->abs(x-max), newFitnesses)
end
if k < 0
k = popSize
indexes = collect(1:popSize)
newFitnesses = fitnesses
else
if k < 50
indexes = randomIndexSelection(popSize, k, rng)
else
indexes = randomIndexSelection2(popSize, k, rng)
end
newFitnesses = fitnesses[indexes]
end
sumFitness = sum(fitnesses)
selected = 1
random = rand(rng, 0:sumFitness)
random -= fitnesses[1]
while random >= 0
selected += 1
random -= fitnesses[selected]
end
return indexes[selected]
end
"""
function selectParentsWithoutReplacement(selector::SelectionOperator,
population::Array{Individual},
nParents::Integer, evaluator::Evaluator, rng::Random.AbstractRNG)
nSelectedParents = getNSelectedParents(selector)
compare = getCompareFitness(evaluator)
popSize = length(population)
if !(typeof(nSelectedParents) <: Integer)
nSelectedParents = convert(Integer, round(nSelectedParents * popSize))
remainder = nSelectedParents % nParents
if remainder != 0
nSelectedParents = nSelectedParents + nParents - remainder
end
if nSelectedParents > popSize
nSelectedParents -= nParents
end
end
indexes = collect(1:popSize)
if selector._individualMode == "fitness"
fitnesses = getFitness(population)
for i=1:nSelectedParents
if needsComparison(selector)
index = selector._method(fitnesses, compare, rng, selector._varArgs...)
fitnesses[index] = fitnesses[end]
pop!(fitnesses)
indexes[index], indexes[end-i+1] = indexes[end-i+1], indexes[index]
else
index = selector._method(fitnesses, rng, selector._varArgs...)
fitnesses[index] = fitnesses[end]
pop!(fitnesses)
indexes[index], indexes[end-i+1] = indexes[end-i+1], indexes[index]
end
end
elseif selector._individualMode == "representation"
indReps = getGenotype(population)
for i=1:nSelectedParents
if needsComparison(selector)
index = selector._method(indReps, compare, rng, selector._varArgs...)
indReps[index] = indReps[end]
pop!(indReps)
indexes[index], indexes[end-i+1] = indexes[end-i+1], indexes[index]
else
index = selector._method(indReps, rng, selector._varArgs...)
indReps[index] = indReps[end]
pop!(indReps)
indexes[index], indexes[end-i+1] = indexes[end-i+1], indexes[index]
end
end
else
fitnesses = getFitness(population)
indReps = getGenotype(population)
for i=1:nSelectedParents
if needsComparison(selector)
index = selector._method(indReps, fitnesses, compare, rng, selector._varArgs...)
indReps[index] = indReps[end]
pop!(indReps)
fitnesses[index] = fitnesses[end]
pop!(fitnesses)
indexes[index], indexes[end-i+1] = indexes[end-i+1], indexes[index]
else
index = selector._method(indReps, fitnesses, rng, selector._varArgs...)
indReps[index] = indReps[end]
pop!(indReps)
fitnesses[index] = fitnesses[end]
pop!(fitnesses)
indexes[index], indexes[end-i+1] = indexes[end-i+1], indexes[index]
end
end
end
parents = deepcopy(population[indexes[end-nParents+1:end]])
return parents
end # function
"""
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] | 2.534091 | 5,104 |
module Trajectory
include("minimumjerk.jl")
end # module
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function lol()
e3::Int32 = 0
e3 = 0
end | [
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function interaction_distance(M::Array{Float64},data1::DataFrame,data2::DataFrame,ndim::Int64)
k=length(data1[1])
l=length(data2[1])
sum = 0.0
maxs = zeros(ndim)
for i in 1:ndim
maxs[i] = maximum(M[i,:])
end
for one in 1:k
for two in 1:k
prod=1.0
for i in 1:ndim
prod=prod*(maxs[i]-max(data1[one,i],data1[two,i]))
end
sum=sum+(1/k^2)*prod
end
end
for one in 1:k
for two in 1:l
prod=1.0
for i in 1:ndim
prod=prod*(maxs[i]-max(data1[one,i],data2[two,i]))
end
sum=sum-(2/(k*l))*prod
end
end
for one in 1:l
for two in 1:l
prod=1.0
for i in 1:ndim
prod=prod*(maxs[i]-max(data2[one,1],data2[two,1]))
end
sum=sum+(1/l^2)*prod
end
end
return(abs(sum)^(1/2))
end
# dim_i: current dimension
function get_IDS_largeI(M::Array{Float64},B::DataFrame,labels::Array{Int64},ndim::Int64,dim_i::Int64)
ids = zeros(length(labels)-1)
M_temp = deepcopy(M[1:end .!= dim_i, 1:end])
for i in 1:length(labels)-1
data1_arr=[]
data2_arr=[]
for j in 1:ndim-1
push!(data1_arr,M_temp[j,:][B[:,1] .== labels[i]])
push!(data2_arr,M_temp[j,:][B[:,1] .== labels[i+1]])
end
data1 = DataFrame(data1_arr)
data2 = DataFrame(data2_arr)
ids[i]=interaction_distance(M_temp,data1,data2,ndim-1)
end
return(ids)
end
function largeI(bin::Array{Int64},interaction_distances::Array{Float64}, threshold::Float64)
sum = 0
if length(bin)>1
for x in 1:length(bin)-1
if interaction_distances[bin[x]]>=threshold
sum = sum+1
end
end
end
return(sum)
end
function determine_threshold(interaction_distances::Array{Float64},T::Int64,limit::Int64)
tert=max(1,Int(round((T-1)/limit)))
return(sort(interaction_distances)[tert])
end
function logstar(x::Int64)
y=deepcopy(x)
res = 0.0
while log2(y)>0
y=log2(y)
res = res+y
end
return(res)
end
function LN(z::Int64)
return(logstar(z)+log2(2.8654))
end
function encode_disc(T::Int64,k::Int64)
return(LN(k)+log2(binomial(T-1,k-1)))
end
function Lbid(merges::Array{Any},T::Int64)
sum = 0
for bin in merges
sum = sum+LN(length(bin))-(1+length(bin))*log2(length(bin)/T)
end
return(sum)
end
function Lmh(merges::Array{Any},interaction_distances::Array{Float64},threshold::Float64)
sum = 0.0
I = 0
for bin in merges
I = largeI(bin,interaction_distances,threshold)
if I>0
sum = sum + LN(I) + I*log2(length(bin)-1)
end
end
return(sum)
end
function encode_discrete_data(merges::Array{Any},interaction_distances::Array{Float64},T::Int64,threshold::Float64)
return(Lbid(merges,T)+Lmh(merges,interaction_distances,threshold))
end
function encode_error(merges::Array{Any})
sum=0
for bin in merges
sum = sum+length(bin)*log2(length(bin))
end
return(sum)
end
# M: original data
# B: Micro binned data
# merges: array of array that form the macro bins
# k: number of macro bins
# t: threshold
# ndim: number of dimensions
# T: nnumber of micro bins
function practical_score(M::Array{Float64},B::DataFrame,merges::Array{Any}, k::Int64, threshold::Float64, ndim::Int64,T::Int64, interaction_distances::Array{Float64})
return(encode_error(merges)+encode_discrete_data(merges,interaction_distances,T,threshold)+encode_disc(T,k))
end
#### --------------------------------------------------------------------------------------------------------------------------------------------------####
#### --------------------------------------------------------------------------------------------------------------------------------------------------####
# Input for greedy_IPD_v2:
#M: data
# nm: number of micro bins
# ndim: number of dimension
function greedy_IPD(M::Array{Float64},ndim::Int64,T::Int64,disc=:km,skip=[],limit=10)
results = DataFrame()
#cuts = DataFrame()
#results_arr=[]
#results = zeros(size(M))
for i in setdiff(1:ndim,skip)
if mod(i,100)==0
print("\n")
print("dimension ")
print(i)
end
micro_binned = DataFrame();
# label for results in dataframe
# label = string("dimension"," ",string(i))
# start by uniform discretization for micro bins, seperate per dimension !!!
if length(unique(M[i,:]))<=5
res_temp = zeros(length(M[i,:]))
for u in 1:length(unique(M[i,:]))
res_temp[M[i,:] .== unique(M[i,:])[u]] .= u
end
results[!,i] = res_temp
else
if length(unique(M[i,:]))<=T
T=length(unique(M[i,:]))
end
if disc==:km
R = kmeans(vec(M[i,:])',T)
cutpoints=zeros(T+1)
cutpoints[1]=minimum(M[i,:])
cutpoints[T+1]=maximum(M[i,:])
centers = sort(R.centers,dims=2)
[cutpoints[i] = (centers[i-1]+centers[i])/2 for i in 2:T]
# discretize the data
lindisc = LinearDiscretizer(sort(cutpoints))
micro_binned[!, :1] = [encode(lindisc,one) for one in M[i,:]]
else
lindisc = LinearDiscretizer(binedges(DiscretizeUniformWidth(T), M[i,:]))
micro_binned[!, :1] = [encode(lindisc,one) for one in M[i,:]]
end
#get the lables
labels_micro = sort(unique(micro_binned[:,:1]))
#initialize number of macro bins
k=T
# determine the interaction distances
int_dists = get_IDS_largeI(M,micro_binned,labels_micro,ndim,i)
# determine threshlod for largeI
t=determine_threshold(int_dists,T,limit)
merges=[]
for j in labels_micro
push!(merges,[j])
end
scores=[]
# practical score at the beginning
MDL_ref=practical_score(M,micro_binned,merges,k-1,t,ndim,T,int_dists)
append!(scores,MDL_ref)
while minimum(scores)<=MDL_ref
optimal_merge = 0
if length(merges)>1
for j in 1:length(merges)-1
temp_merges = deepcopy(merges)
append!(temp_merges[j],temp_merges[j+1])
deleteat!(temp_merges, j+1)
mdl = practical_score(M,micro_binned,temp_merges,k-1,t,ndim,T,int_dists)
# check if the current tuple is the best so far
if mdl < minimum(scores)
optimal_merge = j
push!(scores,mdl)
end
end
end
if optimal_merge != 0
append!(merges[optimal_merge],merges[optimal_merge+1])
deleteat!(merges, optimal_merge+1)
MDL_ref = minimum(scores)
k=k-1
else
MDL_ref=minimum(scores)-1
end
end
results_dim = zeros(length(M[i,:]))
for index in 1:length(merges)
macrobin = merges[index]
for id in macrobin
results_dim[micro_binned[!,:1] .== id] .= index
end
end
results[!,i] = results_dim
#results[i,:] = results_dim
end
end
return(results)
end
function greedy_IPD_cutpoints(M::Array{Float64},ndim::Int64,T::Int64,disc=:km)
#results = DataFrame()
results = zeros(size(M))
#cuts = []
#results_arr=[]
for i in 1:ndim
if mod(i,100)==0
print("\n")
print("dimension ")
print(i)
end
dimension = "i"
micro_binned = DataFrame();
# label for results in dataframe
#label = string("dimension"," ",string(i))
# start by uniform discretization for micro bins, seperate per dimension !!!
if length(unique(M[i,:]))>5
if length(unique(M[i,:]))<=T
T=length(unique(M[i,:]))
end
if disc==:km
R = kmeans(vec(M[i,:])',T)
cutpoints=zeros(T+1)
cutpoints[1]=minimum(M[i,:])
cutpoints[T+1]=maximum(M[i,:])
centers = sort(R.centers,dims=2)
[cutpoints[i] = (centers[i-1]+centers[i])/2 for i in 2:T]
# discretize the data
lindisc = LinearDiscretizer(sort(cutpoints))
micro_binned[!, :1] = [encode(lindisc,one) for one in M[i,:]]
else
lindisc = LinearDiscretizer(binedges(DiscretizeUniformWidth(T), M[i,:]))
micro_binned[!, :1] = [encode(lindisc,one) for one in M[i,:]]
end
#push!(cuts,cutpoints)
#get the lables
labels_micro = sort(unique(micro_binned[:,:1]))
#initialize number of macro bins
k=T
# determine the interaction distances
int_dists = get_IDS_largeI(M,micro_binned,labels_micro,ndim,i)
# determine threshlod for largeI
t=determine_threshold(int_dists,T)
merges=[]
for j in labels_micro
push!(merges,[j])
end
scores=[]
# practical score at the beginning
MDL_ref=practical_score(M,micro_binned,merges,k-1,t,ndim,T,int_dists)
append!(scores,MDL_ref)
while minimum(scores)<=MDL_ref
optimal_merge = 0
if length(merges)>1
for j in 1:length(merges)-1
temp_merges = deepcopy(merges)
append!(temp_merges[j],temp_merges[j+1])
deleteat!(temp_merges, j+1)
mdl = practical_score(M,micro_binned,temp_merges,k-1,t,ndim,T,int_dists)
# check if the current tuple is the best so far
if mdl < minimum(scores)
optimal_merge = j
push!(scores,mdl)
end
end
end
if optimal_merge != 0
append!(merges[optimal_merge],merges[optimal_merge+1])
deleteat!(merges, optimal_merge+1)
MDL_ref = minimum(scores)
k=k-1
else
MDL_ref=minimum(scores)-1
end
end
results_dim = zeros(length(M[i,:]))
for index in 1:length(merges)
macrobin = merges[index]
maxpoint = maximum(macrobin)
minpoint = minimum(macrobin)
# print("\n")
# print(cuts)
# print("\n")
# print(maxpoint)
for id in macrobin
results_dim[micro_binned[!,:1] .== id] .= (cutpoints[maxpoint]-cutpoints[minpoint])/2
end
end
results[i,:] = results_dim
end
end
return(results)
end
function greedy_IPD_clustered(M::Array{Float64},ndim::Int64,T::Int64,c::Int64,disc=:km)
results = zeros(size(M))
SPC = 1/2*(I-corspearman(M'))
clust = hclust(SPC,linkage=:ward_presquared)
clustered_data = cutree(clust;k=c)
for i in 1:c
selector = clustered_data .== i
data = M[selector,:]
res = greedy_IPD(data,size(data)[1],15,:km)
#print("\n")
#print(typeof(res))
results[selector,:] .= Array(res)'
end
return(DataFrame(results))
end
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198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3601,
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72,
8,
198,
220,
220,
220,
220,
220,
220,
220,
886,
628,
220,
220,
220,
220,
220,
220,
220,
4580,
62,
8800,
2817,
796,
6060,
19778,
9783,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
6167,
329,
2482,
287,
1366,
14535,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
6167,
796,
4731,
7203,
46156,
2430,
33172,
8841,
7,
72,
4008,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
923,
416,
8187,
1221,
1186,
1634,
329,
4580,
41701,
11,
384,
30052,
583,
15793,
220,
10185,
628,
220,
220,
220,
220,
220,
220,
220,
611,
4129,
7,
34642,
7,
44,
58,
72,
11,
47715,
4008,
27,
28,
20,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
581,
62,
29510,
796,
1976,
27498,
7,
13664,
7,
44,
58,
72,
11,
47715,
4008,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
334,
287,
352,
25,
13664,
7,
34642,
7,
44,
58,
72,
11,
47715,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
581,
62,
29510,
58,
44,
58,
72,
11,
47715,
764,
855,
3748,
7,
44,
58,
72,
11,
25,
12962,
58,
84,
11907,
764,
28,
334,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
886,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2482,
58,
28265,
72,
60,
796,
581,
62,
29510,
628,
220,
220,
220,
220,
220,
220,
220,
2073,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
4129,
7,
34642,
7,
44,
58,
72,
11,
47715,
4008,
27,
28,
51,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
309,
28,
13664,
7,
34642,
7,
44,
58,
72,
11,
47715,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
886,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
1221,
855,
25,
13276,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
371,
796,
479,
1326,
504,
7,
35138,
7,
44,
58,
72,
11,
25,
12962,
3256,
51,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2005,
13033,
28,
9107,
418,
7,
51,
10,
16,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2005,
13033,
58,
16,
22241,
39504,
7,
44,
58,
72,
11,
25,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2005,
13033,
58,
51,
10,
16,
22241,
47033,
7,
44,
58,
72,
11,
25,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
10399,
796,
3297,
7,
49,
13,
1087,
364,
11,
67,
12078,
28,
17,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
685,
8968,
13033,
58,
72,
60,
796,
357,
1087,
364,
58,
72,
12,
16,
48688,
1087,
364,
58,
72,
12962,
14,
17,
329,
1312,
287,
362,
25,
51,
60,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
1221,
1186,
1096,
262,
1366,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
300,
521,
2304,
796,
44800,
15642,
1186,
7509,
7,
30619,
7,
8968,
13033,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4580,
62,
8800,
2817,
58,
28265,
1058,
16,
60,
796,
685,
268,
8189,
7,
75,
521,
2304,
11,
505,
8,
329,
530,
287,
337,
58,
72,
11,
25,
11907,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
300,
521,
2304,
796,
44800,
15642,
1186,
7509,
7,
65,
1389,
3212,
7,
15642,
1186,
1096,
3118,
6933,
30916,
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51,
828,
337,
58,
72,
11,
47715,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4580,
62,
8800,
2817,
58,
28265,
1058,
16,
60,
796,
685,
268,
8189,
7,
75,
521,
2304,
11,
505,
8,
329,
530,
287,
337,
58,
72,
11,
25,
11907,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
886,
628,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
1136,
262,
2248,
829,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
14722,
62,
24055,
796,
3297,
7,
34642,
7,
24055,
62,
8800,
2817,
58,
45299,
25,
16,
60,
4008,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
36733,
1096,
1271,
286,
15021,
41701,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
479,
28,
51,
628,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
5004,
262,
10375,
18868,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
493,
62,
67,
1023,
796,
651,
62,
14255,
62,
11664,
40,
7,
44,
11,
24055,
62,
8800,
2817,
11,
23912,
1424,
62,
24055,
11,
358,
320,
11,
72,
8,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
5004,
294,
3447,
75,
375,
329,
1588,
40,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
256,
28,
67,
2357,
3810,
62,
400,
10126,
7,
600,
62,
67,
1023,
11,
51,
11,
32374,
8,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4017,
3212,
28,
21737,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
474,
287,
14722,
62,
24055,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4574,
0,
7,
647,
3212,
17414,
73,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
886,
628,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
8198,
28,
21737,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
8472,
4776,
379,
262,
3726,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
10670,
43,
62,
5420,
28,
29152,
605,
62,
26675,
7,
44,
11,
24055,
62,
8800,
2817,
11,
647,
3212,
11,
74,
12,
16,
11,
83,
11,
358,
320,
11,
51,
11,
600,
62,
67,
1023,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
24443,
0,
7,
1416,
2850,
11,
12740,
43,
62,
5420,
8,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
981,
5288,
7,
1416,
2850,
8,
27,
28,
12740,
43,
62,
5420,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
16586,
62,
647,
469,
796,
657,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
4129,
7,
647,
3212,
8,
29,
16,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
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220,
220,
220,
220,
220,
220,
220,
329,
474,
287,
352,
25,
13664,
7,
647,
3212,
13219,
16,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
20218,
62,
647,
3212,
796,
2769,
30073,
7,
647,
3212,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
24443,
0,
7,
29510,
62,
647,
3212,
58,
73,
4357,
29510,
62,
647,
3212,
58,
73,
10,
16,
12962,
198,
220,
220,
220,
220,
220,
220,
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220,
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220,
220,
220,
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220,
220,
220,
220,
220,
12233,
265,
0,
7,
29510,
62,
647,
3212,
11,
474,
10,
16,
8,
628,
220,
220,
220,
220,
220,
220,
220,
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220,
220,
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285,
25404,
796,
8472,
62,
26675,
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44,
11,
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62,
8800,
2817,
11,
29510,
62,
647,
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11,
74,
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83,
11,
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320,
11,
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11,
600,
62,
67,
1023,
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628,
220,
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220,
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220,
220,
220,
220,
220,
220,
220,
220,
1303,
2198,
611,
262,
1459,
46545,
318,
262,
1266,
523,
1290,
198,
220,
220,
220,
220,
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220,
220,
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611,
285,
25404,
1279,
5288,
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1416,
2850,
8,
198,
220,
220,
220,
220,
220,
220,
220,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
16586,
62,
647,
469,
796,
474,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4574,
0,
7,
1416,
2850,
11,
9132,
75,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
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220,
220,
220,
886,
628,
220,
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886,
198,
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886,
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220,
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611,
16586,
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647,
469,
14512,
657,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
24443,
0,
7,
647,
3212,
58,
8738,
4402,
62,
647,
469,
4357,
647,
3212,
58,
8738,
4402,
62,
647,
469,
10,
16,
12962,
198,
220,
220,
220,
220,
220,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
12233,
265,
0,
7,
647,
3212,
11,
16586,
62,
647,
469,
10,
16,
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198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
220,
220,
220,
220,
220,
220,
10670,
43,
62,
5420,
796,
5288,
7,
1416,
2850,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
220,
479,
28,
74,
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628,
220,
220,
220,
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220,
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2073,
198,
220,
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220,
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220,
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220,
220,
220,
220,
220,
220,
220,
10670,
43,
62,
5420,
28,
39504,
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1416,
2850,
13219,
16,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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886,
628,
220,
220,
220,
220,
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220,
220,
220,
220,
220,
220,
886,
628,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2482,
62,
27740,
796,
1976,
27498,
7,
13664,
7,
44,
58,
72,
11,
47715,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
6376,
287,
352,
25,
13664,
7,
647,
3212,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
15021,
8800,
796,
4017,
3212,
58,
9630,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
4686,
287,
15021,
8800,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2482,
62,
27740,
58,
24055,
62,
8800,
2817,
58,
28265,
25,
16,
60,
764,
855,
4686,
60,
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28,
6376,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
886,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
886,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2482,
58,
28265,
72,
60,
796,
2482,
62,
27740,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
43420,
58,
72,
11,
47715,
796,
2482,
62,
27740,
198,
220,
220,
220,
220,
220,
220,
220,
886,
628,
220,
220,
220,
886,
628,
220,
220,
220,
1441,
7,
43420,
8,
198,
198,
437,
628,
628,
198,
8818,
31828,
62,
4061,
35,
62,
8968,
13033,
7,
44,
3712,
19182,
90,
43879,
2414,
5512,
358,
320,
3712,
5317,
2414,
11,
51,
3712,
5317,
2414,
11,
15410,
28,
25,
13276,
8,
628,
220,
220,
220,
1303,
43420,
796,
6060,
19778,
3419,
198,
220,
220,
220,
2482,
796,
1976,
27498,
7,
7857,
7,
44,
4008,
198,
220,
220,
220,
1303,
23779,
796,
17635,
198,
220,
220,
220,
1303,
43420,
62,
3258,
28,
21737,
628,
220,
220,
220,
329,
1312,
287,
352,
25,
358,
320,
628,
220,
220,
220,
220,
220,
220,
220,
611,
953,
7,
72,
11,
3064,
8,
855,
15,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3601,
7203,
59,
77,
4943,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3601,
7203,
46156,
366,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
3601,
7,
72,
8,
198,
220,
220,
220,
220,
220,
220,
220,
886,
628,
220,
220,
220,
220,
220,
220,
220,
15793,
796,
366,
72,
1,
628,
220,
220,
220,
220,
220,
220,
220,
4580,
62,
8800,
2817,
796,
6060,
19778,
9783,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
6167,
329,
2482,
287,
1366,
14535,
198,
220,
220,
220,
220,
220,
220,
220,
1303,
18242,
796,
4731,
7203,
46156,
2430,
33172,
8841,
7,
72,
4008,
628,
220,
220,
220,
220,
220,
220,
220,
1303,
923,
416,
8187,
1221,
1186,
1634,
329,
4580,
41701,
11,
384,
30052,
583,
15793,
220,
10185,
628,
220,
220,
220,
220,
220,
220,
220,
611,
4129,
7,
34642,
7,
44,
58,
72,
11,
47715,
4008,
29,
20,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
4129,
7,
34642,
7,
44,
58,
72,
11,
47715,
4008,
27,
28,
51,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
309,
28,
13664,
7,
34642,
7,
44,
58,
72,
11,
47715,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
886,
628,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
1221,
855,
25,
13276,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
371,
796,
479,
1326,
504,
7,
35138,
7,
44,
58,
72,
11,
25,
12962,
3256,
51,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2005,
13033,
28,
9107,
418,
7,
51,
10,
16,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2005,
13033,
58,
16,
22241,
39504,
7,
44,
58,
72,
11,
25,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2005,
13033,
58,
51,
10,
16,
22241,
47033,
7,
44,
58,
72,
11,
25,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
10399,
796,
3297,
7,
49,
13,
1087,
364,
11,
67,
12078,
28,
17,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
685,
8968,
13033,
58,
72,
60,
796,
357,
1087,
364,
58,
72,
12,
16,
48688,
1087,
364,
58,
72,
12962,
14,
17,
329,
1312,
287,
362,
25,
51,
60,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
1221,
1186,
1096,
262,
1366,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
300,
521,
2304,
796,
44800,
15642,
1186,
7509,
7,
30619,
7,
8968,
13033,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4580,
62,
8800,
2817,
58,
28265,
1058,
16,
60,
796,
685,
268,
8189,
7,
75,
521,
2304,
11,
505,
8,
329,
530,
287,
337,
58,
72,
11,
25,
11907,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
2073,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
300,
521,
2304,
796,
44800,
15642,
1186,
7509,
7,
65,
1389,
3212,
7,
15642,
1186,
1096,
3118,
6933,
30916,
7,
51,
828,
337,
58,
72,
11,
47715,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4580,
62,
8800,
2817,
58,
28265,
1058,
16,
60,
796,
685,
268,
8189,
7,
75,
521,
2304,
11,
505,
8,
329,
530,
287,
337,
58,
72,
11,
25,
11907,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
886,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
14689,
0,
7,
23779,
11,
8968,
13033,
8,
628,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
1136,
262,
2248,
829,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
14722,
62,
24055,
796,
3297,
7,
34642,
7,
24055,
62,
8800,
2817,
58,
45299,
25,
16,
60,
4008,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
36733,
1096,
1271,
286,
15021,
41701,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
479,
28,
51,
628,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
5004,
262,
10375,
18868,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
493,
62,
67,
1023,
796,
651,
62,
14255,
62,
11664,
40,
7,
44,
11,
24055,
62,
8800,
2817,
11,
23912,
1424,
62,
24055,
11,
358,
320,
11,
72,
8,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
5004,
294,
3447,
75,
375,
329,
1588,
40,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
256,
28,
67,
2357,
3810,
62,
400,
10126,
7,
600,
62,
67,
1023,
11,
51,
8,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4017,
3212,
28,
21737,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
474,
287,
14722,
62,
24055,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4574,
0,
7,
647,
3212,
17414,
73,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
886,
628,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
8198,
28,
21737,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
8472,
4776,
379,
262,
3726,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
10670,
43,
62,
5420,
28,
29152,
605,
62,
26675,
7,
44,
11,
24055,
62,
8800,
2817,
11,
647,
3212,
11,
74,
12,
16,
11,
83,
11,
358,
320,
11,
51,
11,
600,
62,
67,
1023,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
24443,
0,
7,
1416,
2850,
11,
12740,
43,
62,
5420,
8,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
981,
5288,
7,
1416,
2850,
8,
27,
28,
12740,
43,
62,
5420,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
16586,
62,
647,
469,
796,
657,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
4129,
7,
647,
3212,
8,
29,
16,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
474,
287,
352,
25,
13664,
7,
647,
3212,
13219,
16,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
20218,
62,
647,
3212,
796,
2769,
30073,
7,
647,
3212,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
24443,
0,
7,
29510,
62,
647,
3212,
58,
73,
4357,
29510,
62,
647,
3212,
58,
73,
10,
16,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
12233,
265,
0,
7,
29510,
62,
647,
3212,
11,
474,
10,
16,
8,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
285,
25404,
796,
8472,
62,
26675,
7,
44,
11,
24055,
62,
8800,
2817,
11,
29510,
62,
647,
3212,
11,
74,
12,
16,
11,
83,
11,
358,
320,
11,
51,
11,
600,
62,
67,
1023,
8,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
2198,
611,
262,
1459,
46545,
318,
262,
1266,
523,
1290,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
285,
25404,
1279,
5288,
7,
1416,
2850,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
16586,
62,
647,
469,
796,
474,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4574,
0,
7,
1416,
2850,
11,
9132,
75,
8,
198,
220,
220,
220,
220,
220,
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886,
628,
220,
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886,
198,
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886,
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220,
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611,
16586,
62,
647,
469,
14512,
657,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
24443,
0,
7,
647,
3212,
58,
8738,
4402,
62,
647,
469,
4357,
647,
3212,
58,
8738,
4402,
62,
647,
469,
10,
16,
12962,
198,
220,
220,
220,
220,
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220,
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220,
220,
220,
220,
12233,
265,
0,
7,
647,
3212,
11,
16586,
62,
647,
469,
10,
16,
8,
198,
220,
220,
220,
220,
220,
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220,
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220,
220,
220,
220,
220,
220,
220,
10670,
43,
62,
5420,
796,
5288,
7,
1416,
2850,
8,
198,
220,
220,
220,
220,
220,
220,
220,
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220,
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220,
220,
479,
28,
74,
12,
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628,
220,
220,
220,
220,
220,
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2073,
198,
220,
220,
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220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
10670,
43,
62,
5420,
28,
39504,
7,
1416,
2850,
13219,
16,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
886,
628,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
886,
628,
198,
220,
220,
220,
220,
220,
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"""
OVGCriterion
Otten-van Ginneken adaptive stop criterion.
Parameters
==========
threshold
Reference
=========
Varanelli, eq. 2.47.
"""
@with_kw struct OVGCriterion{T}
energy_reference::T
threshold::T = 0.001
end
function (criterion::OVGCriterion)(state::AnnealingState)
σ = std(state.energies)
dμ = criterion.energy_reference - mean(state.energies)
σ == 0 && return true
if dμ >= 0
stop_measure = σ^2/(state.temperature*dμ)
else
stop_measure = Inf
end
return stop_measure < criterion.threshold
end
"""
SSVCriterion
Sechen and Sangiovanni-Vincentelli stop criterion. Stops if the same energy
appears a given amount of time at the end of Markov chains.
Parameters
==========
maximum_repeat: maximum number of time the same BSF energy can be repeated.
Reference
=========
Varanelli, p. 26.
