content
stringlengths 5
1.03M
| input_ids
sequencelengths 4
823k
| ratio_char_token
float64 0.4
12.5
| token_count
int64 4
823k
|
|---|---|---|---|
using Benchmarks
using FixedSizeArrays
import Base: *, isless, isequal
include("utils.jl");
include("algorithms.jl");
function graham_scan_vanilla(p::Vector{Point{2, Float64}})
ep = _grahamscan(p)
return ep
end
function graham_scan_wat(p::Vector{Point{2, Float64}})
p = _akltoussaint(p)
ep = _grahamscan(p)
return ep
end
function monotonechain_vanilla(p::Vector{Point{2, Float64}})
ep = _monotonechain(p)
return ep
end
function monotonechain_wat(p::Vector{Point{2, Float64}})
p = _akltoussaint(p)
ep = _monotonechain(p)
return ep
end
p = [Point(randn(), randn()) for i=1:2500000]
srand(1112016)
npoints = [25, 250, 2500, 25000, 250000, 2500000]
nreps = [1000, 500, 100, 50, 25, 5]
for i=1:length(npoints)
# Get parameters
np = npoints[i]
nr = nreps[i]
p = [Point(randn(), randn()) for j=1:np]
gsv() = graham_scan_vanilla(p)
gsat() = graham_scan_wat(p)
mcv() = monotonechain_vanilla(p)
mcat() = monotonechain_wat(p)
println("With $np points")
println(compare([gsv, gsat, mcv, mcat], nr))
end
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] | 2.243299 | 485 |
const JAVA_HOME="<path to java home>"
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# This file is a part of JuliaFEM.
# License is MIT: see https://github.com/JuliaFEM/AbaqusReader.jl/blob/master/LICENSE
import Base.parse
# Define element type and number of nodes in element
element_has_nodes(::Type{Val{:C3D4}}) = 4
element_has_type( ::Type{Val{:C3D4}}) = :Tet4
element_has_nodes(::Type{Val{:C3D4H}}) = 4
element_has_type( ::Type{Val{:C3D4H}}) = :Tet4
element_has_nodes(::Type{Val{:C3D8}}) = 8
element_has_type( ::Type{Val{:C3D8}}) = :Hex8
element_has_nodes(::Type{Val{:C3D10}}) = 10
element_has_type(::Type{Val{:C3D10}}) = :Tet10
element_has_nodes(::Type{Val{:C3D10H}}) = 10
element_has_type(::Type{Val{:C3D10H}}) = :Tet10
element_has_nodes(::Type{Val{:C3D20}}) = 20
element_has_type(::Type{Val{:C3D20}}) = :Hex20
element_has_nodes(::Type{Val{:C3D20E}}) = 20
element_has_nodes(::Type{Val{:S3}}) = 3
element_has_type( ::Type{Val{:S3}}) = :Tri3
element_has_nodes(::Type{Val{:STRI65}}) = 6
element_has_type(::Type{Val{:STRI65}}) = :Tri6
element_has_nodes(::Type{Val{:CPS4}}) = 4
element_has_type(::Type{Val{:CPS4}}) = :Quad4
element_has_nodes(::Type{Val{:T2D2}}) = 2
element_has_type(::Type{Val{:T2D2}}) = :Seg2
element_has_nodes(::Type{Val{:T3D2}}) = 2
element_has_type(::Type{Val{:T3D2}}) = :Seg2
element_has_nodes(::Type{Val{:B33}}) = 2
element_has_type(::Type{Val{:B33}}) = :Seg2
"""Checks for a comment or empty line
Function return true, if line starts with comment character "**"
or has length of 0
"""
function empty_or_comment_line(line::T) where T<:AbstractString
startswith(line, "**") || (length(line) == 0)
end
"""Match words from both sides of '=' character
"""
function matchset(definition)
regexp = r"([\w\_\-]+[ ]*=[ ]*[\w\_\-]+)"
collect(m.match for m = eachmatch(regexp, definition))
end
"""Parse string to get set type and name
"""
function parse_definition(definition)
set_defs = Dict()
set_definition = matchset(definition)
set_definition == nothing && return nothing
for x in set_definition
name, vals = map(strip, split(x, "="))
set_defs[lowercase(name)] = vals
end
set_defs
end
"""Parse all the numbers from string
"""
function parse_numbers(line, type_::Type{T})::Vector{T} where T
regexp = r"[0-9]+"
matches = collect((m.match for m = eachmatch(regexp, line)))
map(x-> Base.parse(type_, x), matches)
end
"""Add set to model, if set exists
"""
function add_set!(model, definition, model_key, abaqus_key, ids)
has_set_def = parse_definition(definition)
if haskey(has_set_def, "elset")
set_name = has_set_def[abaqus_key]
@info("Adding $abaqus_key: $set_name")
model[model_key][set_name] = ids
end
end
"""Parse nodes from the lines
"""
function parse_section(model, lines, ::Symbol, idx_start, idx_end, ::Type{Val{:NODE}})
nnodes = 0
ids = Int[]
definition = lines[idx_start]
for line in lines[idx_start + 1: idx_end]
if !(empty_or_comment_line(line))
m = collect((m.match for m = eachmatch(r"[-0-9.eE+]+", line)))
node_id = parse(Int, m[1])
coords = parse.(Float64, m[2:end])
model["nodes"][node_id] = coords
push!(ids, node_id)
nnodes += 1
end
end
@info("$nnodes nodes found")
add_set!(model, definition, "node_sets", "nset", ids)
end
"""Custon regex to find match from string. Index used if there are multiple matches
"""
function regex_match(regex_str, line, idx)
return match(regex_str, line).captures[idx]
end
"""Custom list iterator
Simple iterator for comsuming element list. Depending
on the used element, connectivity nodes might be listed
in multiple lines, which is why iterator is used to handle
this problem.
"""
function consumeList(arr, start, stop)
idx = start - 1
function _it()
idx += 1
if idx > stop
return nothing
end
arr[idx]
end
_it
end
"""Parse elements from input lines
Reads element ids and their connectivity nodes from input lines.
If elset definition exists, also adds the set to model.
"""
function parse_section(model, lines, ::Symbol, idx_start, idx_end, ::Type{Val{:ELEMENT}})
ids = Int[]
definition = lines[idx_start]
regexp = r"TYPE=([\w\-\_]+)"i
m = match(regexp, definition)
m == nothing && error("Could not match regexp $regexp to line $definition")
element_type = uppercase(m[1])
eltype_sym = Symbol(element_type)
eltype_nodes = element_has_nodes(Val{eltype_sym})
element_type = element_has_type(Val{eltype_sym})
@info("Parsing elements. Type: $(m[1]). Topology: $(element_type)")
list_iterator = consumeList(lines, idx_start+1, idx_end)
line = list_iterator()
while line != nothing
numbers = parse_numbers(line, Int)
if !(empty_or_comment_line(line))
id = numbers[1]
push!(ids, id)
connectivity = numbers[2:end]
while length(connectivity) != eltype_nodes
@assert length(connectivity) < eltype_nodes
line = list_iterator()
numbers = parse_numbers(line, Int)
push!(connectivity, numbers...)
end
model["elements"][id] = connectivity
model["element_types"][id] = element_type
end
line = list_iterator()
end
add_set!(model, definition, "element_sets", "elset", ids)
end
"""Parse node and elementset from input lines
"""
function parse_section(model, lines, key, idx_start, idx_end, ::Union{Type{Val{:NSET}},
Type{Val{:ELSET}}})
data = Int[]
set_regex_string = Dict(:NSET => r"NSET=([\w\-\_]+)"i,
:ELSET => r"ELSET=([\w\-\_]+)"i)
selected_set = key == :NSET ? "node_sets" : "element_sets"
definition = lines[idx_start]
regex_string = set_regex_string[key]
set_name = regex_match(regex_string, definition, 1)
@info("Creating $(lowercase(string(key))) $set_name")
if endswith(strip(uppercase(definition)), "GENERATE")
line = lines[idx_start + 1]
first_id, last_id, step_ = parse_numbers(line, Int)
set_ids = collect(first_id:step_:last_id)
push!(data, set_ids...)
else
for line in lines[idx_start + 1: idx_end]
if !(empty_or_comment_line(line))
set_ids = parse_numbers(line, Int)::Vector{Int}
push!(data, set_ids...)
end
end
end
model[selected_set][set_name] = data
end
"""Parse SURFACE keyword
"""
function parse_section(model, lines, ::Symbol, idx_start, idx_end, ::Type{Val{:SURFACE}})
data = Vector{Tuple{Int, Symbol}}()
definition = lines[idx_start]
has_set_def = parse_definition(definition)
has_set_def != nothing || return
set_type = get(has_set_def, "type", "UNKNOWN")
set_name = has_set_def["name"]
for line in lines[idx_start + 1: idx_end]
empty_or_comment_line(line) && continue
m = match(r"(?P<element_id>\d+),.*(?P<element_side>S\d+).*", line)
element_id = parse(Int, m[:element_id])
element_side = Symbol(m[:element_side])
push!(data, (element_id, element_side))
end
model["surface_types"][set_name] = Symbol(set_type)
model["surface_sets"][set_name] = data
return
end
"""Find lines, which contain keywords, for example "*NODE"
"""
function find_keywords(lines)
indexes = Int[]
for (idx, line) in enumerate(lines)
if startswith(line, "*") && !startswith(line, "**")
push!(indexes, idx)
end
end
return indexes
end
"""Main function for parsing Abaqus input file.
Function parses Abaqus input file and generates a dictionary of
all the available keywords.
"""
function parse_abaqus(fid::IOStream)
model = Dict{String, Dict}()
model["nodes"] = Dict{Int, Vector{Float64}}()
model["node_sets"] = Dict{String, Vector{Int}}()
model["elements"] = Dict{Int, Vector{Int}}()
model["element_types"] = Dict{Int, Symbol}()
model["element_sets"] = Dict{String, Vector{Int}}()
model["surface_sets"] = Dict{String, Vector{Tuple{Int, Symbol}}}()
model["surface_types"] = Dict{String, Symbol}()
keyword_sym::Symbol = :none
lines = readlines(fid)
keyword_indexes = find_keywords(lines)
nkeyword_indexes = length(keyword_indexes)
push!(keyword_indexes, length(lines)+1)
idx_start = keyword_indexes[1]
for idx_end in keyword_indexes[2:end]
keyword_line = strip(uppercase(lines[idx_start]))
keyword = strip(regex_match(r"\s*([\w ]+)", keyword_line, 1))
k_sym = Symbol(keyword)
args = Tuple{Dict, Vector{Int}, Symbol, Int, Int, Type{Val{k_sym}}}
if hasmethod(parse_section, args)
parse_section(model, lines, k_sym, idx_start, idx_end-1, Val{k_sym})
else
@warn("Unknown section: '$(keyword)'")
end
idx_start = idx_end
end
return model
end
"""
abaqus_read_mesh(fn::String)
Read ABAQUS mesh from file `fn`. Returns a dict with elements, nodes,
element sets, node sets and other topologically imporant things, but
not the actual model with boundary conditions, load steps and so on.
"""
function abaqus_read_mesh(fn::String)
return open(parse_abaqus, fn)
end
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] | 2.315065 | 3,996 |
using Documenter
using DiffEqBase
makedocs(
sitename = "DiffEqBase",
format = :html,
modules = [DiffEqBase],
pages = [
"index.md",
"API" => [
"Overview" => "api/overview.md",
"api/functions.md",
"api/problems.md",
],
],
)
# Documenter can also automatically deploy documentation to gh-pages.
# See "Hosting Documentation" and deploydocs() in the Documenter manual
# for more information.
#=deploydocs(
repo = "<repository url>"
)=#
| [
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] | 2.3125 | 224 |
function RTs_SS(a,b,distri,cohmeasure)
# a quantity to mean
# b quantity to test
# Return the mean of RTs and STD
# Sucess after Sucess
output = Float64[]
for i=1:length(a)-1
x = b[i]
y=b[i+1]
x > 0 && y >0 && distri[i+1]*distri[i] > 0 && distri[i+1] == cohmeasure || continue
push!(output,a[i+1])
end
return mean(output),std(output),length(output)
end
function performance(results)
# compute the performance of the model
# need the list of of results for a specific coherence
return sum(results)/length(results)
end
function RTs_SE(a,b,distri,cohmeasure)
# a quantity to mean
# b quantity to test
# Error after Sucess
t = 0
c = 0
output=Float64[]
for i=1:length(a)-1
x = b[i]
y=b[i+1]
x > 0 && y <0 && distri[i+1]*distri[i] > 0 && distri[i+1] == cohmeasure || continue
push!(output,a[i+1])
end
return mean(output),std(output),length(output)
end
function RTs_ES(a,b,distri,cohmeasure)
# a quantity to mean
# b quantity to test
## Sucess after Error
output=Float64[]
for i=1:length(a)-1
x = b[i]
y=b[i+1]
x < 0 && y >0 && distri[i+1] == cohmeasure|| continue
push!(output,a[i+1])
end
return mean(output),std(output),length(output)
end
function RTs_SSS(a,b)
# Sucess after Sucess after Sucess
# same sign coh but not same one
output=Float64[]
for i=1:length(a)-2
x = b[i]
y=b[i+1]
z=b[i+2]
x > 0 && y >0 && z>0 && distri[i]*distri[i+1]>0 && distri[i+1]*distri[i+2] && distri[i+2] == cohmeasure|| continue
push!(output,a[i+2])
end
return mean(output),std(output),length(output)
end
function RTs_SSE(a,b)
# Error after Sucess after Sucess
t = 0
c = 0
output=Float64[]
for i=1:length(a)-2
x = b[i]
y=b[i+1]
z=b[i+2]
x > 0 && y >0 && z<0&& distri[i]*distri[i+1]>0 && distri[i+1]*distri[i+2] && distri[i+2] == cohmeasure|| continue
push!(output,a[i+2])
end
return mean(output),std(output),length(output)
end
function RTs_SS_chgt(RT,Res,distri,coherence)
output = Float64[]
for i =1:length(RT)-1
if Res[i]==1 && Res[i+1] == 1 && distri[i+1] == coherence && distri[i]*distri[i+1] <0 || continue
push!(output,RT[i+1])
end
end
return mean(output)
end
function analysis_RTs_E(result,Rts_coh)
listc=Float64[]
listv=Float64[]
for c in [0 1 2 3 4 5 6 7 8]
tempv = Float64[]
temp=Float64[]
for i in 1:length(result["distri"])-1
if sign(result["distri"][i])==sign(result["distri"][i+1]) &&
result["Confidence"][i+1]>(c-1) &&result["NotError"][i]==-1 #&&result["NotError"][i+1]==1
x=result["distri"][i+1]
push!(tempv,result["RTs"][i+1])
if abs(x) <10
push!(temp,(Rts_coh["$x"]-result["RTs"][i+1])/Rts_coh["$x"])
end
end
end
push!(listc,mean(temp))
push!(listv,var(temp))
end
return listc,listv
end
function analysis_RTs_SE(result,Rts_coh)
listc=Float64[]
listv=Float64[]
for c in [0 1 2 3 4 5 6 7 8]
tempv = Float64[]
temp=Float64[]
for i in 1:length(result["distri"])-1
if sign(result["distri"][i])==sign(result["distri"][i+1]) &&
result["Confidence"][i+1]>(c-1) &&result["NotError"][i]==-1 &&result["NotError"][i+1]==1
x=result["distri"][i+1]
push!(tempv,result["RTs"][i+1])
push!(temp,(Rts_coh["$x"]-result["RTs"][i+1])/Rts_coh["$x"])
end
end
push!(listc,mean(temp))
push!(listv,var(temp))
end
return listc,listv
end
function analysis_RTs_SS(result,Rts_coh)
listc=Float64[]
listv=Float64[]
for c in [0 1 2 3 4 5 6 7 8]
tempv = Float64[]
temp=Float64[]
for i in 1:length(result["distri"])-1
if sign(result["distri"][i])==sign(result["distri"][i+1]) &&
result["Confidence"][i+1]>(c-1) &&result["NotError"][i]==1 &&result["NotError"][i+1]==1
x=result["distri"][i+1]
push!(tempv,result["RTs"][i+1])
push!(temp,(Rts_coh["$x"]-result["RTs"][i+1])/Rts_coh["$x"])
end
end
push!(listc,mean(temp))
push!(listv,var(temp))
end
return listc,listv
end
function analysis_RTs_E_cohspec(result,Rts_coh)
listc=Float64[]
for c in [0 1 2 3 4 5 6 7 8]
temp=Float64[]
for i in 1:length(result["distri"])-1
if sign(result["distri"][i])==sign(result["distri"][i+1]) &&
result["Confidence"][i+1]==c &&result["NotError"][i]==-1 #&&result["NotError"][i+1]==1
x=result["distri"][i+1]
push!(temp,(Rts_coh["$x"]-result["RTs"][i+1])/Rts_coh["$x"])
end
end
push!(listc,mean(temp))
end
return listc
end
function analysis_RTs_SE_cohspec(result,Rts_coh)
listc=Float64[]
for c in [0 1 2 3 4 5 6 7 8]
temp=Float64[]
for i in 1:length(result["distri"])-1
if sign(result["distri"][i])==sign(result["distri"][i+1]) &&
result["Confidence"][i+1]==c &&result["NotError"][i]==-1 &&result["NotError"][i+1]==1
x=result["distri"][i+1]
push!(temp,(Rts_coh["$x"]-result["RTs"][i+1])/Rts_coh["$x"])
end
end
push!(listc,mean(temp))
end
return listc
end
function analysis_RTs_SS_cohspec(result,Rts_coh)
listc=Float64[]
for c in [0 1 2 3 4 5 6 7 8]
temp=Float64[]
for i in 1:length(result["distri"])-1
if sign(result["distri"][i])==sign(result["distri"][i+1]) &&
result["Confidence"][i+1]==(c-1) &&result["NotError"][i]==1 &&result["NotError"][i+1]==1
x=result["distri"][i+1]
push!(temp,(Rts_coh["$x"]-result["RTs"][i+1])/Rts_coh["$x"])
end
end
push!(listc,mean(temp))
end
return listc
end
function load_Jerome_E()
data = CSV.read("/home/kevin/Documents/PhD/PhD_Data/Jerome_17/secondFirst.csv",nullable=false)
#data = CSV.read("/media/kevin/Boulot/PhD/PhD_Data/Jerome_17/secondFirst.csv",nullable=false)
rt_confidence = [] # for confidence task
results_confidence = []
cursor_confidence = []
coherence_confidence = []
reponse_confidence = []
name_confidence=[]
number=Float64[]
for i=1:length(data[1])
if data[3][i] == "confidence"
push!(reponse_confidence,data[6][i])
push!(cursor_confidence,data[11][i])
push!(coherence_confidence,data[5][i])
push!(rt_confidence,data[7][i])
push!(results_confidence,data[20][i])
push!(name_confidence,data[2][i])
push!(number,parse(Float64,data[1][i]))
end
end
#list_coherence = [-2.4 -1.6 -0.8 0 0.8 1.6 2.4]
#list_coherence_string=["-2.4" "-1.6" "-0.8" "0" "0.8" "1.6" "2.4"]
list_coherence = [-2.4 -1.6 -0.8 0 0.8 1.6 2.4]
list_coherence_string=["-2.4" "-1.6" "-0.8" "0" "0.8" "1.6" "2.4" ]
dic_rt_confidence = Dict()
for coh_string in list_coherence_string
rt_new = rt_confidence[coherence_confidence.==parse(Float64,coh_string)]
dic_rt_confidence[coh_string]=mean(rt_new[rt_new.<1500])
end
dic_rt_cursor_error=Dict()
for cursor in [0 1 2 3 4 5 6 7 8]
rt_list_difference=Float64[]
for i=1:(length(rt_confidence)-1)
if sign(coherence_confidence[i])==sign(coherence_confidence[i+1]) &&
name_confidence[i] == name_confidence[i+1] &&
results_confidence[i] == 0 && number[i] +1 == number[i+1]#&&results_confidence[i+1] == 1
if rt_confidence[i]<1500 && rt_confidence[i+1] <1500 &&
cursor_confidence[i]>cursor #&& cohVEA[i]!=0
x=coherence_confidence[i+1]
if x==0 # because 0 is a float in our data
push!(rt_list_difference,((dic_rt_confidence["0"]-rt_confidence[i+1])/dic_rt_confidence["0"]))
else
push!(rt_list_difference,((dic_rt_confidence["$x"]-rt_confidence[i+1])/dic_rt_confidence["$x"]))
end
end
end
end
println(length(rt_list_difference))
dic_rt_cursor_error["$cursor"]=mean(rt_list_difference)*100
dic_rt_cursor_error["var $cursor"]=var(rt_list_difference*100)/700
dic_rt_cursor_error["label $cursor"]=length(rt_list_difference)
end
exp_list2=Float64[]
var_list2=Float64[]
for cursor in [0 1 2 3 4 5 6 7 8]
push!(exp_list2,dic_rt_cursor_error["$cursor"])
push!(var_list2,dic_rt_cursor_error["var $cursor"])
end
return exp_list2,var_list2
end
function load_Jerome_SS()
data = CSV.read("/home/kevin/Documents/PhD/PhD_Data/Jerome_17/secondFirst.csv",nullable=false)
# data = CSV.read("/media/kevin/Boulot/PhD/PhD_Data/Jerome_17/secondFirst.csv",nullable=false)
rt_confidence = [] # for confidence task
results_confidence = []
cursor_confidence = []
coherence_confidence = []
reponse_confidence = []
name_confidence=[]
number=[]
for i=1:length(data[1])
if data[3][i] == "confidence"
push!(reponse_confidence,data[6][i])
push!(cursor_confidence,data[11][i])
push!(coherence_confidence,data[5][i])
push!(rt_confidence,data[7][i])
push!(results_confidence,data[20][i])
push!(name_confidence,data[2][i])
push!(number,parse(Float64,data[1][i]))
end
end
list_coherence = [-2.4 -1.6 -0.8 0 0.8 1.6 2.4]
list_coherence_string=["-2.4" "-1.6" "-0.8" "0" "0.8" "1.6" "2.4"]
dic_rt_confidence = Dict()
for coh_string in list_coherence_string
rt_new = rt_confidence[coherence_confidence.==parse(Float64,coh_string)]
dic_rt_confidence[coh_string]=mean(rt_new[rt_new.<1500])
println(mean(rt_new[rt_new.<1500]))
end
dic_rt_cursor_error=Dict()
for cursor in [0 1 2 3 4 5 6 7 8]
rt_list_difference=Float64[]
for i=1:(length(rt_confidence)-1)
if coherence_confidence[i]==coherence_confidence[i+1] &&
name_confidence[i] == name_confidence[i+1] &&
number[i]+1==number[i+1] &&
results_confidence[i] == 1 &&results_confidence[i+1] == 1
if rt_confidence[i]<1500 && rt_confidence[i+1] <1500 &&
cursor_confidence[i]>cursor #&& cohVEA[i]!=0
x=coherence_confidence[i+1]
if x==0 # because 0 is a float in our data
push!(rt_list_difference,((rt_confidence[i]-rt_confidence[i+1])/dic_rt_confidence["0"]))
else
push!(rt_list_difference,((rt_confidence[i]-rt_confidence[i+1])/dic_rt_confidence["$x"]))
end
#if x==0 # because 0 is a float in our data
# push!(rt_list_difference,((dic_rt_confidence["0"]-rt_confidence[i+1])/dic_rt_confidence["0"]))
#else
# push!(rt_list_difference,((dic_rt_confidence["$x"]-rt_confidence[i+1])/dic_rt_confidence["$x"]))
#end
end
end
end
println(length(rt_list_difference))
dic_rt_cursor_error["$cursor"]=mean(rt_list_difference)*100
dic_rt_cursor_error["var $cursor"]=var(rt_list_difference)
dic_rt_cursor_error["label $cursor"]=length(rt_list_difference)
end
exp_list2=Float64[]
var_list2=Float64[]
for cursor in [0 1 2 3 4 5 6 7 8]
push!(exp_list2,dic_rt_cursor_error["$cursor"])
push!(var_list2,dic_rt_cursor_error["var $cursor"])
end
return exp_list2,var_list2
end
function load_Jerome_SE_cohspec()
data = CSV.read("/home/kevin/Documents/PhD/PhD_Data/Jerome_17/secondFirst.csv",nullable=false)
# data = CSV.read("/media/kevin/Boulot/PhD/PhD_Data/Jerome_17/secondFirst.csv",nullable=false)
rt_confidence = [] # for confidence task
results_confidence = []
cursor_confidence = []
coherence_confidence = []
reponse_confidence = []
name_confidence=[]
for i=1:length(data[1])
if data[3][i] == "confidence"
push!(reponse_confidence,data[6][i])
push!(cursor_confidence,data[11][i])
push!(coherence_confidence,data[5][i])
push!(rt_confidence,data[7][i])
push!(results_confidence,data[20][i])
push!(name_confidence,data[2][i])
end
end
list_coherence = [-2.4 -1.6 -0.8 0 0.8 1.6 2.4]
list_coherence_string=["-2.4" "-1.6" "-0.8" "0" "0.8" "1.6" "2.4"]
dic_rt_confidence = Dict()
for coh_string in list_coherence_string
rt_new = rt_confidence[coherence_confidence.==parse(Float64,coh_string)]
dic_rt_confidence[coh_string]=mean(rt_new[rt_new.<1500])
end
dic_rt_cursor_error=Dict()
for cursor in [0 1 2 3 4 5 6 7 8]
rt_list_difference=Float64[]
for i=1:(length(rt_confidence)-1)
if sign(coherence_confidence[i])==sign(coherence_confidence[i+1]) &&
name_confidence[i] == name_confidence[i+1] &&
results_confidence[i] == 0 &&results_confidence[i+1] == 1
if rt_confidence[i]<1500 && rt_confidence[i+1] <1500 &&
cursor_confidence[i]==cursor+1 #&& cohVEA[i]!=0
x=coherence_confidence[i+1]
if x==0 # because 0 is a float in our data
push!(rt_list_difference,((dic_rt_confidence["0"]-rt_confidence[i+1])/dic_rt_confidence["0"]))
else
push!(rt_list_difference,((dic_rt_confidence["$x"]-rt_confidence[i+1])/dic_rt_confidence["$x"]))
end
end
end
end
println(length(rt_list_difference))
dic_rt_cursor_error["$cursor"]=mean(rt_list_difference)*100
dic_rt_cursor_error["var $cursor"]=var(rt_list_difference)
dic_rt_cursor_error["label $cursor"]=length(rt_list_difference)
end
exp_list2=Float64[]
var_list2=Float64[]
for cursor in [0 1 2 3 4 5 6 7 8]
push!(exp_list2,dic_rt_cursor_error["$cursor"])
push!(var_list2,dic_rt_cursor_error["var $cursor"])
end
return exp_list2,var_list2
end
function load_Jerome_SE()
data = CSV.read("/home/kevin/Documents/PhD/PhD_Data/Jerome_17/secondFirst.csv",nullable=false)
# data = CSV.read("/media/kevin/Boulot/PhD/PhD_Data/Jerome_17/secondFirst.csv",nullable=false)
rt_confidence = [] # for confidence task
results_confidence = []
cursor_confidence = []
coherence_confidence = []
reponse_confidence = []
name_confidence=[]
number=Float64[]
for i=1:length(data[1])
if data[3][i] == "confidence"
push!(reponse_confidence,data[6][i])
push!(cursor_confidence,data[11][i])
push!(coherence_confidence,data[5][i])
push!(rt_confidence,data[7][i])
push!(results_confidence,data[20][i])
push!(name_confidence,data[2][i])
push!(number,parse(Float64,data[1][i]))
end
end
list_coherence = [-2.4 -1.6 -0.8 0 0.8 1.6 2.4]
list_coherence_string=["-2.4" "-1.6" "-0.8" "0" "0.8" "1.6" "2.4"]
dic_rt_confidence = Dict()
for coh_string in list_coherence_string
rt_new = rt_confidence[coherence_confidence.==parse(Float64,coh_string)]
dic_rt_confidence[coh_string]=mean(rt_new[rt_new.<1500])
end
dic_rt_cursor_error=Dict()
for cursor in [0 1 2 3 4 5 6 7 8]
rt_list_difference=Float64[]
for i=1:(length(rt_confidence)-1)
if sign(coherence_confidence[i])==sign(coherence_confidence[i+1]) &&
name_confidence[i] == name_confidence[i+1] &&
number[i] + 1 == number[i+1] &&
results_confidence[i] == 0 &&results_confidence[i+1] == 1
if rt_confidence[i]<1500 && rt_confidence[i+1] <1500 &&
cursor_confidence[i]>=cursor+1 #&& cohVEA[i]!=0
x=coherence_confidence[i+1]
if x==0 # because 0 is a float in our data
push!(rt_list_difference,((dic_rt_confidence["0"]-rt_confidence[i+1])/dic_rt_confidence["0"]))
else
push!(rt_list_difference,((dic_rt_confidence["$x"]-rt_confidence[i+1])/dic_rt_confidence["$x"]))
end
end
end
end
println(length(rt_list_difference))
dic_rt_cursor_error["$cursor"]=mean(rt_list_difference)*100
dic_rt_cursor_error["var $cursor"]=var(rt_list_difference)
dic_rt_cursor_error["label $cursor"]=length(rt_list_difference)
end
exp_list2=Float64[]
var_list2=Float64[]
for cursor in [0 1 2 3 4 5 6 7 8]
push!(exp_list2,dic_rt_cursor_error["$cursor"])
push!(var_list2,dic_rt_cursor_error["var $cursor"])
end
return exp_list2,var_list2
end
function load_Jerome_nextRTE()
data = CSV.read("/home/kevin/Documents/PhD/PhD_Data/Jerome_17/secondFirst.csv",nullable=false)
# data = CSV.read("/media/kevin/Boulot/PhD/PhD_Data/Jerome_17/secondFirst.csv",nullable=false)
rt_confidence = [] # for confidence task
results_confidence = []
cursor_confidence = []
coherence_confidence = []
reponse_confidence = []
name_confidence=[]
number=Float64[]
for i=1:length(data[1])
if data[3][i] == "confidence"
push!(reponse_confidence,data[6][i])
push!(cursor_confidence,data[11][i])
push!(coherence_confidence,data[5][i])
push!(rt_confidence,data[7][i])
push!(results_confidence,data[20][i])
push!(name_confidence,data[2][i])
push!(number,parse(Float64,data[1][i]))
end
end
list_coherence = [-2.4 -1.6 -0.8 0 0.8 1.6 2.4]
list_coherence_string=["-2.4" "-1.6" "-0.8" "0" "0.8" "1.6" "2.4"]
dic_rt_confidence = Dict()
for coh_string in list_coherence_string
rt_new = rt_confidence[coherence_confidence.==parse(Float64,coh_string)]
dic_rt_confidence[coh_string]=mean(rt_new[rt_new.<1500])
end
dic_rt_cursor_error=Dict()
for cursor in [0 1 2 3 4 5 6 7 8]
rt_list_difference=Float64[]
for i=1:(length(rt_confidence)-1)
if name_confidence[i] == name_confidence[i+1] &&
number[i] + 1 == number[i+1] &&
results_confidence[i] == 0 # &&results_confidence[i+1] == 1
if rt_confidence[i]<1500 && rt_confidence[i+1] <1500 &&
cursor_confidence[i]==cursor+1 #&& cohVEA[i]!=0
x=coherence_confidence[i+1]
if x==0 # because 0 is a float in our data
push!(rt_list_difference,((dic_rt_confidence["0"]-rt_confidence[i+1])))
else
push!(rt_list_difference,((dic_rt_confidence["$x"]-rt_confidence[i+1])))
end
end
end
end
println(length(rt_list_difference))
dic_rt_cursor_error["$cursor"]=mean(rt_list_difference)
dic_rt_cursor_error["var $cursor"]=var(rt_list_difference)
dic_rt_cursor_error["label $cursor"]=length(rt_list_difference)
end
exp_list2=Float64[]
var_list2=Float64[]
for cursor in [0 1 2 3 4 5 6 7 8]
push!(exp_list2,dic_rt_cursor_error["$cursor"])
push!(var_list2,dic_rt_cursor_error["var $cursor"])
end
return exp_list2,var_list2
end
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] | 1.771631 | 12,436 |
using Test
using SafeTestsets
using Plots
unicodeplots()
ENV["GRAPE_LINESEARCH_ANALYSIS_VERBOSE"] = "1"
include(joinpath(@__DIR__, "generate_example_tests.jl"))
include(joinpath(@__DIR__, "download_dumps.jl"))
# Note: comment outer @testset to stop after first @safetestset failure
@time @testset verbose = true "GRAPE.jl Package" begin
print("\n* Example 1 (examples/simple_state_to_state.jl):")
@time @safetestset "Example 1" begin
include(joinpath("examples", "simple_state_to_state.jl"))
end
print("\n* Example 2 (examples/perfect_entanglers.jl):")
@time @safetestset "Example 2" begin
include(joinpath("examples", "perfect_entanglers.jl"))
end
println("")
end
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] | 2.569892 | 279 |
# Here are leftovers from my previous attempts to run the EWS from Julia, including programming the gaussian filter rolling windows.
#Program the gaussian filter as a function and run it before calculating the EWS. Note here that the \sigma is the standard deviation of complete time series. The following code doesn't work!
# import Statistics: var, mean, std
# function gaussian_filter(xin, xout)
# sd = std(skipmissing(xin))
# if sd > 0 # std non-zero
# xout[:].=e^(.-xin^2 ./(2*sd^2))./ (sd*sqrt(2*pi))
# else # time series is probably constant / sd == 0, so avoiding division by zero
# xout[:].=0
# end
# end
## Define input and output dimensions
indims = InDims("Time")
outdims = OutDims("Time")
# test the function
test = mapCube(
gaussian_filter,
subsetcube(d2, Scale = "Fast Oscillations"),
indims=indims, outdims=outdims)
# Not sure this is needed. The TS are normalized to 0-mean and 1-variance
# import Statistics: var, mean, std
# std_dev = mapslices(
# std ∘ skipmissing,
# subsetcube(d2, Scale = "Fast Oscillations"),
# dims = "Time")
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] | 2.83038 | 395 |
@testset "Day5.jl" begin
# Write your tests here.
@test AdventOfCode2020.row("FFFFFFF") == 0
@test AdventOfCode2020.row("BBBBBBB") == 127
@test AdventOfCode2020.row("FFFFFFB") == 1
@test AdventOfCode2020.row("FBFBBFF") == 44
@test AdventOfCode2020.col("LLL") == 0
@test AdventOfCode2020.col("RRR") == 7
@test AdventOfCode2020.col("LLR") == 1
@test AdventOfCode2020.col("RLR") == 5
@test AdventOfCode2020.toseat("FBFBBFFRLR") == AdventOfCode2020.SeatLoc(44,5)
@test AdventOfCode2020.toseat("FBFBBFFRLR").id == 357
@test AdventOfCode2020.toseat("BFFFBBFRRR").id == 567
@test AdventOfCode2020.toseat("FFFBBBFRRR").id == 119
@test AdventOfCode2020.toseat("BBFFBBFRLL").id == 820
end
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] | 2.380645 | 310 |
# ---
# title: 592. Fraction Addition and Subtraction
# id: problem592
# author: Indigo
# date: 2021-06-26
# difficulty: Medium
# categories: Math
# link: <https://leetcode.com/problems/fraction-addition-and-subtraction/description/>
# hidden: true
# ---
#
# Given a string representing an expression of fraction addition and
# subtraction, you need to return the calculation result in string format. The
# final result should be [irreducible
# fraction](https://en.wikipedia.org/wiki/Irreducible_fraction). If your final
# result is an integer, say `2`, you need to change it to the format of fraction
# that has denominator `1`. So in this case, `2` should be converted to `2/1`.
#
# **Example 1:**
#
#
#
# Input: "-1/2+1/2"
# Output: "0/1"
#
#
# **Example 2:**
#
#
#
# Input: "-1/2+1/2+1/3"
# Output: "1/3"
#
#
# **Example 3:**
#
#
#
# Input: "1/3-1/2"
# Output: "-1/6"
#
#
# **Example 4:**
#
#
#
# Input: "5/3+1/3"
# Output: "2/1"
#
#
# **Note:**
#
# 1. The input string only contains `'0'` to `'9'`, `'/'`, `'+'` and `'-'`. So does the output.
# 2. Each fraction (input and output) has format `±numerator/denominator`. If the first input fraction or the output is positive, then `'+'` will be omitted.
# 3. The input only contains valid **irreducible fractions** , where the **numerator** and **denominator** of each fraction will always be in the range [1,10]. If the denominator is 1, it means this fraction is actually an integer in a fraction format defined above.
# 4. The number of given fractions will be in the range [1,10].
# 5. The numerator and denominator of the **final result** are guaranteed to be valid and in the range of 32-bit int.
#
#
## @lc code=start
using LeetCode
function fraction_addition(expression::String)
expression = replace(expression, "/" => "//")
res = eval(Meta.parse(expression))
return "$(res.num)/$(res.den)"
end
## @lc code=end
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] | 2.583548 | 778 |
module OPCSPs
import POMDPs: POMDP, State, Action, Observation, Belief, BeliefUpdater, AbstractSpace, Policy, Solver
import POMDPs: rand, actions, updater, initial_belief, iterator, isterminal
import POMDPs: rand! # TODO this shouldn't be necessary, but it is here to ease transition
import POMDPs: transition, observation, action, reward, update, discount
import POMDPs: create_state, create_observation, create_action, create_belief
import POMDPs: create_observation_distribution, create_transition_distribution
using POMDPs
import Base: ==, hash, length
import MCTS
import POMDPToolbox
include("MVNTools.jl")
using OPCSPs.MVNTools
using AutoHashEquals
using NearestNeighbors
using Distances
export MVNTools
export OPCSP,
SimpleOP,
OPCSPBeliefMDP,
SolveMeanFeedback,
FeedbackSolver,
OPCSPUpdater,
OPSolution,
OPCSPState,
OPCSPAction,
OPCSPBelief,
HeuristicSolver,
GurobiExactSolver,
HeuristicActionGenerator,
PreSolvedActionGenerator,
MCTSAdapter,
SampledFeedbackSolver,
SolveSampledFeedback,
InfluenceBonusFBSolver,
SolveInfluenceBonusFB,
Cheater,
OnlyUnobservableUncertainty,
VoronoiOPCSPAg,
OldVoronoiOPCSPAg,
VoxelOPCSPAg
export solve_op,
solve_opcsp_feedback,
cheat,
gen_opcsp,
gen_op,
gen_two_cluster_problem,
gen_problem,
gen_highly_connected,
gen_informative,
updater,
reward,
distance,
build_path,
within_range,
initial_state,
gurobi_solve,
evaluate_performance,
test_run,
initial_states
type SimpleOP
r::Vector{Float64}
positions::Vector{Vector{Float64}}
distance_limit::Float64
start::Int
stop::Int
distances::Matrix{Float64}
end
function SimpleOP(r, positions, distance_limit=1.0, start=1, stop=-1)
if stop == -1
stop = start
end
return SimpleOP(r, positions, distance_limit, start, stop, find_distances(positions))
end
reward(op::SimpleOP, path::Vector{Int}) = sum([op.r[i] for i in path])
type OPCSP <: POMDP
r::Vector{Float64} # r bar
# d::Vector{Float64}
positions::Vector{Vector{Float64}}
covariance::Matrix{Float64}
distance_limit::Float64
start::Int
stop::Int
distances::Matrix{Float64}
end
function OPCSP(r, positions, covariance, distance_limit=1.0, start=1, stop=-1)
if stop == -1
stop = start
end
return OPCSP(r, positions, covariance, distance_limit, start, stop, find_distances(positions))
end
reward(op, d::Vector{Float64}, path::Vector{Int}) = sum(op.r[path] + d[path])
include("pomdp.jl")
include("mdp.jl")
include("action_space.jl")
include("problems.jl")
include("util.jl")
include("solutions.jl")
include("rollouts.jl")
include("heuristics.jl")
include("policies.jl")
include("mcts.jl")
include("feedback.jl")
include("evaluation.jl")
include("visualization.jl")
end # module
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41464,
1634,
13,
20362,
4943,
198,
198,
437,
1303,
8265,
198
] | 2.378165 | 1,264 |
include("Parser.jl")
include("Lexer.jl")
include("Token.jl")
include("Transpile.jl")
| [
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] | 2.6875 | 32 |
@testset "972.equal-rational-numbers.jl" begin
@test is_rational_equal("0.(52)", "0.5(25)") == true
@test is_rational_equal("0.1666(6)", "0.166(66)") == true
@test is_rational_equal("0.9(9)", "1.") == true
end | [
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] | 2.278351 | 97 |
using DrWatson
@quickactivate "ChaosThroughBilliards"
include(srcdir("style.jl"))
# re = billiard_rectangle(x, y)
# si = billiard_sinai(y/4, x, y)
# st = billiard_stadium(x-y, y)
bd = Billiard(Antidot([0.0, 0.0], 1.5, false))
InteractiveDynamics.obfill(::Antidot) = RGBAf0(0,0,0,0)
InteractiveDynamics.obls(::Antidot) = nothing
InteractiveDynamics.obcolor(::Antidot) = to_color(:white)
N = 8
ps = [Particle(0, 0, 2π*(i/N)) for i in 1:N]
# interactive_billiard(bd, ps;
# backgroundcolor = :black, particle_size = 2.0, add_controls = false)
billiard_video(
videodir("circle.mp4"), bd, deepcopy(ps);
frames = 80, backgroundcolor = :black, colors = COLORS,
tailwidth = 3.5, particle_size = 2, res = (1200, 1200),
plot_particles = true,
dt = 0.0001, speed = 150, tail = 10000, # this makes ultra fine temporal resolution
)
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] | 2.421203 | 349 |
# Simple data provider that load text
using Iterators
using MXNet
function build_vocabulary(corpus_fn::AbstractString, vocab_fn::AbstractString; max_vocab=10000)
if isfile(vocab_fn)
info("Vocabulary already exists, reusing $vocab_fn...")
vocab = Dict{Char,Int}([w => i for (i,w) in enumerate(readall(vocab_fn))])
else
# count symbol frequency
dict = Dict{Char,Int}()
open(corpus_fn) do io
for line in eachline(io)
for c in line
dict[c] = get(dict, c, 0) + 1
end
end
end
vocab = sort(collect(dict), by=x->-x.second)
vocab = vocab[1:min(max_vocab,length(vocab))]
open(vocab_fn, "w") do io
for x in vocab
print(io, x.first)
end
end
vocab = Dict([x.first => i for (i,x) in enumerate(vocab)])
end
vocab[UNKNOWN_CHAR] = length(vocab)
return vocab
end
#--CharSeqProvider
type CharSeqProvider <: mx.AbstractDataProvider
text :: AbstractString
batch_size :: Int
seq_len :: Int
vocab :: Dict{Char,Int}
prefix :: Symbol
n_layer :: Int
dim_hidden :: Int
end
#--/CharSeqProvider
function mx.get_batch_size(p :: CharSeqProvider)
p.batch_size
end
#--provide
function mx.provide_data(p :: CharSeqProvider)
[(symbol(p.prefix, "_data_$t"), (length(p.vocab), p.batch_size)) for t = 1:p.seq_len] ∪
[(symbol(p.prefix, "_l$(l)_init_c"), (p.dim_hidden, p.batch_size)) for l=1:p.n_layer] ∪
[(symbol(p.prefix, "_l$(l)_init_h"), (p.dim_hidden, p.batch_size)) for l=1:p.n_layer]
end
function mx.provide_label(p :: CharSeqProvider)
[(symbol(p.prefix, "_label_$t"), (p.batch_size,)) for t = 1:p.seq_len]
end
#--/provide
#--eachbatch-part1
function mx.eachbatch(p :: CharSeqProvider)
data_all = [mx.zeros(shape) for (name, shape) in mx.provide_data(p)]
label_all = [mx.zeros(shape) for (name, shape) in mx.provide_label(p)]
data_jl = [copy(x) for x in data_all]
label_jl= [copy(x) for x in label_all]
batch = mx.DataBatch(data_all, label_all, p.batch_size)
#...
#--/eachbatch-part1
#--eachbatch-part2
#...
function _text_iter()
text = p.text
n_batch = floor(Int, length(text) / p.batch_size / p.seq_len)
text = text[1:n_batch*p.batch_size*p.seq_len] # discard tailing
idx_all = 1:length(text)
for idx_batch in partition(idx_all, p.batch_size*p.seq_len)
for i = 1:p.seq_len
data_jl[i][:] = 0
label_jl[i][:] = 0
end
for (i, idx_seq) in enumerate(partition(idx_batch, p.seq_len))
for (j, idx) in enumerate(idx_seq)
c_this = text[idx]
c_next = idx == length(text) ? UNKNOWN_CHAR : text[idx+1]
data_jl[j][char_idx(vocab,c_this),i] = 1
label_jl[j][i] = char_idx(vocab,c_next)-1
end
end
for i = 1:p.seq_len
copy!(data_all[i], data_jl[i])
copy!(label_all[i], label_jl[i])
end
produce(batch)
end
end
return Task(_text_iter)
end
#--/eachbatch-part2
# helper function to convert a char into index in vocabulary
function char_idx(vocab :: Dict{Char,Int}, c :: Char)
if haskey(vocab, c)
vocab[c]
else
vocab[UNKNOWN_CHAR]
end
end
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] | 2.160959 | 1,460 |
module TestCommon
include("preamble.jl")
using RecordArrays.Implementations: check_eltype
struct Node{T}
head::T
tail::Union{Nothing,Node{T}}
end
@testset "check_eltype" begin
@test check_eltype(Some{Int}) === nothing
@test check_eltype(typeof((a = 1, b = 2))) === nothing
@testset "Ref" begin
err = @test_error check_eltype(typeof(Ref(0))) === nothing
@test "mutable type" ⊏ sprint(showerror, err)
end
@testset "Integer" begin
err = @test_error check_eltype(Integer) === nothing
@test "concrete type is required" ⊏ sprint(showerror, err)
end
@testset "Node{Int}" begin
err = @test_error check_eltype(Node{Int}) === nothing
@test "GC-managed" ⊏ sprint(showerror, err)
end
end
end # module
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] | 2.40184 | 326 |
function forward(backend::GPUBackend, state::HingeLossLayerState, inputs::Vector{Blob})
pred = inputs[1]
label = inputs[2]
data_type = eltype(pred)
n = length(pred)
x_block = div(n + CUDA.THREADS_PER_BLOCK_X-1, CUDA.THREADS_PER_BLOCK_X)
if length(state.loss_blob) < x_block
destroy(state.loss_blob)
state.loss_blob = make_blob(backend, data_type, x_block)
end
if data_type == Float32
kernel = backend.mocha.hinge_loss_forward_float
elseif data_type == Float64
kernel = backend.mocha.hinge_loss_forward_double
else
error("Unsupported data type $data_type")
end
CUDA.launch(kernel, (x_block,1), (CUDA.THREADS_PER_BLOCK_X, 1),
(pred.ptr.p, label.ptr.p, n, state.loss_blob.ptr.p))
losses = Array{data_type}(size(state.loss_blob)...)
copy!(losses, state.loss_blob)
state.loss = state.layer.weight * sum(losses[1:x_block]) / get_num(pred)
# accumulate statistics
state.loss_accum = (state.loss_accum*state.n_accum + state.loss*get_num(pred)) / (state.n_accum+get_num(pred))
state.n_accum += get_num(pred)
end
function backward(backend::GPUBackend, state::HingeLossLayerState, inputs::Vector{Blob}, diffs::Vector{Blob})
const pred = inputs[1]
const label = inputs[2]
const data_type = eltype(pred)
const n = length(pred)
const neg_weight :: data_type = -state.layer.weight / get_num(pred)
const x_block = div(n + CUDA.THREADS_PER_BLOCK_X-1, CUDA.THREADS_PER_BLOCK_X)
if data_type == Float32
kernel = backend.mocha.hinge_loss_backward_float
elseif data_type == Float64
kernel = backend.mocha.hinge_loss_backward_double
else
error("Unsupported data type $data_type")
end
const gradient1 :: CuPtr = isa(diffs[1], CuTensorBlob) ? diffs[1].ptr : CuPtr()
const gradient2 :: CuPtr = isa(diffs[2], CuTensorBlob) ? diffs[2].ptr : CuPtr()
CUDA.launch(kernel, (x_block,1), (CUDA.THREADS_PER_BLOCK_X,1),
(pred.ptr.p, label.ptr.p, gradient1.p, gradient2.p, n, neg_weight))
end
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] | 2.432234 | 819 |
using Oceananigans.Utils: cell_advection_timescale
using Oceananigans.TurbulenceClosures: cell_diffusion_timescale
"""
CFL{D, S}
An object for computing the Courant-Freidrichs-Lewy (CFL) number.
"""
struct CFL{D, S}
Δt :: D
timescale :: S
end
"""
CFL(Δt [, timescale=Oceananigans.cell_advection_timescale])
Returns an object for computing the Courant-Freidrichs-Lewy (CFL) number
associated with time step or `TimeStepWizard` `Δt` and `timescale`.
See also `AdvectiveCFL` and `DiffusiveCFL`.
"""
CFL(Δt) = CFL(Δt, cell_advection_timescale)
(c::CFL)(model) = c.Δt / c.timescale(model)
"""
AdvectiveCFL(Δt)
Returns an object for computing the Courant-Freidrichs-Lewy (CFL) number
associated with time step or `TimeStepWizard` `Δt` and the time scale
for advection across a cell.
Example
=======
```julia
julia> model = NonhydrostaticModel(grid=RegularRectilinearGrid(size=(16, 16, 16), length=(8, 8, 8)));
julia> cfl = AdvectiveCFL(1.0);
julia> data(model.velocities.u) .= π;
julia> cfl(model)
6.283185307179586
```
"""
AdvectiveCFL(Δt) = CFL(Δt, cell_advection_timescale)
"""
DiffusiveCFL(Δt)
Returns an object for computing the diffusive Courant-Freidrichs-Lewy (CFL) number
associated with time step or `TimeStepWizard` `Δt` and the time scale for diffusion
across a cell associated with `model.closure`.
The maximum diffusive CFL number among viscosity and all tracer diffusivities is
returned.
Example
=======
```julia
julia> model = NonhydrostaticModel(grid=RegularRectilinearGrid(size=(16, 16, 16), length=(1, 1, 1)));
julia> dcfl = DiffusiveCFL(0.1);
julia> dcfl(model)
2.688e-5
```
"""
DiffusiveCFL(Δt) = CFL(Δt, cell_diffusion_timescale)
#####
##### Accurate CFL via reduction
#####
using CUDA, CUDAKernels, KernelAbstractions, Tullio
using Oceananigans.Models
using Oceananigans.Grids: halo_size
using Oceananigans.Operators: Δxᶠᶜᵃ, Δyᶜᶠᵃ, Δzᵃᵃᶠ
accurate_cell_advection_timescale(model) = accurate_cell_advection_timescale(model.grid, model.velocities)
function accurate_cell_advection_timescale(grid, velocities)
Nx, Ny, Nz = size(grid)
Hx, Hy, Hz = halo_size(grid)
is = 1+Hx:Nx+Hx
js = 1+Hy:Ny+Hy
ks = 1+Hz:Nz+Hz
u = view(velocities.u.data.parent, is, js, ks)
v = view(velocities.v.data.parent, is, js, ks)
w = view(velocities.w.data.parent, is, js, ks)
min_timescale = minimum(
@tullio (min) timescale[k] := 1 / ( abs(u[i, j, k]) / Δxᶠᶜᵃ(i, j, k, grid)
+ abs(v[i, j, k]) / Δyᶜᶠᵃ(i, j, k, grid)
+ abs(w[i, j, k]) / Δzᵃᵃᶠ(i, j, k, grid))
)
return min_timescale
end
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220,
220,
1441,
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62,
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38765,
198,
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] | 2.215589 | 1,206 |
using Test, Sym
@testset "tracing" begin
end
@testset "match" begin
end
@testset "simplification" begin
end
| [
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] | 2.707317 | 41 |
# Use baremodule to shave off a few KB from the serialized `.ji` file
baremodule SuiteSparse_LBT_jll
using Base
using Base: UUID
import JLLWrappers
JLLWrappers.@generate_main_file_header("SuiteSparse_LBT")
JLLWrappers.@generate_main_file("SuiteSparse_LBT", UUID("8a7bcfce-81ee-5a07-a07c-c8520ac69669"))
end # module SuiteSparse_LBT_jll
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] | 2.485294 | 136 |
# For slope correction on contact frequencies:
const INCIDENCE_GROUPS = 360 #number of τ groups over [0, pi/2]
const MAX_ITER_τ = 5 # (Schleppi, 2007) says "after a few cycles"
const SLOPE_TOL = 1e-3
#fallback
# WHY??
#gapfraction(pixs, thresh) = mean(pixs .> thresh)
#in general specialize on type of input array
function gapfraction(pixs::AbstractArray, thresh)
threshT = convert(eltype(pixs), thresh)
gapfrsum = 0
for pix in pixs
gapfrsum += pix > threshT
end
return(gapfrsum / length(pixs))
end
function loggapfraction(pixs, thresh)
gf = gapfraction(pixs, thresh)
gf == zero(gf) && return(log(1/length(pixs)))
log(gf)
end
"Create rings with similar amount of pixels per ring. Outputs the edges and weigted midpoints of the rings."
function weightedrings(polim::PolarImage, θ1::Real, θ2::Real, N::Integer)
fθρ = polim.cl.fθρ
# create edges for θ rings with similar number of pixels each
θedges = map(polim.cl.fρθ, sqrt.(range(fθρ(θ1)^2, stop=fθρ(θ2)^2, length=N+1)))
#fix possible floating point roundoff errors
θedges[1] = max(θedges[1], θ1)
θedges[end] = min(θedges[end], θ2)
# weighted average midpoints
θdouble = map(polim.cl.fρθ, sqrt.(range(fθρ(θ1)^2, stop=(fθρ(θ2))^2, length=2N+1)))
θmid = θdouble[2:2:2N]
return θedges, θmid
end
weightedrings(polim::PolarImage, N::Integer) = weightedrings(polim, 0, pi/2, N)
function contactfreqs(polim::PolarImage, θ1::Real, θ2::Real, N::Integer, thresh;
Nτ=INCIDENCE_GROUPS, max_iter=MAX_ITER_τ, tol=SLOPE_TOL)
checkθ1θ2(θ1,θ2)
θedges, θmid = weightedrings(polim, θ1, θ2, N)
K = zeros(N)
## WITHOUT SLOPE ##
if !hasslope(polim)
for i = 1:N
logT = loggapfraction(pixels(polim, θedges[i], θedges[i+1]), thresh)
K[i] = -logT * cos(θmid[i])
end
return θedges, θmid, K
end
## WITH SLOPE ##
Nτ = max(Nτ, N) #at least as many incidence groups as zenith groups required
for i = 1:N
pixs = pixels(polim, θedges[i], θedges[i+1])
ind_first, ind_last = firstlastind(polim, θedges[i], θedges[i+1])
cosτ = view(polim.slope.cosτsort, ind_first:ind_last)
K[i] = contactfreqs_iterate(pixs, cosτ, thresh, θmid[i];
Nτ=Nτ, max_iter=max_iter, tol=tol)
# Method España et al 2007. Nϕ different here!
# adj = slope_adj(polim.slope, θmid[i], ϕv)
# # we divide each ring in Nϕ azimuth segments, calculate the slope
# # adjustment and loggapfraction per segment, then take average weighted
# # by segment length.
# segm = segments(polim, θedges[i], θedges[i+1], Nϕ)
# lengths = Int[length(seg) for seg in segm]
# T = Float64[gapfraction(seg, thresh) for seg in segm]
# nz = find(T) # avoid 0.^(negative float)
# if isempty(nz) #to avoid infinity with log, assume at least 1 sky pixel
# Tadj = 1 / sum(lengths)
# else
# Tadj = sum(T[nz].^(1./adj[nz]) .* lengths[nz])/sum(lengths)
# end
# K[i] = -log(Tadj) * cos(θmid[i])
end
θedges, θmid, K
end
# Method Schleppi et al 2007
function contactfreqs_iterate(pixs::AbstractArray, cosτ::AbstractArray, thresh, θ::Float64;
Nτ=INCIDENCE_GROUPS, max_iter=MAX_ITER_τ, tol=SLOPE_TOL)
τmax = π/2
τ = StatsBase.midpoints(range(0, stop=τmax, length=Nτ+1))
Aθτ = fasthist(acos.(cosτ), -1/Nτ : τmax/Nτ : τmax)
iter = 0
# initially start with contact frequency K from whole θ ring
logT = loggapfraction(pixs, thresh)
K = - logT * cos(θ)
while iter < max_iter
iter += 1
sum(Aθτ) == 0 && break
Tnew = sum(Aθτ .* exp.(-K ./ cos.(τ))) / sum(Aθτ)
logTnew = log(Tnew)
abs(logTnew / logT - 1) < tol && break
K *= logTnew / logT
logT = logTnew
end
K
end
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220,
220,
220,
886,
198,
220,
220,
220,
509,
198,
437,
628,
198
] | 1.96345 | 2,052 |
@testset "De Bruijn Neighbors" begin
@test collect(neighbors(DNAKmer("ACG"))) ==
map(DNAKmer, ["CGA", "CGC", "CGG", "CGT"])
@test collect(neighbors(DNAKmer("GGGG"))) ==
map(DNAKmer, ["GGGA", "GGGC", "GGGG", "GGGT"])
@test collect(neighbors(RNAKmer("ACG"))) ==
map(RNAKmer, ["CGA", "CGC", "CGG", "CGU"])
@test collect(neighbors(RNAKmer("GGGG"))) ==
map(RNAKmer, ["GGGA", "GGGC", "GGGG", "GGGU"])
end
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] | 2.013274 | 226 |
abstract type Velocity end
struct NoVelocity<:Velocity end
struct GaussianVelocity<:Velocity
T::Float64
seed::Int64
function GaussianVelocity( T::Float64,
seed::Int64)
new(T::Float64,
seed::Int64)
end
end
struct ZeroVelocity<:Velocity end | [
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] | 2.123288 | 146 |
isalpha(c) = isascii(c) && (islowercase(c) || isuppercase(c))
isalnum(c) = isascii(c) && (islowercase(c) || isuppercase(c) || isdigit(c))
"""
html_entities(ts::TokenBuffer; remove_illegal=true)
Removes entities from text by converting them to their corresponding unicode character.
`remove_illegal::Bool` If `true`, entities that can't be converted are
removed. Otherwise, entities that can't be converted are kept "as
is".
HTML entity can be named or encoded in Decimal/Hex form
- Named_entity : "Δ" => "Δ",
- Decimal : "Δ" => "Δ",
- Hex : "Δ" => "Δ",
However for bytes (hex) 80-9f are interpreted in Windows-1252
"""
function html_entity(ts::TokenBuffer, remove_illegal=true)
(ts.idx + 1 > length(ts.input) || ts.input[ts.idx] != '&' ) && return false
if ts.input[ts.idx + 1] != '#' # Entity is of the type "Δ" => "Δ"
i = ts.idx + 1
while i <= length(ts.input) && isalnum(ts[i])
i += 1
end
(i > length(ts.input) || ts[i] != ';') && return false
entity = lookupname(HTML_Entities.default, String(ts[ts.idx+1:i-1]))
isempty(entity) && !remove_illegal && return false
!isempty(entity) && push!(ts.buffer, entity[1])
ts.idx = i + 1
return true
else
number = -1
i = ts.idx + 2
if ts.input[ts.idx + 2] != 'x' # Entity is of the type "Δ" => "Δ"
while i <= length(ts.input) && isdigit(ts[i])
i += 1
end
(i > length(ts.input) || ts[i] != ';') && return false
if ((ts.idx + 2 ) == i)
!remove_illegal && return false
ts.idx +=3
return true
end
(number = parse(Int, String(ts[ts.idx+2:i-1]), base=10))
else # Entity is of the type "Δ" => "Δ"
i += 1
base16letters = ('a', 'b', 'c', 'd', 'e', 'f')
while i <= length(ts.input) && (isdigit(ts[i]) || ts[i] in base16letters)
i += 1
end
(i > length(ts.input) || ts[i] != ';') && return false
if (ts.idx + 3) == i
!remove_illegal && return false
ts.idx += 4
return true
end
number = parse(Int, String(ts[ts.idx+3:i-1]), base=16)
end
windows_1252_chars = ('€', '\u81', '‚', 'ƒ', '„', '…', '†', '‡', 'ˆ', '‰',
'Š', '‹', 'Œ', '\u8d','Ž', '\u8f', '\u90', '‘', '’',
'“', '”', '•', '–', '—', '˜', '™', 'š', '›', 'œ',
'\u9d', 'ž', 'Ÿ')
if 0x80 <= number <= 0x9F
push!(ts.buffer, windows_1252_chars[number - 127])
ts.idx = i + 1
return true
end
if (number <= 0 || !Unicode.isassigned(number))
!remove_illegal && return false
ts.idx = i + 1
else
push!(ts.buffer, Char(number))
ts.idx = i + 1
end
end
return true
end
"""
lookbehind(ts::TokenBuffer)
Checks if the beginning of the detected handle is preceded by alphanumeric
or special characters like('_', '!', '@', '#', '\$', '%', '&', '*')
"""
function lookbehind(ts::TokenBuffer,
match_pattern = ('_', '!', '@', '#', '$', '%', '&', '*'))
ts.idx == 1 && return false
c = ts[ts.idx - 1]
( islowercase(c) || isdigit(c) || isuppercase(c) || c ∈ match_pattern ) && return true
return false
end
"""
strip_twitter_handle(ts::TokenBuffer)
For removing Twitter Handles. If it detects a twitter handle, then it jumps to
makes the index of TokenBuffer to the desired location skipping the handle.
"""
function strip_twitter_handle(ts)
(ts.idx + 2 > length(ts.input) || ts.input[ts.idx] != '@' ) && return false
lookbehind(ts) && return false
i = ts.idx + 1
while i <= length(ts.input) && (isalnum(ts[i]) || ts[i] == '_')
i += 1
end
(i <= length(ts.input)) && (i == ts.idx + 1 || ts[i] == '@') && return false
ts.idx = i
return true
end
"""
reduce_all_repeated(ts::TokenBuffer)
For handling repeated characters like "helloooooo" -> :hellooo".
"""
function reduce_all_repeated(ts)
ts.idx + 4 > length(ts.input) && return false
(ts[ts.idx] == '\n' || ts[ts.idx] != ts[ts.idx + 1] ||
ts[ts.idx] != ts[ts.idx + 2]) && return false
i = ts.idx + 3
while i <= length(ts.input) && ts[i] == ts[ts.idx]
i += 1
end
for j in 1:3
push!(ts.buffer, ts[ts.idx])
end
ts.idx = i
return true
end
"""
safe_text(ts::TokenBuffer)
This feature covers up for the characters where the main tokenizing function lacks
For example - "........" -> "..." and this is detected by the key tokenizer as a
single token of "..."
"""
function safe_text(ts)
ts.idx + 4 > length(ts.input) && return false
(isalnum(ts[ts.idx]) || ts[ts.idx] != ts[ts.idx + 1] ||
ts[ts.idx] != ts[ts.idx + 2] ) && return false
i = ts.idx + 3
while i <= length(ts.input) && ts[i] == ts[ts.idx]
i += 1
end
for j in 1:3
push!(ts.buffer, ts[ts.idx])
end
ts.idx = i
return true
end
"""
replace_html_entities(input::AbstractString, remove_illegal=true)
`input::AbstractString` The string on which HTML entities need to be replaced
`remove_illegal::Bool` If `true`, entities that can't be converted are
removed. Otherwise, entities that can't be converted are kept "as
is".
"""
function replace_html_entities(input::AbstractString; remove_illegal=true)
ts = TokenBuffer(input)
isempty(input) && return ""
while !isdone(ts)
html_entity(ts, remove_illegal) || character(ts)
end
return ts.tokens[1]
end
"""
function pre_process(input::AbstractString, strip_handle::Bool,
reduce_len::Bool)
This function processes on the input string and optionally remove twitter handles
and reduce length of repeated characters (like "waaaaay" -> "waaay")
and for elements like ".........?????? -> "...???" to increase the performance
of the key tokenizer.
"""
function pre_process(input::AbstractString, strip_handle::Bool, reduce_len::Bool)
ts = TokenBuffer(input)
isempty(input) && return ""
while !isdone(ts)
(strip_handle && strip_twitter_handle(ts)) || # Remove username handles
(reduce_len && reduce_all_repeated(ts)) || # Reduce Lengthening
safe_text(ts) || # Shorten some sequences of characters
character(ts)
end
return ts.tokens[1]
end
function flushaboutindex!(ts::TokenBuffer, uptoidx)
flush!(ts, String(ts[ts.idx:uptoidx]))
ts.idx = uptoidx + 1
return true
end
const forehead = ('>', '<')
const eyes = (':', ';', '=', '8')
const nose = ('-', 'o', '*', '\'')
const mouth = (')', ']', '}', '(', '[', '{', 'd', 'D', 'p', 'P', '\\', '/', ':', '@', '|')
"""
function emoticons(ts::TokenBuffer)
This function checks for the emoticons for the type `{forehead}{eyes}{nose}{mouth}
explicitely in this order, with {forehead} and {nose} being optional
Example:
- `:)`, `;p` # (without nose and forehead)
- `:-)`, `:-p` # (with nose)
- `>:)` # (with forehead)
- `>:-)` # (with forehead and nose)
Also checks for `<3` emoji
"""
function emoticons(ts)
ts.idx + 1 > length(ts.input) && return false
idx = ts.idx
ts[idx] ∈ eyes && (
(ts[idx + 1] ∈ mouth && return flushaboutindex!(ts, idx + 1)) ||
(idx + 2 <= length(ts.input) && ts[idx + 1] ∈ nose && ts[idx + 2] ∈ mouth &&
return flushaboutindex!(ts, idx + 2)) ||
return false
)
idx + 2 <= length(ts.input) && ts[idx] ∈ forehead && ts[idx + 1] ∈ eyes && (
(ts[idx + 2] ∈ mouth && return flushaboutindex!(ts, idx + 2)) ||
(idx + 3 <= length(ts.input) && ts[idx + 2] ∈ nose &&
ts[idx + 3] ∈ mouth && return flushaboutindex!(ts, idx + 3)) ||
return false
)
ts[idx] == '<' && ts[idx + 1] == '3' && return flushaboutindex!(ts, idx + 1)
return false
end
"""
function emoticonsreverse(ts::TokenBuffer)
This function checks for the emoticons in reverse order to those of `function emoticons`
explicitely in this order `{mouth}{nose}{eyes}{forehead}`, with {forehead} and {nose} being optional
Example:
- `(:`, `d:` # (without nose and forehead)
- `(-:`, `d-:` # (with nose)
- (:<` # (with forehead)
- `(-:<` # (with forehead and nose)
"""
function emoticonsreverse(ts)
ts.idx + 1 > length(ts.input) && return false
idx = ts.idx
ts[idx] ∈ mouth || return false
idx += 1
if ts[idx] ∈ nose
idx >= length(ts.input) && return false
idx += 1
end
ts[idx] ∈ eyes|| return false
idx += 1
idx <= length(ts.input) && ts[idx] ∈ forehead && return flushaboutindex!(ts, idx)
return flushaboutindex!(ts, idx -1 )
end
"""
htmltags(ts::TokenBuffer)
Matches the HTML tags which contain no space inside the tags.
"""
function htmltags(ts)
(ts.idx + 2 > length(ts.input) || ts[ts.idx] != '<'
|| ts[ts.idx + 1] == '>') && return false
i = ts.idx
while i <= length(ts.input) && ts[i] != '>'
isspace(ts[i]) && return false
i += 1
end
i > length(ts.input) && return false
return flushaboutindex!(ts, i)
end
"""
arrowsascii(ts::TokenBuffer)
Matches the ascii arrows - made up of arrows like `<--` and `--->`
"""
function arrowsascii(ts)
(
ts.idx + 1 > length(ts.input) ||
(
(ts[ts.idx] != '<' || ts[ts.idx + 1] != '-' ) &&
(ts[ts.idx] != '-')
)
) && return false
i = ts.idx
if ts[i] == '<'
i += 1
while i <= length(ts.input) && ts[i] == '-'
i += 1
end
return flushaboutindex!(ts, i - 1)
end
while i <= length(ts.input) && ts[i] == '-'
i += 1
end
ts[ts.idx] == '>' && return flushaboutindex!(ts, i)
end
# TODO: integrate this with words_including_apostrophe_dashes() to reduce time taken.
# Checks the string till non word char appears, so takes relatively more time.
"""
emailaddresses(ts)
Matches for email addresses.
"""
function emailaddresses(ts)
ts.idx + 4 > length(ts.input) && return false
i = ts.idx
while i + 3 <= length(ts.input) && (isalnum(ts[i]) || ts[i] ∈ ('.', '+', '-', '_'))
i += 1
end
(i == ts.idx || ts[i] != '@') && return false
i += 1
j = i
while i + 2 <= length(ts.input) && (isalnum(ts[i]) || ts[i] == '-' || ts == '_')
i += 1
end
(j == i || ts[i] != '.') && return false
j = i
last_dot = i
i += 1
while i <= length(ts.input) && (isalnum(ts[i]) || ts[i] == '-' || ts == '_')
if i + 1 < length(ts.input) && ts[i + 1] == '.'
i += 1
last_dot = i
end
i += 1
end
i > last_dot + 1 && i > j + 2 && return flushaboutindex!(ts, i - 1)
return false
end
"""
twitterhashtags(ts)
Matches for twitter hashtags.
"""
function twitterhashtags(ts)
(ts.idx + 2 > length(ts.input) || ts[ts.idx] != '#' ||
ts[ts.idx + 1] ∈ ('\'', '-')) && return false
i = ts.idx + 1
last_word_char = i
while i <= length(ts.input) && (isalnum(ts[i]) || ts[i] ∈ ('_', '\'', '-'))
if ts[i] ∉ ('\'', '-')
last_word_char = i
end
i += 1
end
last_word_char >= ts.idx + 2 && ts[ts.idx + 1] ∉ ('\'', '-') &&
ts[last_word_char] ∉ ('\'', '-') && return flushaboutindex!(ts, last_word_char)
return false
end
"""
twitterusername(ts)
Matches for twitter usernames.
"""
function twitterusername(ts)
(ts.idx + 1 > length(ts.input) || ts[ts.idx] != '@' ) && return false
i = ts.idx + 1
while i <= length(ts.input) && (isalnum(ts[i]) || ts[i] == '_')
i += 1
end
i > ts.idx + 1 && return flushaboutindex!(ts, i - 1)
return false
end
"""
ellipsis_dots(ts)
Matches for ellipsis and dots, ignoring the spaces, tabs, newlines between them.
"""
function ellipsis_dots(ts)
(ts.idx + 1 > length(ts.input) || ts[ts.idx] != '.' ) && return false
i = ts.idx + 1
last_dot = ts.idx
while i <= length(ts.input) && (isspace(ts[i]) || ts[i] == '.')
if ts[i] == '.'
last_dot = i
end
i += 1
end
last_dot != ts.idx && return flushaboutindex!(ts, last_dot)
return false
end
"""
words_including_apostrophe_dashes(ts)
TokenBuffer matcher for words that may or maynot have dashes or apostrophe in it.
"""
function words_including_apostrophe_dashes(ts)
ts.idx + 1 > length(ts.input) && return false
isalnum(ts[ts.idx]) || ts[ts.idx] == '_' || return false
has_apostrophe_dashes = false
i = ts.idx + 1
last_char = ts.idx
if isuppercase(ts[ts.idx]) || islowercase(ts[ts.idx])
while i <= length(ts.input) && (isalpha(ts[i]) || ts[i] ∈ ('_', '\'', '-'))
if has_apostrophe_dashes == false && ts[i] ∈ ('\'', '-')
has_apostrophe_dashes = true
else
last_char = i
end
i += 1
end
end
has_apostrophe_dashes && last_char != ts.idx && return flushaboutindex!(ts, last_char)
while i <= length(ts.input) && (isalnum(ts[i]) || ts[i] == '_')
i += 1
end
return flushaboutindex!(ts, i - 1)
end
const allowed_chars_phone_numbers = (' ', '*', '-', '.', ')')
"""
nltk_casual_phonenumbers(ts)
The TokenBuffer function for nltk's tweet tokenizer regex for phonenumbers.
"""
function nltk_phonenumbers(ts)
(ts.idx + 5 > length(ts.input) || !(isdigit(ts[ts.idx]) ||
ts[ts.idx] ∈ ('+', '('))) && return false
i = ts.idx
optional_1_confirmed = false
# Checking for the part 1 of regex which is optional
if ts[i] == '+'
ts[i + 1] == '0' || ts[i + 1] == '1' || return false
i += 2
while i <= length(ts.input) && ts[i] ∈ allowed_chars_phone_numbers
i += 1
end
i + 5 > length(ts.input) && return false
optional_1_confirmed = true
elseif ts[i] == '0' || ts[i] == '1'
i += 1
while i <= length(ts.input) && ts[i] ∈ allowed_chars_phone_numbers
i += 1
end
i + 5 > length(ts.input) && return false
if i - ts.idx > 1 || ts[i] == '('
optional_1_confirmed = true
end
end
if i == ts.idx || optional_1_confirmed
# This is called when either the first part is sure to present or absent, otherwise next one called
if ts[i] == '('
i += 1
for repeat in 1:2 # repeat is unused variable inside loop
if !(i + 3 <= length(ts.input) && isdigit(ts[i]) && isdigit(ts[i + 1]) &&
isdigit(ts[i + 2]))
return false
end
i += 3
while i <= length(ts.input) && ts[i] ∈ allowed_chars_phone_numbers
i += 1
end
end
!(i + 3 <= length(ts.input) && isdigit(ts[i]) && isdigit(ts[i + 1]) &&
isdigit(ts[i + 2]) && isdigit(ts[i + 3])) && return false
return flushaboutindex!(ts, i + 3)
else
if !(i + 3 <= length(ts.input) && isdigit(ts[i]) && isdigit(ts[i + 1]) &&
isdigit(ts[i + 2]))
return false
end
i += 3
while i <= length(ts.input) && ts[i] ∈ allowed_chars_phone_numbers
i += 1
end
if !(i + 3 <= length(ts.input) && isdigit(ts[i]) && isdigit(ts[i + 1]) &&
isdigit(ts[i + 2]))
return false
end
i += 3
j = i
while i <= length(ts.input) && ts[i] ∈ allowed_chars_phone_numbers
i += 1
end
i + 3 <= length(ts.input) && isdigit(ts[i]) && isdigit(ts[i + 1]) &&
isdigit(ts[i + 2]) && isdigit(ts[i + 3]) && return flushaboutindex!(ts, i + 3)
isdigit(ts[j]) && return flushaboutindex!(ts, j)
return false
end
else
function index_including_part_1(ts, i)
index_including_1 = 0
j = i
i + 3 <= length(ts.input) && isdigit(ts[i]) && isdigit(ts[i + 1]) &&
isdigit(ts[i + 2]) || return -1
i += 3
while i <= length(ts.input) && ts[i] ∈ allowed_chars_phone_numbers
i += 1
end
i + 3 <= length(ts.input) && isdigit(ts[i]) && isdigit(ts[i + 1]) &&
isdigit(ts[i + 2]) || return -1
i += 3
j = i
while i <= length(ts.input) && ts[i] ∈ allowed_chars_phone_numbers
i += 1
end
i + 3 <= length(ts.input) && isdigit(ts[i]) && isdigit(ts[i + 1]) &&
isdigit(ts[i + 2]) && isdigit(ts[i + 3]) && return i + 3
isdigit(ts[j]) && return j
return -1
end
function index_excluding_part_1(ts)
i = ts.idx
i + 3 <= length(ts.input) && isdigit(ts[i]) && isdigit(ts[i + 1]) &&
isdigit(ts[i + 2]) || return -1
i += 3
while i <= length(ts.input) && ts[i] ∈ allowed_chars_phone_numbers
i += 1
end
i + 3 <= length(ts.input) && isdigit(ts[i]) && isdigit(ts[i + 1]) &&
isdigit(ts[i + 2]) || return -1
i += 3
j = i
while i <= length(ts.input) && ts[i] ∈ allowed_chars_phone_numbers
i += 1
end
i + 3 <= length(ts.input) && isdigit(ts[i]) && isdigit(ts[i + 1]) &&
isdigit(ts[i + 2]) && isdigit(ts[i + 3]) && return i + 3
isdigit(ts[j]) && return j
return -1
end
index_including_1 = index_including_part_1(ts, i)
index_excluding_1 = index_excluding_part_1(ts)
# Flushing out the bigger of the two.
index_including_1 <= 0 && index_excluding_1 <= 0 && return false
index_excluding_1 > index_including_1 && return flushaboutindex!(ts, index_excluding_1)
return flushaboutindex!(ts, index_including_1)
end
return false
end
"""
extra_phonenumbers(ts)
Extra matching patterns for phone numbers.
"""
function extra_phonenumbers(ts)
return false
end
"""
nltk_url1(ts)
Matches the url patterns starting with `http/https`.
"""
function nltk_url1(ts)
ts.idx + 3 > length(ts.input) && return false
i = ts.idx
# Checking for part 1 of regex
if ts[i:i+3] == ['h', 't', 't', 'p'] # Check if url starts with pattern - https?:(?:\/{1,3}|[a-z0-9%])
i += 4
i + 2 > length(ts.input) && return false
if ts[i] == 's'
i += 1
end
ts[i] == ':' || return false
i += 1
if i >= length(ts.input) || !(isascii(ts[i]) && (islowercase(ts[i]) ||
isdigit(ts[i]) || ts[i] == '%' || ts[i] == '/'))
return false
end
i += 1
else # Check if url starts with the regex pattern - [a-z0-9.\-]+[.](?:[a-z]{2,13})\/
last_dot = ts.idx
while i <= length(ts.input) && isascii(ts[i]) && (islowercase(ts[i]) || isdigit(ts[i]) ||
ts[i] == '.' || ts[i] == '-')
if ts[i] == '.'
last_dot = i
elseif !islowercase(ts[i])
last_dot = ts.idx
end
i += 1
end
if i + 2 > length(ts.input) || last_dot <= ts.idx + 1 || i - last_dot > 14 ||
i - last_dot <= 2 || ts[i] != '/'
return false
end
i += 1
end
# URL is supposed to have 2 more parts.
# Both Part 2 and Part 3 each having 3 possible alternatives.
# Part 2 occurs at least once and Part 3 exactly once.
# After every match of the first part, we keep a track if the second one follows it.
# and store the maximum index in `index_matched`.
# Finally, we flush about the index = index_matched then.
index_matched = ts.idx
while i + 1 <= length(ts.input) && !(isspace(ts[i]))
# Check if part 2 matches otherwise break.
# Part 2 could be one of the three patterns.
# i. ` \([^\s]+?\)`
# ii. `\([^\s()]*?\([^\s()]+?\)[^\s()]*?\)`
# iii. `[^\s()<>{}\[\]]+`
if ts[i] == '(' # Checking for i. and ii. above.
i += 1
(i > length(ts.input) || isspace(ts[i])) && break
j = i
while j <= length(ts.input) && ts[j] != ')' && ts[j] != '(' && !isspace(ts[j])
j += 1
end
(j > length(ts.input) || isspace(ts[j])) && break
if ts[j] == ')' # Checking for i.
j - i <= 1 && break
i = j
else # Checking for ii.
i = j
i > length(ts.input) && break
while j <= length(ts.input) && ts[j] != ')' && ts[j] != '(' && !isspace(ts[j])
j += 1
end
(j > length(ts.input) || isspace(ts[j]) || ts[j] == '(') && break
j - i <= 1 && break
j += 1
while j <= length(ts.input) && ts[j] != ')' && ts[j] != '(' && !isspace(ts[j])
j += 1
end
(j > length(ts.input) || isspace(ts[j]) || ts[j] == '(') && break
i = j
end
i += 1
else # Checking for iii.
(isspace(ts[i])|| ts[i] ∈ (')', '<', '>', '{', '}', '[', ']') ) && break
i += 1
end
i > length(ts.input) && break
k = i # Just for temporarily storing i.
# Check if part 3 matches otherwise continue.
# Part 3 could be one of the three patterns.
# i. `\([^\s()]*?\([^\s()]+?\)[^\s()]*?\)`
# ii. `[^\s`!()\[\]{};:'".,<>?«»“”‘’]`
# iii. ` \([^\s]+?\)`
if ts[i] == '(' # Check for part i. and iii.
i += 1
(i > length(ts.input) || isspace(ts[i])) && continue
j = i
while j <= length(ts.input) && ts[j] != ')' && ts[j] != '(' && !isspace(ts[j])
j += 1
end
(j > length(ts.input) || isspace(ts[j])) && continue
if ts[j] == ')' # Check for part iii.
j - i <= 1 && break
i = j
else # Check for part i.
i = j
i > length(ts.input) && continue
while j <= length(ts.input) && ts[j] != ')' && ts[j] != '(' && !isspace(ts[j])
j += 1
end
(j > length(ts.input) || isspace(ts[j]) || ts[j] == '(') && continue
j - i <= 1 && continue
j += 1
while j <= length(ts.input) && ts[j] != ')' && ts[j] != '(' && !isspace(ts[j])
j += 1
end
(j > length(ts.input) || isspace(ts[j]) || ts[j] == '(') && continue
i = j
end
index_matched = i
i += 1
else # Check for part ii.
isspace(ts[i]) && break
ts[i] ∈ ('`', '!', ')', '[', ']', '{', '}', ';', ':', '\'', '"', '.',
',', '<', '>', '?', '«', '»', '“', '”', '‘', '’') && continue
index_matched = i
end
i = k
end
index_matched == ts.idx && return false
return flushaboutindex!(ts, index_matched)
end
function nltk_url2(ts)
(ts.idx > length(ts.input) || (ts.idx > 1 && ts[ts.idx - 1] == '@')) && return false
(isascii(ts[ts.idx]) && (isdigit(ts[ts.idx]) || islowercase(ts[ts.idx]))) || return false
i = ts.idx + 1
while i <= length(ts.input) && isascii(ts[i]) && (isdigit(ts[i]) || islowercase(ts[i]))
i += 1
end
flush_about = 0
while i < length(ts.input) && (ts[i] == '.' || ts[i] == '-')
j = ts[i] == '.' ? i : 0
i += 1
(isascii(ts[i]) && (isdigit(ts[i]) || islowercase(ts[i]))) || break
i += 1
while i <= length(ts.input) && isascii(ts[i]) && (isdigit(ts[i]) || islowercase(ts[i]))
i += 1
end
if j != 0 && 3 <= i - j <= 14
if i <= length(ts.input) && ts[i] == '/' && (i + 1 > length(ts.input) || ts[i + 1] != '@')
flush_about = i
break
end
flush_about = i - 1
end
end
flush_about == 0 && return false
(flush_about >= length(ts.input) || ts[flush_about + 1] != '@') && return flushaboutindex!(ts, flush_about)
return false
end
"""
tweet_tokenize(input::AbstractString) => tokens
Twitter-aware tokenizer, designed to be flexible and
easy to adapt to new domains and tasks.
The basic logic is following:
1. The regular expressions are made for WORD_REGEX (core tokenizer), HANG_REGEX
and EMOTICONS_REGEX.
2 Replacing HTML entities, tweet handles, reducing length of repeated characters
and other features, make it suitable for tweets.
3. The String is tokenized and returned.
4. `preserve_case` By default is set to `true`. If it is set to `false`,
then the tokenizer will downcase everything except for emoticons.
Example:
```
julia> tweet_tokenize("This is a cooool #dummysmiley: :-) :-P <3 and some arrows < > -> <--")
16-element Array{SubString{String},1}:
"This"
"is"
"a"
"cooool"
"#dummysmiley"
":"
":-)"
":-P"
"<3"
"and"
"some"
"arrows"
"<"
">"
"->"
"<--"
```
"""
function tweet_tokenize(source::AbstractString; strip_handle=false,
reduce_len=false)
phonenumbers(ts) = nltk_phonenumbers(ts) || extra_phonenumbers(ts)
urls(ts) = nltk_url1(ts) || nltk_url2(ts)
# Fix HTML Character entities
length(source) == 0 && return []
source = replace_html_entities(source)
length(source) == 0 && return []
safe_text = pre_process(source, strip_handle, reduce_len)
# The key tokenizing function begins
ts = TokenBuffer(safe_text)
isempty(safe_text) && return ts.tokens
# # TODO: OpenQuotes and Closing quotes
while !isdone(ts)
spaces(ts) && continue
emoticons(ts) ||
emoticonsreverse(ts) ||
htmltags(ts) ||
arrowsascii(ts) ||
twitterhashtags(ts) ||
ellipsis_dots(ts) ||
urls(ts) || # urls must be called before words.
twitterusername(ts) ||
emailaddresses(ts) || # emailaddresses must be called before words
phonenumbers(ts) || # Phone numbers must be called before numbers.
atoms(ts, []) ||
words_including_apostrophe_dashes(ts) ||
number(ts, check_sign = true) ||
character(ts)
end
tokens = ts.tokens
return tokens
end
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] | 2.028915 | 13,453 |
import Distributions: Exponential, product_distribution
"""
$(TYPEDEF)
A symetric random telegraph noise with switch rate `γ/2` magnitude `b`
$(FIELDS)
"""
struct SymetricRTN
"Magnitude"
b::Any
"Two times the switching probability"
γ::Any
end
correlation(τ, R::SymetricRTN) = R.b^2 * exp(-R.γ * τ)
spectrum(ω, R::SymetricRTN) = 2 * R.b^2 * R.γ / (ω^2 + R.γ^2)
construct_distribution(R::SymetricRTN) = Exponential(1 / R.γ)
"""
$(TYPEDEF)
An ensemble of random telegraph noise.
$(FIELDS)
"""
struct EnsembleFluctuator{T} <: StochasticBath
"A list of RTNs"
f::Vector{T}
end
"""
$(SIGNATURES)
Build the `EnsembleFluctuator` object from a list of amplitudes `b` and a list of switch rates `ω`.