"""
@with_kw mutable struct SSVCriterion{T}
maximum_repeat::Int = 3
last_energy::T = Inf
repeat_count::Int = 1
end
function (criterion::SSVCriterion)(state::AnnealingState)
if state.bsf_energy == criterion.last_energy
criterion.repeat_count += 1
else
criterion.last_energy = state.bsf_energy
criterion.repeat_count = 1
end
return criterion.repeat_count == criterion.maximum_repeat
end | [
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function JuliaBenchSIMD( operationMode )
allFunctions = [MatrixAddition, MatrixMultiplication, ElementWiseOperations]; # only SIMD functions to run
allFunctionsString = [ "Matrix Addition", "Matrix Multiplication", "Element Wise Operations"];
if (operationMode == 1) # partial benchmark
vMatrixSize = dropdims(readdlm(joinpath("Inputs","vMatrixSizePartial.csv"), ',',Int64), dims=1);
numIterations = dropdims(readdlm(joinpath("Inputs","numIterationsPartial.csv"), ',',Int64), dims=1);
elseif (operationMode == 2) # full benchmark
vMatrixSize = dropdims(readdlm(joinpath("Inputs","vMatrixSizeFull.csv"), ',',Int64), dims=1);
numIterations = dropdims(readdlm(joinpath("Inputs","numIterationsFull.csv"), ',',Int64), dims=1);
elseif (operationMode == 0) # Test benchmark
vMatrixSize = 2;
numIterations = 1;
end
numIterations = numIterations[1]; # It is 1x1 Array -> Scalar
mRunTime = zeros(length(vMatrixSize), length(allFunctions), numIterations);
tRunTime= Array{Any}(undef,length(allFunctions)+1,length(vMatrixSize)+1)# a table containing all the information
tRunTime[1,1]="FunctionName\\MatrixSize";
for ii = 1:length(vMatrixSize)
matrixSize = vMatrixSize[ii];
mX = randn(matrixSize, matrixSize);
mY = randn(matrixSize, matrixSize);
println("Matrix Size - $matrixSize");
jj=1;
for fun in allFunctions
println("Processing $(allFunctionsString[jj]) - MatrixSize= $matrixSize");
for kk = 1:numIterations;
benchIJK =@benchmark $fun($matrixSize, $mX, $mY)
# t =@benchmarkable $fun($matrixSize, $mX, $mY);
# tune!(t)
# run(t)
mRunTime[ii, jj, kk]=median(benchIJK).time/1e3;
# println("$(mRunTime[ii, jj, kk])")
end
tRunTime[jj+1,1]="$(allFunctionsString[jj])";
tRunTime[1,ii+1]="$matrixSize";
tRunTime[jj+1,ii+1]=mean(mRunTime[ii, jj,:]);
jj+=1;
end
end
return tRunTime, mRunTime;
end
#=
function MatrixGeneration( matrixSize, mX, mY )
mA = randn(matrixSize, matrixSize);
mB = rand(matrixSize, matrixSize);
return mA;
end
=#
function MatrixAddition( matrixSize, mX, mY )
scalarA = rand();
scalarB = rand();
# mA = (scalarA .* mX) .+ (scalarB .* mY);
mA = Array{Float64}(undef, matrixSize, matrixSize);
@simd for ii = 1:(matrixSize * matrixSize)
@inbounds mA[ii] = (scalarA * mX[ii]) + (scalarB * mY[ii]);
end
return mA;
end
function MatrixMultiplication( matrixSize, mX, mY )
scalarA = rand();
scalarB = rand();
# mA = (scalarA .+ mX) * (scalarB .+ mY);
mA = Array{Float64}(undef, matrixSize, matrixSize);
@simd for ii = 1:(matrixSize * matrixSize)
@inbounds mA[ii] = (scalarA + mX[ii]) * (scalarB + mY[ii]);
end
return mA;
end
#=
function MatrixQuadraticForm( matrixSize, mX, mY )
vX = randn(matrixSize);
vB = randn(matrixSize);
sacalrC = rand();
mA = (transpose(mX * vX) * (mX * vX)) .+ (transpose(vB) * vX) .+ sacalrC;
return mA;
end
function MatrixReductions( matrixSize, mX, mY )
mA = sum(mX, dims=1) .+ minimum(mY, dims=2); #Broadcasting
return mA;
end
=#
function ElementWiseOperations( matrixSize, mX, mY )
mA = rand(matrixSize, matrixSize);
mB = 3 .+ rand(matrixSize, matrixSize);
mC = rand(matrixSize, matrixSize);
# mD = abs.(mA) .+ sin.(mA);
mD = Array{Float64}(undef, matrixSize, matrixSize);
@simd for ii = 1:(matrixSize * matrixSize)
@inbounds mD[ii] = abs(mA[ii]) + sin(mA[ii]);
end
# mE = exp.(-(mA .^ 2));
mE = Array{Float64}(undef, matrixSize, matrixSize);
@simd for ii = 1:(matrixSize * matrixSize)
@inbounds mE[ii] = exp(- (mA[ii] * mA[ii]));
end
# mF = (-mB .+ sqrt.((mB .^ 2) .- (4 .* mA .* mC))) ./ (2 .* mA);
mF = Array{Float64}(undef, matrixSize, matrixSize);
@simd for ii = 1:(matrixSize * matrixSize)
@inbounds mF[ii] = (-mB[ii] + sqrt( (mB[ii] * mB[ii]) - (4 * mA[ii] * mC[ii]) )) ./ (2 * mA[ii]);
end
mA = mD .+ mE .+ mF;
return mA;
end
#=
function MatrixExp( matrixSize, mX, mY )
mA = exp(mX);
return mA;
end
function MatrixSqrt( matrixSize, mX, mY )
mY = transpose(mX) * mX;
mA = sqrt(mY);
return mA;
end
function Svd( matrixSize, mX, mY )
F = svd(mX, full = false); # F is SVD object
mU, mS, mV = F;
return mA=0;
end
function Eig( matrixSize, mX, mY )
F = eigen(mX); # F is eigen object
mD, mV = F;
return mA=0;
end
function CholDec( matrixSize, mX, mY )
mY = transpose(mX) * mX;
mA = cholesky(mY);
return mA;
end
function MatInv( matrixSize, mX, mY )
mY = transpose(mX) * mX;
mA = inv(mY);
mB = pinv(mX);
mA = mA .+ mB;
return mA;
end
function LinearSystem( matrixSize, mX, mY )
mB = randn(matrixSize, matrixSize);
vB = randn(matrixSize);
vA = mX \ vB;
mA = mX \ mB;
mA = mA .+ vA;
return mA;
end
function LeastSquares( matrixSize, mX, mY )
mB = randn(matrixSize, matrixSize);
vB = randn(matrixSize);
vA = (transpose(mX) * mX) \ (transpose(mX) * vB);
mA = (transpose(mX) * mX) \ (transpose(mX) * mB);
mA = mA .+ vA;
return mA;
end
function CalcDistanceMatrix( matrixSize, mX, mY )
mY = randn(matrixSize, matrixSize);
mA = transpose(sum(mX .^ 2, dims=1)) .- (2 .* transpose(mX) * mY) .+ sum(mY .^ 2, dims=1);
return mA;
end
function KMeans( matrixSize, mX, mY )
# Assuming Samples are slong Columns (Rows are features)
numClusters = Int64( max( round(matrixSize / 100), 1 ) ); # % max between 1 and round(...)
numIterations = 10;
# http://stackoverflow.com/questions/36047516/julia-generating-unique-random-integer-array
mA = mX[:, randperm(matrixSize)[1:numClusters]]; #<! Cluster Centroids
for ii = 1:numIterations
vMinDist, mClusterId = findmin( transpose(sum(mA .^ 2, dims=1)) .- (2 .* transpose(mA)* mX), dims=1); #<! Is there a `~` equivalent in Julia?
vClusterId = LinearIndices( dropdims(mClusterId, dims=1) ); # to be able to access it later
for jj = 1:numClusters
mA[:, jj] = mean( mX[:, vClusterId .== jj ], dims=2 );
end
end
mA = mA[:, 1] .+ transpose(mA[:, end]);
return mA;
end
=#
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437,
198,
46249,
198
] | 2.228653 | 2,799 |
using BeamformerRecipes
using RadioInterferometry
using YAML
"""
TelInfo(yaml::Dict{Symbol,Any})
Create a `TelInfo` instance based on `yaml` which was loaded from a "telinfo.yml"
file.
"""
function BeamformerRecipes.TelInfo(yaml::Dict{Symbol,Any})
ti = BeamformerRecipes.TelInfo()
# Populate the easy fields first
ti.antenna_position_frame = string(get(yaml, :antenna_position_frame, nothing))
ti.latitude = dms2d(get(yaml, :latitude, NaN))
ti.longitude = dms2d(get(yaml, :longitude, NaN))
ti.altitude = Float64(get(yaml, :altitude, NaN))
ti.telescope_name = string(get(yaml, :telescope_name, "Unknown"))
# Now do antenna specific fields
ants = yaml[:antennas]
diam = get(yaml, :antenna_diameter, 0.0)
ti.antenna_names = map(a->string(a[:name]), ants)
ti.antenna_numbers = map(a->Int(a[:number]), ants)
ti.antenna_positions = mapreduce(a->convert(Vector{Float64}, a[:position]), hcat, ants)
ti.antenna_diameters = map(a->Float64(get(a, :diameter, diam)), ants)
# Validate that antenna positions are 3 dimensional
@assert size(ti.antenna_positions, 1) == 3 "antenna positions are not 3D"
ti
end
"""
TelInfo(yaml::AbstractString)
Create a `TelInfo` instance based on a "telinfo.yml" file named by `yaml`.
"""
function BeamformerRecipes.TelInfo(yaml::AbstractString)
BeamformerRecipes.TelInfo(YAML.load_file(yaml, dicttype=Dict{Symbol,Any}))
end
"""
antpos_topo_xyz(telinfo::TelInfo)
Return `telinfo.antenna_positions` from meters in given frame to nanoseconds in
topocentric XYZ frame.
"""
function antpos_topo_xyz(telinfo::TelInfo)
# If telinfo frame is nothing, assume enu
inframe = something(telinfo.antenna_position_frame, "enu")
if inframe == "enu"
antpos = enu2xyz(telinfo.antenna_positions, deg2rad(telinfo.latitude), 0)
else
error("unsupported antenna position frame: $inframe")
end
end
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] | 2.552457 | 753 |
# abstract type AbstractPlanarPoint{T} <: StaticArrays.FieldVector{2, T} end
# abstract type AbstractPlanarVector{T} <: StaticArrays.FieldVector{2, T} end
#
# struct PlanarPoint{T} <: AbstractPlanarPoint{T}
# u::T
# v::T
# end
"""
struct CartesianPoint{T} <: AbstractCoordinatePoint{T, Cartesian}
Describes a three-dimensional point in Cartesian coordinates.
## Fields
* `x`: x-coordinate (in m).
* `y`: y-coordinate (in m).
* `z`: z-coordinate (in m).
See also [`CylindricalPoint`](@ref).
"""
struct CartesianPoint{T} <: AbstractCoordinatePoint{T, Cartesian}
x::T
y::T
z::T
end
zero(PT::Type{<:AbstractCoordinatePoint{T}}) where {T} = PT(zero(T),zero(T),zero(T))
"""
struct CylindricalPoint{T} <: AbstractCoordinatePoint{T, Cylindrical}
Describes a three-dimensional point in cylindrical coordinates.
## Fields
* `r`: Radius (in m).
* `φ`: Polar angle (in rad).
* `z`: `z`-coordinate (in m).
!!! note
`φ == 0` corresponds to the `x`-axis in the Cartesian coordinate system.
See also [`CartesianPoint`](@ref).
"""
struct CylindricalPoint{T} <: AbstractCoordinatePoint{T, Cylindrical}
r::T
φ::T
z::T
CylindricalPoint{T}(r::T, φ::T, z::T) where {T} = new(r, mod(φ,T(2π)), z)
CylindricalPoint{T}(r::Real, φ::Real, z::Real) where {T} = new(T(r), mod(T(φ),T(2π)), T(z))
end
function CylindricalPoint(pt::CartesianPoint{T})::CylindricalPoint{T} where {T}
return CylindricalPoint{T}(hypot(pt.x, pt.y), atan(pt.y, pt.x), pt.z)
end
function CartesianPoint(pt::CylindricalPoint{T})::CartesianPoint{T} where {T}
sφ::T, cφ::T = sincos(pt.φ)
return CartesianPoint{T}(pt.r * cφ, pt.r * sφ, pt.z)
end
@inline CylindricalPoint(pt::CylindricalPoint) = pt
@inline CartesianPoint(pt::CartesianPoint) = pt
@inline _convert_point(pt::AbstractCoordinatePoint, ::Type{Cylindrical}) = CylindricalPoint(pt)
@inline _convert_point(pt::AbstractCoordinatePoint, ::Type{Cartesian}) = CartesianPoint(pt)
# function _Δφ(φ1::T, φ2::T)::T where {T}
# δφ = mod(φ2 - φ1, T(2π))
# min(δφ, T(2π) - δφ)
# end
#
# _φNear(φ::Real, φMin::T, φMax::T) where {T} = _Δφ(T(φ),φMin) ≤ _Δφ(T(φ),φMax) ? φMin : φMax
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] | 2.216189 | 976 |
Flux.trainable(bn::Flux.BatchNorm) = (bn.β, bn.γ, bn.μ, bn.σ²)
struct Shortcut
shortcut
end
Flux.@functor Shortcut
function (shortcut::Shortcut)(mx::T, x::T) where T
mx + shortcut.shortcut(x)
end
struct ResNetModel
entry::Chain
encoder::Chain
head::Chain
end
Flux.@functor ResNetModel
function (model::ResNetModel)(x::T) where T
x |> model.entry |> model.encoder |> model.head
end
function BasicBlock(
in_filters::Int, out_filters::Int, stride::Int = 1, connection = +,
)::Chain
layer = Chain(
Conv((3, 3), in_filters => out_filters, stride=stride, pad=1, bias=Flux.Zeros(Float32)),
BatchNorm(out_filters, relu),
Conv((3, 3), out_filters => out_filters, pad=1, bias=Flux.Zeros(Float32)),
BatchNorm(out_filters),
)
Chain(SkipConnection(layer, connection), x -> relu.(x))
end
function Bottleneck(
in_filters::Int, out_filters::Int, stride::Int = 1, connection = +,
expansion::Int = 4,
)::Chain
layer = Chain(
Conv((1, 1), in_filters => out_filters, bias=Flux.Zeros(Float32)),
BatchNorm(out_filters, relu),
Conv((3, 3), out_filters => out_filters, stride=stride, pad=1, bias=Flux.Zeros(Float32)),
BatchNorm(out_filters, relu),
Conv((1, 1), out_filters => out_filters * expansion, bias=Flux.Zeros(Float32)),
BatchNorm(out_filters * expansion),
)
Chain(SkipConnection(layer, connection), x -> relu.(x))
end
function make_connection(in_filters::Int, out_filters::Int, stride::Int)
if stride == 1 && in_filters == out_filters
return +
end
Shortcut(Chain(
Conv((1, 1), in_filters => out_filters, stride=stride, bias=Flux.Zeros(Float32)),
BatchNorm(out_filters)
))
end
function make_layer(
in_filters::Int, out_filters::Int, repeat::Int,
block, expansion::Int, stride::Int = 1,
)::Chain
connection = make_connection(in_filters, out_filters * expansion, stride)
layer = [block(in_filters, out_filters, stride, connection)]
in_filters = out_filters * expansion
for i in 2:repeat
push!(layer, block(in_filters, out_filters))
end
Chain(layer...)
end
"""
```julia
resnet(size::Int = 18, classes::Int = 1000)
```
Construct ResNet model.
# Parameters
- `size::Int = 18`: Size of the ResNet model.
Available sizes are `18, 34, 50, 101, 152`.
- `classes::Int = 1000`: Number of classes in the last dense layer.
"""
function resnet(size::Int = 18, classes::Int = 1000)::ResNetModel
filters = [64, 128, 256, 512]
strides = [1, 2, 2, 2]
repeats, block, expansion = Dict(
18 => ([2, 2, 2, 2], BasicBlock, 1),
34 => ([3, 4, 6, 3], BasicBlock, 1),
50 => ([3, 4, 6, 3], Bottleneck, 4),
101 => ([3, 4, 23, 3], Bottleneck, 4),
152 => ([3, 8, 36, 3], Bottleneck, 4),
)[size]
entry = Chain(
Conv((7, 7), 3 => 64, pad=(3, 3), stride=(2, 2), bias=Flux.Zeros(Float32)),
BatchNorm(64, relu),
MaxPool((3, 3), pad=(1, 1), stride=(2, 2)),
)
head = Chain(MeanPool((7, 7)), flatten, Dense(512 * expansion, classes))
encoder = []
in_filters = 64
for (out_filters, repeat, stride) in zip(filters, repeats, strides)
layer = make_layer(in_filters, out_filters, repeat, block, expansion, stride)
push!(encoder, layer)
in_filters = out_filters * expansion
end
ResNetModel(entry, Chain(encoder...), head)
end
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12342,
986,
828,
1182,
8,
198,
437,
198
] | 2.349521 | 1,462 |
# Use baremodule to shave off a few KB from the serialized `.ji` file
baremodule CUDA_jll
using Base
using Base: UUID
import JLLWrappers
JLLWrappers.@generate_main_file_header("CUDA")
JLLWrappers.@generate_main_file("CUDA", UUID("e9e359dc-d701-5aa8-82ae-09bbf812ea83"))
end # module CUDA_jll
| [
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1303,
8265,
29369,
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198
] | 2.512821 | 117 |
using EngEconomics
costA = 15000
omA = 55
benefitA = 450
costB = 10000
omB = 35
benefitB = 200
n = 30
i = 0.05
# Consider do nothing option and pick best from b/c analysis
# 1.) Rank options by smallest initial cost to biggest initial cost
# (1) B
# (2) A
# 2.) Compare 1 and 2
ΔEUAB_21 = (benefitB - benefitA) - (omB - omA)
ΔEUAC_21 = (costB - costA) * capitalRecoveryFactor(i, n)
ΔBCRatio_21 = ΔEUAB_21 / ΔEUAC_21
if ΔBCRatio_21 >= 1
println("Pick the one with the bigger initial cost, i.e. B")
else
println("Pick the one with the smaller initial cost, i.e. Do Nothing")
end
disbenefitA = 400
disbenefitB = 500
ΔEUAB_21 = (benefitB - benefitA) - (omB - omA) - (disbenefitB - disbenefitA)
ΔEUAC_21 = (costB - costA) * capitalRecoveryFactor(i, n)
ΔBCRatio_21 = ΔEUAB_21 / ΔEUAC_21
if ΔBCRatio_21 >= 1
println("Pick the one with the bigger initial cost, i.e. B")
else
println("Pick the one with the smaller initial cost, i.e. Do Nothing")
end
| [
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437,
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] | 2.572193 | 374 |
using Test, Distributions, HypothesisTests, LinearAlgebra
using KernelGoodnessOfFit
xs = randn(1, 100);
@testset "FSSDrand" begin
xs = randn(100);
res = KernelGoodnessOfFit.FSSDrand(
reshape(xs, 1, :),
MvNormal([1.0], [1.0]),
GaussianRBF(1.0),
reshape([0.0], 1, 1),
nsim = 3000
)
println(res)
# reject
@test pvalue(res) ≤ 0.05
end
@testset "FSSDopt" begin
xs = randn(100);
res = KernelGoodnessOfFit.FSSDopt(
reshape(xs, 1, :),
MvNormal([1.0], [1.0]),
GaussianRBF(1.0),
reshape([0.0], 1, 1),
nsim = 3000,
train_test_ratio = 0.5
)
println(res)
# reject
@test pvalue(res) ≤ 0.05
end
@testset "FSSDrand multivariate" begin
d = 5
xs = randn(d, 100);
res = KernelGoodnessOfFit.FSSDrand(
xs,
MvNormal(ones(d), diagm(0 => ones(d))),
GaussianRBF(1.0),
zeros(5, 2); # two test-locations
nsim = 3000
)
println(res)
# reject
@test pvalue(res) ≤ 0.05
end
@testset "FSSDopt multivariate" begin
d = 5
xs = randn(d, 100);
res = pvalue(KernelGoodnessOfFit.FSSDopt(
xs,
MvNormal(10 .* ones(d), diagm(0 => ones(d))),
GaussianRBF(1.0),
zeros(5, 2); # two test-locations
nsim = 3000,
train_test_ratio = 0.5
))
# reject
@test res ≤ 0.05
end
@testset "FSSDrand different kernels" begin
q = MvNormal([10.0], [1.0])
t_rbf = FSSDrand(xs, q, KernelGoodnessOfFit.GaussianRBF(1.0))
t_exp = FSSDrand(xs, q, KernelGoodnessOfFit.ExponentialKernel())
t_matern = FSSDrand(xs, q, KernelGoodnessOfFit.Matern25Kernel(1.0))
@test pvalue(t_rbf) ≥ 0.0
@test pvalue(t_exp) ≥ 0.0
@test pvalue(t_matern) ≥ 0.0
end
@testset "FSSDopt different kernels" begin
q = MvNormal([10.0], [1.0])
t_rbf = FSSDopt(xs, q, KernelGoodnessOfFit.GaussianRBF(1.0))
t_exp = FSSDopt(xs, q, KernelGoodnessOfFit.ExponentialKernel())
# t_matern = FSSDrand(xs, q, KernelGoodnessOfFit.Matern25Kernel(1.0)) # OPTIMIZATION NOT STABLE
@test pvalue(t_rbf) ≥ 0.0
@test pvalue(t_exp) ≥ 0.0
# @test pvalue(t_matern) ≥ 0.0
end
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] | 1.9482 | 1,139 |
# Copyright (c) 2021 Idiap Research Institute, http://www.idiap.ch/
# Niccolò Antonello <nantonel@idiap.ch>
using FiniteStateTransducers
using Test, Random
using DataStructures
Random.seed!(123)
@testset "FiniteStateTransducers.jl" begin
@testset "Semirings" begin
include("semirings.jl")
end
@testset "FiniteStateTransducers constructors and utils" begin
include("wfst.jl")
include("properties.jl")
include("io_wfst.jl")
end
@testset "algorithms" begin
include("paths.jl") #Paths, BFS, paths
include("dfs.jl") #rm_state, DFS, get_scc, connect, rm_eps
include("shortest_distance.jl")
include("topsort.jl")
include("rm_eps.jl")
include("closure.jl")
include("inv.jl")
include("proj.jl")
include("reverse.jl")
include("compose.jl")
include("cat.jl")
include("determinize.jl")
include("wfst2tr.jl")
end
end
#@testset "FiniteStateTransducers.jl" begin
#end
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] | 2.518817 | 372 |
export CUTENSORError
struct CUTENSORError <: Exception
code::cutensorStatus_t
msg::AbstractString
end
Base.show(io::IO, err::CUTENSORError) = print(io, "CUTENSORError(code $(err.code), $(err.msg))")
function CUTENSORError(code::cutensorStatus_t)
msg = statusmessage(code)
return CUTENSORError(code, msg)
end
function statusmessage( status )
if status == CUTENSOR_STATUS_SUCCESS
return "the operation completed successfully"
elseif status == CUTENSOR_STATUS_NOT_INITIALIZED
return "the library was not initialized"
elseif status == CUTENSOR_STATUS_ALLOC_FAILED
return "the resource allocation failed"
elseif status == CUTENSOR_STATUS_INVALID_VALUE
return "an invalid value was used as an argument"
elseif status == CUTENSOR_STATUS_ARCH_MISMATCH
return "an absent device architectural feature is required"
elseif status == CUTENSOR_STATUS_MAPPING_ERROR
return "an access to GPU memory space failed"
elseif status == CUTENSOR_STATUS_EXECUTION_FAILED
return "the GPU program failed to execute"
elseif status == CUTENSOR_STATUS_INTERNAL_ERROR
return "an internal operation failed"
elseif status == CUTENSOR_STATUS_NOT_SUPPORTED
return "operation not supported (yet)"
elseif status == CUTENSOR_STATUS_LICENSE_ERROR
return "error detected trying to check the license"
elseif status == CUTENSOR_STATUS_CUBLAS_ERROR
return "error occurred during a CUBLAS operation"
elseif status == CUTENSOR_STATUS_CUDA_ERROR
return "error occurred during a CUDA operation"
elseif status == CUTENSOR_STATUS_INSUFFICIENT_WORKSPACE
return "insufficient workspace memory for this operation"
elseif status == CUTENSOR_STATUS_INSUFFICIENT_DRIVER
return "insufficient driver version"
else
return "unknown status"
end
end
macro check(tensor_func)
quote
local err = $(esc(tensor_func::Expr))
if err != CUTENSOR_STATUS_SUCCESS
throw(CUTENSORError(cutensorStatus_t(err)))
end
err
end
end
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] | 2.581395 | 817 |
# This file contains tests that have become out-of-date with the
# current code. They no longer run. It may be useful to rewrite them
# and re-include them in the test suite at some point, or perhaps just
# write completely new tests testing the same thing.
import Celeste: detect_sources
import Celeste.ParallelRun: BoundingBox, OptimizedSource,
one_node_joint_infer, one_node_single_infer, detect_sources
import Celeste.SensitiveFloats: SensitiveFloat
import Celeste.DeterministicVI.ElboMaximize
import Celeste.Coordinates: match_coordinates
"""
load_ea_from_source
Helper function to load elbo args for a particular source
"""
function load_ea_from_source(target_source, target_sources, catalog,
images, all_vps)
patches = Model.get_sky_patches(images, catalog)
# Get neighbors of the source from which to load elbo args.
neighbor_ids = Model.find_neighbors(patches, target_source)
# Create a dictionary to the optimized parameters.
target_source_variational_params = Dict{Int64, Array{Float64}}()
for (indx, cur_source) in enumerate(target_sources)
target_source_variational_params[cur_source] = all_vps[indx]
end
patches = Model.get_sky_patches(images, catalog)
# Get neighbors of the source from which to load elbo args.
neighbor_ids = Model.find_neighbors(patches, target_source)
# Load neighbors, patches and variational parameters
entry = catalog[target_source]
neighbors = catalog[neighbor_ids]
ids_local = vcat([target_source], neighbor_ids)
cat_local = vcat([entry], neighbors)
patches = patches[ids_local, :] # limit patches to catalog
vp = [haskey(target_source_variational_params, x) ?
target_source_variational_params[x] :
DeterministicVI.catalog_init_source(catalog[x]) for x in ids_local]
# Create elbo args
ElboArgs(images, patches, [1])
end
"""
compute_unconstrained_gradient
"""
function compute_unconstrained_gradient(target_source, target_sources,
catalog, images, all_vps)
# Load ea
ea = load_ea_from_source(target_source, target_sources, catalog, images,
all_vps)
# Evaluate in constrained space and then unconstrain
# (taken from the old maximize_elbo.jl code)
last_sf::SensitiveFloat{Float64} = SensitiveFloats.SensitiveFloat{Float64}(length(UnconstrainedParams), 1, true, true)
transform = DeterministicVI.get_mp_transform(vp, ea.active_sources)
f_res = DeterministicVI.elbo(ea)
Transform.transform_sensitive_float!(transform, last_sf, f_res, vp, ea.active_sources)
last_sf.d
end
"""
unconstrained_gradient_near_zero
"""
function unconstrained_gradient_near_zero(target_sources, catalog, images, all_vps)
# Compute gradient per source
for (cur_source_indx, source) in enumerate(target_sources)
gradient = compute_unconstrained_gradient(source, target_sources, catalog, images,all_vps)
if !isapprox(gradient, zeros(length(gradient)), atol=1)
return false
end
end
return true
end
"""
compare_vp_params
Helper to check whether two sets of variational parameters from the
result of joint inference are the same.
Return true if vp params are the same, false otherwise
"""
function compare_vp_params(flux_loc, flux_scale)
length(flux_loc) == length(flux_scale) || return false
# Check the existence and equivalence of each source's vp in flux_scale
for i in eachindex(flux_loc)
a = flux_loc[i].vs
b = flux_scale[i].vs
if !(isapprox(a, b))
println("compare_vp_params: Mismatch - $(a) vs $(b)")
print("norm(a - b): ", norm(a - b))
return false
end
end
return true
end
"""
compute_obj_value
computes obj value given set of results from one_node_infer and one_node_joint_infer
"""
function compute_obj_value(images::Vector{<:Image},
catalog::Vector{CatalogEntry},
box::BoundingBox,
results::Vector{OptimizedSource})
# TODO: This stuff is duplicated from ParallelRun.infer_box.
# We should refactor infer_box to return the objective value in some way!
patches = Model.get_sky_patches(images, catalog)
entry_in_range = entry->((box.ramin < entry.pos[1] < box.ramax) &&
(box.decmin < entry.pos[2] < box.decmax))
target_ids = find(entry_in_range, catalog)
# There must be a vp for every patch in the call to elbo().
# So, here we must limit patches to just the targets we optimized
# and pass [1, 2, 3, ...] as the target indexes.
ea = ElboArgs(images, patches[target_ids, :],
collect(1:length(target_ids)); include_kl=false)
vp = [r.vs for r in results]
DeterministicVI.elbo(ea, vp).v[]
end
"""
load_stripe_82_data
"""
function load_stripe_82_data()
rcf = RunCamcolField(4263, 5, 119)
cd(datadir)
run(`make RUN=$(rcf.run) CAMCOL=$(rcf.camcol) FIELD=$(rcf.field)`)
cd(wd)
catalog, target_sources = infer_init([rcf], datadir, primary_initialization=false)
images = SDSSIO.load_field_images([rcf], datadir)
([rcf], datadir, target_sources, catalog, images)
end
# TODO - This doesn't pass right now since some light sources are
# not close to zero.
function test_gradient_is_near_zero_on_stripe_82()
(rcfs, datadir, target_sources, catalog, images) = load_stripe_82_data()
# Make sure joint infer with few iterations does not pass the gradient near zero check
joint_few(cnti...) = one_node_joint_infer(cnti...)
results_few = one_node_infer(rcfs, datadir; infer_callback=joint_few, primary_initialization=false)
@test !unconstrained_gradient_near_zero(target_sources, catalog, images, [x.vs for x in results_few])
# Make sure joint infer with many iterations passes the gradient near zero check
joint_many(cnti...) = one_node_joint_infer(cnti...; n_iters=10)
results_many = one_node_infer(rcfs, datadir; infer_callback=joint_many, primary_initialization=false)
@test unconstrained_gradient_near_zero(target_sources, catalog, images, [x.vs for x in results_many])
end
# Tests that the gradient is zero on overlapping sources after
# joint optimization.
# Makes sure that with fewer iterations the gradient is not zero.
function test_gradient_is_near_zero()
images = SampleData.get_sdss_images(4263, 5, 119)
catalog = SampleData.get_sdss_catalog(4263, 5, 119)
# This box has 3 overlapping objects in it.
box = BoundingBox(0.467582, 0.473275, 0.588383, 0.595095)
result_few = ParallelRun.infer_box(images, catalog, box;
method=:joint, n_iters=2)
@test length(result_few) == 3
# this stuff is copied from infer_box so that we can calculate the gradient
#catalog, patches = detect_sources(images)
patches = Model.get_sky_patches(images, catalog)
entry_in_range = entry->((box.ramin < entry.pos[1] < box.ramax) &&
(box.decmin < entry.pos[2] < box.decmax))
target_sources = find(entry_in_range, catalog)
@test !unconstrained_gradient_near_zero(target_sources, catalog, images,
[x.vs for x in result_few])
result_many = ParallelRun.infer_box(images, catalog, box;
method=:joint, n_iters=20)
@test length(result_many) == 3
@test unconstrained_gradient_near_zero(target_sources, catalog, images,
[x.vs for x in result_many])
end
"""
test_different_result_with_different_iter()
Using 3 iters instead of 1 iters should result in a different set of parameters.
"""
function test_different_result_with_different_iter()
# This bounding box has overlapping stars. (neighbor map is not empty)
box = BoundingBox(154.39, 164.41, 39.11, 39.13)
field_triplets = [RunCamcolField(3900, 6, 269),]
infer_few(ctni...) = one_node_joint_infer(ctni...;
n_iters=1,
within_batch_shuffling=false)
result_iter_1 = one_node_infer(field_triplets, datadir;
infer_callback=infer_few, box=box)
infer_many(ctni...) = one_node_joint_infer(ctni...;
n_iters=5,
within_batch_shuffling=false)
result_iter_5 = one_node_infer(field_triplets, datadir;
infer_callback=infer_many, box=box)
# Make sure that parameters are not the same
@test !compare_vp_params(result_iter_1, result_iter_5)
end
"""
test_same_result_with_diff_batch_sizes
Varying batch sizes using the cyclades algorithm should not change final objective value.
"""
function test_same_result_with_diff_batch_sizes()
# This bounding box has overlapping stars. (neighbor map is not empty)
box = BoundingBox(154.39, 164.41, 39.11, 39.13)
field_triplets = [RunCamcolField(3900, 6, 269),]
# With batch size = 7
infer_few(ctni...) = one_node_joint_infer(ctni...;
n_iters=3,
batch_size=7,
within_batch_shuffling=false)
result_bs_7 = one_node_infer(field_triplets, datadir;
infer_callback=infer_few,
box=box)
# With batch size = 39
infer_many(ctni...) = one_node_joint_infer(ctni...;
n_iters=3,
batch_size=39,
within_batch_shuffling=false)
result_bs_39 = one_node_infer(field_triplets, datadir;
infer_callback=infer_many,
box=box)
# Make sure that parameters are exactly the same
@test compare_vp_params(result_bs_7, result_bs_39)
end
function test_same_one_node_infer_twice()
# This bounding box has overlapping stars. (neighbor map is not empty)
box = BoundingBox(154.39, 164.41, 39.11, 39.13)
field_triplets = [RunCamcolField(3900, 6, 269),]
infer_few(ctni...) = one_node_joint_infer(ctni...;
n_iters=3,
batch_size=7,
within_batch_shuffling=false)
result_bs_7_1 = one_node_infer(field_triplets, datadir;
infer_callback=infer_few,
box=box)
result_bs_7_2 = one_node_infer(field_triplets, datadir;
infer_callback=infer_few,
box=box)
# Make sure that parameters are exactly the same
@test compare_vp_params(result_bs_7_1, result_bs_7_2)
end
"""
test infer multi iter obj overlapping.
This makes sure one_node_joint_infer achieves sum objective value less
than single iter on non overlapping sources.
"""
function test_one_node_joint_infer_obj_overlapping()
# This bounding box has overlapping stars. (neighbor map is not empty)
box = BoundingBox(154.39, 164.41, 39.11, 39.13)
field_triplets = [RunCamcolField(3900, 6, 269),]
# 100 iterations
tic()
infer_multi(ctni...) = one_node_joint_infer(ctni...;
n_iters=30,
within_batch_shuffling=true)
result_multi = one_node_infer(field_triplets, datadir;
infer_callback=infer_multi,
box=box)
multi_iter_time = toq()
score_multi = compute_obj_value(result_multi, field_triplets, datadir; box=box)
# 2 iterations
tic()
infer_two(ctni...) = one_node_joint_infer(ctni...;
n_iters=2,
within_batch_shuffling=true)
result_two = one_node_infer(field_triplets, datadir;
infer_callback=infer_two,
box=box)
two_iter_time = toq()
score_two = compute_obj_value(result_two, field_triplets, datadir; box=box)
# One node infer (1 iteration, butm ore newton steps)
tic()
result_single = one_node_infer(field_triplets, datadir;
infer_callback=one_node_single_infer,
box=box)
single_iter_time = toq()
score_single = compute_obj_value(result_single, field_triplets, datadir; box=box)
println("One node joint infer objective value: $(score_multi)")
println("One node joint infer 2 iter objective value: $(score_two)")
println("Single iter objective value: $(score_single)")
println("One node joint infer time: $(multi_iter_time)")
println("One node joint infer 2 iter time: $(two_iter_time)")
println("Single iter time: $(single_iter_time)")
@test score_multi > score_single
@test score_multi > score_two
end
# Stripe 82 tests are long running
@testset "stripe82" begin
# Test gradients near zero (this takes 100 iterations and is a bit slow)
test_gradient_is_near_zero()
# Test that we reach a higher objective with more iterations.
# This is a bit slow.
test_one_node_joint_infer_obj_overlapping()
test_same_one_node_infer_twice()
test_different_result_with_different_iter()
test_same_result_with_diff_batch_sizes()
end
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] | 2.249959 | 6,045 |
using Flux
"""
`gate(x::T, y::T) where T <: AbstractArray`
* __input:__ arrays of equal length of Float32
* __output:__ array of Float32
The additional `y` vector is used to apply local and global conditioning.
`z = tanh(x[1:f÷2] + y[1:f÷2]) * σ(x[f÷2+1:end] + y[f÷2+1:end])`
"""
function gate(x)
chan = size(x, 3) ÷ 2
tanh.(x[:, :, 1:chan, :]) .* σ.(x[:, :, chan+1:end, :])
end
function gate(x, y)
chan = size(x, 3) ÷ 2
tanh.(x[:, :, 1:chan, :] .+ y[:, :, 1:chan, :]) .*
σ.(x[:, :, chan+1:end, :] .+ y[:, :, chan+1:end, :])
end
"""
`WConv(dilate::Int, skpflt::Int, resflt::Int)`
* __input:__
* `dilate` how wide the dilation should be for this conv layer
* `resflt` how many filters to return for the residual layer
* `skpflt` how many filters to return for the skip layer
* __output:__ a WConv struct
`WConv(x::AbstractArray)`
* __input:__ a (data, 1, channels, batch) Array like a 1D Conv
* __output:__ an Array processed by gated activation, a skip layer applied, and residual values stored in the `WConv` struct.
`WConv(x::Tuple)`
* __input:__ a Tuple of arrays
* __output:__ same as above but also with conditioning array applied in gated activation
"""
mutable struct WConv
dilconv::Conv
resconv::Conv
skpconv::Conv
skp::DenseArray
condconv::Conv
function WConv(dil::Int, res::Int, skp::Int, cond::Int)
dc = gpu(Conv((2, 1), res => res*2, identity, dilation=(dil, 1), pad=(dil, 0)))
rc = gpu(Conv((1, 1), res => res, identity))
sc = gpu(Conv((1, 1), res => skp, identity))
s = sc(gpu(zeros(Float32, 2, 1, res, 1)))
cc = gpu(Conv((1, 1), cond => res*2, tanh))
new(dc, rc, sc, s, cc)
end
function WConv(dc::Conv, rc::Conv, sc::Conv, s::DenseArray, cc::Conv)
new(dc, rc, sc, s, cc)
end
end
function (wc::WConv)(x)
w,h,c,b = size(x)
dat = gate(wc.dilconv(x))[1:w,:,:,:]
wc.skp = wc.skpconv(dat)
x .+ wc.resconv(dat)
end
function (wc::WConv)(input::Tuple)
x, y = input
w,h,c,b = size(x)
dat = gate(wc.dilconv(x)[1:w,:,:,:], wc.condconv(y))
wc.skp = wc.skpconv(dat)
(x .+ wc.resconv(dat), y)
end
function Base.show(io::IO, wc::WConv)
ln1 = ":: Wave.WConv ::\n"
ln2 = "Dilated Conv: " * string(wc.dilconv) *
" dil: " * string(wc.dilconv.dilation) *
" pad: " * string(wc.dilconv.pad) * "\n"
ln3 = "Residual Conv: " * string(wc.resconv) * "\n"
ln4 = "Skip Conv: " * string(wc.skpconv) * "\n"
ln5 = "Cond Conv: " * string(wc.condconv) * "\n"
print(io, ln1 * ln2 * ln3 * ln4 * ln5)
end
Flux.@treelike WConv
import Base: +
+(x::WConv, y::WConv) = x.skp + y.skp
+(x::DenseArray, y::WConv) = x + y.skp
"""
`Net(nlayers::Int; res=64, skip=256, cond=0)`
* __input:__
* `nlayers` how many dilated layers to include
* `res` how many channels to pass into residual layers
* `skip` how many channels to pass into skip layers
* `depth` reset dilation to 2 at every multiple of depth
* `cond` how many variables are included in the conditioning tensor
* __output:__ a NetNet struct
`Net(x)`
* __input:__ array of shape (samples, 1, 1channel, batchsize)
* __output:__ probability distribution (softmax) of wave amplitudes
`Net(x::Tuple)`
* __input:__ Tuple of data and conditioning Array of shape (samples, condvars, batchsize)
* __output:__ probability distribution (softmax) of wave amplitudes
"""
mutable struct Net
input::Conv
layers::Chain
output::Chain
function Net(nlayers; res=64, skip=256, depth=9, cond=0)
i = gpu(Conv((2, 1), 1=>res, tanh, pad=(1, 0)))
l = Chain([WConv(2^(i%depth), res, skip, cond) for i in 1:nlayers]...)
o = Chain(
# x -> leakyrelu.(x),
gpu(Conv((1, 1), skip=>256, leakyrelu)),
gpu(Conv((1, 1), 256=>256, leakyrelu))
)
new(i, l, o)
end
function Net(i::Conv, l::Chain, o::Chain)
new(i, l, o)
end
end
Flux.@treelike Net
function (w::Net)(x)
w.layers(w.input(x)[1:end-1,:,:,:])
softmax(w.output(reduce(+, w.layers))[end, 1, :, :])
end
function (w::Net)(input::Tuple)
x, y = input
dat = (w.input(x)[1:end-1,:,:,:], y)
w.layers(dat)
softmax(w.output(reduce(+, w.layers))[end, 1, :, :])
end
function Base.show(io::IO, wave::Net)
ln1 = ":: Wave.Net ::\n"
ln2 = "Hidden layers: " * string(length(wave.layers)) * "\n"
ln3 = "Receptive field: " * string(2^length(wave.layers)) * "\n"
ln4 = "Residual channels: " * string(size(wave.layers[end].resconv.weight, 4)) * "\n"
ln5 = "Skip channels: " * string(size(wave.layers[end].skpconv.weight, 4)) * "\n"
ln6 = "Conditioning vars: " * string(size(wave.layers[end].condconv.weight, 3)) * "\n"
ln7 = "GPU enabled: " * ifelse(typeof(wave.input.weight) <: Array, "no", "yes")
print(io, ln1 * ln2 * ln3 * ln4 * ln5 * ln6 * ln7)
end
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] | 2.137587 | 2,304 |
using InteractiveUtils: subtypes
@testset "Robot" begin
@testset "test $rob_type interface" for rob_type in subtypes(Robot)
par_type = eval(Symbol(string(rob_type)*"Params"))
params = par_type()
rob = rob_type(deviceID="test", params=params, dependencies=Dict{String, Union{Device, Missing}}())
degrees = dof(rob)
@test degrees isa Int
axes = axisRange(rob)
@test axes isa Vector{<:Vector{<:Unitful.Length}}
@test length(axes) == degrees
@test length(axes[1]) == 2
for i in 1:degrees
@test axes[i][1]<=axes[i][2]
end
def_vel = defaultVelocity(rob)
@test def_vel isa Union{Vector{<:Unitful.Velocity},Nothing}
if def_vel !== nothing
@test length(def_vel) == degrees
end
end
@testset "DummyRobot" begin
include("DummyRobotTests.jl")
end
@testset "SimulatedRobot" begin
include("SimulatedRobotTests.jl")
end
if "igus" in ARGS
@testset "IgusRobot" begin
include("IgusRobotTests.jl")
end
end
if "isel" in ARGS
@testset "IselRobot" begin
include("IselRobotTests.jl")
end
end
if "brukerrobot" in ARGS
@testset "BrukerRobot" begin
include("BrukerRobotTests.jl")
end
end
end | [
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] | 2.373777 | 511 |
import Pkg
const POLYHEDRA_TEST = joinpath(dirname(dirname(pathof(Polyhedra))), "test")
include(joinpath(POLYHEDRA_TEST, "utils.jl"))
include(joinpath(POLYHEDRA_TEST, "config.jl"))
include(joinpath(POLYHEDRA_TEST, "solvers.jl")) # defines lp_solver
include(joinpath(POLYHEDRA_TEST, "jump.jl"))
include(joinpath(POLYHEDRA_TEST, "basic.jl"))
include(joinpath(POLYHEDRA_TEST, "simplex.jl"))
include(joinpath(POLYHEDRA_TEST, "permutahedron.jl"))
include(joinpath(POLYHEDRA_TEST, "board.jl"))
include(joinpath(POLYHEDRA_TEST, "docexample.jl"))
include(joinpath(POLYHEDRA_TEST, "issue48.jl"))
include(joinpath(POLYHEDRA_TEST, "empty.jl"))
include(joinpath(POLYHEDRA_TEST, "sparse.jl"))
include(joinpath(POLYHEDRA_TEST, "sparserect.jl"))
include(joinpath(POLYHEDRA_TEST, "support_function.jl"))
const misctests = Dict("basic" => basictest,
"doc" => doctest,
"simplex" => simplextest,
"permutahedron" => permutahedrontest,
"board" => boardtest,
"issue48" => issue48test,
"empty" => emptytest,
"sparse" => sparsetest,
"sparserect" => sparserecttest,
"support_function" => support_function_test)
@polytestset misc
include(joinpath(POLYHEDRA_TEST, "decompose.jl"))
polyhedratests = Dict("jump" => jumptest,
"misc" => misctest,
"decompose" => decomposetest)
@polytestset polyhedra true
@testset "Polyhedra tests in $arith arithmetics" for arith in [:float, :exact]
polyhedratest(ConvexHull.Library(arith, lp_solver), [
"jumpsimplex", "nonfulldimensional", "simplex", "permutahedron",
"board", "issue48", "empty"
])
end
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] | 2.038724 | 878 |
module Elf
using Base
using StaticArrays, DataStructures
export iself,
elfclass,
endian,
sections,
segments,
symbols,
elfosabi,
elftype
include("./constants.jl")
# include("./types.jl")
"""
iself(bin) -> Bool
Verify ELF Header with Magic numbers
# Examples
```julia-repl
julia> iself(read("/path/to/binary"))
true
```
# Arguments
- `bin::Vector{UInt8}` : binary file to check.
"""
iself(ehdr::Elf64_Ehdr) = ehdr.e_ident[begin:4] == [ELFMAG1, ELFMAG2, ELFMAG3, ELFMAG4]
"""
elfclass(ehdr::Elf64_Ehdr) -> EHClass
Detrmines ELF class(64bit or 32bit) if invalid class error will occur.
"""
function elfclass(ehdr::Elf64_Ehdr)
try
EHClass(ehdr.e_ident[EI_CLASS])
catch
error("Invalid class.")
end
end
"""
endian(ehdr::Elf64_Ehdr) -> EHEndian
Detrmines ELF binary endian. Returns enum represents endian. If invalid error will occur.
"""
function endian(ehdr::Elf64_Ehdr)
try
EHEndian(ehdr.e_ident[EI_DATA])
catch
error("Invalid endian.")
end
end
"""
elfversion(ehdr::Elf64_Ehdr) -> Symbol
If binary has valid ELF version, else error will occur.
"""
function elfversion(ehdr::Elf64_Ehdr)
try
ehdr.e_ident[EI_VERSION]
catch
error("Invalid ELF version.")
end
end
function elfosabi(ehdr::Elf64_Ehdr)
try
(arch = EHOsAbi(ehdr.e_ident[EI_OSABI]), version = ehdr.e_ident[EI_OSABI])
catch
error("Invalid OS ABI")
end
end
function elftype(ehdr::Elf64_Ehdr)
try
EHType(ehdr.e_type)
catch
error("Invalid ELF type.")
end
end
"""
sections(bin::Vector{UInt8}) -> OrderedDict{String,Elf64_Shdr}
Section header infomation. Returns dictoinary represents section name and its infomation ordered by section number.
"""
function sections(bin::Vector{UInt8})
sections_info::OrderedDict{String,Elf64_Shdr} = OrderedDict()
ehdr = Elf64_Ehdr(bin)
shstr = Elf64_Shdr(bin, ehdr.e_shoff + ehdr.e_shentsize * ehdr.e_shstrndx)
for i = 0:(ehdr.e_shnum - 1)
shdr = Elf64_Shdr(bin, ehdr.e_shoff + ehdr.e_shentsize * i)
sname = unsafe_string(pointer(bin, shstr.sh_offset + shdr.sh_name + 1))
push!(sections_info, sname => shdr)
end
sections_info
end
"""
segments(bin::Vector{UInt8} -> OrderedDict{String,Elf64_Phdr}
Program header infomation. Returns dictoinary represents segment name and its infomation ordered by segment number.
"""
function segments(bin::Vector{UInt8})
ehdr = Elf64_Ehdr(bin)
shstr = Elf64_Shdr(bin, ehdr.e_shoff + ehdr.e_shentsize * ehdr.e_shstrndx)
segments_info::OrderedDict{String,Elf64_Phdr} = OrderedDict()
for i = 0:(ehdr.e_phnum - 1)
phdr = Elf64_Phdr(bin, ehdr.e_phoff + ehdr.e_phentsize * i)
for j = 0:(ehdr.e_shnum - 1)
shdr = Elf64_Shdr(bin, ehdr.e_shoff + ehdr.e_shentsize * j)
size = shdr.sh_type != UInt32(SHT_NOBITS) ? shdr.sh_size : 0
if shdr.sh_offset < phdr.p_offset
continue
end
if shdr.sh_offset + size > phdr.p_offset + phdr.p_filesz
continue
end
seg_name = unsafe_string(pointer(bin, shstr.sh_offset + shdr.sh_name + 1))
push!(segments_info, seg_name => phdr)
end
end
segments_info
end
"""
symbols(bin::Vector{UInt8}) -> (OrderedDict{String,Elf64_Sym}, OrderedDict{String,Elf64_Rel})
ELF symbols in the file. Returns tuple of ELF symbols and relocatable symbols.
# Examples
```julia-repl
julia> symbols(read("/path/to/binary"))
(OrderedDict{String,Elf64_Sym}(...),OrderedDict{String,Elf64_Rel}())
```
# Arguments
- `bin::Vector{UInt8}`: ELF binary.
"""
function symbols(bin::Vector{UInt8})
ehdr = Elf64_Ehdr(bin)
symbols_info::OrderedDict{String,Elf64_Sym} = OrderedDict()
str = sections(bin)[".strtab"]
for i = 0:(ehdr.e_shnum - 1)
shdr = Elf64_Shdr(bin, ehdr.e_shoff + ehdr.e_shentsize * i)
if shdr.sh_type != UInt32(SHT_SYMTAB)
continue
end
global sym = shdr
for j = 0:(div(sym.sh_size, sym.sh_entsize) - 1)
symp = Elf64_Sym(bin, sym.sh_offset + sym.sh_entsize * j)
if symp.st_name == 0
continue
end
push!(
symbols_info,
unsafe_string(pointer(bin, str.sh_offset + symp.st_name + 1)) => symp,
)
end
end
symbols_rel_info::OrderedDict{String,Elf64_Rel} = OrderedDict()
for i = 0:(ehdr.e_shnum - 1)
shdr = Elf64_Shdr(bin, ehdr.e_shoff + ehdr.e_shentsize * i)
if shdr.sh_type != UInt32(SHT_REL) && shdr.sh_type != UInt32(SHT_RELA)
continue
end
rel = shdr
for j = 0:(div(rel.sh_size, rel.sh_entsize) - 1)
relp = Elf64_Rel(bin, rel.sh_offset + rel.sh_entsize * j)
symp =
Elf64_Sym(bin, sym.sh_offset + (sym.sh_entsize * elf64_r_sym(relp.r_info)))
if symp.st_name == 0
continue
end
push!(
symbols_rel_info,
unsafe_string(pointer(bin, str.sh_offset + symp.st_name + 1)) => relp,
)
end
end
symbols_info, symbols_rel_info
end
end # module
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] | 2.06599 | 2,561 |
function central_diff(Qbase, Qcon, cellxmax, cellymax, cellzmax, mu, lambda, vecAx, vecAy, vecAz, specific_heat_ratio, volume, Rd)
E_vis_hat = zeros(cellxmax+1, cellymax, cellzmax, 5)
F_vis_hat = zeros( cellxmax, cellymax+1, cellzmax, 5)
G_vis_hat = zeros( cellxmax, cellymax, cellzmax+1, 5)
for i in 2:cellxmax+1 -1
for j in 2:cellymax -1
for k in 2:cellzmax-1
u_av = 0.5*(Qbase[i,j,k,2]+Qbase[i-1,j,k,2])
v_av = 0.5*(Qbase[i,j,k,3]+Qbase[i-1,j,k,3])
w_av = 0.5*(Qbase[i,j,k,4]+Qbase[i-1,j,k,4])
mu_av = 0.5*(mu[i-1,j,k]+mu[i,j,k])
lambda_av = (0.5*(1/lambda[i-1,j,k]+1/lambda[i,j,k]))^(-1)
volume_av = 0.5*(volume[i,j,k]+volume[i-1,j,k])
dudxi = Qbase[i,j,k,2] - Qbase[i-1,j,k,2]
dvdxi = Qbase[i,j,k,3] - Qbase[i-1,j,k,3]
dwdxi = Qbase[i,j,k,4] - Qbase[i-1,j,k,4]
dTdxi = Qbase[i,j,k,5]/(Qbase[i,j,k,1]*Rd) - Qbase[i-1,j,k,5]/(Qbase[i-1,j,k,1]*Rd)
dudeta = 0.25*(Qbase[i,j+1,k,2] - Qbase[i,j-1,k,2] + Qbase[i-1,j+1,k,2] - Qbase[i-1,j-1,k,2])
dvdeta = 0.25*(Qbase[i,j+1,k,3] - Qbase[i,j-1,k,3] + Qbase[i-1,j+1,k,3] - Qbase[i-1,j-1,k,3])
dwdeta = 0.25*(Qbase[i,j+1,k,4] - Qbase[i,j-1,k,4] + Qbase[i-1,j+1,k,4] - Qbase[i-1,j-1,k,4])
dTdeta = 0.25*( Qbase[i,j+1,k,5] / (Qbase[i,j+1,k,1]*Rd) -
Qbase[i,j-1,k,5] / (Qbase[i,j-1,k,1]*Rd) +
Qbase[i-1,j+1,k,5] / (Qbase[i-1,j+1,k,1]*Rd) -
Qbase[i-1,j-1,k,5] / (Qbase[i-1,j-1,k,1]*Rd) )
dudzeta = 0.25*(Qbase[i,j,k+1,2] - Qbase[i,j,k-1,2] + Qbase[i-1,j,k+1,2] - Qbase[i-1,j,k-1,2])
dvdzeta = 0.25*(Qbase[i,j,k+1,3] - Qbase[i,j,k-1,3] + Qbase[i-1,j,k+1,3] - Qbase[i-1,j,k-1,3])
dwdzeta = 0.25*(Qbase[i,j,k+1,4] - Qbase[i,j,k-1,4] + Qbase[i-1,j,k+1,4] - Qbase[i-1,j,k-1,4])
dTdzeta = 0.25*(Qbase[i,j,k+1,5] / (Qbase[i,j,k+1,1]*Rd) -
Qbase[i,j,k-1,5] / (Qbase[i,j,k-1,1]*Rd) +
Qbase[i-1,j,k+1,5] / (Qbase[i-1,j,k+1,1]*Rd) -
Qbase[i-1,j,k-1,5] / (Qbase[i-1,j,k-1,1]*Rd) )
vecAx_xav = vecAx[i,j,k,1]
vecAx_yav = vecAx[i,j,k,2]
vecAx_zav = vecAx[i,j,k,3]
vecAy_xav = 0.25*( vecAy[i,j,k,1] + vecAy[i,j+1,k,1] + vecAy[i-1,j,k,1] + vecAy[i-1,j+1,k,1] )
vecAy_yav = 0.25*( vecAy[i,j,k,2] + vecAy[i,j+1,k,2] + vecAy[i-1,j,k,2] + vecAy[i-1,j+1,k,2] )
vecAy_zav = 0.25*( vecAy[i,j,k,3] + vecAy[i,j+1,k,3] + vecAy[i-1,j,k,3] + vecAy[i-1,j+1,k,3] )
vecAz_xav = 0.25*( vecAz[i,j,k,1] + vecAz[i,j,k+1,1] + vecAz[i-1,j,k,1] + vecAz[i-1,j,k+1,1] )
vecAz_yav = 0.25*( vecAz[i,j,k,2] + vecAz[i,j,k+1,2] + vecAz[i-1,j,k,2] + vecAz[i-1,j,k+1,2] )
vecAz_zav = 0.25*( vecAz[i,j,k,3] + vecAz[i,j,k+1,3] + vecAz[i-1,j,k,3] + vecAz[i-1,j,k+1,3] )
dudx = vecAx_xav*dudxi + vecAy_xav*dudeta +vecAz_xav*dudzeta
dudy = vecAx_yav*dudxi + vecAy_yav*dudeta +vecAz_yav*dudzeta
dudz = vecAx_zav*dudxi + vecAy_zav*dudeta +vecAz_zav*dudzeta
dvdx = vecAx_xav*dvdxi + vecAy_xav*dvdeta +vecAz_xav*dvdzeta
dvdy = vecAx_yav*dvdxi + vecAy_yav*dvdeta +vecAz_yav*dvdzeta
dvdz = vecAx_zav*dvdxi + vecAy_zav*dvdeta +vecAz_zav*dvdzeta
dwdx = vecAx_xav*dwdxi + vecAy_xav*dwdeta +vecAz_xav*dwdzeta
dwdy = vecAx_yav*dwdxi + vecAy_yav*dwdeta +vecAz_yav*dwdzeta
dwdz = vecAx_zav*dwdxi + vecAy_zav*dwdeta +vecAz_zav*dwdzeta
tau_xx = 2/3*mu_av*(2*dudx - dvdy - dwdz)
tau_yy = 2/3*mu_av*(2*dvdy - dwdz - dudx)
tau_zz = 2/3*mu_av*(2*dwdz - dudx - dvdy)
tau_xy = mu_av*(dudy + dvdx)
tau_yz = mu_av*(dvdz + dwdy)
tau_zx = mu_av*(dwdx + dudz)
dTdx = vecAx_xav*dTdxi + vecAy_xav*dTdeta + vecAz_xav*dTdzeta
dTdy = vecAx_yav*dTdxi + vecAy_yav*dTdeta + vecAz_yav*dTdzeta
dTdz = vecAx_zav*dTdxi + vecAy_zav*dTdeta + vecAz_zav*dTdzeta
betax = u_av*tau_xx + v_av*tau_xy + w_av*tau_zx + lambda_av * dTdx
betay = u_av*tau_xy + v_av*tau_yy + w_av*tau_yz + lambda_av * dTdy
betaz = u_av*tau_zx + v_av*tau_yz + w_av*tau_zz + lambda_av * dTdz
E_vis_hat[i,j,k,1] = 0.0
E_vis_hat[i,j,k,2] = (vecAx_xav*tau_xx + vecAx_yav*tau_xy + vecAx_zav*tau_zx) / volume_av
E_vis_hat[i,j,k,3] = (vecAx_xav*tau_xy + vecAx_yav*tau_yy + vecAx_zav*tau_yz) / volume_av
E_vis_hat[i,j,k,4] = (vecAx_xav*tau_zx + vecAx_yav*tau_yz + vecAx_zav*tau_zz) / volume_av
E_vis_hat[i,j,k,5] = (vecAx_xav*betax + vecAx_yav*betay + vecAx_zav*betaz ) / volume_av
end
end
end
for i in 2:cellxmax -1
for j in 2:cellymax+1 -1
for k in 2:cellzmax -1
u_av = 0.5*(Qbase[i,j,k,2]+Qbase[i,j-1,k,2])
v_av = 0.5*(Qbase[i,j,k,3]+Qbase[i,j-1,k,3])
w_av = 0.5*(Qbase[i,j,k,4]+Qbase[i,j-1,k,4])
mu_av = 0.5*(mu[i,j-1,k]+mu[i,j,k])
lambda_av = (0.5*(1/lambda[i,j-1,k]+1/lambda[i,j,k]))^(-1)
volume_av = 0.5*(volume[i,j,k]+volume[i,j-1,k])
dudxi = 0.25*(Qbase[i+1,j,k,2] - Qbase[i-1,j,k,2] + Qbase[i+1,j-1,k,2] - Qbase[i-1,j-1,k,2])
dvdxi = 0.25*(Qbase[i+1,j,k,3] - Qbase[i-1,j,k,3] + Qbase[i+1,j-1,k,3] - Qbase[i-1,j-1,k,3])
dwdxi = 0.25*(Qbase[i+1,j,k,4] - Qbase[i-1,j,k,4] + Qbase[i+1,j-1,k,4] - Qbase[i-1,j-1,k,4])
dTdxi = 0.25*( Qbase[i+1,j,k,4]/ (Qbase[i+1,j,k,1]*Rd) -
Qbase[i-1,j,k,4]/ (Qbase[i-1,j,k,1]*Rd) +
Qbase[i+1,j-1,k,4]/(Qbase[i+1,j-1,k,1]*Rd) -
Qbase[i-1,j-1,k,4]/(Qbase[i-1,j-1,k,1]*Rd) )
dudeta = Qbase[i,j,k,2]-Qbase[i,j-1,k,2]
dvdeta = Qbase[i,j,k,3]-Qbase[i,j-1,k,3]
dwdeta = Qbase[i,j,k,4]-Qbase[i,j-1,k,4]
dTdeta = (Qbase[i,j,k,5]/(Qbase[i,j,k,1]*Rd) - Qbase[i,j-1,k,5]/(Qbase[i,j-1,k,1]*Rd))
dudzeta = 0.25*(Qbase[i,j,k+1,2] - Qbase[i,j,k-1,2] + Qbase[i,j-1,k+1,2] - Qbase[i,j-1,k-1,2])
dvdzeta = 0.25*(Qbase[i,j,k+1,3] - Qbase[i,j,k-1,3] + Qbase[i,j-1,k+1,3] - Qbase[i,j-1,k-1,3])
dwdzeta = 0.25*(Qbase[i,j,k+1,4] - Qbase[i,j,k-1,4] + Qbase[i,j-1,k+1,4] - Qbase[i,j-1,k-1,4])