# Examples
```julia-repl
julia> EnsembleFluctuator([1, 1], [1, 2])
Fluctuator ensemble with 2 fluctuators
```
"""
EnsembleFluctuator(b::AbstractArray{T}, ω::AbstractArray{T}) where {T <: Number} =
EnsembleFluctuator([SymetricRTN(x, y) for (x, y) in zip(b, ω)])
correlation(τ, E::EnsembleFluctuator) = sum((x) -> correlation(τ, x), E.f)
spectrum(ω, E::EnsembleFluctuator) = sum((x) -> spectrum(ω, x), E.f)
construct_distribution(E::EnsembleFluctuator) =
product_distribution([construct_distribution(x) for x in E.f])
Base.length(E::EnsembleFluctuator) = Base.length(E.f)
Base.show(io::IO, ::MIME"text/plain", E::EnsembleFluctuator) =
print(io, "Fluctuator ensemble with ", length(E), " fluctuators")
Base.show(io::IO, E::EnsembleFluctuator) =
print(io, "Fluctuator ensemble with ", length(E), " fluctuators")
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] | 2.37788 | 651 |
export AbEnsemble, BBEnsemble, GBEnsemble, FWEnsemble, SWEnsemble
export trainerr, trainerrseq, testerr, testerrseq, prediction
export normωs, normcardseq, getβseq
abstract type AbEnsemble{F, B} end
#################
# BareBones
#################
struct BBEnsemble{F, B<:AbBasis{F}} <: AbEnsemble{F, B}
prob::Problem
ωs::Vector{B}
β::Vector{F}
end
BBEnsemble(ens::AbEnsemble{F,B}) where {F, B<:AbBasis{F}} =
BBEnsemble{F, B}(ens.prob, deepcopy(ens.ωs), copy(ens.β))
#################
# Gradient Boost
#################
mutable struct GBEnsemble{F, B<:AbBasis{F}} <: AbEnsemble{F, B}
prob::Problem
ωs::Vector{B}
β::Vector{F}
zs::Vector{Vector{F}}
fit::Vector{F}
end
GBEnsemble(prob::Problem{L,B}) where {L, F, B<:AbBasis{F}} =
GBEnsemble{F, B}(prob, B[], F[], Vector{F}[], zeros(F, sizen(prob)))
#################
# Frank Wolfe
#################
mutable struct FWEnsemble{F, B<:AbBasis{F}} <: AbEnsemble{F, B}
prob::Problem
ωs::Vector{B}
β::Vector{F}
zs::Vector{Vector{F}}
fit::Vector{F}
end
FWEnsemble(prob::Problem{L,B}) where {L, F, B<:AbBasis{F}} =
FWEnsemble{F, B}(prob, B[], F[], Vector{F}[], zeros(F, sizen(prob)))
#################
# Basics
#################
import Base.length
length(ens::AbEnsemble) = length(ens.ωs)
import Base.push!
function push!(ens::AbEnsemble{F, B}, ω::B, βi::F, z::Vector{F}) where {B, F}
push!(ens.ωs, ω)
push!(ens.β, βi)
push!(ens.zs, z)
ens.fit += βi * z
ens
end
push!(ens::AbEnsemble{F, B}, ω::B, βi::F) where {B, F} = push!(ens, ω, βi, calc(ω, ens.prob.X))
push!(ens::AbEnsemble{F, B}, ω::B) where {B, F} = push!(ens, ω, zero(F))
getZ(ens::AbEnsemble) = hcat(ens.zs...)
function _correctfit!(ens::AbEnsemble)
ens.fit = sum(ens.β .* ens.zs)
end
function _correctzs!(ens::AbEnsemble)
for j in 1:length(ens)
ens.zs[j] = calc(ens.ωs[j], ens.prob.X)
end
_correctfit!(ens)
end
#################
# {ω} updates for Backprop
#################
function ssedir(ens1::AbEnsemble{F, B}, ens2::AbEnsemble{F, B}) where {F, B<:AbUniDirBasis{F}}
@assert length(ens1) == length(ens2)
sum(sum(abs2, ens1.ωs[j].u - ens2.ωs[j].u) for j in 1:length(ens1))
end
function stepalong!(ens::AbEnsemble{F, B}, ∇L_u, t) where {F, B<:AbUniDirBasis{F}}
@assert length(ens) == size(∇L_u)[2]
for j in 1:length(ens)
ens.ωs[j].u -= t*∇L_u[:, j] # Negative gradient
ens.ωs[j].u = project(ens.prob.Ω, ens.ωs[j].u)
end
_correctzs!(ens)
end
#################
# FW β updates
#################
function updateβ!(ens::FWEnsemble{F}, β::Vector{F}, flush=true) where F #lassoonA
@assert length(β)==length(ens.β)
keep = flush ? (β.!= 0.0) : (1:length(β))
ens.β = β[keep]
if flush
ens.ωs = ens.ωs[keep]
ens.zs = ens.zs[keep]
end
_correctfit!(ens)
ens
end
function fwvanillaupdateβ!(ens::FWEnsemble, iter::Int, C::AbstractFloat)
@assert ens.β[end] == 0.
oldfactor = iter/(iter+2.)
ens.β *= oldfactor
ens.β[end] = C * 2./(iter+2.)
ens.fit *= oldfactor
ens.fit += ens.β[end] * ens.zs[end]
end
################
# Stagewise
################
mutable struct SWEnsemble{F, B<:AbBasis{F}} <: AbEnsemble{F, B}
prob::Problem
ωs::Vector{B}
β::Vector{F}
zs::Vector{Vector{F}}
fit::Vector{F}
ϵ::F
δ::F
events::Vector{Int}
_βs::Matrix{F}
end
SWEnsemble(prob::Problem{L,B}, ϵ::F, δ::F) where {L, F, B<:AbBasis{F}} =
SWEnsemble{F, B}(prob, B[], F[], Vector{F}[], zeros(F, sizen(prob)), ϵ, δ, Int[], Matrix{F}(0, 0))
function pushnew!(ens::SWEnsemble{F, B}, ω::B, z::Vector{F}=calc(ω, ens.prob.X)) where {B, F}
push!(ens.ωs, ω)
push!(ens.β, ens.ϵ)
push!(ens.zs, z)
ens.fit += ens.ϵ * z
push!(ens.events, length(ens.β))
ens
end
function decrementβi!(ens::SWEnsemble, i::Int)
tol = 1e-9
@assert ens.β[i] > tol
if ens.β[i] < ens.δ + tol # Clamp down to zero
ens.fit -= ens.β[i] * ens.zs[i]
ens.β[i] = 0.
else
ens.fit -= ens.δ * ens.zs[i]
ens.β[i] -= ens.δ
end
push!(ens.events, -i)
end
function augmentβi!(ens::SWEnsemble, i::Int)
ens.fit += ens.ϵ * ens.zs[i]
ens.β[i] += ens.ϵ
push!(ens.events, i)
end
function _fillβs(ens::SWEnsemble{F}) where F
# Each Column is a variable, time progresses along a column length
βs = zeros(length(ens.events)+1, maximum(ens.events))
k = 1
for ivar in ens.events
k += 1
if ivar > 0
βs[k:end, ivar] += ens.ϵ
else
ivar = abs(ivar)
if βs[k, ivar] > ens.δ + 1e-9
βs[k:end, ivar] -= ens.δ
else
βs[k:end, ivar] = zero(F)
end
end
end
@assert βs[end, :] ≈ ens.β
ens._βs = βs
end
function ensureβfilled(ens::SWEnsemble{F}) where F
((length(ens._βs) > 0) && (ens._βs[end, :] ≈ ens.β)) || _fillβs(ens)
end
function getβseq(ens::SWEnsemble{F}, fillval::F=NaN) where F
ensureβfilled(ens)
(fillval == 0.) && return ens._βs
@assert fillval===NaN
βs = fill(NaN, size(ens._βs))
for ivar in 1:size(βs)[2]
a = max(1, findfirst(ens._βs[:, ivar])-1)
b = min(findlast(ens._βs[:, ivar])+1, size(βs)[1])
βs[a:b, ivar] = ens._βs[a:b, ivar]
end
βs
end
function getβseq(enses::Vector{E}, fillval::F=NaN) where {F, E<:BBEnsemble{F}}
βs = fill(fillval, (length(enses), length(enses[end])))
for (itime, ens) in enumerate(enses)
βs[itime, 1:length(ens)] = ens.β
end
βs
end
################################################################################
# Exports
################################################################################
normωs(ens::AbEnsemble{F, B}) where {F, B<:AbUniDirBasis{F}} = [normcs(ens.prob, ω.u) for ω in ens.ωs]
trainerr(ens::AbEnsemble, los=ens.prob.loss) = los(ens.prob.y, ens.fit)
testerr(ens::AbEnsemble, X::Matrix, y::Vector, los=ens.prob.loss) = los(y, sum(ens.β .* calc.(ens.ωs, (X,))))
trainerr(ens::BBEnsemble, los=ens.prob.loss) = testerr(ens, ens.prob.X, ens.prob.y, los)
# Vector of Ensembles
#################
trainerrseq(enses::Vector{E}, los=enses[1].prob.loss) where E<:AbEnsemble = trainerr.(enses, los)
testerrseq(enses::Vector{E}, X::Matrix, y::Vector, los=enses[1].prob.loss) where E<:AbEnsemble = testerr.(enses, (X,), (y,), los)
normcardseq(enses::Vector{E}) where E<:AbEnsemble = [norm(ens.β, 1) for ens in enses], [countnz(ens.β) for ens in enses]
# GBEnsembles
#################
trainerrseq(ens::GBEnsemble, los=ens.prob.loss) = los.((ens.prob.y,), cumsum(ens.β .* ens.zs))
testerrseq(ens::GBEnsemble, X::Matrix, y::Vector, los=ens.prob.loss) = los.((y,), cumsum(ens.β .* calc.(ens.ωs, (X,))))
normcardseq(ens::GBEnsemble) = cumsum(abs.(ens.β)), 1:length(ens)
# SWEnsembles
#################
function normcardseq(ens::SWEnsemble{F}) where F
ensureβfilled(ens)
mapslices(β->norm(β, 1), ens._βs, 2)[:], mapslices(countnz, ens._βs, 2)[:]
end
function _swerrseq(ens::SWEnsemble{F},
y::Vector{F},
zs::Vector{Vector{F}},
los
) where F
losses = zeros(length(ens.events)+1)
z = zeros(zs[1])
losses[1] = los(y, z)
ϵzs = ens.ϵ * zs
for (k, i) in enumerate(ens.events)
if i > 0
z += ϵzs[i]
else
z -= ens.δ * zs[abs(i)] # Not checking for Negative β
end
losses[k+1] = los(y, z)
end
losses
end
trainerrseq(ens::SWEnsemble{F}, los=ens.prob.loss) where F = _swerrseq(ens, ens.prob.y, ens.zs, los)
testerrseq(ens::SWEnsemble{F}, X::Matrix{F}, y::Vector{F}, los=ens.prob.loss) where F = _swerrseq(ens, y, calc.(ens.ωs, (X,)), los)
#################
# Checks
#################
function sanitycheck(ens::AbEnsemble)
@assert length(ens.ωs) == length(ens.β) == length(ens.zs)
@assert length(ens.fit) == sizen(ens.prob)
@assert all(length.(ens.zs) .== length(ens.fit))
@assert (length(ens) == 0 && sum(abs, ens.fit) == 0) || (ens.fit ≈ sum(ens.β .* ens.zs)) "$(length(ens)) $(sum(abs, ens.fit)) $(ens.fit ≈ sum(ens.β .* ens.zs))"
@assert all(z ≈ calc(ω, ens.prob.X) for (z, ω) in zip(ens.zs, ens.ωs))
print("✓")
end
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] | 2.060606 | 4,026 |
module TestMSQueue
using Reagents
using Test
include("../../../examples/msqueue.jl")
function test_1()
q = MSQueue{Int}()
push!(q, 1)
@test popfirst!(q) == 1
end
function test_123()
q = MSQueue{Int}()
push!(q, 1)
push!(q, 2)
push!(q, 3)
@test popfirst!(q) == 1
@test popfirst!(q) == 2
@test popfirst!(q) == 3
end
end # module
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] | 2.113636 | 176 |
# Example of robot kinematics constraints reported by John Gustafson
# http://www.johngustafson.net/presentations/Multicore2016-JLG.pdf
using ConstraintPropagation
d = Domain()
@add_constraint d s2*c5*s6 - s3*c5*s6 - s4*c5*s6 + c2*c6 + c3*c6 + c4*c6 == 0.4077
@add_constraint d c1*c2*s5 + c1*c3*s5 + c1*c4*s5 + s1*c5 == 1.9115
@add_constraint d s2*s5 + s3*s5 + s4*s5 == 1.9791
@add_constraint d c1*c2 + c1*c3 + c1*c4 + c1*c2 + c1*c3 + c1*c2 == 4.0616
@add_constraint d s1*c2 + s1*c3 + s1*c4 + s1*c2 + s1*c3 + s1*c3 == 1.7172
@add_constraint d s2 + s3 + s4 + s2 + s3 + s2 == 3.9701
@add_constraint d s1^2 + c1^2 == 1
@add_constraint d s2^2 + c2^2 == 1
@add_constraint d s3^2 + c3^2 == 1
@add_constraint d s4^2 + c4^2 == 1
@add_constraint d s5^2 + c5^2 == 1
@add_constraint d s6^2 + c6^2 == 1
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] | 1.842227 | 431 |
# Use baremodule to shave off a few KB from the serialized `.ji` file
baremodule gdk_pixbuf_jll
using Base
using Base: UUID
import JLLWrappers
JLLWrappers.@generate_main_file_header("gdk_pixbuf")
JLLWrappers.@generate_main_file("gdk_pixbuf", UUID("da03df04-f53b-5353-a52f-6a8b0620ced0"))
end # module gdk_pixbuf_jll
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] | 2.373134 | 134 |
# Tests for the Diffusion module
#
function test_diffusion_basic()
model = Diffusion.Model(N=4, L=2, K=0.1)
model.parameters.K == 0.1
end
function test_diffusion_set_c()
model = Diffusion.Model(N=4, L=2, K=0.1)
c0 = 1:4
model.solution.c = c0
model.solution.c.data[1:model.grid.N] == c0
end
function test_diffusive_flux(stepper=:ForwardEuler; top_flux=0.3, bottom_flux=0.13, N=10)
model = Diffusion.Model(N=N, L=1, K=1.0, stepper=stepper)
model.bcs.c.top = FluxBoundaryCondition(top_flux)
model.bcs.c.bottom = FluxBoundaryCondition(bottom_flux)
C₀ = integral(model.solution.c)
C(t) = C₀ - (top_flux - bottom_flux) * t
dt = 1e-6
iterate!(model, dt, 10)
return C(time(model)) ≈ integral(model.solution.c)
end
function test_diffusion_cosine(stepper=:ForwardEuler)
model = Diffusion.Model(N=100, L=π/2, K=1.0, stepper=stepper)
z = model.grid.zc
c_init(z) = cos(2z)
c_ans(z, t) = exp(-4t) * c_init(z)
model.solution.c = c_init
dt = 1e-3
iterate!(model, dt)
# The error tolerance is a bit arbitrary.
norm(c_ans.(z, time(model)) .- data(model.solution.c)) < model.grid.N*1e-6
end
function test_diffusion_cosine_run_until(stepper=:ForwardEuler)
model = Diffusion.Model(N=100, L=π/2, K=1.0, stepper=stepper)
z = model.grid.zc
c_init(z) = cos(2z)
c_ans(z, t) = exp(-4t) * c_init(z)
model.solution.c = c_init
dt = 1e-3
tfinal = 3dt/2
run_until!(model, dt, tfinal)
# The error tolerance is a bit arbitrary.
norm(c_ans.(z, tfinal) .- data(model.solution.c)) < model.grid.N*1e-6
end
function test_advection(stepper=:ForwardEuler)
L = 1.0
W = -1.0
δ = L/10
h = L/2
model = Diffusion.Model(N=100, L=L, K=0.0, W=W, stepper=stepper,
bcs = Diffusion.BoundaryConditions(FieldBoundaryConditions(
GradientBoundaryCondition(0.0),
GradientBoundaryCondition(0.0))))
c_gauss(z, t) = exp( -(z-W*t)^2 / (2*δ^2) )
c₀(z) = c_gauss(z+h, 0)
model.solution.c = c₀
iterate!(model, 1e-3, 10)
c_current(z) = c_gauss(z+h, time(model))
c_answer = CellField(model.grid)
set!(c_answer, c_current)
norm(data(c_answer) .- data(model.solution.c)) < 0.05
end
function test_damping(stepper=:ForwardEuler)
L = 1.0
μ = 0.1
model = Diffusion.Model(N=3, L=L, K=0.0, μ=μ, stepper=stepper,
bcs = Diffusion.BoundaryConditions(FieldBoundaryConditions(
GradientBoundaryCondition(0.0),
GradientBoundaryCondition(0.0))))
c_damp(t) = exp(-μ*t)
c₀(z) = c_damp(0)
model.solution.c = c₀
iterate!(model, 1e-3, 100)
c_current(t) = c_damp(time(model))
c_answer = CellField(model.grid)
set!(c_answer, c_current)
all(abs.(data(c_answer) .- data(model.solution.c)) .< 1e-6)
end
@testset "Diffusion" begin
@test test_diffusion_basic()
@test test_diffusion_set_c()
for stepper in steppers
@test test_diffusion_cosine(stepper)
@test test_diffusion_cosine_run_until(stepper)
@test test_diffusive_flux(stepper, top_flux=0, bottom_flux=0)
@test test_diffusive_flux(stepper)
@test test_advection(stepper)
@test test_damping(stepper)
end
end
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] | 1.9641 | 1,727 |
using LinearAlgebra
using Random
using Statistics
include("../utils/simula01.jl")
include("../ACQR/ACQR_simula.jl")
include("../utils/byrow.jl")
include("ACQRS_sub.jl")
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] | 2.671875 | 64 |
@export function scr_test(sim; λ=DEFAULT_λ, indices=[37], weights=[1])
ρ0 = sim.γL
ρ0 = sum(zip(indices, weights)) do (i, w)
v0 = eigen(sim.H).vectors[:,i]
w*v0*v0'
end
Γ = sim.ΓR
J = sim.H - im*Γ/2
j, U = eigen(J)
N = size(J, 1)
_ρ_ = inv(U)*ρ0*inv(U')
_Γ_ = U'Γ*U
_L_ = [λ * U'L*inv(U') for L in sim.L]
# function β(t)
# As = Matrix{Complex{Float64}}[]
# for L in _L_
# A = Matrix{Complex{Float64}}(undef, N, N)
# for idx in CartesianIndices(A)
# l, m = idx[1], idx[2]
# if j[l] == j[m]
# A[l,m] = -im * t * exp(-im*j[l]*t) * L[l,m]
# else
# A[l,m] = inv(j[l]- j[m]) * (exp(-im*j[l]*t)*L[l,m] - L[l,m]*exp(-im*j[m]*t))
# end
# end
# push!(As, U*A*inv(U))
# end
# return As
# end
function f(t)
integrated_T = sum(CartesianIndices((N, N))) do idx
k, l = idx[1], idx[2]
x = j[k] - j[l]'
if iszero(x)
return _ρ_[k,l] * _Γ_[l,k] * t
else
return _ρ_[k,l] * _Γ_[l,k] * im*(exp(-im*x*t) - 1) / x
end
end
integrated_S = sum(CartesianIndices((N, N, N))) do idx
l, m, n = idx[1], idx[2], idx[3]
x = j[l]' - j[m]'
if iszero(x)
a = j[n] - j[m]'
return _ρ_[n,l] * _L_[3][l,m] * _Γ_[m,n] * im*(
im*t*exp(-im*a*t) / a +
(exp(-im*a*t) - 1) / a^2
)
else
a = j[n] - j[l]'
b = j[n] - j[m]'
return _ρ_[n,l] * _L_[3][l,m] * _Γ_[m,n] * inv(b - a) * im*(
(exp(-im*a*t) - 1) / a - (exp(-im*b*t) - 1) / b
)
# return _ρ_[n,l] * _L_[3][l,m] * _Γ_[m,n] * inv(j[l]' - j[m]') * im*(
# (exp(-im*(j[n] - j[l]')*t) - 1) / (j[n] - j[l]') -
# (exp(-im*(j[n] - j[m]')*t) - 1) / (j[n] - j[m]')
# )
end
end
return integrated_T, integrated_S
u = LinearAlgebra.exp!(-im*J*t)
A = ρ0*u'Γ
return tr(A*u), integrated_T, [tr(A*b) for b in β(t)], integrated_S
end
return f
end
@export function scr_test_full(sim; λ=DEFAULT_λ, indices=[Int(length(sim.eig.values) / 2 + 1)], weights=[1], α=1.0)
ρ0 = sum(zip(indices, weights)) do (i, w)
v0 = eigen(sim.H).vectors[:,i]
w*v0*v0'
end
Γ = sim.ΓL
Σ = - im * (α*sim.ΓR + sim.ΓL) / 2
J = (sim.H + Σ) ⊗ σ0 + sum(λ * map(⊗, sim.L, σ))
j, U = eigen(J)
_ρ_ = inv(U) * (ρ0 ⊗ σ0) * inv(U')
_Γ_ = U' * (Γ ⊗ σ0) * U
_Γi_ = [U' * (Γ ⊗ σi) * U for σi in σ]
N = size(J, 1)
function f(t)
integrated_T = sum(CartesianIndices((N, N))) do idx
k, l = idx[1], idx[2]
x_kl = im*(j[k] - j[l]')
if iszero(x_kl)
return _ρ_[k,l] * _Γ_[l,k] * t
else
return _ρ_[k,l] * _Γ_[l,k] * (1 - exp(-x_kl * t)) / x_kl
end
end
integrated_S = sum(CartesianIndices((N, N))) do idx
k, l = idx[1], idx[2]
x_kl = im*(j[k] - j[l]')
if iszero(x_kl)
return _ρ_[k,l] * _Γi_[3][l,k] * t
else
return _ρ_[k,l] * _Γi_[3][l,k] * (1 - exp(-x_kl * t)) / x_kl
end
end
# u = LinearAlgebra.exp!(-im*J*t)
# A = (ρ0 ⊗ σ0) * u' * (Γ ⊗ σ0)
return integrated_T, integrated_S#tr(A*u)
end
return f
end
# Magnet
@export function scr_test_magnet(sim; λ=DEFAULT_λ, indices=[Int(length(sim.eig.values) / 2 + 1)], weights=[1], α=1.0, m=0.5)
ρ0 = sum(zip(indices, weights)) do (i, w)
v0 = eigen(sim.H).vectors[:,i]
w*v0*v0'
end
Γ = α*sim.ΓR
Σ = - im * (α*sim.ΓR + sim.ΓL) / 2
ΔΣ = - im * (m*sim.γL) / 2
J = ((sim.H + Σ) ⊗ σ0 + ΔΣ ⊗ σ[:z] + sum(λ * map(⊗, sim.L, σ)),
(sim.H + Σ) ⊗ σ0 - ΔΣ ⊗ σ[:z] + sum(λ * map(⊗, sim.L, σ)))
j, U = begin
tmp = eigen.(J)
map(x -> x.values, tmp), map(x -> x.vectors, tmp)
end
ρ = map(U -> inv(U) * (ρ0 ⊗ σ0) * inv(adjoint(U)), U)
Γ = map(U -> adjoint(U) * (Γ ⊗ σ0) * U, U)
N = size(J[1], 1)
function f(t)
integrated_T = map(j, ρ, Γ) do j, ρ, Γ
T = sum(CartesianIndices((N, N))) do idx
k, l = idx[1], idx[2]
x_kl = im*(j[k] - j[l]')
real(x_kl) < 0 && error("negative `real(x_kl)` found: $(real(x_kl))")
return ρ[k,l] * Γ[l,k] * (1 - exp(-x_kl * t)) / x_kl
end
real(T) < 0 && error("negative `real(T)` found: $(real(T))")
real(T)
end
return integrated_T
end
return f
end
# Calculate the photon emission
@export function scr_photon_emission_full(
sim;
λ=DEFAULT_λ,
indices=[37],
weights=[1],
seed = 824,
W = 1e-1,
rng = MersenneTwister(seed)
)
ρ0 = sum(zip(indices, weights)) do (i, w)
v0 = sim.eig.vectors[:,i]
w*v0*v0'
end
Γ = sim.ΓR
J = (sim.H - im*Γ/2) ⊗ σ0 + sum(λ * Li ⊗ σi for (Li, σi) in zip(sim.L, σ))
j, U = eigen(J)
N = size(J, 1)
V = let
local i, j = rand(rng, 1:size(sim.H, 1), 2)
while i == j i, j = rand(rng, 1:N, 2) end
W*sim.eig.vectors[:,i]*sim.eig.vectors[:,j]'
end
_ρ_ = inv(U) * (ρ0 ⊗ σ0) * inv(U')
_Γ_ = U' * (Γ ⊗ σ0) * U
_V_ = inv(U) * (V ⊗ σ0) * U
function f()
K(a, b) = inv(j[a]' - j[b])
K_(a, b) = inv(j[a]' - j[b]')
output = sum(CartesianIndices((N, N, N, N))) do idx
k, l, m, n = idx[1], idx[2], idx[3], idx[4]
X = 2π*im * _ρ_[k,l] * _V_'[l,m] * _Γ_[m,n] * _V_[m,n]
if iszero(j[l]' - j[m]')
return -X * K(l,n) * K(m,k) * (K(l,n) + K(m,k))
else
return X * K_(l,m) * (K(l,n)*K(l,k) - K(m,n)*K(m,k))
end
end
return output
end
return f
end
##################################################
@export function scr_magnet(sim; E=sim.μ, λ=DEFAULT_λ, m=0.5, n=1, α=0.0, ϕ=0)
ρ0 = let
v0 = eigenstate(sim, E = E, ϕ=ϕ)
0.5 * v0*v0'
end
Γ = sim.ΓL
Σ = - im * (α*sim.ΓL + sim.ΓR) / 2
ΔΣ = - im * (m*sim.ΔΓR) / 2
J = ((sim.H + Σ) ⊗ σ0 + ΔΣ ⊗ σ[:z] + sum(λ * map(⊗, sim.L, σ)),
(sim.H + Σ) ⊗ σ0 - ΔΣ ⊗ σ[:z] + sum(λ * map(⊗, sim.L, σ)))
j, U = begin
tmp = eigen.(J)
map(x -> x.values, tmp), map(x -> x.vectors, tmp)
end
ρ = map(U -> inv(U) * (ρ0 ⊗ σ0) * inv(adjoint(U)), U)
Γ = map(U -> adjoint(U) * (Γ ⊗ σ0) * U, U)
N = size(J[1], 1)
Ts = map(j, ρ, Γ) do j, ρ, Γ
T = sum(CartesianIndices((N, N))) do idx
k, l = idx[1], idx[2]
x_kl = im*(j[k] - j[l]')
real(x_kl) < 0 && error("negative `real(x_kl)` found: $(real(x_kl))")
return ρ[k,l] * Γ[l,k] / x_kl
end
real(T) < 0 && error("negative `real(T)` found: $(real(T))")
real(T)
end
return (Ts = Ts,)
end
@export function time_dependent_escape(
sim;
E = sim.μ,
λ = DEFAULT_λ,
m = 1.0,
α = DEFAULT_α,
ϕ = 0,
hbar = HBAR
)
ρ0 = let
v0 = eigenstate(sim, E = E, ϕ=ϕ)
v0*v0'
end
Γ = α*sim.ΓR
Σ = - im * α*sim.ΓR / 2
J = (sim.H + Σ) ⊗ σ0 + sum(λ * map(⊗, sim.L, σ))
j, U = eigen(J)
N = length(j)
ρ = inv(U) * (ρ0 ⊗ ((σ0 + m * σ[:z]) / 2)) * inv(adjoint(U))
Γ = adjoint(U) * (Γ ⊗ σ0) * U
ξs = map(CartesianIndices((N, N))) do idx
k, l = idx[1], idx[2]
x_kl = im*(j[k] - j[l]')
real(x_kl) < 0 && error("negative `real(x_kl)` found: $(real(x_kl))")
return x_kl
end
Ts = map(CartesianIndices((N, N))) do idx
k, l = idx[1], idx[2]
x_kl = im*(j[k] - j[l]')
real(x_kl) < 0 && error("negative `real(x_kl)` found: $(real(x_kl))")
return ρ[k,l] * Γ[l,k] / x_kl
end
fs = map(CartesianIndices((N, N))) do idx
k, l = idx[1], idx[2]
x_kl = im*(j[k] - j[l]')
real(x_kl) < 0 && error("negative `real(x_kl)` found: $(real(x_kl))")
return f(t) = 1 - exp(-x_kl * t / hbar)
end
return (ξs = ξs, Ts = Ts, fs = fs)
end
@export function scr_f(
δx = 0.0,
δy = 0.1;
t0 = 0,
t1 = 1,
nsamples = 500,
ts = range(t0, t1, length=nsamples),
seed = 42,
α = 1,
)
x0, y0, y1 = let
xsymbol = :δz
x0 = 0.8
y0 = -4.2
mol = Helicene(N=7)
x0, y0, y1 = TheoryOfCISS.find_crossing(
x0, y0, mol,
xsymbol = xsymbol,
threshold=1e-12,
attenuation=1e-2,
rounding = :both
)
end
x = x0
y = (y0 + y1) / 2
cols_up = gen_bands(Helicene(N=7), xsymbol = :δz, bounds=(x + δx, x + δx), nsamples=1, seed=seed);
cols_down = gen_bands(Helicene(N=7), xsymbol = :δz, bounds=(x + δx, x + δx), nsamples=1, seed=seed);
gen_on_bands!(cols_up, ysymbol=:E, f = time_dependent_escape, m=1.0, bounds=(y - δy, y + δy), α=α);
gen_on_bands!(cols_down, ysymbol=:E, f = time_dependent_escape, m=-1.0, bounds=(y - δy, y + δy), α=α);
_f(t, x) = sum(map((T, f) -> T * f(t), x.Ts, x.fs))
f_x(t, i) = real(_f(t, cols_up[1].data[i])), real(_f(t, cols_down[1].data[i]))
f_xs = map(i -> mapreduce(t -> [f_x(t, i)...]', vcat, ts), 1:2)
pol(a, b) = 100 * !iszero(a - b) * ((a - b) / (a + b))
dat = map(x -> map(i -> pol(i...), eachrow(x)), f_xs)
return ts, f_xs, dat
end
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] | 1.58263 | 6,160 |
# Parameters
beta = .96
y = [1.0, 2.0]
b0 = 0.0
P = [0.8 0.2;
0.4 0.6]
cp = ConsumptionProblem(beta, y, b0, P)
Q = beta*P
N_simul = 150
c_bar, b1, b2 = consumption_complete(cp)
debt_complete = [b1, b2]
println("P = ", P)
println("Q= ", Q, "\n")
println("Govt expenditures in peace and war =", y)
println("Constant tax collections = ", c_bar)
println("Govt assets in two states = ", debt_complete)
msg = """
Now let's check the government's budget constraint in peace and war.
Our assumptions imply that the government always purchases 0 units of the
Arrow peace security.
"""
println(msg)
AS1 = Q[1,2] * b2
println("Spending on Arrow war security in peace = ", AS1)
AS2 = Q[2,2]*b2
println("Spending on Arrow war security in war = ", AS2)
println("\n")
println("Government tax collections plus asset levels in peace and war")
TB1=c_bar+b1
println("T+b in peace = ",TB1 )
TB2 = c_bar + b2
println("T+b in war = ", TB2)
println("\n")
println("Total government spending in peace and war")
G1= y[1] + AS1
G2 = y[2] + AS2
println("total govt spending in peace = ", G1)
println("total govt spending in war = ", G2)
println("\n")
println("Let's see ex post and ex ante returns on Arrow securities")
Pi= 1./Q#reciprocal(Q)
exret= Pi
println("Ex post returns to purchase of Arrow securities = $exret")
exant = Pi.*P
println("Ex ante returns to purchase of Arrow securities = $exant")
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] | 2.620253 | 553 |
function is_diag_dom(Q::SparseMatrixCSC{Float64,Int64})
for i = 1:size(Q,2)
if 3 * Q[i,i] < sum(Q[:,i]) + sum(Q[i,:])
return false
end
end
return true
end
function dist_diag_dom(Q::SparseMatrixCSC{Float64,Int64})
delta_dist = 0.0
for i = 1:size(Q,2)
delta_dist = max(delta_dist,sum(Q[:,i]) + sum(Q[i,:]) - 3 * Q[i,i])
end
return delta_dist
end
#function bisection_search_delta!(iter::Class_iterate, kkt_solver::abstract_KKT_system_solver, min_delta::Float64, max_delta::Float64, pars::Class_parameters, timer::class_advanced_timer)
#max_exponent = ceil(log(max_delta/min_delta))
function int_bisection(f::Function, a::Int64, b::Int64)
for i = 1:10000
if a == b - 1
return b
end
midpoint = ceil((a + b)/2.0)
if f(midpoint) == true
b = midpoint
else
a = midpoint
end
end
end
function ipopt_strategy!(iter::Class_iterate, kkt_solver::abstract_KKT_system_solver, pars::Class_parameters, timer::class_advanced_timer)
step_info = Blank_ls_info()
MAX_IT = 500
DELTA_ZERO = pars.delta.zero
#DELTA_MIN = iter.point.mu * LinearAlgebra.norm(iter.point.y,Inf) / 100.0 #
DELTA_MIN = pars.delta.min
DELTA_MAX = pars.delta.max
#Q = get_lag_hess(iter)
#@show dist_diag_dom(Q)
num_fac = 0
inertia = 0
status = :none
tau = 1.5 * diag_min(kkt_solver)
#Q = Symmetric(kkt_solver.M,:L)
#eigvals,vec, = eigs(Q,nev=1,which=:SR,maxiter=10,tol=1e4)
#@show eigvals
#v = randn(length(iter.point.x))
#v = v /LinearAlgebra.norm(v,2)
#@show dot(v,Q * v)
delta = DELTA_ZERO
# see if we can succeed with delta = DELTA_ZERO
if tau > 0.0
tau = 0.0
inertia = factor!( kkt_solver, delta, timer )
num_fac += 1
if inertia == 1
return :success, num_fac, delta
end
end
if pars.output_level >= 4
println(pd("delta"), pd("inertia"))
end
for i = 1:MAX_IT
if pars.output_level >= 4
println("delta=",rd(delta), "inertia=", pd(inertia))
end
if i == 1
if get_delta(iter) != 0.0
delta = max(DELTA_MIN - tau, get_delta(iter) * pars.delta.dec)
else
delta = choose_delta_start(iter, kkt_solver, pars) - tau
end
else
delta = delta * pars.delta.inc
end
inertia = factor!( kkt_solver, delta, timer )
num_fac += 1
n = length(iter.point.x)
if inertia == 1
return :success, num_fac, delta
elseif is_diag_dom(kkt_solver.Q[1:n,1:n])
println("WARNING: Inertia calculation incorrect")
@warn("Inertia calculation incorrect")
end
if delta > DELTA_MAX
dx = LinearAlgebra.norm(kkt_solver.dir.x,Inf)
dy = LinearAlgebra.norm(kkt_solver.dir.y,Inf)
ds = LinearAlgebra.norm(kkt_solver.dir.s,Inf)
my_warn("ipopt_strategy failed with delta_max=$DELTA_MAX, delta=$delta, i=$i")
my_warn("num_fac=$num_fac, inertia=$inertia, status=$status, dir_x=$dx, dir_y=$dx, dir_s=$ds")
#error("ipopt_strategy failed with too big a delta")
return :failure, iter, delta
end
end
error("max it")
end
function choose_delta_start(iter::Class_iterate, kkt_solver::abstract_KKT_system_solver, pars::Class_parameters)
return pars.delta.start
# do something clever to reduce number of factorizations
end
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437,
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] | 2.044793 | 1,719 |
function visualize{T, D}(style::Style, img::Texture{T, D, 2}, data::Dict{Symbol, Any})
kernel = data[:kernel]
w, h = img.dims
texparams = [
(GL_TEXTURE_MIN_FILTER, GL_NEAREST),
(GL_TEXTURE_MAG_FILTER, GL_NEAREST),
(GL_TEXTURE_WRAP_S, GL_CLAMP_TO_EDGE),
(GL_TEXTURE_WRAP_T, GL_CLAMP_TO_EDGE)
]
v, uv, indexes = genquad(0f0, 0f0, w, h)
if typeof(kernel) <: Real
filterkernel = float32(kernel)
elseif eltype(kernel) <: Union(AbstractArray, Real)
filterkernel = Texture(kernel, parameters=texparams)
end
data = @compat Dict(
:vertex => GLBuffer(v, 2),
:index => indexbuffer(indexes),
:uv => GLBuffer(uv, 2),
:image => img,
:normrange => data[:normrange],
:filterkernel => filterkernel,
:projectionview => data[:screen].orthographiccam.projectionview,
:model => data[:model]
)
fragdatalocation = [(0, "fragment_color"),(1, "fragment_groupid")]
textureshader = TemplateProgram(joinpath(shaderdir, "uv_vert.vert"), joinpath(shaderdir, "texture.frag"), attributes=data, fragdatalocation=fragdatalocation)
obj = RenderObject(data, textureshader)
prerender!(obj, glDisable, GL_DEPTH_TEST, enabletransparency, glDisable, GL_CULL_FACE)
postrender!(obj, render, obj.vertexarray)
obj
end
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] | 2.317241 | 580 |
# Julia wrappers around the NumPy API, as part of the PyCall package
#########################################################################
# Initialization (UGLY)
# Linking NumPy's C API from Julia requires some serious hackery,
# because NumPy does not export its symbols in the usual way for
# shared libraries. Instead, it provides a Python variable
# numpy.core.multiarray._ARRAY_API that points to a lookup table of
# pointers to the API functions and global variables. Moreover, the
# meaning of this table, along with a lot of other important
# constants, is defined in a C header file which changes between NumPy
# versions, so we need to do some regex parsing of this header file in
# order to extract the necessary information to call NumPy. Ugly and
# mildly insane, but I don't see much alternative (at least to do this
# purely in Julia).
#
# The result of npy_api_initialize, below, is to produce the following
# tables of API pointers:
const npy_api = Dict{Symbol, Ptr{Void}}()
# need a global to cache pyimport("numpy.core.multiarray"), in order
# to ensure the module is not garbage-collected as long as we are using it
# for the npy_api pointers.
const npy_multiarray = PyNULL()
npy_initialized = false # global to prevent multiple initializations
# Macro version of npyinitialize() to inline npy_initialized? check
macro npyinitialize()
:(npy_initialized::Bool ? nothing : npyinitialize())
end
# numpy.number types, used to detect scalars for conversion routines
const npy_number = PyNULL()
const npy_integer = PyNULL()
const npy_floating = PyNULL()
const npy_complexfloating = PyNULL()
function npyinitialize()
global npy_initialized
if npy_initialized::Bool
return
end
try
copy!(npy_multiarray, pyimport("numpy.core.multiarray"))
catch e
error("numpy.core.multiarray required for multidimensional Array conversions - ", e)
end
if pyversion_build.major < 3
PyArray_API = @pycheck ccall((@pysym :PyCObject_AsVoidPtr),
Ptr{Ptr{Void}}, (PyPtr,),
npy_multiarray["_ARRAY_API"])
else
PyArray_API = @pycheck ccall((@pysym :PyCapsule_GetPointer),
Ptr{Ptr{Void}}, (PyPtr,Ptr{Void}),
npy_multiarray["_ARRAY_API"], C_NULL)
end
numpy = pyimport("numpy")
# directory for numpy include files to parse
inc = pycall(numpy["get_include"], AbstractString)
# numpy.number types
copy!(npy_number, numpy["number"])
copy!(npy_integer, numpy["integer"])
copy!(npy_floating, numpy["floating"])
copy!(npy_complexfloating, numpy["complexfloating"])
# Parse __multiarray_api.h to obtain length and meaning of PyArray_API
try
hdrfile = open(joinpath(inc, "numpy", "__multiarray_api.h"))
hdr = readstring(hdrfile);
close(hdrfile)
catch e
error("could not read __multiarray_api.h to parse PyArray_API ", e)
end
hdr = replace(hdr, r"\\\s*\n", " "); # rm backslashed newlines
r = r"^#define\s+([A-Za-z]\w*)\s+\(.*\bPyArray_API\s*\[\s*([0-9]+)\s*\]\s*\)\s*$"m # regex to match #define PyFoo (... PyArray_API[nnn])
PyArray_API_length = 0
for m in eachmatch(r, hdr) # search for max index into PyArray_API
PyArray_API_length = max(PyArray_API_length, parse(Int, m.captures[2])+1)
end
API = pointer_to_array(PyArray_API, (PyArray_API_length,))
for m in eachmatch(r, hdr) # build npy_api table
npy_api[Symbol(m.captures[1])] = API[parse(Int, m.captures[2])+1]
end
if !haskey(npy_api, :PyArray_New)
error("failure parsing NumPy PyArray_API symbol table")
end
npy_initialized::Bool = true
return
end
#########################################################################
# Hard-coded constant values, copied from numpy/ndarraytypes.h ...
# the values of these seem to have been stable for some time, and
# the NumPy developers seem to have some awareness of binary compatibility
# NPY_TYPES:
const NPY_BOOL = Int32(0)
const NPY_BYTE = Int32(1)
const NPY_UBYTE = Int32(2)
const NPY_SHORT = Int32(3)
const NPY_USHORT = Int32(4)
const NPY_INT = Int32(5)
const NPY_UINT = Int32(6)
const NPY_LONG = Int32(7)
const NPY_ULONG = Int32(8)
const NPY_LONGLONG = Int32(9)
const NPY_ULONGLONG = Int32(10)
const NPY_FLOAT = Int32(11)
const NPY_DOUBLE = Int32(12)
const NPY_LONGDOUBLE = Int32(13)
const NPY_CFLOAT = Int32(14)
const NPY_CDOUBLE = Int32(15)
const NPY_CLONGDOUBLE = Int32(16)
const NPY_OBJECT = Int32(17)
const NPY_STRING = Int32(18)
const NPY_UNICODE = Int32(19)
const NPY_VOID = Int32(20)
# NPY_ORDER:
const NPY_ANYORDER = Int32(-1)
const NPY_CORDER = Int32(0)
const NPY_FORTRANORDER = Int32(1)
# flags:
const NPY_ARRAY_C_CONTIGUOUS = Int32(1)
const NPY_ARRAY_F_CONTIGUOUS = Int32(2)
const NPY_ARRAY_ALIGNED = Int32(0x0100)
const NPY_ARRAY_WRITEABLE = Int32(0x0400)
const NPY_ARRAY_OWNDATA = Int32(0x0004)
const NPY_ARRAY_ENSURECOPY = Int32(0x0020)
const NPY_ARRAY_ENSUREARRAY = Int32(0x0040)
const NPY_ARRAY_FORCECAST = Int32(0x0010)
const NPY_ARRAY_UPDATEIFCOPY = Int32(0x1000)
const NPY_ARRAY_NOTSWAPPED = Int32(0x0200)
const NPY_ARRAY_ELEMENTSTRIDES = Int32(0x0080)
#########################################################################
# conversion from Julia types to NPY_TYPES constant
npy_type(::Type{Bool}) = NPY_BOOL
npy_type(::Type{Int8}) = NPY_BYTE
npy_type(::Type{UInt8}) = NPY_UBYTE
npy_type(::Type{Int16}) = NPY_SHORT
npy_type(::Type{UInt16}) = NPY_USHORT
npy_type(::Type{Int32}) = NPY_INT
npy_type(::Type{UInt32}) = NPY_UINT
npy_type(::Type{Int64}) = NPY_LONGLONG
npy_type(::Type{UInt64}) = NPY_ULONGLONG
npy_type(::Type{Float32}) = NPY_FLOAT
npy_type(::Type{Float64}) = NPY_DOUBLE
npy_type(::Type{Complex64}) = NPY_CFLOAT
npy_type(::Type{Complex128}) = NPY_CDOUBLE
npy_type(::Type{PyPtr}) = NPY_OBJECT
typealias NPY_TYPES Union{Bool,Int8,UInt8,Int16,UInt16,Int32,UInt32,Int64,UInt64,Float32,Float64,Complex64,Complex128,PyPtr}
# conversions from __array_interface__ type strings to supported Julia types
const npy_typestrs = Dict( "b1"=>Bool,
"i1"=>Int8, "u1"=>UInt8,
"i2"=>Int16, "u2"=>UInt16,
"i4"=>Int32, "u4"=>UInt32,
"i8"=>Int64, "u8"=>UInt64,
"f4"=>Float32, "f8"=>Float64,
"c8"=>Complex64, "c16"=>Complex128,
"O"=>PyPtr, "O$(div(WORD_SIZE,8))"=>PyPtr )
#########################################################################
# no-copy conversion of Julia arrays to NumPy arrays.
# Julia arrays are in column-major order, but in some cases it is useful
# to pass them to Python as row-major arrays simply by reversing the
# dimensions. For example, although NumPy works with both row-major and
# column-major data, some Python libraries like OpenCV seem to require
# row-major data (the default in NumPy). In such cases, use PyReverseDims(array)
function NpyArray{T<:NPY_TYPES}(a::StridedArray{T}, revdims::Bool)
@npyinitialize
size_a = revdims ? reverse(size(a)) : size(a)
strides_a = revdims ? reverse(strides(a)) : strides(a)
p = @pycheck ccall(npy_api[:PyArray_New], PyPtr,
(PyPtr,Cint,Ptr{Int},Cint, Ptr{Int},Ptr{T}, Cint,Cint,PyPtr),
npy_api[:PyArray_Type],
ndims(a), Int[size_a...], npy_type(T),
Int[strides_a...] * sizeof(eltype(a)), a, sizeof(eltype(a)),
NPY_ARRAY_ALIGNED | NPY_ARRAY_WRITEABLE,
C_NULL)
return PyObject(p, a)
end
function PyObject{T<:NPY_TYPES}(a::StridedArray{T})
try
return NpyArray(a, false)
catch
array2py(a) # fallback to non-NumPy version
end
end
PyReverseDims{T<:NPY_TYPES}(a::StridedArray{T}) = NpyArray(a, true)
PyReverseDims(a::BitArray) = PyReverseDims(Array(a))
"""
PyReverseDims(array)
Passes a Julia `array` to Python as a NumPy row-major array
(rather than Julia's native column-major order) with the
dimensions reversed (e.g. a 2×3×4 Julia array is passed as
a 4×3×2 NumPy row-major array). This is useful for Python
libraries that expect row-major data.
"""
PyReverseDims(a::AbstractArray)
#########################################################################
# Extract shape and other information about a NumPy array. We need
# to call the Python interface to do this, since the equivalent information
# in NumPy's C API is only available via macros (or parsing structs).
# [ Hopefully, this will be improved in a future NumPy version. ]
type PyArray_Info
T::Type
native::Bool # native byte order?
sz::Vector{Int}
st::Vector{Int} # strides, in multiples of bytes!
data::Ptr{Void}
readonly::Bool
function PyArray_Info(a::PyObject)
ai = PyDict{AbstractString,PyObject}(a["__array_interface__"])
typestr = convert(AbstractString, ai["typestr"])
T = npy_typestrs[typestr[2:end]]
datatuple = convert(Tuple{Int,Bool}, ai["data"])
sz = convert(Vector{Int}, ai["shape"])
local st
try
st = isempty(sz) ? Int[] : convert(Vector{Int}, ai["strides"])
catch
# default is C-order contiguous
st = similar(sz)
st[end] = sizeof(T)
for i = length(sz)-1:-1:1
st[i] = st[i+1]*sz[i+1]
end
end
return new(T,
(ENDIAN_BOM == 0x04030201 && typestr[1] == '<')
|| (ENDIAN_BOM == 0x01020304 && typestr[1] == '>')
|| typestr[1] == '|',
sz, st,
convert(Ptr{Void}, datatuple[1]),
datatuple[2])
end
end
aligned(i::PyArray_Info) = # FIXME: also check pointer alignment?
all(m -> m == 0, mod(i.st, sizeof(i.T))) # strides divisible by elsize
# whether a contiguous array in column-major (Fortran, Julia) order
function f_contiguous(T::Type, sz::Vector{Int}, st::Vector{Int})
if prod(sz) == 1
return true
end
if st[1] != sizeof(T)
return false
end
for j = 2:length(st)
if st[j] != st[j-1] * sz[j-1]
return false
end
end
return true
end
f_contiguous(i::PyArray_Info) = f_contiguous(i.T, i.sz, i.st)
c_contiguous(i::PyArray_Info) = f_contiguous(i.T, flipdim(i.sz,1), flipdim(i.st,1))
#########################################################################
# PyArray: no-copy wrapper around NumPy ndarray
#
# Hopefully, in the future this can be a subclass of StridedArray (see
# Julia issue #2345), which will allow it to be used with most Julia
# functions, but that is not possible at the moment. So, to use this
# with Julia linalg functions etcetera a copy is still required.
"""
PyArray(o::PyObject)
This converts an `ndarray` object `o` to a PyArray.
This implements a nocopy wrapper to a NumPy array (currently of only numeric types only).
If you are using `pycall` and the function returns an `ndarray`, you can use `PyArray` as the return type to directly receive a `PyArray`.
"""
type PyArray{T,N} <: AbstractArray{T,N}
o::PyObject
info::PyArray_Info
dims::Dims
st::Vector{Int}
f_contig::Bool
c_contig::Bool
data::Ptr{T}
function PyArray(o::PyObject, info::PyArray_Info)
if !aligned(info)
throw(ArgumentError("only NPY_ARRAY_ALIGNED arrays are supported"))
elseif !info.native
throw(ArgumentError("only native byte-order arrays are supported"))
elseif info.T != T
throw(ArgumentError("inconsistent type in PyArray constructor"))
elseif length(info.sz) != N || length(info.st) != N
throw(ArgumentError("inconsistent ndims in PyArray constructor"))
end
return new(o, info, tuple(info.sz...), div(info.st, sizeof(T)),
f_contiguous(info), c_contiguous(info),
convert(Ptr{T}, info.data))
end
end
function PyArray(o::PyObject)
info = PyArray_Info(o)
return PyArray{info.T, length(info.sz)}(o, info)
end
size(a::PyArray) = a.dims
ndims{T,N}(a::PyArray{T,N}) = N
similar(a::PyArray, T, dims::Dims) = Array(T, dims)
function copy{T,N}(a::PyArray{T,N})
if N > 1 && a.c_contig # equivalent to f_contig with reversed dims
B = pointer_to_array(a.data, ntuple((n -> a.dims[N - n + 1]), N))
return N == 2 ? transpose(B) : permutedims(B, (N:-1:1))
end
A = Array(T, a.dims)
if a.f_contig
ccall(:memcpy, Void, (Ptr{T}, Ptr{T}, Int), A, a, sizeof(T)*length(a))
return A
else
return copy!(A, a)
end
end
# TODO: need to do bounds-checking of these indices!
getindex{T}(a::PyArray{T,0}) = unsafe_load(a.data)
getindex{T}(a::PyArray{T,1}, i::Integer) = unsafe_load(a.data, 1 + (i-1)*a.st[1])
getindex{T}(a::PyArray{T,2}, i::Integer, j::Integer) =
unsafe_load(a.data, 1 + (i-1)*a.st[1] + (j-1)*a.st[2])
function getindex(a::PyArray, i::Integer)
if a.f_contig
return unsafe_load(a.data, i)
else
return a[ind2sub(a.dims, i)...]
end
end
function getindex(a::PyArray, is::Integer...)
index = 1
n = min(length(is),length(a.st))
for i = 1:n
index += (is[i]-1)*a.st[i]
end
for i = n+1:length(is)
if is[i] != 1
throw(BoundsError())
end
end
unsafe_load(a.data, index)
end
function writeok_assign(a::PyArray, v, i::Integer)
if a.info.readonly
throw(ArgumentError("read-only PyArray"))
else
unsafe_store!(a.data, v, i)
end
return a
end
setindex!{T}(a::PyArray{T,0}, v) = writeok_assign(a, v, 1)
setindex!{T}(a::PyArray{T,1}, v, i::Integer) = writeok_assign(a, v, 1 + (i-1)*a.st[1])
setindex!{T}(a::PyArray{T,2}, v, i::Integer, j::Integer) =
writeok_assign(a, v, 1 + (i-1)*a.st[1] + (j-1)*a.st[2])
function setindex!(a::PyArray, v, i::Integer)
if a.f_contig
return writeok_assign(a, v, i)
else
return setindex!(a, v, ind2sub(a.dims, i)...)
end
end
function setindex!(a::PyArray, v, is::Integer...)
index = 1
n = min(length(is),length(a.st))
for i = 1:n
index += (is[i]-1)*a.st[i]
end
for i = n+1:length(is)
if is[i] != 1
throw(BoundsError())
end
end
writeok_assign(a, v, index)
end
stride(a::PyArray, i::Integer) = a.st[i]
Base.unsafe_convert{T}(::Type{Ptr{T}}, a::PyArray{T}) = a.data
pointer(a::PyArray, i::Int) = pointer(a, ind2sub(a.dims, i))
function pointer{T}(a::PyArray{T}, is::Tuple{Vararg{Int}})
offset = 0
for i = 1:length(is)
offset += (is[i]-1)*a.st[i]
end
return a.data + offset*sizeof(T)
end
summary{T}(a::PyArray{T}) = string(Base.dims2string(size(a)), " ",
string(T), " PyArray")
#########################################################################
# PyArray <-> PyObject conversions
PyObject(a::PyArray) = a.o
convert(::Type{PyArray}, o::PyObject) = PyArray(o)
function convert{T<:NPY_TYPES}(::Type{Array{T, 1}}, o::PyObject)
try
copy(PyArray{T, 1}(o, PyArray_Info(o))) # will check T and N vs. info
catch
len = @pycheckz ccall((@pysym :PySequence_Size), Int, (PyPtr,), o)
A = Array(pyany_toany(T), len)
py2array(T, A, o, 1, 1)
end
end
function convert{T<:NPY_TYPES}(::Type{Array{T}}, o::PyObject)
try
info = PyArray_Info(o)
try
copy(PyArray{T, length(info.sz)}(o, info)) # will check T == info.T
catch
return py2array(T, Array(pyany_toany(T), info.sz...), o, 1, 1)
end
catch
py2array(T, o)
end
end
function convert{T<:NPY_TYPES,N}(::Type{Array{T,N}}, o::PyObject)
try
info = PyArray_Info(o)
try
copy(PyArray{T,N}(o, info)) # will check T == info.T and N == length(info.sz)
catch
nd = length(info.sz)
if nd != N
throw(ArgumentError("cannot convert $(nd)d array to $(N)d"))
end
return py2array(T, Array(pyany_toany(T), info.sz...), o, 1, 1)
end
catch
A = py2array(T, o)
if ndims(A) != N
throw(ArgumentError("cannot convert $(ndims(A))d array to $(N)d"))
end
A
end
end
function convert(::Type{Array{PyObject}}, o::PyObject)
map(pyincref, convert(Array{PyPtr}, o))
end
function convert(::Type{Array{PyObject,1}}, o::PyObject)
map(pyincref, convert(Array{PyPtr, 1}, o))
end
function convert{N}(::Type{Array{PyObject,N}}, o::PyObject)
map(pyincref, convert(Array{PyPtr, N}, o))
end
#########################################################################
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] | 2.243308 | 7,472 |
function allunitsharvest!(game)
#groundlevel=false
#if unit.pl=[2]
# groundlevel=true
#end
#bools=(unit.passover,unit.passoverself,unit.inclusive)
#influencemap=allinfluence(game,unit.groundlevel,bools)
allunitslive!(game)
points=[0.0,0,0,0,0]
for (loc,unit) in game.map
if unit!=0
points.+=unit.harvest!(game,unit)
# println(points)
end
end
#game.points.+=points
game.season+=1
push!(game.sequence,:harvest)
GAccessor.text(game.g[1,2],pointslabel(game))
return points
end
function lifluence(game,unit::Unit)
lifemap=unitslive(game,unit.color)
connectedunits=Unit[unit]
cwhite=Unit[]
group=[unit.loc]
if unit.color==(1,1,1)
push!(cwhite,unit)
stuff=Dict()
#stuff[:loced]=[unit.loc]
reach=makegrid(7)
for loc in reach
u=game.map[loc]
if isa(u,Unit) && u.color!=(1,1,1) && !haskey(stuff,u.color) #!in(loc,stuff[:loced])
lmu=lifluence(game,u)
if in(unit,lmu[3])
stuff[u.color]=lmu
end
end
end
for (col,lm) in stuff
for l in lm[1]
if !in(l,group)
push!(group,l)
end
end
for cu in lm[2]
if !in(cu,connectedunits)
push!(connectedunits,cu)
end
end
for cw in lm[3]
if !in(cw,cwhite)
push!(cwhite,cw)
end
end
end
return (group,connectedunits,cwhite)
end
temp=[unit.loc]
while !isempty(temp)
temp2=Tuple[]
for t in temp
for h in adjacent(t)
# lmt=lifemap[t]
# lmh=lifemap[h]
# canspread=true
if !in(h,group) && !in(h,temp) && !in(h,temp2) && in(h,game.board.grid) && sum(lifemap[h])>0
push!(temp2,h)
u=game.map[h]
if isa(u,Unit)
push!(connectedunits,u)
if u.color==(1,1,1)
push!(cwhite,u)
end
end
end
end
end
for t2 in temp2
push!(group,t2)
end
temp=temp2
end
return (group,connectedunits,cwhite)
end
function connectedunits(game,unit)
(lif,cu,cw)=lifluence(game,unit)
cellconnected=getcellgroup(game,unit)
return (cw,cellconnected,cu) #connected white units, connected by cells, connected by lifluence
end
function influence(game,hex,groundlevel=false,passover=false,passoverself=true,inclusive=true)
unit=game.map[hex]
white=(1,1,1)
group=Dict(hex=>6.0)
temp=Dict(hex=>6.0)
for rad in 1:unit.ir
temp2=Dict()
for t in temp
for h in adjacent(t[1],1,groundlevel)
if !in(h,keys(group)) && !in(h,keys(temp)) && !in(h,keys(temp2)) && in(h,keys(game.map))
inf=1/rad
if game.map[h]==0 || passover
temp2[h]=inf
elseif passoverself && (game.map[h].color==unit.color || game.map[h].color==white)
temp2[h]=inf
end
if inclusive && game.map[h]!=0 && !in(h,keys(temp2))
group[h]=inf
end
end
end
end
for (h2,i2) in temp2
group[h2]=i2
end
temp=temp2
end
return group
end
function allinfluence(game,groundlevel=false,bools=(true,false,true,true))
influencemap=Dict()
for (loc,player) in game.map
if !groundlevel || loc[3]==2
influencemap[loc]=[0.0,0,0]
end
end
for (loc,unit) in game.map
if unit!=0
col=unit.color
infl=influence(game,loc,bools...)
for inf in infl
influencemap[inf[1]].+=inf[2].*col
end
end
end
return influencemap
end
function peekharvest(game,groundlevel=false,bools=(true,false,true,true))
influencemap=allinfluence(game,groundlevel,bools)
brgbw=[0.0,0,0,0,0]
for (iloc,inf) in influencemap
ninf=numcolors(inf)
if ninf==3
brgbw[5]+=min(inf...)
end
if ninf==1
brgbw[1]+=min(sum(inf),1)
else
for c in 1:3
brgbw[c+1]+=min(inf[c],inf[c%3+1])
end
end
end
for c in 2:4
brgbw[c]-=brgbw[5]
end
return brgbw
end
function getgroup_dep(game,hex)
player=game.map[hex]
white=(1,1,1)
if player==0
return []
end
group=Tuple[hex]
temp=[hex]
while !isempty(temp)
temp2=Tuple[]
for t in temp
for h in adjacent(t)
if !in(h,group) && !in(h,temp) && !in(h,temp2) && in(h,keys(game.map)) && (game.map[h]==player || game.map[h]==white)
push!(temp2,h)
end
end
end
for t2 in temp2
push!(group,t2)
end
temp=temp2
end
return group
end
function unitcost(game,loc,unitparams)
distance=nearestwhite(game,loc,loc[3]==2)
cost=distance*unitparams[2]
cost*=sum(unitparams[1]) #only if sum>1?
return cost
end
function unitcost(game,unit::Unit)
return unit.costfun(game)
end
function subtractcost(game,cost,color)
rgb=cost.*color
srgb=sum(rgb)
if rgb[1]<=game.points[2] && rgb[2]<=game.points[3] && rgb[3]<=game.points[4]
game.points[2]-=rgb[1]
game.points[3]-=rgb[2]
game.points[4]-=rgb[3]
elseif srgb/2<=game.points[5] && srgb/2<=game.points[1]
game.points[1]-=srgb/2
game.points[5]-=srgb/2
else
return false
end
return true
end
function updategroups_dep!(game::Game) #sometimes doesn't find most recent unit..? Or is there an issue when deleting units? Maybe fixed. Now it sometimes adds the most recent unit twice...
groups=Group[]
for spawn in game.spawns
push!(groups,getgroup(game,spawn))
end
unique=Group[groups[1]]
for group in groups
for uniq in unique
if samegroup(group,uniq)
break
end
push!(unique,group)
end
end
game.groups=unique
end
function getgroup_dep(game,unit::Unit,color=-1,connectedunits=Unit[]) #why don't white units get added to spawns? Maybe they shouldn't, bug in our favor. They should be available as partial spawns, divide white into 3 spawn units. Fixed some stuff, still good bugs? Well, this should be rewritten rather than bugged down, first a initgroup that gets the body.
if color==-1
color=unit.color
end
lifemap=unitslive(game,unit.color)
if !in(unit,connectedunits)
push!(connectedunits,unit)
end
cwhite=Unit[] #connected spawns
if color==(1,1,1) #this needs to be rewritten to allow colored spawns, sorta works now but... nonbody groups automerge with white group
push!(cwhite,unit)
stuff=Dict()
reach=makegrid(7) #should be minimum twice the maximum ir of all units +1, should be a better way
for lo in reach
loc=lo.+(unit.loc.-(0,0,2))
if !in(loc,keys(game.map))
continue
end
u=game.map[loc]
if isa(u,Unit) && distance(u.loc,unit.loc)<(u.ir+unit.ir+2) && !haskey(stuff,u.color) && !in(u,connectedunits) && u.color!=(1,1,1)
subgroup=getgroup(game,u,u.color,connectedunits)
if in(unit,subgroup.spawns)
stuff[u.color]=subgroup
end
end
end
for (col,sg) in stuff
for cu in sg.units
if !in(cu,connectedunits)
push!(connectedunits,cu)
end
end
for cw in sg.spawns
if !in(cw,cwhite)
push!(cwhite,cw)
end
end
end
body=getcellgroup(game,cwhite[1])
return newgroup(cwhite,body,connectedunits)
end
ulocs=[unit.loc]
temp=[unit.loc]
while !isempty(temp)
temp2=Tuple[]
for t in temp
for h in adjacent(t)
if !in(h,ulocs) && !in(h,temp) && !in(h,temp2) && in(h,game.board.grid) && sum(lifemap[h])>0
push!(temp2,h)
u=game.map[h]
if isa(u,Unit) && !in(u,connectedunits)
push!(connectedunits,u)
if u.color==(1,1,1)
push!(cwhite,u)
for adjwu in adjacent(u.loc)
adju=game.map[adjwu]
if isa(adju,Unit) && adju.color==color && !in(adju,connectedunits)
nsg=getgroup(game,adju,color,connectedunits)
for cu in nsg.units
if !in(cu,connectedunits)
push!(connectedunits,cu)
end
end
for cw in nsg.spawns
if !in(cw,cwhite)
push!(cwhite,cw)
end
end
end
end
end
end
end
end
end
for t2 in temp2
push!(ulocs,t2)
end
temp=temp2
end
body=getcellgroup(game,connectedunits[1]) #tempfix
return newgroup(cwhite,body,connectedunits)
end
function allunitslive!(game)
lifemap=Dict()
for loc in game.board.grid
lifemap[loc]=[0.0,0,0]
end
for (loc,unit) in game.map
if isa(unit,Unit)
unit.live!(game,unit,lifemap)
end
end
game.lifemap=lifemap
return lifemap
end
#lifemap=allunitslive! #maybe not store lifemap in game since it gets modified in so many places. Rewrite everything to work with a temp lifemap. Or rather have a solid lifemap and a temp harvest ledger
function printpoints(game)
harv=round.(peekharvest(game),1,10)
println("Black: ",harv[1]," Red: ",harv[2]," Green: ",harv[3]," Blue: ",harv[4]," White: ",harv[5])#," Total: ",sum(harv))
end
function nearestwhite(game,hex,layer=false) #rewrite to check for spawn? Or spawning is always done from groups? This isn't even needed now that costs are gone
white=(1,1,1)
group=[hex]
temp=[hex]
for rad in 1:1000000
temp2=[]
for t in temp
for h in adjacent(t,1,layer)
if in(h,keys(game.map))
unit=game.map[h]
if unit!=0 && unit.color==white
return rad
end
if !in(h,group) && !in(h,temp) && !in(h,temp2)
push!(temp2,h)
end
end
end
end
for h2 in temp2
push!(group,h2)
end
temp=temp2
end
return Inf
end
function getexpandcost(shells::Integer=6,initlocs=[(6,6,2)],basecost=50) #deprecated
patch=makegrid(shells,initlocs)
cost=length(patch)+basecost*length(initlocs)
return cost
end
function harvest!(game,group::Group)
if !group.harvested
harv=checkharvest(game,group) #some stuff needs to be rewritten to avoid the deepcopy above
group.points+=harv
group.harvested=true
for unit in group.units
unit.harvested=true
end
end
end
function allgroupsharvest!(game)
for group in game.groups
harvest!(game,group)
end
end
function collectharvest!(game::Game)
for group in game.groups
game.points+=group.points
group.points-=group.points
end
end
function newseason!(game)
for group in game.groups
group.harvested=false
for unit in group.units
unit.harvested=false
end
end
game.season+=1
end
function harvest!(game::Game)
allunitslive!(game) #maybe call only when new units are placed. But then placing units takes longer...
updategroups!(game)
allgroupsharvest!(game)
collectharvest!(game)
newseason!(game)
push!(game.sequence,:harvest)
GAccessor.text(game.g[1,2],pointslabel(game))
return game.points
end
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] | 2.1683 | 4,593 |
# fallback methods
# ----------------
set_format_options!(docformat::WeaveFormat; _kwargs...) = return
function restore_header!(doc)
(hasproperty(doc.format, :preserve_header) && doc.format.preserve_header) || return
# only strips Weave headers
delete!(doc.header, WEAVE_OPTION_NAME)
if haskey(doc.header, WEAVE_OPTION_NAME_DEPRECATED)
@warn "Weave: `options` key is deprecated. Use `weave_options` key instead." _id = WEAVE_OPTION_DEPRECATE_ID maxlog = 1
delete!(doc.header, WEAVE_OPTION_NAME_DEPRECATED)
end
isempty(doc.header) && return
# restore remained headers as `DocChunk`
header_text = "---\n$(YAML.write(doc.header))---"
pushfirst!(doc.chunks, DocChunk(header_text, 0, 0))
end
render_chunk(docformat::WeaveFormat, chunk::DocChunk) = join((render_inline(c) for c in chunk.content))
render_inline(inline::InlineText) = inline.content
function render_inline(inline::InlineCode)
isempty(inline.rich_output) || return inline.rich_output
isempty(inline.figures) || return inline.figures[end]
return inline.output
end
function render_chunk(docformat::WeaveFormat, chunk::CodeChunk)
# Fill undefined options with format specific defaults
isnothing(chunk.options[:out_width]) && (chunk.options[:out_width] = docformat.out_width)
isnothing(chunk.options[:fig_pos]) && (chunk.options[:fig_pos] = docformat.fig_pos)
# Only use floats if chunk has caption or sets fig_env
if !isnothing(chunk.options[:fig_cap]) && isnothing(chunk.options[:fig_env])
(chunk.options[:fig_env] = docformat.fig_env)
end
hasproperty(docformat, :indent) && (chunk.content = indent(chunk.content, docformat.indent))
chunk.content = render_code(docformat, chunk.content)
if !chunk.options[:eval]
return if chunk.options[:echo]
string(docformat.codestart, '\n', chunk.content, docformat.codeend)
else
""
end
end
if chunk.options[:term]
result = render_termchunk(docformat, chunk)
else
result = if chunk.options[:echo]
# Convert to output format and highlight (html, tex...) if needed
string(docformat.codestart, chunk.content, docformat.codeend, '\n')
else
""
end
if (strip(chunk.output) ≠ "" || strip(chunk.rich_output) ≠ "") &&
(chunk.options[:results] ≠ "hidden")
if chunk.options[:results] ≠ "markup" && chunk.options[:results] ≠ "hold"
strip(chunk.output) ≠ "" && (result *= "$(chunk.output)\n")
strip(chunk.rich_output) ≠ "" && (result *= "$(chunk.rich_output)\n")
else
if chunk.options[:wrap]
chunk.output =
'\n' * wraplines(chunk.output, chunk.options[:line_width])
chunk.output = render_output(docformat, chunk.output)
else
chunk.output = '\n' * rstrip(chunk.output)
chunk.output = render_output(docformat, chunk.output)
end
hasproperty(docformat, :indent) && (chunk.output = indent(chunk.output, docformat.indent))
strip(chunk.output) ≠ "" && (
result *= "$(docformat.outputstart)$(chunk.output)\n$(docformat.outputend)\n"
)
strip(chunk.rich_output) ≠ "" && (result *= chunk.rich_output * '\n')
end
end
end
# Handle figures
if chunk.options[:fig] && length(chunk.figures) > 0
result *= render_figures(docformat, chunk)
end
return result
end
render_code(docformat::WeaveFormat, code) = code
indent(text, nindent) = join(map(x -> string(repeat(' ', nindent), x), split(text, '\n')), '\n')
function wraplines(text, line_width = 75)
result = AbstractString[]
lines = split(text, '\n')
for line in lines
if length(line) > line_width
push!(result, wrapline(line, line_width))
else
push!(result, line)
end
end
return strip(join(result, '\n'))
end
function wrapline(text, line_width = 75)
result = ""
while length(text) > line_width
result *= first(text, line_width) * '\n'
text = chop(text, head = line_width, tail = 0)
end
result *= text
end
render_output(docformat::WeaveFormat, output) = output
function render_termchunk(docformat::WeaveFormat, chunk)
return if should_render(chunk)
string(docformat.termstart, chunk.output, '\n', docformat.termend, '\n')
else
""
end
end
should_render(chunk) = chunk.options[:echo] && chunk.options[:results] ≠ "hidden"
render_doc(docformat, body, doc) = body
# utilities
# ---------
function clear_buffer_and_format!(io::IOBuffer, out::IOBuffer, render_function)
text = take2string!(io)
m = Markdown.parse(text, flavor = WeaveMarkdown.weavemd)
write(out, string(render_function(m)))
end
addlines(op, inline) = inline.ctype === :line ? string('\n', op, '\n') : op
get_mustache_template(path::AbstractString) = Mustache.template_from_file(path)
get_mustache_template(tpl::Mustache.MustacheTokens) = tpl
get_highlight_stylesheet(mime, highlight_theme) =
get_highlight_stylesheet(mime, get_highlight_theme(highlight_theme))
get_highlight_stylesheet(mime, highlight_theme::Type{<:Highlights.AbstractTheme}) =
sprint((io, x) -> Highlights.stylesheet(io, mime, x), highlight_theme)
get_highlight_theme(::Nothing) = Highlights.Themes.DefaultTheme
get_highlight_theme(highlight_theme::Type{<:Highlights.AbstractTheme}) = highlight_theme
highlight_code(mime, code, highlight_theme) =
highlight(mime, strip(code), Highlights.Lexers.JuliaLexer, highlight_theme)
highlight_term(mime, output, highlight_theme) =
highlight(mime, strip(output), Highlights.Lexers.JuliaConsoleLexer, highlight_theme)
highlight(mime, output, lexer, theme = Highlights.Themes.DefaultTheme) =
sprint((io, x) -> Highlights.highlight(io, mime, x, lexer, theme), output)
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] | 2.403586 | 2,510 |
# Autogenerated wrapper script for ICU_jll for powerpc64le-linux-gnu-cxx11
export libicudata, libicui18n, libicuio, libicutest, libicutu, libicuuc
JLLWrappers.@generate_wrapper_header("ICU")
JLLWrappers.@declare_library_product(libicudata, "libicudata.so.69")
JLLWrappers.@declare_library_product(libicui18n, "libicui18n.so.69")
JLLWrappers.@declare_library_product(libicuio, "libicuio.so.69")
JLLWrappers.@declare_library_product(libicutest, "libicutest.so.69")
JLLWrappers.@declare_library_product(libicutu, "libicutu.so.69")
JLLWrappers.@declare_library_product(libicuuc, "libicuuc.so.69")
function __init__()
JLLWrappers.@generate_init_header()
JLLWrappers.@init_library_product(
libicudata,
"lib/libicudata.so",
nothing,
)
JLLWrappers.@init_library_product(
libicui18n,
"lib/libicui18n.so",
RTLD_LAZY | RTLD_DEEPBIND,
)
JLLWrappers.@init_library_product(
libicuio,
"lib/libicuio.so",
RTLD_LAZY | RTLD_DEEPBIND,
)
JLLWrappers.@init_library_product(
libicutest,
"lib/libicutest.so",
RTLD_LAZY | RTLD_DEEPBIND,
)
JLLWrappers.@init_library_product(
libicutu,
"lib/libicutu.so",
RTLD_LAZY | RTLD_DEEPBIND,
)
JLLWrappers.@init_library_product(
libicuuc,
"lib/libicuuc.so",
RTLD_LAZY | RTLD_DEEPBIND,
)
JLLWrappers.@generate_init_footer()
end # __init__()
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] | 2 | 731 |
using ExtendedKronigPennyMatrix
using LinearAlgebra
using PyPlot
function plot_KP(v0, rho)
clf()
cm=get_cmap("tab10")
pot=KronigPennyPotential(v0, rho)
pf = get_potential(pot)
a=1
xs=-2a:a/100:3a
plot(xs, pf.(xs), "k")
nmax=60
Ka=0
model=KronigPennyModel(pot, nmax, Ka)
for Ka in (-18:18)/18*π
update!(model, Ka=Ka)
ev = eigvals(model.hnm)
for i in 1:5
plot(Ka/ π, ev[i], ".", color=cm(i-1))
end
end
xlim(-1,1)
ylim(-2,32)
xlabel(L"$Ka / \pi$")
ylabel(L"Energy / $E_0$")
title( L"$\rho =$"*string(rho))
end
function main()
#
plot_KP( 0, 0.5)
ylim(-0.5,25)
savefig("Pavelich_Fig4a.png")
#
plot_KP( 10, 0.5)
ylim(-0.5,30)
savefig("Pavelich_Fig4b.png")
savefig("KP_10_05.png")
#
plot_KP( 10, 0.8)
ylim(-0.5,30)
savefig("KP_10_08.png")
#
plot_KP(20.5607, 0.5)
ylim(-0.5,40)
savefig("Pavelich_Fig6.png")
#
plot_KP(10.8775, 0.8)
ylim(-0.5,30)
savefig("Pavelich_Fig7.png")
end
main()
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] | 1.751678 | 596 |
# can we write a function adjusting the states so that process becomes monotone?
# resulting tree should be very close (in nested distance) to original process
#Example 1
trr1 = Tree([1,2,2,2]); trr2 = Tree([1,2,2,2]);
trr1.state = [0,0,0,0,0,0,0,1,3,20,40,2,4,21,41];
trr2.state = [0,0,0,0,0,0,0,1,21,3,41,2,20,4,40];
trr1.probability = [1.0,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5,0.5];
trr2.probability = trr1.probability;
nestedWasserstein(trr1,trr2,1.0) # supposed to be 8.75
#Example 2
trr1 = Tree([1,2,4,1]); trr2 = Tree([1,2,4,1]);
trr1.state = [0,0,0,0,0,0,0,0,0,0,0,1,3,20,40,2,4,21,41];
trr2.state = [0,0,0,0,0,0,0,0,0,0,0,1,21,3,41,2,20,4,40];
trr1.probability = [1.0,0.5,0.5,0.25,0.25,0.25,0.25,0.25,0.25,0.25,0.25,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0];
trr2.probability = trr1.probability;
nestedWasserstein(trr1,trr2,1.0)
# Example 3
trr1 = Tree([1,1,2]); trr2 = Tree([1,2,1]);
trr1.state = [2,2,3,1];
trr2.state = [2,2,2,3,1];
trr1.probability = [1.0,1.0,0.3,0.7];
trr2.probability = [1.0,0.3,0.7,1.0,1.0];
nestedWasserstein(trr1,trr2,1.0)
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] | 1.585298 | 721 |
# details on status codes
const statusses = Dict(
:ok => "successful",
:skip => "skipped",
:fail => "unsuccessful",
:kill => "interrupted",
)
const reasons = Dict(
missing => missing,
# skip
:explicit => "package was blacklisted",
:jll => "package is a untestable wrapper package",
:unsupported => "package is not supported by this Julia version",
# fail
:unsatisfiable => "package could not be installed",
:untestable => "package does not have any tests",
:binary_dependency => "package requires a missing binary dependency",
:missing_dependency => "package is missing a package dependency",
:missing_package => "package is using an unknown package",
:test_failures => "package has test failures",
:syntax => "package has syntax issues",
:gc_corruption => "GC corruption detected",
:segfault => "a segmentation fault happened",
:abort => "the process was aborted",
:unreachable => "an unreachable instruction was executed",
:unknown => "there were unidentified errors",
# kill
:time_limit => "test duration exceeded the time limit",
:log_limit => "test log exceeded the size limit",
:inactivity => "tests became inactive",
)
# simple comparison of two versions
print_status(status, val=status) = print_status(stdout, status, val)
function print_status(io::IO, status, val=status)
if status == :ok
printstyled(io, val; color = :green)
elseif status == :fail || status == :kill
printstyled(io, val; color = Base.error_color())
elseif status == :skip
printstyled(io, val; color = Base.warn_color())
else
error("Unknown status $status")
end
end
function compare(result; rev::Bool=false)
pkg_names = unique(result.name)
primary, against = groupby(result, [:julia])
if rev # hack
primary, against = against, primary
end
# overview
for df in (primary, against)
o = count(==(:ok), df[!, :status])
s = count(==(:skip), df[!, :status])
f = count(==(:fail), df[!, :status])
k = count(==(:kill), df[!, :status])
x = o + s + k + f
@assert x == nrow(df)
print("On v$(first(df.julia)), out of $x packages ")
print_status(:ok, o)
print(" passed, ")
print_status(:fail, f)
print(" failed, ")
print_status(:kill, k)
print(" got killed and ")
print_status(:skip, s)
println(" were skipped.")
end
println()
# list of differences
println("Comparing $(first(primary.julia)) against $(first(against.julia)):")
new_failures = 0
new_successes = 0
for current in eachrow(primary)
pkg_name = current[:name]
previous = against[against[!, :name] .== pkg_name, :]
nrow(previous) == 0 && continue
previous = first(previous)
if current[:status] != previous[:status]
print("- $pkg_name status was $(previous[:status])")
ismissing(previous[:reason]) || print(" (reason: $(previous[:reason]))")
print(", now ")
print_status(current[:status])
ismissing(current[:reason]) || print(" (reason: $(current[:reason]))")
println()
if current.status == :fail || current.status == :kill
new_failures += 1
elseif current.status == :ok
new_successes += 1
end
end
end
println()
# summary of differences
print("In summary, ")
print_status(:ok, new_successes)
print(" packages now succeed, while ")
print_status(:fail, new_failures)
println(" have started to fail.")
return
end
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628,
220,
220,
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1441,
198,
437,
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] | 2.304779 | 1,716 |
# ------------------------------------------------------------------
# Licensed under the MIT License. See LICENSE in the project root.
# ------------------------------------------------------------------
"""
pairwise(γ, domain)
Evaluate variogram `γ` between all elements in the `domain`.
"""
function pairwise(γ::Variogram, domain)
Dim = embeddim(domain)
T = coordtype(domain)
n = nelements(domain)
x = rand(Point{Dim,T})
R = result_type(γ, x, x)
Γ = Matrix{R}(undef, n, n)
pairwise!(Γ, γ, domain)
end
function pairwise!(Γ, γ::Variogram, domain)
@inbounds for j=1:nelements(domain)
xj = centroid(domain, j)
for i=j+1:nelements(domain)
xi = centroid(domain, i)
Γ[i,j] = γ(xi, xj)
end
Γ[j,j] = γ(xj, xj)
for i=1:j-1
Γ[i,j] = Γ[j,i] # leverage the symmetry
end
end
Γ
end
"""
pairwise(γ, domain₁, domain₂)
Evaluate variogram `γ` between all elements of `domain₁` and `domain₂`.
"""
function pairwise(γ::Variogram, domain₁, domain₂)
Dim = embeddim(domain₁)
T = coordtype(domain₁)
m = nelements(domain₁)
n = nelements(domain₂)
x = rand(Point{Dim,T})
R = result_type(γ, x, x)
Γ = Array{R}(undef, m, n)
pairwise!(Γ, γ, domain₁, domain₂)
end
function pairwise!(Γ, γ::Variogram, domain₁, domain₂)
@inbounds for j=1:nelements(domain₂)
xj = centroid(domain₂, j)
for i=1:nelements(domain₁)
xi = centroid(domain₁, i)
Γ[i,j] = γ(xi, xj)
end
end
Γ
end
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] | 2.229008 | 655 |
@testset "976.largest-perimeter-triangle.jl" begin
@test largest_perimeter([2, 1, 2]) == 5
@test largest_perimeter([1, 2, 1]) == 0
@test largest_perimeter([3, 2, 3, 4]) == 10
@test largest_perimeter([3, 6, 2, 3]) == 8
end | [
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] | 2.37 | 100 |
function game_of_life(board, steps)
return board
end
| [
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11,
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] | 2.85 | 20 |
### Fetch required packages and reaction networks ###
using Catalyst, OrdinaryDiffEq, Random, SteadyStateDiffEq, Test
using ModelingToolkit: get_states, get_ps
include("test_networks.jl")
using StableRNGs
rng = StableRNG(12345)
### Compares to netowork with know steady state ###
steady_state_network_1 = @reaction_network begin
(k1,k2), ∅ ↔ X1
(k3,k4), ∅ ↔ 3X2
(k5,k6), ∅ ↔ X3 + X4
end k1 k2 k3 k4 k5 k6
for factor in [1e-1, 1e0, 1e1], repeat = 1:3
u0 = factor*rand(rng,length(get_states(steady_state_network_1))); u0[4] = u0[3];
p = 0.01 .+ factor*rand(rng,length(get_ps(steady_state_network_1)))
prob = SteadyStateProblem(steady_state_network_1,u0,p)
sol = solve(prob,SSRootfind()).u
(minimum(sol[1:1]) > 1e-2) && (@test abs.(sol[1] - p[1]/p[2]) < 0.01)
(minimum(sol[2:2]) > 1e-2) && (@test abs.(sol[2]^3/factorial(3) - p[3]/p[4]) < 0.01)
(minimum(sol[3:4]) > 1e-2) && (@test abs.(sol[3]*sol[4] - p[5]/p[6]) < 0.01)
end
steady_state_network_2 = @reaction_network begin
v/10+hill(X,v,K,n), ∅ → X
d, X → ∅
end v K n d
for factor in [1e-1, 1e1, 1e1], repeat = 1:3
u0_small = factor*rand(rng,length(get_states(steady_state_network_2)))/100
u0_large = factor*rand(rng,length(get_states(steady_state_network_2)))*100
p = factor*rand(rng,length(get_ps(steady_state_network_2)))
p[3] = round(p[3])+1
sol1 = solve(SteadyStateProblem(steady_state_network_2,u0_small,p),SSRootfind()).u[1]
sol2 = solve(SteadyStateProblem(steady_state_network_2,u0_large,p),SSRootfind()).u[1]
diff1 = abs(p[1]/10 + p[1]*(sol1^p[3])/(sol1^p[3]+p[2]^p[3]) - p[4]*sol1)
diff2 = abs(p[1]/10 + p[1]*(sol2^p[3])/(sol2^p[3]+p[2]^p[3]) - p[4]*sol2)
@test (diff1 < 1e-8) || (diff2 < 1e-8)
end
### For a couple of networks, test that the steady state solution is identical to the long term ODE solution. ###
steady_state_test_networks = [reaction_networks_standard[8], reaction_networks_standard[10], reaction_networks_weird[1]]
for network in steady_state_test_networks, factor in [1e-1, 1e0, 1e1]
u0 = factor*rand(rng,length(get_states(network)))
p = factor*rand(rng,length(get_ps(network)))
sol_ode = solve(ODEProblem(network,u0,(0.,1000000),p),Rosenbrock23())
sol_ss = solve(SteadyStateProblem(network,u0,p),SSRootfind())
@test all(abs.(sol_ode.u[end] .- sol_ss.u) .< 1e-4)
end
### No parameter test ###
no_param_network = @reaction_network begin
(0.6,3.2), ∅ ↔ X
end
for factor in [1e0, 1e1, 1e2]
u0 = factor*rand(rng,length(get_states(no_param_network)))
sol_ss = solve(SteadyStateProblem(no_param_network,u0),SSRootfind(),abstol=1e-11)
@test abs.(sol_ss.u[1]-0.6/3.2) < 1e-8
end
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] | 2.151685 | 1,246 |
using VLOptionsModeling
using Plots
# create an emprt contract set for now -
assetSymbol = "XYZ"
strikePrice = 50.0
underlying_price = 50.0
expirationDate = nothing
premimumValue = 0.0
numberOfContracts = 1
sense = :buy
contractMultiplier = 1.0
put_contract = VLPutOptionContract(assetSymbol, strikePrice, premimumValue; sense=sense, contractMultiplier=contractMultiplier)
# setup the lattice (Example 19.1/Fig. 19.3 Hull)
𝝙t = 30.42 / (365.0) # years
μ = log(1.0084) * (1 / 𝝙t)
σ = (1 / √𝝙t) * log(1.1224)
numberOfLevels = 36
# Create the lattice model -
latticeModel_1 = VLBinomialLattice(μ, σ, 𝝙t, numberOfLevels)
latticeModel_2 = VLBinomialLattice(μ, σ, 0.5 * 𝝙t, numberOfLevels)
latticeModel_3 = VLBinomialLattice(μ, σ, 0.25 * 𝝙t, numberOfLevels)
latticeModel_4 = VLBinomialLattice(μ, σ, 0.125 * 𝝙t, numberOfLevels)
latticeModel_5 = VLBinomialLattice(μ, σ, 0.0625 * 𝝙t, numberOfLevels)
# setup strike array -
strike_price_array = range(35.0, stop=65.0, step=0.1) |> collect
# sim -
sim_array_1 = binomial_price(put_contract, latticeModel_1, underlying_price, strike_price_array) |> check
sim_array_2 = binomial_price(put_contract, latticeModel_2, underlying_price, strike_price_array) |> check
sim_array_3 = binomial_price(put_contract, latticeModel_3, underlying_price, strike_price_array) |> check
sim_array_4 = binomial_price(put_contract, latticeModel_4, underlying_price, strike_price_array) |> check
sim_array_5 = binomial_price(put_contract, latticeModel_5, underlying_price, strike_price_array) |> check
# plots -
plot(sim_array_1[:,1],sim_array_1[:,2])
plot!(sim_array_2[:,1],sim_array_2[:,2])
plot!(sim_array_3[:,1],sim_array_3[:,2])
plot!(sim_array_4[:,1],sim_array_4[:,2])
plot!(sim_array_5[:,1],sim_array_5[:,2]) | [
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] | 2.407767 | 721 |
include("drop.jl")
include("hard_drop.jl")
include("soft_drop.jl")
| [
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198,
198,
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7203,
10424,
62,
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13,
20362,
4943,
198,
17256,
7203,
4215,
62,
14781,
13,
20362,
4943,
198
] | 2.615385 | 26 |
using CSV, Distributions, HypothesisTests
data1 = CSV.read("../data/machine1.csv", header=false)[:,1]
data2 = CSV.read("../data/machine2.csv", header=false)[:,1]
xBar1, xBar2 = mean(data1), mean(data2)
s1, s2 = std(data1), std(data2)
n1, n2 = length(data1), length(data2)
alpha = 0.05
v = (s1^2/n1 + s2^2/n2)^2 / ( (s1^2/n1)^2 / (n1-1) + (s2^2/n2)^2 / (n2-1) )
t = quantile(TDist(v),1-alpha/2)
println("Calculating formula: ", (xBar1 - xBar2 - t*sqrt(s1^2/n1 + s2^2/n2),
xBar1 - xBar2 + t*sqrt(s1^2/n1 + s2^2/n2)))
println("Using confint(): ", confint(UnequalVarianceTTest(data1,data2),alpha))
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] | 2 | 303 |
using Test
using Forecast
using TimeSeries
@testset "stl" begin
@test_throws AssertionError stl(rand(100),10; ns=5)
x = stl(rand(100),10)
@test x isa STL
@test x.ta isa TimeArray
@test x.call isa String
end
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] | 2.240385 | 104 |
# type for mortality, lapse, etc
abstract type Decrement end | [
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] | 4 | 15 |
using Base.Threads, SortingAlgorithms
import SortingAlgorithms: StringRadixSort, StringRadixSortAlg, uint_mapping, load_bits
import StatsBase: BaseRadixSortSafeTypes
import Base: Forward, ForwardOrdering, ReverseOrdering, sort!, Reverse
# winner from benchmarks/which_is_the_fastest_UInt_histogram.jl
function uint_hist(bits::Vector{T}) where T <: Unsigned
iter = sizeof(T)÷2
hist = zeros(UInt32, 65536, iter, nthreads())
@threads for j = 1:length(bits)
for i = 0:iter-1
@inbounds hist[1+Int((bits[j] >> (i << 4)) & 0xffff), i+1, threadid()] += 1
end
end
@threads for j in 1:iter
for i = 2:nthreads()
@inbounds hist[:, j, 1] .+= hist[:, j, i]
end
end
hist[:,:,1]
end
# sort it by using sorttwo! on the pointer
function sort_1fasterhistogram!(svec::Vector{String}, lo::Int, hi::Int, ::StringRadixSortAlg, o::O) where O <: Union{ForwardOrdering, ReverseOrdering}
if lo >= hi; return svec; end
# the length subarray to sort
l = hi - lo + 1
# find the maximum string length
lens = maximum(sizeof, svec)
skipbytes = lens
# ptrs = pointer.(svec)
if lens > 0
while lens > 4
skipbytes = max(0, skipbytes - 8)
bits64 = zeros(UInt64, l)
if o == Reverse
bits64[lo:hi] .= .~load_bits.(UInt64, @view(svec[lo:hi]), skipbytes)
SortingLab.sorttwo!(bits64, svec, lo, hi)
else
bits64[lo:hi] .= load_bits.(UInt64, @view(svec[lo:hi]), skipbytes)
SortingLab.sorttwo!(bits64, svec, lo, hi)
end
lens -= 8
end
if lens > 0
skipbytes = max(0, skipbytes - 4)
bits32 = zeros(UInt32, l)
if o == Reverse
bits32[lo:hi] .= .~load_bits.(UInt32, @view(svec[lo:hi]), skipbytes)
SortingLab.sorttwo!(bits32, svec, lo, hi)
else
bits32[lo:hi] .= load_bits.(UInt32, @view(svec[lo:hi]), skipbytes)
SortingLab.sorttwo!(bits32, svec, lo, hi)
end
lens -= 4
end
end
# unsafe_pointer_to_objref.(ptrs - 8)
svec
end
"""
sorttwo!(vs, index)
Sort both the `vs` and reorder `index` at the same. This allows for faster sortperm
for radix sort.