dTdzeta = 0.25*(Qbase[i,j,k+1,4]/ (Qbase[i,j,k+1,1]*Rd) -
Qbase[i,j,k-1,4]/ (Qbase[i,j,k-1,1]*Rd) +
Qbase[i,j-1,k+1,4]/(Qbase[i,j-1,k+1,1]*Rd) -
Qbase[i,j-1,k-1,4]/(Qbase[i,j-1,k-1,1]*Rd) )
vecAx_xav = 0.25*( vecAx[i,j,k,1] + vecAx[i+1,j,k,1] + vecAx[i,j-1,k,1] + vecAx[i+1,j-1,k,1] )
vecAx_yav = 0.25*( vecAx[i,j,k,2] + vecAx[i+1,j,k,2] + vecAx[i,j-1,k,2] + vecAx[i+1,j-1,k,2] )
vecAx_zav = 0.25*( vecAx[i,j,k,3] + vecAx[i+1,j,k,3] + vecAx[i,j-1,k,3] + vecAx[i+1,j-1,k,3] )
vecAy_xav = vecAy[i,j,k,1]
vecAy_yav = vecAy[i,j,k,2]
vecAy_zav = vecAy[i,j,k,3]
vecAz_xav = 0.25*( vecAz[i,j,k,1] + vecAz[i,j,k+1,1] + vecAz[i,j-1,k,1] + vecAz[i,j-1,k+1,1] )
vecAz_yav = 0.25*( vecAz[i,j,k,2] + vecAz[i,j,k+1,2] + vecAz[i,j-1,k,2] + vecAz[i,j-1,k+1,2] )
vecAz_zav = 0.25*( vecAz[i,j,k,3] + vecAz[i,j,k+1,3] + vecAz[i,j-1,k,3] + vecAz[i,j-1,k+1,3] )
dudx = vecAx_xav*dudxi + vecAy_xav*dudeta +vecAz_xav*dudzeta
dudy = vecAx_yav*dudxi + vecAy_yav*dudeta +vecAz_yav*dudzeta
dudz = vecAx_zav*dudxi + vecAy_zav*dudeta +vecAz_zav*dudzeta
dvdx = vecAx_xav*dvdxi + vecAy_xav*dvdeta +vecAz_xav*dvdzeta
dvdy = vecAx_yav*dvdxi + vecAy_yav*dvdeta +vecAz_yav*dvdzeta
dvdz = vecAx_zav*dvdxi + vecAy_zav*dvdeta +vecAz_zav*dvdzeta
dwdx = vecAx_xav*dwdxi + vecAy_xav*dwdeta +vecAz_xav*dwdzeta
dwdy = vecAx_yav*dwdxi + vecAy_yav*dwdeta +vecAz_yav*dwdzeta
dwdz = vecAx_zav*dwdxi + vecAy_zav*dwdeta +vecAz_zav*dwdzeta
tau_xx = 2/3*mu_av*(2*dudx - dvdy - dwdz)
tau_yy = 2/3*mu_av*(2*dvdy - dwdz - dudx)
tau_zz = 2/3*mu_av*(2*dwdz - dudx - dvdy)
tau_xy = mu_av*(dudy + dvdx)
tau_yz = mu_av*(dvdz + dwdy)
tau_zx = mu_av*(dwdx + dudz)
dTdx = vecAx_xav*dTdxi + vecAy_xav*dTdeta + vecAz_xav*dTdzeta
dTdy = vecAx_yav*dTdxi + vecAy_yav*dTdeta + vecAz_yav*dTdzeta
dTdz = vecAx_zav*dTdxi + vecAy_zav*dTdeta + vecAz_zav*dTdzeta
betax = u_av*tau_xx + v_av*tau_xy + w_av*tau_zx + lambda_av * dTdx
betay = u_av*tau_xy + v_av*tau_yy + w_av*tau_yz + lambda_av * dTdy
betaz = u_av*tau_zx + v_av*tau_yz + w_av*tau_zz + lambda_av * dTdz
F_vis_hat[i,j,k,1] = 0.0
F_vis_hat[i,j,k,2] = (vecAy_xav*tau_xx + vecAy_yav*tau_xy + vecAy_zav*tau_zx) / volume_av
F_vis_hat[i,j,k,3] = (vecAy_xav*tau_xy + vecAy_yav*tau_yy + vecAy_zav*tau_yz) / volume_av
F_vis_hat[i,j,k,4] = (vecAy_xav*tau_zx + vecAy_yav*tau_yz + vecAy_zav*tau_zz) / volume_av
F_vis_hat[i,j,k,5] = (vecAy_xav*betax + vecAy_yav*betay + vecAy_zav*betaz ) / volume_av
end
end
end
for i in 2:cellxmax -1
for j in 2:cellymax -1
for k in 2:cellzmax+1 -1
u_av = 0.5*(Qbase[i,j,k,2]+Qbase[i,j,k-1,2])
v_av = 0.5*(Qbase[i,j,k,3]+Qbase[i,j,k-1,3])
w_av = 0.5*(Qbase[i,j,k,4]+Qbase[i,j,k-1,4])
mu_av = 0.5*(mu[i,j,k-1]+mu[i,j,k])
lambda_av = (0.5*(1/lambda[i,j,k-1]+1/lambda[i,j,k]))^(-1)
volume_av = 0.5*(volume[i,j,k]+volume[i,j,k-1])
dudxi = 0.25*(Qbase[i+1,j,k,2] - Qbase[i-1,j,k,2] + Qbase[i+1,j,k-1,2] - Qbase[i-1,j,k-1,2])
dvdxi = 0.25*(Qbase[i+1,j,k,3] - Qbase[i-1,j,k,3] + Qbase[i+1,j,k-1,3] - Qbase[i-1,j,k-1,3])
dwdxi = 0.25*(Qbase[i+1,j,k,4] - Qbase[i-1,j,k,4] + Qbase[i+1,j,k-1,4] - Qbase[i-1,j,k-1,4])
dTdxi = 0.25*( Qbase[i+1,j,k,4]/ (Qbase[i+1,j,k,1]*Rd) -
Qbase[i-1,j,k,4]/ (Qbase[i-1,j,k,1]*Rd) +
Qbase[i+1,j,k-1,4]/(Qbase[i+1,j,k-1,1]*Rd) -
Qbase[i-1,j,k-1,4]/(Qbase[i-1,j,k-1,1]*Rd) )
dudeta = 0.25*(Qbase[i,j+1,k,2] - Qbase[i,j-1,k,2] + Qbase[i,j+1,k-1,2] - Qbase[i,j-1,k-1,2])
dvdeta = 0.25*(Qbase[i,j+1,k,3] - Qbase[i,j-1,k,3] + Qbase[i,j+1,k-1,3] - Qbase[i,j-1,k-1,3])
dwdeta = 0.25*(Qbase[i,j+1,k,4] - Qbase[i,j-1,k,4] + Qbase[i,j+1,k-1,4] - Qbase[i,j-1,k-1,4])
dTdeta = 0.25*( Qbase[i,j+1,k,4]/ (Qbase[i,j+1,k,1]*Rd) -
Qbase[i,j-1,k,4]/ (Qbase[i,j-1,k,1]*Rd) +
Qbase[i,j+1,k-1,4]/(Qbase[i,j+1,k-1,1]*Rd) -
Qbase[i,j-1,k-1,4]/(Qbase[i,j-1,k-1,1]*Rd) )
dudzeta = Qbase[i,j,k,2]-Qbase[i,j,k-1,2]
dvdzeta = Qbase[i,j,k,3]-Qbase[i,j,k-1,3]
dwdzeta = Qbase[i,j,k,4]-Qbase[i,j,k-1,4]
dTdzeta = (Qbase[i,j,k,5]/(Qbase[i,j,k,1]*Rd) - Qbase[i,j,k-1,5]/(Qbase[i,j,k-1,1]*Rd))
vecAx_xav = 0.25*( vecAx[i,j,k,1] + vecAx[i+1,j,k,1] + vecAx[i,j,k-1,1] + vecAx[i+1,j,k-1,1] )
vecAx_yav = 0.25*( vecAx[i,j,k,2] + vecAx[i+1,j,k,2] + vecAx[i,j,k-1,2] + vecAx[i+1,j,k-1,2] )
vecAx_zav = 0.25*( vecAx[i,j,k,3] + vecAx[i+1,j,k,3] + vecAx[i,j,k-1,3] + vecAx[i+1,j,k-1,3] )
vecAy_xav = 0.25*( vecAy[i,j,k,1] + vecAy[i,j+1,k,1] + vecAy[i,j,k-1,1] + vecAy[i,j+1,k-1,1] )
vecAy_yav = 0.25*( vecAy[i,j,k,2] + vecAy[i,j+1,k,2] + vecAy[i,j,k-1,2] + vecAy[i,j+1,k-1,2] )
vecAy_zav = 0.25*( vecAy[i,j,k,3] + vecAy[i,j+1,k,3] + vecAy[i,j,k-1,3] + vecAy[i,j+1,k-1,3] )
vecAz_xav = vecAz[i,j,k,1]
vecAz_yav = vecAz[i,j,k,2]
vecAz_zav = vecAz[i,j,k,3]
dudx = vecAx_xav*dudxi + vecAy_xav*dudeta +vecAz_xav*dudzeta
dudy = vecAx_yav*dudxi + vecAy_yav*dudeta +vecAz_yav*dudzeta
dudz = vecAx_zav*dudxi + vecAy_zav*dudeta +vecAz_zav*dudzeta
dvdx = vecAx_xav*dvdxi + vecAy_xav*dvdeta +vecAz_xav*dvdzeta
dvdy = vecAx_yav*dvdxi + vecAy_yav*dvdeta +vecAz_yav*dvdzeta
dvdz = vecAx_zav*dvdxi + vecAy_zav*dvdeta +vecAz_zav*dvdzeta
dwdx = vecAx_xav*dwdxi + vecAy_xav*dwdeta +vecAz_xav*dwdzeta
dwdy = vecAx_yav*dwdxi + vecAy_yav*dwdeta +vecAz_yav*dwdzeta
dwdz = vecAx_zav*dwdxi + vecAy_zav*dwdeta +vecAz_zav*dwdzeta
tau_xx = 2/3*mu_av*(2*dudx - dvdy - dwdz)
tau_yy = 2/3*mu_av*(2*dvdy - dwdz - dudx)
tau_zz = 2/3*mu_av*(2*dwdz - dudx - dvdy)
tau_xy = mu_av*(dudy + dvdx)
tau_yz = mu_av*(dvdz + dwdy)
tau_zx = mu_av*(dwdx + dudz)
dTdx = vecAx_xav*dTdxi + vecAy_xav*dTdeta + vecAz_xav*dTdzeta
dTdy = vecAx_yav*dTdxi + vecAy_yav*dTdeta + vecAz_yav*dTdzeta
dTdz = vecAx_zav*dTdxi + vecAy_zav*dTdeta + vecAz_zav*dTdzeta
betax = u_av*tau_xx + v_av*tau_xy + w_av*tau_zx + lambda_av * dTdx
betay = u_av*tau_xy + v_av*tau_yy + w_av*tau_yz + lambda_av * dTdy
betaz = u_av*tau_zx + v_av*tau_yz + w_av*tau_zz + lambda_av * dTdz
G_vis_hat[i,j,k,1] = 0.0
G_vis_hat[i,j,k,2] = (vecAz_xav*tau_xx + vecAz_yav*tau_xy + vecAz_zav*tau_zx) / volume_av
G_vis_hat[i,j,k,3] = (vecAz_xav*tau_xy + vecAz_yav*tau_yy + vecAz_zav*tau_yz) / volume_av
G_vis_hat[i,j,k,4] = (vecAz_xav*tau_zx + vecAz_yav*tau_yz + vecAz_zav*tau_zz) / volume_av
G_vis_hat[i,j,k,5] = (vecAz_xav*betax + vecAz_yav*betay + vecAz_zav*betaz ) / volume_av
end
end
end
return E_vis_hat, F_vis_hat, G_vis_hat
end
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] | 1.356213 | 11,291 |
using Base.Broadcast: broadcasted
bstyle = Base.Broadcast.DefaultArrayStyle{1}()
@testset "broadcast" begin
for (mr,br,sr) in ((OneToMRange(10), OneTo(10), OneToSRange(10)),
(UnitMRange(1, 10), UnitRange(1, 10), UnitSRange(1, 10)),
(StepMRange(1, 1, 10), StepRange(1, 1, 10), StepSRange(1, 1, 10)),
(LinMRange(1, 10, 10), LinRange(1, 10, 10), LinSRange(1, 10, 10)),
(StepMRangeLen(1., 1., 10), StepRangeLen(1., 1., 10), StepSRangeLen(1., 1., 10))
)
@test broadcasted(bstyle, +, mr) == broadcast(+, br)
@test broadcasted(bstyle, +, sr) == broadcast(+, br)
@test broadcasted(bstyle, -, mr) == broadcast(-, br)
@test broadcasted(bstyle, -, sr) == broadcast(-, br)
@test broadcasted(bstyle, +, 1, mr) == broadcast(+, 1, br)
@test broadcasted(bstyle, +, 1, sr) == broadcast(+, 1, br)
@test broadcasted(bstyle, -, 1, mr) == broadcast(-, 1, br)
@test broadcasted(bstyle, -, 1, sr) == broadcast(-, 1, br)
@test broadcasted(bstyle, +, mr, 1) == broadcast(+, br, 1)
@test broadcasted(bstyle, +, sr, 1) == broadcast(+, br, 1)
@test broadcasted(bstyle, -, mr, 1) == broadcast(-, br, 1)
@test broadcasted(bstyle, -, sr, 1) == broadcast(-, br, 1)
@test broadcasted(bstyle, *, 1, mr) == broadcast(*, 1, br)
@test broadcasted(bstyle, *, 1, sr) == broadcast(*, 1, br)
@test broadcasted(bstyle, *, mr, 1) == broadcast(*, br, 1)
@test broadcasted(bstyle, *, sr, 1) == broadcast(*, br, 1)
@test broadcasted(bstyle, \, 1, mr) == broadcast(\, 1, br)
@test broadcasted(bstyle, \, 1, sr) == broadcast(\, 1, br)
@test /(mr, 2) == /(br, 2)
@test /(sr, 2) == /(br, 2)
@test -(mr, (2 .* br)) == -(br, (2 .* br))
@test -(sr, (2 .* br)) == -(br, (2 .* br))
@test -((2 .* br), mr) == -((2 .* br), br)
@test -((2 .* br), sr) == -((2 .* br), br)
@test +(mr, (1 .+ br)) == +(br, (1 .+ br))
@test +(sr, (1 .+ br)) == +(br, (1 .+ br))
@test +((1 .+ br), mr) == +((1 .+ br), br)
@test +((1 .+ br), sr) == +((1 .+ br), br)
@test *(mr, 2) == *(sr, 2) == *(br, 2)
@test *(2, mr) == *(2, sr) == *(2, br)
end
end
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] | 1.934572 | 1,238 |
@testset "BidAsk functions" begin
dt = DateTime(2000, 1, 1)
ba = BidAsk(dt, 500.0, 501.1)
@test midprice(ba) == (500.0 + 501.1)/2
@test midprice(500.0, 501.1) == (500.0 + 501.1)/2
@test spread(ba) == 501.1 - 500.0
@test spread(500.0, 501.1) == 501.1 - 500.0
end
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] | 2.057143 | 140 |
using ApproxTools
using Test
using LinearAlgebra
const BitsFloats = (Float32,Float64)
const Floats = (BitsFloats..., BigFloat)
const Reals = (Int, Floats...)
rnc(reals::Type) = (reals,complex.(reals))
rnc(reals::Tuple) = (reals...,complex.(reals)...)
include("tucker.jl")
include("grideval.jl")
include("LogNumbers.jl")
include("LinearCombinations.jl")
include("approximate.jl")
include("inner_products.jl")
include("points/ChebyshevPoints.jl")
include("points/Midpoints.jl")
include("points/TrapezoidalPoints.jl")
include("bases.jl")
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] | 2.787565 | 193 |
# L2 DistLoss
#Pkg.add("Latexify")
using LossFunctions
using LaTeXStrings, Measures, Latexify
gr(bg=:white)
l2fn = "L(r)=r^2"
l2dist = latexify(l2fn)
Plots.plot(L2DistLoss(), c=:olive, linewidth=2, annotation=(1, 3.3, L"L\left( r \right) = |r^{2}|", 18))
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] | 2.098361 | 122 |
#___ ____ ___ _____ _ _ _ _
#| \/ | | |_ / __ \ | | | | (_| |
#| . . | | | | / \/ | | | |_ __ _| |_ ___
#| |\/| | | | | | | | | | '_ \| | __/ _ \
#| | | | |___/\__/ | \__/\ | |_| | | | | | || (_) |
#_______\_____\____/ \____/ _____|_|___|_____\_____ _____ _____
#| ___ \ (_) | | \ \ / / / __ | _ / __ | _ |
#| |_/ _ __ ___ _ ___ ___| |_ \ V / `' / /| |/' `' / /| |/' |
#| __| '__/ _ \| |/ _ \/ __| __|/ \ / / | /| | / / | /| |
#| | | | | (_) | | __| (__| |_/ /^\ \ ./ /__\ |_/ ./ /__\ |_/ /
#\_| |_| \___/| |\___|\___|\__\/ \/ \_____/\___/\_____/\___/
# _/ |
# |__/
#
# This code is part of the proposal of the team "MLJC UniTo" - University of Turin
# for "ProjectX 2020" Climate Change for AI.
# The code is licensed under MIT 3.0
# Please read readme or comments for credits and further information.
# Compiler: Julia 1.5
# Short description of this file: Non Linear Burger Equation
using NeuralPDE, Flux, ModelingToolkit, Optim, DiffEqFlux
using GalacticOptim
using Base
using Plots, PyPlot
#GalacticOptim
# 3D PDE
@parameters x t θ
@variables u(..)
@derivatives Dxx''~x
#@derivatives Dyy''~y
@derivatives Dx'~x
#@derivatives Dy'~y
@derivatives Dt'~t
Re = 2500.0
# 3D PDE
eq = Dt(u(x,t,θ)) + u(x,t,θ) * Dx(u(x,t,θ)) ~ 1/Re * Dxx(u(x,t,θ))
#eq = Dt(u(x,y,t,θ)) + u(x,y,t,θ) * Dx(u(x,y,t,θ)) ~ 1/Re * (Dxx(u(x,y,t,θ)) + Dyy(u(x,y,t,θ)))
# Initial and boundary conditions
A0 = 1.0
muX0 = 0.
muY0 = 0.
sgX0 = 1.0
sgY0 = 1.0
bcs = [u(x,0,θ) ~ A0 * exp(-(((x-muX0)^2/(2*sgX0^2))))]#+((y-muY0)^2/(2*sgY0^2))))]
#bcs = [u(x,y,0,θ) ~ exp(x+y)*cos(x+y) ,
# u(0,y,t,θ) ~ exp(y)*cos(y+4t),
# u(2,y,t,θ) ~ exp(2+y)*cos(2+y+4t) ,
# u(x,0,t,θ) ~ exp(x)*cos(x+4t),
# u(x,2,t,θ) ~ exp(x+2)*cos(x+2+4t)]
# Space and time domains
domains = [x ∈ IntervalDomain(-5.0,5.0),
t ∈ IntervalDomain(-5.0,5.0)]
# Discretization
dx = 0.5; dt = 0.5
# Neural network
chain = FastChain(FastDense(2,16,Flux.σ),FastDense(16,16,Flux.σ),FastDense(16,1))
discretization = NeuralPDE.PhysicsInformedNN([dx,dt],
chain,
strategy = NeuralPDE.StochasticTraining(include_frac=0.9))
pde_system = PDESystem(eq,bcs,domains,[x,t],[u])
prob = NeuralPDE.discretize(pde_system,discretization)
cb = function (p,l)
println("Current loss is: $l")
return false
end
res = GalacticOptim.solve(prob, ADAM(0.1), progress = true; cb = cb, maxiters=500)
phi = discretization.phi
xs,ts = [domain.domain.lower:dx:domain.domain.upper for domain in domains]
#xs,ys,ts = [domain.domain.lower:dx:domain.domain.upper for domain in domains]
#u_predict = [reshape([first(phi([x,y,t],res.minimizer)) for x in xs for y in ys], (length(xs),length(ys))) for t in ts]
analytic_sol_func(x,t) = [exp(-t)*sin(pi*x), exp(-t)*cos(pi*x), (1+pi^2)*exp(-t)]
#u_real = [reshape([first(analytic_sol_func(x,t)[i] for x in xs for y in ys], (length(xs),length(ys))) for t in ts]
u_real = [[analytic_sol_func(x,t)[i] for t in ts for x in xs] for i in 1:3]
u_predict = [[phi([x,t],res.minimizer)[i] for t in ts for x in xs] for i in 1:1]
u_predict = u_predict[1]
#u_predict = [reshape([first(phi([x,t],res.minimizer)) for x in xs for t in ts])]
diff_u = [abs.(u_real[i] .- u_predict[i] ) for i in 1:3]
p2 = Plots.plot(xs, ts, u_predict, st=:surface,title = "predict")
Plots.plot(p2)
for i in 1:3
p1 = Plots.plot(xs, ts, u_real[i], st=:surface,title = "u$i, analytic");
p2 = Plots.plot(xs, ts, u_predict[i], st=:surface,title = "predict");
#p3 = Plots.plot(xs, ts, diff_u[i],linetype=:contourf,title = "error");
#plot(p1,p2,p3)
Plots.plot(p2)
Plots.savefig("sol_U$i")
end
maxlim = maximum(maximum(u_predict[t]) for t = 1:length(ts))
minlim = minimum(minimum(u_predict[t]) for t = 1:length(ts))
result = @animate for time = 1:length(ts)
Plots.plot(xs, ys, u_predict[time],st=:surface,camera=(30,30), zlim=(minlim,maxlim), clim=(minlim,maxlim),
title = string("ψ: max = ",round(maxlim, digits = 3)," min = ", round(minlim, digits = 3),"\\n"," t = ",time))
end
gif(result, "burger_surface.gif", fps = 6)
result = @animate for time = 1:length(ts)
Plots.plot(xs, ys, u_predict[time],st=:contour,camera=(30,30), zlim=(minlim,maxlim), clim=(minlim,maxlim),
title = string("ψ: max = ",round(maxlim, digits = 3)," min = ", round(minlim, digits = 3),"\\n"," t = ",time))
end
gif(result, "burger_contour.gif", fps = 6)
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] | 1.977106 | 2,315 |
"""
read_dump_prim(dump_name::String)
This will read data in `dump_name` file directly, returning a Dict variable, without spliting them into different array.
"""
function read_dump_prim(dump_name::String)
io = open(dump_name, "r")
str = read(io, String)
# Reading all digital data
reg = r"\b[\d\se\+ -\.]+\n"
res = findall(reg, str)
res = [str[n] for n in res]
num_steps = Int64(length(res) / 4)
# Reading Simulation Info
step_vec = parse(Int64, res[1][2:end])
num_atoms = parse(Int64, res[2][2:end])
box_info = [str2float(x) for x in split(res[3], "\n")[2:end-1]]
box_info = Array(hcat(box_info...)')
box_temp = copy_array(box_info)
atom_info = [str2float(x) for x in split(res[4], "\n")[2:end-1]]
atom_info = Array(hcat(atom_info...)')
atom_temp = copy_array(atom_info)
box_info = Array{Float64}(undef, (num_steps, size(box_info, 1), size(box_info, 2)))
box_info[1, :, :] .= box_temp
atom_info = Array{Float64}(undef, (num_steps, size(atom_info, 1), size(atom_info, 2)))
atom_info[1, :, :] .= sortslices(atom_temp, dims=1)
# Rearanging Data
for step = 2:num_steps
step_vec = vcat(step_vec, parse(Int64, res[(step-1)*4+1][2:end]))
box_info[step, :, :] = Array(hcat([str2float(x) for x in split(res[(step-1)*4+3], "\n")[2:end-1]]...)')
atom_temp = Array(hcat([str2float(x) for x in split(res[(step-1)*4+4], "\n")[2:end-1]]...)')
atom_info[step, :, :] = sortslices(atom_temp, dims=1)
end
# Output
res = Dict(
"step_vec" => step_vec,
"num_atoms" => num_atoms,
"num_steps" => num_steps,
"box_info" => box_info,
"atom_info" => atom_info,
)
end
"""
split_info!(data::Dict; id=false, atom_type=false, mol=false, element=false, charge=false, mass=false, coord=false, coord_scl=false, vel=false, force=false, acc=false)
This will split data readed from dump file, adding variable named same as `kwg` before to `data`.
The value of `kwg` can be `false` or column range of property of interest.
# Example
```julia-repl
dump_name = "xxxx" # name of dump file
data = read_dump_prim(dump_name)
split_info!(data, id=1, atom_type=2, coord=3:5)
```
Code above will add three elements, named as "id", "atom_type", and "coord", to data which contains variable in column 1, 2, 3:5 of `data["atom_info"]`
"""
function split_info!(data::Dict; id=false, atom_type=false, mol=false, element=false, charge=false, mass=false, coord=false, coord_scl=false, vel=false, force=false, acc=false)
atom_info = data["atom_info"]
if id != false
data["id"] = convert.(Int64, atom_info[1, :, id])
println("\"id\"\t\thas been added to `data` ")
end
if atom_type != false
data["atom_type"] = convert.(Int64, atom_info[1, :, atom_type])
println("\"atom_type\"\thas been added to `data` ")
end
if mol != false
data["mol"] = convert.(Int64, atom_info[1, :, mol])
println("\"mol\"\thas been added to `data`")
end
if element != false
data["element"] = convert.(Int64, atom_info[1, :, element])
println("\"element\"\thas been added to `data`")
end
if charge != false
data["charge"] = atom_info[1, :, charge]
println("\"charge\"\thas been added to `data`")
end
if mass != false
data["mass"] = atom_info[1, :, mass]
println("\"mass\"\t\thas been added to `data`")
end
if coord != false
data["coord"] = atom_info[:, :, coord]
println("\"coord\"\t\thas been added to `data`")
end
if coord_scl != false
data["coord_scl"] = atom_info[:, :, coord_scl]
println("\"coord_scl\"\thas been added to `data` ")
end
if vel != false
data["vel"] = atom_info[:, :, vel]
println("\"vel\"\t\thas been added to `data`")
end
if acc != false
data["acc"] = atom_info[:, :, acc]
println("\"acc\"\thas been added to `data`")
end
if force != false
data["force"] = atom_info[:, :, force]
println("\"force\"\t\thas been added to `data`")
end
end
"""
read_dump(dump_name::String; id=false, atom_type=false, mol=false, element=false, charge=false, mass=false, coord=false, coord_scl=false, vel=false, force=false, acc=false)
This will read data in `dump_name` file and return a Dict which contains elements
- `step_vec`
- `num_atoms`
- `num_steps`
- `box_info`
- `atom_info`
# `kwg`
Each `kwg` can be `false` or column range of property of interest in `atom_info`. If a non-`false` `kwg` is activated, a element with the same name as `kwg` will be add to Dict that will return.
# Example
```julia-repl
dump_name = "xxxx" # name of dump file
data = read_dump(dump_name, id=1, atom_type=2, coord=3:5)
```
Code above will return a Dict contains elements:
- `step_vec`
- `num_atoms`
- `num_steps`
- `box_info`
- `atom_info`
- `id`
- `atom_type`
- `coord`
Where `id`, `atom_type`, and `coord` are values of `atom_info` in column 1, 2, 3:5 respectively.
"""
function read_dump(dump_name::String; id=false, atom_type=false, mol=false, element=false, charge=false, mass=false, coord=false, coord_scl=false, vel=false, force=false, acc=false)
data = read_dump_prim(dump_name)
split_info!(data, id=id, atom_type=atom_type, mol=mol, element=element, charge=charge, mass=mass,
coord=coord, coord_scl=coord_scl, vel=vel, acc=acc, force=force)
return data
end
"""
function split_atom(data::Dict, atom_type::Int64)
This will return a new Dict contain elements below:
- "step_vec"
- "num_atoms"
- "num_steps"
- "box_info"
- "atom_info"
# Notice
- 1) "atom_type" should be included in `data`, which is created by `read_dump`
- 2) `split_info!` should be called to get details of "atom_info"
"""
function split_atom(data::Dict, atom_type::Int64)
try
data["atom_type"]
catch
error("\"atom_type\" has not been included in `data` !")
end
pos = findall(x->x==atom_type, data["atom_type"])
res = Dict()
# Output
res = Dict(
"step_vec" => data["step_vec"],
"num_atoms" => length(pos),
"num_steps" => data["num_steps"],
"box_info" => data["box_info"],
"atom_info" => data["atom_info"][:, pos, :],
)
@printf("\nInformation of atom with \"atom_type=%d\" has been created!\n", atom_type)
return res
end
"""
function momentum!(data::Dict)
This will add a component called "momentum" into `data` while `vel` and `mass` is contained in `data`
"""
function momentum!(data::Dict)
try
data["mass"]
catch
error("Info of \"mass\" is not contained in `data`")
end
try
data["vel"]
catch
error("Info of \"vel\" is not contained in `data`")
end
momentum = zeros(size(data["vel"]))
mass_diag = diag(data["mass"])
for dim = 1 : 3
momentum[:, :, dim] .= data["vel"][:, :, dim] * mass_diag
end
data["momentum"] = momentum
println("\"momentum\"\thas been added to `data`")
end
"""
function mass!(data::Dict, mass_vec, id_vec=false)
This will add a component called "mass" into `data`. Elements in `mass_vec` are the mass of atoms with id correspond to `id_vec`. If `id_vec` is set to default, it means `id_vec = [1, 2, 3, ... length(mass_vec)]'
"""
function mass!(data::Dict, mass_vec, id_vec=false)
if id_vec == false
id_vec = [i for i = 1:length(mass_vec)]
end
id = copy_array(data["atom_type"][1, :])
mass = zeros(size(id))
for i = 1:length(mass_vec)
mass[findall(x->x==id_vec[i], id)] .= mass_vec[i]
end
data["mass"] = mass
println("\"mass\"\thas been added to `data`")
end
"""
function time_step!(data::Dict, time_len, converter, dump_len)
This will add a component, which is an array contain all information needed to calulate the time in MD_simulation, called "time_step" into `data`.
# Parameters:
- `data`: Dict variable create by `read_dump()`.
- `time_len`: length of time step of simulation.
- `converter`: unit converter that convert time_len to second.
- `dump_len`: length of dump step.
"""
function time_step!(data::Dict, time_len, converter)
data["time_step"] = [time_len, converter, data["num_steps"]]
println("\"time_step\"\thas been added to `data`")
end
"""
function coord_scl!(data::Dict)
This will add a component, which is the scaled coordinate within range [0, 1], called "coord_scl" into `data`.