"""
function sorttwo!(vs::Vector{T}, index, lo::Int = 1, hi::Int=length(vs), RADIX_SIZE = 16, RADIX_MASK = 0xffff) where T <:BaseRadixSortSafeTypes
# Input checking
if lo >= hi; return (vs, index); end
o = Forward
# Init
iters = ceil(Integer, sizeof(T)*8/RADIX_SIZE)
# number of buckets in the counting step
nbuckets = 2^RADIX_SIZE
# Histogram for each element, radix
bin = uint_hist(vs)
# bin = zeros(UInt32, nbuckets, iters)
# if lo > 1; bin[1,:] = lo-1; end
# Sort!
swaps = 0
len = hi-lo+1
index1 = similar(index)
ts=similar(vs)
for j = 1:iters
# Unroll first data iteration, check for degenerate case
v = uint_mapping(o, vs[hi])
idx = Int((v >> (j-1)*RADIX_SIZE) & RADIX_MASK) + 1
# are all values the same at this radix?
if bin[idx,j] == len; continue; end
# cbin = cumsum(bin[:,j])
# tries to achieve the above one-liner with more efficiency
cbin = zeros(UInt32, nbuckets)
cbin[1] = bin[1,j]
for i in 2:nbuckets
cbin[i] = cbin[i-1] + bin[i,j]
end
ci = cbin[idx]
ts[ci] = vs[hi]
index1[ci] = index[hi]
cbin[idx] -= 1
# Finish the loop...
@inbounds for i in hi-1:-1:lo
v = uint_mapping(o, vs[i])
idx = Int((v >> (j-1)*RADIX_SIZE) & RADIX_MASK) + 1
ci = cbin[idx]
ts[ci] = vs[i]
index1[ci] = index[i]
cbin[idx] -= 1
end
vs,ts = ts,vs
index, index1 = index1, index
swaps += 1
end
if isodd(swaps)
vs,ts = ts,vs
index, index1 = index1, index
for i = lo:hi
@inbounds vs[i] = ts[i]
@inbounds index[i] = index1[i]
end
end
(vs, index)
end
using SortingLab
using Base.Test
using SortingAlgorithms
import SortingAlgorithms: load_bits
# write your own tests here
# @test 1 == 2
N = 100_000_000
K = 100
samplespace = "id".*dec.(1:N÷K,10);
srand(1)
svec = rand(samplespace, N);
using BenchmarkTools
# overall sorting is faster too
function ghi(svec)
csvec = copy(svec)
gc()
@time a = @elapsed sort_1fasterhistogram!(csvec, 1, length(csvec), StringRadixSort, Base.Forward)
csvec = copy(svec)
gc()
@time b = @elapsed SortingAlgorithms.sort!(csvec, 1, length(csvec), StringRadixSort, Base.Forward)
a,b
end
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] | 2.029412 | 2,380 |
using ModelingToolkit, Test
@testset "Parsing Test" begin include("variable_parsing.jl") end
@testset "Differentiation Test" begin include("derivatives.jl") end
@testset "Simplify Test" begin include("simplify.jl") end
@testset "Direct Usage Test" begin include("direct.jl") end
@testset "System Construction Test" begin include("system_construction.jl") end
@testset "Distributed Test" begin include("distributed.jl") end
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] | 3.392 | 125 |
#########
#File: c:\Users\digan\Dropbox\Dynamic_Networks\repos\ScoreDrivenExponentialRandomGraphs\src\ScoreDrivenERGM.jl\src\test\test_new_dyn_net_model copy.jl
#Created Date: Monday May 10th 2021
#Author: Domenico Di Gangi, <digangidomenico@gmail.com>
#-----
#Last Modified: Friday June 4th 2021 12:56:09 am
#Modified By: Domenico Di Gangi
#-----
#Description: Test SDERGM_pml for different combinations of statistics
#-----
########
using Test
using ScoreDrivenERGM
using StatsBase
using Distributed
import ScoreDrivenERGM:StaticNets,DynNets, Utilities, ErgmRcall
# sample dgp
N = 50
T = 300
ergmTermsString = "edges + mutual + gwesp(decay = 0.25, fixed = TRUE, cutoff=10)"
model = DynNets.SdErgmPml(ergmTermsString, true)
nErgmPar = model.nErgmPar
@test nErgmPar == 3
dgpVals0 = DynNets.static_estimate(model, Int8.(rand(Bool, N, N)))
@test all(isfinite.(dgpVals0))
@testset " Mappings of static parameters beween restricted and unrestricted spaces" begin
all_perm(xs, n) = vec(map(collect, Iterators.product(ntuple(_ -> xs, n)...)))
listIndTvPar = all_perm([true, false], nErgmPar)
listIndTvPar = listIndTvPar[.!(all.( [.!indTvPar for indTvPar in listIndTvPar]))]
for indTvPar in listIndTvPar
vUnPar, _ = DynNets.starting_point_optim(model, dgpVals0)
@test all(isfinite.(vUnPar))
vResPar = DynNets.restrict_all_par(model, vUnPar)
@test all(isfinite.(vResPar))
@test all(isapprox.(DynNets.unrestrict_all_par(model, vResPar), vUnPar, atol = 1e-8))
@test all(isapprox.(DynNets.restrict_all_par(model, vUnPar), vResPar, atol = 1e-8))
for i=1:10
vResParRand = rand(length(vResPar))
vUnParRand = DynNets.unrestrict_all_par(model, vResParRand)
@test all(isfinite.(vUnParRand))
@test all(isapprox.(DynNets.restrict_all_par(model, vUnParRand), vResParRand, atol = 1e-8))
end
end
end
@testset " Sampling Change stats and estimates " begin
T =100
dgpParT = hcat(dgpVals0.*ones(nErgmPar, round(Int,T/2)),2 *dgpVals0.* ones(nErgmPar, round(Int, T/2)))
# test reasonable sampling
A_T = DynNets.sample_ergm_sequence(model, N, dgpParT, 1)
eps = 0.01
@test eps < mean(A_T) < (1-eps) # density
@test !any(isnan.(A_T))
@test all(isfinite.(A_T))
obsT = [DynNets.stats_from_mat(model, A_T[:,:,t]) for t in 1:T ]
@test all([all(isfinite.(obs)) for obs in obsT])
end
ENV["JULIA_DEBUG"] = nothing
# integrated version
ergmTermsString = "edges + mutual"
model = DynNets.SdErgmPml(ergmTermsString, true)
nErgmPar = model.nErgmPar
model.options["integrated"] = false
model.options["initMeth"] ="estimateFirstObs"# "uncMean"#
DynNets.get_option(model, "initMeth")
dgpVals0 = DynNets.static_estimate(model, Int8.(rand(Bool, N, N)))
dgpParT = hcat(dgpVals0.*ones(nErgmPar, round(Int,T/2)),2 *dgpVals0.* ones(nErgmPar, round(Int, T/2)))
# test reasonable sampling
A_T = DynNets.sample_ergm_sequence(model, N, dgpParT, 1)
obsT = [DynNets.stats_from_mat(model, A_T[:,:,t]) for t in 1:T ]
estSdResPar, conv_flag, UM_mple, ftot_0 = DynNets.estimate(model, N, obsT; indTvPar=model.indTvPar, show_trace = true )
vEstSdResParAll = DynNets.array_2_vec_all_par(model, estSdResPar, model.indTvPar)
vEstSdResPar, vConstPar = DynNets.divide_SD_par_from_const(model, vEstSdResParAll)
# vEstSdResPar[3:3:end] .= 0.001
fVecT_filt , target_fun_val_T, sVecT_filt = DynNets.score_driven_filter(model, N, obsT, vEstSdResPar, model.indTvPar;ftot_0 = ftot_0, vConstPar=vConstPar)
DynNets.plot_filtered(model, N, fVecT_filt; parDgpTIn=dgpParT)
ss_filt = DynNets.estimate_single_snap_sequence(model, obsT)
DynNets.plot_filtered(model, N, ss_filt; parDgpTIn=dgpParT)
DynNets.seq_loglike_sd_filter(model, N, obsT, vecUnPar, ftot_0Fun::Function)
indTvPar = model.indTvPar
vEstSdResParAll = DynNets.array_2_vec_all_par(model, estSdResPar, indTvPar)
vEstSdResPar, vConstPar = DynNets.divide_SD_par_from_const(model, vEstSdResParAll)
fVecT_filt , target_fun_val_T, sVecT_filt = DynNets.score_driven_filter(model, N, obsT, vEstSdResPar, indTvPar;ftot_0 = ftot_0, vConstPar=vConstPar)
using PyPlot
# res_est = DynNets.estimate_and_filter(model, N, obsT; show_trace = true)
fig, ax = DynNets.plot_filtered(model, N, fVecT_filt)
est_SS = DynNets.estimate_single_snap_sequence(model, obsT)
fig, ax = DynNets.plot_filtered(model, N, est_SS, ax=ax, lineType = ".", lineColor="r")
# correctly specified filter
# estimate
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] | 2.255 | 2,000 |
module PCG
include("types.jl")
import .Types
include("arrays_caches.jl")
import .ArraysCaches
include("geometry.jl")
import .Geometry
include("topologies/Topologies.jl")
import .Topologies
include("storages/Storages.jl")
import .Storages
include("universes.jl")
import .Universes
include("neighborhoods.jl")
import .Neighborhoods
include("operations.jl")
import .Operations
include("recorders/Recorders.jl")
import .Recorders
include("spaces.jl")
import .Spaces
end
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] | 2.822485 | 169 |
using GeometricBase.Config
using GeometricBase.Utils
using GeometricIntegrators.Integrators
using GeometricIntegrators.Integrators.VPRK
using GeometricIntegrators.Tableaus
using GeometricProblems.LotkaVolterra2d
using SimpleSolvers
using Test
SimpleSolvers.set_config(:nls_atol, 8eps())
SimpleSolvers.set_config(:nls_rtol, 2eps())
const Δt = 0.01
const nt = 10
const q₀ = [1.0, 1.0]
const parameters = (a₁=1.0, a₂=1.0, b₁=-1.0, b₂=-2.0)
ode = lotka_volterra_2d_ode(q₀; params=parameters)
iode = lotka_volterra_2d_iode(q₀; params=parameters)
lode = lotka_volterra_2d_lode(q₀; params=parameters)
ldae = lotka_volterra_2d_ldae(q₀; params=parameters)
int = IntegratorFIRK(ode, TableauGauss(8), Δt)
sol = integrate(ode, int, nt)
refx = sol.q[end]
@testset "$(rpad("VPRK integrators",80))" begin
sol = integrate(iode, TableauVPRK(:pglrk, 2, TableauGauss(1), -1), Δt, nt)
@test relative_maximum_error(sol.q, refx) < 2E-6
sol = integrate(iode, TableauVPRK(:pglrk, 4, TableauGauss(2), +1), Δt, nt)
@test relative_maximum_error(sol.q, refx) < 8E-7
sol = integrate(iode, TableauVPRK(:pglrk, 6, TableauGauss(3), -1), Δt, nt)
@test relative_maximum_error(sol.q, refx) < 4E-12
sol = integrate(iode, TableauVPLobattoIIIA(2), Δt, nt)
@test relative_maximum_error(sol.q, refx) < 4E-6
sol = integrate(iode, TableauVPLobattoIIIA(3), Δt, nt)
@test relative_maximum_error(sol.q, refx) < 8E-7
sol = integrate(iode, TableauVPLobattoIIIA(4), Δt, nt)
@test relative_maximum_error(sol.q, refx) < 3E-11
sol = integrate(iode, TableauVPLobattoIIIB(2), Δt, nt)
@test relative_maximum_error(sol.q, refx) < 2E-6
sol = integrate(iode, TableauVPLobattoIIIB(3), Δt, nt)
@test relative_maximum_error(sol.q, refx) < 8E-7
sol = integrate(iode, TableauVPLobattoIIIB(4), Δt, nt)
@test relative_maximum_error(sol.q, refx) < 2E-11
end
@testset "$(rpad("VPRK integrators with standard projection",80))" begin
int = IntegratorVPRKpStandard(iode, TableauVPGLRK(1), Δt)
sol = integrate(iode, int, nt)
@test relative_maximum_error(sol.q, refx) < 1E-6
int = IntegratorVPRKpStandard(iode, TableauVPGLRK(2), Δt)
sol = integrate(iode, int, nt)
@test relative_maximum_error(sol.q, refx) < 1E-11
int = IntegratorVPRKpStandard(iode, TableauVPGLRK(3), Δt)
sol = integrate(iode, int, nt)
@test relative_maximum_error(sol.q, refx) < 1E-15
end
@testset "$(rpad("VPRK integrators with symplectic projection",80))" begin
int = IntegratorVPRKpSymplectic(iode, TableauVPGLRK(1), Δt)
sol = integrate(iode, int, nt)
@test relative_maximum_error(sol.q, refx) < 4E-6
int = IntegratorVPRKpSymplectic(iode, TableauVPGLRK(2), Δt)
sol = integrate(iode, int, nt)
@test relative_maximum_error(sol.q, refx) < 1E-11
int = IntegratorVPRKpSymplectic(iode, TableauVPGLRK(3), Δt)
sol = integrate(iode, int, nt)
@test relative_maximum_error(sol.q, refx) < 8E-12
end
@testset "$(rpad("VPRK integrators with symmetric projection",80))" begin
int = IntegratorVPRKpSymmetric(iode, TableauVPGLRK(1), Δt)
sol = integrate(iode, int, nt)
@test relative_maximum_error(sol.q, refx) < 1E-6
int = IntegratorVPRKpSymmetric(iode, TableauVPGLRK(2), Δt)
sol = integrate(iode, int, nt)
@test relative_maximum_error(sol.q, refx) < 1E-11
int = IntegratorVPRKpSymmetric(iode, TableauVPGLRK(3), Δt)
sol = integrate(iode, int, nt)
@test relative_maximum_error(sol.q, refx) < 4E-16
end
@testset "$(rpad("VPRK integrators with midpoint projection",80))" begin
int = IntegratorVPRKpMidpoint(iode, TableauVPGLRK(1), Δt)
sol = integrate(iode, int, nt)
@test relative_maximum_error(sol.q, refx) < 1E-6
int = IntegratorVPRKpMidpoint(iode, TableauVPGLRK(2), Δt)
sol = integrate(iode, int, nt)
@test relative_maximum_error(sol.q, refx) < 1E-11
int = IntegratorVPRKpMidpoint(iode, TableauVPGLRK(3), Δt)
sol = integrate(iode, int, nt)
@test relative_maximum_error(sol.q, refx) < 4E-16
end
@testset "$(rpad("VPRK integrators with internal projection",80))" begin
int = IntegratorVPRKpInternal(iode, TableauVPGLRK(1), Δt)
sol = integrate(iode, int, nt)
@test relative_maximum_error(sol.q, refx) < 2E-6
int = IntegratorVPRKpInternal(iode, TableauVPGLRK(2), Δt)
sol = integrate(iode, int, nt)
@test relative_maximum_error(sol.q, refx) < 1E-11
int = IntegratorVPRKpInternal(iode, TableauVPGLRK(3), Δt)
sol = integrate(iode, int, nt)
@test relative_maximum_error(sol.q, refx) < 4E-12
int = IntegratorVPRKpInternal(iode, TableauVPGLRK(4), Δt)
sol = integrate(iode, int, nt)
@test relative_maximum_error(sol.q, refx) < 8E-16
end
@testset "$(rpad("VPRK integrators with projection on secondary constraint",80))" begin
# TODO: reactivate
# int = IntegratorVPRKpSecondary(ldae, TableauVPGLRK(1), Δt)
# sol = integrate(ldae, int, nt)
# @test relative_maximum_error(sol.q, refx) < 2E-6
# int = IntegratorVPRKpSecondary(ldae, TableauVPGLRK(2), Δt)
# sol = integrate(ldae, int, nt)
# @test relative_maximum_error(sol.q, refx) < 8E-7
# int = IntegratorVPRKpSecondary(ldae, TableauVPGLRK(3), Δt)
# sol = integrate(ldae, int, nt)
# @test relative_maximum_error(sol.q, refx) < 4E-12
end
@testset "$(rpad("VPRK integrators with variational projection",80))" begin
intV1 = IntegratorVPRKpVariational(iode, TableauVPGLRK(1), Δt)
solV1 = integrate(iode, intV1, nt)
@test relative_maximum_error(solV1.q, refx) < 8E-7
intV2 = IntegratorVPRKpVariational(iode, TableauVPGLRK(2), Δt)
solV2 = integrate(iode, intV2, nt)
@test relative_maximum_error(solV2.q, refx) < 8E-8
intV3 = IntegratorVPRKpVariational(iode, TableauVPGLRK(3), Δt)
solV3 = integrate(iode, intV3, nt)
@test relative_maximum_error(solV3.q, refx) < 1E-11
intQ1 = IntegratorVPRKpVariationalQ(iode, TableauVPGLRK(1), Δt)
solQ1 = integrate(iode, intQ1, nt)
@test relative_maximum_error(solQ1.q, refx) < 4E-5
intQ2 = IntegratorVPRKpVariationalQ(iode, TableauVPGLRK(2), Δt)
solQ2 = integrate(iode, intQ2, nt)
@test relative_maximum_error(solQ2.q, refx) < 2E-4
intQ3 = IntegratorVPRKpVariationalQ(iode, TableauVPGLRK(3), Δt)
solQ3 = integrate(iode, intQ3, nt)
@test relative_maximum_error(solQ3.q, refx) < 1E-8
intP1 = IntegratorVPRKpVariationalP(iode, TableauVPGLRK(1), Δt)
solP1 = integrate(iode, intP1, nt)
@test relative_maximum_error(solP1.q, refx) < 8E-7
intP2 = IntegratorVPRKpVariationalP(iode, TableauVPGLRK(2), Δt)
solP2 = integrate(iode, intP2, nt)
@test relative_maximum_error(solP2.q, refx) < 8E-8
intP3 = IntegratorVPRKpVariationalP(iode, TableauVPGLRK(3), Δt)
solP3 = integrate(iode, intP3, nt)
@test relative_maximum_error(solP3.q, refx) < 1E-11
@test relative_maximum_error(solV1.q, solP1.q[end]) == 0
@test relative_maximum_error(solV2.q, solP2.q[end]) == 0
@test relative_maximum_error(solV3.q, solP3.q[end]) == 0
end
@testset "$(rpad("VPRK integrators with projection on Runge-Kutta tableau",80))" begin
int = IntegratorVPRKpTableau(iode, CoefficientsPGLRK(5), Δt*5)
sol = integrate(iode, int, div(nt,5))
@test relative_maximum_error(sol.q, refx) < 8E-12
int = IntegratorVPRKpTableau(iode, CoefficientsPGLRK(6), Δt*5)
sol = integrate(iode, int, div(nt,5))
@test relative_maximum_error(sol.q, refx) < 4E-14
end
@testset "$(rpad("Degenerate symplectic partitioned Runge-Kutta methods",80))" begin
int = IntegratorVPRKdegenerate(iode, TableauVPGLRK(1), Δt)
sol = integrate(iode, int, nt)
@test relative_maximum_error(sol.q, refx) < 2E-5
int = IntegratorVPRKdegenerate(iode, TableauVPGLRK(2), Δt)
sol = integrate(iode, int, nt)
@test relative_maximum_error(sol.q, refx) < 4E-7
int = IntegratorVPRKdegenerate(iode, TableauVPGLRK(3), Δt)
sol = integrate(iode, int, nt)
@test relative_maximum_error(sol.q, refx) < 2E-10
end
@testset "$(rpad("VSPRK integrators with Legendre projection",80))" begin
int = IntegratorVPRKpLegendre(iode, TableauVPGLRK(1), Δt)
sol = integrate(iode, int, nt)
@test relative_maximum_error(sol.q, refx) < 1E-6
int = IntegratorVPRKpLegendre(iode, TableauVPGLRK(2), Δt)
sol = integrate(iode, int, nt)
@test relative_maximum_error(sol.q, refx) < 1E-11
int = IntegratorVPRKpLegendre(iode, TableauVPGLRK(3), Δt)
sol = integrate(iode, int, nt)
@test relative_maximum_error(sol.q, refx) < 8E-16
end
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] | 2.219974 | 3,855 |
## This simple (and naive) implementation is useful to solve the closed-shell linear expansion hartree-fock equation
module QP232
include("./types.jl")
end
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# This file was generated by the Julia Swagger Code Generator
# Do not modify this file directly. Modify the swagger specification instead.
mutable struct IoK8sApimachineryPkgVersionInfo <: SwaggerModel
buildDate::Any # spec type: Union{ Nothing, String } # spec name: buildDate
compiler::Any # spec type: Union{ Nothing, String } # spec name: compiler
gitCommit::Any # spec type: Union{ Nothing, String } # spec name: gitCommit
gitTreeState::Any # spec type: Union{ Nothing, String } # spec name: gitTreeState
gitVersion::Any # spec type: Union{ Nothing, String } # spec name: gitVersion
goVersion::Any # spec type: Union{ Nothing, String } # spec name: goVersion
major::Any # spec type: Union{ Nothing, String } # spec name: major
minor::Any # spec type: Union{ Nothing, String } # spec name: minor
platform::Any # spec type: Union{ Nothing, String } # spec name: platform
function IoK8sApimachineryPkgVersionInfo(;buildDate=nothing, compiler=nothing, gitCommit=nothing, gitTreeState=nothing, gitVersion=nothing, goVersion=nothing, major=nothing, minor=nothing, platform=nothing)
o = new()
validate_property(IoK8sApimachineryPkgVersionInfo, Symbol("buildDate"), buildDate)
setfield!(o, Symbol("buildDate"), buildDate)
validate_property(IoK8sApimachineryPkgVersionInfo, Symbol("compiler"), compiler)
setfield!(o, Symbol("compiler"), compiler)
validate_property(IoK8sApimachineryPkgVersionInfo, Symbol("gitCommit"), gitCommit)
setfield!(o, Symbol("gitCommit"), gitCommit)
validate_property(IoK8sApimachineryPkgVersionInfo, Symbol("gitTreeState"), gitTreeState)
setfield!(o, Symbol("gitTreeState"), gitTreeState)
validate_property(IoK8sApimachineryPkgVersionInfo, Symbol("gitVersion"), gitVersion)
setfield!(o, Symbol("gitVersion"), gitVersion)
validate_property(IoK8sApimachineryPkgVersionInfo, Symbol("goVersion"), goVersion)
setfield!(o, Symbol("goVersion"), goVersion)
validate_property(IoK8sApimachineryPkgVersionInfo, Symbol("major"), major)
setfield!(o, Symbol("major"), major)
validate_property(IoK8sApimachineryPkgVersionInfo, Symbol("minor"), minor)
setfield!(o, Symbol("minor"), minor)
validate_property(IoK8sApimachineryPkgVersionInfo, Symbol("platform"), platform)
setfield!(o, Symbol("platform"), platform)
o
end
end # type IoK8sApimachineryPkgVersionInfo
const _property_map_IoK8sApimachineryPkgVersionInfo = Dict{Symbol,Symbol}(Symbol("buildDate")=>Symbol("buildDate"), Symbol("compiler")=>Symbol("compiler"), Symbol("gitCommit")=>Symbol("gitCommit"), Symbol("gitTreeState")=>Symbol("gitTreeState"), Symbol("gitVersion")=>Symbol("gitVersion"), Symbol("goVersion")=>Symbol("goVersion"), Symbol("major")=>Symbol("major"), Symbol("minor")=>Symbol("minor"), Symbol("platform")=>Symbol("platform"))
const _property_types_IoK8sApimachineryPkgVersionInfo = Dict{Symbol,String}(Symbol("buildDate")=>"String", Symbol("compiler")=>"String", Symbol("gitCommit")=>"String", Symbol("gitTreeState")=>"String", Symbol("gitVersion")=>"String", Symbol("goVersion")=>"String", Symbol("major")=>"String", Symbol("minor")=>"String", Symbol("platform")=>"String")
Base.propertynames(::Type{ IoK8sApimachineryPkgVersionInfo }) = collect(keys(_property_map_IoK8sApimachineryPkgVersionInfo))
Swagger.property_type(::Type{ IoK8sApimachineryPkgVersionInfo }, name::Symbol) = Union{Nothing,eval(Meta.parse(_property_types_IoK8sApimachineryPkgVersionInfo[name]))}
Swagger.field_name(::Type{ IoK8sApimachineryPkgVersionInfo }, property_name::Symbol) = _property_map_IoK8sApimachineryPkgVersionInfo[property_name]
function check_required(o::IoK8sApimachineryPkgVersionInfo)
(getproperty(o, Symbol("buildDate")) === nothing) && (return false)
(getproperty(o, Symbol("compiler")) === nothing) && (return false)
(getproperty(o, Symbol("gitCommit")) === nothing) && (return false)
(getproperty(o, Symbol("gitTreeState")) === nothing) && (return false)
(getproperty(o, Symbol("gitVersion")) === nothing) && (return false)
(getproperty(o, Symbol("goVersion")) === nothing) && (return false)
(getproperty(o, Symbol("major")) === nothing) && (return false)
(getproperty(o, Symbol("minor")) === nothing) && (return false)
(getproperty(o, Symbol("platform")) === nothing) && (return false)
true
end
function validate_property(::Type{ IoK8sApimachineryPkgVersionInfo }, name::Symbol, val)
end
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# --------------------------------------------------------------------------
# ACE1.jl: Julia implementation of the Atomic Cluster Expansion
# Copyright (c) 2019 Christoph Ortner <christophortner0@gmail.com>
# Licensed under ASL - see ASL.md for terms and conditions.
# --------------------------------------------------------------------------
@testset "RepulsiveCore" begin
#---
using ACE1
using Printf, Test, LinearAlgebra, JuLIP, JuLIP.Testing
using JuLIP: evaluate, evaluate_d
using JuLIP.Potentials: i2z, numz
using JuLIP.MLIPs: combine
randr() = 1.0 + rand()
randcoeffs(B) = (rand(length(B)) .* (1:length(B)) .- 0.2).^(-2)
#---
@info("--------------- Testing RepulsiveCore Implementation ---------------")
at = bulk(:W, cubic=true) * 3
rattle!(at, 0.03)
r0 = rnn(:W)
z = atomic_number(:W)
maxdeg = 8
r0 = 1.0
rcut = 3.0
Pr = transformed_jacobi(maxdeg, PolyTransform(1, r0), rcut; pcut = 2)
pB = ACE1.PairPotentials.PolyPairBasis(Pr, :W)
coeffs = randcoeffs(pB)
V = combine(pB, coeffs)
#--- try out the repulsive potential
Vfit = V
ri = 2.1
@show (@D Vfit(ri))
e0 = Vfit(ri) - 1.0
Vrep = ACE1.PairPotentials.RepulsiveCore(Vfit, ri)
rout = range(ri+1e-15, 4.0, length=100)
println(@test all(Vfit(r) == Vrep(r,z,z) for r in rout))
rin = range(0.5, ri, length=100)
println(@test all(Vrep.Vin[1](r) == Vrep(r,z,z) for r in rin))
@info("JuLIP FD test")
println(@test JuLIP.Testing.fdtest(Vrep, at))
@info("check scaling")
println(@test energy(Vfit, at) ≈ energy(Vrep, at))
#---
@info("--------------- Multi-Species RepulsiveCore ---------------")
at = bulk(:W, cubic=true) * 3
at.Z[2:3:end] .= atomic_number(:Fe)
rattle!(at, 0.03)
r0 = rnn(:W)
Pr = transformed_jacobi(maxdeg, PolyTransform(1, r0), rcut; pcut = 2)
pB = ACE1.PairPotentials.PolyPairBasis(Pr, [:W, :Fe])
coeffs = randcoeffs(pB)
V = combine(pB, coeffs)
#--- try out the repulsive potential
Vfit = V
ri = 2.1
z1 = AtomicNumber(74)
z2 = AtomicNumber(26)
@show @D Vfit(ri, z1, z2)
e0 = min(Vfit(ri, z1, z2), Vfit(ri, z1, z1), Vfit(ri, z2, z2)) - 1.0
e0s = rand(2,2) .+ (e0 - 0.5); e0s = 0.5 * (e0s + e0s')
ris = rand(2,2) .+ (ri - 0.5); ris = 0.5 * (ris + ris')
Vrep = ACE1.PairPotentials.RepulsiveCore(Vfit,
Dict( ( :W, :W) => (ri = ris[1,1], e0 = e0s[1,1]),
( :W, :Fe) => (ri = ris[1,2], e0 = e0s[1,2]),
(:Fe, :Fe) => (ri = ris[2,2], e0 = e0s[2,2]) ) )
for (z, z0, j, j0) in zip([z1, z1, z2], [z1, z2, z2], [1, 1, 2], [1, 2, 2])
local rin, rout
i, i0 = JuLIP.Potentials.z2i(Vfit, z), JuLIP.Potentials.z2i(Vfit, z0)
rout = range(ris[j, j0] +1e-15, 4.0, length=100)
println(@test all(Vfit(r, z, z0) == Vrep(r, z, z0) for r in rout))
rin = range(0.5, ris[j,j0]-1e-15, length=100)
println(@test all(Vrep.Vin[i,i0](r) == Vrep(r, z, z0) for r in rin))
end
#---
@info("JuLIP FD test")
println(@test JuLIP.Testing.fdtest(Vrep, at))
@info("check scaling")
println(@test energy(Vfit, at) ≈ energy(Vrep, at))
@info("check FIO")
println(@test all(JuLIP.Testing.test_fio(Vrep)))
#---
end
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# work_hours.jl
#
# Calculate time spent working
#
# Jeremy Rüffer
# Thünen Institut
# Institut für Agrarklimaschutz
# Junior Research Group NITROSPHERE
# Julia 1.4.0
# 30.03.2020
# Last Edit: 02.04.2020
# include("D:\\Code\\Julia\\Jeremy\\work_hours.jl")
"# work_hours(times...,diffs::Bool=false)
`work_hours(\"8:21\",\"12:16\",\"13:20\",\"15:45\",\"16:30\",\"19:00\")` Calculate total work hours\n
* **time...**::String = List of times as strings in H:MM or HH:MM format
==== Summing Time Periods ====\n
\t08:21:00 -> 12:16:00\n
\t13:20:00 -> 15:45:00\n
\t16:30:00 -> 19:00:00\n
Total: 08:50:00, +1 hour, 2 minutes
---
#### Keywords:\n
* diffs::Bool = Show each time difference, False is default
* goal::Time = Target working ours, Time(7,48) is default\n\n"
function work_hours(times...;diffs::Bool=false,goal::Time=Time(7,48))
#################
## Constants ##
#################
dfmt = Dates.DateFormat("HH:MM")
##############
## Checks ##
##############
for test = 1:1:length(times)
# Check that each input is a string
isa(times[test],String) ? nothing : error("All input times must be strings")
# Check that each input matches H:MM or HH:MM formats
match(r"^[0-2]?[0-9]\:[0-5][0-9]",times[test]) == nothing ? error(times[test] * " is not of the form H:MM or HH:MM") : nothing
end
#################
## Calculate ##
#################
println("==== Summing Time Periods ====")
isodd(length(times)) ? endTime = length(times) - 1 : endTime = length(times)
sumTime = Time(0)
for t=1:2:endTime
dt = Time(times[t+1],dfmt) - Time(times[t],dfmt)
if diffs
dtString = ",\tdt = " * string(Time(dt))
else
dtString = ""
end
println("\t" * string(Time(times[t],dfmt)) * " -> " * string(Time(times[t+1],dfmt)) * dtString)
sumTime += dt
end
# Odd number of times given
if isodd(length(times))
checkoutTime = Time(Time(times[end]).instant + goal.instant - sumTime.instant)
println("\t" * string(Time(times[end],dfmt)) * " -> " * string(checkoutTime) * " (assuming a full work day)")
end
# Final Numbers
if goal-sumTime > Nanosecond(0)
println("\nTotal: " * string(sumTime) *", " * string(Dates.canonicalize(Dates.CompoundPeriod(goal-sumTime))) * " remaining")
else
println("\nTotal: " * string(sumTime) * ", +" * string(Dates.canonicalize(Dates.CompoundPeriod(sumTime-goal))))
end
end | [
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] | 2.399213 | 1,017 |
using IntervalOptimization
using Test
@testset "IntervalOptimization.jl" begin
# Write your tests here.
end
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] | 3.054054 | 37 |
using Nextion
using Nextion.Event: TouchEvent, CurrentPageIDHeadEvent, PositionHeadEvent, SleepPositionHeadEvent, StringHeadEvent, NumberHeadEvent
using Nextion.Event.Touch
using Test
@testset "Events" begin
@testset "TouchEvent" begin
@testset "Constants" begin
@test Integer(Touch.Press) == 0x01
@test Integer(Touch.Release) == 0x00
@test Touch.code(0x01) == Touch.Press
@test Touch.code(0x00) == Touch.Release
end
@testset "Event" begin
msg = [0x65, 0x00, 0x02, 0x01, 0xff, 0xff, 0xff]
evt = TouchEvent(msg)
@test evt.code == Return.Code.EVENT_TOUCH_HEAD
@test evt.pid == PageID(0x00)
@test evt.cid == ComponentID(0x02)
@test evt.tevts == Touch.Press # touch event state
end
end
@testset "CurrentPageIDHeadEvent" begin
msg = [0x66, 0x02, 0xff, 0xff, 0xff]
evt = CurrentPageIDHeadEvent(msg)
@test evt.code == Return.Code.CURRENT_PAGE_ID_HEAD
@test evt.pid == PageID(0x02)
end
@testset "PositionHeadEvent" begin
msg = [0x67, 0x00, 0x7a, 0x00, 0x1e, 0x01, 0xff, 0xff, 0xff]
evt = PositionHeadEvent(msg)
@test evt.code == Return.Code.EVENT_POSITION_HEAD
@test evt.x == UInt16(122)
@test evt.y == UInt16(30)
@test evt.tevts == Touch.Press
end
@testset "SleepPositionHeadEvent" begin
msg = [0x68, 0x00, 0x7a, 0x00, 0x1e, 0x01, 0xff, 0xff, 0xff]
evt = SleepPositionHeadEvent(msg)
@test evt.code == Return.Code.EVENT_SLEEP_POSITION_HEAD
@test evt.x == UInt16(122)
@test evt.y == UInt16(30)
@test evt.tevts == Touch.Press
end
@testset "StringHeadEvent" begin
msg = [0x70, 0x61, 0x62, 0x63, 0xff, 0xff, 0xff]
evt = StringHeadEvent(msg)
@test evt.code == Return.Code.STRING_HEAD
@test evt.value == "abc"
end
@testset "NumberHeadEvent" begin
msg = [0x71, 0x66, 0x00, 0x00, 0x00, 0xff, 0xff, 0xff]
evt = NumberHeadEvent(msg)
@test evt.code == Return.Code.NUMBER_HEAD
@test evt.value == 102
msg = [0x71, 0x66, 0x01, 0x00, 0x00, 0xff, 0xff, 0xff]
evt = NumberHeadEvent(msg)
@test evt.value == 0x00000166 # 102 + 256
msg = [0x71, 0x01, 0xff, 0x00, 0x00, 0xff, 0xff, 0xff]
evt = NumberHeadEvent(msg)
@test evt.value == 0x0000ff01 # 65281
msg = [0x71, 0x01, 0x00, 0xff, 0x00, 0xff, 0xff, 0xff]
evt = NumberHeadEvent(msg)
@test evt.value == 0x00ff0001 # 16711681
msg = [0x71, 0x01, 0x00, 0x00, 0xff, 0xff, 0xff, 0xff]
evt = NumberHeadEvent(msg)
@test evt.value == 0xff000001
msg = [0x71, 0xff, 0xff, 0xff, 0x7f, 0xff, 0xff, 0xff]
evt = NumberHeadEvent(msg)
@test evt.value == typemax(Int32)
msg = [0x71, 0xfe, 0xff, 0xff, 0xff, 0xff, 0xff, 0xff]
evt = NumberHeadEvent(msg)
@test evt.value == 0xfffffffe
@test evt.signedvalue == -2
msg = [0x71, 0x00, 0x00, 0x00, 0x80, 0xff, 0xff, 0xff]
evt = NumberHeadEvent(msg)
@test evt.value == 0x80000000
@test evt.signedvalue == typemin(Int32)
end
end
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] | 1.953599 | 1,681 |
# Debug.Flow:
# =============
# Flow control for interactive debug trap
module Flow
using Debug.Meta, Debug.AST, Debug.Runtime, Debug.Graft, Debug.Eval
import Debug.AST.is_emittable, Base.isequal
export @bp, BPNode, DBState
export continue!, singlestep!, stepover!, stepout!
## BreakPoint ##
type BreakPoint; end
typealias BPNode Node{BreakPoint}
is_emittable(::BPNode) = false
macro bp(args...)
code_bp(args...)
end
code_bp() = Node(BreakPoint())
code_bp(pred) = :($(esc(pred)) ? $(code_bp()) : nothing)
is_trap(::BPNode) = true
is_trap(::Event) = true
is_trap(node::Node) = is_evaluable(node) && isblocknode(parentof(node))
instrument(trap_ex, ex) = Graft.instrument(is_trap, trap_ex, ex)
## Cond ##
abstract Cond
type Continue <: Cond; end
type SingleStep <: Cond; end
type ContinueInside <: Cond; frame::Frame; outside::Cond; end
type StepOver <: Cond; end
does_trap(::SingleStep) = true
does_trap(::Continue) = false
does_trap(::ContinueInside) = false
does_trap(::StepOver) = true
leave(cond::ContinueInside, f::Frame) = (cond.frame == f ? cond.outside : cond)
enter(cond::StepOver, frame::Frame) = ContinueInside(frame, cond)
leave(cond::StepOver, frame::Frame) = SingleStep()
enter(cond::Cond, ::Frame) = cond
leave(cond::Cond, ::Frame) = cond
## DBState ##
type DBState
cond::Cond
breakpoints::Set{Node}
ignore_bp::Set{Node}
grafts::Dict{Node,Any}
DBState() = new(Continue(), Set{Node}(), Set{Node}(), Dict{Node,Any}())
end
continue!( s::DBState) = (s.cond = Continue())
singlestep!(s::DBState) = (s.cond = SingleStep())
stepover!( s::DBState) = (s.cond = StepOver())
function stepout!(st::DBState, node::Node, s::Scope)
st.cond = ContinueInside(enclosing_scope_frame(Frame(node,s)),SingleStep())
end
function pretrap(state::DBState, e::Enter, s::Scope)
state.cond = enter(state.cond, Frame(e.node, s))
false
end
function pretrap(state::DBState, e::Leave, s::Scope)
state.cond = leave(state.cond, Frame(e.node, s))
false
end
function pretrap(st::DBState, node::Node, s::Scope)
if (isa(node,BPNode)&&!(node in st.ignore_bp)) || (node in st.breakpoints)
singlestep!(st)
end
does_trap(st.cond)
end
posttrap(state::DBState, node, s::Scope) = nothing
function posttrap(state::DBState, node::Node, s::Scope)
if haskey(state.grafts, node)
debug_eval(s, state.grafts[node])
end
nothing
end
end # module
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2147,
198,
437,
198,
198,
437,
1303,
8265,
198
] | 2.577287 | 951 |
include("./RPSAgents.jl")
import Pkg
Pkg.add("StatsBase")
Pkg.add("Distributions")
using StatsBase, Distributions
using .RPSAgents
function evaluate(agent1, agent2, steps)
# Hàm đánh giá 2 agent đầu vào bằng cách cho chúng đấu với nhau
score = 0 # Số lượt thắng của agent1
tie = 0 # Số lượt hòa của agent1
agent1_last_step, agent2_last_step = 0, 0
for i in 0:steps-1
agent1_observation = Dict(
"step" => i,
"lastOpponentAction" => agent2_last_step
)
agent2_observation = Dict(
"step" => i,
"lastOpponentAction" => agent1_last_step
)
# 2 agent lần lượt ra quyết định
agent1_action = agent1.run(agent1_observation)
agent2_action = agent2.run(agent2_observation)
if(agent1_action == agent2_action)
tie += 1
else
counter_agent1_action = (agent1_action + 1) % 3
if(agent2_action == counter_agent1_action)
score += 0
else
score += 1
end
end
agent1_last_step = agent1_action
agent2_last_step = agent2_action
end
return score, steps - score - tie
end
function compare()
# Thực nghiệm so sánh ThompsonSamplingAgent với các agent khác
# Danh sách các agent để so sánh với agent của chúng ta
agents = Dict(
"random_agent" => RandomAgent(),
"only_scissor_agent" => OnlyScissorAgent(),
"only_rock_agent" => OnlyRockAgent(),
"only_paper_agent" => OnlyPaperAgent(),
"copy_agent" => CopyAgent(),
"freq_counting_agent" => FreqCountingAgent(),
"counter_last_action_agent" => CounterLastActionAgent(),
"markov_agent" => MarkovAgent(),
"thompson_sampling_agent" = ThompsonSamplingAgent()
)
for (name, agent) in agents
ourScores = []
oppoScores = []
usedAgents = Dict(
"random_agent" => [],
"only_scissor_agent" => [],
"only_rock_agent" => [],
"only_paper_agent" => [],
"copy_agent" => [],
"freq_counting_agent" => [],
"counter_last_action_agent" => []
)
for i in (1:5)
our_agent = ThompsonSamplingAgent()
score1, score2 = evaluate(our_agent, agent, 1000)
append!(ourScores, score1)
append!(oppoScores, score2)
for (agentName, agentCount) in our_agent.agent_count
append!(usedAgents[agentName], agentCount)
end
end
println("\n ----------------- \n")
println(name)
println("Our score: ", ourScores)
println("Oppo score: ", oppoScores)
println("Used agents: ")
for (agentName, agentCounts) in usedAgents
println("Mean used ", agentName, ": ", agentCounts)
end
end
end
compare()
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] | 2.202401 | 1,166 |
function interpolate_state(mdp::CarMDP, s::CarMDPState)
# interpolate s in the grid
vspace = get_car_vspace(mdp.env, mdp.vel_res)
itp_car, itp_car_w = interpolate_state(mdp, s.car, vspace)
itp_ego, itp_ego_w = interpolate_state(mdp, s.ego, vspace)
itp_states = Vector{CarMDPState}(length(itp_ego)*length(itp_car))
itp_w = Vector{Float64}(length(itp_states))
k = 1
for (i, c) in enumerate(itp_car)
for (j, e) in enumerate(itp_ego)
crash = is_colliding(Vehicle(c, mdp.car_type, 0), Vehicle(e, mdp.ego_type, 1))
itp_states[k] = CarMDPState(crash, e, c, s.route)
itp_w[k] = itp_car_w[i]*itp_ego_w[j]
k += 1
end
end
@assert sum(itp_w) ≈ 1.
return itp_states, itp_w
end
function get_mdp_state(mdp::CarMDP, pomdp::UrbanPOMDP, s::Scene, car_id = 2)
car_i = findfirst(car_id, s)
car = Vehicle(get_off_the_grid(mdp), mdp.car_type, car_id)
if car_i != nothing
car = s[car_i]
end
ego = get_ego(s)
sroute = SVector{0, Lane}()
# find route
sroute = nothing
if haskey(pomdp.models, car_id) && car_i != nothing
# find the exact route from the list of routes
curr_route = [l.tag for l in pomdp.models[car_id].navigator.route]
for route in get_car_routes(mdp.env)
tags = intersect(Set(curr_route), Set(route))
if length(tags) >= 2
sroute = SVector{2, LaneTag}(route[1], route[end])
elseif length(curr_route) == 1 && curr_route[1] ∈ route
sroute = SVector{2, LaneTag}(route[1], route[end])
end
end
if sroute == nothing
println(curr_route)
end
else
sroute = SVector{2, LaneTag}(LaneTag(0,0), LaneTag(0,0))
end
e_state = VehicleState(ego.state.posG, car_roadway(mdp.env), ego.state.v)
c_state = VehicleState(car.state.posG, mdp.env.roadway, car.state.v)
return CarMDPState(is_colliding(ego, car), e_state, c_state, sroute)
end
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] | 1.899819 | 1,108 |
using Documenter, Includeonce
makedocs(
modules = [Includeonce],
format = Documenter.HTML(; prettyurls = get(ENV, "CI", nothing) == "true"),
authors = "Jacques David",
sitename = "Includeonce.jl",
pages = Any["index.md"]
# strict = true,
# clean = true,
# checkdocs = :exports,
)
deploydocs(
repo = "github.com/jdadavid/Includeonce.jl.git",
push_preview = true
)
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] | 2.454545 | 165 |
module Frameworks
using Printf: @printf
using TimerOutputs: TimerOutputs, TimerOutput, @timeit
using ..Terms: Parameters
using ...Prerequisites: decimaltostr
using ...Prerequisites.CompositeStructures: NamedContainer
using ...Prerequisites.TypeTraits: efficientoperations
import ...Interfaces: id, update!, prepare!, register!, run!, add!
export App, Engine
export Assignment, Algorithm
export dependences, rundependences!
"""
App
Abstract type for all apps.
"""
abstract type App end
"""
==(app1::App, app2::App) -> Bool
Judge whether two apps are equivalent to each other.
"""
Base.:(==)(app1::App, app2::App) = ==(efficientoperations, app1, app2)
"""
isequal(app1::App, app2::App) -> Bool
Judge whether two apps are equivalent to each other.
"""
Base.isequal(app1::App, app2::App) = isequal(efficientoperations, app1, app2)
"""
update!(app::App; kwargs...) -> App
Update the status of an app.
"""
update!(app::App; kwargs...) = app
"""
Engine
Abstract type for all engines.
"""
abstract type Engine end
"""
==(engine1::Engine, engine2::Engine) -> Bool
Judge whether two engines are equivalent to each other.