"""
function coord_scl!(data::Dict)
try
data["coord"]
catch
error("\"coord\" has not been included in `data` !")
end
box_inv = genr_box_inv(data)
coord_scl = zeros(size(data["coord"]))
for step = 1:data["num_steps"]
coord_scl[step, :, :] = data["coord"][step, :, :] * box_inv
end
data["coord_scl"] = coord_scl
println("\"coord_scl\"\thas been added to `data`")
end | [
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] | 2.398121 | 3,725 |
using CalibrationErrors
using Literate: Literate
using Pkg: Pkg
if haskey(ENV, "GITHUB_ACTIONS")
# Print `@debug` statements (https://github.com/JuliaDocs/Documenter.jl/issues/955)
ENV["JULIA_DEBUG"] = "Documenter"
# Bypass the accept download prompt
ENV["DATADEPS_ALWAYS_ACCEPT"] = "true"
end
const EXAMPLES = ("classification", "distribution")
const INPUT = joinpath(@__DIR__, "..", "examples")
const OUTPUT = joinpath(@__DIR__, "src", "examples")
ispath(OUTPUT) && rm(OUTPUT; recursive=true)
mkpath(joinpath(OUTPUT, "figures"))
# Add link to nbviewer below the first heading of level 1
function preprocess(content)
sub = SubstitutionString(
"""
\\0
#
#md # [](@__NBVIEWER_ROOT_URL__/examples/@__NAME__.ipynb)
#md #
# You are seeing the
#md # HTML output generated by [Documenter.jl](https://github.com/JuliaDocs/Documenter.jl) and
#nb # notebook output generated by
# [Literate.jl](https://github.com/fredrikekre/Literate.jl) from the
# [Julia source file](@__REPO_ROOT_URL__/examples/@__NAME__/script.jl).
# The corresponding
#md # notebook can be viewed in [nbviewer](@__NBVIEWER_ROOT_URL__/examples/@__NAME__.ipynb).
#nb # HTML output can be viewed [here](https://devmotion.github.io/CalibrationErrors.jl/dev/examples/@__NAME__/).
#md #
#md # ```@setup @__NAME__
#md # using Pkg: Pkg
#md # Pkg.activate("$(INPUT)/@__NAME__")
#md # Pkg.instantiate()
#md # ```
#
""",
)
return replace(content, r"^# # [^\n]*"m => sub; count=1)
end
for name in EXAMPLES
file = joinpath(INPUT, name, "script.jl")
# Activate project environment
Pkg.activate(dirname(file)) do
Pkg.instantiate()
Literate.markdown(file, OUTPUT; name=name, documenter=true, preprocess=preprocess)
Literate.notebook(file, OUTPUT; name=name, documenter=true, preprocess=preprocess)
end
end
using Documenter
makedocs(;
modules=[CalibrationErrors],
authors="David Widmann <david.widmann@it.uu.se>",
repo="https://github.com/devmotion/CalibrationErrors.jl/blob/{commit}{path}#L{line}",
sitename="CalibrationErrors.jl",
format=Documenter.HTML(;
prettyurls=get(ENV, "CI", "false") == "true",
canonical="https://devmotion.github.io/CalibrationErrors.jl",
assets=String[],
),
pages=[
"index.md",
"introduction.md",
"ece.md",
"kce.md",
"others.md",
"Examples" => [joinpath("examples", "$(name).md") for name in EXAMPLES],
],
strict=true,
checkdocs=:exports,
)
deploydocs(;
repo="github.com/devmotion/CalibrationErrors.jl.git",
push_preview=true,
devbranch="main",
)
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] | 2.385841 | 1,130 |
# Below url is pinned to a specific commit -- if xiaoliangstd updates the repo
# with changes, you may want to investigate them.
mini_cheetah_url = "https://raw.githubusercontent.com/xiaoliangstd/MiniCheetah_description/f8e30e3d9f1077d096483010ddfbaa16e928a37c/cheetah_description/"
urdfpath = "xacro/mini_cheetah.urdf"
meshpaths = [
"meshes/mini_body.obj";
"meshes/mini_abad.obj";
"meshes/mini_upper_link.obj";
"meshes/mini_lower_link.obj"
]
datadir = "cheetah_description"
ispath(datadir) || mkpath(datadir)
download(mini_cheetah_url * urdfpath, joinpath(datadir, "mini_cheetah.urdf"))
for meshpath in meshpaths
meshdir, meshfilename = splitdir(meshpath)
meshdir = joinpath(datadir, meshdir)
ispath(meshdir) || mkpath(meshdir)
download(mini_cheetah_url * meshpath, joinpath(datadir, meshpath))
end
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437,
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] | 2.44898 | 343 |
cd(@__DIR__); include("setups/grid7x3.jl");
## DAG
D = natural_eigdist(𝚽, 𝛌, Q; distance = :DAG)
E = transform(fit(MDS, D, maxoutdim=2, distances=true))
plt = grid7x3_mds_heatmaps(E, 𝚽; backend = :pyplot)
savefig(plt, "../figs/Grid7x3_MDS_DAG.png")
display(plt)
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] | 1.992424 | 132 |
module LabRig
using CImGui, DataStructures
using CImGui.CSyntax
using CImGui.CSyntax.CStatic
using DataStructures
import ThreadPools.@tspawnat
const ZEISS_TID = 3
const PIPELINE_THREADS = 4:8
include("gamepad.jl")
include("manipulator.jl")
include("pressure.jl")
include("zeiss.jl")
include("daq.jl")
#include("camera.jl")
include("gui.jl")
export Gamepad, Manipulator, Pressure, GUI, CImGui, CSyntax, CStatic, Zeiss, DAQ # Camera
function init_subsystems()
Pressure.initialize()
@tspawnat ZEISS_TID Zeiss.server_init()
end
end # module
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3419,
198,
437,
198,
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437,
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] | 2.586854 | 213 |
module DiffusionMCMCTools
using DiffusionDefinition, ObservationSchemes, GuidedProposals
using Random, Distributions
const DD = DiffusionDefinition
const OBS = ObservationSchemes
const GP = GuidedProposals
const _LL = Val(:ll)
const _P_ONLY = Val(:P_only)
#temporary
import GuidedProposals: is_critical_update
OBS.var_parameter_names(P::DD.DiffusionProcess) = DD.var_parameter_names(P)
include("sampling_unit.jl")
include("sampling_pair.jl")
include("sampling_ensemble.jl")
include("block.jl")
include("biblock.jl")
include("block_collection.jl")
include("block_ensemble.jl")
include("param_names_collections.jl")
# sampling_unit.jl
export SamplingUnit
export draw_proposal_path!
# sampling_pair.jl
export SamplingPair
# sampling_ensemble.jl
export SamplingEnsemble
# block.jl
export Block
export set_ll!, save_ll!, find_W_for_X!, recompute_path!, loglikhd!
# biblock.jl
export BiBlock
export accept_reject_proposal_path!, set_accepted!
export swap_paths!, swap_XX!, swap_WW!, swap_PP!, swap_ll!
export ll_of_accepted, accpt_rate, loglikhd°!
export set_proposal_law!
# block_collection.jl
export BlockCollection
export fetch_ll, fetch_ll°
# block_ensemble.jl
export BlockEnsemble
# param_names_collections.jl
export ParamNamesUnit
export ParamNamesBlock
export ParamNamesRecording
export ParamNamesAllObs
end
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] | 2.631579 | 570 |
# Definition of CuckooFilter and associated methods
"""
CuckooFilter{F}
A cuckoo filter. `F` is the type of fingerprint used by the filter (defaults
to `UInt8`).
# Examples
```jldoctest; setup = :(using CuckooFilters)
julia> filter = CuckooFilter{AbstractVector{UInt8}}();
julia> b"hello, world!" ∈ filter
false
julia> insert!(filter, b"hello, world!")
true
julia> b"hello, world!" ∈ filter
true
julia> delete!(filter, b"hello, world!")
true
julia> b"hello, world!" ∈ filter
false
```
"""
struct CuckooFilter{T,F}
buckets :: Array{Bucket{F},1}
max_bucket_kicks :: Int64
max_bucket_size :: Int64
end
CuckooFilter{T}() where T = CuckooFilter{T, fingerprint_type(T)}()
function CuckooFilter{T,F}(;
n_buckets = 1000,
max_bucket_kicks = 100,
max_bucket_size = 4
) where {T,F}
buckets = [Bucket{F}() for i = 1:n_buckets]
CuckooFilter{T,F}(buckets, 100, 4)
end
function getbucket(filter, i)
n_buckets = length(filter.buckets)
i = (i % n_buckets) + 1
return filter.buckets[i]
end
insert_unique!(filter::CuckooFilter, key) = insert!(filter, key, true)
function Base.insert!(
filter::CuckooFilter{T},
key::T,
unique = false
) where T
if unique && key ∈ filter
return false
end
f = fingerprint(key)
h₁ = hash(key)
# Probe first bucket to see if it has an empty entry
b₁ = getbucket(filter, h₁)
if !isfull(b₁, filter.max_bucket_size)
insert!(b₁, f)
return true
end
# Probe second bucket to see if it has an empty entry
h₂ = h₁ ⊻ hash(f)
b₂ = getbucket(filter, h₂)
if !isfull(b₂, filter.max_bucket_size)
insert!(b₂, f)
return true
end
# If both buckets are full, we need to relocate an existing
# item from one of the two buckets and move it to another
# bucket
b, h = rand(((h₁, b₁), (h₂, b₂)))
for i = 1:filter.max_bucket_kicks
original_f = f
# Swap the fingerprint f with a random fingerprint from
# the bucket
rand_idx = rand(1:length(b))
f = b[rand_idx]
b[rand_idx] = original_f
# Attempt to insert the fingerprint into a new bucket
h = h ⊻ hash(f)
b = getbucket(f, h)
if !isfull(b)
insert!(b, f)
return true
end
end
# If we've reached this point, we've gone through the maximum
# number of possible iterations to try and kick fingerprints
# through the cuckoo filter. We treat the hash table as full.
error("Cuckoo filter is full")
end
function Base.delete!(filter::CuckooFilter{T}, key::T) where T
# Delete the key from its first possible bucket if it is inside
# of that bucket
f = fingerprint(key)
h₁ = hash(key)
b₁ = getbucket(filter, h₁)
if delete!(b₁, f)
return true
end
# If the first deletion failed, then we attempt to delete the
# key from its second bucket
h₂ = h₁ ⊻ hash(f)
b₂ = getbucket(filter, h₂)
delete!(b₂, f)
end
function Base.in(key::T, filter::CuckooFilter{T}) where T
# Check whether the key's fingerprint is in its first possible
# bucket
f = fingerprint(key)
h₁ = hash(key)
if f ∈ getbucket(filter, h₁)
return true
end
# Check whether the fingerprint is in its second possible bucket
h₂ = h₁ ⊻ hash(f)
f ∈ getbucket(filter, h₂)
end
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] | 2.276094 | 1,485 |
using Optim
export dmdobjective, projBl2
# This file is available under the terms of the MIT License
@doc """
Functions for evaluating the objective and gradient.
"""
function prox_null(x)
return
end
function dmdobjective(params;updatep=true)
if updatep
return aBFunc(params)
else
return BFunc(params)
end
end
function BFunc(params)
T = eltype(params.X);
# update residual
BLAS.gemm!('N', 'N', T(1.0), params.P, params.B, T(0.0), params.R);
params.R .-= params.X
#
return params.lossFunc(view(params.R, :, params.ikeep))
end
function bFunc(params, id)
T = eltype(params.X);
# update residual
BLAS.gemv!('N', T(1.0), params.P, params.b[id], T(0.0), params.r[id]);
params.r[id] .-= params.x[id]
#
return params.lossFunc(params.r[id])
end
function bGrad(gbr, params, id)
T = eltype(params.X);
Tr = typeof(real(params.X[1]));
# update residual
BLAS.gemv!('N', T(1.0), params.P, params.b[id], T(0.0), params.r[id]);
params.r[id] .-= params.x[id];
#
params.lossGrad(params.r[id]);
#
gb = vr2vc(gbr);
BLAS.gemv!('T', T(1.0), params.P, params.r[id], T(0.0), gb);
gbr[1:2:end] .*= Tr(2.0);
gbr[2:2:end] .*= Tr(-2.0);
end
function bFuncGrad(gbr, params, id)
T = eltype(params.X);
Tr = typeof(real(params.X[1]));
# update residual
BLAS.gemv!('N', T(1.0), params.P, params.b[id], T(0.0), params.r[id]);
params.r[id] .-= params.x[id];
#
f = params.lossFunc(params.r[id])
params.lossGrad(params.r[id]);
#
gb = vr2vc(gbr);
BLAS.gemv!('T', T(1.0), params.P, params.r[id], T(0.0), gb);
gbr[1:2:end] .*= Tr(2.0);
gbr[2:2:end] .*= Tr(-2.0);
return f
end
function bfg!(ft, gbr, br, params, id)
copyto!(params.br[id], br);
if ft != nothing && gbr != nothing
return bFuncGrad(gbr, params, id);
end
if ft == nothing && gbr != nothing
bGrad(gbr, params, id)
return
end
if ft != nothing && gbr == nothing
return bFunc(params, id)
end
end
function projb(params, id)
# define the function and gradient interface for optim
#
function obfg!(f, g, x)
return bfg!(f, g, x, params, id)
end
optimfg! = Optim.only_fg!(obfg!)
res = optimize(optimfg!, params.br[id], params.inner_solver,
params.inner_opts);
copyto!(params.br[id], res.minimizer);
end
function projB(params)
for id = 1:params.n
projb(params, id);
end
end
function abFunc(params, id)
# update P
update_P!(params);
# partially minimize over b
if params.inner_directl2
projbl2(params,id);
else
projb(params,id);
end
#
return bFunc(params, id)
end
function aBFunc(params)
# update P
update_P!(params);
# partially minimize over B
if params.inner_directl2
projBl2(params);
else
projB(params);
end
#
return BFunc(params)
end
function abGrad(gar, params, id)
# update P
update_P!(params);
#
T = eltype(params.X);
Tr = typeof(real(params.X[1]));
# partially minimize over b
if params.inner_directl2
projbl2(params,id);
else
projb(params,id);
end
# update residual
BLAS.gemv!('N', T(1.0), params.P, params.b[id], T(0.0), params.r[id]);
params.r[id] .-= params.x[id];
#
params.lossGrad(params.r[id]);
#
ga = vr2vc(gar);
#
params.r[id] .*= params.t;
BLAS.gemv!('T', T(1.0), params.P, params.r[id], T(0.0), ga);
ga .*= params.b[id];
#
gar[1:2:end] .*= Tr(2.0);
gar[2:2:end] .*= Tr(-2.0);
end
function aBGrad(gar, params)
# update P
update_P!(params);
#
T = eltype(params.X);
Tr = typeof(real(params.X[1]));
# partially minimize over B
if params.inner_directl2
projBl2(params);
else
projB(params);
end
# trim
trimstandard(params)
# update residual
BLAS.gemm!('N', 'N', T(1.0), params.P, params.B, T(0.0), params.R);
params.R .-= params.X
#
params.lossGrad(params.R);
#
ga = vr2vc(gar);
#
params.R .*= params.t;
BLAS.gemm!('T', 'N', T(1.0), params.P, params.R, T(0.0), params.tM);
params.tM .*= params.B;
sum!(ga, view(params.tM, :, params.ikeep));
#
gar[1:2:end] .*= Tr(2.0);
gar[2:2:end] .*= Tr(-2.0);
end
function aBFuncGrad(gar, params)
# update P
update_P!(params);
#
T = eltype(params.X);
Tr = typeof(real(params.X[1]));
# partially minimize over B
if params.inner_directl2
projBl2(params);
else
projB(params);
end
# trim
trimstandard(params)
# update residual
BLAS.gemm!('N', 'N', T(1.0), params.P, params.B, T(0.0), params.R);
params.R .-= params.X
#
f = params.lossFunc(view(params.R, :, params.ikeep))
params.lossGrad(params.R);
#
ga = vr2vc(gar);
#
params.R .*= params.t;
BLAS.gemm!('T', 'N', T(1.0), params.P, params.R, T(0.0), params.tM);
params.tM .*= params.B;
sum!(ga, view(params.tM, :, params.ikeep));
#
gar[1:2:end] .*= Tr(2.0);
gar[2:2:end] .*= Tr(-2.0);
return f
end
#####################################################
# L2 Penalty
function updateqr!(params)
if params.a != params.qra
qrfact = params.qrfact;
# grab pointers
fact = qrfact.factors
jpvt = qrfact.jpvt
tau = qrfact.τ
# prep and call factorization
copyto!(fact, params.P)
jpvt .= 0 # do not forget!
LinearAlgebra.LAPACK.geqp3!(fact, jpvt, tau)
copyto!(params.qra, params.a)
end
end
function projbl2(params, id)
updateqr!(params)
copyto!(params.qrtemp[id], params.x[id])
ldiv!(params.qrfact, params.qrtemp[id])
k = params.k
copyto!(params.b[id], view(params.qrtemp[id], 1:k))
end
function projBl2(params)
if any(isinf, params.P)
params.B .= 0.0
else
updateqr!(params)
copyto!(params.qrTemp, params.X)
ldiv!(params.qrfact, params.qrTemp)
k = params.k
copyto!(params.B, view(params.qrTemp, 1:k, :))
end
end
################################################
# trimming
function trimstandard(params)
if params.n == params.nkeep
return
end
T = eltype(params.X)
# update residual
BLAS.gemm!('N', 'N', T(1.0), params.P, params.B, T(0.0), params.R);
params.R .-= params.X
for id = 1:params.n
params.tcnorm[id] = params.lossFunc(params.r[id])
end
sortperm!(params.idsort, params.tcnorm, rev=false)
copyto!(params.ikeep, view(params.idsort, 1:params.nkeep))
sort!(params.ikeep)
end
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0,
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37266,
13,
522,
538,
8,
198,
437,
220,
220,
220,
220,
198
] | 2.022761 | 3,339 |
# ==================================================================================================================
function solveStokes_penalty( U, P, θ, r, Ucartesian,Upolar, g, ρ, η, 𝓒,
theta, rr, TT,
∂Ωu, ufix, ifree,
PhaseID, EL2NOD, EL2NODP,
KKidx, GGidx, invMMidx,
to)
@timeit to "Stokes" begin
@timeit to "Assembly" KK, GG, invMM, Rhs =
assembly_stokes_penalty(EL2NOD, EL2NODP, theta, rr, g, ρ, η, 𝓒, PhaseID, KKidx, GGidx, invMMidx)
@timeit to "BCs" begin
KK, GG, Rhs = _prepare_matrices_penalty(KK, GG, Rhs, TT)
U, Rhs = _apply_bcs(U,KK,Rhs,∂Ωu,ufix)
end
@timeit to "Solve" U, P = PowellHesteness(U, P, KK, invMM, GG, Rhs, ifree)
@timeit to "Remove net rotation" U, Ucart, Upolar, Ucartesian = updatevelocity(U,Ucartesian, Upolar, θ, r,TT)
# @timeit to "Remove nullspace" U, Ucart, Upolar, Ucartesian = updatevelocity2(U, Ucartesian, Upolar, ρ, EL2NOD, TT, theta, rr, r)
end
return Ucartesian,Upolar,U,Ucart,P,to
end
# ==================================================================================================================
@inline function _apply_bcs_penalty(U, KK, Rhs, ∂Ω, vfix)
U[∂Ω] = vfix # write prescribed temperature values
Rhs -= KK[:,∂Ω] * vfix
return U,Rhs
end
@inline function _prepare_matrices_penalty(KK, GG, Fb,TT)
dropzeros!(GG)
KK = SparseMatrixCSC(Symmetric(KK,:L))
dropzeros!(KK)
transTT = SparseMatrixCSC(TT')
KK = transTT*KK*TT
GG = transTT*GG
Fb = transTT*Fb
KK = SparseMatrixCSC(Symmetric(KK,:L))
return KK,GG,Fb
end
function assembly_stokes_penalty(EL2NOD, EL2NODP, theta, r, g, ρ, η, 𝓒, PhaseID, KKidx, GGidx, invMMidx)
penalty = 1e6
# ============================================ MODEL AND BLOCKING PARAMETERS
ndim = 2
# nvert = 3
# EL2NOD0 = deepcopy(EL2NOD)
nnodel = size(EL2NOD,1)
nel = size(EL2NOD,2)
nelblk = min(nel, 1200)
nblk = ceil(Int,nel/nelblk)
il = one(nelblk); iu = nelblk
# =========== PREPARE INTEGRATION POINTS & DERIVATIVES wrt LOCAL COORDINATES
nip = 6
# nUdof = ndim*maximum(EL2NOD)
# nPdof = maximum(EL2NODP)
nUdofel = ndim * nnodel
nPdofel = size(EL2NODP,1)
ni, nn, nnP, nn3 = Val(nip), Val(nnodel), Val(3), Val(3)
N,dNds,_,w_ip = _get_SF(ni,nn)
NP,_,_,_ = _get_SF(ni,nnP)
N3,_,dN3ds,_ = _get_SF(ni,nn3)
# ============================================================= ALLOCATIONS
detJ_PL = Vector{Float64}(undef,nelblk)
R_31 = similar(detJ_PL) # = detJa_PL*dxi_dth
R_21 = similar(detJ_PL) # = -detJa_PL*deta_dth
Th_31 = similar(detJ_PL) # = -detJa_PL*dxi_dr
Th_21 = similar(detJ_PL) # = detJa_PL*deta_dr
# NxN = Array{Float64,2}(undef,3,3)
# ang_dist = Array{Float64,2}(undef,3,nelblk)
# VCOORD_th = similar(ang_dist)
# VCOORD_r = similar(ang_dist)
dNdx = Array{Float64,2}(undef,nelblk,size(dNds[1],2))
dNdy = similar(dNdx)
ω = Vector{Float64}(undef,Int(nelblk))
invJx_double = Array{Float64,2}(undef,Int(nelblk),ndim) # storage for x-components of Jacobi matrix
invJz_double = similar(invJx_double) # storage for z-components of Jacobi matrix
# ==================================== STORAGE FOR DATA OF ELEMENTS IN BLOCK
K_blk = fill(0.0, nelblk, Int(nUdofel*(nUdofel+1)/2))
# symmetric stiffness matrix, dim=vel dofs, but only upper triangle
M_blk = fill(0.0, nelblk, Int(nPdofel*(nPdofel+1)/2))
# symmetric mass matrix, dim=pressure nodes, but only upper triangle
G_blk = fill(0.0, nelblk, nPdofel*nUdofel)
# asymmetric gradient matrix, vel dofs x pressure nodes
Fb_blk = fill(0.0, nelblk, nUdofel)
# storage for entries in bouyancy force vector
invM_blk = fill(0.0, nelblk, nPdofel*nPdofel)
invMG_blk = fill(0.0, nelblk, nPdofel*nUdofel)
# ==================================== STORAGE FOR DATA OF ALL ELEMENT MATRICES/VECTORS
K_all = Array{Float64,2}(undef, Int(nUdofel*(nUdofel+1)/2),nel)
M_all = Array{Float64,2}(undef, Int(nPdofel*(nPdofel+1)/2),nel)
G_all = Array{Float64,2}(undef, nUdofel*nPdofel,nel)
Fb_all = Array{Float64,2}(undef, nUdofel,nel)
invM_all = Array{Float64,2}(undef, nPdofel*nPdofel,nel)
#=========================================================================
BLOCK LOOP - MATRIX COMPUTATION
=========================================================================#
for ib in 1:nblk
#===========================================================================
CALCULATE JACOBIAN, ITS DETERMINANT AND INVERSE
===========================================================================#
"""
NOTE: For triangular elements with non-curved edges the Jacobian is
the same for all integration points (i.e. calculated once
before the integration loop). Further, linear 3-node shape are
sufficient to calculate the Jacobian.
"""
# idx = @views EL2NOD[1:nvert,il:iu]
# VCOORD_th = reshape(theta[idx],nvert,nelblk)
# VCOORD_r = reshape(r[idx],nvert,nelblk)
VCOORD_th = view(theta,:,il:iu)
VCOORD_r = view(r,:,il:iu)
J_th = (VCOORD_th'*dN3ds')
J_r = (VCOORD_r'*dN3ds')
_fill_R_J!(J_th,J_r,VCOORD_th,VCOORD_r,
R_31,R_21,Th_31,Th_21,detJ_PL)
# ------------------------ NUMERICAL INTEGRATION LOOP (GAUSS QUADRATURE)
_ip_loop_penalty!(K_blk, M_blk, G_blk, Fb_blk,
g, η, 𝓒, ρ, EL2NOD, PhaseID,il:iu,
dNds, w_ip, nip, nnodel, nelblk, nPdofel,nUdofel,
dNdx,dNdy,N,N3,NP,
ω,invJx_double, invJz_double, detJ_PL,
R_21,R_31,Th_21,Th_31,
VCOORD_th,VCOORD_r)
_invM!(invM_blk, M_blk, detJ_PL)
_fill_invMxG!(invMG_blk, invM_blk, G_blk, nPdofel, nUdofel)
_fill_KplusGxinvMxGt(K_blk, invMG_blk, G_blk, nPdofel, nUdofel, penalty)
# --------------------------------------- WRITE DATA INTO GLOBAL STORAGE
@views begin
K_all[:,il:iu] .= K_blk'
G_all[:,il:iu] .= G_blk'
M_all[:,il:iu] .= M_blk'
Fb_all[:,il:iu] .= Fb_blk'
invM_all[:,il:iu] .= invM_blk'
end
# -------------------------------------------------- RESET BLOCK MATRICES
fill!(K_blk,0.0)
fill!(G_blk,0.0)
fill!(M_blk,0.0)
fill!(Fb_blk,0.0)
fill!(invM_blk,0.0)
fill!(invMG_blk,0.0)
# -------------------------------- READJUST START, END AND SIZE OF BLOCK
il += nelblk;
if ib == nblk-1
# ------------ Account for different number of elements in last block
nelblk = nel - il + 1 # new block size
# --------------------------------- Reallocate at blocks at the edge
detJ_PL = Vector{Float64}(undef,nelblk)
R_31 = similar(detJ_PL)
R_21 = similar(detJ_PL)
Th_31 = similar(detJ_PL)
Th_21 = similar(detJ_PL)
# ang_dist = Array{Float64,2}(undef,3,nelblk)
dNdx = Array{Float64,2}(undef,nelblk,size(dNds[1],2))
dNdy = similar(dNdx)
K_blk = fill(0.0, nelblk, Int(nUdofel*(nUdofel+1)/2))
M_blk = fill(0.0, nelblk, Int(nPdofel*(nPdofel+1)/2))
G_blk = fill(0.0, nelblk, nPdofel*nUdofel)
Fb_blk = fill(0.0, nelblk, nUdofel)
ω = Vector{Float64}(undef,Int(nelblk))
invJx_double = Array{Float64,2}(undef,Int(nelblk),ndim)
invJz_double = similar(invJx_double)
invM_blk = fill(0.0, nelblk, nPdofel*nPdofel)
invMG_blk = fill(0.0, nelblk, nPdofel*nUdofel)
end
iu += nelblk
end # end block loop
# #===========================================================================
# ASSEMBLY OF GLOBAL SPARSE MATRICES AND RHS-VECTOR
# ===========================================================================#
KK, GG, invMM, Fb =
_create_stokes_matrices_penalty(EL2NOD, nUdofel, ndim,
KKidx, GGidx, invMMidx,
K_all, G_all, invM_all, Fb_all)
return KK, GG, invMM, Fb
end # END OF ASSEMBLY FUNCTION
function _ip_loop_penalty!( K_blk, M_blk, G_blk, Fb_blk,
g, η, 𝓒, ρ, EL2NOD, PhaseID,els,
dNds, w_ip, nip, nnodel, nelblk, nPdofel,nUdofel,
dNdx,dNdy,N,N3,NP,
ω,invJx_double, invJz_double, detJ_PL,
R_21,R_31,Th_21,Th_31,
VCOORD_th,VCOORD_r)
sin_ip = similar(detJ_PL)
cos_ip = similar(detJ_PL)
for ip=1:nip
N_ip = N3[ip]
# NP_blk = repeat(NP[ip],nelblk,1)
NP_blk = NP[ip].*ones(nelblk)
#=======================================================================
PROPERTIES OF ELEMENTS AT ip-TH EVALUATION POINT
=======================================================================#
Dens_blk = _element_density(ρ,EL2NOD,PhaseID,els,NP[ip])
Visc_blk = _element_viscosity(η,EL2NOD,PhaseID,els,NP[ip])
# Gravitational force at ip-th integration point
Fg_blk = g * Dens_blk
#==========================================================================================================
CALCULATE 2nd JACOBIAN (FROM CARTESIAN TO POLAR COORDINATES --> curved edges), ITS DETERMINANT AND INVERSE
===========================================================================================================
NOTE: For triangular elements with curved edges the Jacobian needs to be computed at each integration
point (inside the integration loop). =#
th_ip = VCOORD_th'*N_ip'
r_ip = VCOORD_r'*N_ip'
_derivative_weights!(dNds[ip],ω,dNdx,dNdy,w_ip[ip],th_ip,r_ip,sin_ip,cos_ip,
R_21,R_31,Th_21,Th_31,detJ_PL,invJx_double,invJz_double)
_fill_Kblk!(K_blk, 𝓒, Visc_blk, Val(η), ω, dNdx, dNdy, nnodel, els, ip)
_fill_Gblk!(G_blk, NP_blk, ω, dNdx, dNdy, nPdofel,nUdofel,nnodel)
_fill_Mblk_penalty!(M_blk, NP_blk, ω, nPdofel)
_fill_Fbblk!(Fb_blk,Fg_blk, N[ip],
ω, sin_ip,cos_ip,nUdofel)
end # end integration point loop
end # ENF OF IP_LOOP FUNCTION
@inline function _fill_Mblk_penalty!(M_blk, NP_blk, ω, nPdofel)
# "weight" divided by viscosity at integration point in all elements
indx = 1;
for i in 1:nPdofel
@inbounds @simd for j in i:nPdofel
M_blk[:,indx] .+= ω.*view(NP_blk,:,i).*view(NP_blk,:,j)
indx += 1
end
end
end
function _invM!(invM_blk, M_blk, detJ)
TMP = 1.0./detJ
M_blk = M_blk .* TMP
# M_blk = M_blk .* TMP[:,ones[1,nPdofel*[nPdofel+1]/2]];
# How to calculate the determinante of several symmetric 3x3 matrices:
# det[M] = M11*M22*M33 + M12*M23*M31 + M13*M21*M32
# - M13*M22*M31 - M12*M21*M33 - M11*M23*M32
# written in 1-index notation:
# = M1 *M5 *M9 + M4 *M8 *M3 + M7 *M2 *M6
# - M7 *M5 *M3 - M4 *M2 *M9 - M1 *M8 *M6
# considering symmetry and using lower triangular part only
# [M4=M2, M7=M3, M8=M6]
# = M1 *M5 *M9 + M2 *M6 *M3 + M3 *M2 *M6
# - M3 *M5 *M3 - M2 *M2 *M9 - M1 *M6 *M6
# and knowing where the 6 different values are stored in M_blk
# [1-->M1, 2-->M2, 3-->M3, 4-->M5, 5-->M6, 6-->M9]
# = M_blk1*M_blk4*M_blk6 + M_blk2*M_blk5*M_blk3 + M_blk3*M_blk2*M_blk5
# - M_blk3*M_blk4*M_blk3 - M_blk2*M_blk2*M_blk6 - M_blk1*M_blk5*M_blk5
# re-arranging
# = M_blk1 * [M_blk4*M_blk6 - M_blk5*M_blk5]
# + M_blk2 * [M_blk5*M_blk3 - M_blk2*M_blk6]
# + M_blk3 * [M_blk2*M_blk5 - M_blk4*M_blk3]
detM_blk = @. M_blk[:,1] * (M_blk[:,4]*M_blk[:,6] - M_blk[:,5]*M_blk[:,5]) +
M_blk[:,2] * (M_blk[:,5]*M_blk[:,3] - M_blk[:,2]*M_blk[:,6]) +
M_blk[:,3] * (M_blk[:,2]*M_blk[:,5] - M_blk[:,4]*M_blk[:,3])
detM_blk ./=TMP
# The determinante is used to calculate the inverse of the symmetric
# 3x3 element mass matrices. The same logic as above is used.
invM_blk[:,1] = @views @. (M_blk[:,4]*M_blk[:,6] - M_blk[:,5]*M_blk[:,5])/detM_blk;
invM_blk[:,2] = @views @. (M_blk[:,5]*M_blk[:,3] - M_blk[:,2]*M_blk[:,6])/detM_blk;
invM_blk[:,3] = @views @. (M_blk[:,2]*M_blk[:,5] - M_blk[:,4]*M_blk[:,3])/detM_blk;
invM_blk[:,4] = @views invM_blk[:,2];
invM_blk[:,5] = @views @. (M_blk[:,1]*M_blk[:,6] - M_blk[:,3]*M_blk[:,3])/detM_blk;
invM_blk[:,6] = @views @. (M_blk[:,2]*M_blk[:,3] - M_blk[:,1]*M_blk[:,5])/detM_blk;
invM_blk[:,7] = @views invM_blk[:,3];
invM_blk[:,8] = @views invM_blk[:,6];
invM_blk[:,9] = @views @. (M_blk[:,1]*M_blk[:,4] - M_blk[:,5]*M_blk[:,5])/detM_blk;
end
function _fill_invMxG!(invMG_blk, invM_blk, G_blk, nPdofel, nUdofel)
# --------------------------invM*G'----------------------------
for i=1:nPdofel
for j=1:nUdofel
for k=1:nPdofel
invMG_blk[:,(i-1)*nUdofel+j] = @views invMG_blk[:,(i-1)*nUdofel+j] +
invM_blk[:,(i-1)*nPdofel+k].*G_blk[:,(k-1)*nUdofel+j];
end
end
end
end
function _fill_KplusGxinvMxGt(K_blk, invMG_blk, G_blk, nPdofel, nUdofel, penalty)
# -----------------K = K + penalty*G*invM*G'-------------------
indx = 1;
for i=1:nUdofel
for j=i:nUdofel
for k=1:nPdofel
K_blk[:,indx] = @views K_blk[:,indx] +
penalty*G_blk[:,(k-1)*nUdofel+i].*invMG_blk[:,(k-1)*nUdofel+j];
end
indx += 1;
end
end
end
@inline function _create_stokes_matrices_penalty(EL2NOD,nUdofel,ndim,
KKidx, GGidx, invMMidx,
K_all, G_all, invM_all, Fb_all)
nel = size(EL2NOD,2)
#==========================================================================
ASSEMBLY OF GLOBAL STIFFNESS MATRIX
==========================================================================#
# convert triplet data to sparse global stiffness matrix (assembly)
KK = sparse(KKidx._i ,KKidx._j, vec(K_all))
#==========================================================================
ASSEMBLY OF GLOBAL GRADIENT MATRIX
==========================================================================#
# convert triplet data to sparse global gradient matrix (assembly)
GG = sparse(GGidx._i ,GGidx._j ,vec(G_all))
#==========================================================================
ASSEMBLY OF GLOBAL FORCE VECTOR
==========================================================================#
EL2DOF = Array{Int64}(undef,nUdofel, nel)
@views EL2DOF[1:ndim:nUdofel,:] .= @. ndim*(EL2NOD-1)+1
@views EL2DOF[2:ndim:nUdofel,:] .= @. ndim*(EL2NOD-1)+2
Fb = accumarray(vec(EL2DOF),vec(Fb_all));
#==========================================================================
ASSEMBLY OF GLOBAL (1/VISCOSITY)-SCALED MASS MATRIX
==========================================================================#
# convert triplet data to sparse global matrix
invMM = sparse(invMMidx._i ,invMMidx._j , vec(invM_all))
return KK, GG, invMM, Fb
end
# POWELL-HESTENESS SOLVER ================================================================
@inline function PowellHesteness(U, P, KK, M⁻¹, GG, Rhs, ifree)
itmax_PH = 50
itmin_PH = 2
# itnum = 0
norm∇U = Vector{Float64}(undef, itmax_PH)
Rhs .+= GG*P
LL = factorize(KK[ifree, ifree])
# LL = cholesky(KK[ifree, ifree])
GGᵀ = GG'
# Uifree = U[ifree]
# Rhsifree = Rhs[ifree]
penalty = 1e6
for itPH = 1:itmax_PH
@inbounds U[ifree] .= LL\Rhs[ifree]
∇U = -GGᵀ*U
# rm0space!(∇U)
ΔP = penalty*M⁻¹*∇U
Rhs .+= GG*ΔP
P .+= ΔP
# Check convergence -----------------------------------------------
norm∇U[itPH] = mynorm(∇U)
if (itPH > itmin_PH) && (norm∇U[itPH]/norm∇U[itPH-1] > 0.1)
println("\n", itPH," Powell-Hesteness iterations\n")
break
end
end
# Ux = U[1:2:end]
# Uz = U[2:2:end]
# M = sum(mean(ρ[EL2NOD],dims=1)'.*area_el)
# Ax = sum(mean(ρ[EL2NOD].*Ux[EL2NOD], dims=1)'.*area_el)
# Az = sum(mean(ρ[EL2NOD].*Uz[EL2NOD], dims=1)'.*area_el)
# A = @. (Ax+Az)/M
# U .-=A
return U, P
end # END POWELL-HESTENESS SOLVER | [
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] | 1.910936 | 8,870 |
module MaxHelpingHandHintsFlagController
using ..Ahorn, Maple
@mapdef Entity "MaxHelpingHand/HintsFlagController" HintsFlagController(x::Integer, y::Integer, outputFlag::String="hints", not::Bool=false)
const placements = Ahorn.PlacementDict(
"Hints Flag Controller (max480's Helping Hand)" => Ahorn.EntityPlacement(
HintsFlagController
)
)
function Ahorn.selection(entity::HintsFlagController)
x, y = Ahorn.position(entity)
return Ahorn.Rectangle(x - 12, y - 12, 24, 24)
end
function Ahorn.editingOptions(effect::HintsFlagController)
return Dict{String, Any}(
)
end
function Ahorn.render(ctx::Ahorn.Cairo.CairoContext, entity::HintsFlagController, room::Maple.Room)
if entity.not
Ahorn.drawSprite(ctx, "ahorn/MaxHelpingHand/hints_flag_controller_inv", 0, 0)
else
Ahorn.drawSprite(ctx, "ahorn/MaxHelpingHand/hints_flag_controller", 0, 0)
end
end
end
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] | 2.633238 | 349 |
"""
Main module for `GigaSOM.jl` - Huge-scale, high-performance flow cytometry clustering
The documentation is here: http://LCSB-BioCore.github.io/GigaSOM.jl
"""
module GigaSOM
using CSV
using DataFrames
using Distances
using Distributed
using Distributions
using FCSFiles
using FileIO
using DistributedArrays
using NearestNeighbors
using Serialization
using StableRNGs
include("base/structs.jl")
include("base/dataops.jl")
include("base/distributed.jl")
include("base/trainutils.jl")
include("analysis/core.jl")
include("analysis/embedding.jl")
include("io/dio.jl")
include("io/input.jl")
include("io/process.jl")
include("io/splitting.jl")
# include visualization files
# include("visualization/plotting.jl")
#core
export initGigaSOM, trainGigaSOM, mapToGigaSOM
#trainutils
export linearRadius, expRadius, gaussianKernel, bubbleKernel, thresholdKernel, distMatrix
#embedding
export embedGigaSOM
# structs
export Som, LoadedDataInfo
#io/input
export readFlowset,
readFlowFrame,
loadFCS,
loadFCSHeader,
getFCSSize,
loadFCSSizes,
loadFCSSet,
selectFCSColumns,
distributeFCSFileVector,
distributeFileVector,
getCSVSize,
loadCSV,
loadCSVSizes,
loadCSVSet
#io/splitting
export slicesof, vcollectSlice, collectSlice
#io/process
export cleanNames!, getMetaData, getMarkerNames
# plotting
export plotCounts, plotPCA
#dataops (higher-level operations on data)
export dcopy,
dselect,
dapply_cols,
dapply_rows,
dstat,
dstat_buckets,
dcount,
dcount_buckets,
dscale,
dtransform_asinh,
dmedian,
dmedian_buckets,
mapbuckets,
catmapbuckets
#distributed data tools
export save_at,
get_from,
get_val_from,
remove_from,
distribute_array,
distribute_darray,
undistribute,
distributed_transform,
distributed_mapreduce,
distributed_foreach,
distributed_collect,
distributed_export,
distributed_import,
distributed_unlink
end # module
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dimension(sig) = length(sig)
basis_labels(sig::Tuple) = 1:dimension(sig)
basis_labels(sig::NamedTuple{names}) where names = names
basis_blade_label(sig::Tuple, indices, labels=basis_labels(sig)) = "v$(join(labels[indices]))"
basis_blade_label(sig::NamedTuple, indices, labels=nothing) = join(basis_labels(sig)[indices])
abstract type MetricSignature end
"""
OffsetSignature(sig, indices)
Metric signature `sig` with offset `indices`, enabling non-standard indexing for multivectors.
Example
===
```jldoctest
julia> lorentzian = OffsetSignature((-1,1,1,1), 0:3) # zero-based indexing
⟨-+++⟩ with indices 0:3
(pretty-printed OffsetSignature{(-1, 1, 1, 1), 0:3}())
julia> (1:4)'basis(lorentzian) # construct spacetime 4-vector
Grade-1 Multivector{⟨-+++⟩ with indices 0:3, 1, Vector{Int64}}:
1 v0
2 v1
3 v2
4 v3
julia> ans[0] # get first ("time") component
1
```
"""
struct OffsetSignature{sig,indices} <: MetricSignature end
OffsetSignature(sig,indices) = OffsetSignature{sig,indices}()
Base.getindex(::OffsetSignature{sig}, args...) where sig = getindex(sig, args...)
Base.length(::OffsetSignature{sig}) where sig = length(sig)
basis_labels(::OffsetSignature{sig}) where sig = basis_labels(sig)
basis_blade_label(::OffsetSignature{sig,labels}, indices) where {sig,labels} = basis_blade_label(sig, indices, labels)
"""
Convert human-readable basis vector index or symbol into 1-based integer index
"""
normalize_bv_index(sig, i) = i
normalize_bv_index(sig, i::Symbol) = findfirst(==(i), basis_labels(sig))
function normalize_bv_index(sig::Union{Tuple,NamedTuple}, i::Integer)
r = 1:dimension(sig)
i ∈ r ? i : error("index $i is outside range $r for signature $sig")
end
function normalize_bv_index(osig::OffsetSignature{sig,indices}, ioffset::Integer) where {sig,indices}
i = findfirst(==(ioffset), indices)
isnothing(i) && error("index $ioffset is outside range $indices for $osig")
i
end
"""
Pretty-print metric signature in short, non-parseable form such as ⟨-+++⟩
"""
show_signature(sig) = repr(sig) # fallback
show_signature(sig::Tuple) = "⟨$(join(map(s -> get(Dict(+1=>"+", -1=>"-"), s, s), sig)))⟩"
function show_signature(sig::NamedTuple)
items = map(zip(keys(sig), sig)) do (label, square)
s = get(Dict(+1=>"+", -1=>"-"), square, square)
"$label$s"
end
"⟨$(join(items, ","))⟩"
end
function show_signature(::OffsetSignature{sig,indices}) where {sig,indices}
"$(show_signature(sig)) with indices $indices"
end | [
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] | 2.492857 | 980 |
### A Pluto.jl notebook ###
# v0.14.7
using Markdown
using InteractiveUtils
# This Pluto notebook uses @bind for interactivity. When running this notebook outside of Pluto, the following 'mock version' of @bind gives bound variables a default value (instead of an error).
macro bind(def, element)
quote
local el = $(esc(element))
global $(esc(def)) = Core.applicable(Base.get, el) ? Base.get(el) : missing
el
end
end
# ╔═╡ 47d5b09c-c213-11eb-2472-7b23859c3caa
begin
let
using Pkg
Pkg.activate(mktempdir())
Pkg.Registry.update()
Pkg.add("DICOM")
Pkg.add("NIfTI")
Pkg.add("Plots")
Pkg.add(Pkg.PackageSpec(url="https://github.com/Dale-Black/DICOMUtils.jl"))
Pkg.add("Images")
end
using PlutoUI
using DICOM
using NIfTI
using Plots
using DICOMUtils
using LinearAlgebra
using Images
end
# ╔═╡ e991c00b-974b-442c-93c6-55a0b12b27ed
TableOfContents()
# ╔═╡ 718b9504-f15f-41d1-b0ea-3346f17b4cca
md"""
## Load DICOMs
"""
# ╔═╡ a45f05b9-08d6-4277-9c9c-205f60b43391
volpath = "/Users/daleblack/Desktop/80.0";
# ╔═╡ 5e69faec-3f89-4f65-aab1-9429ad1e141b
volseg1 = "/Users/daleblack/Desktop/HEL_SLICER_SEG_0/S_1.20.nii";
# ╔═╡ 0399cca4-b251-451e-bf8c-96ccd207a1df
volseg2 = "/Users/daleblack/Desktop/HEL_SLICER_SEG_0/M_3.0.nii";
# ╔═╡ 20b7a9f9-4308-427f-995f-1f29f7f6df23
md"""
## Check Orientation
"""
# ╔═╡ e36c897d-07ae-4f61-8a5d-6dbdc651431a
vol = dcmdir_parse(volpath)
# ╔═╡ 8fe1a81f-be2d-4254-8029-aca2dc07a609
seg1 = niread(volseg1);
# ╔═╡ 6aea2d01-b4d5-442a-aafa-fec218cc4123
seg2 = niread(volseg2);
# ╔═╡ 644c1446-4206-4857-8aa3-ca87e0a6ff99
volraw = DICOMUtils.load_dcm_array(vol);
# ╔═╡ d729e367-61e4-4ade-b898-d66631443236
seg1raw = copy(seg1.raw);
# ╔═╡ f883ae8f-5761-471e-b80f-ec55a1fca07a
seg2raw = copy(seg2.raw);
# ╔═╡ 7d4ddb3d-0d5e-433c-a8f1-6ff77cdfd1c5
md"""
### Without Reorientation
"""
# ╔═╡ 5d6edfe9-9d47-4c8b-ac6b-ebc35e3b9915
@bind a Slider(1:size(volraw)[3], default=155, show_value=true)
# ╔═╡ b198dedc-0e5e-4c69-a755-387de3997d09
Plots.heatmap(volraw[:,:,a], c=:grays)
# ╔═╡ 4d49321a-7dbe-428b-91c4-c9561055ce0b
Plots.heatmap(seg1raw[:,:,a], c=:grays)
# ╔═╡ 55744a6c-a224-42cc-b590-bc7ec4614bc7
md"""
### Superimpose
"""
# ╔═╡ d4bc98e5-e92b-4b24-ad36-081b9c0d8507
md"""
## With Reorientation
"""
# ╔═╡ 670ec570-8a19-479d-a619-873c8fe9457a
volraw_ornt, affvol, new_affvol = DICOMUtils.orientation(volraw, (("L", "A", "S")));
# ╔═╡ 3bb89d52-da0e-42d2-9368-41c576b3ef11
seg1raw_ornt, aff1, new_aff1 = DICOMUtils.orientation(seg1raw, (("L", "A", "S")));
# ╔═╡ 16da0125-d6ad-45d8-ba30-431ff2fdc7ed
Plots.heatmap(volraw_ornt[:,:,a], c=:grays)
# ╔═╡ 8c49c4d7-8864-4a5d-9797-3e2bacb2f514
Plots.heatmap(seg1raw_ornt[:,:,a], c=:grays)
# ╔═╡ bb387fef-0034-467f-8219-232a5942c264
md"""
## Calcium Scoring
"""
# ╔═╡ 64599dbe-4f10-476b-921b-63872d7482d1
begin
ii1 = [1 2 0 1
1 5 0 1]
ii2 = [1 2 0 1
1 5 0 1]
img = cat(ii1, ii2, dims=3)
end
# ╔═╡ 228e1866-5d50-41a6-b5f3-cce2d49cdfee
spc = [0.625, 0.625, 0.5]
# ╔═╡ 1252701e-6812-49e4-9282-7c51dd92f8cc
begin
mm1 = [1 0 0 1
1 1 0 1]
mm2 = [1 0 0 1
1 1 0 1]
msk = cat(mm1, mm2, dims=3)
end
# ╔═╡ 288e776b-04ae-479f-8ae2-b3232037fe87
function compute_calcium_scores(image, spacing, mask, min_vol=nothing, max_vol=nothing)
binary_mask = mask
voxel_volume = spacing[1] * spacing[2] * spacing[3]
agatston_score = 0
calcium_volume = 0
# Find individual lesions (in 3D) so that we can discard too small or too large lesions
lesion_map = Images.label_components(binary_mask)
n_lesions = length(unique(lesion_map))
for lesion = 1:(n_lesions - 1)
lesion_mask = map(x -> x == lesion, lesion_map)
# Ignore too small or too large lesions
lesion_volume = count(lesion_mask) * voxel_volume
if ((min_vol != nothing) && (lesion_volume < min_vol))
continue
end
if ((max_vol != nothing) && (lesion_volume > max_vol))
continue
end
calcium_volume += lesion_volume
# Calculate Agatston score for this lesion
slices = sort(unique(lesion_mask)) .+ 1
for z = 1:slices[end]
fragment_mask = lesion_mask[z,:,:]
n_pixels = count(fragment_mask)
maximum_intensity = maximum(image[z,:,:][fragment_mask])
if maximum_intensity < 200
coefficient = 1
elseif maximum_intensity < 300
coefficient = 2
elseif maximum_intensity < 400
coefficient = 3
else
coefficient = 4
end
agatston_score += coefficient * n_pixels
end
end
agatston_score *= spacing[1] / 3.0 * spacing[2] * spacing[3]
return agatston_score, calcium_volume
end
# ╔═╡ ea475810-4853-4f6f-97bd-d3faf1b646d6
compute_calcium_scores(img, spc, msk)
# ╔═╡ 165084b0-4ebe-4c28-bdae-24278d7497ae
compute_calcium_scores(img, spc, msk, 0)
# ╔═╡ Cell order:
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# ╠═e36c897d-07ae-4f61-8a5d-6dbdc651431a
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# ╠═4d49321a-7dbe-428b-91c4-c9561055ce0b
# ╟─55744a6c-a224-42cc-b590-bc7ec4614bc7
# ╟─d4bc98e5-e92b-4b24-ad36-081b9c0d8507
# ╠═670ec570-8a19-479d-a619-873c8fe9457a
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# ╠═288e776b-04ae-479f-8ae2-b3232037fe87
# ╠═ea475810-4853-4f6f-97bd-d3faf1b646d6
# ╠═165084b0-4ebe-4c28-bdae-24278d7497ae
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] | 1.758145 | 3,407 |
using
Test,
AutocorrelationShell,
Wavelets,
LinearAlgebra,
Plots
@testset "Transforms" begin include("testsets/transforms.jl") end
@testset "Visualizations" begin include("testsets/visualizations.jl") end
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1,
2221,
2291,
7203,
41989,
1039,
14,
7645,
23914,
13,
20362,
4943,
886,
198,
31,
9288,
2617,
366,
36259,
4582,
1,
2221,
2291,
7203,
41989,
1039,
14,
41464,
4582,
13,
20362,
4943,
886,
628
] | 2.75 | 84 |
using Random
using naumer_Dimensionality_2022
using CSV
rng = MersenneTwister(4321)
const max_samples = 10000
dataset1 = samplePSD(rng,max_samples,1,1)
dataset2 = samplePSD(rng,max_samples,2,1)
dataset3 = samplePSD(rng,max_samples,3,1)
function minimumDistance(sample, dataset)
return minimum(sqrt.(sum(abs2, dataset .- sample,dims=(1,2))))
end
function EstimateMinDist(rng, dataset, N, iterations)
total = 0
for i in 1:iterations
mini_dataset = dataset[:,:,randperm(max_samples)[1:(N+1)]]
chosen_index = rand(1:(N+1))
chosen_sample = mini_dataset[:,:,chosen_index]
mini_dataset = cat(mini_dataset[:,:,1:chosen_index-1],mini_dataset[:,:,chosen_index+1:end]; dims=3)
dist = minimumDistance(chosen_sample, mini_dataset)
total += dist
end
return total/iterations
end
dist1 = zeros(500)
dist2 = zeros(500)
dist3 = zeros(500)
Threads.@threads for i in 1:500
dist1[i] = EstimateMinDist(rng, dataset1, i, 5000)
dist2[i] = EstimateMinDist(rng, dataset2, i, 5000)
dist3[i] = EstimateMinDist(rng, dataset3, i, 5000)
end
CSV.write("SamplingData.csv",(dist1 = dist1, dist2 = dist2, dist3 = dist3))
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] | 2.27907 | 516 |
#!/usr/local/bin/julia
# author: zhwang
# date: 2022-03-01
# description: 检查待提交文件中的信息，函数名等，并生成函数信息文件 --- 已有函数信息 ---
function get_info(body::AbstractString)
## 获取代码内容
code_reg = r"## @lc code=start\n([\s\S]*)\n## @lc code=end"
code = match(code_reg, body)
isnothing(code) && throw("未识别到代码内容")
code = code.captures[1]
## 获取函数内容
func_reg1 = r"^function (\w+!?\([\w:{}, ]*\))"
func_reg2 = r"^(\w+!?\([\w:{}, ]*\))[\w: ]*="
funcs = String[]
for line in split(code, '\n')
(func = match(func_reg1, line)) ≢ nothing && push!(funcs, func.captures[1])
(func = match(func_reg2, line)) ≢ nothing && push!(funcs, func.captures[1])
end
## 获取信息部分
author = match(r"# author: ([\w ]+)", body).captures[1]
date = match(r"# date: ([\w-]+)", body).captures[1]
any(isempty, [date, author]) && throw("未检索到信息！")
(code, unique!(funcs)), (author, date)
end
### 检查和准备阶段 ###
cd(Base.source_dir()[1:end-5])
current_files = readdir()
!all(file in current_files for file in ["src", "tools"]) && throw("Script file misplacing: could not find source path `src/`")
problem_path = "submit/problems/"
test_path = "submit/test/"
problems, tests = readdir.([problem_path, test_path])
isempty(problems) && throw("问题集为空，请检查！")
problems != tests && throw("问题集和测试集不匹配，请检查")
sort!(problems; by=i -> parse(Int, split(i, '.')[1]))
num = length(problems)
### 获取信息 ###
codes, authors, dates, funcs = String[], String[], String[], Vector{String}[]
for file in problems
# println("正在处理文件 $file")
content = read(open(problem_path * file, "r"), String)
(code, func), (author, date) = get_info(content)
isempty(func) && throw("未识别到函数：$file")
push!.([authors, dates, codes], [author, date, code])
append!(funcs, [func])
end
println("识别完毕，一共 $num 个文件")
### 检查信息 ###
a = findfirst(i->authors[i]!="zhwang", 1:num)
!isnothing(a) && throw("作者信息错误：$(file[a])")
a = findfirst(i->!occursin(r"2022-\d\d-\d\d", dates[i]), 1:num)
!isnothing(a) && throw("日期未修改：$(file[a])")
println("日期和作者名称检查完毕")
### 检查函数信息 ###
# 1. 检查函数派发是否重复信息
# 2. 输出函数名
func_name(func::String) = replace(func, r"(?<name>\w*!?)\(.*\)" => s"\g<name>")
func_method(func::String) = replace(func, r"(?<sym>[(, ]\w+)::[\w{}]+" => s"\g<sym>")
output = "submit/local_functions.md"
io = open(output, "w")
println(io, "# 基本信息\n## 新提交的函数名\n")
println(io, "| 问题编号 | 函数名 |\n| :---: | :---: |")
for (i, file) in enumerate(problems)
ind = split(file, '.')[1]
func = unique(func_name.(funcs[i]))
for f in func
println(io, "| $ind | `$f` |")
end
end
println(io, "\n## 函数方法\n")
println(io, "| 问题编号 | 函数方法 |\n| :---: | :---: |")
for (i, file) in enumerate(problems)
ind = split(file, '.')[1]
func = unique(func_method.(funcs[i]))
for f in func
println(io, "| $ind | `$f` |")
end
end
close(io)
println("写入完毕，文件保存在 $output 中")
# ## 获取全局信息
# println("检查主仓库的函数信息")
# main_problem_path = "../LeetCode.jl/src/problems/"
# main_problems = readdir(main_problem_path)
# isempty(main_problems) && throw("未能读取到主仓库文件")
# deleteat!(main_problems, length(main_problems))
# sort!(main_problems; by=i -> parse(Int, split(i, '.')[1]))
# total_num = length(main_problems)
# main_funcs = Vector{String}[]
# fails = String[]
# for file in main_problems
# # println("正在处理文件 $file")
# content = read(open(main_problem_path * file, "r"), String)
# (_, func), _ = get_info(content)
# if isempty(func)
# println("未识别到函数：$file")
# push!(fails, file)
# else
# append!(main_funcs, [func])
# end
# end
# println("识别完毕，$(total_num - length(fails)) 个文件成功识别，$(length(fails)) 个文件识别失败")
# for i in fails
# print(stdout, i)
# end
# for (i, file) in enumerate(main_problems)
# file in fails && deleteat!(main_problems, i)
# end
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] | 1.765552 | 2,154 |
function construct_device!(ps_m::CanonicalModel,
device_model::DeviceModel,
system_formulation::Type{S},
system::PSY.System,
time_range::UnitRange{Int64};
kwargs...) where {S <: PM.AbstractPowerFormulation}
construct_device!(ps_m,
device_model.device,
device_model.formulation,
system_formulation,
system,
time_range;
kwargs...)
return
end
function InitialCondition(ps_m::CanonicalModel,
device::PSY.Device,
value::Float64)
return InitialCondition(device,
PJ.add_parameter(ps_m.JuMPmodel, value))
end | [
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7378,
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11,
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198,
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] | 1.662857 | 525 |
#src Generate README.md by running `using Literate; Literate.markdown("docs/src/README.jl", "."; documenter=false, credit=false, execute=true)`
# <img align="right" width="180px" src="https://avatars.githubusercontent.com/u/58918144?s=200&v=4">
# <!-- DO NOT EDIT README.md directly, instead edit docs/README.jl and generate the markdown-->
# # Probabilistic<wbr>Circuits<wbr>.jl
# [](https://github.com/Juice-jl/ProbabilisticCircuits.jl/actions?query=workflow%3A%22Unit+Tests%22+branch%3Amaster) [](https://codecov.io/gh/Juice-jl/ProbabilisticCircuits.jl) [](https://juice-jl.github.io/ProbabilisticCircuits.jl/stable) [](https://juice-jl.github.io/ProbabilisticCircuits.jl/dev)
# This package provides functionalities for learning/constructing probabilistic circuits and using them to compute various probabilistic queries. It is part of the [Juice package](https://github.com/Juice-jl) (Julia Circuit Empanada).
# ## Example usage
include("usage.jl")
# ## Testing
# To make sure everything is working correctly, you can run our test suite as follows. The first time you run the tests will trigger a few slow downloads of various test resources.
# ```bash
# julia --color=yes -e 'using Pkg; Pkg.test("ProbabilisticCircuits")'
# ```
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] | 2.857934 | 542 |
### A Pluto.jl notebook ###
# v0.17.1
using Markdown
using InteractiveUtils
# ╔═╡ 7bd669c8-443f-11ec-045d-bd3b1c74db9b
begin
import Pkg
# activate the shared project environment
Pkg.activate(Base.current_project())
using Omega, Distributions, UnicodePlots
end
# ╔═╡ a5d02f2a-3310-4f7c-a20f-af43332e2306
md"# Bell's Inequality"
# ╔═╡ 88dda8ef-d7c5-44d4-8e6b-13e20dcd2f03
md"### Quantum Model
First let's set up a quantum model"
# ╔═╡ 112d8745-d12e-4e0e-8af5-d069014db356
md"### Hidden Variable Model
In the hidden variable model, both particles contain hidden state that determines their spin when measures."
# ╔═╡ 03441d7c-3421-42eb-bdc4-474d2ab1c70d
# ╔═╡ Cell order:
# ╠═7bd669c8-443f-11ec-045d-bd3b1c74db9b
# ╠═a5d02f2a-3310-4f7c-a20f-af43332e2306
# ╠═88dda8ef-d7c5-44d4-8e6b-13e20dcd2f03
# ╠═112d8745-d12e-4e0e-8af5-d069014db356
# ╠═03441d7c-3421-42eb-bdc4-474d2ab1c70d
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# NOTE: Luxor is using JuliaGraphics/Luxor.jl/pull/48
using Luxor, BitBasis
"""
btable(n, center=O; len=ndigits(n), width=20, title=false)
Draw a table of binary digits with `n` which can be [`BitStr`](@ref) or an `Integer`.