"""
Base.:(==)(engine1::Engine, engine2::Engine) = ==(efficientoperations, engine1, engine2)
"""
isequal(engine1::Engine, engine2::Engine) -> Bool
Judge whether two engines are equivalent to each other.
"""
Base.isequal(engine1::Engine, engine2::Engine) = isequal(efficientoperations, engine1, engine2)
"""
update!(engine::Engine; kwargs...) -> Engine
Update the status of an engine.
"""
update!(engine::Engine; kwargs...) = engine
"""
Assignment( id::Symbol, app::App, parameters::Parameters;
map::Function=identity,
dependences::Tuple{Vararg{Symbol}}=(),
data::Any=nothing,
savedata::Bool=true,
virgin::Bool=true,
kwargs...
)
An assignment associated with an app.
"""
mutable struct Assignment{A<:App, P<:Parameters, M<:Function, D<:Tuple{Vararg{Symbol}}, R<:Any, I}
app::A
parameters::P
map::M
dependences::D
data::R
savedata::Bool
virgin::Bool
end
function Assignment(id::Symbol, app::App, parameters::Parameters;
map::Function=identity,
dependences::Tuple{Vararg{Symbol}}=(),
data::Any=nothing,
savedata::Bool=true,
virgin::Bool=true,
kwargs...)
A, P, M, D = app|>typeof, parameters|>typeof, map|>typeof, dependences|>typeof
R = (data === nothing) ? Any : data|>typeof
return Assignment{A, P, M, D, R, id}(app, parameters, map, dependences, data, savedata, virgin)
end
"""
==(assign1::Assignment, assign2::Assignment) -> Bool
Judge whether two assignments are equivalent to each other.
"""
Base.:(==)(assign1::Assignment, assign2::Assignment) = ==(efficientoperations, assign1, assign2)
"""
isequal(assign1::Assignment, assign2::Assignment) -> Bool
Judge whether two assignments are equivalent to each other.
"""
Base.isequal(assign1::Assignment, assign2::Assignment) = isequal(efficientoperations, assign1, assign2)
"""
valtype(assign::Assignment)
valtype(::Type{<:Assignment{<:App, <:Parameters, <:Function, <:Tuple{Vararg{Symbol}}, R}}) where R
The type of the data(result) of an assignment.
"""
Base.valtype(assign::Assignment) = assign |> typeof |> valtype
Base.valtype(::Type{<:Assignment{<:App, <:Parameters, <:Function, <:Tuple{Vararg{Symbol}}, R}}) where {R} = R
"""
id(assign::Assignment) -> Symbol
id(::Type{<:Assignment{<:App, <:Parameters, <:Function, <:Tuple{Vararg{Symbol}}, <:Any, I}}) where I -> Symbol
The id of an assignment.
"""
id(assign::Assignment) = assign |> typeof |> id
id(::Type{<:Assignment{<:App, <:Parameters, <:Function, <:Tuple{Vararg{Symbol}}, <:Any, I}}) where {I} = I
"""
update!(assign::Assignment; kwargs...) -> Assignment
Update the parameters of an assignment and the status of its associated app.
"""
@generated function update!(assign::Assignment; kwargs...)
exprs = []
names = fieldnames(fieldtype(assign, :parameters))
for (i, name) in enumerate(names)
name = QuoteNode(name)
push!(exprs, :(get(kwargs, $name, getfield(assign.parameters, $i))))
end
return quote
assign.parameters = Parameters{$names}($(exprs...))
update!(assign.app; assign.map(assign.parameters)...)
return assign
end
end
"""
Algorithm( name::String, engine::Engine;
din::String=".",
dout::String=".",
parameters::Union{Parameters, Nothing}=nothing,
map::Function=identity,
assignments::Tuple{Vararg{Assignment}}=(),
kwargs...
)
An algorithm associated with an engine.
"""
mutable struct Algorithm{E<:Engine, P<:Parameters, M<:Function, S<:NamedContainer{Assignment}}
name::String
engine::E
din::String
dout::String
parameters::P
map::M
sassignments::S
dassignments::Dict{Symbol, Assignment}
timer::TimerOutput
end
function Algorithm( name::String, engine::Engine;
din::String=".",
dout::String=".",
parameters::Union{Parameters, Nothing}=nothing,
map::Function=identity,
assignments::Tuple{Vararg{Assignment}}=(),
kwargs...
)
(parameters === nothing) && (parameters = Parameters(engine))
assignments = namedassignments(assignments)
return Algorithm(name, engine, din, dout, parameters, map, assignments, Dict{Symbol, Assignment}(), TimerOutput())
end
@generated function namedassignments(assignments::Tuple{Vararg{Assignment}})
names, values = [], []
for i = 1:fieldcount(assignments)
push!(names, fieldtype(assignments, 1)|>id)
push!(values, :(assignments[$i]))
end
names = NTuple{fieldcount(assignments), Symbol}(names)
values = Expr(:tuple, values...)
return :(NamedContainer{$names}($values))
end
"""
update!(alg::Algorithm; kwargs...) -> Algorithm
Update the parameters of an algorithm and its associated engine.
"""
@generated function update!(alg::Algorithm; kwargs...)
exprs = []
names = fieldnames(fieldtype(alg, :parameters))
for (i, name) in enumerate(names)
name = QuoteNode(name)
push!(exprs, :(get(kwargs, $name, getfield(alg.parameters, $i))))
end
return quote
alg.parameters = Parameters{$names}($(exprs...))
update!(alg.engine; alg.map(alg.parameters)...)
return alg
end
end
"""
repr(alg::Algorithm, mask::Tuple{Vararg{Symbol}}=(); ndecimal::Int=10) -> String
Get the repr representation of an algorithm.
Optionally, some parameters of the algorithm can be masked. Besides, the maximum number of decimals of the parameters can also be specified.
"""
function Base.repr(alg::Algorithm, mask::Tuple{Vararg{Symbol}}=(); ndecimal::Int=10)
result = [string(alg.name), repr(alg.engine)]
for (name, value) in pairs(alg.parameters)
(name ∉ mask) && push!(result, decimaltostr(value, ndecimal))
end
return join(result, "_")
end
"""
show(io::IO, alg::Algorithm)
Show an algorithm.
"""
function Base.show(io::IO, alg::Algorithm)
@printf io "%s_%s" alg.name alg.engine
for (name, value) in pairs(alg.parameters)
@printf io "_%s" decimaltostr(value, 10)
end
end
"""
get(alg::Algorithm, id::Symbol) -> Assignment
get(alg::Algorithm, ::Val{id}) where id -> Assignment
Find the assignment registered on a algorithm by its id.
"""
Base.get(alg::Algorithm, id::Symbol) = (id ∈ keys(alg.sassignments)) ? getfield(alg.sassignments, id) : alg.dassignments[id]
@generated Base.get(alg::Algorithm, ::Val{id}) where {id} = (id ∈ fieldnames(fieldtype(alg, :sassignments))) ? :(getfield(alg.sassignments, id)) : :(alg.dassignments[id])
"""
summary(alg::Algorithm)
Provide a summary of an algorithm.
"""
function Base.summary(alg::Algorithm)
@info "Summary of $(alg.name)($(nameof(typeof(alg.engine)))):"
@info string(alg.timer)
end
"""
prepare!(alg::Algorithm, assign::Assignment) -> Nothing
Prepare an assignment registered on a algorithm.
"""
prepare!(alg::Algorithm, assign::Assignment) = nothing
"""
run!(alg::Algorithm, assign::Assignment) -> Nothing
Run an assignment registered on a algorithm.
"""
run!(alg::Algorithm, assign::Assignment) = nothing
"""
register!(alg::Algorithm, id::Symbol, app::App; kwargs...) -> Algorithm
Add an assignment on a algorithm by providing the contents of the assignment, and run this assignment.
"""
function register!(alg::Algorithm, id::Symbol, app::App; kwargs...)
add!(alg, id, app; kwargs...)
run!(alg, id, true)
end
"""
add!(alg::Algorithm, id::Symbol, app::App; kwargs...) -> Algorithm
Add an assignment on a algorithm by providing the contents of the assignment.
The difference between `add!` and `register!` is that the `add!` function does not run the newly added assignment but the `register!` function does.
"""
function add!(alg::Algorithm, id::Symbol, app::App; kwargs...)
@assert id ∉ keys(alg.sassignments) "add! error: id($id) conflict."
alg.dassignments[id] = Assignment(id, app, merge(alg.parameters, get(kwargs, :parameters, Parameters{()}())); kwargs...)
return alg
end
"""
run!(alg::Algorithm, id::Symbol, timing::Bool=true) -> Algorithm
run!(alg::Algorithm, ::Val{id}, timing::Bool=true) where id -> Algorithm
Run an assignment with the given id registered on an algorithm. Optionally, the run process can be timed by setting the `timing` argument to be `true`.
"""
run!(alg::Algorithm, id::Symbol, timing::Bool = true) = run!(alg, Val(id), timing)
function run!(alg::Algorithm, ::Val{id}, timing::Bool = true) where id
assign = get(alg, Val(id))
if timing
@timeit alg.timer string(id) algrunassignment!(alg, assign)
@info "App $id($(nameof(assign.app|>typeof))): time consumed $(TimerOutputs.time(alg.timer[string(id)]) / 10^9)s."
else
algrunassignment!(alg, assign)
end
return alg
end
function algrunassignment!(alg::Algorithm, assign::Assignment)
ismatched = match(assign.parameters, alg.parameters)
!assign.virgin && ismatched && return
!ismatched && update!(alg; assign.parameters...)
prepare!(alg, assign)
run!(alg, assign)
assign.virgin = false
end
"""
dependences(alg::Algorithm, assign::Assignment, ::Tuple{}=()) -> Tuple{Vararg{Symbol}}
dependences(alg::Algorithm, assign::Assignment, mask::Tuple{Vararg{Symbol}}) -> Tuple{Vararg{Symbol}}
Get the dependences of an assignment and return their ids.
"""
dependences(alg::Algorithm, assign::Assignment, ::Tuple{}=()) = assign.dependences
dependences(alg::Algorithm, assign::Assignment, mask::Tuple{Vararg{Symbol}}) = Tuple(filter(x->(x ∉ mask), collect(assign.dependences)))
"""
rundependences!(alg::Algorithm, assign::Assignment, mask::Tuple{Vararg{Symbol}}=()) -> Algorithm
Run the dependences of an assignment. Optionally, some dependences can be jumped by specifying the `mask` argument.
"""
function rundependences!(alg::Algorithm, assign::Assignment, mask::Tuple{Vararg{Symbol}}=())
for id in dependences(alg, assign, mask)
assign = get(alg, id)
algrundependence!(alg, assign)
end
return alg
end
function algrundependence!(alg::Algorithm, assign::Assignment)
ismatched = match(alg.parameters, assign.parameters)
!assign.virgin && ismatched && return
!ismatched && update!(assign; alg.parameters...)
prepare!(alg, assign)
run!(alg, assign)
assign.virgin = false
end
end #module
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] | 2.601684 | 4,514 |
export solve_SSODE, solve_SSODE_threads, solve_time, solve
const Default_SSMETHOD = AutoTsit5(Rosenbrock23())
function solveSS(func, u, p; method= Default_SSMETHOD)
#= ODE solver =#
prob = SteadyStateProblem(func, u, p)
return DifferentialEquations.solve(prob, method)
end
"""
solve(ode_func::DEsteady, us; ensemble_method=EnsembleThreads())
solve(ode_func::DEsteady)
solve(ode_func::ODEtime)
solve(ode_func::ODEtime, us; ensemble_method=EnsembleThreads())
Extanded solver for steady state and time-series. The ['DEmeta'](@ref) type provides the information of differential equations ('func'), initial variables ('u') and parameters ('p'). With [`DEsteady`](@ref) and ['ODEtime'](@ref)
Arguements
----------
- `func`: DE function
- `us`: Vector of vectors of initial variables
- `p`: parameter constant
See also
--------
[`DEsteady`](@ref), ['ODEtime'](@ref)
"""
function DifferentialEquations.solve(ode_func::DEsteady, us; ensemble_method=EnsembleThreads())
function prob_func(prob,i,repeat)
ode_new = ode_func(us[i])
remake(prob,u0 = ode_new.u0)
end
@unpack func, u0, method, p = ode_func
prob = SteadyStateProblem(func, u0, p)
ensemble_prob = EnsembleProblem(prob,prob_func=prob_func)
sim = solve(ensemble_prob,method,ensemble_method,trajectories=length(us))
return sim
end
function DifferentialEquations.solve(ode_func::DEsteady)
@unpack func, u0, method, p = ode_func
return solveSS(func, u0, p; method= method)
end
function DifferentialEquations.solve(ode_func::ODEtime)
@unpack func, u0, tspan, p, method = ode_func
prob = ODEProblem(func, u0, tspan, p)
return solve(prob; method=method)
end
function DifferentialEquations.solve(ode_func::ODEtime, us; ensemble_method=EnsembleThreads())
function prob_func(prob,i,repeat)
ode_new = ode_func(us[i])
remake(prob,u0 = ode_new.u0)
end
@unpack func, u0, method, p, tspan = ode_func
prob = ODEProblem(func, u0, tspan, p)
ensemble_prob = EnsembleProblem(prob,prob_func=prob_func)
sim = solve(ensemble_prob,method,ensemble_method,trajectories=length(us))
return sim
end
function solve_SSODE_threads(func, us, p ; method=Default_SSMETHOD)
function prob_func(prob,i,repeat)
remake(prob,u0 = us[i])
end
prob = SteadyStateProblem(func, us[1], p)
ensemble_prob = EnsembleProblem(prob,prob_func=prob_func)
sim = solve(ensemble_prob,method,EnsembleThreads(),trajectories=length(us))
return sim
end
function solve_SSODE_threads(ode_func::DEmeta)
sim = solve_SSODE_threads(ode_func.func, ode_func.u0, ode_func.p; method=ode_func.method)
return sim
end
function solve_SSODE_threads(ode_func::DEmeta, us)
function prob_func(prob,i,repeat)
ode_new = ode_func(us[i])
remake(prob,u0 = ode_new.u0)
end
prob = SteadyStateProblem(ode_func.func, ode_func.u0, ode_func.p)
ensemble_prob = EnsembleProblem(prob,prob_func=prob_func)
sim = solve(ensemble_prob,ode_func.method,EnsembleThreads(),trajectories=length(us))
return sim
end
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] | 2.469613 | 1,267 |
@testset "Testing functions on polygons and shapefiles" begin
package_path = pathof(SatelliteImages)
path_len = length(package_path)
assets_path = package_path[1:path_len-22] * "assets"
map_path = assets_path * "/mumbai_map/mumbai_districts.shp"
radiance_datacube = rand(1:100, 156, 85, 95)
mumbai_districts = load_shapefile(map_path)
mumbai_districts_ntl = aggregate_dataframe(MUMBAI_COORDINATE_SYSTEM, radiance_datacube, mumbai_districts, "DISTRICT")
@test sizeof(mumbai_districts_ntl) > 0
district1 = mumbai_districts[1,:] # Select the first district
district1_mask = polygon_mask(MUMBAI_COORDINATE_SYSTEM, district1)
@test sum(district1_mask) >=0
end | [
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] | 2.604743 | 253 |
using Pkg
pkg"add https://github.com/faf0/AES.jl"
#pkg"rm AES"
pkg"add https://github.com/oxinabox/MD5.jl"
pkg"add JSON"
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"""
License: MIT
Julia 1.02
Dynamic programming formulation to count number of matchings of maximum cardinality
"""
# function from the paper
# INPUT:
# G and E - can be provided by read_Graph(filename)
# maxCard - can be prodived by OptimalMatching(G,E)
function Count_Mat_Paper(G, maxCard,E,r,s,aux)
# reset table of results
if aux == 1
global F_table = Dict()
end
# do not repeat computations:
try
# if aux == 1 then that entry wont exist
return F_table[r,maxCard,[if s[v]>0 1 else 0 end for v in sort(collect(keys(G)))]]
catch
# if any of the entries maxCard, r or s is negative return 0
if maxCard<0 || r<0 || minimum(values(s))<0
F_table[r,maxCard,[if s[v]>0 1 else 0 end for v in sort(collect(keys(G)))]]=0
# aux =1 means we are in the end of the recurssion so return table
if aux ==1
return F_table, 0
end
# if not in end of the recurssion return 0 (no need to return table since it is a global variable)
return 0
end
# matchings containing more edges than the ones available
if maxCard>r
F_table[r,maxCard,[if s[v]>0 1 else 0 end for v in sort(collect(keys(G)))]]=0
# aux =1 means we are in the end of the recurssion so return table
if aux ==1
return F_table, 0
end
# if not in end of the recurssion return 0 (no need to return table since it is global variable)
return 0
end
# if we want a matching of cardinality 0, the only choice is M= empty set, i.e., there only one solution
if maxCard ==0 && r >=0 && minimum(values(s))>=0
F_table[r,maxCard,[if s[v]>0 1 else 0 end for v in sort(collect(keys(G)))]]=1
# aux =1 means we are in the end of the recurssion so return table
if aux ==1
return F_table, 1
end
# if not in end of the recurssion return 0 (no need to return table since it is global variable)
return 1
# if there are no edges available, i.e., r=0, and maxCard>0 then there is no matching under these condition
elseif maxCard >0 && r ==0 && minimum(values(s))>=0
F_table[r,maxCard,[if s[v]>0 1 else 0 end for v in sort(collect(keys(G)))]]=0
# aux =1 means we are in the end of the recurssion so return table
if aux ==1
return F_table, 0
end
# if not in end of the recurssion return 0 (no need to return table since it is global variable)
return 0
# OPT >0 and r>0 and min(s)>=0
else
No_r = Count_Mat_Paper(G,maxCard,E,r-1,s,0)
Yes_r = Count_Mat_Paper(G,maxCard-1,E,r-1,Dict(if v in E[r] v=>s[v]-1 else v=>s[v] end for v in sort(collect(keys(G)))),0)
F_table[r,maxCard,[if s[v]>0 1 else 0 end for v in sort(collect(keys(G)))]] = No_r+Yes_r
if aux ==1
return F_table,No_r+Yes_r
end
return No_r+Yes_r
end
end
end
# read graphs
function Read_Graph(filename)
G_tpm = Dict()
f = open(filename)
lines = readlines(f)
close(f)
num_V, num_E = split(lines[1])
num_V = parse(Int,num_V)
num_E = parse(Int,num_E)
for i=1:num_E
v1,v2,w = split(lines[1+i])
v1 = parse(Int,v1)
v2 = parse(Int,v2)
if !(v2 in keys(G_tpm))
G_tpm[v2] = []
end
if !(v1 in keys(G_tpm))
G_tpm[v1] = [v2]
else
append!(G_tpm[v1],v2)
end
end
G = Dict(v=>[] for v in keys(G_tpm))
E = []
for v1 in keys(G_tpm)
for v2 in G_tpm[v1]
if v1 in G_tpm[v2] && !(v1 in G[v2]) && !(v2 in G[v1])
append!(G[v1],v2)
append!(G[v2],v1)
append!(E,[Set([v1,v2])])
end
end
end
if length(keys(G))==0
G = Dict(v=>[] for v in 1:num_V)
end
return G,E
end
using CPLEX, JuMP
function OptimalMatching(G,E)
#m = Model(solver=CplexSolver())
m = Model(with_optimizer(CPLEX.Optimizer))
# only one core:
CPX_PARAM_THREADS = 1
CPX_PARAM_MIPDISPLAY =0
CPX_PARAM_TILIM = 3600
num_E = length(E)
if num_E==0
return 0
end
@variable(m, x[1:num_E], Bin)
for v in keys(G)
@constraint(m,sum(x[j[1]] for j in enumerate(E) if v in j[2])<=1)
end
@objective(m, Max, sum(x[j[1]] for j in enumerate(E)))
optimize!(m)
println("Objective value: ", objective_value(m))
#println("Objective value: ", getobjectivevalue(m))
return objective_value(m)
end
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] | 2.000424 | 2,361 |
function rosenbrock(x)
a = 1.0
b = 100.0
result = 0.0
for i in 1:(length(x) - 1)
result += (a - x[i])^2 + b * (x[i + 1] - x[i]^2)^2
end
return result
end
function f()
x = rand()
y = rand()
return rosenbrock([x, y])
end
g(f) = f()
struct RandMix <: CompilationContext end
allow(ctx::RandMix, m::Module) = m == TestMixtape
swap(e) = e
function swap(e::Expr)
new = MacroTools.postwalk(e) do s
isexpr(s, :call) || return s
s.args[1] == Base.rand || return s
return 5
end
return new
end
function transform(::RandMix, b)
for (v, st) in b
replace!(b, v, swap(st))
end
return b
end
@testset "Rand swap" begin
fn = Mixtape.jit(RandMix(), f, Tuple{})
@test fn() == rosenbrock([5, 5])
Mixtape.@load_call_interface()
@test call(RandMix(), f) == rosenbrock([5, 5])
@test f() != rosenbrock([5, 5])
fn = Mixtape.jit(RandMix(), g, Tuple{typeof(f)})
@test fn() == rosenbrock([5, 5])
@test call(RandMix(), g, f) == rosenbrock([5, 5])
end
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] | 2.099206 | 504 |
function myfuncA()
println("Say hello from function A")
end
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] | 3.095238 | 21 |
using Distributions
import Distributions.logpdf
using Base.Cartesian
using Base.Threads
using FillArrays
export logpdf
export rand
export For
struct For{F,T,D,X}
f :: F
θ :: T
end
#########################################################
# T <: NTuple{N,J} where {J <: Integer}
#########################################################
For(f, θ::J...) where {J <: Integer} = For(f,θ)
function For(f::F, θ::T) where {F, N, J <: Integer, T <: NTuple{N,J}}
d = f.(Ones{Int}(N)...)
D = typeof(d)
X = eltype(d)
For{F, NTuple{N,J}, D, X}(f,θ)
end
@inline function logpdf(d::For{F,T,D,X1},xs::AbstractArray{X2,N}) where {F, N, J <: Integer, T <: NTuple{N,J}, D, X1, X2}
s = 0.0
@inbounds @simd for θ in CartesianIndices(d.θ)
s += logpdf(d.f(Tuple(θ)...), xs[θ])
end
s
end
function Base.rand(dist::For)
map(CartesianIndices(dist.θ)) do I
(rand ∘ dist.f)(Tuple(I)...)
end
end
#########################################################
# T <: NTuple{N,J} where {J <: AbstractUnitRange}
#########################################################
For(f, θ::J...) where {J <: AbstractUnitRange} = For(f,θ)
function For(f::F, θ::T) where {F, N, J <: AbstractRange, T <: NTuple{N,J}}
d = f.(ones(Int, N)...)
D = typeof(d)
X = eltype(d)
For{F, NTuple{N,J}, D, X}(f,θ)
end
@inline function logpdf(d::For{F,T,D,X1},xs::AbstractArray{X2,N}) where {F, N, J <: AbstractRange, T <: NTuple{N,J}, D, X1, X2}
s = 0.0
@inbounds @simd for θ in CartesianIndices(d.θ)
s += logpdf(d.f(Tuple(θ)...), xs[θ])
end
s
end
function Base.rand(dist::For{F,T}) where {F, N, J <: AbstractRange, T <: NTuple{N,J}}
map(CartesianIndices(dist.θ)) do I
(rand ∘ dist.f)(Tuple(I)...)
end
end
#########################################################
# T <: Base.Generator
#########################################################
function For(f::F, θ::T) where {F, T <: Base.Generator}
d = f(θ.f(θ.iter[1]))
D = typeof(d)
X = eltype(d)
For{F, T, D, X}(f,θ)
end
@inline function logpdf(d :: For{F,T}, x) where {F,T <: Base.Generator}
s = 0.0
for (θj, xj) in zip(d.θ, x)
s += logpdf(d.f(θj), xj)
end
s
end
@inline function rand(d :: For{F,T,D,X}) where {F,T <: Base.Generator, D, X}
rand.(Base.Generator(d.θ.f, d.θ.iter))
end
#########################################################
export logpdf2
@inline function logpdf2(d::For{F,N,X1},xs) where {F,N, X1, X2}
results = zeros(eltype(xs), nthreads())
θ = CartesianIndices(d.θ)
total = Threads.Atomic{Float64}(0.0)
@threads for tid in 1:nthreads()
# split work
start = 1 + ((tid - 1) * length(xs)) ÷ nthreads()
stop = (tid * length(xs)) ÷ nthreads()
domain = start:stop
s = 0.0
for j in domain
@inbounds θj = θ[j]
@inbounds s += logpdf(d.f(Tuple(θj)...), xs[θj])
end
Threads.atomic_add!(total, s)
end
total.value
end
@inline function importanceSample(p::For{F1,N,X}, q::For{F2,N,X}) where {F1,F2,N,X}
_ℓ = 0.0
x = Array{X, N}(undef, length.(q.θ))
@inbounds @simd for θ in CartesianIndices(q.θ)
I = Tuple(θ)
ℓx = importanceSample(p.f(I...), q.f(I...))
_ℓ += ℓx.ℓ
x[θ] = ℓx.val
end
Weighted(_ℓ,x)
end
# using Transducers
# using Transducers: @next, complete
# function Transducers.__foldl__(rf, val, d::For)
# for θ in d.θ
# val = @next(rf, val, f(θ))
# end
# return complete(rf, val)
# end
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] | 2.082367 | 1,724 |
module ApproxOperator
import Base: +, -, *, getindex, setindex!, getproperty, setproperty!, length, push!, fill!, issubset, intersect
import InteractiveUtils: subtypes
abstract type AbstractNode end
abstract type AbstractElement{T} end
abstract type SpatialPartition end
include("node.jl")
include("snode.jl")
include("approximation.jl")
include("approximation_mf.jl")
include("integration.jl")
include("operation.jl")
include("operation_thin_plate.jl")
include("approximation_rk.jl")
include("import.jl")
export Node, Element, SNode, ReproducingKernel, getnₚ
export importmsh
export RegularGrid
export Operator, prescribe!, issubset, intersect
export set𝓖!
export set𝝭!, set∇𝝭!, set∇²𝝭!, set∇³𝝭!, set∇̃𝝭!, set∇̃²𝝭!, set∇∇̃²𝝭!, set∇̄𝝭!, set𝒏!, set∇𝑢!, get∇𝑢, get𝝐, set_memory_𝝭!
#debug
include("littletools.jl")
export get𝐴,cal𝗠!,cal𝗚!,get𝒙,get∇𝝭,get𝝭,checkIC, checkCC, checkConsistency
end
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] | 2.45082 | 366 |
# Copyright 2018 Gandalf Software, Inc. (Scott Paul Jones)
# Licensed under MIT License, see LICENSE.md
function _cmp(::ByteCompare, a, b)
asiz = ncodeunits(a)
apnt = pointer(a)
bsiz = ncodeunits(b)
bpnt = pointer(b)
asiz == bsiz && return apnt === bpnt ? 0 : _memcmp(apnt, bpnt, asiz)
res = _memcmp(apnt, bpnt, min(asiz, bsiz))
res < 0 ? -1 : res > 0 ? 1 : cmp(asiz, bsiz)
end
@inline adjust_utf16(ch) = ch - ifelse(ch < 0xe000, 0xb800, 0xe000)
_cmp_utf16(c1::UInt16, c2::UInt16) =
((c1 < 0xd800 || c2 < 0xd800)
? ifelse(c1 > c2, 1, -1)
: ifelse(adjust_utf16(c1) > adjust_utf16(c2), 1, -1))
# This needs to handle the last word specially, if one is a surrogate pair and the other isn't
# It should be optimized to test at least 64 bits at a time for equality
function _memcmp16(apnt, bpnt, len)
fin = bytoff(apnt, len)
while apnt < fin
(c1 = get_codeunit(apnt)) == (c2 = get_codeunit(bpnt)) || return _cmp_utf16(c1, c2)
apnt += 2
bpnt += 2
end
0
end
function _cmp(::UTF16Compare, a, b)
asiz = ncodeunits(a)
apnt = pointer(a)
bsiz = ncodeunits(b)
bpnt = pointer(b)
if asiz < bsiz
ifelse(_memcmp16(apnt, bpnt, asiz) <= 0, -1, 1)
elseif asiz > bsiz
ifelse(_memcmp16(apnt, bpnt, bsiz) < 0, -1, 1)
elseif apnt != bpnt
_memcmp16(apnt, bpnt, asiz)
else
0
end
end
@inline _lenpntfin(str) = (ncodeunits(str), pointer(str), pointer(str) + sizeof(str))
@inline function _cpcmp(a::MaybeSub{T}, b) where {C<:CSE,T<:Str{C}}
len, pnt, fin = _lenpntfin(a)
pos = start(b)
while pnt < fin
done(b, pos) && return 1
c1, pnt = _nextcp(C, pnt)
ch, pos = next(b, pos)
c2 = ch%UInt32
c1 == c2 || return ifelse(c1 < c2, -1, 1)
end
ifelse(done(b, pos), 0, -1)
end
_cmp(::CodePointCompare, a::MaybeSub{<:Str}, b::AbstractString) = _cpcmp(a, b)
_cmp(::CodePointCompare, a::AbstractString, b::MaybeSub{<:Str}) = -_cpcmp(b, a)
function _cmp(::CodePointCompare,
a::MaybeSub{S}, b::MaybeSub{T}) where {CSE1,CSE2,S<:Str{CSE1},T<:Str{CSE2}}
len1, pnt1, fin1 = _lenpntfin(a)
len2, pnt2, fin2 = _lenpntfin(b)
while pnt1 < fin1
pnt2 < fin2 || return 1
c1, pnt1 = _nextcp(CSE1, pnt1)
c2, pnt2 = _nextcp(CSE2, pnt2)
c1 != c2 && return ifelse(c1 < c2, -1, 1)
end
ifelse(pnt2 < fin2, -1, 0)
end
cmp(a::MaybeSub{<:Str}, b::AbstractString) = @preserve a _cmp(CompareStyle(a, b), a, b)
cmp(a::AbstractString, b::MaybeSub{<:Str}) = @preserve b _cmp(CompareStyle(a, b), a, b)
cmp(a::MaybeSub{<:Str}, b::MaybeSub{<:Str}) = @preserve a b _cmp(CompareStyle(a, b), a, b)
# Todo: handle comparisons of UTF16 specially, to compare first non-matching character
# as if comparing Char to Char, to get ordering correct when dealing with > 0xffff non-BMP
# characters
@inline _fasteq(a, b) = (len = ncodeunits(a)) == ncodeunits(b) && _memcmp(a, b, len) == 0
function _cpeq(a::MaybeSub{T}, b) where {C<:CSE, T<:Str{C}}
len, pnt, fin = _lenpntfin(a)
pos = start(b)
while pnt < fin
done(b, pos) && return false
c1, pnt = _nextcp(C, pnt)
ch, pos = next(b, pos)
c1 == codepoint(ch) || return false
end
true
end
_cpeq(a, b::MaybeSub{T}) where {C<:CSE, T<:Str{C}} = _cpeq(b, a)
function _cpeq(a::MaybeSub{<:Str{C1}}, b::MaybeSub{<:Str{C2}}) where {C1<:CSE, C2<:CSE}
len1, pnt1, fin1 = _lenpntfin(a)
len2, pnt2, fin2 = _lenpntfin(b)
while pnt1 < fin1
pnt2 < fin2 || return false
c1, pnt1 = _nextcp(C1, pnt1)
c2, pnt2 = _nextcp(C2, pnt2)
c1 == c2 || return false
end
true
end
# This can be speeded up in the future with SSE/AVX instructions to unpack bytes,
# or to mask chunks of characters first to see if there are any too large in the wider of the two
function _wideneq(a::MaybeSub{S}, b::MaybeSub{T}) where {S<:Str,T<:Str}
(len = ncodeunits(a)) == ncodeunits(b) || return false
pnt1 = pointer(a)
pnt2 = pointer(b)
fin = pnt1 + sizeof(a)
while pnt1 < fin
get_codeunit(pnt1) == get_codeunit(pnt2) || return false
pnt1 += sizeof(codeunit(S))
pnt2 += sizeof(codeunit(T))
end
true
end
_iseq(::NoCompare, a, b) = false
_iseq(::ByteCompare, a, b) = _fasteq(a, b)
_iseq(::WordCompare, a, b) = _fasteq(a, b)
_iseq(::UTF16Compare, a, b) = _fasteq(a, b)
_iseq(::WidenCompare, a, b) = _wideneq(a, b)
_iseq(::ASCIICompare, a, b) = _cpeq(a, b) # This can be optimized later
_iseq(::CodePointCompare, a, b) = _cpeq(a, b)
==(a::AbstractString, b::MaybeSub{<:Str}) = @preserve b _iseq(EqualsStyle(a, b), a, b)
==(a::MaybeSub{<:Str}, b::AbstractString) = @preserve a _iseq(EqualsStyle(a, b), a, b)
==(a::MaybeSub{<:Str}, b::MaybeSub{<:Str}) = @preserve a b _iseq(EqualsStyle(a, b), a, b)
isless(a::AbstractString, b::MaybeSub{<:Str}) = cmp(a, b) < 0
isless(a::MaybeSub{<:Str}, b::AbstractString) = cmp(a, b) < 0
isless(a::MaybeSub{<:Str}, b::MaybeSub{<:Str}) = cmp(a, b) < 0
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5512,
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30072,
796,
269,
3149,
7,
64,
11,
275,
8,
1279,
657,
198
] | 2.053716 | 2,476 |
#-------------------------------------------------------------------
#* EMSO Model Library (EML) Copyright (C) 2004 - 2007 ALSOC.
#*
#* This LIBRARY is free software; you can distribute it and/or modify
#* it under the therms of the ALSOC FREE LICENSE as available at
#* http://www.enq.ufrgs.br/alsoc.
#*
#* EMSO Copyright (C) 2004 - 2007 ALSOC, original code
#* from http://www.rps.eng.br Copyright (C) 2002-2004.
#* All rights reserved.
#*
#* EMSO is distributed under the therms of the ALSOC LICENSE as
#* available at http://www.enq.ufrgs.br/alsoc.
#*
#*--------------------------------------------------------------------
#* Model of basic streams
#*----------------------------------------------------------------------
#* Author: Paula B. Staudt and Rafael de P. Soares
#* $Id$
#*---------------------------------------------------------------------
type streamPH
streamPH()=begin
PP=outers.PP
new(
stream(),
DanaPlugin(Dict{Symbol,Any}(
:Brief=>"External Physical Properties",
:Type=>"PP"
)),
fill(fraction(Dict{Symbol,Any}(
:Brief=>"Liquid Molar Fraction",
:Hidden=>true
)),(NComp)),
fill(fraction(Dict{Symbol,Any}(
:Brief=>"Vapour Molar Fraction",
:Hidden=>true
)),(NComp)),
[
:([v, x, y] = PP.FlashPH(P, h, z)),
:(h = (1-v)*PP.LiquidEnthalpy(T, P, x) + v*PP.VapourEnthalpy(T, P, y)),
],
[
"Flash Calculation","Enthalpy",
],
[:PP,],
[:x,:y,]
)
end
_base_1::stream
PP::DanaPlugin
x::Array{fraction}
y::Array{fraction}
equations::Array{Expr,1}
equationNames::Array{String,1}
parameters::Array{Symbol,1}
variables::Array{Symbol,1}
attributes::Dict{Symbol,Any}
end
export streamPH
function setEquationFlow(in::streamPH)
addEquation(1)
addEquation(2)
end
function atributes(in::streamPH,_::Dict{Symbol,Any})
fields::Dict{Symbol,Any}=Dict{Symbol,Any}()
fields[:Brief]="Stream with built-in flash calculation"
fields[:Info]="
This model should be used when the vaporization fraction
is unknown.
The built-in flash calculation will determine the stream
state as a function of the overall composition '''z''', the
pressure '''P''' and the enthalpy '''h'''.
Additionally, the liquid composition '''x''' and the vapor
composition '''y''' are calculated.
"
fields[:Pallete]=false
drive!(fields,_)
return fields
end
streamPH(_::Dict{Symbol,Any})=begin
newModel=streamPH()
newModel.attributes=atributes(newModel,_)
newModel
end
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] | 2.702104 | 903 |
module BanyanDataFrames
using Banyan
using BanyanArrays
using DataFrames, Missings
include("df.jl")
include("gdf.jl")
# Types
export DataFrame, GroupedDataFrame
# I/O
export read_csv, write_csv, read_parquet, write_parquet, read_arrow, write_arrow
# Dataframe properties
export nrow, ncol, size, names, propertynames
# Dataframe filtering
export dropmissing, filter, unique, nonunique
# Dataframe selection and column manipulation
export getindex, setindex!, rename
# Dataframe sorting
export sort
# Dataframe joining
export innerjoin
# Grouped dataframe properties
export length, groupcols, valuecols
# Grouped dataframe methods
export groupby, select, transform, combine, subset
# Missing
export allowmissing, disallowmissing
end # module
| [
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] | 3.431818 | 220 |
function msa(network::AbstractNetwork,
trips::AbstractMatrix{T},
costfn::CostFunction;
basedon=:link,
errtol=1e-4) where {T<:Real, U<:Integer}
# initialize
flows = allornothing(network, trips, costfn; basedon=basedon)
# start iteration
err = 1.
iterno = 2 # first iteration @ initialization
while err > errtol
## find target solution
linkcosts = costfn(flows).costs
shortflows = allornothing(network, trips, linkcosts; basedon=basedon)
## find stepsize
μ = 1/iterno
## calculate new link travel times
flows += μ * (shortflows - flows)
## calculate error
err = sum(flows .* linkcosts)/sum(shortflows .* linkcosts) - 1.
iterno += 1
end
return (flows, err)
end
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] | 2.286501 | 363 |
# https://juliasmoothoptimizers.github.io/ADNLPModels.jl/stable/tutorial/
using ADNLPModels
using NLPModelsIpopt
f(x) = (x[1] - 1)^2 + 100*(x[2] - x[1]^2)^2
x0 = [-1.2; 1.0]
uvar = [10.0; 10.0]
lvar = [-10.0; -10.0]
c(x) = [x[1]^2 + x[2]^2; x[1]*x[2]]
ucon = [0.75; 0.3]
lcon = [0.0; 0.0]
nlp = ADNLPModel(f, x0, lvar, uvar, c, lcon, ucon)
#output = ipopt(nlp, print_level=0)
output = ipopt(nlp)
println(output.objective)
println(output.solution)
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] | 1.816 | 250 |
# This code is part of QuantumCircuits.
#
# (C) Copyright Rafał Pracht 2022.
#
# This code is licensed under the Apache License, Version 2.0. You may
# obtain a copy of this license in the LICENSE.txt file in the root directory
# of this source tree or at http://www.apache.org/licenses/LICENSE-2.0.
#
# Any modifications or derivative works of this code must retain this
# copyright notice, and modified files need to carry a notice indicating
# that they have been altered from the originals.
module Math
using LinearAlgebra
export dagger, unitary_error, matrix_norm, eye, observe_unitary_error,
min_observe_unitary_error
"The dagger => conj transpose"
dagger(x) = transpose(conj(x))
"Calculate the unitary matrix error"
function unitary_error(exp_unit, mat_unit)
dif = exp_unit - mat_unit
return matrix_norm(dif)
end
"Calculate the observe unitary matrix error, the error which we can obserwe
from quantum state"
function observe_unitary_error(exp_unit, mat_unit, index=1)
# Remove phase
exp_unit = exp_unit[:] .* exp(-im * angle(exp_unit[index]))
mat_unit = mat_unit[:] .* exp(-im * angle(mat_unit[index]))
return unitary_error(exp_unit, mat_unit)
end
"Calculate the minimum observe unitary matrix error."
function min_observe_unitary_error(exp_unit, mat_unit)
l = length(exp_unit)
ret = [observe_unitary_error(exp_unit, mat_unit, i) for i in 1:l]
return minimum(ret)
end
"Calculate the matrix norm"
function matrix_norm(mat)
return abs(tr(dagger(mat) * mat))
end
"Return the identity matrix."
eye(n) = Matrix(I, n, n)
end # module Math
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] | 2.919708 | 548 |
@testset "singular case" begin
# From Nocedal & Wright, p. 288-289
# Jacobian is singular at the starting point.
# Used to test the behavior of algorithms in that context.
function f_sinj!(F, x)
F[1] = x[1]
F[2] = 10*x[1]/(x[1]+convert(eltype(x), 0.1))+2*x[2]^2
end
function g_sinj!(J, x)
J[1, 1] = 1
J[1, 2] = 0
J[2, 1] = 1/(x[1]+convert(eltype(x), 0.1))^2
J[2, 2] = 4*x[2]
end
df = OnceDifferentiable(f_sinj!, g_sinj!, [3.0, 0.0], [3.0, 0.0])
df32 = OnceDifferentiable(f_sinj!, g_sinj!, [3.0f0, 0.0f0], [3.0f0, 0.0f0])
# Test disabled, not stable across runs
#r = nlsolve(df, [ 3.0; 0], method = :newton, ftol = 1e-5)
#@assert converged(r)
#@assert norm(r.zero) < 1e-5
r = nlsolve(df, [ 3.0; 0.0], method = :trust_region)
@assert converged(r)
@assert norm(r.zero) < 1e-6
r = nlsolve(df32, [3.0f0; 0.0f0], method = :trust_region)
@assert converged(r)
@assert norm(r.zero) < 1e-6
let a = rand(10)
A = a*a'
global f_let!, g_let!
function f_let!(fvec, x)
copyto!(fvec, A*x)
end
function g_let!(fjac, x)
copyto!(fjac, A)
end
end
df = OnceDifferentiable(f_let!, g_let!, rand(10), rand(10))
r = nlsolve(df, rand(10), method = :trust_region)
end
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] | 1.996711 | 608 |
module GpABC
export
AbstractGPKernel,
SquaredExponentialIsoKernel, SquaredExponentialArdKernel,
MaternIsoKernel, MaternArdKernel, ExponentialIsoKernel, ExponentialArdKernel,
AbstractGaussianProcess,
GPModel,
MvUniform,
LatinHypercubeSampler,
gp_loglikelihood, gp_loglikelihood_log, gp_loglikelihood_grad,
gp_regression, gp_regression_sample,
covariance, covariance_training, covariance_diagonal, covariance_grad,
scaled_squared_distance, scaled_squared_distance_grad,
get_hyperparameters_size, set_hyperparameters,
gp_train,
ABCRejectionOutput, ABCSMCOutput,
SimulatedABCRejection, SimulatedABCSMC,
EmulatedABCRejection, EmulatedABCSMC,
read_rejection_output, read_smc_output,
LNAInput, LNA, compute_LNA, sample_LNA_trajectories, get_LNA_trajectories,
SimulatedModelSelection, EmulatedModelSelection, ModelSelectionOutput,
AbstractEmulatorTraining, DefaultEmulatorTraining,
EmulatorTrainingInput,
AbstractEmulatorRetraining, NoopRetraining, IncrementalRetraining, PreviousPopulationRetraining, PreviousPopulationThresholdRetraining,
AbstractEmulatedParticleSelection, MeanEmulatedParticleSelection, MeanVarEmulatedParticleSelection;
using Optim, Distributions, Distances, DifferentialEquations, ForwardDiff, LinearAlgebra, Logging
import StatsBase
import Base: write
include("gp/kernels/scaled_squared_distance.jl")
include("gp/kernels/abstract_kernel.jl")
include("gp/kernels/rbf_kernels.jl")
include("gp/kernels/matern_kernels.jl")
include("gp/gp.jl")
include("gp/gp_optimisation.jl")
include("abc/io.jl")
include("abc/summary_stats.jl")
include("abc/rejection.jl")
include("abc/smc.jl")
include("abc/model_selection_io.jl")
include("abc/model_selection.jl")
include("abc/simulation.jl")
include("abc/emulation.jl")
include("abc/plot_recipe.jl")
include("util/lna.jl")
include("util/emulation_helpers.jl")
end;
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1741,
62,
16794,
364,
13,
20362,
4943,
198,
198,
437,
26,
198
] | 2.923438 | 640 |
using SparseTransforms
using NPZ
noise_sd = 0.1
synth_data = npzread("data/1kuh_10_exhaustive.npz")
signal = InputSignal(synth_data["y"], noise_sd)
transformed = spright(signal, [:simple, :identity_like, :mle, :none])
| [
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62,
2339,
11,
1058,
76,
293,
11,
1058,
23108,
12962,
198
] | 2.406593 | 91 |
module Scalings
n_pox_pairs(N) = N*(N-1)/2
export n_pox_pairs
n_pox_dir_links(N) = N*(N-1)
export n_pox_dir_links
"""
Number of links for various network size, in dense regime
"""
denseLScal_DirBin(N, C) = C * n_pox_dir_links(N)
denseLScal_DirBin(N) = denseLScal_DirBin(N, 0.2)
export denseLScal_DirBin
"""
Number of links for various network size, in semiDense regime
"""
semiDenseLScal_DirBin(N, C) = C * n_pox_dir_links(N)/sqrt(N)
semiDenseLScal_DirBin(N) = semiDenseLScal_DirBin(N, 0.1)
export semiDenseLScal_DirBin
"""
Number of links for various network size, in sparse regime
"""
sparseLScal_DirBin(N, C) = C * N
sparseLScal_DirBin(N) = sparseLScal_DirBin(N, 4)
export sparseLScal_DirBin
"""
Average number of reciprocal pairs in Erdos Reny Model
- avgL is the expected number of links, related to the probability p of a single link being present by expL = (N^2-N) * p
"""
erdosRenyRecScal_DirBin(expL, N) = expL^2/(2*(N^2-N))
export erdosRenyRecScal_DirBin
end
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] | 2.307159 | 433 |
"""
Combine a feature along with the number of scenario successes and failures.