"""
function btable(n::Integer, center=O; len=ndigits(n; base=2), width=20, title=false)
sethue("black")
if title
t = Table(2, len, width, width, center)
else
t = Table(1, len, width, width, center)
end
bstr = string(n, base=2, pad=len)
fontsize(width ÷ 2)
# start plotting
col = 1
if title
for k in 1:size(t, 2)
text(string(len-k+1), t[1, k], halign=:center, valign=:middle)
end
col += 1
end
for j in 1:size(t, 2)
box(t[col, j], width, width, :stroke)
text(string(bstr[j]), t[col, j], halign=:center, valign=:middle)
end
return t
end
function btable(f::Function, n::Integer, center=O;
len=ndigits(n; base=2), width=20, title=false)
gsave()
# before
before = btable(n, O - (0, 50); width=50, title=true)
sethue("blue")
arrow(Point(0, 10), Point(0, 50); linewidth=2)
# after
after = btable(f(n), O+(0, 80); width=50, title=false)
grestore()
return (before, after)
end
function btable(f::Function, n::BitStr, center=O; width=50, title=false)
gsave()
# before
before = btable(n, O - (0, 50); width=50, title=true)
sethue("blue")
arrow(Point(0, 10), Point(0, 50); linewidth=2)
# after
after = btable(f(n), O+(0, 80); width=50, title=false)
grestore()
return (before, after)
end
btable(n::BitStr, center=O; width=50, title=false) =
btable(n.val, center; len=length(n), width=width, title=title)
# TODO: push this to upstream: Luxor
function swaparrow(left::Point, right::Point, action=:fill;
linewidth=1.0, headlength=10, headangle=pi/8, target_angle=pi/3,
fraction=1/5, debug=false)
gsave()
v = right - left
delta = fraction * v.x
C1 = Point(left.x + delta, left.y - delta * tan(target_angle))
C2 = Point(right.x - delta, right.y - delta * tan(target_angle))
arrow(left, C1, C2, right, action;
linewidth=linewidth, rightarrow=true, leftarrow=true)
if debug
line(left, C1, :stroke)
line(right, C2, :stroke)
end
grestore()
end
@png begin
t, _ = btable(breflect, bit"1011"; width=50, title=true)
lineheight = 20
sethue("red")
swaparrow(t[1, 1] - (0, lineheight), t[1, 4] - (0, lineheight), :stroke; linewidth=2, fraction=0.25, target_angle=deg2rad(60))
swaparrow(t[1, 2] - (0, lineheight), t[1, 3] - (0, lineheight), :stroke; linewidth=2, target_angle=deg2rad(80))
end 400 400 "docs/src/assets/breflect"
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] | 2.290987 | 1,165 |
typealias MPIDatatype Union{Char,
Int8, UInt8, Int16, UInt16, Int32, UInt32, Int64,
UInt64,
Float32, Float64, Complex64, Complex128}
typealias MPIBuffertype{T} Union{Ptr{T}, Array{T}, Ref{T}}
if VERSION >= v"0.5.0-"
# TODO: Use Compat for this
fieldoffsets{T}(::Type{T}) = Int[fieldoffset(T, i) for i in 1:nfields(T)]
end
# Define a function mpitype(T) that returns the MPI datatype code for
# a given type T. In the case the the type does not exist, it is created and
# then returned. The dictonary is defined in __init__ so the module can be
# precompiled
# accessor and creation function for getting MPI datatypes
function mpitype{T}(::Type{T})
if haskey(mpitype_dict, T) # if the datatype already exists
return mpitype_dict[T]
end
if !isbits(T)
throw(ArgumentError("Type must be isbits()"))
end
# get the data from the type
fieldtypes = T.types
offsets = fieldoffsets(T)
nfields = Cint(length(fieldtypes))
# put data in MPI format
blocklengths = ones(Cint, nfields)
displacements = zeros(Cptrdiff_t, nfields) # size_t == MPI_Aint ?
types = zeros(Cint, nfields)
for i=1:nfields
displacements[i] = offsets[i]
# create an MPI_Datatype for the current field if it does not exist yet
types[i] = mpitype(fieldtypes[i])
end
# create the datatype
newtype_ref = Ref{Cint}()
flag = Ref{Cint}()
ccall(MPI_TYPE_CREATE_STRUCT, Void, (Ptr{Cint}, Ptr{Cint}, Ptr{Cptrdiff_t},
Ptr{Cint}, Ptr{Cint}, Ptr{Cint}), &nfields, blocklengths,
displacements, types, newtype_ref, flag)
if flag[] != 0
throw(ErrorException("MPI_Type_create_struct returned non-zero exit status"))
end
# commit the datatatype
flag2 = Ref{Cint}()
ccall(MPI_TYPE_COMMIT, Void, (Ptr{Cint}, Ptr{Cint}), newtype_ref, flag2)
if flag2[] != 0
throw(ErrorException("MPI_Type_commit returned non-zero exit status"))
end
# add it to the dictonary of known types
mpitype_dict[T] = newtype_ref[]
mpitype_dict_inverse[newtype_ref[]] = T
return mpitype_dict[T]
end
type Comm
val::Cint
Comm(val::Integer) = new(val)
end
const COMM_NULL = Comm(MPI_COMM_NULL)
const COMM_SELF = Comm(MPI_COMM_SELF)
const COMM_WORLD = Comm(MPI_COMM_WORLD)
type Op
val::Cint
end
const OP_NULL = Op(MPI_OP_NULL)
const BAND = Op(MPI_BAND)
const BOR = Op(MPI_BOR)
const BXOR = Op(MPI_BXOR)
const LAND = Op(MPI_LAND)
const LOR = Op(MPI_LOR)
const LXOR = Op(MPI_LXOR)
const MAX = Op(MPI_MAX)
const MIN = Op(MPI_MIN)
const PROD = Op(MPI_PROD)
const SUM = Op(MPI_SUM)
type Request
val::Cint
buffer
end
const REQUEST_NULL = Request(MPI_REQUEST_NULL, nothing)
type Status
val::Array{Cint,1}
Status() = new(Array(Cint, MPI_STATUS_SIZE))
end
Get_error(stat::Status) = Int(stat.val[MPI_ERROR])
Get_source(stat::Status) = Int(stat.val[MPI_SOURCE])
Get_tag(stat::Status) = Int(stat.val[MPI_TAG])
const ANY_SOURCE = Int(MPI_ANY_SOURCE)
const ANY_TAG = Int(MPI_ANY_TAG)
const TAG_UB = Int(MPI_TAG_UB)
const UNDEFINED = Int(MPI_UNDEFINED)
function serialize(x)
s = IOBuffer()
Base.serialize(s, x)
Base.takebuf_array(s)
end
function deserialize(x)
s = IOBuffer(x)
Base.deserialize(s)
end
# Administrative functions
function Init()
ccall(MPI_INIT, Void, (Ptr{Cint},), &0)
end
function Finalize()
ccall(MPI_FINALIZE, Void, (Ptr{Cint},), &0)
end
function Abort(comm::Comm, errcode::Integer)
ccall(MPI_ABORT, Void, (Ptr{Cint}, Ptr{Cint}, Ptr{Cint}),
&comm.val, &errcode, &0)
end
function Initialized()
flag = Ref{Cint}()
ccall(MPI_INITIALIZED, Void, (Ptr{Cint}, Ptr{Cint}), flag, &0)
flag[] != 0
end
function Finalized()
flag = Ref{Cint}()
ccall(MPI_FINALIZED, Void, (Ptr{Cint}, Ptr{Cint}), flag, &0)
flag[] != 0
end
function Comm_dup(comm::Comm)
newcomm = Ref{Cint}()
ccall(MPI_COMM_DUP, Void, (Ptr{Cint}, Ptr{Cint}, Ptr{Cint}),
&comm.val, newcomm, &0)
MPI.Comm(newcomm[])
end
function Comm_free(comm::Comm)
ccall(MPI_COMM_FREE, Void, (Ptr{Cint}, Ptr{Cint}), &comm.val, &0)
end
function Comm_rank(comm::Comm)
rank = Ref{Cint}()
ccall(MPI_COMM_RANK, Void, (Ptr{Cint}, Ptr{Cint}, Ptr{Cint}),
&comm.val, rank, &0)
Int(rank[])
end
function Comm_size(comm::Comm)
size = Ref{Cint}()
ccall(MPI_COMM_SIZE, Void, (Ptr{Cint}, Ptr{Cint}, Ptr{Cint}),
&comm.val, size, &0)
Int(size[])
end
function type_create(T::DataType)
if !isbits(T)
throw(ArgumentError("Type must be isbits()"))
end
if haskey(mpitype_dict, T) # if the datatype already exists
return nothing
end
# get the data from the type
fieldtypes = T.types
nfields = Cint(length(fieldtypes))
if VERSION < v"0.5.0-dev+2285"
offsets = fieldoffsets(T)
else
offsets = map(idx->fieldoffset(T, idx), 1:nfields)
end
# put data in MPI format
blocklengths = ones(Cint, nfields)
displacements = zeros(Cptrdiff_t, nfields) # size_t == MPI_Aint ?
types = zeros(Cint, nfields)
for i=1:nfields
displacements[i] = offsets[i]
# create an MPI_Datatype for the current field if it does not exist yet
if !haskey(mpitype_dict, fieldtypes[i])
type_create(fieldtypes[i])
end
types[i] = mpitype(fieldtypes[i])
end
# create the datatype
newtype_ref = Ref{Cint}()
flag = Ref{Cint}()
ccall(MPI_TYPE_CREATE_STRUCT, Void, (Ptr{Cint}, Ptr{Cint}, Ptr{Cptrdiff_t},
Ptr{Cint}, Ptr{Cint}, Ptr{Cint}), &nfields, blocklengths, displacements,
types, newtype_ref, flag)
if flag[] != 0
throw(ErrorException("MPI_Type_create_struct returned non-zero exit status"))
end
# commit the datatatype
flag2 = Ref{Cint}()
ccall(MPI_TYPE_COMMIT, Void, (Ptr{Cint}, Ptr{Cint}), newtype_ref, flag2)
if flag2[] != 0
throw(ErrorException("MPI_Type_commit returned non-zero exit status"))
end
# add it to the dictonary of known types
mpitype_dict[T] = newtype_ref[]
return nothing
end
# Point-to-point communication
function Probe(src::Integer, tag::Integer, comm::Comm)
stat = Status()
ccall(MPI_PROBE, Void,
(Ptr{Cint}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}),
&src, &tag, &comm.val, stat.val, &0)
stat
end
function Iprobe(src::Integer, tag::Integer, comm::Comm)
flag = Ref{Cint}()
stat = Status()
ccall(MPI_IPROBE, Void,
(Ptr{Cint}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}),
&src, &tag, &comm.val, flag, stat.val, &0)
if flag[] == 0
return false, nothing
end
true, stat
end
function Get_count{T}(stat::Status, ::Type{T})
count = Ref{Cint}()
ccall(MPI_GET_COUNT, Void, (Ptr{Cint}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}),
stat.val, &mpitype(T), count, &0)
Int(count[])
end
function Send{T}(buf::MPIBuffertype{T}, count::Integer,
dest::Integer, tag::Integer, comm::Comm)
ccall(MPI_SEND, Void,
(Ptr{T}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint},
Ptr{Cint}),
buf, &count, &mpitype(T), &dest, &tag, &comm.val, &0)
end
function Send{T}(buf::Array{T}, dest::Integer, tag::Integer,
comm::Comm)
Send(buf, length(buf), dest, tag, comm)
end
function Send{T}(obj::T, dest::Integer, tag::Integer, comm::Comm)
buf = [obj]
Send(buf, dest, tag, comm)
end
function send(obj, dest::Integer, tag::Integer, comm::Comm)
buf = MPI.serialize(obj)
Send(buf, dest, tag, comm)
end
function Isend{T}(buf::MPIBuffertype{T}, count::Integer,
dest::Integer, tag::Integer, comm::Comm)
rval = Ref{Cint}()
ccall(MPI_ISEND, Void,
(Ptr{T}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint},
Ptr{Cint}, Ptr{Cint}),
buf, &count, &mpitype(T), &dest, &tag, &comm.val, rval, &0)
Request(rval[], buf)
end
function Isend{T}(buf::Array{T}, dest::Integer, tag::Integer,
comm::Comm)
Isend(buf, length(buf), dest, tag, comm)
end
function Isend{T}(obj::T, dest::Integer, tag::Integer, comm::Comm)
buf = [obj]
Isend(buf, dest, tag, comm)
end
function isend(obj, dest::Integer, tag::Integer, comm::Comm)
buf = MPI.serialize(obj)
Isend(buf, dest, tag, comm)
end
function Recv!{T}(buf::MPIBuffertype{T}, count::Integer,
src::Integer, tag::Integer, comm::Comm)
stat = Status()
ccall(MPI_RECV, Void,
(Ptr{T}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint},
Ptr{Cint}, Ptr{Cint}),
buf, &count, &mpitype(T), &src, &tag, &comm.val, stat.val, &0)
stat
end
function Recv!{T}(buf::Array{T}, src::Integer, tag::Integer,
comm::Comm)
Recv!(buf, length(buf), src, tag, comm)
end
function Recv{T}(::Type{T}, src::Integer, tag::Integer, comm::Comm)
buf = Ref{T}()
stat = Recv!(buf, 1, src, tag, comm)
(buf[], stat)
end
function recv(src::Integer, tag::Integer, comm::Comm)
stat = Probe(src, tag, comm)
count = Get_count(stat, UInt8)
buf = Array(UInt8, count)
stat = Recv!(buf, Get_source(stat), Get_tag(stat), comm)
(MPI.deserialize(buf), stat)
end
function Irecv!{T}(buf::MPIBuffertype{T}, count::Integer,
src::Integer, tag::Integer, comm::Comm)
val = Ref{Cint}()
ccall(MPI_IRECV, Void,
(Ptr{T}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint},
Ptr{Cint}, Ptr{Cint}),
buf, &count, &mpitype(T), &src, &tag, &comm.val, val, &0)
Request(val[], buf)
end
function Irecv!{T}(buf::Array{T}, src::Integer, tag::Integer,
comm::Comm)
Irecv!(buf, length(buf), src, tag, comm)
end
function irecv(src::Integer, tag::Integer, comm::Comm)
(flag, stat) = Iprobe(src, tag, comm)
if !flag
return (false, nothing, nothing)
end
count = Get_count(stat, UInt8)
buf = Array(UInt8, count)
stat = Recv!(buf, Get_source(stat), Get_tag(stat), comm)
(true, MPI.deserialize(buf), stat)
end
function Wait!(req::Request)
stat = Status()
ccall(MPI_WAIT, Void, (Ptr{Cint}, Ptr{Cint}, Ptr{Cint}),
&req.val, stat.val, &0)
req.buffer = nothing
stat
end
function Test!(req::Request)
flag = Ref{Cint}()
stat = Status()
ccall(MPI_TEST, Void, (Ptr{Cint}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}),
&req.val, flag, stat.val, &0)
if flag[] == 0
return (false, nothing)
end
req.buffer = nothing
(true, stat)
end
function Waitall!(reqs::Array{Request,1})
count = length(reqs)
reqvals = [reqs[i].val for i in 1:count]
statvals = Array(Cint, MPI_STATUS_SIZE, count)
ccall(MPI_WAITALL, Void,
(Ptr{Cint}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}),
&count, reqvals, statvals, &0)
stats = Array(Status, count)
for i in 1:count
reqs[i].val = reqvals[i]
reqs[i].buffer = nothing
stats[i] = Status()
for v in 1:MPI_STATUS_SIZE
stats[i].val[v] = statvals[v,i]
end
end
stats
end
function Testall!(reqs::Array{Request,1})
count = length(reqs)
reqvals = [reqs[i].val for i in 1:count]
flag = Ref{Cint}()
statvals = Array(Cint, MPI_STATUS_SIZE, count)
ccall(MPI_TESTALL, Void,
(Ptr{Cint}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}),
&count, reqvals, flag, statvals, &0)
if flag[] == 0
return (false, nothing)
end
stats = Array(Status, count)
for i in 1:count
reqs[i].val = reqvals[i]
reqs[i].buffer = nothing
stats[i] = Status()
for v in 1:MPI_STATUS_SIZE
stats[i].val[v] = statvals[v,i]
end
end
(true, stats)
end
function Waitany!(reqs::Array{Request,1})
count = length(reqs)
reqvals = [reqs[i].val for i in 1:count]
ind = Ref{Cint}()
stat = Status()
ccall(MPI_WAITANY, Void,
(Ptr{Cint}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}),
&count, reqvals, ind, stat.val, &0)
index = Int(ind[])
reqs[index].val = reqvals[index]
reqs[index].buffer = nothing
(index, stat)
end
function Testany!(reqs::Array{Request,1})
count = length(reqs)
reqvals = [reqs[i].val for i in 1:count]
ind = Ref{Cint}()
flag = Ref{Cint}()
stat = Status()
ccall(MPI_TESTANY, Void,
(Ptr{Cint}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}),
&count, reqvals, ind, flag, stat.val, &0)
index = Int(ind[])
if flag[] == 0 || index == MPI_UNDEFINED
return (false, 0, nothing)
end
reqs[index].val = reqvals[index]
reqs[index].buffer = nothing
(true, index, stat)
end
function Waitsome!(reqs::Array{Request,1})
count = length(reqs)
reqvals = [reqs[i].val for i in 1:count]
outcnt = Ref{Cint}()
inds = Array(Cint, count)
statvals = Array(Cint, MPI_STATUS_SIZE, count)
ccall(MPI_WAITSOME, Void,
(Ptr{Cint}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}),
&count, reqvals, outcnt, inds, statvals, &0)
outcount = Int(outcnt[])
# This can happen if there were no valid requests
if outcount == UNDEFINED
outcount = 0
end
indices = Array(Int, outcount)
stats = Array(Status, outcount)
for i in 1:outcount
ind = Int(inds[i])
reqs[ind].val = reqvals[ind]
reqs[ind].buffer = nothing
indices[i] = inds[i]
stats[i] = Status()
for v in 1:MPI_STATUS_SIZE
stats[i].val[v] = statvals[v,i]
end
end
(indices, stats)
end
function Testsome!(reqs::Array{Request,1})
count = length(reqs)
reqvals = [reqs[i].val for i in 1:count]
outcnt = Ref{Cint}()
inds = Array(Cint, count)
statvals = Array(Cint, MPI_STATUS_SIZE, count)
ccall(MPI_TESTSOME, Void,
(Ptr{Cint}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}),
&count, reqvals, outcnt, inds, statvals, &0)
outcount = outcnt[]
# This can happen if there were no valid requests
if outcount == UNDEFINED
outcount = 0
end
indices = Array(Int, outcount)
stats = Array(Status, outcount)
for i in 1:outcount
ind = Int(inds[i])
reqs[ind].val = reqvals[ind]
reqs[ind].buffer = nothing
indices[i] = inds[i]
stats[i] = Status()
for v in 1:MPI_STATUS_SIZE
stats[i].val[v] = statvals[v,i]
end
end
(indices, stats)
end
function Cancel!(req::Request)
ccall(MPI_CANCEL, Void, (Ptr{Cint},Ptr{Cint}), &req.val, &0)
req.buffer = nothing
nothing
end
# Collective communication
function Barrier(comm::Comm)
ccall(MPI_BARRIER, Void, (Ptr{Cint},Ptr{Cint}), &comm.val, &0)
end
function Bcast!{T}(buffer::MPIBuffertype{T}, count::Integer,
root::Integer, comm::Comm)
ccall(MPI_BCAST, Void,
(Ptr{T}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}),
buffer, &count, &mpitype(T), &root, &comm.val, &0)
buffer
end
function Bcast!{T}(buffer::Array{T}, root::Integer, comm::Comm)
Bcast!(buffer, length(buffer), root, comm)
end
#=
function Bcast{T}(obj::T, root::Integer, comm::Comm)
buf = [T]
Bcast!(buf, root, comm)
buf[1]
end
=#
function bcast(obj, root::Integer, comm::Comm)
isroot = Comm_rank(comm) == root
count = Array(Cint, 1)
if isroot
buf = MPI.serialize(obj)
count[1] = length(buf)
end
Bcast!(count, root, comm)
if !isroot
buf = Array(UInt8, count[1])
end
Bcast!(buf, root, comm)
if !isroot
obj = MPI.deserialize(buf)
end
obj
end
function Reduce{T}(sendbuf::MPIBuffertype{T}, count::Integer,
op::Op, root::Integer, comm::Comm)
isroot = Comm_rank(comm) == root
recvbuf = Array(T, isroot ? count : 0)
ccall(MPI_REDUCE, Void,
(Ptr{T}, Ptr{T}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint},
Ptr{Cint}, Ptr{Cint}),
sendbuf, recvbuf, &count, &mpitype(T), &op.val, &root, &comm.val,
&0)
isroot ? recvbuf : nothing
end
function Reduce{T}(sendbuf::Array{T}, op::Union{Op,Function}, root::Integer, comm::Comm)
Reduce(sendbuf, length(sendbuf), op, root, comm)
end
function Reduce{T}(object::T, op::Union{Op,Function}, root::Integer, comm::Comm)
isroot = Comm_rank(comm) == root
sendbuf = T[object]
recvbuf = Reduce(sendbuf, op, root, comm)
isroot ? recvbuf[1] : nothing
end
function Allreduce!{T}(sendbuf::MPIBuffertype{T}, recvbuf::MPIBuffertype{T},
count::Integer, op::Op, comm::Comm)
ccall(MPI_ALLREDUCE, Void, (Ptr{T}, Ptr{T}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint},
Ptr{Cint}, Ptr{Cint}), sendbuf, recvbuf, &count, &mpitype(T),
&op.val, &comm.val, &0)
recvbuf
end
function Allreduce!{T}(sendbuf::MPIBuffertype{T}, recvbuf::MPIBuffertype{T},
op::Union{Op,Function}, comm::Comm)
Allreduce!(sendbuf, recvbuf, length(recvbuf), op, comm)
end
function Allreduce{T}(obj::T, op::Union{Op,Function}, comm::Comm)
objref = Ref(obj)
outref = Ref{T}()
Allreduce!(objref, outref, 1, op, comm)
outref[]
end
# allocate receive buffer automatically
function allreduce{T}(sendbuf::MPIBuffertype{T}, op::Union{Op,Function}, comm::Comm)
recvbuf = similar(sendbuf)
Allreduce!(sendbuf, recvbuf, length(recvbuf), op, comm)
end
include("mpi-op.jl")
function Scatter{T}(sendbuf::MPIBuffertype{T},count::Integer, root::Integer,
comm::Comm)
recvbuf = Array(T, count)
ccall(MPI_SCATTER, Void,
(Ptr{T}, Ptr{Cint}, Ptr{Cint}, Ptr{T}, Ptr{Cint}, Ptr{Cint},
Ptr{Cint}, Ptr{Cint}, Ptr{Cint}), sendbuf, &count, &mpitype(T),
recvbuf, &count, &mpitype(T), &root, &comm.val, &0)
recvbuf
end
function Scatterv{T}(sendbuf::MPIBuffertype{T},
counts::Vector{Cint}, root::Integer,
comm::Comm)
recvbuf = Array(T, counts[Comm_rank(comm) + 1])
recvcnt = counts[Comm_rank(comm) + 1]
disps = cumsum(counts) - counts
ccall(MPI_SCATTERV, Void,
(Ptr{T}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}, Ptr{T}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}),
sendbuf, counts, disps, &mpitype(T), recvbuf, &recvcnt, &mpitype(T), &root, &comm.val, &0)
recvbuf
end
function Gather{T}(sendbuf::MPIBuffertype{T}, count::Integer,
root::Integer, comm::Comm)
isroot = Comm_rank(comm) == root
recvbuf = Array(T, isroot ? Comm_size(comm) * count : 0)
ccall(MPI_GATHER, Void,
(Ptr{T}, Ptr{Cint}, Ptr{Cint}, Ptr{T}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}),
sendbuf, &count, &mpitype(T), recvbuf, &count, &mpitype(T), &root, &comm.val, &0)
isroot ? recvbuf : nothing
end
function Gather{T}(sendbuf::Array{T}, root::Integer, comm::Comm)
Gather(sendbuf, length(sendbuf), root, comm)
end
function Gather{T}(object::T, root::Integer, comm::Comm)
isroot = Comm_rank(comm) == root
sendbuf = T[object]
recvbuf = Gather(sendbuf, root, comm)
isroot ? recvbuf : nothing
end
function Allgather{T}(sendbuf::MPIBuffertype{T}, count::Integer,
comm::Comm)
recvbuf = Array(T, Comm_size(comm) * count)
ccall(MPI_ALLGATHER, Void,
(Ptr{T}, Ptr{Cint}, Ptr{Cint}, Ptr{T}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}),
sendbuf, &count, &mpitype(T), recvbuf, &count, &mpitype(T), &comm.val, &0)
recvbuf
end
function Allgather{T}(sendbuf::Array{T}, comm::Comm)
Allgather(sendbuf, length(sendbuf), comm)
end
function Allgather{T}(object::T, comm::Comm)
sendbuf = T[object]
recvbuf = Allgather(sendbuf, comm)
recvbuf
end
function Gatherv{T}(sendbuf::MPIBuffertype{T}, counts::Vector{Cint},
root::Integer, comm::Comm)
isroot = Comm_rank(comm) == root
displs = cumsum(counts) - counts
sendcnt = counts[Comm_rank(comm) + 1]
recvbuf = Array(T, isroot ? sum(counts) : 0)
ccall(MPI_GATHERV, Void,
(Ptr{T}, Ptr{Cint}, Ptr{Cint}, Ptr{T}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}),
sendbuf, &sendcnt, &mpitype(T), recvbuf, counts, displs, &mpitype(T), &root, &comm.val, &0)
isroot ? recvbuf : nothing
end
function Allgatherv{T}(sendbuf::MPIBuffertype{T}, counts::Vector{Cint},
comm::Comm)
displs = cumsum(counts) - counts
sendcnt = counts[Comm_rank(comm) + 1]
recvbuf = Array(T, sum(counts))
ccall(MPI_ALLGATHERV, Void,
(Ptr{T}, Ptr{Cint}, Ptr{Cint}, Ptr{T}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}),
sendbuf, &sendcnt, &mpitype(T), recvbuf, counts, displs, &mpitype(T), &comm.val, &0)
recvbuf
end
function Alltoall{T}(sendbuf::MPIBuffertype{T}, count::Integer,
comm::Comm)
recvbuf = Array(T, Comm_size(comm)*count)
ccall(MPI_ALLTOALL, Void,
(Ptr{T}, Ptr{Cint}, Ptr{Cint}, Ptr{T}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}),
sendbuf, &count, &mpitype(T), recvbuf, &count, &mpitype(T), &comm.val, &0)
recvbuf
end
function Alltoallv{T}(sendbuf::MPIBuffertype{T}, scounts::Vector{Cint},
rcounts::Vector{Cint}, comm::Comm)
recvbuf = Array(T, sum(rcounts))
sdispls = cumsum(scounts) - scounts
rdispls = cumsum(rcounts) - rcounts
ccall(MPI_ALLTOALLV, Void,
(Ptr{T}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}, Ptr{T}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}),
sendbuf, scounts, sdispls, &mpitype(T), recvbuf, rcounts, rdispls, &mpitype(T), &comm.val, &0)
recvbuf
end
function Scan{T}(sendbuf::MPIBuffertype{T}, count::Integer,
op::Op, comm::Comm)
recvbuf = Array(T, count)
ccall(MPI_SCAN, Void,
(Ptr{T}, Ptr{T}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}),
sendbuf, recvbuf, &count, &mpitype(T), &op.val, &comm.val, &0)
recvbuf
end
function Scan{T}(object::T, op::Op, comm::Comm)
sendbuf = T[object]
Scan(sendbuf,1,op,comm)
end
function Exscan{T}(sendbuf::MPIBuffertype{T}, count::Integer,
op::Op, comm::Comm)
recvbuf = Array(T, count)
ccall(MPI_EXSCAN, Void,
(Ptr{T}, Ptr{T}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}, Ptr{Cint}),
sendbuf, recvbuf, &count, &mpitype(T), &op.val, &comm.val, &0)
recvbuf
end
function Exscan{T}(object::T, op::Op, comm::Comm)
sendbuf = T[object]
Exscan(sendbuf,1,op,comm)
end
# Conversion between C and Fortran Comm handles:
if HAVE_MPI_COMM_C2F
# use MPI_Comm_f2c and MPI_Comm_c2f
Base.convert(::Type{CComm}, comm::Comm) =
ccall(:MPI_Comm_f2c, CComm, (Cint,), comm.val)
Base.convert(::Type{Comm}, ccomm::CComm) =
Comm(ccall(:MPI_Comm_c2f, Cint, (CComm,), ccomm))
elseif sizeof(CComm) == sizeof(Cint)
# in MPICH, both C and Fortran use identical Cint comm handles
# and MPI_Comm_c2f is not provided.
Base.convert(::Type{CComm}, comm::Comm) = reinterpret(CComm, comm.val)
Base.convert(::Type{Comm}, ccomm::CComm) = Comm(reinterpret(Cint, ccomm))
else
warn("No MPI_Comm_c2f found - conversion to/from MPI.CComm will not work")
end
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] | 2.065334 | 11,403 |
using Bolt
using BenchmarkTools
par = CosmoParams()
bg = Background(par)
ih = IonizationHistory(Peebles(), par, bg)
k_grid = quadratic_k(0.1bg.H₀, 1000bg.H₀, 100)
k_i = 10
hierarchy = Hierarchy(BasicNewtonian(), par, bg, ih, k_grid[k_i])
xᵢ = log(1e-8)
u₀ = Bolt.initial_conditions(xᵢ, hierarchy)
udummy = deepcopy(u₀)
@btime Bolt.hierarchy!(udummy, u₀, hierarchy, xᵢ)
##
@time perturb = boltsolve(hierarchy);
##
@time sf = source_grid(par, bg, ih, k_grid, BasicNewtonian());
##
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] | 2.155556 | 225 |
# Load Julia packages (libraries) needed
using StatisticalRethinking
# ### snippet 2.1
ways = [0, 3, 8, 9, 0];
#-
ways/sum(ways)
# ### snippet 2.2
# Create a distribution with n = 9 (e.g. tosses) and p = 0.5.
d = Binomial(9, 0.5)
# Probability density for 6 `waters` holding n = 9 and p = 0.5.
pdf(d, 6)
# End of `02/clip-01-02.jl`
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# Contains all helper functions for Tmatrix module
using LinearAlgebra
"""
Calculate dot product for two vectors.
# I made this function because the "dot" function in LinearAlgebra package doesn't work as expected for complex vectors.
Parameters
A, B : two vectors
# Returns
scalar dot product
"""
function vector_dot_product(A, B)
return sum(A .* B)
end
"""
Create a meshgrid, to be used with numerical integrals.
The meshgrid created is following this rule:
x_grid[dim1,dim2]
dim1 corresponds to x index
dim2 corresponds to y index
"""
function meshgrid(x, y)
x_grid = repeat(x, 1, length(y))
y_grid = repeat(y', length(x), 1)
return x_grid, y_grid
end
"""
Create meshgrid of θ and ϕ
"""
function meshgrid_θ_ϕ(
n_θ_points,
n_ϕ_points;
min_θ = 1e-16,
min_ϕ = 1e-16,
rotationally_symmetric = false,
)
#TODO can avoid min values w full algebra
θ_1D_array = LinRange(min_θ, π, n_θ_points)
ϕ_1D_array = LinRange(min_ϕ, 2π, n_ϕ_points)
if rotationally_symmetric
θ_array = collect(θ_1D_array)
ϕ_array = zeros(size(θ_array))
else
θ_array, ϕ_array = meshgrid(θ_1D_array, ϕ_1D_array)
end
return θ_array, ϕ_array
end
"""
Create a single index from m and n
We fill the index like this:
`idx = 0 for n = 1:n_max for m = -n:n global idx idx += 1 end end`
"""
function single_index_from_m_n(m::Int, n::Int)
return n * (n + 1) + m
end
"""
Get the maximum single index, given the maximum n.