"""
struct FeatureSuccessAndFailure
feature::Feature
n_success::UInt
n_failure::UInt
end
"""
Accumulate results from executed features as they are being executed. Keep track of whether the
total run is a success of a failure.
"""
mutable struct ResultAccumulator
isaccumsuccess::Bool
features::Vector{FeatureSuccessAndFailure}
ResultAccumulator() = new(true, [])
end
"""
accumulateresult!(acc::ResultAccumulator, result::FeatureResult)
Check for success or failure in this feature result and update the accumulator accordingly.
"""
function accumulateresult!(acc::ResultAccumulator, result::FeatureResult)
n_success::UInt = 0
n_failure::UInt = 0
# Count the number of successes and failures for each step in each scenario. A scenario is
# successful if all its steps are successful.
for scenarioresult in result.scenarioresults
arestepssuccessful = [issuccess(step) for step in scenarioresult.steps]
isscenariosuccessful = all(arestepssuccessful)
if isscenariosuccessful
n_success += 1
else
n_failure += 1
end
acc.isaccumsuccess &= isscenariosuccessful
end
push!(acc.features, FeatureSuccessAndFailure(result.feature, n_success, n_failure))
end
"""
issuccess(acc::ResultAccumulator)
True if all scenarios in all accumulated features are successful.
"""
issuccess(acc::ResultAccumulator) = acc.isaccumsuccess
"""
featureresults(accumulator::ResultAccumulator)
A public getter for the results of all features.
"""
function featureresults(accumulator::ResultAccumulator)
accumulator.features
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] | 3.021053 | 570 |
import Base: sizeof, print, show, convert, read, write, bswap, ==
const Fchar = Int8
"""
FString{L}
Datatype for reading and writing character strings from `FortranFile`s.
The type parameter `L` signifies the length of the string.
This is the equivalent to the Fortran datatype `CHARACTER(len=L)`.
"""
struct FString{L}
data :: Array{Fchar,1}
end
sizeof(::Type{FString{L}}) where {L} = L
sizeof(::FString{L}) where {L} = L
sizeof(a::Array{FString{L}}) where {L} = L*length(a)
# print(io::IO, s::FString{L}) where {L} = print(io, trimstring(s))
show(io::IO, s::FString{L}) where {L} = begin print(io, "FString($L,"); show(io, trimstring(s)); print(io, ")") end
==(a::FString, b::FString) = trimstring(a)==trimstring(b)
bswap(s::FString{L}) where {L} = s # no conversion needed for byte-based strings
function convert(::Type{FString{L}}, s::String) where {L}
spc = Fchar(' ')
data = fill(spc, L)
for (i,c) in enumerate(s)
if i>L; break; end
data[i] = Fchar(c)
end
FString{L}(data)
end
"""
FString(L, s::String)
Convert the Julia `String` `s` to an `FString{L}`.
`s` must contain only ASCII characters.
As in Fortran, the string will be padded with spaces or truncated in order to reach the desired length.
"""
FString(L, s::String) = convert( FString{L}, s )
String(s::FString{L}) where {L} = String(map(Char,s.data))
convert(::Type{String}, s::FString{L}) where {L} = String(s)
function read( io::IO, t::Type{FString{L}} ) where {L}
s = read!(io, Array{Fchar}(undef, L))
FString{L}(s)
end
function write( io::IO, s::FString{L} ) where {L}
write(io, s.data)
end
"""
trimlen(s::FString)
Returns the length of the `FString` `s` with trailing spaces ignored.
"""
function trimlen( s::FString{L} ) where {L}
l = L
while l>0
if s.data[l] != Fchar(' '); break; end
l -= 1
end
return l
end
"""
trim(s::FString)
Returns a truncated copy of the `FString` `s` where all trailing
spaces are removed.
"""
function trim( s::FString{L} ) where {L}
l = trimlen(s)
FString{l}(s.data[1:l])
end
"""
trimstring(s::FString)
Convert the `FString` `s` into a Julia `String`, where
trailing spaces are removed. Use `String(s)` to keep the spaces.
"""
trimstring( s::FString{L} ) where {L} =
String( map(Char, s.data[1:trimlen(s)]) )
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] | 2.45666 | 946 |
"""
PyList{T=Py}([x])
Wraps the Python list `x` (or anything satisfying the sequence interface) as an `AbstractVector{T}`.
If `x` is not a Python object, it is converted to one using `pylist`.
"""
struct PyList{T} <: AbstractVector{T}
py :: Py
PyList{T}(::Val{:new}, py::Py) where {T} = new{T}(py)
end
export PyList
PyList{T}(x=pylist()) where {T} = PyList{T}(Val(:new), ispy(x) ? Py(x) : pylist(x))
PyList(x=pylist()) = PyList{Py}(x)
ispy(::PyList) = true
getpy(x::PyList) = x.py
pydel!(x::PyList) = pydel!(x.py)
pyconvert_rule_sequence(::Type{T}, x::Py, ::Type{PyList{V}}=Utils._type_ub(T)) where {T<:PyList,V} =
if PyList{Py} <: T
pyconvert_return(PyList{Py}(x))
else
pyconvert_return(PyList{V}(x))
end
Base.length(x::PyList) = Int(pylen(x))
Base.size(x::PyList) = (length(x),)
Base.@propagate_inbounds function Base.getindex(x::PyList{T}, i::Int) where {T}
@boundscheck checkbounds(x, i)
return pyconvert_and_del(T, @py x[@jl(i-1)])
end
Base.@propagate_inbounds function Base.setindex!(x::PyList{T}, v, i::Int) where {T}
@boundscheck checkbounds(x, i)
pysetitem(x, i-1, convert(T, v))
return x
end
Base.@propagate_inbounds function Base.insert!(x::PyList{T}, i::Integer, v) where {T}
@boundscheck (i==length(x)+1 || checkbounds(x, i))
pydel!(@py x.insert(@jl(i-1), @jl(convert(T, v))))
return x
end
function Base.push!(x::PyList{T}, v) where {T}
pydel!(@py x.append(@jl(convert(T, v))))
return x
end
function Base.pushfirst!(x::PyList, v)
return @inbounds Base.insert!(x, 1, v)
end
function Base.append!(x::PyList, vs)
for v in vs
push!(x, v)
end
return x
end
function Base.push!(x::PyList, v1, v2, vs...)
push!(x, v1)
push!(x, v2, vs...)
end
Base.@propagate_inbounds function Base.pop!(x::PyList{T}) where {T}
@boundscheck (isempty(x) && throw(BoundsError(x)))
return pyconvert_and_del(T, @py x.pop())
end
Base.@propagate_inbounds function Base.popat!(x::PyList{T}, i::Integer) where {T}
@boundscheck checkbounds(x, i)
return pyconvert_and_del(T, @py x.pop(@jl(i-1)))
end
Base.@propagate_inbounds Base.popfirst!(x::PyList) = popat!(x, 1)
function Base.reverse!(x::PyList)
pydel!(@py x.reverse())
return x
end
function Base.empty!(x::PyList)
pydel!(@py x.clear())
return x
end
function Base.copy(x::PyList{T}) where {T}
PyList{T}(Val(:new), @py x.copy())
end
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] | 2.139965 | 1,136 |
import Gaugefields:staggered_U
struct StaggeredFermion_4D_nowing{NC} <: AbstractFermionfields_4D{NC}
NC::Int64
NX::Int64
NY::Int64
NZ::Int64
NT::Int64
NDW::Int64
NG::Int64 #size of the Gamma matrix. In Staggered fermion, this is one.
NV::Int64
f::Array{ComplexF64,6}
Dirac_operator::String
fshifted::Array{ComplexF64,6}
function StaggeredFermion_4D_nowing(NC,NX,NY,NZ,NT)
NG = 1
NDW = 0
NV = NC*NX*NY*NZ*NT*NG
#@assert NDW == 1 "only NDW = 1 is supported. Now NDW = $NDW"
f = zeros(ComplexF64,NC,NX+2NDW,NY+2NDW,NZ+2NDW,NT+2NDW,NG)
fshifted = zero(f)
Dirac_operator = "Staggered"
return new{NC}(NC,NX,NY,NZ,NT,NDW,NG,NV,f,Dirac_operator,fshifted)
end
end
function Base.size(x::StaggeredFermion_4D_nowing{NC}) where NC
return (x.NC,x.NX,x.NY,x.NZ,x.NT,x.NG)
#return (x.NV,)
end
function Base.length(x::StaggeredFermion_4D_nowing{NC}) where {NC}
return NC*x.NX*x.NY*x.NZ*x.NT*x.NG
end
function Base.similar(x::T) where T <: StaggeredFermion_4D_nowing
return StaggeredFermion_4D_nowing(x.NC,x.NX,x.NY,x.NZ,x.NT)
end
function Dx!(xout::T,U::Array{G,1},
x::T,temps::Array{T,1},boundarycondition) where {T <: StaggeredFermion_4D_nowing,G <:AbstractGaugefields}
#temp = temps[4]
temp1 = temps[1]
temp2 = temps[2]
#clear!(temp)
set_wing_fermion!(x,boundarycondition)
clear_fermion!(xout)
for ν=1:4
xplus = shift_fermion(x,ν)
Us = staggered_U(U[ν],ν)
mul!(temp1,Us,xplus)
xminus = shift_fermion(x,-ν)
Uminus = shift_U(U[ν],-ν)
Uminus_s = staggered_U(Uminus,ν)
mul!(temp2,Uminus_s',xminus)
add_fermion!(xout,0.5,temp1,-0.5,temp2)
#fermion_shift!(temp1,U,ν,x)
#fermion_shift!(temp2,U,-ν,x)
#add!(xout,0.5,temp1,-0.5,temp2)
end
set_wing_fermion!(xout,boundarycondition)
return
end
function clear_fermion!(x::StaggeredFermion_4D_nowing{NC},evensite) where NC
ibush = ifelse(evensite,0,1)
for it=1:x.NT
for iz=1:x.NZ
for iy=1:x.NY
xran =1+(1+ibush+iy+iz+it)%2:2:x.NX
for ix in xran
@simd for ic=1:NC
x[ic,ix,iy,iz,it,1] = 0
end
end
end
end
end
return
end
function shift_fermion(F::StaggeredFermion_4D_nowing{NC},ν::T) where {T <: Integer,NC}
if ν == 1
shift = (1,0,0,0)
elseif ν == 2
shift = (0,1,0,0)
elseif ν == 3
shift = (0,0,1,0)
elseif ν == 4
shift = (0,0,0,1)
elseif ν == -1
shift = (-1,0,0,0)
elseif ν == -2
shift = (0,-1,0,0)
elseif ν == -3
shift = (0,0,-1,0)
elseif ν == -4
shift = (0,0,0,-1)
end
return Shifted_fermionfields_4D_nowing(F,shift)
end
function shift_fermion(F::TF,shift::NTuple{Dim,T}) where {Dim,T <: Integer,TF <: StaggeredFermion_4D_nowing}
return Shifted_fermionfields_4D_nowing(F,shift)
end
function shifted_fermion!(x::StaggeredFermion_4D_nowing{NC},boundarycondition,shift) where NC
NX = x.NX
NY = x.NY
NZ = x.NZ
NT = x.NT
factor_t = 1
factor_z = 1
factor_y = 1
factor_x = 1
bc = boundarycondition
#n6 = size(x.f)[6]
#f = zeros(ComplexF64,4)
#e = zeros(ComplexF64,4)
#for ic=1:NC
for ig = 1:1
for it=1:NT
it_shifted = it + shift[4]
inside_up = it_shifted > NT
inside_down = it_shifted < 1
factor_t = ifelse(inside_up || inside_down,bc[4],1)
it_shifted += ifelse(inside_up,-NT,0)
it_shifted += ifelse(inside_down,+NT,0)
for iz=1:NZ
iz_shifted = iz + shift[3]
inside_up = iz_shifted > NZ
inside_down = iz_shifted < 1
factor_z = ifelse(inside_up || inside_down,bc[3],1)
iz_shifted += ifelse(inside_up,-NZ,0)
iz_shifted += ifelse(inside_down,+NZ,0)
for iy=1:NY
iy_shifted = iy + shift[2]
inside_up = iy_shifted > NY
inside_down = iy_shifted < 1
factor_y = ifelse(inside_up || inside_down,bc[2],1)
iy_shifted += ifelse(inside_up,-NY,0)
iy_shifted += ifelse(inside_down,+NY,0)
for ix=1:NX
ix_shifted = ix + shift[1]
inside_up = ix_shifted > NX
inside_down = ix_shifted < 1
factor_x = ifelse(inside_up || inside_down,bc[1],1)
ix_shifted += ifelse(inside_up,-NX,0)
ix_shifted += ifelse(inside_down,+NX,0)
@inbounds @simd for ic=1:NC
#@code_warntype x.f[ic,ix_shifted,iy_shifted,iz_shifted,it_shifted,ig]
x.fshifted[ic,ix,iy,iz,it,ig] =
factor_x*factor_y*factor_z*factor_t*x[ic,ix_shifted,iy_shifted,iz_shifted,it_shifted,ig]
end
end
end
end
end
end
#end
end
function set_wing_fermion!(a::StaggeredFermion_4D_nowing{NC},boundarycondition) where NC
return
end
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] | 1.693146 | 3,210 |
module ComputExp
using JuliaDB
using Missings
using Query
using NamedTuples
include("utils.jl")
include("types.jl")
include("api.jl")
include("run_exp.jl")
export ComputExpDB,
New!,
Language,
Algorithm,
Implementation,
Problem,
Experiment,
Run,
@run_exps
end # module
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] | 2.205128 | 156 |
module panel
import Term:
split_lines,
get_last_valid_str_idx,
reshape_text,
do_by_line,
join_lines,
truncate,
textlen
import ..consoles: console_width
import ..measure: Measure
import ..renderables: AbstractRenderable, RenderablesUnion, Renderable, RenderableText
import ..segment: Segment
using ..box
import ..layout: Padding, vstack
import ..style: apply_style
export Panel, TextBox
abstract type AbstractPanel <: AbstractRenderable end
# ---------------------------------------------------------------------------- #
# PANEL #
# ---------------------------------------------------------------------------- #
"""
Panel
`Renderable` with a panel surrounding some content:
╭──────────╮
│ my panel │
╰──────────╯
"""
mutable struct Panel <: AbstractPanel
segments::Vector
measure::Measure
title::Union{Nothing,String}
title_style::Union{String,Nothing}
style::Union{String,Nothing}
end
"""
Panel(
content::RenderablesUnion;
title::Union{Nothing, String}=nothing,
title_style::Union{String, Nothing}=nothing,
title_justify::Symbol=:left,
subtitle::Union{String, Nothing}=nothing,
subtitle_style::Union{String, Nothing}=nothing,
subtitle_justify::Symbol=:left,
width::Union{Nothing, Symbol, Int}=:fit,
height::Union{Nothing, Int}=nothing,
style::Union{String, Nothing}=nothing,
box::Symbol=:ROUNDED,
justify=:left
)
`Panel` constructor to fit a panel to a piece of (renderable) content.
`title` can be used to specify a title to be addded to the top row and
`title_style` and `title_justify` set its appearance and position.
Same for `subtitle` but for the panel's bottom row.
`width` and `height` are used to set the `Panel`'s size. If not passed
they are computed to fit tot the `content`'s size.
"""
function Panel(
content::RenderablesUnion;
title::Union{Nothing,String} = nothing,
title_style::Union{String,Nothing} = nothing,
title_justify::Symbol = :left,
subtitle::Union{String,Nothing} = nothing,
subtitle_style::Union{String,Nothing} = nothing,
subtitle_justify::Symbol = :left,
width::Union{Nothing,Symbol,Int} = :fit,
height::Union{Nothing,Int} = nothing,
style::Union{String,Nothing} = nothing,
box::Symbol = :ROUNDED,
justify = :left,
)
box = eval(box) # get box object from symbol
# style stuff
title_style = isnothing(title_style) ? style : title_style
# σ(s) = Segment(s, style) # applies the main style markup to a string to make a segment
σ(s) = "[$style]$s[/$style]" # applies the main style markup to a string to make a segment
# get size of panel to fit the content
if content isa AbstractString && width isa Number
content = do_by_line((ln) -> reshape_text(ln, width - 4), content)
end
content_measure = Measure(content)
panel_measure = Measure(content_measure.w + 2, content_measure.h + 2)
if width == :fit
width = panel_measure.w + 2
else
width = isnothing(width) ? console_width() - 4 : width
end
@assert width > content_measure.w "Width too small for content '$content' with $content_measure"
panel_measure.w = width
# create segments
segments::Vector{Segment} = []
# create top/bottom rows with titles
top = get_title_row(
:top,
box,
title;
width = width - 2,
style = style,
title_style = title_style,
justify = title_justify,
)
bottom = get_title_row(
:bottom,
box,
subtitle;
width = width - 2,
style = style,
title_style = subtitle_style,
justify = subtitle_justify,
)
# add a panel row for each content row
push!(segments, top)
left, right = σ(string(box.mid.left)), σ(string(box.mid.right))
content_lines = split_lines(content)
for n = 1:content_measure.h
# get padding
line = content_lines[n]
padding = Padding(line, width - 2, justify)
# make line
segment = Segment(left * padding.left * apply_style(line) * padding.right * right)
push!(segments, segment)
end
# add empty lines to ensure target height is reached
if !isnothing(height) && content_measure.h < height
for i = 1:(height-content_measure.h)
line = " "^(width - 2)
push!(segments, Segment(left * line * right))
end
end
push!(segments, bottom)
return Panel(
segments,
panel_measure,
isnothing(title) ? title : title,
title_style,
style,
)
end
"""
Panel(renderables; kwargs...)
`Panel` constructor for creating a panel out of multiple renderables at once.
"""
function Panel(renderables...; width::Union{Nothing,Int,Symbol} = nothing, kwargs...)
rend_width = isnothing(width) || width isa Symbol ? width : width - 1
renderable = vstack(Renderable.(renderables, width = rend_width)...)
return Panel(renderable; width = width, kwargs...)
end
# ---------------------------------------------------------------------------- #
# TextBox #
# ---------------------------------------------------------------------------- #
"""
TextBox
Creates a `Panel` and fits input text to it.
The pannel is hidden so that the result is just a text box.
"""
mutable struct TextBox <: AbstractPanel
segments::Vector
measure::Measure
end
"""
TextBox(
text::Union{Vector, AbstractString};
width::Union{Nothing, Int}=nothing,
title::Union{Nothing, String}=nothing,
title_style::Union{String, Nothing}="default",
title_justify::Symbol=:left,
subtitle::Union{String, Nothing}=nothing,
subtitle_style::Union{String, Nothing}="default",
subtitle_justify::Symbol=:left,
justify::Symbol=:left,
fit::Symbol=:fit,
)
Creates an hidden `Panel` with `text` in it.
If a `width` is passed, the input `text` is reshaped to have
that size, unless `fit=:truncate` in which case it's cut to size.
If no `width` is passed and `fit=:fit` the `TextBox`'s size
matches the size of the input `text`.
Other arguments behave like `Panel`.
See also [`Panel`](@ref).
"""
function TextBox(
text::Union{Vector,AbstractString};
width::Union{Nothing,Int} = nothing,
title::Union{Nothing,String} = nothing,
title_style::Union{String,Nothing} = "default",
title_justify::Symbol = :left,
subtitle::Union{String,Nothing} = nothing,
subtitle_style::Union{String,Nothing} = "default",
subtitle_justify::Symbol = :left,
justify::Symbol = :left,
fit::Symbol = :fit,
)
# fit text
width = isnothing(width) ? console_width() - 4 : width
if !isnothing(width)
text = do_by_line((ln) -> reshape_text(ln, width - 4), text)
elseif fit == :truncate
text = do_by_line(ln -> truncate(ln, width - 4), text)
elseif fit == :fit
width = Measure(text).w + 4
width = width < 4 ? 4 : width
end
panel = Panel(
text,
style = "hidden",
title = title,
title_style = title_style,
title_justify = title_justify,
subtitle = subtitle,
subtitle_style = subtitle_style,
subtitle_justify = subtitle_justify,
justify = justify,
width = width,
)
return TextBox(panel.segments, panel.measure)
end
TextBox(texts...; kwargs...) = TextBox(join_lines(texts); kwargs...)
end
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] | 2.566088 | 2,996 |
# Aliev_Panfilov_2D_compute_v4.jl
# ================================ u.p. 5.8.21 / 5.9.21
# Aliev-Panfilov model
# with ODE-solver
# compute a save solution
using DrWatson
@quickactivate "NonlinearDynamicsTextbook"
include(srcdir("style.jl"))
using PyPlot, OrdinaryDiffEq
using JLD
using Random
using Statistics
using Logging: global_logger
using TerminalLoggers: TerminalLogger
global_logger(TerminalLogger())
function Aliev_Panfilov_ODE(du,u,p,t)
d, k, a, epsilon, mu1, mu2, hsq6 = p
uu = u[1,:,:]
vv = u[2,:,:]
uuu=[ [uu[2,2] uu[2,:]' uu[2,end-1] ] ; [ uu[:,2] uu uu[:,end-1] ] ; [ uu[end-1,2] uu[end-1,:]' uu[end-1,end-1] ] ]
# 9-point stencil
diff_term = d .* (
4 .* (uuu[2:end-1,1:end-2] .+ uuu[2:end-1,3:end] .+
uuu[3:end,2:end-1] .+ uuu[1:end-2,2:end-1] ) .+
uuu[3:end,3:end] .+ uuu[3:end,1:end-2] .+ uuu[1:end-2,3:end] .+
uuu[1:end-2,1:end-2] .- 20 .*uu ) ./ hsq6
du[1,:,:] = k .* uu .*(1 .-uu).*(uu.-a) .- uu.*vv .+ diff_term
du[2,:,:] = (epsilon .+ (mu1 .* vv) ./ (mu2 .+ uu)) .* (.- vv .- k .* uu .* (uu .- a .- 1 ) )
end
@time begin
# new simulation starting from initial conditions
# -----------------------------------------------
L = 100 # size of domain
N = 400 # no. of grid points
h = L/(N-1) # spatial steps size
hsq6 = 6*h*h
# parameters
k = 8
a = 0.05
mu1 = 0.2
mu2 = 0.3
d = 0.2 # diffusion
epsilon = 0.002
# initial values
# ---------------
uu = zeros(N,N) # u steady state
vv = zeros(N,N) # v steady state
uu[1:end,1:20] .= 0.9 # N = 400
# uu[1:end,1:10] .= 0.9 # N = 200
u0 = zeros(2,N,N)
u0[1,:,:] = uu
u0[2,:,:] = vv
p = d, k, a, epsilon, mu1, mu2, hsq6
saveat = (0.0, 120.0) # [0. , 20. , 120.]
tspan = (0., saveat[end])
prob = ODEProblem(Aliev_Panfilov_ODE, u0, tspan, p)
sol = solve(prob, Tsit5(), reltol=1e-6, abstol=1e-6, saveat = saveat,
progress = true, progress_steps = 100,
)
uout = sol.u
# perturbation
# -------------
ures = uout[end]
uu = ures[1,:,:]
vv = ures[2,:,:]
# uu[1:30,:] .= 1. # N = 200
uu[1:60,:] .= 1. # N = 400
u0[1,:,:] = uu
u0[2,:,:] = vv
# evolve forward
# --------------
# saveat = [700. , 710., 720., 730., 740., 750., 760., 770., 780.]
# tspan = (0., saveat[end])
saveat = [600]
tspan = (0., saveat[end])
prob = ODEProblem(Aliev_Panfilov_ODE, u0, tspan, p)
sol = solve(prob, Tsit5(), reltol=1e-6, abstol=1e-6, saveat = saveat,
progress = true, progress_steps = 100,
)
# compute snapshots
# -----------------
uout = sol.u
ures = uout[end]
saveat = 1.0
tspan = (0., 200)
prob = ODEProblem(Aliev_Panfilov_ODE, ures, tspan, p)
sol = solve(prob, Tsit5(), reltol=1e-6, abstol=1e-6, saveat = saveat,
progress = true, progress_steps = 100,
)
end # @time
tvec = sol.t
uout = sol.u
nts = length(tvec)
# plot
# ----
fig = plt.figure("AP_2D",figsize=(figx,2*figy))
iplt = 0
for n = 1:9
iplt = iplt + 1
ures = uout[n]
subplot(3,3,iplt)
im = plt.pcolormesh(ures[1,:,:],cmap = "inferno") # "gnuplot" ) #"inferno")
ax = gca()
ax.set_xticks([])
ax.set_yticks([])
plt.title(L"t = "*string(floor(Int,tvec[n])), fontsize = 24)
end
fig.tight_layout(pad=0.3)
path = datadir("Aliev_Panfilov_snapshots.jld")
# save(path, "d", d, "k", k, "a", a, "epsilon", epsilon, "mu1", mu1, "mu2", mu2, "L", L, "N", N, "tvec", tvec, "uout", uout)
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] | 1.96875 | 1,728 |
#============================================================================
I haven't decided yet whether I actually want to use this
============================================================================#
mutable struct GroupedDataHandler{T} <: AbstractDH{T}
df::DataTable
colsInput::Vector{Symbol}
colsOutput::Vector{Symbol}
colsClass::Vector{Symbol}
colsNormalize::Vector{Symbol}
keys::Vector
mu::Vector{T}
norm::Vector{T}
userange::Bool
dfTrain::DataTable
dfTest::DataTable
dfTrain_grp::GroupedDataTable
dfTest_grp::GroupedDataTable
X_train::Dict{Any,Array{T}}
y_train::Dict{Any,Array{T}}
# TODO we need a way of getting these back into the dataframe
X_test::Dict{Any,Array{T}}
y_test::Dict{Any,Array{T}}
# this is where predictions are kept
yhat::Array{T}
yhat_train::Array{T}
function GroupedDataHandler{T}(df::DataTable, class_cols::Vector{Symbol};
testfrac::AbstractFloat=0.0,
shuffle::Bool=false,
input_cols::Vector{Symbol}=Symbol[],
output_cols::Vector{Symbol}=Symbol[],
normalize_cols::Vector{Symbol}=Symbol[],
assign::Bool=false,
userange::Bool=false,
compute_keys::Bool=true
) where T
if sum(!complete_cases(df)) ≠ 0
throw(ArgumentError("GroupedDataHandler only accepts complete dataframes."))
end
ndf = copy(df)
# TODO convert to all non-nullable arrays!!!
o = new(ndf, input_cols, output_cols, class_cols, normalize_cols)
o.userange = userange
compute_keys && keys!(o)
split!(o, testfrac, shuffle=shuffle, assign=assign)
computeNormalizeParameters!(o, dataset=:dfTrain)
if canNormalize(o)
normalizeTrain!(o)
size(o.dfTest,1) > 0 && normalizeTest!(o)
end
o
end
end
export GroupedDataHandler
function keytuple(df::AbstractDataTable, cols::Vector{Symbol}, idx::Integer=1)
tuple(convert(Array{Any}, df[idx, cols])...)
end
export keytuple
function keys{T}(gdh::GroupedDataHandler{T})
if isdefined(gdh, :keys)
return gdh.keys
end
o = mapreduce(vcat, groupby(gdh.df, gdh.colsClass)) do sdf
[keytuple(sdf, gdh.colsClass)]
end
o
end
keys!{T}(gdh::GroupedDataHandler{T}) = (gdh.keys = keys(gdh))
export keys, keys!
function assignTrain!{T}(gdh::GroupedDataHandler{T})
if isempty(gdh.dfTrain)
throw(ErrorException("Attempting to assign data from empty training dataframe."))
end
gdh.dfTrain_grp = groupby(gdh.dfTrain, gdh.colsClass)
Xdict, ydict = getMatrixDict(T, gdh.dfTrain_grp, gdh.colsClass, gdh.colsInput,
gdh.colsOutput)
gdh.X_train = Xdict
gdh.y_train = ydict
Xdict, ydict
end
function assignTest!{T}(gdh::GroupedDataHandler{T})
if isempty(gdh.dfTest)
throw(ErrorException("Attempting to assign data from empty test dataframe."))
end
gdh.dfTest_grp = groupby(gdh.dfTest, gdh.colsClass)
Xdict, ydict = getMatrixDict(T, gdh.dfTest_grp, gdh.colsClass, gdh.colsInput,
gdh.colsOutput)
gdh.X_test = Xdict
gdh.y_test = ydict
Xdict, ydict
end
function getTrainData{T}(gdh::GroupedDataHandler{T}; flatten::Bool=false)
X, y = gdh.X_train, gdh.y_train
if flatten
y = Dict{eltype(keys(y)), Vector{T}}()
for (k, v) ∈ y
@assert size(v,2) == 1 "Attempted to flatten rank-2 array."
y[k] = squeeze(v, 2)
end
end
X, y
end
function getTestData{T}(gdh::GroupedDataHandler{T}; flatten::Bool=false)
X, y = gdh.X_test, gdh.y_test
if flatten
y = Dict{eltype(keys(y)), Vector{T}}()
for (k, v) ∈ y
@assert size(v,2) == 1 "Attempted to flatten rank-2 array."
y[k] = squeeze(v, 2)
end
end
X, y
end
"""
_fix_flattened_matrix_dict(T, dict)
Takes a dictionary with matrix or vector values and, if they are vectors, converts them
to `Matrix`s with a single columns.
"""
function _fix_flattened_matrix_dict{T,K,V<:Vector}(::Type{T}, dict::Dict{K,V})
dict_new = Dict{eltype(keys(dict)), Matrix{T}}()
for (k, v) ∈ dict
dict_new[k] = reshape(v, (length(v), 1))
end
dict = dict_new
end
_fix_flattened_matrix_dict{T,K,V<:Matrix}(::Type{T}, dict::Dict{K,V}) = dict
"""
_replace_values_into_grouped(gp, dict, T, new_col_names, cols)
Takes a dict of matrices and places them into a grouped dataframe. The matrices must have
the same number of rows as their respective groups in the grouped dataframe `gp`.
A new dataframe will be output with rows named according to `new_col_names` with columns
corresponding to the columns of matrices in `dict`, and rows corresponding to the rows
of the original dataframe. The keys of `dict` should be tuples like those produced by
`keytuple`. The type parameter `T` denotes the type of the matrices contained in `dict`.
Note that the implmentation on this depends on the "private" members of `GroupedDataTable`.
"""
function _replace_values_into_grouped{T}(gp::GroupedDataTable, dict::Dict, ::Type{T},
new_col_names::Vector{Symbol},
keycols::Vector{Symbol})
newcols = DataTable([T for n ∈ new_col_names], new_col_names, size(gp.parent, 1))
for (start, stop) ∈ zip(gp.starts, gp.ends)
key = keytuple(gp.parent, keycols, gp.idx[start])
y = get(dict, key, NullableArray(T, length(start:stop), length(new_col_names)))
for (i, idx) ∈ enumerate(gp.idx[start:stop])
for j ∈ 1:length(new_col_names)
newcols[idx, j] = y[i, j]
end
end
end
newcols
end
function getTestAnalysisData{T}(gdh::GroupedDataHandler{T}, ŷ::Dict;
names::Vector{Symbol}=Symbol[],
squared_error::Bool=true)
df = copy(gdh.dfTest)
ŷ = _fix_flattened_matrix_dict(T, ŷ)
if length(names) == 0
names = getDefaultTestAnalysisColumnNames(gdh)
end
newcols = _replace_values_into_grouped(gdh.dfTest_grp, ŷ, T, names, gdh.colsClass)
df = hcat(df, newcols)
# note that it's now possible to have nulls
for (idx, name) ∈ enumerate(names)
orig_col = df[gdh.colsOutput[idx]]
err = df[name] - orig_col
df[Symbol(string(name, "_Error"))] = err
if squared_error
# they haven't written the ^ operator for NullableArrays
df[Symbol(string(name, "_Error²"))] = err .* err
end
end
df
end
"""
getGroupedTestAnalysisData(data, keycols[; names=[], squared_error=true])
getGroupedTestAnalysisData(dh, data, keycols[; names=[], squared_error=true])
getGroupedTestAnalysisData(gdh, data[; names=[], squared_error=true])
getGroupedTestAnalysisData(gdh, ŷ[; names=[], squared_error=true])
Groups the output of `getTestAnalysisData` by the columns `keycols`. This is particularly
useful for `GroupedDataHandler` where a typical use case is applying different estimators
to different subsets of the data. One can supply the output `getTestAnalysisData` as
`data` or pass a `GroupedDataHandler` together with an output dictionary `ŷ`, in which
case all the tables will be generated for you.
"""
function getGroupedTestAnalysisData(data::DataTable, keycols::Vector{Symbol},
names::Vector{Symbol};
squared_error::Bool=true)
by(data, keycols) do sdf
agg = DataTable()
if isempty(sdf)
return agg
end
agg[:NDataPoints] = size(sdf,1)
for name ∈ names
errname = Symbol(string(name, "_Error"))
agg[Symbol(string(errname, "_mean"))] = mean(dropnull(sdf[errname]))
agg[Symbol(string(errname, "_std"))] = std(dropnull(sdf[errname]))
if squared_error
err2name = Symbol(string(name, "_Error²"))
agg[Symbol(string(err2name, "_mean"))] = mean(dropnull(sdf[err2name]))
agg[Symbol(string(err2name, "_std"))] = std(dropnull(sdf[err2name]))
end
end
agg
end
end
function getGroupedTestAnalysisData(dh::AbstractDH, data::DataTable, keycols::Vector{Symbol};
names::Vector{Symbol}=Symbol[],
squared_error::Bool=true)
if length(names) == 0
names = getDefaultTestAnalysisColumnNames(dh)
end
getGroupedTestAnalysisData(data, keycols, names, squared_error=squared_error)
end
function getGroupedTestAnalysisData(gdh::GroupedDataHandler, data::DataTable;
names::Vector{Symbol}=Symbol[],
squared_error::Bool=true)
getGroupedTestAnalysisData(gdh, data, gdh.colsClass, names=names,
squared_error=squared_error)
end
function getGroupedTestAnalysisData(gdh::GroupedDataHandler, ŷ::Dict;
names::Vector{Symbol}=Symbol[],
squared_error::Bool=true)
if length(names) == 0
names = getDefaultTestAnalysisColumnNames(gdh)
end
data = getTestAnalysisData(gdh, ŷ, names=names, squared_error=squared_error)
getGroupedTestAnalysisData(data, gdh.colsClass, names, squared_error=squared_error)
end
export getGroupedTestAnalysisData
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62,
20424,
62,
8094,
276,
90,
51,
92,
7,
31197,
3712,
13247,
276,
6601,
10962,
11,
8633,
3712,
35,
713,
11,
7904,
6030,
90,
51,
5512,
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,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
649,
62,
4033,
62,
14933,
3712,
38469,
90,
13940,
23650,
5512,
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,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1994,
4033,
82,
3712,
38469,
90,
13940,
23650,
30072,
198,
220,
220,
220,
649,
4033,
82,
796,
6060,
10962,
26933,
51,
329,
299,
18872,
230,
649,
62,
4033,
62,
14933,
4357,
649,
62,
4033,
62,
14933,
11,
2546,
7,
31197,
13,
8000,
11,
352,
4008,
198,
220,
220,
220,
329,
357,
9688,
11,
2245,
8,
18872,
230,
19974,
7,
31197,
13,
301,
5889,
11,
27809,
13,
2412,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1994,
796,
1994,
83,
29291,
7,
31197,
13,
8000,
11,
1994,
4033,
82,
11,
27809,
13,
312,
87,
58,
9688,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
331,
796,
651,
7,
11600,
11,
1994,
11,
35886,
540,
19182,
7,
51,
11,
4129,
7,
9688,
25,
11338,
828,
4129,
7,
3605,
62,
4033,
62,
14933,
22305,
198,
220,
220,
220,
220,
220,
220,
220,
329,
357,
72,
11,
4686,
87,
8,
18872,
230,
27056,
378,
7,
31197,
13,
312,
87,
58,
9688,
25,
11338,
12962,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
329,
474,
18872,
230,
352,
25,
13664,
7,
3605,
62,
4033,
62,
14933,
8,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
649,
4033,
82,
58,
312,
87,
11,
474,
60,
796,
331,
58,
72,
11,
474,
60,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
886,
198,
220,
220,
220,
649,
4033,
82,
198,
437,
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,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
198,
198,
8818,
651,
14402,
32750,
6601,
90,
51,
92,
7,
21287,
71,
3712,
13247,
276,
6601,
25060,
90,
51,
5512,
331,
136,
224,
3712,
35,
713,
26,
220,
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,
220,
220,
220,
220,
3891,
3712,
38469,
90,
13940,
23650,
92,
28,
13940,
23650,
58,
4357,
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,
220,
220,
220,
220,
44345,
62,
18224,
3712,
33,
970,
28,
7942,
8,
198,
220,
220,
220,
47764,
796,
4866,
7,
21287,
71,
13,
7568,
14402,
8,
628,
220,
220,
220,
331,
136,
224,
796,
4808,
13049,
62,
2704,
1078,
2945,
62,
6759,
8609,
62,
11600,
7,
51,
11,
331,
136,
224,
8,
628,
220,
220,
220,
611,
4129,
7,
14933,
8,
6624,
657,
198,
220,
220,
220,
220,
220,
220,
220,
3891,
796,
651,
19463,
14402,
32750,
39470,
36690,
7,
21287,
71,
8,
198,
220,
220,
220,
886,
628,
220,
220,
220,
649,
4033,
82,
796,
4808,
33491,
62,
27160,
62,
20424,
62,
8094,
276,
7,
21287,
71,
13,
7568,
14402,
62,
2164,
79,
11,
331,
136,
224,
11,
309,
11,
3891,
11,
308,
34985,
13,
4033,
82,
9487,
8,
628,
220,
220,
220,
47764,
796,
289,
9246,
7,
7568,
11,
649,
4033,
82,
8,
628,
220,
220,
220,
1303,
3465,
326,
340,
338,
783,
1744,
284,
423,
9242,
82,
198,
220,
220,
220,
329,
357,
312,
87,
11,
1438,
8,
18872,
230,
27056,
378,
7,
14933,
8,
198,
220,
220,
220,
220,
220,
220,
220,
1796,
62,
4033,
796,
47764,
58,
21287,
71,
13,
4033,
82,
26410,
58,
312,
87,
11907,
198,
220,
220,
220,
220,
220,
220,
220,
11454,
796,
47764,
58,
3672,
60,
532,
1796,
62,
4033,
198,
220,
220,
220,
220,
220,
220,
220,
47764,
58,
13940,
23650,
7,
8841,
7,
3672,
11,
45434,
12331,
48774,
60,
796,
11454,
198,
220,
220,
220,
220,
220,
220,
220,
611,
44345,
62,
18224,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1303,
484,
4398,
470,
3194,
262,
10563,
10088,
329,
35886,
540,
3163,
20477,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
47764,
58,
13940,
23650,
7,
8841,
7,
3672,
11,
45434,
12331,
31185,
48774,
60,
796,
11454,
764,
9,
11454,
198,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
886,
628,
220,
220,
220,
47764,
198,
437,
628,
198,
37811,
198,
220,
220,
220,
651,
13247,
276,
14402,
32750,
6601,
7,
7890,
11,
1994,
4033,
82,
58,
26,
3891,
41888,
4357,
44345,
62,
18224,
28,
7942,
12962,
198,
220,
220,
220,
651,
13247,
276,
14402,
32750,
6601,
7,
34985,
11,
1366,
11,
1994,
4033,
82,
58,
26,
3891,
41888,
4357,
44345,
62,
18224,
28,
7942,
12962,
198,
220,
220,
220,
651,
13247,
276,
14402,
32750,
6601,
7,
21287,
71,
11,
1366,
58,
26,
3891,
41888,
4357,
44345,
62,
18224,
28,
7942,
12962,
198,
220,
220,
220,
651,
13247,
276,
14402,
32750,
6601,
7,
21287,
71,
11,
331,
136,
224,
58,
26,
3891,
41888,
4357,
44345,
62,
18224,
28,
7942,
12962,
198,
198,
38,
14459,
262,
5072,
286,
4600,
1136,
14402,
32750,
6601,
63,
416,
262,
15180,
4600,
2539,
4033,
82,
44646,
220,
770,
318,
3573,
198,
1904,
913,
329,
4600,
13247,
276,
6601,
25060,
63,
810,
257,
7226,
779,
1339,
318,
11524,
1180,
3959,
2024,
198,
1462,
1180,
6352,
1039,
286,
262,
1366,
13,
220,
1881,
460,
5127,
262,
5072,
4600,
1136,
14402,
32750,
6601,
63,
355,
198,
63,
7890,
63,
393,
1208,
257,
4600,
13247,
276,
6601,
25060,
63,
1978,
351,
281,
5072,
22155,
4600,
88,
136,
224,
47671,
287,
543,
198,
7442,
477,
262,
8893,
481,
307,
7560,
329,
345,
13,
198,
37811,
198,
8818,
651,
13247,
276,
14402,
32750,
6601,
7,
7890,
3712,
6601,
10962,
11,
1994,
4033,
82,
3712,
38469,
90,
13940,
23650,
5512,
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,
220,
220,
220,
220,
220,
220,
220,
220,
3891,
3712,
38469,
90,
13940,
23650,
19629,
220,
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,
220,
220,
220,
220,
220,
220,
220,
220,
44345,
62,
18224,
3712,
33,
970,
28,
7942,
8,
198,
220,
220,
220,
416,
7,
7890,
11,
1994,
4033,
82,
8,
466,
264,
7568,
198,
220,
220,
220,
220,
220,
220,
220,
4194,
796,
6060,
10962,
3419,
198,
220,
220,
220,
220,
220,
220,
220,
611,
318,
28920,
7,
82,
7568,
8,
220,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
1441,
4194,
198,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
220,
220,
220,
220,
4194,
58,
25,
45,
6601,
40710,
60,
796,
2546,
7,
82,
7568,
11,
16,
8,
198,
220,
220,
220,
220,
220,
220,
220,
329,
1438,
18872,
230,
3891,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
11454,
3672,
796,
38357,
7,
8841,
7,
3672,
11,
45434,
12331,
48774,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4194,
58,
13940,
23650,
7,
8841,
7,
8056,
3672,
11,
45434,
32604,
48774,
60,
796,
1612,
7,
14781,
8423,
7,
82,
7568,
58,
8056,
3672,
60,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4194,
58,
13940,
23650,
7,
8841,
7,
8056,
3672,
11,
45434,
19282,
48774,
60,
796,
14367,
7,
14781,
8423,
7,
82,
7568,
58,
8056,
3672,
60,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
611,
44345,
62,
18224,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
11454,
17,
3672,
796,
38357,
7,
8841,
7,
3672,
11,
45434,
12331,
31185,
48774,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4194,
58,
13940,
23650,
7,
8841,
7,
8056,
17,
3672,
11,
45434,
32604,
48774,
60,
796,
1612,
7,
14781,
8423,
7,
82,
7568,
58,
8056,
17,
3672,
60,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
4194,
58,
13940,
23650,
7,
8841,
7,
8056,
17,
3672,
11,
45434,
19282,
48774,
60,
796,
14367,
7,
14781,
8423,
7,
82,
7568,
58,
8056,
17,
3672,
60,
4008,
198,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
220,
220,
220,
220,
886,
198,
220,
220,
220,
220,
220,
220,
220,
4194,
198,
220,
220,
220,
886,
198,
437,
198,
198,
8818,
651,
13247,
276,
14402,
32750,
6601,
7,
34985,
3712,
23839,
41473,
11,
1366,
3712,
6601,
10962,
11,
1994,
4033,
82,
3712,
38469,
90,
13940,
23650,
19629,
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,
220,
220,
220,
220,
220,
220,
220,
220,
3891,
3712,
38469,
90,
13940,
23650,
92,
28,
13940,
23650,
58,
4357,
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,
220,
220,
220,
220,
220,
220,
220,
220,
44345,
62,
18224,
3712,
33,
970,
28,
7942,
8,
198,
220,
220,
220,
611,
4129,
7,
14933,
8,
6624,
657,
198,
220,
220,
220,
220,
220,
220,
220,
3891,
796,
651,
19463,
14402,
32750,
39470,
36690,
7,
34985,
8,
198,
220,
220,
220,
886,
198,
220,
220,
220,
651,
13247,
276,
14402,
32750,
6601,
7,
7890,
11,
1994,
4033,
82,
11,
3891,
11,
44345,
62,
18224,
28,
16485,
1144,
62,
18224,
8,
198,
437,
198,
198,
8818,
651,
13247,
276,
14402,
32750,
6601,
7,
21287,
71,
3712,
13247,
276,
6601,
25060,
11,
1366,
3712,
6601,
10962,
26,
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,
220,
220,
220,
220,
220,
220,
220,
220,
3891,
3712,
38469,
90,
13940,
23650,
92,
28,
13940,
23650,
58,
4357,
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,
220,
220,
220,
220,
220,
220,
220,
220,
44345,
62,
18224,
3712,
33,
970,
28,
7942,
8,
198,
220,
220,
220,
651,
13247,
276,
14402,
32750,
6601,
7,
21287,
71,
11,
1366,
11,
308,
34985,
13,
4033,
82,
9487,
11,
3891,
28,
14933,
11,
220,
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,
220,
220,
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] | 2.138411 | 4,595 |
iter_or_array(x) = repeated(x)
iter_or_array(x::Repeated) = x
iter_or_array(x::AbstractArray) = x
# We treat staticarrays as scalar
iter_or_array(x::StaticArray) = repeated(x)
function metrics_bb(char::Char, font::FTFont, pixel_size)
extent = get_extent(font, char) .* Vec2f0(pixel_size)
mini = bearing(extent)
return Rect2D(mini, Vec2f0(extent.scale)), extent
end
function boundingbox(char::Char, font::FTFont, pixel_size)
bb, extent = metrics_bb(char, font, pixel_size)
return bb
end
function glyph_ink_size(char::Char, font::FTFont, pixel_size)
bb, extent = metrics_bb(char, font, pixel_size)
return widths(bb)
end
"""
iterate_extents(f, line::AbstractString, fonts, scales)
Iterates over the extends of the characters (glyphs) in line!