"""
function get_max_single_index_from_n_max(n_max::Int)
return single_index_from_m_n(n_max, n_max)
end
"""
Multiply the integrand by the `dS` element, which equals r²sin(θ)
"""
function surface_integrand(
integrand::AbstractVecOrMat{C},
r_array::AbstractVecOrMat{R},
θ_array::AbstractVecOrMat{R},
) where {R <: Real, C <: Complex{R}}
return integrand .* r_array .^ 2 .* sin.(θ_array)
end
"""
Multiply the integrand by the `dS` element, which equals r²sin(θ)
"""
function surface_integrand(
integrand::AbstractVecOrMat{R},
r_array::AbstractVecOrMat{R},
θ_array::AbstractVecOrMat{R},
) where {R <: Real}
return integrand .* r_array .^ 2 .* sin.(θ_array)
end
"""
Multiply the integrand by the `dS` element, which equals r²sin(θ)
"""
function surface_integrand(
integrand::AbstractVecOrMat{Matrix{C}},
r_array::AbstractVecOrMat{R},
θ_array::AbstractVecOrMat{R},
) where {R <: Real, C <: Complex{R}}
return integrand .* r_array .^ 2 .* sin.(θ_array)
end
"""
Multiply the integrand by the `dS` element, which equals r²sin(θ)
"""
function surface_integrand(
integrand::AbstractVecOrMat{Matrix{R}},
r_array::AbstractVecOrMat{R},
θ_array::AbstractVecOrMat{R},
) where {R <: Real, C <: Complex{R}}
return integrand .* r_array .^ 2 .* sin.(θ_array)
end
"""
Save T-matrix to HDF5 file, with fields "Tmatrix_real_CELES_convention" and "Tmatrix_imag_CELES_convention"
"""
function save_Tmatrix_to_HDF5_file(T, HDF5_filename)
h5write(HDF5_filename, "Tmatrix_real_CELES_convention", Float64.(real(T))) # TODO: can we store BigFloat in HDF5? I converted to `Float64` because I couldn't read the file when I stored BigFloat
h5write(HDF5_filename, "Tmatrix_imag_CELES_convention", Float64.(imag(T)))
end
# getting indices of T-matrix
"""
Get 2D array, first and second columns are n and m values, respectively.
m = -n : 1 : +n
n will be the repeated value of input
"""
function get_n_m_array_given_n(n)
return hcat(repeat([n], 2 * n + 1), (-n):n)
end
"""
Get 2D array, first and second columns are n and m values, respectively.
This is looping `get_n_m_array_given_n` over all n = 1:1:n_max
"""
function get_n_m_array_given_n_max(n_max)
return vcat(get_n_m_array_given_n.(1:n_max)...)
end
"""
Get 2D array, first, second and third columns are n, m, and idx values, respectively.
The idx value is just the index of the row.
"""
function get_n_m_idx_array_given_n_max(n_max)
nm_array = get_n_m_array_given_n_max(n_max)
return hcat(nm_array, 1:size(nm_array, 1))
end
"""
Get 4 matrices for m, n, m_, n_, corresponding to rank and order of incident and scattered VSWF, represented by elements of T-matrix
## Arguments
n_max : Int, determine the size of the T-matrix
## Returns
m_matrix, n_matrix, m__matrix, n__matrix : each is a square matrix
"""
function get_m_n_m__n__matrices_for_T_matrix(n_max)
n_m_idx = get_n_m_idx_array_given_n_max(n_max)
n_array = n_m_idx[:, 1]
m_array = n_m_idx[:, 2]
idx_array = n_m_idx[:, 3]
idx_max = idx_array[end]
idx_matrix = repeat(idx_array, 1, idx_max)
idx__matrix = repeat(idx_array', idx_max, 1)
n_matrix = n_array[idx_matrix]
m_matrix = m_array[idx_matrix]
n__matrix = n_array[idx__matrix]
m__matrix = m_array[idx__matrix]
return m_matrix, n_matrix, m__matrix, n__matrix
end
"""
using for loops, Get 4 matrices for m, n, m_, n_, corresponding to rank and order of incident and scattered VSWF, represented by elements of T-matrix
"""
function get_m_n_m__n__matrices_for_T_matrix_with_forloop(n_max)
idx_max = Tmatrix.get_max_single_index_from_n_max(n_max)
n_matrix = zeros(Int, idx_max, idx_max)
m_matrix = zeros(Int, idx_max, idx_max)
n__matrix = zeros(Int, idx_max, idx_max)
m__matrix = zeros(Int, idx_max, idx_max)
idx = 0
for n in 1:n_max
for m in (-n):n
idx += 1
idx_ = 0
for n_ in 1:n_max
for m_ in (-n_):n_
idx_ += 1
m_matrix[idx, idx_] = m
n_matrix[idx, idx_] = n
m__matrix[idx, idx_] = m_
n__matrix[idx, idx_] = n_
end
end
end
end
return m_matrix, n_matrix, m__matrix, n__matrix
end
"""
Create m,n,m_,n_ matrices with for loop, and compare the result with `get_m_n_m__n__matrices_for_T_matrix`
"""
function validate_get_m_n_m__n__matrices_for_T_matrix(n_max)
m_matrix, n_matrix, m__matrix, n__matrix =
get_m_n_m__n__matrices_for_T_matrix_with_forloop(n_max)
_m_matrix, _n_matrix, _m__matrix, _n__matrix =
get_m_n_m__n__matrices_for_T_matrix(n_max)
println(_n_matrix == n_matrix)
println(_m_matrix == m_matrix)
println(_n__matrix == n__matrix)
println(_m__matrix == m__matrix)
end
#######################
# TOBE moved to a new package for trapezoidal integraion, that will be compatible with autodiff
"""
1D numerical integral using trapezoidal rule
x and y are 1D arrays
"""
#TODO rename to not elzouka lol
function trapz_ELZOUKA(
x::AbstractVector{R},
y::AbstractVector{N},
) where {R <: Real, N <: Number}
# TODO: small error if compared with Trapz.trapz
base = x[2:end] - x[1:(end - 1)]
av_height = (y[2:end] + y[1:(end - 1)]) / 2
areas = base .* av_height
total_area = sum(areas)
return total_area
end
"""
2D numerical integral using trapezoidal rule
x and y are 1D arrays, z is 2D array
"""
function trapz_ELZOUKA(
x::AbstractVector{R},
y::AbstractVector{R},
z::AbstractMatrix{N},
) where {R <: Real, N <: Number}
integrand_wrt_x = trapz_ELZOUKA.(eachcol(repeat(x, 1, size(z, 2))), eachcol(z))
return trapz_ELZOUKA(y, integrand_wrt_x)
end
#######################
# Calculate orientation-averaged sscattering, extinction and absorption cross sections.
"""
calculate orientation averaged scattering cross section given a T-matrix and a wavevector.
Input T-matrix can be a complex square matrix or a concatenation of separate real and imag parts of T-matrix
"""
function get_OrentationAv_scattering_CrossSection_from_Tmatrix(
T::AbstractMatrix,
k1::C,
) where {C <: Complex}
if size(T)[1] == size(T)[2] # if T-matrix is a square matrix, then T-matrix is complex
return real(2 * pi / k1^2 * sum(T .* conj(T)))
elseif size(T)[1] == size(T)[2] / 2 # if T-matrix has number of columns double the number of rows, then T-matrix is hcat() of real and imag parts of Tmatrix
return get_OrentationAv_scattering_CrossSection_from_Tmatrix_SeparateRealImag(
T,
real(k1),
imag(k1),
)
end
end
"""
calculate orientation averaged scattering cross section given a T-matrix, wavelength and optical properties of surrounding.
Input T-matrix can be a complex square matrix or a concatenation of separate real and imag parts of T-matrix
"""
function get_OrentationAv_scattering_CrossSection_from_Tmatrix(
T::AbstractMatrix,
wl_or_freq_input::R,
input_unit::String,
Eps_r_1::C,
Mu_r_1::C,
) where {C <: Complex, R <: Real}
return get_OrentationAv_scattering_CrossSection_from_Tmatrix(
T,
get_WaveVector(
wl_or_freq_input;
input_unit = input_unit,
Eps_r = Eps_r_1,
Mu_r = Mu_r_1,
),
)
end
"""
calculate orientation averaged scattering cross section given a T-matrix and a wavevector. This works for "_SeparateRealImag"
"""
function get_OrentationAv_scattering_CrossSection_from_Tmatrix_SeparateRealImag(
T::AbstractMatrix{R},
k1_r::R,
k1_i::R,
) where {R <: Real}
T_r, T_i = Tmatrix.separate_real_imag(T)
T_by_conjT_sum = sum(complex_multiply.(T_r, T_i, T_r, -T_i))
k1_squared = complex_multiply(k1_r, k1_i, k1_r, k1_i)
two_pi_over_k1_squared = complex_divide(2 * pi, 0, k1_squared[1], k1_squared[2])
return complex_multiply(
two_pi_over_k1_squared[1],
two_pi_over_k1_squared[2],
T_by_conjT_sum[1],
T_by_conjT_sum[2],
)[1]
end
"""
calculate orientation averaged extinction cross section given a T-matrix and a wavevector.
"""
function get_OrentationAv_extinction_CrossSection_from_Tmatrix(
T::AbstractMatrix,
k1::C,
) where {C <: Complex}
if size(T)[1] == size(T)[2] # if T-matrix is a square matrix, then T-matrix is complex
return real(-2 * pi / k1^2 * tr(real(T)))
elseif size(T)[1] == size(T)[2] / 2 # if T-matrix has number of columns double the number of rows, then T-matrix is hcat() of real and imag parts of Tmatrix
return get_OrentationAv_extinction_CrossSection_from_Tmatrix_SeparateRealImag(
T,
real(k1),
imag(k1),
)
end
end
"""
calculate orientation averaged extinction cross section given a T-matrix and a wavevector. This works for "_SeparateRealImag"
"""
function get_OrentationAv_extinction_CrossSection_from_Tmatrix_SeparateRealImag(
T::AbstractMatrix{R},
k1_r::R,
k1_i::R,
) where {R <: Real}
T_r, T_i = Tmatrix.separate_real_imag(T)
k1_squared = complex_multiply(k1_r, k1_i, k1_r, k1_i)
two_pi_over_k1_squared = complex_divide(2 * pi, 0, k1_squared[1], k1_squared[2])
return -two_pi_over_k1_squared[1] * tr(T_r)
end
"""
calculate orientation averaged absorption cross section given a T-matrix and a wavevector.
"""
function get_OrentationAv_absorption_CrossSection_from_Tmatrix(
T::AbstractMatrix,
k1::C,
) where {C <: Complex}
return get_OrentationAv_extinction_CrossSection_from_Tmatrix(T, k1) -
get_OrentationAv_scattering_CrossSection_from_Tmatrix(T, k1)
end
"""
calculate orientation averaged absorption cross section given a T-matrix and a wavevector. This works for "_SeparateRealImag"
"""
function get_OrentationAv_absorption_CrossSection_from_Tmatrix_SeparateRealImag(
T::AbstractMatrix{R},
k1_r::R,
k1_i::R,
) where {R <: Real}
return get_OrentationAv_extinction_CrossSection_from_Tmatrix_SeparateRealImag(
T,
k1_r,
k1_i,
) - get_OrentationAv_scattering_CrossSection_from_Tmatrix_SeparateRealImag(
T,
k1_r,
k1_i,
)
end
"""
calculate orientation averaged emissivity cross section given a T-matrix and a wavevector.
`particle_surface_area` has to be in units consistent with wavevector units. If waveevctor unit is per m, then the particle surface area has to be in m^2
"""
function get_OrentationAv_emissivity_from_Tmatrix(
T::AbstractMatrix,
k1::C,
particle_surface_area::R,
) where {R <: Real, C <: Complex}
return get_OrentationAv_absorption_CrossSection_from_Tmatrix(T, k1) * 4 /
particle_surface_area
end
"""
calculate orientation averaged emissivity cross section given a T-matrix and a wavevector. This works for "_SeparateRealImag"
`particle_surface_area` has to be in units consistent with wavevector units. If waveevctor unit is per m, then the particle surface area has to be in m^2
"""
function get_OrentationAv_emissivity_from_Tmatrix_SeparateRealImag(
T::AbstractMatrix{R},
k1_r::R,
k1_i::R,
particle_surface_area::R,
) where {R <: Real}
return get_OrentationAv_absorption_CrossSection_from_Tmatrix_SeparateRealImag(
T,
k1_r,
k1_i,
) * 4 / particle_surface_area
end
"""
calculate orientation averaged emissivity cross section given a T-matrix, wavelength and optical properties of surrounding. This works for "_SeparateRealImag"
`particle_surface_area` has to be in units consistent with wavevector units. If waveevctor unit is per m, then the particle surface area has to be in m^2
"""
function get_OrentationAv_emissivity_from_Tmatrix_SeparateRealImag(
T::AbstractMatrix{R},
wl_or_freq_input::R,
input_unit::String,
Eps_r_r_1::R,
Eps_r_i_1::R,
Mu_r_r_1::R,
Mu_r_i_1::R,
particle_surface_area::R,
) where {R <: Real}
k1_complex = get_WaveVector(
wl_or_freq_input;
input_unit = input_unit,
Eps_r = Complex(Eps_r_r_1, Eps_r_i_1),
Mu_r = Complex(Mu_r_r_1, Mu_r_i_1),
)
return get_OrentationAv_absorption_CrossSection_from_Tmatrix_SeparateRealImag(
T,
k1_r,
k1_i,
) * 4 / particle_surface_area
end
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] | 2.307667 | 6,013 |
import Effects: effects
import Effects:_reference_grid
import Effects:typify
import Effects.typify
import Effects:_symequal
import StatsModels.collect_matrix_terms
import Base.getproperty
using Effects
"""
effects(design::AbstractDict, model::UnfoldModel;typical=mean)
Calculates marginal effects for all term-combinations in `design`.
Implementation based on Effects Package; likely could repackage in UnfoldEffects; somebody wants to do it? This would make it easier to cross-maintain it to changes/bugfixes in the Effects.jl Package
`design` is a Dictionary containing those predictors (als keys) with levels (as values), that you want to evaluate. The `typical` refers to the value, that other predictors not in the Dictionary should take on.
# Example
```julia-repl
julia> f = @formula 0 ~ categoricalA + continuousA + continuousB
julia> uf = fit(UnfoldModel,(Any=>(f,times)),data,events)
julia> d = Dict(:categoricalA=>["levelA","levelB"],:continuousB=>[-2,0,2])
julia> effects(d,uf)
```
will result in 6 predicted values: A/-2, A/0, A/2, B/-2, B/0, B/2.
"""
function effects(design::AbstractDict, model::UnfoldModel;typical=mean)
reference_grid = _reference_grid(design)
form = Unfold.formula(model) # get formula
# replace non-specified fields with "constants"
m = modelmatrix(model,false) # get the modelmatrix without timeexpansion
form_typical = _typify(reference_grid,form,m,typical)
eff = yhat(model,form_typical,reference_grid)
# because coefficients are 2D/3D arry, we have to cast it correctly to one big dataframe
if isa(eff,Tuple)
# TimeContinuous Model, we also get back other things like times & fromWhereToWhere a BasisFunction goes
if typeof(form) <: FormulaTerm
form = [form]
end
# figure out the basisname
bnames = [[form[ix].rhs.basisfunction.name] for ix in 1:length(form)]
# repeat it as much as necessary
bnames = repeat.(bnames,[e.stop+e.step-1 for e in eff[1]])
result = DataFrame(cast_referenceGrid(reference_grid,eff[3],eff[2] ;basisname=vcat(bnames...)))
select!(result,Not(:latency)) # remove the latency column if it was added
else
# normal mass univariate model
result = DataFrame(cast_referenceGrid(reference_grid,eff,times(model)[1] ))
end
return result
end
Effects.typify(reference_grid,form::Matrix,X;kwargs...) = typify.(Ref(reference_grid),form,Ref(X);kwargs...)
# cast single form to a vector
_typify(reference_grid,form::FormulaTerm{<:InterceptTerm,<:Unfold.TimeExpandedTerm},m,typical) = _typify(reference_grid,[form],[m],typical)
function _typify(reference_grid, form::AbstractArray{<:FormulaTerm{<:Union{<:InterceptTerm,<:Unfold.TimeExpandedTerm}}},m,typical)
form_typical = Array{Any}(undef,1, length(form))
for f = 1:length(form)
# strip of basisfunction and put it on afterwards again
tmpf = deepcopy(form[f])
# create a Formula without Timeexpansion
tmpf = FormulaTerm(tmpf.lhs,tmpf.rhs.term)
# typify that
tmpf = typify(reference_grid, tmpf, m[f]; typical=typical)
# regenerate TimeExpansion
tmpf = Unfold.TimeExpandedTerm(tmpf,form[f].rhs.basisfunction;eventfields=form[f].rhs.eventfields)
form_typical[f] = tmpf
end
return form_typical
end
function _typify(reference_grid,form::FormulaTerm,m,typical)
return [typify(reference_grid, form, m; typical=typical)]
end
function cast_referenceGrid(r,eff,times;basisname=nothing)
nchan = size(eff, 2) # correct
neff = size(r,1) # how many effects requested
neffCol = size(r,2) # how many predictors
ncols = size(eff,1) ÷ neff # typically ntimes
# replicate
# for each predictor in r (reference grid), we need this at the bottom
if isnothing(basisname)
nbases = 1
else
nbases = length(unique(basisname))
end
coefs_rep = Array{Array}(undef,nbases,neffCol)
for k = 1:neffCol
# in case we have only a single basis (e.g. mass univariate), we can directly fill in all values
ixList = []
if isnothing(basisname)
ix = ones(ncols) .== 1.
append!(ixList,[ix])
else
#in case of multiple bases, we have to do it iteratively, because the bases can be different length
for b = unique(basisname)
ix = basisname[1:neff:end].==b
append!(ixList,[ix])
end
end
for i_ix = 1:length(ixList)
coefs_rep[i_ix,k] = linearize(permutedims(repeat(r[:,k], outer = [1, nchan, sum(ixList[i_ix])]), [2,3,1]))
end
end
# often the "times" vector
if length(times) == neff*ncols
# in case we have timeexpanded, times is already in long format and doesnt need to be repeated for each coefficient
colnames_basis_rep = permutedims(repeat(times, 1, nchan, 1), [2 1 3])
else
colnames_basis_rep = permutedims(repeat(times, 1, nchan, neff), [2 1 3])
end
# for multiple channels
chan_rep = repeat(1:nchan, 1, ncols, neff)
# for mass univariate there is no basisname
if isnothing(basisname)
basisname = fill(nothing,ncols)
basisname_rep = permutedims(repeat(basisname,1,nchan,neff),[2,1,3])
else
basisname_rep = permutedims(repeat(basisname,1,nchan,1),[2,1,3])
end
result = Dict( :yhat => linearize(eff'),
:time => linearize(colnames_basis_rep),
:channel => linearize(chan_rep),
:basisname =>linearize(basisname_rep))
for k = 1:neffCol
push!(result,Symbol(names(r)[k]) =>vcat(vcat(coefs_rep[:,k]...)...))
end
return result
end
Effects._trmequal(t1::Unfold.uf_bsplineTerm,t2::AbstractTerm) = _symequal(t1.term,t2)
Effects._trmequal(t1::Unfold.uf_bsplineTerm,t2::Unfold.uf_bsplineTerm) = _symequal(t1.term,t2.term)
Effects._trmequal(t1::AbstractTerm,t2::Unfold.uf_bsplineTerm) = _symequal(t1,t2.term)
Effects._symequal(t1::AbstractTerm,t2::Unfold.uf_bsplineTerm) = _symequal(t1,t2.term)
#Effects._symequal(t1::AbstractTerm,t2::Unfold.TimeExpandedTerm) = _symequal(t1,t2.term)
#function Effects._replace(matrix_term::MatrixTerm{<:Tuple{<:Unfold.TimeExpandedTerm}},typicals::Dict)
# replaced_term = MatrixTerm((Effects._replace.(matrix_term.terms, Ref(typicals))...,))
# basisfunctionTerm = getfield(matrix_term,:terms)[1]
# return TimeExpandedTerm(replaced_term,basisfunctionTerm.basisfunction,basisfunctionTerm.eventfields)
#end
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] | 2.44633 | 2,711 |
module Day14
export get_inputs, get_solution1, get_solution2
using ..Advent2020.Utils
using SparseArrays
## Input getting
function get_inputs()
test_input1 = read_input(IOBuffer(
"""
mask = XXXXXXXXXXXXXXXXXXXXXXXXXXXXX1XXXX0X
mem[8] = 11
mem[7] = 101
mem[8] = 0
"""
))
test_output1 = 165
test_input2 = read_input(IOBuffer(
"""
mask = 000000000000000000000000000000X1001X
mem[42] = 100
mask = 00000000000000000000000000000000X0XX
mem[26] = 1
"""
))
test_output2 = 208
data = read_input(joinpath(@__DIR__, "input.txt"))
return (; test_input1, test_input2, test_output1, test_output2, data)
end
function read_input(io)
data = read(io, String)
chunks = split(data, "mask = ", keepempty=false)
return parse_chunk.(chunks)
end
function parse_chunk(chunk)
lines = split(chunk, r"mask = |\n|\r\n", keepempty=false)
mask = lines[1]
mem = parse_mem.(lines[2:end])
return (; mask, mem)
end
function parse_mem(mem)
address, value = parse.(Int, split(mem, r"mem\[|\] = ", keepempty=false))
return address => value
end
## Solution functions
initialize_memory(data) = spzeros(Int, typemax(Int))
mask_replace(mask, x='0') = parse(Int, replace(mask, 'X'=>x), base=2)
apply_mask(value, mask) = (value | mask_replace(mask, '0')) & mask_replace(mask, '1')
# Part 1
function get_solution1(data)
memory = initialize_memory(data)
for chunk in data, (address, value) in chunk.mem
memory[address] = apply_mask(value, chunk.mask)
end
return sum(memory)
end
# Part 2
function get_solution2(data)
memory = initialize_memory(data)
for line in data
mask = zero_based(replace(collect(line.mask), 'X'=>'C', '0'=>'X'))
C_positions = findall(==('C'), mask)
num_C = length(C_positions)
for short_mask in 0:(2^num_C-1)
short_mask = last(bitstring(short_mask), num_C)
mask[C_positions] = zero_based(collect(short_mask))
mask_string = String(mask)
for (encoded_address, value) in line.mem
address = apply_mask(encoded_address, mask_string)
memory[address] = value
end
end
end
return sum(memory)
end
end | [
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] | 2.266278 | 1,029 |
# n! means n × (n − 1) × ... × 3 × 2 × 1
#
# For example, 10! = 10 × 9 × ... × 3 × 2 × 1 = 3628800,
# and the sum of the digits in the number 10! is 3 + 6 + 2 + 8 + 8 + 0 + 0 = 27.
#
# Find the sum of the digits in the number 100!
using ProjectEulerSolutions
# Trivial with Julia's built-in factorial and digits functions.
function p020solution(n::Integer=2)::Integer
return sum(digits(factorial(BigInt(n))))
end
p020 = Problems.Problem(p020solution)
Problems.benchmark(p020, 100) | [
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198,
437,
198,
198,
79,
33618,
796,
32093,
13,
40781,
7,
79,
33618,
82,
2122,
8,
198,
198,
2964,
22143,
13,
26968,
4102,
7,
79,
33618,
11,
1802,
8
] | 2.741573 | 178 |
@testset "Group properties" begin
σ1 = Permutation([1, 2, 3])
σ2 = Permutation([2, 3, 1])
σ3 = Permutation([3, 1, 2])
τ1 = Permutation([1, 3, 2])
τ2 = Permutation([3, 2, 1])
τ3 = Permutation([2, 1, 3])
cycles = [σ1, σ2, σ3]
transpositions = [τ1, τ2, τ3]
@testset "Existence of identity" begin
for π = [cycles..., transpositions...]
@test σ1 * π == π * σ1 == π
end
end
@testset "Existence of the inverse" begin
@test σ2 * σ3 == σ3 * σ2 == σ1
for τ = transpositions
@test τ * τ == σ1
end
end
@testset "Closeness under multiplication" begin
@test σ2 * σ2 == σ3
@test σ3 * σ3 == σ2
@test τ1 * τ2 == τ2 * τ3 == τ3 * τ1 == σ2
@test τ3 * τ2 == τ2 * τ1 == τ1 * τ3 == σ3
end
@testset "Associativity" begin
p_group = [cycles..., transpositions...]
for π1 = p_group, π2 = p_group, π3 = p_group
@test (π1 * π2) * π3 == π1 * (π2 * π3)
end
end
end
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886,
198,
220,
220,
220,
886,
220,
198,
198,
437,
198
] | 1.811092 | 577 |
#Adapted from https://github.com/github/developer.github.com/blob/master/Rakefile#L21-L48
#Run this file from its current directory to build the documentation and push the result into gh-pages
last_commit=readchomp(`git --no-pager log -1 --pretty=format:"%h:%s"`)
ENV["GIT_DIR"]=abspath(chomp(read(`git rev-parse --git-dir`, String)))
old_sha = chomp(read(`git rev-parse refs/remotes/origin/gh-pages`, String))
#run(`julia make.jl`)
cd("build") do
gif="/tmp/dev.gh.i"
ENV["GIT_INDEX_FILE"]=gif
ENV["GIT_WORK_TREE"]=pwd()
run(`git add -A`)
tsha=chomp(read(`git write-tree`, String))
mesg="Deploy docs for master@$last_commit"
if length(old_sha) == 40
csha = chomp(read(`git commit-tree $tsha -p $old_sha -m $(mesg)`, String))
else
csha = chomp(read(`git commit-tree $tsha -m $(mesg)`, String))
end
print("Created commit $csha")
run(`git --no-pager show $csha --stat`)
run(`git update-ref refs/heads/gh-pages $csha `)
run(`git push origin gh-pages `)
end
| [
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437,
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] | 2.326622 | 447 |
using Optim, HTTP, GLM, LinearAlgebra, Random, Statistics, DataFrames, CSV, FreqTables,Distributions
include("lgwt.jl")
#data
function PS4()
url = "https://raw.githubusercontent.com/OU-PhD-Econometrics/fall-2020/master/ProblemSets/PS4-mixture/nlsw88t.csv"
df = CSV.read(HTTP.get(url).body)
X = [df.age df.white df.collgrad]
Z = hcat(df.elnwage1, df.elnwage2, df.elnwage3, df.elnwage4,
df.elnwage5, df.elnwage6, df.elnwage7, df.elnwage8)
y = df.occ_code;
#Question 1
#from his solution:
function mlogit_with_Z(theta, X, Z, y)
alpha = theta[1:end-1]
gamma = theta[end]
K = size(X,2)
J = length(unique(y))
N = length(y)
bigY = zeros(N,J)
for j=1:J
bigY[:,j] = y.==j
end
bigAlpha = [reshape(alpha,K,J-1) zeros(K)]
T = promote_type(eltype(X),eltype(theta))
num = zeros(T,N,J)
dem = zeros(T,N)
for j=1:J
num[:,j] = exp.(X*bigAlpha[:,j] .+ (Z[:,j] .- Z[:,J])*gamma)
dem .+= num[:,j]
end
P = num./repeat(dem,1,J)
loglike = -sum( bigY.*log.(P) )
return loglike
end
#values from PS3
startvals=[0.05570767876416688, 0.08342649976722213, -2.344887681361976, 0.04500076157943125, 0.7365771540890512, -3.153244238810631, 0.09264606406280998, -0.08417701777996893, -4.273280002738097, 0.023903455659102114, 0.7230648923377259, -3.749393470343111, 0.03608733246865346, -0.6437658344513095, -4.2796847340030375, 0.0853109465190059, -1.1714299392376775, -6.678677013966667, 0.086620198654063, -0.7978777029320784, -4.969132023685069, -0.0941942241795243];
#look at this to see why you might need this: http://julianlsolvers.github.io/Optim.jl/v0.9.3/algo/autodiff/
td = TwiceDifferentiable(theta -> mlogit_with_Z(theta, X, Z, y), startvals; autodiff = :forward);
theta_hat_optim_ad = optimize(td, startvals, LBFGS(), Optim.Options(g_tol = 1e-5, iterations=100_000, show_trace=true, show_every=50))
#Hessian from ps3
theta_hat_mle_ad = theta_hat_optim_ad.minimizer
println(theta_hat_mle_ad)
H = Optim.hessian!(td, theta_hat_mle_ad)
theta_hat_mle_ad_se = sqrt.(diag(inv(H)))
println([theta_hat_mle_ad theta_hat_mle_ad_se])
#question 2
# now it is positive which makes more sense because increase in log wage should increase utility
#question 3
#part a
#part b
#1
nodes, weights = lgwt(7,-10,10);
d=Normal(0,2)
sum(weights.*pdf.(d,nodes))
println(sum((nodes.^2).*weights.*pdf.(d,nodes)))
#2
nodes, weights = lgwt(10,-10,10);
sum(weights.*pdf.(d,nodes))
println(sum((nodes.^2).*weights.*pdf.(d,nodes)))
#3
# it is close, with more quadrature points it will converge to 4
#Part C
#1
d=Normal(0,2)
N=1000000
x=rand(Uniform(-10, 10 ),N)
println((20/N)*sum(x.^2 .*(pdf.(d,x))))
#2
println((20/N)*sum(x .*(pdf.(d,x))))
#3
println((20/N)*sum(pdf.(d,x)))
#4
#the difference is around 0.2 which is high in my opinion
#Question 4
function mxlogit_with_quadrature(theta, X, Z, y,rn)
alpha = theta[1:end-1]
gamma = theta[end]
K = size(X,2)
J = length(unique(y))
N = length(y)
bigY = zeros(N,J)
for j=1:J
bigY[:,j] = y.==j
end
bigAlpha = [reshape(alpha,K,J-1) zeros(K)]
nodes, weights = lgwt(rn,-4,4);
d=Normal(0,1)
T = promote_type(eltype(X),eltype(theta))
num = zeros(T,N,J)
dem = zeros(T,N)
for j=1:J
num[:,j] = sum(exp.(X*bigAlpha[:,j] .+ (Z[:,j] .- Z[:,J])*gamma).*weights.*pdf.(d,nodes))
dem .+= num[:,j]
end
P = num./repeat(dem,1,J)
loglike = -sum( log.(P.^bigY) )
return loglike
end
#Question 5
function mxlogit_with_simulation(theta, X, Z, y,rn)
alpha = theta[1:end-1]
gamma = theta[end]
K = size(X,2)
J = length(unique(y))
N = length(y)
bigY = zeros(N,J)
for j=1:J
bigY[:,j] = y.==j
end
bigAlpha = [reshape(alpha,K,J-1) zeros(K)]
d=Normal(0,1)
rx=rand(Uniform(0,1 ),rn)
T = promote_type(eltype(X),eltype(theta))
num = zeros(T,N,J)
dem = zeros(T,N)
for j=1:J
num[:,j] = sum(exp.(X*bigAlpha[:,j] .+ (Z[:,j] .- Z[:,J])*gamma).*(pdf.(d,x)))
dem .+= num[:,j]
end
P = num./repeat(dem,1,J)
loglike = -sum( log.(P.^bigY) )
return loglike
end
end
PS4() | [
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