Newlines will be drawn like any other character.
`fonts` can be a vector of fonts, or a single font.
`scales` can be a single float or a Vec2, or a vector of any of those.
`f` will get called with `(char::Char, glyph_box::Rec2D, glyph_advance::Point2f0)`.
`char` is the currently iterated char.
`glyph_box` is the boundingbox of the glyph.
widths(box) will be the size of the bitmap, while minimum(box) is where one starts drawing the glyph.
For the minimum at y position, 0 is the where e.g. `m` starts, so `g` will start in the negative, while `^` will start positive.
`glyph_advance` The amount one advances after glyph, before drawing next glyph.
"""
function iterate_extents(f, line::AbstractString, fonts, scales)
iterator = zip(line, iter_or_array(scales), iter_or_array(fonts))
lastpos = 0.0
for (char, scale, font) in iterator
glyph_box, extent = metrics_bb(char, font, scale)
mini = minimum(glyph_box) .+ Vec2f0(lastpos, 0.0)
glyph_box = Rect2D(mini, widths(glyph_box))
glyph_advance = Point2f0(extent.advance)
lastpos += glyph_advance[1]
f(char, glyph_box, glyph_advance)
end
end
function glyph_rects(line::AbstractString, fonts, scales)
rects = Rect2D[]
iterate_extents(line, fonts, scales) do char, box, advance
push!(rects, box)
end
return rects
end
function boundingbox(line::AbstractString, fonts, scales)
return reduce(union, glyph_rects(line, fonts, scales))
end
function inkboundingbox(ext::FontExtent)
l = leftinkbound(ext)
r = rightinkbound(ext)
b = bottominkbound(ext)
t = topinkbound(ext)
return FRect2D((l, b), (r - l, t - b))
end
function height_insensitive_boundingbox(ext::FontExtent, font::FTFont)
l = leftinkbound(ext)
r = rightinkbound(ext)
b = descender(font)
t = ascender(font)
return FRect2D((l, b), (r - l, t - b))
end
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] | 2.495463 | 1,102 |
export Segment,
Segments,
intersect!,
is_lower_end,
is_upper_end,
contains,
find_leftmost,
find_rightmost,
contains,
is_singular,
min_x,
min_y,
max_x,
max_y,
trivial_miss
struct Segment{T<:Float64}
p::Point{T}
q::Point{T}
slope::T
function Segment(p::Point{T}, q::Point{T}) where {T<:Float64}
if p > q
p, q = q, p
end
slope = (q.x - p.x) / (q.y - p.y)
return new{T}(p, q, slope)
end
end
Base.isless(s::Segment, t::Segment) = (s.p.y > t.q.y) | ((s.p.y == t.p.y) && (s.p.x < t.p.x))
Base.:(==)(s::Segment, t::Segment) = (s.p == t.p) && (s.q == t.q)
get_x(segment::Segment, y) = segment.p.x + segment.slope * (y - segment.p.y)
get_y(segment::Segment, x) = segment.p.y + (x - segment.p.x) / segment.slope
min_x(segment::Segment) = min(segment.p.x, segment.q.x)
min_y(segment::Segment) = min(segment.p.y, segment.q.y)
max_x(segment::Segment) = max(segment.p.x, segment.q.x)
max_y(segment::Segment) = max(segment.p.y, segment.q.y)
Segment(px, py, qx, qy) = Segment(Point(px, py), Point(qx, qy))
"""
Checks if the segment s is purely horizontal or vertical.
"""
function is_singular(s::Segment)
if (s.p.x == s.q.x) || (s.p.y == s.q.y)
return true
else
return false
end
end
function trivial_miss(s1::Segment, s2::Segment)
if s1.slope ≈ s2.slope rtol=1e-10
return true
elseif max_x(s1) < min_x(s2)
return true
elseif max_x(s2) < min_x(s1)
return true
elseif max_y(s1) < min_y(s2)
return true
elseif max_y(s2) < min_y(s1)
return true
end
return false
end
"""
Checks for the intersection of two segments s1, s2.
"""
function Base.intersect!(
s1::Segment{T},
s2::Segment{T},
A::Matrix{T},
b::Vector{T},
tol=1e-9
) where {T<:AbstractFloat}
if is_singular(s1) || is_singular(s2)
return false, Point(0.0, 0.0)
end
if trivial_miss(s1, s2)
return false, Point(0.0, 0.0)
end
A[1, 1] = s1.q.x - s1.p.x
A[1, 2] = s2.p.x - s2.q.x
A[2, 1] = s1.q.y - s1.p.y
A[2, 2] = s2.p.y - s2.q.y
b[1] = s2.p.x - s1.p.x
b[2] = s2.p.y - s1.p.y
sol = 0.0
try
sol = A \ b
catch
@warn "Singular matrix. Check for edge cases!"
return false, Point(0.0, 0.0)
end
if (-tol < sol[1] < 1+tol) && (-tol < sol[2] < 1+tol)
intersection =
Point(s1.p.x + sol[1] * (s1.q.x - s1.p.x), s1.p.y + sol[1] * (s1.q.y - s1.p.y))
return true, intersection
else
return false, Point(0.0, 0.0)
end
end
function Base.intersect!(s1::Segment{T}, s2::Segment{T}) where {T<:AbstractFloat}
A = zeros((2, 2))
b = zeros(2)
return intersect!(s1, s2, A, b)
end
is_lower_end(segment::Segment, Point::Point) = (segment.q == Point)
is_upper_end(segment::Segment, Point::Point) = (segment.p == Point)
function Base.contains(segment::Segment, point::Point, tol=1e-9)
if is_lower_end(segment, point) | is_upper_end(segment, point)
return false
end
y = get_y(segment, point.x)
if y ≈ point.y
atol = tol
return true
else
return false
end
end
function find_leftmost(segment_set, y, tol=1e-9)
ret = nothing
xmin = Inf
for segment in segment_set
x = get_x(segment, y - tol)
if x < xmin
xmin = x
ret = segment
end
end
return ret, xmin
end
function find_rightmost(segment_set, y, tol=1e-9)
ret = nothing
xmax = 0
for segment in segment_set
x = get_x(segment, y - tol)
if x > xmax
xmax = x
ret = segment
end
end
return ret, xmax
end
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] | 1.893273 | 1,977 |
using OrdinaryDiffEq, DiffEqDevTools, DiffEqBase, Base.Test, ODEInterfaceDiffEq
const linear_bigα = parse(BigFloat,"1.01")
f = (t,u) -> (linear_bigα*u)
analytic = (t,u0) -> u0*exp(linear_bigα*t)
prob_ode_bigfloatlinear = ODETestProblem(f,parse(BigFloat,"0.5"),analytic,(0.0,10.0))
f = (t,u,du) -> begin
for i in 1:length(u)
du[i] = linear_bigα*u[i]
end
end
probbig = ODETestProblem(f,map(BigFloat,rand(4,2)).*ones(4,2)/2,analytic,(0.0,10.0))
linear = (t,u) -> (1.01*u)
analytic_linear = (t,u0) -> u0*exp(1.01*t)
probnum = ODETestProblem(linear,1/2,analytic_linear,(0.0,10.0))
probnumbig = prob_ode_bigfloatlinear
#prob = prob_ode_large2Dlinear
f_2dlinear = (t,u,du) -> begin
for i in 1:length(u)
du[i] = 1.01*u[i]
end
end
analytic_2dlinear = (t,u0) -> u0*exp.(1.01*t)
prob = ODETestProblem(f_2dlinear,rand(4,2),analytic_2dlinear,(0.0,10.0))
dts = 1.//2.^(7:-1:4)
testTol = .2
bools = Vector{Bool}(0)
## DP5()
sim = test_convergence(dts,probnum,DP5())
@test abs(sim.𝒪est[:l2]-5) < testTol
sim = test_convergence(dts,prob,DP5())
@test abs(sim.𝒪est[:l2]-5) < testTol
tabalg = ExplicitRK()
sol1 =solve(probnum,DP5(),dt=1/2^6,adaptive=false,save_timeseries=false)
sol2 =solve(probnum,tabalg,dt=1/2^6,adaptive=false,save_timeseries=false)
@test sol1.u[end] - sol2.u[end] < 1e-10
sol1 =solve(prob,DP5(),dt=1/2^6,adaptive=false,save_timeseries=false)
sol2 =solve(prob,tabalg,dt=1/2^6,adaptive=false,save_timeseries=false)
@test minimum(sol1.u[end] - sol2.u[end] .< 3e-10)
sol1 =solve(probnum,DP5(),dt=1/2^6,beta2=0.04)
sol2 =solve(probnum,tabalg,dt=1/2^6,beta2=0.04)
# Should be identical
sol1 =solve(probnum,DP5())
sol2 =solve(probnum,tabalg,beta2=0.04,beta1=0.17)
sol3 =solve(probnum,dopri5())
@test sol1.t ≈ sol2.t ≈ sol3.t
sol1 =solve(prob,DP5(),dt=1/8)
sol2 =solve(prob,tabalg,beta2=0.04,beta1=0.17,dt=1/8)
sol3 =solve(prob,dopri5(),dt=1/8)
@test sol1.t ≈ sol2.t ≈ sol3.t
### BS3()
sim = test_convergence(dts,probnum,BS3())
@test abs(sim.𝒪est[:l2]-3) < testTol
sim = test_convergence(dts,prob,BS3())
@test abs(sim.𝒪est[:l2]-3) < testTol
tabalg = ExplicitRK(tableau=constructBogakiShampine3())
sol1 =solve(probnum,BS3(),dt=1/2^1,adaptive=false,save_timeseries=false)
sol2 =solve(probnum,tabalg,dt=1/2^1,adaptive=false,save_timeseries=false)
@test sol1.u[end] - sol2.u[end] < 1e-10
sol1 =solve(prob,BS3(),dt=1/2^1,adaptive=false,save_timeseries=false)
sol2 =solve(prob,tabalg,dt=1/2^1,adaptive=false,save_timeseries=false)
@test minimum(sol1.u[end] - sol2.u[end] .< 1e-10)
sol1 =solve(prob,tabalg,dt=1/2^6)
sol2 =solve(prob,BS3(),dt=1/2^6)
@test length(sol1) == length(sol2)
### BS5()
dts = 1.//2.^(6:-1:3)
sim = test_convergence(dts,probnumbig,BS5())
@test abs(sim.𝒪est[:l2]-5) < testTol
sim = test_convergence(dts,probbig,BS5())
@test abs(sim.𝒪est[:l2]-5) < testTol
tabalg = ExplicitRK(tableau=constructBogakiShampine5())
sol1 =solve(probnum,BS5(),dt=1/2^6,adaptive=false,save_timeseries=false)
sol2 =solve(probnum,tabalg,dt=1/2^6,adaptive=false,save_timeseries=false)
@test sol1.u[end] - sol2.u[end] < 1e-10
sol1 =solve(prob,BS5(),dt=1/2^3,adaptive=false,save_timeseries=false)
sol2 =solve(prob,tabalg,dt=1/2^3,adaptive=false,save_timeseries=false)
@test minimum(sol1.u[end] - sol2.u[end] .< 1e-10)
sol1 =solve(prob,tabalg,dt=1/2^6)
sol2 =solve(prob,BS5(),dt=1/2^6)
@test length(sol1) <= length(sol2) # Dual error estimators is more strict
### Tsit5()
dts = 1.//2.^(7:-1:3)
sim = test_convergence(dts,probnum,Tsit5())
@test abs(sim.𝒪est[:l2]-5) < testTol+.1
sim = test_convergence(dts,prob,Tsit5())
@test abs(sim.𝒪est[:l2]-5) < testTol+.1
tabalg = ExplicitRK(tableau=constructTsitouras5())
sol1 =solve(probnum,Tsit5(),dt=1/2^6,adaptive=false,save_timeseries=false)
sol2 =solve(probnum,tabalg,dt=1/2^6,adaptive=false,save_timeseries=false)
@test sol1.u[end] - sol2.u[end] < 1e-10
sol1 =solve(prob,Tsit5(),dt=1/2^3,adaptive=false,save_timeseries=false)
sol2 =solve(prob,tabalg,dt=1/2^3,adaptive=false,save_timeseries=false)
@test minimum(sol1.u[end] - sol2.u[end] .< 1e-10)
sol1 =solve(prob,tabalg,dt=1/2^6)
sol2 =solve(prob,Tsit5(),dt=1/2^6)
@test length(sol1) == length(sol2)
### Vern6()
dts = 1.//2.^(8:-1:5)
sim = test_convergence(dts,probnumbig,Vern6())
@test abs(sim.𝒪est[:l2]-6) < testTol
sim = test_convergence(dts,probbig,Vern6())
@test abs(sim.𝒪est[:l2]-6) < testTol
tabalg = ExplicitRK(tableau=constructVernerEfficient6(BigFloat))
sol1 =solve(probnumbig,Vern6(),dt=1/2^6,adaptive=false,save_timeseries=false)
sol2 =solve(probnumbig,tabalg,dt=1/2^6,adaptive=false,save_timeseries=false)
@test sol1.u[end] - sol2.u[end] < 1e-10
sol1 =solve(probbig,Vern6(),dt=1/2^3,adaptive=false,save_timeseries=false)
sol2 =solve(probbig,tabalg,dt=1/2^3,adaptive=false,save_timeseries=false)
@test minimum(sol1.u[end] - sol2.u[end] .< 1e-10)
sol1 =solve(probbig,tabalg,dt=1/2^6)
sol2 =solve(probbig,Vern6(),dt=1/2^6)
@test length(sol1) == length(sol2)
### Vern7()
dts = 1.//2.^(6:-1:3)
sim = test_convergence(dts,probnumbig,Vern7())
@test abs(sim.𝒪est[:l2]-7) < testTol
sim = test_convergence(dts,probbig,Vern7())
@test abs(sim.𝒪est[:l2]-7) < testTol
tabalg = ExplicitRK(tableau=constructVerner7(BigFloat))
sol1 =solve(probnumbig,Vern7(),dt=1/2^6,adaptive=false,save_timeseries=false)
sol2 =solve(probnumbig,tabalg,dt=1/2^6,adaptive=false,save_timeseries=false)
@test sol1.u[end] - sol2.u[end] < 1e-10
sol1 =solve(probbig,Vern7(),dt=1/2^3,adaptive=false,save_timeseries=false)
sol2 =solve(probbig,tabalg,dt=1/2^3,adaptive=false,save_timeseries=false)
@test minimum(sol1.u[end] - sol2.u[end] .< 1e-10)
sol1 =solve(probbig,tabalg,dt=1/2^6)
sol2 =solve(probbig,Vern7(),dt=1/2^6)
@test length(sol1) == length(sol2)
### TanYam7()
dts = 1.//2.^(6:-1:3)
sim = test_convergence(dts,probnumbig,TanYam7())
@test abs(sim.𝒪est[:l2]-7) < testTol
sim = test_convergence(dts,probbig,TanYam7())
@test abs(sim.𝒪est[:l2]-7) < testTol
tabalg = ExplicitRK(tableau=constructTanakaYamashitaEfficient7(Float64))
sol1 =solve(probnum,TanYam7(),dt=1/2^6,adaptive=false,save_timeseries=false)
sol2 =solve(probnum,tabalg,dt=1/2^6,adaptive=false,save_timeseries=false)
@test sol1.u[end] - sol2.u[end] < 1e-10
tabalg = ExplicitRK(tableau=constructTanakaYamashitaEfficient7(BigFloat))
sol1 =solve(probbig,TanYam7(),dt=1/2^3,adaptive=false,save_timeseries=false)
sol2 =solve(probbig,tabalg,dt=1/2^3,adaptive=false,save_timeseries=false)
@test minimum(sol1.u[end] - sol2.u[end] .< 1e-10)
sol1 =solve(prob,tabalg,dt=1/2^6)
sol2 =solve(prob,TanYam7(),dt=1/2^6)
@test length(sol1) == length(sol2)
### Vern8()
dts = 1.//2.^(6:-1:3)
sim = test_convergence(dts,probnumbig,Vern8())
@test abs(sim.𝒪est[:l2]-8) < testTol
sim = test_convergence(dts,probbig,Vern8())
@test abs(sim.𝒪est[:l2]-8) < testTol
tabalg = ExplicitRK(tableau=constructVerner8(BigFloat))
sol1 =solve(probnumbig,Vern8(),dt=1/2^6,adaptive=false,save_timeseries=false)
sol2 =solve(probnumbig,tabalg,dt=1/2^6,adaptive=false,save_timeseries=false)
@test sol1.u[end] - sol2.u[end] < 1e-10
sol1 =solve(probbig,Vern8(),dt=1/2^3,adaptive=false,save_timeseries=false)
sol2 =solve(probbig,tabalg,dt=1/2^3,adaptive=false,save_timeseries=false)
@test minimum(sol1.u[end] - sol2.u[end] .< 1e-10)
sol1 =solve(prob,tabalg,dt=1/2^6)
sol2 =solve(prob,Vern8(),dt=1/2^6)
@test length(sol1) == length(sol2)
### DP8()
dts = 1.//2.^(3:-1:1)
sim = test_convergence(dts,probnumbig,DP8())
@test abs(sim.𝒪est[:l2]-8) < testTol
sim = test_convergence(dts,probbig,DP8())
@test abs(sim.𝒪est[:l2]-8) < testTol
sol1 =solve(probnum,DP8(),dt=1/2^6,adaptive=false,save_timeseries=false)
sol2 =solve(probnum,DP8(),dt=1/2^6)
# Should be identical
sol1 =solve(probnum,DP8())
sol2 =solve(probnum,dop853())
@test sol1.t ≈ sol2.t
# Should be identical
sol1 =solve(probbig,DP8(),dt=1/2^6)
sol2 =solve(probbig,dop853(),dt=1/2^6)
@test sol1.t ≈ sol2.t
### TsitPap8()
dts = 1.//2.^(6:-1:3)
sim = test_convergence(dts,probnumbig,TsitPap8())
@test abs(sim.𝒪est[:l2]-8) < testTol
sim = test_convergence(dts,probbig,TsitPap8())
@test abs(sim.𝒪est[:l2]-8) < testTol
tabalg = ExplicitRK(tableau=constructTsitourasPapakostas8(BigFloat))
sol1 =solve(probnumbig,TsitPap8(),dt=1/2^6,adaptive=false,save_timeseries=false)
sol2 =solve(probnumbig,tabalg,dt=1/2^6,adaptive=false,save_timeseries=false)
@test sol1.u[end] - sol2.u[end] < 1e-10
sol1 =solve(probbig,TsitPap8(),dt=1/2^3,adaptive=false,save_timeseries=false)
sol2 =solve(probbig,tabalg,dt=1/2^3,adaptive=false,save_timeseries=false)
@test minimum(sol1.u[end] - sol2.u[end] .< 1e-10)
sol1 =solve(prob,tabalg,dt=1/2^6)
sol2 =solve(prob,TsitPap8(),dt=1/2^6)
@test length(sol1) == length(sol2)
### Vern9()
dts = 1.//2.^(6:-1:3)
sim = test_convergence(dts,probnumbig,Vern9())
@test abs(sim.𝒪est[:l2]-9) < testTol
sim = test_convergence(dts,probbig,Vern9())
@test abs(sim.𝒪est[:l2]-9) < testTol
tabalg = ExplicitRK(tableau=constructVernerEfficient9(BigFloat))
sol1 =solve(probnumbig,Vern9(),dt=1/2^6,adaptive=false,save_timeseries=false)
sol2 =solve(probnumbig,tabalg,dt=1/2^6,adaptive=false,save_timeseries=false)
@test abs(sol1.u[end] - sol2.u[end]) < 1e-15
sol1 =solve(probbig,Vern9(),dt=1/2^3,adaptive=false,save_timeseries=false)
sol2 =solve(probbig,tabalg,dt=1/2^3,adaptive=false,save_timeseries=false)
@test minimum(abs(sol1.u[end] - sol2.u[end]) .< 1e-15)
sol1 =solve(probbig,tabalg,dt=1/2^6)
sol2 =solve(probbig,Vern9(),dt=1/2^6)
@test length(sol1) == length(sol2)
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] | 1.98627 | 4,734 |
module Parab
export len_curve
function len_curve(n)
# Parameters
f = x -> x^2
a = 0
b = 1
# Calculation
sum = 0
h = (b-a)/n
h² = h^2
x0 = a
f0 = f(x0)
for i in 1:n
x1 = a*(1-i/n) + b*(i/n) # no rounding error by added many h
f1 = f(x1)
sum += sqrt((f1-f0)^2+h²)
x0, f0 = x1, f1
end
sum
end
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] | 1.514851 | 303 |
using LightGraphs
using BALUtils
using StaticGraphs
ba = readbal(ARGS[1])
dia = diameter(StaticGraph(Graph(visibility_graph(ba))))
open(ARGS[2], "w") do io
write(io, "diameter\n$dia\n")
end
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n = 2
μ0 = zeros(n)
P0 = Diagonal(ones(n))
x0 = resample(μ0, P0, 1.0)
β = 10.0
μ1 = sample_mean(x0)
P1 = sample_covariance(x0, β)
A = Diagonal(ones(n))
B = zeros(n)
x1 = []
u0 = rand(1)
for j = 1:2n
push!(x1, A * x0[j] + B * u0[1])
end
A' * P0 * A
β = 10.0
μ1 = sample_mean(x1)
P1 = sample_covariance(x1, β)
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] | 1.706522 | 184 |
mutable struct TServerBase
srvr_t::TServerTransport
processor::TProcessor
in_t::Function
in_p::Function
out_t::Function
out_p::Function
end
function serve_accepted(client::TTransport, s::TServerBase)
itrans = s.in_t(client)
otrans = s.out_t(client)
iprot = s.in_p(itrans)
oprot = s.out_p(otrans)
try
while true
process(s.processor, iprot, oprot)
end
catch ex
if !isa(ex, EOFError)
println(ex)
Base.show_backtrace(stderr, catch_backtrace())
end
end
close(itrans)
close(otrans)
end
close(srvr::TServer) = close(srvr.base.srvr_t)
##
# Blocking server. Requests are processed in the main task.
mutable struct TSimpleServer <: TServer
base::TServerBase
TSimpleServer(srvr_t::TServerTransport, processor::TProcessor, in_t::Function, in_p::Function, out_t::Function, out_p::Function) = new(TServerBase(srvr_t, processor, in_t, in_p, out_t, out_p))
end
function serve(ss::TSimpleServer)
s = ss.base
listen(s.srvr_t)
while true
client = accept(s.srvr_t)
serve_accepted(client, s)
end
end
##
# Task server. Tasks are spawned for each connection.
mutable struct TTaskServer <: TServer
base::TServerBase
TTaskServer(srvr_t::TServerTransport, processor::TProcessor, in_t::Function, in_p::Function, out_t::Function, out_p::Function) = new(TServerBase(srvr_t, processor, in_t, in_p, out_t, out_p))
end
##
# Process Pool Server
mutable struct TProcessPoolServer <: TServer
base::TServerBase
function TProcessPoolServer(srvr_t::TServerTransport, processor::TProcessor, in_t::Function, in_p::Function, out_t::Function, out_p::Function)
distribute(processor)
new(TServerBase(srvr_t, processor, in_t, in_p, out_t, out_p))
end
end
const TAsyncServer = Union{TTaskServer, TProcessPoolServer}
function serve(ss::TAsyncServer)
s = ss.base
listen(s.srvr_t)
while true
client = accept(s.srvr_t)
@async serve_accepted(client, s)
end
end
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] | 2.343182 | 880 |
# This file provide abstraction around performing linear solves (Ax = b)
# and concrete implementations using Dense matrices, SparseMatrixCSC, and
# Petsc (both matrix-explicit and matrix-free)
module LinearSolvers
export LinearSolver, StandardLinearSolver, # Linear Solver types
calcPC, calcLinearOperator, calcPCandLO, # Linear Solver interface
applyPC, applyPCTranspose,
linearSolve, linearSolveTranspose,
isLOMatFree, isPCMatFree, setTolerances, free, # utility functions
applyLinearOperator, applyLinearOperatorTranspose, # LO functions
getBaseLO, getBasePC, getBaseObject,
PCNone, PetscMatPC, PetscMatFreePC, # PC types
DenseLO, SparseDirectLO, PetscMatLO, PetscMatFreeLO, # LO types
AbstractPC, AbstractPetscMatPC, AbstractPetscMatFreePC,# Abstract PC types
AbstractPetscPC, getInnerPC,
AbstractLO, AbstractDenseLO, AbstractSparseDirectLO, # Abstrac LO types
AbstractPetscMatLO, AbstractPetscMatFreeLO, getInnerLO,
setPCCtx, setLOCtx, # matrix-free specific functions
needParallelData
using PDESolver
using ODLCommonTools
using Utils
using SummationByParts
using Base.LinAlg.BLAS
using MPI
using PETSc2
using Jacobian
import Utils.free
# import SuiteSparse stuff
import Base.SparseArrays.UMFPACK: UmfpackLU, umfpack_free_numeric,
umfpack_free_symbolic, umfpack_symbolic!,
umfpack_numeric!
"""
Abstract supertype of all linear solvers. The [`StandardLinearSolver`](@ref)
implementation should be general enough for everything we do.
The purpose of this type is to provide a unified interface for managing
a linear solve, including preconditioning if needed. Many of the
operations on this type are delegated to the pc and lo objects.
The [`AbstractPC`](@ref) and [`AbstractLO`](@ref) types
defined in this module are suitible for solving Ax = b when A is the
Jacobian of the physics. Other methods (for example, unsteady time marching
or homotopy methods) should build their own AbstractPC and
AbstractLO objects and use them with
[`StandardLinearSolver`](@ref).
**Required Fields**
* pc: an [`AbstractPC`](@ref) object
* lo: an [`AbstractLO`](@ref) object
* shared_mat: Bool, true if pc and lo share the same matrix object false
otherwise (either different matrix objects or matrix-free)
**Static Parameters**
* T1: type of pc
* T2: type of lo
"""
abstract type LinearSolver{T1, T2} end
"""
The most commonly used implementation of [`LinearSolver`](@ref).
"""
mutable struct StandardLinearSolver{T1, T2} <: LinearSolver{T1, T2}
pc::T1
lo::T2
shared_mat::Bool
comm::MPI.Comm
myrank::Int
commsize::Int
ksp::KSP # used only for Petsc matrices
is_finalized::Bool
# tolerances for iterative solve
reltol::PetscReal
abstol::PetscReal
dtol::PetscReal
itermax::PetscInt
end
"""
Constructor for StandardLinearSolver.
**Inputs**
* pc: an [`AbstractPC`](@ref), fully initialized
* lo: an [`AbstractLO`](@ref), fully initialized
* comm: the MPI communicator the pc and lo are defined on
* opts: options dictionary
This function throws exceptions if incompatible pc and lo types are used.
**Options Keys**
This function uses: "krylov_reltol", "krylov_abstol", "krylov_dtol", and
"krylov_itermax"
"""
function StandardLinearSolver(pc::T1, lo::T2, comm::MPI.Comm, opts) where {T1, T2}
if typeof(lo) <: DirectLO
@assert typeof(pc) <: PCNone
end
if typeof(lo) <: PetscLO
@assert typeof(pc) <: Union{AbstractPetscMatPC, AbstractPetscMatFreePC}
end
pc2 = getBasePC(pc)
lo2 = getBaseLO(lo)
if typeof(pc2) <: PetscMatPC && typeof(lo2) <: PetscMatLO
if pc2.A.pobj == lo2.A.pobj
shared_mat = true
else
shared_mat = false
end
else
shared_mat = false
end
myrank = MPI.Comm_rank(comm)
commsize = MPI.Comm_size(comm)
if typeof(lo) <: PetscLO
ksp = createKSP(pc2, lo2, comm)
else
ksp = KSP_NULL
end
is_finalized = false
reltol = opts["krylov_reltol"]
abstol = opts["krylov_abstol"]
dtol = opts["krylov_dtol"]
itermax = opts["krylov_itermax"]
ls = StandardLinearSolver{T1, T2}(pc, lo, shared_mat, comm, myrank,
commsize, ksp, is_finalized,
reltol, abstol, dtol, itermax)
finalizer(ls, free)
return ls
end
"""
Abstract supertype of all preconditioner types. Preconditioners can be used
with iterative methods only. When using a direct method, [`PCNone`](@ref)
should be used.
The purpose of this type is to provide a consistent interface for different
types of preconditioners. In particular, it provides control over when the
preconditioner is recomputed.
Users should implement new preconditioners using composition with
one of the preconditioners defined here, namely [`PetscMatPC`](@ref) or
[`PetscMatFreePC`](@ref), ie. they should define a new subtype of
[`AbstractPC`](@ref) that has either `PetscMatPC` or `PetscMatFrePC` as
a field. This allows calling the existing functions for these types
(which compute a preconditioner for the Jacobian of the physics) and
modifying the preconditioner as needed.
User defined preconditioners should subtype either [`AbstractPetscMatPC`](@ref)
of [`AbstractPetscMatFreePC`](@ref).
**Fields**
* pc_inner: another [`AbstractPC`](@ref)
Note that arbitrarily deep nesting of preconditioners is allowed.
The `pc_inner` field can be one of [`PCNone`](@ref), [`PetscMatPC`](@ref),
or [`PetscMatFreePC`](@ref) for a non-nested preconditioner, or some
other [`AbstractPC`](@ref) for a nested preconditioner.
"""
abstract type AbstractPC end
"""
Abstract supertype of all Petsc matrix-explicit preconditioners
"""
abstract type AbstractPetscMatPC <: AbstractPC end
"""
Abstract supertype of all Petsc matrix-free preconditioners.
"""
abstract type AbstractPetscMatFreePC <: AbstractPC end
"""
Alias for any kind of Petsc PC (matrix-explicit or matrix-free)
"""
const AbstractPetscPC = Union{AbstractPetscMatPC, AbstractPetscMatFreePC}
# PC interface
"""
This function calculates the preconditioner. Every implementation of
[`AbstractPC`](@ref) should extend this function with a new method
When creating new preconditioners, this function should generally be called
first on pc_inner, and modifications to the Jacobian should be made
subsequently.
**Inputs**
* pc: the AbstractPC implementation
* mesh
* sbp
* eqn
* opts
* ctx_residual: the ctx required by [`physicsRhs`](@ref)
* t: current time
Implementation Notes:
For matrix-explicit preconditioners, this might not actually calculate the
preconditioner. Rather, it calculates the matrix the preconditioner is
based on, and the solve function calcultes the preconditioner from it.
Nevertheless, it supports the proper semantics for when the PC is updated
even when the PC matrix and LinearOperator matrix are the same (as long as
the solve function is called regularly).
"""
function calcPC(pc::AbstractPC, mesh::AbstractMesh, sbp::AbstractOperator,
eqn::AbstractSolutionData, opts::Dict, ctx_residual, t)
error("reached AbstractPC calcPC(), did you forget to define calcPC() for your AbstractPC implementation?")
end
"""
Applies the preconditioner, ie. x = inv(Ap)*b, where Ap is the approximation
to the matrix A. Note that Ap itself may not be available for some
preconditioners, hence there is only an API for applying inv(Ap),
not Ap itself.
Matrix-free preconditioners need to extend this function with a new method,
matrix-explicit preconditioners do not.
**Inputs**
* pc: the [`AbstractPC`](@ref) implementation.
* mesh
* sbp
* eqn: this argument should generally not be used because all the solution
related data should be stored in the pc object by [`calcPC`](@ref)
* opts
* t: current time
* b: a AbstractVector representing the local part of the solution (ie
eqn.q_vec)
**Inputs/Outputs**
* x: AbstractVector overwritten with result (same size as b).
Some preconditioners may use the value of `x` on entry
as the initial guess.
"""
function applyPC(pc::AbstractPC, mesh::AbstractMesh, sbp::AbstractOperator,
eqn::AbstractSolutionData, opts::Dict, t, b::AbstractVector,
x::AbstractVector)
error("reached AbstractPC applyPC(), did you forget to define applyPC() for your AbstractPC implementation?")
end
"""
Applies the transpose of the preconditioner, ie. x = inv(Ap).'*b.
Similar to [`applyPC`](@ref), see that function for details.
Note that not every preconditioning method supports this.
"""
function applyPCTranspose(pc::AbstractPC, mesh::AbstractMesh, sbp::AbstractOperator,
eqn::AbstractSolutionData, opts::Dict, t, b::AbstractVector,
x::AbstractVector)
error("reached AbstractPC applyPCTranspose(), did you forget to define applyPCTranspose() for your AbstractPC implementation?")
end
"""
This function returns the underlying preconditioner object, ie.
[`PCNone`](@ref), [`PetscMatPC`](@ref), or [`PetscMatFreePC`](@ref).
Note that arbitrarily deep nesting of preconditioners is allowed.
Users do not have to implement as long as the nested preconditioner is
stored in a field called `pc_inner`.
For matrix-explicit preconditioners, this function is useful for getting
the [`PetscMatPC`](@ref) object, which contains the preconditioning
Jacobian matrix.
**Inputs**
* pc: the users [`AbstractPC`](@ref)
"""
function getBasePC(pc::AbstractPC)
# this will recurse all the way down to the underyling pc, which returns
# itself
return getBasePC(pc.pc_inner)
end
"""
This function allows extracting a specific type of pc_inner.
Note: this is not type-stable in julia 0.4, but can be rewritten to be so
in later version of Julia
**Inputs**
* pc: a PC
* T2: a (possibly abstract) type that is contained as an inner PC somewhere
inside pc.
**Outputs**
* pc2: a PC that is a subtype of PC2
"""
function getInnerPC(pc::T1, ::Type{T2}) where {T1 <: AbstractPC, T2}
# can't have T2 <: AbstractPC because that would preclude Unions like
# NewtonLinearObject
#
if T1 <: T2
return pc
else
return getInnerPC(pc.pc_inner, T2)
end
error("unreachable reached")
end
#=
# This is broken on Julia 0.6
# T1 <: T2 implies T1 == T2 when T1 and T2 are concrete
function getInnerPC(pc::T1, ::Type{T2}) where {T2 <: AbstractPC, T1 <: T2}
return pc
end
function getInnerPC(pc::T1, ::Type{T2}) where {T1 <:AbstractPC, T2 <: AbstractPC}
return getInnerPC(pc.pc_inner, T2)
end
=#
"""
Returns `true` if `calcPC` needs parallel communication started before
it is called, false otherwise. Defaults to true. Users should extend this
function with a new method if that particular PC does not require parallel
communication.
**Inputs**
* pc: an `AbstractPC
**Outputs**
* Bool
"""
function needParallelData(pc::AbstractPC)
return true
end
"""
This function frees any memory belonging to external libraries. Users must
call this function when they are finished with an AbstractPC
object.
Users do not have to define this function for their
[`AbstractPC`](@ref) types.
**Inputs**
* pc: the AbstractPC object
"""
function free(pc::AbstractPC)
free(getBasePC(pc))
end
# LinearOperator interface
"""
Abstract supertype of all linear operators used for A when solving Ax = b.
The purpose of this type is to provide a consistent interface for different
types of linear operators. This type really combines two notions: what the
type of the linear operator is, and how it should be solved.
Any implementation of this type should subtype the appropriate catagory
of: [`AbstractDenseLO`](@ref), [`AbstractSparseDirectLO`](@ref),
[`AbstractPetscMatLO`](@ref), [`AbstractPetscMatFreeLO`](@ref)
Note that matrix-explicit implementations can often write a single function
for all these cases if using an matrix interface functions that are defined
for all the matrix types. See [`MatExplicitLO`](@ref)
**Required Fields**
* lo_inner: another `AbstractPC`. Can be one of [`DenseLO`](@ref),
[`SparseDirectLO`](@ref), [`PetscMatLO`](@ref), or
[`PetscMatFreeLO`](@ref), or any other user defined linear
operator.
"""
abstract type AbstractLO end
#TODO: doc these
"""
Linear operator type for Dense matrices. This is generally used only for
debugging.
"""
abstract type AbstractDenseLO <: AbstractLO end
"""
Linear operator type for `SparseMatrixCSC` matrices, which use a direct
solver.
"""
abstract type AbstractSparseDirectLO <: AbstractLO end
"""
Linear operator type for Petsc matrix-explicit.
"""
abstract type AbstractPetscMatLO <: AbstractLO end
"""
Linear operator type for Petsc matrix-free.
"""
abstract type AbstractPetscMatFreeLO <: AbstractLO end
"""
Useful union for all the matrix-explicit linear operator types.
Because matrices have a small set of common interface functions, it is
often possible to write a single function that works on all the different
types of matrices.
"""
const MatExplicitLO = Union{AbstractDenseLO, AbstractSparseDirectLO, AbstractPetscMatLO}
"""
Union of Petsc linear operator types
"""
const PetscLO = Union{AbstractPetscMatLO, AbstractPetscMatFreeLO}
"""
Union of linear operators that do direct solves
"""
const DirectLO = Union{AbstractDenseLO, AbstractSparseDirectLO}
"""
This function calculates the linear operator. Every implementation of
[`AbstractLO`](@ref) should extend this function with a new
method. For matrix-free operators, this function must exist but need
not perform any actions.
For matrix-explicit implementations, this function should be called on
`lo_inner` first and modifications to the Jacobian made subsequently.
**Inputs**
* lo: the AbstractLO implementation (fields may be updated)
* mesh
* sbp
* eqn
* opts
* ctx_residual: the ctx required by [`physicsRhs`](@ref)
* t: current time
Implementation Notes:
For matrix-free operation, this function sets the Petsc ctx for the
PetscMat, which contains a reference to the mesh, sbp, eqn, opts arguments.
This could lead to unexpected behavior if those arguments are modified and
this function is not called again before the next solve.
"""
function calcLinearOperator(lo::AbstractLO, mesh::AbstractMesh,
sbp::AbstractOperator, eqn::AbstractSolutionData,
opts::Dict, ctx_residual, t)
error("reached AbstractLO calcLinearOperator(), did you forget to define calcLinearOperator() for your AbstractLO implementation?")
end
"""
Applies the linear operator, ie. , Ax = b
Matrix-explicit implementations [`AbstractLO`](@ref) do not have
to implement this function, though matrix-free implementations must extend
it with a new method.
**Inputs**
* lo: the [`AbstractLO`](@ref) implementation.
* mesh
* sbp
* eqn
* opts
* ctx_residual: the ctx for [`physicsRhs`](@ref) or the another right hand
side function built on top of it
* t: current time
* x: an AbstractVector (although never a PetscVec)
**Inputs/Outputs**
* b: vector updated with results (do not overwrite)
"""
function applyLinearOperator(lo::AbstractLO, mesh::AbstractMesh,
sbp::AbstractOperator, eqn::AbstractSolutionData,
opts::Dict, ctx_residual, t, x::AbstractVector,
b::AbstractVector)
error("reached AbstractLO applyLinearOperator(), did you forget to define applyLinearOperator() for your AbstractLO implementation?")
end
"""
Applies the transpose of the linear operator, ie. A.'*x = b
Similar to [`applyLinearOperator`](@ref), see that function for details.
Note that not every method supports this. In particular, Petsc
matrix-free LinearOperators don't currently expose this
(although they could with enough reverse-mode)
"""
function applyLinearOperatorTranspose(lo::AbstractLO,
mesh::AbstractMesh, sbp::AbstractOperator,
eqn::AbstractSolutionData, opts::Dict,
ctx_residual, t, x::AbstractVector,
b::AbstractVector)
error("reached AbstractLO applyLinearOperatorTranspose(), did you forget to define applyLinearOperatorTranspose() for your AbstractLO implementation?")
end
"""
Similar to [`getBasePC`](@ref) except it gets the underlying linear operator,
ie. one of [`DenseLO`](@ref), [`SparseDirectLO`](@ref), [`PetscMatLO`](@ref)
or [`PetscMatFreeLO`](@ref).
For matrix-explicit methods, this is a good way of getting the underlying
linear operator object, which contains the matrix in the `A` field (for
all matrix-explicit linear operators).
**Inputs**
* lo: an AbstractLO
"""
function getBaseLO(lo::AbstractLO)
# this will recurse down to the underlying linear operator
return getBaseLO(lo.lo_inner)
end
"""
This function calls either [`getBasePC`](@ref) or [`getBaseLO`](@ref)
depending on the type of its argument.
**Inputs**
* lo: either an `AbstractLO` or `AbstractPC` object
**Outputs**
* the base PC or LO object
"""
function getBaseObject(lo::AbstractLO)
return getBaseLO(lo)
end
function getBaseObject(pc::AbstractPC)
return getBasePC(pc)
end
"""
Like [`getInnerPC`](@ref), but for linear operators
"""
function getInnerLO(lo::T1, ::Type{T2}) where {T1 <: AbstractLO, T2}
# can't have T2 <: AbstractLO because that would precude Unions like
# NewtonLinearOperators
if T1 <: T2
return lo
else
return getInnerLO(lo.lo_inner, T2)
end
error("unreachable reached")
end
"""
Returns `true` if [`calcLinearOperator`](@ref) needs parallel communication
started before
it is called, false otherwise. Defaults to true for non-matrix-free
linear operators.
Users should extend this function with a new method if the default value is
not correct for a particular linear operator.
**Inputs**
* lo: an `AbstractLO
**Outputs**
* Bool
"""
function needParallelData(pc::AbstractLO)
return true
end
function needParallelData(lo::AbstractPetscMatFreeLO)
return false
end
"""
This function frees any memory belonging to external libraries. Users must
call this function when they are finished with an AbstractLO
object.
Users do not have to define this function for their
[`AbstractLO`](@ref) types.
**Inputs**
* lo: the AbstractLO object
"""
function free(lo::AbstractLO)
free(getBaseLO(lo))
end
# include implementations
include("pc_none.jl")
include("pc_petscmat.jl")
include("pc_petscmatfree.jl")
include("lo_dense.jl")
include("lo_sparsedirect.jl")
include("lo_petscmat.jl")
include("lo_petscmatfree.jl")
include("ls_standard.jl")
include("utils.jl")
end # end module
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6044,
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1352,
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7822,
1701,
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428,
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1948,
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3455,
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3455,
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1972,
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8818,
651,
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21982,
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5439,
3712,
23839,
21982,
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628,
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1303,
428,
481,
664,
12321,
866,
284,
262,
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10088,
198,
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1441,
651,
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5439,
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5439,
62,
5083,
8,
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437,
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198,
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2163,
3848,
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21982,
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5420,
8,
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6906,
319,
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663,
4578,
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12429,
20560,
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21982,
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12429,
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4217,
393,
17579,
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198,
8818,
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10267,
7,
5439,
3712,
23839,
21982,
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628,
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651,
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21982,
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5439,
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437,
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3712,
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828,
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12879,
198,
37811,
198,
8818,
651,
818,
1008,
21982,
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5439,
3712,
51,
16,
11,
7904,
6030,
90,
51,
17,
30072,
810,
1391,
51,
16,
1279,
25,
27741,
21982,
11,
309,
17,
92,
198,
2,
460,
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] | 2.915617 | 6,589 |